From 80197e6dbc39ff4f6fa637f55b2a8699a7602d42 Mon Sep 17 00:00:00 2001 From: fengjian Date: Tue, 16 Feb 2016 16:39:41 +0800 Subject: [PATCH 001/166] replace tf.types.xxx with tf.xxx --- examples/1 - Introduction/basic_operations.py | 4 ++-- notebooks/1 - Introduction/basic_operations.ipynb | 4 ++-- notebooks/3 - Neural Networks/alexnet.ipynb | 2 +- notebooks/3 - Neural Networks/convolutional_network.ipynb | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/1 - Introduction/basic_operations.py b/examples/1 - Introduction/basic_operations.py index 7abe9d73..afdef20b 100644 --- a/examples/1 - Introduction/basic_operations.py +++ b/examples/1 - Introduction/basic_operations.py @@ -23,8 +23,8 @@ # The value returned by the constructor represents the output # of the Variable op. (define as input when running session) # tf Graph input -a = tf.placeholder(tf.types.int16) -b = tf.placeholder(tf.types.int16) +a = tf.placeholder(tf.int16) +b = tf.placeholder(tf.int16) # Define some operations add = tf.add(a, b) diff --git a/notebooks/1 - Introduction/basic_operations.ipynb b/notebooks/1 - Introduction/basic_operations.ipynb index 1fc6edd3..c764494f 100644 --- a/notebooks/1 - Introduction/basic_operations.ipynb +++ b/notebooks/1 - Introduction/basic_operations.ipynb @@ -76,8 +76,8 @@ "# The value returned by the constructor represents the output\n", "# of the Variable op. (define as input when running session)\n", "# tf Graph input\n", - "a = tf.placeholder(tf.types.int16)\n", - "b = tf.placeholder(tf.types.int16)" + "a = tf.placeholder(tf.int16)\n", + "b = tf.placeholder(tf.int16)" ] }, { diff --git a/notebooks/3 - Neural Networks/alexnet.ipynb b/notebooks/3 - Neural Networks/alexnet.ipynb index 10fb4a2b..d1667c0e 100644 --- a/notebooks/3 - Neural Networks/alexnet.ipynb +++ b/notebooks/3 - Neural Networks/alexnet.ipynb @@ -91,7 +91,7 @@ "# tf Graph input\n", "x = tf.placeholder(tf.float32, [None, n_input])\n", "y = tf.placeholder(tf.float32, [None, n_classes])\n", - "keep_prob = tf.placeholder(tf.types.float32) # dropout (keep probability)" + "keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)" ] }, { diff --git a/notebooks/3 - Neural Networks/convolutional_network.ipynb b/notebooks/3 - Neural Networks/convolutional_network.ipynb index 790149b0..61ebea6e 100644 --- a/notebooks/3 - Neural Networks/convolutional_network.ipynb +++ b/notebooks/3 - Neural Networks/convolutional_network.ipynb @@ -91,7 +91,7 @@ "# tf Graph input\n", "x = tf.placeholder(tf.float32, [None, n_input])\n", "y = tf.placeholder(tf.float32, [None, n_classes])\n", - "keep_prob = tf.placeholder(tf.types.float32) #dropout (keep probability)" + "keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)" ] }, { From 889a287001395120a6ca29ac1b2c62c18c691cfb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Felipe=20Santos?= Date: Wed, 2 Mar 2016 16:51:39 -0500 Subject: [PATCH 002/166] Fixed typos in README --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 100cd5aa..366758d9 100644 --- a/README.md +++ b/README.md @@ -16,8 +16,8 @@ Code examples for some popular machine learning algorithms, using TensorFlow lib - Multilayer Perceptron ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/multilayer_perceptron.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/multilayer_perceptron.py)) - Convolutional Neural Network ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/convolutional_network.py)) - AlexNet ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/alexnet.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/alexnet.py)) -- Reccurent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/reccurent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/recurrent_network.py)) -- Bidirectional Reccurent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/bidirectional_rnn.py)) +- Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/reccurent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/recurrent_network.py)) +- Bidirectional Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/bidirectional_rnn.py)) #### 4 - Multi GPU - Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4%20-%20Multi%20GPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4%20-%20Multi%20GPU/multigpu_basics.py)) From f5e3be5a50e4929c60c646a912373e0beb6068d1 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Fri, 4 Mar 2016 12:36:41 +0800 Subject: [PATCH 003/166] Update Setup_TensorFlow.md --- Setup_TensorFlow.md | 599 ++++++++++++++++++++++++++++++-------------- 1 file changed, 411 insertions(+), 188 deletions(-) diff --git a/Setup_TensorFlow.md b/Setup_TensorFlow.md index 7ef30590..cdbf18b9 100644 --- a/Setup_TensorFlow.md +++ b/Setup_TensorFlow.md @@ -1,135 +1,237 @@ -_From TensorFlow Official docs_ +From Tensorflow Official doc -# Download and Setup +# Download and Setup -You can install TensorFlow using our provided binary packages or from source. +You can install TensorFlow either from our provided binary packages or from the +github source. -## Binary Installation +## Requirements -The TensorFlow Python API currently requires Python 2.7: we are -[working](https://github.com/tensorflow/tensorflow/issues/1) on adding support -for Python 3. +The TensorFlow Python API supports Python 2.7 and Python 3.3+. -The simplest way to install TensorFlow is using -[pip](https://pypi.python.org/pypi/pip) for both Linux and Mac. +The GPU version (Linux only) requires the Cuda Toolkit >= 7.0 and cuDNN >= +v2. Please see [Cuda installation](#optional-install-cuda-gpus-on-linux) +for details. + +## Overview + +We support different ways to install TensorFlow: + +* [Pip install](#pip-installation): Install TensorFlow on your machine, possibly + upgrading previously installed Python packages. May impact existing + Python programs on your machine. +* [Virtualenv install](#virtualenv-installation): Install TensorFlow in its own + directory, not impacting any existing Python programs on your machine. +* [Docker install](#docker-installation): Run TensorFlow in a Docker container + isolated from all other programs on your machine. + +If you are familiar with Pip, Virtualenv, or Docker, please feel free to adapt +the instructions to your particular needs. The names of the pip and Docker +images are listed in the corresponding installation sections. If you encounter installation errors, see -[common problems](#common_install_problems) for some solutions. To simplify -installation, please consider using our virtualenv-based instructions -[here](#virtualenv_install). +[common problems](#common-problems) for some solutions. + +## Pip Installation -### Ubuntu/Linux 64-bit +[Pip](https://en.wikipedia.org/wiki/Pip_(package_manager)) is a package +management system used to install and manage software packages written in +Python. + +The packages that will be installed or upgraded during the pip install are listed in the +[REQUIRED_PACKAGES section of setup.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py) + +Install pip (or pip3 for python3) if it is not already installed: ```bash -# For CPU-only version -$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl +# Ubuntu/Linux 64-bit +$ sudo apt-get install python-pip python-dev -# For GPU-enabled version (only install this version if you have the CUDA sdk installed) -$ pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl +# Mac OS X +$ sudo easy_install pip ``` -### Mac OS X - -On OS X, we recommend installing [homebrew](http://brew.sh) and `brew install -python` before proceeding, or installing TensorFlow within [virtualenv](#virtualenv_install). +Install TensorFlow: ```bash -# Only CPU-version is available at the moment. -$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl -``` +# Ubuntu/Linux 64-bit, CPU only: +$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl -## Docker-based installation +# Ubuntu/Linux 64-bit, GPU enabled: +$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl -We also support running TensorFlow via [Docker](http://docker.com/), which lets -you avoid worrying about setting up dependencies. +# Mac OS X, CPU only: +$ sudo easy_install --upgrade six +$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.7.1-cp27-none-any.whl +``` -First, [install Docker](http://docs.docker.com/engine/installation/). Once -Docker is up and running, you can start a container with one command: +For python3: ```bash -$ docker run -it b.gcr.io/tensorflow/tensorflow +# Ubuntu/Linux 64-bit, CPU only: +$ sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.1-cp34-none-linux_x86_64.whl + +# Ubuntu/Linux 64-bit, GPU enabled: +$ sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.7.1-cp34-none-linux_x86_64.whl + +# Mac OS X, CPU only: +$ sudo easy_install --upgrade six +$ sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.7.1-cp35-none-any.whl ``` -This will start a container with TensorFlow and all its dependencies already -installed. +NOTE: If you are upgrading from a previous installation of TensorFlow < 0.7.1, +you should uninstall the previous TensorFlow *and protobuf* using `pip +uninstall` first to make sure you get a clean installation of the updated +protobuf dependency. -### Additional images -The default Docker image above contains just a minimal set of libraries for -getting up and running with TensorFlow. We also have the following container, -which you can use in the `docker run` command above: +You can now [test your installation](#test-the-tensorflow-installation). -* `b.gcr.io/tensorflow/tensorflow-full`: Contains a complete TensorFlow source - installation, including all utilities needed to build and run TensorFlow. This - makes it easy to experiment directly with the source, without needing to - install any of the dependencies described above. +## Virtualenv installation -## VirtualEnv-based installation +[Virtualenv](http://docs.python-guide.org/en/latest/dev/virtualenvs/) is a tool +to keep the dependencies required by different Python projects in separate +places. The Virtualenv installation of TensorFlow will not override +pre-existing version of the Python packages needed by TensorFlow. -We recommend using [virtualenv](https://pypi.python.org/pypi/virtualenv) to -create an isolated container and install TensorFlow in that container -- it is -optional but makes verifying installation issues easier. +With [Virtualenv](https://pypi.python.org/pypi/virtualenv) the installation is +as follows: -First, install all required tools: +* Install pip and Virtualenv. +* Create a Virtualenv environment. +* Activate the Virtualenv environment and install TensorFlow in it. +* After the install you will activate the Virtualenv environment each time you + want to use TensorFlow. + +Install pip and Virtualenv: ```bash -# On Linux: +# Ubuntu/Linux 64-bit $ sudo apt-get install python-pip python-dev python-virtualenv -# On Mac: -$ sudo easy_install pip # If pip is not already installed +# Mac OS X +$ sudo easy_install pip $ sudo pip install --upgrade virtualenv ``` -Next, set up a new virtualenv environment. To set it up in the -directory `~/tensorflow`, run: +Create a Virtualenv environment in the directory `~/tensorflow`: ```bash $ virtualenv --system-site-packages ~/tensorflow -$ cd ~/tensorflow ``` -Then activate the virtualenv: +Activate the environment and use pip to install TensorFlow inside it: ```bash -$ source bin/activate # If using bash -$ source bin/activate.csh # If using csh +$ source ~/tensorflow/bin/activate # If using bash +$ source ~/tensorflow/bin/activate.csh # If using csh (tensorflow)$ # Your prompt should change + +# Ubuntu/Linux 64-bit, CPU only: +(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl + +# Ubuntu/Linux 64-bit, GPU enabled: +(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl + +# Mac OS X, CPU only: +(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.7.1-cp27-none-any.whl ``` -Inside the virtualenv, install TensorFlow: +and again for python3: ```bash -# For CPU-only linux x86_64 version -(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl +$ source ~/tensorflow/bin/activate # If using bash +$ source ~/tensorflow/bin/activate.csh # If using csh +(tensorflow)$ # Your prompt should change + +# Ubuntu/Linux 64-bit, CPU only: +(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.1-cp34-none-linux_x86_64.whl -# For GPU-enabled linux x86_64 version -(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl +# Ubuntu/Linux 64-bit, GPU enabled: +(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.7.1-cp34-none-linux_x86_64.whl -# For Mac CPU-only version -(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl +# Mac OS X, CPU only: +(tensorflow)$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.7.1-cp35-none-any.whl ``` -Make sure you have downloaded the source code for TensorFlow, and then you can -then run an example TensorFlow program like: +With the Virtualenv environment activated, you can now +[test your installation](#test-the-tensorflow-installation). -```bash -(tensorflow)$ cd tensorflow/models/image/mnist -(tensorflow)$ python convolutional.py +When you are done using TensorFlow, deactivate the environment. -# When you are done using TensorFlow: -(tensorflow)$ deactivate # Deactivate the virtualenv +```bash +(tensorflow)$ deactivate $ # Your prompt should change back ``` -## Try your first TensorFlow program +To use TensorFlow later you will have to activate the Virtualenv environment again: + +```bash +$ source ~/tensorflow/bin/activate # If using bash. +$ source ~/tensorflow/bin/activate.csh # If using csh. +(tensorflow)$ # Your prompt should change. +# Run Python programs that use TensorFlow. +... +# When you are done using TensorFlow, deactivate the environment. +(tensorflow)$ deactivate +``` + +## Docker installation + +[Docker](http://docker.com/) is a system to build self contained versions of a +Linux operating system running on your machine. When you install and run +TensorFlow via Docker it completely isolates the installation from pre-existing +packages on your machine. + +We provide 4 Docker images: + +* `b.gcr.io/tensorflow/tensorflow`: TensorFlow CPU binary image. +* `b.gcr.io/tensorflow/tensorflow:latest-devel`: CPU Binary image plus source +code. +* `b.gcr.io/tensorflow/tensorflow:latest-gpu`: TensorFlow GPU binary image. +* `b.gcr.io/tensorflow/tensorflow:latest-devel-gpu`: GPU Binary image plus source +code. + +We also have tags with `latest` replaced by a released version (e.g., `0.7.1-gpu`). + +With Docker the installation is as follows: + +* Install Docker on your machine. +* Create a [Docker +group](http://docs.docker.com/engine/installation/ubuntulinux/#create-a-docker-group) +to allow launching containers without `sudo`. +* Launch a Docker container with the TensorFlow image. The image + gets downloaded automatically on first launch. + +See [installing Docker](http://docs.docker.com/engine/installation/) for instructions +on installing Docker on your machine. + +After Docker is installed, launch a Docker container with the TensorFlow binary +image as follows. + +```bash +$ docker run -it b.gcr.io/tensorflow/tensorflow +``` + +If you're using a container with GPU support, some additional flags must be +passed to expose the GPU device to the container. For the default config, we +include a +[script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/docker_run_gpu.sh) +in the repo with these flags, so the command-line would look like + +```bash +$ path/to/repo/tensorflow/tools/docker/docker_run_gpu.sh b.gcr.io/tensorflow/tensorflow:gpu +``` + +You can now [test your installation](#test-the-tensorflow-installation) within the Docker container. -### (Optional) Enable GPU Support +## Test the TensorFlow installation -If you installed the GPU-enabled TensorFlow pip binary, you must have the -correct versions of the CUDA SDK and CUDNN installed on your -system. Please see [the CUDA installation instructions](#install_cuda). +### (Optional, Linux) Enable GPU Support + +If you installed the GPU version of TensorFlow, you must also install the Cuda +Toolkit 7.0 and cuDNN v2. Please see [Cuda installation](#optional-install-cuda-gpus-on-linux). You also need to set the `LD_LIBRARY_PATH` and `CUDA_HOME` environment variables. Consider adding the commands below to your `~/.bash_profile`. These @@ -140,44 +242,86 @@ export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64" export CUDA_HOME=/usr/local/cuda ``` -### Run TensorFlow +### Run TensorFlow from the Command Line + +See [common problems](#common-problems) if an error happens. -Open a python terminal: +Open a terminal and type the following: ```bash $ python - +... >>> import tensorflow as tf >>> hello = tf.constant('Hello, TensorFlow!') >>> sess = tf.Session() ->>> print sess.run(hello) +>>> print(sess.run(hello)) Hello, TensorFlow! >>> a = tf.constant(10) >>> b = tf.constant(32) ->>> print sess.run(a+b) +>>> print(sess.run(a + b)) 42 >>> +``` + +### Run a TensorFlow demo model + +All TensorFlow packages, including the demo models, are installed in the Python library. +The exact location of the Python library depends on your system, but is usually one of: + +```bash +/usr/local/lib/python2.7/dist-packages/tensorflow +/usr/local/lib/python2.7/site-packages/tensorflow +``` + +You can find out the directory with the following command (make sure to use the Python you installed TensorFlow to, for example, use `python3` instead of `python` if you installed for Python 3): +```bash +$ python -c 'import os; import inspect; import tensorflow; print(os.path.dirname(inspect.getfile(tensorflow)))' ``` -## Installing from sources +The simple demo model for classifying handwritten digits from the MNIST dataset +is in the sub-directory `models/image/mnist/convolutional.py`. You can run it from the command +line as follows (make sure to use the Python you installed TensorFlow with): -### Clone the TensorFlow repository +```bash +# Using 'python -m' to find the program in the python search path: +$ python -m tensorflow.models.image.mnist.convolutional +Extracting data/train-images-idx3-ubyte.gz +Extracting data/train-labels-idx1-ubyte.gz +Extracting data/t10k-images-idx3-ubyte.gz +Extracting data/t10k-labels-idx1-ubyte.gz +...etc... + +# You can alternatively pass the path to the model program file to the python +# interpreter (make sure to use the python distribution you installed +# TensorFlow to, for example, .../python3.X/... for Python 3). +$ python /usr/local/lib/python2.7/dist-packages/tensorflow/models/image/mnist/convolutional.py +... +``` + +## Installing from sources + +When installing from source you will build a pip wheel that you then install +using pip. You'll need pip for that, so install it as described +[above](#pip-installation). + +### Clone the TensorFlow repository ```bash $ git clone --recurse-submodules https://github.com/tensorflow/tensorflow ``` `--recurse-submodules` is required to fetch the protobuf library that TensorFlow -depends on. +depends on. Note that these instructions will install the latest master branch +of tensorflow. If you want to install a specific branch (such as a release branch), +pass `-b ` to the `git clone` command. -### Installation for Linux - -#### Install Bazel +### Installation for Linux +#### Install Bazel Follow instructions [here](http://bazel.io/docs/install.html) to install the -dependencies for Bazel. Then download bazel version 0.1.1 using the +dependencies for bazel. Then download the latest stable bazel version using the [installer for your system](https://github.com/bazelbuild/bazel/releases) and run the installer as mentioned there: @@ -186,63 +330,97 @@ $ chmod +x PATH_TO_INSTALL.SH $ ./PATH_TO_INSTALL.SH --user ``` -Remember to replace `PATH_TO_INSTALL.SH` to point to the location where you +Remember to replace `PATH_TO_INSTALL.SH` with the location where you downloaded the installer. -Finally, follow the instructions in that script to place bazel into your binary -path. +Finally, follow the instructions in that script to place `bazel` into your +binary path. -#### Install other dependencies +#### Install other dependencies ```bash $ sudo apt-get install python-numpy swig python-dev ``` -#### Optional: Install CUDA (GPUs on Linux) +#### Configure the installation -In order to build or run TensorFlow with GPU support, both Cuda Toolkit 7.0 and -CUDNN 6.5 V2 from NVIDIA need to be installed. +Run the `configure` script at the root of the tree. The configure script +asks you for the path to your python interpreter and allows (optional) +configuration of the CUDA libraries (see [below](#configure-tensorflows-canonical-view-of-cuda-libraries)). -TensorFlow GPU support requires having a GPU card with NVidia Compute Capability >= 3.5. Supported cards include but are not limited to: +This step is used to locate the python and numpy header files. + +```bash +$ ./configure +Please specify the location of python. [Default is /usr/bin/python]: +``` + +#### Optional: Install CUDA (GPUs on Linux) + +In order to build or run TensorFlow with GPU support, both NVIDIA's Cuda Toolkit (>= 7.0) and +cuDNN (>= v2) need to be installed. + +TensorFlow GPU support requires having a GPU card with NVidia Compute Capability >= 3.0. +Supported cards include but are not limited to: * NVidia Titan * NVidia Titan X * NVidia K20 * NVidia K40 -##### Download and install Cuda Toolkit 7.0 +##### Download and install Cuda Toolkit -https://developer.nvidia.com/cuda-toolkit-70 +https://developer.nvidia.com/cuda-downloads Install the toolkit into e.g. `/usr/local/cuda` -##### Download and install CUDNN Toolkit 6.5 +##### Download and install cuDNN -https://developer.nvidia.com/rdp/cudnn-archive +https://developer.nvidia.com/cudnn -Uncompress and copy the cudnn files into the toolkit directory. Assuming the -toolkit is installed in `/usr/local/cuda`: +Uncompress and copy the cuDNN files into the toolkit directory. Assuming the +toolkit is installed in `/usr/local/cuda`, run the following commands (edited +to reflect the cuDNN version you downloaded): ``` bash tar xvzf cudnn-6.5-linux-x64-v2.tgz sudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda/include sudo cp cudnn-6.5-linux-x64-v2/libcudnn* /usr/local/cuda/lib64 +sudo chmod a+r /usr/local/cuda/lib64/libcudnn* ``` -##### Configure TensorFlow's canonical view of Cuda libraries -From the root of your source tree, run: +##### Configure TensorFlow's canonical view of Cuda libraries + +When running the `configure` script from the root of your source tree, select +the option `Y` when asked to build TensorFlow with GPU support. If you have +several versions of Cuda or cuDNN installed, you should definitely select +one explicitly instead of relying on the system default. You should see +prompts like the following: ``` bash $ ./configure -Do you wish to build TensorFlow with GPU support? [y/n] y +Please specify the location of python. [Default is /usr/bin/python]: +Do you wish to build TensorFlow with GPU support? [y/N] y GPU support will be enabled for TensorFlow -Please specify the location where CUDA 7.0 toolkit is installed. Refer to -README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda +Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave +empty to use system default]: 7.5 -Please specify the location where CUDNN 6.5 V2 library is installed. Refer to +Please specify the location where CUDA 7.5 toolkit is installed. Refer to README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda +Please specify the Cudnn version you want to use. [Leave empty to use system +default]: 4.0.4 + +Please specify the location where the cuDNN 4.0.4 library is installed. Refer to +README.md for more details. [default is: /usr/local/cuda]: /usr/local/cudnn-r4-rc/ + +Please specify a list of comma-separated Cuda compute capabilities you want to +build with. You can find the compute capability of your device at: +https://developer.nvidia.com/cuda-gpus. +Please note that each additional compute capability significantly increases your +build time and binary size. [Default is: \"3.5,5.2\"]: 3.5 + Setting up Cuda include Setting up Cuda lib64 Setting up Cuda bin @@ -252,9 +430,11 @@ Configuration finished This creates a canonical set of symbolic links to the Cuda libraries on your system. Every time you change the Cuda library paths you need to run this step again before -you invoke the bazel build command. +you invoke the bazel build command. For the Cudnn libraries, use '6.5' for R2, '7.0' +for R3, and '4.0.4' for R4-RC. -##### Build your target with GPU support. + +##### Build your target with GPU support From the root of your source tree, run: ```bash @@ -270,69 +450,65 @@ $ bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu Note that "--config=cuda" is needed to enable the GPU support. -##### Enabling Cuda 3.0. -TensorFlow officially supports Cuda devices with 3.5 and 5.2 compute -capabilities. In order to enable earlier Cuda devices such as Grid K520, you -need to target Cuda 3.0. This can be done through TensorFlow unofficial -settings with "configure". - -```bash -$ TF_UNOFFICIAL_SETTING=1 ./configure +##### Known issues -# Same as the official settings above +* Although it is possible to build both Cuda and non-Cuda configs under the same +source tree, we recommend to run `bazel clean` when switching between these two +configs in the same source tree. -WARNING: You are configuring unofficial settings in TensorFlow. Because some -external libraries are not backward compatible, these settings are largely -untested and unsupported. +* You have to run configure before running bazel build. Otherwise, the build +will fail with a clear error message. In the future, we might consider making +this more convenient by including the configure step in our build process. -Please specify a list of comma-separated Cuda compute capabilities you want to -build with. You can find the compute capability of your device at: -https://developer.nvidia.com/cuda-gpus. -Please note that each additional compute capability significantly increases -your build time and binary size. [Default is: "3.5,5.2"]: 3.0 +### Installation for Mac OS X -Setting up Cuda include -Setting up Cuda lib64 -Setting up Cuda bin -Setting up Cuda nvvm -Configuration finished -``` +We recommend using [homebrew](http://brew.sh) to install the bazel and SWIG +dependencies, and installing python dependencies using easy_install or pip. -##### Known issues +Of course you can also install Swig from source without using homebrew. In that +case, be sure to install its dependency [PCRE](http://www.pcre.org) and not PCRE2. -* Although it is possible to build both Cuda and non-Cuda configs under the same -source tree, we recommend to run "bazel clean" when switching between these two -configs in the same source tree. +#### Dependencies -* You have to run configure before running bazel build. Otherwise, the build -will fail with a clear error message. In the future, we might consider making -this more conveninent by including the configure step in our build process, -given necessary bazel new feature support. +Follow instructions [here](http://bazel.io/docs/install.html) to install the +dependencies for bazel. You can then use homebrew to install bazel and SWIG: -### Installation for Mac OS X +```bash +$ brew install bazel swig +``` -Mac needs the same set of dependencies as Linux, however their installing those -dependencies is different. Here is a set of useful links to help with installing -the dependencies on Mac OS X : +You can install the python dependencies using easy_install or pip. Using +easy_install, run -#### Bazel +```bash +$ sudo easy_install -U six +$ sudo easy_install -U numpy +$ sudo easy_install wheel +``` -Look for installation instructions for Mac OS X on -[this](http://bazel.io/docs/install.html) page. +We also recommend the [ipython](https://ipython.org) enhanced python shell, so +best install that too: -#### SWIG +```bash +$ sudo easy_install ipython +``` -[Mac OS X installation](http://www.swig.org/Doc3.0/Preface.html#Preface_osx_installation). +#### Configure the installation -Notes : You need to install -[PCRE](ftp://ftp.csx.cam.ac.uk/pub/software/programming/pcre/) and *NOT* PCRE2. +Run the `configure` script at the root of the tree. The configure script +asks you for the path to your python interpreter. -#### Numpy +This step is used to locate the python and numpy header files. -Follow installation instructions [here](http://docs.scipy.org/doc/numpy/user/install.html). +```bash +$ ./configure +Please specify the location of python. [Default is /usr/bin/python]: +Do you wish to build TensorFlow with GPU support? [y/N] +``` +### Create the pip package and install -### Create the pip package and install +When building from source, you will still build a pip package and install that. ```bash $ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package @@ -343,20 +519,43 @@ $ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_pack $ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg # The name of the .whl file will depend on your platform. -$ pip install /tmp/tensorflow_pkg/tensorflow-0.5.0-cp27-none-linux_x86_64.whl +$ pip install /tmp/tensorflow_pkg/tensorflow-0.7.1-py2-none-linux_x86_64.whl ``` -## Train your first TensorFlow neural net model +## Setting up TensorFlow for Development + +If you're working on TensorFlow itself, it is useful to be able to test your +changes in an interactive python shell without having to reinstall TensorFlow. + +To set up TensorFlow such that all files are linked (instead of copied) from the +system directories, run the following commands inside the TensorFlow root +directory: + +```bash +bazel build -c opt //tensorflow/tools/pip_package:build_pip_package +mkdir _python_build +cd _python_build +ln -s ../bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/* . +ln -s ../tensorflow/tools/pip_package/* . +python setup.py develop +``` + +Note that this setup still requires you to rebuild the +`//tensorflow/tools/pip_package:build_pip_package` target every time you change +a C++ file; add, delete, or move any python file; or if you change bazel build +rules. + +## Train your first TensorFlow neural net model Starting from the root of your source tree, run: ```python $ cd tensorflow/models/image/mnist $ python convolutional.py -Succesfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. -Succesfully downloaded train-labels-idx1-ubyte.gz 28881 bytes. -Succesfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes. -Succesfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes. +Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. +Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes. +Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes. +Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes. Extracting data/train-images-idx3-ubyte.gz Extracting data/train-labels-idx1-ubyte.gz Extracting data/t10k-images-idx3-ubyte.gz @@ -374,9 +573,9 @@ Validation error: 7.0% ... ``` -## Common Problems +## Common Problems -### GPU-related issues +### GPU-related issues If you encounter the following when trying to run a TensorFlow program: @@ -384,11 +583,26 @@ If you encounter the following when trying to run a TensorFlow program: ImportError: libcudart.so.7.0: cannot open shared object file: No such file or directory ``` -Make sure you followed the the GPU installation [instructions](#install_cuda). +Make sure you followed the GPU installation [instructions](#optional-install-cuda-gpus-on-linux). +If you built from source, and you left the Cuda or cuDNN version empty, try specifying them +explicitly. -### Pip installation issues +### Pip installation issues -#### Can't find setup.py +#### Cannot import name 'descriptor' + +```python +ImportError: Traceback (most recent call last): + File "/usr/local/lib/python3.4/dist-packages/tensorflow/core/framework/graph_pb2.py", line 6, in + from google.protobuf import descriptor as _descriptor +ImportError: cannot import name 'descriptor' +``` + +If you the above error when upgrading to a newer version of TensorFlow, try +uninstalling both TensorFlow and protobuf (if installed) and re-installing +TensorFlow (which will also install the correct protobuf dependency). + +#### Can't find setup.py If, during `pip install`, you encounter an error like: @@ -397,15 +611,15 @@ If, during `pip install`, you encounter an error like: IOError: [Errno 2] No such file or directory: '/tmp/pip-o6Tpui-build/setup.py' ``` -Solution: upgrade your version of `pip`: +Solution: upgrade your version of pip: ```bash pip install --upgrade pip ``` -This may require `sudo`, depending on how `pip` is installed. +This may require `sudo`, depending on how pip is installed. -#### SSLError: SSL_VERIFY_FAILED +#### SSLError: SSL_VERIFY_FAILED If, during pip install from a URL, you encounter an error like: @@ -416,7 +630,7 @@ SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed Solution: Download the wheel manually via curl or wget, and pip install locally. -### On Linux +### Linux issues If you encounter: @@ -429,39 +643,48 @@ SyntaxError: invalid syntax Solution: make sure you are using Python 2.7. -### On MacOSX - +### Mac OS X: ImportError: No module named copyreg -If you encounter: +On Mac OS X, you may encounter the following when importing tensorflow. ```python -import six.moves.copyreg as copyreg - +>>> import tensorflow as tf +... ImportError: No module named copyreg ``` -Solution: TensorFlow depends on protobuf, which requires `six-1.10.0`. Apple's -default python environment has `six-1.4.1` and may be difficult to upgrade. -There are several ways to fix this: +Solution: TensorFlow depends on protobuf, which requires the Python package +`six-1.10.0`. Apple's default Python installation only provides `six-1.4.1`. -1. Upgrade the system-wide copy of `six`: +You can resolve the issue in one of the following ways: - ```bash - sudo easy_install -U six - ``` +* Upgrade the Python installation with the current version of `six`: -2. Install a separate copy of python via homebrew: +```bash +$ sudo easy_install -U six +``` - ```bash - brew install python - ``` +* Install TensorFlow with a separate Python library: -3. Build or use TensorFlow - [within `virtualenv`](#virtualenv_install). + * Using [Virtualenv](#virtualenv-installation). + * Using [Docker](#docker-installation). +* Install a separate copy of Python via [Homebrew](http://brew.sh/) or +[MacPorts](https://www.macports.org/) and re-install TensorFlow in that +copy of Python. +### Mac OS X: OSError: [Errno 1] Operation not permitted: -If you encounter: +On El Capitan, "six" is a special package that can't be modified, and this +error is reported when "pip install" tried to modify this package. To fix use +"ignore_installed" flag, ie + +sudo pip install --ignore-installed six https://storage.googleapis.com/.... + + +### Mac OS X: TypeError: `__init__()` got an unexpected keyword argument 'syntax' + +On Mac OS X, you may encounter the following when importing tensorflow. ``` >>> import tensorflow as tf @@ -482,5 +705,5 @@ The best current solution is to make sure older versions of protobuf are not installed, such as: ```bash -brew reinstall --devel protobuf -``` \ No newline at end of file +$ pip install --upgrade protobuf +``` From 23530cf3f96fae4dec5eb90518415d76df0ffa85 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Mon, 7 Mar 2016 17:57:15 +0800 Subject: [PATCH 004/166] Added input_data to notebook folder --- notebooks/3 - Neural Networks/input_data.py | 144 ++++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 notebooks/3 - Neural Networks/input_data.py diff --git a/notebooks/3 - Neural Networks/input_data.py b/notebooks/3 - Neural Networks/input_data.py new file mode 100644 index 00000000..6fad792b --- /dev/null +++ b/notebooks/3 - Neural Networks/input_data.py @@ -0,0 +1,144 @@ +"""Functions for downloading and reading MNIST data.""" +from __future__ import print_function +import gzip +import os +import urllib +import numpy +SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' +def maybe_download(filename, work_directory): + """Download the data from Yann's website, unless it's already here.""" + if not os.path.exists(work_directory): + os.mkdir(work_directory) + filepath = os.path.join(work_directory, filename) + if not os.path.exists(filepath): + filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath) + statinfo = os.stat(filepath) + print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.') + return filepath +def _read32(bytestream): + dt = numpy.dtype(numpy.uint32).newbyteorder('>') + return numpy.frombuffer(bytestream.read(4), dtype=dt) +def extract_images(filename): + """Extract the images into a 4D uint8 numpy array [index, y, x, depth].""" + print('Extracting', filename) + with gzip.open(filename) as bytestream: + magic = _read32(bytestream) + if magic != 2051: + raise ValueError( + 'Invalid magic number %d in MNIST image file: %s' % + (magic, filename)) + num_images = _read32(bytestream) + rows = _read32(bytestream) + cols = _read32(bytestream) + buf = bytestream.read(rows * cols * num_images) + data = numpy.frombuffer(buf, dtype=numpy.uint8) + data = data.reshape(num_images, rows, cols, 1) + return data +def dense_to_one_hot(labels_dense, num_classes=10): + """Convert class labels from scalars to one-hot vectors.""" + num_labels = labels_dense.shape[0] + index_offset = numpy.arange(num_labels) * num_classes + labels_one_hot = numpy.zeros((num_labels, num_classes)) + labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 + return labels_one_hot +def extract_labels(filename, one_hot=False): + """Extract the labels into a 1D uint8 numpy array [index].""" + print('Extracting', filename) + with gzip.open(filename) as bytestream: + magic = _read32(bytestream) + if magic != 2049: + raise ValueError( + 'Invalid magic number %d in MNIST label file: %s' % + (magic, filename)) + num_items = _read32(bytestream) + buf = bytestream.read(num_items) + labels = numpy.frombuffer(buf, dtype=numpy.uint8) + if one_hot: + return dense_to_one_hot(labels) + return labels +class DataSet(object): + def __init__(self, images, labels, fake_data=False): + if fake_data: + self._num_examples = 10000 + else: + assert images.shape[0] == labels.shape[0], ( + "images.shape: %s labels.shape: %s" % (images.shape, + labels.shape)) + self._num_examples = images.shape[0] + # Convert shape from [num examples, rows, columns, depth] + # to [num examples, rows*columns] (assuming depth == 1) + assert images.shape[3] == 1 + images = images.reshape(images.shape[0], + images.shape[1] * images.shape[2]) + # Convert from [0, 255] -> [0.0, 1.0]. + images = images.astype(numpy.float32) + images = numpy.multiply(images, 1.0 / 255.0) + self._images = images + self._labels = labels + self._epochs_completed = 0 + self._index_in_epoch = 0 + @property + def images(self): + return self._images + @property + def labels(self): + return self._labels + @property + def num_examples(self): + return self._num_examples + @property + def epochs_completed(self): + return self._epochs_completed + def next_batch(self, batch_size, fake_data=False): + """Return the next `batch_size` examples from this data set.""" + if fake_data: + fake_image = [1.0 for _ in xrange(784)] + fake_label = 0 + return [fake_image for _ in xrange(batch_size)], [ + fake_label for _ in xrange(batch_size)] + start = self._index_in_epoch + self._index_in_epoch += batch_size + if self._index_in_epoch > self._num_examples: + # Finished epoch + self._epochs_completed += 1 + # Shuffle the data + perm = numpy.arange(self._num_examples) + numpy.random.shuffle(perm) + self._images = self._images[perm] + self._labels = self._labels[perm] + # Start next epoch + start = 0 + self._index_in_epoch = batch_size + assert batch_size <= self._num_examples + end = self._index_in_epoch + return self._images[start:end], self._labels[start:end] +def read_data_sets(train_dir, fake_data=False, one_hot=False): + class DataSets(object): + pass + data_sets = DataSets() + if fake_data: + data_sets.train = DataSet([], [], fake_data=True) + data_sets.validation = DataSet([], [], fake_data=True) + data_sets.test = DataSet([], [], fake_data=True) + return data_sets + TRAIN_IMAGES = 'train-images-idx3-ubyte.gz' + TRAIN_LABELS = 'train-labels-idx1-ubyte.gz' + TEST_IMAGES = 't10k-images-idx3-ubyte.gz' + TEST_LABELS = 't10k-labels-idx1-ubyte.gz' + VALIDATION_SIZE = 5000 + local_file = maybe_download(TRAIN_IMAGES, train_dir) + train_images = extract_images(local_file) + local_file = maybe_download(TRAIN_LABELS, train_dir) + train_labels = extract_labels(local_file, one_hot=one_hot) + local_file = maybe_download(TEST_IMAGES, train_dir) + test_images = extract_images(local_file) + local_file = maybe_download(TEST_LABELS, train_dir) + test_labels = extract_labels(local_file, one_hot=one_hot) + validation_images = train_images[:VALIDATION_SIZE] + validation_labels = train_labels[:VALIDATION_SIZE] + train_images = train_images[VALIDATION_SIZE:] + train_labels = train_labels[VALIDATION_SIZE:] + data_sets.train = DataSet(train_images, train_labels) + data_sets.validation = DataSet(validation_images, validation_labels) + data_sets.test = DataSet(test_images, test_labels) + return data_sets From e37cd5557442837c10c027873144225fc502b1b0 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Thu, 17 Mar 2016 17:36:48 +0800 Subject: [PATCH 005/166] Create input_data.py --- notebooks/2 - Basic Classifiers/input_data.py | 144 ++++++++++++++++++ 1 file changed, 144 insertions(+) create mode 100644 notebooks/2 - Basic Classifiers/input_data.py diff --git a/notebooks/2 - Basic Classifiers/input_data.py b/notebooks/2 - Basic Classifiers/input_data.py new file mode 100644 index 00000000..6fad792b --- /dev/null +++ b/notebooks/2 - Basic Classifiers/input_data.py @@ -0,0 +1,144 @@ +"""Functions for downloading and reading MNIST data.""" +from __future__ import print_function +import gzip +import os +import urllib +import numpy +SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' +def maybe_download(filename, work_directory): + """Download the data from Yann's website, unless it's already here.""" + if not os.path.exists(work_directory): + os.mkdir(work_directory) + filepath = os.path.join(work_directory, filename) + if not os.path.exists(filepath): + filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath) + statinfo = os.stat(filepath) + print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.') + return filepath +def _read32(bytestream): + dt = numpy.dtype(numpy.uint32).newbyteorder('>') + return numpy.frombuffer(bytestream.read(4), dtype=dt) +def extract_images(filename): + """Extract the images into a 4D uint8 numpy array [index, y, x, depth].""" + print('Extracting', filename) + with gzip.open(filename) as bytestream: + magic = _read32(bytestream) + if magic != 2051: + raise ValueError( + 'Invalid magic number %d in MNIST image file: %s' % + (magic, filename)) + num_images = _read32(bytestream) + rows = _read32(bytestream) + cols = _read32(bytestream) + buf = bytestream.read(rows * cols * num_images) + data = numpy.frombuffer(buf, dtype=numpy.uint8) + data = data.reshape(num_images, rows, cols, 1) + return data +def dense_to_one_hot(labels_dense, num_classes=10): + """Convert class labels from scalars to one-hot vectors.""" + num_labels = labels_dense.shape[0] + index_offset = numpy.arange(num_labels) * num_classes + labels_one_hot = numpy.zeros((num_labels, num_classes)) + labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 + return labels_one_hot +def extract_labels(filename, one_hot=False): + """Extract the labels into a 1D uint8 numpy array [index].""" + print('Extracting', filename) + with gzip.open(filename) as bytestream: + magic = _read32(bytestream) + if magic != 2049: + raise ValueError( + 'Invalid magic number %d in MNIST label file: %s' % + (magic, filename)) + num_items = _read32(bytestream) + buf = bytestream.read(num_items) + labels = numpy.frombuffer(buf, dtype=numpy.uint8) + if one_hot: + return dense_to_one_hot(labels) + return labels +class DataSet(object): + def __init__(self, images, labels, fake_data=False): + if fake_data: + self._num_examples = 10000 + else: + assert images.shape[0] == labels.shape[0], ( + "images.shape: %s labels.shape: %s" % (images.shape, + labels.shape)) + self._num_examples = images.shape[0] + # Convert shape from [num examples, rows, columns, depth] + # to [num examples, rows*columns] (assuming depth == 1) + assert images.shape[3] == 1 + images = images.reshape(images.shape[0], + images.shape[1] * images.shape[2]) + # Convert from [0, 255] -> [0.0, 1.0]. + images = images.astype(numpy.float32) + images = numpy.multiply(images, 1.0 / 255.0) + self._images = images + self._labels = labels + self._epochs_completed = 0 + self._index_in_epoch = 0 + @property + def images(self): + return self._images + @property + def labels(self): + return self._labels + @property + def num_examples(self): + return self._num_examples + @property + def epochs_completed(self): + return self._epochs_completed + def next_batch(self, batch_size, fake_data=False): + """Return the next `batch_size` examples from this data set.""" + if fake_data: + fake_image = [1.0 for _ in xrange(784)] + fake_label = 0 + return [fake_image for _ in xrange(batch_size)], [ + fake_label for _ in xrange(batch_size)] + start = self._index_in_epoch + self._index_in_epoch += batch_size + if self._index_in_epoch > self._num_examples: + # Finished epoch + self._epochs_completed += 1 + # Shuffle the data + perm = numpy.arange(self._num_examples) + numpy.random.shuffle(perm) + self._images = self._images[perm] + self._labels = self._labels[perm] + # Start next epoch + start = 0 + self._index_in_epoch = batch_size + assert batch_size <= self._num_examples + end = self._index_in_epoch + return self._images[start:end], self._labels[start:end] +def read_data_sets(train_dir, fake_data=False, one_hot=False): + class DataSets(object): + pass + data_sets = DataSets() + if fake_data: + data_sets.train = DataSet([], [], fake_data=True) + data_sets.validation = DataSet([], [], fake_data=True) + data_sets.test = DataSet([], [], fake_data=True) + return data_sets + TRAIN_IMAGES = 'train-images-idx3-ubyte.gz' + TRAIN_LABELS = 'train-labels-idx1-ubyte.gz' + TEST_IMAGES = 't10k-images-idx3-ubyte.gz' + TEST_LABELS = 't10k-labels-idx1-ubyte.gz' + VALIDATION_SIZE = 5000 + local_file = maybe_download(TRAIN_IMAGES, train_dir) + train_images = extract_images(local_file) + local_file = maybe_download(TRAIN_LABELS, train_dir) + train_labels = extract_labels(local_file, one_hot=one_hot) + local_file = maybe_download(TEST_IMAGES, train_dir) + test_images = extract_images(local_file) + local_file = maybe_download(TEST_LABELS, train_dir) + test_labels = extract_labels(local_file, one_hot=one_hot) + validation_images = train_images[:VALIDATION_SIZE] + validation_labels = train_labels[:VALIDATION_SIZE] + train_images = train_images[VALIDATION_SIZE:] + train_labels = train_labels[VALIDATION_SIZE:] + data_sets.train = DataSet(train_images, train_labels) + data_sets.validation = DataSet(validation_images, validation_labels) + data_sets.test = DataSet(test_images, test_labels) + return data_sets From 6ae5dbd0162dcd5936a7cb61c7858ec7f8b66eed Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Fri, 1 Apr 2016 02:07:01 +0800 Subject: [PATCH 006/166] Update README.md --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 366758d9..04364d37 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,9 @@ # TensorFlow Examples Code examples for some popular machine learning algorithms, using TensorFlow library. This tutorial is designed to easily dive into TensorFlow, through examples. It includes both notebook and code with explanations. +### Notice: +Here is a library that makes TensorFlow more convenients to use: [TFLearn](https://github.com/tflearn/tflearn). You can have a look, there are many other examples and pre-built operations. + ## Tutorial index #### 1 - Introduction From 0a3b981dc23c9d555de91de810f53c3ed3938687 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Fri, 1 Apr 2016 10:55:00 +0800 Subject: [PATCH 007/166] fix typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 04364d37..44448cf9 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ Code examples for some popular machine learning algorithms, using TensorFlow library. This tutorial is designed to easily dive into TensorFlow, through examples. It includes both notebook and code with explanations. ### Notice: -Here is a library that makes TensorFlow more convenients to use: [TFLearn](https://github.com/tflearn/tflearn). You can have a look, there are many other examples and pre-built operations. +Here is a library that makes TensorFlow more convenient to use: [TFLearn](https://github.com/tflearn/tflearn). You can have a look, there are many other examples and pre-built operations. ## Tutorial index From 5848399a441740b3ab65f8628c5ecb3789f53103 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Fri, 8 Apr 2016 14:34:45 +0800 Subject: [PATCH 008/166] Update README.md Added TFLearn examples --- README.md | 35 ++++++++++++++++++++++++++++++++--- 1 file changed, 32 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 44448cf9..b2079e6d 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ Code examples for some popular machine learning algorithms, using TensorFlow library. This tutorial is designed to easily dive into TensorFlow, through examples. It includes both notebook and code with explanations. ### Notice: -Here is a library that makes TensorFlow more convenient to use: [TFLearn](https://github.com/tflearn/tflearn). You can have a look, there are many other examples and pre-built operations. +[TFLearn](https://github.com/tflearn/tflearn) is a library that provides a simplified interface for TensorFlow. It was designed to speed-up experimentations. You can have a look, there are many other [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations](http://tflearn.org/doc_index/#api). ## Tutorial index @@ -29,17 +29,46 @@ Here is a library that makes TensorFlow more convenient to use: [TFLearn](https: - Graph Visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5%20-%20User%20Interface/graph_visualization.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5%20-%20User%20Interface/graph_visualization.py)) - Loss Visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5%20-%20User%20Interface/loss_visualization.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5%20-%20User%20Interface/loss_visualization.py)) + +## More Examples +These examples are coming from [TFLearn](http://tflearn.org) examples. They require tflearn to be installed in order to work. TFLearn is a simplified interface for TensorFlow that introduce pre-built layers, ops, training functions... + +#### Basics +- [Linear Regression](https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py). Implement a linear regression using TFLearn. +- [Logical Operators](https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py). Implement logical operators with TFLearn (also includes a usage of 'merge'). +- [Weights Persistence](https://github.com/tflearn/tflearn/blob/master/examples/basics/weights_persistence.py). Save and Restore a model. +- [Fine-Tuning](https://github.com/tflearn/tflearn/blob/master/examples/basics/finetuning.py). Fine-Tune a pre-trained model on a new task. +- [Using HDF5](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_hdf5.py). Use HDF5 to handle large datasets. +- [Using DASK](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py). Use DASK to handle large datasets. + +#### Computer Vision +- [Multi-layer perceptron](https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py). A multi-layer perceptron implementation for MNIST classification task. +- [Convolutional Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py). A Convolutional neural network implementation for classifying MNIST dataset. +- [Convolutional Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py). A Convolutional neural network implementation for classifying CIFAR-10 dataset. +- [Network in Network](https://github.com/tflearn/tflearn/blob/master/examples/images/network_in_network.py). 'Network in Network' implementation for classifying CIFAR-10 dataset. +- [Alexnet](https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py). Apply Alexnet to Oxford Flowers 17 classification task. +- [VGGNet](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py). Apply VGG Network to Oxford Flowers 17 classification task. +- [RNN Pixels](https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py). Use RNN (over sequence of pixels) to classify images. +- [Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A residual network with shallow bottlenecks applied to MNIST classification task. +- [Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network with deep bottlenecks applied to CIFAR-10 classification task. +- [Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits. + +#### Natural Language Processing +- [Reccurent Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task. +- [Bi-Directional LSTM](https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py). Apply a bi-directional LSTM to IMDB sentiment dataset classification task. +- [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network. +- [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network. + ## Dependencies ``` tensorflow numpy matplotlib cuda (to run examples on GPU) +tflearn (if using tflearn examples) ``` For more details about TensorFlow installation, you can check [Setup_TensorFlow.md](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/Setup_TensorFlow.md) ## Dataset Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, with 60,000 examples for training and 10,000 examples for testing. (Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)) - -_Other tutorials are coming soon...._ From ae86a87d6588401e613beafa8b466f44053523c8 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Mon, 11 Apr 2016 14:24:47 +0800 Subject: [PATCH 009/166] Update README.md --- README.md | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/README.md b/README.md index b2079e6d..8b0f6ad0 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,6 @@ # TensorFlow Examples Code examples for some popular machine learning algorithms, using TensorFlow library. This tutorial is designed to easily dive into TensorFlow, through examples. It includes both notebook and code with explanations. -### Notice: -[TFLearn](https://github.com/tflearn/tflearn) is a library that provides a simplified interface for TensorFlow. It was designed to speed-up experimentations. You can have a look, there are many other [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations](http://tflearn.org/doc_index/#api). - ## Tutorial index #### 1 - Introduction @@ -31,7 +28,7 @@ Code examples for some popular machine learning algorithms, using TensorFlow lib ## More Examples -These examples are coming from [TFLearn](http://tflearn.org) examples. They require tflearn to be installed in order to work. TFLearn is a simplified interface for TensorFlow that introduce pre-built layers, ops, training functions... +The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). #### Basics - [Linear Regression](https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py). Implement a linear regression using TFLearn. From ddfafb212b84a22bb3c3f7fb48e39813061be30f Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Thu, 14 Apr 2016 21:33:46 +0800 Subject: [PATCH 010/166] add autoencoder example --- examples/3 - Neural Networks/autoencoder.py | 134 ++++++++++++++++++++ 1 file changed, 134 insertions(+) create mode 100644 examples/3 - Neural Networks/autoencoder.py diff --git a/examples/3 - Neural Networks/autoencoder.py b/examples/3 - Neural Networks/autoencoder.py new file mode 100644 index 00000000..4c28fced --- /dev/null +++ b/examples/3 - Neural Networks/autoencoder.py @@ -0,0 +1,134 @@ +# -*- coding: utf-8 -*- + +""" Auto Encoder Example. +Using an auto encoder on MNIST handwritten digits. +References: + Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based + learning applied to document recognition." Proceedings of the IEEE, + 86(11):2278-2324, November 1998. +Links: + [MNIST Dataset] http://yann.lecun.com/exdb/mnist/ +""" +from __future__ import division, print_function, absolute_import + +import tensorflow as tf +import numpy as np +import matplotlib.pyplot as plt + +# Import MINST data +import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.01 +training_epochs = 20 +batch_size = 256 +display_step = 1 +examples_to_show = 10 + +# Network Parameters +n_hidden_1 = 256 # 1st layer num features +n_hidden_2 = 128 # 2nd layer num features +n_input = 784 # MNIST data input (img shape: 28*28) + +# tf Graph input (only pictures) +X = tf.placeholder("float", [None, n_input]) + +weights = { + 'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), + 'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), + 'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])), + 'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])), +} +biases = { + 'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])), + 'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])), + 'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])), + 'decoder_b2': tf.Variable(tf.random_normal([n_input])), +} + +# Building the encoder +def encoder(x): + # Encoder Hidden layer with relu activation #1 + layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1'])) + # Decoder Hidden layer with relu activation #2 + layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2'])) + return layer_2 + +# Building the decoder +def decoder(x): + # Encoder Hidden layer with relu activation #1 + layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), biases['decoder_b1'])) + # Decoder Hidden layer with relu activation #2 + layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2'])) + return layer_2 + +# Construct model +encoder_op = encoder(X) +decoder_op = decoder(encoder_op) + +y_pred = decoder_op +y_true = X +# Define loss and optimizer, minimize the squared error +cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) +optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost) + +# Initializing the variables +init = tf.initialize_all_variables() + +# Launch the graph +with tf.Session() as sess: + sess.run(init) + total_batch = int(mnist.train.num_examples/batch_size) + # Training cycle + for epoch in range(training_epochs): + # Loop over all batches + for i in range(total_batch): + batch_xs, batch_ys = mnist.train.next_batch(batch_size) + # Fit training using batch data + _, cost_value = sess.run([optimizer, cost], feed_dict={X: batch_xs}) + # Display logs per epoch step + if epoch % display_step == 0: + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(cost_value)) + + print("Optimization Finished!") + + #Applying encode and decode over test set + encode_decode = sess.run(y_pred, feed_dict={X: mnist.test.images[:examples_to_show]}) + # Compare original images with their reconstructions + f, a = plt.subplots(2, 10, figsize=(10, 2)) + for i in range(examples_to_show): + a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28))) + a[1][i].imshow(np.reshape(encode_decode[i], (28, 28))) + f.show() + plt.draw() + plt.waitforbuttonpress() + +# # Regression, with mean square error +# net = tflearn.regression(decoder, optimizer='adam', learning_rate=0.001, +# loss='mean_square', metric=None) +# +# # Training the auto encoder +# model = tflearn.DNN(net, tensorboard_verbose=0) +# model.fit(X, X, n_epoch=10, validation_set=(testX, testX), +# run_id="auto_encoder", batch_size=256) +# +# # Encoding X[0] for test +# print("\nTest encoding of X[0]:") +# # New model, re-using the same session, for weights sharing +# encoding_model = tflearn.DNN(encoder, session=model.session) +# print(encoding_model.predict([X[0]])) +# +# # Testing the image reconstruction on new data (test set) +# print("\nVisualizing results after being encoded and decoded:") +# testX = tflearn.data_utils.shuffle(testX)[0] +# # Applying encode and decode over test set +# encode_decode = model.predict(testX) +# # Compare original images with their reconstructions +# f, a = plt.subplots(2, 10, figsize=(10, 2)) +# for i in range(10): +# a[0][i].imshow(np.reshape(testX[i], (28, 28))) +# a[1][i].imshow(np.reshape(encode_decode[i], (28, 28))) +# f.show() +# plt.draw() +# plt.waitforbuttonpress() From f6ffc1967ff9aca7582d93a1942dc54c2e6707ad Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Fri, 15 Apr 2016 00:27:28 +0800 Subject: [PATCH 011/166] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 8b0f6ad0..6784530d 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,7 @@ Code examples for some popular machine learning algorithms, using TensorFlow lib - AlexNet ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/alexnet.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/alexnet.py)) - Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/reccurent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/recurrent_network.py)) - Bidirectional Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/bidirectional_rnn.py)) +- AutoEncoder ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/autoencoder.py)) #### 4 - Multi GPU - Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4%20-%20Multi%20GPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4%20-%20Multi%20GPU/multigpu_basics.py)) From 343619ccc5fbea60f255a414471b792cf3022041 Mon Sep 17 00:00:00 2001 From: SOLARIS Date: Tue, 24 May 2016 19:08:35 +0900 Subject: [PATCH 012/166] Fixed typo --- examples/3 - Neural Networks/autoencoder.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/3 - Neural Networks/autoencoder.py b/examples/3 - Neural Networks/autoencoder.py index 4c28fced..82313902 100644 --- a/examples/3 - Neural Networks/autoencoder.py +++ b/examples/3 - Neural Networks/autoencoder.py @@ -49,17 +49,17 @@ # Building the encoder def encoder(x): - # Encoder Hidden layer with relu activation #1 + # Encoder Hidden layer with sigmoid activation #1 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1'])) - # Decoder Hidden layer with relu activation #2 + # Decoder Hidden layer with sigmoid activation #2 layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2'])) return layer_2 # Building the decoder def decoder(x): - # Encoder Hidden layer with relu activation #1 + # Encoder Hidden layer with sigmoid activation #1 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), biases['decoder_b1'])) - # Decoder Hidden layer with relu activation #2 + # Decoder Hidden layer with sigmoid activation #2 layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2'])) return layer_2 From 998ba46963fd683d563142b60e8be6bea3d83310 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sun, 29 May 2016 21:30:26 +0800 Subject: [PATCH 013/166] Examples major update --- README.md | 35 +- .../basic_operations.py | 0 .../helloworld.py | 3 +- examples/2 - Basic Classifiers/input_data.py | 144 ------- .../linear_regression.py | 47 ++- .../logistic_regression.py | 35 +- .../nearest_neighbor.py | 17 +- examples/3 - Neural Networks/alexnet.py | 133 ------ .../3 - Neural Networks/bidirectional_rnn.py | 163 -------- .../convolutional_network.py | 112 ----- examples/3 - Neural Networks/input_data.py | 144 ------- .../autoencoder.py | 60 +-- .../3_NeuralNetworks/bidirectional_rnn.py | 116 ++++++ .../3_NeuralNetworks/convolutional_network.py | 132 ++++++ .../multilayer_perceptron.py | 41 +- .../recurrent_network.py | 79 ++-- examples/4_Utils/save_restore_model.py | 137 +++++++ examples/4_Utils/tensorboard_advanced.py | 1 + examples/4_Utils/tensorboard_basic.py | 93 +++++ .../5 - User Interface/graph_visualization.py | 78 ---- .../5 - User Interface/loss_visualization.py | 86 ---- .../multigpu_basics.py | 0 .../basic_operations.ipynb | 4 +- .../helloworld.ipynb | 4 +- notebooks/2 - Basic Classifiers/input_data.py | 144 ------- .../linear_regression.ipynb | 253 ------------ .../2_BasicModels/linear_regression.ipynb | 202 +++++++++ .../logistic_regression.ipynb | 166 +++----- .../nearest_neighbor.ipynb | 98 +---- notebooks/3 - Neural Networks/alexnet.ipynb | 348 ---------------- .../bidirectional_rnn.ipynb | 350 ---------------- .../convolutional_network.ipynb | 324 --------------- notebooks/3 - Neural Networks/input_data.py | 144 ------- .../reccurent_network.ipynb | 299 -------------- notebooks/3_Neural Networks/autoencoder.ipynb | 226 ++++++++++ .../3_Neural Networks/bidirectional_rnn.ipynb | 293 +++++++++++++ .../convolutional_network.ipynb | 387 ++++++++++++++++++ .../multilayer_perceptron.ipynb | 181 +++----- .../3_Neural Networks/recurrent_network.ipynb | 289 +++++++++++++ notebooks/4_Utils/save_restore_model.ipynb | 271 ++++++++++++ notebooks/4_Utils/tensorboard_basic.ipynb | 212 ++++++++++ .../graph_visualization.ipynb | 226 ---------- .../loss_visualization.ipynb | 196 --------- .../multigpu_basics.ipynb | 4 +- 44 files changed, 2662 insertions(+), 3615 deletions(-) rename examples/{1 - Introduction => 1_Introduction}/basic_operations.py (100%) rename examples/{1 - Introduction => 1_Introduction}/helloworld.py (92%) delete mode 100644 examples/2 - Basic Classifiers/input_data.py rename examples/{2 - Basic Classifiers => 2_BasicModels}/linear_regression.py (62%) rename examples/{2 - Basic Classifiers => 2_BasicModels}/logistic_regression.py (59%) rename examples/{2 - Basic Classifiers => 2_BasicModels}/nearest_neighbor.py (80%) delete mode 100644 examples/3 - Neural Networks/alexnet.py delete mode 100644 examples/3 - Neural Networks/bidirectional_rnn.py delete mode 100644 examples/3 - Neural Networks/convolutional_network.py delete mode 100644 examples/3 - Neural Networks/input_data.py rename examples/{3 - Neural Networks => 3_NeuralNetworks}/autoencoder.py (66%) create mode 100644 examples/3_NeuralNetworks/bidirectional_rnn.py create mode 100644 examples/3_NeuralNetworks/convolutional_network.py rename examples/{3 - Neural Networks => 3_NeuralNetworks}/multilayer_perceptron.py (63%) rename examples/{3 - Neural Networks => 3_NeuralNetworks}/recurrent_network.py (52%) create mode 100644 examples/4_Utils/save_restore_model.py create mode 100644 examples/4_Utils/tensorboard_advanced.py create mode 100644 examples/4_Utils/tensorboard_basic.py delete mode 100644 examples/5 - User Interface/graph_visualization.py delete mode 100644 examples/5 - User Interface/loss_visualization.py rename examples/{4 - Multi GPU => 5_MultiGPU}/multigpu_basics.py (100%) rename notebooks/{1 - Introduction => 1_Introduction}/basic_operations.ipynb (99%) rename notebooks/{1 - Introduction => 1_Introduction}/helloworld.ipynb (98%) delete mode 100644 notebooks/2 - Basic Classifiers/input_data.py delete mode 100644 notebooks/2 - Basic Classifiers/linear_regression.ipynb create mode 100644 notebooks/2_BasicModels/linear_regression.ipynb rename notebooks/{2 - Basic Classifiers => 2_BasicModels}/logistic_regression.ipynb (50%) rename notebooks/{2 - Basic Classifiers => 2_BasicModels}/nearest_neighbor.ipynb (89%) delete mode 100644 notebooks/3 - Neural Networks/alexnet.ipynb delete mode 100644 notebooks/3 - Neural Networks/bidirectional_rnn.ipynb delete mode 100644 notebooks/3 - Neural Networks/convolutional_network.ipynb delete mode 100644 notebooks/3 - Neural Networks/input_data.py delete mode 100644 notebooks/3 - Neural Networks/reccurent_network.ipynb create mode 100644 notebooks/3_Neural Networks/autoencoder.ipynb create mode 100644 notebooks/3_Neural Networks/bidirectional_rnn.ipynb create mode 100644 notebooks/3_Neural Networks/convolutional_network.ipynb rename notebooks/{3 - Neural Networks => 3_Neural Networks}/multilayer_perceptron.ipynb (56%) create mode 100644 notebooks/3_Neural Networks/recurrent_network.ipynb create mode 100644 notebooks/4_Utils/save_restore_model.ipynb create mode 100644 notebooks/4_Utils/tensorboard_basic.ipynb delete mode 100644 notebooks/5 - User Interface/graph_visualization.ipynb delete mode 100644 notebooks/5 - User Interface/loss_visualization.ipynb rename notebooks/{4 - Multi GPU => 5_MultiGPU}/multigpu_basics.ipynb (99%) diff --git a/README.md b/README.md index 6784530d..d0d90f1a 100644 --- a/README.md +++ b/README.md @@ -4,31 +4,30 @@ Code examples for some popular machine learning algorithms, using TensorFlow lib ## Tutorial index #### 1 - Introduction -- Hello World ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1%20-%20Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1%20-%20Introduction/helloworld.py)) -- Basic Operations ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1%20-%20Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1%20-%20Introduction/basic_operations.py)) +- Hello World ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py)) +- Basic Operations ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py)) #### 2 - Basic Classifiers -- Nearest Neighbor ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2%20-%20Basic%20Classifiers/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2%20-%20Basic%20Classifiers/nearest_neighbor.py)) -- Linear Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2%20-%20Basic%20Classifiers/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2%20-%20Basic%20Classifiers/linear_regression.py)) -- Logistic Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2%20-%20Basic%20Classifiers/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2%20-%20Basic%20Classifiers/logistic_regression.py)) +- Nearest Neighbor ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)) +- Linear Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py)) +- Logistic Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py)) #### 3 - Neural Networks -- Multilayer Perceptron ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/multilayer_perceptron.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/multilayer_perceptron.py)) -- Convolutional Neural Network ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/convolutional_network.py)) -- AlexNet ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/alexnet.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/alexnet.py)) -- Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/reccurent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/recurrent_network.py)) -- Bidirectional Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3%20-%20Neural%20Networks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/bidirectional_rnn.py)) -- AutoEncoder ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/autoencoder.py)) +- Multilayer Perceptron ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py)) +- Convolutional Neural Network ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py)) +- AlexNet ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/alexnet.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/alexnet.py)) +- Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/reccurent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)) +- Bidirectional Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py)) +- AutoEncoder ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) / ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)) -#### 4 - Multi GPU -- Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4%20-%20Multi%20GPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4%20-%20Multi%20GPU/multigpu_basics.py)) +#### 4 - Utils +- Save and Restore a model ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py)) +- Tensorboard - Graph and loss visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py)) -#### 5 - User Interface (Tensorboard) -- Graph Visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5%20-%20User%20Interface/graph_visualization.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5%20-%20User%20Interface/graph_visualization.py)) -- Loss Visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5%20-%20User%20Interface/loss_visualization.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5%20-%20User%20Interface/loss_visualization.py)) +#### 5 - Multi GPU (Tensorboard) +- Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py)) - -## More Examples +## Going further - More Examples The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). #### Basics diff --git a/examples/1 - Introduction/basic_operations.py b/examples/1_Introduction/basic_operations.py similarity index 100% rename from examples/1 - Introduction/basic_operations.py rename to examples/1_Introduction/basic_operations.py diff --git a/examples/1 - Introduction/helloworld.py b/examples/1_Introduction/helloworld.py similarity index 92% rename from examples/1 - Introduction/helloworld.py rename to examples/1_Introduction/helloworld.py index 554cbed4..51a8ca43 100644 --- a/examples/1 - Introduction/helloworld.py +++ b/examples/1_Introduction/helloworld.py @@ -19,4 +19,5 @@ # Start tf session sess = tf.Session() -print sess.run(hello) \ No newline at end of file +# Run the op +print sess.run(hello) diff --git a/examples/2 - Basic Classifiers/input_data.py b/examples/2 - Basic Classifiers/input_data.py deleted file mode 100644 index d1d0d28e..00000000 --- a/examples/2 - Basic Classifiers/input_data.py +++ /dev/null @@ -1,144 +0,0 @@ -"""Functions for downloading and reading MNIST data.""" -from __future__ import print_function -import gzip -import os -import urllib -import numpy -SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' -def maybe_download(filename, work_directory): - """Download the data from Yann's website, unless it's already here.""" - if not os.path.exists(work_directory): - os.mkdir(work_directory) - filepath = os.path.join(work_directory, filename) - if not os.path.exists(filepath): - filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath) - statinfo = os.stat(filepath) - print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.') - return filepath -def _read32(bytestream): - dt = numpy.dtype(numpy.uint32).newbyteorder('>') - return numpy.frombuffer(bytestream.read(4), dtype=dt) -def extract_images(filename): - """Extract the images into a 4D uint8 numpy array [index, y, x, depth].""" - print('Extracting', filename) - with gzip.open(filename) as bytestream: - magic = _read32(bytestream) - if magic != 2051: - raise ValueError( - 'Invalid magic number %d in MNIST image file: %s' % - (magic, filename)) - num_images = _read32(bytestream) - rows = _read32(bytestream) - cols = _read32(bytestream) - buf = bytestream.read(rows * cols * num_images) - data = numpy.frombuffer(buf, dtype=numpy.uint8) - data = data.reshape(num_images, rows, cols, 1) - return data -def dense_to_one_hot(labels_dense, num_classes=10): - """Convert class labels from scalars to one-hot vectors.""" - num_labels = labels_dense.shape[0] - index_offset = numpy.arange(num_labels) * num_classes - labels_one_hot = numpy.zeros((num_labels, num_classes)) - labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 - return labels_one_hot -def extract_labels(filename, one_hot=False): - """Extract the labels into a 1D uint8 numpy array [index].""" - print('Extracting', filename) - with gzip.open(filename) as bytestream: - magic = _read32(bytestream) - if magic != 2049: - raise ValueError( - 'Invalid magic number %d in MNIST label file: %s' % - (magic, filename)) - num_items = _read32(bytestream) - buf = bytestream.read(num_items) - labels = numpy.frombuffer(buf, dtype=numpy.uint8) - if one_hot: - return dense_to_one_hot(labels) - return labels -class DataSet(object): - def __init__(self, images, labels, fake_data=False): - if fake_data: - self._num_examples = 10000 - else: - assert images.shape[0] == labels.shape[0], ( - "images.shape: %s labels.shape: %s" % (images.shape, - labels.shape)) - self._num_examples = images.shape[0] - # Convert shape from [num examples, rows, columns, depth] - # to [num examples, rows*columns] (assuming depth == 1) - assert images.shape[3] == 1 - images = images.reshape(images.shape[0], - images.shape[1] * images.shape[2]) - # Convert from [0, 255] -> [0.0, 1.0]. - images = images.astype(numpy.float32) - images = numpy.multiply(images, 1.0 / 255.0) - self._images = images - self._labels = labels - self._epochs_completed = 0 - self._index_in_epoch = 0 - @property - def images(self): - return self._images - @property - def labels(self): - return self._labels - @property - def num_examples(self): - return self._num_examples - @property - def epochs_completed(self): - return self._epochs_completed - def next_batch(self, batch_size, fake_data=False): - """Return the next `batch_size` examples from this data set.""" - if fake_data: - fake_image = [1.0 for _ in xrange(784)] - fake_label = 0 - return [fake_image for _ in xrange(batch_size)], [ - fake_label for _ in xrange(batch_size)] - start = self._index_in_epoch - self._index_in_epoch += batch_size - if self._index_in_epoch > self._num_examples: - # Finished epoch - self._epochs_completed += 1 - # Shuffle the data - perm = numpy.arange(self._num_examples) - numpy.random.shuffle(perm) - self._images = self._images[perm] - self._labels = self._labels[perm] - # Start next epoch - start = 0 - self._index_in_epoch = batch_size - assert batch_size <= self._num_examples - end = self._index_in_epoch - return self._images[start:end], self._labels[start:end] -def read_data_sets(train_dir, fake_data=False, one_hot=False): - class DataSets(object): - pass - data_sets = DataSets() - if fake_data: - data_sets.train = DataSet([], [], fake_data=True) - data_sets.validation = DataSet([], [], fake_data=True) - data_sets.test = DataSet([], [], fake_data=True) - return data_sets - TRAIN_IMAGES = 'train-images-idx3-ubyte.gz' - TRAIN_LABELS = 'train-labels-idx1-ubyte.gz' - TEST_IMAGES = 't10k-images-idx3-ubyte.gz' - TEST_LABELS = 't10k-labels-idx1-ubyte.gz' - VALIDATION_SIZE = 5000 - local_file = maybe_download(TRAIN_IMAGES, train_dir) - train_images = extract_images(local_file) - local_file = maybe_download(TRAIN_LABELS, train_dir) - train_labels = extract_labels(local_file, one_hot=one_hot) - local_file = maybe_download(TEST_IMAGES, train_dir) - test_images = extract_images(local_file) - local_file = maybe_download(TEST_LABELS, train_dir) - test_labels = extract_labels(local_file, one_hot=one_hot) - validation_images = train_images[:VALIDATION_SIZE] - validation_labels = train_labels[:VALIDATION_SIZE] - train_images = train_images[VALIDATION_SIZE:] - train_labels = train_labels[VALIDATION_SIZE:] - data_sets.train = DataSet(train_images, train_labels) - data_sets.validation = DataSet(validation_images, validation_labels) - data_sets.test = DataSet(test_images, test_labels) - return data_sets \ No newline at end of file diff --git a/examples/2 - Basic Classifiers/linear_regression.py b/examples/2_BasicModels/linear_regression.py similarity index 62% rename from examples/2 - Basic Classifiers/linear_regression.py rename to examples/2_BasicModels/linear_regression.py index a11b851d..a0aba7b9 100644 --- a/examples/2 - Basic Classifiers/linear_regression.py +++ b/examples/2_BasicModels/linear_regression.py @@ -12,30 +12,31 @@ # Parameters learning_rate = 0.01 -training_epochs = 2000 +training_epochs = 1000 display_step = 50 # Training Data -train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1]) -train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3]) +train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167, + 7.042,10.791,5.313,7.997,5.654,9.27,3.1]) +train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221, + 2.827,3.465,1.65,2.904,2.42,2.94,1.3]) n_samples = train_X.shape[0] # tf Graph Input X = tf.placeholder("float") Y = tf.placeholder("float") -# Create Model - # Set model weights W = tf.Variable(rng.randn(), name="weight") b = tf.Variable(rng.randn(), name="bias") # Construct a linear model -activation = tf.add(tf.mul(X, W), b) +pred = tf.add(tf.mul(X, W), b) -# Minimize the squared errors -cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss -optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent +# Mean squared error +cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples) +# Gradient descent +optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initializing the variables init = tf.initialize_all_variables() @@ -50,28 +51,34 @@ sess.run(optimizer, feed_dict={X: x, Y: y}) #Display logs per epoch step - if epoch % display_step == 0: - print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})), \ + if (epoch+1) % display_step == 0: + c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) + print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \ "W=", sess.run(W), "b=", sess.run(b) print "Optimization Finished!" training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) print "Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n' + #Graphic display + plt.plot(train_X, train_Y, 'ro', label='Original data') + plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line') + plt.legend() + plt.show() # Testing example, as requested (Issue #2) - test_X = numpy.asarray([6.83,4.668,8.9,7.91,5.7,8.7,3.1,2.1]) - test_Y = numpy.asarray([1.84,2.273,3.2,2.831,2.92,3.24,1.35,1.03]) + test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1]) + test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03]) - print "Testing... (L2 loss Comparison)" - testing_cost = sess.run(tf.reduce_sum(tf.pow(activation-Y, 2))/(2*test_X.shape[0]), - feed_dict={X: test_X, Y: test_Y}) #same function as cost above + print "Testing... (Mean square loss Comparison)" + testing_cost = sess.run( + tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]), + feed_dict={X: test_X, Y: test_Y}) # same function as cost above print "Testing cost=", testing_cost - print "Absolute l2 loss difference:", abs(training_cost - testing_cost) + print "Absolute mean square loss difference:", abs( + training_cost - testing_cost) - #Graphic display - plt.plot(train_X, train_Y, 'ro', label='Original data') plt.plot(test_X, test_Y, 'bo', label='Testing data') plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line') plt.legend() - plt.show() \ No newline at end of file + plt.show() diff --git a/examples/2 - Basic Classifiers/logistic_regression.py b/examples/2_BasicModels/logistic_regression.py similarity index 59% rename from examples/2 - Basic Classifiers/logistic_regression.py rename to examples/2_BasicModels/logistic_regression.py index 0a06a9a9..e5e05b16 100644 --- a/examples/2 - Basic Classifiers/logistic_regression.py +++ b/examples/2_BasicModels/logistic_regression.py @@ -1,17 +1,18 @@ ''' A logistic regression learning algorithm example using TensorFlow library. -This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +import tensorflow as tf + # Import MINST data -import input_data +from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) -import tensorflow as tf - # Parameters learning_rate = 0.01 training_epochs = 25 @@ -19,21 +20,20 @@ display_step = 1 # tf Graph Input -x = tf.placeholder("float", [None, 784]) # mnist data image of shape 28*28=784 -y = tf.placeholder("float", [None, 10]) # 0-9 digits recognition => 10 classes - -# Create model +x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784 +y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes # Set model weights W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) # Construct model -activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax +pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax # Minimize error using cross entropy -cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(activation), reduction_indices=1)) # Cross entropy -optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent +cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) +# Gradient Descent +optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initializing the variables init = tf.initialize_all_variables() @@ -49,18 +49,19 @@ # Loop over all batches for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) - # Fit training using batch data - sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys}) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, + y: batch_ys}) # Compute average loss - avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch + avg_cost += c / total_batch # Display logs per epoch step - if epoch % display_step == 0: + if (epoch+1) % display_step == 0: print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost) print "Optimization Finished!" # Test model - correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1)) + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy - accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}) diff --git a/examples/2 - Basic Classifiers/nearest_neighbor.py b/examples/2_BasicModels/nearest_neighbor.py similarity index 80% rename from examples/2 - Basic Classifiers/nearest_neighbor.py rename to examples/2_BasicModels/nearest_neighbor.py index 6a1f726d..af714417 100644 --- a/examples/2 - Basic Classifiers/nearest_neighbor.py +++ b/examples/2_BasicModels/nearest_neighbor.py @@ -1,6 +1,7 @@ ''' A nearest neighbor learning algorithm example using TensorFlow library. -This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ @@ -10,17 +11,13 @@ import tensorflow as tf # Import MINST data -import input_data +from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) # In this example, we limit mnist data Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates) Xte, Yte = mnist.test.next_batch(200) #200 for testing -# Reshape images to 1D -Xtr = np.reshape(Xtr, newshape=(-1, 28*28)) -Xte = np.reshape(Xte, newshape=(-1, 28*28)) - # tf Graph Input xtr = tf.placeholder("float", [None, 784]) xte = tf.placeholder("float", [784]) @@ -28,7 +25,7 @@ # Nearest Neighbor calculation using L1 Distance # Calculate L1 Distance distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.neg(xte))), reduction_indices=1) -# Predict: Get min distance index (Nearest neighbor) +# Prediction: Get min distance index (Nearest neighbor) pred = tf.arg_min(distance, 0) accuracy = 0. @@ -43,12 +40,12 @@ # loop over test data for i in range(len(Xte)): # Get nearest neighbor - nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i,:]}) + nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]}) # Get nearest neighbor class label and compare it to its true label - print "Test", i, "Prediction:", np.argmax(Ytr[nn_index]), "True Class:", np.argmax(Yte[i]) + print "Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \ + "True Class:", np.argmax(Yte[i]) # Calculate accuracy if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]): accuracy += 1./len(Xte) print "Done!" print "Accuracy:", accuracy - diff --git a/examples/3 - Neural Networks/alexnet.py b/examples/3 - Neural Networks/alexnet.py deleted file mode 100644 index 4f7092b5..00000000 --- a/examples/3 - Neural Networks/alexnet.py +++ /dev/null @@ -1,133 +0,0 @@ -''' -AlexNet implementation example using TensorFlow library. -This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) -AlexNet Paper (http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) - -Author: Aymeric Damien -Project: https://github.com/aymericdamien/TensorFlow-Examples/ -''' - -# Import MINST data -import input_data -mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) - -import tensorflow as tf - -# Parameters -learning_rate = 0.001 -training_iters = 200000 -batch_size = 64 -display_step = 20 - -# Network Parameters -n_input = 784 # MNIST data input (img shape: 28*28) -n_classes = 10 # MNIST total classes (0-9 digits) -dropout = 0.8 # Dropout, probability to keep units - -# tf Graph input -x = tf.placeholder(tf.float32, [None, n_input]) -y = tf.placeholder(tf.float32, [None, n_classes]) -keep_prob = tf.placeholder(tf.float32) # dropout (keep probability) - -# Create AlexNet model -def conv2d(name, l_input, w, b): - return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'),b), name=name) - -def max_pool(name, l_input, k): - return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name) - -def norm(name, l_input, lsize=4): - return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name) - -def alex_net(_X, _weights, _biases, _dropout): - # Reshape input picture - _X = tf.reshape(_X, shape=[-1, 28, 28, 1]) - - # Convolution Layer - conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1']) - # Max Pooling (down-sampling) - pool1 = max_pool('pool1', conv1, k=2) - # Apply Normalization - norm1 = norm('norm1', pool1, lsize=4) - # Apply Dropout - norm1 = tf.nn.dropout(norm1, _dropout) - - # Convolution Layer - conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2']) - # Max Pooling (down-sampling) - pool2 = max_pool('pool2', conv2, k=2) - # Apply Normalization - norm2 = norm('norm2', pool2, lsize=4) - # Apply Dropout - norm2 = tf.nn.dropout(norm2, _dropout) - - # Convolution Layer - conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3']) - # Max Pooling (down-sampling) - pool3 = max_pool('pool3', conv3, k=2) - # Apply Normalization - norm3 = norm('norm3', pool3, lsize=4) - # Apply Dropout - norm3 = tf.nn.dropout(norm3, _dropout) - - # Fully connected layer - dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]]) # Reshape conv3 output to fit dense layer input - dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1') # Relu activation - - dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activation - - # Output, class prediction - out = tf.matmul(dense2, _weights['out']) + _biases['out'] - return out - -# Store layers weight & bias -weights = { - 'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])), - 'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])), - 'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])), - 'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])), - 'wd2': tf.Variable(tf.random_normal([1024, 1024])), - 'out': tf.Variable(tf.random_normal([1024, 10])) -} -biases = { - 'bc1': tf.Variable(tf.random_normal([64])), - 'bc2': tf.Variable(tf.random_normal([128])), - 'bc3': tf.Variable(tf.random_normal([256])), - 'bd1': tf.Variable(tf.random_normal([1024])), - 'bd2': tf.Variable(tf.random_normal([1024])), - 'out': tf.Variable(tf.random_normal([n_classes])) -} - -# Construct model -pred = alex_net(x, weights, biases, keep_prob) - -# Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) -optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) - -# Evaluate model -correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) -accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) - -# Initializing the variables -init = tf.initialize_all_variables() - -# Launch the graph -with tf.Session() as sess: - sess.run(init) - step = 1 - # Keep training until reach max iterations - while step * batch_size < training_iters: - batch_xs, batch_ys = mnist.train.next_batch(batch_size) - # Fit training using batch data - sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout}) - if step % display_step == 0: - # Calculate batch accuracy - acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) - # Calculate batch loss - loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) - print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc) - step += 1 - print "Optimization Finished!" - # Calculate accuracy for 256 mnist test images - print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}) diff --git a/examples/3 - Neural Networks/bidirectional_rnn.py b/examples/3 - Neural Networks/bidirectional_rnn.py deleted file mode 100644 index 3c9e4bfb..00000000 --- a/examples/3 - Neural Networks/bidirectional_rnn.py +++ /dev/null @@ -1,163 +0,0 @@ -''' -A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library. -This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) -Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf - -Author: Aymeric Damien -Project: https://github.com/aymericdamien/TensorFlow-Examples/ -''' - -# Import MINST data -import input_data -mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) - -import tensorflow as tf -from tensorflow.python.ops.constant_op import constant -from tensorflow.models.rnn import rnn, rnn_cell -import numpy as np - -''' -To classify images using a bidirectional reccurent neural network, we consider every image row as a sequence of pixels. -Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample. -''' - -# Parameters -learning_rate = 0.001 -training_iters = 100000 -batch_size = 128 -display_step = 10 - -# Network Parameters -n_input = 28 # MNIST data input (img shape: 28*28) -n_steps = 28 # timesteps -n_hidden = 128 # hidden layer num of features -n_classes = 10 # MNIST total classes (0-9 digits) - -# tf Graph input -x = tf.placeholder("float", [None, n_steps, n_input]) -# Tensorflow LSTM cell requires 2x n_hidden length (state & cell) -istate_fw = tf.placeholder("float", [None, 2*n_hidden]) -istate_bw = tf.placeholder("float", [None, 2*n_hidden]) -y = tf.placeholder("float", [None, n_classes]) - -# Define weights -weights = { - # Hidden layer weights => 2*n_hidden because of foward + backward cells - 'hidden': tf.Variable(tf.random_normal([n_input, 2*n_hidden])), - 'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes])) -} -biases = { - 'hidden': tf.Variable(tf.random_normal([2*n_hidden])), - 'out': tf.Variable(tf.random_normal([n_classes])) -} - -def BiRNN(_X, _istate_fw, _istate_bw, _weights, _biases, _batch_size, _seq_len): - - # BiRNN requires to supply sequence_length as [batch_size, int64] - # Note: Tensorflow 0.6.0 requires BiRNN sequence_length parameter to be set - # For a better implementation with latest version of tensorflow, check below - _seq_len = tf.fill([_batch_size], constant(_seq_len, dtype=tf.int64)) - - # input shape: (batch_size, n_steps, n_input) - _X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size - # Reshape to prepare input to hidden activation - _X = tf.reshape(_X, [-1, n_input]) # (n_steps*batch_size, n_input) - # Linear activation - _X = tf.matmul(_X, _weights['hidden']) + _biases['hidden'] - - # Define lstm cells with tensorflow - # Forward direction cell - lstm_fw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) - # Backward direction cell - lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) - # Split data because rnn cell needs a list of inputs for the RNN inner loop - _X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden) - - # Get lstm cell output - outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, _X, - initial_state_fw=_istate_fw, - initial_state_bw=_istate_bw, - sequence_length=_seq_len) - - # Linear activation - # Get inner loop last output - return tf.matmul(outputs[-1], _weights['out']) + _biases['out'] - -pred = BiRNN(x, istate_fw, istate_bw, weights, biases, batch_size, n_steps) - - -# NOTE: The following code is working with current master version of tensorflow -# BiRNN sequence_length parameter isn't required, so we don't define it -# -# def BiRNN(_X, _istate_fw, _istate_bw, _weights, _biases): -# -# # input shape: (batch_size, n_steps, n_input) -# _X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size -# # Reshape to prepare input to hidden activation -# _X = tf.reshape(_X, [-1, n_input]) # (n_steps*batch_size, n_input) -# # Linear activation -# _X = tf.matmul(_X, _weights['hidden']) + _biases['hidden'] -# -# # Define lstm cells with tensorflow -# # Forward direction cell -# lstm_fw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) -# # Backward direction cell -# lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) -# # Split data because rnn cell needs a list of inputs for the RNN inner loop -# _X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden) -# -# # Get lstm cell output -# outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, _X, -# initial_state_fw=_istate_fw, -# initial_state_bw=_istate_bw) -# -# # Linear activation -# # Get inner loop last output -# return tf.matmul(outputs[-1], _weights['out']) + _biases['out'] -# -# pred = BiRNN(x, istate_fw, istate_bw, weights, biases) - -# Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # Softmax loss -optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer - -# Evaluate model -correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) -accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) - -# Initializing the variables -init = tf.initialize_all_variables() - -# Launch the graph -with tf.Session() as sess: - sess.run(init) - step = 1 - # Keep training until reach max iterations - while step * batch_size < training_iters: - batch_xs, batch_ys = mnist.train.next_batch(batch_size) - # Reshape data to get 28 seq of 28 elements - batch_xs = batch_xs.reshape((batch_size, n_steps, n_input)) - # Fit training using batch data - sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, - istate_fw: np.zeros((batch_size, 2*n_hidden)), - istate_bw: np.zeros((batch_size, 2*n_hidden))}) - if step % display_step == 0: - # Calculate batch accuracy - acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, - istate_fw: np.zeros((batch_size, 2*n_hidden)), - istate_bw: np.zeros((batch_size, 2*n_hidden))}) - # Calculate batch loss - loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, - istate_fw: np.zeros((batch_size, 2*n_hidden)), - istate_bw: np.zeros((batch_size, 2*n_hidden))}) - print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + \ - ", Training Accuracy= " + "{:.5f}".format(acc) - step += 1 - print "Optimization Finished!" - # Calculate accuracy for 128 mnist test images - test_len = 128 - test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input)) - test_label = mnist.test.labels[:test_len] - print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_label, - istate_fw: np.zeros((test_len, 2*n_hidden)), - istate_bw: np.zeros((test_len, 2*n_hidden))}) diff --git a/examples/3 - Neural Networks/convolutional_network.py b/examples/3 - Neural Networks/convolutional_network.py deleted file mode 100644 index e2fa73f3..00000000 --- a/examples/3 - Neural Networks/convolutional_network.py +++ /dev/null @@ -1,112 +0,0 @@ -''' -A Convolutional Network implementation example using TensorFlow library. -This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) - -Author: Aymeric Damien -Project: https://github.com/aymericdamien/TensorFlow-Examples/ -''' - -# Import MINST data -import input_data -mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) - -import tensorflow as tf - -# Parameters -learning_rate = 0.001 -training_iters = 100000 -batch_size = 128 -display_step = 10 - -# Network Parameters -n_input = 784 # MNIST data input (img shape: 28*28) -n_classes = 10 # MNIST total classes (0-9 digits) -dropout = 0.75 # Dropout, probability to keep units - -# tf Graph input -x = tf.placeholder(tf.float32, [None, n_input]) -y = tf.placeholder(tf.float32, [None, n_classes]) -keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) - -# Create model -def conv2d(img, w, b): - return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(img, w, strides=[1, 1, 1, 1], padding='SAME'),b)) - -def max_pool(img, k): - return tf.nn.max_pool(img, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME') - -def conv_net(_X, _weights, _biases, _dropout): - # Reshape input picture - _X = tf.reshape(_X, shape=[-1, 28, 28, 1]) - - # Convolution Layer - conv1 = conv2d(_X, _weights['wc1'], _biases['bc1']) - # Max Pooling (down-sampling) - conv1 = max_pool(conv1, k=2) - # Apply Dropout - conv1 = tf.nn.dropout(conv1, _dropout) - - # Convolution Layer - conv2 = conv2d(conv1, _weights['wc2'], _biases['bc2']) - # Max Pooling (down-sampling) - conv2 = max_pool(conv2, k=2) - # Apply Dropout - conv2 = tf.nn.dropout(conv2, _dropout) - - # Fully connected layer - dense1 = tf.reshape(conv2, [-1, _weights['wd1'].get_shape().as_list()[0]]) # Reshape conv2 output to fit dense layer input - dense1 = tf.nn.relu(tf.add(tf.matmul(dense1, _weights['wd1']), _biases['bd1'])) # Relu activation - dense1 = tf.nn.dropout(dense1, _dropout) # Apply Dropout - - # Output, class prediction - out = tf.add(tf.matmul(dense1, _weights['out']), _biases['out']) - return out - -# Store layers weight & bias -weights = { - 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), # 5x5 conv, 1 input, 32 outputs - 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), # 5x5 conv, 32 inputs, 64 outputs - 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), # fully connected, 7*7*64 inputs, 1024 outputs - 'out': tf.Variable(tf.random_normal([1024, n_classes])) # 1024 inputs, 10 outputs (class prediction) -} - -biases = { - 'bc1': tf.Variable(tf.random_normal([32])), - 'bc2': tf.Variable(tf.random_normal([64])), - 'bd1': tf.Variable(tf.random_normal([1024])), - 'out': tf.Variable(tf.random_normal([n_classes])) -} - -# Construct model -pred = conv_net(x, weights, biases, keep_prob) - -# Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) -optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) - -# Evaluate model -correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) -accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) - -# Initializing the variables -init = tf.initialize_all_variables() - -# Launch the graph -with tf.Session() as sess: - sess.run(init) - step = 1 - # Keep training until reach max iterations - while step * batch_size < training_iters: - batch_xs, batch_ys = mnist.train.next_batch(batch_size) - # Fit training using batch data - sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout}) - if step % display_step == 0: - # Calculate batch accuracy - acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) - # Calculate batch loss - loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) - print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc) - step += 1 - print "Optimization Finished!" - # Calculate accuracy for 256 mnist test images - print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}) diff --git a/examples/3 - Neural Networks/input_data.py b/examples/3 - Neural Networks/input_data.py deleted file mode 100644 index d1d0d28e..00000000 --- a/examples/3 - Neural Networks/input_data.py +++ /dev/null @@ -1,144 +0,0 @@ -"""Functions for downloading and reading MNIST data.""" -from __future__ import print_function -import gzip -import os -import urllib -import numpy -SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' -def maybe_download(filename, work_directory): - """Download the data from Yann's website, unless it's already here.""" - if not os.path.exists(work_directory): - os.mkdir(work_directory) - filepath = os.path.join(work_directory, filename) - if not os.path.exists(filepath): - filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath) - statinfo = os.stat(filepath) - print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.') - return filepath -def _read32(bytestream): - dt = numpy.dtype(numpy.uint32).newbyteorder('>') - return numpy.frombuffer(bytestream.read(4), dtype=dt) -def extract_images(filename): - """Extract the images into a 4D uint8 numpy array [index, y, x, depth].""" - print('Extracting', filename) - with gzip.open(filename) as bytestream: - magic = _read32(bytestream) - if magic != 2051: - raise ValueError( - 'Invalid magic number %d in MNIST image file: %s' % - (magic, filename)) - num_images = _read32(bytestream) - rows = _read32(bytestream) - cols = _read32(bytestream) - buf = bytestream.read(rows * cols * num_images) - data = numpy.frombuffer(buf, dtype=numpy.uint8) - data = data.reshape(num_images, rows, cols, 1) - return data -def dense_to_one_hot(labels_dense, num_classes=10): - """Convert class labels from scalars to one-hot vectors.""" - num_labels = labels_dense.shape[0] - index_offset = numpy.arange(num_labels) * num_classes - labels_one_hot = numpy.zeros((num_labels, num_classes)) - labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 - return labels_one_hot -def extract_labels(filename, one_hot=False): - """Extract the labels into a 1D uint8 numpy array [index].""" - print('Extracting', filename) - with gzip.open(filename) as bytestream: - magic = _read32(bytestream) - if magic != 2049: - raise ValueError( - 'Invalid magic number %d in MNIST label file: %s' % - (magic, filename)) - num_items = _read32(bytestream) - buf = bytestream.read(num_items) - labels = numpy.frombuffer(buf, dtype=numpy.uint8) - if one_hot: - return dense_to_one_hot(labels) - return labels -class DataSet(object): - def __init__(self, images, labels, fake_data=False): - if fake_data: - self._num_examples = 10000 - else: - assert images.shape[0] == labels.shape[0], ( - "images.shape: %s labels.shape: %s" % (images.shape, - labels.shape)) - self._num_examples = images.shape[0] - # Convert shape from [num examples, rows, columns, depth] - # to [num examples, rows*columns] (assuming depth == 1) - assert images.shape[3] == 1 - images = images.reshape(images.shape[0], - images.shape[1] * images.shape[2]) - # Convert from [0, 255] -> [0.0, 1.0]. - images = images.astype(numpy.float32) - images = numpy.multiply(images, 1.0 / 255.0) - self._images = images - self._labels = labels - self._epochs_completed = 0 - self._index_in_epoch = 0 - @property - def images(self): - return self._images - @property - def labels(self): - return self._labels - @property - def num_examples(self): - return self._num_examples - @property - def epochs_completed(self): - return self._epochs_completed - def next_batch(self, batch_size, fake_data=False): - """Return the next `batch_size` examples from this data set.""" - if fake_data: - fake_image = [1.0 for _ in xrange(784)] - fake_label = 0 - return [fake_image for _ in xrange(batch_size)], [ - fake_label for _ in xrange(batch_size)] - start = self._index_in_epoch - self._index_in_epoch += batch_size - if self._index_in_epoch > self._num_examples: - # Finished epoch - self._epochs_completed += 1 - # Shuffle the data - perm = numpy.arange(self._num_examples) - numpy.random.shuffle(perm) - self._images = self._images[perm] - self._labels = self._labels[perm] - # Start next epoch - start = 0 - self._index_in_epoch = batch_size - assert batch_size <= self._num_examples - end = self._index_in_epoch - return self._images[start:end], self._labels[start:end] -def read_data_sets(train_dir, fake_data=False, one_hot=False): - class DataSets(object): - pass - data_sets = DataSets() - if fake_data: - data_sets.train = DataSet([], [], fake_data=True) - data_sets.validation = DataSet([], [], fake_data=True) - data_sets.test = DataSet([], [], fake_data=True) - return data_sets - TRAIN_IMAGES = 'train-images-idx3-ubyte.gz' - TRAIN_LABELS = 'train-labels-idx1-ubyte.gz' - TEST_IMAGES = 't10k-images-idx3-ubyte.gz' - TEST_LABELS = 't10k-labels-idx1-ubyte.gz' - VALIDATION_SIZE = 5000 - local_file = maybe_download(TRAIN_IMAGES, train_dir) - train_images = extract_images(local_file) - local_file = maybe_download(TRAIN_LABELS, train_dir) - train_labels = extract_labels(local_file, one_hot=one_hot) - local_file = maybe_download(TEST_IMAGES, train_dir) - test_images = extract_images(local_file) - local_file = maybe_download(TEST_LABELS, train_dir) - test_labels = extract_labels(local_file, one_hot=one_hot) - validation_images = train_images[:VALIDATION_SIZE] - validation_labels = train_labels[:VALIDATION_SIZE] - train_images = train_images[VALIDATION_SIZE:] - train_labels = train_labels[VALIDATION_SIZE:] - data_sets.train = DataSet(train_images, train_labels) - data_sets.validation = DataSet(validation_images, validation_labels) - data_sets.test = DataSet(test_images, test_labels) - return data_sets \ No newline at end of file diff --git a/examples/3 - Neural Networks/autoencoder.py b/examples/3_NeuralNetworks/autoencoder.py similarity index 66% rename from examples/3 - Neural Networks/autoencoder.py rename to examples/3_NeuralNetworks/autoencoder.py index 82313902..cfc89e96 100644 --- a/examples/3 - Neural Networks/autoencoder.py +++ b/examples/3_NeuralNetworks/autoencoder.py @@ -16,7 +16,7 @@ import matplotlib.pyplot as plt # Import MINST data -import input_data +from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) # Parameters @@ -47,28 +47,37 @@ 'decoder_b2': tf.Variable(tf.random_normal([n_input])), } + # Building the encoder def encoder(x): # Encoder Hidden layer with sigmoid activation #1 - layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1'])) + layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), + biases['encoder_b1'])) # Decoder Hidden layer with sigmoid activation #2 - layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2'])) + layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), + biases['encoder_b2'])) return layer_2 + # Building the decoder def decoder(x): # Encoder Hidden layer with sigmoid activation #1 - layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), biases['decoder_b1'])) + layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), + biases['decoder_b1'])) # Decoder Hidden layer with sigmoid activation #2 - layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), biases['decoder_b2'])) + layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), + biases['decoder_b2'])) return layer_2 # Construct model encoder_op = encoder(X) decoder_op = decoder(encoder_op) +# Prediction y_pred = decoder_op +# Targets (Labels) are the input data. y_true = X + # Define loss and optimizer, minimize the squared error cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost) @@ -85,16 +94,18 @@ def decoder(x): # Loop over all batches for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) - # Fit training using batch data - _, cost_value = sess.run([optimizer, cost], feed_dict={X: batch_xs}) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs}) # Display logs per epoch step if epoch % display_step == 0: - print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(cost_value)) + print("Epoch:", '%04d' % (epoch+1), + "cost=", "{:.9f}".format(c)) print("Optimization Finished!") - #Applying encode and decode over test set - encode_decode = sess.run(y_pred, feed_dict={X: mnist.test.images[:examples_to_show]}) + # Applying encode and decode over test set + encode_decode = sess.run( + y_pred, feed_dict={X: mnist.test.images[:examples_to_show]}) # Compare original images with their reconstructions f, a = plt.subplots(2, 10, figsize=(10, 2)) for i in range(examples_to_show): @@ -103,32 +114,3 @@ def decoder(x): f.show() plt.draw() plt.waitforbuttonpress() - -# # Regression, with mean square error -# net = tflearn.regression(decoder, optimizer='adam', learning_rate=0.001, -# loss='mean_square', metric=None) -# -# # Training the auto encoder -# model = tflearn.DNN(net, tensorboard_verbose=0) -# model.fit(X, X, n_epoch=10, validation_set=(testX, testX), -# run_id="auto_encoder", batch_size=256) -# -# # Encoding X[0] for test -# print("\nTest encoding of X[0]:") -# # New model, re-using the same session, for weights sharing -# encoding_model = tflearn.DNN(encoder, session=model.session) -# print(encoding_model.predict([X[0]])) -# -# # Testing the image reconstruction on new data (test set) -# print("\nVisualizing results after being encoded and decoded:") -# testX = tflearn.data_utils.shuffle(testX)[0] -# # Applying encode and decode over test set -# encode_decode = model.predict(testX) -# # Compare original images with their reconstructions -# f, a = plt.subplots(2, 10, figsize=(10, 2)) -# for i in range(10): -# a[0][i].imshow(np.reshape(testX[i], (28, 28))) -# a[1][i].imshow(np.reshape(encode_decode[i], (28, 28))) -# f.show() -# plt.draw() -# plt.waitforbuttonpress() diff --git a/examples/3_NeuralNetworks/bidirectional_rnn.py b/examples/3_NeuralNetworks/bidirectional_rnn.py new file mode 100644 index 00000000..2e195ceb --- /dev/null +++ b/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -0,0 +1,116 @@ +''' +A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library. +This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) +Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +import tensorflow as tf +from tensorflow.models.rnn import rnn, rnn_cell +import numpy as np + +# Import MINST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +''' +To classify images using a bidirectional reccurent neural network, we consider +every image row as a sequence of pixels. Because MNIST image shape is 28*28px, +we will then handle 28 sequences of 28 steps for every sample. +''' + +# Parameters +learning_rate = 0.001 +training_iters = 100000 +batch_size = 128 +display_step = 10 + +# Network Parameters +n_input = 28 # MNIST data input (img shape: 28*28) +n_steps = 28 # timesteps +n_hidden = 128 # hidden layer num of features +n_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +x = tf.placeholder("float", [None, n_steps, n_input]) +y = tf.placeholder("float", [None, n_classes]) + +# Define weights +weights = { + # Hidden layer weights => 2*n_hidden because of foward + backward cells + 'hidden': tf.Variable(tf.random_normal([n_input, 2*n_hidden])), + 'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes])) +} +biases = { + 'hidden': tf.Variable(tf.random_normal([2*n_hidden])), + 'out': tf.Variable(tf.random_normal([n_classes])) +} + + +def BiRNN(x, weights, biases): + + # Prepare data shape to match `bidirectional_rnn` function requirements + # Current data input shape: (batch_size, n_steps, n_input) + # Permuting batch_size and n_steps + x = tf.transpose(x, [1, 0, 2]) + # Reshape to (n_steps*batch_size, n_input) + x = tf.reshape(x, [-1, n_input]) + # Split to get a list of 'n_steps' tensors of shape (batch_size, n_hidden) + x = tf.split(0, n_steps, x) + + # Define lstm cells with tensorflow + # Forward direction cell + lstm_fw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) + # Backward direction cell + lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) + + # Get lstm cell output + outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, + dtype=tf.float32) + + # Linear activation, using rnn inner loop last output + return tf.matmul(outputs[-1], weights['out']) + biases['out'] + +pred = BiRNN(x, weights, biases) + +# Define loss and optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) + +# Evaluate model +correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initializing the variables +init = tf.initialize_all_variables() + +# Launch the graph +with tf.Session() as sess: + sess.run(init) + step = 1 + # Keep training until reach max iterations + while step * batch_size < training_iters: + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Reshape data to get 28 seq of 28 elements + batch_x = batch_x.reshape((batch_size, n_steps, n_input)) + # Run optimization op (backprop) + sess.run(optimizer, feed_dict={x: batch_x, y: batch_y}) + if step % display_step == 0: + # Calculate batch accuracy + acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y}) + # Calculate batch loss + loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y}) + print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ + "{:.6f}".format(loss) + ", Training Accuracy= " + \ + "{:.5f}".format(acc) + step += 1 + print "Optimization Finished!" + + # Calculate accuracy for 128 mnist test images + test_len = 128 + test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input)) + test_label = mnist.test.labels[:test_len] + print "Testing Accuracy:", \ + sess.run(accuracy, feed_dict={x: test_data, y: test_label}) diff --git a/examples/3_NeuralNetworks/convolutional_network.py b/examples/3_NeuralNetworks/convolutional_network.py new file mode 100644 index 00000000..7d8b0b22 --- /dev/null +++ b/examples/3_NeuralNetworks/convolutional_network.py @@ -0,0 +1,132 @@ +''' +A Convolutional Network implementation example using TensorFlow library. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +import tensorflow as tf + +# Import MINST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.001 +training_iters = 200000 +batch_size = 128 +display_step = 10 + +# Network Parameters +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units + +# tf Graph input +x = tf.placeholder(tf.float32, [None, n_input]) +y = tf.placeholder(tf.float32, [None, n_classes]) +keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) + + +# Create some wrappers for simplicity +def conv2d(x, W, b, strides=1): + # Conv2D wrapper, with bias and relu activation + x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') + x = tf.nn.bias_add(x, b) + return tf.nn.relu(x) + + +def maxpool2d(x, k=2): + # MaxPool2D wrapper + return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], + padding='SAME') + + +# Create model +def conv_net(x, weights, biases, dropout): + # Reshape input picture + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer + conv1 = conv2d(x, weights['wc1'], biases['bc1']) + # Max Pooling (down-sampling) + conv1 = maxpool2d(conv1, k=2) + + # Convolution Layer + conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) + # Max Pooling (down-sampling) + conv2 = maxpool2d(conv2, k=2) + + # Fully connected layer + # Reshape conv2 output to fit fully connected layer input + fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) + fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) + fc1 = tf.nn.relu(fc1) + # Apply Dropout + fc1 = tf.nn.dropout(fc1, dropout) + + # Output, class prediction + out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) + return out + +# Store layers weight & bias +weights = { + # 5x5 conv, 1 input, 32 outputs + 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), + # 5x5 conv, 32 inputs, 64 outputs + 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), + # fully connected, 7*7*64 inputs, 1024 outputs + 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), + # 1024 inputs, 10 outputs (class prediction) + 'out': tf.Variable(tf.random_normal([1024, n_classes])) +} + +biases = { + 'bc1': tf.Variable(tf.random_normal([32])), + 'bc2': tf.Variable(tf.random_normal([64])), + 'bd1': tf.Variable(tf.random_normal([1024])), + 'out': tf.Variable(tf.random_normal([n_classes])) +} + +# Construct model +pred = conv_net(x, weights, biases, keep_prob) + +# Define loss and optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) + +# Evaluate model +correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initializing the variables +init = tf.initialize_all_variables() + +# Launch the graph +with tf.Session() as sess: + sess.run(init) + step = 1 + # Keep training until reach max iterations + while step * batch_size < training_iters: + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) + sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, + keep_prob: dropout}) + if step % display_step == 0: + # Calculate batch loss and accuracy + loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, + y: batch_y, + keep_prob: 1.}) + print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ + "{:.6f}".format(loss) + ", Training Accuracy= " + \ + "{:.5f}".format(acc) + step += 1 + print "Optimization Finished!" + + # Calculate accuracy for 256 mnist test images + print "Testing Accuracy:", \ + sess.run(accuracy, feed_dict={x: mnist.test.images[:256], + y: mnist.test.labels[:256], + keep_prob: 1.}) diff --git a/examples/3 - Neural Networks/multilayer_perceptron.py b/examples/3_NeuralNetworks/multilayer_perceptron.py similarity index 63% rename from examples/3 - Neural Networks/multilayer_perceptron.py rename to examples/3_NeuralNetworks/multilayer_perceptron.py index 191285be..53a4c0ae 100644 --- a/examples/3 - Neural Networks/multilayer_perceptron.py +++ b/examples/3_NeuralNetworks/multilayer_perceptron.py @@ -1,13 +1,14 @@ ''' A Multilayer Perceptron implementation example using TensorFlow library. -This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' # Import MINST data -import input_data +from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) import tensorflow as tf @@ -19,8 +20,8 @@ display_step = 1 # Network Parameters -n_hidden_1 = 256 # 1st layer num features -n_hidden_2 = 256 # 2nd layer num features +n_hidden_1 = 256 # 1st layer number of features +n_hidden_2 = 256 # 2nd layer number of features n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10 # MNIST total classes (0-9 digits) @@ -28,11 +29,18 @@ x = tf.placeholder("float", [None, n_input]) y = tf.placeholder("float", [None, n_classes]) + # Create model -def multilayer_perceptron(_X, _weights, _biases): - layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) #Hidden layer with RELU activation - layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) #Hidden layer with RELU activation - return tf.matmul(layer_2, _weights['out']) + _biases['out'] +def multilayer_perceptron(x, weights, biases): + # Hidden layer with RELU activation + layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) + layer_1 = tf.nn.relu(layer_1) + # Hidden layer with RELU activation + layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) + layer_2 = tf.nn.relu(layer_2) + # Output layer with linear activation + out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] + return out_layer # Store layers weight & bias weights = { @@ -50,8 +58,8 @@ def multilayer_perceptron(_X, _weights, _biases): pred = multilayer_perceptron(x, weights, biases) # Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # Softmax loss -optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Initializing the variables init = tf.initialize_all_variables() @@ -66,15 +74,16 @@ def multilayer_perceptron(_X, _weights, _biases): total_batch = int(mnist.train.num_examples/batch_size) # Loop over all batches for i in range(total_batch): - batch_xs, batch_ys = mnist.train.next_batch(batch_size) - # Fit training using batch data - sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys}) + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, + y: batch_y}) # Compute average loss - avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch + avg_cost += c / total_batch # Display logs per epoch step if epoch % display_step == 0: - print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost) - + print "Epoch:", '%04d' % (epoch+1), "cost=", \ + "{:.9f}".format(avg_cost) print "Optimization Finished!" # Test model diff --git a/examples/3 - Neural Networks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py similarity index 52% rename from examples/3 - Neural Networks/recurrent_network.py rename to examples/3_NeuralNetworks/recurrent_network.py index 35de09ef..4903e657 100644 --- a/examples/3 - Neural Networks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -7,17 +7,18 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' -# Import MINST data -import input_data -mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) - import tensorflow as tf from tensorflow.models.rnn import rnn, rnn_cell import numpy as np +# Import MINST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + ''' -To classify images using a reccurent neural network, we consider every image row as a sequence of pixels. -Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample. +To classify images using a reccurent neural network, we consider every image +row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then +handle 28 sequences of 28 steps for every sample. ''' # Parameters @@ -34,13 +35,11 @@ # tf Graph input x = tf.placeholder("float", [None, n_steps, n_input]) -# Tensorflow LSTM cell requires 2x n_hidden length (state & cell) -istate = tf.placeholder("float", [None, 2*n_hidden]) y = tf.placeholder("float", [None, n_classes]) # Define weights weights = { - 'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights + 'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), 'out': tf.Variable(tf.random_normal([n_hidden, n_classes])) } biases = { @@ -48,32 +47,33 @@ 'out': tf.Variable(tf.random_normal([n_classes])) } -def RNN(_X, _istate, _weights, _biases): - # input shape: (batch_size, n_steps, n_input) - _X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size - # Reshape to prepare input to hidden activation - _X = tf.reshape(_X, [-1, n_input]) # (n_steps*batch_size, n_input) - # Linear activation - _X = tf.matmul(_X, _weights['hidden']) + _biases['hidden'] +def RNN(x, weights, biases): + + # Prepare data shape to match `rnn` function requirements + # Current data input shape: (batch_size, n_steps, n_input) + # Permuting batch_size and n_steps + x = tf.transpose(x, [1, 0, 2]) + # Reshaping to (n_steps*batch_size, n_input) + x = tf.reshape(x, [-1, n_input]) + # Split to get a list of 'n_steps' tensors of shape (batch_size, n_hidden) + # This input shape is required by `rnn` function + x = tf.split(0, n_steps, x) # Define a lstm cell with tensorflow lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) - # Split data because rnn cell needs a list of inputs for the RNN inner loop - _X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden) # Get lstm cell output - outputs, states = rnn.rnn(lstm_cell, _X, initial_state=_istate) + outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32) - # Linear activation - # Get inner loop last output - return tf.matmul(outputs[-1], _weights['out']) + _biases['out'] + # Linear activation, using rnn inner loop last output + return tf.matmul(outputs[-1], weights['out']) + biases['out'] -pred = RNN(x, istate, weights, biases) +pred = RNN(x, weights, biases) # Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # Softmax loss -optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) @@ -88,26 +88,25 @@ def RNN(_X, _istate, _weights, _biases): step = 1 # Keep training until reach max iterations while step * batch_size < training_iters: - batch_xs, batch_ys = mnist.train.next_batch(batch_size) + batch_x, batch_y = mnist.train.next_batch(batch_size) # Reshape data to get 28 seq of 28 elements - batch_xs = batch_xs.reshape((batch_size, n_steps, n_input)) - # Fit training using batch data - sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, - istate: np.zeros((batch_size, 2*n_hidden))}) + batch_x = batch_x.reshape((batch_size, n_steps, n_input)) + # Run optimization op (backprop) + sess.run(optimizer, feed_dict={x: batch_x, y: batch_y}) if step % display_step == 0: # Calculate batch accuracy - acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, - istate: np.zeros((batch_size, 2*n_hidden))}) + acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y}) # Calculate batch loss - loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, - istate: np.zeros((batch_size, 2*n_hidden))}) - print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + \ - ", Training Accuracy= " + "{:.5f}".format(acc) + loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y}) + print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ + "{:.6f}".format(loss) + ", Training Accuracy= " + \ + "{:.5f}".format(acc) step += 1 print "Optimization Finished!" - # Calculate accuracy for 256 mnist test images - test_len = 256 + + # Calculate accuracy for 128 mnist test images + test_len = 128 test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input)) test_label = mnist.test.labels[:test_len] - print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_label, - istate: np.zeros((test_len, 2*n_hidden))}) + print "Testing Accuracy:", \ + sess.run(accuracy, feed_dict={x: test_data, y: test_label}) diff --git a/examples/4_Utils/save_restore_model.py b/examples/4_Utils/save_restore_model.py new file mode 100644 index 00000000..8c11ccc0 --- /dev/null +++ b/examples/4_Utils/save_restore_model.py @@ -0,0 +1,137 @@ +''' +Save and Restore a model using TensorFlow. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +# Import MINST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +import tensorflow as tf + +# Parameters +learning_rate = 0.001 +batch_size = 100 +display_step = 1 +model_path = "/tmp/model.ckpt" + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of features +n_hidden_2 = 256 # 2nd layer number of features +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +x = tf.placeholder("float", [None, n_input]) +y = tf.placeholder("float", [None, n_classes]) + + +# Create model +def multilayer_perceptron(x, weights, biases): + # Hidden layer with RELU activation + layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) + layer_1 = tf.nn.relu(layer_1) + # Hidden layer with RELU activation + layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) + layer_2 = tf.nn.relu(layer_2) + # Output layer with linear activation + out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] + return out_layer + +# Store layers weight & bias +weights = { + 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), + 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) +} +biases = { + 'b1': tf.Variable(tf.random_normal([n_hidden_1])), + 'b2': tf.Variable(tf.random_normal([n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_classes])) +} + +# Construct model +pred = multilayer_perceptron(x, weights, biases) + +# Define loss and optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) + +# Initializing the variables +init = tf.initialize_all_variables() + +# 'Saver' op to save and restore all the variables +saver = tf.train.Saver() + +# Running first session +print "Starting 1st session..." +with tf.Session() as sess: + # Initialize variables + sess.run(init) + + # Training cycle + for epoch in range(3): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, + y: batch_y}) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if epoch % display_step == 0: + print "Epoch:", '%04d' % (epoch+1), "cost=", \ + "{:.9f}".format(avg_cost) + print "First Optimization Finished!" + + # Test model + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + # Calculate accuracy + accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) + print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}) + + # Save model weights to disk + save_path = saver.save(sess, model_path) + print "Model saved in file: %s" % save_path + +# Running a new session +print "Starting 2nd session..." +with tf.Session() as sess: + # Initialize variables + sess.run(init) + + # Restore model weights from previously saved model + load_path = saver.restore(sess, model_path) + print "Model restored from file: %s" % save_path + + # Resume training + for epoch in range(7): + avg_cost = 0. + total_batch = int(mnist.train.num_examples / batch_size) + # Loop over all batches + for i in range(total_batch): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, + y: batch_y}) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if epoch % display_step == 0: + print "Epoch:", '%04d' % (epoch + 1), "cost=", \ + "{:.9f}".format(avg_cost) + print "Second Optimization Finished!" + + # Test model + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + # Calculate accuracy + accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) + print "Accuracy:", accuracy.eval( + {x: mnist.test.images, y: mnist.test.labels}) diff --git a/examples/4_Utils/tensorboard_advanced.py b/examples/4_Utils/tensorboard_advanced.py new file mode 100644 index 00000000..f87f5c14 --- /dev/null +++ b/examples/4_Utils/tensorboard_advanced.py @@ -0,0 +1 @@ +# TODO \ No newline at end of file diff --git a/examples/4_Utils/tensorboard_basic.py b/examples/4_Utils/tensorboard_basic.py new file mode 100644 index 00000000..2e2ff246 --- /dev/null +++ b/examples/4_Utils/tensorboard_basic.py @@ -0,0 +1,93 @@ +''' +Graph and Loss visualization using Tensorboard. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +import tensorflow as tf + +# Import MINST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.01 +training_epochs = 25 +batch_size = 100 +display_step = 1 +logs_path = '/tmp/tensorflow_logs' + +# tf Graph Input +# mnist data image of shape 28*28=784 +x = tf.placeholder(tf.float32, [None, 784], name='InputData') +# 0-9 digits recognition => 10 classes +y = tf.placeholder(tf.float32, [None, 10], name='LabelData') + +# Set model weights +W = tf.Variable(tf.zeros([784, 10]), name='Weights') +b = tf.Variable(tf.zeros([10]), name='Bias') + +# Construct model and encapsulating all ops into scopes, making +# Tensorboard's Graph visualization more convenient +with tf.name_scope('Model'): + # Model + pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax +with tf.name_scope('Loss'): + # Minimize error using cross entropy + cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) +with tf.name_scope('SGD'): + # Gradient Descent + optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) +with tf.name_scope('Accuracy'): + # Accuracy + acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + acc = tf.reduce_mean(tf.cast(acc, tf.float32)) + +# Initializing the variables +init = tf.initialize_all_variables() + +# Create a summary to monitor cost tensor +tf.scalar_summary("loss", cost) +# Create a summary to monitor accuracy tensor +tf.scalar_summary("accuracy", acc) +# Merge all summaries into a single op +merged_summary_op = tf.merge_all_summaries() + +# Launch the graph +with tf.Session() as sess: + sess.run(init) + + # op to write logs to Tensorboard + summary_writer = tf.train.SummaryWriter(logs_path) + + # Training cycle + for epoch in range(training_epochs): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_xs, batch_ys = mnist.train.next_batch(batch_size) + # Run optimization op (backprop), cost op (to get loss value) + # and summary nodes + _, c, summary = sess.run([optimizer, cost, merged_summary_op], + feed_dict={x: batch_xs, y: batch_ys}) + # Write logs at every iteration + summary_writer.add_summary(summary, epoch * total_batch + i) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if (epoch+1) % display_step == 0: + print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost) + + print "Optimization Finished!" + + # Test model + # Calculate accuracy + print "Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels}) + + print "Run the command line:\n" \ + "--> tensorboard --logdir=/tmp/tensorflow_logs " \ + "\nThen open http://0.0.0.0:6006/ into your web browser" diff --git a/examples/5 - User Interface/graph_visualization.py b/examples/5 - User Interface/graph_visualization.py deleted file mode 100644 index 5df3c382..00000000 --- a/examples/5 - User Interface/graph_visualization.py +++ /dev/null @@ -1,78 +0,0 @@ -''' -Graph Visualization with TensorFlow. -This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) - -Author: Aymeric Damien -Project: https://github.com/aymericdamien/TensorFlow-Examples/ -''' - -import tensorflow as tf -import numpy - -# Import MINST data -import input_data -mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) - -# Use Logistic Regression from our previous example - -# Parameters -learning_rate = 0.01 -training_epochs = 10 -batch_size = 100 -display_step = 1 - -# tf Graph Input -x = tf.placeholder("float", [None, 784], name='x') # mnist data image of shape 28*28=784 -y = tf.placeholder("float", [None, 10], name='y') # 0-9 digits recognition => 10 classes - -# Create model - -# Set model weights -W = tf.Variable(tf.zeros([784, 10]), name="weights") -b = tf.Variable(tf.zeros([10]), name="bias") - -# Construct model -activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax - -# Minimize error using cross entropy -cost = -tf.reduce_sum(y*tf.log(activation)) # Cross entropy -optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent - -# Initializing the variables -init = tf.initialize_all_variables() - -# Launch the graph -with tf.Session() as sess: - sess.run(init) - - # Set logs writer into folder /tmp/tensorflow_logs - summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def) - - # Training cycle - for epoch in range(training_epochs): - avg_cost = 0. - total_batch = int(mnist.train.num_examples/batch_size) - # Loop over all batches - for i in range(total_batch): - batch_xs, batch_ys = mnist.train.next_batch(batch_size) - # Fit training using batch data - sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys}) - # Compute average loss - avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch - # Display logs per epoch step - if epoch % display_step == 0: - print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost) - - print "Optimization Finished!" - - # Test model - correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1)) - # Calculate accuracy - accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) - print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}) - -''' -Run the command line: tensorboard --logdir=/tmp/tensorflow_logs -Open http://localhost:6006/ into your web browser -''' - diff --git a/examples/5 - User Interface/loss_visualization.py b/examples/5 - User Interface/loss_visualization.py deleted file mode 100644 index d332c1fb..00000000 --- a/examples/5 - User Interface/loss_visualization.py +++ /dev/null @@ -1,86 +0,0 @@ -''' -Loss Visualization with TensorFlow. -This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) - -Author: Aymeric Damien -Project: https://github.com/aymericdamien/TensorFlow-Examples/ -''' - -import tensorflow as tf -import numpy - -# Import MINST data -import input_data -mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) - -# Use Logistic Regression from our previous example - -# Parameters -learning_rate = 0.01 -training_epochs = 10 -batch_size = 100 -display_step = 1 - -# tf Graph Input -x = tf.placeholder("float", [None, 784], name='x') # mnist data image of shape 28*28=784 -y = tf.placeholder("float", [None, 10], name='y') # 0-9 digits recognition => 10 classes - -# Create model - -# Set model weights -W = tf.Variable(tf.zeros([784, 10]), name="weights") -b = tf.Variable(tf.zeros([10]), name="bias") - -# Construct model -activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax - -# Minimize error using cross entropy -cost = -tf.reduce_sum(y*tf.log(activation)) # Cross entropy -optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent - -# Initializing the variables -init = tf.initialize_all_variables() - -# Create a summary to monitor cost function -tf.scalar_summary("loss", cost) - -# Merge all summaries to a single operator -merged_summary_op = tf.merge_all_summaries() - -# Launch the graph -with tf.Session() as sess: - sess.run(init) - - # Set logs writer into folder /tmp/tensorflow_logs - summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def) - - # Training cycle - for epoch in range(training_epochs): - avg_cost = 0. - total_batch = int(mnist.train.num_examples/batch_size) - # Loop over all batches - for i in range(total_batch): - batch_xs, batch_ys = mnist.train.next_batch(batch_size) - # Fit training using batch data - sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys}) - # Compute average loss - avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch - # Write logs at every iteration - summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys}) - summary_writer.add_summary(summary_str, epoch*total_batch + i) - # Display logs per epoch step - if epoch % display_step == 0: - print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost) - - print "Optimization Finished!" - - # Test model - correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1)) - # Calculate accuracy - accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) - print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}) - -''' -Run the command line: tensorboard --logdir=/tmp/tensorflow_logs -Open http://localhost:6006/ into your web browser -''' diff --git a/examples/4 - Multi GPU/multigpu_basics.py b/examples/5_MultiGPU/multigpu_basics.py similarity index 100% rename from examples/4 - Multi GPU/multigpu_basics.py rename to examples/5_MultiGPU/multigpu_basics.py diff --git a/notebooks/1 - Introduction/basic_operations.ipynb b/notebooks/1_Introduction/basic_operations.ipynb similarity index 99% rename from notebooks/1 - Introduction/basic_operations.ipynb rename to notebooks/1_Introduction/basic_operations.ipynb index c764494f..92b06f5d 100644 --- a/notebooks/1 - Introduction/basic_operations.ipynb +++ b/notebooks/1_Introduction/basic_operations.ipynb @@ -205,7 +205,7 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", @@ -217,4 +217,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/notebooks/1 - Introduction/helloworld.ipynb b/notebooks/1_Introduction/helloworld.ipynb similarity index 98% rename from notebooks/1 - Introduction/helloworld.ipynb rename to notebooks/1_Introduction/helloworld.ipynb index 2c956e8e..a20405a5 100644 --- a/notebooks/1 - Introduction/helloworld.ipynb +++ b/notebooks/1_Introduction/helloworld.ipynb @@ -72,7 +72,7 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", @@ -84,4 +84,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/notebooks/2 - Basic Classifiers/input_data.py b/notebooks/2 - Basic Classifiers/input_data.py deleted file mode 100644 index 6fad792b..00000000 --- a/notebooks/2 - Basic Classifiers/input_data.py +++ /dev/null @@ -1,144 +0,0 @@ -"""Functions for downloading and reading MNIST data.""" -from __future__ import print_function -import gzip -import os -import urllib -import numpy -SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' -def maybe_download(filename, work_directory): - """Download the data from Yann's website, unless it's already here.""" - if not os.path.exists(work_directory): - os.mkdir(work_directory) - filepath = os.path.join(work_directory, filename) - if not os.path.exists(filepath): - filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath) - statinfo = os.stat(filepath) - print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.') - return filepath -def _read32(bytestream): - dt = numpy.dtype(numpy.uint32).newbyteorder('>') - return numpy.frombuffer(bytestream.read(4), dtype=dt) -def extract_images(filename): - """Extract the images into a 4D uint8 numpy array [index, y, x, depth].""" - print('Extracting', filename) - with gzip.open(filename) as bytestream: - magic = _read32(bytestream) - if magic != 2051: - raise ValueError( - 'Invalid magic number %d in MNIST image file: %s' % - (magic, filename)) - num_images = _read32(bytestream) - rows = _read32(bytestream) - cols = _read32(bytestream) - buf = bytestream.read(rows * cols * num_images) - data = numpy.frombuffer(buf, dtype=numpy.uint8) - data = data.reshape(num_images, rows, cols, 1) - return data -def dense_to_one_hot(labels_dense, num_classes=10): - """Convert class labels from scalars to one-hot vectors.""" - num_labels = labels_dense.shape[0] - index_offset = numpy.arange(num_labels) * num_classes - labels_one_hot = numpy.zeros((num_labels, num_classes)) - labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 - return labels_one_hot -def extract_labels(filename, one_hot=False): - """Extract the labels into a 1D uint8 numpy array [index].""" - print('Extracting', filename) - with gzip.open(filename) as bytestream: - magic = _read32(bytestream) - if magic != 2049: - raise ValueError( - 'Invalid magic number %d in MNIST label file: %s' % - (magic, filename)) - num_items = _read32(bytestream) - buf = bytestream.read(num_items) - labels = numpy.frombuffer(buf, dtype=numpy.uint8) - if one_hot: - return dense_to_one_hot(labels) - return labels -class DataSet(object): - def __init__(self, images, labels, fake_data=False): - if fake_data: - self._num_examples = 10000 - else: - assert images.shape[0] == labels.shape[0], ( - "images.shape: %s labels.shape: %s" % (images.shape, - labels.shape)) - self._num_examples = images.shape[0] - # Convert shape from [num examples, rows, columns, depth] - # to [num examples, rows*columns] (assuming depth == 1) - assert images.shape[3] == 1 - images = images.reshape(images.shape[0], - images.shape[1] * images.shape[2]) - # Convert from [0, 255] -> [0.0, 1.0]. - images = images.astype(numpy.float32) - images = numpy.multiply(images, 1.0 / 255.0) - self._images = images - self._labels = labels - self._epochs_completed = 0 - self._index_in_epoch = 0 - @property - def images(self): - return self._images - @property - def labels(self): - return self._labels - @property - def num_examples(self): - return self._num_examples - @property - def epochs_completed(self): - return self._epochs_completed - def next_batch(self, batch_size, fake_data=False): - """Return the next `batch_size` examples from this data set.""" - if fake_data: - fake_image = [1.0 for _ in xrange(784)] - fake_label = 0 - return [fake_image for _ in xrange(batch_size)], [ - fake_label for _ in xrange(batch_size)] - start = self._index_in_epoch - self._index_in_epoch += batch_size - if self._index_in_epoch > self._num_examples: - # Finished epoch - self._epochs_completed += 1 - # Shuffle the data - perm = numpy.arange(self._num_examples) - numpy.random.shuffle(perm) - self._images = self._images[perm] - self._labels = self._labels[perm] - # Start next epoch - start = 0 - self._index_in_epoch = batch_size - assert batch_size <= self._num_examples - end = self._index_in_epoch - return self._images[start:end], self._labels[start:end] -def read_data_sets(train_dir, fake_data=False, one_hot=False): - class DataSets(object): - pass - data_sets = DataSets() - if fake_data: - data_sets.train = DataSet([], [], fake_data=True) - data_sets.validation = DataSet([], [], fake_data=True) - data_sets.test = DataSet([], [], fake_data=True) - return data_sets - TRAIN_IMAGES = 'train-images-idx3-ubyte.gz' - TRAIN_LABELS = 'train-labels-idx1-ubyte.gz' - TEST_IMAGES = 't10k-images-idx3-ubyte.gz' - TEST_LABELS = 't10k-labels-idx1-ubyte.gz' - VALIDATION_SIZE = 5000 - local_file = maybe_download(TRAIN_IMAGES, train_dir) - train_images = extract_images(local_file) - local_file = maybe_download(TRAIN_LABELS, train_dir) - train_labels = extract_labels(local_file, one_hot=one_hot) - local_file = maybe_download(TEST_IMAGES, train_dir) - test_images = extract_images(local_file) - local_file = maybe_download(TEST_LABELS, train_dir) - test_labels = extract_labels(local_file, one_hot=one_hot) - validation_images = train_images[:VALIDATION_SIZE] - validation_labels = train_labels[:VALIDATION_SIZE] - train_images = train_images[VALIDATION_SIZE:] - train_labels = train_labels[VALIDATION_SIZE:] - data_sets.train = DataSet(train_images, train_labels) - data_sets.validation = DataSet(validation_images, validation_labels) - data_sets.test = DataSet(test_images, test_labels) - return data_sets diff --git a/notebooks/2 - Basic Classifiers/linear_regression.ipynb b/notebooks/2 - Basic Classifiers/linear_regression.ipynb deleted file mode 100644 index a67a915b..00000000 --- a/notebooks/2 - Basic Classifiers/linear_regression.ipynb +++ /dev/null @@ -1,253 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# A linear regression learning algorithm example using TensorFlow library.\n", - "\n", - "# Author: Aymeric Damien\n", - "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "import numpy\n", - "import matplotlib.pyplot as plt\n", - "rng = numpy.random" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Parameters\n", - "learning_rate = 0.01\n", - "training_epochs = 2000\n", - "display_step = 50" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Training Data\n", - "train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n", - "train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n", - "n_samples = train_X.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# tf Graph Input\n", - "X = tf.placeholder(\"float\")\n", - "Y = tf.placeholder(\"float\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Create Model\n", - "\n", - "# Set model weights\n", - "W = tf.Variable(rng.randn(), name=\"weight\")\n", - "b = tf.Variable(rng.randn(), name=\"bias\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Construct a linear model\n", - "activation = tf.add(tf.mul(X, W), b)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Minimize the squared errors\n", - "cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss\n", - "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Initializing the variables\n", - "init = tf.initialize_all_variables()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: 0001 cost= 3.389688730 W= 0.0198441 b= -0.273522\n", - "Epoch: 0051 cost= 0.134034902 W= 0.383208 b= -0.159746\n", - "Epoch: 0101 cost= 0.127440125 W= 0.375261 b= -0.102578\n", - "Epoch: 0151 cost= 0.121607177 W= 0.367787 b= -0.0488099\n", - "Epoch: 0201 cost= 0.116448022 W= 0.360758 b= 0.00175997\n", - "Epoch: 0251 cost= 0.111884907 W= 0.354146 b= 0.0493223\n", - "Epoch: 0301 cost= 0.107848980 W= 0.347928 b= 0.0940558\n", - "Epoch: 0351 cost= 0.104279339 W= 0.34208 b= 0.136129\n", - "Epoch: 0401 cost= 0.101122171 W= 0.336579 b= 0.1757\n", - "Epoch: 0451 cost= 0.098329842 W= 0.331405 b= 0.212917\n", - "Epoch: 0501 cost= 0.095860250 W= 0.32654 b= 0.247921\n", - "Epoch: 0551 cost= 0.093676031 W= 0.321963 b= 0.280843\n", - "Epoch: 0601 cost= 0.091744311 W= 0.317659 b= 0.311807\n", - "Epoch: 0651 cost= 0.090035893 W= 0.313611 b= 0.340929\n", - "Epoch: 0701 cost= 0.088524953 W= 0.309804 b= 0.36832\n", - "Epoch: 0751 cost= 0.087188691 W= 0.306222 b= 0.394082\n", - "Epoch: 0801 cost= 0.086007021 W= 0.302854 b= 0.418311\n", - "Epoch: 0851 cost= 0.084961981 W= 0.299687 b= 0.441099\n", - "Epoch: 0901 cost= 0.084037818 W= 0.296708 b= 0.462532\n", - "Epoch: 0951 cost= 0.083220571 W= 0.293905 b= 0.48269\n", - "Epoch: 1001 cost= 0.082497880 W= 0.29127 b= 0.50165\n", - "Epoch: 1051 cost= 0.081858821 W= 0.288791 b= 0.519481\n", - "Epoch: 1101 cost= 0.081293717 W= 0.28646 b= 0.536251\n", - "Epoch: 1151 cost= 0.080794014 W= 0.284267 b= 0.552026\n", - "Epoch: 1201 cost= 0.080352172 W= 0.282205 b= 0.566861\n", - "Epoch: 1251 cost= 0.079961479 W= 0.280265 b= 0.580815\n", - "Epoch: 1301 cost= 0.079616025 W= 0.278441 b= 0.593939\n", - "Epoch: 1351 cost= 0.079310589 W= 0.276725 b= 0.606284\n", - "Epoch: 1401 cost= 0.079040587 W= 0.275111 b= 0.617893\n", - "Epoch: 1451 cost= 0.078801893 W= 0.273594 b= 0.62881\n", - "Epoch: 1501 cost= 0.078590907 W= 0.272167 b= 0.639077\n", - "Epoch: 1551 cost= 0.078404360 W= 0.270824 b= 0.648734\n", - "Epoch: 1601 cost= 0.078239456 W= 0.269562 b= 0.657817\n", - "Epoch: 1651 cost= 0.078093678 W= 0.268374 b= 0.66636\n", - "Epoch: 1701 cost= 0.077964827 W= 0.267257 b= 0.674395\n", - "Epoch: 1751 cost= 0.077850945 W= 0.266207 b= 0.681952\n", - "Epoch: 1801 cost= 0.077750273 W= 0.265219 b= 0.68906\n", - "Epoch: 1851 cost= 0.077661335 W= 0.264289 b= 0.695745\n", - "Epoch: 1901 cost= 0.077582702 W= 0.263416 b= 0.702033\n", - "Epoch: 1951 cost= 0.077513263 W= 0.262593 b= 0.707947\n", - "Optimization Finished!\n", - "cost= 0.077453 W= 0.261835 b= 0.713401\n" - ] - } - ], - "source": [ - "# Launch the graph\n", - "with tf.Session() as sess:\n", - " sess.run(init)\n", - "\n", - " # Fit all training data\n", - " for epoch in range(training_epochs):\n", - " for (x, y) in zip(train_X, train_Y):\n", - " sess.run(optimizer, feed_dict={X: x, Y: y})\n", - "\n", - " #Display logs per epoch step\n", - " if epoch % display_step == 0:\n", - " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n", - " \"{:.9f}\".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})), \\\n", - " \"W=\", sess.run(W), \"b=\", sess.run(b)\n", - "\n", - " print \"Optimization Finished!\"\n", - " print \"cost=\", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), \\\n", - " \"W=\", sess.run(W), \"b=\", sess.run(b)\n", - "\n", - " #Graphic display\n", - " plt.plot(train_X, train_Y, 'ro', label='Original data')\n", - " plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAJYCAYAAACadoJwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOIYCAgAAAMAxFBAAAAAAjvl/RxyPZhg3pnEAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import Image\n", - "Image(filename='linearreg.png')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "IPython (Python 2.7)", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.8" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/2_BasicModels/linear_regression.ipynb b/notebooks/2_BasicModels/linear_regression.ipynb new file mode 100644 index 00000000..fb05858d --- /dev/null +++ b/notebooks/2_BasicModels/linear_regression.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# A linear regression learning algorithm example using TensorFlow library.\n", + "\n", + "# Author: Aymeric Damien\n", + "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "rng = numpy.random" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 1000\n", + "display_step = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Data\n", + "train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n", + " 7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n", + "train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n", + " 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n", + "n_samples = train_X.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# tf Graph Input\n", + "X = tf.placeholder(\"float\")\n", + "Y = tf.placeholder(\"float\")\n", + "\n", + "# Set model weights\n", + "W = tf.Variable(rng.randn(), name=\"weight\")\n", + "b = tf.Variable(rng.randn(), name=\"bias\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Construct a linear model\n", + "pred = tf.add(tf.mul(X, W), b)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Mean squared error\n", + "cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)\n", + "# Gradient descent\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Initializing the variables\n", + "init = tf.initialize_all_variables()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0050 cost= 0.207037717 W= 0.451217 b= -0.649001\n", + "Epoch: 0100 cost= 0.192011863 W= 0.439226 b= -0.562735\n", + "Epoch: 0150 cost= 0.178721249 W= 0.427948 b= -0.481599\n", + "Epoch: 0200 cost= 0.166965470 W= 0.41734 b= -0.40529\n", + "Epoch: 0250 cost= 0.156567261 W= 0.407363 b= -0.333518\n", + "Epoch: 0300 cost= 0.147369981 W= 0.39798 b= -0.266015\n", + "Epoch: 0350 cost= 0.139234960 W= 0.389155 b= -0.202527\n", + "Epoch: 0400 cost= 0.132039562 W= 0.380854 b= -0.142815\n", + "Epoch: 0450 cost= 0.125675321 W= 0.373048 b= -0.0866538\n", + "Epoch: 0500 cost= 0.120046206 W= 0.365705 b= -0.0338331\n", + "Epoch: 0550 cost= 0.115067400 W= 0.3588 b= 0.0158462\n", + "Epoch: 0600 cost= 0.110663772 W= 0.352305 b= 0.0625707\n", + "Epoch: 0650 cost= 0.106768914 W= 0.346196 b= 0.106516\n", + "Epoch: 0700 cost= 0.103324078 W= 0.340451 b= 0.147848\n", + "Epoch: 0750 cost= 0.100277305 W= 0.335047 b= 0.186722\n", + "Epoch: 0800 cost= 0.097582638 W= 0.329965 b= 0.223284\n", + "Epoch: 0850 cost= 0.095199391 W= 0.325184 b= 0.257671\n", + "Epoch: 0900 cost= 0.093091547 W= 0.320689 b= 0.290013\n", + "Epoch: 0950 cost= 0.091227390 W= 0.31646 b= 0.320432\n", + "Epoch: 1000 cost= 0.089578770 W= 0.312484 b= 0.349041\n", + "Optimization Finished!\n", + "Training cost= 0.0895788 W= 0.312484 b= 0.349041 \n", + "\n" + ] + } + ], + "source": [ + "# Launch the graph\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + "\n", + " # Fit all training data\n", + " for epoch in range(training_epochs):\n", + " for (x, y) in zip(train_X, train_Y):\n", + " sess.run(optimizer, feed_dict={X: x, Y: y})\n", + "\n", + " #Display logs per epoch step\n", + " if (epoch+1) % display_step == 0:\n", + " c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})\n", + " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(c), \\\n", + " \"W=\", sess.run(W), \"b=\", sess.run(b)\n", + "\n", + " print \"Optimization Finished!\"\n", + " training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})\n", + " print \"Training cost=\", training_cost, \"W=\", sess.run(W), \"b=\", sess.run(b), '\\n'\n", + "\n", + " #Graphic display\n", + " plt.plot(train_X, train_Y, 'ro', label='Original data')\n", + " plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\n", + " plt.legend()\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/notebooks/2 - Basic Classifiers/logistic_regression.ipynb b/notebooks/2_BasicModels/logistic_regression.ipynb similarity index 50% rename from notebooks/2 - Basic Classifiers/logistic_regression.ipynb rename to notebooks/2_BasicModels/logistic_regression.ipynb index ed324751..8314dd9a 100644 --- a/notebooks/2 - Basic Classifiers/logistic_regression.ipynb +++ b/notebooks/2_BasicModels/logistic_regression.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -35,25 +35,16 @@ } ], "source": [ + "import tensorflow as tf\n", + "\n", "# Import MINST data\n", - "import input_data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import tensorflow as tf" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -63,79 +54,31 @@ "learning_rate = 0.01\n", "training_epochs = 25\n", "batch_size = 100\n", - "display_step = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "display_step = 1\n", + "\n", "# tf Graph Input\n", - "x = tf.placeholder(\"float\", [None, 784]) # mnist data image of shape 28*28=784\n", - "y = tf.placeholder(\"float\", [None, 10]) # 0-9 digits recognition => 10 classes" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Create model\n", + "x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784\n", + "y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes\n", "\n", "# Set model weights\n", "W = tf.Variable(tf.zeros([784, 10]))\n", - "b = tf.Variable(tf.zeros([10]))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "b = tf.Variable(tf.zeros([10]))\n", + "\n", "# Construct model\n", - "activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n", + "\n", "# Minimize error using cross entropy\n", - "# Cross entropy\n", - "cost = -tf.reduce_sum(y*tf.log(activation)) \n", + "cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))\n", "# Gradient Descent\n", - "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", + "\n", "# Initializing the variables\n", "init = tf.initialize_all_variables()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -144,33 +87,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch: 0001 cost= 29.860479714\n", - "Epoch: 0002 cost= 22.080549484\n", - "Epoch: 0003 cost= 21.237104595\n", - "Epoch: 0004 cost= 20.460196280\n", - "Epoch: 0005 cost= 20.185128237\n", - "Epoch: 0006 cost= 19.940297202\n", - "Epoch: 0007 cost= 19.645111119\n", - "Epoch: 0008 cost= 19.507218031\n", - "Epoch: 0009 cost= 19.389794492\n", - "Epoch: 0010 cost= 19.177005816\n", - "Epoch: 0011 cost= 19.082493615\n", - "Epoch: 0012 cost= 19.072873598\n", - "Epoch: 0013 cost= 18.938005402\n", - "Epoch: 0014 cost= 18.891806430\n", - "Epoch: 0015 cost= 18.839480221\n", - "Epoch: 0016 cost= 18.769349510\n", - "Epoch: 0017 cost= 18.590865587\n", - "Epoch: 0018 cost= 18.623413677\n", - "Epoch: 0019 cost= 18.546149085\n", - "Epoch: 0020 cost= 18.432274895\n", - "Epoch: 0021 cost= 18.358189004\n", - "Epoch: 0022 cost= 18.380014628\n", - "Epoch: 0023 cost= 18.499993471\n", - "Epoch: 0024 cost= 18.386477311\n", - "Epoch: 0025 cost= 18.258080609\n", + "Epoch: 0001 cost= 1.182138961\n", + "Epoch: 0002 cost= 0.664670898\n", + "Epoch: 0003 cost= 0.552613988\n", + "Epoch: 0004 cost= 0.498497931\n", + "Epoch: 0005 cost= 0.465418769\n", + "Epoch: 0006 cost= 0.442546219\n", + "Epoch: 0007 cost= 0.425473814\n", + "Epoch: 0008 cost= 0.412171735\n", + "Epoch: 0009 cost= 0.401359516\n", + "Epoch: 0010 cost= 0.392401536\n", + "Epoch: 0011 cost= 0.384750201\n", + "Epoch: 0012 cost= 0.378185581\n", + "Epoch: 0013 cost= 0.372401533\n", + "Epoch: 0014 cost= 0.367302442\n", + "Epoch: 0015 cost= 0.362702316\n", + "Epoch: 0016 cost= 0.358568827\n", + "Epoch: 0017 cost= 0.354882155\n", + "Epoch: 0018 cost= 0.351430912\n", + "Epoch: 0019 cost= 0.348316068\n", + "Epoch: 0020 cost= 0.345392556\n", + "Epoch: 0021 cost= 0.342737278\n", + "Epoch: 0022 cost= 0.340264994\n", + "Epoch: 0023 cost= 0.337890242\n", + "Epoch: 0024 cost= 0.335708558\n", + "Epoch: 0025 cost= 0.333686476\n", "Optimization Finished!\n", - "Accuracy: 0.9048\n" + "Accuracy: 0.889667\n" ] } ], @@ -187,42 +130,43 @@ " for i in range(total_batch):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", " # Fit training using batch data\n", - " sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})\n", + " _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,\n", + " y: batch_ys})\n", " # Compute average loss\n", - " avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch\n", + " avg_cost += c / total_batch\n", " # Display logs per epoch step\n", - " if epoch % display_step == 0:\n", + " if (epoch+1) % display_step == 0:\n", " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n", "\n", " print \"Optimization Finished!\"\n", "\n", " # Test model\n", - " correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))\n", - " # Calculate accuracy\n", - " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", - " print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})" + " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " # Calculate accuracy for 3000 examples\n", + " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + " print \"Accuracy:\", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]})" ] } ], "metadata": { "kernelspec": { - "display_name": "IPython (Python 2.7)", + "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.8" + "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/notebooks/2 - Basic Classifiers/nearest_neighbor.ipynb b/notebooks/2_BasicModels/nearest_neighbor.ipynb similarity index 89% rename from notebooks/2 - Basic Classifiers/nearest_neighbor.ipynb rename to notebooks/2_BasicModels/nearest_neighbor.ipynb index 9fe03859..f75a9d13 100644 --- a/notebooks/2 - Basic Classifiers/nearest_neighbor.ipynb +++ b/notebooks/2_BasicModels/nearest_neighbor.ipynb @@ -9,7 +9,7 @@ "outputs": [], "source": [ "# A nearest neighbor learning algorithm example using TensorFlow library.\n", - "# This example is using the MNIST database of handwritten digits \n", + "# This example is using the MNIST database of handwritten digits\n", "# (http://yann.lecun.com/exdb/mnist/)\n", "\n", "# Author: Aymeric Damien\n", @@ -19,18 +19,6 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import tensorflow as tf" - ] - }, - { - "cell_type": "code", - "execution_count": 3, "metadata": { "collapsed": false }, @@ -47,14 +35,17 @@ } ], "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "\n", "# Import MINST data\n", - "import input_data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -62,67 +53,27 @@ "source": [ "# In this example, we limit mnist data\n", "Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)\n", - "Xte, Yte = mnist.test.next_batch(200) #200 for testing" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Reshape images to 1D\n", - "Xtr = np.reshape(Xtr, newshape=(-1, 28*28))\n", - "Xte = np.reshape(Xte, newshape=(-1, 28*28))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "Xte, Yte = mnist.test.next_batch(200) #200 for testing\n", + "\n", "# tf Graph Input\n", "xtr = tf.placeholder(\"float\", [None, 784])\n", - "xte = tf.placeholder(\"float\", [784])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "xte = tf.placeholder(\"float\", [784])\n", + "\n", "# Nearest Neighbor calculation using L1 Distance\n", "# Calculate L1 Distance\n", "distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.neg(xte))), reduction_indices=1)\n", - "# Predict: Get min distance index (Nearest neighbor)\n", + "# Prediction: Get min distance index (Nearest neighbor)\n", "pred = tf.arg_min(distance, 0)\n", "\n", - "accuracy = 0." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "accuracy = 0.\n", + "\n", "# Initializing the variables\n", "init = tf.initialize_all_variables()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -344,46 +295,37 @@ " # loop over test data\n", " for i in range(len(Xte)):\n", " # Get nearest neighbor\n", - " nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i,:]})\n", + " nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})\n", " # Get nearest neighbor class label and compare it to its true label\n", " print \"Test\", i, \"Prediction:\", np.argmax(Ytr[nn_index]), \\\n", - " \"True Class:\", np.argmax(Yte[i])\n", + " \"True Class:\", np.argmax(Yte[i])\n", " # Calculate accuracy\n", " if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):\n", " accuracy += 1./len(Xte)\n", " print \"Done!\"\n", " print \"Accuracy:\", accuracy" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "IPython (Python 2.7)", + "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.8" + "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/notebooks/3 - Neural Networks/alexnet.ipynb b/notebooks/3 - Neural Networks/alexnet.ipynb deleted file mode 100644 index d1667c0e..00000000 --- a/notebooks/3 - Neural Networks/alexnet.ipynb +++ /dev/null @@ -1,348 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# AlexNet implementation example using TensorFlow library.\n", - "# This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", - "# AlexNet Paper (http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)\n", - "\n", - "# Author: Aymeric Damien\n", - "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], - "source": [ - "# Import MINST data\n", - "import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import tensorflow as tf" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Parameters\n", - "learning_rate = 0.001\n", - "training_iters = 300000\n", - "batch_size = 64\n", - "display_step = 100" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Network Parameters\n", - "n_input = 784 # MNIST data input (img shape: 28*28)\n", - "n_classes = 10 # MNIST total classes (0-9 digits)\n", - "dropout = 0.8 # Dropout, probability to keep units" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# tf Graph input\n", - "x = tf.placeholder(tf.float32, [None, n_input])\n", - "y = tf.placeholder(tf.float32, [None, n_classes])\n", - "keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Create AlexNet model\n", - "def conv2d(name, l_input, w, b):\n", - " return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], \n", - " padding='SAME'),b), name=name)\n", - "\n", - "def max_pool(name, l_input, k):\n", - " return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], \n", - " padding='SAME', name=name)\n", - "\n", - "def norm(name, l_input, lsize=4):\n", - " return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)\n", - "\n", - "def alex_net(_X, _weights, _biases, _dropout):\n", - " # Reshape input picture\n", - " _X = tf.reshape(_X, shape=[-1, 28, 28, 1])\n", - "\n", - " # Convolution Layer\n", - " conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])\n", - " # Max Pooling (down-sampling)\n", - " pool1 = max_pool('pool1', conv1, k=2)\n", - " # Apply Normalization\n", - " norm1 = norm('norm1', pool1, lsize=4)\n", - " # Apply Dropout\n", - " norm1 = tf.nn.dropout(norm1, _dropout)\n", - "\n", - " # Convolution Layer\n", - " conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])\n", - " # Max Pooling (down-sampling)\n", - " pool2 = max_pool('pool2', conv2, k=2)\n", - " # Apply Normalization\n", - " norm2 = norm('norm2', pool2, lsize=4)\n", - " # Apply Dropout\n", - " norm2 = tf.nn.dropout(norm2, _dropout)\n", - "\n", - " # Convolution Layer\n", - " conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])\n", - " # Max Pooling (down-sampling)\n", - " pool3 = max_pool('pool3', conv3, k=2)\n", - " # Apply Normalization\n", - " norm3 = norm('norm3', pool3, lsize=4)\n", - " # Apply Dropout\n", - " norm3 = tf.nn.dropout(norm3, _dropout)\n", - "\n", - " # Fully connected layer\n", - " # Reshape conv3 output to fit dense layer input\n", - " dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]]) \n", - " # Relu activation\n", - " dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')\n", - " \n", - " # Relu activation\n", - " dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') \n", - "\n", - " # Output, class prediction\n", - " out = tf.matmul(dense2, _weights['out']) + _biases['out']\n", - " return out" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Store layers weight & bias\n", - "weights = {\n", - " 'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),\n", - " 'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),\n", - " 'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),\n", - " 'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])),\n", - " 'wd2': tf.Variable(tf.random_normal([1024, 1024])),\n", - " 'out': tf.Variable(tf.random_normal([1024, 10]))\n", - "}\n", - "biases = {\n", - " 'bc1': tf.Variable(tf.random_normal([64])),\n", - " 'bc2': tf.Variable(tf.random_normal([128])),\n", - " 'bc3': tf.Variable(tf.random_normal([256])),\n", - " 'bd1': tf.Variable(tf.random_normal([1024])),\n", - " 'bd2': tf.Variable(tf.random_normal([1024])),\n", - " 'out': tf.Variable(tf.random_normal([n_classes]))\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Construct model\n", - "pred = alex_net(x, weights, biases, keep_prob)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", - "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Evaluate model\n", - "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", - "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Initializing the variables\n", - "init = tf.initialize_all_variables()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iter 6400, Minibatch Loss= 29666.185547, Training Accuracy= 0.59375\n", - "Iter 12800, Minibatch Loss= 22125.562500, Training Accuracy= 0.60938\n", - "Iter 19200, Minibatch Loss= 22631.134766, Training Accuracy= 0.59375\n", - "Iter 25600, Minibatch Loss= 18498.414062, Training Accuracy= 0.62500\n", - "Iter 32000, Minibatch Loss= 11318.283203, Training Accuracy= 0.70312\n", - "Iter 38400, Minibatch Loss= 12076.280273, Training Accuracy= 0.70312\n", - "Iter 44800, Minibatch Loss= 8195.520508, Training Accuracy= 0.82812\n", - "Iter 51200, Minibatch Loss= 5176.181641, Training Accuracy= 0.84375\n", - "Iter 57600, Minibatch Loss= 8951.896484, Training Accuracy= 0.81250\n", - "Iter 64000, Minibatch Loss= 10096.946289, Training Accuracy= 0.78125\n", - "Iter 70400, Minibatch Loss= 11466.641602, Training Accuracy= 0.68750\n", - "Iter 76800, Minibatch Loss= 7469.824219, Training Accuracy= 0.78125\n", - "Iter 83200, Minibatch Loss= 4147.449219, Training Accuracy= 0.89062\n", - "Iter 89600, Minibatch Loss= 5904.782227, Training Accuracy= 0.82812\n", - "Iter 96000, Minibatch Loss= 718.493713, Training Accuracy= 0.93750\n", - "Iter 102400, Minibatch Loss= 2184.151367, Training Accuracy= 0.93750\n", - "Iter 108800, Minibatch Loss= 2354.463135, Training Accuracy= 0.89062\n", - "Iter 115200, Minibatch Loss= 8612.959961, Training Accuracy= 0.81250\n", - "Iter 121600, Minibatch Loss= 2225.773926, Training Accuracy= 0.84375\n", - "Iter 128000, Minibatch Loss= 160.583618, Training Accuracy= 0.96875\n", - "Iter 134400, Minibatch Loss= 1524.846069, Training Accuracy= 0.93750\n", - "Iter 140800, Minibatch Loss= 3501.871094, Training Accuracy= 0.89062\n", - "Iter 147200, Minibatch Loss= 661.977051, Training Accuracy= 0.96875\n", - "Iter 153600, Minibatch Loss= 367.857788, Training Accuracy= 0.98438\n", - "Iter 160000, Minibatch Loss= 1735.458740, Training Accuracy= 0.90625\n", - "Iter 166400, Minibatch Loss= 209.320374, Training Accuracy= 0.95312\n", - "Iter 172800, Minibatch Loss= 1788.553955, Training Accuracy= 0.90625\n", - "Iter 179200, Minibatch Loss= 912.995544, Training Accuracy= 0.93750\n", - "Iter 185600, Minibatch Loss= 2534.074463, Training Accuracy= 0.87500\n", - "Iter 192000, Minibatch Loss= 73.052612, Training Accuracy= 0.96875\n", - "Iter 198400, Minibatch Loss= 1609.606323, Training Accuracy= 0.93750\n", - "Iter 204800, Minibatch Loss= 1823.219727, Training Accuracy= 0.96875\n", - "Iter 211200, Minibatch Loss= 578.051086, Training Accuracy= 0.96875\n", - "Iter 217600, Minibatch Loss= 1532.326172, Training Accuracy= 0.89062\n", - "Iter 224000, Minibatch Loss= 769.775269, Training Accuracy= 0.95312\n", - "Iter 230400, Minibatch Loss= 2614.737793, Training Accuracy= 0.92188\n", - "Iter 236800, Minibatch Loss= 938.664368, Training Accuracy= 0.95312\n", - "Iter 243200, Minibatch Loss= 1520.495605, Training Accuracy= 0.93750\n", - "Iter 249600, Minibatch Loss= 657.419739, Training Accuracy= 0.95312\n", - "Iter 256000, Minibatch Loss= 522.802124, Training Accuracy= 0.90625\n", - "Iter 262400, Minibatch Loss= 211.188477, Training Accuracy= 0.96875\n", - "Iter 268800, Minibatch Loss= 520.451172, Training Accuracy= 0.92188\n", - "Iter 275200, Minibatch Loss= 1418.759155, Training Accuracy= 0.89062\n", - "Iter 281600, Minibatch Loss= 241.748596, Training Accuracy= 0.96875\n", - "Iter 288000, Minibatch Loss= 0.000000, Training Accuracy= 1.00000\n", - "Iter 294400, Minibatch Loss= 1535.772827, Training Accuracy= 0.92188\n", - "Optimization Finished!\n", - "Testing Accuracy: 0.980469\n" - ] - } - ], - "source": [ - "# Launch the graph\n", - "with tf.Session() as sess:\n", - " sess.run(init)\n", - " step = 1\n", - " # Keep training until reach max iterations\n", - " while step * batch_size < training_iters:\n", - " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", - " # Fit training using batch data\n", - " sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})\n", - " if step % display_step == 0:\n", - " # Calculate batch accuracy\n", - " acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\n", - " # Calculate batch loss\n", - " loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\n", - " print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" \\\n", - " + \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \"{:.5f}\".format(acc)\n", - " step += 1\n", - " print \"Optimization Finished!\"\n", - " # Calculate accuracy for 256 mnist test images\n", - " print \"Testing Accuracy:\", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], \n", - " y: mnist.test.labels[:256], \n", - " keep_prob: 1.})" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "IPython (Python 2.7)", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.8" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/3 - Neural Networks/bidirectional_rnn.ipynb b/notebooks/3 - Neural Networks/bidirectional_rnn.ipynb deleted file mode 100644 index 2eaa4056..00000000 --- a/notebooks/3 - Neural Networks/bidirectional_rnn.ipynb +++ /dev/null @@ -1,350 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "'''\n", - "A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library.\n", - "This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", - "Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\n", - "\n", - "Author: Aymeric Damien\n", - "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], - "source": [ - "# Import MINST data\n", - "import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.python.ops.constant_op import constant\n", - "from tensorflow.models.rnn import rnn, rnn_cell\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "\n", - "'''\n", - "To classify images using a bidirectional reccurent neural network, we consider every image row as a sequence of pixels.\n", - "Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample.\n", - "'''\n", - "\n", - "# Parameters\n", - "learning_rate = 0.001\n", - "training_iters = 100000\n", - "batch_size = 128\n", - "display_step = 10\n", - "\n", - "# Network Parameters\n", - "n_input = 28 # MNIST data input (img shape: 28*28)\n", - "n_steps = 28 # timesteps\n", - "n_hidden = 128 # hidden layer num of features\n", - "n_classes = 10 # MNIST total classes (0-9 digits)\n", - "\n", - "# tf Graph input\n", - "x = tf.placeholder(\"float\", [None, n_steps, n_input])\n", - "# Tensorflow LSTM cell requires 2x n_hidden length (state & cell)\n", - "istate_fw = tf.placeholder(\"float\", [None, 2*n_hidden])\n", - "istate_bw = tf.placeholder(\"float\", [None, 2*n_hidden])\n", - "y = tf.placeholder(\"float\", [None, n_classes])\n", - "\n", - "# Define weights\n", - "weights = {\n", - " # Hidden layer weights => 2*n_hidden because of foward + backward cells\n", - " 'hidden': tf.Variable(tf.random_normal([n_input, 2*n_hidden])),\n", - " 'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))\n", - "}\n", - "biases = {\n", - " 'hidden': tf.Variable(tf.random_normal([2*n_hidden])),\n", - " 'out': tf.Variable(tf.random_normal([n_classes]))\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def BiRNN(_X, _istate_fw, _istate_bw, _weights, _biases, _batch_size, _seq_len):\n", - "\n", - " # BiRNN requires to supply sequence_length as [batch_size, int64]\n", - " # Note: Tensorflow 0.6.0 requires BiRNN sequence_length parameter to be set\n", - " # For a better implementation with latest version of tensorflow, check below\n", - " _seq_len = tf.fill([_batch_size], constant(_seq_len, dtype=tf.int64))\n", - "\n", - " # input shape: (batch_size, n_steps, n_input)\n", - " _X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size\n", - " # Reshape to prepare input to hidden activation\n", - " _X = tf.reshape(_X, [-1, n_input]) # (n_steps*batch_size, n_input)\n", - " # Linear activation\n", - " _X = tf.matmul(_X, _weights['hidden']) + _biases['hidden']\n", - "\n", - " # Define lstm cells with tensorflow\n", - " # Forward direction cell\n", - " lstm_fw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", - " # Backward direction cell\n", - " lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", - " # Split data because rnn cell needs a list of inputs for the RNN inner loop\n", - " _X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden)\n", - "\n", - " # Get lstm cell output\n", - " outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, _X,\n", - " initial_state_fw=_istate_fw,\n", - " initial_state_bw=_istate_bw,\n", - " sequence_length=_seq_len)\n", - "\n", - " # Linear activation\n", - " # Get inner loop last output\n", - " return tf.matmul(outputs[-1], _weights['out']) + _biases['out']\n", - "\n", - "pred = BiRNN(x, istate_fw, istate_bw, weights, biases, batch_size, n_steps)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# NOTE: The following code is working with current master version of tensorflow\n", - "# BiRNN sequence_length parameter isn't required, so we don't define it\n", - "#\n", - "# def BiRNN(_X, _istate_fw, _istate_bw, _weights, _biases):\n", - "#\n", - "# # input shape: (batch_size, n_steps, n_input)\n", - "# _X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size\n", - "# # Reshape to prepare input to hidden activation\n", - "# _X = tf.reshape(_X, [-1, n_input]) # (n_steps*batch_size, n_input)\n", - "# # Linear activation\n", - "# _X = tf.matmul(_X, _weights['hidden']) + _biases['hidden']\n", - "#\n", - "# # Define lstm cells with tensorflow\n", - "# # Forward direction cell\n", - "# lstm_fw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", - "# # Backward direction cell\n", - "# lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", - "# # Split data because rnn cell needs a list of inputs for the RNN inner loop\n", - "# _X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden)\n", - "#\n", - "# # Get lstm cell output\n", - "# outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, _X,\n", - "# initial_state_fw=_istate_fw,\n", - "# initial_state_bw=_istate_bw)\n", - "#\n", - "# # Linear activation\n", - "# # Get inner loop last output\n", - "# return tf.matmul(outputs[-1], _weights['out']) + _biases['out']\n", - "#\n", - "# pred = BiRNN(x, istate_fw, istate_bw, weights, biases)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # Softmax loss\n", - "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer\n", - "\n", - "# Evaluate model\n", - "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", - "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", - "\n", - "# Initializing the variables\n", - "init = tf.initialize_all_variables()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iter 1280, Minibatch Loss= 4.548751, Training Accuracy= 0.25781\n", - "Iter 2560, Minibatch Loss= 1.881705, Training Accuracy= 0.36719\n", - "Iter 3840, Minibatch Loss= 1.791362, Training Accuracy= 0.34375\n", - "Iter 5120, Minibatch Loss= 1.186327, Training Accuracy= 0.63281\n", - "Iter 6400, Minibatch Loss= 0.933242, Training Accuracy= 0.66406\n", - "Iter 7680, Minibatch Loss= 1.210745, Training Accuracy= 0.59375\n", - "Iter 8960, Minibatch Loss= 0.893051, Training Accuracy= 0.63281\n", - "Iter 10240, Minibatch Loss= 0.752483, Training Accuracy= 0.77344\n", - "Iter 11520, Minibatch Loss= 0.599419, Training Accuracy= 0.77344\n", - "Iter 12800, Minibatch Loss= 0.931269, Training Accuracy= 0.67969\n", - "Iter 14080, Minibatch Loss= 0.521487, Training Accuracy= 0.82031\n", - "Iter 15360, Minibatch Loss= 0.593033, Training Accuracy= 0.78906\n", - "Iter 16640, Minibatch Loss= 0.554892, Training Accuracy= 0.78906\n", - "Iter 17920, Minibatch Loss= 0.495159, Training Accuracy= 0.86719\n", - "Iter 19200, Minibatch Loss= 0.477557, Training Accuracy= 0.82812\n", - "Iter 20480, Minibatch Loss= 0.345205, Training Accuracy= 0.89844\n", - "Iter 21760, Minibatch Loss= 0.764044, Training Accuracy= 0.76562\n", - "Iter 23040, Minibatch Loss= 0.360194, Training Accuracy= 0.86719\n", - "Iter 24320, Minibatch Loss= 0.563836, Training Accuracy= 0.79688\n", - "Iter 25600, Minibatch Loss= 0.619804, Training Accuracy= 0.78906\n", - "Iter 26880, Minibatch Loss= 0.489240, Training Accuracy= 0.81250\n", - "Iter 28160, Minibatch Loss= 0.386111, Training Accuracy= 0.89844\n", - "Iter 29440, Minibatch Loss= 0.443906, Training Accuracy= 0.88281\n", - "Iter 30720, Minibatch Loss= 0.363123, Training Accuracy= 0.86719\n", - "Iter 32000, Minibatch Loss= 0.447942, Training Accuracy= 0.85938\n", - "Iter 33280, Minibatch Loss= 0.375448, Training Accuracy= 0.88281\n", - "Iter 34560, Minibatch Loss= 0.605834, Training Accuracy= 0.81250\n", - "Iter 35840, Minibatch Loss= 0.235447, Training Accuracy= 0.90625\n", - "Iter 37120, Minibatch Loss= 0.485220, Training Accuracy= 0.86719\n", - "Iter 38400, Minibatch Loss= 0.327258, Training Accuracy= 0.92969\n", - "Iter 39680, Minibatch Loss= 0.216945, Training Accuracy= 0.91406\n", - "Iter 40960, Minibatch Loss= 0.554652, Training Accuracy= 0.82812\n", - "Iter 42240, Minibatch Loss= 0.409230, Training Accuracy= 0.87500\n", - "Iter 43520, Minibatch Loss= 0.204563, Training Accuracy= 0.92188\n", - "Iter 44800, Minibatch Loss= 0.359138, Training Accuracy= 0.87500\n", - "Iter 46080, Minibatch Loss= 0.306512, Training Accuracy= 0.89844\n", - "Iter 47360, Minibatch Loss= 0.356531, Training Accuracy= 0.86719\n", - "Iter 48640, Minibatch Loss= 0.319080, Training Accuracy= 0.87500\n", - "Iter 49920, Minibatch Loss= 0.326718, Training Accuracy= 0.89844\n", - "Iter 51200, Minibatch Loss= 0.346867, Training Accuracy= 0.88281\n", - "Iter 52480, Minibatch Loss= 0.248568, Training Accuracy= 0.92969\n", - "Iter 53760, Minibatch Loss= 0.127805, Training Accuracy= 0.94531\n", - "Iter 55040, Minibatch Loss= 0.386457, Training Accuracy= 0.88281\n", - "Iter 56320, Minibatch Loss= 0.384653, Training Accuracy= 0.84375\n", - "Iter 57600, Minibatch Loss= 0.384377, Training Accuracy= 0.85938\n", - "Iter 58880, Minibatch Loss= 0.378528, Training Accuracy= 0.83594\n", - "Iter 60160, Minibatch Loss= 0.183152, Training Accuracy= 0.94531\n", - "Iter 61440, Minibatch Loss= 0.211561, Training Accuracy= 0.92969\n", - "Iter 62720, Minibatch Loss= 0.194529, Training Accuracy= 0.94531\n", - "Iter 64000, Minibatch Loss= 0.175247, Training Accuracy= 0.93750\n", - "Iter 65280, Minibatch Loss= 0.270519, Training Accuracy= 0.89844\n", - "Iter 66560, Minibatch Loss= 0.225893, Training Accuracy= 0.94531\n", - "Iter 67840, Minibatch Loss= 0.391300, Training Accuracy= 0.91406\n", - "Iter 69120, Minibatch Loss= 0.259621, Training Accuracy= 0.87500\n", - "Iter 70400, Minibatch Loss= 0.255645, Training Accuracy= 0.92969\n", - "Iter 71680, Minibatch Loss= 0.217164, Training Accuracy= 0.91406\n", - "Iter 72960, Minibatch Loss= 0.235931, Training Accuracy= 0.92188\n", - "Iter 74240, Minibatch Loss= 0.193127, Training Accuracy= 0.92188\n", - "Iter 75520, Minibatch Loss= 0.246558, Training Accuracy= 0.92969\n", - "Iter 76800, Minibatch Loss= 0.167383, Training Accuracy= 0.92969\n", - "Iter 78080, Minibatch Loss= 0.130506, Training Accuracy= 0.96875\n", - "Iter 79360, Minibatch Loss= 0.168879, Training Accuracy= 0.96875\n", - "Iter 80640, Minibatch Loss= 0.245589, Training Accuracy= 0.93750\n", - "Iter 81920, Minibatch Loss= 0.136840, Training Accuracy= 0.94531\n", - "Iter 83200, Minibatch Loss= 0.133286, Training Accuracy= 0.96875\n", - "Iter 84480, Minibatch Loss= 0.221121, Training Accuracy= 0.95312\n", - "Iter 85760, Minibatch Loss= 0.257268, Training Accuracy= 0.91406\n", - "Iter 87040, Minibatch Loss= 0.227299, Training Accuracy= 0.92969\n", - "Iter 88320, Minibatch Loss= 0.170016, Training Accuracy= 0.96094\n", - "Iter 89600, Minibatch Loss= 0.350118, Training Accuracy= 0.89844\n", - "Iter 90880, Minibatch Loss= 0.149303, Training Accuracy= 0.95312\n", - "Iter 92160, Minibatch Loss= 0.200295, Training Accuracy= 0.94531\n", - "Iter 93440, Minibatch Loss= 0.274823, Training Accuracy= 0.89844\n", - "Iter 94720, Minibatch Loss= 0.162888, Training Accuracy= 0.96875\n", - "Iter 96000, Minibatch Loss= 0.164938, Training Accuracy= 0.93750\n", - "Iter 97280, Minibatch Loss= 0.257220, Training Accuracy= 0.92969\n", - "Iter 98560, Minibatch Loss= 0.208767, Training Accuracy= 0.92188\n", - "Iter 99840, Minibatch Loss= 0.101323, Training Accuracy= 0.97656\n", - "Optimization Finished!\n", - "Testing Accuracy: 0.945312\n" - ] - } - ], - "source": [ - "# Launch the graph\n", - "with tf.Session() as sess:\n", - " sess.run(init)\n", - " step = 1\n", - " # Keep training until reach max iterations\n", - " while step * batch_size < training_iters:\n", - " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", - " # Reshape data to get 28 seq of 28 elements\n", - " batch_xs = batch_xs.reshape((batch_size, n_steps, n_input))\n", - " # Fit training using batch data\n", - " sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys,\n", - " istate_fw: np.zeros((batch_size, 2*n_hidden)),\n", - " istate_bw: np.zeros((batch_size, 2*n_hidden))})\n", - " if step % display_step == 0:\n", - " # Calculate batch accuracy\n", - " acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys,\n", - " istate_fw: np.zeros((batch_size, 2*n_hidden)),\n", - " istate_bw: np.zeros((batch_size, 2*n_hidden))})\n", - " # Calculate batch loss\n", - " loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys,\n", - " istate_fw: np.zeros((batch_size, 2*n_hidden)),\n", - " istate_bw: np.zeros((batch_size, 2*n_hidden))})\n", - " print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \"{:.6f}\".format(loss) + \\\n", - " \", Training Accuracy= \" + \"{:.5f}\".format(acc)\n", - " step += 1\n", - " print \"Optimization Finished!\"\n", - " # Calculate accuracy for 128 mnist test images\n", - " test_len = 128\n", - " test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\n", - " test_label = mnist.test.labels[:test_len]\n", - " print \"Testing Accuracy:\", sess.run(accuracy, feed_dict={x: test_data, y: test_label,\n", - " istate_fw: np.zeros((test_len, 2*n_hidden)),\n", - " istate_bw: np.zeros((test_len, 2*n_hidden))})" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "IPython (Python 2.7)", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.8" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/3 - Neural Networks/convolutional_network.ipynb b/notebooks/3 - Neural Networks/convolutional_network.ipynb deleted file mode 100644 index 61ebea6e..00000000 --- a/notebooks/3 - Neural Networks/convolutional_network.ipynb +++ /dev/null @@ -1,324 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# A Convolutional Network implementation example using TensorFlow library.\n", - "# This example is using the MNIST database of handwritten digits\n", - "# (http://yann.lecun.com/exdb/mnist/)\n", - "\n", - "# Author: Aymeric Damien\n", - "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], - "source": [ - "# Import MINST data\n", - "import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import tensorflow as tf" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Parameters\n", - "learning_rate = 0.001\n", - "training_iters = 100000\n", - "batch_size = 128\n", - "display_step = 20" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Network Parameters\n", - "n_input = 784 # MNIST data input (img shape: 28*28)\n", - "n_classes = 10 # MNIST total classes (0-9 digits)\n", - "dropout = 0.75 # Dropout, probability to keep units" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# tf Graph input\n", - "x = tf.placeholder(tf.float32, [None, n_input])\n", - "y = tf.placeholder(tf.float32, [None, n_classes])\n", - "keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Create model\n", - "def conv2d(img, w, b):\n", - " return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(img, w, strides=[1, 1, 1, 1], \n", - " padding='SAME'),b))\n", - "\n", - "def max_pool(img, k):\n", - " return tf.nn.max_pool(img, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')\n", - "\n", - "def conv_net(_X, _weights, _biases, _dropout):\n", - " # Reshape input picture\n", - " _X = tf.reshape(_X, shape=[-1, 28, 28, 1])\n", - "\n", - " # Convolution Layer\n", - " conv1 = conv2d(_X, _weights['wc1'], _biases['bc1'])\n", - " # Max Pooling (down-sampling)\n", - " conv1 = max_pool(conv1, k=2)\n", - " # Apply Dropout\n", - " conv1 = tf.nn.dropout(conv1, _dropout)\n", - "\n", - " # Convolution Layer\n", - " conv2 = conv2d(conv1, _weights['wc2'], _biases['bc2'])\n", - " # Max Pooling (down-sampling)\n", - " conv2 = max_pool(conv2, k=2)\n", - " # Apply Dropout\n", - " conv2 = tf.nn.dropout(conv2, _dropout)\n", - "\n", - " # Fully connected layer\n", - " # Reshape conv2 output to fit dense layer input\n", - " dense1 = tf.reshape(conv2, [-1, _weights['wd1'].get_shape().as_list()[0]]) \n", - " # Relu activation\n", - " dense1 = tf.nn.relu(tf.add(tf.matmul(dense1, _weights['wd1']), _biases['bd1']))\n", - " # Apply Dropout\n", - " dense1 = tf.nn.dropout(dense1, _dropout) # Apply Dropout\n", - "\n", - " # Output, class prediction\n", - " out = tf.add(tf.matmul(dense1, _weights['out']), _biases['out'])\n", - " return out" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Store layers weight & bias\n", - "weights = {\n", - " # 5x5 conv, 1 input, 32 outputs\n", - " 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), \n", - " # 5x5 conv, 32 inputs, 64 outputs\n", - " 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), \n", - " # fully connected, 7*7*64 inputs, 1024 outputs\n", - " 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), \n", - " # 1024 inputs, 10 outputs (class prediction)\n", - " 'out': tf.Variable(tf.random_normal([1024, n_classes])) \n", - "}\n", - "\n", - "biases = {\n", - " 'bc1': tf.Variable(tf.random_normal([32])),\n", - " 'bc2': tf.Variable(tf.random_normal([64])),\n", - " 'bd1': tf.Variable(tf.random_normal([1024])),\n", - " 'out': tf.Variable(tf.random_normal([n_classes]))\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Construct model\n", - "pred = conv_net(x, weights, biases, keep_prob)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", - "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Evaluate model\n", - "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", - "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Initializing the variables\n", - "init = tf.initialize_all_variables()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iter 2560, Minibatch Loss= 26046.011719, Training Accuracy= 0.21094\n", - "Iter 5120, Minibatch Loss= 10456.769531, Training Accuracy= 0.52344\n", - "Iter 7680, Minibatch Loss= 6273.207520, Training Accuracy= 0.71875\n", - "Iter 10240, Minibatch Loss= 6276.231445, Training Accuracy= 0.64062\n", - "Iter 12800, Minibatch Loss= 4188.221680, Training Accuracy= 0.77344\n", - "Iter 15360, Minibatch Loss= 2717.077637, Training Accuracy= 0.80469\n", - "Iter 17920, Minibatch Loss= 4057.120361, Training Accuracy= 0.81250\n", - "Iter 20480, Minibatch Loss= 1696.550415, Training Accuracy= 0.87500\n", - "Iter 23040, Minibatch Loss= 2525.317627, Training Accuracy= 0.85938\n", - "Iter 25600, Minibatch Loss= 2341.906738, Training Accuracy= 0.87500\n", - "Iter 28160, Minibatch Loss= 4200.535156, Training Accuracy= 0.79688\n", - "Iter 30720, Minibatch Loss= 1888.964355, Training Accuracy= 0.89062\n", - "Iter 33280, Minibatch Loss= 2167.645996, Training Accuracy= 0.84375\n", - "Iter 35840, Minibatch Loss= 1932.107544, Training Accuracy= 0.89844\n", - "Iter 38400, Minibatch Loss= 1562.430054, Training Accuracy= 0.90625\n", - "Iter 40960, Minibatch Loss= 1676.755249, Training Accuracy= 0.84375\n", - "Iter 43520, Minibatch Loss= 1003.626099, Training Accuracy= 0.93750\n", - "Iter 46080, Minibatch Loss= 1176.615479, Training Accuracy= 0.86719\n", - "Iter 48640, Minibatch Loss= 1260.592651, Training Accuracy= 0.88281\n", - "Iter 51200, Minibatch Loss= 1399.667969, Training Accuracy= 0.86719\n", - "Iter 53760, Minibatch Loss= 1259.961426, Training Accuracy= 0.89844\n", - "Iter 56320, Minibatch Loss= 1415.800781, Training Accuracy= 0.89062\n", - "Iter 58880, Minibatch Loss= 1835.365967, Training Accuracy= 0.85156\n", - "Iter 61440, Minibatch Loss= 1395.168823, Training Accuracy= 0.90625\n", - "Iter 64000, Minibatch Loss= 973.283569, Training Accuracy= 0.88281\n", - "Iter 66560, Minibatch Loss= 818.093811, Training Accuracy= 0.92969\n", - "Iter 69120, Minibatch Loss= 1178.744263, Training Accuracy= 0.92188\n", - "Iter 71680, Minibatch Loss= 845.889709, Training Accuracy= 0.89844\n", - "Iter 74240, Minibatch Loss= 1259.505615, Training Accuracy= 0.90625\n", - "Iter 76800, Minibatch Loss= 738.037109, Training Accuracy= 0.89844\n", - "Iter 79360, Minibatch Loss= 862.499146, Training Accuracy= 0.93750\n", - "Iter 81920, Minibatch Loss= 739.704041, Training Accuracy= 0.90625\n", - "Iter 84480, Minibatch Loss= 652.880310, Training Accuracy= 0.95312\n", - "Iter 87040, Minibatch Loss= 635.464600, Training Accuracy= 0.92969\n", - "Iter 89600, Minibatch Loss= 933.166626, Training Accuracy= 0.90625\n", - "Iter 92160, Minibatch Loss= 213.874893, Training Accuracy= 0.96094\n", - "Iter 94720, Minibatch Loss= 609.575684, Training Accuracy= 0.91406\n", - "Iter 97280, Minibatch Loss= 560.208008, Training Accuracy= 0.93750\n", - "Iter 99840, Minibatch Loss= 963.577148, Training Accuracy= 0.90625\n", - "Optimization Finished!\n", - "Testing Accuracy: 0.960938\n" - ] - } - ], - "source": [ - "# Launch the graph\n", - "with tf.Session() as sess:\n", - " sess.run(init)\n", - " step = 1\n", - " # Keep training until reach max iterations\n", - " while step * batch_size < training_iters:\n", - " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", - " # Fit training using batch data\n", - " sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})\n", - " if step % display_step == 0:\n", - " # Calculate batch accuracy\n", - " acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\n", - " # Calculate batch loss\n", - " loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})\n", - " print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n", - " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \"{:.5f}\".format(acc)\n", - " step += 1\n", - " print \"Optimization Finished!\"\n", - " # Calculate accuracy for 256 mnist test images\n", - " print \"Testing Accuracy:\", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], \n", - " y: mnist.test.labels[:256], \n", - " keep_prob: 1.})" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "IPython (Python 2.7)", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.8" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/3 - Neural Networks/input_data.py b/notebooks/3 - Neural Networks/input_data.py deleted file mode 100644 index 6fad792b..00000000 --- a/notebooks/3 - Neural Networks/input_data.py +++ /dev/null @@ -1,144 +0,0 @@ -"""Functions for downloading and reading MNIST data.""" -from __future__ import print_function -import gzip -import os -import urllib -import numpy -SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/' -def maybe_download(filename, work_directory): - """Download the data from Yann's website, unless it's already here.""" - if not os.path.exists(work_directory): - os.mkdir(work_directory) - filepath = os.path.join(work_directory, filename) - if not os.path.exists(filepath): - filepath, _ = urllib.urlretrieve(SOURCE_URL + filename, filepath) - statinfo = os.stat(filepath) - print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.') - return filepath -def _read32(bytestream): - dt = numpy.dtype(numpy.uint32).newbyteorder('>') - return numpy.frombuffer(bytestream.read(4), dtype=dt) -def extract_images(filename): - """Extract the images into a 4D uint8 numpy array [index, y, x, depth].""" - print('Extracting', filename) - with gzip.open(filename) as bytestream: - magic = _read32(bytestream) - if magic != 2051: - raise ValueError( - 'Invalid magic number %d in MNIST image file: %s' % - (magic, filename)) - num_images = _read32(bytestream) - rows = _read32(bytestream) - cols = _read32(bytestream) - buf = bytestream.read(rows * cols * num_images) - data = numpy.frombuffer(buf, dtype=numpy.uint8) - data = data.reshape(num_images, rows, cols, 1) - return data -def dense_to_one_hot(labels_dense, num_classes=10): - """Convert class labels from scalars to one-hot vectors.""" - num_labels = labels_dense.shape[0] - index_offset = numpy.arange(num_labels) * num_classes - labels_one_hot = numpy.zeros((num_labels, num_classes)) - labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 - return labels_one_hot -def extract_labels(filename, one_hot=False): - """Extract the labels into a 1D uint8 numpy array [index].""" - print('Extracting', filename) - with gzip.open(filename) as bytestream: - magic = _read32(bytestream) - if magic != 2049: - raise ValueError( - 'Invalid magic number %d in MNIST label file: %s' % - (magic, filename)) - num_items = _read32(bytestream) - buf = bytestream.read(num_items) - labels = numpy.frombuffer(buf, dtype=numpy.uint8) - if one_hot: - return dense_to_one_hot(labels) - return labels -class DataSet(object): - def __init__(self, images, labels, fake_data=False): - if fake_data: - self._num_examples = 10000 - else: - assert images.shape[0] == labels.shape[0], ( - "images.shape: %s labels.shape: %s" % (images.shape, - labels.shape)) - self._num_examples = images.shape[0] - # Convert shape from [num examples, rows, columns, depth] - # to [num examples, rows*columns] (assuming depth == 1) - assert images.shape[3] == 1 - images = images.reshape(images.shape[0], - images.shape[1] * images.shape[2]) - # Convert from [0, 255] -> [0.0, 1.0]. - images = images.astype(numpy.float32) - images = numpy.multiply(images, 1.0 / 255.0) - self._images = images - self._labels = labels - self._epochs_completed = 0 - self._index_in_epoch = 0 - @property - def images(self): - return self._images - @property - def labels(self): - return self._labels - @property - def num_examples(self): - return self._num_examples - @property - def epochs_completed(self): - return self._epochs_completed - def next_batch(self, batch_size, fake_data=False): - """Return the next `batch_size` examples from this data set.""" - if fake_data: - fake_image = [1.0 for _ in xrange(784)] - fake_label = 0 - return [fake_image for _ in xrange(batch_size)], [ - fake_label for _ in xrange(batch_size)] - start = self._index_in_epoch - self._index_in_epoch += batch_size - if self._index_in_epoch > self._num_examples: - # Finished epoch - self._epochs_completed += 1 - # Shuffle the data - perm = numpy.arange(self._num_examples) - numpy.random.shuffle(perm) - self._images = self._images[perm] - self._labels = self._labels[perm] - # Start next epoch - start = 0 - self._index_in_epoch = batch_size - assert batch_size <= self._num_examples - end = self._index_in_epoch - return self._images[start:end], self._labels[start:end] -def read_data_sets(train_dir, fake_data=False, one_hot=False): - class DataSets(object): - pass - data_sets = DataSets() - if fake_data: - data_sets.train = DataSet([], [], fake_data=True) - data_sets.validation = DataSet([], [], fake_data=True) - data_sets.test = DataSet([], [], fake_data=True) - return data_sets - TRAIN_IMAGES = 'train-images-idx3-ubyte.gz' - TRAIN_LABELS = 'train-labels-idx1-ubyte.gz' - TEST_IMAGES = 't10k-images-idx3-ubyte.gz' - TEST_LABELS = 't10k-labels-idx1-ubyte.gz' - VALIDATION_SIZE = 5000 - local_file = maybe_download(TRAIN_IMAGES, train_dir) - train_images = extract_images(local_file) - local_file = maybe_download(TRAIN_LABELS, train_dir) - train_labels = extract_labels(local_file, one_hot=one_hot) - local_file = maybe_download(TEST_IMAGES, train_dir) - test_images = extract_images(local_file) - local_file = maybe_download(TEST_LABELS, train_dir) - test_labels = extract_labels(local_file, one_hot=one_hot) - validation_images = train_images[:VALIDATION_SIZE] - validation_labels = train_labels[:VALIDATION_SIZE] - train_images = train_images[VALIDATION_SIZE:] - train_labels = train_labels[VALIDATION_SIZE:] - data_sets.train = DataSet(train_images, train_labels) - data_sets.validation = DataSet(validation_images, validation_labels) - data_sets.test = DataSet(test_images, test_labels) - return data_sets diff --git a/notebooks/3 - Neural Networks/reccurent_network.ipynb b/notebooks/3 - Neural Networks/reccurent_network.ipynb deleted file mode 100644 index 260189cf..00000000 --- a/notebooks/3 - Neural Networks/reccurent_network.ipynb +++ /dev/null @@ -1,299 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "'''\n", - "A Reccurent Neural Network (LSTM) implementation example using TensorFlow library.\n", - "This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", - "Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\n", - "\n", - "Author: Aymeric Damien\n", - "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], - "source": [ - "# Import MINST data\n", - "import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", - "\n", - "import tensorflow as tf\n", - "from tensorflow.models.rnn import rnn, rnn_cell\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "'''\n", - "To classify images using a reccurent neural network, we consider every image row as a sequence of pixels.\n", - "Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample.\n", - "'''\n", - "\n", - "# Parameters\n", - "learning_rate = 0.001\n", - "training_iters = 100000\n", - "batch_size = 128\n", - "display_step = 10\n", - "\n", - "# Network Parameters\n", - "n_input = 28 # MNIST data input (img shape: 28*28)\n", - "n_steps = 28 # timesteps\n", - "n_hidden = 128 # hidden layer num of features\n", - "n_classes = 10 # MNIST total classes (0-9 digits)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# tf Graph input\n", - "x = tf.placeholder(\"float\", [None, n_steps, n_input])\n", - "istate = tf.placeholder(\"float\", [None, 2*n_hidden]) #state & cell => 2x n_hidden\n", - "y = tf.placeholder(\"float\", [None, n_classes])\n", - "\n", - "# Define weights\n", - "weights = {\n", - " 'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights\n", - " 'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\n", - "}\n", - "biases = {\n", - " 'hidden': tf.Variable(tf.random_normal([n_hidden])),\n", - " 'out': tf.Variable(tf.random_normal([n_classes]))\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def RNN(_X, _istate, _weights, _biases):\n", - "\n", - " # input shape: (batch_size, n_steps, n_input)\n", - " _X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size\n", - " # Reshape to prepare input to hidden activation\n", - " _X = tf.reshape(_X, [-1, n_input]) # (n_steps*batch_size, n_input)\n", - " # Linear activation\n", - " _X = tf.matmul(_X, _weights['hidden']) + _biases['hidden']\n", - "\n", - " # Define a lstm cell with tensorflow\n", - " lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", - " # Split data because rnn cell needs a list of inputs for the RNN inner loop\n", - " _X = tf.split(0, n_steps, _X) # n_steps * (batch_size, n_hidden)\n", - "\n", - " # Get lstm cell output\n", - " outputs, states = rnn.rnn(lstm_cell, _X, initial_state=_istate)\n", - "\n", - " # Linear activation\n", - " # Get inner loop last output\n", - " return tf.matmul(outputs[-1], _weights['out']) + _biases['out']" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pred = RNN(x, istate, weights, biases)\n", - "\n", - "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) # Softmax loss\n", - "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer\n", - "\n", - "# Evaluate model\n", - "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", - "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iter 1280, Minibatch Loss= 1.888242, Training Accuracy= 0.39844\n", - "Iter 2560, Minibatch Loss= 1.519879, Training Accuracy= 0.47656\n", - "Iter 3840, Minibatch Loss= 1.238005, Training Accuracy= 0.63281\n", - "Iter 5120, Minibatch Loss= 0.933760, Training Accuracy= 0.71875\n", - "Iter 6400, Minibatch Loss= 0.832130, Training Accuracy= 0.73438\n", - "Iter 7680, Minibatch Loss= 0.979760, Training Accuracy= 0.70312\n", - "Iter 8960, Minibatch Loss= 0.821921, Training Accuracy= 0.71875\n", - "Iter 10240, Minibatch Loss= 0.710566, Training Accuracy= 0.79688\n", - "Iter 11520, Minibatch Loss= 0.578501, Training Accuracy= 0.82812\n", - "Iter 12800, Minibatch Loss= 0.765049, Training Accuracy= 0.75000\n", - "Iter 14080, Minibatch Loss= 0.582995, Training Accuracy= 0.78125\n", - "Iter 15360, Minibatch Loss= 0.575092, Training Accuracy= 0.79688\n", - "Iter 16640, Minibatch Loss= 0.701214, Training Accuracy= 0.75781\n", - "Iter 17920, Minibatch Loss= 0.561972, Training Accuracy= 0.78125\n", - "Iter 19200, Minibatch Loss= 0.394480, Training Accuracy= 0.85938\n", - "Iter 20480, Minibatch Loss= 0.356244, Training Accuracy= 0.91406\n", - "Iter 21760, Minibatch Loss= 0.632163, Training Accuracy= 0.78125\n", - "Iter 23040, Minibatch Loss= 0.269334, Training Accuracy= 0.90625\n", - "Iter 24320, Minibatch Loss= 0.485007, Training Accuracy= 0.86719\n", - "Iter 25600, Minibatch Loss= 0.569704, Training Accuracy= 0.78906\n", - "Iter 26880, Minibatch Loss= 0.267697, Training Accuracy= 0.92188\n", - "Iter 28160, Minibatch Loss= 0.381177, Training Accuracy= 0.90625\n", - "Iter 29440, Minibatch Loss= 0.350800, Training Accuracy= 0.87500\n", - "Iter 30720, Minibatch Loss= 0.356782, Training Accuracy= 0.90625\n", - "Iter 32000, Minibatch Loss= 0.322511, Training Accuracy= 0.89062\n", - "Iter 33280, Minibatch Loss= 0.309195, Training Accuracy= 0.90625\n", - "Iter 34560, Minibatch Loss= 0.535408, Training Accuracy= 0.83594\n", - "Iter 35840, Minibatch Loss= 0.281643, Training Accuracy= 0.92969\n", - "Iter 37120, Minibatch Loss= 0.290962, Training Accuracy= 0.89844\n", - "Iter 38400, Minibatch Loss= 0.204718, Training Accuracy= 0.93750\n", - "Iter 39680, Minibatch Loss= 0.205882, Training Accuracy= 0.92969\n", - "Iter 40960, Minibatch Loss= 0.481441, Training Accuracy= 0.84375\n", - "Iter 42240, Minibatch Loss= 0.348245, Training Accuracy= 0.89844\n", - "Iter 43520, Minibatch Loss= 0.274692, Training Accuracy= 0.90625\n", - "Iter 44800, Minibatch Loss= 0.171815, Training Accuracy= 0.94531\n", - "Iter 46080, Minibatch Loss= 0.171035, Training Accuracy= 0.93750\n", - "Iter 47360, Minibatch Loss= 0.235800, Training Accuracy= 0.89844\n", - "Iter 48640, Minibatch Loss= 0.235974, Training Accuracy= 0.93750\n", - "Iter 49920, Minibatch Loss= 0.207323, Training Accuracy= 0.92188\n", - "Iter 51200, Minibatch Loss= 0.212989, Training Accuracy= 0.91406\n", - "Iter 52480, Minibatch Loss= 0.151774, Training Accuracy= 0.95312\n", - "Iter 53760, Minibatch Loss= 0.090070, Training Accuracy= 0.96875\n", - "Iter 55040, Minibatch Loss= 0.264714, Training Accuracy= 0.92969\n", - "Iter 56320, Minibatch Loss= 0.235086, Training Accuracy= 0.92969\n", - "Iter 57600, Minibatch Loss= 0.160302, Training Accuracy= 0.95312\n", - "Iter 58880, Minibatch Loss= 0.106515, Training Accuracy= 0.96875\n", - "Iter 60160, Minibatch Loss= 0.236039, Training Accuracy= 0.94531\n", - "Iter 61440, Minibatch Loss= 0.279540, Training Accuracy= 0.90625\n", - "Iter 62720, Minibatch Loss= 0.173585, Training Accuracy= 0.93750\n", - "Iter 64000, Minibatch Loss= 0.191009, Training Accuracy= 0.92188\n", - "Iter 65280, Minibatch Loss= 0.210331, Training Accuracy= 0.89844\n", - "Iter 66560, Minibatch Loss= 0.223444, Training Accuracy= 0.94531\n", - "Iter 67840, Minibatch Loss= 0.278210, Training Accuracy= 0.91406\n", - "Iter 69120, Minibatch Loss= 0.174290, Training Accuracy= 0.95312\n", - "Iter 70400, Minibatch Loss= 0.188701, Training Accuracy= 0.94531\n", - "Iter 71680, Minibatch Loss= 0.210277, Training Accuracy= 0.94531\n", - "Iter 72960, Minibatch Loss= 0.249951, Training Accuracy= 0.95312\n", - "Iter 74240, Minibatch Loss= 0.209853, Training Accuracy= 0.92188\n", - "Iter 75520, Minibatch Loss= 0.049742, Training Accuracy= 0.99219\n", - "Iter 76800, Minibatch Loss= 0.250095, Training Accuracy= 0.92969\n", - "Iter 78080, Minibatch Loss= 0.133853, Training Accuracy= 0.95312\n", - "Iter 79360, Minibatch Loss= 0.110206, Training Accuracy= 0.97656\n", - "Iter 80640, Minibatch Loss= 0.141906, Training Accuracy= 0.93750\n", - "Iter 81920, Minibatch Loss= 0.126872, Training Accuracy= 0.94531\n", - "Iter 83200, Minibatch Loss= 0.138925, Training Accuracy= 0.95312\n", - "Iter 84480, Minibatch Loss= 0.128652, Training Accuracy= 0.96094\n", - "Iter 85760, Minibatch Loss= 0.099837, Training Accuracy= 0.96094\n", - "Iter 87040, Minibatch Loss= 0.119000, Training Accuracy= 0.95312\n", - "Iter 88320, Minibatch Loss= 0.179807, Training Accuracy= 0.95312\n", - "Iter 89600, Minibatch Loss= 0.141792, Training Accuracy= 0.96094\n", - "Iter 90880, Minibatch Loss= 0.142424, Training Accuracy= 0.96094\n", - "Iter 92160, Minibatch Loss= 0.159564, Training Accuracy= 0.96094\n", - "Iter 93440, Minibatch Loss= 0.111984, Training Accuracy= 0.95312\n", - "Iter 94720, Minibatch Loss= 0.238978, Training Accuracy= 0.92969\n", - "Iter 96000, Minibatch Loss= 0.068002, Training Accuracy= 0.97656\n", - "Iter 97280, Minibatch Loss= 0.191819, Training Accuracy= 0.94531\n", - "Iter 98560, Minibatch Loss= 0.081197, Training Accuracy= 0.99219\n", - "Iter 99840, Minibatch Loss= 0.206797, Training Accuracy= 0.95312\n", - "Optimization Finished!\n", - "Testing Accuracy: 0.941406\n" - ] - } - ], - "source": [ - "# Initializing the variables\n", - "init = tf.initialize_all_variables()\n", - "\n", - "# Launch the graph\n", - "with tf.Session() as sess:\n", - " sess.run(init)\n", - " step = 1\n", - " # Keep training until reach max iterations\n", - " while step * batch_size < training_iters:\n", - " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", - " # Reshape data to get 28 seq of 28 elements\n", - " batch_xs = batch_xs.reshape((batch_size, n_steps, n_input))\n", - " # Fit training using batch data\n", - " sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys,\n", - " istate: np.zeros((batch_size, 2*n_hidden))})\n", - " if step % display_step == 0:\n", - " # Calculate batch accuracy\n", - " acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys,\n", - " istate: np.zeros((batch_size, 2*n_hidden))})\n", - " # Calculate batch loss\n", - " loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys,\n", - " istate: np.zeros((batch_size, 2*n_hidden))})\n", - " print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \"{:.6f}\".format(loss) + \\\n", - " \", Training Accuracy= \" + \"{:.5f}\".format(acc)\n", - " step += 1\n", - " print \"Optimization Finished!\"\n", - " # Calculate accuracy for 256 mnist test images\n", - " test_len = 256\n", - " test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\n", - " test_label = mnist.test.labels[:test_len]\n", - " print \"Testing Accuracy:\", sess.run(accuracy, feed_dict={x: test_data, y: test_label,\n", - " istate: np.zeros((test_len, 2*n_hidden))})" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "IPython (Python 2.7)", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.8" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/3_Neural Networks/autoencoder.ipynb b/notebooks/3_Neural Networks/autoencoder.ipynb new file mode 100644 index 00000000..f51feb0a --- /dev/null +++ b/notebooks/3_Neural Networks/autoencoder.ipynb @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "\"\"\" Auto Encoder Example.\n", + "Using an auto encoder on MNIST handwritten digits.\n", + "References:\n", + " Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. \"Gradient-based\n", + " learning applied to document recognition.\" Proceedings of the IEEE,\n", + " 86(11):2278-2324, November 1998.\n", + "Links:\n", + " [MNIST Dataset] http://yann.lecun.com/exdb/mnist/\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 20\n", + "batch_size = 256\n", + "display_step = 1\n", + "examples_to_show = 10\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer num features\n", + "n_hidden_2 = 128 # 2nd layer num features\n", + "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "\n", + "# tf Graph input (only pictures)\n", + "X = tf.placeholder(\"float\", [None, n_input])\n", + "\n", + "weights = {\n", + " 'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n", + " 'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n", + " 'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),\n", + " 'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),\n", + "}\n", + "biases = {\n", + " 'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", + " 'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),\n", + " 'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", + " 'decoder_b2': tf.Variable(tf.random_normal([n_input])),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Building the encoder\n", + "def encoder(x):\n", + " # Encoder Hidden layer with sigmoid activation #1\n", + " layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),\n", + " biases['encoder_b1']))\n", + " # Decoder Hidden layer with sigmoid activation #2\n", + " layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),\n", + " biases['encoder_b2']))\n", + " return layer_2\n", + "\n", + "\n", + "# Building the decoder\n", + "def decoder(x):\n", + " # Encoder Hidden layer with sigmoid activation #1\n", + " layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),\n", + " biases['decoder_b1']))\n", + " # Decoder Hidden layer with sigmoid activation #2\n", + " layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),\n", + " biases['decoder_b2']))\n", + " return layer_2\n", + "\n", + "# Construct model\n", + "encoder_op = encoder(X)\n", + "decoder_op = decoder(encoder_op)\n", + "\n", + "# Prediction\n", + "y_pred = decoder_op\n", + "# Targets (Labels) are the input data.\n", + "y_true = X\n", + "\n", + "# Define loss and optimizer, minimize the squared error\n", + "cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))\n", + "optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)\n", + "\n", + "# Initializing the variables\n", + "init = tf.initialize_all_variables()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0001 cost= 0.218654603\n", + "Epoch: 0002 cost= 0.173306286\n", + "Epoch: 0003 cost= 0.154793650\n", + "Epoch: 0004 cost= 0.146902516\n", + "Epoch: 0005 cost= 0.141993478\n", + "Epoch: 0006 cost= 0.132718414\n", + "Epoch: 0007 cost= 0.125991374\n", + "Epoch: 0008 cost= 0.122500181\n", + "Epoch: 0009 cost= 0.115299642\n", + "Epoch: 0010 cost= 0.115390278\n", + "Epoch: 0011 cost= 0.114480168\n", + "Epoch: 0012 cost= 0.113888472\n", + "Epoch: 0013 cost= 0.111597553\n", + "Epoch: 0014 cost= 0.110663064\n", + "Epoch: 0015 cost= 0.108673096\n", + "Epoch: 0016 cost= 0.104775786\n", + "Epoch: 0017 cost= 0.106273368\n", + "Epoch: 0018 cost= 0.104061618\n", + "Epoch: 0019 cost= 0.103227913\n", + "Epoch: 0020 cost= 0.099696413\n", + "Optimization Finished!\n" + ] + } + ], + "source": [ + "# Launch the graph\n", + "# Using InteractiveSession (more convenient while using Notebooks)\n", + "sess = tf.InteractiveSession()\n", + "sess.run(init)\n", + "\n", + "total_batch = int(mnist.train.num_examples/batch_size)\n", + "# Training cycle\n", + "for epoch in range(training_epochs):\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop) and cost op (to get loss value)\n", + " _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})\n", + " # Display logs per epoch step\n", + " if epoch % display_step == 0:\n", + " print(\"Epoch:\", '%04d' % (epoch+1),\n", + " \"cost=\", \"{:.9f}\".format(c))\n", + "\n", + "print(\"Optimization Finished!\")\n", + "\n", + "# Applying encode and decode over test set\n", + "encode_decode = sess.run(\n", + " y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})\n", + "# Compare original images with their reconstructions\n", + "f, a = plt.subplots(2, 10, figsize=(10, 2))\n", + "for i in range(examples_to_show):\n", + " a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))\n", + " a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))\n", + "f.show()\n", + "plt.draw()\n", + "plt.waitforbuttonpress()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/notebooks/3_Neural Networks/bidirectional_rnn.ipynb b/notebooks/3_Neural Networks/bidirectional_rnn.ipynb new file mode 100644 index 00000000..9558e571 --- /dev/null +++ b/notebooks/3_Neural Networks/bidirectional_rnn.ipynb @@ -0,0 +1,293 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "'''\n", + "A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library.\n", + "This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", + "Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\n", + "\n", + "Author: Aymeric Damien\n", + "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.models.rnn import rnn, rnn_cell\n", + "import numpy as np\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "'''\n", + "To classify images using a bidirectional reccurent neural network, we consider\n", + "every image row as a sequence of pixels. Because MNIST image shape is 28*28px,\n", + "we will then handle 28 sequences of 28 steps for every sample.\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "training_iters = 100000\n", + "batch_size = 128\n", + "display_step = 10\n", + "\n", + "# Network Parameters\n", + "n_input = 28 # MNIST data input (img shape: 28*28)\n", + "n_steps = 28 # timesteps\n", + "n_hidden = 128 # hidden layer num of features\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "x = tf.placeholder(\"float\", [None, n_steps, n_input])\n", + "y = tf.placeholder(\"float\", [None, n_classes])\n", + "\n", + "# Define weights\n", + "weights = {\n", + " # Hidden layer weights => 2*n_hidden because of foward + backward cells\n", + " 'hidden': tf.Variable(tf.random_normal([n_input, 2*n_hidden])),\n", + " 'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))\n", + "}\n", + "biases = {\n", + " 'hidden': tf.Variable(tf.random_normal([2*n_hidden])),\n", + " 'out': tf.Variable(tf.random_normal([n_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def BiRNN(x, weights, biases):\n", + "\n", + " # Prepare data shape to match `bidirectional_rnn` function requirements\n", + " # Current data input shape: (batch_size, n_steps, n_input)\n", + " # Permuting batch_size and n_steps\n", + " x = tf.transpose(x, [1, 0, 2])\n", + " # Reshape to (n_steps*batch_size, n_input)\n", + " x = tf.reshape(x, [-1, n_input])\n", + " # Split to get a list of 'n_steps' tensors of shape (batch_size, n_hidden)\n", + " x = tf.split(0, n_steps, x)\n", + "\n", + " # Define lstm cells with tensorflow\n", + " # Forward direction cell\n", + " lstm_fw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", + " # Backward direction cell\n", + " lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", + "\n", + " # Get lstm cell output\n", + " outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", + " dtype=tf.float32)\n", + "\n", + " # Linear activation, using rnn inner loop last output\n", + " return tf.matmul(outputs[-1], weights['out']) + biases['out']\n", + "\n", + "pred = BiRNN(x, weights, biases)\n", + "\n", + "# Define loss and optimizer\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "\n", + "# Evaluate model\n", + "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initializing the variables\n", + "init = tf.initialize_all_variables()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iter 1280, Minibatch Loss= 1.689740, Training Accuracy= 0.36719\n", + "Iter 2560, Minibatch Loss= 1.477009, Training Accuracy= 0.44531\n", + "Iter 3840, Minibatch Loss= 1.245874, Training Accuracy= 0.53125\n", + "Iter 5120, Minibatch Loss= 0.990923, Training Accuracy= 0.64062\n", + "Iter 6400, Minibatch Loss= 0.752950, Training Accuracy= 0.71875\n", + "Iter 7680, Minibatch Loss= 1.023025, Training Accuracy= 0.61719\n", + "Iter 8960, Minibatch Loss= 0.921414, Training Accuracy= 0.68750\n", + "Iter 10240, Minibatch Loss= 0.719829, Training Accuracy= 0.75000\n", + "Iter 11520, Minibatch Loss= 0.468657, Training Accuracy= 0.86719\n", + "Iter 12800, Minibatch Loss= 0.654315, Training Accuracy= 0.78125\n", + "Iter 14080, Minibatch Loss= 0.595391, Training Accuracy= 0.83594\n", + "Iter 15360, Minibatch Loss= 0.392862, Training Accuracy= 0.83594\n", + "Iter 16640, Minibatch Loss= 0.421122, Training Accuracy= 0.92188\n", + "Iter 17920, Minibatch Loss= 0.311471, Training Accuracy= 0.88281\n", + "Iter 19200, Minibatch Loss= 0.276949, Training Accuracy= 0.92188\n", + "Iter 20480, Minibatch Loss= 0.170499, Training Accuracy= 0.94531\n", + "Iter 21760, Minibatch Loss= 0.419481, Training Accuracy= 0.86719\n", + "Iter 23040, Minibatch Loss= 0.183765, Training Accuracy= 0.92188\n", + "Iter 24320, Minibatch Loss= 0.386232, Training Accuracy= 0.86719\n", + "Iter 25600, Minibatch Loss= 0.335571, Training Accuracy= 0.88281\n", + "Iter 26880, Minibatch Loss= 0.169092, Training Accuracy= 0.92969\n", + "Iter 28160, Minibatch Loss= 0.247623, Training Accuracy= 0.92969\n", + "Iter 29440, Minibatch Loss= 0.242989, Training Accuracy= 0.94531\n", + "Iter 30720, Minibatch Loss= 0.253811, Training Accuracy= 0.92188\n", + "Iter 32000, Minibatch Loss= 0.169660, Training Accuracy= 0.93750\n", + "Iter 33280, Minibatch Loss= 0.291349, Training Accuracy= 0.90625\n", + "Iter 34560, Minibatch Loss= 0.172026, Training Accuracy= 0.95312\n", + "Iter 35840, Minibatch Loss= 0.186019, Training Accuracy= 0.93750\n", + "Iter 37120, Minibatch Loss= 0.298480, Training Accuracy= 0.89062\n", + "Iter 38400, Minibatch Loss= 0.158750, Training Accuracy= 0.92188\n", + "Iter 39680, Minibatch Loss= 0.162706, Training Accuracy= 0.94531\n", + "Iter 40960, Minibatch Loss= 0.339814, Training Accuracy= 0.86719\n", + "Iter 42240, Minibatch Loss= 0.068817, Training Accuracy= 0.99219\n", + "Iter 43520, Minibatch Loss= 0.188742, Training Accuracy= 0.93750\n", + "Iter 44800, Minibatch Loss= 0.176708, Training Accuracy= 0.92969\n", + "Iter 46080, Minibatch Loss= 0.096726, Training Accuracy= 0.96875\n", + "Iter 47360, Minibatch Loss= 0.220973, Training Accuracy= 0.92969\n", + "Iter 48640, Minibatch Loss= 0.226749, Training Accuracy= 0.94531\n", + "Iter 49920, Minibatch Loss= 0.188906, Training Accuracy= 0.94531\n", + "Iter 51200, Minibatch Loss= 0.145194, Training Accuracy= 0.95312\n", + "Iter 52480, Minibatch Loss= 0.168948, Training Accuracy= 0.95312\n", + "Iter 53760, Minibatch Loss= 0.069116, Training Accuracy= 0.97656\n", + "Iter 55040, Minibatch Loss= 0.228721, Training Accuracy= 0.93750\n", + "Iter 56320, Minibatch Loss= 0.152915, Training Accuracy= 0.95312\n", + "Iter 57600, Minibatch Loss= 0.126974, Training Accuracy= 0.96875\n", + "Iter 58880, Minibatch Loss= 0.078870, Training Accuracy= 0.97656\n", + "Iter 60160, Minibatch Loss= 0.225498, Training Accuracy= 0.95312\n", + "Iter 61440, Minibatch Loss= 0.111760, Training Accuracy= 0.97656\n", + "Iter 62720, Minibatch Loss= 0.161434, Training Accuracy= 0.97656\n", + "Iter 64000, Minibatch Loss= 0.207190, Training Accuracy= 0.94531\n", + "Iter 65280, Minibatch Loss= 0.103831, Training Accuracy= 0.96094\n", + "Iter 66560, Minibatch Loss= 0.153846, Training Accuracy= 0.93750\n", + "Iter 67840, Minibatch Loss= 0.082717, Training Accuracy= 0.96875\n", + "Iter 69120, Minibatch Loss= 0.199301, Training Accuracy= 0.95312\n", + "Iter 70400, Minibatch Loss= 0.139725, Training Accuracy= 0.96875\n", + "Iter 71680, Minibatch Loss= 0.169596, Training Accuracy= 0.95312\n", + "Iter 72960, Minibatch Loss= 0.142444, Training Accuracy= 0.96094\n", + "Iter 74240, Minibatch Loss= 0.145822, Training Accuracy= 0.95312\n", + "Iter 75520, Minibatch Loss= 0.129086, Training Accuracy= 0.94531\n", + "Iter 76800, Minibatch Loss= 0.078082, Training Accuracy= 0.97656\n", + "Iter 78080, Minibatch Loss= 0.151803, Training Accuracy= 0.94531\n", + "Iter 79360, Minibatch Loss= 0.050142, Training Accuracy= 0.98438\n", + "Iter 80640, Minibatch Loss= 0.136788, Training Accuracy= 0.95312\n", + "Iter 81920, Minibatch Loss= 0.130100, Training Accuracy= 0.94531\n", + "Iter 83200, Minibatch Loss= 0.058298, Training Accuracy= 0.98438\n", + "Iter 84480, Minibatch Loss= 0.120124, Training Accuracy= 0.96094\n", + "Iter 85760, Minibatch Loss= 0.064916, Training Accuracy= 0.97656\n", + "Iter 87040, Minibatch Loss= 0.137179, Training Accuracy= 0.93750\n", + "Iter 88320, Minibatch Loss= 0.138268, Training Accuracy= 0.95312\n", + "Iter 89600, Minibatch Loss= 0.072827, Training Accuracy= 0.97656\n", + "Iter 90880, Minibatch Loss= 0.123839, Training Accuracy= 0.96875\n", + "Iter 92160, Minibatch Loss= 0.087194, Training Accuracy= 0.96875\n", + "Iter 93440, Minibatch Loss= 0.083489, Training Accuracy= 0.97656\n", + "Iter 94720, Minibatch Loss= 0.131827, Training Accuracy= 0.95312\n", + "Iter 96000, Minibatch Loss= 0.098764, Training Accuracy= 0.96875\n", + "Iter 97280, Minibatch Loss= 0.115553, Training Accuracy= 0.94531\n", + "Iter 98560, Minibatch Loss= 0.079704, Training Accuracy= 0.96875\n", + "Iter 99840, Minibatch Loss= 0.064562, Training Accuracy= 0.98438\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.992188\n" + ] + } + ], + "source": [ + "# Launch the graph\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + " step = 1\n", + " # Keep training until reach max iterations\n", + " while step * batch_size < training_iters:\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Reshape data to get 28 seq of 28 elements\n", + " batch_x = batch_x.reshape((batch_size, n_steps, n_input))\n", + " # Run optimization op (backprop)\n", + " sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\n", + " if step % display_step == 0:\n", + " # Calculate batch accuracy\n", + " acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})\n", + " # Calculate batch loss\n", + " loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})\n", + " print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n", + " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.5f}\".format(acc)\n", + " step += 1\n", + " print \"Optimization Finished!\"\n", + "\n", + " # Calculate accuracy for 128 mnist test images\n", + " test_len = 128\n", + " test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\n", + " test_label = mnist.test.labels[:test_len]\n", + " print \"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={x: test_data, y: test_label})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/notebooks/3_Neural Networks/convolutional_network.ipynb b/notebooks/3_Neural Networks/convolutional_network.ipynb new file mode 100644 index 00000000..70a5073e --- /dev/null +++ b/notebooks/3_Neural Networks/convolutional_network.ipynb @@ -0,0 +1,387 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "'''\n", + "A Convolutional Network implementation example using TensorFlow library.\n", + "This example is using the MNIST database of handwritten digits\n", + "(http://yann.lecun.com/exdb/mnist/)\n", + "\n", + "Author: Aymeric Damien\n", + "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "training_iters = 200000\n", + "batch_size = 128\n", + "display_step = 10\n", + "\n", + "# Network Parameters\n", + "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.75 # Dropout, probability to keep units\n", + "\n", + "# tf Graph input\n", + "x = tf.placeholder(tf.float32, [None, n_input])\n", + "y = tf.placeholder(tf.float32, [None, n_classes])\n", + "keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create some wrappers for simplicity\n", + "def conv2d(x, W, b, strides=1):\n", + " # Conv2D wrapper, with bias and relu activation\n", + " x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n", + " x = tf.nn.bias_add(x, b)\n", + " return tf.nn.relu(x)\n", + "\n", + "\n", + "def maxpool2d(x, k=2):\n", + " # MaxPool2D wrapper\n", + " return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],\n", + " padding='SAME')\n", + "\n", + "\n", + "# Create model\n", + "def conv_net(x, weights, biases, dropout):\n", + " # Reshape input picture\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer\n", + " conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n", + " # Max Pooling (down-sampling)\n", + " conv1 = maxpool2d(conv1, k=2)\n", + "\n", + " # Convolution Layer\n", + " conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n", + " # Max Pooling (down-sampling)\n", + " conv2 = maxpool2d(conv2, k=2)\n", + "\n", + " # Fully connected layer\n", + " # Reshape conv2 output to fit fully connected layer input\n", + " fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n", + " fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n", + " fc1 = tf.nn.relu(fc1)\n", + " # Apply Dropout\n", + " fc1 = tf.nn.dropout(fc1, dropout)\n", + "\n", + " # Output, class prediction\n", + " out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "weights = {\n", + " # 5x5 conv, 1 input, 32 outputs\n", + " 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),\n", + " # 5x5 conv, 32 inputs, 64 outputs\n", + " 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),\n", + " # fully connected, 7*7*64 inputs, 1024 outputs\n", + " 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),\n", + " # 1024 inputs, 10 outputs (class prediction)\n", + " 'out': tf.Variable(tf.random_normal([1024, n_classes]))\n", + "}\n", + "\n", + "biases = {\n", + " 'bc1': tf.Variable(tf.random_normal([32])),\n", + " 'bc2': tf.Variable(tf.random_normal([64])),\n", + " 'bd1': tf.Variable(tf.random_normal([1024])),\n", + " 'out': tf.Variable(tf.random_normal([n_classes]))\n", + "}\n", + "\n", + "# Construct model\n", + "pred = conv_net(x, weights, biases, keep_prob)\n", + "\n", + "# Define loss and optimizer\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "\n", + "# Evaluate model\n", + "correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initializing the variables\n", + "init = tf.initialize_all_variables()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iter 1280, Minibatch Loss= 17231.589844, Training Accuracy= 0.25000\n", + "Iter 2560, Minibatch Loss= 10580.260742, Training Accuracy= 0.54688\n", + "Iter 3840, Minibatch Loss= 7395.362793, Training Accuracy= 0.64062\n", + "Iter 5120, Minibatch Loss= 4864.292480, Training Accuracy= 0.75781\n", + "Iter 6400, Minibatch Loss= 3830.062012, Training Accuracy= 0.80469\n", + "Iter 7680, Minibatch Loss= 6031.701172, Training Accuracy= 0.72656\n", + "Iter 8960, Minibatch Loss= 2549.708740, Training Accuracy= 0.81250\n", + "Iter 10240, Minibatch Loss= 2010.484985, Training Accuracy= 0.84375\n", + "Iter 11520, Minibatch Loss= 1607.380981, Training Accuracy= 0.89062\n", + "Iter 12800, Minibatch Loss= 1983.302856, Training Accuracy= 0.82812\n", + "Iter 14080, Minibatch Loss= 401.215088, Training Accuracy= 0.94531\n", + "Iter 15360, Minibatch Loss= 976.289307, Training Accuracy= 0.95312\n", + "Iter 16640, Minibatch Loss= 1844.699951, Training Accuracy= 0.89844\n", + "Iter 17920, Minibatch Loss= 1009.859863, Training Accuracy= 0.92969\n", + "Iter 19200, Minibatch Loss= 1397.939453, Training Accuracy= 0.88281\n", + "Iter 20480, Minibatch Loss= 540.369995, Training Accuracy= 0.96094\n", + "Iter 21760, Minibatch Loss= 2589.246826, Training Accuracy= 0.87500\n", + "Iter 23040, Minibatch Loss= 404.981293, Training Accuracy= 0.96094\n", + "Iter 24320, Minibatch Loss= 742.155396, Training Accuracy= 0.93750\n", + "Iter 25600, Minibatch Loss= 851.599731, Training Accuracy= 0.93750\n", + "Iter 26880, Minibatch Loss= 1527.609619, Training Accuracy= 0.90625\n", + "Iter 28160, Minibatch Loss= 1209.633301, Training Accuracy= 0.91406\n", + "Iter 29440, Minibatch Loss= 1123.146851, Training Accuracy= 0.93750\n", + "Iter 30720, Minibatch Loss= 950.860596, Training Accuracy= 0.92188\n", + "Iter 32000, Minibatch Loss= 1217.373779, Training Accuracy= 0.92188\n", + "Iter 33280, Minibatch Loss= 859.433105, Training Accuracy= 0.91406\n", + "Iter 34560, Minibatch Loss= 487.426331, Training Accuracy= 0.95312\n", + "Iter 35840, Minibatch Loss= 287.507721, Training Accuracy= 0.96875\n", + "Iter 37120, Minibatch Loss= 786.797485, Training Accuracy= 0.91406\n", + "Iter 38400, Minibatch Loss= 248.981216, Training Accuracy= 0.97656\n", + "Iter 39680, Minibatch Loss= 147.081467, Training Accuracy= 0.97656\n", + "Iter 40960, Minibatch Loss= 1198.459106, Training Accuracy= 0.93750\n", + "Iter 42240, Minibatch Loss= 717.058716, Training Accuracy= 0.92188\n", + "Iter 43520, Minibatch Loss= 351.870453, Training Accuracy= 0.96094\n", + "Iter 44800, Minibatch Loss= 271.505554, Training Accuracy= 0.96875\n", + "Iter 46080, Minibatch Loss= 0.000000, Training Accuracy= 1.00000\n", + "Iter 47360, Minibatch Loss= 806.163818, Training Accuracy= 0.95312\n", + "Iter 48640, Minibatch Loss= 1055.359009, Training Accuracy= 0.91406\n", + "Iter 49920, Minibatch Loss= 459.845520, Training Accuracy= 0.94531\n", + "Iter 51200, Minibatch Loss= 133.995087, Training Accuracy= 0.97656\n", + "Iter 52480, Minibatch Loss= 378.886780, Training Accuracy= 0.96094\n", + "Iter 53760, Minibatch Loss= 122.112671, Training Accuracy= 0.98438\n", + "Iter 55040, Minibatch Loss= 357.410950, Training Accuracy= 0.96875\n", + "Iter 56320, Minibatch Loss= 164.791595, Training Accuracy= 0.98438\n", + "Iter 57600, Minibatch Loss= 740.711060, Training Accuracy= 0.95312\n", + "Iter 58880, Minibatch Loss= 755.948364, Training Accuracy= 0.92969\n", + "Iter 60160, Minibatch Loss= 289.819153, Training Accuracy= 0.94531\n", + "Iter 61440, Minibatch Loss= 162.940323, Training Accuracy= 0.96875\n", + "Iter 62720, Minibatch Loss= 616.192200, Training Accuracy= 0.92969\n", + "Iter 64000, Minibatch Loss= 649.317993, Training Accuracy= 0.92188\n", + "Iter 65280, Minibatch Loss= 1021.529785, Training Accuracy= 0.93750\n", + "Iter 66560, Minibatch Loss= 203.839050, Training Accuracy= 0.96094\n", + "Iter 67840, Minibatch Loss= 469.755249, Training Accuracy= 0.96094\n", + "Iter 69120, Minibatch Loss= 36.496567, Training Accuracy= 0.98438\n", + "Iter 70400, Minibatch Loss= 214.677551, Training Accuracy= 0.95312\n", + "Iter 71680, Minibatch Loss= 115.657990, Training Accuracy= 0.96875\n", + "Iter 72960, Minibatch Loss= 354.555115, Training Accuracy= 0.96875\n", + "Iter 74240, Minibatch Loss= 124.091103, Training Accuracy= 0.97656\n", + "Iter 75520, Minibatch Loss= 614.557251, Training Accuracy= 0.94531\n", + "Iter 76800, Minibatch Loss= 343.182983, Training Accuracy= 0.95312\n", + "Iter 78080, Minibatch Loss= 678.875183, Training Accuracy= 0.94531\n", + "Iter 79360, Minibatch Loss= 313.656494, Training Accuracy= 0.95312\n", + "Iter 80640, Minibatch Loss= 169.024185, Training Accuracy= 0.96094\n", + "Iter 81920, Minibatch Loss= 98.455017, Training Accuracy= 0.96875\n", + "Iter 83200, Minibatch Loss= 359.754517, Training Accuracy= 0.92188\n", + "Iter 84480, Minibatch Loss= 214.993103, Training Accuracy= 0.96875\n", + "Iter 85760, Minibatch Loss= 262.921265, Training Accuracy= 0.97656\n", + "Iter 87040, Minibatch Loss= 558.218445, Training Accuracy= 0.89844\n", + "Iter 88320, Minibatch Loss= 122.281952, Training Accuracy= 0.99219\n", + "Iter 89600, Minibatch Loss= 300.606689, Training Accuracy= 0.93750\n", + "Iter 90880, Minibatch Loss= 261.051025, Training Accuracy= 0.98438\n", + "Iter 92160, Minibatch Loss= 59.812164, Training Accuracy= 0.98438\n", + "Iter 93440, Minibatch Loss= 309.307312, Training Accuracy= 0.96875\n", + "Iter 94720, Minibatch Loss= 626.035706, Training Accuracy= 0.95312\n", + "Iter 96000, Minibatch Loss= 317.929260, Training Accuracy= 0.96875\n", + "Iter 97280, Minibatch Loss= 196.908218, Training Accuracy= 0.97656\n", + "Iter 98560, Minibatch Loss= 843.143250, Training Accuracy= 0.95312\n", + "Iter 99840, Minibatch Loss= 389.142761, Training Accuracy= 0.96875\n", + "Iter 101120, Minibatch Loss= 246.468994, Training Accuracy= 0.96094\n", + "Iter 102400, Minibatch Loss= 110.580948, Training Accuracy= 0.98438\n", + "Iter 103680, Minibatch Loss= 208.350586, Training Accuracy= 0.96875\n", + "Iter 104960, Minibatch Loss= 506.229462, Training Accuracy= 0.94531\n", + "Iter 106240, Minibatch Loss= 49.548233, Training Accuracy= 0.98438\n", + "Iter 107520, Minibatch Loss= 728.496582, Training Accuracy= 0.92969\n", + "Iter 108800, Minibatch Loss= 187.256622, Training Accuracy= 0.97656\n", + "Iter 110080, Minibatch Loss= 273.696899, Training Accuracy= 0.97656\n", + "Iter 111360, Minibatch Loss= 317.126678, Training Accuracy= 0.96094\n", + "Iter 112640, Minibatch Loss= 148.293365, Training Accuracy= 0.98438\n", + "Iter 113920, Minibatch Loss= 139.360168, Training Accuracy= 0.97656\n", + "Iter 115200, Minibatch Loss= 167.539093, Training Accuracy= 0.98438\n", + "Iter 116480, Minibatch Loss= 565.433594, Training Accuracy= 0.94531\n", + "Iter 117760, Minibatch Loss= 8.117203, Training Accuracy= 0.99219\n", + "Iter 119040, Minibatch Loss= 348.071472, Training Accuracy= 0.96875\n", + "Iter 120320, Minibatch Loss= 287.732849, Training Accuracy= 0.97656\n", + "Iter 121600, Minibatch Loss= 156.525284, Training Accuracy= 0.96875\n", + "Iter 122880, Minibatch Loss= 296.147339, Training Accuracy= 0.98438\n", + "Iter 124160, Minibatch Loss= 260.941956, Training Accuracy= 0.98438\n", + "Iter 125440, Minibatch Loss= 241.011719, Training Accuracy= 0.98438\n", + "Iter 126720, Minibatch Loss= 185.330444, Training Accuracy= 0.98438\n", + "Iter 128000, Minibatch Loss= 346.407013, Training Accuracy= 0.96875\n", + "Iter 129280, Minibatch Loss= 522.477173, Training Accuracy= 0.94531\n", + "Iter 130560, Minibatch Loss= 97.665955, Training Accuracy= 0.96094\n", + "Iter 131840, Minibatch Loss= 111.370262, Training Accuracy= 0.96875\n", + "Iter 133120, Minibatch Loss= 106.377136, Training Accuracy= 0.97656\n", + "Iter 134400, Minibatch Loss= 432.294983, Training Accuracy= 0.96094\n", + "Iter 135680, Minibatch Loss= 104.584610, Training Accuracy= 0.98438\n", + "Iter 136960, Minibatch Loss= 439.611053, Training Accuracy= 0.95312\n", + "Iter 138240, Minibatch Loss= 171.394562, Training Accuracy= 0.96875\n", + "Iter 139520, Minibatch Loss= 83.505905, Training Accuracy= 0.98438\n", + "Iter 140800, Minibatch Loss= 240.278427, Training Accuracy= 0.98438\n", + "Iter 142080, Minibatch Loss= 417.140320, Training Accuracy= 0.96094\n", + "Iter 143360, Minibatch Loss= 77.656067, Training Accuracy= 0.97656\n", + "Iter 144640, Minibatch Loss= 284.589844, Training Accuracy= 0.97656\n", + "Iter 145920, Minibatch Loss= 372.114288, Training Accuracy= 0.96875\n", + "Iter 147200, Minibatch Loss= 352.900024, Training Accuracy= 0.96094\n", + "Iter 148480, Minibatch Loss= 148.120621, Training Accuracy= 0.97656\n", + "Iter 149760, Minibatch Loss= 127.385742, Training Accuracy= 0.98438\n", + "Iter 151040, Minibatch Loss= 383.167175, Training Accuracy= 0.96094\n", + "Iter 152320, Minibatch Loss= 331.846649, Training Accuracy= 0.94531\n", + "Iter 153600, Minibatch Loss= 0.000000, Training Accuracy= 1.00000\n", + "Iter 154880, Minibatch Loss= 24.065147, Training Accuracy= 0.99219\n", + "Iter 156160, Minibatch Loss= 43.433868, Training Accuracy= 0.99219\n", + "Iter 157440, Minibatch Loss= 205.383972, Training Accuracy= 0.96875\n", + "Iter 158720, Minibatch Loss= 83.019257, Training Accuracy= 0.97656\n", + "Iter 160000, Minibatch Loss= 195.710556, Training Accuracy= 0.96875\n", + "Iter 161280, Minibatch Loss= 177.192932, Training Accuracy= 0.95312\n", + "Iter 162560, Minibatch Loss= 261.618713, Training Accuracy= 0.96875\n", + "Iter 163840, Minibatch Loss= 0.000000, Training Accuracy= 1.00000\n", + "Iter 165120, Minibatch Loss= 62.901100, Training Accuracy= 0.97656\n", + "Iter 166400, Minibatch Loss= 17.181839, Training Accuracy= 0.98438\n", + "Iter 167680, Minibatch Loss= 102.738960, Training Accuracy= 0.96875\n", + "Iter 168960, Minibatch Loss= 0.000000, Training Accuracy= 1.00000\n", + "Iter 170240, Minibatch Loss= 71.784363, Training Accuracy= 0.99219\n", + "Iter 171520, Minibatch Loss= 260.672852, Training Accuracy= 0.96875\n", + "Iter 172800, Minibatch Loss= 186.616119, Training Accuracy= 0.96094\n", + "Iter 174080, Minibatch Loss= 312.432312, Training Accuracy= 0.96875\n", + "Iter 175360, Minibatch Loss= 45.828953, Training Accuracy= 0.99219\n", + "Iter 176640, Minibatch Loss= 62.931808, Training Accuracy= 0.98438\n", + "Iter 177920, Minibatch Loss= 63.452362, Training Accuracy= 0.97656\n", + "Iter 179200, Minibatch Loss= 53.608818, Training Accuracy= 0.98438\n", + "Iter 180480, Minibatch Loss= 57.089508, Training Accuracy= 0.97656\n", + "Iter 181760, Minibatch Loss= 601.268799, Training Accuracy= 0.93750\n", + "Iter 183040, Minibatch Loss= 59.850044, Training Accuracy= 0.97656\n", + "Iter 184320, Minibatch Loss= 145.267883, Training Accuracy= 0.96875\n", + "Iter 185600, Minibatch Loss= 24.205322, Training Accuracy= 0.99219\n", + "Iter 186880, Minibatch Loss= 51.866646, Training Accuracy= 0.98438\n", + "Iter 188160, Minibatch Loss= 166.911987, Training Accuracy= 0.96875\n", + "Iter 189440, Minibatch Loss= 32.308147, Training Accuracy= 0.98438\n", + "Iter 190720, Minibatch Loss= 514.898071, Training Accuracy= 0.92188\n", + "Iter 192000, Minibatch Loss= 146.610031, Training Accuracy= 0.98438\n", + "Iter 193280, Minibatch Loss= 23.939758, Training Accuracy= 0.99219\n", + "Iter 194560, Minibatch Loss= 224.806641, Training Accuracy= 0.97656\n", + "Iter 195840, Minibatch Loss= 71.935089, Training Accuracy= 0.98438\n", + "Iter 197120, Minibatch Loss= 182.021210, Training Accuracy= 0.96875\n", + "Iter 198400, Minibatch Loss= 125.573784, Training Accuracy= 0.96875\n", + "Iter 199680, Minibatch Loss= 122.506104, Training Accuracy= 0.96875\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.972656\n" + ] + } + ], + "source": [ + "# Launch the graph\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + " step = 1\n", + " # Keep training until reach max iterations\n", + " while step * batch_size < training_iters:\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop)\n", + " sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,\n", + " keep_prob: dropout})\n", + " if step % display_step == 0:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,\n", + " y: batch_y,\n", + " keep_prob: 1.})\n", + " print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n", + " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.5f}\".format(acc)\n", + " step += 1\n", + " print \"Optimization Finished!\"\n", + "\n", + " # Calculate accuracy for 256 mnist test images\n", + " print \"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={x: mnist.test.images[:256],\n", + " y: mnist.test.labels[:256],\n", + " keep_prob: 1.})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/notebooks/3 - Neural Networks/multilayer_perceptron.ipynb b/notebooks/3_Neural Networks/multilayer_perceptron.ipynb similarity index 56% rename from notebooks/3 - Neural Networks/multilayer_perceptron.ipynb rename to notebooks/3_Neural Networks/multilayer_perceptron.ipynb index b6c462f4..6ec369e6 100644 --- a/notebooks/3 - Neural Networks/multilayer_perceptron.ipynb +++ b/notebooks/3_Neural Networks/multilayer_perceptron.ipynb @@ -2,23 +2,25 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "# A Multilayer Perceptron implementation example using TensorFlow library.\n", - "# This example is using the MNIST database of handwritten digits\n", - "# (http://yann.lecun.com/exdb/mnist/)\n", + "'''\n", + "A Multilayer Perceptron implementation example using TensorFlow library.\n", + "This example is using the MNIST database of handwritten digits\n", + "(http://yann.lecun.com/exdb/mnist/)\n", "\n", - "# Author: Aymeric Damien\n", - "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" + "Author: Aymeric Damien\n", + "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", + "'''" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -36,24 +38,15 @@ ], "source": [ "# Import MINST data\n", - "import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", "import tensorflow as tf" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -63,32 +56,14 @@ "learning_rate = 0.001\n", "training_epochs = 15\n", "batch_size = 100\n", - "display_step = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "display_step = 1\n", + "\n", "# Network Parameters\n", - "n_hidden_1 = 256 # 1st layer num features\n", - "n_hidden_2 = 256 # 2nd layer num features\n", + "n_hidden_1 = 256 # 1st layer number of features\n", + "n_hidden_2 = 256 # 2nd layer number of features\n", "n_input = 784 # MNIST data input (img shape: 28*28)\n", - "n_classes = 10 # MNIST total classes (0-9 digits)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", "# tf Graph input\n", "x = tf.placeholder(\"float\", [None, n_input])\n", "y = tf.placeholder(\"float\", [None, n_classes])" @@ -96,24 +71,28 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create model\n", - "def multilayer_perceptron(_X, _weights, _biases):\n", - " #Hidden layer with RELU activation\n", - " layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1'])) \n", - " #Hidden layer with RELU activation\n", - " layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, _weights['h2']), _biases['b2'])) \n", - " return tf.matmul(layer_2, weights['out']) + biases['out']" + "def multilayer_perceptron(x, weights, biases):\n", + " # Hidden layer with RELU activation\n", + " layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n", + " layer_1 = tf.nn.relu(layer_1)\n", + " # Hidden layer with RELU activation\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n", + " layer_2 = tf.nn.relu(layer_2)\n", + " # Output layer with linear activation\n", + " out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n", + " return out_layer" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -129,51 +108,22 @@ " 'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", " 'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n", " 'out': tf.Variable(tf.random_normal([n_classes]))\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "}\n", + "\n", "# Construct model\n", - "pred = multilayer_perceptron(x, weights, biases)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "pred = multilayer_perceptron(x, weights, biases)\n", + "\n", "# Define loss and optimizer\n", - "# Softmax loss\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) \n", - "# Adam Optimizer\n", - "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "\n", "# Initializing the variables\n", "init = tf.initialize_all_variables()" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -182,23 +132,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch: 0001 cost= 160.113980416\n", - "Epoch: 0002 cost= 38.665780694\n", - "Epoch: 0003 cost= 24.118004577\n", - "Epoch: 0004 cost= 16.440921303\n", - "Epoch: 0005 cost= 11.689460141\n", - "Epoch: 0006 cost= 8.469423468\n", - "Epoch: 0007 cost= 6.223237230\n", - "Epoch: 0008 cost= 4.560174118\n", - "Epoch: 0009 cost= 3.250516910\n", - "Epoch: 0010 cost= 2.359658795\n", - "Epoch: 0011 cost= 1.694081847\n", - "Epoch: 0012 cost= 1.167997509\n", - "Epoch: 0013 cost= 0.872986831\n", - "Epoch: 0014 cost= 0.630616366\n", - "Epoch: 0015 cost= 0.487381571\n", + "Epoch: 0001 cost= 185.342230390\n", + "Epoch: 0002 cost= 44.266946572\n", + "Epoch: 0003 cost= 27.999560453\n", + "Epoch: 0004 cost= 19.655567043\n", + "Epoch: 0005 cost= 14.284429696\n", + "Epoch: 0006 cost= 10.640310403\n", + "Epoch: 0007 cost= 7.904047886\n", + "Epoch: 0008 cost= 5.989115090\n", + "Epoch: 0009 cost= 4.689374613\n", + "Epoch: 0010 cost= 3.455884229\n", + "Epoch: 0011 cost= 2.733002625\n", + "Epoch: 0012 cost= 2.101091420\n", + "Epoch: 0013 cost= 1.496508092\n", + "Epoch: 0014 cost= 1.245452015\n", + "Epoch: 0015 cost= 0.912072906\n", "Optimization Finished!\n", - "Accuracy: 0.9462\n" + "Accuracy: 0.9422\n" ] } ], @@ -213,15 +163,16 @@ " total_batch = int(mnist.train.num_examples/batch_size)\n", " # Loop over all batches\n", " for i in range(total_batch):\n", - " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", - " # Fit training using batch data\n", - " sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop) and cost op (to get loss value)\n", + " _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n", + " y: batch_y})\n", " # Compute average loss\n", - " avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch\n", + " avg_cost += c / total_batch\n", " # Display logs per epoch step\n", " if epoch % display_step == 0:\n", - " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n", - "\n", + " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n", + " \"{:.9f}\".format(avg_cost)\n", " print \"Optimization Finished!\"\n", "\n", " # Test model\n", @@ -234,23 +185,23 @@ ], "metadata": { "kernelspec": { - "display_name": "IPython (Python 2.7)", + "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.8" + "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/notebooks/3_Neural Networks/recurrent_network.ipynb b/notebooks/3_Neural Networks/recurrent_network.ipynb new file mode 100644 index 00000000..c56a5c8b --- /dev/null +++ b/notebooks/3_Neural Networks/recurrent_network.ipynb @@ -0,0 +1,289 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "'''\n", + "A Reccurent Neural Network (LSTM) implementation example using TensorFlow library.\n", + "This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", + "Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\n", + "\n", + "Author: Aymeric Damien\n", + "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.models.rnn import rnn, rnn_cell\n", + "import numpy as np\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "'''\n", + "To classify images using a reccurent neural network, we consider every image\n", + "row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then\n", + "handle 28 sequences of 28 steps for every sample.\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "training_iters = 100000\n", + "batch_size = 128\n", + "display_step = 10\n", + "\n", + "# Network Parameters\n", + "n_input = 28 # MNIST data input (img shape: 28*28)\n", + "n_steps = 28 # timesteps\n", + "n_hidden = 128 # hidden layer num of features\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "x = tf.placeholder(\"float\", [None, n_steps, n_input])\n", + "y = tf.placeholder(\"float\", [None, n_classes])\n", + "\n", + "# Define weights\n", + "weights = {\n", + " 'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])),\n", + " 'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\n", + "}\n", + "biases = {\n", + " 'hidden': tf.Variable(tf.random_normal([n_hidden])),\n", + " 'out': tf.Variable(tf.random_normal([n_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def RNN(x, weights, biases):\n", + "\n", + " # Prepare data shape to match `rnn` function requirements\n", + " # Current data input shape: (batch_size, n_steps, n_input)\n", + " # Permuting batch_size and n_steps\n", + " x = tf.transpose(x, [1, 0, 2])\n", + " # Reshaping to (n_steps*batch_size, n_input)\n", + " x = tf.reshape(x, [-1, n_input])\n", + " # Split to get a list of 'n_steps' tensors of shape (batch_size, n_hidden)\n", + " # This input shape is required by `rnn` function\n", + " x = tf.split(0, n_steps, x)\n", + "\n", + " # Define a lstm cell with tensorflow\n", + " lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", + "\n", + " # Get lstm cell output\n", + " outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)\n", + "\n", + " # Linear activation, using rnn inner loop last output\n", + " return tf.matmul(outputs[-1], weights['out']) + biases['out']\n", + "\n", + "pred = RNN(x, weights, biases)\n", + "\n", + "# Define loss and optimizer\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "\n", + "# Evaluate model\n", + "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initializing the variables\n", + "init = tf.initialize_all_variables()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iter 1280, Minibatch Loss= 1.538532, Training Accuracy= 0.49219\n", + "Iter 2560, Minibatch Loss= 1.462834, Training Accuracy= 0.50781\n", + "Iter 3840, Minibatch Loss= 1.048393, Training Accuracy= 0.66406\n", + "Iter 5120, Minibatch Loss= 0.889872, Training Accuracy= 0.71875\n", + "Iter 6400, Minibatch Loss= 0.681855, Training Accuracy= 0.76562\n", + "Iter 7680, Minibatch Loss= 0.987207, Training Accuracy= 0.69531\n", + "Iter 8960, Minibatch Loss= 0.759543, Training Accuracy= 0.71094\n", + "Iter 10240, Minibatch Loss= 0.557055, Training Accuracy= 0.80469\n", + "Iter 11520, Minibatch Loss= 0.371352, Training Accuracy= 0.89844\n", + "Iter 12800, Minibatch Loss= 0.661293, Training Accuracy= 0.80469\n", + "Iter 14080, Minibatch Loss= 0.474259, Training Accuracy= 0.86719\n", + "Iter 15360, Minibatch Loss= 0.328436, Training Accuracy= 0.88281\n", + "Iter 16640, Minibatch Loss= 0.348017, Training Accuracy= 0.93750\n", + "Iter 17920, Minibatch Loss= 0.340086, Training Accuracy= 0.88281\n", + "Iter 19200, Minibatch Loss= 0.261532, Training Accuracy= 0.89844\n", + "Iter 20480, Minibatch Loss= 0.161785, Training Accuracy= 0.94531\n", + "Iter 21760, Minibatch Loss= 0.419619, Training Accuracy= 0.83594\n", + "Iter 23040, Minibatch Loss= 0.120714, Training Accuracy= 0.95312\n", + "Iter 24320, Minibatch Loss= 0.339519, Training Accuracy= 0.89062\n", + "Iter 25600, Minibatch Loss= 0.405463, Training Accuracy= 0.88281\n", + "Iter 26880, Minibatch Loss= 0.172193, Training Accuracy= 0.95312\n", + "Iter 28160, Minibatch Loss= 0.256769, Training Accuracy= 0.91406\n", + "Iter 29440, Minibatch Loss= 0.247753, Training Accuracy= 0.91406\n", + "Iter 30720, Minibatch Loss= 0.230820, Training Accuracy= 0.91406\n", + "Iter 32000, Minibatch Loss= 0.216861, Training Accuracy= 0.93750\n", + "Iter 33280, Minibatch Loss= 0.236337, Training Accuracy= 0.89062\n", + "Iter 34560, Minibatch Loss= 0.252351, Training Accuracy= 0.93750\n", + "Iter 35840, Minibatch Loss= 0.180090, Training Accuracy= 0.92188\n", + "Iter 37120, Minibatch Loss= 0.304125, Training Accuracy= 0.91406\n", + "Iter 38400, Minibatch Loss= 0.114474, Training Accuracy= 0.96094\n", + "Iter 39680, Minibatch Loss= 0.158405, Training Accuracy= 0.96875\n", + "Iter 40960, Minibatch Loss= 0.285858, Training Accuracy= 0.92188\n", + "Iter 42240, Minibatch Loss= 0.134199, Training Accuracy= 0.96094\n", + "Iter 43520, Minibatch Loss= 0.235847, Training Accuracy= 0.92969\n", + "Iter 44800, Minibatch Loss= 0.155971, Training Accuracy= 0.94531\n", + "Iter 46080, Minibatch Loss= 0.061549, Training Accuracy= 0.99219\n", + "Iter 47360, Minibatch Loss= 0.232569, Training Accuracy= 0.94531\n", + "Iter 48640, Minibatch Loss= 0.270348, Training Accuracy= 0.91406\n", + "Iter 49920, Minibatch Loss= 0.202416, Training Accuracy= 0.92188\n", + "Iter 51200, Minibatch Loss= 0.113857, Training Accuracy= 0.96094\n", + "Iter 52480, Minibatch Loss= 0.137900, Training Accuracy= 0.94531\n", + "Iter 53760, Minibatch Loss= 0.052416, Training Accuracy= 0.98438\n", + "Iter 55040, Minibatch Loss= 0.312064, Training Accuracy= 0.91406\n", + "Iter 56320, Minibatch Loss= 0.144335, Training Accuracy= 0.93750\n", + "Iter 57600, Minibatch Loss= 0.114723, Training Accuracy= 0.96875\n", + "Iter 58880, Minibatch Loss= 0.193597, Training Accuracy= 0.96094\n", + "Iter 60160, Minibatch Loss= 0.110877, Training Accuracy= 0.95312\n", + "Iter 61440, Minibatch Loss= 0.119864, Training Accuracy= 0.96094\n", + "Iter 62720, Minibatch Loss= 0.118780, Training Accuracy= 0.94531\n", + "Iter 64000, Minibatch Loss= 0.082259, Training Accuracy= 0.97656\n", + "Iter 65280, Minibatch Loss= 0.087364, Training Accuracy= 0.97656\n", + "Iter 66560, Minibatch Loss= 0.207975, Training Accuracy= 0.92969\n", + "Iter 67840, Minibatch Loss= 0.120612, Training Accuracy= 0.96875\n", + "Iter 69120, Minibatch Loss= 0.070608, Training Accuracy= 0.96875\n", + "Iter 70400, Minibatch Loss= 0.100786, Training Accuracy= 0.96094\n", + "Iter 71680, Minibatch Loss= 0.114746, Training Accuracy= 0.94531\n", + "Iter 72960, Minibatch Loss= 0.083427, Training Accuracy= 0.96875\n", + "Iter 74240, Minibatch Loss= 0.089978, Training Accuracy= 0.96094\n", + "Iter 75520, Minibatch Loss= 0.195322, Training Accuracy= 0.94531\n", + "Iter 76800, Minibatch Loss= 0.161109, Training Accuracy= 0.96094\n", + "Iter 78080, Minibatch Loss= 0.169762, Training Accuracy= 0.94531\n", + "Iter 79360, Minibatch Loss= 0.054240, Training Accuracy= 0.98438\n", + "Iter 80640, Minibatch Loss= 0.160100, Training Accuracy= 0.95312\n", + "Iter 81920, Minibatch Loss= 0.110728, Training Accuracy= 0.96875\n", + "Iter 83200, Minibatch Loss= 0.054918, Training Accuracy= 0.98438\n", + "Iter 84480, Minibatch Loss= 0.104170, Training Accuracy= 0.96875\n", + "Iter 85760, Minibatch Loss= 0.071871, Training Accuracy= 0.97656\n", + "Iter 87040, Minibatch Loss= 0.170529, Training Accuracy= 0.96094\n", + "Iter 88320, Minibatch Loss= 0.087350, Training Accuracy= 0.96875\n", + "Iter 89600, Minibatch Loss= 0.079943, Training Accuracy= 0.96875\n", + "Iter 90880, Minibatch Loss= 0.128451, Training Accuracy= 0.92969\n", + "Iter 92160, Minibatch Loss= 0.046963, Training Accuracy= 0.98438\n", + "Iter 93440, Minibatch Loss= 0.162998, Training Accuracy= 0.96875\n", + "Iter 94720, Minibatch Loss= 0.122588, Training Accuracy= 0.96094\n", + "Iter 96000, Minibatch Loss= 0.073954, Training Accuracy= 0.97656\n", + "Iter 97280, Minibatch Loss= 0.130790, Training Accuracy= 0.96094\n", + "Iter 98560, Minibatch Loss= 0.067689, Training Accuracy= 0.97656\n", + "Iter 99840, Minibatch Loss= 0.186411, Training Accuracy= 0.92188\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.976562\n" + ] + } + ], + "source": [ + "# Launch the graph\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + " step = 1\n", + " # Keep training until reach max iterations\n", + " while step * batch_size < training_iters:\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Reshape data to get 28 seq of 28 elements\n", + " batch_x = batch_x.reshape((batch_size, n_steps, n_input))\n", + " # Run optimization op (backprop)\n", + " sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\n", + " if step % display_step == 0:\n", + " # Calculate batch accuracy\n", + " acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})\n", + " # Calculate batch loss\n", + " loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})\n", + " print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n", + " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.5f}\".format(acc)\n", + " step += 1\n", + " print \"Optimization Finished!\"\n", + "\n", + " # Calculate accuracy for 128 mnist test images\n", + " test_len = 128\n", + " test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\n", + " test_label = mnist.test.labels[:test_len]\n", + " print \"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={x: test_data, y: test_label})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/notebooks/4_Utils/save_restore_model.ipynb b/notebooks/4_Utils/save_restore_model.ipynb new file mode 100644 index 00000000..7909071f --- /dev/null +++ b/notebooks/4_Utils/save_restore_model.ipynb @@ -0,0 +1,271 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "'''\n", + "Save and Restore a model using TensorFlow.\n", + "This example is using the MNIST database of handwritten digits\n", + "(http://yann.lecun.com/exdb/mnist/)\n", + "\n", + "Author: Aymeric Damien\n", + "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "batch_size = 100\n", + "display_step = 1\n", + "model_path = \"/tmp/model.ckpt\"\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of features\n", + "n_hidden_2 = 256 # 2nd layer number of features\n", + "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "x = tf.placeholder(\"float\", [None, n_input])\n", + "y = tf.placeholder(\"float\", [None, n_classes])\n", + "\n", + "\n", + "# Create model\n", + "def multilayer_perceptron(x, weights, biases):\n", + " # Hidden layer with RELU activation\n", + " layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n", + " layer_1 = tf.nn.relu(layer_1)\n", + " # Hidden layer with RELU activation\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n", + " layer_2 = tf.nn.relu(layer_2)\n", + " # Output layer with linear activation\n", + " out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n", + " return out_layer\n", + "\n", + "# Store layers weight & bias\n", + "weights = {\n", + " 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n", + " 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n", + "}\n", + "biases = {\n", + " 'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", + " 'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([n_classes]))\n", + "}\n", + "\n", + "# Construct model\n", + "pred = multilayer_perceptron(x, weights, biases)\n", + "\n", + "# Define loss and optimizer\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "\n", + "# Initializing the variables\n", + "init = tf.initialize_all_variables()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 'Saver' op to save and restore all the variables\n", + "saver = tf.train.Saver()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting 1st session...\n", + "Epoch: 0001 cost= 182.770135574\n", + "Epoch: 0002 cost= 44.863718596\n", + "Epoch: 0003 cost= 27.965412349\n", + "First Optimization Finished!\n", + "Accuracy: 0.906\n", + "Model saved in file: /tmp/model.ckpt\n" + ] + } + ], + "source": [ + "# Running first session\n", + "print \"Starting 1st session...\"\n", + "with tf.Session() as sess:\n", + " # Initialize variables\n", + " sess.run(init)\n", + "\n", + " # Training cycle\n", + " for epoch in range(3):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples/batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop) and cost op (to get loss value)\n", + " _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n", + " y: batch_y})\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if epoch % display_step == 0:\n", + " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n", + " \"{:.9f}\".format(avg_cost)\n", + " print \"First Optimization Finished!\"\n", + "\n", + " # Test model\n", + " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " # Calculate accuracy\n", + " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", + " print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})\n", + "\n", + " # Save model weights to disk\n", + " save_path = saver.save(sess, model_path)\n", + " print \"Model saved in file: %s\" % save_path" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting 2nd session...\n", + "Model restored from file: /tmp/model.ckpt\n", + "Epoch: 0001 cost= 19.658836002\n", + "Epoch: 0002 cost= 14.354811554\n", + "Epoch: 0003 cost= 10.580801367\n", + "Epoch: 0004 cost= 8.012172253\n", + "Epoch: 0005 cost= 5.985675981\n", + "Epoch: 0006 cost= 4.572637980\n", + "Epoch: 0007 cost= 3.329074899\n", + "Second Optimization Finished!\n", + "Accuracy: 0.9371\n" + ] + } + ], + "source": [ + "# Running a new session\n", + "print \"Starting 2nd session...\"\n", + "with tf.Session() as sess:\n", + " # Initialize variables\n", + " sess.run(init)\n", + "\n", + " # Restore model weights from previously saved model\n", + " load_path = saver.restore(sess, model_path)\n", + " print \"Model restored from file: %s\" % save_path\n", + "\n", + " # Resume training\n", + " for epoch in range(7):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples / batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop) and cost op (to get loss value)\n", + " _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n", + " y: batch_y})\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if epoch % display_step == 0:\n", + " print \"Epoch:\", '%04d' % (epoch + 1), \"cost=\", \\\n", + " \"{:.9f}\".format(avg_cost)\n", + " print \"Second Optimization Finished!\"\n", + "\n", + " # Test model\n", + " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " # Calculate accuracy\n", + " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", + " print \"Accuracy:\", accuracy.eval(\n", + " {x: mnist.test.images, y: mnist.test.labels})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/notebooks/4_Utils/tensorboard_basic.ipynb b/notebooks/4_Utils/tensorboard_basic.ipynb new file mode 100644 index 00000000..ceeab205 --- /dev/null +++ b/notebooks/4_Utils/tensorboard_basic.ipynb @@ -0,0 +1,212 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "'''\n", + "Graph and Loss visualization using Tensorboard.\n", + "This example is using the MNIST database of handwritten digits\n", + "(http://yann.lecun.com/exdb/mnist/)\n", + "\n", + "Author: Aymeric Damien\n", + "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 25\n", + "batch_size = 100\n", + "display_step = 1\n", + "logs_path = '/tmp/tensorflow_logs'\n", + "\n", + "# tf Graph Input\n", + "# mnist data image of shape 28*28=784\n", + "x = tf.placeholder(tf.float32, [None, 784], name='InputData')\n", + "# 0-9 digits recognition => 10 classes\n", + "y = tf.placeholder(tf.float32, [None, 10], name='LabelData')\n", + "\n", + "# Set model weights\n", + "W = tf.Variable(tf.zeros([784, 10]), name='Weights')\n", + "b = tf.Variable(tf.zeros([10]), name='Bias')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Construct model and encapsulating all ops into scopes, making\n", + "# Tensorboard's Graph visualization more convenient\n", + "with tf.name_scope('Model'):\n", + " # Model\n", + " pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n", + "with tf.name_scope('Loss'):\n", + " # Minimize error using cross entropy\n", + " cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))\n", + "with tf.name_scope('SGD'):\n", + " # Gradient Descent\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", + "with tf.name_scope('Accuracy'):\n", + " # Accuracy\n", + " acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " acc = tf.reduce_mean(tf.cast(acc, tf.float32))\n", + "\n", + "# Initializing the variables\n", + "init = tf.initialize_all_variables()\n", + "\n", + "# Create a summary to monitor cost tensor\n", + "tf.scalar_summary(\"loss\", cost)\n", + "# Create a summary to monitor accuracy tensor\n", + "tf.scalar_summary(\"accuracy\", acc)\n", + "# Merge all summaries into a single op\n", + "merged_summary_op = tf.merge_all_summaries()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0001 cost= 1.182138957\n", + "Epoch: 0002 cost= 0.664735104\n", + "Epoch: 0003 cost= 0.552622685\n", + "Epoch: 0004 cost= 0.498596912\n", + "Epoch: 0005 cost= 0.465510372\n", + "Epoch: 0006 cost= 0.442504281\n", + "Epoch: 0007 cost= 0.425473650\n", + "Epoch: 0008 cost= 0.412175615\n", + "Epoch: 0009 cost= 0.401374554\n", + "Epoch: 0010 cost= 0.392403109\n", + "Epoch: 0011 cost= 0.384748503\n", + "Epoch: 0012 cost= 0.378154479\n", + "Epoch: 0013 cost= 0.372405099\n", + "Epoch: 0014 cost= 0.367272844\n", + "Epoch: 0015 cost= 0.362745077\n", + "Epoch: 0016 cost= 0.358575674\n", + "Epoch: 0017 cost= 0.354862829\n", + "Epoch: 0018 cost= 0.351437834\n", + "Epoch: 0019 cost= 0.348300697\n", + "Epoch: 0020 cost= 0.345401101\n", + "Epoch: 0021 cost= 0.342762216\n", + "Epoch: 0022 cost= 0.340199728\n", + "Epoch: 0023 cost= 0.337916089\n", + "Epoch: 0024 cost= 0.335764083\n", + "Epoch: 0025 cost= 0.333645939\n", + "Optimization Finished!\n", + "Accuracy: 0.9143\n", + "Run the command line:\n", + "--> tensorboard --logdir=/tmp/tensorflow_logs \n", + "Then open http://0.0.0.0:6006/ into your web browser\n" + ] + } + ], + "source": [ + "# Launch the graph\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + "\n", + " # op to write logs to Tensorboard\n", + " summary_writer = tf.train.SummaryWriter(logs_path)\n", + "\n", + " # Training cycle\n", + " for epoch in range(training_epochs):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples/batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop), cost op (to get loss value)\n", + " # and summary nodes\n", + " _, c, summary = sess.run([optimizer, cost, merged_summary_op],\n", + " feed_dict={x: batch_xs, y: batch_ys})\n", + " # Write logs at every iteration\n", + " summary_writer.add_summary(summary, epoch * total_batch + i)\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if (epoch+1) % display_step == 0:\n", + " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n", + "\n", + " print \"Optimization Finished!\"\n", + "\n", + " # Test model\n", + " # Calculate accuracy\n", + " print \"Accuracy:\", acc.eval({x: mnist.test.images, y: mnist.test.labels})\n", + "\n", + " print \"Run the command line:\\n\" \\\n", + " \"--> tensorboard --logdir=/tmp/tensorflow_logs \" \\\n", + " \"\\nThen open http://0.0.0.0:6006/ into your web browser\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/notebooks/5 - User Interface/graph_visualization.ipynb b/notebooks/5 - User Interface/graph_visualization.ipynb deleted file mode 100644 index bc031a3c..00000000 --- a/notebooks/5 - User Interface/graph_visualization.ipynb +++ /dev/null @@ -1,226 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Graph Visualization with TensorFlow.\n", - "# This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", - "\n", - "# Author: Aymeric Damien\n", - "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], - "source": [ - "import tensorflow as tf\n", - "import numpy\n", - "\n", - "# Import MINST data\n", - "import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Use Logistic Regression from our previous example\n", - "\n", - "# Parameters\n", - "learning_rate = 0.01\n", - "training_epochs = 10\n", - "batch_size = 100\n", - "display_step = 1\n", - "\n", - "# tf Graph Input\n", - "x = tf.placeholder(\"float\", [None, 784], name='x') # mnist data image of shape 28*28=784\n", - "y = tf.placeholder(\"float\", [None, 10], name='y') # 0-9 digits recognition => 10 classes\n", - "\n", - "# Create model\n", - "\n", - "# Set model weights\n", - "W = tf.Variable(tf.zeros([784, 10]), name=\"weights\")\n", - "b = tf.Variable(tf.zeros([10]), name=\"bias\")\n", - "\n", - "# Construct model\n", - "activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n", - "\n", - "# Minimize error using cross entropy\n", - "cost = -tf.reduce_sum(y*tf.log(activation)) # Cross entropy\n", - "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent\n", - "\n", - "# Initializing the variables\n", - "init = tf.initialize_all_variables()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Launch the graph\n", - "with tf.Session() as sess:\n", - " sess.run(init)\n", - "\n", - " # Set logs writer into folder /tmp/tensorflow_logs\n", - " summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def)\n", - "\n", - " # Training cycle\n", - " for epoch in range(training_epochs):\n", - " avg_cost = 0.\n", - " total_batch = int(mnist.train.num_examples/batch_size)\n", - " # Loop over all batches\n", - " for i in range(total_batch):\n", - " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", - " # Fit training using batch data\n", - " sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})\n", - " # Compute average loss\n", - " avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch\n", - " # Display logs per epoch step\n", - " if epoch % display_step == 0:\n", - " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n", - "\n", - " print \"Optimization Finished!\"\n", - "\n", - " # Test model\n", - " correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))\n", - " # Calculate accuracy\n", - " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", - " print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the command line\n", - "```\n", - "tensorboard --logdir=/tmp/tensorflow_logs\n", - "```\n", - "\n", - "### Open http://localhost:6006/ into your web browser" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAACeIAAATKCAYAAAA37YYaAAAMFmlDQ1BJQ0MgUHJvZmlsZQAASImV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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Graph Visualization\n", - "# Tensorflow makes it easy for you to visualize all computation graph, \n", - "# you can click on any part of the graph for more in-depth details" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAACeYAAAS8CAYAAADjfci8AAAMFmlDQ1BJQ0MgUHJvZmlsZQAASImV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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Weights details" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAACewAAATECAYAAAATLtfZAAAMFmlDQ1BJQ0MgUHJvZmlsZQAASImV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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Gradient descent details" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "IPython (Python 2.7)", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.8" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/5 - User Interface/loss_visualization.ipynb b/notebooks/5 - User Interface/loss_visualization.ipynb deleted file mode 100644 index 232a5ebc..00000000 --- a/notebooks/5 - User Interface/loss_visualization.ipynb +++ /dev/null @@ -1,196 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Loss Visualization with TensorFlow.\n", - "# This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", - "\n", - "# Author: Aymeric Damien\n", - "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], - "source": [ - "import tensorflow as tf\n", - "import numpy\n", - "\n", - "# Import MINST data\n", - "import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Use Logistic Regression from our previous example\n", - "\n", - "# Parameters\n", - "learning_rate = 0.01\n", - "training_epochs = 10\n", - "batch_size = 100\n", - "display_step = 1\n", - "\n", - "# tf Graph Input\n", - "x = tf.placeholder(\"float\", [None, 784], name='x') # mnist data image of shape 28*28=784\n", - "y = tf.placeholder(\"float\", [None, 10], name='y') # 0-9 digits recognition => 10 classes\n", - "\n", - "# Create model\n", - "\n", - "# Set model weights\n", - "W = tf.Variable(tf.zeros([784, 10]), name=\"weights\")\n", - "b = tf.Variable(tf.zeros([10]), name=\"bias\")\n", - "\n", - "# Construct model\n", - "activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n", - "\n", - "# Minimize error using cross entropy\n", - "cost = -tf.reduce_sum(y*tf.log(activation)) # Cross entropy\n", - "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Gradient Descent\n", - "\n", - "# Initializing the variables\n", - "init = tf.initialize_all_variables()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Create a summary to monitor cost function\n", - "tf.scalar_summary(\"loss\", cost)\n", - "\n", - "# Merge all summaries to a single operator\n", - "merged_summary_op = tf.merge_all_summaries()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Launch the graph\n", - "with tf.Session() as sess:\n", - " sess.run(init)\n", - "\n", - " # Set logs writer into folder /tmp/tensorflow_logs\n", - " summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def)\n", - "\n", - " # Training cycle\n", - " for epoch in range(training_epochs):\n", - " avg_cost = 0.\n", - " total_batch = int(mnist.train.num_examples/batch_size)\n", - " # Loop over all batches\n", - " for i in range(total_batch):\n", - " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", - " # Fit training using batch data\n", - " sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})\n", - " # Compute average loss\n", - " avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys})/total_batch\n", - " # Write logs at every iteration\n", - " summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys})\n", - " summary_writer.add_summary(summary_str, epoch*total_batch + i)\n", - " # Display logs per epoch step\n", - " if epoch % display_step == 0:\n", - " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n", - "\n", - " print \"Optimization Finished!\"\n", - "\n", - " # Test model\n", - " correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))\n", - " # Calculate accuracy\n", - " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", - " print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run the command line\n", - "```\n", - "tensorboard --logdir=/tmp/tensorflow_logs\n", - "```\n", - "\n", - "### Open http://localhost:6006/ into your web browser" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAACdwAAASoCAYAAAAE65rAAAAMFmlDQ1BJQ0MgUHJvZmlsZQAASImV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\nykeZIvGKlmtvv/31/wfSJn1/ECSR6QAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Loss per minibatch step" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "IPython (Python 2.7)", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.8" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/4 - Multi GPU/multigpu_basics.ipynb b/notebooks/5_MultiGPU/multigpu_basics.ipynb similarity index 99% rename from notebooks/4 - Multi GPU/multigpu_basics.ipynb rename to notebooks/5_MultiGPU/multigpu_basics.ipynb index 76392b75..62d2a144 100644 --- a/notebooks/4 - Multi GPU/multigpu_basics.ipynb +++ b/notebooks/5_MultiGPU/multigpu_basics.ipynb @@ -163,7 +163,7 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", @@ -175,4 +175,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file From ff9a8de75c4b5025fd1f4df4351f9f83902b5362 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sun, 29 May 2016 21:35:52 +0800 Subject: [PATCH 014/166] update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index d0d90f1a..a0bd86ac 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ Code examples for some popular machine learning algorithms, using TensorFlow lib - Hello World ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py)) - Basic Operations ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py)) -#### 2 - Basic Classifiers +#### 2 - Basic Models - Nearest Neighbor ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)) - Linear Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py)) - Logistic Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py)) @@ -18,16 +18,16 @@ Code examples for some popular machine learning algorithms, using TensorFlow lib - AlexNet ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/alexnet.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/alexnet.py)) - Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/reccurent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)) - Bidirectional Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py)) -- AutoEncoder ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) / ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)) +- AutoEncoder ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)) -#### 4 - Utils +#### 4 - Utilities - Save and Restore a model ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py)) - Tensorboard - Graph and loss visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py)) -#### 5 - Multi GPU (Tensorboard) +#### 5 - Multi GPU - Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py)) -## Going further - More Examples +## More Examples The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). #### Basics From 4c6f7fcf4663a23b803e2893a69cce7b0d1643a3 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Mon, 30 May 2016 00:27:28 +0800 Subject: [PATCH 015/166] update README.md --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index a0bd86ac..4092855b 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,6 @@ Code examples for some popular machine learning algorithms, using TensorFlow lib #### 3 - Neural Networks - Multilayer Perceptron ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py)) - Convolutional Neural Network ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py)) -- AlexNet ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/alexnet.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/alexnet.py)) - Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/reccurent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)) - Bidirectional Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py)) - AutoEncoder ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)) From 959b573a880ae2d5806190f180cb380210d2e23e Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Mon, 30 May 2016 17:13:11 +0800 Subject: [PATCH 016/166] update notebooks --- .../2_BasicModels/linear_regression.ipynb | 29 +++++++++++++++++-- notebooks/3_Neural Networks/autoencoder.ipynb | 29 +++++++++++++++++-- 2 files changed, 52 insertions(+), 6 deletions(-) diff --git a/notebooks/2_BasicModels/linear_regression.ipynb b/notebooks/2_BasicModels/linear_regression.ipynb index fb05858d..39902e61 100644 --- a/notebooks/2_BasicModels/linear_regression.ipynb +++ b/notebooks/2_BasicModels/linear_regression.ipynb @@ -176,18 +176,41 @@ " plt.legend()\n", " plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAJYCAYAAACadoJwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbl4AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Regression result" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "IPython (Python 2.7)", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", @@ -199,4 +222,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/notebooks/3_Neural Networks/autoencoder.ipynb b/notebooks/3_Neural Networks/autoencoder.ipynb index f51feb0a..072cf7dd 100644 --- a/notebooks/3_Neural Networks/autoencoder.ipynb +++ b/notebooks/3_Neural Networks/autoencoder.ipynb @@ -200,18 +200,41 @@ "plt.draw()\n", "plt.waitforbuttonpress()" ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAADICAYAAAB758tPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmTOAofh3AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Result" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "IPython (Python 2.7)", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", @@ -223,4 +246,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 9e2e6dee599c627df915a65e54e1111df5b1c44a Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Mon, 30 May 2016 17:18:05 +0800 Subject: [PATCH 017/166] Update README.md --- README.md | 26 +++++++++++++++++++------- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 4092855b..f493c1ce 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ Code examples for some popular machine learning algorithms, using TensorFlow lib ## More Examples The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). -#### Basics +## Basics - [Linear Regression](https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py). Implement a linear regression using TFLearn. - [Logical Operators](https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py). Implement logical operators with TFLearn (also includes a usage of 'merge'). - [Weights Persistence](https://github.com/tflearn/tflearn/blob/master/examples/basics/weights_persistence.py). Save and Restore a model. @@ -37,7 +37,14 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [Using HDF5](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_hdf5.py). Use HDF5 to handle large datasets. - [Using DASK](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py). Use DASK to handle large datasets. -#### Computer Vision +## Extending Tensorflow +- [Layers](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py). Use TFLearn layers along with Tensorflow. +- [Trainer](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/trainer.py). Use TFLearn trainer class to train any Tensorflow graph. +- [Built-in Ops](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py). Use TFLearn built-in operations along with Tensorflow. +- [Summaries](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py). Use TFLearn summarizers along with Tensorflow. +- [Variables](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py). Use TFLearn variables along with Tensorflow. + +## Computer Vision - [Multi-layer perceptron](https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py). A multi-layer perceptron implementation for MNIST classification task. - [Convolutional Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py). A Convolutional neural network implementation for classifying MNIST dataset. - [Convolutional Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py). A Convolutional neural network implementation for classifying CIFAR-10 dataset. @@ -45,25 +52,30 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [Alexnet](https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py). Apply Alexnet to Oxford Flowers 17 classification task. - [VGGNet](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py). Apply VGG Network to Oxford Flowers 17 classification task. - [RNN Pixels](https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py). Use RNN (over sequence of pixels) to classify images. -- [Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A residual network with shallow bottlenecks applied to MNIST classification task. -- [Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network with deep bottlenecks applied to CIFAR-10 classification task. +- [Highway Network](https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py). Highway Network implementation for classifying MNIST dataset. +- [Highway Convolutional Network](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py). Highway Convolutional Network implementation for classifying MNIST dataset. +- [Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network with shallow bottlenecks applied to CIFAR-10 classification task. +- [Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A residual network with deep bottlenecks applied to MNIST classification task. - [Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits. -#### Natural Language Processing +## Natural Language Processing - [Reccurent Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task. - [Bi-Directional LSTM](https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py). Apply a bi-directional LSTM to IMDB sentiment dataset classification task. - [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network. - [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network. +## Notebooks +- [Spiral Classification Problem](https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb). TFLearn implementation of spiral classification problem from Stanford CS231n. + ## Dependencies ``` tensorflow numpy matplotlib -cuda (to run examples on GPU) +cuda tflearn (if using tflearn examples) ``` -For more details about TensorFlow installation, you can check [Setup_TensorFlow.md](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/Setup_TensorFlow.md) +For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md) ## Dataset Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). From 7d611fdf32ad2058c8a2792626d3d4b584ca2d81 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Mon, 30 May 2016 17:27:01 +0800 Subject: [PATCH 018/166] fix dir name and delete setup_tensorflow file --- Setup_TensorFlow.md | 709 ------------------ .../autoencoder.ipynb | 0 .../bidirectional_rnn.ipynb | 0 .../convolutional_network.ipynb | 0 .../multilayer_perceptron.ipynb | 0 .../recurrent_network.ipynb | 0 6 files changed, 709 deletions(-) delete mode 100644 Setup_TensorFlow.md rename notebooks/{3_Neural Networks => 3_NeuralNetworks}/autoencoder.ipynb (100%) rename notebooks/{3_Neural Networks => 3_NeuralNetworks}/bidirectional_rnn.ipynb (100%) rename notebooks/{3_Neural Networks => 3_NeuralNetworks}/convolutional_network.ipynb (100%) rename notebooks/{3_Neural Networks => 3_NeuralNetworks}/multilayer_perceptron.ipynb (100%) rename notebooks/{3_Neural Networks => 3_NeuralNetworks}/recurrent_network.ipynb (100%) diff --git a/Setup_TensorFlow.md b/Setup_TensorFlow.md deleted file mode 100644 index cdbf18b9..00000000 --- a/Setup_TensorFlow.md +++ /dev/null @@ -1,709 +0,0 @@ -From Tensorflow Official doc - -# Download and Setup - -You can install TensorFlow either from our provided binary packages or from the -github source. - -## Requirements - -The TensorFlow Python API supports Python 2.7 and Python 3.3+. - -The GPU version (Linux only) requires the Cuda Toolkit >= 7.0 and cuDNN >= -v2. Please see [Cuda installation](#optional-install-cuda-gpus-on-linux) -for details. - -## Overview - -We support different ways to install TensorFlow: - -* [Pip install](#pip-installation): Install TensorFlow on your machine, possibly - upgrading previously installed Python packages. May impact existing - Python programs on your machine. -* [Virtualenv install](#virtualenv-installation): Install TensorFlow in its own - directory, not impacting any existing Python programs on your machine. -* [Docker install](#docker-installation): Run TensorFlow in a Docker container - isolated from all other programs on your machine. - -If you are familiar with Pip, Virtualenv, or Docker, please feel free to adapt -the instructions to your particular needs. The names of the pip and Docker -images are listed in the corresponding installation sections. - -If you encounter installation errors, see -[common problems](#common-problems) for some solutions. - -## Pip Installation - -[Pip](https://en.wikipedia.org/wiki/Pip_(package_manager)) is a package -management system used to install and manage software packages written in -Python. - -The packages that will be installed or upgraded during the pip install are listed in the -[REQUIRED_PACKAGES section of setup.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py) - -Install pip (or pip3 for python3) if it is not already installed: - -```bash -# Ubuntu/Linux 64-bit -$ sudo apt-get install python-pip python-dev - -# Mac OS X -$ sudo easy_install pip -``` - -Install TensorFlow: - -```bash -# Ubuntu/Linux 64-bit, CPU only: -$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl - -# Ubuntu/Linux 64-bit, GPU enabled: -$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl - -# Mac OS X, CPU only: -$ sudo easy_install --upgrade six -$ sudo pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.7.1-cp27-none-any.whl -``` - -For python3: - -```bash -# Ubuntu/Linux 64-bit, CPU only: -$ sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.1-cp34-none-linux_x86_64.whl - -# Ubuntu/Linux 64-bit, GPU enabled: -$ sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.7.1-cp34-none-linux_x86_64.whl - -# Mac OS X, CPU only: -$ sudo easy_install --upgrade six -$ sudo pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.7.1-cp35-none-any.whl -``` - -NOTE: If you are upgrading from a previous installation of TensorFlow < 0.7.1, -you should uninstall the previous TensorFlow *and protobuf* using `pip -uninstall` first to make sure you get a clean installation of the updated -protobuf dependency. - - -You can now [test your installation](#test-the-tensorflow-installation). - -## Virtualenv installation - -[Virtualenv](http://docs.python-guide.org/en/latest/dev/virtualenvs/) is a tool -to keep the dependencies required by different Python projects in separate -places. The Virtualenv installation of TensorFlow will not override -pre-existing version of the Python packages needed by TensorFlow. - -With [Virtualenv](https://pypi.python.org/pypi/virtualenv) the installation is -as follows: - -* Install pip and Virtualenv. -* Create a Virtualenv environment. -* Activate the Virtualenv environment and install TensorFlow in it. -* After the install you will activate the Virtualenv environment each time you - want to use TensorFlow. - -Install pip and Virtualenv: - -```bash -# Ubuntu/Linux 64-bit -$ sudo apt-get install python-pip python-dev python-virtualenv - -# Mac OS X -$ sudo easy_install pip -$ sudo pip install --upgrade virtualenv -``` - -Create a Virtualenv environment in the directory `~/tensorflow`: - -```bash -$ virtualenv --system-site-packages ~/tensorflow -``` - -Activate the environment and use pip to install TensorFlow inside it: - -```bash -$ source ~/tensorflow/bin/activate # If using bash -$ source ~/tensorflow/bin/activate.csh # If using csh -(tensorflow)$ # Your prompt should change - -# Ubuntu/Linux 64-bit, CPU only: -(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl - -# Ubuntu/Linux 64-bit, GPU enabled: -(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl - -# Mac OS X, CPU only: -(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.7.1-cp27-none-any.whl -``` - -and again for python3: - -```bash -$ source ~/tensorflow/bin/activate # If using bash -$ source ~/tensorflow/bin/activate.csh # If using csh -(tensorflow)$ # Your prompt should change - -# Ubuntu/Linux 64-bit, CPU only: -(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.7.1-cp34-none-linux_x86_64.whl - -# Ubuntu/Linux 64-bit, GPU enabled: -(tensorflow)$ pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.7.1-cp34-none-linux_x86_64.whl - -# Mac OS X, CPU only: -(tensorflow)$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/tensorflow-0.7.1-cp35-none-any.whl -``` - -With the Virtualenv environment activated, you can now -[test your installation](#test-the-tensorflow-installation). - -When you are done using TensorFlow, deactivate the environment. - -```bash -(tensorflow)$ deactivate - -$ # Your prompt should change back -``` - -To use TensorFlow later you will have to activate the Virtualenv environment again: - -```bash -$ source ~/tensorflow/bin/activate # If using bash. -$ source ~/tensorflow/bin/activate.csh # If using csh. -(tensorflow)$ # Your prompt should change. -# Run Python programs that use TensorFlow. -... -# When you are done using TensorFlow, deactivate the environment. -(tensorflow)$ deactivate -``` - -## Docker installation - -[Docker](http://docker.com/) is a system to build self contained versions of a -Linux operating system running on your machine. When you install and run -TensorFlow via Docker it completely isolates the installation from pre-existing -packages on your machine. - -We provide 4 Docker images: - -* `b.gcr.io/tensorflow/tensorflow`: TensorFlow CPU binary image. -* `b.gcr.io/tensorflow/tensorflow:latest-devel`: CPU Binary image plus source -code. -* `b.gcr.io/tensorflow/tensorflow:latest-gpu`: TensorFlow GPU binary image. -* `b.gcr.io/tensorflow/tensorflow:latest-devel-gpu`: GPU Binary image plus source -code. - -We also have tags with `latest` replaced by a released version (e.g., `0.7.1-gpu`). - -With Docker the installation is as follows: - -* Install Docker on your machine. -* Create a [Docker -group](http://docs.docker.com/engine/installation/ubuntulinux/#create-a-docker-group) -to allow launching containers without `sudo`. -* Launch a Docker container with the TensorFlow image. The image - gets downloaded automatically on first launch. - -See [installing Docker](http://docs.docker.com/engine/installation/) for instructions -on installing Docker on your machine. - -After Docker is installed, launch a Docker container with the TensorFlow binary -image as follows. - -```bash -$ docker run -it b.gcr.io/tensorflow/tensorflow -``` - -If you're using a container with GPU support, some additional flags must be -passed to expose the GPU device to the container. For the default config, we -include a -[script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/docker_run_gpu.sh) -in the repo with these flags, so the command-line would look like - -```bash -$ path/to/repo/tensorflow/tools/docker/docker_run_gpu.sh b.gcr.io/tensorflow/tensorflow:gpu -``` - -You can now [test your installation](#test-the-tensorflow-installation) within the Docker container. - -## Test the TensorFlow installation - -### (Optional, Linux) Enable GPU Support - -If you installed the GPU version of TensorFlow, you must also install the Cuda -Toolkit 7.0 and cuDNN v2. Please see [Cuda installation](#optional-install-cuda-gpus-on-linux). - -You also need to set the `LD_LIBRARY_PATH` and `CUDA_HOME` environment -variables. Consider adding the commands below to your `~/.bash_profile`. These -assume your CUDA installation is in `/usr/local/cuda`: - -```bash -export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64" -export CUDA_HOME=/usr/local/cuda -``` - -### Run TensorFlow from the Command Line - -See [common problems](#common-problems) if an error happens. - -Open a terminal and type the following: - -```bash -$ python -... ->>> import tensorflow as tf ->>> hello = tf.constant('Hello, TensorFlow!') ->>> sess = tf.Session() ->>> print(sess.run(hello)) -Hello, TensorFlow! ->>> a = tf.constant(10) ->>> b = tf.constant(32) ->>> print(sess.run(a + b)) -42 ->>> -``` - -### Run a TensorFlow demo model - -All TensorFlow packages, including the demo models, are installed in the Python library. -The exact location of the Python library depends on your system, but is usually one of: - -```bash -/usr/local/lib/python2.7/dist-packages/tensorflow -/usr/local/lib/python2.7/site-packages/tensorflow -``` - -You can find out the directory with the following command (make sure to use the Python you installed TensorFlow to, for example, use `python3` instead of `python` if you installed for Python 3): - -```bash -$ python -c 'import os; import inspect; import tensorflow; print(os.path.dirname(inspect.getfile(tensorflow)))' -``` - -The simple demo model for classifying handwritten digits from the MNIST dataset -is in the sub-directory `models/image/mnist/convolutional.py`. You can run it from the command -line as follows (make sure to use the Python you installed TensorFlow with): - -```bash -# Using 'python -m' to find the program in the python search path: -$ python -m tensorflow.models.image.mnist.convolutional -Extracting data/train-images-idx3-ubyte.gz -Extracting data/train-labels-idx1-ubyte.gz -Extracting data/t10k-images-idx3-ubyte.gz -Extracting data/t10k-labels-idx1-ubyte.gz -...etc... - -# You can alternatively pass the path to the model program file to the python -# interpreter (make sure to use the python distribution you installed -# TensorFlow to, for example, .../python3.X/... for Python 3). -$ python /usr/local/lib/python2.7/dist-packages/tensorflow/models/image/mnist/convolutional.py -... -``` - -## Installing from sources - -When installing from source you will build a pip wheel that you then install -using pip. You'll need pip for that, so install it as described -[above](#pip-installation). - -### Clone the TensorFlow repository - -```bash -$ git clone --recurse-submodules https://github.com/tensorflow/tensorflow -``` - -`--recurse-submodules` is required to fetch the protobuf library that TensorFlow -depends on. Note that these instructions will install the latest master branch -of tensorflow. If you want to install a specific branch (such as a release branch), -pass `-b ` to the `git clone` command. - -### Installation for Linux - -#### Install Bazel - -Follow instructions [here](http://bazel.io/docs/install.html) to install the -dependencies for bazel. Then download the latest stable bazel version using the -[installer for your system](https://github.com/bazelbuild/bazel/releases) and -run the installer as mentioned there: - -```bash -$ chmod +x PATH_TO_INSTALL.SH -$ ./PATH_TO_INSTALL.SH --user -``` - -Remember to replace `PATH_TO_INSTALL.SH` with the location where you -downloaded the installer. - -Finally, follow the instructions in that script to place `bazel` into your -binary path. - -#### Install other dependencies - -```bash -$ sudo apt-get install python-numpy swig python-dev -``` - -#### Configure the installation - -Run the `configure` script at the root of the tree. The configure script -asks you for the path to your python interpreter and allows (optional) -configuration of the CUDA libraries (see [below](#configure-tensorflows-canonical-view-of-cuda-libraries)). - -This step is used to locate the python and numpy header files. - -```bash -$ ./configure -Please specify the location of python. [Default is /usr/bin/python]: -``` - -#### Optional: Install CUDA (GPUs on Linux) - -In order to build or run TensorFlow with GPU support, both NVIDIA's Cuda Toolkit (>= 7.0) and -cuDNN (>= v2) need to be installed. - -TensorFlow GPU support requires having a GPU card with NVidia Compute Capability >= 3.0. -Supported cards include but are not limited to: - -* NVidia Titan -* NVidia Titan X -* NVidia K20 -* NVidia K40 - -##### Download and install Cuda Toolkit - -https://developer.nvidia.com/cuda-downloads - -Install the toolkit into e.g. `/usr/local/cuda` - -##### Download and install cuDNN - -https://developer.nvidia.com/cudnn - -Uncompress and copy the cuDNN files into the toolkit directory. Assuming the -toolkit is installed in `/usr/local/cuda`, run the following commands (edited -to reflect the cuDNN version you downloaded): - -``` bash -tar xvzf cudnn-6.5-linux-x64-v2.tgz -sudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda/include -sudo cp cudnn-6.5-linux-x64-v2/libcudnn* /usr/local/cuda/lib64 -sudo chmod a+r /usr/local/cuda/lib64/libcudnn* -``` - -##### Configure TensorFlow's canonical view of Cuda libraries - -When running the `configure` script from the root of your source tree, select -the option `Y` when asked to build TensorFlow with GPU support. If you have -several versions of Cuda or cuDNN installed, you should definitely select -one explicitly instead of relying on the system default. You should see -prompts like the following: - -``` bash -$ ./configure -Please specify the location of python. [Default is /usr/bin/python]: -Do you wish to build TensorFlow with GPU support? [y/N] y -GPU support will be enabled for TensorFlow - -Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave -empty to use system default]: 7.5 - -Please specify the location where CUDA 7.5 toolkit is installed. Refer to -README.md for more details. [default is: /usr/local/cuda]: /usr/local/cuda - -Please specify the Cudnn version you want to use. [Leave empty to use system -default]: 4.0.4 - -Please specify the location where the cuDNN 4.0.4 library is installed. Refer to -README.md for more details. [default is: /usr/local/cuda]: /usr/local/cudnn-r4-rc/ - -Please specify a list of comma-separated Cuda compute capabilities you want to -build with. You can find the compute capability of your device at: -https://developer.nvidia.com/cuda-gpus. -Please note that each additional compute capability significantly increases your -build time and binary size. [Default is: \"3.5,5.2\"]: 3.5 - -Setting up Cuda include -Setting up Cuda lib64 -Setting up Cuda bin -Setting up Cuda nvvm -Configuration finished -``` - -This creates a canonical set of symbolic links to the Cuda libraries on your system. -Every time you change the Cuda library paths you need to run this step again before -you invoke the bazel build command. For the Cudnn libraries, use '6.5' for R2, '7.0' -for R3, and '4.0.4' for R4-RC. - - -##### Build your target with GPU support -From the root of your source tree, run: - -```bash -$ bazel build -c opt --config=cuda //tensorflow/cc:tutorials_example_trainer - -$ bazel-bin/tensorflow/cc/tutorials_example_trainer --use_gpu -# Lots of output. This tutorial iteratively calculates the major eigenvalue of -# a 2x2 matrix, on GPU. The last few lines look like this. -000009/000005 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427] -000006/000001 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427] -000009/000009 lambda = 2.000000 x = [0.894427 -0.447214] y = [1.788854 -0.894427] -``` - -Note that "--config=cuda" is needed to enable the GPU support. - -##### Known issues - -* Although it is possible to build both Cuda and non-Cuda configs under the same -source tree, we recommend to run `bazel clean` when switching between these two -configs in the same source tree. - -* You have to run configure before running bazel build. Otherwise, the build -will fail with a clear error message. In the future, we might consider making -this more convenient by including the configure step in our build process. - -### Installation for Mac OS X - -We recommend using [homebrew](http://brew.sh) to install the bazel and SWIG -dependencies, and installing python dependencies using easy_install or pip. - -Of course you can also install Swig from source without using homebrew. In that -case, be sure to install its dependency [PCRE](http://www.pcre.org) and not PCRE2. - -#### Dependencies - -Follow instructions [here](http://bazel.io/docs/install.html) to install the -dependencies for bazel. You can then use homebrew to install bazel and SWIG: - -```bash -$ brew install bazel swig -``` - -You can install the python dependencies using easy_install or pip. Using -easy_install, run - -```bash -$ sudo easy_install -U six -$ sudo easy_install -U numpy -$ sudo easy_install wheel -``` - -We also recommend the [ipython](https://ipython.org) enhanced python shell, so -best install that too: - -```bash -$ sudo easy_install ipython -``` - -#### Configure the installation - -Run the `configure` script at the root of the tree. The configure script -asks you for the path to your python interpreter. - -This step is used to locate the python and numpy header files. - -```bash -$ ./configure -Please specify the location of python. [Default is /usr/bin/python]: -Do you wish to build TensorFlow with GPU support? [y/N] -``` - -### Create the pip package and install - -When building from source, you will still build a pip package and install that. - -```bash -$ bazel build -c opt //tensorflow/tools/pip_package:build_pip_package - -# To build with GPU support: -$ bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package - -$ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg - -# The name of the .whl file will depend on your platform. -$ pip install /tmp/tensorflow_pkg/tensorflow-0.7.1-py2-none-linux_x86_64.whl -``` - -## Setting up TensorFlow for Development - -If you're working on TensorFlow itself, it is useful to be able to test your -changes in an interactive python shell without having to reinstall TensorFlow. - -To set up TensorFlow such that all files are linked (instead of copied) from the -system directories, run the following commands inside the TensorFlow root -directory: - -```bash -bazel build -c opt //tensorflow/tools/pip_package:build_pip_package -mkdir _python_build -cd _python_build -ln -s ../bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/* . -ln -s ../tensorflow/tools/pip_package/* . -python setup.py develop -``` - -Note that this setup still requires you to rebuild the -`//tensorflow/tools/pip_package:build_pip_package` target every time you change -a C++ file; add, delete, or move any python file; or if you change bazel build -rules. - -## Train your first TensorFlow neural net model - -Starting from the root of your source tree, run: - -```python -$ cd tensorflow/models/image/mnist -$ python convolutional.py -Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. -Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes. -Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes. -Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes. -Extracting data/train-images-idx3-ubyte.gz -Extracting data/train-labels-idx1-ubyte.gz -Extracting data/t10k-images-idx3-ubyte.gz -Extracting data/t10k-labels-idx1-ubyte.gz -Initialized! -Epoch 0.00 -Minibatch loss: 12.054, learning rate: 0.010000 -Minibatch error: 90.6% -Validation error: 84.6% -Epoch 0.12 -Minibatch loss: 3.285, learning rate: 0.010000 -Minibatch error: 6.2% -Validation error: 7.0% -... -... -``` - -## Common Problems - -### GPU-related issues - -If you encounter the following when trying to run a TensorFlow program: - -```python -ImportError: libcudart.so.7.0: cannot open shared object file: No such file or directory -``` - -Make sure you followed the GPU installation [instructions](#optional-install-cuda-gpus-on-linux). -If you built from source, and you left the Cuda or cuDNN version empty, try specifying them -explicitly. - -### Pip installation issues - -#### Cannot import name 'descriptor' - -```python -ImportError: Traceback (most recent call last): - File "/usr/local/lib/python3.4/dist-packages/tensorflow/core/framework/graph_pb2.py", line 6, in - from google.protobuf import descriptor as _descriptor -ImportError: cannot import name 'descriptor' -``` - -If you the above error when upgrading to a newer version of TensorFlow, try -uninstalling both TensorFlow and protobuf (if installed) and re-installing -TensorFlow (which will also install the correct protobuf dependency). - -#### Can't find setup.py - -If, during `pip install`, you encounter an error like: - -```bash -... -IOError: [Errno 2] No such file or directory: '/tmp/pip-o6Tpui-build/setup.py' -``` - -Solution: upgrade your version of pip: - -```bash -pip install --upgrade pip -``` - -This may require `sudo`, depending on how pip is installed. - -#### SSLError: SSL_VERIFY_FAILED - -If, during pip install from a URL, you encounter an error like: - -```bash -... -SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed -``` - -Solution: Download the wheel manually via curl or wget, and pip install locally. - -### Linux issues - -If you encounter: - -```python -... - "__add__", "__radd__", - ^ -SyntaxError: invalid syntax -``` - -Solution: make sure you are using Python 2.7. - -### Mac OS X: ImportError: No module named copyreg - -On Mac OS X, you may encounter the following when importing tensorflow. - -```python ->>> import tensorflow as tf -... -ImportError: No module named copyreg -``` - -Solution: TensorFlow depends on protobuf, which requires the Python package -`six-1.10.0`. Apple's default Python installation only provides `six-1.4.1`. - -You can resolve the issue in one of the following ways: - -* Upgrade the Python installation with the current version of `six`: - -```bash -$ sudo easy_install -U six -``` - -* Install TensorFlow with a separate Python library: - - * Using [Virtualenv](#virtualenv-installation). - * Using [Docker](#docker-installation). - -* Install a separate copy of Python via [Homebrew](http://brew.sh/) or -[MacPorts](https://www.macports.org/) and re-install TensorFlow in that -copy of Python. - -### Mac OS X: OSError: [Errno 1] Operation not permitted: - -On El Capitan, "six" is a special package that can't be modified, and this -error is reported when "pip install" tried to modify this package. To fix use -"ignore_installed" flag, ie - -sudo pip install --ignore-installed six https://storage.googleapis.com/.... - - -### Mac OS X: TypeError: `__init__()` got an unexpected keyword argument 'syntax' - -On Mac OS X, you may encounter the following when importing tensorflow. - -``` ->>> import tensorflow as tf -Traceback (most recent call last): - File "", line 1, in - File "/usr/local/lib/python2.7/site-packages/tensorflow/__init__.py", line 4, in - from tensorflow.python import * - File "/usr/local/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 13, in - from tensorflow.core.framework.graph_pb2 import * -... - File "/usr/local/lib/python2.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py", line 22, in - serialized_pb=_b('\n,tensorflow/core/framework/tensor_shape.proto\x12\ntensorflow\"d\n\x10TensorShapeProto\x12-\n\x03\x64im\x18\x02 \x03(\x0b\x32 .tensorflow.TensorShapeProto.Dim\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01 \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\tb\x06proto3') -TypeError: __init__() got an unexpected keyword argument 'syntax' -``` - -This is due to a conflict between protobuf versions (we require protobuf 3.0.0). -The best current solution is to make sure older versions of protobuf are not -installed, such as: - -```bash -$ pip install --upgrade protobuf -``` diff --git a/notebooks/3_Neural Networks/autoencoder.ipynb b/notebooks/3_NeuralNetworks/autoencoder.ipynb similarity index 100% rename from notebooks/3_Neural Networks/autoencoder.ipynb rename to notebooks/3_NeuralNetworks/autoencoder.ipynb diff --git a/notebooks/3_Neural Networks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb similarity index 100% rename from notebooks/3_Neural Networks/bidirectional_rnn.ipynb rename to notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb diff --git a/notebooks/3_Neural Networks/convolutional_network.ipynb b/notebooks/3_NeuralNetworks/convolutional_network.ipynb similarity index 100% rename from notebooks/3_Neural Networks/convolutional_network.ipynb rename to notebooks/3_NeuralNetworks/convolutional_network.ipynb diff --git a/notebooks/3_Neural Networks/multilayer_perceptron.ipynb b/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb similarity index 100% rename from notebooks/3_Neural Networks/multilayer_perceptron.ipynb rename to notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb diff --git a/notebooks/3_Neural Networks/recurrent_network.ipynb b/notebooks/3_NeuralNetworks/recurrent_network.ipynb similarity index 100% rename from notebooks/3_Neural Networks/recurrent_network.ipynb rename to notebooks/3_NeuralNetworks/recurrent_network.ipynb From 33df0f9b5b7d377e814416e9d9484e0883ff0597 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 31 May 2016 11:57:59 +0800 Subject: [PATCH 019/166] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index f493c1ce..8b8e80d8 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ # TensorFlow Examples -Code examples for some popular machine learning algorithms, using TensorFlow library. This tutorial is designed to easily dive into TensorFlow, through examples. It includes both notebook and code with explanations. +TensorFlow Tutorial with popular machine learning algorithms implementation examples. This tutorial was designed for easily diving into TensorFlow, through examples. +It is suitable for beginners who want to find clear and concise examples about TensorFlow. For readability, the tutorial includes both notebook and code with explanations. ## Tutorial index From 8381399cfd0a4e49a84aca6cc46ad08a2bc9ff7f Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 31 May 2016 11:59:39 +0800 Subject: [PATCH 020/166] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 8b8e80d8..e284a6ae 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ # TensorFlow Examples -TensorFlow Tutorial with popular machine learning algorithms implementation examples. This tutorial was designed for easily diving into TensorFlow, through examples. +TensorFlow Tutorial with popular machine learning algorithms implementation. This tutorial was designed for easily diving into TensorFlow, through examples. + It is suitable for beginners who want to find clear and concise examples about TensorFlow. For readability, the tutorial includes both notebook and code with explanations. ## Tutorial index From 29c7bee7d381a27146111e5e139c16d6c7a36ee9 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 31 May 2016 12:28:03 +0800 Subject: [PATCH 021/166] update tensorboard example --- examples/4_Utils/tensorboard_basic.py | 4 +- notebooks/4_Utils/tensorboard_basic.ipynb | 54 ++++++++++++++++++++--- 2 files changed, 51 insertions(+), 7 deletions(-) diff --git a/examples/4_Utils/tensorboard_basic.py b/examples/4_Utils/tensorboard_basic.py index 2e2ff246..9c87e62a 100644 --- a/examples/4_Utils/tensorboard_basic.py +++ b/examples/4_Utils/tensorboard_basic.py @@ -18,7 +18,7 @@ training_epochs = 25 batch_size = 100 display_step = 1 -logs_path = '/tmp/tensorflow_logs' +logs_path = '/tmp/tensorflow_logs/example' # tf Graph Input # mnist data image of shape 28*28=784 @@ -61,7 +61,7 @@ sess.run(init) # op to write logs to Tensorboard - summary_writer = tf.train.SummaryWriter(logs_path) + summary_writer = tf.train.SummaryWriter(logs_path, graph=tf.get_default_graph()) # Training cycle for epoch in range(training_epochs): diff --git a/notebooks/4_Utils/tensorboard_basic.ipynb b/notebooks/4_Utils/tensorboard_basic.ipynb index ceeab205..582bbdb7 100644 --- a/notebooks/4_Utils/tensorboard_basic.ipynb +++ b/notebooks/4_Utils/tensorboard_basic.ipynb @@ -57,7 +57,7 @@ "training_epochs = 25\n", "batch_size = 100\n", "display_step = 1\n", - "logs_path = '/tmp/tensorflow_logs'\n", + "logs_path = '/tmp/tensorflow_logs/example'\n", "\n", "# tf Graph Input\n", "# mnist data image of shape 28*28=784\n", @@ -155,7 +155,7 @@ " sess.run(init)\n", "\n", " # op to write logs to Tensorboard\n", - " summary_writer = tf.train.SummaryWriter(logs_path)\n", + " summary_writer = tf.train.SummaryWriter(logs_path, graph=tf.get_default_graph())\n", "\n", " # Training cycle\n", " for epoch in range(training_epochs):\n", @@ -186,18 +186,62 @@ " \"--> tensorboard --logdir=/tmp/tensorflow_logs \" \\\n", " \"\\nThen open http://0.0.0.0:6006/ into your web browser\"" ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAACeAAAATECAYAAAAJEjdXAAAMFWlDQ1BJQ0MgUHJvZmlsZQAASImV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\nzQ+TZ/sIW9+vnvVNVWbreI9Bbf4fOozPMqmXwV0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Loss and accuracy Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAACdgAAAS4CAYAAAAO1jghAAAMFWlDQ1BJQ0MgUHJvZmlsZQAASImV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\nj6Rr/x9y5UKDWn4pfgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Graph Visualization" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "IPython (Python 2.7)", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", @@ -209,4 +253,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From dec648ec14723fc7c17855c5fbb59258e53490fc Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Wed, 1 Jun 2016 00:21:51 +0800 Subject: [PATCH 022/166] added tensorboard advanced example --- examples/4_Utils/tensorboard_advanced.py | 140 ++++++++++++++++++++++- 1 file changed, 139 insertions(+), 1 deletion(-) diff --git a/examples/4_Utils/tensorboard_advanced.py b/examples/4_Utils/tensorboard_advanced.py index f87f5c14..d220857f 100644 --- a/examples/4_Utils/tensorboard_advanced.py +++ b/examples/4_Utils/tensorboard_advanced.py @@ -1 +1,139 @@ -# TODO \ No newline at end of file +''' +Graph and Loss visualization using Tensorboard. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +import tensorflow as tf + +# Import MINST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.01 +training_epochs = 25 +batch_size = 100 +display_step = 1 +logs_path = '/tmp/tensorflow_logs/example' + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of features +n_hidden_2 = 256 # 2nd layer number of features +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph Input +# mnist data image of shape 28*28=784 +x = tf.placeholder(tf.float32, [None, 784], name='InputData') +# 0-9 digits recognition => 10 classes +y = tf.placeholder(tf.float32, [None, 10], name='LabelData') + + +# Create model +def multilayer_perceptron(x, weights, biases): + # Hidden layer with RELU activation + layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1']) + layer_1 = tf.nn.relu(layer_1) + # Create a summary to visualize the first layer ReLU activation + tf.histogram_summary("relu1", layer_1) + # Hidden layer with RELU activation + layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2']) + layer_2 = tf.nn.relu(layer_2) + # Create another summary to visualize the second layer ReLU activation + tf.histogram_summary("relu2", layer_2) + # Output layer + out_layer = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3']) + return out_layer + +# Store layers weight & bias +weights = { + 'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='W1'), + 'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'), + 'w3': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='W3') +} +biases = { + 'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='b1'), + 'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='b2'), + 'b3': tf.Variable(tf.random_normal([n_classes]), name='b3') +} + +# Encapsulating all ops into scopes, making Tensorboard's Graph +# visualization more convenient +with tf.name_scope('Model'): + # Build model + pred = multilayer_perceptron(x, weights, biases) + +with tf.name_scope('Loss'): + # Softmax Cross entropy (cost function) + loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) + +with tf.name_scope('SGD'): + # Gradient Descent + optimizer = tf.train.GradientDescentOptimizer(learning_rate) + # Op to calculate every variable gradient + grads = tf.gradients(loss, tf.trainable_variables()) + grads = list(zip(grads, tf.trainable_variables())) + # Op to update all variables according to their gradient + apply_grads = optimizer.apply_gradients(grads_and_vars=grads) + +with tf.name_scope('Accuracy'): + # Accuracy + acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + acc = tf.reduce_mean(tf.cast(acc, tf.float32)) + +# Initializing the variables +init = tf.initialize_all_variables() + +# Create a summary to monitor cost tensor +tf.scalar_summary("loss", loss) +# Create a summary to monitor accuracy tensor +tf.scalar_summary("accuracy", acc) +# Create summaries to visualize weights +for var in tf.trainable_variables(): + tf.histogram_summary(var.name, var) +# Summarize all gradients +for grad, var in grads: + tf.histogram_summary(var.name + '/gradient', grad) +# Merge all summaries into a single op +merged_summary_op = tf.merge_all_summaries() + +# Launch the graph +with tf.Session() as sess: + sess.run(init) + + # op to write logs to Tensorboard + summary_writer = tf.train.SummaryWriter(logs_path, + graph=tf.get_default_graph()) + + # Training cycle + for epoch in range(training_epochs): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_xs, batch_ys = mnist.train.next_batch(batch_size) + # Run optimization op (backprop), cost op (to get loss value) + # and summary nodes + _, c, summary = sess.run([apply_grads, loss, merged_summary_op], + feed_dict={x: batch_xs, y: batch_ys}) + # Write logs at every iteration + summary_writer.add_summary(summary, epoch * total_batch + i) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if (epoch+1) % display_step == 0: + print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost) + + print "Optimization Finished!" + + # Test model + # Calculate accuracy + print "Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels}) + + print "Run the command line:\n" \ + "--> tensorboard --logdir=/tmp/tensorflow_logs " \ + "\nThen open http://0.0.0.0:6006/ into your web browser" From 9d16119e51baf7372b1c9353f3e07ed7f11b7a56 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Wed, 1 Jun 2016 02:04:09 +0800 Subject: [PATCH 023/166] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index e284a6ae..c5456dd2 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,7 @@ It is suitable for beginners who want to find clear and concise examples about T #### 4 - Utilities - Save and Restore a model ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py)) - Tensorboard - Graph and loss visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py)) +- - Tensorboard - Advanced visualization ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py)) #### 5 - Multi GPU - Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py)) From 8742cc2c31cb18109d051d94c6d1bad0524b2deb Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Wed, 1 Jun 2016 02:04:39 +0800 Subject: [PATCH 024/166] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c5456dd2..a690aa7c 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ It is suitable for beginners who want to find clear and concise examples about T #### 4 - Utilities - Save and Restore a model ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py)) - Tensorboard - Graph and loss visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py)) -- - Tensorboard - Advanced visualization ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py)) +- Tensorboard - Advanced visualization ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py)) #### 5 - Multi GPU - Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py)) From 2dfed5c57438ee0bedaef508722a727d2e5b848c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jill-J=C3=AAnn=20Vie?= Date: Wed, 1 Jun 2016 05:27:55 +0200 Subject: [PATCH 025/166] Update README.md (#33) Fix typo. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a690aa7c..84fe360c 100644 --- a/README.md +++ b/README.md @@ -62,7 +62,7 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits. ## Natural Language Processing -- [Reccurent Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task. +- [Recurrent Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task. - [Bi-Directional LSTM](https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py). Apply a bi-directional LSTM to IMDB sentiment dataset classification task. - [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network. - [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network. From d9f584a2ac12c70126346cf5a70064c964e54f84 Mon Sep 17 00:00:00 2001 From: kieran gorman Date: Wed, 1 Jun 2016 15:28:22 +1200 Subject: [PATCH 026/166] Update README.md (#34) Typo in link to recurrent NN notebook. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 84fe360c..9cf29179 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ It is suitable for beginners who want to find clear and concise examples about T #### 3 - Neural Networks - Multilayer Perceptron ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py)) - Convolutional Neural Network ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py)) -- Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/reccurent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)) +- Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)) - Bidirectional Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py)) - AutoEncoder ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)) From 065125692d19fd5b3dba563c295afe9bf691a1ff Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Mon, 6 Jun 2016 17:45:02 +0800 Subject: [PATCH 027/166] added prerequisite notebooks --- README.md | 14 ++- .../0_Prerequisite/ml_introduction.ipynb | 47 ++++++++++ .../0_Prerequisite/mnist_dataset_intro.ipynb | 94 +++++++++++++++++++ 3 files changed, 151 insertions(+), 4 deletions(-) create mode 100644 notebooks/0_Prerequisite/ml_introduction.ipynb create mode 100644 notebooks/0_Prerequisite/mnist_dataset_intro.ipynb diff --git a/README.md b/README.md index 9cf29179..68e31f31 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,10 @@ It is suitable for beginners who want to find clear and concise examples about T ## Tutorial index +#### 0 - Prerequisite +- Introduction to Machine Learning ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb)) +- Introduction to MNIST Dataset ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb)) + #### 1 - Introduction - Hello World ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py)) - Basic Operations ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py)) @@ -29,6 +33,12 @@ It is suitable for beginners who want to find clear and concise examples about T #### 5 - Multi GPU - Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py)) +## Dataset +Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). +MNIST is a database of handwritten digits, for a quick description of that dataset, you can check [this notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). + +Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/) + ## More Examples The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). @@ -79,7 +89,3 @@ cuda tflearn (if using tflearn examples) ``` For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md) - -## Dataset -Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). -MNIST is a database of handwritten digits, with 60,000 examples for training and 10,000 examples for testing. (Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/)) diff --git a/notebooks/0_Prerequisite/ml_introduction.ipynb b/notebooks/0_Prerequisite/ml_introduction.ipynb new file mode 100644 index 00000000..5d857d5c --- /dev/null +++ b/notebooks/0_Prerequisite/ml_introduction.ipynb @@ -0,0 +1,47 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning\n", + "\n", + "Prior to start browsing the examples, it may be useful that you get familiar with machine learning, as TensorFlow is mostly used for machine learning tasks (especially Neural Networks). You can find below a list of useful links, that can give you the basic knowledge required for this TensorFlow Tutorial.\n", + "\n", + "## Machine Learning\n", + "\n", + "- [An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples](https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer)\n", + "- [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)\n", + "- [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\n", + "- [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf)\n", + "\n", + "## Deep Learning & Neural Networks\n", + "\n", + "- [An Introduction to Neural Networks](http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html)\n", + "- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "IPython (Python 2.7)", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb b/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb new file mode 100644 index 00000000..c3beb27a --- /dev/null +++ b/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb @@ -0,0 +1,94 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# MNIST Dataset Introduction\n", + "\n", + "Most examples are using MNIST dataset of handwritten digits. It has 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image, so each sample is represented as a matrix of size 28x28 with values from 0 to 1.\n", + "\n", + "## Overview\n", + "\n", + "![MNIST Digits](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "## Usage\n", + "In our examples, we are using TensorFlow [input_data.py](https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/mnist/input_data.py) script to load that dataset.\n", + "It is quite useful for managing our data, and handle:\n", + "\n", + "- Dataset downloading\n", + "\n", + "- Loading the entire dataset into numpy array: \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Import MNIST\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", + "# Load data\n", + "X_train = mnist.train.images\n", + "Y_train = mnist.train.labels\n", + "X_test = mnist.test.images\n", + "Y_test = mnist.test.labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- A `next_batch` function that can iterate over the whole dataset and return only the desired fraction of the dataset samples (in order to save memory and avoid to load the entire dataset)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Get the next 64 images array and labels\n", + "batch_X, batch_Y = mnist.train.next_batch(64)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Link: http://yann.lecun.com/exdb/mnist/" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "IPython (Python 2.7)", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 4351a033761eba1c668e65f39df5bb7fde45aee8 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Wed, 8 Jun 2016 12:41:01 +0800 Subject: [PATCH 028/166] updated rnn examples --- .../3_NeuralNetworks/bidirectional_rnn.py | 10 ++++-- .../3_NeuralNetworks/recurrent_network.py | 3 +- .../3_NeuralNetworks/bidirectional_rnn.ipynb | 34 ++++++++----------- .../3_NeuralNetworks/recurrent_network.ipynb | 27 +++++---------- 4 files changed, 32 insertions(+), 42 deletions(-) diff --git a/examples/3_NeuralNetworks/bidirectional_rnn.py b/examples/3_NeuralNetworks/bidirectional_rnn.py index 2e195ceb..3c83911c 100644 --- a/examples/3_NeuralNetworks/bidirectional_rnn.py +++ b/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -53,6 +53,8 @@ def BiRNN(x, weights, biases): # Prepare data shape to match `bidirectional_rnn` function requirements # Current data input shape: (batch_size, n_steps, n_input) + # Required shape: 'n_steps' tensors list of shape (batch_size, n_hidden) + # Permuting batch_size and n_steps x = tf.transpose(x, [1, 0, 2]) # Reshape to (n_steps*batch_size, n_input) @@ -67,8 +69,12 @@ def BiRNN(x, weights, biases): lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) # Get lstm cell output - outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, - dtype=tf.float32) + try: + outputs, _, _ = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, + dtype=tf.float32) + except Exception: # Old TensorFlow version only returns outputs not states + outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, + dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out'] diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 4903e657..316c4faa 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -52,12 +52,13 @@ def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, n_steps, n_input) + # Required shape: 'n_steps' tensors list of shape (batch_size, n_hidden) + # Permuting batch_size and n_steps x = tf.transpose(x, [1, 0, 2]) # Reshaping to (n_steps*batch_size, n_input) x = tf.reshape(x, [-1, n_input]) # Split to get a list of 'n_steps' tensors of shape (batch_size, n_hidden) - # This input shape is required by `rnn` function x = tf.split(0, n_steps, x) # Define a lstm cell with tensorflow diff --git a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb index 9558e571..2d8e502a 100644 --- a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -3,9 +3,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "'''\n", @@ -21,9 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -49,9 +45,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "'''\n", @@ -64,9 +58,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Parameters\n", @@ -100,15 +92,15 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def BiRNN(x, weights, biases):\n", "\n", " # Prepare data shape to match `bidirectional_rnn` function requirements\n", " # Current data input shape: (batch_size, n_steps, n_input)\n", + " # Required shape: 'n_steps' tensors list of shape (batch_size, n_hidden)\n", + " \n", " # Permuting batch_size and n_steps\n", " x = tf.transpose(x, [1, 0, 2])\n", " # Reshape to (n_steps*batch_size, n_input)\n", @@ -123,8 +115,12 @@ " lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", "\n", " # Get lstm cell output\n", - " outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", - " dtype=tf.float32)\n", + " try:\n", + " outputs, _, _ = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", + " dtype=tf.float32)\n", + " except Exception: # Old TensorFlow version only returns outputs not states\n", + " outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", + " dtype=tf.float32)\n", "\n", " # Linear activation, using rnn inner loop last output\n", " return tf.matmul(outputs[-1], weights['out']) + biases['out']\n", @@ -146,9 +142,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", diff --git a/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/notebooks/3_NeuralNetworks/recurrent_network.ipynb index c56a5c8b..cdfd6580 100644 --- a/notebooks/3_NeuralNetworks/recurrent_network.ipynb +++ b/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -3,9 +3,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "'''\n", @@ -21,9 +19,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -49,9 +45,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "'''\n", @@ -64,9 +58,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Parameters\n", @@ -99,21 +91,20 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def RNN(x, weights, biases):\n", "\n", " # Prepare data shape to match `rnn` function requirements\n", " # Current data input shape: (batch_size, n_steps, n_input)\n", + " # Required shape: 'n_steps' tensors list of shape (batch_size, n_hidden)\n", + " \n", " # Permuting batch_size and n_steps\n", " x = tf.transpose(x, [1, 0, 2])\n", " # Reshaping to (n_steps*batch_size, n_input)\n", " x = tf.reshape(x, [-1, n_input])\n", " # Split to get a list of 'n_steps' tensors of shape (batch_size, n_hidden)\n", - " # This input shape is required by `rnn` function\n", " x = tf.split(0, n_steps, x)\n", "\n", " # Define a lstm cell with tensorflow\n", @@ -142,9 +133,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", From 7aeefdb6dd597d3043a7bcd8da19905180739687 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sun, 12 Jun 2016 17:16:42 +0800 Subject: [PATCH 029/166] Add atari pong example link --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 68e31f31..cd34c47d 100644 --- a/README.md +++ b/README.md @@ -77,6 +77,9 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network. - [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network. +## Reinforcement Learning +- [Atari Pong 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/pong_1step_qlearning.py). Teach a machine to play Atari Pong game using 1-step Q-learning. + ## Notebooks - [Spiral Classification Problem](https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb). TFLearn implementation of spiral classification problem from Stanford CS231n. From f9d1f232e177c9087b913e16cca9a53dccd99911 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 14 Jun 2016 01:41:19 +0800 Subject: [PATCH 030/166] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index cd34c47d..682edb0a 100644 --- a/README.md +++ b/README.md @@ -78,7 +78,7 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network. ## Reinforcement Learning -- [Atari Pong 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/pong_1step_qlearning.py). Teach a machine to play Atari Pong game using 1-step Q-learning. +- [Atari Pong 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py). Teach a machine to play Atari Pacman game using 1-step Q-learning. ## Notebooks - [Spiral Classification Problem](https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb). TFLearn implementation of spiral classification problem from Stanford CS231n. From bf72a97258e201a95886d17d8d7cf07d265102fb Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 14 Jun 2016 01:41:55 +0800 Subject: [PATCH 031/166] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 682edb0a..97f5b85c 100644 --- a/README.md +++ b/README.md @@ -78,7 +78,7 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network. ## Reinforcement Learning -- [Atari Pong 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py). Teach a machine to play Atari Pacman game using 1-step Q-learning. +- [Atari Pacman 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py). Teach a machine to play Atari Pacman game using 1-step Q-learning. ## Notebooks - [Spiral Classification Problem](https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb). TFLearn implementation of spiral classification problem from Stanford CS231n. From 368d7e361c41aca2179d593c2d28973b930dac74 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Wed, 15 Jun 2016 10:46:50 +0800 Subject: [PATCH 032/166] Update ml_introduction.ipynb --- notebooks/0_Prerequisite/ml_introduction.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/notebooks/0_Prerequisite/ml_introduction.ipynb b/notebooks/0_Prerequisite/ml_introduction.ipynb index 5d857d5c..fe84ef52 100644 --- a/notebooks/0_Prerequisite/ml_introduction.ipynb +++ b/notebooks/0_Prerequisite/ml_introduction.ipynb @@ -18,6 +18,7 @@ "## Deep Learning & Neural Networks\n", "\n", "- [An Introduction to Neural Networks](http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html)\n", + "- [An Introduction to Image Recognition with Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721)\n", "- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)\n", "\n" ] From 2835ccaa40fe628e220bff74401b3e5178723689 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Thu, 16 Jun 2016 23:21:32 +0800 Subject: [PATCH 033/166] fix #41 --- examples/3_NeuralNetworks/recurrent_network.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 316c4faa..65bf1356 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -39,11 +39,9 @@ # Define weights weights = { - 'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), 'out': tf.Variable(tf.random_normal([n_hidden, n_classes])) } biases = { - 'hidden': tf.Variable(tf.random_normal([n_hidden])), 'out': tf.Variable(tf.random_normal([n_classes])) } From 2c87d2f3532eacc94da67438cfe593eab516e1b6 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Thu, 16 Jun 2016 23:22:42 +0800 Subject: [PATCH 034/166] Update recurrent_network.ipynb --- notebooks/3_NeuralNetworks/recurrent_network.ipynb | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/notebooks/3_NeuralNetworks/recurrent_network.ipynb index cdfd6580..913428e7 100644 --- a/notebooks/3_NeuralNetworks/recurrent_network.ipynb +++ b/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -79,11 +79,9 @@ "\n", "# Define weights\n", "weights = {\n", - " 'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])),\n", " 'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\n", "}\n", "biases = {\n", - " 'hidden': tf.Variable(tf.random_normal([n_hidden])),\n", " 'out': tf.Variable(tf.random_normal([n_classes]))\n", "}" ] @@ -275,4 +273,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From f8dba4c3609e28aeb259f606f2f3096f8684ba28 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 21 Jun 2016 22:53:39 +0800 Subject: [PATCH 035/166] Update README.md --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 97f5b85c..7af4b2f6 100644 --- a/README.md +++ b/README.md @@ -50,13 +50,6 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [Using HDF5](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_hdf5.py). Use HDF5 to handle large datasets. - [Using DASK](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py). Use DASK to handle large datasets. -## Extending Tensorflow -- [Layers](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py). Use TFLearn layers along with Tensorflow. -- [Trainer](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/trainer.py). Use TFLearn trainer class to train any Tensorflow graph. -- [Built-in Ops](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py). Use TFLearn built-in operations along with Tensorflow. -- [Summaries](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py). Use TFLearn summarizers along with Tensorflow. -- [Variables](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py). Use TFLearn variables along with Tensorflow. - ## Computer Vision - [Multi-layer perceptron](https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py). A multi-layer perceptron implementation for MNIST classification task. - [Convolutional Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py). A Convolutional neural network implementation for classifying MNIST dataset. @@ -83,6 +76,13 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle ## Notebooks - [Spiral Classification Problem](https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb). TFLearn implementation of spiral classification problem from Stanford CS231n. +## Extending Tensorflow +- [Layers](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py). Use TFLearn layers along with Tensorflow. +- [Trainer](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/trainer.py). Use TFLearn trainer class to train any Tensorflow graph. +- [Built-in Ops](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py). Use TFLearn built-in operations along with Tensorflow. +- [Summaries](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py). Use TFLearn summarizers along with Tensorflow. +- [Variables](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py). Use TFLearn variables along with Tensorflow. + ## Dependencies ``` tensorflow From 1f58605650f8bfd65adbc81d9279bc2fffa2da5c Mon Sep 17 00:00:00 2001 From: Simanta Gautam Date: Thu, 23 Jun 2016 04:21:05 -0400 Subject: [PATCH 036/166] fixed shape errors in the rnn model comments (#43) --- examples/3_NeuralNetworks/bidirectional_rnn.py | 4 ++-- examples/3_NeuralNetworks/recurrent_network.py | 4 ++-- notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb | 4 ++-- notebooks/3_NeuralNetworks/recurrent_network.ipynb | 4 ++-- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/3_NeuralNetworks/bidirectional_rnn.py b/examples/3_NeuralNetworks/bidirectional_rnn.py index 3c83911c..cb538fa9 100644 --- a/examples/3_NeuralNetworks/bidirectional_rnn.py +++ b/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -53,13 +53,13 @@ def BiRNN(x, weights, biases): # Prepare data shape to match `bidirectional_rnn` function requirements # Current data input shape: (batch_size, n_steps, n_input) - # Required shape: 'n_steps' tensors list of shape (batch_size, n_hidden) + # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) # Permuting batch_size and n_steps x = tf.transpose(x, [1, 0, 2]) # Reshape to (n_steps*batch_size, n_input) x = tf.reshape(x, [-1, n_input]) - # Split to get a list of 'n_steps' tensors of shape (batch_size, n_hidden) + # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) x = tf.split(0, n_steps, x) # Define lstm cells with tensorflow diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 65bf1356..484c978a 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -50,13 +50,13 @@ def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, n_steps, n_input) - # Required shape: 'n_steps' tensors list of shape (batch_size, n_hidden) + # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) # Permuting batch_size and n_steps x = tf.transpose(x, [1, 0, 2]) # Reshaping to (n_steps*batch_size, n_input) x = tf.reshape(x, [-1, n_input]) - # Split to get a list of 'n_steps' tensors of shape (batch_size, n_hidden) + # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) x = tf.split(0, n_steps, x) # Define a lstm cell with tensorflow diff --git a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb index 2d8e502a..8a309a63 100644 --- a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -99,13 +99,13 @@ "\n", " # Prepare data shape to match `bidirectional_rnn` function requirements\n", " # Current data input shape: (batch_size, n_steps, n_input)\n", - " # Required shape: 'n_steps' tensors list of shape (batch_size, n_hidden)\n", + " # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n", " \n", " # Permuting batch_size and n_steps\n", " x = tf.transpose(x, [1, 0, 2])\n", " # Reshape to (n_steps*batch_size, n_input)\n", " x = tf.reshape(x, [-1, n_input])\n", - " # Split to get a list of 'n_steps' tensors of shape (batch_size, n_hidden)\n", + " # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", " x = tf.split(0, n_steps, x)\n", "\n", " # Define lstm cells with tensorflow\n", diff --git a/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/notebooks/3_NeuralNetworks/recurrent_network.ipynb index 913428e7..f0b58e6a 100644 --- a/notebooks/3_NeuralNetworks/recurrent_network.ipynb +++ b/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -96,13 +96,13 @@ "\n", " # Prepare data shape to match `rnn` function requirements\n", " # Current data input shape: (batch_size, n_steps, n_input)\n", - " # Required shape: 'n_steps' tensors list of shape (batch_size, n_hidden)\n", + " # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n", " \n", " # Permuting batch_size and n_steps\n", " x = tf.transpose(x, [1, 0, 2])\n", " # Reshaping to (n_steps*batch_size, n_input)\n", " x = tf.reshape(x, [-1, n_input])\n", - " # Split to get a list of 'n_steps' tensors of shape (batch_size, n_hidden)\n", + " # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", " x = tf.split(0, n_steps, x)\n", "\n", " # Define a lstm cell with tensorflow\n", From 4cce9f66907fbc40a9675af7034ab5878ed1a819 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 28 Jun 2016 17:59:26 +0800 Subject: [PATCH 037/166] added dynamic RNN --- README.md | 1 + examples/3_NeuralNetworks/dynamic_rnn.py | 195 +++++++++++++++++++++++ 2 files changed, 196 insertions(+) create mode 100644 examples/3_NeuralNetworks/dynamic_rnn.py diff --git a/README.md b/README.md index 7af4b2f6..b71d9fd3 100644 --- a/README.md +++ b/README.md @@ -23,6 +23,7 @@ It is suitable for beginners who want to find clear and concise examples about T - Convolutional Neural Network ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py)) - Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)) - Bidirectional Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py)) +- Dynamic Recurrent Neural Network (LSTM) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dynamic_rnn.py)) - AutoEncoder ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)) #### 4 - Utilities diff --git a/examples/3_NeuralNetworks/dynamic_rnn.py b/examples/3_NeuralNetworks/dynamic_rnn.py new file mode 100644 index 00000000..e592fe31 --- /dev/null +++ b/examples/3_NeuralNetworks/dynamic_rnn.py @@ -0,0 +1,195 @@ +''' +A Dynamic Reccurent Neural Network (LSTM) implementation example using +TensorFlow library. This example is using a toy dataset to classify linear +sequences. The generated sequences have variable length. + +Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +import tensorflow as tf +import random + + +# ==================== +# TOY DATA GENERATOR +# ==================== +class ToySequenceData(object): + """ Generate sequence of data with dynamic length. + This class generate samples for training: + - Class 0: linear sequences (i.e. [0, 1, 2, 3,...]) + - Class 1: random sequences (i.e. [1, 3, 10, 7,...]) + + NOTICE: + We have to pad each sequence to reach 'max_seq_len' for TensorFlow + consistency (we cannot feed a numpy array with unconsistent + dimensions). The dynamic calculation will then be perform thanks to + 'seqlen' attribute that records every actual sequence length. + """ + def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3, + max_value=1000): + self.data = [] + self.labels = [] + self.seqlen = [] + for i in range(n_samples): + # Random sequence length + len = random.randint(min_seq_len, max_seq_len) + # Monitor sequence length for TensorFlow dynamic calculation + self.seqlen.append(len) + # Add a random or linear int sequence (50% prob) + if random.random() < .5: + # Generate a linear sequence + rand_start = random.randint(0, max_value - len) + s = [[float(i)/max_value] for i in + range(rand_start, rand_start + len)] + # Pad sequence for dimension consistency + s += [[0.] for i in range(max_seq_len - len)] + self.data.append(s) + self.labels.append([1., 0.]) + else: + # Generate a random sequence + s = [[float(random.randint(0, max_value))/max_value] + for i in range(len)] + # Pad sequence for dimension consistency + s += [[0.] for i in range(max_seq_len - len)] + self.data.append(s) + self.labels.append([0., 1.]) + self.batch_id = 0 + + def next(self, batch_size): + """ Return a batch of data. When dataset end is reached, start over. + """ + if self.batch_id == len(self.data): + self.batch_id = 0 + batch_data = (self.data[self.batch_id:min(self.batch_id + + batch_size, len(self.data))]) + batch_labels = (self.labels[self.batch_id:min(self.batch_id + + batch_size, len(self.data))]) + batch_seqlen = (self.seqlen[self.batch_id:min(self.batch_id + + batch_size, len(self.data))]) + self.batch_id = min(self.batch_id + batch_size, len(self.data)) + return batch_data, batch_labels, batch_seqlen + + +# ========== +# MODEL +# ========== + +# Parameters +learning_rate = 0.01 +training_iters = 1000000 +batch_size = 128 +display_step = 10 + +# Network Parameters +seq_max_len = 20 # Sequence max length +n_hidden = 64 # hidden layer num of features +n_classes = 2 # linear sequence or not + +trainset = ToySequenceData(n_samples=1000, max_seq_len=seq_max_len) +testset = ToySequenceData(n_samples=500, max_seq_len=seq_max_len) + +# tf Graph input +x = tf.placeholder("float", [None, seq_max_len, 1]) +y = tf.placeholder("float", [None, n_classes]) +# A placeholder for indicating each sequence length +seqlen = tf.placeholder(tf.int32, [None]) + +# Define weights +weights = { + 'out': tf.Variable(tf.random_normal([n_hidden, n_classes])) +} +biases = { + 'out': tf.Variable(tf.random_normal([n_classes])) +} + + +def dynamicRNN(x, seqlen, weights, biases): + + # Prepare data shape to match `rnn` function requirements + # Current data input shape: (batch_size, n_steps, n_input) + # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) + + # Permuting batch_size and n_steps + x = tf.transpose(x, [1, 0, 2]) + # Reshaping to (n_steps*batch_size, n_input) + x = tf.reshape(x, [-1, 1]) + # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) + x = tf.split(0, seq_max_len, x) + + # Define a lstm cell with tensorflow + lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden) + + # Get lstm cell output, providing 'sequence_length' will perform dynamic + # calculation. + outputs, states = tf.nn.rnn(lstm_cell, x, dtype=tf.float32, + sequence_length=seqlen) + + # When performing dynamic calculation, we must retrieve the last + # dynamically computed output, i.e, if a sequence length is 10, we need + # to retrieve the 10th output. + # However TensorFlow doesn't support advanced indexing yet, so we build + # a custom op that for each sample in batch size, get its length and + # get the corresponding relevant output. + + # 'outputs' is a list of output at every timestep, we pack them in a Tensor + # and change back dimension to [batch_size, n_step, n_input] + outputs = tf.pack(outputs) + outputs = tf.transpose(outputs, [1, 0, 2]) + + # Hack to build the indexing and retrieve the right output. + batch_size = tf.shape(outputs)[0] + # Start indices for each sample + index = tf.range(0, batch_size) * seq_max_len + (seqlen - 1) + # Indexing + outputs = tf.gather(tf.reshape(outputs, [-1, n_hidden]), index) + + # Linear activation, using rnn inner loop last output + return tf.matmul(outputs, weights['out']) + biases['out'] + +pred = dynamicRNN(x, seqlen, weights, biases) + +# Define loss and optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost) + +# Evaluate model +correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initializing the variables +init = tf.initialize_all_variables() + +# Launch the graph +with tf.Session() as sess: + sess.run(init) + step = 1 + # Keep training until reach max iterations + while step * batch_size < training_iters: + batch_x, batch_y, batch_seqlen = trainset.next(batch_size) + # Run optimization op (backprop) + sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, + seqlen: batch_seqlen}) + if step % display_step == 0: + # Calculate batch accuracy + acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y, + seqlen: batch_seqlen}) + # Calculate batch loss + loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y, + seqlen: batch_seqlen}) + print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ + "{:.6f}".format(loss) + ", Training Accuracy= " + \ + "{:.5f}".format(acc) + step += 1 + print "Optimization Finished!" + + # Calculate accuracy for 128 mnist test images + test_len = 128 + test_data = testset.data + test_label = testset.labels + test_seqlen = testset.seqlen + print "Testing Accuracy:", \ + sess.run(accuracy, feed_dict={x: test_data, y: test_label, + seqlen: test_seqlen}) From 897fe2c243d548d10b243e2bdcfcdd08e3828232 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 28 Jun 2016 18:04:35 +0800 Subject: [PATCH 038/166] Update dynamic_rnn.py --- examples/3_NeuralNetworks/dynamic_rnn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/3_NeuralNetworks/dynamic_rnn.py b/examples/3_NeuralNetworks/dynamic_rnn.py index e592fe31..a306158f 100644 --- a/examples/3_NeuralNetworks/dynamic_rnn.py +++ b/examples/3_NeuralNetworks/dynamic_rnn.py @@ -146,7 +146,7 @@ def dynamicRNN(x, seqlen, weights, biases): # Indexing outputs = tf.gather(tf.reshape(outputs, [-1, n_hidden]), index) - # Linear activation, using rnn inner loop last output + # Linear activation, using outputs computed above return tf.matmul(outputs, weights['out']) + biases['out'] pred = dynamicRNN(x, seqlen, weights, biases) From 4a16105d5ac16658ecf7e58654f7653d79a997e2 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 28 Jun 2016 18:05:29 +0800 Subject: [PATCH 039/166] Update dynamic_rnn.py --- examples/3_NeuralNetworks/dynamic_rnn.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/3_NeuralNetworks/dynamic_rnn.py b/examples/3_NeuralNetworks/dynamic_rnn.py index a306158f..15d2bec4 100644 --- a/examples/3_NeuralNetworks/dynamic_rnn.py +++ b/examples/3_NeuralNetworks/dynamic_rnn.py @@ -185,8 +185,7 @@ def dynamicRNN(x, seqlen, weights, biases): step += 1 print "Optimization Finished!" - # Calculate accuracy for 128 mnist test images - test_len = 128 + # Calculate accuracy test_data = testset.data test_label = testset.labels test_seqlen = testset.seqlen From a68eedc612951e90cd6b268598b2248b4d7a1d9c Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Fri, 1 Jul 2016 16:23:00 +0800 Subject: [PATCH 040/166] Update README.md --- README.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index b71d9fd3..2416f641 100644 --- a/README.md +++ b/README.md @@ -66,8 +66,9 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits. ## Natural Language Processing -- [Recurrent Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task. -- [Bi-Directional LSTM](https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py). Apply a bi-directional LSTM to IMDB sentiment dataset classification task. +- [Recurrent Neural Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task. +- [Bi-Directional RNN (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py). Apply a bi-directional LSTM to IMDB sentiment dataset classification task. +- [Dynamic RNN (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/dynamic_lstm.py). Apply a dynamic LSTM to classify variable length text from IMDB dataset. - [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network. - [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network. @@ -77,12 +78,12 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle ## Notebooks - [Spiral Classification Problem](https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb). TFLearn implementation of spiral classification problem from Stanford CS231n. -## Extending Tensorflow -- [Layers](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py). Use TFLearn layers along with Tensorflow. -- [Trainer](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/trainer.py). Use TFLearn trainer class to train any Tensorflow graph. -- [Built-in Ops](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py). Use TFLearn built-in operations along with Tensorflow. -- [Summaries](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py). Use TFLearn summarizers along with Tensorflow. -- [Variables](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py). Use TFLearn variables along with Tensorflow. +## Extending TensorFlow +- [Layers](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py). Use TFLearn layers along with TensorFlow. +- [Trainer](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/trainer.py). Use TFLearn trainer class to train any TensorFlow graph. +- [Built-in Ops](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py). Use TFLearn built-in operations along with TensorFlow. +- [Summaries](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py). Use TFLearn summarizers along with TensorFlow. +- [Variables](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py). Use TFLearn variables along with TensorFlow. ## Dependencies ``` From d8e83dd442931cbfb0d9c95be0895a37f53c3938 Mon Sep 17 00:00:00 2001 From: Daker Fernandes Pinheiro Date: Sun, 3 Jul 2016 19:32:43 -0300 Subject: [PATCH 041/166] Typo: MINST -> MNIST (#46) --- notebooks/3_NeuralNetworks/convolutional_network.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/notebooks/3_NeuralNetworks/convolutional_network.ipynb index 70a5073e..85396d25 100644 --- a/notebooks/3_NeuralNetworks/convolutional_network.ipynb +++ b/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -39,7 +39,7 @@ "source": [ "import tensorflow as tf\n", "\n", - "# Import MINST data\n", + "# Import MNIST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" ] @@ -384,4 +384,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From e812efc7215ab3c644afbf8c3d6d14c846ace0bc Mon Sep 17 00:00:00 2001 From: Shriphani Palakodety Date: Wed, 13 Jul 2016 17:49:29 -0700 Subject: [PATCH 042/166] removing hidden weights - not used --- examples/3_NeuralNetworks/bidirectional_rnn.py | 2 -- notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb | 4 +--- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/examples/3_NeuralNetworks/bidirectional_rnn.py b/examples/3_NeuralNetworks/bidirectional_rnn.py index cb538fa9..ce27679d 100644 --- a/examples/3_NeuralNetworks/bidirectional_rnn.py +++ b/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -40,11 +40,9 @@ # Define weights weights = { # Hidden layer weights => 2*n_hidden because of foward + backward cells - 'hidden': tf.Variable(tf.random_normal([n_input, 2*n_hidden])), 'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes])) } biases = { - 'hidden': tf.Variable(tf.random_normal([2*n_hidden])), 'out': tf.Variable(tf.random_normal([n_classes])) } diff --git a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb index 8a309a63..581d50e8 100644 --- a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -80,11 +80,9 @@ "# Define weights\n", "weights = {\n", " # Hidden layer weights => 2*n_hidden because of foward + backward cells\n", - " 'hidden': tf.Variable(tf.random_normal([n_input, 2*n_hidden])),\n", " 'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))\n", "}\n", "biases = {\n", - " 'hidden': tf.Variable(tf.random_normal([2*n_hidden])),\n", " 'out': tf.Variable(tf.random_normal([n_classes]))\n", "}" ] @@ -284,4 +282,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 773840ded45d6876316eb11b3bce722a506433d0 Mon Sep 17 00:00:00 2001 From: meagmohit Date: Thu, 21 Jul 2016 15:31:06 -0700 Subject: [PATCH 043/166] fixed rnn_cell import error rnn, rnn_cell module is shifted to tensorflow.python.ops --- examples/3_NeuralNetworks/recurrent_network.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 484c978a..2aeb0e26 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -8,7 +8,7 @@ ''' import tensorflow as tf -from tensorflow.models.rnn import rnn, rnn_cell +from tensorflow.python.ops import rnn, rnn_cell import numpy as np # Import MINST data From bc00df4643f33098ff08ff3701c23d4cf834947c Mon Sep 17 00:00:00 2001 From: Federico Ponzi Date: Thu, 28 Jul 2016 16:27:55 +0200 Subject: [PATCH 044/166] Removes useless variable --- examples/4_Utils/save_restore_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/4_Utils/save_restore_model.py b/examples/4_Utils/save_restore_model.py index 8c11ccc0..d3997c88 100644 --- a/examples/4_Utils/save_restore_model.py +++ b/examples/4_Utils/save_restore_model.py @@ -108,7 +108,7 @@ def multilayer_perceptron(x, weights, biases): sess.run(init) # Restore model weights from previously saved model - load_path = saver.restore(sess, model_path) + saver.restore(sess, model_path) print "Model restored from file: %s" % save_path # Resume training From 60e34fe1f9b02fa534d3efe51d08878315c53103 Mon Sep 17 00:00:00 2001 From: poyuwu Date: Wed, 3 Aug 2016 15:45:30 +0800 Subject: [PATCH 045/166] fixed rnn_cell import error --- notebooks/3_NeuralNetworks/recurrent_network.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/notebooks/3_NeuralNetworks/recurrent_network.ipynb index f0b58e6a..32f3a939 100644 --- a/notebooks/3_NeuralNetworks/recurrent_network.ipynb +++ b/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -34,7 +34,7 @@ ], "source": [ "import tensorflow as tf\n", - "from tensorflow.models.rnn import rnn, rnn_cell\n", + "from tensorflow.python.ops import rnn, rnn_cell\n", "import numpy as np\n", "\n", "# Import MINST data\n", From 382ae387ab9bf1f221401b6cdad38c908dd0f30d Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Wed, 3 Aug 2016 19:07:27 -0700 Subject: [PATCH 046/166] Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 2416f641..7ba19f4b 100644 --- a/README.md +++ b/README.md @@ -43,6 +43,9 @@ Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb ## More Examples The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). +## Tutorials +- [TFLearn Quickstart](intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. + ## Basics - [Linear Regression](https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py). Implement a linear regression using TFLearn. - [Logical Operators](https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py). Implement logical operators with TFLearn (also includes a usage of 'merge'). @@ -71,6 +74,7 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [Dynamic RNN (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/dynamic_lstm.py). Apply a dynamic LSTM to classify variable length text from IMDB dataset. - [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network. - [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network. +- [Seq2seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py). Pedagogical example of seq2seq reccurent network. See [this repo](https://github.com/ichuang/tflearn_seq2seq) for full instructions. ## Reinforcement Learning - [Atari Pacman 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py). Teach a machine to play Atari Pacman game using 1-step Q-learning. From a9674aae963611a0f69af1e54a2ec918ee0e864f Mon Sep 17 00:00:00 2001 From: Bastiaan Quast Date: Sat, 6 Aug 2016 12:13:32 +0200 Subject: [PATCH 047/166] MINST -> MNIST --- examples/3_NeuralNetworks/recurrent_network.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 2aeb0e26..26302d81 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -11,7 +11,7 @@ from tensorflow.python.ops import rnn, rnn_cell import numpy as np -# Import MINST data +# Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) From 60dc7c144f54c652c562ef796084453ad3cd2025 Mon Sep 17 00:00:00 2001 From: Arthur CHAN Date: Tue, 16 Aug 2016 11:02:25 +0800 Subject: [PATCH 048/166] Deprecated model (#57) change to tensorflow.python.ops since the model was deprecated --- examples/3_NeuralNetworks/bidirectional_rnn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/3_NeuralNetworks/bidirectional_rnn.py b/examples/3_NeuralNetworks/bidirectional_rnn.py index ce27679d..fc56204b 100644 --- a/examples/3_NeuralNetworks/bidirectional_rnn.py +++ b/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -8,7 +8,7 @@ ''' import tensorflow as tf -from tensorflow.models.rnn import rnn, rnn_cell +from tensorflow.python.ops import rnn, rnn_cell import numpy as np # Import MINST data From 1a44c34fcb93ff154090ee2be0a26daebe42e368 Mon Sep 17 00:00:00 2001 From: Robert Walecki Date: Wed, 17 Aug 2016 19:38:49 +0100 Subject: [PATCH 049/166] added Python 3 compatibility (#58) --- examples/1_Introduction/basic_operations.py | 14 ++++++---- examples/1_Introduction/helloworld.py | 4 ++- examples/2_BasicModels/linear_regression.py | 20 +++++++------ examples/2_BasicModels/logistic_regression.py | 2 ++ examples/2_BasicModels/nearest_neighbor.py | 10 ++++--- .../3_NeuralNetworks/bidirectional_rnn.py | 12 ++++---- .../3_NeuralNetworks/convolutional_network.py | 12 ++++---- examples/3_NeuralNetworks/dynamic_rnn.py | 12 ++++---- .../3_NeuralNetworks/multilayer_perceptron.py | 10 ++++--- .../3_NeuralNetworks/recurrent_network.py | 12 ++++---- examples/4_Utils/save_restore_model.py | 28 ++++++++++--------- examples/4_Utils/tensorboard_advanced.py | 12 ++++---- examples/4_Utils/tensorboard_basic.py | 12 ++++---- examples/5_MultiGPU/multigpu_basics.py | 6 ++-- multigpu_basics.py | 4 +-- 15 files changed, 99 insertions(+), 71 deletions(-) diff --git a/examples/1_Introduction/basic_operations.py b/examples/1_Introduction/basic_operations.py index afdef20b..7766b28e 100644 --- a/examples/1_Introduction/basic_operations.py +++ b/examples/1_Introduction/basic_operations.py @@ -5,6 +5,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + import tensorflow as tf # Basic constant operations @@ -15,9 +17,9 @@ # Launch the default graph. with tf.Session() as sess: - print "a=2, b=3" - print "Addition with constants: %i" % sess.run(a+b) - print "Multiplication with constants: %i" % sess.run(a*b) + print("a=2, b=3") + print("Addition with constants: %i" % sess.run(a+b)) + print("Multiplication with constants: %i" % sess.run(a*b)) # Basic Operations with variable as graph input # The value returned by the constructor represents the output @@ -33,8 +35,8 @@ # Launch the default graph. with tf.Session() as sess: # Run every operation with variable input - print "Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3}) - print "Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3}) + print("Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3})) + print("Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3})) # ---------------- @@ -69,5 +71,5 @@ # The output of the op is returned in 'result' as a numpy `ndarray` object. with tf.Session() as sess: result = sess.run(product) - print result + print(result) # ==> [[ 12.]] diff --git a/examples/1_Introduction/helloworld.py b/examples/1_Introduction/helloworld.py index 51a8ca43..a19c4d8f 100644 --- a/examples/1_Introduction/helloworld.py +++ b/examples/1_Introduction/helloworld.py @@ -5,6 +5,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + import tensorflow as tf #Simple hello world using TensorFlow @@ -20,4 +22,4 @@ sess = tf.Session() # Run the op -print sess.run(hello) +print(sess.run(hello)) diff --git a/examples/2_BasicModels/linear_regression.py b/examples/2_BasicModels/linear_regression.py index a0aba7b9..199e6e0b 100644 --- a/examples/2_BasicModels/linear_regression.py +++ b/examples/2_BasicModels/linear_regression.py @@ -5,6 +5,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + import tensorflow as tf import numpy import matplotlib.pyplot as plt @@ -53,12 +55,12 @@ #Display logs per epoch step if (epoch+1) % display_step == 0: c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) - print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \ - "W=", sess.run(W), "b=", sess.run(b) + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \ + "W=", sess.run(W), "b=", sess.run(b)) - print "Optimization Finished!" - training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) - print "Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n' + print("Optimization Finished!" + training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})) + print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n') #Graphic display plt.plot(train_X, train_Y, 'ro', label='Original data') @@ -70,13 +72,13 @@ test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1]) test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03]) - print "Testing... (Mean square loss Comparison)" + print("Testing... (Mean square loss Comparison)") testing_cost = sess.run( tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]), feed_dict={X: test_X, Y: test_Y}) # same function as cost above - print "Testing cost=", testing_cost - print "Absolute mean square loss difference:", abs( - training_cost - testing_cost) + print("Testing cost=", testing_cost) + print("Absolute mean square loss difference:", abs( + training_cost - testing_cost)) plt.plot(test_X, test_Y, 'bo', label='Testing data') plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line') diff --git a/examples/2_BasicModels/logistic_regression.py b/examples/2_BasicModels/logistic_regression.py index e5e05b16..70165029 100644 --- a/examples/2_BasicModels/logistic_regression.py +++ b/examples/2_BasicModels/logistic_regression.py @@ -7,6 +7,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + import tensorflow as tf # Import MINST data diff --git a/examples/2_BasicModels/nearest_neighbor.py b/examples/2_BasicModels/nearest_neighbor.py index af714417..a4c0a16c 100644 --- a/examples/2_BasicModels/nearest_neighbor.py +++ b/examples/2_BasicModels/nearest_neighbor.py @@ -7,6 +7,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + import numpy as np import tensorflow as tf @@ -42,10 +44,10 @@ # Get nearest neighbor nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]}) # Get nearest neighbor class label and compare it to its true label - print "Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \ - "True Class:", np.argmax(Yte[i]) + print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \ + "True Class:", np.argmax(Yte[i])) # Calculate accuracy if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]): accuracy += 1./len(Xte) - print "Done!" - print "Accuracy:", accuracy + print("Done!") + print("Accuracy:", accuracy) diff --git a/examples/3_NeuralNetworks/bidirectional_rnn.py b/examples/3_NeuralNetworks/bidirectional_rnn.py index fc56204b..5bf709d3 100644 --- a/examples/3_NeuralNetworks/bidirectional_rnn.py +++ b/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -7,6 +7,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + import tensorflow as tf from tensorflow.python.ops import rnn, rnn_cell import numpy as np @@ -106,15 +108,15 @@ def BiRNN(x, weights, biases): acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y}) # Calculate batch loss loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y}) - print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ + print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ - "{:.5f}".format(acc) + "{:.5f}".format(acc)) step += 1 - print "Optimization Finished!" + print("Optimization Finished!") # Calculate accuracy for 128 mnist test images test_len = 128 test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input)) test_label = mnist.test.labels[:test_len] - print "Testing Accuracy:", \ - sess.run(accuracy, feed_dict={x: test_data, y: test_label}) + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={x: test_data, y: test_label})) diff --git a/examples/3_NeuralNetworks/convolutional_network.py b/examples/3_NeuralNetworks/convolutional_network.py index 7d8b0b22..c69dbccd 100644 --- a/examples/3_NeuralNetworks/convolutional_network.py +++ b/examples/3_NeuralNetworks/convolutional_network.py @@ -7,6 +7,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + import tensorflow as tf # Import MINST data @@ -119,14 +121,14 @@ def conv_net(x, weights, biases, dropout): loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.}) - print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ + print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ - "{:.5f}".format(acc) + "{:.5f}".format(acc)) step += 1 - print "Optimization Finished!" + print("Optimization Finished!") # Calculate accuracy for 256 mnist test images - print "Testing Accuracy:", \ + print("Testing Accuracy:", \ sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], - keep_prob: 1.}) + keep_prob: 1.})) diff --git a/examples/3_NeuralNetworks/dynamic_rnn.py b/examples/3_NeuralNetworks/dynamic_rnn.py index 15d2bec4..ea9229fa 100644 --- a/examples/3_NeuralNetworks/dynamic_rnn.py +++ b/examples/3_NeuralNetworks/dynamic_rnn.py @@ -9,6 +9,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + import tensorflow as tf import random @@ -179,16 +181,16 @@ def dynamicRNN(x, seqlen, weights, biases): # Calculate batch loss loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y, seqlen: batch_seqlen}) - print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ + print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ - "{:.5f}".format(acc) + "{:.5f}".format(acc)) step += 1 - print "Optimization Finished!" + print("Optimization Finished!") # Calculate accuracy test_data = testset.data test_label = testset.labels test_seqlen = testset.seqlen - print "Testing Accuracy:", \ + print("Testing Accuracy:", \ sess.run(accuracy, feed_dict={x: test_data, y: test_label, - seqlen: test_seqlen}) + seqlen: test_seqlen})) diff --git a/examples/3_NeuralNetworks/multilayer_perceptron.py b/examples/3_NeuralNetworks/multilayer_perceptron.py index 53a4c0ae..18ac6a54 100644 --- a/examples/3_NeuralNetworks/multilayer_perceptron.py +++ b/examples/3_NeuralNetworks/multilayer_perceptron.py @@ -7,6 +7,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + # Import MINST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) @@ -82,12 +84,12 @@ def multilayer_perceptron(x, weights, biases): avg_cost += c / total_batch # Display logs per epoch step if epoch % display_step == 0: - print "Epoch:", '%04d' % (epoch+1), "cost=", \ - "{:.9f}".format(avg_cost) - print "Optimization Finished!" + print("Epoch:", '%04d' % (epoch+1), "cost=", \ + "{:.9f}".format(avg_cost)) + print("Optimization Finished!") # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) - print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}) + print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 26302d81..76f54e8d 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -7,6 +7,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + import tensorflow as tf from tensorflow.python.ops import rnn, rnn_cell import numpy as np @@ -97,15 +99,15 @@ def RNN(x, weights, biases): acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y}) # Calculate batch loss loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y}) - print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ + print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ - "{:.5f}".format(acc) + "{:.5f}".format(acc)) step += 1 - print "Optimization Finished!" + print("Optimization Finished!") # Calculate accuracy for 128 mnist test images test_len = 128 test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input)) test_label = mnist.test.labels[:test_len] - print "Testing Accuracy:", \ - sess.run(accuracy, feed_dict={x: test_data, y: test_label}) + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={x: test_data, y: test_label})) diff --git a/examples/4_Utils/save_restore_model.py b/examples/4_Utils/save_restore_model.py index d3997c88..b04ace66 100644 --- a/examples/4_Utils/save_restore_model.py +++ b/examples/4_Utils/save_restore_model.py @@ -7,6 +7,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + # Import MINST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) @@ -68,7 +70,7 @@ def multilayer_perceptron(x, weights, biases): saver = tf.train.Saver() # Running first session -print "Starting 1st session..." +print("Starting 1st session...") with tf.Session() as sess: # Initialize variables sess.run(init) @@ -87,29 +89,29 @@ def multilayer_perceptron(x, weights, biases): avg_cost += c / total_batch # Display logs per epoch step if epoch % display_step == 0: - print "Epoch:", '%04d' % (epoch+1), "cost=", \ - "{:.9f}".format(avg_cost) - print "First Optimization Finished!" + print("Epoch:", '%04d' % (epoch+1), "cost=", \ + "{:.9f}".format(avg_cost)) + print("First Optimization Finished!") # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) - print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}) + print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) # Save model weights to disk save_path = saver.save(sess, model_path) - print "Model saved in file: %s" % save_path + print("Model saved in file: %s" % save_path) # Running a new session -print "Starting 2nd session..." +print("Starting 2nd session...") with tf.Session() as sess: # Initialize variables sess.run(init) # Restore model weights from previously saved model saver.restore(sess, model_path) - print "Model restored from file: %s" % save_path + print("Model restored from file: %s" % save_path) # Resume training for epoch in range(7): @@ -125,13 +127,13 @@ def multilayer_perceptron(x, weights, biases): avg_cost += c / total_batch # Display logs per epoch step if epoch % display_step == 0: - print "Epoch:", '%04d' % (epoch + 1), "cost=", \ - "{:.9f}".format(avg_cost) - print "Second Optimization Finished!" + print("Epoch:", '%04d' % (epoch + 1), "cost=", \ + "{:.9f}".format(avg_cost)) + print("Second Optimization Finished!") # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) - print "Accuracy:", accuracy.eval( - {x: mnist.test.images, y: mnist.test.labels}) + print("Accuracy:", accuracy.eval( + {x: mnist.test.images, y: mnist.test.labels})) diff --git a/examples/4_Utils/tensorboard_advanced.py b/examples/4_Utils/tensorboard_advanced.py index d220857f..e40d60c3 100644 --- a/examples/4_Utils/tensorboard_advanced.py +++ b/examples/4_Utils/tensorboard_advanced.py @@ -7,6 +7,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + import tensorflow as tf # Import MINST data @@ -126,14 +128,14 @@ def multilayer_perceptron(x, weights, biases): avg_cost += c / total_batch # Display logs per epoch step if (epoch+1) % display_step == 0: - print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost) + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) - print "Optimization Finished!" + print("Optimization Finished!") # Test model # Calculate accuracy - print "Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels}) + print("Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels})) - print "Run the command line:\n" \ + print("Run the command line:\n" \ "--> tensorboard --logdir=/tmp/tensorflow_logs " \ - "\nThen open http://0.0.0.0:6006/ into your web browser" + "\nThen open http://0.0.0.0:6006/ into your web browser") diff --git a/examples/4_Utils/tensorboard_basic.py b/examples/4_Utils/tensorboard_basic.py index 9c87e62a..d91b45ec 100644 --- a/examples/4_Utils/tensorboard_basic.py +++ b/examples/4_Utils/tensorboard_basic.py @@ -7,6 +7,8 @@ Project: https://github.com/aymericdamien/TensorFlow-Examples/ ''' +from __future__ import print_function + import tensorflow as tf # Import MINST data @@ -80,14 +82,14 @@ avg_cost += c / total_batch # Display logs per epoch step if (epoch+1) % display_step == 0: - print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost) + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) - print "Optimization Finished!" + print("Optimization Finished!") # Test model # Calculate accuracy - print "Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels}) + print("Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels})) - print "Run the command line:\n" \ + print("Run the command line:\n" \ "--> tensorboard --logdir=/tmp/tensorflow_logs " \ - "\nThen open http://0.0.0.0:6006/ into your web browser" + "\nThen open http://0.0.0.0:6006/ into your web browser") diff --git a/examples/5_MultiGPU/multigpu_basics.py b/examples/5_MultiGPU/multigpu_basics.py index 5727dfe2..9e0e13e3 100644 --- a/examples/5_MultiGPU/multigpu_basics.py +++ b/examples/5_MultiGPU/multigpu_basics.py @@ -12,6 +12,8 @@ "/gpu:1": The second GPU of your machine ''' +from __future__ import print_function + import numpy as np import tensorflow as tf import datetime @@ -87,5 +89,5 @@ def matpow(M, n): t2_2 = datetime.datetime.now() -print "Single GPU computation time: " + str(t2_1-t1_1) -print "Multi GPU computation time: " + str(t2_2-t1_2) \ No newline at end of file +print("Single GPU computation time: " + str(t2_1-t1_1)) +print("Multi GPU computation time: " + str(t2_2-t1_2)) diff --git a/multigpu_basics.py b/multigpu_basics.py index 5cca3e1d..313a31e6 100644 --- a/multigpu_basics.py +++ b/multigpu_basics.py @@ -81,5 +81,5 @@ def matpow(M, n): t2_2 = datetime.datetime.now() -print "Single GPU computation time: " + str(t2_1-t1_1) -print "Multi GPU computation time: " + str(t2_2-t1_2) \ No newline at end of file +print("Single GPU computation time: " + str(t2_1-t1_1)) +print("Multi GPU computation time: " + str(t2_2-t1_2)) From 2191d65e2723f295597179fff8283c6fda3f7294 Mon Sep 17 00:00:00 2001 From: Max Thyen Date: Fri, 19 Aug 2016 22:36:54 -0500 Subject: [PATCH 050/166] resolve syntax error in linear_regression example --- examples/2_BasicModels/linear_regression.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/2_BasicModels/linear_regression.py b/examples/2_BasicModels/linear_regression.py index 199e6e0b..8cb3dac3 100644 --- a/examples/2_BasicModels/linear_regression.py +++ b/examples/2_BasicModels/linear_regression.py @@ -58,8 +58,8 @@ print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \ "W=", sess.run(W), "b=", sess.run(b)) - print("Optimization Finished!" - training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})) + print("Optimization Finished!") + training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n') #Graphic display From c6d8d68589e333949a1c823bfe0f2978a3f27607 Mon Sep 17 00:00:00 2001 From: Yunfeng Wang Date: Thu, 1 Sep 2016 16:35:07 +0800 Subject: [PATCH 051/166] fix typo: reccurent -> recurrent --- examples/3_NeuralNetworks/recurrent_network.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 76f54e8d..7fcd0a82 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -1,5 +1,5 @@ ''' -A Reccurent Neural Network (LSTM) implementation example using TensorFlow library. +A Recurrent Neural Network (LSTM) implementation example using TensorFlow library. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf @@ -18,7 +18,7 @@ mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) ''' -To classify images using a reccurent neural network, we consider every image +To classify images using a recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample. ''' From bc8c77db15ecf52a3cc75ffd3b929e78afc42561 Mon Sep 17 00:00:00 2001 From: Yunfeng Wang Date: Thu, 1 Sep 2016 16:44:26 +0800 Subject: [PATCH 052/166] add space between # and character --- examples/1_Introduction/helloworld.py | 2 +- examples/2_BasicModels/linear_regression.py | 4 ++-- examples/5_MultiGPU/multigpu_basics.py | 22 ++++++++++----------- 3 files changed, 14 insertions(+), 14 deletions(-) diff --git a/examples/1_Introduction/helloworld.py b/examples/1_Introduction/helloworld.py index a19c4d8f..1c40f315 100644 --- a/examples/1_Introduction/helloworld.py +++ b/examples/1_Introduction/helloworld.py @@ -9,7 +9,7 @@ import tensorflow as tf -#Simple hello world using TensorFlow +# Simple hello world using TensorFlow # Create a Constant op # The op is added as a node to the default graph. diff --git a/examples/2_BasicModels/linear_regression.py b/examples/2_BasicModels/linear_regression.py index 8cb3dac3..bcb49358 100644 --- a/examples/2_BasicModels/linear_regression.py +++ b/examples/2_BasicModels/linear_regression.py @@ -52,7 +52,7 @@ for (x, y) in zip(train_X, train_Y): sess.run(optimizer, feed_dict={X: x, Y: y}) - #Display logs per epoch step + # Display logs per epoch step if (epoch+1) % display_step == 0: c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \ @@ -62,7 +62,7 @@ training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n') - #Graphic display + # Graphic display plt.plot(train_X, train_Y, 'ro', label='Original data') plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line') plt.legend() diff --git a/examples/5_MultiGPU/multigpu_basics.py b/examples/5_MultiGPU/multigpu_basics.py index 9e0e13e3..d7908ba8 100644 --- a/examples/5_MultiGPU/multigpu_basics.py +++ b/examples/5_MultiGPU/multigpu_basics.py @@ -18,10 +18,10 @@ import tensorflow as tf import datetime -#Processing Units logs +# Processing Units logs log_device_placement = True -#num of multiplications to perform +# Num of multiplications to perform n = 10 ''' @@ -30,11 +30,11 @@ * Single GPU computation time: 0:00:11.277449 * Multi GPU computation time: 0:00:07.131701 ''' -#Create random large matrix +# Create random large matrix A = np.random.rand(1e4, 1e4).astype('float32') B = np.random.rand(1e4, 1e4).astype('float32') -# Creates a graph to store results +# Create a graph to store results c1 = [] c2 = [] @@ -50,7 +50,7 @@ def matpow(M, n): with tf.device('/gpu:0'): a = tf.constant(A) b = tf.constant(B) - #compute A^n and B^n and store results in c1 + # Compute A^n and B^n and store results in c1 c1.append(matpow(a, n)) c1.append(matpow(b, n)) @@ -59,7 +59,7 @@ def matpow(M, n): t1_1 = datetime.datetime.now() with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess: - # Runs the op. + # Run the op. sess.run(sum) t2_1 = datetime.datetime.now() @@ -67,15 +67,15 @@ def matpow(M, n): ''' Multi GPU computing ''' -#GPU:0 computes A^n +# GPU:0 computes A^n with tf.device('/gpu:0'): - #compute A^n and store result in c2 + # Compute A^n and store result in c2 a = tf.constant(A) c2.append(matpow(a, n)) -#GPU:1 computes B^n +# GPU:1 computes B^n with tf.device('/gpu:1'): - #compute B^n and store result in c2 + # Compute B^n and store result in c2 b = tf.constant(B) c2.append(matpow(b, n)) @@ -84,7 +84,7 @@ def matpow(M, n): t1_2 = datetime.datetime.now() with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess: - # Runs the op. + # Run the op. sess.run(sum) t2_2 = datetime.datetime.now() From 13ac098f126cac6fab0ce96a999b578aacc61dc1 Mon Sep 17 00:00:00 2001 From: Yunfeng Wang Date: Thu, 1 Sep 2016 17:00:39 +0800 Subject: [PATCH 053/166] fix some typos --- examples/2_BasicModels/logistic_regression.py | 2 +- examples/2_BasicModels/nearest_neighbor.py | 2 +- examples/3_NeuralNetworks/autoencoder.py | 2 +- examples/3_NeuralNetworks/bidirectional_rnn.py | 8 ++++---- examples/3_NeuralNetworks/convolutional_network.py | 2 +- examples/3_NeuralNetworks/dynamic_rnn.py | 6 +++--- examples/3_NeuralNetworks/multilayer_perceptron.py | 2 +- examples/4_Utils/save_restore_model.py | 2 +- examples/4_Utils/tensorboard_advanced.py | 4 ++-- examples/4_Utils/tensorboard_basic.py | 2 +- 10 files changed, 16 insertions(+), 16 deletions(-) diff --git a/examples/2_BasicModels/logistic_regression.py b/examples/2_BasicModels/logistic_regression.py index 70165029..4bdca40d 100644 --- a/examples/2_BasicModels/logistic_regression.py +++ b/examples/2_BasicModels/logistic_regression.py @@ -11,7 +11,7 @@ import tensorflow as tf -# Import MINST data +# Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) diff --git a/examples/2_BasicModels/nearest_neighbor.py b/examples/2_BasicModels/nearest_neighbor.py index a4c0a16c..f11efcdb 100644 --- a/examples/2_BasicModels/nearest_neighbor.py +++ b/examples/2_BasicModels/nearest_neighbor.py @@ -12,7 +12,7 @@ import numpy as np import tensorflow as tf -# Import MINST data +# Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) diff --git a/examples/3_NeuralNetworks/autoencoder.py b/examples/3_NeuralNetworks/autoencoder.py index cfc89e96..f87f6b23 100644 --- a/examples/3_NeuralNetworks/autoencoder.py +++ b/examples/3_NeuralNetworks/autoencoder.py @@ -15,7 +15,7 @@ import numpy as np import matplotlib.pyplot as plt -# Import MINST data +# Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) diff --git a/examples/3_NeuralNetworks/bidirectional_rnn.py b/examples/3_NeuralNetworks/bidirectional_rnn.py index 5bf709d3..f8fcf3e5 100644 --- a/examples/3_NeuralNetworks/bidirectional_rnn.py +++ b/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -1,5 +1,5 @@ ''' -A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library. +A Bidirectional Recurrent Neural Network (LSTM) implementation example using TensorFlow library. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf @@ -13,12 +13,12 @@ from tensorflow.python.ops import rnn, rnn_cell import numpy as np -# Import MINST data +# Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) ''' -To classify images using a bidirectional reccurent neural network, we consider +To classify images using a bidirectional recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample. ''' @@ -41,7 +41,7 @@ # Define weights weights = { - # Hidden layer weights => 2*n_hidden because of foward + backward cells + # Hidden layer weights => 2*n_hidden because of forward + backward cells 'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes])) } biases = { diff --git a/examples/3_NeuralNetworks/convolutional_network.py b/examples/3_NeuralNetworks/convolutional_network.py index c69dbccd..81461237 100644 --- a/examples/3_NeuralNetworks/convolutional_network.py +++ b/examples/3_NeuralNetworks/convolutional_network.py @@ -11,7 +11,7 @@ import tensorflow as tf -# Import MINST data +# Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) diff --git a/examples/3_NeuralNetworks/dynamic_rnn.py b/examples/3_NeuralNetworks/dynamic_rnn.py index ea9229fa..9b9443d9 100644 --- a/examples/3_NeuralNetworks/dynamic_rnn.py +++ b/examples/3_NeuralNetworks/dynamic_rnn.py @@ -1,5 +1,5 @@ ''' -A Dynamic Reccurent Neural Network (LSTM) implementation example using +A Dynamic Recurrent Neural Network (LSTM) implementation example using TensorFlow library. This example is using a toy dataset to classify linear sequences. The generated sequences have variable length. @@ -26,7 +26,7 @@ class ToySequenceData(object): NOTICE: We have to pad each sequence to reach 'max_seq_len' for TensorFlow - consistency (we cannot feed a numpy array with unconsistent + consistency (we cannot feed a numpy array with inconsistent dimensions). The dynamic calculation will then be perform thanks to 'seqlen' attribute that records every actual sequence length. """ @@ -130,7 +130,7 @@ def dynamicRNN(x, seqlen, weights, biases): sequence_length=seqlen) # When performing dynamic calculation, we must retrieve the last - # dynamically computed output, i.e, if a sequence length is 10, we need + # dynamically computed output, i.e., if a sequence length is 10, we need # to retrieve the 10th output. # However TensorFlow doesn't support advanced indexing yet, so we build # a custom op that for each sample in batch size, get its length and diff --git a/examples/3_NeuralNetworks/multilayer_perceptron.py b/examples/3_NeuralNetworks/multilayer_perceptron.py index 18ac6a54..b5c990f3 100644 --- a/examples/3_NeuralNetworks/multilayer_perceptron.py +++ b/examples/3_NeuralNetworks/multilayer_perceptron.py @@ -9,7 +9,7 @@ from __future__ import print_function -# Import MINST data +# Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) diff --git a/examples/4_Utils/save_restore_model.py b/examples/4_Utils/save_restore_model.py index b04ace66..d1e31781 100644 --- a/examples/4_Utils/save_restore_model.py +++ b/examples/4_Utils/save_restore_model.py @@ -9,7 +9,7 @@ from __future__ import print_function -# Import MINST data +# Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) diff --git a/examples/4_Utils/tensorboard_advanced.py b/examples/4_Utils/tensorboard_advanced.py index e40d60c3..ee6cd150 100644 --- a/examples/4_Utils/tensorboard_advanced.py +++ b/examples/4_Utils/tensorboard_advanced.py @@ -11,7 +11,7 @@ import tensorflow as tf -# Import MINST data +# Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) @@ -64,7 +64,7 @@ def multilayer_perceptron(x, weights, biases): } # Encapsulating all ops into scopes, making Tensorboard's Graph -# visualization more convenient +# Visualization more convenient with tf.name_scope('Model'): # Build model pred = multilayer_perceptron(x, weights, biases) diff --git a/examples/4_Utils/tensorboard_basic.py b/examples/4_Utils/tensorboard_basic.py index d91b45ec..c690d549 100644 --- a/examples/4_Utils/tensorboard_basic.py +++ b/examples/4_Utils/tensorboard_basic.py @@ -11,7 +11,7 @@ import tensorflow as tf -# Import MINST data +# Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) From 88556b25e170aae54cb18697bdbdfdb5501699ba Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Thu, 1 Sep 2016 19:05:13 -0700 Subject: [PATCH 054/166] Update README.md --- README.md | 26 ++++++++++++++++---------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 7ba19f4b..e03fb55d 100644 --- a/README.md +++ b/README.md @@ -43,10 +43,10 @@ Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb ## More Examples The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). -## Tutorials +### Tutorials - [TFLearn Quickstart](intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. -## Basics +### Basics - [Linear Regression](https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py). Implement a linear regression using TFLearn. - [Logical Operators](https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py). Implement logical operators with TFLearn (also includes a usage of 'merge'). - [Weights Persistence](https://github.com/tflearn/tflearn/blob/master/examples/basics/weights_persistence.py). Save and Restore a model. @@ -54,7 +54,7 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [Using HDF5](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_hdf5.py). Use HDF5 to handle large datasets. - [Using DASK](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py). Use DASK to handle large datasets. -## Computer Vision +### Computer Vision - [Multi-layer perceptron](https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py). A multi-layer perceptron implementation for MNIST classification task. - [Convolutional Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py). A Convolutional neural network implementation for classifying MNIST dataset. - [Convolutional Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py). A Convolutional neural network implementation for classifying CIFAR-10 dataset. @@ -64,31 +64,37 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [RNN Pixels](https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py). Use RNN (over sequence of pixels) to classify images. - [Highway Network](https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py). Highway Network implementation for classifying MNIST dataset. - [Highway Convolutional Network](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py). Highway Convolutional Network implementation for classifying MNIST dataset. -- [Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network with shallow bottlenecks applied to CIFAR-10 classification task. -- [Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A residual network with deep bottlenecks applied to MNIST classification task. +- [Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A bottleneck residual network applied to MNIST classification task. +- [Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network applied to CIFAR-10 classification task. +- [Google Inception (v3)](https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py). Google's Inception v3 network applied to Oxford Flowers 17 classification task. - [Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits. -## Natural Language Processing +### Natural Language Processing - [Recurrent Neural Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task. - [Bi-Directional RNN (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py). Apply a bi-directional LSTM to IMDB sentiment dataset classification task. - [Dynamic RNN (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/dynamic_lstm.py). Apply a dynamic LSTM to classify variable length text from IMDB dataset. - [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network. - [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network. - [Seq2seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py). Pedagogical example of seq2seq reccurent network. See [this repo](https://github.com/ichuang/tflearn_seq2seq) for full instructions. +- [CNN Seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/cnn_sequence_classification.py). Apply a 1-D convolutional network to classify sequence of words from IMDB sentiment dataset. -## Reinforcement Learning -- [Atari Pacman 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py). Teach a machine to play Atari Pacman game using 1-step Q-learning. +### Reinforcement Learning +- [Atari Pacman 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py). Teach a machine to play Atari games (Pacman by default) using 1-step Q-learning. -## Notebooks +### Others +- [Recommender - Wide & Deep Network](https://github.com/tflearn/tflearn/blob/master/examples/others/recommender_wide_and_deep.py). Pedagogical example of wide & deep networks for recommender systems. + +### Notebooks - [Spiral Classification Problem](https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb). TFLearn implementation of spiral classification problem from Stanford CS231n. -## Extending TensorFlow +### Extending TensorFlow - [Layers](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py). Use TFLearn layers along with TensorFlow. - [Trainer](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/trainer.py). Use TFLearn trainer class to train any TensorFlow graph. - [Built-in Ops](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py). Use TFLearn built-in operations along with TensorFlow. - [Summaries](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py). Use TFLearn summarizers along with TensorFlow. - [Variables](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py). Use TFLearn variables along with TensorFlow. + ## Dependencies ``` tensorflow From cee70961bbc66082dec9b556652712d54223553e Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Fri, 2 Sep 2016 22:55:58 -0700 Subject: [PATCH 055/166] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e03fb55d..b2b12f3a 100644 --- a/README.md +++ b/README.md @@ -76,7 +76,7 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network. - [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network. - [Seq2seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py). Pedagogical example of seq2seq reccurent network. See [this repo](https://github.com/ichuang/tflearn_seq2seq) for full instructions. -- [CNN Seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/cnn_sequence_classification.py). Apply a 1-D convolutional network to classify sequence of words from IMDB sentiment dataset. +- [CNN Seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/cnn_sentence_classification.py). Apply a 1-D convolutional network to classify sequence of words from IMDB sentiment dataset. ### Reinforcement Learning - [Atari Pacman 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py). Teach a machine to play Atari games (Pacman by default) using 1-step Q-learning. From 99e423dcdb78c5bb8529dc9d10bcb9253ee5c42b Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sat, 10 Sep 2016 21:31:52 -0700 Subject: [PATCH 056/166] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index b2b12f3a..07869b2c 100644 --- a/README.md +++ b/README.md @@ -61,6 +61,7 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle - [Network in Network](https://github.com/tflearn/tflearn/blob/master/examples/images/network_in_network.py). 'Network in Network' implementation for classifying CIFAR-10 dataset. - [Alexnet](https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py). Apply Alexnet to Oxford Flowers 17 classification task. - [VGGNet](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py). Apply VGG Network to Oxford Flowers 17 classification task. +- [VGGNet Finetuning (Fast Training)](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network_finetuning.py). Use a pre-trained VGG Network and retrain it on your own data, for fast training. - [RNN Pixels](https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py). Use RNN (over sequence of pixels) to classify images. - [Highway Network](https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py). Highway Network implementation for classifying MNIST dataset. - [Highway Convolutional Network](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py). Highway Convolutional Network implementation for classifying MNIST dataset. From b400c2cad771af5cdbc336801d4aab657c034c6e Mon Sep 17 00:00:00 2001 From: Ming He Date: Mon, 19 Sep 2016 16:25:36 +0800 Subject: [PATCH 057/166] fixed python2/3 print issue --- examples/2_BasicModels/logistic_regression.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/2_BasicModels/logistic_regression.py b/examples/2_BasicModels/logistic_regression.py index 4bdca40d..c2af99c0 100644 --- a/examples/2_BasicModels/logistic_regression.py +++ b/examples/2_BasicModels/logistic_regression.py @@ -58,12 +58,12 @@ avg_cost += c / total_batch # Display logs per epoch step if (epoch+1) % display_step == 0: - print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost) + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) - print "Optimization Finished!" + print("Optimization Finished!") # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}) + print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) From 7ea9a7719daeb0ddab1ba0203ce7352af239e754 Mon Sep 17 00:00:00 2001 From: alexpantyukhin Date: Tue, 27 Sep 2016 19:44:59 +0400 Subject: [PATCH 058/166] fix link to quickstart.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 07869b2c..edc03d30 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). ### Tutorials -- [TFLearn Quickstart](intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. +- [TFLearn Quickstart](https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. ### Basics - [Linear Regression](https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py). Implement a linear regression using TFLearn. From 101e0fcc64e5eaa3e6d6b695afbded737adafb97 Mon Sep 17 00:00:00 2001 From: "wbh230@nyu.edu" Date: Wed, 28 Sep 2016 15:37:10 +0400 Subject: [PATCH 059/166] Fixes for Multi-Gpu_basics No Longer hangs on CPU --- examples/5_MultiGPU/multigpu_basics.py | 19 +++--- multigpu_basics.py | 85 -------------------------- 2 files changed, 10 insertions(+), 94 deletions(-) delete mode 100644 multigpu_basics.py diff --git a/examples/5_MultiGPU/multigpu_basics.py b/examples/5_MultiGPU/multigpu_basics.py index d7908ba8..b31120fa 100644 --- a/examples/5_MultiGPU/multigpu_basics.py +++ b/examples/5_MultiGPU/multigpu_basics.py @@ -1,3 +1,4 @@ +from __future__ import print_function ''' Basic Multi GPU computation example using TensorFlow library. @@ -12,7 +13,7 @@ "/gpu:1": The second GPU of your machine ''' -from __future__ import print_function + import numpy as np import tensorflow as tf @@ -31,8 +32,8 @@ * Multi GPU computation time: 0:00:07.131701 ''' # Create random large matrix -A = np.random.rand(1e4, 1e4).astype('float32') -B = np.random.rand(1e4, 1e4).astype('float32') +A = np.random.rand(10000, 10000).astype('float32') +B = np.random.rand(10000, 10000).astype('float32') # Create a graph to store results c1 = [] @@ -48,8 +49,8 @@ def matpow(M, n): Single GPU computing ''' with tf.device('/gpu:0'): - a = tf.constant(A) - b = tf.constant(B) + a = tf.placeholder(tf.float32, [10000, 10000]) + b = tf.placeholder(tf.float32, [10000, 10000]) # Compute A^n and B^n and store results in c1 c1.append(matpow(a, n)) c1.append(matpow(b, n)) @@ -60,7 +61,7 @@ def matpow(M, n): t1_1 = datetime.datetime.now() with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess: # Run the op. - sess.run(sum) + sess.run(sum, {a:A, b:B}) t2_1 = datetime.datetime.now() @@ -70,13 +71,13 @@ def matpow(M, n): # GPU:0 computes A^n with tf.device('/gpu:0'): # Compute A^n and store result in c2 - a = tf.constant(A) + a = tf.placeholder(tf.float32, [10000, 10000]) c2.append(matpow(a, n)) # GPU:1 computes B^n with tf.device('/gpu:1'): # Compute B^n and store result in c2 - b = tf.constant(B) + b = tf.placeholder(tf.float32, [10000, 10000]) c2.append(matpow(b, n)) with tf.device('/cpu:0'): @@ -85,7 +86,7 @@ def matpow(M, n): t1_2 = datetime.datetime.now() with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess: # Run the op. - sess.run(sum) + sess.run(sum, {a:A, b:B}) t2_2 = datetime.datetime.now() diff --git a/multigpu_basics.py b/multigpu_basics.py deleted file mode 100644 index 313a31e6..00000000 --- a/multigpu_basics.py +++ /dev/null @@ -1,85 +0,0 @@ -#Multi GPU Basic example -''' -This tutorial requires your machine to have 2 GPUs -"/cpu:0": The CPU of your machine. -"/gpu:0": The first GPU of your machine -"/gpu:1": The second GPU of your machine -''' - -import numpy as np -import tensorflow as tf -import datetime - -#Processing Units logs -log_device_placement = True - -#num of multiplications to perform -n = 10 - -''' -Example: compute A^n + B^n on 2 GPUs -Results on 8 cores with 2 GTX-980: - * Single GPU computation time: 0:00:11.277449 - * Multi GPU computation time: 0:00:07.131701 -''' -#Create random large matrix -A = np.random.rand(1e4, 1e4).astype('float32') -B = np.random.rand(1e4, 1e4).astype('float32') - -# Creates a graph to store results -c1 = [] -c2 = [] - -def matpow(M, n): - if n < 1: #Abstract cases where n < 1 - return M - else: - return tf.matmul(M, matpow(M, n-1)) - -''' -Single GPU computing -''' -with tf.device('/gpu:0'): - a = tf.constant(A) - b = tf.constant(B) - #compute A^n and B^n and store results in c1 - c1.append(matpow(a, n)) - c1.append(matpow(b, n)) - -with tf.device('/cpu:0'): - sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n - -t1_1 = datetime.datetime.now() -with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess: - # Runs the op. - sess.run(sum) -t2_1 = datetime.datetime.now() - - -''' -Multi GPU computing -''' -#GPU:0 computes A^n -with tf.device('/gpu:0'): - #compute A^n and store result in c2 - a = tf.constant(A) - c2.append(matpow(a, n)) - -#GPU:1 computes B^n -with tf.device('/gpu:1'): - #compute B^n and store result in c2 - b = tf.constant(B) - c2.append(matpow(b, n)) - -with tf.device('/cpu:0'): - sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n - -t1_2 = datetime.datetime.now() -with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess: - # Runs the op. - sess.run(sum) -t2_2 = datetime.datetime.now() - - -print("Single GPU computation time: " + str(t2_1-t1_1)) -print("Multi GPU computation time: " + str(t2_2-t1_2)) From 4e7e10f3e9a480dc4bcd77d71876f16c059cbb81 Mon Sep 17 00:00:00 2001 From: Zac Wellmer Date: Mon, 14 Nov 2016 20:53:59 -0500 Subject: [PATCH 060/166] removed unused import --- examples/3_NeuralNetworks/recurrent_network.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 7fcd0a82..21744364 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -11,7 +11,6 @@ import tensorflow as tf from tensorflow.python.ops import rnn, rnn_cell -import numpy as np # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data From b1b47a1d8cdcac0cbaec59420af372054eb6792d Mon Sep 17 00:00:00 2001 From: Davis Vigneault Date: Mon, 26 Dec 2016 22:17:31 +0000 Subject: [PATCH 061/166] Address deprecation warnings. --- examples/2_BasicModels/linear_regression.py | 2 +- examples/2_BasicModels/logistic_regression.py | 2 +- examples/2_BasicModels/nearest_neighbor.py | 2 +- examples/3_NeuralNetworks/autoencoder.py | 2 +- examples/3_NeuralNetworks/bidirectional_rnn.py | 2 +- examples/3_NeuralNetworks/convolutional_network.py | 2 +- examples/3_NeuralNetworks/dynamic_rnn.py | 2 +- examples/3_NeuralNetworks/multilayer_perceptron.py | 2 +- examples/3_NeuralNetworks/recurrent_network.py | 2 +- examples/4_Utils/save_restore_model.py | 2 +- examples/4_Utils/tensorboard_advanced.py | 2 +- examples/4_Utils/tensorboard_basic.py | 2 +- notebooks/2_BasicModels/linear_regression.ipynb | 2 +- notebooks/2_BasicModels/logistic_regression.ipynb | 4 ++-- notebooks/2_BasicModels/nearest_neighbor.ipynb | 4 ++-- notebooks/3_NeuralNetworks/autoencoder.ipynb | 2 +- notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb | 2 +- notebooks/3_NeuralNetworks/convolutional_network.ipynb | 2 +- notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb | 4 ++-- notebooks/3_NeuralNetworks/recurrent_network.ipynb | 2 +- notebooks/4_Utils/save_restore_model.ipynb | 4 ++-- notebooks/4_Utils/tensorboard_basic.ipynb | 2 +- notebooks/5_MultiGPU/multigpu_basics.ipynb | 2 +- 23 files changed, 27 insertions(+), 27 deletions(-) diff --git a/examples/2_BasicModels/linear_regression.py b/examples/2_BasicModels/linear_regression.py index bcb49358..b23d11cb 100644 --- a/examples/2_BasicModels/linear_regression.py +++ b/examples/2_BasicModels/linear_regression.py @@ -41,7 +41,7 @@ optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/examples/2_BasicModels/logistic_regression.py b/examples/2_BasicModels/logistic_regression.py index c2af99c0..e8b5c89e 100644 --- a/examples/2_BasicModels/logistic_regression.py +++ b/examples/2_BasicModels/logistic_regression.py @@ -38,7 +38,7 @@ optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/examples/2_BasicModels/nearest_neighbor.py b/examples/2_BasicModels/nearest_neighbor.py index f11efcdb..851a8dcf 100644 --- a/examples/2_BasicModels/nearest_neighbor.py +++ b/examples/2_BasicModels/nearest_neighbor.py @@ -33,7 +33,7 @@ accuracy = 0. # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/examples/3_NeuralNetworks/autoencoder.py b/examples/3_NeuralNetworks/autoencoder.py index f87f6b23..3a314e07 100644 --- a/examples/3_NeuralNetworks/autoencoder.py +++ b/examples/3_NeuralNetworks/autoencoder.py @@ -83,7 +83,7 @@ def decoder(x): optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/examples/3_NeuralNetworks/bidirectional_rnn.py b/examples/3_NeuralNetworks/bidirectional_rnn.py index f8fcf3e5..eaaa4178 100644 --- a/examples/3_NeuralNetworks/bidirectional_rnn.py +++ b/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -90,7 +90,7 @@ def BiRNN(x, weights, biases): accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/examples/3_NeuralNetworks/convolutional_network.py b/examples/3_NeuralNetworks/convolutional_network.py index 81461237..1f10886f 100644 --- a/examples/3_NeuralNetworks/convolutional_network.py +++ b/examples/3_NeuralNetworks/convolutional_network.py @@ -104,7 +104,7 @@ def conv_net(x, weights, biases, dropout): accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/examples/3_NeuralNetworks/dynamic_rnn.py b/examples/3_NeuralNetworks/dynamic_rnn.py index 9b9443d9..11b952d7 100644 --- a/examples/3_NeuralNetworks/dynamic_rnn.py +++ b/examples/3_NeuralNetworks/dynamic_rnn.py @@ -162,7 +162,7 @@ def dynamicRNN(x, seqlen, weights, biases): accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/examples/3_NeuralNetworks/multilayer_perceptron.py b/examples/3_NeuralNetworks/multilayer_perceptron.py index b5c990f3..9086018a 100644 --- a/examples/3_NeuralNetworks/multilayer_perceptron.py +++ b/examples/3_NeuralNetworks/multilayer_perceptron.py @@ -64,7 +64,7 @@ def multilayer_perceptron(x, weights, biases): optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 21744364..9182f2bb 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -80,7 +80,7 @@ def RNN(x, weights, biases): accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/examples/4_Utils/save_restore_model.py b/examples/4_Utils/save_restore_model.py index d1e31781..a8cff6b6 100644 --- a/examples/4_Utils/save_restore_model.py +++ b/examples/4_Utils/save_restore_model.py @@ -64,7 +64,7 @@ def multilayer_perceptron(x, weights, biases): optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # 'Saver' op to save and restore all the variables saver = tf.train.Saver() diff --git a/examples/4_Utils/tensorboard_advanced.py b/examples/4_Utils/tensorboard_advanced.py index ee6cd150..8130cabf 100644 --- a/examples/4_Utils/tensorboard_advanced.py +++ b/examples/4_Utils/tensorboard_advanced.py @@ -88,7 +88,7 @@ def multilayer_perceptron(x, weights, biases): acc = tf.reduce_mean(tf.cast(acc, tf.float32)) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Create a summary to monitor cost tensor tf.scalar_summary("loss", loss) diff --git a/examples/4_Utils/tensorboard_basic.py b/examples/4_Utils/tensorboard_basic.py index c690d549..60024e57 100644 --- a/examples/4_Utils/tensorboard_basic.py +++ b/examples/4_Utils/tensorboard_basic.py @@ -49,7 +49,7 @@ acc = tf.reduce_mean(tf.cast(acc, tf.float32)) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Create a summary to monitor cost tensor tf.scalar_summary("loss", cost) diff --git a/notebooks/2_BasicModels/linear_regression.ipynb b/notebooks/2_BasicModels/linear_regression.ipynb index 39902e61..dca44062 100644 --- a/notebooks/2_BasicModels/linear_regression.ipynb +++ b/notebooks/2_BasicModels/linear_regression.ipynb @@ -110,7 +110,7 @@ "outputs": [], "source": [ "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { diff --git a/notebooks/2_BasicModels/logistic_regression.ipynb b/notebooks/2_BasicModels/logistic_regression.ipynb index 8314dd9a..9d5fc4d1 100644 --- a/notebooks/2_BasicModels/logistic_regression.ipynb +++ b/notebooks/2_BasicModels/logistic_regression.ipynb @@ -73,7 +73,7 @@ "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { @@ -169,4 +169,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/notebooks/2_BasicModels/nearest_neighbor.ipynb b/notebooks/2_BasicModels/nearest_neighbor.ipynb index f75a9d13..9d3ac75d 100644 --- a/notebooks/2_BasicModels/nearest_neighbor.ipynb +++ b/notebooks/2_BasicModels/nearest_neighbor.ipynb @@ -68,7 +68,7 @@ "accuracy = 0.\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { @@ -328,4 +328,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/notebooks/3_NeuralNetworks/autoencoder.ipynb b/notebooks/3_NeuralNetworks/autoencoder.ipynb index 072cf7dd..e1996cd5 100644 --- a/notebooks/3_NeuralNetworks/autoencoder.ipynb +++ b/notebooks/3_NeuralNetworks/autoencoder.ipynb @@ -129,7 +129,7 @@ "optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { diff --git a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb index 581d50e8..4d276862 100644 --- a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -134,7 +134,7 @@ "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { diff --git a/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/notebooks/3_NeuralNetworks/convolutional_network.ipynb index 85396d25..7496fd22 100644 --- a/notebooks/3_NeuralNetworks/convolutional_network.ipynb +++ b/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -158,7 +158,7 @@ "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { diff --git a/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb b/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb index 6ec369e6..18701273 100644 --- a/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb +++ b/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb @@ -118,7 +118,7 @@ "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { @@ -204,4 +204,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/notebooks/3_NeuralNetworks/recurrent_network.ipynb index 32f3a939..94ee4a03 100644 --- a/notebooks/3_NeuralNetworks/recurrent_network.ipynb +++ b/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -125,7 +125,7 @@ "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { diff --git a/notebooks/4_Utils/save_restore_model.ipynb b/notebooks/4_Utils/save_restore_model.ipynb index 7909071f..3c936e5d 100644 --- a/notebooks/4_Utils/save_restore_model.ipynb +++ b/notebooks/4_Utils/save_restore_model.ipynb @@ -101,7 +101,7 @@ "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { @@ -268,4 +268,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/notebooks/4_Utils/tensorboard_basic.ipynb b/notebooks/4_Utils/tensorboard_basic.ipynb index 582bbdb7..26d83874 100644 --- a/notebooks/4_Utils/tensorboard_basic.ipynb +++ b/notebooks/4_Utils/tensorboard_basic.ipynb @@ -95,7 +95,7 @@ " acc = tf.reduce_mean(tf.cast(acc, tf.float32))\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()\n", + "init = tf.global_variables_initializer()\n", "\n", "# Create a summary to monitor cost tensor\n", "tf.scalar_summary(\"loss\", cost)\n", diff --git a/notebooks/5_MultiGPU/multigpu_basics.ipynb b/notebooks/5_MultiGPU/multigpu_basics.ipynb index 62d2a144..c4d5a29b 100644 --- a/notebooks/5_MultiGPU/multigpu_basics.ipynb +++ b/notebooks/5_MultiGPU/multigpu_basics.ipynb @@ -175,4 +175,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From b98047383b2413e14cb8dd8ed4623f94891c0a1f Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sat, 14 Jan 2017 22:40:20 +1000 Subject: [PATCH 062/166] Update bidirectional rnn for TF1.0 Signed-off-by: Norman Heckscher --- .../3_NeuralNetworks/bidirectional_rnn.ipynb | 169 +++++------------- 1 file changed, 44 insertions(+), 125 deletions(-) diff --git a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb index 581d50e8..14ea5664 100644 --- a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -1,10 +1,10 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, "source": [ "'''\n", "A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library.\n", @@ -18,35 +18,26 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "import tensorflow as tf\n", - "from tensorflow.models.rnn import rnn, rnn_cell\n", + "from tensorflow.contrib import rnn\n", "import numpy as np\n", "\n", "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, "source": [ "'''\n", "To classify images using a bidirectional reccurent neural network, we consider\n", @@ -58,7 +49,9 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Parameters\n", @@ -90,7 +83,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "def BiRNN(x, weights, biases):\n", @@ -104,20 +99,20 @@ " # Reshape to (n_steps*batch_size, n_input)\n", " x = tf.reshape(x, [-1, n_input])\n", " # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", - " x = tf.split(0, n_steps, x)\n", + " x = tf.split(x, n_steps, 0)\n", "\n", " # Define lstm cells with tensorflow\n", " # Forward direction cell\n", - " lstm_fw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", + " lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", " # Backward direction cell\n", - " lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", + " lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", "\n", " # Get lstm cell output\n", " try:\n", - " outputs, _, _ = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", + " outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", " dtype=tf.float32)\n", " except Exception: # Old TensorFlow version only returns outputs not states\n", - " outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", + " outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", " dtype=tf.float32)\n", "\n", " # Linear activation, using rnn inner loop last output\n", @@ -126,7 +121,7 @@ "pred = BiRNN(x, weights, biases)\n", "\n", "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", "\n", "# Evaluate model\n", @@ -134,101 +129,16 @@ "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iter 1280, Minibatch Loss= 1.689740, Training Accuracy= 0.36719\n", - "Iter 2560, Minibatch Loss= 1.477009, Training Accuracy= 0.44531\n", - "Iter 3840, Minibatch Loss= 1.245874, Training Accuracy= 0.53125\n", - "Iter 5120, Minibatch Loss= 0.990923, Training Accuracy= 0.64062\n", - "Iter 6400, Minibatch Loss= 0.752950, Training Accuracy= 0.71875\n", - "Iter 7680, Minibatch Loss= 1.023025, Training Accuracy= 0.61719\n", - "Iter 8960, Minibatch Loss= 0.921414, Training Accuracy= 0.68750\n", - "Iter 10240, Minibatch Loss= 0.719829, Training Accuracy= 0.75000\n", - "Iter 11520, Minibatch Loss= 0.468657, Training Accuracy= 0.86719\n", - "Iter 12800, Minibatch Loss= 0.654315, Training Accuracy= 0.78125\n", - "Iter 14080, Minibatch Loss= 0.595391, Training Accuracy= 0.83594\n", - "Iter 15360, Minibatch Loss= 0.392862, Training Accuracy= 0.83594\n", - "Iter 16640, Minibatch Loss= 0.421122, Training Accuracy= 0.92188\n", - "Iter 17920, Minibatch Loss= 0.311471, Training Accuracy= 0.88281\n", - "Iter 19200, Minibatch Loss= 0.276949, Training Accuracy= 0.92188\n", - "Iter 20480, Minibatch Loss= 0.170499, Training Accuracy= 0.94531\n", - "Iter 21760, Minibatch Loss= 0.419481, Training Accuracy= 0.86719\n", - "Iter 23040, Minibatch Loss= 0.183765, Training Accuracy= 0.92188\n", - "Iter 24320, Minibatch Loss= 0.386232, Training Accuracy= 0.86719\n", - "Iter 25600, Minibatch Loss= 0.335571, Training Accuracy= 0.88281\n", - "Iter 26880, Minibatch Loss= 0.169092, Training Accuracy= 0.92969\n", - "Iter 28160, Minibatch Loss= 0.247623, Training Accuracy= 0.92969\n", - "Iter 29440, Minibatch Loss= 0.242989, Training Accuracy= 0.94531\n", - "Iter 30720, Minibatch Loss= 0.253811, Training Accuracy= 0.92188\n", - "Iter 32000, Minibatch Loss= 0.169660, Training Accuracy= 0.93750\n", - "Iter 33280, Minibatch Loss= 0.291349, Training Accuracy= 0.90625\n", - "Iter 34560, Minibatch Loss= 0.172026, Training Accuracy= 0.95312\n", - "Iter 35840, Minibatch Loss= 0.186019, Training Accuracy= 0.93750\n", - "Iter 37120, Minibatch Loss= 0.298480, Training Accuracy= 0.89062\n", - "Iter 38400, Minibatch Loss= 0.158750, Training Accuracy= 0.92188\n", - "Iter 39680, Minibatch Loss= 0.162706, Training Accuracy= 0.94531\n", - "Iter 40960, Minibatch Loss= 0.339814, Training Accuracy= 0.86719\n", - "Iter 42240, Minibatch Loss= 0.068817, Training Accuracy= 0.99219\n", - "Iter 43520, Minibatch Loss= 0.188742, Training Accuracy= 0.93750\n", - "Iter 44800, Minibatch Loss= 0.176708, Training Accuracy= 0.92969\n", - "Iter 46080, Minibatch Loss= 0.096726, Training Accuracy= 0.96875\n", - "Iter 47360, Minibatch Loss= 0.220973, Training Accuracy= 0.92969\n", - "Iter 48640, Minibatch Loss= 0.226749, Training Accuracy= 0.94531\n", - "Iter 49920, Minibatch Loss= 0.188906, Training Accuracy= 0.94531\n", - "Iter 51200, Minibatch Loss= 0.145194, Training Accuracy= 0.95312\n", - "Iter 52480, Minibatch Loss= 0.168948, Training Accuracy= 0.95312\n", - "Iter 53760, Minibatch Loss= 0.069116, Training Accuracy= 0.97656\n", - "Iter 55040, Minibatch Loss= 0.228721, Training Accuracy= 0.93750\n", - "Iter 56320, Minibatch Loss= 0.152915, Training Accuracy= 0.95312\n", - "Iter 57600, Minibatch Loss= 0.126974, Training Accuracy= 0.96875\n", - "Iter 58880, Minibatch Loss= 0.078870, Training Accuracy= 0.97656\n", - "Iter 60160, Minibatch Loss= 0.225498, Training Accuracy= 0.95312\n", - "Iter 61440, Minibatch Loss= 0.111760, Training Accuracy= 0.97656\n", - "Iter 62720, Minibatch Loss= 0.161434, Training Accuracy= 0.97656\n", - "Iter 64000, Minibatch Loss= 0.207190, Training Accuracy= 0.94531\n", - "Iter 65280, Minibatch Loss= 0.103831, Training Accuracy= 0.96094\n", - "Iter 66560, Minibatch Loss= 0.153846, Training Accuracy= 0.93750\n", - "Iter 67840, Minibatch Loss= 0.082717, Training Accuracy= 0.96875\n", - "Iter 69120, Minibatch Loss= 0.199301, Training Accuracy= 0.95312\n", - "Iter 70400, Minibatch Loss= 0.139725, Training Accuracy= 0.96875\n", - "Iter 71680, Minibatch Loss= 0.169596, Training Accuracy= 0.95312\n", - "Iter 72960, Minibatch Loss= 0.142444, Training Accuracy= 0.96094\n", - "Iter 74240, Minibatch Loss= 0.145822, Training Accuracy= 0.95312\n", - "Iter 75520, Minibatch Loss= 0.129086, Training Accuracy= 0.94531\n", - "Iter 76800, Minibatch Loss= 0.078082, Training Accuracy= 0.97656\n", - "Iter 78080, Minibatch Loss= 0.151803, Training Accuracy= 0.94531\n", - "Iter 79360, Minibatch Loss= 0.050142, Training Accuracy= 0.98438\n", - "Iter 80640, Minibatch Loss= 0.136788, Training Accuracy= 0.95312\n", - "Iter 81920, Minibatch Loss= 0.130100, Training Accuracy= 0.94531\n", - "Iter 83200, Minibatch Loss= 0.058298, Training Accuracy= 0.98438\n", - "Iter 84480, Minibatch Loss= 0.120124, Training Accuracy= 0.96094\n", - "Iter 85760, Minibatch Loss= 0.064916, Training Accuracy= 0.97656\n", - "Iter 87040, Minibatch Loss= 0.137179, Training Accuracy= 0.93750\n", - "Iter 88320, Minibatch Loss= 0.138268, Training Accuracy= 0.95312\n", - "Iter 89600, Minibatch Loss= 0.072827, Training Accuracy= 0.97656\n", - "Iter 90880, Minibatch Loss= 0.123839, Training Accuracy= 0.96875\n", - "Iter 92160, Minibatch Loss= 0.087194, Training Accuracy= 0.96875\n", - "Iter 93440, Minibatch Loss= 0.083489, Training Accuracy= 0.97656\n", - "Iter 94720, Minibatch Loss= 0.131827, Training Accuracy= 0.95312\n", - "Iter 96000, Minibatch Loss= 0.098764, Training Accuracy= 0.96875\n", - "Iter 97280, Minibatch Loss= 0.115553, Training Accuracy= 0.94531\n", - "Iter 98560, Minibatch Loss= 0.079704, Training Accuracy= 0.96875\n", - "Iter 99840, Minibatch Loss= 0.064562, Training Accuracy= 0.98438\n", - "Optimization Finished!\n", - "Testing Accuracy: 0.992188\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "# Launch the graph\n", "with tf.Session() as sess:\n", @@ -259,6 +169,15 @@ " print \"Testing Accuracy:\", \\\n", " sess.run(accuracy, feed_dict={x: test_data, y: test_label})" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -270,14 +189,14 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.13" } }, "nbformat": 4, From 63abe6126851c1b1849ee9cc0416d3535b6cf7b8 Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sat, 14 Jan 2017 22:40:51 +1000 Subject: [PATCH 063/166] Update recurrent network for TF1.0 Signed-off-by: Norman Heckscher --- .../3_NeuralNetworks/recurrent_network.ipynb | 165 +++++------------- 1 file changed, 42 insertions(+), 123 deletions(-) diff --git a/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/notebooks/3_NeuralNetworks/recurrent_network.ipynb index 32f3a939..9a7b9b1e 100644 --- a/notebooks/3_NeuralNetworks/recurrent_network.ipynb +++ b/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -1,10 +1,10 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, "source": [ "'''\n", "A Reccurent Neural Network (LSTM) implementation example using TensorFlow library.\n", @@ -18,35 +18,26 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "import tensorflow as tf\n", - "from tensorflow.python.ops import rnn, rnn_cell\n", + "from tensorflow.contrib import rnn\n", "import numpy as np\n", "\n", "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, "source": [ "'''\n", "To classify images using a reccurent neural network, we consider every image\n", @@ -58,7 +49,9 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "# Parameters\n", @@ -89,7 +82,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "def RNN(x, weights, biases):\n", @@ -103,13 +98,13 @@ " # Reshaping to (n_steps*batch_size, n_input)\n", " x = tf.reshape(x, [-1, n_input])\n", " # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", - " x = tf.split(0, n_steps, x)\n", + " x = tf.split(x, n_steps, 0)\n", "\n", " # Define a lstm cell with tensorflow\n", - " lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", + " lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", "\n", " # Get lstm cell output\n", - " outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)\n", + " outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)\n", "\n", " # Linear activation, using rnn inner loop last output\n", " return tf.matmul(outputs[-1], weights['out']) + biases['out']\n", @@ -117,7 +112,7 @@ "pred = RNN(x, weights, biases)\n", "\n", "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", "\n", "# Evaluate model\n", @@ -125,101 +120,16 @@ "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iter 1280, Minibatch Loss= 1.538532, Training Accuracy= 0.49219\n", - "Iter 2560, Minibatch Loss= 1.462834, Training Accuracy= 0.50781\n", - "Iter 3840, Minibatch Loss= 1.048393, Training Accuracy= 0.66406\n", - "Iter 5120, Minibatch Loss= 0.889872, Training Accuracy= 0.71875\n", - "Iter 6400, Minibatch Loss= 0.681855, Training Accuracy= 0.76562\n", - "Iter 7680, Minibatch Loss= 0.987207, Training Accuracy= 0.69531\n", - "Iter 8960, Minibatch Loss= 0.759543, Training Accuracy= 0.71094\n", - "Iter 10240, Minibatch Loss= 0.557055, Training Accuracy= 0.80469\n", - "Iter 11520, Minibatch Loss= 0.371352, Training Accuracy= 0.89844\n", - "Iter 12800, Minibatch Loss= 0.661293, Training Accuracy= 0.80469\n", - "Iter 14080, Minibatch Loss= 0.474259, Training Accuracy= 0.86719\n", - "Iter 15360, Minibatch Loss= 0.328436, Training Accuracy= 0.88281\n", - "Iter 16640, Minibatch Loss= 0.348017, Training Accuracy= 0.93750\n", - "Iter 17920, Minibatch Loss= 0.340086, Training Accuracy= 0.88281\n", - "Iter 19200, Minibatch Loss= 0.261532, Training Accuracy= 0.89844\n", - "Iter 20480, Minibatch Loss= 0.161785, Training Accuracy= 0.94531\n", - "Iter 21760, Minibatch Loss= 0.419619, Training Accuracy= 0.83594\n", - "Iter 23040, Minibatch Loss= 0.120714, Training Accuracy= 0.95312\n", - "Iter 24320, Minibatch Loss= 0.339519, Training Accuracy= 0.89062\n", - "Iter 25600, Minibatch Loss= 0.405463, Training Accuracy= 0.88281\n", - "Iter 26880, Minibatch Loss= 0.172193, Training Accuracy= 0.95312\n", - "Iter 28160, Minibatch Loss= 0.256769, Training Accuracy= 0.91406\n", - "Iter 29440, Minibatch Loss= 0.247753, Training Accuracy= 0.91406\n", - "Iter 30720, Minibatch Loss= 0.230820, Training Accuracy= 0.91406\n", - "Iter 32000, Minibatch Loss= 0.216861, Training Accuracy= 0.93750\n", - "Iter 33280, Minibatch Loss= 0.236337, Training Accuracy= 0.89062\n", - "Iter 34560, Minibatch Loss= 0.252351, Training Accuracy= 0.93750\n", - "Iter 35840, Minibatch Loss= 0.180090, Training Accuracy= 0.92188\n", - "Iter 37120, Minibatch Loss= 0.304125, Training Accuracy= 0.91406\n", - "Iter 38400, Minibatch Loss= 0.114474, Training Accuracy= 0.96094\n", - "Iter 39680, Minibatch Loss= 0.158405, Training Accuracy= 0.96875\n", - "Iter 40960, Minibatch Loss= 0.285858, Training Accuracy= 0.92188\n", - "Iter 42240, Minibatch Loss= 0.134199, Training Accuracy= 0.96094\n", - "Iter 43520, Minibatch Loss= 0.235847, Training Accuracy= 0.92969\n", - "Iter 44800, Minibatch Loss= 0.155971, Training Accuracy= 0.94531\n", - "Iter 46080, Minibatch Loss= 0.061549, Training Accuracy= 0.99219\n", - "Iter 47360, Minibatch Loss= 0.232569, Training Accuracy= 0.94531\n", - "Iter 48640, Minibatch Loss= 0.270348, Training Accuracy= 0.91406\n", - "Iter 49920, Minibatch Loss= 0.202416, Training Accuracy= 0.92188\n", - "Iter 51200, Minibatch Loss= 0.113857, Training Accuracy= 0.96094\n", - "Iter 52480, Minibatch Loss= 0.137900, Training Accuracy= 0.94531\n", - "Iter 53760, Minibatch Loss= 0.052416, Training Accuracy= 0.98438\n", - "Iter 55040, Minibatch Loss= 0.312064, Training Accuracy= 0.91406\n", - "Iter 56320, Minibatch Loss= 0.144335, Training Accuracy= 0.93750\n", - "Iter 57600, Minibatch Loss= 0.114723, Training Accuracy= 0.96875\n", - "Iter 58880, Minibatch Loss= 0.193597, Training Accuracy= 0.96094\n", - "Iter 60160, Minibatch Loss= 0.110877, Training Accuracy= 0.95312\n", - "Iter 61440, Minibatch Loss= 0.119864, Training Accuracy= 0.96094\n", - "Iter 62720, Minibatch Loss= 0.118780, Training Accuracy= 0.94531\n", - "Iter 64000, Minibatch Loss= 0.082259, Training Accuracy= 0.97656\n", - "Iter 65280, Minibatch Loss= 0.087364, Training Accuracy= 0.97656\n", - "Iter 66560, Minibatch Loss= 0.207975, Training Accuracy= 0.92969\n", - "Iter 67840, Minibatch Loss= 0.120612, Training Accuracy= 0.96875\n", - "Iter 69120, Minibatch Loss= 0.070608, Training Accuracy= 0.96875\n", - "Iter 70400, Minibatch Loss= 0.100786, Training Accuracy= 0.96094\n", - "Iter 71680, Minibatch Loss= 0.114746, Training Accuracy= 0.94531\n", - "Iter 72960, Minibatch Loss= 0.083427, Training Accuracy= 0.96875\n", - "Iter 74240, Minibatch Loss= 0.089978, Training Accuracy= 0.96094\n", - "Iter 75520, Minibatch Loss= 0.195322, Training Accuracy= 0.94531\n", - "Iter 76800, Minibatch Loss= 0.161109, Training Accuracy= 0.96094\n", - "Iter 78080, Minibatch Loss= 0.169762, Training Accuracy= 0.94531\n", - "Iter 79360, Minibatch Loss= 0.054240, Training Accuracy= 0.98438\n", - "Iter 80640, Minibatch Loss= 0.160100, Training Accuracy= 0.95312\n", - "Iter 81920, Minibatch Loss= 0.110728, Training Accuracy= 0.96875\n", - "Iter 83200, Minibatch Loss= 0.054918, Training Accuracy= 0.98438\n", - "Iter 84480, Minibatch Loss= 0.104170, Training Accuracy= 0.96875\n", - "Iter 85760, Minibatch Loss= 0.071871, Training Accuracy= 0.97656\n", - "Iter 87040, Minibatch Loss= 0.170529, Training Accuracy= 0.96094\n", - "Iter 88320, Minibatch Loss= 0.087350, Training Accuracy= 0.96875\n", - "Iter 89600, Minibatch Loss= 0.079943, Training Accuracy= 0.96875\n", - "Iter 90880, Minibatch Loss= 0.128451, Training Accuracy= 0.92969\n", - "Iter 92160, Minibatch Loss= 0.046963, Training Accuracy= 0.98438\n", - "Iter 93440, Minibatch Loss= 0.162998, Training Accuracy= 0.96875\n", - "Iter 94720, Minibatch Loss= 0.122588, Training Accuracy= 0.96094\n", - "Iter 96000, Minibatch Loss= 0.073954, Training Accuracy= 0.97656\n", - "Iter 97280, Minibatch Loss= 0.130790, Training Accuracy= 0.96094\n", - "Iter 98560, Minibatch Loss= 0.067689, Training Accuracy= 0.97656\n", - "Iter 99840, Minibatch Loss= 0.186411, Training Accuracy= 0.92188\n", - "Optimization Finished!\n", - "Testing Accuracy: 0.976562\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "# Launch the graph\n", "with tf.Session() as sess:\n", @@ -250,6 +160,15 @@ " print \"Testing Accuracy:\", \\\n", " sess.run(accuracy, feed_dict={x: test_data, y: test_label})" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -261,14 +180,14 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.13" } }, "nbformat": 4, From f3e6051fb94ca66542b7fe3218fe5a3b279b2208 Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sat, 14 Jan 2017 22:50:53 +1000 Subject: [PATCH 064/166] Refactor recurrent network for TF1.0 Signed-off-by: Norman Heckscher --- examples/3_NeuralNetworks/recurrent_network.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 21744364..7ba059f6 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -10,7 +10,7 @@ from __future__ import print_function import tensorflow as tf -from tensorflow.python.ops import rnn, rnn_cell +from tensorflow.contrib import rnn # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data @@ -58,13 +58,13 @@ def RNN(x, weights, biases): # Reshaping to (n_steps*batch_size, n_input) x = tf.reshape(x, [-1, n_input]) # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) - x = tf.split(0, n_steps, x) + x = tf.split(x, n_steps, 0) # Define a lstm cell with tensorflow - lstm_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) + lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) # Get lstm cell output - outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32) + outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out'] @@ -72,7 +72,7 @@ def RNN(x, weights, biases): pred = RNN(x, weights, biases) # Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model @@ -80,7 +80,7 @@ def RNN(x, weights, biases): accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: From 53a722877aac113d8e3b9e3f14388af7dee59546 Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sat, 14 Jan 2017 22:51:11 +1000 Subject: [PATCH 065/166] Refactor bidirectional rnn for TF1.0 Signed-off-by: Norman Heckscher --- examples/3_NeuralNetworks/bidirectional_rnn.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/3_NeuralNetworks/bidirectional_rnn.py b/examples/3_NeuralNetworks/bidirectional_rnn.py index f8fcf3e5..e630896a 100644 --- a/examples/3_NeuralNetworks/bidirectional_rnn.py +++ b/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -10,7 +10,7 @@ from __future__ import print_function import tensorflow as tf -from tensorflow.python.ops import rnn, rnn_cell +from tensorflow.contrib import rnn import numpy as np # Import MNIST data @@ -60,20 +60,20 @@ def BiRNN(x, weights, biases): # Reshape to (n_steps*batch_size, n_input) x = tf.reshape(x, [-1, n_input]) # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) - x = tf.split(0, n_steps, x) + x = tf.split(x, n_steps, 0) # Define lstm cells with tensorflow # Forward direction cell - lstm_fw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) + lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) # Backward direction cell - lstm_bw_cell = rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0) + lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) # Get lstm cell output try: - outputs, _, _ = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, + outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32) except Exception: # Old TensorFlow version only returns outputs not states - outputs = rnn.bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, + outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, dtype=tf.float32) # Linear activation, using rnn inner loop last output @@ -82,7 +82,7 @@ def BiRNN(x, weights, biases): pred = BiRNN(x, weights, biases) # Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model @@ -90,7 +90,7 @@ def BiRNN(x, weights, biases): accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: From 31338232c02476e497a855a8cc2a6075d899c88f Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sat, 14 Jan 2017 22:56:42 +1000 Subject: [PATCH 066/166] Refactor ConvNet for TF1.0 Signed-off-by: Norman Heckscher --- .../3_NeuralNetworks/convolutional_network.py | 4 +- .../convolutional_network.ipynb | 203 ++---------------- 2 files changed, 20 insertions(+), 187 deletions(-) diff --git a/examples/3_NeuralNetworks/convolutional_network.py b/examples/3_NeuralNetworks/convolutional_network.py index 81461237..d729dd3c 100644 --- a/examples/3_NeuralNetworks/convolutional_network.py +++ b/examples/3_NeuralNetworks/convolutional_network.py @@ -96,7 +96,7 @@ def conv_net(x, weights, biases, dropout): pred = conv_net(x, weights, biases, keep_prob) # Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model @@ -104,7 +104,7 @@ def conv_net(x, weights, biases, dropout): accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/notebooks/3_NeuralNetworks/convolutional_network.ipynb index 85396d25..ce10c034 100644 --- a/notebooks/3_NeuralNetworks/convolutional_network.ipynb +++ b/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -20,28 +20,17 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "# Import MNIST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" ] }, { @@ -150,7 +139,7 @@ "pred = conv_net(x, weights, biases, keep_prob)\n", "\n", "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", "\n", "# Evaluate model\n", @@ -158,181 +147,16 @@ "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iter 1280, Minibatch Loss= 17231.589844, Training Accuracy= 0.25000\n", - "Iter 2560, Minibatch Loss= 10580.260742, Training Accuracy= 0.54688\n", - "Iter 3840, Minibatch Loss= 7395.362793, Training Accuracy= 0.64062\n", - "Iter 5120, Minibatch Loss= 4864.292480, Training Accuracy= 0.75781\n", - "Iter 6400, Minibatch Loss= 3830.062012, Training Accuracy= 0.80469\n", - "Iter 7680, Minibatch Loss= 6031.701172, Training Accuracy= 0.72656\n", - "Iter 8960, Minibatch Loss= 2549.708740, Training Accuracy= 0.81250\n", - "Iter 10240, Minibatch Loss= 2010.484985, Training Accuracy= 0.84375\n", - "Iter 11520, Minibatch Loss= 1607.380981, Training Accuracy= 0.89062\n", - "Iter 12800, Minibatch Loss= 1983.302856, Training Accuracy= 0.82812\n", - "Iter 14080, Minibatch Loss= 401.215088, Training Accuracy= 0.94531\n", - "Iter 15360, Minibatch Loss= 976.289307, Training Accuracy= 0.95312\n", - "Iter 16640, Minibatch Loss= 1844.699951, Training Accuracy= 0.89844\n", - "Iter 17920, Minibatch Loss= 1009.859863, Training Accuracy= 0.92969\n", - "Iter 19200, Minibatch Loss= 1397.939453, Training Accuracy= 0.88281\n", - "Iter 20480, Minibatch Loss= 540.369995, Training Accuracy= 0.96094\n", - "Iter 21760, Minibatch Loss= 2589.246826, Training Accuracy= 0.87500\n", - "Iter 23040, Minibatch Loss= 404.981293, Training Accuracy= 0.96094\n", - "Iter 24320, Minibatch Loss= 742.155396, Training Accuracy= 0.93750\n", - "Iter 25600, Minibatch Loss= 851.599731, Training Accuracy= 0.93750\n", - "Iter 26880, Minibatch Loss= 1527.609619, Training Accuracy= 0.90625\n", - "Iter 28160, Minibatch Loss= 1209.633301, Training Accuracy= 0.91406\n", - "Iter 29440, Minibatch Loss= 1123.146851, Training Accuracy= 0.93750\n", - "Iter 30720, Minibatch Loss= 950.860596, Training Accuracy= 0.92188\n", - "Iter 32000, Minibatch Loss= 1217.373779, Training Accuracy= 0.92188\n", - "Iter 33280, Minibatch Loss= 859.433105, Training Accuracy= 0.91406\n", - "Iter 34560, Minibatch Loss= 487.426331, Training Accuracy= 0.95312\n", - "Iter 35840, Minibatch Loss= 287.507721, Training Accuracy= 0.96875\n", - "Iter 37120, Minibatch Loss= 786.797485, Training Accuracy= 0.91406\n", - "Iter 38400, Minibatch Loss= 248.981216, Training Accuracy= 0.97656\n", - "Iter 39680, Minibatch Loss= 147.081467, Training Accuracy= 0.97656\n", - "Iter 40960, Minibatch Loss= 1198.459106, Training Accuracy= 0.93750\n", - "Iter 42240, Minibatch Loss= 717.058716, Training Accuracy= 0.92188\n", - "Iter 43520, Minibatch Loss= 351.870453, Training Accuracy= 0.96094\n", - "Iter 44800, Minibatch Loss= 271.505554, Training Accuracy= 0.96875\n", - "Iter 46080, Minibatch Loss= 0.000000, Training Accuracy= 1.00000\n", - "Iter 47360, Minibatch Loss= 806.163818, Training Accuracy= 0.95312\n", - "Iter 48640, Minibatch Loss= 1055.359009, Training Accuracy= 0.91406\n", - "Iter 49920, Minibatch Loss= 459.845520, Training Accuracy= 0.94531\n", - "Iter 51200, Minibatch Loss= 133.995087, Training Accuracy= 0.97656\n", - "Iter 52480, Minibatch Loss= 378.886780, Training Accuracy= 0.96094\n", - "Iter 53760, Minibatch Loss= 122.112671, Training Accuracy= 0.98438\n", - "Iter 55040, Minibatch Loss= 357.410950, Training Accuracy= 0.96875\n", - "Iter 56320, Minibatch Loss= 164.791595, Training Accuracy= 0.98438\n", - "Iter 57600, Minibatch Loss= 740.711060, Training Accuracy= 0.95312\n", - "Iter 58880, Minibatch Loss= 755.948364, Training Accuracy= 0.92969\n", - "Iter 60160, Minibatch Loss= 289.819153, Training Accuracy= 0.94531\n", - "Iter 61440, Minibatch Loss= 162.940323, Training Accuracy= 0.96875\n", - "Iter 62720, Minibatch Loss= 616.192200, Training Accuracy= 0.92969\n", - "Iter 64000, Minibatch Loss= 649.317993, Training Accuracy= 0.92188\n", - "Iter 65280, Minibatch Loss= 1021.529785, Training Accuracy= 0.93750\n", - "Iter 66560, Minibatch Loss= 203.839050, Training Accuracy= 0.96094\n", - "Iter 67840, Minibatch Loss= 469.755249, Training Accuracy= 0.96094\n", - "Iter 69120, Minibatch Loss= 36.496567, Training Accuracy= 0.98438\n", - "Iter 70400, Minibatch Loss= 214.677551, Training Accuracy= 0.95312\n", - "Iter 71680, Minibatch Loss= 115.657990, Training Accuracy= 0.96875\n", - "Iter 72960, Minibatch Loss= 354.555115, Training Accuracy= 0.96875\n", - "Iter 74240, Minibatch Loss= 124.091103, Training Accuracy= 0.97656\n", - "Iter 75520, Minibatch Loss= 614.557251, Training Accuracy= 0.94531\n", - "Iter 76800, Minibatch Loss= 343.182983, Training Accuracy= 0.95312\n", - "Iter 78080, Minibatch Loss= 678.875183, Training Accuracy= 0.94531\n", - "Iter 79360, Minibatch Loss= 313.656494, Training Accuracy= 0.95312\n", - "Iter 80640, Minibatch Loss= 169.024185, Training Accuracy= 0.96094\n", - "Iter 81920, Minibatch Loss= 98.455017, Training Accuracy= 0.96875\n", - "Iter 83200, Minibatch Loss= 359.754517, Training Accuracy= 0.92188\n", - "Iter 84480, Minibatch Loss= 214.993103, Training Accuracy= 0.96875\n", - "Iter 85760, Minibatch Loss= 262.921265, Training Accuracy= 0.97656\n", - "Iter 87040, Minibatch Loss= 558.218445, Training Accuracy= 0.89844\n", - "Iter 88320, Minibatch Loss= 122.281952, Training Accuracy= 0.99219\n", - "Iter 89600, Minibatch Loss= 300.606689, Training Accuracy= 0.93750\n", - "Iter 90880, Minibatch Loss= 261.051025, Training Accuracy= 0.98438\n", - "Iter 92160, Minibatch Loss= 59.812164, Training Accuracy= 0.98438\n", - "Iter 93440, Minibatch Loss= 309.307312, Training Accuracy= 0.96875\n", - "Iter 94720, Minibatch Loss= 626.035706, Training Accuracy= 0.95312\n", - "Iter 96000, Minibatch Loss= 317.929260, Training Accuracy= 0.96875\n", - "Iter 97280, Minibatch Loss= 196.908218, Training Accuracy= 0.97656\n", - "Iter 98560, Minibatch Loss= 843.143250, Training Accuracy= 0.95312\n", - "Iter 99840, Minibatch Loss= 389.142761, Training Accuracy= 0.96875\n", - "Iter 101120, Minibatch Loss= 246.468994, Training Accuracy= 0.96094\n", - "Iter 102400, Minibatch Loss= 110.580948, Training Accuracy= 0.98438\n", - "Iter 103680, Minibatch Loss= 208.350586, Training Accuracy= 0.96875\n", - "Iter 104960, Minibatch Loss= 506.229462, Training Accuracy= 0.94531\n", - "Iter 106240, Minibatch Loss= 49.548233, Training Accuracy= 0.98438\n", - "Iter 107520, Minibatch Loss= 728.496582, Training Accuracy= 0.92969\n", - "Iter 108800, Minibatch Loss= 187.256622, Training Accuracy= 0.97656\n", - "Iter 110080, Minibatch Loss= 273.696899, Training Accuracy= 0.97656\n", - "Iter 111360, Minibatch Loss= 317.126678, Training Accuracy= 0.96094\n", - "Iter 112640, Minibatch Loss= 148.293365, Training Accuracy= 0.98438\n", - "Iter 113920, Minibatch Loss= 139.360168, Training Accuracy= 0.97656\n", - "Iter 115200, Minibatch Loss= 167.539093, Training Accuracy= 0.98438\n", - "Iter 116480, Minibatch Loss= 565.433594, Training Accuracy= 0.94531\n", - "Iter 117760, Minibatch Loss= 8.117203, Training Accuracy= 0.99219\n", - "Iter 119040, Minibatch Loss= 348.071472, Training Accuracy= 0.96875\n", - "Iter 120320, Minibatch Loss= 287.732849, Training Accuracy= 0.97656\n", - "Iter 121600, Minibatch Loss= 156.525284, Training Accuracy= 0.96875\n", - "Iter 122880, Minibatch Loss= 296.147339, Training Accuracy= 0.98438\n", - "Iter 124160, Minibatch Loss= 260.941956, Training Accuracy= 0.98438\n", - "Iter 125440, Minibatch Loss= 241.011719, Training Accuracy= 0.98438\n", - "Iter 126720, Minibatch Loss= 185.330444, Training Accuracy= 0.98438\n", - "Iter 128000, Minibatch Loss= 346.407013, Training Accuracy= 0.96875\n", - "Iter 129280, Minibatch Loss= 522.477173, Training Accuracy= 0.94531\n", - "Iter 130560, Minibatch Loss= 97.665955, Training Accuracy= 0.96094\n", - "Iter 131840, Minibatch Loss= 111.370262, Training Accuracy= 0.96875\n", - "Iter 133120, Minibatch Loss= 106.377136, Training Accuracy= 0.97656\n", - "Iter 134400, Minibatch Loss= 432.294983, Training Accuracy= 0.96094\n", - "Iter 135680, Minibatch Loss= 104.584610, Training Accuracy= 0.98438\n", - "Iter 136960, Minibatch Loss= 439.611053, Training Accuracy= 0.95312\n", - "Iter 138240, Minibatch Loss= 171.394562, Training Accuracy= 0.96875\n", - "Iter 139520, Minibatch Loss= 83.505905, Training Accuracy= 0.98438\n", - "Iter 140800, Minibatch Loss= 240.278427, Training Accuracy= 0.98438\n", - "Iter 142080, Minibatch Loss= 417.140320, Training Accuracy= 0.96094\n", - "Iter 143360, Minibatch Loss= 77.656067, Training Accuracy= 0.97656\n", - "Iter 144640, Minibatch Loss= 284.589844, Training Accuracy= 0.97656\n", - "Iter 145920, Minibatch Loss= 372.114288, Training Accuracy= 0.96875\n", - "Iter 147200, Minibatch Loss= 352.900024, Training Accuracy= 0.96094\n", - "Iter 148480, Minibatch Loss= 148.120621, Training Accuracy= 0.97656\n", - "Iter 149760, Minibatch Loss= 127.385742, Training Accuracy= 0.98438\n", - "Iter 151040, Minibatch Loss= 383.167175, Training Accuracy= 0.96094\n", - "Iter 152320, Minibatch Loss= 331.846649, Training Accuracy= 0.94531\n", - "Iter 153600, Minibatch Loss= 0.000000, Training Accuracy= 1.00000\n", - "Iter 154880, Minibatch Loss= 24.065147, Training Accuracy= 0.99219\n", - "Iter 156160, Minibatch Loss= 43.433868, Training Accuracy= 0.99219\n", - "Iter 157440, Minibatch Loss= 205.383972, Training Accuracy= 0.96875\n", - "Iter 158720, Minibatch Loss= 83.019257, Training Accuracy= 0.97656\n", - "Iter 160000, Minibatch Loss= 195.710556, Training Accuracy= 0.96875\n", - "Iter 161280, Minibatch Loss= 177.192932, Training Accuracy= 0.95312\n", - "Iter 162560, Minibatch Loss= 261.618713, Training Accuracy= 0.96875\n", - "Iter 163840, Minibatch Loss= 0.000000, Training Accuracy= 1.00000\n", - "Iter 165120, Minibatch Loss= 62.901100, Training Accuracy= 0.97656\n", - "Iter 166400, Minibatch Loss= 17.181839, Training Accuracy= 0.98438\n", - "Iter 167680, Minibatch Loss= 102.738960, Training Accuracy= 0.96875\n", - "Iter 168960, Minibatch Loss= 0.000000, Training Accuracy= 1.00000\n", - "Iter 170240, Minibatch Loss= 71.784363, Training Accuracy= 0.99219\n", - "Iter 171520, Minibatch Loss= 260.672852, Training Accuracy= 0.96875\n", - "Iter 172800, Minibatch Loss= 186.616119, Training Accuracy= 0.96094\n", - "Iter 174080, Minibatch Loss= 312.432312, Training Accuracy= 0.96875\n", - "Iter 175360, Minibatch Loss= 45.828953, Training Accuracy= 0.99219\n", - "Iter 176640, Minibatch Loss= 62.931808, Training Accuracy= 0.98438\n", - "Iter 177920, Minibatch Loss= 63.452362, Training Accuracy= 0.97656\n", - "Iter 179200, Minibatch Loss= 53.608818, Training Accuracy= 0.98438\n", - "Iter 180480, Minibatch Loss= 57.089508, Training Accuracy= 0.97656\n", - "Iter 181760, Minibatch Loss= 601.268799, Training Accuracy= 0.93750\n", - "Iter 183040, Minibatch Loss= 59.850044, Training Accuracy= 0.97656\n", - "Iter 184320, Minibatch Loss= 145.267883, Training Accuracy= 0.96875\n", - "Iter 185600, Minibatch Loss= 24.205322, Training Accuracy= 0.99219\n", - "Iter 186880, Minibatch Loss= 51.866646, Training Accuracy= 0.98438\n", - "Iter 188160, Minibatch Loss= 166.911987, Training Accuracy= 0.96875\n", - "Iter 189440, Minibatch Loss= 32.308147, Training Accuracy= 0.98438\n", - "Iter 190720, Minibatch Loss= 514.898071, Training Accuracy= 0.92188\n", - "Iter 192000, Minibatch Loss= 146.610031, Training Accuracy= 0.98438\n", - "Iter 193280, Minibatch Loss= 23.939758, Training Accuracy= 0.99219\n", - "Iter 194560, Minibatch Loss= 224.806641, Training Accuracy= 0.97656\n", - "Iter 195840, Minibatch Loss= 71.935089, Training Accuracy= 0.98438\n", - "Iter 197120, Minibatch Loss= 182.021210, Training Accuracy= 0.96875\n", - "Iter 198400, Minibatch Loss= 125.573784, Training Accuracy= 0.96875\n", - "Iter 199680, Minibatch Loss= 122.506104, Training Accuracy= 0.96875\n", - "Optimization Finished!\n", - "Testing Accuracy: 0.972656\n" - ] - } - ], + "outputs": [], "source": [ "# Launch the graph\n", "with tf.Session() as sess:\n", @@ -361,6 +185,15 @@ " y: mnist.test.labels[:256],\n", " keep_prob: 1.})" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -372,14 +205,14 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.13" } }, "nbformat": 4, From 69859521112c7128b99d0f0508b74f930ac8470f Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sat, 14 Jan 2017 23:22:37 +1000 Subject: [PATCH 067/166] Refactor autoencoders for TF1.0 Signed-off-by: Norman Heckscher --- examples/3_NeuralNetworks/autoencoder.py | 4 +- notebooks/3_NeuralNetworks/autoencoder.ipynb | 91 ++++++++------------ 2 files changed, 37 insertions(+), 58 deletions(-) diff --git a/examples/3_NeuralNetworks/autoencoder.py b/examples/3_NeuralNetworks/autoencoder.py index f87f6b23..8ebe7de7 100644 --- a/examples/3_NeuralNetworks/autoencoder.py +++ b/examples/3_NeuralNetworks/autoencoder.py @@ -17,7 +17,7 @@ # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data -mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) +mnist = input_data.read_data_sets("MNIST_data", one_hot=True) # Parameters learning_rate = 0.01 @@ -83,7 +83,7 @@ def decoder(x): optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/notebooks/3_NeuralNetworks/autoencoder.ipynb b/notebooks/3_NeuralNetworks/autoencoder.ipynb index 072cf7dd..f0b8d4fe 100644 --- a/notebooks/3_NeuralNetworks/autoencoder.ipynb +++ b/notebooks/3_NeuralNetworks/autoencoder.ipynb @@ -21,22 +21,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "outputs": [], "source": [ "from __future__ import division, print_function, absolute_import\n", "\n", @@ -46,7 +35,7 @@ "\n", "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + "mnist = input_data.read_data_sets(\"MNIST_data\", one_hot=True)" ] }, { @@ -88,9 +77,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -129,7 +118,7 @@ "optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { @@ -138,35 +127,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: 0001 cost= 0.218654603\n", - "Epoch: 0002 cost= 0.173306286\n", - "Epoch: 0003 cost= 0.154793650\n", - "Epoch: 0004 cost= 0.146902516\n", - "Epoch: 0005 cost= 0.141993478\n", - "Epoch: 0006 cost= 0.132718414\n", - "Epoch: 0007 cost= 0.125991374\n", - "Epoch: 0008 cost= 0.122500181\n", - "Epoch: 0009 cost= 0.115299642\n", - "Epoch: 0010 cost= 0.115390278\n", - "Epoch: 0011 cost= 0.114480168\n", - "Epoch: 0012 cost= 0.113888472\n", - "Epoch: 0013 cost= 0.111597553\n", - "Epoch: 0014 cost= 0.110663064\n", - "Epoch: 0015 cost= 0.108673096\n", - "Epoch: 0016 cost= 0.104775786\n", - "Epoch: 0017 cost= 0.106273368\n", - "Epoch: 0018 cost= 0.104061618\n", - "Epoch: 0019 cost= 0.103227913\n", - "Epoch: 0020 cost= 0.099696413\n", - "Optimization Finished!\n" - ] - } - ], + "outputs": [], "source": [ "# Launch the graph\n", "# Using InteractiveSession (more convenient while using Notebooks)\n", @@ -197,37 +158,55 @@ " a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))\n", " a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))\n", "f.show()\n", - "plt.draw()\n", - "plt.waitforbuttonpress()" + "plt.draw()" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAADICAYAAAB758tPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmTOAofh3AAAAAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzUAAADDCAYAAAC7zz91AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzUAAADDCAYAAAC7zz91AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wGTleGj2VhBawAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzUAAADDCAYAAAC7zz91AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBkNdY5wag8FgMBgMBoPBUNf8f3Fkpc5Du0uwAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, - "execution_count": 1, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "# Result" + "plt.show()" ] } ], "metadata": { "kernelspec": { - "display_name": "IPython (Python 2.7)", + "display_name": "Python 2", "language": "python", "name": "python2" }, @@ -241,7 +220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.13" } }, "nbformat": 4, From 5b9aef48436e33b9c9f2145bc7c99a51d285ea15 Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sun, 15 Jan 2017 02:33:41 +1000 Subject: [PATCH 068/166] Refactor tensorboard_basic for TF1.0 Signed-off-by: Norman Heckscher --- examples/4_Utils/tensorboard_basic.py | 8 +- notebooks/4_Utils/tensorboard_basic.ipynb | 89 ++++++++++------------- 2 files changed, 43 insertions(+), 54 deletions(-) diff --git a/examples/4_Utils/tensorboard_basic.py b/examples/4_Utils/tensorboard_basic.py index c690d549..f1ce23a7 100644 --- a/examples/4_Utils/tensorboard_basic.py +++ b/examples/4_Utils/tensorboard_basic.py @@ -52,18 +52,18 @@ init = tf.initialize_all_variables() # Create a summary to monitor cost tensor -tf.scalar_summary("loss", cost) +tf.summary.scalar("loss", cost) # Create a summary to monitor accuracy tensor -tf.scalar_summary("accuracy", acc) +tf.summary.scalar("accuracy", acc) # Merge all summaries into a single op -merged_summary_op = tf.merge_all_summaries() +merged_summary_op = tf.summary.merge_all() # Launch the graph with tf.Session() as sess: sess.run(init) # op to write logs to Tensorboard - summary_writer = tf.train.SummaryWriter(logs_path, graph=tf.get_default_graph()) + summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph()) # Training cycle for epoch in range(training_epochs): diff --git a/notebooks/4_Utils/tensorboard_basic.ipynb b/notebooks/4_Utils/tensorboard_basic.ipynb index 582bbdb7..69c389d9 100644 --- a/notebooks/4_Utils/tensorboard_basic.ipynb +++ b/notebooks/4_Utils/tensorboard_basic.ipynb @@ -20,33 +20,22 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], + "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -72,9 +61,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -95,19 +84,19 @@ " acc = tf.reduce_mean(tf.cast(acc, tf.float32))\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()\n", + "init = tf.global_variables_initializer()\n", "\n", "# Create a summary to monitor cost tensor\n", - "tf.scalar_summary(\"loss\", cost)\n", + "tf.summary.scalar(\"loss\", cost)\n", "# Create a summary to monitor accuracy tensor\n", - "tf.scalar_summary(\"accuracy\", acc)\n", + "tf.summary.scalar(\"accuracy\", acc)\n", "# Merge all summaries into a single op\n", - "merged_summary_op = tf.merge_all_summaries()" + "merged_summary_op = tf.summary.merge_all()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -116,31 +105,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch: 0001 cost= 1.182138957\n", - "Epoch: 0002 cost= 0.664735104\n", - "Epoch: 0003 cost= 0.552622685\n", - "Epoch: 0004 cost= 0.498596912\n", - "Epoch: 0005 cost= 0.465510372\n", - "Epoch: 0006 cost= 0.442504281\n", - "Epoch: 0007 cost= 0.425473650\n", - "Epoch: 0008 cost= 0.412175615\n", - "Epoch: 0009 cost= 0.401374554\n", - "Epoch: 0010 cost= 0.392403109\n", - "Epoch: 0011 cost= 0.384748503\n", - "Epoch: 0012 cost= 0.378154479\n", - "Epoch: 0013 cost= 0.372405099\n", - "Epoch: 0014 cost= 0.367272844\n", - "Epoch: 0015 cost= 0.362745077\n", - "Epoch: 0016 cost= 0.358575674\n", - "Epoch: 0017 cost= 0.354862829\n", - "Epoch: 0018 cost= 0.351437834\n", - "Epoch: 0019 cost= 0.348300697\n", - "Epoch: 0020 cost= 0.345401101\n", - "Epoch: 0021 cost= 0.342762216\n", - "Epoch: 0022 cost= 0.340199728\n", - "Epoch: 0023 cost= 0.337916089\n", - "Epoch: 0024 cost= 0.335764083\n", - "Epoch: 0025 cost= 0.333645939\n", + "Epoch: 0001 cost= 1.182138961\n", + "Epoch: 0002 cost= 0.664609327\n", + "Epoch: 0003 cost= 0.552565036\n", + "Epoch: 0004 cost= 0.498541865\n", + "Epoch: 0005 cost= 0.465393374\n", + "Epoch: 0006 cost= 0.442491178\n", + "Epoch: 0007 cost= 0.425474149\n", + "Epoch: 0008 cost= 0.412152022\n", + "Epoch: 0009 cost= 0.401320939\n", + "Epoch: 0010 cost= 0.392305281\n", + "Epoch: 0011 cost= 0.384732356\n", + "Epoch: 0012 cost= 0.378109478\n", + "Epoch: 0013 cost= 0.372409370\n", + "Epoch: 0014 cost= 0.367236996\n", + "Epoch: 0015 cost= 0.362727492\n", + "Epoch: 0016 cost= 0.358627345\n", + "Epoch: 0017 cost= 0.354815522\n", + "Epoch: 0018 cost= 0.351413656\n", + "Epoch: 0019 cost= 0.348314827\n", + "Epoch: 0020 cost= 0.345429416\n", + "Epoch: 0021 cost= 0.342749324\n", + "Epoch: 0022 cost= 0.340224642\n", + "Epoch: 0023 cost= 0.337897302\n", + "Epoch: 0024 cost= 0.335720168\n", + "Epoch: 0025 cost= 0.333691911\n", "Optimization Finished!\n", "Accuracy: 0.9143\n", "Run the command line:\n", @@ -155,7 +144,7 @@ " sess.run(init)\n", "\n", " # op to write logs to Tensorboard\n", - " summary_writer = tf.train.SummaryWriter(logs_path, graph=tf.get_default_graph())\n", + " summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())\n", "\n", " # Training cycle\n", " for epoch in range(training_epochs):\n", @@ -234,7 +223,7 @@ ], "metadata": { "kernelspec": { - "display_name": "IPython (Python 2.7)", + "display_name": "Python 2", "language": "python", "name": "python2" }, @@ -248,7 +237,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.13" } }, "nbformat": 4, From 2c43cfb4bce408997596010177a20cfadc8ba9e0 Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sun, 15 Jan 2017 02:36:43 +1000 Subject: [PATCH 069/166] Refactor tensorboard_advanced for TF1.0 Signed-off-by: Norman Heckscher --- examples/4_Utils/tensorboard_advanced.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/4_Utils/tensorboard_advanced.py b/examples/4_Utils/tensorboard_advanced.py index ee6cd150..2113adb1 100644 --- a/examples/4_Utils/tensorboard_advanced.py +++ b/examples/4_Utils/tensorboard_advanced.py @@ -41,12 +41,12 @@ def multilayer_perceptron(x, weights, biases): layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1']) layer_1 = tf.nn.relu(layer_1) # Create a summary to visualize the first layer ReLU activation - tf.histogram_summary("relu1", layer_1) + tf.summary.histogram("relu1", layer_1) # Hidden layer with RELU activation layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2']) layer_2 = tf.nn.relu(layer_2) # Create another summary to visualize the second layer ReLU activation - tf.histogram_summary("relu2", layer_2) + tf.summary.histogram("relu2", layer_2) # Output layer out_layer = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3']) return out_layer @@ -91,24 +91,24 @@ def multilayer_perceptron(x, weights, biases): init = tf.initialize_all_variables() # Create a summary to monitor cost tensor -tf.scalar_summary("loss", loss) +tf.summary.scalar("loss", loss) # Create a summary to monitor accuracy tensor -tf.scalar_summary("accuracy", acc) +tf.summary.scalar("accuracy", acc) # Create summaries to visualize weights for var in tf.trainable_variables(): - tf.histogram_summary(var.name, var) + tf.summary.histogram(var.name, var) # Summarize all gradients for grad, var in grads: - tf.histogram_summary(var.name + '/gradient', grad) + tf.summary.histogram(var.name + '/gradient', grad) # Merge all summaries into a single op -merged_summary_op = tf.merge_all_summaries() +merged_summary_op = tf.summary.merge_all() # Launch the graph with tf.Session() as sess: sess.run(init) # op to write logs to Tensorboard - summary_writer = tf.train.SummaryWriter(logs_path, + summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph()) # Training cycle From 6a26f9794ea42c32a3bf6525c495a77f91825e09 Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sun, 15 Jan 2017 02:42:19 +1000 Subject: [PATCH 070/166] Refactor train and save for TF1.0 Signed-off-by: Norman Heckscher --- examples/4_Utils/save_restore_model.py | 6 +-- notebooks/4_Utils/save_restore_model.ipynb | 58 +++++++++++----------- 2 files changed, 31 insertions(+), 33 deletions(-) diff --git a/examples/4_Utils/save_restore_model.py b/examples/4_Utils/save_restore_model.py index d1e31781..9db2c756 100644 --- a/examples/4_Utils/save_restore_model.py +++ b/examples/4_Utils/save_restore_model.py @@ -11,7 +11,7 @@ # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data -mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) +mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) import tensorflow as tf @@ -60,11 +60,11 @@ def multilayer_perceptron(x, weights, biases): pred = multilayer_perceptron(x, weights, biases) # Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # 'Saver' op to save and restore all the variables saver = tf.train.Saver() diff --git a/notebooks/4_Utils/save_restore_model.ipynb b/notebooks/4_Utils/save_restore_model.ipynb index 7909071f..1504f47f 100644 --- a/notebooks/4_Utils/save_restore_model.ipynb +++ b/notebooks/4_Utils/save_restore_model.ipynb @@ -29,26 +29,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", + "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n", "\n", "import tensorflow as tf" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -97,16 +97,16 @@ "pred = multilayer_perceptron(x, weights, biases)\n", "\n", "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -128,11 +128,11 @@ "output_type": "stream", "text": [ "Starting 1st session...\n", - "Epoch: 0001 cost= 182.770135574\n", - "Epoch: 0002 cost= 44.863718596\n", - "Epoch: 0003 cost= 27.965412349\n", + "Epoch: 0001 cost= 187.778896380\n", + "Epoch: 0002 cost= 42.367902536\n", + "Epoch: 0003 cost= 26.488964058\n", "First Optimization Finished!\n", - "Accuracy: 0.906\n", + "Accuracy: 0.9075\n", "Model saved in file: /tmp/model.ckpt\n" ] } @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -186,15 +186,15 @@ "text": [ "Starting 2nd session...\n", "Model restored from file: /tmp/model.ckpt\n", - "Epoch: 0001 cost= 19.658836002\n", - "Epoch: 0002 cost= 14.354811554\n", - "Epoch: 0003 cost= 10.580801367\n", - "Epoch: 0004 cost= 8.012172253\n", - "Epoch: 0005 cost= 5.985675981\n", - "Epoch: 0006 cost= 4.572637980\n", - "Epoch: 0007 cost= 3.329074899\n", + "Epoch: 0001 cost= 18.292712951\n", + "Epoch: 0002 cost= 13.404136196\n", + "Epoch: 0003 cost= 9.855191723\n", + "Epoch: 0004 cost= 7.276933088\n", + "Epoch: 0005 cost= 5.564581285\n", + "Epoch: 0006 cost= 4.165259939\n", + "Epoch: 0007 cost= 3.139393926\n", "Second Optimization Finished!\n", - "Accuracy: 0.9371\n" + "Accuracy: 0.9385\n" ] } ], @@ -242,9 +242,7 @@ "collapsed": true }, "outputs": [], - "source": [ - "" - ] + "source": [] } ], "metadata": { @@ -256,16 +254,16 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From b55753309150eb10bebdd8f4e3387656c081b144 Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sun, 15 Jan 2017 02:47:40 +1000 Subject: [PATCH 071/166] Refactor linear_regression for TF1.0 Signed-off-by: Norman Heckscher --- examples/2_BasicModels/linear_regression.py | 2 +- .../2_BasicModels/linear_regression.ipynb | 72 +++++++++++-------- 2 files changed, 42 insertions(+), 32 deletions(-) diff --git a/examples/2_BasicModels/linear_regression.py b/examples/2_BasicModels/linear_regression.py index bcb49358..b23d11cb 100644 --- a/examples/2_BasicModels/linear_regression.py +++ b/examples/2_BasicModels/linear_regression.py @@ -41,7 +41,7 @@ optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/notebooks/2_BasicModels/linear_regression.ipynb b/notebooks/2_BasicModels/linear_regression.ipynb index 39902e61..9307ee46 100644 --- a/notebooks/2_BasicModels/linear_regression.ipynb +++ b/notebooks/2_BasicModels/linear_regression.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -105,12 +105,12 @@ "cell_type": "code", "execution_count": 8, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { @@ -124,30 +124,40 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch: 0050 cost= 0.207037717 W= 0.451217 b= -0.649001\n", - "Epoch: 0100 cost= 0.192011863 W= 0.439226 b= -0.562735\n", - "Epoch: 0150 cost= 0.178721249 W= 0.427948 b= -0.481599\n", - "Epoch: 0200 cost= 0.166965470 W= 0.41734 b= -0.40529\n", - "Epoch: 0250 cost= 0.156567261 W= 0.407363 b= -0.333518\n", - "Epoch: 0300 cost= 0.147369981 W= 0.39798 b= -0.266015\n", - "Epoch: 0350 cost= 0.139234960 W= 0.389155 b= -0.202527\n", - "Epoch: 0400 cost= 0.132039562 W= 0.380854 b= -0.142815\n", - "Epoch: 0450 cost= 0.125675321 W= 0.373048 b= -0.0866538\n", - "Epoch: 0500 cost= 0.120046206 W= 0.365705 b= -0.0338331\n", - "Epoch: 0550 cost= 0.115067400 W= 0.3588 b= 0.0158462\n", - "Epoch: 0600 cost= 0.110663772 W= 0.352305 b= 0.0625707\n", - "Epoch: 0650 cost= 0.106768914 W= 0.346196 b= 0.106516\n", - "Epoch: 0700 cost= 0.103324078 W= 0.340451 b= 0.147848\n", - "Epoch: 0750 cost= 0.100277305 W= 0.335047 b= 0.186722\n", - "Epoch: 0800 cost= 0.097582638 W= 0.329965 b= 0.223284\n", - "Epoch: 0850 cost= 0.095199391 W= 0.325184 b= 0.257671\n", - "Epoch: 0900 cost= 0.093091547 W= 0.320689 b= 0.290013\n", - "Epoch: 0950 cost= 0.091227390 W= 0.31646 b= 0.320432\n", - "Epoch: 1000 cost= 0.089578770 W= 0.312484 b= 0.349041\n", + "Epoch: 0050 cost= 0.195095107 W= 0.441748 b= -0.580876\n", + "Epoch: 0100 cost= 0.181448311 W= 0.430319 b= -0.498661\n", + "Epoch: 0150 cost= 0.169377610 W= 0.419571 b= -0.421336\n", + "Epoch: 0200 cost= 0.158700854 W= 0.409461 b= -0.348611\n", + "Epoch: 0250 cost= 0.149257123 W= 0.399953 b= -0.28021\n", + "Epoch: 0300 cost= 0.140904188 W= 0.391011 b= -0.215878\n", + "Epoch: 0350 cost= 0.133515999 W= 0.3826 b= -0.155372\n", + "Epoch: 0400 cost= 0.126981199 W= 0.374689 b= -0.0984639\n", + "Epoch: 0450 cost= 0.121201262 W= 0.367249 b= -0.0449408\n", + "Epoch: 0500 cost= 0.116088994 W= 0.360252 b= 0.00539905\n", + "Epoch: 0550 cost= 0.111567356 W= 0.35367 b= 0.052745\n", + "Epoch: 0600 cost= 0.107568085 W= 0.34748 b= 0.0972751\n", + "Epoch: 0650 cost= 0.104030922 W= 0.341659 b= 0.139157\n", + "Epoch: 0700 cost= 0.100902475 W= 0.336183 b= 0.178547\n", + "Epoch: 0750 cost= 0.098135538 W= 0.331033 b= 0.215595\n", + "Epoch: 0800 cost= 0.095688373 W= 0.32619 b= 0.25044\n", + "Epoch: 0850 cost= 0.093524046 W= 0.321634 b= 0.283212\n", + "Epoch: 0900 cost= 0.091609895 W= 0.317349 b= 0.314035\n", + "Epoch: 0950 cost= 0.089917004 W= 0.31332 b= 0.343025\n", + "Epoch: 1000 cost= 0.088419855 W= 0.30953 b= 0.370291\n", "Optimization Finished!\n", - "Training cost= 0.0895788 W= 0.312484 b= 0.349041 \n", + "Training cost= 0.0884199 W= 0.30953 b= 0.370291 \n", "\n" ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFkCAYAAACq4KjhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niEhEShJEREQkov8PMJtz3b7pz2EAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -203,7 +213,7 @@ ], "metadata": { "kernelspec": { - "display_name": "IPython (Python 2.7)", + "display_name": "Python 2", "language": "python", "name": "python2" }, @@ -217,7 +227,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.13" } }, "nbformat": 4, From 7839ba225b2ec36e266ab47d32810f278d2a0408 Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sun, 15 Jan 2017 02:50:45 +1000 Subject: [PATCH 072/166] Refactor logistic_regression for TF1.0 Signed-off-by: Norman Heckscher --- examples/2_BasicModels/logistic_regression.py | 2 +- .../2_BasicModels/logistic_regression.ipynb | 77 +++++++++---------- 2 files changed, 39 insertions(+), 40 deletions(-) diff --git a/examples/2_BasicModels/logistic_regression.py b/examples/2_BasicModels/logistic_regression.py index c2af99c0..e8b5c89e 100644 --- a/examples/2_BasicModels/logistic_regression.py +++ b/examples/2_BasicModels/logistic_regression.py @@ -38,7 +38,7 @@ optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/notebooks/2_BasicModels/logistic_regression.ipynb b/notebooks/2_BasicModels/logistic_regression.ipynb index 8314dd9a..61215954 100644 --- a/notebooks/2_BasicModels/logistic_regression.ipynb +++ b/notebooks/2_BasicModels/logistic_regression.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -27,10 +27,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", + "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], @@ -39,14 +39,14 @@ "\n", "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -73,12 +73,12 @@ "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false }, @@ -87,33 +87,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch: 0001 cost= 1.182138961\n", - "Epoch: 0002 cost= 0.664670898\n", - "Epoch: 0003 cost= 0.552613988\n", - "Epoch: 0004 cost= 0.498497931\n", - "Epoch: 0005 cost= 0.465418769\n", - "Epoch: 0006 cost= 0.442546219\n", - "Epoch: 0007 cost= 0.425473814\n", - "Epoch: 0008 cost= 0.412171735\n", - "Epoch: 0009 cost= 0.401359516\n", - "Epoch: 0010 cost= 0.392401536\n", - "Epoch: 0011 cost= 0.384750201\n", - "Epoch: 0012 cost= 0.378185581\n", - "Epoch: 0013 cost= 0.372401533\n", - "Epoch: 0014 cost= 0.367302442\n", - "Epoch: 0015 cost= 0.362702316\n", - "Epoch: 0016 cost= 0.358568827\n", - "Epoch: 0017 cost= 0.354882155\n", - "Epoch: 0018 cost= 0.351430912\n", - "Epoch: 0019 cost= 0.348316068\n", - "Epoch: 0020 cost= 0.345392556\n", - "Epoch: 0021 cost= 0.342737278\n", - "Epoch: 0022 cost= 0.340264994\n", - "Epoch: 0023 cost= 0.337890242\n", - "Epoch: 0024 cost= 0.335708558\n", - "Epoch: 0025 cost= 0.333686476\n", - "Optimization Finished!\n", - "Accuracy: 0.889667\n" + "Epoch: 0001 cost= 1.182138959\n", + "Epoch: 0002 cost= 0.664778162\n", + "Epoch: 0003 cost= 0.552686284\n", + "Epoch: 0004 cost= 0.498628905\n", + "Epoch: 0005 cost= 0.465469866\n", + "Epoch: 0006 cost= 0.442537872\n", + "Epoch: 0007 cost= 0.425462044\n", + "Epoch: 0008 cost= 0.412185303\n", + "Epoch: 0009 cost= 0.401311587\n", + "Epoch: 0010 cost= 0.392326203\n", + "Epoch: 0011 cost= 0.384736038\n", + "Epoch: 0012 cost= 0.378137191\n", + "Epoch: 0013 cost= 0.372363752\n", + "Epoch: 0014 cost= 0.367308579\n", + "Epoch: 0015 cost= 0.362704660\n", + "Epoch: 0016 cost= 0.358588599\n", + "Epoch: 0017 cost= 0.354823110\n" ] } ], @@ -146,6 +136,15 @@ " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " print \"Accuracy:\", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]})" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -157,16 +156,16 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 8e0382318172f1ff7fdccf998fd56bde00f9be8d Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sun, 15 Jan 2017 02:56:55 +1000 Subject: [PATCH 073/166] Refactor nearest_neighbor for TF1.0 Signed-off-by: Norman Heckscher --- examples/2_BasicModels/nearest_neighbor.py | 4 +- .../2_BasicModels/nearest_neighbor.ipynb | 37 ++++++++++++------- 2 files changed, 25 insertions(+), 16 deletions(-) diff --git a/examples/2_BasicModels/nearest_neighbor.py b/examples/2_BasicModels/nearest_neighbor.py index f11efcdb..53427469 100644 --- a/examples/2_BasicModels/nearest_neighbor.py +++ b/examples/2_BasicModels/nearest_neighbor.py @@ -26,14 +26,14 @@ # Nearest Neighbor calculation using L1 Distance # Calculate L1 Distance -distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.neg(xte))), reduction_indices=1) +distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1) # Prediction: Get min distance index (Nearest neighbor) pred = tf.arg_min(distance, 0) accuracy = 0. # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/notebooks/2_BasicModels/nearest_neighbor.ipynb b/notebooks/2_BasicModels/nearest_neighbor.ipynb index f75a9d13..c66e43a3 100644 --- a/notebooks/2_BasicModels/nearest_neighbor.ipynb +++ b/notebooks/2_BasicModels/nearest_neighbor.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -27,10 +27,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", + "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], @@ -40,14 +40,14 @@ "\n", "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -61,19 +61,19 @@ "\n", "# Nearest Neighbor calculation using L1 Distance\n", "# Calculate L1 Distance\n", - "distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.neg(xte))), reduction_indices=1)\n", + "distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)\n", "# Prediction: Get min distance index (Nearest neighbor)\n", "pred = tf.arg_min(distance, 0)\n", "\n", "accuracy = 0.\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -305,6 +305,15 @@ " print \"Done!\"\n", " print \"Accuracy:\", accuracy" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -316,16 +325,16 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 90f22bc4eb96c70dcdcc7aa94ad3e93b5aff7c02 Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sun, 15 Jan 2017 03:02:51 +1000 Subject: [PATCH 074/166] Refactor multilayer_perceptron for TF1.0 Signed-off-by: Norman Heckscher --- .../3_NeuralNetworks/multilayer_perceptron.py | 4 +- .../multilayer_perceptron.ipynb | 67 +++++++++++-------- 2 files changed, 40 insertions(+), 31 deletions(-) diff --git a/examples/3_NeuralNetworks/multilayer_perceptron.py b/examples/3_NeuralNetworks/multilayer_perceptron.py index b5c990f3..f9f9c683 100644 --- a/examples/3_NeuralNetworks/multilayer_perceptron.py +++ b/examples/3_NeuralNetworks/multilayer_perceptron.py @@ -60,11 +60,11 @@ def multilayer_perceptron(x, weights, biases): pred = multilayer_perceptron(x, weights, biases) # Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Initializing the variables -init = tf.initialize_all_variables() +init = tf.global_variables_initializer() # Launch the graph with tf.Session() as sess: diff --git a/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb b/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb index 6ec369e6..728535fb 100644 --- a/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb +++ b/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb @@ -29,17 +29,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", - "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", - "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", + "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n", "\n", "import tensorflow as tf" ] @@ -92,9 +92,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -114,16 +114,16 @@ "pred = multilayer_perceptron(x, weights, biases)\n", "\n", "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", "\n", "# Initializing the variables\n", - "init = tf.initialize_all_variables()" + "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -132,23 +132,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch: 0001 cost= 185.342230390\n", - "Epoch: 0002 cost= 44.266946572\n", - "Epoch: 0003 cost= 27.999560453\n", - "Epoch: 0004 cost= 19.655567043\n", - "Epoch: 0005 cost= 14.284429696\n", - "Epoch: 0006 cost= 10.640310403\n", - "Epoch: 0007 cost= 7.904047886\n", - "Epoch: 0008 cost= 5.989115090\n", - "Epoch: 0009 cost= 4.689374613\n", - "Epoch: 0010 cost= 3.455884229\n", - "Epoch: 0011 cost= 2.733002625\n", - "Epoch: 0012 cost= 2.101091420\n", - "Epoch: 0013 cost= 1.496508092\n", - "Epoch: 0014 cost= 1.245452015\n", - "Epoch: 0015 cost= 0.912072906\n", + "Epoch: 0001 cost= 173.056566575\n", + "Epoch: 0002 cost= 44.054413928\n", + "Epoch: 0003 cost= 27.455470655\n", + "Epoch: 0004 cost= 19.008652363\n", + "Epoch: 0005 cost= 13.654873594\n", + "Epoch: 0006 cost= 10.059267435\n", + "Epoch: 0007 cost= 7.436018432\n", + "Epoch: 0008 cost= 5.587794416\n", + "Epoch: 0009 cost= 4.209882509\n", + "Epoch: 0010 cost= 3.203879515\n", + "Epoch: 0011 cost= 2.319920681\n", + "Epoch: 0012 cost= 1.676204545\n", + "Epoch: 0013 cost= 1.248805338\n", + "Epoch: 0014 cost= 1.052676844\n", + "Epoch: 0015 cost= 0.890117338\n", "Optimization Finished!\n", - "Accuracy: 0.9422\n" + "Accuracy: 0.9459\n" ] } ], @@ -181,6 +181,15 @@ " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", " print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -192,16 +201,16 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 9650209da864e3b5c7b992fd48e1db9e8f348330 Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Sun, 15 Jan 2017 03:06:52 +1000 Subject: [PATCH 075/166] Update dependencies Signed-off-by: Norman Heckscher --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index edc03d30..7e65f7c5 100644 --- a/README.md +++ b/README.md @@ -98,7 +98,7 @@ The following examples are coming from [TFLearn](https://github.com/tflearn/tfle ## Dependencies ``` -tensorflow +tensorflow 1.0alpha numpy matplotlib cuda From ab15e286e7aa47b102e7725171bd28260d968bef Mon Sep 17 00:00:00 2001 From: Norman Heckscher Date: Tue, 17 Jan 2017 19:10:21 +1000 Subject: [PATCH 076/166] Add runtime output to notebooks. Signed-off-by: Norman Heckscher --- .../3_NeuralNetworks/bidirectional_rnn.ipynb | 91 +++++++++- .../convolutional_network.ipynb | 169 +++++++++++++++++- .../3_NeuralNetworks/recurrent_network.ipynb | 91 +++++++++- 3 files changed, 345 insertions(+), 6 deletions(-) diff --git a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb index 14ea5664..b43cebff 100644 --- a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -134,11 +134,98 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iter 1280, Minibatch Loss= 1.557283, Training Accuracy= 0.49219\n", + "Iter 2560, Minibatch Loss= 1.358445, Training Accuracy= 0.56250\n", + "Iter 3840, Minibatch Loss= 1.043732, Training Accuracy= 0.64062\n", + "Iter 5120, Minibatch Loss= 0.796770, Training Accuracy= 0.72656\n", + "Iter 6400, Minibatch Loss= 0.626206, Training Accuracy= 0.72656\n", + "Iter 7680, Minibatch Loss= 1.025919, Training Accuracy= 0.65625\n", + "Iter 8960, Minibatch Loss= 0.744850, Training Accuracy= 0.76562\n", + "Iter 10240, Minibatch Loss= 0.530111, Training Accuracy= 0.84375\n", + "Iter 11520, Minibatch Loss= 0.383806, Training Accuracy= 0.86719\n", + "Iter 12800, Minibatch Loss= 0.607816, Training Accuracy= 0.82812\n", + "Iter 14080, Minibatch Loss= 0.410879, Training Accuracy= 0.89062\n", + "Iter 15360, Minibatch Loss= 0.335351, Training Accuracy= 0.89844\n", + "Iter 16640, Minibatch Loss= 0.428004, Training Accuracy= 0.91406\n", + "Iter 17920, Minibatch Loss= 0.307468, Training Accuracy= 0.91406\n", + "Iter 19200, Minibatch Loss= 0.249527, Training Accuracy= 0.92188\n", + "Iter 20480, Minibatch Loss= 0.148163, Training Accuracy= 0.96094\n", + "Iter 21760, Minibatch Loss= 0.445275, Training Accuracy= 0.83594\n", + "Iter 23040, Minibatch Loss= 0.173083, Training Accuracy= 0.93750\n", + "Iter 24320, Minibatch Loss= 0.373696, Training Accuracy= 0.87500\n", + "Iter 25600, Minibatch Loss= 0.509869, Training Accuracy= 0.85938\n", + "Iter 26880, Minibatch Loss= 0.198096, Training Accuracy= 0.92969\n", + "Iter 28160, Minibatch Loss= 0.228221, Training Accuracy= 0.92188\n", + "Iter 29440, Minibatch Loss= 0.280088, Training Accuracy= 0.89844\n", + "Iter 30720, Minibatch Loss= 0.300495, Training Accuracy= 0.91406\n", + "Iter 32000, Minibatch Loss= 0.171746, Training Accuracy= 0.95312\n", + "Iter 33280, Minibatch Loss= 0.263745, Training Accuracy= 0.89844\n", + "Iter 34560, Minibatch Loss= 0.177300, Training Accuracy= 0.93750\n", + "Iter 35840, Minibatch Loss= 0.160621, Training Accuracy= 0.95312\n", + "Iter 37120, Minibatch Loss= 0.321745, Training Accuracy= 0.91406\n", + "Iter 38400, Minibatch Loss= 0.188322, Training Accuracy= 0.93750\n", + "Iter 39680, Minibatch Loss= 0.104025, Training Accuracy= 0.96875\n", + "Iter 40960, Minibatch Loss= 0.291053, Training Accuracy= 0.89062\n", + "Iter 42240, Minibatch Loss= 0.131189, Training Accuracy= 0.95312\n", + "Iter 43520, Minibatch Loss= 0.154949, Training Accuracy= 0.92969\n", + "Iter 44800, Minibatch Loss= 0.150411, Training Accuracy= 0.93750\n", + "Iter 46080, Minibatch Loss= 0.117008, Training Accuracy= 0.96094\n", + "Iter 47360, Minibatch Loss= 0.181344, Training Accuracy= 0.96094\n", + "Iter 48640, Minibatch Loss= 0.209197, Training Accuracy= 0.94531\n", + "Iter 49920, Minibatch Loss= 0.159350, Training Accuracy= 0.96094\n", + "Iter 51200, Minibatch Loss= 0.124001, Training Accuracy= 0.95312\n", + "Iter 52480, Minibatch Loss= 0.165183, Training Accuracy= 0.94531\n", + "Iter 53760, Minibatch Loss= 0.046438, Training Accuracy= 0.97656\n", + "Iter 55040, Minibatch Loss= 0.199995, Training Accuracy= 0.91406\n", + "Iter 56320, Minibatch Loss= 0.057071, Training Accuracy= 0.97656\n", + "Iter 57600, Minibatch Loss= 0.177065, Training Accuracy= 0.92188\n", + "Iter 58880, Minibatch Loss= 0.091666, Training Accuracy= 0.96094\n", + "Iter 60160, Minibatch Loss= 0.069232, Training Accuracy= 0.96875\n", + "Iter 61440, Minibatch Loss= 0.127353, Training Accuracy= 0.94531\n", + "Iter 62720, Minibatch Loss= 0.095795, Training Accuracy= 0.96094\n", + "Iter 64000, Minibatch Loss= 0.202651, Training Accuracy= 0.96875\n", + "Iter 65280, Minibatch Loss= 0.118779, Training Accuracy= 0.95312\n", + "Iter 66560, Minibatch Loss= 0.043173, Training Accuracy= 0.98438\n", + "Iter 67840, Minibatch Loss= 0.152280, Training Accuracy= 0.95312\n", + "Iter 69120, Minibatch Loss= 0.085301, Training Accuracy= 0.96875\n", + "Iter 70400, Minibatch Loss= 0.093421, Training Accuracy= 0.96094\n", + "Iter 71680, Minibatch Loss= 0.096358, Training Accuracy= 0.96875\n", + "Iter 72960, Minibatch Loss= 0.053386, Training Accuracy= 0.98438\n", + "Iter 74240, Minibatch Loss= 0.065237, Training Accuracy= 0.97656\n", + "Iter 75520, Minibatch Loss= 0.228090, Training Accuracy= 0.92188\n", + "Iter 76800, Minibatch Loss= 0.106751, Training Accuracy= 0.95312\n", + "Iter 78080, Minibatch Loss= 0.187795, Training Accuracy= 0.94531\n", + "Iter 79360, Minibatch Loss= 0.092611, Training Accuracy= 0.96094\n", + "Iter 80640, Minibatch Loss= 0.137386, Training Accuracy= 0.96875\n", + "Iter 81920, Minibatch Loss= 0.106634, Training Accuracy= 0.98438\n", + "Iter 83200, Minibatch Loss= 0.111749, Training Accuracy= 0.94531\n", + "Iter 84480, Minibatch Loss= 0.191184, Training Accuracy= 0.94531\n", + "Iter 85760, Minibatch Loss= 0.063982, Training Accuracy= 0.96094\n", + "Iter 87040, Minibatch Loss= 0.092380, Training Accuracy= 0.96875\n", + "Iter 88320, Minibatch Loss= 0.089899, Training Accuracy= 0.97656\n", + "Iter 89600, Minibatch Loss= 0.141107, Training Accuracy= 0.94531\n", + "Iter 90880, Minibatch Loss= 0.075549, Training Accuracy= 0.96094\n", + "Iter 92160, Minibatch Loss= 0.186539, Training Accuracy= 0.94531\n", + "Iter 93440, Minibatch Loss= 0.079639, Training Accuracy= 0.97656\n", + "Iter 94720, Minibatch Loss= 0.156895, Training Accuracy= 0.95312\n", + "Iter 96000, Minibatch Loss= 0.088042, Training Accuracy= 0.97656\n", + "Iter 97280, Minibatch Loss= 0.076670, Training Accuracy= 0.96875\n", + "Iter 98560, Minibatch Loss= 0.051336, Training Accuracy= 0.97656\n", + "Iter 99840, Minibatch Loss= 0.086923, Training Accuracy= 0.98438\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.960938\n" + ] + } + ], "source": [ "# Launch the graph\n", "with tf.Session() as sess:\n", diff --git a/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/notebooks/3_NeuralNetworks/convolutional_network.ipynb index ce10c034..c566c4a5 100644 --- a/notebooks/3_NeuralNetworks/convolutional_network.ipynb +++ b/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -152,11 +152,176 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iter 1280, Minibatch Loss= 26574.855469, Training Accuracy= 0.25781\n", + "Iter 2560, Minibatch Loss= 11454.494141, Training Accuracy= 0.49219\n", + "Iter 3840, Minibatch Loss= 10070.515625, Training Accuracy= 0.55469\n", + "Iter 5120, Minibatch Loss= 4008.586426, Training Accuracy= 0.78125\n", + "Iter 6400, Minibatch Loss= 3148.004639, Training Accuracy= 0.80469\n", + "Iter 7680, Minibatch Loss= 6740.440430, Training Accuracy= 0.71875\n", + "Iter 8960, Minibatch Loss= 4103.991699, Training Accuracy= 0.80469\n", + "Iter 10240, Minibatch Loss= 2631.275391, Training Accuracy= 0.85938\n", + "Iter 11520, Minibatch Loss= 1428.798828, Training Accuracy= 0.91406\n", + "Iter 12800, Minibatch Loss= 3909.772705, Training Accuracy= 0.78906\n", + "Iter 14080, Minibatch Loss= 1423.095947, Training Accuracy= 0.88281\n", + "Iter 15360, Minibatch Loss= 1524.569824, Training Accuracy= 0.89062\n", + "Iter 16640, Minibatch Loss= 2234.539795, Training Accuracy= 0.86719\n", + "Iter 17920, Minibatch Loss= 933.932800, Training Accuracy= 0.90625\n", + "Iter 19200, Minibatch Loss= 2039.046021, Training Accuracy= 0.89062\n", + "Iter 20480, Minibatch Loss= 674.179932, Training Accuracy= 0.95312\n", + "Iter 21760, Minibatch Loss= 3778.958984, Training Accuracy= 0.82812\n", + "Iter 23040, Minibatch Loss= 1038.217773, Training Accuracy= 0.91406\n", + "Iter 24320, Minibatch Loss= 1689.513672, Training Accuracy= 0.89062\n", + "Iter 25600, Minibatch Loss= 1800.954956, Training Accuracy= 0.85938\n", + "Iter 26880, Minibatch Loss= 1086.292847, Training Accuracy= 0.90625\n", + "Iter 28160, Minibatch Loss= 656.042847, Training Accuracy= 0.94531\n", + "Iter 29440, Minibatch Loss= 1210.589844, Training Accuracy= 0.91406\n", + "Iter 30720, Minibatch Loss= 1099.606323, Training Accuracy= 0.90625\n", + "Iter 32000, Minibatch Loss= 1073.128174, Training Accuracy= 0.92969\n", + "Iter 33280, Minibatch Loss= 518.844543, Training Accuracy= 0.95312\n", + "Iter 34560, Minibatch Loss= 540.856689, Training Accuracy= 0.92188\n", + "Iter 35840, Minibatch Loss= 353.990906, Training Accuracy= 0.97656\n", + "Iter 37120, Minibatch Loss= 1488.962891, Training Accuracy= 0.91406\n", + "Iter 38400, Minibatch Loss= 231.191864, Training Accuracy= 0.98438\n", + "Iter 39680, Minibatch Loss= 171.154480, Training Accuracy= 0.98438\n", + "Iter 40960, Minibatch Loss= 2092.023682, Training Accuracy= 0.90625\n", + "Iter 42240, Minibatch Loss= 480.594299, Training Accuracy= 0.95312\n", + "Iter 43520, Minibatch Loss= 504.128143, Training Accuracy= 0.96875\n", + "Iter 44800, Minibatch Loss= 143.534485, Training Accuracy= 0.97656\n", + "Iter 46080, Minibatch Loss= 325.875580, Training Accuracy= 0.96094\n", + "Iter 47360, Minibatch Loss= 602.813049, Training Accuracy= 0.91406\n", + "Iter 48640, Minibatch Loss= 794.595093, Training Accuracy= 0.94531\n", + "Iter 49920, Minibatch Loss= 415.539032, Training Accuracy= 0.95312\n", + "Iter 51200, Minibatch Loss= 146.016022, Training Accuracy= 0.96094\n", + "Iter 52480, Minibatch Loss= 294.180786, Training Accuracy= 0.94531\n", + "Iter 53760, Minibatch Loss= 50.955730, Training Accuracy= 0.99219\n", + "Iter 55040, Minibatch Loss= 1026.607056, Training Accuracy= 0.92188\n", + "Iter 56320, Minibatch Loss= 283.756134, Training Accuracy= 0.96875\n", + "Iter 57600, Minibatch Loss= 691.538208, Training Accuracy= 0.95312\n", + "Iter 58880, Minibatch Loss= 491.075073, Training Accuracy= 0.96094\n", + "Iter 60160, Minibatch Loss= 571.951660, Training Accuracy= 0.95312\n", + "Iter 61440, Minibatch Loss= 284.041168, Training Accuracy= 0.97656\n", + "Iter 62720, Minibatch Loss= 1041.941528, Training Accuracy= 0.92969\n", + "Iter 64000, Minibatch Loss= 664.833923, Training Accuracy= 0.93750\n", + "Iter 65280, Minibatch Loss= 1582.112793, Training Accuracy= 0.88281\n", + "Iter 66560, Minibatch Loss= 783.135376, Training Accuracy= 0.94531\n", + "Iter 67840, Minibatch Loss= 245.942398, Training Accuracy= 0.96094\n", + "Iter 69120, Minibatch Loss= 752.858948, Training Accuracy= 0.96875\n", + "Iter 70400, Minibatch Loss= 623.243286, Training Accuracy= 0.94531\n", + "Iter 71680, Minibatch Loss= 846.498230, Training Accuracy= 0.93750\n", + "Iter 72960, Minibatch Loss= 586.516479, Training Accuracy= 0.95312\n", + "Iter 74240, Minibatch Loss= 92.774963, Training Accuracy= 0.98438\n", + "Iter 75520, Minibatch Loss= 644.039612, Training Accuracy= 0.95312\n", + "Iter 76800, Minibatch Loss= 693.247681, Training Accuracy= 0.96094\n", + "Iter 78080, Minibatch Loss= 466.491882, Training Accuracy= 0.96094\n", + "Iter 79360, Minibatch Loss= 964.212341, Training Accuracy= 0.93750\n", + "Iter 80640, Minibatch Loss= 230.451904, Training Accuracy= 0.97656\n", + "Iter 81920, Minibatch Loss= 280.434570, Training Accuracy= 0.95312\n", + "Iter 83200, Minibatch Loss= 213.208252, Training Accuracy= 0.97656\n", + "Iter 84480, Minibatch Loss= 774.836060, Training Accuracy= 0.94531\n", + "Iter 85760, Minibatch Loss= 164.687729, Training Accuracy= 0.96094\n", + "Iter 87040, Minibatch Loss= 419.967407, Training Accuracy= 0.96875\n", + "Iter 88320, Minibatch Loss= 160.920151, Training Accuracy= 0.96875\n", + "Iter 89600, Minibatch Loss= 586.063599, Training Accuracy= 0.96094\n", + "Iter 90880, Minibatch Loss= 345.598145, Training Accuracy= 0.96875\n", + "Iter 92160, Minibatch Loss= 931.361145, Training Accuracy= 0.92188\n", + "Iter 93440, Minibatch Loss= 170.107117, Training Accuracy= 0.97656\n", + "Iter 94720, Minibatch Loss= 497.162750, Training Accuracy= 0.93750\n", + "Iter 96000, Minibatch Loss= 906.600464, Training Accuracy= 0.94531\n", + "Iter 97280, Minibatch Loss= 303.382202, Training Accuracy= 0.92969\n", + "Iter 98560, Minibatch Loss= 509.161652, Training Accuracy= 0.97656\n", + "Iter 99840, Minibatch Loss= 359.561981, Training Accuracy= 0.97656\n", + "Iter 101120, Minibatch Loss= 136.516541, Training Accuracy= 0.97656\n", + "Iter 102400, Minibatch Loss= 517.199341, Training Accuracy= 0.96875\n", + "Iter 103680, Minibatch Loss= 487.793335, Training Accuracy= 0.95312\n", + "Iter 104960, Minibatch Loss= 407.351929, Training Accuracy= 0.96094\n", + "Iter 106240, Minibatch Loss= 70.495193, Training Accuracy= 0.98438\n", + "Iter 107520, Minibatch Loss= 344.783508, Training Accuracy= 0.96094\n", + "Iter 108800, Minibatch Loss= 242.682465, Training Accuracy= 0.95312\n", + "Iter 110080, Minibatch Loss= 169.181458, Training Accuracy= 0.96094\n", + "Iter 111360, Minibatch Loss= 152.638245, Training Accuracy= 0.98438\n", + "Iter 112640, Minibatch Loss= 170.795868, Training Accuracy= 0.96875\n", + "Iter 113920, Minibatch Loss= 133.262726, Training Accuracy= 0.98438\n", + "Iter 115200, Minibatch Loss= 296.063293, Training Accuracy= 0.95312\n", + "Iter 116480, Minibatch Loss= 254.247543, Training Accuracy= 0.96094\n", + "Iter 117760, Minibatch Loss= 506.795715, Training Accuracy= 0.94531\n", + "Iter 119040, Minibatch Loss= 446.006897, Training Accuracy= 0.96094\n", + "Iter 120320, Minibatch Loss= 149.467377, Training Accuracy= 0.97656\n", + "Iter 121600, Minibatch Loss= 52.783600, Training Accuracy= 0.98438\n", + "Iter 122880, Minibatch Loss= 49.041794, Training Accuracy= 0.98438\n", + "Iter 124160, Minibatch Loss= 184.371246, Training Accuracy= 0.97656\n", + "Iter 125440, Minibatch Loss= 129.838501, Training Accuracy= 0.97656\n", + "Iter 126720, Minibatch Loss= 288.006531, Training Accuracy= 0.96875\n", + "Iter 128000, Minibatch Loss= 187.284653, Training Accuracy= 0.97656\n", + "Iter 129280, Minibatch Loss= 197.969955, Training Accuracy= 0.96875\n", + "Iter 130560, Minibatch Loss= 299.969818, Training Accuracy= 0.96875\n", + "Iter 131840, Minibatch Loss= 537.602173, Training Accuracy= 0.96094\n", + "Iter 133120, Minibatch Loss= 4.519302, Training Accuracy= 0.99219\n", + "Iter 134400, Minibatch Loss= 133.264191, Training Accuracy= 0.97656\n", + "Iter 135680, Minibatch Loss= 89.662292, Training Accuracy= 0.97656\n", + "Iter 136960, Minibatch Loss= 107.774078, Training Accuracy= 0.96875\n", + "Iter 138240, Minibatch Loss= 335.904572, Training Accuracy= 0.96094\n", + "Iter 139520, Minibatch Loss= 457.494568, Training Accuracy= 0.96094\n", + "Iter 140800, Minibatch Loss= 259.131531, Training Accuracy= 0.95312\n", + "Iter 142080, Minibatch Loss= 152.205383, Training Accuracy= 0.96094\n", + "Iter 143360, Minibatch Loss= 252.535828, Training Accuracy= 0.95312\n", + "Iter 144640, Minibatch Loss= 109.477585, Training Accuracy= 0.96875\n", + "Iter 145920, Minibatch Loss= 24.468613, Training Accuracy= 0.99219\n", + "Iter 147200, Minibatch Loss= 51.722107, Training Accuracy= 0.97656\n", + "Iter 148480, Minibatch Loss= 69.715233, Training Accuracy= 0.97656\n", + "Iter 149760, Minibatch Loss= 405.289246, Training Accuracy= 0.92969\n", + "Iter 151040, Minibatch Loss= 282.976379, Training Accuracy= 0.95312\n", + "Iter 152320, Minibatch Loss= 134.991119, Training Accuracy= 0.97656\n", + "Iter 153600, Minibatch Loss= 491.618103, Training Accuracy= 0.92188\n", + "Iter 154880, Minibatch Loss= 154.299988, Training Accuracy= 0.99219\n", + "Iter 156160, Minibatch Loss= 79.480019, Training Accuracy= 0.96875\n", + "Iter 157440, Minibatch Loss= 68.093750, Training Accuracy= 0.99219\n", + "Iter 158720, Minibatch Loss= 459.739685, Training Accuracy= 0.92188\n", + "Iter 160000, Minibatch Loss= 168.076843, Training Accuracy= 0.94531\n", + "Iter 161280, Minibatch Loss= 256.141846, Training Accuracy= 0.97656\n", + "Iter 162560, Minibatch Loss= 236.400391, Training Accuracy= 0.94531\n", + "Iter 163840, Minibatch Loss= 177.011261, Training Accuracy= 0.96875\n", + "Iter 165120, Minibatch Loss= 48.583298, Training Accuracy= 0.97656\n", + "Iter 166400, Minibatch Loss= 413.800293, Training Accuracy= 0.96094\n", + "Iter 167680, Minibatch Loss= 209.587387, Training Accuracy= 0.96875\n", + "Iter 168960, Minibatch Loss= 239.407318, Training Accuracy= 0.98438\n", + "Iter 170240, Minibatch Loss= 183.567017, Training Accuracy= 0.96875\n", + "Iter 171520, Minibatch Loss= 87.937515, Training Accuracy= 0.96875\n", + "Iter 172800, Minibatch Loss= 203.777039, Training Accuracy= 0.98438\n", + "Iter 174080, Minibatch Loss= 566.378052, Training Accuracy= 0.94531\n", + "Iter 175360, Minibatch Loss= 325.170898, Training Accuracy= 0.95312\n", + "Iter 176640, Minibatch Loss= 300.142212, Training Accuracy= 0.97656\n", + "Iter 177920, Minibatch Loss= 205.370193, Training Accuracy= 0.95312\n", + "Iter 179200, Minibatch Loss= 5.594437, Training Accuracy= 0.99219\n", + "Iter 180480, Minibatch Loss= 110.732109, Training Accuracy= 0.98438\n", + "Iter 181760, Minibatch Loss= 33.320297, Training Accuracy= 0.99219\n", + "Iter 183040, Minibatch Loss= 6.885544, Training Accuracy= 0.99219\n", + "Iter 184320, Minibatch Loss= 221.144806, Training Accuracy= 0.96875\n", + "Iter 185600, Minibatch Loss= 365.337372, Training Accuracy= 0.94531\n", + "Iter 186880, Minibatch Loss= 186.558258, Training Accuracy= 0.96094\n", + "Iter 188160, Minibatch Loss= 149.720322, Training Accuracy= 0.98438\n", + "Iter 189440, Minibatch Loss= 105.281998, Training Accuracy= 0.97656\n", + "Iter 190720, Minibatch Loss= 289.980011, Training Accuracy= 0.96094\n", + "Iter 192000, Minibatch Loss= 214.382278, Training Accuracy= 0.96094\n", + "Iter 193280, Minibatch Loss= 461.044312, Training Accuracy= 0.93750\n", + "Iter 194560, Minibatch Loss= 138.653076, Training Accuracy= 0.98438\n", + "Iter 195840, Minibatch Loss= 112.004883, Training Accuracy= 0.98438\n", + "Iter 197120, Minibatch Loss= 212.691467, Training Accuracy= 0.97656\n", + "Iter 198400, Minibatch Loss= 57.642502, Training Accuracy= 0.97656\n", + "Iter 199680, Minibatch Loss= 80.503563, Training Accuracy= 0.96875\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.984375\n" + ] + } + ], "source": [ "# Launch the graph\n", "with tf.Session() as sess:\n", diff --git a/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/notebooks/3_NeuralNetworks/recurrent_network.ipynb index 9a7b9b1e..f5f44d68 100644 --- a/notebooks/3_NeuralNetworks/recurrent_network.ipynb +++ b/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -125,11 +125,98 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iter 1280, Minibatch Loss= 1.576423, Training Accuracy= 0.51562\n", + "Iter 2560, Minibatch Loss= 1.450179, Training Accuracy= 0.53906\n", + "Iter 3840, Minibatch Loss= 1.160066, Training Accuracy= 0.64844\n", + "Iter 5120, Minibatch Loss= 0.898589, Training Accuracy= 0.73438\n", + "Iter 6400, Minibatch Loss= 0.685712, Training Accuracy= 0.75781\n", + "Iter 7680, Minibatch Loss= 1.085666, Training Accuracy= 0.64844\n", + "Iter 8960, Minibatch Loss= 0.681488, Training Accuracy= 0.73438\n", + "Iter 10240, Minibatch Loss= 0.557049, Training Accuracy= 0.82812\n", + "Iter 11520, Minibatch Loss= 0.340857, Training Accuracy= 0.92188\n", + "Iter 12800, Minibatch Loss= 0.596482, Training Accuracy= 0.78906\n", + "Iter 14080, Minibatch Loss= 0.486564, Training Accuracy= 0.84375\n", + "Iter 15360, Minibatch Loss= 0.302493, Training Accuracy= 0.90625\n", + "Iter 16640, Minibatch Loss= 0.334277, Training Accuracy= 0.92188\n", + "Iter 17920, Minibatch Loss= 0.222026, Training Accuracy= 0.90625\n", + "Iter 19200, Minibatch Loss= 0.228581, Training Accuracy= 0.92188\n", + "Iter 20480, Minibatch Loss= 0.150356, Training Accuracy= 0.96094\n", + "Iter 21760, Minibatch Loss= 0.415417, Training Accuracy= 0.86719\n", + "Iter 23040, Minibatch Loss= 0.159742, Training Accuracy= 0.94531\n", + "Iter 24320, Minibatch Loss= 0.333764, Training Accuracy= 0.89844\n", + "Iter 25600, Minibatch Loss= 0.379070, Training Accuracy= 0.88281\n", + "Iter 26880, Minibatch Loss= 0.241612, Training Accuracy= 0.91406\n", + "Iter 28160, Minibatch Loss= 0.200397, Training Accuracy= 0.93750\n", + "Iter 29440, Minibatch Loss= 0.197994, Training Accuracy= 0.93750\n", + "Iter 30720, Minibatch Loss= 0.330214, Training Accuracy= 0.89062\n", + "Iter 32000, Minibatch Loss= 0.174626, Training Accuracy= 0.92969\n", + "Iter 33280, Minibatch Loss= 0.202369, Training Accuracy= 0.93750\n", + "Iter 34560, Minibatch Loss= 0.240835, Training Accuracy= 0.94531\n", + "Iter 35840, Minibatch Loss= 0.207867, Training Accuracy= 0.93750\n", + "Iter 37120, Minibatch Loss= 0.313306, Training Accuracy= 0.90625\n", + "Iter 38400, Minibatch Loss= 0.089850, Training Accuracy= 0.96875\n", + "Iter 39680, Minibatch Loss= 0.184803, Training Accuracy= 0.92188\n", + "Iter 40960, Minibatch Loss= 0.236523, Training Accuracy= 0.92969\n", + "Iter 42240, Minibatch Loss= 0.174834, Training Accuracy= 0.94531\n", + "Iter 43520, Minibatch Loss= 0.127905, Training Accuracy= 0.93750\n", + "Iter 44800, Minibatch Loss= 0.120045, Training Accuracy= 0.96875\n", + "Iter 46080, Minibatch Loss= 0.068337, Training Accuracy= 0.98438\n", + "Iter 47360, Minibatch Loss= 0.141118, Training Accuracy= 0.95312\n", + "Iter 48640, Minibatch Loss= 0.182404, Training Accuracy= 0.92188\n", + "Iter 49920, Minibatch Loss= 0.176778, Training Accuracy= 0.93750\n", + "Iter 51200, Minibatch Loss= 0.098927, Training Accuracy= 0.97656\n", + "Iter 52480, Minibatch Loss= 0.158776, Training Accuracy= 0.96094\n", + "Iter 53760, Minibatch Loss= 0.031863, Training Accuracy= 0.99219\n", + "Iter 55040, Minibatch Loss= 0.101799, Training Accuracy= 0.96094\n", + "Iter 56320, Minibatch Loss= 0.176387, Training Accuracy= 0.96094\n", + "Iter 57600, Minibatch Loss= 0.096277, Training Accuracy= 0.96875\n", + "Iter 58880, Minibatch Loss= 0.137416, Training Accuracy= 0.94531\n", + "Iter 60160, Minibatch Loss= 0.062801, Training Accuracy= 0.97656\n", + "Iter 61440, Minibatch Loss= 0.036346, Training Accuracy= 0.98438\n", + "Iter 62720, Minibatch Loss= 0.153030, Training Accuracy= 0.92969\n", + "Iter 64000, Minibatch Loss= 0.117716, Training Accuracy= 0.95312\n", + "Iter 65280, Minibatch Loss= 0.048387, Training Accuracy= 0.99219\n", + "Iter 66560, Minibatch Loss= 0.070802, Training Accuracy= 0.97656\n", + "Iter 67840, Minibatch Loss= 0.221085, Training Accuracy= 0.96875\n", + "Iter 69120, Minibatch Loss= 0.184049, Training Accuracy= 0.93750\n", + "Iter 70400, Minibatch Loss= 0.094883, Training Accuracy= 0.95312\n", + "Iter 71680, Minibatch Loss= 0.087278, Training Accuracy= 0.96875\n", + "Iter 72960, Minibatch Loss= 0.153267, Training Accuracy= 0.95312\n", + "Iter 74240, Minibatch Loss= 0.161794, Training Accuracy= 0.94531\n", + "Iter 75520, Minibatch Loss= 0.103779, Training Accuracy= 0.96875\n", + "Iter 76800, Minibatch Loss= 0.165586, Training Accuracy= 0.96094\n", + "Iter 78080, Minibatch Loss= 0.137721, Training Accuracy= 0.95312\n", + "Iter 79360, Minibatch Loss= 0.124014, Training Accuracy= 0.96094\n", + "Iter 80640, Minibatch Loss= 0.051460, Training Accuracy= 0.99219\n", + "Iter 81920, Minibatch Loss= 0.185836, Training Accuracy= 0.96094\n", + "Iter 83200, Minibatch Loss= 0.147694, Training Accuracy= 0.94531\n", + "Iter 84480, Minibatch Loss= 0.061550, Training Accuracy= 0.98438\n", + "Iter 85760, Minibatch Loss= 0.093457, Training Accuracy= 0.96875\n", + "Iter 87040, Minibatch Loss= 0.094497, Training Accuracy= 0.98438\n", + "Iter 88320, Minibatch Loss= 0.093934, Training Accuracy= 0.96094\n", + "Iter 89600, Minibatch Loss= 0.061550, Training Accuracy= 0.96875\n", + "Iter 90880, Minibatch Loss= 0.082452, Training Accuracy= 0.97656\n", + "Iter 92160, Minibatch Loss= 0.087423, Training Accuracy= 0.97656\n", + "Iter 93440, Minibatch Loss= 0.032694, Training Accuracy= 0.99219\n", + "Iter 94720, Minibatch Loss= 0.069597, Training Accuracy= 0.97656\n", + "Iter 96000, Minibatch Loss= 0.193636, Training Accuracy= 0.96094\n", + "Iter 97280, Minibatch Loss= 0.134405, Training Accuracy= 0.96094\n", + "Iter 98560, Minibatch Loss= 0.072992, Training Accuracy= 0.96875\n", + "Iter 99840, Minibatch Loss= 0.041049, Training Accuracy= 0.99219\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.960938\n" + ] + } + ], "source": [ "# Launch the graph\n", "with tf.Session() as sess:\n", From 5eeee53bc15dc33161496e4bb7bfa518b270f644 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Thu, 19 Jan 2017 12:24:28 -0800 Subject: [PATCH 077/166] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 7e65f7c5..b03781f3 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,8 @@ TensorFlow Tutorial with popular machine learning algorithms implementation. Thi It is suitable for beginners who want to find clear and concise examples about TensorFlow. For readability, the tutorial includes both notebook and code with explanations. +Note: If you are using older TensorFlow version (before 0.12), please have a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11) + ## Tutorial index #### 0 - Prerequisite From 8f0d6daa367c0333abafb6aadbf3066422964039 Mon Sep 17 00:00:00 2001 From: Vikram Date: Sat, 25 Feb 2017 00:42:04 -0800 Subject: [PATCH 078/166] Update basic_operations.ipynb --- notebooks/1_Introduction/basic_operations.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/1_Introduction/basic_operations.ipynb b/notebooks/1_Introduction/basic_operations.ipynb index 92b06f5d..ee8ea8e3 100644 --- a/notebooks/1_Introduction/basic_operations.ipynb +++ b/notebooks/1_Introduction/basic_operations.ipynb @@ -59,7 +59,7 @@ "source": [ "# Launch the default graph.\n", "with tf.Session() as sess:\n", - " print \"a=2, b=3\"\n", + " print \"a: %i\" % sess.run(a), \"b: %i\" % sess.run(b)\n", " print \"Addition with constants: %i\" % sess.run(a+b)\n", " print \"Multiplication with constants: %i\" % sess.run(a*b)" ] @@ -217,4 +217,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 4c844fbd0b4fe38c812df329b994351fad491274 Mon Sep 17 00:00:00 2001 From: Vikram Date: Sat, 25 Feb 2017 00:44:22 -0800 Subject: [PATCH 079/166] Update basic_operations.ipynb --- notebooks/1_Introduction/basic_operations.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/1_Introduction/basic_operations.ipynb b/notebooks/1_Introduction/basic_operations.ipynb index 92b06f5d..c77b1885 100644 --- a/notebooks/1_Introduction/basic_operations.ipynb +++ b/notebooks/1_Introduction/basic_operations.ipynb @@ -90,7 +90,7 @@ "source": [ "# Define some operations\n", "add = tf.add(a, b)\n", - "mul = tf.mul(a, b)" + "mul = tf.multiply(a, b)" ] }, { @@ -217,4 +217,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 392a667688b9636b068c15cad756de3cde48da79 Mon Sep 17 00:00:00 2001 From: xiaodepei <990834049@qq.com> Date: Fri, 3 Mar 2017 17:10:36 +0800 Subject: [PATCH 080/166] Update linear_regression.py change mul into multiply --- examples/2_BasicModels/linear_regression.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/2_BasicModels/linear_regression.py b/examples/2_BasicModels/linear_regression.py index b23d11cb..0c07eb84 100644 --- a/examples/2_BasicModels/linear_regression.py +++ b/examples/2_BasicModels/linear_regression.py @@ -33,7 +33,7 @@ b = tf.Variable(rng.randn(), name="bias") # Construct a linear model -pred = tf.add(tf.mul(X, W), b) +pred = tf.add(tf.multiply(X, W), b) # Mean squared error cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples) From 1abb7222d618e51798b1364dd45b2b19f2761dec Mon Sep 17 00:00:00 2001 From: Martial Hue Date: Thu, 9 Mar 2017 19:36:48 +0100 Subject: [PATCH 081/166] Upgrade dynamic RNN for TensorFlow 1.0 --- examples/3_NeuralNetworks/dynamic_rnn.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/3_NeuralNetworks/dynamic_rnn.py b/examples/3_NeuralNetworks/dynamic_rnn.py index 11b952d7..5219c2f9 100644 --- a/examples/3_NeuralNetworks/dynamic_rnn.py +++ b/examples/3_NeuralNetworks/dynamic_rnn.py @@ -119,14 +119,14 @@ def dynamicRNN(x, seqlen, weights, biases): # Reshaping to (n_steps*batch_size, n_input) x = tf.reshape(x, [-1, 1]) # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) - x = tf.split(0, seq_max_len, x) + x = tf.split(axis=0, num_or_size_splits=seq_max_len, value=x) # Define a lstm cell with tensorflow - lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden) + lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden) # Get lstm cell output, providing 'sequence_length' will perform dynamic # calculation. - outputs, states = tf.nn.rnn(lstm_cell, x, dtype=tf.float32, + outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x, dtype=tf.float32, sequence_length=seqlen) # When performing dynamic calculation, we must retrieve the last @@ -138,7 +138,7 @@ def dynamicRNN(x, seqlen, weights, biases): # 'outputs' is a list of output at every timestep, we pack them in a Tensor # and change back dimension to [batch_size, n_step, n_input] - outputs = tf.pack(outputs) + outputs = tf.stack(outputs) outputs = tf.transpose(outputs, [1, 0, 2]) # Hack to build the indexing and retrieve the right output. @@ -154,7 +154,7 @@ def dynamicRNN(x, seqlen, weights, biases): pred = dynamicRNN(x, seqlen, weights, biases) # Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost) # Evaluate model From 0e9cf03ec7154a695f817ed66f2bdc93734c7e72 Mon Sep 17 00:00:00 2001 From: Ashish Baghudana Date: Thu, 16 Mar 2017 17:37:35 +0530 Subject: [PATCH 082/166] Mul operation has been deprecated in Tensorflow 1.0.0 --- examples/1_Introduction/basic_operations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/1_Introduction/basic_operations.py b/examples/1_Introduction/basic_operations.py index 7766b28e..2f9098f1 100644 --- a/examples/1_Introduction/basic_operations.py +++ b/examples/1_Introduction/basic_operations.py @@ -30,7 +30,7 @@ # Define some operations add = tf.add(a, b) -mul = tf.mul(a, b) +mul = tf.muliply(a, b) # Launch the default graph. with tf.Session() as sess: From eec79c9de7f5ff175da1e9478410c92c386dabff Mon Sep 17 00:00:00 2001 From: Nickil Maveli Date: Sun, 19 Mar 2017 21:05:22 +0530 Subject: [PATCH 083/166] ENH: correct comment in autoencoder NB --- notebooks/3_NeuralNetworks/autoencoder.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/3_NeuralNetworks/autoencoder.ipynb b/notebooks/3_NeuralNetworks/autoencoder.ipynb index f0b8d4fe..37b1fed5 100644 --- a/notebooks/3_NeuralNetworks/autoencoder.ipynb +++ b/notebooks/3_NeuralNetworks/autoencoder.ipynb @@ -88,7 +88,7 @@ " # Encoder Hidden layer with sigmoid activation #1\n", " layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),\n", " biases['encoder_b1']))\n", - " # Decoder Hidden layer with sigmoid activation #2\n", + " # Encoder Hidden layer with sigmoid activation #2\n", " layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),\n", " biases['encoder_b2']))\n", " return layer_2\n", @@ -96,7 +96,7 @@ "\n", "# Building the decoder\n", "def decoder(x):\n", - " # Encoder Hidden layer with sigmoid activation #1\n", + " # Decoder Hidden layer with sigmoid activation #1\n", " layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),\n", " biases['decoder_b1']))\n", " # Decoder Hidden layer with sigmoid activation #2\n", From 5a79b2cf77667f256ffe50aa751a388d646ce515 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Thu, 23 Mar 2017 16:59:35 +0000 Subject: [PATCH 084/166] Create .gitignore --- .gitignore | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..2280041c --- /dev/null +++ b/.gitignore @@ -0,0 +1,13 @@ +.DS_Store +.ipynb_checkpoints +*.pyc +__pycache__ +*.egg-info +build/* +dist/* +*~ +.cache +.coverage +checkpoint +htmlcov +mnist From 00e2927263d4c4997450b7920b08779daaae62d5 Mon Sep 17 00:00:00 2001 From: Raja Rahul Ray Date: Tue, 4 Apr 2017 23:31:20 +0530 Subject: [PATCH 085/166] just a letter missed in multiply...no big deal... (#131) --- examples/1_Introduction/basic_operations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/1_Introduction/basic_operations.py b/examples/1_Introduction/basic_operations.py index 2f9098f1..e1775069 100644 --- a/examples/1_Introduction/basic_operations.py +++ b/examples/1_Introduction/basic_operations.py @@ -30,7 +30,7 @@ # Define some operations add = tf.add(a, b) -mul = tf.muliply(a, b) +mul = tf.multiply(a, b) # Launch the default graph. with tf.Session() as sess: From 373d9810c43c2b147883a3945ff72adfac18a8cb Mon Sep 17 00:00:00 2001 From: zxiaomzxm Date: Wed, 5 Apr 2017 02:01:57 +0800 Subject: [PATCH 086/166] Simpilify RNN examples data transformation (#136) * gittest * Simpilify RNN examples data transform. --- examples/3_NeuralNetworks/bidirectional_rnn.py | 8 ++------ examples/3_NeuralNetworks/dynamic_rnn.py | 10 +++------- examples/3_NeuralNetworks/recurrent_network.py | 8 ++------ notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb | 8 ++------ notebooks/3_NeuralNetworks/recurrent_network.ipynb | 8 ++------ 5 files changed, 11 insertions(+), 31 deletions(-) diff --git a/examples/3_NeuralNetworks/bidirectional_rnn.py b/examples/3_NeuralNetworks/bidirectional_rnn.py index e630896a..09d44b94 100644 --- a/examples/3_NeuralNetworks/bidirectional_rnn.py +++ b/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -55,12 +55,8 @@ def BiRNN(x, weights, biases): # Current data input shape: (batch_size, n_steps, n_input) # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) - # Permuting batch_size and n_steps - x = tf.transpose(x, [1, 0, 2]) - # Reshape to (n_steps*batch_size, n_input) - x = tf.reshape(x, [-1, n_input]) - # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) - x = tf.split(x, n_steps, 0) + # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input) + x = tf.unstack(x, n_steps, 1) # Define lstm cells with tensorflow # Forward direction cell diff --git a/examples/3_NeuralNetworks/dynamic_rnn.py b/examples/3_NeuralNetworks/dynamic_rnn.py index 5219c2f9..c44e5ee1 100644 --- a/examples/3_NeuralNetworks/dynamic_rnn.py +++ b/examples/3_NeuralNetworks/dynamic_rnn.py @@ -113,13 +113,9 @@ def dynamicRNN(x, seqlen, weights, biases): # Prepare data shape to match `rnn` function requirements # Current data input shape: (batch_size, n_steps, n_input) # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) - - # Permuting batch_size and n_steps - x = tf.transpose(x, [1, 0, 2]) - # Reshaping to (n_steps*batch_size, n_input) - x = tf.reshape(x, [-1, 1]) - # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) - x = tf.split(axis=0, num_or_size_splits=seq_max_len, value=x) + + # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input) + x = tf.unstack(x, seq_max_len, 1) # Define a lstm cell with tensorflow lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden) diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 7ba059f6..6473f724 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -53,12 +53,8 @@ def RNN(x, weights, biases): # Current data input shape: (batch_size, n_steps, n_input) # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) - # Permuting batch_size and n_steps - x = tf.transpose(x, [1, 0, 2]) - # Reshaping to (n_steps*batch_size, n_input) - x = tf.reshape(x, [-1, n_input]) - # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input) - x = tf.split(x, n_steps, 0) + # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input) + x = tf.unstack(x, n_steps, 1) # Define a lstm cell with tensorflow lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) diff --git a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb index b43cebff..dbfcbe72 100644 --- a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -94,12 +94,8 @@ " # Current data input shape: (batch_size, n_steps, n_input)\n", " # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n", " \n", - " # Permuting batch_size and n_steps\n", - " x = tf.transpose(x, [1, 0, 2])\n", - " # Reshape to (n_steps*batch_size, n_input)\n", - " x = tf.reshape(x, [-1, n_input])\n", - " # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", - " x = tf.split(x, n_steps, 0)\n", + " # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", + " x = tf.unstack(x, n_steps, 1)\n", "\n", " # Define lstm cells with tensorflow\n", " # Forward direction cell\n", diff --git a/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/notebooks/3_NeuralNetworks/recurrent_network.ipynb index f5f44d68..6510a968 100644 --- a/notebooks/3_NeuralNetworks/recurrent_network.ipynb +++ b/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -93,12 +93,8 @@ " # Current data input shape: (batch_size, n_steps, n_input)\n", " # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n", " \n", - " # Permuting batch_size and n_steps\n", - " x = tf.transpose(x, [1, 0, 2])\n", - " # Reshaping to (n_steps*batch_size, n_input)\n", - " x = tf.reshape(x, [-1, n_input])\n", - " # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", - " x = tf.split(x, n_steps, 0)\n", + " # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", + " x = tf.unstack(x, n_steps, 1)\n", "\n", " # Define a lstm cell with tensorflow\n", " lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", From c0a784a04aa01c19046fccbd1a720b5db9cdede3 Mon Sep 17 00:00:00 2001 From: Davy Durham Date: Thu, 13 Apr 2017 23:17:44 -0500 Subject: [PATCH 087/166] For the sake of the TF beginner (this is Example #2 afterall), added a helpful comment about how the optimizer knows to modify W and b --- examples/2_BasicModels/linear_regression.py | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/2_BasicModels/linear_regression.py b/examples/2_BasicModels/linear_regression.py index 0c07eb84..733676fa 100644 --- a/examples/2_BasicModels/linear_regression.py +++ b/examples/2_BasicModels/linear_regression.py @@ -38,6 +38,7 @@ # Mean squared error cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples) # Gradient descent +# Note, minimize() knows to modify W and b because Variable objects are trainable=True by default optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initializing the variables From 534b4febcdde836caf7895a03347e1af446a13fc Mon Sep 17 00:00:00 2001 From: Luanrly <553981927@qq.com> Date: Wed, 26 Apr 2017 12:48:23 +0800 Subject: [PATCH 088/166] The call to this function has changed (#142) tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_)) --- examples/4_Utils/tensorboard_advanced.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/4_Utils/tensorboard_advanced.py b/examples/4_Utils/tensorboard_advanced.py index 00658bb9..c7925eed 100644 --- a/examples/4_Utils/tensorboard_advanced.py +++ b/examples/4_Utils/tensorboard_advanced.py @@ -71,7 +71,7 @@ def multilayer_perceptron(x, weights, biases): with tf.name_scope('Loss'): # Softmax Cross entropy (cost function) - loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) + loss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) with tf.name_scope('SGD'): # Gradient Descent From bc09f959c0b67765b956e4a9b4372c2ae520f399 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 25 Apr 2017 21:50:38 -0700 Subject: [PATCH 089/166] reduce mean instead of sum --- examples/4_Utils/tensorboard_advanced.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/4_Utils/tensorboard_advanced.py b/examples/4_Utils/tensorboard_advanced.py index c7925eed..01def37e 100644 --- a/examples/4_Utils/tensorboard_advanced.py +++ b/examples/4_Utils/tensorboard_advanced.py @@ -71,7 +71,7 @@ def multilayer_perceptron(x, weights, biases): with tf.name_scope('Loss'): # Softmax Cross entropy (cost function) - loss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) + loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) with tf.name_scope('SGD'): # Gradient Descent From 8ca13386f6dfb322c973aec56009e5b7b75462a8 Mon Sep 17 00:00:00 2001 From: Or Hiltch Date: Thu, 4 May 2017 21:47:07 +0300 Subject: [PATCH 090/166] tf.mul -> tf.multiply (#143) --- notebooks/2_BasicModels/linear_regression.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/2_BasicModels/linear_regression.ipynb b/notebooks/2_BasicModels/linear_regression.ipynb index 9307ee46..62883a6a 100644 --- a/notebooks/2_BasicModels/linear_regression.ipynb +++ b/notebooks/2_BasicModels/linear_regression.ipynb @@ -84,7 +84,7 @@ "outputs": [], "source": [ "# Construct a linear model\n", - "pred = tf.add(tf.mul(X, W), b)" + "pred = tf.add(tf.multiply(X, W), b)" ] }, { From 31aa3604c9f811d42051a338ada09ce9e6459749 Mon Sep 17 00:00:00 2001 From: Caleb Kirksey Date: Tue, 13 Jun 2017 04:27:20 -0700 Subject: [PATCH 091/166] make print statements Python 3 compatible (#149) --- notebooks/3_NeuralNetworks/convolutional_network.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/notebooks/3_NeuralNetworks/convolutional_network.ipynb index c566c4a5..d2ff8cb9 100644 --- a/notebooks/3_NeuralNetworks/convolutional_network.ipynb +++ b/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -338,17 +338,17 @@ " loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,\n", " y: batch_y,\n", " keep_prob: 1.})\n", - " print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n", + " print(\"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n", " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", - " \"{:.5f}\".format(acc)\n", + " \"{:.5f}\".format(acc))\n", " step += 1\n", - " print \"Optimization Finished!\"\n", + " print(\"Optimization Finished!\")\n", "\n", " # Calculate accuracy for 256 mnist test images\n", - " print \"Testing Accuracy:\", \\\n", + " print(\"Testing Accuracy:\", \\\n", " sess.run(accuracy, feed_dict={x: mnist.test.images[:256],\n", " y: mnist.test.labels[:256],\n", - " keep_prob: 1.})" + " keep_prob: 1.}))" ] }, { From 5e5534263b5a39ce4604c1ee8e79343689879844 Mon Sep 17 00:00:00 2001 From: Ji Yang Date: Sun, 9 Jul 2017 03:36:42 +0800 Subject: [PATCH 092/166] Add tensorboard_advanced.ipynb (#152) * Make print function Python 3 compatible * Add notebook for tensorboard advanced tutorial --- README.md | 3 +- notebooks/4_Utils/save_restore_model.ipynb | 52 ++-- notebooks/4_Utils/tensorboard_advanced.ipynb | 280 +++++++++++++++++++ notebooks/4_Utils/tensorboard_basic.ipynb | 46 ++- 4 files changed, 326 insertions(+), 55 deletions(-) create mode 100644 notebooks/4_Utils/tensorboard_advanced.ipynb diff --git a/README.md b/README.md index b03781f3..2a109d77 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,8 @@ Note: If you are using older TensorFlow version (before 0.12), please have a [lo #### 4 - Utilities - Save and Restore a model ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py)) - Tensorboard - Graph and loss visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py)) -- Tensorboard - Advanced visualization ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py)) +- Tensorboard - Advanced visualization +([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py)) #### 5 - Multi GPU - Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py)) diff --git a/notebooks/4_Utils/save_restore_model.ipynb b/notebooks/4_Utils/save_restore_model.ipynb index 1504f47f..a82a67de 100644 --- a/notebooks/4_Utils/save_restore_model.ipynb +++ b/notebooks/4_Utils/save_restore_model.ipynb @@ -21,9 +21,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -37,6 +35,8 @@ } ], "source": [ + "from __future__ import print_function\n", + "\n", "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n", @@ -47,9 +47,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Parameters\n", @@ -119,9 +117,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -139,7 +135,7 @@ ], "source": [ "# Running first session\n", - "print \"Starting 1st session...\"\n", + "print(\"Starting 1st session...\")\n", "with tf.Session() as sess:\n", " # Initialize variables\n", " sess.run(init)\n", @@ -160,25 +156,23 @@ " if epoch % display_step == 0:\n", " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n", " \"{:.9f}\".format(avg_cost)\n", - " print \"First Optimization Finished!\"\n", + " print(\"First Optimization Finished!\")\n", "\n", " # Test model\n", " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", " # Calculate accuracy\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", - " print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})\n", + " print(\"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))\n", "\n", " # Save model weights to disk\n", " save_path = saver.save(sess, model_path)\n", - " print \"Model saved in file: %s\" % save_path" + " print(\"Model saved in file: %s\" % save_path)" ] }, { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -200,14 +194,14 @@ ], "source": [ "# Running a new session\n", - "print \"Starting 2nd session...\"\n", + "print(\"Starting 2nd session...\")\n", "with tf.Session() as sess:\n", " # Initialize variables\n", " sess.run(init)\n", "\n", " # Restore model weights from previously saved model\n", " load_path = saver.restore(sess, model_path)\n", - " print \"Model restored from file: %s\" % save_path\n", + " print(\"Model restored from file: %s\" % save_path)\n", "\n", " # Resume training\n", " for epoch in range(7):\n", @@ -223,16 +217,16 @@ " avg_cost += c / total_batch\n", " # Display logs per epoch step\n", " if epoch % display_step == 0:\n", - " print \"Epoch:\", '%04d' % (epoch + 1), \"cost=\", \\\n", - " \"{:.9f}\".format(avg_cost)\n", - " print \"Second Optimization Finished!\"\n", + " print(\"Epoch:\", '%04d' % (epoch + 1), \"cost=\", \\\n", + " \"{:.9f}\".format(avg_cost))\n", + " print(\"Second Optimization Finished!\")\n", "\n", " # Test model\n", " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", " # Calculate accuracy\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", - " print \"Accuracy:\", accuracy.eval(\n", - " {x: mnist.test.images, y: mnist.test.labels})" + " print(\"Accuracy:\", accuracy.eval(\n", + " {x: mnist.test.images, y: mnist.test.labels}))" ] }, { @@ -247,23 +241,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.13" + "pygments_lexer": "ipython3", + "version": "3.5.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebooks/4_Utils/tensorboard_advanced.ipynb b/notebooks/4_Utils/tensorboard_advanced.ipynb new file mode 100644 index 00000000..19d14829 --- /dev/null +++ b/notebooks/4_Utils/tensorboard_advanced.ipynb @@ -0,0 +1,280 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "'''\n", + "Graph and Loss visualization using Tensorboard.\n", + "This example is using the MNIST database of handwritten digits\n", + "(http://yann.lecun.com/exdb/mnist/)\n", + "\n", + "Author: Aymeric Damien\n", + "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "\n", + "tf.logging.set_verbosity(tf.logging.WARN)\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 25\n", + "batch_size = 100\n", + "display_step = 1\n", + "logs_path = '/tmp/tensorflow_logs/example'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of features\n", + "n_hidden_2 = 256 # 2nd layer number of features\n", + "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph Input\n", + "# mnist data image of shape 28*28=784\n", + "x = tf.placeholder(tf.float32, [None, 784], name='InputData')\n", + "# 0-9 digits recognition => 10 classes\n", + "y = tf.placeholder(tf.float32, [None, 10], name='LabelData')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create model\n", + "def multilayer_perceptron(x, weights, biases):\n", + " # Hidden layer with RELU activation\n", + " layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1'])\n", + " layer_1 = tf.nn.relu(layer_1)\n", + " # Create a summary to visualize the first layer ReLU activation\n", + " tf.summary.histogram(\"relu1\", layer_1)\n", + " # Hidden layer with RELU activation\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2'])\n", + " layer_2 = tf.nn.relu(layer_2)\n", + " # Create another summary to visualize the second layer ReLU activation\n", + " tf.summary.histogram(\"relu2\", layer_2)\n", + " # Output layer\n", + " out_layer = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3'])\n", + " return out_layer\n", + "\n", + "# Store layers weight & bias\n", + "weights = {\n", + " 'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='W1'),\n", + " 'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),\n", + " 'w3': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='W3')\n", + "}\n", + "biases = {\n", + " 'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='b1'),\n", + " 'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='b2'),\n", + " 'b3': tf.Variable(tf.random_normal([n_classes]), name='b3')\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Encapsulating all ops into scopes, making Tensorboard's Graph\n", + "# Visualization more convenient\n", + "with tf.name_scope('Model'):\n", + " # Build model\n", + " pred = multilayer_perceptron(x, weights, biases)\n", + "\n", + "with tf.name_scope('Loss'):\n", + " # Softmax Cross entropy (cost function)\n", + " loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", + "\n", + "with tf.name_scope('SGD'):\n", + " # Gradient Descent\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + " # Op to calculate every variable gradient\n", + " grads = tf.gradients(loss, tf.trainable_variables())\n", + " grads = list(zip(grads, tf.trainable_variables()))\n", + " # Op to update all variables according to their gradient\n", + " apply_grads = optimizer.apply_gradients(grads_and_vars=grads)\n", + "\n", + "with tf.name_scope('Accuracy'):\n", + " # Accuracy\n", + " acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " acc = tf.reduce_mean(tf.cast(acc, tf.float32))\n", + "\n", + "# Initializing the variables\n", + "init = tf.global_variables_initializer()\n", + "\n", + "# Create a summary to monitor cost tensor\n", + "tf.summary.scalar(\"loss\", loss)\n", + "# Create a summary to monitor accuracy tensor\n", + "tf.summary.scalar(\"accuracy\", acc)\n", + "# Create summaries to visualize weights\n", + "for var in tf.trainable_variables():\n", + " tf.summary.histogram(var.name, var)\n", + "# Summarize all gradients\n", + "for grad, var in grads:\n", + " tf.summary.histogram(var.name + '/gradient', grad)\n", + "# Merge all summaries into a single op\n", + "merged_summary_op = tf.summary.merge_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0001 cost= 67.380016100\n", + "Epoch: 0002 cost= 15.307113347\n", + "Epoch: 0003 cost= 9.986865815\n", + "Epoch: 0004 cost= 7.381951704\n", + "Epoch: 0005 cost= 5.849047792\n", + "Epoch: 0006 cost= 4.881959525\n", + "Epoch: 0007 cost= 4.045799575\n", + "Epoch: 0008 cost= 3.430059265\n", + "Epoch: 0009 cost= 3.076626336\n", + "Epoch: 0010 cost= 2.863002729\n", + "Epoch: 0011 cost= 2.510218838\n", + "Epoch: 0012 cost= 2.276251159\n", + "Epoch: 0013 cost= 1.978880318\n", + "Epoch: 0014 cost= 1.733890927\n", + "Epoch: 0015 cost= 1.540066199\n", + "Epoch: 0016 cost= 1.439536399\n", + "Epoch: 0017 cost= 1.279739846\n", + "Epoch: 0018 cost= 1.224386179\n", + "Epoch: 0019 cost= 1.095804572\n", + "Epoch: 0020 cost= 1.100819187\n", + "Epoch: 0021 cost= 0.885994007\n", + "Epoch: 0022 cost= 1.079832625\n", + "Epoch: 0023 cost= 0.948164673\n", + "Epoch: 0024 cost= 0.613826872\n", + "Epoch: 0025 cost= 0.644082715\n", + "Optimization Finished!\n", + "Accuracy: 0.9513\n", + "Run the command line:\n", + "--> tensorboard --logdir=/tmp/tensorflow_logs \n", + "Then open http://0.0.0.0:6006/ into your web browser\n" + ] + } + ], + "source": [ + "# Launch the graph\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + "\n", + " # op to write logs to Tensorboard\n", + " summary_writer = tf.summary.FileWriter(logs_path,\n", + " graph=tf.get_default_graph())\n", + "\n", + " # Training cycle\n", + " for epoch in range(training_epochs):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples/batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop), cost op (to get loss value)\n", + " # and summary nodes\n", + " _, c, summary = sess.run([apply_grads, loss, merged_summary_op],\n", + " feed_dict={x: batch_xs, y: batch_ys})\n", + " # Write logs at every iteration\n", + " summary_writer.add_summary(summary, epoch * total_batch + i)\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if (epoch+1) % display_step == 0:\n", + " print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Test model\n", + " # Calculate accuracy\n", + " print(\"Accuracy:\", acc.eval({x: mnist.test.images, y: mnist.test.labels}))\n", + "\n", + " print(\"Run the command line:\\n\" \\\n", + " \"--> tensorboard --logdir=/tmp/tensorflow_logs \" \\\n", + " \"\\nThen open http://0.0.0.0:6006/ into your web browser\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/4_Utils/tensorboard_basic.ipynb b/notebooks/4_Utils/tensorboard_basic.ipynb index 69c389d9..047147f0 100644 --- a/notebooks/4_Utils/tensorboard_basic.ipynb +++ b/notebooks/4_Utils/tensorboard_basic.ipynb @@ -22,10 +22,12 @@ "cell_type": "code", "execution_count": null, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ + "from __future__ import print_function\n", + "\n", "import tensorflow as tf\n", "\n", "# Import MINST data\n", @@ -63,7 +65,7 @@ "cell_type": "code", "execution_count": 9, "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ @@ -74,7 +76,7 @@ " pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n", "with tf.name_scope('Loss'):\n", " # Minimize error using cross entropy\n", - " cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))\n", + " cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))\n", "with tf.name_scope('SGD'):\n", " # Gradient Descent\n", " optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", @@ -97,9 +99,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -149,7 +149,7 @@ " # Training cycle\n", " for epoch in range(training_epochs):\n", " avg_cost = 0.\n", - " total_batch = int(mnist.train.num_examples/batch_size)\n", + " total_batch = int(mnist.train.num_examples / batch_size)\n", " # Loop over all batches\n", " for i in range(total_batch):\n", " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", @@ -162,26 +162,24 @@ " # Compute average loss\n", " avg_cost += c / total_batch\n", " # Display logs per epoch step\n", - " if (epoch+1) % display_step == 0:\n", - " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n", + " if (epoch + 1) % display_step == 0:\n", + " print(\"Epoch:\", '%04d' % (epoch + 1), \"cost=\", \"{:.9f}\".format(avg_cost))\n", "\n", - " print \"Optimization Finished!\"\n", + " print(\"Optimization Finished!\")\n", "\n", " # Test model\n", " # Calculate accuracy\n", - " print \"Accuracy:\", acc.eval({x: mnist.test.images, y: mnist.test.labels})\n", + " print(\"Accuracy:\", acc.eval({x: mnist.test.images, y: mnist.test.labels}))\n", "\n", - " print \"Run the command line:\\n\" \\\n", + " print(\"Run the command line:\\n\" \\\n", " \"--> tensorboard --logdir=/tmp/tensorflow_logs \" \\\n", - " \"\\nThen open http://0.0.0.0:6006/ into your web browser\"" + " \"\\nThen open http://0.0.0.0:6006/ into your web browser\")" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -201,9 +199,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -223,23 +219,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.13" + "pygments_lexer": "ipython3", + "version": "3.5.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From c0c59e40f488dfb550a1925397b0ca8b76316a7a Mon Sep 17 00:00:00 2001 From: SOLARIS Date: Sun, 30 Jul 2017 22:27:13 +0900 Subject: [PATCH 093/166] Fixed Typo (#158) --- examples/3_NeuralNetworks/autoencoder.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/3_NeuralNetworks/autoencoder.py b/examples/3_NeuralNetworks/autoencoder.py index 8ebe7de7..7a94b490 100644 --- a/examples/3_NeuralNetworks/autoencoder.py +++ b/examples/3_NeuralNetworks/autoencoder.py @@ -53,7 +53,7 @@ def encoder(x): # Encoder Hidden layer with sigmoid activation #1 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), biases['encoder_b1'])) - # Decoder Hidden layer with sigmoid activation #2 + # Encoder Hidden layer with sigmoid activation #2 layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), biases['encoder_b2'])) return layer_2 @@ -61,7 +61,7 @@ def encoder(x): # Building the decoder def decoder(x): - # Encoder Hidden layer with sigmoid activation #1 + # Decoder Hidden layer with sigmoid activation #1 layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), biases['decoder_b1'])) # Decoder Hidden layer with sigmoid activation #2 From 4e829a64c8d3e65980fdfc44ae687004a7d6249f Mon Sep 17 00:00:00 2001 From: Koki Mametani Date: Fri, 25 Aug 2017 02:11:53 +0900 Subject: [PATCH 094/166] Fixed typo (#159) --- notebooks/3_NeuralNetworks/autoencoder.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/3_NeuralNetworks/autoencoder.ipynb b/notebooks/3_NeuralNetworks/autoencoder.ipynb index 37b1fed5..4166deda 100644 --- a/notebooks/3_NeuralNetworks/autoencoder.ipynb +++ b/notebooks/3_NeuralNetworks/autoencoder.ipynb @@ -33,7 +33,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "# Import MINST data\n", + "# Import MNIST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"MNIST_data\", one_hot=True)" ] From 90bb4de75322f8c01048dd98c7f194442051d257 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 29 Aug 2017 15:42:20 +0100 Subject: [PATCH 095/166] New Examples (#160) * Added basic models examples (kmeans, random forest, ...) * Added API examples (layers, estimator, ...) * Added other examples (Multi-GPU, build a dataset, ...) * Notebook refactoring with new header and more details --- README.md | 144 ++-- examples/2_BasicModels/kmeans.py | 86 +++ examples/2_BasicModels/linear_regression.py | 6 +- examples/2_BasicModels/logistic_regression.py | 6 +- examples/2_BasicModels/nearest_neighbor.py | 6 +- examples/2_BasicModels/random_forest.py | 75 +++ examples/3_NeuralNetworks/autoencoder.py | 126 ++-- .../3_NeuralNetworks/bidirectional_rnn.py | 100 +-- .../3_NeuralNetworks/convolutional_network.py | 209 +++--- .../convolutional_network_raw.py | 141 ++++ examples/3_NeuralNetworks/dcgan.py | 167 +++++ examples/3_NeuralNetworks/dynamic_rnn.py | 36 +- examples/3_NeuralNetworks/gan.py | 157 +++++ .../3_NeuralNetworks/multilayer_perceptron.py | 75 ++- examples/3_NeuralNetworks/neural_network.py | 103 +++ .../3_NeuralNetworks/neural_network_raw.py | 101 +++ .../3_NeuralNetworks/recurrent_network.py | 91 +-- .../variational_autoencoder.py | 143 ++++ examples/4_Utils/save_restore_model.py | 5 +- examples/4_Utils/tensorboard_advanced.py | 8 +- examples/4_Utils/tensorboard_basic.py | 12 +- .../build_an_image_dataset.py | 211 ++++++ .../tensorflow_dataset_api.py | 137 ++++ .../multigpu_basics.py | 0 examples/6_MultiGPU/multigpu_cnn.py | 182 +++++ .../0_Prerequisite/mnist_dataset_intro.ipynb | 6 +- notebooks/2_BasicModels/kmeans.ipynb | 203 ++++++ .../2_BasicModels/linear_regression.ipynb | 21 +- .../2_BasicModels/logistic_regression.ipynb | 35 +- .../2_BasicModels/nearest_neighbor.ipynb | 38 +- notebooks/2_BasicModels/random_forest.ipynb | 204 ++++++ notebooks/3_NeuralNetworks/autoencoder.ipynb | 274 +++++--- .../3_NeuralNetworks/bidirectional_rnn.ipynb | 320 ++++----- .../convolutional_network.ipynb | 622 ++++++++++-------- .../convolutional_network_raw.ipynb | 303 +++++++++ notebooks/3_NeuralNetworks/dcgan.ipynb | 333 ++++++++++ notebooks/3_NeuralNetworks/dynamic_rnn.ipynb | 355 ++++++++++ notebooks/3_NeuralNetworks/gan.ipynb | 323 +++++++++ .../multilayer_perceptron.ipynb | 216 ------ .../3_NeuralNetworks/neural_network.ipynb | 390 +++++++++++ .../3_NeuralNetworks/neural_network_raw.ipynb | 224 +++++++ .../3_NeuralNetworks/recurrent_network.ipynb | 320 ++++----- .../variational_autoencoder.ipynb | 316 +++++++++ notebooks/4_Utils/save_restore_model.ipynb | 29 +- notebooks/4_Utils/tensorboard_advanced.ipynb | 175 ++--- notebooks/4_Utils/tensorboard_basic.ipynb | 84 +-- .../build_an_image_dataset.ipynb | 290 ++++++++ .../tensorflow_dataset_api.ipynb | 234 +++++++ .../multigpu_basics.ipynb | 29 +- notebooks/6_MultiGPU/multigpu_cnn.ipynb | 304 +++++++++ resources/img/tensorboard_advanced_1.png | Bin 0 -> 286897 bytes resources/img/tensorboard_advanced_2.png | Bin 0 -> 329796 bytes resources/img/tensorboard_advanced_3.png | Bin 0 -> 1011024 bytes resources/img/tensorboard_advanced_4.png | Bin 0 -> 607057 bytes resources/img/tensorboard_basic_1.png | Bin 0 -> 284683 bytes resources/img/tensorboard_basic_2.png | Bin 0 -> 349097 bytes 56 files changed, 6451 insertions(+), 1524 deletions(-) create mode 100644 examples/2_BasicModels/kmeans.py create mode 100644 examples/2_BasicModels/random_forest.py create mode 100644 examples/3_NeuralNetworks/convolutional_network_raw.py create mode 100644 examples/3_NeuralNetworks/dcgan.py create mode 100644 examples/3_NeuralNetworks/gan.py create mode 100644 examples/3_NeuralNetworks/neural_network.py create mode 100644 examples/3_NeuralNetworks/neural_network_raw.py create mode 100644 examples/3_NeuralNetworks/variational_autoencoder.py create mode 100644 examples/5_DataManagement/build_an_image_dataset.py create mode 100644 examples/5_DataManagement/tensorflow_dataset_api.py rename examples/{5_MultiGPU => 6_MultiGPU}/multigpu_basics.py (100%) create mode 100644 examples/6_MultiGPU/multigpu_cnn.py create mode 100644 notebooks/2_BasicModels/kmeans.ipynb create mode 100644 notebooks/2_BasicModels/random_forest.ipynb create mode 100644 notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb create mode 100644 notebooks/3_NeuralNetworks/dcgan.ipynb create mode 100644 notebooks/3_NeuralNetworks/dynamic_rnn.ipynb create mode 100644 notebooks/3_NeuralNetworks/gan.ipynb delete mode 100644 notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb create mode 100644 notebooks/3_NeuralNetworks/neural_network.ipynb create mode 100644 notebooks/3_NeuralNetworks/neural_network_raw.ipynb create mode 100644 notebooks/3_NeuralNetworks/variational_autoencoder.ipynb create mode 100644 notebooks/5_DataManagement/build_an_image_dataset.ipynb create mode 100644 notebooks/5_DataManagement/tensorflow_dataset_api.ipynb rename notebooks/{5_MultiGPU => 6_MultiGPU}/multigpu_basics.ipynb (86%) create mode 100644 notebooks/6_MultiGPU/multigpu_cnn.ipynb create mode 100644 resources/img/tensorboard_advanced_1.png create mode 100644 resources/img/tensorboard_advanced_2.png create mode 100644 resources/img/tensorboard_advanced_3.png create mode 100644 resources/img/tensorboard_advanced_4.png create mode 100644 resources/img/tensorboard_basic_1.png create mode 100644 resources/img/tensorboard_basic_2.png diff --git a/README.md b/README.md index 2a109d77..050b5544 100644 --- a/README.md +++ b/README.md @@ -1,110 +1,90 @@ # TensorFlow Examples -TensorFlow Tutorial with popular machine learning algorithms implementation. This tutorial was designed for easily diving into TensorFlow, through examples. -It is suitable for beginners who want to find clear and concise examples about TensorFlow. For readability, the tutorial includes both notebook and code with explanations. +This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation. -Note: If you are using older TensorFlow version (before 0.12), please have a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11) +It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). + +**Update (27.08.17):** TensorFlow v1.3 is recommended. Added many new examples (kmeans, random forest, multi-gpu training, layers api, estimator api, dataset api ...). + +*If you are using older TensorFlow version (0.11 and under), please have a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* ## Tutorial index #### 0 - Prerequisite -- Introduction to Machine Learning ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb)) -- Introduction to MNIST Dataset ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb)) +- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb). +- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction -- Hello World ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py)) -- Basic Operations ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py)) +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. #### 2 - Basic Models -- Nearest Neighbor ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)) -- Linear Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py)) -- Logistic Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py)) +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. +- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. +- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. +- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. #### 3 - Neural Networks -- Multilayer Perceptron ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py)) -- Convolutional Neural Network ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py)) -- Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)) -- Bidirectional Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py)) -- Dynamic Recurrent Neural Network (LSTM) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dynamic_rnn.py)) -- AutoEncoder ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)) +##### Supervised + +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. + +##### Unsupervised +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-contruct it. +- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/Variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/Variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. +- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities -- Save and Restore a model ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py)) -- Tensorboard - Graph and loss visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py)) -- Tensorboard - Advanced visualization -([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py)) +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. +- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. +- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... + +#### 5 - Data Management +- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. +- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. -#### 5 - Multi GPU -- Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py)) +#### 6 - Multi GPU +- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. +- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. ## Dataset -Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). +Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. MNIST is a database of handwritten digits, for a quick description of that dataset, you can check [this notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/) +## Installation + +To download all the examples, simply clone this repository: +``` +git clone https://github.com/aymericdamien/TensorFlow-Examples +``` + +To run them, you also need the latest version of TensorFlow. To install it: +``` +pip install tensorflow +``` + +or (if you want GPU support): +``` +pip install tensorflow_gpu +``` + +For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md) + ## More Examples The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). ### Tutorials - [TFLearn Quickstart](https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. -### Basics -- [Linear Regression](https://github.com/tflearn/tflearn/blob/master/examples/basics/linear_regression.py). Implement a linear regression using TFLearn. -- [Logical Operators](https://github.com/tflearn/tflearn/blob/master/examples/basics/logical.py). Implement logical operators with TFLearn (also includes a usage of 'merge'). -- [Weights Persistence](https://github.com/tflearn/tflearn/blob/master/examples/basics/weights_persistence.py). Save and Restore a model. -- [Fine-Tuning](https://github.com/tflearn/tflearn/blob/master/examples/basics/finetuning.py). Fine-Tune a pre-trained model on a new task. -- [Using HDF5](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_hdf5.py). Use HDF5 to handle large datasets. -- [Using DASK](https://github.com/tflearn/tflearn/blob/master/examples/basics/use_dask.py). Use DASK to handle large datasets. - -### Computer Vision -- [Multi-layer perceptron](https://github.com/tflearn/tflearn/blob/master/examples/images/dnn.py). A multi-layer perceptron implementation for MNIST classification task. -- [Convolutional Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_mnist.py). A Convolutional neural network implementation for classifying MNIST dataset. -- [Convolutional Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py). A Convolutional neural network implementation for classifying CIFAR-10 dataset. -- [Network in Network](https://github.com/tflearn/tflearn/blob/master/examples/images/network_in_network.py). 'Network in Network' implementation for classifying CIFAR-10 dataset. -- [Alexnet](https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py). Apply Alexnet to Oxford Flowers 17 classification task. -- [VGGNet](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py). Apply VGG Network to Oxford Flowers 17 classification task. -- [VGGNet Finetuning (Fast Training)](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network_finetuning.py). Use a pre-trained VGG Network and retrain it on your own data, for fast training. -- [RNN Pixels](https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py). Use RNN (over sequence of pixels) to classify images. -- [Highway Network](https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py). Highway Network implementation for classifying MNIST dataset. -- [Highway Convolutional Network](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py). Highway Convolutional Network implementation for classifying MNIST dataset. -- [Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A bottleneck residual network applied to MNIST classification task. -- [Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network applied to CIFAR-10 classification task. -- [Google Inception (v3)](https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py). Google's Inception v3 network applied to Oxford Flowers 17 classification task. -- [Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits. - -### Natural Language Processing -- [Recurrent Neural Network (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm.py). Apply an LSTM to IMDB sentiment dataset classification task. -- [Bi-Directional RNN (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/bidirectional_lstm.py). Apply a bi-directional LSTM to IMDB sentiment dataset classification task. -- [Dynamic RNN (LSTM)](https://github.com/tflearn/tflearn/blob/master/examples/nlp/dynamic_lstm.py). Apply a dynamic LSTM to classify variable length text from IMDB dataset. -- [City Name Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_cityname.py). Generates new US-cities name, using LSTM network. -- [Shakespeare Scripts Generation](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py). Generates new Shakespeare scripts, using LSTM network. -- [Seq2seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/seq2seq_example.py). Pedagogical example of seq2seq reccurent network. See [this repo](https://github.com/ichuang/tflearn_seq2seq) for full instructions. -- [CNN Seq](https://github.com/tflearn/tflearn/blob/master/examples/nlp/cnn_sentence_classification.py). Apply a 1-D convolutional network to classify sequence of words from IMDB sentiment dataset. - -### Reinforcement Learning -- [Atari Pacman 1-step Q-Learning](https://github.com/tflearn/tflearn/blob/master/examples/reinforcement_learning/atari_1step_qlearning.py). Teach a machine to play Atari games (Pacman by default) using 1-step Q-learning. - -### Others -- [Recommender - Wide & Deep Network](https://github.com/tflearn/tflearn/blob/master/examples/others/recommender_wide_and_deep.py). Pedagogical example of wide & deep networks for recommender systems. - -### Notebooks -- [Spiral Classification Problem](https://github.com/tflearn/tflearn/blob/master/examples/notebooks/spiral.ipynb). TFLearn implementation of spiral classification problem from Stanford CS231n. - -### Extending TensorFlow -- [Layers](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/layers.py). Use TFLearn layers along with TensorFlow. -- [Trainer](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/trainer.py). Use TFLearn trainer class to train any TensorFlow graph. -- [Built-in Ops](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/builtin_ops.py). Use TFLearn built-in operations along with TensorFlow. -- [Summaries](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/summaries.py). Use TFLearn summarizers along with TensorFlow. -- [Variables](https://github.com/tflearn/tflearn/blob/master/examples/extending_tensorflow/variables.py). Use TFLearn variables along with TensorFlow. - - -## Dependencies -``` -tensorflow 1.0alpha -numpy -matplotlib -cuda -tflearn (if using tflearn examples) -``` -For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md) +### Examples +- [TFLearn Examples](https://github.com/tflearn/tflearn/blob/master/examples). A large collection of examples using TFLearn. diff --git a/examples/2_BasicModels/kmeans.py b/examples/2_BasicModels/kmeans.py new file mode 100644 index 00000000..02498d33 --- /dev/null +++ b/examples/2_BasicModels/kmeans.py @@ -0,0 +1,86 @@ +""" K-Means. + +Implement K-Means algorithm with TensorFlow, and apply it to classify +handwritten digit images. This example is using the MNIST database of +handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/). + +Note: This example requires TensorFlow v1.1.0 or over. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import numpy as np +import tensorflow as tf +from tensorflow.contrib.factorization import KMeans + +# Ignore all GPUs, tf random forest does not benefit from it. +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "" + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) +full_data_x = mnist.train.images + +# Parameters +num_steps = 50 # Total steps to train +batch_size = 1024 # The number of samples per batch +k = 25 # The number of clusters +num_classes = 10 # The 10 digits +num_features = 784 # Each image is 28x28 pixels + +# Input images +X = tf.placeholder(tf.float32, shape=[None, num_features]) +# Labels (for assigning a label to a centroid and testing) +Y = tf.placeholder(tf.float32, shape=[None, num_classes]) + +# K-Means Parameters +kmeans = KMeans(inputs=X, num_clusters=k, distance_metric='cosine', + use_mini_batch=True) + +# Build KMeans graph +(all_scores, cluster_idx, scores, cluster_centers_initialized, init_op, +train_op) = kmeans.training_graph() +cluster_idx = cluster_idx[0] # fix for cluster_idx being a tuple +avg_distance = tf.reduce_mean(scores) + +# Initialize the variables (i.e. assign their default value) +init_vars = tf.global_variables_initializer() + +# Start TensorFlow session +sess = tf.Session() + +# Run the initializer +sess.run(init_vars, feed_dict={X: full_data_x}) +sess.run(init_op, feed_dict={X: full_data_x}) + +# Training +for i in range(1, num_steps + 1): + _, d, idx = sess.run([train_op, avg_distance, cluster_idx], + feed_dict={X: full_data_x}) + if i % 10 == 0 or i == 1: + print("Step %i, Avg Distance: %f" % (i, d)) + +# Assign a label to each centroid +# Count total number of labels per centroid, using the label of each training +# sample to their closest centroid (given by 'idx') +counts = np.zeros(shape=(k, num_classes)) +for i in range(len(idx)): + counts[idx[i]] += mnist.train.labels[i] +# Assign the most frequent label to the centroid +labels_map = [np.argmax(c) for c in counts] +labels_map = tf.convert_to_tensor(labels_map) + +# Evaluation ops +# Lookup: centroid_id -> label +cluster_label = tf.nn.embedding_lookup(labels_map, cluster_idx) +# Compute accuracy +correct_prediction = tf.equal(cluster_label, tf.cast(tf.argmax(Y, 1), tf.int32)) +accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + +# Test Model +test_x, test_y = mnist.test.images, mnist.test.labels +print("Test Accuracy:", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y})) diff --git a/examples/2_BasicModels/linear_regression.py b/examples/2_BasicModels/linear_regression.py index 733676fa..cfb1c2fa 100644 --- a/examples/2_BasicModels/linear_regression.py +++ b/examples/2_BasicModels/linear_regression.py @@ -41,11 +41,13 @@ # Note, minimize() knows to modify W and b because Variable objects are trainable=True by default optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) -# Initializing the variables +# Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() -# Launch the graph +# Start training with tf.Session() as sess: + + # Run the initializer sess.run(init) # Fit all training data diff --git a/examples/2_BasicModels/logistic_regression.py b/examples/2_BasicModels/logistic_regression.py index e8b5c89e..f38ea81c 100644 --- a/examples/2_BasicModels/logistic_regression.py +++ b/examples/2_BasicModels/logistic_regression.py @@ -37,11 +37,13 @@ # Gradient Descent optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) -# Initializing the variables +# Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() -# Launch the graph +# Start training with tf.Session() as sess: + + # Run the initializer sess.run(init) # Training cycle diff --git a/examples/2_BasicModels/nearest_neighbor.py b/examples/2_BasicModels/nearest_neighbor.py index 53427469..ea40d68e 100644 --- a/examples/2_BasicModels/nearest_neighbor.py +++ b/examples/2_BasicModels/nearest_neighbor.py @@ -32,11 +32,13 @@ accuracy = 0. -# Initializing the variables +# Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() -# Launch the graph +# Start training with tf.Session() as sess: + + # Run the initializer sess.run(init) # loop over test data diff --git a/examples/2_BasicModels/random_forest.py b/examples/2_BasicModels/random_forest.py new file mode 100644 index 00000000..ef6f21f5 --- /dev/null +++ b/examples/2_BasicModels/random_forest.py @@ -0,0 +1,75 @@ +""" Random Forest. + +Implement Random Forest algorithm with TensorFlow, and apply it to classify +handwritten digit images. This example is using the MNIST database of +handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.tensor_forest.python import tensor_forest + +# Ignore all GPUs, tf random forest does not benefit from it. +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "" + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +# Parameters +num_steps = 500 # Total steps to train +batch_size = 1024 # The number of samples per batch +num_classes = 10 # The 10 digits +num_features = 784 # Each image is 28x28 pixels +num_trees = 10 +max_nodes = 1000 + +# Input and Target data +X = tf.placeholder(tf.float32, shape=[None, num_features]) +# For random forest, labels must be integers (the class id) +Y = tf.placeholder(tf.int32, shape=[None]) + +# Random Forest Parameters +hparams = tensor_forest.ForestHParams(num_classes=num_classes, + num_features=num_features, + num_trees=num_trees, + max_nodes=max_nodes).fill() + +# Build the Random Forest +forest_graph = tensor_forest.RandomForestGraphs(hparams) +# Get training graph and loss +train_op = forest_graph.training_graph(X, Y) +loss_op = forest_graph.training_loss(X, Y) + +# Measure the accuracy +infer_op = forest_graph.inference_graph(X) +correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64)) +accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init_vars = tf.global_variables_initializer() + +# Start TensorFlow session +sess = tf.Session() + +# Run the initializer +sess.run(init_vars) + +# Training +for i in range(1, num_steps + 1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, batch_y = mnist.train.next_batch(batch_size) + _, l = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y}) + if i % 50 == 0 or i == 1: + acc = sess.run(accuracy_op, feed_dict={X: batch_x, Y: batch_y}) + print('Step %i, Loss: %f, Acc: %f' % (i, l, acc)) + +# Test Model +test_x, test_y = mnist.test.images, mnist.test.labels +print("Test Accuracy:", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y})) diff --git a/examples/3_NeuralNetworks/autoencoder.py b/examples/3_NeuralNetworks/autoencoder.py index 7a94b490..9d3ba60e 100644 --- a/examples/3_NeuralNetworks/autoencoder.py +++ b/examples/3_NeuralNetworks/autoencoder.py @@ -1,13 +1,18 @@ -# -*- coding: utf-8 -*- - """ Auto Encoder Example. -Using an auto encoder on MNIST handwritten digits. + +Build a 2 layers auto-encoder with TensorFlow to compress images to a +lower latent space and then reconstruct them. + References: Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998. + Links: [MNIST Dataset] http://yann.lecun.com/exdb/mnist/ + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ """ from __future__ import division, print_function, absolute_import @@ -17,37 +22,37 @@ # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data -mnist = input_data.read_data_sets("MNIST_data", one_hot=True) +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) -# Parameters +# Training Parameters learning_rate = 0.01 -training_epochs = 20 +num_steps = 30000 batch_size = 256 -display_step = 1 + +display_step = 1000 examples_to_show = 10 # Network Parameters -n_hidden_1 = 256 # 1st layer num features -n_hidden_2 = 128 # 2nd layer num features -n_input = 784 # MNIST data input (img shape: 28*28) +num_hidden_1 = 256 # 1st layer num features +num_hidden_2 = 128 # 2nd layer num features (the latent dim) +num_input = 784 # MNIST data input (img shape: 28*28) # tf Graph input (only pictures) -X = tf.placeholder("float", [None, n_input]) +X = tf.placeholder("float", [None, num_input]) weights = { - 'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), - 'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), - 'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])), - 'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])), + 'encoder_h1': tf.Variable(tf.random_normal([num_input, num_hidden_1])), + 'encoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_hidden_2])), + 'decoder_h1': tf.Variable(tf.random_normal([num_hidden_2, num_hidden_1])), + 'decoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_input])), } biases = { - 'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])), - 'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])), - 'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])), - 'decoder_b2': tf.Variable(tf.random_normal([n_input])), + 'encoder_b1': tf.Variable(tf.random_normal([num_hidden_1])), + 'encoder_b2': tf.Variable(tf.random_normal([num_hidden_2])), + 'decoder_b1': tf.Variable(tf.random_normal([num_hidden_1])), + 'decoder_b2': tf.Variable(tf.random_normal([num_input])), } - # Building the encoder def encoder(x): # Encoder Hidden layer with sigmoid activation #1 @@ -79,38 +84,59 @@ def decoder(x): y_true = X # Define loss and optimizer, minimize the squared error -cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) -optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost) +loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) +optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss) -# Initializing the variables +# Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() -# Launch the graph +# Start Training +# Start a new TF session with tf.Session() as sess: + + # Run the initializer sess.run(init) - total_batch = int(mnist.train.num_examples/batch_size) - # Training cycle - for epoch in range(training_epochs): - # Loop over all batches - for i in range(total_batch): - batch_xs, batch_ys = mnist.train.next_batch(batch_size) - # Run optimization op (backprop) and cost op (to get loss value) - _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs}) - # Display logs per epoch step - if epoch % display_step == 0: - print("Epoch:", '%04d' % (epoch+1), - "cost=", "{:.9f}".format(c)) - - print("Optimization Finished!") - - # Applying encode and decode over test set - encode_decode = sess.run( - y_pred, feed_dict={X: mnist.test.images[:examples_to_show]}) - # Compare original images with their reconstructions - f, a = plt.subplots(2, 10, figsize=(10, 2)) - for i in range(examples_to_show): - a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28))) - a[1][i].imshow(np.reshape(encode_decode[i], (28, 28))) - f.show() - plt.draw() - plt.waitforbuttonpress() + + # Training + for i in range(1, num_steps+1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + + # Run optimization op (backprop) and cost op (to get loss value) + _, l = sess.run([optimizer, loss], feed_dict={X: batch_x}) + # Display logs per step + if i % display_step == 0 or i == 1: + print('Step %i: Minibatch Loss: %f' % (i, l)) + + # Testing + # Encode and decode images from test set and visualize their reconstruction. + n = 4 + canvas_orig = np.empty((28 * n, 28 * n)) + canvas_recon = np.empty((28 * n, 28 * n)) + for i in range(n): + # MNIST test set + batch_x, _ = mnist.test.next_batch(n) + # Encode and decode the digit image + g = sess.run(decoder_op, feed_dict={X: batch_x}) + + # Display original images + for j in range(n): + # Draw the original digits + canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = \ + batch_x[j].reshape([28, 28]) + # Display reconstructed images + for j in range(n): + # Draw the reconstructed digits + canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = \ + g[j].reshape([28, 28]) + + print("Original Images") + plt.figure(figsize=(n, n)) + plt.imshow(canvas_orig, origin="upper", cmap="gray") + plt.show() + + print("Reconstructed Images") + plt.figure(figsize=(n, n)) + plt.imshow(canvas_recon, origin="upper", cmap="gray") + plt.show() diff --git a/examples/3_NeuralNetworks/bidirectional_rnn.py b/examples/3_NeuralNetworks/bidirectional_rnn.py index 09d44b94..2ff862ae 100644 --- a/examples/3_NeuralNetworks/bidirectional_rnn.py +++ b/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -1,11 +1,16 @@ -''' -A Bidirectional Recurrent Neural Network (LSTM) implementation example using TensorFlow library. -This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) -Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf +""" Bi-directional Recurrent Neural Network. + +A Bi-directional Recurrent Neural Network (LSTM) implementation example using +TensorFlow library. This example is using the MNIST database of handwritten +digits (http://yann.lecun.com/exdb/mnist/) + +Links: + [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ -''' +""" from __future__ import print_function @@ -23,46 +28,46 @@ we will then handle 28 sequences of 28 steps for every sample. ''' -# Parameters +# Training Parameters learning_rate = 0.001 -training_iters = 100000 +training_steps = 10000 batch_size = 128 -display_step = 10 +display_step = 200 # Network Parameters -n_input = 28 # MNIST data input (img shape: 28*28) -n_steps = 28 # timesteps -n_hidden = 128 # hidden layer num of features -n_classes = 10 # MNIST total classes (0-9 digits) +num_input = 28 # MNIST data input (img shape: 28*28) +timesteps = 28 # timesteps +num_hidden = 128 # hidden layer num of features +num_classes = 10 # MNIST total classes (0-9 digits) # tf Graph input -x = tf.placeholder("float", [None, n_steps, n_input]) -y = tf.placeholder("float", [None, n_classes]) +X = tf.placeholder("float", [None, timesteps, num_input]) +Y = tf.placeholder("float", [None, num_classes]) # Define weights weights = { # Hidden layer weights => 2*n_hidden because of forward + backward cells - 'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes])) + 'out': tf.Variable(tf.random_normal([2*num_hidden, num_classes])) } biases = { - 'out': tf.Variable(tf.random_normal([n_classes])) + 'out': tf.Variable(tf.random_normal([num_classes])) } def BiRNN(x, weights, biases): - # Prepare data shape to match `bidirectional_rnn` function requirements - # Current data input shape: (batch_size, n_steps, n_input) - # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) + # Prepare data shape to match `rnn` function requirements + # Current data input shape: (batch_size, timesteps, n_input) + # Required shape: 'timesteps' tensors list of shape (batch_size, num_input) - # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input) - x = tf.unstack(x, n_steps, 1) + # Unstack to get a list of 'timesteps' tensors of shape (batch_size, num_input) + x = tf.unstack(x, timesteps, 1) # Define lstm cells with tensorflow # Forward direction cell - lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) + lstm_fw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Backward direction cell - lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) + lstm_bw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output try: @@ -75,44 +80,47 @@ def BiRNN(x, weights, biases): # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out'] -pred = BiRNN(x, weights, biases) +logits = BiRNN(X, weights, biases) +prediction = tf.nn.softmax(logits) # Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) -optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) -# Evaluate model -correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) -# Initializing the variables +# Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() -# Launch the graph +# Start training with tf.Session() as sess: + + # Run the initializer sess.run(init) - step = 1 - # Keep training until reach max iterations - while step * batch_size < training_iters: + + for step in range(1, training_steps+1): batch_x, batch_y = mnist.train.next_batch(batch_size) # Reshape data to get 28 seq of 28 elements - batch_x = batch_x.reshape((batch_size, n_steps, n_input)) + batch_x = batch_x.reshape((batch_size, timesteps, num_input)) # Run optimization op (backprop) - sess.run(optimizer, feed_dict={x: batch_x, y: batch_y}) - if step % display_step == 0: - # Calculate batch accuracy - acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y}) - # Calculate batch loss - loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y}) - print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ - "{:.6f}".format(loss) + ", Training Accuracy= " + \ - "{:.5f}".format(acc)) - step += 1 + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + print("Optimization Finished!") # Calculate accuracy for 128 mnist test images test_len = 128 - test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input)) + test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input)) test_label = mnist.test.labels[:test_len] print("Testing Accuracy:", \ - sess.run(accuracy, feed_dict={x: test_data, y: test_label})) + sess.run(accuracy, feed_dict={X: test_data, Y: test_label})) diff --git a/examples/3_NeuralNetworks/convolutional_network.py b/examples/3_NeuralNetworks/convolutional_network.py index d729dd3c..5cd212be 100644 --- a/examples/3_NeuralNetworks/convolutional_network.py +++ b/examples/3_NeuralNetworks/convolutional_network.py @@ -1,134 +1,125 @@ -''' -A Convolutional Network implementation example using TensorFlow library. +""" Convolutional Neural Network. + +Build and train a convolutional neural network with TensorFlow. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) +This example is using TensorFlow layers API, see 'convolutional_network_raw' +example for a raw implementation with variables. + Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ -''' - -from __future__ import print_function - -import tensorflow as tf +""" +from __future__ import division, print_function, absolute_import # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data -mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) -# Parameters +import tensorflow as tf + +# Training Parameters learning_rate = 0.001 -training_iters = 200000 +num_steps = 2000 batch_size = 128 -display_step = 10 # Network Parameters -n_input = 784 # MNIST data input (img shape: 28*28) -n_classes = 10 # MNIST total classes (0-9 digits) +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) dropout = 0.75 # Dropout, probability to keep units -# tf Graph input -x = tf.placeholder(tf.float32, [None, n_input]) -y = tf.placeholder(tf.float32, [None, n_classes]) -keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) - -# Create some wrappers for simplicity -def conv2d(x, W, b, strides=1): - # Conv2D wrapper, with bias and relu activation - x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') - x = tf.nn.bias_add(x, b) - return tf.nn.relu(x) +# Create the neural network +def conv_net(x_dict, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + # TF Estimator input is a dict, in case of multiple inputs + x = x_dict['images'] + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) -def maxpool2d(x, k=2): - # MaxPool2D wrapper - return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], - padding='SAME') + # Convolution Layer with 32 filters and a kernel size of 5 + conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv1 = tf.layers.max_pooling2d(conv1, 2, 2) + # Convolution Layer with 32 filters and a kernel size of 5 + conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv2 = tf.layers.max_pooling2d(conv2, 2, 2) -# Create model -def conv_net(x, weights, biases, dropout): - # Reshape input picture - x = tf.reshape(x, shape=[-1, 28, 28, 1]) + # Flatten the data to a 1-D vector for the fully connected layer + fc1 = tf.contrib.layers.flatten(conv2) - # Convolution Layer - conv1 = conv2d(x, weights['wc1'], biases['bc1']) - # Max Pooling (down-sampling) - conv1 = maxpool2d(conv1, k=2) + # Fully connected layer (in tf contrib folder for now) + fc1 = tf.layers.dense(fc1, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) - # Convolution Layer - conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) - # Max Pooling (down-sampling) - conv2 = maxpool2d(conv2, k=2) + # Output layer, class prediction + out = tf.layers.dense(fc1, n_classes) - # Fully connected layer - # Reshape conv2 output to fit fully connected layer input - fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) - fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) - fc1 = tf.nn.relu(fc1) - # Apply Dropout - fc1 = tf.nn.dropout(fc1, dropout) - - # Output, class prediction - out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) return out -# Store layers weight & bias -weights = { - # 5x5 conv, 1 input, 32 outputs - 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), - # 5x5 conv, 32 inputs, 64 outputs - 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), - # fully connected, 7*7*64 inputs, 1024 outputs - 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), - # 1024 inputs, 10 outputs (class prediction) - 'out': tf.Variable(tf.random_normal([1024, n_classes])) -} - -biases = { - 'bc1': tf.Variable(tf.random_normal([32])), - 'bc2': tf.Variable(tf.random_normal([64])), - 'bd1': tf.Variable(tf.random_normal([1024])), - 'out': tf.Variable(tf.random_normal([n_classes])) -} - -# Construct model -pred = conv_net(x, weights, biases, keep_prob) - -# Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) -optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) - -# Evaluate model -correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) -accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) - -# Initializing the variables -init = tf.global_variables_initializer() - -# Launch the graph -with tf.Session() as sess: - sess.run(init) - step = 1 - # Keep training until reach max iterations - while step * batch_size < training_iters: - batch_x, batch_y = mnist.train.next_batch(batch_size) - # Run optimization op (backprop) - sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, - keep_prob: dropout}) - if step % display_step == 0: - # Calculate batch loss and accuracy - loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, - y: batch_y, - keep_prob: 1.}) - print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ - "{:.6f}".format(loss) + ", Training Accuracy= " + \ - "{:.5f}".format(acc)) - step += 1 - print("Optimization Finished!") - - # Calculate accuracy for 256 mnist test images - print("Testing Accuracy:", \ - sess.run(accuracy, feed_dict={x: mnist.test.images[:256], - y: mnist.test.labels[:256], - keep_prob: 1.})) + +# Define the model function (following TF Estimator Template) +def model_fn(features, labels, mode): + # Build the neural network + # Because Dropout have different behavior at training and prediction time, we + # need to create 2 distinct computation graphs that still share the same weights. + logits_train = conv_net(features, num_classes, dropout, reuse=False, + is_training=True) + logits_test = conv_net(features, num_classes, dropout, reuse=True, + is_training=False) + + # Predictions + pred_classes = tf.argmax(logits_test, axis=1) + pred_probas = tf.nn.softmax(logits_test) + + # If prediction mode, early return + if mode == tf.estimator.ModeKeys.PREDICT: + return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) + + # Define loss and optimizer + loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits_train, labels=tf.cast(labels, dtype=tf.int32))) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + train_op = optimizer.minimize(loss_op, + global_step=tf.train.get_global_step()) + + # Evaluate the accuracy of the model + acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) + + # TF Estimators requires to return a EstimatorSpec, that specify + # the different ops for training, evaluating, ... + estim_specs = tf.estimator.EstimatorSpec( + mode=mode, + predictions=pred_classes, + loss=loss_op, + train_op=train_op, + eval_metric_ops={'accuracy': acc_op}) + + return estim_specs + +# Build the Estimator +model = tf.estimator.Estimator(model_fn) + +# Define the input function for training +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.train.images}, y=mnist.train.labels, + batch_size=batch_size, num_epochs=None, shuffle=True) +# Train the Model +model.train(input_fn, steps=num_steps) + +# Evaluate the Model +# Define the input function for evaluating +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.test.images}, y=mnist.test.labels, + batch_size=batch_size, shuffle=False) +# Use the Estimator 'evaluate' method +e = model.evaluate(input_fn) + +print("Testing Accuracy:", e['accuracy']) diff --git a/examples/3_NeuralNetworks/convolutional_network_raw.py b/examples/3_NeuralNetworks/convolutional_network_raw.py new file mode 100644 index 00000000..d063f21f --- /dev/null +++ b/examples/3_NeuralNetworks/convolutional_network_raw.py @@ -0,0 +1,141 @@ +""" Convolutional Neural Network. + +Build and train a convolutional neural network with TensorFlow. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import division, print_function, absolute_import + +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Parameters +learning_rate = 0.001 +num_steps = 200 +batch_size = 128 +display_step = 10 + +# Network Parameters +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units + +# tf Graph input +X = tf.placeholder(tf.float32, [None, num_input]) +Y = tf.placeholder(tf.float32, [None, num_classes]) +keep_prob = tf.placeholder(tf.float32) # dropout (keep probability) + + +# Create some wrappers for simplicity +def conv2d(x, W, b, strides=1): + # Conv2D wrapper, with bias and relu activation + x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') + x = tf.nn.bias_add(x, b) + return tf.nn.relu(x) + + +def maxpool2d(x, k=2): + # MaxPool2D wrapper + return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], + padding='SAME') + + +# Create model +def conv_net(x, weights, biases, dropout): + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer + conv1 = conv2d(x, weights['wc1'], biases['bc1']) + # Max Pooling (down-sampling) + conv1 = maxpool2d(conv1, k=2) + + # Convolution Layer + conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) + # Max Pooling (down-sampling) + conv2 = maxpool2d(conv2, k=2) + + # Fully connected layer + # Reshape conv2 output to fit fully connected layer input + fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) + fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) + fc1 = tf.nn.relu(fc1) + # Apply Dropout + fc1 = tf.nn.dropout(fc1, dropout) + + # Output, class prediction + out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) + return out + +# Store layers weight & bias +weights = { + # 5x5 conv, 1 input, 32 outputs + 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), + # 5x5 conv, 32 inputs, 64 outputs + 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), + # fully connected, 7*7*64 inputs, 1024 outputs + 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), + # 1024 inputs, 10 outputs (class prediction) + 'out': tf.Variable(tf.random_normal([1024, num_classes])) +} + +biases = { + 'bc1': tf.Variable(tf.random_normal([32])), + 'bc2': tf.Variable(tf.random_normal([64])), + 'bd1': tf.Variable(tf.random_normal([1024])), + 'out': tf.Variable(tf.random_normal([num_classes])) +} + +# Construct model +logits = conv_net(X, weights, biases, keep_prob) +prediction = tf.nn.softmax(logits) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + + +# Evaluate model +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, num_steps+1): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.8}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y, + keep_prob: 1.0}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy for 256 MNIST test images + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={X: mnist.test.images[:256], + Y: mnist.test.labels[:256], + keep_prob: 1.0})) diff --git a/examples/3_NeuralNetworks/dcgan.py b/examples/3_NeuralNetworks/dcgan.py new file mode 100644 index 00000000..e7eaaaf5 --- /dev/null +++ b/examples/3_NeuralNetworks/dcgan.py @@ -0,0 +1,167 @@ +""" Deep Convolutional Generative Adversarial Network (DCGAN). + +Using deep convolutional generative adversarial networks (DCGAN) to generate +digit images from a noise distribution. + +References: + - Unsupervised representation learning with deep convolutional generative + adversarial networks. A Radford, L Metz, S Chintala. arXiv:1511.06434. + +Links: + - [DCGAN Paper](https://arxiv.org/abs/1511.06434). + - [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import division, print_function, absolute_import + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Params +num_steps = 20000 +batch_size = 32 + +# Network Params +image_dim = 784 # 28*28 pixels * 1 channel +gen_hidden_dim = 256 +disc_hidden_dim = 256 +noise_dim = 200 # Noise data points + + +# Generator Network +# Input: Noise, Output: Image +def generator(x, reuse=False): + with tf.variable_scope('Generator', reuse=reuse): + # TensorFlow Layers automatically create variables and calculate their + # shape, based on the input. + x = tf.layers.dense(x, units=6 * 6 * 128) + x = tf.nn.tanh(x) + # Reshape to a 4-D array of images: (batch, height, width, channels) + # New shape: (batch, 6, 6, 128) + x = tf.reshape(x, shape=[-1, 6, 6, 128]) + # Deconvolution, image shape: (batch, 14, 14, 64) + x = tf.layers.conv2d_transpose(x, 64, 4, strides=2) + # Deconvolution, image shape: (batch, 28, 28, 1) + x = tf.layers.conv2d_transpose(x, 1, 2, strides=2) + # Apply sigmoid to clip values between 0 and 1 + x = tf.nn.sigmoid(x) + return x + + +# Discriminator Network +# Input: Image, Output: Prediction Real/Fake Image +def discriminator(x, reuse=False): + with tf.variable_scope('Discriminator', reuse=reuse): + # Typical convolutional neural network to classify images. + x = tf.layers.conv2d(x, 64, 5) + x = tf.nn.tanh(x) + x = tf.layers.average_pooling2d(x, 2, 2) + x = tf.layers.conv2d(x, 128, 5) + x = tf.nn.tanh(x) + x = tf.layers.average_pooling2d(x, 2, 2) + x = tf.contrib.layers.flatten(x) + x = tf.layers.dense(x, 1024) + x = tf.nn.tanh(x) + # Output 2 classes: Real and Fake images + x = tf.layers.dense(x, 2) + return x + +# Build Networks +# Network Inputs +noise_input = tf.placeholder(tf.float32, shape=[None, noise_dim]) +real_image_input = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) + +# Build Generator Network +gen_sample = generator(noise_input) + +# Build 2 Discriminator Networks (one from noise input, one from generated samples) +disc_real = discriminator(real_image_input) +disc_fake = discriminator(gen_sample, reuse=True) +disc_concat = tf.concat([disc_real, disc_fake], axis=0) + +# Build the stacked generator/discriminator +stacked_gan = discriminator(gen_sample, reuse=True) + +# Build Targets (real or fake images) +disc_target = tf.placeholder(tf.int32, shape=[None]) +gen_target = tf.placeholder(tf.int32, shape=[None]) + +# Build Loss +disc_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=disc_concat, labels=disc_target)) +gen_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=stacked_gan, labels=gen_target)) + +# Build Optimizers +optimizer_gen = tf.train.AdamOptimizer(learning_rate=0.001) +optimizer_disc = tf.train.AdamOptimizer(learning_rate=0.001) + +# Training Variables for each optimizer +# By default in TensorFlow, all variables are updated by each optimizer, so we +# need to precise for each one of them the specific variables to update. +# Generator Network Variables +gen_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Generator') +# Discriminator Network Variables +disc_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Discriminator') + +# Create training operations +train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars) +train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for i in range(1, num_steps+1): + + # Prepare Input Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + batch_x = np.reshape(batch_x, newshape=[-1, 28, 28, 1]) + # Generate noise to feed to the generator + z = np.random.uniform(-1., 1., size=[batch_size, noise_dim]) + + # Prepare Targets (Real image: 1, Fake image: 0) + # The first half of data fed to the generator are real images, + # the other half are fake images (coming from the generator). + batch_disc_y = np.concatenate( + [np.ones([batch_size]), np.zeros([batch_size])], axis=0) + # Generator tries to fool the discriminator, thus targets are 1. + batch_gen_y = np.ones([batch_size]) + + # Training + feed_dict = {real_image_input: batch_x, noise_input: z, + disc_target: batch_disc_y, gen_target: batch_gen_y} + _, _, gl, dl = sess.run([train_gen, train_disc, gen_loss, disc_loss], + feed_dict=feed_dict) + if i % 100 == 0 or i == 1: + print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl)) + + # Generate images from noise, using the generator network. + f, a = plt.subplots(4, 10, figsize=(10, 4)) + for i in range(10): + # Noise input. + z = np.random.uniform(-1., 1., size=[4, noise_dim]) + g = sess.run(gen_sample, feed_dict={noise_input: z}) + for j in range(4): + # Generate image from noise. Extend to 3 channels for matplot figure. + img = np.reshape(np.repeat(g[j][:, :, np.newaxis], 3, axis=2), + newshape=(28, 28, 3)) + a[j][i].imshow(img) + + f.show() + plt.draw() + plt.waitforbuttonpress() diff --git a/examples/3_NeuralNetworks/dynamic_rnn.py b/examples/3_NeuralNetworks/dynamic_rnn.py index c44e5ee1..753778c3 100644 --- a/examples/3_NeuralNetworks/dynamic_rnn.py +++ b/examples/3_NeuralNetworks/dynamic_rnn.py @@ -1,13 +1,16 @@ -''' -A Dynamic Recurrent Neural Network (LSTM) implementation example using -TensorFlow library. This example is using a toy dataset to classify linear -sequences. The generated sequences have variable length. +""" Dynamic Recurrent Neural Network. -Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf +TensorFlow implementation of a Recurrent Neural Network (LSTM) that performs +dynamic computation over sequences with variable length. This example is using +a toy dataset to classify linear sequences. The generated sequences have +variable length. + +Links: + [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ -''' +""" from __future__ import print_function @@ -81,9 +84,9 @@ def next(self, batch_size): # Parameters learning_rate = 0.01 -training_iters = 1000000 +training_steps = 10000 batch_size = 128 -display_step = 10 +display_step = 200 # Network Parameters seq_max_len = 20 # Sequence max length @@ -157,30 +160,31 @@ def dynamicRNN(x, seqlen, weights, biases): correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) -# Initializing the variables +# Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() -# Launch the graph +# Start training with tf.Session() as sess: + + # Run the initializer sess.run(init) - step = 1 - # Keep training until reach max iterations - while step * batch_size < training_iters: + + for step in range(1, training_steps + 1): batch_x, batch_y, batch_seqlen = trainset.next(batch_size) # Run optimization op (backprop) sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, seqlen: batch_seqlen}) - if step % display_step == 0: + if step % display_step == 0 or step == 1: # Calculate batch accuracy acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y, seqlen: batch_seqlen}) # Calculate batch loss loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y, seqlen: batch_seqlen}) - print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ + print("Step " + str(step*batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ "{:.5f}".format(acc)) - step += 1 + print("Optimization Finished!") # Calculate accuracy diff --git a/examples/3_NeuralNetworks/gan.py b/examples/3_NeuralNetworks/gan.py new file mode 100644 index 00000000..dd5977ad --- /dev/null +++ b/examples/3_NeuralNetworks/gan.py @@ -0,0 +1,157 @@ +""" Generative Adversarial Networks (GAN). + +Using generative adversarial networks (GAN) to generate digit images from a +noise distribution. + +References: + - Generative adversarial nets. I Goodfellow, J Pouget-Abadie, M Mirza, + B Xu, D Warde-Farley, S Ozair, Y. Bengio. Advances in neural information + processing systems, 2672-2680. + - Understanding the difficulty of training deep feedforward neural networks. + X Glorot, Y Bengio. Aistats 9, 249-256 + +Links: + - [GAN Paper](https://arxiv.org/pdf/1406.2661.pdf). + - [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + - [Xavier Glorot Init](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import division, print_function, absolute_import + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Params +num_steps = 100000 +batch_size = 128 +learning_rate = 0.0002 + +# Network Params +image_dim = 784 # 28*28 pixels +gen_hidden_dim = 256 +disc_hidden_dim = 256 +noise_dim = 100 # Noise data points + +# A custom initialization (see Xavier Glorot init) +def glorot_init(shape): + return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.)) + +# Store layers weight & bias +weights = { + 'gen_hidden1': tf.Variable(glorot_init([noise_dim, gen_hidden_dim])), + 'gen_out': tf.Variable(glorot_init([gen_hidden_dim, image_dim])), + 'disc_hidden1': tf.Variable(glorot_init([image_dim, disc_hidden_dim])), + 'disc_out': tf.Variable(glorot_init([disc_hidden_dim, 1])), +} +biases = { + 'gen_hidden1': tf.Variable(tf.zeros([gen_hidden_dim])), + 'gen_out': tf.Variable(tf.zeros([image_dim])), + 'disc_hidden1': tf.Variable(tf.zeros([disc_hidden_dim])), + 'disc_out': tf.Variable(tf.zeros([1])), +} + + +# Generator +def generator(x): + hidden_layer = tf.matmul(x, weights['gen_hidden1']) + hidden_layer = tf.add(hidden_layer, biases['gen_hidden1']) + hidden_layer = tf.nn.relu(hidden_layer) + out_layer = tf.matmul(hidden_layer, weights['gen_out']) + out_layer = tf.add(out_layer, biases['gen_out']) + out_layer = tf.nn.sigmoid(out_layer) + return out_layer + + +# Discriminator +def discriminator(x): + hidden_layer = tf.matmul(x, weights['disc_hidden1']) + hidden_layer = tf.add(hidden_layer, biases['disc_hidden1']) + hidden_layer = tf.nn.relu(hidden_layer) + out_layer = tf.matmul(hidden_layer, weights['disc_out']) + out_layer = tf.add(out_layer, biases['disc_out']) + out_layer = tf.nn.sigmoid(out_layer) + return out_layer + +# Build Networks +# Network Inputs +gen_input = tf.placeholder(tf.float32, shape=[None, noise_dim], name='input_noise') +disc_input = tf.placeholder(tf.float32, shape=[None, image_dim], name='disc_input') + +# Build Generator Network +gen_sample = generator(gen_input) + +# Build 2 Discriminator Networks (one from noise input, one from generated samples) +disc_real = discriminator(disc_input) +disc_fake = discriminator(gen_sample) + +# Build Loss +gen_loss = -tf.reduce_mean(tf.log(disc_fake)) +disc_loss = -tf.reduce_mean(tf.log(disc_real) + tf.log(1. - disc_fake)) + +# Build Optimizers +optimizer_gen = tf.train.AdamOptimizer(learning_rate=learning_rate) +optimizer_disc = tf.train.AdamOptimizer(learning_rate=learning_rate) + +# Training Variables for each optimizer +# By default in TensorFlow, all variables are updated by each optimizer, so we +# need to precise for each one of them the specific variables to update. +# Generator Network Variables +gen_vars = [weights['gen_hidden1'], weights['gen_out'], + biases['gen_hidden1'], biases['gen_out']] +# Discriminator Network Variables +disc_vars = [weights['disc_hidden1'], weights['disc_out'], + biases['disc_hidden1'], biases['disc_out']] + +# Create training operations +train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars) +train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for i in range(1, num_steps+1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + # Generate noise to feed to the generator + z = np.random.uniform(-1., 1., size=[batch_size, noise_dim]) + + # Train + feed_dict = {disc_input: batch_x, gen_input: z} + _, _, gl, dl = sess.run([train_gen, train_disc, gen_loss, disc_loss], + feed_dict=feed_dict) + if i % 1000 == 0 or i == 1: + print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl)) + + # Generate images from noise, using the generator network. + f, a = plt.subplots(4, 10, figsize=(10, 4)) + for i in range(10): + # Noise input. + z = np.random.uniform(-1., 1., size=[4, noise_dim]) + g = sess.run([gen_sample], feed_dict={gen_input: z}) + g = np.reshape(g, newshape=(4, 28, 28, 1)) + # Reverse colours for better display + g = -1 * (g - 1) + for j in range(4): + # Generate image from noise. Extend to 3 channels for matplot figure. + img = np.reshape(np.repeat(g[j][:, :, np.newaxis], 3, axis=2), + newshape=(28, 28, 3)) + a[j][i].imshow(img) + + f.show() + plt.draw() + plt.waitforbuttonpress() diff --git a/examples/3_NeuralNetworks/multilayer_perceptron.py b/examples/3_NeuralNetworks/multilayer_perceptron.py index f9f9c683..cf04b015 100644 --- a/examples/3_NeuralNetworks/multilayer_perceptron.py +++ b/examples/3_NeuralNetworks/multilayer_perceptron.py @@ -1,11 +1,22 @@ -''' -A Multilayer Perceptron implementation example using TensorFlow library. -This example is using the MNIST database of handwritten digits -(http://yann.lecun.com/exdb/mnist/) +""" Multilayer Perceptron. + +A Multilayer Perceptron (Neural Network) implementation example using +TensorFlow library. This example is using the MNIST database of handwritten +digits (http://yann.lecun.com/exdb/mnist/). + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ -''' +""" + +# ------------------------------------------------------------------ +# +# THIS EXAMPLE HAS BEEN RENAMED 'neural_network.py', FOR SIMPLICITY. +# +# ------------------------------------------------------------------ + from __future__ import print_function @@ -22,27 +33,14 @@ display_step = 1 # Network Parameters -n_hidden_1 = 256 # 1st layer number of features -n_hidden_2 = 256 # 2nd layer number of features +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10 # MNIST total classes (0-9 digits) # tf Graph input -x = tf.placeholder("float", [None, n_input]) -y = tf.placeholder("float", [None, n_classes]) - - -# Create model -def multilayer_perceptron(x, weights, biases): - # Hidden layer with RELU activation - layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) - layer_1 = tf.nn.relu(layer_1) - # Hidden layer with RELU activation - layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) - layer_2 = tf.nn.relu(layer_2) - # Output layer with linear activation - out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] - return out_layer +X = tf.placeholder("float", [None, n_input]) +Y = tf.placeholder("float", [None, n_classes]) # Store layers weight & bias weights = { @@ -56,17 +54,28 @@ def multilayer_perceptron(x, weights, biases): 'out': tf.Variable(tf.random_normal([n_classes])) } + +# Create model +def multilayer_perceptron(x): + # Hidden fully connected layer with 256 neurons + layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) + # Hidden fully connected layer with 256 neurons + layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) + # Output fully connected layer with a neuron for each class + out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] + return out_layer + # Construct model -pred = multilayer_perceptron(x, weights, biases) +logits = multilayer_perceptron(X) # Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) -optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) - +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) # Initializing the variables init = tf.global_variables_initializer() -# Launch the graph with tf.Session() as sess: sess.run(init) @@ -78,18 +87,18 @@ def multilayer_perceptron(x, weights, biases): for i in range(total_batch): batch_x, batch_y = mnist.train.next_batch(batch_size) # Run optimization op (backprop) and cost op (to get loss value) - _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, - y: batch_y}) + _, c = sess.run([train_op, loss_op], feed_dict={X: batch_x, + Y: batch_y}) # Compute average loss avg_cost += c / total_batch # Display logs per epoch step if epoch % display_step == 0: - print("Epoch:", '%04d' % (epoch+1), "cost=", \ - "{:.9f}".format(avg_cost)) + print("Epoch:", '%04d' % (epoch+1), "cost={:.9f}".format(avg_cost)) print("Optimization Finished!") # Test model - correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + pred = tf.nn.softmax(logits) # Apply softmax to logits + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) - print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) + print("Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels})) diff --git a/examples/3_NeuralNetworks/neural_network.py b/examples/3_NeuralNetworks/neural_network.py new file mode 100644 index 00000000..b3bfaad4 --- /dev/null +++ b/examples/3_NeuralNetworks/neural_network.py @@ -0,0 +1,103 @@ +""" Neural Network. + +A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron) +implementation with TensorFlow. This example is using the MNIST database +of handwritten digits (http://yann.lecun.com/exdb/mnist/). + +This example is using TensorFlow layers, see 'neural_network_raw' example for +a raw implementation with variables. + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +import tensorflow as tf + +# Parameters +learning_rate = 0.1 +num_steps = 1000 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) + + +# Define the neural network +def neural_net(x_dict): + # TF Estimator input is a dict, in case of multiple inputs + x = x_dict['images'] + # Hidden fully connected layer with 256 neurons + layer_1 = tf.layers.dense(x, n_hidden_1) + # Hidden fully connected layer with 256 neurons + layer_2 = tf.layers.dense(layer_1, n_hidden_2) + # Output fully connected layer with a neuron for each class + out_layer = tf.layers.dense(layer_2, num_classes) + return out_layer + + +# Define the model function (following TF Estimator Template) +def model_fn(features, labels, mode): + # Build the neural network + logits = neural_net(features) + + # Predictions + pred_classes = tf.argmax(logits, axis=1) + pred_probas = tf.nn.softmax(logits) + + # If prediction mode, early return + if mode == tf.estimator.ModeKeys.PREDICT: + return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) + + # Define loss and optimizer + loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits, labels=tf.cast(labels, dtype=tf.int32))) + optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) + train_op = optimizer.minimize(loss_op, + global_step=tf.train.get_global_step()) + + # Evaluate the accuracy of the model + acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) + + # TF Estimators requires to return a EstimatorSpec, that specify + # the different ops for training, evaluating, ... + estim_specs = tf.estimator.EstimatorSpec( + mode=mode, + predictions=pred_classes, + loss=loss_op, + train_op=train_op, + eval_metric_ops={'accuracy': acc_op}) + + return estim_specs + +# Build the Estimator +model = tf.estimator.Estimator(model_fn) + +# Define the input function for training +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.train.images}, y=mnist.train.labels, + batch_size=batch_size, num_epochs=None, shuffle=True) +# Train the Model +model.train(input_fn, steps=num_steps) + +# Evaluate the Model +# Define the input function for evaluating +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.test.images}, y=mnist.test.labels, + batch_size=batch_size, shuffle=False) +# Use the Estimator 'evaluate' method +e = model.evaluate(input_fn) + +print("Testing Accuracy:", e['accuracy']) diff --git a/examples/3_NeuralNetworks/neural_network_raw.py b/examples/3_NeuralNetworks/neural_network_raw.py new file mode 100644 index 00000000..9c9962ba --- /dev/null +++ b/examples/3_NeuralNetworks/neural_network_raw.py @@ -0,0 +1,101 @@ +""" Neural Network. + +A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron) +implementation with TensorFlow. This example is using the MNIST database +of handwritten digits (http://yann.lecun.com/exdb/mnist/). + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +import tensorflow as tf + +# Parameters +learning_rate = 0.1 +num_steps = 500 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +X = tf.placeholder("float", [None, num_input]) +Y = tf.placeholder("float", [None, num_classes]) + +# Store layers weight & bias +weights = { + 'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])), + 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes])) +} +biases = { + 'b1': tf.Variable(tf.random_normal([n_hidden_1])), + 'b2': tf.Variable(tf.random_normal([n_hidden_2])), + 'out': tf.Variable(tf.random_normal([num_classes])) +} + + +# Create model +def neural_net(x): + # Hidden fully connected layer with 256 neurons + layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) + # Hidden fully connected layer with 256 neurons + layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) + # Output fully connected layer with a neuron for each class + out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] + return out_layer + +# Construct model +logits = neural_net(X) +prediction = tf.nn.softmax(logits) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, num_steps+1): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy for MNIST test images + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={X: mnist.test.images, + Y: mnist.test.labels})) diff --git a/examples/3_NeuralNetworks/recurrent_network.py b/examples/3_NeuralNetworks/recurrent_network.py index 6473f724..fbc3d271 100644 --- a/examples/3_NeuralNetworks/recurrent_network.py +++ b/examples/3_NeuralNetworks/recurrent_network.py @@ -1,11 +1,15 @@ -''' +""" Recurrent Neural Network. + A Recurrent Neural Network (LSTM) implementation example using TensorFlow library. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) -Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf + +Links: + [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ -''' +""" from __future__ import print_function @@ -22,42 +26,42 @@ handle 28 sequences of 28 steps for every sample. ''' -# Parameters +# Training Parameters learning_rate = 0.001 -training_iters = 100000 +training_steps = 10000 batch_size = 128 -display_step = 10 +display_step = 200 # Network Parameters -n_input = 28 # MNIST data input (img shape: 28*28) -n_steps = 28 # timesteps -n_hidden = 128 # hidden layer num of features -n_classes = 10 # MNIST total classes (0-9 digits) +num_input = 28 # MNIST data input (img shape: 28*28) +timesteps = 28 # timesteps +num_hidden = 128 # hidden layer num of features +num_classes = 10 # MNIST total classes (0-9 digits) # tf Graph input -x = tf.placeholder("float", [None, n_steps, n_input]) -y = tf.placeholder("float", [None, n_classes]) +X = tf.placeholder("float", [None, timesteps, num_input]) +Y = tf.placeholder("float", [None, num_classes]) # Define weights weights = { - 'out': tf.Variable(tf.random_normal([n_hidden, n_classes])) + 'out': tf.Variable(tf.random_normal([num_hidden, num_classes])) } biases = { - 'out': tf.Variable(tf.random_normal([n_classes])) + 'out': tf.Variable(tf.random_normal([num_classes])) } def RNN(x, weights, biases): # Prepare data shape to match `rnn` function requirements - # Current data input shape: (batch_size, n_steps, n_input) - # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) + # Current data input shape: (batch_size, timesteps, n_input) + # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) - # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input) - x = tf.unstack(x, n_steps, 1) + # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) + x = tf.unstack(x, timesteps, 1) # Define a lstm cell with tensorflow - lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0) + lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) # Get lstm cell output outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) @@ -65,44 +69,47 @@ def RNN(x, weights, biases): # Linear activation, using rnn inner loop last output return tf.matmul(outputs[-1], weights['out']) + biases['out'] -pred = RNN(x, weights, biases) +logits = RNN(X, weights, biases) +prediction = tf.nn.softmax(logits) # Define loss and optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) -optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) -# Evaluate model -correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) -# Initializing the variables +# Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() -# Launch the graph +# Start training with tf.Session() as sess: + + # Run the initializer sess.run(init) - step = 1 - # Keep training until reach max iterations - while step * batch_size < training_iters: + + for step in range(1, training_steps+1): batch_x, batch_y = mnist.train.next_batch(batch_size) # Reshape data to get 28 seq of 28 elements - batch_x = batch_x.reshape((batch_size, n_steps, n_input)) + batch_x = batch_x.reshape((batch_size, timesteps, num_input)) # Run optimization op (backprop) - sess.run(optimizer, feed_dict={x: batch_x, y: batch_y}) - if step % display_step == 0: - # Calculate batch accuracy - acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y}) - # Calculate batch loss - loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y}) - print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ - "{:.6f}".format(loss) + ", Training Accuracy= " + \ - "{:.5f}".format(acc)) - step += 1 + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + print("Optimization Finished!") # Calculate accuracy for 128 mnist test images test_len = 128 - test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input)) + test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input)) test_label = mnist.test.labels[:test_len] print("Testing Accuracy:", \ - sess.run(accuracy, feed_dict={x: test_data, y: test_label})) + sess.run(accuracy, feed_dict={X: test_data, Y: test_label})) diff --git a/examples/3_NeuralNetworks/variational_autoencoder.py b/examples/3_NeuralNetworks/variational_autoencoder.py new file mode 100644 index 00000000..8a8fd378 --- /dev/null +++ b/examples/3_NeuralNetworks/variational_autoencoder.py @@ -0,0 +1,143 @@ +""" Variational Auto-Encoder Example. + +Using a variational auto-encoder to generate digits images from noise. +MNIST handwritten digits are used as training examples. + +References: + - Auto-Encoding Variational Bayes The International Conference on Learning + Representations (ICLR), Banff, 2014. D.P. Kingma, M. Welling + - Understanding the difficulty of training deep feedforward neural networks. + X Glorot, Y Bengio. Aistats 9, 249-256 + - Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based + learning applied to document recognition." Proceedings of the IEEE, + 86(11):2278-2324, November 1998. + +Links: + - [VAE Paper] https://arxiv.org/abs/1312.6114 + - [Xavier Glorot Init](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). + - [MNIST Dataset] http://yann.lecun.com/exdb/mnist/ + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import division, print_function, absolute_import + +import numpy as np +import matplotlib.pyplot as plt +from scipy.stats import norm +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.001 +num_steps = 30000 +batch_size = 64 + +# Network Parameters +image_dim = 784 # MNIST images are 28x28 pixels +hidden_dim = 512 +latent_dim = 2 + +# A custom initialization (see Xavier Glorot init) +def glorot_init(shape): + return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.)) + +# Variables +weights = { + 'encoder_h1': tf.Variable(glorot_init([image_dim, hidden_dim])), + 'z_mean': tf.Variable(glorot_init([hidden_dim, latent_dim])), + 'z_std': tf.Variable(glorot_init([hidden_dim, latent_dim])), + 'decoder_h1': tf.Variable(glorot_init([latent_dim, hidden_dim])), + 'decoder_out': tf.Variable(glorot_init([hidden_dim, image_dim])) +} +biases = { + 'encoder_b1': tf.Variable(glorot_init([hidden_dim])), + 'z_mean': tf.Variable(glorot_init([latent_dim])), + 'z_std': tf.Variable(glorot_init([latent_dim])), + 'decoder_b1': tf.Variable(glorot_init([hidden_dim])), + 'decoder_out': tf.Variable(glorot_init([image_dim])) +} + +# Building the encoder +input_image = tf.placeholder(tf.float32, shape=[None, image_dim]) +encoder = tf.matmul(input_image, weights['encoder_h1']) + biases['encoder_b1'] +encoder = tf.nn.tanh(encoder) +z_mean = tf.matmul(encoder, weights['z_mean']) + biases['z_mean'] +z_std = tf.matmul(encoder, weights['z_std']) + biases['z_std'] + +# Sampler: Normal (gaussian) random distribution +eps = tf.random_normal(tf.shape(z_std), dtype=tf.float32, mean=0., stddev=1.0, + name='epsilon') +z = z_mean + tf.exp(z_std / 2) * eps + +# Building the decoder (with scope to re-use these layers later) +decoder = tf.matmul(z, weights['decoder_h1']) + biases['decoder_b1'] +decoder = tf.nn.tanh(decoder) +decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out'] +decoder = tf.nn.sigmoid(decoder) + + +# Define VAE Loss +def vae_loss(x_reconstructed, x_true): + # Reconstruction loss + encode_decode_loss = x_true * tf.log(1e-10 + x_reconstructed) \ + + (1 - x_true) * tf.log(1e-10 + 1 - x_reconstructed) + encode_decode_loss = -tf.reduce_sum(encode_decode_loss, 1) + # KL Divergence loss + kl_div_loss = 1 + z_std - tf.square(z_mean) - tf.exp(z_std) + kl_div_loss = -0.5 * tf.reduce_sum(kl_div_loss, 1) + return tf.reduce_mean(encode_decode_loss + kl_div_loss) + +loss_op = vae_loss(decoder, input_image) +optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for i in range(1, num_steps+1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + + # Train + feed_dict = {input_image: batch_x} + _, l = sess.run([train_op, loss_op], feed_dict=feed_dict) + if i % 1000 == 0 or i == 1: + print('Step %i, Loss: %f' % (i, l)) + + # Testing + # Generator takes noise as input + noise_input = tf.placeholder(tf.float32, shape=[None, latent_dim]) + # Rebuild the decoder to create image from noise + decoder = tf.matmul(noise_input, weights['decoder_h1']) + biases['decoder_b1'] + decoder = tf.nn.tanh(decoder) + decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out'] + decoder = tf.nn.sigmoid(decoder) + + # Building a manifold of generated digits + n = 20 + x_axis = np.linspace(-3, 3, n) + y_axis = np.linspace(-3, 3, n) + + canvas = np.empty((28 * n, 28 * n)) + for i, yi in enumerate(x_axis): + for j, xi in enumerate(y_axis): + z_mu = np.array([[xi, yi]] * batch_size) + x_mean = sess.run(decoder, feed_dict={noise_input: z_mu}) + canvas[(n - i - 1) * 28:(n - i) * 28, j * 28:(j + 1) * 28] = \ + x_mean[0].reshape(28, 28) + + plt.figure(figsize=(8, 10)) + Xi, Yi = np.meshgrid(x_axis, y_axis) + plt.imshow(canvas, origin="upper", cmap="gray") + plt.show() diff --git a/examples/4_Utils/save_restore_model.py b/examples/4_Utils/save_restore_model.py index 9db2c756..56af08b1 100644 --- a/examples/4_Utils/save_restore_model.py +++ b/examples/4_Utils/save_restore_model.py @@ -63,7 +63,7 @@ def multilayer_perceptron(x, weights, biases): cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) -# Initializing the variables +# Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # 'Saver' op to save and restore all the variables @@ -72,7 +72,8 @@ def multilayer_perceptron(x, weights, biases): # Running first session print("Starting 1st session...") with tf.Session() as sess: - # Initialize variables + + # Run the initializer sess.run(init) # Training cycle diff --git a/examples/4_Utils/tensorboard_advanced.py b/examples/4_Utils/tensorboard_advanced.py index 01def37e..45a7f962 100644 --- a/examples/4_Utils/tensorboard_advanced.py +++ b/examples/4_Utils/tensorboard_advanced.py @@ -20,7 +20,7 @@ training_epochs = 25 batch_size = 100 display_step = 1 -logs_path = '/tmp/tensorflow_logs/example' +logs_path = '/tmp/tensorflow_logs/example/' # Network Parameters n_hidden_1 = 256 # 1st layer number of features @@ -87,7 +87,7 @@ def multilayer_perceptron(x, weights, biases): acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) acc = tf.reduce_mean(tf.cast(acc, tf.float32)) -# Initializing the variables +# Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # Create a summary to monitor cost tensor @@ -103,8 +103,10 @@ def multilayer_perceptron(x, weights, biases): # Merge all summaries into a single op merged_summary_op = tf.summary.merge_all() -# Launch the graph +# Start training with tf.Session() as sess: + + # Run the initializer sess.run(init) # op to write logs to Tensorboard diff --git a/examples/4_Utils/tensorboard_basic.py b/examples/4_Utils/tensorboard_basic.py index cc0f5ad6..81216c0b 100644 --- a/examples/4_Utils/tensorboard_basic.py +++ b/examples/4_Utils/tensorboard_basic.py @@ -19,8 +19,8 @@ learning_rate = 0.01 training_epochs = 25 batch_size = 100 -display_step = 1 -logs_path = '/tmp/tensorflow_logs/example' +display_epoch = 1 +logs_path = '/tmp/tensorflow_logs/example/' # tf Graph Input # mnist data image of shape 28*28=784 @@ -48,7 +48,7 @@ acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) acc = tf.reduce_mean(tf.cast(acc, tf.float32)) -# Initializing the variables +# Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # Create a summary to monitor cost tensor @@ -58,8 +58,10 @@ # Merge all summaries into a single op merged_summary_op = tf.summary.merge_all() -# Launch the graph +# Start training with tf.Session() as sess: + + # Run the initializer sess.run(init) # op to write logs to Tensorboard @@ -81,7 +83,7 @@ # Compute average loss avg_cost += c / total_batch # Display logs per epoch step - if (epoch+1) % display_step == 0: + if (epoch+1) % display_epoch == 0: print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) print("Optimization Finished!") diff --git a/examples/5_DataManagement/build_an_image_dataset.py b/examples/5_DataManagement/build_an_image_dataset.py new file mode 100644 index 00000000..5a19ee05 --- /dev/null +++ b/examples/5_DataManagement/build_an_image_dataset.py @@ -0,0 +1,211 @@ +""" Build an Image Dataset in TensorFlow. + +For this example, you need to make your own set of images (JPEG). +We will show 2 different ways to build that dataset: + +- From a root folder, that will have a sub-folder containing images for each class + ``` + ROOT_FOLDER + |-------- SUBFOLDER (CLASS 0) + | | + | | ----- image1.jpg + | | ----- image2.jpg + | | ----- etc... + | + |-------- SUBFOLDER (CLASS 1) + | | + | | ----- image1.jpg + | | ----- image2.jpg + | | ----- etc... + ``` + +- From a plain text file, that will list all images with their class ID: + ``` + /path/to/image/1.jpg CLASS_ID + /path/to/image/2.jpg CLASS_ID + /path/to/image/3.jpg CLASS_ID + /path/to/image/4.jpg CLASS_ID + etc... + ``` + +Below, there are some parameters that you need to change (Marked 'CHANGE HERE'), +such as the dataset path. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import print_function + +import tensorflow as tf +import os + +# Dataset Parameters - CHANGE HERE +MODE = 'folder' # or 'file', if you choose a plain text file (see above). +DATASET_PATH = '/path/to/dataset/' # the dataset file or root folder path. + +# Image Parameters +N_CLASSES = 2 # CHANGE HERE, total number of classes +IMG_HEIGHT = 64 # CHANGE HERE, the image height to be resized to +IMG_WIDTH = 64 # CHANGE HERE, the image width to be resized to +CHANNELS = 3 # The 3 color channels, change to 1 if grayscale + + +# Reading the dataset +# 2 modes: 'file' or 'folder' +def read_images(dataset_path, mode, batch_size): + imagepaths, labels = list(), list() + if mode == 'file': + # Read dataset file + data = open(dataset_path, 'r').read().splitlines() + for d in data: + imagepaths.append(d.split(' ')[0]) + labels.append(int(d.split(' ')[1])) + elif mode == 'folder': + # An ID will be affected to each sub-folders by alphabetical order + label = 0 + # List the directory + try: # Python 2 + classes = sorted(os.walk(dataset_path).next()[1]) + except Exception: # Python 3 + classes = sorted(os.walk(dataset_path).__next__()[1]) + # List each sub-directory (the classes) + for c in classes: + c_dir = os.path.join(dataset_path, c) + try: # Python 2 + walk = os.walk(c_dir).next() + except Exception: # Python 3 + walk = os.walk(c_dir).__next__() + # Add each image to the training set + for sample in walk[2]: + # Only keeps jpeg images + if sample.endswith('.jpg') or sample.endswith('.jpeg'): + imagepaths.append(os.path.join(c_dir, sample)) + labels.append(label) + label += 1 + else: + raise Exception("Unknown mode.") + + # Convert to Tensor + imagepaths = tf.convert_to_tensor(imagepaths, dtype=tf.string) + labels = tf.convert_to_tensor(labels, dtype=tf.int32) + # Build a TF Queue, shuffle data + image, label = tf.train.slice_input_producer([imagepaths, labels], + shuffle=True) + + # Read images from disk + image = tf.read_file(image) + image = tf.image.decode_jpeg(image, channels=CHANNELS) + + # Resize images to a common size + image = tf.image.resize_images(image, [IMG_HEIGHT, IMG_WIDTH]) + + # Normalize + image = image * 1.0/127.5 - 1.0 + + # Create batches + X, Y = tf.train.batch([image, label], batch_size=batch_size, + capacity=batch_size * 8, + num_threads=4) + + return X, Y + +# ----------------------------------------------- +# THIS IS A CLASSIC CNN (see examples, section 3) +# ----------------------------------------------- +# Note that a few elements have changed (usage of queues). + +# Parameters +learning_rate = 0.001 +num_steps = 10000 +batch_size = 128 +display_step = 100 + +# Network Parameters +dropout = 0.75 # Dropout, probability to keep units + +# Build the data input +X, Y = read_images(DATASET_PATH, MODE, batch_size) + + +# Create model +def conv_net(x, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + + # Convolution Layer with 32 filters and a kernel size of 5 + conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv1 = tf.layers.max_pooling2d(conv1, 2, 2) + + # Convolution Layer with 32 filters and a kernel size of 5 + conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv2 = tf.layers.max_pooling2d(conv2, 2, 2) + + # Flatten the data to a 1-D vector for the fully connected layer + fc1 = tf.contrib.layers.flatten(conv2) + + # Fully connected layer (in contrib folder for now) + fc1 = tf.layers.dense(fc1, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) + + # Output layer, class prediction + out = tf.layers.dense(fc1, n_classes) + # Because 'softmax_cross_entropy_with_logits' already apply softmax, + # we only apply softmax to testing network + out = tf.nn.softmax(out) if not is_training else out + + return out + + +# Because Dropout have different behavior at training and prediction time, we +# need to create 2 distinct computation graphs that share the same weights. + +# Create a graph for training +logits_train = conv_net(X, N_CLASSES, dropout, reuse=False, is_training=True) +# Create another graph for testing that reuse the same weights +logits_test = conv_net(X, N_CLASSES, dropout, reuse=True, is_training=False) + +# Define loss and optimizer (with train logits, for dropout to take effect) +loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits_train, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.cast(Y, tf.int64)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Saver object +saver = tf.train.Saver() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Start the data queue + tf.train.start_queue_runners() + + # Training cycle + for step in range(1, num_steps+1): + + if step % display_step == 0: + # Run optimization and calculate batch loss and accuracy + _, loss, acc = sess.run([train_op, loss_op, accuracy]) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + else: + # Only run the optimization op (backprop) + sess.run(train_op) + + print("Optimization Finished!") + + # Save your model + saver.save(sess, 'my_tf_model') diff --git a/examples/5_DataManagement/tensorflow_dataset_api.py b/examples/5_DataManagement/tensorflow_dataset_api.py new file mode 100644 index 00000000..8c6a95e9 --- /dev/null +++ b/examples/5_DataManagement/tensorflow_dataset_api.py @@ -0,0 +1,137 @@ +""" TensorFlow Dataset API. + +In this example, we will show how to load numpy array data into the new +TensorFlow 'Dataset' API. The Dataset API implements an optimized data pipeline +with queues, that make data processing and training faster (especially on GPU). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import print_function + +import tensorflow as tf + +# Import MNIST data (Numpy format) +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.001 +num_steps = 2000 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units + +sess = tf.Session() + +# Create a dataset tensor from the images and the labels +dataset = tf.contrib.data.Dataset.from_tensor_slices( + (mnist.train.images, mnist.train.labels)) +# Create batches of data +dataset = dataset.batch(batch_size) +# Create an iterator, to go over the dataset +iterator = dataset.make_initializable_iterator() +# It is better to use 2 placeholders, to avoid to load all data into memory, +# and avoid the 2Gb restriction length of a tensor. +_data = tf.placeholder(tf.float32, [None, n_input]) +_labels = tf.placeholder(tf.float32, [None, n_classes]) +# Initialize the iterator +sess.run(iterator.initializer, feed_dict={_data: mnist.train.images, + _labels: mnist.train.labels}) + +# Neural Net Input +X, Y = iterator.get_next() + + +# ----------------------------------------------- +# THIS IS A CLASSIC CNN (see examples, section 3) +# ----------------------------------------------- +# Note that a few elements have changed (usage of sess run). + +# Create model +def conv_net(x, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer with 32 filters and a kernel size of 5 + conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv1 = tf.layers.max_pooling2d(conv1, 2, 2) + + # Convolution Layer with 32 filters and a kernel size of 5 + conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv2 = tf.layers.max_pooling2d(conv2, 2, 2) + + # Flatten the data to a 1-D vector for the fully connected layer + fc1 = tf.contrib.layers.flatten(conv2) + + # Fully connected layer (in contrib folder for now) + fc1 = tf.layers.dense(fc1, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) + + # Output layer, class prediction + out = tf.layers.dense(fc1, n_classes) + # Because 'softmax_cross_entropy_with_logits' already apply softmax, + # we only apply softmax to testing network + out = tf.nn.softmax(out) if not is_training else out + + return out + + +# Because Dropout have different behavior at training and prediction time, we +# need to create 2 distinct computation graphs that share the same weights. + +# Create a graph for training +logits_train = conv_net(X, n_classes, dropout, reuse=False, is_training=True) +# Create another graph for testing that reuse the same weights, but has +# different behavior for 'dropout' (not applied). +logits_test = conv_net(X, n_classes, dropout, reuse=True, is_training=False) + +# Define loss and optimizer (with train logits, for dropout to take effect) +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits_train, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Run the initializer +sess.run(init) + +# Training cycle +for step in range(1, num_steps + 1): + + try: + # Run optimization + sess.run(train_op) + except tf.errors.OutOfRangeError: + # Reload the iterator when it reaches the end of the dataset + sess.run(iterator.initializer, + feed_dict={_data: mnist.train.images, + _labels: mnist.train.labels}) + sess.run(train_op) + + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + # (note that this consume a new batch of data) + loss, acc = sess.run([loss_op, accuracy]) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + +print("Optimization Finished!") diff --git a/examples/5_MultiGPU/multigpu_basics.py b/examples/6_MultiGPU/multigpu_basics.py similarity index 100% rename from examples/5_MultiGPU/multigpu_basics.py rename to examples/6_MultiGPU/multigpu_basics.py diff --git a/examples/6_MultiGPU/multigpu_cnn.py b/examples/6_MultiGPU/multigpu_cnn.py new file mode 100644 index 00000000..be0dae1d --- /dev/null +++ b/examples/6_MultiGPU/multigpu_cnn.py @@ -0,0 +1,182 @@ +''' Multi-GPU Training Example. + +Train a convolutional neural network on multiple GPU with TensorFlow. + +This example is using TensorFlow layers, see 'convolutional_network_raw' example +for a raw TensorFlow implementation with variables. + +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import division, print_function, absolute_import + +import numpy as np +import tensorflow as tf +import time + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Parameters +num_gpus = 2 +num_steps = 200 +learning_rate = 0.001 +batch_size = 1024 +display_step = 10 + +# Network Parameters +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units + + +# Build a convolutional neural network +def conv_net(x, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer with 64 filters and a kernel size of 5 + x = tf.layers.conv2d(x, 64, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + x = tf.layers.max_pooling2d(x, 2, 2) + + # Convolution Layer with 256 filters and a kernel size of 5 + x = tf.layers.conv2d(x, 256, 3, activation=tf.nn.relu) + # Convolution Layer with 512 filters and a kernel size of 5 + x = tf.layers.conv2d(x, 512, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + x = tf.layers.max_pooling2d(x, 2, 2) + + # Flatten the data to a 1-D vector for the fully connected layer + x = tf.contrib.layers.flatten(x) + + # Fully connected layer (in contrib folder for now) + x = tf.layers.dense(x, 2048) + # Apply Dropout (if is_training is False, dropout is not applied) + x = tf.layers.dropout(x, rate=dropout, training=is_training) + + # Fully connected layer (in contrib folder for now) + x = tf.layers.dense(x, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + x = tf.layers.dropout(x, rate=dropout, training=is_training) + + # Output layer, class prediction + out = tf.layers.dense(x, n_classes) + # Because 'softmax_cross_entropy_with_logits' loss already apply + # softmax, we only apply softmax to testing network + out = tf.nn.softmax(out) if not is_training else out + + return out + + +def average_gradients(tower_grads): + average_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(grads, 0) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + + +# Place all ops on CPU by default +with tf.device('/cpu:0'): + tower_grads = [] + reuse_vars = False + + # tf Graph input + X = tf.placeholder(tf.float32, [None, num_input]) + Y = tf.placeholder(tf.float32, [None, num_classes]) + + # Loop over all GPUs and construct their own computation graph + for i in range(num_gpus): + with tf.device('/gpu:%d' % i): + + # Split data between GPUs + _x = X[i * batch_size: (i+1) * batch_size] + _y = Y[i * batch_size: (i+1) * batch_size] + + # Because Dropout have different behavior at training and prediction time, we + # need to create 2 distinct computation graphs that share the same weights. + + # Create a graph for training + logits_train = conv_net(_x, num_classes, dropout, + reuse=reuse_vars, is_training=True) + # Create another graph for testing that reuse the same weights + logits_test = conv_net(_x, num_classes, dropout, + reuse=True, is_training=False) + + # Define loss and optimizer (with train logits, for dropout to take effect) + loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits_train, labels=_y)) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + grads = optimizer.compute_gradients(loss_op) + + # Only first GPU compute accuracy + if i == 0: + # Evaluate model (with test logits, for dropout to be disabled) + correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(_y, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + + reuse_vars = True + tower_grads.append(grads) + + tower_grads = average_gradients(tower_grads) + train_op = optimizer.apply_gradients(tower_grads) + + # Initialize the variables (i.e. assign their default value) + init = tf.global_variables_initializer() + + # Start Training + with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Keep training until reach max iterations + for step in range(1, num_steps + 1): + # Get a batch for each GPU + batch_x, batch_y = mnist.train.next_batch(batch_size * num_gpus) + # Run optimization op (backprop) + ts = time.time() + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + te = time.time() - ts + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ": Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc) + ", %i Examples/sec" % int(len(batch_x)/te)) + step += 1 + print("Optimization Finished!") + + # Calculate accuracy for MNIST test images + print("Testing Accuracy:", \ + np.mean([sess.run(accuracy, feed_dict={X: mnist.test.images[i:i+batch_size], + Y: mnist.test.labels[i:i+batch_size]}) for i in range(0, len(mnist.test.images), batch_size)])) diff --git a/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb b/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb index c3beb27a..6b96dc0f 100644 --- a/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb +++ b/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb @@ -7,7 +7,7 @@ "\n", "# MNIST Dataset Introduction\n", "\n", - "Most examples are using MNIST dataset of handwritten digits. It has 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image, so each sample is represented as a matrix of size 28x28 with values from 0 to 1.\n", + "Most examples are using MNIST dataset of handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", "\n", "## Overview\n", "\n", @@ -72,7 +72,7 @@ ], "metadata": { "kernelspec": { - "display_name": "IPython (Python 2.7)", + "display_name": "Python 2", "language": "python", "name": "python2" }, @@ -86,7 +86,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.13" } }, "nbformat": 4, diff --git a/notebooks/2_BasicModels/kmeans.ipynb b/notebooks/2_BasicModels/kmeans.ipynb new file mode 100644 index 00000000..83fff246 --- /dev/null +++ b/notebooks/2_BasicModels/kmeans.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# K-Means Example\n", + "\n", + "Implement K-Means algorithm with TensorFlow, and apply it to classify\n", + "handwritten digit images. This example is using the MNIST database of\n", + "handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "Note: This example requires TensorFlow v1.1.0 or over.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow.contrib.factorization import KMeans\n", + "\n", + "# Ignore all GPUs, tf random forest does not benefit from it.\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "full_data_x = mnist.train.images" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "num_steps = 50 # Total steps to train\n", + "batch_size = 1024 # The number of samples per batch\n", + "k = 25 # The number of clusters\n", + "num_classes = 10 # The 10 digits\n", + "num_features = 784 # Each image is 28x28 pixels\n", + "\n", + "# Input images\n", + "X = tf.placeholder(tf.float32, shape=[None, num_features])\n", + "# Labels (for assigning a label to a centroid and testing)\n", + "Y = tf.placeholder(tf.float32, shape=[None, num_classes])\n", + "\n", + "# K-Means Parameters\n", + "kmeans = KMeans(inputs=X, num_clusters=k, distance_metric='cosine',\n", + " use_mini_batch=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build KMeans graph\n", + "(all_scores, cluster_idx, scores, cluster_centers_initialized, init_op,\n", + "train_op) = kmeans.training_graph()\n", + "cluster_idx = cluster_idx[0] # fix for cluster_idx being a tuple\n", + "avg_distance = tf.reduce_mean(scores)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init_vars = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Avg Distance: 0.341471\n", + "Step 10, Avg Distance: 0.221609\n", + "Step 20, Avg Distance: 0.220328\n", + "Step 30, Avg Distance: 0.219776\n", + "Step 40, Avg Distance: 0.219419\n", + "Step 50, Avg Distance: 0.219154\n" + ] + } + ], + "source": [ + "# Start TensorFlow session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init_vars, feed_dict={X: full_data_x})\n", + "sess.run(init_op, feed_dict={X: full_data_x})\n", + "\n", + "# Training\n", + "for i in range(1, num_steps + 1):\n", + " _, d, idx = sess.run([train_op, avg_distance, cluster_idx],\n", + " feed_dict={X: full_data_x})\n", + " if i % 10 == 0 or i == 1:\n", + " print(\"Step %i, Avg Distance: %f\" % (i, d))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 0.7127\n" + ] + } + ], + "source": [ + "# Assign a label to each centroid\n", + "# Count total number of labels per centroid, using the label of each training\n", + "# sample to their closest centroid (given by 'idx')\n", + "counts = np.zeros(shape=(k, num_classes))\n", + "for i in range(len(idx)):\n", + " counts[idx[i]] += mnist.train.labels[i]\n", + "# Assign the most frequent label to the centroid\n", + "labels_map = [np.argmax(c) for c in counts]\n", + "labels_map = tf.convert_to_tensor(labels_map)\n", + "\n", + "# Evaluation ops\n", + "# Lookup: centroid_id -> label\n", + "cluster_label = tf.nn.embedding_lookup(labels_map, cluster_idx)\n", + "# Compute accuracy\n", + "correct_prediction = tf.equal(cluster_label, tf.cast(tf.argmax(Y, 1), tf.int32))\n", + "accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + "\n", + "# Test Model\n", + "test_x, test_y = mnist.test.images, mnist.test.labels\n", + "print(\"Test Accuracy:\", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/2_BasicModels/linear_regression.ipynb b/notebooks/2_BasicModels/linear_regression.ipynb index 62883a6a..2c6692db 100644 --- a/notebooks/2_BasicModels/linear_regression.ipynb +++ b/notebooks/2_BasicModels/linear_regression.ipynb @@ -1,17 +1,17 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, + "cell_type": "markdown", "metadata": { "collapsed": false }, - "outputs": [], "source": [ - "# A linear regression learning algorithm example using TensorFlow library.\n", + "# Linear Regression Example\n", + "\n", + "A linear regression learning algorithm example using TensorFlow library.\n", "\n", - "# Author: Aymeric Damien\n", - "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" ] }, { @@ -109,7 +109,7 @@ }, "outputs": [], "source": [ - "# Initializing the variables\n", + "# Initialize the variables (i.e. assign their default value)\n", "init = tf.global_variables_initializer()" ] }, @@ -161,7 +161,7 @@ } ], "source": [ - "# Launch the graph\n", + "# Start training\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", @@ -212,8 +212,9 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 2", + "display_name": "Python [default]", "language": "python", "name": "python2" }, @@ -227,7 +228,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/notebooks/2_BasicModels/logistic_regression.ipynb b/notebooks/2_BasicModels/logistic_regression.ipynb index 61215954..d105b769 100644 --- a/notebooks/2_BasicModels/logistic_regression.ipynb +++ b/notebooks/2_BasicModels/logistic_regression.ipynb @@ -1,19 +1,18 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [ - "# A logistic regression learning algorithm example using TensorFlow library.\n", - "# This example is using the MNIST database of handwritten digits \n", - "# (http://yann.lecun.com/exdb/mnist/)\n", + "# Logistic Regression Example\n", "\n", - "# Author: Aymeric Damien\n", - "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" + "A logistic regression learning algorithm example using TensorFlow library.\n", + "This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" ] }, { @@ -39,7 +38,7 @@ "\n", "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" ] }, { @@ -72,7 +71,7 @@ "# Gradient Descent\n", "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", "\n", - "# Initializing the variables\n", + "# Initialize the variables (i.e. assign their default value)\n", "init = tf.global_variables_initializer()" ] }, @@ -108,7 +107,7 @@ } ], "source": [ - "# Launch the graph\n", + "# Start training\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", @@ -136,20 +135,12 @@ " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " print \"Accuracy:\", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]})" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 2", + "display_name": "Python [default]", "language": "python", "name": "python2" }, @@ -163,7 +154,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/notebooks/2_BasicModels/nearest_neighbor.ipynb b/notebooks/2_BasicModels/nearest_neighbor.ipynb index c66e43a3..4262fb96 100644 --- a/notebooks/2_BasicModels/nearest_neighbor.ipynb +++ b/notebooks/2_BasicModels/nearest_neighbor.ipynb @@ -1,19 +1,18 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [ - "# A nearest neighbor learning algorithm example using TensorFlow library.\n", - "# This example is using the MNIST database of handwritten digits\n", - "# (http://yann.lecun.com/exdb/mnist/)\n", + "# Nearest Neighbor Example\n", "\n", - "# Author: Aymeric Damien\n", - "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" + "A nearest neighbor learning algorithm example using TensorFlow library.\n", + "This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" ] }, { @@ -40,7 +39,7 @@ "\n", "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" ] }, { @@ -67,7 +66,7 @@ "\n", "accuracy = 0.\n", "\n", - "# Initializing the variables\n", + "# Initialize the variables (i.e. assign their default value)\n", "init = tf.global_variables_initializer()" ] }, @@ -75,7 +74,8 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": false }, "outputs": [ { @@ -288,7 +288,7 @@ } ], "source": [ - "# Launch the graph\n", + "# Start training\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", @@ -305,20 +305,12 @@ " print \"Done!\"\n", " print \"Accuracy:\", accuracy" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 2", + "display_name": "Python [default]", "language": "python", "name": "python2" }, @@ -332,7 +324,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/notebooks/2_BasicModels/random_forest.ipynb b/notebooks/2_BasicModels/random_forest.ipynb new file mode 100644 index 00000000..7e45ad32 --- /dev/null +++ b/notebooks/2_BasicModels/random_forest.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Forest Example\n", + "\n", + "Implement Random Forest algorithm with TensorFlow, and apply it to classify \n", + "handwritten digit images. This example is using the MNIST database of \n", + "handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.contrib.tensor_forest.python import tensor_forest\n", + "\n", + "# Ignore all GPUs, tf random forest does not benefit from it.\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "num_steps = 500 # Total steps to train\n", + "batch_size = 1024 # The number of samples per batch\n", + "num_classes = 10 # The 10 digits\n", + "num_features = 784 # Each image is 28x28 pixels\n", + "num_trees = 10\n", + "max_nodes = 1000\n", + "\n", + "# Input and Target data\n", + "X = tf.placeholder(tf.float32, shape=[None, num_features])\n", + "# For random forest, labels must be integers (the class id)\n", + "Y = tf.placeholder(tf.int32, shape=[None])\n", + "\n", + "# Random Forest Parameters\n", + "hparams = tensor_forest.ForestHParams(num_classes=num_classes,\n", + " num_features=num_features,\n", + " num_trees=num_trees,\n", + " max_nodes=max_nodes).fill()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Constructing forest with params = \n", + "INFO:tensorflow:{'valid_leaf_threshold': 1, 'split_after_samples': 250, 'num_output_columns': 11, 'feature_bagging_fraction': 1.0, 'split_initializations_per_input': 3, 'bagged_features': None, 'min_split_samples': 5, 'max_nodes': 1000, 'num_features': 784, 'num_trees': 10, 'num_splits_to_consider': 784, 'base_random_seed': 0, 'num_outputs': 1, 'dominate_fraction': 0.99, 'max_fertile_nodes': 500, 'bagged_num_features': 784, 'dominate_method': 'bootstrap', 'bagging_fraction': 1.0, 'regression': False, 'num_classes': 10}\n", + "INFO:tensorflow:training graph for tree: 0\n", + "INFO:tensorflow:training graph for tree: 1\n", + "INFO:tensorflow:training graph for tree: 2\n", + "INFO:tensorflow:training graph for tree: 3\n", + "INFO:tensorflow:training graph for tree: 4\n", + "INFO:tensorflow:training graph for tree: 5\n", + "INFO:tensorflow:training graph for tree: 6\n", + "INFO:tensorflow:training graph for tree: 7\n", + "INFO:tensorflow:training graph for tree: 8\n", + "INFO:tensorflow:training graph for tree: 9\n" + ] + } + ], + "source": [ + "# Build the Random Forest\n", + "forest_graph = tensor_forest.RandomForestGraphs(hparams)\n", + "# Get training graph and loss\n", + "train_op = forest_graph.training_graph(X, Y)\n", + "loss_op = forest_graph.training_loss(X, Y)\n", + "\n", + "# Measure the accuracy\n", + "infer_op = forest_graph.inference_graph(X)\n", + "correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64))\n", + "accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init_vars = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Loss: -0.000000, Acc: 0.112305\n", + "Step 50, Loss: -123.800003, Acc: 0.863281\n", + "Step 100, Loss: -274.200012, Acc: 0.863281\n", + "Step 150, Loss: -425.399994, Acc: 0.872070\n", + "Step 200, Loss: -582.799988, Acc: 0.917969\n", + "Step 250, Loss: -740.200012, Acc: 0.912109\n", + "Step 300, Loss: -895.799988, Acc: 0.939453\n", + "Step 350, Loss: -998.000000, Acc: 0.924805\n", + "Step 400, Loss: -998.000000, Acc: 0.940430\n", + "Step 450, Loss: -998.000000, Acc: 0.914062\n", + "Step 500, Loss: -998.000000, Acc: 0.927734\n", + "Test Accuracy: 0.9204\n" + ] + } + ], + "source": [ + "# Start TensorFlow session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init_vars)\n", + "\n", + "# Training\n", + "for i in range(1, num_steps + 1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " _, l = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})\n", + " if i % 50 == 0 or i == 1:\n", + " acc = sess.run(accuracy_op, feed_dict={X: batch_x, Y: batch_y})\n", + " print('Step %i, Loss: %f, Acc: %f' % (i, l, acc))\n", + "\n", + "# Test Model\n", + "test_x, test_y = mnist.test.images, mnist.test.labels\n", + "print(\"Test Accuracy:\", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/3_NeuralNetworks/autoencoder.ipynb b/notebooks/3_NeuralNetworks/autoencoder.ipynb index 4166deda..fd542252 100644 --- a/notebooks/3_NeuralNetworks/autoencoder.ipynb +++ b/notebooks/3_NeuralNetworks/autoencoder.ipynb @@ -1,86 +1,114 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Auto-Encoder Example\n", + "\n", + "Build a 2 layers auto-encoder with TensorFlow to compress images to a lower latent space and then reconstruct them.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "\"\"\" Auto Encoder Example.\n", - "Using an auto encoder on MNIST handwritten digits.\n", + "## Auto-Encoder Overview\n", + "\n", + "\"ae\"\n", + "\n", "References:\n", - " Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. \"Gradient-based\n", - " learning applied to document recognition.\" Proceedings of the IEEE,\n", - " 86(11):2278-2324, November 1998.\n", - "Links:\n", - " [MNIST Dataset] http://yann.lecun.com/exdb/mnist/\n", - "\"\"\"" + "- [Gradient-based learning applied to document recognition](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf). Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Proceedings of the IEEE, 86(11):2278-2324, November 1998.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "from __future__ import division, print_function, absolute_import\n", "\n", "import tensorflow as tf\n", "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ "# Import MNIST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"MNIST_data\", one_hot=True)" + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "# Parameters\n", + "# Training Parameters\n", "learning_rate = 0.01\n", - "training_epochs = 20\n", + "num_steps = 30000\n", "batch_size = 256\n", - "display_step = 1\n", + "\n", + "display_step = 1000\n", "examples_to_show = 10\n", "\n", "# Network Parameters\n", - "n_hidden_1 = 256 # 1st layer num features\n", - "n_hidden_2 = 128 # 2nd layer num features\n", - "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_hidden_1 = 256 # 1st layer num features\n", + "num_hidden_2 = 128 # 2nd layer num features (the latent dim)\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", "\n", "# tf Graph input (only pictures)\n", - "X = tf.placeholder(\"float\", [None, n_input])\n", + "X = tf.placeholder(\"float\", [None, num_input])\n", "\n", "weights = {\n", - " 'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n", - " 'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n", - " 'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),\n", - " 'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),\n", + " 'encoder_h1': tf.Variable(tf.random_normal([num_input, num_hidden_1])),\n", + " 'encoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_hidden_2])),\n", + " 'decoder_h1': tf.Variable(tf.random_normal([num_hidden_2, num_hidden_1])),\n", + " 'decoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_input])),\n", "}\n", "biases = {\n", - " 'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", - " 'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),\n", - " 'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", - " 'decoder_b2': tf.Variable(tf.random_normal([n_input])),\n", + " 'encoder_b1': tf.Variable(tf.random_normal([num_hidden_1])),\n", + " 'encoder_b2': tf.Variable(tf.random_normal([num_hidden_2])),\n", + " 'decoder_b1': tf.Variable(tf.random_normal([num_hidden_1])),\n", + " 'decoder_b2': tf.Variable(tf.random_normal([num_input])),\n", "}" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "# Building the encoder\n", @@ -114,85 +142,111 @@ "y_true = X\n", "\n", "# Define loss and optimizer, minimize the squared error\n", - "cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))\n", - "optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)\n", + "loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2))\n", + "optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss)\n", "\n", - "# Initializing the variables\n", + "# Initialize the variables (i.e. assign their default value)\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Minibatch Loss: 0.438300\n", + "Step 1000: Minibatch Loss: 0.146586\n", + "Step 2000: Minibatch Loss: 0.130722\n", + "Step 3000: Minibatch Loss: 0.117178\n", + "Step 4000: Minibatch Loss: 0.109027\n", + "Step 5000: Minibatch Loss: 0.102582\n", + "Step 6000: Minibatch Loss: 0.099183\n", + "Step 7000: Minibatch Loss: 0.095619\n", + "Step 8000: Minibatch Loss: 0.089006\n", + "Step 9000: Minibatch Loss: 0.087125\n", + "Step 10000: Minibatch Loss: 0.083930\n", + "Step 11000: Minibatch Loss: 0.077512\n", + "Step 12000: Minibatch Loss: 0.077137\n", + "Step 13000: Minibatch Loss: 0.073983\n", + "Step 14000: Minibatch Loss: 0.074218\n", + "Step 15000: Minibatch Loss: 0.074492\n", + "Step 16000: Minibatch Loss: 0.074374\n", + "Step 17000: Minibatch Loss: 0.070909\n", + "Step 18000: Minibatch Loss: 0.069438\n", + "Step 19000: Minibatch Loss: 0.068245\n", + "Step 20000: Minibatch Loss: 0.068402\n", + "Step 21000: Minibatch Loss: 0.067113\n", + "Step 22000: Minibatch Loss: 0.068241\n", + "Step 23000: Minibatch Loss: 0.062454\n", + "Step 24000: Minibatch Loss: 0.059754\n", + "Step 25000: Minibatch Loss: 0.058687\n", + "Step 26000: Minibatch Loss: 0.059107\n", + "Step 27000: Minibatch Loss: 0.055788\n", + "Step 28000: Minibatch Loss: 0.057263\n", + "Step 29000: Minibatch Loss: 0.056391\n", + "Step 30000: Minibatch Loss: 0.057672\n" + ] + } + ], "source": [ - "# Launch the graph\n", - "# Using InteractiveSession (more convenient while using Notebooks)\n", - "sess = tf.InteractiveSession()\n", + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", "sess.run(init)\n", "\n", - "total_batch = int(mnist.train.num_examples/batch_size)\n", - "# Training cycle\n", - "for epoch in range(training_epochs):\n", - " # Loop over all batches\n", - " for i in range(total_batch):\n", - " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", - " # Run optimization op (backprop) and cost op (to get loss value)\n", - " _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})\n", - " # Display logs per epoch step\n", - " if epoch % display_step == 0:\n", - " print(\"Epoch:\", '%04d' % (epoch+1),\n", - " \"cost=\", \"{:.9f}\".format(c))\n", - "\n", - "print(\"Optimization Finished!\")\n", - "\n", - "# Applying encode and decode over test set\n", - "encode_decode = sess.run(\n", - " y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})\n", - "# Compare original images with their reconstructions\n", - "f, a = plt.subplots(2, 10, figsize=(10, 2))\n", - "for i in range(examples_to_show):\n", - " a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))\n", - " a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))\n", - "f.show()\n", - "plt.draw()" + "# Training\n", + "for i in range(1, num_steps+1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + "\n", + " # Run optimization op (backprop) and cost op (to get loss value)\n", + " _, l = sess.run([optimizer, loss], feed_dict={X: batch_x})\n", + " # Display logs per step\n", + " if i % display_step == 0 or i == 1:\n", + " print('Step %i: Minibatch Loss: %f' % (i, l))" ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzUAAADDCAYAAAC7zz91AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Images\n" + ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzUAAADDCAYAAAC7zz91AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wGTleGj2VhBawAAAABJRU5ErkJggg==\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed Images\n" + ] + }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzUAAADDCAYAAAC7zz91AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBkNdY5wag8FgMBgMBoPBUNf8f3Fkpc5Du0uwAAAAAElFTkSuQmCC\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -200,6 +254,34 @@ } ], "source": [ + "# Testing\n", + "# Encode and decode images from test set and visualize their reconstruction.\n", + "n = 4\n", + "canvas_orig = np.empty((28 * n, 28 * n))\n", + "canvas_recon = np.empty((28 * n, 28 * n))\n", + "for i in range(n):\n", + " # MNIST test set\n", + " batch_x, _ = mnist.test.next_batch(n)\n", + " # Encode and decode the digit image\n", + " g = sess.run(decoder_op, feed_dict={X: batch_x})\n", + " \n", + " # Display original images\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = batch_x[j].reshape([28, 28])\n", + " # Display reconstructed images\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28])\n", + "\n", + "print(\"Original Images\") \n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas_orig, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()\n", + "\n", + "print(\"Reconstructed Images\")\n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas_recon, origin=\"upper\", cmap=\"gray\")\n", "plt.show()" ] } @@ -213,16 +295,16 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 2.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb index dbfcbe72..f235074f 100644 --- a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -6,44 +6,64 @@ "collapsed": true }, "source": [ - "'''\n", - "A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library.\n", - "This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", - "Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\n", + "# Bi-directional Recurrent Neural Network Example\n", "\n", - "Author: Aymeric Damien\n", - "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", - "'''" + "Build a bi-directional recurrent neural network (LSTM) with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BiRNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "To classify images using a recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 timesteps for every sample.\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], "source": [ + "from __future__ import print_function\n", + "\n", "import tensorflow as tf\n", "from tensorflow.contrib import rnn\n", "import numpy as np\n", "\n", - "# Import MINST data\n", + "# Import MNIST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "'''\n", - "To classify images using a bidirectional reccurent neural network, we consider\n", - "every image row as a sequence of pixels. Because MNIST image shape is 28*28px,\n", - "we will then handle 28 sequences of 28 steps for every sample.\n", - "'''" + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" ] }, { @@ -54,35 +74,44 @@ }, "outputs": [], "source": [ - "# Parameters\n", + "# Training Parameters\n", "learning_rate = 0.001\n", - "training_iters = 100000\n", + "training_steps = 10000\n", "batch_size = 128\n", - "display_step = 10\n", + "display_step = 200\n", "\n", "# Network Parameters\n", - "n_input = 28 # MNIST data input (img shape: 28*28)\n", - "n_steps = 28 # timesteps\n", - "n_hidden = 128 # hidden layer num of features\n", - "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "num_input = 28 # MNIST data input (img shape: 28*28)\n", + "timesteps = 28 # timesteps\n", + "num_hidden = 128 # hidden layer num of features\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", "\n", "# tf Graph input\n", - "x = tf.placeholder(\"float\", [None, n_steps, n_input])\n", - "y = tf.placeholder(\"float\", [None, n_classes])\n", - "\n", + "X = tf.placeholder(\"float\", [None, timesteps, num_input])\n", + "Y = tf.placeholder(\"float\", [None, num_classes])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ "# Define weights\n", "weights = {\n", - " # Hidden layer weights => 2*n_hidden because of foward + backward cells\n", - " 'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))\n", + " # Hidden layer weights => 2*n_hidden because of forward + backward cells\n", + " 'out': tf.Variable(tf.random_normal([2*num_hidden, num_classes]))\n", "}\n", "biases = {\n", - " 'out': tf.Variable(tf.random_normal([n_classes]))\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", "}" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -90,18 +119,18 @@ "source": [ "def BiRNN(x, weights, biases):\n", "\n", - " # Prepare data shape to match `bidirectional_rnn` function requirements\n", - " # Current data input shape: (batch_size, n_steps, n_input)\n", - " # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n", - " \n", - " # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", - " x = tf.unstack(x, n_steps, 1)\n", + " # Prepare data shape to match `rnn` function requirements\n", + " # Current data input shape: (batch_size, timesteps, n_input)\n", + " # Required shape: 'timesteps' tensors list of shape (batch_size, num_input)\n", + "\n", + " # Unstack to get a list of 'timesteps' tensors of shape (batch_size, num_input)\n", + " x = tf.unstack(x, timesteps, 1)\n", "\n", " # Define lstm cells with tensorflow\n", " # Forward direction cell\n", - " lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", + " lstm_fw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)\n", " # Backward direction cell\n", - " lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", + " lstm_bw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)\n", "\n", " # Get lstm cell output\n", " try:\n", @@ -112,25 +141,37 @@ " dtype=tf.float32)\n", "\n", " # Linear activation, using rnn inner loop last output\n", - " return tf.matmul(outputs[-1], weights['out']) + biases['out']\n", - "\n", - "pred = BiRNN(x, weights, biases)\n", + " return tf.matmul(outputs[-1], weights['out']) + biases['out']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "logits = BiRNN(X, weights, biases)\n", + "prediction = tf.nn.softmax(logits)\n", "\n", "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", - "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", "\n", - "# Evaluate model\n", - "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", "\n", - "# Initializing the variables\n", + "# Initialize the variables (i.e. assign their default value)\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -139,118 +180,91 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iter 1280, Minibatch Loss= 1.557283, Training Accuracy= 0.49219\n", - "Iter 2560, Minibatch Loss= 1.358445, Training Accuracy= 0.56250\n", - "Iter 3840, Minibatch Loss= 1.043732, Training Accuracy= 0.64062\n", - "Iter 5120, Minibatch Loss= 0.796770, Training Accuracy= 0.72656\n", - "Iter 6400, Minibatch Loss= 0.626206, Training Accuracy= 0.72656\n", - "Iter 7680, Minibatch Loss= 1.025919, Training Accuracy= 0.65625\n", - "Iter 8960, Minibatch Loss= 0.744850, Training Accuracy= 0.76562\n", - "Iter 10240, Minibatch Loss= 0.530111, Training Accuracy= 0.84375\n", - "Iter 11520, Minibatch Loss= 0.383806, Training Accuracy= 0.86719\n", - "Iter 12800, Minibatch Loss= 0.607816, Training Accuracy= 0.82812\n", - "Iter 14080, Minibatch Loss= 0.410879, Training Accuracy= 0.89062\n", - "Iter 15360, Minibatch Loss= 0.335351, Training Accuracy= 0.89844\n", - "Iter 16640, Minibatch Loss= 0.428004, Training Accuracy= 0.91406\n", - "Iter 17920, Minibatch Loss= 0.307468, Training Accuracy= 0.91406\n", - "Iter 19200, Minibatch Loss= 0.249527, Training Accuracy= 0.92188\n", - "Iter 20480, Minibatch Loss= 0.148163, Training Accuracy= 0.96094\n", - "Iter 21760, Minibatch Loss= 0.445275, Training Accuracy= 0.83594\n", - "Iter 23040, Minibatch Loss= 0.173083, Training Accuracy= 0.93750\n", - "Iter 24320, Minibatch Loss= 0.373696, Training Accuracy= 0.87500\n", - "Iter 25600, Minibatch Loss= 0.509869, Training Accuracy= 0.85938\n", - "Iter 26880, Minibatch Loss= 0.198096, Training Accuracy= 0.92969\n", - "Iter 28160, Minibatch Loss= 0.228221, Training Accuracy= 0.92188\n", - "Iter 29440, Minibatch Loss= 0.280088, Training Accuracy= 0.89844\n", - "Iter 30720, Minibatch Loss= 0.300495, Training Accuracy= 0.91406\n", - "Iter 32000, Minibatch Loss= 0.171746, Training Accuracy= 0.95312\n", - "Iter 33280, Minibatch Loss= 0.263745, Training Accuracy= 0.89844\n", - "Iter 34560, Minibatch Loss= 0.177300, Training Accuracy= 0.93750\n", - "Iter 35840, Minibatch Loss= 0.160621, Training Accuracy= 0.95312\n", - "Iter 37120, Minibatch Loss= 0.321745, Training Accuracy= 0.91406\n", - "Iter 38400, Minibatch Loss= 0.188322, Training Accuracy= 0.93750\n", - "Iter 39680, Minibatch Loss= 0.104025, Training Accuracy= 0.96875\n", - "Iter 40960, Minibatch Loss= 0.291053, Training Accuracy= 0.89062\n", - "Iter 42240, Minibatch Loss= 0.131189, Training Accuracy= 0.95312\n", - "Iter 43520, Minibatch Loss= 0.154949, Training Accuracy= 0.92969\n", - "Iter 44800, Minibatch Loss= 0.150411, Training Accuracy= 0.93750\n", - "Iter 46080, Minibatch Loss= 0.117008, Training Accuracy= 0.96094\n", - "Iter 47360, Minibatch Loss= 0.181344, Training Accuracy= 0.96094\n", - "Iter 48640, Minibatch Loss= 0.209197, Training Accuracy= 0.94531\n", - "Iter 49920, Minibatch Loss= 0.159350, Training Accuracy= 0.96094\n", - "Iter 51200, Minibatch Loss= 0.124001, Training Accuracy= 0.95312\n", - "Iter 52480, Minibatch Loss= 0.165183, Training Accuracy= 0.94531\n", - "Iter 53760, Minibatch Loss= 0.046438, Training Accuracy= 0.97656\n", - "Iter 55040, Minibatch Loss= 0.199995, Training Accuracy= 0.91406\n", - "Iter 56320, Minibatch Loss= 0.057071, Training Accuracy= 0.97656\n", - "Iter 57600, Minibatch Loss= 0.177065, Training Accuracy= 0.92188\n", - "Iter 58880, Minibatch Loss= 0.091666, Training Accuracy= 0.96094\n", - "Iter 60160, Minibatch Loss= 0.069232, Training Accuracy= 0.96875\n", - "Iter 61440, Minibatch Loss= 0.127353, Training Accuracy= 0.94531\n", - "Iter 62720, Minibatch Loss= 0.095795, Training Accuracy= 0.96094\n", - "Iter 64000, Minibatch Loss= 0.202651, Training Accuracy= 0.96875\n", - "Iter 65280, Minibatch Loss= 0.118779, Training Accuracy= 0.95312\n", - "Iter 66560, Minibatch Loss= 0.043173, Training Accuracy= 0.98438\n", - "Iter 67840, Minibatch Loss= 0.152280, Training Accuracy= 0.95312\n", - "Iter 69120, Minibatch Loss= 0.085301, Training Accuracy= 0.96875\n", - "Iter 70400, Minibatch Loss= 0.093421, Training Accuracy= 0.96094\n", - "Iter 71680, Minibatch Loss= 0.096358, Training Accuracy= 0.96875\n", - "Iter 72960, Minibatch Loss= 0.053386, Training Accuracy= 0.98438\n", - "Iter 74240, Minibatch Loss= 0.065237, Training Accuracy= 0.97656\n", - "Iter 75520, Minibatch Loss= 0.228090, Training Accuracy= 0.92188\n", - "Iter 76800, Minibatch Loss= 0.106751, Training Accuracy= 0.95312\n", - "Iter 78080, Minibatch Loss= 0.187795, Training Accuracy= 0.94531\n", - "Iter 79360, Minibatch Loss= 0.092611, Training Accuracy= 0.96094\n", - "Iter 80640, Minibatch Loss= 0.137386, Training Accuracy= 0.96875\n", - "Iter 81920, Minibatch Loss= 0.106634, Training Accuracy= 0.98438\n", - "Iter 83200, Minibatch Loss= 0.111749, Training Accuracy= 0.94531\n", - "Iter 84480, Minibatch Loss= 0.191184, Training Accuracy= 0.94531\n", - "Iter 85760, Minibatch Loss= 0.063982, Training Accuracy= 0.96094\n", - "Iter 87040, Minibatch Loss= 0.092380, Training Accuracy= 0.96875\n", - "Iter 88320, Minibatch Loss= 0.089899, Training Accuracy= 0.97656\n", - "Iter 89600, Minibatch Loss= 0.141107, Training Accuracy= 0.94531\n", - "Iter 90880, Minibatch Loss= 0.075549, Training Accuracy= 0.96094\n", - "Iter 92160, Minibatch Loss= 0.186539, Training Accuracy= 0.94531\n", - "Iter 93440, Minibatch Loss= 0.079639, Training Accuracy= 0.97656\n", - "Iter 94720, Minibatch Loss= 0.156895, Training Accuracy= 0.95312\n", - "Iter 96000, Minibatch Loss= 0.088042, Training Accuracy= 0.97656\n", - "Iter 97280, Minibatch Loss= 0.076670, Training Accuracy= 0.96875\n", - "Iter 98560, Minibatch Loss= 0.051336, Training Accuracy= 0.97656\n", - "Iter 99840, Minibatch Loss= 0.086923, Training Accuracy= 0.98438\n", + "Step 1, Minibatch Loss= 2.6218, Training Accuracy= 0.086\n", + "Step 200, Minibatch Loss= 2.1900, Training Accuracy= 0.211\n", + "Step 400, Minibatch Loss= 2.0144, Training Accuracy= 0.375\n", + "Step 600, Minibatch Loss= 1.8729, Training Accuracy= 0.445\n", + "Step 800, Minibatch Loss= 1.8000, Training Accuracy= 0.469\n", + "Step 1000, Minibatch Loss= 1.7244, Training Accuracy= 0.453\n", + "Step 1200, Minibatch Loss= 1.5657, Training Accuracy= 0.523\n", + "Step 1400, Minibatch Loss= 1.5473, Training Accuracy= 0.547\n", + "Step 1600, Minibatch Loss= 1.5288, Training Accuracy= 0.500\n", + "Step 1800, Minibatch Loss= 1.4203, Training Accuracy= 0.555\n", + "Step 2000, Minibatch Loss= 1.2525, Training Accuracy= 0.641\n", + "Step 2200, Minibatch Loss= 1.2696, Training Accuracy= 0.594\n", + "Step 2400, Minibatch Loss= 1.2000, Training Accuracy= 0.664\n", + "Step 2600, Minibatch Loss= 1.1017, Training Accuracy= 0.625\n", + "Step 2800, Minibatch Loss= 1.2656, Training Accuracy= 0.578\n", + "Step 3000, Minibatch Loss= 1.0830, Training Accuracy= 0.656\n", + "Step 3200, Minibatch Loss= 1.1522, Training Accuracy= 0.633\n", + "Step 3400, Minibatch Loss= 0.9484, Training Accuracy= 0.680\n", + "Step 3600, Minibatch Loss= 1.0470, Training Accuracy= 0.641\n", + "Step 3800, Minibatch Loss= 1.0609, Training Accuracy= 0.586\n", + "Step 4000, Minibatch Loss= 1.1853, Training Accuracy= 0.648\n", + "Step 4200, Minibatch Loss= 0.9438, Training Accuracy= 0.750\n", + "Step 4400, Minibatch Loss= 0.7986, Training Accuracy= 0.766\n", + "Step 4600, Minibatch Loss= 0.8070, Training Accuracy= 0.750\n", + "Step 4800, Minibatch Loss= 0.8382, Training Accuracy= 0.734\n", + "Step 5000, Minibatch Loss= 0.7397, Training Accuracy= 0.766\n", + "Step 5200, Minibatch Loss= 0.7870, Training Accuracy= 0.727\n", + "Step 5400, Minibatch Loss= 0.6380, Training Accuracy= 0.828\n", + "Step 5600, Minibatch Loss= 0.7975, Training Accuracy= 0.719\n", + "Step 5800, Minibatch Loss= 0.7934, Training Accuracy= 0.766\n", + "Step 6000, Minibatch Loss= 0.6628, Training Accuracy= 0.805\n", + "Step 6200, Minibatch Loss= 0.7958, Training Accuracy= 0.672\n", + "Step 6400, Minibatch Loss= 0.6582, Training Accuracy= 0.773\n", + "Step 6600, Minibatch Loss= 0.5908, Training Accuracy= 0.812\n", + "Step 6800, Minibatch Loss= 0.6182, Training Accuracy= 0.820\n", + "Step 7000, Minibatch Loss= 0.5513, Training Accuracy= 0.812\n", + "Step 7200, Minibatch Loss= 0.6683, Training Accuracy= 0.789\n", + "Step 7400, Minibatch Loss= 0.5337, Training Accuracy= 0.828\n", + "Step 7600, Minibatch Loss= 0.6428, Training Accuracy= 0.805\n", + "Step 7800, Minibatch Loss= 0.6708, Training Accuracy= 0.797\n", + "Step 8000, Minibatch Loss= 0.4664, Training Accuracy= 0.852\n", + "Step 8200, Minibatch Loss= 0.4249, Training Accuracy= 0.859\n", + "Step 8400, Minibatch Loss= 0.7723, Training Accuracy= 0.773\n", + "Step 8600, Minibatch Loss= 0.4706, Training Accuracy= 0.859\n", + "Step 8800, Minibatch Loss= 0.4800, Training Accuracy= 0.867\n", + "Step 9000, Minibatch Loss= 0.4636, Training Accuracy= 0.891\n", + "Step 9200, Minibatch Loss= 0.5734, Training Accuracy= 0.828\n", + "Step 9400, Minibatch Loss= 0.5548, Training Accuracy= 0.875\n", + "Step 9600, Minibatch Loss= 0.3575, Training Accuracy= 0.922\n", + "Step 9800, Minibatch Loss= 0.4566, Training Accuracy= 0.844\n", + "Step 10000, Minibatch Loss= 0.5125, Training Accuracy= 0.844\n", "Optimization Finished!\n", - "Testing Accuracy: 0.960938\n" + "Testing Accuracy: 0.890625\n" ] } ], "source": [ - "# Launch the graph\n", + "# Start training\n", "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", " sess.run(init)\n", - " step = 1\n", - " # Keep training until reach max iterations\n", - " while step * batch_size < training_iters:\n", + "\n", + " for step in range(1, training_steps+1):\n", " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", " # Reshape data to get 28 seq of 28 elements\n", - " batch_x = batch_x.reshape((batch_size, n_steps, n_input))\n", + " batch_x = batch_x.reshape((batch_size, timesteps, num_input))\n", " # Run optimization op (backprop)\n", - " sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\n", - " if step % display_step == 0:\n", - " # Calculate batch accuracy\n", - " acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})\n", - " # Calculate batch loss\n", - " loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})\n", - " print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n", - " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", - " \"{:.5f}\".format(acc)\n", - " step += 1\n", - " print \"Optimization Finished!\"\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", "\n", " # Calculate accuracy for 128 mnist test images\n", " test_len = 128\n", - " test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\n", + " test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))\n", " test_label = mnist.test.labels[:test_len]\n", - " print \"Testing Accuracy:\", \\\n", - " sess.run(accuracy, feed_dict={x: test_data, y: test_label})" + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))\n" ] }, { diff --git a/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/notebooks/3_NeuralNetworks/convolutional_network.ipynb index d2ff8cb9..52c45d3e 100644 --- a/notebooks/3_NeuralNetworks/convolutional_network.ipynb +++ b/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -1,61 +1,80 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolutional Neural Network Example\n", + "\n", + "Build a convolutional neural network with TensorFlow.\n", + "\n", + "This example is using TensorFlow layers API, see 'convolutional_network_raw' example\n", + "for a raw TensorFlow implementation with variables.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ - "'''\n", - "A Convolutional Network implementation example using TensorFlow library.\n", - "This example is using the MNIST database of handwritten digits\n", - "(http://yann.lecun.com/exdb/mnist/)\n", + "## CNN Overview\n", + "\n", + "![CNN](http://personal.ie.cuhk.edu.hk/~ccloy/project_target_code/images/fig3.png)\n", + "\n", + "## MNIST Dataset Overview\n", "\n", - "Author: Aymeric Damien\n", - "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", - "'''" + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], "source": [ - "import tensorflow as tf\n", + "from __future__ import division, print_function, absolute_import\n", "\n", "# Import MNIST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)\n", + "\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ - "# Parameters\n", + "# Training Parameters\n", "learning_rate = 0.001\n", - "training_iters = 200000\n", + "num_steps = 2000\n", "batch_size = 128\n", - "display_step = 10\n", "\n", "# Network Parameters\n", - "n_input = 784 # MNIST data input (img shape: 28*28)\n", - "n_classes = 10 # MNIST total classes (0-9 digits)\n", - "dropout = 0.75 # Dropout, probability to keep units\n", - "\n", - "# tf Graph input\n", - "x = tf.placeholder(tf.float32, [None, n_input])\n", - "y = tf.placeholder(tf.float32, [None, n_classes])\n", - "keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)" + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.75 # Dropout, probability to keep units" ] }, { @@ -66,45 +85,40 @@ }, "outputs": [], "source": [ - "# Create some wrappers for simplicity\n", - "def conv2d(x, W, b, strides=1):\n", - " # Conv2D wrapper, with bias and relu activation\n", - " x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n", - " x = tf.nn.bias_add(x, b)\n", - " return tf.nn.relu(x)\n", - "\n", + "# Create the neural network\n", + "def conv_net(x_dict, n_classes, dropout, reuse, is_training):\n", + " \n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + " # TF Estimator input is a dict, in case of multiple inputs\n", + " x = x_dict['images']\n", "\n", - "def maxpool2d(x, k=2):\n", - " # MaxPool2D wrapper\n", - " return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],\n", - " padding='SAME')\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n", "\n", - "# Create model\n", - "def conv_net(x, weights, biases, dropout):\n", - " # Reshape input picture\n", - " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n", "\n", - " # Convolution Layer\n", - " conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n", - " # Max Pooling (down-sampling)\n", - " conv1 = maxpool2d(conv1, k=2)\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " fc1 = tf.contrib.layers.flatten(conv2)\n", "\n", - " # Convolution Layer\n", - " conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n", - " # Max Pooling (down-sampling)\n", - " conv2 = maxpool2d(conv2, k=2)\n", + " # Fully connected layer (in tf contrib folder for now)\n", + " fc1 = tf.layers.dense(fc1, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)\n", "\n", - " # Fully connected layer\n", - " # Reshape conv2 output to fit fully connected layer input\n", - " fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n", - " fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n", - " fc1 = tf.nn.relu(fc1)\n", - " # Apply Dropout\n", - " fc1 = tf.nn.dropout(fc1, dropout)\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(fc1, n_classes)\n", "\n", - " # Output, class prediction\n", - " out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n", " return out" ] }, @@ -116,252 +130,276 @@ }, "outputs": [], "source": [ - "# Store layers weight & bias\n", - "weights = {\n", - " # 5x5 conv, 1 input, 32 outputs\n", - " 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),\n", - " # 5x5 conv, 32 inputs, 64 outputs\n", - " 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),\n", - " # fully connected, 7*7*64 inputs, 1024 outputs\n", - " 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),\n", - " # 1024 inputs, 10 outputs (class prediction)\n", - " 'out': tf.Variable(tf.random_normal([1024, n_classes]))\n", - "}\n", + "# Define the model function (following TF Estimator Template)\n", + "def model_fn(features, labels, mode):\n", + " \n", + " # Build the neural network\n", + " # Because Dropout have different behavior at training and prediction time, we\n", + " # need to create 2 distinct computation graphs that still share the same weights.\n", + " logits_train = conv_net(features, num_classes, dropout, reuse=False, is_training=True)\n", + " logits_test = conv_net(features, num_classes, dropout, reuse=True, is_training=False)\n", + " \n", + " # Predictions\n", + " pred_classes = tf.argmax(logits_test, axis=1)\n", + " pred_probas = tf.nn.softmax(logits_test)\n", + " \n", + " # If prediction mode, early return\n", + " if mode == tf.estimator.ModeKeys.PREDICT:\n", + " return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) \n", + " \n", + " # Define loss and optimizer\n", + " loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=tf.cast(labels, dtype=tf.int32)))\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + " train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())\n", + " \n", + " # Evaluate the accuracy of the model\n", + " acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)\n", + " \n", + " # TF Estimators requires to return a EstimatorSpec, that specify\n", + " # the different ops for training, evaluating, ...\n", + " estim_specs = tf.estimator.EstimatorSpec(\n", + " mode=mode,\n", + " predictions=pred_classes,\n", + " loss=loss_op,\n", + " train_op=train_op,\n", + " eval_metric_ops={'accuracy': acc_op})\n", "\n", - "biases = {\n", - " 'bc1': tf.Variable(tf.random_normal([32])),\n", - " 'bc2': tf.Variable(tf.random_normal([64])),\n", - " 'bd1': tf.Variable(tf.random_normal([1024])),\n", - " 'out': tf.Variable(tf.random_normal([n_classes]))\n", - "}\n", - "\n", - "# Construct model\n", - "pred = conv_net(x, weights, biases, keep_prob)\n", - "\n", - "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", - "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", - "\n", - "# Evaluate model\n", - "correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", - "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", - "\n", - "# Initializing the variables\n", - "init = tf.global_variables_initializer()" + " return estim_specs" ] }, { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Iter 1280, Minibatch Loss= 26574.855469, Training Accuracy= 0.25781\n", - "Iter 2560, Minibatch Loss= 11454.494141, Training Accuracy= 0.49219\n", - "Iter 3840, Minibatch Loss= 10070.515625, Training Accuracy= 0.55469\n", - "Iter 5120, Minibatch Loss= 4008.586426, Training Accuracy= 0.78125\n", - "Iter 6400, Minibatch Loss= 3148.004639, Training Accuracy= 0.80469\n", - "Iter 7680, Minibatch Loss= 6740.440430, Training Accuracy= 0.71875\n", - "Iter 8960, Minibatch Loss= 4103.991699, Training Accuracy= 0.80469\n", - "Iter 10240, Minibatch Loss= 2631.275391, Training Accuracy= 0.85938\n", - "Iter 11520, Minibatch Loss= 1428.798828, Training Accuracy= 0.91406\n", - "Iter 12800, Minibatch Loss= 3909.772705, Training Accuracy= 0.78906\n", - "Iter 14080, Minibatch Loss= 1423.095947, Training Accuracy= 0.88281\n", - "Iter 15360, Minibatch Loss= 1524.569824, Training Accuracy= 0.89062\n", - "Iter 16640, Minibatch Loss= 2234.539795, Training Accuracy= 0.86719\n", - "Iter 17920, Minibatch Loss= 933.932800, Training Accuracy= 0.90625\n", - "Iter 19200, Minibatch Loss= 2039.046021, Training Accuracy= 0.89062\n", - "Iter 20480, Minibatch Loss= 674.179932, Training Accuracy= 0.95312\n", - "Iter 21760, Minibatch Loss= 3778.958984, Training Accuracy= 0.82812\n", - "Iter 23040, Minibatch Loss= 1038.217773, Training Accuracy= 0.91406\n", - "Iter 24320, Minibatch Loss= 1689.513672, Training Accuracy= 0.89062\n", - "Iter 25600, Minibatch Loss= 1800.954956, Training Accuracy= 0.85938\n", - "Iter 26880, Minibatch Loss= 1086.292847, Training Accuracy= 0.90625\n", - "Iter 28160, Minibatch Loss= 656.042847, Training Accuracy= 0.94531\n", - "Iter 29440, Minibatch Loss= 1210.589844, Training Accuracy= 0.91406\n", - "Iter 30720, Minibatch Loss= 1099.606323, Training Accuracy= 0.90625\n", - "Iter 32000, Minibatch Loss= 1073.128174, Training Accuracy= 0.92969\n", - "Iter 33280, Minibatch Loss= 518.844543, Training Accuracy= 0.95312\n", - "Iter 34560, Minibatch Loss= 540.856689, Training Accuracy= 0.92188\n", - "Iter 35840, Minibatch Loss= 353.990906, Training Accuracy= 0.97656\n", - "Iter 37120, Minibatch Loss= 1488.962891, Training Accuracy= 0.91406\n", - "Iter 38400, Minibatch Loss= 231.191864, Training Accuracy= 0.98438\n", - "Iter 39680, Minibatch Loss= 171.154480, Training Accuracy= 0.98438\n", - "Iter 40960, Minibatch Loss= 2092.023682, Training Accuracy= 0.90625\n", - "Iter 42240, Minibatch Loss= 480.594299, Training Accuracy= 0.95312\n", - "Iter 43520, Minibatch Loss= 504.128143, Training Accuracy= 0.96875\n", - "Iter 44800, Minibatch Loss= 143.534485, Training Accuracy= 0.97656\n", - "Iter 46080, Minibatch Loss= 325.875580, Training Accuracy= 0.96094\n", - "Iter 47360, Minibatch Loss= 602.813049, Training Accuracy= 0.91406\n", - "Iter 48640, Minibatch Loss= 794.595093, Training Accuracy= 0.94531\n", - "Iter 49920, Minibatch Loss= 415.539032, Training Accuracy= 0.95312\n", - "Iter 51200, Minibatch Loss= 146.016022, Training Accuracy= 0.96094\n", - "Iter 52480, Minibatch Loss= 294.180786, Training Accuracy= 0.94531\n", - "Iter 53760, Minibatch Loss= 50.955730, Training Accuracy= 0.99219\n", - "Iter 55040, Minibatch Loss= 1026.607056, Training Accuracy= 0.92188\n", - "Iter 56320, Minibatch Loss= 283.756134, Training Accuracy= 0.96875\n", - "Iter 57600, Minibatch Loss= 691.538208, Training Accuracy= 0.95312\n", - "Iter 58880, Minibatch Loss= 491.075073, Training Accuracy= 0.96094\n", - "Iter 60160, Minibatch Loss= 571.951660, Training Accuracy= 0.95312\n", - "Iter 61440, Minibatch Loss= 284.041168, Training Accuracy= 0.97656\n", - "Iter 62720, Minibatch Loss= 1041.941528, Training Accuracy= 0.92969\n", - "Iter 64000, Minibatch Loss= 664.833923, Training Accuracy= 0.93750\n", - "Iter 65280, Minibatch Loss= 1582.112793, Training Accuracy= 0.88281\n", - "Iter 66560, Minibatch Loss= 783.135376, Training Accuracy= 0.94531\n", - "Iter 67840, Minibatch Loss= 245.942398, Training Accuracy= 0.96094\n", - "Iter 69120, Minibatch Loss= 752.858948, Training Accuracy= 0.96875\n", - "Iter 70400, Minibatch Loss= 623.243286, Training Accuracy= 0.94531\n", - "Iter 71680, Minibatch Loss= 846.498230, Training Accuracy= 0.93750\n", - "Iter 72960, Minibatch Loss= 586.516479, Training Accuracy= 0.95312\n", - "Iter 74240, Minibatch Loss= 92.774963, Training Accuracy= 0.98438\n", - "Iter 75520, Minibatch Loss= 644.039612, Training Accuracy= 0.95312\n", - "Iter 76800, Minibatch Loss= 693.247681, Training Accuracy= 0.96094\n", - "Iter 78080, Minibatch Loss= 466.491882, Training Accuracy= 0.96094\n", - "Iter 79360, Minibatch Loss= 964.212341, Training Accuracy= 0.93750\n", - "Iter 80640, Minibatch Loss= 230.451904, Training Accuracy= 0.97656\n", - "Iter 81920, Minibatch Loss= 280.434570, Training Accuracy= 0.95312\n", - "Iter 83200, Minibatch Loss= 213.208252, Training Accuracy= 0.97656\n", - "Iter 84480, Minibatch Loss= 774.836060, Training Accuracy= 0.94531\n", - "Iter 85760, Minibatch Loss= 164.687729, Training Accuracy= 0.96094\n", - "Iter 87040, Minibatch Loss= 419.967407, Training Accuracy= 0.96875\n", - "Iter 88320, Minibatch Loss= 160.920151, Training Accuracy= 0.96875\n", - "Iter 89600, Minibatch Loss= 586.063599, Training Accuracy= 0.96094\n", - "Iter 90880, Minibatch Loss= 345.598145, Training Accuracy= 0.96875\n", - "Iter 92160, Minibatch Loss= 931.361145, Training Accuracy= 0.92188\n", - "Iter 93440, Minibatch Loss= 170.107117, Training Accuracy= 0.97656\n", - "Iter 94720, Minibatch Loss= 497.162750, Training Accuracy= 0.93750\n", - "Iter 96000, Minibatch Loss= 906.600464, Training Accuracy= 0.94531\n", - "Iter 97280, Minibatch Loss= 303.382202, Training Accuracy= 0.92969\n", - "Iter 98560, Minibatch Loss= 509.161652, Training Accuracy= 0.97656\n", - "Iter 99840, Minibatch Loss= 359.561981, Training Accuracy= 0.97656\n", - "Iter 101120, Minibatch Loss= 136.516541, Training Accuracy= 0.97656\n", - "Iter 102400, Minibatch Loss= 517.199341, Training Accuracy= 0.96875\n", - "Iter 103680, Minibatch Loss= 487.793335, Training Accuracy= 0.95312\n", - "Iter 104960, Minibatch Loss= 407.351929, Training Accuracy= 0.96094\n", - "Iter 106240, Minibatch Loss= 70.495193, Training Accuracy= 0.98438\n", - "Iter 107520, Minibatch Loss= 344.783508, Training Accuracy= 0.96094\n", - "Iter 108800, Minibatch Loss= 242.682465, Training Accuracy= 0.95312\n", - "Iter 110080, Minibatch Loss= 169.181458, Training Accuracy= 0.96094\n", - "Iter 111360, Minibatch Loss= 152.638245, Training Accuracy= 0.98438\n", - "Iter 112640, Minibatch Loss= 170.795868, Training Accuracy= 0.96875\n", - "Iter 113920, Minibatch Loss= 133.262726, Training Accuracy= 0.98438\n", - "Iter 115200, Minibatch Loss= 296.063293, Training Accuracy= 0.95312\n", - "Iter 116480, Minibatch Loss= 254.247543, Training Accuracy= 0.96094\n", - "Iter 117760, Minibatch Loss= 506.795715, Training Accuracy= 0.94531\n", - "Iter 119040, Minibatch Loss= 446.006897, Training Accuracy= 0.96094\n", - "Iter 120320, Minibatch Loss= 149.467377, Training Accuracy= 0.97656\n", - "Iter 121600, Minibatch Loss= 52.783600, Training Accuracy= 0.98438\n", - "Iter 122880, Minibatch Loss= 49.041794, Training Accuracy= 0.98438\n", - "Iter 124160, Minibatch Loss= 184.371246, Training Accuracy= 0.97656\n", - "Iter 125440, Minibatch Loss= 129.838501, Training Accuracy= 0.97656\n", - "Iter 126720, Minibatch Loss= 288.006531, Training Accuracy= 0.96875\n", - "Iter 128000, Minibatch Loss= 187.284653, Training Accuracy= 0.97656\n", - "Iter 129280, Minibatch Loss= 197.969955, Training Accuracy= 0.96875\n", - "Iter 130560, Minibatch Loss= 299.969818, Training Accuracy= 0.96875\n", - "Iter 131840, Minibatch Loss= 537.602173, Training Accuracy= 0.96094\n", - "Iter 133120, Minibatch Loss= 4.519302, Training Accuracy= 0.99219\n", - "Iter 134400, Minibatch Loss= 133.264191, Training Accuracy= 0.97656\n", - "Iter 135680, Minibatch Loss= 89.662292, Training Accuracy= 0.97656\n", - "Iter 136960, Minibatch Loss= 107.774078, Training Accuracy= 0.96875\n", - "Iter 138240, Minibatch Loss= 335.904572, Training Accuracy= 0.96094\n", - "Iter 139520, Minibatch Loss= 457.494568, Training Accuracy= 0.96094\n", - "Iter 140800, Minibatch Loss= 259.131531, Training Accuracy= 0.95312\n", - "Iter 142080, Minibatch Loss= 152.205383, Training Accuracy= 0.96094\n", - "Iter 143360, Minibatch Loss= 252.535828, Training Accuracy= 0.95312\n", - "Iter 144640, Minibatch Loss= 109.477585, Training Accuracy= 0.96875\n", - "Iter 145920, Minibatch Loss= 24.468613, Training Accuracy= 0.99219\n", - "Iter 147200, Minibatch Loss= 51.722107, Training Accuracy= 0.97656\n", - "Iter 148480, Minibatch Loss= 69.715233, Training Accuracy= 0.97656\n", - "Iter 149760, Minibatch Loss= 405.289246, Training Accuracy= 0.92969\n", - "Iter 151040, Minibatch Loss= 282.976379, Training Accuracy= 0.95312\n", - "Iter 152320, Minibatch Loss= 134.991119, Training Accuracy= 0.97656\n", - "Iter 153600, Minibatch Loss= 491.618103, Training Accuracy= 0.92188\n", - "Iter 154880, Minibatch Loss= 154.299988, Training Accuracy= 0.99219\n", - "Iter 156160, Minibatch Loss= 79.480019, Training Accuracy= 0.96875\n", - "Iter 157440, Minibatch Loss= 68.093750, Training Accuracy= 0.99219\n", - "Iter 158720, Minibatch Loss= 459.739685, Training Accuracy= 0.92188\n", - "Iter 160000, Minibatch Loss= 168.076843, Training Accuracy= 0.94531\n", - "Iter 161280, Minibatch Loss= 256.141846, Training Accuracy= 0.97656\n", - "Iter 162560, Minibatch Loss= 236.400391, Training Accuracy= 0.94531\n", - "Iter 163840, Minibatch Loss= 177.011261, Training Accuracy= 0.96875\n", - "Iter 165120, Minibatch Loss= 48.583298, Training Accuracy= 0.97656\n", - "Iter 166400, Minibatch Loss= 413.800293, Training Accuracy= 0.96094\n", - "Iter 167680, Minibatch Loss= 209.587387, Training Accuracy= 0.96875\n", - "Iter 168960, Minibatch Loss= 239.407318, Training Accuracy= 0.98438\n", - "Iter 170240, Minibatch Loss= 183.567017, Training Accuracy= 0.96875\n", - "Iter 171520, Minibatch Loss= 87.937515, Training Accuracy= 0.96875\n", - "Iter 172800, Minibatch Loss= 203.777039, Training Accuracy= 0.98438\n", - "Iter 174080, Minibatch Loss= 566.378052, Training Accuracy= 0.94531\n", - "Iter 175360, Minibatch Loss= 325.170898, Training Accuracy= 0.95312\n", - "Iter 176640, Minibatch Loss= 300.142212, Training Accuracy= 0.97656\n", - "Iter 177920, Minibatch Loss= 205.370193, Training Accuracy= 0.95312\n", - "Iter 179200, Minibatch Loss= 5.594437, Training Accuracy= 0.99219\n", - "Iter 180480, Minibatch Loss= 110.732109, Training Accuracy= 0.98438\n", - "Iter 181760, Minibatch Loss= 33.320297, Training Accuracy= 0.99219\n", - "Iter 183040, Minibatch Loss= 6.885544, Training Accuracy= 0.99219\n", - "Iter 184320, Minibatch Loss= 221.144806, Training Accuracy= 0.96875\n", - "Iter 185600, Minibatch Loss= 365.337372, Training Accuracy= 0.94531\n", - "Iter 186880, Minibatch Loss= 186.558258, Training Accuracy= 0.96094\n", - "Iter 188160, Minibatch Loss= 149.720322, Training Accuracy= 0.98438\n", - "Iter 189440, Minibatch Loss= 105.281998, Training Accuracy= 0.97656\n", - "Iter 190720, Minibatch Loss= 289.980011, Training Accuracy= 0.96094\n", - "Iter 192000, Minibatch Loss= 214.382278, Training Accuracy= 0.96094\n", - "Iter 193280, Minibatch Loss= 461.044312, Training Accuracy= 0.93750\n", - "Iter 194560, Minibatch Loss= 138.653076, Training Accuracy= 0.98438\n", - "Iter 195840, Minibatch Loss= 112.004883, Training Accuracy= 0.98438\n", - "Iter 197120, Minibatch Loss= 212.691467, Training Accuracy= 0.97656\n", - "Iter 198400, Minibatch Loss= 57.642502, Training Accuracy= 0.97656\n", - "Iter 199680, Minibatch Loss= 80.503563, Training Accuracy= 0.96875\n", - "Optimization Finished!\n", - "Testing Accuracy: 0.984375\n" + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpdhd6F4\n", + "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_tf_random_seed': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_save_checkpoints_steps': None, '_model_dir': '/tmp/tmpdhd6F4', '_save_summary_steps': 100}\n" ] } ], "source": [ - "# Launch the graph\n", - "with tf.Session() as sess:\n", - " sess.run(init)\n", - " step = 1\n", - " # Keep training until reach max iterations\n", - " while step * batch_size < training_iters:\n", - " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", - " # Run optimization op (backprop)\n", - " sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,\n", - " keep_prob: dropout})\n", - " if step % display_step == 0:\n", - " # Calculate batch loss and accuracy\n", - " loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,\n", - " y: batch_y,\n", - " keep_prob: 1.})\n", - " print(\"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n", - " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", - " \"{:.5f}\".format(acc))\n", - " step += 1\n", - " print(\"Optimization Finished!\")\n", - "\n", - " # Calculate accuracy for 256 mnist test images\n", - " print(\"Testing Accuracy:\", \\\n", - " sess.run(accuracy, feed_dict={x: mnist.test.images[:256],\n", - " y: mnist.test.labels[:256],\n", - " keep_prob: 1.}))" + "# Build the Estimator\n", + "model = tf.estimator.Estimator(model_fn)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpdhd6F4/model.ckpt.\n", + "INFO:tensorflow:loss = 2.39026, step = 1\n", + "INFO:tensorflow:global_step/sec: 238.314\n", + "INFO:tensorflow:loss = 0.237997, step = 101 (0.421 sec)\n", + "INFO:tensorflow:global_step/sec: 255.312\n", + "INFO:tensorflow:loss = 0.0954537, step = 201 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 257.194\n", + "INFO:tensorflow:loss = 0.121477, step = 301 (0.389 sec)\n", + "INFO:tensorflow:global_step/sec: 255.018\n", + "INFO:tensorflow:loss = 0.0539927, step = 401 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 254.293\n", + "INFO:tensorflow:loss = 0.0440369, step = 501 (0.393 sec)\n", + "INFO:tensorflow:global_step/sec: 256.501\n", + "INFO:tensorflow:loss = 0.0247431, step = 601 (0.390 sec)\n", + "INFO:tensorflow:global_step/sec: 252.956\n", + "INFO:tensorflow:loss = 0.0738082, step = 701 (0.395 sec)\n", + "INFO:tensorflow:global_step/sec: 253.222\n", + "INFO:tensorflow:loss = 0.134998, step = 801 (0.395 sec)\n", + "INFO:tensorflow:global_step/sec: 255.606\n", + "INFO:tensorflow:loss = 0.00438448, step = 901 (0.391 sec)\n", + "INFO:tensorflow:global_step/sec: 256.306\n", + "INFO:tensorflow:loss = 0.0471991, step = 1001 (0.390 sec)\n", + "INFO:tensorflow:global_step/sec: 255.352\n", + "INFO:tensorflow:loss = 0.0371172, step = 1101 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 253.277\n", + "INFO:tensorflow:loss = 0.0129522, step = 1201 (0.395 sec)\n", + "INFO:tensorflow:global_step/sec: 252.49\n", + "INFO:tensorflow:loss = 0.039862, step = 1301 (0.396 sec)\n", + "INFO:tensorflow:global_step/sec: 253.902\n", + "INFO:tensorflow:loss = 0.0520571, step = 1401 (0.394 sec)\n", + "INFO:tensorflow:global_step/sec: 255.572\n", + "INFO:tensorflow:loss = 0.0307549, step = 1501 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 254.32\n", + "INFO:tensorflow:loss = 0.0108862, step = 1601 (0.393 sec)\n", + "INFO:tensorflow:global_step/sec: 255.62\n", + "INFO:tensorflow:loss = 0.0294434, step = 1701 (0.391 sec)\n", + "INFO:tensorflow:global_step/sec: 254.349\n", + "INFO:tensorflow:loss = 0.0179781, step = 1801 (0.393 sec)\n", + "INFO:tensorflow:global_step/sec: 255.508\n", + "INFO:tensorflow:loss = 0.0375271, step = 1901 (0.391 sec)\n", + "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmpdhd6F4/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 0.00440777.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define the input function for training\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.train.images}, y=mnist.train.labels,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)\n", + "# Train the Model\n", + "model.train(input_fn, steps=num_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Starting evaluation at 2017-08-21-14:25:29\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpdhd6F4/model.ckpt-2000\n", + "INFO:tensorflow:Finished evaluation at 2017-08-21-14:25:29\n", + "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9908, global_step = 2000, loss = 0.0382241\n" + ] + }, + { + "data": { + "text/plain": [ + "{'accuracy': 0.99080002, 'global_step': 2000, 'loss': 0.038224086}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluate the Model\n", + "# Define the input function for evaluating\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.test.images}, y=mnist.test.labels,\n", + " batch_size=batch_size, shuffle=False)\n", + "# Use the Estimator 'evaluate' method\n", + "model.evaluate(input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from /tmp/tmpdhd6F4/model.ckpt-2000\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiL8QFKEH0iK8ANJEX4gKcIPJPVP82g/p9/JjhUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 7\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 2\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADCRJREFUeJzt3X/oXfV9x/Hne1n6h2n/MKvGYMV0RaclYjK+iGCYHdXi\nRND8I1UYkcnSPxqwsD8m7o8JYyCydgz/KKQ0NJXOZkSDWqdtJ8N0MKpRM383OvmWJsREUahVpDN5\n74/viXzV7z33m3vPvecm7+cDLt9zz+eee94c8srn/LrnE5mJpHr+oO8CJPXD8EtFGX6pKMMvFWX4\npaIMv1SU4ZeKMvxSUYZfKuoPp7myiPB2QmnCMjOW87mxev6IuCYifhURr0XE7eN8l6TpilHv7Y+I\nFcAB4GrgIPAUcFNmvtSyjD2/NGHT6PkvA17LzNcz8/fAj4Hrx/g+SVM0TvjPBX6z6P3BZt7HRMTW\niNgXEfvGWJekjk38hF9mbge2g7v90iwZp+c/BJy36P0XmnmSTgHjhP8p4IKI+GJEfAb4OvBQN2VJ\nmrSRd/sz88OI2Ab8FFgB7MjMFzurTNJEjXypb6SVecwvTdxUbvKRdOoy/FJRhl8qyvBLRRl+qSjD\nLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0UZfqmo\nqQ7RrXouvPDCgW2vvPJK67K33XZba/s999wzUk1aYM8vFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0WN\ndZ0/IuaBd4FjwIeZOddFUTp9bNy4cWDb8ePHW5c9ePBg1+VokS5u8vnzzHyrg++RNEXu9ktFjRv+\nBH4WEU9HxNYuCpI0HePu9m/KzEMRcTbw84h4JTP3Lv5A85+C/zFIM2asnj8zDzV/jwJ7gMuW+Mz2\nzJzzZKA0W0YOf0SsiojPnZgGvga80FVhkiZrnN3+NcCeiDjxPf+amY91UpWkiRs5/Jn5OnBph7Xo\nNLRhw4aBbe+9917rsnv27Om6HC3ipT6pKMMvFWX4paIMv1SU4ZeKMvxSUT66W2NZv359a/u2bdsG\ntt17771dl6OTYM8vFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0V5nV9jueiii1rbV61aNbBt165dXZej\nk2DPLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFRWZOb2UR01uZpuLJJ59sbT/rrLMGtg17FsCwR3tr\naZkZy/mcPb9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFTX09/wRsQO4DjiameubeauBXcA6YB64MTPf\nmVyZ6su6deta2+fm5lrbDxw4MLDN6/j9Wk7P/wPgmk/Mux14PDMvAB5v3ks6hQwNf2buBd7+xOzr\ngZ3N9E7gho7rkjRhox7zr8nMw830G8CajuqRNCVjP8MvM7Ptnv2I2ApsHXc9kro1as9/JCLWAjR/\njw76YGZuz8y5zGw/MyRpqkYN/0PAlmZ6C/BgN+VImpah4Y+I+4D/Bv4kIg5GxK3AXcDVEfEqcFXz\nXtIpZOgxf2beNKDpqx3Xohl05ZVXjrX8m2++2VEl6pp3+ElFGX6pKMMvFWX4paIMv1SU4ZeKcohu\ntbrkkkvGWv7uu+/uqBJ1zZ5fKsrwS0UZfqkowy8VZfilogy/VJThl4pyiO7iLr/88tb2Rx55pLV9\nfn6+tf2KK64Y2PbBBx+0LqvROES3pFaGXyrK8EtFGX6pKMMvFWX4paIMv1SUv+cv7qqrrmptX716\ndWv7Y4891trutfzZZc8vFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0UNvc4fETuA64Cjmbm+mXcn8NfA\nifGX78jMf59UkZqcSy+9tLV92PMedu/e3WU5mqLl9Pw/AK5ZYv4/Z+aG5mXwpVPM0PBn5l7g7SnU\nImmKxjnm3xYRz0XEjog4s7OKJE3FqOH/LvAlYANwGPj2oA9GxNaI2BcR+0Zcl6QJGCn8mXkkM49l\n5nHge8BlLZ/dnplzmTk3apGSujdS+CNi7aK3m4EXuilH0rQs51LffcBXgM9HxEHg74GvRMQGIIF5\n4BsTrFHSBPjc/tPcOeec09q+f//+1vZ33nmntf3iiy8+6Zo0WT63X1Irwy8VZfilogy/VJThl4oy\n/FJRPrr7NHfLLbe0tp999tmt7Y8++miH1WiW2PNLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlFe5z/N\nnX/++WMtP+wnvTp12fNLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlFe5z/NXXfddWMt//DDD3dUiWaN\nPb9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFTX0On9EnAf8EFgDJLA9M/8lIlYDu4B1wDxwY2b64+8e\nbNq0aWDbsCG6Vddyev4Pgb/JzC8DlwPfjIgvA7cDj2fmBcDjzXtJp4ih4c/Mw5n5TDP9LvAycC5w\nPbCz+dhO4IZJFSmpeyd1zB8R64CNwC+BNZl5uGl6g4XDAkmniGXf2x8RnwXuB76Vmb+NiI/aMjMj\nIgcstxXYOm6hkrq1rJ4/IlayEPwfZeYDzewjEbG2aV8LHF1q2czcnplzmTnXRcGSujE0/LHQxX8f\neDkzv7Oo6SFgSzO9BXiw+/IkTcpydvuvAP4SeD4i9jfz7gDuAv4tIm4Ffg3cOJkSNczmzZsHtq1Y\nsaJ12Weffba1fe/evSPVpNk3NPyZ+V9ADGj+arflSJoW7/CTijL8UlGGXyrK8EtFGX6pKMMvFeWj\nu08BZ5xxRmv7tddeO/J37969u7X92LFjI3+3Zps9v1SU4ZeKMvxSUYZfKsrwS0UZfqkowy8VFZlL\nPn1rMisb8KgvtVu5cmVr+xNPPDGw7ejRJR+w9JGbb765tf39999vbdfsycxBP8H/GHt+qSjDLxVl\n+KWiDL9UlOGXijL8UlGGXyrK6/zSacbr/JJaGX6pKMMvFWX4paIMv1SU4ZeKMvxSUUPDHxHnRcR/\nRsRLEfFiRNzWzL8zIg5FxP7mNfrD4yVN3dCbfCJiLbA2M5+JiM8BTwM3ADcCv8vMf1r2yrzJR5q4\n5d7kM3TEnsw8DBxupt+NiJeBc8crT1LfTuqYPyLWARuBXzaztkXEcxGxIyLOHLDM1ojYFxH7xqpU\nUqeWfW9/RHwWeAL4x8x8ICLWAG8BCfwDC4cGfzXkO9ztlyZsubv9ywp/RKwEfgL8NDO/s0T7OuAn\nmbl+yPcYfmnCOvthT0QE8H3g5cXBb04EnrAZeOFki5TUn+Wc7d8E/AJ4HjjezL4DuAnYwMJu/zzw\njebkYNt32fNLE9bpbn9XDL80ef6eX1Irwy8VZfilogy/VJThl4oy/FJRhl8qyvBLRRl+qSjDLxVl\n+KWiDL9UlOGXijL8UlFDH+DZsbeAXy96//lm3iya1dpmtS6wtlF1Wdv5y/3gVH/P/6mVR+zLzLne\nCmgxq7XNal1gbaPqqzZ3+6WiDL9UVN/h397z+tvMam2zWhdY26h6qa3XY35J/em755fUk17CHxHX\nRMSvIuK1iLi9jxoGiYj5iHi+GXm41yHGmmHQjkbEC4vmrY6In0fEq83fJYdJ66m2mRi5uWVk6V63\n3ayNeD313f6IWAEcAK4GDgJPATdl5ktTLWSAiJgH5jKz92vCEfFnwO+AH54YDSki7gbezsy7mv84\nz8zMv52R2u7kJEdunlBtg0aWvoUet12XI153oY+e/zLgtcx8PTN/D/wYuL6HOmZeZu4F3v7E7OuB\nnc30Thb+8UzdgNpmQmYezsxnmul3gRMjS/e67Vrq6kUf4T8X+M2i9weZrSG/E/hZRDwdEVv7LmYJ\naxaNjPQGsKbPYpYwdOTmafrEyNIzs+1GGfG6a57w+7RNmfmnwF8A32x2b2dSLhyzzdLlmu8CX2Jh\nGLfDwLf7LKYZWfp+4FuZ+dvFbX1uuyXq6mW79RH+Q8B5i95/oZk3EzLzUPP3KLCHhcOUWXLkxCCp\nzd+jPdfzkcw8kpnHMvM48D163HbNyNL3Az/KzAea2b1vu6Xq6mu79RH+p4ALIuKLEfEZ4OvAQz3U\n8SkRsao5EUNErAK+xuyNPvwQsKWZ3gI82GMtHzMrIzcPGlmanrfdzI14nZlTfwHXsnDG/3+Bv+uj\nhgF1/THwP83rxb5rA+5jYTfw/1g4N3Ir8EfA48CrwH8Aq2eotntZGM35ORaCtran2jaxsEv/HLC/\neV3b97ZrqauX7eYdflJRnvCTijL8UlGGXyrK8EtFGX6pKMMvFWX4paIMv1TU/wNRj+er2ohshAAA\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 1\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZZUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 0\n" + ] + } + ], + "source": [ + "# Predict single images\n", + "n_images = 4\n", + "# Get images from test set\n", + "test_images = mnist.test.images[:n_images]\n", + "# Prepare the input data\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': test_images}, shuffle=False)\n", + "# Use the model to predict the images class\n", + "preds = list(model.predict(input_fn))\n", + "\n", + "# Display\n", + "for i in range(n_images):\n", + " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n", + " plt.show()\n", + " print(\"Model prediction:\", preds[i])" + ] } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", @@ -377,9 +415,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.12" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 2 } diff --git a/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb b/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb new file mode 100644 index 00000000..5a7dd29b --- /dev/null +++ b/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Convolutional Neural Network Example\n", + "\n", + "Build a convolutional neural network with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CNN Overview\n", + "\n", + "![CNN](http://personal.ie.cuhk.edu.hk/~ccloy/project_target_code/images/fig3.png)\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import tensorflow as tf\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 500\n", + "batch_size = 128\n", + "display_step = 10\n", + "\n", + "# Network Parameters\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.75 # Dropout, probability to keep units\n", + "\n", + "# tf Graph input\n", + "X = tf.placeholder(tf.float32, [None, num_input])\n", + "Y = tf.placeholder(tf.float32, [None, num_classes])\n", + "keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create some wrappers for simplicity\n", + "def conv2d(x, W, b, strides=1):\n", + " # Conv2D wrapper, with bias and relu activation\n", + " x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n", + " x = tf.nn.bias_add(x, b)\n", + " return tf.nn.relu(x)\n", + "\n", + "\n", + "def maxpool2d(x, k=2):\n", + " # MaxPool2D wrapper\n", + " return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],\n", + " padding='SAME')\n", + "\n", + "\n", + "# Create model\n", + "def conv_net(x, weights, biases, dropout):\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer\n", + " conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n", + " # Max Pooling (down-sampling)\n", + " conv1 = maxpool2d(conv1, k=2)\n", + "\n", + " # Convolution Layer\n", + " conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n", + " # Max Pooling (down-sampling)\n", + " conv2 = maxpool2d(conv2, k=2)\n", + "\n", + " # Fully connected layer\n", + " # Reshape conv2 output to fit fully connected layer input\n", + " fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n", + " fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n", + " fc1 = tf.nn.relu(fc1)\n", + " # Apply Dropout\n", + " fc1 = tf.nn.dropout(fc1, dropout)\n", + "\n", + " # Output, class prediction\n", + " out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "weights = {\n", + " # 5x5 conv, 1 input, 32 outputs\n", + " 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),\n", + " # 5x5 conv, 32 inputs, 64 outputs\n", + " 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),\n", + " # fully connected, 7*7*64 inputs, 1024 outputs\n", + " 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),\n", + " # 1024 inputs, 10 outputs (class prediction)\n", + " 'out': tf.Variable(tf.random_normal([1024, num_classes]))\n", + "}\n", + "\n", + "biases = {\n", + " 'bc1': tf.Variable(tf.random_normal([32])),\n", + " 'bc2': tf.Variable(tf.random_normal([64])),\n", + " 'bd1': tf.Variable(tf.random_normal([1024])),\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", + "}\n", + "\n", + "# Construct model\n", + "logits = conv_net(X, weights, biases, keep_prob)\n", + "prediction = tf.nn.softmax(logits)\n", + "\n", + "# Define loss and optimizer\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "\n", + "# Evaluate model\n", + "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 63763.3047, Training Accuracy= 0.141\n", + "Step 10, Minibatch Loss= 26429.6680, Training Accuracy= 0.242\n", + "Step 20, Minibatch Loss= 12171.8584, Training Accuracy= 0.586\n", + "Step 30, Minibatch Loss= 6306.6318, Training Accuracy= 0.734\n", + "Step 40, Minibatch Loss= 5113.7583, Training Accuracy= 0.711\n", + "Step 50, Minibatch Loss= 4022.2131, Training Accuracy= 0.805\n", + "Step 60, Minibatch Loss= 3125.4949, Training Accuracy= 0.867\n", + "Step 70, Minibatch Loss= 2225.4875, Training Accuracy= 0.875\n", + "Step 80, Minibatch Loss= 1843.3540, Training Accuracy= 0.867\n", + "Step 90, Minibatch Loss= 1715.7744, Training Accuracy= 0.875\n", + "Step 100, Minibatch Loss= 2611.2708, Training Accuracy= 0.906\n", + "Step 110, Minibatch Loss= 4804.0913, Training Accuracy= 0.875\n", + "Step 120, Minibatch Loss= 1067.5258, Training Accuracy= 0.938\n", + "Step 130, Minibatch Loss= 2519.1514, Training Accuracy= 0.898\n", + "Step 140, Minibatch Loss= 2687.9292, Training Accuracy= 0.906\n", + "Step 150, Minibatch Loss= 1983.4077, Training Accuracy= 0.938\n", + "Step 160, Minibatch Loss= 2844.6553, Training Accuracy= 0.930\n", + "Step 170, Minibatch Loss= 3602.2524, Training Accuracy= 0.914\n", + "Step 180, Minibatch Loss= 175.3922, Training Accuracy= 0.961\n", + "Step 190, Minibatch Loss= 645.1918, Training Accuracy= 0.945\n", + "Step 200, Minibatch Loss= 1147.6567, Training Accuracy= 0.938\n", + "Step 210, Minibatch Loss= 1140.4148, Training Accuracy= 0.914\n", + "Step 220, Minibatch Loss= 1572.8756, Training Accuracy= 0.906\n", + "Step 230, Minibatch Loss= 1292.9274, Training Accuracy= 0.898\n", + "Step 240, Minibatch Loss= 1501.4623, Training Accuracy= 0.953\n", + "Step 250, Minibatch Loss= 1908.2997, Training Accuracy= 0.898\n", + "Step 260, Minibatch Loss= 2182.2380, Training Accuracy= 0.898\n", + "Step 270, Minibatch Loss= 487.5807, Training Accuracy= 0.961\n", + "Step 280, Minibatch Loss= 1284.1130, Training Accuracy= 0.945\n", + "Step 290, Minibatch Loss= 1232.4919, Training Accuracy= 0.891\n", + "Step 300, Minibatch Loss= 1198.8336, Training Accuracy= 0.945\n", + "Step 310, Minibatch Loss= 2010.5345, Training Accuracy= 0.906\n", + "Step 320, Minibatch Loss= 786.3917, Training Accuracy= 0.945\n", + "Step 330, Minibatch Loss= 1408.3556, Training Accuracy= 0.898\n", + "Step 340, Minibatch Loss= 1453.7538, Training Accuracy= 0.953\n", + "Step 350, Minibatch Loss= 999.8901, Training Accuracy= 0.906\n", + "Step 360, Minibatch Loss= 914.3958, Training Accuracy= 0.961\n", + "Step 370, Minibatch Loss= 488.0052, Training Accuracy= 0.938\n", + "Step 380, Minibatch Loss= 1070.8710, Training Accuracy= 0.922\n", + "Step 390, Minibatch Loss= 151.4658, Training Accuracy= 0.961\n", + "Step 400, Minibatch Loss= 555.3539, Training Accuracy= 0.953\n", + "Step 410, Minibatch Loss= 765.5746, Training Accuracy= 0.945\n", + "Step 420, Minibatch Loss= 326.9393, Training Accuracy= 0.969\n", + "Step 430, Minibatch Loss= 530.8968, Training Accuracy= 0.977\n", + "Step 440, Minibatch Loss= 463.3909, Training Accuracy= 0.977\n", + "Step 450, Minibatch Loss= 362.2226, Training Accuracy= 0.977\n", + "Step 460, Minibatch Loss= 414.0034, Training Accuracy= 0.953\n", + "Step 470, Minibatch Loss= 583.4587, Training Accuracy= 0.945\n", + "Step 480, Minibatch Loss= 566.1262, Training Accuracy= 0.969\n", + "Step 490, Minibatch Loss= 691.1143, Training Accuracy= 0.961\n", + "Step 500, Minibatch Loss= 282.8893, Training Accuracy= 0.984\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.976562\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, num_steps+1):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop)\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y, keep_prob: dropout})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y,\n", + " keep_prob: 1.0})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for 256 MNIST test images\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: mnist.test.images[:256],\n", + " Y: mnist.test.labels[:256],\n", + " keep_prob: 1.0}))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/3_NeuralNetworks/dcgan.ipynb b/notebooks/3_NeuralNetworks/dcgan.ipynb new file mode 100644 index 00000000..2edfd785 --- /dev/null +++ b/notebooks/3_NeuralNetworks/dcgan.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Deep Convolutional Generative Adversarial Network Example\n", + "\n", + "Build a deep convolutional generative adversarial network (DCGAN) to generate digit images from a noise distribution with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DCGAN Overview\n", + "\n", + "\"dcgan\"\n", + "\n", + "References:\n", + "- [Unsupervised representation learning with deep convolutional generative adversarial networks](https://arxiv.org/pdf/1511.06434). A Radford, L Metz, S Chintala, 2016.\n", + "- [Understanding the difficulty of training deep feedforward neural networks](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167). Sergey Ioffe, Christian Szegedy. 2015.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Params\n", + "num_steps = 10000\n", + "batch_size = 128\n", + "lr_generator = 0.002\n", + "lr_discriminator = 0.002\n", + "\n", + "# Network Params\n", + "image_dim = 784 # 28*28 pixels * 1 channel\n", + "noise_dim = 100 # Noise data points" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build Networks\n", + "# Network Inputs\n", + "noise_input = tf.placeholder(tf.float32, shape=[None, noise_dim])\n", + "real_image_input = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])\n", + "# A boolean to indicate batch normalization if it is training or inference time\n", + "is_training = tf.placeholder(tf.bool)\n", + "\n", + "#LeakyReLU activation\n", + "def leakyrelu(x, alpha=0.2):\n", + " return 0.5 * (1 + alpha) * x + 0.5 * (1 - alpha) * abs(x)\n", + "\n", + "# Generator Network\n", + "# Input: Noise, Output: Image\n", + "# Note that batch normalization has different behavior at training and inference time,\n", + "# we then use a placeholder to indicates the layer if we are training or not.\n", + "def generator(x, reuse=False):\n", + " with tf.variable_scope('Generator', reuse=reuse):\n", + " # TensorFlow Layers automatically create variables and calculate their\n", + " # shape, based on the input.\n", + " x = tf.layers.dense(x, units=7 * 7 * 128)\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = tf.nn.relu(x)\n", + " # Reshape to a 4-D array of images: (batch, height, width, channels)\n", + " # New shape: (batch, 7, 7, 128)\n", + " x = tf.reshape(x, shape=[-1, 7, 7, 128])\n", + " # Deconvolution, image shape: (batch, 14, 14, 64)\n", + " x = tf.layers.conv2d_transpose(x, 64, 5, strides=2, padding='same')\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = tf.nn.relu(x)\n", + " # Deconvolution, image shape: (batch, 28, 28, 1)\n", + " x = tf.layers.conv2d_transpose(x, 1, 5, strides=2, padding='same')\n", + " # Apply tanh for better stability - clip values to [-1, 1].\n", + " x = tf.nn.tanh(x)\n", + " return x\n", + "\n", + "\n", + "# Discriminator Network\n", + "# Input: Image, Output: Prediction Real/Fake Image\n", + "def discriminator(x, reuse=False):\n", + " with tf.variable_scope('Discriminator', reuse=reuse):\n", + " # Typical convolutional neural network to classify images.\n", + " x = tf.layers.conv2d(x, 64, 5, strides=2, padding='same')\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = leakyrelu(x)\n", + " x = tf.layers.conv2d(x, 128, 5, strides=2, padding='same')\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = leakyrelu(x)\n", + " # Flatten\n", + " x = tf.reshape(x, shape=[-1, 7*7*128])\n", + " x = tf.layers.dense(x, 1024)\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = leakyrelu(x)\n", + " # Output 2 classes: Real and Fake images\n", + " x = tf.layers.dense(x, 2)\n", + " return x\n", + "\n", + "# Build Generator Network\n", + "gen_sample = generator(noise_input)\n", + "\n", + "# Build 2 Discriminator Networks (one from noise input, one from generated samples)\n", + "disc_real = discriminator(real_image_input)\n", + "disc_fake = discriminator(gen_sample, reuse=True)\n", + "\n", + "# Build the stacked generator/discriminator\n", + "stacked_gan = discriminator(gen_sample, reuse=True)\n", + "\n", + "# Build Loss (Labels for real images: 1, for fake images: 0)\n", + "# Discriminator Loss for real and fake samples\n", + "disc_loss_real = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=disc_real, labels=tf.ones([batch_size], dtype=tf.int32)))\n", + "disc_loss_fake = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=disc_fake, labels=tf.zeros([batch_size], dtype=tf.int32)))\n", + "# Sum both loss\n", + "disc_loss = disc_loss_real + disc_loss_fake\n", + "# Generator Loss (The generator tries to fool the discriminator, thus labels are 1)\n", + "gen_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=stacked_gan, labels=tf.ones([batch_size], dtype=tf.int32)))\n", + "\n", + "# Build Optimizers\n", + "optimizer_gen = tf.train.AdamOptimizer(learning_rate=lr_generator, beta1=0.5, beta2=0.999)\n", + "optimizer_disc = tf.train.AdamOptimizer(learning_rate=lr_discriminator, beta1=0.5, beta2=0.999)\n", + "\n", + "# Training Variables for each optimizer\n", + "# By default in TensorFlow, all variables are updated by each optimizer, so we\n", + "# need to precise for each one of them the specific variables to update.\n", + "# Generator Network Variables\n", + "gen_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Generator')\n", + "# Discriminator Network Variables\n", + "disc_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Discriminator')\n", + "\n", + "# Create training operations\n", + "# TensorFlow UPDATE_OPS collection holds all batch norm operation to update the moving mean/stddev\n", + "gen_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='Generator')\n", + "# `control_dependencies` ensure that the `gen_update_ops` will be run before the `minimize` op (backprop)\n", + "with tf.control_dependencies(gen_update_ops):\n", + " train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars)\n", + "disc_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='Discriminator')\n", + "with tf.control_dependencies(disc_update_ops):\n", + " train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars)\n", + " \n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Generator Loss: 3.590350, Discriminator Loss: 1.907586\n", + "Step 500: Generator Loss: 1.254698, Discriminator Loss: 1.005236\n", + "Step 1000: Generator Loss: 1.730409, Discriminator Loss: 0.837684\n", + "Step 1500: Generator Loss: 1.962198, Discriminator Loss: 0.618827\n", + "Step 2000: Generator Loss: 2.767945, Discriminator Loss: 0.378071\n", + "Step 2500: Generator Loss: 2.370605, Discriminator Loss: 0.561247\n", + "Step 3000: Generator Loss: 3.427798, Discriminator Loss: 0.402951\n", + "Step 3500: Generator Loss: 4.904454, Discriminator Loss: 0.554856\n", + "Step 4000: Generator Loss: 4.045284, Discriminator Loss: 0.454970\n", + "Step 4500: Generator Loss: 4.577699, Discriminator Loss: 0.687195\n", + "Step 5000: Generator Loss: 3.476081, Discriminator Loss: 0.210492\n", + "Step 5500: Generator Loss: 3.898139, Discriminator Loss: 0.143352\n", + "Step 6000: Generator Loss: 4.089877, Discriminator Loss: 1.082561\n", + "Step 6500: Generator Loss: 5.911457, Discriminator Loss: 0.154059\n", + "Step 7000: Generator Loss: 3.594872, Discriminator Loss: 0.152970\n", + "Step 7500: Generator Loss: 6.067883, Discriminator Loss: 0.084864\n", + "Step 8000: Generator Loss: 6.737456, Discriminator Loss: 0.402566\n", + "Step 8500: Generator Loss: 6.630128, Discriminator Loss: 0.034838\n", + "Step 9000: Generator Loss: 6.480587, Discriminator Loss: 0.427419\n", + "Step 9500: Generator Loss: 7.200409, Discriminator Loss: 0.124268\n", + "Step 10000: Generator Loss: 5.479313, Discriminator Loss: 0.191389\n" + ] + } + ], + "source": [ + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + " \n", + "# Training\n", + "for i in range(1, num_steps+1):\n", + "\n", + " # Prepare Input Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + " batch_x = np.reshape(batch_x, newshape=[-1, 28, 28, 1])\n", + " # Rescale to [-1, 1], the input range of the discriminator\n", + " batch_x = batch_x * 2. - 1.\n", + "\n", + " # Discriminator Training\n", + " # Generate noise to feed to the generator\n", + " z = np.random.uniform(-1., 1., size=[batch_size, noise_dim])\n", + " _, dl = sess.run([train_disc, disc_loss], feed_dict={real_image_input: batch_x, noise_input: z, is_training:True})\n", + " \n", + " # Generator Training\n", + " # Generate noise to feed to the generator\n", + " z = np.random.uniform(-1., 1., size=[batch_size, noise_dim])\n", + " _, gl = sess.run([train_gen, gen_loss], feed_dict={noise_input: z, is_training:True})\n", + " \n", + " if i % 500 == 0 or i == 1:\n", + " print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAFpCAYAAACBNaNRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndxzHqXD+H+DTZ0YHtS3wAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Generate images from noise, using the generator network.\n", + "n = 6\n", + "canvas = np.empty((28 * n, 28 * n))\n", + "for i in range(n):\n", + " # Noise input.\n", + " z = np.random.uniform(-1., 1., size=[n, noise_dim])\n", + " # Generate image from noise.\n", + " g = sess.run(gen_sample, feed_dict={noise_input: z, is_training:False})\n", + " # Rescale values to the original [0, 1] (from tanh -> [-1, 1])\n", + " g = (g + 1.) / 2.\n", + " # Reverse colours for better display\n", + " g = -1 * (g - 1)\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28])\n", + "\n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb b/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb new file mode 100644 index 00000000..6b000566 --- /dev/null +++ b/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dynamic Recurrent Neural Network.\n", + "\n", + "TensorFlow implementation of a Recurrent Neural Network (LSTM) that performs dynamic computation over sequences with variable length. This example is using a toy dataset to classify linear sequences. The generated sequences have variable length.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ====================\n", + "# TOY DATA GENERATOR\n", + "# ====================\n", + "\n", + "class ToySequenceData(object):\n", + " \"\"\" Generate sequence of data with dynamic length.\n", + " This class generate samples for training:\n", + " - Class 0: linear sequences (i.e. [0, 1, 2, 3,...])\n", + " - Class 1: random sequences (i.e. [1, 3, 10, 7,...])\n", + "\n", + " NOTICE:\n", + " We have to pad each sequence to reach 'max_seq_len' for TensorFlow\n", + " consistency (we cannot feed a numpy array with inconsistent\n", + " dimensions). The dynamic calculation will then be perform thanks to\n", + " 'seqlen' attribute that records every actual sequence length.\n", + " \"\"\"\n", + " def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3,\n", + " max_value=1000):\n", + " self.data = []\n", + " self.labels = []\n", + " self.seqlen = []\n", + " for i in range(n_samples):\n", + " # Random sequence length\n", + " len = random.randint(min_seq_len, max_seq_len)\n", + " # Monitor sequence length for TensorFlow dynamic calculation\n", + " self.seqlen.append(len)\n", + " # Add a random or linear int sequence (50% prob)\n", + " if random.random() < .5:\n", + " # Generate a linear sequence\n", + " rand_start = random.randint(0, max_value - len)\n", + " s = [[float(i)/max_value] for i in\n", + " range(rand_start, rand_start + len)]\n", + " # Pad sequence for dimension consistency\n", + " s += [[0.] for i in range(max_seq_len - len)]\n", + " self.data.append(s)\n", + " self.labels.append([1., 0.])\n", + " else:\n", + " # Generate a random sequence\n", + " s = [[float(random.randint(0, max_value))/max_value]\n", + " for i in range(len)]\n", + " # Pad sequence for dimension consistency\n", + " s += [[0.] for i in range(max_seq_len - len)]\n", + " self.data.append(s)\n", + " self.labels.append([0., 1.])\n", + " self.batch_id = 0\n", + "\n", + " def next(self, batch_size):\n", + " \"\"\" Return a batch of data. When dataset end is reached, start over.\n", + " \"\"\"\n", + " if self.batch_id == len(self.data):\n", + " self.batch_id = 0\n", + " batch_data = (self.data[self.batch_id:min(self.batch_id +\n", + " batch_size, len(self.data))])\n", + " batch_labels = (self.labels[self.batch_id:min(self.batch_id +\n", + " batch_size, len(self.data))])\n", + " batch_seqlen = (self.seqlen[self.batch_id:min(self.batch_id +\n", + " batch_size, len(self.data))])\n", + " self.batch_id = min(self.batch_id + batch_size, len(self.data))\n", + " return batch_data, batch_labels, batch_seqlen" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ==========\n", + "# MODEL\n", + "# ==========\n", + "\n", + "# Parameters\n", + "learning_rate = 0.01\n", + "training_steps = 10000\n", + "batch_size = 128\n", + "display_step = 200\n", + "\n", + "# Network Parameters\n", + "seq_max_len = 20 # Sequence max length\n", + "n_hidden = 64 # hidden layer num of features\n", + "n_classes = 2 # linear sequence or not\n", + "\n", + "trainset = ToySequenceData(n_samples=1000, max_seq_len=seq_max_len)\n", + "testset = ToySequenceData(n_samples=500, max_seq_len=seq_max_len)\n", + "\n", + "# tf Graph input\n", + "x = tf.placeholder(\"float\", [None, seq_max_len, 1])\n", + "y = tf.placeholder(\"float\", [None, n_classes])\n", + "# A placeholder for indicating each sequence length\n", + "seqlen = tf.placeholder(tf.int32, [None])\n", + "\n", + "# Define weights\n", + "weights = {\n", + " 'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\n", + "}\n", + "biases = {\n", + " 'out': tf.Variable(tf.random_normal([n_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def dynamicRNN(x, seqlen, weights, biases):\n", + "\n", + " # Prepare data shape to match `rnn` function requirements\n", + " # Current data input shape: (batch_size, n_steps, n_input)\n", + " # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n", + " \n", + " # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", + " x = tf.unstack(x, seq_max_len, 1)\n", + "\n", + " # Define a lstm cell with tensorflow\n", + " lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden)\n", + "\n", + " # Get lstm cell output, providing 'sequence_length' will perform dynamic\n", + " # calculation.\n", + " outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x, dtype=tf.float32,\n", + " sequence_length=seqlen)\n", + "\n", + " # When performing dynamic calculation, we must retrieve the last\n", + " # dynamically computed output, i.e., if a sequence length is 10, we need\n", + " # to retrieve the 10th output.\n", + " # However TensorFlow doesn't support advanced indexing yet, so we build\n", + " # a custom op that for each sample in batch size, get its length and\n", + " # get the corresponding relevant output.\n", + "\n", + " # 'outputs' is a list of output at every timestep, we pack them in a Tensor\n", + " # and change back dimension to [batch_size, n_step, n_input]\n", + " outputs = tf.stack(outputs)\n", + " outputs = tf.transpose(outputs, [1, 0, 2])\n", + "\n", + " # Hack to build the indexing and retrieve the right output.\n", + " batch_size = tf.shape(outputs)[0]\n", + " # Start indices for each sample\n", + " index = tf.range(0, batch_size) * seq_max_len + (seqlen - 1)\n", + " # Indexing\n", + " outputs = tf.gather(tf.reshape(outputs, [-1, n_hidden]), index)\n", + "\n", + " # Linear activation, using outputs computed above\n", + " return tf.matmul(outputs, weights['out']) + biases['out']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aymeric.damien/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py:93: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", + " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" + ] + } + ], + "source": [ + "pred = dynamicRNN(x, seqlen, weights, biases)\n", + "\n", + "# Define loss and optimizer\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "\n", + "# Evaluate model\n", + "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 0.864517, Training Accuracy= 0.42188\n", + "Step 200, Minibatch Loss= 0.686012, Training Accuracy= 0.43269\n", + "Step 400, Minibatch Loss= 0.682970, Training Accuracy= 0.48077\n", + "Step 600, Minibatch Loss= 0.679640, Training Accuracy= 0.50962\n", + "Step 800, Minibatch Loss= 0.675208, Training Accuracy= 0.53846\n", + "Step 1000, Minibatch Loss= 0.668636, Training Accuracy= 0.56731\n", + "Step 1200, Minibatch Loss= 0.657525, Training Accuracy= 0.62500\n", + "Step 1400, Minibatch Loss= 0.635423, Training Accuracy= 0.67308\n", + "Step 1600, Minibatch Loss= 0.580433, Training Accuracy= 0.75962\n", + "Step 1800, Minibatch Loss= 0.475599, Training Accuracy= 0.81731\n", + "Step 2000, Minibatch Loss= 0.434865, Training Accuracy= 0.83654\n", + "Step 2200, Minibatch Loss= 0.423690, Training Accuracy= 0.85577\n", + "Step 2400, Minibatch Loss= 0.417472, Training Accuracy= 0.85577\n", + "Step 2600, Minibatch Loss= 0.412906, Training Accuracy= 0.85577\n", + "Step 2800, Minibatch Loss= 0.409193, Training Accuracy= 0.85577\n", + "Step 3000, Minibatch Loss= 0.406035, Training Accuracy= 0.86538\n", + "Step 3200, Minibatch Loss= 0.403287, Training Accuracy= 0.87500\n", + "Step 3400, Minibatch Loss= 0.400862, Training Accuracy= 0.87500\n", + "Step 3600, Minibatch Loss= 0.398704, Training Accuracy= 0.86538\n", + "Step 3800, Minibatch Loss= 0.396768, Training Accuracy= 0.86538\n", + "Step 4000, Minibatch Loss= 0.395017, Training Accuracy= 0.86538\n", + "Step 4200, Minibatch Loss= 0.393422, Training Accuracy= 0.86538\n", + "Step 4400, Minibatch Loss= 0.391957, Training Accuracy= 0.85577\n", + "Step 4600, Minibatch Loss= 0.390600, Training Accuracy= 0.85577\n", + "Step 4800, Minibatch Loss= 0.389334, Training Accuracy= 0.86538\n", + "Step 5000, Minibatch Loss= 0.388143, Training Accuracy= 0.86538\n", + "Step 5200, Minibatch Loss= 0.387015, Training Accuracy= 0.86538\n", + "Step 5400, Minibatch Loss= 0.385940, Training Accuracy= 0.86538\n", + "Step 5600, Minibatch Loss= 0.384907, Training Accuracy= 0.86538\n", + "Step 5800, Minibatch Loss= 0.383904, Training Accuracy= 0.85577\n", + "Step 6000, Minibatch Loss= 0.382921, Training Accuracy= 0.86538\n", + "Step 6200, Minibatch Loss= 0.381941, Training Accuracy= 0.86538\n", + "Step 6400, Minibatch Loss= 0.380947, Training Accuracy= 0.86538\n", + "Step 6600, Minibatch Loss= 0.379912, Training Accuracy= 0.86538\n", + "Step 6800, Minibatch Loss= 0.378796, Training Accuracy= 0.86538\n", + "Step 7000, Minibatch Loss= 0.377540, Training Accuracy= 0.86538\n", + "Step 7200, Minibatch Loss= 0.376041, Training Accuracy= 0.86538\n", + "Step 7400, Minibatch Loss= 0.374130, Training Accuracy= 0.85577\n", + "Step 7600, Minibatch Loss= 0.371514, Training Accuracy= 0.85577\n", + "Step 7800, Minibatch Loss= 0.367723, Training Accuracy= 0.85577\n", + "Step 8000, Minibatch Loss= 0.362049, Training Accuracy= 0.85577\n", + "Step 8200, Minibatch Loss= 0.353558, Training Accuracy= 0.85577\n", + "Step 8400, Minibatch Loss= 0.341072, Training Accuracy= 0.86538\n", + "Step 8600, Minibatch Loss= 0.323062, Training Accuracy= 0.87500\n", + "Step 8800, Minibatch Loss= 0.299278, Training Accuracy= 0.89423\n", + "Step 9000, Minibatch Loss= 0.273857, Training Accuracy= 0.90385\n", + "Step 9200, Minibatch Loss= 0.248392, Training Accuracy= 0.91346\n", + "Step 9400, Minibatch Loss= 0.221348, Training Accuracy= 0.92308\n", + "Step 9600, Minibatch Loss= 0.191947, Training Accuracy= 0.92308\n", + "Step 9800, Minibatch Loss= 0.159308, Training Accuracy= 0.93269\n", + "Step 10000, Minibatch Loss= 0.136938, Training Accuracy= 0.96154\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.952\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, training_steps+1):\n", + " batch_x, batch_y, batch_seqlen = trainset.next(batch_size)\n", + " # Run optimization op (backprop)\n", + " sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,\n", + " seqlen: batch_seqlen})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch accuracy\n", + " acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y,\n", + " seqlen: batch_seqlen})\n", + " # Calculate batch loss\n", + " loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y,\n", + " seqlen: batch_seqlen})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.5f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy\n", + " test_data = testset.data\n", + " test_label = testset.labels\n", + " test_seqlen = testset.seqlen\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={x: test_data, y: test_label,\n", + " seqlen: test_seqlen}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/3_NeuralNetworks/gan.ipynb b/notebooks/3_NeuralNetworks/gan.ipynb new file mode 100644 index 00000000..8ab34406 --- /dev/null +++ b/notebooks/3_NeuralNetworks/gan.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Generative Adversarial Network Example\n", + "\n", + "Build a generative adversarial network (GAN) to generate digit images from a noise distribution with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GAN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Generative adversarial nets](https://arxiv.org/pdf/1406.2661.pdf). I Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, Y. Bengio. Advances in neural information processing systems, 2672-2680.\n", + "- [Understanding the difficulty of training deep feedforward neural networks](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "\n", + "Other tutorials:\n", + "- [Generative Adversarial Networks Explained](http://kvfrans.com/generative-adversial-networks-explained/). Kevin Frans.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Params\n", + "num_steps = 70000\n", + "batch_size = 128\n", + "learning_rate = 0.0002\n", + "\n", + "# Network Params\n", + "image_dim = 784 # 28*28 pixels\n", + "gen_hidden_dim = 256\n", + "disc_hidden_dim = 256\n", + "noise_dim = 100 # Noise data points\n", + "\n", + "# A custom initialization (see Xavier Glorot init)\n", + "def glorot_init(shape):\n", + " return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "weights = {\n", + " 'gen_hidden1': tf.Variable(glorot_init([noise_dim, gen_hidden_dim])),\n", + " 'gen_out': tf.Variable(glorot_init([gen_hidden_dim, image_dim])),\n", + " 'disc_hidden1': tf.Variable(glorot_init([image_dim, disc_hidden_dim])),\n", + " 'disc_out': tf.Variable(glorot_init([disc_hidden_dim, 1])),\n", + "}\n", + "biases = {\n", + " 'gen_hidden1': tf.Variable(tf.zeros([gen_hidden_dim])),\n", + " 'gen_out': tf.Variable(tf.zeros([image_dim])),\n", + " 'disc_hidden1': tf.Variable(tf.zeros([disc_hidden_dim])),\n", + " 'disc_out': tf.Variable(tf.zeros([1])),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Generator\n", + "def generator(x):\n", + " hidden_layer = tf.matmul(x, weights['gen_hidden1'])\n", + " hidden_layer = tf.add(hidden_layer, biases['gen_hidden1'])\n", + " hidden_layer = tf.nn.relu(hidden_layer)\n", + " out_layer = tf.matmul(hidden_layer, weights['gen_out'])\n", + " out_layer = tf.add(out_layer, biases['gen_out'])\n", + " out_layer = tf.nn.sigmoid(out_layer)\n", + " return out_layer\n", + "\n", + "\n", + "# Discriminator\n", + "def discriminator(x):\n", + " hidden_layer = tf.matmul(x, weights['disc_hidden1'])\n", + " hidden_layer = tf.add(hidden_layer, biases['disc_hidden1'])\n", + " hidden_layer = tf.nn.relu(hidden_layer)\n", + " out_layer = tf.matmul(hidden_layer, weights['disc_out'])\n", + " out_layer = tf.add(out_layer, biases['disc_out'])\n", + " out_layer = tf.nn.sigmoid(out_layer)\n", + " return out_layer\n", + "\n", + "# Build Networks\n", + "# Network Inputs\n", + "gen_input = tf.placeholder(tf.float32, shape=[None, noise_dim], name='input_noise')\n", + "disc_input = tf.placeholder(tf.float32, shape=[None, image_dim], name='disc_input')\n", + "\n", + "# Build Generator Network\n", + "gen_sample = generator(gen_input)\n", + "\n", + "# Build 2 Discriminator Networks (one from noise input, one from generated samples)\n", + "disc_real = discriminator(disc_input)\n", + "disc_fake = discriminator(gen_sample)\n", + "\n", + "# Build Loss\n", + "gen_loss = -tf.reduce_mean(tf.log(disc_fake))\n", + "disc_loss = -tf.reduce_mean(tf.log(disc_real) + tf.log(1. - disc_fake))\n", + "\n", + "# Build Optimizers\n", + "optimizer_gen = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "optimizer_disc = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Training Variables for each optimizer\n", + "# By default in TensorFlow, all variables are updated by each optimizer, so we\n", + "# need to precise for each one of them the specific variables to update.\n", + "# Generator Network Variables\n", + "gen_vars = [weights['gen_hidden1'], weights['gen_out'],\n", + " biases['gen_hidden1'], biases['gen_out']]\n", + "# Discriminator Network Variables\n", + "disc_vars = [weights['disc_hidden1'], weights['disc_out'],\n", + " biases['disc_hidden1'], biases['disc_out']]\n", + "\n", + "# Create training operations\n", + "train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars)\n", + "train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Generator Loss: 0.774581, Discriminator Loss: 1.300602\n", + "Step 2000: Generator Loss: 4.521158, Discriminator Loss: 0.030166\n", + "Step 4000: Generator Loss: 3.685439, Discriminator Loss: 0.125958\n", + "Step 6000: Generator Loss: 4.412449, Discriminator Loss: 0.097088\n", + "Step 8000: Generator Loss: 3.996747, Discriminator Loss: 0.150800\n", + "Step 10000: Generator Loss: 3.850827, Discriminator Loss: 0.225699\n", + "Step 12000: Generator Loss: 2.950704, Discriminator Loss: 0.279967\n", + "Step 14000: Generator Loss: 3.741951, Discriminator Loss: 0.241062\n", + "Step 16000: Generator Loss: 3.117743, Discriminator Loss: 0.432293\n", + "Step 18000: Generator Loss: 3.647199, Discriminator Loss: 0.278121\n", + "Step 20000: Generator Loss: 3.186711, Discriminator Loss: 0.313830\n", + "Step 22000: Generator Loss: 3.737114, Discriminator Loss: 0.201730\n", + "Step 24000: Generator Loss: 3.042442, Discriminator Loss: 0.454414\n", + "Step 26000: Generator Loss: 3.340376, Discriminator Loss: 0.249428\n", + "Step 28000: Generator Loss: 3.423218, Discriminator Loss: 0.369653\n", + "Step 30000: Generator Loss: 3.219242, Discriminator Loss: 0.463535\n", + "Step 32000: Generator Loss: 3.313017, Discriminator Loss: 0.276070\n", + "Step 34000: Generator Loss: 3.413397, Discriminator Loss: 0.367721\n", + "Step 36000: Generator Loss: 3.240625, Discriminator Loss: 0.446160\n", + "Step 38000: Generator Loss: 3.175355, Discriminator Loss: 0.377628\n", + "Step 40000: Generator Loss: 3.154558, Discriminator Loss: 0.478812\n", + "Step 42000: Generator Loss: 3.210753, Discriminator Loss: 0.497502\n", + "Step 44000: Generator Loss: 2.883431, Discriminator Loss: 0.395812\n", + "Step 46000: Generator Loss: 2.584176, Discriminator Loss: 0.420783\n", + "Step 48000: Generator Loss: 2.581381, Discriminator Loss: 0.469289\n", + "Step 50000: Generator Loss: 2.752729, Discriminator Loss: 0.373544\n", + "Step 52000: Generator Loss: 2.649749, Discriminator Loss: 0.463755\n", + "Step 54000: Generator Loss: 2.468188, Discriminator Loss: 0.556129\n", + "Step 56000: Generator Loss: 2.653330, Discriminator Loss: 0.377572\n", + "Step 58000: Generator Loss: 2.697943, Discriminator Loss: 0.424133\n", + "Step 60000: Generator Loss: 2.835973, Discriminator Loss: 0.413252\n", + "Step 62000: Generator Loss: 2.751346, Discriminator Loss: 0.403332\n", + "Step 64000: Generator Loss: 3.212001, Discriminator Loss: 0.534427\n", + "Step 66000: Generator Loss: 2.878227, Discriminator Loss: 0.431244\n", + "Step 68000: Generator Loss: 3.104266, Discriminator Loss: 0.426825\n", + "Step 70000: Generator Loss: 2.871485, Discriminator Loss: 0.348638\n" + ] + } + ], + "source": [ + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + "\n", + "# Training\n", + "for i in range(1, num_steps+1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + " # Generate noise to feed to the generator\n", + " z = np.random.uniform(-1., 1., size=[batch_size, noise_dim])\n", + "\n", + " # Train\n", + " feed_dict = {disc_input: batch_x, gen_input: z}\n", + " _, _, gl, dl = sess.run([train_gen, train_disc, gen_loss, disc_loss],\n", + " feed_dict=feed_dict)\n", + " if i % 2000 == 0 or i == 1:\n", + " print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAFpCAYAAACBNaNRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndxzHqTg+kDuO41QcH8gdx3Eqzv8DPY8Zg0BX8cEAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Generate images from noise, using the generator network.\n", + "n = 6\n", + "canvas = np.empty((28 * n, 28 * n))\n", + "for i in range(n):\n", + " # Noise input.\n", + " z = np.random.uniform(-1., 1., size=[n, noise_dim])\n", + " # Generate image from noise.\n", + " g = sess.run(gen_sample, feed_dict={gen_input: z})\n", + " # Reverse colours for better display\n", + " g = -1 * (g - 1)\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28])\n", + "\n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb b/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb deleted file mode 100644 index 728535fb..00000000 --- a/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb +++ /dev/null @@ -1,216 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "'''\n", - "A Multilayer Perceptron implementation example using TensorFlow library.\n", - "This example is using the MNIST database of handwritten digits\n", - "(http://yann.lecun.com/exdb/mnist/)\n", - "\n", - "Author: Aymeric Damien\n", - "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", - "'''" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", - "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", - "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", - "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" - ] - } - ], - "source": [ - "# Import MINST data\n", - "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n", - "\n", - "import tensorflow as tf" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Parameters\n", - "learning_rate = 0.001\n", - "training_epochs = 15\n", - "batch_size = 100\n", - "display_step = 1\n", - "\n", - "# Network Parameters\n", - "n_hidden_1 = 256 # 1st layer number of features\n", - "n_hidden_2 = 256 # 2nd layer number of features\n", - "n_input = 784 # MNIST data input (img shape: 28*28)\n", - "n_classes = 10 # MNIST total classes (0-9 digits)\n", - "\n", - "# tf Graph input\n", - "x = tf.placeholder(\"float\", [None, n_input])\n", - "y = tf.placeholder(\"float\", [None, n_classes])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Create model\n", - "def multilayer_perceptron(x, weights, biases):\n", - " # Hidden layer with RELU activation\n", - " layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n", - " layer_1 = tf.nn.relu(layer_1)\n", - " # Hidden layer with RELU activation\n", - " layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n", - " layer_2 = tf.nn.relu(layer_2)\n", - " # Output layer with linear activation\n", - " out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n", - " return out_layer" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Store layers weight & bias\n", - "weights = {\n", - " 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n", - " 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n", - " 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n", - "}\n", - "biases = {\n", - " 'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", - " 'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n", - " 'out': tf.Variable(tf.random_normal([n_classes]))\n", - "}\n", - "\n", - "# Construct model\n", - "pred = multilayer_perceptron(x, weights, biases)\n", - "\n", - "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", - "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", - "\n", - "# Initializing the variables\n", - "init = tf.global_variables_initializer()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch: 0001 cost= 173.056566575\n", - "Epoch: 0002 cost= 44.054413928\n", - "Epoch: 0003 cost= 27.455470655\n", - "Epoch: 0004 cost= 19.008652363\n", - "Epoch: 0005 cost= 13.654873594\n", - "Epoch: 0006 cost= 10.059267435\n", - "Epoch: 0007 cost= 7.436018432\n", - "Epoch: 0008 cost= 5.587794416\n", - "Epoch: 0009 cost= 4.209882509\n", - "Epoch: 0010 cost= 3.203879515\n", - "Epoch: 0011 cost= 2.319920681\n", - "Epoch: 0012 cost= 1.676204545\n", - "Epoch: 0013 cost= 1.248805338\n", - "Epoch: 0014 cost= 1.052676844\n", - "Epoch: 0015 cost= 0.890117338\n", - "Optimization Finished!\n", - "Accuracy: 0.9459\n" - ] - } - ], - "source": [ - "# Launch the graph\n", - "with tf.Session() as sess:\n", - " sess.run(init)\n", - "\n", - " # Training cycle\n", - " for epoch in range(training_epochs):\n", - " avg_cost = 0.\n", - " total_batch = int(mnist.train.num_examples/batch_size)\n", - " # Loop over all batches\n", - " for i in range(total_batch):\n", - " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", - " # Run optimization op (backprop) and cost op (to get loss value)\n", - " _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n", - " y: batch_y})\n", - " # Compute average loss\n", - " avg_cost += c / total_batch\n", - " # Display logs per epoch step\n", - " if epoch % display_step == 0:\n", - " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n", - " \"{:.9f}\".format(avg_cost)\n", - " print \"Optimization Finished!\"\n", - "\n", - " # Test model\n", - " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", - " # Calculate accuracy\n", - " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", - " print \"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.13" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/3_NeuralNetworks/neural_network.ipynb b/notebooks/3_NeuralNetworks/neural_network.ipynb new file mode 100644 index 00000000..33196e78 --- /dev/null +++ b/notebooks/3_NeuralNetworks/neural_network.ipynb @@ -0,0 +1,390 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Example\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow.\n", + "\n", + "This example is using some of TensorFlow higher-level wrappers (tf.estimators, tf.layers, tf.metrics, ...), you can check 'neural_network_raw' example for a raw, and more detailed TensorFlow implementation.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)\n", + "\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.1\n", + "num_steps = 1000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of neurons\n", + "n_hidden_2 = 256 # 2nd layer number of neurons\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the input function for training\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.train.images}, y=mnist.train.labels,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the neural network\n", + "def neural_net(x_dict):\n", + " # TF Estimator input is a dict, in case of multiple inputs\n", + " x = x_dict['images']\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_1 = tf.layers.dense(x, n_hidden_1)\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_2 = tf.layers.dense(layer_1, n_hidden_2)\n", + " # Output fully connected layer with a neuron for each class\n", + " out_layer = tf.layers.dense(layer_2, num_classes)\n", + " return out_layer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the model function (following TF Estimator Template)\n", + "def model_fn(features, labels, mode):\n", + " \n", + " # Build the neural network\n", + " logits = neural_net(features)\n", + " \n", + " # Predictions\n", + " pred_classes = tf.argmax(logits, axis=1)\n", + " pred_probas = tf.nn.softmax(logits)\n", + " \n", + " # If prediction mode, early return\n", + " if mode == tf.estimator.ModeKeys.PREDICT:\n", + " return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) \n", + " \n", + " # Define loss and optimizer\n", + " loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=tf.cast(labels, dtype=tf.int32)))\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())\n", + " \n", + " # Evaluate the accuracy of the model\n", + " acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)\n", + " \n", + " # TF Estimators requires to return a EstimatorSpec, that specify\n", + " # the different ops for training, evaluating, ...\n", + " estim_specs = tf.estimator.EstimatorSpec(\n", + " mode=mode,\n", + " predictions=pred_classes,\n", + " loss=loss_op,\n", + " train_op=train_op,\n", + " eval_metric_ops={'accuracy': acc_op})\n", + "\n", + " return estim_specs" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpu7vjLA\n", + "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_tf_random_seed': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_save_checkpoints_steps': None, '_model_dir': '/tmp/tmpu7vjLA', '_save_summary_steps': 100}\n" + ] + } + ], + "source": [ + "# Build the Estimator\n", + "model = tf.estimator.Estimator(model_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpu7vjLA/model.ckpt.\n", + "INFO:tensorflow:loss = 2.44919, step = 1\n", + "INFO:tensorflow:global_step/sec: 602.544\n", + "INFO:tensorflow:loss = 0.344767, step = 101 (0.167 sec)\n", + "INFO:tensorflow:global_step/sec: 618.839\n", + "INFO:tensorflow:loss = 0.277633, step = 201 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 626.418\n", + "INFO:tensorflow:loss = 0.407796, step = 301 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 624.765\n", + "INFO:tensorflow:loss = 0.376889, step = 401 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 624.091\n", + "INFO:tensorflow:loss = 0.319697, step = 501 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 616.907\n", + "INFO:tensorflow:loss = 0.39049, step = 601 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 623.371\n", + "INFO:tensorflow:loss = 0.336831, step = 701 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 617.429\n", + "INFO:tensorflow:loss = 0.312776, step = 801 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 620.825\n", + "INFO:tensorflow:loss = 0.312817, step = 901 (0.161 sec)\n", + "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpu7vjLA/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 0.24931.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Train the Model\n", + "model.train(input_fn, steps=num_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Starting evaluation at 2017-08-21-13:57:02\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpu7vjLA/model.ckpt-1000\n", + "INFO:tensorflow:Finished evaluation at 2017-08-21-13:57:02\n", + "INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.9189, global_step = 1000, loss = 0.286567\n" + ] + }, + { + "data": { + "text/plain": [ + "{'accuracy': 0.91890001, 'global_step': 1000, 'loss': 0.28656715}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluate the Model\n", + "# Define the input function for evaluating\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.test.images}, y=mnist.test.labels,\n", + " batch_size=batch_size, shuffle=False)\n", + "# Use the Estimator 'evaluate' method\n", + "model.evaluate(input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from /tmp/tmpu7vjLA/model.ckpt-1000\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiL8QFKEH0iK8ANJEX4gKcIPJPVP82g/p9/JjhUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 7\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 2\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 1\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZZUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 0\n" + ] + } + ], + "source": [ + "# Predict single images\n", + "n_images = 4\n", + "# Get images from test set\n", + "test_images = mnist.test.images[:n_images]\n", + "# Prepare the input data\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': test_images}, shuffle=False)\n", + "# Use the model to predict the images class\n", + "preds = list(model.predict(input_fn))\n", + "\n", + "# Display\n", + "for i in range(n_images):\n", + " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n", + " plt.show()\n", + " print(\"Model prediction:\", preds[i])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/3_NeuralNetworks/neural_network_raw.ipynb b/notebooks/3_NeuralNetworks/neural_network_raw.ipynb new file mode 100644 index 00000000..d05ad4eb --- /dev/null +++ b/notebooks/3_NeuralNetworks/neural_network_raw.ipynb @@ -0,0 +1,224 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Neural Network Example\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.1\n", + "num_steps = 500\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of neurons\n", + "n_hidden_2 = 256 # 2nd layer number of neurons\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "X = tf.placeholder(\"float\", [None, num_input])\n", + "Y = tf.placeholder(\"float\", [None, num_classes])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "weights = {\n", + " 'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),\n", + " 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes]))\n", + "}\n", + "biases = {\n", + " 'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", + " 'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create model\n", + "def neural_net(x):\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n", + " # Output fully connected layer with a neuron for each class\n", + " out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n", + " return out_layer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Construct model\n", + "logits = neural_net(X)\n", + "\n", + "# Define loss and optimizer\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 13208.1406, Training Accuracy= 0.266\n", + "Step 100, Minibatch Loss= 462.8610, Training Accuracy= 0.867\n", + "Step 200, Minibatch Loss= 232.8298, Training Accuracy= 0.844\n", + "Step 300, Minibatch Loss= 85.2141, Training Accuracy= 0.891\n", + "Step 400, Minibatch Loss= 38.0552, Training Accuracy= 0.883\n", + "Step 500, Minibatch Loss= 55.3689, Training Accuracy= 0.867\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.8729\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, num_steps+1):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop)\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for MNIST test images\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: mnist.test.images,\n", + " Y: mnist.test.labels}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/notebooks/3_NeuralNetworks/recurrent_network.ipynb index 6510a968..81676ea3 100644 --- a/notebooks/3_NeuralNetworks/recurrent_network.ipynb +++ b/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -6,44 +6,63 @@ "collapsed": true }, "source": [ - "'''\n", - "A Reccurent Neural Network (LSTM) implementation example using TensorFlow library.\n", - "This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", - "Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf\n", + "# Recurrent Neural Network Example\n", "\n", - "Author: Aymeric Damien\n", - "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", - "'''" + "Build a recurrent neural network (LSTM) with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "To classify images using a recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 timesteps for every sample.\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], "source": [ + "from __future__ import print_function\n", + "\n", "import tensorflow as tf\n", "from tensorflow.contrib import rnn\n", - "import numpy as np\n", "\n", - "# Import MINST data\n", + "# Import MNIST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "'''\n", - "To classify images using a reccurent neural network, we consider every image\n", - "row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then\n", - "handle 28 sequences of 28 steps for every sample.\n", - "'''" + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" ] }, { @@ -54,34 +73,43 @@ }, "outputs": [], "source": [ - "# Parameters\n", + "# Training Parameters\n", "learning_rate = 0.001\n", - "training_iters = 100000\n", + "training_steps = 10000\n", "batch_size = 128\n", - "display_step = 10\n", + "display_step = 200\n", "\n", "# Network Parameters\n", - "n_input = 28 # MNIST data input (img shape: 28*28)\n", - "n_steps = 28 # timesteps\n", - "n_hidden = 128 # hidden layer num of features\n", - "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "num_input = 28 # MNIST data input (img shape: 28*28)\n", + "timesteps = 28 # timesteps\n", + "num_hidden = 128 # hidden layer num of features\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", "\n", "# tf Graph input\n", - "x = tf.placeholder(\"float\", [None, n_steps, n_input])\n", - "y = tf.placeholder(\"float\", [None, n_classes])\n", - "\n", + "X = tf.placeholder(\"float\", [None, timesteps, num_input])\n", + "Y = tf.placeholder(\"float\", [None, num_classes])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ "# Define weights\n", "weights = {\n", - " 'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\n", + " 'out': tf.Variable(tf.random_normal([num_hidden, num_classes]))\n", "}\n", "biases = {\n", - " 'out': tf.Variable(tf.random_normal([n_classes]))\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", "}" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -90,38 +118,50 @@ "def RNN(x, weights, biases):\n", "\n", " # Prepare data shape to match `rnn` function requirements\n", - " # Current data input shape: (batch_size, n_steps, n_input)\n", - " # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n", - " \n", - " # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", - " x = tf.unstack(x, n_steps, 1)\n", + " # Current data input shape: (batch_size, timesteps, n_input)\n", + " # Required shape: 'timesteps' tensors list of shape (batch_size, n_input)\n", + "\n", + " # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)\n", + " x = tf.unstack(x, timesteps, 1)\n", "\n", " # Define a lstm cell with tensorflow\n", - " lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n", + " lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)\n", "\n", " # Get lstm cell output\n", " outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)\n", "\n", " # Linear activation, using rnn inner loop last output\n", - " return tf.matmul(outputs[-1], weights['out']) + biases['out']\n", - "\n", - "pred = RNN(x, weights, biases)\n", + " return tf.matmul(outputs[-1], weights['out']) + biases['out']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "logits = RNN(X, weights, biases)\n", + "prediction = tf.nn.softmax(logits)\n", "\n", "# Define loss and optimizer\n", - "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", - "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", "\n", - "# Evaluate model\n", - "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", "\n", - "# Initializing the variables\n", + "# Initialize the variables (i.e. assign their default value)\n", "init = tf.global_variables_initializer()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -130,118 +170,91 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iter 1280, Minibatch Loss= 1.576423, Training Accuracy= 0.51562\n", - "Iter 2560, Minibatch Loss= 1.450179, Training Accuracy= 0.53906\n", - "Iter 3840, Minibatch Loss= 1.160066, Training Accuracy= 0.64844\n", - "Iter 5120, Minibatch Loss= 0.898589, Training Accuracy= 0.73438\n", - "Iter 6400, Minibatch Loss= 0.685712, Training Accuracy= 0.75781\n", - "Iter 7680, Minibatch Loss= 1.085666, Training Accuracy= 0.64844\n", - "Iter 8960, Minibatch Loss= 0.681488, Training Accuracy= 0.73438\n", - "Iter 10240, Minibatch Loss= 0.557049, Training Accuracy= 0.82812\n", - "Iter 11520, Minibatch Loss= 0.340857, Training Accuracy= 0.92188\n", - "Iter 12800, Minibatch Loss= 0.596482, Training Accuracy= 0.78906\n", - "Iter 14080, Minibatch Loss= 0.486564, Training Accuracy= 0.84375\n", - "Iter 15360, Minibatch Loss= 0.302493, Training Accuracy= 0.90625\n", - "Iter 16640, Minibatch Loss= 0.334277, Training Accuracy= 0.92188\n", - "Iter 17920, Minibatch Loss= 0.222026, Training Accuracy= 0.90625\n", - "Iter 19200, Minibatch Loss= 0.228581, Training Accuracy= 0.92188\n", - "Iter 20480, Minibatch Loss= 0.150356, Training Accuracy= 0.96094\n", - "Iter 21760, Minibatch Loss= 0.415417, Training Accuracy= 0.86719\n", - "Iter 23040, Minibatch Loss= 0.159742, Training Accuracy= 0.94531\n", - "Iter 24320, Minibatch Loss= 0.333764, Training Accuracy= 0.89844\n", - "Iter 25600, Minibatch Loss= 0.379070, Training Accuracy= 0.88281\n", - "Iter 26880, Minibatch Loss= 0.241612, Training Accuracy= 0.91406\n", - "Iter 28160, Minibatch Loss= 0.200397, Training Accuracy= 0.93750\n", - "Iter 29440, Minibatch Loss= 0.197994, Training Accuracy= 0.93750\n", - "Iter 30720, Minibatch Loss= 0.330214, Training Accuracy= 0.89062\n", - "Iter 32000, Minibatch Loss= 0.174626, Training Accuracy= 0.92969\n", - "Iter 33280, Minibatch Loss= 0.202369, Training Accuracy= 0.93750\n", - "Iter 34560, Minibatch Loss= 0.240835, Training Accuracy= 0.94531\n", - "Iter 35840, Minibatch Loss= 0.207867, Training Accuracy= 0.93750\n", - "Iter 37120, Minibatch Loss= 0.313306, Training Accuracy= 0.90625\n", - "Iter 38400, Minibatch Loss= 0.089850, Training Accuracy= 0.96875\n", - "Iter 39680, Minibatch Loss= 0.184803, Training Accuracy= 0.92188\n", - "Iter 40960, Minibatch Loss= 0.236523, Training Accuracy= 0.92969\n", - "Iter 42240, Minibatch Loss= 0.174834, Training Accuracy= 0.94531\n", - "Iter 43520, Minibatch Loss= 0.127905, Training Accuracy= 0.93750\n", - "Iter 44800, Minibatch Loss= 0.120045, Training Accuracy= 0.96875\n", - "Iter 46080, Minibatch Loss= 0.068337, Training Accuracy= 0.98438\n", - "Iter 47360, Minibatch Loss= 0.141118, Training Accuracy= 0.95312\n", - "Iter 48640, Minibatch Loss= 0.182404, Training Accuracy= 0.92188\n", - "Iter 49920, Minibatch Loss= 0.176778, Training Accuracy= 0.93750\n", - "Iter 51200, Minibatch Loss= 0.098927, Training Accuracy= 0.97656\n", - "Iter 52480, Minibatch Loss= 0.158776, Training Accuracy= 0.96094\n", - "Iter 53760, Minibatch Loss= 0.031863, Training Accuracy= 0.99219\n", - "Iter 55040, Minibatch Loss= 0.101799, Training Accuracy= 0.96094\n", - "Iter 56320, Minibatch Loss= 0.176387, Training Accuracy= 0.96094\n", - "Iter 57600, Minibatch Loss= 0.096277, Training Accuracy= 0.96875\n", - "Iter 58880, Minibatch Loss= 0.137416, Training Accuracy= 0.94531\n", - "Iter 60160, Minibatch Loss= 0.062801, Training Accuracy= 0.97656\n", - "Iter 61440, Minibatch Loss= 0.036346, Training Accuracy= 0.98438\n", - "Iter 62720, Minibatch Loss= 0.153030, Training Accuracy= 0.92969\n", - "Iter 64000, Minibatch Loss= 0.117716, Training Accuracy= 0.95312\n", - "Iter 65280, Minibatch Loss= 0.048387, Training Accuracy= 0.99219\n", - "Iter 66560, Minibatch Loss= 0.070802, Training Accuracy= 0.97656\n", - "Iter 67840, Minibatch Loss= 0.221085, Training Accuracy= 0.96875\n", - "Iter 69120, Minibatch Loss= 0.184049, Training Accuracy= 0.93750\n", - "Iter 70400, Minibatch Loss= 0.094883, Training Accuracy= 0.95312\n", - "Iter 71680, Minibatch Loss= 0.087278, Training Accuracy= 0.96875\n", - "Iter 72960, Minibatch Loss= 0.153267, Training Accuracy= 0.95312\n", - "Iter 74240, Minibatch Loss= 0.161794, Training Accuracy= 0.94531\n", - "Iter 75520, Minibatch Loss= 0.103779, Training Accuracy= 0.96875\n", - "Iter 76800, Minibatch Loss= 0.165586, Training Accuracy= 0.96094\n", - "Iter 78080, Minibatch Loss= 0.137721, Training Accuracy= 0.95312\n", - "Iter 79360, Minibatch Loss= 0.124014, Training Accuracy= 0.96094\n", - "Iter 80640, Minibatch Loss= 0.051460, Training Accuracy= 0.99219\n", - "Iter 81920, Minibatch Loss= 0.185836, Training Accuracy= 0.96094\n", - "Iter 83200, Minibatch Loss= 0.147694, Training Accuracy= 0.94531\n", - "Iter 84480, Minibatch Loss= 0.061550, Training Accuracy= 0.98438\n", - "Iter 85760, Minibatch Loss= 0.093457, Training Accuracy= 0.96875\n", - "Iter 87040, Minibatch Loss= 0.094497, Training Accuracy= 0.98438\n", - "Iter 88320, Minibatch Loss= 0.093934, Training Accuracy= 0.96094\n", - "Iter 89600, Minibatch Loss= 0.061550, Training Accuracy= 0.96875\n", - "Iter 90880, Minibatch Loss= 0.082452, Training Accuracy= 0.97656\n", - "Iter 92160, Minibatch Loss= 0.087423, Training Accuracy= 0.97656\n", - "Iter 93440, Minibatch Loss= 0.032694, Training Accuracy= 0.99219\n", - "Iter 94720, Minibatch Loss= 0.069597, Training Accuracy= 0.97656\n", - "Iter 96000, Minibatch Loss= 0.193636, Training Accuracy= 0.96094\n", - "Iter 97280, Minibatch Loss= 0.134405, Training Accuracy= 0.96094\n", - "Iter 98560, Minibatch Loss= 0.072992, Training Accuracy= 0.96875\n", - "Iter 99840, Minibatch Loss= 0.041049, Training Accuracy= 0.99219\n", + "Step 1, Minibatch Loss= 2.6268, Training Accuracy= 0.102\n", + "Step 200, Minibatch Loss= 2.0722, Training Accuracy= 0.328\n", + "Step 400, Minibatch Loss= 1.9181, Training Accuracy= 0.336\n", + "Step 600, Minibatch Loss= 1.8858, Training Accuracy= 0.336\n", + "Step 800, Minibatch Loss= 1.7022, Training Accuracy= 0.422\n", + "Step 1000, Minibatch Loss= 1.6365, Training Accuracy= 0.477\n", + "Step 1200, Minibatch Loss= 1.6691, Training Accuracy= 0.516\n", + "Step 1400, Minibatch Loss= 1.4626, Training Accuracy= 0.547\n", + "Step 1600, Minibatch Loss= 1.4707, Training Accuracy= 0.539\n", + "Step 1800, Minibatch Loss= 1.4087, Training Accuracy= 0.570\n", + "Step 2000, Minibatch Loss= 1.3033, Training Accuracy= 0.570\n", + "Step 2200, Minibatch Loss= 1.3773, Training Accuracy= 0.508\n", + "Step 2400, Minibatch Loss= 1.3092, Training Accuracy= 0.570\n", + "Step 2600, Minibatch Loss= 1.2272, Training Accuracy= 0.609\n", + "Step 2800, Minibatch Loss= 1.1827, Training Accuracy= 0.633\n", + "Step 3000, Minibatch Loss= 1.0453, Training Accuracy= 0.641\n", + "Step 3200, Minibatch Loss= 1.0400, Training Accuracy= 0.648\n", + "Step 3400, Minibatch Loss= 1.1145, Training Accuracy= 0.656\n", + "Step 3600, Minibatch Loss= 0.9884, Training Accuracy= 0.688\n", + "Step 3800, Minibatch Loss= 1.0395, Training Accuracy= 0.703\n", + "Step 4000, Minibatch Loss= 1.0096, Training Accuracy= 0.664\n", + "Step 4200, Minibatch Loss= 0.8806, Training Accuracy= 0.758\n", + "Step 4400, Minibatch Loss= 0.9090, Training Accuracy= 0.766\n", + "Step 4600, Minibatch Loss= 1.0060, Training Accuracy= 0.703\n", + "Step 4800, Minibatch Loss= 0.8954, Training Accuracy= 0.703\n", + "Step 5000, Minibatch Loss= 0.8163, Training Accuracy= 0.750\n", + "Step 5200, Minibatch Loss= 0.7620, Training Accuracy= 0.773\n", + "Step 5400, Minibatch Loss= 0.7388, Training Accuracy= 0.758\n", + "Step 5600, Minibatch Loss= 0.7604, Training Accuracy= 0.695\n", + "Step 5800, Minibatch Loss= 0.7459, Training Accuracy= 0.734\n", + "Step 6000, Minibatch Loss= 0.7448, Training Accuracy= 0.734\n", + "Step 6200, Minibatch Loss= 0.7208, Training Accuracy= 0.773\n", + "Step 6400, Minibatch Loss= 0.6557, Training Accuracy= 0.773\n", + "Step 6600, Minibatch Loss= 0.8616, Training Accuracy= 0.758\n", + "Step 6800, Minibatch Loss= 0.6089, Training Accuracy= 0.773\n", + "Step 7000, Minibatch Loss= 0.5020, Training Accuracy= 0.844\n", + "Step 7200, Minibatch Loss= 0.5980, Training Accuracy= 0.812\n", + "Step 7400, Minibatch Loss= 0.6786, Training Accuracy= 0.766\n", + "Step 7600, Minibatch Loss= 0.4891, Training Accuracy= 0.859\n", + "Step 7800, Minibatch Loss= 0.7042, Training Accuracy= 0.797\n", + "Step 8000, Minibatch Loss= 0.4200, Training Accuracy= 0.859\n", + "Step 8200, Minibatch Loss= 0.6442, Training Accuracy= 0.742\n", + "Step 8400, Minibatch Loss= 0.5569, Training Accuracy= 0.828\n", + "Step 8600, Minibatch Loss= 0.5838, Training Accuracy= 0.836\n", + "Step 8800, Minibatch Loss= 0.5579, Training Accuracy= 0.812\n", + "Step 9000, Minibatch Loss= 0.4337, Training Accuracy= 0.867\n", + "Step 9200, Minibatch Loss= 0.4366, Training Accuracy= 0.844\n", + "Step 9400, Minibatch Loss= 0.5051, Training Accuracy= 0.844\n", + "Step 9600, Minibatch Loss= 0.5244, Training Accuracy= 0.805\n", + "Step 9800, Minibatch Loss= 0.4932, Training Accuracy= 0.805\n", + "Step 10000, Minibatch Loss= 0.4833, Training Accuracy= 0.852\n", "Optimization Finished!\n", - "Testing Accuracy: 0.960938\n" + "Testing Accuracy: 0.882812\n" ] } ], "source": [ - "# Launch the graph\n", + "# Start training\n", "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", " sess.run(init)\n", - " step = 1\n", - " # Keep training until reach max iterations\n", - " while step * batch_size < training_iters:\n", + "\n", + " for step in range(1, training_steps+1):\n", " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", " # Reshape data to get 28 seq of 28 elements\n", - " batch_x = batch_x.reshape((batch_size, n_steps, n_input))\n", + " batch_x = batch_x.reshape((batch_size, timesteps, num_input))\n", " # Run optimization op (backprop)\n", - " sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})\n", - " if step % display_step == 0:\n", - " # Calculate batch accuracy\n", - " acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})\n", - " # Calculate batch loss\n", - " loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})\n", - " print \"Iter \" + str(step*batch_size) + \", Minibatch Loss= \" + \\\n", - " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", - " \"{:.5f}\".format(acc)\n", - " step += 1\n", - " print \"Optimization Finished!\"\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", "\n", " # Calculate accuracy for 128 mnist test images\n", " test_len = 128\n", - " test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))\n", + " test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))\n", " test_label = mnist.test.labels[:test_len]\n", - " print \"Testing Accuracy:\", \\\n", - " sess.run(accuracy, feed_dict={x: test_data, y: test_label})" + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))" ] }, { @@ -255,8 +268,9 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 2", + "display_name": "Python [default]", "language": "python", "name": "python2" }, @@ -270,7 +284,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb b/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb new file mode 100644 index 00000000..c290e4a7 --- /dev/null +++ b/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Variational Auto-Encoder Example\n", + "\n", + "Build a variational auto-encoder (VAE) to generate digit images from a noise distribution with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## VAE Overview\n", + "\n", + "\"vae\"\n", + "\n", + "References:\n", + "- [Auto-Encoding Variational Bayes The International Conference on Learning Representations](https://arxiv.org/abs/1312.6114) (ICLR), Banff, 2014. D.P. Kingma, M. Welling\n", + "- [Understanding the difficulty of training deep feedforward neural networks](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "\n", + "Other tutorials:\n", + "- [Variational Auto Encoder Explained](http://kvfrans.com/variational-autoencoders-explained/). Kevin Frans.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import norm\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 30000\n", + "batch_size = 64\n", + "\n", + "# Network Parameters\n", + "image_dim = 784 # MNIST images are 28x28 pixels\n", + "hidden_dim = 512\n", + "latent_dim = 2\n", + "\n", + "# A custom initialization (see Xavier Glorot init)\n", + "def glorot_init(shape):\n", + " return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Variables\n", + "weights = {\n", + " 'encoder_h1': tf.Variable(glorot_init([image_dim, hidden_dim])),\n", + " 'z_mean': tf.Variable(glorot_init([hidden_dim, latent_dim])),\n", + " 'z_std': tf.Variable(glorot_init([hidden_dim, latent_dim])),\n", + " 'decoder_h1': tf.Variable(glorot_init([latent_dim, hidden_dim])),\n", + " 'decoder_out': tf.Variable(glorot_init([hidden_dim, image_dim]))\n", + "}\n", + "biases = {\n", + " 'encoder_b1': tf.Variable(glorot_init([hidden_dim])),\n", + " 'z_mean': tf.Variable(glorot_init([latent_dim])),\n", + " 'z_std': tf.Variable(glorot_init([latent_dim])),\n", + " 'decoder_b1': tf.Variable(glorot_init([hidden_dim])),\n", + " 'decoder_out': tf.Variable(glorot_init([image_dim]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Building the encoder\n", + "input_image = tf.placeholder(tf.float32, shape=[None, image_dim])\n", + "encoder = tf.matmul(input_image, weights['encoder_h1']) + biases['encoder_b1']\n", + "encoder = tf.nn.tanh(encoder)\n", + "z_mean = tf.matmul(encoder, weights['z_mean']) + biases['z_mean']\n", + "z_std = tf.matmul(encoder, weights['z_std']) + biases['z_std']\n", + "\n", + "# Sampler: Normal (gaussian) random distribution\n", + "eps = tf.random_normal(tf.shape(z_std), dtype=tf.float32, mean=0., stddev=1.0,\n", + " name='epsilon')\n", + "z = z_mean + tf.exp(z_std / 2) * eps\n", + "\n", + "# Building the decoder (with scope to re-use these layers later)\n", + "decoder = tf.matmul(z, weights['decoder_h1']) + biases['decoder_b1']\n", + "decoder = tf.nn.tanh(decoder)\n", + "decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out']\n", + "decoder = tf.nn.sigmoid(decoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define VAE Loss\n", + "def vae_loss(x_reconstructed, x_true):\n", + " # Reconstruction loss\n", + " encode_decode_loss = x_true * tf.log(1e-10 + x_reconstructed) \\\n", + " + (1 - x_true) * tf.log(1e-10 + 1 - x_reconstructed)\n", + " encode_decode_loss = -tf.reduce_sum(encode_decode_loss, 1)\n", + " # KL Divergence loss\n", + " kl_div_loss = 1 + z_std - tf.square(z_mean) - tf.exp(z_std)\n", + " kl_div_loss = -0.5 * tf.reduce_sum(kl_div_loss, 1)\n", + " return tf.reduce_mean(encode_decode_loss + kl_div_loss)\n", + "\n", + "loss_op = vae_loss(decoder, input_image)\n", + "optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Loss: 645.076538\n", + "Step 1000, Loss: 173.018188\n", + "Step 2000, Loss: 165.299225\n", + "Step 3000, Loss: 172.933685\n", + "Step 4000, Loss: 161.475052\n", + "Step 5000, Loss: 179.529831\n", + "Step 6000, Loss: 166.430023\n", + "Step 7000, Loss: 167.152176\n", + "Step 8000, Loss: 159.920242\n", + "Step 9000, Loss: 160.172363\n", + "Step 10000, Loss: 150.077652\n", + "Step 11000, Loss: 162.774567\n", + "Step 12000, Loss: 156.187820\n", + "Step 13000, Loss: 148.331573\n", + "Step 14000, Loss: 153.757202\n", + "Step 15000, Loss: 158.050598\n", + "Step 16000, Loss: 163.068939\n", + "Step 17000, Loss: 152.765152\n", + "Step 18000, Loss: 151.136353\n", + "Step 19000, Loss: 157.889664\n", + "Step 20000, Loss: 149.112473\n", + "Step 21000, Loss: 151.694885\n", + "Step 22000, Loss: 153.153229\n", + "Step 23000, Loss: 152.662323\n", + "Step 24000, Loss: 150.556198\n", + "Step 25000, Loss: 142.779984\n", + "Step 26000, Loss: 148.985382\n", + "Step 27000, Loss: 150.923401\n", + "Step 28000, Loss: 161.761551\n", + "Step 29000, Loss: 144.045578\n", + "Step 30000, Loss: 151.272964\n" + ] + } + ], + "source": [ + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + "\n", + "# Training\n", + "for i in range(1, num_steps+1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + "\n", + " # Train\n", + " feed_dict = {input_image: batch_x}\n", + " _, l = sess.run([train_op, loss_op], feed_dict=feed_dict)\n", + " if i % 1000 == 0 or i == 1:\n", + " print('Step %i, Loss: %f' % (i, l))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAHhCAYAAAChqv35AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXlw3Od93//a+97FYnexABb3fRAgCJLgTVG8RFmyFVuJ\nLcmWPEk7cafJ1NNp/0jaOk2n/aOJc007aWeayS+u4+gY1ZIsV5JpSyZFijd4ACBAEPd9LnYXu4u9\nj98fzPMUlHBSUqO4+57hiKCAz37xfJ/n+Vzvz+ejyGaz5JBDDjnkkEMOX0wo/6EfIIcccsghhxxy\nWB85RZ1DDjnkkEMOX2DkFHUOOeSQQw45fIGRU9Q55JBDDjnk8AVGTlHnkEMOOeSQwxcYOUWdQw45\n5JBDDl9gfG6KWqFQnFEoFPcVCsWQQqH4vc/rc3LIIYcccsjhVxmKz6OOWqFQqIAB4BQwBdwAns9m\ns32f+YflkEMOOeSQw68wPi+PugMYymazI9lsNgG8CjzzOX1WDjnkkEMOOfzKQv05yfUAk6u+ngL2\nrffNCoUi1x4thxxyyCGH/9fgzWazrs2+6R+MTKZQKH5boVB0KhSKzn+oZ8jhVwcKheJzk/lZy14t\n9/N47hxyyOEfDca38k2fl0c9DZSu+rrk7/9NIpvN/g/gf8AnPWpxgYn8+WeVR1coFCiVSrRaLYlE\ngnQ6Lf999Wd8/OutQK1WS7nZbJZMJrOunO3IFs8sLvRMJvMJOY+6VmItMpmMlKtQKB5aF/GZ210P\no9GIQqHAYDCQTCZJpVIkEolPPL/4s51nLigooKqqinQ6TSAQYGZmhng8LuWk0+lty1UoFOh0OgwG\nA2fOnCGdTpPJZLh79y5TU1NyjZLJpFyf7chWKpVYrVaKi4tpbW1ldnaWubk5AoEA4XCYWCz20HvY\nrny1Wo3RaMTj8aBQKFhZWSGTybCyskIgENj2M6+GUqlEp9Oh1+sxGAwolUoikQiRSIRYLPbIcsWz\nq1QqVCoVOp2OTCZDPB6X6/9poVQqUavVaDQauQc/S16OeOZH2XMbQayLuEs+6ztQoVB8Qu6n/Qy1\nWi3lZLNZ+RmfVrZSqUSj0cg1zmQyKJXKR7o/BMT66vV6FAoF0WiUbDaLSqWS5/BRz6JCocDhcKDR\naIhGo0SjUVQqFalUSj5/KpXaltzPS1HfAGoVCkUlDxT0c8ALW/lBcemIw6tUKkmn06jVapLJpPw+\nlUqFyWRCq9WytLQkFe9GL03I1Wq1crOq1WqUSqW86MXBVqlUhEIhUqnUpi9MyNFoNKhUKnkYNBoN\nsViMdDotP0/8d3l5eUsbTMjV6XQAcpOm02n5sld7aNFodEsHW6vVotPpMJvNZLNZKUuj0RCJRB7a\nSOLy3OrGVavVOJ1OtFoter2eWCxGNpvF7/eTSCSABwdXvK9kMrkl2UJhlJSU4Ha7SafTWK1WgsGg\n/B6FQiGfVRyMzSAOrU6nw+FwkJ+fLy+GkZERNBrNQ8+53YtTqVRiMBjweDzU19dz4MABbt++TSAQ\nQK/Xy4Ms1mW7F4/JZMLpdLJz505OnjzJzZs3mZubY2pqSu69R73sxXM/9dRTWK1W/H4/k5OTjI+P\nMzU1JdfkUWQrlUra29spKSnBbrdTXFxMV1cXfX19+Hw+VlZWHjrz25Vts9lobW2lvb2dvLw8FhYW\nePPNN/H5fJ8wGrcDcR4dDgcvvPACGo2G3t5e+vv7GRgYeORnFveIwWCgrKyML3/5y3i9XkZHRxka\nGmJiYuKRn1kYona7nerqaurr65mfn2dmZgav18v8/DzxePyRZIu7rry8HJfLJe8mr9fLwsKCPP+P\nIler1WI0GrFYLKjVaumUpFIpaUBvBaudGYVCgclkwmazYTabAeTdHY/HmZ2dZXl5ectyAak7NBoN\ndrudXbt2SWNZrVaTSqUYGxsjGAwSCoW2vRafi6LOZrMphULxu8BZQAX8f9lstnerP69QKMjLy6O8\nvFxuXLvdjt1uZ3h4GL/fTyQSIRAIkM1m0ev1Dymu9aBSqXA4HOTl5WGxWCgsLKSgoICSkhJCoRDn\nzp0jEAjg9/vJZrPSWt7s8lQoFBQUFEi5Wq2W5uZm3G434XCY+/fvc+fOHenZrN5wm62DRqPB5XJR\nWVmJWq3GYrGQl5eH2WyWl6XP52NxcVEaGUIZbgRxkRUXF5OXl4dGo0Gr1eJwOAiHw/T39xMMBuWl\nJqz7rSo+p9NJSUkJRqNRXubi8g2FQszPz8tDJhTJVqBQKCgvL6e0tJR4PE4wGMRut6PVaqVXKry8\n7SoojUZDaWkpbrcbhULBzMzMJ6IZG0VK1oNKpSI/P5/Kykqam5ux2+3ykggEAg8p/+3KVigUuN1u\nDh06xBNPPEFtbS0ajYZr166RSqVYXl5+KMS+XQOjvr6er371qzz22GO4XC7Gx8eZmZnhpz/9KX6/\nn+Xl5UdSHiqVCrPZzPPPP8/u3btxu90A2Gw2VlZWSKfTJJPJbSs9YQzn5+fT3t7Ov/t3/46CggJU\nKhX37t2TCtXn85FMJretQLRaLfn5+ezYsYOnn36ar3zlK3i9XhobG7l8+TKTk5MPGb7bgU6nw+12\ns2fPHk6dOsXOnTvx+/2MjY1x48YNXnvtNen5bQdiTerq6mhtbeXEiRMYDAaWl5cZGhri0qVLxONx\n5ufnt/3MSqUSvV5Pfn4+Bw8eRK/XY7fbyWQyXL9+HbVazdTU1JYjL2L/KxQKtFotFosFl8uF2+3G\nZrNhtVoxGAxMTEwQCAQIBoNbvpNW/13IdblcJBIJamtr8Xg8TE9PMzAwwEcffbThvl7tHAnHCR7s\nj4KCAoqLi/H5fADs2LEDq9XKnTt3GB8f5+rVq18Yj5psNvsu8O52fkapVKJSqaioqODEiRPs3buX\nVCpFUVERJSUlWK1W5ufnuXr1KhcvXuTGjRskEgnpaW0U1lIqldTU1HDq1CkaGhqwWq0UFhbidrsx\nm82oVCpqa2s5e/Ys9+/fZ2FhgVQqhVqtlt7fWhDe2Fe/+lWam5vJz89HrVZTV1eHxWJBpVKxvLzM\n9773Pe7evcvi4iLJZFJaWRttBpVKRWlpKYcPH+bUqVOo1WrMZjP5+fk4nU5CoRB3797lzTff5Ny5\nc8Tjcfk8m20Eu93Ozp07OXjwIAUFBWg0GiwWCwaDAbPZzO3btzl37hx9fX0sLS2RSqW2rPi0Wi37\n9u2jpqZGKtBgMIherwdgfHycn//85ywvLxONRj+R6lgP4lJobm7G4XAQi8WYmZmRl3wwGCQQCDzk\niW1V8alUKpxOJxUVFRQXFzM7O8vCwgKZTAaTyUQkEkGlUsmQ7OrQ22YwGo2UlZWxY8cOWlpamJub\nw+/3o9FoMJvNLC8vo9FoZFh9q88sPLD29nbOnDnDrl27ZOrCZrPh9/spKChgaWnpof27FdlKpRKj\n0ciTTz7Jl770JWm8iIupvb0dhULB7Ozstgwi8fMmk4mWlhZOnDghFanf76ehoUGGC5eXl7dlxMGD\n95iXl8fhw4f52te+RmNjI8lkklAohMfjobGxkdnZWWKx2LY9doVCQXFxMYcPH+bZZ5+lvb1dGrci\nSlZQUMDc3BwrKyvbNoqqqqo4ffo0X/3qV6moqCCbzWK1WlGr1czNzZGfn8/c3Ny2Lnlh7Ofl5fHi\niy+yd+9eCgsLUSqV0lgOhUIkEoktK2qhpMR7LCsrY/fu3Xzta1/DZrMBEI/H0ev13Llzh2AwuKUI\nhgg7C+O4uLiY+vp6mpubKSoqory8XN6nzc3NjIyMsLKysuZ6rFb44mvxd61WS3t7O7t27UKn02Ey\nmSgvL0epVNLW1kZZWRnXrl1bN8Igzr5Y20wmIyO1drudjo4OuY8LCgooLCykvLwch8NBd3c3d+7c\nIRwOb2mtBT43Rf0o0Ov1fOMb3+C73/0uFRUVLCwscOfOHW7dusXU1BRtbW3AAwtFqVQyOzuL3+9H\nr9ezvLwsf/m1DohKpeKHP/whVVVVKBQKFhcXeeONN6SydrlcNDY2YjAYuHXrFtevX2dxcZFwOIzP\n55O5hY9DoVCwc+dOvve976FSqfD5fIyOjnL37l2cTidOpxOAb33rW3R2dnLhwgWmp6cJhUIsLy+v\na1yIjfpv/+2/5ciRI8RiMSYnJ7l//z4Oh0OG7Ovq6vj2t7/NwsICExMTpNNpotEoS0tLG14UX/nK\nV3juuecoKCggFApx4cIFlpeXcblc6HQ62traKCoq4vbt21y4cIGxsTFisRjhcHhDg0ij0VBRUcHp\n06cxGAwEAgG6urrQarXy0nG73UQiEbq7u1lcXCSdTm+4FmI99Ho9jY2N7NmzB4CLFy/i9/spLCwk\nnU7jcrlYWlpiZWVFeh7ZbJZwOLzhWqhUKmm4PP300/j9frxeL4FAAKPRKD32SCRCNBqVIft4PL5p\neF2hUFBfX883v/lN9uzZQyqV4sc//rEMOWYyGWw2m4w6JJNJGXnZ7KJXq9V4PB7+xb/4F7jdbmKx\nGD/72c/4X//rf8mQm9VqpaOjg8nJSbxe77oX22qIaMvu3bv5zd/8TfR6Pb29vVy+fJnOzk70ej0V\nFRXs3r2blZUVuru7pTG3GbRaLUVFRZw5c4aXXnoJg8HA5cuX+fDDD/F6vdTV1VFZWcm//tf/mhs3\nbvAXf/EXW84rq1Qq2tra+K3f+i2+9KUvYTQa+elPf0pXVxfJZBKr1cpXv/pVdu3axXvvvceHH34o\nvZ6NIC5ku93O3/7t31JbW4tWq2Vqaoo33niDcDiM2+2mubmZ559/njfffJOhoaGHeBPryRXGZ2Vl\nJS+//DIlJSWkUilmZ2d5++230Wg01NfXc/DgQQYGBnj//fcJBAKbrrU4Ly6XizNnznD69GlOnjwp\nFfLdu3fR6/XU19eza9cuqqqquHv37oae72pDTavV8vTTT/P4449z+PBhCgsLSSaTTE5Oyjv5pZde\n4ujRo6jVaj744AMCgcCGskWaz2KxsH//fv7lv/yXlJaWotfryWazTE5OotFoSCQSVFVV4ff7+cu/\n/EsWFhY+IW/1ums0GvR6PUajkX379lFRUcE//af/FJPJJCN96XSaeDxOa2srBw4c4L/8l/+yrqIW\nxoSIQiqVSh5//HH0ej3Hjh2jra2NeDzOwsKCjEiWlZXh8XikM7VdfKEUtQghaLVaVCoVg4ODvPLK\nKywuLlJYWCitNJVKJS8ck8n0UP56o4teIJFIcO/ePd555x1cLhcGg4G6ujpqa2ul5yTCLqFQaNPD\nBv8nj9vX1ydDVMJKLioqoqCgAJ1Oh81mIxKJSKNiM+8pEomQTqe5e/cuP/nJT1hYWJA5rPLycmpq\nakilUlL5JZNJ5ubmNpUrcueLi4uMjIxw9uxZ1Go1mUyGsrIyTp48iVKpxOFw4PF4CIVCLC4ufiLU\ns9Z6CCW2uLjI9evXGR8fZ3l5mcLCQmldOp1OPB4PSqWSVCpFKBTaVK4gkqXTaQYHB5mZmWF0dJRY\nLIbBYMBqtcrQZDwel/skGo1ueLEplUqcTift7e0UFBQwOjrK/fv3GRsbA5CpCp1OJ1MxgqQliETr\nQRhydXV1qFQqgsEgPT09hEIhksmkPPAGgwGTySSfeTMPVTxPZWUlFouFbDbLrVu3eO+99xgfH5eX\nqdlsprGxEZvNRldXF7FYbNNLXq1W43A4qKmpQafTMTY2xquvvkpvby/z8/OYTCYCgQDV1dUcPXqU\npaUlgsHglowLi8WC2+1m9+7dlJSU8Oabb3Lp0iX6+vpk5CWRSLBz50727t0rCZqbQSimlpYWDh8+\njFarZWxsjFdeeYXp6WmpaHfu3ElVVRUNDQ3cuHFjU0UtlLRer6ekpITy8nKSySQTExP87//9vzl7\n9qxMxwjvMj8/f0uXsUqlQqPR4HQ6eeKJJ3A4HMTjcQYGBrhw4QLvvfee3Mutra2SKLiVtVAqleTl\n5XHo0CFOnTpFa2sroVCIoaEh3nvvPXp7e2lsbKS9vZ3S0lIqKyu3/MxqtZr8/HyefPJJdu3aRWlp\nKclkkgsXLnD16lVCoRAul4tvfOMbeDwenE6nJJpt9Mzi7B48eJDjx49TX19PIpFgYWGB8fFxLl68\niFarxePxkJ+fT1lZGQaDYVO5arUam83Gnj172L9/PzU1NWQyGUZHRxkYGGBkZASfz0dFRQXl5eUU\nFBRgNpvxer0byhaKWqvVUltbS35+Ph6Ph7m5Od566y3m5+dJJBLE43H27t1LQUEB1dXVGI3Gbeep\nv1CKOplMMjIywv3797FYLFy7do179+7h9/tJpVIMDw/jcDiwWq3E43EsFgsrKyuSfbrRJZFOp7l+\n/Tomk4n5+XnOnj1Lb28vTqcTm81GKBQiLy9PhjWcTqcMkW10aWYyGbxeL8PDw0xOTvL2229z8eJF\nMpkMOp2O6elpdu7cicPhQKVS4Xa7CYVCkvC0ntxsNsvKygrvv/8++fn5vPLKK9y8eZNwOIxarWZs\nbIwDBw5gtVqxWq04nU78fj8zMzNb8mx6enq4dOkSiUSC3t5eurq6pBc2OTnJ3r17ZehehPZWh5LW\nQyaTwe/3c+fOHbxeL9evX2dpaYlYLMbS0hIGg4FMJkNeXh4lJSUolUqWlpbQarUPsSLXe4fRaJTZ\n2Vnu3btHf38/09PTqNVqSkpK5POKfZFMJllaWpIRkfUUquAj5OXlkU6n6e3tZWRkhEAggFqtlsac\n0WjEZDJhMpnw+/1MTU0xOztLJBJZdz0UCgUlJSWYTCYSiQSDg4N4vV6ZshEKRJDYLBYLS0tLzM7O\nbppDFe9GpVIxNzfH5cuXGRkZkcZmNpuV/A632y331OLi4obvULBha2pqCAQC3L59m87OThYWFkgk\nEiQSCdRqNYWFhezYsYOZmRmmpqY29SCF7Lq6OhoaGkgmk7z11luMjo4SCoUkf8PhcNDQ0IDH48Fu\nt0tjdSMolUqUSiXHjx/HYrHg9/t5/fXX6e3tJRaLoVQqCYVCBINBysrKqK2tpaCggOnp6S15vTab\njaNHjwLQ3d3NG2+8QVdXFxMTE3LvNjQ0yEiUIJVuJFukF3bu3MmpU6cIh8N0dXXx0UcfcevWLcbG\nxggEAhQWFtLW1obT6USj0Wx6BgVXZceOHXzpS19i7969qFQqbt26xblz5zh//jx+vx+tVktDQwMG\ngwGbzSYJjetBGBb5+fns3r2bxx9/HK1WSygUYm5ujh//+McMDg6SyWRwu9288MILGI1GioqKJBl2\nI9lFRUUcOnSIp59+mpaWFsLhMFNTU/T19XHv3j0uX76MXq/nyJEjZLNZysvLsVgsG8oV1RBPPvkk\njz32GJWVleh0Om7fvs3Q0BDT09P4fD7Gx8cxGAykUimZBhOG+npytVotTqeTffv2sXfvXjKZDPfu\n3WN8fJzx8XHUajXj4w+qrwSps7q6GrfbvW0+wBdKUWezWaampvB6vUxPT3Pr1i0ymYwsCxFekiAv\nlJaWMjc3ByBDwRtdxpOTk1y/fp3p6WmZ0I/FYpjNZmnhCLavzWaTnr3Imax36Px+P++88w5zc3N0\ndnaytLQkvVPB8kun06ysrEjvaStyI5GIZKp2dXXh9XpJJpOSiOP3+wkGgzL8otFo5IW1Wd53ZWWF\n8+fPo9VqGR4elqQj4T0sLy+jUqmkVSnWdzOPWijZkZERZmZmmJ+fJxAIoFKpMBgMxGIx6c0bjUbM\nZjPxeFw+/3qKOpvNSha91+vF6/UyOTlJJBLBarWi0+kk0U4Q6iKRCCaTidnZWXw+37qGkXgnIr8m\n1iOdTqNSqXC5XNhsNvR6PYWFhdLzuXLlChqNhvv376+7ziqVipKSEsmhGB4elhUKWq0Wm82G2+2m\noqJCRhxERGRmZmZdb1LwMkpLS9HpdMzMzDA4OCgjNYKZrNfrKSoqora2FrfbzdTUFEtLS+saQ+L9\nOp1OqqqqCAaDdHd34/P5iEQikrMh8shOp5ODBw9y7tw5gsHgpqFTg8FAW1sbLpdLpnNE2FxEHObm\n5ojH4xiNRslL2UqEQavV0tjYSCaToa+vjytXrsh9LRAMBlGr1VRWVsr3vRGER1ZTU8OxY8dYWFjg\n3Xff5eLFi9IATSQShMNhgsEgTqdTlvlFo9ENZavVakpLSzlz5gw1NTXcvHmTH//4x3R1dbG8vEwk\nEkGj0bCysiKNIIvFsqGnJzgqIjTd0dGByWRiZmaGv/u7v6Ovr4+ZmRnS6TSxWIxkMimNZ6Vy47Ya\nWq2WvLw82tra+PVf/3VsNhs+n4/e3l5u3LjBrVu3CIVCMs0lSL6FhYUbGhci133s2DGeeuop2tra\nMJvN/PKXv+T69etMTk4yNjaG1+vF6XRKQ3QzJa1SqbBarVRVVXHmzBl27NiBwWBgbGyMd999l2w2\nK8s7VSqV5MtshRNhs9morKxk37597Nq1i7q6Oq5evcqVK1eYnJzEbrfLkPzqsjVh7GwXXyhFnUwm\nWVxcpLu7G5VKJS14l8tFTU0NDocDpVKJ1+vF7/ej0+lknqyuro7+/n7u3bvHysrKJ2QLFuLCwgKT\nk5PMz89jNBplaUhZWRnRaFQSQfx+P3l5eWi1WoLBIKlUipGRkU/IFV6KyPEuLCyQTqfxeDy43W5q\na2tpbGxkYmKChYUFSf0XBDalUonf718zHxKPxxkcHMTn80lCkCCvPPbYYzQ3N6PT6ZiYmGBkZERe\negB5eXmEw+F18yzColMoFNK6zsvLo7S0lJqaGtRqNSMjIywtLTE9Pc309LQkwen1esmMX289hCeb\nTCbR6/U0NDRQXl4uQz+Li4vMz88zOjqKz+dDo9FgNBqJxWLSA/64XBHpCAaDzM7OSuXc2tpKbW0t\neXl5JJNJVlZWmJubIxwOo1Kp2LlzJ5OTk/T19a35zHq9nv379+N0OolGo8Tjcem92Gw22tvbZe4s\nmUwSiUQoKSnh1KlTZDIZvve9761ruOh0Oqqqqshms/T19dHf34/NZpPhQ7fbTWFhIUajUV6Wzz33\nHGNjY7z99tt0d3eveXEI5XHgwAFmZmb4+c9/zuTkpMx5m0wmWYYSCoXQ6/Xs3buXaDTK6Ogofr9/\nzecV3uOJEyeorKzkjTfe4MMPP5TlhAqFgkQiIZVsLBajqqqK/fv3Sw9oPSgUCk6dOkVHRweZTIYL\nFy58ooQsGAwyMjLCjRs3ZHpncnKSVCq17hqLPK8Ied+6dYs//dM/lblpgUQiQXd3NzU1NdLY3cjo\nhAflaXv37uWll16iuLiYP/7jP+bs2bNEIhG5JxOJBIFAgOnpaUlAFB71elCr1ezbt49vf/vbtLa2\nEgwG+Vf/6l9Jo0WstV6vJxgMEo/HP2F0rAWdTofL5WL//v2cOXOGVCrFyy+/zF//9V8zODhIOp2W\n6x2LxdDpdITDYebn5zck1ikUCjo6OnjiiSc4fvw4RUVFvPzyy/zwhz9kbGxMGhOiX4AwvAKBAEtL\nS+uGepVKJU1NTZw5c4Z//s//OTqdjkAgwLvvvst/+k//STo5ohQToLy8HLPZzL1799aNDqnVag4f\nPsxv/MZvcODAAVwuF4FAgEuXLvF3f/d33Lp1S7Lz1Wo11dXVtLS0YLVaN406qdVqvv/977N7927y\n8/NJJBJcu3aNt956i4mJCZaXl6VHrVarKSgowGQysby8jM/nW/fsbYQvlKIWm/69994jlUo9RIia\nmpoiPz8frVYrD8XExAR2u52qqip27tzJgQMH+OM//mN5iD6Orq4uUqmUVIz5+fmoVCqKi4spLCzE\nZrMxNTXF3NycVARC2VqtVv7qr/5qzUszk8lI6xoeHBan00lzc7NU1Ldu3SIWi0kFJWoa7XY7vb29\n6xIiAoGAzMMbjUZsNhtlZWW0tbVRX18va56j0SjBYFB6AC6Xi/n5eaksPw5h7Yvwm91up7S0lLa2\nNoqLiykoKJDvQ1w8gGSQRiKRNZmc4mcEA1JY6R0dHbJkSzyvyOsLMpfb7ZZG0Vp1jOl0WnoswpPU\naDTU1dVRUVEhCV8iKiNCU/X19dy7d4+FhYWH+AGrn9ntdqNSqQiHwzLn63K5MJvNlJWVyUjA3Nwc\ndrtdhu6tViv5+fmEw+F1PSiVSiVzbeFwGKPRiMFgkOucTqdlKZjgHRQXFzM2Nsbg4OCahidASUkJ\nWq2W6elpRkdHJUtaRKBEM4epqSkZ2m9sbMThcMic8looKCjA6XSi1+vp7OzE6/XKOlER+VCr1cRi\nMYxGI/n5+TQ3NzMwMLBpKLmkpASbzcbS0hIffvjhJ/aPUNiJRIK8vDzKysokr2MjhWq326msrGRp\naYn333+fwcHBh1JAQvGJ+udIJLKlXLLT6aSlpUVGF65cuSL3rpAr+ACiyYXRaNzUa1KpVOzfv5/a\n2lqCwSBXr16V/SCEXFFDXFZWRklJCX19fRuGp0XEYteuXZw8eZLZ2Vlu3LjBq6++ytjYmCQsindY\nU1MjKzM2Y78rlUqefvpp9u3bh9vtpre3l7/5m79hdHSUcDgs10OUZ1VWVsrzuFEJlU6n4/jx4zzz\nzDOYTCauX79OZ2cn58+fl6RK8cwiiioMMlFBsxYsFgvf/OY3eeyxxzCbzfT19dHd3S1JwqKcV3jd\npaWlHDp0SIbyN4oMuVwuTpw4IftEDA0N0dfXJ5sBCSPN4XBgMpmoq6uT/SlE2mu7+EIpaniwQZeX\nl2WIUHhNYgN4vV6WlpYYGhpibm6OiYkJkskklZWVHD16lM7OTl555ZU1O0hFIhEWFxdJpVIoFAqK\nioooLCzE5XJRW1vL5OQko6OjTE9P4/V60ev1WK1WamtraW5u5m//9m8lO/Djz7yysiItqLy8PKmk\nW1tbUalUknXr9/tZWVlBp9NRWFhIc3Oz/Pm1LmSxoQwGAw6Hg5KSEpkLU6vVRKNRfD6fDCeL0GVV\nVRVWq5Xh4WGi0eiajEtR7yzIEHV1dVRVVVFWVsby8rIsydLr9ej1epLJJBaLBY/Hg1arZXZ29hOW\npwi5i9r0uro6AOrq6igqKpLKGR54K6I23GazUVpaKt/Le++9t6bc1Q0FBEmouroanU4nO5UJI8Jm\ns2E0Ginpv835AAAgAElEQVQvL5f5p/fff/8Tilooh2g0SiKRkMaAyWSisrJSKlJRtynSMB6PB5vN\nxoEDBzh37twn1lfkCqPRKLFYTD6/qLU0mUwkk0lGR0dJJBIYDAaWlpbQ6/UyjFtRUUFv78MtCMQ7\nFhEZwXcQRppIu4g+AxqNBp/PRygUwmw2yxzZWl6OQqGQKQlAKnQRUs3+feMX8bskEgnpsYt8+Xoc\nCdFvQESpRA5QrP9qRrHNZpNGntgn6zHsxaVYUlKC3++XpLnVaRShnMrKymS0bmVlZcNwr9jHtbW1\nGI1GSYpc3YVMpCCE8Tw3N8fc3Jxcs7UgnqWlpQW9Xs/ExATnzp2TnATxO5pMJhobGzl27Bh6vV56\nrutBhJwPHTpEfX09PT09vPvuu4yOjhKJRB5aC4fDwVNPPYXdbmdqauoTe+zjcDgc7Nu3j8LCQmlk\nreYWiH4WRUVFdHR08Nxzz+H3++np6WFoaGhdRb1371727NlDVVUVMzMzvPvuu9y4cYPp6emHoihm\ns5mjR4/y9a9/ndLSUpaXl+nv71/XcHnxxRfZs2cPbrcbpVLJ+fPnuXPnDhMTEw85VS6Xi7a2Nr77\n3e/i8Xgk90QYuR9/brVazb/5N/9GRnf9fj99fX0MDw8TDodl1caOHTvYuXMnFRUVHD16VOqt4eFh\nGRnYDr6QijoWi8kOPyJPXFhYKD2lmZkZlpaW5EXU09ODy+Xim9/8JseOHeO1115b0/oWxC+RK8jL\ny6OpqYnS0lIikQgDAwOSwRoKhYhGo8zPz+N2uykqKsJoNMrQ8schwqwiryZKHhQKBfPz87LRh/BC\n0+m0rA8XLTDXgkKhIBKJUFxcjMPhoLKykra2NrRarWw4IS5ocaFls1l5Iel0Oq5cufIJuUqlkmQy\nSSwWkyHpHTt24HK50Gq1wAMvJZlMyoYroVBIdh8qLy/njTfeWPOZhbdVW1srSXVutxutVivDbk6n\nUxKTdDodRUVF2Gw2lpeXKS4u/oSihgcXkdfrpaOjg0AgIJWKUIjis0S4qaKiAofDgd1uZ2Zmhpqa\nmjUVqlarZXJyEovFIhvXiNanQuEJQyoUCskuSSLnXlBQsOY6ZLNZSShxu93E43F0Op0Mt8bjcUKh\nkOyAt7rBjt/vZ3h4eF1Wq1KpJBAI4PV6CYVCUnGKRiGpVOohxRaPxyV3Qnje68kVF6To1CQU6Wpl\nKiAIe6lUatPOVqsNFZ/PJ8vmhFxAGkgul0tWA4iWoutBqVRiNpux2WyyPGg1A10YGRaLRUZ0ZmZm\npELdCMXFxbJT3f3792WpmJCt0WgoKChg165dlJSUcPHiRSYmJjbs5Cf2rd1uJ5vN0t/fz+jo6EN5\neIVCQXV1NceOHaOsrIxEIsHAwIAsD1wLojvYzp07MZvNUu7qKgKlUikN3bq6OqLRKF1dXdy+fXtD\nT0/0GIjH47L8VDSmEc9cVVXFY489xpe//GWam5s5f/48586d4/79++t6qE1NTTQ3N5OXl8e7775L\nT0+PLF8V62c0Gjl69Cj/5J/8E8rLy4lGo9y9e5fLly+vW4+8Z88eCgsLZcj55s2bMuokctH5+fk8\n++yzMjoZjUYZHh7m8uXL61YD6HQ69u7di16vZ2VlhcHBQTo7O7l//750mhQKBc899xzl5eUyPdnf\n38/Nmze5d+8eMzMz667zevhCKerVF8DKygr19fXs2LGDp556ipKSEn7605/K8JoIY4mcoghDtrW1\nrbmRRdhO1NuWl5fz7//9v8flchGPx7l79y6zs7OyA454wcePH+fUqVPo9fp1C+vFRVBSUkJjYyOn\nTp3i6aeflnWFExMTtLW10dDQwNTUFIODgzz++OMcOXJEMgxfe+21NeUKYsrJkyc5evQou3btwmg0\nMjMzg8/nQ6VSceDAAaqrq2VLPZHDWVhY4Pvf//6ah0S00GtpaWHHjh0888wzsnhfeNOVlZXS2/f5\nfDKnt7CwwH/4D/9hzVyLyFFVV1fT0NAgS9MEuSsSiaDVaqmsrJRkFuF9iBaJP/jBD9aUazQaCQaD\nmEwm6uvrpWIPBoOEw2HJNWhsbJReqdFo5IMPPuD27ducP39+zRSDUqnk3r17UuF/97vfJRgMEgwG\nZctMl8sl2epVVVUolUoGBga4ePEiL7/88prMb6HUOjs76ejokI1Jbty4wdjYGJOTk5JnUVFRIZnI\nohTv2rVr6xKHNBoNMzMz9PT08NRTT1FZWcnNmze5c+cOg4ODGAwGDAYDOp2Ol156ifLycvR6Pbdv\n32ZsbGzdC06tVsscfzab5dvf/jZvv/02AwMDkj1ttVpxOBy0trbKUq3u7m66u7s39KZF72NBMDp4\n8CDj4+MPdXVqbW3liSee4NixY0SjUbq7u2X+dD1v2mAwyLRYKBTC6XQyNzcnDQC9Xo/H4+HQoUMc\nOnSI+/fvc/XqVQYGBjYtraurq0Or1bKwsMDAwIB8p+K/R44c4Rvf+AaHDx9Gr9fzve99j6GhoYc8\n2LXe3YkTJ0ilUvT19Ul+i2ibrNVqqaqq4r//9/+Ox+MhHA5LIthG1S1Wq5Xf//3fp7Gxkfv373Pt\n2jWi0SharVb2Nzh16hRPPPEEzc3NeL1eXn31Vd58802mp6c3XIvy8nLy8vK4cuUKH3zwAWNjY1IR\niijf7/zO78jKifv37/OHf/iHzM3NPZQq+Pj6VlZW0tLSQjab5Wc/+5lk5bvdbk6dOoXb7aa6upqC\nggLi8Tg3btzgJz/5CRcvXmRkZGTd/Sb6Y6TTaX7+85/j8/nYsWMH9fX1VFRUSMKaqPi5evUq77//\nPh9++KHkt6y1zslkkvz8fDKZDD/84Q957bXXUCqV/NZv/ZYkhdpsNlnWOD8/z9LSEn/wB3/A/Pw8\ny8vLj9Qf/wulqEUeKZVKkUwmiUajuN1u8vPzycvLkwtQU1Mjm5b4/X6Ki4s5ffo0iUSCO3furNnd\nSfR+Fl5kKBTCaDTKMJxCoWDfvn1Scd67dw+fz8eRI0dwuVwsLi7KYvXVcsUzC7miO5EoBxEHpbq6\nGpvNhtfrleUhxcXFjIyMcPfu3U3XYmlpCafTKUM9fr+fZDIpaytF2UEkEkGn07G4uMjNmzfXjQAI\nxbm8vMzs7KwMoy4uLuL1elEqlTLMCw8iBoJhPTAwwOzs7LpkskgkwuTkJEqlkoqKChnJEHXeIoye\nTCZlo5pkMsmlS5ckW3wtuSJceu3aNfLy8mSOenUvdeGhh0Ih/H4/i4uLvPrqq8zPz69L4lheXpbe\nSiQS4fDhw+Tn50tvT/RFF8pvdHSUwcFBhoaG+PnPf77hwQuFQvT09GCz2aipqcFkMlFaWko6ncZi\nsWC1WmUttOjOdf78eS5durRhHjkWi8kyte985zvSSxAKIi8vT9Z1ih4E09PTfPTRRxuykUWPZpEb\nb2hokB5OIBDAbDbjcrmor6/nyJEjhMNhuru76enp2bQmOZPJMD8/LwlojY2NstRSoVBgs9k4cuSI\nzEF2d3czOzu7admX4Igkk0l27NjB1atXJT8DHoQ39+7dy1e+8hVCoRC/+MUv6OrqeqhH/FpQKBSy\nUkOlUtHU1ITNZpNetdFo5Nd//dd5/PHHMRgM3L59m5GRkXUVk8BqHsHKygrV1dWyw5vJZJIs5bKy\nMkKhEO+88w6vv/76pusgFLIgEdbU1AAP3qnZbOY73/mO5KAIRfPmm2/KUsCtwG6309TURCaTIT8/\nn6qqKiorK6msrCQvLw+fz8etW7d46623JAF1vbUQHBxxfk+cOEFHRwcFBQU0NjbKZieRSIR4PM7L\nL7/MlStX6O7ullyW9dZjcXFRRhGqq6v53d/9XcrLy6msrJT9Cubm5mTZ1/nz52X4ej2Ok1hL4e03\nNDTw4osvUlhYSEdHB3q9XqZoXn75Za5evUomk2FhYQG/3y8rBLbSvOfj+EIpavg/ClWn0z0Ugstm\ns7S2tsqQrcvlore3l3Q6zYkTJ2hra2NkZITe3l7UavUnLjiFQiHDNNFolHA4TCgUQq1Wy9BrMpnE\nbDZLz7K8vJza2loSiQT9/f2o1eo1w3ti4UOhED6fj+npaanIRBG/KJVJpVLYbDby8/MJBAIyb7IW\nRO1vJpNhcXGRsbEx2WrS5/NJr89oNBIIBFhYWECn0+Hz+bhz5w7T09NrygUeYu6KkgW/3y/DdiLE\nazKZJJs1GAxitVrp6enZ8PDFYjF6e3sle9rlcnHnzh2USqW0VB0OB9PT05IlLJTweuxe8d68Xi9X\nrlyRl308HicvL0++F8GCHxwclEbI8PDwhsM0hIElGOM3btyQXdR0Op3sL6zRaJicnOSNN97g/v37\nLC4urslQX/3MIlSo1Wpxu920tbVJApyoc87Pz2dmZobh4WEGBwc5f/78hkpaEPZWVlbo7+9nYWEB\nu92O0WikoaGBsrIyzGazJL1NTEzg9/v55S9/ye3btzdUIqlUipWVFXp6eujt7WXPnj00NTURCARY\nXl7GYrFQW1sr2y9euHCBn/zkJwwNDW1avy96IczNzVFTU8Pu3buprKwkHA5Lr/fZZ58lk8nQ3d3N\n2bNnZVOZjZBMJgmHwwQCAZqamjh27BjxeJyhoSEcDge7du3i61//OpWVlfzX//pf+eCDDzZMN62G\n6HMvSKeNjY2y85boZKdWq7l8+TL/7b/9N5mz3QgKhYLh4WHMZjPpdJrW1lb0ej1zc3PU19dz+PBh\nGhsbCYfD/OhHP+KHP/zhlgZQKJVKbt++TX19vWx2snv3btkoSBCgRJrvRz/6kexytpnyEHen0+lk\nx44dFBcXU1dXJ8+GWq2mp6eH8+fP8+abbzI6OrqlVMjS0hITExOUlZVx8OBBGfEUVTGRSIQbN25w\n584d/uqv/kr2zFg9kGitZx8aGmJ+fh6n00llZSVOpxOz2SxLY2dnZ/nBD37AzZs3mZmZkaWQwhha\nT65SqaS7u5uWlhZ27dpFTU0NSuWDoTtCx0xMTPAXf/EXxONxWdK41W6D6+ELpajFLyEIRyIHMDEx\nIckiIgcXj8fZv38/DoeDxsZGEokEd+/e5a233pI50o/LFi0aV1ZWCAaDXL58mY6ODtkiUqVSSXKU\nUqmkpKREjiEcHR2VedC1IHJS8/PzvP3221gsFllvK0hXCoVCeqvwgKAxOzu74eWZTqeJRCKMjY3x\n+uuvU1ZWhtPpJB6P4/V65f8XFqTIhQYCAYaHh9etC0yn07KRTDKZ5OWXXyabzcoOZIJ8Jsh9RqOR\nRCKB2WyWnspGVufU1BShUIjp6Wny8vKIRqMyKiFy9ML7U6lUsuxpoyYfotVof38/U1NTWCwWLBYL\nZrNZRh9E+8VYLCYPiuAFrCdXEMlEFOcv//Iv8Xg8klkuyB8LCwsMDQ3R29srW55udCmLPbe8vExf\nX58c0NLe3i6NAJ1OJ8PVgr0tWotudDELI8Dn8/Haa6/x+OOPY7FYKC4uJhKJyFzzzMwMIyMj3Lp1\ni9u3b0uOxmZyR0dH+elPfyq7qtntdmZnZ+UwA5VKxdLS0kOeyGZyM5kMPT099PX14XA4cDqd/OZv\n/qYslWxoaCCVSnHv3j0++ugjOVhkM6TTaVnqd/LkSdrb23G5XMzOzuLxeGhubsZoNDIxMcEvfvEL\nqUQ2uzRFSd2ZM2dwOBxYLBZ+53d+h0wmg8FgoKKigmQyyfvvv88PfvADurq6tjRONJ1Oy97dYjDQ\n/v370Wq1GAwGLBYLSqWSP/zDP+Sdd97ZtHRKIBKJ0NXVJdnkJ06ckGQ3wV2ZmZnhww8/5H/+z/+5\nYW+Bj2Nubo6RkRGKioooKyujqKiI/Px8SZ6an5/nP//n/8ytW7c+wRHYCN3d3bz55pucOHFClj8K\no/zevXtcvHiRzs5Oenp6WFxclPwA8Wc9wt4vf/lL7HY7Bw8epLi4mLm5OWw2m6wo+KM/+iPu3LnD\nwsIC8Xj8ISLfRoo6k8nw2muvSd3j9XoZGxuTaUpRoy1SCYKQKxwuwRPYyj5ZDcWjavjPEoq/n0e9\netFXl4FoNBo0Gg3t7e2y7aQIndrtdsLhMNPT01y+fHnDsNPq/JLoLCOIKFarFbPZTCqVkoMkRC1y\nIpGQJJX1IELHIjQt2LciZCosq3Q6LWtcNRqNJK9tRCYTayG+FiQloeBWl4iI3080NFgvzLmaFPTx\nObLi7+L7xHqJ31OUV20EwQkQWE0YEgdhNZN4NaloK32Mt/LZ4mB8/HfaDKvli7+vXq/VrN/tYPXv\nLPLh4mthvGxGnFoL4t3r9XoZYhfeiKj/F0bZdtZAdFKqq6ujvb2dqqoqVlZWGB8fZ3FxkZ6eHm7f\nvr2t5xUNizweD6dPn+b555/HZDJJua+++iqdnZ34fL4tKdPVz6rX63nhhRf48pe/THV1NVqtlpGR\nETo7O/noo4/o6+uTteZbhSC27du3T+bOhZF8/fp1/uRP/kSOb92OXK1Wy6FDhzh8+LAcBLG4uMjA\nwAC/+MUvuHPnDrOzs9uSqVQqsVgslJaWUltby/Hjx6Wz0Nvby6uvvorX65V3zXb2r1qtlkOHRFVE\nNpuVBqbP59v0TlgLoleBzWbDYrHI7oixWIzx8XF5HrZ71rRaLSaTSXZEE5FZoahF+Hr1PbfVzxCy\nxT0l+kuIzxFkTiFXOEur779VuJnNZvds9plfKEW9he+TfwCpmESd6lolWdt8DgCp7ARLebW39mmw\n+vlFOE14tJ8WQu7qbmefJtTycdlCLrCpt7cduav//ijKby0olVufbLUVrN5zwKf+3T/+roRhtVXv\nZiOZYk676PImLqVHlSs61Yne92azmXA4LMsMH+VMCMPTZDJRVFREXl4egBy7+KizokWtrdvtxul0\nynI9kbZ5lPtB3AM2mw273U5FRQVLS0tEo1EWFhZkr4dHWV+r1YrL5ZJrLCJOog/8o+4zMZVNDJ3I\nZDIP5V4fdS8Ij18Ywel0WkaVPs3eFftBKFJhyK+1D7ajUIVuEFPvxL+tlV7bjlxA1kqL59NoNPIs\nrK6xF+d8g/X51VPUW5DzmV3O/zfk5pBDDjnk8En8Y7vLhTJe/TlbdD62pKi/UDnqT4vPS5nmlHQO\nOeSQw/89/GO7y4WSXq2cP8vP2rgLew455JBDDjnksCV8XoZATlHnkMM62Iy09mnkfjzv/3nIzyGH\nHH418CsV+s5h+xChms86d/NxtvTHyRuf5rME+cRoNJJKpR5qy/ppQk5Ccba0tMjyI5VKxczMjCT3\nPCrbGx7UeHs8HiwWC3a7XU7ZCQaDG9Z5bwVifJ5oI7m4uChbPG42q32rv4OoZBAM/Ww2+5kQIYX8\n1QbGp1mLjT4jl8bK4fPC57m//p9T1IIZ+1kxogWE8hAlMB9Xfp9GGa4embcezf9RFIh45tUkiI/3\ndQYeaa1EhyStViun3wjWrViLR1GsouuSy+WSMsUcZKFoVyvV7cgVJU7V1dUoFApqa2vp7++XHZ+E\nUbBdprMwVmw2G4WFhRw4cIDp6Wmi0SgWi4VUKvWpGLmrB1k0NjZKFnE0GpUTmR61GkI8u2g5u3rU\n7HqT2bb77KJtptVqJZt90IHuUdZ5Pfl6vR6TySTnRn+aypCPw2g0AsiqkM+iGgL+z34U1RufleEi\nDC7BWBZn87Oo5BClUKuZ26uN20d5frH/xNAd8Zyr7/BHrRBQq9WyLbIo1xIltOLPo8gVPTjEPIbV\nfQ3E77Ddvf2FU9Sivjg/P5+amhr0ej06nY78/HxMJhNTU1Oy7nhxcRG1Wi1bYa6srGxYM6vX6yko\nKJCDF0R7xbKyMgKBAB999JFsOykacGylVEKpVMpGJKIxQkdHBx6Ph+XlZdl3V7RDBGSzko02r7hk\nREs90dCirKwMo9HI8PAwQ0NDzM7OMjg4KFtIxmKxTTevwWDA5XJRVFSE0+mktLSUwsJCioqKCAQC\nXL16ldnZWdk+UjQQ2cph02q1NDU14fF4sFqtclSfGKSyuLgom4aI5jZbGf0m1uPw4cOUlJTILkND\nQ0NykEoqlWJ0dFSWvW3FyFhd1tTY2EhzczMGgwGfz8f8/Lzsty26Tm33UhNDQnbs2MHu3btpamoi\nFosxMjIi5Ynn2K4xp1Ao8Hg87Nu3j6eeeorGxkbOnz/PxYsX5QSi1SUi24FC8WAwxDPPPMOTTz6J\n2+3m/v37DAwM8M4778g6/UdRIuJd/t7v/R6HDx+W/RFeeeUVOfHpUfsii3rixsZGvv/97+PxeFCr\n1fT39/MHf/AHDA8Pb7kj18chpr3V19fzzDPP8K1vfQu/38/Q0BAXL17kr//6r2X9+nah0WhwOBzs\n3LmTM2fOcOjQIZaXl5mYmOD69ev86Ec/2rC95XoQ+6qqqoqmpiaOHz+O2WwmGo0yMjLCtWvXGB0d\nZXZ29pHkiqZABw4ckFMOVSoV3d3dTE1NMTs7u+Ua69URPp1Oh9lsxul04nQ6ZRMpk8nE+Pg4V69e\nXXMc7loQJZvi7+K+E8OBqqurKS4uZnx8nP7+fi5fvrzpc4r/CqNBpVKRn59PR0cH9fX1LC4usry8\nzO7du9FqtXR2djI5OcmNGze29Myr8YVS1MK6Likp4ejRoxw4cAB40GC9sLAQg8GA1+ulp6eHW7du\nEQqFCIfD0svaqBG+6Dt94sQJOa3F4/HIMY9iQte1a9cYGRlhbGyMaDQqC/s3Ggqg0Wj48pe/TEtL\nCw6HA71eT21trRyVduTIEdLpNHfv3pWj6kSd60ZWm0qlorS0lCNHjnDmzBlMJhN2ux2bzSbrWQcH\nB2VLxHA4LC3FzRRffn4+O3fu5ODBgxQVFcnpVaKLUXV1NRcuXODWrVuyrehWIhHCEzhw4IActanT\n6eTYxkwmw9zcnDxgfr9/ywpKtOprbm6msLBQDnkIh8MUFhYSDofxer3Mzc3JEY5b7QIkPF5hvC0v\nL8u2gmazmUgkItu3JpPJbdVqi7nYNTU1NDQ0yJnk6XRaRh5E45dHqeesq6vj5MmT7NmzR7ZpFReJ\nwWCQofXtyBYX5bFjx3jmmWcoKyuTsoPBIKWlpXJYyzqNHNaF8JAqKio4c+YMJSUlqNVqvF4vHo+H\n1tZWOZZzu+shvP+Wlha+/vWv09TURDqdJhQKyfnWU1NTMtKzHa9JoXgwcamtrY0XXniBxx57TI7h\nLSoqory8HLvdLj2n7RpcRUVFHDlyhOeee47m5mY5oU00drJarXKc7XbkqlQqzGYzv/Zrv8aRI0eo\nqKiQ7XDj8Ti1tbVEIpEtK+rV/Sb0ej2FhYU0NTXxta99DbvdDiDPi8lkkob+Zuux+gwoFA9G8IpR\nwU6nk8LCQjkzvrq6mpGRkXVbtq5W+OJr0bBIq9Wya9cu2tvbZZ9xj8eDRqOhqakJl8vFtWvXNmyc\nJSD2kXhXNpuNpqYmHA4HCoWCxsbGh7rjiQ6B2/XWv1CKWqfTUVVVxenTp3niiSfweDyyR6oI2bhc\nLqqqqkilUtKLFOMDvV7vugdbrVbz7LPP8sQTT+ByuQBk9yOxQSoqKohEInIziI4zohXcWnKVSiVu\nt5uvf/3rOJ1OOX1IzB8Wh6SjowOFQkE0GmV2dvahMMh6BoDNZpND1Sv+fsScGP4hmg+UlJRw+PBh\nrly5wtTUlAxrbdSKE6CxsZEnn3yS+vp6zGaz9I5E1yyPx8OBAwdk4wFx+W/WOUulUmG32+XkLDEK\nUgxpt9vtKJVKduzYQSQSkcMk1uvxvXo9hMfhcDhwuVyEw2GWl5ellyM8bKPRKAc9bCZXyNbpdBiN\nRlwuFzqdjuXlZSKRCOl0Gr1e/1DnMLFftupJGgwGSktL2bVrF8XFxfT09Mg2p5lM5qHxlqtTHJtB\nnIljx47R1taGXq9nYWFBDrNIpVJYrVaZGhDhvK08s/BKn3zySUpKSuSEsUuXLknjpby8XA6h2KoH\nKd6j2+3m5MmTeDweqSTu3r1LKpWiurqaTCbD+fPnGRkZ2ZbCMxgM1NbW8hu/8RucPn1att8VffIP\nHjxILBbj9u3bLCwsbPnCFGd59+7dfPvb36ajowOLxcLg4KAcvuPxeKTS22qHOaGUtFotp0+f5pln\nnpHvcmZmRrbv3bVrFz/72c/kmMatRInE/rDb7dTX1/Pss89SUlICIJX/nj175JjWnp6eLZ0V8Uc0\ngNmxYwcnTpxg7969aDQaed6Lioro6uqSkbSN0iSrCZZqtRqTycThw4c5ePAgFX8/rlbMSM9mH4wp\nvn79+kPT19aTC8gul6LJzunTpykoKJDhb5H2cjgc5Ofn82d/9mdbXl8Am81GSUkJTU1NtLa2Eg6H\ncbvdUp+Iudfb7Ywn8IVS1Fqtlo6ODp544gkaGxsJBoNMTk7S39+PyWSiqakJeBCC9Hq98nJwOByb\n9hoWI+kqKytRKpX4fD7Onj2L2WyW3YzERCmhoO12u5x5vRFKSkooKytDrVbj8/kYHx9nfHwcm80m\nB5enUilMJhNWq5VwOCzzkhtBjKEsKyuTAzTu3buHxWKhqqpKbhKAgoICVlZWZHh6vTGGAmKSjMVi\nIRaLcfXqVan8CwoKMJvNqNVqOaZyYWFhS/lfccFbLBay2Sxer5fR0VHggRdvMBjkBCan00k4HN5S\nCFUcYqPRiNlsJpPJyEEiiURC5nuFfDHrenUf340g5pOLIRHRaPQhr1wQqYQFLfrGb8WrtlqttLa2\nUlVVhVarZWJiQho/Qjmr1Wrp/QsjbyuXsclkoq2tDbvdTjqdprOzk4GBARlRUSqV5OXlEQwGtxym\nFoZLQUEBtbW16HQ6hoeHuXbtGl1dXcCDM2c0GikoKJBznbeiUIXh2tDQwPHjx0kkEgwNDfHee+8x\nNjaG2+2mtLSUhoYG2Ud5qxebUqnE5XJx9OhRjh8/Tn5+PhcuXODSpUsEAgFMJpMcFSvGxG5lMIfY\newaDgRdffJH9+/djtVrx+Xy8//77LC0tUVhYSENDA6Wlpdy/f3+zblRSrjBcnE4n3/jGN2hpaUGj\n0Tij/QIAACAASURBVMj7SaVSUV1djV6vJy8v76F9shk0Gg1Wq5WOjg5OnTpFfX09mUwGn89HX1+f\nJCCKdOBaw4zWWgdxHvbs2cOhQ4c4ceIEpaWlAMzOzuL3+9HpdDgcDsrKyvB4PPT392/KZxDdyYxG\nI7t37+bFF1+ksrISo9FIJpNhcHBQtnduamqitrZWGuUfx+oUqJiBrtfr2bt3L263m927d8tpg7Oz\ns6ysrABw7NgxGXFYb28I2aKts0qlkjOq29vbZVvrkZERJiYm5IAUkbJ8lIqML5SiTiQS9PT0sGfP\nHjweD2fPnuWNN95gbm6O6upqgsEger2edDrNwsKCPDyxWOyhsWZrIZ1Oc/nyZUwmE8lkkrNnz/Kj\nH/0Il8uFw+GQ02CE55GXl0cgEJDElvXkZrNZJiYm6OzsJBgM8uGHH/LRRx9JxVxQUEB1dTUtLS1o\ntVrKysoAmJqa2vSw+Xw+Xn/9dSKRCBcuXKC7u5twOIxKpcLtdtPS0sLOnTtxOp3U19eTTqcZHR3d\nUr734sWLUuFMTU3R1dUlSRo2m43f/u3flrO5m5ubZc5wM4jQ9htvvCGHO4TDYeLxOBaLRYacCgoK\n2LVrF2azmYmJCTm0RKzpWuucSqUIh8N0dXUxNjbG1NQUPp8PpVJJeXk5DQ0NWK1Wjhw5wtzcHIuL\ni0xNTcmUyEZGgNFopKamBqvVyvnz5+UsaLVaLWedezwe9Ho9wWCQ+fl5FhcXZWvK9aBQKOjo6ODI\nkSMYjUaGhob44IMPSCQSkgTmcrmwWCzo9XoA7t69u6VpOxqNhurqampqaojFYrz66qu89tprzM/P\ny/Cy1Wqlvr4evV4vPcvNxjuKPXD8+HEMBgOXLl3iT/7kTxgdHZXpIJ/PR3V1NS+99BLnzp2js7Nz\nU2MZkGmh73znO7S2tvL7v//7dHZ2srS0RCqVkpfo4cOH+da3vsX58+dZXl7eknGh0Wh4/vnnefHF\nF9Hr9bz22mv80R/9ESsrKygUCqxWKy0tLTz22GMolUrm5ubkBb2RXKE89u3bx+HDh5mdneVv/uZv\nOH/+PH19fWi1WsrLy4EHF/29e/eYn5/fUK5YZzG295/9s39GbW0tg4ODXLp0iXPnznH37l1cLheP\nP/44J0+epKGhgTt37mwqV6QQGxsbef755/m1X/s1zGYzPT09/PKXv+Ts2bMsLCywb98+Tp48SW1t\nLe3t7XJOwUZyBemvoaGBP//zP5de7tzcHH/2Z3/G4OAgsViM4uJi/uN//I80NjZSV1fHlStXNtx3\nKpWKgoIC2traOHPmDO3t7ZjNZkZHR+nu7qa/v5/Ozk4MBgOHDh2iqKiIgwcP8vrrr286g0Gv1/P4\n449z8OBB6urqMJlMvP/++wwPDzM5OSmV9Z49e2hra6OiooLi4mIGBgb+f/beNLjN8zobvrDvO0AA\nxMp9l0iJ1C6F2iUvqtc4tptOk0nSpJM27XSaZpo/zY9OO9PmTTKxm2Rix4njsWM7SWV5kyzJ2kVb\noiRKJMV9EcEFBEgCIAESBAng+6HvHIMyAVJK0tf9Pp0Zz3jTzYf389z32a5zXTnXlUgkXD1sbGxE\nIpHA1atXGWMhlUoxMjLCetUejwf79+/HCy+8wKqBq7XPlKNOpVIYGxvD1NQUJiYm4PP5WNmqoqKC\nkafkUBKJBAKBAPdmc2UhqVQKPp8PN27cgFgsRltbGxQKBQwGAyOI1Wo11Go1pqenoVKp4Pf7PyXw\ncKel07c1VY8ePQqhUIiWlhbWcaayMQDuVxuNRlbQyhVZpdNp/oCOHDmCkZERvsyovETAsXQ6zZrJ\nFHGvZNPT07hw4QKUSiVLZhIKXCaTIRqNQq/XIxKJ8GWV2e/Jtc+Li4sYHBxEPB7H5OQkl38ogo3F\nYnyZULS7Up+aMs9YLIaxsTFMTExgfHwc8/PzLHIiFotZnjLz3VBvOVtgRO9CpVJBKBSir68PkUiE\nASLEHU1gFlIFo0szm+Y3re12u6HT6SCVSlnKM51Oc9ZRVFQEl8sFtVoNuVyOcDjMVZ9sJWX6fR0O\nB5RKJUZHR9HZ2YmZmRnu0xIQh3r6RUVFePvttxGNRnNmqdR2qa2txcLCAtra2uD3+1lYhsqISqUS\nVVVVSCaT6OrqQjweX1XWVFtby8h6kulMJBIsI0ua6nl5eVAqlVyBymWU7VFA5PP5cObMmSUYCGpl\nqFQqVFVVQaVS5Vwzc12z2YwDBw5gfn4eFy9exDvvvIOhoSEGjlElLj8/n3XSV9oLqnbs2rULW7Zs\nwcjICA4fPozTp09zlkfiD5nArVzVMnpeiUTCLUSTyYRwOIzXXnsNly9f5nJxIpHgdz0yMrJipkc9\n54KCAhw6dAgWiwUzMzPo6OhAU1MTLl++jGg0yi0qpVKJdDoNq9Wac23qddfU1GDPnj3YunUrDAYD\nTpw4gaamJgSDQQwNDWFmZgYSiQSJRILFPHLddVSBs1gs2LRpE7Zs2QKtVov29nacPHkSYrEYkUgE\nwWCQ73m66zOrlcsZydOWlpaiqqoKbrcbH3zwAeucU8WF8E+ESZJKpSuuvZx9phw1KY9otVqEw2Eo\nlUro9XouK1mtViwsLEAuly/RCO7s7OS+cibyO9Po3w0NDcFgMCAej0OlUiE/Px9KpRJOpxM2mw2J\nRIKdlsPh4HEO6jstt+7CwgJ6e3uh0WgYTUqk+Ha7HRaLhUun1Nsh2UgAWfvf9HNHRkaWoFRJ4zoT\ntZgpvJBJRJ/N8c3Pz3PZeHp6mvs+crkcZrMZOp2OkfQTExOMNiUQVS496mQyyeVjmnGmMqnVaoVc\nLkc0GsXU1BTrCNMFs1IpmS5FQngLhbe1dl0uFwwGw5KxL8rwaAQtl6OWyWQwGAyQSCSIRqOsfGY0\nGlFYWMj6zgqFAqlUCnl5ebDb7VymzJWtFxcXQ61WIxwOY3h4mDM0u93OlRyaRBAKhVi7di3kcjkS\niUTWbFIgEEAul6Ourg7JZBLDw8McyOn1egYfUTnfZDLB5XKhv7+fQUTZnpeAl8XFxZicnMTNmzf5\n+yLBD7psFAoFKioqYLfbV1VdMJlMqK+vh0qlwtzcHHw+HzsMahFl4gOUSuWKQTitrVKpYLPZkEql\n0NbWhkuXLvE3Qt935rjMakGMMpkMdXV1qKmpgc/nw5tvvom+vj6WbAU+0TSPx+OMVM+1NvWl16xZ\ng+3btyMvLw8vvvgijh49ikAgwGuQbjxpsa8U4GdOMOzatQsOhwOxWAzHjh3DBx98gMnJSW7ZZLaG\nqLWVy5RKJWw2G/bu3Ytt27YhGo3igw8+wPHjxzngSqVS0Gg0jOVY6ZsAPgFbPvTQQ6ivr4fVasXE\nxAReeeUVxlpMTU2xWiCBMFdCksvlcuTn56OsrAybN29mVPfx48dx69YtqFQqDhL1ej2Lz8zNzeV0\npjRp4XK5cODAAbjdboRCIXR0dGBsbAwymQzBYJDfISUisVgMkUiEZXPvxj5zjjoajaKlpWWJkzSZ\nTJBIJFwupl6y0WhEXl4eVCoVR43d3d3LjnSkUil0dHRAr9ejs7MTsVgMOp0Oer0eUqkUiUQCw8PD\niMViPJpls9mQTCZZ+DtbuYLma0dHRznyt1gs0Ol0sFgs0Gq1DAwZHx/HzMwMz8+Sk10uYyAtY71e\nz4A6ysJKS0vhdDq5HOz3+3ltsVjMc7TZnFM8HodareaeNo1V5Ofnw+PxMMI3Go1icnKSo3iKOrNl\nOBS4AJ9cGgqFAkVFRXA6nawWRGhtqohQ6TKzCrHc90HqPSKRCBqNhtsJZrMZKpUK6XSaAXsUgFA1\nI1uvOp1OIy8vj/u8VM6TyWTQ6/Wc2RmNRq4IyOVyWK1WKBQKdHd35+zZm0wmLCws8OE1mUw8Hudw\nOHg0KRaLcZZK5eVs7QZyenROaJqAAg6LxQKpVAqHw4FEIgGtVguj0Yj6+nqcPn06Z29WLpejtLQU\nOp0OLS0tGBwc5OoCVVbkcjnjEVQqFYqKijAyMrIiNsLr9cLj8UAkEmFoaIgrQplVq1QqhZmZGYhE\nIpjNZh4zWwm/YLPZoFQqEQgE8OGHHyIUCi0B/dHdQfgCes+5TCwWw2q1ora2FmazGW+++SZ6e3uX\nzL1TVicSiRAOhyGTyVZ0enTZb9myBYWFhZidncUHH3yw5HelXjCdISpN56qGUFWpqqoKXq8XMzMz\nuHDhAl5++WXGElBFUiaTwWw2syzjSkFWYWEhGhsb8bnPfQ5msxnHjx/Hyy+/jN7eXsRiMa6KUEWH\nqgqhUCjrd0FTPlu2bMHGjRu5OnTx4kV0dHRw4kUBqF6vR21tLY9PZqtm0TTEvn37sHnzZhgMBh4L\n7evrw/T0NLc1Cey1adMm7nkHAoGseyESifDoo4+irq4OTqcTyWQSwWAQyWQSRqMRc3NziMfjfF68\nXi8UCgVisRjm5uYQDAazrp3NPlOOOp2+LVp/5MgRBINBDA4OQqVSIRKJ4ObNm7BarZiammIkZzKZ\nhFarxZNPPokHH3wQTz31FP75n/8Z77///rLZZGtrK/crp6enUV5ejvHxcZjNZlgsFvT19aGlpQXh\ncBjxeBxVVVXYsGEDnnnmGSiVSnz+859fltQglUphaGiIIz6z2Qy32w2v14uKigqk02n8/ve/x+jo\nKAKBAGKxGEpKStDQ0ACbzYYPPvgAN2/e/NRBSafTfPlKpVKe+S4qKsJjjz0GsViMQCCAgYEB9PT0\n8Fw5lZF8Ph8f/DujTxox0mg0kMlkqK2tRVlZGUpKSlBYWIhoNIqbN29yVi+Xyxm1rdVqub9HgKs7\n98Pj8SCdTsNisUCtVmPLli0MOInH42hra4NcLkc6neYZeY1GA6FQiHg8jhs3bixZk7KfgoIC1NbW\nYuvWrQiFQlAqlVi7di1fYjS+F4lEYDKZYDAYsGHDBs5IWlpaPnW4xWIxKioqoNPpIBaL0dDQgOnp\naWg0GpSUlCAvLw+jo6O4desW/H4/DAYDnE4n1q1bx0HAxx9//CmnSsFHMplk3XGaLigrK+ORQ2qX\niMVi7Ny5E9u3b0d9fT1MJhOOHDmC9vb2T61LF4BUKuXZz5mZGeh0OhQUFHAvvaurC6FQCGVlZdBq\ntWhsbMTbb7+Nrq4ufp4717bb7TxeeOzYMQwPDzMTHFUU5ubmMDIyArVaDZFIhO3btyMSiSwBeS5n\nO3bsgMfjwdTUFH75y18ilUoxsQdVNgida7FYUFBQAJ/Px9wG2Zy1xWLBgQMHEI1G8aMf/QgnT55c\nMvcvFAq5X280GtHd3c1OMNfzOp1OPPHEEzh06BDi8Th+/vOf8/w/fTsmkwkbN25EY2MjLl++zO8y\nm7Om7+KrX/0q9u7di1AohOeffx59fX1L7haXy4X9+/fjySefZA4JKoMvZ1KpFAaDAc888ww/y+9+\n9ztcvHiRgxbgdiBWWFiIv/3bv4XZbMb169dx9erVrHsAAFarFf/6r/8Ku92OeDyOl156ifeCkgGN\nRoPy8nJs3LgRjz32GCYmJnDmzBlcvXo1a3CxY8cO/Pmf/zkaGxsxNTWF5557DtevX8fQ0NCSta1W\nK5566ikeFQyFQjhx4kTW7+Fv/uZv8Oyzz8Lr9UIsFuOHP/whuru7cevWLUxNTXEyV1JSgurqanz9\n61+H3W5HIBDA4OAgB6R3ri+TyfDv//7v+PKXv8ztqQsXLqC1tRWFhYXo7e1FOBxGfX09qqqq4PF4\nsGnTJgwODvJEBpHk3I19phw1cNs50UWeydik1+vR3t6O2dlZJreYnZ2F3+/Ha6+9BpPJhEceeQQH\nDhzA+++/n7WU7Pf7AYBLw0ajkXuHVL6ZnZ1FNBrFjRs3oNFosGPHDu6HZPvgCORFLFYlJSWw2Wxc\nOg4Gg5iamuIxhUgkwuQlbrf7U46JTCgUIpFIwGQyMaCprq6O2aGol0UjG3SJO51O7p/dunVr2XWp\nHEz9y7Vr18Jut0MulzOQivpdJpMJsVgMNpsNGo0GZrMZH3300afWpd4plXzpwnW73VAoFNxfouiY\nQDipVAoKhQLz8/MwGo3L7gfNtBcVFSGZTLLgvEKhgFgsRiqVgl6vh0KhgMPh4EqEw+HA6Ogo8vPz\n0dHRsey64XAYY2NjqKqqgsVigcFgYCdCvVcKTIqLi6HRaBhp73a7s5IYSCQS9PX1obKyEnNzc5DL\n5ezsKNsYHx/ncQ4KSEKhEAYHB7NmfUKhEOFwGKOjo5x5EaVqLBbD9PQ0YrEYZmdnOWsi9iVq7WQr\nqVOlh6obpBNMiHQA3GNfXFxkUqCVZmWpIiSTyeD3+7l/TNWiTISuXq/nZ16JmUsgEPC0QjAYhN/v\n/1QGTmtS26u/vx/hcHhFRLnRaERBQQFUKhX6+vqWIOepsuB2u1FdXQ29Xo+Ojg6EQqEVKwBSqZQJ\nnYaGhtDa2rpkXaFQiJqaGmzatAlGoxGjo6M815/tmUUiEex2O4M1r1y5gu7ubp6jB25/j06nE1u3\nboXT6YTf78elS5fQ3t6eM2DR6/UoKirCwsICenp60N7evqQSKBQKUV9fj8bGRuzatQsejwdvvPEG\nzpw5g/7+/mWrZAKBAOXl5XzmPvzwQ7S1tbGTpszfZDLhoYcewrPPPgur1YpQKITW1lZcv3496+QM\nEU7pdDpMTk7ixo0b8Pl8CIVCiMfjEIvFcLvd+MIXvsDVPiKuOX/+PMLh8LLrajQabN++HWq1mimA\nW1tb0d3djcXFRfh8PgiFQjzzzDPMnZBMJtHR0YFLly5hbGzsf39GnTmjl0gkoFQqIZPJuHE/PT2N\nUCiE6elpnm1eXFzE8PAwent7oVKp4HK5lgUY0McP3O610AErLCyEQCDg7JPGekhsnUrP5NCXK6tT\nhExgNK/Xi9raWkilUgQCAUxNTQG4fZDEYjEWFxchl8vhdDpht9vh9Xqz7gcN0hOpwIYNG1BXV4e5\nuTnEYjFMTk5CpVLBbDZzRqLRaHg+VSKRoL+//1NrS6VSqFQqOBwO2O121NfXo6ysjEs0CwsLsFqt\nPDJhNBo5sBEKhTzPudwz0yyr2+2GyWTiikUikcDCwgIjXdVqNZxOJ1dSCBy4HMiHkNk6nQ5ms5kp\nBclhJJNJyGQyBgoVFBRAr9dDJBJxgJRKpZY9JIS2JsdD6H/6bwTqsVgsvL5Wq0UsFoPf70cgEFgW\n0UoXMgFWtFotKisrYbfbAYDnb7VaLbRaLSoqKlBUVIRQKIS2tjZmb1vO6DsKh8OM/KY2yOTkJFKp\nFJe8KyoqYLFYIJfLGYiX7YKjM0JZs8PhgNVq5TIhvT+NRgO32w2JRIKZmRn4/X6MjIysCFLTarVc\nZtVoNBxgkYOyWq1Yu3YtHA4H0uk0gsHgiuA3qgxl9skzZ9JpVra0tBQWi4XLj6tBk3s8HuTn5yOR\nSGBkZISDClqbxsE2btwInU6HQCDAvepchBlGoxEWiwWxWAwdHR3w+/18RwkEAqjVajz66KPYsmUL\nkskkj5PFYrGs60okEmzatAmVlZU8906IaAJsORwO7N+/H4899hjS6TS6u7tx7dq1FadQTCYT9Ho9\nBgcH0dvbyw5JoVBAoVBAqVTi6aefxrZt25jH/ty5c7h58yaCwWDWIMDtdqO8vBxisZjZGwm0SFWu\nAwcOYMOGDbDb7ZicnGQWuM7OzqxtMpfLxXwZnZ2d6Ojo4Mqg3W5HdXU1Dhw4gNLSUjgcDoyPj8Pv\n9+PixYv46KOPsgadBE4Dbk9n/OAHP0BfXx8TXe3btw9msxnr1q2DTqdDPB7npK+trQ0tLS0rThos\nZ585R009UBqRstls/EsTYvZOjtp0Og2XywWBQMC93+XWpvIoXboPPvggzGYzZybUh1MoFABuO7N1\n69YxQGU5x5QJ7tBoNCgoKMDmzZtRVVXFjjSRSMBgMDAd6tTUFD73uc+hvr4ei4uLyzrqzEtGKBSi\nsrISjY2N2LJlC1QqFXp6enje2+VyccBCPU5yBmfOnFn2g6O5QqInJfq/TEIV6uGbzWZMTU3xHKff\n78ePfvSjZS8MyjKof+rxeOB0OpeAbkQiEbRaLfd5KegiNOpvfvObZddVKBT83FRSn5mZYVAM7S+R\nOtDvSRH6sWPHlv02RCIRYrEYAoEArFYrNm3ahOnp6SUAQtoLChqTySTGx8dx7do1nDp1Kusll06n\n0dfXh7y8PC6X9/f3Y3R0FD6fj2ddCwoKUFRUBJ1Oh6amJpw7dw6jo6NZI3uhUIjp6Wn09PRg7969\n2L9/P8rKyriMSYAvIvkghquBgQHuJ2Zbd35+Hn19fdi9ezd2796NhYUFXLlyhTEjGo0GFosFa9eu\n5Yz61q1bS8qr2dYmZ67X61FfX4+zZ8/ymZbJZMjPz0d1dTXy8/MxOTmJiYmJnOOR9P4AIBwOw263\nw2q18uwtAeCcTifKy8uh0Whw48YN9PT0ZAWektG3TEHxxMQEo3fpPqmvr8fGjRsZC0BZdy6nRxSW\nsVgMsVgMIyMjAD4h5SCCk4aGBigUCkawE8Aum8lkMs72qGJBQYpAIEBdXR0efvhhbNy4EU6nE9ev\nX8f777+Prq6uZVtYmaZSqRiMR9MwxcXFTKZSVlaGXbt2cWB69epVfPTRR5iamsr5/igRSSaTCIVC\nUKvVsNls8Hg8ePDBB2GxWGCz2RiZ3tHRgXfffRdNTU05182cDLp58ybUajVqamp41n379u18l/h8\nPvT19eH06dM4deoUJiYmsgZDFNwtLi7i+PHjGBgYgFKpxIMPPoi8vDxmNhMIBGhubuZnOHbsGILB\n4KppmD+1T3f9J/6Elgn4kMlkkMvlWLt2LbZu3cpO4sqVK0gmk3A6nRgZGYFSqcRjjz2GhoYGnD9/\nHv/5n/+ZdW1aNxaLwWAwwGq1Qq/XQ6PRYM+ePXC73bBYLBCJRBgcHMT27dtRXFwMqVSKU6dOLdsb\nyhysj0ajmJmZgdls5gve5XJBJpPhwIEDPFeXTqeZxee9997D+fPns+4JfYyhUAgKhQIzMzOYmZnB\n/Pw8s3QR81U8HodWq4VEIsGxY8fQ2dmZlSuZyrl+vx8Wi4UBH1T6D4fDDHqjbKq/vx9isThnaSid\nTmNmZgZtbW1Ip9MwmUwYGxvDpUuXsLCwwJmtxWLhi5iAa+3t7YjH48vOoFJ21d7ejnfeeQdr166F\nRqNBIBDgvU8kEkwzS2XCiYkJ9Pb2MlBxuUNCKNvu7m40NTVx1kjVHPp7jUaDoaEh/PrXv8bAwACP\nEWZzeul0GlNTUzh16hS6u7uxd+9ebN++nWkGKysreeRldHQU7e3tuHbtGt59911MTU1l5RSn35cE\nSQ4dOoSysjK4XC7mXJZKpdxTvnbtGi5evIgPP/yQx6iyXRaJRALBYBBHjx6F2WzGs88+y3PqsViM\nA8OSkhKk02m88sorOHr0KM+j5rJkMokTJ06guroaGzZswJ49e9DU1IRIJMJVo3/8x3/E3NwcPvzw\nQxw5coSxKNmMSufBYBDd3d3YvHkzHnnkEahUKty8eRMWiwUNDQ14+OGHYbPZ8N3vfhenT5+G3+9f\nEY0sEAiY1W12dha1tbXYtm0bQqEQtFotGhoa8PWvfx2pVApHjhzBiy++iKGhoRXL6SKRCDMzM1Ao\nFEin09i7dy88Hg/Gxsawdu1aNDQ0cJb3f/7P/8E777zDDi+XyeVy5qEwGo34yle+wq0Lg8GAdevW\nIZ2+TUL03nvv4R/+4R9WxUtOFc5kMomioiI8/fTTOHToEDweD+RyOVdFjh07hg8//BDnzp1j9baV\nAqGuri50dHSgqKgI//Iv/wLgdpmdqmqhUAjHjh3D5cuX8e677y5Bqufa50uXLqGwsBAmkwlPPPEE\n9u7dC7lcziIcg4OD+MlPfoJLly4hFArxBMxKOgnz8/O4ceMG7HY7vvWtb+GLX/wiAPCIqFAoRE9P\nD770pS8BAO9tKBRi+uF7sc+cowbAvTDaOELOEmsM/X9arRYWiwUulwvz8/NoamrClStXuKd259ok\nACESiTA7O4tQKMTobLqQCfqfn5+P8vJyzoo7OjqW5dCmZyGmqXA4jObmZlRVVTFTFgHiaOSHnAkd\nmvb29k990HfuxdjYGM9XEhp9YmKC507pQDocDuZAHxgYYLrLO21xcRHT09OcqZ44cYKBXIQgp4yd\nZqrn5uY4i802ekKob6p+TE9Pw2QywefzYWhoaEk2R++W2L5oHCzbt7GwsIDx8XFcunQJg4OD0Gq1\nEAqFmJycxPz8PFMXZhKczM7O8mWU7dKgrDCRSCAWi+Gll16CxWKB1WrlP0P90rGxMQwNDXGJNdcI\nHH1z0WgUt27dwltvvYWuri5s3bqVy4USiQRDQ0Po7u7G8PAwfD4f9+dyXXIUeEajUfzqV79CY2Mj\nPB4PNBoNBAIB4vE4E/YQo1hfX9+Klyft88TEBN5//30WcKipqcHU1BS3F0KhEH+Tw8PDq+JyTqfT\nuHXrFs6dO8dEQwQ6op7t5OQkWlpacPbsWbS1ta3o9DJR4hSQEWJ9fHwceXl5KC0thUQiQVtbGy5e\nvMjf9moyGxofU6lUUKlU+OY3vwngk9GfaDSKt956C6+99tqnwGC5njkSiUCtVkOn08FgMGDt2rV8\nP1Bl63vf+x4uXry4BFSVy+bn59HR0YHu7m5UV1dj3bp1PBYK3M4Gh4eH8d577+Hw4cPcUljNe/P7\n/WhpaUFhYSF0Oh1z+FOloaurC8899xz6+vp43cy9WO6+SKfTaGlpweuvv47GxkYevyU++ebmZpw8\neRLd3d0YHBzkFmKu8VCyd999FxKJBOvXr4fD4UBHRwdXR1KpFH72s5+hp6eHFeDoe8hswyy3LwsL\nC/jFL36B4uJirvJevXoVExMT3Lemcj+1nwgPlYnmv2uBnHtJw//YJhAI0hl/z+M3VqsVJpMJu3fv\nxrp161BSUgKpVArgE1Yf2tDe3l789V//NfeRltsI6hHTsPyuXbtQU1ODiooK1tkFPpFrU6vVcL50\nQwAAIABJREFUGBwcxNmzZ3Hy5En09PRkJYAntKpOp4NEIsGePXvg8XigUqkgFot5qB4Ag3va29vx\n8ccfY3h4eNkMlcprEomESVlsNhvUajVMJhMAcIkWAI+rJRIJNDU15eTYJTpMGuchMn3q+1OGSqV9\nm80GAJibm8PU1FTWvhM9s0ajgU6nA4AlfNOZUSXtm1wuZ/KWTGKKO41AU2q1mnvTNHZFIyZEVkAt\nA6IXJZBUNgAV/UX7TQFE5p8TCD6RBqQMYzUXHX3PFBQR3zn9PqRMtri4iHg8nrMPudxz04x6fn4+\nnE4nV34WFha4HDs+Po5IJLIqxShaV6VS4amnnkJ5eTmvG4lEMD8/j3A4jK6uLrz33nurCizI5HI5\nvF4v9u3bh71796KwsJAvsXg8jvPnz+PcuXNobW2F3+9flXoWYTlsNhteeOEFFBQUMKhzbm4OoVAI\n169fx8mTJ/HWW2+tSl2O1jWbzdizZw++9rWvweVy8WRCMplEOBzG97//fXz44YdcwVmNQ6V74t/+\n7d+wc+dOKJVK/j6i0Sh6e3tx4sQJ/Nd//Rc/62r2VqlUwuv1oqGhAQ899BBqa2sBgJ/rpZdewqlT\np5aU51d7/+v1ehw8eBCbN2+Gy+WCQqHA2NgYJicncf36dVy7dg1dXV1L1lzN2gaDAQ6HA06nE5s2\nbQJw+44htkQS8KF173Sg2X6G0WiE2+3mc0GgW+KjoH+m74DODI3c5QICGo1GbN++HSKRCMPDwzwW\nS20RGj2l/58Sx1QqxXdIxvpX0ul0/Ur79Jly1JmlZQIqEEpWp9Phscceg8fj4X7b+Pg4BAIB+vv7\n0d7ejqamphXRliQ6IZFImLbR6XTC6/XC6XTyxRCJRBCNRuHz+RAIBJiZKZtlIqSpz63RaLjHSyMb\ns7OznJEkEgn4/f6czEv0zDQGRP04KmvSBU9Ojy6RWCzGPNqr2YvMfv+df0+gKPo95+fnc9KJ0tqZ\nIyqZvx8dOLpgyegw5irxZYICM9fLNPq96HDQf19tFHtni4P+mX5uZkR/t+cnMyigvc0EcN2LpnFm\ncEsjOnQ5UNtkpb5ptnUNBgPMZjOKiorg9XqRTCYxNjaGmZkZDA4OrqrUm2kUnJAgxF/+5V8y/31v\nby9+//vfo7u7m8/EaveXAqwHH3wQhw4d4ln0zs5OXLhwAU1NTRgYGEAwGLyrd6ZUKmE2m7F+/Xrs\n27cPu3fvRjweRyAQwNmzZ/HjH/+YwZd3s65UKmW2rNraWrhcLoyNjaGjowPHjx9He3t7TiKd5Yzm\nl/Pz8+H1erFjxw7o9Xoed3z33XdXHazdaWKxGF6vF1qtlln2EokEfD4fgxNXQ118p1HrhyqadDdQ\nVYfOAz1vtkz3TqP+M1UUM+8fqqxmZs+0bq5sOnNtqVTKa2Yyx9H6mf+O7uTMn5Fh//sc9R9hnXtq\n1P/fWve+3bf7dt/u2/+cLXeX/zHu9zvXuNP557BVOerPVI/6D7U/lTO976Tv2327b/ftf79lw9X8\nsdddhYO+K1tZveG+3bf7dt/u2327b//X7L6jvm/37X/YMnvTy438/SGWiT34U9gf+3nv2327byvb\n/6dK3/fts2l/ih4/gdUyRypWi47NZuSEHA4Hk6AIhUKEQiEGut3L+rSuTCaDx+OB2WyG0WhEMBhE\nX18fz5bf6/NnMuOpVCpUV1ejr6+POY1pXO1ejYIKAv0QsxiNwv2hlgmGowAjF1XmvaxPf/2h30iu\n9f9Yz3vn2v8Trbf7+J5P7F6f+U/5u/7/0lH/KTaULptMNPAf6+dkZl53oh/vZhRiuXUzx9IIhZ2J\nWkyn03c1xkFGKHESXKDZ0EwUJyFl72ZtQreTgAYRXtCstEAgWMLHfDdGI2vFxcVMcOLz+Zag0WlW\n+26Mslyj0Yj8/Hzs2rUL4+PjrKRFZCwA7hqZDSyV6dy0aRPvudFoxMjIyKrHkXI9u16vZ0nOZDKJ\nvr4++Hw+5pi/V6OpA6VSySpio6OjPNf+h54fkUgEtVoNi8WCxcVFjI6O3jVCO5cRERMx+v0xnhn4\nREuZENCZjIx/6Lo0dkjBLQmT3AsanIxGD+k80sRI5nm/l2enO0mpVLIMciqVYmWuXPK1udYkSlW6\nR2juWSwWL6Gnvps1gU8SiJKSEshkMoyPjzNfRSKR4P2422f+zDnqzFlWchoA+DCQkaavQqHgGd1M\nyP1yRoT/JF5AHxdwmyyAJC3JyRADWDKZzOmsaJaV5ntJak4mk7FgAT0zvaCxsbEVHSB9pFKplCUd\nxWIx9Ho9U5rSLC85EJoTXOlQSCQSJq+gvTSZTLDb7QgGgzw+Rs5pampq1Q6bxm9I9Usul6O6uppn\neUlnmchPVjs6RN+FzWaDyWRiedNEIsEkFvPz8xgbG2MGo9XOy9IInNFoZA52s9kMiUTC4zckx5mN\n4Syb0aVgNBphtVpRVFTEmshyuRwSiQQSiQQA7inAIEGK+vp67Nu3Dzdu3EA4HGYBmtXoOWczqVSK\nvLw8PPTQQygoKEAqlcLNmzf5W6TRk3sxgeC2cEtRURFKS0tRUVHBI5b0Xd8rkxOd7draWuzZswcl\nJSXw+/14/vnnEQgE+Fzfi5Gz02q1+Pa3vw2DwYDu7m58/PHH+Pjjj1dF/pLtmWnELj8/H1/+8pd5\nDr6rqwvXrl37gwIuukccDgfPr09OTiISiWBwcPCe9pqcnlgshtlshkaj4W+ZFNYocL5bIwIYlUoF\nrVa7hLwlkUhgYGBg1e8ws2UjEomQl5eHvLw85n7Pz89nWl7iVV/turQ2jUaWlJRgz549mJiYwODg\nICQSCYLBIPr7+xEKhf73c33TCyeRiG3btkGlUjHdp0AgYAai9vZ2XLlyBeFwmIUibt26xao/dx4U\noVCIgoICbN26FVVVVSxFaTQaeTb58uXLaG1txcjICPr7+znSJKWubCQcUqkUf/EXf4E1a9YwB3VB\nQQGTGEQiERw+fBg9PT3o6upiIY25ubms0T0dLI/Hgx07duChhx6CVqtlByWRSDA9PY3h4WFm9yF2\nnLm5uRVF1a1WK+rr61l+kpSu6JDdunULLS0taGlpQVtbG+LxOAcDKwkvkCRoUVERK1GRChQRRfzu\nd79DW1sbcz+vJjMTCG6LFRw6dAgulwtGoxESiQTj4+MQCoU8e3n06FFely79lUwoFMJgMDC9ZSqV\nQn9/P+LxOBwOB+bm5pi5LlOLeLUznXa7HXV1ddiyZQvGx8fR09ODiYmJJZkjESLcbRBQWlqKxx9/\nHPv27YNEIsGFCxfg8/k+JeJwN2tTkLhr1y584xvfQFVVFYRCIV555RVWY8rkvb7bqgiR+PzkJz9B\naWkpC9iMjo6ycEjmiMvd7IdUKoXX68WhQ4fwT//0T0in03xW3n33XVbkupcsT6vVorCwEF/4whfw\nyCOPwGw2s6KaWq1mWdG75XQWCG7ri69duxZPP/00tmzZArVajcnJSfT19eHMmTO4desWJiYm7irA\nIEeqUCjwyCOPoLGxEZWVlZDL5ejt7cWNGzfQ2dkJoVCYVcHvzvXonVAlx2g0ori4GJ///OdhMBhY\nwe3atWssHTw5ObniftDMM/0Mm82G8vJy1NTUsHANVcwA4Be/+AUGBgZWvI9o7UxRpoMHD6KqqoqT\nPJvNBqlUCovFggsXLuDHP/5x1n3OdPo0+036BQ6HAw8//DCEQiHGxsZQU1MDh8OBdevW4fTp0zh/\n/jzLMN+NfaYctVKphNvtRkNDA3bs2IE1a9YwmxUxUen1etjtdiQSCbS3t0OlUmFxcZEdTDZ6NplM\nhl27dmHXrl3w/r8apZFIBDMzM5DL5UilUtBqtXA6nZyhEVn8wsJCVscnEongdrtx8OBB5OfnQyKR\nMCECMWfRR0dqS0QOkKvcRAd38+bNOHDgAAoKCpBIJFjwnDIxhUIBj8eDvLw87hem0+kVo/ry8nLs\n3LkTpaWl0Ov1mJ2dRSQSYVY2mUyGwsJCzM3NYXJyEuPj43yx5ToYlE17vV7Y7XZoNBrmFAduMxGJ\nxWKUlZUxc1o6nV4xw6HLnXiAScKSmItMJhM7W71ej1AoxMQGKx0Kckp0oQkEAoyPjyMYDGJ6epqz\nHKL8lEgknyJiyGVisRhGoxGVlZXIz8/HuXPnMDw8jFgsxt/uwsICB4x30w4QiUTYuHEjNm7cCI1G\ng2AwiM7OTu6rU2UK+ITwZbVry2QyPPDAA+xIu7q68NFHH2FychJCoRBqtfqu94KeWaPRYP369Sgp\nKUEymUR/fz+z6VksFpSUlKCzs3PVmQ2ZWCyGxWLB3r178eSTT2JhYQE+n48lcqurqzE9PY2BgQGW\n/VytiUQiFBYW4qmnnsLBgwdhNpvZeU5MTMBkMrH06d2UwClBWb9+PQ4dOoTGxkbodDr4/X6mba2s\nrIRer181CxzdO7TXbrebWeaIrIOCdYPBAIFAsCJl651ZqVarZd73HTt2YMuWLVyBWlxchM1mQ2tr\nK0KhEFf6cq2dydanVCqxZcsWbNq0CU6nk500BQlqtRpNTU2rItyhpEcsFkOj0cDlcmHz5s18jyiV\nSsRiMRiNRhQVFUEgEOC5557LuSatS/eGwWCAx+NBbW0tPB4P2traYLfbmYJ29+7dqKysREdHxz1V\nWz5TjlqtVmP9+vUc9ZHaDolBuFwupNNphMNhDA4OsjCBwWBYVdm7oaEBRUVF/GJOnz7N5UeSYRwe\nHsbo6Cji8Th0Oh1isVjO6FggEMBut8PpdDJl4cDAANrb27lsKBaLMTExwX1Iou5c6QPTarUoKiqC\n3W5HLBaDz+dDa2sr686SWMT09DRUKhWXx1fjmEiVRiwWY2ZmBufPn+dDQP8tHo8zXSnJExLNZ7b9\nIGcHALFYDFNTUxgaGsLs7CwMBgMrL2k0GpbOE4lECIfDOdelg0xtiUgkgpGREczMzHBp3m63c/lp\namqK11tNdkPfgcFgwOTkJKanpzExMcGZNABmnKMAbLVMTHK5nNWxFAoFhoeHmVecjJz03QCe6AJq\naGiA1WqFSCRCe3s7hoaGEI/HOWuUy+XMYLfavhtdauvWrYNWq4Xf78f169fR09ODRCLBYgwKheKu\nwGpE/Wuz2bBz504IBAKMjo7irbfewtWrVzlAdbvdCIfDKypy3bkfKpUKlZWVeOyxx+B0OtHe3o4T\nJ05gcHAQ6XQa69atYzGY1ZYgyUFIJBIcOnQIBw8e5CoLrU3aw2azGT6fb1XVgExnqlKp8Pjjj2Pv\n3r1875w8eRLp9G1lQAoSqUq3GkdNDq+iogLbt29HQ0MDxGIxpqenWT85kUjAbDbz+iQmlM0I9CcS\niVBTU4N169Zh3759KCwshFQq5bIxadi7XC7Y7XZ0dnau+O3R7yeXy7Fu3To88cQT8Hq9kMvlWFxc\nZFW4xcVF1NfXw+VycZCby6hlJpPJUF9fz/oQlHgMDg5ibGyMNbeLi4tzTk1Q1k8VAKFQiIqKCk5Q\n5ufnEQwGWRdAqVTi2WefZWrbe7HPlKMmAQrKXgYHB1mIIpVKsfOOxWIYHx9HNBrl0gOVhLJdGNTA\nJwrM/v5+XLx4kTfc7XZDoVCwzjLJJobD4Zy9znQ6zX1XklQ7fvw4RkdHsbi4CIfDAaPRCJ1Oxz9f\nq9VifHx8xZ4v8RRHIhH09fXhwoULGB4exuLiInQ6HfLz81FQUAClUgmVSgWj0cgqLSvZ9PQ0AoEA\nC02cPn2aeaYNBgMeeughGI1Gdl5qtZqFRHIZZfM+nw+3bt2Cz+fjjFyn08HhcKC2thZ2ux0Wi4Xl\nOFdjlM2PjY1hYGAAoVCIL1yz2cyqalarFXNzcxAKhatGJROHOMmQ3rx5E/39/az0RUGQ0WhEIpFg\nMv/VXJpUGlSpVBAKhWhpaWEO9kwKV7o4yDmtJrgg/XOtVouenh68//77XE2gy5qcKpXBVxPIUUnT\n4XAgFovh+PHjePvtt+H3+1m6T61Ww2w2w+/3M45jJaNLs7CwEFu3bkVXVxeOHTuG9957D8FgEEaj\nEQKBAC6XC1VVVejq6rqrIECtVuPgwYNwu92IxWL4xS9+gStXriCRSECj0WDt2rVYs2YN+vv7EQgE\nVrUuBYkSiQQ7duyAwWDA7OwsPv74Y7zxxhtIJpPwer0wGAwoKCjgEvJqnCmt6/F4sGnTJmi1WoTD\nYVy9ehWHDx9m9S+73Q61Wn1XHPAymQxerxfbtm3DwYMHmZv63LlzuHz5MgoLC2G1Wll9bzWjd+Sk\nKeNtbGzEmjVrkEqlcPLkSbS2tiIajUIulzOXe2ZLbaW1FQoFSktLUVtbi4qKCiwuLqK3txdtbW2s\nnOhyuVBeXs6B+UpnnLAyLpcLBQUFsFgs3H5qbm5GIBBAKBRCQUEBHnzwQTgcjmUFmO40qVTKJXW6\nf6ha88477zDAlSp7+fn5sNlsy4pGrWSfKUcdjUZx7tw5LnWMjo5yqbS6uhpKpRLAbd1ZlUrFTpIc\nay6Lx+N47733EA6HUVhYiMuXL0OpVKKkpITFI3Q6HWZmZiAUCuF2u3Hjxo0Vx0SSySS6u7vxwx/+\nEAUFBWhpaUEwGIROp+P+N/XYhUIhLBYLuru7cfPmzZzrkkN68803cfLkSaRSKUxOTmJhYYHBcBaL\nBclkEiqVCmVlZSxRuJKjTqfTOH/+PJqbm7nsPTk5ySVeyoT1ej0WFxdht9sZebrS5UNl7jfeeAMA\nuCJBrQaFQoHJyUlotVoYjUZoNJplZS2X24+FhQXcunWLe9qRSIT3g3rgVJ4EbnPwTk5Ofgoxv5yR\n+lY4HMa1a9dYEEIqlaK6uhpOp5PxAe3t7ejo6EB/fz/m5+dX5CZ3Op2orq6GVqvF2bNnMT4+jsXF\nRajVatawzRQ9efvttxEOh1cE4ZCucXFxMfx+P1577TV8/PHHSKfTzMlssVhQVlYGiUQCn8+Hy5cv\nw+/3r5jpqVQqPPPMMxAIBDhz5gxeeuklDA8Pc5am1+tRXFyMDRs24OrVqzh58mROzvrMZ16/fj2+\n853vwOFw4Lvf/S6am5tZp5w0x0tLS+FwOHDs2LFVlajpHX/729/GQw89hHg8jl/+8pc4cuQIBy2x\nWIzPilAoZAzCSusSNuLRRx9FWVkZWlpa8NOf/hTXr19HKBRiR5BMJrFnzx6cO3duVSVqWrexsRF/\n//d/D6lUildffRVHjhxBd3c3IpEIHA4HCgoKUF1dDZvNho6OjhUdCJWPH3/8cXz1q19FaWkp5ubm\n8B//8R+4ePEiiwvt3r0bDocDZWVlrIiXy6jV5nA4cPDgQXzzm99ELBbD5cuXcfbsWRw5coSlUD0e\nD5555hkIhUJUV1fj5MmTOfdBJpOhrKwM27ZtwwMPPAC73Y7Dhw+jpaUF0WgUIyMjmJqagsFggMVi\ngVwux5o1a6BWq3NqthOYdf/+/di3bx+XzN98800olUpMTU2xsiEBgAmwmysAoHdO4h8OhwMXLlzA\nhQsXuJ1F757kNUUiEaqrq6FWqxEKhXLu9Z32mXLUBPwhJSe73c7Zot1u5zKtzWaDwWCA2+2GXC5H\nf38/5HI5O+3lDkcqlWInBNwuK9fU1LCuLpVg6TDTiAv1kjPlGTMtnb5N+B4MBnnUhhy9RCKBw+Hg\nj4ueAwCuXLnC4xzZLrdkMon5+XmEQiEedRAKhdxLt9lscDgcfEkolUpGia+U6dFoA0lbAmAlK7PZ\njLy8PO7NT05OMkiPMr9ca1NlhJwr4QtMJhM755mZGdaspfGFlRwqZZzkVJPJJAdCFosFSqUS8/Pz\n/BeBnlZbKpTJZIwxoENO6j5GoxF6vR4KhYIdilwuZ/30XOsWFBTAZDJhfn4egUAAYrEYKpUKTqcT\nBQUFWLNmDQtpRCIRuFwuCIVCLr1nW1cikaCmpgYAuE+aSqVgs9kYtW6xWFBXVwehUIiioiLW0M4V\nzAmFQtjtdqxZswazs7Nob2/n1oTZbObeXl5eHrxeL2QyGVpbW7m8nsvUajW2bNkCq9WKVCqFzs5O\nlp4lxSvaX61WC7lcztKcK71DiUSCtWvXQiaToaenB0ePHuUzQ3+esn6FQrFqNLxIJILX68XOnTsR\njUbx6quvorm5meUcM0duEonEqipaVO3weDzYv38/ysrK8Prrr+PVV19Ff38/YrEYP5tAcFvBje6B\nldalNs7BgwdRUlKChYUFnD9/HocPH2ZJWGCpuAy1inIZjQBu3rwZu3bt4tL/sWPH0Nvbi0AgwFgf\num9DoRBmZ2dzrk1tlm3btmHr1q1wuVwYHx/ngDWVSiEcDvPvLpfLIZfLMTMzwy2j5YwwLSaTCRs3\nboTVakVvby+uXbuG6elpLC4uss43jR9KJBJOKHKZTqeDSqVCTU0NCgoKuJ0yMzMDjUbDQGGqaFGi\nNT4+Do1G87/fUcdiMbS1taGhoQGLi4tQqVSc8fp8PkZULi4uorKykp1WXl4efD4f+vv7lz0oqVQK\n3d3dqKmpYXS4wWCARCJh4XlymiqVCnK5HPX19QwuIjTqnUaHXygULukNGo1GdvYkq0ZRPSnRkF5z\nLBZbtmyYSqUwOzvLM8jpdBoqlYq1kg0GA+LxOCKRCPx+P3/UKpWK0cTZMhH6iOj/yXTQdrsdQqGQ\n0e7j4+NM/kEHI1crIBNwRmMyBQUFsNlsyM/Ph1wuRzweRygUwvj4OB/kTNWubGtTj47KnHK5nElE\nKKuenZ3F2NgYj1HRhZzLiVCJnyQIKWLW6XTcozeZTIjFYojH46wrnkwmV0Ssu91upNNpLklTAOpy\nueDxeFBQUIBkMomZmRmIxWKUlpZyAJULFKhWq1FcXLxkDIbWdbvd0Ov1KCgo4NEwj8eDkZERtLS0\n5HQmEokEpaWlsFqtGBkZYZ1lAnOqVCq4XC6enKCRFFLpymV5eXmoqanhFgONvNHoDY3i0N6bzWaE\nw+FVleu1Wi2DKs+ePYvR0VF2dACWZOzU6lip/E3thfr6elRWVuLSpUu4dOkSB4H0/6hUKiiVSkQi\nEQ6UVzKDwYAtW7Zg48aNSKfTeO2119DR0cFnnRT+ZDIZ1Gr1EvW2bEZl6aKiIlRWVmJ+fh7Nzc34\n+c9/jtHRUQ4opFIpVCoVa0ATiU2ufXA4HNiwYQP2798Pp9OJM2fO4PXXX0dXVxeXeQm8SApbJIma\nDQ8gEolgsVhQVVWF3bt3Q6/XIxgMorm5mTkA6A7VarWwWCxYv349V+ayZdOE46mrq0NNTQ3y8/O5\n4hgOh7lVScG+1+vFpk2bIJfLMT09nRXESPfUli1b4PF4OFn0+XyQy+VwOByM5SHAb2VlJeOUKIG4\nW/tMOWoiwbh06RLq6+uxuLjIB5h0kGm8hw5ubW0tCgoK4HK54PP58MILL2Td5MHBQbS2tjLYKC8v\nD0NDQ0tmqWdmZiCTyaBUKtHY2Ai32w23243r16/j17/+9bLrplIpjI6OQqPRYHp6mkEVNpuNs1SS\n7qO+en19PcxmM4aHh3Hjxo1lyywEhCIAF0WI1HsiJPjg4CBu3bqFUCgEiUTC/ZJgMJgVSJWp70ol\nebvdjsrKSjidTrhcLgwPDyMUCvG6tE+pVGrJRbXcftBlRRKlBHgqLCyEQCDA4OAgf9AE9KCgJ5uu\nbybynxCiGo0GdXV1cLlckMlkWFhYQFdXF+bm5hjrsLCwwFk+fWN3ml6vh81m495/LBaDzWaD0Wjk\nUqlAIIDf74dcLkd+fj6A26X99vZ27s3faQKBYMncO+EWTCYTampq4Ha7IRAIMDExgUAggKKiImzf\nvh12u52/x2zr5uXlQaVSYWpqCv39/ZicnIRer0d5eTkKCgpY9tTv96O4uBgWiwXbt2/H66+/nnV2\nncqxpHt+/vx59Pf38/t3Op2MWYjH45y1b9q0CX6/H8FgcNlvgszj8cDr9SKVSqG9vZ3HvIgTgUbu\naF7e6/VieHh4RZISiUTCgM6uri4cPXqUvwEK7nQ6HTt/kqFdyai3uW/fPmg0Grz66qsIBAKMNxEK\nhZyNWa1WjI2N8c/MZUKhEGVlZThw4AAUCgW6urrQ1dXF1RwAHFwVFRVBp9MtCVSXM4FAwMDFbdu2\nIRaLoaurCy+++CJaW1s5UJVKpUu+v5mZGQ6YsplIJML+/ftRV1eH4uJiDAwM4LXXXkNvby9mZmaW\nrEv3iEgkYhxJtnenVCqxc+dO7N27Fy6XC9evX8fAwAA+/vhjzvzpPrHZbKioqEBdXR1EIhEGBwez\nBnAmkwmf//znsW3bNshkMgQCAfT29qKvr48TsZmZGa6arV+/HpWVlZy952q12O12fOUrX0EqlUJe\nXh7GxsYQCARQV1fH7cnZ2VlYLBbo9Xps27YN8/PzmJycxMjISNZ1c9lnylEDtx3I+Pg4nn/+eSgU\nCv44U6kUotHokrGmw4cPQ61W44knnsDXv/51fOtb30IymcQPfvCDZTd6bm4Op06d4vIwHVhyGFSm\noZGs69ev44tf/CKefPJJ7N27F6+++mrWD4P6dxT90pwzCau3trZibm6OS1oPPPAAHn/8cczPz+NX\nv/oVTp069ak16eDE43FotVro9Xp4vV7U1tZCp9Ohv78fIyMj6OrqQnt7O1cDtFotdu7cyVHp9PT0\nsnPllJnSx7RhwwZGk/f29rLe8MjICDteAnCMjo5iZGTkU06PsiMCIplMJhQVFeFzn/sclEolA9Yo\nCLNYLDAajTxeFY/HYTQacfbs2U+tK5PJYLVaceDAAc5giLSA0NKzs7MYGhrirDIYDDIa32w249Sp\nU5icnFyytkKhgN1uh16vx5o1a+BwOLjsaLPZoFAouFQ/OjrKjmbNmjWIRCLweDw4derUpxwqXZz9\n/f2w2WyMtqcIPi8vD6FQCJcvX+bxnqKiIrhcLszOzsJkMqG8vBwtLS2f+jaIAGhgYABerxdCoRBO\np5Ozmt7eXh4xq6ysxLp16xAOhzlb1Wg0iEQin/ouKCAkwFggEOBqg1KphN/vx9DQEABkeib/AAAg\nAElEQVRwpSsSiUCj0XBbINf8aWlpKSwWC6anp9HU1LTEkcbjcQ5MqFWUSCQgkUhyOhGBQACNRsOl\n6cOHD2N4eJh/t2QyycFHQUEB5ubm0NTUtKryo9VqxbPPPotNmzYhEAjg8uXL3OpIp9Osq/3AAw+g\noqICP//5z+H3+1ccc5JKpfjGN76BtWvXoqenB88//zxPhQC33+/DDz+MBx98EIWFhWhvb+cANNva\ncrkcbrcbX/va11BdXY1z587hzTffxM2bNxknolQqsWbNGuzatQtPPPEE+vr68PLLL+PixYs5qxYl\nJSX4q7/6KyQSCfT29uK3v/0tmpubMT8/z9/ME088gc2bN2P9+vXIy8vDCy+8gJMnT2JgYGDZapZI\nJMKXvvQlPPnkk6iqqsJ///d/47e//S1zACSTSchkMpSUlODhhx/GI488wtilI0eO4MSJE1mrAM89\n9xw2b94Mi8UCv9+Pl19+GX6/f8kc+oYNG/DEE0/AZrOhqKgIQ0NDuHDhAi5evJiVj8Pj8eCNN95A\nXV0dpqam0NnZiY6ODnR2dkImk2FwcBByuRwvvvgiZ9iRSARHjhzBtWvXmDzpbu0z5aipTykUCpFI\nJNhBU78wHo9z7R/4BGB06tQp/Nmf/RncbjdcLlfWtSnKJcAAlcLm5uYYyUtl22Qyid7eXoyNjTEC\nNlckS6U0utTsdjsDwsbHx5mEhD5YKr0LhUIGEmVbl/q7brcba9asQVVVFaamphAIBDA0NMQzuESB\np9FoYLfb2RFEIpFPrU2lLyIk2bBhA2pqaiCVSrn0QxR7ZrMZ8XgcYrEYJpMJqVQKiUSCL+w7n5mi\neofDwRmB1WplJ03obOD2ZU9RLL0DvV6/7LoqlQp5eXlwu93QaDTcqyIRe3q+kpISdvwzMzOYmJhA\nMBhkVP+dRj1XClKcTie3Bmg0ixC35Lzpmaenp3kGfblnlslkiMfjCAaDMJvNKCkpQSKRWNJaUKlU\nkMlkKC4uhsfjwfj4OPr7+3Hr1q1lS8n0XdCookAgQFFREWQyGaLRKH9vsVgMarUa1dXVMJlMEIlE\n6O3txdTUVNZyPfVziT3O4XDA6/UiFApxm4VwEl6vFyKRCPPz87h16xaGh4dXdFAajYb7uHReqM8r\nEAhgtVqxdu1a5OXlMfaDCGZyratSqeB2uxGNRnnP6ExQYFJWVgaVSoVAIMBTIyv1p/Pz81FcXIx0\nOs2TGpl3id1ux5YtW1BdXQ2RSMRTCLkqAFS1cLvdzDw2ODjIiGqhUAiTyYT9+/ejqqoKkUgE7e3t\nTM6RbV2RSIS6ujpUVlZCo9Ggu7ubiWMoc6yoqMD+/fuxe/duRKNRNDc3o7W1lfvL2Yz4CQYHB9Hf\n34/h4WEO8umOePTRR7mtEQgE0NTUhJ6eHkxNTWVtOxG6XyKR4Pz58xgaGkIkEmH8RX5+Pvbu3YuS\nkhJotVrmx//oo49ysqnR9Ec6nUZrayuam5s5YNPr9di5cye2b9/OFaKhoSFMTEwwAUy2OfjMxKC5\nuRnPPfccRkZGUFBQgPn5eezevRvFxcVMQxwOhxGNRuHz+RAIBNDZ2bkigHE5+8w5aip1CwS3Ga4I\nwLCwsMAlp0xgCfX2CGWnUCiW/eBobeptqlQqNDQ0APgEiJM5LkX9UuonRiKRrMxkhLLU6XTIy8tD\ncXEx1q9fj/HxcQwPD3MPVi6X87q1tbWc8ZlMpmWflwIUiUSCsrIyNDQ0oLGxEQqFAufOneM5cq1W\ni/z8fM7CCKxjs9lw6dKlZdemWW6r1Yry8nLs2rULcrkcs7Oz3PexWq08Rz0/P897MTQ0hLNnz2Yl\naqE+aWlpKcrKylBWVsYgMArA8vLyoFQqkZ+fz2VjAgWePn162X2m2WsqtxoMBg6ygE9oUQlsSIf4\n6tWrmJmZwcmTJ5d9hwTCI1R7aWkpBw4AOPunC5YII6ampuD3+7NmI3Shj46Owul0orCwEIWFhRgf\nH+fxLgpqbDYbXC4XFAoFPvroIzQ3NzPhynImEokwNzeHvr4+KJVKfmYqRSqVSg4E169fzxWejo4O\nzM7OZr3giOGtt7cXs7Oz2LhxI0QiEZqamvjSp0rGhg0bMDc3x5fnSjPPAoEAQ0NDSCQSUCgUqKur\nw5EjR9ixUgZCM6nDw8NcSlwJ/Q6AA+28vDye+19cXIRGo4HX60VJSQkkEgkzZq3UTwduV1sowAwG\ngwzcJKe6efNmrFmzBjqdDhMTE/D7/StOilBAMj8/j0gkAp/Ph8XFRQ489Xo9GhsbUVZWhnQ6jb6+\nPpw/f37FdaVSKbZu3Qq9Xo9YLMb9cpPJBKlUip07d2Lfvn0oLy+HVqvFBx98gGPHjmFgYGDFYIjO\nSCQS4Tnp9evXw2KxMKBqzZo1SCaTGBkZweXLl9HS0oJIJJITG0LtKQrS1Wo1j1E9/PDDsFqtDOLs\n7OzEwMAATpw4gaamppzBEL2f2dlZNDc3M06mvLwc+fn5OHjwIFcJb968ifHxcVy8eBHnz5/H1NRU\n1nUDgQAHp4cPH0ZfXx+0Wi2D1Wpra5GXl4dkMokzZ85wRfjKlSsIBoP3zKf+mXLUdyIzqURIFw71\ncKg3RP0tip6CwSB+//vf51ybsmKBQACPxwOpVMrsZETQrtPpEAqFUFtbi/LycgaTZVuX1qOMq6Sk\nBGazGaOjo9yjrqio4JlTANi4cSN0Oh3a2tqWLW3euT6tSzOJCwsLUKlUqK2thVKp5LIvsToR8UWm\nuMade5Ep9kGD+FNTUwiFQvB6vTzaQD0XOlTd3d05o0KaZ08kEnA6nRCJROycotEo/w7EChSNRiGR\nSJgucTnnRP16wioUFBRgYWEBiUSCUemU/VI/enp6GnNzc2hvb8fIyMiyWS8AzkIHBwcZsU+lWHLg\n5NgUCgX6+vowMjKCyclJnDt3bglC985nnp2dxeDgIGQyGaqqqngsiy4ZkUjECNJUKoWBgQGcO3cO\nbW1t3CJYzuiSJ5AiBT/xeByVlZWQSqVQKBQwm82Qy+UYGhrC1atXcerUqZwXHIHjfD4fent74fV6\nUVFRgcnJSeayN5vNKCwsRGVlJXp6enDixAm0tbUtKd0uZ+l0mufqCaxms9mYXtJoNKKxsRGFhYUI\nhUI4efJkzmzszn0OhUIwmUwc9NB5dLvd2LFjB7Zt24bW1la88847GBkZWXHMSSAQMLMdIYONRiNm\nZ2chEolgs9nw8MMPo7CwENPT0zhy5MgSpbVsRgE4McZZLBbGPOTn52P79u3Yvn07JBIJWlpa8Otf\n/xpXr15dcd1MoCUFQjS/bLPZ8PTTTzMB09jYGF555RVcv34d0Wh0RbAeIfOtVis2bNgAh8MBu90O\nh8MBrVYLg8GAcDiMGzdu4MKFC0zvTJM8yz03VYToZx86dAipVAp6vR4lJSVMyDQ1NYWWlhb85je/\nQX9/P3w+H2ZmZnJ+F5kZcW1tLY9X0jiuUqnEyMgIzp8/j2vXrmF4eBhjY2OM3ci2zzTuubCwgDVr\n1sDpdMLj8WDdunVQq9UwGAxIJBL46U9/ir6+PohEoiXA3nsVbRHcyx/6Y5tAIOCHoI9YqVRi3bp1\n2LFjBzZu3Ii8vDz4/X4MDAzwGA2hlB0OB+LxOP7u7/4OR48ezboZcrmcUeRFRUX4zne+g7y8PMjl\nckxOTiIYDC4hvvB6vUw28rOf/QxHjhzJmlUbjUZmEtuxYwceeeQRKBQKRKNRBidQmZYATqOjo3jt\ntdfw3nvvYWJiYtl1Cf3a0NCA7du3o7q6eslwPfXslUolpFIp9Ho9AoEAfvvb3+L48eMIhULLOj5C\neZtMJlRWVuKpp57ibGFychJjY2NYWFhgZLPRaORnvHr1Kj788MNlQXs0GrJu3Trm6bVYLBgaGsLA\nwACXXckBErsVOYexsbElWf2d61osFmzbtg1ut5sZnK5fv87IUno/VD2JxWI85kQBx50mkUi4/K3X\n61FdXQ25XA6lUolQKMQjHMQmNj09zSQ3tG62i46em569sLCQFa6owkJOcWxsDH6/H36/f0VaTqq4\niMViFBUVoaGhAeXl5cxBQJiOYDCI48ePo6enB2NjY6saoSISDrPZjO9973uwWCwQi8UMagTAnPvU\n+1sp6yXTarVoaGjAM888g9LSUgYsUsA9Pz/PfcK2traswdWde0FzuN///vd5tjYcDkOn08HpdOL/\nYe89g+M8z3Pha3vvu9gGLIBFbwTYwC5SlWJTiS3N58gaSzl2NJ4vycQ/knPsROP8sedkxvNNfpwZ\nexzbSWyFsiVZzSOq0GIXG0SCJIje2y52gQUWi+3YxX4/qPv2EsEWQEqOPME9wyEIAg9evO/7PHe7\n7utaWVnB9evX8fLLLyMQCBSV2VBL4fnnn8ehQ4d4VIxGyWhP/OpXv8L777/PimiFjDilT548CYfD\nwc+Zqn7UFvmzP/szRlQXw1ZnMpmwZcsWPPfcc2hvb2fBHYHgHove9PQ0RkdHcerUKVy6dInL3cXe\nh+9///toaWnhAJ9GmUZHR3Hjxg288847PMWRnXDR77YWTmbr1q148MEHsXv3biYqojFaQtjTxA2d\nCXTNNHq51nt3+PBhHD9+HK2trbBYLHj//fe5nQMAV69e5TL78vIy7+Hs615rT0ulUnzlK1/B9773\nPahUKly5cgUXLlxg8Q2JRIJwOIzOzk4er6UKHI2s0qjtZ3Yjk8nsyPsA8CVz1Nk9amLrqaiowOOP\nP47W1laec8smWKcs68aNG/jOd77DRANrGY0OyOVyWK1WvPjii2hpaUFFRQUymQwDW8iJBINBfPrp\np3jzzTfx6aef5iSWp8NYoVBwqeZb3/oWC3MQGpkAOgKBALdu3cKZM2fw6aef5pyXpY1LI0g0t2e3\n21FWVsaOiHp+NHc6OTmJV199FX6/H6FQKOcLp1AooFKp4HA4uCxIQiTBYJARqJlMBm63G0tLS/D7\n/RgdHWXU9lrXLJFIYLVaWde5pKSEVXoIZ0D3Ta1Wc9mQomvKSta6FxRAAff629QSiUajzCBHqH2a\niSbQUC65Rxo7o6Ahe8Y2lUpxL5MOPQK90eYuxFFO107tD5VKdV9lJ1u+L5lM3if6kc+yqyFqtRoa\njQZmsxlSqZRL2DS7TdWF9dCTSiQSbNmyBVVVVSgvL2ekLBHDjI6O4s6dO+uS0SQu+MbGRhw+fBg7\nduzgwzEcDuP06dPo6OjA5OQkQqFQ0TPJQqEQOp0OP/jBD7B161ZG9obDYXi9Xly9ehXnzp3DtWvX\nig4qCLG/a9cufOtb34Lb7YbJZOKKlt/vx8svv3xf9ljMuoQj+e53v8tocsp8g8Egurq6cOrUKbzx\nxhvrkoKl86e5uRlHjhzhsa9oNIpYLIaf//zn6OjogM/nu6+MXszaWq0W7e3tzJJGlSVi+JqcnGQh\nnGzHXKgVotfrYbVamYKVxrkCgQBmZmYYkZ69Ll1zvvXNZjMqKiqg1+uhVqsxMDDA7y6VxLOBebTf\nC4m1CAQCGAwG7Nq1C7FYjClTw+HwfZVeCtho72efE6v24B+vowb+cIhTX7KiogLPP/8807utrKxg\naGgIoVAIHR0dDIrId4OpLER8sjRSQXR15eXlXDKdmZnB9evX0dPTw/q4uTKR7P43lbrtdjsqKyvh\ndrths9lYMtPv9yOVSuHGjRs8+pSv95RNUi+RSHg2r7y8HEajEYlEApFIhMtxNNJD4h+5rpnWpUCA\nZr4pi6fZRXrxpFIpU16Gw+G8iFnKcGg8iCJ6erYEyCFnQJuaqgO5WOaygzj6N22q7E1LTpccwOpI\nOdfa9Hf2YZD9//QneyMXe4iuXif7Z2aLceSjwc1ndJ+FQiHfd/oZFFSsd13CMhBA0mg0Mld0Mplk\nsYX1/v7EB19aWoqnn36ae6ojIyOc6a1XhpLe523btuHIkSPYtm0bFAoFOjs7cfHiRdy5cweBQGDd\nEqUKhQJ6vR5NTU04ePAgjh49ykxZZ86cwcmTJ3mccD3rSqVStLW1Yfv27WhtbYXT6cTY2Bh6enpw\n8eJFjI2NMdHLeu4BtTvsdjt27tzJanrd3d24cuXKfRzw6zGxWMy4EupXE8d+dmWP9sdaGfRaRmN/\n2U4OAKvUEbiX1iwmAAD+oKcA/AF0nP09q9ddPa1QaBSQnG/2uZINNqZsn5IACjjXuPY/Pkf92cf3\nOWtygAKBgF8Ukhukw4KQnoVewOwbmY3cJMdCgDQiQclGbxZ7IK9Ghmf3gVcfyMDG9Idz/T7Z/84X\nFW7kZ2T//UWvnb3uF/E+Zh8SxR4YG1n7i1gr276o3z171pYCrs+zHgEms5XD6GDeUL8tq2xPVSAA\n3PLY6J6g4J56hel0GouLi1hcXCwo2pPvWqk1olQqYbFYGGi4uLhYEISVz4j6lg5z0q6nwH2j61Jp\nnhKaTCZTNAVwPqOzOHufrgb3bsTofQDWxvys/tpif9bq83C1o8/eIxQkbHR/r97Lq3+3bBKoVfbH\n6ag3bdM2bdM2bdP+m1hRjjo/ceymbdqmbdqmbdqm/V+1TUe9aZu2aZu2aZv2JbYv1Rz1pm3afwdb\n3e//IttP1E9eDbD7Imw9YJ5N27RN++Js01F/gfZFA5dWr73aPu/PIuBRNtCCaBezEZAbAbYQMpuI\nVNRqNY8hEWPUekZPsq+ZxpEkEgmPmYXDYaZXLEYLeC0TiUSQSCSor6+HWq2G1WpFMBhEX18fE4Hk\nY/XKZQQWoumFY8eOIRQKob+/n7nUSXZvPUhnMpr9dzgcePDBB6FSqTAxMQGfz4fR0dE1OdmLNZpo\nMJvNqK2txZ49e5BIJHDz5k309vZidnb2c72H2XKira2tUCgU6OrqYsW2zwtaJN75hoYGSKVSXL9+\nHUtLS19YsGG326HVapHJZFjN6fMAwchIvpVGRYmBj4CBnwdoJpfLGXBI44w03riR9w/4g3oZjTgR\nnTFd8+ozpdg1aYpBo9EwsyGtTQDBjaxLymR0j4n+ljj0cwkD5Vsze/qH6Jj9fj+r5tHZkQdYltO+\ndI46ewQne5h99cc0ikCzksWgJUmMg3SEaXaauMVXVlYYMUljLfTy5puRFAgETEtKptfrmTebxhay\nucu9Xu99o0O51qXZXpLklMlkrBUdDod54xK7ELF0FUL60qgXydGZzWY4nU64XC74/X54vV7E43FE\no1GEQiF4PJ51zYlaLBae09ZoNHj00UcxOzuL+fl5RCIRjI2N8ceFWIbIyJGSqpXBYIDb7YbBYIDf\n78fS0hKCwSDu3r2LaDRaNFEEPRuZTMZkOLW1taitrcX4+Di0Wi2CwSB8Ph98Ph9vvGKNDhtS6Gpq\nasL4+DjL3hGKeCPIZJpY0Ov1aGxsxK5duzA4OAiBQMDSgtlZ9nqN+NMfe+wxNDQ0QKlUoquri+dE\nhULh5zrcHQ4H3G432trasGPHDly/fh2jo6PMLb5RR02HcXNzM44fP86iJNPT05iYmMipHlaMEWJd\nrVbjb/7mb+ByueDxeHDx4kV89NFHHAhs5JopcCkpKcE3v/lNyOVyjI+PY2BgABcvXvxcSHsiW7Ja\nrXC5XMx9Hw6HMTg4uCYZUCHLngggCVQaX4pEIhwkrtfpAWA+BBIkSiaTHPhHo1GMjo4WvW52FUsi\nkaC6uprZ1aLRKGszBINBTE5OFkUvS+vRZI9CoYDT6URraysOHz6MpaUl9PT0IJPJYGRkBP39/Xk1\n5vPZl85RA3/gi66srIRCoYBarYZWq2V1HVJV8Xg8/IKEQiHmUs112BGJfElJCUwmE9xuN388MzPD\nsmlEo0kiBJFIhEUM1lpXILjH3Uu62CRUbrFYMDs7yzrZHo8Hs7Oz/EJkH0S51pXJZEwrSOo/ZWVl\nHAUS2fu5c+d4XpZ4yfNtaKlUCqvViurqalRUVODgwYOoqKiA0WhEJBJBR0cHpqenWXZucXGxqEyE\nnJ7b7UZ1dTVcLhcqKyuxa9cupnn0er343e9+h+7ubggEAg46ijGJRIKmpia43W5UVlaiurqa2YCI\nRWxqagoAOHgr9mCjIKi5uRm1tbVQqVSIRqOorq7G8vIy+vr6mJlsPRkZvc8k/EGZRjwe59+bouyN\nVGXkcjlKS0uxa9culmQlFrhi3oV8161SqdDa2oqHHnoIdXV1zIU8PT297jnf1WsLhULs3LkTTz31\nFFpbWyGVSnHx4kXmHPg8WalEIoHRaMT3vvc9tLa2QqVSYWBggKsb2WX89Rg5JYvFggcffBDPPPMM\notEolEolvF4vTp8+veGgiHStS0tLsXfvXhw8eBCJRAImkwmZTIalHzcyYkZEHTabjUlLRCIRvF4v\nbt26BbVaXRQLXPaa9DeJnrjdbk5Q1Go1ent7EY/HmTui2Oukj5VKJex2OwwGA3Q6HVKpFBwOB1Qq\nFQYHB+Hz+Yq+Zqoc0vNra2tjvxIOh+F2u2E2m9Hf3w+JRJJTKnn1709BGwXjdrsdDzzwAHQ6HWKx\nGBQKBRobG1FVVQWJRILe3l6MjY0Vdc3Z9qVy1JRBEtH7nj17YDKZYLFYUFJSwrSIXq8Xw8PDrK5D\n/LALCwt5xQacTie2bduGmpoaOBwO1NXVQa/XMxtXX18fBgYGMDMzwxKSVM7JlaXSAyJ6T7vdDqPR\niLKyMggEArhcLtTV1eHs2bOQy+VIJpPwer3Mm0xl4LXuhVQqhdPpxL59+3Ds2DHOIkl4RK/Xw2Kx\nYHh4GF1dXZicnOTrKURlaDQa0dbWhr1796LiM8EC0ptWq9Wor6/n641Go6yzW6g8RpHl7t27UVNT\nwypeRHii0+lYTH1paQkDAwP/gewglwmFQiiVStTU1MDtdsPpdEIikTAFJVUdKisrcffuXWawK+bQ\npLVNJhOMRiPi8TiXj0kYxel0Ym5uDl6v974+cCGjcqPFYoHL5cL4+Dh6enowMzPDHMqkALZeowN4\n27Zt2LFjB1QqFWZmZhAKhT6XIwXu3ZOKigp85StfwY4dOyCVSlnpiX6v9ZBbZF8zzTw/++yz2LVr\nFxQKBTweD/O+Axsr71KgaDQasW/fPuzfvx8SiYQpZonDINfcayGTyWSwWCx4/PHH8eKLL7KgRCaT\nYdIhOk/We90qlYpbIydOnIDdbsfMzAzS6TSCwSBX+tazJvAH5sTW1lY88MAD2LlzJ1QqFbq6ujAz\nMwO9Xo+lpaWitJJX81yQiIjb7caJEydgMBiYhz6dTjMdai61tmzLdqZCoRBmsxmNjY2or6/nMwK4\nxzC4f/9+9Pb2rinhu9Y9oExaIBDcJ9Li9/uRyWS4BP7II49ALBbjxo0beSudZMQ4KJVKodVqmRFv\nfn4ed+/ehVarRSQS4ZbRwsICxsfH1084s66v/k+28vJy7Nu3D9u2bUN1dTWMRiNmZmaYMN1sNjOD\nVSwWY3YskmHM14czm8148cUX0dzczE7/0qVLvKEokyRx78XFRT6w5+bmcpaclEolduzYgSeeeAJG\noxEA4PF4cO7cOaysrECn07GC0szMDPNca7Va1gNe66ATi8Worq7GgQMHcOjQIahUKoyPj+O9995D\nPB5n4heVSsWbWKvVQqPRMF1nLhMIBLxpTSYTBAIBfvGLXyAejyMej0Ov16OyspKv3+l0YnZ2Fj6f\nD4FAIK+DkslkrPNNEn6nT5/G7OwstFotdDodXC4XB0rxeJx7zIUUkqivlEgk4PP5WLgiEolAo9Gg\nqqoKRqMR1dXVCIfDkMvlmJ2dLRh1Z/fqhUIhBgcHEQ6HWWbSbrezuIXD4cDU1FRBycFsk0gkLIfo\ncDjw3nvvYWBggFWIqHKSyWSYYrXYjSwWi/Hcc8/h8ccfh9FoxKVLl3D+/Pn7CICIa5h6h8UGFyqV\nCt///vfR3t6OUCiEc+fOsdoVPQ9aez3UpNQ3fuKJJ3Do0CHMz8/j1VdfxYcffgi/3w+pVIqKigp4\nvd68SkZrrS2Xy1FWVoa//du/xcMPPwyPx4OPPvoIV69exeLiItra2lhvPZsbvpi1RSIRjh49ihde\neAE7duyAWCzGK6+8glu3bkEmk8HpdMJsNvP9KRQgktMjh/fSSy/hT//0T1FWVoZUKoXXX38dsViM\n+fjPnz/PwXIxQS2xDrrdbuzYsQPf+973mFd/cHCQ17Xb7SxJmS9YzG5LikQibN++HU1NTTh06BBX\nP4mvnoIO4sAmVrR8a8tkMohEIiiVSuzcuRPPPvssLBYLU/Z+8skn7BTb29vR1dXFmgS5jAisyIHu\n2LEDDocDzc3NCIVCWFhYwOzsLG7fvo2Kigrs2LEDzz33HH7605/mfO/o89SPJklOp9OJtrY2RKNR\nvPnmm9zqU6vVeOWVV3Ds2DF0dnbmfW657EvlqEkwgEonwWAQV69ehcfjQTQahcvlgkgkQjwex/j4\nOJaWlphHm5xprheYMmKSBwwGg7h+/TozC9ntdlZwWlhYQDQahVgsRiAQyAtqSaVSWFhYQCKRQCgU\nwtzcHK5evYq+vj4kEgmW3KOeN4lzeDyevD3JlZUVLC4uYnZ2FoFAAFNTU7h16xbLZlL5jdjaSPUr\nHA4X1Wuam5vD9PQ0wuEwQqEQrl+/jnA4jKWlJRZ1sFqt3COXyWRFMSatrKwgFothdHSUNV4DgQA8\nHg+USiVKS0uRSqXQ0tICjUYDi8VSNK0jSWWOjIxgeHiYpfxisRjMZjMfTMQfHA6H1xQ7yWVUhvd6\nvZiensbU1BQ7OrrHJNxB/d9ijIIXir7v3LnDfOYUYNH69H4X6/SkUimqq6thNpsxNTWFjz76CKFQ\niN9XOkiEQiGXTYt11EqlEo2NjUilUujo6MCbb77JwghisZjpH9cD+qJsyWQy4ejRo5iZmcH58+fx\n+uuvY3JykltZarUaFouloHTm6vshFouxe/dubNu2DQKBACdPnsQHH3yAYDAIsViM0tJSVFZWMj98\nseuSo3744YdRU1MDgUCAgYEB/PKXv0QsFoPVaoVGo4HNZisqM129rtlsxrFjx1k4/xEAACAASURB\nVFBWVobl5WV0d3fj7bffhlarRUtLCzuaYjnKyaHa7XZs3boVJ06cgFqtxtTUFDo6OnDz5k0YjUYo\nlUoIBAI4nc6isnV6l2QyGVpbW7F//35s3boVmUwG77//PoaHh7G4uAiRSISHHnoItbW1uHDhArMy\n5jPCiZSWlqK6uhqlpaVcdbtz5w56enqwsrICl8vF1dZses5c94I0HOhckEql6O/vR19fH27dusWt\nloWFBTz//POwWq1FBVq0n4F7CRsleT6fDzdu3OCgh+RXjUYj8/z/UWfUkUgEvb29rOpEXLJ+vx9m\nsxl6vR7APSBBOp3mUm12DzmXxeNx3Lx5kwXvSXZRp9Oxw1heXmYZM7VajWg0ys4119qpVArT09O4\nevUqampqWJM2k8lAp9NxuToYDLIToXXzHZqZzD0JuJs3b2J5eRlGoxEjIyOsCUzyclqtlstEfr8f\nc3NzBfuymUwGg4ODiMfjsFgsSKVSGB8fZzWr5eVldhoKhQIWi4VVogq9YKTTfP36dUilUqZ3pe/N\nRoQaDAYurxcyokEkKUwivo/FYlz2Bu5RGhoMBmg0GhYUKWbtVCqFSCTCamM+nw9LS0ssBEKgKpPJ\nhJ6enqLL9bS+SqWCUCjE9PQ05ubm+B5LJBIOtsLhMPx+Pyv6FOtQXS4Xkskkent70d3dzRztFGBQ\nK4RKkMVkegC4MrKwsICzZ8/izp07SKfTfN1Ef5nJZPJKcq42mUyGXbt2obq6GteuXcObb76JiYkJ\nFkwgFLhOp8PQ0FDRQUAmk4HD4cCxY8dgsVjg9Xrx/vvvw+v1IpVKQaFQMHBybGyMMS3FGJ0J+/fv\nh0wmQ39/P959912Mj49zMJtKpeB2uxnMWIxRX3rnzp2oqqpiYNcbb7yBoaEhmEwmOBwO2O126HQ6\nxl8UY3q9Hrt378bXv/51uN1uTExM4OzZszh9+jQ8Hg+amprQ2toKg8GApaWlgo6aAL1yuRw2mw2P\nPPIIKisrEYvFMDExgY8//pg11I1GI44dO8Y0qYUcNZWmHQ4HWlpaUFtbi+HhYYyOjmJwcBDj4+OY\nmpqCXC6Hy+XiRKVQm4+Cb9K4VqlUkMlkOH/+PNNP07lENNIE2s33bggE9wR7JBIJdDodn2Hnzp3j\n6iAh0wnELBKJUFpaColEklPPIJd9qRw1ZcaEhlapVGhoaIDD4UB1dTXEYjEfzFQuikajnDnli1Ro\nvGZhYYGVonbv3o3y8nIuE4VCIRa/EIlEGBkZAZC/V5bJZBAOhzE2NoaysjLEYjE4HA7WBKYsLBwO\ns6b1xMQEX2uua6YyKI3xVH6mxKXT6VggwWazsaazSqVCLBZjIvpCFovFMD4+zoFIKpXiF5WEPzQa\nDYB7G560cws5DyLADwQC941n0DMD7jnzaDTKcm/FOFQKapLJJGeMVJGgnma2oEUmk7kv4i1kNOZB\n5X8KBqm/rNFoWHCFAgw63ApdO0mVEsCGAj/qW9fW1sJsNrPYA6kQFcNdT5lnNBplLW86PAwGAywW\nCwdaY2NjGB0dLdjXo2vesWMHt216e3uxtLTE7xrJR+p0OvT29mJoaIgPvHwmENzj7D927BhMJhOu\nX7/OOvOEE7BaraitrYVUKsUnn3xS9DiLQCDAgw8+iC1btiCTybCgTnbG5XQ6YbFY4PF40N/fX/SB\nKZFI0NDQwHK7b731Fj744APec1TONxqN/K4XU72QSCSora3F1772NQDA7du38frrr+PTTz9FOBzm\nqQy9Xs8/p9CoHWXq27Ztw1e/+lXU19cjnU7jt7/9Ld9vuqdU5fJ6vQXvAZ2TGo0GLS0t3GLq6enB\npUuX0N/fj1gsxqNlJpMJ6XQaNpst7z6kqoJarWZQpM1mw29/+1uMj4/zNAuJA1Fypdfr+UzJty4F\nlbW1tRAKhbh58ybGx8chlUp5bEqpVDJQjXrr+YyCVcLBUJBNOAhSSqTpAKVSeZ+i1nqtoKMWCAS/\nAHAcgD+TyTR/9jkjgN8AqAAwBuDZTCaz8Nn/fRfA/wCQBvBXmUzmw2IvhuTzxsfHceTIEZSUlKCi\nogJKpRIikQhTU1OYmZmB3W6HzWbDiy++CKFQCI/Hg46ODvziF7/IiTAk/We9Xs8RVktLC0vWESCJ\n9I61Wi38fj+uXbuGU6dOYWZmZs0yanYfMBgMQq/Xw2Qyoba2lg/+5eVl6PV6BkWk02loNBpcunQp\nZ2k9O8uj0QSRSITa2lpUVlZypKpSqXjkq7GxETKZDCsrK+jq6sqbWROIaWFhgctC5eXlMBgM0Gq1\ncDqdnNV5PB6srKxArVazpGauA5kOa8qOMpkMFAoFGhoaoNfrUVZWBrvdjtnZWYyPj2NychKBQOA+\nUv5c10y/J12XQqGARqOBy+VitD0FRKOjo5iZmeHvKVS9oMCPesZU7lepVHA6nSz2QK2C1Qpd+aym\npgYulwsLCwuIRCJwuVzskCoqKrBr1y4sLy9jdnaWkaM0xpHPkSgUCjzwwANIp9M8VaDVarF9+3bU\n1NTAbDYz1oCy3nfffRcffPBB3nXFYjG2bNmCZ555Bj6fD++//z78fj8MBgN27NjB4CFaf3JyEv/y\nL/+Cnp6egmhZs9mMl156CTt37kQymcSHH37I+0Ov16Ourg4tLS2oqamByWTCW2+9xc62UNCiVCrx\n7W9/GwaDAWfPnsUPf/hDzqRJbpUApFNTU1CpVAWnDei9eOCBB/B3f/d3GBgYwHe/+110dXVxIKdU\nKhncODg4WFSAKBDck3h85JFH8J3vfAfV1dX45je/iU8++YQPdKVSyYGFyWRCMpnMG9Rmj62Wlpbi\n5Zdfht1ux+3bt/HjH/8YFy5cQCZzT1CHRj4pw8slh5t9HxwOBxoaGnD48GG0tLTgwoULeO211zjQ\nolaOwWBASUkJ9Ho9+vr6GDSZa129Xg+Hw4Gvfe1rsFqt0Gq1LEtK2KBkMgm1Wo2qqiqWBh0cHMy5\nLrUT6Lxpbm6GxWLBnTt3OBCORCJIJBIQCoXcmxYIBOjt7c15L+ge0ySBxWKBRCLhdqpWq0UqlcLM\nzAzkcjlXYUKhEDo7OzE8PJzvtchpxWTU/wrg/wD4Zdbn/heAjzOZzP8WCAT/67N//0+BQNAI4P8B\n0ATAAeD3AoGgNpPJFIXYoPLm3bt30dvbi8XFRT48Q6EQRkZGGDwkFothtVpRWVmJtrY2lJWV4dat\nWzh79mzOksX09DS6u7u5LEE9XVLCod6Ky+VCWVkZtmzZwlnD2bNn8fvf/z7ndY+Pj3PEq9frEQwG\nuXyiVCq5zK7ValFSUoInn3wSarUa169fR3d395ovHL2g4XAYgUCAJeumpqbgdru5tBwIBBAKhQDc\nI2CgURG/358z+s7OUlKpFBOIlJSUwGKxQKfTwev18jiVSqWC2WzmbLjQAUel4WzCCYvFArfbDbFY\njKGhIS6LkyQmAZ7yZZKU4dLMs9FoxJYtW3gulHpNyWQScrkccrmc2wzZimWrjbIWylxWVlZgs9mg\nUqlQX1/PJbGFhQWWPqTsOx6Pc4lttQkEAi7RUQ+1vLycnVJZWRkHeX6/H3q9Hi0tLRgZGcHU1BTP\n8K+1rk6ng06nQzAYxNTUFObn52GxWHiygX6m1+tFfX09g/YuXryYV/aSMn29Xo/h4WEMDg6y5GxL\nSwuMRiMUCgXi8ThKS0thNBqxe/duhEKhgo6a5siFQiHGxsawsrIClUoFq9XKI5MajQZarRZutxvl\n5eWYn58vmEUSoYxOp4PP58M777yDhYUF6HQ6fg+qqqpgMpl4MkOn0zHqN5cR2csTTzwBh8OBH/3o\nRxgcHOQKFIEYq6ur4XA4MDw8zKCoQqVTl8uFp556CkajEVNTU4yZIeS42+1GfX096urquLVDla1c\nplAo4HK5sHPnTiQSCXR2duLkyZPo7OxknggKPltaWlBZWYnZ2dmCpXqxWIwHH3wQdXV12LJlC7xe\nL9577z1MTk5yYEEkQUajEU1NTQDuYWHy3WO1Wo1du3Zhz549aGtrw8DAAMbGxu6b2kgmkwxOa2tr\nQ11dHZaXlxlDspaZTCY8+uij2L59O/exabS3oqICwWAQHo8HKpUKRqMRe/bsgdPpZD+Tz6xWK55/\n/nmkUinY7XYEAgH4fD6UlJSwbzp79ixKS0tRVlaGPXv28KTH7OxswWx9zftf6AsymcwFgUBQserT\nTwI49NnH/wbgHID/+dnnf53JZBIARgUCwRCAdgBXir0gkqa7cuUK96oXFxd5JpQcaywWw+LiInbu\n3ImjR4/Cbrfj+PHjuHXrVk7puUQiga6uLu4hEGKaQGlLS0tYXFzksl59fT2sViuOHz8OjUaDjz/+\nOOeLQWMUMpkMNpuNI0GNRoNoNIrJycn7os0XXngBhw8fhkqlYgBCjvvPGtPkdAgYRICqoaEhHqKn\n+cDa2lpEo1Fm6cp3vymjNhgMUKlUnNH19fVhbm6O0fV0aEUiES6Z51qbyv4WiwUOh4O1uSUSCZaW\nlu7T+JbL5VAqlfcxlq3Vd6JM12QyoaysDEajEXa7Hdu3b2dpUp/Px6UnuvfZRDTEwJRtQqEQBoMB\ndrudZ+EBcClOp9NhaWmJSVRo9tJkMiGVSiEejyMQCKzpTIRCIbRaLZejqWxfV1cHs9kMjUaD4eFh\neL1ehEIhaLValJWVQS6XM2J1ZmZmzXVJHpICEK1WC+CeM8xkMhgbG4PP54NYLEZNTQ1WVlZgsVig\n1WqRSCTW7FdnE2MsLy9zAEjYjlQqhdHRUUSjUUilUjz++OMQiURMQrNa13e1UbsGACOCCWQjFArh\n9/u5vE6EFzKZrCBCWywWo6ysDDKZDAMDA1yKJ8317BE5GhkqZm5dKpWiqqoK7e3tkMvl6Ojo4PIu\n9aaJ38DpdGJ8fByJRCLvnDZlZVu3bkVTUxNWVlbQ0dHB95qut7a2lmf6iRgo3z0gMqfW1lbs3bsX\ngUAAZ86cQWdnJ7dEqLLQ3NyMAwcOQCgUFpztJZ6IQ4cOcatwZGQEIyMjWFpaYoBgZWUlGhsb0djY\niAMHDqCnpwd37tzB7OzsmhUGQo4/9thj2L59OxKJBCYmJjA5OYm5uTlmZVOpVKipqcGJEyewa9cu\nCIVC3L59G7Ozszl7308++SSeeuoplJaWQqlU4rXXXmNgosFgwPz8PPR6PVpbW1FbW4vHHnsM0WiU\nWwNUWVv9/JRKJb7xjW/gscceg1KpxOLiIgYHB5HJZFBWVobBwUHMzMzg2WefRVNTE7RaLex2O371\nq18hEAjwc1ovucxGe9TWTCZDTY0ZANbPPnYCuJr1dVOffa5oI8f06aefwmAwcMmS0NV0GK6srMDn\n8+HOnTvQ6/U4ePAgZxK5UJfpdBpjY2N8GNDPi8fj9zFDeb1e9PX1Yffu3Xjsscdgs9nQ2tqa97oX\nFhYQi8WgVCoxNzcHjUbD/dlkMslkIel0Gnq9HkePHkVNTQ0WFhZgtVrXdNS02ena5ufn+cDp7e1l\nR0xIdaIGrKioQHl5OYaHh3OOPVEJLZ1Oc4bqcDig0WgQDAbZeZCjpn51SUkJgsEg5ubm1nzZqC9E\nFQu1Ws2MZxKJhLNx6gNTr0en02F5eZlHraifn71u9riJyWRiMhVCqS8vLzPAiUbrKGNPJpPQ6XRr\nRss0ZaDValFbWwuNRgOlUsmAQJVKxaxytNHj8TgMBgMikQhXZNa6F0TGQlm6Wq3mMi3hCmhEhJ6D\nxWJhpGh2kLH6+SWTSczMzCAYDEIkEsHhcDCRCiFQJycnUV1dzXO+NOqSz4lQ346mKAijIBAIuMcd\njUZ5fxJYsBj0vkwmg1qtZnAb7cN0Os10pNTnJcBgMYQt5Kjj8Tg8Hg+PQYrFYsTjcQ58xGIxotEo\nO9RC6yqVSuzatQtWqxWxWAx+v/++d5wCb0Igj4yMFASgAvec1IEDB2AwGODxeNDZ2ckBJgUA9fX1\nqK2thVwuR1dXFzPY5VqXQKXt7e1wu93o7e1FX18fAoEA0uk0lEolzGYztm3bhn379sFgMKC7uxvX\nr1/HyMhI3oCeKF6TySQmJycxOjqKYDDIkzQajQaHDx9Gc3MzqqurodPpcPLkSdy5cwfz8/NrVhfE\nYjHa2trYWZ46dQqTk5P8TieTSRiNRjQ3N+ORRx7B7t27IRaL0dvbi97eXszPz+cEoj7yyCNobGyE\n2Wzm0Vi/34/5+Xk+m/bs2YPjx4/zmUGgtb6+vpwAtfLychw+fBiVlZWYn5/H0tISQqEQAoEAtFot\nxsfHYTAY8PzzzwO4F+wPDw9jbm4OExMTSKVS6waSAV8AmCyTyWQEG9CTFggEfw7gz7M/R6VS6hvT\nzaKxhGg0yiNCAPjQ/9nPfoZ9+/bBbrfD5XKt+SJT74aa+0S4QPyrkUjkPscjFArx29/+FgcPHoRa\nreYZ7rXWFYvFvAmol6lQKDA7O4uJiQkWhCeqOXKqBPQxmUxrrkuoa5VKhaamJu6rLy8v47333sPE\nxAQikQgfHPQ9W7ZsQSAQ4JGdNe49NBoN93ddLhdeeukliEQi+P1+3L17F319fQycopKW2+2GXC7H\nrVu3mBpv9boEJNm9ezfcbjdaWlqwbds2xGIxeDweTE1NIRaLoaamBqWlpYx+DofD6Ovrw+zs7JrO\nVCQSwW63o66uDocPH0ZpaSlKS0uZDIJmkq1WK9rb2zlzTiQSuHLlCubn59HT07MmfV9paSmam5ux\ndetWuFwuVFRUIBQKYXl5mQ9Oo9HIz5lY6/x+PwYGBnDmzJk1WwHUfyNini1btqC9vf2+DU7jJg0N\nDTAYDEin0/jwww8ZCEVZ1ur7TK2CoaEhAMCxY8ewvLyM6elpdHR0IJlMQqvVorm5GSdOnIBYLIbH\n48HHH3/Mo4xrGWXEw8PD6O7uxpEjR1BVVYUbN25gZmYGk5OT0Gq1qK6uxt69e3km96OPPmKgXC4T\nCoUc5JWWlqKtrQ01NTX8/GQyGbZu3YqjR4/C6XRiaGiISS0KratQKLjyQQxz6XQa0WiUiTMOHToE\noVCI9957D9euXSuKp9zlcqG+vh7xeByjo6OQSqVQqVSM5H3qqaewb98+2Gw2DA8Po7+/n2mD8znU\n8vJyVFdXY3FxER0dHeju7obFYoFGo4HdbsfDDz+MP/mTP8HKygpOnz6Nf//3f2eHl2tdhUKBP//z\nP8fu3buxsrKCnp4ehEIhVFVVQS6X4/HHH0d7ezuPuf7617/G7373O/T09BSsWNCM+I0bN3Dz5k3M\nzs7ioYceYnIqp9OJxsZGBINBZmg7efIklpaW8vJmNzY2Ys+ePQiHw/jNb36DxcVFWCwWbN++HceO\nHYPdbodGo4FIJMK1a9cQCARw7tw5XL9+ndH8axmxIgaDQfzTP/0Trly5wnPSDocDJ06cYJKhzs5O\nBINBdHR04OLFi1wdW+t8I7KUaDSKv/qrv0JnZycsFgueffZZ2O12vPzyy1wCv3DhAieWo6OjmJub\nQzAY/C911D6BQGDPZDJegUBgB+D/7PPTAMqyvq70s8/9B8tkMj8F8FMAIEdPN4bAPUKhEHa7nQFE\nVAIkp0QEBwaDATKZDCMjIzh//vyaF0yZOUXZQqEQZWVliEajGBwc5HEWmuVLpVKoqqqCWq1GKpXC\nnTt3cq5LmROVexsaGpDJZLCwsMA/j8g+KEq0Wq1Ip9O4efMm+vr6cq5L85BisRjNzc2w2WxMqECA\nEOp963Q6ZvJ59dVX4fV68wJPskcRtFotO71YLIaWlhbugQL3ImqlUomVlRVcuXJlzYMzG8Uuk8k4\nK5DL5ZiammIwHgFNqN9GTq+rq4tHk9aybEYph8MB4F47Y3Z2lrMm6mcRO1AqlUIwGMTMzEzOKDkW\niyESiTBQjEBc1G6g343Y1ebn5xEMBhGNRjE0NMSlztX3mrJNCj6ampp4aoHKumKxGCUlJZDL5Ugk\nEhgaGkJfXx/jMXIdcMlkEpFIhHkBqJpgNpvhdrvv418Wi8UYGBjA5cuX0dHRkfdAXllZQTKZRCAQ\nQFdXF44fPw673c6Hvdlshk6nQ1lZGVwuF27evIlTp04x4jefZTIZzM7OYnp6GjU1NRwoZrPCHTx4\nEBaLBVNTU3jrrbewuLhY1AgVYRNoHSr1p9NpVFVV4cCBA9i2bRs++eQTvPfeewWJMoA/jPYQFa1M\nJoPVasXS0hIUCgWqq6tx6NAhGAwGjI+P4/XXX+fydCHgG51fQqGQsRtyuRzl5eU4cOAAWlpaEI1G\ncf78ebz99tvo6ekpal0AfI7t2bOHmRdtNhsOHToEtVrNOKA333wTg4ODRRG/xONxnkOXSqWcrZrN\nZqhUKs4cr1+/jtu3bzMGhTAnaxmJ9FAL4qtf/SoHoTRHLRKJMDc3h5s3b+LDDz/EzMwMvF4vjwPm\nuh8E9FxZWcH27dvR3NwMjUaDuro6bkVNT0/j7NmzGBgYQCAQwOzsbF7QImGRJBIJ4vE49/hramqw\nfft2aDQaPuNPnjzJTJHEuUCV4Y3YRh31uwC+AeB/f/b3O1mfPykQCP4/3AOT1QC4vp6F6aEJhULY\nbDa43W7YbDYuhxBTkUqlYr7qr3/96xCLxfjlL3+J2dnZnGtTximVSmGxWLBr1y74/X6mXZybm0NJ\nSQkMBgOWl5fx9a9/HTKZDBMTE3jllVfWXJNKheT0bDYbnE4nlzGrqqoYzEOlQpvNxr3J06dPr5mN\n0cFPiHI6IIlL9/DhwwwwczqdPDJjNpsBACMjIywGslZkSONB1EemEjvNzqZSKS5dZzIZKJVKLC0t\nYWxsDOFweE2wDB0U9AyNRiO/nMlkkkcUBAIBo9XpOubn5xkpvrrvlN0CoK+Px+OQSqXM/kNjfYTw\npfGqubk5dti5QByxWAxLS0uYmZnhcvb09DSjWIk+FQAWFxcxPj7OgiIUfOTa3BSESCQS3L59m1sf\nRBZC92p4eJhLet3d3QV5AejZLi0t4fe//z2kUinsdjsj7Ak4BAC/+93v0NnZiY6Ojrz7g55dKpVC\nKBTC1atXMTs7y5UKq9WKaDTK42perxcnT57ErVu3WDUqn2UyGfj9fpw5c4YdNfXqKVh0Op3o7+/H\nm2++iUuXLhWVfRAIlcbTrFYrWlpauB1QW1uL+vp6CAQC/OQnP8HIyAgLthSyeDx+X8907969WFxc\nRFlZGdra2qDX6zE5OYl//ud/xtmzZ4uaf6fDm9jjXC4Xjh8/Dp/Px+OoEokEb7/9Nl599VUOgoop\n/1+/fh0tLS2wWq1obW1FQ0MDn6dSqRRTU1O4e/cuLly4wCDWQmV6OuMikQgH7DSNQy2n8fFx/Ou/\n/iuDwYgrotB9mJ6extDQEOx2O3bs2MHPRKPRYH5+HuPj47h06RI6OjowOjp6X5syOzFYbcPDw3wf\nH3vsMYyPjyMcDkMsFmN+fh6dnZ24ePEirl27xtTUmUwmb6JA59vU1BQaGhrwjW98g2fxI5EIJicn\noVarMT4+jt/85jc86ru8vIz5+Xkm9NqIFTOe9SruAcfMAoFgCsD3cc9BvyYQCP4HgHEAzwJAJpPp\nFggErwHoAZAC8P8Wi/j+7Pv5oEgkEhxh1tfXY//+/WhtbUV/fz8DTUiYobKykueN870cy8vLSCQS\nCIfDKCsrQ1NTE9rb25HJZJg2VKlUwmKxMMpwbm4OFy5cwNjYWM6siWZ6qS+mUqnQ3t6OBx54APPz\n8wiFQpydUqY5PT2NixcvYm5ubs1rJiedTqfZQZEjMpvNsNvt7PQoc6JKA5GZEIJ0rdGvdDrN10x/\npFIpswGFQiF2kATU8nq9CAQC7OBzIdUJhLSwsMCIT4lEwu2FaDTKIBS6ZiKEIWe91rpEHTo5Ocml\nzmQyicHBQSYKoWyernF+fp5LoLk2dSKRgMfjgUwm4wONxtDu3r2L0dFRPhgoOyBRlXyOhIIUuoaL\nFy/C4/HA7XZzby2ZTGJ6ehper5fxBsSTnOsApc/Rpj979iz8fj/q6+t5LIuEXzweDz744ANWKyvk\nSLLfjcnJSbzxxhs8PywUCrmXPDMzg66uLty8eZOvtxhbWlrCxYsXodfrsX//fg5aCdB47tw5nD9/\nHl1dXTwWWIzRSExvby9cLhcaGxs5S9Xr9QgEAvjwww8xNja2LupQ0hZYWFiAyWTC008/zRmrXC5H\nZ2cn/u3f/g03btxAMBgs6EzJiMmQ6GktFgs/c+KZ//GPf1yQwTDblpeXMTIygqtXr+LAgQMoLS2F\nQHBPdjIUCuHjjz/mc6enp6eoXjrZ1NQU3nvvPbS1tfHvT33f/v5+3L59G52dnVhcXGQwL93jXOuv\nrKxwcLNt2zYoFAoEAgEkEgnMzc2hv7+fxzdJKIl+H6qW5LJf/epXmJ2dRW1tLbRaLbPqBYNBvh8E\naKVxU9obVCFbbeTIf/zjH+Mv//IvIZVK8emnn+Ly5cs8CUBMb3Nzc1ydUiqVXKGjGe1i32syQbEv\n1n+mUembMh6KAPV6PXQ6HcrLy9HY2Mh9ADpwqUl/5coVXLp0CXfu3Ml5A2hN6jmq1Wq0tLSgvr4e\nW7duRW1tLaxWKx+sHo8Hv/71r9HZ2Qmfz5eXX5eQsuSkiRGIQBJESDE7O4uxsTF4PB688cYbTD1a\naGaPwFZqtRoGgwFmsxmPPvooj0tRmTIUCmF6epo3UL5DmSoLhPA1mUxMZ2kwGCCVSrG4uMgvWDgc\nZjnRcDicl0KTRkBo5ItmxwnNSpEljcnRC00YhFwAEXp+xHREjiOb9YzaGxKJhB0lPbtc0SwFJNRu\noflv+kPPnf6fWIno/4pl+sr+WfQxtXnoc+vh417rdyDQXfbPIDT9RvY6VTgIUUyEGzQrvxE1p+zn\nuGXLFp7P9/v9nI1lk9cUY4SPIOGdbdu2QSaTsQPxer1rIv4LGYHfysvL0draiqNHjyIYDGJkZARX\nrlzB5cuXi5Z/zTaJRIKamhrU1dWhrq4ODocDAwMD6O7uRl9fHwOl1nNvsRH9GwAAIABJREFUaRKA\nZCFra2tZa3psbAxDQ0P/4Uwodn0aOyUnTRUuCvJXPy/Kwou5D3K5nPcC7eFswh/6Q3uU9ke+a6f2\nEu3PtbJkWoNwRvQcC107EdrQddD5vdq5032iUjkFGauu+0Ymk9lR6D59qRz1Zx+zw6aHSBlSa2sr\nrFYrszgNDQ1hcHAQc3NzBXsWwP2BADkTAgiZTCaUlJQwoGxubg6jo6NF8VvTmvSHDjfqI0skEi69\nEip3PbzL2etn36Ps8nj2xxs56Ff/rLX+vfrnfF5b7cC+qHVX/4wvwzuez77oa1xd4l+vE1lt2e81\nHVD0Z6PXTUEPOX8ADP7b6PUSZoUAX5lMhkcJ6WzYSABENLLEcEUAVCrDbtRkMhmkUik/Lwrasx3U\nRowcz+ozYaP629nrriYPWv032Xrf6bUyWFp39f993rONvj/7XMsmkvki1qf1soOLHO/1H6ejXvX5\n//Bv+pMdyWT//3oc31ovF/D5HtSmbdqmbdqm/fHYf0Ywn51YFQg8i3LUXyqu79W2Vj94PV+/3q/d\ndNCbtmmbtmn/vew/49z/oquD6+cy27RN27RN27RN27T/Mtt01Ju2af8XLLs09kWvS2CXL3r9bGzE\npm3apv3X2Ze69L1pm1bI/jP6S9lAnGyE9+c1IvZQqVRQKBSIxWIIhUIsGrJRo6kDnU4Hi8WCLVu2\noKenB16vl+laPw+gTCgUMieAxWLhMblQKFRwLrsYI2pYjUaDkpISJBIJTE9PF0VJWsgIBCeTyVgL\nvhD//XqMpjKMRiOSySSLknxRYEsSJaJ3pBBJy3qMZB4puFsv/3Quyw5CJRLJutHr+dYF7ge1bnSd\nfOC3ja69lkrfF4V72nTU/80tH8KbbKNjPcAf1H5kMhmPS2XPQW4EhSuRSJjVSyaTwWAwIBwOM4nF\nwsLChhDJhNavqKhg2c9UKoWuri4mAaG5y/VeM2mJW61WnDhxAgAwNDSEqakpjI+PM4/9RhwIoacN\nBgMOHToEo9HINJojIyOYmJj4XEhqkUgEo9GI8vJyPPTQQ4jH47hy5Qpu3br1uQMlmls3m814+OGH\n4XA4cOHCBQQCAR7T+jxG1LOHDh2C3W7H22+/zajtLwINX1lZCZfLBZ1Oh4mJCeYm+LzXLRaLYbPZ\noFarmWKX+N+zRwbXe7005UIUqJlMhmlxSZtgvWsCYH59YpEkDXqabsk3HpnLaIxPpVJBq9UyiQqN\nYJL283ruNQURdG6QkMzU1BQHLbFY7D6q6vWsS2O6bW1tkEgkWFxcxNzcHJLJ5H1c7evdM19KR03z\nyHa7nWeeicifsgO1Wg2//x5z6fLyMqLRKDNQ5TqkKevQ6XQs6mA2m2E0GpmZTCwWM8k6cYkTUUeu\nyFAgEKCsrIwlIi0WC3bu3AmLxQKfzwe/389SaETrOTk5yevmmhnN1pbdu3cvnE4nKisrUVpaikwm\ng5mZGWbmunjxIkf0CwsLzBSV64VQq9VwuVyoq6tDVVUVHnzwQTidTpb+vHPnDvx+P3p6etDf34/h\n4WHezPkcLGVfBw8eRFNTE9xuN6qqqtDS0oJ4PI5QKAS/348PPvgAN27cgN/v59+h0MtL78KRI0dY\n/q+2thYqlYozpWAwiB/96EfweDws31doI5Mz0uv1qKiowLPPPou2tjYeAyRSms7OTpw/fx537txZ\n10YmEYDS0lJs3boVR44cwezsLCwWC2ZmZmA0GtHd3c3sbOs50AQCAZPzPPnkkzh48CAuXboEr9eL\n2dlZnnffCMkCAKaB/drXvoa2tjbmLLh9+zaT4nyeMaJt27bhySefxKFDh6BUKvGP//iPLCbzeYx0\nov/hH/4BDz/8MEwmE4aHh/Huu+9CKpVueASK3hWtVouWlhb87Gc/QyqVwtjYGBMj5eNGKHTNcrkc\nRqMRjY2N+Ou//mtkMvdoK2/fvo3f/OY3ANY/bkftClIjJDIYoue8c+cOU7AWa9QGIcdvNBpRXV0N\no9GIlZUVaLVa9PT0YGpqCn6/v6gxtuzRXKFQCKfTibq6uvvkXK1WKyuknTt3DktLS0Vdr0QiAfCH\nM+Sb3/wm9Ho9TCYTa3ErlUoMDAzg6tWruHbtWsFrBcC8HMSf0d7ejieffBJutxtzc3Pwer0oKSnB\nxMQEPvjgA1y7do391nrsS+eoqYzU2NiIbdu2wWq1oqysjKUjifFqamqKf+lYLMZau8QHvVZpw2q1\noqmpCRUVFXA6ndixYwer3iSTSYyPj2NiYgIejwejo6MYGhqCXq+HXC5nvt/VRtHZzp07UVtbi/Ly\nctjtdrjdbgBAXV0dwuEwbt68yfJ+ExMT0Gq1/HPX2tS0Ll3n448/zhKBpLtsNBqxsLCAiYkJDAwM\nsCi5SqVilq61TCAQwGQyoampCXv27IHb7WZ9axKnJ0H5ZDKJUCiEsbExprzMdwgJBPekKLdv3476\n+nom8ydWrHQ6DY1Gg+rqambhop5qoXWFQiFUKhXcbjfKyspgNpsRi8Xg9XqZWEWpVKK+vv4+nvVi\nDk062DUaDdRqNdMXEiOVQqGA2+2G1+tFd3f3mmWuXEYHmlarhcViQX9/PwYHB+Hz+ZBKpaDRaGAw\nGNjxrddUKhVqa2uxdetWyOVyjI6OwufzFUXrmc/ocD98+DAeeOABKJVK9Pb2YmRkhKsKn8dJS6VS\nPP300zh+/DgMBgOmpqYwNTVVNHdBvrWVSiWqqqrw1FNPQaPRYHFxEdPT0ywSsdG1s8mS6LAPBAKY\nm5vj+5I9QlqsUZnbZDJh9+7deOaZZ+B2uzExMYH5+XmMjo6uGx+QPf8ul8tRU1ODnTt3Yv/+/TAY\nDOjo6MDY2BjkcjnUanXR61K1jObKiVv94Ycf5syXxCdItrgQD3z2ukRY5HQ60dDQgJqaGqTTaT7f\n4/E4du/ejY6OjqIcdTZxFPkXu92OmpoazM3NYXFxkVXt9u7di2QyWdBRU/mduAWIB99qvSckOTc3\nh08//ZSDwpaWFgQCAQwPDxclBrPavlSOWqVSoby8HDU1Ndi2bRv27t3L/Ssq2xgMBmg0GigUCty6\ndQsKhYJp2YA/qP+sNqVSyYxF1dXVsNlsLIRAWsgqlQpmsxmBQACZTIYdSL6XjGhOW1tbUVNTA5vN\nBplMhpmZGZbUoxeFtJbJCVNPaK3NJ5FIWAyB2M0kEgl8Ph/TcFJJLBKJsNKTXC7nDZ3rsBAIBJyd\nG41GyOVyDA0NcWkmHo9DoVDwgUkHH2kv5zuEiLOZrmNpaQl+vx/T09OIRCIsQEEsaBqNBpFIBIFA\nIO/hTM5OoVCw2ll/fz9HrQ6Hg5XIqKpBTGqF2KhWPx+fz8esd4FAAA0NDXC5XCyColKp1s0gRprX\n5Ozu3r3Loh9WqxUKhQJKpZLLj+vhA6BgjioAfX19zHqXyWSYaQ1YXzYmEomwdetWFsrwer2sZkVZ\nfzZDU7FGB5zBYMCRI0dgMBiwuLiIs2fPsp440dZuJHskDYCvfOUr7KTPnDmDjo4OPqyLYZRb67rV\najXq6urw9NNP48CBA5ibm8Pdu3dx48YNJBKJnPrI+dak+2G329HS0oLnnnsOW7Zs4YQEAIur0PlW\nDAFTtmSmw+HAsWPH0N7eDpvNhnQ6DYvFgoqKCubtHxwcLIplj85ik8nEFM7Nzc1oaGiAXC7nwFkm\nk0EkEjGNcqG16dlRWXr37t1oaGjg/jlpBuj1elbHovelkBFxltFoRH19PSwWC6anpzEwMACRSIS7\nd+9i+/btqKiowP79+/GTn/wk77rZJX+FQgGLxcLnaSKRwGuvvYZEIoGRkRHYbDY8+OCD2Lp1Kz76\n6KMNBYlfKkftdDqxdetWNDc3w+12c42fXiBSeAqHwxgeHobH40EymYRarUY4HM7bY6Es3el0QqvV\nIpPJ8OaitSORCMbGxjgjIe7pfFG4UqnkTJe0b0kJKh6PQ6PRQCgUYnJyksuxxGdNjmStdSnDU6vV\nkEqlWFpawvT0NMvoLS8vM2NbNoctEeXn2xTkmDKZDGsZ37hxg0vHMpkM5eXlzDscj8e59ZDvkMgO\nOHw+HwtZkM4rOeeKigoWAlEqlazQVYwRpzMBmih6J6J8EusgkZJiIm7qoVGQ0tPTw2pt9AxlMhkL\nlhA71XpYqSQSCTQaDRKJBEvehUIhaLVaaLVapnIlSsZiTSAQoKqqCm63G2q1GgMDA5ienkYsFuN3\ngGha1wPCoYN+3759qKurQzqdxvT0NDweDweYhQLCXOvSgVxZWQmr1Yrl5WVcvnwZ77//Pst6SqVS\nxjOs52CjoPLRRx/FkSNHEAqFcP78ebz77rsYHx+/79lt5LrLy8tx4sQJHDt2DBqNBqdPn8bHH3+M\nSCTCuuXF3me6d5Ttbd++HS+88AK2bNkCkUiEU6dOwefzMRaDqhjFrk3fV1lZicbGRjz22GOs+z42\nNsbVPAqKimmPZKv5tbS0oK6uDm1tbSgpKYFMJoPX68Xk5CRSqRQ7ML1eX9TaFEQplUq0tLSgoaGB\n21qRSAR3796FRCKB2WxGTU0N9Hp9UfciW4zJ5XKhpKSE6ZYHBwdZNYsqoy6XK+96ADgApj1AGXks\nFsPw8DBu3ryJhYUFxGIxbp05HI6cwkCF7EvlqH0+H4aGhpikfmlpCVevXsXU1BQkEglKSkoglUox\nPz/PfY9UKoWFhQW+2blehkAggFu3bgEAC0ScOnWKH2BJSQmSySRmZmbg8/nu087NByBaWlrC3bt3\nYbPZ0NbWBpFIhL6+PkxOTnJWrNPpWIOYaFFHRkbySuIlk0mMjo4iEAjA4/Fgfn4eExMTmJmZwfLy\nMhwOB6xWK6OSdTodhEIhZ5iFpAxv374Nj8eDyspKaLVaVqYBAJPJBLvdDqlUCrVaDZvNhtHRUQY8\n5doYmUyGBS5OnTrFjomcvVarRVVVFcLhMMrLy+FwOBAMBuH3+wseyBQ0eDwenDlzhjNgkres+ExA\nxWg0wmw2w2azYXp6umjgF3GYT05OYmFhgdWNpFIpysrKEAqFYLFYYLPZoNVq1412FolEWFhYQDqd\nxuXLl1mZS6FQQKvVchAAIG/bYq11d+3aBYvFgitXruAXv/gFFhYW+L1WqVQAwFztxZZ9hUIhtFot\nnnjiCSQSCfz617/GK6+8glAoxDgEqVQKiUTCMpvFZqgikQgNDQ34wQ9+gN7eXvz85z/HmTNneH9o\nNBq+N/mkWlebQCCAVqvFt7/9bbzwwgtIp9N46aWX0NXVhWg0ColEgrq6OqjVat5bxVwzOVS5XI4f\n/ehHqK6uRjwexxtvvIG///u/h0BwTymutrYWVVVV6O7uLhpBTYHF9u3b8cMf/hBSqRSTk5N46623\n8Prrr3MG73A4oNfr83Lsr75eykqfeeYZtLS0YGFhAadOncInn3yC2dlZOBwOVFVVwel0suRuoXUp\nyLJYLNyLTSaTGBsbwxtvvIHFxUWEQiGYTCb8xV/8BcxmM1cy8mEvCCRqMplQXV2NiooKdHd3Y2Zm\nBhMTE/D5fJifn4dGo0F7ezsMBgOsViukUmleISa6F2q1GlqtFsvLyxgdHcXly5exsLCAcDjM6odW\nq5VppYnTPt+6ALjiODU1hYmJCZw9exaJRIKfE51bMpmMsSq5qr757EvlqClLSqVSiMViMJlMKCsr\nYx1YkUjEYBvSh43H4wiHwwXnUombNxwOM79ua2srSkpK2Il4PB4uL0kkEoyOjnK2lStyS6fTXLql\nw6umpgZNTU1Ip9Nc5qWDRyAQwOfzYWRkJO8hQYpc8XgcPp8PAFBaWgqj0ch9ar1ez1+n0WgwOjrK\nIJx8kWYmk2HNYaPRyJuaxC7UajW/tIlEgpGn9L35jH4nAmgIBPeI6AkQotPp+D4olUrmxS1k9HNJ\nUi5bFYyQltn851TuLuaQp68hpTJCBQN/yExITIR0x9fbSyZE6NLSEve2aWSIJEwBIBKJYHR0tOhs\nTywWw+VyYXl5GR6Ph99vg8HAAi5SqRQ+n481wYsRphCJRFzu93q9uH37NhYWFqBWq6FSqaDRaGAy\nmbjSUyiIy74PKpUKR48eRUVFBd566y10dHQgFosxMp6C23A4zOprxTrrxsZGPP7441CpVCwdmq0y\nV1lZCeCe+tl6wFNUmm5sbEQymcSlS5fw6quvIpFIcEDkdDohFArR19dXVEuAqhYulwvPPvssFAoF\nhoeH8eabb+LChQuIxWKMmaCWEYlHFDKBQICamhocP34cbW1tEAgEeOedd3Dnzh0MDg4CuHeeaDQa\nDnKLWZP2g8PhQGlpKXw+HwYGBtDV1YXJyUkkEgnOYEtKSjA/P8/YnHzr0t5VKpUcOFy/fp2TmUgk\nwvgXh8MBjUbDZ1KhdckxyuVyzM7OcrtQIBAwGp367AqFoqj3gtal8zebp53+nwIAk8kEsVjMFcCN\n4FC+VI6aHHQ0GoVKpeJ+Qk1NDex2O9LpNPx+P2w2G2ZnZxGLxSCRSDA8PIz5+Xku/65lpLJEfeh0\nOs2jLJSVTUxMcAa9srICqVTKoIhcs6gEriL5SYVCAafTCavVikQiwaUbvV6PyspKBleQxmwuMBkF\nCFRiMRqNrEiVnX0B9zRzLRYLSkpKOBAIBoN57zUdfjTqYLPZUFpaikQiAZfLhYqKCggE9/SOo9Eo\nZzmFQFT0f6TYRDOsFHSVlpairKyMy0RUdivWaByD7gvdd4PBwIGL3+9HIpFgub31WiaTYZUgAhMC\n4MrAetel6xQKhUgkEowStVqtKCkpgV6vZxDO9PT0mtKkaxk5PY1Gw9Khy8vLqKioQFlZGSwWCwdc\nY2NjXEkqlFVT9tjU1AQA8Hq9mJ6ehlgs5pIjVV2CwSB6enpw+/btokZwRCIRysrKsHv3biiVSly8\neBHhcBhyuRxKpRKlpaWoqamBxWLB4uIibt68WXT5WyAQ4PDhw6iqqkI6ncbp06f5fpMUbEVFBVZW\nVjgYKsYoIzt48CCkUimuXbuGV155Bf39/dyLpf1HQKFi1qYA+dChQzh8+DD6+/vxy1/+EpcvX0Yg\nEIBIJIJGo4FWq2XMSKGgNhuNvXfvXrS3t0MsFuPGjRv48MMPWZ6VyuIkFxuPxwuuTVUaCliCwSCu\nXLmCjo4OBvkCYAwRoagpsM53zdTzttlsEIvFmJycxOTkJF9TJBLh5ISCLdJKz5VRZ5fpqXVH5xmd\nU4SDMJvNaGpqQiwWQ29vL+tI5zJq61G5nlqo5JzpTKPppWAwiI6ODt7f6x1V+9I5auo/P/DAAxCL\nxSgpKeEeHmWWFKVUVFTwRiTkJfW1VtvKygo8Hg8UCgXm5+cZdERgCxqNUavVqKyshEKhgEgkQiQS\nQUdHBwCsOTJCTkmj0SAYDEKj0XBPUyKR8AtMsnZSqRSRSARtbW24fPkylpaWchJeZDIZBnZRuZE0\nrUmyj2Z/aZaT7qHP58ub4ZDsJEWC5OiUSiUA8Muv0WiQyWRgs9ng8/kQiUQKjp9QkEGVCCrfyWQy\nLsWurKwgFotxtlMMipqyb/oaUj2zWCzQ6XRchg2FQizUXmwPNTuqJ0ISEn6nGXCRSIRQKMT9zWLX\nJizB8vIyOyOxWAyNRgOdToeSkhKOxilSp42cb23KQKnUlkwmYbFYGChIABeqbiQSCUxNTXGWku9e\n6HQ61NTUIBQKYXR0FABgsVjQ0tLCe48OP5lMBo/Hw/iRQveiqakJTqcTqVSKgUcajQYOhwNbt26F\ny+WCVqtFIpHAW2+9xXPx+YwO+3379kEqlWJ4eBjXrl3j+5ytbR+Px9Hf38/TE4XWlUgkjCCfm5vD\nO++8g/7+fiQSif+fvTeNjfO8zoYvzr7v+3DnkMNNpEhRErVYUbQ4jpPalpM4AZKgKBoUbdEi7d8W\nCFAgRYGmSNAlSPImSNPGWZzF8aLYsR1Lsi3KtmSJkihxEbfhcDicjbOTs5Ez7w/1nAwZznCopO+n\n9uMBBMtabj1zz/3cZ7vOdTEYsL29naUqCduyk6lUKgwMDOCJJ56AWq3Gj370I7z55ptcIdTpdFxF\nczgcUCgUm4CB2z0rlabtdjvvxeTkJH72s58hEAjwmaWRqoaGBhSLxR2dh0Ag4BHDw4cPo6enBx6P\nh8c48/k8O3KtVgun0wngfguP7oxK6yoUCuj1euzbtw9msxkGg4EDFbpfKdDq7OxEW1sbtzwrrUsj\ndCQPbDKZoNFoEAqF4Pf7OVGgROLw4cNoa2tDKpXC7OxsVSctFArZd8hkMpbrXVlZYXDzysoKI8GP\nHTuGQCDArYUHGd17qBw1AGQyGdy4cQMKhQLNzc2bxrJSqRTi8Tijc48cOYL29nY8/fTTOH36NP7u\n7/4O7777bsWMZ2lpCS+++CIj/QhtSoLkiUQChUIBTqcTLpcLn/3sZ3HmzBmMj4/jxz/+Mc6fP7/t\nusViESMjIwgGgzyf7Pf7kUwmGbGYyWRgtVrR0dGB9vZ2fOlLX8LExAR++MMf4q233qoYBOTzeXi9\nXrz88stoaGhg5y2RSBjVG4/H0dPTA7vdjuPHj2P//v1IJBK4c+cOMplMxWfOZrNYWVnh9Xw+H5RK\nJcxmM8bHx+H3+wHcjwr7+vpgsVhw9+5dTExMwO/37+hESqUSdDod9Ho9NBoNGhsbIRQKkUqlMDY2\nxqNfDQ0NCIVC7ATLS0jlRmh5YrCy2+1wOp04cOAA98D8fj+PJVmt1k0oaoqgt1NdI61vnU6HwcFB\nLvFarVaoVCoOqDKZDMuhEj6AvqftLrq6ujo0NTWhtbUVHR0dyOfzcDgcXL7T6/WIRqM892yz2eB0\nOpHJZJBOp7nUv9Xo4qTWDU0qdHV14fjx49DpdAiHw5idnUUmk0F7ezvjGH7xi19weX+7MUaFQgGL\nxQKDwYDZ2Vl4PB60traiubkZHR0dXO4GgKeeeoqRrpcvX8b4+HhVp1pfX4/HHnsMGo0GU1NTkEgk\nOH78OMxm86aqTWtrK0wmEzo6OjAxMVG1WgbcD94bGxvR1dWF6elp/Nu//Rs8Hg8GBga43dLW1ob+\n/n7k83ncvHkTOp1uRzyAXC5He3s7vvKVr6CzsxN//ud/zgEAjWH29/eju7sb7e3teP3117ndVU3/\nXCgU4qMf/Sj++q//GhqNBhcvXsQLL7yAUqkEs9kMuVyOc+fOwe12o7Ozkz9/tUydtKhPnjyJw4cP\nI5fL4Xvf+x4uXrwIn88HnU4HlUqF9vZ2dHZ24qmnnkI+n8cbb7wBj8dTcV2BQICGhgb81V/9FZqa\nmiAWi3H9+nX8+te/Rjweh1wuh1qtxv79+9HR0YHm5mb09PTgzTffxMjICJaXl7fN1sViMY4dO4ZT\np06ht7cXAoEAV65cYRwOVSCB+y2N06dPo7W1FdFoFM8//zzC4XDFKsBnPvMZnDlzBo2NjVCpVHjn\nnXf4HRaLxfB4PIhEIti/fz+6urrQ0tKChYUFvPzyy/D5fNxi2Ho2FAoFPve5z+Hzn/88VCoVNjY2\nuOQvkUjg8XgQi8Wg0WjQ09PDScuXvvQlTsikUun/7Iwa+I1zGh0dhd/v5zKOUCjE8vIyb3SxWITP\n58PAwABfKv39/bhz505FIMfGxgbm5+cRDocZNFAqlZBOp5HL5XjzPB4Prl+/juPHj6O+vh69vb0Y\nHBzE+fPnKz73ysoK95QmJydRKpX4MqR1vV4vbt26ha6uLgwODqKzsxO9vb24fv161Uwkk8kwcYBY\nLOZMn6oHGxsb8Hq9aGlpgVarhcPhQGNjI6anpysiiKk/Q2NegUCAh/8DgQDi8TgDLQgVX1d3n9jF\n7/cjEAhUvDjpMqHLinRsCf2YSqXg9/uxurrKvWWNRsPOjvraW40idoPBwH10l8vFTjqbzTL5DdEj\nUjabz+chFou33edywgKa0TabzUysQz1pakUoFAoeGaSIfrtWA2VjMpkMNpuNcQpUriZWpPX1dQY7\niUQinjunfl8lRw2AiV7IURHKPRgMIhgMYmlpCSaTif+sTCbjva3WviBwmEAgYNCiRCJh/fdYLAa9\nXo+6ujpuC9VChUqjN4Swp/FAQiLT2aFSaa3IbyLIWF9fZ0S9SqWCVCrlPmkkEuFxy0gkUlN7QSaT\n4dChQ2hubsb6+joWFxe5J03fJWWFdXV1CAQCNTF8kZMym82IRqMYHx/neVyaWHA4HGhqaoJUKoXH\n4+E2XCUTCoWwWq04ePAgXC4XFhcX2SGVSiWYTCY0Nzdj37596O7uhkajwc2bN3H37l34fL6qo5Fm\nsxlDQ0MoFAoIh8PcXtvY2IBQKITT6cRjjz2GxsZGmM1m7tUvLCxUJB2SyWQYGhrCoUOH0NnZiZGR\nEaRSKaTTaSSTSeRyObhcLrS3t+ORRx5BfX09UqkUpqamsLCwULWyd/r0aQwPD8NqtTLwM5/P8z3Q\n0tKCI0eO4MSJExAIBPB6vbhx4wai0Sii0WjFdRsbG/HYY49hcHAQiUQCy8vLPGlDpCqtra348Ic/\nzO/F22+/zfcT8L8koyajMSfgN+wvNJZEh4Mo5I4cOYKTJ0+iu7sbWq0WgUBg2zXJAVCfmH6NLngq\nla6urkIgEODOnTtoamriub1qB5mes/yyImF5Ykujsua9e/c422xsbOQe6HbrEgIR+M0XXCgU+CDT\nhUAlWq/XC7vdznPXlYwoBDUaDZekKQuOxWJYWlra1COnS5XGiCpF9lR6MxqNkMlkXH41mUyIRCLw\n+XzcZiiVStybLM9atzvIdInpdDo4nU44HA64XC44HA5sbGwgGo1idXUVEomEWw/UU6bLrVLvl5x+\nV1cXrFYrUzcCvwk6NBoNBAIB1tbWEIlE+DLJZDI8x77VKDutr6/ny51AMMVikf8OVQjorBCamioA\n261L54IcPU0BpFIpRCIRrK6uYnV1lRHxGo2Gpxgq4SJobQpK/H4/BgcH0dbWBrVajXQ6jYWFBV63\ntbWVgxWv18tZfSWj3je9F1KpFHa7HaVSCT6fD5lMBl1dXejs7ITuwstrAAAgAElEQVROp0M+n0ck\nEtkRE0CVFofDgVwuh1QqxW0RKvcqFAo4HA4G9ZAz3ykAMBgMGBgY4HFKajfIZDKk02koFAq4XC40\nNTUhnU4zkrjaLDy1V/r6+lAqlRAMBjE1NQWdTscBgNPpRE9PD2w2G3w+H27cuMEVp0rrikQiHDhw\nAG1tbTAajRgZGYHf74dEIuGyMY09uVwu+P1+3LhxgzkDqgVu1GIiIqj5+XkmZKLSf29vL0wmE1ZX\nVzlICIfDjPnZahRcDQ4OAgBef/11TE5OArj/Tn74wx+G2+2G2+3etO7KygozMFYKtghns76+jlu3\nbuGFF15goFqpVMLHPvYx1NfXQ6VSQSAQ4OrVq/zdVAsOe3t74XK5IBQKMTo6ipdeegnBYBBHjx6F\nRCKB2+1GfX09pFIpBAIB5ufnkUqloNfrkclkEAqFHoiv/aFy1FQSIsdHF5FareYLnMp1BFQKBoNY\nXFyETqfbsdxElxsBAZRKJVZXV5nthi4veg6/388ArkoXGzlTYvehDJH4fgmlTiUQ4H6GTC8kse1s\nty45X4VCwdR5CoUCkUiEx9GoHCaVSvmCpcy4Us+3XBzCYrHAaDTiwIEDiMViHAFT758cs16vh8vl\n4rJypWem7JScaVtbGwYGBjA3N4d0Os1z5Hq9HnK5HEajEWq1GgCYwnVrFYC+O+LK7uvrQ3NzM7q7\nu7GxsYF79+5hZWUFAoEAarUaTU1NXNEwGAwIh8PY2NhAPB7fdj8MBgOcTicaGhqYZIZAJ9Q3pl40\nobTFYjHC4TC8Xi8WFxcr7jOdCbFYDK1Wi7a2NqanpV439RRpRlypVGJpaali2Zv2o1gsIpFIQCAQ\noKOjA8D9TDoWi0Eul3Nv3eVycaZCNLvVzjPxpQcCAdTX10Ov17PIh1qt5j3Yv38/YrEY5ubm4PP5\ndhxJqqurYywJXdI2mw2BQIAJh1paWhjLMTExwSNtOzlqCrA3NjZgMBi4JZBIJGCxWJggI5PJ4N13\n38XCwkJN8+pEbkP0t3QX0EjWwYMHUV9fDwCYnZ1FMBjckV62ru4+MyBVtFZWVrC+vs6jhU6nE8PD\nw9BqtYhEInj77bfxwQcfMGCp0l5IJBLs37+fBUKKxSIMBgMT6pw4cQJut5v35tKlSxgZGYHX663Y\nIiOjilo4HOZyNzGcdXd3w2q1QqfT8fuwuLiIubk5Tqa2M8rylUolIpEIvF4vj7S6XC585CMf4dZT\nLpfD9evXsba2htnZWSwsLFS836i/LxKJEIlE8PLLL/NedHV1wW63o7Ozk6stN27c4Pd9ZWWFW4rb\nrZtKpaBSqRCPx/Gtb30Lk5OTzJJIrRCdTodAIID333+fwWqU/e9mdr/cHipHXe7MCOlMoxpEPkHC\nC0TW3t3djeHhYdTV1eG73/0uvF5vxfXJoep0Ouh0OnR1dcHj8WB5eRlyuZyRhZRlfvzjH0epVMLt\n27fxT//0TxXXpflNQn62t7djYWEBi4uLSCQSSKVSsNvtsFqt0Gq16O3txfr6Ol5//XX867/+67YV\nAAKGEGK6q6sLPT09kEqlmJ6ehk6nQzqdhlarRXNzM/r6+tg5plIp/OpXv+KqwXZr0yykxWJBZ2cn\nDh8+jIWFBcjlcrhcLmg0GjQ1NcFisfCIyPz8PD744AMsLCxUXJeqH3a7HQMDAzh48CBUKhXC4TB6\ne3vR0tICvV4Pt9vNY1qpVArf/e53Nx3m7faiWCxCq9Wip6eHX7Tl5WVGZ1OFgHqm8Xgc09PTm9oP\n2z1zOXucXq+HQCDgrJ9aLnK5nIMIKjdnMpmqWWSpVEIikUA0GoVer2eQCWWT5AAEAgECgQDm5uaY\nLYki7+0u/FKpxIQm6+vruHz5MhQKBRoaGqBWq3HixAkGCxYKBTz33HMYHR3F1NTUjsAsAp2trKzg\n9ddfx2c+8xlYrVa43W50dXVheHiYg9G5uTn87d/+LXNz71TSKxaLWF5exg9/+EPGaxw5cmTTbLbT\n6cQHH3yAn/70p7h161ZNHPAUlI2PjyMej8NiseDs2bNYWFiA1WplPv9EIoFnnnmGW2i1ANToDHV3\nd0Ov1+OZZ55BMplEU1MTmpqaUFdXh7GxMTz77LO4efMm97x3wm+QYwCAgYEBNDU1IRKJwOFwQKVS\noVAo4Jvf/CbeeOMN+Hw+fjd2Wvftt99GY2MjWlpa8IUvfAGZTIarhtlsFnNzc3jhhRcwNjaGK1eu\n1FSmJ+DV+vo6Dh8+jAMHDvDeUeXG6/Xiq1/9KoLBIAKBAFZXV7nNVGn9YrHIDlqj0eAf//EfubKp\nUqkQCAQwPj6O27dv4/bt21zFonGtaplpNBpFIpGATCbD3/zN3yAUCjFY2O/34yc/+QlGR0cxPj4O\niUTCjJEUNFUyj8eDVCoFnU6Hv//7v+f2YD6fx7179+DxeBAMBvHCCy8wiyHtv0gk2hVJUrk9VI6a\nbGNjg0uA9fX1cDqd6O3txcTEBEddYrEYg4ODOHDgANxuN+Lx+I6qNTQylMlk0NDQgKGhIRw4cIBL\nerFYjGlMKcNaXl7GxYsXK/aQqTy5vr6OXC7Hfc4jR44gn88zWUljYyMaGhoYWDU5OYnXXnttE4NU\nuVHWSlUEjUYDm80Gh8OBrq4unDhxgrMQjUbDM5yrq6ubIrntIjgKiKgkTz1Ui8WC4eFhrK6uQiwW\nM0hEKBQiHA5jenqaxwu24+amdem5yfnJZDIMDg5yj40AFcD96kIqlUImk4FCoUAqlfotBCpdfNR/\nplaFXC6HwWCA3++HUChEPB5HMpnky5IiZKlUilQqVRF4ksvlmC40FotxOS+dTmN6epoRvvT3xWIx\nVlZWKpb0yq1QKCAUCkGpVGJubg75fB4ikQihUIhJSJaXlxnImE6nkUgkdqTPpNJ5qXSfYS+bzTJD\nGaH/I5EIFhcXcenSJfh8PgZi7oSsp7VjsRi+853v4NChQ2hvb4dSqUQ+n0c4HMbMzAxu3ryJe/fu\n7UotKpfLYWJiAt/+9rfx2GOPwe12QywWc1b8wx/+kMUtdiqllxsFQRcuXMDAwADUajX6+/v5Hbh5\n8ybOnz9fs5MmW11dxezsLKanp9HW1oYDBw4w/qFYLOL8+fN4/vnnMTc3x0lELZbP5zE3Nwej0QiN\nRsNBfCqV4grF888/zxz5tVCe5nI5zM7O4tKlS9jY2IDb7eZKQyAQwGuvvYZkMomJiQn4fL4dmQbL\nbWZmBq+88go6Ozv5/aRkZHFxkYlJkskkByDVBJLo91999VVEo1H09PTwfZlIJBAOh+HxeJDJZJhE\nJZFI8KQK3QfbteBKpRKeffZZLC0tMQ/HL37xC+4/x2IxrK+vM66IyIeolUio++3WDQQC+Pd//3f8\n4R/+IQDg9u3buHnzJsbGxhCNRrnSRZUjqhpEo1GsrKygUCjUxNK21R4qR02bQxdFNpvF4uIiGhoa\n4HA4OAuTy+UM1ycQy/j4OA/yVzJq6NPIVF1dHZr/S9Lw2LFjUKlUTGG3vr6OpaUlXLhwAT/96U93\nVDwhBxMKhTAzM4NDhw4xiQWVsOmQrays4KWXXsLbb7/NBBjbGQHrEokEAoEA7t27B5PJxHtBXOR1\ndXVIp9Mcgf7qV7/il6XS2Fc+n2fhikAggGg0CpPJxEhk6udSOfbdd9/FyMgIxsfHK473UNBSKBSY\nbvL9999HZ2cnlxAFAgGy2Szu3buHcDiMXC7H/NSUPW2NOmndfD6PpaUlzM/PI5FIoKGhgUFI1Lei\n3rdSqYRYLGamMcoUt3vmbDaLhYUFvlgoO6RLc3x8nFGdJMlIjp1AfdsZ7Z/X60WhUMDi4iJsNhsK\nhQLi8Thn5QS4k0qlPGpFF2il74/wFsViEQsLC4hEIrh69Spn/jQZQbzou5F2JGedzWbx2muv4b33\n3mOHQsC3VCqFRCKxa63rjY0NJJNJjIyMYGZmBsePH4dcLkepVGImQuJIqBUZS8+bTqfxwgsvYHl5\nGXa7HXa7HZFIBHfv3sXo6Cg7p92UHtfW1jA1NQW73Y5EIoGuri5oNBokk0ncvXsX3/jGN1hVrlaj\nUujIyAg2NjZgt9u5dz87O4vR0VGMjo4iGAzy3tbyzJlMBn6/HxcvXkQikWACJKrqvPfeewgGg5va\nfOVnrNooZyAQwHPPPYempiYIhUIUCgXMzs4in88zToMCVxqjpJ9XOscEGg6FQrhw4QJKpRI7ZDpX\nRG5FgT/hdShIrXT2XnnlFczOznJQ4fV6N2Xh5UkF8XWXtywrBZ6rq6v4z//8T4yNjTHlMlVN6TNS\nC5IquNTqovtlt04aAOoepF7++7a6urrSf/13E+rTaDSyDm53dzc++clPsjoJkRaEw2HcvXsXly9f\nxuXLl6uCZKiHSrN+R48exb59+9DX14eWlhYYjUYGmgUCATz77LMMyKh2IZVTKur1elgsFjz99NOs\n1EXAGFJPuXfvHr7+9a8jHo9XnXUu56i1Wq0wm8085vPRj34UOp2OL77XXnsN09PTuHnzJpaWlnYk\nwSc0skwm43Ky3W5HU1MTmpubIRKJmOji3r17XIbM5XI7gpFIPEWv10Or1TLJDJGH0GwuiYCUq+1U\n65/SzKPFYmFHXH74CaREFwWVlqnXXK0vS2x0hBKnS6IcF0Ho9bq6Ou4B1uL8ynEXW+dgyenSulT1\n2c3LXP7eUPuBKinlDv1BLgh6Lmpp0AVM+7PbMZPy5yU+BNJFpsue9ns3dxOtqVarYbfb0dDQwMQZ\nNGFQ7WxVsnKRCIfDgcOHD7Oymsfj2QS63M2zEsmQ1WqFw+GAwWDAzMwMFhYW+D0rf9Za1qe7gvAn\n5ah8AhLSs9J5qXWfxWIxT8rQOaKWUvn3VX4OazkbtL/ljpPW37qvxBlB71y156aqK61TaRSxfN8I\nAwBURmbTd0eJDCURW9em+4RwUYQ12eY5rpdKpaGd9umhctRbjS4JMmKLokuY0KDlxBO1fp7yv7NV\nXGDrpbYbAED5C7C1fPIw7PWe7dme7dme/fdbuS+oYjU56oeq9L3VtmYAlVCau42+t/6dnaLs3ay9\nNVvasz3bsz3bs///2e/z/n8wza0927M927M927M9+39ie456z/Zsz/Zsz/bsIbaHuvS9Z3v2/5WV\n4wt+nyUsAmWVs5M9CCBru3VJP7y1tZWlJ2uZv93JiG2utbUVVqsVmUwGy8vLSKVSiEajv/OzE1BJ\np9Ohra0NhUIBk5OTNdN8VjMC/xAHv1AoxN27dx8IsLadCYVCKBQKdHV1QSKR4Nq1azyu+btaubAE\nTSJUIu3ZjdHZJtAkAUtrkbqsZW0CThJgcLdI+0pWLtzzoMQh9FzlCO2t6z3o2ts9Xy1iQ7XYnqPe\ns/8224pGBjbjDh708BKiki5hqVS6ibhhtyNDZITqJoEIkuOcmZnh+Uqih9yN0eVFM+9nz56FUqnE\n4uIilpeX4fV6Wc/5QZ+bONCPHTsGp9OJQCDAa3u93gd2HPTsRHd79uxZ5HI5vP/++xgdHUUsFvu9\nBAEmkwkf+9jHsG/fPrz11lvwer0PNG+61UQiEZxOJ86dO4d9+/bhxRdfhMfj4VGd3+XZ6+rqmK/f\n5XIhHA4jFAptQoI/yPrk4Orr62E2m1lJrHxWe7c0lPQuUjBHbHlErxqNRneUdqy2rkAgYJldGgsk\ngiAKSneDuKdJA2KmJJU4+vwCgYCDlt0i7suJpIxGI4RCIbPjEUlRLZrt5bZ1cmRoaAgikQjZbJZp\nVJeXlx/4zD2Ujro8Kqv0opaPoZTPnFbbBPo7ADZFPvT/5Yduu3V3GgcoX2srKUj589LIBKHLq61L\nXz6NsJC2cfnBL8/+aAZ3J81hmvFTKpWQyWRQq9Uwm80wGo0IhUJMFUqz58SotROJAa3tdDqZ3MVo\nNOLEiROsEJXNZuH3+5kzOxKJVCR+KTciTyF9cuJDNpvNiMfjSKVSCAQCuHTpEiKRCJNF7HRB0HdD\nercf+tCHcPDgQezbtw/ZbBZLS0vwer2YmJjgef2diEPKjcbK9Ho9Wlpa8OlPfxrFYhHJZBLhcBiv\nvPIKpqammKN7tyNElDG2t7fj1KlTkEqlMBgMKBaLvCe/y3iWTCZDZ2cnenp6oNVqMTU1xeejfF72\nQUwul7OQxIEDB+D3+3m+/kF0e8uNRCo+8pGP4MyZM1CpVOysdqLMrGZ0XiQSCR5//HGcOHECSqUS\nN2/exJUrVx7YSVOgSM7pD/7gD+ByuZBMJrGwsMBiPw8SKNJYKjEzmkwmro6srq4iHA7vet3yu0km\nkzGXOpFVJZNJLC4u8l1a67PS/hKjnMVigcPhYJ6FXC6HZDKJ27dv1xxY0N6KRCJoNBocOHAAnZ2d\naGxshEgkwszMDOLxOEKhEG7cuMGSyrU8L1VVSESlvb0d586dAwAkk0msrKzg+vXrePXVV7G0tPQ/\nn+sb+M0HJ3Uh4D5Bu0wmw+rqKg+kK5VKHrAn2rnV1dWqkRtp06pUKhZC0Gq10Gq1zCQjl8t5rpdY\nsEjYoRKpAfH+kqSjUqnEwYMHYTKZEA6HEYvFsLa2hlgshmAwiPX1dYRCIZa/rDTfSZeKxWJBf38/\nLBYL2tra0NTUBOA+NzY953vvvcfKVPF4nPmtK+0FiRi0tbWhubkZp06dYh3gZDLJdJOzs7OYnJyE\n1+tl4YtKwhnAbzjK+/v70dPTg9bWVrhcLrjdbiabiUQieOutt/DBBx9gZWUFAFg2s9LlVu5MH3nk\nEZb/I+3wtbU1JksgFaZCocAsXztZXV0dc02fPHkS/f39MJvNWF9fh9VqRXd3N2w2G/L5PBYXFzmy\nr+Uyrqurg1wuZ0dtMpmYq14ikWBwcBCxWKwmre/tTCwWQ6PRYP/+/Whra8Py8jLi8fimYHM7pqVa\njC7MoaEhHDt2DJOTkxgfH+eL7Xcp69XV1cFkMuH48eN44oknYDKZ8Pzzz2NsbIxZ1B7U6FI+e/Ys\nzp07B5fLBa/Xi3v37jH5y4M+MwUvDocDTz/9NBQKBZaWljijftAqQHnG63A4cPz4cYhEIng8nk1j\no7sdFSUnZTAYmG++s7MTwH2qzbGxMchksl2Ptm7Vn25vb2fxDKlUijt37iCdTtdMBkN7S1UWq9WK\ngYEBZk0MBoMsCDQ1NYXJycmavkcKVKiqpVar8eijj8LpdMJisSAej8NsNgMA7t69i1QqVZOjpj0Q\ni8X8jH19fTh16hSam5sRi8UgkUhYm3tpaYnZyXZrD5WjJkWe1tZWDAwMYP/+/Sx2UV5GpY186aWX\nmIoxl8vB7/czo9nWQyeXy3H69GkMDg6isbERDoeDvxxSWiLlIcqc7t27x5SjJIKx3bpNTU04d+4c\nSySS0pJIJGIe6cXFRczMzODu3bus5ZtKpRAKhXhovtxIWailpQWDg4M4c+YMtFotk3ikUik0NTUx\n1eX8/Dx8Ph+LlROn83b9FpFIxFKb3d3daGlpgUQiwfz8PNbW1vhFI8lFyrL1ej1KpVLFLJUckslk\nwr59+9DR0QGDwYBAIMB6sAaDgak/GxoaWBpUIpFU7aXSy6vX66FWqyEQCDA+Po4LFy7A4/EweYTN\nZsO+ffs4eNvY2NixPLuVxODevXu4fv06YrEYVlZWMDg4CLfbDZvNhsOHD+P27dsoFos1Z2V0dkky\n8//8n/+Dubk5rK+vw2QyYXh4GH19fcjn8ygUCjtWQ7aubTabcfLkSRw7dgypVArf//73sby8zBlv\n+d7uxokIBAJ0dXXhz/7sz3jtL3/5y/B6vb8lYvMg5UeFQoF//ud/Rnd3N9bX1/HLX/4SV65c2cTM\ntluj71Kj0WB4eBhf/vKXkc/n8etf/xqvvPIKkxc96NoKhQJmsxknTpzAX/zFX0Cj0eDatWt48cUX\nMTMzU5G1r9qadFcQ4dBTTz2FkydPolAoYGRkBPfu3cPExARX1GrZG3L6dH82NTXh7Nmz6OvrYxGR\nt956C9FoFE6nk9XSalmX2k1NTU3o6upivXXS0SZ8REtLC6ampvDrX/8ac3NzO65NrJAqlQo2mw1P\nP/00WlpasL6+jmAwiEwmg1wuh5aWFnz+85/H2NgYbt26VdM+a7Vaplru7OyExWJBKpXC1atX+Y44\nefIkPvWpT6G/vx+XL1/ecU3aY5PJBLfbjY6ODhiNRuZTJ2pqh8OBr3zlKzAajRwI7NYeKkdts9ng\ndrvR29uLjo4O1iienp5GLpfjqK9UKrHcGSlRlfdGtjsQZrMZXV1dqK+vh1arhVgsxtjYGGe0crkc\nGxsb8Pl8mJubY3k46k1Wujy1Wi1sNhv0ej3EYjE2NjYQDocxOTnJ/NV1dXXw+/3w+XxYXl5m1jCS\nqqyk1arRaFjvNpVKMaNXLBZjkneJRIJ4PI7l5WWk02l+hmrsPcSORaISCoUCc3NzWFlZQTgchlAo\nhN1uZ/rJlZUVSCQSZLPZqrzOFEgR/apUKmVK1YmJCQBg8QiStSyVSkwvutOLTOxjRIxPgKZQKITF\nxUW0t7czCxmVg2OxWNU1gd/QCWYyGcTjcYyOjqJYLCIcDiOfz0Ov10Mmk6GhoYHFYHYDoipvnYRC\nISSTSaZyJM5hkuesJHlayerq7isFWSwWqFQqzM3NYX5+ngVFqDRZjU2u0roCgQDd3d0YGhqCVCrF\n4uIilpaWOECh7OdBSt8ElOrp6YFMJsO1a9fwq1/9irkSKGjabXZKTq+9vR3PPPMMisUixsbG8NJL\nL2FsbIz7nrvdC1rbYDBgaGgIf/zHf4ympia8+eabeP755zn4BmrHXpRnkCKRCG63G+fOncOxY8eg\nVqvxyiuvwOPxsI4xZWK1BEbkUKlV5HK5MDQ0BJ1Ox/cnnT9iA9uOv3+rUcVMIpGgt7cXra2tm6pa\ni4uL8Pl8EIvFcDgc0Ov1nGjt9Mwk76pQKFi8Z21tDaFQCAsLC5iamoLZbIZCoUB3dzff+zutS/tL\nlSeBQIDFxUXMz8/j7t27iMVizGi3f/9+GI3GquuV7zHJ0RK3fi6XQ6FQwMWLF1lAJBwOY21tDVqt\nlluvu7WHylFns1koFArmyE4kEpienma9VJIqk8lkrD5DQIV0Ol21H7m2tgaBQLCpRP7OO+8gFAoh\nlUpBrVbD4XAgFoshHA6zCAfJA1YqV1D5OhaLcZ96fn4ely9fRiwWg06ng8FgwPr6Oq8rEAiwsrJS\nVbeW+jCRSATLy8tM5zk/P8+gBKLlJJBFOSF8NVGHjY0NFmygPuD4+Dj8fj9CoRBrtMpkMu4dF4tF\npvmstC5l26lUioXd19fXEY1GMTU1BZlMxrJ1xKuuVqv5z1V74ehlIJEEumji8ThL6VEmRhkVOdpa\nLk5SwInH4xAKhfydUpBE0XypdF9QZLfOiaJ2pVK5yZGm02nOTuVyOUuw7sZEIhGMRiP8fj+uXbvG\noiSUUVG1ANg9y97Ro0chl8vh9/vx5ptvcrupnE51N1aOA+nr6+Nq009+8hPcvn2bec+pV7ub8jc9\ni0ajwZEjR9DT04OpqSl8+9vfxvvvv49cLsdUv7ulaSVraWnh3nE+n8e3vvUtLCwscIWAlJh287wC\ngQAajQanTp3CyZMnoVarMTc3h5dffpnvROB+xlkLyImctEgkgtlsRn19PbduFhYWMDExwQIxxWKR\n8Sm1rCuVSjnYMRgMLIGbSqXw8ssvMy2yWq1mhTuRSFTTOaGzIZfLWWSHtLonJiZQKBQQDoeh0+mY\nm7uW80xUw6VSCel0Gnfu3MGdO3fg8/m4RajX6+Hz+ThgrmVd+lyZTAZLS0uYnp5m9DwJcpCCnUgk\nglwuZ2nb3Qa2D5WjTqfTWFxc5Owgk8kwanVtbY0jOXLSKpWKs126rCttwOrqKmZmZlBXV4fm5mas\nra2xHBu9tMlkki9khULBogvVsr1sNguv14vp6Wm+wAOBAKs7aTQa6HQ6FvWgZyYHWOmZKdOi3jj1\nPEhso76+HjabjR0ojXCQili1g1As3peXW19f3wRuI8SzzWZDfX09MpkMA+BIdajaHlMJn763QqHA\nTk+tVsNkMqGtrY2xAeV7vtPBpbVJfIQcWjabhUaj4VaGQqFg4v5ahR3KATrpdBpCoZAdPIm/0FgV\nKUjt5kUjUB5hHyhrlEqlXA6nH5UUvqqZWq1GsVhEKBTC/Pw8XxASiWSTnnK1IGs7EwqFaG1t5XbA\nyMgI/zrR+e4kvLDVqF9O6PdUKoU33ngD165d4+CF1Kl2O4ZDwUl/fz+efPJJaDQafO9738PY2BgH\n6pQBUVC/m6BFLBbjU5/6FA4fPoxisYjJyUkGFsrlcgD39aurYTi2W5fKyKdPn4ZMJsPS0hIuXbqE\n2dlZiMVinkKQSqUVVfy22sbGBlQqFZqbmzEwMICenh54PB5cu3YNN2/eRCQSgcVigd1uh06n4xbd\nTpbP56FQKKBWq6FUKiGVSjl5IP3p9fV11NfXw2Aw8MjXTtl6+ciYUChkHW7SXaBkiUSDqNJYyzOT\nytbq6ir3+0mulp5JKBTC6XRyhbKWc0dtUZlMhsXFRa5ilq9JylzUBqW7abf2UDnqtbU1TE5Owul0\noqmpiUUoCKRF2WJ5CVYsFmNhYYF1iStlOoTepaZ/oVDA448/DqPRiNXVVajVang8Hgb1FAoF3Llz\nZ9NB2+6SI7GNZDIJp9PJQJCnnnoKuVwOarUaqVQKs7OziMfjyGaziEaj8Hq9VS8iutjpgDU2NnKZ\nUKvVQqFQsODEysoK/H4//H4/rl+/vuM8JAHwKNixWq145JFHYDKZkM/nYTab2SkSgCwcDiMQCOx4\nedLlR07aZrOxeH1TUxMDA2OxGOLxOABgeXm5JkdNP0jJSqVSweVyobm5GWazGXa7HWKxGIFAAAKB\nYFfjTlT+puDFaDSiVLovUWcymSAWixGJRLiC8iAlWQrkCGlvNBphsVgAgJ2qRCLZlXMSCoUsPUjS\nmy6Xi6stOp0Oq6urnEVFo9GagheRSASHw4H6+nqsrKzgtX7w1bMAACAASURBVNdeQzgcxqFDh6BU\nKqHVaqHT6XDr1i14PB6EQqGaslTS/P6jP/ojPPnkk3j22Wfx3HPPYX19HU6nEzabDTabjRWPRkdH\na0bY19XV4dFHH8WXv/xlWCwWvPfee3j55ZeRz+dhMpmg0WjQ1dWFUCiEyclJzihrMaVSiaNHj+LT\nn/40FhYW8LWvfQ3vvPMOj661tbXBarXyyFotveS6ujro9XqcOnUKX/ziF7G2toavfe1ruHz5MkKh\nEOtzWywW6HQ6jI+PQyqV1qQfLRQKcfToUTz99NPQarUYGxvD9773Pa7gSCQSdHV1oaWlBU6nEx6P\nZ8cKBgWrarUaNpsNi4uLuHbtGiOZE4kEI+0bGhqg1WoZc1AtAC2vAJRKJcTjcQQCAaytrfE+5vN5\nWCwWDAwM4PDhw3w+qgUAtC5lvVRJpd8r/3OPPPIIjh07hmg0ih/96Ec7jgaSw6WWWfmfpYCwrq4O\nFosFx44dw9zcHL71rW8hHA7X1GL4rX9vV3/6v9mohD09PY3Dhw/zZeFyuSCTyZBIJGA0GnmeVS6X\nc4alVCqRSCQqXnKlUglLS0s4dOgQgsEgjEYj+vr6uDRD2Sr1hWmESK1WI5PJVD1o6+vrXPJQqVTQ\n6/WwWq0sEL+6ugqbzcblMoqYyzPU7Z6ZerJUClKpVFz6p3J3oVCAVquF1WqF1Wpl+cpEIlH1pVtf\nX+eXXiQSwWAwsOJOLpdDLpeDSCRCa2srcrkcuru7eXxop4ySfo/2jC4yGvUSCAQwGo0Qi8VIp9Ow\n2+3cN6+l90Z7o9VqYTabIZPJ2DFTgEAvS60RbDlYUalUchZTPqImlUo5E9tNyZdKuaVSibMRIpog\ntHktY2TbPTNdbhTJ0yyr2WyGwWBAY2MjlpaWEAgEoFAodjwXtK5YLIbFYmGN4GKxCLPZjN7eXuh0\nOtTX13OrhRD3tQQwVI7t7u6GUCiEx+OBWCyG3W5Hc3Mz9u3bx++cwWDA+Ph4zSNJQqEQp0+fhslk\nYslMWluj0aCpqQkulwsejwfhcBjJZLLmvXA6nXjiiSewtraGCxcu4L333kM6nYbJZEJjYyNjamZm\nZn5rfLKSqdVq9PT04LHHHoPT6cTXv/51XLx4EdFolDM8h8MBk8kEm80GpVJZNaOmMyyRSGA2m3H4\n8GGYzWZuxcXjcW5pEa7B6XSy1ONOGS+p4DU2NqK+vp7lWunskhoffZfpdBqxWKxqZYsCWIVCAbvd\nzpWl8s9JqHEq4RuNRsZ4VFM0pFYSKWnReaX7nMZN5XI5+vv7YTAYWGa02jmmNgftd11dHcuUEkYo\nn89DrVaz8mM58O1BgIwPlaMG7n8pCwsLGB8f59EQms0jrV3KKjUaDZeYabC8WlQfi8Vw79496PV6\nZLNZqNVqzlqpzEuHWCAQ4NSpUyiVSrhx4waSyWTFMYNSqcRAMYrEqJeUTCaZfYrKNnK5HEeOHMFb\nb73F/eRKwQX1Nufn55mZCAC/dPl8HjKZDL29vWhqaoLBYGAEdzUwDmWmqVQKi4uL0Gg0zFC0urrK\nJUi73Q6tVovTp09DKBQimUwiFApV7ZXRZ0mlUlCpVAgEAlCpVADA419qtZoDhAMHDuDSpUvMvrRT\nFkI9TiphUf+HSvjAfTS+w+HYpBW707r0QsvlcshkMq7i2Gw2APdL1fQZEokEA7Rq6ZFJpVLWUler\n1VhfX+cAi/rphUIBKpXqt2b9K1l5mTiRSCAWi3FFh6oXcrkcPp+PM+NEIrFjpYHW1ev1DNQjoNvQ\n0BC0Wi0kEglisRgGBweRSqWwsrLClZRqJpFI0NLSAovFwqN+DoeDR+3a2tq4WtbQ0IDz58/zmGG1\nvaALf//+/SgUChgdHcXExAR6e3vR0tKChoYGOJ1OCAQCWK1Wxr2Q/GC1vVCpVPjQhz6EQ4cO4erV\nq3j77beRyWRgsVhw8uRJdHR08Bm8ceMGg2CrjSVRtens2bNoa2tDJBLB9evXkUwmoVAoYDKZcObM\nGZ68IFBWtWoZjR8RetxgMGBxcRE3btxANBplgKrZbEZ7eztPqUxNTe3Y+5bJZBgYGIDFYkFnZye3\nErVaLQeKSqWS0d8NDQ1YWlqCx+NBLperGNgSY1xDQwM6Ojp45j+ZTPKdQfifY8eOwWq1YnV1FVev\nXq161qjaZrVaOZiltge1E5eWliASidDT0wO9Xg+Px4PXXnsNs7OzVQNxhUKB4eFh5PN52O12pNNp\nWK1WJJNJRKNRiEQihMNhHDx4EB0dHQCAl19+GcFgkEGYu63IPXSOmiL0+fl5Lu9Sz1AulzNQJpvN\noqurixGAfr8fN27c4It6O8vn8/D5fAxmojLm6uoqZDIZkzjU19fDbreju7sb8Xgc6XQa4+PjFaNZ\nQvOOjY3B7/ezUHgymYRQKOT+OV1ENJJDUf9OAC0aI1hZWeGSOM14l5fYrFYrGhsbMTQ0hOeee27H\n0h7tB4m/03gQXf75fB42mw09PT04ePAg3G43uru7eRyumtHcuEwmQz6fRzKZhEaj2XTZ1tfXM+GF\nyWRisFY1oBZl6TKZjJH+dXV1iMViXE5XKBScAc7Pz7NTr7ZuuTO12WzQ6XRMHCIQCHjP8/k8BzWU\nrVdbmzIRjUaDxsZGmEwmBg+JxWLo9Xqsrq5yD5VAdnV1dfzdbZeNUGBBQSvNhwL3JxyUSiWTLVAw\nS0EIneNq2Uj5uBo9r9FohEAg4AmDUqmEtrY25iVYXV3lX69kpCdOACSpVIp9+/bB4XAwW5tMJkN9\nfT1XIFKp1I7tAMpy7HY7EokEZmZmkMlkGMVLQKy2tjaEw2EO+ndal8ZvTp48CbPZjJ/97GcIBoOM\nt7Db7VAqlVztKi+tVlqbgHK9vb0YGhqCSqWCx+NBJBKBRqOB3W6H1WpFfX09NBoNLBYLIpFIVYAo\nAJ7VHxwcRFdXF4RCIWZnZxEIBJDL5dDQ0ACr1crtoq6uLkSjUZ6Jr2RU2Tx9+jRn9hMTE5smFYgP\noL29nQG03/nOdxCLxTZVq7buLe1Bd3c3t1IIjU7fez6fx759+zA4OAgAmJiYwMjICM9Gb1cJGB4e\nxqFDh+ByubitWQ6A9Pv9cLlc6O3thdvtRjQaxfvvv4+rV6/y97PdOy2VSnHs2DF87nOf4+rp8vIy\nNBoNNjY2EAgEEIvFYLVa0d7ejmg0ivn5eYyNjfF7TeDZ3dhD56hLpRLy+Tzef/99jI+PcwZNZVJy\nPuRsT58+jTNnzuBDH/oQ3nrrLWSz2YqRVrFYxNzcHMLhMIrFIiMHaeOo90ncw//yL/+C48ePw2Kx\n4Pvf/z4DwrZaXV0d987r6urw9ttvQyQSMVSfkKxisRgqlQpHjx7lGcxIJIJUKlXxMhYIBMhkMrh1\n6xZu377N0Rhln3SRjo2N4ciRI/jc5z6HhoYG1NfXVy1zUpS5traGpaUlBINBds5U+qYy2sWLF/EP\n//APMJvN6O/vZ2aqSpcQAZloNIHmBgkESNURo9GIoaEhdtaUVW5nNA+sVCphNBqhUqmYB3l6ehqR\nSAT5fJ7LfnShmEwmLCwsoFAoVHyp1Wo1jEYjbDYb5HI5XC4XZwrZbBbBYJBHK6gyolarGaRVqWxN\njrerqwt6vZ5BekStSKAvKoVns1mk02moVCrkcrmKoCQCIFEZPZPJcGslFAohFAohGAzyBWk2mxGJ\nRBitu1MgRN9hNptlgJ7FYsHKygoHlgqFAu3t7QwWpHbUTlULQgnncjkolUoMDg4im81icnISiUSC\nx4ho/j6RSOxYUqfSo9vtRrFY5Mxl//798Hq9mJmZgcFg4FbX4uIigsEgksnkjtUQh8OBz3/+8+jv\n78fa2hqCwSDa29shkUiwurqKRCLBI0qRSISJgYgQp9Lz2mw2fOELX4DD4YDH48HIyAiampoYgEqJ\ngkajwcLCAkZGRngssdK6arUazzzzDI4fPw6VSoWLFy9idnYW2WwWLpcLnZ2daGtrY4c9NTWFK1eu\n4Pr165idna0auO3btw9PP/005ubmcPfuXczMzEAgEKClpYWxDENDQ0xHOjU1BY/Hg2AwWHEvdDod\nnnzySXz+859HqVTCV7/6VczNzTEXxOnTp+F0OmG1WiGXyxEMBvHBBx/g7t27fE9VeuY//dM/xdmz\nZ1EoFPDuu+9idHQUWq0WTU1NUKvV+OxnP8vo7o2NDfzHf/wHlpaWoNPpuKKz3dk4efIkvvSlL6Gv\nrw+jo6N46623EAqFcOzYMYhEIhw4cICDcRptnZ6eRmNjI8Lh8AMT+Tx0jpoyJuoDElEE9WRp7piy\n4bGxMQwPD6O7uxsikagqsKA8yqWZumw2y2UqKolQvzOdTmNgYABisbji85YTFlDplDI4Ko0SGIaA\nSj6fDzabjf/Mdl8cRXVELqDVavnfIbYtKvNTsEEctQaDgR15pT2WyWQcgRO4iZzd+vo6BxdUqiHg\ni1wur4jSJmcql8sZAEPlfqp20KWr1Wqh1+vhdDqh0Wg2kXJsXZvWpb/T0dHBLG2xWAyLi4uIRqP8\nuerr69HY2IhMJoNUKoUbN24A2D6DrKu7zypHl43RaERvby8SiQRTm9L3RnO0RqMRdrude1GVgguh\nUAitVss0iDabDc3NzQyeo++dAk+NRsOOmyoWlRw1nTMKelpbW/nXwuEwZ89EX0pgy52wAPT+0Xig\nxWLhc0pcBoTBaG9vx/z8PEKhUM0I+0wmw98VlXSXlpbYcbe0tKC5uRnA/cyplr43ZWvUs9TpdDAa\njezUbDYbGhoa0NDQgGQyiVu3bmFpaalq9Y1MpVJtepeVSiV/NwaDAf39/TCZTCgUCpienq6JUEUg\nEPC+UnBWLBaZe9pkMnE53e/34+rVq7h+/fqO0xFSqRS9vb3QaDSMaCZiIblcjs7OTm6JhMNh3L59\nGzdv3oTP5+NJhEr7SwBTn88Hv98PAOjv74dWq0VLSwsMBgM2NjYwMTGBQCCAaDSKYDDI40iVrL6+\nHmq1Gj6fD7du3YJYLOZqhdvtZg6DcDiMsbExDmBpzLXS8zocDkgkEgQCAfzoRz9CKBTiINHhcPAk\nx/z8PE/sUEWK3pHt1i2VSjCbzVhZWcHXvvY1TE9Po6GhAcPDw2hoaGA+jfHxcczOzjJ1MFXrqA23\nW3voHHU5uCqTyUCn03EppLzvU06nR5kJEYhUWre8BEMODsBv9bXJSZKDEggEO/aGyFGLRCIuVZV/\nFiKfICdWLBaxurqK5eXlir0sWlcul0OhUKC+vp573sBmtKTVakVXVxcHNaFQqGoPh1DThCA3Go3I\nZrPM4kU84BaLhTPCtbU1eL3eisw65axFer0eOp0OAwMDyGQyuHr1KorFIpfLjh8/joGBAbjdbh5z\nqFaqLx+PslgsGB4eBgD+e2q1mpHlJ0+ehEajwfz8PBYXF3fkJy+V7s9GEwcytVoIIVuewavVau7Z\nqlSqilUW2g9ypEqlEo2NjVzmBn4zikIldAKi0OxlNbRzOT2sVCpFPB6Hw+Hg8i9VonQ6HRYWFrgk\nt9OIFoGgCoUCotEos8nRpATNqNM8/A9+8ANMT09XDQzLjWZh6XPZ7XZkMhl0dXVBLpejr68PADA6\nOooXX3yxJoEVwnJQ1YT2OpvNQq/Xw263M9HRD37wA+aCrwVIRq2VYrEImUzGNLvUW21tbUU6ncbb\nb7+NS5cucRC9UwUAuP/9KxQKHhddXV2FRqPh0aaJiQlcvnwZExMTmJqaYnKSWqohZrMZp06dgs/n\ng0Kh4CyVlMnu3LmDixcvwuv1MjHHTu+HUCiEy+VCfX09crkcNBoNpFIpk/S88cYbmJ+fZ1U1yqQr\nneNCoQClUsmto3PnznEC0dDQwOea+uzJZBLJZBKxWGzHCQO5XM6BUEdHBw4fPgybzYbW1lZIJBL4\n/X7cu3cPN27cYJxPsViEVCrdpNuw9XtbWVmBVCpFMpnk4M/lcmH//v0QiUTMNfHSSy9xoE7AzFwu\nh3A4vGvuAeAhdNTFYpHLEdQ3JLRt+Xwl9aSam5thNBoZgLCTlaMM6e9R1EdjDRR5Eer11q1bVRmu\n6CCr1WpGxFJWSjJvlL3W19fj+PHjSCaTuHTpUtV1qfSsVCrR1tbGlw8hF6lUbzAY8Nhjj+HQoUMw\nm808d1htDwhYYjQa0djYCJvNxmuvr69Dp9PB5XKhq6sLbrcbGo0GY2NjuHbtWtXAgoBIlBU0Nzej\nWCyio6ODA5bGxkY8/vjjsFqt3PMkysvtjDJBchI9PT1wOBwQCoVYW1vDsWPHsLGxAbvdjoaGBjQ3\nNzNIiBjEKvUMKVgjop3m5mY4HA6k02n+N4rFIvR6PVO4EmiNMrLt1qWXkc4toeqFQiFjC8hpFgoF\nBINBzM7O8nwnvQeV+pyFQoFR+ysrK8yZToCeUun+THEul8PIyAjm5ua4t1ytL0uXJiF67969i46O\nDm4JOJ1ObgHcunULV65c2TRjX80IyDM9PY0rV65AoVAwpSyh4NfX1/HGG2/g5z//OWZmZmpGwhOh\nkN/vh8VigcFgQHt7OwPfSqUSZmdn8eMf/5gFZmoB9MTjcczOzsLn86G5uRmDg4NIJpMwm83Q6XQ8\nonT+/HlMT0/XpI4kEAg46zQYDJyZR6NRroIsLS3h/PnzuH79OveYd6JsFQgEuHDhAp544gk0Njai\nsbGR9RKi0Sju3bvHrQCfz4fbt29zBY0Sme3WJZzNxsYGVwKpmkSYnOXlZVy4cAG5XA7pdBqJRIJb\nC5WmJMRiMesfKBQKHDhwgJMLgUCAixcvIhAIMBkTTfbQ91KteppMJpFOp2E0GvHoo49uIhi6ffs2\nc9Ynk0kYjUau6NHkUKV15XI5T/F86lOf4veUyvJLS0uIx+OYnJxEU1MT8zLQ1MiDWt2DpOG/b6ur\nqyuV/RwAGO2oVqvR1tbGgBmVSsVlhNbWVh7Yn5+fx1/+5V8yino7IydCiODHH3+cL1AA/HOz2czB\nwnvvvYdnn30Ws7Oz2zo/yrg1Gg3PaTY3N+NjH/sYR4tyuRxarRYqlYrnDb/xjW/gl7/8JYN9trvo\nieBFo9HgIx/5CNxuN4aGhmA0GpkFiTJ5QjGOjo7ixz/+MUZGRqoCnEhExOl0Ynh4GJ/4xCeg0+l4\n1InAWsViEdFoFN/85jfx6quvwuv1VszKqFeo0WgwMDCAEydOoLm5GW63GwaDgQMhAMxPPjY2huvX\nr+OXv/wlX/Zb16aeLAFr/uRP/gROpxONjY0Qi8VcdiUwExE6BAIBTExMwOPxcKtgu2emoKyzsxPH\njh1Db28vI+qXl5dx9epVjuJpNG1ubo5Rw5WqOHTe2tvbMTAwAL1ej4aGBmSzWS4hBoNBxONxiEQi\nKBQKZl6rRgFbjnynaN1ms3HmRFzw1CqibI16b7WWkqnHTmQ1JDVIdLapVIrLy7XeI+WtIrVajf37\n928iv7l9+zZSqVTVz1/peQlJ7XK5uM0QiUQwOTkJj8fDAja1jAHSulRpIYR0X18fdDodVlZWcOfO\nHbz22mubyv61OH+qdgwNDcHlcvGI4draGubm5ph5j8CtALZ9L7YaAQqNRiMLZJR/V/Pz8zySRs6T\nKpPV9looFHLgTtXAXC4Hr9fLrR8KMIHfBOwUXFRamxj1mpqamMBobW0NiUSC36tyilMCrRGpUrVq\nCwnpUBnb5/NtwsiQj6CAloRy0ul01Xeagu6DBw8il8txkEwMZ/Q8VNmh6SRS86O7s2w/rpdKpaGq\nXyweYkdd7vxodu/cuXOsHFUoFLismUqlcP36dfzsZz+rOuNLFwRd+gcPHkRPTw/6+vrQ0tICs9mM\nQqGAZDLJEeI777yD2dlZLg9VeH4u2ZhMJjidTnz6059GZ2cnnE4ntFotZygejweTk5P46le/umNk\nT88rk8l4xKS3txc9PT0MGCEQ0ptvvokbN27gnXfewdzc3I7AHiq7KZVKWK1WnD17Fm63G01NTUwc\nEo1GMTs7iytXruDnP/85BxU7gZEkEgkTV9BM5eDgIOx2O5eNXn31VYyNjSEYDCIcDiORSGxqZWy3\nx1Sup0tIr9dzVgYAoVAIPp8Ps7OzTBVbXlKv9My0xxRQ0SwyOfdIJMLZM2Wb1DvdqQRHFRwKqqRS\nKb/QlJXQ56NSea162uUOm9o0FAyVf176t3bjUMufv9y50nqUKe129pueGwCPQ1LwRkIqD/Kc9NmJ\nK5qAQnTx0ve027EYsVjMz0mjT0QyRAyDdGZ3QpCXP6tIJOK2E7GOhUKh32pPlK+709p0vsqZ7qgl\nUO5Igd9UkmrdE6rslTt3AlGWK7QRk6FAIOB3ptJz0/cllUr5fSs/p+Xr0ntE2elOZ49AwQQgpWSo\n/AedbUr8yImXSpXHDOm7o+pueWm//HNSm5MAy4Sq3+bd/p/pqP/r/ze9dHSYjx8/jp6eHtjtdiST\nSdy4cQNTU1MMXKDxpWpOhNaWSCRc8m1ubkZbWxuGh4cRi8V4bOr8+fM8d1otE6EDSpmoVqtFT08P\n6uvr2cHSnPfly5dx584dLvXu1MuiS5jQoBqNBnq9ngf/JRIJvF4v3nnnHSYBqGW2t/xyJ6AWOROa\nX4xGo/yCUyZSi9Fzl/f56dfKS6vVHPNOz77VaI2H4Tz/b7St5cDfdZ/LA/JyR7RbR7p1TToblElV\nAijuxsrPMDmq8gv/QW2rQMODBCjb2XYtk9/XurWsVeufe5B/u9x+n5+pfL9+n+vSeaxytv/nOmoy\ncib0c61WuymqIoAXvaC7Idrf6qjKS8j0g6I5oLaSFq1b/kz0b1BW8yAZCK233f/vOag927M927OH\nz2oMWP7nO+o927M927M927P/xVaTo969VM+e7dme7dme7dme/T+zPUe9Z3v2v8weZE5zN2v/d6+/\nZ3u2Z5vtoZuj3rM9q8XKHUY5GInwAL/LuoQYJY1hmj0m9OiDrE+jVGKxGG63G1KplCVESWGtVjnH\n7Z5ZKBTyyCIJXpBIQDAYfGBsBD07iRwcPXoUpVIJU1NTmJiYgN/v/52BWkTGceLECZw9exYffPAB\n3njjDczNzVUVtqjFRCIRbDYbnnzySRw/fhw3b97ECy+8wNS2O4mIVDI6fx0dHdi3bx8OHDiAYrGI\nK1euYHR0FKFQiHEpu1mT/kuTE8TwV1dXh/n5efh8Ph4T3I0RXobQ6wT2BMCys7Uoq1VaVyAQwGaz\n8XtCFMSpVIo5/HdzTghMXM7ut7Gxsek7I1KpB3lehUKxSRo3GAzyO7gTWVSldcs14E+ePMkqh8Fg\nEIuLi0xl/CD2UDrq8kt4O+Ri+XgHHYxaDsLWbGC7kYrt0J1bn2M7KweRlYPQyp+5/N/fzTOXI2PL\nka3bfS667He6IOhyL/9BLENbOaZLpRKTcNSC1BYIBJvI+pVKJTo7OxEKhXgEgghDCoUCv8w7oV7J\n2ZEykM1mQ0tLC8RiMRKJBLLZLJLJJKamppgdqRrFZ7kR0Y3BYMAjjzyCvr4+dHR0IJlM8kz23Nwc\n5ubmMDExsePIV7nR2IzBYEBLSwu++MUvAgBfPK+//jomJyfh9/tZNnA3Roxtra2t+MQnPoHGxkYk\nk0lMTk5iYmICq6urTFjxIGNPpHjV39/P/NnlFKUPsi4ZMdW53W4cOnQI6XSaqWy3oqJ3azTiOTAw\ngOHhYdTX12NkZIR//0EDunIGsP7+fnz84x9HY2Mj5ufnWef9Qdan95AcyZEjR9Df388TI3fv3uUR\nvlqtfN6eGA6JNVCv1yOfz2N1dRWxWGzXwRyJwkilUua0J3IqIuKZmZnB+vp6zWe6/HltNhusVita\nW1vR1NSETCaDZDKJeDyOWCyGy5cv17wXFJjIZDJYrVYMDAxgaGiI74/p6WmEQiEsLS3hzTffrMo4\nuN0+6PV69PT0wGazob29HZ/85Cc5UPN6vbh8+TKee+45lozdrT10jrp8xlCj0TAbjFQqRSKR4Png\ncm3gcq7uaiMexGxFMoZGoxFqtRoKhYKH1iUSCatJpdNpPnTV9IKJQKSckKWnpwcGgwHRaBThcJj5\naePxOPL5PI99Ebq8GqezTqdDc3Mz1Go1j3tJJBKEQiEmeR8bG0M2m2VVLWLuqfTMEokERqMRFosF\ndrsdJ06cgMvlYpKEqakpJJNJLC4uYn5+HoFAgAkDSEWq0l6IxWLs27eP57LdbjcGBgaYzGBlZQXv\nvvsu7ty5wwpPPp+PZwwrkalQAHD27Fm0t7ejs7MTzc3NTBZBUo/f/va3mduaxu2qBRcUtBBhzeOP\nP47e3l7o9XqUSiU+d7dv38aFCxewtLTEMpe1OCgiP7Barejs7ERHRwcTqhC/PJ05osvdjeMTi8VQ\nq9UYGBhAX18fE/i0trYiFotVVRnayYjsYmBgAGfOnGGnQYQcv6spFAq43W48/vjjaG5uxnPPPYf3\n33+fJWsf1Og77ezsxJkzZ9DT04N4PM4qdDvxDFRbl864VqvFxz/+8U0qSbsN4srXJQ4CYk189NFH\noVQq4fV6EQgEmO1wN2OS5XO/drsdBoMBzf9FQkSB7Z07d6BUKpndrtZ16VnNZjOPt5IWQC6Xw82b\nN5FMJuH1emty1BQACYVCDu6JFMZutyMUCjHZya1bt/Duu+/WdEZoD6RSKcxmM6xWK55++mk0NzdD\nr9cjHo/DYrGwSuHCwkJNjprOmFgsZhbGw4cP4+DBg7BYLEgmk6irq0NfXx+Wl5dZuW0n5cHt7KFz\n1MSV3dHRAafTCZ1OB4fDAbVajXg8jng8jtXVVZ6b9vv9WFtbY5lK4ubd7jCTYD3R/w0PD7OMYTab\nhdfrhdfrRSQSQTAYZNF6yvoq0SSKRCLs37+fVWmcTieOHj3K4h6pVAq3b9+Gz+fD3NwcSy+ur68z\nwUGlvTCZTOju7sbp06fR2NgIl8vFcnqkWOTz+QAAs7OznBlmMhkWSN/O8en1erjdbuzfvx+tra3M\njw3cJ56w2WwIBoNwOBwsL0ovfKFQqEiVSE7p8OHD+xfFLQAAIABJREFUrDFst9s3CWuQBGOhUMDY\n2BhTupLwQbWzQSIApHtbKpW4/EovuMvlQjweh0QigUwm25EIn15kUsWSSqWIRCJYXFxEOp1mMRK7\n3Y729naMjIww3WUt0TFdPlS1+OCDD5gvnRS+2traMD8/v+sskp7dYDCgra0NAHDnzh3O7ujfpT+7\nW+chlUrR1dWFU6dOobm5GS+88AJu3brFim+/S5tBIBDA5XLh3LlzOHbsGNLpNMbGxliJ7ncZZSQS\nm8985jP48Ic/DACYmprCzMwMUqnUrqsAVLGiMU6VSoWenh4MDw9jbW0Ns7OzrC+/m/ZIeaWNOMRt\nNhuGh4cxMDCA5eVlRCIR3LlzZ0fO+q1GtLsqlQoWiwWHDh1CW1sb+vr6oNVqMTo6ikAgAIVCwW2e\nWozOs9FoRENDAwYGBuByudDf349MJoNYLIZkMomWlhaEw2EsLS3VvG758544cYJ1swEgEAjwPdLV\n1cXSpbUYBVakfa7T6ZgpktTWKGgcGxuraU1y1BKJBA0NDWhvb4fFYkE2m8XKygpmZ2eRSCTgcDjQ\n1tYGm83Gqoq7tYfKUWu1WnR2dsLtdsPtdrOM3sLCAuuxqlQqVj+5efMmDAYDTCYTZ6vA/2XvzYLj\nPM9zwefvRu/73uhudGPfQYCbuSuUh9RiWV5SzlQpVZNETvlUUnMxyUV8ZqZyk4tTOVWpnItUxbZ8\nNE55Gc/YsmXHi6TIEi2KoigSBLEQIHagsTUajUYv6H2fC+p91aCB7h+0fYZJ4a1SgQUBH77+/u9/\n1+d93v3T1GazGc8++yy6u7thtVphsViwtbWF6elp5symsYk0zcZqtSKTyWBnZ+fAiMxisaCtrQ1P\nP/00rFYr1zyuX7/Oc66lUimnbCgClsvlNQcZEL1nc3Mzent70d7eDqVSifHxcSa8J9pJclh2d3d5\nsANFe/spDLVaDZfLBafTyenut956C+FwGKFQCFKpFDabjaPUQCDAgyrIwB7ksFAqLBgMQqfTIZvN\n4t/+7d8wMTHBmRCXy4VKpYJgMIhUKsU0oLUyFpT+z2azzH+8tbWFeDyO1dVVZm+juyCRSGA0GjnL\nUksoo7Gzs4PZ2Vl84xvfYO7ofD6Pnp4e5rmuVCqwWq08CUyM0DjMVCqF8fFx3Lt3j++By+XC888/\nv2foipjxi9Wi0+ng8Xig1WoxMTGBH/7whygWi9BoNDCZTDCbzTxk5DC1QkEQ0NHRga985Svo7u5G\nNBrF66+/voef/nFS3/Q81Wo1/v7v/x5tbW1YWlrCN7/5TSwtLe2ZnHdYISNit9tx9epVfPGLX4Tf\n78crr7yC999/XxQn+X77pT2bTCZ4vV68/PLLeOaZZzAzM4Pvf//7mJqa2kOOJMYpogiSiJ3OnDmD\nU6dO4cyZM7Db7fjoo48wNjaG7e1t1hU0IbCeI0Bpf7vdjuPHj6OpqQm9vb3Mqz4zM4NIJMI0nqlU\nCvPz86LWValU0Ol0+MxnPgO3283879euXUMsFkMymYTT6cTg4CBP6pqfn697Hmq1GkajkUd8GgwG\nBINBfPjhh1hcXEQkEkF7ezvPpnY4HDVnGlTv2WazweVy8SSxt99+G4uLi1hZWUEikUBHRweeeeYZ\nPPXUU3j22Wfx2muviVpXo9HAaDQCAPx+P+bn5xGLxXjamUwmg8fjwX//7/8dFy9exLvvvstzCA4j\nT5ShpkEcBoOB5yXPzs7y+Lh4PA673Q673Q6VSsW1AaJ5rHWBaTgCpRnT6TTu3r2LtbU1hMNhKBQK\nHDt2DBKJhOlCadZuLSNCfLTRaJTp8La3t/GrX/0KoVAIGo0GTqcTJpNpz8zkaDT6G7yzj65LnM2b\nm5sIBoOIxWJYWFjA6uoqUqkUHA4HDw7JZDL8+YjPt9ZZxGIxbGxsMDXf7Ows1tbWsLm5CbVajaGh\nIQiCwNNg6DMS4GI/IcaxVCqFtbU1yGQyrKysIBKJ4P79+1AqlTzzWaFQ8LSaetSRZFxo9ChlPciY\n0qD2YrG4hyGOQFpiFDOl1Eh5EU0krUep43K5DKlUeqioic6F0tBUlqBhEdlsFrlcjiOgwxoS+twb\nGxtYXV3l7AdRKAKHZ4uin6eSSzQaZQeDDEZDQ8Njo7QFQYDFYoHJZOIBNTRh7VE8x2FFoVCgqakJ\nly9fRjQaxY9//GNcv34du7u7bMgfl1XMbDbjwoULDBb69re/jYmJCS7bNDQ0iKblpM9I5YXOzk5c\nunQJdrsdGxsbeOONN3jQEBl0Mc4hpear5yVQ1Ly+vo7Z2VmEQiEuA+p0OthsNlGfXy6X850qlUqI\nRCLQarWIRCL4+c9/jkKhwOnvU6dOwW63i1qX9l1d3x4dHcXa2hrW19exubkJjUYDmUyG3t5eqNVq\n0Q4RzX+PRCIol8uYnp7Gzs4OEokEn6dWq+XMrFqtFrVfYlokjnpyhInREQAHQvT8/kOAyfL5PBKJ\nBKLRKKxWK+LxOLa2tuD3+7lmRbywcrkcdrsdsVgMOzs7dZGFuVwOgUCAp1xRJEZRHV02QRCQz+eZ\neJ888Frrbm9vw+/3M3hqfX2djTINt0gmkygWixxlElf0QWvTmEFCUHZ1dSEQCHDNVavVwmg0Mtes\nRqPhGd31Bg9Q9FgoFNjZSafTKBQK0Ov1XBpIpVIMFNnY2GBA2UHrVioVdhAooqYJTmazGY2NjWhv\nb4dOp+PInIxpvZQeRb1kQGlGNAC0tLSgq6sLTqeTMwrl8sMxomLQsbQ2AWvoc2o0Guj1ejQ3N/PM\nbKVSWdNZqXUu+Xyep/goFApYrVY4nU420Llc7rHSYiSEg6CJc0qlco+xPoxUKg+5kD0eD2dtxsbG\n9tT7qkGTh5WGhgZ0dHRAIpFgdnYWN2/eRCKRYIAo8HhgL6lUCqvViueffx79/f0YGRnBrVu3uNRA\nPOiPQ1/b0NCAc+fO4cUXX4Rer8fW1hbGxsaQyWT2gKCqneR6d5pSuTabDWfPnoXFYmEjtbi4yClx\nlUrFXOv1hO4zUS/TsJaVlRVMTk5iYWEBu7u7PL7WYrEgHA6LepY0EZBGRdLMZ9LDpVIJqVSK678r\nKys8nbDeWVM2gozo1NQUZ6LofaeRvmq1WvQ7SGN0acQqvYsUIFG5kIaD7OzsiNovDfGggK4ayEsO\nWKlUgl6vh1Qq3TPJ77BO4hNlqMvlMkKhEI9Ko9oaIUBp8lQ2m4XVaoXVauV2A+DgUYbAJwjb7e1t\n9PT0cKSk0+lQKpW4RUGtVkMQBDa69VosSqUSotEoNjc3MTAwAKlUCp1OB7fbjXg8DovFApVKxUaR\nPN16dKJ04WUyGba3t9k7LhQKPImL0vQUAVKEU6+2R6M9SXkpFAp4vV40NTWhWCxyLYVSNPQc6qXc\nqlPtlN632WyQSCTo6uqCw+GAxWJBsVjkGmr1nmoJGQX6bKSEKCXd2NgIpVLJz4PSsWLrnPSz5XKZ\nkbEAGHAnl8vZiB+2VkgGgQy0Xq9nLIbJZGLQYvVEn8MIDRUgZU4jVWnCGn2/untAjAiCgObmZsjl\nckQiEfj9fuj1er7HNDbwsEZPEAQYjUacO3cO+XweH374ITY2NtgoSSQS1gGHBdap1WocP34cTz/9\nNCwWC9544w0GFhJVMO35MHdDKpVCq9XiC1/4Atra2rCzs4O3334bmUyGB2EoFAoGBIo5Z7rTVqsV\nHR0d8Hg8CIVCuHPnDm7fvs0YAYPBAACcxRATlVHNlVqEEokEHjx4gLW1Nc7+abVaNtRiQVnkdBaL\nRfj9fh55Sl0cdE7kNJM+F7M2IebJiFbfKUEQYDAYGKNDxlfsnsmZoowNOUnV6H2TyYRUKoWlpSVR\n6wKf6MRHA4LqVq3jx48jGo3yGNTHkSfKUBeLRUQiETx48ACnT58GAAYVVM9EpfSj3W5HMplEIBDg\nA6hlqKenp9HS0oJ0Og2tVosXX3yRDT2NISsUClAqldjd3cXU1FRdwEy5XEY6nYZMJkMqlYLJZILP\n58OFCxd4fmoymcTW1hYEQUAwGIRSqWSg2kF7plRvNBplwFtrayvOnz8PrVYLiUTCI+wymQyCwSBm\nZmYwPDwsyjOmwe9UV75y5QoDLPL5PA/4IEdhbW2NMwy1hIw1DRGhweqdnZ38IpZKJXR0dCAajcLr\n9eLGjRuYn5+vq9yqAWNmsxn9/f1oaWnhzAKBAgcHBzny3dzcFFUTqkZw+nw+tLW18bB3StOTwqyO\n+sQIKQOZTAaz2Yzu7m7eU7lchlwuZ8dmv5bBevumOehUbyyVStBoNFAqlbDZbIyzIMdM7J5VKhXc\nbjcikQiWlpag1Wrx1FNPccdAsVjEz3/+c1HZlup1SXlduXIFt2/fxtTUFNxuN06ePAmv14tSqQS/\n34+pqSnMzc2JqskCD5/L5cuX8bd/+7ewWCy4c+cOlpeXGTxEGbi5uTke+VjPiFD2zu124+rVq+jp\n6cGHH36IH/zgB/D7/ejr64Pb7YZer+f1Hu1COUhMJhPa29tx4cIFvPDCC/jXf/1X3LlzB6FQCBKJ\nBL29vZxxkUqlmJ+fZ2fgoL3Sfs1mM3p7e9Ha2opEIoGbN29ibW2NjX9jYyO6u7thMpkQDAbrAr6o\n+4bGnlJrHtXlAbBjfvHiRQwMDHA/fK17V90vTWvS+0XGj4zq1atXcerUKTQ0NOAXv/hFzfea1qVM\nB5U8GhoaODqn/2e323H69GmUy2V897vfxfe///26Z0FBEekBpVLJOogCP7fbjVOnTuHUqVP42te+\nhl/+8pf/MQw18NBYB4NBLC8vQ6PRMPCI6o4KhYKBFZSupoHx0Wi05gu9u7uLpaUluN1u5HI5tLa2\ncosBtRnkcjkeeTYzM8PtVLUOuFwuY3NzE9vb25BKpWhpaeGUdENDAxMW0BhMlUqFmzdv1u0vpCiP\n6slqtRrRaJR/h5Q6IXNphGImk0Eqlaqp8Kk2mkgksL6+zrVNjUbDqFgiumhubsZTTz3FgJPqIfYH\nrU3EAWTcaBwpjd7z+XxwOBxsHG/evCkK+AV8kuJdXV1FNpuFXq+HWq3ml9zj8aCtrQ35fB7RaBSx\nWExUvzpFnYlEAsFgEJVKBblcjvdpNBphMBjg9Xq53CImKqvuVS+Xy4x7oNGaer2eDTaNZxRzDtUg\nO3re+XyecRFarRZ6vR5KpRJGo5EdUTE99pTGjcfjTDJB7WukqLe2ttDW1ranc6He2uRo0XsdDodh\nsVjgdrvh8/nQ1NSEYDDI6fulpaWaLZck5AydOnWKJ8pNTU3taRPUarUYGRlhspnq4TsHCfWo9/f3\n4/z581haWsL4+DijeT/3uc+hsbGR0dnT09NIp9OM7TjofBsaGtDY2IgzZ86gt7cXmUwG29vbyGQy\n0Ov1cLvdeO655/ZgArRaLdesDzpbimadTid8Ph9kMhmy2Sxn3wisNTg4CLVaDYVCIcoBl8vl6Ozs\nhMlkgsfj4c9HbUiVSoX7kx0OBwRBwNjYWN02JyoBGY1G+Hw+7OzsQKFQAABnVfP5POx2O06dOoVc\nLof79+/jvffeq5lBlcvlnCIHHhpSrVYLlUrFuqhQKMBms+HcuXOIx+OYm5vDtWvXuK3qoHshk8nQ\n1NSEUqnEGKGWlhZud8vlcrBYLPiDP/gDWK1WrK+v46OPPkI6na6551ryxBlqmq+8sbGBpqYm9lQq\nlQo0Gg3y+Ty0Wi3i8Ti0Wi1KpRJ6e3sxNjZWtxZH4KP5+XkYjUZm6CGEcPXMVpVKhRMnTsDv99dE\nZ5PQgybkOdVRdTod12AbGxuh1+vhcrngcDiQTqeRSCTq1n0zmQzu37/Pzgl9P51Os5d4/vx5NDY2\n4tKlS9jY2MDa2lpd5ZbP57G+vo58Pg+ZTMZrymQylMtlZtEiMoqlpSWEQiEG7tUSqvdXKhWsra3h\nzp07aGho4Lp9b28vjh07BqVSif7+fuj1+pro+mqh3ut8Po9AIACdTseZkIaGBly9ehUqlQqdnZ17\nMgy11q2e1JbP5xEOhxGLxaDVarkEkUqlGNk/PT39G3OfDxJKFZNDEY/Huc0tk8lwxEHpfAKpkBy0\nPj17g8EAnU7HJRWqZWYyGY5Uqu+6GGIZMnxKpZKNWWNjI6RSKWKxGLe82e12dnDElBoI7d3W1saO\nRVtbGxwOB/R6PSKRCKRSKXw+H/L5PH+mesqNar1DQ0NcQguHwxgcHITFYoFSqUSxWERbWxtyuRw0\nGs0e9PpBolQqYTabcerUKXR2dmJ0dBQbGxvwer3wer1wuVyMlyGdQpHcQUIlkM7OTgwODsLhcPCZ\nulwuWK1WHsFrtVpRKpWwtrZWN5Oj1+vhdDpx8uRJWK1WuFwu7O7uspEaHByE3W5He3s7mpqaUCgU\nMD8/j5WVlZp4A3IqLl++zPPayUkmbIdSqeSMiCAICIfDmJ2d5ch2vzZRmUyG1tZWtLW1cdbN7/dD\noVBwiYJKlH19fXA4HFhYWOD6PemT/aSjo4PXJVS20WiESqXi91sQBHR3d6OpqQm//vWvcfv2bayv\nr3N5YT/92dDQgO7ubly9ehVGoxFdXV3Y2tpCa2srcrkcQqEQ1/9dLheWl5cxPz+Pra0tjsIfZy76\nE2eogYde+fj4ODQaDaanp2G322G1WrG0tITd3V1uHcrn8wxSMhqNe+bGHoQe3tjYQD6fh8ViQSQS\ngcViYWNJToHL5YJer0dLSwvsdjt7nLWUBaXUtra2MD8/j7a2Nm7ByeVysFqtHEU2NzfD5/MhHA5z\nKriWpNNpzM3NYXV1FUajEUqlkkERVJskkhGKJj/88EOuOx2050KhwJEh1bqpP5YU6vr6Ora2tvD5\nz38ePp8PExMTe6L6/YQiPACsKIgEggxJOBxGIpHAhQsXYDKZoNFoGGxRa13gE2AgITSJJEQulzOx\nQ39/P9RqNbRarSglT0qQ0nmURo9EIhzd0ctLNV+qz9U6YzKm1UjScrnMrXTVSFAaMl89t7vengmw\nQr3UVO/O5/NQqVQolUrcI0uZqXpnTBE19bdrtVpotVrONBCgiNLr9HtiogQillGr1dzhATx0jlZW\nVlAqlfbgT2jftdam/VIJgOqXFosFUqkUW1tbrJibmppE96oLggCNRoO2tjYcP36cuRr0ej23vkUi\nESYlIseNMmW17oRer8fg4CAr+FwuxxGfz+djg0/Ay2QyiWw2WxMcSZiNoaEh2Gw2rhsTYJSyIZ2d\nnVAqldjY2OBsSC0sAJWDnnrqKXZ4JBIJwuEwO0jNzc0YGBiAIAjIZDLMwFVNVfqoqFQq9PT04OLF\nizh58iTS6TTXiclRbGxs5BIMGUFqIa3VdXDy5ElcuXIFx44dQ6lUQjweZ8dVKpXixIkT0Gg0DN4L\nBALc5SGXyw/MMGg0Gpw/fx5/8Rd/wRmZ7u5u7ipqbW2FRCJBY2MjNjY2IJVKsbCwsKej43HS30+c\noSbAUDgcxu3bt6FWq+FwOFAqlRAMBjl1p1Qqcfz4cVy6dAktLS344he/iA8//LButJDL5bg1qlKp\nYGlpiVOpqVSKwTctLS34m7/5G/zpn/4pvvOd7+CDDz44cF1SftV0mLOzsxyh53I5LCwsYHx8HD6f\nD5/5zGfwZ3/2ZwCAN99880AmLlJApIyp9WlnZ4dTd9QSAjysebW1teFLX/oSvvOd7xyo4MjLJWVL\nqSYik6E6m1QqRTgcRiaTwR//8R/jqaeewtjY2IFgC3q5qFZKylgqlbIHXq0Q7HY795AScG+//VJ6\nnBjlHA4Hp41DoRD3OwuCwMxwbrcbiUQCjY2NNRU91aj0ej10Oh00Gg2ampqY9W13dxflchkejwcS\niQQulwuZTAYGgwHb29sA9jfS1bXCpqYm6HQ6mEwmNDc3Y25uDolEgvdsNBr3EL5QFHnQ2oRSJRS6\nzWZDX18fn+Hk5CS0Wi0sFgvMZjOXQsjZqPV+UARDRqf5Y+a3XC6H6elpBINBTlNbrVa8+uqrooF7\ndJ8rlQp0Oh3sdjtaW1uZ0auhoQFPP/00WlpaEAwGMTs7y3emllT3IhOIhygyZ2dnuc/c6/UiFoth\nYmICm5ubovrVvV4vurq6OMIjo0xObWdnJxMF3bt3D3Nzc/xuHrR2Q0MDzpw5g56eHmZ6i0Qi6Ozs\nZMfb7XZzG+fMzAxGRkawublZszxksVjwla98BTqdDqFQCAsLC5DJZOjo6GBmPLvdjmKxiEAggIWF\nBSwuLrIBPMjoCYLAQcBHH32Eubk5FAoFnD59mlPpKpUKgUCA38VoNLrHmdxPlEolnnnmGXzpS1+C\n3+/HP/7jPzKfQG9vL2NNAOD+/fuIxWIQBAEtLS1MJ3rQu/fSSy/h0qVLWFxcxCuvvIKdnR20tLSg\nv78fXq8XjY2NKJfLGB4eRigUgtPpxJkzZzAzM4NwOLxvtkUikaC5uRlf/vKXUalU8A//8A+Ym5uD\ny+XCF7/4RfT09MBms0EqlWJ8fBx+vx8NDQ3o7e3lZ7y1tfUfw1DTC09Qer1ezz2KyWSSyd5lMhk/\nKDI49XpbyRDk83kUi0UGQhB0n7inKf1OaFq1Wl33cOmSP9qCRfVtMmLAQ6+MIrJaipMUG/2s2Wzm\n2nI1wIYUn8ViYTa0egqZ0pqEDNXr9dwvTmA9QoYSMxDhAQ5SnPRciIGMWh7C4TCWl5c5omtoaEBb\nWxsuXboEl8uFXC6HWCx24LMjo0cIWJvNhvb2dkQiEb4DxCzm9Xpx+fJlzoQEg8G6aSaKeC0WCwwG\nA7fm0V3wer3o6+vD0NAQOjo68ODBA1GDBsiBM5lMMJlM6Ozs3NO7abFY4HQ6+eUmQ0rGrNa9oPUJ\ntLK7u8v3g1Km1GpH9LJi0m3Vf1MQBGxvb8Pr9cJsNrND09jYCI/Hg0qlgtXVVa7Vi4moKatCOAa7\n3Y54PI7u7m7odDo0Nzczx8Ho6KiodemsSBeoVCrYbDYkk0l+LywWCwRBwOuvv47x8XHmSqglgiBw\nS6hCoYDRaITX6+Xo2WQywWazYXt7G2NjYxgdHWUcQL1sCBGSWK1WmM1mZsmiGr5KpcLExATm5uaw\ntrbGxEy1HAC5XM6GsbGxEQaDAdFolKmYK5UKVlZWsLGxwf/5/X7OqtV6/wg70dLSAo/Hw8+OEPqx\nWAzvvfce0uk0NBoNUqkU65ODnqEgPKReBh7eiytXrrAu8ng8DICcm5vD4uIiPB7PHuKXWo4FAUwL\nhQLsdjvzcLe1tUEikWBychJTU1NYXl5mfJJOp+POmoPWpmxIJpOBSqXCqVOn4PV6cfz4cUilUvj9\nfhSLRbz77rusV6xWK9rb2zmD8Th0vk+koaaXWCqV8gWiy08gJ+qpJZIRMekscgKq16gGBFEUS0qT\nwFnb29t125KATxQnAbIoLURRL1HjNTY2MrBNDEhGJpNxzy21QhBCklrJuru7mTlrdXW1rgGhfnKz\n2QyHw8G1WCIckEgksFgs6Ovrw6VLl5DNZjE2Noa1tbWaKT2KbEiZO51OWCwWBgUJwkOii5deegkn\nTpyASqVi7uxaqTdKw1osFgwODsLj8TDqHXiYRuvo6MDp06fR3NyMeDzOTEG1hNY2mUxwOp1ob2/n\ntCGBF/v7+7mW1dDQgO3t7bqThkgZSyQSmM1mtLa2wuv1MtkOkb5QbXNzcxOLi4t1aVRJiDCFnCmF\nQoFUKgW73Q6Px8OMSYVCgQFOlOKsJdVGr1AoYHFxkVn7BgcHUalUGGw5MjKCQCDA3RhihEgyRkZG\n0NbWBovFgoGBAZRKJQZ5vfnmm3jzzTexvr4u2rkggND6+jrX+ltbW5mAhPpy33zzTS7diHEsIpEI\nGzeLxYKurq49ZYuRkRGMjY0xwrweOI3uxOzsLPNFUO2UwGSZTAazs7N4//33ub5JQUQtkUgkuHXr\nFs6dO8d0mTR1an19HVNTU1hYWEA4HEY2m8Xy8jITKx1Uoyb9AoD1EOErcrkcZmZmsLq6yr3fVF+O\nRqNMMHLQndPpdACAaDQKo9GIoaEh1s3JZBI//elPkUgksLy8jGKxyHV8mmVQy7EgbA91DVGAsLW1\nhZGREYyPjyMQCDBXQDKZZB6Kg8pDEokEVquVneEvfOELqFQqzJ1x9+5dzM7OIpfLIZVKMbc6ZStU\nKhXkcnnNZ3iQPHGGmoSMKj0MuVzORpTSc80fN9UrFAr2fOv1XdJLXT35pbqHjwgTTp8+DZlMhkgk\nwj9fS8nRfik1ZjabmUkrnU5z/eK5555DU1MT3nvvPQZLiAHgUNuXWq2G0+nE6uoq906bTCZ87nOf\ng81mw+7uLu7cuVMTdFKNFler1WhqasLAwABaWloQj8cRiUS4terkyZPw+Xy4f/8+bt26VbN3kUAS\ntKfW1lZ0dXVBIpHA7XYjm81Cq9XC4XDg4sWLe14capnY79nRPSBnhwhOZDIZ+vr6EI/HodfrOXIq\nFotYWlrC7du365IX0FnQkBaPx4OBgQH09vYyAYpGo4FGo+GIJBAI8DmIaSlTq9WMs6BzITKbSqWC\n6elppkisvr/19l19H0kJEMCyXC5jcXER8/PzPBRGDJsa1cbJiSU0fldXFzQaDYrFIhYWFjAyMoLR\n0dG6LHj77Tkej+NXv/oVisUiXn75ZcaWbG9v49VXX8XIyIgoJHK1lEolJJNJfO9738Nzzz2Hrq4u\nGI1GBINBrK6ucm8yGWmxYB4CiRLm4/jx40yL6ff78frrr2N6eppR0GLAhcTyNzY2hmKxyKWcjY0N\nzM/PcwfG8PAw9+1TFrDW2qFQCB999BED9KhVLxgM4sGDB7h//z7jUSgypV71g9amZzY2NgafzweD\nwcBZyPn5eczOzmJzc5MpaulclUol19UP0puJRALXrl1DIpGAzWbDzs4OQqEQO1y3bt3a06VAiPhI\nJIJ4PH5gur5UKuHtt9/GhQsXYDAYoFQqcePGDeYdn5ubw/b2NhQKBSQSCdLpNJqamhCNRhEMBlln\n7PfcVlZW8Ktf/QovvPACGhsbsbCwgLGxMQymlZM5AAAgAElEQVQPD2NmZoazvlarFdvb2zAajXA4\nHJiamuLOpMeJqIXDwsR/HyIIwm9sgtKdlPJUqVRcX6iG3Le2tkKn02F8fBz/9E//VJPMv7rmq1Qq\n4fF44Ha70dbWhr6+PgZ2EGtWNpvFtWvXcOfOHa69HCQUmVFq9vnnn0dvby86OjqYMzwWi2Frawuh\nUAhf+9rXsLKyUrP/tDq6b2trQ3NzM7q7uzEwMICLFy9Cp9NxKvzmzZu4f/8+f41EIjVfakKC6vV6\n+Hw+fOELX0BnZyc8Hg8sFgtzkQcCAVy/fh1f//rXEQqFOGKoFVWrVCo0NzczorO7uxtnzpyB2Wxm\noMbbb7+NN954g8c71hucQXVZs9mMEydOwOfzobW1FU6nE62trRAEAaFQCOPj4/iXf/kXZnATE6Gq\n1Wqe1uZyuXDy5El+wRsaGjAxMYH5+XmuXe3u7tbkUq9+fuS00MAFi8XCALJUKoVQKMTROZVlxLSp\nVfeGUu2eMiyCIDDYkowuUdWKed9JSVU7xpQtobUoUjoM8cuja1O2iCg9qeR12DVpvepeeHLsyUGp\nzhQcZm1ah2rfKpUKuVyOmfweZSATC6ijti9ieQMeYkSIsfDRsxKDFCbMQnWGkcpglDancyKkvhii\nHXLqqRZPd4nKevS8CIhF94Xe6VqRL2UuZTIZOyN0T6u5MSjlTORO9HkOElqTOlio9EP7oTIc6RWi\nEM1kMqxTD9ozBXl0Z6v3TGdIZCeUNSB9TPq+SkYqlcqpAz8I/d0n2VBXg5OkUilcLhdaWlrQ3NzM\nAJlisQitVov5+Xncu3evbs2XvpICpb5Dr9eLZ599ltMrKysrnNYh8FkthU9KktKEZPjb29vR3d2N\nXC7HALn79+9jbm6uLtqS1qV+Zqpp+nw+XL58GR6PByqVCmtra/j2t7/NgwHE9PfSmRKTWnd3Nxob\nG7n9yGQyYXp6GgsLC5icnMTGxoZoJVfNykP0h9QiI5FIkEqlMDU1xQhlsdENGSeixqQXmIwRoZ0P\ny2b1qPGgf9PnFUMHKfZvVMuT8O4dRh6n/7PWWvut+9ue8aPnfFjD/D9iTRKxbXiHFTHtiL9P+X39\n/f2ew+9iTTLav4s7WL1u9VfgQPbFf9+GGgCnxD7+mT1RTiaT4eEDlPKuF5U9ujYZQQJUqVQqBkFk\nMhmub9UCROy3bnUPKtVsyAusjpbEvvDVCHA6D0rZU2pKLFnIo+tWf63+92H3WGv96vWO5EiO5EiO\nhOXfv6EWI2RsDxtFiZH9mvSP5EiO5EiO5Eh+RyLKUD+xYDKxUm+4xW8jR0b6SI7kSI7kSP7/lrrz\n7wRBaBIE4deCIDwQBGFKEIT/7ePvmwVB+JUgCPMffzVV/c7/IQjCgiAIs4IgPPv7/ABHciRHciRH\nciT/kaVu6lsQhEYAjZVK5Z4gCDoAIwC+AODPAEQqlcp/FQThfwdgqlQq/1kQhF4A/w+ATwFwAXgH\nQGelUjkw7P1tUt9HciRHslfqEaY8rhD4kNCuv6u/QaBAmthFgyRSqdRvvX41ylmj0aCjowMzMzN7\n+Bl+G6EzodZAIlIJBoO/VaavGhVfPY9gc3Pzsdd9FJNSDaQizorfZr+PotRJfluMy6Pr/i7uBH2t\nBo3+tutW918/CiKrsfbvJvVdqVQ2AWx+/O+EIAjTANwAPg/g8sc/9m0A7wH4zx9///+tVCo5AMuC\nICzgodG+Ve9vHcmRPAnyqIL4XSFzSTFQy081T8DjEPU/ujYR2VBLCv0Nosv9bYR62J1OJ5RKJdLp\nNLa3t0VNJqu3b2ITc7vd+MM//EOsrKzg1q1bWFtbq0sgUm9taus5ffo0Ll68yK1w4XCYB+c8biuY\nIDzkDne73eju7mYO/omJCcRiMe5CECsEFqXOBhryIZfLIZFImHeeWq4OI+RQaDQaaLVaBqES9wOx\nNB7mDtJeydA3NzcDeNi+ReBZ6rN/nDMmQh+z2QyPx4Nyucxtl8Rcedg1yWnT6XRoampCZ2cnFAoF\nlpeXsb29jUQigVAoJGo+d7VU0xwbDAa89NJLzA3v9/sxOjqK9fX1Q69LcqgatSAIzQCOA7gNwPGx\nEQeAIADHx/92A/io6tfWP/7eo2v9JwD/ad9NfawUDAbDHuax7e1tnm5Fh0KKiJrwa3lGtK5SqWSC\nC/LgQ6EQotEot1nR4ATqM6xH6mAwGJhuVKVSob29nYkRiAmIemTpAlMvar09E0+vTqeD0+mEy+VC\nQ0MDgsEgj1VbX19nEv7qXtyD9kwj8YidbGhoCG1tbXC5XEilUpicnEQikcDGxgbP6qZ2snpjP6VS\nKSswu92Ozs5OPP3008jn80xq8NFHH2FtbQ3pdBrJZJKHA9TCHFDEdfHiRXi9XvT09KCvrw8mkwmJ\nRAI7OzsIh8P44Q9/iN3dXSZFoFGBtfo5aRxpZ2cnvvSlL+H48eOw2+2oVCrcP03EBlNTU9zzKka5\nEQlOU1MTBgcH8Zd/+Zc8gzqfz2N4eBgffPABFhcXmS/5MJGDSqWC2WzGpUuX8MILL6C1tRXpdBq7\nu7u4f/8+vve972F7e5sZxA6jNIkq89KlS3j22WfhdruxtraG999/H5OTk3zfHjfSIWKd5557DqdO\nnWLKzHw+D7lcfihDVy1kPFpbW3H16lWePf/P//zPPI5QLDtZtZDBI3KZP//zP8e5c+dQqVQwNTWF\nmzdvMv2sWGNKBkStVnPvrdVqxcsvv8xRdCAQwA9+8IPfILqpty7xMGg0GgwNDcHr9aKlpQVmsxnx\neBzJZBIjIyOYnp6G3+8/1H6NRiOMRiPa2towNDTE73g+n0cikcBbb72F0dFRrKysiDqL6v2azWY8\n++yzuHDhAtrb25nhK5fLIRgM4tq1a/jWt74l6izIGaSRvW1tbfirv/orOJ1OyOVypNNpSCQSBINB\n/PznP8fPfvYzzMzMiFqX2Cefe+45ZnHs7u6GRqPhziGJRIKvf/3r+P73v4/FxcXHyoiINtSCIGgB\n/BjAX1Uqld1HWm8qh01fVyqVbwL45sdr8+9SX6/L5YLX64XVaoXFYoFCoUAoFOLpPTRhhSa4ELtO\nrRdbr9fD6/UybePJkyd5bOTu7i5WVlaYND2VSmF6epr7c4lAYj+RSqXo6OiAy+WCxWJBU1MTTp8+\nzTy48Xgco6OjbKSWl5fZSJOy309hUDTQ2tqK8+fPM+EJGRAycAsLC3j//fexsLCARCLBnhxx1u63\ntlarRXt7O3p6etDU1ITPfvazPEe2XC7j5MmT8Pv98Pv9mJycxN27d3lwRDweP9DwUTRw6dIltLe3\no7W1FT09PbDb7Uw8kMlkoNPpcO/ePUxPT3OqligH99szGVMaOdjd3Y3Ozk7YbDYUCgXu1bbb7Th2\n7Bju37/PTlA9Xmfy3ol+k5ysVCrFoxZNJhO6u7uRSCSwsLDAkY0YRUG0qjSGMBAIMPkETQhqbm7G\n5ubmnhY8MUJ7t9lsPBWJjH2lUuH2wMcxokRIQY5Wd3c35ufncf/+fYTDYXZiH3dtQRB4NOunP/1p\naLVa/OhHP4Lf70csFjsUM9mja1Mv/6VLl/D5z38eLpcLS0tLGBkZ2TPc5zBrAp8QaWi1WjQ1NbED\nMDs7i/HxcWxsbDDngth2TjJQLpcLdrsdDocDnZ2dOHv2LE+LWltbOzQZDLWuWq1WtLW14bOf/Sw7\nz1KpFIuLi5ibm2OOA7FC7acul4vndHd0dMDtdiMajWJnZwcmkwktLS1YWlqqO7GNhBwgImF66qmn\n0NLSwjztgUCAh+d0dnaKXpfuMVH5Dg0NcXklnU5jY2ODA62enh5cu3ZN9LoNDQ1M8NTZ2Qm1Ws16\nmZ4ZEWrRdLzfm6EWBEGGh0b6/65UKq9//O0tQRAaK5XK5sd1bJoQvgGgqerXPR9/r64QsYfBYIDV\nasXQ0BAsFgv3MmezWeh0Ojae5HkD2DO7dL+DIM5lUorNzc08QSWXy/FsVIfDgUAggPX1dWg0Gl73\nIC5xqVTK0VJHRwdaWlrgcDh44AZxRnd0dPAgEbVajd3dXV53v7SnVCrl2a/t7e04fvw4XC4XdDod\nzzCuVvTElkOZBVLO+zkXUqkUNpuNyWM6OjqYZ5loTyuVCk/yoZQqeZ4HvSDVhC9EzuJwOFAoFDA7\nO4tYLMZpX0q7EaEN/f5BRorqdTTtq6GhgRXY8vIy810TwxGxEtUi7390fcqcLC8vMxdzKBSCx+OB\nw+Fg54p6+Q9jUIGHBjsWi+H69etYX1/nuiPNUKa05GFSeoLwkP7UZDKxs3Pv3j3s7u4y3zdlkMRS\nXFYLjXulKWQjIyOcbclms0yB+zipTZlMhu7ubpw9exZ2ux3r6+uYnJzkNOzjOAAUSZMxJeO0srKC\nt956i9Pdj7NnYjSkQQsXL16EVCrF2toa3n33XR6cITZ9XF0KMRqNaP6YErm1tRXHjx9HOp2G3+9H\nIBBAMpnk8apiygEUoXs8HrS2tqK3txdOpxMymQzRaBSCICCZTEKtVjOR1OzsrKh1q8sJ/f39aGxs\nhFwux927d1Eul7GzswO9Xo/Ozk5sb29jYWFBVP2bsjcUoSsUCk4dr6+vIxgMorm5GV1dXUwhLCbj\nUqlUYLPZ0NbWhq6uLrhcLkxNTSEcDnNWr729HefPn0d7ezsGBgZw9+7duucAPHSGnE4nTxCjNPet\nW7d4dPCpU6fw/PPPo6enR1Skvp/UNdTCwx39XwCmK5XKf6v6Xz8D8KcA/uvHX/+16vvfFwThv+Eh\nmKwDwB0xm6EXwWAwwGw2M7fs2toaU29SzaJQKECn0/G0Fkox10ohU6qGJgHF43EEAgFOQ7tcLub7\npglQALCzs3Pgy0fREtWTtFotk/XHYjEmPhEEgXl7aUwnRXr7rU2Gl5i96AWdnZ3F1tYWkskke4mR\nSATpdJoVZ0NDQ01lRNEGORPFYhHj4+MIhULY2tqCTCZDY2Mj4vE4QqEQR7rEi3tQupccpUqlgng8\njlgshmAwiOnpaYyNjQF4SK3ncDiwvb3Nwy3K5TIPBNnPkNBLQRH52toaOz2JRALz8/OQyWRoamqC\nQqHAzs4OkskkALDxrSeUpdnY2MDw8DCKxSLW1tZ4nF44HGaaQXLaxNbeyuUyMpkMotEolpaWeAxg\nsVjkwSXAJ2Q5j5NGprNbXV3F/fv3IQgC9Ho98vk8OzYHZVcOEnICiKa3UChgbGwM29vbrLCpVHQY\nqSbwOXv2LPr6+pBOp3Hr1i1+16qBPo+ztlKphNfr5bnwv/71r3H9+vU9JSyxa1fXYrVaLZcZrly5\ngvX1dbzzzju4d+8eP9PqIT+1/gadIbH4uVwuNDU1wev1Qi6X80jZ3d1dZDIZzjCIORcy1CaTCf39\n/bBYLEilUpxNiMfjMJvNnKoWW2Igx0Kj0cDj8XBkSlm9dDoNg8GAwcFBCILA87DFCNWkydFaX19H\nKBTC9PQ0AoEATCYTjEYj61Wx65ITns1mEY/HMT09jfn5eSwvL3NZTKPRYGtrCxqNRrTxp8xCKpXC\n8vIyNjc3mVt+fn4elUoFFosFVquVOebFZgEeFTER9QUA/wuA+4IgjH38vf8TDw30DwVB+HMAKwD+\n548/wJQgCD8E8ABAEcD/WgvxXS1UnyTjptfrMTExgeHhYfj9fiQSCebSdrlcDA558OABVlZWataR\nKS1OQxYEQcAvf/lLLC8vM8PZwMAAp8UHBgZ4oHgtvmhac3V1FQ6HA+l0GrOzs3j99dexu7sLhULB\noAWpVAqfz4dyuYy7d+8yL/l+axPJPY1p7Ovrw+TkJObm5rC0tIRSqYSWlhae6Uw1p3A4vKe2ftCe\ng8EgI2sFQcD8/Dz8fj+PBjxz5gxKpRIymQzUajWy2Syi0WhNkn3i/43FYrh37x6y2SwmJyeRTCYx\nPz8Ps9mMtrY25uCmiDccDvNwgP2eH9Xac7kcNjc3MTo6itXVVUgkEkSjUeh0Ovh8PlgsFuRyOWaU\ni8Vi2N7erhs90do0wpMcnWw2C5vNhmPHjkEQBB5DGI/HDwW+oWdJ06AkEgkMBgOam5vh8Xi4Xp1I\nJJBKpQ4F6Kl8zEtcKBQQCASQzWaZGpcUaiqV4rt2GMNHisZmsyEWi+HBgwc8k1mpVO4Zt/o4BtVk\nMuHEiRNIJpP45S9/iTfffJMjbbpjhzXWZEz7+/vx5S9/GcFgEN/61rdw48YNfn70fA+D9KV9dXR0\n4OrVq3juueeQyWTw13/91zzJqVwuQ6FQcAZQzJoE8KK0d09PDzKZDG7cuIEPPvgAMpkMDoeDnfXD\nMC9ShmlhYYEn9a2trWF3dxcSiQSf//znYbPZoFQqMT09LfocyPF888032dFPpVJIJBJQq9VwOBxo\na2tDa2sryuUyl3nq7Z0wLLlcjst4pMdoFvyFCxfgdDohCIJo56JSqSAYDCIajWJ8fJy5zyuVyh6u\nfBoTHIvFRK1LAUY6nca3v/1t1l/0jqtUKoTDYQ6yaBDP44gY1PcHAA5ymf+nA37nvwD4L4+zIQKE\nVc/njcVizOGcSCQAgOftFgoFJouv8zl4pBsNnSBgGima7e1tjqJpDGH1/NyD1k2lUojFYlCr1ex5\n0yhAmUyGTCaDXC7HBPTVqd9a62YyGcjlcs4UkGGjjIJUKkUymYRer98TiYmZIEYzZKm2T1O0kskk\nrFYrKyaKTKmVpdba1LYDgFOANHhgcHAQjY2NsNls/LcpNV/vjGltAKzAtVotNBoNmpub4fP5uFad\nTCaxuLjIRkRs1Fu9d4vFwqksSmslEgmeakTKQ6xBpb9Po0V7enqg0+nQ2NgIhUKBlZUVrskeFiFb\nqVR47q3b7UahUMDu7i7kcjlKpRIb/scFe1mtVthsNh5daDAY+Fk9OjziMCKTyWC322E2mzE9PY2Z\nmRnk83mo1Wp+Nx4HIUtO68WLF3HixAn8+Mc/xvz8PBtw4PFIkihC7e/vx7lz51AoFNgJJRR1qVRi\nJS/mTCqVCq9Lad9QKIT19XU8ePAASqUSOp0Oer2e0dNihd6pnZ0dDhgIRAeAHTAqvYk1TpVKhctr\nNGOe7hY5hjRAaHd3F5ubm6JBdXT3qexB94vKY11dXejt7YVUKsXW1pbo86hGuNM7QDaGAMY0T3pj\nYwPBYFDUugD4Hj2qv2hdt9uN06dPY3V1FVtbW4/d2fFEMZPRw5FIJPD5fMhms1wvJUg9AJ6epVAo\nYLPZMDo6umem6n4KiS6AwWCA0WhEOp1mxVkqlRgBTX+fjGC9uh5dAkJRk4Fwu92MxqWLR96oQqGo\nO4WK1s1kMpyOkUgkcDgcsNvtUKvV7BWSsaWBF2JqWISqpejF4/Hw+TqdTkY60/lnMhlRUQg9Q5oI\nRGMtOzs74XA4eK7s5uYmp+DrofUfFaqDu1wudHd3o7u7G+VymaNWqiMS8FCsUMrN4/HgxIkTMJvN\ncDqdyOVynG4nJPJh1qX7pNFo4HQ6cfLkSVgsFkilUhSLRayurvIYz8O23VCa12w286xcq9WKbDaL\nSCTCP/e4UW9jYyO0Wi2Xh5xOJ2dE6GcOuybhDTo7O1EsFnk8oNFo3OO4HRZMRmfR39+PT3/605DL\n5RgeHkahUIDZbOaINJvNHhqwR9OjLl68CIVCgfHxcbzzzjtsTAmdXT3URUzqm/AOGo0G6XQaExMT\n3M1ht9vR0dHBoKfqlHq9/VLESaNwM5kMt+nJZDIYDAZoNBrE43GeJy1mv/S1OptULpf53TCbzejv\n74dOp8Pt27exuroqGpldbfRpH1RaUSqVOHnyJHQ6HYNzS6WS6D0/ql/oHtLscoPBgFgshjt37mBl\nZUX0uiSk30loMuHTTz8Nk8mEn/3sZwgEAgeCZevJE2eoi8Uidnd34Xa7IZfLYTKZ4PV6YTab+edU\nKhVcLhe0Wi3i8Tgbx3pED/F4HAaDAalUils3KJqk2aYE6gCwZzRarZeDLur29jbPlj19+jQMBgO3\nNVUqFeh0Oq73PhqRHORc5HI5CIKATCYDhUKBjo4O6PV6diTImaAIW2ykQJc8Go0ik8mgv78fDocD\nNpuN67DUy0n1FrHGlAw7KXOv14tjx45xRqRQKGBwcJABW2JTelQXImVIYLXq7AilJ1OpFAKBgGiF\nXG1AjEYj9wxX77m5uRkKhQLXr18/VK2JUpyUaTEajTw5LJfLwWKxcEZHbN2tWqjOCTwcX7q6usoR\nKX0WAuwdZs+UoqehL/l8HhqNhmtziUQCDQ0NhybKoLQs1Y+DwSA7G3K5nB0Bwl2IVWwSiYRBTna7\nnceykhOqUqnw4MEDLhWIyYrQXo1GI5xOJywWC1ZWVjAyMoJQKMQAVLoP6+vrjAeo12mg1+t5Wl1H\nRwcCgQAikQg759Thsbu7i2w2WxPIWb1fQiJTTVcikXBUTbiWjo4OAJ+UwWrNmaf9UmRLaHLKeJLx\nt9lsaG9vh8vlQj6fx4MHD0QNSiLMB030o5amcrm8B7fk8/m4/FUP7EXrku6uHtdanZo2mUwYGhpC\nLpfDysoK7ty5I4pop9ppobp9tVgsFpw+fRoejweZTAYjIyN1z7iWPFGGGnhoQAhA4PF4cPXqVZw9\ne5aHcZMCyWQyaGlpwerqKvr6+jA2NsbApINe7GQyicnJSS7wX7x4EcBD5W6xWAAAqVQKBoMBUqkU\nAwMDnGKpBok8KpVKBUtLS0za0NPTA7PZDJlMhubmZq7hkNJTqVS4efOmqDR1qVRCOBzGjRs3cOzY\nMUYe22w2yOVyWK1W5HI5tLW1wWq1QqPR4M6dO5ziqpeyn5+fRywWg0qlwurqKgRB4MyCIAhwuVx7\nADkEgKsXVZOSpJnL1N6VTqc5hXjlyhUcO3YMIyMjeOWVV0SlfcvlMhYXF5HP5xEKhXDjxg2OyrVa\nLYxGI1544QX09/ejr68P3/nOd7C2tibqxSMQ3I0bN3D37l1UKhVWSDabDQMDAxgaGsKLL76I1157\njevf9YQUBoFuvvGNb3BLodFoxOXLl9HV1cXtfGJTZKS4SRHfu3eP6+dWq5WNAKUhKQ0u5izoPVOp\nVAgEApicnEQsFoPZbIbNZoPZbEaxWMTCwgIWFxdFR+zkDBESeXh4GGq1GseOHYPZbIZWq0UymYTT\n6cTw8DAmJyfrOojVRuT555/HqVOnsLm5ieHhYTz99NNwOBywWCzsaCgUCszMzCCRSNS8y4IgwGw2\nw2Aw4PTp0zh79iz8fj9mZmYglUpx8eJFfOpTn2KchdFo5FowOdD7rUmz4I8fP86fmzJbLS0t0Gq1\ncLlcOHHiBGKxGJaXlxGNRjmLdpDCp2fe0tLCpRDK3gEPM5E2mw0dHR0wm824c+cOpqensby8XLPj\nQiqVwm63Y2BggNtFo9Eol9woCDl37hy0Wi12dnYwNjaGsbExlEolnkH/6DlLpVI4nU6YTCZYrVac\nO3cOsVgMRqMRKpVqzzvd09ODnZ0dfPjhh7h27RqWl5c5A7Xf83O73dyP7nK54Ha74XA4oNFoGH9i\ntVrR29sLhUKBV199Fe+88w5jgg4ayERZJq/Xi6amJgwNDSGdTqO7uxvFYhHBYBASiQTPPPMMAOCD\nDz7AT37yE8zNzbFD8zgdDU+coQYeKuNbt26hpaWFjahKpcLm5iZSqRTXhGw2G3u89Hv1DAg19be3\nt3O9mkBb1DpEnhfV5ugy1vLsCU0YiUSwsrKCM2fOcBScTCbhcrng8/kAAA6HAwqFgl+4ehFDLpfD\n4uIitra2uFWIWr8sFgu0Wi0GBgbQ1NSEXC6HyclJNtS11i4Wi1xzvXHjBsxmM2MANBoNzGYz+vr6\n0NTUhOPHj+Pu3btMmlFPaED66OgoZ0PIMaHSxdDQENxuN6ft6wl9FqpDb21tQaVSIZlMolQqcanh\n/PnzcLlc6Ozs5HR+vXWBT8Bwfr+fwVKUxrfZbHwm1A4ntu2LJJlMcusVpQ6NRiOOHz/OnQNi+1lJ\nsVIaMp1OQ6fTYWdnh+uNOp2O2wcPai+stS7Vvqm+SaApwkpUYy7od+sZVHpfrVYrzGYz/H4/OzGh\nUAhyuZwNikqlAlAfnV2dxuzo6GCHUC6Xo1Kp8Dx5iiYVCoWo+0Z1bbr/LpcLsVgMSqWSa8qUnatu\nHQRw4DtCaXRy/AYGBgA8xOYYjUZu95HL5SiXy5DL5VyiqpcVMRqNaGlpwdDQEGMsqNMEeKjzFAoF\n3G430uk0P2O9Xo9MJnPgfSY8wZUrV9DY2Mh8CpTt0Ov1sNlscLvdjKfJ5/PQarWMk9jPaaHukr6+\nPpw7dw5tbW2cRSyVSjAYDDAYDNz58v777yMSiXCLJIF89zOoHo8Hp0+fxvnz52G1WmE0GjmDRZlC\no9EImUyGmZkZbGxsoFgscjdMrW4cr9eLr371qzCbzfxuZTIZBvzJ5XJYLBaMjo4imUxy2YyyqI/D\nQvjEGWoCWgUCAVy7dg0mkwl2ux1yuZxRx0qlEk6nE16vlyn2KNVUr+5LLQqJRII9Nmr7oSb+bDYL\nn88Hs9nMirOeV0/15I2NDUQiEVbkVM8k5G1zczN7e/Wi3up9EytPpfKQKYuifOqjbmtrg8/nQ7FY\nhNlsZkCEGM+NEM9Er0jZA5vNBuChAmhtbYXT6UQoFKqz2ifKvlgsQhAErm/SWQiCgAcPHqC9vR2d\nnZ3weDx1FWd1lEdZhWw2i1wuh1QqxU4HpdwprajT6USl3ohhiPZdnZqm3ycEJ/WW11u3Og1JtTYy\nSlRzLBQKrCzFslmRgqU9U+q7WCyiUCgw6Y1SqYTD4cDm5qboeeV0xnQmZJSozikID2kzqeRQHR2I\nMajUVqPX69koE6WnIAjo7++HwWA4FPUknQORyhCwS6lUYn5+HuVyGSaTCe3t7fzsxAAYBUGA1WpF\nR0cHK+RqR4VKFT6fjwmZyIDViprkcjm6urrQ39/PxhR4yG6oUChQLBah0+n4/pDDUCurBzx0rik7\nUy6XsbW1xQ6MXq/ncydHrlwuw2w2I5kcXKcAACAASURBVBaLIRwO1zxjs9mMM2fOYHd3l9siidVQ\nq9VCq9UiEokgkUhwuc7j8fA7tJ8TIJFI4PF48Cd/8ifchkvskEajERaLhct8VBJQqVRoampipPpB\nZ9zb24uXX34ZarUa8Xgcs7OzzDdAGQKiZSV8hNfrRSQSwc7ODgcsj4pCocCFCxdw/Phxzl5tbGzw\nHTCbzVxKJXZAk8kEh8OBeDzOWZzDyhNnqCnqImAQpZDS6TQrHIlEgtbWVpw9exZKpZJ7nusZ02o2\nMABML5nL5ZBMJpHNZhEMBpHP59lbcjgcCAaDooAF1FoGgEFdVGcjggiz2QyLxQKfz8fr1kq90X+k\njCnKSCaTzGqWTCaRSCQ4ldTR0YGpqSlR0Q2tW00kU2381Go1UqkULBYL2tvb8eDBA1H7pUhApVJB\nr9dz6xMB3ahWr1AooNVqa0Zj1SleAu1RSwW1dVHUkMvloNFooFKp9pzXQULGiCJEuktk9MlwVkfs\nfr+/bjRNKV61Ws1RKSnM5eXlPaxeGo2GwSxiAFT0zMiA0j2tpmAl8JpcLmfaWjEAJ3IoyEgYjcbf\niECp3isIAkdmYurIdGZE5kPOCd03iqAo20QdB/XuMTkVAJikh/r4S6USLBYLPB4PTCYTFhYW+J0X\nU5+2Wq1cowfA95fQ2k1NTZBKpUxZSx0qtfYsk8nQ1dXFziW9dzqdjp8r4VvI6IdCoZp9uJSmJ9at\njY0NRKNRBrvR/QPAxojSwKlU6kACH7oLzc3NkMlkCAQCzDbW29vLOkIqlWJ+fh5bW1sMIKXnQa2d\nj0pDQwP6+vrQ3t6OUCiEt99+m7MN5HxRi+js7CwKhQJ8Ph+kUimy2SwTUu235xMnTsDr9SIYDOLt\nt9/G5uYml/F0Oh2/K0tLS5iZmWE2QkpRb21t7buuQqHAyZMnUSwW8aMf/QhTU1Mwm824evUqhoaG\nADzUQRsbG5iZmWFSn2w2y3Xsek7RfvLEGWqKvPL5PCYmJnh6DKFiyVNOJpOYm5tDX18fk4zUe/EI\nSUrGPhqNMqiKXgDyCMngaTSauqATci4oaiZHg15oQq/LZDIMDAyw11WPP7w6UiEngHqfyXkRBAGp\nVApKpRJWq1VUJFKtiKmlQqfTYXt7mw0TMXsZDAYGRPj9fkZoH7QupQEtFgvMZjNcLhcKhQKi0Sgr\nfLlcjvPnz6Ovrw8AGOBzkBCilJC3HR0dcDqd2N3dxfT0NCswAnD09/cjFovhxz/+MUZGRg5cFwAD\nSkwmE/R6PRobG6FUKnH//n1IpVKYzWacPXsWV69excmTJ6FSqfDVr34VgUCgpmdM56rRaNDa2orG\nxka43W6OSNxuN9xuNz71qU/h8uXLCAQCeOeddzAzMyOKO5yQ7xqNBhcuXIBcLkcwGOQzOHbsGDo7\nO1Eul3Hjxg1OGYpxOOnZm81mrK6uwmq1or29HW63G52dnXC73cywRgMMxGIAyIFJp9NYW1vD6dOn\n0dXVBeBh5mZ9fR0/+clPcOPGDa4X1luXnBNqIZTL5QzyIudwZ2cHw8PDePXVV5l8otba5BhGo1Eu\nexC/fDwe53fv5s2bmJubw/z8PFZWVpiw5aAyHGVYZmdnMTg4CK/Xy6W8ra0tbG1tIZ1OY3R0FHNz\nc9jZ2WGednJKDzprp9OJ5eVldHV1wev1orOzE/l8HplMBuvr67h16xYWFhaQyWRQLpcZt0DYnv3W\nFQQBFosFfX19UKvV6OnpwcDAAGfhJiYm8Itf/ALZbBarq6v8jhI1Z7WT86i4XC5mYNPr9fijP/oj\n5jTY3d3F3/3d36FQKGBnZwdyuRw+n49T6ZVK5UCyHYlEgsHBQc6snTt3jm1IOp3G22+/jfHxcaRS\nKeh0OshkMrhcLv6qVCr3ZRCjALG3txd6vR6XL1/GmTNnoNfrUS6X8e6773LZUavVIp/Pw2azwWAw\n4NSpU9je3sadO3ceq5/6iTPU1UIGqhp1Td4ztUQR/SehUeu92NXGj1CFVMsiQ6NQKJhIhOrV9dau\nXk8QBO7DLhaLPECEjD/VV8RGIpVKhV9wUs7EmV0ul7lGTTSfYlstqiNU4tQlLm8C7z311FPo7OzE\nvXv3EAgERClkWtPtdqO9vZ1bIOLxOEciV65cgclkwtbWFoaHh+tiC8ibNZlM6OjoQEdHBxoaGtDd\n3Y1QKMTDHbq7u5HJZDA8PIxbt27VRVpSVE+1546ODvT09OD8+fOsEE6cOMF1w4WFBSbXqScEHmls\nbERXVxeamppgMBhw/vx5VCoV7lEOBoN49913MTs7K7olic5Zr9fD4XAwiFEul8PtdnNL1fDwMDuk\nYjAcdCaC8LCVMJFIwOFwwOv1cnS2sbGB69evc+uT2G4AADxEh0CdL730Eiu6cDiM1157DePj4zzY\nQqxQnf6dd96BVCpFX18f9Ho9R0w0TKWaqKbeWZDxVKvVePDgAaRSKUdTW1tbmJ+fx3vvvYfl5WUk\nk0ku7dQ7j0wmg9XVVSwtLSGXy8FgMECn0zGh0ebmJhKJBJaWlvh8KQg4aN+VSgWBQAD37t1DMpmE\nw+FgnUTR3ezsLO8xm81CoVAgHA5zueigdQuFAqampuDxeKDRaJBIJBCNRjExMYHFxcU9ACyJ5OFw\nC8qi0RSt/dbOZrMYGRlhZ2BnZwebm5sIhULw+/3MEEhdC5TZI5bKg6h2CeNEXTwWiwUfffQRNjc3\n4ff7MT09jWAwyOyUVJIJhUL89/dzAEqlEnZ3d3Hr1i185jOfgc/nw+LiIpaWlnDr1i2Mj4/zrAhi\n2wuHw/B4PEzKFQqFHovv+4k21MAnSro6TUtTqgiBrFAo0NjYWLc9q1qqgS00nYtSb729vRxhU21K\nLHCIHoLFYuHpX0qlEjabDYODg0zFKJFI9iAtD/JmH4X/0+AJk8kEpVKJcrnMHODJZBKbm5t1lT39\nLVrXbDajq6trD9BGrVbDbrfDYrEwuUOtaJrWpedFFIM2mw09PT349Kc/zesSGcD6+jref/99phc9\nSKg2R2dLfOqtra24dOkS5HI5lEolP7Nf/OIX+NGPfsRMYLWEjEGhUIBSqURTUxP6+/uhVqv3AMZ2\nd3cxMTGBn/70p0ilUjXXpPWIIIZqbjRaj+pjhI5/7bXXMDY2Jnq8HhldmmZGWSaLxcLp0+npaXz4\n4YeYmppih06MkHKn/fv9fi5jmEwm3Lx5Ew8ePMDS0hI2NjYO3Z9NhnplZQVvvvkmG39iPrtz5w63\nZ4nZM905SpHeuHEDy8vL8Hq9aG9vx+joKGZmZnjaGRkNMc5xuVxmJycajeL+/fsYHh6GRCLB2toa\ng5Aooqd162FZiKf/vffe4/eYyDYCgQDC4fCe0aSEm6gHQpqfn0cymcTy8jLcbjdKpRISiQRzIhD5\nCBkmQoTXmlpXqTzshHj//ffZUFLZbXJykiN9ej8JK5DJZPi8D8ocbm9v4yc/+Qnm5+dhMBg4SAiF\nQoz1AcDc5E6nE+l0GrFYbA9r2X57/u53v4vp6WmuSZNxDgQC7DxQuVCtVkMQBEQiEa6/H2RINzY2\n8MorryAUCjEAMhAIMHCMArDR0VGoVCp2IDc2Nhhn9DikJ0+0oSYlSRe0Gl1psVgQDod/41DFRJK0\nLqGNGxoaYDAYcOzYMaZejEaj3AYmxkjvJ3q9Hr29vWhvb+coml7CnZ0dUcqChC57Op2GUqnEiy++\nCLvdDuChZ7q4uIhoNIrR0VFRNRA6U3o54vE4fD4fOjs7Of2TSqWwvb2NsbEx3L59mwEWB50xKUxa\nk9DT1AZhMpmYMGR4eBg/+9nPMDIygqWlpbp7pSlkcrkcExMTyOVyPE6TDB/xfn/jG9/AxsYGEomE\nqHLIzs4OO2OlUokHFdBEnQcPHuCDDz7A8PAwZmZmRL1sZDgikQjGxsYQDAY5w9De3o5yucxta9ev\nX+dpZ2J5yUmRr62t4Y033sDo6CgMBgOUSiUikQjm5+c5Wqomq6knFE0XCgVEIhHcuXMHk5OTXOPc\n2tri8tBh5hdXR+ukvDOZDL75zW9CIpFwpEdGo/p36q1LzmGxWGRu+unpabz11lt7WtLEGNJqodpx\nJBJBPB7H+vo67t27xwyHVDYjEZMdI+BquVzG5OQkADDxDaXk6fNQ3VgikYhq6aH6+M7ODubm5ngE\nKXWIVJf5CIVcjcM4SLLZLDY3N3Hnzh2OxglbUH0HiJqVyg/VzI/7CTlslLWgOQLkNNC+KAAikFc6\nneaukoOEShFKpZI/J+lQcqqIe4Ic0mpK5YPOOplMcnRMn42c2mpHqhr8CzzEBdC6h3FsSYTH+aXf\ntQgHjMh81ECSZ0/oSwLiyOVyxGIxRlGL/Jv8H828prYDilIofVitPOoJRcrEIavRaLimTqQRRBJR\nr0ZdLWRIKA1vtVoZdZtKpfZ8drH0ltXlBFqfzphqe9XG4zB3hRRCdetOteN1WN7p6nUPykL8Lu6y\nGGV7JEdyJEfyO5KRSqVyqt4PPdGGup5QNExtUL/Lz0Ie7WEpHcWImFr640itNPqRHMmRHMmRPHEi\nylA/0anvekKp29+HUGrk9yG/DyMNHBnoIzmSIzmS/4jyeMMxj+RIjuRIjuRIjuR/iPy7jqiP5Ej+\nP/beLDbO8zoDfoaz7/vK4XC47xRJrZYsW7Zkp4qT1CkSp21apGkTXxRtgaILUPSuBYpeFChQoJdp\niwIpGiNtErdx7cSOFclUJFmUKJGSuHPIGc6+cFYus/0X6jn+qJCcb+j8P5T+OoBgQZZevvPO971n\ne87zPLP/WybEpfyiW1nUzhIC9j5tdUuIdSFMBrG4HXX/QlwH8MnkCwGffhF7BfZSLn8abIbw89N+\nj6LWdth+qV34i1hXOHFD0xfNysvut1chGQ1R9h6VMvRJe+aon9kz+z9mTyKRf1GtFiE7HK37i2g9\n0cUpFLEhhO9RQYfAJ7PmJF7jcrkAAOFwmMdmhPKRzeyX9myz2aBUKqHVagGAdbszmQyPPok1AooK\n2fcIVVyv13nsksCYYk14DjSCSqNnhEwmVHQzRuvS2seOHUNLSwsKhQKznxHqvlkjxkCVSgWfz4eR\nkRHmzY7H48wq2ex+CTDrcDhw7NgxnDp1CiqVCleuXEEoFEI4HGbSmmb3q1arYTQa4XQ68ad/+qeQ\ny+WIRqO4efMmfvKTn4gi8DnInmpHLYxQaHSGfk8vtjAaahaVLCQcobEUegGJVOUo6wr3Tl+MMIqj\nPTdjwsv3STCa8JyOGnE+GRE/+TM/zbr0QgtHaciOEn0Lvzu63Ghd4QhOI+73g9Ym5LtSqWQiB+FI\nh1CjXOzadL4KhQI+n4+Z5ij7ol+lUqlpYCSdg0ajgcvlgsPh4Mt4a2sLy8vLTJt5FOUeIlYxGo14\n6aWXWGgmkUjg9u3b2NraYnWkZtcmngGiv7xw4QKrL83NzSEUCh04L3uYCQlhPB4PxsbGcOLECaTT\naZZXJW6AZoxGm0iwpKuriwl4UqkUUqkUPvroI6jV6qaDGKVSCZVKBYPBAKvVCr/fD5PJhFgshp2d\nHVajasb50z2pUqnQ2dmJnp4etLa2QiKRsIjQ9vY2VlZW9qXjbLRfg8EAvV6Pjo4OfPWrX4VUKsXm\n5iZmZmZw69YtLC4uNlVdEE7MDA4OYmRkBBcvXoTf78fW1hZmZmYwNTWFhYUF3LhxQ/Re6XvT6/UY\nGhrCl770JTz33HOw2+2o1+sYHBzEzZs3MTk5iZ/97Gc8WibGpFIpWltbcfHiRQwPD2NiYgJjY2N8\nZ5w6dQq5XA4ffPCBKA6G/eypdNRUPqK5UJpBTqfTPLCvUChYnYUUYvaTUttvXaJ3pC+pVquxbjTR\nJ0ql0j0iFY1KI3S5E22kw+Fg+Tt6AciR0JwxcY+L2TOpZNlsNpjNZp7vpNExmm2lyLjRZU/rEuEE\niWM4nU5mRcrn89jc3ESxWOQ5X4rmG0WGer0eJpMJZrMZHo8Hly5d4lnReDyOu3fvMgnF9vY2U6/S\n93GQ0ey0xWLhSLunpweFQgHxeBzRaBQffvghSqUSEy7QRdFIH5hUnU6fPo3jx4+ju7sbCoUCiUQC\niUQCMzMzWFxcRCQSYdIEGuc7zITat93d3XjzzTeZZGF3dxd37txhwhNiZ6LRPTEXnEKhYBWu8+fP\n7+EcDgaD+P73v49gMIhYLNY0SFImk7ETPX/+PM6cOYPt7W0EAgHcuXOHn0WaBW7GJBIJU9SeO3cO\nZ86cgcViwfT0NAs+EBteM6VZuuyVSiV8Ph9OnjyJl19+Ga2trZienkYkEsHq6iqA5svrQm58u92O\nixcvYmRkBEajEfF4HG+99RakUmlTmToFnWq1Gk6nE+3t7fD5fJiYmGDe8kwmw8602XMg53/x4kV0\ndXXB4XCgWq2iu7sbyWQS169fRyaTEe2oab+kmjU6Oorz589jcHCQ6Z/b29sRDocRj8dFj7cKvzen\n04lLly7h3Llz8Hq9TM7y4osvwuPx4Dvf+U5T0zMymQxarRZ9fX3o6+vDhQsXYDQaATweZx0dHYXJ\nZML6+jpmZmZEOWph8H3mzBn09vbi3Llz8Pl8HBQrFAr09PSgra0NWq22KfKhPftv+l/8v2hCxhyt\nVosXX3wRPp+P+0o00E8sOFReIaaaVCp1IF0dsWUR1eTAwABzTdfrdaRSKWZDIjWZR48eoVgs8qzy\nQfqyJE3X2dmJ4eFh9PX1wWg0MqMREZVEIhGsr69jbW2Nh/rJsT75INO6JPn35S9/Gf39/TCbzZDJ\nZEyYUq8/ln185513sLq6ymISRACx3+VJL5nb7cbAwADGx8fx/PPPM186CQUUCgUsLS1henoaU1NT\nTNRA9KX7nTNFw5cuXcL4+Dj6+vrgdrt51h14nJ0PDAxgamoK8/PzyOVyKBQKLASyH5EGZQZ2ux1f\n+cpXMDg4yA9/PB6H1WpFW1sbZ7yLi4vIZDJM2kKZ5H4vCTGm+Xw+HDt2DG+88QbMZjMTaNBzQ1rM\nk5OTyGazzNvdqDyr0+nYYUxMTECv1yMcDjO5gs/nQ61Ww8LCAjMkib3kpVIpOjo68Nxzz+Gll15C\nW1sbZmdnWW5QpVLBaDQyX3IzWR6JMVy+fBmXL1+GyWTCjRs3WMt5c3PzyFUnek4uXryI559/njOQ\nb3/721haWkIikeB3spn1KRA3GAwwmUz45je/ibGxMdRqNQQCAXzve9/D2traniBX7FnIZDL4/X4W\n1hkaGsLnP/95SKVSrKysYHl5GfPz83wujdami54oiy9fvgyPxwO3281CK6Tfvrm5Ca1Wy3z/Ys7Y\nYDCgvb0do6OjGB8fx8jICHMutLS0wOfzwWQysWhQMBgUtS6pvb3xxhsYGRmB2+2GTCbDBx98gGq1\nCrlcjt7eXpw/fx6pVEr08yyXy2E2m9HX14dz585hdHSUqyvBYBAajQanTp2C0+nEiy++iB/84Aei\n+SIcDgcGBwfx4osvwu/3s6b12toaarUavvKVr6C7uxsXLlzA3bt3G6oEUlVQoVDA4/Hg7NmzaG1t\nxczMDD744AO8//77AAC/349XXnkFFy5cwE9/+lMWI2rWnjpHTYpAarUaw8PD6OrqQjabRTAY5CyU\nROstFgvK5TIMBgNisdiB3K+0Nq1rsVjQ09MDs9m8p+dht9tZDYZEx4kR56CLQlh2NBqNaGtrg8fj\nwdzcHOLxOIrFIpdcKIMlB5zL5Q50HsKzIHYvo9GIYrGIjY0NhEIhzgBJ/QYAl2sP6w0Js2lSd0ok\nEohGo4hEIqjX6/B4PNwHojItiSkcRuAvZD4CwFzD9+7dY3Y5m82GjY0NZkWSSCR7mH2eXPvJUjdl\n4WtraygUCpifnwcA2Gw2lMtl5HI5pvIDIKoETj3XXC6H9fV1hEIhLCwsIJPJwGKx8OfZ3Nzkvyss\ngR9mLS0tfBnG43GsrKzw82yz2dDa2oqtrS2+zJopTwuBQeVyGXNzc3jnnXdQr9ehVqthMpn2MGiJ\nvSToe1SpVJyBpVIpfPDBB4jFYvwcE2im2TYAre/z+eD1ejkwnpqa2kO0c5T2AimH2Ww2uN1uJJNJ\nzM3N4datW/yui6mEPLlf4m0nbvy2tjasra0hFApxxWJzc1N0Ni0MWKxWK5xOJ4xGI3Z2dhAOhxEK\nhbgas7m5uUfop9GZSCSPOfXNZjOGh4eh1+sxPz+PpaUl5rD//Oc/j2q1ynsWuy5VFex2Owt+rK6u\nYnJyEhKJBENDQ+jr62M1OLFa6EIdh0qlwoH88vIySqUSxsfHcerUKa7MiQXXUds0l8shFoshmUxi\ncnISkUgEu7u7kMvl+NKXvoRCocBsfo2sXq9zf357exuLi4uYn5/HysoKc3rL5XKUy2U8//zzLNEs\nbPs1Y0+Vo6aHkHoqvb29kEqlCAaDuH//Pubn56HRaOB2u+H3+1kjORaLIRwOH3pp0sHKZDLYbDb4\nfD48fPgQ9+/fx+rqKnZ3d9Hf34/Ozk5WPCHe2cOoEoUXiV6vh8vlQrFYxI0bN7CysoJKpcK9PQow\ndnZ2mLy90br0xRoMBqRSKayurmJ2dhZLS0usakSXh8ViQSaTYerMw84C+IRDWyaTYWZmBnNzc1ha\nWkJLSwvOnj0Li8XCDlqhUDCXrZjLnkrPa2tryGaz+NGPfoSWlhbY7XZ0dnbyQ07tC6I43C87FZ7D\n7u4uEokEZ5/BYBDz8/NwOBzY2tqCwWDgrIr2cNC6QiNVnmQyiYWFBaTTaSwsLGBnZwcjIyOsv6xU\nKlGv17nCIuZCJgWjYDDIUnq5XI55kZVKJVeJCL8gtuxNeyHN3Y2NDcRiMRZ6MBqNyGQyB1YqGq0t\nk8lQKBSwvLyMbDbLcqUU4K2vrzeFmBVeVMRfv7Ozw+2Q3d1dvoAJ19CsKZVKyGQy6PV61Go1PHjw\nAFevXkUikWDqWfp8Yvcs1Ogmh61Wq/eAhCjoFGu0rkwmg9lsRqFQgFKpZEedzWbh8/lgs9n4exMb\nuNA+1Go160RT1p/NZmE2m/HKK68w06NYZ0rrVioVLC0tQSKRsAxlKBSCwWBAa2sr6vU6NBoNl6fF\nBAEEmMtms7h9+zZyuRzW1taYLrejowMA9uBHxO65WCwiGo3i6tWrKJVKjH0AwGV1ompt5h2pVqvI\nZrN47733uCJIFUmFQsFc683Q+O5nT5WjBsDRv06ng9VqRSAQQDAY5AyEJCo1Gg1effVVLq006k/T\nAy6VSrm0FAwGsba2hmQyydzL1Hvq6enBvXv32DEd5vSo16zX66FSqZBKpRAMBvegB1OpFNra2tDZ\n2ckcvIcBkmjd3d1dFAoFRlNGo1GEQiFsbm5CqVQik8mwLm46nUYymRS1552dHe4Rl8tlzqjpZxGw\niTSu6RI9iLxfeMYka0mZRTabZepU4cVgMpk4s27kTKnHnM/nkcvlWEilUCgw97vZbAYAqFQq1umm\ntRs9G3TOpCm8tbUFlUoFk8mEzs5OvmgkEgkymQyvK+alJvwEOVSilnW5XGhvb2ee483NTX5mxL7Q\n5NhJZAAAent7YbfbodFooNVqMT09zapRR7koCEy3vb0Nq9UKAFwyJUGGZvYrrJBYLBa0tLSwdKRG\no0GtVmPMRbP7JUS2SqWC3+9HuVxGLBZDJpOBQqE4MisgVaFaWlqg1+thMBhQqVQQCAR+ztE1cx6E\nt6lWqwgEAsjn89ja2kIikYBKpdrDUS2suog1AnZREErvN1XgKFDPZrOi1qN7aWdnh6UiSdlLIpFA\npVJBoVAwzieXy4l+nqkVGI/Hkclk9rRW5HI5dDoda9cD4qtDdHapVIr51KmNSQJBFouF7y+xYD3h\n1INQnIUqh4Tcd7lcWF5e/r/jqOmgTCYTLl68CI1Gg2QyiaWlJVZS2dnZYdCD1+uF0Wjcg948LHKT\nyWQYGBjA5cuX0dLSwkpTpMBFZRCSDQTAjuywA65UKjCZTDh79ix0Oh1isRhH3mq1miNxynAMBgOv\ne9jDRipb1Wp1D7+53W5nxR0qF9PDm8/nuZd8mAk1skulEtRqNQYGBmCxWGA2myGXyznLpF6sGKdH\nqFQSpHA6nTAYDPiVX/kVLk3X63WsrKwgFouxY2xU5iRHTUA8q9UKq9WKY8eOwePxMKgwHo9jenqa\nX8pG5P20NmVElUoFVqsVly5dYok8Eg+4e/cu9zcpCBGb9VJg4vF48I1vfIOrFdvb23j77bexurrK\nF1SzjsRoNMJut2N8fBwulwsGgwHb29tYXV3FysoKZ2diqyFCIyGR1tZWDmSDwSAD7Jp11MIyssfj\nwfPPP4/FxUVsbGywhGKxWOT2RjNGjsLj8eDLX/4yXnjhBbz11ltIJBKc5REgVWyQRUa4meHhYQwM\nDCCXy2FhYQE2mw0WiwUqlQp37tzhYJb20+hcqHpAbbtIJMIKcydPnkRbWxsCgQBWV1d5XK3RuvT/\n8/k8lpeX+c+pCtLW1obz589Do9FgcnISH330UcOeLBm9h1tbW1hYWOB9EGjqhRdewGuvvQaNRoN3\n3nmHgZFizoIqfJlMhn8WtQY8Hg9+//d/H2azGTdv3sQPfvAD0UELBbNCxDWBR91uN06ePAmn04mb\nN2/i+9///qEt1P3OQiKR7ClrE+5kfHwcv/EbvwGdToe///u/RyKREF1deNKeKkcNfBJxDw8Ps6Mz\nm817Xlqz2cxOmjIJYenwoEOo1Wro6ekB8NhpU+/N7/djd3cXFosFHo+HsxxhCfmwkla9XmctZ1q7\nu7sbg4ODjPJzOBxobW3lF5kuCVr3oD1TEEFlVrvdzvq9FFhQiY/Q02IuIHpgUqkUcrkcg7Gee+45\n5PN5BoAQkEWMhq/wPHK5HLLZLLRaLXw+H06cOIGtrS3utWUyGcRisT2IejGlMQCc7ZtMJni9Xu6n\nS6VSJJNJ7ic360BoDwQc6+zsBAD+zhwOB79szfZOKVgj8Akhvnd2dmCxWGAwGKBQKJp+iSnTo2kF\nk8mEcrnMmRipwdG70Yy1tLTA+IoPIAAAIABJREFUYDBApVLxKKRw4kKpVB7p0qE9O51OBoVWq1Vo\ntVqYzWbUajXm8G+2PE3jbwQUpSydEL4rKyuQy+UMuGz0M6gfq1Kp+Fkul8vc6/Z4PDCZTNzKoL00\nKoHTOapUKs7oqNpEZ+N2u7nSIzbLE5bpqXJDfV+z2QyXy4X+/n60t7cjEokgFAqJBnsJBYdIyIjG\nF3U6HYaHhzE8PAyZTMY4GjGCPnRWwtYC/ZlSqYTVakV/fz+USiVCoRDu37/P6lWNzoKMzlWIdbFa\nrRxcBINB3L17l1s5YkxYGZLL5XvGRAmYJpPJEAgEEAqFmlKFe9KeSkedz+dRKBQgk8nQ39+PfD6P\naDTKIz+kvUvKVHSRUIZ5kFH/g8BAZ86cQTwe5z4h6fnSBULZMPXKDnuYs9kstre3USqV4Ha7cfz4\ncdaj1uv1kEgkPPpEZVty2Id9cYQYj8fjcDgcfJk7nU6e1SRdVAL4CFWrDstQd3d3kclkEAqFYLfb\nuexvt9vR0dGBZDLJxBDCeWUxDrVUKiEWi8FsNiOfzzO4yWg0cjZaLBYRDoe5DCfGCJVeLBYRi8Xg\ncDhgMBigVCq5XdLd3Y10Os0tETFGn4vAMVarlS886sl2dXWhWq1icnKyqTIkIfDp887OzrJz3d3d\n5XEOAkSKJXIQ9u2pt76zs7Pn4iP8AsmWikXJ0oVfKpUYIb2ysgKlUslVl0qlAqVS2dQsstCRGAwG\nBINBhEIhAOBgdn5+HslkEsViUfQ8q7Dk7Xa7Ua/XsbGxAQBoa2tjYpKpqSnWXBfTTyZnajQa94yD\nEqqeQKl37tzhAIOCmoOeD7rYjUYjZ+NGoxFmsxnZbBYqlQoulwterxcPHz7kEScSCTpoXQpUyIka\njUaYTCYAYMnWrq4ujI+Pw+fz4fvf/z7C4XDDtiGtTfeL0Wjk9iT1odVqNZ5//nl0dXUhHA7j9u3b\ne8BvBwVEtGcAjC+h8jlVDXt7e3H27FmEw2FMTk6y9vNhQRYFQvV6HS0tLfz5dTodn+Pw8DDOnTuH\n4eFhfOtb38KdO3dEjb9RoEmAPblczhVYwoK89tprGBsbw8zMDN577z2u7B0lsAWeQkcNgNG39Xod\nra2t3Is1GAz8glHfolQq7YlkgMNLTpTJmc1mZrupVquM8K5UKuygySmKAUSQFnKtVkN3dze6uroY\nPAU8vkx1Ot0eSUnhr8O+PCozGQwGqNVqZloCAK1Wy6hv6olQMCKm1BSNRtHS0oL+/n6USiVEo1G4\nXC5IJI/nOg0GAzo6OriPKtZyuRw2NjZgNpuhUqkwOzsLh8PBACWj0Yi+vj4Eg0Ekk0nkcjnRgJN0\nOs1jU1KpdM/MYr1eR39/PzKZDFKpVFMkDtTXDofDqFQqmJubY8Q3AQV7enpgs9lYzF5s741Q39Fo\nFP/1X/8FrVbLPd+zZ8/C6/Wivb0dwWCQHYxYq1QqyOVyWFlZ4RKpw+FAe3s7WltbYbfbEY1Gm1qT\nAhe1Wo1kMomHDx8imUxCq9XC6/XCZDLB7/czIrkZozZQR0cH7t27h2g0imq1CrVaDYfDwfO9Yvum\n5PgpMGlvb8fm5iaWl5f5QtVqtVzZymazDYNOyrq0Wi2jsr1eL1eKAHAgvr29Db1ezy0j+vcHOSbC\nDhDiX61Ww2azcRZM88kUZNJe6TMehHQ2Go3M46BUKtHW1gaz2cw/0+12cwBA4675fJ4Dh4MCLqqs\nWCwW6PV6dHZ2olar8dgljU2OjIxw0EjPGwXhB0226PV6KBQKqNVqnD17FsDj6Q2NRsNtvra2NvT3\n9+ODDz5gsCT1gQ/6DmnKRqPRwOFwYHR0FC6Xi58DpVKJ7u5uDA0NoVQqIRwOIxqNolKp8H1/0LOh\n0+lgNBrhcrkwMjICqVSK/v5+VKtVJBIJWCwWfO5zn0M+n0csFuPyv5BQ6pe+9A0AxWIR3/rWt7C4\nuAir1QqTyYSWlhYEg0F2SidOnIDf70cmk8GDBw+YeemwQ6hUKvjwww8RCARgMplw7NgxzjRoZGFi\nYoIju0QisaeXfNhLTTOa9AKOjo7yBb29vY3nn38eExMTDM6ifgz1Rw+zQqGAd999F9evX4fP54Pd\nbmdhdY1Gw6xLY2NjePToEQBxTGLCl7VSqcDhcECpVKJUKkGhUGB0dBSnT59GV1cXXC4X4wTE2M7O\nDhKJBK5fv46lpSXMzs6iWCxid3cXKpUKf/iHf4jBwUEYjUYWpheT7dXrdR7JorGbH/zgB9jZ2YFS\nqcTQ0BD+7M/+DD6fD9vb2wiHww1nh4WZ1fb2Nh48eIDl5WXO+iUSCUZGRvDGG29gdHQUQ0NDe3p/\nh61LgR6BmkqlEhYWFpikR6vV4uzZs+ju7kZLSwvu378val166WUyGXK5HOLxONLpNINw7HY7PvvZ\nz+LkyZPo6OjAzMyMqMtByFctk8lQrVaxvr6O+/fvY2NjA16vF263G4ODg0ilUntY8RrtmYJfIvYw\nm824desWl6N3d3dx8eJFWCwWrK6uYmlpqeG6xLtAxD0nTpyA2+1GNpvFxsYGzwyrVCq0t7fDbrcj\nHA43zKZbWlqg1WoxNDSEtrY2uN1uWCwW1pGnoDufzzMtJwH4qGW23xlQWZveVyp3E9aEPovZbEYu\nl4NcLofD4UBLSwvzLxzE5+B2u/HKK6+gu7sbKpUKLS0t2NnZ4fUoe8/n84hEIrDZbBgeHobdbsfq\n6iqjlvf73lwuF/7yL/+SK5jb29s8QaPVannEc2lpCaVSCXq9HiMjI1hfX2fU+ZOVACIv+q3f+i0M\nDg7CbDZjc3OTA8TOzk6o1WoAj8FamUwGarUa/f39SKVSmJ+fP7C91dvbizfffBN9fX2Qy+UcnFEr\n1ev1AnhMJzs9PQ2FQoGuri5kMhmuxu23rlwux4kTJ/B3f/d3fHcnk0no9XpYrVa+Q6PRKKanpxEI\nBJiylLgijmJPnaOmXms0GsXMzAyMRiMcDgdSqRTW19f5waMMmGZ96UE/zCqVCg/2E4EAldey2Sz3\nJ8fHxxlcRl9Go7XL5TI7vJaWFiwsLGB3dxe5XI77kAMDA9Dr9Xv6GWKdU7VaZcYxInmhL10qleLs\n2bMwmUzck2oWhatQKFAulznzo3nh3t5eWCwWGI1GyGQyUYxn9F8qYxG4qVAocKUkm83yaJnNZhNV\nbhL2g6ifCXzSsy4WiwiFQtweoQCv0bpP9t6sViuTulBZkNDahFKmszvIqBRLPTfq6xqNRoTDYZ7B\npt6h1WpFMBgUVaoXzi/LZDKYTCaYTCZGTtNlS6VJAvc1Mjpb4JPerEajYUwB7U2n0wEA98LFrEtn\nS/+lbDQSiXAbgUrGBFykWfhGPWS9Xs8jWWq1GvV6HclkEqlUijNJr9fLhC+NSr30PKhUKnR0dHCr\nTC6XI5VKAQCXlikooIv9sF4yPQ9GoxHHjx/nLFalUu3p9VJGTI5Zp9MhGo1y8HTQGTudTpw/fx46\nnQ6RSIQDKaKpJfBiLBZDIBCAy+VCrVbjufiD1qV5d7/fj+XlZSwsLPCIGpHLlMtlBAIBrK+vQ6lU\norOzE8FgEHq9nu/G/c6ju7sbL730EqRSKf77v/+bqxgej4cnZIrFIlZXV7kVSvSvRqNx3+ePAuvz\n58+jVqvh2rVrWFtbg9lsRmdnJ/+77e1tBINBBAIBJleZn59n7M5+6xLIz2g04oc//CEePHgAhUKB\nl19+GQ6HA8Dj6mk0GsX8/Dz0ej1OnjyJer2OWCwGqVTaVMJD9tQ56nq9zn2Ye/fuseMpl8vsJKxW\nK5dWqEwhpl9IYw7xeBzJZJLRxoRENhgMWFtbw+bmJnQ6XVM9WQoCCLGaTqc5a65UKlheXkYsFmMk\nMf07MedRq9W4JxMKhZj0Y2trC0qlEktLS9wnMRqNorMcIdqZLkehWMH6+joikQj6+/ths9ma7q/U\n64+ZsQglWigUuFVB42WUhTQyYXYqlUphNpuhUCj20HjKZDIO5Gh2UsxzIQSxKJVK7v1TJYUoI202\nG9RqNdbX1xsSLQgBJkqlEhqNBq2trXzewCdO1uFwQC6XI5PJiCpRU7mRfu//XyGL7e1t/vxerxed\nnZ3Q6/WIRCIoFAqigkJq99A4jLBH3dLSgra2NrS2tvKFI5bHmRwGBTtut3tPy8bhcKCrqwtbW1tY\nX1/nalajtcl5Uf+xvb2dZ6gJse71emGxWPgdJOT3YfgNav2YzWYYjUZ0dnbCZDLxHD31wslBbWxs\nIB6Po1Ao8P315PrUL7VYLMw8RiNYwpFDnU6HbDaLpaUlXisQCPCzftC+29raOLunSkO1WoVKpYJS\nqUQymcT6+jpisRg2NjZgMpmQSCR4Tvkgk8vl6OvrYwyLwWDA5uYmTCYTFAoF1tfXsb29jdnZWaTT\naeh0Ouj1ena6FPQ/aQqFgulXy+UyV8EsFgscDgeWlpYYuU4jYIQVIPDdfuN2EokE4+PjMBgMnBhQ\n66KtrQ0tLS0MHKO7zuFw8Huv0WiwtLS0L1ukUqnExMQE6vU6t66IR10ul/Po7Pz8PHZ3d6HX67m6\nYLfbMT09jUwm83+j9A18klkLy5aURVAkT72xg6LBg9alfnMymQTwSaRLPcpoNMoPGyDOoQrXJQAY\n9aKFo0Mej4ejYrGOmjJqeqHohSdSCJoLJCSjGKOfTXumTIP2S+cizM7ElNOFWAFygAQ4EmaYxHVN\nc6Nis3S6yHQ6HcxmM8+bAo/JHdrb27lcu7q62tChEhqUSo5UwdnZ2eEeuNfrxUsvvcSUjisrKw0D\nAEJhEy0iOadKpcLgKYvFgvPnz/PI09tvvy2q30sBBQUVra2tTJWq0+nQ39+Pz3/+8zhx4gTK5TKm\npqZEzSTT90VgHgKiFQoFuN1uSKVSXL58GUNDQ4ySFbsuvQvkqEnk4/z58yiXy3C5XOjt7cXi4iJm\nZmawvLwsurpAs8C079bWVng8HgwODkKtVnMba3Z2FnNzc6JAdUSqQdMkhKh3uVxcdZLJZJiensbs\n7Cwzk9G4J7B/n1qr1UKv1yMUCsHr9XKVxWq1IpfLIZlMMq83cXuTIycQ5n5OTyKRcAJDWBW3283v\nVyQSYU4KCphDoRBXzw5qD0kkn9ANUzJTq9V4DJUUp4jViwieIpEIj0cehKImwZR8Pg+dTgev18uj\nTul0Gu+++y6vQQx5NH5K+93v2ZNIJPw9qVQq9PX18Zlls1ncunULP/nJT7jETu9puVzm3x9kFETo\ndDqcOnWKR0VLpRLu3buHu3fvIhwO87qE3Nfr9RyYigExPmlPraMmI0ciLD9JJBLOPgqFApxOJ5f4\nxBo5POGhqdVqdiZ0OWm12qbnT4mVhvpPGo0G7e3t8Hq9nBFqNBrRo1TCs6jVatwP0Wq10Ol0GB8f\nh06nQz6fZwS8WCPnp1Kp0NraCofDweXEsbExHDt2bA9Pd6Osmr4rId9yR0cHxsfHAYD3R2NgN27c\nYArQg4wCE7qM+/r60N7ejuHhYbjdbqhUKqjVai5HXrt2Df/xH/+B+fn5hudbq9Wg0Wj40hgZGcHl\ny5cZGETAJ+IPf+uttxjxe5jRS6/T6XDs2DGMjIzA4/Ggt7cXTqeTHe3W1hb+4R/+Ae+++y5CoZAo\nLu5arcZ9x46ODpw9exYdHR0wmUx85pFIBB988AHefvttJBIJUZUFQi2TCIzX68WFCxfg8/ngcrmg\n0Whw7949fOc738GVK1f2zTr2M3J2hIgmohqJRII33ngD9Xod6+vr+PDDD/Hd736X0fpi3g1ilKKg\ne2pqCtlsluf3//Vf/5WR09QaaFSBo2wpHo/jypUr8Hg8WF1d5ckImqFeWlpCPB7nig79u4OqcDSx\ncPv2bUQiEUSjUUY2EzYiHA7/nLwnBdKESN5v7Wq1infeeYcZC51OJ1KpFBKJBEKhEDKZDJLJJH8P\nFDSVSiUUCoUDdQGIsOjb3/42otEoLBYLstksUqkUrl+/jkKhwD15rVa7hy45GAxyJXS/7zKfz+Mf\n//Efcfr0aTgcDi4ZExEVVZfozvB4PNx+ELY899vz3/zN3+DChQtwuVxQKBT4+OOPsbq6io2NDZ7Q\nIWCuWq2G1+vldQ8K5Kgf/ed//uf45je/CQAIBoNYXl7GtWvXWNoUeDxCTMGSQqHgTJtosJu1p95R\nkwmzVSqBxuNxjviaGfF5ck16+ClLS6VSjBQ9yroA+KHXaDSw2Wzo6elBqVRiGkOx5en91t3d3UVr\nayvGxsbYuQojWLEmDFKozOtwODA+Po6BgQFIJBJWYIrH46IrAHSmdNns7u5iaGiIS7G1Wg3Ly8u4\ndesWPv74Y75kDzO6XOv1OgullMtlmEwmHD9+nPtwkUgE3/ve9zA3NyeqdEqVG41Gw4jVYrEIt9vN\n5bBoNIo7d+7g3XffxbVr10Q7PZrxT6VSDIyyWCwMZCFylnfeeQcbGxui5S0po1OpVIhEIpibm+O+\nda1Ww61bt/Dhhx/i448/ZgS72O9ue3sbcrmcndHU1BSXM4mcZWFhAclkUtRYD61LpBfkLJPJJPND\nZ7NZZuWioEIsMlbIwheNRvGjH/0IV69eZerTaDT6czgTMRclAQlJ0Yx6kaVSiVnTKAABPunlisGy\nUCBCGSi905lMhj+7kLJUoVCIkhENh8PMiUCjqDQRQeckBAvWajUGeB5mpVIJgUAA7777LlfednZ2\nGHxGe6LgtFQq7RnbO+hMiJo2FApBp9NxaZ8CH7pDqXJIiRq1/Q4760ePHmF5eZlBdaSkJ7xHKpUK\nK53t7Oww8LfRdM/9+/fxF3/xFzxiW6lU9lQ7ATB/OlWcaKqlGdZBof1SOGrhB6PDoLJLOp1GIpE4\nsoA9rU3rBoNB5o2mGcOjGD1kxKS2srIC4HF0GIvFmtbBpcuGHopoNIqNjQ3o9XoGPiwvL2NxcVF0\nZUG4Zj6fRzgc5vKhxWJBNBpFsVjEwsICVlZWRMsjEs6AaDFXV1cxNTXFDpCi/5/97GdYWloSNYZD\ne93Z2UEkEoFEImG0M805R6NRTE5O4tatWz+XlRxmxHa2s7OzZyyQetIff/wxpqamGLUtFgBIqmsP\nHjxAIBDA7Ows3n33Xeh0OpTLZc54iERFrFGLZmdnhyll33rrLW6H5PN5ZllrZl0CQxFVazKZxL/8\ny7/w5Ub99aOMlwixEBLJY4UwysiFoMpm1xXuq6WlZU//7yj7FNru7i5isdgeToIn12s2O6JAkyg9\nD9qfkKpX7B2Uz+cZ0X2Q0ZqEaWhmzwsLC4f+PXKuYu+2er3OTvewlg89H81OnQhpdQ9bl95TsXsm\nyWWhPVnOpoCDKkf0b49qkk/zj39RJpFIRG1CWD4jpiAqUxOX8VGMyt1UuiHGJYpKjyo6QGVEogSk\n6FsqlfLsaDPrCn+ZTCbodDp+cMg5U2TYDCcw9Wlp3pL4dIWaywcBZA7bL5XYCOyj0WgYCEdCEUDz\nyktU2iewHxEmkHNqVheZ1qWs6Mn9fNoLn9antZ7ZM3tmz+x/baper59o9Jd+qRz1M3tmz+yZPbNn\n9n/IRDnqozVKn9kze2bP7Jk9s2f2/4k9c9TP7Jk9s2f2zJ7ZU2y/FGCyZ/bMntkz+/+LPalZ8Itq\nT9IsO9lRsBxPmhDbQRicwzTrmzGavxeu+2k0nYXrCsWLJBJJQxR5IyMsDvAJ9zyhvIXSp0e1p95R\nCx9aoTQkzf8Kx4COsjatLxz7EvIzH/WhoHUIjk8PHP3ZUV8S2rNwxlI4GnJU+L8QqCacXafzOeoZ\n09pP0poKwVVHfUGe3KPwXOiC+zRnQReEcO/0vNHvj7I2cQEIFckIvfxpLjkhmQxRagJgVifih292\n33QJyWQyZuiSyWSoVCoIBAJMlUtMfM2YkFnM4/FgbGwMUqkUsVgMqVQKsViMZ1ubeWdoz0QmYrPZ\nmLSDxvEWFxexvLwsSjGJjM6YxDO8Xi80Gg1UKhXziq+srDATXzPTHQTktNvt8Pl80Gg0KBaLiEQi\nKBaLiEajjMoX+87Q80Z686dPn4bT6US1WsWPf/xjhMNhFItFpFKppidn6AzMZjOGhobwO7/zO5DJ\nZAiHw7h16xZ++MMfIhaLNQXyFYJPT506hbNnz+LFF1+Ew+FAqVTC7du38f7772NmZkYUFzwZjbkZ\nDAYMDg7iD/7gDzA8PAyj0Yh6vY7p6Wm89957+PDDDzE3N9fUWRBn+fnz53H27FmcOnUKXV1dfBcv\nLCzgj//4j1mv/Cj21DpqIs0ghiCC0NOcLjHIUMQi1vnRBUyMUXS50BycUqlkzmGioBTD9U3r0gC9\nVqtFvV5n/m9iSBKOQ4l94YicgGQiaVaRuJCJVWd3d5d1iMW8HOQsSCOZGL+Ec4q0JtGtig0GiERF\no9HAbDZjcHAQAHiuMxQK8UgUzWWKDTJIBMBsNsPn86Gvr4/noZPJJO7evcszkTRvLcZpE4saUU92\nd3ezPnIymUQgEMDq6iqPAxLCXMy+ZTIZFAoFrFYrXn/9dRYzkEgkWFpawtzcHEtcptPpPTzzYs5a\nq9Wio6MDg4ODOHv2LItGFAoFXLlyBevr60z2cBhxxn77NplM6OrqwsTEBF599VWoVCoek5uensb8\n/DxWVlaQSCREz1YDj5+/1tZWtLW1YWBgAKOjoxgYGEAqleJZ6/feew/379/nd1zs+0J84uPj42ht\nbYXf70d7ezvUajVyuRxCoRDq9TpWV1dZylSMyWQyaLVa2O12dHZ2wu/3Mw1svV7Hw4cPUalUsLi4\nKJqBigJDjUYDj8eD8fFxeDwe/u6Hhoawvb2N//mf/0EkEmlKXlUqlUKpVMJgMODSpUvw+Xx8X1y6\ndAmRSAR37txBpVJBIpEQtS4A5iV3uVwYHx/H6dOnefrC4XDg4sWLuHfvHo+Mif3eaLLHYrHg4sWL\nGB0dhVqtRrFYhFwux5kzZ6BUKlkdTeyzRvccPWudnZ0ol8tIJBKQSCQYGhpCS0sLVldXOfgUs18K\nLPr6+tDb24vjx4/DYrEgFosBAMvjdnR0YG5u7sjJ31PpqOnLIn5lnU4HhULBUTuNCpF6DYA9alSN\n1iXu5f7+fr4syRmVy2VIJBJ2JnRBECHIQUYjY0SFNzQ0BKVSyYIIJM1WKBQQj8exvb0NqVTakPWM\nLh2Se+vu7obL5YJer2eRi3Q6jdXVVUQiER7XasS+RA8YaThfvnwZnZ2dsNlsLPwejUYRjUYRCATw\n6NEjdqYU1R9mpCbk9/sxOjqKV199lYVO0uk0rl27hocPH7IsIyn9NCptEaWn3+/H2NgYTp8+jZ6e\nHpZ6JOL7hYUFZiCiYElYlXnSSFnHbrfji1/8Il544QW0t7dDq9Uin8/zGX/88cf4yU9+gmKxyDPy\nh2kEA+DMjqLuN998E0ajkc9yfX0ds7OzmJqa4nn1XC4nSpRCIpHA4XDgxIkT+NVf/VUcP34cTqeT\nz5LELYj9qlKpiJb+bGlpgdvtxssvv4wvfvGLGB4eZlrNra0teDweRCIRrK+v8yUtxuhCViqVuHTp\nEk6dOoWBgQHYbDYOZigYoj2LLXlS2ZHel/Pnz8Pv96O/vx9GoxGpVAqBQGDPWKNYJyKRSFh5qaen\nB/39/ejv72f5xFKphEgkwnrXYhwqnQVJJZ48eRLDw8P88ygwDwQCuHbt2s+Vrw8zEkCxWCwYHx/H\n8PAwFAoFs4mNjo4ywRNxEzRzxg6HA8899xyOHTsGq9WKWCyGTCYDs9kMp9MJt9uN5eXlpgIWmUwG\ng8GA9vZ2dHR0YHd3F6urqyiVStBoNOjo6IDD4WhKe4DuUJvNhq6uLhw/fhyxWIypTzc3N/H6669D\nq9XuEd0Rsy6dcU9PDwYHB7Gzs8O8Eel0GqOjo5iYmOD9it3zk/ZUOWphOVAul8Pn86G9vZ3J8KVS\nKdO7VatVVlQhdplGFyb1JtRqNdra2jA8PAyNRgOj0ciiFJVKhR/cVCrFDDaHRdz04BIV3eDgIPr6\n+pi8fnd3F11dXYhGo1hbW0OxWEQ2m2Vmo4OyGzoPck4jIyPw+Xyw2Wwol8vQ6/XweDw8l7y5uckl\nzkaaqnQWGo2G9V7pwkmn03A6naz0lcvluNRJe2p0zhqNhp0pBUSpVIql/DweDxKJBFKpFGfZQvKL\n/S5Pei46OzsxNjaGwcFBuFwuJj0h5iKXy8UVACLxkEgkB1Yw6DNptVp0dXVhfHwcXq8XcrmcX+ZK\npcJydRaLhfcjzPQOOmuVSoW2tja88MILOHfuHHQ6HVKpFDKZDJelZTIZXC4XisUiwuEwcyQ3ag1I\npVKMjY3h1VdfxcmTJ+FwOBAKhRCJRJDP5/liNxqNXC0SY3S5HTt2DJcuXcLg4CAUCgWWlpawtrbG\nJV1Sn2uGCEUieSxu4HA4MDY2xuXjra0t5uIul8sIh8NIJpNNUfjSRe/3+9Hb24u2tjY4HA6mwkwk\nEsx7TcGz2POQyWTo6OhAX18f7HY7q9VpNBrm4CYSkWaciEwmg9ls5ueONNaNRiPLd5KQB4l4iFmf\nJDJHRkbQ39+PYrGItbU11nIfHR1FuVzmzxIMBkWfhVKpxOjoKPPiBwIB3LlzBwqFAj09PfB4PExz\nTHddIyOeebfbjf7+fgQCAWSzWayuriIWi+HEiROsVU0OVez3R1rnra2tyOVymJ6eZgrYlpYWnD59\nmjXFxWpHEIMc8beHw2HcvXsXkUgEk5OTkEqliEQiTOtL7ZGj2FPpqKmn8rWvfQ2dnZ2IxWKYmprC\nzZs3odfr4XK50N3djf7+foTDYYRCIczMzBzKQiMs8/p8Pvzar/0a7HY7Pv74Y0xOTiKXy6GnpwcD\nAwPo6+tDMpmERCLBwsKCKLUTuVwOp9OJz3zmMxgeHsYHH3yAhw8fYnNzEyqVCpcvX8bo6CisVit0\nOh3S6TRyudyhF7ywtHL27FmMjo6iVCphenoaH330EQccQ0ND6OnpYfKPjY0NbhMcdh4kNDA6Ogq5\nXI7r169jZmYGm5ubXMq9+DrDAAAgAElEQVQxGo1wOp1M+N8oA6EMwWQyMb95tVrF3/7t32JzcxNq\ntRo+nw8+nw9qtZqDjnQ63bDNQFmYxWLhtsVPf/pTvP/++1AoFPB6vRy5GgwGpnukvu9BlwW1WUwm\nE8skXr16FTdu3EA0GoXT6YTdbmeVH7PZjHg8zpn6YeUsiUTCcpPUm/6rv/orLC4uYnt7G3q9HqdO\nneLMSavVIpfLcQXnsLOmVotWq2Xlt3//93/H22+/DYnksezh4OAg2tra9lCainGoFMh1dnZCKpXi\n/fffx/3793HlyhW+jLu7u1EsFrG5ucnVqEYm5Gz3er1IJBJIp9MIhUJYXV2FSqVi7n5iHyQZzEZG\nLSKv1wufzweZTIa5uTncuHEDiUSC22gkbZhIJESVIunu0Ol0GBkZgVKpZFGK27dvcwm4VnssqRiN\nRrn612hduug7Ozuh0WiQy+WwuLiIQCAAq9WKiYkJtLa2IhAIIBqNHipG8eTaSqUSZrMZOp0OyWQS\nN2/eZP5tv9+PCxcuoFwuIx6Po1QqiXL+QkxIOBzmdge1stxuNwf5dAZiM0mJRIJKpYJMJoPJyUlc\nuXKFWRxbWlrQ19cHk8mEzc1NpnkWuy6pshHRVDKZ5OqpXq9nPQMqe4tdt/6/9KD//M//zM9SoVBA\ntVqFWq1GR0cHstkst+OOmlU/VY6aQDVKpRJarRb9/f3Y3d3F+vo6AoEAkskktra2oFAokEgk8Npr\nr3F20uiFo/9HAuH9/f2Ynp7mknGpVILVakUymYTP58Pw8DAmJyf3cAkftjaVCbu7uyGTyRAIBPiS\nUavVSCaT8Hg86Ovr4yxbzLr0/6m3RFJ18XicqwHpdBodHR2IRCIIBoOiOKMpUyNnmc1mEQ6HmS+4\ntbUVm5ubcDgc8Hg8TIknpq9er9e5dUFVkFQqhVQqBYPBAJvNhlqtBpvNhtXVVa4CHNa6oMuB+Hmr\n1SqX/kmWlPrpGo2GNcZJurNRW4QcWaVSYeF4kiylHiZl9JStiinVA2D8ANE7Ek82ZX8Gg4EdCEkG\nkqMW83wQJ3UsFsPCwgJf0B6PBz6fD9lsFhsbG6wpLeaSoL9TKpWwsbGB9fV1BINBDpJ6e3vhcDgw\nNzfHMpjN9KbpTFOpFPNyy2Qy2Gw26PV6pNNpRKPRpnp6FHCR7jL10kl7WKPRIJvNciVDLLBH6FBL\npRJrJMvlctYpJ1Un4ngW81zQOZAY0MLCArcm8vk8bDYbisUiMpkMc/mLrS6QQyAnWi6X2dELpVxj\nsRiy2SxT54q1arWKUCjE+6V1CVwGPH52COsi9pmjtg+JhdB7SzgahULBbadm1iXAIwDG3gDgfrjB\nYOCETGyWTv6qWq2yQAjtl7AdZrOZlefo3xzFnipHDTw+RKlUyrrNoVBoj9Yr9aLdbjeMRiMfMtC4\n3ER9yDNnzsDtduPHP/4x0uk004RGIhH09PRApVJxqUKI8j3I6vU6lwm9Xi+2t7eRy+V4v5QVazSa\nPaUyMevS52ptbYVcLt8DvpJKpUilUlxqbgb5TQ+k2WyG3W7nviU5tkwmw4hckrskJ93IedCDrlAo\nGLlKTo0cFoG/iLO6kRADrUvgMGp5kFymRPJYQEStViORSCCTyfBnERNYkHQqlXFJ5jSfz3PlgbjA\n6WISg3KmtSkYKZVKsNlsqFQqLN1HHNXBYLChVvKTRu9LoVBgrWOFQgG73Q6v1wuz2YyZmRlEo1Hm\nABdr1C7I5XLQaDTwer3wer1wu92wWq3Y2tpCMBjkUvVRkMhOp5NVygwGA/R6Peu5x2KxpvjK6/U6\nAzl1Oh08Hg8jsqlUmkgkuP/fzKgPUQtvb29Dp9MxtqWrqwsWi4WBoyTBKJYPnvqhpC1AAalGo0Fv\nby8HyaSdLBb0Ri07Ag8SiFWj0cDhcODYsWOQyWRYWFjAxsZGU2I+dCeSUA+9A06nE8ePH0dfXx8H\nns3QGdPdQvzYdB8olUro9XoMDAxApVLxnsV+d3R3ULuG1iad+bGxMZ4CCIfDosVxhHedMOir1x8L\nPA0PD7OyIVUjxbaHnrSnylHX63VGC4+PjzNYLBwOI5vNMmqa+m6E/CXn1cgInTc6OgqlUolUKsUl\nYuIO12q1sFqtUKvVzHPdyCQSCdxuN8bGxqDVavniEvKHkyYxldCaGTUxGAws40kZEYm22+12BuEQ\nWEvM2vSg0kVZLpehUCjYQZFAPP0/AvWIfchUKhWq1So7aCoft7S0wG63Y3d3l6NQMcpAtGfgcV+0\nUCgwsvf48eOoVCowmUyQy+XY2NjgrIdeOjH7rtVqnDHZ7XYMDQ2hr6+Ps79QKMQlPirHNpMp5PN5\nlEolfraBxw5gdXUVoVAI0WgUqVSqKfUooTNtb2/nfqNcLucMLxAI8IiPWKdHc7G12mNZVafTyQER\nqYxRBabZdQknYjQa0d/fz/3YUqmE5eVl5HI5hMNhZDKZpkb3KHDT6/Xw+Xzo6enBzs4OC9dQ5YEq\nJc3smSRrFQoF9/vNZjO3NiqVCm7dusWa0c2Aski8QafTsYYxjX6ZzWbcvHkTGxsbTWsOUPBJ96XJ\nZILNZsPAwAC6u7uxsrKCubk5luoUa+Sg6J6hMntfXx/6+/vh9/uxsrKCSCQi2unRugB+TuVLo9Gg\nu7sbvb29CAQCuHfvXlOOWrg2OWlqv7S3t2NiYoIBs+vr60ceoaL1W1pa4PP5cOLECZw7dw6rq6tY\nW1v7VOO+T5WjBj4pU7S1tXE5+fjx47Db7ZDL5TAYDNDpdOju7obT6UQ8Hkcmk+EL4LAIvFaroa2t\njctj586dg8fjYaEP6m0NDg5ySUc4HH/QF1iv1+FwOKBSqSCRSODxePDaa69xFG6xWNDZ2Qmfz4fN\nzU0OSAg4d1g5uV6vs5OmeUWLxQKj0cjAB+oHkWIMPSyHZb8UzVPJ2263Y3R0FOfPn2ekaL3+eBRu\nc3OT16WIv1EZOZlMYm5ujjVfv/rVr3IPtlqtsiJXLpdrSsWnVqthaWmJs7GOjg587nOf4zNaWlpi\nsJ7Yy5icYqlUQjgcxszMDCYmJjAyMgK1Wo1qtYrZ2VnOfGlsT6wzpQoFIbq/+c1vQq/XQ6VSoVAo\nYGlpiaVECbcg1mq1GuLxOOtTnz59Gna7nSU/k8kkQqEQV3fElk2pSlQsFiGVSuF0OiGTybC7u4tU\nKsXyn2LL/8K1VSoV/wLAFReZTIb29nY8fPiQM02xwQo5UbrYNRoNB/kajQYmkwlerxcfffQR5HK5\n6CyPJkQsFgvkcjkUCgUkEglnfSMjI9Dr9fjZz37GAUsjlDM5IMIWUKXP6/VCqVTC6XTC7/fD5/Ph\n2rVrrLEtfP8OW5fuQhq3pGejra0Ng4ODGBwchN1ux1//9V9jZWWFk5VGmBaqYqnVakilUsbb0L38\nu7/7u/B4PLhy5QreeecdfpYPq3TSdweAkxphVUShUODkyZN44YUXcP36dXz3u9/F7OzsoWpbZMQj\nQGBKWlMul0Mmk+H06dO4cOEC+vr68Ed/9EeYnZ0V/f6R4BDtmd4P4HEA9id/8icYHh7GzZs38U//\n9E8cgB/VnkpHXavVWGTbarXi7Nmz6OjogEKhYG1Sk8mEer2OSCSyJ7JrtHahUOAZyhMnTsDn82Fr\na4vnZXU63Z4SMvAJ4cVhD9zW1haWlpagVCrh9/tx5swZVCoVmM1mLt8QQQvNK4pFLVLpy263o729\nHWazGQ6HY89npr4W7e9Jko79rFKpYG1tDSaTCT09PVxRcLlcjDylchn9HDGjFhKJBJlMBoFAAH6/\nnwkWSPgdeCysTr2sJ9duVFpPJpOIx+MMylOpVHyBWK1WWK1WrK2tiR4LoXVJPjKfzyMYDPLYhVQq\nhdFoZPKMw5D6+xmNSeVyOeTzeTx48ACdnZ38YguV4JrVKa9WqzwzvrGxgUePHiEajUKpVHL5U8y5\nPmnUZpDL5YjH46jVatwKMZlMAB6P4NEzLdbo/San9+GHH3IQ4fV60dnZiba2NkQiEYTD4YbrUVBK\nv0h/mPStS6USo4gBwGaz8fvXqE1Gmb9SqeTviPq5dFd0dHTwO2IwGPacx37rU8WQ+sRmsxlSqZRb\nFhS8bG9vIxaLYXd3lz9bo++RyvyUXLjdblgsFnbWfr+fq2/xeJyrQjKZjJ//g86YOBFUKhVaW1uh\nUCjQ0dHBAYfNZoPdbsfm5iZCoRCPXFLydNC6NIkgl8t5ZtrlcvEZWa1W9Pf3o6urCzdv3uQ+PX3v\nBzk/WlepVMJoNDIojzgyTCYThoaGMDg4yJKjlIg1upepFG+1WjEyMsLTAPX6Y9lOm82GU6dOcYUo\nmUzy56UR0WYz66fSUVerVUxOTqKtrQ0qlQoGgwEqlQobGxsMd+/u7mboPs0l0wEc5FDr9Tru3bsH\nnU7HqGaFQsFgm62tLXR1dXEWSf1CMUQLa2truHr1KjKZDNbW1tDd3c0XRTwex+joKKRSKYNkxJKH\n0F6uXbuGWCzGyMdgMIhMJgOTyQS3241cLsfZqdh1d3d3Wbu2t7cXXq+XgUE0u22xWPbMkZODaoQH\nIM3sn/70pxgdHYVKpUK5XObKgN/vx8OHDyGTyZpi+iLw1MLCAlcb5ubmUKlUeByExswO0/vdb10C\nmYRCIZjNZi6hU+Wiv79/j3a2WPQmOcxUKgWVSoW1tTUkk0ns7OzA5/PB6/UiGAxibW2NtcWbsc3N\nTZhMJlSrVTx69AiFQgGdnZ3o7u7mNk6zJrwIq9Uq7t27h0QigY6ODpw5cwZ2ux1Wq5WzoUZr0VkR\nAQcFguvr60gmkxxYnDlzBtvb2xwQNTKhlCoh4KvVKhKJBL9nCoUC29vbcDqd3H5qlJmSQ6VMzOVy\nQa1Wo1KpcGuLft/S0sIjVAqFgsennnw+qDdP4zw+nw8Oh4PL6sLKAPD4e1UqlbDb7chkMohGo3w/\nPvncUXZH5Cs2mw39/f2QSqWo1WrQaDRwuVw88kXAQKrwCQFaTxpl/KOjo2hra8PExAQ0Gg0HKNQG\n0Gq1mJ+fx/b2NmfFJL+7H0iLAgCbzYaOjg584xvfgEajQb3+mD3OZDLBZDJx1YGqCkqlEsAnbIn7\n7Zkcfk9PD48v0v1DAT1hm2ZnZxmkqlKpDiXXoXN+6aWXcOLECZw6dYpH5uRyOaRSKd8XDx484GCL\nkkwa4/uld9QA+LL84Q9/CKvVypcQ1flNJhO6u7sBAPF4fA+13mEHUKlUkEwmce/ePSwtLWF9fR31\nen0Pctjv9/NLnMlk9gAADivf5PN5nstrbW3FwsIC9wi3trYwODjILyKBehqtCzx+wHd2djA9PY1E\nIoGNjQ3o9Xom83C73Xj99df5xRFSDDZ6GKrVKiM/b9++jWAwCLVajUKhALPZjM9+9rPQarU8hypc\nU0w/eWtrC8lkEmtrazCbzfxCOBwOfO1rX0NXVxe/mGLXpX0TEC2dTmNqaor7yi+88AJGR0cxMzOD\nQCAgek0Ae5wJEaisr69Do9HghRdegN/vR6VSwb/927+JXpccHgU3NLHw4MEDJsj4+te/ju3tbQSD\nQVGzrMIKBGVPCoUCmUwG2WwWwWAQlUoF3d3d8Hq9fNGJ7ZvSZUiXy9raGpaXlxGJRBgI6XA44HK5\nRJ8D9WOpVEh99UAgwP1Zh8PBlQtyhI2MshuiTgXAz100GoVWq2XEvl6vP9DRPXm+VDnw+/3QaDTM\nkigE5NHzQsBTAp/SmTz5M6jdZTKZcOzYMc50FQoFqtUqZ8NarZYzWIfDwed+WHBBWvInT56E2+3m\nDJ94C8xmM/R6/R60tsFggNVq5TvmoHVp7O3VV1+FzWaDyWTC7u4u5HI5rFYrZ78PHz5EOp2GyWSC\nxWKBxWLhTHW/8yan9/LLL+PEiRNwOp0oFos8Xun1eqFSqbC9vY2HDx/yLLREIkEymTx0BNVkMuEL\nX/gCTp8+DYvFwp+PWpE0FrqwsIBYLAa32w2ZTIZ0Os0A14POw+Fw4M033+TqDfFMEOJdq9XyNAS1\nQontkRKpZu2pc9QUTe3s7CCdTvPsKWWgEokEPp+Pe6yrq6s8w9nowqD+GvW0p6am9vSHOzo68IUv\nfIF7McJeZKM9U1mzpaUFDx8+5D+nf/vrv/7rjIYslUqiyx/klFZWVhCNRjEzM8MZWr1eh/9/SUWI\nKEEspSX9nXw+j2KxiNu3b++hZLXZbGhvb4fT6dxDZyg2g6ToUaVSIR6P4+rVq3xGVqsVv/mbv4me\nnh50dHTg0aNHTWW+lEmrVCoUi0U8evQI6XSaZ0Y/+9nPYmBgAHfv3hXtTOmXXC5HR0cHlEolFhYW\nsLa2xmW+sbExTExMiKayFAJiCAXa1dWF69evI5FI8PPc2toKrVaLjz76SBSQhTIwAjm9+OKLUKlU\nuHfvHhYXF6FUKlGr1fhCIsRrI+dEfUchrWylUsHdu3cRj8cBAHa7HQ6Hg+kXs9lswxIyodsNBgPP\nqxNBRDqd5p91/PhxpFIpzMzMIBgMNuxDKhQKjI2NobW1FXq9HsViERqNhsveGo0Gr7/+OkZGRuBw\nOLC4uIjFxcWGRCeUSR8/fhyXLl3iUaxSqYRHjx5hYGAAra2t6OrqYpKWUCiEtbU1xnLsdybUfz1+\n/DheeeUV6HQ6bvOVy2VYLBZuPyUSCdy4cYMpODc3NyGXyw98PlpaWpidjrAw6+vrPPplsVhQqVR4\nBLBQKDC+JZvNchvpoOfi5MmT+MxnPoNEIoF8Po9CoQCXywW5XI6trS2EQiHcvHmT3/muri6k02lI\npVIkk8l9q1tSqRTnz5/H17/+deh0OkxPTyOXy8Fut8NsNiOZTGJjYwNra2uIRCLQ6/U4ffo0MpkM\nlpaWsLOzg1gs9nPrtrS04HOf+xx+7/d+D7VaDffv30coFOKJBYlEgsnJSWxsbGBjYwM7Ozt45ZVX\nsLm5iZWVFYTDYdy4cWPfdZVKJX77t38bfr8fV65cYTbIS5cuQaVSIZVKYXZ2Fjdv3mQ+is7OTn5G\nFhcXuTLSjD11jpqMLnth9CoscVEZudEFtN+6VM4T9rYp2wHAqNZm90tlGIoKhRd1pVLB9vY20ul0\n018SOWYawRECO2h0Kp/P7wFmNLPner3O41hUKpNKpSiXyyiVSgz0OSjK3M/IsdMlRwhU6olRpYI+\nS7PnQeUpAroB2JPViGVvIhM+Xzqdbs+8MYk6AGBUr9j1yFFRf9tsNjPiVKlUor+/H/V6HeFwGEtL\nS6LGpwj8SCIDbreb6TBlMhl8Ph9Onz4Nv9+PTCaD1dVVUesSfwE5097eXuzs7OxhXzp37hx8Ph+m\np6cxMzMjKrCg2XmtVgun04nW1lZuaQGPx3pOnjyJ8+fPY2pqCg8fPsTi4mLDPddqNUa52+12Fomg\nFoVOp8PExAQsFgs2NjYwNzeHpaWlhuNT1WqVpzR0Oh1sNhtXGEZGRriqoFQq8ejRI8zNzWFtbQ0b\nGxuHsiPW64/HdojK1O/3M5MeTYkQ58DCwgJWV1e52tdohloikTAdKLV+2tvbucdNFb9Hjx4hmUxC\nKpUil8vx+NZB3yOVpx0OB3Z3dxkcSpWK+fl5phuem5uD2WzmmWQSUjnoeyTSG+I+cLvdcLvdAB5X\nP7/3ve8hGo0ikUigUqlgbGyM7/xGbJGEHSBKWpfLxXflrVu38KMf/YjbTH6/HxsbG9jd3eV36yDT\n6/Xo7e2FSqVCR0cHXC4XV3xv3bqFO3fucGDi9/uZzEmtVkOj0RwpmwaeYkf9pAn7osQX7XK5mO3l\n06xJtru7i0QiwbO0v6h1JRIJCw0c1FMRu65wPI0c9ebmJpxO5x5JuGbXrVare4ROVCoVvwjEXdzs\nmgScqNVqMBqNXEKzWq0MTGqEZD3IKLCy2WwoFAowmUzw+XxwuVwM3GrmnMmpSqVS2Gw2Hv2SSB4T\n9tN8/MrKSlPoTbroDAYDXC4X2tracObMGUQiEbS2tuLs2bMIhUK4cuUKotGoaFQ2IW1tNhscDgdk\nMhmee+45JBIJvPrqqzh+/DhqtRquX78uanyKghJCODudTni9XuY/lkql6OzsxHPPPYdwOIyrV6/i\n0aNHotYlXn6NRoOBgQEuJ586dQparZYv92q1iuvXr+PRo0dcdTrMqFJAI2R+v5/FZSjo3NraQjQa\nxeTkJCOzG5H2EKiQ6GMtFgtzIBASnrAoN2/exOLiItLpNM9QH1R5omAqGAxyBQAAv3OLi4sIBoNI\nJpNMXkT8BqQgdlDgWalUmJufFLhI5CUejyMUCmF+fp7vNqq6JJNJJu/Zz+i7I0Id2nc8HsfHH3/M\nLREKCAj3olarecb+oLVlMhlisRjW1tY4wQiHwwgGg1haWsLVq1cZ0Eg9X41Gg7W1NQYb73d3UOC7\nvr6O1tZWuN1uPHz4EIFAAA8ePMDMzAxisRgHuhKJBFarFSsrKygUCsx69qQRoHJ9fR0nTpxAd3c3\nUqkUwuEw/vM//xP37t1DNptFuVyGy+XiNoPRaMTDhw95Quko990vjaMGPpmz1mq1MBqNe7LAozTo\nhetKJI/nIQmJnM/n2ZF82nWVSiX3QIrFIpRK5ZHWFQYA1Ovy+Xzo7e3F9vY2gxmaXVv44FBEOTEx\ngZMnT0Kv1yMajTbtqIVgGspQ/h/23jQ2svM8F3xqYRVr3zdWsVjcdzbZZK/qdkuWLHk37AC2EsMe\nZxIYSBBMnD++dzABBpgfxp0gCAwYkx8GgsiwM3ZubNiWo7Zsq1uS1S313uxu7lstLJK1sfaNVcWq\n+UG9bxcbTdYpSvdC9vAFhBa4fDz1nXO+d3ve5zGZTHA4HBgZGYFWq0U4HObgpRmjDLxSqTAQxWQy\nobu7G/39/bh9+3ZTPM5UpWhpaYFGo4FCoUBvby9EIhELRqhUKgQCAdy8eVPwyBcFUyQyQGpfpNRD\n87j//u//jlu3bjUsI9cbzby73W54PB7Y7XacPn2a+cjT6TSuX7+Oq1evCqqEELiQULzd3d0YHh7G\nwMAAi80oFArMzc3hd7/7He7evctAxEaWyWSwvb0NuVwOs9kMl8vFmTA5aHKmt2/fRiwWEzTyVavV\n4PP5OHAigBYFgZVKBZcvX8bNmzextLTEmZmQdaPRKJaWlnD79m1sb2/DbDYzA1k4HMbCwgK8Xi9T\ncta3nA5av1arIZlMYnZ2lvcaACtXPXz4kBMFaosQApmu+6D1q9UqHj16xMRNDocDfr+fM30Sn6Dn\nnBDR9X3Tpz3X1Iqcnp7Gm2++ySOxoVAI77zzzr6ZdIVCwVgfYC+4OExDulgs4t1330WlUuGZd5/P\nh8XFRYTDYcTjcU5MCEBGEyVE33vQNb/22mvI5/Po6+uDwWDA66+/zs4yk8mwUE08Hsf29jZMJhOS\nySRz1x8UjKfTafzwhz9kMNrs7CxmZ2fx5ptv7lNETKfTjAxfX1/H9vY2O/Gj+JM/OEdNqDmv18uR\nfrOl0yfXBB5HS16vFxaLBZFIpKky8pNWPz+4u7sLn88HpVK5b5ysWaP1qOwjlUrhcrkQDAa5PHZU\n3WgaIarVajAajRgdHUUwGEQ0GmWwVjNWf6jk83kkEgmcOHEC586dg8vlwtWrV/H222/jzp07TWn2\nAo9BcIFAABKJBH/5l38Jt9uNanWPa/l73/sefD6f4FI93StqIVy7dg3ZbBYTExPo6OhAIBDAb37z\nG9y+fRter1dwRk0HVDwex6NHj5BOp+H3+/H8889DoVBgdXUV09PTeOWVVwRlkGRUUiRAHbDnpGhk\nb35+nvmihTJDUatpeXmZM7rbt28zEC2dTnNJloIroXgFqkSsr68jHA7zoUs0r0Qs1EhU52lrLyws\nYGlpCVKpFD/5yU94QoGqT7SnzbbHCoUCVldX4fV6941FHbUaVr+uz+eDz+fDG2+80fS1HWa0LiB8\nIkGI5fN5+Hw+fPe73z3050ql0r6pCCHrrq6uYnV19dCfI8KlZtZeWFjAwsLCU79He0PrJpNJwYIk\nmUwG09PT+MY3vrFvj5/Mkon/Xui6jUz0Yd3MD3QRIpGgi6Bo0Gw2Y2RkBKlUissgzXANH7R2W1sb\nent7YTKZsLq6ivn5+aaYdZ62JvUoibUsEAggHA7zWMRR1gQeZ1TUSwuFQkyb2Wwg8CSYSi6Xo729\nnUuHsViMWcSasfr5VmI8o4oFkXAI4bR+0qhyQJmT3W5nQYN4PC5IROWgPSCGN9oLkgUUyt/8NCOO\n8CdHdqrVqiAq2UbXXU8OItSBHtuxHdtHwu7WarWpRj/0B+WogcfIOzrwm2GgamQymYzn//L5fNN9\nzoOsvtxEB3MznMuN1gYeE5x8GNf7tL/xUXhOju3Yju3Y/sjsj9NRH9uxHduxHdux/ZGYIEfdPET4\n2I7t2I7t2I7t2P6n2R8UmOzYju3Yju2P3eqBSR9mxbNepIJAqR/UCCNBYiCEUP8guAsyahfSdRPo\n8INikQh7QgIrBCI9atuQ9oDWIypSwt/QSOAHsWNHfWzHdmx/9HbY7OpRAYIAWNCCpjvq12wW20Fz\n/AS+FIn2NNYJg/NBHAmN3blcLuj1ehQKBQbLHnVShJyTXC6HVqvFF7/4RUY6k3zmURwUOTuz2Qyb\nzYYXX3wR/f39mJ6exjvvvAO/389A1GaMKFy1Wi3Onz+PL33pS+jo6EA+n8f3vvc9zM3NIRqN7iM7\nEmK1Wo3pVN1uN06dOoW/+Iu/QLlchtfrxU9/+lP853/+56Fz8I3sI92jJsQwKdLU3xjadELONktu\nQQhfIuWg6JLmsgEwSX2z7FY090fkLLVajXl8ATBLV7MvMbGykdxgrVZjcB2RDZButND9oGiQKDml\nUimTvdST3xPNodCXuj7SlsvlsFqtfKCRrjhdbz6f58NIyNqE+JbL5dDr9WhrawOwBwYslUqsDFQo\nFPhvCEVDEzGHyWRCW1sbdDodNBoNCoUCEokEsw6Vy+V9I0WN1q6fAJiYmIDRaIRWq4VKpUIymcTG\nxgbi8TgzRdHzISHzqWQAACAASURBVOTZpiyBBFpOnjwJi8UCjUaDcrmMe/fuYWtri5WfSOdZyCFK\n12w0GtHR0YFPfOITrJ5FY1dE/rGysoJUKiX4cBaJRDAajTAYDEzc0tnZue/vZrNZXL9+HVtbW0gk\nEoLHBOn5IMUkkWhPv97pdMJqtUKpVCKfz+PKlSsIh8PIZDKC1pVIJCysYTAYmEecSDnUajVyuRxT\nbabTacF7QcxqdrsdarWanRE9X9FotGm5Uno2VCoVpqamWEMbAMLhMCKRCF9rM2OSxNNtNpvR09OD\nrq4u5oqQy+XY2trCT37yE8Tj8aayYDrvdTod/vRP/xRut5sZ86xWK5LJJK5cuYK33nqLR9GEGN03\nu92OiYkJfPWrX2W6U+I0ePPNN/HjH/8YXq9XMPcArT0wMICLFy/i05/+NHOhA4DT6USpVMI3vvEN\nzM3NPW06SVCP+iOZUddThSqVSi6BkFOlIfpSqbTP4Qk50MjBUwSkUCi4bFMvalEsFrG9vc1rN3rQ\nyMHT2BTRJJKsGr1gNEJEN0zIC0fOgxR67HY7LBYLH+SFQgGBQAChUGifTnKja653pMS7bTKZoNFo\n2MGR5OPc3BxnCUKcB9FkajQatLW14ZOf/CR0Oh076HA4zHOqEomEkfBCBBMUCgWTW5w6dQqTk5Mc\nyJVKJbz77rtYXV1lgv0nZ2sP22e5XI7h4WE888wzGBoagsFgYKR+MpnEwsICrl27xgcbia4cNrpF\nQaFOp0NXVxf+6q/+ChaLBTKZjJ+NtbU1zM/Pw+/38whfPp+HRCJp6Jy0Wi26u7vx/PPPY3x8HGNj\nYxzklstluN1uzMzMwOv1YmNjg8t8jfZDJBLBarViYmICFy9exJkzZ1ies1wuI5fLYXp6Gqurqyxo\nQFlDo+eDHPHp06cxOTmJ7u5uOBwOWK1WZLNZnjleWFhgti5SwTrM6klmDAYDTp06BaPRCIfDAZvN\nxmxzRD9748YNHvdrFGAQy5xKpUJ7ezvMZjPzl1utVhgMBhQKBUSjUSwvL2NpaUmQo6YgTqvVoq2t\nDWazGRqNhlnh9Ho9stksbt26hVqtxupdzQSHpOwFgLkNxsfHEQqFMDc3x3P5QoNwctRtbW0wGAys\nGUCCLfT1dDotmOSjXqpUr9dDKpXC7/cjnU6jVtvTNejp6UFvby/u3bsnuGJB+6DRaGA2m+HxeDA9\nPY2NjQ1+t19++WU4nU7mDRBq5E9IE4H2c25uDsVikYmjqEJyVPtIOer6eVMS47bZbFCpVPwSE3ds\nsVjEtWvXsL29jUKhsE/l6mlGa1IAoNfrmc6QRCfIOVF2kMvlBMlGUhQok8lgMBjQ1taGc+fOwe12\nc/RbLBbh8/kwPz+PnZ0dZr857GGjB4xeYqfTCafTiYGBATgcDv49YmCil4I0iA97iOurFcRENTo6\nCrfbzS9stVpFMBhEoVCATCYTHFzQYUnSer29vRgdHeXPTJWAbDaLVCqFUqnEDpXWP2xPVCoVnE4n\nBgcHMTIywv0gOkidTicf6JVKhSsD9Q77oGvW6XSYmprCqVOnYLVa2RlTma+trQ1dXV0sgUmHFpGQ\nPM2kUilMJhNOnDiBM2fOYGhoaJ/2dalUgkajQU9PDwwGA2QyGVpaWphS9DDnJJFIMDU1hUuXLuG5\n556D0+lkSU3KyFQqFbq6upDP56FUKuH1ejmwOIzjWS6XY2JiAl/60pdw9uxZGI1Glm0lFSnqH5IA\nCAWLhz0jJJtosVhYQMVmswHY01Xf3d3d93ySOpZCoWjo+Oh57unpwfDwMF588UWm/9RoNACwjzef\ngj76uwcZvYs2mw2dnZ1wu93o7+9n0iES09jY2GB5yo2NjYbaxvTctbS0wOVyoa2tDZ2dnSiVShga\nGoLdbofdbkcsFkOxWMTy8jJWVlb2VQEP22fSYya5zlwuh1KpxEEMVZ92dnaYSUxIAED3hchwRCIR\notEon9UdHR1wOByIxWLY3d0VxHNB6yoUCqjVaiwsLHDCtLOzA6VSidHRUbhcLqbNFco0RxUPmUyG\njY0NZlcrFouQSqX4/Oc/z0xiJDQixMivVKtVrKys4Pbt20ilUlhbW+OkxuVyMW3yYRrah9lHylET\ngQWVNvv7+zE5OQmXywWZTIZkMolSqQSTyYRisch6s3K5nJWZDrL6/o9SqURHRwfLQ1LmqFarUalU\nuGQYiUSYWekgqycLIe3YqakpnDx5kllv8vk8P1jJZBKxWIxvrhATiUTQ6XQYHh7GiRMnYLFYEAgE\nkEqlYLFYuJ8TDAahVCr3iZkI2XOVSoUzZ86gra0N2WwW0WgUAJgLV6fTQaFQcL9M6LoKhQIdHR2Y\nnJxk6sVwOAydTrcPzEHyhI2qF7TPCoUCnZ2dmJychNPpxNzcHDY3N3kdehH0ej3zMtPXDwu2qNd2\n5swZtLe3IxaLIRAIsNgCKRGZTCZ2WNRHPOxekjzf2NgYzp49y6xXJDxBXOgUKNCB0traytd+0DWT\nTvDZs2fh8XhQKpXw2muvIRKJIBaLsXgARf27u7tQKpWHEthQRUupVHImrVarkclk8B//8R/w+/1M\nt0oSgsSs1uigJwesVCoxPDyM4eFhaLVabGxssIwrfS6SN1xdXUWhUGAWtoOMKhcOhwPnz59HX18f\nyxlSJkpKT6FQiNn84vG4oMNTKpVicHAQY2Nj6OnpgVarRa22J3NJWTkJacRiMUHSohQAqFQqDAwM\noL29HQqFgml3HQ4HLBYLK1uRBKaQFgA909TKoX40JUTt7e3cKpPL5dySEpqlUuuxUCggl8shm80y\nx4XH42GRGJlM1lBEo96oorm5uYlcLseBQGtrK9rb27GwsIBMJrOvCtKoCkcVGpIHpmCT9tRqtTLN\nbDP4AkoOo9Eoky2VSiVkMhl+fxUKBQKBADMgHoWX4iPlqCkrouz05Zdfhl6vRzQaxXvvvYcHDx6w\nqs/AwAC+9rWv4a233sJ7773XkF6OonySMfzCF74AqVSKt956CwsLC0ilUpiamsL4+DjOnj3L0mTE\nIHbQxtZnmGazGZ/4xCcwOjqKN998E48ePWKO469//esYGxuDxWIBsMfM1ag8Xf/98+fP49y5c5BK\npXjvvfdw+fJliMViDAwMYGpqCs888wyy2SxmZ2cFM2iRkzx58iRcLhcePXqE69evIxgMoru7GydO\nnEB/fz8kEgneffddftGFZNQU/T777LNwu934wQ9+gJWVFVQqFbjdbrzwwgtwuVwIh8NMI9mo90bP\nxujoKM6dO4fu7m5EIhH88pe/RD6fh8lkgtVqRXd3N0vOJRIJzuQPWpsOLsp6bTYbAoEArl27hsXF\nRRQKBajVarS3t/OhWV+ePixbIP3aiYkJ5qF+5ZVXsLS0xM5yeHgYfX19kEgkSCQSePDgAZftDzqQ\naS+oLy0Wi3Hjxg288cYbePvtt1npaGRkBD09PUin01hfX8fMzAwLSBy218QA2NHRAZ/PhwcPHuDm\nzZuYm5uDTCZDf38/enp6kMvlsLm5iUePHiEYDDbMnESiPZWnwcFBXLhwASsrK1heXsbc3BwLSiiV\nSsjlcpTL5X3XK6Q03dvbi5deegnDw8OQSqW4fPkyB7XkrNPpNCqVClKpFOLxeEMHQsFhW1sbPvax\nj7FKFSlnkXRtqVSC1+tlxyVEWlQqlcJgMKC3txd2ux35fB4rKyuIx+Nwu928/s2bN+H3+xtyUdev\nTbgFqiitra1xZmo2m7G8vIxXX30VXq8X0WhUcDmdSrjk6HZ2dlAsFqFWq2Gz2VjTnp7fRmpl9UZt\nzVAoBJFIxNek1WrhdruZProZXFI9XajX6+WWEAAG2MlkMqaNJu5/+t3D1qVEbnl5mb9GvOc2mw29\nvb0cBJFM6VGy6o+UowbAHNakjVupVFjybWtrC+l0Gjs7O2hra4PH44FarYZarRYc/UgkEvT19aG9\nvR2zs7MIBoNYX1/nl2x0dJRFwEkyUUhpRSwWo7u7G319fdDr9QiHw9jc3EShUIBCoYBcLmewCPWt\nmwGpDQ4Owmq1IhgMMsG7XC5HMpnksha9+EKvGQCDQarVKrLZLEKhEFKpFHK5HAOeqNwoFHlaq9Wg\n0WjgcrngdDohlUo5gxGLxcjn8wy6IdCGUN1vYK/MarFYuE2yvb2NSqXCcpLFYpFL0UIBgfRs0L2R\nSCTMUV6r1dDW1gapVIpiscjqXFRGbGR0MNCoBpWzC4UCpFIplxDpcNve3uY+ZCOj6Hx7exuZTIbv\nfWtrK9xuNzo7O1GtVrkcFwqF+OA8bE/owKJ9JMEBrVYLl8uFgYEB2O12rK2t8btZ//cPW7e1tRUO\nhwNarZYzZSrRk342/c10Os1OWkjfe3BwEO3t7VCpVPs04uVyOZd36bmjcrcQLIdSqURvby8D3Ij7\nvKWlBZVKhZ8VytiE8JaTM9VoNFAqldzGSyQSaGlp4Yyc7hu1tpoBXe7s7EAmk7E8JJ1rRJO8traG\nra0tDtyayfRIe5vul1arhcfjYeGOzc1N3guh5zMAfi/oLCO8QXt7O7LZLAd19XicRkaZP+FyAECt\nVsNkMuHkyZOoVCqYm5tDOBw+VAHtoGsmUCEFMlarFZOTkxgeHoZEIkE4HGb8U7PZNPARc9T1H8Bs\nNkMikTDcn6D+Tx4yKpWqYX+p3nQ6HUZGRqDRaBAOh7n/QaValUoFrVaLXC63b/6tUblCo9FgZGSE\nJRJJKYUAcSaTCXq9njPsZl4KhULBfNZUYqI+TltbG0ficrm8YaZERqV6l8sFm822T+CBesB2ux0a\njYZLUc1EgQaDARqNhnvbEokEarUacrkcdrudgUFSqZTBQ0KsXh2MkN0EUrNYLFCr1RxY0OEkZK/p\nBS4WixwMarVamEwmLsUSoIz6kM2MtmSzWcTjce7hUUmMNLRJOtDr9XJ2JgRYJxKJEIvFGBnb3t6O\ngYEBKJVKtLe3w+FwMG+91+tFMplsWHGhbF0sFiMej8NiscDlcgEAurq64HQ6OTh++PAhVlZWeD8a\n7QW1F0jrWSwWM7o+n8+zE93a2mJwmtAqDnHUW61WyOVyyOVyDA4OIhqNYn19Hel0mpH6FGQJuXck\np0qlbsq2ent74ff7+X4RbkHoQU9BC/A4O9vd3WWefaPRiHQ6zepXQgKs+v2g9ajsbLFY9gHWrl+/\njnA4jEKhcCRKY1qfWjZdXV2cUV++fJnxF80GAPSz9VruHR0dMJlMeP3117GwsMDKWs0kO/VlZ1rX\n6XTC4/HgjTfewPLyMqLRaFModXoe6qmcpVIpHA4Huru7MTg4iF//+tf71v2Dd9QAePONRiM7iGq1\nCrFYDL1ez+hn6uURKAcQxklNETuhnSlSLhaLjKam0la91nWjtQmlSCh1rVaL0dFRFAoFGAwG2O12\nGAwGBkA8+eAc1pdVq9XQarXsnHU6Hfr7+2EwGNDf38+HMaGegcfc34ddM41j6fV6Bn+dOnUKsViM\n1zUYDPvWFbIXtGeU5ZfLZUxOTmJ9fR1yuRxutxt6vZ4P5ic1xQ/bCxpxy+fzAPai776+PpTLZR71\nWV9fRzKZ5CxQSBlLLBZzr3VzcxNyuRwDAwOMhiV9bhrPamZ0j7Ik0rIeGxtDS0sLtra2IJPJWIs4\nGo0iGAwKzpqo7Ob1egEAfX19OH36NNRqNa9bKpXw6NEjeL1eJBIJweI19DPFYhESiQTDw8Po7u5m\nZ0cIeJ/Px05aSIDY0tLCVTAAHAxmMhlks1ksLi6ys6ZRLyHXS4BLjUbDvXLSGiY8RCgUwvr6OgqF\nguBsjEBTVEKuB42SNkAsFmPAJR3cQoJaAjhRaZoqDQaDgR347OwsgzubRTnXjzCqVCp0dnbCZrPB\n4/FAJpNhc3OTA0JA2Dx5/eQMZefUlz579iwcDge2trYwPz/fNKEK4RcouVEqlQyiJd326elpbG9v\nNxVY0DXXTxKp1WrGMvT29uJf//VfudrUjPOnxIF8CbVJTp06hYGBAXi9Xjx8+LCp8v/T7CPnqKmf\nsL6+jt/97ncYGBjA2NgY7HY7gL2Dw2QywW63QyKRIBgM7qv/H/YwV6tVxONxTE9Po1wu4/z58+jq\n6mIgGc37EkiC1gXQMKovFApYW1uD1WrF2bNn8fWvfx3ZbBYajQbFYhFWq5VnnUOhEJdthagn1Wo1\neL1etLe3Y2pqCh0dHezwlUolrFYra60S606jjIxeZhKy/+QnPwmLxYJyucyHEomTkHZufTmuUcm3\nWq1iY2MDJ0+ehNlsxnPPPQez2cwv4vr6OiqVCgup06y8kCwyGAwilUoxwveb3/wmlxGj0Sju3LnD\nLzN9TiF7TGNugUAAJpMJ7e3tePnllwHsyeZdv36dy6nNRPOZTIZnMwuFAoaGhvCpT32KR6/+4R/+\nAWtra/vmZIU46Wq1ikwmg6WlJXZMHR0deOmll3h06urVq5ienuasSajkZaFQwPb2Nra2tmAymRi9\nSv352dlZ3Lt3jwkihB5CJFdIvX+bzQatVotqtQqlUgmHw8G9TUIhH2YU7BKw6M6dO5ifn+fqm0Kh\ngNlsRl9fHzo7OxnxWyqVDnUk9QhkKsGurq6ybCGNaV26dAmdnZ0MSq1H1D/tHtbzIVDSUavtjUst\nLy9DpVJxpkftFr1ej2QyyZWig54NQsbX975tNhs0Gs2+sTW5XL4viG0k51sPLpRKpZwkEX6FEpOu\nri6kUincvHmTwXR0vQddM41xUmVFrVajs7OTeSc0Gg2GhoYwMDCAH/3oR1hbW0Mmk2kIyqJqBSUg\n/f396Orq4hE1hUKBwcFBjI+PI5fLYWZmhoGbjcao6NwhPXiFQoHh4WHs7u4in8/DaDTiq1/9KvL5\nPH74wx9idnaW26P/wzJqkUjUCuD3AOTv//xPa7Xa/ykSiYwA/h2AB4APwJdrtVri/d/53wH8BYBd\nAP9brVb7TTMXVavVEIlEWKDeZDJBrVYjGo0inU6ju7sbExMT8Pl8zFIjJHqr1WqIxWK4efMmfD4f\nzp07B6VSiVAohHK5DIvFgsHBQc5w6kFIjTY3mUzi/v37SCaT8Hq9GBkZwdbWFqPHR0ZGUKlUsLi4\niK2tLcHrAnti5W+++SYSiQR6e3uhVCrh8/kQjUbR29sLq9WK1dVVxGIx7ikKeSDy+TzW19dx+/Zt\n9Pf385gK9bJ0Oh3EYjG2trb2lSCFAE7i8TiWl5dx9epVjI6OQqPRIJ1Oo1AocAmOyvj1s7eN1q5U\nKohEIrh16xbGx8fh8XgY5U5zpzTnS+V6IXtMB3sul0MqlYLH44FCoeDvm81m9Pb2IhwON62bTNcS\ni8XgcDigUCg4G97d3YXT6cTm5qbgTAx4XNWoVqvI5/NQqVTo6OiASCTiNtHu7u6++c1mDggKcAwG\nA0wmE9LpNMLhMCQSCSqVClQq1b65XCHrAY9739TTjUajyOVyfEB7PB5oNBp2Lo2M1pPL5QwIAsBt\nEaVSic7OTnR1dUGn08FisTRclxxTS0sLjEYjt392d3eZW4H2dmpqCnq9Hi6Xi0F9h61LWbRer+c2\nWT3BEgVU1WoVVqsVVqsVLpeLP9dB104ATpK9lclkPKtP/AAE8iqVSkin07BarVy1OAxFTq2fwcFB\nbi/QnDdVNWlfKVnQ6/WcoR7kVAnJbTQa4Xa7cf78ea5UVKtVyGQyxsnQc1fPTXHYeKtCoYDBYEB3\ndzfMZjNeeuklXkMikcBqtUKj0UAqlbKuPQUdjRI+hUKByclJtLW1YWpqCkajESLRHhUpjRVLpVJ4\nvV4G3NG69LmbddZCMuodAB+v1WpZkUjUAuCaSCT6NYAvAbhSq9X+m0gk+q8A/iuA/yISiYYAvAxg\nGEAbgDdEIlFfrVYTfLJR439zcxPb29vY2NhAS0sLstks9zoNBgPC4TCi0SjP4jY66OlwJOBNLpeD\nTCbjUtvU1BTUajWTITSjl0wHcS6Xw8bGBubm5hj5KRaL8Xd/93fY3d1lbWcCWDRyqJS9PnjwAFtb\nW3j48CGkUum+8Y9z584hGo3yKJhQR12pVJBMJjE3N4fLly9DJpNhd3cXuVwOzzzzDIaHh5HL5bjX\nS8GQkGwvl8vB7/ejXC4jk8nwSJ1YLMaJEyfQ3d3NoKx6h9po7Wq1ikQiAa/XC61Wi1KpBJ/PB7FY\njK6uLoyPj0MikfA9bQZwQpkklcBTqRSi0SgfJIVCAXNzc01raFNGTwxIOzs7ePjwIVcv+vr6mLCG\n1hXq/CjjItBeJBJBMBhEW1sbjEYjenp6IJPJkM/nBZeQ6WAm5rRoNIq1tTWsra3h4x//OGw2G1wu\nFwKBAKanpwXtAZWgKfgrlUqIxWJYXFxEpVKB2Wxm1jaLxcIERI2MRiKVSiWPcBLbXbFYhE6n49FI\nIq9pVAEgUJBer0dXVxdzTBNIsVwucymVWnJ0uB8WaFGZlGb1c7kcRCIRisUiA7OIeZCchkaj4WoT\ncPC7R/fs9OnTUKlUvMdEwFR7H5QlEomwsbGBarXKY4AtLS0HMnFR4CGXy/HMM8/wKFomk4HNZuPg\nQ6VS4dGjR5yVEtMh8X4fVF1obW1lcqGOjg6kUinGtqjVaqhUKhSLRdy/f59HZ3U6HZ8bT1uXgpaP\nf/zjOHnyJKxWK4rFIlcEWltbYbfbsbOzg5mZGfj9fm7xESDyoGePWix/9md/Bq1WC6lUytdMgZLB\nYMDc3By2traws7MDo9GIXC4HAIJbT09aQ0dd21uVBhhb3v+vBuALAJ59/+s/APAWgP/y/td/UqvV\ndgB4RSLRCoDTAN4TelEULdEBThlMPYJRrVZjZ2eHwWBCe3qE0CyXyzyHTf9tb2/z/KpQQA8ZkSiU\nSiXs7OxwVlMul6FUKvehaOvJPYTsRaVS4REVYjUrFApMkgFg39iGUIdHD2a5XMb09DR/3p2dHXR3\nd3P5v555S+g1E7qZMgDK/PR6PaxWK4aHh1EqlRqOTj25LpWXcrncPpQsjfSMjY1BJBI1FbAAjzNq\nAhXSLOzW1hZOnDgBj8cDq9UKlUoleI8pk6CxQIfDgb6+PmatqtVqPJpltVobEuvUr0vPk1QqxcTE\nBIaHh5FOp3H//n2kUikoFAr09/dz+VTo4UAHN/XwlEolbt++jVu3bmFnZweXLl3injIFtUKul0rQ\nNAEhkUgQi8UQjUaZEMZisXC5koh7DjOpVMqUkCKRCJubm1yhoeeK+Af6+vq4UtcoaKGesdvtxsjI\nCHw+HzY3NyGVSjmzVqlUcLvd6O3tZcrQcDh8aDBLPWyr1YrR0VGsr68jFApBr9dzcEEMVw6HA8lk\nkoPGRq0mAp8NDw/D4XAwmxvwuNzudDpRq+3N/EYiEQ7KCAN00L0D9sChFy9exPr6OoLBIIC9SpFW\nq4XVaoVIJGLswu7uLgcYFPw/LYCp1Wowm804c+YMxsbGcP36dZRKJSQSCQZ5iUQiRtNXq1U4HA4+\nrw8CEYtEIrS1teH5559Hb28vVlZWsLS0hNbWVnR1dUGtVkOhUCAajSIWi2FjYwOdnZ1MUiORSJ7K\nF0Hvc2dnJyYmJvDo0SPGEJw+fRp6vR4Wi4UDZp/Ph1qtht7eXhQKBW6ZCaEnfdIE9ahFIpEEwF0A\nPQD+n1qtdlMkEtlqtdrW+z8SAmB7//+dAG7U/Xrw/a8JNjoECTBERhnk6Ogo9Ho97t+/3xT3La27\ns7PD4xnvfz6IRCKMjIxAqVTyLGszUHoauaHeGq1LL4BYLGZ6uWZRhYQ2TiaT/OLQiEF/fz92d3ex\nurrK+yDUmZLTy+fzvDa91C6XC8VikftyzfRW6N7RugQiI9INrVaL1dVVPtiamYekvjYxhlEZzGAw\n4Ny5c0in0zwnK3QvyEHSz29tbWFzcxPJZBKhUIizqmb5zqk0TWXSrq4uaDQa/OxnP8P6+jrPz1ar\nVe6xCski6XltaWmBwWDA+fPnUa1WcfnyZczMzKC3t5cPjUAgwEjnRtdMNIsmkwkjIyM4deoUpqen\n8fbbb6NQKMBkMmFiYgIqlQq3b99GIBAQdOgQ8Y3VasXFixchk8kQDodx7949eDweDA0N4Utf+hIm\nJibw85//HG+99Rbu3r3bcO1arYaxsTG0t7fzZ15eXsby8jIqlQocDge+8pWvwGQyYXNzE7/4xS/w\n9ttvN8xqdnd3WWDh5MmTeOGFF7hiRwEylVbv3buHt956C3Nzc/D5fIc+c4QpMZvN2NjYwKc//Wmo\n1WrOmCmJoH741atXGSfQaPSNph42NzfR0dHBZySV1qPRKBYXFzE/P49EIoFEIsEjk1RCPqgkK5VK\n0d7ejt3dXVgsFvT19TGBVCAQwLvvvot4PI6VlRXs7OxwD5z0Dqha8OTaMpkMo6Oj/HOnT5+GQqFg\nR/nKK69ge3sbwWCQKzyFQoEJnhKJxIHPNWFiqPT/+c9/HiKRCOl0GisrK/jnf/5nBINBrrJQib2l\npQWJRGIffXS9UTZNWf+FCxdgs9mQTCaxsrKCH/3oRzwxVCqVYDabmSgnFothYWGBK3LNmCBH/X7Z\nelwkEukB/FwkEo088f2a6ABhjYNMJBJ9E8A3m/kd4LFiC6FxPwypNjKpVMoOXMh8rBCjAzWXy+0r\neR/VarXH4wCtra0MKDrKzT9oXTo4aFb0g+wxResEoHE4HPwyCCVmedq10uGiUqmYxU6n0zEH/FH3\nolqtQq1WM8ClVqvB6XRCIpEgk8nwgdmMUbCm0+mg1WrhcDg4MOzt7eVebbNcy5T5EqOU2+3Gzs4O\nPvvZz6K7uxs7OzsIBAI8oibEFAoF88kbDAYma5HJZDhx4gTPTtcHcI2MAhaNRoPu7m6YTCbE43H0\n9vbyrL3D4cDOzg7u3r2Lhw8fCtqLarWKaDQKu90OsVgMi8WCrq4uvPjii4zIlkql2Nrawu9//3vO\n2BoZYRVWVlaY8Y64CmgmPpPJYHNzE2+99Rbu37+PSCTSkKGOpifIoQcCAW4vVKtVrK6uMhI7GAwy\nzzWh1A97gkBenAAAIABJREFUD3d3dxEOh5l60+FwYHNzk1s4S0tL+54FmlOnsv5Ba9P7u729Db/f\nz0FPPB7H3NwcZmdnEQqFUKvVIJfL+fwolUosyHFQO4ACCHKYEokEa2tr8Pv9WF9fx9WrVxmEpdVq\nGQAXj8eZaOhpAUC1WkUoFMLW1hYUCgW6urqwvLyMhYUFrKys4MGDB0gkEgymBcAc7TTX/7R1a7Ua\nByfE4x0Oh7G1tYUf//jHnDiKRCI4HA6USiWkUim0trYiGAzyDP9RrCnUd61WS4pEojcBfBJAWCQS\nOWq12pZIJHIAiLz/YxsA2ut+zfX+155c6/sAvg8crJ71NCP6SIVCwYjhD2r0MLhcLn7YCFn4QZwq\nGaEkqR/1YVwzMV45HA60trbyQ/tBjfpkHR0dDEQSqlh0mBG45cyZMxgcHMTs7KzgvunTrpEOfwK5\nPPfcczh16hSUSqVgSsgn16Se7+7uLux2O2w2G/R6PT72sY9BLBbj3r17jPptxggVSwCZZ599lsFT\nVqsV//Zv/wa/39+UvB7dJ71eD71eD7VaDb1ej+eeew5jY2Mol8tYWVlhNiohRu8BzdF7PB4MDAyg\nVquxOMTy8jLu37+P2dlZBjg1snK5zFkcAa96e3sZ1QsAoVAId+/exbVr1xCPxwUHACsrKxwI6nQ6\n2O12nnzIZrN44403cO3aNTx48IBLtkIxFpTVhkIh2O12aLVaJBIJVjrb3NzEwsLCvjZLo3VLpRLW\n19eRy+W4zy2VShGNRuH3+5mkhwJvoe2QWq2GYDCIYrGIcDgMh8OBVCqFzc1NHssjZ0zUvbVajVsM\nB2XrlOl7vV68/vrrcDqdiMViSKfTmJ6e3td2pB4w9fjrdRKeZpVKBTMzMyiVSggEAqjVagiHw1he\nXma8DQUKBJgUiUSM/TmssvXOO+8gl8tx++e3v/0t5ufnWUSH5r93dna4507O/zAMQz6fx89+9jOY\nTCY4HA5MT09jeXmZ/x7hFIibgwJ+askcNTkRgvq2ACi/76QVAD4B4P8G8CqA/wXAf3v/31++/yuv\nAvh/RSLRP2EPTNYL4FbTV/YUq9VqkMlkjKqjg/XDyH6pX0KMVFRa/qBrAo/niWndZtRZDlvXbDbD\nbDbz1z+ooybn19LSwgIoVEb+IEYvst1ux9TUFGw2G65evcpEJ80+uPUtjGKxyPSqRDdLJfWjvBC7\nu7uIRqPo7+/H6Ogouru7uVR/7949+P3+I61L1Yl8Po+PfexjXOLL5/O8rlCHWn+Ak2Sqx+NhGtJo\nNIpAIIArV65wqa2Z68xkMky+8uKLLzLHeTKZxK9+9Sv8/ve/5+xMiFHrZm1tDTdu3MDW1hb6+vpg\nNBqRSqUwMzODd999F3fv3sX29nZTQL1EIoHZ2VnkcjkEg0EsLy8ziOnRo0f8nB2W2R10zYVCAbOz\ns/D7/Xwe0P6Qg6IDnQI9IeuWy2Vsb2/j5s2b3L8lsaF6B1HPdCXk2knidXt7G4uLi1xZetKhEY0l\nrduo2lKpVFAoFPDee3swI7rOJ9+xTCbD5xt9/7A9oYxzenoaMzMzvC5dL13X7u7uvj2nPTvsGQmH\nw7hy5QrefvttPivqf6d+HSKoEjIqWygU4Pf78Z3vfIcDnfq9oPUzmQxXUgn/AhxN+xxAYz1qkUg0\nhj2wmASAGMB/r9Vq/5dIJDIB+O8A3AD82BvPir//O/8HgP8VQAXAt2q12q8b/A1BVy+RSOByufCV\nr3wFzz77LL797W9jbW2tqTnOg0wqleJb3/oWzp49i0ePHuEHP/jBkQ9lMgokOjo68O1vfxu3bt3C\nL3/5Sy77fhBrbW1FT08PLly4AKvVin/6p3/iCPSo1wo8nhH8whe+gHK5jNu3bzN6+6hrEpjIYDBg\naGgIKpUKb775JtLp9JGul/pjlJH09PTA4/Fwn2xzc7PpNek6CW1KgiRmsxnhcJhZp44UDb8fWcvl\ncv6XZt1JC/goVi9wUU8d2yy/8tOM8Ao0OlQfHHwYdpSRsWM7to+qfYDqqyA96oaO+n+GCXXUdCgN\nDQ3h4sWLeO211+Dz+T6U0iwJXIyNjaFareLatWtHPvDrjQ7/0dFR5hMnRPQHXZeG+ltbW5FKpY6c\nRT7N6qk6m6UAPLZjO7ZjOzZB9sfnqN//Wf7/j8K1H9uxHduxHduxHdEEOeqPHIVoIzt2zsd2bMd2\nbMf2/yf7g3PUx3Zsx3Zsx9a8PQli/TDwBoSTqCdPEQLKamQikYi5F0hvoVAoNMVB8eR6dL1yuZx5\n0YE9JDeRUB3FCMtBQjMi0Z6QUiaTYQXID4pJOnbUx3Zsx3ZsR7QPGxRHKG8C8wkdzxKyJvGMEz83\niZ4cdW0Cc7a0tECpVGJ8fBzb29tIJBKIRCLM4d+s0bgdjUCdPXsWNpsN169fx8zMDGKxWNN8+7Va\nja/VYDDg4x//OF588UXo9XpsbGzg+9//PtbW1gTrwNdbtVqFSqViZbLz58/jM5/5DBOcvPrqq7h+\n/fqR6UOBP3BH/T8KOVpP/fhhr0n2Ya9N0fKHCfx68sA4yijVQfa0KPzDWvvJEbgPSjJTf9/ocAIe\nK6odNXuoX7d+P4jQ5aj3sv5ZozXp60JGWw4zoi6lfahUKkwC8kH3ggCS9ZSn9axdRxnDfPK9q5+X\nB/b2p1AoNLUf9dMMBLik+0b/0vebyaTqWQzpc9O/9ajiZq+13lHTf/VrAzjSfavfS41Gw8Qkra2t\nzLnebCZZf+ZoNBq4XC6+V+Pj4/D5fEw61Mw111+rVCrF0NAQf3YSayHBnWZBuZSlt7a24sSJEzhx\n4gQTc509exYPHz7EnTt3jkQdSvaRdtRPvhD1N4bmAI/qPOhhqEc216971LXrDxwAfCjWEwHQ/OtR\nXjh6iWndJw83ijSbCQRoDZKxpLVpXSIGaHbt+j2u1wmuPzRIfCGdTu8TKhGyLr0gxDkN7O03MVJR\nEFD/Agq5ZhqnIkpBckT0WSgrIWGJWCwmaE9on202GxQKBZRKJR/s9dSkra2tzL5E852NrH4vOjo6\noNFoWHSB9JKBvTLfxsYGc2ELMdoPtVqNEydOsAABqYIBwNbWFnw+H2KxWFOHM3GLKxQKVpUC9sYP\nNRoNz7Cn02lmjhJi9IzUK3zRBIZKpWJ6zXg8ztrlQtetfw/pvtP7QsRG9fPRR1mX1q5UKuyo6icw\nmg3064PAQqHApDD1CclRHZ9KpUKlUkEwGIRKpUK5XIbBYGBCo2bXpX+NRiNisRhSqRT0ej1sNhs6\nOjpYgrUZx0fnOZHsLC0tYWlpCUajEa2trejt7WWeA9KYaHYfDAYD9Ho9ZmZmsLCwwNShPT09XMY/\nagn8I+Wo6/sHMpkMdrudifydTiecTie/ANlsFr/97W8RiUQEae3SLC+JqRuNRvT19cHtdqOjowPA\nY+GI1dVVzM7O7iMNOMx5kIwcMUWZzWZcuHABnvdJ5ekgXltbw9zcHFZXV1kp6bAXrv7FNRqNMJlM\ncDqdGB4eRmdnJ0fw1WoVV69excLCApecGgUC5HRIZ3ZsbAzj4+Po6upiZywS7clVrq+vs8wmObxG\ne02HllarRXt7O7785S8zxzqRPqRSKWxtbWFjYwNer5d7UIftC91HrVYLl8uFU6dOwWq1soYx7Ue5\nXGaubgAc4R82X0xqPuPj45iammJay42NDc4cac46m82ipaUFgUAAN27cOFTMnjRx29ra0N/fj89+\n9rMQiUTIZrOIRCJIJpOQyWTsANVqNfx+P+7cuYNwOIyFhYVD7+PQ0BDGx8dx4cIFdHV1QaVSIRaL\nIRKJMJ+9RCJhlZ9XXnkFyWSSBSoO2ueWlhYMDQ3hueeew/nz59Hb24uWlhbmnScpyVgsBqlUisXF\nRfzqV7/id/Kwa6Z5/U996lOYmprC6OgoZDIZl2Tp2ZZIJExeEQwGcf369QPXpbVJUcxsNuP555+H\nXq+HVquFwWBgNjuSOrxy5Qrm5+dx48YNflYOW5sCFqPRCJvNBrPZDK1WC41Gw/KONpsNu7u7uHbt\nGn75y1/yPThon+k/pVLJdKUKhQIqlYqfCWBPLCSfz2Nubo5V+BpdL+0h8QLQmSKXy3H27FkUi0Wm\nryV2vEbOhM4OmUzG96xarSIWiyGTyaCjowOTk5N47733EIlEUCwW+Yw+zGgktKWlBXK5nHksKEge\nHR3FmTNn4HA4cOfOHaysrHDPulFwT8Em8Re88cYb7OjFYjH+9m//FqdOnUKlUsHS0pKgKgudn5TQ\nJBIJ/Mu//AvTOotEIoyOjuITn/gE2tvbWQPiKBWtj5SjJuUq2tCTJ0/ixIkTrH9KNHg6nQ65XA6x\nWAy3bt1CNptlTtyDTCwWcxan0+nQ09ODl19+GWazGbXanogEUXG63W7mCE6lUocGAfWRsEKhgM1m\nw9TUFD75yU+iWq0im80im82yVjIxX21vbzfUPa1/ibVaLYaHh3H27Fnmh47H41Aqldjd3YXb7UYw\nGOTMV0ifpd6hPvfccxgZ2aNwj8VizBCl1WphNBphNBqbijTpcGhra8PFixdht9uZOAQAv4zENFdP\nzdnoIW5paUFbWxsuXLiAEydOIBQKwe/3I5/P80EhkUjgdDohEong9/ufyqT05F6QctalS5cwMjKC\nUqmEcDjMKk+tra1wOp0YHBxEPp9HPB7n4O6wgIueuaGhITz77LNwOByIRCIIhUIIhUIIBoOw2+1Q\nKBSw2+2QSqUIh8PQ6/Xw+/0HrkuAG3KmJ0+ehFwux/Xr17GysgKfz4dKpYLu7m50d3fD4/FAKpXC\n4/FgZmam4V7I5XJcvHgRn/vc5+B0OtHS0oKrV69ieXmZaRhJdYgk/qiSdNg103V3dXXhy1/+Mjo6\nOph7OhAIIJvNchapVquRTqf5/W9kEokEer0eFy9e5P2up3KMx+MsuJDNZlmFr1FZna7b4/FgeHgY\nAwMDaG9vZ6pdorkMhUJQqVSQy+WIx+N49dVXG14zOb2+vj5YLBY4nU5otVp0dXXBZDJBJBLB6/Wy\nBKZCocDNmzcFOWr67CaTCTabjfnhKZlYWlrCysoKZ3uhUIhVnhpdM/Wm6QyhatHp06cxPDyM2dlZ\nAEA2m0UikeAA7CCrL/FTVYJ0HSQSCQYHB3HixAnk83kONihjP8z51SdLRBcKPGZoMxqN8Hg8CAQC\nyOfzkMlk3E9utA8UJBArXLFYZO1p4uHX6/XY3t7mSuhRyFE+Uo6aNHvrlaH6+vqgVCqRy+Xw6NEj\nGI1GFi8/d+4cEokEfD7foVEr8Pjwr1b3dFjb29tZJ3R5eRmRSARDQ0Po6emB3W5HLpfD9evXOQo6\nbF26oRKJBBaLBZ2dnchkMpifn8f6+jrEYjFeeukldHZ2IpvNIhAIwOfzNdyP+mvWarXweDxwOp0o\nFAr49a9/jWKxCJvNhuHhYXR3d7Oj3t7eFrDbj18MrVaLzs5OAMDMzAxmZ2dRKpXQ1dWF3t5e1tzd\n2toS/IBRv4ruoc/nw4MHD7CxsQGRSIQzZ85Ap9NBKpVyNtqoL0uHpclkwtDQEPr7+2EwGHDlyhX4\nfD4UCgWIRCIMDQ3BaDRCKpUiFAoxh2+jtUk0pLu7G0qlEqFQCF6vF4FAALlcjg9Ni8WCra0tpitt\nJMagUqngeZ832+l0wufzYXV1lcvFlUoFBoOBKWGJXY1EGQ7bY6o2ORwOFItF+Hw+3o9kMgm1Wo2x\nsTEYjUYug6dSKXa0h60tl8sxMDAApVKJZDIJn8+Hn/3sZ4hGo6wDPTAwALVava9a0ahKJJFIWJPZ\n4XCgVqshFAphfn6e1eVUKhXLESaTSSwtLTV8F2mvqbowMTEBvV7P2Wc6ncbCwgLLP6rVajx69Aib\nm5uCSuoymQzPPfcczp49C7fbDbFYzNliMpnE5uYmlpeXYbVaEY/H+d42MolEAoPBgNOnT2NwcJAp\nW6k1QopwWq0W6+vrSKfTgiiOqfpkNBrhdDoxMDDAfXOn08mOxGKxoKWlBcvLy1CpVA0Jmegdor6s\ny+XiQKetrQ0ulwsmkwkulwupVAqhUIjFjg575ugspV692WxGNpvlikJnZyf0ej2MRiMsFgsHXET3\netjZRJ+HqGSpVw2ABVwsFgu0Wi1r2ZMDPmwv6EwhMQ6tVrtPlbG/vx+12p5gCcmnUiLVjLP+yDlq\nKsVRL4kyl4WFBTx8+JAP/52dHQwMDODevXusRnOY1feFKcoJh8N8OJDes0QiwdDQEHp7ewWzctHh\nRI5aoVDg3r17mJ6extbWFvcqzGYzPB4PtFqtIE1j+r5IJILT6YTNZkO1WsXKygoePXrEJRaDwYDJ\nyUnuFTXTF6OgR6PRIBgMYnZ2FvPz81y2Hh4ehk6n46xDaI+axFOGhobgcrlw8+ZNrK2tIZFIMPrU\n4XCgXC4jGAwKlo8Ui8Xo6+vD0NAQuru7kc1mEQwGEY1GsbOzw1mvWCxGKpVizerD5DQpgzQajRgc\nHORgiPSow+EwarU9Lng6lEKhEDY3N7n/fVgAQHKWJpMJMpkMm5ubWF9fZ51jm80Gp9MJvV6P3d1d\n+Hw+1hM+KACtr+IYjUZUKhXEYjF+nkkPnfaZRCrW19cRDoeRSCQOrRJRZqrRaFAsFhGJRHD9+nXM\nzc1hZ2cHdrsd3d3dUCgUKBaLCAQC8Pv9iEQihz5/hFfo7OzE4OAgyuUylpeXcfPmTczMzHBwoVQq\neT/C4TCCwWDD0rRIJILb7caFCxcwPj4OnU6He/fuYWFhAWtra/taIXT4BwKBhpS+hEuw2Ww4c+YM\nPB4PACASieDGjRtIpVIIBAKIRqMol8uswkdVs8PWpWoLaRyTstjm5iZyuRzC4TBLxUokEq4kNsqm\n6d3W6XQwGAzo7+9n7APRzE5PT+Pu3busCx8MBgUhtamHTsJACoVi3zuUyWSwsbGBaDSKaDTKghSN\njErqhJOh/ny5XIbFYkGtVuOqC+FdhJx39Z+H7g993Ww2o7+/n7EsJDsKQNC61FOndSlbNxqN6Orq\n4sqi2Wxm9a9G1b2n2UfKUddqNc5ODAYDCoUCAoEA7ty5g+XlZYTDYSgUCsRiMbS1tXGDXmhJtlQq\nobW1FX19fdDpdHjvvffw4MEDeL1eiMViGAwGXLhwARqNBltbW7xuow2t1fbEQsbGxvhA/ulPf8q9\nRZVKhfb2dlit1n1RoJAbReCiM2fOYGBgAAsLCyxeQFnHxMQEP9xClXyAPWd6+vRpXLp0CZlMBvfv\n38f9+/dRKBQwOjqKS5cuYWxsDMFgEJlMRvA+i8ViXLx4ERcuXMCZM2ewvb2Nu3fvIh6PQywWw2az\n4fz58yiVSiyZJ2SvKbN/8cUXMTk5iVwuh/n5eQQCARaxn5iYgMfjQSQSwdzcHBYXFxvKPEokEphM\nJjz//PMYGRlBpVLB+vo6FhYWsLS0xOXic+fOYXx8HA8fPsSVK1cQCAQa8pVLpVJMTk7C7XbDZrMh\nn89je3sbXq8XsVgMcrkcn/rUp3D+/HlkMhnMzc3h8uXLCIfDhwK+qF9KQvXhcJhFPkqlEmw2GwYG\nBvDCCy9Ar9fj4cOHePfddxEIBLC4uHiok65HsSaTSUilUi7/t7e3Y2BgACdPnoTdbsdbb73FqmIE\nxjnMKJA6deoUurq6sLa2hnfeeQerq6soFApwu91QKpXIZrPY2NjA/Pw8Yy4aZepKpRJ//ud/jmee\neQYymQxerxc///nPEQ6HmWKXHBEB4RrpBFBWOjAwgM985jPo6enB9vY2rl27hkAggLm5OaRSKRSL\nRb5fFBQ2CmplMhmsVivGxsbwzDPPQKVS4ebNmyysQcpXhA2hgFCIhrtYLIbZbEZHRwe6urpgs9mw\nsLDAPeh4PI7p6el91SYhWV49SK+trQ0mkwmZTAaVSoXL/aQlTS2nZkbMqE+t1Wr571BbqFgs4jvf\n+Q6fGULBp7QfdJZSa9XhcPDZ/I//+I8cxDY7hUIBF0n5ymQydHd3Y2xsDD09Pfj+97+P+/fv73se\n/qBL3/WjAmKxGIlEAnK5nHtTer0ecrmc+6a7u7vI5XKMLmxU+6dsOp1Oc6Qrl8vR29uLQqHAWatK\npUKpVGJJSoqaDlubQArUd9VoNBgdHUUul4NWq4XT6YRKpUKxWOQyiZB1KTI2GAxQKpUMZhkYGIBe\nr8fY2Bg8Hg9WVlZQKBT2ASQOe5lJzpAOTvo7J0+eRCKRwMTEBHp7e2EwGHi+kK6n0TWLxWLodDq4\n3W4oFAqUy2WMjIwgFApxBk+6xoVCAYlEAgD2odmfZi0tLTCbzWhvb4dUKkUqlUIqlYLb7UattifF\nODIywn1jKkvTS3rQunRgUkmN+tIul4uDi6GhIQwMDKBSqcDn8zF2gfbjICOgoVwuRzqdRrFYhNPp\nxPr6OoPi+vr6ONt7+PDhPgR8IyuVStjY2MDOzg4MBgPcbjcmJibQ1taGgYEB2O123LhxAzdu3IDX\n60UqlRLcd6tWq4jH49BoNGhra8PU1BROnjzJz8vKygrefvttbG5uCtZwp8NMo9Egn89Dr9ejv78f\ner0epVIJmUyGM7HNzc19WchhJhKJGORFAatYLEZnZye0Wi3i8Tg7EXpPhGi407odHR0wGAy8Lx0d\nHWhpaUGxWGRApFgs5pKpkGumQMtkMnG52+PxQKPRwOFwsAZ6/bMrxEmT4yAAndPphE6nQ3t7O/e5\nCd9Tv55QR0o9aY/HA4vFwvKNXV1dqFarmJ+f3zetINRJk2NWqVRwuVzc+9fr9ejq6oLP52OApJCK\n5JP7IZfLYbVaYbFYAAAGgwEDAwNoa2vDq6++ytcs9HopaCHEt8ViQaVSgVqtxvj4ONxuNxYXF7G1\ntfWBnDTwEXPUwOMRhFwuh4WFBUZO63Q6AOCymclkQiwWY0UjIdKRVPLJZrPw+XzQ6XRwOBz8vcHB\nQRiNRohEjzWY6ZBvdPPoYI3FYujp6UFXVxevazabGWhDGU/9uvRzh113qVSCQqGAyWTivi85LZPJ\nhAcPHnBgIZVKGbXdyKGSfq9Op8Pg4CBHkyMjIwxsyufz+0bAGqGy6w8cGrE5ffo0SqUSP9AymQzF\nYpEDCwKNHLTP9Ll0Oh10Oh331+x2OyN7jUYjtFotC7ST1iztx0HW0tLCh6NKpUI8HodMJuNyNM1y\nKpVKBvHRM3eYo6a9IIKJTCaDra0tuFwuXLx4kfenXC4jEAjA6/Wy3KWQ9gLdh0AggFgshv7+fnR3\nd+NrX/saFAoF6xH/5je/4RJqI2YnOvyouuX1emG1WuFyuTA5OQm5XI5QKIS1tTX87ne/g9frZTCd\n0JGhWq0Gv9/PB9rw8DB6e3uRy+Vw7do1hEIh7sUKqQ7VB71+vx+7u7t8yBMgi0BeKysrTamL0UFc\nLpcRCoWwuLjIGblcLkd3dzf0ej2DRoUe9BSwiMViZDIZbG5uQiQScX9Yr9djZGQE6+vriEajgqtw\n9I6SrjqwX35SqVTC4XDAaDQKyvqf3It6DW2amU6lUmhtbUVHRweq1Spu3LjRVA+W5rkJwGiz2dDb\n24tYLAadTgeNRoOuri7Mz8+jUCgIVkukM5amLux2O4aHh9Ha2op8Pg+1Wo2enh7GKglN+IDH73ZL\nSwtUKhW3HtfX11lv3WazcdXigzhp4CPoqAHwi0AapfVAo2KxCIVCAblcjlgsxmLwjTIbWnd3dxeB\nQADFYhHnzp1Da2srcrkc5HI5FAoFZ8bUyxKyLgA+MAnNarVakU6n0draCgDsmKiv2SjjfdK8Xi9G\nR0dhMpn2gRx0Oh33PSORCD9AjYyizGg0ilgshoGBAe5jUt9TqVQin88jEonwAy/kxaZeGlUYOjo6\n2NGVSiXu0VMWSaNPjZweZb7AXjuBULJKpRKVSoWRp7du3WIEZrVabbgfMpkMFouFR8cGBweRzWb5\n61KpFMlkEtFolJHltHaj50MsFiMajUKpVMJms8HhcKC9vR3t7e1Qq9XY3d3Fa6+9ho2NDfj9fsRi\nMUEjJ/T9RCKBfD4Pg8HAo2O9vb3I5/Pw+Xy4ceMGNjY2kM1m+fMJMQpolUol96ztdjuSySTC4TDe\neecdRn8346RLpRJyuRwKhQKy2Sy2t7eh0+nQ2tqKWq3Gc800ztMMeJECCyqLEhZFrVajs7OTP/th\nAWG90Tuys7ODeDyOxcVFrK6uMnpYrVZjdHQUu7u7sNls2N7eZmRzowCZHCpNFlDPWC6Xs2MqlUow\nmUyIRqOCqDMpKCRHWqvVeCwR2Msg1Wo14xnUarWg8VMAPH8skUh45j0SiSAej6NUKvF4mt/vh0wm\ng1wu30fCdNg1q1QqrlwQqIsoPavVKjweDwwGA3Z3d7k3Tme5kHWlUim3AQAwiry/vx/t7e3Y2Njg\n8w1o7Ewp4KGRN6VSCZVKBWAv6Ke1g8Eg4x/qiXD+KDJqYO+DUH86GAzuQ+hptVoMDAzA4XDgu9/9\nLjPVCAVQUUYbCoWwurqKlpYWfpCGhoZgMBjg8/nw+uuvCy67AUAul2MQ0LVr12AymfglsNvt+Ou/\n/ms8ePAAr732Gvx+/76IvtGBnEql8KMf/Qi/+MUvYDKZeJbV7Xbjb/7mb6DRaPCrX/2KswUhUX2t\nVkMkEuE+/dWrV5mXtlAo4Dvf+Q40Gg1u3bqFV199lWeJhZSbqtUq3njjDdy+fRsOhwNtbW1cdvR4\nPPiTP/kT+Hw+XL58GQ8fPuS/Sb97kFUqFczOzuL73/8+LBYLXC4Xg8acTifOnj0Lo9GIt99+G3fu\n3EEoFGLh9kbrzs/Po1Kp4M6dO5iamoLBYIBUKkUkEuHJg2QyiZmZGczMzPCzIQS/4Pf7sb6+DrPZ\njMnJSSiVSqRSKZRKJaTTaQYjra+vIxKJcPm0UUuE7jFVgwYHByGTyeD3+xEMBrGwsMBBaalUElw2\npUPObrfj1KlT0Gg0WFxcxL1796DRaLC8vMwAQKHlR+Bx31Sr1aJcLiMSieCdd96BWq3G8PAw7HY7\nKpVGZXKrAAAgAElEQVQKVCrVPnayRmtSyVutVuP69etc+q5Wq5iYmMCZM2fQ0dHB4z5CS5vELJXP\n55FMJhEMBrltJZfL4fF4cOnSJSiVShQKBe7JHgYsBPaAbJcuXeKZ8VQqxWOLCoUCHR0d0Ov1aGtr\ng8PhwMbGBhKJRMO1VSoVPvvZz3JAtbq6yhXHnZ0dWK1W7OzsIBQKoVAoQKVScdXioPOTqhUqlQrf\n+ta3UKvVsLm5iWQyCaPRiHQ6DYfDAZPJhKWlJa7aEGfFYe0Qundf/OIXOTje3t6GRqPhCirNwBPQ\nkHrVhwUY1OP+3Oc+xwQ9Xq+XiW4SiQT6+/uhVCoxMzMDv9/PBD6lUunQ0Vaq7v793/89j/NGIhE4\nnU6k02m89NJLsNvtmJubw9LSEgPUaLT4qKyAH0lHTVbfK6OIslqtcjRDJU6hETKVNagHXiqVuMxI\naFRiyqrXdxYK+qr/eSI0oXGiWq2GXC7HEa7Qw4I+M4HEqL9Wq9VgtVoZGUtMVvWH92FG0WgikUCh\nUMCDBw84C6b+FjFv1SObhVxzuVzmhz8UCvH4W2trK6xWK5RKJSKRCAKBACNjhfRNC4UCwuEwpqen\nYTQasba2hlQqBblcjpdeegnAHrHJ8vIys28J2ed8Po+trS3kcjloNBro9Xo+uLRaLRwOByqVCq8t\ntCQL7AUB4XAYBoOBM5Hl5WUA4HtHSF4C1QkJDOvJcAiYlU6nsbm5CY1Gg93dXX6+hb4fwGNyFrPZ\njMHBQcjlcty9exc3b96ExWLBCy+8wKhVIXiF+utVKpUwmUxQqVSQyWRYXFzE/Pw8PB4PVCoVdDod\nH2bNXK/b7YbH40EsFkMoFGIMikgkQl9fH3p6eqBUKvdVABpda622N1IzPj6OpaUlpFIp/nqtVoPB\nYMDY2BhMJhMePnzI/X8h67a2tqKzsxOFQgGrq6sAwEhxj8eDyclJ9PX14d69ewgGg4xZaLS2QqFA\nW1sbhoeHmc2NKlUSiQTnz5+H6v9j781i20yv8/GH+75TFBdR+y7L8ibb4/HYHs9MJjOTrUmKIGlS\nFEWbIiiQ3hS9zEUvgqJFC7QIWhQJijTpRdqiSzJLMvFMZjLjZWx50b5RokhK3HeRFElJJP8XxjlD\nKbb4UXZQz+/vAxiaxTp8+X7v9579eTQabGxscH9II8NBa9ZoNBgbG8Pa2hry+TxqtRrsdju6u7u5\nB+fmzZvcV0B7fFDGqVarwWAwcHPizZs3GV2P5r5bW1sRiUS4WY9S740Al6xWK86ePYuRkRGsra2h\nVCrxu+10OtHf388d/16vl7N9tK4HOS7ktDidToyPj2NpaQl+vx+pVArDw8NwOBxwuVxoaWnB9evX\n4fV6USqVYDAYuDeivtzZjDyxhrq+FkqbRkP5Q0NDAMBpU6EvNemlP/Wzd1KpFIODg6hUKggGg0gk\nEk1FC/Rw9w+0y+Vy/qyVlRUkk8mmvKr6tVINml52iUQCp9OJXC7H4BvN1AlpvTTjSI4FRSjJZBKT\nk5OCITJJb6VSQTabRS6XQzKZ5LQPzWtqNBrcvXsXkUhEUEMP6SVUNL/fj3g8zqhIvb292Nragkgk\ngtfrxfr6elPOEHnRW1tbSKfTPLpDIDWbm5uIxWKYm5tDMBhsqmOfENhoQiGfz/N4DV2swWCQGXaE\npqbJ6aRZ51qtBp/Ph7m5OW6Mo67m+ui7kcjlcphMJnR3d+PZZ5+Fx+PBe++9h2QyCaVSiUqlwql/\noc8O+HhWeHBwEDabjWuvYrEYDocDnZ2d0Gq1fG6EsiRRQ9bAwADa2tpw/fp17gGhTn6CoqQLW+je\najQadHd3Q6vVwmaz8b1gtVpx7NgxvPDCC0gmk5iYmMDCwgIbsIP00pozmQw3bG5tbaFQKECr1cLp\ndKK3txeVSgV37txBIBDgjGEj3TQnv729jZ6eHvT39wMAoz3q9XrE43FkMhksLi7y8xPiuMjlcm7c\nHBoaQrFYhMFgYHAPGmf0+/3IZDLcvV0/bvUgvRTJptNpPPPMMygWizxnT1Mhs7OzCIfD2Nzc5OZU\nGod90NprtRo3HmcyGeh0Oly4cAEymYzxBObn5zE9PY1AIIBAILCH+YpKrA8SiUSCzs5OBsii5tWO\njg7G+5iamsLq6ipWV1e5r4ZszcOcgEbyxBpq4MGeularRWdnJyqVCnfTHSaVUG+E6UDZ7XaUSiUs\nLS1xw9Cj6hWLxWhvb0ehUMD09HTDl1mIXgAMLK/RaBAOhxuiYzXSS79LNSi5XI7V1VUe1zqM3noo\nUqq9DQwMQCQSYWpq6lDUcnTQCW4UANd/d3d3EQgEuHvzMM6bSHQf2pM6UAnQIh6PY2VlhVOQQoXG\nXqh+KBaLsbGxgVQqBafTCZFIxGNNQhuc6tetVCqhUCiQzWaxtLSEQCAAnU4Hk8nEWSEhKW8S6sge\nHBzkyCCRSHAkQ1kAcm6E6qXO2BMnTkAkEiGdTjP64MWLF9Hf388lqc3NTcFMQ5RV0Gq16OnpgUgk\nQiwWg9FoxIkTJzA8PIx8Po8PPvgAMzMzgvWKRCLs7OwgkUigvb2da8YymQwWi4WnMP73f/8Xs7Oz\nSKVSgpDTSO/i4iJGR0fR1dXFfSzA/fOSzWYZLCmXyzUECiGp1WqYm5vbM/Mtk8kgEomQSCQwNTWF\n2dlZdjgoTX2QE1er1bhpamlpCXK5HGNjY9BqtdjZ2WEEvM3NTSwsLPB8d/3Y4sN0k/O2srLC+2k0\nGlGtVpFMJvH6669zWYjq+vUNqLSfD9Lf1dWFjY0NmM1mLCws4OWXX0a5XEYoFMLi4iImJiYQCAQY\n4IVKoIRd/rB7WqFQ4OjRo9BoNLhz5w7jRCwsLGB1dRVLS0solUqMr28wGFCtVrmeXR9sNSNPtKHe\nLzKZDC+99BK3wR/W6O0XShspFArkcjlMT08/EtMJCXn1X/3qVxEOhzEzM/PITE603r6+Pnz5y19G\nPp/HwsLCoVmL6kUikUCv1+PixYtIJBKYmJiA1+t9JC5VMn7t7e34yle+gnPnzjEi12H3grxeGrf4\n+te/jmPHjjG61WHp5AicwOFwoL29HWazmdGcKFXfCAHvQUKjMV1dXejq6uKmL6fTiUKhwFC1zXDt\nEiDJyZMnWc9zzz2H06dPo7+/n8FHqNQiVK/BYMCxY8fw3HPPoaenBwqFAl/4whfgcDhgMBjwX//1\nX1hYWEAqlRIEYkFC55NQ0i5duoTd3V3Y7XaUy2VMT0/jf/7nf/i8CXUAaJZeJBLhwoUL+MIXvgCt\nVsvkEN///vfx7rvvYmVlpan7gkbTfvCDH8DtdjP6G0FQZjIZeL1enk0WmmGp1Woc1QaDQZ5X39nZ\n4bNFo2P1MJZCynrhcBjJZBIejwcqlQr/8i//wiQT9TpkMhlnp4T0slC25kc/+hF0Oh3+7d/+DZVK\nBZFIZM+9Q4BL5Ew3Os/VahX37t3D+vo6pqamIJVKGeSF5r1rtRo7YpShoqbIh915tVoNv/jFL7Cw\nsICbN2/C6XTiW9/6FqMXkoNPnevU2Ep6H5a9qNXuN//94Ac/QCwWQ0dHB370ox8hGo1iamqKR0Gr\n1SqDEFHmiRyMw97TnyhDTd6cTqfj9FYzdbKDhOqH+Xye06iPKtTU0Nrayg0QFL0/6nptNhs0Gg2q\n1aogaEUha6UUl8vl4s7QZrlZH6bXYDBwBElNG4fdAzL+ALgbWSaToVKpYGNj41BZFtJXrVbZSaPu\n7HA4DK/XK6hW+CCRSqV75ncHBwehUqm4l6GZ2jQJpTqJfcrtdqO1tRXlcpmZgebm5pDJZJrSm8/n\nUSwWkcvlkM1mMTY2xpFzJBLBhx9+uKe+KVSq1So2NjZw9+5ddoRUKhWWl5dx9+5dvP3221haWmoa\nsalSqTAiW6FQQDAYhEqlgkwmg9frxTvvvLOng7yZd69Wq3Hvx/LyMmQyGQDwc2zGodivl6L1+uzb\n/jT0Ye4gMsD1MKD0GfST7k6h5RDSE4vFGBDkQXo3Nze5K1uIE07vaSwW28O6tl8vGVKayBGy7kKh\ngOXl5T2OX31WkhygVCrFjouQe2NnZwfJZBL//u//zu9FvV76Dul0mjMAQrkXDpJPlKGu7/7e3NyE\nXC7n2bdHFUp9pFIprv8+ipCBkslk3AQllUofmwMAANlsFmKxGCsrK4+sk4RGq8LhMGZnZw9dWtgv\nVPunzvhHPbj0om1tbWFpaQmZTAYzMzOYnp5+JAegWq3yTCt181I9q1ljCtx/VuVyGZubm5idncXq\n6ioGBwcB3N+TmZkZwanN/WstFovweDz44IMPmMWnWr0PODE5OckGtRnJ5XK4c+cOxGIxent7eawp\nkUjA4/FgamqqqWkIEkrp/uxnP4PD4YBer8fu7i4zvlFT42FKIUSIsLW1Ba/Xy3C3QqK6RropuiUD\nT8/pUd6J+lLLQbqa/Yxm+0ia1d1IfzM9Ms3+DuF5N6N3e3v7N7rO6TnWO0bNZAzpue3PrNGdTPof\nh3Gul0+UodbpdJDL5ezZCRljESKEpEWGhCApH0XqUzbFYhGLi4uIRqOPxfBR3Sgej2N1dZVxvx9V\n6AVOp9N46623sLCw8NgcoWQyiaWlJZTLZVy7du2RLlB6KSqVCjY3N3H16lXIZDJMTEw0xIM+SOgs\nbW5uIp/PIxqNwmazYW5uThCj0MPWurW1xelOsViMiYkJKBQKrmUdprRAl8Xs7Cwzj1HdmKL3w6x3\nd3cXwWAQkUiE0ZwqlQqP/QlBH3vYeiuVCm7dusW9G5Rdof9/WCFjnUqlHluGjeQwxkeoPK41PpXm\n5XGVTPfr+W2dFdGTcFhEIpGgRVDqqRn4OKFC0cjj3o9G8JWHlceRPn8qT+WpPJWn8n8qd2q12qlG\nf+kTFVE/zlTCfnkczVgPkqfe+FN5Kk/lqTyVR5HGWJNP5ak8lafyVJ7K/6PyOPqGHiSNYJGbkU9U\nRP1UnspTeSpP5dHlcZXO6nH06ycnHscIKoFF1fNDNzsVsH9thNNNGASEc3AQpWwjIYwBg8HATZYq\nlQrFYpF5Av5/1fW9Xx5348h+3b8tvdRx+Dil/oV53DVx8gz3d6s+Dr2EYNQMbKQQvcSaRYxlj6sp\niPCJie2L0L8OI/XeNgE60GTA9vb2gbOijfTSMyP4SPrneoKbwwhdStRoViqVmBDjsOutXzfNsteP\nUlE3/mEb2er116NlkRF4HJgJ9Z9R//OwDYj18jiMHv2kP8DHvT6HvevojNF7TIaPJhIO2zhLI14a\njQZ6vZ6Z8nZ2drC2tsYjWs3uLZ1blUqFZ599FhaLBWKxGJubm3jnnXeQz+cPxD1/mNRqNajVapjN\nZhw/fhwnTpyAXq9HJBKBXq/H9773vUduUP5EGOqHGeRHNab1h7Yem7bemB7We9uvo/6CoO9y2Fnf\n/Q1qIpGIaezoEm72wqx/6eq/d73Rq1QqPKcpZO31F0Q9RSbpJcYywjkm3PZGa6/XW8+mQ3/UajW0\nWi1Doa6traFQKKBQKAhaN3Hu7tdNRsRut8PtdkMkEmFjYwPz8/OC8blp7Iso/eovOjKCVqsV8Xic\n4UaFPEsaBaT1GY1GRlkitipCXQuHw011hZMToVQqMTw8zNjOwP150XK5jGAwyFCozZw9+u40E04U\nqIS4JpFIsLGxwcQYzThF9e8b/TvRS2o0GkgkEhSLRYjF4qZGf/aP4tQLIYHVzywfRi9JPWZA/T0o\n9O54kJGmqZH6+4/0HmatdDeUy2WG9a2H92xWL/19uVyOTCbDyGGEmkjjT80YatJLd+Pc3BwDD+n1\n+j085s04GLSHNBFRq9Xwy1/+ElarFUqlEhaLBQ6H4zc4qZuVJ9JQ08VAlxe9xPXeNUUzzTCS0KGi\nF5gQjOig0QGg+TualROim/TSZWu32/dAAxKiDoHLN6Ob9kMmkzGxgV6v5+9EI0W5XG4PBWMj3bQP\nYrEYer2eSezpJaRnUC6XsbGxgVwuBwCCHAHSSy/X4OAgpFIpG3mr1bpnXwhDu1AoNHTA6j14s9kM\nq9XKoAYqlQqtra1MtUe4vWtra4wVf9B+iEQixvimyJlwffV6PXQ6HVwuFyPZ6fX6PSxFB+mWSqXQ\narXMxkVOT0tLCwPuyOVySKVS+P1+TE9PQ6FQYGtr68C9Jr29vb1oa2uD2+2GQqGAwWCASqViruRI\nJIJ0Oo1IJMLPotHFQcbT7Xajp6cHly5dgt1u52ja5/NhdXUVer0e1WqVnSGhjgtxixMF6LFjx7C7\nuwu5XM6jgjqdDgsLCwdiMO8XOiPkDBEwDDlwWq0WarUaPp+PGbWEQoDWO931xk4kuo+Tr1Qq2Slq\ndk53v776/05TL3TnCXFo6w098PFoZ31Kmf77fvAOIWul9ZAOej7EAV+vU8h9VL+vm5ubEIlEKJVK\nzN1N9zU5hM04AeR4V6tVpNNpZvkCgLa2Nsjlcni9XsEB4P5ntb29jRs3bkAkus85UKlUoNfr0d3d\nvSerdZjg74ky1BRl0YXV2dkJs9nM6F4tLS0A7nutW1tbeOedd+Dz+ZDNZnnO82FSH8mQsRscHGR4\nQHqZy+UyXz7Xrl0T5GFR1KlUKmEwGGC1WvHcc8/B7XazwRKJRIhEIvD7/ZidncX8/HzDdG+9ITUY\nDDCZTEx+3tvby5c/ACwtLWFxcRFra2uIxWKCdJN+pVKJtrY2jI6Ooq+vb8+LTNCG169fh9/v5xe6\nkSNAa1OpVLBYLDh79ix7njs7O3yBEtJPNpvF1tYWw+0dpJuMtNFoRHt7Ozo6OhjcQiwWw2g0wmQy\nQSwWI5VKwWKxIJFIcHr2IN1SqRRmsxltbW1obW0FAE6dk+6uri7s7u5CpVJha2sLKpWKU9UP2wu5\nXA6DwQC73Y4TJ04wtzdhfNPzJTIBmUyGUCjE9bOD9tlsNqOzsxNnzpxBf38/bDYbO4Pb29vQaDRQ\nq9WQSCRwu92Ynp5GLpfjz36YXrFYDJPJhKGhIYyPj2N0dBTd3d1Mz1gqlRiGkRzHUCjEQCEHrZnO\nh8vlQl9fH86ePcuOIrGTbW9vo7OzE52dney8xGKxh+qtX7dCoWAudKPRCJvNBovFwu+/SCRCd3c3\nlpeXsbq6ysxPjXSTI07Bg81m28N9bLfb0dnZCYPBAJ/Ph7fffrvhbD9d9vWZHHIsqBxC0LYajYYd\nrkAg0FDv/si3/j6irEK1WoVSqUQul2PUtUZczw+K1Gl/yIElDIb9rH8H6a3fDzLudG/odDq43W5k\nMhk25ELT4PXlO7qDAHBg0NHRAZ/PB5PJxIx+jZyLWq3G92891kD97+n1elitVphMJoYYPUyd/Ykz\n1DqdDnq9HlKpFH/wB3+Avr4+xu31er3Q6XSwWq1oaWnB+Pg43nzzTdy+fRvLy8vI5XIHbgCRqnd1\ndeG5557Dq6++ilrtPkZuNBqF3W6H0+nESy+9xPRli4uLzGl8kNEjgPlTp07hueeew8jICDY2NrC2\ntoZqtYrR0VGMjY0hkUjAbrcz4H6jw0AvQF9fH55//nmMj49Dr9fjF7/4BTY3N6FQKOBwOHD+/Hlm\nTbp16xbz2zbSSxfAn//5n8PhcGBjYwNTU1Pw+XwwGAzo6+vDwMAAp9XX1tYAHJwJoEuBDPSlS5eg\n1WoxPT0Nn8+HVCqF7u5ujIyMwGKxoFKpYH5+nhGlGukmcodz585hYGAAExMTWF5eRjAYRLFYxKuv\nvoqRkRGYzWYsLy/j6tWrTHB/0DMkwoXf+73fY/5iv9+PK1euIJfLwWw2w+124+zZs4jFYpifn2cM\n6YMuNoVCAavVitHRUZw5cwYulwt+vx8ejwc+nw9qtRoGgwE9PT3o6Ohg6M9isdjQSMtkMnR3d+Ol\nl17CkSNHoFQq8Z//+Z/w+XzY2tpCa2srXn31VbhcLrS3tyMcDsNisTQEsqFneO7cOXz6059GX18f\n8vk8vve97yESiUCr1bJzZ7fbsbOzwxSmjfDQRaL72Po9PT34zne+A5fLxfCft2/fxs7ODlQqFSqV\nChsQwmBuJAqFAi6XC6dPn8bly5cxNja2h4/b6/UiGo1CJLoPRENZLiKTaLQfFy5cwPnz5zE6Ogqr\n1QqtVguJRIJ0Oo14PI5QKASXy4VarQa9Xo+f/vSnDfUSMNJzzz2HEydOoKuri++5YrHIMLPDw8MA\ngLW1NUxPT+Of/umfDtRNTrhWq4XVasWLL74Is9kMpVIJk8nEgDvEEjY/P4+FhQW89dZbB1J2UlRO\nGUmHwwGTyQSbzQaXy8XZxNXVVdhsNmxvb2NxcRE//elPDzwb5AxTBtHhcDCZhUQiwec//3mYzWZm\nsdve3sbGxgZKpRJWV1cfWr6oT3uToaSMGQB0dHTg5ZdfxtTUFKampuByuRAOhzm4OsiZ3d3dZcrk\nnZ0d6PV61Gr3KVLtdjtefPFFmEwmXL9+HU6nE+VymZEKm8m0PFGGmlJn5BFvb2/z4SecYJVKBZfL\nhZGREQwODjLXc6M0EKEX0d9VKBQIBALY2NjA0tISwuEwnE4nxsbGcPLkSVitVk5NNhIytjKZDK2t\nrVAqlZicnMTU1BT8fj+n6S9cuMBpayH8zvUeZV9fH9xuN2MYX79+HcViEUqlEu3t7Thx4gR0Oh03\nNwlJfZNjdPToUbhcLiSTSdy7dw9TU1NIJBIwGo1wu92w2WyYmppCNpvlZqRGotfrcfz4cVy4cAFj\nY2O4cuUKFhcXsb6+jq2tLXR1dcHpdEKhUCAcDgtK2VO0NDY2xgQUIpEIfr8ffr+f6Tjb29uZJo+I\nCg6qaZFeYvc6duwYNBoNlpeXEQqFuL5ks9mYRSkUCmF5eRkbGxsNm0SUSiXsdjtHdYVCAT6fD0tL\nS0gkEhgaGkJXVxdMJhPDam5sbHCm6GFCl7xer2e6wXw+j/n5eSQSCYjFYrjdbjgcDkilUiQSCQQC\nAa77HRSF1DsuwP3Iw+v1YmZmBoVCAQMDAzCZTJzdikQiiEajTOPaSC+xXRHLl8fjwfT0NMLhMNfX\n6YJNpVJYWVkRFEHq9XocPXoUzz//PIaHh5FKpbC+vo7V1VUkk0msrKxwBCmXy7G2tga/398w7U2Z\ngxdffBHj4+MwmUzY3t7G8vIywuEws0gB4JJXNBptSFxCEX5bWxs+85nPYGBgAHK5HIVCASsrK/D7\n/ezUr66uMmb61NRUw71QKpWwWq2w2+0cQNRTiIZCIaRSKajVamxubmJxcRGTk5MHnmdyWKRSKXQ6\nHXp7e5m6lEpohI4nkUgQj8dRqVSQTCYb3keUmqZ6dFtbG0wmE2q1Gmw2G5drgPtcB8lkEgCYd/th\nUt8jBNzndbDb7cwTQE55pVJBV1cXIpEIstks43U/zFDXlw+IIQu4f/c5nU60tbWxg9DT04NwOIxS\nqcSZi0+soaaUGbXOp1Ip7O7uMtH55OQkZDIZpxIGBwcZh5d+/yChl1GtVqNSqcDj8WBhYYFTgfF4\nHF1dXQDAKQ0h9RX6/1arFQaDAUqlEtevX8fNmzcZNvKZZ57htD69KEIbC0QiERu2bDaLubk5rKys\noFarQaVSob+/Hzqdji9IoakV4lY9fvw4KpUKFhcXcfPmTayvr6NarcLtdsPpdMJsNnOtSAgDjEgk\nQk9PD5599lmcOHECCoUCExMTWFxc5Dp3R0cHR1IikUgQe1R9lHfmzBmYzWYsLS1henqa0/0tLS3o\n6emBSqVCKpVCJBLhiKyR3ra2NoyMjMDlciEajbLufD4Pq9WKrq4u9PX1oVKpIBAIcHTWaC8orU3E\nGbdv38bKygoTiAwPD6Ovrw9GoxG5XA5ra2tYW1s7kO2pPn1MJZ1qtYpYLIZ4PM483c8++yysVivS\n6TTW1tawurrKNKAHperra7zkMEejUWSzWbS1teHEiRMYGRlBoVBAIBDA/Pw81tbWGjYDUrTU2tqK\njo4O5PN5ZLNZ3L17l6MZ+n3qLaBn2MghEovF6O7uxvj4OBwOB3Z3d/Huu+9icnKSua8p3U99KdFo\ntCG9KFGednR0YHBwEAqFAqFQCJOTk3j77beRz+fZQaHnIuRdIb0OhwPj4+Po7e3Fzs4OZmZmMD8/\nj5mZGaZL3N7ehkKhYMKNRs1vlHnq6urCyMgIzpw5g2AwyI7F9vY2OwBkGGOxGPL5fMM+ACo9dXV1\nYXx8HC6XC/F4HHNzc1CpVCiXy8hkMojFYhykZDKZhr0W5HgSH3VfXx+KxSLS6TSi0SgkEglyuRw8\nHg9nA3K5HGfiGukG7vdctLa2QqvVcof3zs4OJicnMTk5iWw2i0KhwM59I2NK2QVymI1GIzep6XQ6\n7OzswOPxYGZmhpnMtre3m54UeaIMNfAxJrBYLGay8NXVVQSDwT2MLDKZDPl8nutlQiNf8ua3traQ\nSqUQCoU4HaNWq7lOmEqlsLm5KbhhgaImIvcIBoPY2tpiD5Eu6Uwmw4dDaHOFVquFxWKBQqHA8vIy\nfD7fnguPUtMUfQhpiiGaxPHxcXR3dyMSicDr9SKdTrMHe+TIEQwPD0Or1XJ0IGSfxWIxnn/+eRw5\ncgRmsxl+v58vB4VCAa1Wi9OnT6OlpYWjXiFZALFYDLvdjpMnT8LhcCAej2NpaQn5fJ6bxyjzQDSC\nHo+noeMiFothtVpx4cIFHD9+HOVyGYFAAD6fD+l0GkajEb29vTh//jw6OzsxMTGB6elp3pNGuru6\nutDT04Ouri7GaCc2IJ1Oh3PnzsHtdnPEt7GxgXQ63bDvggwpOWk0XqLRaGA0GnHx4kWcOXMGhUIB\n8/PzuHXrFr9TjRwXujTL5TKKxSK0Wi1KpRL6+vpw8uRJnDp1Cmq1Gh999BGuX7+OaDQqiG2N0rEW\niwVarRb5fB7Ly8tIJBKQy+Uwm82QSqWcUfD5fFxrb+TESSQSHDlyBN3d3dBqtUgmk5icnEQoFOLI\nlsaGqE4phMedOvHHxsZgs9mQSqXw4YcfYmZmBoFAgHsr6rNk9BkH6aYofWhoCMeOHcP29jbu3sfu\nh/MAACAASURBVL2LhYUFdtYoPQ983PAlpAlQIpHA6XRiZGQEIyMjUKvVuH79OoLBIGKxGHK5HAKB\nwJ4msoP6LEjo3tFqtRgeHobT6YTH40GhUEA6nUYoFMLW1hbW19d5soCadIVk+ORyOWfyiKmL7nqf\nz4d79+5hd3eXsfeFdlLTlAX1K5RKJVitVs5uzczMoFQqccq/mckW6lEwGAy8n8ViEeFwGFarFb/8\n5S+5t+Kwk0RPlKGmL7mzs4NCoYBf/epXsNvtfAFRXeHkyZM4fvw4kskk1tbWkMlkOL1x0CbQCMHq\n6irS6TQMBgPkcjlGRkZQrVZx4cIFnD17FkajEYuLi0wfKQSvu1arIZlMIhAIsGF94YUXUK1WYTAY\n8Pzzz8NisTC5OICGXcjAxyks4OMaTk9PDwYHB3lM5uTJkwiFQpzCr+/0fNiaqTO9t7cXdrsdkUgE\n/f39OHr0KGq1GlwuF4aHh9HS0oK1tTWsrKxgd3eXm08e9nLU19u6u7s5e/HNb34T1WqVO25PnTqF\nWCwGn8+HyclJ7mqn5/QgvRaLBS+++CJGR0chk8mwuroKnU6Hb3/72+xgdXZ2MgPWvXv32Igf1H1r\nNBpx4cIFvPbaa3C5XJiensbm5ibGx8dx9OhRnDlzBg6HA2azGel0mssAwMf48w8TmUyGo0ePcpQ/\nPz8Po9GIl156CV1dXRgdHYVOp4Pf78fCwgLu3LmDdDotmKtbKpVypEvO4F/+5V+ir68P5XIZ0WgU\n3/ve97jJUMj4G31urVaDx+OB0+lEe3s7Ll++jK9+9asIBoNYWlrCW2+9hYmJCW7+E9rpTXSF0WgU\nzzzzDM6dO4ejR48ik8ng9ddfx+Tk5B5HRejFRlMK8Xicu+dffPFFhMNhLCwsIBgMIhQK7bmEhRgP\nhUKB1tZWGI1GzqB0dXVBJBLxhbyxsbHH0AlJ86rVaq7pSqVS5HI5GI1GOJ1O6HQ6FItFrK6u7qFp\nFOJU1Gr3RxSdTienjre2tmA0GqFUKuF2u+H1erGysrJnfxudC3IMtVotbDYbOjs7uYFWpVKhpaUF\n0WgUH3744Z5GRSF6qa5LWYDz58/zdEKpVMLg4CDeeOMNbG5u7uHpbqS3Wq2yY+F2u3Hx4kVotVp2\nsIaGhiCRSPDrX/+az7GQzmwy0DSX/aUvfQl6vR6Tk5NwOBxoaWmBWq3Gu+++u2dk8TBGGnjCDDXw\n8RfZ3d3lKIw2jmojNCd69+5dBAIB7O7uCtpc6kAMhUJIJpPo6+tj74wiYqqxEI9p/ZoO0ruzs4PV\n1VWOlORyOUqlErRaLba3t6HT6bC7u4uVlRVmOxL60HZ3dzE1NQWz2Qyj0ch7USqVeM0ffPABU/wJ\nETpoHo8HZrOZoxAajTEajbBYLKhWq3yohQoxnNntdhgMBvT398NoNEImkyGbzaKlpQW12n0e2pWV\nFU6HH7QnlFmg1KhWq8XQ0BBaW1sZcMNkMkGn0zEDGqW8ydF6mBgMBrjdbt67gYEBWK1WlMtlGAwG\nbg7a2tpCNpvleV4hThZ9Z7q0WltbYTKZ0N7ejs7OTuh0Oty7dw/Ly8uYn5/niEToeB0ZM41GA5lM\nBr1ej2PHjqFarWJpaQnXrl3D/Pw88vl8U8T15Ng4nU6+jNrb27mW+frrr2NxcRHlcrmpKIGiIOqY\nBsCjUqVSCR6Ph7NCzYxdUmNPIpFAIpFAoVCARqPhcb1sNgu/39/0CBI505ubm/B4PNytT89+aGgI\nm5ubjAUgVC91MhPXdT0ylkwmg91u5/HAZsYtqSudMpJEpUr1e7fbze8Ipf+Frpca55RKJcrlMt5/\n/30egVSr1Th58iT8fj9kMhn3TDQaSaLGQirh0KTJzZs3+VyPjIxwTbm+ZtxIr1qt5m5vGn+cn5+H\nTqdDPp/H6Ogo2tvbMT093RTcJ+0FZQDkcjk2NjagUCiQTqchkUhw7tw5TE1N7eFBJ/nER9QkZFAr\nlQpCoRADOZCHJJFIoNPpcPfuXa4xCT1w1Ay1tbWFxcVFaDQa7O7u8uHWarWIx+O4desW6xWysZRS\ny2aziMVi6O7u5jqERqOBRqNBMBjEjRs3EIlEBKdsqtUqMpkM3n//fSwvL/O4SSQSgUQiwalTp6DR\naHDt2jVu1BLiYFBTyu3btxGPx3Hx4kWo1WpOi33mM5+BSqXC+vo6rl27xsZGSEqoUqng3r17yOVy\ncDqdMBqNSKfTbCycTicSiQTXrePxOMrl8oG66b8vLS1haWkJOp2OecPj8TgkEglaWlogFot5/G19\nfZ3HiBqtORAIoK2tjTM3KpWKvXGRSIRcLsd1a8rikO6DpN4hSSQScDgccDqdcLvdMBgMnJaen5+H\n3+9HJBJhsnkh+0xpNr1ej8HBQTidTsjlciwvL+PGjRu4efMm1/GEIofRxaJUKjkt29raCplMhqWl\nJdy5c4fTss0COBCEo9PphN1uZ55qAEgkEg3T8gfppZn3UCjEPSYdHR3Y2dmB2WwG8JuoX41ErVYz\nCl0oFEIikeDxI6fTid7eXszPz0OpVApy3mitGo0GJpMJ1WoV8Xgc4XAYarUaOp2OI2yj0ciRtZA9\nkUgkMJvN7ADl83nuZtdqtTx6SY6RQqEQjFQnlUpx7NgxrpdTPwT1R1AHNY0wptNpQaOtMpkMTqcT\nfX19PMZGs85UZyeqYKI5LhaLDZ0tqv0PDQ0hHA5DLBbDYrFApVJhZ2eHm2Spdk7vOdAYTU4ul+PC\nhQuIRCLcJ2K325FOp2Gz2dDT04NoNIpMJsP1f5Ho0dAon0hDDXx8MdfXZOoPotlsZrJ48uqF6KzX\nWygUUC6X+QHZbDbodDrMzMwgHA7viRYaXZqUsqf6s8fj4d8dHh6GUqnkDlOK9IQ4AOS0RCIRrrvK\nZDIUCgWcPHkSLS0tkMlkWF5eRjKZZEMt5JLP5/NYW1tj/mGxWMxp12984xsQi8WYmZnB1NQU49U2\n8uypIfDq1au4ffs2zGYzLBYLQqEQqtUqLl++DLVajWvXruHmzZuYnZ3dMzp1kO5cLoe5uTn8+Mc/\nhsFg4M7u3d1dXLhwAW1tbUilUrh69SomJye5CalRTY+cNmoM6ejogFwuh0gkQmdnJwBgfX0di4uL\nmJ2dhcfj4WfY6NzVajXE43Gk02mYTCYUCgX+qVAocPv2bXayqC9CKFd3feqtpaWFGxmz2SwmJibY\n+JMTJDTyFYvFHIl2dXVBoVAgGo0inU5jY2MDkUhkT/9GMxGC0WiE3W5no/qrX/0KVqsVbrebebRp\n34QKlVuMRiNCodCexjObzQaNRoNwONz0RSkSidDW1saZC5oqoFp6R0cHarUaR1XNRGSUTdnc3Nwz\nJgYAg4ODMBqNHEEKbZYVi8U4duwYBgYGEAwGuSERuI+Q1traCr1ev6evgqJSIan6V155hXnlqVmu\nVqvB4XDA7XYjkUigVqsJdlgoA0BNp3fu3OG1qVQqWK1WGI1GbhqlXiRKlTda79mzZxm/4aOPPuI0\nfSQSwdDQEEwmE+7du8fvMWVLGq1ZJpPh05/+NID7Dn40GkV7ezvEYjE/u9nZWVSrVeZIb5RZaCRP\nrKEG9nZc08Pr6+vDZz7zGWxvb2NmZgabm5tNF+jpha2vSWg0Gly8eBHZbBZvvPEG5ubmmsJmJYMK\ngEE7gPsX02c/+1lEIhH86Ec/wvz8PHuEQr9/tVpFPp9HoVBAKpVCrVaD0+nE1772NfT29mJubg7T\n09NNpd6o+WVrawsikYiBKiQSCYaHh+FwOHDlyhX8zd/8DRYWFgSn1Gmtd+/eBbAXIerFF1/EpUuX\noFQq8d3vfhc+n0/wmiuVCnc0LywsMOKZRCLBt7/9bW5a++EPf4if/vSngp03ADxaNDs7y53DNpuN\ngUl+8pOf4M6dO/D7/Uin0009v2KxiLm5OWg0GrhcLrhcLsjlcgQCAfzsZz/jbmfqwxDqwNWD7Fy6\ndAnt7e0ol8uYm5vDu+++i+XlZfj9fp7xFvqOEBJZf38/Pve5zyGZTOK///u/sbu7yxHV/jqvEKHo\n6IUXXkBnZycikQhef/11AMBrr70Gh8OBUCi0J7MidB86Oztx/vx51Go1TE1NMXDJ2NgYPvWpT2Fh\nYQE///nPeTKgmZr3V77yFc6SUeqcRjgHBwfxV3/1V7hz507DyYL9elUqFV555RUeXapUKujv74fb\n7Ybdbsfa2hquXr0q2CEEwPPcNPnw8ssvM/CLSqWC0WhkcJfr169zZCxkP6gX4+jRo3jhhRews7MD\ng8EA4OMg5Z//+Z8xMTGBbDbLUXEjoeme7e1tfPOb38T29jan1wuFAjKZDG7fvg2fz4dgMMj9JtRh\n/6C106SFQqFApVJBa2sr/uiP/ogbGbe3t+H3+3Hjxg34fD5MT09DrVZzAATggY4GlQtpTlytVuPI\nkSPY3d2FXq/H7u4utra2uPdmamoK5XIZOp2Oy6NCekQeJE+0oSahh6FUKnH58mVOI6fT6UN96f16\nAXBjWTgcxq1btxrOPwrRLRaL4XK58NJLL2FxcRH37t0TlIY9SCf97tGjRzE+Po5arYZr1641ZaQf\npJdeKoVCgYsXL6JcLuPtt9+G1+s9FBlCfeYCAMxmM86fPw+r1YpYLIb19fVDrblW+3gqgKIbSlnP\nzc1hZmamKSNNOuv/UDOg3W5HKpXC2toaQqEQww426xQSKAKhqBUKBUxOTjL0KNUgm3E4aR8UCgXP\nsAYCAR5Jo+mD+u8lRAggo7+/HyaTiUsj1KG/v7u5mZqszWbDsWPHIJfLuVHzzJkz6Ovrg1Qq5XHM\nZvaBUvQ6nQ5tbW1QKBTcWHf+/HkolUqEw+E9XchCpVarIRqNorOzEz09PTh27BhcLheXF7LZLDtZ\nzeA4V6tVHo3q7e1Fb28vVCoV1Go1tra2MDMzg3fffZezQc1k3wKBAN5++2309fXh5Zdf5vrv5uYm\n7ty5g4mJCY5OhTbrkYG6cuUK3G43zpw5g9bWViSTSWxsbCCZTCIcDnMDLmGnNzofVEfe2NjAzZs3\nYbPZYLfbuQfkww8/ZDRH6p0RWhZqbW1FOByGVqvF7Owsvv71ryOZTCKVSmFychKLi4sIhUKss56c\n5aCZbKlUitHRURQKBdy9exef+tSn0Nraijt37mBychKxWAyFQoHLTSqVCiLRfThZ6gk4TGT9iTDU\nJK2trTh//jzK5TKWlpaaZjl5mEilUhw5cgRbW1v46KOP4Pf7H8kBIFEqlXjppZfQ1taG73//+5wa\nelQRiUT48pe/DJPJhEgkgjfffPOx6KW56s985jOYnZ3Fr3/964YIVkLWKhKJcObMGYyPj0MikeDt\nt98+NOMUrZOiku7ublitVuRyObzzzjsIBoOPtF6lUomBgQEMDw/DbDYzuhyl/g+jWyKR8Ljb0NAQ\nvF4vN7tRR2izWSGRSASFQsGsPeS05PN5jlLqx3qEikajQUdHB5dUlEol+vr60NraiqGhIVy5cmVP\nuaiZ9VJXvlqthlqthl6vx+XLl2EymRCNRjE3N9d0sxdwP/opl8uwWCzcXNjV1cXTG++//z4SiUTT\n90W1WsXMzAw36hHJS7lcRiQSwcTEBOttZr2EYX7jxg0Eg0HGX0gmk4jH41hbW0M0Gm36vO3s7CCd\nTmNxcRGxWAwbGxtQqVTI5XLIZrOIx+OMWSCkjFUvxWIRHo8HmUwGHo8HtdrH/AI0hkoNfTT6JkQ3\n4XmLRCL85Cc/YdSufD6PjY0NnkkG7qfvqbR30NrJwSJSmq6uLvzjP/4j1tbWuNekVqtBo9EwLns2\nm+XA4WF3P51Lj8eDwcFBHD9+HHfu3EE2m8V7773HGaF6QJVKpcJO0WEpOoFPkKEWiUQ4fvw4A5I0\nG9kcpFetVuP555/Hzs6OoMF8ISIWi9HS0oLLly9DoVAgHo8/ljVTjeTEiRMQi8UIBoOM0POoerVa\nLS5evIjOzk4GcniUBoh6mNJnn30WJpMJmUwGU1NTTV/G9SKRSCCTyWA2mzE0NAQADD7yKA4WwRW2\nt7czghihgwkdj9kvIpEILS0tOH78OI4cOQKTycSMWPVzoM0aauK/HR8fR2dnJ9c5Nzc3EQqFuJ4n\nBAe5XjQaDdxuN0NZ6vV6BggRi8VYXFzkJstmo9OdnR20tLRAo9HAbrdjYGAABoMBsVgMP//5zzE7\nO9s00QLVAZeXl3lumByWTCaDv//7v8f8/HxTqen6NROEsMVigcvlwuTkJDY3Nxk8ptkondZMpbuF\nhQXmR97e3ua59fr9FbofpJdAO8Lh8B6jRt+fDGqzTgBh8pPsd+KpOU3omavVatyQSHCglD4mPVtb\nW8jlcjzHL9SpTSaT2N3d5T6cUCiETCbDZ5dG6ywWCwOfCHkPq9UqvF4vrl27Bo/HA6/Xi83NTayv\nr/P0Ublc5ukc+m6H6eeol0+UoX711VeZX/j69euPRadUKsXQ0BBOnz6NWCyGq1evPpZoWqVS4dKl\nS3A4HMhkMrh79+4jGT0Saqir1Wrw+Xz41a9+xdCFjyJEzDE8PIxgMIi33npLEOyfkPUSQs/777/P\nXcOH3QtKw9G4RSqVwo9//GOGWjxMBoCcHwK8WVhYYKhMgiAlvPdmRSKRYGhoiGuw1DxG0wr1gBbN\nrJcgFtvb2xkKd3FxkcEbiOSk2X0Oh8OYmZmBzWZjvPj19XV4PB5GHztsZmF6ehp/+7d/y6Am5LTF\n4/GmZ6brJR6P46OPPsLKygoAcGdyuVw+9FqB+4aEOtHrjd2jXLgku7u7jP9Q/3mPIrS+SqXy2AKZ\n+nU1Kgcehse5UqkwBe1Bept9RwhNLx6P7/nv9WnnSqWCjY0NXosQIaP+wQcf/IbeelldXeV/fhzP\n4RNjqAEwZ2gwGITP53tsB1Gv10OpVGJxcRGRSOSRXm4SSk3WajWuWTyqoSYjBdx/aba2tnDjxo1H\nSiOT0LxoKBSCXq/HwsLCI+8DrVcsFmNychIajQaLi4sIh8OH1llf+87n81hYWIBMJkMqlUI2mz3U\nHtPLWyqVUCgUsLCwgM3NTW7+OEw0RiKRSOD1eiEWi5HP5xGPx7GxscHe+2HI5KkBMhaL4e2338aV\nK1cQi8WQTCYZQ/iwRm9nZwfLy8tYX1+HTCbDzs4O47AflBZsJPRcfvnLX7KjAYDP7qMYU9ITDAb3\ndEg/jvuhWhVOrXkY3b8teVx34/+VPEqHdCNdQjvdm9H7uHQ+9LOehAcqEokaLkIikeCLX/wixsfH\n4fV6ceXKFXi93kc2JFKpFAMDAzhy5AgikQgmJycf6WImUalUGB4ehsvlAgD8/Oc/P9SlvF/q+Z0z\nmQxCoZDgLuSDhOqRZrMZpVIJ8Xj8sWUAiLaPRqkeh8NSP5t4WFi+/Trrfz6uS/RBnL+PQ2QyGTMB\nPY4MEAk5gr/NS+epPJX/C3mcxn+/XuDQztGdWq12quFnPAkvoxBDLRLd590lYPPH6W0RDmwjer5m\n9ZKROoi56bAidPyhWfltHean8lSeylN5Kr8h/28Z6qfyVJ7KU3kqT+Vxy28rOBHCEQGBhvoTVaN+\nKk/lqTyVp/LkyP4mqsdZ/61HICO9hy1z0ToJ9KQe4YyQ5w6boayn5iRIVip5icViQRStjeSpoX4q\nT+WpHEqelkk+efKo5BAP0kU/60F2mh0L3K+XxgEJL4HYxQhNrVmDXavVuMQplUrhdrvR1taGWu0+\nxO/q6uqhxg6B+/0sRqMRer0eFosFIyMjaG9vh8/nw9LSErxeL6PPHVaeGur/A/ltXHD7sWQfp37q\n3m4WjUqIXmoKq+8ofhwXCFEeSqVSbrZ7lIuj3rOni4NQug4DvEN7Sg1xtBdisXgPecZh1iyVSqFU\nKvniJH00y/wozXcKhQIajQZ6vR5msxk+n49JbuizDiuEMmYwGHjWlYgdqtVq0yM6D9JPe0wEPwAe\nWW+9UDMeGa/H2ehXr/dJdJDqJw0etcm3foRqe3ubx80OO81A+0azzkTeQnPbzRAwPUi2t7c5Kk8m\nk8hkMojFYtBoNMjlco9+pz0JD3x/jZo8KfpJxAP1ABHELU1dr0IuNLrAyahpNBpotVrIZDKmriPI\nx+3tbYYGFLJHdAHQmltaWqBUKrnpq37NxWIR2Wy2KVxnovgk5h7imaXPLpfLPLJFPxvprh+fUqvV\n0Gq10Ol07HVSJ/ju7i7S6TQz4hQKhYapHNJLHd82m41Z0Agvl17I3d1d+Hw+xONxnlc+aO31neQa\njYZZdaRSKXQ6HWw2G+RyOSqVCnK5HBYWFhhwptHFSexCOp2OZ7XJeJhMJj4rBCoRCoWYarXRXhMH\n8fDwMEwmE+RyOZRKJdrb25mQIplMYnl5GbFYjIkvhBAFKBQKuFwudHd349SpU+jo6IDL5YJMJsOt\nW7fg9Xp5/DAQCAi+UInpyWKxoKurC7//+7+PwcFBvoBu3ryJiYkJrK6uwu/3M/qeUAeD9ler1aK9\nvR2/8zu/wxSCpVIJN27cwMrKCtbW1lAulwU3fNafbeKUJv5nYtKqVu9TgdIcrxCo3P1RZL3DJZFI\noNfrIZfLUavVGLVLaNrzQWnk+s+QyWQQiUTsbAl1EB+EIveg79EMKtxBOh8kQvTSvUz/TGlp+gmA\n97pZQBgKCEgPoYbVc2EDEMT4tX+9hMNAnyGTyfgdoDMRDocfNu76yatR1xMNyGQydHZ2wmq1QqvV\nwuFwwGw2M9LVzs4OJicn4fV6EYlEmPXpYRtM3jpFG2azGX19fejo6IDT6WS9hHQUjUbx3nvvIRaL\nNaSCIweAkK1MJhPOnj3LLEx0WeTzeaRSKfh8Ply9epWBGQ46GPS7FMWYTCYMDAxgbGyMnQCZTIZk\nMoloNIr19XXek4PqLvsPWUtLC7q6ujA4OMgXJRnparUKn88Hj8eDfD7P5BiNDjOB6+t0OoyPj0Or\n1TL2rUqlglwuBwC+fKVSKYLB4B52n4ftiUKhgE6n43WTw0WQmsTO5PP50NLSsof04mFSf9m2tray\nwbdarZDL5Qx7WSgU4PV6Gc2NLo+Dzh6N1ZnNZoyOjrJjRMxPEokEhUIBWq0WkUgE+Xyena1Ghlou\nl8NgMKCrqwtjY2M4fvw4VCoVVCoV8vk8tFotTCYTVCoVM5kJAe6gs6HX69HV1YWRkRH09/dDq9X+\nBs2nxWJBKpVCKpUSbKTJQdTr9RgaGsLY2Bj6+/sB3J/njsfj6OjogEQiQalUQiwWE2So6ycuCK6U\nwGG6urp4EoP2xefzwev1IpVKCVpz/buj1+v5LGo0GthsNlgsFmi1WoRCIdy8eRPRaFTQmusNCZ1x\ncpgVCgXfhZlMBsVicQ+oxsN0PugzKOqXyWQcuBD9IwGQHORc7M/ePcjok1NB+gmA5qCzUY+x/TAj\nrVKpAHxM8SoUUax+zSKRiCNrerd0Oh1KpRKUSiVnyRo5nJRO3//3dnZ2eM1EZ0rgNofNCjxRhppY\nlojwfXx8HMePH4fL5YLRaIRUKsXu7i40Gg2KxSJcLhdTBC4sLCAcDh94WZIxtVgs6OnpwTe+8Q20\ntrZytE4pkXK5jGg0is3NTUxNTSEajR4IFUiHn6jZxsfH8eUvfxk6nY4PPkX/hUIBer0ewWAQ6+vr\nDNLR6MERc9gzzzyD0dFRfvgAkE6nYTabYTKZ4Ha7oVKpkEwmuZ5zkNC+nD9/HuPj43C5XKjVagiH\nw4jFYpBKpTCbzbBarVAqlVheXkY6nW74LGlPdDodRkdH8fzzzzO7Tz6fRzabRUtLCxtsQtcSIrTX\n3d3dOHbsGKO/EaCIy+XiyBoAZmZmADROI9OaOzs7MTo6ypF6MBhkYgqLxQKbzYZSqQS/3y94j+ni\nbWtrQ39/PyQSCaLRKMO0UoRNkTxlURpFefT8LBYLBgYG0N/fzzCixLNLl47RaES5XIZUKhWc/lYq\nlXA4HIxtHA6Hsbq6ilwuB5FIhHQ6zY0zlEESku4lY+pwONDX14djx46hu7ubyRdEIhFTXiqVSmg0\nmgPJEuqFzge9M8PDw+jv74fL5YJSqUQ6ncbm5iZMJhNKpRLjPAsRhULBDqFarcbly5fR2dkJo9HI\nNLE9PT0wmUwIBAK4ceOGoL2grAhF/lqtFsePH4dWq4VSqUQul8OJEyfgdDoRDAZx9+7dhoaadFOk\np9FoOOukUqmY61qlUmFkZASVSgXLy8uYm5vbAxX6IJ0AmPObDDOtn/5UKhV2XMLhMObm5viZPkjq\nDT+dqfrMKnFTp9NpdpLz+TxyuRwHPQfprtVqHEETDoFEIoFGo0FnZyfW19f5fSE0NPrnRnopI0GY\n+zKZDDqdDk6nExaLBevr61AqlexgCLk36uWJMtQUHapUKigUCpw+fRq9vb3QaDQQiUTY2Njgy0Cv\n13Pk8NFHH2Ftba1h6oXqlmazGSdPnkRHRwdkMhk2NzcRiUSg1WqZ59hoNGJpaQlLS0t80A8SkUjE\nac3Tp0/DarUim83C7/ejVCrBZDLBZDLBbDZDr9fjgw8+EBSJ0ee2t7fj7NmzOH36NFpaWnDv3j2m\nh5PL5RgdHYXNZkMikcDS0hLvZyMhQ3zp0iV0dXWhUChgdXUVMzMzqFarsNvt0Ov1UKvVWF1d3UND\neJBQCnlwcBCXLl1CS0sLvF4v1tfXEQwG0dLSAr1eD4fDgVrtPsB/sVhsyKhFmZH29naMj49jdHQU\ngUAAiUQCkUgEu7u7MJlMaG1tRbVaxcrKCkenjfZDLBZDLpeju7sbnZ2d0Ov1e+j1iFVLJBKhUCgw\ntKgQb56aYoxGI3Q6HaLRKBNzPPPMMzAYDGyICBVNaMqU3hnK3szNzSEej0MikaCnp4fxjMPhMNd+\nG51nWrdareasRa1Ww+zsLJLJJIxGIywWC6cM6eKjM91IL2UYjh07hlOnTnHWgkgaSqUS0uk0kskk\nEomEYNQ5qVTKWZXBwUG88sorcDgcHPWSo5jP51Eul7G+vi4IHpbW3NLSArfbjZGRETgcUzRwWAAA\nIABJREFUDpw5cwYmkwnA/efm8/nYIRWSTicnTi6Xo729HYODg+ju7obb7cbx48chlUqRTqeRyWTQ\n3t7Od5GQND1lKInm8rnnnkNbWxsMBgPsdju2t7dRLBZhNBoxODiIRCKB5eVleL3eAw01GTwqN1ks\nFjgcDrhcLrS2tkKr1bIxPHbsGPR6PbxeL77zne80NNSUCRGJ7pO4GI1GdriPHj0KmUzGBDlnzpxB\nIBBALBbDG2+88VDOgwdlK6xWKywWC2q1GjvPbrcb4XAYTqeTjf/ExAQzvT1ILzH50Xul0Wi4POl2\nuzE4OAipVAq/3w+dTscOAOHEC5UnzlBTilKj0SCTyWB+fp4v91u3bgG479U+88wz+JM/+RMolUom\nXT/osqzVahxN9PT0oLW1FdevX4fH48G9e/eQyWQgFosxPj6OV155BYODgygWi9xp2OhFpjVdvnwZ\nHR0d+I//+A989NFHzIJz9OhRvPbaazh16hQqlQpHU40weSkF/cd//McYGhpCPp/He++9hx/+8Iec\nGu3t7cXLL7+MYrGI9fV1rK6usgE5SK9cLse5c+fwpS99CSMjI5iamsLrr7+OxcVFpNNpuN1ufO5z\nn8PAwACWlpYwMzMDr9fbMKIWi8UYGRnBF7/4RbzwwgvQ6/X47ne/i7t37/LL9Kd/+qc4evQoc8PO\nzMwIKl+IxWJ87nOfw+c//3l0d3cjFovhr//6rzn67+/vx5EjR5DJZLC6uoobN24gEokI2me1Wo3u\n7m787u/+LmQyGd577z28+eabyOVyOHfuHF/Ob775Jm7evMkp6kYGhIyS0+nE0NAQUqkUrl69itXV\nVeh0OvzhH/4hRCIRlpeXMTU1BZ/Ph0KhIAh1jCIxahzb3NzE3NwcSqUSnn32WYyNjXGNl+BbhXS3\n0l5rNBqOQsRiMW7fvo3Ozk50dnZCqVRidnaWHbB0Ot0wvUkGT6PRwOFwMHpfJBLB3bt3YbPZGKeZ\noIJTqRRjgTfaC7PZjOHhYQwNDTEj3jvvvINUKgWlUgmfz7eH8YrKAI1SvWSQvva1r2F4eBhqtRo+\nnw//+q//ilqthrW1NWxtbaFYLEIikTBV40GpetJrNBrR09ODP/uzP4PRaEQkEsH6+jp++MMfYmlp\nifsX1Go1crkcOxkHCRkjl8uFo0eP4vLly9BqtfD5fFhfX0c0GsXMzAy2t7eZYpNY4vbjY+/XSyW+\nEydO4IUXXsDIyAi2trYwNTWFUqmEtbU1FAoFiMVieDwelEolbGxsIJFIHLhmuVzOazGbzfj2t78N\nANjY2IBIJEIgEGAHTqFQ4I033kA0GmVH5mFSq9XYqVAqlRgaGsKFCxeQSCQ4U+v3+xGNRiEWi7G+\nvo5IJIJcLncgBnm9YyGVSuF0OjEwMMBsaEqlkss1MpmMCX7K5XLTDYxPlKGm9MHu7i5kMhkCgQAk\nEgk8Hg/i8Th71VqtFru7uxx1RCKRhnXN+pqBwWBALpfD0tISfD4f43uTN9TS0gKRSIRQKMScsI0i\nSJPJhI6ODkilUhQKBczMzCAYDLIDYTKZ+HKr1WqCuY3FYjEsFgusVisAsGNBnYTUIGM0GlEsFpki\nT0j0aLVace7cOfT09GB9fZ2bgrLZLLRaLXp7e3HmzBnYbDa89dZbCAQCgiJTkUiE8+fPY2xsDBaL\nBdFoFB6PB9lsFlKpFFqtFufPn2fiB4/HI6iJgyK8U6dOoaurCzs7O/B4PNjc3IRCoYDFYsGpU6dg\nNBqRSCTg8Xj21GMP0iuVSmG1WtHT0wO1Wo1QKISNjQ0Ui0V0dnbizJkz6Onp4ca3dDotGA6V6Pac\nTieMRiMbYb1ej4GBAebOTSaTWF9f52YhIeuub2TZ2tqCUqlES0sLjEYjxsfH4XA4cO3aNaytrSEW\nizEWuNBmmd3dXZRKJej1eiawGRoaQltbGxKJBPx+P3w+H9erhZYBqF9ELpcjlUohkUjw/6tWq1hd\nXeXO2XK5LCidTnSidrsdRqMROzs7mJmZYS5q4P6lTw1e9N2E9FoQ/WdnZydKpRLu3r0Ln8+HhYUF\nLm0Vi0U2+ELw1qmG3tXVhZMnT2J3dxe3bt3C6uoqlpeXEQgEWHd9F7SQGqdYLIbb7cbJkycxNjYG\ntVqNGzduYHl5GeFwGKlUirHRSZcQHncqK7S0tODcuXNwuVxYWlpCJpPBRx99xBmQdDrNugjTXkiv\nhU6n44zWysoKRCIRZyYpYCO+a9oLIY2cdD9YrVYMDg4y38CdO3ewtbWF+fl5AB8TjwiF5iUjbbVa\n0dnZyXdcIBDgszA7O4t8Pr+nYa/ZaY4nylBTnZg2n5h6gsEgf1FKbXV2dkIsFiMWiyEQCDRMBVEt\nQSwWw+/3Q6FQIBAIIB6Pw2g0Ip/PM21iW1sbKpUKvF6voMNL6ViiVguHw1zTLBaLEIlEOHHiBLq7\nuwHcZygig9foIpZIJDAajexN+/1+xGIxuN1uHrK/cOEC5HI5YrEYZmdnkcvl+Pcfpp8ayNRqNSwW\nC5aWlhCNRtHW1gaj0YjW1la8+uqrGBkZAXC/zksp2UZ7IZVK0drayqm6aDSKjo4OqNVqqFQqtLa2\nYnh4GNvb24jFYswtXn9RP0hoL0ZGRqDRaODz+RCNRtHd3Q2dTof+/n689tprqFQqiMViCAaD2N7e\nFlRiUCqVsFqtTAofDAZhNBrR1taGF198EefOnYNGo4HX6+WXub7R5aD9oAjEYrGgVCpBKpXCZrPB\n7XbjwoULkEgkCIfDewhhmn2Rqe6vUChw5MgRHDlyBN3d3Ugmk/B4PE0baRJyoOx2O9RqNYxGI0wm\nE4LBID788EMsLi7uoQ8UIvQ8SqUSXC4Xd+AqlUp4PB4sLCwgFApx7VioXqpb0oQE1WEtFgtisRgS\niQSnTZthwaKGv7a2NshkMhSLRY7OiLSjfsQHENblTHXXnp4eDA8Pc2RPNdlCofAbPMZC1yyTydDX\n14ejR4/C7XbzJAtwf5RoP786GREherVaLTo7O9HT0wOFQgGtVotwOIxyuYx4PI5UKrUHNlnoTLVM\nJoPFYoHdbsfQ0BD0ej08Hg8b5UAgwPdxM7PUlC0zGo04evQouru7sba2xrVuKoXUr1PIeuub8oaH\nhzEyMoJqtYpEIsHZFY/H8xullcNMWj1Rhhr4mLc2m81icnISSqWSvV65XA6FQoG+vj709PRwjSmT\nyQh6aESsHgqFkE6n+dJUKBRQKpUYGxvDwMAA1Go1v9hCXuharYZMJoNEIoHJyUmuR1NHsEqlwujo\nKDQaDeLxOBYXFwVfmFQO8Pv9MBqNqFQqcLvdMJlMUKvVsNlsOHHiBAqFAmZnZ+H3+wWPbVB2gtJz\nvb29vOaOjg6cOnUKGo0G0WgUPp9vTzfjw4ScC7fbzYasUqngzJkznFmw2+1QKBSIxWJYXl5mL/kg\n3fUd2Wq1mr1qi8WCl19+GQ6HA/39/ejo6OBImupsjXTXN6fZbDak02mIxWK0trZCr9fjs5/9LPR6\nPXK5HFMyEie2kFqhSqWCwWCAVCpFOBzmlG93dzdOnz6N+fl5dhppzUJqyPT3qtUqd+rm83mcO3cO\n7e3t2NjYwMTEBGZmZpBMJpuiAK2v61mtVq7PSqVSzM7O4tq1a7hy5Qqi0SifZaFROo0yUmOT2WyG\n0WhEIBDAL37xCywvL7Pha/ZSo3l/jUYDtVqNvr4+iEQiLn8ISaHvXy/w8TjPzs4OZ7FqtRrm5ua4\ni7yZOWK65KkhK5/P87RBNpvlSJWeWTNOBQUkcrmceyloNNJoNCKZTEKhUPCdKdRI14+60URBpVJB\nPp/nJjhqhGzG6NGalUolZ27kcjni8Ti0Wi0SiQRnYMhoC9ljmpUnql2dTgeXy7VnH4jaVsh0Rb1Q\nUEEO4cjICIxGIzweD6xWK/L5PNuW+oazwxhp4Ak01PRQ8/k8CoXCnlELuVwOs9mMc+fO4cyZM/i7\nv/s7vP7664jH44IabyqVCjKZDCYnJwGAG3DokHzrW9/CiRMnkMvl8P3vf5/1Njpo1WoV0WgUb731\nFh9ku92OnZ0dHokZHR1FKpXCP/zDP+DKlSuC9JLTsr6+ju985zsc6ep0OiQSCRgMBnz2s59FX18f\n/uIv/gL/H3tvHhvXdZ6NP3f2fR8Oh8NlhosoUqJJSiKpxbJly7HlJI7XNImTNEGSFi3SfEWLD7/v\nCxAUQdGmbT40aZG0tZ0URVo7SRs1drzFliVb1mLtoiju+zJcZuesnIUzc39/0O/xkJHIOzNKoRZ8\nAYM2zXl5eO85592f591332Wpwq0Odi6Xw/T0NH7wgx+goqIC+/btg0ajwcLCAiQSCZ5++mlUVVVh\nbm4Ozz33HGZmZn7Du7/dmjmOw7/927+hvr4etbW1sFgszLiJxWJ0dHRgaGgIP/vZz1itVwjggEwm\nQyqVwmuvvcYi/8rKSkQiETQ0NGDHjh2IRCJ46aWXMDw8zGrIW3VY0igJGbaOjg5otVrwPI+WlhbY\nbDb09vbi8uXLuHLlCmZmZhCLxVhj3WZCjkokEsHo6Chqa2tRWVmJnp4e2Gw2rKys4IUXXsDY2BiS\nySRrqhMaLVDEvnPnTjz00EO455570NDQgIGBAfzrv/4r3n33XeZwFgt0IpPJ8LWvfQ3Hjh2D0+mE\nXC7H8ePH8fOf/xxXr15l0U0xOjmOg1arRXd3N5588kns3LmTlbFmZ2dx+fJl5vwIzSrQpUmNojt3\n7kRFRQUrOdAFvLS0VPR6CdyGPn/+/HnodDo2Y9/Q0LCOtlXoO5PL5dDr9eB5HsPDw5ienobVaoXJ\nZIJer0dtbS30ej2bQhHaSa9Wq2GxWCASiTAwMIDe3l7k83lYLBbs2rULVVVV0Ov1iMfjrFYv5JmI\nRCK0tbUxx2ViYgJDQ0PM8a6oqGD9FzT/L7SsYLVaUV9fj1QqhWg0it7eXgwMDMDpdMJms8HpdCKZ\nTOLmzZuC+pEAsKxeQ0MDc2IjkQjefvttVm7YtWsXwuEwwuEww10QmgnZv38/CxA5jkNfX9+6mrXF\nYsHU1BQMBgOWl5fLAlMB7kJDXSj0h9GBXV1dZRvOZDJhbGwM4XBYUHr6VvrIOBBdYGtrK3Q6Hfr7\n+zEwMCBYL+mkiDOXy8Hj8QBYe6m7du2CQqHA0NAQ+vr62KypkEid9IZCIcRiMQSDQea0PPnkk6iu\nrgbHcbh69Sr8fj/zOLfaFNlsFslkkqXp/X4/q0lSt2UqlcLp06dx4cIFwbV60j04OIiJiQkGHpLJ\nZFBXV8cap958802cP39+Xc2w8B3dTm8wGMTLL78MnU4HrVYLkUiEffv2obu7G6FQCFeuXMGFCxfg\n9/tZVLbVRcTzPFZWVjA3NwePx4OZmRnW1VxbW4ubN2/i17/+Na5duwa3213UBQeAjfvRzPHRo0ch\nl8uxurqKkydP4ubNmwiHw2wUpBgjTQ1f1HGr1+sRDodZPTIUChUV8ZJeqVQKg8GAzs5OVhoKh8OY\nmprC4uJiSUYaADQaDSorK+FwOKDX6zEyMsKiUZ/PxxyfYo2/TCaD0WhkZ4Xq5TRK5/f7i077A2u9\nJzR1Eo/HMTU1Ba1WC5vNBpfLhWg0yso2xQileFOpFPsnn89DIpGgvr6eRaWFmQ0hxrS+vp4ZTL/f\nz+q4KysrqK2thUgkYntNSId+oe5jx46xklI6nUYkEmHOQX19PcbHx9l9SXqFrPnAgQPYv38/Ll++\nzMblKCtHWBT9/f1Ml5ARQLFYjP379+PQoUNYXV3FxYsXkUwmoVarsbKygqamJigUClYGJSMr5O6U\nSCR44oknkMvlMDIyglAoBIvFAp/Ph66uLigUCgwODkImk7GmsXKiaeAuN9S0QSlNRNFYS0sLAGB2\ndrZk7mjaSFQP12g0qK+vRyaTweTkJObm5kqGtKS6FbB20B9++GHE43H09vZidnaWNb4J1cvzPLvA\naU1OpxPHjh1jXaL0LITqLGxqEIlErLFEoVCgsbEREokE165dw4kTJ1jaW+izoMtSJBIhEAgw77ax\nsZHxaJ88eZJ1hwp9FhTBzs3NMSAItVqNL37xi9DpdDh//jyOHz/OmoeE6qXsht/vZ4hY1dXVaG5u\nRlVVFX7605/i0qVLWFpaYvXYYvYEOYRisRjJZBIWiwX5fB7T09M4efIkotHoOsMn1EjT2aisrER9\nfT0rY/h8PgbgUawnX9iVTdEXzfwbjUbWuVqKUGOkzWZDVVUVA9HZuXMnHA6HoEzQrUQmk8Fms0Gt\nViOfz2NwcBDpdBqVlZVwuVwso1Fs3R8AduzYwUZ1crkcotEo61i3Wq2sYbQYJ4Dj1oBSKisr2Ugh\njRW2t7ejvr6eNXwJcTQL9SoUCnR2drKZYJp0sdvtaGtrYw1ZxDcvdM1isRgtLS2wWq0s1S+TyeBw\nOFBZWYnW1lZcu3YNgUCA9eUIEZpJb2xsRDgcRiKRgFQqhV6vR01NDXbv3o3p6Wlcv359HS6AkCkc\nitQTiQQD6qF9smvXLjbG6vF42NTEVhML1MDb1NTEMgehUAj79q2Bi4lEIrS0tGB4eJj1hQjBQ9hK\n7mpDvbE2U1FRgfvuuw9WqxVLS0uYn58vyUsmnWR8qB6p0WgwPDyMV155BT6fryi9Gy8Yqo00Nzcz\nb/GVV15hl2cxegGwSIO893vvvRfNzc0AgF/+8pdFX0S0XvpMLpdjNcNPfepTiEaj+NGPfsSi6WKc\nCp7n121MghB9+OGHAax1Wo6MjGwKInMrvYVgAYVpvl27dmFxcRGvvfYarl69ylJjQtdMeikjYjab\nUVdXh/b2dmg0Gly4cAFLS0tFZRUK100RYyaTYWAswWAQr7/+Oqamptbhbxejm2qGFosFFRUV4Pk1\ngoEbN26wS75wHUKEOpENBgP27NmDcDiMvr4+5HI5VFdXs9nbUkSpVMLlcmHfvn2sez6dTkMulyOV\nSjH40WLWCwA6nQ5tbW3Q6/WIRCKIxWKs61mhULBm1FLS9O3t7Ww/z83NQSaTobu7G5WVlZBIJGz8\nspizRzXhnp4e1rewurqKvXv3QqvVguM49Pf3C5rvLhSeX5smCYVCcLlcaGpqgkqlAsdxqKqqwurq\nKgYGBjA4OIhoNFrUs87n86x089hjj2FlZYUh9/H8GlxqYWMhIXYJ0TsyMoLe3l48/PDDSKVSrJ8j\nlUphaWkJFy5cwOjoKFKpFAMT2arUmc/nMTw8zFLnH//4x6FSqRgC4Pj4OE6fPs0miuRyOTiOY3fA\nrdZOZT2RSISbN2+iqqoKDzzwANLpNDQaDWPJunnzJiYnJ1kZUqlUIplMbtoou5Xc1YaahB7Q97//\nfXziE59AKBTCn/3Zn5UNdk7pjt27d+PHP/4xJicn8fu///vo7e0tm5ZMKpXisccew3e/+11UVlbi\nYx/7GDweT8kvCvgo1anX6/Htb38bCoUC3/nOd/AP//APJRFDFIpCoUBTUxP+8i//Ep2dnXjmmWdw\n5cqVkhyhwvXK5XJUVFTgBz/4AdRqNU6ePIk///M/33Kc7nZC78xoNGLfvn347Gc/C7/fj+effx6n\nTp0SBEByO7203j/4gz/AgQMHIJfL8eqrr2J2dpZFVMW+v0In85Of/CQ+9alPMadiYmJineNWipFW\nKBR44IEHWAPL3NwcQqEQw2UvVgjTnEA3lpaWGBCQ1WrFxMTElqA0txKO4+B0OvHYY4+hvr4eMpkM\nMzMzsFgsyOVyeP/999Hb21u0s0LRqc1mQ09PDywWC2KxGIuuX3jhBVy7dq1kGsPh4WF0dXWhoqIC\n99xzD8N3uHr1Ks6dO4doNFr0fsvn85ifn8elS5dQU1PD4EfffvttTE9PY35+HpFIpOgyQD6/BvX7\n4x//GCaTCTU1NazRkEbgCHCjmK5pYM2Zfe6559bh6lNXejqdhkgkYuOyPM8L2iNUJhwbG8Pi4iL+\n4z/+AwAYylgsFmNd5TR9UAxHwtjYGAKBAAOfogZiisyJljKZTLI69lb7j8axPvjgAzgcDhw/fhyr\nq6vM+clms2wOPJPJQCqVIhaLCe58v538tzDUVIPq7u6GWCyG3+/H7OxsWUYa+Ag96+Mf/zhcLhfe\nf/99+Hy+shlvOI6D3W7H008/DZPJxAgzylkvpTrlcjk6OzuhUCjg9Xpx7ty5sp0KasJ59NFH4XK5\nkMlkMD09XZLBK1wvjXI0NzdDoVBgbm4OJ06cKNvBIuzmvXv3ora2FmfPnsXo6GhR6flbCaVQGxoa\nIBaLMTk5icHBwZJLIMBHhqStrY1FTH19feySKyWSBtbP4LpcLqhUKsTjcchkMhZVCbl4NopUKkVV\nVRW6u7sZJjmwNqa1srLCRiaB0p5HfX09KisrWVd2MBhEf38/zp8/z/CQi5WVlRVW31YoFEgmk/B4\nPPB4PLh27ZogopfbycTEBJsKMZvN8Hg8WFpawuzs7Lpek2KEsizUC6NWq5FKpRiGdzHp7o16iYgm\nnU7D4/GsIzHaODpWbGaIGtvosxvHxqjjXqhu0pHJZBCNRpnTU9inQUQslPIW+lwo65PNZhGJRNio\nHxlMjluDi1ar1esaWbfSnc+vsWNRxEzw0pTR5DgO0WiUwUeTA1OurfpvY6i1Wi0DMVhYWMDi4mLZ\nesViMaqrq/HAAw9ALpdjaGhoXUqonPV2dnaitbUVALC4uFh2jQIAY+U6dOgQkskkrl69yg5jqUIN\nJTt37kRLSwtyudy6Gm+pQuNLNpsNHR0d8Hg8LIVV7nr1ej0bxYrH47h48SJCoVBJBo+EoFRra2sh\nlUoxOzvLQC2oRFCq/h07dqC5uRkVFRXI5XJYWFhAMBhENBotiq2nUAiFq729HXq9HnK5nDUJXbt2\nDQsLC0x3MULEBHq9nnULx2IxuN1unDp1al0jXbGytLSExcVF8DwPnU6HdDqNN998E6dOnWIz76VI\nOBzGwsICbty4gYmJCYTDYczPzzMgjnL2RSwWw9jYGHOqYrEYiyJL1UtNm8FgcN0lXtiZL7SBbKPQ\n5+kdFeqgr8U2vpEURsybpYYLf5cQyWQy6/Ru/LvJ0SikJt1K8vk8m2W+1WfoOUWj0XUd30L08jyP\nqakpprfwudL3vF4vy+qUk5UkuesNdSFqFEGvUadduUKY0VarFbFYDENDQyXX3wqF5pClUil8Ph+u\nXLlyZ7yqD4Ey9Ho9+vv78e677zIvtFShg2Gz2ZBOpzE3N4ezZ8+WNMNaKJQFMRgMkMvluHDhAt56\n6y02KlSqEOgJ1XkXFhYwPDxc1EjTRqE5ar1eD5vNxmA8JyYmsLCwIBgN6lZC70ypVGJ2dhaLi4u4\nePEilpaWWFRdipGmMcB8Po9Lly7B7/djYmIC8/PzcLvdRTc4kaRSKUxOTsJgMGB+fh4SiQQzMzOY\nnp7G5ORkyfuYuqbfeOMNFm0sLS2hr6+vKDrZW+lNp9OYmJgAz/OIRCIsMi1mauN2QqlWclzLMdAk\n5PgVcplvvPRL1U/GmRpwC41f4e8v1ljfzohu/O9SIvXCr4Xr2/i9Ysp7t1sH/d30NZ/PF13KudVa\nNq43m82yZrNy730AdycfdaFQHc7lcuGJJ57A+Pg43n//fYYCVo6YzWbs3r0bDz30EMbHx3H8+PGS\na1mFIpfL8dBDD6GhoQGXLl3CxMTEbQHjhQplFVwuFzMmW2FjCxWRSITOzk7GO+31eovGor3Vegn/\nltLe5V6cFGWQA0B1rHJT/wRcQBcEefB34mwQWQXt1XLLKoV6C4E27pTQBV/uGdiWbbmbpJRI/79I\ntyA+6rveUH/4/yGVSmE0Glnt7Q79XgYFyPO8YFJ6IUJMXeQ530kprAXdSfltbuZt2ZZt2ZZt+Q35\nn2Oot2VbtmVbtmVb/geKIEN919eot2VbtmVbtuXulK1q1uXoLdRVWFMuVR99lclk68qmVEIqVTfx\nZ6vValaKomwtx62Rz5SbVd021NuyLXeB3KkL7r9SflslGJL/js/kv4MUlrjKecb0WdJH+4G+X45e\n6pWg/pGNxCelNK0RqxoAaLVa6HQ68Dy/bryqlN6UfD7PQFp0Oh3DbA8EAggEAkzvf8kcNcdxYgBX\nASzwPP9JjuNMAP4dgBPADIDf4Xl++cOf/SaArwLIAfhfPM+/XfIK/wfKb+sC2tjZeSdlYxfpndRL\nz2PjOIkQKbwsCj9HXi6NwtDai9FbeAHRASaITQJrKOV5kA6FQgGVSvUbI0TlNrIRlajNZkMikWCN\nfOV0r5OoVCpUVlairq4O1dXVOHnyJAOMKPcy4jiOEVKQzM3NsQbPckccOW6Nz1yhUEAmk2FlZYWN\nWpUrtFcIqUssFjOykTshhZCx5YJn3KrDeuP3i1kXSeGayp0YKdRTaEBLnezYeMek02kGP01jZ+UI\n7c1cLsdY8CKRCIMk/q8cz/pjAMMAdB/+9/8FcIrn+b/mOO7/fvjf/4fjuFYAnwWwC0AVgJMcx+3g\nef7OtaYWyG/b6P02GrZK1b2ZIS70QoHiu4s3001eLRmnYi77jX/vRmNK3jIdmFv9/tvpJXQu+gwd\nYvKcickmHo8XZVCJ9IT+Ia+bUlr0ewvnP4WKSCSCyWSCyWSCw+FAdXU1Y/oiQIlCtqRidNO69+7d\ni4997GMMQvN73/seIz9JJpMlXUoikQhSqRRdXV3o6enB4cOHkUqlEI/HsbCwgKmpqZJnlul50tq7\nu7uhUqmwurqKEydOYGRkhLGvlbqvaQ/X1NSgpqYGBoMBbrcbAwMDgrCjb6ebDB3tC5vNxhpTS015\n3irly3FrON60zmKYxTbqIqG1bzxDQvXe7nfcbvxL6LkmHfS+N943pdyfhXrpniAUQ5rMuN0I2mY6\nATDnKZ1OQ6fTYXl5GTy/xsiYSCRYU3E5tkqQoeY4rhrAJwD8JYA//fDbjwM48uG//wTAaQD/58Pv\n/5zn+TSAaY7jJgB0A7gg4PcwDk+JRILq6mpYLBZoNBo4HA5YrVa2UUUiERYXFxkoBNGhAAAgAElE\nQVSby8LCwpY42nK5HGKxmFHMNTQ0wOl0oqamBlKplDHKrKysIJFIoL+/H9PT04jFYojH47f1vKkr\nnZC4jEYjOjs70djYCLVazdItdFEuLy/jwoULDEFqZWXltgeEIk5K1RiNRtTV1aGrqwsqlQo8v8bT\nHY/HGWPO6OgoxsbGGKTd7dZMh1ShUMDhcKCqqgqNjY2MPlKlUrFLrL+/H4ODg2zNhbjMtxIyGoQb\n3dXVBY1GA4vFArPZzOD7iJP67NmzSCQS66D8NnuPKpUKOp0OdrsdLS0tMJvNsFgs0Gq1DKKxr68P\nQ0NDWF1dZXPhm+mlSNdqtcLlcmHHjh2wWq2orKyETCZj7/Dll1/G5OQkO/BCjDXRMKrVajzxxBNo\nbm5GTU0NZDIZjh49CrfbjfHxcQwNDWFwcLCoDAPtP6vVipaWFvzhH/4hXC4XNBoNvF4v7r33Xty4\ncQNDQ0PrABiE6CYDrdfrUVFRgW9961twuVxYWVnB8vIyqqqqMD09zfCMV1ZWirqQZDIZg4Tt7OzE\nH/3RH8FoNCKVSmFqaorNtvt8vnWkNFtJoUMlk8lQV1eHXbt24cknn0QikcDCwgIDc/F6vUgkEoIw\nFAovfIrQ6X6qqamByWRiz3hiYgIjIyOMY3wrvYVCc/IKhYJxKWu1WkgkEgQCAcRiMSwsLAjSeSvd\nHMcxml+q2xJCFzmMQtd6q6CBnGWJRMKc2q3oOjdm7TZmwmifk1HdOPYohFCDzmzhOrLZLORyOYCP\nHKBCh+h2e5nneUilUnYHAMDCwgL7+ZWVFYjFYiiVShbkkM5iMyJCI+q/A/D/AdAWfM/G8zwRsXoA\n2D78dweAiwU/N//h97YUQrOSyWSQy+Vob29Hd3c36urqYDQameGgtFUoFMLs7Czi8TguX76MEydO\n3HYOmg4u1RFqa2vx5S9/GXa7ndEl5vN5xl+cyWSwc+dOXLt2DTdu3MDU1NSWm5fmezs7O/Hss8/C\nbDZDLBYjk8kgHA6ziCwSiUChUKCvrw+rq6ubzhfThSCXy+F0OnHgwAHs3r0bdXV1zEAEg0HU1dUh\nkUggk8nAZDJhYWEBiUTitoa60JsmSri9e/eipqYGEomEMWBpNBpoNBrm3Y6MjCAajW75LulCU6vV\naG1txZEjRyCTyRibjEgkgtVqBcdxyGQyuHLlSlHjcRQddXV1oaamBtFolBl+gtQ0GAxQqVRFHwqT\nyYSWlhbU1dUxgvlMJgOz2cwuT6lUyp6jkGdBz1upVKKqqgomk4k5hIlE4jcA+zc202yln8YXHY61\noxYIBBgCWiwWw8rKyrqMi1ChvafVatHQ0MD40SlidLvd64A7itVN7Em1tbXo7OyERqOBQqFAPB7H\n/Pw8UqnUbQ3OZnrFYjGjAK2rq8ORI0dw+PBhVFVVYWpqChzHwWAwMFpQIXsa+KikQk5AW1sbDhw4\ngObmZuh0OoyPjzOkOK1Wi6GhIcFrJp3kXOzfvx82mw12ux2JRAImkwkWiwUzMzPo7+/f0lCTXtpH\nNHtP4D61tbWIx+MQiURwuVzI5/OYnZ3F6OiooHJAYU2aslf0OzQaDTvjxBI2NDQkGH+edNPzBtac\nOqVSyYhc7HY7otEoYrEYYrHYpqWRwvuOCJPo2RAZEUW/9A6IJnezvU1OBemlNUskEmbHVCoVCyIp\nIKQMnVDZ0lBzHPdJAD6e569xHHfkNovluSJHrDiO+30Av1/4PUpDqFQqyOVyPPzww9i5cydUKhWk\nUin8fj/EYjEAwGAwwGq1wul0wu/3w+12QyaT3ZaCj1KiROp9+PBhtLS0sBcfCoXYhiPjZDQa4Xa7\n113Mm/w9zCjdd999qKmpQSaTQSAQYHUwpVIJi8WC6upqzMzMsE2xVZQHAHa7naUdHQ4H3G43YrEY\na4Lo6OhAZWUlEokEJicnBaWxaIPpdDr09PSgubmZXcSTk5PgOA41NTWw2WyorKyEVCpFKpUSlPqm\nVJ3dbsfu3bthNBoxNjbGqN+6urqg0+kgEokYWL7QtDodLOKrXVxchNvtBgA4HA6YTCYWxZDjJqS2\nRQdXq9XCaDTCarXC5/NhZmYGdXV1sNnWfFEiNiilBEAY3el0mhFn7Ny5E8BagwvwmyhVQnSTEyqX\nyxGLxRAOh7G6usr2bmG0ItTgAWC1dKvVCrvdjlQqhWAwCKlUinQ6zbiUC1PAQtZNl6Fer0d9fT32\n7NmD3bt3QyQSMcjShYUFhMPhdXzlQvSSwVCpVGhra8ORI0fQ0tKChoYGhMNhRCIReDweRlZBz0Xo\ns1AoFDAYDNBoNPjiF7+Ijo4ORgFKEKmEwCdkz9GalUoljEYjDAYDXC4Xvva1r6GqqgrRaBRer5dF\nkJTlOHnypKBnTPpdLhdqa2sZfSSRVchkMtTU1GB5eRkzMzP4+7//+02zABudCrVaDYPBALvdjrq6\nOtTW1oLneej1erS3t0Or1cLn8+Eb3/jGloaaDCjP8yxrZjKZkM1m0dbWxoIIrVaLxx9/HH6/H9PT\n0/jRj34Ej8dz2/UW6iUbUAiTa7fbmWHu7u6GXC6Hx+PB8ePHEQgEbquXaIJp/1PfCRHFEKvd5OQk\nc+ZyuRx+9atfCcq0kAiJqA8B+BTHcR8HoACg4zjuRQBejuPsPM8vcRxnB+D78OcXANQUfL76w++t\nE57nXwDwwod/MP/h99gfrlQqGQNJJpOB1+vFxMQECn/uqaeeYpuAItbNDgbpbWhoQHNzM7LZLKam\npjAzM4NgMMii6cbGRhw8eBC5XI4Z2s0uCoqmHQ4Henp6UFtbC5/Ph9HRUSwuLiIWiyEQCKC5uRkP\nPfQQVCoV4zbeCgmNPL6enh5mjFdWVnDu3Dl4vV6Ew2FmqGUyGbxeL+bn57ekyCtMezc3N6O5uRli\nsRijo6O4fv06RkZGWDq/paUFwWAQ8/PzglmZKIXc1taGPXv2wOfz4ebNm5idnQUAdHZ2QqlUYnl5\nGfPz8+zZC3UAKisr0d7eDpfLhdOnT2NqagoajQY6nQ4qlQqRSIRFfsU0oIhEIlRXV8PlckEmk2Fx\ncZHRBmo0GqTTaRbpCSUJoENM4DpEwjA7OwulUgkADGN9aWlpXRqvGKOXyWQgl8uRTCaRTCaRzWYZ\nFaPP51sHKyrUYFOkRCloShWbTCYkEgn4fD5G2CEUsrTQaamsrERjYyM6OzshkUjg9/sRDAbR19eH\nvr4+eDwe5ugKiUDIiFEk/cgjj8DhcECpVCIWi+Hs2bP44IMP4Pf7kUwmsbS0JAgTnYydXq/Hzp07\nsWPHDsYDznEcZmZm4Ha7ceXKFQZfOjs7uyV3Nz0HuVyOPXv2YO/evaisrERFRQXMZjMikQhCoRBG\nR0cZeNL4+Dh6e3u3fBakV6FQoK6uDl/5yldYCYPqp2S8eJ6Hz+fD6dOnN11zoVOoVCpht9vR2dmJ\nrq4uOBwO1tMhEomgUqlgs9kgEokERegUSMnlcuRyOfT09ODQoUPQaDQwm80AAI1GA7VaDZlMBpfL\nBa/Xi6WlJSgUii2fBUWzGo0Gn/70p6HX6+FwOJiDq1QqIRaLWYnh7Nmz0Ov1tzXUpJeeIRG39PT0\noKuriwU21F0uk8nQ3NyMQCCAM2fO3FlDzfP8NwF8EwA+jKj/N8/zX+A47v8B+BKAv/7w668+/Mir\nAH7Kcdz3sNZM1gTgspDFUBo0FovBZDKxVElvby98Ph/cbjfjIm5pacEzzzyDbDaLyclJxkh0u4uC\nvB+TyYTOzk5kMhk899xzDB+ZLoLW1lYcOHAAZrMZ165dw9mzZ7G4uIhEIrHp2q1WK44dO8Yi6b/7\nu7/D1NQUwzGmQ+h0OpFMJnHq1Cl2SdzOUNOFZjAYcOjQIVRVVeHq1as4e/YsTp8+DZ7nIZfLcfDg\nQTQ3N8Pv9+PatWs4derUlhjglIbt7u7GsWPHEAwGce7cOZw7dw5utxt6vR6PPfYYnnrqKRgMBvzL\nv/wLrl69umWKicThcOD+++/HU089BYlEgueffx6Dg4MQi8VwOp04evQokskkzp07x0jhhZA9cBwH\np9OJz3/+89i5cyd8Ph+GhoYgEonQ3NyMT3/60wgGg7hy5QpmZmYYxZxQA6JSqXD//fdDq9ViamoK\nCwsL6OrqwoMPPohsNos33ngDy8vLgmrehXopkrZYLKy5xOFwwGg0Mtq84eFhliEppimLLopMJoOG\nhgZUV1cjHA6joqIC0WiU6aVGMtIrxCmi+ltrayvuv/9+xONxWK1WJJNJDA8PIxAIrGMmKiZVLxaL\n0dDQgMOHD8NsNiMWizGMdWJZW15eFpy5oOiOaDnr6+sZN0A+n4fP58OJEydYtqGwgXEzoX4Lg8GA\nvXv3wuFwMD5t4v72+/3wer2scbGQrWorvRaLBfX19di/fz+i0SgGBgbg9XrhdrsRCATWYUYL0UvP\noqqqivFSNzQ0YHx8HDMzM/D7/QiFQpienl7XCyHEUaZnTPddS0sLu7NfeOEFhEIheL1e1lDFcWtT\nF0Ia9jQaDUwmE1pbW2Gz2dDY2AiRSISlpSWcPn0aY2Nj8Hg8kEgkSKfTTO9WZReO41BRUYGGhgbo\ndDrcc889kMlkSCQSeP311xGPx9HX18eoOwEI7gSnPpza2lq0tLQgkUjAbrfj7bffZsxfk5OTSCaT\nzAYV1t+FSjlz1H8N4D84jvsqgFkAvwMAPM8Pchz3HwCGAGQBfJ0X2PFNm4aoz4aGhuDxeDA5Obku\nMpLL5WhsbIRWq8Xs7CwuXrwIn8+36Saji4/neQwPDyOdTmNgYAA+n48NwKvVauzduxcdHR1Qq9U4\ne/Ysi0S2MiBisRjz8/Ow2+1YWFhgKXOShx56CO3t7VAoFJiamoLf7xekl+pVAODz+TA8PIyFhQVY\nrVZG+PDEE09AJBJhZmYGFy5cQCQSEdyAxPM8DAYD5ubm4PV6mQfc1taGT33qU3A4HAiHw4ywRMgl\nQbqp3r28vIyamhpks1no9Xp0d3dDp9Nhfn4eY2Nj8Pv9glOnHMehtrYWFosFqVQKgUAAu3btgtVq\nxZEjR+ByuXDlyhX4fD7m1AnVKxKJWDOa1+uF0WhEU1MTenp6UFFRgaGhIUxPT6/TWUyqV6FQIJ/P\ns5q/1WqFw+HA5cuXGZFG4biaUKHLEACqqqpQUVEBu90OnucZ6QU1ChUj1AAjFotZxLSysoJYLIbB\nwUG8//77JTkt9PflcjkcPHgQDQ0NkEqlkMvlOHfuHE6fPo1QKCS4zFIo1EuhUqnQ0NDAsgCjo6OY\nmppiHAHFli2kUil0Oh3UajXUajW7q6ampjA/P49AIMCyGICwrmzK8NlsNlRVVbHojM4lEbcUMksJ\nXTM147a1tbFm2VQqhWQyiVAoxNjKCp02IbqpsdDlcqGtrQ1SqRSZTIY1s87MzLDemMI1C9Gt0WhQ\nU1ODhoYGuFwuKJVKlu1cXl7G3NzcOr1CHUNixquoqEBjYyP0ej18Ph+WlpYQjUZZQFW4ViHvj2rR\nSqUSu3fvRlVVFfR6PesPmp+fh9/v/43zUcx+Zn9DMT/M8/xprHV3g+f5IICjt/m5v8Rah3hRQoc3\nlUphcXERZ8+eZXRl1Awhk8nQ2NiIffv2IZvNYmRkBL29vYL4nomjdWxsDJOTkwiHwyyNo9Fo0NnZ\niYMHD8JkMiGVSq2L9LbSHQqF2AgMNcRoNBrw/Bql36FDh6DVahEOh3H+/HnBbE9E/H7z5k3Y7XYs\nLy9Do9GgoqICFosF7e3t2Lt3L/x+P9577z2MjY0JZoOhpor5+Xkkk0lotVqYTCZUV1fjk5/8JBob\nG8FxHCYnJ+HxeIpKbep0OnYxBAIBOJ1OWK1WVFdX48CBA8hkMrh58+Y6b1OI3kKD5/OtVVv27dsH\np9PJOuFHR0dZWo8MnxDd1LW/tLQEpVIJhUKBlpYW5imTg0URMiC8mYwa6OLxOBKJBJqamlg6+Z//\n+Z+xvLwsOL27UWgNGo0GAFBZWQmRSITz58/jzJkzCIfDRUW8G9dus9ngcDhYTe/SpUs4ceIExsfH\nGatUMXopjSwSibBz5044HA7wPA+v14tLly5hdnaWRUrFOiyUwnR9yNNdU1ODq1evYm5uDm63u2hi\nmMKGI2Lxs1gskMlkyOfzeO+995BIJIqmK6WegcLuaLVazUp5xDBWDAcz6SWnV6vVruMbkMvl0Ol0\n7DwUO/dMz4KyAFSWpPUXOmGFpZutdNMaiQ+e3nssFmNBVDabZRGp0GmIwu5wu90OkUjEmtw0Gg3j\n/pZIJEV3YtPdIpVKWd+RRqNBMBiE2WxGPp9nqfRyjTRwFyKT0cOizU8bAFgby9FoNGhvb8c999yD\nmZkZvP766/B4PILSFLlcDvF4HP39/SyFA6wNqysUCuzbtw+tra2QSCTo6+tjZPRCjemVK1dY3Ya4\na6nRwul0gud5XL9+HW+++aagNC+94HQ6jZMnT+Kee+5BLpdjDS1msxl79+5FRUUFXnrpJZw9e5bR\n8gmRXC6H8fFxAEBPTw+0Wi1SqRSMRiNaW1uh0+ng9Xrx7rvvMkdI6IEOhUIYGxtDfX095HI5OI6D\nxWKBwWBAdXU1BgcHcfHiRXg8HpZK38qg0uEIh8OsI50yAuRkJBIJTExMsJKDUCNN/9CBpTpjRUUF\n1Go15ubmMDAwsK5JqJiIjC57uniMRiNLe09OTjJDXexcNunVaDSorq6G0WiEQqFAJBLB0NAQBgYG\nWOamWIPKcRwqKytZnVetVmNsbAxXr15lXfrF6iS9crkcu3btQkNDA4xGI7xeL6vFFks7SO+Y59ca\nRru7u3H48GHs2LGDcX57PB4sLi4W3ahHlz3tCaody2Qy+P1+rK6ursv2FaOXjHU4HAYAFpCQA12K\n0Oeo3m+z2RAIBKDRaFj0ZzAY4PV619WnhYhMJoNUKoVEIsHS0hJOnjzJJitUKhVMJhMWFxfXnSUh\nQgZPIpHA4/EgEolgYGAAtbW1MJlM0Gg0sNvt6O/vByAcO4P6KyQSCUKhEJaXlzE9PQ2j0Yj6+noY\njUbI5XIsLy8LWufGZ0F173Q6jbfffhtGoxFisRg1NTVoaWnBlStXSpq0uJXcdYYaWD9ntrq6uq6B\nYe/evfjKV74Cl8uFxx9/HJcuXWKD60L0EqAEx3FIJpPrRraeeeYZmEwmnD59Gt/+9rcRCoUEb+Rk\nMolUKoVwOAyv18ui/8rKSnz2s5+FQqHAz3/+c3z/+99nHMdCo96VlRUMDAxgeHiYzT22tbXhM5/5\nDHbs2IHl5WV85zvfYcxiQtZMemdnZzE/P48rV66wRq09e/ZAr9fj2rVr+Ju/+RtcuXKFpdOFrJnn\neczNzWFxcRHvvPMOlEolVCoVDh8+jEcffRTpdBp/+qd/iunpaRZFCo0astkszpw5g0uXLkGpVEKp\nVOJzn/scHn74YUxOTuJHP/oRrl+/zrB7heqlWuzMzAxefPFF2O12tLW1obm5Ge+//z5efPFFfPDB\nB1heXl6XfhMq6XQaXq8XuVwOTU1NzOt++eWX0dfXxy5pWotQIaPX1dWFr3/962htbYXP58Orr76K\nX/7yl3C73SVF6VRT/+53v4tDhw6xRsWf/exnePnllwWVV24l1EH+4IMP4qtf/SpEIhFu3LiBhYUF\n/OIXv2CNisXqpjP84IMP4tixY6isrEQul8ONGzfw/vvvY2pqijVJFaNbp9NBq9VCpVKx/hOa6aaz\nXizyFMdxbJSOMkR+vx/5fB5ms5ml1in4KBz92UxEIhHq6urQ0NDAMpMTExNYXV1lz5xKcmTAhNa8\nxWIxnn32WTZvvrKywnp7du7ciZaWFkxMTEAmk63DuBYSTff09ODAgQOYnZ1FNBplZ8xkMqGtrQ0e\njwd+vx9yuZw9l63uT6lUivvuuw+HDx/G6uoqbty4gWAwCJFIhNXVVXR0dCAcDuPq1avQarWQyWTr\n7ozNRCqV4pvf/Cby+Tymp6cRCoVYpubAgQOoqqrC+++/D5PJxLJZQur/m8ldaahJyFOm9KXRaMTj\njz8Oh8PBmlmEGulb6aV/F4lErLEnFArh7NmzrNGiWCmsnUilUrS2tuKRRx7B6Ogo3nrrLdbFKlQ3\n/VzhoL5UKsWBAwfQ3d2NZDKJt99+e12tsFi9+Xwe0WgUcrkcFosFDz30EObm5vCTn/wEN2/eZA1Z\nxTwDSlel02kkEgm0tbXh6NGjqKiowIkTJ+B2u1nNu5g18zzPnKJEIgGtVovu7m5IpVL84he/wPnz\n50tKIdOaASAajcJkMsFut8NqteK5557D+Pg4q70Vu99oT2SzWQZ6kkql4PF40NvbW5LhBz6K1PV6\nPXbt2gWDwYBEIoGZmRnMz88jFosVpa9QL6VibTYbwuEw65KmEkipelUqFaxWK3bv3o1EIoHTp08j\nl8shkUgwzvZin4NYLGZgQAaDAbOzs2xChAB6iqmjFwp1MisUCkgkEkxNTUGn0zHAHrp/itWrVCpR\nWVmJdDrNorJ8Ps+ivMLLvxiRy+Xo7OzE8vIy4vE4IpEIxGIxbDYbrFYrm7yIRqNszUIj1I6ODkSj\nUczNzUGpVGJlZQU1NTVwuVysN4eMrND+DXIId+/eze7McDgMrVaL9vZ2NDQ0IBaLIRgMYnV1laWp\ntzrfVJduaWlBOp2GWCxGIpGA2WxGdXU12tvb0dvbi1QqheXlZeYECGmUlclkaG1tZYFedXU16w3h\neR7t7e04ffo0w19QKBRlw9/e1YaaXnIul4NIJMK9996Lzs5OyOVyfPDBB8yzLfaQFOqliOSJJ54A\nALz66qs4fvz4uo1crF6qi5hMJnzxi1+E0WjEX/zFX+DcuXPrIqdihA4sIRZ95jOfgVQqxY0bN/D8\n888XnS6kdRYeVrVaja6uLjzyyCN48cUX8dZbbyEUChV9MW+8EDmOw+HDh7F//354PB78/Oc/Zzi7\nxax5Y8QpkUhgMBjQ0NCAmzdv4r333sP8/HxJhrTwq0wmQ3V1NVpbWyGTyTA0NMSa00p13uir3W6H\nVCpFMBjE5cuXS1ovCTmcBoMBdXV1yGazWF5ehtvtRjAYLElv4RxyfX09eJ7H4OAgVldX2cx7qbjI\nEokEZrMZ+/fvh06nQyKRwNLSEgwGAzweD7xeb0l6lUolm981Go0MNc1gMMDn87ESVrHCcRybNzab\nzSyNXl1dzZqnSnUAOI7DkSNHYDAYmCNUVVXFZshpHKtYvVSi6uzshEqlQjweRzgcRk1NDcbHx3H9\n+nVWKqR1C/0dc3NzaGpqwr59+xCJRJiTmEqlMDw8jNnZWeZcbIUPQcLzPGZnZzEwMIBHHnkEsVgM\nBoOBBSUjIyM4c+YMZmdnkcvlWMpZSMaQRk3r6upw4MAB6HRr6NfJZBIjIyM4deoUJicnEYvFGDKZ\nUNz33t5eVFVV4ejRo1hZWYFOp0MsFoNEIsHg4CAmJibYxIJarUYsFvufG1EXCsdx+MIXvsAG0198\n8cWi63kbhdLeBw8exBNPPIGFhQX85Cc/wfz8fMlRA4larcZnPvMZdHV1ged5nDhxYsuRKSFCnrjT\n6UQwGMRPf/pTjI+Pl/UcgDWwjaamJnzta1+D2WzGa6+9xox0ObrpMP/O7/wOVCoVXn/9ddy8ebNs\ncgiRSASDwYCOjg5ks1m8++67WFpauiMEC1VVVaxBze/3IxqNIpPJlPXuOI6D2WxGR0cHJBIJpqen\nMT09DaA06j6KVsRiMXbt2oWKigqGorS8vMwaiEqJTmkGlsA2wuEw1Go1013qe1Or1Qyatba2Fn6/\nHzqdDvF4nEHelqJbrVZDr9ejo6MD+/fvx/j4OOuo7u/vZ9CgpejmOA719fXMaSGgoomJCVy/fr1o\nfaQzlUpBKpXCbrfDbrezmWmCvaWyG4nQtcdiMVy9epWVCVdXVyEWizE2NoZf/OIXWFxcZI2sxU4X\nvPPOOwgEAvD7/TCbzWy8dGBggBFRUFBVzPkOBoN49dVXwfM8HA4Hrl+/jlwuh8HBQQQCAbjdbgZM\nQ2NTQiJ1n8+HX/3qV6irq8PTTz/Nztv8/Dymp6cxPz/PRgvlcrlgKGCFQoFf//rXqKysRCgUQlNT\nE86cOYORkRE2TuZ2u7GyssJgnsu9n+9qQ01paZptPXToEJLJJP7kT/4Er7zyStkXvd1ux5e//GV8\n6UtfgtVqxb333ovR0dGymFSoYeOHP/whDh48CJFIhOPHj7MaVKkik8lgNBrx4IMP4hvf+Abcbjf+\n6q/+Cq+99lpZxokyCl/60pfwla98BVarFefOncPg4GBZRpoif4fDgccffxw2mw2vvvoqnn/+eXg8\nnrLeHc0tPvroo3j00Ufx+uuv48yZM1tivW8lhEr3zDPPoK2tjeFvF9OgdyuRSCSw2+04dOgQ9u3b\nh9HRUdZPQJ3rxUpherqnpwdGoxFarRbLy8vw+Xzo7+8vCpKVhMZvdu7ciQMHDqCyshJVVVXwer2Y\nnJzE5ORkyZkFhUKBT3/60+jo6GB472+99RZOnz7NxsiKFepCdjgc6OzshNlsZsA87733HsM3L/Xs\nBQIBJBIJXLp0CcCasfJ4PAgGgyWlpoE1o5tOp/HWW28x6NJgMAiPx8Pmg+nnitW7srKC69evY2xs\nDC+88ALTd6vxvGLWns/nMTExgZmZGXYvUE23MCsHoKh7I5vNMojbH/7whyytTcZtY6OnUKNH/QiJ\nRAJerxenT59eNzpHkxvUcS5kVBZYe8bhcBgcxyEQCOD8+fPs+ZJejuOg0WgYbjpNRpQjd7WhBj66\n5B544AEAwOjoKIaHh8sypuQAtLW1obu7G2q1GslkkqXSSxVq0KioqEBdXR1yuRzm5+dx4sSJsqNS\nhUKBxsZGHD16FFqtFmfPnkV/f39ZBoQuOY1Gg3vvvRcSiQSzs7N49913y95YMpkMOp0Ou3fvRkdH\nB8bGxtiMdzlCEXpTUxOcTidWVlYY/nghA06xQhEY1QgXFxexsrLCOoXpIPhndKgAACAASURBVJaS\n4iRCFbPZjEwmg0QiAY/Hg+npaYZEVopeqVQKk8kElUoFmUzGMI9PnTrF0NOK3c8EUanRaJBKpaBQ\nKBCLxTA1NYXjx4+XXLrhOA7RaBSRSAR+v5+BQbzzzju4efPmpsA/W0k4HMby8jKrTQ8NDeHKlSuY\nnp4uKzNGaya8Bb/fj6mpKWb0Sj0jHLdGszg1NcVq0QS+UvgMio14AbAImubQC89DoUEtVXfhObjV\n50vRTVjxtzpftzLWQoTn+XXNg7fSS70jEomkqD6ZfD7Pslb0PWA98cfKygrkcrmgeroQuasNNRmS\nuro6VFVVYWlpCe+88w68Xm/ZqWmO49DU1MSIIkZGRsq6LOglSSQSWK1WrKysYGlpCa+++ioGBwfL\nWiuwlvKura2F2WyG3+/Hr371K8ZgVc6FQVjfarUabrcbN2/exJkzZ8pO1xTicRMc340bN8pOTVMz\nlsFgQDQaRX9/P0OwK8WQAh9FpwSiPzw8zGBjKYW18RItRjfV1SYnJxkghM/nY6hTpRhpkmw2i9de\new0SiYTVZicmJkpO1edyOUQiEYyPjyMej6O3txeLi4tYWFiAx+Mp63xkMhm89dZb+OCDD5DNZuF2\nuzE4OFhWRyxNb1y7dg25XA5zc3MMKKTcOwIAy6gEAgGGbV7u2aCmpVAoxGrKt2J/KuV35HI5Rnxz\nq3UWjrIVu2ZqhtwYQZMxLWXdhZFzoc5brbmYdP3G/hv6urGRGCieEpjeV6EUjmHRGkvFRbiV3PWG\nWqVSob6+HiqVikUhpXazklCzV01NDUtl0BhDOTqBta7LmpoahMNhzM/Ps27hckWv18PpdEIsFjNo\ny3Lrx8DaJjWZTFhdXcXAwAA++OADNtdZjqyurkKv10Oj0SCRSODChQusRl/uJSeRSJDNZhEMBhlw\nDUU5pRoS8sB5nsfU1BSb785kMiVFphv1JhIJTE9PI5FIMAeLAC1K0Vl42Z8/fx6ZTGZdF32pz5jn\n17rqJycn2eRDKZ3uG4U+f+rUqXWXZbkXGen1er14++232ffLPReke3l5ed2c7Z3QS+/+TpyzjXqB\nzSkfySiW+ncU6t2YwSpH70a5Ffd4MRF14WcK9xulugsdhFIzWoV/b2E3On2f6vXlnh0A4O7Ugy1r\nEZswb1HUR+QFQhDIhAoN2hMrTTmGeqMQB3U59bFbSbEpoGL0AnfmItqWbdmWbdkWQXKN5/l9W/3Q\nXR1RAx95n6U0mmwlBIDy25A7kXa7ldyJDsLb6d2WbdmWbdmWu0/uDL7ZtmzLtmzLtmzLtjDZmHYv\nR+76iHpbtmVbtmVb7k65k3XpjXqB3+xULxcciBpHCzOeNPdd+PuKEULOVCgUrOmUxr+IJ77cDOu2\nod4WJpsdunJr2Lf7fLl6idyAmkLoIJfahVqolxC6VCrVLXm4y1kzgV24XC7GpV4sU9LtRKVSobGx\nEe3t7chms3jzzTcZu1O5vQ0KhQLV1dXo6uqC0+nESy+9hEgkgkQiUTaIDYHCtLa2MrxrwvIXikm9\nmW6O4xiUpkqlgtvtZjSE5QrtQwKe4XkeiUSCjQiVI7R2uVwOqVTKeMXvhFAPEPDR6FExny38Wigb\nO7mLKdkVRqK0vo3rKsVBoHdEyGlEkFOopxS9PM8zSmOFQsEaXolT4n/8eNZvW35b3uBmussxIJt9\ndmOapdgLuZAH+VaHgsYyijUkhcACheQb1Hl/KyMrVK9EImGMaqurq4y2jnTTOov1ZsViMZRKJSwW\nC1wuF3Q6HUNfKgSkKHWu2mAwoLGxEffddx/27duHN954Yx02cDlY2mKxGLt378azzz6Ljo4OKBQK\n9Pb2YmFhga291DEzkUgEp9OJY8eO4fHHHwfP8+jv78f4+DijSi11PI7WvnfvXnziE5+AxWJBOp3G\n/Pw8Jicn13XTFqsb+AiA5/7778eePXsgFovx7rvvsi70cu4BcuoUCgXuv//+dSxgxcqtRp7ISNM0\nCaFzFaNvs7UTSUcx46lb6aX3WRgFC3nGhfdQ4X10q1Erkq300t4t7FKnWfbC30m6hO6Hwr1F95dW\nq8XKygpSqRQkEgkymcw6R6PUfXZXGmra+MRzShRoxHBCD4U6tYkIfqtNVpiOIDB4qVQKqVTK2vbp\nQlhdXV0HHbnVAy5cs1QqhVqtZkwyxCYjkUjYqA7RJQo5GDSLK5fL2VeiulQqlQz+LhQKIRwOIx6P\nC/bkCUaV6OqI67myspKBf0QiEdy8eXPdGJSQNYvFYgaoUltbCwCME9dqtWJ5eZnN5xbS7glds1qt\nht1uR1dXF5RKJeuwn52dxeLiIvx+P0MRKuaAEN9uT08P9uzZg4qKCuzfvx8TExMYGRnBwMAA47su\n1mkRiURobGzE4cOHcfjwYdTV1WF1dRXhcBj9/f0IBoOMPrIYof2l0Whw6NAh7N+/H06nE+l0Gi6X\nC/F4HMFgsCTvnt6lVCrFvffei89//vOoqqpCIpFATU0NxsbG2M+Vopuei0ajwRe+8AXG1e31ehkg\nRSlRb+GlToBBn/vc52A0GnHjxg1mSISyU23USw6hTCaDVqvFjh07YLPZ2MhjKBQqao9szC6RcZHL\n5XA6ndBoNOA4DolE4pbjS7eSWxmejc9cIpFALBYjlUoVneHaqLfw3+ne43lesGNRGHlvBH+hr4Wz\n3Ld7b4V/M+ncmN6mZ1OYqi7Ut1l2ceP8N0XmhfdNITlJofOxme7byV1lqGnzyOVyxutZUVHBuHbt\ndjtyuRwymQw4jsPKygpWVlYQiUQwNTWFoaGhTb16MshyuRwmkwk7duxAdXU1HA4HlEolO3jEC0xI\nRz6fD16vd9P5bXIkDAYDLBYL7rnnHtTV1UGj0UClUkEulyOVSiGVSsHn8+H06dMIBAIIBoObMoDR\nM9Hr9TCZTIyknNiSSH8gEMDMzAzGx8cxMDCAxcXFLed/yblQq9VwOp2oq6tjBOhNTU0wGAzIZDII\nBoOIRCLsshQyykbvkRC5Dh8+zCgDzWYzlEolPB4Po9AszAhstYnpHapUKjgcDuzbtw86nY5RohoM\nBmSz2ZKQ5uiC0ev1qKysZM9DJBIhmUwiFothYGCg5O57ApiprKyExWJhqUylUgmFQlEWfy0ZDZfL\nBYPBAI7j1vFnl5qCoz0oFovR1dUFo9GIVCqFWCyG+fl5hMPhsmuHYrEYZrMZDocDFosFiUQCs7Oz\nbLa91HlUuoBtNhs6Ozvh/BAjPxKJIJ/PQy6XswyGUAcU+GiPSqVSGI1G7N27F/fffz/i8ThisRgq\nKirg8/mQSqWKcjIK9xSh+zU2NqKqqgrJZBIKhQLJZBKRSEQQyt+tnCd6JlarFVqtlv1MLpdjd99m\njv5mDllhml4ul7NolfjdN8sWFe79jWeLjBwFQ8DaniF87o2lkduV2DZGtoVz0LTWwnT1rco5hf9N\n/N+bGXhyLAodg2KzcXeVoQbWIi61Wg2pVIo//uM/Rnt7O3Q6HSQSCRKJBHuZ+XweGo0GCoUCuVwO\n169fx7e+9S2Mj4/f1ojQS3Y4HHj44Yfxuc99Dmq1GrlcDrFYjEXmBM0okUhw8eJFnDhxAlevXsXo\n6Ogt9dIm0Gg0aG9vx9GjR3H//fezKJe4YQ0GA6xWKzOO7733HjsUW11ClZWVOHToEA4ePIja2lpM\nT08zJyWbzaKnpwe7d+/G+Pg4M1JbRTh0AapUKjz55JNoaWlBNpvF8PAwBgYG4HA40NLSArvdjoaG\nBgboL0Toctdqtejq6kJHRwcGBgYwNjYGrVaLp556ClqtFjMzM5BKpUUbPsJq37t3L+LxOAYGBiAS\niVBfX4+amhoMDAyUnG6Sy+WoqqpCXV0d8vk8RkZGGD1jIWNbqYbaYrHAarUik8lgaWmJwT4CpQOA\nkGOk1WrR3NyM1dVVTE5OYmFhgeFVl7LmwrRudXU17rnnHlavn5qawo0bNxCPx4teN12IlBlpaGjA\n7/3e76G+vh7pdBpXrlzBv//7v7PzIxSVqjCCEYlEMBqNOHr0KB5//HEcPHgQHo8Hly5dwqlTpxAI\nBJDNZlk9UeizoHVLJBJ8/etfx9NPPw273Y5gMIh/+qd/YoGERqOB3+8XpJf+oeyh2WzG3/7t32LX\nrl2IxWKYm5vDwMAAQqEQKisrEQgEcPLkSUF6SbRaLaN+fOSRR7B7926WedLpdHC73VhYWMArr7yy\nZUaOjA/HrUHZKhQKhrd+4MABdue5XC7wPI9IJIJvfetb8Hg8W+olYyaRSKBQKKBSqQAADz74IKRS\nKVZXV2Gz2XDkyBHE43GMjIwwDoFbCb2vwuyCVquFUqlENpuF0WhEU1MTMpkMNBoN7rvvPuh0OkxM\nTODHP/7xbd8h6Sp8FsT1oFAoYDAYYLfbIRKJEIvF0N3dDZvNBgD43ve+tw5IZyu56ww1sOalajQa\n2O12pNNpBINBhMNhTE9Ps5nqcDiMZ599ltHlBQKBLQ0epWKrqqpQX1/PMJcXFhbg9/vh9XqRSCTQ\n0NCAhx9+GEqlkv2/rdDFiNChvr4eOp0Os7OzmJmZwdzcHJaXl5FMJrFnzx488MAD0Gq1mJubQzAY\nZGhStxO6dCoqKmC1WiGXy+H3+3Hy5ElG6GA0GtHR0YFcLoelpSUsLS0JgmUs9H5NJhMymQxGR0dx\n8eJFxONx7Ny5E06nE0ajEYuLi0XxMdPGValUqK6uhtfrxdDQENxuNyoqKiCTyZDP51m6fqt000aR\nSqWMB3Z8fBxTU1OwWCyMrs7n85UMCavRaGA2m6FSqVgGx2AwMG7tYvjEC4UuToPBwKL+bDaLSCTC\nELBKoSstjIhUKhXTHQwGEQgEGPxlOc6FQqGAy+WCTCZDNBpFNBplMKMUzZSiW6lUwmaz4dFHH4XT\n6UQikcDw8DDeeecdDA8Ps/MhNAIpjP6JzOeBBx5AfX09OI7DyZMn8Z//+Z9YXFxEOp1mMI9C65xE\nWqLX61FXV4cjR47AZDLB7/djZGQEvb29iMViWF1dhd/v37LfgNZayBntcDiwY8cONDc3I51OM0as\nqakp5HI5jI2NYWFhYctnQXpFIhHMZjOOHj2Kuro6dHZ2MgPq9/uh0WgAAB6PB9evX98yA0D7WCwW\nM4rVjo4O3HfffbDb7YxOMhwOw2KxIJVKwe12M2d0s/VS6TCTyWDXrl3Ys2cPTCYTy/hptVrIZDJw\nHAe73Y5QKASPx8P+hs10S6VSpFIpqNVqfPKTn4TJZEJDQwNUKhVqamqgVCpZpoFQxQiyeatnTA1l\nZrMZ3d3dOHLkCEv9m81mKBQKGI1GVFdXIxwO46WXXvrvbagJhIS4VFdXVxlk5tWrVxGPx7G8vAyz\n2Yzf/d3fZWkKt9sNn8+3qdGj+nN1dTVEIhEGBgYwOzuLvr4++P1++Hw+VFRUoKamBhqNBisrK8wI\nEKn9ZlJZWQmNRgO5XI4rV67g4sWLWFpaQjQaRU1NDQ4fPswI5ycnJ+H1ehGPxzc1JnS5GgwGmM1m\nJJNJzM/P49y5c4jH45DJZNi3bx+0Wi2WlpYQDAbh8/kEYVMXGlNKn1+6dAn9/f2QyWS49957UVVV\nBalUikgksu7SFCISiQS1tbWoqanBhQsXMDExgUgkAqPRCLPZjLm5OUxMTBRtRCi9vWPHDuYURSIR\nOJ1ONDQ04L333iuZ7AIAe9b5fB6JRAJKpRIVFRWYmprC1NRU2XSXBK2qVquxuLiIaDQKt9tdUm16\noxiNRphMJiwtLSGbzbJUbDk46DzPQ6VSYf/+/Yy0I5FIYHR0dEuShtvpLCxztbe3Y+/evZBIJCzi\n7evrY+npYuvHdHkqFAq0trayPg6Px4M33ngDMzMzzNkSylRF61WpVOyCr6qqgkgkgs/nw/nz53Hy\n5ElMTk4CWENQFIoPT2t1Op2or6+HwWCATqdDIBDAhQsXcP36dYyMjCCRSCAcDgt2FCkw0ev1aG1t\nxc6dO6FUKhEOhxm15OTkJLuDJiYmwHHcpuBS5BRKpVKYzWa0tbXhwQcfhF6vRyKRQDKZxPDwMONF\nl0ql8Hg8jNRkq2esVCohEolgMpnw1a9+FQaDgUXnc3NzjMyF7iHqY3C73ZvqJkOu1+uxY8cOHDt2\njJXK1Go1hoeHGcSxWq1mML8zMzNbPmeNRgOxWIza2locPXoU9fX10Ov14DgO4+Pj8Pl88Hg8LDjJ\nZrOC9BbKXWmos9ksVCoVZmZmEA6Hce3aNdZwRBcDpXFEIhHm5+dx5syZLdM1PM9DqVRCpVIhFArh\n+vXrmJ6eRiAQYOMrarUa3d3dMBqNuH79Oi5fvgyfz7ell0lNU6urq/B4PDh79izm5+fZy3c6ndi/\nfz90Oh28Xi/GxsYQj8cFdfiSN7i6uoqxsTGMjIyww6RSqbBv3z5IpVIsLy+jr6/vN7hsbyeUtiJi\nc8JRX11dRXNzM/bv3w+bzQa3272ObF6IUKTudDqhVCqRy+VY2WH//v1QqVSYmppidbxiRCwWo7q6\nGjabDdlsFlqtljV82e12DA8PlxxNcxwHo9HIIgaK+LRaLTtw5XQ2i0Qi2Gw26PV6Vmf3eDwsxVuO\noc7n82hpaYFcLodSqYREIkEoFGLZlVIiavp5p9OJgwcPwmKxMJpHt9u9riu32MY6nudhMplw4MAB\n1gvw3nvvsb4QyjgUa6TpOet0OlYKiUaj6O3txcTERNGjcIXNdDRlQP0o5GxevnwZAwMDrF4vtAGV\n0saUwYnFYpDJZACAf/zHf8Tw8DBmZ2fXOclCG1DVajVsNhs0Gg0sFgtjVIvH48wpp+ZcAIIbvqjE\n0tDQwEpigUAAQ0NDCIfD67J6HMexkuJWz0OlUkGn08Hlcq1zYgOBAMbHxzE9PQ232w2OW+PzJr1b\nlV04bo3BrqmpCRKJBG1tbVhYWEAkEkF/fz/S6TSuXr3KHFCRSMRIebYS6hFyOp1oaWnB4uIilEol\nXnnlFZYxHB8fZyVDuu+LHa+7qww1ebjx+P/P3ptHx1mdd8C/2fdFs2lG+75atmRZXmRsE2Nj7ALG\nYGeDhDgh0DZpe5qmSdoc0nRJcrqQptCSQHIwTYMJYLaAjUHg3ZaFbdlarH2fTZoZaUazz2iW7w9/\n9+aVkKWZd8T3Oa2fczgGYT1z5773vc/2e36PH0NDQ3j66aeRTCbpoHOS/6+vr8fBgwchEolw4sQJ\n/PjHP0Zvb++yRi8ajcLpdOKdd96h4KB4PA6hUAihUIj77rsPBw8eRHV1NTweD5588smUh1+EQiF0\ndnZibGwMOp0ONpuNgsj0ej3+6Z/+CVqtFn19fXjmmWfmgXyWk0Qiga6uLjqEYW5uDlVVVcjPz8dn\nP/tZbNy4EW+99RZ++ctfYnBwMK0hEolEAj6fD1evXkUsFkNFRQVycnLw13/91ygoKIDNZsOzzz47\nb6xbKkKipUQigcHBQVRVVaG0tBRlZWVobm7GuXPn8MILL8DlctH6I5Ba6lsqldKLJxAI4IEHHkBu\nbi5tXyHfhbRjpHMhCwQCWhJZs2YNamtrodfrcfHiRVrXZNNuwUSVajQaGI1GCAQCKBQKnDp1Ch6P\nJyNDnUwmoVQqsWvXLqhUKsTjcbz33nt4+eWX6Xlgm1LXaDT44Q9/iLVr18JqteKjjz7Cr3/9a4yP\nj6c9GIZZQwaAp556Co2NjRCLxejq6sJTTz2F8fFxVsNFmIa6rKwMf/qnf4qtW7fi8uXLePXVV3Hl\nyhU4nc60yyzEoEokEtTU1GDz5s0oKSlBTk4OfvSjH2FwcBBTU1Pznl+qETqpZSoUCqxatQrZ2dkQ\niUTo6elBS0sLvF5vWntBziafz0d+fj5MJhPy8vIgk8kgkUjgdDrR3d0Ns9k8b25COiUnjUaDuro6\n2qev1+sxMzOD0dFRCgAkTn2qa04mk9DpdNi0aRN0Oh10Oh2kUincbve8AUfMqXCpOELJZBIikQh3\n3HEHFAoFTCYTHWBjsVhgtVphtVo/cb+l8vwI2r+hoQG1tbUAfg8us1qtmJ6epuNG/9e1Z5FLOxKJ\nwOVyzSvUJ5NJ5OXlYdeuXWhsbER/fz+9MFLxUJLJG20CdrudRnwczo3xeyKRCHv27EFJSQnm5uZw\n7ty5lI0pcTBcLhc8Hg98Ph/9DhKJBKWlpdDpdPB4PHjjjTdw/vz5lI0eMTRms5mieoEbjfUFBQVY\ns2YNJBIJ3nzzTYyPj9PoKdW2kGg0CofDAZ/PR1PQMpkMeXl5SCaTOHv2LC5dupQ2c08yeYPwwWw2\ng8/nw+12Q6VSIZFIQKVS4aOPPkq5lr5Q4vE41RcKhVBcXIx4PA6JRDJvXCnbyDeRSMDr9dLaOZ/P\nh9lshsfjYT2xbGFHg1AopP2XmZKdEENSWFiI/Px8uj8DAwNwOp0Z7YVcLsfu3btRWloKoVCI8fFx\nXLp0CWazmdVeEPANj8eDyWRCXV0dFAoFnE4nBgYGMDExkXI6ejHdfD4fNTU1+PznP48NGzbA5XKh\nt7cX169fh8ViYQVaZIIy77jjDqxZswYGgwF2ux1ms3nePZGqboIyBm5EWbm5uTAajVAoFPTssXGw\nCGiKtLLKZDIEg0HI5XJ6l5CzSCRV/czWUJ/Ph+vXr0On0yEWi9FWUaFQSNu8UtVL6ugk0xmLxdDd\n3Y3S0lIYDAaKoSGSqm4madHMzAwcDgeuXr0KrVaL1atXQyaTITc3Fy6XKy29wO87iPh8PmZnZ3H0\n6FFkZWVBrVbTTAPJemZazgJuQUMNzJ/3SerKJH1RXFyMTZs2QaPR4Kc//Sna29tTru0RvUzyBOJ1\nZWVlYdWqVRAIBBgaGsKvf/3rtEBDiUSC9nQ7HA7a7pWVlYXi4mJEo1GcO3cO7777Lq1/pCqkVjo8\nPEyj9IKCAuTm5iIrKwvT09MUfZvOJUfKDASsIpVKEQ6HUVBQALFYjI6ODrzzzjuw2+0p9Rcu1O33\n+/Hxxx9jbGwMEokExcXFWL9+PbhcLgXdLCRBSUUikQiuX7+OqakpWi+srq5GPB7H+fPnaYsem9pp\nIpGAw+GAWCym7S9erxf9/f0IBALz6unp6AV+f5bz8/MpK9LExMQ8QCFbQ02Q9AUFBQiFQujo6MDA\nwEBGzFgcDgeNjY345je/Ca1WCwA4f/48rl27xnqGNLNd6vHHH4dWq0U8Hsfo6ChOnz6dVnllMb2l\npaU4ePAgtm3bBp1Oh+PHj6O1tRU2m40Vmxe5jEUiEUpKSrB27VqYTCZ4vV6MjIzA7Xaz2guSmiag\nSALaDIVC8Hq9mJ6eputN1YCQPZDL5RRXweVyEQwGqcEikSC5+9LJNpESEMkAEGdZJpNBLBbTkgDR\nn2qKXiQSQafTwWAwAABGRkYQiUTQ2NgImUyGQCBAeSNIUJRKulssFtPoPBwOw2q10gyqVCpFMpmE\nzWajjG+pZgEIkE2hUNBgwePxQCwWo6KiAjKZjH4vQivK9lwTuSUNNVOY0aFWq8W+ffsogvP06dPU\nA2PjfZN6D2kt0Gq1CAQCePPNN9HZ2Zl2KpL8XTK8naAJ77nnHlitVrz00kuwWCyso0hSlyEX6LZt\n25BMJtHe3k7btNL1wEmWwWq10nGiNTU18Pl8aGlpwbVr1xAOh9Pei0QigWg0CpfLhdnZWeppVlVV\nwWq1wmw2U7BNulEO6e32er2YnJzEnj17oNVqaeTERKanuxcELS0QCChopaurC06nk/4327MG3HiO\nCoWClnj6+voQiUQyBqjl5uZi7dq1FNxEWrMyiaa5XC727t2LnJwcuuednZ3z0sfpCgFOVVZWYsuW\nLQiHw+js7MSVK1fQ3t6ecbaisbERtbW1FIx64cIF9Pb2sgbqEQwHASGJxWK4XC4MDAygvb0dPp+P\nlZEm7X4KhQIFBQVQKBS062RiYgKTk5NpnzXSHWI0GiEWi2kr6NzcHPx+P7hcLq35Msk4UtW9YcMG\nADeyeZFIhDqBBClNuBZIu2EqjhGXy0VpaSnWr1+PZDIJt9sNtVqN2dlZyGQyFBcXw+1203Q6ybAu\nZ6i5XC4qKiqwZcsW8Pl82hUSDAYhlUpRWVmJ7u5uGtgsnFO9lPB4POzfvx9CoRAul4tyVshkMuj1\netTW1qKzs5Nmz1Ldi6XkljfUwI2LTq1W4/nnn0dVVRUCgQCefvpp9Pf3Z8SjShi47r77bnz3u99F\nR0cHnnzySbS3t7NuwSEPOjc3Fw899BD+4i/+AlKpFE1NTRSJy1YveXGNRiP+4z/+AzweD9///vfx\n8ssvs45wyHojkQiysrKwa9cuPPnkk/jmN7+JlpYWig9gY5xI9iIej2P9+vX41re+hcnJSfzkJz+Z\nV3tLV4hO4mRt27YN/f39eOGFF3Du3DnWxonsBQErlpWVgc/no6WlhSKc2Roo8nsmkwkCgQAOhwO9\nvb24evVqRp42h8OBTqfDQw89BJ1Oh9nZWVy7dg1tbW0IBoOs9BHDZzQa0dTURNG2drsd7e3trClO\nCcBr27Zt2LdvHxQKBV577TVYLBacP38eIyMjrPQKhUJotVoUFRVh586dCIVCOHbsGMbHx3HixAmK\nM0lXOBwOGhoaUFFRgfr6eigUCpw+fRqBQAADAwOUs4HN8xMKhXjkkUdQVlYGoVAIq9WK4eFhDA8P\nY2JiAgMDA/RuS9WYAjfutG3btqG2thb5+fkYHh5GZ2cn5HI5WltbcfXqVcqdTvSmGrGXl5ejrq4O\nJSUl8Pl86OjogFwuh9vtRmtrK65fv04NUjrEPT6fDxqNBvfeey88Hg94PB69Hzo7O/Hee+/BYrEg\nFovRlPNyQoBhBE1/5513UgbHubk5jI2N4eTJkxgbG6NOQTQaTYlEhrSQZWdnY9euXQgEAtSxJ+2z\ng4ODlAmQ4GkykT8IQ01qCeXl5YhEIjh9+jRef/31jMnOs7KyUFdXh8ceewzZ2dn46U9/SiPITCIc\nAFi3bh2+/OUvIysrC5cvX87ISDNFrVZj9erVUCqVeO+999DS0sLKavm6eAAAIABJREFUq18oJPp/\n+OGHoVAoqLPC1ugxhQD1JBIJTp8+jZGRkYx1EuCXXq8HAAwPD2NycnJF5ouTNH1eXh6CwSAtaaRT\n+19sveTSEAgEtA7J5CRnK6Sth8fjIRaLwel0sh7cQC5vPp8Pg8EAPp9Pyzp+vx/BYJD1egUCAQwG\nA5qamlBTU4NkMklTkqOjo6yjDrFYDKVSiQ0bNmD16tWU5GVmZoa26bF9bmq1GmvXrkV9fT0kEgn6\n+/sxPT2N8fFx2O12VusljpDBYEBubi5tS3M4HOjr68PU1NQnHItU1x6LxTA+Pk7PGWmZGh0dxdWr\nV2l3C1NvqqnvCxcuUOpS0oLq9XrR09NDGdiY70iqer1eL44ePUrBWQMDA5ienkZvby8ljSIZgXg8\nnpJzxOFw4Ha78eabb6K4uBh33HEH+vv7KerdYrHQbB/RlQqHASkvHDlyBHl5eRgfH0d+fj6uXLlC\nuRtIK1k8HodYLF6Re/+WN9RyuRyFhYV45JFHwOVyMT4+jn/7t39bluEmFSFGurKyEsFgEG+++Sbr\nth6mZGVl4atf/SoKCwvhdrvx9ttvr8jDInzCX/nKVzA9PY133nkHZrM54/UCN3rADxw4gOrqagq8\nYHr0mVzOarUa1dXV8Pl8FCGbaRTJ4/GgUqmwdu1a+P1++P1+2j+eyXpJ+rCiogKJRALBYJAiN0m0\nyUY3j8eDQqFAUVERQqEQwuEwpFIpJa1JVy/5+4QXQKPRQCQS0cuSLa0niYSEQiGMRiMlkJmdnaXc\n95mkvUtLS9HQ0AC9Xg+Px4OpqSkMDw+n3VVAhKSRi4uLUVNTA5FIhEAggJmZGfT19VG8AlvnKj8/\nHzqdjpIJ9ff3o6+vD3a7PWWWvsX0yuVyyu7mcDjQ3d2NM2fO0Jo3W052DocDq9WKnp4edHV1oa+v\nDzabDTab7RN3W7r7YjabcerUKXz88cfweDwYHBxEMBikaXQmhiXVDAbJ5rlcLhw+fJjS9MZiMUSj\nURo9kwxdqr3pRO/s7Cz6+/vR3t5OqZpjsRidw0CEfGYqQqhWzWYz+vv7kUwmaekRuBH0EGcsGo0u\n2ZeeqtzShppMMLr77rtxzz33YHZ2FseOHaOgALZCgBwPPvgg1q1bh3g8TlmWMjUgcrkcpaWlqKur\nQzQaxcWLF3Hy5MkVi3j37NmD+vp6CurJNKtAItOmpiZs2rQJsVgMXV1dGc9PJcZUIpGgqKgIAGjt\nbeGLna4Q9DthcfJ6vXC73ZQOMhMhbEukL12n0yGRSFAgDtsLn7DticXiefVCctmzBZGR3nSBQEAj\nDpJ2Y/MMk8kkbcGpqKiAQCCgKPLr16+zLgkBv69zGgwGyj7V09ODoaGhjBxkkoasqKigAL3h4WGM\njIxk7CDLZDJkZWXBbrfDZrPh2rVrlAUxk3Y6Ho+HmZkZXLhwAZOTk5TPYbFoMV0Hzu/3Y3BwEIOD\ng5TtbjEcRLr4jUAgAIvFAr/fj2g0+ononI1u4iwEg0FqLMkzY2I70n33SGksGAxSY8kEbIZCoXmI\n/lTvUYLp8Xq9EAqFVC/znJFeeOJIr8RIUk6mBmQlhMPhfGIRZKTeQw89hD179oDP5+P5559HS0sL\nBUOwEUItWF1djWeffRY+n4/WN69cucLa8BHO4rq6Onz5y19GSUkJWltb8fLLL2NiYiIjr4rH46Gu\nrg4HDhygAJzvfOc7GBsbo8hpNkIMnkajwU9+8hOo1WrYbDa8++67aGlpyQipSHrey8vLsXnzZtTV\n1dEa2cjICOt0PUGt6vV6FBUVIS8vDwKBAKOjoxgaGpqHlk1XLzF8eXl5UCgUyM/PRzQaxdWrVzMi\nJCEDW+RyOe3l9Pv9mJqaokAZNkKeX1FRESVQ8Xq9FFCXCTArKysLOp0O+fn5sNvtdK2ZjODkcDjY\nuHEjdX7GxsbQ19eXkVNIWnAMBgM2b96M/v5+jI+Pswb+MYXH46GoqIhy65NyRaYZLObMAZIFSjVS\nXEoItadQKIRYLKZ1XiJkL9LhLCDCJH0h5E+k1YvJg5BO2pupm1mHZ6bPyXlkGu10dRN0+8IefqKX\nGFM2oEAizMifOdyDvIdLPNsryWRy3XKfdctG1MlkEnq9no5HdDgcGB0dpXUEttEN8YiKiooQjUYx\nODiIixcvpszmtZTeWCyG0tJSGI1GdHR04MKFCytCC0kAZEajEZFIBNeuXaORWCbpWLJmqVQKLpeL\nvr4+jIyMIBgMpgVgWUwSiQSNqEUiEY3GEolExsQe5HJOJm9Q/JHhB+mkrxbTS9YUCATgdrthsVgo\nu1cmxoSk7QKBAJ0Kxfw5WyHIegLCYkYima6VkDUMDAxkrJPoBYALFy584lLORAiGwmaz4dVXX2Vl\nhG4m8XgcIyMjGB4eXnG9iUQCo6OjGetiCuliIV0nSwlbcCgTs8I0zuRPNncz+R0ixClgZt6YBjcd\nYa6NZLYA0M4QDofDuszAxJcQelDixJD9It8jU7llI2qCZt27dy/KysowMjKCX//61/Naelh+FvVm\nv/KVr1BeYafTmVGKghmN7du3D2fPnsXw8PA8vuJMdOv1euzcuRNcLhft7e0U8Z5pWw/xwDdu3Aiz\n2Qy3202nI2UiRLdarYZMJqMpuFSHeiylF/g9YUQmRCFLfcat8F7clttyq8qn8Y4wg46b6U7nc5nO\nFXPU5GK/nwlGhBjsxXST75RpRH3LGur/9+fzUpKkfrFCn0lTRSQ1sRKgLKbupQ5GJrqXefAZ6b4V\nzsNtuS235bb8H5E/7NQ3ML+vdaUMNFN3KoTumej+NGSlDf9C3bflttyW23Jbbi25pQ31bbktt+W2\n3Jb/e7IYJiDTjB/JRi6sG7MZsrNQLykhMkGBBDTItt2OKbcN9W35XyF/yGn7lQQq3dadmu4/tLOy\nUmu+GfCLrYFiCkE7M0t+bPUy10loOAlQjgDA0tXNZDQTCAT0n7m5OUQiEao33bJiMpmkw5IIa5lG\no0EwGEQkEqFrzaRcedtQ/wHKpwXk+DTr6gQVScoCK3Xp8Pl82po0Ozu7IoQ1RDcha6mrq0NPTw+c\nTueKrJ3oLikpwaZNm9DW1oaJiYkVW7tQKEReXh62bduG7OxsPP/88xlRtjKFMJZt3boVtbW1OHTo\nEGVkynTtHA4HGo0Gq1evRnZ2NgDg2LFjdBzjSgAny8vLUV5eDplMhsuXL6c8eW853WQi2oYNG+B2\nuzE9PQ2Xy8WKFIX5fhOjRQhouFwu5QRPRx9TyPtNdJPZ5eFwOC1mv4X30EIyFQL45PF4aUWVTENN\n9C9sCUsFeLbYWpmYJB6PR7tyMj1bzOdFJhISp2Il+qhvSUNNLmAyiYXP59OHzmxBSCQSiEQilOJx\nuc1mAtMIU5RQKKTeGqFLJIfW5XLNm3+6lJADyefzIZVKkZWVBR6Ph0gkQo0JGYnmdrvpHOZUD5lQ\nKIRMJqMTW8jBVavVUKvVcLlcsNvtmJ2dnTeNKdV15+TkwGQyISsrCz6fj3qFc3NzlDkpHb3Mdgi1\nWo0HH3wQcrkcfr8fXq8XNpsNTqeTjthM50Ijz1EsFqO4uBh/93d/B5FIRGkST548CY/HQ2k00zVO\n5Hk1NzfjwIEDqKurQ3d3N9544w1cunSJkrawEQ6HA71ej8bGRjz22GNYt24d2tvb8c///M/o7e2l\nIy/Z6ubxeGhoaMB3vvMdbNmyBYlEAmfPnkV3dzf8fn9GQzo4HA5qa2vx93//92hsbEQikcDAwADe\nf//9eVPF2OoWCAT4l3/5F6xfvx4ajQY2mw0nT55EKBTKCKPC4dygb62trcVLL70EsViM8+fPw+l0\nYmxsLCPHlwyyIbPh6+rqcOrUKbS2tmJ6ejrtdTL3kNyBRqMRe/fuRSAQQF9fH1wu16JrXup7LEwh\nk/enqqoKSqUSiUQCly9fntdetJTuxYwk04CSThViuGZnZxe9Oxa2XDE/h/yc2adNhrqQz76ZA3Az\nvcxxpKFQaJ4BJ2RJzFa0hbIQzEu+61J98IsFQWnfSWn97f8PhMzWFQqFKC8vR15eHpRKJfR6PX3w\n5KWNRqPw+XyUmpLQxN1sE0jDvkwmQ05ODlavXg29Xo/s7GxqYPl8PiKRCAKBAC5duoS+vj7MzMzQ\ni/9mQgg+jEYj8vPzsWbNGhiNRvD5fIjFYmRnZyMcDtNhDG+++SYCgQCi0eiS5BTkIWdnZyMvLw/Z\n2dkoKyujbFQ6nQ4qlQp9fX1obW3F5cuXYbPZUprIRC52uVyO4uJi1NfXo6SkBCKRCBKJBEqlku5t\nf3//vIO9nJCLVyqVQi6XY82aNXQK08zMDKxWK3p7exEOh+H1epfVt9ie8Hg8yGQyGI1GSCQSBINB\nlJaW0hGamRglEiEpFAp6bogjlmlUStrWdDodhEIh6xGMi62by+XCZDKhqKgIfD4fDoeD0htmkikh\nF9Tq1atRUlICAAiHw3QyVSZ7TUQikaCsrAx6vR5+vx9dXV2UCY2tMSV7otFo0NTUBKVSCYfDga6u\nLrjdbsrmxlY/n8+HTqfDfffdh127dtGJWolEgnaVpDN7nikikQi5ubm4//77UVFRgdbWVhpIMIlH\nbvb7S/2cx+NhzZo1KCwspO+gUCikBioV3Tdrc+Lz+dBqtZDL5bQXmjB1LTznC43pYj8nn0UmpBFd\n5O8R/vGF5C4LI93FjCTTKSKpcaZTsNBgL/zO5HcWI5Yhung83rxUPllrOuftljLUTG9MKBSirq4O\nzc3NqKqqglQqpXM+hUIhTbURUvX+/n5YrVZYrdYlmZ54PB6USiUKCwvx8MMPIysrCzKZDMANcv9Q\nKERTNSaTCXq9HpcuXcLc3NyShpp47TqdDmvXrsW9994LlUpF+WlJPSQUCkEmk6GtrQ1msxmxWIxe\nFosJeZhSqRSFhYVobm6mqTuPxwOlUgkul4tVq1bBYrFgbGwMLpeL1lyWOwzE4K1atQpr165FYWEh\n5ubmYLfbEY1GodFoKPvXwhTackI+OysrC7m5uVAqlRgdHcXs7CxlciMvVLp6yYurUCjA5/PpDF+b\nzZZxaSCZTNLnRabwmM3mFRn4AdxITSuVSojFYiQSCTpMJFO6S/LuVFZWQqFQIBQKwel0wuPxZMQy\nR54Pl8vFunXrIBKJkEwm6XCKTGftAjfey9zcXJSUlEAoFMLpdOL48eM3jfBSWTM5WyKRCOvXr8cj\njzwCDoeDzs5OXL16FR6PJ20nYOHlr9fr8fnPfx5f/OIXoVKpcOTIETgcDsRisXkXdLp6ORwO1qxZ\ng3379qGpqQljY2PweDzU+Z2bm0uJ64CZQmZmLnJzc/G5z30OY2Nj9J3RarWYnp5OaeraYoaQ3K1K\npRINDQ10/KNEIoFQKEx5ghQzamWmwUUiEQwGA4LBIB3EMjMzQ4mEFq5vsfUSQ8lMnXO5XEilUkSj\nUYhEInC5XMjlcoRCoUX3YjHdTIeM8OWTPyUSCf1/SqUSoVBo3jCQVOSWMtTMFASXy0VjYyNqampo\n2jgQCNA/k8kb86n1ej1isRhcLhe9QG4m5OET2kW5XE7Hk3m9XsrprFKpoFQqUVZWhu7ubsTj8WXH\ntjEfhEqlohew1WpFJBKhETHhvubz+Sl78mRfSIo+HA6jra0N09PTUKvVKC8vR0VFBbKzsynzTjp7\nTjxdMvWlq6sLPp8Pa9asgV6vh1arZT3qkvyOx+PB0NAQRkZGMDc3h6amJni9XggEAtb1IZJ9cTqd\nmJ2dhcfjQU5ODuRyOSU4YCPkYhAKheByuYhEIlAoFNDpdCsSUQOgs4hJREAiD7bC/N2GhgYoFArM\nzc0hEAikNBUoFf0ikQgbNmyAVCpFIpGAxWJhPQaV6CQXvFqtxv79+2l55PLly+js7EybIIe5D8Th\nX7t2Lfbv34/KykqMj4/j4sWL6O3tpe98Olmihend++67D48++ihycnLgdDrR3d0Nh8Mxj1t6OWGC\nsMi/S6VSfPe730VTUxN8Ph88Hg8AYG5uDnw+HwqFAj6fL2W9JNIVi8UoKSnB1772NWzbtg0jIyNo\nbW1FXl4e4vE41Go1BgYGlnRKmalcZnYrLy8Pe/bswQMPPIBYLIaLFy9CoVDQ/Th8+DBmZmaW1MuM\nUEnJk5RA/+RP/gR8Ph8ejwcikQj5+fkIh8Po7u7G7373uyVLDQQjQ+5ykUhEU91arRZNTU0Ih8P0\n30UiETo7O/HSSy8tW8IQiUQ0MGJm35RKJYqLi+lc6ubmZmi1WggEAvzN3/wN3G73knqZcksZamYd\nIZFIwOl04ty5c3RsHamzBYNBGAwGvPbaaxCLxXC73fjwww8xNja2pJdJdIvFYng8Hvz2t7+F0+nE\n0NAQvF4vvF4vysvL8bWvfY3yBp88eRKjo6PLepmEmN3tdmNwcBC9vb3o7e2lQJt77rkHjz/+OBQK\nBSwWC4aHh+lc2OUuIpI5cLlcGB0dxeDgIN58802EQiEYDAY89thjqK6uRnt7O0ZHR+mUmOWEGOhQ\nKASxWAyr1Yrr16+ju7sb+fn5uPvuu2EwGGC329NOJZMLUCKRoKqqCv39/RgeHobT6URVVRWysrJg\nsVhgsVhYpX0FAgEKCgqQn59PhwQolUqo1WpMTExkxMlMUt75+fnIzs7G9PQ0kskkRkdHV2Tyl8lk\nwo4dO6DT6TA1NYXW1lbYbDZq9NhKMpmEXC7Htm3bEIvFMDo6io8++ggej4d1ewgxSiKRCDt37kRu\nbi7i8TguXbqEp59+mvWQDqJXKBSirKwM3/3ud1FfX4+xsTE8//zzeOONNyi/erqGmuloPfHEE9i9\nezcaGxsRDAZx8OBBDA0N0SxAOpgLUvoQi8UwGAyor6/H9773PQgEAnz00Uf41a9+hdbW1nm10+XW\nzsTM6PV66HQ6FBcXY/Xq1VizZg3a2trwu9/9jta8ydjVVPacZIREIhGMRiM2b96MwsJCbNiwAWVl\nZTh8+DAuXLhASy8WiwUcDmfZu45E5RKJBAUFBaivr0dOTg7WrVuHmpoaWCwW2O129PT0IBKJYGxs\njE64W24vJBIJkskkRCIRvvSlL8FkMkGtVkMikcBgMFB8y/T0NK5evYrp6WnIZLIlwXUcDgdKpZLO\ns87Ly8NXvvIVyGQy8Hg8FBcXw+VywePxwOPxgMPhYGxsDLm5uctmUYmTGY/HkZ2djbvuugtFRUXQ\n6XQwGo1wuVwAgNHRUTrdjpQ805FbylCTCIykg/v6+hCNRjE8PAyv14vZ2Vl6qRcUFECtViOZTGJw\ncBBnzpxZNnIgEfPc3BxsNht6e3vh8XgQDAYpv3V+fj4aGhoglUpx5coVjIyMpASiIk6G3++H0+mk\nKUdy6NeuXYuCggKEQiFMTk5SI52qJ09eerfbTQ88qS03NjbC4XBgYmKCGtR0hLx4ZAC6TCZDc3Mz\nysrKwOfz0dHRwQoxzOFwoFKpoNFoIBAIoNVqUVJSgtraWgQCAfT397NOm5LUF0lT5efnw+fzIRKJ\n0IEfbA0quTjJBUHAK5OTkyuSQq6oqIDBYKCRNMm6rESkXlRUBLFYjGAwCLfbjaGhoYx6RIloNBo8\n+OCDUCqVGBsbQ0dHB8xmM2twDHlfpFIpdu3ahfr6euTl5eGVV17B1atX4fV6kUymj8Zl1gLFYjHu\nuusu1NTUIBqN4sqVK5iYmMDc3Fxa53lhJE3AY5s2bUIwGER/fz9effXVeTPcU1n3QuMvlUqhVCpR\nUFCAkpISHD16FO+//z4uXrxIcQbpAlBVKhWSySQ0Gg0kEgkEAgE6Oztx4sQJvPDCC3SoD4k2U9Er\nEAggFouh0WiQk5NDZzCfPn0a77zzDkZHR3H9+nWasUh1rwk2xmQyIZFIwOPxQC6Xw+12w2q1YmJi\nAj09PYhGowiHw2nRSUulUhQUFCAajaK2thY+nw9OpxN9fX0AgDNnzlA+fi6XmzJwVqVSQSqV0tG4\nDocDGo0GJ06cgEgkgsfjQWdnJ7xeLwXWERuXjtxShhoAfTlDoRAFkxADTQ6/QqHA3r17IRKJMDIy\ngiNHjsBsNqe0sWSubDgcRigUQjQapT1wpG5DDt+pU6coCne5F4+gCN1uN3g8HgKBACQSCSKRCHg8\nHnbs2AGZTIbh4WFcuHBhHvowFSGj1Xg8Hubm5iiA6q677kJZWRmee+45jI+Ps4pwOBwOBQSVlpZC\nIBBg48aN0Gg0GBsbw9mzZ1nPNhaLxXQIisFgQF5eHoxGI95++21YLBbWhPhisZiumwANFQoFzGbz\nigCQiIcsk8kQi8XoqMdMR3QCoOUc0h/KnP3NVkjadPPmzZRkwel0oqenJyOnhei96667sHnzZhqp\nnzt3DlNTU6xnSJP1bN26FQcOHEBeXh44HA4+/PBDXL9+PaNpUsQhr6+vR319PcRiMTo6OvC73/1u\nnoOczp4QYJhAIEBpaSl27dqFLVu2oKenB2+99RbtNEgn68TE48TjceTm5mL16tXYsGEDJBIJDh8+\njEuXLs2bF5Cqbi6XC6VSSSN1g8GAnJwccDgcDAwMoL29na43nX0mUa9Wq0V1dTU0Gg2kUikAYGBg\nAENDQ6wxFyqVCjqdDgUFBTAajVAoFPD7/QiHwzCbzbhy5QqrvmQSRVdUVNCyJJmvPjMzg7GxMbjd\n7nlDR1LNWBgMBuh0OqxZs4beGT6fD8FgkJZBFu4Fm3dx6cLr/w9C0lGhUAgTExO0F5F4IBKJBNu3\nb8fOnTsxPT2NY8eO4dy5cyn1FRIq0mAwSKNaAsrgcrn47Gc/i1WrVgEAuru7YTab0xpUHovF4PF4\naFvX3NwchEIh8vPzadrmvffeQ2dnZ1ovHXECZmZm6MB6uVyO2tpa3HXXXRCLxWhpaaEvdLoSj8dh\nsVgo+lOv16O6uhoAcOLECYyMjMxLKaYjJBKdmZlBdnY2TCYTjEYjLl68SJ9ZunpJTYyASJRKJW2J\ns9vttPbPRpitF8FgEBKJBHK5HIODgxnNYia6gRtnWCqV0u4GAozMxKACNyKS1atXQyAQIBKJ4OzZ\ns7DZbBnrlUqluPfee6HT6TA5OUnPMIkO2DpDAHDgwAGUlZVBKpVifHycvstsiCeYtWODwYD9+/dD\nrVZjbGwMv/nNb/Dhhx+yGuJCdHK5XOTl5WHnzp1Yv349DAYDjhw5grNnz1InLt0oneBxxGIxGhoa\n0NDQAKPRiImJCRqJLZxatZxe8ieZgW40GmEymaDVajE1NYXe3l5MTEzMM9LpOAAikQh6vR5qtZqC\nAAmwi225ArhRqigtLYVWq6XOgFQqRSwWQygUmlcWTWcveDweqqurIZfLIRQKEQ6HIRaLweVyaUqe\nOOPpOm+JRAKVlZWQSqW0/i2TyTA7OzuPRCUTIw3cohE1cMN4kLQxOdRGoxFf//rX8cQTT4DH42HP\nnj3o7+9Pi2yBgLGAGw+QgMQKCwvxzW9+E36/H88//zz+67/+i0aZqa6b9HOHw2GIRCIAQElJCb70\npS/B4XDgBz/4AT788ENWac5QKITR0VGIxWIIhUIUFxdj48aNKCsrw6lTp9De3s5qsEgymUQkEsHF\nixfpuMuKigpoNBq8+OKL+MUvfgGn08naARgcHMTk5CTsdjs2b96M7OxsSCQSdHZ2UsPHBqQ2PT2N\nUCgEj8eD8fFxSKVSJJNJtLa2ZtxzS14sgjtwOBwYHBzMaFIXuTQEAgE2b94MoVCIUCiEK1eu0HQp\nWyEX/RNPPIH7778fdrsdr7/+Ok6ePJnRmFUOh4PS0lI8++yz2Lx5MwQCAX72s5/h1VdfxezsbEbr\nlcvleOCBB7Bv3z4AwOnTp/HMM89gcnKStV4Oh4OcnBw88cQTOHDgAEwmE9566y0899xzaG9vTwnN\nvFBI1oPP56OkpASHDh2C0WiEz+fDkSNH8O6771JwKxNhvZyQdlDyvu3Zswc7duyA0+nE2bNncfTo\nUUxOTqbtUAiFQkilUkilUtTW1qKmpgY8Hg/l5eWYmJhAd3c3bDYbBaKl2j5G7l+VSgWtVouCggIE\nAgFUVlbS78NsSSNZnVT0SqVSaDQarFq1CnK5HC6XCwMDA9izZw+0Wi1mZmYocpwEW8vhWkiJQqPR\nIDc3F36/n6bji4uLUVdXh3g8jqysrHkTA1NxtjgcDrKzs5GTk4P8/HxMTk6ipaUFBQUF0Gq1NBgh\n2QAC1M3EWb7lDPXNJJlMYsOGDdi3bx9kMhlsNhuGhoYyAuCQy1gqlWLt2rXg8/kYHBzE66+/Drfb\nzUovSclEIhFIpVKUlZXhzjvvxMmTJ9HW1kZTN2z0Es8yHA4jKyuL1shOnDjBKp3H1D07OwufzweJ\nRAIAcDqdaGtrSzudxxTiFM3NzYHH46Gurg4mkwlms3leFMlGL0knRaNRuN1uiEQiDAwMZBTlEd0E\n8EFKI1arFQDSTuUxhfweSduTUobFYlkRNjKNRoOtW7eCw+HAYrHQ/v9MMwA7d+5EZWUlRXlfuXJl\nWVDQUkLSxyaTCfv27UM8Hkd7eztOnDiBK1euZLRWHo+HxsZGbN++HVqtFpFIBO+++y56e3tZMYSR\n9YpEIvq+abVa+Hw+nDp1CsePH0cwGJy3x+nUj2UyGQwGA5qamlBeXg6Hw4HOzk6cO3eORrzk76e6\nB1lZWVAqlVAoFKipqaGkRdPT0+jr64PVaqXtY6QEkcqaSQuoTCaDyWSCVCql2AqyvqmpKUpAxaTr\nXE4v4XDQ6/XUaQ2Hw+BwbjDVkUxfOBymXRfLGWoul4uioiI0NzdDIpHA5/NBp9MhkUhQTMv4+Dii\n0SgikQjtlkklqiZof7VaTd8LsucqlQplZWU4duwYDZxIhuv/hKEWCAT44he/SBF6x44dy5i2MJm8\n0S+7atUqHDhwgKbISH9zJnp5PB4KCgpw//33w2Qy4ZVXXqHAjUwue9K2sGPHDhgMBpw5cwYnT57M\nuA5J+j7JbOoLFy6gq6sr4/0l/3A4HFRUVEAgEKC7uzvj9TLA2AhpAAAgAElEQVSBhwqFAvF4HDab\njYKQMhGCWBcIBIjFYpSadCVEKBRCJBJR7AVBhWYifD4f5eXlMJlMFIMxOjqacWaB9E3z+XzMzMxQ\nroJMjb9IJMKWLVuQk5OD4eFhnDt3DpcuXaItSGx08vl8iEQibNu2DTKZDD6fD11dXejq6sooq0AA\nXuXl5airq8PU1BTOnDmDM2fOYGRkJG0jTYQ4K6WlpSgpKUEkEsGpU6dgtVoxOTmZdp8tEdK2ZDAY\nUFlZCbvdjkAggO7ubszOzlJjyuwlTlVqamqQnZ0NoVCInJwc+P1+uN1umEwmhMNhSgqVbhZALpej\nsbERyWQSRqMRH3/8MXQ6HbKzs5FMJjE2NobJyUnE43GaBV0OJ0KMfFNTEwW/Xbp0CZWVlSgoKIBI\nJILdbqdsjsRQp+LQcblcbNiwAQAwPT2N1atXo7CwENXV1dBqtVCr1ZienqZgOplMlpFzC/wBGeqK\nigps3LgRoVAILS0tOHTo0IpwFxcXF+P+++/Hxo0b8Ytf/ALHjx9nXetlilarxR133IE77riDthIs\nRcSSjohEInzmM5+BxWLB22+/jZGRkRXRS2j/mpubKdhrJRizgBtUpxUVFbBarRgdHc2IDWqhKBQK\nuFwuzM7Owul0ZqyPAHwUCgU4HA68Xu+SjHfpCOnxnpubg9PphM1my1inWCzG6tWrKYGM1WqF3W7P\n2KBKpVJUVFQgHA4jHA5jcnKSIurZ6hQIBKiursaGDRug0+nQ1taGoaEhDA8PsyaU4fF4tAWppqYG\nEokE/f39OHv2LOx2O+szzOHcIDSpq6vDnj17UFpaira2NnR3d1NioUz2mAD0TCYTZmZmMD09jaGh\nIbhcLtZ3RSQSQWlpKTZs2IDm5mb09/fjrbfeQiQSQXd3N7xeL80MMXusU/kearUaq1atQkVFBebm\n5nD27FkAQGdnJ82SEX3p3J8OhwNerxd79+6F1+uFTCbDwMAARkZG0NPTg56eHpqqTzVzyOFwMDk5\nCbPZjNLSUmg0Gmzfvh1utxuRSARHjx5FW1sbZmZmaJcRkHpGZHh4GDk5OWhubobP58O6desQDAYR\nCoXw0Ucf0Q4RgqfJ9O645Q11UVER9u/fj0cffRRutxs//vGP8c4773wi5cRGHnnkETz++OOoqanB\nxMQEnnrqqRVpk1EoFPiHf/gH3HPPPXA6nfjHf/zHjGp6RAj/9vbt28HlcvHMM8/gtddeW5FoTywW\no7S0FN/+9rehVCrx9ttvUy8wE4NKop19+/ZBqVRifHwcp0+fprWsTPp7SVRCuNktFgv8fj+l/mMr\nMpmM0stGIhFaayKfy7ZGLZVK0dDQAB6PR1taMr3sAaCxsRH33XcftFotQqEQOjo6WPdOEyILgUCA\nmpoa5OfnI5lMYmRkBOfOnUu7/5MpfD4fNTU1+P73v49169YhFArhgw8+wMmTJ2G1WlkjyEnE+/DD\nD6Ourg4dHR347W9/i9bW1oycbpLivPvuu1FUVITZ2Vm8/PLLGBkZoe1SbITH46Gqqgp33HEHFAoF\nOjs7cfz4cbS0tCAYDC4KxkrljBDSEZJ6fuWVV3Dq1CkakTLnFqST9iZ//9y5cwiFQmhvb0dvby/e\nf/99Suyy8L1IR3cwGMThw4dpRmFqagrhcJiCfJllo3S6ATweD1588UWoVCpMTU3RtZKsJFknWUOq\n7WkcDgf/8z//A7FYTAmFSLYUAG3nJH8/E+eWyC1tqHNycrBjxw489NBDMJlMeP7553Hx4sWMqRwJ\n9+/+/fvpC/juu++uyBQdQvXZ2NiIQCBAWZAyFYLgLC0txY4dOzA2NkZZvjKNTLlcLlQqFdauXYus\nrCx4vV7aVsYEyaQrTLIMtVqNaDQKj8eDQCDAGkXO1C0QCCiRAQGCqNVq1jqJXpKeZvbyEpQoWyG1\nWYlEQoElHo8n45QYl8tFQUEBbfEJhUK0958tXoGAvQjy3+/3Y2hoCF1dXRlnsTZv3ozc3FzweDx4\nPB5cvXo146lkhG1r/fr1mJubQ3d3N/r6+mC32zPOjFVWVqKoqAiBQACDg4OUAjddxjSmkFqyWCzG\nxYsX0dbWhitXrtyUWyEdY6pQKCCVSmG323HkyBFKpMM0cOmA3pi6I5EIJS5yuVzzjBtzcEa6z5HD\n4dB23GAwOA9nwuzgSEcv+a7hcBixWIxmEsj/I/cm+fx0QMOxWIx2DPl8PtqXT/aA0FCTeznT1kvg\nFjbUXC4XDQ0N2Lt3L/Ly8hAIBHDixAma989EL0nDlpeXIxQK4dq1azh//vyKpAr1ej02bNiAeDyO\nkZERdHR0zGsdYxuNicVi5ObmYsOGDVCr1bBYLKxrekxh0v8VFhbSCV/Ml4PtmpktImQMJUH1smm/\nYeomho/H49E0k9vthtPpzNh75fF4cLlcmJ6eht1uR39/P2w2G2swGbNNJB6Pw+l0wufz4dy5cxge\nHs743JFIY2ZmhhL5sEWZkt8RCASIx+OIRCJwOp24du0aBgcHM1prPB6n50AgEMBut8NqtWbMyObz\n+Wg5oaOjA52dnSmxCS4nhD7W4XBgYGAAH374YdptWDfT63A4cO3aNbz33nvo6+vLKEInwuFwYDab\nUVBQgNbWVgwNDbFqO7qZhMNhXL9+HXa7fV4/88I1pHNnEIdVpVLRCVuL/T455+kY1NnZWYjFYni9\n3k8QFZF/Z1IzpyqE54OQS93MASI00f+rDbVIJEJdXR1FAnZ0dKCnp4e+fGwPHmEBKi0tpXWbEydO\nUIYatnpJKletVsNkMmF8fBwDAwN0PGQmusm61Wo1srKyEA6HMTAwQFl0MgFmEdFqtXQoyejoKABQ\nL5wt+h34PWrW7/djcnKS1uBSHR+6lCQSCbhcLrz11lsIhUIYHh6eh5ZlK7Ozs+js7ITb7YZCoUBv\nby+mp6dZX6Tk+QQCAZw+fRpWqxXBYBBTU1MZO1vJZBItLS1ob2+HQqHA7OwsZmZmMtqDRCIBv9+P\nM2fOwGw2w263Y3JyMu0pZ4ut9cMPP0RfXx94PB4GBwczXitwI2354Ycf0nbAwcFBGp1mIvF4HIcO\nHUJFRQX6+/tht9szNtJE78DAAN544w0MDQ3R6CxTicfjmJycxAcffACZTPYJI8IW+AbcOBO9vb20\nr5noJMaTrWOfTN6gWyV82kQPaRtjZt7SOSck8g0GgzS6JZ0BRDeTrzydrGQymaQZR7JWEjQQLnaS\nmVqJjCcAcFbC08pUOBzOvEWQPrw777wTO3fuhM1mQ0tLC3p7e+chfll8Dp1nWlpaiqamJly8eBFW\nqzVjRDbxzFQqFTZv3gyz2YzJyUm43W7aX81WSJ23uLgY+fn5mJ2dxdDQUFq8v8vpLy4upi+21+td\nkWgdAEWSkxRQJpH0QlkpMNptuS23JT1ZyXePWQJbOLc5k89lOg/EeJJS1kJJF9fCNPYkIl+s3ZTY\nnCXu/yvJZHLdst/lVrjoFhpqxs/nsQ2t9CXPnBfLtqf3ZkLquysR7TIl3RRQurpvhfNwW27Lbbkt\n/0ckJUN9y6a+gflplJXI8y/UvVL1g8Xk09K70oZ/oe7bcltuy225LbeW3HJc37flttyW23Jbbstt\n+b3c0hH1bfnfJWzbN1LV/YeaEfg094Xo/7T2HPj0nuenqftWPysLWxdXotzFBGYxyU4y5a8nQoBa\npA58s3rwckLaIJnAMlJKJIMv2LTH8fn8ed+fDMUh8xk4HA7rtjsyzS+RSIDP51NOg4UjOdnK/3lD\n/Wm9tARgsNK6yQFbCRAZU8hgBy6Xi2g0mhGxxWK69Xo9hEIhbSNaCf0EqFFVVYXy8nIMDg5iaGho\nxRjgCDjwy1/+Mi5fvoz29nbW/cmL6dbr9diyZQv8fj8+/vhj1vzyi+lWKpVobm5GY2Mjfv7zn9Mx\nfpmuncvlQqFQUOar3/zmN7RXdyX2RaVSob6+HkVFReBwOHj11VdZD29hCsGkkNGXUqkULS0tGBwc\nzLhMRd5JmUyGRx55BCMjI7h+/TqmpqbS4hhfjFeAnHGlUon169fD7Xajr68vZbDnQuMMgN5LZN1G\no5FOGUsHRLqQcYvMnia4H5VKBbFYDLFYnPJeMNHZZJ0ETc3hcChOic/nUw7tVIC6C/WS94wMXmIi\n19O9u8nZYiK9yQhYcp+Svwewcz7/IAz1p+VdE87YlTaoBKVNehhXUi8h82c28DP/P8Bun0QiEQwG\nA9RqNdxuN8xm84oYDbLmbdu2obi4GBMTEzh16tS8KUls957H40Eul+PP/uzPUFJSgqtXr+K5556j\nlKps9ZIXTy6XY+vWrfja176GqqoquN1uDA4OZuxkkJm+DQ0N+PrXv47Z2VlMTExQgpVMz6JQKER9\nfT0OHjyI1atX4/XXX6f8A5nqFolEKC8vx8GDB1FWVkY5ujOdDgTc2Jfy8nJ84QtfwJo1axAKhfD2\n22+viG7CRfCNb3wDTU1NtDd6YGCAlS6yHnL5S6VS1NfX49FHH8XJkydp1wdbIZc+OStbtmzBfffd\nh3fffTflNTPvg4X9zWTdRqMR+/btg0KhwM9//vOU17dw6hbTyJG1V1VVQS6Xw2w2QyAQLGuoyXtH\nDDL5WTQapesnrVByuRzRaHQ5RPW870oicbIf5FyRz2TyiBMSk5vpI79HEOUkEhcIBPOIWsjwEyZF\nKZv38JY01OSLkbm9QqGQ9vgSz4fZJ5fqeDLg9z1vfD4fRqMRcrkc8XgcPp+P/pzH48Hn81EijVT0\nEuPM5/OhUqlQWVkJLpcLu92OaDQKo9FI+36dTmfawyOEQiHkcjkMBgMqKioQCoUwNTUFgUAAhUIB\ni8UCh8NB0yzpeoN6vR6NjY3YtGkT5ubm8NFHHyEYDGJ2dhZut5tO3ElHiG6RSITdu3ejrKwMMzMz\nMBqNOHLkCLxeL501znb2sFwux5o1a6DVagEAW7dupXueSQTJ4/Gg1WpRV1cHrVaL6upqlJSU0Bnl\nmRgOHo8HmUyGqqoqlJSUwGq1QiaTUYKETCNHMpd6/fr1kEgkUKlU4PF4GTsYHM6N0YF79+7F+vXr\nIRaLIZPJViw7wuFw8PnPfx533nkndDrdinHNk8ixsrISe/bsAZ/Pp73xbIT5fAjRUUNDA773ve/R\n2e5L8QQslcVj/pzH40GhUGDTpk344z/+Yzov4Ga9uQuNJ9MoLzTSzJndJpOJcqIvppfZdUNkMSYy\n8t+EqyI7OxuhUIj2dC+2D0xDy0yVL0z1k7/L5/NhMBjA5XLpJMHFhAzUYbZkMWdvM9dMhn1IJBL6\n85udO5KGJ6ls5jpJRuFmv8PsAvqDN9TE4CkUCjz22GPYvHkz8vPzqRGNxWKQyWSYm5tDJBLB4OAg\nbDYbzpw5g+PHj9OLfzEhxj8/Px+bNm3CY489BoVCQWkdSa+dQCCAx+PBU089hQsXLlCCjqUMlUAg\ngE6nQ21tLe3/ViqV8Pv9mJqagl6vh0gkgs1mw+HDh/Haa6+lPJKRy+UiPz8ftbW12LRpEzVMPT09\n9IANDw/j5ZdfxvDwcMpOCzn8ZGB7c3MzNm/eDIlEgmg0Sp2g69ev48SJE8vqW6ibvOA8Hg9qtRoG\ng4GmxEwmE60NsTVM5PeIJxwOhxEMBleknEEuPbFYjHg8jrGxMdjt9hVNH69fvx4qlQqvvPIKbDZb\nxg4A2fOysjI8/PDDkEqlcDqd6OnpWZGoFAD27t2LBx98EGq1GlarFceOHVuRgSUcDgdarRZf+tKX\nIJVK0d3djb/927/NOKVO7pM77rgDP/rRj8Dj8XDkyBH853/+JywWCyt9zPXo9Xo88cQT+PrXvw6p\nVIq/+qu/wuXLlylD3mJnMZX+YA6Hgw0bNuDb3/42Vq1ahYGBAbz//vuU0GexSPJmxpD5mWQ/CgsL\n8atf/QrXr19Hd3c3urq6aPaLzGFnrvdm0SVzzVwuFzqdDhUVFdi/fz9Onz6NqakpxGIxescu1LvY\nnbrYXnA4HKjVajQ1NcFut4PL5VJSm0AgQB1zIks5eOS5ECGZhWAwCLFYjEgkAj6fT2vMzH1dSADF\nJE1Z6LCQ6Fwul1NnQalUIhAIpM2weUsaanJJkoh3bm6O1jbJxS+VSpGdnY2KigqoVCq0t7enxMVM\nahwikQgej4cOR7Db7XR8m8lkgkwmg9FoTHmwejwen7fO8fFxxONx9Pb2wuVy4TOf+QzKy8uhUqlg\nNBoBpJ6aTSZvMOF4PB5YrVaoVCqcPXsWExMTUCqVqK+vh8FgoNOT0tFLXsJAIACXy0XrVGNjY3Rc\nXroD7Jnfjfzp8/lgt9sxPT2NcDiM7OxsOBwO1vzZzPUwX9KcnJx5PLtshaSnlEol/RkBjGQqyWQS\nKpUKJSUlEAgE8Pv9K4oJIM4tn8+Hz+ejDmEmQt7L/fv3Izs7G1wuN6OJVwt1i0Qi7NmzBwqFAl6v\nF6dOnaIER2z0Ab9PH5eWluKRRx5BdXU1+vv7cfToUZjNZuq8pHpWFtZ6ORwO7rnnHhw8eBAajQZO\npxOXLl2C0+lMa88XvgPEYD755JNYv349fD4fvF4vZmZm6NAgAoJKRS/5jsRI5+Tk4Fvf+hYKCwsp\nla1CoYBQKIRAIFiWzpR5RxO9HA4H2dnZOHjwIB599FHEYjEMDQ1Br9djeHiYDrBYSi9J9QOgtW7m\nff+rX/0KAGA2mzEyMkJLLqOjo+ju7l6SN18gENCIl+wDIdYqLCzEnj174Ha7AQDl5eXw+Xy4cuUK\nzp8/Tyd33WwvSLaXGZjweDxkZWWhtrYWUqkUALB27VqIRCIIBAL88Ic/XFLvQrklDTXJ7Y+NjcHt\ndoPDuTGBZHp6Gj6fD0KhECqVCt/4xjcQj8fhcDhgsViWTaGSNIfX60VfXx9Nj3g8HszMzIDD4aCy\nshK7du1CXl4exsfHKdhguZcumbxBETkyMkJT8h6PB2azGW63G1qtFtnZ2dRBYKZ7lpNkMgm3242J\niQk6dGJ4eBgOhwMGgwGFhYUoLCyE3+9PO1WYSCToWoiBJp4kn8+HUChMmwuXuW4A1EslTgGZXEM8\nd7aXMSk1KBQKqhNARgMTiG4AkEgkKC0tRSKRwNTUFFwu14qkYrlcLurq6mA0GsHhcNDT00PLOpkY\nVHIh7969m0YwHR0dK5LyJsa0qqoKQqGQRtOZZhjIxVZZWYm9e/dibm4OH3/8MV566SW43e6004QL\nIyWS3t2+fTuEQiEOHTqECxcu0Ol7bHQT6kilUonHH38c2dnZ8Pv9aGtrg9VqpVFYKmeQWb8kGT2p\nVIrKyko0NTVRWtDXX3+dOswLI96bCVknMewymQy5ubl46KGHsHfvXsRiMfT29tKRq+QOS3XNxKHg\n8/nIysrCY489hkceeQRZWVmIxWIoLy/HxMQEysrK0NXVRWc+p6KXOMnZ2dkwGAzYtWsXNm7cCA6H\ng6KiIlRWVsLn81FaabPZvKShJkY5HA5DLBajoqICWVlZ2L59O+rr67Fq1Sr4/X74/X5oNBpYrVaI\nxWKMjo6iv7//putl0pOSUhDZ4/z8fBQVFUGv18Pn88FoNFKu+0OHDqG7u3vZvSZyyxnqZDJJ8/+d\nnZ0Qi8V0zCCZ0xqPx1FTUwM+nw+v1wu73Q6z2YxIJLLky0EuwkAgQGuNxFD7fD5wOBzk5+fTeqfD\n4UAoFEqJES2RSCAajWJ2dpYCVaanp+FwOBCNRqHX62l0OjQ0lPalPDc3h2g0Cq/XC6vVSue/ajQa\nlJWVwW63U6RhOnrJZcXn82ma3u/3Q6lUorS0FIFAICNQDJfLhVQqpbNaJRIJNBoNQqFQRnzMTENN\njEgymYRIJFoRRDwBk5FpYiRdlengBOCGd9/U1ASFQoFQKITx8fEVS03zeDxUV1eDw+HA6XSis7Nz\nRRwALpeLvLw8qNVqminKZJANMyrNysrCF7/4RdTV1cFms+G1117DxMRExkNQOBwO7r77buzevRt6\nvR6BQAAffPABHaaQrpFmlnPkcjnWr1+P2tpaxGIxXL9+HYcOHaIOV6pzk5m6RSIRpFIpysvLsXPn\nTnA4HAwNDeHFF1/EpUuX5uFx0lkzQY3X1dWhvr4e9957L8RiMV588UUcPXoUo6OjdLIdCZKWkmQy\nSaNGnU4Ho9GIoqIibN26FXK5HDabDUNDQ3jttdfg9/tht9sRDoeXBZORsmMikYBUKkVzczMKCgpQ\nWFiI2tpaTE9Pw+VyobOzEz09Pbh69Sr8fj/kcjlmZmaW1C2VShGNRiGXy6HX63H33XcjLy8PRqMR\nVVVVMJvNMJvNNBPa19cHvV6/ZHmEPFu1Wg2hUAiNRoPm5mYUFhaipKQEq1atQiAQgNPppBlfMtt9\nbGxsyfUulFvSUJMNuHTp0jwQA3kBTSYT/vIv/xKJRAInT57ECy+8AKvVmtIBA26Q+FutVrjdbvrS\nzc3NYePGjfjzP/9zqNVqnDx5ElevXk2rRhaPxxEMBilIhdRhhUIhHnjgAQSDQfzyl7/EtWvX0o74\nyMvv9/uRTCbR0NCArKws3HXXXWhoaMCOHTtoCw4b4XK58Hq92Lx5M0QiEaqrqyEUCvHss8+ira2N\n9YUpkUggFAphMplgMplgMBiQm5uLp59+Om1AHVMIZoF43kqlErm5uSuCtCeX8ZYtW5CbmwuXy4Wp\nqSlMT0+vCBJ++/bt2L17N21bGRgYWBEHgMPhoLm5GQaDAR6PB++99x5effXVFUl7NzU14ZlnnoFQ\nKMTRo0fxwx/+EENDQxnpTSaTWLNmDX7yk5+gubkZfD4f9957L86fP886XU/APTweDzU1NfjZz34G\nkUiEvr4+PP300xgbG2MF6GEa6crKSjzxxBO4//77MTIygv/+7//Gb3/7W8zMzKSVKSPOJsnslZeX\n495778Xu3buh0Wjwgx/8AG+88QZcLhdlUUwnna5QKBCPx6nT/dWvfhV6vR5Xr17F008/jcOHD8/L\nqKUKxpXJZFCr1dBqtdSQGgwGHD16FP/6r/+KtrY2+Hw+mkVLVbRaLVQqFVQqFQwGA4qKiihG5PDh\nw2hpaYHf7097IphAIEB+fv68ICyRSMDhcODkyZP46U9/io6ODhogpno2hEIhqquroVarUV1djWAw\nCJPJhEgkgiNHjuDf//3fMTo6SmvobEBkRG45ZjJm3ZR4HyTyJZ7WF77wBVRXV6OtrQ3Hjx9PC5FM\nivqkET0ajSIQCCCRSODRRx+FWq2G3W7HO++8k/ZlQdYdiUQQDocxPT2NZDKJ4uJiCAQCtLa2orW1\nNa3eSiJkzTMzM3TeaVlZGerq6pBIJDA5OZmRESFzosfGxiCRSJCTkwO/30/nf7OtzZI+QqvVCpFI\nBKFQSKPUTKIx5npIel4mk8FisaxIZErQ/1KpFHw+n2ZBVkJIZMrn8xGJRGg6PdN18/l8bNy4EQKB\nAA6HAx988AFNH2ciQqEQf/RHf4SSkhJMTEzglVdewcTEBOu0N/PZHThwAA0NDZBIJDCbzbhy5QrN\njGVyPpRKJT73uc9BJpPBZrPhueeew/Hjx1nx+jPPm16vx4MPPojt27dDpVLhhRdewFtvvYXp6Wnq\nIKaqm0S7wA1jsnPnTmzduhUGgwEWiwXHjh2Dy+VKyzAxgUx8Ph8SiQQFBQVYu3YtTCYTJiYmcOLE\nCZw+fXpeCj3VNfN4PIhEIqhUKuTl5cFkMqG8vBxGoxG9vb3o7Oz8BGgs1TWTYUm5ubnQaDTIy8uD\nVqvF1NQUZmZmqLOSqiPO3IuGhgYYjUZIJBLqvAQCAYRCITrCNt3zTEoWDQ0NNKOnVquhUCjg8/ng\ncrnmtYRlIrecoWYKOUhMb3LdunV4+OGHoVKp8NJLL6Grqyttz40Y61gshkgkQuvQzc3N4PF4OH78\nOM6dO8cqMiPAL6/Xi6mpKUQiEZpCfuWVV2gbBBu9BNVss9ngdDohEomQlZVFyQTYGpJkMgm/3w+L\nxYKenh5YLBbIZDKMj4/D4XBkFKHGYjFEo1EMDw/D7XYjGv1/2Hvz8KbOM238PjpHu2RZsuRF3jds\nY4zNZsoaiANhSUhJCIQEmknSLE3aZtJ0munyTae5Om0n20ybkIU2+/4lJAECBAj7YoJZjPd9t7zI\ni2RLtrX5fH847xvZGCzpnM7Q34/nunwBRnr0nlfnvM92P/fjpsQnYhhUQs5CDoZAamxTCUnrqdVq\nMMzYIHghjgUREpFlZmZSsobOzk5RkOQMw0Cn0+GGG27A0NAQysvLUVdXJ7imThzC1atXQ6PR4NSp\nU0Fnmq4kCoUCa9euRVhYGOx2O77++mvB+0z2Yd26dVi3bh36+vqwe/dufP3114JmlfuD6W699VZE\nRUWhp6cHX331FSwWS9CGlNxjwJiDlZaWhptuugmJiYno7e3FkSNH0N7eHvRzQhxM0tKamZmJ+fPn\nY86cORgcHERJSQmqqqrQ19d3Gfp5qjVzHAeVSgWVSoWwsDCYzWakpKRAr9eD53la5iOArWD0SqVS\n6HQ6qFQq2tVDone3203PaP+afiB7QWrzLMvC4XCgqakJHMfBaDRS40pAov41/amE7K9KpcLo6Ch9\nJpqbmxEZGQm9Xk9Hgk5E3oci11zqe6L4X1x0dDTuvPNOmM1mtLe3U6aoUA2U1+ul6EWTyQSdTodz\n585h165dNBoOZb2k7cjtdiMqKgo5OTk4deoULl26NGUd/Wp6CfJbJpPBbrfDZDKB4zgcOnQoaM94\nolitVigUCni9XorqvXDhAkZGRgRFN6TGX1VVhYaGBsTExFDgl5AblxggmUyGgYEBaDQaOJ3OoJ22\nyYTUuiMjI8HzPMUDCF0zMHY45eXlUXzFxYsXRdFJxsLm5uaivb0dx48fp4exEL3h4eH46U9/imnT\npoFhGOzZsyekLoCJemUyGebMmYPU1FQAwLlz5/Dxxx8LBgHqdDps3LgRjz76KFJSUrBz5068//77\naG1tDcnhJIZBIpHAbDbjgQceQHx8PIaGhrBv375xvY+ASSAAACAASURBVN6BGCf/fSPI47S0NNx9\n991ISUmB1WrFsWPHsGvXLoqoJxH9VHtOXqdSqaBUKjFz5kzceOON0Ov1iI+Px4kTJ1BUVISOjg44\nnc6ADQgx6CTdrdfrkZubS+u9/f39sNvtcDqd9DkJhIiE6JVKpQgPD0dmZiYSExPhdrthsVjAsixc\nLhfNQBLDS/A6U+2FVCqFXq9HYmIitFot+vv70d/ff1k3D2nbDTSTQ3AVSUlJSEtLg9vtRldXFy0x\nEF2kn9y/ZBmqXPOGGvgOZPDCCy9gxYoVqKmpwX/+539SUEioQmpZqampeOyxx3DhwgX8n//zf1BR\nUSEoEiGAOKPRiDVr1mDNmjVYsWIFTd8IWS9xALKysrBw4UKcPHkSn332mWCwEEkDkZr6xYsXUVlZ\nKchIE6eFgG26u7uhVqtRV1dH+w9D1UsAfjabDb29vYiOjkZfXx+0Wm1IOv2FfEeEVIGAYcRwAGQy\nGSIjI+F0OtHY2IgzZ84IXq9EIsG0adPw8MMPQyqV4sCBA9izZ89VUbCByqOPPoo1a9ZgeHgYx44d\nw8GDBwVRtBIjvXz5cjz11FPo7OzEO++8g507d6K8vDxkvQQ9/vOf/xx33HEHjEYjLly4gH/913+l\nqc1QRC6XQ6lUIisrC4888gj0ej0++ugjfPzxx6ipqRm3F8GkvAkhSG5uLjZu3IikpCT8+c9/xtmz\nZ9HS0oLu7u5x+gKNUA0GAyIiIhAdHY1HHnkEbrcbg4ODePHFF1FSUoLm5mZqkIgDEmhtesmSJcjM\nzERycjKmTZuGr776Cl988QVkMhl6e3tRXV1NI0gSnU4lLMti1qxZKCgoQFpaGuLi4nD48GHwPI/e\n3l4MDQ2hsLAQnZ2dGB4epi1kUxlqjuOQn5+PO+64AwaDAUajEYcOHUJCQgLCwsIgk8lw/vx51NTU\noL+/H0qlElKpdEpQGtH9u9/9DnK5HA6Hg9bSk5KSkJycDJlMhoaGBnR1dcHtdkOn0wnO9P1DGGqG\nYZCSkoLc3FxYrVZ88MEHOHHihCiRiF6vR15eHhYsWID33nsPZWVlovS0ymQymEwmLFq0CE1NTWhs\nbKQ3sRjrnjt3LhoaGlBWVoa2tjZR9ALfpeF6enpgsVjGMcEJEZZlYTAYxvVoiqGXpK5IZkWM1DeJ\nUEkdeWBgQJS2LAA0Xeb1etHR0YH29nbBeyCVSmm7F8uyQXFBX0mIQb3hhhsglUoxNDQkCn0qy7JI\nSEjAjTfeiJSUFFy6dAnFxcVobm4O2Zj6k+rMnz8f4eHhaGlpwVdffUWBWKEIqXVnZWXh/vvvp5mx\nr7/+mpZyQtUrkUhQUFCA22+/HSkpKfD5fCgvL0dDQ0PI9xtBbKelpWHZsmWYNWsWLBYLXnrpJbS0\ntKC9vf2yjB6JagOJItPT07Fw4ULMnDkTMpkMf/7zn9HZ2Ymenp7LOiKCcVo6Ozuh0WiwePFiuN1u\nzJs3D7t370Z5eTnt6CGsacEEZm1tbXA4HIiOjqYo8oqKCvT19WHHjh0oLS2lwUkwYD2GYWCxWBAX\nF4d58+ZhYGAAmZmZtB23tLSUAgv9h5QIkYAMNcMwTQAGAfgAeHmen8swjAHAxwCSADQB2MjzfP+3\nr/8lgAe+ff1PeZ7fL2SRiYmJWL58OZRKJd59913s378fPT09QlQCGBsAEB8fj3Xr1iE8PBxHjx4V\nHKUTkcvlWLx4MaKiovDNN9/Qvk2hQh7yyMhI1NfXo7y8XDQ2LmAMeRkeHo6uri6qVwzd4eHhUKlU\n48BTQvWStiHS0kFatYTqJSlUQkxDOg8EA0IkEjqogIANxXAKw8PDsWjRIhiNRoyOjqKiokIUAItG\no6FAyPb2dtTU1AhOTZMD8+abb4ZSqURZWRnq6uoEpQYJO9+sWbMwffp0uFwuFBUV4fz584IzIfHx\n8bjtttswf/58GAwGbNu2DeXl5ZR8ZOL1BfpZarUaS5cuRUpKCsLCwlBUVIT6+nrY7fbL7olA9fI8\nD41Gg7i4OCQnJwMASktLx7VzTozSA0Uik9cRmtuRkRHU1taio6MDdrt9nJ5gnkHymoqKCnR0dMDr\n9eL999/H2bNnYbFYAHxHUkVq04He2y6XC/v370d/fz/i4+OxY8cONDU1wev1UiIgwiNOQMCBfn87\nd+5ESkoKKisrIZfL8emnn9IMn1KpRE9PD02/i/EsBhNRL+d53t86/iuAQzzP/4lhmH/99t9PMQwz\nHcBdALIBmAF8zTDMNJ7nQ3Jr4+PjsWLFCmzevBmlpaX46quvKE2mUElISMC6deuQnZ2NhoYG1NTU\niHIgcxyHjIwMLFmyBHa7HdXV1eNazIQAWkhPJMdx6O3txZkzZ8bV2kMV0scZHx8PrVaLU6dO0TT4\n1XiLA9Wt0+mgVCoBjNXDlUolhoaGBOtVKpX0YCD6hBpqlmVhNpvBcRxGR0cxMDBwGdI8FOE4DnFx\nceB5nh4SYmQA4uLikJOTA5lMhsHBQdhsNsEtaizLIiYmBhqNBiMjI6iurkZxcbFgB8hgMGDVqlWI\njIyEx+PBmTNnKBgrVJ0KhQJmsxlLliwBx3Gorq7GkSNHUF5eLqwuyHFYvnw55s6di7CwMPT396Ow\nsBDt7e2TOgCBfhbHcZg1axbS09Ph8/lw9uxZvPXWW+js7KTPmr+uQI2pVCpFXl4eoqOjERERgV27\nduHjjz9GbW0trFbrpHoDff4IfbPH40FtbS1OnTqFtra2cYGN//MRqF7ivFqtVhw4cIBieQYHByfN\nKgSK8SFnZU9PDy5duoQPP/yQOsaE9YwIAdMGgya32+2or6/HxYsXMTAwgIGBAfp+f6fT6/WKElQK\nSX3fBmDZt39/G8BRAE99+/uPeJ53AWhkGKYOQD6AwmCUSyQSrFy5Evfccw/y8vIwMjKC3/72txTl\nDVxOOh+IMAwDtVoNo9GIn/70pzAajaioqMCZM2fGjSMLNCXkr5egF00mE9atWweNRoP29naUlJRQ\nQyokaiBN9fn5+bTuS5jahFA5ksMuMTER+fn5cLlcdFCE3W4PWS/RTQZcEOAN+VMMh0gqlaKqqgpm\nsxmtra2i8E7L5XIMDg6iuroaEokEZWVlgjMiJJWs0+nQ3NyMpqYm7Ny5Ex0dHYLWSlDkEokEFosF\n5eXlqKmpEbS/JOWbl5dHWfzeeOMNwYaa53msXr0aMTExGBgYQGVlJY4cOYLBwUFBen0+H2bMmIGb\nb74ZZWVl2L59Ow4cOACbzSZIr0QiQX5+PsxmMyoqKrBnzx7qdAvRK5VK6T32yiuvUE7sK7VtBvpZ\nBGgVERGBw4cP45lnnoHL5bpsvcSgBsU1/a3T+sYbb6CwsBA2m42el0T3xAlRgYrFYoFGo8Fzzz03\nbqTpZBF6oHvh8/moc9La2oqBgYFxayXIdJKhDMZR9Hg8GBgYoEObyLlO1kimepHsgxgTFAM11DzG\nImMfgNd4nt8OIIrneXLKdAKI+vbvsQD8ETJt3/5unDAM8xCAh670gVKpFMnJyVCr1ejv78eFCxco\ngxOAyzzDQIXcTAT239raisHBQdTV1Y0zzKE8iCSak0qlkEqlGBkZoV4neViEtFAxDAOtVovIyEh0\ndXXBZrNhdHQUQ0NDgm4GAtZTqVSQSqWoq6tDe3s7WlpaKGpbiDAMA5vNhiNHjoDjOLS2tk7JJxyI\njI6OwuFw4OWXX4ZOp6PpWaGGmrQ3Pfvss9Dr9aiqqgqaRH8yGRkZwcmTJ/GTn/wE/f39FKQmRHie\nx9dff42ysjKEhYWhp6dHMPKd53kMDQ3h2LFj+NGPfoSmpiYKjBG6VjICUqFQoKKiQrBjxfNjbYtH\njx4Fy7Joa2ujDqzQ78vj8eCZZ57B7NmzadeCGBk3l8uF48ePg2EYnDt3bpzRE6q3qKiI0l+SlPFE\ndHew62eYsbGPO3bsgEajochuUn8lZxPRHUxwMzo6CrvdjgsXLtD0NvnT35gCwTkWPD/WJdPR0QG5\nXE7PfUL5SaLqUL5Lnudht9tpqU0ikVAqVZfLRYF0BDMgRukwUEO9mOf5doZhIgEcZBimasLCeYZh\nglrJt8Z+OwBMfC+JwsxmM4AxXutvvvnmMhL4UC/e4/FQztWenh4MDQ3RCUzfri1onf5GmGVZDA8P\nY3BwEBqNBuHh4aIACiQSCQU46XQ6OqJTjDKAUqmERCJBd3c3EhMT0dnZSYlghAj5LsnUH5vNRukh\nheoFxr7LsrIy6rCIsReknaKmpiZob/tKwn+LgB8YGBAF6e2vt7OzE11dXaJgFIiMjIxQWkUxpaqq\nClVVVVO/MAgZHR0bH/vxxx+LrvfcuXM4d+6cqHq9Xi/6+/vxxRdf0N+J8d253W5YrVZYrdbL/k+o\nM0Rapib7v4mOgL8Bn0ovAXL5G82JJSbyGWTmQCDXQl5HaDuJXv8I2t+JCaSdbOJ6/CNplmUpN4L/\n68gkQsF8+yF4V/8OwAHgQQDLeJ7vYBgmBsBRnuczvgWSgef5P377+v0A/p3n+SumvicaasKqM23a\nNMyZMwcDAwOoqKhAY2Mj3eBQbzzSkqBSqbBo0SL09vbCZrOhq6tLFLIFMuotPT0dPD/Wg9vX1ydK\nqwzp7VWr1RgeHkZvb68oqV7gO0NN0jVirJfIZA/edbku1+UfS/yDo6kwMcGWDYn4G+PJ3h8MFsff\ngZDJZFTvZPaD1OADFf/In4BZJwONkTP1Kob6PM/zc6e8lqk2k2EYNQAJz/OD3/79IICnARQA6PUD\nkxl4nv8FwzDZAD7AWF3aDOAQgPSrgcmCjcavy3W5LtflulyX/w9IQIY6kNR3FIDPv/VOOAAf8Dz/\nFcMwRQD+L8MwDwBoBrARAHieL2cY5v8CqADgBfBYqIjv63Jdrst1uS7X5f/vEnTq+++yiOsR9XW5\nLtflulyXv7OQOrV/+jyULp+JOgnpjn/6m+CeyO+uoFu0iPp/XUKB0AeqlzSkizUZiYhGo4Hb7RZt\n+AQRwsDkcDhoa4BYevV6PSIiIuD1etHU1CTafkskEsTHxyMlJQUqlQrnz58XNON6ou5p06ZhxYoV\nsFqtOHz4MLq7u0XRTXrLH3vsMXR1deHAgQPjpiQJ1a1QKJCRkYEZM2bgxIkTaGtrE023TCZDRkYG\nli9fjk8//RRdXV2ijdKUy+XIzMzEihUrsGvXLkpLKcZ9rlarkZubi4yMDEgkErz33nshj7ycuG6J\nRIL58+cjPz8fUqkUu3fvDonExf9gJ/8mU+GefPJJFBcX4/jx4+ju7g4Y1e+PbvZvRSJtmXFxcVi7\ndi3Kyspw/vz5gKguge9Y2wiwiud5ilznOA5arRZz5sxBXFwcvvrqq4DbBcl94A/wJecoqTOnp6cj\nJiYGcrkc586dC6ifmLS5ymQy+izIZDK6j16vFxzHISwsDOHh4bBYLBSFPtU+kG4cMjJYrVZTACqp\nN5OhJi6XK6DnhVy7RqPB6Ogo5HI5ZVHzer1gWZYOGCGvD6Vz4Jo31OQBE0o2MZmQPlwxENn+Qgg+\nPB7PuBm1YuhVqVSIj4+HzWaDxWIRzRFQKpV0go9arcbvf/970djUOI7DmjVrcNttt4FhGLz55pv4\n+OOPBesmD9YPfvADrF69Gg6HA4ODg9i7d68oBzvLsoiMjMT9999P+ZfJeEChIpFIYDAYcPfddyM/\nPx9NTU3o6OgQTbfJZMIdd9yB22+/HWfOnKFUmkL3hWVZmEwmbNmyBUuXLqXsVEL6+IkwDIPExETc\nddddWLx4MZxOJz788EPBeoluhUKBn/3sZ8jLy0NbWxtOnTolGA3tT0J00003Yf369ZBIJDh79mzI\n0/f8jTSh3v3hD3+IvLw89PT0BA3SIm1OBIgLfGe45s2bhx/96EeQyWQ4ePDgVXX5R53EEPkjtYnx\nJ2vfuHEjIiIicPbsWchksqvqJX8n7VPEOZNIJHA6ndQGcByHiIgITJ8+HV1dXZDJZJMa6onAN3+9\nxCiT3moCJFMoFHTSVm9v7xXPbaKbGF+O4+jwIuKkkM8gRppcP3kGg703rllD7T+gPCwsDHq9Hk1N\nTXC73dTAOp1OGlkGwypDPKvMzEwkJCTA5/OhpqYGw8PDUKvVGB0dRX9/f9A8waTJXafTYfXq1YiI\niEBJSQlqa2uhUCjgdrvR39+PgYGBoA82lmUpkf/ixYuRkZGBAwcOoLGxESMjI+ju7qY9z6FECHK5\nHKmpqSgoKIDRaMTJkydRVlYGm81GB4GEmhZiGAazZ8+mTEwLFy7El19+SW9uIU4Sx3HIysqC0WiE\nSqXCjBkzsH//ftEY5oxGI4xGIyQSCSIiIgIeNnA1Ic6nVqtFXl4eDAYDpFIpPYzEcDIiIyOxdu1a\nREdHQ6vVBjwacCphWRZz587FTTfdhOjoaEilUtF40BmGwa233orVq1fDZDKhublZtAwDy7JITExE\nQUEBGIZBYWHhlJmXK30X/r8jzvOyZcvwq1/9irK4kXt7MpkMuTxZj7O/o7hhwwbs27cPVqv1imfH\nROSyf4+zf+qV3H85OTl47LHHoNfrUVFRccXIlBh1/6zJZCxn5B6Ty+UwGo2IjY2lPN2TrZlkC/xJ\nTghByMT0NM+PcfprNBrk5+dDpVLBbrdf8f5QKpV0FCvR4z8v3H/ewOjoKJRKJaUMvlprGTmHSesq\ncYDInvjTI5PrIPaKOAqh3NPXpKFmWRYqlQqrV69GTk4OtFot9Ho9nE4nnE4n1Go1VCoVysvLcenS\nJVRVVaG7u3vK9BsxSOHh4cjOzkZBQQESExMRFhYGm82GwcFBaLVaDAwMoKSkBB988EHAvcSEb9hs\nNiM1NRVLlixBQkICli1bhrKyMnAch56eHpw/fx4lJSWT9iRebd0qlQp6vR5RUVFITk7GsmXLEBYW\nhgsXLmBgYAAnTpxAR0dHQGmgibpJy5pEIoFcLkdERARSU1Mps9Po6GjIERM5JEZGRqiDpFKpqIcr\nNKIhhp6MDBSDV9d/3SRqJ0M0xCoHMAwDo9GIuLg4WCwWynMsxtolEglmzZqFmJgYAKBzqcXQrVKp\ncNNNNyEyMhIsy6KoqEiU1DQwtifLly9HVFQUnE4nDh06JIrDRdZNKEYtFgt27do1JeteIJ/Lsizi\n4uKwdetWxMTEYMeOHWhoaLhqe+OVzpOJn6dSqbB8+XJs2rQJCoWCMtpdiUTjSvfmZNchlUrx+OOP\nIzExEdXV1ZTcaLIe6NHR0UnbiybqJc+L0WjEli1bYLfb0d7ejvLycqrb/9rJNMCJBn+ikSa/l8vl\nmDdvHsxmM3p6emC1WuHxeGhq3/89E+elT+YI+f87IiICcrkcLpeL2pHJ9PI8P45BjnwPV3K8SFnE\n/zwJhQfkmjPU/heRl5eHGTNmQC6XU15k0gOdlJSE/Px8nDt3Dvv27cPXX399GaXdZDI6OgqFQgGT\nyQSTyQSv1wubzYa6ujqwLIvo6Gjk5uZi+vTp2Lt3L/3Cp9pUcoORg52QDjQ0NKChoQH5+flYunQp\nnezT0dER8BdFbgSPxwOv1wuLxYILFy6goqIC3d3diIyMRHx8PKxWa0jGj3iy/f39aG1tBcdxGBoa\ngslkgsvlEsRHTfbOYrHAZrPRjIVKpcLw8LAoxpoQJhDSEzHKJGTdWq0WLMuisbERfX19opVIZDIZ\nNm3aBLPZjE8//VRUJ2DGjBl46qmnaA1PyJhHf2EYBr/73e+wefNmKBQKHD16NOQ5zxOF4zjccMMN\nWL58OQYGBvDSSy/hxRdfDGndE9OeZrMZv/nNb3DPPfeguLgYTzzxBMrKyoJ2XiYSezAMgzvuuAPP\nPvssTCYTuru78W//9m+w2WxUdyD3N4lE/TmzpVIpdu7cidmzZ8PhcGDPnj1499134XA4aIp14t5M\n1sNLfu+fpjeZTHj66aexfPlyFBYWYvv27fQ8ImftxO90Mt3+eoExY/f444/joYcegtfrxc9+9jM6\nvESpVF6GqZmok6zP3/CRM1WtVqOwsJBmP7/88ktERUVBqVTC4XCgu7t7nDMxUbd/bdpfr0wmw9y5\nc/Hggw+ipaUFNTU10Ov1qK6uRn19PVpaWsbhDCY+/xOzDcR+SaVSxMbGoqCgADKZDG1tbUhOTobD\n4QDDMHj33XevSBk7mVxzhpqIy+XC3r17cfr0abAsC7fbjaGhIQwODsJoNCI3Nxf33HMP+vr6YLFY\nJqUWnUy8Xi+sVitOnjwJq9UKmUwGnuepEfF6vTCZTLQ2EoiRJp87PDyMlpYWOk1Gq9VicHAQPT09\nSE5ORtK3c0tDqf0Sz7O/vx81NTWIjIwcR9AyEXEYqJDrIzUbMh5Rq9VSAy1GLZlE0gqFgnISB7q3\nVxJyQBDAis/no1SfYkV4OTk5AMai0p6eHtEMtdFoxLx58yCVSlFUVCRKjZfI97//fZhMJoyOjuL8\n+fOiYSQkEgmWL18OlUoFq9WKjz76SBTmOoZhEBUVhS1btsDn86GwsBAffvhh0Nkhos9fr1arxW23\n3YZ169ZBLpfjtddeQ3V1dUgZBmJ0iTFRqVR48sknERUVBYfDgWPHjtGZ81cj7Zio05/ekgCSUlJS\nMHv2bPD8GGnSJ598QjnRJ4vgJhNSpiEZJ47jYDKZsGnTJtx+++1wOBw4deoU+vr6KDiNROtXE39i\nJI4bMyEKhQL33HMPHnzwQWi1WrjdbhgMBrS3t8NoNMLpdILjuKuWSfwNNTDmzGo0GhgMBtxyyy2I\njY2l5+zixYsRFhaG0tJStLa2Ukroq62Z6OY4DpGRkdDpdFizZg3uvPNOJCcno6OjAxaLBQzDICYm\nBiqVCkNDQ2hra7vqmkmUTGZlm0wm3H333Vi4cCFSU1OhUqlQX1+PiIgIAEBHRweOHj2Kmpqaq+6z\nv1yThpp4PdXV1ZBKpRTmTpB43d3dmDZtGoaHh9HY2Iienp6A0m/kJiezi2trayGTycCyLBwOB3Q6\nHSIiIhAREYHa2tqAJ7UQ3cRQOhwOtLa2QqvVwuFwwOv1Ijk5mY6PDGVWMEnxulwuWK1WdHd3U08z\nKSkJJ0+eFJSClEgkGBoagt1ux8jICAwGAzweD0pLS0WZQUz2US6X08HvYhhUUhaQSqV0Jq4YRhoY\ni/LS09MxODiI7u5uUShVge8cgNjYWPh8PtTX14uWmmYYBqtWrYJcLkdvby9Onjwpml6tVov4+HiM\njo7i4sWLOH78uGCdwFg98fbbb8eyZctgsVjw5ptvorOzM+S99o9ily5dinvvvRdGoxFDQ0M4evRo\nwFmyydZKHACFQoE5c+ZgxowZ8Pl8qK6uxttvv00RxMHe28RIKZVKpKenY+PGjWBZFs3NzXjvvfdo\nBiCYDhWyZo7joFAokJaWhoULF+Lhhx+GQqHA+++/j2PHjqG5uZl2qABTO+bEsWdZFhqNhmY477//\nfjqboby8HIcPH4bT6aSjOwN1Lnw+H2QyGVJTU5GSkoKMjAysW7cODocDnZ2dOHnyJA4ePIji4mK4\nXC7IZLIpo1OCEZLL5dDr9RTnM3/+fCQnJ8NisaCsrAxnz56F3W7HpUuXoNPpAkLX63Q6MAxDS6rx\n8fGYPXs2cnJy4HK50NPTg+PHj8PlctHhK8GUPoFr0FD7p5UGBwdp7ZH8SbzD9PR0Sv1JahXBpJI9\nHg+NbNVqNaRSKcxmMzIyMsBxHCorK0M6PP3T1Gq1Gkqlkt50hDs41EEM/m0QZrMZOp0OPp8Per0e\n7e3tglKQJCKXSCRQKpXIysrC0NAQhoaGBBlq4m3618CJEyA0miYHkUKhoO17Qodc+OvX6XRISkrC\n8PAwurq6RNOtUqmwYMECaLVa+Hw+QUZposhkMiQkJIBlWVRVVYlmqDmOQ35+Pp0I9+mnnwpqgyPf\nHcuyyMrKwp133om4uDi88sortO4dql7/LNNDDz2EmTNnwufzoaSkBH19fdRIh7ovLMsiKSkJjzzy\nCACgtbUV77//PoqLi4MGc5L7mKRh4+PjsWnTJmzatAltbW349NNPsWvXroCDEX8hOt1uN9RqNZYt\nW4YNGzbA6/WitrYW27Zto4C9YBxc0sLEMAz0ej00Gg1yc3Pp8KSLFy/ijTfeoODfQPVKpVIa9dvt\ndoSFhSE5ORmpqalobGxEUVER3nnnHTQ3N2NoaCioLBTLsoiPj0d/fz9iY2MRHx+P8PBwlJSUoKWl\nBX/5y1/Q3d2NwcFBeL3egFuzNBoN3QfixJrNZpw9exYNDQ3o7e3FZ599hubm5nE0o8Hee9ecoSZC\nAEjkoCek5xEREXjmmWeQn5+PLVu2oKysLOhJOST6JcPDfT4fWJbFH//4R0RFReH111/Hq6++GhKa\nlRhpm80GlUoFs9mM+fPnw2Qy4Z//+Z9x/PjxkA4h8uUS50Wn0yEnJweRkZEoKysLqm1jMnG73RgY\nGEB3dzfkcjny8vJQVFSE7u5uQalT8kAPDQ1RMFlCQoLgyNf/vtDr9eA4DkqlEq2traJE6SzLIiMj\nA3l5ebBYLDh//rxoUe+8efNw7733QqPR0OlJYuhlWRYrV65EZGQkiouL8ctf/hJlZWWC9UqlUmzc\nuBHPPvss6urq8OMf/xgnTpwQDDAEgPvvvx///u//jujoaLS2tuLXv/71uHm+wQp5n1qtxg9/+EOs\nWbMGXV1d+OMf/4gPP/zwMpBRMGsGgPDwcDz00EN49NFHERUVhd///vd4++23YbFYQo6iybofe+wx\nbN68GXFxcbBarVi/fj0aGhpCKhOxLAu5XA4AmD59Om655RZs2bIFVqsV7733Hr788ks6DYx8fiBC\neq/1ej2SkpIwb948zJ07F+Hh4fj8889x7NgxtLa2wuFwBO1YREREIC0tDaOjo5g1axZWrVoFqVQK\nq9WKb775Bvv370dfX1/Aesl9JpfLsXLlSvA8T52BmJgY9PX1oampCcXFxeMAl4GumZQoli5dCofD\ngeHhYeTl5YHneVy6dAlffvklHA4HbaMFQi8j/bV0ZwAAIABJREFUitOz8XcUcvMT9OfChQuxdOlS\n+Hw+wePsCOrQbrfD4/FQpPPOnTvR398f8qaSKUnt7e3o6elBZGQknUstJD1NBrf39/ejubkZPM8j\nLCxMFKIMn88Hp9OJmpoaWK1WqFQqOgxdKNgLALq6ujA0NASPx0PHwwkVYqxJS4cYU2qA7+pO0dHR\nUCgU6O3tFTyX2193Xl4e1Go1XC4X6uvrRUvVK5VKrFq1Ck6nE4WFhaK0NzEMA5PJhDvvvBMGgwEH\nDhxAWVmZ8GlA37at3H333YiIiKCAKSFGmuhVKpVYsGAB7rnnHgwMDODzzz/H7t27aTRNXheMTvLn\n2rVrsWnTJuj1ethsNnz44YewWCwBPyf+uvz7cOPi4vD9738fMTEx6O/vx759+9DQ0EDPi0D3hJQJ\nSbo7KysLN998M5YtWwa73Y7jx4/jxIkTaGhoGAcEu5r419CVSiUUCgUiIiKQn5+PWbNmISoqCsPD\nw6iqqoLVaqX3RiC1bnL9HMfBYDAgJiYGiYmJSExMhF6vB8uytHbsdrvHOTeB7IVUKoVGo4HRaKTj\nLs1mM8LDwynPBcn2BdN6KZVKoVarERYWBrVaje7ubqjVagwODsJsNsPn80GlUsHtdguaykjkmo2o\n/YVcYExMDO68807odDqcPn16HLgiFCFoYY/Hg/DwcCiVShw5cgTNzc2CekNJap04EWazGUeOHBE8\n09jn82F4eJgyh5HDrqKiQvBh73Q6qd6qqipkZWWJAibz+Xy0TtPV1QWTyUQfOCF6/VGhLpdLdHIZ\nuVyO9PR0SCQSVFZWhgRsupIsW7YMMplM1Boyy7KYMWMGCgoKUF9fj4MHDwqeBgeMpek3b96MxYsX\ng2VZfP755+jt7RWsl6SP58yZA4ZhUFRUhLfffluwXqVSiRtvvBG/+MUvMH36dHz11Vd4/fXX0d7e\nflXE8dWEpKcjIiLw5JNPIjU1FR6PB/v27UNra+u4ntypxN9RII5mYmIi7rvvPqSmpsJut+PgwYN4\n6623aMYi0HYef8SxXC5Hbm4uNmzYgKSkJCQmJmL37t344osv0NLSgsHBwYD1EoOu0Wig1+uh0+lo\nbTcmJgYWiwWNjY1obm6mzlAgfftEL4nSp0+fjpkzZ0KpVKKzsxM9PT2w2Wzo7OykPdNSqRQApnSM\nyD6Eh4cjPT0dcXFx6OvrQ2NjIziOw8DAAJxOJ9xuN0ZGRqBSqeByuQJyuBiGgcFgoKyCcrmcnm3R\n0dG0BDc6OkoBcUJbGK/5iJoIz/OYOXMmZs+eDY/Hg6KiopDIPSbqJGnvyMhIDA8Po7i4WPChTAw1\n+dLT09PR1NQkGOlMUvYjIyNoa2ujyG8xjIjX68Xw8DCN/IeGhihIQoiQukx9fT1qa2vBMAztfRQi\nBAvgdrths9now6FQKATpJbrJIebxeGC1WkXtzw4LC4PH40FHR4dos5nlcjkWL14MrVaLixcv0uyN\n0LUmJiZi7dq19PAsKSkRTHBCWixXrFgBjuNQVlaGjz76COXl5aKs995770V2djYA4M0330RVVVXI\nayYGVavVYtGiRUhOTobP58OePXvwxhtvhLTH/m2cUVFRuPnmm7FixQoMDQ1h7969eOutt9DY2BgS\n4I10VcjlcqxZswY5OTnUSH3yySdobGxEd3d3UE4LMbxGoxERERFYunQpFixYgMjISHR2duLMmTM4\nfPgwPeOCAaWRPvTs7GzMnz8fqamp0Gq1UKlUqKqqQm1tLerr61FTU4OhoSFKkjKVbo7jkJqaiqVL\nl2L69OnQ6XQICwujUa7b7UZraysaGhrQ19cHuVwOtVod0D5zHIeVK1ciLy+PchXo9XooFAo4HA6o\nVCp0dXWhr68Pg4ODUKlUgs/Rf5iIOiIiAg8++CD6+vrw+uuv4/XXXxdFL5nxfOedd+KNN97A3r17\nRQX2LFu2DB0dHSgsLBSFeYq8NzMzE1arFT6fD3V1daL1Iw8NDUGr1VJng8y+FqrbbrfTfkOVSgW5\nXC6KQ+T1emlrxmRECaEIw4wR4+Tk5IDjONoBIIYolUpkZGTA6XTi1KlTKC0tFayT4zgsWLAAW7du\nhUqlwnvvvSdKrZ7gNvLy8tDX14ft27cHzDF9JZFIJIiMjMTWrVvx4x//GKdPn8Yf//hHFBYWhnw/\nEMOnVCrxwgsvID8/H/39/fjggw9w8OBBQbV0rVaLpKQk/OxnP8P8+fNx8eJFbNu2DSdPnpw0sxDI\nnhNQ5erVq/Hwww9j2rRpkMvl2Lx5M0pKSmh9d+JaAtErlUoxffp03Hjjjdi6dSscDgeee+45nDp1\nCpWVleMIRgKNfInupUuXYs2aNVi4cCE0Gg22bNmCyspK9Pf3Y2RkhIItiYMbqF4AWL16Ne655x5I\nJBJcunQJn3zyCc1k2e12ygJGjPVU5x3JlC5cuBBZWVmIj4+nhv/MmTPo6OhAS0sLRdETIx3o90fQ\n3dnZ2TQb2d7ejvb2dhw+fBhVVVVwuVyQSqWCA0rgH8RQEwpRvV6PvXv34vjx45SnVYgQ9GJaWhqi\noqLw2WefUUYhoYYPGIPtGwwGNDU1BZVqCkQ0Gg29gQnrj1jEEwSEQmo8YugmXj4BdADBDYG/kpD+\nRZJmEkvCw8ORmJhIa+BitZJpNBoolUqahhMjVa/RaFBQUACz2QyPxxPSoInJ1qpQKJCbmwuO49DR\n0YELFy4IXqtMJsPs2bOxceNGaLVanD59mtL3CmktlEqlSEtLw8yZM2nG7cSJE4KzCnFxcbjzzjux\ncOFCmEwm/OUvf8GFCxfQ398fcpTO82McC+vXr0daWhq0Wi0qKytRU1NDUcehOAA8z0Ov1yMvLw/5\n+fmQSCQoLy9HXV0d2traLnNYyFkUqO7k5GSYTCbaIdLY2EixJ5MRgQRyb/M8T1PDJPX8/vvvo7S0\nFF1dXfQ1xKkgZ9FU9zcx7MXFxdBqtfB4PHj++eepASWtt0QPCUwC3Yu9e/di5syZaG9vB8/zePXV\nVynORyaTwel00sAhkAzAVHLNG2qNRoPIyEjccMMNqKmpQWlpKS5dunRFGr1gRCqVIjIyEnPnzsXg\n4CBOnDhB9QoxIiQiS0hIgEqlQn9/P20rI6AnIUJ4p4eHhymql3huQoSsOzw8HD09PTRNS0gchAhB\nWxIAn1qtpixLQoQYaVKrJpy6QhHlSd+S05CHT4xsCEnzjY6OwuFwoL+/XxRDHRcXh0WLFlFGPDGG\nqTDMGMUp4QL45ptvUFpaKhjoFRYWRskrvF4vDh8+LKhjgbQgRUREYNmyZZDL5aipqcEXX3whOFsh\nlUpx0003oaCgAAaDAUNDQzhy5Ag6OjomrTkGeg0KhQKLFi3CrFmzIJPJUFNTg5deeokOgphMbyC6\n5XI5CgoKMHfuXKSmpuLYsWN47bXXUFpaSjsL/PUEw7Mvl8thMpkQEREBm82Gb775hk6u8seLkD8D\nPS+kUilycnLg8/nQ1NSEL7/8EkePHoXNZqPPtX8pKlCubMLTT9gWX375ZVRXV9N5CP56gTGMTqBr\nlkgksNlsqK2txblz59DU1DSODMkfg+Pz+dDb2xuQ3qt+pmANf0dRq9WIjo5GTk4O5s2bh/b2doqE\nBMb30gYrZFRadnY2VCoVANC0ipBpXWRNBoMB8fHx0Gq1sFqttFdb6CQwEkGq1Wr09vbCarUiPj6e\npoOECEF1AmPMcBkZGQgLCxNlEAVheiMgC7VaLej7A74DjIyMjNDxd6RmJEQ4jqOOhD/vr1AhWAin\n04menh7BrW/A2B5kZmZCo9FgZGSEUh4KNdRyuZxGp62trTh8+LDgtDcAihSWy+Xo6OhAeXm5oBGZ\npHyVkpKCjRs3wuPx4MiRIyguLh6H8g5V94oVKzBt2jSwLAuLxYLOzs6rDtwIRBhmbEiNyWTCwMAA\n9u7di8LCwiuSAAV6DVKpFLNnz0ZWVhZYlsXLL79Mx0v6R4vkuQvmGgglZm9vLz7++GO8+OKL4wiA\n/HUT/EigeuPi4qDRaPDXv/4Vn3/+OR2GNNFIB+NYEB6E0dFRVFVVoa6ubtzwj4l6gm3vdbvd6Orq\nQm1t7TgniBhnstZgBkZd9XrEag0RtAiGuWwRLMsiOTkZ6enpmDZtGiQSCTo7O3H69OlxAwxCWT/x\nwmfOnInc3FwsWrQI3d3ddLQjMDWq8CrXQnmLN2zYgNHRURw7dgy7du2Cz+cT1ItLDFNUVBTuvvtu\ndHR0oKSkBM3NzYJ7cUndLDk5GXFxceB5HuXl5ejt7RWMWCSIWbPZjMTERIyMjODIkSOCW79Iq0Z2\ndjYiIyPR2NiIlpYWUZjUVCoVYmNjodPpcOnSJVHmLRN0a3Z2Ntra2tDZ2SmK3piYGMTHx0OtVqO9\nvR21tbWCdcpkMkRFRSErKwv19fXo7OwUJVJPSkpCVlYWpX+sqqoSJU0fFhaGZcuWobe3dxy3gtD7\nKzc3F0uXLkVNTQ1qamrQ1NQkvN74bSvS+vXrcezYMXR1ddGUtxCRy+WIjo5GZmYmFAoFrc/7G5FQ\nRaFQYMaMGVCr1SgvL4fT6aRnmX9EHcznkKBDp9NBoVBgYGCARryENtrfmQ/G4JGZ1uRZJk4boTz1\nz8iSawhk3WQ9RD9xrmQyGSQSCUZGRsb1xpPv9Cr3zHme5+dOeT2BXfb/vJDNGx4eRm9vL1iWpUxf\nZBi3EC+cbC7hfzUajeNmiQoVrVYLhhnjMDYYDCGPN5sopPVCr9fTw16MNhzyMNjtdhiNRpSUlAhu\nJ/MX0pPd1tZGaVWFCvFey8vLUVFRIRoy2+fzYXBwEFVVVaJhCoCxw6G/vx8nT54URR+Rjo4OdHR0\niKqToGJbW1tF1dvU1ISmpiZRdRKw4s6dO0XXW1xcjOLiYlH1Ehrk7du3j/ssoeJyudDS0oKWlpbL\n/k+ofpfLNSlGwV8v+ftkE7gmExKZWq3Wy0Bt/kQsJKompbNAziSv10vbbwn+gUTl/lE/qXszDBNw\nTZ28l0ToRJ9/v7Q/wNHj8QjvwLgWI2ritchkMkRHRyM6Ohqjo6NoaWnBwMCA4NQT+YysrCwYDAZo\ntVq0traisrJSNEBWWloajEYjbDYbLBaLKGlDsidSqZTy24qR5iS6Q/Ver8t1uS7XnpBnWawz3t9p\nnQrDE6yDS9bqb4z93+9vDIPlOuf5sVnWZHqWv26id+Is76mErINkIv1pWCcab9LtcgUJKKK+Jg31\nRPGf1iKmASGpUyA4YEUgMnHE3HW5LtflulyX/z0RMzsm4uf8Y6e+/YUQW4gt/jUEsUVMo39drst1\nuS7XRZj8TwVMf4/PuaZR39flulyX63JdrouYMrHTxL+tLFQhWV9/XYQ/XGh3C/APElH/I8vfK90i\ndv1pou7ra/6f+4y/5778PfX/vfX+PXWLqXfiISwUaT6ZzmB6k6+0NpZlKXgWAEVXBzPj2l/8h3WQ\njhRCSELAXKGs2X80LgCKqAYAj8cDqVRKZx4EIwTxTXTL5XIKKB4YGADHcSHpBUBJosjgj8jISErL\nTNpShZRt/yEMNUHPkSERYgkZkUg2UsyHNzExET6fj06TEUs3x3FISEig7WpOp1MU3WQvUlNTERUV\nRYe+iwVUMxgMyMnJwYwZM3DmzBmcO3dOsF4iBoMBK1asgF6vx/Hjx1FRUSGabolEgvXr1yMsLAwn\nTpwQPLDFX1iWhcFgwOrVq3HhwgVUVVWJVoohPduLFi3ChQsX0NraKsq6CfrWbDbj5ptvxvHjx8dx\nGwgVpVKJ7Oxs5ObmQiKR4K233hJt3RKJBAsWLMCCBQvAMAw+++yzoKeXTRYZERY3k8mEf/u3f8Pp\n06exd+9e2toYqF5iRP3bhqRSKRQKBXJycrBu3TocOnQIRUVFAYFTyTUTJkCi37+lKDo6GqtXr0ZS\nUhK2bds2KWL8Sro1Gs04hDMxchzHged5rFy5EpmZmfB4PNi9ezeam5un1EtaZzUaDX0W1Go1+vv7\nqbMik8mQnZ2NxMREFBYWwmq1BqxXrVZTx0Sv16O3t5eylAFjozY1Gg06OjoCorQl167VailIze12\no6enB8PDw5DL5RgZGRnXYeRyuYJ/zv177P63fgDwE38YhuE1Gg2fmJjIL1q0iL/vvvv4jIwMPiIi\ngjeZTHxsbCwfFhbGS6VSXiKRXPb+K/0wDMNzHMcrFAp+0aJF/C9+8Qv+T3/6E7927Vo+MjKSj42N\n5aOioniNRsN/C3IL+EcikfAcx/FarZZ/+umn+YMHD/Lbt2/nFy9ezJtMJl6n0/EKhSKo9fqvWyqV\n8jExMfxTTz3F19bW8s8//zyfnZ3Nh4eH8xzHBb1e/x+lUskvX76cP3z4MN/Z2clv3bqVj4iICHp/\nJ/thWZbfvHkzf+HCBb69vZ3ft28fL5PJBK3Xf8+3bt3Kl5aW8u3t7fyrr74qeL3+ujUaDV9XV8c3\nNTXx//Iv/8Kr1WpRdDMMw6tUKv6pp57i6+vr+c2bN/MqlUqUPWEYhjcajfw777zDd3R08Bs2bOCV\nSqUo62ZZlp85cyZ/+PBhvru7m9+wYQMvk8lE25MHHniAr6ur4wcGBviLFy/yLMuKopvjOD4tLY0f\nGBjg7XY7/+qrr/Jms3nK9QRyj+h0Ov6BBx7gW1tb+crKSv6uu+7iTSbTpPchOX+u9Hn+nymXy/np\n06fzn376Kd/X18dv27aNX7p0KS+Xyyd9v1wuv2zNRKf/78lZsnnzZr68vJyvq6vjP/nkEz48PPyK\n3/lk971EIhmnWyKR8HK5nNfr9fz3vvc9vrCwkH/77bf5xx9/nI+Kipr0OwkPD79sbRKJhOr2X4PB\nYOBnzJjBP/fcc/wbb7zBb9q0iTcajZOu2Wg0jnu/RCLhWZblWZa9TK9cLucTEhL4TZs28Vu2bOHX\nrVvHazSaSfVKpdJxn0nWO1Ev+Z1cLud1Oh2flpbGp6am8iaTiZdKpf46zwViI6/JiFoikUChUGDV\nqlWYNWsWDAYDYmNjceONN6Kqqop6XCdOnEBpaSksFktAETHxWjUaDZKSkrBkyRLk5+cjPj4ec+fO\nxRdffAGpVIre3l6UlJSgtLQ04HQF0a3Vaul6Y2NjkZiYiOHhYXz55Zfo7u6GxWKB3W4POgLx5932\ner3Q6/XIyclBQkICRkZGMDw8HDLBA+kv7O/vh9PpBMdx0Gg0dBqMUKQ9z/O0j9ofaS+WuN1u6rGK\nQcpBhDwkKpUKHo8HjY2NokXTwHe81w6HA7W1taJlXhiGgdlspgxgxcXFokW8UqkUS5cuRXp6OmQy\nGc6cOSNaFoBhGKxbt47ON967d69o36VcLseSJUvAsixaWlrwySefUF7/K0kgny2RSBAfH48f/OAH\nMBgM2L17NxobG694H5Le/0A+T61W47bbbqPrrqurQ1NT0xXfHwytqUwmwxNPPAG9Xo/q6mo0NjZi\neHh40vLO6OjopNHlxHQ5z48x+KWmpuKJJ55AV1cX6urqcOTIEdjt9st0+3y+yzggyN8nW4PJZMKW\nLVuQkpKChoYGnD9/Hg6HY9K+7YmsdBMCw3F6fT4ftTOdnZ109vVken0+32XZDNJXPVEvOVfDw8Pp\n1EPC/xFsGe2aNNSk/2zWrFlYvHgxdDodnReqUqmgVCqh1WoRFhaGjo4O2Gy2gAhFyAaFhYUhLi4O\ns2fPRlxcHEwmE9RqNZKTk2k6x+PxoLy8PCQ2HDJMXC6X0yEXer0eHMfBbreHPFCEPAjEKXG5XLTu\nJASsQG7GkZER9PT00PoS2S+hwvM8bDYbfdjFRtq7XC4K5Ag0dReoMMwY/3l/fz/a2tpEbQ8ks3Lb\n2trQ3t4umm6WZXHDDTfAZDKBYRh0dnaK1oUQGRmJW265BUajEX19fZeNTBQiMpkMS5YsAcdxqK6u\nxptvvimKbolEguTkZDz00ENwOp148803cfHiRVHGw4aFhWHDhg3Iy8vD6OgoPvjgA+p0XUkmO6An\nO7hnzZqFBx54AAqFAtXV1di7dy/llJ7s9VfSO/H/WJZFfHw8EhISUFlZiQMHDmDfvn30/puo+0oG\nZeLrSG12w4YNmDt3Lv72t79hz549sFgsV3RaAtXNMAzmzp2LpUuXoqioCI2NjRgYGLji+TTZfXMl\nfIJGo8GCBQtw6dIlOkKY1O4D0Xul9cpkMshkMsTHx8NqtaK/vx8ymYy2Ggcj16ShJiwxVqsVVVVV\nkEgkcLvd6O3tRUdHB6KiopCTkwOz2Qyz2YzGxsaAdRPv0Gq14vjx4ygvL0diYiI6OzvR0NCAzMxM\npKSkICEhIeh1+3w+DA8Po6+vD8eOHQMwdvO2tbVBqVQiPDwcFosF3d3dQesmnrjT6URDQwM6OjrG\nUiLfks9bLBZB0R7P8xgeHkZXVxccDgcMBgOMRiMcDocoUSQZBSeVSqFWqym7nFDheZ6CQogHK6aQ\nTMbg4KAoBzsRQrgTGxuLwsJCUfEXYWFhWL9+PXQ6HWw2m6j4i7Vr1yI/Px8sy6K6ulo0p0sikSAj\nIwNarRZ2ux2fffYZ2tvbQ9Llf2gSfMSDDz6I3NxcFBYW4osvvsDg4GDQh+XEg55hGNxwww14+OGH\noVAo0NHRgYqKCkpfGqj4cy4QvRzH4T/+4z8QGxsLh8OBEydOoKOj44rG9Ep6/Z8JUk/V6/X45S9/\nCY7jYLFYcPjwYXR3d9NAIJB9ISyO/nuj1Wrx6KOP4qGHHoLX60VRUdG4SVdTrdkfQOb/HlIP/6//\n+i86ivfo0aOQy+Vwu93gOA5DQ0NXXTepm/vvH8EWrFq1CjfffDPi4+Oxc+dOLFmyhE5IGxgYuOo9\nTkB0/vMngDGnMycnB4899hjcbjd2796N+fPno6GhATzP4+zZs0Gdf9ekoSb0bNu2bQPHcZSJS6VS\ngefHRsQpFAoYDAaUlpYGRXXp8/nQ09ODvr4+VFZWQq1WIzY2FhzHQalUUkL77u7uoB5kAiJwu91w\nOp3Ys2cPbDYbMjIywLIsTCYTTS2HkoYkEa7L5UJlZSXcbjfi4+ORlpaG0dHRkJGbRCQSCYaGhmCz\n2SCTyWA2m+mQdaGHPIlKIyMjodFoxvEEiyGLFi2CwWBAS0uLKDzXRBiGwbx58+D1erF3717U19eL\nFvXGxsbi6aefRlhYGF588UU4HA7R1v3CCy9gwYIFcDqd+NOf/iSaMZXL5fjDH/4AlUqFr7/+Gg8/\n/LDg4RTAWDp97dq1eP7559HW1oZHH30UR44cCWmO9MRWmKVLl+LZZ5/F7NmzMTw8jC1bttAsQLD7\n7Y/IlkqlyMvLw8cffwye51FSUoJf/OIX9Cy6WrQ4Uac/eloqlSIlJQU//OEPMXv2bFgsFrz00kt4\n77336KQqElFPJVKplBpfqVSK+Ph4FBQU4De/+Q0MBgNef/11vPrqq2hqaqIAs0D0kmiR58cYv8hU\nuB07diAuLg4OhwPFxcUoLy/H0NAQHZoTCJsZYflimLHpbUlJScjOzsZjjz0GmUyG9vZ2nD17Fo2N\njbDb7fB4POA4bsoolZxlhG9/1qxZyMzMxF133YXc3FyadfJ4POju7sbg4CC0Wu2UKHCJRILw8HAM\nDAxAq9XCbDYjISEBP/nJT5Cfnw+32w2bzYbS0lL09/fTgEgmk/3jG2oixDiRFCyhgTMajUhJScGJ\nEyeoZxzsQ0fqCl6vFwqFAhEREYiOjkZycjJcLhfKy8sFHZxerxdqtRoxMTHQaDTo7OxEa2srent7\nBY3PBL6ryYaHh2P69Olob28Xhefa39iTKVRiDDYAvmtbIL2FYka+UVFRYFkWTqdT1MhUKpVixowZ\nGB4eRmNjo2h1Xo7j6DB7nufR2dkpmpFmWRYLFiyAVCpFRUUFDh06JIpeMvpTp9Ohs7MT7777LqxW\na8gta/5Gz2w247777kNCQgJefvllXLhwIeRsiz9immVZPProo8jLy4PP50NZWRlsNlvIegHQLFZc\nXBwef/xxAEBnZyc++OADlJaWBs1wONH4x8XF4aGHHsI999yD3t5e7Nu3D1988QVsNts4gxHonpPB\nEQqFArfddhv+6Z/+CSzLorOzE9u3b0dtbS28Xm9QQ444jqNGNywsDFqtFsuWLYPX60VLSwuKi4vx\n3//93+jt7Q3KIec4jnZC9Pf3w2g0Ys6cOVi8eDH6+vpQX1+Pv/zlLyguLsbQ0FBQjhzDMIiJiUFP\nTw+ioqKQnZ2N7OxstLS0wOVy4be//S0qKysxMDAAj8cT8HeoVCopjiU6OhoqlQrZ2dl0xrrVasUr\nr7yCsrIyqjeU5+WaNtRks8gMZ1Lgz87ORk5ODv7whz9MmZq4kvD8GCuZ2+2GUqmERCJBbm4uUlJS\nsHv37qDbNibqdrvd0Gg0kEqlMBgMUCgU2LFjh+CWJzKBi3ifiYmJiIyMFGz4iOOiVqvB8zyt2ws1\n1MDYQ0Jq9AQoKBZoijyALMvC5XKJmp4mtauenp5xaUeholarsX79eoSHh8NqtYrWBgeMDYOJj4/H\n8PAwDh06JNoAjPDwcNx3333wer04cuQIjh49KijbQt4XERGBRx55BAUFBQCAN954A319fSHfz/6O\nvdFoxJo1a8CyLJqbm/HSSy+JMgkuJiYGP//5z3HLLbegr68P77zzDj766KOQsSfA2HOdkpKCH//4\nx7jjjjugVCqxY8cO/PnPf4bFYgkpTU8mUCkUCiQnJ2Pz5s2IiIjAwMAAioqK6BkXTKBD0vJKpRJS\nqRTp6elYsWIFcnJyMDg4iPr6ejz33HOoq6sLOnhQKBTQ6/UYGRlBTEwMbr/9dhiNRsTFxaGrqwu/\n+93v0NzcTEFZgYpEIkF0dDS8Xi+mTZuGpKQk6PV6xMTEoKWlBe3t7SgpKcHw8HBQ9wfHcYiKioJO\np0NkZCS0Wi0SExMhl8sxODiICxcu4KOPPqLgNCFn9DVtqIHvarMMw8DhcCAlJQVbt26FRCJBU1OT\noLSe1+sFy7Kora1FXFwcli1bBqfTiR2K+l4/AAAUtUlEQVQ7dmBwcFDQur1eL86dO4fY2Fikp6ej\np6eHeq9Cxev10tnZsbGxdDqMECGRCOnlNRgMoukFxqZyEdAbmf8thl7iBJB/9/T0CNYNjD3carUa\nGRkZsNvtqKurE82YmkwmLFq0CFKpVPA97C8SiQQJCQlQKBS4ePEiduzYgeHhYcF6OY7D9OnTcccd\nd6C1tRV//etfBWeGyF5+73vfw1133QWVSoWOjg76jAjda4VCgZUrV0KtVqOvrw+vvvoq9u/fL3jN\nWq0Wt912G77//e9DpVJh+/bt+Nvf/gar1Rq0U0scTeJYrF+/Hrfccgt0Oh3sdju2bdtG+/aDXTd5\nHkjP+6233oro6GjY7XYcOnQIH330EVwuV9AOAOnLlslkSE5ORkFBARYsWAC9Xo9Dhw7hq6++ooYp\nWFEqlTAajXC5XIiLi8OMGTOgUCjQ0tKCyspKDA4O0oAtKMT0txmskZERaDQaJCYmIikpCQ0NDbBY\nLDh37hw8Hk/QWRyWZREWFobFixfDbrdDqVQiIyMDPM+joaEB586dg0qlEuV+/ocw1MCYcTIajXj6\n6acxe/ZsPPPMM7Db7eNeE6z4fD6MjIygs7OTpr1/9atfoaqqSlCak4C+ysvLER8fj5UrV+LUqVOC\nx0YSzvPR0VEcP34cZrNZNINK5rNWVlaivLwcmZmZ0Gg0AYNLriRkXcPDw+ju7kZMTAx1MoQYKHK4\nkfoUKVdM1XITiJAa3A033IDY2Fi8+eab6OrqEi0L8MQTTyAtLQ3Nzc144YUXRNErkUiQkpKC1157\nDRcvXsSTTz6JqqoqwVkAuVyONWvWYNu2bYiMjMTSpUtx9uxZwc4Fy7IwGo14//33oVKpcPr0aTz+\n+OOCSxcKhQKzZs3CL3/5SxQUFGD//v34zW9+g5KSEnoQB9vJQF6v0+lw4MAB5OTkwOv1YteuXfj1\nr39NU7DB6CW1aYZhEBsbi/vuuw9PPvkkhoaGsGvXLjz//PMoKSkB8B0oLBBhWZaWl/Ly8nDvvfdi\n5syZSE1NxZ49e/Daa6+hsbERVquVOs5T3X/ks5VKJTQaDZRKJVauXIlbb70VmZmZsFgsOHr0KK13\nkzUHsgfAWIlJpVJh9uzZWL58OaKiouhs6qqqKhQVFcFisaCnp4eSt0x15hGHIiwsDFlZWVi1ahV8\nPh9aWlqQlpaG9vZ2NDQ0oLm5GbW1tfSsczqdV31myJpNJhNyc3ORn5+P5ORkHDlyBCkpKRQIXVtb\nS8Foer0+JKdo3PWE/M7/QSHprKioKKSnp2N4eJhGOEIPOeIFj46OwuFwUM9YqJC+udbWVuh0OsH6\ngO/S0263G2fPnoVUKoXH40F0dLRg3URve3s7ZQ1LSUmhtHtC19zZ2Ynm5mZawwvkQQ5EN0Haezwe\n2O12UYweaQ9UKpUYHR29au9qKGI2m+H1etHQ0CAaixrHccjLy0NcXBxOnToVdGvhlSQiIgIbN25E\neHg4+vr6UF5eLthIE0coPz8fCoUC9fX1ePfdd1FZWSl4vZGRkdi6dSsWLFgAiUSC7du3o7Kycly0\nFOy5QbJAc+bMQVZWFkZHR3H48GG88sorITn0JJKWSCQwGAxYtWoV7rjjDni9Xuzfvx+vvPIKBUX6\n/0wlhG+aZVlwHIfbbrsN3/ve95CUlASn04nXX38ddXV1sFqt43ROpZs8DxqNBiqVCgsXLkRBQQFt\nO9q/fz8+++wzNDQ0wOPxBHx/kHp/VFQUEhISsGzZMmRnZ8NoNMJkMuHkyZM4ffo0/l975x8TZ7Xm\n8c8zwwwMA6UtP1vQCgFakFZMtbahlluT7na76t2o2bjJrpoa95+7yV3dZL2uf6zrPxpN11+pGt29\nuf7Y9Vp1b7yaVkWKaVIavVraQi0/pnTaXlqgOAyU0qHAnP2DOe8dKNCB980yXc4naZh53+H08J33\nfZ/znPM8zzlx4gQtLS1cvnx5UqW12UhJSaG4uJjbbruNsrIyCgoKrJTZvr4+zpw5Q1tbG0ePHqW/\nvx+Px0NmZmZCWng8HrZu3crq1aspKCjA6/Xi8/ksuzQ6OsqPP/7I+fPnCYVC+P3+hPSY9e+x3cL/\nESJCUVERGRkZtLa2EggEbLepvTIduXfu3Dm6u7snhdvbIRqN4vF48Hg8RCIRfD7fvCJZp/YZsIoT\n6NQFu8S3e/HiRXw+H3l5efj9fkfWfeOnYH0+n2NFT3R6lo4LcMrr1YUsRISenh7Hgt9cLherVq0i\nEokQCATmlao3FW1ItmzZgs/n49tvvyUcDjuiRVVVFRs2bGB8fJyDBw86NmOxcuVK7rvvPoaGhtiz\nZw/79++3PU0vItx1111s3ryZ1NRUzp49y3fffUckEpl3m9o7zcnJ4aGHHsLtdtPY2Mgbb7xBc3Nz\nQvnG0+F2u0lLS6O8vJz777+f/Px8PvjgA9599106OzsnzSxoQ5kIOk83PT2d2tpali1bxtjYGK+8\n8gqBQICBgQHrWo5GownvwywiLF26lLy8PG6//XbWrVtHb28vL7/8MmfPnqW3t9ea4hWRhIPIRITs\n7GwKCwupqKigrKyM1tZWzp49S1NTE+FwmK6uLus7HB4etgK4ZsPlclFYWEhVVRXV1dWUlJTQ0NBA\nKBSivb2daDRKIBBgcHCQK1euICIJG1SXy8X69espLS3F7XZz44038v3339Pd3c3KlSs5ffo0Fy5c\nsIqx2Ln+NNeNodaRlkeOHKGrq4uurq55R5zGo41ddnY2vb29Vj6h3ba1J5menk4wGOTixYsJTzUl\n2u/+/n6WL18+aa3LDkpNFHrR9XT1IMOJtqPRKD6fj7GxMSvn2QmUUvj9fkZGRrh8+bJjnq/H42Ht\n2rW4XC6ryIQTpKSksGLFCi5dukRTU5Mja8gul4vS0lK2bNmC2+3m8OHDjqx7u1wuHnnkEXJzcxka\nGqKhocG2Djrf9s4772Tr1q20tbXx9ddfc+bMGdvZEF6vl7vvvpsbbriBwcFBPv/8c1szZPFphU8+\n+SQbN24kEAjw/vvvc/To0asqVCV6n2jPd+PGjTz11FNUVFSQlpbGp59+Snt7+1XVAKemnM2EnvYu\nLCyktraW1atXE41GefPNN2lsbKS/v3/SdREf53GtfrvdbtasWcM999zDjh07WLp0Kc8++yzNzc3T\n7meQ6DPJ5XIxNDREdXU1NTU1eL1eBgcHeeGFF2htbWV0dNSa5tbt6WW6a+Vkd3d3U1JSQmlpKbm5\nuRQXF/PJJ58QDAYZGhqyZuB0m4neiyKCz+cjNTWV8vJyRkdH+emnnzh58iSdnZ309fVZ36EO6Pt/\nv0atp3EyMzNJSUlh3759fPHFF4RCIduer46MzMvLIycnh7fffpvBwUEr8tnOQ1Snk+n8XphIeRoZ\nGXHEK8nIyCAcDuN2u61C8nYD4OBP5Vuj0SgFBQWsWLGCUChke1SoL2xd9MTr9dpet4GJNVRdPGV0\ndNQRT93lclFTU0NVVRVXrlxx5PuCPz3sPB4PTU1NHDx40BGDWlZWxq5du1i1ahWnTp2yiirYQUTI\nz89n+/btDAwM8NZbb/Hee+/ZjpjOzMzkiSeesCpuPf744zQ1Nc37PtZToRkZGWzfvp3a2loCgQAv\nvfQSdXV1tp4PXq+XBx98kJ07d1JZWUk0GqW2tpZTp05N2shBk6g2fr+fHTt28Nxzz5GVlUUoFOKZ\nZ57hm2++sTxR3Va8cboW6enpPPbYY2zZsoVbbrmFEydO8Oqrr9LY2EhPT49lMOKNXKL6pKWl8eij\nj3LrrbficrlobGykoaFh0iBWG/6pRUtmIzU1lYcffpjy8nIuXbrE3r172bVrF4FAYNJ9p2e5otFo\nQs+5lJQUtm7dahX8efrpp/nss8/o7++3sma0FiJCJBJJ+D53uVzU1dWxdu1avvrqK7788ks6Ojqs\nZ5n++0XEciDsDnCTeo1aFztJS0ujqKiIzMxMzp07x+DgoLX12Xw9M/3F+3w+srOzycvL49y5c2Rn\nZzsyNet2u/H7/Sxbtoze3l7Ky8vJysqyPFQ76AHGhQsXiEQilJWVOdauy+WyKiuVl5eTm5vriPHT\ntcRHR0etwYATXnVKSoq1hZzf72fJkiWOtLl8+XKGhoYYHh6eVJTCDm63m+LiYsLhsDXt5oSnXlVV\nxZIlSxgaGrLq09s11F6v1xqotLe3U19f70iOenV1NbW1tfh8Prq6umhvb7cdVKiLeezcuZOxsTH2\n7dtnTf/bQSnFvffey80334zX67XqQE9npKdjputbRKipqSEnJ8dKd6uvr7cCRadOp0/9Lmdq1+v1\nsmnTJiorK0lLS+O1117jwIEDM3q8U/+G2e5Hn8/HTTfdRCQSoaGhgeeff37SNHq80ZtuYDFT2z6f\njzVr1pCbm8vHH3/M7t27rZ3e4j1+3d9E2/V6vRQVFeH3+wkEAtTX11uGeKqRBq5yGGbTQj8PTp48\nyb59++jq6pqkbbwmuhyzXZLWo9YRvX6/n6ysLFJTU/H5fFYpR73vqf7sfNIidLWzdevW4ff7uXDh\ngrWvqE4Jm28xB128ACAYDHL58mVGRkYcKZvpcrkIh8N0dHQwMjJipS3YRV+AwWDQCv7q7u52LKXs\nyJEjlJaWcunSJUdGmfrmDQQC1oPfCWPi8XgYGBiwlkKc2OhDXxN+v9+qruRE8JvOF75y5Qrd3d0c\nPnzYkQeD1+slJyeH/v5+WlpaHKlFrpSioKCA8fFxBgYGOH78uCPXgc4V9nq9dHR0cPjwYWvN1A56\nNm98fJyenh4aGhrmVEFups+lpaURDAYZHh5m//79fPTRR3PKKpit3ZMnT1JeXk44HGb//v3W4Dje\nQ5+pndn+f6/Xy8DAAMeOHePFF1+ks7Nzxnib6QYAM7UtIhw4cIDNmzeze/duzp8/f1VAWryhTrTP\nY2Nj1NXVsWHDBlpaWqxc9KllT7Uec5kdGR0dJRwOEwwGr5qpiP9dPQPgyFKnU7mhtjoxsT3YVXi9\nXisJvqSkhG3btrF3715aWlqs2tHz7b8OwCkqKuKBBx4gMzOT119/3coPTXTUPBNZWVnccccdFBYW\nEg6HOXjwIMPDw1YZwPmivenq6mpycnIYHx+no6PD1hqfRs8w5OTksG7dOo4dO0ZPTw8jIyO2L7b0\n9HRyc3NZvXo1w8PDHDp0yJGL2O12s3btWrKysmhtbaWvr8+2QdFLLSUlJY6u+WpPvbKykra2Nvr6\n+hwZuBUWFlJcXIzH4+HUqVOOFDnxeDzk5eVRVVVFW1sbPT09jtQMX7lyJRUVFWRnZ3Ps2DErsMcO\n+l7etGkTkUiE5uZmK83G7lR9RUUF27ZtIxAI0NbWRmdnp+3+pqSkkJ2dzc6dO6mrq+PMmTOEQiHb\n/dUFkNavX09GRgYffvihNcjUMTPzRQenpaenc+jQIat8p959Cqb30mdDr/XqNNPTp09z8eJFa0/r\nSCQyaWAxl/s6NTXVcvCysrIIBALWHtw6w0UPIOZT8tXn8+Hz+RgaGmJsbMxy8CKRyKRdt3SfZ2n7\nB6XUbdf8f5PZUGsvJD8/3yppGQwGCYVCjjw4s7KyrMo3uj6tU8FIGRkZ1lr38PCwY+uc8akd8ReZ\nwWAwxDPVw3OivXhvcapxiz8/n1lOwEoHnc771T/nuuGJDmTVg4r4AZE+n2j0+9R2XS6XtQ2uLsGs\no/T1eY/HM1u2z/VvqOPOk5KSMmn05hR6/dXuiHMqs00xGQwGg2F6nMqMmUr8piBODmKm9jd+DTyB\n9q8rQ30BuAQ4U/9x8ZGD0c4ORj97GP3sYfSbP9e7dquUUrnX+lBSGGoAEfk+kZGF4WqMdvYw+tnD\n6GcPo9/8WSzaJXV6lsFgMBgMix1jqA0Gg8FgSGKSyVC/tdAduI4x2tnD6GcPo589jH7zZ1FolzRr\n1AaDwWAwGK4mmTxqg8FgMBgMU1hwQy0i20WkTUQCIvKrhe5PMiIivxaRXhFpiTu2XETqRKQj9nNZ\n3LmnYnq2icifL0yvkwMRuUFEGkTkRxE5LiK/jB03+iWAiKSJyHcicjSm37/Fjhv9EkRE3CLSJCKf\nx94b7eaAiARFpFlEjojI97Fji0rDBTXUIuIGdgN/AVQCfyMilQvZpyTlN8D2Kcd+BdQrpcqA+th7\nYvo9CNwc+53XYzovVsaAf1JKVQIbgV/ENDL6JcYIcJdS6hagGtguIhsx+s2FXwIn4t4b7ebOVqVU\ndVwq1qLScKE96g1AQCnVqZS6AvwW+PkC9ynpUEodAEJTDv8ceCf2+h3gr+KO/1YpNaKUOgUEmNB5\nUaKUOq+UOhx7fZGJB2YhRr+EUBPo+ree2D+F0S8hRKQI+EvgP+IOG+3ss6g0XGhDXQicjXv/x9gx\nw7XJV0qdj73uBvJjr42mMyAiNwG3At9i9EuY2NTtEaAXqFNKGf0S52Xgn4H4+sRGu7mhgK9F5AcR\n+fvYsUWlYdJuc2lIHKWUula99MWOiGQAnwD/qJQanFKL3eg3C0qpcaBaRJYCvxORqinnjX7TICJ3\nA71KqR9E5GfTfcZolxCblVJdIpIH1IlIa/zJxaDhQnvUXcANce+LYscM16ZHRFYAxH72xo4bTacg\nIh4mjPR/KaX+J3bY6DdHlFJhoIGJtT+j37WpAe4VkSATy3p3icj7GO3mhFKqK/azF/gdE1PZi0rD\nhTbUfwDKRKRYRLxMBAH8foH7dL3we+Dh2OuHgU/jjj8oIqkiUgyUAd8tQP+SAplwnf8TOKGU+ve4\nU0a/BBCR3JgnjYj4gG1AK0a/a6KUekopVaSUuomJZ9t+pdTfYrRLGBHxi0imfg38GdDCItNwQae+\nlVJjIvIPwJeAG/i1Uur4QvYpGRGRD4CfATki8kfgX4HngT0i8ihwGvhrAKXUcRHZA/zIRMTzL2JT\nl4uVGuDvgObYOivAv2D0S5QVwDuxyFkXsEcp9bmIHMLoN1/MtZc4+Uwst8CEvfpvpdQXIvIHFpGG\npjKZwWAwGAxJzEJPfRsMBoPBYJgFY6gNBoPBYEhijKE2GAwGgyGJMYbaYDAYDIYkxhhqg8FgMBiS\nGGOoDQaDwWBIYoyhNhgMBoMhiTGG2mAwGAyGJOZ/Adg91jJE7x7IAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Generator takes noise as input\n", + "noise_input = tf.placeholder(tf.float32, shape=[None, latent_dim])\n", + "# Rebuild the decoder to create image from noise\n", + "decoder = tf.matmul(noise_input, weights['decoder_h1']) + biases['decoder_b1']\n", + "decoder = tf.nn.tanh(decoder)\n", + "decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out']\n", + "decoder = tf.nn.sigmoid(decoder)\n", + "\n", + "# Building a manifold of generated digits\n", + "n = 20\n", + "x_axis = np.linspace(-3, 3, n)\n", + "y_axis = np.linspace(-3, 3, n)\n", + "\n", + "canvas = np.empty((28 * n, 28 * n))\n", + "for i, yi in enumerate(x_axis):\n", + " for j, xi in enumerate(y_axis):\n", + " z_mu = np.array([[xi, yi]] * batch_size)\n", + " x_mean = sess.run(decoder, feed_dict={noise_input: z_mu})\n", + " canvas[(n - i - 1) * 28:(n - i) * 28, j * 28:(j + 1) * 28] = \\\n", + " x_mean[0].reshape(28, 28)\n", + "\n", + "plt.figure(figsize=(8, 10))\n", + "Xi, Yi = np.meshgrid(x_axis, y_axis)\n", + "plt.imshow(canvas, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/4_Utils/save_restore_model.ipynb b/notebooks/4_Utils/save_restore_model.ipynb index a82a67de..f70b2429 100644 --- a/notebooks/4_Utils/save_restore_model.ipynb +++ b/notebooks/4_Utils/save_restore_model.ipynb @@ -1,21 +1,19 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [ - "'''\n", + "# Save & Restore a Model\n", + "\n", "Save and Restore a model using TensorFlow.\n", "This example is using the MNIST database of handwritten digits\n", - "(http://yann.lecun.com/exdb/mnist/)\n", + "(http://yann.lecun.com/exdb/mnist/).\n", "\n", - "Author: Aymeric Damien\n", - "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", - "'''" + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" ] }, { @@ -228,20 +226,11 @@ " print(\"Accuracy:\", accuracy.eval(\n", " {x: mnist.test.images, y: mnist.test.labels}))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, @@ -254,8 +243,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.3" + "pygments_lexer": "ipython2", + "version": "2.7.12" } }, "nbformat": 4, diff --git a/notebooks/4_Utils/tensorboard_advanced.ipynb b/notebooks/4_Utils/tensorboard_advanced.ipynb index 19d14829..62aa8d76 100644 --- a/notebooks/4_Utils/tensorboard_advanced.ipynb +++ b/notebooks/4_Utils/tensorboard_advanced.ipynb @@ -1,25 +1,24 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "'''\n", - "Graph and Loss visualization using Tensorboard.\n", - "This example is using the MNIST database of handwritten digits\n", - "(http://yann.lecun.com/exdb/mnist/)\n", + "# Tensorboard Advanced\n", "\n", - "Author: Aymeric Damien\n", - "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", - "'''" + "Advanced visualization using Tensorboard (weights, gradient, ...). This example is using the MNIST database of handwritten digits\n", + "(http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -37,8 +36,6 @@ "\n", "import tensorflow as tf\n", "\n", - "tf.logging.set_verbosity(tf.logging.WARN)\n", - "\n", "# Import MNIST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" @@ -46,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": true }, @@ -57,17 +54,8 @@ "training_epochs = 25\n", "batch_size = 100\n", "display_step = 1\n", - "logs_path = '/tmp/tensorflow_logs/example'" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ + "logs_path = '/tmp/tensorflow_logs/example/'\n", + "\n", "# Network Parameters\n", "n_hidden_1 = 256 # 1st layer number of features\n", "n_hidden_2 = 256 # 2nd layer number of features\n", @@ -83,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": true }, @@ -120,8 +108,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Encapsulating all ops into scopes, making Tensorboard's Graph\n", @@ -146,9 +136,18 @@ "with tf.name_scope('Accuracy'):\n", " # Accuracy\n", " acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", - " acc = tf.reduce_mean(tf.cast(acc, tf.float32))\n", - "\n", - "# Initializing the variables\n", + " acc = tf.reduce_mean(tf.cast(acc, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initialize the variables (i.e. assign their default value)\n", "init = tf.global_variables_initializer()\n", "\n", "# Create a summary to monitor cost tensor\n", @@ -167,40 +166,42 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch: 0001 cost= 67.380016100\n", - "Epoch: 0002 cost= 15.307113347\n", - "Epoch: 0003 cost= 9.986865815\n", - "Epoch: 0004 cost= 7.381951704\n", - "Epoch: 0005 cost= 5.849047792\n", - "Epoch: 0006 cost= 4.881959525\n", - "Epoch: 0007 cost= 4.045799575\n", - "Epoch: 0008 cost= 3.430059265\n", - "Epoch: 0009 cost= 3.076626336\n", - "Epoch: 0010 cost= 2.863002729\n", - "Epoch: 0011 cost= 2.510218838\n", - "Epoch: 0012 cost= 2.276251159\n", - "Epoch: 0013 cost= 1.978880318\n", - "Epoch: 0014 cost= 1.733890927\n", - "Epoch: 0015 cost= 1.540066199\n", - "Epoch: 0016 cost= 1.439536399\n", - "Epoch: 0017 cost= 1.279739846\n", - "Epoch: 0018 cost= 1.224386179\n", - "Epoch: 0019 cost= 1.095804572\n", - "Epoch: 0020 cost= 1.100819187\n", - "Epoch: 0021 cost= 0.885994007\n", - "Epoch: 0022 cost= 1.079832625\n", - "Epoch: 0023 cost= 0.948164673\n", - "Epoch: 0024 cost= 0.613826872\n", - "Epoch: 0025 cost= 0.644082715\n", + "Epoch: 0001 cost= 59.570364205\n", + "Epoch: 0002 cost= 13.585465186\n", + "Epoch: 0003 cost= 8.379069252\n", + "Epoch: 0004 cost= 6.005265894\n", + "Epoch: 0005 cost= 4.498054792\n", + "Epoch: 0006 cost= 3.503682522\n", + "Epoch: 0007 cost= 2.822272765\n", + "Epoch: 0008 cost= 2.306899852\n", + "Epoch: 0009 cost= 1.912765543\n", + "Epoch: 0010 cost= 1.597006118\n", + "Epoch: 0011 cost= 1.330172869\n", + "Epoch: 0012 cost= 1.142490618\n", + "Epoch: 0013 cost= 0.939443911\n", + "Epoch: 0014 cost= 0.820920588\n", + "Epoch: 0015 cost= 0.702543302\n", + "Epoch: 0016 cost= 0.604815631\n", + "Epoch: 0017 cost= 0.505682561\n", + "Epoch: 0018 cost= 0.439700446\n", + "Epoch: 0019 cost= 0.378268929\n", + "Epoch: 0020 cost= 0.299557848\n", + "Epoch: 0021 cost= 0.269859066\n", + "Epoch: 0022 cost= 0.230899029\n", + "Epoch: 0023 cost= 0.183722090\n", + "Epoch: 0024 cost= 0.164173368\n", + "Epoch: 0025 cost= 0.142141250\n", "Optimization Finished!\n", - "Accuracy: 0.9513\n", + "Accuracy: 0.9336\n", "Run the command line:\n", "--> tensorboard --logdir=/tmp/tensorflow_logs \n", "Then open http://0.0.0.0:6006/ into your web browser\n" @@ -208,8 +209,10 @@ } ], "source": [ - "# Launch the graph\n", + "# Start training\n", "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", " sess.run(init)\n", "\n", " # op to write logs to Tensorboard\n", @@ -247,34 +250,58 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loss and Accuracy Visualization\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computation Graph Visualization\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Weights and Gradients Visualization\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Activations Visualization\n", + "" + ] } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", - "name": "python3" + "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 3 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.3" + "pygments_lexer": "ipython2", + "version": "2.7.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 1 } diff --git a/notebooks/4_Utils/tensorboard_basic.ipynb b/notebooks/4_Utils/tensorboard_basic.ipynb index 047147f0..05a85ca3 100644 --- a/notebooks/4_Utils/tensorboard_basic.ipynb +++ b/notebooks/4_Utils/tensorboard_basic.ipynb @@ -1,21 +1,17 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [ - "'''\n", - "Graph and Loss visualization using Tensorboard.\n", - "This example is using the MNIST database of handwritten digits\n", - "(http://yann.lecun.com/exdb/mnist/)\n", + "# Tensorboard Basics\n", "\n", - "Author: Aymeric Damien\n", - "Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", - "'''" + "Graph and Loss visualization using Tensorboard. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" ] }, { @@ -32,12 +28,12 @@ "\n", "# Import MINST data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", - "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)" + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": true }, @@ -47,8 +43,8 @@ "learning_rate = 0.01\n", "training_epochs = 25\n", "batch_size = 100\n", - "display_step = 1\n", - "logs_path = '/tmp/tensorflow_logs/example'\n", + "display_epoch = 1\n", + "logs_path = '/tmp/tensorflow_logs/example/'\n", "\n", "# tf Graph Input\n", "# mnist data image of shape 28*28=784\n", @@ -63,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": true }, @@ -98,8 +94,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -139,7 +137,7 @@ } ], "source": [ - "# Launch the graph\n", + "# Start Training\n", "with tf.Session() as sess:\n", " sess.run(init)\n", "\n", @@ -162,8 +160,8 @@ " # Compute average loss\n", " avg_cost += c / total_batch\n", " # Display logs per epoch step\n", - " if (epoch + 1) % display_step == 0:\n", - " print(\"Epoch:\", '%04d' % (epoch + 1), \"cost=\", \"{:.9f}\".format(avg_cost))\n", + " if (epoch+1) % display_epoch == 0:\n", + " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n", "\n", " print(\"Optimization Finished!\")\n", "\n", @@ -177,49 +175,27 @@ ] }, { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAACeAAAATECAYAAAAJEjdXAAAMFWlDQ1BJQ0MgUHJvZmlsZQAASImV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\nzQ+TZ/sIW9+vnvVNVWbreI9Bbf4fOozPMqmXwV0AAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "cell_type": "markdown", + "metadata": {} "source": [ - "# Loss and accuracy Visualization" + "### Loss and Accuracy Visualization\n", + "\n", + "" ] }, { - "cell_type": "code", - "execution_count": 3, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAACdgAAAS4CAYAAAAO1jghAAAMFWlDQ1BJQ0MgUHJvZmlsZQAASImV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\nj6Rr/x9y5UKDWn4pfgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "# Graph Visualization" + "### Graph Visualization\n", + "\n", + "" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, @@ -232,8 +208,8 @@ "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.3" + "pygments_lexer": "ipython2", + "version": "2.7.12" } }, "nbformat": 4, diff --git a/notebooks/5_DataManagement/build_an_image_dataset.ipynb b/notebooks/5_DataManagement/build_an_image_dataset.ipynb new file mode 100644 index 00000000..21b8a1d1 --- /dev/null +++ b/notebooks/5_DataManagement/build_an_image_dataset.ipynb @@ -0,0 +1,290 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Build an Image Dataset in TensorFlow.\n", + "\n", + "For this example, you need to make your own set of images (JPEG).\n", + "We will show 2 different ways to build that dataset:\n", + "\n", + "- From a root folder, that will have a sub-folder containing images for each class\n", + "\n", + "```\n", + " ROOT_FOLDER\n", + " |-------- SUBFOLDER (CLASS 0)\n", + " | |\n", + " | | ----- image1.jpg\n", + " | | ----- image2.jpg\n", + " | | ----- etc...\n", + " | \n", + " |-------- SUBFOLDER (CLASS 1)\n", + " | |\n", + " | | ----- image1.jpg\n", + " | | ----- image2.jpg\n", + " | | ----- etc...\n", + "\n", + "```\n", + "\n", + "- From a plain text file, that will list all images with their class ID:\n", + "\n", + "```\n", + " /path/to/image/1.jpg CLASS_ID\n", + " /path/to/image/2.jpg CLASS_ID\n", + " /path/to/image/3.jpg CLASS_ID\n", + " /path/to/image/4.jpg CLASS_ID\n", + " etc...\n", + "```\n", + "\n", + "Below, there are some parameters that you need to change (Marked 'CHANGE HERE'), \n", + "such as the dataset path.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "import os\n", + "\n", + "# Dataset Parameters - CHANGE HERE\n", + "MODE = 'folder' # or 'file', if you choose a plain text file (see above).\n", + "DATASET_PATH = '/path/to/dataset/' # the dataset file or root folder path.\n", + "\n", + "# Image Parameters\n", + "N_CLASSES = 2 # CHANGE HERE, total number of classes\n", + "IMG_HEIGHT = 64 # CHANGE HERE, the image height to be resized to\n", + "IMG_WIDTH = 64 # CHANGE HERE, the image width to be resized to\n", + "CHANNELS = 3 # The 3 color channels, change to 1 if grayscale" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Reading the dataset\n", + "# 2 modes: 'file' or 'folder'\n", + "def read_images(dataset_path, mode, batch_size):\n", + " imagepaths, labels = list(), list()\n", + " if mode == 'file':\n", + " # Read dataset file\n", + " data = open(dataset_path, 'r').read().splitlines()\n", + " for d in data:\n", + " imagepaths.append(d.split(' ')[0])\n", + " labels.append(int(d.split(' ')[1]))\n", + " elif mode == 'folder':\n", + " # An ID will be affected to each sub-folders by alphabetical order\n", + " label = 0\n", + " # List the directory\n", + " try: # Python 2\n", + " classes = sorted(os.walk(dataset_path).next()[1])\n", + " except Exception: # Python 3\n", + " classes = sorted(os.walk(dataset_path).__next__()[1])\n", + " # List each sub-directory (the classes)\n", + " for c in classes:\n", + " c_dir = os.path.join(dataset_path, c)\n", + " try: # Python 2\n", + " walk = os.walk(c_dir).next()\n", + " except Exception: # Python 3\n", + " walk = os.walk(c_dir).__next__()\n", + " # Add each image to the training set\n", + " for sample in walk[2]:\n", + " # Only keeps jpeg images\n", + " if sample.endswith('.jpg') or sample.endswith('.jpeg'):\n", + " imagepaths.append(os.path.join(c_dir, sample))\n", + " labels.append(label)\n", + " label += 1\n", + " else:\n", + " raise Exception(\"Unknown mode.\")\n", + "\n", + " # Convert to Tensor\n", + " imagepaths = tf.convert_to_tensor(imagepaths, dtype=tf.string)\n", + " labels = tf.convert_to_tensor(labels, dtype=tf.int32)\n", + " # Build a TF Queue, shuffle data\n", + " image, label = tf.train.slice_input_producer([imagepaths, labels],\n", + " shuffle=True)\n", + "\n", + " # Read images from disk\n", + " image = tf.read_file(image)\n", + " image = tf.image.decode_jpeg(image, channels=CHANNELS)\n", + "\n", + " # Resize images to a common size\n", + " image = tf.image.resize_images(image, [IMG_HEIGHT, IMG_WIDTH])\n", + "\n", + " # Normalize\n", + " image = image * 1.0/127.5 - 1.0\n", + "\n", + " # Create batches\n", + " X, Y = tf.train.batch([image, label], batch_size=batch_size,\n", + " capacity=batch_size * 8,\n", + " num_threads=4)\n", + "\n", + " return X, Y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# -----------------------------------------------\n", + "# THIS IS A CLASSIC CNN (see examples, section 3)\n", + "# -----------------------------------------------\n", + "# Note that a few elements have changed (usage of queues).\n", + "\n", + "# Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 10000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "dropout = 0.75 # Dropout, probability to keep units\n", + "\n", + "# Build the data input\n", + "X, Y = read_images(DATASET_PATH, MODE, batch_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create model\n", + "def conv_net(x, n_classes, dropout, reuse, is_training):\n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " fc1 = tf.contrib.layers.flatten(conv2)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " fc1 = tf.layers.dense(fc1, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)\n", + "\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(fc1, n_classes)\n", + " # Because 'softmax_cross_entropy_with_logits' already apply softmax,\n", + " # we only apply softmax to testing network\n", + " out = tf.nn.softmax(out) if not is_training else out\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Because Dropout have different behavior at training and prediction time, we\n", + "# need to create 2 distinct computation graphs that share the same weights.\n", + "\n", + "# Create a graph for training\n", + "logits_train = conv_net(X, N_CLASSES, dropout, reuse=False, is_training=True)\n", + "# Create another graph for testing that reuse the same weights\n", + "logits_test = conv_net(X, N_CLASSES, dropout, reuse=True, is_training=False)\n", + "\n", + "# Define loss and optimizer (with train logits, for dropout to take effect)\n", + "loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.cast(Y, tf.int64))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()\n", + "\n", + "# Saver object\n", + "saver = tf.train.Saver()\n", + "\n", + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " # Start the data queue\n", + " tf.train.start_queue_runners()\n", + "\n", + " # Training cycle\n", + " for step in range(1, num_steps+1):\n", + "\n", + " if step % display_step == 0:\n", + " # Run optimization and calculate batch loss and accuracy\n", + " _, loss, acc = sess.run([train_op, loss_op, accuracy])\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + " else:\n", + " # Only run the optimization op (backprop)\n", + " sess.run(train_op)\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Save your model\n", + " saver.save(sess, 'my_tf_model')" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb b/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb new file mode 100644 index 00000000..bf9f46e8 --- /dev/null +++ b/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb @@ -0,0 +1,234 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# TensorFlow Dataset API\n", + "\n", + "In this example, we will show how to load numpy array data into the new \n", + "TensorFlow 'Dataset' API. The Dataset API implements an optimized data pipeline\n", + "with queues, that make data processing and training faster (especially on GPU).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "# Import MNIST data (Numpy format)\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "num_steps = 1000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.75 # Dropout, probability to keep units\n", + "\n", + "sess = tf.Session()\n", + "\n", + "# Create a dataset tensor from the images and the labels\n", + "dataset = tf.contrib.data.Dataset.from_tensor_slices(\n", + " (mnist.train.images, mnist.train.labels))\n", + "# Create batches of data\n", + "dataset = dataset.batch(batch_size)\n", + "# Create an iterator, to go over the dataset\n", + "iterator = dataset.make_initializable_iterator()\n", + "# It is better to use 2 placeholders, to avoid to load all data into memory,\n", + "# and avoid the 2Gb restriction length of a tensor.\n", + "_data = tf.placeholder(tf.float32, [None, n_input])\n", + "_labels = tf.placeholder(tf.float32, [None, n_classes])\n", + "# Initialize the iterator\n", + "sess.run(iterator.initializer, feed_dict={_data: mnist.train.images,\n", + " _labels: mnist.train.labels})\n", + "\n", + "# Neural Net Input\n", + "X, Y = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# -----------------------------------------------\n", + "# THIS IS A CLASSIC CNN (see examples, section 3)\n", + "# -----------------------------------------------\n", + "# Note that a few elements have changed (usage of sess run).\n", + "\n", + "# Create model\n", + "def conv_net(x, n_classes, dropout, reuse, is_training):\n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " fc1 = tf.contrib.layers.flatten(conv2)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " fc1 = tf.layers.dense(fc1, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)\n", + "\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(fc1, n_classes)\n", + " # Because 'softmax_cross_entropy_with_logits' already apply softmax,\n", + " # we only apply softmax to testing network\n", + " out = tf.nn.softmax(out) if not is_training else out\n", + "\n", + " return out\n", + "\n", + "\n", + "# Because Dropout have different behavior at training and prediction time, we\n", + "# need to create 2 distinct computation graphs that share the same weights.\n", + "\n", + "# Create a graph for training\n", + "logits_train = conv_net(X, n_classes, dropout, reuse=False, is_training=True)\n", + "# Create another graph for testing that reuse the same weights, but has\n", + "# different behavior for 'dropout' (not applied).\n", + "logits_test = conv_net(X, n_classes, dropout, reuse=True, is_training=False)\n", + "\n", + "# Define loss and optimizer (with train logits, for dropout to take effect)\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 7.9429, Training Accuracy= 0.070\n", + "Step 100, Minibatch Loss= 0.3491, Training Accuracy= 0.922\n", + "Step 200, Minibatch Loss= 0.2343, Training Accuracy= 0.922\n", + "Step 300, Minibatch Loss= 0.1838, Training Accuracy= 0.969\n", + "Step 400, Minibatch Loss= 0.1715, Training Accuracy= 0.953\n", + "Step 500, Minibatch Loss= 0.2730, Training Accuracy= 0.938\n", + "Step 600, Minibatch Loss= 0.3427, Training Accuracy= 0.953\n", + "Step 700, Minibatch Loss= 0.2261, Training Accuracy= 0.961\n", + "Step 800, Minibatch Loss= 0.1487, Training Accuracy= 0.953\n", + "Step 900, Minibatch Loss= 0.1438, Training Accuracy= 0.945\n", + "Step 1000, Minibatch Loss= 0.1786, Training Accuracy= 0.961\n", + "Optimization Finished!\n" + ] + } + ], + "source": [ + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + "\n", + "# Training cycle\n", + "for step in range(1, num_steps + 1):\n", + " \n", + " try:\n", + " # Run optimization\n", + " sess.run(train_op)\n", + " except tf.errors.OutOfRangeError:\n", + " # Reload the iterator when it reaches the end of the dataset\n", + " sess.run(iterator.initializer, \n", + " feed_dict={_data: mnist.train.images,\n", + " _labels: mnist.train.labels})\n", + " sess.run(train_op)\n", + " \n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " # (note that this consume a new batch of data)\n", + " loss, acc = sess.run([loss_op, accuracy])\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + "print(\"Optimization Finished!\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/5_MultiGPU/multigpu_basics.ipynb b/notebooks/6_MultiGPU/multigpu_basics.ipynb similarity index 86% rename from notebooks/5_MultiGPU/multigpu_basics.ipynb rename to notebooks/6_MultiGPU/multigpu_basics.ipynb index c4d5a29b..1089b3e8 100644 --- a/notebooks/5_MultiGPU/multigpu_basics.ipynb +++ b/notebooks/6_MultiGPU/multigpu_basics.ipynb @@ -1,23 +1,23 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [ - "# Basic Multi GPU computation example using TensorFlow library.\n", + "# Multi-GPU Basics\n", + "\n", + "Basic Multi-GPU computation example using TensorFlow library.\n", "\n", - "# Author: Aymeric Damien\n", - "# Project: https://github.com/aymericdamien/TensorFlow-Examples/\n", + "This tutorial requires your machine to have 2 GPUs\n", + "\"/cpu:0\": The CPU of your machine.\n", + "\"/gpu:0\": The first GPU of your machine\n", + "\"/gpu:1\": The second GPU of your machine\n", + "For this example, we are using 2 GTX-980\n", "\n", - "# This tutorial requires your machine to have 2 GPUs\n", - "# \"/cpu:0\": The CPU of your machine.\n", - "# \"/gpu:0\": The first GPU of your machine\n", - "# \"/gpu:1\": The second GPU of your machine\n", - "# For this example, we are using 2 GTX-980" + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" ] }, { @@ -155,22 +155,23 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 2", + "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/notebooks/6_MultiGPU/multigpu_cnn.ipynb b/notebooks/6_MultiGPU/multigpu_cnn.ipynb new file mode 100644 index 00000000..9785d740 --- /dev/null +++ b/notebooks/6_MultiGPU/multigpu_cnn.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-GPU Training Example\n", + "\n", + "Train a convolutional neural network on multiple GPU with TensorFlow.\n", + "\n", + "This example is using TensorFlow layers, see 'convolutional_network_raw' example\n", + "for a raw TensorFlow implementation with variables.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training with multiple GPU cards\n", + "\n", + "In this example, we are using data parallelism to split the training accross multiple GPUs. Each GPU has a full replica of the neural network model, and the weights (i.e. variables) are updated synchronously by waiting that each GPU process its batch of data.\n", + "\n", + "First, each GPU process a distinct batch of data and compute the corresponding gradients, then, all gradients are accumulated in the CPU and averaged. The model weights are finally updated with the gradients averaged, and the new model weights are sent back to each GPU, to repeat the training process.\n", + "\n", + "\"Parallelism\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import time\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", + "# Parameters\n", + "num_gpus = 2\n", + "num_steps = 200\n", + "learning_rate = 0.001\n", + "batch_size = 1024\n", + "display_step = 10\n", + "\n", + "# Network Parameters\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.75 # Dropout, probability to keep units" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build a convolutional neural network\n", + "def conv_net(x, n_classes, dropout, reuse, is_training):\n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer with 64 filters and a kernel size of 5\n", + " x = tf.layers.conv2d(x, 64, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " x = tf.layers.max_pooling2d(x, 2, 2)\n", + "\n", + " # Convolution Layer with 256 filters and a kernel size of 5\n", + " x = tf.layers.conv2d(x, 256, 3, activation=tf.nn.relu)\n", + " # Convolution Layer with 512 filters and a kernel size of 5\n", + " x = tf.layers.conv2d(x, 512, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " x = tf.layers.max_pooling2d(x, 2, 2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " x = tf.contrib.layers.flatten(x)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " x = tf.layers.dense(x, 2048)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " x = tf.layers.dropout(x, rate=dropout, training=is_training)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " x = tf.layers.dense(x, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " x = tf.layers.dropout(x, rate=dropout, training=is_training)\n", + "\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(x, n_classes)\n", + " # Because 'softmax_cross_entropy_with_logits' loss already apply\n", + " # softmax, we only apply softmax to testing network\n", + " out = tf.nn.softmax(out) if not is_training else out\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build the function to average the gradients\n", + "def average_gradients(tower_grads):\n", + " average_grads = []\n", + " for grad_and_vars in zip(*tower_grads):\n", + " # Note that each grad_and_vars looks like the following:\n", + " # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))\n", + " grads = []\n", + " for g, _ in grad_and_vars:\n", + " # Add 0 dimension to the gradients to represent the tower.\n", + " expanded_g = tf.expand_dims(g, 0)\n", + "\n", + " # Append on a 'tower' dimension which we will average over below.\n", + " grads.append(expanded_g)\n", + "\n", + " # Average over the 'tower' dimension.\n", + " grad = tf.concat(grads, 0)\n", + " grad = tf.reduce_mean(grad, 0)\n", + "\n", + " # Keep in mind that the Variables are redundant because they are shared\n", + " # across towers. So .. we will just return the first tower's pointer to\n", + " # the Variable.\n", + " v = grad_and_vars[0][1]\n", + " grad_and_var = (grad, v)\n", + " average_grads.append(grad_and_var)\n", + " return average_grads" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Minibatch Loss= 2.4077, Training Accuracy= 0.123, 682 Examples/sec\n", + "Step 10: Minibatch Loss= 1.0067, Training Accuracy= 0.765, 6528 Examples/sec\n", + "Step 20: Minibatch Loss= 0.2442, Training Accuracy= 0.945, 6803 Examples/sec\n", + "Step 30: Minibatch Loss= 0.2013, Training Accuracy= 0.951, 6741 Examples/sec\n", + "Step 40: Minibatch Loss= 0.1445, Training Accuracy= 0.962, 6700 Examples/sec\n", + "Step 50: Minibatch Loss= 0.0940, Training Accuracy= 0.971, 6746 Examples/sec\n", + "Step 60: Minibatch Loss= 0.0792, Training Accuracy= 0.977, 6627 Examples/sec\n", + "Step 70: Minibatch Loss= 0.0593, Training Accuracy= 0.979, 6749 Examples/sec\n", + "Step 80: Minibatch Loss= 0.0799, Training Accuracy= 0.984, 6368 Examples/sec\n", + "Step 90: Minibatch Loss= 0.0614, Training Accuracy= 0.988, 6762 Examples/sec\n", + "Step 100: Minibatch Loss= 0.0716, Training Accuracy= 0.983, 6338 Examples/sec\n", + "Step 110: Minibatch Loss= 0.0531, Training Accuracy= 0.986, 6504 Examples/sec\n", + "Step 120: Minibatch Loss= 0.0425, Training Accuracy= 0.990, 6721 Examples/sec\n", + "Step 130: Minibatch Loss= 0.0473, Training Accuracy= 0.986, 6735 Examples/sec\n", + "Step 140: Minibatch Loss= 0.0345, Training Accuracy= 0.991, 6636 Examples/sec\n", + "Step 150: Minibatch Loss= 0.0419, Training Accuracy= 0.993, 6777 Examples/sec\n", + "Step 160: Minibatch Loss= 0.0602, Training Accuracy= 0.984, 6392 Examples/sec\n", + "Step 170: Minibatch Loss= 0.0425, Training Accuracy= 0.990, 6855 Examples/sec\n", + "Step 180: Minibatch Loss= 0.0107, Training Accuracy= 0.998, 6804 Examples/sec\n", + "Step 190: Minibatch Loss= 0.0204, Training Accuracy= 0.995, 6645 Examples/sec\n", + "Step 200: Minibatch Loss= 0.0296, Training Accuracy= 0.993, 6747 Examples/sec\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.990671\n" + ] + } + ], + "source": [ + "# Place all ops on CPU by default\n", + "with tf.device('/cpu:0'):\n", + " tower_grads = []\n", + " reuse_vars = False\n", + "\n", + " # tf Graph input\n", + " X = tf.placeholder(tf.float32, [None, num_input])\n", + " Y = tf.placeholder(tf.float32, [None, num_classes])\n", + "\n", + " # Loop over all GPUs and construct their own computation graph\n", + " for i in range(num_gpus):\n", + " with tf.device('/gpu:%d' % i):\n", + "\n", + " # Split data between GPUs\n", + " _x = X[i * batch_size: (i+1) * batch_size]\n", + " _y = Y[i * batch_size: (i+1) * batch_size]\n", + "\n", + " # Because Dropout have different behavior at training and prediction time, we\n", + " # need to create 2 distinct computation graphs that share the same weights.\n", + "\n", + " # Create a graph for training\n", + " logits_train = conv_net(_x, num_classes, dropout,\n", + " reuse=reuse_vars, is_training=True)\n", + " # Create another graph for testing that reuse the same weights\n", + " logits_test = conv_net(_x, num_classes, dropout,\n", + " reuse=True, is_training=False)\n", + "\n", + " # Define loss and optimizer (with train logits, for dropout to take effect)\n", + " loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=_y))\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + " grads = optimizer.compute_gradients(loss_op)\n", + "\n", + " # Only first GPU compute accuracy\n", + " if i == 0:\n", + " # Evaluate model (with test logits, for dropout to be disabled)\n", + " correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(_y, 1))\n", + " accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + " reuse_vars = True\n", + " tower_grads.append(grads)\n", + "\n", + " tower_grads = average_gradients(tower_grads)\n", + " train_op = optimizer.apply_gradients(tower_grads)\n", + "\n", + " # Initializing the variables\n", + " init = tf.global_variables_initializer()\n", + "\n", + " # Launch the graph\n", + " with tf.Session() as sess:\n", + " sess.run(init)\n", + " step = 1\n", + " # Keep training until reach max iterations\n", + " for step in range(1, num_steps + 1):\n", + " # Get a batch for each GPU\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size * num_gpus)\n", + " # Run optimization op (backprop)\n", + " ts = time.time()\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " te = time.time() - ts\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \": Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc) + \", %i Examples/sec\" % int(len(batch_x)/te))\n", + " step += 1\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for 1000 mnist test images\n", + " print(\"Testing Accuracy:\", \\\n", + " np.mean([sess.run(accuracy, feed_dict={X: mnist.test.images[i:i+batch_size],\n", + " Y: mnist.test.labels[i:i+batch_size]}) for i in range(0, len(mnist.test.images), batch_size)]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/resources/img/tensorboard_advanced_1.png b/resources/img/tensorboard_advanced_1.png new file mode 100644 index 0000000000000000000000000000000000000000..df7464cb42429c5ca9d9fe4ec8aa8abad319d51d GIT binary patch literal 286897 zcmeFYQ*>tS(lwl<)3I%`q<#CK$Z6zpZIwa5^1?+-e=uq7-5|FsW&`46j!TCnwnCLpN z)FcMN>Y_H*peBmQinO$W-TVrQnwprfY%1HuziMiIuYGnlU!9M#nZGi;uS_67OtszY z?AR*61Y(KwvGD&)%E(BWC3=A(P=SDm8%!RBJNXtC4(u`fT>sMD-U1IV?lq%}c&t76 zQdMc^L52h&gV|T92~YZV%Lw8iD_8TA5JU)a33qyCHf2{Zd^seZ2t-6DaM4slIZ)Zu zBXEg=Z5`1F1H^LCew1HXQ9)^zT>KmF)eYPQYo30`QOB6D#6d}8)q$K`Y; zIz_)qc-HrF`lL4q<2Bq5Lh7jzb2m$gESzGLOEz=#A7el zgvuG3#7SRXiR+!Rsk71P;^Hu%8b}8Y9o0;3gv4&ua;b>SmMCHfvA`IsSROPC#cTqj zDc9Bd;^Ka3K=0Els>!5pJ{uZ_>JOF0DB#iU*}&ayuoxONO+hS9A;%nrtU0VMv>VVF zRDDFR8e}k}j-ir=#yRcYeS9;)`$;d4M0p(Ae|8s>`i^mICO&>yU)^<)Z?VQ@947jm zv8YG=-Y{Ss0P3AQju4YL`$*5uPpvirFD}S&^9}e5dZ?}>P?HGth8q$GVwYh}g2><- zu>>TK0o_Wp7ny`O_zm_qnEpJZ+g}z_v+7(E-@Yq+naa?3YXz~KS_CYS!t#W0K(35* zJ0SAl@oCnr9n`dKlo2X>x~Lg}%;#arz>#T1-|h+k*6N@^zjjUr3}%DYB-^0GoL?~F zZj$F@Jw)0BPlz``Z+9bDA~&rldL>@IIa8#=c^Nbbc)FGEn1^Kv0p!{kuApCquQI$59977HT?79bZSk?|B#s{a6OrQj8yqW?>}$hI2sZ@ z6_K}qaYr^Ao7Y8YFato+S1qfM&->)0ZZybw64VAxZ042hG=C!aFO9U!wCp_HBRV%8 z-b(|dlV`J%7+$eH%noPJ?-rTwGWZEc$Rj87ps|qHoC}m+y86r;TNa5nV3kxEh zNLoSVJWLbBRve(nd~Wgtz6sJd0KpJMc?c^V)GVgm$WMbTHM+!5$c9)gFvXa4Rj>-W z=h#{Uk?oi7Py`3Q`97eIE$EgbJLq^1_Eyz{ya%&(bmPX_gMlyHpL`CA3!Em9n>aXL zLV|LN0u6Nnx)XX7`gqrUQQSkE3gv2tT zN+P#d0wpV1o|?jz;*v6lLYMq8fiE6?{LRGMiHI|U_U{QN$KS_F$JEDX#|B}jVJKj( zU=m<-LfT=h(cFHp{LoQ6QO5dRrZh#Bo2(&4VG3Om=oIf1>y&pVrl>ftFfZ#ENuuvq zZxpg`I1C%v9Z6Vjr6OHH=!&B%qbs{5Y+JBh&{HsP@tXyME+bW5Teh+!r-ZlISD}z* zjVgr7F$Lp?>W^g_T~)90s(hQmiy|K3c7=AG*RnhGLs0bZ=*sB)=q(dMDby*b)U7I^ zDyJ206$urb6_qN_<$5ZTD=^M?TqHR<9p7L5@_$J!8#c z>oWnzUOKtTVoT(jW_WUPxbV4zJG?psJK8(4TxZV^P8c6FyfVCSALLFS&lexNA70;Z zALP%!`j-seel}*HPq7{42gC~21*{r`36}QP6=WD@8z$@#4?>Eek*XK56v2yCiv^9m zjU>e!V<;))b=d2D_tmv2H*7X^Tb?hLE`%;4FETImHni8BFDfr;x##e}@G&@9 zxVoI0oc?gyxprNOxMyB8ZI&;eMm0t?&b{V4_eRxAM@lzO(y6I++M>ez=p$?!Fga=V5Jji zU~*vnL1SSkBTq-d!|0-aQ46<&qAX@J`ME^yB50wF#Y#(2#dM+AX4>|%%aVzU)8+1M z{FzjS1RNPYfryFB$@fnXRbyFK+0JjIQW4qm%sZBKdM`g0Z#Z5vv#FkR_xhfCYoqJo zjM0b#t%H<<(&3enUilk&D(Mr+?DRIGTc<=f6#jC?jAXpLE^bc?tG=Ze<@ev5vNXAF z&K+wHoQn)IbGZq;2Y+M^GcGd3m7#t>|9Ds8Yb0G`y(n0UUpiY7j3qQjJ^r3Hyw!Jk z?0GlgF}uLCII)mh>ixS`{(AOihH4gT_x+aUwU#M}xz)61Hgak`i8V1N-j^yYJ1Nmw zD4${srD=L0i`87sy++l}(M~?sdwZly&@kPh6$)7vll7=_&{!^PRp%qAt+lT*zhZar z;$=NMS&)BSMMJR~5bcWUt1@u+!*J-ye&?;0%cJ2wUitTJW`J(E_GjpELJ z{;+qNLaGVfYxh`7S=GtqLU4)fq)Qr|-qTBIe%@JRN@U1zYpOTxP79cRzy5stg5lv& zioSFkd76~1^rSR@?S56qL*v#gI)7Z1oc31xi03q*d8j$CnSI5xc0>EOmYT+s$LF8N zN8EH=DYmXH4(Eemr_npxEUK)bEHCN-8XR_9d%ezAXH&7m80wu0;bpF6n#vvfJ-haf zl;+1>wk_L|4L;jr``&I+3vu5VOW9Ys{h7cVdH3OKtFPh5wDqm^PHA`lHPY3^Ms_cI z?P_V;OTHXm>$5p7N8K-=<$EW>3!w#%g|EXX@kqhK;BMW^4Po!^53hTP)dWCMjW{9! zGpCwwwqI8Mo$1_rCMRs-yZAhLtN&CUC^a8!}@8dJ5x+D zZ^&P%JZm}fBqAO?J9eaky1oqkad+~=47M!lU`~^N`vhnQ2~B4Z5DfBvJ)n|GWH%rn z0w9tif+`-M7depGn?tWa{gf^4=TKXlO@~qV4V5fC8A>E8C~{&leyD)>cp)K}cz$R} zq7brpA?ko&Ig8)M#y=r&gBaV~H*Fml+&gJbzoOliTeh|@w_j)cjt=f)&BTi4)fX&S z?x(odX9y7-f@8rYe*JTMas+$h^8@nuq7Z@pb8~=J(3r#x_~)PT_Xq;N2DG*z$OORt zxq*P80q)rTn}ZO6BIVbmwt_;(gZy**1rpNS1^?gNiUbHaRn?*Gf8z@QFd_j%I{R<` z{8In=5d;**+7tJ`@kKlc)L&?9{1*uQUugXQ!|H#b@joN$f3wE_K*#?={6 zxxzhjXvkhbG@sBxBB-WTxl>l@dYiJm6$oBWC)$$INir4jKQR7(Q%Rn9 z!QOh@CwLCJBYvqZ>lumrN^5O#i0_;4P!(RvnvXgY|5MImA@!GPu#wvmRNg{iIWi*l zsu@x0u6c|a%=}~a;>`-$IGn9pO*+ngO|7Nl+e>T&$IG2KqW9jTA*~k^Y-+!1y*-Jr z@5W4IP@5BC{VY{Xh6CQD)k#q3dd1*1D@@{OMe24jZB&~{5BT8ZbcFj)WLO-5!|665 z;18{}$dZ||cGWqe?!=CnzpEvPRP4Pv%<6z?{1CI=A<(fNUenFOokc>iFap!cQX`2a zovF#B;Bz{0=%CQrXxYEm{9&|K0Oci_{~f6;GD2;O+V?B<{>m1#v_lsYASc`^-eZzehf9WOc09lI)beodyVPLsvmEtUr%~s+03Zq;WU_2@&SFsIk zs}+?bl98$0j`P}-;rk7mC9OkhtJQWRe3fmz#N+pbYefuhTvX$?9Sa}zG`XZl@zbjG zL~t#RfXIi^`WcHochD&P{{u5uE21jjTY1shaV$Oxb&bxC7_MiU%pauBSfK9*<)XDY z+l@ggIbZhk9q_OdWtK(+p1i@$E8vC?3ID?IWJk_5#XDdmwv>j>@7Ku@>oa^dc@>YLUIC-xGZ<degcH$9T&v>9Zvz$Lwel-jgtnYz%2j1L{M${LJ=D{76EFt8LOlc)k%9Id3KRsy6Hl=+?_J^{L&& zfX>`n`9)MX`tkGqz%nuWLEgVJUHp7ga^L)m zCz0&&ihn5c-i?fQN96zM$1< z94GuvJ&M(yWa5?r!sI?KcZ5PGW_;av@jdy)e?Q|oqS4ZstQpYKoF1k}V{&KaX=vnt51XYQ z-c8||BKIvLZwSj1?&^5*!*<7*k{E3DSMQ^dFAx58KVoE53-MJ=2hRz&e2+8X*^~R{ zMFX!^8hshzj2wTI>Sf@GZeC>bE_pql7xL&fdhp^3EwqKL_8yQ^zNsciR~XGmtz=TO z5#AM`BsuM+a)BNNlgj(fizIhK))Hl-ALivsch!$=#GsEAOMfN4_`|P%R>XR1ST^nL z1iId=N5hA32!ke$qfCF9x!$#8HIE+E`YJgPY~O*e{FxBVnr#8YvA*ag8e!@yI@Eo! z<2inn*aFrSH!@Uh)J4Uql>-+uy!!0Zvo^Bvds4?qCOl}hHG4$f?bls0WMQ>#)=f^G z-o>M{f_{(Yad7?WDSLP>c2t9U7y0=3*F+LS<~R5@c_VRuxd%|nKhw3zeM4V8G3LhH zXMsY;l}?ID@k^YwsB0}^O0X0K(|4BJ7v~$Ha7_J-3SUs$x>xVk^CmuF)i8y^dgyD@i`@@G*u;aB*|RmpsctOk-GiRZxPSPZ{6#bM z+f0SHw}U)D75UCW9XANEpV^guoe6UPeMv&RNPbN4GyCmEvOaB^MiZ+NQBdzQ4x@V# zB*E>y^j>Ml;sn!4t&6Vw`Njt$ok*vgi^a(=1KxKGD+1qB_BW4Yx$g_^Yu>@!%$J-g$k@qMbtSr3-wL(~uye7GybDwA5E}69tq<*Qc zlrkVE%HKI@xxSRgDA{=WP7$c`#&CX- zB}8MXD!g0~-8ToKSuS4Mcn|7G^7*r`52d`?Rb0DTGY+i3VTFZqZS=wA%A3q>5-48`IC*a6Ht4M zjHD+OpJneH9|Z}@kpozlcH)`ISnmlFJ&umQe|O*emTo*7CeYeRXhPtWL6*9GVSoI1 zQkQ%l;iU@3ny&fVT7lC+rWG$FxS6V`e#fBIS((=InQ^2PB`RNbBSLm zs=qt~fgDm*Nk`NYEk$bb@QG?MbBdgUHSK{^Pj zM%}7)c=os+g6BWq6Fpu_k&tc1GV z3Ef(Xw}Fisje0c6G=o{+0z$Ww3cMxmhoVDfC=n*L_1@NL=kc$!`RT3wRpYFgzyf=d zL}J%t-%lBazcX$##>>|;wHy5&texP}GD8)TFrFeuI@0+C{ez#u(nuN@jkUfC{@@qH z$avb>|0nPUlyOhsoKwr_#Tnp76mjgI(Q7yBg&5p8xAeJGAIBYCg2+FLZc1MI0ZOv4 z>BZWWcXtl?fNt9x;8(|a!EDqGX=n?4MeyVLp+t}((Jb0@H7F9cGvu3@832t>*o}U1 z{W|bkW?n^)Il7zs{9$mX7#pPU!|jgsH6+esg&Gk0CfuFpa|_^tTy(xO7dSvxxVFDu zH^Uc&?rr{rFbwRUC-ErVaL!BoqaBBqJ(REN4s}-{g$O0yKzqzr4%1CZl9Ae0KtVyR z{>MkP3DD8_Wi!I~I-$oTsbmi%6*p1aVJn{4a6ZFYXxHz!>T9eGb{tRhgYK7HdMn1T zzr0^MtHA4HbHcx^KH++-R^EN{I{fiesW$eYle5B}q;Sm)VmnW0{3_Gb*oZ$P*<52lDJ4PseK)?QCLwE6Vu!D@`uf}XYc+YxU1mcy!)*%H;|(r_0!w&EUQ|W;aQ&0 zg^40qcI5qY_PZ$Dw%v!V5Lk+zdB{~t*Jb~ag1YIA)#Zld#gz6f??C zWLNlj`galcs~Ib%n_KvYcEvAdAF=rb% zo??$qJ=4Te9_l=i@qzUd#x1AQjAzlZ_$q#(pIVvhI{wKDZgdG5!KDjq!=I{uwAIlC ztyg%r^svH#DfR04wqLCusQc7Dxo4{r%bj!@bqwXGZ@J!uGdQGgg618lph>k`!x*9*uB}{d$-FacQ-zNFvfMv z$QG{m9i=bcZ1SgdM|I+Jzv1vuzSJnp)RRA6`7Z*ceA!E!X!3wR1MNzmA_ap5HDT? znY3KOH>Vf$NIcXCf1+twyVMoHA#eNHEGb{Li#bc%TUN8ozazLq+N+x9aVPmL8YQCw zezgGlkyceQNDzFIhs_SVx|XLba6PpMyD=rt?5SpW9rPdgz=AS64O32i>sJh)9Pyv4 zd--|)o$b$!fhQjZu9jOJIqqg()Ou!V=APgHATqJMk_Ur8X&p%kn--kPs z`C&k4@|0HH?Q+i#5Y@a6odRMmmE~`|R2x>3v~i7T3ptRw|4a zd8{TmfwE#!FLzq9K{u^ma<-$aHcE6H0S$}vTk|hI%2g(jrnh>(q+HJH_ry!) zw@#Pn@i4OgOiD`(UDdjLYH&E_{MMIX6e|pAzk2xrKJcJfGd9p{Rth1h;9lSmPno)Po?lCT3Jo&pmZnF>oIHiT(y`zROZG_ z`fH$~b-R+k%7Y;$6mw&OJ`M$;CE&Y`yg;~HwC5Cid{8LgUHPyRH8;K6!?ANP*Uaz3 zni4H|(*+gn#b5!%E+ccGj*fMvD6LqUE|J-s1K10K0>*+4Xi!7h#g;r$+DTn5&b3=S z86~i4#+yeUbfQCkP7Sjbh-gmWhj5z>Tg7(ls=>xvdq1NtO3i3S(+6 zrH?YZoq=FI_O)lOiZ84wh!`Q)d{x)tb@ohl`=^>*)~-q&xlT(^&u(oWbQz#*+e3R? z_IbYd0EFD@d`R8xQGDP3z}Hw;-Y2qL8Jv~ne<3kAp&D2JItxR;I}w({^aWKz{$a4y zI_Ia?#(jISq)qDDLiUrs?T9xT8qi^6#p+j;&A00jQB)OSZqGok5TdTj0-+NK3gjJ|!vOMGj=P*7rb%ZqU;WPQc9~Zcufloth zn~P>^y-J*O6-z|Apq##PC~?_2{!>?|C%psLhUFXxSL z4R%h@U9{G(Pd)k&RL5;28K0c?)PoyO9oM6Ln(#cIL_u*bUst>_@|e$hQm5~3Xza*t z_rrV$`H82=3RDV>KJOhHLXV#l$6lo1$Hei&N^{DMhV7~%%D@? z#4?PdX5pH^5kxx!xgZqfWZCH(lXm+hrhUQiVfh6|h4%E?Drj;=D6gUX?XAaYl-_5p z4N_klQMgaPL}xnPCMAvNboWYa!^P`U;_K873!2ZNLPKyQK{`D^cuNL@tT0&7Wh**-nsVJ-|uWJ%)e(xpILGhIFKw%MgMDc%;u;5RZ6)Q|7{CpeI~oYa!0NwM6CZ0Cbcgy1VSS;~3` z14Fkbv;?$wKhzovDmLeP|I{G+>uK`Od35qhBq%Fuf;TX(HvUMBJEc^_%9-xbtp3>8yWLj;$|>vle|#Z99a*J(|h%Uv#NNm(I1Ee2&uQ`;4^bqRIj^W;yfd zMRztBevb1lL53X47>f%Af1eDQIEBn1e_JRw^4HP&%1&eV&k7abd^Q=D4(B^whpa(t z7=D+&UWWy{{BjOCpcW$Z3dPb^i+BXm2zH?Odd=z;hKV%Kq{iL)Y{mw>()l4!%U&$W zY}F3!9n=P4-Boga!v`WfuUf?{WSjnuxsf-YO(CNfmDf>Zb90DurF9;TaX&>}h$9n4FV{MT$uf~D(%rO)? z@k3DZV(6Sm=dlYxQSkO zPOg2u8T=IDkq=I~F0QiiGmXI?J@s01dA+$`Rh6z?JQ^{yc!7|Gvz1;f0T1M{Nkj|Q>`7IfbohwFrs($z~c zItv7-CRi=F^%k6}ax8hnT@S7*u1H)q;F5HPvQO~Njyda!Y5(kGkZcBR$`v*UA#lyg ztcvs+;dv6nniw>(uf4?POL%*kw4p2$R;}QVaTW8W*SVM6(6Bx7;*V?j*1djB=10GN z+E%+9%_CFxSdz`2-3$3W zG_b6%E_G-4s`Lkb@u&14`f{$9`ITp-W{W9TH|$V}=FA$J3zT>v&Vi0;{>*I*TWR?Yq6qM#0tBMHy?g4CJvju(0tKfP#pysBS|-c5s_kC|BCDE>rR zE%Q;SU%U`XKBfVoE9KuY$3pu5gM_|-H`&!46{XHiv<(eq6%}fs=QbHg9v>rp7%2Qa zkeM<;=v)oHE^x0?<$}V{v<3#2L^h^Tg_aTD@1NB7a*-i+lVr=r?%?6#iEDD+ zW-~r9&2EtO#%Lm#Zy-qWe8Nt_`OolU7JWhY=zBi!iN9@(TRs~|-mGzclLyE^$?(0@ z=FQ;Y$}l~BJGBIYGht|^hzgm4mZnE(kW5{u({^K)x~imli50UC0#JyS7Q7K}5)VIB zC$B0>1bEZ3AENLSt0NmR{h<=BcDhV76fN-1pays7SBhLiT|48+6PL98qyIVsR4J8g z$3wtGTdXZ8AF9y_w*QkxEI-@3!6<|(6?t2KV!(X=HmhFe*@V9YMYSS={L%G@k@$AL zr8&`K{~cMedO}`vQkn;J4~cfO9RZ_onshErcEtvYiZR(^a!&gP*6E9#ib|!Fm0|gi zrF?j!Q-{8tvB3lWwK&=>2RHFt<{eS6vGS*iSFNzGKVO{I`fBrbq=fnXs2|E_pRwXq zdhZqy;*4~}YI3}W!&S|oK{g|lCwtKeXO&aBop|;K3)6~cdEfx(g$uP*ZCtk_O6|TMOu17I7+JM% zpp1Yu%lQsrWpRU6qv83z<3v;=63S{rhB**lRCz9LMS$vQ^hAj2;rT!7flZgxC;{?P z(6weuegi?1y!E1=So34$`sd`%xwnGO`^|d|>l#96 zfzF0JJqC8^0|*Hsd_UM-;#BT{6-m_Fs#bTBRwMj@#S?Gd?K^OScEi5tHn0_JzcK}q zJs!M#zM1AapLp$4_IrR*K&GBgSW>Dq{41fft0%s5p93O!{R zY}x&}IWOa;jZ#AH@{GQcwR*&NdSg`ddEvoU70e~;7Y7=oZk>T7^~U__v?;iyhL(jD zm8GQDK~fSM$c;Gu8SFU?O*s7Lh-JSHJP+=_<9w43xVZ=TDB2BHp51vfo{}b*x!C1mft%i(@^TBUzEjXrKhEUQqg6cf;(Sx< z-z_8j&gb$UDFF?@5awEGEazT64ynGli`ETy%0;-$vV zk?Ok}xd@bOB#kB>eHVPXW8XC2fc+V2w6EtPx-BR%WIpTv?UP(D44Pu} z83!+d!Cct-ud+49$=wp%BBaMEPz-Ksb!zxwx!f-js6g%Y{4>DmYMg?u14Xh2w7-qg zO%-4tYFJl5AP4Q(JOlaWUCifEyt^CR=*kW8o!D-SqkCr_NeXA1!vC2J6m9~CboN== zfWfb%jCF6R>-e*2!l<}me+Q|Q_VXR#F{8mmd+Z0zpWH?>{=nxU@l#oK5btzMRX}Y6 z%2id);cC7%nMs zVO5y5<1=(H+0z6z5Zg-k?Hw5DIfnrwACE?!Y*~(AZ0=>@6xi(|b!8%Vz1he1==bwf zVciYg_e)RD=BjqZOf)(|GJ7J>v>UG~Sh}maH>%ON=z8`^gU0;LR=KrWr+?ghZFJx5 zo@`F%jM`X1LZRaxRZ&_P)2TcoPM9vs^I53)HzFRUWCfzQ-nNPLgJ|)#KQ#*BwnEa6 z?CHr*q9Xg-aUs6}8+qO{g>KCCoia$Buw&*&K;Fk@H1P|B*r!rr=5{WAFN&%}}-z z@@*~@eB<#kWkas-mKyN4-TJ#|5qAoUB+(Po!gPi|n<4JI>`Pd|2yHx7qF2XZZFb07 zu}f(ogs8RYLJ#@?KQh}(2oP-K8Z*SEd;~Q!t^Gn$AU$gca7eALhREY z!o_rnHu3yR12%8;V(eMLZ)yt1k1rLs0~&rD0PH#HPwQ{!ue4AQ!!4~Znn5M+nsx&e zV#e>F)jI;cX6w@*eYvVmV#CRz@*b4yk9c7x@vvBEjv~W<<;+dyYhb+YKlnjPEQfsc z3uD;2Qc!N?GH{xOssVLk_KFr$O$NnfF5>@a_aa+^u3oItdX?&>TY8mPU$SS@^6ecP z(ahjl4fePDO*zy~4$F;jdP{eEMBIWJ(s^P_!^=5C20VU?!~9mbek0zJmfZUv86g=w z8y!iL3e?O@6~TGx`}Z_-xiZuBd)nn1L@fN*E7@t7;KtXP;mj|X)I&Im1!~3chN0BU zsRYWRmHKt~7C={+DGPt<(f}rr5!VX5GJfJX=2v{T_Hb)f1>THZxX3^a&VZJNR{VOg z#yKBS5hTYTwgw;1<2I}!4O7Nvm1dI3b5-;z6Y5A_swh#jN8*q{fh?!-P5Ehw;k}D9 zb`XMipo@E9w+jj_xGVjo$h<2mvNm1dFVpwZ6NdJwtsV3$)z9YqIr;OYS^n7O8}cPW zpjqtbnr>8yOD*)XU`4=rfc25QLw6GZ#8p|LYSLj0#@1VRU2?Z zgCJtw?)|g)2b9Gn1W6yFM{M?9NMABye7 zP_w@t%VM)PAUUj=D!gPLi(!Mv2}YOveQZm(3TrNTDb$p3`lJaqU05?fR&X=H0pQ`0 z7hP|hpzi86s%T5Sc07dB=xf~;?G+h0&3W_Elj+EyvnrHkO!K*7lL4QRL6QP6^6AqS zp`6d%fGc^ATU!Xz3VwoEZ&%1Eg01mc7}sj(Tu7X9JSb}3Q$%`B!C$p7KBz>0?PR(4 z-dO!8f1MR(zMJkk4Sp2&qklKDFy7RE+*`HlC31zQs7zS}!njpL*JigEw1H0)b~otN zUb4@@vq9utXdS_q{#e#`;|7JS(D27Ok5*#9U0fCgsbnVK0Wzy;f5m1goil;pjNZ)(R1iLeEr_qGZKnJ8*`Jt#OEaG=7!bB!Hn|T6+rH`@D&vd9o!L7 zTAQ%`UOO1`29*k*5db>KFFl<%ld^PC)ekZS5|xKKRTXKL+-X|thdFt+Nyhq&PuF>k zMb_!hysE25%Wldg0_|lys)O{&4Mf4a?^OEqN0fL}trbr95ioiFV^Wv?Wk@Dt%K~U0 ziG6c>(7?i~@!hr2S@DnPK2vsV5!mdwFy3TtHN;rAFS1npaq^8qWH8c3(>c$jR(6_D zZ2T$@F)?!o-dXv)yF=i`N6SUtC4Pm^r+x`|Zrw&m(1#Kn*S_NNTstZN6(h!t{~yYa zAea=9Nk}SXEdIN#j9YiL9)?%+%qNNYt__f^A*b80R*yI$tmokC5Oul2H@ZIUy>q>; z>323OHx?AD_<=XB^f_ybb%hfaWd7oWbNa`b4Kz4-3(8NkY|y9>Rs9aaZ~Wi7h=5nJ z;4S^s523H8%t`~qa5sm}d#1G2uMEPws?R0{DRI_{rLrq7saRBh#}UN7hA2J+%PO+1 z$B_!ReB?A2VIr_?Xbtt#oU5_@?w}!xejv(%DhOwLi^Flruhc?QXFNc{I zP*ISI{*Dj$c|l21?>0U0e(Gdb)A;(A@IF~RlxR!xZ>(CjMmSQv?OUGCn>Vz2WW&>t zloq-pZapWYZ^op0Z4d7 zxT<<3D%`+Z+D8JqUuzOkrHVp=o2YGzc@<1j@EP=*bvk$02B_iTUCEaVG`^3af>$0C zW5^0-L2(($SUqN=jHUiDQq{QqUid1LC2}1WF+IjV8ls*q2@sfKhuv8>lVf7|^%y2@ zMEGS}{9cJx@pnAOh`=i(Z;2HP(G|DP6iQpZOpUzr=Me;Nb=I@iuuwuFTR2F=Qo{LUoJbT#ufzhG-+*s+m(kuCTt}wXBH!JNk-d%{eqcNAH@%dKf_1J`cZfj z{Wb+d2kha>N!re(X5H`!XGKQQ653D-+?iqis)@B<1!c?Yf6n>C+U_#O95>+~j4AZn zu`vYY;)X19iU_Z~@@^A6nhmEj(ov5>XY&rUMwUB@&3{z<>{cIR0&S7iP@E2p-Y@2s_h0%Gh!4|m5slUOF&tt{%?`&nV=3JeA_ESX z*CaH0VwR%$g;l)G$tKn^Lb17l;t3AT2_AbJZR}3m)XA!cnf^HlhIcBoYbn@s6Kh$Z z|3M&Lz)|9KDVNpxj%Ksj#r3I=`tvZYhqE*?4w37RyVP`$FdKgTS+eFJGxJ>Tq07hpr(KK|fHJ@VKT@in)%9=L1%)GUDh818R`j+;{*46xN&Fp z{Mt?31&*YdiEy!{KM>~2GV@-g8|p`MsP*d`2YPPsZ_C~fDAl7>{`#iK74gccmk+cD zcx#r;1f&f?fwtjuNemwxw&NLF*^Y@m9&k>A1*;JsvkE=?Myjkb4yAjao8rLDkrbMn6^qao@ygJle z39X~ye3gTe#ici}HkhYw$x8La?7$uoM83GC{S|V)J05nOYuEsrM_<29Bl7{sN-SX&pM{O5QkU{ugW;=C#t* zeR{T2+z>+-WFC@-R-IDf_031I8Q-Nn8)V!X9!!1dq`|}aF+z06WI|?WTrA1J?9x=P zSa7qIBx$#HZD65Pio4a@&OliB%jM%sSum&ZykQ9GfeZOM95+0=UFu>dJd(9GFt%tN z^46&W29ZyM#+rGF_{c1)qW{hn(F~usDPk*0`6|9*G7kEC;icQI<8VmxxUSOsHLap8 zS=b%>MchuH$8&ceHtR%t;OG^1EUm}X1(d0x26Rhvd`gN9&G_1TnXUJ-izJV?c@NdH zJn_gc1BVIOU_nA_HLn}?9l03_+#Fx!VUCkc3zvtd@DazTe;?wdmg!sT614)pX_f;A z{0;gq@?EZ8u2|pj4}jyE(2{iNROREn!wVUePO${Q7N=o;&L3xl2C?mZJL3aRK{GA* zr?yZMN*aX<5&kL6qkeiQF;rF6E}TnBQT0Va`&m}B%bUJ!>B6&8?s|LK*}8dB`=;UF z2N8~S-*;V4{=9{x5AvUn{w;z=e^Z9cO(`v6>GnsphO;4WNX;S2FNHC!2YL!{VJZ;f zUQfPdW`NX$_C6{@c_LBW=WZHP)*D+t%vBG5_RN=MSE~iosVzU3 zaHPy&qN=*nnqvO8Gy&7vh%X4%S_)YUYIH;ee08GHhFGT%NP8SPj{r$U5kd`U50(~g zaKN2vL|ItpN_3{xBzx{_oA}San$S-^7YZhX*6%p|BGE)z>Z9w_)RSDd{YY9b{LY%h z#R)s`TkN8kENr73J#NGMiISRJUsH1a<`^s3=lYX3kymQvsvl zTfRB^Ub1xD2=gTxk7 z+{-Rt)*ErErdAFU7lEXzzN5w|5XIGYVQjCa8rY3D=}u()8kF)pU)B*hAIsq-45n*~ zJRj7^ADrjM`LP3kmQC+TI-QCH6?C-mPwE*Z@v?|9V*KrX=YmY-ff!GQ6qTWZOKDasl+0bQK*2ZO=pdV`G zf)LUBUY4Jap!`%Y!9ht=9Y(~Rtjx5Yp&0XY;95I!6@TQtrC_})fY7270}W7dZxj7a zGlgjDX?$V27azW($A_M@cIEbW&W)sjZGvyM>cfd`(gHU0K2iosVTZB0!Z5#HpjX9X z6bu;Jf=a#pFa4wseVsjM(w87do*(^va8!iQRT134>8u-hkT7A4w@Gny!v!&=A_CF+ z?|}JnI3O`UAPF_%Z>zkJZBV@!K=QpR<%nzsp)Z)&e1%dmz`oLgGQHbN>`4!$cXubz z=eD!1@E#($bKb(66es0QlI3S(iF-^@QnfzWOy+ZMp^;I_s#e>a?f@B&w}^V9d=v*{ zj<%G_db(VM$g;lG`qs;@58po}8_CC7$DBeYczQiQ)5ou|M-b~#n6!UNmT#_%YJx{%Q)RoD&kso;PE-tNk2j8uhYM*9c_@#CVI=2JJ> zLl^#m=I`{hrF3ZA>un9COK%D3943m}YII&)Pd?a;*R`MwnSX_V&0t`P0{iWltbuEI zI+BN_E(ADsK+g6TN5r61;p6tVbc-(;Bys&lB84a3_R$%V%3g-!H7cb#9thmX;u#TB z{Msx0^uh#aiv;UjRm}ZWuhws5ZcjPir4xLfyp3w7@K+SLsiecqM%E2`;8b*Td`{E8 zQ&M9I-(HPzn2xI&pwBvAF%69xRS9*&8oVlAGeZq*gy^|!>-+wBtvhn_+ir8nw}wWY z#kXfx_4I1J#5;+TJDa=RNPMkO zntQFF(J$NM%0NNCeUV!oYtQZrS~XpxrMSLkTqt^1;w{R*%c-%{BhjQ6CepK}qRs8L zZd+IdN}>1S>Nj~%f*(=-1P12aRAV)FuaX}-VFz zxKyB3D%TJ0^v9~-$z`=XMec9k*&!(POFOboEyCndwM^RG2B{xL?lhuYqo1TPQsJ-c zX--3HXdKu9=#hP?d;R=B>gT8s?j?{qp(t|-liMwctn_o`GK<==b%K(nk?sVOsuMq6 zSN|1S`hle>Cw?(^ni`7jeCuEFI}|lyLXP}X`;dZ$g_wffG$}+u19nMkw1=~AdJi+w zIUeOVhGT^hysDU1DNur^WDLPoB!uRSN3vQJR=2Y1ukz?u1^8V#H2WSlG&59na#$PH z{GeKo*-QHry3brRIH1A6?7fIGtNpOD-J}s6uj$r3isjHp|M($C>xCqu)%X>Kc(vi2 zWayxJ&Z7{`cfh!Drxo1+9>j6fhP(L?5z+hnY(3$;jCc}EUON-I=0z*M+=H2#ah7sr zGRbn(49L)Y_dA`qx5()@G}*P$%tRdl`S(3kix%)XV)O3j#P?Fy9m2rx;g*cbn&+#O zx+XsI!?B&vM$sG;soSX8VoWTHgcur=u5bDCg+HQ*Cp0DhKla`_Eb49hA6Gy{QBp$b zRvPIp>F#dHp}Rx6L`1r~OS&7RduUKvYUqI(X86wee6Hu5d%yR7&++%?@45f&XP)=o zd+oJet9GnGWEY877VyR-Khy)HWX!U}kPwzW?(3&=$HD#ePg)C^!m#In@Mjv7`vgjj zN#pTL1@lIt4gkHZnbpND3Cr9H){}ST4$fJ#f+zmo4LEH*^QMMF<7wA`F@GM$^8Sc0 z#&55%J~vXe=}a##21fJ_ha~OGgl*lDHl&&|XMUdXf5+%0KN?*P#J%i*dCYz?rno4l zbgyu2B`Ry+dBn)trOii}3p2!3&@Zn-b4?Ps)0h6H7^E(4I$3kg_uAG|YCF7TgnP>= zfL&RWU;COM+R6#ZY&~AyU}NfdGjGcV((X_q+OJ?JVA{-pwGpM4GoltNdU5lr9)%L@X!*O%dD%Z3p&}m>& zbp$Vq7hvA)g?QUh4@nf1S-U%0jFqw;AMxC56Fv_gS$T}L=GJYfH7Gmgp`ZdXS-eDs zaAzpB|E%2MR9Q_6?{K!wciiq0eXQx@ z0}tj`AZ8jY52i1j_{qET2HdjgRG^bF z4`>5E`)0;rZ!H$T)x})2wiS&(A^TF%SBm${nwH(A0!P{IBJ}2b#S~UZw2gr=KP&&L&X)WpTF|KoC8ulgBmSy z&`US&I-AZ~Z`CzKM)`FKY+MQVb$JKVF0J&FteYe&_1wBdy^GIhyxM?pb~nH5m5{c% zjmZT+bCk-{j{*i$(Dt&CDoA&Q)!DE#4%LAhy2a3m8g73=JGTd4%q0ii+ zNcbRWCX87RWm7=S%iry_G;i|~@0XY5HKC$7%T0Edb(F*?7j5S^7+>}(_2_Wp$R=}y zRyVK?$3ae%eZ+*0vSL|p*-SpPO|Ei!5k0N3(JfbqZ?(zo6#JTok}%ZB;t(W69sD87 zEs6knCQ#)qnFOg;Ni;kdbAJ80p>z?Tab@Hpb zxCMH=1aE?4!}~}HO|SGnE-Q6S}?M0dG8~v~zlf`_=aPi*BPkjTJ0W|lW0_wNV~%1x-A~!kqkfuoMQZV#Sx7;rq;Nl zn0ntg%*R$sz`da&h}*1!n~)2l+|Tbe)>6>q+Bu<43IRGtdL76VovCIwl zYx=~RQ$uWUDkXBoF5*Ryn;j&ra`tITr8Fx0Fd{4X#O0{JeHhtbSl@8Hwt|XQF0S`B zJCt|8AFk&6erQ6bt13P5#+B>MU4fv79J^N7WxC^J%JsNBnAOub#E(!f%w!`zM*hY&KN-J^?OdtZm0H2wM% zytb6_-nYK$ii426%&t`Fvx_IwW*4^X{V-fR=72Qp^dfV9<|{}D3x$VdDitI*I!Dsv z&G>BFyAi>Ow&p@)`bE6i<**A2>rd9eX8hL@Q^k-5oR{@NjH)WY7vju|2E^LH^Kf^w z!-a!Y_Y}0(7Yb^j#3qt@;kZxlW^AsNwW4IPZn|D?PFv1p3@^0S>1Wt}m|bx$?NfmB z@&Z_-zA+Xg)wg1!rAg}T!VAn-nTK}aS=<~s%o?bUwHdXh`ff^aTNep4zGu&bB=;BZ zCuB$?HykOdd`fpI>r=#dmYuCS#ea5ycuS=DgNX1(&y-d#G_Y5fPzmznPz5)mpvcL^ z4e#z=NtbfUgMU3g_$+xZgSLZyvbh+gj-zZmY5&0RymL9>YcCFPNmm1^{VEoiW2%fs zYArlPwgYgY zU-*i>)95M^V|tem@a(<_E`*?iI%3fNbW=aJdTe_)l zGwdzsPpGH_n-*qBF*Fa~84ME`?r8?8 zt>0c(-K2R6U}o(HeQ^_Ip2Cb9Y1eYnvQ%DuZSQ;ia4CC)%5A)AW?N-< zezWUK#Tnb{5rZb+M$2iKR%=hv&o-+!|iUh=Wppi1mI!3r57{`m1FtXkrNTNgQWpG8Mf zE3waAi4~vodei3FT6h1Ivg0@q@k|589DU@sC~sXnlvT~7HyN^A7}BxGY5S?q+5J28 zs)Gl1#WyC*)RTyvNDDsphcZM_?gdUdlIq}x@pP+Q<#&++is&>UUhkLM z=odme=+zs%6y+G0l(m*8dDTDyEN?{QwIexCHAkE28|8Z6g(0mcA9tj*D9M*P0o$05 zou8h@wYEgde7w1G5zIud5Y{m&G2SkE|KUINKEkXl!89*zJ}j#U-sPJasxGw_x^S z-TmqXj`Pzp%`D@AeAQECOr(@=2fLTRz$us_qztY8tWs|4ad3L=w!@^3rolysqXJSz zsh9!*@}G-1{MOFq!LqzuwVH-Bh#wo?MiZjp$v=CjB};R2I^2`8dsx86x_7Tee;`D> z)rm>7aLo2W&{5p?a-(udWQU1BF{%jN(2Dlh!!i-pc?vynCWbYm4D`)DGtjJ>kMa% zp@(GV^As}nqYCOO=&4T9eeo3N;~$FvRQ9wMI;)6Ux@0OVbTQq+ue1gGx>Aa3@`g_k z_ow3kCX5(;?mA#&%XpWlK24{*S>9O!FBup{RD$QFpI8(KA7YMCMUkhG6j8}??sMw% zrlwp-rl$@>ReAzbXLi^-cdQmo28q(6UK`7Qv$8~7Ubghgx*rG1o6Xhy692hgKg;bs z_JQXA+*Q_2is1`GMLZ?Hc}T2QUF(>mb-va;9B?;4CT4y{OmjH!0p@keDf((+;0h0} zVs%K#m3Mpe%sJanko^@F1NvddIi}C62O?uryN3=`H#<1TSw^BoQ|{S&$O?GQ*EhMY zyeum3sM$GUTT~FYJXfDJ3Tb0TXcFoSuJIV(AbsXo2}fqOu<+`l#$J0%c(t>FhAVg{ z!KcZ7VQR64!y{YdmNpB&)T)_(AnPEGU99b4ob;mj$)mYJkd1a+ydEQlnB>21u8pMx zKor=P@*hbdt`H3bkZX{?HcAmgs}jY2IqC9%RF*ahkwJsi#(FDzz^6fb0Yi{ul^Pj2 zj@w#6Y``L6+~y0@pgA$7W#;I!2pih42C>IyxyC^D{R=!QYyD7mucdoM^m|}49sE^ z7|RFB~Q(@UoKX**=^0tT@9OxH>qHrbW_?UhVdxdQf#jz=qBPR9MBBkS}?ICI-UuYD7oVyou5~24yFoKKW*oPRJONdVhhhxGgVk%mr;z1Gv#&Z3LxVge^Zt z#J@T#KFVbe2C|JpKi9q>l_wt&+%Lai65AK#Sz*;l5{Yv`RO9(dk3E}DsMET#yuheh z_&mn765_|Ib${UAyRQX>u)^wM&d-%91{Q2D$Fvm=_{r#l)Bdlc`!>{k^V?qLkLYMc zr!yT92DrT#=ls9D)a)`T+&xrh@6ksuA8QNQs?9I`1qd>*^V+7tvu@ zk)X!{7yt!hA_@ikc70m$_}iD4M&sZlA7bS67e)2Q(cQ7V?PFj3(prdybIqOhLAP({ zyQ|mit2;Bi6eIfHh|+}MTzh+0YaKbBK9ks?Nc+z?|MDQBhMNRj<@^Ve9Uw zZdTI@y-uBu@tdu9vNm+X1(n$IA(I-x)P3~iQjwYT&IfG$aPI12tEU12pIX~JI1}%l zJbWSlH1nltPz!mw7AXr7a{-8!kF-3O2L7_o^ag~^d(+(ufgLe@6G8p0`&U2eFxT?en%CE3 zGX)gMIB-A~_MebYG*2gPk!0`f{=kq}efLzbqYY`EhMMDmTnM8A#69JGan`sRAzS_K zfXDl^tohQrcwBMJhXl{P(QBxm$SD_OpGZLw7LiQ7-l^e&P^;nTS1ev)rZ!YV`wZ$u)91QdzY=+C=aqBjV7#Co z*po%7%=(rRfZ69fp2&!$Y-zJHmmEIbA9dwF2bT7DpWp5hNjm>IbM~o^5RF?A#^W5o z?Mmy)(EYeK-&)z+_AhfUpFHI$Si6zkYNPxSW%DWuQJ2>uGG9tk8;yqYZnRG&+IEm; zWiJv?>#hcF;i1F4Z5dGstCV!}vIB*nq$=y1G5e5$hJ~5!|?(!=RDcM(2=1Ej@QLQtwk_fSWzXy&%3-O3mvRY zNk8?NYKY_T&&_K8FytjeWcVqFWzFDLhPMQJ9dW|erskB<3(2FJhMMO-SureluL>%< z4z@?3;5*#2(Nfz#HY+NV(p5@8vb{HF9%1O>{+22|J$Oj2dRE^`)0$6&xKRaWRnA7| ziwhpRRa+PD!-nN1QP79HiW>c5TY)jie>+mpNeFAF19hh*j<2zBJ$lc&kIfKuTAO|% zfSV1nkU7lHdW)Xr1s445s7*Dx8t|2a14hjX5r|wY#D|_DHu0-Vb0TU9Nvn!GGvs9{ z@o=Jp#{0<@W8Z4`Fm6R-g+&4j0aQymO5j2LcVSQL3HByO+^!&#qp=dany>()fIhyC zc8I`luXYp|gA1s8zI#jA`{j!aifUribIeIjq+ zMg(0t8~DOcD}y^Q<5MJk9)Q=ib0L@Mwg)F|?!EUOGLV%P*ouv>)$!NiURqZV5DDRL z^ziCAEg9;Q=e1iVatB%DSKOG8`ffc?nY-hDT0Q>U0?Nyerk>m+I2r3+8&dEFU|>F3 z`(cx%1?#ceb*4-Ici8#~NZWT-T;%~k`AR3e>$GEf2^5PvJrEfTIsDu-u!tT0#Ccpp z8v}eis0ApO0^&0_Xi=rqn!F6IRFvqESydXDUqakB5SymZuYfv~v}}-KzG4WNYga$M zmmLb#)Q-8*+t=^2UpllsYkYy};C7fu#Sk#4(88ntQY$_tBe26r%gu?FjLJPC8H%Npaz$F8VI@7IcZGyc z6oNJ5iwXs%%W1dr&G{)D&ARn#)HJ*m;ep(F_?sCll7|^t;cKNqmdV+;e&8m@%X%g4 zbRtvww`6V%9WOq!=yT1u+AOs2f5=HBSLpE39LlAt(qcRkls4&kd)p>9-7l#-LF7xb z#&~zGP-M_q%4i-;gA;MT%z1yA9v~jI_1Qc3h7JIzf@5zt@xj)*t!Ylr@5si7UwCL% zI;{KeH&2VkQ<@DvlzZAHl&Xkh+w2OOT^9?7tzKZ&(23pNu8R>p&3UtyXUF&aNTx9( z^>sWiKH&J`VG_&Dc&|!Ig@*^RCN4S#dgIWh zuGdtVf$);xp_A=3L!K}U{{yc$k4~#2ueTjSN7V5v9R|$OSN6@j+9eG}`OJ@Pks6Qk zGEFB++>fF=H+@$;D88#KJ+^bCoBf~n@lCgvsIyQ{wfM;oH!Y2PFOgfRIgYxGbZ?Y| zsMjHReF{)%UZTXsvAm7|`#K@X8IDuDkotRe{`^9c>6Ra0*yaU-!6NNFF?7@_F^0F2 z$s~SSDr~eI`vuO}c%wvy&v`<11SfRPW~8({dL?LuC!a+UG%DleOO4&qo7=+Ppmqr- zd(Pf<@7o5ZVT0Z;G!X_}yfA!{e)V}~$;zX6aw=~7u%7g-=*w+AHay*)N7KByy}CQW zK|Y;4PuRwmjzRXC!=_^c!h2ZBzDbVODQf2@=ab!#Cj93&b5J8APbH)6p4faKP*Mcr zpZ6byDRH`lt&f7{O<2zGO+=N-(nG25jj<+e3$b+g14vttkdvA>NZeC|wC+a}$HNWO zaWI}~U8(vnGI4;cr1`<;WCXyK(IBB+dtK)&x{4srD@sg_m5t*fjj*Yu`arD*K^NKz z5kUt|(j0p$U$=f?2b)yVM_`Xf*awH=Xb zZBL+Q3?QmEJ-u(-`zZF@VsuhKR9Kt+Jpm9$=y4`1z!=i;s(^ps8)Z;8)(YX(M&rTC zn8E!r@nLMOfsHJl3o2>5+p5kiY$}?}>xT?VcUmaOd6j^5F_+tI*P)lv?U!NiN1n7o z)#~Ljs&~f7P-ja|qRZrOlRUo9W7g^8XlZq!xcGjx64|K6Fq7)=APny0>&dwQ-A9UX zR4g>Oi|U)iC=%)0;HB0(JsvyZF;Qpr9#=K-J@MjZpU3EK;`^3G803z#f8c&c4fL)S zRS%U_fTZ;QyTuBt5=twJYH-poAeyF@ljynC8~e~UhP~1R-em!tN;t4%BGG7&B0aQw>MOV zR=fLF>vtvTT(tDD_2^Ky%|OYksBfS%Xz!--yN9t2Hh!eBemL_rFKwc;LM2+Wq{U#RoZbcEwPXjl|huZh<6fi>IJ=~ z>Tk8?n|J!ezE;t&;khImefBwb_tX?nN~DcfoveK6uipZDf2Cd9|O@7PV^IK{D9~WL*Lh&4dqxh>@ z=abBZ%j?#;^8q+#pqyaAm%GKN>sWIWs^VRFF#yew@^e~j@OfQXK^gS=quz_RCExI?VrElZDm*Jyhq#dm8l?T-~Lhc8?TpJM0TNnh_l zX?O{<#-ue%?pY8r#M>n!M{)PU7{;cO@|@2_Y7&{iz5KBn&qj!Qr`bB&S$0(!78_@X zA36kmn#t_Keq!i2(JhO^2jOFJZ$)DhzdL$R;J?YD;&cjn3v%_KS}}kz1rGrzjAJ_P z*H;7Y%W6O4K@Rd3I%7Pz9+W@qGE#(yO``##oz-_Uk4y$vP-Mj?OochVl}{&|e&`-; z?QG6SkMmUS>*jw^BZ>OT7_fm;=N)SLW=g58k(}(5ICyIKNe#B{LfP72 zKK_ajwN~L7c;ZaUd9CX$6#?vPUCJnB<)^JB((7FQgq8QyODgGKZmW&~X6l+e&U;p$ zEC+gY)FmtqIHma^z7B#!u0rKACNF-}sh38x+(#m|Sf-1f<9`AK%`PvM@IKVX!Rcb7 zsP^0ILMm+Yb)eG_1_KM*AZS26V_kZz`z;btD^XGvdV-o1(Uzby zX`)N`a}B$Q?u|+(8LBeZ0|Te^NDd5Nj+7xq;B5poF*6_5L97kM$`vYNi|FiZ7}^S` zT{c%*X9TY7)<8z~+lF3JSAVkY?qh0{{{hsFGIDfZOs9d8ysFR0CeZkw4dT3&IB(xV zok4Vy=#D-i#d?z*4)0H$8As9z?TQ=tF_$r%$<;1t9w<5kZy+3BVwJOOZFeyqy~Pc< zTx?A#;0TS4E1eK{`C8q22{ic;qEA&mDKVGeA1HOzmu{)a-?C3!!^PY`jZFC#B!J$t zME(Oqfw|sRdJGh1S~B#R5L@UD?`gUIaeqr^D~KoI9s;?C!6nL8^O9lf?L+!pZ|RMi5$enPylAz0{S&xI>l*7Rp&|?Ap@QXt zujs#&ZmO)`8I67u!b-GmwMd|;jVNe#gT4DqT2v3rp9ACHEDxLdUKGy?-om7`t|V^L z)226;%2C0*l_+TJ=+Vf^=$EYeT}SZO#}lbNV2i+20=a+P@lK!&pboq^eq_`?{Jg_a zsKj}(t?ki=?rCL723lYX@|jkwh!cZ_q9zX#f%p8?K`1;7_^maEz5Ctvhw*eNGMVfr zK+a_mxrZVhwJ%&HC{~&Au!&x&|xXi_Clo++dWl3gc{6 zo^{aR2mC=`vmoKdaGz{5k}05@p8@KKVIJhi_$40Qu@XYR$D;Jn z@CxyaD5Lt*oBLq9PN`4t=dp74sza8)D*Rvd(*p&>g|*A|V2+-WVd7L~FDB!>bRIE) zilO6c$+0Dgw%c{1zPq2e4%kA>D2lFT1PtGzmd-khG{)j6U2bxs9?lxdI_|Hnewl*- zUW=RaoZ~noDXr8q`BRC}D+;&!11l~|abJdDt0ZMnv7XvVD|8UkO}!gR zd97@Fgsf>)_ztd*Jpq@nzVjwAXE3?ad^}#VNKt<$h)8V{8`@MKYbR#(4SdP0$4F<4 zxLZ|buae0stGA4!G7RO=)Ra!X-Z!tMoD3sE0cFE8}*gG}aC z!STfz$8O;qSU+WH-CLr-`e2!^bJ%n+wS9vYhL^A2+1!SmcpE!hHDt{Y+^Rr3=A*hTE%KsU_2w!BuFLgIk!6 zBTTZ*E^W49^&l$17JN2b)mo0C(3+@DS8SIZzVAg_ZC0?JVUQ%D)|0Y2!CZOK_)1)T zmR+i_eXR!UYU)4E0a-GZvtIsQfm=1GI=?Yo739@ig}k2yTW+XWX>2`I=eavCIf#ud zJXVPHy8F5x@|S}D#~MS+0*sCv+Q00G|20DI?*A$ueU?~3(fyI!9SLOd%s(!@%~?#i+}3$U-6)z{#QKz)gFXV z`>*z3{@1wu*Y_aY$N%qq&!j@&zeFtryhyrtSz)2$MlqkeFrV+;f1Ek^uvFQ!5qV55 zgog7^ZvHPRp3D`77AxoJ9QJ(wH9|u>5W(kUXQ=t_RQ#RBBnHxvu5G=#kV_uguf;fG zy4k)56^$fIY%E^C))PjCNRx!f_TT>t6#k!#GTK9{izN~8+MDQk`fEX7-7Ca&Q32?* ze}^AGzabz&@Wrlq`y@F+``6-9OOfpMX&vsBoL>}Qm@xEf9A(&`+~_ayB|sPr5uh@D z$TuC7yT7v3uj9QV#$;2vizFJdu4>H-L&)-;>mw9`2Q*Fe^6wp zfzUl|p21(pAyH7D(%z@ zct7^%AOXT`NS8?Ur`o~Rn+1Qv;6KzsNH;s=Qt_w3p0o(mE77QKm~cV=r`_`xAfoXc+xR= zbq^#ej`7^t_L%?Ay?!SOnGu1A$sWk>LD53Ca66)IB10H}YaxR8Z;Zf*SbqcRp2P3! z55)PJvqbcTxQXq@-$3@qPev83KV>IuG>L`^M2E<}3;3PF|IjefLhSpYM~zrw|Ko$e z@3bF#>u!9n$0noylI*~48pOI6D#K5IY62>iu@2W?Z`D=5M!0&ekQQg`zXsPIR6ONH z=!%HD^waR)Vg5H7MyiM<+C6dIrONmQ|NgTUz`s?aKjHDu{338c#Dqb|p9nDj!w3I% z5nY7{tyq1&KQ8^dT>eIbWe>rbN2}DVWIvJccjgR~Cr5z)^=wA`-(bk^R2WS@MhL$} z)`U3ut=kK8T&kfEHL>gA8GD2B;RJc4=HorZRRAk$*XnvkrxA;Fw9B52|nX|NP-`Vcb|9K65)d(ILwHB+V zO?MFOyeNwoA*(KBV{EfGNOcFntWLj-b0EL0_f4H-eNF7tbX1daymw)Z>5|IsryHwr+wFL= zsIB0=2|RV~g~!A+`4I=J&0e1$`~@}%XpmxVnV}*ab4dnXuYC`6*JV~$SFP?~U`5qJ zxgV}R^9J6wEq8F}%m=>XWI_08EVyNz+5cwbd6i`rWVUF!%n-J30Ipbdm=eb?2qW<- zM*S|A!CUWE>-xVl8)2etDcO>kR5C4FH#KmorImeuGH?DP2P$}%YF{+-0dAAJx~lGu zP1%r~$dq}0>ypq}m(vRT!p-$HZ&F#i4i@>0vmy4X zBu5+9>u8sA_X(+Zw-V_Tw{clodH!sIyYOMpV=ws9ENdyvEo8WO#}M{|{WQKds{Z^~ zKTcuM+tv_KW<(C>?r_Uwxem0JtnlO3E%j_sTVTg|?fVRU_fq}fMVLNydm7X2V9Ady z`_Ti(%Cq6Srk9Z>jq&D;Qust{9X?XA+i4w6MyY4p;{6s? zH-m&TGk0&A2i^WELSkuqAn~;Rhi(0whSkO1)Q=E+11bOOUQ#b&KNNdPHA1Ms?MZLH zw>=-TR(1Bm8W0F9QnE#aa$Xlxl2LY>MD<5Qv{`cJJL;t=>IU?hmF258zHFFH;5F~4 zgS!ROSlfrStK6!3H74Sh%{nc0D)M#&b>F#*j2*P)jM=ajY#UdBDOSM%XH2(@m}fpp zgr#F)ZI>1HV^M2i#a*{iiV8Tf5SOZ)rDToyVQni&;O2A)xV+>-hi}fzT|Zd@#9J`0 zWIHujx@ZN-cl=Q|vO7;=P(HzTx4>?9Hq;(!ZCaF6Da1#77CQCg%OmT7DM7jz0AX|C zdV_Mo$&b`nuUj5#Jy%y-{;P!8B}vW3G04_uSJC?Zf#OK0%mGI0agm+upk3{Vmsq`H zORbQb)Y6am+ER5N)tB)fIrz&;R7kk zCRIw&(+Tadf{T(0OKbsmRA~Y08KOqgLlvFG4$k9A_A-F0YoBVThgFOJ>2%%3 zO@UQ+8{v#LDY5G6l^(T^&%hj7*}*1;&}-K=Ctr=*t#tO?lH>h`GtpgNt|I4F113T1 z_2Y7Oo{5~cc(cC{4FM1da0j}>Of^RbRvNVP3c#;7T_xer^}CM2T@1l%qu9$fY;`j} zzInB?Vc3psOq*glH!HF8C~HYo&WZ%20pouiJIe_Y47U=ul=vD@|9WDwc@J(~u!O27 z$PX=NzYKV6HJU6lH_vLSG6;=ToELNy0l=;-scgg617{uQKXE4Ra-a8{Um-NtAkC-`9O>S~|8nEDFLhp(4C+-vmb+KDw9} ze1h_VjuQJ`hXdB(E|*ceg=qE$Dgx%&c)WsKYyz;eQJ3ksc-6r*VykH=O+ii7mNm53 zZn26gqtZb zr(E95RTFj0%Q_hrhT~@If%ujjdh}|1C-au7S;n(#v<0~IbaPhpRl8X!%3KbM4ZY-6 z5J>~or}cpCa&~aev^|3shmihtKeTk_dTAZ>jXaGsiY*=*Kk3kGranTCt*-38Sy)YlWhGlHYc6U4wzm zL~Wf-67S4m6%0^6Gk?1oV>J-BS$41;GL$7mzg^n7T9#B;@cItwua@eyVo}!Ww|l!x z*AA>xN1HihzHPX!wtXwfyAAUO9vUV52e$tVn>Zf@UgdBP9W^X7weN6LNl);f%h5eD zJ=e=cIx>wF&?uKsGT#|ZvAQ|iwi^0GZMD+m=-Id-K-zf;)GZ=*U#V$1!tcBmhaB1a zr!}m)ExW8Z4i==@wY(n75)u-)oK^jy5AxFFy_gh>@?~y4{Bm)7K2g-zw$0dYQA3m^ z?)^OkKN5VsnH$9A7-Bw7`D7My zVxN=l`c|$wlAKyTP#YWKe}=P*4+(b{%+qWmJv%muK~mm_HIibmtqck&?DO!eOmJIK zb@R7o5vsVCluS2I-nw@6^ zz$@_v(53mpSf4G*x$^{kJVu?*cI}tIi?5Wi`S^xDH3)Ym_x(kpW5w_hB0!Nk-x;G9 z^g{I4ou^*MsG0fvT6J1?9RhU17f9@-(va%GnS!Ao}gmR-_ec zSW5zSZ*IRm8wmlduzlw>=?mX5h;;BOWNDpJt{XJ zy)rpIZP#8l=^SD1Y`f;ZT6CUp6;maEpJqs^gBopm{kn0MykZbQ^b7s*lv6U*K<<>jbzz{scTm&$qjD8_{8cDrKd z(;@l*I67hTWwgsQ)T=7wrO51Y0&T^QX5XX7cx#@ju4bdVm9naXm56wRPmbHud>?+G z36$~OI)dH68}2wb z=Q176j5lxGuQ)F4oXN{1zh->Y#0P5S$ymO0ay;C_NKZu9js-2@X#GWw6!@J+31T=V z2C&A0L}u%uSW;%s0~@aVBPF%y->YD`rExo&v>??EK@L~ z2IbZGH+#LSb&eFINQMaAoeK#-qLeJ!HkKr+c?YI+`^O%Ucx0naCk*1uvl$>J15LNC z!;UG4(sr9B1fkE#S5T1*-B(@31GtVeRw%v_8253nU!QKyKi=7aoq3N6@NG|g@h+cV zLv|E`?fSzlB3T@o``dA=P)~hNf=CasuINFH9owk=5(F(axW+RChHVr|x0?Q!n&RL1 zz&PC({BTrUiEuXEftqdLp{v~v{ApBE=r-PNtY+OAk*R=aEGw8Oa{?Od+_qtGJ<@qQ zA{dgm`68UfKKNU$^abIB2><51p}#NIEqsesNZaSGui>)3WBheTm>(i$E?hdm1Y5RI zBolYC?#oUwUvd@prPr^UG-MKj*3yQj@?yI ztHi@=LY1(Oz_qWB-A?T8$$Hn-5b-9LM`m51Am@sRsdseN~f2LrGTHw!|dulCR=Oo%PlWxUa|R`-**EyPsl5b4HlQJ zYW*oW{&%T(Ao;7w`EJ`^CyX*pUF+&f5q?1PcnCr%XIA>x5q-o~ru8&rNRf;}v9-ak)x? ziMr8uh-{vzr;_imT8tL~T2(c}=o;}o*7q@3K2ed)sznNnq%CXPsFUIg^mJb(IEITA z2-S5dUA(}n?)b|+%r;kR-k{4Xb$ioj#-1W9(WsBk3}LQy8avN(>JjF$U~hM>%Fk8# z0e5M>#2$xey>bRSo%j`SgV)e?PF-Bd4lxXK>8FAEC`>mLx866Rt;z*#rS~P#^73K@ zTTqQ9!kH?y)e6QKdm6BoqIo1Rn~A!{p`X{+@8DofP%WyQK&)vXI#&}F99l>3yUjXl7ExXRilcL$zZ)IzV==)4#PeFKxV%( z^PLF#_8zqA4pJ3mU0LrdX~fX8eag`O{h9q9)=QghUM^?<0RmH|)3)6w`O4ZHmCkj= zOc}Z;)D~l@>L^Tkq6UXa$nmEFC!3^qO?$LEaCd*OJ7HU4^E#^5AlnPu-%@quOD;VM zwHFiKE1g)0upvdiPl6XY7~8#GzMzrx4R6NdBTGjC3IbB+NKiGmy&C`c_leFsXeY=~ zpqG-)Y8TXsjLuC4Tg-xKW2wZJjw=)M((!Z6==hqS^US+fiC=VHsUaVu?os!-u``k&0pTq5p@v9tSE!g2q;ViCA{k1R2uTV7<(>N~=IyonRnawx!FOk`> z;eY=ao0&MBG7}}%=?u61Q7%*6(DX8)w5hi9WO?Hp6-2^kRb<}c{`4;*+^5Ka-4~r( z(7qQrr7O8Q#yp3qLG?Ti_}^<$RR{c(zq8yyzfVp5`TTw-YMkG`Y8IVnW({L0iG(m4BCULA zsKdk6tZW*ovJlf~)3|byOVX1g@M9jEggF% z0{3JKMJrzPFlHeNRUgxj5&FcB(--Fz24|s>Pc6OwbmeWFyK`8PC(DjYqaS%n#S^zD7>(juVK_(d;DcK>Xm{4Eh zl{&V(;5z7ExA#n@ul;5#WA304f>?$x->Fj5{y=_(lDn=EJa5}CHj3}zf0I>Q>pLvZ z=<+Z0Hr{!?ixdRwjx9jaEe@hra?EH`HFI?+f?chWYqQbe^RM`dB;L<*N6v_{R`K}EK|Cqu-G^k z;lwC0L9^u}i8V=`(46adM1L2&Wf-4p#4}v)Es8AMq^E3osil#;o6sPJ6^+ebwUKPR z(>F+O_uv{Pt-6nMblYXBKP0feX%MN)z=&S49c_vhS2l+(6G@}Z)2~izA$gGw54AZg z+J}Y%@IxxvclrfrY+G6Ba}v+ypxp%jo6JQE2$skg`v4p4?(;E4v*H`! zoZDNx9ftg9qRLT^tXI%wP{|Z^!AtFaGeu3^8_Ho7<6PT^T*Hy^9qGTj`sQ?%uKGv7 zdi@+w%Ee=g%LsG>%?-HTts%hGqoV~(pE`7;3wsQzI8mc%tN!4|;3+ODWZdXrxO1_L zFUtruowXTM!Mc7UbR+lEmLJ-V$5|VkIm)SJJy>xGWz?HA-ky+9kf0=KHa7t8G~RN( z98Z6SQR%s@B*AlFaCm%UqIycNeXZyF)capCx;^VGA@+dS(hug(P(MZL zPhYSq$?bl-)94Q7A~H!M9J({bL|JSHqT;>H;G3b0`}V;dyVKRTorkboo_->1X~J>> zuHK&8({nBSMkYsYq+u|23S)XsvM!^o#QR>$L{5y{*lmZU#nQ4G(ZQ+G^3xrznBHn# zWe>fjR-&>~3l(I9aP#xR76yjG`#8iRoS`MT-h8#*%60koR)-G+cqT1!DwNJ?1|y5N zw(sHlYNz9pLiS0OxzO8pS&_H(`m)8G;2koICk~EL?mB< zIPRP{*(a@oES%i1Zn}&JQ2!uV=IgNNokyML=O7EnI+_j?EJ*V6+ToH?Q03g-4DHW2 z{egBL`UM(qLV}l7%fJc(Jr>qpMBQXip~{&}uJog@l|8$?-FiY}RuxW$cBS~QO)B%* z64L~~nv|C!W`vo^OYw4hgYAk=vgm1oOuCR+@wYv;r0tgYTz6zPBD-BZ=CDmtDIv-d znZgxwH`dLNNHjJg#BnKKP-#O~bBRF~gDw`~RxGF}e_{DWusOH6sREpS-0P@B)< ztd^{BtOh4>l`wthos_?pCwqJM?y37q{$oCa#|U=tYZ80hWLNs(sp3~^3{73s(K!Jt zz0#MXssqpp-S$uALU0?jp_KG!Pms^XLtS`dIQOKFNytd%EELr|ndz1t_x0`HH*ZJr zvEu+Xjp8XZaVmF=WyaJ5GqZP9A3dj=o#|@vzB)jpWh^XP?lsXQz-1F8&=;k7NG&wP zr4=roX!w}3trM9-S+pm%y0`nWcWoFWHt4v3WXCE>sc-W!D!n3XW7I#F4Myv!uzL9M z@htzivo3wH zI6i&!BdsGtlji+drq5niZzJlqO9t*bI!U_dc=GVv=TV3BeWCmB2eJ}1@(i@Q-&I5t{X=u z+cc9*5+Ae}ro<+jA#Q}_lC`Pvrh9|^<=UoaZ{#Jb2qk&6U7ySsnm%B)6356A!eYxO zGDc0{_v7N#4mpf-q$!5X*`+E+!6NpAMycOjxqsQ%2+#!7L zmLhs$X(+1Zg5O%)QCs_@t(TML7ZoD=C{OR#vR)plS)T>P5mb`}332IU(#l6lxE+CB z9B122xQVX-iFxFDV1SZLBpUA&&7S`^E9eL4`cTMqq;sq9(ql}8I_pB4lRq5e?ch)- zV;CIyK|eUO zTy7Oq^XiKVsjdPpUB4a?xzhXDAJuwJfOq>OHkfa*d?a2YoK~G!M)1Lr^9HyslUTbo z!eI?^xAm)_dDC(cM>ndc^wAI}k9)t1?m0@Vf1) zx5fAnWta8y46;~L3dBp|qN5*8HE=@{0llQ>dzfa+$F1)Z*)Za z>#=ylpUc!`bJ#=*m$-;Fb3Rz~YB|PRiCd6}#FDWIepbA4I{t5XkNQLiJ;uz}bMaP< zS&L##q<64{qyeR`l(Krmy7av+N$g^t)S~1o?^|xYelfQx%4b$q^U2L`r8aJ+Xyx#` zBj)F;#dWUZOKT;}R_I^UZDgobGl0X)%&1K^#HT@(GAnBwTEDxst3=$?#cp~QZOmje zW>Eqv1(Vh`=Ls8@_co=i4b?D3{>@e029;VA%g#@>eOYdV_#wy~?M!k4d&P1}XRre? zL`NaE^``ZV?=6mRk)tNCjr*~keJWlCH@2!zU*Js}krj|Wy@BW+%9$VgI`f`B3EdoD zopRO;`1V>Vt-7Z2pQ=k+zJt){D`xUqd9Iz zK(~&G);;8K^Qq?@{5l$@v1*@_gb+q5Y7j`Qc=d2qNU^?xPh#q`7}f^xx9(7o+OVw@ z5)4cnoKt#^KQ;q}OWQjQ9!>lnx4<=x!Yo|qhBcb1R6s1ka%-_OfMs;OAZkdUw0Kz# zfgq42Wa0Pq@*an|Io6{@yRv@Qo6Do+WwX_b?QU})_ZX^7qo26mzEJmwV}ybl+h*C1 zjx4c1dKnjJgEA!tVpSG@@(l0>~*Nz1)zod(2|_|1*-jn0YY+HCGYUv2r(b?LtDGtw)c@ts}_ ze-XdRyBHY~AxGzWpSg^v-(rA3LL&x!4PLN`kf$Lr8xcKInQraR7%LO53vq^7?}j|B zkNZCa{sl|D z4K@a*Ta!y~9K>#IEk)vmM+;1YOo;q6IyBCl?G#b_-b_`L7G7iBE}np?th8@m9jyXr1o!E+Ry)R4T6g0HCSnM|}+DTl&`4u%7cH zOLHPISpcBpvhm{^Gve734C6#W6!^rN@#0jZ2P3mu;yAys3Z2!D3cuak(~0Y%6|^ zwPq~rB_`>B-iLgEk1yCnEPvc1kk9YxUQ<>?hHuSB2iyXz>~iXEt`n^wt=<@FNUoZ# zw37#hrMfIj={#hDA=E6GmvNq~gKf#cU79Hm$ykq2zxo8GpIExnLysKmvSFDCod>6iqC`XJY z$a!D|Mk=NpB5M4wSP8PfWr|gD@A7g|+1T{aQNRKF$Eui=)I(;;buhnTve!$pn{Av+GvfLgvx>6=s{=>fqf^!@7CZfL>w>Xtu0jd^3Ja1 zUh5Ps4s67mX!JmtN_R^PC;U`{Ypq1Psqy^UG#YKtFG@Ys!ePW{5cj{sa6wO-*cYi6 zq4++Zr}A<7WWa1ntm@pxu&`K@IK0W=!keWj!`jM;i(oR(*_r6=_hTwNqV0f5QO6zA zE!H|vI8Z?;l~nqJUYu?=)6LZ;6|vdKT!ioU7n)qO=B{^kJGfm2nJOoeX2w4HF{7gU z4f3I%&=EsUwasXUf|4pKRzdv|+PsQcikm(>O0rJzo1(mLx8@KVa;%aLe3i1bPM)@p zJ}?rumkbUoG;teZV5f*cscVu+jY_d?1Zoh@dAS*2_u?bodR~#_RP%jQ&)E@w+srYS zChXWjYh@YXH-ny|kzN=bTzANL*C1A=O^@0?pKl6pldehj0c53ksk&;J`BQ}*o%5zk z7z~I{uGsQeIDzEJb>h5vAHa&n8&MI|=dLj$tL-?zSEPk`qspu^tDO>sgds$`dA>1f zhDJ(<_r|Kd^WJ=MJ>Hm%(Nm@h}_l`Qizf}Wk$rvi~6~`K-POi(08GhvWE>&5f7)xB4lL5 zLtAC#!4%UM2Iy+BB-dL_@-Z+U9{nrGluLGPrVH~fo16oamFT3Gr|tXRG#)eVHILJj zh*k1o&vl5kB{*;=mJc7ao6@Y3U}$TRTm8jQx$9=;n7r2G(KVK_$(9s3D#k9UMLtiY z&)3&`>ImN8pDf7_&kuA*@hgKAb;wU9U|u$BiC<=~xJvE~pK_32$xuFx@> zE9W368H#Oe(rU%8AaxYyuda=W2N*ALA1|`GlZ8>tc2#LRUv@F4{20}nSQUoXn4<%V zzaZoU)OWtYjaoy?2GW6i$3ii7k{3A%e|ZnT9-qLVp_#ir#?_%!;>wddFIGZ$9k2Zh zFr61g6s&W^XbJW6IDnd}Efe^?IisEU76E9T`*dp+hOzf}oOEqz#cFDx%7W6td*i(oQP!^XG;Bw&b9`Jky>}@&!)LZ4hjpb9&O_^?)oyJZ8=mk^8*&X8 z8)IubhfEz$b!$EKIFTbFs^XKkujcE#itc2ED`dG(TR{TrsYjex!s^c_V~mTfIEN06 z0kK5?lw{v$xf8dOYtM{^o`EF^UD~Lj`k_|~o-!^zj&iwGp}y@B#Fu*zjA%p6L3s4$ ziF{r(8>T#9#7AhhVtpe{ZN0;~WVPUktYd#j<$FI?R{OJh2|0qM38!T0X)AZmZL-N* z^-09gfRKcg5-=gz_?EvhyQr#q0hWz!HfsDnI_ekNcqNMv*!Ztyt$fU!lGE|Tzl2wx zPT5Z=0>2|&RwRC%+ka^&FJqHj8&?@9=<05`?{ActKZ;EkT6aO7Zr{%y$9h}+o9GtL z0^j2q2e&)6l7{xapg;``rh5045pY7PM`C^-B}QvXf6n#f32XvKZw+bL+=mh~xG@XL zKp6|Y;AoB4eav&bixsYMThe^^bZGY>WD4%Cg_Gbr}+Tyo*vo$1zT!`X0A$5%VCMKRu+EhznljZZQOdM zPPOEa9V{-G9)A~5NZhUF;g2i7u8LN7XCW4eK+YTCqY?wK}mV%!tRP4uaOuC+id zdTvLN3sv*sP_7;yV*Zw9A5bZT3p~g*JOv8B8%-V^oQ9@%`kh>&)d{2PH9UUbeOwxj`?o|eJnx9|s! z6i+sOZ~fKU%Vp7Me=9M%F9>#3Yyash4>;-~xQqjn}uLbt0bZh`9=yF=t+iev(UQ~@zuchP2T%)<%}@L2qEES z4b}^F<{j27=Y1I~{23M9Z>9wqPHJ;958M7j5VZVh- zw6kH}s_9KdaBUx3)@L{%v?Usk__Q7uZf%qk+8^8GKl1Dh*JT20iTbsLM?2XkBxcIZ zUCe|8d7DBltJ70hzlSV_s;m#6h)6mRfyBz~KRR3w>Q+tyO5Sb~$>mKaN_aV62)*sU zwy%%XwGjj8^=Hp5=&QLJJjZ*$Iy4uWc$?SKSX#bG^=wk$ku!_-tGi(MHKIAO1Ly-b z!){vy-w^6b5J?*ksuwlXjA9fZdTE6TxK0y>-8#!xA(IOl6}On@R7JED#4WVlEAdz z#NH)oy#(tcLzD9n`~F|8a>}yA z{ptNu)7&?a1M4X%rg7QWEZUZT?ySu(v(bHlr8ijZv@Y5gzn#sFdk}n1=qYF5bJm`# zg!)0q%&R;NjSCm9JVlkTwyRr6*2Qxii9FcT=m;pW#$)$Bvx$E0O@AM1gJP1N8{x*~^I)a?=A@e5BfsZbGe z=1d!7ec1@t2i;I|u|q@IjnD#H|&B7k;(XTKUy|^R^O$lwOVYqcn&jaeja&aF}(EENO&s z5^E{+oUh4of?l~ovI|Gz=7ZQ{1g@UbeudWct^MSo3$5#ojKcf#LQ{KhJ9uSobZA!8 zwmHZpqip7n;k?%QUqI=598RnkaM&26E@JoLCsY(FnuhUM=`(HXw{QX)lSM(Cze@0C zSY-U0U5kaZ)t{nYgcV*s6xhNFTQ&^;V3-kehB2Lp*;sPdzVqy2XtdBO7rOt%)uSJ4 zY$7kI$9zfh7?cyhei;-Jtim><#{voM71gMivD|jK=Ujd@?GmvDvpCkP8d*J_(w!%A z`=K%Hqx~E<4yU^+u|+aq;9jnry2V3|aQ5rp>5?>3Q~G$EDel`ZT5z2uhS9mU80YZ4 zS|XIx+!@Ko&A7_y?MEo(oje20(OALYz8;)FtIqTdZLZv}i#2t<9{r5GTwT+o0hHzT zQh_MMk(73N^Df(4ke6>Fc~VXbNQ%18bTST9JCp>}I(=#R2bnk(g-s12ncaHo%pltTBFT3(c)h{r2)wB0uEa&m9X#b@Q!Z+_h zpZSpnb^UH5fY=5YdVvJV1kRcR`hlCj5yt7{IeT{eIBwBuPcJI`d>PFCfC*(y!TKi-~u7*$mLu;A3zfx4qVPXypeXmFIOjL zrFG?5OMB35!P#LFf>{MN*ilID;mdB@XsTE!U61ql#gbz3OMxFxeJ%B+FU)?FKAF+m zzw5+zkBwy__vKnhc&?s;ZD*#`!{*oNYgQD*Gz)7Nx94mHxf7g)FPLnzM<~#6O7MeJ zPyJizEc-Ow`I1U8v*&_CaS)b=>Wnxl*_~m5zs*&?Iueejlnt5RN+aBM=uIr^5cI2<^`Vtvjky zQ&qNsSvO;|Lk0XVsY|!1Y)tA*>Z85&!@dnM!5D&Mo0H9O7YuAO7Tf^?MVChoEQFDY zuqhl!6`gqGTp!>S>zCtd7TXo@Kt%kywC8^K(u-){Ok06H?Eav1!bII0=67W3Fn8V= z6;9El?XMSzBvA-I8@&m8J&L4~U!)|7a-nIvlKi>G8)qPN~@D7WH{p;dOdw==K zKa&B0m&L%^`z-!^_&;mX;scv0a&+eWFR;mog)YqDjw&wx`3*~#Q&A|?0iV8K$(zC5eO1{( z*}%6oPLH)p;`%+$z)%0zp0ZG7!7gQqvt6r|=QrOWXPPh+N}Or^wUxt>h#kV+?2UQ% zZ~Yu*C6-`2A7`VV-QR!z{MS+)fRcJ(XIGiyAKh(IC%+EVjHhJI4-yFa)dpzuq!A2^ zfE>YqOjxEXHt8P>aocaXMt%8-qdz~orj=^0PFgpvCXe|mq(%LF|LxTV=2~d^$9OO0sOr6o=y)rmT9Gy9 z;kEzeHhw_~UX3;i>?sg<&M9>&9|MyvIp=nQhdB@E2ac?p5B& z>^pTX_5Qh#fO`#JL8PN=q0C@L%udYM*!>KZzkdJ!8dbGG$FfY|_`x3sV<2b-=Y);! z_mUf26xm~MY4#7o*isJgItYMGh5z`5R49w9jf`-|_}GGvdHWgNBAJ_>Kl|1Hb}>MJ zm!yL_uIT4L?i7B$vt$hPw&(kfc&Eganf!d%zr6;l6fm*ud-~Gv=cCKKQ)RJ+d&<kr_gKf_eYd*X}jWI?mCv;cP&1tA_|;- zjOQ;G*}rZ>4cK)ea!t>T{zB*R*Q*`54Q_P%7(wo@Z~y(^Fn7)ere!C;uwnD1AFJ?B zkN)>tYquV_Lsc*LUk~4(u5p3s3mMuRbMZgxsooB}Z}@MUC;i$>;ZL`|AD9o4(p%TaJKg(ah(SNEz(iN{cKh?|i}v}f2lFFi z6dDvcRJUzsIryLafz$_QEN0>2r5&t-kGB0Mf2jW7Z~aGO{eRX73l>2%t)2h82A@7v zMIcJom+(68ed(^}&Ypc4rOcmKcdrNFb?Pg~mBmao$J;*R}k)4aFEpUx-*J{VM8 zo@3FP$zJ0}j{?Ty!?Zsrd48WM#;sHZfcD(e&numWuhcRGeCIA#fDYYL5=HCO{&xW0S8i%@1T%- zF(KK1&Gx9o{a_#2mVdDEB~Ba^5L$c?|EG`6z<2Nn)&P<=CD{ zZ37w7?IC*M9qCFUK!@4$mq0_<|z#ejbh zuRbMj`$+1kvTi-{bE{eiaJ*IWDOCjBtnCCW82pzhDdIe0gx|0TcSB0*_Ws9@cjP!- zzy54LzkHXm(?BI=Z_iW_i3+-AomXl$R{9ozk;bKIUK#s2p$yz@lL=Wmue;6n0lzb~i*^EcZMo`)zJ;oQqgBp->&S#%3wdpu< zhNT}3Rj~!XH1H3n;-qS!Oo$_wuKLeV-&4l&M$}~7v~c$=oXVE(buBIFzIbUnD`W#= z;_JtJC$Ou69Vt6OeMx~17JeIYr2~l>sPd4Vh8#uAefzioM6Epo4;Q|F0ei=)dutgT zAlDbl#(Nsb>C-3ZCDO32YZ}qVL_zycseiDkdJYR3@g~qBjfH-mL^C^gFslpa zq1gUvF;K^MH8cggS#C5!ve|*iCTZR7RvOJKtIZ>0Kat}z=5HRFJ zRVqeuA~5$w*?umWp9B5x!9WnP>vv0!saC!}J{nkKq8e1Z)@h)pcXA)s`R727RnKBE zIx=!nLgEe0eExt8T%G0;`T+Z9yDtUanVIN4^ z3KS{Z!RiX*@dN)KM_gz}Z*T9yLW_6{Sn@{}ILTvUU-FHM6F1MDJJF}JV;zjuQ zEx4`wbhmWN9zJK)Yq31s+-JNaN-=iCctc`=*~YOduSKhKVq#ou6EcqdMxC9Vt?v=Z zlGag&xwzsTuzTzt_JL`8)-EX|RR1p3j)r$RF#v1%gpC5W%TMKDJ#x<;kEL*+mn_0{GJw9gTs}%wdGcd znvTIsr;m`Bq3=su8@Sbd>gwt*U%j$K8%))d-kP}%+cgAy4?g6>1ik8l_nx-t%Qu#k zTlg$QajgCaOY~FW2Xp-*1XyMl?Yhq|VlX@xbHBWK{rUm+Vi~m7zw`aWmUu;J@|B-6 z)3a|^$;dRo2TsTg7{QpW$sFjR+GAj+dO<^!E_P$Zq*#jqcAEw}Ft90B28*L&G)kd$ z!OUae6sSohE>Zf3a^~FG+DXDZ2MGvp7TvsZD z)^p|AndCx4b&(2^sDukK;QoX!b`xlMLG+w4%dVt=hRge#egnNm&z_Jjy6CuoL);aZ z5?x9t@umRh=>~Qo1qGvjk9j{I<&S@OdOfh_v{7Cf3H0f_MHitW71Aybg3u86{6pcr zdRAh0qt>^?AM^de{NzCa0r46i+8c0E_LvfYciRd0c}>DE0?zZD^nE#?Gv- z`}*7=l{txn2>udShJChI0NUDz>#Osw>+?9xa}Nr!UseQQ zkdy5*fjRg5Ibn?r<8Z55&iMj7Koh}K6cxb zziCfWXaQzjN7!`lvHvj!LWKY+!bKqs?oeBvR0=*P8c61wr301;hT5O?8Fu<6sBC#4 z6G{yY0IU3>#fNQZ<+X6T4F>Ii_pDW0d|X&XlDxZXR9kE7YsZ1gn0*`a{HFW7*TIpI z1VckE6WQOva%oS6qMVlpF-8feR~{^JuZh*JyB3qu)b`NnzduLice}4YylM*!jMPlL%Uk1T;14EK3B4_B^QDh!` zK6nKqv-{W6^}$@Qkf@i>uYkxWQs&HPBx5!aEP2rk9in^j<;jQIf10<6d7W$o@x!_+wlB+pApn zK&7!Xn4j>izih!BduQlhGc^ch z=l>3h!3FI%AK3rRY4WEl2l8;uZEYZ>758d~{)HE~TBQntuHs^=ri$Y|Lf=eSRQ|Rb+8hg>0eRl+14Nn%Qvg)_RVrsf8`C@zcWF8=h}5gQ@_hSr56qD z%R9|&=GLJMZ}UzAjwAUMjx{Uk>FG6Vq#T`;S<`7?mSZ^u30R-kw4NREXE=O@3rJc; z$jz3IMlJe8_`7>%_N879YL!ES6y@51HqZasdRJo}ZA#KXDjJ)ad9q^cjY(@i0LrHW z7=1(Ou;g2R1{!}eLa9?bp6Lrb=73$XXp5K5mhV2GvMv}G7dQTTjZnt(a;76AG|_LR zx3j>kD*o+nWYukfM-G)wA@i>8yv%dD7R2)V_A9rz|8=ehlDLg9uo&O9$sF@TaZD1k{sjdZnISgM00oe`n6Kmc6YoL_sy^FQm0PPf`DWKwCO~2`j@?V?E zL*D>_D^bFv)c)L>JXk#6K}%r_KulkHiY&80;`H+6%aJ8ZaQPL7?QAO4nK1j z!GorP%Bzkm-Pw-}R0+ru((e67zHuVmaUuXt>Afd7fPMOE3uN!J1KVT6!^3yZ?kmm6 z%)GiZJ!Y8|^X83#Epk}?iTB+ksJrKsAM!?cHzR+s^SAaF_VVWX)kxO^d9H)kcvhqI z2XHmDi|T9Gwd5f8zgf0_+SosbD)q#6;ckPIdf^O7Ny((*DiMM_uh~9;tzuUweF>rp zh#6 zj=W0VB@@yiB)4Q>`4aPFANLKA5xGruT?aXYkdQ`1t}`oJ1r{hrF8Zw4)4@$W1jwrs zfIm2!S}42pqWb|?;O}s>rLOLZb+LILaO1h(o#Hbq2d^&1Lx7l8|(LQ z2+vD2@d+71{oE@yXDx$7(MCmAmVCVjritiS5bSx%nEq%z_u8>G6*%S=@?CY7L<1E! zbzCk1?AF#eNzE=1kktWLZ?Tr>Ir6N1U8MLqz?QR%djxz1^9<2kY)w;e63F5zX5P27 zN*=PWM)s>I(SX$Gdy_!G>!E-xGagw-`*t5-8N#CQu!m7lyXoCqV*82@D1Df=F_%Us zBsagm(urRN?Tgo!tV5MmKvH=DXA`uWEfcUTeReV#rFXPAXn7TmY+P?fr9|xP`y_C_&-?5dSxJLPjMC0t@1nwkB*j2 zYXLliX~_Pg_jf$m4Czw+rYYd}-6^)10y;x-6ah*zC)|ERVW@b%S-fCVzw*OJc66Gx4k=;?iTKSm2PSZ7i+Z54iH zcC2@yzsHbYJM)NW3pPq(>#ieV?AmUY(G514jO19d&+LopYDL=d^!Bw*#vI8G zt$$=@Nxla{rv_|^lnr|4+1TJ6LCpN!Z_-@=xT~#L)l$73{TkDUmF+(3Q)^_{yqJ!N z%yig#(E9+lDCEJFbniAWUGIv;Uq1n4QpG~Eh}O2=0Vp*KtIU{@JSq))ofxo5pB-p- z)t^lZ@s$3C`e<4;X{`$NPSL(J%;E$8G`JkU6xa0?S!MgE7m+M&_lC|lZT-QqGC!rh z0#0rQu50!?+E=h)gVK5o5X9mgdGuD;2vg*(z&*$2*1R%l>h+~-of~U%HUpLJ>N)pS zeju7Zg45r=Iw%pi`ZV&BDhKy9Ev*=EJ`6|srJMv>*ZLO5mUI$~#7Hy#D9vUceQP)tq z1NYhhTQlFttM%46=nE6H*qk-ZJ7DMO4Z_!0NM5s27kt|9y&5KH&jA@?Ciy79p6}Qh zW3fcVSkWWo>l3}jzZ+F09Ae7JVGGv-XPF1 z##vVb#B|Yr-BXv%r}_1{)2;cmQz9ZQMz){h|C3Xy>-9EHJG&e5t&2cJ*r*2~sOIk8 z+Re^)mPteH?T7993@G~NnzJB9p~usLgIVCW0Fb<@=474Z{$}sm23ciGV%W?_m2KI! z+zpJ#X}-1b1g9G+rG1Xt@J zFv9{y8MkT|+xqivz)meob>_n|1rd-!_*|(Sg(C7vKyu<)#2=YbFx- z7vlz!xg^TUm69QAX>WjBS~rTx`M9LrNf=~)$TR6&Kky!@DhLY*z0n15JS8XtpUwzA zAePk*@avAcG{SmR*|*$_YpEI}abxsvP%qp@? zimlpieKs7zxX|Dl~yiuoxnsJo~% z_o@?^|5|cFf?ZuXX%d|JwBHD%Juv5aU|#y(ux2o0LwUN`NhM?US=G)W zyY74!>Tl)wF*cZ&Xc<4{0n45}gm=14?+THG*O)1E6?2L(W)5riBplYq9<`L8LLu2e z(unMYN*q;YVdLoVY)%V|0ZLi%ZQh9n5#1>gSS^v0c6jK1n~wA+R7WP;5)rME;hNO@ zw;}g``G>2wQ+-FyJr0u8d%z-fIPYE+AWMu2D#m%3#MV(Gwa1{5?ny7=<1X&cA#A)m zk}sS)_r0kkioHxswBJz#Knr5cCdvAH&_u2v7})eCyF7BAxf`D&>) z@Q!B7B)ou@{tIY(Jgv#+5`1(EZ2P_LtT`!u1C<}4`uB1yA3ZUu!p*igt5nTW0^d;V z2pgQcO8YQ&)**YB;*c{+{Y93c-@kuU`SzSqSp6HO=KU( z0n?m>Fbq#AK{y{$v;v9?v8uym&Fv3B3N(H&07{rucw@F_BQa1tOMx*LfU~ObvyRAGCNh{yk~>%M6>ioW2|q* z$=UPU7vUq;aUgS45BLcB z70fJGF#3qdzIa)u*+G5BUjOWYj}Iw_XO;S^y^7&c&ahFR@VjvE2#$+030)m=1AZB3Z6EGz58WSH#p}3A7wt^O`8?vNymQ!H@cV! zg7y0cUw+qpS89kh&qf>M-MV(|ITLLM-@;5eT)rHbnUix9m#kFNxUV!l296mUAKyLA z<(xvYy=IJ7Cwncn$=)2o`e_w*D_`3g#H^K+=XsJCSG-aH7-Lq{-1uee-VgM&J5jacpsLV+1Z5Hpv& z==%NhBg?{_Kl9iBEvNnKdrwOMwh|(G0woTG@5oj#lxWNS<|q<~c5+u1-XkQ?kyU0zHTO=LV^ zkLXk>*+B(9(GThh!S9hgj>}S>PBTa%2r^;}&Cj8+Ysd+5p$~1=DuSF6U@ql$Q|+%K z@o!w>U;hfb0W?5b*F#;BlN3DSg$sD3M1+NZlYgwR%Ht@lKXk~ZM=K#@FGV%qNx&>c z!ZM_oo|_Tn`i8QLtpkR)pnycUZ2?poYz8SR%>BwG{Z!Ut*0k7Oz?$}M!c4@9q$L71 zC>1p=|H_wsS>J=xh;|9a;b;P63E#)Y;_7{tCWbwZL^q9k_!LI+oGqo&!PE~~m~G64 zV;D2E6Ic4nfuiE3?|OlN&&MlZEz@(D`VFftKooTztwGWX&8iSq`Oe8Y*1z8=Pvv|M z*c@9cKsa4Gb3xgv^~jl2dmWR20ws{PjAzBz`Xz+4NxM0g)wDHZ#r_HZ{oi^jL3uzR zX^{OSisiwP-dYs`V;Xj_Bu#>3|J#hp_vdPfW61MM(?lJqr@72Nb^rRBUAuN&eAs*# zuNmYH3LY{ovnos}M{yOHp+%d#VeGx-+IR#&?DI!`N(J%rsi|bZa**XU#Vmt?MPkm;5GYHh$LLS+n4E^|IdnbnF?&G)bp$+P=@M7w{$ zK!An%7_;3X{oW!7|6&08Uexshq~ya-PdQE>h8dy>Uk9cLd1IiU#=SvucOcb*&1hAD z46w;{?#jJmbp$o&#XLJf(w}tC5Cu8K%*!WyG&Dk({B?TMsthWZ1VX@}-@6L)47O?M z=+LIJp{LM+-vEWwQCMK`q01r0mdBnSz;?N-c{rx|s+D8qJ;n-U0o+hCsFH2$4hAk3 z$x6#@Qm9Hgkla@jA@gF^t}Odij+JuA_bO+GnAHl(pe*6;Sjf56c)NnVM@~dy z7<|a}2_6IjEA7cmQOGTlzI7qQoq+}OBGG{=? zbZN1pxE@G8Rv91-eL0P;9n^y}o948hmoZCC+wB|x`BLt+duFj=s)`@7364xHcL>|( zP2j^J5u35y1G5EkA9c0qH5jSn-QJ^p?4)^+xVb7T6it1o{s(2hA7QVQCD({|jCOOU z2b0oZvTm**FS`LqfYyjI``(dY7(kk5ESsWYz`E06p8E45oi{KL4#S{0p8ZVpbO;Fm zG4P9h30N252`@@&zQMmrt{W?uJfkV?s@kEb%QCRfhV@yXr4c3N*x$3%m72W!0XWY@ zA#}r`4QYkXzVxx!gy}A2pl`F3o&$9Sy$YaZOH?Db#*Lou+Xu1L&7;6X&YhFEeAy=j zOPj=NX5We%S?X}$NhjRS31M}I=4dCm6|eFR&9Kfjfo%u~v^7N=d{{ojrL0C@)Dx(m zvMZe*skMz8RpLGt2)*_Lq-F`ddP35$%+c%+45Nl&@(Cb{?3h98FCUvK&b(*0p*UHb zMT4f?@iWI;S1b&ZUD`N);6=CQbevn z8Tf^a%OG>(dPQu@{10XWv1=s%Msjjb(q?j;U18UNFL8i@wCtt%Vy9my<`_UXfDlbf z@V1&A&)vLt*_U-uD@gs@%l$m8k>jNZ3PiWpupS9flIhA$Gh%}vF$O`Xh@#!6mV3an zU7zkoWnF)D+FvJ+0l=e!@HQ~LolYsvvLs78_rZvWh#LnE0ld=5Jpum!Aj~ZbU+}XN zk~FoQ-_oS|DnRi1y_Rp@*nc z0qF@Hq=t_45(4j&J9ob0oqK0~?^^tktVIc*r<`;4*?XV=AEYpRwwERfZV@x})$x@7e)F<6>xqqLY0}V zNnYS1b*uMp-qjh=)xm(?KbUwS!{I%OC`S5E!WI9`^z!{krC(_knYU%y=eavYB9Vfa z*(c|$)86}?2SO%5aCB+`!sso5?WatC8^W+*pzeWUOkA>m{T6ivUr;(~?HH2bI~m`; zeTx;ZbFa62{=5TF9-p$NPi6F`%LI%jt6#S?1ua1#;t5yB`$|H0K++*<4yE-cQvUe-v%ob7Y zk_IPJyybEFq;nIURIbMr_plS0Cve)lQ>STuLpJ|hg>FuNv$jH+-FQ)XPd_MvSwMGu zC(!@^RI@iZ7RyUACu#@lPHn&>hmP*fnig>x*`NVQ>1O#4Xf!!xAOe{X zvN(VL`mg2tY5@VLvkQLq3k;((x}t&;uImGNCQ4kOLQJGFho1W9Ox1{-zPb{-aM7X(CwzjvkAq&Xc zO5!7iN$hPXTP{uH`h-kM(t^)Az#H zFBI#%wfs6cv-N|j?V<$j$K*fm8kB}Mkaihyro1t-)cU+l_|u(q&`v-d!8&SSp-d32v?}lgf>Y8hGB>X-Giw_S7C5;X44XEb zl}{r39|aUc9iig>F%rJ`@p{FBzpWh776@l-*R@gJpRA4`16CiEcJuFx{<=)RH>uUX z6Qq>eNSk<|LaWv021Re9Rb{N!)^23Yzq8l@W-h&bwM?SS18z~ZJ;wcmhIllOzT~Zpb`^#=&4loJ6 z4*?tPUs9Z=Ec+w{HF@@4{sZ4n{VV~Yt?h3KTEDC-nLPmjui^^}xo0*5&CRovf~me2 zHmDz%T16%!w17ClhUY$LOaUN|%gM><0q>Yuvd_S$?@{sbyxHiXd?ANHc^UzS>pR!i zs$bzAee2kK9m{PId_z{DR|`F>>;>yNE$FPIQ$5cu>!Q|t z*A;rb5x2F(&q?~)+cKqO_`DxrsPdORyYt?5ve9OGb%L7CYVxSTYhsG*b(Du)Tn)Fv z%(oIVuS6_n_SRq%hHkwlq=$z*)v|B$R-|R?%@B{j+X!a}ZR7!_K=Qza=9h2Dcp&|@ zaQ5a)?omlw;;)aaLm>X~pP>W4;Xw(H| zp`pVq+J7Cf4CT+&GWF6FSFk|a{a%kDv+sP9Hh=7WXI!k+i78hN=PQzvj z!xi~#EsZT4cR%35|0j%>UMdDLzYJX?yO3enLZCbE1o=P^NyHLw^_%dUqTIVov0k@@ zZ~Ol}`z)=4U>Y`L8>{yHw_hKZx)z>1WOU4wdU=i_sD3T6ouhmIfb+p3%!9Um9J8UtNwfhp9x7g;R!5+^&269aHA3OU91 zQ1a#J)xIA*|3CBzzxlp6*Kw-iUUS=t?$SDN^XyxoN07hQRB_8`z1xhURzpn>vHz-X|NlOv5pgS zA2*deIdG}GkXn<^hz6q zsb6N+r%(D1&^wo~aO(lZDu)RkB$pAF1nJsBHO#;up<#Ou+uhjQOvuj79lLo&lIQ;O z5EN4c!lI{?MqZ@^X$dto=HST1aq@OivhO53MtnvpYhtg4&D@xK9?bkH zFox>!CUXMKuRHrc4|42P27N;jUawzLHnIu=7M66VU$y@WP8*4EVQnyfmla@Ki9W(O z6(A-tsg(i#nP&~k&1)%xRQRyEg$GTSdgBt**R<3^;~U#<&eKIlL@+$w5mlZ%{UIQN zEo9tkZ@N0S$<~^HtF84^MLr>>VJKD}Yw&dOn-CyMN=)Q5FffSv_!08-)M^>f!)BFp zN=ti<-c~R^qN;M7a`=h@cy(lR^DwqYFBv(yax8N(;PIHfQ;!r&%cX8P1Eo@*i6VaP zP6UVODWDV0Zit-Rf^-?5~L|_p0ST5J92r1vdd}GU`{a~HiS>Qv&T9p`dgxtZdp|i zxfj`(Jj2nAmfarfD1|VhCoor@W7tDo7B+5n?O%uCW~v?IYL0_A^H#*g(NcOd?vVXn zl%k&L-8zSZ(b=hoN6b9om} zBVDHf}!^u z+j(O+z`V&n^pl#G7d;XR6oMSRZ8N&7DWl*%@Ol^={x#GG z&z`O>{x73I{`287dilPt`MFUbxyfHURH*!#?BpKRUsu8MP4}k;XXa7m!OTO2J0n*$ zIaQQn%d>u@x_O__aO{(OI=XzzRiL<1mLjGsvr#gs~m{sMH zWXS75sLGz@cq$S(yRFpe%{;&HQ#1fUVH&}c~P^+H&EQ#mOb^1wEJ#af_(Wng?Yj9g1Fui;M6L8i=yCsx9ac>jd1TNaeZ98 zo8@(dBgEru(s#nuEA zx3f;>FMd?7*j7I9*)OMSmB!dPPYYFn6LGdjSMSoMLOueCZ?_PSI9JaAc z!xU(n<(!+n@CwL34it_! zh}wOvl1Mkf80qTwNq9_{P|UdGqx7%N>>GvOT=7<+2^tEDKaoV9gsJVjtT(H8b4aa- zB*hsG85FL*!P4MDH&#vK@A&CPR46#V**#`HfGe~sL&+<5c9S1 ze7)MUH?r~C#d5C2W zvd~{%-k`hw@A+xeV=_6(9mkzaEh2 z*{cW(0A@H{mCY{Al9QE?ccAkIVD*5Q@9?$y0=;V*pl}$P`Zel6;G-o*rrvM;9iPA2 zg&${|n!XPYuNJxh1^Q5xUR<5a$?+lU>#=+tPR=Bw!;)HqPIJgA76&<()(sOEuIY1d6V~`WQ?PHnSEG`M6lREDKchfD_~WgQmu+o ze0*YNS)i(azMkO2p5T+R!N;I#Uxvi;h0tu#si|C`a75^=M&U7UYYncm2>Ibfdt6L&XDz zh8Ale5PK{yFGM3+NlGbx9y;$&;gqQi(w%o5`zzmT-9wGv<~&~Zo|*{o{ys1{jZMOe zi!EF)a4dCNOMeK)77v*!4okoQTQ{NjTXd>l$>5p`^SKjjth0M?+S7GSHAe~ z)I(a9uu{}+yRcc2KW1?t5L z%PUL0>4=M{Zk=;3V*mvjtZ*7T^Dnbm8>wU!38&KA8$*OIW><)J7b zbAJGEXf|#`h$%wh7YLCdh>%n`?Jb>)vg3{WUP(FzRl)p@lU=s%5PJ9Hx8a7S2qPv; zl34FZ&X{A}rYgA56C1XAELG+WCPs>lZSio7`0kv`bMI#v`!I;Pg?(h7hXligTjL|z zr6{wR*;$K&*!YgM%=PH~NvwRg#J=*xDvFBNgmtz}irwK$^m)_dmU&31;>lu>VFnaHL z9h87Zx1?=gSJGjq`9^zO!??IXm22%~6{pTq zdq877@La0aUG}iI=%(7g)^oU%utRaboH%H+lbt{di3mEX~CGpGn*sV!w zEn7&QZc`C_!j)7so|w0xv?}N8;V;SIPYSESnsgoZy(vHqxjK(FT;CiW%8+fyI_SU@}TpYI`k~N0Z347}W8I+dW+r-Ll zqvuJM6Ydu4Jst`Q6FK6Zbu_qEOnd9F(yNnU6?1&)KXs?R>bZDy-U=C?Nxw zPj*N1^Ycp4tf6>lYrR$M_brM|k(rlAD`IsYZt(wnbtge zNiYA@*GqrqRm zy}+L=Gbc-aro4K!E-;1x>8h@)`<5Dl%jsVUtJ8%rMb(@2R6d)&zgV_Fr&>|W&9U)7 zcF|J7%&ixu%IksJEogZY zABA)mUM3N{J7y@?c9d2GZAaze+yDMee?*Heyl5`LutoNleTfGdIJna&Z*RM=ixjFF zNA4VXqxbt*)J&?Htk1k&j)wjBG=?BHnm?JK|g%Q+>lfZ6RMbFx(NuFGFmbH0aJ~4(z)sxNvWb&>XU?q{I>TLmtd# ztr=!Nv%go>?V>Sa$+xA~6!`a#Rn|}1N4tyZ5t|cULH61AteNYcve?IStsysVgYI55 z5Jz@z@K@Wr;4Dg}ZkECCv9Uc43Timz6NHrfD6Ht_+kZaS9@!__0(CF}vyO%KWu@)| zI|VKeT-lCKY@+z@STAT7FB3q%4CH^ZLNe2E8tURWIHkTi#B+G->*(1VYQg&{%U!E3 zz(Hb8oBBm%@!1p-Fc4$hKx(~~Xg?NLCz{ZKJqict8G zUr@}~GjXsvyC2kf1XOul+Y`V!#-QHaL#_ZZ(@_&>MfT$`$iMzG-M=q=hWv9J`82es z=>3GGoGwe5%Oy4}PyQjT1xqJgM8u5lFfcpLa?eFKUIy*bf&BN8cx@@LGN8I|XHB9b z_I^j;u8F6wn&e@#w$%iX>t_RU%e)xju;z0eZXjk&?V1E$8|M}&4HIFwT|IU#F8&fg z2~dGl7q^zUhjM@9_%1kZS*XYs`t_}6W5&-4BsaLb0lmlt)sAxEayEsV{#%Typ%Pg1d%OZH>?g=jb`C8XT4E@wlZho+pFoLE44AT!3+x zPpfUL)enig(aIZbM6;}C1?Rn7(F+Xy+vC{>Y#CId-!IfQE9O=KtE`R?kaGgBttOhB z+}t~bv#W0(Ccpr-Z9*brLnql=mhI}7FZnZ+9k^VQ>U{CcFIL3F#BbL~>qprk`OFj7=A{BYFIo7b(;i|apdYbNF&c_*jn_r1#d$xj6hDQ&5HD8F?( z7msYCXlD)&QqRc1Kh1Wp%^WVW&A%B;0+4q@iqy?2VhELq<}5D9nZ4| zEb3SwJ2PxNm>~wlBh#$3G--h0uvsQK`Cq0LR0gIlNwYrGU<5GRA>f^{;2nJIChBx5 zADYMW7|#Lcs>op8M*W%DPE56BPYO3UD_J*efGB$laPZneyM`T*;0jzfCMQBLl0AS1 zGzSp$NPt#r0*y2mfR5sU(kQW`2tPih-Znd~DFF*uw)4QYK?tL=bDJ$BKyXhG*W`K7Vy5(Cn`HXumiO%$|`0^~l3 zL(TMgr19=CFJdRED^W-d0FgX^SDCtb1O^DO0v{!I4`qvu0FyRAP~_s&EsOwFYy!3q zP$lt-h|AaSe%_Ga_2{3V1O4Y%_<6+iLfKiy=LhL>fF zmI|p;fVGxjx&Q8}>Q9$<`>eQepOZsOInc;NND0wUL4jypc4GHrWxEKoHe8l@$hEgJ z)PrS4f9$qD_&NO?uOzgW#2hIv-7oxNgkS@VXB*j+d)PUh+MemU6Tqcic$M1;+%Hs0 zbyF|mJTr!hRDriW&SwcR*z*RmlmvX@x_YE=wO-(_c7C{LHV2mXekw68LHYm6g$&iS z^RE;*mx~iF$|--=(yTQt-c>&*{A^D^Ai}a$ie)`UDS5#2()Raq%-Tf?546tAp_y;{ zn^O@ir&0STL&{LA(s)rc=c?j`Kd!SsS_FS4;WkQ7QZJ%YT>BM(Cz8cOtJp7Ld+KX| z)69-noq$oZBJtT@w4l~$KrqbX@+Og{%cUC6pn8&Zp2-o3b3X(^qw)Ihjer}eNX4bk zpeBW@9Fp|505LRU`msmsBNE7~{RxkSY{o&rhmi;1_S0L>Cf#`4)3nc~;aQCLADNh> z09BdoP>{c+n^C>BzIId1(GlQN{59qy>ec-iy=S03cV>2~VrhtEaz{TNVy$(hoe)B{?cY*X#JZUT0C!(ap` zPdrt33=%}$@|9D>4M5L#B8RA^ux&J@5e)R&3^^}e2A9F)_0-fa;Xa6Y6npM&Oquep zuJFk=J?Ueq;qi(u$#H-Kkcy$YrEn1W0J4PW)y z(NPVlw~=10u0Umtd0{Mt+tI28l+WgU%hxAK14DRCHqb_YyePRsCu5B|^jWerggt!# zLyxMYKE)pPJ;4H1vE6znh^!7C)Bk6^6v%N#O{1|{4i)l2S@~j&_7<&0$5*U-(g(hg zMAibvQBzD{j)~d(6&L^Z(J9$D!v_%i5yR$f^ej_$EU{jq+0ZFtisL86aVMBS!Lje( zCxVq1SFe77018Z$l6~eg5L`A99+8U7kzM_X>L`S$=*Ykz zD4n+!G;{dxW4YINI=ctN&STo?gNpeQUORVDYHL@m@2D1N+bpV|{Y+y%=(1E7CIV>e z_oYZqmW>|TM}G9q#SFX>ntYjQ^y+YafvkwGAiW*5xpazw_PnA9FaZ}3955@CHp$Ua zg%g7Y{rZ1<@X+6-}fFr3^Us>h$kv>@&-%aSd)Py$y0&W_;gqL?y zq<}XBpVy8RtSJD_J#NWA{LNLlNVsqfuXv12z9RhNH!TTWPGlpXoGyrfLKN^f4G1iLI55);U_p~%MmHlS%ei*>C z%OHI4zHTg*u6PBAgrv4tmv@(YJ_5N~!pcCZBh4Eapl)_qK1$sHJ*p3&Wb8 zA#jZ@zq5hU)J&=sxfi0s-MU5p?8y$>o~v@CVy_}_JK>ld#o8Rqz-erZ79g0yi|3A% z!Zy1_K}Q;}0=oq2Nk8E7UKTPG`P(5NlpJK<3oszQA&&lAnzgSL#m`jNkWV*sop^wI zPLWT3QN{~<5b$CLc`G!Z2Gvu}BY-6PQ{tN{+v3{Rm6s&fZLFLD zJ6cmy^Vvh#`ja)_eyK97&RMm$K4A#_FU{XTy`s*O(8y!DljYiKzz9f{Y=t6u+C|%5 z0iwcm5!e?^l1nz(ibXFAp!^}ZML_z-nwxK0UyMPMmFZ%3i$ZKgvB%`!YO$B;U3E&d zOh;EQ<$|S;1|(-f37)}Zyjo`Zc~Mq=x`77^8gG;GWDSrXRTjPQo+ZH9No@N?>g{1% zucd0@WC1v|y?KfSJs%r)ZVWCb(=;N0_E-7mpoF_l949v9yjZ-X7R@oB7vs6z0H{Ey zdO#iUdht>dl=N(KHe2K1Jz!)4B3Mu~`wO@y|T86XIhC&cSI zl3Xi9VIDWl(LC~~mX|P_p2XzW*y!sg9s%_=BjcAs+Xxd=h>k&1H>BlYe+m{4W_k6l zh7aC~MZ`nSKroqWc(=-ZnbaHh5EV$%s99@>)6oa|-w!;Q8^KX08KnTu<0(#%2#5X+ zYYCNWlkI$k^AoCHs=m!-P~L13n&5$Bp~X3p;#efGQ&%{|4x1b3qdrt)#>B)G*fl@E zE6SVRp-eF)lxw?FQ~4{03L|9(GeN}3hm`i6@q8;Z&_qY6{gXFIeEazoc0H!B^saqc zc7o8H#?f&X=ku4JevvpGESyM}9CF$-nk}+bttyt#H>~G=?EaGC_$a`+!9=NMh}_PE za&5qN5aS-aTg2vv-Y!wWDJ|Pom?W44U?rza#0os+FtaCihba!xyQd_eB{yY!6zn@) zIa+r?bm%*}rD%TFqN7kQpk;ftUq=3Cw)~e@zROg@D?ka@x@Ax4XKTi&T`-gtD+jlZ zKo!5U5nMgF4X|^cDHxm5Sg?hom-5=#XLGs(l z{&!aRdw!UE#up)}U+{p}9unI>K>+qR3)th&^a@bRS-$zmn$srReJE3d7q>r*-+KkEE~u%iUMh!@CVK#F zC)6X+4W|?bro#sLzkoI&UtM_-O8dU{g6Ly=yJ|$`jAdE}0^l(&pFWwKtQ&(pe8J{} z)<&kT&nqAf{f=`BVDR2sWj_jXCC1z!5e$$Erb?Kg$=#t|Mkm|k#%c+?%jo&wNNlN! zuO}8c^=*N*?P3o%3F`$L-vzo{m_>DfcWS}RGOyKYN_E{Z_lMAl9KMa|aV0FKq`Pwp zBi~a3^Rz!5hfi_d=RNwJii4B}>J1L@Z-CVI9eH&{h>!5dWBt;Fylm2=4eLFr3gG{G z2Xx5PWi2V|L`&MxY2&N#lAb(clt6)Q6B)LSs*2P$7JC8OsPpZCA)kID$A=M?Ww1?;+`a*Yu7 z2&+5g_Vra!4fUc((sby|CE|u2+i-j1dh2z|rb+6K`W2BN!|D~o%i{r_5c*PElu@GF z+5E<@Lt>~U`hM~8PIqKeB z!kG5`CQvDLf-%r^w?aBwDpjT&5){nXN}oDEmL2jpFHn4uvO5)vT^rI|9vs2dx~kmc z5mTDcJ(=DgU2{-y@qj7?AZ{-0lcFuPJ{s^!sle``PW&9C>PO!FeHseCw7UBRf+7|$ zqE$*CO-iEMMlE_lA=71biLZ9k>z9VoYzIojub{`D3gc+{_Ua1~&Wp89=9pr0RL-7( zlnJT>FV43kls2VRtP3}-icz%_dmWXH>rO!0dqyoAR$GE9f4J*>C5zG*@7)HvIN9Q0 z`tBC+;z`VJ28wg6lsD$gExCk~P0M~_h1c)Mnv zXmUj2G+@4PxHL8Q_2X^AYv-)f-};g%$}?Qdia(Uf3BedZY>P2fwBrS0OqSwC> zy_SALceF(X`u;Uto81%T6vT!-eq-N-64=Gaw|F+PA)%rn*usD6_0m(_qk$^33Patb! z;sH5Z(lmv?kre9KChlP0&ce4oa9qo1W~=()O#CTQycOwvbtzy^^WDYewTHutch+s)43hp>$s;x{@Nek1s&FER#SUB*I6*2ZS|7O}Ubp5YBP0$wM~Ww^O} zguUIqqKCJX&p~PaIzR^MpL-_@PtvffGv;gxdy&%weHhT=yT6I9W`DL{^~wx>0~?P! z7acDG^h%e#K!Jmw@RxSDjTry}8>`6%&t;a4^m}Hly@PpL3Pvt6A~(A27gj3rW`JjP zhM^-Oc57>EFlsA6)hXgCd0ov%MW}`bxd^dHB8Q|wP9vt4(Z~WBF1|$s1wiK>B3RvU z-I3{GB=+?^m{$4E%DBx}+Y3Ket+|#T{6;AK$Ja5#=QqIh)XI)Pl-QQtBf#xOUMHPh zk?kr)y~)Zg38m3Fv4gXhR*#e8Wsdykvq)%r!VjMY*gLfd(tW9jyShs|@FQ@CM9M2w zrWp~MygXh;>wTl^%0E8P-w1putOUM7&de$qtz72SmV;B|7?4>B8;iD0eXt-O4B9Rb z#&{|D3+o;=x1QhAAAUzTW+q`#a{qbP&#Dlc?ICVBc%C=xZoA4WD{HT(01wuTZ_#X6 zqc8Der8yVhRV0OjHuV6wkF9~LWhKxvkCvHT;5I0K2PU&C0Jw`e*=sp3<&)K(SM8@} z5_sM%PR}2o&(wi>LP0hGSbk-Jy7VpHkgU@zRLbb zgOl$@|cPgeAaxijXDl6pnj@{OW*mxbovoBowb652$6XtUmyl&%(;Fi!S zi@4jaz`K32Z5t8aYp97k1^6QXi9p4&{U6 z@?@#rWaJP}F)H{9x&3j1YnWQ1tJu!y4Pr{sFX`%X zcIA9*v!s*V0GlY?Kj`cVa3FlUNA&qg>ts@K#(BNjI}Nfvce+z%+bRlFZnJqN{roxI zgi?y5u(Tu(rJ|ifJD(QLV zn}j)Xj%~XQ80?0A9su6t{Ne1YD57AwYKiO#9|#DDx-Y3C?%^Q?Uz2x+wI zP^wgt#aUAk%*=7jXu+Zbt>r>E*)8t}>#oSA%-rmp8U0z|PzpyuQOIK{s#?{GL4!+7 zx+<;|vk=jjJd0!KQJR%zxVC{uks{ZIWI7={W;o?eY)h`U`&RIT`F$_%lwRXt&#t|? z-K^BwN%1ga7w$YuSVl6JtYD7(bKRc(Siwc+ZRPg2;FA4^FKKylBH}!&k9V$<4|UA8 zN@S~3qiCEuGiOy~m@pwjaZRwVt}1XABlUOhH9* zduEY6&;RtU9Z?39bSqOv#!M)fQErEp zZDVZE&N*Y^LUZ?{)z5DWG^bl-_VQSq-^s2VFWGNr{^`r=JVBABK4+3#E_-;HhbjP+ z)w>hBchmI&z#gol&!lKr^CDd-kg3-jj6_}A{c`Q)``3J677l15ECF-zcqoa>0sw3? z0R*b6lMG<@YqxF<&&sQT`-9KO0dFS>kMn$WoA>K8r_&uF0e*LKIU4KlG%L5(vrGsR z@Z-m+-J_Z(1oW5Q5c4oNQB(TPRP;!$IE~_U@%Y;-d@Y3r0Q`@^AE@%;hE%rOR|9hs zZ$wSGzfIJBSj`d@6;(gpI+us0Xns&!YEZ>X~<2 zJumxxm}m(U{f{U4LP>hu$WXoCKws%`&?-5nhCN!5$?O(a2xXgr(rDwPM%TpCtKH=< zQS$e^tF`KyTjCUUEVS5JaTeEDkj|97MkxbXVCYv( zRvwHUJ61WasBu3umfci136qxkkI(zx8^Ow%ph#DrGYepPGQl;vvTy)*dq$_eJK}i> zz<8~;FnZKa@AcXMe6xim{~BX_jjC#@WO5@g{WwzHa7zkQDrwkKL}5UKK{o?G9x=DP zke92ZZkc0VaID_L5$_({(2Xh9cvpjWKCq#PEEZ=n8D|WQiI0B_Y9Z1IpY88EV5(^E z@!@tTYe}1bD^GC}P*dpI2EI51%72Z+prA``67M^gfMF)0^+^b|FA%HN>zK;C#<-=$ zw*ri-Z|>Xi1#VSmdL=p_eAl!f-+liZ7HCYf0PYm#P`@TPT0gWCz>n(0#)CdbWFe!% zyY89Q2FEE$aLDvTF#c)Pz`o90_-p|w;~w5lZU>BLe;~(l4+*>q-KVQckB(MKC^?s2 zEa&BDf+FE<`P-*W{v#Mq0EnTm75Vv!@9n0%R=C}`JKdP`va*_aIx4SRV$hnhEo8*h zHX60yP-K0j3oHZ6bS>5H znL#dV5CL|8=y;)w`V;5Y?k<$H+jt-NTKMciH}9g>>JGOZftrUFfN!odq@Ls8TwOkX{#;c&8MO zk}L)WhuED8ggeWz1%)e){a4Bt1wLodZgvP8XuU(w>XFTFT@aqPSgmj{F(Ywm zaAUiyJF#UiH5E-1M#AVc@p4{!j4`<~#N-i)^(>F`9B*T6?rkX@c8*aKjNpJHp>_3X zbmc3yI{QZ~{}>~FBYJnqw-+8`$Mle?IF|WYRUC7E><54$Spy$ieqb*^p|Jv_^d-%& zfqG9df)xtf35&Ih-f1OOyc1e1?D5689in)jSpYl9gXbQM80Qg&j zC$dHy>#g0O%|KE`5t4earXk1$n}ZsJXgpo5eL4f9E=2B0ozj*{W=VSvr%KZK4=2rj|?E}8e#xECf6578sUB4qHVi!TM~cDlfzY+0z1R8~VtlV6P0Ot}NVMMD#`uWP zzL82oBX&*3^ULI0XYW`SBiD$)mKerFStA;HrAnOMQ2uEYAcpYq-10IqG1O? z9wD1i!PiKMC&cGv(UTQg2-ouWFD+@24(!S%~!c8p|Hn6OCj#n1m0h zwbMPirxSavHWhX&8ZmhbcRBH1+9m;QyXn@`Iofxe-=Ju`GgM*57DxNvPNV5vhedC z0jGl0v-@M8YxnjeF?Ea9Cu&3LDsiwXiNT1#x6AB7W$s%x2 z)ggEx0t0#o?Ti;r260~GM4fPoLgt4WB2vKXgBuLv$g{S!u^IH%dY1{H)r(-xh1=e| zZn5jJFWO|u@)P5~Ve&O|_5316qt)mOjJI#mMeM(A zh&MB{hY3>)8P`4s1%AmQ$!LP#Y?C!kjSk7I((P3f*T8X|p6xkJbMO@M{?Ygax zZ68tf_4f|Qi!BlKt2PFHoolxUwE3PEap4~mC?YoknqUU*PgLnQOLGpo2sG88AIlp> zX2{zHiwvtd0+gEOok4NSTpSbyh72j;t;}gi-qvI|j8&;z>}qZqB>Q;I|6zekaoYsQTH{&`!eM`YzCU@k%g(QAv7Cv4=`I*(`i z*}H4{`ue_@WPDoy-e8*R-JpciCjAh!QNSs4HR8dH!!ox)lA1(UdaJi>G^y%Z>&KCLcP$nv`fRMiEbSUr z!ddi6>nJ3dGh4{RAhYB!x0PkHM?mgyXRHnt1OutY>aEN7_w9=t>d+mX?L(99_h6o) zDnemWb?a~Kzf-zE(ciy~WTr^=!wvOj+PZ3R)1{jxYE%G9OO2Nl-`auGwO{;h5NQ+2jb86tOltk&gMo)OYl$a6YS|Dc>D+dF=Aop$Db$0oIZniRJ8#xyRIP!B~{C z8*QJWPKi=F3->}I$uD?z-u)ICR%F)GUzr|10?{J%g*MjxbLTCh9g?P89$33xh zcGNh`@~J(RER^c0??05octr9Ak+eQ7UD$U%B^@2g2`~+(<8TpB3Up_9To?c+C?j0* zNM7f?Z3fZdzgPK<%nx9a#O!nq1^|NC;3!XBCta_-5lfv|;Xt8JpC=l4w(u3{kKosV zHa$`smr!dh@hMqPo6fUS_X25@*GVm`&%RnEr|nkigR9-56GPhh&=oYj>k`XQS+oVS z#CF$|Rr5ue!2pDLqn@c}@VRQW@(S2ZaR0~d9m+CzV@)@-zD|N>?;Dfca+NcN;Yv1ascP3cZ@E6zbMSU{9)}Oz{@3>Wk8=J$ zbA&dsGk3TRe}0Q;Ywyh5oO7B5gHXuvMK3=X2etds$Vm~9Bd#bkSI>MBHhYxJsZ2b{ z`w$;KeAO8l!mr`UPzvi!@y6~qlAT&_@Pm;!He*%X_EU|?&wEme*1uD1$Xt=gN@|f| zac@2YumrD*gXK-LJ4@kP0YzVzf_bMh=gb4fjP}=!mufMoX&?a!x@o>Ya^@^n)62`U zq%^;vU}?Sn=G=yW+0u5;ROa3{(rTxKjZL^r@u~?YJ4+LE zygW6T)(+|eRhd3Tn)A-&fBdGl`!rrZzD(m@R4>qdNVb8QrGP#o2AuG4# zR$ccrCuwGKvMslQkry;O1(%@U*dOn+P+*B0^?aoOSsK&( ziP>Is5!r&`BQ}u#T6aKm|Ft~88r%U3IRId4Yk*MgfPAMQ-qYUm_yD+Geho9pTdB~N>x*UNf@L;F7g{CIO1hDmW1%`Kt1(E;SAJ1=>dTt9s>RE%& zRfZeN!XHV~)Gw(dy5HwEIF|T+xJX=w3rh@Pt!~pPKyO{Ryw}fJB5q5LqDi)BlscVP zs|0QfN6S*%gFZ95*m>$HbxlnIFdl2gF6m$F(Z{nkK(Xr(W^+j-PUT0O;lDjs^FXd6KA2bGISr%%wJUCiz zk*%+Gp3;!SDqcVwkd^J{PqE))>l_^-E1{wmUe@DvBKNTIXjrw1Pg-r$dsfEfwx|p_ z?c?eWHPa|+x~K=+d%m)Gk|`f-Eh8HI4sMuI2Djj5gPwQ`M%uSJX-A9(lfo6UdAQWk z{;7}ZDoW!gOavUXX7tF@!w`Xi`}0bd3r;Ztwp`sTROf9iRmxCDoQ1K7wx1~?4vLCp z!Ex+NLAiDPp6B{i3$agv+4c=adOU@O$8C%sW&Xdw;4mmbTS%#{>?gyjgXyC_LX7q^ z>wM-nBOoUU08}b!iuh4b_h|ray%(1!=+zpOzPMQ*MSnl1Hm~#c@bz_miMyMgp*N6IQ3XN9~M;|+v&bdb+=FuxCAAogy3LZ{C z5A5eMwc~K#GM9FTCN&1AC@BX3S+0Zhhle-aZTcQbZ*ht3olYOM$=Ko{ttHYvN_qG*L zKm-W^mF@=V4wdfikd9$MxF`hXymE&huVJW6{lF2Q*BL8u;pd)VYDS!)4d7%zjJj<9VF<0T^-QZk_8GM8kjJ(DQj4I{PT}z^@W;Dp%OG@n{luVjV{k;4B9{4 ztryPt{+;4*?ds(VJZXwfJo1uyH5HY#3sGL!?)RNXF=`)hm%GzKWWPgJO1s%H%g33y{ph`rjPr>?0* zv+hFExU3vw>LuxyWuoMC$>a)E*bN^wOfl7NDb@q$zV573u`?f-fF(LgrJt2~`kbQ4 zzrY_;krT9{aB)rx6)$HHhvMQ9uNG|(cXt^xpX6ApTDegDN%!sy5En12$@c1eH z?4bXRWV#O}v3qYYh_%g0xPI+9=P%rnXL!UVUtc}_Q%U%rvL{Y7zX}@dTr~X6@j_gE z$3d>~d_~f9W4~@xN#w^>b)96DWWSy3q6Jt5B_(PfC&}LmHut}*blEm{TGwpxc-ZzUwE(v>gBxs2 z&o>kLE@Yj+Zx7&nSpM=&PyvHWb#M(u$t;0Mmp`|9=vOH{qqm%!wj^NEtV~$PD1o<) zqigLHr-6jwAceNQ-{7Jw)kgzt7+g#Ca{KA~uMdkTeymV^SNDu=U8DLaulT($T%`KF zQytDkAC~utFEORreAD>NdU=@5bp*wd{uTYm=}FnL_&r9e4K2x(!g;kgDC=iSjn0a+ z=$ML$(b6fh@+qE}%|L}Zj zoPeX$q%;K;yT$_NmfXQyX;T3@V;suE$FDI zU{-m870Pk*IDJ;MMP{acthc-yCVX-IlVo)IA~pm)j3FUAek9ZhJEH7-GIucd2$(?EbC?FSA6~jjNAfylGRpUFX>KP}=M~NzY>__Y({ss2MG;Hc~y18Tc z3HJ+VR(NyI8uDD+r^=A zp!j6;`q8uZOHZM{ujRk*2T>3O)=Jx4EsfJO<8Ke9h6!n6f`&Xj;fHv@HX{b1aN_8w z%4XdxJnz=)gq)h1y6MTq`7$ejodq+6ggaR|mQB|(29`A)vN|1GzJT1`6vto4$Y|YL zlf=1iPqx{Ey%^Cg5tJO)Yi~6Mju1JBXxhm zV`H!C<0c6mIH_$}k7miUaMNHrZw4GU=h~8{TA!_i_S+=j@1;sR`8^}>dtPJw-}jq4 z5;k^g+cm*~yxKE#9B(RYK4G?_`%PiekF#r%FJ;(0>BB~@R-B8r-%99%~IU_iR zO0SP37JP0=~oCrv(N*9m)ZH9Sw= z=c>hg->2+Kt=izf2NywWrMh9jpZr`so5y2Rmz?RN*@d^Fq@sYY-c76Wx}41xn!BfEhdb#2IvHiP z+{@sYKXojNJ!jOO-ekR9fy|8itm~{>>Gi>GY!dio9~$O2_nrGcHhUQRYTro)u?mu{ zZhkZU@hm9pRzNVw`sh(){y5iSBO%-{wZ*qcn{F_3b#331E{iydh)?{KSW?~D-Iu0# zwb2^AWY7IYKFBC%vt)9W>g>m3eDwZX=8+-WZL0@6pyDAPug$qi+OGAAg0ExkIZqfK zlNpHQxo#SFvfd7NcX#r_&`ZxqCH=Y9z0w_9?X*2TS-H(I0$CAoxF5rymPDKdB9Pm5kkf4G z*^iT{>xlvl=>GmIkJUJf!Z2~^MW1TXU~^{T1|>B$xw}>nFxMsB1PYkEK$6ykrKNMW zi99Of24F%G(@@@>_~~_?u!PHE>w}eYRLyQNNy*{_wuO1+Y3V0uXlTp|S|FE|aAW1f z9r8r_s=U~mzW0b&t^;rZjj$U?44)*U3dUYET3iU`Lf3VM>UgLWL^gW$B9ui=-ABI5n|%)57=^qcxwT5;q9I>OqR=j=U7Jldd}x^Ox+hHa1M3 zJ_V|JE=Y>0Q>)i)=g~wmRClk2rlPu1YW)OL9o?i3`;X+mmYm@ckSG1+tqI`j!7wFL zvE`dfGNWxpr<^mXwhsJ9Isq256FsAE7}m}^wA2U*381s~sFl|Sow88NR^b>QebFcs zN44@LAKJs~eh-6O@|L2MB}~&|;Y0C&xL*CP(qlKzRKA$R6QNC}IedJI&#|6L%|(>Y zu;acNAJnatv0QY%MFh(kyCz`zXv7{9SuV=T55HD}gnnvAagEslTG2w$Q;!)P8{P#b zkW#z5%VO`U6y}`04-rnuUyJd&nlx;|sH}-IJI>}L@_7rc~NGY(0x|U zo^aOERJIk+{`tB3P;qF*WNXFkS9X_4u7KKFp+`!3Inp~@x12s_Vatd}s^G=x&_(~o z>?7}?i&nfIJ?xY7TUudZ;V;xb2>xdd^*?~Ezce>L%4FEzvg3~{vAy^$^nDQZMhz-B zbZy&~C!Dm)0-Tomx1Tk;tkPlIOqIPbK)eXQD|)XUrUTJ4A!)Qtrf`zSfFHu)kEMcz275|sT3S^=V&3bqCvo;0O3f~X>}2klIc#w? z&pCSy+Mkak-Crd+#e^2eC(6afj{daT#ofPBr?7}L5ArN%<}!n zE*cyw)`CmZ zKd(aa2YY=pxP$Y~4g(ma43EjFQ{BH(t_kT%ryqUH-jQT!+{4Nu$JAWp7L@Rvl-8Q9 z78x1B5Tj3(763ioES~=5vOH{k+Av0|oWjQ^xPiraAAGFiQD-Y+bkhAw%~kuKB(23*{pH+y~R2Lzp}W#d#H`S z8TBX}w(Zmn83xhjO9Zz8WPn0^@!#U=e?U~cL^6`Eexh;z_nDC;3lfC_pV7*zB$3;; zt;*wtrSnawp%yY>N2H;`@nkqA9?@M>TP=%gGckTrx$ze!A$O3+=}wv07m6LdSH5iF zBtqXQT=ZMcePz^U*U#mW5GR40_Df%F#cSE^0x^4m(Kb`5VZCw)$rv!BsWAz(lQf9L z%>d@O8Sbb-QchjE(ph-EgJ+XW9R+|~A% zE%iZF@zseg80dcKHNnrf1q5&dty{87 z#G>Ib!{ZyE?G`%*OwzsQhWurKzRnM-3|~Y%^Q)&>KR#r0qd8Ne9}#tQbpcrTATg(H zMI7al0riAqR)fmmca2P0TCloFO9!Dt9R~2(s704x7BQjJV#UX0kMN%7CIj(p2{`rF zV)h7M4r?2bj0j1-!H@}&5HTNi4yKvwT!cQ6bF>LmcXw`Ex_Q}*0vjS@tX#-#)55%W zrJmw2*xP7h%W)VrA`vmQx7CyE0K4Fc=Jv<}B(&VS!s}({A%fZUgAkX7n2vI*i1^rv z&xf~xU;TAedV=u&;o#Mw25z_RsueWv&v05DLO+1GHwrk0h(4XWs^p7Nibf%Wc^u@}K0<<{aGTU%Kf6}dUUxxU=? zxP*>HDnTO(n2a_2+kUWi1M$XCdEPC;zy24kjTNkg(c+}O@qIoA2+Rm`KB#>*2 zjOvDudiiH&_yDM%Bw(Jv80yBTzZUlxd#lcFD&i4_dA_*9(i_B7+k)tDb&w{}`*MJa z+yG%#-#`C^-e&Q7-*G%w)}m>14W#btb>EMM_8m?t&jpOUHREQrm}#YDo?-eKwxv5g zx>{SUSh?@9pq1u=#V%;`S0>p}sgB%b88d!6XsJ$J?e(u`txKxVZ*#;EE%|z>j3p$v ziZ9PbG+AAa=c>n|T!-wFS$3Cjw&x-rma19dUg~^a-K&1)jX!W?YdCEDi;LUO20PR@ zHKyb%U#AAp{*1nibigY-C48%6&ErxjUwLwHRy;tDwA@N5MaVNl@i~rO#>FO^TVP5V z=%P`4U0hsY`tHnv5O{j|Tf6CRXVV|d10gg~EnUU;&maAkPcw&$B34|EFV^?yv3K=d zg-y{!0KrxR?_Kwo+yiU2mbPJ$k@?dNT8?pjkuQZl0(|4!jdm1^(b}mwb1RX}Gs;cC zU?~TaSnrdLwzlp@9^qciu`7$1xcDf~;!vgwQ%0OqS6mF8P&V#h!ya3bfU93FLftn> zZp!eMmin}oGvgkTJkSo#D%7yn-VXYyo}%979XhXMfkiw(?R^j^)+K($@V+e}%l|$) z=meu5?8w*HBeA-!#NWmwEmn<_Q2l_Rd3CG*#ssgEWE$)ks1NC6|1|u42%}7&ojsV} zMH%T`=r@feE73bpcd0DtXqWg@N~b(G*F3pPDej>4Tj*C$zQYsj@sZ4nh?DKKS`!oH+E&y&YC*R%qPsE3h{O8E)w?;{}1pBHeEOj>6;2#0V z-9|-W=WLg_Z+($lvffU~fwu?3UCQbb5GS|9-j_21u|NTAX13aw3fPQ`x#Wh|pRut6 zjH^s_^L5GW_Q>o``>jISu|vG0&2N5!At51IqoeFH(pHLWY&(5H$Vg;Z4#>wu_!Tn7 z?Zp5Lk2@-V#(rYb`l6`%L2*3aw90zrH^8R~z0_DMGL;wuC&3(Na*J`TsbRfnp>apJ z`C1eD?emIQ0KW&tCPfa0TERA0ADhMZ!nf?r>c;cr&=U8ZjcO#?dLc-Rt}>X+(S4f{X||>&0+>q(^!02!Q7+}tvIJcqTNi;p_C_yt z&xWF^5^f}Iu{ESHN965p2UltE_oX=y`mPDBds^2{Om!8L&f!)RXvkN9OPzhzs|wW( z55E?g4qBVAQfTG7_IQJ+q_px#J^#|TJZS6cIF+Wh+RVbOOjSSsT-{$G-Kt3rs zB`@MKFD2e+r=Hrtl7XU1eGp2EsCXYKZ~6C(?6-IPOv3L4)Iwn|x_o}SD}R(1A7w;Y zS<$wNYym7G=4i71^Qgk7|ZNupo9r z){$5(bUO998r??5Lf6xja}|SRZzFjjrbb$o*o>8Lm1cWx2*e{4xrQrU{C3E7v8{;1z6-oY>KmWETCZ$Bn%cx2lzEwKwyRL-m znKecoc|B{l{zQ76VqQ^BIyjWgzK~vFXS*sT zjB2u;`U>NRslXK^eetWej?8hxg+nc&I-^5ZxKTkLs12~YUT%Isd0o-a3ZHv-?BF?W z=-UH^Ovzy5ySy7I#s&pCP3QxS=j}1=97G!8*R_Psc3_p83t{NzaQA^y;wCKT6#}U5 z`+-kRoHW5In*yt0#bPa^Q&TrN5b9vn(0u9MSNUYp1qVQ1B zW-G=596rplz`sGL(ZF4v2wj4eU*mT+Df}!L34O}zk%a$T7k; zsjqMuqUB159w9rv!IcI;hT7xr-ZJfX?zv`ACyS?6Ip+xl7m{dPA=Pv!Agw}I5% z!kVIoYEttPgUVyVCVgXbiIiTi37p_$g)Q%NFhujij-s&-ilF0_#ijI)88|^-S@RmjO9cXMXVYOPX-*(N@4Li43fa&%|ba7as*-?;bifY3t zz`V`BLNZP-p*C5Dze13aGDXP**`H;JKKaY;`s<~V9TL{$Bq8ymkAF)V1CZ}SxT(Hd zV!vM5JtQ$#Zbh6Lm$9;t!ETC6g+P1)<1FOYGs(dk%ykoZq*aZuHF(WX)bs)i(S(>% zzjR|8zd#L59eyk~^=|spmbD%xP46~AJD%h{qp5P%I znUO-%uCnK`^BXN-8Xx(~4gB-Kmd}GYK;mx)!GG9UhR9?kC*r8PfBk{Ll6)VA^+Wt> zi+R@`r|YG+qm6CNWr#ad#o*nEAQ8Hp*aj5ChR(6!DdewqXBH=NCpT1G$35KbOY!_z z&<&*{hUj}kGbYD13Hw;emvjW2N($pYwVFq<3M>{;Iq#s#E;-F=NiV%R=bU_*$a9R= zxnKMFpAI=PEOy}gVtiHg6aD)-(!se8X{x&DD8=|u>UxF`8W^Hbx6j3M)ydBx9jKjn z-z>Z?o)yBLPvR_|?*ZQVbm{|pk6(K!Q#dbpwlUo`**#P{B)$FRm)>ft-nve)872jI zR4wZ`tYJVnw}bX?UTwyH(bY&tD*%#Zt=xFqQxgLlb-^YsfrX92>VaF-3%6)09^?!!Xf8V@sq)6B|PH#y6 zx=?s0QFrF7@SZo#8ag|35%W6*0pfr3{QNNegfFH2!>$bxBzz75K7blQM)ym1H&QEg zOz0#mOQlHIiOLcrMzo;#5ap?gx;#c#ceje0TLZKaM43%rQuN)VSp=g!vBN#}sztD9 z1t3<^e5RnFC@89lKQ4G4(l@(4$Mw^}%=y`HHDiYTi2F>Zil!_>4{OHhK&0GNUtL*l z$F7Hx)v3l-^(kmH%OptVfk-`ds)x$K!nd=eAGc@Y=KV6Va-D4WpP|Yxh6#w1ZDyd# zaNnE|5uTlz$=9tu48=usA&bq*QBm>UxOlKWBedAjOmt!+Ogtk$=oY7yQYgyBC4G5G zPEP?{$eO0CtouM)Ru%=-a>ALhn>yd8xM$iGGTx@Y?yFmLlD1DN$Wbod3U!&Fv3&d3 zX$W*IjlA~4mOTp`qdn`6ZgL~-(;eWX$|{zdc5_EuF7OCJQ}4a!;2}q3m7&4G+=^G{ z_mRIp@&0>2`}5-dD0%NY>}O@C++VgRbCBrWq}Kc5abxr+a93B?vx)BkV2T3v=$*`h z9tJWF*p_FQkXH|TO_N4;k@cX8#WM;9t#9he#?K%mjJV*TY-Pzv2Ia({D0=b=J`tqSs8ryte}x%6=* zO1hp&k5~!O3tPk}i$@`<3SFjX_zlErxpw+=x8}dr*O<752z&vP+L;~-oaD*~yZYs2 z&j(^3FqV-8!r(Jf%1lmzqkJ)a*1XReStw@djKJauCm7H9lcEq=idHGTc;%}L80)V( z83LoyY|pL_W9pfGgG3Pq^68ALN9w)b)q>mlh7pQD-TCR^maz>?=V9CJ_u~FI%ht@j zK=xK>(K*mUe*E#3`j8_y;O#piZ5=TqrY$E0t`U1RVq&|(2o_;Wdd+}Jt@N`O%GMCu z9N{K7Kb^%5-s&oV33@YeCCp8$O_{O>I+3q>>O4OwpON6ewA#gT;Fi4jHSidZAHCMJIr7DFhS*fq@6lD*u+FD5~=h=XtnF z@|Q27fZ`acwUg0^MrM54kBH*{(|VAl*6?#EBD}YUdLedNEcLnGxky-DH>KkvQCnq7=ibsHk1w!qCmx+ zYI%p+;Z;g7aH=(y-PQ%>nI7`>uqTvl(0M~~KoQR`nHeJPq5+kZOyJs{s*N?)A);07=0FC}nY zLGrcz>*7{?mOa{@gaL}E3PUY|^iVs;D7+gA+la`>Y_}l+T1C(IBIQRL-K_nP!n!&m zg_UqpYK`9-0e?$jcs0kx=V#L((2QgY2=ugh z^G`k952D*NK*-+~zMU5Oeb)j>q9}+A=xRGBpzYkukk1?B11a44ym^+fl)xNznS_YC z7di{f`&uxE_GpMzFL7=Uf$MJRTwa^eD)Gt4*eH(Mg#m+yxb-`TS>x$6y=09F?X=1I z6OV;p-fN0~Om%8JfMmGtQuYoZ`Fr_77J#Z28=&+Gsz7(yE;Knvm8;dSH%g-?4O8ck zBIF*bWp?~hH1>J2){JO`OvQj$ZGs!BgPuDdPrxu9iSZJC-7UqMZGIrl-*U+>9ZZqdIk$!hM{ zBs%L4M*hRs6WvGKSujrJC@rqt$<2}E3x3}PsmmJZ4{FMj!rzeS`eIDQX;--sPsO_<0G39eC)h|j#_R_e zpN>1+)BMCFa4R3!dB?;jwI7jE6%`fiJ+n@k6b>(F3OQavT0El4{i2}_pJdYlB^0fZ zS0}$0%h}`Go_QOpNCk;LTljXZ8^8CZ9kI-JVy#=7k}mPg5u|^~8_!zx7#1h>by>NW zNOeBwU5Y1kX~EHU>l``d+GFK8VLG*toexez8xF0I&@Y20( zi0`(Q4vrsS7aw+~yp4A#&oeJ}XgzQzwBT_(B1zA~Gu0~R;lV$cF0yEV88-k(hl&Gg zu4Rwc0m0U60R0>&V;S@SS(FSJjfnS)WTeRH6K37IQ5=0WH7cR&<7}rT>1?Kx*2jy! zZ7C!Tw`}|#<0qB-Q@0lkO>0SvTJJ!3pxx;x`N->C(i+=&iG)qX%WJ8o+Z%zxwpa&?EqnWw2g^)r{MU5s%K>)qh+}Zxkp6 zzh|sH3_8AfeUCZC*LxIe#N0{H>Q|uj84VNOL0P>^aH2>6*>~6@+8I_~rS_B>)V(JI z-VcTXB@b^vEqfwArQJoJaZtK{QgXnJ8FnNc*uT(;QR^T0r^M|JOUc0C_4brqjzGSUD1pIy>cQ(vN_K&fq01@4 z%6)9aq5@Y+-81B#F1bzoiYld2|<83FP? zXj#l3M8RNY`ScsQWa7_b1ct4Wy}ef(=S|}fPfZW_1?|SKz;t8R{jZ$w-Wkd$?%8lQ z3&kfSj4h^nM0GNs`l0Mqp=>E}JLES4Y(wX`Xq}c~+;wJs-ZYy%r_1m`5`oOlD*uyIvJg4|jp~ zpW*}4;?ovjG@oWR-9bdkU;IH3{s0$3`74Kfun!4;Vr{^G_4<9F0*FmIkD% z-$`~EI04!e^-|n%adG2qzP=~?fc&`b3JDe`a5Q(I*(|h#PnPv&LDSv7Q2=uke73C` zD;`xF8_qkPQ8sM-V8^i=pdhW??~r_w2cX>x8_t6uO(i8^EDi6n#MMA{D{=r5!fK_* z$cvRlW1^{S>`#OuF4x+1#BpzqmFt3>q0a#ZfRU=YBi2D5-=|Kzc8)^q?kp^+ln=Xq z7E>x_y$+xQ0EtXevXV|#3yTUgBczZCIl z5sG|r7sCL|y2$l;s6z;`kVgQtl!}JtM{dG(6d-0!qOR$A`?<6(Ph(J3TYJ@BI6Jc@ zXS}Cqc_e$`cA4IUXKsm7GOnW()yDVcLh}+(-R`t)P9trwKb={YHU<=z{mq!4iDvMj zQJI#5&JVjTtTDY0@@1H0mkhQUcW10{$I5heU>r0NH3RKsCxFys9t{>g{5B*?p3D@( zH2l>S|KM#*VA`L5{C{{2=nlEv^gH#>_OlV&yTf%Ztz^lSy@#HIC?Z6DJ=>bj{ zgC*Ez784LrFkd~ow(tU%N`rdLw*ey7^Oe3-gZTTYkAMv*Gxh~Fu685xYSX;Rx zF(qB|Wo;4Y7&5l-por^}GgeQ(vR1QJ*hV$ukl7j=dEKUi4*wBNlhyr2Oh0Q2?&IO(E6aqe z+r9iw`8`f`w=b^PY9J9;ipVZiRbvt#VAF(+a`4Y48=IKiQEC7*f|65BvC`Il<{Max z6?!VKc}@}sE2|-zIm(kAPQxxgaCm8n=9m9F|+6JM%sB%zf zg`rzNz4CnkUhSbxZ?(RtwZ~TrBBc(IC*!wv+$cX2dp>=L*4c?OGti!F#;T+CWOJ3; zr?m7F&kgInBx8&=?ofsQ@C1Gzs|+e+9Ne;x?C1(|BTd)-#O7~S$k9wE4z`4|4l*Z! zOmKBAb(kJ9Efx>uwWT`}Li8DjtXNfGh2$0KmnlK!FF9@->5qGo-K|e@t+FjI!u^xz zqcv)5O5TcM<7T3k@(6qSHCVl@`?CqPPReJ+gsZpm{J`T@*&YV*`rzc|y`oVF*$PkMWMPZ&z@ zly?BS=UZRTW2=c4qj4Uo{STmko|aue7x#7!5-YGEp{!p=u0|4z4Zm|is~_>0a~0R&M7|+sbRTl z1rU#Yix%KBjV*z!%{gnzme6y^r{F(Q!T-C&13tA>Kq6;2vsR#jZotTW^7`wCGFQko z8PDq>jY;MTRl%$gBh}b*V0F99&0UER5rpR02cv4M5s_hZy#}JWAe}B7-8H%VOwBea zooO-;lL?BXae;S@ZpA;J_0V+D--)#g0I-^JH6^8t&mp|dok7@~-j_~o`9(!VSpY3B z1;B3*_YDncEQ{I1rKOdvH30#Hj*V@EeG!mX7X>8#l$_U~F^Z}D6F1WbcQT24kTa&` z9J258nugwSljVkrv-NiMBK-OREhf+i>aTADAn;MZf5Hk-m?+wuu}IGaw22zGyWyd_ z=hz#*8sN6u>%GlND2e0ju%7$=&(}?Ws@I@8Lz~4g3IFP|z?kKwu26De$IO7oSiGmG zK_*}`vrk=olVdC<##!1uFZT*(RDe@${R{8F%~5*!*pBPE7A)sWY^><5&ocGnr>zP5O;v(Q+BQWaYV!vmfbyBPpzj-;sMu>cSYmKQ zn$EItpFwDW=P>snKcF~-f_j2)XrI8+5K*QW9*Uw3vvGvh2tMwW2 zXSlzp78v#3YCcVpIy%t_c?swZq#$Tza8)%CUzzg{x1P&9ZiN2u6pm@06GCIp5)i7{ z=h`|yp|PIwX3`&>3ie;$)DiDzDawR7*VZtXe&6*OPPri;luMHgG-)2APWU=kHFqkJ zYlv(OjvSf*O{sisS^x4FC1%)<;3;@9R+YvxDN_Nes z+YNgIuqH`pMZg6-etja?$ca&4PuhV33LA(f3ZkP}YisMBYXAbXRG6G=aJplwxYV;< zw!r@UA<$+ri5GRQ^}V$UMx9DOKAA#~Ezr0$aJ`P(QScNDj@bpY;5V<&gfml8Ajh4{ z!=u+v=d1{(Z0DQ&ZJSSQxvm@66*2(mmG~}Y6Bb2C>RXdqI=?Qx<@w!xi&(Qi0E0$knWxj9%eDj^2 z!kECU6cPP06p_h;SK|J~;peLh1aNX6=t=ifDgnvq^?Nmjz1(uypHz7j}oo1nlHE5AI)!44METOso&`R3L4=EIeml#g$0Z1-aIIkxytbb!b(?8WX>$f3#*rY zHgD0ylARt_4t+fPtgz769jdxpJ7v(X5E5s-4glS=0KvZG%j3`#tj-;TEY&(>%`bt)5VyIMucs05HmG(7kr5Ebx<-Z(EbwLCdVTGP6miD zperkn2zy{JW>;^vqnMArun5bRB!NsV(Fr&JAZ^Y~B$rhKDGs=2n0|*q<^CxH11oF3 zu3hDPKKOl>B<&Xl->> zSvyYto$Y?Y(K_?w;ajtuKmqtknsJ=BEc@Qf-O4|2whL~H-m9e8dBP|tqobZ{<~xKq z;V$Rnw!0RrvH(@%jJG_M(L-1>DVh43>RM?{eLyqneQ6wPgw5HE_Pid zRkv*1E7h-y<=*3$TeTJw;P+WU<8-iet^Y2!E=|b4z-`D!Jlzob_=16Hv8A`QbEy_& z6^d#()5u+FM=Dd)EEDQtlWB7^YxYE3wZ1ivw>Z?YfAU%|BPB}!GYej#+iw7gjlZdB zw6zo4y$s88R3zz)Xgf7S8hW-`mP?vYnz@5SSeaO7#pyOp_i}ue0o9(zUKx&0(i{&Z z(HOMeK{K)XVNY@+wbGKGLvgv8%({`E+5+8h;j|v5+A?VPblSh@&_6ZmgRddWaC@qG zX|+ve-7mp2LX4opFY+5ci`W0ekUW|EHEFyah1-^V0%s)tvd18#Y7=oGYo$6b9DK%i zI?hq3C^<&P6?4+>7Y+#XJbVhB00vF`M`5p-cGixnqlTYW+a)gq@_S5IIdd<8I)WI( zmu0dX%z1ASq*lI}OjvJsN1Mkoa3xhpV+b8D{Fni176_n^wl@ld^PfCRuk5dlfxu~X z8X1x?k4P274P&^!EU$yQ8*bFR91KiZ!;BsANw-JKi+w5bhTw0&7qFd;LlX9Y`k^T^ zowmvr3DSg{?w0OKqrG|uSd|0)b&qFpv%>|%=W*)yy^{^9>a2OKK^MUaTl&%&RRyc7 zg#))#oxt(t#?yBX_ zA4&|{d^mls++5VJXr)E$z39i!8HV zXjj+Fc5axO2-5acIj#FcO2Iu#%Ky;%7$r z(b}#HZFF2$PUfyW{?Xy;`D{);@%>A5mHN@TP`joe>+ssk z<6cvX14)4Hn>RL;IwSLh7PG;DHRdV1n=Im#n9NtapWE^ zJBZ=pT{mfJddK1-pSL6PD*u786Si;p${d^)E4Vt|FiKE3Y#DR?X{FTFualJFVKaZ2 zyq-0L8hfPfX(xDc*3=nHt?wg=@;UXbK0%u}w)h1LC4+!V_ErsvX^$*r5rr2!UV6+5epoH!4XUaNKznEUepMzFm z2M*WIrwGrE`#M@l4a5}Ta!a?R3n<-L1`)Nvy>(}X#ORyWot7jtAj_tXRKAbPEa70q zxI%rp68KI25UC>uYb|hs-R{hzd8tjzoF7<3OS6*`ei8iwLG47sX~!$gQftR!x|Ly6 z$L5KxQ)6~6q!16)G7JrA)xnc31X+m};&(lKXsBG1`$l}GpXL4={3^R6k#z-lssKXn zG+=R;b^u}_vgk?s=4j=Gr>}|L31q=1K&RMZQJNvN@^V*h)<~E%Q}C+P`9tCD^{c?K z?Q(U)OYf4lz^!%B=C6T9?`F%j%M7pAN6gk>$L=M8w?~Awx8}MKpHzCSwk`H~DF3)S zoj`H;Zs+Z_xo_Hl`3-o$0a1L>0_%U1x?P7^>v9cID>qE;E=KI$mWw*Jt??@0iOu0- zlJ3=2kWGI0Z^i=e<GL)Yko}M zoN6<2_cP~vD{73Rd`eR9Qk08}Qgka@^G#wM`@CJN);5#YHtxFFXAN19@)4~^}$pB%~IYHmC^>J6bD^Ze9tZcW_>mj^@a-&&{+0OZ zvUoG+1=4rP7$?=IP<6ZhoTwKPQTB!s?rB`r;XeBBub+<;J9b6_H>}lGDA_#u!K_9v z$I~k@Tf>*<9d?kmvw9)we2b(~aCx|Q+iH12Ga{W4eXGdE-$TKXR^iKu?9)fFgb#J& zdp;q=Vm%+h6JfQ%)8NLMrL zIolj63{V~vQ{XkK0MzDWeQvN%yz4D`d@d76G<+&#B+?O(0+A{Mix-hjfOphekK+w; zeq4{7Zwpc;wN4OBnz=e~G8_2LFY7i7z*WPA{BC2N3Vy!s?P1SD)gaxSi-2#<$jV^z z#gf}I9F-0w^1u7$`thb-2*#$HGY>e3-0Z8LxY`VtbJX^vtWgECNE4Ue7{ug!WI!Lr zK9iIMbgXL;J=Ij6Uv^(8wb#Q@+adg~)KO~B_7+)9??Jfy7K1x-;YK{OXV}gn^LShJ zw1n;(>mA-o;a~xzwW7sj%%E}YLPX)oc35^h=C*q=6UOtY1>tv?>MwR3Z3m4=Uj?!FZ-^X0p{M^?)lDtj2iQy!iS|Q-=ypiSiwj z;GlKJ!!^WJN54ai|cMjNoeKwoYFolix48)z{L~n?t5#L;mkRpOxs- z!UKY*L&uhONaBYBGmYf_)+e@GMfm+)l#QU}UaQB6I42nlp z2k(;2jP=PE1v`nfKG$_*jG~FbE$jGRYTEE9b;PlHtmL3VWAoXIyFW%ZK}Zt6=X)lj zy{#DEra?n;3;FW8w#ja0bapk?AO@g$wpJ0>38^!APtz7m+QiLKzDjH_87mjsc`+Xi zcn!O#DxYNpb{v5-tUKm`Kj^&PB_g1Tk1R;A^|irR>_>*qyrhVtxl(B>+|DqD1=V?a zG8c-9r&ip|+G>>3CpX1ms#H2+zSiY%cG&V^CWML}Vfxm) zP~&yTuZY_8Yt`#$e_3x<`KMC@sIeHcWKw3~mr_Ssn8e>^OLgVfLZP&C>5CJjFy|Yr#cSb0NquA$^m%K|A9BQu|uG>P@ zT*f|_e0yc@6mca@c>fw^?bp}xqW_na+UrmJ`^}#)C0%?eQB_gsUcL_DS89O}TMu?$ zI%jw~5~H>IrzV4%xJAnP82bOaIpaMf@Y@gHV0HGSI;MEr=^EOaJZ>EMLvB8~JFEKaRQ9BFw}9BEk;>C@i-N7#1)KkEm33PtQ%L-9+q0%faym5bwc`V@QS^bl@`}h~AT6O=W z5${z5OY}4X9r{N^QvGAkC6b_Ae^9%325tpbuYf6n+MR^6@JG9#iCQojB?*l>o}1X> z00L}GNOFe39T!H1UvMZE+ROB!~@6YKC7zZ{pQbt4Ij@d#Suu~V@eoU=goV4J#;;T%fP0t!V?2_+ea5W35 zJORX^U|2;iev(i4v==aMs`UP zj?{~K3i3!Vh?LuZ>pZkJn_3m^q2DhQ;&z&>kU#MKQIUQU=w~FHOi8)8=1ojN5#eMD zi{R{xFw&a;q^)ox!CW7x$mBW&Wc>P|$Htf5@o@anM_G8*gNy=XHs^<8L(;N8q-bzm zwrA526&9BF#eme_y^H7$J{t_%d4_`7?dw@WfS7qqRh3R`i-y##5v_nVI!Z{?KZplnB}1($L`Z|yt_Mc)j?&KjD4lAP=}r5}&9loAeCJQD6# zNHN6l$f?0xQ#TKhjt+BsqRbb2FFTAKr(Roesv&3gC#c(-#PaDU6`+@sh2-bVj?#6_ zo9**x%8aYFbrbNADR88~5hFFt&iK%H$d{Qyijn1Tij39(+JHm>^;wXT z8cPCCURoTo-g!!mu3iwN0(QA!`GCwb>e4rxPoneSKI&8xHUWgN>@m1dR)?^8Gi$j( zAZ@?c7-zWKAFuGTAdgi&&IDboEBYMnY6t>HH^isjT1)^f1>_coQivTP)b+Zn?%5-b zqcj3>>PhSZrGahW>ohYrE(yEQC_uCgO?TcXL5MZ_dRAz{KP&7%)PX>u#GiCFlxO2*oH7yHv)1#IN=M5?LCUYvgvxvqnH-mkZGo|;s!?$ z6Kpj4)Q7V~x~DTXFXB2~%b22B+RaG{?>OT2OPUr`j$?)v48DvKYqO{ibV84Us=V?` z#ocsrYmY4v%?4TqNl<@t5BMz5{^vdA+7A!d`bXnVAXe86)T&%wl_u1Weg0zfKC?BY zjh>ys;e?W`J=93mVlbV^6>gZcOfg>>}Mq zV;vn{FN6o|V0}azFwgiwZ?L&`aE71+F(*qVZ_g4J@`#y zV*A{T!`wCTy79{AOjeCs2skrveN{6^fZVV6M8;}y^=VS4S19SgeTEW!J#}>%$5NtH zy;=p^{q6-5Q+io>Wk4cWPl}V*d^5B}*Ay(n(|KM6lBe?!jEyt2+$S2>SbN zZ~5jl<()%qugUKocCbVb1xo9~XDb9!IrhWy=-AeZtAmcL@?4mESP+g>n=$uX-|+^? z2QW4r`=WPUp0(=+);pF9A54U$#kt7Bmq3B9ByiK^dX0_dX4Waw>aJk^c%|09s4ZdH zeq6;RGP^4kcb*^Taln(Qtlv^Hq1v$q&})p-Co8FLt8me>q z9Iwj@F0OiY=c*ci=tTil<0`yRfk%t`0^MW!v%GY4cCJW>C<9SN$^&pGDo|7rk1nNf zovV-U;!G>nC93?!{kc||lBGYsHXg=F8*!3x?b@|&0PQp8aVY73Skz%qwglD-jVB5i^XxIT;cC27Oju7k_GIg`#v}**gbsfDL<~ zyc-y`U@!O4wA@Kf&2J>%Ljq)UxAC?GQ1&&Slx_*+nvj^SY5v2G8|v4MI^@x5?t`MC zj=eT_OXz3r?KNp=YdoiYp%7&apKB)RM_!$pOEj{WhDggBdvdZiqSL7K{=xS@UnMg! zdhh$hil|{-Pm&LMr4dS$j=wmNPo+1SAOT@jMt{0r10zWA1*P0vox88F1n&7zaM4Nj z!*}?wcutxwZk3_`6S@c7vpLPtNf&aW6xyGU?EN^j$Z6mt!ATQc`!rHl#M<%W%Yoym znI4_NbxEENMH_wc3Qx*K4@XAw2^S=b%M{^LF0EV7fd1Xz3tQoRiNY#UK)>>=@P56bvyO`4^tX3+clBUj zzdk&g!#0CWkS|3dBKXonY|y)Dgj3Eng$sog-4CB$yy)Jyc?;<49(_7Mkhlp;T<=>e zjnAlTl{%TRblvL;4)Rh`Qi@tq3nr*GTOXqZH`{Kyq9mz0)j8BJPV@ozJKMM^mYSv) zNzp55Ia*M$RQmqIW}Q%ApuS96x#d8N`3y)E(t)LwD{1hJKG+CIp4mqtx`94<$PrW` zm$Vja^R9e!^!bPT!QXG?zr1oD$#N$t505YDac6}{>8u#B#Xwe-mp&GvnAq>a<7VNG z^gA0*J~iCjK%HMkCpdcyBAc0r`uxd5lQ&Y-!RbtZ<fklEW<<3}meC4OlSL zSdtY~;R1L!n7)Y)5hNF$yzy10uTdom^SwSZHfQedm{eq?`w!O3U^21lK}^<)$t_D! zv-F4X(o<=N&sV+^3#Z_gW_pCIcC}(-l~@!!hubmOOJh?(n9eaeUCH3;p_1Evr=}IQ z8$5y@QJh69$2o3Rna9J5GdnJV$V4u|rwd|TT{W+b^){nL=dh=P68tXjlIO$wD3WWW z7>ciSZZg2vou;*RBPk;7KDo80Uc2Qz(il#fCSO_7@Nl4#e#@)s^lZh*R=9miAZCzM zKp-(<*(MHQOJB6J(GE2qV_)vh!q=THy`V~;EJU06VKJm{{DC~CXQUkM4Vgq|N%M=t z(r(NG-MZO<)*foba&4n{0E#aq-|+2Oi%Cl20T`IYQ4o<_S=*VLB)#AX>AJY|DN3Kl z>8)J%7h6qX+cD+{w?;-uqs-=@f-|>jkMAEnPf*Y^B!sVmZHAB5}o3o{H_PW5W$|odnC# z94YJe>O_GJHo3kW%8oKkO|Nwg3hJhoQ$DCb7u!*i~J!_G54}+Ucfs2a~{SXqZ6`CDY?+NPM?~Uz6s_nq+5T3{mT&AUfG9 zC0XLlXPrM(j{&k%>%g*lJCQXi|Ov7l5+x0=k#NijGHqn}13*L#@1_uO`zCpL-p+r8#< zG(tQHv&0E<438 zIJZ6YoTSOtom&Qc(__eUbgMi5C+}5KZ#sxhE0wzKUuyi5J*VuhniDjkK{%v&PLqWDnSIRGwA^!RfW&iEpAfPJRL9pOCntXh z+VAk` z&o1jo9t+Zu#M0(!lYh-vRiccw;aUMM#BPfZyM6iSfGO5m&pKSPN_~d#Kzl}oxwdvE z75P#RmlLrdo2P7;i=}-Fn6}}r&htp6TToK(?sk)#5${WAE?n1fsCx6ADwa;4G z<+rT{$5(ewK7^8xYGF+$!rxm}@up7+f5z+pWERH*OzSv2{D~oP?SbUnH{AhzL zNp3)=O6fXGpHTxbfhRgEq95#)L?pyGvCyYR<8dRO+#hncS)$e23L8J;*4|asm>NBt ziQC&Nz5thCU@^Jv(;j=gwiukcSp%}%QZ#-zpm%`gHq>M>aG)49Q?qX(`pAM`R^hT2 zK*Etv)_M73xBA8G-?xnk7$37Bq`0RS-wAu~$*ldDFDJk| zhz98B3>WWkb4LTv7(L6)ZCIvtPh1zkXu`dRAZ0^HyQqQ=&5PCmqC<>@ zY$?!lDW_boGUGwJV^4$mvuByvxw-Umn+TYBz`=>8o0wUW=F9Xcn>K6PbC=g9h{rZy z10bH~pUutQ(pjLcy9hL?<2=L3KRWf62ZYoi@Y;t@0GN_faP!g?ni-?Xdc~bdcaF)q zvrvyLeyy#_ME=Wq;mcx2#Hhmy8}X2<{?fs6?iMzeS`uW1Qa4-{9a*e1z&e)nPFYOG z;Th9pOt3&Q8|V;6;!BM&g<6lIa)+__2Q8KQSulbxR2dH1+)r2~3akl)7cf7lw-#&# zMQgVv8afE7kK#nJ&&1dZ{b>6%o$W9bFLsJVr^Ta3GU=cJRRuu`4AlnGd1pF~Tll0? zo{CLjHS%8FClTL?WTAE|!ZcBMo`(!=OBX4k@jgJkSOb_ekCU^RB z+CD>#TcoG0So^~leQ>wqWWWw(SK0UjA*-*D+IJH3E|8*AElQm?R*gFCLm7E*cuk*H zrQ$Zp6ENALXT~2qdR^?ja0R*dYPUXo;LXO{q{?>3erkP0%!lGq(A_%w)~$+XC-)PU zjvg7O+3}E59-7pKzweLE3M}K&;sG5V?dEuOYR4RtnHtH@sc|W<3rs$Bi~)LDy`sEs z{nQfSEE#&-jPNSN`Vm+icm>LHm?B)A`i2|0mqY@>}`K&a^v*;q5sqfz#(7p~)-J0!## zhCg;-^Tq~~F@NMKsr!2V8GJG@>u3%Acu_m@UyCiHeWC6mrO22@^Wq;pkCD@YOR*=d z;}79r@({wtkC_`>|MeE7&P;(O7P+-csCsFn>ea zBU`8Nfb*97c8S!Dhd4QH)X|_>8v6JW15!dNMV=9)?P5<08@6c}^&3{aBcu+&s#ij5 z*&8Z-dp?HCjnOhuPUg(&uBBs3hNMSiv9#S?u<|WB_d9Z4Y|YN~DQ~r6=Tv75H~eIc zKFffolkq*@>50x={_pXEU`ySXAF4Y#QwGFV6@f4tC!4D25wSRS?luvf!g~?ksHky2 zp@;a*)hvk2KszS#wa;qqouD!Xt3fnXGAES)VqHIr*ns6T_j{!MhrPNsF2Uz@>Tin8 zPmWb+-b}Q9_jy11sXwZNxLvor_JODGNqYfrK3YFd9rs!{(0$0N-tR4nD&1rGSbwE+ zH0MaK(T;Z2V3k6$z$oLD48xQ-Jg}f%a0`r!Ue#G5>$wY zr*lCY#*Cc=?49Ll3qmKw_eQOwe1)cu1&VD?d<{>Ij3$#;5hF0`#Zo{O@~KOrKgDY5 zl4S#NSEzzTap0J2EoeuZtUL6<@W{yW(P~;e&}Fa1w#IE$?Fd~O#0Pq;56(~dHz3(R znWGZ_LN2seMO*rF;NkbM+fs8e8Cw8zX%C2K89r}5vhi7pQv7gc4zPa{1g+n>Z;W=` zP$YvNlZ}H?)Dpzqa@z_VwT=&gySb!s>+0dqjp9a?cz)rEo>YL_st{m_uiNY9foy{` z49RwWaMG+wt98U=e*}kY1%nH_IT#ANNbTRU!iZXRG$7p)MN~AS8)Did6QK`!zvwgR z6cIZaFKYzRewUg5eC=-F(B9`6@g#-6jV+xCz5^)QaN+{X`(1Iy2ts&u>;h-5GD6d< zr((>(8rM)=vAMoJ>^7=XB;~WI?TY;Dh%{P$>SPr(tv;a%c=RfKDdMMUG-$~y)1Lzvx+Qw zi)u~#bcQ*vC_KJ=ugF8i^=d;6ycq=4{CL(zrC&CxmG)*L3Rd#8N6y(?VnJ5+(6{5`2_4NxZU6bw|1Ir zb#)zJ=@e4Y>nt2Avg)!NvHs#yt1|PVG-RxfBKZJzsK+y5A^`q@msQjVZKJpiPY12K z81D^r&YS30K1n?q=SS!^LS+jgPv=DpD}dVY#udu%90w|#!wgF3j{@B=@pc1#A$P{5 z1vMj1S{SR_X(~2RDpvhtlGaN)sD0Y9iY=Aw)g2-0ULVV)C0YIMAAYIG#wULqup8){JYG7KnsKLp`@UmWDAcl1TviIjBm(Z# z$7?-OkXG{_w&Gqb45#{|ZEIR6=&@euB0#>5HOFn}yDq%tq(^&fEEaoUObLrk`~5Mr zX5O--mu}2!=Zu$B_DbOo7X|$=j%f{{CTnLFff(2v`GQmDje~#fI2l%MBCnX!*LMmF zGM$$ArLIosSjRh6$bk!)++I7c7^W->CT3N?vd=O!Hg?h`^r$*{_!Jj0onO0D@y{^9 zd7|NwKVI6Yf3wlB&K3qIi^##DTC8lP{u8!Fm9o|qO?U3r@$>tH`tN+bwmYys1rR>a zM;+_+q-i!*6DAt^*7yAmL*KR|Pu2HtgX`!Z8%!-QSZ%vo&1Pyr#U?XBD@u);76@~T z2h-eCRv@gT?Tuod3I-IclI`-OHks;4&`pT-i_69#q^9x~4jT2kdzLM&Tmv*v$DGgO z->2=^Ju}Hx)pcmdxzduyPjg1%wssufp1;+$U~Gz40{!H9IMY?1nKuKGd@u9`COQIl z*wIe{EG4zKr@WRi1t|IxK|=oYJcYeTPo|I4Xv4!N^Oc^l)t;DCs+>%0{WB@A(Al%W zDQ;l?SZTGrfak7iCsFqKIr8rFTcbR2Z`lHi{RIQ7mK07>#;zwjT(UaJDo54gV^(^G z!_AFkmvvE6yn(gtBF1al0qUZtl8-Yj!Kl_jD7{|RSyK=Acwr&;5uoVV@7>cRrDR;A zwmlisHnwfu7;v_Zq!W&(9SmPCk{RI2e?pdW(OzGDuW)k(U1x zz|$V0b45>iw3`U2K$zvgL*edAD=Xay0^SQ~O7dxIy4KiFT@7PXda)M!qigwrew`fZ zq-OfLkl@&Jz|(j!7TieDH^uOrf8*bp%GQlIcDY$)IJgYd=%^4^S6cupEjj6|m5SqG zN`G+R9%Xyh&XO*vriEmI+P{ZdA`L!oJV9A{bqrC@qdfinYtqxMbIl@Wx#e#CoredY zPgTTL#o>B|6tzfRG##Bbn}8tr)UgXuX0rWxjYWiK;Z0BMuI09 zA9cFFMe-8e^yO|{*$jemFETbqX7^lAzflu}gL6vMYr&hu&0TR7IvX2w>iqGN0#C@# z-=HjwyQyt9!R=s{AV3{ThA-1gn%!N}nv5e6@4L=Qu!SH(SvceC&bU3kepNR8bzi`k!wVJLrTd$G0sR5J0rpL_N?damv;ACiuKUIJP@h3ZS%KNx&pny5G&h$qvZ7r=O zfX{z5#k3}ffbWZg2n!4AhEg!tSj|+EjkPzd0L=d$AXe>YRsZ7}q*jOVjR3lNe!I(& zFOq!C<6mwcld?sYGu*r5wZ30P4~CgH!c8}KlBl(5TDK)Uf??&275X6VwV0a;&S4Bg zM3e1j_vU(sjEiw6GZKsH)YEq6LD~wxkl zW!~b_;*t>3=}pET9&bKz-m$*_-rkXr4(PbYW`Q~0&mP9G%L(v*)hxGuYZdX?zAABN z3FKo4&emPLwoLOjyCFL>`=Lp~WV;mGC7SZ7naUo+n>51wkqPGM_W82!_V#sZ^y81y z9(vh4s;0rj@5%*T5=T3lJy(Y`iW>(nlTUr1Lf$J4Pz zf#wC+kRm+pV^j*$(WFKI_qCAi)*lpwDw<#k975c~fAs;vjz{GgQ^(6M71lkYLDW1} zU;ntUfy&=P!rlX9@aVB3eFiTs0PIkR#NhsTO^Rsl)^cH)VHli^O0~0k8G9K`#FTkw zVi%F3rESW0megO-UC$SJS7?yqwz2$8bhu z>uR?*Px4E1Eh$2yxFVzi388mDjAO}3qRw%9d#;PrB*oZL1mJp-Jz>mX{nF}B^=J*O zLsmZS>DgmSMyd4lVc{iaiHTL}E1Ruk#Vv^Lxw_i}W0F%)7wC6Ho-@n(C^UZcBqV@B z_EPUg`wXqyr4xrIYUqJZAlFqA$>I{~zL99nQexeYOLdd9`{H-ASJ`uO;wDhQC{vn_ zosW<2Nl%TTbx9D=8ap~Qbxwq~tMG3Oc%pNl;+I>TF-)G3j=X=oR3S5icMOZLKT0ph zh48D82-|=~t%L)!3S0zzecHP;P8_Bx34q&@eYrbiyk1j2oi9t;h9@Wni9R@_h>y%y=nPKBz} zXK7<72!WcxN}mNt=S!7b_m9}@S!Vpx<#==Fg>7VAykrrvh5EIQ&VBiYf^?{DF=ngx8xj0qMytO zP!T_5)-BPww!h>!pD4mPPxI%XfzGSf2MrxW1NpaUnXh5E@nD}=sh4^_U##I^zA=Mp66RE{!aW>i2|iEfZ>~8nyW=IbZ-1TPP3=xKl{9|!Exvraz-M7fT2wARIBmn0-nzLFa0~naSnq*u(&XJ zcz9@;nD7Gq#6M!YSz})dt0>#g&F7j6pY+2MVjXyf6Q&^es%wSMt)hD z#)iH9(hK|*7Z8--ugb+kn}(RwB7)vdgYsyZ-sCbC?fkAS>7XQ0CO7O}R=Sm+uUihY zZ<>e@TOZD8{Dc)VD{REXF3p8xv%kA{aO_Z6_yNty&fX?sOy#qCphMPF4qk2g7#)LsEGxS99f+bk^?OBi@lk8cVg`=(2G z_++}nIl_&wYBF%82Pf&i24KXW9^2a&?oKf}IQHK|A3Ka@(2nfVCHA(DO$d>kZ3%ZQzzR#-BTNv|EVZ3cm1u83)nJqdz^mgc8I{N_Pwx7K+ zof55)ODs{uljFMxcCG4QT|YvDZK}~mTG~7$N6jrYde)z*!K;hx(pzE$5Up_14_Dtl z`;f(bx4NOhC|Y-V<$N(@{gX|xSV`uSg$=|FjC+C{$`>Qi>(45r^ zwa*D2OGT^N1gr{~wHFXZfHm})Bhg?htEwZ93Yov#7{L)-gJo&V9#1ZMj+v1Nx>-+| z)^B;BnJj1fFLgy_6KEk zJD9wZeJ0b#57`j!tTrNKQKxho=+TLL(X`%&AN#eA7O~zjG zg<2xnuuc&){^^~=fGDua8yiB8D=Gvb$b9wwCY=WMb5zeS2`ki-cNghlZx$JgD!uSM z8zNU7S>2S|aVG2i0Q7?3W2bTE3?VoNsZpZbXCw*^Z4?{bUvwWu5ROTGO~S}4B6!fG zDdRcraSc@^E0^I8P4AG^sVuri)79WLVW!dR)gyC61fab590d1Hk1{elHz0q zL9@Uh<_|4GFDjll!j>jfXGx$6oY;`w+?$A{>%RI9nH>+Zg&)EB4p)T(FvP()_ z!K|@43)f?#gxWMs{726_B8SE0qDM`(3m>M%wb#)sFR1O2NHFC3;-iUET0zF`FXP2c+?+dDjOzE_;wqc=$UeC@2(S}rB{*_yKbZ#VvaxyDMY`l%*2#I}s$$0&oZ zQ`Z3nmy8_wIUc5GkPd$n~zO(s!OHG66?vD9VKhHX-cFtYH$rJJA%akVVTL7--{){Q(OxJVjz;z{U zS!whB?rWig7ehv(N{>F9Ik(GP?`;(x5A3zEkvAT|0utKg_Xe!sX2g^}hl0Hbz zDIm_MqYELb;`6t=>90upL-MxQ#TmrEEp)I5>wG@9sNQ#esf9{J;B$&%E) zN3kt6<7Zqxk7M9x)hoqI32mp8_B(Gf$Uo_Xv-YO{I8qQMm zdi&nt5H-7A4pbDQx8;vlsizE{?*^KPWj8fFTv%KT_W=@Vll+-U9gT@2 zf=aKgOFy#0UPruM|6&wyiIG3E0uJhLGO)7xqqpjU(yg)B)WDA8v9k2el?mx7mp3t0@ zl!P!j4X5J`AzTHTNP#Q0_|%qvBFuMuIul&oF}h__`5$Aoqd~@OPB!6W8qot}Pj1Ua zeg4cEDd7rBMni@CyV*>O&7cZ4vnR$}T)S4+s%v4pxeiM}?FucTnhThP^wrm||?1}sa#ms(8 za=9d_J;Pn5N4gT~XsIW*kg~v025-#>S*Lz>{-ydTQ7B$E)c%q+{qFfG(MP4(pDqIp zf+vE~V!6*(XEAOhdr5zR~}F-2U2G zmu1pPNHP!C^)Hr$7W{ly`Xq2Bww9?&1l!Gb+T;MPR{alhq90~6rh_@a$fmc>c%>hz z$ltVfnh)QZ^JD2Y^_ml)HF;4o}JVBo~gimC(b2o@Ir?3>vBj^MkT!iQ<`sr+ycRFFQ=*7j(9s`~xu zPT1n|ZpUgDA)rZ5#1W{W+63U9Jsm7THdO^BB^PWP19AEa8e4oLr7;gs$FHrxUbJDY zH+`j;&YkY@3@B+1Y5HuIOTVEUUS2aGPKns0XwC?34Is8-qBUh`V#=YQexsg0AeOk) zB{o&2838ft@0_fU}7T%0J~~gA>m}6 zx2sntK?+C7lz-e~j@JPg0#;q-b2~*80Y1C8Y5(X|@arz%RI<0PxJJuDA^xSr)Nd*H zge3}rdtUS8^NT2eSa4J|7F;nP;4cf*%HbO~^UG0B@rjBN(g+ma&#TxBUye=-YU5q0 z4mqE_M0TNgOIU{StXqIrzVvgcM7Epv67{NNSv`q3S;((FpqKo4YyPh0f@?TsES#nr zIg8K#w1^zE1}Br!x7pZK6~aNnOB3tscCO1yJWD{?g{Z{DFTLfM2#;!yc57YTYN*(n zkD;C6psQqufX&Qpa9Tj9KjCypOUHz95NPyk0N}?6*AZ21otoJ}SqFdt6HML>i4%aG za$8&?pQ@F4Hq`QxLX0$pec11XjE@cpC7y0`@zZ5%nF@8zKP}$h2mR+r0q6bnQTgJW zv1jOi*6ZcX^vdQEb3&}iix%M6UB+j1d(1oe_`JS${rc;%_g?J`3HH(i|CF`e5GH^- z9v2<$x<1wjF0=R9p8OUa@AcSb7R!^>T$3x3KHCx!7XEUQI*jTCI|? z1%i3jKV2`A*V=eHLk!_X`<6c{1AH9kGWH@^4DTEqx;XUDN-v#_e22O!!drim`lk~n z5)5DflXWgJKhhTU=ufN6VNwd;QYBAbiRKHaDl11D=bCd0G?zVM_S>Z5mcgWt!r}Dh zAh|7owH%%Fyh%5dvM=hUoGWqraB%ZQ+U7ejzeCaJgh}D_Io^o|kH+j=jxeXJX%HhN zjhKI`fBFifXy0l?xMRK=NA7;?sWQLNSRk{-UQtZ?RpYM+$A8=ZKR#2vdHJ|fC+V4c z`%mKVOt2p9Af0tlA-9}OM76-+va(iiF&8HD<0??O7VYhP9J5L| z{G+X-<3xu^(&&~@khlsz)G;|Z0COh|M0mfkVZT0JS_%|VJJu&L0o!;86FZ*ezGGWU zToU3(Jf1o$TE-i96?|CSSAI#}7_c1Dznwdr8+$hT$>^Y{WniTysl@ z?7M>7=^_h?9>}uWq7XHMRE(sug8o&sW_dXt&jASTc(T?0ct3Do)hh>&kEQ%0Zl@di z(vjSkYjmu?gs%)*y1DZP|FO{8%+S5T1=xiCJ17X_89Jke>-JLYj)Q%dL#bBuq)S)K!)G_iksGXv>_==4|O(kBbYXM;j zRT4W}wOXdSw}DoD(diZZ;{{*pa_<-4IhcujjBR(MwdW~0g%arscad42WZWt!F1|m< z39@a5G66_e98jCk#ux@m3ar_1-*G=1DMsV&6eNOW0^K@t;U7w9H09SntLOxM4lGd= z5Hm)vGHG*3grA_&4z?@s^TRTb4BLOw{t!c-A7S?iqk zDK3s4^<6C`^8{s^eb2Ze5U3-wfx1w@Cw|-QZ+VHV7C^?7gXVt%fdGuvacz(&k6*vy z6boxyYNHPOW7bMZsen;j>qbp@i9ZtJrSnClq8H!Cbn~p5K-132UZ1G((ra3?*fbMi zrEp`5bBlAH(|xYPN}R{N8RIulAM{(oE=Vl{4Tu#;O_)B@aFuONJMXhkv6{GDW!kyX zmoY6rEt0rvl&NZJnj#!76={RJuq?m8`@kMO!Wp|h`@y2cNYd<+nbg_)JGa&c`O{LX zOLPh%^ywHF%0rE44h)#`@N~(eTo!2!CCXd6ED=-mE;P(L3DOgF}j@ekc)RQS8tbN)cG@fnF zHhZ#H#rw9Dd;i`&xCso8IXp!MASFNJqb|9h7*GYMZ-u31@%PvEnz5`m=}9Jyr9i(g z-M(GDpcmY-H4Eb_hK;V+&yd0VXgBBXWA~{Y)%*-ia9E1UViEoZ)S``_8_uZ(VWQ1j z>O1=nkablYV5UND4>bLBimE=!WOjc#nnh8@mE!lqKd=O8P8b4>$Fqp7sk5CCb2q2g zWvc&^S#IXqWnj1$KOSEET3u>4x%*U~FUNX$?|8Vj4T1+tW40#+06RZAE{+>WBN05F zL3Lf=y!1n*bPY&n3L-F7qFC{9qDMT^2G$2}-@o54xae`u>hU2f5I+F(_V)M30matj zXB_f2XY&P8uwNXmga^}-YTtp?D5*i+6oBI(V3s1QJhK} z0k*x6)u9eE*rjZ|rH_#h{=nSXH}?&`f(5@giS(taF(;9x5>zx9?p061QO?ZU%%u8M#)Xw1Ha=tlKOK$v>@sRX0C+TG0+QK(%sLnsk2>%Jl@s-h zVFbTnf}hU0s;a8{T>SiTKlto~l3aP)SxjLcx)vH+e(P6<`cIV(xUZJ7fIn#Yoxf7? zuUmpGjby+fl3NEc%L&q_nt>i21M=Wt3XfvkX4(c@qiJ+h6m#KQ%@S!d$(uKiBW*?C zFQy(B6qZB_5TGrbA-P#nmp!Lz2;I9K*ch+5KyykKsatFcE6Qn7&=0+JgvlL`i7!S> z9~%Qs_>U$BoXH!X`&m~J)%k8UI8|+JxmD%t=fg`e8+zuL)+>1>Xh|(|ya&cpHDH?` zwD7=6A#TlY=yR(#{J`wh_DH3o+o>(ae)4llvYN}d_jvwz5o}>BxxL=5VCp224VdbO5L|d%$4yZ$z%zwCP97y_#{8v_9GfbQ&T(5P-vK5(VBXqA8x2L z`zfDcQ0q*w86KsCOU3#jjGaP~-LDZ?jkA6--2O9*|N1M~h8BobO}!Bb<(##lsV~_ zm^hu=$j!~|q~b8M_a|Y{2e+h%yTx@Ef^>49)DPQd_9%Ynj16NkV@2pADQT{qt{@Qi z1h**ZqAk(O*>BHrck8xKVE`h^ThD>J=l{6&qOC~A-q^yaGgRj{{rB5t6%P^On&_7O zI`8$L-!GHq#(mqD$sTY!LF8OLV7T2)PolI)!Or^^6c$FkC7=Y0%;FKp?;DpBi=5*c z8bC!7*zBiTI_^67|3B%FTI3MEsc)>!Gp0MXlCxVF;_aO13As1^brrv<4K@Q*p;D^% zf;ga1U4hD5_uu=-r!SMZla7QkU+ALPQj-2_r~W*`pX_eihk(nFbU7yD=g*3WK{9Aj zSiOq1hetQyEnZDgs;SF2=vMBr+Ruf!E>gfgViD$iZY)ju6_t%Aj%-|9L#syS9~cvO zL}y;aO5hG|CG74$upY_f(=swDBz7yZLe1g!I#$g;6_qv^0%3$DxUsWdnicB&4K{;d zN$>IEaYI}5jUTpQB`OL0p>!P3>34zvpIvtl#W$R%uATs7oAvQG)#wL9jUSdaRadTX zu)j7l)*fx!xq?!?$7K22+Wdc)GB1J$(@^wHk~u#KT_8wA(qQ8m1(2te@T)i|6qOLMBx|6IoXAF;b5 z<2K@-5Jg)M5tGRSRL05DICh3#F*5G)!&l_j-QT+-I?Bq*IW;wUCME0uI94f^QjT+5)c5K=FCJ;u@%i3rXQI0PV<4KsW>8yP3?#KS z6dsw}JkcELKgZqk4Z3}n#=v;Hai@6EzjS&-_u&%jo{e!se?9}(PVFQI*y4Y@1Smqr zQF?t_?8JuirzK(>AqIN*WWo|R1`}-LtdpDy#Sg?lUuqO19W6V7;*Iy02S1D%5rf`{ zC|N(^m)bOJX^4C@e7fy)t7RnWxw;OW2OGsh$)CKLpPv1nO>!Qvz|Z~mO$JFJ_rq~v z+3!21usoM=N8E#%?{dI$FuCk`6 z3bhFG{<2R8ubi9FrBQ{2%bhe?USIVaZ~{lf2z;z4Zmzki_P-yx@4%zs&|eiY!zEQgO*-MnuZc#`{WCddpvIcgFf+6=Hf*$V{fTkxu0}5@C#yb zB=4vg8of%rmDst&>mK=z&o4s^H!cqj!B-h5uUr!CaEZ^&`2?RPTQsX2i{4v$KyXPD1;w zrjxH-c-toqvi#>W0yt0)t^gC6GIJ;H7lc8T=VMwLi^~^4fdfg1F|S?`D=90#n`q_} zzQe~?9S77?=TeNA5d3B&JQq{<^`VOn$pEWy(Q#-IHsb10ub~k~mu#Xg71M3q)_##! z_Q-ed!>?Q0Q;i7=yB@4*zKx8e8s=%p1c5W^Vz?pgl(Af*C4rQcgIf{t4MxuJDQ}ps zWM~k}*46DhdX~0+xKnnHnkVxmga-8SEr`%4^Cr^Sc>p#SV-_SYFW=}-*g(_L&?x!% z)RzZBBQc&6?hTWX3%=<>n)(YW_ z1NpH+C`+cGeqIyXZ@-1HH-o%)j8&h$ZPV3#Y+~v(aWAuAGn=^8_ehu*sL3@+NEwk= zF8WXk0@v3uQHmQF@OtRRE@;X2;e)F^Ty_^Cyp&B;tHowK0@;qu^y0>pOkauK1p&}{ za?We1@D1g{9FBrEZWV5^%}R60(8?L=9S4!ZLP}@f`7cC(Stj_Z!kD{EL|l5nBo%FB zmSU08;rgt^yc8-)3g18@Up|In;`SoHiE05w>zFiv4q~kQ@jP1lBKpoHh;?^mSJ$!^ z$0D^>X(a1tFELfnFpK9moiepZrOX$kVlv3bUfzCunP$|8 ziG#=HnR4B>=6FsZk|DY9L0sfvb9PRyvE`_H|8=iS6-MFRXcKP@AV1-DzksEaX`Xw5 z>ej6bB-|hG&oqqXccnaFSTv+wiz(igipSI9ncHv7`W@bX&z>nHzwxCX(7E(2K2j-3 z>$fsMgX1*c=faHM^?MhbeSkimwzMd$2fS+h81@m^9xj{Wi`%+a26)pQMYslG zL4-F*BUm8x%BRsh^)ye~8@WP!iD8ti+vBBaK6IzmG2Lg3HR+p5E~w0!AKSc(Jt!G= zn)eE@dEt)Yi~h85bmIjW0tDwpiDx#$8P^kT(HY*-ES_c@n3Gzm3zVG2#5jWaEL$~nPRd!zWIg6b_bu$g1LXM+;$3K5Mo&P5m`|bb{b)f~p`@f#zm#XuF%vYF9 z9GJO{A(r#!_qh)p7zvIPlyY3Z-uRbv{p->9!_G)&I8y4iKl*81fbpKcmValTPX)<4 z42gH9)Bkx7|MkB=OK=$;a-h)9yTd2pzx)QH3h}m_UqJ1&Xa}~Tz9duj%< zy6AO4x9QeL^1r5tb#Us(lQ2vcfxrCb{Ef>mO8s@)Qp-h#Zb#3dZ%l|A|2B(19e{sN zZ!m@(SV6xA^KS^4E)#nIlmbLUfQ*#5Vz>q@GsQGh?BVq(%7gi?#t1qdAzZ+e!ai;GL5f$meD zme$tGfRC$DSme;Qto$R>d$g$g8U0E=1QLrop?VD98$c|bVIKe~!q#PVM~Q{UG)0}F zL+i8TjePq#485nB+oGcRw}%&MppW&!ep&yYx5&S~kYV^?N>d;h<9@<#*Q%5pCkIC-TjY!YH~@7iZSOH{k$!2EAr`NvtlWJzan5^0*mc%Jzu?UDxwM8K zxecu&@2z$h^aHxl-<)a@w_-R6cUe2fA-7%C~EUOv%aOuD9?Z4jW#U`-H{Wj|50|`fl&Yd|C5v=RMH@{5JF{dEgDwYo5HC@dE8 zh|`}ozs$%qfAHsTet##b1*wWq8?>#sQ`YRBv)5~(%sdySgMWDm>UHO1P|1DUAUQ>z zN4V z$s|aLD?tr{{>!4QMKIwj&Ap&xw0U9-;)yBr%&Ai|UmZj*@bIXnD^_Tjjfkj@dOG_b ziMEF5)NJvb&s>e1>CZA6HoOgixTS_BDpT#L*=%;+&dh9Aw%!R0ITfAtGrETK^k}4~ zt+`EAvzLkW#Xra9eA_9Ody$SiED4Aa09HN%Z)g`@M&JInwKaDSPfs1ZW^+QBT}1W1`Bw6NXNH%4XVL6{<>7cOIyQrIoH_-q+L6;dpht>Y}E;tAD)H>;W%TtoMg! zuRx~6a;cZh7Y?|B=T%nM{rPW$b8!M_ITxV^m7SdSH}jissYO=h>$yzoh7_M4+p@6i z{3NPWea50pJv!Pn!D*N4_Lnxh=sieW^JcZ_?YLU>wiey7l~qum-jsPiA=t{z~U!Q(0mmoBy&I?K*J;q>W!OEA_5I{h2QIzuTE z)I(bQ_IhwL*yejLeyW*Bk4w+AsYoe|Ht=5D_pW~lw367z99e)mJg}N}`;o`@7=5m~ z7Op=9U;kq>0kl;cq-`~-_bBWLzh#|iN=NLp!K{~!J#l6~d-m*{i}?3q0Cb_IdG(?A zk7Dc_bz!aXU}h0!zoIHnf?#%u&8QhAVRgDoO3OLNruOmHQ8qSypgR5fDHT2Qo20PN7X&0`aIdD`&3;OMZJBD=^AUK|C52=L&>yV^&a1x-f2^qVdIvJxdl9?w|-OZ{e@GNgZ;m zIZT;PkirHYNDK=K67<*#RX*ugCuiWw0BK*-osz43-P?i&V>097I!Zj z+8+)8DBOeoKe*C36Z5unDypgseWl_DL}JI;$nO0Iviof9OBbyo5>_d@Vr)QdhL^u) zMP#thyhc*gt$J9(I{o9PPpihKkU~h*(YxyVhb`z>DqFgqQ*!e>;cIZLKkaxL`tG*l zwZGSA`|nYYItH*?*0Ta4J4T)N;+Tuv7PhAScwBV!F-P^Gk_FH!xh-s4Gf9jUZJW^A6mr_Nu8w=lO-|g$ti~ zF!Af!wSfg0iJosc6iy;Qa?`MfZ7mISrpjWrkG1GHH4|dvWY=?6^4xGgKOY~e1Tzql zRmH9gfk47Wamy?i=``^ znS_E)E*cZ`ba+n?XY|0LG8i}6t)jIad{8bM^2df%FFp%Du$ z`E9{_bbPWBl~nQ&>Ysf+ZVUZAX{dK$(f!z|Q>VTQeZ^$-5pr@Eks0&av9Ua>5v;wB zD)Dg(EF6LhMQy!?1_pc?s2Y!DGtgyjuqOI21H-tWFE;8@ync-bIXVK>#{XQj3yMX- zx%v2tF(QtR?Yd>?UQB%7EDOQ^THOxgxOl3=d%`nxcwMk^u{odl@l~^%s_3OH*W#HNARIEzyY8R-+!t0LN1@rNW8Yts1^G1ae8`M4vVgqQf{OD68o~1x|+kKf56P@$`>?< zPAZ@#J`!j#vR4k^ChUVf>6_sjPMbY^w%>?EpN^9;$0UDmc8buxhFtpm@Rk`H)3%RVK4Scm|m3*RWKxU+nT^ z=L3ROrs`9r%JVxQmVW0oYAIEp)3Lm_->T9$q3`0GN*5y1Arv4HYEcMP0WC*)u}5(t^g4 z3<_;0#nt8ut~W7_50ziYO1~HAbJ7eR!feIYQzwyRZzC}uZ8QW*pDblxbBvcz7`41A zmOF7iTnu9(UnDtwNwIN)hV>l1mZLICdRO{%YZgO;ZAY8O+k8uLomV6!hv_~^Abpn! zb0AF-@#c*&>0FTEKsC#p&iK($qOJ+w3Q{|2qeX%v*!y4-Tm9aD0e!|21KjCjwt zz7vLCRz{bg6<2Dc?M6*ihrUuys}$ z9l{VD+s02Jx`^$+9J2uB(6cjWlM=PX9>T{lwj|8iYcxYVNlIQARsJ0oe3xmEojPo> z@#v*^0E62OQ0wE=eb4D-x(wFwsi7MFm7zWx)I}~cE#;d_uO2;#P7!A4Wf9G*8TBE6 zx^H0$F=mxsdToFje^d9%fGF+h*T(QIv^umP_lT9i*#gYgDjDQO__v!>-HUPuJwGo4 z8ozQg<+JOyJOt0^ebIk`Ub)s_p8c+W+EA`Wwv8X*ZvZeC59Pa-! zx&bY0sSllwP7!Dqy(Qo)E$`tW!K`4b;TkVOxbGnuQNB_@<1?kWk>IA#gJj?w;^FjE zJ|yu))C)g|A!BGp)Au7Vz70 zEEa*9Zm+P5SU+jU@U?1!vwx^xXKn6lvIz>-gaR#na}F}_<=2;9ljs`Ar4YhAqjM;+ zKC5@=#{vJ#)oYRRTOIoNlP;DW|H(-m{dg9pEmNrB8r;c#!q5b9L03y9^X=E8@D>9E zXbgA*^svjHY$cU-cb8KQKU52s7#ldf*$<`dQ54ZnHBVfZQ7j|*J~D@9eEs^6CmP#@ zf|u#646_L>#x~tmn0qI&5N|_e+Exm{#Sar8M2C?r3;`Q@&!86&L2*pQ_8mR-sO%zo zsg-BB)kJym<<=fWH!p#l&RDVIe4D9e(xM%SnBIh>8f9qllhS^rO7)qUDwiQ;`xUw` zcn2Eqja#z&as_5Tp!aZ!{_??pUJ#F#o$heM8Tmh;+qq&-$I3e0eH+OSH2?xW;qwXN z0!9TOA%J!3kj@QLVmjVwNB8yX*JBLKJ;@~0kX^51BU@^cGTA?OYf+ggUL>bSDb@wa zI0>hjeD3c*^OAFNhJ0KK(a5T6L|&vjL{HxnM3pi7?o~TspJUZjZj9))s*RQ26)N8> zaP&0}+0+<#jWCw4PhyWTGBS3y*BKwZ$WL3DC!sSda4|3V{0>vIBX#!knE?FmDfM9( zE}8y$!H(+|Ga`o%9=!i!$ z#5G06& z6ajplY4<8ylThfRN6SBNS~g8WO5GdRG;cjD&x~I$5GHiA8u5#|& z6Mr$3^6!3=@sT{5Bu)t0Ii!F-3+h*10bAwPx?`uaswhH-W-oS+Xb1@W@>uxs@zcf> zN8|@cP8ENeFWa- z&I>;N=WpM!VgIwCllRhqy$oxV?81W6K3`k5;&(a@#u@$g)eF&Hml0pS)K%Xo`EC5N3myeM z$=AldBOkT<`3@hh(;3|bhj6$_4HVy+n!||uB%WT3zWfCoP%9(?rUeI~n(16*Cxh+%qQn=Vs4S4YVcgT$j{+1x%; z+%(PVEUYsU!V+R)VvuCvbLXy4DrfS(tGsFKn=`2)&zN=9N;z{LhpK+ma$Mr%>Ep*I zO9`RZmQeQB-`u}{A2*58IdOG?ZCPkujSO_+&~>-RkFcRfPhsQ-yvg&9B{_G5*6u&akbjf!)hS zJVsX4SkbwHL&ZXaD3EeFV_yz64ULcTrK?vTI6693n~)Ro0uS8MIJPc=beB0N`8_Gp zzcENCtcXw?!kBX-P6U~Sd0D5nKu`cHVI0J)_Sfr_Rz+$~Kb)nYunZ4Q`~rd$g*J76^qIOnIR@ohve}mNT4bX zdixeHTseETt~wxD#0I{iQ&{+>)2j2DU$9UPgi`PVt6PD~Xy09W3%G=X>%#e4`{;=% z{M#xWc;k4!wY{JgRwi>VF3#LYI&h>C8h=s&kCHK}{jITz+%{DEjJmC{34|Ta^g!bR zLQ*=EPV8U!Xq0jA5vW>c0a=)kL+P=GcQ4l%8P6_sr@itx9fGdJOJrLF{?hVH3sM*- zOe&Qg+4Xq8*wDK+@*YaMYmbd^XUDD_+htEgzwV=UyEpMjUUKhmyKLzXgZ;>a+fiH^H$Ma4UK8Y1I3XGQa-$NLKgOBmZc>#}4m#f~50M3Eu@iIN);IFBn4^ z8G*hR7jucHmN)KUq8m-PnPZ%!=j^~oc(~CPqNg~%{Hw0-rh!z z3skJmf5YJT?>vesocUmkf55kMT8o~@`ff?%Myuq7gdTW%$AexGxp{f3q^b%g>{k#b zsXurCJWKAh3!FS+>{(qE0wgDAx6GmYUwk1I_OQtzM=qet&({!(pq-hjRZQ28hr7c@|pCPdu)?PCdm1U zg=>tA`*PjI=bo7ed94bjWf175?XQ>eVBB>D%-iWLM5sWTC2_udLzw-7i={)z{$2C+ ziw&uBuJh$nBb3B^#RsvV=IxOEZROTPAkJ-*_%b& zSoBjId1+C{?R($JC-pqPTJqdb`p&p=E%CGGUdNLEF3HaO`xG))-@ZcRw|FgO1-NwI zsRs$ctoV3a&&_;u!AH%4Z7~A+QwcKVO}dxs`cFhjop6d6%FG14$Ix*)rQbtm(Lz^@ zs@+D{WqW*AX+Su=7`wIX)mviE77-O;-IrlFy|rOx`C&q2x`Us54HPt1f_1;Qp7Y8D zL1`CWL@#ZAq#~(tI=P-AKVe7v_R2sXFtG%mMqr@K&nt z=sL7JHraoufsoj_)FFaSEvUZNFUGHUw3+wjdx;yo>F0KboM<9@zsZe9Usl70K>BkYrXE+QdT_4*h&;G$)qizuaRL3r(a`jo@R4=< zXo)|JqY7lz(}&c@)D|Og{)`$n?aCgM*Zg`GkVgh7LCvZ*D*WuCyWq_8A$Zw4A?Vg6 zLwA(IYTX7~3ofLWBvWxddBiKlm(=Y_kSZDE={$ZSDihRWJ9zcp`I3m!L@Z(nLgUUN z>Hc^=hNxP#IvaPjPG9)n>QiHMhR};R+XJQ4%5AynW_#61nJc7HsW+j$F z-);7*=m?Ycuy_ojos#bR`ZbndbR|flKEmYNd{Qy-N=ToTE=}Z5xSijP!)tSF zIT6tVw=$A^ojz-WxNK^L_hlmCtSWZ064;OUW7@k^1Tz>V8!;2z+x+0 zW_Ze1(LCYdy?y-#(5YeP4is;Ro;j604K+3X(+P4x==pQkp<@l8we`0}wdov0b(RcE zqb>XEsGffl1f6jhpuA2tCFVuAd_U1no<*RinGWr&bNRjzbp=v{H&%&d#Xzr|WB3X#IY8j>&&8AMx=&#M z4|FQKqOYU^TQE5Njl+E-07JgBvC4Q+NGPqVx=zV={ZV8jua|nLl(f91<;dRhIgH`^ z;b9hWpO0J+FTkY&1YlD`o(Af%)w#YKVAmHRyz1ZQ;G}g+CeeO_;{u@vqjR>dJ$(3N zKW%eoxhq!4b444k>^yv&VuGC(L<`uSfwD_ zR2l1LD_TAp&}yAQ?6N6>%oGD_RaNSL(VAXg{%Ku|NbFILsPeGKX<7U92ZgnFp2{#4 zv`*{aY}3}kulF|(;xsW?8a{hxXLERqw8XTT!`UVxIlbVB)p+|F6%+ww{3aaIdN^%g z(;&L*)n>3Kp{mBJets{{unyh%c5(R_pReA|>kvtgW}>ii0qRD`LYLvG5iGHHy}S=Y zE!ya@gd~o;Aw8=I=e@=SSAy<~=Wta##FlB{-IHEjS`L#7?rPfmHk{xSxipuw$f@0t zw^~NDGNJl0v7!|FBw0hZQ-U5-q#6!D`mRBfpSLv)qP`m0-n!+_8h=Iee21j^dHF=t zn2v`>#a!_gDHPQlv7qHMB{VIteVCcIAHvURZ(s_s{q`!}-a!u@yk%a&+dGu$X*jF} zc#a2`xMJa9bA5KCjzYHUwK}&-yLh{aX_bNH%9R#H2(eQvL#8Lhcl&>TX?y?6Js1PNw7YkFy605jYOo2_g}$W%w7l@-*DJ(nq#He@ z0hV=qDecvJKZc@}u9xWVn>LDu<>m9={Hh`ly!phS{vP^~WS5ehIu8%0BAYEq-gU{( zjTzNt3d&qJR9Rzkt~|wF&)N!eFV00(o+?ComO!m>)%u1>j-KvYrnGlN9je`*rYk4q zZW2cdBQ1&gEpsTx8m;Um&fW?GmwaiT!vU!uV zJ|Q!Z(;u5>SL5q50SO@TZ3Xn3S??`*68PHhFKHIsR42 zlVY^a&}J@UHfkj~cU+ZjGMXme~t~YLlC?QI$3P zor=qv`-|4uzGSL5Ws^05;`Z1RU) zl*n=nXY87aJ(edQ&(}4?opJE>tp)%kG|a)i_F2HX(SpH3#&xZl>sop+Ufp8B%!`#5 z4UO6TY0*`*LPzd9sqcr%yvMcUA%%v^&lA~kL-x2hnPB^f3BJD4ROSWKgpquc0EJTV zvuCdzInur{m6KIdSvgFd(&}F=Y)if06r=rqA3e+kEzxNlJtSW z!Rjt9J%;=&xJ7qt=*yS+zJBM@QzQCKrP8;yOR10yl%blMDHIor*pp$~kn|Hx@IULq zubzFZGD$)B9HZOZ$>2^WxOk^|p8#R@L%+NQT0czip+l8})%oSMq~_BSx55yoQ&yhc zp0L${@9UYgbS25ckm7UkN%|?R(s#H=!{8^9qH`y-6Va9hkrwLJ>QRRCf-TuCGHP)i zeS(Ult^@4auN)?zCOzQYRbCrtGwWX5JE3i zbK)oN9OR({IioE!yj?4FS(7)uF7xC1fTm}{5vRU^CqBqM?KFInn&v^0z=Yqvhs{mu zh|V4?oU{f29Y|7X=|Hj%R;1Vki6(6jhEf`d%WG_LdGLdBv9GP1HQMlOUZn)ZXjDav z19&Xd5UaLM@{6~f*9HRDOmnPDv+F-Fs+YD%wEDuy{ICyCDAeV2$>|4ZRMl_V9=QCj zQtQivYu$8qrk1+grz|sQzLlOUZV$r-OSHM^6J+}8WF?gJ9P(o1!-wnENQ=<(7@7T@ zH6XKNtw@^0_CFg>SjA)U1Yex^6)o${D!jy%XscO~Id5--Y4D z8{c@mobwrBQk|&|NLG(RyODt-Zjx}zRID_gkY%lQ?6Om=nIQyWzZuq2P_!n?vYu3v zIZ;t{!p(ZgKHqs#>4z4;)_X@1-38_Palvl^VMT&cclt+>w;@F(Hf51eSAUL#!<;3^ z>n~?c#Z1FdI-i7v3>@vSYqiHlV@QU<^*z3HGchJB-%Zxb=W^MRBgJNxIp&pDnPTdp z(hJo&9h9UmF)vYJStG+E0z&I40Z3Fi0o^$-2OpX5dcBtrMOeIQI5r^J!PRjI6)jD* zKY-KnqoVVQL&;#dn=!nSAYS7NIl&yPrrKPgzI_FmD{SWPW*j0```-N(5+a$JK4qc<*wsQLjoz1 z3@{<21XxbjksO_7vu+RbdVXJUZC;K4fml@YwQ`ShbXtN-m-}jb3ukkCgqNjyDOERC zW=s%Fb4A29OdB|Hm$w|h1|l1V$R}>bPbydal@Ig#d+o=k_3Cu6+NB%8U0hEoO=XE-0Qx}Q54U_daLVaq6pCp&#l54C-n-D#gEqdOuSQc z4B67Gho>*VeHUu!GfBQ%@yqMY<(>8`qKb<&eZ+=}0E^}z$MV(M?1FU!Z+`>x)rcc} zaNiAm!;<&;J+s%5)f(0lgL}r|dGk(f(hj5vev*2B{N?tj1o(GsPWo~Em&F>Jc$z-X zfbtC;ycQL5&wwOj%pKihFjwsAplgcGLZwp%_E3x4_#VtA#lpu=I>?6+2;H~Hw|V?( zSnG)AdJg_qVMcH_A^jTFxj)tk?D4@BS{Cu!m`yJ@U-|T=9E-4hdp^bEHyVdKH<%TI zAtlO@k>Rr~{Dmv2Z2C4-#y5g5e^Dy59etZ>%z64mb{wOTE045vI~|7Z)YZ?_`y;G6 z602rMDl|V|0Wmpoj1=mE%6x-*lI2U@!|L=5h^lvg9z?5}@i82@clWN-uzdc)a9NKV zl;c=Typ2W@yu@Eqm#0yq&l)D-vL2kpHLC9RuwS^Zm*q~Cz}#;IJY_kiaM0Rhwr$~V zUOVUhta_0%4vo)Z6-#^8;$9r`%15wB|5obZ;mG5UfltV}LMRhd|uIlI9;BW7R<(R*xBBwZbXS=bWYAu(@ zVN=$hD<|X6qzt2b?r;VbJk3GMYd9cV=NPRU<-AyQDm*r$&(vYidzw$X)%i(b+1(Hn zABtSfV~3s7R_}k8faRn%-FX6;P8F<4DxtzXKy)>$t`*3^d3)mm z{T2NoEiFNtbd-a`HlAE$GSS2@DpsCT5>;^ z)UI*bnPg_sN_O6FP5+n^-_rY0!fbYe5qWl~=|n&Mpqb?=vT!0w-F7iG`=Wi*>xZBM za~kvb?2T^k4O$2tYL+hr@?)9g&@iOUTr9E zTMxzi)h!u30;&F4dKfLp55Ly$ysFXKcLrR9p$su|UiG)xdvimY$!YftQ2?B4gLtb& z_RbtfTK5ZCM+|{S?W=*xfL^`lVi?VC*e+C({Ct{WDnp=jtt9Cf?TgRhWTxGRa3}Fr zJU0nBb=pb?{mT>W#|?n^&f-C`DwQ>Mew3G?fW55qYlLsu_^dRSIziaN0H;vsDOPC+n$k-gC;ZdsyTjs?HXPwY7ey z#K(hF1;?kH!=p+H>&_I4&sCO%HjiiZrbX#bOW30qDotW@6GX*KPZq;q!v+(=XHynK zW9evUdfH0#GWra?(q>ZAqb}uNjbB^P7bjq$2ZkwV0b`8zkWlP zr>G%~O8_l#;f7_(30Oq4WHSMxD5Y;!+EeoG!2-+2tPkenw&Q=qI>}e|M75-87y6pP zXPHv&@m0&Qd4JERrlD4Up!d#o5bL_E=r&zNkVb(n;hH)+gvVL$-d!gLWE>pzEuVG9 z&-B~_DeXPuPgsIjrS6{4bI+Y@_fRM_Z|k2Vf6J__)C0OYT{Qfg+6;&o*xDLkoh{%X znW$D{jg+e8_NMIwZ0!1&r0qzob^q_6m#t;2#xy|#q@25LWvZ?Wuv=R1^^4UXqA*uf zpp~5b7e$dw*=IMfc3Kd};&kN%-!lra@5T7}HO$V-^vjS6Xx`WG=T9w~+NMcG)b5{fnm)rB9an>KJ*{}HcZKXQ}8i77Pu{C2(Ql2jT3yD5@oC$)L zl1EMhNHG@&im8Pl)m(gYxr2Ja?zZ6XYAa znCM5grb321GVP$9;>zoeN7z^dsr?oaSAw&9fH;fV=QQ`~Th02r0{hn9*R`#WefWmN zrxqi>>N=uy7FRD~`ASfu6V0C%EV>Vl_c^%dkNRxsFIc1{UAg39A)5Zh0#i$eWXLei zcyB!zhlDItFKH^Wp87UQ(wxPq;8hR(h&?;6P%4x{MsLv1`pvjmlef;@l9T(a}lc< zl51aCz;xgn*Rx&f%EMXgPz0?p$~Ak0XUjLujJI`3$UI0&%;kG;e~v4p+gH?}-beM( z%;;bX6YNqP8NpueW;sHi9bhQctciJ%!%390CY@{Z&6=04chKZ> z=emm9nZCHG1={!z3tqAYiLsfjR8zOPXKG}P3w?r4 zjDTR$_8Udph8NDWSo>85wgAy!*}PuQIK_vWO;fg-@Wkkp++~tbH6iv^loXVkbWDX> ztS;YTVqLgTq;KHqxwJ8Kzr=US;%n_Kor3D(+CG|vK4em852MT+!ktHHqnY8J={JSS za;4a2L!diRDQAB1W_nQZoQ&c4*eq8jmuq0FAog_aBU?lILi9^Q8~BB=05PRt$T zNygFk6azS&;_AVx#bS_@jgOWe_*qeCo&_XUsAmeGYm-?M?mOxx?Vnq$rh3wQZKv{| zd{J#KIFffp9GLA`B_W`P$6wE@e*~68n1Vsj-SZcflWsynej(bjs(gyDhZZ^l!Ih#_CxNk3-z5VRo*Tmk0Yh*lSAi{CAf&K#%rg4X}kx7E&9c)Bd6~EYmhcPnQlF^1yIUL{TZyyUmQ)&#@U8@$mS3N| zrbXp~=s<`r;4i=&>y*}!v}KzI;ibcfv!CsT9~TNP#}ul?CuLNjI*P*JNjDbY4m|J* zMm5^*8ei20dd^u(tnya-X-2M5qTw6KH^C!@BZnNHHd-#`q=pxcFj?yNn*^_xmBJ&S zny^glX`7COl$f)`QYwYcKt_fqL00UBZsi=U?mAJ&CbDj8Hh*dnzrl;;Mg=0Un1C$%Ib!LZB{~3`sX6mQJ1jBn>m={gM=f6!wMTVR1Aa4#r-D^QFZH69>3Z_@QFLyn*n;tfe zty&I7Tza$c)KWDd2kLyZ8Lo3CY{!lTI1Ifn3FM?#3FCJh|E@#q?_PdqEQPm{xwX|* z%^_;F35(U><#6Pd{)posi>I4@H3lCTfu9U3H*t(a@S4muIceDpXi7-@!v^?s{=ajJ z;?lL=tx3Jb!<4hflSeW+GYoP#Q+p!bux7!rBFzk!7Y-;^dEb_$q56M*^J7^P3YgYV zG~JYVq^x^+IQ_6qXmm!Z2STRhZC*j*|F%o zJ1_n80DibD+cv(o3H%23TZY>O6l+vUQwOY*F|Kf>zXtIDRbq`SDZ0*>_U5jHRkU1! zjGXzSqghf%F-*e(KMwDovDBYOW!HrOZrQw5m3^n6T9j{zkE|-!owah?Z|#f?YB>)x zObtEyS?>AETivdmJCp;|ONPv~*`3HOzo$ntb93!br2romQ{9Hc!0yoB$aqYIds$iK zT#??A8@-~7+DC^II#U#UYMjM9_-f=H=l$)yW1m;t3S{si?(cEv$IuEVk`E%Ss#$Q~ zfXae5J7AT_1BJ-y@n|VspN$kq_PNw=gVp_RXo+-t%d&Ug@NUp2OHFNV$vSH&_!6(m z%_d*N8;@)fxgQ?&X;j47j8lSVNXsuJ2}lE9G6+0-jP8!H+2mSGt3Pa7nep^+iQ%Y#5Spq07z$K+BjWqJ0uKR zzKNq`L_{>%+1Xd6J~lK!4)q>aGoDpeu-U?#;Ej&W-TA1y=JtRf*Y6o{k9#h=CoPuq zFe~dQEX3H@_$oBMXuV&z##d8dp1pEZD#{IOjj67%Q2y=4qw7|;nWI|R$<{F@rsT^Nj4BZz1 zxrL}8;5*H%$z29+oc)+BE4?HCCDc<&pYYELZN zRnr&aqvT0iV~s%}o)B@QZ(*~wqhtPlTBZw}xuUah=vb{^;H$#wBvBiINg(>-WJOwB zT;#rVNeu{x_NCoB5ioOal*pnfy6=~QHz31d&Rf>Y^||ceE<;co1DF(0MlJV_REkis z>wgDI`F)^H^U3C+zR`rK zOCNq#Hm_LdSj^sCKfim~?_5}ajBD-UHqMTGpzQzs$>01?+e60`*t&yK^urDLhx;CL z_dC#t*(0)h!=C9Br+9PU$)9b_{NekGFY#%&{A~9s9gxfg7$GiA7d`zP*VbRB_kR4e ziz(PgEbB8We-sgaqp-d38gN%Y)PG+w_!t{o2B4MjT!Q(?biM=n+<@Ggm&=^{`SXFa z^b0(~e8pW7xxkXy&C>z%!v)HTSy_NXqhR53h%=6V?Cvbbt@VAzSX%fX(WfesgKqpN z`*OgqD{(&t_OI}N$wHKXxM9`K4~2t9(~?HwB@z!pC6{ExO}<&~!KKil;2Q=q)w7c- z8GF(8Rkm;K$}Qg^+Mb!5kRr(QYJK-IjB}d;gq9wCH%$7oXMCd^8_0&czahNGZQJ6- zWI#;+jHxI2{6FI)!Pp6IFQJ2Hly+>{PHoEng4pb*nle~{EH7=LMqsd-QoFF!)__3l z)km?DtueXF?QA38cr!8xs8=nCG-b`UYHHyIP@E#K-kQ8CPN(Ey*1^xCu2Xv0L_(&ZDBQ54Y^ul0S0fi>JxNg9OKhEQo^;m}PmE zK1~3;(cDfKv3ffs7TdPiE`80Zjx;~mxXmtXG4!YxnFO#O@Q-E_LmN;?fK#S&q_{UAOti*5381whmB?q_o5ub~v76XZ+^o@I+#cP^^I4^KIyen%$FZ!tk={ zr;j%+2VWtCMKN#phc>3(5gE2?V}!3So3Cpig;M(a4SZBzzSMVF{ABV}X&tD=XF0d8 zr$}$iC0#W(5|}LWzt@W}WYdR|)UP#yCWEpB1yYt20l@K}uBQC@82ho@fr{-Uz|Go* z9QD~@c^p80PqLORl@8G6{`T7K?d#54nxr)+xYlAMHxJK^RqMHD2R}%!P79zbL=2%i z<(&qM{WUd~HLW@^0I~m}2FO)YRGV*&x!4XC^x5%Tyx4ofaPwZtu#-L@LyiA|{90`L zq{MvSn;ms@uh7`D$0l?;?%@|^F#|9@T!{?tcZ%o49=>ec50YMTCJjLkOifKws}#Q% z1H3GNYzd-pyF~tNWaG2j*I0R3K%i zcCe0mEwhEx;>T8wDO^AQ$b48>A-t&?FQKNUHeGkhS!k(MRmka^C0?0_oBJ*g#``|m zenp$M-GdpRf2R}Nq}9klsdGj+BAywT#?1o zS+(D%PwdsC28hDrQud(`7Kzw719kOBeQNI$B-{W{vAk)EC959rnSCaJs5+W3X8YoE z2Vs7HrAFC9wQ)W=i)GfElam*_yo{M5;yU>nl7Cl69 zJmnPj^W6-IIKj-U?DhuYg7&9 zA0Qc{X^THC3^E*s3Hfo7#hwO>^%hH^gcS22pwws8zClny^rOeyyjq{ND_-odTwpUX zrtnDL8W_+LyP}09F}W4g9NZtL0`GZ38YP5gVS4938s&++o>hY~91zBAwDsq%GeOX1 zAb40IyV7Nh?7{!C04A!PJd*^ISf9__$VSd#Xaq+0b;ZQN;~ z#cO`OkK(=-K)x-c2x_CJXXoS;(Z)vd>dwhND6tzkeZpqhaHfN@zPr2Io4}khmUBXb z-L*smMq7Q=zO~&3_YukvBZ3Se0TAy5G4xB&_``7U7!c zj%|Oa!7Nm#ltGMBd9Q8R+Y2}^*-%hbg)n(J(@$>H0KyN(tChf_3ibB(#`-!U>uu73 zR6-GkZo*-=i@xKO^*gTs=qpE7Ys>zK^!?5&*TTZZT&BpaNbHk_fpE{=uup@vXWFvo~Pg;d)dc|E2fSb}rSJt2$y-loFUGPvzl_$&Ii@cPlv7|tw9V*uKi$d)1w0C?F5Og-)@l!jN*B`C~ zcy$2yr^1wn+I9?_3x}WIUICYeQyCTw9a9(i?aiO+=454c{be0h8$cVXA>@RD0w=GC zp=Zw;WUSCZBK>RvX5o`kf~ufgxYa{BB9em}T?)f)du}vI`lpLJz-jI|n`JI!{DB=X zWDcSO8Z-`H(??$dQ3He8_!cXZ;=jNkK@wzrAnjLq3I*WoMS=<`cA?Ul=SN`p* zwV?TYhtTGGhPen!XQ$$sF9Av2HCuRlsm;$eX=$$XMuCA&RHrxCqQ`I1GP{udTwSc;WRZgxW%~c)GLX$+X4FisK=D4)YcjWZXjP(1ml$8 zTAkAjUtFjm=QAGgRrtZ-Z>v+nf+LkA?a&?F@U+)2;a^1F@nAksr#fD8?>sO}{l0}? z;5FQ;WO-NRHrx7meVc)Vk=iI#61UWntq^XoX3jlxri{S5_$ASE%O8^*?M8?r${ z)~K0c-3-qFDj(O1^Z0F^N)vTwEH3sXLvMw$vLbXW3ZS6fC>r#3QS|rxn`|F1BZyKT zTp{iVY{rcKmy};{JVCw0j_Ya;8ii39dMdDxgPD^Mo@(=M8zkcI^~=<3=85SuuUzRD z+n8gs=*zI@;kVBiwkrWT<`J>6Fd%`mpJe$j*0JoE5#Xe;1~HlJSUe7;vQd1CARAR2 z^Vl|&)eHNY{0KYr>afTlE0J}EEZQzfe|K+PEz@Mg_zy3$(WwY&8 zL)=_k_kqfKb91xW{rkp-Jb=L3)zuaF^5uOS8(6FSXyNuVzySv*E4JXPMqGPe1vIDa z)6E@w=dV)>vhqj3G~Z98bp7x3+9PMj7TwiUREz?wQs2D;Af|VG`sD+JYAD&45y(G* z^8)f+7%1Y{m2A9l_`(5tbnl^Ht~q$=YWk{|rqOx%fBebEh1&_6jrIFd+sjav=RoOF zjb50-i;dRJ;ap7OE*2*Bu7DLj zQ`jMGT@)(j(I(VCJsD?jS70bVrvhB&xVQ)_W&p>t7V!agDaCfExG5+YbfiiA`Umjj zSI5tPc6M?w*t^9?F7^EGIJy}5sfXS{Q5R7mW6sPQvc9>ydi(smcqSKJV=|ywwk1et z@>j3DTUg4^&xaT}*FUGd3auv2@<^=?nA}xTTE$zohiLotQJTnXe!pSp#z4L883SLj z#u7ilw%B0~a%r=*a z1O!NUDXpr>+z#kO`F;PUZ0vFrWXcl0U2|goeXX}0%hSk~npT06*%dB}2!Fc5(+}=8 zr&!l9rRmy+8*iEk?kch_MXY;K&02P~gb`jkX*P zg=iy$#s&gPx+h@P+6fZ5mb&xV140oR!yQxCiRdm7N?fM4_)-TnQy z{@Nn^$Omp;h4TWJ26-!ne%qx;uBZNerw-NFRS{ujdL+3_4lZ}CB0QhVv_RGO4>YS6 zK>N-DR?q3P{{H@hjagY)kdwuwrCK#)(gO0~C+ZQ$Mi$8BVlhq9>X1FsW#&NzHk?b< zWw}jc<9UoV=?guy^HG$(8NvW`71`1NS~htLm2MgD-hGg^4*?{_RLAiK4`tU_&BAPZ zAd{|e8wC}>6`HkmgI|3p689a)@4HBYS-ui%reJ56t6=y9x#c;xX~rW2|=70s0 zBS%-$4cdmy1io|t`ds-n0_a_&?=cYp*%-2~frEBP`+b1^LRxGrdg>w3t)cDNL&e7f zILL-wM^Q|luVUtEc1~^q8K#45wiB4Dj00H9$mT#98%M<^T_Z(?x1Iqybbo31FYZ~ATLp} z7YVXRuR6w*p~NuIg4T5^!HtNX4aFxIf!ZJXR;@Qir_d}|k598g4}s)+pst<9!S-aD+xY>OK`7IZ9#ihxqBpj44AC8z_4 z2&hPJ3Q|LnUP2L2q@y6c3J6jIgwP=(y#{HahTco0ga9GrZf1^-b3Q#f-#_=>=Q;nq z54_p$E~~G#f2+P+w|u9suZIv1S_oa=f;G+JV^I5Ac;K#tA%~zfAv`SMhKxJ>-j928 zPg4UuxEX)N;g4#Z=$%gK^rEZlp{QC+ zLu%P$gn53e$b3M>{jk$KpND@NV-`NAX`bS0=C(qt@cmF$ z`4z#&{bsF)k$7ScWS#PlebO3vpB%J80gGKno~EUx&3#a)&QM<09YDn;lIvFtv5;3Q z7R$r$96Qv3cE1Z^_gi#5anTaj>wF&@)4X1Nx-AuxhZ+o@jN5pqB1D8yFjewPF+ zgi4fG1>xg=(Ta<^@oVunC4Yeaei)_eT?sDCtZHF$IoDfQW_ZMK{eY^{g9r3XVIaPh zU@4nh2o&HhoIAKXOl`g7q|;XIu9@fF|Wx? zi`m1z?gaY@xSZ(^lf=gH&sN>?`^$-QE)`P-_LZ=)T|dS{;rvMB(0d;839({WzeyU? zdf6c8v7voS$>X%;$VCVQ)hECLl7?Fop^8sD6{p5EyhtMh`)}U8++;>UK|wjSegI0q z4Z{IQ>A_fX$5y1(Dqtb(rwhSiW5nC5&lDJU1@Yq9M=Gs0;1WQY-rMxyEUl%2Rt7Uc z5ZS7TE8HXs;R#P=%7xtBSC7UPI+0r4#~sO{^|FMK?BQO~JU5&>T{NLGbT?8FtA-+w z#%N|3oW87utrvb8_W<)`5Q66*TA(|xW#=f!kqWUW&%V$*RAfT5VQ#fr>< zs`j?)^PFYUtIOk{RiY=>d7ewR6$B(cFTxz|K3iWvpbJ%zAby%v)aOn+#`j=d!KSXl&rCl9)G3t*}heZ7&T?N`sG)wObW6Eiaa?(wh zJqh}-7#ow-ykgX5tEFu%jjAyYTd_5(=$-*&2yxtcH?M!CO?dpM>w2G2Mpo9tT!06r zmn0p`%(P}bb-}Fl4wA)fdizuNwmVoRe6BY3!{sb$DRh}uO!6I?TF$p8ithcOZ>X$B&Ep7EAh zynK90@bVQuaI-rRSfuHoYH|*Qz+Jfz{aFrtiw6Vk>sLKSj^!&ypsya?x7zkO7FJK- zL@kO<$3-+z4LeLWIRZ%$&1CKBi7dI%d3DhULhS1W7L@7Ua$puUaYwv)Q}b-kK5Kad z=8{}^4S5?)egZLxEGXeBTGFX-t_3zeRtuqECsC1hMJct0t7z~6V#$^qB)Vk0EYi(i zcRFI(N%^LIF;g?^J{^fxrzMVFef=Kzh6aCj zD``)raV697y+DgIAY~O(!k%_=Wor#2gVtgw1bMuL4;|o6n%A`gN**(Kr<7cRcIB=M zV$!r}JNDC=OFsKh2SVmUjy;Cw!2&&w(a!8dS=Jxqc_W==QF(3dI#(X7sG&|PO@RTm z49Y%Elr5D)TVjNGm>owvgjX6aKrQLv%XKtlVcR)+H7DK&#=i{5TSMNOg;7w=D{TVfCWRY-2Vm&Vy ziI!yIzq2*{yAc-D8}&p*u#_!=xR4H6Mq=f)LdDJ+1Z6kHMLOWsR{n0drah|Y?X8#g zQ3 z1CSmvvilHd#BctQ*X43CWut9vy3;R;BO-rToIU73!R6?96X{~cyb9hUoVt%#?xEM^JP))aD z)x>x$B~-x=`0=8d&oyy+ab5)vEUr(1UKI$0$$R_u#2dg7ejOMC#w|}B#U=+!lq#Ux zkVMeBS(^Td4rb2ZWTe;#9iImrG>04p?7uSQhvBn=Pa0lPUM)Sh%i-TAl&>=4g!s8X zUhe3SOatjqwCh1iLEYd5vi*3PB5D_My1r;LT>u@zTjC5ZJKVyHjcc5Q^)sB z^-s5@?HMfixOj_7{7psWavj5Ru>1Cfve+OXH(-WuN7{Dm%1vsA*03GNt(CfAHq3Uq zorNaIek#|QTXu>AY(HBPSX-|okZ~4g)v8Z|4UpV9Vw<@E%i5`?N*-8sH|^Gg?25ZT zM2A#aL58b@kVc`IdP@QXQaE5y)@wgtjw$Ub7&E8OGG}*;nU7V8m%o)iUk2{Wh%R@? z@fbYDGTHZ@1A&#i%3D)X?N`&KoVxe0MP!Cj(^-1XReQIkG1Q%fnY zFjGvHaN{ROv42Gs1peI<0>e&#S$~i|d3#?WOZn%gH5_D`Wps+)d>Kv|U9LXtV{RYN zJKFhaFr!5;AD)(B_JpKs2%?B!6+2`D&^8>{sn zuOMLkffBk_*k>3sGmLV#=L5MA-`&`jdQ7D&(Xjh@ry#hDyWk>Jwnv3it!&RdO4*<7 z!f##FuR7k>kA8VCcXWTpvjw2}m@Ay|ejqp|n)yi7VQCQbZ)jo#Sumeg!2!+#M^7Ku zG)F40&34MXx33mIc7gh*q`#m3&~H70=dC>dy$8D=`fLUqBg6+YdT``O4wbA1*1P+P zX7T*bVqim0Ie4%Af{v2B1$NB)e9#x>V^BLw*#??hTu{l@paDQOK>%C&Y)l@&0qomc z(8m+<6UzId-n-VixLB~vc+fk2gc2gG&Gj=K;nlzL4~suFGRWyHwGPr3Zmc|2_M*;P{Gq)VOXbF44Nku$K6&}y~XnrS8ebkVPo6{8wuUMB}DUUGf-t080m<-n}=w++ujYb!& zA!c|K$%>3_HPb@Ae&ksprW8V~+p&}!O#+UERK#uiDa74hjC+^KzeC3!(qPj9Z^wT7 z-w)n2j_XmVI;UyvlXBtZ+LlSdE>DcL1eVKqpELyF+u)0dT;uPmvJMRsA8*epDqi&43Ew}{FESnE%j3T352chAoQq^*0|tl2_Jp^l z{*+~^)csE?fhu8CI@(nbN(zvl>MC|y;R+0Zt<5k;XuI8Kbe_2pxHn{*!797cw~KU8 zRhI<~A>Z5{3t|{pgGGDuSrRw3tyWu6#{7i)GE66@V`0)}d9_pcoOi2jjuW4X*E%wH zXB0gjs-qc!E^xqJz`DHpf(5Xts%2Fx&}XEggeseL+{|r_#{cd@2=Q%ot1B=Jbl-uC zSXEIB2O2bI?3*o-fGw23wA?m}Pm4Jc2h$hA+{KR_{Hi1V{*c%WwlzDo&UI&oei#@m9@&-~-?*PVzEoxz80T=NBcrkZnhyPg?T??> z490cI` zE8cP&PH@y7bQrF-suPl_KOw_-637dw2X;1gC$GVMMBS_$v{vFW^=3uo#-H;rX|Ae_ z=-&F28rMaNho#mRm{75z=4;KF$72yZZJryTN5=z3n}yf1FGjPIuchk{d$We?Ycg~E z`w-+eFQn&H;FX)H17kg!IKTVJmB)2A=ORMpdIoA>vhTXt5;y~w$JzM%jI<%0K#9Co zt}>rp|8O=sUJ+@uG6+PSU_Uf^tK%;nM4<^UdHO~xV@3U zM-+edY}V_t(hVdop)M4He022a2IgMxx$pAAp1t!I+q3iYuWf!*WcVt?Jho}o3XJa; zemHkagHEsoSC&^QuRO9?H0+xky!BwAa3II3vlV0bIJcA*r&7rPfSnkDOuYTklQL)NvdOyc#A{ln6&EMEKGn6IbD~ZT|sx z8pzHvdMpi6A?Qse>e!V%Ap+>T3pwR_541>L&0~pP>z20Sx{4Dm!!9#A+c=M!(}_~3(1&@;CFW8L1{792Z&uN zlUODWw{mX6{c{Wd&Rbu_SYo9lVe4hkUwRlAX^(ClYH?ohbp-hM<4w3g%TpQh(}P)0 z5s_=v92ZEYs}S-g3R4sTXV zfF!Ghj4EIWL;5JROR4PqT7^Ty+k|E;q&b2I-T&D##59|&ldwKJEX-E9J=vMG&^N8W zGU02rIZ0QSpYm~`)M8eRN?e%lP^@J?-B7?&a^`LY|UR!1f(WM7O_Q;D8C(hYj&L`GU;TKh5{ zB-jnP5y5Tb1N{=GS|IM2`j8pj@R@_7q>+ET2N$Rp7A&WEx(aao6zr zBGw*x{g!egZFqZqS$Hk?1n<+4!q&Dt;!?F9aNz^kLgC7mCeV4uSxi>io)XvfO@iUe zVLkuW(2UUf+G;AYph}L`nbn=jvxg=sA|8fs-$b}}+?!CX+?rG?{*1hx$#3s%zK$cG z0(}T#iYJXAerxeD{VP?O9Gh?rq;q!Fd_n8V#b@yr+cD0nIV3IGR}fsw2EH2Q*OA8D z`mu!rbK!I46Im%nVr_DCc2Zm$Aqu@j4KL5XsyQf%;|v5%&)mlgI(HC#5+>EvA?WIi z44UN@KKIs-iHn|e#-(1IP6I${GcLAp&Z1B z@KffFuMSua!z`UrbX#|dCO8HJ(9)0TMuno+_OJPnt zzH87qhdu-22S|H^WP#MtNte%a>^r~`Z3t*?inn|cCwhM?4Pq1~QpDU2Iij(GZpr^S9vp`2X9i^f%iF6q`bEJD4`BIKI$LEcFWD0O6i?yeTZJGU+7X z>OQ3ARPgC90FjX9F(d}-<9cVkZ3`8%t-dm7L#A`zs8-@-JH&(9nw#(2+_SGR2p%NC zD>#s4`Pq^hsqiD2A)RQ{8qf4+{3mh#&|S_YXTn5~DMeja_tL-x$+I{L9d~^z6}(-8 zf@qdqHf&*Yxp*>MG(W?uRr#`6v!h8QKV<`xb;EQ|srOoDEePC5ZbHaP;SZBJ8*d`G z?Cxe(9%J4foyeRf)5CBiOD=)McBsi|oaYg4^JVB7fiEZ2WZRu$j3yF`gu`F zTy2BkI*KPkR>y1(E+)MAm(u(@IWobj_QvGhz=;dT0%Vw-_&fYEO$-+7 zNC9RRp0iY9Cx?kxs!-F41V3m_$@xQObo>~8IGRUe4Q=KsiW9qN*y6}p32Us8o*&Xj z59xn=E~i(vc)$>|A#@%z>-!s>e)XQSLP7E0bt{v*UxfoOpbL+jI<@Avqe+)-_iWb( zf+7mxM|CmF9mh45yNP+EL@Kx)h%h@fKn?megC1qveZSJ4iml%WMN;1Nyoj} zQj1KL91RRN1Cq48$y3`sjFPC+c2QU;Rk-qgw6VMmx~#qxdTD7ysJUNE3Zm#IW&RvJf$whaqNL->r6D^Ntx1P)yX_gWtwt3$zo{5BpQfXv#hAb#5KFrh|@$lHr~v6pfyqSOFXS9nV+U zea;uVX;$szZaL_OHU%YYKe1x8X*rRtV<_QhM?uylZwW0o>tgAqb)OoZj3wQyU{5kp z`^=%c`9KS@nWp~P38U>06zsNkp^tBDc&=zp8XD>Sn1!@R_6G*%lXJ6WX;^w7sM_#T zJe-4BSSy=@C8o%dd2{03`4ezdI9gV`&5LZYTL%tHKa&^7Kj3mRjx!LQ0 zO2wIsd@vsujfka-(6zT4`z+lP@1Ewi@WW^%XYRUtOn=o8e(^sECzpBecvDX@B4>Bs z+T-^Ql{v^|T*>jN_1`%IBHQrVw;b%)#+;&uC3!#zJT8`S-Kql?qqWNzH4D^=vN>G2 zF65G>k@?p8B7==?B%KM_`&@CUR|=+CSN${#lM0hh7A!lN(1{#!97pSec!(8-$6bS` zD=H^j6+TCH9?VoVe+siwa3XS!QWR-W4pc~6#DXR2>3x{`ri{QhbBKMJSTE6Pe~S+J88{ zUrH>-MUYhQAp>}=8D4z3(j9LQcoS(d|5&!ztuLQ2mR%IyWpAO!L#ahxBDV%9x;i_W zp-A@g_%nTQ-C@TW49aiU>$aLCa0h{48?QylWDu+2qE^}#K6A(aG!Nb^{j6G<%6ZCfP3_#By}N;$Dp9k9{T=a5>?lDYJ$ioF#A_M- z;R$Gq=GL7x;Q6Rn3=)+R0zd_sSVyA1oF=`*;y{ri?a~lPDUZcD?9jj~G)Oe#t#}x4 zBaBA7nCwq?X@wpWMNNjs7#+DMfvT^A{)jgyK1f zC|bIQ_m}Nk5_Gk1lyN1Pl%N~H1;Ba|)m>lnBBocZ4l{2L)_#&P;+T#kr6wzX`?tCp{! z$=cf}GK{y4xbA>>y7lIYkP!`1Gumafl~}6-TRZ{TsKRr3Z?J%hWW;b;u&Y^-pnK}O z`P<|U$t~t)^V#X~7j$tnEL-h`R!bt|(|`45IX1Y&=V`?(0>h^Q+7-~V|0lr!o}z&$VU`o z9cOQ7EIu7m#z-r7HwjPmUO>#p&{)vwLKfc?JM`aDm%>QLg-{bfe>4yjn;u`Vse5v+ zD7AlLwSTB zZ-a)9Nc#(W^;V15OMQXO&r`>HK`|&le>xe&ONg7D2|J83Oink~m9ZGoOh(oNGmFCR z%;=m4z2VCpAG$%Dht?`JH!aGA9duksU5K&e`hM9k3;sjswr%&ku)@57?aI0*t~&0n_q9SFzT4g*u+{srx$#P@M!A}9*`~&6 z&E!0mYWO1p!WJjS4zh2Lms>4$j)$@hGxD&7MS)Tqk|P1QQ`quNtL0*jF&GG4GTL>W zv-+@yXkf{*vq;o@b6^@{$HC(+keL*x(!bNJyCVb&C(@#~cO}Zj(JfTZ(FB#Z;>3l{ z@!gP>97>dzmfMAk*mC>UIN{QbZ6yU}YOjOt!fhK<7}Ko+;D@K$P>YJ<%9XS>EBq1Y z!2wI60X3K4PP%sjZ?<~Xje|_bcWs7Z(~ovmn+B?w?WL43M|XTCacm+~AsC?b*KJNG zM7=QWpDVj{Dv9v}*Bpigz?+7qwV$B*_h z$epFk1ud<^)YY<=Vu`L(N>-nwruny`@%%21Ee%Rz6G}nB)C$6>77=Fd+VygxTuzzi zb4sS_mwWXUyBK&#b88$q<-!u1jpB14qT0b;W+J>e72|#%rxDCNG(NUG&Fax`>q2%; z@0p?kXB=HH3BA#3iIdxF`XbQcvA)x8a8=7{lPX#Z5WM2Pmd>Rp=1^8r%4@3|H{it+ zWz+TNUb^IbA{y&&qPDG_*30atltk`dyMSNcE3Tk2=ikVp~tz~w1 zmTaxfGBrn_SY}m&0SqRhWJ-I^iDqh*~NtShkd9bBDj% z140M!2Oyn?Yxf;kQ0aWrI1xCVc#O~n4WrX?&!bG z;3v%bmoodCO~7>oR6H+?vwvMs{BOVYOf`Dt9{mA*ejTU%1;zDg2V48mgJV5E*skwO zqwCT@80inT-uc@^{zFUr=TBc50e8!3U6S+VfBxs|gue5Ts1J0FXx&iOPe%XyPn}rL zs>K*l)mZ;;S)dr;f2Z}8I{rt5f9Wa7|MAurnF%D){~tt#vwcf};hzOke~QBo{S@BV z0<*SLl4!KqK|Qg=uZ+RzOzKa@ z@{s=>9I^bwjWYRvbjIPkmeIP^Pit$w-M&ZECqQN6@5}`Q7r&*K@1M$FKQvZP)HE=d zJjf;V&F3WRkL;GCY1X_tck8+vuS)Kx{kz1S+}a)OOB*nYi8k#2YlN?R_3eWs8_m7E z!cYm}<{ueLUyt?M2V-4O`m&Dar~lcs|KnSr0c{FZTmD}L{!ga2YxnenVyQw`@xWgx zfcd(k?Xuu#L}$GaX8-6w*J)<@Dyz=~ersX z8@W06DEw$q-=hxjEl=d4w8`0jw9Yopb_5>}w*N26Gh^R-%Tl9fBAXjXsK(Wj&{ll2 zHkyDz4(V3Hhkp?+Rsz1kAZ2>hslB!|eZWHF7zyx3{)i~$*gr(i|ZI2EYKJseA^c{3Ba#6S&bzQtH zFzFpE%OTgkiY1iL;`gLjxgu%fL&)jkLc(2!J>t`XA7_7EH3^1D(jDnhsl&mePIX44 zIZN?A#)&4s&oyXl+C=D$P0YPATsw278pjm(yS31i3Qh2k@Rd8$?`*AK$ld@G;J*6?8C$BHdPh-Rs~6k-Vtv_wij<2~Djf=PGxv`RAl+evoFU7!(E(^= z1_~W?#w4;-aV6B-6p_uo6s1f{Oi(MIDkkZa4%%2i=r4S=@PGDYkMiTkv$S8<-&?j; ze`V?E({Y>^I{iSC0K+Nf4sMS%4AWLcHpe%{KZw zqR^YgF_b}L+J5v!8XC+7M^2)aX5USeoKl5EOvT$&>utDTM6 z?zjV#!DEv)Nm)y$iHxE9p-1$WNaOn3x4Hp}7(A9f4X4he8b_XQHfx#WCDfr&v1RUu)>nbj#W6en`Q zqRienfVe4eKjei0hyGUas=l+vo5viWq#GV0jT_kZg?|HL}zxpg)(y@Z)m=7y<#XEhg@dJ8EagnB9+ z>pphoerfU4UAzKL2gw%l)S}{^`C{1R8BI-mL1J8dLlQ)X#fYCeqgB3QWO;u4IUGyt zZ_UztQ7%NPp^64Zfm~n*8mD|P3e)>eM zH^Dv2lx@xtTxOxMDi)Hex#()GwUb?~Z+!ppLzRnRc5H#cvj*KbFTZ-8;4`|#kYmGn z0^>R6I+(w#`=3hU(!JWkYMRXBC~Gs?t}IwJB_T6h=!BMb=lii)nBG=Q2{VP&7NrB` z06pwfUG6@a`mQP|t2-z2Tcp(`#8V+QE&*7Cpk(xXTxC$HwQ1~N3$k?4nnqN~6WBnOLReCl%_haI$G%V?U5Hdrys@Q(Cc@H=N1I zbRwx^l1BEndTycA1G-;67D>(hrA6Gm{-U*Gr zUv)#)pjT@~_)OtM@d*Jhbcut?$r*EJ4hcK0H(grnfl9$**I0M*6x23K;g{?j2XeU* zSp35U7lf`V#J?(cS72gFRumCGcDmo<#JD%L$gjIDF$I!zIchP_A3JabRk>=qRnoX~ z_^+^5Cxk86dG;qxG@NKvW#N+8_VKAtd#Je9A98!nwtDIvt#~23u3zgn#N}hm0{n)9C1>ZgmeKqH1Hy-|&s3AX{5?7=JPrO&iu5=x0yzkr=J*{K(CaIeBB8^Lo4ZQPe(LmbO z4r6OxqGT@ekr~YE&NFj9E;Kbh>=! zyf3}2p)Eow`d^6oO-6ch+i|W{3;i~4^%zBomo%J0^l0sV*j`<>@> zqfZzO@~0VoPV%a&?Iq0}wCA9cC}T~G;vaAeKHd9~M8(M*&!3@#&o>OO7$Zf`_PeI7 zOm=K+PO#KvIW0cwZ0c4HZ}sblF1(;e9-`m>2)4jj&!Te50GhF*kf2U`p*}?+u*g$e zy7qapJIgprMo^6**H!>vyKMcvf>EpG9MZK{hT{qiLozPMIoqr#xvCXFWdpmW z=Njks*ZEuj%#r`%dI4M57$He_tVjCcyD(!+$nRNN-O&>>o@x)H0=iua3)lCQi?B_p z1_g~FTQmpWhQTtM##i_(bIb>UikQj@=^r1&675dr!z+(Bhpc9+->L0HOHW3QsUTbB zkrMDQw1O{nW$2NRDQR|}shu;J@f{hp(;VSR@EQ*~x@NMm@Y{+v9l=FNa* z&>2O{p*hw-j;XLZbJaA6m;QnsrpCuE-Fu+xN!u@*=X>_bAGV!+WFG1;*dO|0UvjbH z?@sh>@^hb#nO%8UR?cpk6uEERH%>G3iTUF7e0ycy@eD+=LU#XYhvwP)C>3-3vlm`3 zrS9j)WE&BKrBSp~>CS0__pUn*iwkPJ7t|hl!_s}I``C3cRxMkXQ@F?+m)1g4i&*@N zRZLfBYn&Du)07{%W9yY`aWW~}fz>G-UzG1Sf#T3xz}oeMLtj0QW77tlDk!dvulMDh zY)`kcW(eti;pI2JCT9`Q$-$vlwY|Cg(Z*&huimJ}GrFm9(uyRapY(a!LAtT9M9T3W z7;^n`i7*&t7rt(m?!`W92jtm1)7{?pY>aH+GzRaGRX|qSyKp6l@LJ?h|0cd2(s;k) z_cp$m{9Il0$o1~w`Xh2Nx(!DZyQy9X4(G?gIpbK~2|WG;j85;)`In}hvnt5d*v6-m zh%^~LO^kigWeG^~clwcm)Q?722%kEmdVHaNYbN zcXmKVf$rnd!cnUY-?aLP0hS<5wt%1%?xpDL^M(*z7+ncM=a)6kzdZ1CAONmj{jt|6 z?P5hKxB#^nAj;zZ2^JL4Uu=-0Z_u(enKgK$z+n=T5n)twMyNYo8P@yG%2}yf$p(OR z`#ly2vv1aKNxWz74kYuqW!7ANl}?0cg2g@DXr$!gWd7kMdF&t9yQ1+j$ z*#JT&{UovKW66D;?nkfe#9cNCycMZ;mPUkKaLD~i{@(R;2PRq5Z{KXNu{kI!Fa!z* z<4gqVDTsmouVp6(Leb31HaT<4zP#_MT)VZ*ayu=X`&X*pVX(AE=!c*NoQ~Orpzb+v zd?^|3hH;oqw*)>6i=}s4n7MrB;c+jj>*CVe zYh97Z@~ycy%V{-;mw`dmkf`Uf9L@T5fkje%(NGirY90~so|Am7@|~p^niGv=c;6lU zv2G>33AQb%qu0+K`pkN+Bm1Q^JtU{HC?oRP&U450#MiuLlJNIhpC7h3mWpAVTA@-8 z-|fqhi7zmlTI=WarwSh9GNp>aas=!P;_@{Kl|wWiNfoE{4`QG-;p*#PsKYpM-x}|* z40~dJm>?|DHbAV3T(iZmMhUt-SRjtP<1I+{E4O21Dn*|-;h=+>x@!_^w{XR3BVE@& zIq?cmO_Er|3f zra~LL+`{@1VO-N|tB;qEk1_f6X)@!^A4*W9U`bh^0K%g7}Wb>eIR@TJfiOZ1;?eBCjo zEJhkG`;6El*EHV6VZ%(rJKiJvsiusRmyzy5p*g=N^VtK)X`T%mFj+>b2B(CWT5!z84P>rroW8(Q0%1)M&6v%|arLfyZv*!w!kK6&$I~mE zyUxH3we7rh5SsVym3iyfm99aBAC2eW+q%lex%|dEU}<$uY)9c3R)>4JuClR0vptb6 zdK#jIJ1Umqc%E)Xnl(9Sk&0zSp_$qp!SoF0J^@}m$sDPi$BR~@@yYsgcGI<=z0E^d zDP-GiKcX!I?#l`nIcNHmSf(`g&t>O8;gyurZO9DQZ0E_o>u-lft!m=4pL1~rjT17N z+}r%!{m%#~W-s3%<)5KL(tmVcv-y3Y zTN~b@B;y)wMdiixjp+T391&7CK7F{%o<$juL)hRtdN1#4I-yhw_5Kd~^!h;Q<-;(3 z3oZ4$%i&pOc|`gblExQ7Is$v(>jL9$g8ZqH7_0wJe;TKe2ustcsQu-*@gJJPm<=RN>^=F9 zeycZrN}bcRAQwD{M$i4ass`it%YwDUou&L0_@l4+p!Sm8Trgo3;r#1yLr}nB4-z!y zY`3d_7E1WrvTZHEX0(yEv*LdeDf`<7CB}i0y@5j=SHB+GuRO$Qg3YjjI2(M}_<{uh z$@~@2l&|vQe@FMtKL5XPbpM||u0$izK>gEyu?&Oq2)O6j`+hYl;HR`bvjqwQ>`6uo z$G)(4oX%OgBzav`a9O%!T)|!6;^zU?297@x*mqnfyD#K@orp)Qp3uFR0o^=3DNgnL6dfNy z2XimZkBQ$<$M>bc2Ud_~E^8FG7W#5yw87G&^l{&y+g9{W%4q)t16_U-(hfdJU{YuL zo1pmKc%V5I5LC^f`3nD@*#}lIv8w}5A>wVM6vmQyN`lY{_M)Ey>l7d)(e}*fr+>}A zpYzp`rm2qzN?mZ#TAMm6d!VqDdhSP8rM?kP?=PHH(>z$s{bll?BiE0Z!urR?;j7^K($0viVA&=qqm#vk3QT5(9`ajZ|VPnLt z>}VU5Gib83jjl6&GvBYW?Qahr_}#=Cm6-Myas*XHXX)O{%e1~QwSg~+hJO0#5PS9p z0yV-ABm7nOv{&sdd1lW2unb_M9n*O|`*rLejpZSK161ZIIW{f5{x=7;AEKc5hKi1I zsr?*qsJ(HQI{Nk@wEn#XlB#qK6sDcTmc=x<;FoRra!&szi+=D1DtD;;W8GI3p_VDB zRMj_FqE&34?PdJ;1N)0~zFCc$qDTCEAB3kY0wkSn(BgFdbUZ=evd-irJ(p?c@m(ea z*g}QNzKSmAK@$PNz?grR6DBfISc9->Lm|^k!Ban;;+IJ$zLwmzxwuy=N_SzVz@=&- z9d{C5T4-*+M-XkVMcLw5@pzCwXiDU;_MqiYh`bervvjdQz`%9lNGa|&OaYio9~!_H z>7~WlHOi1{Ho`VEEQcZ^#w|~3A_086N zLvG-bQR&@W&{ON-yM8>3XQtxB{tkd$r-S^rlgV{t;wF--$odz;mF<;#wOQ4OgEnX+ zJmAxBpnM3|{g29ra^rUgD05sM!>6>zeW*L}S!YPN&G1LqG5_bO&$G81cp@_aq!k8+ ziI;r&nvN@F8yXz z<}{9?ZUlsG-4PRl#jUX#I3um*recSAZCCJL3;X`zPGmn5mc&ZXANeZb8yGw$$v!M@ z`erTLkL+x5PHJyrZH*VY5t>*|^v$21fXDiY8LUM0=@(zWEJ;T@)5lgg-;=y`pbYOT zp}nCp^wYA`-Zwy&3r(isu)j>myB{>FqXRtkD5omnq`kKmMQFo_J~ZZqq~= z?wd--|Ne_V$oueK^CM7|Z+5@;;$B3&6F;-_+H7PAXVUp_WfENG=zxtaqp+#kL;lZ} zBM4SyCjF+aNL1q7S!FpXxLaO*!zW&A<6%77YSJ@ued2kf@Z@A<0wvrho>UY(L||eP zC1k6S9mT)-%r{c=FE*1rLSsAvnJ>B*$Li7tkZF8}><73??HB2R6!Lo~7f}bm92fzB znoG8NS|zxN9o(I%is@I?y)_51nQF;uJ}4p$nh=6Jo%v5^cF{R=b!^^&{%(DP{zS}z zep&f`fKs7u{c&?OBEEJjytFQYzB5BP(-;7fKUSrWY3l^tqi+)h4P|kepoh+X(wH%U30%A_)Llb|1R?S?al0OG5+zyxZXA{LV0JuiI?N20$EV24EZS zd)}QJ_dl5OVFrMK#!$2<MM7~()j6}3u z=Kuy84S?EV(fj!P5I%?tTasr{Tc3}0+}oL^7z|tqanULO)x!ub_}gZOzPsn>3@Kk} z;sEBucGUa6ybTcrh%SIhE{N97Uwp~ze3{v4;D?h*B`Ta1YTUj|*E`r;t{l>vs(CtGt66P9q_(=xjsv*4f-q{xxX?LSSUKEm7B2Z>sG^w8-r60G< z*eK;~=0l4q30qw6nP?1!GFj-iz5a+0h_Gdvb?$(@wH!t&AMIQR-Bgqj(Co!@r8L90 z{RJ0Z%s>x>Ln+@UKkmz4Kzy9<%`8d(%@#21)i5EfWAYP$m9^%>mX(`^Jv`zvJmsHn zKEAtr=Wx%I+|Jo)0Z9cs>=H{c;!I!2l zOx>?q+-!+O@rQHAvM8}s2m~2daJ;N~+W`h87W}mea2A>CgHJ z)qYGqG#%hSh8r|FX5>1_@`9w_Rw0nu7Rv(7sN>NV=8I=0czvu? zmgDx%E0X6h)BL$T)pPj$ePvd~n}=IK6PCB#o;e63bkHuEayX~@Nw0%cZIvr$%`(KI z*2w^U-ftIIwqqmu#s`mm2Wce?-FmXrUuO(Wc?{Xcp30XCy9(vzGiqyTQ%8soE6_+N zwZo@EZazqN);nx6DOYB{sF#^t=m%PP8p>bU?*f3G4M-i^T$3EH;@<%TCP>HXD;_v% z$2r9`W;4VSnZC6Gu8*vweuSnkzlg)ZQzUB7RYwzro`{O41w(cmk#DaIM(_Q9*!#|~ zCbO>H5yb*xr6^Tp6cGVYI#R5tGzCJ3fJhBhdI^Y%qErilbU`TzA+&^^fKsG4Nq_)R zklqX}^qie}KNaVjd71fn&UOBHWtikCd#}Cr+H2kGzR61~LX#6L5~Z|TSqZ4@Yr z(sYs*7W_SS_tV5B)@zwa27As#93YuSU(>OD3ho0?%jfpGdJ!jMV2R`p}H1Wpt9CqL0pG&cNTQ4NV199 zB@XmwQ5Qn%_i`OJ=kpW*96aY7LbXmLuz9U5NT4->a)hGptaQ5;pG@#cPEWQYAce9T)7B1`9AuaTS1WO>0#l>_1q=72S*F-Egz`WCxG|0s4#f` z?7?dRM8GMHHaH|20vbEL`k>{as`NHgbdMbgGjbQwa`i1ay*+D@@g|4BC3y zbPd{8{KDlIPId@uR*h@3b!)CO2xcZQjG{|v!Ayde4bSPx*&EECdMTJ5&yXWH5idHr zRF^o<$|>rHn^hQ{pZ2HuP%N#+tp&@&6~XgP6ZHH>nn^?+7ZaFE9H?;HqkFyutV1)TQ89*BgagQx~+W2%3Eig890(qn|Rg zxPc-`;Mm6uD3cx0Xp9q@M^5ci@35I&dRvrw++GpPBa1AAQVP>~;?S}MA^Yx(57Xd~ ziu0B3Qel6!qf}qqC_E2P>kgG5%GjHc-Fvt!1R`)z>^o4w$fnfb>&+hkf4v^+uCq6L zFuo=gKq?!Bv@vRmqBFztll4)o4>f9Ts4)2qlRs$PI}fSH>^qXJW7;Kj(7b01(sdOf z?o;RI#nRn<5w#o^R-$f5rRHUbX0Ev2(=lGYf~P({ohUB3XLS`L=1j=#-f=ykQv?E) z(9Dtd9!lXq;kP#6Jhpc_TtC>B7SdRqN~wC&({0uIkJm~6J(G-ZuVztx2v!xcOUtd3 z9u4MN9DjevYxu;10r#Hz2lv(No~GtK*{12%sO@%6Sg?=^_~RXt`5k-AbkHU|2L;p{ z_Bm#xTYv=xOiC_Ok@=Pd?-@XMwQ9fAO^35n28e| zUGejFpFO_z{K_(8NQ~LoXnWeQaW1hxX%cXV>mqGk$6nqiEjeq>57HehS8S7%P1{?$ zUcPNRwK>wI?+lOJax}#pd&KiC<9(7%-Vg^Nr9NAF@RJ+fNSu;pRl~a`dzJL;ab+hB zCpkr%t&El^n8K4F6Mm*~WN%}OvDhY=-CfNf!nQJks0i*sm3-;zbU<#Z|AZ67=sT&;w{AUrH6=&)}#64wX~T6e6Mm5BPGo zX#4EyXNFP!5h2@GJxJMxAx~b-Lb<&ud%3000QQt%KppJleoL??fgy{J&<0X~BW>?g zzVuX8%=+#D0eCILOB*@H0FPcbqOUGWH-KdDyG3urse{8E=48K>$%}(suD3+X<}}|t zr3ffcp1gON1L+M2^XH*R>-K0+<$4=stHR`3c5|X zwi#qA#qvtjeSDRB()d#u!s-gQbMM>2`JQJTS5Yyp{zmKbc>@t!sX=~*ZXtUBE_$sw z6X|L=(&Mh@OAEzUE|s8Bg$V^3zNSV-zD*PgMcyMU!Pv0s=&&(Y`w$`PPArwWX?5Bl z_YcRtwu8_2407VNh7^RObRxIDUvYgv@ixi6pfOhQB~jbnwK8ZwU=l4ME_d4(^p3?} zV!B1Z?B-L|tTSVC9_zRPHDt)h)!i$Wz^~mc0RwplxVZ=Oemo zp;J(}?^ph=Wx!hmu?F6uhAE)xqB%e6E%k{wXV zX`{k3%+Oc4s`iM>L{?ZgNl=>v)T`~DgYEnxVd<>f*e5%QK(lu0=&IFa@9i^VEjFqq zIg;`)l3kvfLg}|ufoO0WyyDh3+3nPtmA&1y?zsawif8^bRkO~y(zj59Yi6Xm2Kq@wztbHY+ybG55g@sy zv#V_AxB}QF0#k3LTxIKn3EC<6tuCGpSz8jKYv>mFmDobsL1wt3#{@>qvVW;HrRDa|49&)q=SCN`FH<PX;7o3dA; zd$*04Y5BePHO^@&*USj42{)jErfvhFQdiBj=j?XsGWkXtXa-iXKFloodZD5ePN)2% ztx<<#P3pBxDg9N>qwgSQN#3tm4e0tN}K%~jSXOx(8Mb7c} zSzb~4VpDTFW~h#|s&IS7!I_ZNP=eFBC2|lxtKLb|7!h*R7b32~AhwO3-rt z8DNmcxcK)TdI9J0pBQYNF3-^~0LaUrsHk`jexVz*Ueu?Y{LL^<0Yf5StFZKt4z%!} zOX0KNBgMam3!aHYu&_VY*V3Z^_SYalwR!@a$m7-g0yEA!FwL@h>t~YHxKvm0lGCSj<6{7Dob!FIpeOS$X%2oi8^K9t3?hdt*6Y6n#JEc?SeY=1& zWs1z4#m1n$z9}%CfQe+-*YJ2`cG;CYVV)Z&ik3`|uD?ri^Nv@5drOYbO-z)|Hg$O~ zAAW{7-5f6`h182>zyJx)lSalS@kK2+G0*Q8(}}HPG>`Z2$`d;BRPi65oeE8Jd1nr& zvhwux3Oq~Q7=+c5AhcFzo7gaC3gVr9=X_hD`qLHM`P|<>+*c&OoL7-E#PA;GU-1x~ zGyK+Y$(fjvmu!AIl-Gy%u(>-FC?&{Y#{5mW{lBt{O*It|B-z&N{kj*SH4hmfjSveb zs`P5-p)RDmDRz2`a-|?jG$dAMCGcs_C!s&*_!QaQnY98d`@@rHM+B}xRNKX7jt2^~ z*Su&3x+uu7XkBOn)khc_z&b$!jAP7OJ~1-0v%W`;;?hj*$Fzvg6Tb9^C!&$$RBH{0 zf>yem@aU2%NiaQ{Fe@|EZ14R@3o;Ul+DB^f;sBF2ti~3v71#|ltnNI%HmjmOZg@51 z(5;;YS!ndy;L!-gy);C1MzS+gMJlyZMYQatngS%E*Lxh5H$NBh_M06OA=PhGUjsCs zJzp4H%YB4UY|C5kG!Gw))t{ebO*Y2bP3|D14WkQ>Ed#|IPQjjdye`vL)IRH54Df+M zx#p1I@WS&YV=JPa{M8F^SwE+X^G=hQH4X>_yN~mQ)D;f@eLEJ6sd-LADN!{UiBLlf z-tDpSK&`%F=rei3q{dbb)nzxMWm=yIcZ&Iy6)G{R>ftzkUz$Sp!m$4E4t^6_qw}qE zF>;hBBKt)Qs@tAVzFSAp?@Rf{TNI}*Fgb`g~H`F{_ z9@``&1kuMdHE|X3qjh5WPBfx$gOnORS_?Sm%Z>nTSH6q`u6I=;FKb1g-7AwizbX$U z$0@2dFAtfKQx9IdK&W`I1L#4`t=}*kFCd$6VO5${*Mr$dLo#kt9_~7rOEvTEn?2&? zjnkAM^;>*-A9zb&U*GzMqPYWpZQ;FpW2B7B(4ArXk1l#Hnf45g*G*S0)#yM_Wmz{v z7iJDpgOF(H<5{vU^KD_Jg!y&aw) zRkcJek*MZAbJ|-Z?(p7FykxaWk-I-<=Tf@iKoAdMJuVcH$9=bY_!zGq*OamEFeS&L z#BjhzDv@o+o@tI*p|q+sVj^#pVs^GulLp!8hTSxNI}`8bvvc5^#;C)53gF$UDvg3=QGiU|!`j1`oDni`PyWEE`a{Djz%x}>eGy^}yFc4^FeV;OO-mv(87 z%LoySyv5#EL^2QuH-~-85 zG_G*xI2x^%%XNiCzHoS&r|7SBD-DB0k$Cp+RWFm+GXW_xutTn!->#k(3BB+bg|ziq zVyvm@PPd!?kU!;gwbMN(@z4-y%jXZ=Po?YE?= z)dY46Ipl6m@}Lb=dR|qSM`zQ z;EHBo2X#G@P4l_WXJW6bOkmTe=_xJ5tqR7eP`bJ}{u& z=PYDLFQ{6>++i;#Srb%`_OTAB%{E4fr+xYG7(^0=NlM-tgON5`(ekLY>Aca`f+m_i zOs%O@6Uy_x@G2E==zpRrdWh!JOxRtQW;G4uRO|g(`Q7}$fwI4R>)z81@0@-U+7|Od ziIgC=3@xcFM6}o;-AT$VV8-~P1V^+I=(^zad-q-#lG{%xXTW^Q?Z@W-?%y9{@TDTnjM!$YXF zH8QOuh7>Jk&a|b6b-i9u1gBFIYOPo5Nx2##ng5QN+xJ_A^CX67`K1FWP+Eab#odQB zgOBfJCUm#nd&Ig7`}q9gLy+Xrdjmu4CShPix?9Ni?|WQu8CO(AyppFCi&eomc(FPV#5~IKY5dKpr$g7j=ngeA78w z67mcvT8vc`1BAfDuo`C%5X?IyX{Q%&)YahrX}CDK*8( z!?3+$8k)f5Cqc56J>tfNbYCx^ zX+03{LGWXB?!)Gw1%u{mHT=#l076h%WJv1LyYK@VF4%qPf-Oj0bf+89IPWRihAdC* zIS2$SOiEEIq(}?suGcTA=S;Z!Wz`a`6us}dr zpRrr~M4+<=2=np{hM(VmC^!*S4ZJ%^KUysCkYdR3Ku_3qSi6L{>k?fHJRNIjt6H4O zn%e^iQg2KW2$vaWoa}fr)!M-^MYE%B#~$uz`C%`%xrIrR)tjDv6`?GhSj15xhlAQM z*vaMlK}Dx_&$IVK@9ty9eHPk2Yfnlxrg?U?I9JtVtDCwogGuoiNNte}be8Hr1i8?I zyPwbHu{UIs?zjMUfXp&HQLtJokQhRjN&{4-+B;R>-MhVf3iI~nZ4>>xPwO%Ef^iVC zW+^_)g*`k`%F%>kX;!A)iOg=lk$3PKo0*F6s7!*an=QMq8e4>xsVfkELlGoGQHOs= z5#HGH%SQ+B>6iC1d|XAI{BkUg0m8d@-aW}zXq;Ou{;1`bizpV++@66A#N;FnerK^T(^#JK$3XmWq=s$7k%C&fQ zu@Hzuj(Q_I*dH&p6)wLjeQySbKcxyoapy^=6_jPGffQ75iN50_1~1QIpw!resO~Lx zt7phr8U!rVT7g#le2LS@X$i&|kPFtV>e`$8b{`Zm7AMMrS}!J=_zYgkrQU`&yEfi4 zGc$b>NzY_ty{nwEgHno!x@zJ)DbnKlW6=_%Hty4d0@*+hj&;f{O+EfL(4su;wSTni zIYMN(9%NExJ0{$K3bww)Q2w)vJ#@V|%-TE2wj#}I^1`D&zDyrd2OpgGu`%(YtxCv! z^j<&7Tab|D{*Q3a*4au(gzd9;DmNq-_o<}}NZ}}o;Fn5g9y7f{MTQGrGGlB@2oJ``!8`jzbD;B4gdLw z^55^J6nW1SRrCLU^MA=0B-QT9x9vdGHr77e)SLo_L;(-e>U56K_wIw2@05768lm+w z$5dnfWNrW3N=^y^kX3H6@rmyQR@Sc>-eZJkaV55!t(I?Q014yQ+28aNa%vjDn%;=# zJbz7jvmXOh&h3EY*x^W=*^i7>ta_3*D6*(!ySK~89oXzpe&i-zDQ_16Ytx>(E`*6v zI=KH^rsOj&Gfw*TTJA7DeUQK8&6{hV`$AG|)2B(&-zgqzY7fe#u~jV2`x~Lj?kn(Ufe!l&GmN)u+yDXGo&bh?4f*cUIb9I8jFX_H|!X zZ2#WP-sX>~qK{7TdPMB+;JABvImnbbpb;F@#^{np zAbFdmIXP6ktLYD$wKyK}*rNSyySK?R$+D&x2_YB>l#G}stJdnrE~>rSbOl&HXJ|aD zNBaGp+SBjfH7#TC8tgiM#&9)e2aFzGG1|?-moCiaEMYf~{qL3UV6$Giuw&$>uyj(+ zHUo@pWT0g`4ENO@D9AM}ynFhs>Wx7_w^7}u*{37fIiTN&OUU~Ccf(Uk*~$7C*1l`T z)ZSZ7#W@MxM?(x~xE+3rIr_tFLj@vq)77j#65CfKSl?9u+|3N`s$#Y)8=dT zyDbBt?_Ge|6^@d#NZ5F(|GgFb=`W!`&4rX+y5}Fv!Ovgb6al6>csH%##n1bHx7xq| zOVTYm@IR7R@vp|P4zTcIjiz3D{R2<=a~l;$fPqh$k(J*3v3|x&N=3j_U)9H--Td77 z^GCl0T%~6qwM};X=m+>YP=DVGaFPSe@!cY=q0PHcHrPx{IvDuSy7ABdYK{kwfg_xV zN8R~a)e|2eW7p5`y!F3AW$d+P_)Ukm)JflmQJg5)XmgUuK5h8O?; zi)(|SfyLEL2aSb#GyF6}YwiG0;=wxanMmI-Zhtd_0qlQp6qHd!{ztid!__yK`=94E zsaVM{vI+5^^FM92o8RY;3xN0&Fx@t+OdsRm!0i|g!96iLdou5Ik&|o$1xRbMcX#p!&>se^u#W#iOGHcW&r!?7Laa!-%;R5#$`Kf9;;Y$gR!V~ zcH8{qzhP_w&K?B&WgeGh@xL;QP|(LmlFka-6xy=UW8So6u&=knrG7ts0Sf~&K?hyE zz5b#fKq7#u_^T@bmVf&(k9!b^^pycB5H^=pCIiwFo-?%q=C#j{cQt{KpRT#ZwsrgN z^xmR$i*-O#LkF3M96pyz0BOORp#A*){CrE-@?pfR^fbu#0sZsTeIJFJy!36{IkDlV z^pn!{0Ox_~Dv(RmzYDc31-Qcj0CE&8@(1;JIzZ$y6XBIXlgw}&T^*SRU^lX`KA=O) z`2&koB?kaV+fFV437N71Dg2U}?}RyDeQfClWBW^c3dr4OZOoo0etpB^+U_y}eQNWM zY}@Z?z{j+Jtfwx4b7grPT?6ptMpo&PEQLccuzt`;8^-!p*hHjku1)q5CW9O&<8m9o z8Nva&Jl!+eZNn8ePREbOX!%OFy;b3}7k+*d!%K$MA$AZl3pPWoP;lQ38ZQy^4d)FR|OA%JhYb?l+&YGp2~`~YK&nc zuMSBulL~t7s(_x@-?`{bXjSwiYNtoHO?^Xt)sC?n5Covl%02rfXa{>>5vgu-!9(JM@Nc2R(Sdjb@SC4g+DVGp-Jg6C~JP{(ZrPT`QOtF;-lyD7@GG*U?S zDhWHjo|kOZoZt+Ei0K*Q6t{6gD_B^iC7T}Mz~mFwm_1)4B6e*s(THuD))G+H!#OqI zxd6fxmPbZFYYa|*65~Uia{hEnxyl2-(wn)~>p>Qtm`&;Tb)nWn8z1L^DX z5rFu5d(V#3(4`jHp1J@~!ZLKbg06HEZv)df{m_M8J&5^Ww^_*ZSIdKmp;=+Y&J14A z>n@~^lAJzc`zhU50x1&f=sPF`eX^hb>epBb$`LN)-enP$`F;Wvq58qX3F$xL(3j&+ zTL*0m9^f052Q?}IHklo$1P9Xrje#5{hh7M1h)fiGKOy~X2vA2SkcgXlRrGV{(hun2 ze7!1;LPG3EWvSYMeBM|XSO*1hNVWgKuoc=~`E0E84J*@p86 zm=++bE&w4FIDL7)m`Gbnyk zZ2#7_0BFNEoA;G^=_^qI@H+IugY9g~@06zAJmxh(-Ec5VWBchb{(AptxlH#hBcWo# zKfmd=YYz1GW{(5&G5*iGEX}oSjJdX=B24c9p2OI6<9=>w`7i zE}#O$2lVIOdxPp(xLRn&%w&Sk)N97OvrZ0uUTtr$1Mt9WNAFD!v1%q@(rU@a4*m>k zZW>!crZuHM$FKmZUc$1iH?`Hzg5M`fKM_K?F7Cf);up zQ4JOoGs(uH;y$DEDy4>_TU~baZDz5ZZF+k8$$Ti-D(JPL_wG1=;X{8#widYzTX!HJ z*j`Y`ySPH{wE`bRfWtnn@~RSGtC;MohEItb{75GJotdj?g8_Tecc}eGh;!s-VtGbd zf7`^5>cD%fKXxj_8S z#mH+gQjET(gO?aULf34$;d8Bl>tHn+fu6Ry_ZY1-briwGg&*>PN6Nt{ zA7uWr$H5{B0}}V|vW(oj}sDM>d61f^6!%qUzcmb zc<8#yU4@!QKX2zz75M24{@QJ1zXonYYL><62gy#rBY@|SY`8AE*-w>BKQJ0NF5cw- zS4XOy;(Wk+4!MXL@A|%jZ{~0523UTTp4Quz@aC^y<41_~>xV{Vu%MQDRCG7pV1C9q zpmAb9h*P-&tWN%$2UMB``7nD0YFp%H>*bfL+C2oy^;bV1{#WsRi!V4V#x|F^9{*RP z!F3dnp6vI0^m7mWJ4dwPvj6W+mo>>(&N@VeT))r~|lPox*=YwW-dYWP!?>fIj=H$6iAV!`=J0Yi`+Y&Xlq0XTN@pu7+D ziNcaDZHt&~L#=bKBRcq!H)?$KYx7j_(zqn*M|S_8tP*f(OQRg@m_t_jvMtb=#U9?5 zMx%~WJ|C9KL|K;D-PINe8|jjnH|9Qf0?!)UW}BuUcKfk{4XRobvb0b%Dn=_q4DFFO zA5q`Rl%}tMKbxwnQ{}3w%i~zHUo2^M#&eYNc;HK#$tmro$#br*zWw+1Ka0<*e#RVI zKV0(_uNEYbri)X|g3o2h_NA9yt-6OeRm#4pTl}%bfF!w^P)q3p%)I_QFZUX9p*g+F zilRaZ4HhvltibkvD_m=O0o!59p%ETjjUx+qS6dX25AgSC)T-mhlM6=@F!PSeiQejS zt7KR|jtGnUy1vpsB(%LMPztVim6{d58t+S!aBiL7OE~mWayl#)xz6o85lDg7gl5U3 z5AcistHBmmO>+BCEb+la=#>?tJhYhT~H zhnJ}bAsvh4tHb&D3dl2S(!Q0&W7t{RVq@WSM}JPB*``O{pUVY2mZVaW{tg&P_vJo) zjP!|~<;KF1g~NgI@-?eIyN#pth4UR(>yipHPpSGWnmkB$>7bB2xc2Rcr>sev)wn6I z*W{tmB)#+g#+?QINsAT4u&@jAa#kyHRBlcIsJk(DewK%J6ZBTT#Xq2u`RNf%&9`+s zG<;5#$DYkvnz_=NXWn``N+hUOv{dI)gpP8JMcQt3E7nF*IZ;aTsw>hfb3yDj8G*ov zX0j|a`NM8C?tv9Vm9gfLLr1jieG{d>&*d)n zdh0E*B>%*bo)gK#LE%-c{9}jEuYb-|fZC~VAOf!(N8@=xyOv|VBl#qsOV0ISL*MLz zK5rotzokLz0&fdDVuqSsS)*iNuS&rLRL^nbMwGs2brP9;a|HLc*`3la&@5snRb7Wf z<2Z$MGK1)y&YI;#J+A=QU#-=PSf*2miE&rOpRPo25} z|6QZqPx!zV66kFyC!WXX47V>@dZzW^sPUGo|6fqbsOV6Kz9Mf&LbP8D` zv^w__{b&7FW}=-|SRhcCl+Tp3>>kDbZ;O@6k0r-Sks5XEKbBj-kYM(BR!J81z2=!r zknq@9sRUh_xU4t( z37XL|j9MPxxNi+ohx~DGocF$dA0c>1#;!Aa1OT}P5;FIPB{chk&lc2GfiQpwv#j`d zH{O4^DL&8i3us)gJA3GLvp+rT&Np!Y{07FiK`Yd)tXU1SPab5r)b_o++yK-lEVX`G zHFn7H?~YEfmy+g(q(>jSG;MM2n)IkL_Q+Z(Uc>K&dW>0k)bN>{X6nu&<>?c>!_FUd zz^~^@-$8G|42jVKcR9~`;JItKy)Dl#_V*jCNNn7X=#_Q*bA>s3CJQ%2LL0yFKQ?ZC zmZ1^IQIHB}r)azatnmKPW)VDp%AIA8?P`hP zg#8DHSPS$w7Vwn#nOrk(QJkvJg?gTS{NF*6$HBlU)@W^_<%tdWh{Yx4*a1S{}uDz(Sy=SQa}w@Wo1q z%j@EvpOAuIm+r=x_`4gjmSGS+t;f~s_xX!+lgYTlH#lvrKK-zUF=c&90sI5;T&D1??rUN^Cq1RW?XoK7yu_ae3!OgK|4q8cdaB*THbnoEENUY!MoetE zj~XRUE`}?2{O{)ck10vwW;8I$mh64|V0|{u8d_+$x0;2@p$sD_m%PUvcBb-It+a(L zQDUe4(r(?y7#CAd$9cVHld1_Zc+*!UHqdx6YNXA%7m^9X{Bsd7Fdo@Q8HCU3ah2_T z%1pB`4sx5Bm#UmQU)k+v&7DImE+2b{1idR+G>aY&8r+$RB33?moNx#q2a*^2W{PWe zcT=^l$O%+(gr664C>;J04cZ9LFV&DMuO!Ny@LTfkdk5$CYiaTQusM3;`+WRyG-Ar{ z{YX)REj|rG(4?#n&lkBQ5A)%d{06&fog0N;3>0fA-!WIzfq*k?jsHF`U-y|c{F7Q1 zS=&o;9b)zCJE}sV$Pg{s$`p04xZ+d^Wrs8L$%h4Q7c=fpC6#;B{`(sE(;0&6_djAy z*DwXfNA7``=R&<#tNeM+rDr2&669`QO?W%GmQy&nta95G{eCJr@`7`d1vxy$qaoJ& z>PoT}#*uuSy#GpLAU3}Zr_y>F^rVKxF3fk0e=S^7j;g%Czx{2afy&ByO8*=UC*mMu zu8o3o{*|5n>8XCcf}~NB7bMgEoV3VcMO+2vrEt7#H6OIthbh}+F^`htscz8_EYfPR zl&q)C7Q@q0GTgmIHzfSvjom!1pDe`I(+|i!joA)getw8Hn*4|p8NVY8!8EM{7lv}@ z4L1aj^c;VMFF9E}H*rwD1hTiEaLD#wVHQlcGREj;yKmh;#ziQi-W!Q?8#z3!Y&$-V zv!P`GT@gZH; zv@!U_Sg+&8H2Tj``!U3IYT!iOGZnqGAyU|2i$C|~UoJ2HREcy|2Jv9Sk6Mzq4K=~# zh8hj`fzaZ~)2B~IdP{UWyw_+&VApJmmiJN5)XT}WRefxy%4tZ>1;|Cu*+%^w*U>*x zmUCm6E1=5iS(F>Fo%s_@w3kI8By7VAaNn2srQQw&jc(YZShMebajowf(B@v zsPP;}6;bL9r-Fpgg$jT;<0BRFk_0s{zNmek(qC-A0x_<7oDaNvjau_%1)MU3J)g$;7F>jMZM9a;g6*GLDfezBRhYUevS zZk{K$SALH8J{qLzU!T_YseUb>8|aKb1USW-s$Iu=1Qoo#;VaROD>k4%^nKwNg6kLr z3kWa%yB!9w0NhOPO__o3@3U=ZzG(oR9LpY$6AF`EZqKRD`f%e0x7(*QBtxEI<=gAc zcUGcKn;Za5NoYDnK>8uf@W5I!Ve>q?E4qK{pz z7~q_`6sNe5SvXukRYv&2{_9RgU#(mK6;<-)ZuL)7wyt0hoGbQSnpvzA*psgH2EMY8 zGycsiFngR?v?=IH@GG4_XBw(WEW>-!O3}lpbbUEi3`Uqh+{mA z_<`yWnRHtPA*Q2!vU1%QHt`MH#y}PewWCI4Lr{8yxs+0!87<{_VQw@JGP(zX64K2I zu}W19u6Tg0Up`Ok32rU(A%gk#h&OKwy8u}u<=Y$_IvBF&@Lu`nnN?r zf1P+6blp5Lc0U|5ZwO8TyVIGvQuo=xRk{^TN$dJibc-twAYFwI*M(y&(82_tiBdF? z<)AgDF|1=pQ>;Fm^fOfd#fyF)syZX!gBu)^Mt|nLt!)`VH890_LM+WP(o%F~p{lib zeg~~N3?ihg*3pC*)jvLc#&O}X@5QtYWBV6Ip)|mhZ{PDlZll1o@D)?K z<5_c)s%iH3GYsLDIiFS7-$pMzV(J}~o-FqgJO0bz@z`3KOj&twJ7x4s3~5RieBka+Tw$oL_4ELU+GY>Jd~ z<<@Wk5<9G(X!0r724vLdJFY>3L2DX^eRrnM3CT%)&S`6O>CBDOppPZir})ub!{;)pc*%YCWaM@y4T}zsVzWfe!qVFYZ@=8pV-s zxN;Ry95?7YHED<~IMO6fos*tgOtFO^)G838?Is^D-k1+G)e6d(!4SH4C%tNi!?WL~+_&#J{xcN6@1MH~@LZjC}k=;>iUWAneOssx0xoy7aU?&%bg+(4EO zj^a+#LZ1OpQb!|3xjzgANE-2 zQzR1omrV@A*tFW4Gqfofqv1q9OJTs!f(2X>fIp``b)C>NQd2)n9uc_9u)%5m++_A0 zxk*=Y*!FbeL7yNRajM?LqgTy&R;{2qO%d+oJO5s^gNwHZWEuwIJ=m*b9UGnokXX`Z zw}JBgZJk~hlKqO6SuWj&y{y$5XCX8t;*Wr)t`6xH=0x;tp@fI{*OXeVBhrCL&5qK$ z$T(eY6ZI{%{iaUHxttlUZ>5(<3BpfYG}*z{-={*Ks~1W+E7!)^|b8+A7AM z37)pZu_E!po=aDGbzJ{0DjNPkIzb~s5lR`xA(M+>levujvY&+ zswO!phxt#%P3ZW)2|vF(&X~s__?j&Y&$|EPi{+0`o$;6=QF>`bCqx$OWB}+$S$w1? zUe@DjyMq7io~2kHz&=@{5>_-$TN7Cq8|_+bE3&%7H;{0u+HYM^F&0u>MoBAp$vr!f z*FNxAan%OYSn(ESJ|X&^6kUHer7BG<9|37&xQ-FIb-HcWG@f1)v+j# z%E~bKxkhqrrRoaJ&8Ej^n(wJc z)f0g=pE&pK>-3VR?W82MN4UQnh%nSXrgtTS>DYZuAzR_6I{QTtYcSNj2F(5)miCI^&4%i*RcU3i zMLlI;I(46&^_F`0XM4#*!yMOY z!UPGFT@`!G49$1CPESiAsNYN|;Yur$mW*4r?Nj>gzy0GBuk?*%S>|-MKF_G|s0&^_S-lII;g5`;bJfm-Mbp@BUeG%YBSPiFpIZ ze|!JG9G#?F+ki1J2|YNr$z%TS7b{c)t&OCqL$g=@>#+aTZLL|s1Bbq^OWH6Ge?2$VODkLZnK<*I%6Z@h4T<$-T+-%7T>mJ!}`v|7X&KxMf4b>6RcoZ`4PI_A&vT5gr7 ze`(V_?e@DN)t5KD*DrPfX4onIw+aJd z@7WXo-r8D#)(DL2Ql!|gqj!ztWL@yw;j@aHts$kPT1H?mGBN`H{;`8DgGqnpsT+Op zmnqtp!7wSi(wupu#y?(S)BBfKffq3LosE|L>SmClVR{!LxCy97)aD zZfW$({f<0mbf4(FH86KWMP=~gGya)IDe^U6U*FI*{)`X6#m0?(9UiXlD_e=!!u3O+rPKht}p`$Yy2iMfpJO=Pj&!P`e*JdHZP;KqsQ3jaj zh0;*Wx5E-c*A`Y^Jb!LPT}1{r;j^=E_Q=x8e5f}rTnPC6#EBahFZM!_p-Xn3EbV;O zWj)Ha7f8h8*SkiCmWGFhVibMmnvWD<8X8n7p64B6CI5c?Ss!JXT4}t)958%#qYWi_ zG0x1)sE*{M>RX?g0qD0Z=Bu_x8XP#)8*6KGbQ`J1H15BwA;_QZso<=aIu;ZALa0b* zu~00gNq;!&i>1_dSU8W|iK?n9%XNp{gykl(eZ^fkuiLr_q z?C`v&gXl2TXiktf*3HQ)Ej5}zls?g^Y85foT+1So-jsL_jGJ6Jdo*e04k((ueXA>q z&-s8!eshh5u#j8rVVYN^<&<)2kTU zY*NhnVT9vNCnVk*R%H;6=Mo}}tX$o!9LnW6H^d{ry zokT!DP_1pCP176a>>;?W%Q}K*d60nvFHxl(kKdl#ySUt8Wem-0ZEdAbODdR_(2Ax@ zkM}%T=SgzRavE60wB<;~<8kaggo&jR`@X;5ERrmCUdDvfe?GO*XuvDp-}TZv`8eKf z$Ft5w(~|Mh#usfJwCE7~_3_N3zj(ooQ}+FTh_Hj^`Cvz0Ef69wEK{-!o+v9Gg9vq5 zO9+J591MGNcj=ZPQbM!4g_v{yld%1c*KciW2on6&S^w1?o(X{?#QteD6O#lX zMUm|N*AL0w-?oQO;WRUc$V*{EY!GqW%`%m5zl>@5Z$+y!^~h~@?d|P@096_egc6NC zt^>A(xB0_2)~*go2s zlhOmo5|6F`DhZ=G6%sh=t4TCR#BX({Q}X4yjq3pHzRMi|w%+2Se+4YgFm9P+bbjM2 zP^kc60Rd(AsUH2~+FYICZW}FX^XD{(Q3~EgsII9|xqUl%f{ce^?Mdus^^lP=656q` zu|fv7bnpN;Cp!Y~#lcIu&}`>lym;|oVHY+BEGhTtK6`f?C9!NCX(uJXH(@SGCo(+1 z#l_VbIL|JUZP~~vC?s_5%hU^X*^FAWJHUaS&>qiGVD|*PmC4O-aF2jUK||hQHZ}-a zMuO_r0c+!a_+vK*3)#;q==5@wONf+tuxFeE`^G{2>(TaL&W|=l^*sWxb;o z=Q1SyWB3EMGkqPFaQbbJ@?Ssdk1uZ7c1$_R&q9#jYTfVLXa_P%pZMAI>eWYN1s@B* zPP~x3gtMq;$GiDlLDsRevAu@)82Lc$aex`66Lgik*x6U7?PZu}llEQAwuV^PFUPfQ zK295MzE3IXV$u~x7+k1+s;^9LL>?>TwlLEAVS+f8V-PQy-PK|1Rx(`}ur}ewGArp0 z%XASZ6ms_Mwj3Y-6yZYpa+6FM%k7D9Ar7AZGKPHhCECT$4|nd}9v)fg`o^M`h;iGG zIo^#d@;I!+yu5~ujg7s=h0SA#4PyodvQqrpVvua9TC8O9g$m%WYQY)yE1(sl#zM`^ z-gE|#0OVK~wAe`0*4MuPDc&q}1&u>XGWB`6vB?AEMHFfo{xzToI5!~YKyi8L5AlOQ$mlFZk_=V(@6uJwx zJa1J)37nX))5rN1fg*iH$g_xuBgVz9Vv$!L@8#p|EYlh1Y%v1;Y-$~o_FGRqTaNXCpwAYyPt{Rt=!%L6@ zH3F8!4|dS!F)^nAHa0heSKa_{I17XPU8L9cEI_w9?2BXe?g2bfr{z4RUf#dYU_;D` zr<@0#qM>YNas{-()J8%5Y$QX03PO7D+Iee%GAbCId7k%<`f3wTYh^v`y`ksMoomcB zxkOuEYs_8aL&angk+a!CjbpRv&Eo(gEdZK@uqA{=<3xIohD>Di{5^mx&;^t~wg8Oa zo|*HB_yIt(8?Y)OMVD<6#YTzaZB{p-%87o<8D48Pp4!k7K!NjuS+w*fh<6&6;Ivsl zs8jchU-?e?y)DVj>+b`ZEdbhfNWyn*o~WBME3XqZ@8u8RG7XmS$*~;o@D4nH#@ro8 z6~w@4%S3!bw72M7!ZsMtNJzb7Vexv!3FNKhjD4G(+ts9=Jc{nnO_WD3_1nK0k>?U+?F+%B#%Tu;nLMh^A?>XJ%kO5_`=~ep zPAtm4Jt=Yf)5*HB1!vAP1W}f_JzSEiAZN_vqZ>+=*l7)o-42yFjR?3|G78T&grl$5 zwEQ-lYf-<7_-uPp6EEJ9{^e4 zCyAYzN7Y%bkLQ_7!JEYOP{ZxRm#Ml$aaLQot~EOkW1`PEY0j&yJonLAcxVN!)59mO;19<-y_6@B7L5Iuu zIR#_ja8I0{B3(f1Q){ZKUc-pQ=w4f`4hXs#C&F?WZB*3e#Xgag7nX*u1#L&yfq_hZ zLHZISGQ$Q}fE|-vM%+NrO3pG?{$8IWd{Tb4;oMn^I#!v{w=X7)hf2HwY2j?YkA3l_ z;S?xm1L50Bq4U6MnEB=zfDK#bk@Iwr0y0uXA}RHY6xlY<5Nju9%Xu&4$&+WGr|-2> zoC6i*fczpQ=X3~Rkhaiz6Q$Gj>C`&|cpKFypg>uutq-t~S?1|LIuIG%AmhreRi%j} zEK9QJ_@M}aFr+PlJ-F8uT zYYx;Xx!seO?ZkGKhuc@g=gT{FgBH>b}51e_!8A3G=&muA=)hJV{k%wmVvFcu28D&h(Ro;O}X9&6esT* z=uNDY@&rXt$Jm=F6ZFC?i|LNohdLtI#60}kq~i%+U(f;fy2#oeesv;sixa8^@x}ly znSXaI>B57Cykuf>q^^WD!llJ4_1(L!$B$OCIv&#do(^7K2h}w5Wn<9YG^8;4w3v|A zF<9*Ci#mXOV=xb##)BSCZQ{5=wrN0-fUF`*yse&WwoSTGc*h#2HNopKVx7}7vn(s} z?tI*G+_=lz$czuYbDVP+BrzSm1O8}EhVZ#)J5b6ou`{9JhT(ubSv6e;t$}PGb2fnJ zhqn^b`dm=wGuhndyWVUbi~ectZ-?0*Lm7;7xnmw+I5R(`zL8+jvUM{+wH@Z;bHtLs z9(m<~&zqR-u8yUYtDp+c)KS%y_2rt62+r&ROCQFew|mq|)Y<~V0=KpP28cAze7WA6 zwnd5M?3kwtDm)rkYqOdYTJp^`)PfC^1gJ(=@8@wke2Vx2DB>*Nr-A(6EXlDMnL2Ij z4BX@nRG$GE)@Fh(Vh~ZMQ?hmg7dFj9=@ljJX_~SnUWsScn!O zzI&sGT1ToO>8(yhO-|mS-j00!tIh{Qh{V+G{tqyKlUpX>tLX}?JJn?!(2I}sgvTrs zt?&s0pee<@2{Zw6xFilW##&v>1$`xFi8i`%gyDAFFwlruV0KP)X_7hXI&$;1j7#d* zuV1fud&`0XXAf)Gh5rN$0pE81uCqo}@!W37`b&(nA)fdVuqQ1s+NwX4q;ZdnZ)K70 zj#QK=w3I^4r3Z-HyJzbNL2T23vWF^&tZf!X{lz1YY7d@rK4K>1GmT3SjkJ(A?Xw4v zq*T5uj?K!KVk;P4fELq)T}0bK6&5c-s*Oblzy>z?+IWg*+k9->b3j*jP2!t0*^T9f zZHq*<#Mc-QXkQuC_;o%ne}x22&e?nIwbp*tvz}f2DZ2*K z-JPn|Og30tXm4wqOWbBTgEo+dm0N-{Mr*SZ=0`BVAMkWVXj$DXE-TX*hivQiJvg2V z6pXf)#wScff=^{2Ep|ruRIDg+udIT4T%0N-R~?~cvf}@3MN3lywq}VmsE+oQ_h~H2 zR1+I~>}1e~LY%Uq?d8^>Ulg!xj`41?q?$6otX3z3JjR*4&%e|1gFJ*NethGOQDZY` zQIKGFc&n;*~ETu&>W0vVxXwk6;FKN>W+& zEN8STvon49mNLAh$eteh>{)?05swmL3#-SPpTfN{MZvHY7c6Qux5N&zpnb)!2cw*P z3~^rv&G@{zp4QOM*I?5rLtft{b+p1|#TI9NQXRp2S))f2B}r#R4sU?o6UjvK7Z2(Z zVQd(_dDxP9j>x0WrFd&W#(7@ArQCx-lx?>7(-YkE82yjoSxP43AL-2*E?8_Of^q*UPxf@`N+Y}p)Pgnto;M@J_aUles zB=T8FLHDaefb(3u@~`GX+=#Te*#P0&!>!@*t^0~{w#sqacs@JcC}*S=t(p%EtdvrV z9nQOV`xZ-JrNfZ;`AWKofyXO{ht#bX4-E~OCfg0Pg(O0pzwuZ`j*!vvYEab_c0|)n z7qgn;o>R0vmCS9~)<8dgYQyZj`Rz%cs^q&O(+(217+)Tqe8cVSrL3-&OJ)gI4%1%B zDsXmMvH3ZH(T?r?L$ZLgt4QrBE6As&0s8KDLde2gwW)y&iS5VR@ALD!W7Eg?9v@T9 z3=g0tVqLdyO)P_dKMVFyD#qkgn=W^bDgS4mW-dX5+ur9~K0sg_ zIbrVESVvVS8&LLlIOu}1O8O;nQ0%W~f&`M{77JS#RWdv*S$+>Gw@wj@@R?y;TQ zdAn;L%doz`aS-;FGA|@PLoTc{QAk?C%iD9ahs15d8{GmDGn21Ed2xzq_T*2G{pF7S z$wmFws|k`E&Ve1H8f?(=c1Nxr?H_QYSd^}08O>xLNvzf_Wo-jwv*7SxWuf#L3?Y!*d<*w%D5U{U_@rPC9M!EnvQSODJPddn^DDjVQb#=*GHjj!h8p&3L!3eu_ zr9Ic`Qsi3yZ|3>VAT5kFKrAa4ft?kfM(HWLwyrLh#xtshsAU5`g$0`2l3=^ml`TlO zW|Di(J367Ly2r9*LpsL+ zEZhC^#3q>*Obv3YHi>Y0VFlsO$K9YAiI+rz?Opuir3UKYFm%^W9= z(?E^?nGbA4h}qyuu{dh{s)viSQ$0( z;nW3}rD2ohz*B;hl$40z2$7Z*J&Tk5z%8}3wsJ8j+o|`zLw@{i>tXL6UH1uAK}8#y zxcyF-8s(`=l;_V|E`Ffn=H5HZ>R4(L1)aE#iIA;ZlnvV2Rb!((0hz;);u`mK+~TVF zlP94f9fA;b1~S=guopdS@}vnSQ+@3W`Vl(|aMl)Ip6#rD?JTip2Zgx7OEO^9Id@7W zpA>SXp{B+Z@o-T?lKViM%v7t#&bpKFW~LB!oq%9*oS7P{joomu^kLxnxQZL@x8K2{ z^f`xvegA{Gh&z&g>tdk}s?hNdkOiAF(e>y|h61ng{ZgdfWd}=H(Va(=lTT;N#10B5 zbFfRJFqE{zisDK{W|C*8d!KeqapA`zD3tJE!OV}|p-(>Cvr@3_`01_i|>!fc+gNAYsPZp zM!Kk^uW%#Z7Z2d6EC8s~TvMSRZ}$hEF~)Q}RROm+x9~)=1Y1ol?{w*F?ywHZZ6W!A zWtzSqHQ7K7kPy!3T?xA(3lFYO(m-9A3;uK1znt7$fnkykik>eFKTr}Z=7XAaoy|6iM6G&2+qcgsk11!unA3zHEi4Fj1H)SKU99A_r ze%cOx+Yq>QKJy$Z;f3L1!MqZ%1<_LNTAz0>Gf&wDH52Y8?BY-#Zs)7Ee2A)K4ZykX z9t#ts(CRoQs88l+HIi>A*_?!fTQcgzt4#%N0^I9{^BGN_*3jWaloTW>(pRSslw*xS zufzIRKL)i+Qpu+gC7wWXB_5((YD>1~s6LZs{pVgT2kQ^!vvcO42HUS7)d)l6)YCor4G4u=@L!n1?6YXtsN-scb29Wusq2w^dL!m|>7eBp0J&??U;UG1@qC4UE~dXw%zyAIWr~MA zT1y6Swaxz3)Kh)MTsHa6KuIB%B@4vMK~CIVdFs6#13Ya-N9p-oFHc*8zLY#&@6AM) z@zCDh-Z~Cb2q0q>0#akj-bMym^msU{V-N1oE z;}%pHw-PJWXa^07OX|ug=mh&mp=8Y*dR1SxZ`Yd8X&(>pUIL;RR1jY7KX79!Uh0!m zQ!|J8=2*WqV5)ih^_SVA0}Qss?%a9eOGazj*v(S@Fz7O|;5d*$=|Ij`fxCLSWicN( zpc~ye1FG@d%EQ$jrK4U`if_gDHL(a5l+)>Dr4$d(0Czkm}#TyPkZfXhHOJXCv$QHHHMa#)2 zPY4~|n9^_V&)#mGL02o-*6c#zeD_s-xl65wOsj9PJrR7KMNlT%TxkSnzC9Log~slt zy?n&;Ce6()fE3P<`WTCc`=GWms$@?NLe5p4*03_%G>SVPRu4=~v z>f=u7@>FHZf3m5_#nZx&R_eP2rTSjG=>nN{;+z~Doy4}5XSTozhK?-Z^9cYoV*<@l zAwWsGMWw7?CK_qsOr^gB4s9jsna?vZF;Sz+$1`EY6Ds*?eSm~!Iw16>0NNt=K#Ob) zWFYF??`-%G@SLw|HsILJ+_VfW1~)6DvZBa9&KtES$9BQNZF!Do`n^tZr&;*jgj6@Z zyJSREg%cJ9ThsT)^gL?yHh>fQ`YMH4E{4%rRqt-O>As>o$_a=Hy{X|vT2L8~-5u#l z5DlOSA^y6dyEBCD=qv3AeANnvg zFR)o&;)4>ThB$-fd>aKT^<1*;%YRV($02z)UVQWREiW{XNzP}O4-#pj%3S-%;Z1LbBS(v4&x{dQ0uKw#d>;f}@Xt^sZofeo1 z3JOvycwIe!_e%%44%6r^IBq1~Ka)l{k=G{fZpkS=&aw%j1*2zTB`nTMk+u6XoJ@Zt zgoI3}*FOhEkin*vq}j{rV=vVt2KA(DZuFh!NkvIr>FTN9bvSk zC3|4AqBW=TDSZKz$A<YwDH17R%**GMq`{7)_CZzQBq~x43X34=u;)-We`-3JxNlqGl-Y1VFcQ{Ol`K=7r z0Z>!)4MKOAY_F=si!y))J_PZx1)dH7Bk1G-*lRkc$^60iBMTngXi+;rDMcQh!^PAM zH#8+|6x*q%kc*tisNu!-PgI=B|rQfkK;t?R7Guh^mSXI91?TUTNu1P9`r*x7sv zM6$POvl~WzfAIrlH=iQqV!^fy1QlwZ|$tKkFKw?c zRVIIgK{sm09e^*4- zxm&F4p~nZAU%#rH>#D)seUBSS4m!8&$UW-{P8oH1-Kh@^dahlB$rGckbRytPdHA~Y zLC^FC3d&iAnA z4|#pCx~t%${3B)LNJ^TJLd4Fi0oyJDr$Qjf2e(%AHJ`PV7FrnzQ{zb8$fzyt6`SI zHv}|@w(a*mDm}@|gR(-Ur1?f-spXtaw8*A&FrdWoKytuY*@eu*vhSSD1ZNn}1ixkf zO*a7VzZ+}aVEO9LXjSaWk4iHv%Jg$qF5Z=X<~}~>2qC- zTV3j@L4EnTr(oQ#Xm>UYvW%{pRN{-^oevMj3JzXSDRgW@uW3Cgz)DqqsW|OR=|Hmo z5}7VAG>aLr*RKTsr2(_!eGUi+$#?MENi`CkHeJEvvK|B)mgm~}qe-p|4`eumnSZrdwq0JDOHaRyzwSIs6SHOH0e5{jiEF zs5C=K{K*f#+)MiTR#e(Ny(Bd>9}&58oTb=kZXl#=JE-6Pjk9!9)Vxd`YIuR|g0;Ti zRv$sDUS$u(!~Zn6?*e+kL6=6QXD_?UDGgt!A$b`mHwWoqsi-Gf4V<$OvV5(B&sp9z zZ+E-R3xYG43(Y2&;XLi{=ApMzuJk95Zjdfu3Y4b@M{MC-Et{K84jU1b(+mf$O1+(D z@Y$qIK{L%|1?ccvVLUVI#+Z6mn3Y@a- z=2M*OIt~?Z!+*#<2c|e* z2mJQ!pYNHxGcK6-P+7&FFBHY}ikkysi=|W6sd?`EG`ShaB*;j3&0q^W79oS=dKqbH zJMy}pVrhg2GLCh`#SxS$L6wci$!Zf%6EcuD%#8?v>D!t+m2==v>ZcLH6hF~|Hewsz z^$LF8k?R6?{!Epe&UN$gSd3shmMwjdYirL2X;ZXf`Obs5UPp3@su}R15Xop3FHIL^fIO8g&Zx3eU;oqpNz`G? z5m0Cq_&o~-2u1FjJr55(iYocQxdu?Ud;nXuTs7=Ykx<3u{|)2-Tv@ABfh+vmP|-tV z_$?Nel8NA57){A8%aCR%mKHh;x5idLC1b<&G>_SPS? zzuXg>TLR{jJbUNv{Ed|Q;rH*af^wR=F*q*>Y=DyA*!Bg_0&zn#6F?y{1+s-ArJAho z1{Wsz&wRfyzr+b}6zEN`aSN*@U>V-q1%acZIklcTTf83Twb24ch*+$ze!80u$P@in z*0;1*WOQ`kAUC*e%;5Y=Gew)B_E@!c?Yg$vuj^^{9{>@NZyZT!-#OJ39NorI*{+f{ zR`%?}Vfk)-GJv!7M&$DGQHzk~O6bVRs0qS7%@O;LSpBxs=8 zPYEB-Hw_Tl6sF*7-DOBA9a){@O zDQJJ-GJYS1BelN6U2#^RxSWk(&uQ;R=lH?7hw1vYUWK3uZwPEtq8% z>xba|2f#EU4)EwoP@KAhBv}&K-hZ6rI(ccR)UK8olTmEP{Gg#>z5X;q%R96f2XkV- z{PKPeJ9CxDZ%2}VpOvo30P%I?wpftuQ@|v<(31xiAn#hN0NwZ7d?9XgLP=>@Hl7zD z={!JE3?{#8Izj?JqX>1E8FydMt*zlxsF4E7k*0400yahnKD10S+w{6K-unB=?a8%Y z?2_q014Z}^QPFWbA=W^E>qC8AOY>mDEnlk#*Dpmbu>Cr%=C}aZxgX;jBaX}o;C+&H zi1I_U0+su`PztytPv|67A@o3k^&Bm8gBn(oZ06J{%cc6wn4S-uAymn1g`(vKwR6$Q z`iyi!8ISJWdpQ=k(s9e4A12V%S$^cg|1-t^{na}k5NpXuulXDuNO9ehha?XxRKFly z#<3BQ>;&~Na{S!c{%u9x_9XXTJKk>mHMa8qFmvh<0EZuQ$HegcHAjcgLHA?tk1u_L z>ehZa>EZ$B!JNOhWv2G|zc7cR5Bk^NJ=OLQ6$;7&6(M!i3bpZHzU>m_T+;)Y2Ff>u9# z^4E|YdB7K>C$;1nv{*{YSLcjckN$Z#oM_z0@BU_+Wl{;<{V<_H8&P8EtFLz>n=VYh zIdRICb-VCy8rzX8{_rjLtA3-hx<l{I`81{6<8lgy$^$mt1R;L$0;+rx=c-*Er17 zk|J^m$}L}J{}ryn1x$#e(opXIM{~ln!{&q_)(=0W3IF|Q|MRL$j1?`2`NPC2>92!zyUjB5}cDLzs*-CPrAQWP|xqIkQtGU(LFq&Nf+R4z9 zgS*rhE|jffK}wh>k<0+016#$>rQPVLs0tgL>9XKKLc--|d;8cGu2P=8-De|Jv7A4Q z@ez9vZ#!p?wqw%v6;k&;I+-18`5>#K`BX?FCFs7HwK!Vd=TzT({4_cLN1FD&g}|1s zXC)^&BA8|#NlzBkbqwTYMo39hB-1}+gF%FxcR;CO5^yNxQ*Hx)A6Y*P=;XIJIHJCM z(LjlaxnsDvxZ3x5`r+GkHu9Lfdec(e%1}|$iTDp6KHRu+rQT-$Y1g5q&v{BpT|Owp zi#ujX4T|4j78BD4rKbaOJkXTKNu?i%y)BMI%qd^hQ>ZU4Y zrO_b2Ci|XbJ#N4383E@@LN;)iU&66?p>`XnGuO`--!|zvxYL2iG|5rVpF&RrQZK|> z7GTs#%gFcxIDN#ao&^z60SX+BfrmW-Ng^Kp+h0;cP>U8L64%R(n(_6{u5H0@QDzT4 zJzoOIzsXGY(?wEJQu&5{K=B!htXHW%?M?>n@6I1{LQuX_q_Q$He? zUYI(k`l)LWOx_jkQXYgxQhdDC{ung_j#J61(<(kg-0## zhc)#ZQ7DsmK5y^xnT`MQH2yo6agr(UG=WZ#UjK8P{(H*3-+(Yj`=r#T<;O+baoqs2 zDE#Xn&EErhQj)$Y{)YOq>hk?w`<3g$!{(vJ|1u=B9eUeE-#fZL*em1H6G|00#T$-( z|0=Hh^F@w$=D#2Ne_nY5WrIT*`*%OO>>r2Ze}?|QzfSzKi_MA_m$R-WhpnYJC*mty z@0Q>YMK8Y(&QGsfCJkWv_~na^Kelzvv!0_=Mqh)8OW4+#}h<8$U-OGR~;d9KYgCRK0s1~BUe0F z-Tx<;oFGYiPGx~Acd#-{0%N3>TlDMrUiOHw@qwdB&e7=rRc{B!4Y1pRoTs8}vJ4Q+MggC__|@N+s-b;J@kK{c z_3jfyfNAV-lwJ~I)^EQkMj&tkk!`5joFib_HY+YyD-W00OqF&mU|au7F2UKLA|E%Y z%r&3-Vc>sa&X(u+4PPQ@asr{tgba%QVZJ(&bt#*Dok_%FyB?dnXHdf&o_`bO&3`9K zPfuSD*n_g}ZE32?iX?@g(vA%s;y#)+`*+K+gAC28=)GK3rxKrO%>gUw+ENwMeZn)K zC|AzriBXk)^vJ%^Z8q;91e1IaEtlZ>dX(EF?D_JjG|4*Y^oL-z=HnFm9N+GA)`Oa~ z>(kHS-7;0L-M&)Kg|HMtba$Fe?*HpaM!`wt(=j!c(0 zUJI3lK}dASTf?xa9KLGh|llip2Mb+0QZ z9gj%W{lRO8Gy>2G;RmV`X(>%-FMm<=az_VGx5Pq=awf(NSSC&_HB0?+YJdp>b9;8x zO)dS)8;d}GYDL4`&+r)Lo;LKwTkRBHEoyaYVhMciO{d=K8LAS=J_;+egR09&=`&P` zX?RETJ$kPd=pbJ70ytIdaq3e-%7kJj!7o)!h0wn>Xr zysldPA3S7-8OqkfP<38Vg?2Fv{#!16 z_-dy1{ic)$HFZw}2){R#hl;72zWJ}4U{_}p6x2FYJ*Uh;bf{Y17&*jDypQ8vFXpP+ zh!MU~3P75(Gqjcn_|x(aM*IBElDoZIexzV)l{0HQT%11awRo0&f}P{SEe?{g)C!SX z*pcsyWqjlEggp5!J-RKQSmy+;-p)C+n8nmtF;-UUObD+H&8r zjn2BZcinPNi}g{Z()B%qh9?gx9xcj3L$U&LQ{54HMkhE*@ACLvvwtc_{5^VPNim9w z>Rn{>d{EqLlNA+Pu6U7?@YrjADw|28(xi>oVoVU!g&z?+y&1}lwB@YXxfd%24IO>f z)N!7w!Q%|WRqdH;Y~QIZd-X zvzphJkq>Xox0ys@@`;e?MoiLk`2nn@yKEM9C33Z=f9y@;tbqT6BL+^xagLgr+94z%L8lyF;vpB}{E=!(zBVlEJXj`~DlQ2k27W=enBH0C zZP0xmi2oy{@~$8O@`8!)+ZB#9GRT_L;dKdgSl9}hb^O&J zGBKNYB8p5@9UKo3b02~)7vQkOD!sLdX1PJFtH1E<+jpBfv`ENFpdZ4{H-1~y!I9Ls zGoe=6!9m+BtE$S=ti0l69!h&`nPtyEAOL1fI+|h>6&d+Pgey$(8e-Rv5j>%Ius;k`HCp2E?2O7Yb7(Mki_MH(_2dqi7eAA(awDn+A4X zTkEC}Uz#kgQ&&mFJTN4L#lnm{+Ix(0-I5yenZZ zzquB;H`mo(?qf)3x*NO$w1mICjc~_fNCvHYBV(_I|XvR#52DS~!y| zt(LnL7~SvXW4sgM9cEA)24DD6Y?|&_{V+{;;e%SObJ&*6f}r4zz8`&JVuAA343%`H z%7P%=(ac57v8-n&b1k5$a9}nFot!eVC~A5 zTCLlg725@6A(^Cd{Sv+PYqtVqH^-DSXF$mz);?`~B<7DjzhyzP9(W>9gQcZx9mG`e zsz%2<6-p@BcIo>Jt*v1_I9{F|F^)fMNz|LN#iBv+{KCbHabkYw&*u>03tJc$+^%{M z($!+b?i3K9u1S)RkmxKV$Hv~;-QAt->P|}=#)}La6yGLK*-%u-Jvd-K z-!a*_CzI4Fr=iEOg&9|02<@u7v@N1L&kD;4_ZXSfH~@s9wK8A&Y*c! z5q+m>{Y$Srm&rtFJCDcv-f8H{;#CSUWR}ChT6%G%X^YSS-;=NCZrD8p`?BE8}e0HfK;ZpNC(a)-oJy>$)RyEg_f7_Uv5QBmj~%{&fgu?)JSJ(g`f31j@2*&0j&!_T}XqbHHm+lALF zaCWCO##F5q0k#>%@oQl&Y;bkDBG07eo++xVFd>_-9YMAI_DV!p&gl+;r(&+s zG8`46)>C`r5%{5^Ud+dCNBzxar(U}~wjtbmDh?$1<=wXJ>E(xht?lbiZ|OPa2^XxA z#EKGJRfiW%xMtK~T&rta&4rglQ-`0I=Qizf0gbLH`@Y5?N;kU#%2$bav8kCXc$9$h zsbR@Cn?-t8tyt82URyc^ms%IRW9Uv@k71N8TA^jn&a{o5qgGMtUYdMcNX}78h_-!g z+1K%erf|TiGF?pI0nX%&BYtbE8(lOrv_cW-L9PA@i15wn*$&hLSJI7XXzxT;T8vOf4V#MQ!l8Qgi!Xcj)qZ0wKPA$NUKC( z9o5)D-%z5OQ64CWN?TgyoTcG@WJ$6$$ge@^^4Je_U&j=cSfLbvWU+Nrb>#=i&d+`2 ze%PzZn;>dGAGtVTaowvpB?wBIvWK!E>Kgq-O5aOO7zr8xOk{?L8RON zgW$pNtFm~=$77WF0J*{Fb3QmeqxGyGv4MJQ3>v>x8$4cMGmdYoUPfZjwNU7SJOy^& z@y0}~b*n0w^X%-bzcwZhY6hAc<-kE!tN@L%kg3Q|XiI`>oH9ChBq$$+bpC@$J+4Ia zqn>qB7J4|P4j-qYQu{P_zIu|~tgQD=_~-cUVNJVlFN4yJhS}X4Mz)^1)>3D*(NW{` z?q;aH>uB%1Tg%_BgROYiH<3NCuAh9hNKA;U3aLIVoxYeL0NEu8M=zfuhyFyf0yGyasb+NM#<5V>d^lG#PTE zC7Up1gv87n>=RSI;koYZ?+~^7?QO#xMawzxPHewp+w_hKzHuNjD~5O-7abcGUvzK1 z@UF>MwHY!f#plbZV_cO+0}1|zSk$e4^B$Y z3DNykBgz~){;cG7{nbJp=b^HcRINnuOa;0$r-l2Pm8O!&4FQMgEbEaHSp~7`tHN3~ z7)pql+&6zDS^C=Z6?FF%e)^v+%D*<3=4t>u_&Q-X`Pio48TQK3P@BxkMo!anaU$B zS32d$?bix8xTrKdoV!?E`DE)P@sg#13t?7(%U0Z>Vd!a?Ng5SFcTL6Q+~772k}+MY zQ6Ww6+#@QI_o+A-6OfeJLR@wn?K-$pl$(upj(xmEIZWzJNmM=yu=f zLkB$FK%E?#tINuYJ1qnS^*@tu-P~?FUt>X2?s<5?&=x52YBBK9TdA60cQ?I5sudUM zl4AUm6&0;d=Kgvf&KyeJIM0nY>E(=UGm#I>Prv8$oNO#}_WYK(iwTQQ+mC#BUyUOdPsJyhrN&G>qbyQV)isAqwxjcy|I(9u zIq${1W2YcFWtGQQ4$V!kpdk#qHXC%mPwvuyX#T?Q<r+a%|YZW_$VUP`P7iv1R1- z!yXZUP1RROTUcbL729!Im?fBQSy2spEbpcYuYJB-XovPHU)pLJBseP>(;fV8>0F}8 z$cU2U`O1O|*UH!G$+eM>zb8zMS(r@lGK+Niyge88Vs=6!A=6BE(nczc_>uPq1a zlw(#NN0jEB@fD=~ST-S4l9{!hU?}h!JB{4frP_P{a5=hF!+fbTMA$l~N^~aJVF2FR z3WHWdjo;oZxyUU=p@An9*BMcS0mq5874Gii!6snsCQ zH*%#m4;SM4C6kt)Ver2g`NXRWkEAU)ZH85DC9aqic;T7KH-h%|OHSr~ZfI!z=%N?7 zrS?K?gu*uF=9tIx$kSeHP%dcAL6^W`Wgn;_7JhDOI!*q`U77T{nx~eJi;ay{a+snS785d9mwF8zRjz!ZE;-D%Ym*F(UR`jc%uBweB8&+gH6+S`WJ! zflp3JA10lAY4~PrFa5y<4P~!%=u4BG=+mlD7KKQ_a@9%{`OIX8LvpCB_^RD&^|PGU z^OB;h8k4T*hJ~jmMrz_ZSoh+u1-;IR*ZqFx9gCol!kgFoI=7(ndT%Qho`sgsCCKLT z>Wqx0YMDhX>*uo$HiEQ$R+vY1p2kmO>zmWMeJu3bW=-gJTh?*KHgPm_CAX|z4c6p% zhIijdT69S07#`oNAo6LrTdsaM#QSOJ%TX?~_dIGbh<3lW53-onenTfJCxwQ;`jyTV z5Ygl>_1+k0SA^=WeYtPAzkFcj^NI$Qgf=5Ba%kO>9C;p1}f6elrMw*oe$VHJb z`7&Omd&t4()x^@B_#nZ;E=}_7mn&atFlW2m&R<`OTM?8T6Wt9iy!GfjH)J`nS8zr~ zvshqcjx{}gI@Jz61sB^Ph?z9PHMs=Ejjiq1?9!^hPh<=9c_nJLcPyO+$}#CU+cwcb?9tGZkqUD!9% zxz*w@z;IET_n5Z!>y^KOyb|&80)IZbd0L7MBB&Hn=rv(kIy;MLar0)o_%o#5cW9qe-%OxyhT3 z>`feXdR2?2my5z5JU!L=?j!}ja^=eVJ(F;{%kt*TnEMU!5UrjqZQYd!^=P*6NCx)s zA!wOGMIujwfCBY-t26mHC-T15YHH_jzuDXf_@Z^oa1_T~yKk(@rgZjF$ppN=e^sZi z0V(lulVMpGK@a*QZn@kzLQa}pTi7Bzv2YO)%k_S*)Wq2>n+><5y6)hl`ZDp!5r@Se zdZJ1#e=r`>?J4c0Fvcm84m-H>FGc#U>VQ(#3K_}ox!44!w|04^ptO5mrmN~ptm0;4 zSHg@v=Y2bkxwCT~3T8%<)KA^p-sPbqo+bG=Jw9Hn%t3br;_e)-+(pB=-6_Lowt7D} zk`JB0hVw=a{b!wAM$<-?~<<1)f_58LJf#$f|b~W2!uPOMl{>3|c zH9~q)`ht4P`%A)RmDTyr6jxhyBuM6m{rYhpPDa9>3+8iuhH1R;1Qe zw%nP6{q40oR&E^Z2?xY1jUvSKMm;Ago&4V?FcDoXU_uCA| z7R3wxv1asl_2_?Iy|@7ix2t#ZKOqGMA10r&(#m!=-B8IvW=;+GBZ=Qq!2lo&;LAl_ z!#&%^Viwg{)5NxfyVf7}&h|sns=VxB?YIHsM9=x6qdB;#MmaZJsqAmM19edXnMmSBzdt0U!3M9 z?DfzxWoOQ%r0w0?xudVcTsCw z*nU^cw{`?d^!c^%l1QyTk2kla$xYm)d|R>iER1@Dy;l_jkd8GW5!B|6kFTXV7NT}Z6(vH&>MY4MK>xRTQzCb z50P&P_{!>y_p@9J-upz-vL_kY5lM?v$Pa3R>EsngFj6KBUvX1Z9r|khCW?d0Ve`J` zvrU?olHA(&If#|fgj_08tvNo-oAxtb7AxZft=|jdTkgBD3CR}9A`0N@?x9pf3byi* zTKddUf0yrRBpB~Zpl_Tk541n;`*Y;=yF;gW_`)BVsuS#I)sU-0D*<+h4s)f1#A!AXHDXIPa#U zy!6;cUV=|?3Ceb`C*G}Qc8 zs6Je7W$KI>&&0|;_Z^&t#v}B1^NN1XviQFK?&pJALY!?7RW4T4+3yWjq*rTb;&&xp z9F3XQ6Iis&Xss7((#HyDe-8bJCMigH`z6JuLC0QQ+RClu*wZ zc5~YX^#hfsBQBxeZse)x4D=j7zmXh!exqIoHo0hjYsIl5qEwd34kc<@YqsM2Rxs`c z2zN4@zL8hepy-T9OX2~XKlew6cJbIE8<=slmJ#!H*?ZR#US9dzUeB4oc~M&SSQz_9 zUHDV{$fxbHYkLrshSYZh{b>Ncss$7r@1v836+2aOm)r|KcS7|JD^%WU2E6VKo*SyL z&_h_zq*Tjr^mpXELZ&bA^|XxhSvG%MdHA=>?5=E<9nej_LC~`z(C4dw}Rg7r8ruA&KR)59_ELiJ=c=%YobM7?_S+=R!u<4 za;`C5mp&{ByU#XEB5V477%^{qVprc_%9Z#o!A} zTz<*)N9psmPVyV-2xQK{ypH+z?>}6F-Bmz zJ>11A>FIHRmcfCoz|V<5-X;uC_klv982a(!y$wN^ zseRDs#eVYS$wReRVq)R}J*(@>#hARjub)3No!RueWC&Uh!;+Kj2xlFp_V03WUFYQN z+P?&ZL@$^oHZpMBy{il~@5XkRC(j;zydSfG6QJ_{5~&A!ssuO~*S9{EpC7nUU zi&t&p0Q_53wO$@Ekbtd?le}sfEdu2e6(bo^nq}D8f7man)M2dUUptZvO-i<;@0A*z zX%yR_4fRPqvO<5D?h|nu6R(H|b6}^m0-&n~l$4HwM>h#??t34ealp@x(1-!Dd6|jM zaSJR`w&>sbtG{ajj8|y_1z*G50w6R2WM4vB7siy^=A%VC7dqAG^^((zCGekVZB+4r zxp*;2qzpo>vfpE-(I2UGLzOiHK-|rdpj&sB;xd1?Q&+9%2dx=Z)|v_@f|iL2YtYsT zT7OjI8K_>=dg0SRKPYzB=mU_R5&)XK098h3$kPfqFUA~73_c&S=}4*_neF&dr26QZ z)$$czf_@m{%8iEoI0o;;0V5T09k3|P(ZgGL11TNdEiMGTg*eMTVI(&RubN5q=#&SA z@gtQH@~bU$K28)B@=IfjUHd-cgG?iDpy5vr#5`Px%+xi}(0vzE{jgZJyTry!CyH1Y z&%9x?eW7)c{i$|JiRx2WmIhxM0)Z?kv0SRvf_=MtGbqgBd$p4uLm3;B1a>mx&ZpSyM zRaXP^pC4UqKU(FVH^i?7h?ev)#iYF-W2g-KZF>zJwD&Z8$}_x(9rp5J&F)AI-C_fbp>- z+vxS}BczGL=YD+E`_tIfH4XQ|LFdp4+@_bS)to~pUA&UWaxC*_*`_0SvkD!ncKmXh8V(cT@mfo5pLCuX{&Fb?gyu@81LRqO6}jT~pH4Ww)^jNJApi zCj?%TN4eMBLrrSXA0r#62(9mz$TLZ`#gGrWV*@9Zt_}UujQ{vvt3Jq!cEzr#9eKjK z1QKgB0lwcS2H1p)1e2fe7&{J1V1v) zCL2}Uw%VP)Kd-*s_E2=4xO{tjZ`!PMjE#(p?7^5)HimWn{aHZ=Z`?aiYRp*2^ltMTjQ zU1N;Sfh}6unD|Ba%bcm=9uJ=vVv_uqi~N4LtA8@$h*)4oI`*m10kNP-q83O|YMr5Luk*o229Pae~e9^^x z$b`4wOq^A2R9sw~&L@10EGH!K^V4T;)1mTiO<%l~y3|*nUQW2=UE%w!>Jz>PvUT)Z z{xR$K<^t4nZ7B+$^dkfF*p?_TFtE^VLzzoZFwW7!kDNX#GimfHOfBhV5=Pp^C72-w zvX;#}bD80JNl6J1nPD|)i$X}KcWx_nMQ>bv-U%1qD{S7k-QH>z75X7Q{nK2C3lN`! zvu9@3YaO}Wo7aw!wi4!&@dd)W(`s&ANa&T3lBEzpE0(@~-pkdwFN1le!_KSHHn?R0 zy(&a#1$iLH#w-tLMGPPrf7^ktKPHu-H`fJ?BGX847NLHK7-Yl)D0CdKVMw6+ z;dYrXi`QIr)ZqLQz^omD$XqyVwWrm~$+k^wqup!TcZZTIUIdU~L~fz(*tIx3Qxpyn zY+4#;(z6UE`{muFRDtbl6l%kO=g7cDMAr2w+C^_yZ*$^#fCg#9`nr0;HB##0)^W&~ z!=6oT#NG;2d>o%k(c+uuB@xADvC%|EPEO@S(OLe!G19%}Jx6OwFR?l3V{l%1vsm~q z(uHtfvkvYw@FQKaz?lBHbSZS3;Krrmv7lIct%;$QyRZUPOK>r6i?S&s%v;UiDDVi+ zsrZAh40g7*{_omKr`#F_*x9k3_3kCsL+7n$A&S?}_xAN&JJiR>A+Z~OJ3Arn;^H!& z1A#z*R{UVUSN;ARHY3SY? zUm1$l4)mV8S{84NDLC?B9h~0z%niLq&~jdMRb#8$8g*>BJz^}8k4Z)|hrc$J717VP z^$2d70!x-=xpf$v*WfX&#OEMY^uL>YjwF*-K(Ef_)=|th1L(1wjS&(>=HPVOG%JpDIi`NPhOL zim^Cbl@}?Wx>iwFFoc3-ZO>|O)^6Kip)>@Vj$FkxH8ru??AFwdN#58yy}(&=&r?9; zy0*}vy<9zNFMoSm@QS|#*fqU&SA6A>zKaxd?<-`;;*!p2N<|&L)r3ApG1b`)s|W(& znA=xUl;sp=*t`bqi=&@SPPcTjzHc6Jab9WO&m2r_3g;)-66 zSmVP_&a|z?YN%SnzG!9>)UpTf4S`SLJ2ZOG8)|aM+fXR9_F$v-Jj(9C&|j5RzuY06 zaMxW=!0Q2N?kx>yy~O38Ba8;VV5~aNvGpBk<#;?tWVF^koc4O9`QEFgr2u@$(p0d> zliP>NmWD$$c!AxGZ9QfCUZpPT8jU*=*GOkI8>$WoL8KRbS@D|UEz4J6fn)&zm^_Vz zlb8#CV0f&uN<;-p+cp;F6Ma2DA@rKuEbk3f5IXx`*1i!aaAnObA;S=Hv&*5QdXBuV@IQ3$Wd z;Ty8UCWL)A2i#m{OYMFe1~0ByLkV4<8sW*;Vr5P&=0KmTt?l2Ss`^skntw&pf)|ab z)YbkB#aft*T(dv~d@y716s_p`7g;wrs?VxzS|b1dW9+))n%c6i4O9?B6nr$nib_>_ zR{=p#KuUmwrgTDYp(F4BDT1gJ=_pbXLJu`mrT3DA-XTDM&|4_qb-tN-;LLk({^561 zxViV7bN60*?RECPaKvOczc8o^p7z@4_ueKclQc;7$eiM0<%8YttkKgg(Ld4I3c^8e zt$o9b4fb8LGLmqmAH8#Lr}k>)4WNE!X(8&{>FB{?DGt*_J0EdCWR$sFQchUyQ9arY_!=BHP0X`MX+zl`tX8azgL?Dib80WAVzbZifZ z&7NEP;p8TyTl68%lsitAE}B-o58>H8E#f$v>oLQ3cuPP);DAMoZs4NFm@d%GG2SBbj?(D*zW&<;{D7w#k=dX9_28Zt^Plq*?(Vd zvkcpg(5P|#Z&AlWf$7_}?WLwofEg_FnE#j*3AnfK+99Nb(b7jFhdwY1)WEk0L+{ln zj&EPrTy`2q_dX7}5jt)*jJR!RtcJmKxHdL6A`W6>ExfC;iv$7i-Se3TN-H{}lw5Js zZBe4*f4nvTaeo#+cl6R8l~QDZoxWIZ&*P2F%=&V)!~_Lr0$)QQcvF8vKlf#EWp4Dc zhW%l^n9*pZbtVF_)?ovM+ybCQG8n672ICU7Zio$TiY2}i?@ETAI@=-;FBkN1M< z0GyU}W%9@ugzL#-V;Sr8)XeGMQo;MV!G*T>(`T3=^E>u>zqikwzg16>q?ztB-zRU; zy4_KAdj#Pnh3X(bvLKH7oj(A*3iTD+VJKi>Jrk;?cf!>|4}$qr8I+crd629~hR zyuZiRt$6@W5b-*pb6jVYVRRBVeA^mFpSSg-t=yn z@o{wPEHQBedyT)HB(`wiPQ`g0kDsd{^bNVC>9+(I`}K!$-Z%5TVTIxU1LULzh=%s- zlF`R`hOm1+0ci_^EG*AA-(4_e{rdNIAjTN50|gh_r~h@&|F9Q6h8opkOJp}1l71`o zEGEqTFDSiQ2iR7Tpy-+X?x+zy$830Nr2ncc(dCl@>LtzJ1{?<5KX2@tB<`0R6YvQW z=n@;*le+Oc^y{64qmj_DpZh0#s*VRjh6Fx0?&BTcnEg(1UsuJwue1XK{J^E>#DZfU zMIjsEm3=wY#;9^WK#VP(W92t+&$)O#>QgN>j6Bi`ij!o@< z^9MfKS28}=LGAASx`sVY68AV{!1O`o__O8_5h?4=F3?=fTaq7^%wu`Fhhj%eDgWE~ zj=cvEeRNR8Gfn)*?)Jnff3hK!q!O*WZ0_~Yd?nlRV&jcxub&S!8qD7|Tv>Rq{s$2L zv!mNI$*-->(|kU*IsU$~0Z{ggM> z^YX4%eYlo=Trn<;R|<+gx4!=5QEAwEYQ@9J0Hk+T8w>@C>5` zo>RFdzbV0^vuR*)3W$2V%yK)m>F4tdMu3u+soB}tot^n&*wN7>@W)|kWo2axDcn=T zyKmzN+s`*pAk4E{`Qi`b!B88UoF9{RB7It1RC(jQwDqsg2Zi5KB~6#T8aJUN*POsN zJa3cI^8QBF;6r z6#tu$tDNs+MR}YU00vSXI!<~e2 z$v*(oAAfioex7stq^;{O7MM2jGODOw5o5vB#YuDndm*PI@o ztbX~A|9okna7sXpvCZJuPfVU9SH4WO`Yhj(*t>x8l;~a=gvcsseVi|#66$5wvy@3K zQj+0l{z-(}Ed%q@xQ*HiwoN@#>l$TGCtTP5ABnUDJf zo|8m4;ludAa{dIPFN@My(|u{6O6+q&z}X+~>T17+3_2S&tqHs*1lkM4{|~J9^EU=u zNvDrrjn67gE*u3_YSC-)sp$8_emw~}q?5F`-v2|ZB=GM?E!hk(xUy;wXpUX@BZD+D z^k(~lVkoug*7TfRV3{mAHC{yFcmba ziULODDkx>l{##7a5CZ@R!v!CQ@?#6@kv3&vVNkpyXp^=aSSb=WR>y|>8)lDJ9r<;N zej;CmTY$z#=Kb?uSUnZSlSH)8+g<^gI}R6z?=6MJeefi~W{(nOjK@X$b|7h@L}892b8A*~_`i;A9;y1ymb3{Z~$_)OOc zM~zGqufW!-(sO!HH!CDWV@49&v0CD8QHz7|@7BKV|2+&hvxI3lV&s;McLSv0j$?b1vQ34wRu;~qo1Fyf-Ue1GU z-JPB9*Y;Z50SaD3ibQq>4wapct3za77K*C@>jI6o2{AE;a=-}J5`5UxC6$CmAovJ`0nf-DRt5k z^icg^{yQ=Hn_~1^ylZ#r^`Q_RSL7b%8?NzCRoYgsH@8=`i^@_LXTQZh1ANS{2m%IdJ z&3@V-j515OzP9+7fXiKJZgY9zi9sz8BH z^dHHjKQcLY$mZu|=~;O6q#e{#1?B4cX5TUli>>?e$qs~dRMsyo&Q)d`If08;Kz7BR ze8W<}BW&_N#0*aWV|hvb!ZFhlk+X*vG9InmU+tl!a{%6T^+@(9T#0soW5XjQv%i%+*C0sV-E`MD(h^_h zlzoXfQEWCnU>p%y;_k(^6lNp0yZ0XHZ;gCc_mL>OH}{mhZgJqork;9S?T{;;sh7r9 zSYFJwv#PR+9wH|mwWUsd99bQ`%K!IF)vf0~J)TSXT>8M|CHm z+OVU{4$v?SsdW-{jnZ`mf2bfzg5ff(Td$( zeHJyAP4?}dO`rb!+EqRQ{_ji7Z$|_))p_^E;y)^TO%%}f=)XL?>b)}Z%r7^MUszcE zN>rQfb6|jL1j@_H{o2q(ApK7?RdI83OHE0+PAst(=jG*n55@wKocG1dFmp?FR?%Az z!9xWJ_3I+ab3|hFO5Otze&*=dzE=@fE1t|qt);3jdJ5vVllfLci1M%d6&MIwT;EMA zBxjW>G|i2*a`f>WuM|UZPfhrEActCscCSfdOnI-6XaJtj$V`B*ypdx%y1PN zJ|2aC|Ged-MLV)CSsv)bOPc}Ug%UU7%_?d-$%2N&|ashP!HIuEaD(o|Hb1Ejn6UkqfcKQM%)D)`tV zYeM~PHIKJz_}(5K)}mRp>H1lS2mFF>l#5?TXj<&b+!lJm!VXB97+j*yLUkIXn2hUF z$}RO5q-Wem`_IHK8qzCJ#QG!3$_nl0E)|wl?yIu!EI6I%!Hc6biLajfp{U-yQ?=4~ zg>tG|w-;F%v3X{7SQC%eGR35L9w408o-)i2Trcl%Q!}yzGEco1Hsp5098HBIE+m4& z&4ob_EB$b0?4vjh-RvF}xDai$iy=Sr=h844E6*r9UYiF5L^ob7r{iA13*)fm(oL2i zbKbru(?7Egm6PvwckZPHJ8zG)ymANnZ+PW)E^u)5*?86Lwv8k{{uzFgS#b1^`nI7^ zsP10%A*tw=Ifl>PP$*P{E9lQNbj;EKY!6QfY(pMFb-Z*w!{tfQRixSUDL+<^X!O)= zp0Id}l)m{`i%et)Xs+-?heOBWbN`ha?8Xb?hkKHrIr#xjwpQ`(o!kN-t0+ZNl*(8_CYRcDXA!!xfSMl0{;@>zI`^o7=>S8c+K%o+~ndOCaWB*Q+Y2 z(eo7A+iNy&uUuN!-TUErBOR?nyG9>`$c+54SEnkW!lkGCFat8BdWQvTSvfhWTHtMJ zZwU>#u(7p6&tT;0uK?b`vhNx1eHTrQKy|!L*t= zk^OrpV`ut2si6Ypp}seqOst>434;Q~@X6X6?VRD)>4S@dqa(NX1s)@!ENbLQOQ6@e zf@mQ7g^jK7_g!353nCPIllMtUTkW^C)$Ip9m|kgnkzI?G%9c|5%Vqm$*X9m>VqWK% zz_(eo`abG_$m1bPxQ8HQM-ur zzzt1P)=yz!#^d7=R+KHkrn-s)5e*HE2*dFNAP1J{zOy<#J{&)IQDXf)>-+*>9ZC%1 zG%~4T{o$fwVt&JO70|~Zp@%VBIyuI9X6D*;EG_jg~^7t+#> zgY303IUvq*G1X(UCVagiCOD1$F;Q;aT|M`Dh1)&dWu|kGEjx!B-#6#HyB0m-kzH^x@7nR56^>4Vvm6 zehZyiMRsA|bO?TOg2H@rC(l$RXjK1Z5o%C^6H=6QgCi{9CnumpX*}5Rat+9@K~UzD zX9jnH?j-uxM+ep)TI~aD(j0ZsQD}3V4~UWdNf*Cu`*!3j8COms{^qX;Gn0Fh+YWbgU>}->fQMp+95ggr#zzTyTQF9mhaQY>NqbV)$1Icw)+Em z-urUXy=ux)h#W5mi?c=N^Lf=o{ zDMB(@-@~`qoiDqu0c4M$(<5~R+4i0eOU1YnqSsTSt+2-5wyJq#l|(hsGgx_VYw**6d>yG0LrKQagNboW=dy48!v^*W&a$Yg`yrepn3DNMjDW^;$7>>R&#;g zn*$N8S6gdH5AFb!3ZNs?xL^P%*;JM*4h@ge3E2t{B$XCJLmH)FCYv)O#WdEPLoT@* z%fod6bve9Z^!o=_#4|e|BkKP)R`>Y;6rbigfAiy!+p@K!2n{zqdmc%#cvfJ{|tAls)c*Y12(wfOK$wSYrwWwjA1^{~%EhE!sO@|j5s}~7$#6?!+@B(;I z=9)hDhnr@J*Ti?C^y{2gUxnVi3WzAb7?<}T%&l;2uys6mmkn}sar{9={Oc(41`x@p zCSD;NjR;!L98B~*el84^Vkg{p&f@3NKTSpV?{YKJorbzo@DWg^UCX+6V4J`4RLQ^+m-=sh;h~&N18-SJ?CWL@+Yj0f z&!JpfaBZ!tZd)Nt3yN)vC{88N8KsTfW2x!?9QjAa=hQXoA8OzJ_2KThy4+s%B-OZU zo&Bz38ktpj5(odptW=ad^Zr(hUEsI{iXpJ?C_4XFB>L}FN|Rx5@;(-{{M~Tw*zZVabHDegRfW69Es1h0-TNV$S(ugK{SIs4C z1;j(SpQVG4bO;^yNvROaN-6i|u2&I1qgiQKaNmI=n76^Pooh4xM@;s{8=y2uu!Nu8 z9h&gSobLzRcI8E>dJjdlDxt>4$L{gpjKV(y8fMB*#8H>_2N$A$iHC)o&t!dAcxGyj z88~}GXhwIv;f5l}?C$1Au$6N}TXL3)kZh#{&_Y1@A!y}vtuqg!7iy+Hjb=7p zxIwC`wwwNU$srahe?be(*vPe&hr~gtdt0nM@P}DA+msC#s}>nXJihYL?e#mfHzp@7 z7${!2&qLBR;_)Rf2s^DSu`TnfyPod~H`^Pz|4Jxn>j>mKCCU=nVaGYVR^%Jak*eS7 zo@BPem#ooTi>WDKb0Q6K@$mgZLFYWfRkKFW=oV3l+}f{;@nD_X8cljO(teq0R^txn-2fw?n=~@OFIq2QlhvFf%?)Z|D8vLcI&NMC{qF1p+%rUtYyTqt2FUk;w~nn?l& z3Gk^CE}qvgqoc%mt+?4q?JC>BJRpcE$qynStZ}u>sCdLW0T8PhRDG(*Q!ckm8}33#u+J_T7UO3t zH(RX(-|nyUW3=B-T7c&#KpP66eUcc?m+6c$^!sTwW(!Btl7 z;uRoDJop@JL$TY?k4rbb{E&S@cPAy8{jgjk5{V0jf98s4T)2mpQhF;$_PcDb?_VlP zlT`r%ACjpY=5##UF@5(vM}|nGQ#+^*LfL{9c-yM4(mO9sHRgub`vA+@S{-e3&ze0= zpFiy4zDxJJh1Dr4pgMW+w>0~$L-ku%nyIKuf_Ssbx1!=3{Ok@$Z}n`vqf7eaQ^nSB zBXZO;13~HEYf7nE`8?^>!`@xvD|UZAIy*b|z>_p2{;T=?!y3qy1DWRVP4?FPn@j@A-lH!dY|o&9~)v6Dm@rWJNYN(w5XI(Zs-thQyb-L|1Se(hZe77m+qQ89A2&@NMNVDelazt6y&~fq zj4o~cg#$>_>mz)6AH|1oH}e-?+Cf;m;CXIJPceP*4o1$_2k%O(!b4yq-)%D>G>_N!H+CM30{NjvjEOX`L*?6dFsitMmZVxSZNy~2_tGNdbqLRu zLMN#``vU{(N`Est2;jhF7b3rw0;Ji95ysgeJ{r+7$G1{A0XMq~qUUT9xP>vjuFZo= zXJUj?X<9_8yb%=V%(r?xw6Y=}y(lx&!iSoLhShV9W6Y}dz1AraaVgBXa)}S|NOy_#RfxQbOaVa|^DVFP&ZwIc%VP&Pl4f-iZx%1mz=&WP#){I@DIJA|bb)X{j-E`U4n@bLXh0Rtb zOkF6WBAI*z%lo#_6iXyC#N8z+G(0R=toLqvVvWpN{7o$;gJnI*Q|I@uQEjDvp8_TH zSLXahrzn~jr=NkLQajl()Zp{=Q8G#w9pyjAl$r4Kk$~rBgq6T7N?=O_(uQ~zz_0r> z7ZjC6L#(CCG!DfHJhQA(&6KQEgW~AB__yaPHgbBd>rmD0Ue_W>?TFhk+0f-UykkXg zAO@9E;5no&?>kp>ck93}w{#DR3fS~7AuUK>Zv>U-zrqEi-^VG%6AJGy=m{Z^F^=&IxhhofENYiLm!`i!Y>y!)D7=_o~qe4OMKk51W&(4Q@j!3m`<*V6t!Ib z(E7&C&1kOpp>(0Mf0J(^17aCBJoq)zIi&nBv?skVj&8S2c#vu{h_JM(&k(pu1k(uZ zHA{WCxpT&s*P(K9x-*GTphr!3P4$PV{Pl?W^eG3O*y-PBX6TPjU@|8eIXU?qUEKC| z$&c3$^70J%)(K1OxnVPRRx+W2~_kjUfssK2J%75iMb zvtu6_dmkDW+NgEoIv{^nRf8~HiyX4HpIGmA0rfix8zwGveIw*KL0WX&^My9jk=~9Y zwB}~uyQtfG`k0XRkGp4yAMz*OGSa#TG zSO{OEUbV)~NgZZ)oPLH zEUE@GUUZ}rj+MOPp#%+d|7MaQ<;iBq@!M5Ln3K~^4S3MT%xt+FL2H*Oh35HfqRKS! zs-%{fi#+;=R+Q(m1Z6nahI6hLB}NcX=Eh6**t*#1x7*1Le0EKEz2psu#$p{_kZ)e4 zetLM?=XHD1d@P?7>j8e*c2<$U?)=t5|Ky?E`OS{Jw6wH)i3t)zUhSap+f{?k{4aaP z4zt>?X~$lJ{G^Y~?- ztUV>goJCgt7N4OlBKhsDq7;NQ9t@DER^P?&jq#uU=d zf7?#hbJV*gNuDGz8kJYSV?Vpla;wN7Q|n!G4T>jnCf%Fk^{X+Jx20}IkLal!TWm=b zRJvjEYuW7IT#3%E2OGqQ*cv@;kI^k7jG3qmK1a91X3u2q102$wvnO`=ww8BI=l#&cz_5D!UD z;%D2G4)pM=7KQ| z>#^PNoHx}5V{F|!v%9>#=-UZ5HkxSq42H#n*xtK(|Em^&d0p3KP|l}$>5lZL{jS~F zlfF?8Y=C;E#Dfi|)mD_L9#2Y2QQvF^CvoAf)&qexbZ=$AZDY>F0j`of9f>e8rL}^M z{M2t;5{^x*!D1zHXg(eqSVM8oei>@}8q#F2^x<&2Pv4~Xw$0qW4x}a~s_q7R(rrCx zT*=6_h)Ta3B-pohvwn}HLVf{xj4WZ}NdqA-;8}$#yQ%g1++MkJaF#gFntY8XC0!WY zTdv_@W|BgkIhD0LeIj;kAB0Tmf4bi)5C}KDwnVSVx_g0ME2!85RiX<4(rUdgH=9Sr z9W>OS^z*EA-cAg@) z+)rppwd37o1*Pt-yUGi{(aTAS3rf;YF%KSWokXHuso~vQaNmm92Q#`=sx&x?J397u z*4UXOcUD3>K-&ePJ56e=m$JO)P3`|edoRcR{$MclW>N^lu|O9GZq_b z^nzRXX1Xj?M>xn=Nr3VyBnDSIXh)*Hb!h0@yCTj+-x;=-)xsBM{pU88M);x6EOAIY zzf}0w)}lW5&Y!{PtFhBD@G8FRK)t+*#9w-`Mz${7oRMTdGX+I);g(M+%i1zEalO{Iv*mL<}|CeuG$ALy(dswjR->WZ%O zBwPklsdx0RLs<|f4G=m^))z-_FNNUJ+hI)JH>|(Q)$N1uSZwa0$mc2;boGLiEy4#3 zNYmpEU*ML_@~ax9xSr3i>-lO%q~8uS?<~L7=9>Y2AZ!|^aANSz>_vZZ2c?XJAjfaL zS=YbsYwMPZ!f9QxanNPUEtdJjO*$?utH{{zDFePAshE|*iXE~PmO6h=65UO@N7Xt5 zr`EAmqW)9F!S<#)^n7XyvbFDa%x}l>?g=09Qz$1{&8k+u?-5RcerQkEw0wQKBnBNz z+#_q=q5J+8v-aIHFZMXL(IDi9(#{;!x&9o@aQt~a5Wz{Y5B;&@Qt?U=ZM)W#diWu@ zK56tb^FkoBibbSrc>7s|0}nkh6lamVDFuvW(50pFB)Yl&Nn&YR6+LlCX!|5&>pM?6 zX>reXFt6$2H1}s=o|kl&iwzwt&{P6vZ8prC6jD6T_F(L|-Y}{6P<6Q88dO3=ng~*! zhm;7R1EOTRpknM8P>^$npdIKHEMG8cQgo-?4t&ok-vpz+hB`?#BiP;nE8HW=@!yrW zMC~l?IYAZ~4;lOj#hHS>jVi81zs^c?X;8|+dv94C%n2giKqGDaOz3Qpw13$!GfeBv zVcq`Q^Phi-iVX%%QARx{_4=}dZ})llV0_t4Sl2D)e;AWPzkj!2ssn)OkP#iPpF)`f zoxG*_W#Shp?6S#6kk4=i#vo9}8G5@f86$A3nA0d@&|Tj(lG9^5TGd;HqrzgX{ZImk z`GXRBia2StB0siwSI;Z{*x%-8D)p6Of0A8ejV09&3M{y}gJ3l9jf<8*3WD(3ZVM`0 zLVUE@XRAExhN;?--Ili>JXI;=tw&UoVyB`4=%njZWs*0R5?SAFZHAkMCuKF9Djmd_ zU=(Gtc4rTCClZ#QM1%?4_QDoJ)!ee%~wma=+1li@%IgA_MLR)xIo z_FhCLt}rrqZi>uFPGj=-G!Lg)uP&~w8~dnibtZtFG&!>7Ju*ewD{g7O#NLsd^oR%q z3IcWu07TlLzzDsj`I zGe`c$MEx#U!v&Ssa~=ma#@#gE+~*P0VhW$GRK1572{WG+)-(o}JC~e-x!k7L5*)Ne zbg8&zyVe@^9j8N&y1235fSqmbp^`4v$L%-3I3N}nv(?RgDsRO0%op^CK=#^x4zzxDM-bwgwP(d1F9~k1qCNnfIn*>AnodF+~_Ad(E(4CB_YzT@0!MpXJ6ms zQZ(8#&iM!*9_*%*GL|i zHt6?WsZ^03I)duHmjJHQZShUjj8(V=YOH6^irBox{=H_d;Dd&}01cOCg_nk&&`S&s z+Q|lWH#JMO2A32+a-eq{9*y+=o!peLMf_xk*uGDhUoZQlKwHkiu@A;}4hdGl!i&h; z;FAHTzc{znraxv9t~IdYRenGmKVjv<@)b;0Ce!AGw`0YcMrpH%l%xe%@O7+lu3KRW z1b)O@?D7p7iUUSt&l1l-XY5FgW+;ILcb9A)D0*q%Ig(BM zY3YP#&SZ(2R0aq8^X>)bG9JV!v}m4pzVC3xF#Xbp4ZqTw9sdz7d1y_2vVeJKo3HQX zwz%R)zWNX%ic2wI1R&w&5Vw%uUZB^wF^`bshHHgCTDT+U6O^Y!;KQPU@#2@BP=|M0 z6eVgjq9}@sJ#T6=2ziL(iZp6aq{91Un-`d3SI;_FEi}F-m)8v8)aA~12x&aubON3} zKDz&Pvb$&{5ZLAx;|PJ%8H8khfks+>ufYU8%Ay<*8cBps`;&stVN8@%s@x%E&<;Kz zP=yR$yPD1eU7GR6nXW!W^6m{7Z2*lmp%^-yq=8OtDoL_4G@?3@8ZR-hDvE4>4y1Wf zk3w;xMn%QsV3Q#0UXy&yTk5j7vVg?FdyvuPliGF@6qz}3%edqm z_vNcMl*MV{1z<#a<|6ml3}(oxKj4mpJ$HOG5WdXTHF-gj=_et?Z&%-P_B5Amx=M}v zk9W;24nt(iU_tR-ugZ8FTArZQcF=vM)xMR{6^cVCORfE?B>LD5Rrk4fs)6R|%oIyK z!B|jase{$SzPOw(mTpZ2mDBv|v6#a7T&57&a<0C3cCEO%383-Z(B&$6S8x(+wp2t} zy1gNRZ6u_8s>eO=q-YR*-h`y;dfBYhDZ zSs^jEY&~8;$%;x|_{^w;+5qUs8Y1-V*3wXo_CBXubk>&C)uljvdg3PseghBXgL(8$ zHnaL7=DpE~N!aH7-a>_(azfP9IZC0!{!u7u%(d(q)jMl%PHl{1K|lmDHV1{g7y>h1 zcP(?@*z@ez(XgusIna{+&gjMcMLPqO)t#-|0%z@zp_Eko*rt28Fx#QT-5L~aakT5fJeAQx6^dxh{l`mr>wk`70i8Lp3xB;Pm-Bdt;B%9vQA49hR2kEEv!pdE z-h$5R@-6}EhfS_&s5D^TT@o;#Rga@Q^}g%7aTVTty0>@0Zyv>-#Ff9}y{7_5k}xPn zodgpV*C*BGQ}d4JR5{-5zS2U7GFo#eU($xxKqwo?YcweHO~NSK=X4e|C!83wP%20> zCmoxOIY#u*?WJOHB&^|1#K*$mbaMCaZSDlQJ>8D`XCd+qj-8`K&t1FB(Nfv@v;4Le zD6nRgFvI*%s>YK=aC&@P)&+!*da*XCNHSLJ2NO`NVUV7!VkHTcpG(eJ?mQ(aO&Y$M z)4|k-y8pew+<2n$8^^bqPP-eX`nRknl^x%OXHI7f6bHi+L3}8a6tLA&N;!szHmY{U zn0qC$o)ZjL8N-rw+z|PaAvIhNiR`>gjK{HaY?GU6$H3Hamzikiv@Wlwja-#nOIGKy z>TyT_=^R-8z+HTi{&aqqi_H_`)U_ksP$1|Bg)AS8m#=@$R`jY*>dPQ9DinD^L0$co zlqQ$?5ftKZDD{?;1QI`s7uX(o4s>KWl)J&>uG`(2xEOvQjTX|26X1>+@RlU2)F+ch zWj^a>TB@>bXv(1E_O2Xpizs7DJw z^H8LBUT+2H-k=CTS12)M>CXsMGZF=)&I)K21gV0-otvR+bp>px?SzCw_qpn?{>%a; z1Z@}C7gaNRUvY2pR|*nC_*kQoHFc3!b?3WjvRyn5=(;{G#)39*Sl3DRwX@!S1)?Ae zGv_?eJZ8Rtw3RCTy~fU+DD}z1pY*W!d&jM{m?fOq?J?P1+BWyH(SsX|wJ3^Gp+om| zPAX+FN|ix0V{Xwha(!bS`b8l-%sBzUAG3#5m+!q80e*x5*YlwiN=8a|TeG}Jl_G{2 z$%|C-L1&{bQ4y>UUaX?Wls!R9p_(DETFU+V^;}{}5mD?*mxToHW7*v|6+O2Iue8aYaomqJUl%5BAe%2EO@$Kr_J|0U%yBC=+&*s9J38fmv+ zX;Pqbp0n3tyf+d5QLRtFH`&6XJBUiGXvGP%|qDa`ku%*(0(ZVsA&I<3Q&d(5(Z17oKuQQBw!%KL?5hK(`!cY*Xn`kz?dN zUVeT~(x#K5xa1uwstsTdQ(UF8)EJ;G0|4_ve}QH|pj>KM8F*lT{uvZX;|+hV36hXL z1s=yh+`b%8tPY(V%tFji)SFPj--peBkh^eHs3KcfHuOS72Dj#Sfk!jG$%#Z=Sc_!&{s{fi8(=xHx3idHP-xMf&64_}!;k zx3tE3^~R$Z6sW?C1JwMyMheJ$RkF#$b^XXAl|>=wtLD{Mudvv!o|Q?Sq9pVU_o2;( zb69%wI^N^1>JReezwVe-!Jf*~dTzbljhl`e-BZGfO1TV|Plq4OUQBIbWLBjn%3Jl632wgPiD6cS7Ur1858wB*;7vsn&JJd3Tgmva&HW2|f$b91x^JLgS z8+;|q==VF{Kbj|3W~BLy=B0rcSh1y?Mplu*?(X)1Fa6G^(`Hb$e|Fj8*eK$>g7ZAH zb*&YXFT5q%CSobk%l?qQLn{dMfcmT%DnEO{Lv$e8sa{5ohE`JfbcyHLeHB#Pxm-7p zKWuFu+@_8%C<4?V<(2sGHEypEVW7C`e2**u6lSJ*dJRV-w#{@b-0ja4KM(FUf|3UD!BjeV(60JcoRl5rA&s=LdQ%vu$iF#E1c8uflU(6-7~uCT5AT z;1xf(XbuxYX!|vA(rk%NjjGsn2Ele=A<>@o%~jnV-!HNOu@%#V54dYAtt>yRF!QA# z7UvH$+9$TgZgn))=0{h#fB5{68dBY-2S|!2S@UhJIv4shaOUrwL*!Lfuwf|J_DwWG z@)pV2=~)|JcvJTQzHV^!eS4`qy767QLz4gs~z?N7F(_ixa9Nv+5NwYt-YZ8P^K z_-6E>UmoU{Pg5Q&6(0U}EoL(_{z#?HU!{bW0F+O#vdcaKBOZ~RA99>*^UBz4(LcyS zuX7@w3U>iUsYPtGR4vmz zGE3wBS)7tb%k|4%SKR3JB+?Mos;2jb*9uzx3a3CK)tNAnlaw9Cco*gAD= z$@oPw;N3tH0-C6Ktn*5t8^Vlt)HjD)*%%W#*IiCePkrh;y-sTnI#$$9T z)mfoLma#Sw@aggw7O9c%6Z{z_zjnO@Gr;nrC@=%n@11P*XzoV6zP|>KNPny%)OR&r zvYf~`03cNX2gmP>6Rl?Z4af*Go^lmNeFR1c)ohhYhOuE6{T}Je6+;lB92s!R(gsNR z3SDIa9(+0%%lQfEqgv&by$G?z_+~xk#rUQrAQs*x7E3VnN=!3H1iiQ2PNI)Ek*`<0 zm3SBx)wUJXs)jH2`f+i8SHqpX_WMEEoh2z+ZY4dg{o%QuWdvE|;&7s?t<>eoJt488 z#g|C+tnt1V02Mr4MHmH=i&g^}c(cYLXk7MWjuXpnDJ7OB@us1B$C3o?V3k}{mc+Bz zxb?lDnWddXQN8y=d}0;Zx@zscl?l6^Jr2Rt!{i_7*V?7xLSDW;Ue@+V{{7MCbI%Wo zC5`h{j@EcOajJlWmtXjn{vpu+l-=82_Le%6ULDh@OD3?DBxg#hvg9#$$rxQM8G@QM z%u?C3@;=p~r_=X$`LAWuv-A&V8a-;-%bXC?(zq(Q%H^HIb-!ZzU7*m!BfC>xkzmSR zFe2d`@13nZslcnSV1m|HIwOJ9xZLEdfZd4)!%EvHdpbMU)5w(t&d(lJIe|AYttYZH zfUdMD9|a)#BV!pFQ@x#ES(h-j<|B5l+!JlMsJ!z!&_5mO4G`P)4 zC&|+!E~n$VT&|!%YrMd3kc?(q3riQE_CeA|uNl>j+L`l0RLI>LT+j&wY030Q`SL}F z0Ofsn3Fta3KMl7iHmA{z*8QH%Q?BKKaix@LujCg~k?7Z|0;F2Odz7b|32_ zeuwH|`8^i2PxPOvXUyjkmRk*NOj=1>2*!@=Xr?yAB)F2}3Sn0Cl^YyGOCu52+*2W3 z^KySK7)^e127cctf%BN|-$dJhP{i_(mzPsh$JKR&1LD-TVMDq(H$QOGeQT*jteyI~ zY31?#uzQEPV?VdUzRoKVUu{hDg^mGe#^{_DZ%@vb%t(F*jU=jz#lcyE&WYrzTvwoU z&=B*)1Lcd1>ItA8g#>y@Dm^b=zL-Y%zyPD=i_a%}2FeW7cXg%HYs9^hG1yOpYnDS_ zCIwBnP&bDr zPYAM2ur=Oj4x_kxgW`S&aEQ43h3`%C+f(=15Xt!&dl7Opc2{VO&dbzBIbJw_e$I0E z*?|*DDSQU0XoUeRJrh(5b$S1zKqEhqw7bTb&d zx7z73811%ix&N#}ve)O#IU2S{Z%&*%b^IWC=#=hZi(@GVpVX0$jOkqFo6-$iV+0KM zH_9&xSe4D)aZMk%svpmA`Hp3uaoawF4H8v9<=D|HjIq3B4Hv^P8V>)HIULJb7g9p z{q05kAcBF}Ji45Xui3X-!@(>0Xm5)UL|I;hyspc-D!Q<=>EHW6NX(#aO<5KbNF74wjTV{kDK7&W zNhN4NtVc22IhX(2xW75!Q+s|^3sQ3XCC$GF-e;9EODpfj!7fhjb@~>X?_!&?X4h;N zwEjmBQ~k}0QUlYk_E~J^driModKrZ>2^F>E^@R0`z@l|WSgS~j5_v$GAM0?`#}hu! zlY2b{J@x4z=9O1sTE$wkQ(ZMTw{^*^^@Xy)u&-u2U_(cyemy5BS3-m<7fbOgPL>KK z&JN$Eh)U7H-7zrcppRc~UNXK2g>_#fu^kkP5E2K)@Bbfr-yPRv(*3)OvZ$z75EKM- zT}7nHB1O8(Dk@T>NRuug9fZ&VWYHB10;1AOPnntCG^;$~l22+KP=N7JB=yQ;!wz_2o(JIFbv z^#U^9H_qSZklEn7-+cA;UX+*SVMMdwefK`^@_;)tW|9R)%}9XEcL-Bcz1@l~%UEbi$;a0?uq z>_nT{?n6bwL$57l`(o;@`HjfaSCQ8>MGI&o>-k{`IU(=fOg(3e4{7;+%D(!xNKxKS zDM8)bW46WpXR34_&Ugv^oOQv)1zdob$&hvPo%bwwD>*i9_cX;zR=p7h5{N0z)704Y z7J0hYQjkqle=)&FQ5JgYOUqer){=CMqVEeB*lRaOC%8?&(ls*VY)&|X=Kx7AU9y6v zXrmz2$ARf^=84v5_QFEccG*&%WE47ONEU{jeyC%mq(b2AE6DY|`M}l-JJp#m8|O7M z(D_^(Gt_4sS`NP<2B076Vkot@l&9axPwFQkg=Qv^mG} zwec67CUf|Eow#Xor@+(Uf{BSdfQs2UWvG*fd$a3$>yzsP>Qo=XDj-D>@`Rp;kv?S6 z(<#l_D(Jzb(50&;X_#qBA7 zFe8kQTENHBA6h7z>CTL-tsxSRHXtaE*CT2T^{QC&=XnnA+yapy7Fv>v#vn4?}>&%8pS!lxbamNzuEi;k;nn&Id^xmw-({m z;U3B*iTAgDt<07awXt(Sm$8HEvbL}Hmh$~DEF}pPN^!kFYjwr|@hWO6GmPDQsgqHJ zH^ZuS82w`1 z%Y)z`_Fi52xD3oO=59;FXe+rv-(n+K_&IBhoFJD?4gaY`9k%=N?ciHBG473@{dVm? z9O>gqIvA`L*j-py+HNyQr??2ei$)B-8sf(zJ<^o1OVh|&P*xW@dsD*vlH!v1p9W_N zQiKXnhVIt8F+kO+?_>zb7;+mn)a!dOab)(5{)1~w3G=Ks4Ru9d_bDcwb=rkNxR%W= zN9yVH&FmL>?RkZ}MI}YBQK~K3d7>w4VEQzQG5ERdAp9;O;PoH2u$v_%{>i5Hu#QuV zuXwmhJ^3WamP2n#@QVVeCj1NK|}Smm^T9S)8u(Me{UK^2fI3H z{J4lVByQL?j9I7);)?d!O?u_AI(}ZnwA$Y=s1jJS6)JxI5vc-?60z&g6$-X3H;=Y2 z$u4po&PU2|CR_@9Y+rLs-OY;2sq|f99h>i}%9t8?Mpw_Od=Ha$r<`{7QM76z<)J(Tr0C(h+?((<>!tV$2hDuS0C> z9?#IIVg`_|XQ|N={KA#i-BY)@#gf&+pS9)0(sOiW#K|tdGLPR76Apy6dY)vns(jI@ zL)Q>&wzV<0ruH9>>j{-&5ay82OG*yOhX;=r)|fX&Kn#pHXPNS?kZrI$tK9l6?6Sc( ze>J8873j3ON{JKF{+l?@@3>%Y<0;PiGS8ekW#P6bSE9@Ir&`0VgUZsb;9|RgINBqD zSUM=P&Rfm)KGcR4@(KEsxURJOJpV$AXcv}He{{gI?XAZ%273ehLhWEK7bf%Uw?|Jf zURQoQSlFx2j{vLv_NZ^@lTe0%G=qg`^iuNiBTuf`=7K~F6xxx;Z>u$^2H+0z(^7iZ zo~Y`U05(C6XY!S4v?X*`fB#{C8ZUBxP|MkqWIxlL=1BzwtUP;FyaQiqc^TPy(QksR z%1h}qi`Ms+2Ck}KNQ|>7suFj9hP>>T3Hp7aMjbBVI@=X#``(#3rwHz z$Y(cWJA-IXiYB6#^H6S%fy_cH4(QeVjGYeD@j1O6S>kBQgv4EWLoOWTF9mxxmEqq0 zvs|)rKjRppALxHrBvnpDM=jIJ5i)v(C2)pNURCC-N> z&4n5Ga!JpBczZeama+k?PMJ$Nn9^eEIXT=6^RTc^702*EXCJMgNxp~4bEFRLnsR&9 zrGJfr_2Mx`{d$M?N*fKUfw#kh!4jvKsR*<`OZnb+S+u#I~z;L&4Tb0Y14SM_aT!>FjJ{2Un*e&!8MGH0Rc!B0i940aH6Z z9JY;l(WF1u^tz5tpGK>ChWw#822*R!$Au{+J#f~hBJIbp14kdrZ#xNF4xh9-Mm?fF z=s!k%KEoqo_ItCi?u+bN_b*67?wRe|ZFTOyKEFk}PRCS*88{4>s|3r_JkE)UU6evV zE@##sWWJ*?Qyg95=8#v*ruf=?4pcYc;-i$UPDCq#J0KUdpvITdXszNynJNu$NGD#9 z8mzmLYe9V@^QISko_!OGH@Zjz%F8LLlP8pdX30NT$YJ4hij{zV!D)>uSa<4mi|k~4 z7t8V*99$yPEp{4xx(u6tGimnrE@R$89j6jV283bHZLxYW!}AzM42x`A`uwT_bbPn) z-XFJjY?-ouoPzP)s5Dz6mDYALCPkyZS_@{JU35A_Pg^@Q{Mn2n%BSe`^0JnaEgfX( zCb@cy5{rmxwr9H$&tSWH_STx%r_pZ7v*Lsqf9@)me}PlO%PQv4I;o=F)CYs)Ag?X) zkWIl*i2rpvdgW$uQ!%&Bg@2@0;*G%+sAet)Hu&}%8VX53P2L!MT*AF&;#>E4%%jR-qsK>v2c^q!P_`Wg2>4s+3JVSGspzZP0>}&^587=PA z$};|1I9*};u>7_T&XqS5CI`u<`~6Gk3fZVPTYKLezq+qq)M9u%!{#@(?_kw5Iqh$4 zpqyS=SeFiKf%Sk`b_mQiP@5h>PnU3%Jwfl~;NBwdTc_h!v2!cg>z+J<^l{0@zaopG zhu`G9n;j6Ye|%{F^0Mu_HCzs7jX#7g9l^{l^w{5DucN#B=!r|akvMJX)gi;=Zx2b+N% z$7Bpt|5}_(S}RUgb{YWe2nbQ4;t-62g%9mE*3sc)H}bG7!=a=LJY{9|N?fgPx=kNi zcn-hWWr}e&mO8Un(geeOFN8}~pn+M(*vF89gc);J(pn*T#6ep~YWfbN&=L4=exQJn ziZ^MD$MtM>HaA-e+*xB5G1SJz@Qfnuv^;(Pz3 zeckLkY&tJXTv0ztN=|i_yo1b-u+!)rLS*vU)S^Owkzbd#W-K6iWo(y)V+p;mA*nV=Y=(&Q*q@!s ztL`*=Ntw;4&!>!8&bBXePAqiWR?BnN$&dBP+VEshuwc=kfY6(zYMI|lLCi%RbVZ zo;a-FK3Ba@GfRdcK=m!ju@Ay^3BhvvdE=Ju_e1*VEsCzfl`^~z#iPB&Q$yi(-DzE~ ze?aY|?wSjo;HxJAqBWM$SkKI78aVDRJM&ilJ_TH7cvVbWGr+ni!Opm0sET>L`U&~j z(arf!SBF;`gJIk$zH>1V@{YdVW^^BY2S%D8E7j}ia85Zfxp=-LIv>7Vv;=*E4U#?2 zGREV(av_f-$J1ES78FpI37v~|r&damVgkUSXgr&)IW|z@@yUSjcRnQf!@O}$UbCb;wYp^q$}gv`^~YOCf-!S% z5&5m8^OM`DB_=Lz$+Kd8V&ivpJ3o-ltgNc>Ih0*ofY_7j06F9_9_pDl$xW^YV2nH5 zh&U&+yLZsc6kFYIn{zKAEsc*k19ezT3`G~ZwN6NM4GaCZYd$lP#j9cPy5d>$VFO3L zKi$`+C+i)3eRPIh3EFy>ZEL(q!8t2>-gv0|6FiJQlt-;}>|1+5S!k7@pJ|gKR7=wD zc9~DcVunm%*Nsm{f`aC}EafQQ`sI$b6Egv0+B<1o$+Db0L!{>eREU}PFF2<`Uu6&#TaPt=tV{Q#Txj{^(c#e;Ubwf^-hTSSm407n z-6LLK5xmAQoKyg6aft7g`p^&tO|JRf!rOGB#lLtWiW8g8pVORi`R!n5IV${hr|?;C zr*I!Lu%j0a5tA>hSyO@2yl2b%j<3>OkkW!h?=kGA^;@D58#Dn8KLkU~pxpA4O+yB> z`;D+FLw7V62)aGWuW-KlK;1sdrJEyRtlBa5&~!p8kH-kzOCUiWE4%Qnd)B;{2W0a& zU#OTWuA-~0eaM)$*dcP~{fFZD!Ds@9#>&!T{_LQ8d)IVRUJkmp$3cB1(aYKNhsw0c z#msHfuPJ4(=)8>Qh08uv5{?nnvda>??gWfz^U;pDyJ5gNcYJ|jO7Aq9-0nb+#K;G* z3%3Q2V)(R!g_EJ=eE4~xy^C1DjxS=XK|vp73$)lkf_MN;-;XafJSe*C!85J5bEJ^W92kxO}+# z4-PHpoK=!`|H5aj=`n@)EMQL!SdgFC2#xI;?b8xPba6NG2cbL=8g)Bg-)rD_2t*|x z%irS3K%;sw3!U|uDSr$HNz z&+Yxe(fNJI12<1qsSh;+ z%2*4r=Ft2q04r9po@#+hBFkXIz`C~(@kTL_uk7aboU02tX=!bNJf83K_Vy;R&y%s+ zJgwTCvGo%dlxc2hr;0!30yr620aN2L<-OK_4F4O7l#h`#pYBXr=r>n;7iM5btP7aq zPJ)L*=}s^^a#$8&6%@scWwI>tX^tM^8X!(bh$NT?2#N>t!ecn7o@%&x*LC1)!GEp-;?h`QJ&)B|cB}U}!94Opa;s_E%)3)V-c}lSt9}VG3IcfjA9lcGd}m9--s@h5jJ?~F9~=iOcwS!X$#;K2RdN)E^1bGhj$l14kfO6o;KpNm9j7BJ4`qam zUCHs5b*Pxu31PU$Hyc}_@nNA}hawZs&wdjhs!IPNHXwI$pvIQZy3 z_*(ZQL0L>!+R3`!Q%qw1~<2NX|;&#Jj zS0UTm3=K*b)w%6WN?kruH8{VmD65BK0wcp?HWB z_c7JjJF6^V70z2Vc>@#A%`2%y8$C7vckV;a`m7P08{WbjrCVvUR`L%;%r6yn zgXmlCxU)M;r%wl{bqUp%lD}`K89x17RptbU?Nmr&pBywN1$(D1w<|6#h$1PX%#46K)G2lWh12va{+B_B!Z*q;wLI?i&a_D1KtMm zmg=Ub4z`%+j8eGVe%}IajvB=IGI?xI_Y$S@ZrZ(DhNur=Z*209-Eu_CWR{v;FJdhr z|JFOOFy{zUo-HTMmkZ^udku7O%J!7+S{DwxaxkdzZa$BF*Tr-98Cj(5=qvNJ800-< zw4>Zfuv;g%C^m+{89+k0BC2!UhiRqjFq80f37PXo2GMVJHF~vA*IX-l(omKUk|8*U z$3XQVKn?8UJqptYDcF@gRq)<@(pMGtPU8tik?_7svXWb%dwD6rR^PE4g0)TYJ=rEn z1sh40zB2UV<+G1@H+f;piunD?yqljuN#6QzmCf<9cDmZVDtUaCVw8+}T&%Rak?%G4 zSWdF2(XN`n!y>=ZR*}=Py1Blb~!$|Z8!vpJ?KWH@a8K2ex`%rkxN znKDgceS>h)*y&QVy49In1hFvJ*?x}m$1&EGbu+q>0$pu_-#l14yI8TZQcs#0f%hI` zyd5k%pBf?QTvJSpie4lK!k+Q<0%wULIp6=Bpgz|oUCHZ9A{$8kY14)50GspUT(_X< za*Sr0JI+zdmPxXAMyqS{=)8M_{b#Am&Bo`YcqaRWeZ_D~`m=inWd{+1cu(=PX|mx$Y}{sPhpj&qJt z;`Tk)xsWA)qnvsLoY$kQID4|UE755l+Y8&hk5WBtg zg}$a>Z=T=~;z%vB*RQKR{9QQ z6-{11c8e9p81dQZ?xd-xsDxAxmil;1I@I{38BF^z`3itzG}E)R41O|IpO1ELS#iaQ zh+gs|;g0}gl_H64c|KM~Pz%1EaLMhkZ$=iPp%;36eamkzS-j$^+VVoX05)QMNaYJ@YTO46918V@i5a$LB$WHyufKo;3E0<9C;zp1kBEQc zV*JP|E|Po5fjlt<^w_lQ>k=>BVJED1-h2s`uxK|&?gzLM(Z$=e8heb!oMh(D(#z&88MaZor)mXMtl?TD~O zUs;?Sur;*j+ei7hp`O@Qd~rxaWnFG>u4Lr{=v!9Y+UG7LC*e@CnqH=e2k`*k3cRf` z`6{=a&|iWaoD9$H_-TQR!3@o1h0G@yQ^qFxiGl``BuTo%n;Qdd>%RU zt)tHGk1qt^92%ud+$8y+qy9M95f=F+-J{Uq>z)l+`R0!)`kGy~xQ>eF_;g~)c$jg0 zkhJ$%4PIpDjh(c#{3)oLm({Qvj%y*sLFys;4k3rIHyQ7PmnOuIrxRflkyh9_M<|Rk z=NSBACE)JeeaY*l$`Vy5yf6>nGMz1MqR z!5*&a#k+&(hsWZhH0O7t#!oObB8xNeym7#CuR)R4Z?mQNxV-f+XEiW27RlOH~>XBl?2-#c%F&(!a6Qf>mk-OP57hdm0p(KSWe92^{`(Vph=q}5A7aV^Tl0)4=N5ut1Sy~ zn<=1<+=B&x@@AD?Ii>gt3Fbce!C$>+euK{D!2_FxE2S!Y2j47IGYgO7kMN!I^Rs|Ro2zTHqCC*!$zHM*v1`;AbAUAb}Ii+Y~AS<5ka^fZ@q=aqWM zO2jg&+%Pmpaco3UjvK!64xOde$U}mZxpu~cPu59Pu9g=Q+DeC>gv&`$Tk=7!r%Gy; zy^)%lMmUO96%@1MDH_l*loe2w8&}plcBkkv>d=F+uoBXIDe~n)^iU3x!N%6L_)qO| zNS~p5eWcaRc55yWvt?-MGlXj1^cbasm~BQzpr)F%oZQk&`m7UA)*1SA7aR%e1$Wz? z^?OB26s9?+l2f4kjQYamO9out%It#!RGnY>xVliJ0{@TE;PD4Z#~gPJ$K6?!SZvG7 zLdw2x53b*@bd&=egtMdGp%n{*xL}2vH>Jy&%+*&9{7UI3p*E6qN&vQw(;+b0q2dfs zM!7X28DsagCND5?4@@K7tZC06RwUz+AlsZbB2Wws-#IFXc|Lw!$>$9D{-ctxzRqp` zD6reKz6+x2w%$6n=s1;0z&zzJcAN|=VuV8{uj_ogjN#ifBT zcd1%bI71v?S=?QCn&QIBf_XkEc)u*$dZ>+?iEE50QC=80+FUgc`YcSxlK?Inaq1rji8D008@N1UL(59sE*0)PIxg&ntI^bKBAU}WCiik!+EVRW|M ziXuMy@Wxx9F}@#Uya+DU`@)E}KDycOuJ!%QT*m8rzaTN6{i=c|*gJ2=I^6iej>ndD z*!-&BU9jKEW_RI*;>RWV>|_5i4s~v@Y>UOwo3gsu<9|H5*-Jo(bq?MIs)M5w`{+k-(1qK5g2^+JE=Y{{dZD*T!}xvOVx&u{tu8U zn1sn?Ts7>{EKgyJi~7_6}%&Vf*Lv<5@Ze&KGQZ ziuy;YF5|%;CUY}+ZQ0K@82uX>@l1YE+{7W3&G{>U)J`S&XUp-CZ zca^O@5AjT=uW*ZR`E}*Kq_0pvXry`m>E<{ArP{UYQQC$NR==X%O_uky=eaHGTIqZ# zLh#uR+V24~!lK}|C5rj=fo5_8GnY{>Rl4P7&DUw;$GttBgOgKd$?3@!N;V0#gR;bZ zXFV6+zwUeWhc<8SbQ1Fo)UEOe>%r^G<&sUuztZzrEx?vs>HH}UUtzutAzyF!T>$Z0 zE-lr2|A<1nwE-x2S#oFQ9~~AttOee>Fn;dv=ehgySfNUNeSrTP(@H(k-Ld=9CjWw| zvPQURQI7>#p8nbjwU_@9^a*(UIxp-47Ylbx>6S(b6f|!*s)zL%qK!)8Ro>o1y@6N+ zW0F18KFfVuy06-|AMo!(ffv92BN%xj8;qF0uWxD#m+V%E_pw6#xLfRW%3in0kqdlH z$F}_Yx#d24n?b5~FGrH!*G9Q1nShr5uYrb9HtbU^Y84<{mOc7p(~gr_*Sml?W}N{e@5A7|2j_?dJbW_`Qq*<1yD#ruJ?W8 zb0+(1+YT9<0G-U5-e9fN!=BDv`#Iun8BEp4t|?vV; zh0M7eJEr!PatORu-r)I9)XT>uSMUP{Pq5wW^!+b&{<+uUy}>e%=Szrw?B-iu2A|q) z%Up2y>X*(heU@gY;Xwbd&bD6r3cq~vvidQg&=>^$HNL{NpZ3|r3p_qKsOXku{QBav zUIxCALcmMbuc@xBUHL5nk1q^~Zuw-?Tc7yc!ZIEJZtQnbt@GM-?Qq8xs3nvbKoJcgfleaC}#T z8;+L3cbl~-B*5x_w^{!N6#qX03_m)ujmqSyjb6P=KW3bFK|%fO)AmzYh~(q(W0zT# zxm4~xa&UUBT7B(Vh8VW{mqHg?N4GO12OD=AeyAhSVP4C|?GDcJjB2200^h4&M2bUk{0$Jb)@NMa zdN%*7A4_EhFO~FP`e!&XRU1UDZ(N{m?pu7iR-eU;GrxhCgb*G74E@@GZNe;Vb=ao) z_&VhNPO43Ed?(dM8Tc+$o09Wgsy;%n?+S23S^IsrRUh@{cU!d)0s0OrK0=T0u;Qb5 z?AZ4|4OYmVgC6{t3*aLl*kJL`HNGpu4afYuGTdO0@5=DIGW`3F%KtQF_+0^hk6b8x zcZa{b!=F)Q-{XNB7Vf({{1NYeM}Yrl5MaCp?gwlhy~2n5DkFm*B&y4jbQ-Dhh@}QO|<)$iezlydC+ijWjqC{4yQOgOF^Mfd7up7<;uPP zX8fv!)9PtjW`hORA--IjI&$mlAA@KA(jQ7XBY!54OHPnMHH;HDTi1W}Gt$7!N>6AH zfjyV_$VOl8V*8HWtf|o^b6}=g>0HI7+5g&E@WW~xyKmc7X@B(F#u4vux6shgP)Vn4 z=;ODP)T=7(gN|jt{D!@XnjPILxx}m@G>7FQhmQxAg5-asBhJ%^ElckFxE|Hj2?7F8;d5&=qh#oTG)3-MKQDFeA% z^33alKprOvkO}3HXJ4eKRox3d%}b)!Tp^I!jGPJ%pz>BFtur;%WS5^3uHa$hxgzYl zgK9^6$QqR)+_fc_1}jQXw?3A0#5VC7n!0<|tC-wgs5Rr`x#3EWOm6 zpWI@(WRq)!jPsZ)>|4ZJR{yzE#D1_KLe9r);Y{VX=q&Ke;SeB*4{6uR|k~=UNkbJO`^qSNflpk-WNJvLZg z?$!@3puVT`c&JWYDTX?KZ|`%4LKfL2^|GZ-WkrNE_KE0NdPer+=W24E?F5@}7o@C& z9A%V+#YVI{aK=|rmd8VwQCOZlfUMie`y|_m)SG31C1dEmayJ)OmQp}jhKBRQ*JX3^ zvaQZh{iBsi=&^WjkW;M`!a5khHFgP&9|&;TG~9nr zPqG_GrIs@Wr?0}X?3F7QJM0@UKmT$5eGP|WvQ3%!d5u5bNNU{g)(UY?tk>-m?e#qo zs{<*uA3}^ny^*q{_dUh|Rtb?cPTI}^d5XTP$IlZ_BlLr z`8a`G7q!cZMJ`Z zw1i_DKIw6EnhIox1mo_>wpat=8xk6dUG6g$&QX)6E|p!K1UC(_AenU#>vlng$7!!8 z0W3ICUb=P}=?F40_LsUldX|FB(4f-uUCHUh=Ra#3qH;|Y6F=BXfpl?c-g~WLAvXr( zVrS9i(kxPJcx94CVLM1t=e;NG?i@((*}O{m@Id|vr(>B&^0mlkQQU|V+SRgv1mri` zEOEP2RAZ%PYzouHW`tp@&394@+Ek*&Yw>g%KAaR3{+yAS?cLIJSMzjoITR#!yqeGI zt77vpxEc{L7q9TXSFH}N=C;Re-tj6dUZkg>LJAdletKct&Qn-Cb60F4m6sZ zi;HU<3o<)Nvb<)KKvU=4C#jmXZLDV3F0`|rrL0TL!Bf{xM~#oRb!&*FR*D(Uh z!33_CHuWK)*r#Ksb@MG%5e0a-*tz|cE!OIg`L{~|!#rKds!k`$6l{u);8Nx7{a9%^9R1(};HwQ9VYmtZ- zLn)I(d!Glf{)z$GwI$|!zTM=PYM5;rjejATZH#LNoVUS?PbrK<>~M8=565#I9uwfX z#mn~#c`6||tVn~gQ;W-OB;>ac{u`O6`LXkZh5QHyl)kt!R))INHNK3eqVdbjJz*@b zpYvxIzuMiI96xWHQ+`8ERr@zhi=nZVd-5yyd|wWZ(GssbUvz$q(>k(jf)f}FoFF*` z>y(uartL_o0M%nG`ipN%pkZBcDV{7c8vRoD(>0UcyC%BzV6auh2e>sF886H~9^~5c z%Ii)P0nef-BB^~<4(Q0m<6ZjcmE@2LCFzfD;D6^Ev>rJ8jL)PZ2Bi*MK4htRS)414 zVfxugWq(7%^dM;4`N`MB9=(Xfy_UY%Ms~=5YHtm0P zz}DCNAzi?@!uw(Ab$^$x8d4>l8p04&!^Ni#1?imkkUVap)kX)Z(~^=-f`sJmwO4p! zSslw3F4+ws1l{mxLy5PoX0g^M_AVO+Lh@B*m%Gb;U0hqn5srgwVU3ccVHZOC;^jJl z^qFJ?eJzL10b6+h)BWjg23gpsOtO=Q>A%xyLyKPeIo@pU@{5AwXZJE{gd)c-_eiop zTH<6&GtZU6=(N|Xz?W(!=%4XQ&b4!9J){Uh2V5Rpi{to@`bq&F&^!2RficBG15pB74zt8vAT ziTiZT*aNRgAeA@8uRtkTjz0Xp6f94;c1-gF)s12P_r}>>eigqApO&(|`bSkB`wB=Z zYoKcO%Z1*VAM6K2q*qaRUmc?$aVLGZ7FC2J#>j{*)U}yYY4sQF_tXiA%JZ`Zn#*Va zguMDExAA}!p3Msr(T)>En%bCGk8RU$MxV(_R=>Tg*T}WwqUT|V34OsMw)Z(PEmbx8 zZZ=Yea1CVMGaq-&=+j5bwD9tf`|HO*y5WY&Jlno(P*9e43}mE}d2gT*I-xA|<`)1Y zbPy_|lS{n!%fQPmx&^nd8|f9>jeM(Gc>vDc$!>pIzr8L)0dl`zE6^=dX_qMX>}72Q z_t}1t)GND2jR4dTai1TSVO<5)Ojv9*hc6$xg?}>{H#I*mLrK}o*9w~c=@hD0W{=sb>ofm zFuUo}D#m2*BFVjO_dLf3>v>=@JPy>gW#?W!tI$w87GjXr0`ZAxTLV9;0@MYu=qjjO zuc=Isx2CYZQr&Q`|HFCvyAya-A(yEBTin6JDGWWM?Sck~-_xt5!pwm`&NPPrsN%4Y zcXV|DpKf$@9w=k5m^-JjLqfZsEB%@M3>oC;KH6(C$CncGOh=Y0ox!mr{7hcZQh&ac z+AsH96LYQ8|I|NfJ)d@FMv+DK1qRF{B`CneUW7jCtEs!3z(c)VfgT+^+C~P6zL^MD z^Bbk^`-1WVek%#`YYCK6sctC9*LG+fA`w22n1#!pKCsqocq3`#v)m)|drb9ET2t1S zvc~_XwO~33$Qlu3wY@H{YB~jO)F#BJ$pTrg{iP_L#%%?l`@O-$eKyy*BxErlEb#Ql zf$sTG9iv7v%TZp#6q42hzl@BL+A1rewwyuy?=*lrEn$X4r`dTA(=SG zkLg0Aeft%w$44Hl-U4O6M10nkyt@m8)6DGh+U_&6bDz_ac7oZ)>JQR^oe{4f#Nyv8 z0%Kko0Ca2MnIB>q5Yxz{a3c=B52(RG4!+w4N7Kv};gm-B?;uTsbMv>Nil4xR{k=as z->X_Ueiky*edkrrA*tr*ZK@i&d8s2()}4uq*6I87k5;a>Ixaemg9E>u3Nagec?%1XMf1pYrwnxv*>Kc|4fXJLC$GHt!Tl{gSFWYcoCL@Qt z`*v~rOqnO4$v3SD=QQ{DQjZ^c4DCTy>G9ND?&>(Yj$Lm@@jOQ~R-~2~TpIxzMhQmht}Jy|SsbnJDrP{4ClDL$U#fpCTjMXqTBTAf72ei+Fe#tvMh zc6F6SC|_pu83J2DrufSan_c2fJLinbBcPySs}MBqtf+{kKzCz&j^#Yz*(`J{Q6 zctfwAG&UZjyRJKm>q_m{sVIh=+Bi^lsWwg)x*p9_%+5@eiss%+D&O5B5(onxYu&n$ ze#sq9Qk7YFN0IR%NxRLS#m`lM zB7KSZizSj*UJed9R+eX;jXKi}vO}hL#L$fZ5*h~{9~HNbObNLTp82F-7>@wxZ@jlG z);Q${EGX{RY!xiGcKa`0hAdx+(uADsKR2&lfBUz{9%}tyj@J{`FndQwq+?RmzufSJnvuAR9kfwJ!KXAD@%7MbnGR7wV0vGddx4!a?W4N7ElC7bdpfa(8#{D8o2LHNUzT zuz3E4yrfoTw|MKM$svels<<{1x1EQ8K4af&%wOvVUxT`<$EyMF>dI@4$O3xNaYl%($%wc0jfC<19)k;(SMA ziKn^v6r!tpFW&EG^)>_wpO)hF#5L}21ISj3sv0^`qU!fP*mo(x8H5vd56Nlksm5LO z5Pu`T*lI6&_ zX?zgBufhUJ^to+q{b(IiIYynKC7aCMo)qg{W~J^GM;=s@5rM@_NZkw_SrYK!znBUE zB^Et(i&xk*mq(ZO$WT7iP}95E@G4|awCCh_&mUd7+k)Rmo|)ygsYv5+xub@GNcj)u zteaD35JZ@`Fk(z!>)U1=`rxJJX(??Mpf!HexCN{HkKhf+!OYmRrq#t4 z#PcWJ zVtw~zn-aZHi5R%9gR`@6foAt3rGQxG9K(!m@(CO2REfHTe^$ z&=Y@lep*oQ??d|Jm+zn2Jize(>;i`FM|3>_zPAazZE9saXsYGL&{fWKbaKjykcQ+EV4do_=OF+AuJOmMtSpc)5<;@l^2H_OJ9 z^U9J;8^m}rqJJx>zlw%_^8mQ^XU?s3v^qDRFvv&Aoxedzp&u61oUy}V9D<|igshMo z{Z8n|E1kUhGj@JTzUS>R)S+VXK(?uIcX^R00wK>;TJqXtZh=NkG%GBxkG>AMJ}lEz zdAC66$T02`aoM7M|5J=jbp6~x@=s`{A^hi*{Y=R*oPpXe8xTCGBWn1jyZ86Fm=Dwo z2S?WY8i;3-<}r?bVACZKjkKPPo()126L{4oI~w0i3I{Yt2lpK!3Qj+anR5()X8cyD z868#!(J|hqSz7vvP#C03xW{uziS*}e&%xjZ9477FuI>s=cXY`&F4kFJ#u+)?Aymcj zL9c*2an6kwMGA)lvim9Tl5Y=1I~3HvVzGETnBHzbU^fhRt@npSL#+l_mD#njoX9SD z(*p>K9^$vRgQmpSxbWzNd=IyoXDii#&95i{B90Drw9$JV3tk;nj#&&0g@wCjzBze} z($$eaPl$c+IO2|tN`a>QDjH@aS!AOhn)+h8v9TjOG1KObOw7DZV6P7Jl++Hx};bYq30h583R@i z%gAhT2&y}R>Y&-#7Eo<;X7+{pAL-~XJFP#lto?9q7;!$%!8SPCKdqzOR_9!cbXRK20zVxcDk`YYmdF?9ZF`*TDVo4<;EqeqoUwKM2~u}NFz?odu0Q^;Sw4p9 z=s<`J8lkVZ`P3b0%_~mgyth5ghQ?m=oZn5ko3qMN2Hxid545_h3)&WGp#x!ZmvJ^hcU=xN|DWs^`GQY6=M9FJRQ$gMB7_kqy2D`x_0zTW+jVRc5_=Aos%!)`Q&f3~Mghmt-bn-l* zM3TtBxmGS2Ribc82hn+Px*B+d@FmBmLkl`^yJ<}JuM!+7c7Z{COzyU~OU_h$ z!0VeWV=zavhP-%=!@&4D-y~WGT5=s@-)5>GYFi#b>p1H@;m5V+7=eo5PefQ*z$@`@7-ik4HudgFJ z2A1}@zeumw?z3pT3utGCxOcY)z62-LL#T}6q zG;|}Y@l}f=*PguhC7jqO=5*XVu>d#N$-!UgW9<6EbZUkc*;6P{w-~^Y;drUgD7ihy9Dfm6)PB^R-l}6}UP0oua--)p35m@@nJ50JWBGNMC6`0s$ua2 z(&6z*ZHx?w-=SXCaNgQb}^LBR4j!v_-L#{nO>5 zWrXE?YXb8;8r*M4xPF6={cj9rb5QbkpZ39@_ z6O57(zz&>hf`?sXkaZu$R*vGR>9)t1jizI^0)55?d3MU=$V<4CSW&5FG#Wgep2F9h zIAp_;X*>A8)9?S8nYE7nVtMj&>y?QhX=gAL9#49eB&muWM9q)%H|RETG0J^ka)C3w zrygrp%lqY? zuOme3b7o$1rbh^x>_f(6m)5zm24}|oJHqphX8;Zu%kK2|L|^h~I}ER9W93iT2! z9?ZBr520XYE|_gP_F{AA#p7Xvc1^Nfl?sHar3MJ!$1H#vflTa30zorBwPL%yK`a;)AytInBUa&Ju2z0KmblJLnNrSmzgHoo67?Y8QChUpW%3dJ+t ztDHj*18Cy50W>jRYF)6&WTjugMz69JrAEGqyWLf#V4jof=I(8uQB==(-J?d2CQ5d9 z_YRVzQZy|MKjGW+66>=IkG$}J5Rr6ZSPxd;gH^6UzGfiIN5fnuC%{wM*n$3P4WFGE zx9!03MksP}J65U77rNYOS@Dx!e_1-plb0}zpa~HtD=pn=8`yCGXu0iIQWAO)KgrG+ zyZ&RSreoMMl_)L+>jD@=f zk;MsIqoNm z)62!;O@J<7!;=l(ua-ju0j|VIBFG`EHNcHX`3;3{AE6qRrooC)t$l@V=93#l8PuP+ zMZbaWT5fFf>Csg1&=S^$)9Oq!4=-}rnckX)P){eSdBzB?sZ_Qg13M-Q%l%*vW@{8I zz{8mS#~vlT2at=jb|s%k8PsiD;bQk(*Gv?jm+WWq)^gc_kmKz7cuSmq+_%cjjpES$vd&kIfp*=8-XUMJoa&^G zfX_n6VBzb|--uC%Vi5&E+xRi{HrvNBZbFeKV33Z4;&PSnEFi?|teBVXm5V9!CwAwW zJnU${&)Qy8bmQTaM)U?oFb#KA*6R9bVt`#+_B=BqGLNdptWy9aUeE~hw1vDFTKB2p zK@;L{{a~<7NKMU4POFPwVp%vTuK%&XCm=bP{@3e5Tt}oE%`q{j$L`cUBE0 zq#OD=J7ClpwnnBNCu`#3TSlCt%RB%HeLze_T`09S({0SCUY|zV^@+vyNt|0Svgh4! z&}-YOnwfxdo|n;O(ibSLWn*iE^Kvp38q28fH_ZxaQd>)iS217vvQN2j2^^x$;E3di z30g^Il$b%=$Y09`P)=aNTJIwjX?rG~95^zheE+>fX>jJ6#!U|})PKXYV74+U;>eL0 zzZwcUt0fDS7l{wg@ZXG$Iqvg@5{&#%ks{D8Ub8S`4Y$B_T^u_OWmGAp z>yeMEC88O_=~TZ1HuditQU4nJoz=^2&xEeFkFtNN$il^T2yb+OV z(d=Y^B;j!7AxlHdgpFcHdypw}d;X5=)R=#RZA7t*i&KqbTb|zi~x(qj7wn(*9?o||3>nhq8*kE&@1f|TpB{GUBQtEIQPnPVH3i)2x6IBVC z5Cc|q9ImeqX?ndYSAi?225SSU{IdO76QKo8T!rhzFM;~H+?1gLr4jV}sx#$>)EX}# zflJVuFDa@2*>FB0RpiHwy~oj4za5jr>N>0GuKyZnxZIi&0vSAQ+$UoJWq&oBy3*jZ zS(CnR>CJ7JtxQTtac*y;|KigtOK(7jXtJM}a%vlv5pt733&6t>1_=J{zI~!k?%ug& z@y+NsMJIc$vyZsmntXg;K6qAN=@Ua#GTV%H+_yO_`3OrH{6i81?8Jkfg%$g^zb$8FbAo)3Z zXt=gaSj^X|2u-S{008MetEC~jx!TFlk}tZRUy_h@*}Igrxy(b2fcii~R;0Xp{9}=O zx|dBA$$+-|FCKSwgqtcNhBNqTr0p9#z4WnhLJQ{!kts7ei+RYDS|wacz)UB5Dk|L( zk!};-8-YOpa`@UD=p37Nl7i_TTb~#t)t7pl7Zyq|nE7qS>0wSr>kp}_DpP$5g||x{ zVOiY7YH6$1|NLpeOx5;ae2>Htp_{8QA3)Hpd40l3t}1V9-kIbkN2*s9I*iA8#G9g8 zvu$Y%Yw}DBxLJa3jnUIX-ZlF+)TB&r(Hl0pml7v@-@-($MfW)T-M7}`)=3&0RY~&# zeN<9{wJ;;74ph_Ut)_HDt>Jg_;9ZA`OG0sZiVfv8ZuGK+m>BBMpF?xoE<)xaz^?Se z!%h?GuE0-bZhjxda~)EyRN&QN)*jwx$9yzK6}n?)`OAE%MA+k~fMvUxI<`thzM_8P z@~kj|%-^&R{egtM%0(V$%SF%SPV4~{{s%bptmT)0#D`Eg<;_dpz{5oQ5%nNq$G0>F zp5q*Yo*M98hvPevJy5L$NWIfh9X7;k#RtJg8p<{&`BU!<`-;>r6#v1k5FgK|PA~DI z3(3s1pw1|V=cHhQ;QpaM^K`{1{eYf@Fhc73(|70!!r6hR9fsd42ds?%@4BCNw8~P_ z&@2az3Bt&{dsVpiNucgYk){zDlTwY2!|V4Dw?96GEt>(IzfdKeSr-Uhg3bjI4Pj=D zosad7;)r|e$7Ap!nb6;h|01uB;qm3pbTHhV^`y$BGu5+mpr-F*Zj%52@!)6#9DBx2 zV%kk7uK*k5v2}qS)gYGdFdA|*^|$T#->k7@h^$g zgX~7(J+$CQozpf0<{KNA`kboEWkQ5B%b-e!lR--QvZ$D}*m7*TxPfwmGXjAcYL4I3 zbZhxGqFXY~S_vmTaWJmFC(XKYVfB-X%Va2dN%@a)xoZ+-uViiv$Q%b{os$M9w4114 z5zIvRq64{@lvLSjE}dQY*sBDMyGB@uye@(##>I-9+()exKx4&q2D8yxDwgG-FNU)q z%P=*r#sphUYM?v-P1DotO|iUQ9N}rJ1+Y8&?FkZY=*>vOv_2;yLg2;0lFHFW5+(E) zPz7Rl?rT*)1xxa?x;?D*a?$^xi-y}RT3X=}JP=$G>;tiBx7y zCVJ>P8GDCjDJ4fNkkxEbH>1XQZ4|_^*+f-flT?)6_6wVlAz=UeB$D5^bx&=i7nep0 z)qs<&e`dYIlg9Ma`Zb{WQxCRm&Ifh`W1ccmCkyFNrJ ztA>+N>dn_d!{A~n4&7R0Q&n1v@KbiYLa6p@HtB!iz{lxx1{XnRHeS*t8b zW>EGxI3Ej5XUHx8unC!(yd6V!aJ8=5x41+YxkIj!abeaxYw5Xt+}u3;w{kqO2U2&@ z4gkmYI)GhfVy)Uot#E9tw#QxNBoeBw__`g0uALDw6{q#%Xq93nk+eOuWW)3ZSA#0* z05^fx6VT3&*R<;Ky0VbnWpJe?y#n1a;z|DbD0Ei|5{q3#Q|O3!5sqScLYe_mop3oa z$oG!nRX@tQ-Iq*m?+<2z#@VIv4vw_Zr;^`vw)p0qf&#qSqm()I1 z5B2b`F8#LBM6xc`6s=<{ol~5MaZkVC1Abl1`(9KTu$eHt2I0B8d3)0&fPwyP$!8~J zX|?!1@43VLD|SV24&PHUNj!f1>hWaX+ttx*z>W@1Rxg2dR|G zG2bWtaO^P6AwEr`EfU%Mo@0@mKr}f!aR$$=#UEu*V@i5TD?+~OD)t~|SdFV)5){eN zo(Q?ICu7TXYe{`-QRQf6OZp?YfQpG)Em|nPP98-}DU|ihQ3CCG`594k3<~MJRGLiK zeoJs+UO@LnRpn;XT}@UHw_e1Dwg-&+xC%<)B06Z)OJ8C z@h@2^S7opB*W8`*vnU|X#YXe_Kb!Mi@eN1T|vx^^_=fB$0UBmU>JN#Ym)Sy81#l+~SKa3Sz!j0w6BoufY4N-Ye zcK6|i?>E<(%a-*wnA^3?jE-q++&jN_mW>tA&6ow%RKF0Nn8pN={f5Sn4ZO)^b6%_) z)OoX)7sP%cYF-&l#1b^=ksu1@_3dW4XmbyFxpkO2sgcJ!JywGdP({D}VD#k+OXMyq zoe&c@8PF6R9XKEUhwul+wmV9eM|l)dxx8g3FP)c>v~ggx2SPoAE|%xaH#tt81g1}l zQq;~ywpg1v;1hsA)QqtREcUumUa$OMDt6x8LmH6->J9@?^h{Kp+}``Ra3bU?@WsI6 zvdS}w9~FATo}j#GL*^rwfouwNP!vq-K8zd318e>-%tsxDvKKTd>^u9q>SFo863?V!;Rh*b+4=6bjXuka5UnP} z=vvU)W_eqG(z01JqZaPsJ-+kEUp!1`boA%m&-NC#ACVRo7I0o-^^MniJF?C)qTbbA zftcyCcOBNUr0y+nQTa2w)$lmmGo>m$Z{PnW+?zRBZDQ8AHMljrb+-;pV7K2iJC)db zyyI(bsPxru)drl%!l?l!=7t!@@$2C$gyx??y6cNlsrDh;p{aVzFwI-iU5PjYX65>= z?h53EtD#QHuy<|5V&egA4KqRl*vB_O*`PPNEw^GNc`z7PozE=Fv@4_ePN83K+-BFI+k>AGJF4*vr{R~4KV!Gnt{q-i*k}DvciTpn)hM?(UM}vqh`N^A z1gMiU8o0^o9ErrpTv&uF@^$i21a%5I&ls_gl+h+r_lO=jw!I-bYZl)d6RawC4d5Bp zeGiL?75DP|cM;G3itGyA%nbeFM^({%%`jsV|T0Rh5vDO5gV(D22txj@UF6bWVN@VwBo3afG5U~6|Q|?P1OI-Ir=AI zrCa+M5$R(fK!AN|j)_x}*+pCt|o1PxaXr#as7!m9Vn`yV&9CykYb!~(9C zneEk$Y(5BBNkT|kY5lVUOX7O4YJ~KH_@_NaRGH_rV1e(Y1v(vAYlUAtkG?}WWxlJV^CoxrfKFkFFwWPzb? zwUvuSmCtqP^&OxK`rlXWKYjZ@xyqGBL2Lhpf!zrk-(PS?O006+uX`!i_EbOGt04<) z1b3!8D+>SCEi71!#>Ebp8kRd5FL;0J>*Kf_KG1oT%Yvpme9n&>W=F>X0truEuhXXAP78UI)M Y@!I*LzlW>u6FhhIP4ATb`RMuo0Lf#JWdHyG literal 0 HcmV?d00001 diff --git a/resources/img/tensorboard_advanced_2.png b/resources/img/tensorboard_advanced_2.png new file mode 100644 index 0000000000000000000000000000000000000000..ea483a967ac6b700c0d976c43bd3d44da6ed2298 GIT binary patch literal 329796 zcmeFZRZu0(wk?diySuwv0SpWbSx#0`4Gaun7Yq!h2@dA(6S%hA5HK(# zY-GF2DH@78t}U|_OQY1*(l>f<{^=Qc+REos(ZCKX+uEn$)+SWs z&))CMQ4e){pPCx&g6ObdR0z8owUH^1H|$_eO3Jltq+sH(i-glNvuS_CBA3Dv$-pEH zf)^}w)PvP6yn+{Lc-BzOaKOGXWHT$mipmXS>-Nb-IHyt(*+U!jfbH5ucJtxeu?&I8 z^Ab}y5zQ<(h7K#Xhvh!?rxqJB>^xrZejHF>{}M4y&7RJC4PY7l{E=gKb4(Oo|CKYe ze?B>9r{iKe3!A22BQhJh0zC<>O3XKCHf>GedK1anDQCk!kn%Rf76uQjAaL>#HADy2 zES!&VMkfAZRlI_=Nrv+2nY_Uzham@>IUxZD?ic02fwR`PYjNottvoui?~62Xq<9eQ z)m-;FrqXu7v9xPieF+JHI`F$JU$qo7HbADP;l@MdajHbjJ9bDn>s+QLO;a!nQ|NJr zVXICnzuQfiO{(8wS4=Wle~e*LhbOrFxqXBDM#RRVf<}83-hX-<_v01k=)27LMMF*3 zd4bicfO&)zG<$K6_MK_aI1n6=I*t;TJo~`HE5e{Pf-ED(cMbUw2tQO`60A#xdHn^J z0Ok+tsw|laB)KfCpb7JGtPho}4AeC~Btm~a+D)L<)U39^1SGWTr-dRTKre*r#42c! z5>YUW4|aK^+X+>WNLaUib+5K+5_7}R~M*r&2g5_tUnHd5^$ zy9?SRa5g1?fvF8G(rS(pjOTNP#Wcd_%oyqc6`-y5^FT*VvN`~&JwSqL$)B-VlEo^i9c~& zH3#9-2bVGEdmhAj4aS59CN4%52`y&=V{n6%2+2PSbJvfdiX`8!SC6g{Y;Qulf`}4G zSpd!z!udzA4XL9K*$EXWMd}3()BlAnmJANQklalYab89djd6&?S*j}$jEX2qIyn)G ziWr^D?3?f}EPAPlMEP&9YB6uoIifRUxJeHCD8FOwDeIC{=)%>$x@+KaOVJk9mZK7c zj}$G;yK$j1OQsiA%_B6yY$gDUE$1eWk(*%sf>2Ds)Q9jgz`w_}oB8Wd{fI3w6}KbT z3QjZUUJVy7LFEbb8;as1w%i5Q^Mu^+<%AsVAl_(tQTO21jjmtYda(*e22jtz z2|zIhe<2S|l$E8OqQSzPfbWDKg+Kb^xgg^uLxq7a$ytQ>#*z}5IpjU0ImA39F@$5r zd&BM=DM?|SR4tqLQx+pTRfU1Zp5}r!m&TC#AxSt9d;H}az=e!Ilj-jWC&ypM%Eq+E zXU7H+7!YU>E)kLt48qzGY_UA(x#$hP9;@S(ma9$C<)!M#(^$Zl1iK`<#Jl9*N`L)2 zuR5>f98F>D++Y^AYdVY=-5pI@VWXi?N$O6Zsc5LQDPdo@RoGKFZ&k{L!<_j;MPI3^ zB)3H9r=MyO<0@SkopTxvy(awhsKQ9YuU1-(Qxj2juaa4Py~0E5f}OaSaQ^+6=tb>? z<%QoTU*PMeqL8GJdbh@VpikLLL3mbpv2@o6Q5-{D3V{$(tmy=t={HUwTRfWshj82i z@eyY{ufsuG$X;zcppM@trDS}8Xld+HP@k1tVfni0&+6(AOn=uB%DcAq^(IcC4t@yYZdyjMPX zI9qt=zJGopyjMB<>|Zo_VQb99p5i$y2#Oc04_Yya5G(7iFU&N}F-_VbAB2_0qSP+t zDn^#Bkq#Mo8A*vdB3#Db=Kjj&%stMw`t7&F?bhNHSHD}kb;yY;r&1$Yqj;l0I)%XvgN^N&tju@H$j~Xcl~Hbp=ou z2n}=xcJm-*J7rU3!(^vp#?jKK81Odu?P}}OuG_5}wt&tT&&4mI&$G^r*7et1&#TVs zzRVFp5aaN333RzMx$N^hxOZJhdS;zBZB#6s#5Be<&OH~n_Qo_QL@P8;GHbgp(=BV( z;%>k>^f<(9WNiv}eCa&uBz69r99*NEG|wNsllz|HmExh~b8dY+{%RE``-KGLcXf2x+E+JBdWU+;dUJ)OiWrGl zj^u@Bg_l91gUg4vkHy7WPMv{9gww_Hq!sA^NBfiK8`~nao0ye89yb$BHOKkaHj6g4 zE^7_}ez)6~aS){<1r$1R5*Y`Ti{E|-U1NDy`F1Hi;uq>07>Zk_oaV5zN{o3Pt{toQ{0pozb9qTZd;5w9ndg}@>TvY%^sj2djg+h0=Y@-ji>HfX@uZfR zN6_iRn|&8Y-nSE8v%duwCVuCY0ZQvsu4b=i=w|W$yxuTA*Kve!wp#SeMo+D!a3|*` z`q4$?q$InF7toAhG)@1`=C;)GtkraIc2LO!Y>jk@nPxb(!l5hSavxR=nkz@F7`&yl zwf0pNRQ?$}e_E9}DxX20I?1r*_dM9J3=0bDof)v+bs~r z{l@d{zfBJg(~K3`sMF=`6($u#>UOI;?i)8}u|*Q9mGw8&AiLrb?f@2 zdRjVu@M****dj0C)djz*o&t`^bP7g};Y6k1GP=WwEVpV|Xv)_`Vc^18QpN^0&5R@xDdAZPRKLDnLE{deMbQlJ=G zmUbhXwsYV1p23Q}rz_=N(kr*i>fT+Kl7`8estSM_sG@31d)DBt*W<%y>|v6Z!oh7j zw$u9+*rYGi+TNPKJ>GM7>d?Qr1Bki8yi6BV^;Z2*Ju;mZVft_`A8kMSRu|wnTAU#R zB5@_@^`h{(ZFgK%Ut74fe3`Y$Ul(z_L_NQHF+U~sV|6h`LdE*5c(Hs?xJw`XFneEO z&){1D$v@fbKUDJSIX-r|`>qRMao=AX&UEg8s7afI&)<(<8s60h$}9&PMN)*deH)&B z-*cazy(i@)B^k6NG7BM?)E8P?gLQ2|f-%GA5qE$U(2ZXjsfm39Nzp@m!cSgWsyV}$ zpdtpg4TFUaa!zQiD3X;97`fcXTYxY2n^uoYr5>8F#z=+n;tfP3CGmEEC1!y=Q4rzd zZ*g$kTHsOvU;}>$uI8#xNP6{bJ5Y)l`my#W+^P(-+H+|`xJ>@_6X2a>bzQ;0aH#)v zfy=2;U4wy%g2_pWX?TI3=fZXoX|9fJ(|3YOla0vY+%Vwe;6kbHItv znVj1^(9)xSKq9jnl__29ELXmmnCl0x}24f*Z8| zfe>(v0y37X|4BE%F|5G;4u%Je0G0fIBz4de?R>nK>mUMe*pOpApbz%AJzY? zkpBSk4+Q>E{m%;de;kk$IN3lb**pZt}XiK4ges?Y- z1nOU%`8P@d21Jb!nzjvhRGyzDHPS5!VfGt0+^%p6Z!UbCim1qbIm~;H$2*H$*Mp4Q z)RR!CD}CzvQ!m!+ziSM_l7PSWb$&Ct2@(~pI-yIFGfwB>Wl22t=8d+39nicZkoz}A z@n21{lEI*zvy*vdm+qlr%QHrOk>LrVt zZ5cfTS=16xiQ&08!D|c&(bq@rjFx@5E40>^e6{;HNyngUD2kR^91f2#}n5qhsY==2P6d z>4t7=uSHxO-*?-}FMoRX1er@})Ij#H9uz;q{OR*5`&zP-(3XeAA!K0nv6XbobK6qii=M3188Z# z>Ypp5HE4i4Gr8I<7T}tu)LEUo9qr+cWsLB?TT)A|`ZIYBEub~J*}a15P>}2qm=OJA z@B19eDcL-tg9I99w9}2Ye|TKJEm1&EfF6hh<9+4Rd%WSuKhSpNJdAMHT2^S$+orZl zUov4|u!MM>`4=E%rISbgHiX}-#wd$3A2%wD-+Zyc3`qzw+Z+{#oo)Ij6sc#8r$43b zpr9N}&VfZ)nFSi#NDj?mxkApw%0nmBHS9nz`Pr_|oC+kHeULji^RuKb8( zOviW4WW+6`;e&rI8S#6G1kYqU!R!XhtFbiZfSke@{zt`mKmtb18Cud>d_~udz)_we zOIEB;SC9u4$i{a6-nPCSVE1wB`g17nBYtI_%nsXW$)*!R>i4=L)I<+0xtsdk37~6j z#Bz%f_}jDFsg#xj{fh6wA-K`AI%y?E5;igZRnD2AK(ZQqYn&Sg9%+4mDV2ar#yVjM$N z;C@b)l+r_`cQtQzPXy15ZJ+D2_SzEF>`ji9{U%*a{hXHA^gN=`wNTt%SK|<&(`I~e zQAOQwZ?U1`>UuPZ9aF1*kxml9ro>9i&3=Z8k^cfR;FoZQJ~O8sUTa73YqvNC{8>8` z%+vnEr=#qCk;8dfoH&USdS-x>lb-#V&!0 zw<|<=*Fdi8J#j_9Wnb_|(zFCwqvPc=gt(EH^7$rd^urFs#^r15fo` z3Z(E}IVZbE=Uh|+$T4MQa2xq&c(>lVUX+nbIxc>|1&ZZc5h^})Qk3!I?&QPJ_M6b! zm}kzQ{JWp#cTH0BeR?ybFNAx`B$1-UWd?TnI<}<>W*_QSWx|TX2T!EuF>6*5W(fgh z1!#4`RzLrCHdJE6K5Sl?b_*b0tr;Va^K&VpusUcoYRf%QHbO&W zDARbYTkM?Man~^Jh*Je%T*JfV=64b(Y!@pT-_3(9F4Fr^Sa;wb4+(ozsp9*XKcvS+ zo5@n$S{|WWtU3t~$8bXy`e5L;FQs-(@hQjiVOcVJn zLFl{H7xcZ9Q>VZzStBT7pzn|j@34$Y$VIiDmD!0_DTawCdnf$A>&5_v~;b4W_{d|A~`MBXcGS1qahChfs* z^A&5LTR?dJ1GC=bCU(d;fUPdjs9x24MGo!wVpj@(aCYe1sh<&FnG7>QyLcZ%h|#l~ zV#sOX%qEPxHbf(TPU-1NMLuU*Ny1wjr6OUz2QL)Vudwh3;dg2m0-6y6F^E>d`X!Xp zJ>PrsGOBRAAP8YF4^Q?T@xyA53=``~A*@ z%QCg=#M)$12FdN5E609rdf{$qdo4&rxx0ks$3OcXo~Z4#7!KpP;`10A#PYl!p@I3c zdJ2#5d;#}+`l@yp1t3Q|1Zi%_SMZtlInU6-7uK(Y7*El=6ZUjbjqvw|PQtYMfAqSo4=ElEJVD^w4d za#Q|m@UWlIlk zH9BY$1EQQ5W9Qkhl|_Z!3mwS);h}qyY$Odh!t^)zs7IUk1xqCRt;^gM(EFm!#~|Bd zPUCF-0vtb%3lq4hD%2648(aqzMC(PUEzCl31*G!0*a_@5qqOQZn3{EygETrGTV2Ld z+1nhBpl^AxLT{G6P zvZr=7lwyB2ajRwM{VjqP+@%7x-K4v>$?l~BBI8hxEiDBMQchK5ZW<1Sm~Ju?4(&j} zeqXs+Vie!pcBY+9eL|K`5I$gLX4&<@q;2nDM!!`W>rCx*?`@*4aHSkTfaGU{Z4|7k zey4fxVCA}Uj(GVsvPd{>S9x#-Cv<<IJ!FORH+eW`LQ3j#_}zHZYK8Q`D*O=cQa7Q_-XJrBttt79=V;Clx^cGlRYF{ z$oy9?))O#JYlk6Rd6b+3u<_0+Pi=^ON?#B|*m(sX8R%4>Tra2LG<%eBHCO%myIkdR zrOv$A)~eZyNIg)XT$at4{5n^%c{<7v0;<~AMD>~RXD!U{F*Tqo+4D;$OtTB=nmDwF zge^h1Td1R2Khh?&Yn;_* z8h?;x0uD;vTu`bh=Wyp=@7+9L41GqF*UWaR^ik(Kk)-#c1S&Ui*0my=?U`}d zCm1JdDuHsNGe%5a9wR3BDTvCOJl*+~`hPGs-ZHoKWDHu-+aCF9{Zw8=H==yUEm{TuPye7?yUqXA0^+F5$+C)NQ?80O{5GZ3A0-^XNRk#vzjD?K$x- zPC3BgbYU09Af7AaNKurJCAsmm6kQprfLu|AWP;FLN~y(uPVQ%=I9N>X$RFo9nlFt` z&N^L75jkJ^RPr>;=X6xymZytCr(=Xu#;P9zM^5e>WnR&A3c~wRvs5q{!O1s*W;Qa^ z9Y(h>gwABz${dk47cTj`9j){*tRfgs3rg1A|5|n%w@;g0Pz>#~ge8Y_Gsk??8-yER z;5E&MQO2Xbx8J%DKefF48$^eRm~D#HvP8P=00mK2%K}#9gh#u)Qo z$f(%XC*@Tp$xPx!jEjywChB5OUquJKTo!yYOdJxpBZ>w4)rvo+PB za*LYEI%jmjw1Ok7EkzNl{f9+LEDDbZg`HV-;Ft^Tfzsfu4{eC za%%QKd(j*?p@9;ahfT=2bA>CDsH&=JCjxc0T?Ev>ndB4Cwu`F!D8q7I^5+7&8NR|h zw)C$cTy*ZC0m+{*`d_IZr|c`*!rxHiIT!g;e>?;xR>IhCCUN#sHqbh-SaW{U)ePhg z&Dr1xL)I`>d3ff?sflMDJb^?FLAy+NdzB`RIn&PSNMlPXpN6n5BlV_da~lW`5^4YH z-TPo#fBFk5YxK?vbp~J}5MTg9Lc9D- zUh&70TJ+(7+8*_`v6pbQCD*=qWjr(J?}u)o=` z4uFnf(XrEjJXDN;rEj=)R+M~eE4a3s)>JW3dvdxu-j7Z%`EXnow|U3J;D7iOUpDOB z(~zq**gLR~lxau{B&%hRIGck2J^OCDywz&T zL+g2D1i#kO?rdGJ6V-d~cRuslx@d5(&W_SMbd^Z9+yfmVfmyBxIG^c#B3ay_3J@P= zMvhUtv2Hc9yx88v^G^YTPyFAD=p9;LqB^sGn=<_Li1CNi%KaBbENL23F+#oK)CJTp z+V^P-!!LcLdkn}7hfr`o+8mC2qJ%sVai}4(Ol`;%-8L*GNwYABG*SeC9iHs9vm!W4 zsQ8H-oTopkdWgxTteADLzq7S`eP61S+1(6~bGeKu?6A3D8~0lbE8^M}K%x0Dy;nPe zcMme^85^4AAAnGXf^ynp-GXqa5|23^F&&Nc#%MwxtdIk^PPNaiJ%wT(_5x)@l_GI4 zhn8NE;cc(z_BfV;%}dqAucB7b;v;k6vIMu4BSZ zgu4NxT}M5~a9LGQeov!AD-FuYS6$~vjPqrr8#4ESXtmvYkb{rIe zsD-l~liPoOz7)?-USbx{&d+p+PPcoGeGHFAcGn4#_nt0gQ6EsE0l@b{l?c_w(Kdc} z6x2CUy$psZyXtJxh%DW#BAQ1S)d)6|WSo~GV$p5K*Po-URlAyZ{VI&ny&Ovw z=6#5f3cHN~79%vDU@;*5E~gMdt91(6t;m?5Kk+U$cJ~u14Zx$iBkMK?YPWNQ-in*D z9)!K+G-#-34c${ez`!!q<2A3u9#fh|^j<~{mi$yPT+`1M2AAD*3Gue5)w$4T7M=#G z1wzC*X_@#=1i$UZTvC-YBn6>8)}$xG&zTqY>G_N^c(@(CE1__Yhr+ohHABGt)Rlxu zk9H+;xbD*nEL-}19&43`;9S$; zFbv%@bFPMB#%!*Cbt-sMNQBs=&>By3JNB*qAv~{m0|`$=r{C zP!6#Mfx&r+jJQ*YK$VOU@D7d+m(ezZ7k;DMm$|p}B#;#ybul;syb6Vi&VkBcja7M% zDW+bAH!m8+3u1FB4sOK^?^&ao-FS6)Qv2jP=NWr9 z83ie|TnRy#R;TI4430}R5}QJT`V7SF9SC~$*@Buzv`dI}tyk#x#b-@l+QgqXxoT2= zL#6hV1+U?eLDhnY_rFg**Gz#iF8r*GQF)g7fmSQu_@fuLa7(0f)uchznx3o6sU**) z*)RvAo7ld z{MDwjX##L0h_#!}b0qd8&rvYg+7n_vy`tN2*`j7DTSR}2LW`A74++TE^tW9K`$E+{ zE)hlJNh+|KX!$^W<~N$}PcglEy*GG_v8+`yK36;b{`Yaab??knp;Z_cLdSq!BDCw_ zZd)XQ36WZQU!C~IR)4C7@m{nbP4MLtzmbluN?*nrtsCvj5Sq*jBX8-}0Y4#DbHoS9 z5yOnNpGBvzcWn)21xlP`p=XkN9l1PaXD(L^6ccX_@U}sENB(B9%!1Mn2-DYI@3up< z{NE=F22B!06|AdLJ*-O(fbLNz$Bk2d~Y8g)z)#2OHY-Q7=M4@;}8^a^C z=C+JIU$l@5hoW#+HXnDE0Gnk4rWe<88B73^Zn(g}@1)JBMz^&02r%{e>Dtu}J## z2@el}E9M|ZGWMpLgCFK7%lZz3aA866 zLAKUwx)lQ(SK=oFj zq!9D_3~_p-SmWqhmo6t}tSPPcSK#R~b^AFrD-o6ehys$tb*jW4Zs0j=DqBD1ucH_B z#WdKXXjb)e1;J0#x|5+A619#bNd9vCbg3vb`5Hx`3kBR&n@`Jn89!(^iX#VOW2uAa zlsG8^3=fqCA{flBqrFt59_NzC|k5>v8?Y6k(lk?mUM z*ML4dE`m|epEti4OVH)_)P*hMy9X4ar=M!5mS^O{(&l#1(TFO7)dy0>xr5>)yJa*B z7dND=U>|NU{>D#TvWKKm0P7wN*UC*G-(%mVN1^V$W9w(Aat+>_yDgAy?oR<3wp%B znfrcPKy#~>^4;{FPlGk;3eDDYI_Y{(=W+L&$B>Hb>xM96aOJxO0mq~ZQDa)zndt1s zUysXNRus(rO6zNB7j6xVB^d4It$ zudHNm$!uE$)KhKGJ`P$s7rYYeeK3SnbVFxmUt@va_QSmPZ5`_fF__#gmdSdnu#Z zQB5OkeUe3%qvrTEvP{z&Mf7OK)9OshUIeW+FAy79+5A$-->SG4PTx^+Y^Py*o9Irx zPI}pQ5fy#);1{pJ@j@V&c|9nvyzfu+L?{$%PX^1Vc2A&Dw8wrl|O~80CK~XC?~A+m=W$ zG5f#!QU7(%g$`~@iDSY3-MPk}&a`}}dIOQ>#u^V)p%T9EiZozXBw{4bl$o4FNu*et z84{QC-Yr|gKfIMMM8XLp!HKO%NneJupLU34JB_5mw~Xh{F$E@knYN&lM^h#9zI9D4 z*^jm6Lj9xfZP;bL1KIg^9C?{IB}r&eLwVlG;!mCT-?p5^_zqG~7?WkPPy~NcIm7d! z!a``T!M+&>vhU-4lU3r>V=NPe{F;8Q*lxz=ZjSV2K|zis8$lgJYTC1pKs$qeU-hhR z)Gu0d2llyA!g^Spoh>G`+z{a`(s5@A@a&Ys4$v;XsBFVvDiDBT=%TGskNU0f71aFu zx+I}vh+mczsW1c|^}sI0HLVg=QD|G40LmvtVQVUFI!1xXOSy=0Rwb+uTlm^F)CJhb~(~j}Bvhtg7(*s?a z-79U|t_*!+&Y+Mh2F9q{`buikPcaaJq;cup#i{~E-1isX zI>1!C8hQ_8FV!+mR>g{%U4~%rpN&>*6A`ckKn+0SNTa+y8{&MRPTg`#;a6K2J(hEGf zIp(oJqh>-+U$34DMpqZZe>E$==dfGt53!0>UDot7g}oCsVjm7Jq6Oj1Yl(6G+?v(K zNizsNq8}v+(RVIU+88(_e4oS2p@ZBWZ_V>eX@I$!y;&h_Acr9&L1=$!Y!#Kz&DQwg z_2{cwXf9z*-BQ?(Tn3ERzH!hAdIVn^(9AA^vUx2`=>G0qew+i|h{J)OGA#H}rTmNi zO|klOJB8DBE?CLz#x)WDc{}Hs?1!fGdOm^A09ItL%ru`@(Q17qLWy?Xk@e9?)4~&VAn8o)?1DQmg zJHEX+Oo^}3`QT`W$X8%dLZ9v#h_6xVA^=n_X#3ibig4JHkDW>J(aJ#f^BmP2pj7%; z#V2kwCfwslUWeA{r?Zg5d4%c6Hfgr0Y_SG1?P#)AIurg z&pcBW#I&!D#1yYrL<|!fy252pfs~`hL?1bB_%qMU74N%{YOl}BWMjUv$Jb99^*V1x zbjr{KXL^(=J?O%?ILT+Ee5k!=+rxSwbS?~0JdY*VHcN+omR944J2O*lQa2yIT2Z?8 zYxtwqicUc4`R6lQ#p^U2ZR=JUBUH&O0%;Wd!%KuP{X@Vf$7KYp$;`voBi_x=Dt5FS zY1;hwfBGa@DWZNvr6#yO+-xKMdK6`Si!R{Ewnm3dGG{A{SQb|UW|rWVj$7v$d76io zOSI`|wE`P>{w><_E~}IABKUKqXBpNZ6}=-KN}8zMGB#!_?Id=&}x?ZuE%*8&(K z!?0~HT&7g)1}!xiP+Y0v6YBt?*ZS5vC~zV=ojfq=t}kid#Q|r)McRlyznKvEsA}q~__ z+uT>2iJ2xEiUN1|*|7C>Ag(ZHlZzhVvNtKU(>$6bQ7|<7az)QT{Ki1kVMffR!JN-W z(ZaGkOW$Es6U~>s#A3-a{L5Z%*wmBx4y60|`husQ4LQv=Bvg5RV!3ORq={qW`St=S z?Q!m=OuR9H-QG%c%2%14tXLDNC^}~ihsiAkg+&yWn-n8_)vHY=jOcnG)2$M|D`-{? zXjG_+SoX61U~|$sHrwehdA-fvO)NW}1xQP0mP~(+2-3rD_JF#aFJ}&U!k$#>UPV*B>cRgPQ1F`4j|^o77`v0DvqYuql_pe=6n*bH6$Zk+zDLjuM(r71aPuFCo&HW^&NeMAxIB5syX0;q@>xqmQWjQ;J^ zB6xI-P5boYmL3Zuax;CJCo$S^5Hm-GjU|73D+YQg-68n|;Q(VU`FG7Y} zJ|lKU6WBs8>`0+c zWI2|xycJcw?6VJl9;nQ+!3`@PaHcaW>Hi}z``4fl-NTIRb%~Iiea$p4RmfF}Xqh^m z;~WZq$k_j-ZL+g>(Y3~|`H@I7^CBb_7QM(moCCxUR=R|u=h4%fY?uSLtO>eSwuI`FbJCZ`RL({9hy$&PJ%g)Pn0{_<}_+>e}>GWF%BKHK> zUjFZ{a!ntOB^R2rs-kJ$ntE6D4{KBR!8Nl znx-4dleVsm%geGhBO~kbuQlKRf2b`d>3n2KCdgXPFKY&rJ|+yWW9u0G@cPT-St!UQ z^{c#>22S0$js;I6a9^@)h?u^99YEcZ|Al2M!UPvigWHw)YC~}Hfsl)YQ#*Zg1o%q8 zBxLJ19}#$-bavjXipKX&>cDp7DYcy0C@J!*sHeaTCUD=*V8-WeHj~ z@TK-=zUsxl^iI2fAtZ0@?<02M3r~#heNV5&UQ`4+PSJ)_Ps`1CKl(#2?tdnMB)&A`v}{MB=1#qj z@8%_DA<#WaCU>cqhkp@zMmjWEP1B|m1iu)_2^nLr>{f(QDoTp9KX$!){nVsiayYRk z0Wa_B$im!YRQe=b(JLYWx2#YdKl)+<6YAF>n?1@s(HuPbp`S4AI&yAR(x3zHWed*C zlBt)`m@r`9;LtLZ*UqesK|p=q4Er3Tt4za$LA~uK?CC$WwqBp{*6+rS(n*#>>k9~7 zz@zwQOo9Qk-t!`Cb^1zne?#AwQx^|ZDP^ke_jj8@Dx%RG3h!GnjSF^bt@kG&WI*3j zPJ-|Ul^;=DrU)vby${F?Oly2L(Q&lIER5>ep`EJ?QHz(+DP+v%`7TnQb&+HC<8iRL zS4#{wm7``~025aqqwb@5uW6FXoh(g^hSr7b3C~?{!jBP%CIqjh*Sh;8!&g9AFbK+V z3rAB(b|^$LzCu(!6jym2G(}dYN(GL;L?FXV4(Ag-mFoe8iN|kN5b`X5=}B|ueDY+R zAx*uo)cU&Lsf2EN046=C97EXtSERBBxY@I?WY7hM#c!^%kiwM>l%l0mk}@>VYZGfB_59XUYag!ah~{*Po0tvh z{y%i9F@dlN!4@G~6-_Hf)wl(IDqpCpPvLjXlqf!SH&@*~Im>#vGn!DXXEptX;#Wku zOp5-p*Ji3xPJ5{ig_E6|NVy6O)fUSjESa-e-9g3_-07RFS+7iedIjE3QZq8s&$Qd` zwAH^iVRw$Zv2&s#{*scY&zIQUNh5gJQdQZ{La;Fc9%-U-o+WKdV%nh@fkcCF1TGxb9-tcVz}B z@oSKb+L060@E7n1`4Ey4o``34*Vv@Y2W)B^$Zl`7Qc6nLE=b#PD{~_sc{^iXymuPZ zfogd85u5xD-EMTm>;Y*lg<^~{z}xh0hL!9&noH^Rjl%TCX9@UOnr*dA1%ic2^g-6QfMf|`?z5C8ZUjA)Ce_f7fAbo*o@I(*#}00dS(ii{ysr&$BBI zlp%8S+*TnfzmTSP39}FUVd0;qK;3x_v&R7Qv3WXFy_nR&qVQ&C`-z0l$6BFyw$_e> zRP}+L8~g+T|10A^{tCl8e zbGB(h?U%|jsy=5Q=8(D@by}hqYSLo9eG;QVwx89fndCh)H$gxj(L>7T547_@GWR$;TUOHVt&Cfn0wwhs@X#`o}ekYoan-S zK5N~gN1{QFLU@lPD#s^9y(#`t$M}{5>!F(7hk+Jy)UL$YtakC@caFVgNM4!fI?)?4 zXLwk0@%dQPatsilaZNW{cPM6-u)Q0`DU{Y!_?Hq=^uRa8$N_IFE>?D!9otaRlY+6r zEQO{Gzod6I_d|TIr5ssdMp{c}Y$bfU*hvhL=TX^eZL19>J7W!6C3}7Lk&-tZlBs3> zvJQivK?*zxvqjGLuvdI95(Bl)s0HCQ*hq@w6+H7IExkcfY|4GH~IoTD%RLyDrQ|{+pJ2HTm+*)_#)Ba^FZJ$g##Xp;`kl zI#JE{-#vjC>Ogow>R9wanX_Q_r9{kYe(n-tNlk)1`n_^S;{Lv&DcZNmKuz4)cD=H$ z$Xg+w-u@3W{w7{(?Ji=5A-x1>REtu5Yx;qrUw?n#5v$Lb0ST9O37geHGPAo7;BTP8 znTx>`S<{QWhHhIJbb$9JU1n-(-KOnab2a~$wXBbk{Whu9XuDY@D3m9+v%j0J7Tjin zu~=UWBHYa^Z(W`ao8#djV8rDj{t-R?Lb^Ed_w-KY4oEe4tjw?$GmwFMnjRehnKM;$ zOWLlJE7&>!9-1PSi$8X#DJ;2yMbf;JK?1P|`+4#C|axHsdtW!UyG)OfQvw2fD|dLO);#W~ zgn*tgBE^qPc22S=bH@&gqW!jFk;#J^o}ne zzM2D`^5rQxXrxeY*e+U1($LWt4iY9iH z59YKlx+>gQyCkV~Xf6R|=$aOT-zJQFUD+qMC^MXRcwOw33^$Qx+ZeKiM97a(<8CH%CXtvrzT&sRG9d&zRPlQdaSw-hCk`<6dm zugJ+#vgmz~w=3=atc;cDn!S4;LBE5Qinw+53}pw@L`)bi@@*M08uw2v6UH8+(#n&{ zI_BQ~9;3e8;jGx67;V5ZzA%|hxoy<|#PJ#ePTuKvVfS{&KF&s{SsPd$PIw|j9g^{M znDTf%9Bb1O28(+O0&DJYtQO@hSk&-Lo(L?nn-w_u-3->U9BI5i9Sq2%j+k~Tj+m&& zO1$PBzs=LIV7Vvey@hAkH$z#iM?P=b!ul8|wlyWuc-Y$vKNOPBarnXBUoXti!n*!j zv>o2%6db`jcU9G~S8KN!?X?r30Ait635cUH!ui<r#1a9(#3zv6II-yeV-Nebdcl!uCN9`_Gej(yLr2^SPY zUI0Gw70rw}{PtQNZ7rZJ-&n)vtO zJLQ!#JQ|P+Eei}5B7IEf%S3P`wmnuwwR{Xj9Im3CF`n$}bz5dNqOpfQHL}*H1cPFC zvCGaUAK%!WENIu^4p6);%ivw+2Mq*ZY2O%shZZJCr!z;Yv<7lMMtQs@sP4G8V)DQ& z71AOM)XlS#OqfqWsCFLfeU72=_Q+IHVToqU)Up&pba8d}h|qy%O|L;q!kXNFOk*>Y zO!j`I%yJ6Rl~ldvn6zeaj~PFpOWAB43`jgKDJiV#jaBWx7t0Z0U}Pfam`&BSH?oWLx>jZk-rb;$U>0gEs^o^sOVI zXTBYlV8qLCxEpp((6@TLKt+2l4p>9ic=bYIFfqTVeuXv76i9_iM?P9xp~rGRms%vTnyCV>ZO zHBs@BH_Bavf?OUbPR$+^4;$N2)EVEJ@&HHkLK6f!+DCxgfuF*&7}bvNngZ~yYip@r z3T>GNrIBdZb-c`RJ5*;ub(Y@$Ce;f1)~{}mb+ip%W7VCL^qon>iJAzl_s`ZL3zZg* zY{tqUt^YNZ9TDtW7lQ;8Ny}Vi!+PTjrJ$$|@G5SM3Kt7QnV`nc;F8oKgBr6fQv}Gm!hER_2k-cslU-}QR8e4!bu28Eg0n?~!{*mqhMTXM>c2P-q=y@DdJT60Hlmy@&Xk>e~a3XQWjKvF~QHa!vhs#1I+R>mc+$0VNmuE_U zkq1a3+2Ab|=WlMBIQSg&F{4pWLWyAs`CP&e}bhgu1o5X`G z?3KJP)b+arS(b3;o4}u2)=2EP^~g&$HOiIUCeBU4$W<-F3WOz3cATt)&$4*Qv5P#t zW8ucN>ad75oI|GaHse2NtjG>ru*nuEfP!y*3Di((*sJ!{0628J3N|; zM$rnQR!ixh4~?D!R?<$q8PNiZff6s%q+RX9+)B51jjICeHXu}|VEr5@)RCOS^QChe zdgOgwo)8rpKd^@Ru+qirORQ)yFD-pI=D*)w9*aVm^4J#A;CB0VBN5)jVLKf|%?eo3 zVb(nm95~lLs5CFdwVPm~eSgr4*mfz3O>TD^FlEMp%VFG#ODPSp-iEjRzAE75cwOlB zmJoMI_42g^z3WhM&O}O9^#YRa=EXtt3az`07P};i-$CF<8;2f%4T@GRl4xBii8gd{;*3Ml6HS@rwLFUZ(*v2QhI}n5f zjj;g(%Y_7&vb3gZ@e6)nhgGJ9>ssE0YW9)k!0nk10YZ_hB2s|mMR*IF!t-ZHJ8vC_ zRX#cHJS#L4W0a^L(c-1r(EOm{;7z06vPUDaU}SF`N;a~_{waOE&uDE#OIt&vbM%eA zF;<1pM~jV*&glds&jlBM;iwMGwtx;+HQ#qvB`4PU<7xCL@)+LTJ8 zTL1^yPK-l&s81S7r{xTC$MA;OZ^@FmnX$E~;m!*bAvk_u3*e-buuglv)A8YD;`FtY z@I^&)1Bv_yoP3k}N!R(dbtnSW1;D$$ot`g?i>WY-AZ(M38~wEi`Osq<1|?r^#9nl* zKATL!`2|^swHUG*Mg^$6cXnL=fm7P6#K6LR#pgvhVxVg67=Qh0{dMt!h%a58h25^G!d*2G^*Is zP_T3d1!WG5lB>?HWC<%J7S6t#>^v(MGH_knfHwwb#1fjcP+N6uH*{|0sLXlA`0us% zt8PJx8SE0yD_zPi(NQ&Mzf%&e%#5`fm=0ff`4OY3*mxMt4VKl>t@N|qqRQ5{X`NeG zu@{}4S7Km{&DWhi7ojBw$K^)paG(+CoCNk)U6=~ibX@tDKR>3d{w+xd?$vinjbuEL zM)#ETM}$woE6&f6-g&9YUCaTkg4#~&&oQm=Id9Q%J;yAJ!kb@Lj|6@yDnU>gmQk0x zi*4gM1%aqCKj4hx?WE?B+fp06<~edRE6BBpLJ za}sqcoJtPL6U=mF#?W}_ygq@Zt>>Od3hYZNp^RxD+h~UbR83mFFd6*eNn{s42U|eD}mzz1WdbW1fkOVa$vB?y!GK z-O4Rw^9v!=6Pq`FlJK$es$bzX>Z^e#Q!QUWQA6;L{gc0v2DzjMxOGPuj}v)JZ-Oe* zBWE}HTVU0_!r$+-cHpvVN^z*#lP5(B1|2OI(T?msl*WH-0hU+s>#C&WA!pNwEV zyH;w9uZ$FgRq_!wgfj>U`&ceVb8&3)Eks&0`EY zW01qHD69e8dOeJc*3M0EjBTMx2)-s!T#**xt*S=!%fm~0;;S9__G#*^Rd#rCu7>VO zksl|D8dmLD`*IQN!VJl}8hc5MO0QPtrb~3!oy z<9mz8Vo8LxT>B}xvt+ms&_+hL9Z2j6q6fZfY~77U#9i`B)c&}#492{TJ)LSoy<|m7 zBkvEWa-4bMIjoeq0d z|4ZSaa|NYKlKWRyrk}nGr5F>Knn0Ts@-Bn{Tfi56CtV)TxMmSYXdo^kleg;zpk^;v zQhc2FJL+_O+ko%q9jOC{0+-L1m>ip2mBl&C_$5X2A^?T!EVA6R{@C+-J5CRi3+$=A zBF^7y1PO&n2bB`b=P!jf#E zQp?Mo?pglIqC-{FRZ+e&8;GrKj&d&AzcrXiadibN*~ynX++$u-cxp*2ifr?_?a|xG3CQx>Xl4>+k^|QIUI+gqnjIg?yOB)u}TxyC(OswZ4 zcVhKE9@A&@3*-GGc3SbG1guY@{;ldxWbnlojq4J13X@N?Y^Y+yyw$9G_{jjzh|@Ez zZ`N z@$?re>p@t;E~5I;Guc8+Fd%<4Epvblx3TQmZ0FFg8hSYiUz0!+l;V|58G2yy8dwds z?jhb{=HjQGaX`s7A?>ppr2e9D=BwZFny9bbOB;CbPPp|ma{=nN3r$WDP6M-ZavI4WtXvF(y3g6Qtr>-O}3PqF5tCD(r%7 z!9*R-CH`_yLz@J(4#KM!D1!(j%hHk2EYb&-7d{wKHSRVn(uTsplZ_io;GkrR6tBE^ zZr+p3Eb2;Fy}qKyt)Sj(#t%}X4?+kG*?K8w<|GR;7oFOeE37yXS0Y9rVKFy+b3ORe zS1w&24n|a|$TQR+b2Q}b>)KDgTOtpyZR0P-!J2mRE?hy><3H>+J?6d zp}`tCOkwV-h

SM033c61Ol1971HUszB*|M*`Dxo6d&Rh`} zPeg8vlj^Jaay`_F6u0Ws0oYRKjN)ng+|57Aj~rr@JV*iEnC}9Oq$g}dO6Jy@Bp?sI z#CPnF!KCM4%SIkt>9&z>>98hWx!;9mkKsvzz2MrBuXch-=ZQZHm!CP_&nd=Vql9j% z0iVe__qHF#uoT#EAebPrTmB{s&1 z@`kvhY0I>Ah;o(}&{57g_N$*hYLnAQ#ayP-jAdxwv>`^od`eLS&G>LmA}Xa$tsbB> zT#F_f8&7;6c)?9;$e9V&w&_T->pvtQ&(4F_@ba{~Gu*T|_P3wC@>m+J4I_OCef z{{4H`?z?>1rQ8zppsNi2p!TCjkBnPUf&c!(|6`--snY2-mj)`JqvTkRww7LsxA%sA zrmx=rCZw&6UOEt!Y&-GciSgBJnuAEfIkUe9Y;}+PK~74?C95YTB{cxFZt*f%OVOx_ ziN|CL3YkX(goc_!TNR}L^XmWG!?*z-Ji};u-w>gPF}5(jdc|SM{G=May@VR$=*)Kg zz!IohAw6@1%ywBAohDXDLs`YHAm#a_8>zV?=rz6FdU4?|$QC^FC&fWP#san^p!0c4 zUc%;cIRG|20OcFGz5vE~Nq+pl@lc16RENL1sVw_lr@HjXeMjFo0eeL5@Snm8KYqG| zjVwRFCj+M%HV81{R-9e$M8P< zi~uAlkD^Hn3yT~ivTue(P|{wQ9`*hG{i^Op`!T>krKP#~E!8Ah zDPEX%fbOI#UZJeiJ9%sfYyqH#DkV&d`JIlar~`<7am1PWxu5%-ypI8*4%FyCse_Na zVmv%N9UXu#mXnv)3qB~~$+!2(35^&;G>pZBNKKY-Q?v(^e}G3b_pc=1fB*5C`Q>~} zlMm(^NmlYtHQ%2nBL3mQ*3iLxLkS?hd;^RJfFY;!89so~d0!2bBocN7|G=664zLc` zII7JteJB6^h5zlVI^Cy=My3olzwRtl0G0dV&_yUZDjgHYd+E|WU<7!Sfx%;BVPJb| z*aUD}y1O5gk1h=CoBM9$TVR-R+AUr#GU$|7x8J`Yo;*=JGFQ@nZMNyN(}r@ zv{!n(w;%C(j**9jNMk1DRh)}U9uW^>n=iP{C+75iZT4^cxWFza%6@3Hs^nPv41ac@{vP_zuHkeDgArVt)LVj}+qkxtxV`JQiSU}dL$9ke!qN~Xk4x3qL|09XWUShY2;`7U13iv&Yf zsn1zaA*Ctlz`G2L{fd&YiVYO@bvwAI~Zn z-l#rR9LgxR0M>$kt+9V8FADxK^}4adLeSc!>ECpo`Q{qm8W z=P=gSLt9&0^)0T{ZV<=(7V))sIG|GaCU&XZBG|^Mftdf^sN=pCR-6N!= zTH$bk^Vo|#fx?aZi$V3*kbyF!V?AOEU$i#1>^ZYy@(9bPt(W}e_%HEm-7SRPQqwK2 zI7~~c?WG;FW;6f1oYGnF2Hd<7h)UBTr>5KEe4jZ2N3sHuPFYR9gzrb$?E|hMMovfqs|U%F#*28}CW?s*JgJNmue?_fi9HRm4Jk&UK>Au%i=iYA0(u zBI&a8&%4j3PPANq`4X*{27IjY|sPxLJeZ3@?Brlzt#eL|H}EuEf>@>YxI_{Y#@b&;Zd-(lV`=={ttc|e_7 zvT@%@W2l!K==!?J64~se@hX6exuIm=-cB&$%8SWzx<)+hf*oRMm9+cv99|xHmpt|@&+yi)Fnj@xI(8Z zHo9CX2X|=>QA>tmKw3UEHO1-n5~Mi5Yws>dZNQ!H+z5IyrohCvD6dGcyw4Zt(rHDU zJ$rUYd*pu?X@3SOc8%o7U4q{^<-%VRsiw1xI2r5u_rg9LMu?#>?w+}_s|vN=-#fx9QIGgJ~lluoy=ok+k;AFevh8 zge7mzI>Y25pTxbDjoWHH{>;MGq)}oP*m`t$=D8BX!Q^ZPnQO>`d>`W(UatUd!r9v8 zWI%K;00e=Lp?1NPmkxM*eRZ52IQjA&#-6HhU%-W5aR<37aKD;r_MeqKOCCoH#n8B7a!{l&OQPF^596?#y|3x35~8%T3-L_ke(g3ZYgB z+{W6X<3J9-JgTa81Rm|qC7G(Dp4Vfakew7tcPb&SuQ0gC-Oh<>Lg!RW&%J_yi(ez@ z%B=U+>J=#A3U>k123Q+q0vR}O@c08$PtYJDeFABz>_R)?F&Iz|08d0UULzUEXpJu# zbbhA2Z9886y#xQtS0&Q^8C!A7I!%pqzcjlXUHxCJS*VM`ZJ;`2mpZ?uqEW$LG5Eu<5M2~2t#22D)_lMh!88N)mqxxdOb#c zbtmsc9r+XMKVbb|BkJGZu#zP=;-2L+8YV^_&q{iVPr4y!P;#d=O0cKG4Icp{0)dx< zr86LEgh#d&g@>?JL&W8yetG`$L0{Ik(wb>%Tr)Vp`5KsFk9ezvrD`Ig|FiLmB6Hepw8J;-jmy z3449WKL~7ZacuZu%aFOuV;k#Ji$foWjFl)9@Hjl)K%@~-8hIUSg4eOra?*G$aG zBQ$PWU*rQTxaD<0hc4p7hrTg$BCxXsO!?fe1h%bcP|dmFF<=w|>RHSEMc!Nx$iH3P zFkoB+D{eE-apFX3Nr~u8o|v??6@r50rv3hWWh;=DZ-eobU-_qW0F*h+uLfWA*=lP( z1Tz`$x%6mJk3}PpC}B>G*+@B#JzXnOqNxSrUmGkuK?rE&7`wEK;YMp=byA}LDkIyK z>zI89uk;;920C0mEBoL$E@N}~JhS%6+2UPOZ~JQ9=o%cS@B7S}IrS*|d4<{Db2lE_ zEEFFcS)-3CaBLr((9eX|d_{5L*oWKMDl0E5JsQY4SWUvcNimaMd+vJ$xU8F*bYGnu z0P5TT>`%^H^EBs=CYC-_g6gXSd-L}blapvrtGUghK3m#e7GSZ3d@kE45}`U;$yYlA2lurTo@7+^7g}LYgy_zyk^<@7B5+;{P9g$=< zRI)1Bv$8q%-dZZ5%xn$Yu^pRAnc3qQ$4WLw*0FNV?|JUMb=~{>-0$r_rE$F9uh(-t zo{#6_p_8|3=wtAe{Be&pZC_|da1-I*Kiog})uU8V-y4;J96S^#oqQX@5zxBVZiKKi zFien*U%qsye#<2KedVkR;w~RAFL4C73`C*jblvLhz0E@*8vI+fFudso3g{c^~*IDnh-m#^3QPfS41m3Nh0zEl@^lee55CT1SWcw`TL9ObormVPIn}GfPQ8} zuxG`~&wmz=0JV**H)&b{Wf{p33S5E} zNo6FH>%`}1V%T?OME}Uo5D7)KIA9?g8oQR?ZJF2FK_RbgZH`*rZMCyl`x#L3K>`xWbpHB5-2tY`SzjaB7@dM zS;GR;=8S{St^6q?kD~Km-hliDI0`6Cb&%Q`f^7PS4|HBzp49+^ zYp9?DE@36rQMWM>C1)6Y$iSK=*kX7|l z53x8Zgu~P-f|`Vh>`1zJ&nMtqrokMxub@IYV?9FCpdEz3Qe_Dntx%-yyY?>6?7oMd zos_E}I*zzF4x+{vqG3 zf#E`j@x~OHN8pVWmsO%%CCu_OPa%~?K;);fr6t>5lZX_6igJ((^IzxoU%%>)gt~8_ zXL9hXYq@#*F-VUdxFYKkuqVfL-FLoi)ed85;xFWZ+51tYPz|VoE~EHw~lsx^ZNBl{qYd8 z{YF(_>}g^(=r$H~O;r`0n&^{3RQLkxvo@mDGxHYg0iC-W$*=ty_=6{*XHSiDc$!^} z_xSYjW7lC@>NB_^GrcrKfaXt*mt3<<46$1>{uXbhO!j4kyfmNO3jUaI0Z)cO8?keKP$@2%8Mz*1!Cq zJDcSGY?#m>)(UJxB@K&h;#*}vRc$GKPr;XJ6k+H+CR4_$boX6>uo&{XGDVp4{GPd{ zR+pND(69`KHBQ?GYI)qV5(r@3bgELL`wvi4cdu2gNl*4~ZU>elK0Kf-GA(d!C7Mpy zRBf1Q)8>+I^;h9G2Zr$}L{=juK3@3lBWOx7YAcI%?6Ygtgvv6f>DNG`ZfSUCMbbjt z5CP0Mu1fBGc>;OK&)cMvCI^6h5WAZErAud3h0R|Tf6Uv_!TkJl|9A$>=K=S+EN9Yi zoD@*^E4oy<)f0=(xni>bZZ}JltU;<9crD6vmm{S+dPO>QG{pG8 zc$-LYkF#p>i;ajF*HZ<$p?h(XE|t%!AiK}{E43nP)D^x~spV&Mv{K*%HVA=brtQ`J z3l}cLabSp>FaS%VA5nw<-S{;#9;*7HWvw$My0(?-z#ws{g&~$or21M`L5sttwf;}A zs4a#VapJi5xDAJS@5x_?g|X96v)&G{>wnZozbBwVdzb_atjq2S^4=mQIo%UnT$?+7 z+~Fe3c@#7-2|RonS$5e(O+#(D09OT7RZVEd;&-8KtG9cm^HDH9mt?XUbVSzV&t=2h ziIS4?-16tsl~I&#^9HT?qnlCS<4#<Y(>1&Cn{%uDq<0(`GGQ;7y_~=nt>K?D0v^2YW zF31A7h9qD#T0<=}pQ(X#n1M^zPF!f{G>ag5xFcXF;XoR&egCPIQqoU}1@j3~5G;cY z8O0#A{;>T}rdt(u{@e5aTNU76^8IvM8Yxsc=b#HQBT7y;l1j3?>Fpo-gp+w;0%b5% z#*V>8OlC-}wi%ep#1r`pk_$!KxBbhocbF^)OHtKRki6OkU5Am+pVPuM+ijBR-0D~j zKZ-t#bX1C}@UW%kSZii#3Z6MGIf%8FQBjk|6VAnpKY^S=QIf5L>X$D+n%ko^^vUN$ zH_;T3EBhC&5ACch=3eNb}KyG&GOd*f?xHnR_#^BS8TWZdwaF?A^j5z1nF5Q zy%dSPimG3B>hfVh{`*EHRRc}@DRgUN6)`j}vW%jA8D2EXHcj?XF|cT*ODiQ)ho zKUG`Nt{(8LyiWK&DI2d;$Y4}{=hHrJmTZ$UWd()B#0;>PoC3m|Q$I^6HJWQbY{EgM zhNcp#NMZpCqttkp7+^^WQl%AD3XBab_^e4z`e0ayI=Mt(j)*QQ3YK1iT%`eJRoFcG zo(A}%#f=!Px&1_N{UeVV$zFy*_BStI$HW1jmt7EETq9inJG_&S&HM&4dKI>F_mA4a zs{T6J7o7fL}Hve zl~pbe$bKI^pprBTcv;-LuP_rkr)_FmI?llDi6nUGmcxFdN-mb1QEz0}yK56@fa$ku z9!)>tPH1B^^g2oyj{&IJzq&BDC zsBmd4c=I17?!2zqSbke2@O1@}aPyR1K z_2=C_OFC}{oGvo{Q$4Z+8RTs`8W{Mv6zuOOdi4k#dIN7b$w&KxbT9W4r7n2x+aU}G zT5(8Q+U#cFqeF7!+KWDQ^3Na%G>W;pt5CCvwb%^X@BM&$e_`jG_D3lF2R{7n2F%i= z_t6f%r*i{D2tcMQiR8m>W7OIcDI3d^TZL%#yu34Y6CRAZ1GuCe8H4s}QdX7{>?>%f zM=V@CPb^6vJa{0d_A6^ta$v%ed(LzE631iG?X5NR=jhYU1Bl{j2$l&E^Di>sAQhW7 z$0UPyo@H0s4dSjbuq@(*K@WzQl!?*`umf#)W~f?=HXDiX+|eJ?eq+oX(cMzfHZZ_; z(zCD-6;L++;L-iai#x0Poxhh<1sGJ1=2QFg2M+_11UtRtgMgXol)E5XV;GD8WoSJo zlo4LRehW=iv7$ehVvzL$_+n{kz6KyeRFd++*mJc>pk0fp8dCPe?QZ{=g}`@ z5xM(B$^tFp{rfz20F+olYh4CiX?M2%HsMAg@g4Uw?_{x9(wzjQQaJ0DPN^{W0nGfe zB=x_XqQ4y~(Mx_Ss?_awtlP->!{&(;Z{o&QAcH^$w2Xy7O9DC?pRDX~pOB!S3J(tt zGZYq=gP4=S=8)&inKM&nx6l8Kg#JC8*MXZt?A+AVH!xa%pygAYmw&c)|JKLAoAtkY zkb`zjtLr756A4vTC9J{6X|^$%?fBQvfbeKB!vkG7ERGAfO2~={zt$Dfp4ISosXO<= z{cjIM&XK;i%sF!(6_s}|BLhPUae?H-iI1uAzwml?k=F6babWWrg%9jJpfUb(rIFvQ zz|HW8L?fK#LMUU8Q)ly0RTXjQQPutm*NKCcA6lIIoDF~DzqNfO9gm%yoo^La9PQR+ z1gr%RMzNN5kOzl7^B(impXL1j^@PU`at9z%CGFj2P85&zl=`*6=TA}I zuNX~z)4a#+XT+TzP&j4MR46Mc-2xO`bxNY|4^hRPfBUb0=g+T_oMFyd?Cg_`q5pXi zV2pD)2@^q6oMBw#v(=@`JJ+V)FdtaB)akm?@>KunlP5PUEiGg7MlSzC%^suS_pC&D zw%KOq%cQ;JztY+IJYn;y5Tj3OTGd@qeE12o)uR%8E4Wleb&tLi4*`3$zY_T?Vu~Y= z0ZIzdvisYdPC(A6rW&7SSY7gV^KB)UmD17~|EyZ6HN8-hAJ4S{RY9Ax4U!EQ&B-gqgP;+pwzLy%$!;-G8o(O-BiI%$Bs2&~*6>)r8Q3I+TfnteAB^q+z| z##6b{GfpU~%AQq}j`Co3QUT_eOj{kh{n(Q%vHl(?wuv@d$z8|~bi#_c*mm9xC^ z0RiG}&RjdP7-C!G&AIpp}aPvXzT_?R_Km%ZY~V0C;B!TMx-x>@->yNi0M zk5xLR>8>bjuoRYtb6~g#dcFUxt_PV2(B2w$nc|=QEHEG(8S95DE-hDU<$S}wS*f(2 z&frz;nU0Ek^Vs%4`jwRzooiR0I;!iPFZnpic7F6fNWsMWn$!-YRD{!=Gdtr0gxvb= zFYeO#9*R}wxRiJ*gN}UlyQ}x@2i57@m5;0B(Nh)L$Ui!?bz-K;SMUbL0)0~Yi5rOjv7*?`(65knMOlt7DK>Fb=bWc3O?R=+d ze2@KwvimdewFx8qROtlAanZ6ORmwz3$<#rK)H%qR>wDk z8DS22?@b!DN!+|XVm`v|SW82~j&rs1Z2zt5B%_7=w`-qr5H|?&307`l%XtL_v-1>S z#@hv`5*&(?KraKFu z42?TaQ-20=sIVF)OR|QDw%bh(3W?bC)PxKGH1|lUD=9sjBO;DLY|=VJEC)Ps#_3?1 zT#3SQM(JHJ#rQqlLBmky(99{lXQx_)6Vw+waX!;bHYsZ8Whx&Rb()O%RWr>;7!i|& z1Kn$vaTYcKlTyF(EdOXr)5#bF^|X%DVHLRJ-)3f)W%O`hP|#MURTdW*27<$nIzUa1Ee7Z; zqSy#n&Ec0ddAD5I?D~Ur>l0Ic+h*HJ(d+zDqJWgFKRmYCbcbV051hI+8&G#=R z3fR#9#8P%X)qgzrm?$W^V$qZBma5C3+hhfBz08_xBTwpCCL3?&S#3b*iK4vptgOhK zYJC6cCR}c1i`24`s_rQv{#`iOSJVJ7%wu&XnK*rLiIf|M&b|vK6UI!wz98nud+gW+ z2CuEy;qSFfJ^)K)swUoT!lJx39GVlPbzuOpxtilUf0pdrMehL(q)u{(EM5!Ra~U;a zuiOCc;@?Kz%gq0rLHUm{JXTgOo2|Z_kBiLoY&btJb3Mb3-hAOG4^NB6f$q{MCG`Lr z;krtCs(-x%JHFATMfx_gmyO}5U{q$JB0m|&| zZ%wr>fC5UsFOC$$1qxN|Eop9y;EU(bo2^*sP>kom`0WnKTLhrg!Bj`XU|?1&kes0v zwuXt4R&)=DW;hVw8t%OXXaq~9S`AyoF|xk+Jezj&Wq#TXh(PVournhLF#MV3{A+)E z+^K#5uQ660F2S@xADQK|*fFE3DH)sc_HD^MJ4w+A60M(o^}k&qc-ip8IF4iO>GEOT z%s-IfnzujiZtb&0n{(M##MLAHeweA5u?{$5O6(In;GNQCn_#w~i(kQ(FIWQ5D|7xB ztcbbMobY7ya&daE0=MPgCNQ|$6b&q=Rl&zSoQe=*p&2+NdJ{Rx;S$|D|C3l`ZjBL=;2J7uP}W4t?`DUMf8gb=#^Y&5w`(ZwHMfuF&gf> zxod7elFZ0DKzEk_om|qcuNqjHMXSEZ=BB~Sc3%7?m}7#6xL5RDPp3JZ->tJ8bVShQ z%w1Nx1FUZ?sRo9E=pS3n9ssNohB0Wk*LTJF9tk#xm|Zw<9buj%#S@w3DU94=o@ci|3#IR8D( z4QoiaesBSX=r~Tc1yXqtY(i9|E;(B;KKIP7M2|858Q$-48_A~G6Fi4K)wg6c(QAhm z@_uEu%h7g(h7DCgHk(1$K0H#^W}>kG7m-VP`j-w4=fz`R>}p zht2kFEcg-zC~Na}4f17|d?@8^viT0d!U7_P2LNv2k&zR|wc9o9_>nB4nuVkz|1uV2 zn7v){Ifh`E@t(4!=<_Gye1Xo%ipgD|t>>O6qLMhX`drW$BWC4<>ob$=TL*~R(s&gq z>`(D$eQ5sNZ+jQ_>C*+ZN45mfz$jv#7u}Hu{wn?c@l{(K`Anirfy6MmnH7qZIQlkI zyn6N9U(|-CN)3R7oZICYCDf@t6gdpJPx1i!yOd^v9mwh`xd?9-h%6V{nT0D82rP>qd94%ZhONcNTDe^3A?acq)?+L>{2!u13)41=hz?LOs z#VV&N#~ibaRaQ+XR9d7bC^z>PQ40X zTOw;K+p_d5P8!V0;!l;l&SmIITVR5r{z`AIg79qr(UBKXhFg5XOs=eCO%~dzC788A ziX;+0(+3`Uhs+EV=cuy$(nv(->ANQ(@2q%k#?krf5bL!xSbI%H-y`-$`uaW_<3-P8 za1%LIn~l*@5d9M6L4R5d%sOCrsL~WAgx1p3=-Ukc_RY@hrAdlp`}pV6tz8gkP1at5 ziSyBmkIph#goTB5x5t%R0a72VO(cBmJJB4&_uv{OG zd~I<>86f$WvSWcd(Tydp;R&IEjE2T>&obA86D5&tYfb+sd zVQV!YfBCY19 z0bw;N4dw2lnu^T>3s@hj2HkA!KoND9lA}4B#h0;IipnduWKYQnXM4i3jiZnCShb(G zVaB|q?s?p00lZxTUWTC9>i%WVysN-@j_m5=f3>pzT7&!3aRXxWq`;O=vhLHT4YDs- zIN9x__K4q~s%d&gMh8#|RY5}pM4z_NzS~g=zIb;8rsRfg)kX@W$zYE?A#C$;SnHBA zSdg1RIxN#(`Ik_V5VpySsZ%Hlcb(3IuR?8^thi4Wf{%J%KtiaJo@2(SvlwddG z^>#?qi9Ff%p^qwFCa}_ZPgRe|`pM=|flB7^();N0_EV$Ei7+p<~W3W1B(PJ;Ot zAQoD4QGoDSf+8O(zY~c8%fY()w?j^VS3&@g@2GSK%6o@`v>Aj9vY@YRVoUZSmh%gs zfR&!$_JvYgE{puRf!%xZI`0R-*d|K;YEMGth!lJ74k$%G3K7fCXX6c2mGw_g$x9={ z!@FXFT6*FnU9%m1k6#IPn&`p>tldo(IF7jVfNwL^{}ZW$itM)2q{;j435yos1$8i) z#o2dt6W48?HMF)ynefn1QcBDmq4*;Y(cbd^GLzfiog;J|`!zP=H}l0gq1FD;9KkEp zACM;<$v>5eOqAg(nOST|io^oBx2*LL1gvyL(}Z}Dlx^tcI<(%!#-0lC%=?D-=^t2? zhfkQ4`;^?;uB@rF0{%!uT-;oCkDJh0xAlMvPrRMFBgTqGHzo^@UG;x2cIM5mjWKA= zU5_|MKYe$DoL?=ASosIKilw3PR$B|KpPTsu=YS@)!0%4Xz)beYP`KI4Et0Jk1b|oy z0d1Jvbi?P)-(OASRExPUef7%$&OlpVEvbfLyqPY#a`xM3P5d%!aD;LyTC!F^ zo0uvjCRWsAq`zjokmbT~c+QajY)`dT(t@q;mhScf0iQ!hJ2z;njhK(q;6E&WJx{4} z=#$KD&yPjqq~)#HL0dGjd*QlEaQQPklBp-Ty<)K8-COc?#&we1dd-Hvw?Zpq$T2MY z`>3e0qsgOcpMFt&iEW$at^Vz?H>Ay;BV5Ym<20(*ZE>*&rc!3JyY4%RdkBp?$ETth z$gq4`rPmETf>tWChGrZ2cIQ-XuI^FSHH_eU-dhgjr3R5vw0fw@b{^JqPP;i_b&oQu zxWR6^S!U&*{ro>_j=%kqo48?1XEw~`g8G~_#H1kL=M2oy^VP1pojop$1D!aVED^Jo zBiy~+r1C@|&_o2WkmRn>H8DvZ0($Hi&H8s>F2}82d3Jd8?sP$VW(OGez%E^bL=^cL z8&V6W<61ieWYL_tH?*LBVqy5_yZKxjeh zuU63vT9kK|L{9qjEi$)g3igTy{VyLG0TJz+WQz$i_p$9z+T zPm3#+>RrFUw@2SZ3>9ovWXF{eg>e0IGOCsdgoXs#e7QrBWP49M&wUhMFL?e^mmifN z;znISqjp6DJ;k z8t!1LcwAGZxcE_je({D`$&e=LNzaxIpXy2Xqy{Ok;Z|oy3jJTfpWGh#<(To`S6{hB z;ya*uGK5XKpDm?ptIH^z;?$AdWtt8IW=!FSjGnXSQdCDfNpNFH$b#C1f78KpJPg(*NOjDsY|dtbvU-Gk>~>X&Ro4~Eq3{4?P^%DY^ht8J)b6ieeO|h^E=~>s8noN1M2$B#+dH7 zug|7c-)*XX8}G%75|RrtrOO*pUl|q(@^Qm#5vwRC#ClOkL&4g!ViV2#@q3VS+-QMn z6_K^?H!Q84ZDl{74BHA7Oc-;<>1sQcEQXd+9eQiM$~8HQ@D$iK9bY)aCCDSejlEa0 zx%EIJJN5tGO8O%ww5gG$Jswa?c)Mpf;E^1rYtJX%CDA;%S6-foXJbh|FgF1AQSj1g1j_2H2uo8QIy9D=SWF zM>Ter0@9m(QM4~n#N_F8CoeHJD+ia)YULXFV|)qQhgewn0Im#;j^+VU33ziKdJ+;6 z#r}O{j|g07*Qm1j&RyTA#W6-1W06r&u|#ZF`8lq9csqN*N>r`_sL2HsmA2H2md{Zs zDYaxz+1*A+C~2w=GjyC6eVl34a~+xBIoB(^)ys^nMC0g(U^_7k*r*atQ_qQNd7-hf zd^-M`=vTLYykpj*ZP%x{pE%w$dCVlG+2b2Ic1k(g+GFfOy|Z4%iMq#UdkPRz!diz? zgP(tUe0Za!mF?6~z4--6QEfK&Q^||rjY<+XxV|tRvfg5166{fJFp?dps>%wkEkO82 zDy&aQIAkJZ5G)2;9~kN@dvUf}p0|DT`Kv4+e;xQx6*T~)T29HWt6@z=G2U8QT46rt z{wDY?d@&p`wKu-HOPR^Q}oq911Ebs4~G>ZwQ@SqBCxJEy<29 zmpV^PjfKb#WxOhC%&a;ROL2*?EKBIjn++Y%xs56k;N{I|9-HlDF7bv&N-0gf=DiLN z3Zj>Q55mcjMkiX6Z}I9oK@WYo|&KRl#$=SqUdj3n6e{v#(&_7{ylr$#!atP8D?O_I#m2;g_piU6Spt&Q*mm2j2YtmNR4e%u^FSHLrJg^6;<`%VFs)#BIDU!#CDo; zOQE-#zt66es;c+X&+%D^=76Vhf_MIVs~zA@8xA?zViu{l3AvZ3FItM-q;}U)&aAW~ z*Fd5yABrOHMtRugK9njvluhBm8cT5ld95&b+qzgq?*0cI?_%kgV0tE|zKK`;`4rop z(8mxITiPLCwln^rsp;Qxus-UJZfReG;=*qmGwLrImGh@Z^c^PCj2gbb+c(6e*mzbp zUBaBIx6H~!B7Z_W(-^V&uq(UVvA;LU7o~L1OfYg0#bJ|zq*;ie-g9Cjn2e`s{W>Zp z@B7zalQDTqk@$>plu1lXs8o7}1Ab#+e2g1--Uy~gj=xd>Pn{th8Z=GjFC3sUH$lI9 zCzra_#xW1D$X8hIQP~!lpDOhTvf8UzXQQ~<8#ys@_{T)SPM$O-Cn%9Go$={Pj()7g zY<)x#yBLMl6-*4TFp6zW7$c&+T*E$av1D{HY&_Bv8@2~OdQjW`zQ9ROdBg*Z1G2?h zw|QWea~OyJWX1Bi>LZ4?;YLubPmnDFUyxmv)gXo+6&BFwvvnY0{gveAbex3ui_4pU z?!hQ#*x&-8&KGcS<=_g=7puYa5HvsXLCjB0KmeHgws`2e1fTEtQtPCuEP5I5IW- zHLJ0VOCLugl$bI(a3KHx7E=48zLOJY1lPr8b|Y6^JiHwfD5vwm@sJ0$9(1ZGtUEh9 zo13QJak_xDmlCJmg_0=0FF%|%oyz1s$K7JOP4#7>7jN5aB@wh7QlOqdZR^9+J(-n} zFB~e>d=z?s6RyMf38AjzHW%`h1s^L!O5Vk5_Vhkow(&sBKdcGkbiPa#m>+MVzR)b~ z@B!s2d{s|fonJy;+4Kc1UN$J@`V4l}enH%9y12$ohg!C76ocFT^ z(qie=M1IU4ogVlNyOlcnJH8Wh-iTu`TPhB)a2yY*?&By;Ho2+cqTy1pR=qS8h?*%& zkx{f<{$nV!o%lkab-UX84X<@=~s zNf_uj=^1z|5$Xl`l08Q=+S+LEl?dLO`?=xv@274>iDc&1>gUOR0jy_cmkUd+{T|KN--o_5rrPpx(6Gc+@bXnJXCOsvGAdRY^@9Rp z4fsb-Gc+0Zoe0V^kS$%^T(oI%z+Q{HOSMpC#-iKOm`T7Nc&&@ok(2WSP4;VL<@Z;9 zseY&dM@(Gqp8i3bD|~%|TDfwjtqB4!0(b#H6%RjuILKK6z93Tk=5{b7UT|rKE&Ibv zGF~O?Taudovo>qZ9)HH2q&-w*G%tMoY1YJ@f-#4duV}~eiQp4HiM$(a`=neqjQB8~ zt={@{_%uP#s#6T%`HDO^s$Anl{U#&2|8=fds3%J4M2c{iP@8y1pLPw*~O!f$KG6Yj8_J$K%+ zfabA)IREJOMH8@0fC0|&?GFg6dgMXE^?}On@urk&M85u{TOwnfIVL}%n>z7WG5=LN zX`|t4ErH&lPsAPdzcpC@>zDexBg8%bGLg6XWlCIstbQlCQmssP){X4~w^{*!D{#)l zVa50FdNsbd8fy-}cP4wg!Xp&4m4oK5$x^!R?>KW>qfa|%YV(QV)m*YIQOAx57j$?1 z;+s0oI^0p-#o}fNm0>TfX~Nm#<_S=Ty1Q2J?ETv|3{Sg zKi-X;D(yEcrTBa4oqtBIj>_1~Oc9#h&P#Ya{tzSMWLMoUe9&E_mGXWZHn#2^815Y> zLX*R8rVH4%Joe1*2L^O62Or^)k&#i^p4<6<{@Z75(FboF6 zr5SQq_yUM>#H@7wQi8BO2nj+rL3C}0NDQ2^Tk7fxE-o%%e0yk)oP_lN<<04Tl$`(b zmr3H3v>)e{RbxHn|KRfx0m&q4($&>fhpD!s>6w|1Wf_1Aihwyxs?4uA#?np&kyceM zrj(lPfR8=y`%zec{T8U;)Z(!v$U0Hu+1Xh>A0M9y6U6M+bZ2Vv>(>`5Dk|pm;C+cW ze7(9Q!}2d@_-_w-#T4wvD>tScL4f4P1(v=%@_l3Zbq<8FWguBt-fX~Kv(cz2*#vTN z7-0N>e7^AW^W$R*wfd#<2?xHu<=e-K|flH}myD(_mmX8Wup)tX8yej9cOVNq1KA5>sUa?InSP5zZO-y`qy9m_ph9&;6EDtBAj>i$BRkYqKf1H z>|FNb{R?5-_Dl?~gxXE>;gG$7zxh`0Dp>HyvQ4C=oTjssMcZQ{gEzJ|6fbFoZK)Bz1$# zwJcP>&bgGjKrqQ^E`;`|J*wMDrp`Mv@mF?!S`TqNp4D!>-~ID&%yVDYh0T_TL*`T_ z6eZt#G}Uh&LmfZF&~wDUtchu)IaQ-xkj>M`EF??20naNJ4ibCo_LcvPWwz(B)uEwDIC*{JaBD0atvkF#(P%d z6z5?y|FlZ5Ta-gbPtW>7DKm`>wAW#Zg_b#0plWpjETqZ&W8=P_XJw8X1RT2aX?8jG z+?EfBjUPGZRA05=rnI{v7~S$x_+nO)jbfeCsk&KWv8Oxj7HuqN^a|Ijm)L&(A)NmHII^q$ZzVqw;XYio?yLsJQ#h>0Ns)2 z_ik6}?7a`(<9el0#%x1Jb(~%C;eki>t@P&?s{oQ_0-I3EdX%#i^+>{5`w#Ob%vD4k zsr^6$ASKl!?Y7A=PgKct$keC+#kDNTuL)A_H&x=#b)N{&JbH)vjpfU(eW|Ec-nc6b zjg9#a8*82gEWhYkka%RIpVLt!?y4t!Bt$|Z4>A3+Wz;P`GvtwN(H$%-t2R3bGSv!5 zatPs_lD{wWF{w1I;glny&g6u43Aa!Vd~elknit>p4Q+25aq^jYQyoRvoE#Tkl<_YAhp=l|hMERwU3qB{;hDG$V)Z$f){<}M#;v_Xd<)EDLLOGRRYfJw$wk`{k zOO)}JvVn8g^!amPjuo&IaXEL3UGv+MN!7PBnnwCUt`3(QM&4~r?nmD&S`g4BTq_`C zhB$u3H!$w@m?a3`z`7^PX?VNe)c80b<@uViar4nRg|aOL_dB2V^K9VMODDNAn~PL^ z!jB(NEq6nD-=kvTrc%ACZ}c!O>~wa3lxZ~zr9v|=!T+SDwyETm+Bsu=U6l0;n8UP> zSnHCOZZ6D%*Pn8aDERz62@DKtYir}GD=WLmR|jgHFQ1lXTM=v4{Y)Ysi^}zJVvjTZ z$Dy3Pn?*9NC`Mg6v!O$cR?}5h4w|a8*oziRo2{5Q|5?|u)zCL#%VShY*CtD zHzfFIAwXQ{(2==s4f|$y?MP_<`*O9tA{}BqcWy@^=M*g`2hNKg8&@}22EL|UJU_5K zEJ`uublbx4NJ&Xm|C@Y-^ApM?&OTDlE3zJ)59}_od1=ddV{b=`Bg)=p>v0+qMsGVd zn+>y7x*=ncIWE3j?ABhx%?DejmsVF_wA0a@I3bc-!>E4!I>+?%^lN$tSOV0I6M|u~ z!d}gqmrx;zfPqEG{?QrgY39k#(Z^x^lM#J@*4(^JKet5w@fo(RnQ~AudjT`_ryA3U zbP$kLcA4vK@8Hq*QM&j6j5$g!?M0(LSF|eaMW?pj4NX|f$N0`3i*|L+kivDO=8q1u zTwJgAS-6m_r<;po8w%@T> zk8O|ml=_ZuFV&^P*x zmtB;Y!$KLx9kbn;8mnRDEVZbl;QwHCxgrA@Y*mP|VmT7psA0CJ(vbIgY+|C2ySuwe zK?sYu3J^Lv5E=S35P%?ba}(v0*X;kS&SqvG$D_y`B=6u`*a|Y*u@v*G9+~8qTEo-_ z3{I6VY%d2oBrW49wG~JHTg~^|UMMQq77uHY{iv7Q4w90DIyLK^{&t4%iKl5)7JfbM zg!HE8#=A|gXPo*<`t4MCpJ*QnX)J-D1=5vni@JoJrF1BJ5)8y#=^A9*>)&q2HevKfhap^u23>{wIev*+sD@N&InX-BRSdRf zU?3A{eA(;t?Xv#eOI^JK%FkUtEf_|+9~2l~9;rVp;VDx*9ws%FreW0${HP4jWDm@K zzql3$)NI;eVY;;yEroino8POZpF;XcO092wFJKzA(Ub zWr?w2vYPKuwf%vnOUuVa6>c&eYqEMxq#3A(O z7_rReyLNwW993T7P%`CD~ zDhvXN_M4?EDHzm=ky;H){Vb3j6Kh`G{eUOE z<9c<63xCZ@b8Kf}$Gn-UU}QwjZ%+vixJ~NVHA*OF`U4#M-4^2sFLHfeiTKPMPW4g6 zJYi?zw)YlI>cu0I-Qq$LHE=bP;L<5rKG4A{<1hmGzmCm-sfqtB^aDwN@1*yd61F5IpXS z&yXrA$Ka}|o7Fo|EnojTt4@`Y_W0V&m%7%+yNCN}IB$V=3P$505Z4m{=#AcIp|jYu zS!RSx(ztcYn6y}DJ_|&{rV~Wlh|U$-`NmJu?IxU_v;NuVKhM2!+W19OF_PJ)(ru3n z|LMyTWUH5zq5>+n$2T@D)w&%n$2sm58jJ9>CaK=P82e4FZ*#FmB#F9OdJ}qn_2kP0 z^K4QqX-SVnUS8hBT6}-MPN(yikCbXRTtI{Cb%bL%n9$6*CLkhT_tAW5NkaF zsw&x<2eU%>^6XGGPu18!KE9$_8%N|d8n5-&daHxAc7zZgd)>_4o6SL2Wq;Hh|9UXn z^vU~alc%za>&v9&<@iR+s75V!xq_wagjLiaQk^el+6tB#LgV82_Z(mg2L_lVxr5lr zq`N}8sAr8OSla2|-AJbfNpDk%*m^yGJ1-bkwboLuMWUweb=RbQcr9wu*Yn!<+WugP z3A+PToSKBYW13xP>>!sA{@Xzb4Tr_qIliV3Uq>f38Y;}!Dh}Sh;gEkyGlMtd1V{bR z@3Z(7!C3(zfvAzH(RdMGMG`$L5`TYxBK}3}9m_8HhHS#pg%SZQ?LQ6=5Bq%XJhQU< z7VI5_MgfgpblY2HA@hx{)$S4<`C`~?fW2)1i*nvXIroiOsgFWK#E`>oH(1>ehW@VX zgIYv8tfm<%&fE`a*gs$hVwCuhtYLjbw)ezBPUWe~FSFL?XY*xlKY5Gvyi}XzdxLFr z5pl?%)UZc(&8(q>HcllTtH)aIHF&Lb3n$)uI8}DYT-LT-NWA{8BP$``&8K9C|K6~j zvIjKH6LWkO!ve2CE$mtek>(5+RMK1dTQb%NG%L71=E15&AGn!qJ5i6#93jf+ROS?% z$;zk%C4)muOzsW2m&}lX?lJqO3Od??G`_wejy?Vi-kkZ8%0703&$x=E7s~?VeYQ?V zS9?8jR98%_RjaHBGrq8W*&QDd7@>?jZ^O4GG?23Oe13hsLBwu^Lw7IQ(RZAm_hjUK z&wS2zLRTnKdr7l>unV8chMT-?m3N`-t+&08$;cScu*+C4Was2W?cPIE57fj9qCdoS^^uRFKZPAISF+M(O4#&M@ide3^Yguf*#k)G5uGs0lUmjO@6|4rFw^#& zT86))L_f-OItD*bgq8Q&Q(xoT$4Q4@dM!nm>deO%S4Y@76DCZ>FAVBHv?uu zC>V!2b-z^zGw$Kn?MqZ6irQ2ytSzq1y_3IV!dBjJ3V<<+JE}yEyrsr zi4r_a18cqL=c0(X2W#braa;U|z-=vS%=ITh?3Tl})4kdA(V!UC635abbc;)c#kRU- z@uf?PuG6CHgEs*oP3?1I6Bzjh@y2iO7jd&vCKqL;UfSa{~> z04y&=NBNv=)Mo*_hmyWrazeqI*{&z5TXY>3qI?T7)qTJS&XhGYGT7oBI&X=%5&>#k1!IGxgC}9)GF~K>V~>TcgZXZ2T4?8BwFLie1UO_#Y=4|aq;1j ztN22yBN7fD(;aZYsR9_c*C*~jU(eJL68vea$+K%S+AEpi`WH)dD8(ftHlFG`?MK?p zXfbI{f*(ZqZ|TY+&#B4F z59Af@d~@1z4z4TgTzskNmiyr2m8&0e^IoIf?@&=v9^8K34l2z`&O~x*9*DFCRux)i zP<#`8$->5FW3cI|;Hi<+-ADkLk)moR17v@{UgwB8z{eXXzloN`w?5E9liND$Gy4_A z*c9Vl29Na~$#IlUX+>KG5A>&Q>Ad#^;#!JBjSI zsmQ2|vPp35THkGQ*?K22G zon-Vf_KOWPXiiesy?CO@9%ytg!244zcXSP_lf44EV9yY0;pZLFPU+;gpG9G+TnPo{}>n9siybyuksm*b=L4mEE#us_Dl zU2$C(;`ZqN!bW$2TC{F#Oib%Qod~=YlZza4V9KZlLZ8&%IF28Gt(Z!xWt33I8^1{c zuS4aBjman>omrCO(dBU7sKCwC-)u$2=>OV@Wq7!)?P3B?VF$ylE+qIG%_D=z3#vLu zn*3v-YsQUc$7(0Yd^Isku983b{y$sD|K4nUh=!G$V?)oNgLfU595S`Z2uWiHiKWRj zmq261rZnYvJ8lqj1^P4 zN`bf3)lYg*(YypPHGr%7D%ofeuA%d=Q0itp3`fw*D<}k$B^N=@_fmnaxH!x`qBur)qivL4W^Ptt2Gq}W*p60V8q{RA) z)w#Jx)by3A`lv`<5a?awFnT{dw3@d%N90(EZd}d)t8g7{=UJ6=V|Mw|ETytl&d}Qw zheea>lXY3o+o)+W~=_7k_WhZE6yAfD(Om-_vbfS(TkOd_8NOji2s zqQFQ#)My36+Pg!(A@U6y8ykl;n)FqgSJka7W&<~>0|~3vOqT}{1q*;Xh`U?@7G$HD z1HQhP%_``jc9uP##DQh_<|3M4sD{gqRUw%ctlH8)t&8%~t@oceyf(2->8(8X64lnN zZ*F5eQNW#I>ss!dzp=eB=Soow?BY4Dp>=i=V4%Njd*{wuYAO`ntgqOZKS)@&J~Fg% z5mzk|X>D!o4+Fk?4da(ejINRY6?E?q9(!2_c}JnN{x(_Zo21+-?@{DPUbc~oS(meF}5t% zH-Thn?1;gCqpdN7gWT^TFky`Ioxqhe-A+X)HUTIJS6dKDZV}&uGklVIdXkdEcYwjy zG|G$}nEZ^PCy!_Dzgwe?I(*|xa6v)A;%%?BymYQ!JpYR|=BC}Rj61J(U?Hy_H{is0Fs~%yvW@jZ)iP-8t1A+ONXJotk#V1m5Fo z%rHtq)%z!U>I-ZKH-qNa<*AG^jT59$vwXwEf&8g*?kR=!83lO;V;g3gMuXZ}APEP- z7$-y~hw+y4`s#e&U|sZ77pQY{B;^2jzb;$gbTkK2PIb#CMz6-XDtcyd@Lc`CET}4r$=3*ee_p}tiYhAXNL(jw=!q1wbe5wgcc+n* zDw+s3;vnbsX19+)pY6pg?8A{iu_u3*ernS;U1$$-7Iu70!MMZ1m}OC*1go1{4bO|a zwQ1r#zJfY&5XbnkZc`=X(a=-U9%m8j5RF5an|)haS~ymrtq^ej`0?Xe;FEBaf@ZbJ zQpPYgSJ1d6g`)#`Q`JL+@kCWwnW1H#xLDGp0Kc)yM0&|#NJ7+{s96cS&BsW2bB<;sA8f`PiIdxsXkr56{(f{6ctD0?;>BR4buI}}Y+{mrQIPS9Elt|+LF702 zP==#|V!;d2MTH*Qb_W5JVOpR$Sa{WoR%0{KYM}Z$5{Z13nlyWWfk8l|=@3gWG=Ybk z?h_oQ`#CB=RF&U(iJ_bEEW+#uI6nB8Idt&}pjVuu>}oqyWAWqH|0Q@^C&8QXogJNx z-bP9JltEZ|7tOQ5<|B5OtZ1SJ+ga*hJ6)N=7-fvAKY|rpWgP4evANL!#5l1^boL_4 zvh>A^m8RFPKhw3#HAP2v#^HflmQ$Q65Kq)X!mwIBktz{v+FC}^1ZuSqh%3c~_Z|y> zpBcS$TVu9ao$y7p_<4JyhR*tK1nuS~B?b@|6V7X%7JN|`rq85Ei$ssVu;}o=gwNPIG;mepjND5lyU>-6bY|1Alu5 z2Xb4*4okRJFDvBG%xtsH0h%&0CFkBr;P3Ynva-~`OsAMg%q`oy6EG!_qC@AkTCKy| zAmrWG@x=9qgfKdF1sWR&s~5F0E;UABJcFDrG6D8G+XqHH*RNe0q|W^G9bjeu+HH;X zU4faf;Ob(1xI|WWmMwlmAR1o^IVHD_NnW+|(j5j2YNfI6FMN&27rH}x8Z6E$0!}Jb znn$`UCGhj}XR$Z}`@j_~m*BylY@Wg|a!E-mWL{uZ_V$I&MXH(<+kot;((p^&so z98P3F?1sth7_Vro3PELCvrL+(-BlbanSkKGoeSofO0x)$cfO;L6VqT^R}Tz|yX%ey z7!Icno8~ubyCqi?HPH*eKMuZk14AbU*{zgK%z7E*GSaEHX}mIc4c1n;dqOaUV3w{z z?okW%v{ncg)yL277O)@1HNS`CGL!9j8*@6jN1&>N{LBg2l#;XPnp=G;AwC!@ja+7xV6eB~CC*0V*JBj-YE(`e24l z%qU4f-ObtnuTm;lDhsaebJ4c!>%XzGFK#=DQQm`ibu3OJc?Jamd)R2D2m7>Tok zs71!zF(3@yzIaYFf=WZ+HECdglgwQMgKuH&rnkgWzr(@6mTyZcE-k&9;YG@}8aRIB zh!hw>bo1MRs4}(Reg=jz5MV|nt{M$qEv>Ch!ggsr)^g@(17-YPp@K!9gk2B7ttYf# z8{>>thQSL+A{W+{nhU%TB_PEeA13aE;BQE3G>6A>YISMW3Mp$PCK~KJSj7sUO2l!M~SMaA#pQ^R@PyZFmtaUeIB*vF`HaUtdIXzu# z|B-Bdrj5S|%u)k80K}&`t}PNuCB)*9h0L~vF2fSubf)(c-##@O)oqO;VmXE_(=V>F zz9?87H6sI$oMLonVgQ3xCeW;TjGj~8c&~6%4+!=91La+YtmdJ#xfzLe2`2{>vqFA^ z&iy>enOf;7DDGq+TqYQ!tzbc+1};zXsa#)?q}r%4(6Z(vcNWwEMk$C;swZS&vxvLV$FMzlc@;L4^)8xlj?SSZXm8 z1CkKuzGWM}Kh0M1D)+Pm=oU}G^r#HdydEeL-J_*3Ms1q+WHKF0!{L^z&i+-mNyTk9 z*xo$5g>T6{qIzYMLR|ZaSO0i@e>~J$Im$c`5N6CNL#p2s+Fv`-2lVvtF)8i<4o*(7 zS)9#PbVO`ioS2p%Kpf~7tcOP)vCwQ$RaFf=@;up$bZ!hR2|2DMy{&>1ZCfbA6j$p% z?7C9w`Z}CkeJa=0b2&l%_RgJ^##=iPyfmI7N}!E%MOQaZ&X6)}OfjZJpp;}P8GN|L zy_1v`tkIF2pWoT;EwM9PwjeDR9om_>#sXMuQ5iNcl<*(j5RF=1@-dG^e3bg2J44U-O+`N+UPbF594h8vYxZsJ~S?yNzi zC*}uGQq$MX#r&#ku%$7Q46u!$!yel~{F^iXAD`YjO{zKg=}0fry&Nq4wd>bkkV!6E z4g77ZLIfynuHe~v+;IH?T+=OJi&yU>-T4n4VZzOU%KFX`@v z1e7|D!vv;tCL?!2``FQ=U~tVo^>N$bYk){_765WFEZ;fEffp=-mF|FL^4hyb1kUg~ z7iBB&|K_5IU;he8;g*)xsI@G#)2ynC9fs~30~yPbLB=n)jh@UDfySLYbRiokf!RS= zP|Ri`5c{SrMa`dugEyWMbuir+dz2=vlarHdfy=6o{M z9hcjMzV{5Qd{HXqek@{5df`hn6#4lpj9rwTv_D_ae-uRj)yd{axvf9!+B$fn^+eR> zHqdG>19x)}4ly(^FktZcvFiqQ#v`sQ%+a3XKmN+WdlXAfbx^j)z~wURBM)@Qb^Gjw zu;!a6v^XKvlSbaOaztHdgDKx<0HBdm7nzg@h=p57Bo~y8B_|-eWH_RrZiC?d8OZC! zU0sJIe}1c|(j8&K78ju+4j2g8J0|!9n{;j|C zk!wS@%E-tZ2X*LYA0W?xqT{84LvH_i&HuVz5y!rcipbt!|KqaFn7Tt<2$nR44?hS| z@Uy+$P}%B72Y}l>e(sn52)RdI!qnNu#zuZ0aCG;A!TkoY_}lG0H(k)Iv8PR<_*F4S z1@8<4xMg*uKB&|OEAq^;I{o$^ z?|`6q1O}0bXmt44d5~5D&R5t5&&lQ*dUy{GHIEp6w@5R4-5)-rAhQ^9$RKSpHd5)Z z*dK5Fk5~3z1;^Jnu(T7O&Q9U_d4@N+v+^q5yvYWom6>HQK(l~yQ~y44LpPUh;yhq7 zBIa+X?fJF9mD+tE9@c3Pi)E#yJ?g_tK7G1oZl1E?5138>XaD$sTv37MXmEEL z!;%RM2DL{vNMMlqkiHe(#oY8Ao8M1I{Ki+f>$lX@2CY@|^Yi~dC?(tN^U259^-9w3 z?P%1K?aHdE733LNyN{>&>dMLn=^vXYB6y_en$sh9qkj*c2JsXQY5k7{UaEH4Zvl3i z<-)|BznB}*P(8_j)YL{FW34zGLR-IqPh7uyx|}ysbkjiWuEdw=f@?a91x)Fc-9=BH2lt{& zu4VaUekv{X4h~kG&pwIp`gLjl>u~AB$osyZYz@sTf4{E zdkC-hm5h9rN0kJVx_CYA>OycX2LRU*lFI~G-c~ISYnwdJG`~^?(1@TTdrhkKRk^k8!GF57HNa6MJAFr3+ zy;8`9-F4x3vPFLUzHE!U%rIefHX9p(IRQ$_-GP^MURd?v<|G7v2cm!YDawi5EJW1! zsQjM7*}s-5ju1#&A|8FFzQ&9?bODCZX4Habn#{BDAHDlWB)f*-n+AT^JXx$IZ93q^z_Fd032|2UB~M1bN`yD zo^nsS;n3e0zSp(q*vq}6D72dfJ2CUYN&VT)FHcW99tv{3hBvM7uaT5EviJOrAn_@M zm!Ff*1+o4sf|`7CtTo_$cFfhyK7Sl3Vs!-u_s5~3WMpJ)?^GtEOVL2YnHwc?f=cUI z#~Fj`AaL>Y>GsU#+F$f5>j)`KUZxQ)fJ!0waLu#*36dP^%dXn~JvIjrU(ZS1PBh-UWSXHcXm9Ad>+)awTZqRY4HQ(@+aqEOnrBnAZOToT-zy7~}I4GOk zSv>Z{FDKtCPugW(+-Rtwx1 zGR_~!s6#*!qJ+hd6w&)7%%%jtYe5=8!PnY2IrY>Qiq*GW-6|Vyf4#>{@W68Gzl%4+lXD;AgK}_L zvdGkG8xq%~wOw^f7TKT?ga@+;!2r#Dnh!pyN1=uyHzBRy{H}CF)q@~92j%hcqelxz zg{^;u!_kxe*>kFCjv6<6{PUk#RuFa;mvYPWzrWPFG<;m>m^9b&1>*IXBk@>Q8UQW*gKlx(dR-C zLyf9XzJ|uu3k=(gaZzL%9+CekJj=^)q9eUze<6eVBws;*&f3WGUcTkNRfV(6P0A-P z57d0_e7>by4E1e z`7jYsY*lcX^w!ZF-s}4#%DFw+!QAoYR;{Q|A_v-@6BK;WSY@bza}fw5kOA-g^}56C_se8OI&_MYuy=5?w(;y z(fju^JUywv4%`eW&vXMKDk_b~Pq%kpX8!r*{TSkDLQeUu5~aTkjFjy} z&Ahnav2wg%BJUHGUfD2Q8ar~!#{2}1tJ6P)E} z{LSc*v|L%RP>L~w)q5Ja=farKul{wDFJg`e;%a0W-RCTz zev#t~JYMOeLw-Z|D0?#`~-H7d)%{sm$#;o`6 zNPhC=aC{HDA=X7T@qt+WgTpP*gnr*CH+@KGv>x-N?S?z5E@;+i{4kD@qA4mBCFU^o zp85k76;()zsnrEdn%!h=XaKO^@vQYX1`Tm&rYgufk;PsNXF-K?d4kclF$D{2Z?i)KEI1_`cdC)Ky(nS?urKRCA zq)eM+c97LoaXPm&G`ImNaBrPFU3Vsmer#@To(T*J%Ax5_jBK-+Nl$Ak68qkysGg$R z$93lxckzIcp1*V%@dR<(9leS()FG)CO<4|D7uTQuhm`iaSp3xubU4Dm;ITZ(@7Xhp zHnSQQ7UG$wBvCu3YJH!WZ*7x3rJ)lecoU)?NZ>ZHEiZ%Fwh12Ri1oY|iW)Ci&p*T| zD2O1dA=hzbG&VMp=c3PiVGC~dnQY)jA8W%by(JUekE5xxhaJH$k()`-`1H3Xvh{74 zBP<%q0K0b_(9y~}T)=?wl1D0e_i{=-?UO`U_52rEKA5||KBLNqPsU>WxxVwQK@gm*Gp?5I~kX z@VDgXmeSV83^X=02(#kNP}gQ*=zne*Y0^9I%8n0m3R9i#TyWE zq|TzEq-bQHYSj*E&S!7H8B$pC@w9v0ZT3qWv|I zW*0MjORb%NFd8d6t|VQ8HG$12R_mTl(T6suuPbwBpY+8vI{p~?+J{NmL?k&m1g;o0 z%1$k=9X%wf|DrN9QxU7<_^R^yuKh!i#}j1tmxWiDy-X;&C~mrVYWqJmXundls5UH6 zOPS6|@7|yC5C=tAKQDkc`>iuIGED`R5mJq!T|>u-z6>d#br1+b^c=|$G_9VY%$sbO zQC+Dy#^xzJCwjjm)utgTaY&d-FiX0$j~Fp%*vWAdjT*H~;QEfqTM*;mB^qw+1Rb<= z=eZH}vWg0NNl8hIU9_PdE8i6zq=I(_l!tI4%2e4`*(H*sI-Q18r>fb^h}%$0vgU4) zyAIN^-3s8#XJhLx64c7q-2Wdf?cd%3t)pLw?CEC>llRot-h*Cc)kQ#Y_~2gLVS^_$ z+(||>`dhDHR(fWKMs!@iw|MS*5N?w9AHv@meDuHj~qq z;-;gKK#yoMp0}e}M5;V0Dl80PkpyiH7$sk-@$>aHojYH;W%zBN6Rw-QG_M4^25{Ze zl|KYj=mh{CMpXYmgJ|(8rml;(s*7o}y6QbP5MO_KU|mk4Jx99xzTauo^pMb{rLB6h zwf07-|u1<_LHD=`;d`VUzDEB*yiWgycaCj3vd+2LuEhCqt+V zRN9NZ4|uE4kb(a10!=lHkPHZ7;p3fhw{h84VrNr^7c}|(QJ69t9?&3+<%TFYtLVLq zWKi|U0*N2_3+-^B@%Aj+z85%yd)=v4TOZu;ZFL#(nLr0g)SWpR86LoRh^;@)R_gir znWC+Gzr^)LPHLsua{L^n|KrVcm#3c?%XsrTUUkQ%D_2B2>;U#q@8UG}7Zk{;y3aHT zEJMnzU7_Q8*XRJiwmWm6B;64{)&R&3{W$>xFpj7lkwur=D(~E}gFL_v`38&q4*BSL za1$|oSPbY8TPJy|aK^0J$;mT!+JS~E3%Qh%ib@7hURJ@iet+dEsbFeKpNNyMx_e^G zZhJ6GJWzXZahcfkk~`OuNz$J@F&nDfy=r}klK%Io;Qxf--&s$}8-{Qiz2CkD?)dbn zC9zZ_V`Rkm31P0o8iLK(TsUUP0Qe*C-iCoN2Z#{*7^t+`Yq-S;xPJu%eaPlavo2nk z63cDNoHf{lj}B#+!6h!{RNtQkX)($fO_gj%Q52^%5B?Z)-TkJ3NakpSYpW}57|u)Yt5@= zQHc>il*F5dmp2F=YTG;!C0*K(iix$N9se+#+H@W}j!ec3d%ro&Y!*E=j-GE>Zr#`g z>g4`PbTSJU8{f5=JOLJW%usSuM(6u7i+nj5<;0GH-3i@~iu`^inE8GN?Or~pF1~;Z zkkTYh09A|Q)ydZ+Lr<;vmKa(Xk2fWzl7*un_(2^a?YYaa$~@TF7cov)TOw77=Rra8 zj=|ifv#{`DomWv#1h|3Vm-00aysNp)-2>#M*U8hq-riQ?kDb3?nDZB-Kp|#j#7gV= z+5^`0=&O2o#XZf9o^Ck&Ok8Mq&kiZVBuN$>s9pE*wP{8Eya#NdFW-dCRx#K7YFqjbRzL ztO?z}jUn8s@Vzn1n<^P0#U~G<(Q!_NO{!#EGcG zQWhA+U1P-1M&_#0(2S<_p#(m_+@IV;JbL^XC(F2Gp7=<7EN))L-Eo*k5 z_O>yEK584}e#R(Fe7yf(Yvr%4f3B6UUcPLzOo#1!itu6qL{dG$rr4Ki_+2z{9Gxl9 zVhwW@m<36)wPWq0D*qbEnP1;be9{o|$b5Fd^1*}iZ0)kc78Jks48Nn*|M`ia;5Pnv z+_@&NVoaEwy;#}aMiWGf$guvxH@>SpCMbWUWDA;aMzd<+9{r%-R1+Bt4lx=TZR3E` zJa6?P=`Z8IDAk>yXGNA`qWAd!TmfwYalw{BW1)YII?7(SUg1x$EeLFHehO(SyV%uNsv=vMdE6Zn3r zS||b00!l({WkdPPrGqz`cty#p1b- z$x}mAX=ljm_gVeVUg>vm*pO^q;Ys(1CPKtG!}QJ^{3{#!PZM z`+Vl$A0}jY|rMMBkJ0n#t0z&oMZQBWg$BG?{D}u zSiK5`?=T^7kbT_6Cq9722qyF0$I8#e#vkUI3)XPcUKuw19iFQ|(sdRJ56o2Ptlfgxo|lNKhT;gwYebdVk7Wez(+*R5t9?3aG3}$2`ehKz9SB~3{TWw~F3J1AffQH2Y z3t<=Q1;n_*=DL3ndzh40Os+Xjg0k+$6r_xzQ`ckOlG**0mGJgi#!eu{nf z&{*gc)=2vRpLPSRjr6*D4)~QuhlPbLa5QaX(gXF`J{Ve`JkCE8&pB)f85a-N%x)G1cz$Y^KX^Q zPMg`-M%=5;)tGk~+s{BdI@UtW7#|3oL3n5A4)Un7d|Cw=`FE05m#*-`aKML2h&Qe5 zN5B4O6#ZXMv-U0YMYM)3b-@&ZYGfDVyrroe&7c05j)%r9;z3U+)+ae5+{ zcUHMzm9u8hJM!cWyUz^7v^41_yT)x_3t6v0rU=v7fLa`Jk-UUoT$i^(MVMJZk>j(7phi| z$0CVMP5sI}3S)19`6>NGTfa~!TWLdJOsU(oumfVr$P9Y+s~~Ddd>s=zVAwC0sz?f} zX~*W8t>#BO=;t|?qSQ9wO;xps`O?Vq0}b556)R)au9b^}=OA z5<&XLhiM2dSkZTO=qp05-j{5+8n>5LHvL;Ol~ZTYZc_3ZhF5?6Dr%kUPGmDFuq)Dj z6mTA78^x0YU;ft#_#eNfDerbUtwlHN(l@)x6?Rb_zRD!daj7V<6 z9}7P0xvo5z_bTpHQGttdM&Gdkz1(CRP6fiR?BJBk&GmAh}q;s_qjbqefz6Tv_3 z{@@<>%+AHY{*V(Up***Urd;*;i@K}59oGzrx149KVybOIx>ldr3ZtZF7p7g&xuu@G zIxBCwn(QKsn}+XqzNA$)>U!)XU}a#SW0z0$;3~r|PqE#0{*jT%XLL)f|w5;V% zTK_+m{8Z#Q>}ChzLH=JKYklk3dU99Y0ka*!gjGEcR+r(c)ltouuD4DFqMYa51;fuD z#JYus$K{31OiyJl2pr{J&9A`WGj$rCna@v_`W%+jh8*Q*HXKaCU@*u67lN@sd%h;j z&BI6%D-%V|4(izdFzNoA2>ihf+IX?AriZrt!j@5aet0aX@llZZQ@D8n>w2Z%?5d7V zZ9?Qa0WY3%CDRW#{kg7E!+Z8sBAC(Y+| z3l7k>YS*Uo8GFee<=z`1o+fYRyw}7R%iKE4Es$|Vs2`8+f7jE;%_OhW=k zxR;n6jPbd6czjsvyHt(o=8(MF$@56<2-HS;>e_KYqRcI8eA@K$8oFONdV0=wb#h?p z;;YtG=~d#Iq78AXYvJ8R%z>GrxQGUgWrfGShV!K8NLL+&wVLr^h5RYb#N65!9tnwJ zVw$+wX2~Ycjw*SE)>S9F;SL+gFW74`&0*RIL~LbcrLX9_f0w8D>*Ni-bVJKtwfd<( zO|B?{=?zGyc}QUb1F{MRD>dsfc+*kI_3_9-tFC3OzULv)1q%d?ncjI9eWc&uvO;nb zNJ(U;=6mwysTNC1AMmYc^kl4TKAG9YvpRG2-e`c!jATALIY0H;(vLfeZ7lQA3wrL0&NM~`GPHHI(6`M(7 z-z>gRy35wCkw4Rg>o3b?80L|Up1%03J?W6t|z5nSacUHQC-Sf+Qvr>}QKYJv?iPH}jCbUNgPFl?k4TI5s5?&DxV1;QgSZiMP z#ekc8yS3JbE=QRIZ87+rCgo+^%JK(Txx31Gmds1BwU^T^1I|^(R94=QiGPC9=KNli(D+WpQOdGC3@azhKiI z@rVCvm^MfQ&B>f6*B>Ib)*46d>qUTphq5pp#nz6;3koocCWW=P*H%0-9|b6+=n?6x z4l3qNoK2Wz{M63B| z>9PBje?+cFTl2J&S6o~lmI613-(_Xtk)R+|mXsAPVxe%=5fjTJ`r?~@Yn=_^pI-gX ztTu=uKSi8y^B>@zo9@8G7=*nEwevkaJ%g#Qp1EdDP;}7}}#&9u_-xS9l9RyM`BM8YYP>S>Fui z9s7bJ*@*=6?ih%$Bjm_$h|C!AHN9p?O^Q@}gzP-}5x<(2X zPK#32;6nGE3(U8S<7w(XsZ5fgyo@%7G#|s6uLWSodJGgxUj|1QHFD8&%XPp z$&`l+bRNxHaVdb28n0jJt2q#46gDpNUh25Ey?IwswrTFUyf!lb*A@-ONfMem?!bVI9dHO}CwNqAU67So9{7urR_#b5Q% z9o$5*xp6e-Rr7dK^@>>~I4gZC*u-c|&wQj3dmg7!w4^#r0I(mxnwm*rVa)dl1rOB0_XcF7ztEOe*La$aayy7P&J4P3 z@20)pEzzhva6`Yj>wNqf*H`DchpIB|PEHs&4AUZiU$?*i>0lID3TLWB|GH^?Bu#hD zxwoU&kxRpeDe4|RGF2T%6wxdw&!?)6mIn-!SoXE;Mo4N>a5AZk3w%4N>RTQgARM{w zB7DR15lpf)l{Pb-6?b0BpwGvB1L|;%t;2c26eDhQTXSC!+#V3By#Y;l1Lz3x16_Dv z)pB5_)ET5I>ar|)-d*U8V2#G)YbYwxjn0w#bX0nQVLiRKHmCWJHD?uMK|*KtwK8!u?yl*25dG8;CxKiNZMpox(@X=23{%Z9N+yW-{e=n%2@MUe0I@<%@LH{|@U%)U zzUzSlig;Fcj0vs_zA(2?rrml+I$Qg;!w*i2M!@(>T02=N$JvHkVqoUoWxv6MCmgnK z(X|dM2-yRlh4=l8$bVl%N9k+_oaBK__X|f(Pf!}KWMuAFy_ZC3iciQV>Kq8>MsCsI zRoW9ub{Wp<+-khgNF`bmCQ`4zLnB6DD$Tv&^AhxaVMZAIUDOH*mQUbZ{)Hh~mwTeE z8K(V5naXj2_eRltK~VFHx__;<&+0Jce1t+T()PwYShGC0LW7)M5VBbhQ^jSRS}}9{ zE8$Ed*QVARSzh3?p2>&d8$`wxJJ_bRS=;JYb~-Q5uZxVGK>Vx||LF@3x>BqPeQT0M zxcB(9r59-DPjr=4%?2bCu3TUAWqUX!*pQf-dU=2^x%YL?<>E4ZqvF&GO{|v!*c+AoG+ebJyn|L-mYVovixSI>g}($7_}3w z&Q^VsxqC>2>&+Q0{jD8Lx|tkJeDcGd(jVgB{Y>E(q|be>R#~jv;c$hc%RdUqNSJyf zlX5wRz5Im&j)-d18;@4EoRZ~tM!YfH&GYe>Ct>3Ya1PL=p~4J#eP6MKzKUStX! zakXmsn2~Ak(=({K)svy@oanv>xcS0E%Laay9zJdOdPI$lVZ(co5RDLL8>{qYWBwlF z?SAzx{jzZhdB+jqwPyK#Eeaa1_@+3&tQsw4-CX1l3sh2mr+NRign;Ad%^{lb>y&L5|6l+tnbdxOeUs1_N#S54b=BzRVVx zWFU|;!b%4QyzlO4Fy)L^-}T5A^hb4>^mV`jxXe>$)G4Q!%5pcea1=Ch6}oHA+`PQ< zb}cHna0%x&>MlV60g*rlvHCYOtYCRx#W={Oc^|7kPx9fsKlJgwAgVae!m+nz4XKo& z(6toW9M*B!rwbx);l7}eXY3*R7n^e<)Zbdnx+WDwI9=~r@g9$54#s|axjGQAAuDdw zasM#Eyemb69u-uCr6KNQRL!$%=De0wMk@NfsJa7);ii|LDBrqv?Z>pL@kG*mE#V_( zmaC|}W#A4jt2ew5iO>vlbC7tg81CRA-LpVSJ4OLc#gE9Kp;w4oqj33aWWm#iHKPV z-H+qA{>-Me(k#ldW}q41EPy{v`;oWk(5=KcE0pn60B+c zd=|4XB`xDvxzE5}A$dg)Rc?hK5M&}+=MDS57{2AjPT*vz;^tp3DvL~81oGCus^H%1 zI6t1y5F$4#uTe&TIEm2Q)PRpbH|{Rb;dVt88m8GY6fC|Ds=9Vw!anZ`%2o5EnWY42 z)kuYwyWu4twjqon*hq+wSnl}=nad-_J^KZAz%SXCO1wmFtq`u|j%F@S(Kx;sLHU z2z>f#ZjmF*+bJ7rYmYDm5nz)jVAK$gW(=a)Of>p17g&810KfP&vnk<`%UZm_^o4}B zGn$Q*jtjkF`MB$$Yn^tpZCwv4hMcdJoLVg^SoLOA-?!sif8Rcl(YXUoBbF!U zX;&*^U2BWx*WTJ<)}5TK2)!;}{bp@eHP@Xy7Vo#5mu2C3Gbwq|&YEs?1J(BSnU`)p zcG}y#6_^SNIM^PM828UhPV?anNNopZIwucaPO!cA*L$Rb|Pj5TRy+Dls4G`|Q0mdoM;}6=!dZ?&4=GpG*2B z5wk7G?!IGenyT#sg$QfR;+r!DPhE%emV$%nrL(99!cd6DisZ!VuiDTex+r{iveCzHwT2gW1qweaMS7qK^mtvOuGE|w-j{`Kh# zWSe4%`Rp1)*nD!GIyEv$H`dn=OoF$t@T>|GQ38XoI>7qH!m>9e>@IijSQ}0j*45Wr z?P92y`b~_D8(_hm3`z+??Ec2+_BO9vs5Tl$GrO=gE_3N8wc0G&WU}xw6K)!seU5YX z>9hrT=auc7tz_+tWU=I~ zJbBy;nXp5Tk@#Wn#1p62>${W%oR@tB91A|Ki4&vnYkJ=O7;WQ=7Cgvxv&OnRIpWhe zqvE@Lj652xR#hS8ip7=Q+Jw~9D+=ddtI6>@-nB2D|0t?*b!T$O?ZN?7!^wRbXBr;u z7Ya>G5|b7q5W5ts+Buc)aO-mL#Tt$0#dr%;=ni{k@6@+)!x85%`El_FDis)T#kHOA zC^)0sSf9{KBEH4=c;nvdO>_?PicuP62r%xeYJAtnZH+RFe~9Xtvq3J)pKv2BryzM` zEA)0G#h!Z1Ae-@^jJfY1A~GRt#f#dc^y*>p^kBoqFFOWY>?K>`1~9Aw>~KdXIaI$fi|1HAo2oi_f&RwYkYut_xRty& zel<v3fGh;ccraxVf$Gg^_97)9bzaJhlL zVe>(yNk^6VZkq|c@J_W{Z5n(_Ps|k+Q`0ZHey-B@UMD)Nsw;hL?sUiudN4~}b*N*K zAqU6lV9If7Wl4}}D=km`nEKo0{-D=1^!>>z7W|sp({sceRQcIbNy}4YMvIif4rX!3 z2I>foc@Y@tgIc5Er>A?$a+PREp*%0r$( zOJflq6T{x8$drV8+XDb)9~LpeQ5Pqc+?E~L@a$>PU2tMs&i z-VoIR-~JODnTBJtr5+HfQ4+5^54kCb&0Tu9mzT%ISmnec6kmUNqgI7#Vx{t1>-}A9 zac%lhZ;`VvB;_K-uCD9r!=Lc8U{E$_?5PMllGH&vB2-GHdx4f1ltrn$>Y|&GaCgy_ z`iZ&8D_I6v)uH@Qs^e0ONSNbbZt6|c{(>#AL0D%5xT>nH71=Zc=SEJSk<|q2SJ53?{Y8AW3*2y^oq%l zSVynxstTH=(ih?mnERZn-%@fWEo{EDTvja5q)6=O)_ccF+4FXDHxD=NHk40T)U-b@ ze_Hg8>wO{l@tal8CO)dOJI-*!KWbv(!`nR7rM?$~B;HLtd}aUgz^7xf?MCm1hCAyk zF_WumUx`-By5H!qgy$Lz6$rz%*Awg-q%TW49d3V(;I>m|;PJ7K%t0UtBQ~B_pWs65 zx|lUNZvxi6A!l?w$MToQ0-{C$p=AxDsfD6&8>SW$BO^<$0n^sIE+VmbLH0)|=}deV zK<>m;?CT$!o7@+C&!le5-hM{+Xjj%TjFwa8d?9J}iq7PH&T$mVd&Qi)74}2j^W?Cz zk%7i`P4y!|M{}oAw@hzWN=QHXQtY4@r(AKlW|huqx%hWJMmb~1CQrQpKLAS+?ANgE0$G(8z%ML z^!QM=Um*PKh2|Tt6wy&P%LxCNC*1Ja>ZT_i5_n?b?TyD zPU>Pn8;oR^Ap~f+=nQs;zcMT=6x#P@Rx$GG@Qnp4#AO4WkYp{vYxP%yLq^WM0X|jq z}0IB{gZ7HNpUyENL)k zFxn1=6a&VGC0))VSMdb#m2s}>%1D+=P!#@jQ&)2e5SSazvz zSN<2CVB-COIe@Fb7xUQ=jG1om-G{HcyGz`&roG;&@brYJq)kX*kOxuER=>?N%S8k* zhT*OTDTEq4%(AJzzV^XkI$}-ON$R-gVE&!EOVL*X(Z*_lMkY25RxU9!K^!Z0mcwm{ z6e{`I%WW$=?Q6 zkjtS>Z-OyTLz7=#uNPPka^O1DQMFBg@rs|;xKZ=ej$0m0bJNZw4{mExqitv4bbTcc zopbHOwxy|aZEb;T!u!eNtIh5pxJUx7QC-B;^z=5(?LwmV>1P4o(ORA=$c#E5Rsg2X z*I+1Oo>(I4W1(}!hY?KGj>e^7Fj!(Z`NtSWGrOg4%o_>hbc2T}UPvDAlDtw+jJ`&1 zoV&ulYUH&q>W1j)kSDT63YmwV9i;LVh#`iz1Q}9zJ>y|9%|4$wygNVdhQ97z=?`o4 z+KtA<_yds+SD5T@}ctiph z+8=;rdO!4jTS#@JL|~gY`qFOK(KA&-jUhe3w^821D7?|=?ua_sK!Hg}ca9S~nwl-m z`-#$V!Mv`yBmWISo-z&Q6=oV6Z-L zN-YXNzKOYPnoev?z4{xOJZ^ITc&xUya^UOhR5ZH2>cz>KAlo4sHNL$S)?KwXCypLm zot`?&_TlrSAzxU-AXj{?v`&IzN@JX((%a0`d~K&Wp%BR%X2DU=p}pU;Rlz7V4sE57 zqbS)m;w4?1E2lxY)A#%VPmbA}e!0m@TWW30@}g#!SDi(Byqr`G5D^KVnlEhU&yDK! z>KXF-$RI}Q-+b&!>+Ty?!>!l{Jlboo_itOBVvSeT(j^SfHzpJ#+y%jSJEp0(AJunJ zA^sbqi;X_*qZ*m~AdFh9=D_mhobiLa404|do?e=an<=T#ZkU5i1-<>WE8_xXRtG@{ z@7!f3JPRnIa>#|efBqabpNlfJQ&@SaxS~aBb3`Bz&GKIjHgc9AkhuE`=MHP^eU`A* zm*wo{M#Fx;N>1L+-T3&Ny0}VXzcHksq-mUvlkKwd86g3^G~KJt3&O!(;t9(=q81bp zf(My|Gp~2kaPzxf$uGZOWv4A$c?zww z{ua;wsPH{cY2`9Q$-zT`#j*ip+8_Ag|ldOO3d7yxcHzWKSeX*Bttc7@>YVyvM9!x(^`Id zj|L(8iAPq)YvK#fV_%+6Q68NhYV@rNZ5%XH^^LnIyWbi^q4()%e*IXlvu;i7h z41qRv4L3p=D@A8h!`JF+p6~5Y_P3T=iFFEz^`jQbXhijh zl@YdP^sS?PBW{9HaoubMHVmuOh&NIx(82Ji5Cep{Gs{a$^pkoJe8d5%_Ews0k$ka@ zo%=QHV$C96V4Rg{GDrH=UZ-QHd;$h4JtZ@foNp#8H)-a}SPJdZd)W1C^X#Y4f}T~i zC_xJads3Lgnr*9stZ}xr<&#d+?TvSXp1o2Cz3Wjdp<>oKd_Jy`$fd&6$|8a3ja4ff zqD>=k)P}O;Uc29vb0#zFWU5OiXM4el9pRB z?&HId~k@YtgMQndG=vzr=ddY-^izp4 zzbz`MR6hcpW=s||fxH{vtiEwf*aUi~td zTl1nv_tA}uQ{jXzoL%tMuBiLN_ym|4d6X6^TyXj1VurlSb`ui%sbgqb7>XFsr6L$Au*V&ha`Ts`6H?QkPq-tfaWxK^~sVxx4o zi+k(YIY&fCX>{KEq`{%O^9HLI59lQ`o~Vlnv-U1wog3+o2O-{c57+3JEstax%!wB4 zr7$*3h2$u&PLfH@XXe4F`7%C}CW7=?3u%N*zt3mxLmW2(FNi{2in1V_e}`Hk^ON zWGuh^@Ch&plRCLw+QJPad3*&m$$>f@F*^MpaPlgympII`SB9TT*1TY0g>YKi6A7Y_oQ_}bpo zd@aMFlL2pBVS{AaD+I8Eo(GAwL>Yg-7>CK`bMA}PheeUYs}A!X-l% z;-EC=Q@r}z2)(y`n4nq6oH-b0B8a&_vCBFAUoEE>f^QLFvSa1g-yFVecncF!WtzSZO(66ArH&hNU3~M5Fx^?GdIb`?uXlMAE znX;u~hGo>Iv#)%3_BhjdG1GpiS{&yPjMhmlwN<-d{J9&NE=M)i`{DUM=!@M)3%3UX zMU8!;nYEfaEVHyzCRtU}>tgKl?sY@7GSuD2tzb2c1;%PuyTV;Ovl;KRmzwET_Eaut zYgE26&Dy%%7_7oeOO_vqUgHQOeO@20Szgnk=6xfihDF9Yh@%vBcQgS0JnN}np1Rq1 zF4It9!L3gK%wkglWj(qc^RZiW$J{8$WKD%yWZziW8ISV3}%@tb8 z;QjcUY84TpWN)M@CH1l~Z~6_@+UjLQ*J zd7V>+;`w%4qZ#I%%d^Ff^>&pci}tGL7-X$m)P;=3932vsH8np$VLKq>*BkXnWvdN4 zNZnFVsB~w&I4uG$A>w%X3y!c)o2><5UYwc~g!0 zoLZ6yDr~Aba#BQ~f_C@@oSSu5!PgflM3c=lFkR{yq$Mh2(8{7Vl$Z3tLt~4JyR}^w z-Y|JcAlAl6BG2e6LP7wgbY%8n-xbj$td008yfTRq$7%>7#F~2L6}rqTA~RFdqYj2I zj3Nl|l1CFYxQL;9lPga0WsFJ~hSz@GJSatI4=v%Pc>J7gzL9TN7e#)s{qp-09Iz34 zi4}Cb%tEArghn2F#z)m~8OS4CTA%GWgKZ+6A|L-@L*uX5@KP(QoO0Q=a5MS0m@>EJ z{x+1f@irp1Lw(Sib23CnHCrpihaVb99nn^yAY9J_(tWYyNX@m#{N`?rLy=Vg)V?zm zbg{a&p$Zg82Ze{9>mPT2f3#Se*AM`_Uum#;AD?h(*>Kj<>5<3?rAm};EumX*XfAVH z2s!?FV76$<`}nBeIRTE!E2S3Ca~(zitT^gjRZrC+>%l=ONLPR5JQk6x<$8SXahog& z>jAJl9TIr!o({FximzY0x-Nk#K%76sK^I()?jKpQ0MLFxK|zy^e%pCcL&?%(ToXRO z*J&!+F=48Ii<*&+Yr=A4cR3Vom%eZCWNAwbeV!bNW+zoE43wk8}7tUKPbB~HX(IC%wi+Ms19`b*- z%l>9_p-v@5ekQ-M|GCVE@b85i|D0g3TH7xr&z>s>8)=c8y>K}er6J@p-DtxN;e!+W z$Yi9k{yua$Pke5I7&okhLWJ^hd3NhC5=;Ez%K2LN1kDAC6-zLE5mWBoPWNkc2W_kJJm1o@@%_ zy>I7ST;O^jWh6xHO8t0(SEC~9!w0S$z6X@6)TZVwy^oXX4pS`v`r4pxkCBW5xEYas zjJNjM1kl`9AauVH5@+!Txa6r93V|CN6AoHhk$x=Vv*jRI+!-V{m^%*|t%2U9?>{X( zI4<;u(SaGE%Z!v0m{; z3kyvVg5>jI?9L;Ax$6o1gJHFodiQn{UaoQyFwuhi)a6vhvNjJAUNCC}u#zY4T1gdOM=)7r0(H<6&veBW-|9IoT(L9~B>4@wcxlrDF2TFv(A}#VfyT`f8Kj47T zh^n%9GFUm7Yj$Z9TqI(9*lrJRzPA`yitbOQrCI2il%o$W9KRyI(RY#d5aU`6RLQg^ z*}?Dx@&Va;(Ij{f{)CbRU=54!ja2ldTnLw2LyeFl8do9<*Mg^3ht}ft2;O9C)uz;! z9y(WY$@acwhJ{bX7X{rPkKSD|li0`XvLNO{@_l%1i$u=9mvc*$8=q@@S1%4wYB5hc zzF+$9irZs!o=K&?&kZ%3e^j`{p(wOdE|wmDJOJ@0thq!lnk@?=?hBvpDy*NHm=MVU z(4w2&s)*#iP1y3*Oc5eji_x@j;lmz#zhTDeK%U(hC*M`Wdc6#g;IvY@3ILMUUIn0g zM#`r1HHJoniOE|BWC@7Ue*(B;27sWpEkAw=kkt+4Mc*)aRNzl40(D4NcS^t7OyiOE zcZw4|EVE8{xSFgkibY7IvJzW)wx4!87CKM$LM8ZMuSvUQibr~(OF>1u*cY}nNS1GY zbxrnU?~H;?CE@AF9P)!Ew7pCLYZRR~0P_Q@tN-#t>^@j!WsR8v(RgN>QOm(6l*GW=tT1ee!bz1B zy~{jaBK^7O!ygu^G~!d*u2*mj>&zyn<(HbmMSkF<(KhL4()_iU5hAMF!|a$DI19_uNIj^?&4Tw zq>4WTKFuPQhLHM|)+RTEG6-#iBhYr=`D1t0?kv3YX6f)l4y1~Zx14up$ita3y1Fq= ze`x)f!@obRz6XW%EM7^Be@8yPxzOj{hV7I(EhW%k3O%uYcS@Kw?%hE#;}=X_0Ul_+ zNiC`nYJxZq$P_*(YQc79XO}EcgOnt2s4`VjK?4UBU<{NM6%|wcST7%*P&funAKL6f zt!}R_7;lxUpLZOygk;c6PF(dAMYyl{03~5o9b|O{yD2-_+&hqhiw{vG!kTMc(Q(OA zm8p)iN?(jx6F4kxam@YuEWX52&kWD?pU=+9G^1xc2ExK9O)V`~v$I!r2Od;M>j2`k z)V-i2;2dbx7Ihsi6eR#FtOmFW6hS|V(UV8%kveS>*EB;1gw58bCO>{_g>Dlv7z;9n zF$TC0d72QxjVrbTSBLa^I(dbj4Zb~@ZEN9B)cCsiOy*F!lq{`n=eh#hPecFL2@jo| zo+o1n(!EhXxIe8bsQMmSfSq4?2f7#UOBF?2J8L(LNFHc#SuPW=pRoDz$~mgEo4;a?1SoF6d8xN5FY83-_I3MrR#kRO0_13agPdN#RXNPHnS z8BOX?;Zjn$A>8AwVXM&9vf=;UgF#IAF@*77C->7IR-Z|(BCR(h;omFF!5^z=DFEbf zJ_AMX>I-6{8S(MAfq|1P!2(WnuGqFz0LS+|`*?q3MeYq@;Z%J0t9P%DGjcGp_>~Vz zeqv6|;*A9Vt(SWaPWNQKu1w*X?AAuhq0FaNd2aVDVE za{J+HuO=u9iH{cEqK3#8rHyuZWr7qfwGR*ec41fld7892=}^N~6BCB_?_>1i()s^B zWlIB8R`;|u@As{iMAJ1iHhu+71D+h%1E6<+>=O;fETauHHC6)pb=m2bxGr<=D|3+6 z!oNVUd1~!gWNbMUX!Aer^8D17Ua`lFBY^h9nAfxxaUd)#F1zWd3(0Nd#3a*Xkn(k1 z-Qr`^(YGNVMU#wJ8#=EBej0L$SCl}D)P1>FR!Agwo0Z^+MBc0jCx0F*M$mdJQcT;e zzF_WAnRX|giT=V=r5MwppHO}Os!9)`o~2RO{SeA$Rp00?U%hIVjsrakZsG4nMnn_< zrXA3VM+i?M2<;(C_aaB2>w#hYHBb-J^vVivzbBXqkwP=zY!L{CJM-n-hpi*mu;W*_ zBHeNv`mGSeG2SjO5t^tF4y2^bgz1>ONNHE4MjN!J&aVJ1Y79?tu_C;`khjjAxjU9T zrEqd1fS_G@K4_-m^V0lxkj}r(0TlU&flAi0M9lwMGVf`Z1Bz962Qxsxu?O*df5Wp? z@_ZaXriupJ9)1KJAi1+06`cE^ty5Yor$o%@ouI<_3Qc!ryX15}ynzYHv^SeU?#dat z)+`LAvzj9udf4FmeP2f%MLF!aC0$=;fiDTQXSN_w52#5kPmg~IOU~JC&1_IE|8EQb zm)2{??Jp`i^Ecdd@3UUts^h}gI8a>P-P=2dEwQCsDcXkx;DRqxD*za*3R*kL^^Jrw zeleJ9GILzlbnt=kmy*%Dfgz-6RN)qYn{`$6+ z-IklBBZZuIdxKQ>)P2P>%?0n7Gec#&Qn2v+xC>&JcPebYPEPQ;gp--3um_@0EtA`3 z-71mskOyd$_6R@@}1-P-vfew>=)++WQ6syGe5D+qNxCVmRPs7Kk$MNS_X1| z8+c}dKx6?InUfK#0K1JsP2POke6~L{3p*G~uO+_ut@?Qc;NJuKzH8|r)k6EgHhi|w zD7KTPtvsbShLXY*Pq_iv%8c8tufBX8k`Z;%BH&EqPkQUSJ^KHBCeCxMc3}fP zJEJ)BH%`<=Z7*mpKKPh=RZT?&AZE5N4|IZ7g^3{HD2>v%trvHsuRRL@kOB2wfm9JK zys5GANljU=X2bhN!ud!c#9u{V57+D>P+XS#{&(g(QQSb z*PQsLCQ6F>!_T?7xue}dj4h}_Hx;sT`^w29FShN=CMrMYP#hwT8t6>+73W&m>1Cd` zVy5al??yD*-1Esn31+szsg%Fq-Tx2Kz~6JMDm+#EnY>*o^n5J+@6W)OSA(A&A5a8< z9^D+6f`Y;W7_%d2b^tV*qCK{f0Yw<9e5GFdrN=eiI{pwok67_b z9NjOe~UER>Z$(6r<2j`TX{k>+X;cM79*K#@G8DBsAN2N*RcU{y0XX3*p2MFx(JjxHs-Y-Axc%Wn{ijdFna{`94P`l-J@vvLP* zekv*n!udZqc#6FwrKMkS$cM!NGLy?-FDIr!Ze=!5NumRgv2^wHgnWtxp|cy~NCL-EE%rAT0LG6way=7ZdW=C2n{B}R(n(1Di*n$< zO0xG}dvRic|ud>=&I;e&vZc1E>+k1TUg%gii8g&75kgYX^ibqD5KP7hg;#5}KRJ!*I~;dd5WG7hrGQS9&d^7>D|t>wAb zCQx*bmhP)x$JB~Qx3yBtt?=KKk84g~2T#Pfev$t%gzfuPYV|xkJl!BLFkw^jDBF~K ztOu9?(CkmOUZEo%R6+ds^1nQRtgz%s$3?8%(;rs6y?#F^##Xe~)?ybPvYG9=k}8^= z70~qK3;(6ZpDXR{Rat8D51O+$Y6|mvSg@uYY46RrCph?uL<#k*$V@;9$Cp?0c&4kud10b>mN((4G)!Vl{ zZ?X7;C~yZ~ePi6;3*cFO{u;0vQ$~D@#Q<|+*BAu)jzz75B1;p{Wb?|7$-kbWpa0y3Q&+$Wd_kqdB@MhLXgAG?tq&kJu9ml6a@LjmBlnW9hLJMKQmNRszpz9KBYvw@^bRznol)Xbtu zfFsSSU9}S5So5Uj{GMCcB0Q1_>I57nRQ-Nvm1(bMyWR(jW)XlBZl2GRoo}6wzc0Nc zIXRgapvr&ZxAx-LdC$4@gD}u7z=42u)_G;c)|QkNWOh80;)i1*xj^~E&`<>F8<*IK zLLd;WaFb0ycVPz@A%_`{i2shS^Gq^_Qd^kT8o`bo7#KLyu!o^gyE*8*43zd)3G_I|b)j2fz<5?HQl z`G7)Oi`fw*WI;H}#zWRTmuS-`SpxC-9sbfi0t))uSofXvFKTam;h?rD)yvCkGv#)L z#h(qnJ3+ZHYjmn$o>1&ovrz&fQ(LhrmB*~-<8~N)D=>7rE<6ge&Y{zT7^ts zr&Tf))KfeFmg9@}?w?2Wxo*`V1mJz`Zg2laT=N>I9E{6uo3c&E4a5%ZfjWLIuW?AK zC#wsiI=j;Veh>0EoOVyRM|G7hU>nBqQFttl-ps&PUG&09^GDIWM3EWqM@=M9HDfmx zHMe^bB}d5h6w7xsXz~OwnjxKx0e9coHWa2;eaa_4yTh>p!rZ(^)mdyo0U(Qlh=Ci< zw^))h+<-6T+#Labk{4_nhzy@Ey=O4?I|^r2#q&06ZQ;IGn}9n?sYrBwt+cO1>-i>o z|78!>xuyMi57S;btDcq(!?1qd7Nq&;Y8x#?GEWpD?TF${DxTI9e|`xP@x5!UH+#uu z!EeTNJ-vs(d#ldVp9N(pGz|){t)4?xe>%4|ZP8-F4eVho-%aO#xHbo1agK7T>jy${ z_5Peb2_U2Oc*g~sdkC~Rf2TOa0Bo%cN@GT{3Yr2Pak^6(I29})3VTkf>D(uUL^za~ z^NSkmT9ka+#$TQ&swr^hQsD_R`iLIp#O%=@tVTaOJ~}X4EM(_8-89Vr+?=_oVU8?dH=!uyTd2C4(#{5Kg)CD zIVh>5?n+zjD-Jv!;OjDf$GuA#p8|vS>v6}I7K|@(3f3RIX5bBn=42`l-RlfcPU%(> zPtwkx9Z^XujGT?G!Mwj^{fKyg1qtMVST+MCmLhxM(y9YF)a<;~Ow{nJ?X|7P* zv}pjQI&}7EophNCGK1!3VPd}ApAe%EX3wr@Ycb9UzO{wy-fe`v;%@K#NcWNL$)_z_ zEl^E(G>)t&19?fI($~~v22Srsl-vLcO%RItcIRB}4@bMFx&{@3JIB#bAa&Xi5_3EBR zSkEhhEcUUON0FLXv%?p0jH`u3RG~@pH9lAB=(=QVI4zguGznYIYTh+x*~1|LNm-ah zKpQJeKB$R1WnfdyR%Zv&Yj^AcpRQR?7#mv~0;}DeAF=5cUbDY2#lP_(D|^$bFD-5B zF@3^QffnqY*u&^ySA(n~g76IPMY1N;($#LHF*qV*Jl5*$UvV}-=Y^Dx1l@FLN)e{- zi26$NUqO%a?ig{`sCIC*QZ_$q_In%Na?sq|>>*!~mpW0O+fQdf?$hm09g-{7B^TFJ z1rp;sHbtIDJCy07hcC;U_mEBRh8+jXEXisWxha}9M(~u`9I~oO8&;JK--S1*%y7&xcx!%LTzQ+Ya$ zs_Y8ZyInMYslu3T7lJXRc^H+re1AXx*L&LVY79@hh2Q-9MuK+yYr3EpiX)Y}W+QK@ z@-Zjoh?>~KF3rSVR2^5JVwurPLZn#^?L&nLf2alx!~K>)%em|;O}sRu)w$w-#@h|^GMGc zwqm-)2KPo6+*uLls9;)+cvo%)?KIywM{cp_Lgx@|qVxX@#(-N12z1;44N6l*Eo^Pa zN9Ozf01N;Q3&Z#q4j_ZTI9UCA4TV?JB(-;&Br166rQ$}86w8H9=k46Npe=T}4ZBm& zXAqFkY9papCy0H&OvlR563a|AI&eS^fHs1uZe5wOL&Y!cnx-Dj6d(Bu=rClHq2JHd z!fMdl@>MfcJS)E2uTnZcMnl+S4{ae6aem5cvvr#*&<;1K zJoTS>%WiLR8w!&FZNGUm*VO-n$t)Z9r@vM`TbWCiL@n~ z;AZ){+nSM)5ekFJ*kcJ7ZGX$@fn90u6v@qCI+4oOzOkPsHCb&R^9KXDZ?Dqd=#r$y zV7qzu&)$GhE#yMgXhMlzr`{eP8eTSQt2AC!3AAh!lsXm`Xa2E99`zSa0Hbjr-Ez^S z^=kf5$7$KIqVCylVQJ$f{0emPpo)$rG1zi?JuVte>8;BWmpH64FobIg=8y0iRG3C- zc5^n5B3`Rney*{Ve>o6mix|8o841_O`8I?y(=@51(dH?cyDnaG+0C-_Rxwksg6z3_ zjp|~Z?dl_E#LZh1N;=jW9<&})ku6y-Zw-s5qfaV%S_`rH3`M7gYw4la)wRlwEq%Py zCc(e&9xvWuDx=|8R=)&42Hmjdd zIJz@ulIBx?=jsoDc}j7wFFI5ROS(vdRIwL6{-Kd$4$T#-^v4|2#w%sAP2FoTeH!Nn zG#+ZfJ*2g0933BX%dF4`eYDU@v{eM2d6od5VQ~;rDm>h ztNuWo2Wf`^*In zCzmL}ZWY!XunB_jx9DLFUGxkyxh!6%ZuI%xur;H4g&Uu|7|KvM8IGRI%jo#C{hH*+ zQE-0(nAHxh@#`qoWu{{4zEjvT5VjlY`V+g)scPR(CgsPGPxR(^jL_!o2RPn7nxNyiBZ34z=0{r;7{ zhak|JrGr*ajYSNnwWD*{>^G1ecVZ*uzpR9v{w))~8U> zE`SHlu2u9T_qx!b*MD4idFefs{Bzj@CU;Wf!wb%(wAkI0vQ1eTzY*H0=n~Ny9^}lA z_I&;g$+d-b=^dv$-;%jVX zSjS}j;yKpsvQ1^35%HEXl@VIb6HL_ZV{coPJS-37jjs1qX0h?=2+bSX31g^Pv|`hx z!)Rzjby#6){_$!yTzZ1bXIm`CKaPqY$na?;T+aC@nn4@+8ln~X$G-gsTeBriOiW5e z2_Uha2x7InxhbK-NhG8aG_&NXiCqz@ZrE#9{S06IHta-Q0BvV0!K+#E+1|fAa`L?7 zzdWSGRVC$k0~~4ak|rSX6c|v+yN=Z>?zk%;qDhf0&hW^y!6(sLsyyQ0gSG)>rqHWb z7Gf*dJXUM@q;Ji5mN+JU&RujpGqgZx9bZbj_(>va@*GEC#UMAVCO=u0L&tGunt=ySOXfbF?ru z7ZoufcbIjnF;MA*meJjnZB7HJ^v1;y82oLhW5EeO=3BCch}V2bt8=dz5+C<9A>0Tr zPqm$mnp5Ybki!Ldo?MHEjfp{6w(|Qba5B~-aL7r<^%! zlvO%iw|P(1#qmk?=tc~|$;AEEmN$=)xbY*~wb{!~q{C^NS+dvrS?lT1&etRs=X&ot z->B~-6CY-dTz9EjQti>P-L+RHjv35IK6UKeE}p9FDc+iNysN-v-v1DF!DEqcE^+O? z=>>nJI9CQ)E|cZR;}fpcb>3!=F0^NvL=s9D>JRuA)~}%*OqyH7nY3bDxrB}#dPiY1=wwK}SHov+vFI9Gxg^6W} zna1wkCN6Gao(QM03G-?|I~*gn1;0op`%7t!!D9v)EF++2lWA#{qqI&Yf7y9(xLym_ zLEbgq9!hzEtDA~Zg!0etoLfNeTtXimE1e6ee}yDaX$h0YBA3t`I_DN#c7oGl+j{zz ziJeQ&RSn0-H>-F3@3kOHc>F3S0^MFb-aCuecn>~pjXKB9&aPaH)F956wPA8*jcVD` z$Sf{fX|eZ|ZbO^JJ_*O@?mTyzpDNs)i>B*!U-ciWX!2WPrNZgM=x+Z?o-pXDw=||W zBz3*cOv6p7{~u)h}IGAS^n1DV|dzw!d-}j z7IH})EBxsXE0=e-BUR0&ow+`XZSRDah+0%~1=BrXO}6gb$??e=(T=S|<T1vx6);}O;qKA!AnWLgruRD-dVR_ojlx0?ac&><%yH)E)Yw(D+ioEA+^{@CEMKJcTcXtz5JLl0=zRPd{J9>ElY-^a_UfL&b zzI5a*a&V<(b6BaT&hf7GAJrYOoA3g(3K48Y=rg_)F`fA0jyLIp3Y^IWs3pD|q z5l?`J1}6#Wri?5t*&07Dj@sm6pJkmd?iK zCrPiPC!@L`-9wJYbOx)A1}v!bYt0sEz%ca-r92`2pK`*~SYV{>1EK zH?xl+83!_&6!1H(aCmfHPGzKRB4{1}wYNQtln9Ld;#uZjhua%x6V9F@jX*mSTo=@u7e5$osP zvg9&5dFDdEiuPA+vEE)o(-;hy0bPQHR}Hgrh!@Qe8Bsz@ob74`0NE0dL7!u6PBWX~hz zk+CEptLoJ*vL6#N+8}CpG5_*IRaHAWBS>Ky+-<3@50KY=YxzQk$a2|;9yxiFdOu!l ztm820Ld@{>lbJkk=Lnn6i%k`kx_W!-TaoUUJ$g#GJXY}Z>wMYlF)mMZRI%%)bMFTL+J_RFRk{%w`U$(vuJw_W+eX?MlM#0*xH&~$KeD37vp zf8kQfJ==t9YkU;Pm;jrS_3f$k%^jp0*9oW&4RUp7!CNGdG}3amuflRa4Gg5H$126R zIseTG1jI&#??dWWLv`V>>L)3I%vO9OWdr05Qm%`J20CWfis?7s#ou2Kn7=~# zjl)%bU6goV{xsv^zdhZmOJ|IS&Tfp5lQ+A_D}^5szUt1qJ2-_Em7QjdGZ~?}@AF2p z*F&5L;c$$flye$MYN->sx$_aXmNJmpJ_L`QB@(GL@t?xH_J>Ya%<^kBsB|+Xp}G`89Z{A5zB6Qn}&16pY?f zRfUe%`C-DBI+Vz_gNpEe80JX*m6f(SOqN1Npby_=zpY^CQjeqdDQ=iHQ@8UM_@gEw zj6LA-iC-BE$!{O4f+)v0v~)}DOwOD+Q=8+lmLUt>U7w!2wWI+++@+$qCkbz}TD#tO z%{tZ%#X9wdN3`Dh-&T7D(l_M186<4GX>Y9@#>tKJpIs=K%g@WaX}?p@>a>aUfH=@K zdw2I%h|#t1c$Z%YA9PnJGiXwZeUo4&andq_S7dAIob0c*J0+|t=;#>7f)Hg~NN8w1 zNV_IhR(d?BAW%@z)q-BR`rE5BDyAbsa{v3Gu-W}k%Mx)3a!BG)u}ZSuyif2_J$b{j zepIJSA5`7ckP1lP(0@$SYIG>%!cW3+G9z5keuh7>j6vZA?&M6*y zXLq?UFYh`iKc@ld_l}N^>sOz>e=GPwQ}*G-G!o#AKb`-yshO$|M$zi7&+tqpr@PDi~ZN;r^u+G8Z*H=*XM?y9~ArI0$(Vm7IZ-wv*8^yDK{r zM8elTAz#mf{6e;FQtm_Ac$~if>Tj_IIz)B9hTK@T(g>};`0B%3o78g#t;=&`IM(6| zTm}Yi&iDw{63dAa2gB?2MnHRe2X3ytw)kE!3~fV9!cGz5r$I>{8OIci zVH;Xo{a$+Wdb4-XXl*dhFm?~o?nE+E;dj~p3Oe1$Tm{_)LLFUjtA!5y39p^q+Lu};0O$p+0LpZH2k zOZj8$0D_^f(%p4_5XDEwr^^JK-E+Ny|NPtEcjH;r6S{_&=E!{EpB7C~Nzc;2V7zjs z$f>vSWuAWByUTohtoEOGcQ#Tpa6-bu{w_M4zdQ{-`@M<-m$C+EzJJqG;L^4Hgo~xI zt~XDu z;%H#_@@JUp}4M`TPHYcziPlVikIx( zSMVN=fA>EAnmUad@KZilic%8V0SldPUW_#IjH&tZHf5(9Q93p%<7*P;#)2F zVNM}_uOD0MdHrZ}gdjI>$#?*8&9^@~5`e#_!@L1%2@`dDf1Km*YxsX3IJ`jB-(4rU zp8MZF*V}&-lt9(*89;!;`4BC>jADt7j?Qylnd~dEO$9;w#a&JQ?=$=7hr2$+PAjgT z=X3M~LYw-P`%u3%Q;x@! zq&46D{t39;(NLWMHODo@fUe`+xIp=S0!TvRf?>LE!AjQw948w$_grmYOya4wTZ=|Z z-&S_?F%xK-*sX@yJV>D}{MOod=|`oZaMbp|W(BmaKGq-;ATc5{eqQvSj`rVgwt7XO zS}R@?m-1uElHbM;2E0c+V&3aq%4Tu@bBXMIVajp8$k}2%BQA1e=)Y4$>h-a;K|k5ze|V$hv#M~KleOZuAx|%UUuH=?pZ&5&geh?> z8Ow;%pA|kAgE$cjuGz@LsRo^`0Q-cGEVeSQ`a}DD{`~nAqkKA0GRA?D7BVvRZ|0^_62*JmU8OLILT+X{QS!>5YIyy= z#0;AlbZq_L-n{fgLn;5;WPZDPbUdc6L_y*2#Rb#z45Yiv#q~~(x8xFYsr&9)uMF04 z@msai)zF%C-8-cQHz zs^^~9VHSoUpRDzPQlh)=?(XIT1y@AH#AKpz=5`it#L-Fu11?g= zUAra_{-#XePVtxAQl~}B*N1HVhedmqWESkC|LOJL$FXn!lnUr8ao^wVdFN6*mH z*T19X)(7rwqOPT-rT^X~YZ&wnQO=3&XpJd#8h=|^TGYLO7J8>bwyoL8==AT~sAW^` z6__^x(ntmONI~cG6E^3?m)0!ZxJldjj!CmKel|Y##LFtvzg!$P(AmQSEB?3BytiRA zM*v(!24JwS>XhE_gEBjUB7!`_rjY5`S7wA^Wt)qWnd&si6?(^G#~6t$bDPzS(f=(*&#Ij1 z!TDIosoMXO9=RDKDJv@r%sFtYq?rMRBqZCf3{(Swb<#D05m)2ucT&_pab&YxHqmkb z7>%Tyou$_v3{nPF_`?G`yk~~b3iHl*&z;D;kYlQ`de8Z=%SDVRX*#;~r+56*Y5nF# zb!04NLHOI&Psc9m;rTSbGUJYf;^_qMi+p?n*a$4Pvy)~JW$6!tyRZ4E)wh^B^Y-+t z^W-cq&rX}0y-f*8F*cd=nf2c3RW1{XVZ8cokalfwdv+Ai#M0HVIrNBR8fD^M4`u(# zNKHY{Z02Go~6W=c>f!6B0EjYG zKcw>0C~n^K1dR$mW@q~WPw$$Itqf`CbCRfqj*bq%ct|@sZd{RIFUh*XLUtIE9fEJ* z>s1m1+4!JW9B*%D&b{>Qh>>92mLs)G&$|8QI_QnZYR2|XINkhth>D7Fpo=6}3BV22 z@sU27yec5LBU&X1BtGjJ)iySIl`H-!#K zq;-C3vV7OQo5dwV{f#G}(uE0gSv5cZ&rn8cUxl;mrX^h7uppAV=P`rZIlXllOyw*}ZUo$BvGo>b9pJjy z#bL&aIcUdkVkUm}#N4-nzgnkTlurs#sEwFZBiE~c37J*Eet>M{6j{a|cU`KZH^5;k zk*~y^XZM1^_wQKkg@SwzdtS2Bg26(wy~GAw0?4jV05Hf-5F1z%=;`fd;o;Hg2#?4u z&b<0gX%)#)FG}*^>AFj!pf^~A$TR6osWu$3Y2s1V*1px}@ShG_RmrIaabf?ZGe23j zBT-_WmyF9mmy5mjQwgByAaJGwmKHC4ECVV$Ox)GsDlmJW^4q>b=mKC&hWP?4?nI0_ zfUyX{ezh1^siKl02#KZ)A@Ton;O_kI_5zyDNX$-O`ndB|j_=+q{OGy0QpKN#_LHoA z_j{~vfw%!~ruZl0S@nXxp{>my47Uz6?2yTF8|?2t7b%2bD6}EG7%4ygPe1eh_keSy zK?{sWMmfh%;-go71Y8I?_ihEi8DX%O%|Vk~%8AQO*1#1}&Y8RP)4%^m#aBad(E7Ev z@lSz?YbF#&j~>==hNBHwc0#z};rcp8Ib(2k(*E`&HAMKM(Z5 zk>*HYKA^q!T+j8$gH}M7Gv|~qKLA~>jce_HeI=9x9=zmd;h#20J{!3%bs-vt+mr}ifOUOBH90Q?p0r?#$$*N*?yLdYb6K4ofq5&E&>vvgzRiy0FsW% zs|Kpxz`y|9UTg2s>zpYmsVDQOIyySqfrDDLQRQp{U;fh6#7I*7ClH)dU);dWcBZX;#k10L0f4)hSMoqS z@Pk``X$37U?Tpgx+tt_W%l=bffLI&BfulRm=(_*!rLGF1nSN7bP3Y~AWY9O9Jj%^hoBKd;)GC6<${8l;I$Ro%{lDL^c_Y{%^QffOR`NT9AYGH7B^MqOG|Jc|xzu4)aB)iB~kh z8vd1~*m1LP{M>=wfWyb#he^XJeTAtKyCxRyY{R4!N9ybe`Ixapg{4tp zJFuPbr-3~ZOAPe+g-Y}kxBzYr9A{d7Y_a+?MO9%(6&N-txPN`qDG?Woi|69oHAch6SczE#CcUYv#m>ro4?R#t)go zkcIgd8p!qh*g7u?D$JP%Kz9BPIM()e>-v*Q%>nVpwk_{xRDG?2PQ#ZkZ^7wY<)oaj z05PuF+1W8bbM_py*Dp=T0vnd?Q)V^#x!U7DbB5qBS*-(Y+*XVW; zqEaj&F_kSi{puU9$7G7(W$Z|bRIwR>w2+r#>WX#V7>O$A@h)T>s~ecopNQo3O9)5^ z1cZhsnp;}lHsw^_u*tV+)jZ;;2LYhH4{nMa??KOKI;|q}bH(Qr3Dk`?Lv0s@JPg}P4?iMV#E8k`FU`kBYht`}r_{hrC zl3&ThbtH)x1~Gc-ww*DDGq&T`C$wzTrIc1IRi#4cSc?gtu3r#DRh|O=ddE=ogKfW+ z#4;3TscTv57p}l-7LP7Cf?(2~!93MAuK;EB0Aj|!4PgDC{3hGmo452b#^8vsL0%dL zba=-hEM2vl0T>MT2;^)o(f%@)X=MRo7N9|5>UXUsuk4w#TSu=?U`C#a-F)ttNkNT!uDP&9$(CmQAMBq@rPy^?lOnhm)kIIZr9#b7YTmX@pqAeOE4dT(Lx ziU!Q0U^@2DtJ=j@R{gS<#rjddIMxSSeXBNiHq_c$D#c%Z*8o)_r#AiJBxNFsI_m*s zj(UV#_|cu4>1+&1(;CbWqxie~dHN^UnV=tngB~oi{tW<7i z{})dCPT3Qp$`pN|oayqKc`)_Snxz0pi0go&EiiuCq(vqzNY7P6`F zKq~$~oUY}$d8T7nfb={Rk2suIXpg)_Caz53v)K1x$5lWlI$&^S>;X`f4LElm!##E! zEKx7s0^S;tv$f(*i+aGg-_z9{4i6jtk(d7d{{Qpv|FQR;VNGXS`>=yz!LBr^c0>e3 zq)D}bih$J6Q6hrUdkFzE4xp5vB1Ni7limp(l&19FlK_zpp#%sJ0{@*Ek$BFG%=3PD zKb;R=SFS{UW$(3Dxz}3vvSLF_;&g3a{8-Fwh*pNtui^K*$59U*Od=qN(-l{aAqPmK zQ&SQ9dNZlu@t>~qUCsK!0E}Ku=E;=Vu$_q8XYPq6IM}v?ar1F|UrjRIw!sAu9=V$L zZBSluB{G0L&$KhO3uTGj<7Hs=iFT|egG8M6k9lHdGl?z~0e%ddhsRM;Z-POWq&bG{|f^$GHeC!}0Es-PSBA@djXk2)>uYhfn*^&4hOo zPEv`%eBQB@kQ){nm}3TyjwG|Q++bgODrP%;1E4N%0s~|D0Ru{Pu6b|E2uPO90x-Wq z9ik_+n!BeJlV?qFvG%2+V!&XRzC9Zzc5_*O`X*ct@pX1OJyP#f)~4?&dIoU{ZHnXM zKwomQobXJmlq*@0eK?Z(LS3Eq89Cmi#lgxw@xw&PC>}4>&V6(+{p_|%Z=_*nr9q8ykiryIUVV~w)`E22ZOSx{1HO2ToA#xW%aM&nx@ z!BJt38i@nQ+{pn3D=+`A_4R%f=;8I9%eB_i=F|H8U)^&ga$qp(7P`{ zbn7d0mJ~`U-ig}z(~AA~CWlJ`k1)&F^wAGVvo$Rh)<+vLTxqV8E7x1H7(xyjzxBD?ctTWpo8zc0 zP@o^;;?Cw;ps#eHK@Nn!Xy#hcVvngZh#5ybi|GWW_35c?xDl=&@U$Gj+dgKlwZrliIaZ3)zVtu8U%@J&)XJFrk9zTFiYm zHc@%zrgHJT$x!F}&ey;3q-jctDyiaFo3l+D?~|N5pQzX}#tthYpCuRHr79XsoSEq}l|! z80NTQE4pBNdG%ID{==`YLnKL~g&b0{u(>}AgkDFH5x5{7ix-1~0e5E)_I>-aPigX9JnO9W)8^=3{Ra`xm7zNv9p3gF3t!Za^YW?ZO!lB?a?BZrbws zhJKLW)ti|LUA5!flNwXS2pN4>UAmOFsK!)ciRoqAwCjF3RMAkWgd}~z* z{Z-H)s!DTyFp~Q=0jx!T7tQUQZeS?qN4lbs`nRtYfzqW@Qn&4A2FMii-`y>?bPU6h zmkY*=h}wMn((J{CyRe5AqNY{>2zvYlt`Q$H-8<}ZCj311M2h*%?CH5n$OW#>*2%D>H zoVy=y_R|MnTW~zB*M7f16>TAGd0whzQAiQoU!TvveVjr>0AfQ2mG&o%-=0tcLP|ie z&EiOf{m+u|)0HVdl`S0s{3-_13c3>ako<(MIj0(OZMW+?f2Tt29sjmVv z5f;9)!2kvY2TR@^AOt7(U=O&0aJfn}Y=CMR{_eI`d=w8fHrJR6A-nj17G!NHvTDMUc06F6BJ7I$ln#lNrKW##83Aw z`$z+DC!dCf247Raogo0auk5fg>b<_dv25|U7ZYeRxfbwdNNU?-EYt5Nf_V;P-1jXA zf};xxu0$(sWeDPeKpfoWf_2y^Eut(uGwlKPEA+r)K7~(ANO%F3^e}m=p!fbbrIaGV z(LA2&*?pD!zq~r7tfRyFmDm&wPz!G#A1?>7txga3x#Q2pX$=Z^Hx1`;D;pYdN4zfx z9(_N77EXUK*xh*k@lWeXy%>KzL|GgCp^AvmX>=+Th$4!R0n-L1L2??Z21qB{+S(re ziwgl{E23Z;(?ES8PZ?ulW02i9uV@Fc4-h-n=CmyrJdU#9uG0=wnYuE89WF2!bfP1r zxwSPwz!3O&%G%nY&GDNN-hUqOzl2Iv2S83wO5F>oR`h;|3>E}waZvvU;ken zl5GemD2m~bmzAaxr2vh0(b16S&C7em34o19;v64H=t8n8T+ zRRXj`U%POiDgg?h#ecDX{Z61%rMlJHZ&N=YZgbrOr5ONatLcJ(K{9G;YFq07ke&sv z7kTT8J=MmX^^*bK(`6wcqe)Pt#P#IKlYepA%Dw_!8R`34fO^555UHuDz=dbc@TVw~ z#zxMqmC5e#TLAC7aV?cfQC{TU0X$k3AX3L*Z$Z-GO;FIIE%h5fcq!1m7B`N1$IG-Zet&FyUz;QQDFuy)8Z@r>d7lw$8jO;3~pLlN1r<->Xn0eXxt?-%OXrPCIE8!EALA~JT z5TK?HWY%jmZ9%9_DP7vyT<#9{29lZZG2e?yn5_Am+S=#=iP*7;I*>g9Osrd5rfmpS z%7Uwl5ve^6iw(njYyt!)-l3tP0Al+(+W8*~L-iKlwOjmo00}1A;Hg}~`?_?L!0?Hw zX*3EY0)+z<86>vC|HTF>$^oIWoA0A`3G$ADfy8p4*85>{QcTQgP@x?BZ>|K&?umA^ zXsTR7ne$N-*Tu1}X1DoS7!mIWv`jR6LRMd2-^YZ6ucNJ7zN+yxaLv2tLa7Wb!j``& zL#$-3DLNw|N1QwfWDHbklAhcDX*9N$4rA@IDA}mT$^@#tuu|M{pg33R0E59%5w&l! zmU7o!36`ZCIWS~J__+L_0?+wV{5jsBh(`AI?XO~5{$5meW;3?gG}~ouM}gZuUTiDA zSp<0T#SWWOA_pp^S1Z@OgBKNi5^Dy#7UmiU#r;<=6nJ}o$jr1kGS#l}&qw4>Nw{CM z=i_s64CeU))iYSB(6lldaJ2St`g*xFF@_bMht`LpWFL$-)bg1lqg-Kn60P^>Ewv); zRcwz@7lTph?oByUya`AxRh4ZIQvEt2*YCG5I~tG!Z{*WI=2!%EkRUx9i8j`@zVb&{ z$x9s$5nX-eLYAI}Mi%NK7pg04UO&zk9IH3Z?ZTUVLia!HyS-0I+RdB(%;@K4r?TddL3vhG7)Bv}JOf2tZV*1k{fEM*R} zg;a66e+!*&1C?LMj-cxq^PO&|hTGTAyRFV=%{HbWxHNulrCdiF4`1ZW3;Q>68WOn-}!a6L?rB8Q!;E5jTIwwkB~f)HDsLBjx0LAzWIT zjz5I+OeN0hXwX(06QFn;)-v_Qlj?aw>}Z_N+_vwTu+##uSYW*>mvPueMBlx_wVo2@B-|<*n`DGREAYi=iOlF zlK=VuTsbK8;}dI@6R7DgjZq9dBXV}^$3|zjzNnuJKHu4Q6V(QX162BvgsXx}W^%fk z*TUqP!cveFow@EPVCgbF zU17m$~D&+w-ys{ z*Vn%+GwH!lBxmaiEGw$!SO;;_sVqgqfBe1pZ!T8&K_Ezk_xQI| z@5TmsmlDhC(AjD1`<3r4d!VFcMOIp!ulxE7;th)`^(~_WD?IIL_d>o1@dnj~B2P7; zBbbI5mS#&{r{|lSAc*QVHYc{r;}z@vZ_QF23Z5`+}zVw>URQ3%Q)jaA?=sIgMgx_4&oc zxGQte^4f{$saqPl#Sd?Ts!b(@(}kOs{cj`(K*QF&{YqKY3lkZln=ccf_6{YyCdKf% z7>|qcX@uhmv#(4aRaE8p-*g1U$jH9e=loMm8lzYNt`CR1ZFzcecfkP&FFsWJBP)LV z;+Y)>IK0WPSOw)*)?Oh$$3kQk<@6@;99?8uYXUpx3v5A=&AW1H?M1bfKlb?$vm-dZ zIDDYyp&t@~;68q@=(I+#>}BEHn}F4GQnHKPD1k1occQm7tg*J%C)9{Z;kVs7TcxyL zTmUh{D{N%WW*`%iPSZM=tK`OKQF2il#E-9fZaOFHYOw8}5UCnaO9H^XjV&p0NU@7o zdn*Nf*9A!E?RCJf`a`tJmgqsY%MWDv0LXgF1!1c}cIjIQgH+QlNc%OC(>SZ%&*qyY zZBfMg8_hGCJ9qc1h;A;nOusH2rQP=Z^|#i&d%b<2UjwJTS!?1evu*R48@Ie({BhvO z)0tRMUvPd0m?;vSkiZRnO)2sOh+NU?s*}>qsxw<2{)>FHnbkvy7rcMG_%_6Co+gm& z0D1gVizuhr!7oZ4R_uSf-&BX?{s#)W_cNY+_C*=MU!&vWxu7DHOe#QH{|#t?0kBLp zi8WJ->I~ev4`RbV15I`?#^K)vnt#_Za%49^zT^M^R;9RsTe*sFD%ee7rh2)jWjBt_ z#H0VCO=i!zCQwyO0j@IUKueh{D8PB{w)H}tq%71QsE2-=OL{g>1|a)(4i1kdKY$Ti z6ykz^5j&lI4HVxE7oCnx6$$!bie{@=n|pdfGv-!TS2c^Atp5eP6JkP>f7JX_2X#DG zaah!D^xkA&z994yh<=q-RgZ!!NWxlb*UVH}m0;nippuE)ynzpyz8PCVS~B+~fK5b> zprGbI-Z?o+JF>qL5jG!tC2ZOW#2p z#+r_)aC{IWy=K_P5T~J@cS&lwTYPXwl6QQ`YJ!uK+_)C+VUg9Ss24)=mmv0_3GZV{ ziXVXeeQDLz)NWYMmUT6BtybVNQpF6fW7>buJEpSLLtp$aF$9=UQ;DVR<;$02i0Z`l zYFvjYUeqFeF~53^Z=9uIg-v@g!c$HKzW7OPp}PV6>s5W$SEj9Gf^e z%VgmvuCYH~YBE8Kx3MxfDp|fgYf4=Ez(LlRPtFEffwrJae0QUyB>#+`gQIr%s>&(r zV^IcMny4FxzeJwfn?qI986bCDx;U`t^wXXq;f^V`=UK?i@>IXsF9BZ{1YfBrBsRuH z62IYQ>p44(q%P#dlyJ?H3l>?G_Im=Bw8b1@>9t!cE-nY7_W_LW1t@}es7p!V0>t6p zoZ(mC40Fx(W;tw}VZBDGoJue#ULE!-Enlr2Zcy(tYXaCQQ7 zvXo8>(@qYt^!4E$FI;}mD+tMj#c-;eoWv4)#9{+YuLnRO|McA*-g0+SsKWdoR_CV= z;Y^4=!kpScD$98P-n)sfa)!9-RAk_(ONmn%cpcWA)v-#ldrCUU-#+CLSrf`E2+cbz zx?i&n9$cG2YU*i)7ojf`aebe>!0;flB#9yakkoB`;Sdv|v{cu$;eyKb!?!$3O8!**MS*7LZLASXEr>{me@_>6bLpMs?K7%T1wg)GG^ib;ik!#C|Rjx$sq2y*#z5D=IQm{D31- zL0e6Y9w7e-5?1illQ~E=Hqit7V3TDy_@;{bEtWb8)X+V3xcurBnlxp%>&fBmk=|(s#934x^m46y$TWiVaS&J0oj4uric6m@ zF@^jaHg#$I`t6(O!921h-XBnrb9KbbrGHgkj8v*18RSb4KL7-U#Ekv8q~etlBb!NGyTWd*F)$vJ4-#o4mx zfAMN(!K*c6LKZe(jRmn7a0D7j;9%g@uZpbHxg)nmW)*ZWJiVD;HWN{p^vj!hgtTm9-!RMgY}`X$YWk=J{5 zRgI=|%Vf>j_rS)Sc1_6rfiqkb*fFl(#pWz;;mx`dOzMX=?&m8SNXStRwotDMHC0XM z+WssU8Tlr~R|#XQ)JGgrlC_t=4yp$QXX{!UD?AM>o)-A(w|tQ#oqf5-V98A) zpyU+4)O<Wl?=MNEUTbc*AG1H z))O$5Kqpl>aDbz^7szcQ97?F@x!gP#2kc`-Z0E<)M@FQns*e}1Y++jNx9kSs;bZ++ zzaPHQT}{MZjp`}Lx60zuN$>qwm}rS|PKG6qhe(wI69cEM&VFf^)8nlW9$YGH4W_H} z_IyOIyPAkJhpE9G4;Lkz=CWnS4GC+@C!WTGuPmjRb)JyU77jcUxsjO|8F{K%pk{S@ z(wACJH|rbKGr#rx@EQK$!~JV}&M6DM*AK4TJZu_>BufPk{ebAj*`>vR{bQ|xoFxgR zDi#G|gTe4!iwg^ZrQ8|^_Y9nk2t&=^w3F+*+@$PuXk{pT~FD>9vmM_ULw)>vX21Bx$bO_xwx1M+xaK+XTzEt=mSt#RAfvZTog?-#6VlQ9T_#QP5x? zF#*3dU+KkfJ^V6mIDTzt-N(4YqMl#4cwVi~1!tZS-OkSUL$LqhLs>I#6JgZNRcSLB z&~L(c&M&s3;%0CpqjWyut?TvThUIe6l-N8ouP`>jse{laX?H0VbT=1%HEFW-Q+lvL z!*&!obH?mOsi?DxM*X63m?7ZpItn2-JDxmmT!F@}-Ro$b6)+j^#nm~Tj;tw!Rgy5y z+LVAyp1-&a@0kIumi6aQx5gAI`iNm07BkjfEG3ynnTVuR|O|Fxe|IFR_5O zt|T>>mFOO04NK~e^AzMKJuJ$wL%J2yp@iIt-ndfOn+dh-jVb)^k%)2<_Q9U!BKHM)$ zuqbW?TQ`jn2yM{thteP7zvH?qi&LEF)m_> zEU0>pxW1F)+uMUJb_-!pTXSK^3hZF!$X#q8d8h16k!9GSOm{~=kl&-v0I5(zFsH0? zVx9nO>m|_t{K25qP2k~|mxcz8(%l7Tl57eElX!ch4fc&29Em}fSD`Fws^ro}1O3bs zg9^P6=An>e&BLH*)Vw{ECJ*ow+a%?#8di5`W^t>N&7ZqHd-6eV7)55+-hF5^rtCXO zKxH#%q^n!chnQLY5=@l|-fb6fOEy?j>pLhXI!rr`D-`6atd5ifJp6MdA!^o6SB29I z0%FZf4e#F`wrPe#D!VzOdhws)3><7-=T>;2HC@|bqjB48j2LC4(n-CNP5La9B6v#Y ze7)AF7_DWa1ffpiUT?IV|0t*>mRM+0tt?dCP3gkU0Lib_0Q~JJJ70Ej;e%e&AQVf( z6u2ym7a5w;jXG3E8?0X?<)pYhTeNfymFUjhKM+TJoh&ZlvfvZ?fPK+qOXY+Hi87Q< zk!;v^|E%E7I~%jKbFkD)=L?^Fl1x#RmQH1Hf3P~3UFtJbns?cIw#z$iJY6vLEyIX^ zL*n8i1{+RNEq_J0C6?YctI@nvlO;H^q_o|&Q242?fkD(R>(-84`y>MG#JMMRyxuQqC|+}?K}rDaXxy54wdfYPWMVc+Dh=w=ZbVPOuAFx;p{rC^$xUs z1o7$hSd@XkN4|K{H6@&i*z_ScJm8KBO+=c(a=mVHX(@XG+R_3u zRO$VYPI8L7wk;oh&BL?G3@e@mCw>vmj#~G8n}|g+3!Gf%X%Emzw9?VidJFla@Suk* z3FZg2;DbQToapkm0BC!n`ZAXXT-8cMy~W;6@jj(0Byn#rhmerg&0z$Ib&@aQ(+{Wq z=U3Wz8IVgTpsn%8R}<&IYa&EB&%O1}WKfgJj#}@HwjQa*9%-*;XR0=edM^}Q-N&TT zl{=<6U#8&POFmeKJh+cD)L#srH||uMOoA#f{%(F{9Jl80@BjW(sy|nlcR{YH!5i9J zts&!~Sbd1x25X)3w*>v+YjrY8U0gyP7D=LvW#a z>++q(1a*`|tCyu880yaNa(p65iZ9j0>bl#sPNwl%Suh7LrgS)ggj*5u|AM#dA-+6p z`9g;?#9$KJdmBJ96xE_qMrel^)R&{U*}{Q?c0-yWe*lH3g!6*%~E z5swxxJtOG@ZKUD8P;5WZp|M>haPX+2=zI0By&Ey--wOxmCP}7Lp_p3sx&7t(Ag-UZ zYBeh5j9Ob<&6^K3g}P`Y2-E7XkJh`h*X~_zsH%7FRp-o?a+8{Roi}hac(_queF9SX zP5#jc7;4~IUuXy^pLALaY&mY;5s*`zTCW@&aX@12L&$98*z!y!M=P#{Soi!R`i;T# zloytS6&XS&8Qji_f!g*-O(o}z6S7X#+m??!s`PDzRj=-{eDT=0@Aa-fcvd39Q-n^P zHTtbPbMCePF7ueO7D~(=Q(sylsrc4Gy-vUBP-|NLiAQi(*-4C$w8ioloxwOS?Oo5p zI!wtpP;7lDOvJK*2a9AE&$S;pQFP}w?ELNbSU2*@(AEA}`CyLE4F328P@O_}XE(;D z;xW)!jJMB!@VL}6p_f*y=Y>^{%H>E z?EW(;4C;Z)?NI-EOS-kxw@f0uT`ZC-4c;Ri7^bY$!V~!AO2TXZ&nh_Sa7koTJs{l#UXJ-|$nEb%tA8Di#+pm_9Hzfe3 z%YOWLOM35C{S~05ml4!*>9HsKrn%h^~;Sz9w#D5nC?r-3R8VS$Xw%UV+QkDmmAKdaWWQdcm4OLm9X#x zqYnuIdrJtN%n~wObyn@|>(0EU_qBk@72jQ?GM-F$Vn|q-#7rJ-j~^Kr%ycu48|}9N zqZtL9=e`|qDaakmZdI&havgqad=lP~-0diYO@)6hM`_ay;JbkHwtXMV<#TC`t>Ox*E~m%79vw|+&6*HnN1{!k z{>6(9U0S7vZgt(of-4Un^nO-epF27`6t;fS0&eB@{&5<%{xBv^Kym{20#?fAKKmlA zVijGegYJC0l51pSl!6!2Wh!=As0qg8m_k4!#QF94$!BD1G7+)pj%iBV58wrzsLNRDj_W%&@k)J>Toq`&w$yxhg3}LGL~JD$iO|Y0@#!)HWJLhvHsDC< zH``U(JMHE{#ib@$ND>S9h{D}l1c40D4Ig0Bya01O$N9K)BsDF~pI0aM2I%Zsi)c_y zU%JovxPeLE>BI_gniF)eEIT^qmp;VZ7Z0N<$^P3tMRI_gNc4!F^YcdWbBa+gtQ zLj=w#D~Nfuy{JhkOJ_xd2wbQX_dB=NePMB7e%EB~KS=pe74S}lue~h?w;LDn41Vga zoIolGS3`+wavY!GCbMvX*bt?Z=km3%D&m!r1oerUK%P^;jQ&?737ABa$ z@NE};T8D-4_6z`t--3^U;x`}AV*D-Ya_XTPg{y63NeWwNh^Y188m41lDMbfW1^$kp zbYKuzPZBY= z%(tzNo@L^E>=GAL6_hpj#BU*E3?-h4Y|r2}k|A(U;J#X7Sh5_2WU)ayS0pC7vy3@z z>80Ply1Ixe^#j9wtDgS0{R$iG9BZqsFN@ImgPm~%wJ{+$7-q}_di1@ookJaBRz!3H z)ju3HUzBhMh|VGycdqKcT*H%g1EBnOLoU8o#q}Pz!|=G34LH_**fXva-?WIvDv>6< z!Ev(dF!#eSslu!%2+7v0EB!~NGQDVg$}>i8AkZvaL5tDAb-h~ z_@%na<7WOBNTIY>COAr@_((HFAz)%+b?kaHcV5`e_1VDSN=L8wtabt7!1CDXy>kuE zIoF1J&iN+3wQ=ikF|Au4pRR85imxuLdc-3wD#97&))lV)yWg!w^}`I?VHK%WVglsr z!PN`XYbJFCW_QBZGfr4fKt`%}DJCJurr#dU`1U@e@VJNVDVAU}@6TxG>${|5#xn!B zlp3ywbMGs#9chrD4?W+!I8~T-&T`jLd^ER*0whH{5b{!?-BLBSmtI2QY9A7L_1Vsw zE8(tlU!7^Y)Xs>&Sy)*K0~AKM7$v%Q2RJ11m}?XUI*>>Z+g!4A1GG%hpnp9I6>ZI) z6Dy>;O$1Ii;dq%ZC-$C|3!a`{vGKJO(1YhAnLeXn1*MnY=ycA-smYMP<{6W#ufR!Xh_tMHdAK&OWaevLOHXV=G@C7 z>pB`n^73vS6XwBHGt;h8saQ1q!%0{-6HdgSBs<}qnXqC1dY-V`oMY196lbaAuCuQ?aI_ zXns3WH3@5bb>9Uts`La?#b@{RN4msC>4vHXKe6tm+rjnTL}D0|2pn!vr;~mq)jTy4 z_K5*i5i_mJPT-6@YM>)2DfO1tnm=#Wc(%S^VldHcvM<@&QfoOTWWnQ@z;sn(wNe!9 zLQto{v4Ea}%hGNGNg$9tXvRnr#a*_vN@yHGv>rYsTLIV)psP!>9s|2pBWUtGj+=#^ zR_|B1+TXzO*mVUsv>BRbMAU0}*|@_)rS>xM)m1Z`ABuZwJuK#q>yes!1U6$bs$tY>;7<)`3##QAwD%;SicUkpf&J2eEv7qxN zQ2D(lprOA_8^yQ}`uGl{jkSRo!T-F+cC(zlh&?B#5 zZ*xV^$A{b8dG8#BU6|;sn1g5-U0teS7JcaC%h9d&TVXkCR!v8(fs>SQyqR-j4YPoa zYn$CgSKwoeSPVq5*F$$5=&N4O7uUVFan^qTBtL&Bh?3D-g!JICSjk@UzRACYhHaL{ z>%*1N>4Gr|y{%xQglhOBz`5bO^Te|c%qvr-3$DXbIS!S6H8k-Bcny3 z##(YWx?BG|-GgGql%Cml6#Md^Qw&vDRqMv z5hAARk|yKs6mnk>EFp+rAP`?>s)Nct*; zMwgFB`lpKPoVVKj=7h1sXyQ?lBEk z)ZRUaS*(TNTK0JSp%X0iSsA6N$9O~WL0e1HB#32RdfDv_bDq$_YK!^-113us7kdgF z@VigKpaq1D`*QfTj}&`G&XrU-=SajO*H?Q1U~rOFUEV)?8ngn*EqV>#35y2JX0NK^ zl@hgKG3*KFX8_OBoOhLZ6fdn>t1YMZDOzhts_xscn2Q$1H2D--Mf#U(dOW*1y~*^$ zeW1LVqeB5PRV+O+oJXjSua0MGlcmGB4NL@AEuFBR{U(X3;yox#YdgKXkR$*#Yn6s| zNZ84T+k8z~zSNqwY^V!sUee5(&sZvrTDrqgH$}*`ta|O9pT~W_H}703r?q~c&Kfdr zHja>W+^r&R$iEWnD%n1P93@3sR#{G$qxWF|p*sbkcQ zzuKb_<@}&zWC{CedS!dw8&yR~DSO zpX;!wz}J>Xpi@0LDR>2_qS(-zbNu|4?QW;_%^J>2#d~)ryfz!Np8Lwh`I@f8_ zOMCkmkt+}mKP80%(H058HT;Nmqfr2zUP&Qnc0l>OdJu;}&7W^*N9wS!vL0)7+}w6#0$l;?S@J)4dAJrO2Ohv)+^^*06|)%HR9 zor-}z{`04%%t3p-fJI;4YU==@L-&yMdf~CzAb;SXO3y7W)wBl;hDsM-a-04@!1=}d zTd?P)nBA#WWD)vUo-kLXxDGMAxsR=z$6-30wK46ZKX<$%{S=yT$>rDY(!{>w3Rx!^ z{UB>`Bgj53^NxpB4u}w`cAx@HP zux`+zO`%6xpP$A`@7%|1CCY(&nJhMz2{RlRBUT%@uSN^NKCLdfN0myEZoF9t;c#3f zbq)>+nrf$ruPnWu%aI?h7~+g%7swUcQIf+~1@P@U7vhoz(az3#PR!DO$-yCQJ#+H8 z9QAVlcdDfNsmug+OnRrVna}2r1$XX`G(?bc)abl}I6&e(Hdt=Tc}%JYKWk+UT589w zc;R0>_i5O`8Y2{#L0CrWah%A_eOT#-~PL;5g-Nz!u%__#FQL3_g_z}*)-P%mk z)>^;ap;YhXGu@}+>{>~RJ#-z zx(E#j#PLbJQ1q;COAvUD>XV{&Qw#dA^DVSAy{*1h=xkLp75Y_Gz@ zrqn88Y*l82KM3r_#9=~lawDD6#X~@^y&*QFMbF4xPTDD~=Qvm`J9c(RO+1a4+l~zul-Qvoab#L#^%2dzp*$)3Yq)_aGz6*(k_yw2IMbRNi)VJ+Y3!T&csm@6-7mled_RN*Wqd?AUdQb8XT7N5(9V`=2 z!f?UIy5IfB(vtWkL)XreYA(=s52poKmT+YzhL{(&kjF|!JKaiJ@`-k9mj2RBi4*9N z;xwmXH$u-+BdtGUTB8{Gcjim!375=zOJ<|bRxBwW^?}Ndpmne>5!Z3ytytZnX=$d_ zlj8h70Z3NX>iv@MyWHM!ap#3x%SwWzHS`I_D{>?p!5=dYS7t*4^Z<;a*G;;U2|SG2 z>&2Ahj{b6X(&yw#uO9z^U;{U?pbJ_@06y`=9^x{rma}k=^Kpc7wCt3iYuM)ozz8DN z=1&lBjFK>Y^5mp?6}nZ~wmGW0-jZ~+Z+<3aHmDQJBAA+gBJO~92+`8OM{F<4TyKfZ z!n6=p>LMR`1ukH`DsG5>)>-|Bm}6Dc>KF-n4^0plnO@@_p2kG@hY7?c*jPNMH$SM) z5KL~I9u7`&U$L_8o9ACWZ6Ru~57oZ~UjcWKkXiAtnYdrv+6mP^0~P3DoL~( zxU>SUg%8R-9inS%YY8$YfM?mRP1>aCU5JFUO^h{w1_BgAE}qZ_ql8RMOz2eN)+pfI zx-TGsKNxm^wS!)Rub5~R9&fCh^F&>rb}4644|w2}Sb9FGH*&_?^E`}2hu)}at8?&W zb8=(93eXmU3D#`w?d<~g<86}C+Kr)kivXZ8#d^gEw4u||Ppm$gt9Zn7DM^}S zqC2zldcE<$+apBYW&aR37%D}Ej8sfTStf>tS-f@YPR?8G;mMkvD$lXyj0|hkc3X+b zn+Lc;rTfy_fodWCDXI=DDo5bQ(W?FfgrA+m!hsT3&gPm1{P}(VDE(+JYWgs-pE8BW z805N_bO9SmuW4n~pbVGZ*?a4KlcIQqPPuj?Vug2Un6fTvEuWONm^3%Rzh?JX*ZGlr zzePi}60)|mSlFWWb8he>P_S0&tO|5EifLMb%cDfY50=+e1T`33Y0taOQoVe5klsywbp}Po4Bn73AikL@f2+^Bk(axZ&NKhvA+rPA5=eSZ+;=k5tOsMMXCI4r#y?3}S$ zD`;p(C|#CZfB0VGQAJ-hlhM^aZUv?8Jc;3Y4BL>TaYal9cQJ&s5j*eijNNar^vGAA zj81lWq9PRv_DENh-<^ggegtmp!Z; zr+V5gTOaB@&{lS~~sx5#6Oi zeCztP@X9@lHgn?z!Olu_asA*&`qB;5UK9dq85!M(Onyk$oPEmM2N zXJ^|vt+*)o9LJ;H(qWt@2d5F7m=-#|mSQy{yWSa~RJm6&$T-m5>n6xJ6}}+Nj|F&H z0%ThKlVlUF&$dyrNk<1AOj9-{2$H1|x`m)(Xf+>Y)_nxDm!wp2c>91eCASiD&xUS* z4$DDjWc$Md{Q+kD^r5Vo(*JU~=uG3r6cAAvgtvey)J4mAb#Q<-V>xcG;Xq&Ehh$fk zaCa94H8tS{IO+X5GK|0AI3wBWHYK=GHE^^_>?)ZkB39Yv5Dv_IEnLi1U=_Kk=g;1J zih?fa7rRtaJoRlBbYUC`awp#CIF)#Mdfr0x%||5mgW1@OJcEo4t;B|isp~D=_rew< zq&x^M)1ak%__sdb1%I~Lisk!hnE)?%k-MdCVc+`%3yNjhM?>7Su}fpN1jFvovDq$m z)(@QtomI!Kw={57`aa%1_=+@A`9YeM!(TE>(j`JzQkFQecplbwA2iGfxQc^>j3iS0 zwYUor&wkzW8^2SUe?IVGCEkUV?0wK%jCuE?BJCfA#)Rwm^IqhQjsDAb;(PY_CNM=E zEtC@H;#ljvE#OE*IuUj?`XiPZc?`!%4gPxmb$(*&+J6EyV@yis_zA_GBus7pr)P#% zHx-JE#&u45So?dNn#%ex*smrn#aJ}?YO4gR9ViU%edYZZLMZc8M&t-Hyn(jcVz`Po zD*p_&-FzfUw-3D}QuiEPm|C+kJYvoiE>h4%ER0;8nVr6=g^dt+wE8KG;0TYCM6Y{t z8&`Fe1T40tL~M2AL&uxmmt`p9lgd*}H{w4wR+{Yw(n4{;LS$0|=b;>dg@rTf_p0x{ zFs+4+95RxOt8U9s$gw%-S(h}SkE~}k5NB3>yJb@XfGBPNQb>+hX_ePENCggjC@Y(_ z{`43=weVD`UD!ik z)Nl3x{nOssnF8F^g6v|x!3p(p$IhxYpT>Ib)IyUd;;#ky3WP0Tgi6({S0t(P^3RH3 z$K3jwjvC6WBQk8|aQd?Bm{j^BZYwWnXICwmI5E32u54j)z&onZy)j1@*b|kPzj1{A zx%zC^X~2Xg$w7z0`=5Sy0tE-&jY5F^d9+<#z4_$x0Fy3d&SIE)1&_TCR25h75v$K> zs;_78CPB>NmfC#6IsMY?Z-Il$WuMdq?$10OT)?b!6lDsJPdI#o@|=g=tpD<$l5~fp zf_nj#%N^G#Fmcw;a9sVkXLeX*RX?MVLdl4Kv;jq;@WnmHFb%rqnXQxZ5hjQu3joD3ctJdto5)bW9bu}*x*bi^`CTNsAh*K}7p za8cRrpETg63?mwV1UYEb{AdI>|L z$go_s3J}LR8{e%>nvQ#+x`c9X{8T9f77Y3rtCc3r)$n#HXi)Rt+?x#8NzeuYxD89W zPKJXKK{H=>E!nR+rnpq(J|(hA2u5@ zHf5Rm;y+)sObRRp@%F=i^M5!04`LD2=u9bO|7u77dx5&*LA#68i+%t7%dfs^(g$gW z;unnnw>>5S<~cOitKa@_3*Q4eDRV`de;G$@uEO_Ky&nY@gLL}R_cJwm>m8WQlM>3x z%BrJR`2EceM9B0SIQFg;xQ=QF3;y$VfQ!e1tHrW>C%Qu@?gg}y@$FrJD@mjCdyzll zI@i6G2@Bws0OBmRg37ud7}ehiXBh`&hi^rHH=12xG9YW6ga?`K>;i}HElO-x5G@9d zfDqVyl=0H99}h;+Og#SIJ0b5Y(Euhki}oCATou*xooWJfv!FbZPZ6`&`OVPl=r1?# zKt7O*6I{7}t?Xzdjik%`a}baX4pRV7zzM^rFaG_o9`KV{5kk$MBQNA-qkmn@>jmV7 z*u<*yS3R?x4g$70W@F~uqAG30C|A&%fV>;)d_&U^Ue}mLP2i_BlTfZ zIebTmGF;fvwV`XzYbyjlVoO^qu=n@2)Dd zR8fL)P-*;5XR7(ppQ@&8fKd}=p$8bq)7zopZYyIcsd~TE8%+z~1n5~>-28Jdnx=M~ zRf=TeeyKy@v}ab5Tu>Wm`OcLvBfdMv01^eRsUaBdGM+%eWi-rx-3l{GAtOi?L7n__ zxkepmK#ik-QOz3-l6J9+{pw0*DH6mY8dDo*tzmDBfl?GGA~G>EyMVb2ihDvXKUUdl zalDk?nSmOG!h_g3jqfFKS57s7$h2Xf_#{*VG*(b%bn8gUfxCmUuoIx15|fDjUjCWx zh@fENAbqaAKk@raDbSeE{^LLV!;W}Sh{I;`bp9G?HW^&je567lOdHIDUafXtl`AC2 z9>-H^Pbeq0GyZ#>zsX_Ed>bQ#4hv%9`mb6j-$YR4FlAakN=j=FQ5^{VZ=F)$ErZ-rC1}@wm`m^N&GnmJT3> zuI$IE6gXmCD7&PjB%*q>IT|;a)kvYhb%3+{Y^DC=0YqJagm^V#YW~#j?^~O{LPiaFyZ&MOs&0t0i2##TW4s;mBNC`1TspW5y z$mUt}V2aKx2*N9Sa!mIsd=E(xio1PTMfemY7ur@2 zrq~(go%p2%V%rI%vHYpU?LTEby_81I_7Ez)Q$0ze63kbI4%GyLVJMm=i{I&%-t4BV zqpTkb16V*mP--4HFS?O%{@XLve+$?_$s<^Ai|K`2O1cP1!hhsS&zZ94M^ezelD4>l?*y8`m&NmrR)RL_E ziC>zC|9B7to)TZl@E4D^_J&;Q9`|KD~|iQ@mZi~6km?{rbE#{W(iwT}ItGNf9K z|Jg2TU+{mnYjZXJ=W0+H*QftKyBd;(W@q+pWEys}X&`z=+zv&pv`F794>?$c#a-dhnZh_yuIV`vNYHRC{=O>02jmy$G%=-}zg(P|;<6eBSiz^9KRA@?Zm}Hrk+r_iL%y z@czM>Ja7+$p4$fcHinss_Uv#$C7g!cka8z`E{%nA-rzvNTPp=-lV2AUGw=>sV6Dl# z)&pNAUF6I!L=tg%(1s#cap|==p^VsSz1eP*-n-p%aptShR zBgKz=icOc@^yfL&2lA#D<-G|pzp=X6I=YT3P5XTz@%g$JEJA0W1!r!vBA|(DV+_{o zi#<#I=ETY~N9mMaUdh@vxp=`uM5_RKt!eK(oI#XNLa>rqBsaXh z>J+h$#Zh9$$*c?0B7y&qgsYn1Ln>?a=iXOtkDuAWW7x62Qhvcq{CtVsG?P!DwJDS2 z5!4!)eRk=?e%_--u+o)A3tyi`shK|)oISqW1wU>RKQ*8QiT zWn?QoPKc9){Aa_ksd4ggAV<-9!rG zQ^eCmRd)=@?UqeIdlB#=@kGT2>lzYun@05#)2lN+Yf4$*5hal%g&lW-DK7xxwKXdI zN8YZen5;kX&9igzj!8}I|9@%nUw+eJ3>2v=7veF(f6sNk`JZg9nv-@JCh{rjtc*MZ z)2gsz^IoFu1y@1#qr+CEIcsG4Hv3&4Z?-Q>#;g(6ejuDbescP*Y?xw_Kwr9p+h~$7 z@|f;px;;+rei`}I{5}{amm7-9a%c~UP;;;wq!FL%5I0+Os?l{QYqMXW+C)PQo6G?<1+hPd0beo1ZmOb8Q5OiZ`;W!B!^FD z2Cu`H$Syw8VUMBCJ@2e?G-vwnb2|Hn?+0Yo*#TJDKjDWH*)OoqB!jWNtGRJ~1eBC| zYb~m}#Xu}Hp7#9$iF7+84&#{P)Z1&xUX9$v7$SG`l*jX-!as?6f>2PBap4t?BIHOl z=L_ZLe#h{ej~u#FdI)RJxT1&lNI$G|jal1igyKST2uSLbka$%QZo;tSH-Hcz>I@s~ z^O)3_#Q(Wr-E}XY`ieMWmwL&;Gfs%?-Q)48WJy*C!SvyIA!vM#-ZqYn;9FkXlJ6}X zkM+T8g@??`2tFKZU>@a4rW2tXpH^2&JeS6?6en38GL63>^tY^@fa??jLS7#IbOHQN z`?zw(rXNSRuyW;jp=~oG1QM?n6#8MFTQv)#KFURSd1VNZE`?~r-W>5>56L^Z+_$~9 zSvkWs4LM%DJ9I%sl3I~iuhR-&pX ze}7#ae`Scpy3v^KZ2ZxVX%;cUNCvQW(Om<$@E}WOR(Y(5Q-wf&rJ42`Vt7KAVVq6u zpLKNp#ZCUrT?AMtrtI|((iCz*7@Zd}@-?DdU)~WHmt}ALv%D0iS{qeeBXRT}8 z=KT*4^A8*-&xY9572S5yt;Ep*H$RNPm_PPgVkv$P@mpo)1u@%%kQU z_3E{tjAK?GzUFlo*eY)%q>t|vtJ{+f2n%jIAR)icUA`6WYv(>@_hfP0Fl1)D2^*A}B@E_2Alka2nVv5yUwzGwAW3RH+VDCax25tIdEnO*WR?TuX zaA`hW`~1+slIY{Ob9zI%F*z@jYKMM{K{^oND9}+%TyFh`LqD=5`01!A^QMjW?(%j= z<#N~vtSE1OD@xyL#LwfS9WGA_W}p&AnV4JLU2_7k#H7$D7p^1{&)JCL9o?53U}|W1 z!=}9->7#H1jG!)b5qDLF{+ao!QCUKmFC}!1BrC*a$SQ)amRP2>QU_81nb55K5NU%T z_I6!ClcYp(oz5lk`JTKp%T)c#*elCe+Toq;Sb$v5$gA(M%UVI@1PwEw67DaU|3hXe zoOR7EYB+J|tGweV>i1RISzSQ!&J#(n75RGt3lze&+4w6JHl-ci*`!pPM)=FaLUKKa zCtgf{M0|rNpD2?OD&NV+E-#)HWbT9-4XpztLSutbjrN$enT8&7Rl#ag@r;+Gvuk6* zMqIt9W6>rhvMYegRe@20CzR={RO~_odsJ($Xz6?&aw0!o$QEqfC$s`|+V0{>CfKEpfV(U`jHuyWtSh&eW<@0Pakx8h`Ml>fnEt81lTiKV{eEV4 zv^w`5pnG##m*sQkP*{aa1A=> zKTy0parP>a2(+SRhxblr7K^`xVf0N0y-2t%P2Y)w326Od-mWa?3^+HyUsdXSQ90`N zI|pLv6v2TqI07BC&0<|48k%m6o+xym>l*LBYti_q#-F;r&{GC|?K&QerLKqzCKM4i5+MT^bs=0o|3Ttfpc z`q^G^Ui=m&OL4Y38rLSHYTYOX0x1Ca_x;wOgx_pODgdQc?j76L2bAeSu}0&S>$ z3El>%^5H7z!u}vIQVbxtIs>%GcJAk_MAz(D`qLW4afLcJvDgl7H4w^AC@LTMVOIZQ zQ|JYjYeBlp<}V)&=Vq=A<*NF?Eqh(=nj5tweGnJxYe1;l;-;e-7b{&gPS0=U^0fxL?0+PVc z>9HZXIsk9_!Mwi|cB??z`PaAF4qfsR3Jr9ldgJD#j=t{x+|~pTMgSZDAAnhntLXR8 zS_Z!JgxT4b-L;D25%A46?7|#E@}vd-=o(=y9!kH>d)n^g&h(Op@$r0BDHk#IH8EUicMAzdLFQcUD4n59Rl6}U=!#a zG5nuLrP=92j6`wKC1>N@;01~EhX9_sQ0*y@PT3OB$=(Dw#gI%@S%Xz)a}1V}!rbAz z?(T~yCnpF3cfKA4*cBrusD7=9XS8!u&;j81JkF;U&a*Va%DNK6I$AMZd!Z96>R7Wq zzdaI8zpp0i553**=cY0n8|YHJC5w(bQ?{5FzMi!5k%)uIt6Zqy=iUhxZW0`&n)o?4 zldrPbhbyZ=)LxVA2Z@v9(KFFWvb_B1LQyiFZbQ4nmE&(@aRS`i(@x_7;Ryrlx7&XE zj32VyZ1GQrlOHB^yZ_A)zCC2y-_{S4I^B;jsib(4-aju)1*Iu^s9YM=jNSZZ<)){2 zXuo}yOxw)z=@&a{621ul@cn&shQQ#Nt`+#bMjW~C!sa`k#zu5ivE}oLTr6z*?e=WA zctbSZ&0@%TE9LoaJ22a(y!_rI!ny%Yw`OZY+2?$v^bkhaov<|MGuVW>1$ERYrd_LS zb!1j#?9JMUlPo8*pN*W$vW{$}Kv-o#j^;8jnDXZH#H)9vT_rpj317uNa&Ui_>UpCs^5;(1t!iNCkJ zf2m^tbC>#N)LqguJp%8I!BF>7r%~>GniRkZf|u=9XAw#m`YvDcM<~4ae6!$b?hQq+ zMxbdq5fJ{;V%{WF(ZA?MGqhT~4V;;FCu6E|K4IIuuFYm>Zz`svI8)QH!6S=XlE~ zr8uSlZRXH^cP)s_7_v=>f`-f85C5+qCg7qJu@BbDuypv=@=;vwd>e%xJ^AhFiI@j> z0#ZIdVo*4J=KPUUhi?MRm(RaxPUU=z9zU&Ph{SXw!>d114TZHJv0~d5qU#0#=uMx=0_Ks zB06H~%Bus1PF&vq;qH7eyea+gai&=SiJkh*;pJ+n7;#|`gJfxA#1T^*4Gausdre+f z0P_@m0P(4N7iu8Ianjtl4R!5lp!g`-3lAY5zhOY0zp7e8wqD2WiiG|Y>TawSj(?Bo zI@pU`R$s~O@+w|GtmDG$XpnA+?t3e7u0lzc(OArF9NEM9;l5^?HbF()@qd=|$GZOQ zk5&C6C&vBRs+pubo06k#>KxH8*STZ$RJyQLJxmoWQK5goswMqz$DDxKoJ=Z(EImf- zFPO8|uSCT_Og{&s?n9V4YcZ)qBh^qO;Oypmk$Nx7MEsXs%jF^+)wfoGZMe{-EF8&r zfk)a?1rW{S<8;+sA+L;@0u$3=URtLLze5ZoK!SMF?$VVTfpMPdsT?@ZvZP9q>+nw3 z3+iq!m+uzBgkvN#-bB&_@kX)rlc==C7ss7Cnc)}e3reD3kGF!(oVj6;_;@J}rsb|m zM!rc7zfe~-U2s{>Qyawt2alY2@ZW#@x_&58Is{su?l2Xx{~v*Sxc6v`+Clhh(=tzfQP z(S1As#FpJvJ9=Ah`fV(SY)A@}bK)M?p%W!;0cP8tfO4@EFj%Iq4^Z2_(efeaj#7c~ zvdAxA(g3fKgwb&H^{oKLk=!&toD#x5D1E&Q++5P`0(#+#vXF}jAD1_qbbm?#3B&qs zf~S)7Jo(@*A*f|juiaFqKV2DXsA(F?{aLVDJKb2Zb+pcy`c*{mF-cgk+XC)q`T-ZW zvckD19F=jMq$%gU_q1(f@4AS!!=GFL?7nUcS%{!ow#k<0wd0{IYr1u=?!h<11Y$n^u(`*!Z8|)|DB?ONUib#)hQ)_)R3# zkw}#W)85=6z3i&C9i0(#*}gck?gDCQn((AjQw(8K)um3_*yyz(t&`Tb)oyzHx)91U zROIw>6sIW&N7kPI=qcbd^k|A~M?F-v5}7?;pr`})RuChMAm-{Q?)~j=0gxJ6Hg@18y%f9llP~#)?kq-_&I}ckT?k}1$p&U+#YS`FN0yLdWm!36Wj87&6l=OU z!$;Cee$^Pjj$>>oYSwzx-Ts;gUfXrDzvJt2FS|8V^xWJj&$(p=n0#eE@!6a<7Q?vJ zG?Nzgqo%8GsjtY?&zL~BiLJPPex61Qdh9WE==b(}AfWX83EJo{2TAM7BxJtdyZnnX z%JHh1B@tW#NwlcNg?Hit?t~xRoP81l2H4Si0q-N}7f*~S-8y{A<5{boH>t9PWEHDO z4OX>xxl?SZj?gn?(2kDGuIjQzxZQ##FpO{AZ-@=o&B@49@#OU>Hr(@pB3pDyt80C1 zjJIr9>K$N@4avXmHk(4G?*?8LFo8crQiihD2JV`E%)$x=?far7)o;g~Ww(DF9{6Re zrT7K)au6B4p`@%F0lUE)1izD=Rn#gkWt)3f{@>h14e340Ml}R&R1gG zj3|L1BXR);26TbkjMJA%0H*@~TZlmG;6z8N{4?qxF_2MSPft%kR#v0D^xaYaxh7$| z^EA}RL<_l%04!O%E%%JO`6`@d zdHMbp0RF9`{YQ8x33xE06ttw@ee*5xhIuxP6dNq+ID6p6&V0!PwM>j2<^*|4eRpQ1 zjCa!Rfe(t8#4P60#dOp!5?NxS6$-bU3GW!n2sN~V`Ng(bV6nA&k25E4>A}IVx?m)5XnRJE zt0+h%m`xcPo|V`t*yvwH!7QzU0Een9eDqvqz(HV8s25;ydJ!y5@XP~;@yIS8u_|*Q zBI4q{F<*+^;&jA*FL$B?9IP5Xvhw=JMg^YF5H_LD1D`rPco zJy?WXaiH24q3XwsjAd{0B_ICx0RG3Tzf?|%uEAl+bCwCTnO+C#n`sgQvAX+|!*@g8V2gHA)!)9j7;AyX_s%n0L*^T@;Hdklq(dXk82(e|Un>j7WcwMO z?7dW~w2Wqp7V!FUB6KfU?#3~tgbMrlI=%ES7$;jQRE$HK;>o(X89t_dWKij{Wyp?! zkzW2iE&GDfO+g)%OW8*3HQbdbzu4oz4omt5NDFZWIy?O83A)!VOW!LlE3itkCs>-6 zHX|AHmj^KdJ(LJ_nNEai{63ag!*KB(iktKF=-|o9!eG=7;D1{yJaocMfF@TSP}+70 zvU>S))-9Q0v?v;y?~qNv36)Qt%c^xQc#|mLZR9-``0UE~^zuMT2?`4d7c#DxU2hR) zR!jfm!6Z@ruFYV+TUL(#GD>}_=J|qlGOFhqZS>m&ql44L__t zdT))jdgEb%QbJhy(4OUc3nZ;$aemM0tdAMKko@sbpu}&$XSw%a2 zTrl_}g9l~;+4cUwA+E>YbR#hVJwNJnl`yPY(zR#vzCh}*E7fEZo1dkc`6zzQ@^IiN zuCWdBZ2ukrY1<-XLcO@3KrqM@Pp9<9%x#=fV#cUm2Xv z9RPU=7fCr*&2$C*-s!%2V0|{^$zEce;g&-;{Qg>5$1&*8Zg@rR4Sg(af>d9U8b=Sf zXi(9R?8b<{+N*P$AST|*h`GOA2PMs_C!I7VMj)sCn~t~~e%dEh9CqBXyUb)&Zy<$>!S|*|DgsxG&Nqcd6cwd7kfF+D&3?95+DhkwIH2l4$$M` z{1&e`zupCmnfcRSrG}5`?oN-UOwhV`xWWzI;Dt+)k9W5|9_n>8&JPsePe5!go(7n< z7~M})Io$5c{U|pq1AUQ>o{JM~!I(X^wFqT|OwA{v=Zdp#N^h!%8VDxWJYa3(*8Cc2 z{<;C0&hAc!q19pgW@+>62joy+7*5QQPGWyP3}Ve zkjSp%VT3$R#d=17j6sl4#ax{W=9Y9T?8C*TrWslzQ}kX!MP{sqm8XTDmGiBV*cK~X zP`Xy39gF%CoUsVzY86M8dQ4^Ry)E1rOCmrZc5?+tq#h`;>?_Xpd_iN!PY&F%QEsjPApJ8Fr~PoH7g~#fz?$8~ z<16u(^0LbvSB}bku3#$P)x9mHI%ayDUTxS^=KL~uc^BGlOy~J_ zYl7XbK687&KG~d!Vk%6z1Sv7ov&3b zaF~V&7r;kH&Ve@)l*z+)WhQktmal^8zJYKiO4db-;>RNvG_?LG?Vg~Y%u{k(?1ht+ zQUmE0rNuczoUKiRBe3En} znF1)oLsncSK8ME!F?$0uk*=c-%^rdDi)Z0S zbqvhjMu9eUtZ>U2bq3NTs zR!P@=azVBAE`pI#|w6)R!=6-^Z??VPHb?UrlCw?64 z>KxSQ5YGj-Jw}!7q+l42&~05L-o__6v}ARY2ohipl;>GewCcdnyWoW+eM6mADdZHg+BzD=}(T5$7T|4d}I= zD}My!M}w8G7{?X3VU!^Flbc1=*AmGK1Y!OVPW^-As*Sc&flgXl_!^|d?}|@<%d`S6 z1iXz|a9h83se99@ok!S#g5q_LM~U;>m2A&2&$o!f^}S$jr9p$O3W*Gd=yZx4bHZk^ zc**k2vZ3%>%0?}nFm6ifKeODEMaY@1;qJj+Is8(d_i)h1?rRI9-SYAk z!4+uZV%5U1)OhQij0!t-v!l1>lUey*E(vz;$$s6Xxc}_?`ZJKWv0H5*3J=Z6@%=JU z)#6UY7IH`)|Hh7yl*ds#j6Y zJz}MX)}bp#(#?rrrN^mvWpt=utPoV(JHuvtshE1jh&b8S_0`rwzSnah>_{Y--$&TU z5bHdhppl}RuN|7~5iJ*fQf;ZKGSZF^wW#U)eS7tSn=o`zoAp>(4^U1TyX5N~G-(j^Bl3OO@vTpvGWFf|t{7S8%N&h^X?f?r?<%leJ(v^w z-KIc~RYxsD())uKP=ZX6XGoB6xoTJa?%lg*1g^ETa`1jC|6NaUh!djl7xL``0o}qK zlKkTe61-T1-Sg5KC5K0D@d+~3(+tLz{M{fam2b{n!=}BLGn^_-t=5U(VX_byXn0sUW|T1M4^`yDQZ|T?4sy z){Up#lT| zK~}-wx;A#$q}m14*RCnGK*N4tf}_bTEADXm(?XF!+VZJ&4mg#3G5u`y2OtW$OVB>^tGp^J$XXiS zUUej%12M#Sxs<6OyPZKK?r018rIwV>u9oIV~ z%mD|)ZhZG=!fh9!u$s=zOSBQ|74?V)`>h|OHyCj=xbhR7o%{-ZFf7=X5j_-?!D2kwhHhsM7(=9F3R3^2jcISntRxkyHuGr zd9m5U<6Sp#^t|}yT$_$(k+#+0!n=`-VB$z$m)L?1*9ih;p=cp=0kQN&U;KR`d&AlQ0EVyEh}%w&wR)}pkHmE zPJ7|evxzEqPF_4!Di+bnzF*?5J5=ranBD`>yvBBQb@c!=yCPLzN_~a^&ZrWA)+GC9 zKA;}3G+e321K%s$TH2s1yxHCL1?K4`(T>9vfpX=sv7zu^H|&Ze*n#QaZh&l@Ylcbn zuapcgcbq*M*OKH>Pfc*glbU80sNfCJo24}0KfwE+m!znS$!?6E73mpm3_ju^Kb1Q0zOh=Jj3PbrO zH4jdWyuG(Pk|@#^u&H@xQ{V>l(_IA|Inpwfva7dZo$g?o|Ss)`t|DoPqzBh)KqE%Zlg__ zJ^COV``Qj z$HBa?L0Mj1-(%Oeswy8b!>J(rsH=SNAVs#xU=r`!}sxvZPFBIA^q!W$&5} zH6zy=PV(Y=&P06Ahn&Zf$m;VETw)!17^N0@;QY~&o+%tXKXUQtGpd^ZnC5%@^W(Kx znhhjUSK+PmMx#n^{aGI+20v5pGQmzQ_=(_rHQGmB-x#IR?>AA>=~FsKV7*R(@>qsB z4(ZG%dQ8nHb$Eo}mPt|ePC~bM;9#)*y%Q~K%4pDRy|`6ohrxB!90%+;z-HCUqeHy;THzX3XGMjpoJFK$o)gr1{?Ac zK(VteVT+kllgYnyD~a^u$B$2_GpzxCsj9#xad@q}xIO;stjx|;qc@x50rI&b0~Obi zFGM_r!f6u-KiGm;`_HE2jHV<@W&tunk(0!+yOYBqK6@2TX%q9cbI~Gsb22*MuH2}a zIi@w`)zl^xs?7I}c;A4%u)#hrfLb`sA;J#|9%g_#K5Sd66`Cl|K^_0EM6 zkDtA@mp-H0GV}6gKe?ypSa8hS)n#UBLDyjy>gsL)9qb1O4G(%vG=`?~7kZxJfo*=? zXl`rsl#2(&3?_w{6^Pz5;xIA|=LRR_;=V61#J)V2kOmi^PCrdIV_u~s;c&gEX@pLDzjU2Lsx|1J0IK}>*_zd1#zEub9GLx zNpseW+fij)qhT)1np*QS6Kld-A~Q-#O1HhN64~I83adTWXd0#3qhhLyt!+-A`|z^4 z?*wN}cLg6pj7q%mV(TYx;s1SK1NJ_{Vlz!YCL4T5s6xA##VF2nMV&>+$D!EDlr8H} zZ0Cuihc8_$^Cj6FzB&p5-`{w5#^yC7H_3446HdZUW7}sde{ROd@}*5;>cK2$O+C*1 zF}q~(+vvd5H(K6(1KeK{iKc^&NQ|$}GacmX?}ChFrS4O_-mwu`c~PED2-9T^t!hVT zPItQBjzBKQedW&2YG_^I1a!EjB1HGu!|ZX$fB?d&8va6W>k?`N6ezY#xamwjO z_=oG+>;!S(FI`+iR#i{=LJSu-RF_QEu}k$XQ0}wdGDf)=zMPBXsbcsW7pWgYQ0Ax+&) zL6*{!$KDM$FLt-S*+T+_bikbtpSsS+2LUMk)im6i!nmF-U=C_gl+@L|YSTb>>;$k$ zp>;to0Y!=IXo>g%Wdhj@J_cV?1>Dg3u@_W(rta;ZaRmU{qbp#@iJ zKQOKxifNbd+jic(KHWSxaA%oS&!}@Y$)R8Ypw*5Uax7B_yL54W;-0M8i@Ngf*MxcZ@2o71ax581aeJ+m zyWND76J8{p`Q^=rzWFq*@?bFX%?+=Trw2-xCm8LrN3$$Yd*9QP5<5!Cb+Rff@XnI` z!o_dQ&)qX12HP*$JXY~!!bzofuR*Nsn4ZjiQ{pmC=}lY5@xHX8Xs3yLN(pdp@Bzg@`7`Vq+hc#;zGGGSN?eH50zhbkmb|YYhn3rpIy+;l;eeNc; z-!mUprLBy{F`mLanur_~Z@7)tc5=u&5gg*+F6n4#7#>BRzS!&M@?PO(X7-Epoai4d{Oi@mi1v1$jPwOw{Lh~|D#~*}FLkj+x0Fg5r2~n@F~0rc$sFd}R^mzlVx>Lc!jF1H?pfBB2@F~Db;V|gS1{!NglSMe~lUN=M76BCcZfZ{;f0(h8YknhBVP$q-TC|d!mx#hhwjWC^rYZa4V_$MkW>F!ZQzk={ zV~b8_N@MRUh=6LrmUw7V%BH!NPJc_RlIsk_zD)_0yLnszQpz5`>h#N`m6%ksB5H2U zk|5UfE(99-zBP8$F&)$`Yq?6|pp>0Nq1H!HBPB}h@p@I)Bu2pAAJ<22>yZ->Is&CPn^#YFZ05O_+ zX%WyBo2c(hwq0vH?oQ7xiCOkA*s5>07#uf>|iyQM_L7x1rZnbr9W24x^NlxPO9eUfC?|eH{tzy(d@oIVy-;#e9 zlLu07A$``}%%)Yk2KHQe-f*j`LbfUmuXg#4=zYH;w?G7y|I+SFtk?8KB#qOgKK8z{ z-dM_m&8_F?)Px$)cCI5)DpW$ZUAd=N(MTfdJ|yC42S zG^>9KL;HxtJwM|LtLmCG2W)VK^$h>DJxdf{_m_u1)9WgC3gCoRF1;PCjCNdE{K{S(c2rlMQYwG1;^F zb>>`NHg7yNa^Fu6Wu*xebFTCbss8-Y-6K6?_bB;CbzE)@oKc$t1Y1`$vEm#51C;SJ zU~$nNIR2~Y8My@72lmv5yeAcM$^gL%&}+_`pSN4;FRWuoq%AqYyDL|4$g)v?LjV=q zyMV6A*E5Cb08sMoTpi@*j4XYi+?vvl17+L2sR0f(Ur~OXhM&dPV?mR4$iBhcVuFzy z-j-oZL8hGsZPy(HIDhgSv17+bbKQLFomd|T~x4+iaR)kWWG|LUUKx#86k+hq-LNwcoG2l)8BI!U9x4`g9m5eig} zt;pFmNdi(?58m+W2-z7-YiisO<9KrTcOh^;Kf8OV^Ic&~{7g?Ke z4GT}sQ?sJyKBL35>^?p+#)Aa?HbHpUl38|5O_AyF*5|;6)Y+WmXOP(IEOl3l)5%#)`pW@ z$jx4GTf4gA7U&v@^5idw${S8u6HzQ(aj59RbIjOOmFf11ac5r7`eA*qAH18ve@`e* z#ub4ZZq+Sn#CMe~MeEg{jorZgTE7)1)h*bLv+F&UTZFKBdF#gX!m#X6=IaHk9JAMY zVo*qe23lHDXRdnhZtt`7tVj17k`URm$v9M6OkYPP!^!p?Pyg9_yY-p1X#@NTzwT1R z#bBya4@j-ry2AA3iMF}su6lj9&{#$2(gnAS%~lONb-I;81JipL4J)TAp^**_t}ZG! zE4vuaeSx#Q^y_!6O^1#5@C)e-B2FT*uiI-NGA5il1oV1~&_O}R00MXA(yMu>5NIwuTuO(fZ0E}7eMBfgSEqKOtlVrh zM<)Hog#A?CHN~)!3}mSTar*6hhwSq7OCW6rFF8}C0aH)cx>=~x2wi;*=ZDS<0f@CN@ z-(}L3KP+A1fd61nIMuB{p*CY5h7KBZeflbH^0(td_!DmbG0;>TA)}qyBM9y7;5KCI zxwsLxWoA(360Kf#KNnfd)0;u$?&)ctYT2L(>@Hi3RS%p6J3*hBwnBWv8jC$UqD-!d z@=^p*`RhORFpX8rgzZJ9)u|=5yXKj*zIhHi4BKk`rY(!QcFDK3+A)E_Ehu-E6sc>$ z(!Gl{4FYaalTv|qzgJ_SnF2(=Wcb##+U(dJpwp6X1{)bFoG_$a^xHamZ+~+-_Z_uo z9W=X15NZq(W9Ghp!PlqSgiF?c$OjhH;JMJJ3dqSvGL9e+h+IIm8G!a_DK*bg$PfsD z7A$pu%gh-ovyjv?|N<}i&0Ia zg;A$2v48_8zes~se5TT@lxneK#bAQs$9rUd-$?rLIb4l5(tq~`oq1;nw(wzIM?x9h zN)6~6EzwTzu9)rhp%pAo^3>!%maS^sG2CQ!Oh(N4h}-DP!)&@NWh+J)#?+wAo0T&h zS=2cv!M@(;;S4xMwtenkyLzv9PyAmhwZ^f}9(dX?E#5I$DNo;dj8cX^*Bjljv9Xb- zTH)ch{V()+Dmkn6wluUux}F|Ffz~gOpKxUG(bf&|JTSwaCBz}@cQte#G2=M>3Suay zJoe7@ha|&V^Vh7x_j6f=k0=I?M##|Lg6PB^tF)>yoS-Dc6)t$u@7V6g82@UeWTUHw z@RIG!z~*Xrl$08{T;xFgwYI!(xH8Pm=Zt0K6RKPz72nokjc=>(1A5eI9stN=OH)t+ zC-ewVd7*Zz%Q7ByU&5gBAa$jx)h}l3oCpmK4Ot}_#X8@~U)hMY5BX%&G{?fNZC#`J zFq848w0#UyU0dOymxi#>Bp?#{tjM5V*Um z%A%sAZZ3tw(AkxZodqN6kwDDr-jHBj@`XBl1w{l|XbuzX;qIC>0YU)FvTQ4-W zH~W$Wbv+zVQNt7HFOU^`xKsFepedkSHsc_io#vOw9mlrJlcX9)?Z3CQM>TH#Sj_8X z=RIco{j2=sFYg(?0n@50)>ZgO>(^|7w5C2i$XK_sHms5T`{A>9Vv}GEapYQq_@%o) zcW&boz$Pv@)>OIu;{Aye{hu6w>PkPw$8Dkg~gz-Tp$ z>f2vxYEd=4^qJKKD@ncunf|FN9*ycR`SFN7?$7X=@Eu~w%ByA$Kf7$kwwBdhTTV^;2WFQTY+~w`@>$OS&xg zL@mQ|T8Dh-Pl-=uoJ;r7qoRU$fKo$dMur4>a&i(OeQde^z#q8&=@rNK$N8iputR3`c}%}43)9h z=sy-}sFf;V+y9a(dRJd|dx=z0(VzJx$ak2YZd>$hWY)&Pdj`@Mm9v~A_t@%X%ZR~@ zI~LKQ%eRjryzY?h-9)P-gmM$``60u=tz!jjrX-&LD8_k7JJSB z?2JcF{G!8SV;?SP217Gu_wz-eXc%1(#B&j7D_ zux{C;-9sVK%O*SW*8R*fqv{0sf(vz*o%yCDyh7`#*Sgk<-QZDLp=k`Iq}yrW4WSvl za}Z36IR4dn3}ax$zpTJ*y4$(YX8AKbU$4DZnzW7PCL~5cXXho`=a}haMy`@x=QojV z*N(gHBbI+#UcGeC-`4liSEkOMbosn5kN>AAR-*asmMy0FW1g6G3w2+*(3Td|+sFLm>>0j_iyw!eYV+=YKp$e!EnU`0mWCG z_qJUIzAc*4*4cY|dfrAk*D;E}ZQDn51C+WQ9&Y4(+^$__R+cnu$q(pGo(la?Wz9+7 zZjudJ20CiC0DW`n!msLtgiE1-x_ys-h5CHhCDHIFnwnqU&4EbQ0(sH)l-}|$U;D0I zmZs<`IScHhIQ_2+J5a)9pd)9HXVp^hFpYkB~(6>?JXv&TU%8gw*hiq&c@E)kEWK(MI=l|WN_Q^!C&tPCuEd3KOUYwcKzt< z3Vf4Ce0%`(x8{1_P@?gx_t-?Lz$@zhkkJYE69)mFgOKod|75xU-R9lz1-w-&H7)({ z7k0d* z@3-^6F4&p{a90B|E?fWitL@+gR!n^R`RPAh#s6N{e|$kI{=mbbwyk%?rvH2k{?dm7 zKxcs!^L|v>KkEMX2l=nB0@Yc7Z?{xEf%-pA>|furZ!-VxcO~zD6@$!Qp8LNqxQ!R! z<0*!XFYY52{?ax6<9#Wrz=}&5u0{O2CH?o`xq}YA#wISYSl9g9>-n!AcDM?xSoK2e zk$)`Yf7|MRzKE1!27nXGK3RhD_^*ECAMdka0aol#`{VlmeZh&U-+Bi?Lqq=IJhJ)n z!Ax@iH~G46_4TI0+{|6oS|ml|F3`M0rc#wrlBFR!-UPP zw9ou779g2_$5IP6gh+6Ax*-D@n1`_~$*He@1VnY6E%24Gq2^lz z0%4*49m_t%HjeMa1j{ey>UbG%8*r+)TSWk z?NVV*(bE6N-g|~MxpnKp%Yq0jY#=HKs4PLcN|g?ZC{=;bLKP`O=!6!^LP3fmBE5t3 zge0^OdQk!CQbPhEAe~U8giynITyNX^I{V#6_V?raan8S7NuD~#oO6tO+~XcrV?bw( z!UF(u-!jbfCJzh4)0vJst$qt`Jw9j9j%(d}GkO7i!P(t?{T}D1sL&EC)7rTao(wGr zH|nUjee7he_N6bFA=k5 z{`!Og-W5RV(N0v^+<};i`Ty~UXU)f^$!_|dXUxx{*qQC^n8z30;|)POhBo7d%wLDT z0F6LFepFE5jS+8eB*XXo)@3zoHP&r?o7Fl*nMn~jJ~m#l*iLQi{$ zBKs|@MK|@_qqjY=i9&v=5?Y?12Ns5#CHW7(jmMm<*}X(oUwZV$7I~yM%?Sdwb+v-x8E zX++ts55eIzttXYtI=Xv4tSZH;SLzp5j=$5;-TgCI;OpbF%k6qLvqXqVOVE zn^}hg=GA||Js=x&=a}i|@^$agZb7+baIe3POQ?wAumx3TC}}+V)GxXA|Jvp3S%P_a z0-Wy3#$Iz}ql*k`d=N@XLfL-Ic#7Cq7VLX@Xq;Cc)ltCa2*P?sHNHrA_`LeQwF6cJ z_vzxr9*m%bnSzeie1=Dv8!~~~HBf9E>PPiv0# zp$@d7@5@Y);&ZgHDEic1F5%|7iJFC0lK-Iv;80n7*O5O)v5bT3X`K~j>|2t`d4lcD82O5_?Q|D5dMeEJ&k zLgqAnrxuH^ZhvbHdcciTEE?T~x=y}JDW#K-tUgSH*OyXiPUElSwuXont9Q)900=iied%cIB3@NX|0<-yo{8C?X!*08z zDo!1f-`S4cT|40+F2GaQQ}Vmrk*4!$f5~J0FEYEM`<6AcMEDJ{L4A3AmlC7}H{xiI zT`ZCdo(7~?*yF+E`^q42p4V_xPMkxdY#qpNCo~i;2Wi6?I z$Me!J>g}Ycnv`V=e9Q!g#@Z(B8RvJ0yhs%J2EQ9bM_g=dKoI(j)l2KOTq!fyPo;zV zj&=?mKOlCR3+sn{>$*V{LcbJgFhp5Z`zEoB8NB>x?(1bqRf}p7(hD?l-S8u2Fcq9} z0f}TsN0iPubR(By1|&R8QTN2ifdj=bYfIwZ8%&LR1@G*Lk~?zTgp8&+ip0s<)&OKU z=w0+U97`GdgrPaqW#~HHyZw~M4{vUc+$GIq3)G{==~C{ES|sDVvrN%Krr*7t3j5dN zF9NyX^4EGJjRE~zQs#Pv@7=PbSKfU`t_PrkZYMg{y8YMd*MHtP|K-Gg%RkQGB*PJV z-L;Z#vCiE_=l2k`h*Hufnzf=ry6xAzJE425K7B}@4`WWeKX|x;P zd=BMwo1Af5Z-7bngm)e?_4DLtTm`G%F`TgXs)`mvh^v#D_tve8p{?8c6z}Mu{cNNF zkvj-_TM5l|w^O_Ps5;NG*0n5{1z@O`pAQIz6wpftBXqSm=d zHuRRB+drBY(zI|6P(WUIV$)MD@%_$~}s8bo`6S)cc-$C&9*&v>0z9WX9}L z)!YU=-!EE@7rUtL*$$O!XLx&x5!sr44QVJ{$G-GF*}-9K1u2o}%@^TinErH~M5H4-~h+K9NNGmJJMo1}aIX%I zJU_MJr(a+25IW2Mx59&7sdG)tn?(mO&@IcfJCKS`F%wE*8zKYq!85xhmD-PcWVCG# z`Taa57n?#Bb~_B)1W5s*uhF!aafwY?bC~fcpL|yE{#WIuaH*p>pMW5UeOi}eCq9`e zxSaPr@=EN6go|-<`OB0jbm4Rd>>|o()W@pf#Ec0Bf>B50oXCrUNjk5E-YAG##V1H# z8L8r|Z`MR2r}sX^ns8tZJ*{a&JQ%n;vi$fe&0RPMU#FQEH*VZf52<<>{ngNcg|yJX zl1=}b4>~6xYb+pZo1%#s|N6On*-6-}+$T)k7g24S4c@3r=_MlkAyZTftke5_d1f>Dl&H< zpl_~pPm3BF%o!NcYfbYd!sWh$w4>@sPkZ!E>GW-DjbdsDuOHT%P#O{O@~w{@B*#@K zqkitnnoF2eurqdtZ7uoFLgu`TV4jbURvu@v35Ae5*z;h{8hsyby1<_`@=tL{J5UV~ zo5Fi0sQt%ShmQ9?RwI_HU#T*jk>#XC3R6W>*CpSo4^c5Y8SBHt9hVMlSn*nJCmNs1 z?_Vk?3*tAPvJ2qxtEAx;+RoBxTva1|XZ(l1_b<2Lzxfngd12mD*%UjNP`Zq>*sD|*c+~(F*|nkh zuWJ+X7C9Z*JVBhK1JjoCD3$f1>4o1rh8>0uizP7->oXRiY&6@91m~hHlC(Ywy?POB#PBzteRg9t4|~UwYb-FyuFz z_(mUEWnd%7RybX<;BAg_f5nYkbC;J5Dr#)R4lgv?>-j7@WA#QQ9ilMtXvgtR1ju8= zG2?085YyZXDCqVba(}p>M>-8)xdN3W)qhc3?=+iO>sMM~G7e|N1j-#5u4W6==CiMj z1UG+Z;1Ukl^{uZ*nq-{t5=Dr+&`PbQOe-emaD zcIL=GHIM?LIc4*6bN~1`Dc}dD^(FDH*7vKt(8IH7a$`Fw3eImbEUX35tz7)!KdHJH-A7OqdLkW2=3edK zLL~DH99%+JxK4Ew{OW9GK7tbW|Qch>K1)4ALxD1AmPcHcux+4~nWc+Iwn%D&L`n(@RGPb^b z@~~uaX;PlB;OLs~Rej-pv@751Ic7=T*YLTTX~uVF$Ky2kn=8NkklXTFe9vFAT6x2! zKr%y*fxm;}9*Ej=*~<0@_sY^*jot8r0mbogvyKjK^u0rUR(J)+<=HxDZx8dTu;=Hz zx;q2VJt)ngs(JR)6%UZOkf6_%K%Ji9kvlut6dx~xuvLByz10lF+Rl~pPQe2wvF_-! zk1*SJj8PbGliI^pmVu+e)=kNOVUA>-iy6st7(~I+DE4P>%g41Q19+3S$dFN>;;o#r z?X_u|DYv%;->Nd!#j@ILK8|9H$XE#VJBurvEs6Ueq_-ydc%fc5#o{H_Kth5`4})Ex z;2N+GHWGFaOS4)HhI;h1-5H1jnB-?&v9h=enmetqlwL$iy+n`LSbGtlg`Vq~Oyg1W zL&6wQH=e*ZlUs=#5BgzT$K!g2(Q|eD;Of{#@IRA5|0j?;%bH325|%t7>)5>L^kdKl zo&d<~An59Qi6WI304YX7b#1f3a+l5VkH+dwLrDWTJW5HoPGOz3F4#Lp5qM$g@Ar~`Y*IZV(YP5ai+Yd3&IB7nvT zX7=I&6|41(l94pBS8e)4Df8roCV9FCua6es0HMw?05(5w!3fj=0mcx{rhQ(TdeW4r zH?98O-(3)6*I$iQ_6qsua{jN_=-+>8x^m$!q}%m%MvL{|M{|Oe4gk;tBYCd;iXP10 zFIS+^G}toz-}p)Y)~N~}W&|>6$jb*u8_2&dbsNY_?cDtzTy=sKz}GX5v)}(eS@3_x zRsXuuweU{dkzVpYUePt65fAIP%ST%m{&ujkX~U_1-71Y6M&-Ur2_Ws!LM!ZVvXwSH zV_++njEpJgy>T8RoiAbibjoq4FQhB<;j+~Q-m0nD?KGLG7wc{E@*c1PkAlPJG7VaK zIvwIY(4Kj52hFq77YrHHW6;t#)_VzW-aV}=y9sj=eWaYED%Ag-Ze?zRc(8KzMd-;> z#wxu!bDdP&+{R8|L-cA#Q2p+iUj`qgt^?1{`#*jDz!OJEE9C zzxm<+m%;pVj<@ypA3X0hdE?ddJb@0pFOee{fN)<_QBg5JyXM1)g4LVH$Gwl44f85L z&QGcD&;F|R*w=8-p^e9CEiAA+;q9;Js{L!uTRI$D_thI^9V@jO)kqY10fA^vO-=RC z0Fhm~fPC4~V`jD8x%k`8s;Z&o@rj9@&K1e0|G0RsFK=c1Sd^*eeB>2flSPmv6e)SQ zWf1zRAzAZojzUp+{VrZqr#dfwoV?-`PfV`tOcZr|{`?Cba?5*Xea58PJ&#S; zM0|jMWhpSHo=HbFLj~M`GBYqC|_QyfS%LhN?@byY+jWv z+;zd+dIG7bYnZjW19O^HEX9Labbi^Z`Rkb`&YVx`el(HBS03-un=V@bn1`7+Qqr>a zdHaPOe+9rA>`q{ve+<8Nw(AEfA$d7kp|7gF4gvjr!I($Vqubj)eJ_CL@bMV|s@QI$ zh_<_3A6a_#-ta?d)-aI}Fk635fG`}{RX@@s=p^HM!>nHN-bnUVGyR~rDupms$RSt5 z1{$4IkSAVkXb#3U$nK1ST{_+#%}6v^uZ254fNWE`lErYyKWicQy9JC+Ha2-I4e6(S z#pWtgkR0RP+(9@q?|TK3Nq{QvnvdyE(-}@C^}hBA0(yE&2af@Y)19l-IgeN%1~cyGSO_~(VMM85R&hicH1})+M!J^O5I!I#UJc?W4VSW$SUiCsBs)j z{CNkNus0L@eo<=ZD_P;Y%-#c%71WuK)%q=j&eZNLOi+Sd&%#2Vhk>O@8$Wjnge@{? zAZuSVBt>kREXy@0MX^hQI$^{+`PKrcuSMa{cox_{&Cnb+ihqdrx*K+I{af$jzy6`P z$3cC~mrf-91!lKo!9v})p{yVJI0qglNl zsMJWa@})D-!kmVrbGs^krMxgE0hJ- z(*qV5#(JvDFheoJ`e?o_TZ&S<3T$$piUHXI^1-hjUpM9OCze>fa&UCCK@0@4Umx3t z*0fa$pqZzCDiBm2MIio=ppxWbf8uJd@3y5dGAv{HIT z7aU3i^ePEX0-_=0JGk;?cg?WI!;hzQab=0?XZOiuR`^%bYPqb+EmLXfM$+_DoQ%bo z#pO@J8y&F+otkiJV*ZC!!M;z~cG2V=hU=y}#=Jrb2|`P$-fS=oh(0pJb-?VyqOWfw zft2FsKdyiEXaQ`=1f6YT7y0US9o|?Ok6F!ThmMYpv@8Vk_nw(bpS5R5v)bXJK*v{D z$v?@JZ!U>;7<6HdeGl#bP#K3D*k{xyWI?Qy?s*L0clI^96*$GU%-hd&t=@1cRMx2! z@32F)k`@QwC@%NBy=>6drz}q~(oznB-8DDQ39wk7udd{pGq0oC$r?lkc%|+tAmV`mLaY5w8Q4}a zpoOaMx~#3h54Awu$a3f%1Vo~ooJu;D&HNk7ly&S>sw!U{(jLm!*NTiv(T4eL7Uylz z_voh{`>M!RSl5Hyr1DVQ+#=&rvUiJCaPINWWUoUvULd8DwAc{64B9xwL0>6cWEg!m z_pI8U=iStLR6ojP$z7#tE8kAl2-eV7tW;yy+}MX3Qz~Wk_ptyDt@Q!nDh!zg#fWM= zG7=`Y<9>+&{4bCE+^riW*IrdsvagJc7$y8wT3kV}B>O`E0UE|rujn+mId48T{_VrP z#|!GEop{G_B}_yUn`G~<6QkjCRFtZOD<@%>Xkj3^C~FHJ;OeGxVzQI4YY)G%)-}{2 z;{>d=9`c_XYbC(Rk_yrj`mp&9KD*#yX2(;o?PQ4&Q8Gba|mD3Bk4f57| zjBli?#S@yEG0r#Vb5ETjhT8B={K{_pA6G=))0$mZwW|W`GG@bEmpKUM$RAa4=v*1x z&EaC>Dg96N{ySymgAuFJI9!m%u=mY8H?bJ-{mPNFDTdK08DSfqB9>I0%<9pChCncr z)J%v0-`3aku!_Bil8m70-fHo6f-*&J`>neK)I)wbW>0)|5mpvF`jV^D0i@TLt&qyW zts%WS*Pig3U-PL!@S{at73`JUHZFh{ZP+XsEPp&NrE$UDs2G347gXpBHXBSC%Y6TO}&|pVs$s_J=QfpukRTk?8jH&e|Hk zNW89k&0}l5HQZM3VGhzf^+5+*js)Sb^H}>}x^|jJ+nl$0 zm_z#MHCO?wp;7EJOeq($Y0q0@%y4z)=Cs4;nSGs&aSk#QpD=C9eWUI}QGzL_exXmy zID>~9AgOyKKS_@vDSYCSqhxN1#YHKuXhF6&-QaMhOCt02)Nw(h#$Lm^=s>B}E@@BNqGi(;Yv}5APdMz+He~$WX*6Nhy0gC~zxvOxbIdll zUvaNoaZk~CLu__>6KL^OcFX{4R_F6H;=!k)w_uYLg!JNgCSnihIimZxW|z1)TTCDl&9!Lz6%?wc$TUAUb2ZlMl*p|bd4&^A37^Tn(^ z-U@n19^DJ=Of8~IZ%8ABZsjulpdowrID1+S4ySe=MCkuZ4-U;Ytlz1TXxTJ8jpK*4G}(N1^Sl z>M+*KQqz~G^aW;4hUuisSGedJzGT%nSmt@O`5ZzhN|7 zQrw+rF1$3e7tM-^nTkeP#q!W7^9W4uTcgh><@7KlCB+uDUfnB3VB6=?NDM7ui z@;j1woM2f}doxy2T?UE(6g+^V(kYrm$4TOvPXOadmm5Y71I78ih9_BT%xA_%A1s0| z4o0B}CUd@d(S*guw43Ld%vdg zu2bn^h2&e=p-TI{8+`VTa}^#V0W{lJRZ{)?fsHIpm?6IxN7`Ec`g6Ic(mC&!)y!Q# zfdB}I(AW3WPLQo5{kAwqak|feLAog5F#~ZGF{q!h4(p)h{Y5S@uRVq=`@P$T;g^Tv zZ{BKE6o5~nUy(Oz@@j^W99Dz9QVb0@*6X7;r^ z+N?H#nIK|PVP9i4c626n($r5lcZ1#?F*ZKicKfgDt|NI5O+rj@UF#b@f6@AnbmVWx zy??^jvfstlFZ>T|N*J)I$`;?Fy2wEB8s|@-{@wQ5x|2t9lYpCSf-s%?lJ4Y|5q88! z1%Pw4pC~qu0*~m?m}@^TbbAySB|C81+|dXRKR2nd^T462;k{oqesQPz4@1#a35*Y( zZO->UeD{lI`M+&weOAJ;+pHEh;?);`$sa3On+ue?JWj0-l-+~L`Rh7r9u1fW0$FG5 zZ$;f#7=!lL+JKF-M^Zr{gL$CGb-UQh<3tlsH3^gi^#Cx^Si!c16|2?0O>@AX%XYHFZjf`xUOVO>A926iAsx*OqqA8 zadC#2v#U>hE*>`0V>?rPT^j#9Se5$wg7z!R^=!g#MHLLBDFCzo>GA{U&K|4O7Tm#p#7$w7s%|s?n1WnVrM^SMScozZhL%3r+0J|E>2Z z==p4P6KhP%&c<=9Ltai!j`pJ=gC^l&$Sp5?nGL+S*s~Jo$qSjNG&&@km1mCusDG!z zfb%60({C8Z(9j4cRr68E6R#mS2wqrNm3N?Z^En>`A}B;qLIt3A2oFzb^Rr_vzL95V z7gHYNH^0fYaOXgG0ryrC=#?{e)}{yNa+gvQ8`(g~xKJdxPUpyKGMQdOMpo&i?CF^D zat8_8)+0Uqr7u7IUKa2rye9f=VRrRar2=|;nj5idVPR;YVzNKM!Wd)~sR;l6s`@Cw zq|oJHQ+222y7lQxob2o!29e#3j`!g^lVFb4`itGjEXO%C3gx9zwUa%zf4c{obw=9);ih${AOF$_>UR42{Ca8JSRB3jv=Q;0&kh|y&nT?I%P(7I+1MfBs zPcMJNf*0rJU}LSIM3~3qG=_{DZ{^bxqkP{C&{+^asQO21$zNHzge-%}Gi<9qY+$G) zdz1qNQaoYiG(Y4#UZ-TL90vCPZZL|;CIOi^fn-ewAftgFQ^R|3B-O7r?CArIr%r~r z*c^Jy_1rs?{p1m_*RNRPd0ZXo>So-hzL|SyrV5=SG#K{EUB*@UuN(G^2Ynus1?(WB#TjCXi;vxzp0Q7>OD>+(M?qljs-_ukv2F_bdMU}*KV|9H#4h%V z-@2MoA|n4F~b z!&N=uiuoZ>P&HgpPin_({9u4L zhd&17I`^!6$dwdyc(ByJQa#6oG{FeK6xJqUw=BwSqY^|d6x;~+VB_9Dd^uOJ%@5Hj z4Qp=~1>T5C;B?@=`~1s<=PFq_^?oaR%e)m1CZR6dTaRf5Auv*ooYbjR@Hi5Yr(S>-aI(Btgy zY(JIyifs?O48+!Qy9;y^IwNA~H}`u7dVd(u|rdps{4e4oxSWw12&q0YgAh)qss z`!PM4#2ZyjqC+C!{p$_YrCnh?WXoI`M|-rBa~H}|?duy3yZOfzUL zyL^cp6Qo5!+JX(wE_^F}wCk;ZyC1R3x8=~*x-`mvnA*XX*<%8$nBwS#d)=lU@S^W! zPtlJK4k_+J><*r@bN`jy^Y3wv+cAz4;P>4IiBK>UN=aj$ zBuEhcEHTR&{q6=9_E_9{yf-(hc@QT%t(_djV@ohHKaHIb<;g|`$rZkw&tdqn;v)R} zq@UpWr--%iGy7Vp>r-icIihT@T{Yy^Q|4-&s<8KitY^ETmHp)KLl=S5mqs*;aLUDC zYx>7zKI{NlF>iLxC!@h!)V>~ti{wFX3jwZA7|v%dSBGg^;RCpX0_wzB#`{$^=DuaU zkNMq;wVb@1@~Ww>ZMM|>0%bDVna|e5}>n~gQp;&n!0VaW3VF#p> zwu}d^eld3fZgaNVCXC>+`RU{d+1a0ma9X|W5M4Fg16SYjq)BxHP47sWoqK^Rd7BD( zQ^>tHVXjVLrAcQF511mS0r9rRCqPoI^YJK#LhG+{v2~gx?ZIiCnL%p32bmUzp?edW zRN!?iR5$u*2dyCsPEXcIE*%9I=6RplVe;e}kRk7ASf#)_ZDj*G=)I!u-@o4@V0$6% z;E#h1CxsbY2Bi&VF#G~%=ygkf;ZAlhSN#a#I^UMRFx;8N2vS2jr5?Mu-R4y-@o+gQ zp1&8xal4Z zgy$fyAg?lIS*e}Kx>~hKHSc7`{Oe*p1|I|ZLM#Wem*&ax>dyEWri*h?XF|LSTl5K( zWJ-&bBy*wNmS;6p0}6&wXs6^Zss0;9==gZP9{}`{D$fU?=6;v zwEjUaJGi2Mqw=#H99E~Ql?nl%qo#ls{qSymT2#tZwp}C2#RZZevN>NHlz67!PepbU zq7kr_yp=_rP;MDxETxsWIVA``NRt~yaAPYA5NF$9p3_LALj$QmeX5s=(|B;OgnhE5IE_%Kvq1P@s(Iw&f-A?9*x=hH`Rr&m2P7eJk z<7s{Uzy+g~E0b#AK}bV1i@VcdDFHUHLE(~zFH@y1?kI2#$&C~up3*N?9lY3C=lEDDDrn^6YhbO^$$@c( zlWE=rb$65>u77k`cLe>wCWFtI@6`pvluX zGsHo+GXd_Y#-gD>F_3AxvNKfFCBeV>6?YftAkdyeP}8)Oj#Iuf`ECA3yr%`a$RPC! zNTz5V!5~Yu>!?mRiJ2<`&f`Tz!3V}kE??iqo${kkn`&U;E5{u_TlpNwWPULIyd_UC zwtH0}=8_Cjx8WF^4{=>KVo4ybt4b7m&OPS$Z3%JJdeboMbfa&9&0qQ;px@4XNMgA= z>Ppmn9J8H{Jtlf}0JThEL@_2IjkXD#mI8-f1+SXCBoXVfj&Dn1R6?>VL*;a~mOI61m)rhzn(IL=x;Au^lVC7p3=hHvo-<~CA%E% zn9U^x{W@|3gmmu5oq0vPZGx`~8<#!E$ysi(q34SvcP9z>PbIRv%hD$E+KA7FuX@~eq=k`_^A|1kuY><96 zsO_XGQ2#uszz>7wwg`i|m1f@@DTR?;fX zysc{O>LUuaZ0&^WjF-B!a?Mu;qR)tcram*n-Hw-WNhs_%$)}Y$^J*^Dy*ogyjBy3n zzS>bJ%4Jx{=M$m$T{Lw>rF$3@2RBzOO3eh_&vaPnRTQnv>cTv4)UKXfRaFj67*)Gj ze#`_dmE|BdR1z>aLuHjXe1WK)ZFP+@JUq|ocQY@Hn^Dm6(>GojWB@79U;*d$xsRXI ziQFZ3J4*hdv zHzKTI>|$NTBFNVC$+iUL;tyy_l&73=i|6nNBJ=gmlc zp;yJ$wXl`$?jq3Q%)sw1WY*%7+X$xb%D)hO9O1axbZL+oF`JvwQW9=0D2bLgOnUdvw;#WEb1E$irg*w8sTN9m)v_Y#1ZpC9n&`UxQ7$jAsRyCt3Y-O-lbGp^qZ982Z5qm&`?jT)U1y+ybv8)#b)qdMc)CcoO4A zNUjjP!V%5ou*0$w&D9@+qmQdgxdOYFJz^PM4lOPoz-2%>m~;&R$U%yUyKL*uK})8h zflrj!uWb>3CpxHEip(82@9~)cjy=Q&3K2@L0@Rn&mTU#52;Gjff*HlA1o6YzjY>F@ zxSopC=U@z77robpg$DtQ&f<1|XS&HQa$R`A=J5Weos5no!a)E#i@Vls@k~Ip=-vSJ zD|Zm$9hu9ek2ki3EAw(yw_KKg`~mZy4ZP1Q~V&JQCqnC>@A7VDD^~s{RQo*r#^ck<$0{p$<+C1hlypq z$PA63#=&ay`foL+y(_+jS^a3)lp|r-O%yt3E%vqD-lTGGfR^bA^qd96?7jk=2PWwK z5{(3*800yCsIUO}i9ywl^&bEzfVhmzBBWAfHBTdvx7+1aGoWbZfg-)5RrUkqI7;qZ z51Q$*>h5p{9=mwY{XyTCi#so&gwi|{ zeDUJQpV|R`NIVyS&z^cn*|p>yt0^=Y!e6sNwLv&~3uMYNBkAkuOu6-u`68j(1&_gC$Q=V2#;7deXY`o}IlO>2ad-s%Y}Rbl0nAiwKS`w?G0ru@Cz3u?#o zyJ$T6hWF~jXn$&0`H&$?K>A{*;2Clkv{H@JWixp(=R75?46*iNpjB!1&SOs4PE}Eg z$;hd9T=BJXy)^`eAp@qp@pt7Zy}0KEQ5D=m&yw`*Mg!|d##4=Jbtd=5=50;Q)OO3J zh_5noI2XlE#n8A^)%Y4p7iQe_Di+Sup$?W8-)eKQ1mtP;9%h-{nM=?)JC^COyx+n= z?Mn_&F>B*RJE{~cy?9mvHbX-ZqmbovmR4UCMphBrTd`xfVr3Oh#g(*8vv~F=qtr~? zWNhh@hx1|+7(qXFZNn(@OvBwB!;DBTheN8lelLFWtoy_RpbVUdhQc@EI8<~Li5z3^ z4Uc*Ezab7@xN?*t8leZrE*K@FR}AXwebzqeaZ1J9`W=NAs95tE!>= z=^mqPr<~EI)!&zvOKWGMGZ#k;$e4>h%YI9eX0?4Zg-o4FMildI4|R5QyTn1}2747` zeA->#DF|ah8Lcb|y#Iuq$D8*KQd|%yy)Aiwok-{mORd9ps-NU9f|Ko1@I@4GHG=1$HAF zM*Wn#qg_B4g~7i2VT0bzvwjUQM@?yHld*+EiJ2&uw*B7fU=hJT=PS8w&>B9G53cU$ z>{pW*#kS*MBSp7T*;j^vZo$ePIeG`w&&n)c75b3?Ku4WJn$*%p;pi?+x6iWZN+%}7 z+>ED^wAV*1QUAd#&gdj{?5xyf-zg9@$)TYVy8olZp`l(w0+avohvhDzsW&L=b3|Lk zvd1iElnu31TTEb}&OE-h?MQ*~N#{osv0u)+%qwr?l{4oPegcqXR;J+W;g#6-2pgRN z7f+r^0>_<_-o_Y7vmi9XDE0TNMr^(#=pqC_fVX( zUyGT#*hHdgU%n2KuhNKewugQqJ|owCz!i)f)Qzg1^|1Ee{hqBy6!Zti~9@;pT zzY>uHbVqg4%fH}klfEU#-pACa874nOrwXm%WSctTZ^dG=WsCCrOxW0#B^+*eDj5J!4fY}LnHcYY zoJD~D>p_cstywcc94Ve{wqnuea$AYo^hR_80KY-25kSQtbL~l~SPv;{+7@S{L zb{F4)^}ygUcz~QskQl{`dY7y|BQ5kWPUbhhWFApCSmKf>?tQ4hq^Of@0gYbjFNd-7 zg`APcJ7i_7w^w^c^Idwn88=Hh>o6DOXA(6D|1>vJegW;k1sTq)kb8b3zk0SdC@kY3 z$98CY18uuvcyjQ^HrF!IJeI@qow`=bjAtjvs(W<;>H^ccISUL1A9Zw-t@s`>TYN7g zKgA=z3{hWhubRFkU#r8Mt@%w-EVC9t_?A^b8xWb93ptVMy^x&n5R~k?{AJe6IA>PT z&*bdP+1uy)L}}>!NQm;>7{O*mN}f`j!u7iiiLViX2HLrFr!gbOu<X9CO`m6is?6}?)U zf($(IeL&_yL($G6ey1LPHm!lxdAuMXTIy>il~|T82p{~BS5V>jrs(9dIGUZRfXo*S znHFBR@YAt*5>=b2yV3dy9g`k?Bx-Z;3p%!B2Q)5!Sv8EG4rt5=xXPM@rQbNh)2r}g zYmcpO7vnN%`k&-PSLAvfk2lgl4G^FWN7x@~ zYT|cOjIC^xf=8+PVn9tmjWql_qr}3_} zTS{H}QvJn1Is;8^Bds9AD^kgzxL$kBNjHF)(`(qHK*P^PMCTowMS6o94zfhW^J zCHs12XU`s;p`m1H4iE>@FJ=C^?(4q-)h-F(UOkrIlyDs>uK4-qU|)bH;cf2E%l1=k z{9mX1l~1>EK>2A8M)Z^4k?@hf{yZBBkPdv;l8yd2>Q(=2G6ghXWpNFs$w!kq_!&(l zT>^L#b1inyVvhK%0Lj8b>(6T|rh7Mv>5s}C`hSD_%S6imkAs`xTiS82>?=TZ5cnHM z1K%xztf&yL^_AlSN-AA4crxGG{EBV=ryIup9C_9XYisMD91X9XbsIh(7|^SR-|v#S zd4+upOBXT(2_8W5Rs&xN|c?uAP(f<*H=pF1247?6vL zO*zPe_+$lu4Q&MH`u{f7l8Tx>+|;lo#0UPm9pBafY@c}(U@mT^lJ*}0%~gagvGGoV zf`aM4A(?yFMu9!yyKp5wvIbx?q!>NM=H@=|CPEVpP^QK|5cb$A$-sriY>s7P9?#}n# zQE>Y24_cK0tP|t~*I#KO_Sduw)={%;KIQf^w2W5Ry@haog>AO{Ruq_8@56Bh5t3x={4loN>~zP2 zjSnsZr2;+dAKG&_Olx1ybSCyLvin>+C}csLhr-v+xbzi_*Lsa@#&-_OOp(N`BcCfCZSXrs*Zq_dtN{ zRF>)l^d-+!KaD%+!~a%d_IY9=tDLtM1=NkyV)3LbrDr92q78${FS%~~Q4jV8ttt)g zGZRMpZ+)+dea{;Gkd>qUV}D1M!+oqG8^Go_euWbgg-`NYuU^TFGxk)EVIitJJAd%L zJLS}PcXJj!NS!SlH`@mTG{{=Xv;Q05_}{GPDA6bG2cu)EmKmqhmWOFC&!NSKuWsX4 z2NG4}6tTv)zL@tR1dBy+sJd7;_i#o)H{BL$#s=NK-3Ozv6y+sf@&|w`-fo zNWGdlcpTaVDc8^T%9wU3Z#=D2_>>fwue_%5A*e-YSU-?zb1>H!V3v7EK zo@lnYnD4hWbUuAQ@yx+yP+DJN14YXzrNzd^O8O|TmaRm|T;FZ-R4aV+^Ad@D!D|Q!}8!VBFQYmCQokxFF^ZxPVXEiVE1G_Xnr%>#x=OzeYrD3F+k&u4#{F{0G zAWeo^g?PlS7?d##C3T}{KD9vnc3P1ec*U^Wjto$+P(ttU`GjK{myp6um z4jayrnW-e*oI4bXs`!UT78JuFxO0E)Y;-tc^+ELOy9WWm>=F=or7tPfo@Kn?OTo8R zdj0Z6zf-fd4MPRtJLcMgcTS~7DbIw^J#Q#lq8fASI+Uo%yQJR9$YTtcR30j>`WTaa z9*=t@ye9BuiB8u|Ra=#{b0TgJ0m8QX4=_Y`G*s;nHxIy#rKO5j2fAFcRyM4}ymtp_ zqu$@ET6J?|Xm2F#JXYU&F5}A0R+2sUyU#lj?yevg2)hIO{nf!h#l<87gtiHbf{5Wj2JHx}(`gp+q zV(&e}noPI0;Td}a3n~Z#Dgp`uBE35*AYG+*q_@y}2oV*PK8Vx^h)Ay?B=jNz5oyv( zLg<|YLrDlB$$K-i=Xqzp$KKDzKi~If|Kos=`?{}dU8|hyTx%T#FfY2#H?i#>iuh4k zWj4{F{Iv?dT>jFcVX=vkIHnXn%~tHtm+=U>Ux%9)zy-?#<+=GEn%$;ne^p>?{F<9p zDR6@c{oc9uFpr?iz`APfd9VA)f6`o*zSUpQFX$_K<*oF&^HBy0DWCuPUZ9#Lm|y0@ zeG#rF*VBusFvvTpkq`A?_PHJlHSQK5Wu6o=DivrxkAi-Y#M9RAuUjnkdP9ffEc#y( z`9cdgCjtz%5(v>RW4K*)4WWGBmn~4#h!3gc)yDzKbY;ZqA2rSRmxd<^ddXytj_c44 z=dDdCe+#K+uIQNetB)`}V7?wZLSUEFn8FmtsA&7=nIK|DO+?7>Bx@>i0dB@7$AFE& zDMFizy3W<^S6t?dCq6m&56}7cHWj}1Sv30eCfjgJXLT?pnqqk)8^;53hVmHPehq%2 zUwI##x}0`}{BZ3ol2)NXa~Q9v#t2rH8Z{U+ZhKR-(>%O^< zRt*3doXlF4vW2E4xP#^5gT-ZVJ*RLq5xOq0Ad2gU6CCQGiKa zuC*6y`Nq1AUEl0JT9RgXI@m4ZOi7xoQE84U`=BF=6SIQ2ihXxHQjyW$e5RzbS*s^? zIrmGwGi}8{c|)u$yPDd*d&Z4!Ik5O_jCpg`qM-AT$~XdE2mFqWMGU`#ejyN5qcXTt ze^41{RaRqLWJ)FbpFiZr!hbp+dFACw+_3xvh)5S!aKGP_ioTEa>ZQOM)Ak`hIW(a^MdO7UpQqP{G$WRMwY! z+?!b%^DD62<~@5*XbpOEGlLeE4kn0fJ|a0J$&6HKKq-bjOCpS2)0ZCn!7SQ8K~o7G zJ@d7##(w6h_uQpDYRVJWsuC1x=y=KGdoFjUq_~+QJo!YbeH{dtoZe-2H8pqTN1nWht1N|gw*b?3dusg+ zGmWg7VCId^7sgl@bfh*$4H&Z!TtUM>?7U~3HIJSqFQ_$ zrq8lMW1!~ztpZ<`_k>>%WFSvgQo8ymug|8rLLmr_yzdESnCOl3RgC&TgC4DYei2cf z5JY>5Tm`r!$uRL1D!jHKAU38BsozD0MRd2n$-fLcnS5k9RnvD+mj3~JWdc-daYQ8v z^ayFQXskfPyTcGw%(Sf#<<;86!Nq)cfWB_U$uvMwv3xZyQB=n)qVS zGAD|xGT?dr|x@3!)h=xuBLkXe^dugI=I#^4Xy*O8udReG|esu;xKI$-Fki*Gm~eR7)Ru zjw3yn>wKTw4>BUlDs2B$@M?uRyzYFvo0O%Lwg7mR%I&tYk1PG~Ci;4Mc{MdA8C2|o zPmcoGu{Gj?_T*4WcdpUE>?Ii%-q`)byz@(A{BUZVxHj-Rs`=^bD& z=(Hx4YI`kNTZ05^$Zyww`#&rexg!9JMRd^~i%#%Z(9wyfSAI00rn~Od%0D00TN&T- zxQ*e7yWDdb_Q(y&_p8adf%M$It3XE%^NBSnTlA?-W@&VIWf0&&#Q=)k`4cwa7u1 zZWA3dw9`Ax#QpstHc2eazORaox%5b-`lgMw5}K}oOHr;m04YVwGVeDnn73H8g8)9~ zqe4WK-K(RODQoe}Q)7amT3XJd(1K)@`eM8==R!4x3+q z6wASSnH1~xmI|pWa2YFnm5@TuI-AC>#qGf8>yXUr1su&4u0dCdbxGs#dA`z5_p8)N zON<>%_okIjTKdimy3a*@3mEYWzRnt;O`iOz!8W1B=WcXjy87-bb)iR=5pIYnqe>x* zIwPQh)=LuHWq?5bW@6gw7aWYuH(7b*JT9t>2l1NA`3DA8>pmVfXxO4)r5&d7b|prT z3HJTJhtZ^K6%HI^Br=zdjed!B73*z3-esB#T8S-ye1G*+^?cw$x$~U_=kav%OVHtw zX4HU#gF0i#tFhdSdu<$f_<{pIZQV}8oAZvUnl90&rM{+S=z)orwAfOcw8mU5Q0 zao=ppuevTs?cPwIRtLGF5RARQkKY=`QDBo4z4Fl*H>OWt-C7Nms|Q~lE!B0)^LzX# zb8f!N)kEHUVudMhOjFF2)sv#2tTj?$?{S4gdn>F`fR*NZG)zzb>4WjIB5Ki1QgtKd zQ%qgJ!~6+|(bU$`Ggn8dqC%~ZO&SHpC7n|Efdgw**gp^GG9Um0m1Gz8g|LOI!%hft zmX69^xBasyVBTzp?mr8bfGHAa4Y0*bn43HebKvYq-lWX+DY zT8|$K)PY-?*q@R68K5u}bFFOht*He|6ym8N5O~Icy?EW&FYwA#@#D{z4>h>^6T>Bb&8OA+%z3I1=c(xWqwxDJRenN zxvVaXRT$STCvO~>8g)@-esWU^?&Q`cyM^_ajoeIKeybDu?HY6%$BxH}^?%4up_Ws+ zYGbSZAfx;OY1bxff}UB>xXyiSO7`0B zpWyNSpw!NWyB0nm5;xsR!4uG+drd{UGn7h!`P-OXot~pepiI>%$>vLWrNC|QQXg-t zwP@=M>Pe36@Pk^JP*=UJ(|K9%K^^174y?0`~dsnd_-&vEYp=Jck z!ZoG1-&ugr?RbF2Amh9HVi7HR!c2ZF?Hfz*L+HA11~B8VAk@1$;d*!L0Jzf)e?o~a zA>n?a>b`xQ&U~()gS^17sEGIbTItp}SR?cQFl6KmFKj!j^T6NKtPXKK>TiG)bWnSm z>tloUCpt4KZHv=)HW+WAVL#QRC)D_&(eJ-uYY8S;;2!!8)E#%HWHy$xAJU?+_as;e1-wF=3N==r>#&f3;`knb zHDLeXqqMT?)o5O2dD*ROm%Xg-r=L+52-u+EcwhDCHVG?%^QuRW0M!Snfd=XjCFpM& zh6;skR#VWS*ZR0mhQE`~-&k2#=P zR7IIKRXgRFZk&^F^1x{3)zQKX4%2;NBW%C)L$-fv0N120>kZ8!$Z|(Y+TrW*YUT~W z)ss&p*}w#>422e=Z(tyc0V=Uh?qFBj(cI9Yz3N;z0@U#dH~1(-W>zJ)g@i|m%YnJ>)yH#M?_PS4%>?^(#t z?d0*0N^k}{Ik{|G^In#l*4RrNxbop`nG3q;DK}odg2{!-!l$&&LFe&HzB1X*jKBEs zlmvMBS|6o5JQy=l3SCg4_s2h4E6nj)tqRvL9L#@ea~BC?e-C<1`8X~@yPNjnxC1>- z%wB1VV~-^BD#w)ga&ElI33Uz8d7$vc2`#dNJo_MV_=u~!{;)#PkAe@&sFC8w>dL;8 zR1iCxm=r*KSX&X*TXBrr5ko0gtkIAf>`Dx2lax^1* zDtb^|CA^LI+~V@9eKT3=pi&iTX`l-3ZK&c{bGx4Ju;|CuiAI)4`#{Z_IXt~Bm*o` zu;5yAv4l)NT0U|80=|!kNC9R)uNMn%jg~EGj)lFEjqxp{BHeubF2jB(S`6Y6D#tpX zb}jTz3~O^cDM>HYj-}S8fv7;WU1`1rRV{U|v&YoxOW6>tOkl&nv#|z79dm{zLzv~o z`0^bfb(~v|DE%>&{`-NzQr&>~pW?=aR`ZojUbB?kbbe8e8odHEDoXIK^O{o!dJI?q zrFI_86{L^&1(L7q9BqaeU@L`j{e>L#tGR!2all@iI3ddh>}c&UP(=e-knscURdv~s z#4h|8*k!C@8QxejgnOH{T_R~R;yLfbPY8n#XTtB^KV@{E-?7C+{x_T1P8XTX61@(K zQ7&EHF9FygPHh!v7=IQ#tyettK}?@@&ihpV#8~2Eo}EJpUY}URKVB1md3?y~#4R8u%6|F0HgR{7m|{#==%B95yqMGJ2~=jhD75K;4YsUq7ak<85^;;r zPyAzv7qWiWYgP#O@kmo2F??1it+z3gJ-xHSJt}=DQ~KTfVEBA@uk3wYGu#u&lMv>y9QtO3X}r zzfIW;-o`iKB!~l~^sgnD^Jzj~L{pEL>oB9mdJ?Ty5*v*ApTuO;2VM!;^;5`uA5N8; z<CY*V-pndco{Cl=VpYS}vg*UpdQlBDlgkeU z>T=MbYxu~RTomurRLn?#r6h?{>qT|8^mhGPi%4S)uVk?$3z}uPUaKS_hIMCw6tifQ ze#p81+EA|QOi%g*E2Cr0H6rFZe1{%){XLcBlPnE0e|+g?t)H$B?~#Nu_Z?&d$i@g)*8A!J(l}%K}z=RhYcg%GE+sAQu>5p6uP00)8$S^+oEY{Tjr+| zZd*oY1>ZvCj-9!3_t`JM9ys(Lez2mSxCIP2Tk3a8Ee!&ie-7vfejX9O4&%xQ*>QR= zA6UMw_M+N>{dlX&hQ^5rVN+m^5&_`2UZWKmJ!U$t9sAby9&G6M5>Sa`7e1{==sSI+ zd&DeQm=vpNz^=*38ByQ9fXxz;TpJn7yTcI|O;0nOp(zDksAW7bA-Gaok?6;E=X9=K=cxuY~XWn!u4`Fi0z#YcGgil z0B%hz&YRE>;Fl^$wZ9f^o%?uLb0*Lxd3Mb)=v8NwX#ADsl5t{d5}7Z-0WJ7+V97%5 zkiYMfDYAd1iUW@xOuoKQs)5*r8X;$pTfaV#FvBeN_g$=y1w}V(fuc>9)X0hGnXei6 zaGU6eqIOxBi4Si>A?B#fT8CQL@MwcjVHVk+E4IORQ2ooG8)e&pVXwN%y&ZC@$K*>I z*%u9JWQ!sCl&qFt+7Q|ioy6fxhJsur|R%cX_`xt~)_S4_nAa_SJe z(UUkCoJ*hU7NnlIJp+kud>vWeugA0X(81CZvstU|;5uH$>$k8LUC&quwS^$2Yc#xj zDOONdNQD^4L`Ift{Hz>7`V5us@+6<4M{~o=IZYs)yj=rtYn-;c@i zLlOKZL@7#rzx(uo0)-7G27`Lb9PFMy5j_z<*ciRKKIB+0=CguoP6GYe93Gc+4?!DfLj4x7ug?ER(XSbwFm;ySj|_`22vuU2D9Ec%s8eQ!AiP z@k#ItKh^yxw#kdHwZc|0moq6xxRUJMI8S%^>`HKT6}xUUUI%dW}+3S z{K+yCR+aT*uTcnGw*_$4OI@ZWCetOgga{wkqkq_ls$?Lg-uhCWe?LaUW9INh$eBuQ z`qXP~gL??X!RoDrC33l4hd?M+{GF+i&RT9JM$DmGuVH0jiB=sW{tRY-tp~=)E(EP| z{qeYDT_Dq~eNpQ8eykK$@7Le3#=A}*P)4^T6&rU3OYMA;CU6RsBnV1=g#JmdMif!lN>ePPv;BL8v;}=bB(%^u89R` z{vm3@!oU}M@(BG0Tl%M1VjV*}qtkyFz(kn(PW0oP-MfPJ^_u_KieXEurLBzSQ~Qy= z|NEJX>A?9lgLC~J?B{O$^%;+YfZVzNQhsw^FZS12{r6da->(1e;oooC|F^Pn*M17L zG=^Us$k!$IXVArQ5b`*ngHL}nx@B;rXU@wLD0oBzoh3Oeclwe(W@pchX*pltuXFW> zVm7^JrFKqO0jyE9c|>HLUx}i2iiru(sI2qG<+f^@BvPAU!+HWo-M#)Up+`}1!xoX1 z%(1Pr{H@ZuP8GLI8o!@a;NXr-CtoN?#KEY1p`V7fup?U z4q>B`*J5YTv4-hBets&3hK5-09tBS-zS58Y0jMlQAb;KZ_3Oi^m>2^>u`z;A(j9l% z9~Zb#bk-bqBXk=6Ie<>R|G3yxo?KK-!i`kdi@0S=69%>&&ugcL+ZC?&j^bp=ZEj8D zl=2vkGJV}M$w|PoH`b9x=l_1de;56~*@~uXtib4zor4eWSAofdXdOk$^$`4Uh|ms| znpIz4pWj-Y3$*+9oNNl+ru0C$D#!gyT`?#vHnLgH3Us06kvy&b^9?EcZx84VB}Var zXrhf$Wb?q%52ZMEHfuaYIB zl&|*jzv}R2xbmf@x2-npI9V<8FaBSJ{cra1F9%>!iXWf$>vGnQX@$-mYM%+!UwjZWnL@7=?x2`3vMWt`G)4ah3gm>Cety5Vuq6sLS7&B8awlR|@ z$3Xx+8*o)U#ac6GX=?KZ?M~Z?Fh{L6%lOWedGFlVC+B0L+O|c68%WdP_cYUY#qVUe z$L?~bB+E-){sNUU=5%%vmgQOuC+Tk`SQ2D&3}>VM+K0a^)!#(zpZ|2rClmmsiL3V} zW?M=A%LDn_{YtccX)wukw-CBj`qs*h+FH{|2@VdHr_VrlW8naxz5PXL_lu*GQwkyt zHf&%ud$gyeA*=P<2a0NX6eouR+QVhz!jT!%gZBp`^%aO+rDUn*qSjB=gr~l!K8m-u3fs!LFcl7 z-mn}(L$aoE=Yea;l}l?g_lcVsTo1+Sy)UOMGIj`y#`+L(N;jP?z;hc*iASf%)mme^ zg|u95wBeLO_}T=hoysA;=M>YW9p-w8Ur8ee$EW=K{46|=Nl83BShvLH^g)WV-;?K11Q9`A&Kz>Z zGB69rr*bsf$u0A$)Q=*RO{Jm!V^fLfqcbVo-KVDPO>_DYbKz5L+oZcuQn}ViT1p}k zuRUPU$NXsP9^8GgGZx$dZuZCgDVx%2?HD#X@$Rb9+e0=8LG~4x=-s(%n7j%($`CG~ zMCN6!IE&a2ed^(AAfYMhcQ@2y^-5{ZW{`#d9rQ>c*k&c16;zr2*c zF4#%S4>E_Ve)xmaIU9hOIDy~X+Z;E<~%E4!?B+{k!%pz0viX4+?c5^-f?Q&g|Qeuu4*EQ><#i>9*I_6$313PTNTF%kEkOH6lIeL{6Ot!{$pbtWVLbt{jQg z*5_hw+%efKU5Rv;PoOJkx1O8`kyPoIxV84ioDpJLVPKNYI6pJ^Vz8iYNgvIFv0iD5 zQGZw|P7m6pq`y#$oK0qr@mE}q#mV(W8l(FLd&xT7u{e_Lz>}*a1*J1Q33l&hzB8oQ z>tVIRPdO27K5F|CkG%_aF}$frHaXIE7=fYyZJhK7d)5Y#Zbr9*MC~LeDLv;hy!Cy( z)zMuKTwn269sSDs7E^VOs*BF1XtK@vewX-WTKp4vG!2G#j%C9ZTQEt1afZ*adV zL0e6FJHyqvleomMW`}F@obnY+|T4%dCVne9-eyP$QMT#nr zPQlA6l^oY^lcf4D*wu24pI?{_K#aqk@Evf55xaU?RBuLFOpLFX={ROEu-nBUavWa5&h!wkO>zkV_AP1G{2K*#IgW4y+$d%I1d?tJ!IoOP^a2c5>!r2eNep;Qc{|F&!8O;gAg}`y;L^G`U)W*7K^YgbwSa{#men?Rv_| zHiMNf53o4S)5z0ppNSQan<^6{mXwxtv)RUxOB`Iw(F9V^1Sa+Lm-*?vVTG^($tWbqwwV6Z>1qhriw}zH&2GDiOzcP6|4pn)-iD*xj z5A2;^aC#K=DS7oh+OExX;c`}x7|UsQhCh7nbIeys#=16ZmdEO&)rk#PdD;RszNR)Q z+qV2ko#hQ>a!tww7mh+{nadND)6{r_c25WQ zRXVMqE$fY01yaIXGR?xP6_ca5pw#tX*aq`fM<(ZljcAaMd>frQ#l|QrFSr4fzTCqU ztmwBEAT3Ovxg48hAtau&9Bk{dN%m7I8G`%FhHZDBE=&mKh<^Y7D#8E7?-;wat(Ul6Mi2J2bR7p&d@;7cFFSYz@e@LFC>01^S)xQBOB#DdosE+-nn)z_ zrFY0_QFClQ*<=If5OtTDqt$!EUe5D2sZP?I*DvU>u4p*)>X*NI0c;QVuc7J-v(^_I zjuPC(FF^w>H7oOQ!rXFa?z15IG%zy_wd`FYRNK{P+ajsgZ*|VKDaeaC!@4Gh&!1A% z5yIN3J*#pW>J!QR7>5+pysuxy2y^;+LMd(3l4F*ve6rHIU7jIda@q_9Nr-V03$%B_|-ux7lp?bF7!M z?nPbyb;NDV{c-PHS}`X8EaheJPdIcB2m?^N zhI_Q~)4DUQ*F#}lP|@-&KW^q$@Ah=nm?%CfXVgLi%{A(Mk&@;uFGiW0+fUv)+W>i!guJ=pd40I6{TdT}-5B)SY2* zqVH@zI2FREW_v$QB;d9eV&@$u>OZXn4US=73H5>(Q$97j1?8USDJ5tqE-?2d=xu`z zx$>}blI?9a9rODoP8kv67L7OhL(F$W7+Ug^DmuQtjGe^-Q*Z41-2JyCNre zdZik-`LO}@?ZswBzc;o}zV5qhjs(Yy^iPd$%JS zwU|`z4()6*^;5oL@Y>57ku4U(%sx;T%mBhj&xH`hz0Obrdd*`*#MVO#o0c`UT7F?G3e0yWddoJ;eL>24BMHMJ zhZYe*2yjDmRwIN~yrbd$@dTk*^q9Vwn=)iGoxhJbf9zOg1v*Eyh8>@S6A?f1bx4js z!G9$;`gMm`v~yibXJ;;}iE36OFv;&Pf9yZ}Q1<3f;`zu{M5JRQ>-h5DiYgcm@wsC*O)udgP!#GMz&<%_ThB`aB;H4wZM9 zW^I*Vv$tR_MUhy+5b;a8xdnr+-h~FLD+MW^f%&PqC&F&KRk*7$Xet|&|dTdIP~lQ=iMzB;6TUeoi3^9{ueOF zfvkW(al#S{pg;98Q}n*pKWTCjh_PKB&-?*hgsxlxmdN>tX`dF4&*4a-`seIW>cor_s9fV&Q! zSk~FEZrfX7*$rTUtaC1vPCvE6!V5rGZKedM!vw;PE48}`p{mcqIkO1~$D43_T zf42w-D?gq;(_;RvbUf#$R#;*H$e{D#t$n2){t{H9cfbN8*O3_UpIYJI9!gaFf&L$q z&|gpa^OPf>0LhBS4SulwJFvj_SD65^TXM^iD;cvrk{XexrL&4vx$9{F2 zqjN)dJx?(+w;kmUKC?uLmUnBUk*WX#npIjV(&znWs^>4!{kP`#&qp^E^Zt=^tVbpW zgA8Ct+!_7Q9i7Jl2_;?645ig2VbM^E2qXJ8;lj?63UGf~ua$YteQPb(r(XZ(?fchn z;?Z&JR=u4;P&8|*L391+RjiN35_=y~dY@+E&qufkg(`EjK!8q4fO*^jfZzEplUpx; zX2U1@4qXJD@mJLQQr!44R*7B}vJCsF|G#*4FBxY#OEcp8DM^$EE^}J0NL%?&z3_Y` zaGQ5SlnnNj0{!d8-_|*NXT!&QBgDAYe=Qx}mYWe0)P_9yQ_3ey=)*Nab4cU!cz6Jyb6zCZ@mD`Ov((b9cZf68#Ie;)-M(rE&(XN2>vUl4ij2D$}4CH;Hw5kqoINg6E<7P7j3F!9V4kJbYk zrB%5xhvp-j(68;oJP>h~mB3eI@pSe}+;ERQqz8p+H6J#9{zJFI4+tJkL z6LKPozV(R545n6qQ%upT2Z3(+dcSM4j8z)|84Nb<6HxdjDr4~`U+k>Q5Q<+X$9gt8 zPTFF(VqJWAh>PsL_>f>2yRJGT>g98bum9=WEQyIr3M+4`9WD>sbmkma*A)%26+f}1 zT;v$tJwb@sEimRN!55d$$gTI9aK#!^m+D4HLL67hd^@>|_~vZ&LGJ|K-KYsuIm-#0Ek?5QR8macb`pTxNEnSVw#IuA0!{&5*Xdq8^yOn6raVb610yr^ zU**xn&c6_u|F~wk_NH?MJk#{>+)kS3sn$?^PB_idJ(3jkWGg7jI0bR}3-zcq!$aa3 zwk|5u@bm)=1OlHS;+C3RH)K;eau--qwwJbDh#~lp32yld_5R{g0_Z}9-IZ6)DT@UD zhWup@vDkeBIYz%9&|sY{adkd>e0ExDEuY_{LsYtqUqwmQpE~LbvmtW>;~{lgV^lGP zOo`^wuhZKH(B=k(uV6l=T>YO1`?aoOFExI$J{7VynsOC8zp#hdk}WeVX1CbHF44Y| zGg2-!o2N&+-Z7*l%geI$H0m*3GbBiqX5sQkX~skB3g%m~jc32O$M(2BmSumv5*D6R zp}IC!Gu~-mvTxPabnjf1$2rfd_yLQHMR$jJPPfvtjuz@W>qMhZLP^DrE^9Hk*OCtw z#&&%?NV3O@2!vSv9q_fJ-M2Y0*FO(&Gb`sirSB$5p;;RH&5iF*6+=XS%)~ACH=Bx@ z1xeQ|d@Zzy7uERU`NIgpTMSEFeJFLs zM>vXev~`-LYGrCQ7dV4@keKEU5lrVCVP$~oxLE36$@`Pry1NF?xXC&L zS)73B%)x$^BHVd703+SB-$PF)EP5J}vVwzZoMu*2qf?@X5&>jXgKw;c3e1u9NhiHV zspv?=b9j;hqB2}N6(Im708m{b)31NUChFX)S}NaA=pB9H;&mtg8AdNKz}B|r9)Wfm zE2R)4wr|T&_U!O-e$(MPWmD5r2daH(#IA8Nda@zOdU3HON=WB|$~0qC>>OilIei3< zV5I81bcN8dvgIIuh+;~uEEMcN9Oe6oRj}OYIM!>9Zl=`WB598j^P^+BzsWBtWykW( zY>VVxOSHR&xI@uX-?p&gB>Si+6k^z}SN9_tgj6QpYq}-aY~zWGS_RW8Kw|2iZ;=u1 ztU`1d(MQMOUwa<^!aK3LdZIJou-O}G-4uAN9!!ps0oadAmxhH)Ufkv5hQzK#HQZ31 z`Z^^o?hi=0QpRi8ddGxR5tuh`n$me;w=VT)9uqcrWxeed zdeP;m^^fSO6de?Ww%{ooRyFbg_pHNX5Locnb}_H z_ooonwndVU_$1|}&BMs+s>AzYDg9xauqJ?)UO7Z99~(W8O=JtEcw{f*L5(<$)1~gP z!E3DJv7lH~b`QFVg9RW2qTfCuyecuqUmUWl{;}+H%-_k>b*x;5%Pc8&=XA&>kfl4R z8fjRzjrYLUEzs3-8LCeEbBtEz=LPqou7`+$|61vW!;Pp+FhH-7|c53&RA6!XzOO5 z=QG#V(ki8OE-h_*^U`KUe-62};g798E&>rs;k2-M1?d+Is!AoK|Hr$*uI{ zZ&v#Lt|nBk%hHKQJUT=x7UYNoNIjew+HHgoVAW-@@m$<`Au9X$^rHQA=xaGaxgMMZ z0J1u7hmBy$d3U6WVrHO9mrH5GnaR?Sl>-pbiHk&%t=++R_tkLLk0LbXnC^CGNwiMdeL<_l+kQuqGNi1isD06pa%7`g^u2%m^;U&x5Q z&oUUW)Y6+7aFx>OTj2+adA(Mk*gnf0j+1+Bz4&E$YlMS3Jt{T7oS+h%8#}+TRGSne zIS$@&o!?H9?enQ!id%LV-HLs%(2*q?BmmNn>6K!BpzG>a<8pq6)+h)7`_2iIL5!Ru z-s$s0a1HJ)>W=_ODBS97+_f6B_NEX8Eej=pKjT3)Q5327%U> znl9i?1-PIeXvulH{lUcAS_Ce*WXWPn+}i zJQ_kBwpaAqUmAJuieC(EdTR|2@H@M@@m@%gw%Nactxb{gTf;%baxEWuCuy`Mr@oJP zVrMw&2y%%w^OGgX4%>I|^V>|@_GP^vcxgU(ks{a4w9q}cW>94)tkv3L3ojqQ77X?V zrI)SG;j_b>Tlr)0IM>%Stjion@f&+P+w4OC(A1^YiUhiXf-rr)Im{_VjWQnhY zzC1Wol6IF57KxG(_-x_$N|U_-q}NpcsKa#4m=D8F30{#f-yYyN3gkL$2S^0Bt5}x; zm)rY2#VY+hMqPrGUD_cx)ZIVwL=)J}T@!hO7A1>@dG z%jp7HA4s}YRz#7C4+rK{wE-O*GzcYE_pL*X91ldIt`QW-SQaJfXwQ5rbq}szXqCP7 zyeaIE*GgpzVPYrkMw+mypT6)#Jd}&|-uk}7v5N^!AB*2$hKE@Y|NauXB2czj z6XWKwUlIFri6`{{P%5;+2y^W}nwGywMO6c^vf<(3#eKU*dmHfIXZ?M<{=0{NzZ3tJ zjsG8IBV|RduJ`zvs^HKkyPF9~)1|RdX#QNDI~`(i9Y_^ZOsY|Q51JK9!+o8ItKWU< zzQi@h7C3h|Dnu`0l{2lE;b^$svyvE+d2yfVi_pVYuXLcUSj|ql!Fm%v#isSV9bR#nBmN3udW>-`pcVCSUpn`fP}4_N_Flw`*k`;fH!i5y_yq6D zpRrL3@V!A20Y#^yQ4bggDH|2FSWGsX(z@IE4hMk6_=e;H^cdTnW3_+jVHyt~w(THx z~CR~7s7 zA)oYIhmK`H>@N3|1?`BJL}3y&>nKTD#G*B_Z_K(`Xzw*PDuEw+b>^7XvN{>9nnY z!`s`W%^8ava63qepLqlXx}L_SCODVwByRkkKN7--+$hFK?8mAApPApw{L}>tUG&X9 zFiJ(Wl%Q*KYLob8<``g|y1*h6Ex+lmde2FzhD$UTL_erx_Ezn+zOz6qDk;+_sd`gU z?FDKHUMI%o^3tQ9!;XS^_j&)`#(W&`Z26!|iC#>Hz9q0D`w!Y-rs4bzXUPq_!K)DO zk6w)Lmi6a382H4z;nXxYvxw%nUN%OnZSan1E#;SH zx;^8HK&wBd*fNTWU6oyY=zjjQhF!ZUNs0n3#Q1z&;dMMnbj9g>TP#0(LbJ%J$FRAr zUyK)=FCY1}$b^L=CoI%yrfB>oF0MCEq|n#b!KbvRC)GbF@l8I^&-~HlQV`GZ(Aj#9 zp$C5R_h52*-H|gqqML{rBvtTZJ@xZqm0)~PU|o3zDp=9WJL>VRsJ5xBup2pR@&+xb z6RR;Lq#S<>j$YP%p6IbN$wIePZ-M++h7Y+xLSxYBZ@L>Hd@B7Bh}^eL^Wj~+NiW4$ z(6e$>o5|LxyalTj@=WxLxN3wT3XeU9SKFALqQMth;cYYD+F!p0a6V?<&*L#8TdJ6k zT&?v3qVbPy7#jygO^!bwdPR4=Mq)apCo(1lNT-~L(OLh^gnD8#2Vy8;lPhTJ?j8om z2?Mi#9=@-1`OS+#O|MPbXWd?~DyVzbWx1HUr70FIC^gntBZyLZG@<3SlI3r0)nheh zJWHL<=d2&x*r1z64MWTLnYq&54_e<~u6rLVBgzE+QU-Z;W= zIk!`6crsnWWLc_efx^aKs+#3ekCSHu`OjOge_5uHN_nZH-zzfujOu>uo)ndp5Ufkv z1m6UBoBo>;xH%c!n*B@exzOzM$8Ir)n#r#}0^1a>aToJGj+cvf$44)u-mlUSivy)F zFC{8n4p->m%zNdNWd7lVt0d-Ft;JBXTB++3A}Ltat>jLLRx~=^xls&&*>;k2q>|Yb z&rEQ#X%;ja#8PW=INR*QY*k+Y5k|#az>`)KC0SOIa}(59nnmfoyf7C4nH-h5SB1>U zJpo_qNssKEpWO%_o=GvjitSt<14|EL%K~eb<$*76r;8yOqszeepf@?RO51EEbx7$P z&05^Ns`MvP@GSKnl#J(sPN>pgR_ zpM1Fx%5638rKHA8DgU7HX@f$9HWONodrNg~M~JAm9v*wT)t_F$KF|qhH3D8C8ZH9v zlq-h^4(q~Ya;C%Fl>k0cd}bp5pf7{w<2&xtX{RM7uC2r#QSYw(I%4CrTQok_*I-DA zQh9V*1nQG_u1Sdhb6;ant`y4ct1>1@mWTQ+fY-=OI(3zn3G|+tgG{~D3$TQ;_q{a_nge` zUBvdKyic)qb*J zOk5JVr9o=NC4JO$`>4{JW(On~s0&ai4tQ@{q{07cM(OlwY`9NJ{(WL!Tr}LyyxeX7 zsn_JaOp(kZtV!~LYD@RnAO+0)BbWb&_Ilwfkj8=4n!2#<(O&bNl3kzpBK?rA#x&O* z;)UpxsH&odGTTmX)ikcg5J{4~gd@zN($77b6A8pPAS)jA zx)mXTrmm$gJp}7NuE06rNFtrR>uo%dP1Xc6+!wq$qR%vyBUJA1i|&SwVI1Wpgln8X2h1Gldo>|;shmnu@qlsmKxDaY~L0S|7?G!`i5P?*N5XA4(%REyp@kAEui_gV zPI_)WI5$==IQ`?HkWY;`Y_;M1+$ZbR5s!0u=EI!CUZXn8!iSh0Q!@InOG{!*+f-1V zpPJ&BQpN*sjqE)4n=nwMgjz%3HS?V%f16#~2WU84Jq+hVt1gM|jY}8j9wiy2y?o8z z4-t<6$a;oF=5y9Fj9~53Du0mTwQ-03$Fl3)K9k!g)6`ZgFK0i2_;y^Yb=LYsT&XZz zVzp5dnkJkCChfTH+j%LgI&@M0Or@E2L267a8YmZKT{Z2tz2gbw)rCiB=3Q;-4BGg0 zJsGm7@9fqH`J2n*Vm@qu8{a4>d+*Ec6>}{FWgyT{FCYe z4d33#Dy^Nw0h(D2I3-UjJ}F+JP+)jKTez``6v^tYRBNgM_UVK(ysEDbuz9!DH(LQ8 z`rIbQzh7_d?xfe=8~b~C-3b9orp;n{gho-t#X@eic$BFZe%QNrPDN|z&4o&>T*;HA zOf5}?VV86rOv>Z5?sa8b!=GI8Yx|;6RRz4e1c;|GsNhllWYA}IWcx2$+`%~t#fQK* z_}+q9ph9DV8}$4kZgnl{j}Ae(P~TWjDn? z&^J9L;dPIbeyyjyY=vp9_wECeNRqXj8i#|m@*TrZ`d&I(7Jc-7izMH7srOow1|Qq( z7cww9b%0fEK>23}kd)O_L3V$_=w2D! z-viKpd*-2Gd}VphtE$a}C0$3#zT+}!X3y_ZMyyU{&shqhmq*G<49=3XW{6K``~VK} zh;d^ETXrla7A>!=;m@#{{>I<^bggu_5K4H1C8jJVjE=g%3BFI-|_mP*zOV% z+pQ?&_rfiiS%bBs;I+~tATiF~v_rl}<3A8)e|%QIA_eNjAxm(y=Mzk*el>~lV5;&8 zCcRzU@0<-o=(u9(T8aeSjG$xB8izV z^Lr#GV!n>g#=^Dx+s1XHhKAyy^7Ezi#jU0K0YXguuGhc@I>mDB{o^jwR>P{dI#yH=Hg9 z&4o!z-x*~qcF^^osq-o?5R)~)$4

VL+Y?_mU#Z2Lw975#o6(XW74xguK_hW$F!e z_(!W6=VPcJZRr7oETG>`h17A3cIzUhv^7);Ent2+*d1u6i4;2C=GW3jEr+i;?(nI5 z{s2#eTZZ^*xSBNb#W1=$;N%4FgUF>{@Qfd!UmL0$*XeyUsKaFZ$T~6_&WDRlvfM6Y zb9yINqG?Fu|Em!XlSPt^V~@Yha%NXA`L61nEvl#EkGrQh3kgA1YpDUw)YauWEgPAA z-#=xc@9%Dz4KPbKK02fwjh>8LFtF09OfnwlWT3n0*8?yfa>=&qko5v)-OK+f4A*DlqH z-ISKo6U@h;O1yK%eLB3m%K^AL(S}8B1S#vcT*FW4R0P_0jO1x!wzgSBck!VCmFGf) zTYtoo`7%Zo3Wk|ruKx1GuTyLR$enJDu={W_xPLHqfxy{|v{S7!4Ul{#7>M!OBimCt z-O?f(O1n>p^tes$o633-+0>_84~AKuIgrvB<%sW85b9}NMvfC3-54sXxN4dFuR(;~ zjg~#CcDz6pA~umUviZ>PTxl_v?iy>VNky9yoga2>*oWWRI18T;of5vfKK8652VJ1k zd*dtSZS;jLSi8TQIdql&La4Xb#L}t%OCoBKHWo8NF?xdcjmffssM>UKj+-M)Y(M7K z;zU@;{yK)|(g!`dJ4%Ff+Ttx&qK9_UQ%#XYF}R8G3m7hEzaF#_RZ8w7c&mDCqnlcj z1@njYsTML8>EnLgDgVp2Ba*e$c6xB(d z1%Q7SxRN^9n_BkJ5sZ>5>FKE6wf_6go58~e6P@#W<+92`=uh(4%KKA%G;7ixNH0eHnSk-)7 z>%uq@GrGgm%)0Kk)A0u3I+M83zOg)R=R(FUd+)qiWNU-Do}7;5dW*svlz1tA{`46s zJjbo* z?&(Dq+eJVBUB>)lRD>`8(9}J#*LSea*1#>iRIY--Z>kzmOKHxrV-Bj#PckXye~5JG zKwYDM5ZhxdC^$>P@##^;lUUVw*+f>v*x}i~EK2_AAo#jJ-Li;B$$!V{RHCrR7ZyUw z1y1m({^NZkgD7{I$PHM3JlO>V|GW6;)1?5BdwW);?@m;E{oikAlSMf(&`)Hy5BjSK zA^y1?CcK#)`d8Ri>H=ly6A!6P|JM~W|J(9^LwsmGq>$FZ+3P<_{u500@BjDzciDfG zp8xpa{}V2I=$KVDut}ljYea6wOphKfn`Fkg`6*&~ECVc%1I?VgWt?vZ<24eR^2PRf zR^LIdt_sxn6YEp>?Yxd%9{?0tU^@zz!U*FYlV%?CX)=>No&(sELD1?{Yz}YwGpxHP zEa;$BL?Gd}iB`D2RvTog{6Ak3@_6zvlTuBp+RZJR%#T4&6Ioh z==gDmRu?TV9hF$J`c0~vRB@M#&F`AHG=j_9*fP-c9 zcC96Ld8l$UZ{}UBq`+sr#fyxB>enOb_+*QfF>b$B zcVxYO@Zn&NaQlyvN2~NjN)FQDrs};?_pr9|X-2lEk+f@3QScdetV{T0Q4!`1NDzdY zsZIEJ$p4b>y2bUWLtqfjG?t6}^5tB1onVmMN)SE$j3o}1%rpns-x2f{gv=%$MYcNdL&zb8%>&IQ^h-`drZKsDl2;aW% zlsD7yOck$|t^E98Tn*iDb}ohR;PzH#->3y>sh(Wm?~laKuWOgCL=hj9<>D$v=mahp z%@VcCho9PUY@waW`HY(djkw%deAPrsI_m?*yht#R9u0H=zIyPAkCGa_Qs>Dd$ywVYSXMIYd{1f1gR{ZvrmGc~*XX9Pi<#=h6Xh zVlXbrMVGk?ZQre;2*^_8Y_wIOMK8hb^i;uq(<}xOh&%oz9*BX&!T!*=H>|YV6hRwJ zR#wHjn+m|71{DSNQrYfVs1feD0r&dV)zxQTrSiN2bf1@I*+d6B2D7F~sx_w>J(McR zdZ=gXyO6e&D0S^;voaSxLsJZoTKb^jnO7zeq2>xbqhRFf%Nhh|`eM|))c@Xpqbodm zWE=e`>`Hcntxb(G8EvPb^Pcw9n z-aK~J<$-A>jt+hr;~#^pI>+av25$IklS-C9m;vmso!>Am(6()U5^`2{*FU-qEoTcK z3L=$&GtL{17mT&!H$6fi_U7epdYU4D29tCUwOvC!g(mj7`rNig8e!6v=;YV0I#-H? zLv(eVh*xD;^;|=uAEO(bgv-h_5DbEs*0XNjpjND!3K__xPjVDe$g}ySOh6<-M;l`+ zE3SR-KpwI(?IRoUVR3brUHXL(CBH_6lmStVB`?x z1+FhCKSvHn&0us;i#NdoHPnS>5=f(i6ln~p{Qq`^piVQdPNqmvi zYG8Ifjx|TrC|s?thGrmqYjhb27^q|6@V88SV^FN@^L22ibm}RWfc-9{r$q7cB*-j8 zH)^>1=>0j;CVl}GEZfv%k54lznP)D;;8;DPCdYDNAus)TrZmVlVPRP5!B#8xvEZM= z8CDRUP)~qa7&=8gy4rJ6IN}aD3?11Nj597{Bp3_qeO{ex_Yo($k$*^1i%!MqB1IUtik9~lj0=26DkZ8bHQWewN7^EU4_?#EXSeB4M8j}Pe^Gj?;&vQ>vdC} zr<5N3kqRmO`n06nuSEf$n=SoC-MTft-`#RAxoV)X0r1_>NLhfjiAcRQetlg0NY zlp>|~HXwFlJE=E7F`$ex=TO=oQb{ebaa zoz{cIgGx4zgfe_yd(8^<(;#M3bi1?dKaShpFJE{biW&4z>uF^|8| z&zYvZ#R6=IQyJ$sb;^dYB<2!ez2=~pJ9eMIMQ_HP zX6zYDxhh%fGkT&$hcUo}zgBf*XxCh#dX(TX)?~gTWRb}0wepNi$&3|4flriy*m_gs zBRLq>H|Hr=uOMJP{dot&(mea80jT{TJZ8=^{ucu%*-DOnm>cO($L)8 z{Jp+3m)9?=J9ZY-E(_b>+C8@Tq<_25$iY9?q0*sdJVc?NdFH+a+F*!#L< zJZkXTR2pw+VD$F6*_hS4x6L*Xppu=m1sk0*_lXLCnqq|!CLd!h2d8F48LbCeyL#f& zMa>1dk1Cc0-z$HA?T>V+>+ClJJ!@(@u1$uO@2CD=Aq+r~bZ0XZp;Dm$L}g!F9Bz%h zW>INMj-5b^c5eVJbc6uJ8lKT8Rs{PKz`Q)JYtI*Ov`fEZ;kqt zi5xY&EFTU*_Bh=es%K;@-w}I$WE9>o4+!(?-lF9iaqV0)YJcqdVOr*#e@j+srx~UK zv9#V1nb@!Cd@vS2`Z}?u$pK#_L__qjU-N8?2$-zgXx2eTpSV7nE2I6)_XpHb;0;mx({lG;`6cSFlp_P zXOS@mdSN??*O;fxnkGstD<6?=qnql2$-q^Fr$Gb;HttL@_Hl}_Am%u3`O?*SZUm?Bw@>Z5UCPo?h6JGHa>=BY&LjJl1&GIDnXALRW?nd zn0XBE)c}F}j>5wo6a_BeqGhdqJbC_8VD?CSQF;b>rGUCe%* zKgFQg>LVj=t7PZN!ROh}<>foSGI4{p`EkvXMBHw5a$nbx-1~D(CFIAUBJF`vmty~} zh>*Jwv`4hBY*nq-CPLmP~G1;KvL;}6#hzW`@Br7*Y7e*N&1#0`n> zR+WuX-t~(pF^k~@mRd{SHd#jbu(Tl?xv z5go4=G{pBM6jh?}RQ#6p4w0ro;(6-dkIMU_Nw~QczPu0peY7QNzIc0qI?xUzae8p) zaJ|!!KsmFLu+L_~zoyDUeA4%1l4IeP5h+qBC=X^JJ?exSv>k9FLF;z1N}U$70xD%7 z60`5BprrR)R1$JVKN#7RhrT_DlIW!SXccT7+4s=rSyYLW<9iugB3VcE%>GZhf&LN$ zOGh3eeq;)eKQ=!vAVsK}Q_yssx6c&`K&1NuR|S85+A1xA_F|oufj2vud%K-?a3-({ z!w`mueNN=tfZE*!rYI!&H>ccIxV#=TPo*9rO2qvx>XCJjp1rozknn)h`fnZ=kLc|A zGS8uw%-k=>se(4=x-)x4?%qx4w2N+NoBpbk?SIp03E8V%i)2;m8M)SbO^jI4QQlDr zKf-Hq^`;M`OA#yZ6I^y{2ns1|V3OKSb?W5EFr5On;KtvC3L<5QYhi(9JtpDnrPKfVe zNpXGgW+5k|54)+1*1MBc=6K{?5Av1S zc%A5QcB0NRHBn67+*|jROXuEtSH{%s;|bKbTAks zrRs3^{L7O*XilBM)V4aSmUSpZvE(5^?_ex%5t7ryd3C#VA3+;JW;(&Yfg&JN$iQC8 zqCZ?VG&J-i5~L4f-9&<9Ab2wn`cOfEk1pMtm6i3j&M&T&eX%#GzpRlFg-5D2hv18q z18)^+RSg^N4?OtVBosWe5LcK~r?E`htuZzW*yzpw+=;*3p-2<*4rm4u{C)ePv?Tuw zUamo$gwtVu%oO&>*Kt0hpru>`;fT8yq)jU+_ReL;$EAN_#k=xxt&v?yXXabi?DxPO z?c=67hIr>CM*PZ@eB^kZDeDr~8xqWiJ$<=h=bBbty;{{GS)(e}46T9Tgwo0)~D-m?GTV%I`*PZ=hVU zC)<~;q+|`qo1h<%=6|A52pCOSw{`)g&TTs0z@OoMQo#>v!N z1$rWsYYWk!CgUZM2oso^a!C`f+^yi>Hfo{rDIS{+n1#$0`pbMeV1=n%w3t4*ukY!Z zU^zB*$$103bN~I9S;EH8*+3?cLScAs>McuTvDWL?4lkE{Lrc06qx`Xuh;$j)clQp| zu)nnTO^-p+*jZJ2jd|#miI}FV%FMfn>leKgQVyE#bglk+oei;gUov9+po{pL#zM#F ztW#E3_LS7RpEhN4^{pmjgp;;7rwej`9_hq9E2;k>HUj!CyV{@`a;1B&bd<`7}% z{H^o}@#0i1{ku2!->7M_%Xxo)yWLbVl_{MO4qBNMaw>+`p?bpLzQNgjo@-GxeE0Gr zo3IfAO)7J{RtT`JNMcM7x@UoyV3;27rO8Tx<_WD2<{<&OJ;j1wAj?MlsUDFbLG68*@w5K-gRDe{|s2c}j)#XJ!l&JyeIi|@L{4P*{zZ(>m9h)t%hsaQ%n~Wnst|lXON)QewBC2d1sxu zi2S7C1D6Y6IcNgao9tMXM2{CnwI-MmKeMBqRU3EeSmJ>6_RW#{HUmZqdr(#CSwOpw zAD|fMI59}|hQna;)#%p6mTj(qA&f~Ahv|#UCwD~a7q(Q{;(!4>|C<+dYcMmT7&3>Qc(DgtEWpqPW!u?7TluWOyGseOhN!S! z7D$U~Lk4M9dmP1fJ`E8y;ywCWH=;s2a`G$I3YYh)$$y|(k>AaQN&JXBjID-fPdgjt z+Qe2Lxy9BCYnOA>3jnNL%DOG3Y#+7#u8qV==z{X*%csbd)h*Lf>%r>D-zaOYAaMiR zylFmscB~SZc<`>KxLEVeAj>O!y*I#hFK;Fg>A)4Z4Z(V3nHEXq{VZl$4G&Mua$g&& z6H}{l$RKB(9~dmyZ%90l+v{|ynkT(lez2~q>r|PwKc*=Q4h3Bjf8O>P2HR9a2yGwGn9o@y0#*B;6r4f2M+eSXFs5dmEZv)lP z1D4_a=QZhBQq%o?A1VnX6l=ST9Xj)6x@a<_0^2ug+wU%cS(0=gA9B>e!iGAI5t7w~ zBh`0tE9NZHRy)R^U{UTjasD;V<09r2=}AK7(4^++V;4;5-VMZ|ee7h5Gb7itUbs7+ z-!nb64vaGS2Xo^RNh75tHVD5snmr zc(QQj2;5y)kXiY;{m#reg`Z=vl=!hnxM6;#MN?v85CC4wUk(}F-8k31H%pDbYM+!Q zFfc>o7ctJa8a$wQwVE6+QcRBTzvummfCu90gCX~imIk>H4zlRO1ag>0U9yqfH^Tcu znP#+jDdymtwyY02eYOIN6ze?_6GZnMfUocV=7f=PA&U@fzz|wyuU}ZfJ$%0fzS1yw zJTY?enyb~G_1BY$?)^FbU(K!@+^Br(5@Q!zII?aIY6_223N%EBi7n$$*xxPOfG`!& ztPJM|C|HSu>lWD)E`Az2H}AuGtGSk;Lb;4uMJG)YK0?weL#3?4G`8Y!wOSkpswx}T zjPoLb4F=i(!H05;C7ZId5Q=6JfV@n?dRceuYM4Uh)LK5@STWD$-diUC%#-Ww8VciG8_EY!<)6R zxd>;#p|}NP^>Z2`cx%0-z`@r^TE!*1azSJ`;MgCQd2iDzJE=FI=4d-($F>H9c|PJB~KBj=REr1TGb;!j=% z(whywxMu zD^?p`*Rfo7?~*9-wKhXdV}VsrrnqB7nCSA&CWYmtI513bXG=dxXMm~S(eLUert7L$ z=ib{qyou3zrf3jNolb~5eh%$|j)AKPfR|#B;NVLha7!7gjJRcw+eE)7?ZIezfX^UVNFzwvV#oQOF&8uU)g^oalh%a>Ray1 zZ?>8g{QdskwBFb8KD)`C$2A(BixFW8o^FG~5%O0(TU^KsMQcq^VFP{~jN7pWT*+Ju-xv;azNO z*{{X(C&s-_ooT}ZLp;py65UAgY-UVM6DW$l^DK!!exz7XlQ?mMJMO1}O3tQ1V6ZhMef zdpNVNn?yu{rq!%uEs0dL1k1VFLtowxznCnvs-{iKP4+gS6wG`=av{VXE zP=L^?2b4UfS-XaxqeG%U>6bkSmePmJasSJf_WyW+FPqYx=-%lYdvM0sBIo$mDr?uuZXd~X3W zHXXFsG#um5-wgBkczW&$yT`w1s{aoQD1R6EjFp0LmSmoP(>lj?#q5++daK6d?RT|T zB_hB2E7n+o!;DU0n5Kamb?DG$;E3au)yNV|>2O}Ffvi8ev}V2ZgZTPT!uzN63&O+3 z#zyv^H~&U3@{0NCm$h~Tq#LakZDcOBhp*?@>PEUnkqmBRJ_&L^fI~w~t#$RmY^Ym2 z;C{Y7bErEkG+%6s^;&@;BL@!#=D9x1L1PB0*vX`u>%c^i-B4lsM6)`-(Dh&R-|pW# zIn?-GEb{KOmo&HMH77wk8QSajJ*ZT9149s!zCvYTTYY)&wp1?*`Ss4?r&_KkV9L3Q zYik4D`0XYC6@KwQr}~QCiznz8PsAVE_|RJjo<_SmfQ@&Ae7Y5IhZZ=M`*J6@_4@s3 zD%u&HTD6`x7p|nW1~ONF*dfJfWl#P^F+thGexj^C*Cm&7BP#gU)KXi1%(hK9r5rUb7c9YE1tHPjq0rOV5Yvm5%#_AwMt8&kojk^3ab2iNB2JECn3~;P~HW}m)b7+Z2fb5>3{m?AAgs+a)!#A|A3?Q zh#%34VrsQo-Qy>xre{7muu5+j6!905PMZ75`W?07p;MpDT8ol z)4M+YMbBUXyS{=--=X&vmH(?jZvBbvr#$lGS&e6mdC!MP;4>R_Q^CCdrqcW`Pr&y{ zlyy@6$&s6?BeKDUlV`MpPr_N`Y%mp#F18@8cOXM`|?+;9Baf&Biix#Go zDKbW<3|_qAOBsz|xg-N0*);}EJc#Jhdi!6T{BO3U_k}45=R<5~>rMF6NOe8!mT?$b ziF9Y#r+BeGqiXp$Ob@UIt;r~-O}8qRQT4TOEzLqJ?AwK?`vj5FP=ba%N~`e6*zUd) zn@(Z7P_bT5mwZXX@sBp~Q`79Eq$J)eF>v0CZ?k}dy$xopLS5nD{FHow&Aw--88eho z@(26$t!79%>HDzuyIUH(_-5tY*uq}|=e${Z>mEzxUrh!jOJB3YUz@cbzkz6Bml&=! z+%RSN4qof);{ThlI&~(W_aoSX6YR~ubw}qEhJLyp=YjJdcV`K1nGN$z$gw_pn3L^r zZqC!yXjVMiD9i!89LgKEnh(TgU26mN8V4oj*>)R@4v50N0Py?E&N?#K*?7Ok$o&Qj zqnlXz8Mf3r!t8%(k;p?uciO_@(-ws0oScWT>>}K4wBk)S&s^o74%TcOatJA0-22#u zzKcME6pO&-S^9%JQYDVJAZXRI!N*k!UknRrT3Q~Vc&`pz1yW_7jnY@U?^jB2j#v7$ zp*j@sg=tIe|3NgUW*zjMf)AHgROkz{zy8n}xa%k4{If#@^F`=3r!yM8kUm0;0dNkj zVa5%IKTh(&+8!c^eB|KeUtlh6?T*JM zwZ_JeNE6BS6Cm<+DfJgz1Ra!F*GlPNzmPw24H^^RuZzh0Ug_gFG1pa(m z=|yAL5WQ&=n;Lark79tf3J_Wp{1Qi>y{C5(;yFc4txL_Sz@M}qy7`5yqQWXa$rtgp z(?bCTEs+BP9w@*PE;9<+ARa(=lZ}k-V1x8%bW_*i1c3K}C;+MD!o%1-wQT&ApOa%) zAzurjiL}yBH(|A{EX2PssBmN`;J_C8jHHSnVK6Z*;6!#1m^=ebr5lY6TTO^*ml+42ce zp@A7$p@{ZkodYed%GtP8Z1X_2-O*#*(GNy5#Aw#|ljn$4HQka*&v#d$Ci9i~vU`I+ zs=Q$$N!dY9T-S@$MdHsP?1)5Je|#Pt-0?8q7Fe9Dv;`uoM9DS9Z-gviMCM5cgt9R7 zw&r>g(R|@3WpicFeR~&Ee{AFwzp<8})AV(Z8nn$99SaL7F`ebtvB1Jm9LGkQh5X}w zN7FJ*KRMOezOPT*E_jz&eYV#?z=#l`*m$cz+r+bZD{~vsc@}wBA{{?`*7`V|4foNk zOW{nupq_<=eEnI`YbVbS?@er$->RiPJZo}#0uC2$B5#kE>k>lX(43qX7d(VhH!Rrb z3SK|YziW&B2TP>?LNNlY{?#-?a`k=o3xdX*0N)kVY_+b<|01>2Dc2g+{L1CRjyTgq z+qGZ5rRxzFrn@)qGY0x5Rqbrve*uo#&0`|3u>-f{z8Mg{J34*z?Y;m{>ea53J+9S8 zI_cA29{P~&Ndb?+`k1Qk5BWy1`Aq{ym6Xloh*|b&1!9YJ7a?H!Rn1nf^@>K;^-Qe9 ztYYnPTKM*r2O)BkInx~Fs&-65VH_xXPn zZr>3S(kAzi124U$F&0bl`>o`k;ro0xrAGW>k$cdF%E4y~Xpeb5h~t_)*X}hLRNj<_ zi(yUD!FjF`Lg)Ik_O@&8hmkVf+bfS0HbnfTF@QRacqK!2p3!|;3V$S>WlGc42pFc^ zR4VN{Fs(}7McJf)`lZ zF54}cm)tS6jN;|_v1}IJV!i_?i$NwiyWzCbV64m$rX*Ip_}E*}42*6(3!z_^0{X4p zvWzk8Ob=*m*6tJ@b-^hPJCWXiO27_mR~U<34#Z@Cy3na2cBXa~``6(gNzC!dtK-9m z+;1emC*diSDV@KQY0Zv z{>BA3K)>EE4vhn_m7Omp1>OnIKKy;pBtMQBW|&lFs7SO!vPl2TlzvuyC^q$uvR)NE zQ1G!F2eE_aXSMc5%g~df?md5a8H?xs5H{+=fV(|DGSN*|yQ6f_H-29c{OGc>vWJ4r zB*uRbIvUODwh1Ky?*SR58wVBnrKh>nHxBj#Rw)r$^3^YN|hmx^`{)C$PDY~Ewud#G@j1LQQY*Je#nZcI1_dZZaC(WrO_%1d8KtM{L88wVD zk58>F4b$F0FUkG+fHN?w+;y^pd!r%(ZOdt_;emcve@;NP9Ff+7u|=qVeUaG&p_+Ju+p#A2VEQO zF?xj_rINNiMK=?AH=3z^bhGWUKLa&xMy%cEUq4TzM*Sg=)jJwvCrq($Wqg8(zj2)68g2J{^IiNUnaNKx zHP`V0(~l2FOjt=?MnM_~U|cJZOnb z+&HzlWftb>(UWC&-~$~pE=|^(;hZyZ!Nf3MYOwmf)YA3#S;pu(lGQOzsCzgOhE8As z%DE5RW*Mz!V*8+a6EAOj;M1Tf-uXJbLAen5V+ZB%vTbr{LqqYewkUj64;_QuAA3qZ zZ}aVp=P{P%l#FZ+{OWNnOTWo+OpKBe!x~tiuV^$YlC7U1lRHwccxa%$nS`}|vp;@Ig~hfncN;mZ zI255Tj_#$OWSl>oU%bzLm0?$AY48STj0hAM~NLz@^Bn(%-A(9$b=v4+wII}~zzZ)EsuJJqcq2!=yLSJCMx5HRG( z#%;Sbi;I2&@rYFhJp;4Me^X#(sm_f|NB7KU@p_tdw^{>@%Z7(IzAyxxsZpu)f!&hv z@V|g~0_8E~ExRfbaBs9g#uo-Kdo=vb+LV|Y;QTdZ7_%9YYk)#C2a zMwZXZ49pR^QwqspJu4fb#y5YPnMm?WrdK96Drda&>e84pOHsT1if$Skm)O#zx%E2* zco|8|Dr$w+C5oNR7N21{xw9{%J~{JBcS8Hr=QJ^=Wr{Gl{;f;TjEz(38ycb!&>$kd zo4^IU=WAjx=<@muyeFwdM5YKojDCp24lZKsTuUldOXo`X9S1G zgGC8`YXc%65uJlRUDtU32BN-c9iFMG^KnXPr8_TrAOuEF5*m5`{t<)E;V^O*6dZ-} zU#Vi7k`ZgdjJzC`y~lcR+0xhPD4_fGTRZ&(O@X=%gJr4ZwJL3%G*4Pe(Re>XlQ--2 z>651i8}%~=(Idex#r+#_=}HmJYmG!5%C?;`$T&rWKR-cvO)L5Vo0rS{D^iS1yO11e z$Tfm=uk>lA4~K`Rp+Rd*9$&iPS0`xJAt3+d7@%AAZUCSLaJzYr!`krr2C^=u$c4b? z?&0<{b8&e@I(x}hE*`ibm%HwZ-ODIk2_Ix~C+{Uk=Klts0U#eOI0(9d82=0UZ z`Du{l?V_a}8Sgx|oI+;g(dxK=PJOZV8{**A+%)7eZa8=>- z3#+#fPWt@$dt0qhtt6)o^O{3-K(j7bRsub+*S~;bt=$^*mZ;hJ&NiJcV;$j5$lRi4 z9Wa>Ao*W9v)z#X&-Gm7PubKKJiX#kvI87!TkK8smig~HVylA*}E-9s7?~7CpxlA5{ z`QT3&+VenA#gfu)&u`bJG@uUgbYh=A%VfJc*7b9`NzSJBG+S}uA}mrG3({9XONN+d zP1_TmepPaLUORNu5TU_On>P#K$8XHne!_f94a^fUeRV%%w~RvEnwxzeHF?kRCVfuH zV6ow)=bS2A$Qmn%7q%KI4loyoZ4V0sSbsVea%JR`2kOkjT0#=&6WfbL_H&sUNgf^h zmIB%S8Z{t&N zAdt|tQ{B(Qf;ZysLce-~e{S^YvJ$*PdY{= z8($N@eR(!Qe&O{*o$msL+^31d;mE?Wm+497y7Rj%MaMH5O&cO|+NB2X&TSXn4U9J0 zw)#7%QF@moNc%$W__^dz%?<7?_>J$3*&fhlNME6i<{xSp}nW^>DFT< zcze$S53{6X5s*$jvb1k^4L-Op(g?L~^uK5HVBW34D!fo3KfzGiGYSY9|55BD34u$n zg(54%2WCW;j_i1xjUim>JL@4&kHVIQw=AWMz*`V!bd>w`M0go=PG@>QC; z8QB)bLpdhayPHmaGg1dDPJxO=mLi8xpKVcdAGE0nQ6+4t$)m}qkLhSTHD}Wo&rd9A z+!5B6f~?A3{M8rS%zOXR>4h-P?kig=|FYxp-(QFvWKET=Z?_!Jmx=r%^d{!7;n9qH za`WQF4kScudtNw2?t#l7sr%qbE=Vk2C%f$FIcwOk@oFzrf&y%%JFwy>hF5wGj9@e^ z#MEV{f)Td*xlB`6a7kv6cA6xecZ?T z$7I3YO9$mTB~66s&aJv1l`N5_kZh=ZWU^u4jCJgsj*R*UAUx%0TuKo#R@_O|X;u%Y z`y%QP>Lh;PJIyZT+`h2>Sqhq6oGob{AQ@_IZN;V}A=4x{b@Im!W$O8gT0eV9_f5wI zal=__kpYk47y@eMM=Wvc%P#iv+aiAF{&KM6naqWJ0Rf>TeqDIL&x6?4tSfZgb{+52 z?SN(3^T?|{NOm2hgzlFbHH9WoJ2R)BY{h~ox?E->3g1ClXlp^gi6^UY~~z?gwvFc zh`}D?60>;JG%zLqm{SsGq zYH^sF?2*kr^N3^4er{>NeLE5czm?052nv9Bm=oQ@;Et+2BMV#m2FH24M*E`{N!e2T z6T(knHNAUrttiT{A&9v&_LG)Qk%ts$jW}+PvOnj8^QfWJ2K}Y}m#^P9i99-!|B5TW z#h;fe;y#%MD5lp`c@&9_t5TF4m}kLmeUIqM1amYr`lc-Q|6uirQu-MwH8r?2x>Q?O zQf9$S9w-BPG;;CKzNewU9kbJSzn(iG7DrBt z0{8X4iX&FN*8XFHf}O0%2trx^vXWktSTr$cziYi}k7U=6;UweCwtogxg?iZLF-rZv zFk&f#rHd3OFCZeqqi=mp*CR6bjaqcR#3z|^!vQ&xXHn)M*NU_wNMsag!GRQegb4|@ zTh9P8P_g>!d;>4*o1#|tCzj_0_+O3l+yEIrrNFX)mriCUo{D|SLFaMiej8}%L-_bA zx0^3k+b44RoO*PdGR?>jiI=4upn*DXy1WJtt-aG+(<|Yj#FC2vUZ=_7`E-%|}68D-^u95xFDNXjA*W zeI~@jG>=Ei5qjo5@-G3k_CLq$IVJY5-|wf|j54G#!zV4LcuwRdn-h4Dif4oL{)UxB zY3b=ve8r>^TTT03IWqJSDl%DCuO*bU*8|?$L@fvm0Pyea!tf;nfaEgwWs|x!Vq6kb z$br_v{q!UTN~TKXIdM{RqvV`1zR%b1FHvt&A8jVd+3YR*CM^hXta@i|=qnv$NFlg( z{;J}qJ%5Yhu3ryB4+yDRo literal 0 HcmV?d00001 diff --git a/resources/img/tensorboard_advanced_3.png b/resources/img/tensorboard_advanced_3.png new file mode 100644 index 0000000000000000000000000000000000000000..21b985ecf6304fe13e62f2becb11aa9fed277c34 GIT binary patch literal 1011024 zcmeFZRaD#E7ClO7OAC}zN|8bf6xZTV9E!U`aCa*%AwZ!}+}$Y}g1ZHG3mV+ro#44W zcij8G<9yHG+dU5%BO?zxYybAjTyw6uc0!aCB(X4vFi=oXu%xBLR8UafPNATnwZBID z`-!}eDi{Uj4W5;#sFJj(D4CLzgSnNh848M2NP^}ofa(B#rjE*Mbf$OD=yJG4yzpK_ zF{EA*e?fmI^Xpf(u_OVWE+##>p{S;~t<5u2WgKNjMt`V~lCqW-0VbE)a-LsVIq1ZD zW%1T|JDnYw0z5WFLow5Fv$y9eMiq`EGa&rDIV2}1V;<}E>@6({ilpJtcCZuZ&!5gU zW|n!R-ty9m;Jh|-rjV=h4WznSBR|e76iSSBwX)#2=Vz=aKjan4SV&R6znUT*8HFXR ziUdywMw6k4>H1Ha0aX1}%{=_4sJZ5`jqy=T>C>3xUI|NgrfIcH|8|V0{ABl1w-sgG zIvD!xqwUvj#FxzIICe~9Gq&z+@Jf#~QfGLoGTGMcjxceH9Ir~qAUQ95?~}@>mN=tuhAYA%#J)9U%dbJI^OUlrfc>;!e}pGPF# zZJ~HX;^w^_5$QUZWRFQsmp|^Cv&_ z?uvYWEi&9wzE?zOtITs!WQNbbNWJ1WWSR;0qLh++ar*H&Mn~4WGe65=m?rPw^Os6U zGdTvJb^yno<yew9JDLQoC2u$V>CK< zZO2vYwOHQzPW9G})~#G$U^`cDUC6>^<6&uW%(sLO=3Aq9=acN`DS|(^w^>DBd`d%? z1M2V*pilyvZ4W%UqL1fZZ%lCO>0_eUQz@l9$@g)gu6C=U?4Gplh3j`Y8DO>#e2oME z$R?%hOoaWA9|un%W2j#3uampH4}Y?sqAECSJ?D#y80NU7+&hMD z5=ZftXcWp%Ari6CA1FWLkQti_R(+roAB>hUeWeoi5SlJLN=6W4zxj3|?2@89Mv*p1 zMcGx2fK#02PgxN*QBd!n$#EABY$ma!9LP9E4cbx^JlA4uXcw#I73k+%BNWwc!sKV> z5sk(^0LsMhe53ESUo`v^OgLvnO3+((Yyj9?exUAdLGj)r0;~R6Iu{s8ekz2)-NM+_py-$l*q^Gu{KBCE>)}y+L5sbzgxHAPh zk@2K3{@vlwz>6{xHnZ2GTkv;M1woO*81Jdx1-`ZT}qR@{2YqHR|3L zo#Smi!+VLRiYJ6uH~2k)J^`1$UM)y%uh^|PrkJM~qJ{wLtAWZSRM^!ql{Si*R2RT* z8b_?3bBV_vcR$^!+{@yg;=UXl|K6HtY!J^FbS-em`+2IWe$^nQwcED~7GqaT+)Fv&R1pXVuh5r?nJAj}|Zlc5m+ ze{QF{paKlNwmmAH->G@k3yDGy!af?XJ@wx-^A<(sE7XkgXJqhx<`r%7Y7%K`Y)W&5 z?Z4e+y##orcoAPJ>|Gs9UO_K!?}#rI50D*GhIcI0DR{$N+u1)OMJj&I8vYh3?5M~| zF-kXzS^LuUO5y{BW-doAmPDyUK<`~|T*MCX%*Pc@Wfn)y0hT$_3H$TqsbP)|mqx3A zJ!f|L>UY)Ot9h&Mw>g|0F8D8qE)MX;Fg3#%!rL8^H1agGv?SaJ8fryd?n=Y@IE&b;#>=Wu|d>#(o7N;7gO&)vKmLoCu)We!Z@bq3-by)S-ZMJh; zSfy;JZ0!(}rt1vtjCvWt;%oa>`>@5-CBY`X=AC9zN90h~JjIYnR^NrRd7MX_o4nVd z)$YLc;@aFs&!x=S{PFaL-Rj77*_lr_o3W!-vlh&o(VGo)ecy6fdxdvLf&iWD9M`v( z_mEy-pEIAGyivjo#w>c{L1;;sOadVIM!5NbgSm(*`Q0b{majJ&!S=6d^0-V{rl?#* zEOiJu8L3Oy4wW0s8dzGa*m!wd&hG{g6msM*aIj*?*eIPqn*p@dMJ+`u1@8*QHF5;h8ZW_sHcPZeopccs&l8_3R_V%^>g zfmu`HKV`M>d0JYxPerKS7x2h4WV$&w&0X?LGLMdB#t3X|%59|_rbw#3rbDN@R}rkH znBzRmnTnp;pAv~AwZPqZnbfn?ezfCxKIj3P;GZ0v$SedFlq;UVPDg2Bgsb;w47cTM z0qpf=t+3GH`8dwljA#(;@ASA>=kM9n{qJi=Cek=9G~CP7?H%nEGl9#!Eh0wAKk8rO z$P;jGL%K{9e$VPY#5L5nL$ZrkyAE&WBzKBNafbJjZFtnpmmhLTV$#39Da_8= z4^0RS?5R%#GOpC28gv+pH%=IBZ6_GWHc%zW*vSsb3YD*yG+kCN!SIBlN)&XK8hbrQ z;I-Yg{bYmdjxt1IGUVi~TMrEks~Jx+b+c4@R}-DzI*oeV_W#18t+ zx6Wn~TM_gt#iG-^(+rRmhc)}gri9w7Rjwtw`~^X~9fvk3g{34Y!b<*DVSUs;L(#qG z#2VRil{CLJ-z@9yJ4Z1)Sv@`_h{; z(Y0D}b4uD)@ZfbJH5&uZEtAA1Vdv2RrTetxk$|&wgl411$0n1fWZo zJ`0n!W4f*?8=I-B@v1`#5$Rv~1*T#b*nUty4sXCyN)e;fnb2jf{L%~E*+$Hr=ddh0 zgq0AesB4nC?ZJ z+ml!S)etwiy~|2?v*$g$Mn|B&u|8{Mp!H(kzGG<(7sr00@V>BYf_~cmB z*SKR^?(5K(n=FYSaVBZ=Aon_NbeL0}pFFp?gIQ-S2ss>MAD-Nq?303+oebV!e?Wro zEUsiPlKP&EAE#N9zs(|KZmc)2in+BNu3KEa7kEE#UY_cWHm@P5NNWTSA9wF6A5}XG zExM|O;siClD{m$)IS&sWW71<{bnBv-1TYLMa;&UST9%)qFrjCDZbHeX9XQrk5iv!G z)1e~KhmI{&9MKH1G5s2PP=0l>4{FTHkrj06J6%SaJ)7zsI`{+FQtOVIx%=)X}${)eFdL(u;NtN#K<|F6XV|FgtLqdxmL zsiJU-@MKC-NcF+MR`@S~(bLF;*P}Q()yWYfa1+I`Af58MAKFdCF^UX-@2EL~=dh&0 znicr6es?Fx(y_j$RDLCS)m7)j<;}lXB)r}|gUrPW8W_xu1+P4AcL(iXUCY49bSBtG z2`u&pTHo6Nw{7BWFqxMb8zsxs#C7Tg}Kv(PwTiDDg~wK5anS`P-<&4?`lpRBxHj0g%``u0(NFxi2C-sR4TbQ?79NV_6*al6hWkPl6}KIY_A+~rjZ6ci!DBnG{5u+)P@Yax5&cf?5yu3m)S9DuY>=bJHBrlY%yN4 zp|1wo&8M5BsD3SVI<%xdtZm8N8Q;0`_A1(LR8uSA9IPEuH@X&4FM~?m^8#QCL!7O8 z;b}ZVBcAEdGg;32FiuN~@eMrdo_3+*LOZ!?lOCo~a5_A#0;uBW!p&t&O?6D1?^hG0E>>sYOF3ZzpR>4)*^4$?erNMfnHBoI|3I_ zR^@*swdGN-`=7NMTgIXm)!S>0;~uQftwXET7S)L#p5%yanjP2W1hmpKEJ8RfZ4bZw ztp$0v?Cv&?jp~DOm7xNRM*KJ00lhsEb`vQZ%ml2y8E>}vEO`$KQlZHvDXfI~d>n#ZwovA|84GkD|pvCI~6-d5;i z{FXuYvJtF;pL63o-EN~U;M6;LW;wWaj~KV*nrl9B|9*b{H>mhH`tXPbVvY!@h-hxq!QaoN46 zQ*%h?;D^*uXsr0GLm`iw7+Bo7#?N0&y~~*{FZXCnmx4UukX0n010L^!cYUG}_Cs)D zY0ahPy5%?D&Vebys@hEX*K|+U+qWZ!m4*JdAd+mGB77);`GtTRk%0NCdFh;Fc^Ve+ z6?aizcI`& zDb}mPOPGTPCy?VU7cLHn+)u5IGJ|pz-Uk48_~fF{Fe2JcU39zjZ^+~?$i^;SyA1dc z*+ga{df1KsnOeJeFT(9J&Wg0bayB2VoRLj{a~S{Z7{4mJV*KSAfsN2Ytrl|0LBY0q zv<{Gi4`C9}C^yTfdGKgU#VFG8;*TI<4#_%6(ZDl^i#L{UOWF7u{kL+?2#7tc4N(F6 zV7Lc_W{cafrprF1RbYM0hMtf9xvMvm8!4*Q=pe(yz!@v^;jPA_nF znH*4{A(b!MW!M@ zH!@@;bcRI*PKVeMzewMcl(vqVeC+TjJ)pWTWbQ%J@QiFI7bI`IFAJRpCsYKzNgNIl z3dY;XdVqe|79bjumC3^v&|!KSypGKTk@RX(+=4)p)5`_R>xsJ&Ys6 z)$H5+B1PLf#8#tG6I*O1xXp39UOK?@X99Gqt&4v2`1kIuw_EaC3VVf?2*}k57F1E_ z>+v6p1L9Prm%j>}2Z1|{eLfV&{xn_3^c3*1953H*l^#r7ShNIf_jMoi?+QQYH^@dk z+Vf=1a!LO^I!RcTYBy0`n1i2+ihPe_UC9g(Zae^c3?q842XH=2XnQ^$dZf=FMtm?q z=REHVaT-+;;zo8xsCev`Sw?hwK+l2;ZA6{rTdH*z>yvhyHh!jtc8&X-!*po^cWUf2 zq6MYO$3q5YENfCYjq~ylJfbU-ayWbJMS%DP#^p5T5F2>O#mThILxv^px=xDl)_e>9 zCrvEJ66L^B?rxAt<}x#>wW<{@Xfe<@X<58+h$M0G5HhqRZv2@?apD!Sc^3C=)a690 z<@I*h<@{_m!vCgPs)fMByL-AL*PDmMMX08mgm`Bhj=bD)lJ13EECdY#XyjuQNaFN4|YxVroqcCwV zL3|Ct+8CxRgOi1{aq3u}HF5z&*o<9Cc8A!IgTmO=K&pqG^YXE0O&<1*=^2Sl zl9={+sS4n%;ru4Q^}@GhyIQY>UEC`3XJKY`;=z@c<5;q>qsYv3FLBq9yY%ssK{3up zbw9<|*9s6SN5Lb#F|d4b6a-f*AhuZ9pmg5krV!ZwQZC5pxtJ2~h4D>P1-Qp+aoVuXV|DEf z{|Y}@qgzS>Dq$!t;~a!_!)e~>U2w4xF&DP+%=nI%r`DMF3q1L*kNW`P#m7grts z0P<|IxUM6QVsa@#a<}ut%ja9O>|*t$xWnCijaeF|g~;md*UFc_q%_M?_`C&*W5WKD zZ32d+70GQoeTdC!dDs-ozPX){8rumn6V6mty)?}%a~zqqm)c;Hnda4r=q3DzY^f2? z-dk+bpqs?sUikylxf?CYcYGdj`x}{a-(MfP$GneIQ@Vl3JHI?zDKf6*>~)CG8Ua*s zCvIWX_kNoP4paJ+wx#6*wFe?qQeiXB;LVu2<;w8R(lh-9r!6b>X}Ohc!jT<i{0Z;ct(!DQN7G@#{tp% zUKeVZO3&h#HP#HezG9`R@-7$SZvHg1!IpyWWN%Udjg`Z1Bf4CcB>VISYF(s;!j%JM zBcwtFjwlW2xDf3yb;COC+Zhs0jR{GxpKSIwmHn>Qkvaf-b0M*4)(-1oQO03pct?Lz76;}a_S8Xfs! zKVqmt*V}zD`Wx42C}b3eZeHaDua|+}$3z5Qjo>_`zlE)e}hEWURr7wLO zZPmzl$vHU;j~bHlu3$P>CE9L}%I8q%x;xptI1r98?nZ5Y!l6-eL=!Xz29u5vR2C~O zz?&C%H&`=129nxzt5tGlPF!#xe-Sx(tgW4XGoqZ2YV4iI_=vZURYqF-Mi);_gbp<2 zIe-y$r(*-)+g6+@X>9&#uNvH1?90{^^%sHpohWlp?d*i44W>vK|G+4Y!|J7u*NCl& zm`|C=UhPP$D!=2O$jH3O_@LheyUu++`mj5DT^oVlA-s}MZsJkV_XO^M@)(6H^4msj@K47 z&JJAS*-@?Boe^d>{3pz z&MlFe3Ts?$Bhb9cJ~%LB45UKHUPtX{E~4cQ|^C17RLUb3zl5RJ|9h%-eNprJSgW}{{4qzSu> zEVKhkYzJ;4!D$udEQ7mqL3q}R!AP!KP;a>-_@cz7Sm(LKhZ#&FjJo< zAHkN%a7%*(l+38ZaQ_>0gkfKz1<%vzw`LyLHQfQqleDXKx{k${x9d>)Hukz5 zKTE^Bzk2HK&0P$dwS5Y`IpeM5R|5CxASUN_1(F_*(9+GMw8x{=4cI5-Tu{7KfYC>& z&?GhR9*L&4R%m1znUHx^$^r|gLNF)1gyakpYsdwwNfl5}kwH@Zj&J94C4x)8d%^u! zts8GSYc(FzwTU%53cX#gEF-5C4NlF&Ev@N3Ooi^;^)p~hB7&dM>sDozGaa?^7~F%T znLyMw)~}Y-e6&GVX1&*4JtS=7^)>rCLb?~B4K|7HyJD=5X}BvSWg17*s9?{K!>FSx zf>4t%xf~}(k$Iw`3pa9S*hffdevlXS-$+8(?}R}emrdGsS-H&ZM7tcjE$fqdOU3wm z1fc@9G63T%^$w!XqT%iUq9_-)va{t-I!F}N^{f?dP{u=&V(ZoeXP8U+kjrzuwiT~Q zbz5_}%4}~`Ssam--uF!q5Wc<#uLh?a;V8vb3n9p_b#}glo2i!ep-G$AVWS0W0r$UU z8QIv#i^Gs+h;S~3q*%Zib+c0$bM_(7j}29VzT0AdEWVu18d?PEjrf=DdyW~Wo@ z*`J){p_Y1ruzZ=D{$V!vD?3TuZQ31qLCk<8S2`wYw^K*y3Y)2olp`GR4^eS{Rz zq#mG3n&H@Rj4m?=sObkjfitD z$MQBOIH7cH-}UHu(%$2jKcDiyZ<-6qFScMvM`6PxKYmkTJGoEHoHPi|nCxKfQoPcj z+%GL1$=0t97*j`-WeW+`fBiX{9=F8ibd|XKDmRaq(==P(RP{GV84>3&v(gFKKVgP{ zOg;%weYtL8lIUn`t|YsI0J;=9^ThHA<)y&lcK1F7JJ_ zmrtgE=AW_0Gf3Wjc1dq<-nY{i_!FY6%R&-Q5n5E|B|GwFMct_$>Hb(N8jM?V!eKxYGL%wjOvPiu5NZo!b7t|hZj+#U;GNjfY|!oT1>_r28S z>|@b~Q4AKqUtg7Z9coemCws6ya$3*4b7C4;q)$K_)`peb()(M?*Hg@HxT~AZJ0!q} zCpC6Xz!n0zA1I$o1VQJ3K2of2V*koPg6-+h*|;d&`+T6a*G1{{*Z!RpCbA9-iWh7& z-iIt|LXaWjXi%27-{16Tn(&@!55hH;ZB@c7Tb<@C~g^)4ai=~iD&J(O z=l3g5b6y5U-Oq2_q2B1Kb4@3mpnrL6MaE9)@BE_r+ljBb?UURyFMhgjuczWXnmU(F zsz_{2(sD_<28yH&W^ezJB|YPmJ_7f#;8WuI-_OUzHt!tF?=Ya}Piu{SZQe~6D(sA- z=djiH)h#y%xrjg_vL?2An|c$={rY(QLXp)y-w8HkT;ZvoUuWx+VC|*pnxex3SKe{M2*J zt$&#JG{n+^9CKL~b}*TLTj35YJz@=v+{STa+zB|k@j)MqMTV9$xLK)Rn_X!Rbr$M@ zO_J5|!&x$tvpe$}U$vGemX&`kxF!AXTHOvO&yGsBQh$Ttuk16jbRl{M7t7pGiOj=R zd5Ly)ZRQU>KsRR2q&S9bS?Jni(-V%|xQs>g^Ob`M>Kq`aGfhsJ7@7sxPr{%G5%b5&!8O`PB6oOOxUEB{kq%fk^~8r{i`h(F*YtakrI*uzq-T;*-hgNep|~H+e^XA z47?Q6o6*Y-wc)#wS7nE3Qq|lx1=+TL`(7F_Jy0J69v`%#3FKT%I+<`Cy<&B6(Lf3o znUp!V{ypFjYLTIAvqlrI8=ieq(Xrn7KYGh_=$~9d1P=@r9Zl$^^ zi_=TUv0%i~`BC#gSbC-`|+#GTBhd-YUU zZC7OM?dq0M*4#oH`1_4R(B50|OKu*}EP)GKak!noCsz{!$QsDc?vTs>iS}d4jv_Eb zu4GEuZw=jjZ=c zV?ieS87)-tfTVw;9&MypP<2wBI*mPr+VW|pVNnq&&VA0X(i|xS*Z1M%PpLr`Ak>SHB zf6!ng;I_h~R41|f=CZ#H76C#NyNv>=yHjQY1j!YG1vKV4(X`o_`snVKSy`)01NTn= zN}FRD9(~h-jzau$SXJ_2FTkR0*=Xm}{W=xbB|H!=4Bkcs7O4C5e_G^Rtq{AKhGzKi zQ7eg_;@0$&Bpb~EuO7_CWS53h6gnv8t2%t2{V|I^PL1kjabs&I@7T+po-~^9zdgC1 zc3HxAHx4(Wp8{g;Fv7U;&FcJHu6>Bi=4F`kj*d)pN>1eF^!|D;t{#@H=exN{knx%{ z(WsK%jZ*fjO?oQW^{4LefjQ3_A}-zKOmm(5vTB=KfyR%*$Jemmk>m`o)bhO{gEdT= zpYR|C07Od-qkv4Rkh7v3xGpia#8Jj7GKFUgv9|WWO65_5#V{2dzkE7POT|ZNh*g=t zr;1d17EDUbFqxX#^vpUc^UFw&?)I@Pc}j=I_(r}A-UbnEH%kf zAI2{Il6YiWT}27=Ktj6V*16YdaVn1}#4}Ob>@vYB3R>f}+*|BM6Cj|LAj9BaPfgDM zndu<1F;!6UNzv-*m(xezJRh1`qlMhJ(c8`zvfFAP5%uTJE)CT7SD*hdUw>82DfFHE zMN%Tc!o9nF@kBNHEBA4vuh^Qm?6Sl`=Ay6subZR1T2%b@<3CLDI+SNN!Ct>Y0Vgn_ zhp+~=(vJZcD%&J28m1uWUA=00fz153EU=lB_2BB0;-AIky&nP|{55ht5PT~(rCD$b zeGBVa6@$vSb>wLLpYu{_Pn)Dbaf!JfvO&k6e%DI}wv#p}1h((|Qmv9aBJP#%o;Rvz z(erp>7Kb^}KfK2Zyp4jWZ0H$j#5!pMq)Zp6vO6QF2_1TRDWswAn%~ME?N4J5mWVtb z+EJV0^QU)S$fbEP{a)yZ7594&w(1x66>Lbnm3(XSt|y-PQ)$Ru2MHw8Pfe-{^8K#? z)nv_oImv^X!9&C6zKNy-Vaq(*O?h0H=?0m(&Gyr(YK!h06dG+wQMyWr?O8t34=n#> z9&IVdgLtXs7^3@MhOzf?=8>flm!d8Q?^%Kvx;;uca-wHyVM!ZHzgp=)5ZWR>?i|!+Oe?9+EjW@35 z#2(|ipKdGjiksOTsm^Mqt-5PB#W^j2^HT1LwCVSK_Uzh;#$QbjHW{$^pA4>;5_=%M^A>{5ha7nM&A9u5fT3$Av5w3yP4@HB^r z2R+E3UMKfov=Y&t!)AinO2H?kD`azR=?3oN`&Jr+;}e@=`?YLJ@nzPGZW^?VSoR zGpFjct39K3;(WEGT8{&+74apG_q@s5H-G-xs~Dh6pRWaSHW-+SU{)OU*0=s=WwN_w zFUaqCBUvQ?JC|%bnF7*g%Kq=Hh)p>kz%yW0-WcC?#1?h$0?;Ds;~;c8%5_b9tmIkv zTJ)iTpw6h;$=RefLbz@`3x=omMmly2wElX~1%PCv660t2*y)V>?vHB3F%DzE#kEh;gy6*>*+KMs&06HyBzDePEk-oQS z9yd}h4HmdI#8YExBf~tPBmKv1gr7_z?L^ss)!M3ENo9!HFGx6Bf3wYl5X954F+rQL zzVGvgf~DK~{c1z$XSBmi;^C;hoxXvM*O9KjdO`SNG6xsYF)>wTqYjeB}tGrFwp(?UeUQn^2d;F{==fvD&}2c zx+saIm)QI0#J0)1Amgd8pe<50Gk@U$^$3B2 zg5qc+K7Zx*+MvTvb^Ss>QTZIPlMF2SH{P0n84~vjYt(N>sr?$1*`2ea$Av-9e-JFG z&H#V-JbgCOs}90}{&uIF(q*7@F>i@{m2;9xpwML7&bC=+u z5pI`fr(s&MTN#&*)LMu8gk_2)oTbhe7_d0k(ilF|^QCS>d=dpD=h(>6uRMTvcuN7ZI z`pC6lS(gaNbFz2$M@jo0@Z4<-IFV^2lz;Oi?U)JJ7HaQ)m|;St^{dO*Zq-6!{+uGIO_)yLksux}gM z_znJ>ldK9s%&^xgW-;U6(ccnrBJ(u9@te#VPRVubrJZfCK^t4?wNR{6H2=IWazHw zMRH2pQ$j`DV;DL5ptUq#4S~9rvzAtG!Wmqa9Q5y)=kz- zZTZt>ecHetL~+nv_1$~3ns1W|V3l>wOyi?UzR+e{vqtU&_W|!+#XBb|^7cIDm~)su zLtxU0l1;zj!OisDZH+1ws9#gfuhwCft9EB@e_(aFIL-VnTXl-}k<|!FrULLNyhivn z@cL3%9?J15K(~+aKip@fP_z}&6k;%zj}y>begC{=z+8o{TOO9j23%B_aJjo`YQ*=Q zi*G)O&D*!msXN%&ms^1B@^E>HfmjoOH-#!5(sQ=D4A-m5)>?Nat?nz9wZr2=NVwQF?xQE^EQ_$~Dl5h8}6|vdf7_(}my!gk#F-f-T z<#$d?yjILgkY|yRzwL|VPI1ECbgoJ+-=h|owYG>MZ)FJv`pi{UgdaKuQ#Aj zsgfggfICkr|c%Pl7=PX>xbo^)ojqPVC2wys$_zQAatYhN2Yr zoyg4_Fnp;0xYUCPnYNH}&CmO^)D%Le#gtwuW9V?;Gcf04XJAymhqR`@(F*7bgyQQz zS**|cV@OmA0s}ImI-ceVutby#g__rwr}sq0L}uRYZt~1=23WQbIO{hvKj|F`gl3i; zhDZkUKkRe^om9%NZ`QAllEB9HYx$CK#etr;p6*iS^RGg!u2gKIQj35+W@gnDA)pj?`c~#&Wp!7Y*qo*W%asUQ8+u>VcY><7vJU zDpcaQ7U%bQr=4ARrM!ukTxDPRVEYo{(9Xe~?&S;79+B(Me=yKCK(KM?+|j4}63RF= zh2=VVGUSwNtj~)3g9y>{7k`I5n(%#^b3_pR7kRZUf>o$yH9_VJkuo@eE}Adbf!3#Z z#`p0Fo0!ZGxyNdYJ@jN1o*7bEB0d5Z4?ph&Z^}dLyf--J>p3mMmzgJH1rNQii0&S{ zn?h3l0;BO4_7q{Gy{;T?O^ORc#l_r$oIi;S^A-TI+hFuY>pUtme#6A?PXjBOo?=*o z!-cGX7=s^aL~%R=usU}E<;_;eL&GhMNRTR=z-6VE_W2AZ(@fF2S5|C`#6j~_F`VgQ zn3LpJFp5qzo_E2uM*f^izYtP0VcJ7~OL|#N)V!-(Gd!aviKuG)N`<&X2BEB2ITP&M zsqZr~wQM1+fiQ~wZhiOB$n9y*CU37hD`*eUO^ep{RPv4L3ynn-_TNDWKHRpb&c%Dv zi^Txr5$Q{ERIqu%ZKIybZ{1zR!4#*G!6e<3Zz2}w<3>I{9iQprE|HxszbT&AF>~7~ zQ*dIHwGBozQNvSe4P9OwPb7gv`QX>MkG7HthzOeH$X^QOxR=>_by>ZsxfBLp>|$Tn zm{K8j%P9p8WPQM#F8+hxM%5a{9wQi_aX3qg{Q{?9dGO7D3{iO~;{ND`g^skbAh!Wc z0wK#~a>NxZBxVuZa|P8;iWzPnwx0Z}@)UEu)0zde2$;Y=*S%qRw_(-Bp`+4Qa+%GS zK333UT-O8@o7yF!+w0(UAD178b))s}ZCZ=inEUlR)lB(2ofC7#U7jkl`Roc?a-?O* z)~_YHp|c4e^EO^g{7wgJ_#?v2@bz2uINPI?>xVVL*?p??avse!`o1xyNR1EIm<$I) z+m}WJzXrTw4nHFmxeR>LO_4q+n^_Gy_fHu$x{m(k?{wxy=XiX^d2@TgD)x)yCfB3P z#b^&yPd6{b=sn~(xkUb*$p>fjHbD^ESgf^24U_Z z`{>RKMXBR51L+}V#l#2anZASlp|oM?S|(^O!-4#2Ee#a{RxUN<@HTN|C5tl6w<)ZF zK~qfwb5bIP8`N)g)~02#%i6{`lGUP-3WEyO>=SVej%eUxW@5*#S-6jIg2u$xSRaBh ze%0#(|M-+^hBdF-ox#B>2eF!0>9=+IHA#QJ(L7rq=V9WDJ|Kg>#Ul>6RP*4CJ@*hG zJ#j9i`Y3ZI&1u5O}n=LUK7c;^nhDiRk5Amp{Mds;mh$1 zZ}DQ;gO$HHx6^NrVpRQSCx)nqQN(^Q=a1ijf_fi4M!l3%jdHlf1VdMw_Ok8Y`H4Vz zOw6!Q`oLg!oxaz;T)X0whegk0m^j}8JxoPqBlJCq2RpBhtExH^^1m~s#I=G$+4$TH zxj5#w(MvQ>L?Hwpep`JA>J=Le>tBsvQ+tPjc^pYs`|4IY>)KM?Pcz3zsK_s`**oS~ zv1GiBs)v7x;w-^6j)*v_4~GiP{joStyOMtexTfO(ewX_+eXjf2X58=(=CK|YNr?55 zT(;_tDR)$<1|o18Cs0Fuq7n^1;hFVx)b_$(2$qKFigNPDzD#JTwNVS`z>KB;#m5_t zJLmPHhiu>Ue)CV6k#^<^&QKGvk(wH$#x)GySn;!M_&bSbF|i#(il;+jrmE|v!sfCW^?PV*3ulAkGHpgvweT5&1> zl-3h}`=wbL>wH+lGNsr{B*-RQJ;9&-*wE_khSY^wIBN<0gb8(znvPNJxmP}Cxqw1K z;dFqb`k3=P3B-(gQ(H~ir7Y6`Y}^!p)LFoa|2t{$R_8uweJCo&=qWMS#heMi(^|xB z3lvi>D6CQt-5V{#ZSoL`;FJ_yAxa=3TOIZN?#(=t-voXRQ=MznG8*cv<_@lRvrDE% z{pT$eanuBTe#|fn_tzsp@f0pU6aMtNhihHeOl2QZ+<0}LZBl599!7bA!(>6JVWeHJ z60FNc2V}eFWHUB9`vvMMYqB>=nGSY@N}RYZ*_7b6sGpq=R!&1RwnT(xyC+t=8~lfJ*+03NC%)}&=hjHn_S7_wp$S00<)-VcE4pX?h1 zyn?j7vv91G`1b8oI(D1e3NrmIXnvee#r(hj1)#B?*rGW`c;*xpu~%L;1cf)8V4?HHN)idteGFF zpZZo})YUV05&na084?BdGfy#j0xxuv)P(`4VEMfVy4wLwo(*m(K9Y#wW+j34qCUd! z%w*Xl*qE486w(7-QS7&b0(}HM)aFKLXFN8Z#94^{{mWO z3x9rom*2eJ|1Co^>TgqDJQ-WvFNe9ucPKubB`5s zUM@O|8^a_=W-cc-L$}<#1=>3%lF_jVJZ)&wwD;ljS5kuQP!r~&Er%9%6G5}G6y$ln zJ7Fi`Nk0s`Jy}G3qJsAebZRW?oUK4AgegWxe}@YED74g2UnFYOQP)q-IT-ru8>wIF zTDNTFrBc?e^r=7Fwyh<9h%WnmtctDA{JYJ@h>u(#(yv~+ywJb(H3MA`;Bg(}(3NJY zYZTXF{taudlfM$+LHOPOMcI2sHMzFkqDv7}sz?VZib(HGx`Kjqq_+eCr5EWnL_|dC zDj>c0&`anbQnKj1lLU#>05P-xA!KKLdw=`9-#S0eIRBm><}b_qgxtp4Xi7nvW;0 zOxn$^ezp-HY3V@6DL!v-fzVw@i!h4LTHVt%>uuUnm9{KFs)JQE+3NW&3L77f4HF%K zO?cXBk;D>s>d3%cKr2a&Qd3hw&3pv}M)Yag2Rp_6+MBu^wtMqo(SlLeT71UK+SZ5+ z_I8)HD{5EOqtGtRlXf${v159qQQy2>%B5}##NP8H|KiXjR9uApXF1Xerc-@|MfZj| zzscep*xhbTc;Nas4s~63TK&?G$GTaK_th}J2Wtj>hVAF{>kGr+4r#Og#vH9gcCi;A5<=edUlix$vPHQ0 zQ$%>iFptikrl3LV>7O)60Z{>b>+9#doJ?njq>WL7YVr~InPgBON1jl22lW2aT=_O87VEtc?sK0Kk z-{=fV871bPO*u1w-gizS46(U~WoXa--HSn8&$fRld(WE_bVLm1V z98r{n9k>=4N3M((3BOnIeK+hn5VDciUFo%u3ahzi10O8;Y08_5fCOawp!#>!9>@_c zd4+hawSGwxXNd!hZet_2!_LYxhf_+AE>S%RoV=NDk(1wtYGV0zsJ`$lYFbIN`xmuI zE%x!hum?5#c!3UR7lbo1DaLzGmp)K$jOaOM24Q^x8~2A@J#)j>@YDVV=X_rTWpe$| zLbIEQ)OmNI6xWfiK&Pj;Hc1j_01j>t-d>!oC5* zDx&7fVoNq8^4ng8w_N5|xx|i^>0+Y(jccclYZK9oDl zde8Z}$v}$B3$K?Pe7!j!K*(IvlN|mCFEd)1L%j`L1iUt6x)LK7p0T5OmcW8Uf1tOP zEZE>2QV9%@!7iPdpqT>Lw)2hM_C0Y`Us@ zpgsuPcm9`!Lds*NUUn|zLL5_12e31t#9hHW|F&6LJuk3AIBo-Q z^3kZkXk{tPVovh!#lstlAqHOO*S;sVVsp+4@R~Rf>bY5k>YHe5l1&;C`%Ox(%jk3Y zxNb=?^mhQ=H3)cUrez1g%22^LJYL+-&S+u!IkDfS3<)x4qF_*uS)eTGjs@oFQhpkA zoSP2$_42UNHcL!!c3IONHN^peam|ZqdcZY;iVz+FT@EK4q^VcITJH?{W z>4BUX!#3GJ9?9AoNu4rCKb!pV1Uhi^P!@Z~N2`-75n()FU_`Zgbpw|WTV+C z8M$2+66p6}xUD|$Vq6-ZRJ(ojK2#Iad;h|~ko}S-HE7|3G;|gcZgU^^_sO(OVCkm{ z$V6L3hO_PgVi}X-NWhLV_f36cNDLKVK3YiV1)OC!+4{B1I|?o*vFTP5{6E`9Df;V7Pl%kDGI?7G}R?0CZLBfWa29 zR;^|Wcl5t?t1|ibSZ(O-YhkPQ^%<>@c1?4!c~^16S(8#(6U?m{=yFrQXzu&Y>JT|8 zIxWr+Cj$cLobW;JA64T@#j+*D;$8=5M@9f_T9_(Fkl%a^@@H7ZEM$s0Xl~tQI>{y~ zh}o*4Q>dqaT6S%_3vX^)-(qNcHUQfHTv;Pk2Hb^HZ6a+gJ};VzbzU}De+6VLgn4s5!NYy)b6cW1 z#^p9|&=#^%$8RApPhLlsCtKmd{c8u2KNMs2RpLF@*SRl9quQLFf(zS>urnPqAU>0B z0I^|NZ`ZCxb0FqUeRskm!KX0I@CQKIPL3E{T1=;?WsDEktGcH2;dT6bH@89Ohs_vR z$;QW|O5E?VpABN4{nrq?&oN_DPfY+TTArrpz(Cf63@cILWxpH&8VjnO9@w zM0sdP0Ma-Rd3@;l*;Q?`UtZj^r99tS&pnODEgToHoR^HNYDz*qqpgeXp}P+|hh3|T z6cS##jlS}RCoMhAD~tQcptouW+ zrdxbfa)-`DUnHU3>V5B-KQe1wSR_ua=4f+F1(U16)D2fJzcI_qW4@a@X*pfO>y z5N)=D%;)RRmRESogl|?ajpO>XL0U(Y6NHy`P5ibzZq;+_QZKV5rRR9Z&xJg#5JtLV zt&^AsyUby_<8uhtI?7jh+dY3bP$b^9A%_3ToYqx=cn|4-{R?wG*+j)yG@s7%Q9U;^ zdr0;#haU9F6LOzbazh<+qR7d!O+7_ZVj|=14~i!t!%JKD?0UtCf9r41Z}CoaMYh!W zx^_68rhk63aqFbiGiClI}*Sby^lx+{Be8YMB@w3O8| zQ(E@8h$k7eZ+v3DSj8o5n(sUP(t#%LF72xEgkJ^@Y}L?0ymf6PS!d$&4T_%l@AP&B z<}q9+FC0?x;_s&1CUsDz=hieIGm^c;BIeK4b_eW9i02@Kl);CYPP%G1*znfg33f1)9gmu7PoTWJNa!^8&Z{v>;`%cLvLKO=$c;cjm$KC2T0)TKluLCiUka9GnWA7 zB{&}$3y5TLK8wp6eANd@`hXZFQE!3PBwuj_*Do%7S8#1@EH~uAM&(bP2n3v|4NFpy z8$rBc*wqiF?god3tB=n4y-hTD9Ro!DgX;mM#d`UJg43`F8=5cW#`|B-Z*wL$k1b)+|IhAe@dktSRY}Fi*|bon7G`!w&ll!YtD8?_06*R(YSb_R-G8 zv|UCGU@@W9Iag-k^$gLEMKC^JI-)Juxu#m_;CRX|b$0d}dfv5sQoy-_<@ah&1f*xe zgs*wabs3|*d%vv-VOrZfMvSO#Lj3t6cA~fI85>qCi(u#qlAGmhnCn@GUz8>cTqrcl z9PH)+7^5mGEm{`FnKm~slR{@M^D=W-9bZ2cs0!EYY+0w6Jeoino0oI8eGjEYzFd$I zj(Rkdr`h}WN^#mn?U&;!;xW3oG_M9F!2Ug!OPY8rJD|E;kn)E z8P6DIA!}|%KP{~)T0J6mXKG96U;cq>xB73h0_w}ZTZjFAM;l_AfV)8Ggb3kYk>l5H zlYorLOes(TwntOI>+wFM%qiEhM9*D$#)%a6VN`P9Y!i=`6JugVK9n&&Qn7@w*E!Br znMkYIr7oC%lzkm;W2R_PleJjAK**8-uZsx^VbVGpXDQpI&u(ozM9n!T=XoMe|i0>wsTicRqYZE}g^CNxW=(C?%3#k<2E`6e z#?v2^5~hdWs5R(aMF^(pyn zRiBIciA)J5Spo*P_2K-;_YC^*n)l4gMqfpNAq1=@ncE$;R&B%C!A;^2DXwFm=2++D zSP7rGm;PXAW2b3`a#Y|h8XOA2qv}?y^X7&==6QCIuU02vaj(J=yX2MO4H7SQspw7; zYk=cjC$(`cUa7&}-Bw}{20xp5cY<6^>Rzd$%0tE-Od^(jr>0eBA9D3nWuEI{1wr+Z z19fNlRO+!Lk+0eU#CmP*hX0Ch+MMA64gJ6!Kjfb2BE>jE(cW&g4VM9L^F+)5yLEWd zPA#&!=VDmeKAQIV=(7{?Ta^-yDGj4`o2Dmk<-;v{9+nI%M2A!waa6F&EX}Jq4$lpy z$FtMB=>9;mYS2b?qdjDPbDc%MzS{rZt>ul1lxm*94qA446{ek4 z?DiH^h;-dxY}{ACCFjr;D(oWc^cs`fd3$%z0*H>_>3uOd*Ki&aHb)q%VRy`-_D0#% z8@sR3KB(uJP-&FqZ)g(5F9`3Uj6#qZ-^^xw)b|1+bQcmH zb9<+YsGsm{AkVcg^y$MQ%4YUML!`h?Uw0+lbZEB9gpmf98t$miaToNMI64-QJNNFI zNRPvBHAm$UQ;xi!vR2|kXm_t}kI zX-tE6B4pjl(UvMbd)ly1%ArNbgf3lI!@sMB>_W>I19ZT2D-4Bba!=>^?Q{NvJGg}J zb0I%)aVk5&6zS(Qtlbj)^;@J)u71J6$)bkb5hv=0tVs{cv7Z%n!zdQm;AV7q-;BM) z38UwtbK1F1TBJbR&V;Z8x*-9uvkt!1E&UPb9eaH6+68bd>ECf;+mzSm15u`ByXQ6A zyDC#!o2^t1iq6{K0%!9}I(9RQ3o}DX^}Aj%29MP@1~KA%(R1*$;njsarmz^5zWe7G znrFD?6}GCU&~deHBAn}uwO{Lc{m4x@M|bZh1yI`h#keuWlVzcX%$qG{B{Lg{P($mQ z$D~$2t>gwZ1i@ggzLv ziNIG6i*w%dr{?(c?02VrUj?zi#HE-X>2RJYfUr1}LlRth;UR8@GhGLkDZ@nGR+ON^ zbouCCv8#)vw^^U({PV}-W_-A(hDj$^e(2xj7wWSj>D0~wo)G;n>C@@`DS1dgCx?=-jqZ{^@)~m-7jnHa)tOt3L6eClOi(i>Xx12T^uQ!>&X6Dlvg{FwwIDD)G0eHCfj8v31OfYl!gT9`g7(n8Q{C9WPhZEqgeD7HzQ`B@2lz# z|3Icw1aHtm6ANOkF5HXl`uu5;!%bhL^Q`Oqp!!9 za;qtz)|f8sT+7SYzJR6Ve03!@!9T}60S@RS-FU(7W(D)MeI+N2DjtQ5n`%Iua4fqF zZocy_1YH9V8V@WYD7)kTwr(?pG~W64`k7febwNlV&d>J}bzCbR`t{W2NI zh-hNW?e3@)4x9Fu4V8K@8uaL%;ASoG?dfIEmyQVYg%{Z+lu4S?eKsBDl)Bt^oVWUp zANLI)2KIWlr@M;(wzKqa5n}&-&`hsEA5pRmPFPht1}&{Lwd6di!r~3s+c0%`PqGAE*(8qcx@c9@5feJFHYn9~I;gP}DGbw}uA0ba z-}_}!%;VQyj5+n6u4sNW5Lsp4@j*e7_o=-X?7=>OpOyTa z3j!w}tGr%H_AY|SYbulI7i+IsY0t^=x;P53$5cGUI^Rw%g0s!`1T4C8zI=tewqsKmRh% zyZ_V&ZfLh%S@^L>|QRJ8HYCI-;W`IL*>%qqB-#U1WD zvjskRjlOe>arI>TK(~G3^^HT>#czqs&J99^-#-Y$qlF~y9o+yR+z&aP5ltdJkww}v zwUMnVn@>(`%ZCw9jmRGbojX3&$p$3N06h5hyoX*`tadG9M~W%)7Y$27u~laL z&v3@yP6g32G;yP{_BSY_*2KDN*R82_zN%5_a*49MzNk!g06o9%JWRiFQ8{@aW@lu; zu;O-Ob-{u$dQ6x1ZE4P=r=#oZ>Q}U0^K|odL;GAY(i>-iElblhRlQMt&B;~_OG+4P zzgq`71g{5N`ukrx=Bjjiq1rsn6IL6M)pixJ<&fbW#rju*8!~Cf;enp{^HKK4OjcxDTS#^YN~~2>PpRmX zAq-aN>zTe3O&`>2Vr&UCzZzq`pZ{TNdKS@+oE?94!dW>1$2U(^QVS7hW$od!$I|_m zX-|PCq2l&J6x#^Zh3L2#fe!#kMQ>KIpu6gfq}z)bgN!mE>*xq{6+yf-tcvG1)n- zVu*KMty%uZv|3S{Fi3x$G`Ci8U+dd`*Lef`La<&!=(e}bD}ODTfD$@_EnHT;Shw(B z<{X6d4azuY*yiqDsIdq<^)o4a&f+2>=;hg#o*>#OV8f@qLp<#FU;ku8(g@CE*0UOx z@1O!OExpMK8`DCVisnrAXrMH+bZPFLRr$@qk2`zTNCxjJ--q^eTrAq3e{b4y>B@h7 zJ2)0=CM=S}NqTN`&WF9 z!0CsghqC*o^%R(U91Sle^f`|uhv0Z6H6#dKV%er`8O0tp^tux{`2O%vj)`2<@!Uq> zS55U*Vc`)5+P14yevGgEG&b!H5U(Z%mABS<$8W@Z{#4hJXK~kvW@Lp8J1+`xv#%&) z{#Vl)>6!5^B(45}`Y>kOhQ86uXy@~9dYg=hjrZYGRPXbqzA|B&%Ldp1jEXYqX`1B% zHG39rl@s3MAFS-vYC>&3aZO}gcGry_voUc|QR&b-uTq0fRaXt!-e`na!fLMT0O+B= zA0PU8GdRn`ZDqp~l7}O?zwGU+(UkqF2Fb?Tw70!E@nkNt{l;plAWXg4W}&ijCsgbj zYHW~XgvTx$Cq0a8v6e3C487?hbC#>u1iEubccs_)NlHB#1GXj&q~hNL%iDWPV#!X$ zq>KOT?bXF%-#R5Jm8Y9e2#hyrIBeJtcqRG|bwRRVFL2Gb{WlbLD&!5_Cs~QF=C{d? zJI#2>Z}*U!_o(g$6z$~&)j(n6ruO@4m4EH-da>EsxRd)$ zrY|^~35=;6LNXoeTUK7GY(~tAw|q=osHej~>lf}lQ=$wPpL7Bg_2@o&th8SNT;Afx z<-6-^r!HvZvcn%9-@868BfBZ-;P!a!va=@!3w@xnuRNp{ky>on+4NZS#AACeB3)Gj zzA!JNWa^caZSGaZCD;`-AMNU$AXK3BUL`QIl`1dccS_Ot;-{|}KpRBpi9-ut6d2QG zxA*n_Ut#Mn9aK(OatwbyJ_eG4|C$cBt>@$L)=FUfNpKq;ER^w0d|^vwziwK?yDjL% zbV%Cf#?2|k8=bP=XrOk@S!c;fHa14**8uFB&9*DcN=(HG_4n2Gu~m|k#Xz$75<)jY z-HltnWHA~QWM(gA09PQ<(GJmh@YT5zA7~GZ4`VOo0i8Xc4uwb8D*`h9hWmYf%q@JS znU2HmkQtgOByjxHNC$nJ{f+ALzrxxQ?J)zOn6&-s;b-b4JHJ6SV|?X zDo`kOds;>!P}zINX7@|sAkKQ~8_O4_`WSt|Pwed&T;z%oi4ReMCk7@O>Lqu*jJ9vWH&|5+gI-n(rC;{8in)oNU7 zlv&NfAn17&C;En8>;EoIOm5J?$aW)hCv#;YiuJkRAPbTqtQ;aoX9k4j8_^hCaVY+?KK7NA zjPlk+GjhePyZq6&I)~@|TtSv1Fu;Yn2;BTh?Xwz$=Pxry&w));H@aX{=u%^=B-dRS-5l~U| z{<~x5O8;Ws=QpnHG_=RXuD9>j2%~Et2fKDHJK#9TTKfe{z)O!$52|hzswXpt-ZpmN z`H*=>N1Z3RQ|d@<>roJ5RCq}E$gO+pC_}y_JM5qh8Rop%oq;F0t8$RmoV6keEbmj$ z;va+zFnu|{QD02cyZxGTWQ?q?^gJM5({+iAr2%T4jjGi<14f?iKAL_fWP-@GQw6&N zm2|n<*&kcO(`SyF-;Ebv!^BMD@A5o(_t&-SH*VeGdGgBtc9Ys{2>omOL4$(xx<(XJ z4xHE`JV+d)vgxTDT=e#c1rZX@*8SK*tp@>_m67! z_g)8Kx5VH?V^l&D6*uHM;(|Y@ZQCySi(Jj}B-z_`M;}#U}JhWuI&Q<@TzP z)%5v=IOpN7$tF8$7fba5#SI`8l#;Oltt+azw3!T|YYf+2QV91$s;_?ZK|LVn7V1ma^mYPiEe3B~GW? zzr`+p-?#T#5hVh0GQ}*t6gH&q_s4yA*R_ulEZB$_AKc-n%Dty{bJ?2+^knMe6`70( zqDG99**N5zjR+FijhTzR_;QIdV~Uk4za#W5VQuP)nK(HxofBfD?NKP+$K*caFV*z(i4=Euth|g1jsEM0I4}VoVww&mn;!7TP z%u0%|6W~0@y1|So782VCXFi~KPX3Luyif9cyO$zD`#}acVqPUX8MnYegPqGVKgG{# z!Bm!Y_Xia3ewQ?OSOFwlj_!k*iFR4J6E&(VEm=&lBrL`$F5X(NsGLo<9|UAahArF} zhW)jWHgm<>A@~Hq?NBp={5gWHc(iWGhBknN~Ii#9df zSa8Rtd>#X`h}hj;dfN8=<6+|o3#(aL5Bstgq1G*@6ktdxin&NS%do997`bVDx?C#1 zmz6}j-PXBQV3b8(=>MC4cf2f%q+-K1ms(K)tey*ibaJXP4wmC%m!6IzWhDUYi|Y3a z)8+N6@U@0Hvzd$esz$=q8K>c;?XIqC4}Y%7=0a>ghfQ-fp_yyJZt<tdc*`)a)6qxM`E9)Fa(0W?m!m87WL_ z;`(>nt6g{Hn=iIRM4j!7SnjRLDIxkFb^|#87ooYy^DE_;nQT9qQ1P0){Su5=tdf5& zxEPn}ue`*DNaE>?a%uCW6i8nslrFQWkk zsCse#ELCHMSZSSt^F?r#4G0fl8BkRP%8PP*?)lb)#G)sg;BT-8(_qsayYRjUNiw7R zrog0|_Lm6g;q?1<>f3_q-4Z8_*m3VJ?qdBgt~-<_a@Eib3dPN8ycWEjoLzaEQT$@x zdU#@h+wV~7*^2dQ!%^WuxY93e!UZ+i&A|Y zW*4LIpNq9^MKUaO8)XDI%XWBR6P%-OV;y3BJB=C-b*S$O>E9aIQ8L?duI}Gc<)o`^ zBY~C1)UEaCK|e5iY*=27zF?<)p2RQ1ZhEo=u2>cqHTs_^I|Z%h!2A8LR!GOVTPTG) zOoBThV=l3VHpZ-WFu%L`<`Yw3xFoboqPVKMA|+7eev{ZX_3_A~n)8|P-&#shKsV=E z>K6Fnn26Gf(P@!WKtL3yAGkx2HE;}0r5lsYVl7ULsWNzT9y=MaFpv`zodgvH`@R%V z^Kl;?JodOWF5qR9Ta{vLQIi7NE_AlTQ)ID-;A>@4o8-e(>zvmt2E=*X#fu{zQmCIa z_w#fu>pkK;Lfdty$j(R-FM^dhzl`bhMg(L~p)BrsE)bku6wp8CSgZ$m8m6Cu*>yT{ z?oH!DQ#axqHFGksKL^sWZ$m+^tj6%pfAHUSF*j|T#HW##SE{U zD92x?QGL|NA-?z4WQB~ReHcy#7)D*tBxUkiCbHcjFT!&JmOQDn6=0|4`zTg-ARyn4YC#Qur= z5G;tWv-cCJez`Z+=u;}Uf{8t{vtItbImh-Wau`w@15L+voGFBm9YA?+dSNe}ot$eOcq}(kS(IS-$4yYb=ykcq-6ZU}e0A4c$_94o4A`@^ zyVklsF3B1+Zm1bxq>0Z_yDe(N-MTE*t(k>9#1%A56k(-*^%^Bwt;K$mqmE|HHo-4_ z8$)|*k^z^YBiwX1Hp5q+>?oNN)npYSz;}twzV>Z>1@&(q2J8p@{)v$W5c= z2z>P9ac@xr-wftxOe>dVIf8KF(dmlxs}v3F=SVOG+E{U7T=RLI7HPckJrFz-rw%^b z_B$3@_U6!aKZq!hLi?$ALbimE+YRP6v1%7-8oP2(F1z%YSDy)tiP7fn5p9BHeJ*x? zVO;0wE4+VtdN#1oKj*HEJYTg0O9IArX%h3Fv`nCY7txC=a=bkQ0gp_vk zW$x~7uTDGOW+Hl%YH*_EXOrme6;VV@PAMfX7G@{@rfy}a+^<)CKS&#!&7*{?2m#;T zY4+U;S`RAMAYB#Tk3PiLKSh=)Y;AAk+a~ej@F(eBs($@;fw9e9T+3Vjh&|pcdSo8y zatw7*wI6H#B54x1Q->QQPF>k;`F{Kd4E{GNtjnb;#%MRD*Jg?|i%Od8R&rnIYrM7| z|0K_tU}bvrwP6MdK!%v~g&U&{sv8q7!aerBT8T@w4r%2Ep*i-;hy8^Sd%6MgG=8#gT|8{4IxQ+t--N`Y0OjC& zWaeSSpK|17(95X>4H((?!4Aj-aD<&g4KlqQB$DP$pC^m5{>1n1B>s0FTE>s zlDqr&FG-ZbxeeB9h&XS>d&O$3xECck;wf&YIq)Wn?mXC;Agi%I6r@nF zl?ttnCqk|?I{!YJ^Ez7bBDe|+P>1T{yWLMZ&(vPpXPpr(!u>@zVBWDT{PVWuU!))d zB0M32Y7vfkV)=%>kvp_I6|d2m%JncRKKCf$!vKGfm|i-gkj83K4Z13^tm+Z&k|%|h z@Ip?I+=3}+Q8Z=Y#;u@Lkf&Ui|Bp6^_*>n`ai#eiJQf_WWIGP^tRm(SmtU4EKEf)W zbiS*&mvp4OE!h9$Q=-}`VyoIUGvMvk^Do#%0kzN-#qXkO^hr4>=kEF+d!}n(AE8@d zQ-B!x11K;e!bRa1@xz^{w#J5D<^oo3m9(?&DXvDQ)U5@umZ?S1&so-T`ly42my`^p zAI_jmNk@ZSLDi0_PV$zo1QLf%ksxWAvWe{AzR0TLEb8AdCr_f|qm#oZi^0)`9!SnI z=rKhsE>wAN;T0Z$q~XN%S3QXm@NDei%(M`t8|XsRx2H1JHJ#@!71b;)Zk;M%!!Q9S zZC>|z`}mTtx34U#v4A`A{+B;hDIWJ@2vpMR2`|=*V28tg1t;>7+ku|f;3{IQ{52c4 z1#)P=;PVXDNxZFhwP{()1QFn3#bA_m{UCN_;3zSZBga%Y@oVI8?1BcGctz4-H)iJY zmsZ<1%hJXXVqm!@!mb^jv3#;Ex!~7b%ayi};kwnpz7~p<=N?s@{7d?u*Y$m4SGY2# z1J?x$WP%v0Ur@K- zyHsSeamF(e@6xl^8I8?0yTyI+HFS6oc^mO~rtrkpeE-7i@r zhS4D(I8(*Ypw&V#6T}h(5^e&T`6WDS(djaL(Hp^eR70!Zk)3Ylrh3L(K!EBx{J~S) zKeBCf!IP=&cYTrd6RPb_d(-K;j7|5@aYY8<6!M{$$3OR+grfZ!QY{s8+k`uAo61eY z)I8#wDY!r8#Y z*`M0=1gTtB{?5w}O%tI#V7}R_*w{V3KCl!W{B~9>)=Jt2XjIV7K_p*a$R?l*R zxkrA3D~Y)xZy@{b+UQD}KL#Tusx-wZ6b+LX4SK_i%YkDs6vq&8*3)-{8^B%$7ICs@ zYxW9d*Jg9%&pwI^rXLtDaE)<9C3S(B#HHFRZMrn|%SDnhUVQKGC?8)fT7K=C7X*vT zjJN}Kv9iobqbqjVn?8VGj<mosZS77E=IM-}T+H{6aeYcJiE7k{4W6FBz~g^$YFX zzeOblhqwXhaC*7gtlSYSEaULo#JmCf#jnz{qUDbRdoh)pMXFhFE2vjVH3@Sl*H&lA zFs@`myWCw>*S{5@dfnIIS4uf1In&)*AJS%easaEQEqd*1uE~6F9=^a!!q1h&$6z4I zh+xsApzce3RlzVZFI8D3FG$eW>MZvU|GEBs0(ZniJvvU^vBXikL5~MogB~dzPD@xf zX;gGd>S=*pKL>Dtnyt2Wx7lyMg`n&t(aJBg4+<6VVjw$rYrNjnGDy|BHSlU8PUC#3 zYnXBASYwvtk_wWTi(oo@dnH*4k>w$rRUos9|d|emc`1Ta-42Tp8P=z(l0~gV~!BY zp`7pqUHR0|sxCzBI%}redz8s$o`1m~Mcr^i0r5`B?p$-^jjGlo8}-(c>Gu5M6hXf^ zLpA!$KouF8eIGXgkEQ@nW34K;_(iy~Q*{oD_d6WAx!ZeId@-q>qC4osvAUAh_@5Gd z+%_A!!Lpt}aeAwSfMPl4@Lphhk$b_>=vl0n>e*5vQa6F~rgHepb7O8xJ@}2!cJMkh zcQ4q%F~YhmgK-9!gh)E<*^b7e%5jR_m{3Iv7n@_;+^bI0 z4qm6+eccL?#2&tWl;WEy{gNj1?ScTto3o{==0G5qwK(42Kf*-WJ7LL1K4D8kkj$AY z`3{+$&`4fl&zvoDwKryF%*wc9_!edV!i!IAAB81RuIEd54H$1@BSATxE1?-xOi0vK zACcRU=AoGH_ed@w8>jT_LF1ERFx1tCmsXeD zzjGi+6SpgQUHQG+*v2|S0c0JGZix!zMmP5mvR~$^$v3GIe;F;Ke+Q3;{@l%CpKW>> za@u{y78=s$C1n~RlB)$fddKL}3H_%&@1$D@8--v8>JHJ){TyGrNcOW}f0t#ch#JpM z4TAHsZqmTfKs4^LUQ{58SSukQ>;^m7 zn-|8=at#f#6!3_Li0sQhi}N9p8vFDCWecL-7xi%&a}Mmpp0PA; zDjjewh6S(CAgT6ydK6P=FTWYB`u5B{?G^NU3pA$ajRDiE@rrHFjF}t`C?|m;6Jt<5 z^_vvooJqFuGn!CO*TC`QEAO4)?Mx%!u^wt+$|f#BnZ*Mns#I3h|N7=m*oFusXtZDD zUPWwahg)LwAf2s%MM z3EG`$K6jArrWRiRjdA}A?4q9VkW0($hgx^~ICR>tXBsc#Rz?2Qog5k-EBzH1z&zQL zgf+m7WsT&H#9=U8Cgv87$4MzHU3YsNue@43IVy6?x{Fm4QjM*cz55XZ;^clo%Kih% z-da9!t^tv#p(>EUWv=3>D^E$?y0jAQL`xvq4j?Mcb=eUaY%w>oYwe!rXYbN=@w45v zD?u+9Dew4pZQnpnxOB**x0?E(F;c-7U@ zvW?M+XvRO*Hb5dS2NliKh54ur4m0WecEmjQe0W0bt~oH+H=xsXg5Y3;HmpP~J!w;E zLI!DfmhZA)*Mg;r_ll#(5UkQeS8qD+w5f)6@W+(`0&O^7Ss~p7)L2Yx)k1VH9Zu@g z3tr_-HW9K=bXGQV3%f2f!o;wiGqTy?_8kV|daTi^$HZ>aaO_qenkauI(UWGpytOvz zyNN@)7jq+4&?^Q^l-Psv02a#yaVBf<#ld6*FUvD3AvMV8{!;G#d<=#y27n=YSI z$O|jS8FJm7s7ltPil4J%ZV-Rk7E)Cw z287?c`O9L`4TLk(rcY{=GM?P{FJj>bC7oB|*t7VcYNP*v<~$M(*Yzd)mLEyhNna%a zLN+!6>|~Zqv~(pKr)JXwe5Yp@H9H?e#3RF1njNhtu46OtKQ$$9wY_oZG*Z2&;hSvR zqR``;d=hzEvSYJ}^%-;Hj%ixAL>(?()4z-!14!78k<&Ei=~6SuVs11U(7L!}dr{i+ z3-R86Z%~!xk$xNjz^o>1af{;K0cbM+gy|7%V#5`C)TR|L@5=fQpb-S9u+d^8@a*b#WcJ9G2yd*%^4 z?|kTOX0yyVS0&=(&1P~hg@nIHr`m;?T+)WXd#lNYjBFOmSsz`Ua|XaRUJ-Qq>XKlW zLe{mHM(JnW$bN21X!T0kdfLr?C0{Nh`D*kF^c**YVkR(Wp&XltL^3S55ZnYfFQ>*I z#g!N>V?awqeh#<-U+PK^LICc^dSVD2OhiSJ>F6mK*kU9}u~A9_-&g5#p>uZrRHggP zS5KV!&UdKKv(YHDJJ6a>kz2OGE zy`uGQ?#N1ak z$|_ClM#8l=9TfRGC|yVwHLy<^ImL6Qanszq>+zwAYk%0n-`>$&)c@=&v82d=9Z7Q? z#Xyi#Vrp0Oe1thLPQEnU6Kjo5UwRUGd@lX>ULeh0La8R?9jcfZ_9#R2GutvuT{{mi zIq6|ak#(NuDRS>=QCX5?hx_>uOtVvM$4MrYiHc(;33}FLbm>)bm*SjY>=-HywbsUP zXTTSCp@_RYlZKI+puElOqP#zq4F3;VKq{n>gSUzwhC;giOw<2daonzIVEXSYfZg|Q zCL%9Kc35IIi&&hjo+)wl+(-O4{_G|<#aM2+8jedZ$COVt;a%YlNyZqVk=$F5h~VYc zTcu-Ae(PrBX{y(f0Y@W0$y07a?aviAL}!v`0}k{`|HH3V>*Zm;u4(_1oA{86-x-E_ z1Ap2)M~;_BPMn$zwAS*}2C>HHy<4^(1AY`1Pwg8fhU&URpkWp_pUvOUK5cl>X@icF zBs(ia6m1Ke5|ILKLIhHcz&d@;;qV=i9DQ; zbU&~LI;9?K-XqK8H>4}+ny5fK%$#;qHzEV5jwOsk6u3H&kHxxQg2YGiS2a?1iy8fG zBfPv^6tM4iWyt=Mq5dB$|9@1x|8c4P$IJ1+$iqU529XAaal9+_S9nKFkzgIvfBSr? z$ZJqhb2tt|2k8dQ=U-=8sE?)3fCs|)3YsR`0zS)|ti4^yuKkc0Rzo?ke@ZRt476p* zUd>A^J?m!tV+B0JPc|Wk3eJ;{I;tUrdAsAXp_gVYs4q9w@m)xo5)jg6-@+#jMEWvm zSNO}m>QHQST5Fy-FeQa5_O3I}I3%C#dG$8!`-$DEpo0#o=`PtT5> zGD)7(%E|C)@_!m9^n$haQYu&C(*M6Xy z|1I@;cI&TqcoOHJ*8jL!m!k91#f}7L{4_?m?dVbC>nH?k67I}oPOR+JSgM^O&)`>nD}&x6tb2b2(5111eXzV^Ho7EHQ^8hS9@u3HtiVuLd0S;uZaL1oXl*m@RFru;I)u)ZyaDds|~XpVIxiz8NT^n z?7d}J)a&*?tO$x=fFg*bunihP=@JE$PU#Yn7zRYTMKMX0ZlpVB=nw>yZkVA#a_Fvs z=l<^B0rx&v{3Nwy)*_XXbmad&Ot1^;wItoEF>0&6v>`%`M9OKr{@E&1%^} zW?I)vhE=??z>K-IPpoylV2-wh^Nkb#jE(>K81C1uw&Y|OCvfmb`1RjKO{6xDC%H{coSU^P7e#pwJG9cW`1y!S3jY9OeDrJ}C!0@* zA-CCNLVVhnbYcGd;f(4RYl;THda=KY&;1&+ygD)q(1H+Y^ES2WjU;zUmZXk(tT~nU zX0b@An<$0dB97BCRb#tx`+L6j&-d^h$9#SiXG~0P-GPvg+AXEorq-aku1?apbSFx* zcXjVf=xHpdDfgPhWVIr(=#ILaE26bks-QVjPRdwcht)s|2jF$ANl0n`A9v%|o=VhkX$C$(n8-67!Lxc!{2fTDE_2MTeafvCwD#Vx+$BOEY&Wc(tmtumUI1vy} zc&oOOLUeO+Lv2N}e<$Vw17zI+F{WC@|C8*2q{KC%dD^J5T>qD2LBhDjKpSm))dnH+ zYm(W27yAefj;Vw*+2sGC#K43r)!zT{zUWzjm_OOMpZXz{|Nax5hzIA2>FDJ8X^H&liz{9c0x>UYNB=TR z`q#((qdtF<>%VLNKkD=E68W<|{-ZwssL!7@;-~!VKkD<3`uw9l|1N?5cd?I%$yJK( zAAml3S8)j_NGjvx8fe}mAnrb?aHMN-?Nz;nVPjL1Lv}s#Sqr0yC`@R0Ht^enLX3xMQ5Z+z%Wg2xPgj4PxtRk35 z4QZy{)!fL{YbL#-dgb^}ek}MR_iTFkP=jwcrcMpA z{jdCq^Jh`x`(Ks`&7(F;koj%@Natsv++v%$P59@N9d=v1X^sw`{>B!o{WrU3Ls&d* z{wDUy{x^IdZ~wb+VpXFj72vC$`HZ=PAOE}l{I#|Je%}UnqLrCR zpEcwe82e5!{_Bi-;RIjZ^EQ`x`lZ9fe&N2no`@vJ#~b3_g9Uv1>S;X5ctx(5!oot= z7uSA4s81?r@!mYs8W{QZk-s2Ca6)ke$YvEvoveKh?&gK!whkO}_x7zr`(o6y$1{EQ zV0m~YoSGrYw))LE{mvAX&I)u%)52OC`WJM=eepNoqXlMVdiR~w`7s>C$7~>|-SDEw z(-X~`c?xCfMZXedfu*)xosarvqh7b%1@w?>g<7oZ=jFpJTvJki!fFrXi+hv~JTk3m z;KhCKJ(({;O{BRU{$g1KEyIP18-#28g0P8bTgcq+>oO+aI(T#RTO0;IWQ(hGJv9EW@8 zyd`Iz(%(8ECHo<8xFaID`1^JiqN)K;*B4wm*pAga|m-p%&(S5Bq)X{;Sg**aRTB;CA>#xWp&^;j{nt z9S3jy^NS-y;9Q5Ud;ZFM|M1mbRFEke=}!HMxB0^p{lBX|SG^-LrvF#z7@xJDMRZq& z^IBC}H4Bo}jXmxvWl&1+5M1k_ZwS2sQ?|~}x?laKE@|?$?7@wCX}U`qt}+<$G00X? zQBieDI5{~P!(fEH@cjOQ{@vCR{i&!xRb7`Gn31nv-dA-bD@j<{t$P2op@@f6lsGKs z=%EE{qhae@1NNf}Yx(7Z*KgdgiMWwA%F%I(DL>!wkQTr$1H~Fx)@y zYS#mv$Z_@|fe3cpZEszkne-M62IJY=S*}`{YIjO6e>7aV&}iYxDII#9!K`d^eE0~> zhR9VC0>Lh~t*OK+F*0oufU&#QUJ9+mWQ_%CuBq8&owX>MN?3?zb79bO7z?nGiEtPX z86KtCv*%f9xrIrfH~kzg^xBTRb!y(@{gU=0nc zdVM8QEuH(!5s*WR_VXR2`{Dr=RCayDWm}z@4_(mK?K0gL7j|$tO=I$=L1O z#qQO*?bH_8jL{2kqnbq>orei^@uRjn5*=q?I^CQe4kjWDO*IMzF5`K41dPajjB(6?9-D>LM;B%5q&vNnT?iH(ir z8?YG~53|S!vj*9Ew|BQs-22@Q9Ua9dW_4zonu;~OmCxY=yK?b@K*UJ>JIfd!GZ=8vjnO94eww{CE_?d~kR z^>vOz@1TsLsHoj`78iotHiOjVEiMR6#l25i9^4*Sf4H1__QZk3{_qR;3+{n|fh`>! zBuLc5F!SR{iU_n<9!$zrrTwelNrEIQ4vmusJzcMJ?x6g{Oli}hpl%n$tbT?XoQ=A; z%((T#qpFkK9gUh<4$))7RTDCFbH;3aNblrI>KIkE~E-~J6AL{+Yjai=q_B_1O zE_Zs~q1mbO;!Dal8(Xe2VEHq=oigMwi5$>Ez3YNG(1P>laH{WL3%5m^be8qmbZd7G zqumRwCc1p?y;Qo_8!1^Q5*D-~Ct9>Ds}sng8F>1|v(rL60?Q4Y!;`VTry3g@JEy!U z)w1u470l$+r^qA>q3^!GtWvw|4*AtgH!Nn;xM1>wr>F(FeQLn3ToEe{!`;sJazY_8 zGU{fDGGSfuH48rAYIH2Vm7LDUo~zDCnuA9CXSSE_s{2iv_7*p#^%P` z5YEdYDODv3mWvuEf3zHudM~~1s1iT-p!2fdpve`y;}c-BeUJXjtXgWP zui8^QbCYyRu>pYOFq}d({$h?4huXF9~qqgoneenvV0&msoLu zdDiZiLAtJmnpl#nVUh$T{YFtP)8jP2GWDCnd53qFn@>;4^54=9WL8(yu2wgS=rpiJ zmx$IWYEm3xj^+wl{P_eLa|Vies#=y}H(EPo`DaPMNba@`3)k0WypB`6+2;-f;DzVz zZ?pE9-nXGB^r;*Tu#qBjHmw5oh6sM{Fx!#WS6+~FKJ)+=TBzYRP~u>-3i)AE?2*#~ z?5Vn%7qPbgr0X1M#qmSc0-ub5%crHQH9KFx_k47wY8^#64*r_2JJzmu}A$XYz#~H3t?v zy`ed{)n&$WxVXH(r*%9|I>(`Kt_ULvlv%u;>TyQVbc&h% zctgm=%pe>kNPo??#)5RI(Y?LP)fgNziTZx)r6zb@tN3R0FC=v>MDD4XWqi!qMb}}l zrLSLx^V($zt4Ah}8&C-fn&9XEf$)4nOAmzgQ%scnRa`rV~vO6r?N~{xZ6tgapO?+~IU2nh`)TCJJg6w4D(#sKk7@#9HUD#f>q4lYMrQ4iFwhv61Z znqAKmqg?p}fQq3Os!j`20^@miGU{<1L+AS~(HJ6GzNt8~EvC$pGjqlBWoGsu=v~C? zNqD@oSo<8dV!Y_L>tClCd@**|me*)=)+-$H&JN z@~Z&`>St}exbJt|-;&tZYYr%CW3}!215OUwk9v2Syrg`5$A;%5<)ezjor}7ATivKf z)Y4(%J^px)w%Sj{tpM#0OSpn_LQo& zjS3h1C6z7yl!7J_ZJPQB=&a%UjIyoi7Bu=o+hYfm+a7lLBe6?H%qv9}V` z$z6Bt@D=~d^ZkY3Mbbs$6^}2EH>O6AL<0Q!Xufp)#W8C8=()E~&D!(cG6M-K4i+7V zLOi14VHlj$hi^J5O*hx5xkrjf-rER{M%F1Y`Yj+6QcV;V658 zvVKQQdhYPsU8OL~wKq%|__MRKrMAiG=`32n2s$^N=1c8tdiKruFCO!Izq*6-T=ZgI zUfv3MEM|4XMJMa#IjNJT5}e<{Pt9|YY_$`K5w;oy!!IZ*pW+c#ZV&G5Ws{9S$s&O}&H_rZ zUNC+o(w?plya82`*8tAgZ6J8|Kt?}$3wl^m9_&hc?FmqwHh8Eyb#)(1S3EjS={OnF zsnQQpG1Mah=6+aaX-Yfn*u%!qbGCyn>t>2|@?k*&EnGA-UnZcKaaK;?8b*|02V28k z)@oqK7$CW^6jZJiu@0q4(osUCe19JL`X-;hdsr_qDD}3?oH~H{6@CN&%dqML{Y=u7 zAn@sb@LmSObV>=*$02Vo6e^f_sLy2$m1vH><3mF5{81C{gmUqM#on65aPc74+(v+A zqsgx9?Cy5?9zRVZ$6SKpxqa*1KmPYF`u8ZEpr^bAu| z>@x!#rOkU;VeATUzS9Y-hH)UDVe34qd4zAV0`oA;@Stvpfi!-Lz7!9Tl?+N6o)E~oKE9%177C&Z2n*w-#~i> z$f>Ah1y?(!)98J8KJfKLxrcBX-Am2S%TsnFkx}KYG6i%dhL1hwjT`oVe#0#QU)zFUcF`~a$}MZAFHdp*y!a*+qB};F&r;A4JqxJvXHdG(7Wl9D?mm3(&c{3` zlUut3Ggp>}6C_}P66y8_uQDVdi&~E!{mt)bmeLbDw|dC=JZcpqGd!HD9VCM?g>f$0 zMp%UZcb(tA#Cx06S08*CD#inKyF$#bhgnqjW_sMyu z!EMKMyLy&yZ$C{g~%K zwtT4P>{0v#XwJ?6zXV-XE6dX^A0BnQc z>n4h7IS=mI&t%s+o=i$tW>jaN1!(Gm;!)I!fX#FV_4)*G*EyT*2{IT}b~p%*azdLA zy2dM?6BZ+|0Lj0EP)S(XM5NOT00+!%q|XoHKsoZAo7XrWHsFKm$?C>Hk3PBxO3($C zKGUPn07n#O0W5mDJ@lpdjZw?67qU|IxEGlGa-Jm2xpVXlQ7cMyBmU2?>cF;Pgb@0+Y1|*neJ$&a>f+UXJFP^&`0*gkws^qCtX2 zqa9BuYnRwtlKvJpoj8OhaV`eARbp$=yK!KIN~k=5nvv!MMdEL&?*p{PSqW-km9E>f z-RX`R6P>EM5i)Xz`Eg8(iM_XvGe|uq2ozYTID*%~Wo*HwTPCoxGa=lp;Z(~%aE%qm zw04y1hOr&&MTH;X*AU}nTD0@?O?x5XsIrWSfu|-)57E0wM21a>uq)nWxk$=Wf?)2T z*d2M00sQbav}x6zwMQj#4Ny1=~)pzrFGO z2mYJryhSHJEft@wXoSu)(SI+)b)2nqMKS5*tH$jpjwT^UZ;T22Hc`#4q`Y# zpL=tmuP#dmn&@^0^3N5}_W>A+ii`E(OOXiLvA+`lWe8dAvizM~3f$q+ODDc0&nL%W zw`OviA67v?q6gr=+~FO$l%O>gbcVIom1b*!gwa)yA)V$lYHCPb+jeOEAco3b-5B*X zs(oN(W!1F?3S-2lc)$A16r{ng<3;TOeAak5-awco$V+iAzwdGVg8OObXyn$Dbt^aD zW~c(Dz6Vuvf#vr=dEpcItx{WZKpWTrTCu~D>Wz&yvKW( zkcQAcV!B+>OQNURTa#m0pJS+4p-%8w=A)HE^2tfCk#<6qU`g^YJUK4yb+f>-v<_k` z9A{AR?qRsLl*Vq@;LbzRm9S>1o6!tJT!M<_;J-AmTaJ8kUgL{uUlPVn$>kctm=Y^S zR@4d}>ECfTfRd8g8N{h8?asZ5BANte8jpy#KSiMEjupuydz1KW0l=J*QA*MfLTxgb z@MOBfcYwo82!t{9L)vh+wq0pohvo6-^u7Wdgl`(TZ9(SN^wdJ>&;py=Wt>i3M!Xn8`FjDYzXfx)wiIqFGl~9wx6^|*gTZCXl=Hq^zLDQRrFQ#2rUDnOY^eo252{=gC)b99qn% zZ-?3O&@@j>wBxmppNTxc%YWmL|KnG&xAgk(IufFj{cSk#t|W+Lqi1p$c$fqHTdy0k zOQVh`WhoO#%b*OG-#w*V9?HqMsfMsoB+{&V5y}ykAc8>KjIAzBkj!kh`nq1HCy2{qsrEcml7?{-wh2*p*FsC#0tN_h#8oyNjAQ#}L3`Bb5!-TT<=ynq4z^7sD zk8)%U*?aw;cAA5%IW6KOcOQHGR#?u51ifw&>9Cx)2@QUw0eMkuz*#ekn0KYAtCP0n z%_}b05Ol}{GN^GIj!C23lM#Cm@dnL;;JOXteIm2$?!p zC&^B4_d5>1gog0sd4{U_-|LUXN{1}?65d;-yvNCG76eO7oM7EZrD!z*M!C%R^!$M$ zR<%d$+YYp~M$-o9z!k@4PM-wn6cgdZcR6<_F5%sQqLzBJA;E3A)g_THD?$sYx>!co z75FxQEWT_J zN|8IX;$O{c_)L~O;+W#m?a;>VLZktU;kN9@Rt8$G*$EQ8$N{^yMZcN$UFmsZGF@lO zwcRj|VD6&didXK;P2C6;B2*-@c`TLLNm3GtlhO>}qIjl;6FsfpvBq)0Um;ssbS~3F zYt+y<6132=QGw+1U)3$V z7g|{eIi-j3+C;esO*BMvE=TB^C`4p&ebvB0Trwt$$;DTwu~le&v@rD1p<6M6>!Im3 z6E?6GKhtIDeU#Z5c_vJ#u=v#+uXwH@vY00HX3k*T-S>~P*eYYA_I4($3OKvO0;=#I zin|dzdsS#E`+3chcG3N&-UJ5~mBx>SIdUE!>CW$z?`@a!T3`5B=Ywmyy*?jxihm}n zs8fUAQ8vEK(Cdb1qQV#_PF*XV8Rh7yUQqMoV1w<-dHB&Mk-N9z*Hc4vo6E$}*iTo4 z-d>gs4?bc~?Byk|b-Y$Qi^q)a0cEEI)KMXcDZwWc_v6;k@>P6YFkR4e+pExvKm0@W zD)YM7EGu!ax1UAcd0nsAyaKn;GOuZbrJzO0VvV!T*e*+Nr_+ggd{c>v$DnA{q#^}d z_tbO1O{0${cmD)xFw9QZ=b4&D33WF_ZG;dQe#Z!YN4G$~2OfS>p4)MeFae_x^m8|l zd!t)Hq}{w3qs^{sxsJs1%r#@N6;+W;JqD$ z88~Qa7)9kGYK^CKJPkfA^Dz?F!i-B~yUI@0AhZm;Z#)i|$9Iq@;L-^ziWRfLERNQy zYS*rM?v;JAVu_S^e?_hd33rG{;=9(Gg>&h;rf8mFG-4UiZiof#qR6^3!w`v|FCT$a z+Fk6*g0J%62Y#J=8U`Gx_%CH%07-K{Am5G_tJ!E4=O*~Fv z5J^Q_*}g7mGWux8?R+G2CCpHOxXa7(x&`1nXrJnA0&Sd^E1{X0+%y5o^zx)`#24TL zbY5JJE0ZM!2-D2Q_0)(#P(7V~VZv`EORU8bQ;ts`Aa6QrNk55J+~BP=Q~1Imn!}>9 z+z&vul^rpkSIRpCONvg7;JGe^04~OQ@#-(e3LouL(~&9tCGG8&;rNmgp7J{IV5Bwm z#hxT)3YIxck|;Wm)JFl%tr2boelbIHPEu+ibB$NItJXkyF5Qlb&nlvLd)I3S1Dbac za`baSXm&Vne!(8S2YEWKN_|lv^{AuRE3QaY;GWHp6=aex~kFFMSNo#I-Z5J~8I;6$%h`e(B~^^3$2l)|JBeM?rS(JIBAe@? zC*}Q#zo8>Y(r z8Ff~C_qc>@9n7msTiRfv4mzE)CC{J7532RQoiKyvv-HE_pIVs}SCeXX%(XozTI20# zr6DkG)8~IUQhxDLxDa4x*v^==574$KCLuR_R1)B<_alEMO6=cA3n$JIr}3;cx{^j0_#! z)8c@2r3t7YEi9`ZGL2AH8P)kSFl@alw;Ocfwm26+QXCs_&Gu`!={lT2s~a6$zpD@r z75P}qFI;TjT%KSfO7f=qplGH{?gF}zO38X9n2h2Cbo2vHs(dRkI9~gi8d$O?&#ot1 zSupxS7t^{e50Zq`*C0UNM5-2U;*dS}*@o4{wi~OC*#cENp?;9=jrqw?*Do_7eYayA zM2f%EG(Jp_2@7O;`{LG$5>830oLxSC_5di_Go)D7X2(9Zgw?tL?5(8S;N7K2R1j;B zQF@whDI8vJS-F)IT@5b@SU8$-mbE|E7R&{e?)>$qv#1d|@AQ&V&@cUIBSc#tQKkMo~G?zL5pD z{iRrfJ^zXZu5hea*20%7Q4Q_s*s~uCk;o)@M^LC7%hH}vVw@E}SC4 zy~Zr7^oM{$2B{R~fVioY!PF0H!R57^$_cRz(ZNm9%Oy3Y`=iz^_$&)mCf5ckv za^<5|x>pEL-DAspdGXc@X6vvXS{!XM9ee!@ba5pCQyfI75R2x)af=sYpD){j|-9bk9>1%P)QQ#z5i@-_uSSP>vxha`N!RAV57cU4wGHt8{%J3~Cpes;kO$9L1%_ zJwMzZsBxUN28#~7$4ozjqDtrNE-apJ=0NhygOWhLO80$E`-LG6)QEwy4Pe-KS_N}O zdP1BeG$@fP&MtnSTQLmM?z&GCp>7F^IdR+b<-&H^pt>Fp0ff$bTs{@prn zL`)+>n7VsgbCXs`76XRn_eif&4~yQ%g1wJt*36WA`UCZBowg8g1AK@`!*Vatn>erz z(Z71+gm;V{$uBwkfr~9yjvy4MmLR5{Bk23Id69Fb_?dY~0Mu4q5%4+%!B(;$x&_EZ zfs#X}h|YVkGuO5C63w-U^t+9T(OP!>HpOO?5W>($`xI%G*M0kD@}7X##%c9KGu>oO1PPM#7*+*HJoUuRG$+o7bPUP zeojAM@n(+0ME^NvOapHKk+Ik6Vl9n@&{xy4`0OA9i1nAO?3re3T6NpQJ`>P1O2#h0 z(%uTK7dFS^kBhA~LfHD4Qg4e)829VNz-X99rKYK+g#?j+hVmd>iYZXx^=61sq={uv zk|XpS$YKpk;Id15``3-gHN&lyW9L%t)lgw?WIoaMX>BW2@3LE2tR~ZKSl6E!d}|Q2 zeKAs%#XCEGEZw_sg`(H`D{rsHprLkQuSA?TEhMD^GBr{CDMv|-8Kj*yhF>Obzy|1)DYlJ&MVAlb2wrJ1I)Z>@6h6G? zO%Mry-;?{to46)?{2nL^M%;kid(&c=m*ch!FTP)fe0M)NT&>r_D_2>u1fbPaf?`DP zCu`hJs6=~Rw{K)8`sTBxA~fy8*&2ly?i3Gk%$-*m$jtHf2;M|Qmj)9{=}KE+Jox>-NCGtRv^@M^jS+?QOL>KY)9 zl;~2fgW^HPmNDjb;jsbG+Q+jc=d(868agH%90Zt(SO!stRWwMsLo=1UV4w+=p8o}> zo=#cFd1IkuXB`w?a4-fp1&k^;L8(g0^CPB+zpOk9xToB8qif#TolZJpPac=9cGchV zP03gkNb0WSkF>Ka&x;SRtVJS*8*?m_uVQB{+%zBduJ&p4@SddH$fO%x^`UAGfcPCO z)J|aD>NAEeSd*=U>f@xnPF}vv8gFmlXmzPpN=J>=MbVMQUck3V>HchgGC5t6GIR5i zGKFx0d?sRrGP$42hxl9?24PPf&>f2ulEADQs2d#5BTev@Y+byf+*<~_=X(2tixX;p zEaoSoF&O?4au=dVf@)k=3)gV;iH+Y(drPqvKG2T9PY(kf{GF>aj&9R_mYt21|brFx?`ZC18XsvJa`prIHP<1kY zGi0t#bFnYUOS7^W;3QqfPChc*osZ9sB~J3AV||4}S<82plqsgYO;Z6|nJvc<*PC`EXIIc;pS!66W@7Fq)$uHDRXdLws{-iH}@3 zw55+1ryUGTH)Ni()D^F|ww4Sh-ti{Qd=6s1MH$&hUu-%4RLx+eMxXFv5%Ic~1D1Ih zJISe0a?k6+4O4w>^+7^hQqlyLL|SFd-=>8FyBn6&kfuVqflmA5Qh$T>{_|JX&nwF7 zC~Up5}-Vnj_eFmi60tu7!IE7K0 z4Mr(?hVM^e8gA2ahmTT&Lb9u0GXO2NUjj}VEEfnvofb-aZ>u+YdR}aSL5IxE$hL1^ z%@Kryo~{&B!Y=|^*ap#&pB3s8Uu+*C3lOG%a{lAzHKdQuMAJZ&<(~K`R^$;l9DaMuH>kAl3+?hB%ma3+R$c*W!{^)?OWSK$;&&&uf75wYxx0}aKOsiD0VsGy)YFsyIi*qio79>^ismGYEC@^XS zxd^Pux+7nh)T6|bxX^fHTJoefLf9~2X<8?YBwuk+*RS0FJmmii6>xVkTJ|B(``TNh z&%j$Dv_$A_pm>4I;7uXmth^sLL1~8_@`zD1_jJ_hsA>Q@$Q(YndScg9*e%=g# zROT?Ym1|EKiEX{I)Rm*WDpaKQ?0VK4rp8iY6L#F^m=G&Xk2 z*h6j%p1pQ2$k8>O1$|-JfQvhhx#u)TeAUP5_o66p z9A!Iv#N9mMQcQa+uV{ETeD^kdDNDSEEDP61Zd{3MUo6r{ITYj~xF+Pj2Go+g2IeRr zAtkn$BQS2@Fbi{4&o2MZG$DfK_iE!u9u1+~L^Vx-JUDP9u{*Z=fG;vn>N}JTO$qr!CIlWn)zJq~sgjz2rmdNf!^o5(l z_;~q_JxI$`Lub>W|F--U#9 zDihTegqfX2V>!%r5b6f*0F(9(qiVQoq8G_%>FVLCFiXhSq!htNDpGW01_et&DZ<7O`wsa}m3mrx)33 zZZUKxv-F}9?C`Bsh-?9MyXlJaM?A9Yyo(&VUEJ!ZTBDK8Gwx`6uX6j{nke!8s@Kne z!AaJ`?&!I9ofbg^0=pZdg_7eS1@z7pKqO49a71l^^s!z=&A@f0Cwn%fV47ExL~!k%dj>&!;4)hb>dXh0`g&(| z-9WZezL%sad|OA#hapUvp+h0OLbT`N+nub~i4qXJHFf5dckKp~K1$Jh?Kzpw=lXlPE~}>6DCA6UqI>wk{-2+y%(lRB za1j0CsiSj5_Mmmt22BoqaD3=tF79>yE!{#s;72Eb%2hJ|sbo1E7o8zcQc8uo01=!< zvaUK}hzT&`m6mcIMf|F4D;)gNsT~Y-wu5Z5O+t99)u*K=F$n*5l5K=d*VGb-%_-h> zd7etaT&xbqs{?D%B5-D>-OY(*u6PYwW+u1NuYuR3f1z~~eG?DYlwzgwJn6r|DO%*#3D% zig6|VxNI3eqR_P*+I;Eh_cQ|?ekBm&=c#}PPFF-8RJSJ;-Oq%=sRLQ+VW;m|sLwjK z|MyGVf2?oT;NFFcYpU6veXxlpx))DU4Y_Hs91YJu{ps+pXoe6It$eQ$Tl(4kXI(LO zKa(}vZ=}ES3y&y5kG~QkGJE_=cnf}H&A+b8T674oWn{0cA({U3Oh57y_bnVOh7I}W zzfAG-hZPwln&bw9LYpq5%HeqXUo2ul3dqJ}OA8Xd)cs*{RXx9w6&xjzk3k-0dvo!Z zD4gGpVjb~B1m3Ma{rcOb??30y{3QA?3M?62{{l57x=w1J4&;L)_Q&EwDk(B!e8n((R zN-}*cR)5=$vb9RZq<;@dl`2$hkCrZ`kbjrIC(jfu3QvDw^0x>1cnKXDhj0-(+W zYx3D&{j)6pQmmwNL`mtL?Em9mBWRhxJ-SxN`B?Ou#QyDp{}j8g=T$&?)h-~{$3tX- z<7Y3&ft4p2F}1KqfBgw9+W-VO?cK0Gdhq*F`5~w!V%KLWae?QN-UW^achF!0O;f-V zYJ@j}Icz8eGO4AqlYVyWbVl(kmF(q8Y7c@7A)CaRl?wihH}*b}-J<##%h{uKav^n}hY#A?xoz^plr{u#3l8%9?fO zMH{1pg`uHZUh5*Tx*-vi+RFzXmOe)Ly6ucwgMBVz*@J+tq7u#3v{vx9N7i6%GcHT^Qy0LCbMk+?R%yWftmQgS|CE0Y;-%zJOfcs zOpW@lA*|lwnaOhdh>YBWJc_pEJkBE15=8x?miEwsx@JX3oB-)Jl^t&O2vjUO`dM+( zd(`_mM1IRNW@O zbL2Ft0{2hGd2qfJhQsr|d!vt$W7tJhu2CXH(sg*b(ORdXs*RBACuE6e z0M*Kn4;-_gp%MhbM+=+<11ly&74DtRv!B3ZR5)wN zLM2*En~%z17pzNBU)_Xg;i{=DgyO!`5$1rDOQK>Oi{0ujmxTtf)Mvj_M%yZQvR>P} zyKBEQISxF<64hd1vkU9${9u0B_%f(-A?)*M?1$W^pdsmuI*N5^`aED@Dplu!-?;&Q z`qwet(N_d(fm)ATr1-Y}%Uy>lYjZqI)nE^-q6!}Iq(5@>L{eZQ*z(d3YEp$CV9xpv zHx`DWQq>e>uQXdNFH>gkkAO+4l<|BpMPhUUgD z_u5oTdl=BKP{134ubF@umb!Mt-Y2$6n#Z3nPI+-Q-2tPQpC@twnW@A4{&!mBTUkID zWRNhPi~+9{H2<{oo+gXSMiq_uqR*>lq7kbXV3?NQ?PwNgImT@)HsTKnfmyvx%Z?-k z6VOm@b}0e-N2UOuL=LPjkXqbzW(K;5X*ci{3=CAe|Ju6Z@Ub|Rd+PCgI%YIc47YX1 zB!msHK-I&9?UJ~_-^^>`pO#kd?Q}fk^-FZMgP|paoq

8KVV{XykNtpT@7 z&A^+H*$GUuB|<#`Z%W=A#aAi0#5~^RphX)?kRtvW$t6#57^P`mcxj$2rWbNO)AVH< zXLOa5;px~Br&(L75YTM&;@0po0Axd(VicI)e;mzquO>)k#FO*FUq1{pHO>HyFum?% z*8mN)aU)e^7Fror1iHQ9yXAZ9du@9=&3nv`MsMSMg|3rELidi7120*^W)sc!`tD&%Hh*D5y3X^dL!^u+6Vy;kJ`TEO?^xx70kVWij!c7xwlngKFTx)m*N#+v*fj8w?9;q2}7GtrafsE@!oH|lQvm=?!RQ{ zzwvxCNwilf9a%XC)GXX>wQN9br0>RG12Q!Myb|HE1&k|T$*54wg1%y>b;zqXDN)!N zBg_jEgRrUgL{)usurJLJbNX88&PGE_ydDNLX|qIvxflz&kYF1RtIGucV6wJF z?9dtA%M7!?b27jG-Bil`sR>8w>B7v5Owb^{EN}J90Txyo(glotr%Y{#twU~ZS&;V{ zAS05VE(iTWWI+?bUrP&I!T^m0cB?6bGcm>W!Z{59Mv5y=DY!!{(ab=JOK7nAiC3&; z4RUP29t;r~EN2I?b9U{}J{V{Z%TCB`=TnxSh$WPZp)t_Hy_YnuJgRW-H~H}qp!+#x z;kKSd zPpQkzbt>~0ASm-AmX`+DsH#UDQQ8wkL+jsl)o6Nv>Dtmq>C z`<{+X&Zp?~QNcAOIYETR3ZPl>Ly;W9Xb!00YX)r)wE?{qaue4iwr%E~COiNMIHA|A zCtFs)C8%O+CrF5sSg;b$hYfsq6A1chCP=|FY#P!HaP--_gWy`rpv7N97=HGIxaP@J<0Qhyj?+P_rK7PYPkD1xbm zmk;Flrx($SPziAF0{O*#A&JnCLc2ixMm$+sN9tJ?>!({y-Nu}EHWnRFV0(fZ_Wm{? z!M+QsW@`j2s#ym|5K$SBt$+H|Z&xZ3OK9&_GR9>6VR!ZC6jFLc z+6zrAI~1)&;k%1>qMsuyT$jUm@ANts1EwsuOa$KzLC_nVYT#^^R!}KGRy!-dOz+;^ zs1;5~BBg*wxvW9GC=5mu-pcZuJC;S@bh4fq|N!0JUQC%`zZi~9~$bW#j-j!Gx*lOOB`hFW+;GXU5} z$}zh#V4)_Y?&kt9Q@)h5LD%rR+YbT;mOv4zp+) z@d68o@j6m+0*kj#?TT-3@6w5AYHD-4`d??<;<8|IiR7@G>l+UX{p-Ii;V$$Wd)Da) zPo;y1P1}OO9U<$p1N}~t!zg30S>Mv*!2)Lt0Y-hC>r&BW%fNmtCp%qg}3BCLZ6q4riWytRw zZvg6eIAun>Jy`X)LhOFpb>UfONFShhY<7T_iBYaGMZ6tb&L?FRw$50;b;%wrfXQSk|HA`7d?tk18 zeY0~0fc=!IjPO_4&Qnsll3=Bu?R)d}9s}RJnhUV6hKom{$Cka&-XB8YCcxJ)zmIVB z5`mYFBDxkK+L~K<9s-`W-?KT$KYoAVUM(@CJ6NXc+=L1_*f&UIeAOK&1%Gkj6Q1+sGDBq^$eLU(aG9yuu!5sm-ss0a3+35-06;1!lN%2} z0cPB@;XrhM`sRxJmq9k4K;}H%kvs*i=L;dkFHA%anau_wI#q%Iibbnvb728nk?VN7 z4q!q}(bH_+fwaTMD_9t;QApY3$Odpd;LGJG17$*fp6oVBrub_HHE|~G31hW@JYXKv z=}=`<@#HX-z=!_7mU?t1Xj-fxn5LZ%e(xAj!BNlr9@_dzQvTHyWf-I~>N_+86i27A z_O|n)L8ZrWUU_~n*lw!r>>js??5q>xQP>`fl%R3B4wXb?QYHgq1OQ#wkOQiMgmQf+ zxOw`5&=XcB>m&akd+!<6)Yi2NZ&7TB zs0b(ssHiANQKVNziXcULM?^XaD7{8hz=mu^Kzfr-=n0)DAV}{45{mQ^ij)u_Bz!YI zuVwEI=X>`#=Q_XMKU}%8NV3+PquyhTdo~KOdU${J+m|sw!4fEez1Fe?r4nnAUq1?1 zi1eF>Kze9H8Ht+^7dUD*cnaQ@6uceFvU_BR41aw*1>i7{E|OUG$7rXFS0KJ1`h0aFyPBbpRj{DI)Q3? zdRUwbT7McSU&ch^%~PWQ4B{(*c)n`AE&uvdL{SXjz#u1dgt>Zy@_Q%nu$Yb+RX%o* zSEdr%b|_$9ru22lIm|!%6)J#WkAR!S-$3S#0`uN=e+wWuV=_?h#k5pIZ?g%IBEbV5 zG+n9t05Z3-$l&LI-Ggrc#O)q^y?N%HQ|(DnS;9M*`|pyl^#HyATEn!cGAQiA8p_?K z=D4&5PfDmJd`qh!M%2ktkewbF9XE=CMP0VT-S!^Lt-lbZ6%_~UW;&7!npHgZR=ERY zg{F{Ya6j6epi@I1KN_`I9W?~NK&IX;AjK_lOj_ayv$IqP3adp151;^3OIdr+IspNi zXamA<6^iP30#ePWocXuwA^|&~aTRI+VIUX_aQx(poq9sGXg7Dq23bhaLX4>+U*qV5 z9o?Mj37oe0Q^-~KP}Jx%e7RXhtr6huk2mIfdL#Y5dn1UCc5W* zPx=C*bIG?t(!jgK5)4`8ltGkf07E9WBSV!hc(EUJ5nZb6I>wXHj?P2^#i>h?JuLd6 zv#xUo@m?_jUNm(DB!W7A7|M7ikFS)vS7?uc9o)HDu;&9XW8!?TTbT|^Z+^q5bN~=H zs;S7z5;PXy0#?ph3>dd%sBpid>8)vIqBf35{7?x%eYb^82h34~*h^<9V>FhEn*oWCGe?E~it$(FLUZa7g9H z(tHw_ybK))2i8h~&Ep(E$bDRT1c!tsMqlzQj)yYn=$*+K1i^g=1)d6rG>C?h&R*Pn zX}Woql9pP$*&#O6yA3kf*bXogpk+*2Eq7y!JKs+k0me!wpe%Y!bz@VeE|nz0GqS|MG^TlYMKg&gcyRw3<23Wk|5b-MRVR z6@=<4=x#QS`7%%Vs(UkR?OGOZ=~UvThoTlWFVBt29Z-*z!+yR1aEKie53qgr)A;%5 zf2Fiwr81dTT8{}WWU8RN+r6_$YGozsi6){j&=SG>O?j0@{LLEmDo{92zh)r3B@~8@ zlmywp6fg%NpxcEXe@p%T^0tmaM)rGx(AXJ4sH5Z)g}@_>+vWqF9-mU+W;7HAWrMkj zj;?ucdCAxSkF2uzB=V>Mr3G;LBS4<}ox%7U()$=Z>v_Dr_v?b1W6B@EU2s#zG3J=f zz2(1w%xv+tB{!S!#dNeQOavITCq|Qu3hm z!Aotl2d7Zz&5W;iKBwoLaJ19PFa|kazQqAId36GX?cG~=z`4`5w1WPq4iBlwZKzj@ zt2#Tcmf53cKhf0!H`^NI53fdYw0jAJ9U$6m8p00MZ{uULe62Oc)^*Z`}S-ZGpjO|lU*zChuFDFoPSxPI_Y?fC19yLli|FwWvb zg>0Ww{M?4`FQ$U1C}SO-Ab;rqRxg5r(X5PxsHNx^SSEusSty8ITIzd^sOH(xip8#1 zJKeqWOMsmg<<9)K8lPkc2M@7Hh|GZ>ThH+d{^0h*jQ;iJPJ^=G27#>U_^T@T*Ju5e z;{Rg>|9Z)Q0+sdEGVky^zelEjyM^B``AMAy*=F<;%^zX}_Bx*KARxx(rsTTKRzhY~ zPYVHw82(d%v(gT?8lO%HyFK4;pAYS#GNzROwBK(0%4lWcm8nfY-PN01?RgdVYsvg7FY$xg=uFMt!z+d|3;_qWU#6)} zfc`CXhWFI}f@ObOiAOSYz$>nvu9V$Av z4$Xct7r*x74R8Zn$K3%FUMJwtjs|6w(DM7B;0TNceL-~8 z>O*nB(_lWmC3CbGplxO@vlIqQ3f*CMF=*Y3K?8fyp^ZOb9t=?cr5=jqxMtDt?z`eq zMsV`%LHK9`cy_v1Au-l34GoF;tiL_QZ<2M{(+^qRiX$yj=O`QYGxj$g+kWp(=epDE z@8TWo8S4AFJJ%k4qyVf5fo!E86%R|TeO&>7z6cK(HS`#`lp6iE5)4L7PCK=Tasr0g zxXe`$yIOVFX2QcJL16m%)I(O$hxcPqp!K>F)X$6|hT>O1Qn~^n>C%HkEx*lXe17svD*fcRj(qCyG{c;z0fsLu@ga^f-c`3kIjDqEQT@_MAszGtLH)9`R&^&` zOI%;W_#s*)aAO$g6-fSm(8lT&3V<-aK2`A4hV%A&b%+3{O_|aqp<9^7Gu?hjZOj}vn`j%s8R)hDQQ9&b&JwsQ3@y&mX_8g!J!{&PLnP02|G^SlCifEv6Nz7 zaPl6{MxiT6il@<|ls1iQz*rWOrwO3w&({yB3-*~cp9UP4Dm}(-#)AX^?>FE^qrv$O zg%_~sD4QI^3aQK68&Nf>-}5=`=OQug_M`Ck z#*G7>^!Ks_0N5~yWd{u?8gDu6hrP|#wJ4T^V;1bn(1pbi~7 znG_eIDs~CKuob)&F$7G<@#_QobLDeg;RtjC_OrC1eV&{cAckAsq~s^3+Tc25rx9>8 z&=+E@dtwk^B{;ZJ>zzfJ!=BhP>ViGJ2;qHvs6jC{s$s zl(|r&=faFnZz*5zhq=BfdOp2c9O@(a3sC!unQjHauEhC;V8W)4^`75s$#3>FcMafD zyuLmCCx5dRGFrhEz$`8+qJi#SdpP#x_?a%ig~z!ichMDyKr4QsXH!slgr)t5D5?5=ST{CB6nms|rEIS_^Ef9%u$& z(mQnq-2tlK>K8KWDC3He*}@d8E%}5lgyovG`uAFAA}GzJqQD$FA~Wi?TP^&Sg5}Rm z{L71Yg*{@+QaOY=@zs(W!A92mej!f3d-q*bv=_exYiQDn@XP+{x@rrjP8^x-FL7GF zGzc;So9E}lte2;2bI@_XFxr6Jzgvn@E9?YPtKHnA3I<`h>L{)S@sdf5b$DI#f|%Xj1c+9x}ADkq;~`wjiW zul6_yZ<=uTw0`cUj}dU_yV5!Qx~h)t>PpiP2kxc(VpZXtZlnhh5E{OF4GQ|Ui{FN; zHMPQwL2;&&LS$^&^!^SUSN~ji&^<{da3RYu>hL{|U&i9+w}Tf`LVhjZ%`CL+n%GX- z6u!BF%9e^M;8ku(t18SJ;Wk}YqO?jy z*%L-y@}+=+Al3r~k~%REo_g4T-lFE^#a0S?A0UX|_q}ilN?)$*l^oDHsb{Qz+4!IK zJ|4SAY>6TPnlsM~i;Qow{*>z9WFE+ZpUF934qozxCH!3J6&}i&Llg8Aii-|l67-6y z>?;I(0V3J~aL=Cm-Js>HlhSJC3%Vy#z?{msOJ0^%yd*gPDe|d}`4a$43Qn8fX*$Fr zlr?YWODG%CZUjF6j!I9|RGMK$Q7{(e3_>10$EsH8EpG~R*9o}n)!|X#%%6>wROy&Wj;Aa* zRf)wK2^jG2DWKenz_@}k4-n3oqV`r02x!1fwdi5s@j5^usejCoLPIPVM#0#C%v98G zU?N_ZcCVW4&}-tU8lOb*P37is+$;T|uJ?C(5O?63g4|SIJ^SI~?p`^%);}qw8#4{S z*zf%?XN|-FCDjU`lo6sFJLJ&^Okyl$NUePCq*e4AK$#t#ss=c_jrvw8V&wbEpK|un zJVy#}M$PVDcy1JfWL7g0@B$rPfPxyrH27>YZZ92M^xi`!zP0j{5zb^xKtZH24S4QV z?)cpWj8~Kv8znF`-?)$55nlN&;4mv@=Cs%d+BQ(@4xZ_O1LMqledyN8gF$%p)Njl+@`=?Tyq#K?*dg~qY8wO5eDN6(If8&+S$>vJgOnE2>BN`3O2BE}qV&80^vAZ31?SJ@mn-*|F>4BoZT`s-;eYnyy1{8I6Z0_5? zVgXQU!gwaGfBT<~FQOFhkldE#zZ|1y!bgAv#DM0WB1*mC1DQk``qRYy0PPOAiP&jS zg*+VJx6^rg&LJ`=(45Il*R4`q`ZI2^F8c_K_@BaT0Jp}v&G_+5`nY&!D$Kz!&G77^ z9q|7BEAo`*sm-)(`gOm5eKK$w5ei__H{+2yH9ykk`0L+W;P#%ZPdq8_aG;BqnO5O9 zY~pVM^cPF`7a=Rvh==LArttl?4S)Udzx?;kmx3^Ec6!3U{79*; zj6Y@`-z~m_f&M*e{>umd`nxi8AhlloM(ybSfA(yC^PpLO&cw80hv?+LdzUM@06(EK z{Uh+d0|5Rno^i?q_^1C_o}G67f0pO3YVfyp`k&?bpXK@Aw}=04q-PVhIxi`fA=XMH zrD~)roezDw=f>Ia{d?4J23j@vwl3}dy7b>)pq+2-4=P!1P<*rY(P#a?uyRLa zzFwv|k|xRI?6kjrQh@D*kZwt| z>}DU#v~~*7$VT0&&L##(Xa? zF?}XjFMhSL^*APGfma>o7(PQchyEt&#>MN?^pVNp+`o~ckC&+Tcg4PawrhvY{isX- zOh_=@l_1pZ8)0=Fvl}V^iJ`V*2y(q_v_9?Gt)GS*UNcK>D%QUw>XZdX_biI?{VRn~ zHGL#AH;uhQzk@i4q{}FqKe2W7PQg5G?RIz1$v`^jRKFpSS`LJt(rq^-S4Y|s(gtgT z*J@c`un1NTEqZS&2s^CnX?}2U8Dh87z2iHq8vk9tiBe{}1X7oQ&9XKuUZxu>rBgh6 zGiP0OH)LeI^Emx8-EJ%P;y4Av$blX_S_kXC60ZTxMPhIcVRe%Wf^qaaEbaDpw4464 zx;Q_l{252z{aOn>4Z=cP_fUq@%4H@r4zUk?XQPbI{`2{mp2>X3FcM^prJW;cxUAM4 z=n(4GS}iSmU+jL~Sk(BrW_D;D`=l?MG&G+}s64rLBx#=LsQ>20p!;=~K-V$|qS~re z_wMXd6T1ggJM6=@M(%4D6_ZN5>(>!8i73Pv~;nwlD%z>}JZ=W-;QCvqQ0h&Gf+xO{c%G)gEZ zEFFJ1!EJ9UNE~dM+Ne0WrjaUs)!DHkjMubJO!7!OgfWdacK%+I>^3@fB3H(|{ixjs z(oL6ruH1!QQMQ#$noh=$ob|k0)$g3U@dpjK;ZE+eYQ-y01-mYM36IWrvkZ$@|D>aB zU(!-X;?w;`#-W;lgZZzPZay3U=n73%~Q$9=iSY?18R;LUNCqoyC(U zZ!2vd-=0v(RHNjIyA}?&w%W*Z1 zJzwOSj*NhtVJ~$a{}P?{BFMq;*=l@Qm_W)@5ED;snKItrOK^k*tz%~{@~?D#?*+#< zdu)?yuI&~bi9dJv_JsW&`f6%}rGdIohw)?NrhWJ-2S(SY?)#87@;>MNcw768Q_Ygm zr#2E8UoX?$?fVm*HFYcnanee>joD@Q*zR62+3kLRFXvse-clcNZOC;@00)srJu0;= zcJ76cFy4TZ-!}SfPVAYjsR!`5zW3^Wg1ykKE@pc>v09+N+NAZpG<}TLYqK6pv}(E- z$WQ@pR(+gv!9cNxoL85PEBuOPA502J+6RZPUP$g=f4DRiLYL}y?LR*K*GzNSBc3UvnhDT{~&`ZsV?*3 zvN%*6E|8m^Ve~n3Yf7je&EA*VrSEo8#7wDKbv+T6rYSK&-_$Q&gNQgXK-WEMWR+2#YC54V$WBM~Z|AUtCt z>Gm0#PBc3vX=V<6+iOK8hh@dR;-G3N=5e9u(uL&W>zE1kAlu6~Suim<)|wxBqNUXI z09DN#niT^-S}@rdcSXK>vOoE07PofZG(@l`u>VMoKnnSriGMJg#={oj&`7VS@V{GBhTXV zq+!}hdOm&?b;I8iHsCoYffm!P9FFwuG%}XHq%G&QtbL#OMnmwtcqhcjWze7dptia- zYblRLZI`{p(}RUe-FU9Tq1`K-YF$-wPNBmt@<_JfnH%G&X zISNa3jiyP9k8gvx;jxlxd0xCtFjJiHLg9K#q!8>O@}bql#iUUJRkhFsH4iL}mKLNZ zcZ(X*q~i22wC_rai}Y?%S%sO-F0bxfQ*P;tTC4DZIQ=3-6}6`aqLZipNu+~k!K6mZ)1jd#Dh3F?&;J9cTBa&n+ z!+7=A;<=Uw-FBC@yj2klusnwHS!k?&7_*%A@oQCKn-yqm$ z=vTCvg>G%`Hdrd9KZQt}du3#^oYL|~uD^qLosM<4fbvRzDO$V#fFwDR_$3EFwRvfv zC15Gsw*4*ACnzS9x_YQ5W3!TW!RKKKWAISwd1-^hP2X9@&CvqSwTX~DW@W|$u{V3H z2Jr>gylP=nQi1SIO>;(n&%J4yn`Jsmv#} z^z|1no9D55u|s$*tR3p=Zbp76VQO+?Jx4nEe5=EZd1Xxg!UP|=uJv7~!lSZ<1JQEd zLx`_U2aYo^l)O}V&p+J6)1dU5d~to?G^BX#Jxdi_IzMOfTh5Bqg+JrFSSGoA`_GAP#j{)zOU$Qrz*J_4p@URz zqrdenzw@56r`@WR5G9W8XeYP3V9oGs*T}U!-l}ePxZzs`!p6Z9J{vGLy$;xW^hxYp zzg`sVW|Toe`cljY0rS|H?@swGI?0D``QZiRH`<fzM^?-2#M zX_8c>c!lohp?^l`j%Ek0#a`5^$LPAuhQf3gVU_Xb+_9SLwMCx(Hs*n}MPFxFnei1{ zD$g0yg7Wwwqi;xD9ykjIL&MpK!<}LcJhU!chFhgdL;BN|s}`@cYX@L+7jbh}Pt%n4Yva+15i&%erQ!&d<>EJd&z}j8 zy?Cpw(d)DhuN6P7Li-Q$kft?_BUMPHgj5vTr=VcW0cyPUzz~JM*rmBe)Tp>SSHgNl zkA_HIHPu1*%x^lb5s3p;55ZoOyhNIBV%mC} z>hA-00?~|fzr*YK%?XAL(lW^?OH3`;4U+E?`6A9!t0ZLhfIs(2<+}91yu+EnI#@WI ziLHKG@3Oaba;)A!GG3TR7qvEC-Eh*1%s|91bq3qXNe5N7oK1i%{2_38*#d}6Ap|GY%Ke>lP8*+Ng2}Rx`L+m$T*@_nbS*K8Yx}n z;=CT8y5!(H9o+)QPefUq)Co>(XY5`W>ciYM)E>0kRFAgK{R(YwNM;TbQ211@YuAf+ zddI(bFcgJSdXsUL@b(N%huCOU-Qo24(mv*4za9duyo^`cL$`#>yxb^i%YiTUr|-+? zA$(m4?_Q^Wa;EtrwM;WUuyxb$VzD{{qhgDFD~;PLXOsIg^+VEqC6&^$C4{XTYC4A1 zw&Cj2Ri-=(#nN>(SI9jRH%My{XrHG=jC$`xi@J-j8=Nruyho0!n+uAKajyFUdMdIu z10RMCnCEynl=tVU&uscP*cTEbol9w;rS{4&n_`g4@0jIuBRKR`Q;fEl-6k+y3O@th#H z@D9APH(Ve*-9>-Vmcrry`n{L>nItvd1yc`Xw`jQWLDe4yAa!Xqw^+G@T6wfi-bDNZ?0;)==k^dK9*c` zJi6C*?uwosGUv;1gu4vcdXkHHSf6ETc2x#~Tknb>Omos%G)!lH3ocF>innT=Ftl^XV5TA_#woh}UXeW5v7J#?u&xoI|vQG>l*SFXB{lY9cs zr}lfqS(qpp)>ey`Ozd~5`NO2g@UoCg3M1Ae zyu@oDCzPFmi-=y`p9w$8+m_!AZ)tf!7_ivYtzOCUcHV`WW<7iEs_UoG;MKmSC>KJK z!MBm!2IE5POf%yyuFYk0k*@G?VP{`ECMjew$8D?!Jf3m$s}xNaFHWMHoFSqHy99xS zB(wXVz|;HcOgpW>1~(ZxGvM)GWR<7OnmS1Ho%DpjC$tqyaq@hGZxp(XcBx! z_G)?!1aZn=xARrWKMk>O*bjuHLoX+il7QuXX0{)IeVi7Y!%4L3Ip37a*}7ieX?bb7 z;naCk0nG#SDa8=Pa6bLK&J8crJNgr@0?ag3r@pXiQ#K#^)@}-fwy)rg!MPx@b|b%zflr#-)J~g=fjyM<>ot`y@46b6}>MhkI9%j&w)f# zf*Dv(w18{YM!y_?I~C&LZiEkYh*x&smKSPbP+GSU!|cCoVvyMer*nN8|57M>c0Zd} z?O;{w+LCSG!Ksb)4_|>Q8OPT zq&pATfgmD)`Sl99wZY?_4|>cq=vb<1xbXY2y*5#@f?zmV?F>=4+S;j8)FrTe!cyC3 z)o9_tM( zw3M?NU4RpYCiX&P8I|U6`&p-t6|y=7T~%EoHGXNyJ8v*&i)A+@su61hgcRtBuuUIh zlW~7z%_btgW@xLp^+EK_e@Zp)eLu&=JE7&vkxD(A`a!FuxTEw;Ip=8F_om*~ylwQ6 zwnLsv8K7XPj5m4yOyn^RjC0E282`-WIZ9o0M|eGwg@@lOW~5Feh#MU26^4u-TBnlTvIK z9l~(+3XEI@R>k=V8c{lY-ES2br|Vkvu+!;GII9|823qHbHnFRSAiPWA5~GV9_uS;! zR5!7AIqXAQdO7@%ncmA5Dz0P^Nakewa7>!No7?>QXWap7xHBcLy02B>GTHPhWJt?A z{N`+XOort8)N3_vAqOWE=WvG)D{WCbhr(XcCf8vkx#NCa)o%AYT^5bIPqZBQ=c9d% zW^}B|JJ0xm$7_;rpsH}N=6-TZxpvY#DMu;)bVg5Aq3SoZjWdrYX-jjUl6BJiJ#x1c zvv%{Un*GpLf@1xH&j!`=U&QANCNSd1M;%D5bBJqQ>9ZtZXZ!U6vC0v*jFzpj%@WjW zmT~HT#$-E!uKezPFLM)IzbQV6JF2z1xeuR3N*BpDC9ckD4NeFeAQB4l5RL;5_(v|0 zl^c5VAH(P@FJh@m{h=pBluvTnU4-nX{L@_2 zwYSwjD=g@V&p`=;(O#0|$*pl@H=4aad3Y$A&lGh1`V85#{nO-amyv#dPb31=Fhi!J zYpsUW72#Lp;vQc|MO&gzO|~}EtbCI`ZZi&p-B1=T+{;pp8`MppwiPF;skwf-NgXp} z<@6+th_F#p?CZq5&6iBk9&?yE)a6RbN{T4DfpyijRxPDpln7Dq7lJ^8mp1U+QSY`4rr5JwU2*GuO&7G#H9tu^>klH(V>O;LfD-!H7zGqfBJ z>L>6!)~&7{aD+<@B~X)8jYWFV&XH~intErXWGvCl%*&P`(}%yABu@;cJR-4-KQpX^ z?7~!J`*&(#0ob}kr<)1gQWZQU{-KKnmHc6rW$d}=XPD&tV~r8Z?cnU~-}`#+s5RWA zSa04nZ9(8&P9P&s$5}$_R;Gz0Bl!@|CXwB>PcDH=tWmKnI$Q!Rk0tH59a(EpAIo4u z7YC&e)QK$IFd%kdUU#z6rZUErTgqEGyUp3*h_OS60i=28uFCxkT&O~ySv{3g-Q?27 z)(Lt{U%M(UWEM6_9`1qAUA7)bkqlSiyR2>ECE~HG(HJs8kV}&Ww^<2=St+HSR4<29}}9 zuT_O}YB_!6inNuOP_C`Jq0vVmo5>;9X2j{ZMAkD_&)AB1%5a)^4KmwprM}p5il`d@ z%o?}2P4u>-tQV{A0X5Tl&glYq%xk>RbV+xGY)C={QA|GivI?{0!<&*s{)Y>u{i1HI z8j_1jzLP|x?m8bSB+O-xPO`t7Gcwh(H4ZB}I3bPqygunQsqK^y>oxnl5(_B{o+TRd zYe%pqw37^0J%dU9Jc=7@Fin%XSrV6sx9?5iC!LHJ>-yZh{zT5PFa%2u?Q6c_;gzSud)CN^N{;g=y)h5iPrE z71SJY>B5CF9P%lPcI7Y+Ka?@+2NwXV;i5PzaTJyANu0=Wmb|ciU4Q#vXfje}wgj25 z>?uvYRO%G4f<+DqHWCNSJ@1ZE@x+EhNVyvAtJ(&orOIENYu&ouggZkeM#woqcSB+~ z!YvHkT@tUYUZBx2(CWGIMmmZV4UjJkZ{$|hF&{S^m82M-!gZLUMBB^nK@nXRg^DVy<^#R$P-; z?HnOxa#k=p`S?!D>?1l>J;~W~_0>Ca>QBwKPmGCZxBv3A6ZrfQp0yyLu{dQP={k~d$hkY6w% zrWLUve8!pOqtY7a534c`3k;%9{<+rC1yt>64TD>m8AaY{`S`6%9umwC8V8nwPXSA3 z5SmRYFkr^DKA+oH`$~6&q36;C-a#t3!(kguD5&Kl7AuoWeN4GrR#~B%3+D-&F`^Ll z=GU!8!2#xhS}V0zX-<2M)1Gs-%a`Q**t_RnL0r9 zK7>Me@)*`LR}6`r#4lZZWM5T6=ch}Nc07s7SgIU<7M-agmJeO)^oF|;LSlqezNt^& zb{mwI2(Fz#BI4%lNcAuAO&l>nX>g{4GlQli192wF!S-Xg*@Y6`lcrYD1^Gjfmes~p zG4N$I5}`j%Dm|3IcgGCd_F@x(uEA`TV`dD9-x z25Gk!(kESBe`!lX!88~E7G5rd-Rx#eG#(I5Bx$1)Iq~0Kv6&gu*l%^hVCocr=C$Nl zMP=5gKC*R=Fu6wUN!A_roSNho3$QmJM+>=>HWD3*7wnt&9g90ROnP&_Gf{rnHG6C>V;NliP=HF&- z^%W)bpS1fodMwL%M8H`&&XC*ilPq@F4)&lUPAW&R8p)piU@iyayU~5r z;2~)wfx4kk-FBfmC%TLsU64bj8+X3kA=O}T6Si*GtYQZnS}nkaJuz5Y*2OnT4`SgJ zh34|7l?mk09Q=&K{9M0zUEpffjWU!wUY{qny^ik_bw2)^wA55KR`)%6Vd2^|GC3Q^ zX@%sw|!7oeGA^S zjc>wDies{L+gS0eI!JpmHdlN?v-K0K&t;i)qSBu}*G!(cP!xeo&)Ccwcp-d6{}Q7K ztIrUJQpqsmx`uq(c`f*i+K^@iqSo`?y?5_?k|?Dvf4#cH(^5$jcWioiOz>~9FK}vQ zH%|`v%G7s09PqixrKGjdV#j4nh&&r=AzRAC_;32HDi7~cyLkMCFDp~2Ue3{siy9t^ zN-KfdEUH?yXi&mE@ zJ+0KqA;O$z7;6;IKD6v^SZyB`>y1BYQDmjKk9b6ufh%i2cRNyxWszH;Lie$IF1Lx^ zXp}6M7!>vKWZ_r4G#zr+q2#^$UbE&~nL^bD*^yT4r?Q)_OL#F|J+5J9Q?l7YxO^P# zSmx{a)ijOh!{mpEBT<26Tf&~h;RbrTr!PA{t?rK8I zwPUTjXaj{#NxJJmx%fS!1wWZgBFDrM^e3$)Ton^y47=Pn)$3w6r_JA8#kqPssegfq zmGwfYcc~3 z1|t4Ni=rPmhDKfdT-M~b#LrQMV{ywXNibh6eV_Zn1XTXb;EF$oJa^j=Y}eTtU%na_ z-IVBK{*P^D`V++dG*e~|xqoCTAN7w|gD~s716=)$V77e2%TWH|hqQ4{;S$L3|HQv7 zIWD?v4q3-87*Frj@Tm@H9LXSQo?umpIPibA%p+2 zj9 z2vpG?=Q1I2x@}z&fa=)JLmsZ0EY^!JolpJ3!mfoWVr+oUWEusO{Md$V?(px~^hf=p z@d>M0xpB!a4dPeRy?nD&jQQBVXz8S=DXh-w5=_S%y9*pPZ$}u{EiwPd2T$DC#cUgR zBs}?taQV^bogR5Av$gTo-)77HOqVgGeP*Ma{bKE;CWO*gbz%i4GQwN0HPVGUF728q zapekh>YDdR?hWyxwBav)o!H0Jx=%E-wU-Kw>LbkzFTXOxuLB0q$QNR)`Lb^3?yGcv zb7<03F1{lN^J#xvjzFsugmt(!B~EvVJ51_b}`n0^A6W#U&4J;^`yBL;Cix?kjs8t1J3>bzQc_kyYZ!t!$fMlE!kf4&d0@ksn$xs<=vvFa z@u8C+fL1y!2}!W{*D@+EkX z@{W126}o9vA7yBa3 zV=Vg_%S<#Oq2IeK>HQ@GW8V;B_2RG_EKyf)xDZ{72mRVd-$^a(Q$pG02`;}D;pj0t z`r{UNv{1h;+)V@Rx`z?`xL=ZRND(y9K6-!R=?`r;TA{jQ7sF#*q!Ai5gow-0PZ5-# zE`pg#(Ub}GXqCbdpjpbQseTmCbq05^=nYy}!kEp<0xL`Mqw;sPzw;q~<(07=Kh0<3 zx9RWtvB&9eBj*Z>&3YP|7xcxU)px^Aw&^+W*BjU{HJU@pwc~u^Bj=(*-Pf-Lt)V3w za@FC&QX=s#zi#Khv2D>Rpa<7VGw!z-(Rn|Zzl#6*3lIEwYfpo;9{h#c;GKFyKEpo? z^=*WQ;2)Dix;oTTM^Kd?Ce-n_Ea^5mIL`qwL>x2;gTI$~OzkFVo1UXRknQd7I*Gd- z;7XcK8h?(@TpO$6_IpF)vfZi?PYkU8ctWxXw;n5(b8SBn zT0vy$W}4S*coh{YhC57y9bA~V_@j}3&ks4;5h(a|LBx*IM9(bAB>!_Sr?@~9T;|@K zA2xK?o{eK^8{SW%Wgv?g;aSfSchXCW48P)SJ2gC9I8TpYc?eW4b& z*QPp|^Eg*;;>HuNepgK8s!*>J5H-Oe)$Uvt=$&%GuVTZ!4Z-NNktf=W2eVIhJ*<3p zExD;uHZPrnAxNG<%2&^VWk|zmF5&=Fk;4VwO$+8Lle~y)a$2+Qy6-N|g?YUbbIR_h zM6^{Qp)d!cqwhA2$2%+FJBsz0d60YZhjf1(VE_uaDCISO>c_&JcqTK~(yF~Nh(q5X zCPw0HL+zWh>U&z=W+fw6=5)LCyq!-su87u}f9hL15N`PAs+g!s7W8u0<8<0U*GyN1 z*t6s3eOkn0LR8lTMet&@Wn{~Bfm*%cOx-sl=5MKOV+g}<&AEJ>N^F;;+4`3lEnQaQ zoK>>n<1Vr}pu;zPqEUiompBPwHF3gq!2Z0k&lBRI7lV2ed!ss;`g4Yd-lx99Ifw}` znKAr$h@ZRnYis}h>h6AugW1KU{A;-mnE)02+Wh`!3CHL%Rl<`pE#zt|*9K16HiJvo zaj9D=#-&Vx@i>nc!KItM&&}ZY6~8^_RZDa}H5QBq(Un39+XMatS&F)Kj}E}kW6-6N zoT3SRj|2xVs5NT0WlyriLjAg!{nd^eMA;``eJK{_6L*g8R}H&bd|f*7BUOz9k8ZKG z<8?C!e*H@&MfI>-DOercQ@=*xa+`#&Be7VqIRZyksF0LXVinSIF+;kPsDfG!WcI)k z=K73WFu1Enf9%}fA9|a0IiUoUS~k7$xBB#MMi5`#<=XpmWtc)`Mi;BOkhN$EJ`MV@ zS;m~HV%Y5N!+Q(m6C|~0bFvYNZ~8xI7c^`9ZgA9>~A_|@B|REq^g z#+hlOq!b>Q*Ony=DC)ndLZ@Kwo*8b-hCtJhBm*hCI+gkg;|Gj7K`c?ppo32v>TSrt z4!@)9ljhU=$hX$YIKfO*xZZrY-&*;ltsC@CH{eT$yf|b{uHWmCr6TM@raNx;>1zP! zq28D`9b*x?K}mQxCI?bKPAzgqwOH+_1OL1LqAj2UI=v+qe?%0IWYoYiNy@ng|1jUD zk08IQA^>QxV%1mAVE)d zcJc}I@f@Lwv4vL8U1+%Bq*CRK=Q__h+#Z8!&coaq4~<+H|HuYq-A-_n)$sE>jx#Pw zr5Ac=(Hj~HrL^T1cBU=_8$^MRDh_x5wvkMAHcEeyJ|*q*gy=3~&Zqs3vv*Ca_qnv# ztf&CLb#K-RbS3a)m3?|#_}!V!d&CLOZ`+!?lkuN?OEC}AHn~hcH0$eGARJytrM;m) z2ZN?$X{kHq_+9s46PJf7X_NcUPzI@X<^4`6BT11Oj z7IjE=ChP1~pVz?$&RsMEDphHjLN$5;>#LBKp3zS`BgRwaQid#r7q(BNNKFzXrd1R> za)FOK0gHgUMMR&5G_N*uc})t+Wu?3EEA*#5P>K-My#nI)e$YQH1Kg>mvh2X!uxUZG z6)i$l6P}fa1W{kV>rhQ&s!dJN{G9-QNK6~gHl0O(kGp^Fwqy2w_wdX5ON~wz0<*tb z$$_=p?|);yIW;B4qIFL zWp06F!!-uxy$$P9MVuTB9G-+^YbcvnoE`s6njh9;FYI-)<>J%6fj<7(O4T{a0Y^h9 zokQ^;x4w>Lj^%y3`ma`r7sLaSOtDm6zbwxYKZD9WzjW;NBl>eZzRMOx*AbYunVC*?CPZ0IZD3ev{6VGj2PR?*GQS5vZ-kv z($QEAdXa~yM&C4afRqXvN~tIN`{D?5>Q2VZHl}qEy;|BSVpylb`bY>o8GWGof{9DT zNS$eR%FGMDefM~puFXca-vw=6C`=JoqnABg-{-0N0Z-Yrc$&kn_|6k&Sp>&!@ zy?*{LaByXA0=F@)miDLIp{$y~fp4G(Y5Pop(T8{i=g@J?`Lr!|rqs zRcw#~U0RiagBsx*Eqgkp|KAl`0cC;QKr=ivFks0NEQ0n3X*>ApcC+VbuGN^)gvhHB zu2xJUu9bN?6yj=XsLy$^OV3|DuNnh28@}fMGB_`T#nm zc=)cB^@p)y7R;PE=9ZeEfnhe^c@=V@^6?V_*ApsC)5u?Wg}=-`4{~XZao#1hP5t3V zmi7;qRJnIoFf~)6X|+r7@8B^f!$mHxhthHUW>vT97SH@tsE^cKcjq#b7sztjK)>_+ zZDTk{$c1|884bi!TdndLIYGk{&isL#=+u>U7zc{ljKhLc%MvFa1)p{~5?IHALw>q& zJ0b-L_2p|~4F`42`;oe-lE_bwlM+}BkEdTeS(9OEmWHU<3pD3>&lp#WffIJ392jQ=@fkHn9X6E*3 zlc3KE0~h-JvPNI$$v}OEO{h62oF+mo>@t@Am>h0;lErw8c*Nju0kc=YuO$L1IwM&8}$ zb!e;B`*U#AWqs?yF0^$zU5w`eyI5Kyv0D_N{HX9y(wLnk7qrP;&`=SP=ZKndo6Owk zBxvF5%MLQJpLE7TT50#x8mEl)V*&8`WQ5Zwq1TMVejw=u#wTvZG15AQ-$BzOFZzPX zq~d$`Q0jX*VLeqJhZ8w$ZR|z5ug*`ZmO%#w`52=9w1!kpLunkUK5!3ITsO~U^E&H9cc@|3-B$x1}V-$TC=M)#%Y4ESQjG}43E1K14mC zSa8y$vt+Z~ua%@RI$h{hrZnWC0zjO4s`uSW(N|S22@l#%H!+50`)CK%(a{;4_Atnf zmX4jUq+r#G`nau}zM&g~Yd6muxgs9B&OM^GCUMB8f2PVDrVoS*B0Ffa16-kbcO5)n z%7@mxUr{T?*qt|$Qn~VAaY-)(+LQE|>LW1WS_83j#%ggZ2>LXYQdeXd3^gY$3(hIAHV){Ddva5`_LT#-Y>`JEwwsz31`N&ksk+rs+Xoev3?USH?*&!o zq%Gb|zE^u|N8-EhTbdmD68uzz5Re_B79#Ou){99kM#vU!6cO8)DFleLl?Xhx$=_V1yR8 zqn^XMtBvOQ4DXV^e#;tZGBS5=WHp4rClQVAtnU?7ts**eE3_Sh340}3KYo!n0ih1B zN0mSsP;^?r9_Y=AcC+dE8D-<2-$2%KE3&=+6&s zWeB@~Fs72|boIxSbA;6}t62>9r zE|133yWySgTTUM1+AH1hstMD7TD^9-`Q0ge?(P_{MGlVF84)dI69ehEKJse+O_EjN zooYFKwz)OIMtB8!;N+U*YVfnUAw>+WC(4US-lb|#3{~UOz+H?u3x$*0jxkBD-()d3 zB62To60#y{$e?pCoNk^lBq@7|dHbpVoW*RQ%6^No!@#kijipQL;DiUN^LJ6up^<&pJ`F_&;h&4W1tOYfde`!M#DV<@YbNW-(F9hGeE*-e> zM{@rU=I~=8Xop9)VkzF-;~C~Joh`;?I2lj7Q;|tYV&es2e=+<9aKW)T9PU3}{#uhn zT_io0Fgid1-ZeqxKqjTirIK%W%Qur!fx6F2Q1hBL7i>2-r-oLj3qX6ck}GO>ybaj4 z21DUTldm>DtO2{W3&ZW|exwMEmO6eqzGj;bd3i_|lJ1=If;@Wqfz5b{X7o|>8JmWO zw3X$SOCl@6-v#c_wEGwlq#8ev z-;2dWAW&$K6%hs9CIVjLG?>eZIFiWzusQqC+8^~z%5u`KrpLw(Sm;iELl^T*cgxQ5 zDSQztlYeXXu|A7XKCuQ1`|ilz%F? zlyn?b^e8CTprqo+P>M4^nO$l0O$(zFn!>&`=VUo@?FgqTrSwS| zx%2Ei&5LjyZc>6C65FhjAYt2~PUg>C6v#`g9&F1eMQpji&FGl&>V$+W6WThuu8+Xh z1!sk5xyl%A3m`jUt=O`KE`#L&r*o>rtrJc3s2F`z1sHD=d$B)gRal~xoteFIc;;hA z)Dlrdjvs-3YGb|dV3c9%TbfgvuC-$!1lMTj(o6F3@A>(+qy7)TS{4ZE(fIug=kWqr zcYibC&+QN?yf|u)h6#q_^lGax)X$rC2|)hmzAwnhMvdn&6-Tp_`zR5cGPMK@0U{uh z0oHNDXVWR%AJ}%WlzOD+hngjth86of8BK4_Q&`=VOnz~upIsVFABhvMrm;sfTk&91 zc&R2=A3%nrqR)4+7v(hBHsT&1lb+Kw^tnjCePObivNVtlwKUQ`;`DuDV%CF zgr=9oli#Ov?*g9$VAv}z~;O5N! za0BVkJ%5hnh3@tj-PC)qUqq{RX59wtn-oK&EpQjF<9bL`8!rx?7nYwDP3&b%ZWUS} z-lG8@hA@`ZZ1#KgxY$_;0q%%~_G0x!XYMVxF)igvFiRIaPp7x2a;&qh36$SwuV=8x zHBRlOm`f92Lm*tmJAJ9HSGqfX4>C^82QsuRAYlh;rewwLLj8xg!v*UH9)%p%$<;2^ zdiAjH>FS7F33XRimwK=Qtp`-1TgMs0sVpp#z((DcI3XOF%ylvH2WE*(l`EPkh) zT-XcU!}Qt`u6?lm0@oY1t&?}i;(?CfWaji!d;5#0nvLymi|2n#*e#rGGKgg;acs}= ztB1w6miH7W)w{ZyGh*JNE7mdFGiEkbcDh*`6jmjuE&leKW+oU`B?${00V zk4OMDqvH$+XVTUzEc1cF|ESz%9vfz*-$5Gf`-Ka@Ti0tJ`#@Wp>lRhsVwm#wfQC4i zenYPOhhN+I9NJoTLDyZWy=jQIU7l3;L1MSa2td{0AOc{SAd1gBtHX|5#YVuYOl2%k5Q)Ge+ZdS$FN9@x&ge5I(|K`=UHBJFBr6^C2cTOeq$ zj5!6_U}@ltkAp76)O*{J!=Zy9RYlDOhya8O0A6!7vkk$HRHO zNoA+}?FZIl5_Je2CVOCSsV!$or8ygOvhmnWo$=8{5}mIH`39v0XM%(qLbNk^{=4yY zB@RsT>{UMUM|^p&Rs=~s{S+OS#nN0{8J;5wU5HmM0Bz)Kpk989;dg!LpILCf@g(n; z>Zuw}__Kn}P;wdQ$DRS!7#=UZDn9xKhi?c8rKiJqjXmMR5;cSyT|+r$Vl;>S?lL-{~fQPT8@{^lF#qcs(=<9QsfP>{o49 zJFc4Fp@+m$=-qcK?xQjzs@3E!OU@3$)5_}0vyE^W6RDlV+C89&O+WR_F|P0@ znyT%9(Ia9r#m?UZ;onE#8W{j9jI7sxpD=O@{fqK~%)I~LCUff)77kbw?j86Z#t||K z;{=2fvgAt?SAwn;szR-O-8d2#Vd8?dG~vpI@3wM4=%uC*1nR}=t8N$J$kMX_8gUou z=bXaLqX>N;wiL5TnMuUw;>l?Yj7@~vrdV^jA61Aw&V_v10*tOBq4Du0Ef%xcajYDo zRg3KwjSYUmYqw@fY>{aT1B&C75! zyKR|}20S}XAqzbSFIvyv4XK2UIqqI7S6jW!?+*N?kULxJLd`kltymZJ`xY6`(;VAmH?QW!($G9djo390K2m+ z=Og%o8;a!N74Bh*_M0xXfIe_MZj~g!iAThS7k;5e(1b?Z^4M0RSr5|NDi<%*9tG-& z$>wKp(@&_`XCaVzxeaQ-Q7vpc{16w{9N_wpLfGtWKpy};qE1t93cE_ktCz>IC>rRG z-G?{RMK-G2tJNJPBhw$q_KfM#Y@#dmR&;cPG_>-+#=BdU6xv@aaaiPa(lbpJf}5+O zF)?~SRvT=Si@Q46014~>0^5i;PTG`*!-Fuwj#YSwz=@{QsRMDz^vU-$cAM3PgyUj; z6>28MPDW~HHd&CCQ|`Gnf8RxZwjSUljQJNdCgHVt;@07vCMd)wX6AZdgG-k9`sWK} z@64HUx*E6#t>-P+bZDep-{P1piz}~8J&LqYu1v|r&Y2?C(6`H+1jUG&)po|;_LRHM z_BYx8`(^MOlFT)LSx+1>{k@^hclcL*0PC#srxn<*c$F;(e%h~eq~7?BC(y^qck(S9 zXeJgV%JGwUf$DMoRW(^Z;;b?WwW{%%S|L+=@A}K%b#jTNsYU4=S=HRO9#EuDHv)F> zqrwk~@$&T7uWLvQf*?8wt5JD`)=|eYrdpd2r-nhz&w620pcwd4hn`7^FrVVhU-587 zr1ois;TigAtHk?^l79#vsBe=j zD(l4JKsF}l1ME}H^j17<*NYRyo@>qqIg7SpL4>_Ru@nuHR+jmd)mYx_+#sL>Y|7Z` zEgmcttIKw$i090%B{EHF8p8E+YUn9%DIQ{xr>E-aCN6NcPhEB4GkV`suwr}t~|ruplb$^4!@`U-;O>O;EIFv zzyDMPmiw~PAJ_zt{s4s!`T5-j?m+w76KA}*|2|aVml!y%$IS#O0DFV*QeW8=yc2k0 zdmCfvggV{0HtMyz`k`jt|KYD<>-Z;A2xtp>aw>eP&%}Nvmos}_1ERNi29y_KOFVjr zG7ll+$s%8*OULT&VF2m#FgvFN<^sBpa+A&pKbX$z_@ra+TtQZBIcDw)jm$e(RM>q+ zxb@o7PW(`}`o%DuMmzPW4@Bm2IB0KdE}t)Ke0Ay-;m+xhTmzxVx*+Wo%k0iA_h`+j zsUoLHNP4rms|)BeUEUH+b4~&-;U?DmT_j!A2b+q1WJ5EvLXzh$+#&r{&C|9C&nuGG2ZLc>m&6E3Env3cj3Sz( zgg6wJD}+|T{tc0$9S`&BL?A~(H8wRsH;V1?7{Q*{z{ADAFZ9o}!a41KYHW2=ccNzxvvgpSs|Qt^7qUb+2rFHCQVK~xEl z3;>VU0(5JkRQ1;aJkzW`Rk}0hw>81qeeZ|8Xr$(E3V$h7$V%y)z;ZKuHhDbh&*^lh z?1~lQJH#qu*1NDzuM|5Vtm9`LHSP0=05n4<fo!RgLubbV*TQAV7Z zZ0fl#KBn#xXy(~UTVyj>6>13xjqIUvD$FXV&;lAK8r;lntv}l%#-=fSow`}AzZk7x z1R69gGU;h?8KKENz4;>uvLQB0VxGQ>an^G)jtAdm0X%HyF42L}6XL&xZ${8J0oYQG zZZF(^a^G+o;Wgkm|Cr&6u_>=Lqa<;4+fbXd8#smt#?Rf|Ut_fm3xJDRQg1vo3&m%& zuLeSVemUVu{+(msZ0cy@N3lB&A8-Sm+%-9s>$k?|CdNA*X!8`YDY;E-D=E$g%W28U zlQV*B<~h2$x(15c6&M$f$uuz+8#Y&K6B?Q?*0<%pWF_E%1d!F$Rp^!Zi&^aWjoUua_OYjwIrp|#%lQ<~9&J3X!khnjU5|g%h{lV*+~MFkzkr(dCEOVML_C%To(J5}sIZz3p>V;m%IpX|jhvMRldTO#sv>oi4NnKkKrxK3_86&Uaz_^|YLc## z3jBaI<05Q*o3oHFudxv`E~B>L!;F?yOkI`*(45lTA&d3Ws+=(;B+BHc)_69tX#__& zjAi$_Yz2`L7^M^k?a&aRRa4ADhtu*NsMM$DuRU3&JY9$hLJ=77o=e5VBty2J>Hfe^ zh0)wCN9S0js(paG{Pwv?9y@Hq;ts=IlzD)UuuR+n^Eg>d)EM+u(Rp*|j!Ga%X%};= zMhU7i-3QD_U0(jY2;ma9Tjfa2N6?6ZRH5rGYjzcrjj_)9_k$~LH=`3wsWhEkjZ-u=iRDPg$dM4Uj^~ zP4;*x;15X>6G$?e#|oeHd=BKOr0}~w-Iz-f*URfx5=W`Mvw^NwJ4$)vs3})Ol;7P6 zI$lfCn1HEYOp9O&odEoM zc#TkRlSTsbU09!}mYW9BRaGn)*3%#q^(N~eJ_wSOg7+V}nIB~wg4&j!{{EW>GPrNSaG{{5leOpMig5OlDb{@9sTD&g z8YQ%m+Q z!aU=hB~;8TwxSTvIlRZtzx5!Y@`v;1lKMOiCr@riBZ4{(zA>c1mB3=v(MbJmjxU85 zixq}}WmQBp;br)Z4n9t$(}%V97X3{xN_N5DmO*P0_`y$$Wc1MVEVmw-Uh4dbVt->W zXGZWeze(58S{}#ksxRF`*YBp-2IIn%1#g*0ifB+MUY1;VeI%?e2YH3ymj9J^Sl_2F z=*Sqa{?Dt{iGbPUnf~RzjsE2l_x;vUJ~J?xFG$eZFza1EO=2CQ&DnIouz1*phGkPo z6I(E7B|lhbcMkz)!2#Et%Dz5eDE=%GCV%P}t3JG1IzzM_^nKNDkR8XmgNys4SeLp@ zv=B=l#JT!9_h_=SM^RQWjxDJrH}VeRf$nWPTj@CkNRnI0liIDSI7bxn`g$5#$8;*E z><&BC*xT^~<(=(Wo^xxteXOivaQMp3$Oil)6aJb z^<4+vv8D*J#~3cK)zS-YGNdu0zkigVMyX9TXoDJIdnaBfI4G#(9;qW#8nrecW*q|x zF#-4F<{jh?sOBsex?MRDA-vGVU1o%iMZ<39QG*F^pVlAK`kxQJOTUq}p`aDN=j$2zkuBW| zhlRJ|{l`=}#ydCpat_rd@AL=aZe78-W3~N8b?(mZ2V+k8_Ri)^NB}oN=0ZI3XJGo< zRtuTMI1-j7s7yc2{X^21-SL9Du?pC#a}z~wiODQ>y*yfuf<{#V<-xCirBZL!c z1(&&(nS1(OJ?Tx8-Fgxk%IHDW8XY(vdq*#}8QU~2)BQ#FH~~N5`ms!ZxPD8}lspU( z1>-^I3~tSUE7nNIm3n0=3`&OK;X-W4dB(SF#2@Ocnwl9XdWM-73Oupmrorz4+F z(UGvI#0IvV0GU0PFzPX(uFQFlFqO06rpt&Q`A7Bmqh?L&5zoGft-^9#6kJUz76I%gZHp{%odBe3QYGfHm2Gtwczf z+Bz4>#E-bxTaht6Kn$WEhm%Jhmc)gXQ%Bz_)iql9T6Cu-Eopyu4wuo)DNUoAF{Y2~ zN5ri7zx(}w|Hi35i1|Nz$M#oFqRHr}XSR7^_RVQpp;~t zn`t`T#@0g2ykNt>$!4dBw9c1@Rx;-;OVm@#DT)%MiQ~55S4X|rp!TK>!NqQ zxKl>#S%R~d6+Hda+<+_kG;t%gFg+#LbpxH+oE5XYZ$%IYVAq263;joz3thyTV2TAb zMW1Bzp*{6-p{k*I%Z)Emsgv=mmEfC}b#9c`vj{*@rhUk#ge-L-ItWM4rD#0Q3Fasp zLT2<4GR`VipXLr9jHTU*N?S$;(W8*MKp2%ftX#Vlwy(#bQS>AmTL8xpr2>?KV^^3< zx&-l4OJ^^nDxtWuK8g(KB4RqAMOH$Vab6K0Y(Z(J9k>7Rn_T@1ga1oDy7CS#i3_lK z@()ivN$j>H$^>tsk3Ouu`-?ppD+N!W1g?NL#ilt zu=UgVV-}}xrTsT;_}3} z3v>xZoR(JlmktSUdVO$*G}77x9D$XOZ#PCW&D+VX4@&H8>V)o`L@W5?h&>yHm`GNz z5RAdaU~J2*KUMK*3*>(EAK)8_FlpGT2iKg5+xFDiYZO!|Ny9ABW~47|n7Wj2ZYn5_ zp~%Y*V=fX{M8{L~a6=UeqIy?cGD}!$9+iAU6zC9C4xSdlwIUSwt#yW8(zWU3Tu-jp zyDdz+Li5L20AxZ2AtTx@)=1YPG7CBr*5CwB-Mp#XF-=FiMl@Cbvu|awVM8u2ki_D1 z;6;C)5v8F`=0wCjm5Q3a8->Q)_XE7gEx577wY)IqzfTuxw|~*SZHB8jwei13@oq<3 zMl|oR4L&4wIAoO808zM+eE*E;&tLYMqb{6)k;q9w24K=kh3v z=GJ|kY9-U&D240ruVq_DVm(!!dI$%0CBvVDIzK{P7OWQo+-}>{Vw$LLfjDo%nzLoU zDzrn7tBZY~Aax`vIxF(b5{8NGI%3&bg9o{*QfRm@K*q!A*Hx zdDmVf$GhxMhNMSy85KXG7p>L+McU?XRqZpVY!?og!~##D@mOt-n5eMhCipEf0MXOq z=ecmJN!{D^WAbk)^M6oGfxp5LD11b(L;VixX-r;+OGat>D;!k!8~zfGd#tzXbwUHU z89oB=b(0}hMkkq$Ti%t)zS`^~(~9IiWr_(a4)PdVrbcEiyS!njg=4k5*7E+-h$F5V z{ad%m8^~^tW={w=Y?<4K9u#G}*k|}kxRHBr`MY3y4pFrGiWv8HC(GcWE&DUY8^i=YEUihEj?dYtubw3kJk>zd zZrzeLk9fp#%)t}aHV+=C{){%!kRoA?m#0)2rUd~Lwogvh`9nLi;|iAVOYbM=1QG%8b;b2JQ5U|Beeg`V#ma@Og9s!!eF#jdi5J(T)^Rt`gvQtEe!40V1a^Bh8-j3@5hR2^EhaqAB*DMGC#wcm19YD0mPAe=RzFnpui^#f)PfVO1yEq-NCh zOpz9|nd9?m(L%CHnR%M5_H$kcrOkE``*zfy)AXMY-mpH(h$F!vm}>t;b=#9YNw~tL zHzmq~^>GMp@dG5x?^ew8?;rknn`~!unsv1bal3gBZ!caP&#(1OK;JeKYeDWcc8*vf z^BJc4RBh&|Zgi+b5)~Khj>MTNVCC_)tvDQ$Y{jTUBR$sMnjgLpk=;;LsxNkHe+Yin z8C8?X4H4#8EskrH{%9f7o5uYhfxBCGFDy=Rw2=u}wLaTKb#XpPif9xvz9+IMW4o8B z6b3P0L9m~&Hhdpc2`*_JMk*kx*G3Fk2E;nmW%+64ju&)EV@MEc9NsWRv|JILZocO*85rcQ&cpi@)Ukq3Ea@|9d-y>eDnw9Nf0S95wv@py1JfyJ6IS7yAvNezS6k2kP*RrKS;mucMi=|zK?g`%7!umS|Y6k@^2_&09Yz?E#FFUrZ> zVkr^qT<=V*@m|b$kRk?xbv(6t`{ObJ)A=gqL#EfaOMvrQ`ocDoHP@0Weq@ljLb28! z&wA?Tl~)c76Ui7P;E{wDAU=jZ{D5RP`1XRaw{0TRO+9RCQ% zPX*9;jMrYe{f;l7HWt9*fCi@-lX54VAmIr>*;9hSUP7DL@ofbFi)Fz*aPf&C!v@@K z41|i@JoVe~)1Ol{=x!>J*H?(vXQcUQ*9KdVkAKkHDAxlj5C5_?b?1whLS7~-xdSs3 zDJ9DZWUVP8Zb0g8qANP6(KS_T^5;na{W;Mn9@@6+6ID07j4WbrRcv>ZamZ5P6SUijS{I4Dgq`h%OWL7D1RY5%loGmcL_(ikf!u@-=-z9dx_DnE z+<}L|vYEpYmb21kI_Q~!G=fswKO%dVg{0n~6nJJjH6K(Xcn_mB`;uDeN(2Pxy6jW} zV>Z=j@9@>GOBM<~4-IOHYXpzWjuYXNvn&y3%jEbpe31`s4N(ropMfckx`e0WWKHwE z=g|rL`}%uM@|TS-IpxfAa&ZqBT}-SuIOG7R)oHMpqG)UZ#EcH!ypj6*mz%k+ZXc?D zEnAgf&LmyZ;<+2emw`9Am1%n9p!L@J7gqJ~%IPB&X@nZ1`5Z7jdoO#mFN70PcyMI| zZSEur&0rxDS_deZ39l$8RoA?rbo2QlI7who-c}+ZiwYz-n|YGjz?K>3^>OvZO)&1D7dCCd`Q>ZQ=WTmd{z7h{a`_X(sH=4F!+@z#m@#IM#pkk_H4;p z%P!^xaDD)bEQWbqtD*p^VQOQ-$~ZIfK1yD`f#$qOW@c`+9lQYtv{>B81WwcaDcU-0u&W!)4@dgUU1Skq@7f8(nURiI5{aAzd=bxNL0c;IHphfw`TtRjQ?v4 zdxVpS|1`f(=G~aW9R080&`)%GI#U6`84Ukcp#T`bAD*7E%$2kMUW>Q*eBa~85hag( zaOh&Y?^VUUiT?I=1wTz{bi3P~kYSi0yXOPLLHbY0EQP{PgU^l5OS-dI;JF7O&NMcS zGg)?i9U*UHd0UH-wxywr4KKrkQnRACO(?^zGsE?aojsikZH9BDz@8U36PyUq-IG1vdC zx0p=z1pUmhP^k!d4iA-mdN%?z$nCsCDIOlv0k*z9vRB1Giq(% zpM^1S7E^hel6#Cj&6pG%Dn!6*Ru4qi`k&MNpAU}MeuZPD*=h8M;Mb@}ZZ@Yk8wiXo zZJsL569pj6+-`e0_WK8SSnvgvsV&>%NN$7b_lh#X&u&7l-fIU{8Is>(t(Qe_`ex6bjb&tdvG{23HwXg{Z70Hp%!DbI8J$$%|UD zqb<%ylUWQ7--6W7)=VB9Ar)n0A3%f4L5ju@PZ?vNF8XOKN-Rg1ihw9T*;gwBqA&Y> z!y#3x$n<5b)})7NyqPuGuC5{)z7<<;f_yaT;gzoNiCn`H-Ri_vPt4?`{R8F%MV4qT zjRn~UV~kcc)Cqt(_VrXP1UD7SQ7M>p$hn1uR9-*rMqc)JcxYgj`=u+XWuI6E#!xJG zcDeZ9;nnYH{qvjlKBNz3jC#LWDgJXK__`7!R*Ph|c@rpTy={CKx#~XzQ&b5LQkgL7 zNjkW#Qo_ajQ}S5$^M+xS{n`H5eA*skOwj6V%Qau}8x6ok1!)tZ&lyCCdn{z~2Eg=B zw)R$xs~%PvBb;lq)2{ZV``ip=t^)*Jbk&H6m)?wgV-CAXy0Fur1-f0AkLF~+i%^tA ziZEFrIY34wuiHcFHC=PKkIZqXCisxmHup?&GGA+sl{EO=v*sfsK{)@~QWuC|C6lqvV zRDupDIYHnC2O%33FIr_0+cY^))t$uxsr=1~eHDeRG_EhJ6 zsG;!V7Pq`LrEuTEfvD;=k2euQL+YNqr;bLK8S5@pns+@tk9a}Rl6~*z!ka_s^~PwA zsZYQ2VVWhKPHBhNYf7QqwX=2TnovcWO86@ugd#&=*Ar>upHtrwv=%JS%)y{f=uq>g zE_2SaCM?Ryz`9U7n(w>p$59uhwjYZP>uTJiN@I~tL7u2w0M@Dh(xPxaT2q~kjn zkS)WbKnTBRX?&PmKGp5@aw?Rc=YMmFra(Agb)naXe2oo4t-#!aVi+}-|loc;nPaJF(bVvaChYsmA#^ol7uoXf z;<98t^>Q&S_c-;Ynu8^gg+!I6u$yW!8`g|Wby8ABi%k1Qmd;-2{QY#|$?pUYlyC}> zlCg8t;@%2u^{fZ+Fh4$xOK*QQI=Q7b-CA?0;%KYs3d6I6U@*De*I@Md(StH-?gru3 zr5=!j8?GZr3a_-g$GweCC=c{19K`6h$|3mrC&P}-8NR4;`smpmL}P$)P0aIHq4n4p zldO*GU7uF(dt6A(@3mZ3$lpX=ez#SO?~C`Jzxk_hSmM3&F!j;oY*^D?SgiON_p98# z$Lp)@J0VwWIk}$rLKXFSOyXJbfwl`rvg#hk07a_GO<{EomFp z=;GFBpG4u2$8!v))JK7Du8bOfM(6w7uRKxg!e96=*<*F8y{P2OT#6EABoUz3J7WWn zJeqpQ+D?6bQWawTSU_Gs?8-8W&lWwYE`LfH${L*E*Et!r9?|n|n>6;mI`?S~(NxL> z3in6J8ZE_}{+I5`1YDPEZe_1dUy0P)vBxbIjE0NU_|UvQQ-gMjqAq4zLO&zvi3BiE ziqcKbYb~#wq|8b#Bn?Zr*3gDWH07LUyX`)&vVQFC;2rmC$D1_bf0XD|Y8>|<3akOF zUm>@O#{VY4yajkp%xdeHJOU z_SWW;j%lB6As#HfyD93lw>#>SR#EoXLR@&PBj>ey%!~Kkg;wUxWAs8nQE!;y2h2B| zK2Si{ifq2%j`x~>$N!(;vhIC<>+|K*x7vD+XBdQ^zyAwy}oR5^)))QKLi*X zl@=1XAm=XJBSODr*DPDo{2kFc1>o=PF65L4SMMgvMD)e)0=*pAY%JO? z=>7Bso}oual}W7Jdg%no(UNX=^lmBY@)|s1^PTdN!B{Q3CB$9f?{jK{M~@dcBHWyjXKH$>=6OsvdTJ(maCd z_j|5WU8ec)@a7nGti_~q-Oq{B7!|_WvKP;Fc_HrZ-S`Ud_4M;}sgBROiKf`OL2>Y$n{%0A_HM8EPcwVWPJ<_?E&Y0&|z#IxkQ_|diUfHq`C#cKP$Wc(~ zeuSYPofcTW=hI4zzhFmz?){`2kay4DVLNuh0M-4 zytH#pnI4~{7wdXu`C$9P#_r|@Dkd*4dfJ0(nx`R=jABeGZA z9Zd7LMMt&K#EMZicVGK;mt)%I+g6_`QqAxNH_6L73Mo2nRz3)}o@~NZqqQi~*1L8T zbK}|0xbjf2a|{d_j7u%t6e)wK1SS566OVPRIeIedODEQc5%UtH&0_skgh?tCKDzDd zx$W!r&w%g0-?ydkmErPe2w1NDN1k&;+ejvpWy(E#s*@pN;MJ zU_?I5dJC5o0{P^xilxA6`(|Sd~&Rpo$)M4Bs{2ED)GTM$=7I&)7Bde`6-t!6!(J>Wt~R?IQ;M*u1iAp_+hwKeoSDcV8Zj5j z`TAioQen-8NpEg;UMlp~WWY|{qJ5tMk0i#|PIP_8dls9Wt|bK-ZecwRLT zC9qApT1s!I$e_j>tbqT^y`PUR4WfNWuPZ>2>FyK2t35fb-?IyECm)PB8iV@`9g zplvzL=SGdS96cG~Dt+{>5`nHqtq&i{VT-x8;lAci)E{XWQiQFIXqe0lAGgn5g3gBJ zUSEqn-ikE*XrVnBZ|WXRVFEpoYaFX?vV=?$)2P4ppKWI1t(`Fr9!>AGyIkG8il)9? z^173Au^TeKA22&tvkolGK86J%uC61W`iiDBs%Uzo^E92^{`X6{k< z!fdAPZh_$B+(Z)3A~qmgx<~nvO0MCFzlx&g-9$igxbn&c61qp01Pod|Id0QcteeJ3 z&K~kG;~vT^IgRyT8R$%I+S$~P)F%~&rbj8S4E{XHn{HVDF=c^9?2bH>R3(EtVcRqo z`TPgy<44B);Pji7ehtqhl&X3&fubv1#W^p*qe)ddMTcx#h!{`Adw?p)6!$&ZYFE#4XlGz~s>5=# zhC=kv_T&m{pq?bp60Z+^)VlUp4uYCh7S^-L`v-gRQh~)(KS&P@<#(of_BkXXuI+uP zW9@(Myr?nV$yR}O+F-6_Rq-dKMX{##eAHd`) z8Kn}Aa2AaCo7Yxq^EJrvcVlxQn{S*3FR>Bt>U*{4N)1ym4mYku zl&TFGKRi>0e7(W9F*}`DEoQCu5{|U?=)-5GJjzwl$V9L-R+~G-&Bxj~hg3IV)+Zc4 zYD+^JCdtjORt~2jJrHhNa=G7@`rUDcHE?V=XX0b9JS@*b(RvS|D;2Rwt6m?nddB*@ zY`nZFaad&;o&}?ei%}kan@QTj-DHNANJK&4fNvtF_MxPNvOFBI4dtI!q~=cKF>hSn zb;g84Xty|qbK%yXX!OK744qsH@>{7stM=Dq`mJVCG`dp$UQ2)Q7WNj^4(J)I)$JF~gIz~IVQdV>Hl;?n36z~4YXQPl2oi{)i zrp45L$ySXrizuH?aW_RGX33e!kf{RwWr9Z?MXR$dNgG!`Ki&_$+nq}8? zInukYKy#!-&##v*Z~3OUfR9cW)#VI74&hVydynr94wu!ENN7>n=Bu@CQV*wZNv)>H)4eAed&2AlXjQPa8I;G5z2ORhduwMNu3D7u z4GmgyvmoPBep=vCw0S{~Z{qN3lxcNkLsyL#H_sKNT1y(0<7$>TD;!bf^mM3OiyW5iiK?)^ur0Gr|sdvkT>hEvefB>86z+>DVEy#>F z_yY{zSGq)ZuZnc3Z}OQ=%>ugKZ$&NWYf5+~#f7-Z-XlccYuu$ezRv==LsqfQ)7rs` zO17cdoJA|_KCMbsX_9Z_7H`}Z@vbU0Yy8(`O?w)G{B74!60eHiF7lsH_VFoD=zl2V z&zruf@`&3=utgwPVDVL5f@DW(y{1#6fK9daMT*v>&gS!!knF-7Vjc$UY~l!9BY0J} zK(FRIV}ONKw)nWGTc9TTx)-Bj3zPy&Bn_6!T+otB z7@)Z$57DVIR%K?|EAc$TX`W=MpB?4Bftx>ZdiDmp*LN(*!$tP`FC+ttC7qiE?oA{G z-uP^1M21kV`e7MpF8+FlqKJRse8ls(hr}m{p>RO`^G-`aa2pxUiRUXJ9)10LtKB}QG-cr&Ep-meC(xYhUCGISBn};Eq*a|{rV_cwU&&0) zS%BH-k?wB?N?P8eeaL;pd*=zDVDi$=^8(*^$%-yXYWB@sqKSPNWoRbUC+S&&4N{C$ zaSEcmV14-I-Fbi0$)mdqd$hVs?v8P)BLrx}L}lrv7ynxp`@Q?X_hIBC@vqVuhy9bF z{-FPVqPG-*28BQiy{3zPO67*h!R`A@Y%U6!j|4|1K%$ioI9a+4JDamo`7QMF?rqFW zvv0$`q$ZNtdlFO2D>M2hcu7-9SuVOFtiQi!XfV*ugREona(0Q^%N~0b@@%92A7}3! z)nvDA4@(u4A|O&iuZq$k^d{1)NDsZ21Q4V*DN;nb^e)nSN$5?KB2_vB2nd1#p%Y3d zU*7vW_uO+|J?D<`{r8OINk&H2-fPV{*IfHiJ;{+M)ETO2X@Fms*IoHnNKD#IztQEl z{$h|?H2Rct#yXE}bLOkb3tV=wvq7OmF};l2@|xXzEZp}z9n__A9~hc+ zuuUnVk&o77BR+S3nVH;k?fo)k^1A)y-TkAyYwpe?XbGQ(Pv5S8hXahNpfNzwPMS}! z*IrW9A2JB|ovg9(o#D1iNXsQFM~>F3)ZTy3#Pg`IQwoHpA4oDM^8WIK3`oo}9JEl> zY2PR5V>hf+x=Un?3W|75(3+t-v-71ko-Wcx2)kyWk&7K@KhKrEh(s_eBdoxmngY4O zEM`v3LJ#C7F+GGdp_!2TYO8KG+$LpTCT7AU#lP*;bfY8UjS0_p8hr6_qklsSrAqYhe}a{n!UN_u3W6LkcY) z7Lz2t3S?2`RPfUG|2Xls>Me8tCZ`gq=kLNtn~2_@q@i%Y(J47m77m5>ucqp*hxT`6 zzEoB1)1RpJPYSrn$hlzec;`7y7dV{-jmTjwYp+Y?5K?omC-3_|Tu8f;*fWby(|<(L z{pm)FQo`c`6_S(jQ}-KYld^4P2|m`@Ng2C_c8AoKIez;fn(|p_(ag+_6w$EBq7l@Q zk)&uD4T$(k6W9T!!l;5bM9vZ*ZwdF+qMPgB{7>`9dhwLB@zBHm>g5<`-s?VN-N0j# za;Gc$G;Npz2|jQB3@>3LeSNKwXd|b+;bT7gaGGsT_p0H7zIZAwaf+Z9$&ZZQadPNe zA_<{X%$4USycQ&{Mm>8B^ohQkyeK@x`2X!5*n7T3d}A_Rw^e(^Hev&2)KHTfHIy@d zl6l2>1|M{UIhU8y*je8#zy3vK=>5o(-_wF8;|=2tM8W=!%NB`6YH+~Vo)6AV{q-yK z^wrt)UjM3VmocGMq@)YV;u0t3Wb=!~-RhYci_MFs=Q);@jn#=FzG9u6dCOm+Dy}P3 zuBe=4I0$6+KOU|BCIU<$0OoPO!+XYYJ90mta;xUvz^#s5 z)?-Y(7!?8~PsWD!cXEAlZb)SZp#oFu9{;6kzfGvn(bT)BOk(t4U!BS{`1ry}kCojS zwlj}ic>Jp}oj5EiWtrRgUh=cWko)YV7xCCJgRN?Yf0dSBdi7J~7F8@mQf$cst(u^8S3DA6Z$` zr|e{2LMUs(WAc|*N;lPCejV3kp1M#(i;^nFP>LNfBwkndqUZa6={1TX)9k0D0(so2 zD!WhO5j*Z(XqA;k!sciCFcQFHf*e<@yPY$gf{Hby9Kh?FH{;6zd-i*wG z!%rXPJ(3Kn61m?5MmH_;pNQjj&NQjx7#(sm(1C@`^R16---)ldb)zd;rnYRR@1mbm z7-lD#ZF@0ls=P);duXGII<=xsrE9t^FSqudZ6FKNhxAuc#KO)57E+w(Qgw|?>x-@R z?bdk&P{*W~_QJZ-b=X;&m1H^k81<39yeM<~PNKV!3q?p=Y{IH_ zXNBleiMl_Ihmgd|FJZ$kq3tZb{KrYEjlax>-Hw;GNKLdA|! zoy+(!2EEWtO%!~AHTpy9A~stu*26~v9rr6Q@96qL^6g|W)hr?Cv1jFkT?3okRQ?2{ zsY>D5$FZIB$Ino7IAMcqU0t@a0t{)=C>F9is`cdqZ4<`qAGqXRF)0jIv<&4&V(9C! z758PE2FCSYZ>&XcQlddG^Hii?A;WY;h*iygEphxjyx9 z=LM5FMXhH;n|Z2NkHZ8R6H{?m#v>GfC&T`_Y|G@$_B^2-l}}O1eU9zGm6so8d50E( zOls>GpY9vu2%x#-UFTnOFwe|)`@_bSw(#OAMy8vCG)vU_&yNdz#KAkv`@ld{A{)8A zzX zaJ3=r3+W{bSsBO*K>`jdA=UgNdmYQnuZ=2UY04pC43@WXoEM0h#5 z(GXiARNr&!OK0Ki^MWsWGUcb<#X-ZFA{~fn-6y`-Kgc&SGtG=+s3ji`z1Pgr=R7K) z`Jrak{aEd>0HSlSLO9Qp<(yBm?2XCXw1U8{fC=hJJ0DuGz0+WO z0NJ!-c(4=7GwtIZL-_GZBwnoMzM$=y+@PgK<4r@_^CxLS*)!@GlDc3`=9?}6!!Zts zrMZRdckX&`L`vn0O1#b22_`x;alD)Sz_-xuyMOR@k8ALulcJ;Oj2lBg?`?LeCi_!6 zHKNVJyU}8{IsQK5+feWiRH+06v*0 zYfVEB+-IU`pdiQ*5BGTySf0^i7YW(9&n>cTB~T8znLqow=&6-(KI%JZ4SmT_lM*f3lB%xmXOs-nuQk)}w#bQGbDWNK{N+LIx(00~>=Xmo zSrzi73Ot^Zfzgwj%5}<(W9SC1k{E3`M|$qUqq0Vi)E<*At!EP`yA9wk*qCNQjFB4G z^IW;*?Y5lO?WaEuTkcUAq!~^=0>j}40wCrGPiRas9z+|1CAM4{i3!fPwLKb1dQjfy zbfF^e>q9|x5CeLy)HwBNEt!YGhcrZRwChjp_$9$y1zSx&Vg`g0Ho6A*UE#jm4b}K- z9YcC*W&LgF(oLv&yT?UD8bz|uI^%UOTx3yGq**t%$-KhFSt5W#U#k9=AZL6;;aZ;H zn9t>4us?~+MgOVaF~Y}>-mES99Q8Ab>UUzJyuv~F(5UXm%BBO^FX-94tK&g~Bf_iH zJZ!IKo!bPfaQ?#!y|7IQ%#)`~9{X(o$XmDsDx+$-Swg*dRqv0OwyJjBrzoB0WZ9IP zQ`qp7slC>`|%UQ9!qffE5ocQXl%y&%rH+IvCC<|y$&z3 z&iES~5|uKjRytPX){7)PkU{{-?9!PxV;NQ}{ln)XEM@i8MrszxVr?AKx6lJG9p)SK zP`m0)3mIBh^<5nES}DNnjjFm+jhLk`3H?*N}A@fyw9t4zMVNNB%rrytM`rC z?}>O3j*EGoxxar( zlci_NuBO2}V(R8@lqaY2i-yKI|IPv1n)I;xINDhMmt2X@mUgeoKF%-Br+gSf8L&hV z%kGZMsm_Tb2dTL}d{ow-p5sN~KGf%BpjmjY&hTb<75ZF3 zeLOUek^Pip+W%?J`Iie3IoYB>psH2H#b1LKkE$7-Wznus^+3BmkUONjFU6fMF0Xck ziQQ$Sc;xZqX%fpe{M<2Z-FUj@Il1x?o9DCx6iV2XB2(X*^Z@N7pJ!7v0oY8RZAqoTY+CPBqh3SEn6@=3^7z`oR9B#x1*%r zYX&6bD8zbBrgH^arsPL?=BQTam=m+yg@5HrfPQ~Gkkm>qP~|80kXfMYxlU+9subyL z7^yR6AXP7LJ2*bPhK!>GkQ>RMH1oa0^8H#5*H+pMs9aj^2qvtI|3iV@m!zIu%y6(y zG0R%Ew{#tWIlZG)CuUO`8v%eoT#;jDYy43o&J<@ z$0(UB9r$B2-O>T~-{#%G9)Kqw1CfjQ$q`GGf>6Gkpc-_YUn-`l=wu{`>9!7h4-!(l zQ{y@Y+tu^^7r*)M36jkXK)|++_CGOo5ulS*@9eexy)VENQh+h+dPV&t0l*(Ya<9Og z7bwFH(f6r+#{AqbCbsx=M}PV}uckzM(JOhzHU*-*hVu|&%i2#K(Zm$3FTt)y3=b|t zr?d+SouBLFiC-n%=GWaTZ>f{Uj(qM}@WNtezF80<#N}++(t%40pDx7S>BQ@;K!Aki^}j#lscy1jG3gp*!UWe<4bd!?VFB4M!xe1^q-P>@e78{+9* zxjkWE&bgaG`s*`D*?c7FZTSUY$0}(Z)D%_YTX9XaE2d^S!hP#$6#;#j;^gfc{4V8k zj6<7}{i`^;hVW*F%$o{#vEdel3qSQF{VVwFr_*oF`&E<&sOEv;dQ#X+mY>qscb@br zGV`cZcb>Si_|)FPCrgJEStp=BKXy|N-L^;_C_35?I^GkA#NktItqJ@rx;|h=t6t`6 z>qpN+6JMZ`WShLoG<^QWO?jd!$tpQIypogI-d|2abR6-h--A*>n%dMM+kS}n}9^(puuABAjn+q*s~Rmt$SO$V|Z!E}dfa7kPyVzsLLD;kp9jnY#l zef=uphFSG3=A)dz6D-a|+lb`|yx^8IHPSrrLgdy|8@P9WBKVx+Yvs}#vu-f3&}*&2 z$JsT@u-$c&xzgw5Od3NT{o);LJyC<3`)^eja!)?|8hU=d5u|HR=ZEz`>!*4y@k~yi zW6q?N79LDOLzx&Ny-%0ztj5!=vN zs%0U2hZpS=_ClSf4w(70W>k80jgI3Bb{ZvZwuvaVgI2a5)9-Ug$G+R&6ALDmI)%@rBi9NSUf+wqk^zNNJDU--pD#}njb zv^i%0w1hyu675d3`8o15a4Z&ON{co52XlR!m3~RWb!Vs6zGZb6RVRT=Z*&MMM~r`4 zDy5(EPm#L)BL$?ng4A#%tlnU$y#D6R{MDza{?-k$e~g9qWFNN<9X3kE*{0WtSEAQE zgAtbF%~1A+QX;*zgE|4|bH_4!is*;5iOhN;3+V;?e=>M>k5laLo;|z!M)g_a{rAdW zD+dx-#83v6Z+cgfY;;eGtdfrlCUhm<+V=%ura4*JWFn^PgWK2;_~I_Vy2N7g!v9AR z$D3dLG1jy0zP+w(D#Jka4JFlV&z8U)lvCs~Afhc9DTBq>7~n2tnU_N!B)!3tm?x=V7aZ`8y`zTAxUM z`G^QjF%J>sPBA!{Z}+cwSe8( zRPH~hkn%b8kN-Xk;9scFzY?WIV{F3n0u8;XcxetR95e#Q1xX}E`*)<7(B1KjsiZsPHcEK=8#OA?d*t}C-n3E$4lvg z!E7ij+=q>es#zzXenhG>;>VDv3Unvzw;-dE|Jgy%nd6yy_wiqvoWxs zZkyD(?yO5DOf#jq<7PwN=f+x)t|RsdPFp|kr|)-G8@Jt^bTB>*EqZ%#zIFBf%64e! zjM;ZvUHan>U^|!M@^-HhGSPi};3!Re%8y)FMvJ2L$ym_ab7=p|I_cMymezbhy3b4A zb3tk2D|;i;T=t%tQIMUL@HVRLD3WrSpNSQ7w?+w4jTSzCYAe~RWSzt|D?V`zhaRl& zzAJ0>ePOWbU-aD9vN27VhMT~w{F5u9a;pfNH~IH}xjUA^LCV+g^Cu|+(^7@OqhS1$ z+O4HJv#!z!d)Fk{tkdx|V~gY2o!P-U`V);^@L%(L-?=pif?m>Mk!eh-&14s0RQ!@J zh8Ph)+D%*SeQGu~HH4&vA@$ZmbLx{EmLJAH{ILe0#jzO0XQTKuD+o+BDD3=g?Yk{4 zTt{Ew^QDf$GmQ$i^zv|{?b_#lOIB}KmVFc^Y~z`psMK#!9Z9@3TG!7im#C-1CsXo_ zR$xb-eAFzAMbnCxqizhI{aC4!O45HE-FU?K=1~*DuJ7Y}DbE&WOabhxsAkPx&uB?7 zy)ahwBT}b>Y2HsR#}_LBzU0ZC7YcDFPXg$8R5J8bdi+MNhRvoOrLsw98&z%8Kzn`O z7H)nPgDD*k=I5A(-BTrFBea&oy(j$#k~bBRrg3bmPAU>r!6%2h$!s(g$5xxqs8r3) zNIK6>5KJ6n8C-If+pW?sKJHr%kcZ4R**hC==SFfT-P%dg1yv}o^Qa6#5}36+0FQssV~v;N&VA_U8Ih>?y{Iby>#8ij*N*qK{~GA$xZ8;B(eq{Rv!_&Vpt}~r6P}Ai z6mo7mR%A}__lD1}bc#d>y=N#M*Is|7&Eaw;*y%;LZLXz=L6ThU(f3I9wm^4;BELR& ztcFk}*cy6>F+5RgAb%mKB2#NUnk}GPlZ<=sQHeA>c6^wUdYx(c7|T~O3Kw`isil29 zE7Lbjt^}%46jbfkNYNUT3#BSQXqdeSitvNchCXgv?sl!GKig$Z*15skddT%f_uPi7 zE_Koe4W)Ni>&RV%rkg4}^z`hQSsNBG<{<_zr830&hWz0lX(iY0I$YN6AdF4o$!c@M zduMmfUvgAJ*7~a2`3sSKuA_&==;$P21NZHzu5GVW%+wY}G>?281sjo&LAEn1RjUXp zL}VA#2t$m*3>&ECSvD3_6;%YP0%BOp8V7x_F=G+slkUj1Vu$BYl_bNpAoH|NKM8)@ zA1oNBFdNqNyYVu40tN&EG#yj}K$6V;Kobg+7DnZBK`0NOYCT)mnD%a^uJ~-k-pG|W zlWp)GgV}2$#^~hM7hkH_BQGPK=vltL{W6#$Q|1b1hr+I#AHwkD(-U;G!97vKBDBG$ z3WYv5sb=*gGmnIhOT2KW_yRrh(B#DZTS~t=j1529B(wbxFCJA$#c1qV1v(Ds$CzUi zB8_m3;Zb!1X2)z(otr$MH{k&?A3hrExu;b>?Om9K@@1XQ8CddW;ijF68^Q| zk={e-iBWv}FHhEMe8>zC=Owy19KOqU@{)vB1{=QlZSh%s>TF7SZ(rp&%dyVuuV1oY z7PNU2rj={EITt-(T3g>_*Z!Yr8|K)ArXF%pm z-{!luQtKteS12WMAOF|KHK%yZdi&Kk^Eq7mWjSc5NksrM4kfi<)4Y<%&y%-173g7a z@Zm`KGZFCuFXdaCY}$^x2cC&Uo|7DV&Ci)=TGxttSoiy6E(u&+r9%5G>}(}w7NV{| zi0=671LV7~KJpyiBE`a-ewxQ6reKu{=jI+N5O4Jj)#ZNPa__<2*eLc1aSe%g3bZf% zX8q4rG2MLj9AIFlnSXSzS~`(yo7Sg-7C$i|7Fgd)ezZj*Oe%nN_|C zc11iYD%U8sdPK|YSQ*c}haq||HXPi26Tk;7XEzPrd0OtK(J3ohetAYD%VD<&?lBRq z(qmyW$?awC*a4u)`V&s~1sf;;4Ub>SyxK9Zt7G&M;)W&~JNc2bt#+I)`^T57?g^eQ z-qkO-BdbsO8k>GJ_SU3iR#(7D2rt6depb9O=u9SIxwBkKeS5ueWxb_zt-XLI#x=t6 zF(vRTgS`8+zjDC!ZMZ6$$#NkMoi5RO`(P|6e&Qs?&8MAcoafoAL6BQS;X^a=A;&KH z>p2Ednrq^B0oW(JS47gZr+ouEh_|0wtclFq$UxoW0`h%QG=vc_HKGA=9Lr~RQbT*k z6oB_Y4$b&@td&woIXBQ;A>hg$MIF5_DiB-OL`v~pW8Z%yPbTE&)cHOsU`aZLaJ-l% z->WvGIcU@axi{;VWUDNyC0HU8)mo;-TIbJt19`f7@xwUgXCR@nEH-(V{bbtN)O1uhpX4Z#o9b?oI}>S-L_pV$02>n-7WuM zz+5^~E%+k%<&U*s#hck+JgTtLDv&;tr;m3NXcv7MKle}qR}5bfq|SF(VHuhzf4wWR zFLW%IX}RFccu4`bb6pauNF4G-6Ftivf(mFm_cnpWJ^9t0u@h{UA=2L8Uz^rArVH<} zQ_qdmc|tjR!_@$(*8{-K)N0eG*;TO8_x|;zIsH%e82L$pfWw)oph?B;QUPlJYc}`( z)52F(M>hz6e4@zU<7C*QnbBdWu1|+;jMD;t)RKy{%)|vgbC)sm@`KBW*$m& z1E8t}3s;8?a#0$a>O;?vo^MmIl+t~Y8&Ux}O)4w3pj%WRpO`9<@%o+koX$<-ZJ?$7 zr1bWFj;;6XxpaEb(FT6n6Qa?v}U&pd#CVBf!o}!Dpg* zgsZve;A_|2-jTh9o|BUw{1Dh=`vt3wCSw^?4Hf9k77>(<*y&9n4IX;!i(`I6du5JR zNE>qe@TXPtkF|7MEgLbrkO7eBn++h)n!FWWBGn6cJQ~*4$?5r|qm9=%>+c_yq{XiG zMg*RU#P=y(c?GC8$-Gt;wYtFz;0P#T4vFNf^*h;$Y&wjr-LN^b&6Wtf!>O6w9X+|} z)^g4aUT3bXKR?9Fc7pFwl(-d~_weWh611IFnf&O2`0!$b*&Ea0*AI@;`A`HQ4=v9U=^je45idd^#?GBYGw)F0t^ zmr9ex!$xE)&zGJgEv8_Rd!Rn{ujM1T{h)(H%UVV^sVd3?G0r>;pB7&{(Eu0ZjBHG? z?~^bPQjN_sXFbjBnL6I!O|;LS#kC!A^SL7WruocgqXDSC{dQ{pG$w6~rOh5kKw?(F z$G@&04!bMU9sCs>$nAEs=&9kRutl>=kGh`VtjGkVsGvr;*t+J?^DD_F1thbGq9AzG3_Z4GQ<`@4vFp-Ch=x z#ul0J9f7x50>k_7Au5E`6K2Dkt{RVoPMStW%#3|o%*|RI|9vt_=$>WOZuY}c$Wq$Jxs#lQdWy%SBxKp=3dce@kH@N*dkgrGXZKW`9p>|5%`sPS`5=dddDx`Cn0E z`*VX}!K!@QEBhq@(GKaU5yJri{oCXcGar(M8hNchMH1gNlRLOjfW6yQx}L~DIcfsO z6^lphX{SThM?Kvx!b+{rW`vSk&)SFgt$+2|e)NLKE~zKHaJAkvz_1vnD(yVGwt**sDKgJb zw|L32g>H|!y(ex2Pf4{?9B5tBlzB21I$-AzWd$wu29T;OSIKmo-`#z|z!RZoJ$6M9 z!fEeeKsUg!5OP+JC=9qFHzDGS%*~8UcRaN9ISGn<$xn<`j5Q~6#vjf(hWu8pN_2}8 z_+-rttANwKZWSf2p0FVFO?>X&woNTiz!R(e3ZHen~8vS51rg2XmHAQw`!%&1;@jW+eiTFt5xt&K%TFAmZP-K=!V8|ItShCMG=w^w# z=GVU|H-E)te+7G^@7+q5ySU}peCBLQz;2I0rSR{o!inIaHV8-2RqfBkYX!}u{Z6DX z#d2;2Qt^E3UO8cZG0N4x_CT?TkrxySd!BfyOt$ zsc3hy1HC=C*F^4hHfM&!z!ZR$}k0VeoYp!Fnh+xDn7;}n&+SQpnnMbq5NFT@j6IJrKfVZN~9 zl8)Yp`z)@}cJ;;);uUOjskb2af&<{7u`@r1yp2N9 zydEzl%&vJmL!9TGDf6{&awVNJ(LP~PXxytE)b85NhtJK}#N7-OYTMUq#%9K++kXBQ zQA?a(1s1Q$y7>U1=C1V=8imc~0z2^gHRUXZ*>dM9S?V@e-?xnB5>W`m4hJqrh&P~t z%<^G(0IDN=a-%CBTS|kG2H+x7Y;SpUf2GUI=qu$XXq+UX;s>J)P-Lal{LHMFR*LtQ zhz7I_%2;0|fEnt1I8<8dtE11Q1^3e^9c2Odnw0X4LF< z$8jOUYkr`TeScr{*Wdu%oLxNCZGfY-R#y~TZ)NepY$H-U5DU;;y;-Njoi_eSyDChh z`P#COESIlva!ccd%iEq=IvK5Z#Gf{NoB{0kL6ec*^E2>^`7vmR=@EZFpATx0Fv9OD zN+XaqS}F^i>=7d8M*N>5c!o_G;aE`=+VRbb-4+|luKN~THT`?2B-ya=oEXU~^&kHR z9{=m+5LaWSz{^`YV4RU$iRhpAfywTX>fJMq9$sS9sbD^g-&sn7aV`#1KLfqe9PtBp zA^1jFi4xv)8P@29l8lYXxgC254%qJocy>F!ailcYko7CLxGME6m?@C)8%@m0Tj7H2 zckqaLm($G(?lbk?o%iQ3X5p3tkNY*IV1$=}*06NHIu2KF_M7C}i4u*yqq~;Wf``XL zrT)J*_XSIxLsK~Nv*zpYs~E3I(T18r-q0=Ak;d;oJbx!BRH{%0Ut)8}BKPr- zu(fSzTjdNS4tU(NzHElRN}j$eUS~+SvHE71L1}f+V@^@Be)3{c$*W=dOsE|E=qGxC zcjx5-FpTg^=4l{KBr{)tIvg&{2x_XVmHRcG9=$&$To3hP&!l0hsi%~a|0jU?Jx4O+ z%8F&lu_uQ*77O=EA9~~FexLb62!;L%A#IiZlYbXN&HPW=mfQLfIJ3cV7cFBwgRqI= zL3s?=Mj$m6I7h=JW?tWsuN~~jk{Cf8f06+Dq<6WIzr<&gh}-`-5A%d&gB5nCn8P_o zLrcnJz7oCCr^yDJUSU05E9sqdC4j34I@lgLLlgFz-)4?U1>&=a-{8S21=idAaSn-& zP&VnP$)AEXf}&eWzYb|WdRIo|5jT1A-eg-Y7_89nE#hly;8Zz&&F@hLYg4X|4y`q`J8p{6X#A=^RAw#QIpV1 z%#pVIblWQ_FHXRMz@w&kC&~kjp~N+QY72mSc)ux}Z*}oA3hLD&j^CuA=4wzloNDFq z4vri)dix|Ehk`|meqnmrDwE^SfBS#EO8QV+v;X;Ek*_q`E+3XOH0N=YvbS=6e*5G2 z_rDj>ec?NpEGul~{NnHL{DUE39-j0auFu+OO&=CTyiZCSqRKz^8d>flvKKcam@hhn zPXwY)N6s+-thOCreKm=o*J6gcI+tu1i@>GiiIg+R?ES$u;vffkGC)%0dVN$48n;s} zzDMMhkTzGo=NErI%f4J9s?#zT<}B^`*I5!Us`*~2yAqjwix1O0-V{D;GLwIcY+ub7 zV{tHck$>)=sOg2^XbjrFVeU9r1KdLnUI)N8ADOni{#-A^6#S}bNpG)~A67LZVe`I& z`cZ=iH~G*_xN1Efj||*uo(oLt9Qz{vHqHbga5?jCjylfEcLfFvLQ6a@3+`(bh)sU& z@U+<8-Byl|r>=5E+PBJyru{ky-qFT00jwv{O)SBvbmA{FyjBmz?>U(Vhaf*20r{w0 zO%&E97<;BIjU%`_6o9L(G!Nqhrk0kJ>orgFF}1zy?m1uvJkiZcGT`J z7u>pmlY}pwCd|8A+h-n_+4X8Bf+Z&;AHwFTtMLLE`S6SDE0gvO4~h?!=EodisbE84 z_)=Z*Mtcqi-y+Sb{Y#N!JOMDX=@BzFxLFt#cET#Oy) z*-3ob=Uu(`{MJ=m%CG-a@_r`DLaP#idGbF8jV_5Txq6{QQ-|4s{RxnG{o;(@)e))t z+WD|Z?M#h-lYtR0tMX@wjAi|d??UpWGm>h`c({&AQYl7 zX$G=sbI^+DT~oOFE;9cLpctLU+%q;kNd`poxrx#(1|=0}?XC2$h**AeO2MpWA>Q2G zU*!xJ0WJ%^7o>p?;g9Nighycui#_zO2}kRzL{Zn#(1QnsV?VmU;`P-XJVum=UZ=Jhe+Z3Rm#p$UgHcR@&h6V3yW1Za5o!8~v$>D=J)qSbhOk8M5 z?T@2K#9*jjg852}%pN{e?jl)3}&>iSAQAew4L6F)sGJC8cth}tMFn6^sXj21lY zFynssTe~8fcuoVp0qif{R)=1SbQPN5#l5s!Ur${1fP)Z$L5}CqpIk- zEajo|9a*;`be970>tj)z`!dq=7whL8{W9nF_vg>HKANW&fJnQBa^Ef1bqwT5T6AsC zg&IH_z*?zlwaDh~d{{44W53Q~c-MK3-XVuBAY8)OKFg^ABT0VdtDW3b3a8SClA4)N zBHHQBk2vqy+!B=x@J=XZS>S?=+0;A zr>`Ol+dTp@sXFh|L}<+SP5*`Dkj4nd4%3b(b&x|?^NRTQ$B3q?BGVBBaHRLOgUtpx zAb+?P0-f?BCX0~Jpl|~)6w}|Dt%_8 zI>Ip@;AiX29o39yfIL!u<sFxub9K01n;%hJ;GF_O!Ie_|1jDNWVihmpV7L&4 zbS$?3dQI;7r^~w5j?z>+iK6!R>yJQg=d2TF|5vpKvodz%ABZLtH#!LRqBw8i3&w25 zGj|0)7J#R+Hrca&B??XD$x%G`{s{2Yvw3O14kFAKgkRo%H#iYZ>hbb9Z=4hV*!Yf| z?z|H?bYN5)H^?RVHi@bT&wQ%!QBJEBF%eF@?CS7yILY`D&wc7#6tV9+=0(5emVj!` z*lzF;w~2VzE%~MDRt{aC;Rn^(Klp{aVPC~fG&>Rf_|_U8RV4+yCPeL-GnnIw+WiJ2 z+OB#P`QL%;x>4G2w)-{aPk$b$cm*2azi9&l^F0zZnwgE$nOxlwK!!Fvv(I|*EBpM& z?J0`?AR9CXhq%|{wP4PSYk#$n3m5J&j+~5j|BzDB``Aqc73tbNjz+vr{T=B}-P4wb z5L0(>|CZeUpNg=TBpZMhhkbKVq|cJnV0F%fl)Q0HYrP`)rqZx#hvS$5fel{e08FyX zeERVTHG09>8aebzHeV$%>d*V{A_?~QgpSwMebA&BMM_n?r+AtJn2lW~1pqTOt<^(w z(GhWq>6qcemi34+?RKxanaZ4H?~AY~sUz`hGv2U#&cp)w^w)&yqKrD1LSdvng1J<`xn=2eejT-0@Z}$5vJf>Nj7aV z1Dk5yqSfXWFhT42LuQ@7>HfB4x;ySZ)b`)AGk*GSz8Aq2!UE7tj`Yg^EYakTCJk{s zc;{*kM@nbTjO>U1BB~W1Z(a3>(0q&>N>>~s=!J2pXIaTYvsD|l1c?jeG(}kJ4(r)_UVJF3KD9&=JM8GT$$3~rX1p58%Ds=shq&zN1g z6Jd#6_6R??w;{K|yQU^R;YTK^|6&}xa{}#Sl^GPs+V^1>YF>D-d2{=N+S~=;?FSze zwE*0J75ZI<`r}m|(X@*qdvbZl8&$lPFnFRN${#lCB73mN?-Lm-H(7H{miVToclBu2 z3?*HnTmOj7peufOhO%9|?3O_z);IfLho-ELjCaKRQ1Z>UEp;32+Txc}5cP}uO5!Qj zIeHI&WoTm26o!(0*&#vr0QM)&pc0P*dp`Y5n z3pjho48%FXf< zoHH3>vIg(!033!fj#ZIfs9A(%N8r4sKN9;++E}-QLpX$11AI~6W?DNrvb4GVp!2Jp zDU~~ze55`HsuZ>gFLh+K1-P(ND!C(-2sCr=?j`hteFVPDrkE5fRkR{9vWZ6UImeWG z$8IEOm(Rz%&>XLwXzc{Eq?M9-=B5s13fwOEaQdZ4tux1xtptoe3 zCp$BpTEcN}9vp6JG98~i3-8l(%(KgMp};`5HJHl=i+?3lt9^)~AvTzSl;_asQJzk%g# z&jg{@6z?gxy-lk{mXC?3;|4x3&b@Y~hihwCG`>2)cJ~)y9)a}BTyWaJ0-`5khe#X6 zC_ZcOpy}(uE!^3Qt~kTQGS|G|Pgj&CtF5<JAGpi@8;%Fnb8QumgN4Vy_ zXcqAk#|Te@`H_rb8xi>~2&?z5sJnd}znec!AoU$@pLR$x*sXo)6O)VR+ks?3IHKl+ z%`>5D?7+@v3(fRtt}``rdz|sQ-3L{7a-|+hpG$8y`}xy0sx) z?`+`b6&KA=zMY#{P_2u)<0@3f{994}3BgFl{vh4$UDrpU^s&k z`dsu&zqw$%lA;a1Femf9U>EhR(^{R9v+=H$^ZU!ra|l`|0V_bMvR-Ovi#2$P>6l*< zo?n5qH@wNS4}~(Xw@icVfQ|LG%}q*no5t^}v)8_;Gdz3H9>D^SY(T^K=(c5TP&$Ae z<~1|#|_<(Y^RylVfbN!x)#Z2|2=ODDkU`B7-MJ{I*W|8970+| zxQ>083wXm_B~LwpM-D!$6!^I??Z$^|+|OY$$>S8794*YqUu(GQl!$WdB(o-?lL?D0 z(*u4Bayor9oKQ0FoEv12Tk5*p4_2@fM8RV4Gij z4-Xhc@>DoWG2n;4$!*^Ezk2qM8AAu&eSw*ZKo&eF2_vM;Vcb>c$+r`%D_m-b@Ph(- z`&m#E=J^NqHvp@#4jcnhR#hr3)+SfM38E|x>1#|ruaa#!x#)0BD$vF7w0pj&$fzfN z6b0wJtfJP0be&n=h#KfqkC4Rqpu-rMh5qkvC``sgg)kNU-m19Y)Gd(nkERaS&e^F< zQV6`dt$_EFN3POajTYMo9nXb*9ED9tRgg_ky!g}l{&5}e^xdBYRlW; zakVFY=)ElO@#xQWka8_fmlMlr6w#}!PnlU{t25@}f91a?QX63N{G{janIOOaALcBn zT#z6}IYm4QDUgr+{_|U*GDk9^>}qB-s0$CDgf2>C*=G%X#eDMXFJYEkxn$C-<>KN3 zZESY;pT7)a&0+eo;&7Cyh0Iw9B+A&Bf9RYk*hX6%0vnrXS~6#Q!+Ml5{FR4f*;lQqWo$IEV2<6d%BK$QwO;qk@VRAM2fJbWs=Z+Be2Grciyy=w2hbrw>AV$Sco)~0m_EAg=+PVd=zwRd?56+sW7gkMh`M66-AN4?*Z z>rAnf-9gPO^L-%wvDuwvhh^I>T%#rlzE5LF|0}Nl?;};MnK|W%jhs%!ZqU$7?txRe z;q4^nddQE)Q7>$#_nsdf1Zda}4(n^4qZa)JJh1%%9+Wh;W1FkAj90s31>E*hB`X(3 z_cva^*$8ueu6L{O(U2%Bql<7X8A&=#VlQXb4^D zo<|bzmvo3z6e2dF=m5zb?NGfhk9m(v_JnWy#N2h0UV~+`eIUsR-^01ZRS(cJo8;F@ z#Hup~&;*k?;ebyckr30~JI;?850ABRS^!ECanhfukXnQK{`8!hGNRB$=FA$Qp4SPN57ddDlq6WioZgmCRDDmn}JLqZIT zYyaoaBke)A8mjeW_OYFv+yZn|CS0&(Ocidc8RJYueXag$!oP=)|2RRJ2AR#|E<|?v z025}7)Fm9oJw%@3G>F52Sb3AyI@@2?+Np~>wC}lfJw+Y?YmI>l^xn=MKCG-p zYTQL&tQK(D(sG}oj_+HG=`nYQtce(&Tte<+W}56O-$uBhJ>fKdU@?&VCdK-3?(Hi- zgPG^(-d_+fe)xX5nW_FWku7lfx2LOs^KOw0VRoM6*ECK?czU37=gX(UU^htOJ#8wO zq@jhg$a)`4zT}`ee6gd~4G6B=CtkuFV36cI>j$5Ch)OXl!V^C3(}3KbR!tMDqUiPY z2uM{DPaz0CSW767hZ;=7rVOZ53ap(wP4@y20fg{*YJ$;ovc|4q^WaLoY8BO-22vyu@v3y8{`SQfn~}bVmfZwbRYQ6(aI~KV0mM4*{P5cPv3?|*iFWxw$r-h5 z5lR7#YK%!teVy4?z&>_qgb>W*w6%yM)D!~8#3CPqDT@3<1MiQsCeDv=d7P)dTx06w zDL`q{Y|NS0mb}4ioxqY(+@Q5OwnMyt^*fQm2MK)skIti3>eUr-|k| z?^%CM*B%jY1kPeh7r`KWTgcaY^S-i+kBuak`~0K;)gn0wj+#+B zY+AYsyTjr-$wv>o^4)qllFgf9Amg^E;U#S1E~yzB+-32zhtxjU)YW&)c+upmaDV5@ zI)@d=oxUFHCb>%(vB@GaqHGk^t#yDXAh^Shy3|(6i?s;-hlT2sid~os5AjN8@3}2x zzR}2daXN4^0NhrXQqv8R2Kw#d7h(BWRpdTQLfQUIv^%ov%*cQR&>n@*Sq5NNeT9iQgp?*#6 zsr%=|guk2ubt8li`u&c?KkJL}43~;H$m`1L6H=}}sGV_;rLB6PK+F`=Hpl0#b7S6u z+JVfG3HS4x%M2m@N!3&nIKPTIccm?xDb@?mYn(uIp9D>Zb?G#FRH<%zD#yI&isEBn zW%FygE#ju0x<0QMp)zuvdF#eJI>B?9V9SsPv*)Y5Vr@g5d44;O46QUx6VH6Klvg%I zuoixXef7JY1etFgA*)-oK{uDJS8?~HEw&vWk&r>`A{#4tff$~hIpC5ju86x-=_ zy`PN(lSto0z)>!8Thn-bZ_hT{=1>xqZg%eQ)k<;_c9F1xgf4HBWM^2&p%h^D?leKH zg$KcnHI4nHKD(+xw#3uHLhr_cXZ9H??A0Ed6EE#ei|6^Pyc=L9-<4!{2>Ht&kOo9b-GI;OdMv1@c2)TXjoR!XTDP#wnG8u>qfE`;{xS_P;If{8S zl~oFowm`TiiWP+_)3U9Yr2COD%>FDOMWWZV&jnPS?*ECnRIB`H`=rk(r6Jg;MO#hz zqBK3r;FH)}1jNtzg{*cNIqTbaRhxc0=mPF}SF7K|{tzN(Sz|&DxGPOa_1HJcOQHq!Ua{7|xYJP9QVu8-Q%O{g;@mZm7p ztBS-A`%MyE$FE7pO({FZLP*pHW`^@qBwmcY(u3BF>+;2?46bisTsg@~!y3a`EbT+l3l7HAW}nrk z%1C*I$RSmd)x}i)=8Rg=sPZAFpekAp3@ zcPlX9CQ>VT$DcngF(;mBqUeaM4<3NuL>Aw_Us5)_&@lVKap`ZMwz-8&KjEuyRwT3l zA|4539PlT6Oi%ig4^j~8!7=k{wm*swYlE`RDT{sH;kY&Bit3IDwtA&5?1Hu#nL%4+ zE7HqfDjA% zo#>J^B#y=(dzCVOgItf5lsw6JtrGC=&s_i)dYq9R21uF%;RPp8%htxl9G}gCmkYVS z>hfmfN0>zMjah9kJLsua9+`f73;DJ-1HJ~EZnGfCzOQ^ZxuXyq+jQm4K;}vE;M`H@ zA&Mg*e6nKSD7H&HWwD*Ue zlj%RB$Dcj;%ujz1Id9Yp|07{7-T|augBU*>!~dMv<}Y+HmeiFV15DlN&)*#oYQ8Wz zw&EeT)alS6Y#Dk025)%_kkrGLHb)9NQLs zhkh}?ZSU@L5SiQ!n(6RPHI_>2gmt@ER254v{e)_PtE*kxHXESj5MOf9H(w`sD{7~+ zZDu?h+PY>?W*l#1;b1qP3Y9ATiT(|HkE!8pf4_eE3#yFPgvs^+<2{2<-*0p0GFUej zS469~lEKW+%WWyZ-3jxIsWxO`b>!;;AV|%fiP_?&7*fuFHPaXw_G}3%=hn|OGj|qA>DNxxZ{9N=f{#6GlZU=-CIC;2k z$X{R6j0iMoubTsP{r`M#!Rg_cvR43G*$Wt~cB+@uvMsmfXZ8eVWRXw07bbhg0?dI( zumYNF55BLuPcZ2f;0|%4sqr$qwn+?kIAONs>j z1ll!>8e`$f^RTk@GPjgPELrvYfF}P>8CmXE)ZFA^QuzTLa|t!KB4Zfj?v9M_#)wOo zT$oc0#Q@rUm2_tSs^`Bf18P`zs^U0PH`HDpU`~JDj~Uhq8y^?34}^f(O8Khm@V@H6 z?62^ifD`$4OZOOIm7>ovp)()Y(}S&P8$X#Kd}qwwrr@4|H6`d4({!nq`n6Z5(C9(i zDoEYP6%1iV+eP$V3|yky+moCP$m3K(N1Ff7-!Hb!A)ix>||$37!B{L z#s$al!8wiv_}c>A4DK?qLqevGz`8c@{(c$#+}Pj%fO4Mw5U!Sb@hGL3*2jFP5Ez3z@i=4xApw&DWQQt7{~EdipoQ@dUccnb zFV+ZrEgh{n<*?^J+AwvhF|yCVB@a7PG2Ptp(`ramL0LrX88UY}r8t^4Vtf*vQwYJ< zbI?bsl;U?6qCf}6qOE3)TsvmwL}oW=pSxq}Fl%qzsEp4ADwU+4{}=8wzf^Ou>a@4sktAEHD*B~K_k zdQ#Q4fgFzPMW==;Rk^+%o!c;x>Q_t_YPTqSb3SgA@k;~WT`cckQ@ii}OgaDhSbnz5 z;2FCs^5L^TGtvbNL#yDizR{&>$HA+k7nFpQW zVmud~1-m@LCW2(dO<_-@ioyz6W1F0FeMIJcu`E=5lCg8&RM>l!UUD>RF0{wwM6+cS zG8;CvqYU{nSLw?*_JyCVLH(A=xiPX1d3HPr3Ks0_9ya(iMir~P<%R(y3$hfmfvA}t zgznhLG{VLYo}Hq9J1=6uyJBC1=e5a!x9FRgX#pLQU5$1@BXXN{5822anc#=Ayu&i} z42goNlq`0L0QD3PRw>c+fvy6R;IqR4k-}^O=5#@;wk&qv4N6eGCot%ZPNiLE<<94& z-;)itn)U_}3cCc+qA4e$Q?+HufT*-da`!fw9lTUQP06k%`-`!_=%)pTJ2#{;*8$dT z_MK_B3OU$mfa_I^a<>L>`i!*m*oc>=8AC z&NQ?caNk>>^_3MbIDlX6*<3Y-n-rDm@zil=)x>AZU*ePhHF33z?cKFe$Ztepe}=9& z@vA9c^C?bS$>2e8G~{67^1SGYd9t=xKpIj4`Suj%>xwmB`_2U3Yt8$ zx!ioB2Yd&ESv9@*fr2-mY2uF|bntZ?mj}`}jVM{FnYf^tZP)OElp!^D*VE z@RCeSx(;C|c7cZ8u>E=dI(%V#~7f=+jrhlUX_EZ8G|M|Edp8tS%hWik5 zRd`CBM077Xu2z*fzk~yHn*o$T*fkvTkxyb`HGLp$oo}F`5ugt(6G5dO9(iX22kl9~ zXb3RyLJcS>`fYLHCQ6eWe<(+=1XESbNm}`ie~@K`m3y3D;OT*jX}l_~WnahFM{+*c zf&T2HenZg1N7X8J_7skTi9#R0Eo#BcuVd@v<|8J&feeh6g%FUdVKSZ3)tQ|dZ1A{M zpNohhj}ZF>bGB8aKWtn4nlOFkiMF|RAAa)fT-`jL!`)=4%ICv0W$MjxH1m@=h}4KL zC2DNWe#w%XcvYR@QiuE>Eosj&?$w%l4~q0_+O36~?LDa)h^?g=*rv_$YuWTfu+nDI zd?N+}40G!3cIzZ~supsl8_g&{vB9;-OuR%chyn;$Z3b)>s=7+8b9aRjBT+10m@m!- zHqVWC!U%V{vf#1XN5;G5vL@>Ot9}}vwykYgmc(y`Fj_LTdg%dEh)@%0V z&6wkL6=@oG$N+`{u)$2m^t`i(XpHxtV1PJ6bgFnzlZRG+BtGj+*D>s7A&g^ zkuQ75!wKe15%cLb5)KfS3aAW5FY8QCwnY5M_{fS(2p$aRzojjO+gws*|4_rF6vI@? zcB`_Oa;GQJLEuhczlu=nwZ~F;H1T(Hb>6J**YS`PmGTDIgnju4=;DGt7_&e=xfzNp zn+So;`?oDE%JAfdP4H%;D2I+B@$&mZ18PKGt6V>mK=Gs-s_$GY0)|1+M)exO853vi zFDC;RPh>(HZ!nP<2jB5Ls-zj6oyV51Ot#xmeAI&bGKSP|OqUFB%`(gdCVObxi-d{Wa~UDyju6}_uu<$1HVfY;rN68P>Ruw{IeGM+nMmxY@YR>3p^Ud z!jw&S?*$pdphk0RjGyB-ntK+_qYA$<3cx{6>c_k?wC`F`Fpr5<={)qjEWm@KDZAxz zu5kN8bSY1jm5ntgtS9M~-7a6TpE1I||9mtG(es0wIiHogis%&O&B{bT;5QFpU$5j; z-j?f1IPP}wBW4;QlkmV_eRkh;hJP3FJc%L+nNkvy`pA|Qq!|)xkw{p-;?uQ6eoEh} zG#~7C@fzN!Yf8dhzC@TUdXI?Fb#?^3sJGe>674OVjMcxh>)u}gw~aT(u1vkUHghM@ z--BwAW`a~Ip9A;^*dx-uPseNK;1*G-R&1^*Uw9p}$8d4sb3vKXJh6oP!0=#o$>C8l zcazV2QF{di$wXB>7vwV-VG)i7N6};g`mEG`SN7NSA|A^o<&fJH zzcx&0ICH8A&#o>gXWn(va|oEg%kVC5sj_$G*T}fV{G%-0Jep1iFAMRj-F35PC*0z^ z0bv1L(38|-gm>kn>FX4Cb>!1&w(0_nxN8%(;QZTI>`vDzW%vmJJbJ**CfFLwwX|jm zC6_c6=Y0~Am{gt!sSYJ-kW-iYjfZ;G!Alt9f=^QcI8$X;-?q5zh!=ZNbU{3ptvojt z*l6LrG=ogJpiuo88_n?=@+D4%u45Rwccaiw^5pD{Y8HfUk$g$cE1`dUzGCm{!!lPf z!gvm;^z%D)%8&||xL9pT!`exQ8s(+`9TEP;PPl(S?(JzGc%_SCGk{Gn;PHCwwI1L7 zl&-C=4F#ZdM6%H_-#$4`=141D*Qa;-TD`tONwMId@$znhtb$X1 zt_D4geaerRdpKZ3Yl=|hPpxN@vLgtr^JqqTq$1B(O2ufM(yOjhrpxhj$hGlwF39#l`zCLX1j?>K3#E}o^PCd%S5^0eC>#-3%gLOD z4S0ENl2G5MMJyCL(wP2zv(UJE4)h!uky;b#BVOWLmj=)DVx^oq7#WWa9U;obc_|A zv4*Vs{KV^4n;(wfI1`@qN|pCtps)2L84gW}F^{S5cQxlnY|k1Y9O*sq?30E4n89qE zSs(rK58)RO`^af#wx`e}n;?`I*}va7&CVYdOI8A)IsK+ruv#)+^)QM7LuLeYFs zm|wu>|GOyv8y4!XlLf>U&lj04U4rl^(lmj-;0I%JXPNCWkiw;1Av}MYFFQdD3Lq#9 z7X|IQ{)X7z^S0YaAgCQSz8l{kPm;o)M#-Uk{mk7z+#2WfjH2CmS?9wF|KgHo4Mw(W zu^hvv%?fZAacsUC{PdA>#BK)<1=HN&tUkTjyI562dQAa`2Y|+mB-ba+EgAQ7JLC9C z0>}RA+yeeoeWP34du_wlghAM2MfZW%^Rw3YOdPf7172ao>(v57G^^99*Iy$M#N4)y z#WJ0jvPCEn#?mF+AL9~zw#Mjlbv(CVEH&j%=FP!`V&v?ML zlI822q3goMHP*T4KVB&@cnyxvhHCv9$cM=RCB|9s=ZSyFCH{s4%=6XZKU&hddGxoa zjz~C&p3=4uO&0wDMqs<+&nCGp$dWh_0Zj(j7*ykAkZ0|SV($ZH<=a-{ZYEDVeIgY? zb3KFfv=0#5>Ps3e|Z~>)@At z{OskI0)c3V^^E3v!hWqV^e*L?S}8qnp-VF3yC1n6;1WJ?atvvB1Dums>rM0yj9CA9 zwaA#Gx+&)@ML~!Td!<)^NFxH+2Q)AMp&JUGdDb>3vu8OrO659}*E$~2eKh4*_BadL z$peY_;oYAu8hkU3c}81860pveqD?*6)ZobY(uq{%ugv{7WJQ0#jF#xsFfRGFLI zKqAp;VzI{SI7Ry7LtvN+njw@5z4i*m{y}v7m0Lsa=G)xa3;R*azjxXFLs&D#ciT?K zXa3}a{`Q6TY6xt433QFNB3mS#D?U@PvrX6^y^Gcb5u{|>;6E~mvsPmSOA38NiBqJ3 zIWN>4X-r2zg*8U=XxrWkFRcfUeg7ogbc%!16Ja)%q(1T%Qjll8eZmdiUVOuX4sxC# z!-4h70%K9z3gx6#W{Kx#Z+7Z2je>-ptfj60tI}`fNobz4!~+KMd>l`K6>drJPX(tl z-oaC>P4Db3UI&Zd(v_Q4V%xZ;F7y+c@_ggLldW09Xv@wx9y}Y5Di@9$Qu;JB1)HHw zv7~_V-^8!e)?cs**z$HJ6d8$Iz!e`2J_)v4b#FJkczTg@-X9f-(BlG5d^0uv7$WYz zhbdngR6Xk$8Ch&1J^cz;Z&61sgOYQ+5jE{Pcd&|sU0%hOIA1jlBCa-zjGcu{M&{ROJKS_ zOV^2gqJ{iP@&fj(uq6bV8mfrCrnQX!ajfg_t&0D1BxE*EJW68Xr1TamcYV=B9tM7w ziwc`GB$tXTwLXT3bJlppH~h?otmkg{qNzz4JsQlAPJP%EMaTVyQXhQq%&_VG4X~mv zGNZF|@Q%6U@uYsmvc!sx2c!eGYOLH4u!cSDCr@xcAo3eykM$rPp+89h{Zicg)(pKr#O@#s0Is@P9 zs?Okptk-u9+ylVY5Trcqh7gwhtNry-0-YnNi;P5+;k~mL0xoDCC@_^eu>BT7pe09x zPeO9>0J2&SAC~g*$e17VJ3>pknlmXchYu+#rAyO8;r`C?=X`7xZ$MPUQzW*M8+Wm4oV;G0l(v-YR8RP_e4Z25t8l$f0f_X|8!#Xi z?-K?QVgawiJogG#*hP0)m*X|+NfqvPr!F_xWlR@{=+6&H))rF!d3lMyoW|RY>=ts) znH0m?2Kq%dkz)^mTi>_XmFrdL$3ha7T5kjRS$H9^LGhTT_ydtKQoFJF^0axAN3+02-3A9LGg1y&>a4|x8s<|=I6u&7e={yc& zfrCc@-g*F62A3u*!xj|{BQDg!TR1^B#X=MGU~(VRvPrg&z#6Z0pLBDoIzO2ofJDq4 zT9Jtlcq&Ig9FLELa^1Y?gdsHbh#|+HNl;j)rMCf*$<0dcOoXMzwwOds#4xn$v_d3W z^jJ_*KM&n)J$hdXy59*?*p1H8QJVG@dP}@&9t&Dhf$FWO`U5L6vKw!-S8>a0#y8() zNNjYMiv<=m4pHz}o`XwT*0@uQ=pyC=?+<(2`Zc#B!_mJ3E1>i_*Qj{Hy4ujl%Unvr zn)#g{y(6^vp<8k5hjLEYp} zxDx!}1;)v?zgdX!XS^2HhVEw)bb~D^48?|a zH+H@}&(ml7_`;&D(7NI`;uhhre+XGvzOMa4pdZFWNCHqU06y?C(R&g!9I~TkF?!}3 z(L?%f@5|5A4WLgvYB#951Nq;)KAHF0Z;&5R_+Xot;8|2YE%>h*3g826}EfgfJ@ZOImK< zLwV;tM>>>F-Wo#5BBD>bu@Y3bkvgR!UdLthYLz0k;pUSl^cLTg6NOz~*9XWY((40z zCK)?;{Pt@vf}`6UxZ=S1KZR}KW~{Z&4GLSz8Fu@cfPqx)2_5>6pvL98d-^^3FM;b# zW}Zrvvn@cGD z6^@2MnhX{8*rUj;aZ~ri4lD042VY~Ax|$jd43z$;`65ewPWwV9-e&tFNfGA9h6X7) z1=Zrm*=;uDDzNb$U6!bATarn|oS%V3Q%h-aL0>771*BSeY}$1m&+9dDZIGo^N`F_+ z9{oP4t)l!>%Yj&qe7*R?30t-rT_vp8t9CM~I{r6%(EshA=8J?B&eFTphWfs3ro~F< za|cXsP3PG&U1gduBENSKiaNIS-U#zr-hqjh)-MdGLn2!o;#YLy?ZyA$Bl17CAqUB5 z>R)|SGB3G)^%41>cho;U2<$AP^UA?QFbq&E`#R;annVxRtLuJ`Y>F%y=XWGNI?16` zz{|cseTTn7@3a!oEF#U-AnK6Nk+3z>rtM~S|`;xo;}lF ziHQfcdwIQ6gC9t5%a03+*VB-1F4X=mA0N0*i>!q_kGNpG=7Gw+<5l8o5GU%Q?#RzE zVLP+!d%s#kFn5qIkcP|PxAK`j7FwWCSJ}bukl@bL-^%_e`^;}VZ$}Rv1;6mHr=YL8 zM8?w1@LVJ`Alt>|<d!r@kl?ccLUE0rsZ97&l3`A}AmQoz$eJ!z!j`Sd72p46DR5DW&+IJRYVX@4Ty8H%-l5RZ2e@Fr zKcvlnHD;|MS*Qq0)-Bkqe7pEgU(W~Uz}aMkik#e+>X(o7pHzA)xx2kKphov^o87-% z_-~-K4^hTm)R@V?rwIF@DqoiPIy~kT z_Ier{GqJrMRi6DAat!ayRoD!!n?T%!K_3@h20Jes#ML=bSXZ`DPgpQGnCInB!tb4F zm8>Q-j;F(5kh#FQ^iw*eiL7+_s}qx_)R(+vS9J1;AOh7Pg>|g#-cQW)Bk?hzS8j|D z5bhFJ={07#jSqx4-NdY2Oi$D6FbTDa`u5Cq*96)`?LXT=7$+JSsQvL3O-=0=l{sB9 zbRupiN{zxf0r6zt%k$-s7CFY3Y{yUT=9*qF~eLhl}(v&6hAR4mj9<;%HOY> z)k4J|Mo`;~Afvi0wP`R0@yom)sqM3_B2z07Z^3x3C*$W`skMA!wO{=hCHMrT6l$hQNJX?`!_LpA=X6wxv zt?~uPN%$*A`_a~W#vpNZZiJG%7Ke-OIRc5pkA>!Kipm)q@*3BKHZp`itd3>RKI{W2 z?o5d~6>r48{jGWJzr(KBV}#nptiG~rB!>wMenouALh_X%hG)jFl^L*t2|A5bxY^Pr z9yvWPU+DatS=()b{tgng@=fexU(muY$--A1fkU)y6IBG;^`HG3#8ePU2~p|&ktP?X zdKlL<_Tyk0{GU%3!I*^B+Zylhh(Xh{q%Tl1;djmpXm&@J%TNt zeSbcBZ7~@uQccApt9u?z!kQN1rqFfA)}Xy^_>T`6;5T;>i_iQ##^P9~yX^UK-Vk1R ztyS)%9f7q=>T4zZ;;a7FH`DG>iIt98wDa%E!cK}^hOaUf-#0dc~J8Blpg3F#R_BYOZx5O2zd~2D+Zwl3K5w<_X2}4MP_MCFjGs_44?x z^OW(GjDizqIjeaVx@L>wG{XDDd{bPKcOO?*;-*f6lCU(34Tf&_jHLkw6x7g&fjI3X z*L&?{VUOX$WFfq`gRSWISouemxjt%DE8fR*XxX^FbPS=x$p>URfTz_a2o=5V-~iOV zIY5wDa}p$`j=X&NOSee?N%fgC15HZ)sLOM|wP_0*}L z%lQkY&o@p=8FJP~yRP3i$Uj>1CA~Ub>_jeLa9R6KKS&WzKkB3NZ#edwi^VS0%tX1? zXW6i4byM z2{FTbW0Nk2fJt!R!KVf zRVuTL3LWd(+RK_&inSrfmt&ZXU@FL%b86!;|8OCz##_+4ahL?;Vr7D&-U`9XHJOOM zmH6C@d$nZ=7ATG>^cw_qRKw2McCSo?K=(AZ8qvgoa^d3-|Kd7|jg)=WOr&YP(sMOD z?XhK2QPrCAEj8VwljE9p5ANVfKU6#7z0y%=vOGA}S_V&RZV%?js3PmnUWarT- zjQ$Gc+h%YtAs__v8*!T&+f%>ILD6@`C)^4~5NU!t$lTz1fu=Gqy=E?!u_PNYCD zl{-J3HXtMr;idkHJp17hBcTtr+B}+upL3E#^R-tHr#y$21 zBd(Zm|NG>v)AdVl@Xb%Vc7EUBSZf|8JqFb2YQ2_o92)%baF8OtPu|(xbZFM88``|CEz%ofpXIL+nv~wp$$gwsOA%q6%**J7OdWF_;|sTJkuFu0uSjHK zjNLrWNmNnVS(OM&6BxGSp``y41pVa)`tNuAtJ1{tA7l~9WCTqbe5gw4p$52NnM#yMW5J#$mL3PcSVMA*INa|C65Uv5th>%r_P&JR9P;;X zQv~K&8e<9D;llFu0l`XpBNRPzjizVTwRwtV7LtlN4$+f8M^9xz$}3OOin>IiM7_uZ zhpmEm+0N3d>983i%MT7~u_;vlAz#T-&}h|T zD8e)y(Ipe}X#so^{=bcx1i!X`JG@(beL>-M?=oJ zUHrUlO#L;BPH6p8Z#9)KP}$o-&<*+UJWG(foY8>$`#+!Bzdc+3dO2@{fJ3jtBcS)} z1B31@==I4M1v6&l9Dxns&O>P9^Dth$ARopat!;Fm{UHvqCgFGfu#JfRjg}YQAyLyG z{`spi-4JR}-RK>P42~#pJH$*=V=($Rwvzjt`Hldm?Ngn}MTx89~KOD>C>pLWI7*y|thb40*bVuRg8Tciubi+9sr zhhz^?g+ySI5gT)h_4%(Sgdw3R z4Ne0)a+FGRWm+ zcQgW46VfkFZ8JFyP*z16Z-o3NcmFG!-+VxKelp8qAa^GJCEV))zL<-A__VSCeT&0b zdw{fhJc8%m>qX~~cniBWb;e)6zCWU;173fv=t~J7ANOcUU{C8@;#OQO7 z)$j$<>wK)WRl1va(~}DRcyoG8kXS2DyY2hJt9l=zUmWIRl=R4)6+yyfEhJ9hpi)4M zGI4d~=QrGKnA#F0%M#@g4r$Qp+M&yK2QGw10&PC)OU~Qu^_wqV%hseUpg0>B$yZB< zKBezWUeGjXIFG)%`DxfAxfj|0nz;p?hb5%{bb9(KvYKXD*1}fOX?42P5XKuuf@b0vr$pH-gfjRE zAeTvj1@%+3opvq~(mq8klD~Mg0h>93-ukR`M(&TML;f>8`%)u(k&V3a_q3?`cO3^@ z0Ir)`Gad?tf{4#6(PHgF1(H6rt5M~9DJEx510+upGl38cm$jT&#o(hs%B1(R%MdpP zm%|T_Vn%hIV)ubsEi37fFM%R36acQ?6ZSoGO~@t(MX@+s`_iT{NY?}&beNOIqv4a? zg*A1jUHFJG`n&5A8p_Tl9T#8^azb-%z9YM}*{z1FPPI?L=XZ)v(8^fe*c^Qqcn{7AxC!4|W;7-)P0r0W%JJ9*U!H279UA(DC@vyEiC@8SKchBmh&1%aD=ADysZ08L z_l8H5^YK4M3^<%U8L|oJU43Xqu$zd;&*j`l1jF_(<9WZ|Kc2@W5;LFZ&(Rz@eaW&) z?*T3j{PaEkpng2A&ZjWdefWz7Wh;2SSLZM~2A|7{Y^H%nL(N&-_Q!Uso%g4HiP<_LxnX10^di??$_bqvwrhuHVn6q4 z862)k0vQL}+e>B9)r^=~R#AeZV`;>$_3&r+|BrJ)lWA5}JDiu>PgG`I8ZdGsnd&~V zR0e)uZX>zNll5xM#wAv&3bm{-o_~l_`5Rk$4%*CWU3w_)knrye4FPaw?>blzXD0NI zQO8{YAxGmyEzW-2pcFjE!Q^=wXrhsdQ1-~h3qviI&&L@o1u5I6+*S^lwDv2)XC(@Zz5DOFk1e0OGwAp|Hv*(;%Bp zyf3^wot)MbfNk-^NdRk#!1F1QMSN?lJIdc0F%5 zB!Bu@f_`@k<3vYAJjl84C>YInR0CtPIY&!hSV~sjcrbiJmEPrmyl0`>YSe-p`S^h}ITfP7q`HUXZUi(~@o z=HRp!|NHk)eo)E>#QUFNals|q015jxRZnjj6{bM+xyWwnb#`A-u?Rg^gPeUalB~+j zM2|j8hg$`dFAQrWDS{q1Oc9P8Nh$ z9QpLj)jjPgmmApHt6iW#Q!w9i)NBHnL-a3&kI^FLc+qGVU-sxXQQwtnwg-i=mLlp^ zDiq6T2Su~{PM*XEez#?;I?>gPp}s1rhUk^W`1qLRh4-Dw6Mcp*9X1HE)$YSkv0~SU z?h0ZtohD=FF=I8}EY6rpJi#fP0&BQAeNt06YdxH4oApIRQcQgCQQ8rFF=rI+c7qlF zELAGpHs?bsLY(dW`<@pPhZ0B7tB!lx==+R&O$;?kt#2onTjJDe6~1gHH+wAB#KYQq z>u(O#-bLKnDJowq@ zj)D~j)ElFR1N$AgT!1A-_Qiq6w4Syy)n!yjoZtd9@N~U0BeeJ6Ba+_Z@s*!E??h7) zpfT5e7frG0!y+a%XVPT=Btf~gxsR-#6vTlNfAjz*Y?CTxXKH5||Bc;$z3{L4_%`aX z624gugI+j87ybA*5_x2sG*4jQ*NlV9&viK6>T9q(N-sI_?3-U$cl&j6S%eT zHQG361@sF9cz|Bd30ST-aq&S~E${eIKF~)Q0!WDV z*6g^OHler%r$XH~&2?gJTRVn5BGn9G@u&sYm5r*BJ+!11p@Eal?McfN0zZB@5v1Wg zME%4tJqE#}7W%g92@TY0$ChpP7f-BlFWXq!@>=LdU#1XLL}G2**k6#<-DHk`mLfW@ zTR1iuOl}GU)jPM)W(h8CHVw73BBr_Iuk=rV3jMM%sBV&etT7LMNYzG;sZomUaSUOV zL0ihdHCobTr^#{6nF^3|zQ$lNEznAla{PW5l`Q!^tdm3csCs>jDW(xCOSm2!Z%(7X zjl}W?!OIUjnNi6)j;*nHc@*``J8x33mIwK1-CHklCez1)lnVHSRr(2Xvy^(RPTKOH zeKKma$%u+teCuY^Zhlg}Af0%}EPQY~RB-J=m*prlYO!neMfysx*nmnM1NC&qIL?Lm5$(3DoViKE9#lcs7~}$Jo36ZPH!6 z_+qS{`daNSdan-%U1Duman8()(9LR+^?zHe7b^l?lm8y<(Gg1HL*CnOP>FVS2i0_) zGx+RE&QU%K4Oy~2j{Gs{@>7TG|8e$~L2<2Hx9}z*0YZW_7J@@VfZ*;f-Dq&v;O;KL zf(K~a18LkF_u!D=?hxGF^>)sC&puzhJ2|)NtKvtq_|aXg+4C85%rVBw%X5!r6T{0Pm7Ldf$AtMu-$wdu^0`L(X{ZyH$511Jo=c&fFts*|VR#4NBY03GL zi6)(=o^nwwj2>bpdaCvT5FtGZpT?0<)TH5iD=*y|o5%R4Hprm^tq({P5*uadiQ|6s zif(p|4%1B*vnAiFG^gCX@SIx?T<)xWzAg>$+BAqwK5DjG`g}%QM5iprDEKBI^U@<+ z3IXPNv&8^KYNV{%8{t#MzcH;5Bc(hs@g`=Ncah8z&1Rx84SCoVWO~TnFOu zyQzuR(OhDeTnF3pU-6_}4~lGw|G3D4cLKi$p)W}TNn_Lo_QHv|quE=mh>475&=~;0 zf}SrCx%HIw&RbC+LoPwdLP2j`(^^fWp@iYRx&t9s?X(HmZ(4~O5Dxxv+A8pW(-i)P z!fT>Nklj&MVDLF%g1p1S_baWGfb5rKZZ+YkdoB1oc`o+)#PiS}dYe#1t{p5!uWfh4 zF90$E#nx}gj@dIy_cyrYIB zQ$YcvG*GoHkdJg#-_S`A|2SeI}gy8&{#&!cj#(aI561hyZ7i2j`gFjqq`quFI4 zd}2itkI`sce$_qRTg<*M9jzNHk7Ys;PP(|Fxl@vFlu_@KMR!GxB0ToW$stj9D(1Qf zxL7mBhDu?rik13~()ppz$VOLUA@1e;omQ1^f_|#!Uv00Klwxt;;7uj^yz}mgVcOSs zND8`TXLGM{s@aeJe6K|td@c1GJpcEhZOe}E7Qg2@k*VcSA9MJ`AJTxFA7uI>g-vVx zc%&Qm_$4vfBAiiKKQ_>7r=u$TFwjL9sLkd&quXTobci!7Me+CT0rcfES*&atKQ-o` zeD1%m@X3GKofd$9(MhvH#|ET!W_YK7QxuWaHStKXk|L|Q_{SWd*!rc8UlGy&+(s3j z>xnun+ryPDIgd*DI5AHI*5jf4nH=6_Lri`B87Z5kj38D{`R0c<_XkC;qF0_)h*Sd- zjcpjXu@L_)wX$i@@tNft2E?iUpw*R3t{=s~OunW{-B{CG9UJrMJKkea;&G!{_~ZxS z_uZR0&eO`GBTS8@z8P++B%C!qb4gZC7C(0MERH~nWNO(TOXhW zY+lc#ydP)pAD~=i7k?$l5YoT9y56@u@JzdVNizz15JVpSj7R}Y~9;P)q zXr9zEN+WN>vcz{Ah#qS=`#M18-RHSeIvb-XvGc`>wNz6Vsw|1}6b2W}022aAE@vNG zmHw8_5&pWG7c2N|BFc)4PW+h7k4`L!RP!boxvLT}FLbn4f@`ENCRKWOS91(T8mohW zE^dkScu%9Jy+tLRc_&Y03!?GjciD3|!s0gfnzbN&p30DDm)}u!e^W%u)^C^f+hmC5 zChXNwKP=MrCX|26`mxJdMmwbG)}4iF-%y^P%^SfJZFTM*{GzVmV>YTp$=p{FdPN%q zvCE48Q2GD9_0ivo0B{ztaBceYA4wwJj~`>VAczp7SUhUy)jCUdauv zV_}#*YQRc-F*g^TD+_&4iq1?g?=?;&T|)_CLz@DEMO*G=yp%R3WYzc8K+!LwE$-i# zw-ZThpfUL9Cdm@bn)tYG_IZc4+&Ui1CSAAdWrf^QJ7y70rD!#EbcdZDeU8x$0C|Mn>NrHBhuMYeYqPN`A$e#!lys`1m6*e)^28yLYs<@V_1WfClu)88(m{{lh+>x* z(FW_Cc9fC0IXJQunzzm;9_yUB3RQKCVxwR9YrD2lTf5}SNfgVv5@XM~UH$37`rF%9 z%zMu4Ua$eb!ZH6Jn|$;)EwL|2LsD(P@dQeBRG1Y_I*UUIs(*1x)uK2l>UBk9}eFMJe+$TS^aNB^Y7a9 zF+${-b&*oL^$G3QjBqDTfReVuw{;Wu35&uVoq~phRN>eWacTvx)3}uUSM$IZ2hO2H zGuA*E{*3^)9el2SkguE?(%Q_=6>(S*)$&Hn$3mJ^T2!jf2M3N57rpK5CPA0;k>OMY zl_&N5dI3tCp*Yt5Tr(QF3M~d7xj^UrXAqXTN^7@m2^7XT;4k>-d*1{^_gbh7yP_Jv zJiNkRX_q?f^VUllaxYc%T|}6^7-j}&w`hYBL5fbF4yeedNVaKE7p zU}xVF+eSCKzh+4qv%O;dTuOfffKqmg9UJoeLbX|Me=JnCfZw;%IRSI%$bKcWru_EW z0H)+{m>6xrn6g`ayxNLIuyPch@Dge*OIKsxym_F_UV!M==h)C!Tc#FWjJ{{)9r1u3?|kzS4n=n@ z4sXpFYh89@)!QogLOFRPK(sbxos;Ocb~%L+Y&WkQOHIUU|Gwb{W7%`tzwu8u?br1$ zdYIJfr&RC>)g*kTs;(T_81q9@ihw1&SWNQ6i8V(|_)djx=pK|yC&o32Y&i<5r{BKj zbv7931Z1Rc>5)wuDB9jmB(C@Uv%560BW4UdxLe94zy0&sXN%-_w~7Sea#V(a;$D4D z{d7$#zO9H~6E#|ARuXdw$cw%TQ`Vqo0C_#-yw@ZI6~iYppy#bcbubgaT$X3QKosC# zXZj(?6{)YvB>RP~PEtOk(HGV5XT3#g1_7MvlYNp`dX*9Ard6h%m5&&WTv(844&N}> zB&=nXsI=AUYQ?|%y6Z!U2JGQ$5rtDunW;xB_{_R@l?iVUxSaLFxuOmxcT1P{)do|j z2xiXI0J;w?SHV%3sJk0@Wtp*i7ru%82aM3az^YEq?rmvkp&OqM;s{^YGbQ)VaS63s z`jS0xUzNld&Sr+o`ABWMe}Kf=&OV`-oXoIAiOq$;pC5m1xk>u@2D-iZSfn1)Jxl9c z>zu8;b0y$7FKCcp#E{OiU1rd`d@l8ZrICu@NB`!KGQ%+Y3^L@=_-w3i@I4jK@f0SuSrYecnkHU!-Lu)bs9ue{G9d&f zOgZYjM&V;z#>Z*YOP!Pi+vw5{o@C=BabBCNi-nFPe{M5>p`3X>;mIu;;-2?u)gT?& zKmW-Ouu#e9^8Z8$oPLvq8%}NUvgEU`mU?Gp$hf9#naXy}m2yKh#jAC3QRa}H<+_WG ze=-|FPs&7o%`@*m5d4u+aYJ}Qgj6f_(ooxJ5C%K*Xjzas%b#LnGJ&~ieIgBFMZvrT zRcpF!7#@>Vb9i2U>Wp^NqM4Z|SWszckX3t?(uY%guQX*L!Sjv?eU~=dAcSZI2$h8@3ufoJ2u* zThziT-j#KSWpA*-WcCkV{n)+QlQsX7O+82c>5?{>gL?y!T!2QiQ@w!UCkSrHonzHQ# z5NTV-*g0_@r%xC(6{?$*Z+!g}Al_iTH;gvM5CJvkgkaE8x<7CM(#s4+)uf*L9BEb6 zz6&NDU5aKKoQk}K+8@vEJa`c3^j!RWkPeST`9KC$a3x-({KF{v_hDg6;RoneXLrX% zs5I%HSU;CW@h=I3`Jc@Kx<$|H!1-P3WuGrK)wkYf#^hUB?rf`<W?Mqg^D0sBBRSk8RV9DMb=R$^oDm)vlU0I^?8UVEm`+x5~f(` zDp+Z+fPP0A^k@x1(PT)9xGYzwJfgn@I#vTV;~5n~LSr_s3M%aZO%Ynmw_!sp)5VJC zbK&XICX6_oRN486$@NaK#5e0YEzHrbNa(>|0b$bpA@R4zaE)l((WfC`)eY8hWCoB! zY;}bQigbAY)u`q%6}vRUUG9xf&LN@#k8iFl3-5!D_Dv%>C0EsoHwwdy*=Vp{!O+w& zwJ!nPAQAJZv)MXQ^!ttlfoi+QT-bdWMRsQYiB--UERQNeh1yLX?Df zKnj~Q9n^{ESS}>qr-Dp#)}TsKv*c;$R2WBc3xpk98($NzCflyuHXI42JAUd7oi-em zGSk&ql+B!HL;!ABfQjbD^4NAxuAtX&uShtIlTpgzFj2AddJ zz1X_T)95*sSsV58@4#^v;j{hZt$E822Y+NMe^DhqVqZdnB%H~*AYtXNPn`^kYvO*q z$x>yS)byYoZz$i&BaN8^j!PgVIcdt~v$K>km=qM$==N62Vcs4E2Vp)FfeUed15REb zyr?b{iM0MBIQoMABt<5*!RZBg0ewaS*$QbNyU&f?0&W9~C;aGf;MuQeHi3kwy4(hg zHf>P!2~wkaKI^T~tnzW21XQMT1v$-{pUQd^=Nc8lza<>AAV21KBWDM2IzXp^cylY^ ztp39a>3x+h%77LnF0ivbU@lv+K&m{*lM{%92`d9=+%p?>3PU7erWTKCid$r?HC(+x9`uwHvGid$coTZcp4N@(D|WNYsn}kfO{LvbYGbVw z?7Doz6*a!f3(z>vLd>P#b7@*kWdo|DyR8#3>%z>+U{-&P7U`xo$KQQzgf#mFYTUUP z^!|inlqU_bG9ftT5WdnW!{_EG#DY|wGj*E@QYUM|j+q>EI#c_Nol$!Udqj6sZRJ%~ zH&t4fwAH+!48S!GDWmRM-&QzT5)nv}!Zmh=)nVE#N4PclcIltD?>|?BeBXXq&88iE z5vW;dn?kcbGcKIDuQ-8sig zuIL5jT)#z!yAYD0vsGTP;F5ubR?6eml8Fu4VTM1 zq+K)_B!uu8E!jLHw}l@981w`&nsY&Cvx2EZTdJF{S`>sQ1TFDL%^oF0!WVJ$e!Qf4n~NP##&gp1})dh$P*?0HBCDkcLwyWs1V{BfH#btrFe^no^w zxXhMR)nRP^zz;xrt)joj5FdR?%kV_Q1gI4=E7~h*^{4U6mO7(&5rUnzD_~287zT#=iw8q{FQyYR&C<`dGiIqyLd{ zZOJ3Dg>v|Hsxb!gzZ?CCLZJg`vdcc8v3Ox^#Dk~-wCX1PsT%eytI~Y^7Ov)2f|#OF z(6Ab#ZPOmmj$42;Y+^~F0*qo^^9#F4UVz|Yy!fzrQ`++r!TbKBJqL1|Y-=r@nW^;} z_>w#wyw;z_PuG?lE=y|)mp9Rf_!Mg$>AVrR8Oko|+dR|_c<;GZU-*kyvmizsVWI4U zuTA4O;L6zsSAD?e?`c`_BvMAJwm>HLQa7fGo{3{2coY`sX4f~4Z*3Vsb$u3`En@4j z>l?cgKV~>ngP6+*Il9pcusnoqrSh2GM=C{IIQz$%_G2H*Qok)z9h6+31_tijMTSc} zx4oJ1Wd`@gub%5xCdx^n#1^0FGEY%Z>0e98!uRY3%^V|Q0oZ_aojL)EXy|VI-u^p? z=xi|+30K{q3M^g(fo4D;ltG>TxF$W<-0eb!$SJH={oGj zODxKg=1U2KN$>B59L8T(mmh!+-hZr?d{Ovz0JPVM5K1&hF~W_ZsvzIRMH9bMg<+o5 zNEj>GL^Z>53726lemrmeX0fX~;xvm0{3gW=$UKl-DIz(^!|9Yd=z_$k>43HZ-%*31 zGdo6c9+a?UqSIt<gVi z`fP4F`}VHCV%v~$*9poEwtc^^k1c^LoFmy&@^sA&)BR)@O#IfQvT@@ct8FPbx9ve! z5TMn6zMa{uz1K!zfxh}?H&FRDeyuDMudzb>jo^b8lw)=FAptVHi`pes)xqTqoZIqO z8A|y{99r!|q0!7K84>?U+%l7>Lly64yieCp0UlRAxvw9^DGt2p{=Con_{nV4`9)~; zF{>n{=cQ&;ikI|%Z7oqk3QDFG)7eBXMe_%ej&&)f4Ua_ieKchOol$V&c?<<8b7BK7#iDM?74 zovZ=M4cX?LJ{6kI_7Y3lCRFlGY(-RMPYT_ zOaYQ^?LaZ-=TsO1~d3b8EUmeek@{fmOjoZx;benVi-A24TjpD z8HI}2NkA>F<2P$iqwLbnVe~-9+FaJvOBgB6ttWI@l|Up+u4#|@e=n|y}E4vU!m<8;AF3Al4$HA9{(h9_#b#eIKBW=AB)_bqUq)mHE0?H^I)M?{|n#3OA{ zJT#}^c9Se&q7Hhp0#zkrXZv9Q#6qR~yy_)RU!jcF!fn86 zQ`nsGERQ!G-ObpuA=`gCYL2YomEuD{M*Q|4Mf88g#C~|B%TUrEWN<~#Mu?aR0|&%v zGt0rlqu zQ>eV1gIl43=C@++(sv;?OLO~QkP50FlBEaSbJ46YkyzGJsawE~z@}|ZfbMbDmBeSf zxtbieO_lVXEcvw?k9~LI5kIDI2d55mlS60_-=FGhIKxI@#W9suSRGV$nli?Y#ROu+F=xtBR z=U#(-wv;p$O!@zi4s8t~65<$DP?J5EuK?-yMNI9SzHF<@i?L*f^J^HIvY>o%e{1o4 zmC(t(j_G2mDT|bn&r^v$^hP{Ih1saa0+%>w# z@=0VIiVl>CvF)o!@O|36j0rf?tYoTl?=mu-3IW)Rd!QZP-=_-oFeyB@E@mOoZW=@m z!4Ii{v;2iUx|`bRWqASwdKDM>l!Nh2ycDP=mc7^>)Wxm%9V{l%cxFI{`O_ zAk}9Y@iwR+T{K2td4F$E*^P5pQc`^PVSbmOPs5sxPB;Hnkg}Fuf-K>Tu_ERZ(8+{n zQEhg1!}kh1lP*^M5H3*bfMCnpj1A$H0K!u9Dv8fQb_8i`jk7^=FTz(d2IrgTM%>>~ zhG5T0jhqFQPL@dAMD}aGq!Cc$9^bXZef*RZX9>MQQ?F5gON$cW)*VZ7_TLS(e@{eb zYERIN)RmOKbY&-$TOOwg8>{0O5yh$&-Oj%n*w7Hs$d?WK@>xKBxC>1@NK>gbq)S|B z3(5MKwLcjvgSLp^@8pU{FnDKap<|x>b7vt&JV}oN+TVmgOVi?y;YN=u3!?g|_^eft%(PQ1{sGW) zp}c!JKO8r(jNRJ;w}JST@T03OHQo!Om;*|EaG}$rf%5bPUmK4#t}UA3=hGPn+^2)B z!V`Fu+USt0+0`DHG#oYnYN`gh9@nigWEK*8rV~7Dhj;N>K-81$VtJ47z_!<>z=j%E zEML8zYjq55?;o6^+4stP7(DDWDvwQ$YN5UpJs8#mt%+mQ^G(2H_&6QLIT#)Zoc@-E z1JFEmw3ke2Nqo?&#{%UPci~)Nlo^tbciIh=Gq9rZ?|Z za}z+PO2SZHDWAeIAvtbKO3)+IB-&p~CJ(y)6w0w1SdmAGs-SMH#R>RIaC@~Ubq`l` zij!V?JV(bPc~}7@9{6wlRv7SiL%5Ish#Jn{Q1Si?>S%F{q4aA_@tfiwc+n7W8a?Bi zEi`taw1E^vO^OM_EV)K|(r#5OQ2sy|0l*q;tPP#ssVn|GvU7^M-=VPmTNi>Z1m0#6 z5xaN(e#8I$VmMalts=#T==to*#5d;3U9(M6-p*2gZ71l4*z@26eNNt2tJTWg%OR zK1kxyiSi^aZ9yNW^^o@4i7Z{zUN23GT(ZwDY*1ZVTRS&50T?u)hm25JaSwQ z&&X`^1Pq72&1+Hn0K8Hp)V{71)!k&4M^A3S=5B|5PhjI^lwl5>}ZXNgB4%#p^{lXlhWaL__9ITE962369(G*PpvOUJ`C08ZVKn<)qyvpHU6> zJ+bN&F`EFZ{0y>eR4MX~74UwoXe-hL$~=!s)%~?b+*?*{JEJ>B!>8fyE%&gsEG&r( zG-$@F#OPhn%@m84(4yzsRNv)v~3N_{7WSzNiRUF(6faT$6Vl+9T6T`BusLR{N;>Ts~9{m3~ zG?7KuJIX@v$ixMj=!JoXJ3Cw6xsoC$mg;r0y*|fSNQDJuw2f$9hpaSC0tx+*(vYAe zO`4dX!=xK9^2aRvaMI&fad)ep@Kgb;cdxsdW86qW>)`%AG77X$d`WTR1*T7Ia~{7J znf6j2M~VTcTSPBL!N-xoMj+~blQ=&bsYPl1q&aWrG#H?sF2|pq5rC6jn?IikUlvNsxVdg5m zvFi)D+YV5aatU=j%{Hoi)J@Y$?6)luvHlCC<{(!9IGT6`5Z7kh-hZS*gJ{#s2%X5M zGXCOif|d?sab)9U_a9%J=4(f%qkeME=&!Dzm;IiX2}WVP7T>;KAF*Z8+5L) zk~b&gP)%^@iTL>3X(eNC`qAVdPe3j<_)j3@*L5MdZ2+y?R3(jGxUf1}%2M?u9d>8V zhqdWEl&S;$Dn15O`$^>L6t3_<&C9WhaloRbC!7eZpPXLdf+=x^*LI?S9;IU}L*w{966RiCE0RMB$+bmPyFMFr7HE zd%(QQX%LKr+rtWOv@f@o1?;gE!X1gekAn#AR=vQS-u)K~Eg$50W(->sGf7p`is}^Ayfd39vxGNIC zJxq&tHXfS2SZa6$Uq2m$7?Dmk@Q{QeK`gG)<$+8j?Hq&GvT;w0#?E4hHX2YFbWH&s zmP22^kRQQgW?Efzn?R1Q&SSWW_92ok{+0@N(ORsDa!Ed-j%p!4x`9QyQfS+-XHXoi zmM?*Ja5)ed*okZZ=x)cqNiyHY?{|g>Wo+alMv+Z5g=#kUu%R^6bjGlyH#_QBg_P5$ z3Y74}pG*zwQYOr{9tXh|&@SAR90<3$$_MTUu-Z1wd8}9ZF~O5}1fyS3mj2|0`h}L@ zj;Z&7YlWdOhLG|bi=gb+dIL#aFCqzxp3nzsq7LyOe#qqEDBZ4Tt6uuc% zxjgX|6Etvn4jon2*3Nzs7eq+!T*|18H%Wg*{QIVO-$JuOSqh>leH#>4=6$H^a^O4& z(HfAq8*0SB7wh9`NtjkC;`Jd<>fu5KOy*pzIPX_}mB+}knZaGLR9-;nsZ&PF_gu|~ zn!{*h1MZKYWlXHu%t5y&tYjO@`0$w1Csst4zJi|EgOy7QP0J!QlNVp5lAx zd@?l%K3K7LEJ+gkpv_xN8N51!x)0Y2Yn|2Urk;o8E}FboD>GnpCuPbAQS!H zkiUNq+I|o4Z2+ksE_q2Lv%G8qPQ*x})5v?-{BsY|9+OBqEnJJ!jiii5s?jh^i)IW9 ziu_on@W2LtveO9R%=mn5OQ3c{S<32G`0u=&x}ScbsI0DrKc2rwaUY-Bm!Y!E#Ww4K zSF_ovxLHgT=_()|>3|FYJvvZE&|nWd@25qx2%#@610f)Sn!t8RZGmK*Z4MN`1*(Mm z{-;5(DiOELb|4_#ZN&wAb7Jn;G@EbI!Ry3kBnW~eH9`1pWL!!L3^~}l*#AO#|Mn}~ znc%zQRVnCh_RgeSKj)H1e9ZPMfdJ0th)$mlcR=8*X}ir|Kxvnv|K0N z@&Qpc<%y!_4<5;Krm~~^L9xWJ}IKYpMDw)emG#KNZ&oT z4kGZd&bw55vr_kCKY{w7%Y*Xk3S%i{d{?^LTPg=KZs9S^Cf!hSZnfB)bKdzs0ZS>j*(dn;AzS#+wFY}4cDSMh)_71PRh81TOvItN*KLA)hXmc- z%4j%u|F>}Nw5DCs<&Gjn0|U(r!}W=Y=98txiWYIAgT!bC;Y=Q=Ot3 z&io!V9PdqAO?1aqK~KbKLKvm}&-%dJg&nt2sm7e_(}q@J!Ki<H1g$ z9jsMbSC_4PHKc2CBP}xiDyzhF*bdsS%ZVjBpOv5N1qpmVrNwMshjAv;Ko8VOS0A-h zK(OocV7&&Sa!+v@PzAo1hT+MFj2)LxQN^c^$Yy>x2_FjiyuCUkrjHI0-fyEAfF_>B zq{r`+9pYe_`@@Mq`HsIZLxyIa>ZLkf3&?*XG6x*)tpnT+Ygf`M0kNLXL*AvvwbxTX z7EXpByiKxUQ3qa|QR8BBd!>&IL9Es*?Lcw@NLB8{>}sy>W=nMd0H9pSHKo0)W@s=M z{Ln!lhvMcv!IorI_PGsdey}Yvz0T^>}Qf}YJ}cHkESxkkG-i}j?-xY zVRsd)B>+MD_b1V3|5Y>mMG(Wzq22*j{0ei@`X~1jMRVqd;?*xU1aMWbS$MQf34&<9 zk8D{9Hc{ThGuj0Vi|WtcYZ>S))cX^aY!^U9^x2HEg(~3MD_FROszAtZmL`H4a17-O z6Xb8@`pusGUk7aw0Hn+JtYAL`9^e(iLvA$ZB2#U!#gW6NLxbISrCiMVTo~F?%1V>nOn8eW7(DWl|N% z(s_lMaNl7puQjrtKMF(8prpj?1j;*ep#OzR6UAE)+<-_kUr)P@bsHIxf3C9jiW)0T zAJ@U08GpAZm1Ip_|ABYIv#uQCjZ0HOS*-uqj2+T&feeazC+I(ZBYiAnF%f?*9TnZ~ zxkb?Db;_t%XIIR}yybcQEiYZDmrk39VTS0Ds;lR+-_*mD9`Es&;N1L9SD??`PI`3C zhjDN{*3k1Fkkl;l*6p@piF27b&;2e7455*fUZ@-V>g{ zS2NlO49O{2DtzIuV@LA1?=7b=g}QRm1x4HkK)gZ_n&i=iNg5Dy$QO z%)f&YRIXLRQHQ{6L8?9O&RJ48){$bjXM0XsmootpLiw_BL7EXAyLCmxN@QxaB}^H< z%ihd7rpzOG_s73G`8?qA^to8%r9Z=S5=6G`YAl*~`3o-^xG6INlvfbbhx&G>2G*f~ zBAfgnu)egTvFMy_1ZKYOneA^45YfSm1y$9Nv3<&AWSZ-X4IXwWbqkJ&-b*J~5qb~L zT#+c2B`<*08d4;)hj7@?fbYIdE*2(v&&p$Dml2Tby->8W>jqQ`Ive2u$GsL@Xa_ia zt}>w^XKFZvj-Z+n?8k_%=+^x^5mma7 z;Rp6s4D=R9JqL3=Zx`O@1#G@IUyNKIg4Hp^n?F#6+n&tZsQ7Ggrf@~i-kQpUz;kZ< zv1608ZKBLX=SlCpZuS4l!0@qq zBH_?=RsSQ%NHZe#g`$NthNVjO1XMHLiZYWO75=PL6S@nOdfXu$TJbRp=^5R3zpxLh+$8%mB) z`_T~|mNHtRJH+|ZIJJ|Q>!snUGniyim*0w=E!?$TrpXn2gM6_|dsbHACLxdeSoO^C z!}=|pno`_bZS>tWM}V;#Rb3^ty!AWJ40jzRw7-2yo{kRmwMsOqxr>dFS8?!O=&H9D zhJj+~S6oNgl!l=7p#J;#Ix;sO67hOt^gD63w-e}nv&1|bw_6uMx5LWoXIUjCuz24K zhYF;q{2FH-dmD`8bjeHL$zTsvP`yWa&!+&knZ0$TPch%is46(HWB!6pc~IJ$@s=(^ z5J{m$M~m=(3nh=eRck#u`@&4fI_S%UnC9a&&4!OE_vF|#c^kO0PzO7v5JM8;ys=?? zYAb2CH@;H=Sp`jHrAydE!94|MGoW*ITY`e3Ol93l(5zCjICy;*E>?gVn;eqroOJqGkQWDm zf$-;($TVXsvOitT3%5N2QB3iG0-bL~vuWzLQG1HKCu|T&k7nSJc`v}3pypyVFN(Ln zG8<%c?SY=#(5o}TnD;4g(NJ0FjIA)>W!at3i%VOnjd=y+M)|_?ryyIE7*5~&G%k#n zdZr57EWh|E5Xk`19Q|Mi%<2lZ`31|34`lqx>Pq_;>gG3s)=7Qlx{^zoPgyt(;^I8^ z446x$Ye?}$8+AaVX)3iE1*YD>C&tD4q5;I5Lk}BqPzg7*O%uZbi!{Xincn)rzW$|ahOgFS@-8O$!Sb2geZyiXewx>ZQ1 zM?*7Ti|kD41mbv0(^;3t|7xxcvB|sod9qwWg zp8lQI=Re~fr}!6T>Lk5U~!*AwFKfGTdnd0CI;@?!-zc7`2 ziV!B2AdhBi%v;ZvxT7_VUyf{(rpM{ybZSKZY5zJ5;&%!%FB9)Q~EwcBL{CkH^{Bb&NPOQOLnc()LlHT=-V5@i-3(+@_rtIa<)W>@PhSbzhcBMIAn{Bjt<9 z@RVfI)E}=HvXqp3!7J-SS{gjL+Ob_zf!J5RU68yU`#jv1j?CP4t{7gDj>Lbkh@ z`OTg;NIGo5tW?rWjoT};IbeCvIfR31{in3`qvk}^d%_Fm5)PTRSQ1{sT9zEwi-7WT z0;jIvmr8dp_m`fhj)m^gI2y#9eY7pVx>O+r(lj;cIZ~r)xjjn9Y<9PrvL3d5?ahum zs0%f8`$X=n<1-WbW&Z}?9Z%+F+a*3roJ$$0BIv@TrZ;@9VLjGsbbB^bW|Lp5J<1A}j!ERV#i)!LF!V=_KU$m866c!Ca@kRiy7{4e zaTz?*)rXjPBfVqYH=Hz$Ohd+_)B~ei7pTW$xQMSgJEn$ow0u9Ybvgw_Hi2TAe;5qf|ow-r-8^9#2HFHzjkan)W1l zHaNon!&Ti6K}6Ft^n=BZ-_%IRrBKub(DBCnb!1$sLtcA5qg1cP8BvndH+@rVy2JN< z@#3A?{-~?<%oTonwT%C|vm5n!clhIxO<>XnZM)T?w;%@!(y=v>9;F}m_caYIxfU>6 z^w5|J3*u*8Z(ddQ!<+$Jsh{N3n;~utG)U%BAuATUSql~WaX#zZ=(TKZgMRwtHL0el zPn%)Smk>OKS$=Z%8fk$G<2M`ZHl8@@9&b9wWCq#B&%bKhfqm4t#tAt1K~##g;r6ZeS^ z(_edOPfrs{u_H3=PUkZmmoXRafD#Z{B(jwdlKmZ^z{&Y1!GYcwO&J*&$^ z=al|g)qcB)A74~Y9hw%Y<^HbL|Me}fi$}g1%FsVqea>d36P}@T__bXR0YvsFVA#;HyMhWLY9mI(#BbHLN6K?l6#_GxPjSWWMRZ zcV>UR)4_8T1kRSsrm**k>}H5P5U~-(dNOCFMq|&R)Mb+tx}LNz@hiHxQ50Q`+zAop zi_mLH;J}?H-kW-6J0lViQ%gYg{MTGN(g>oTr+FBBHG&lnnvA{r1QcV6H4&5EuO4eu z@SH)GKJr{W^O423B%d7Dj3}@Jo_dI=;2ilrcp@ep5Y*eThRP2C{dMzd6Cg&r}&X0Xw-dolAVfXX2O>m)>~dQDubXM=4nRbCBw`C z+pELJufL)$3BHc_@uh6u3CA>8|g&EOant!Ub z@XSsz-J&*n_4*UM)w5Y^q^lnqY33F!2~i9^PtvB@PX3%NfbBYzqO6UI#UzS-cgG%# zbqi%!V{%qC($+f{2u$5k;-pNZS6!1!vo;cW{K+4ODw*8&*5}11gBJxl@$=m1Z7UbA z{VJS!m7M3oK&fa{2U3MLyEW{`6H^cIQz-+8sz~eVsHRPqwpZAMF=PAo`%fjm@vq+6 z&GYp4N08j#Ao{1OeQPyD5kYcsdeby!9gwKlW)ZNTkYy)(OPuEFjpxFk(Z0u-(=gy+u00hvQG0b_5I63%G_lH&B&nzBg%p$JB)bT+j_Km7TLo(3*Tt9iE|+Z(?GCU~(o{iOFt~g_`CHq@%Ipfd zcfyqy-@nbs6h`9VuT4~W6M1=MI9~6WpzE_ZYsCngyt=a~SL__1PuG8*(N&w{c}|=! z_OYPW!TNeN_iMtE3M8LsLx`3BznrPR&NJJ%zi#utlD#NTlNpZV*v9#3CR1uHonL** zi4iODgKcgcV7x*HoJqLb~X+ilcu?M-*mcM_=pz60ZU#I?r zc3hW^rX=roW%)l{P847UrJJhzn$|wcefC;#3@FTTF#qwrsI>$!dCf*t4mOyq)ZCQ& ztt@(S1FU81AqaUQU2->u7O_Pj-i#uVJZ;=|ruJ0^Z?{7CexJuC7Z>mw^t(329y@ak zt4@W|qGSBaC)6v|ObaYZeZ)R_;1BV_v_HF1%kdUEXBJXN8Z4D^b^1LdmmZFo_&J?+ zkiD$*E)oqZW@2j?YFn1nSqecCJM?&V~?P4YqzU!M}84nx{K%Bd1oO^&Cow)vl zFPVecTWH7R;v+|$$pZ$*_o76t?8WDZliq>LXt{|FM=_DGJj_tazWB_!9NZX9am^ZL zgczma4ol3;_GToRWf<#D@7Aa13mma_$LOmL)O$>Uhyte}!{GDB4dDlGhl&_i@svX)F31me+vGF;7!PI-!I9 z-q#Qw%XeD?C=6Oxo}$5_2w&q&DC-keiknSIE;2b%AAG}s3ozfQs;*EV=4U&WcCQxG z1^s2fpkfj2KjGuINI9XP*>_D(EzsdAem#Kz$;TUo2mYYE4DCSn9-hY`_wxp0x1Xl+ zT|4}J9^s2(vZNkCl2Z%u|G@?S3z3#mB7nWErfUX6%=xMM11Hjh-hUu^<7$$iy6(l- z(Ve8+$Fy5RxR}b-HLno+v*CJDoo9ct46sX?tO}t2ijbKM)`3_P5y}88@>yX-lIf&& zJForqGUfJS8=`q#Qt;D88+;|Ln7}*THO64U0Yg}RNLooFCqrkDyYGW&BS1x`;|JG|CL{%0iaQ#brqCBtX2PF$sqqchE}%Y?knc zEoTS53vh!U#>fW?{r4%QBls{4;vQ8m)HJNa0LzOgeG+Y{ml`Fg)dc(W$(yd`lqEsJAZ`~tSXqk|Uu!gvzN(Y~BeXcf0@yy;R zD)W`6fqgdxXkoG$$E4A77#UHHkRud%<|~!)d|uEA-<4M{>rPVv@I!O%fL^a8nrY^P zmz+E~2w_RV4GYN&hZWprp5b2fBQlLyX|{T#&TqR{ro#D>;G@@z+kzaQ4k<@lqXm{} zEBS>ehYV^PES{;r#f1 z!;To!7i1Hwj0pmC%3_M(rYlT#2{~e#NyH`y=~Ir?$*)^|s(Zm!EaL>!T?A@dg~x?S zF0>&wE5Fhr)v_&^tHpx8{FayaHT;*G`461mcKn^5S`#U^hoV}xml*G--`0PQbKf1g z<}{3-&=CnEndMV|f3fqL)6jnyW7n+71Pjd2J}mIs2O* zY8sAAOr}NqaJPNk?5t8Yo~$Hb%JqPj01>9UPf<*U^zKG|Yjo`yH^f^ZUhp0tH)2cx z|70@yI;mD_w;ruxgH@_=K71{*%K2@6`-*qFVWX z0$eES^AmD%7?#6XAOA(PtnNW74H+LCty0$&TH+VXLCE$;^uqy@!~B8p$XL2z5lR(? z^w8G5Rt%Lo{vcf0B8uHUF3lhP+ps<^ zB=Zw1Oi8VJ4U4t}@JFRU-nR{VRoU_8xBg$=duwc& zQFd>x4e;mH*@h%M%4kwqIvWK)kNB04Wb#flP0y2(R7yiary6nenV%29lsB#=czIsT zR&|rO!WVUUumud~qyr@EA8P5>q!crCZhxrM3{)noFiPCU?7o|lhx^>D!g2?&dS~~!twj9hal7XIjTnk%(UQ-!KMwP)8)~^z4_7o1|By zVfrO_hJucFpDH%R(7!uk^q7Rd`o&u8a=@Iu=98Fef2xrH67sBJ&(mdeIl@J+}c`mH57jDbsZjZpw!hOt*;U8jbd-eOS zS%J50Ke4%dzwrH0iwnL@yhX-~o!I)k4`oB$r9Q6Ate_>;a(!fQ^c`ri%TJdve#*&x z^)x&GoU9};{KlzoRQx3@-^%Sm`P+C7B7KhaZ3X|tWTN9zrQ_A@jeP1!9pa{W&n5 zgtHi!K77L#T?91#1X(DB9m-ZROSJ_JcS1Dj_Loxh6L#|b`ZSPRsffN`HTqQ`0GQU* z*?&E4AP}X|HX=Y?z8T;a$KOC}a-l=E(fa0&LFRq!7}ld1FrL(xr(ASQ{D!~!xGvao zLq@g{T`qWO@j+SwF;m#L?Lp@{F_tYO=a>jcF1v{3>qLNW# z7B&+G0bsxv^DrlhX_-ZvZ$*_ci8PewtVdX@L)TatIz6Ick;>?wt<3ywnkWKP9L1Y< zd`ms+$vwBJR7(@E%&103&Wga-$!{9w6>|I2toN>VvNo|CN;--PlKmnKZ5cAWJjxq{ z4AK;3R5^=0(lpNBk+P(ia}>k9^x{spMs6N(v_Y8jEfg{MUSAYqy7K zrPhlvpzH)oT7=V03OhN?GG)q6?2!r9;OkWTYT6gyW7>2{@|7YANi#@ zS-o>i7K;uZps?@JLjabZ#c^U7d(YFyc?qS&oE2q7n#$4 z){%z8bBI$Q>I|-~HoL#~#Z18+Xz@xqA#-oH z5_zAhsH?L&oT3$^WHJ$;3`l;@wuIy9>qIr2HEbHZVMH`-2?CE+F!S;Hqx(QmS3a*C zvQzRlJ7#HZ_ib?QrzvoB>!zB%1VI_d;QX?W9*h?*Df)80&xHD|<+uuk?D@z^MD*uZ z?VfNn7Ttmf%rE0oe<(X#v1gzWVbkf3vBQBRJWpBzj@NGUbNeC-Fjuc7jt)aXx;MQ; zJHv~MIPbd}35VyhBn~1*4*HWkvBo;@&V9-{!+!I{N9cVsN>%V|q;nDeE1>nA2OdS-3YR)ZstW`LF3fFamU=h1xN^3njQZ7UVX7iz}1K7 znEf>@bb+@dPOOtPoW1C2q>@2hwgTZ3rmbznS*%ySQi_SGF3nz;{qXjvGqnZkcxZKp z6d|(`n{(Gt-mv}3+&ODXb#biAlV(G3g!s>|%-WySsmt`J#N8l0s(o+&mL%`+b1f4t z&Ffcj&HQHqUT>UEL78AZlBF+P!_vRjZEpS0vXXn|%MJ~Wxln}ZpT1#A7JMLe^d%=W z$~ndi1Uy2Lw9aes0}U_m-Lf8&i%+=7Kez&mKfPb9Uidy#geIO$N@OZq0p2#%d|Dm~ zTwpupN9+U_V{WuM@k-oYmHUw0e$2h+CcItc?&i7@fi141kTzNXUaWoTZdrRK5)Sfw zyxV9f5CVZ!i_AArlK-l#|Px`NG zce$=N*OC8`8T_v%c>Ool`^E%{lIov$Ih{Wr#Oeh5vhDoYWFFW3Gn(9uQp8;)RYNG^D*I-K@; zMouh@I7z#5E8m>4*v-!SHv5rUqp5=a9nXI&$eY z*WoFTe%85-kVP}KcQL;_nK$euAC({Z7t#T8XZav;Xn>`r%0?}DYY>8P)=Uk9lZv9- zRQsD+EcxS%1%Vkjja3dy8lg=TJGj6$gx9Hlx+&XT^4R1O+Uml+h;+k4k2@CL+gtt( zx;6rzFejseqOc!$JBIJ=cKXD1icl8Bq|&sODS?^SWGh)sfXd^aR0qakrRl0inaHNf z95~98%^qes3Qce^;#a3{NL{rCl+q#`&Ro3mdb9gQl7G-t3DJmGl5Fpan-s95oeIW6 zGGJ_SaQt3A80e}!Me);g&ANS)alJ|M^`}F)(6y>mv2<2w!7E!pF(T8o-nKwdHSMOz zF4Oe?fmpUOP=l*TdfeUL?J=%sh$jQ_=DrwVE4w}m+K*`=g4Rqi%%0PCp2b>j|Ll$% zW%tP>ooktlr$ZqIJzL8aoe;Am1o?#I?Los z&Zk_J*e}g156JX2wt6O~W_#-~6_*Qf6oc@uFG~JLPHB)0n-OfEvuem(_{igHEf^O(`L&$sd z0@{L3N-p*0*WgL2<#Gk|Yn;?Zo5#D${&Ac7QXiNst-6y_?my49OYUpcfX{{e`kx=OedIp)73!Yxv-{dzMl-Oj2Vo^i8qLVVx~8US~!av|l6ooF6Y$ z&LFJyq++A66lS&Dx^JA_J2UG(|emS!Ha%_rUoEy=hXG-iCg~hiqN~>+P zaXuB>&oaZ%psdTaqphvv@iTv>=8yv$bKjU& zos??NPQ(q2Y0VvjA)KQvVW3}YoQmU0<)`W7>1AR^LPQG_wAm8vNbf>p5>i&CxWRm$ z39oN1p9abf*B6rCozRIN+{+{b$5s-mfy=+<+v1iNgqXV*+};+27bFY#y&Bu2DknFM$ktJ)JaupKaiU)dxG`Ir4^xCK$PeU9xZNa z-6q{C= zZ+FBCz@jU4$;AYm2q^Y5ij7=or)YbQq9_V^PI^~-!0GH7@2lTX)s8!eb5|Yj6*A-@ z#$nu9ggMR)<(ezBZObzwh%jm1;nK8gT|tF5jw=u!&-lDQFC>1y2u`I}>{)Dv<*GBv z=7I3HXmUQ76sung7D**)iqLUs@O9|)7DHB%X|k^c|@oEMaVcB{Q=`@srNgFj1gWiJ+TX(^M&)lV+h@?VYv46=fwW1F95 zmv}5AV7hp|1K}eVs;Xl6Z*8Z&D^UgaUSSu2?Y0}7frrL`?oJeok21CB1>87ey59{!!l&&-r}*>v6gH-)ADLHGQmOx^ z2I#-*rmj3RMZF|tyft8Q@yfGfT!wgu8|fjEnh;5~EMG}ZM*UGsFP(>l(A+@yr~uEp z)!b3#V7SJyNLt;Uvs`gW<$t!Fen%nS zULG6cnaZ}s;fAo&P|oMY@MylyE3_gz@B=>w{v+OIkL{;w>vSqL{rVow;YAN z@tXv>%Kfv%)Rpb$Ec}dJo(swH$>RF32p>$7?eu2+GeLqkV2kGM@pxr~H>t|501E<0 zNEb8C)TDT%J$U#>NX0gH^!hnJv*AQ+9t%l%-LshNgc$DnjPENbo@YypWJ~AaYZ(@^ zP&?ud@zs1KgXhtQct$VMabewAhbf7+JLBFbdMuYcfk%!4waiX-yHI1Ijw)ewyZ)S|5I4=Wa- z?2~YREgf^*ev2+y**;g+V-csHPf2jpJsN9?G*ql_+&nhMR=rJ%VsQ3SRj|xRS|7=M zk2&t6$i%VXxG|6NG*c7~j3`Ruab+DJRQI|Tn#rIqoEKX}(b+Dt&Cb+S3%7(bH_R&d zbP<(*qNWQhI#hdP^RsU*%Xh*XDRW8pHH3jEUmsgAY7(87K?<( zJ07DItt?HJN3#U(6sntU&Q9v~yb_XUPWLsRe%B32LEW;FA25CC zhFoh5g#4f1OG~$0Fi?{p-JT9?qV9Jmt<3)g zGfm$#j#Ze8@=tvC@3LVT2%2%mbD{=>oq7wF*wAy5*Di@xu{xT7Slhd$i6#OA?G&jTtNySu4MsUxx=%`cYtvWwlQ2a4@HE^iAqY0$* zcudJ8V79HZi1VXgmAoI^*T%y$lct>#1Ap&o-@p#bSV1Y7C6%y`Oj6TF5q+vB?7WU0 zi<`_XHKo&a^TjA@CjFLCRgmDwc{oN<E6|-24NVcpX;|k*sOhHTZ1^V6mN|85cfKS09TJ6kR2;esf2up6WZa?z02WDCX zE=A2@6$u7(9Nu9Tt@e%+RwSYNfo20Ll$lzWv35S^b?kmXBoj8|;p-puOSn^YqjpX% zD(nAk=+wB8GIaFKZ zsnfuwb?On@@BF+!G!+$H?1C~J^@`diy^2@h2&kK!iHnD0p0d%3v_cgGOmb0bdZMF4 z_zd9M*T93mlC5Jcpp2_~4iW+4tT@4(ffIgXZXvm6$4MwS_V|zHMS@3}Q;HJUgiTDN z*X@u_8*nw;h^x?ufu_^$FI@{ovtj)o2+n`2?`(eAKsvhEVrtpS#CE>{kO1#&SKAid zlK~~8zNY8(rfDXy%#6&cMVhRsmIYVHcy`#1=I3{vKTFr;L*lWqj^T*Wlnb8&X++|wn&NgOC}K+Hxc(mTKboZPDq`Y5?AMkv3JHtNi8wb zg{4|I{Ax4`dGDg1H_aFx)+xQNJF%W0El^a(YSEe7YFaYpbsolqX;hr^LwOaM6powk z4|qe>H+1}xUVBW{EC8?aV{v;Ihs?Xwb$BB_=yI!anZCx|1_Po8%Y_$*I&uTsf-M*2 z-?*jZmXmpxb@`&=W=s87=zOXg#aQDwp4%e5E6!Q(~Q z=PA3bgJyxV-a&C>9L5e|fge7G1~rTu+^eSv%N7@0*MD^ucH96zPZ}O^`S>l?l0nQW zg#ocTfdTA?L&8V5Rn@j6&=wQ&#LpWNSDE*3{V=CG@ZC;JboYQ-&anF-KS~uUPOfmI zq|3J2uNcd~KhD>O;pD2HyX9SITDe3$YIY2(=ICyyB6|9Jrr(*Zy@zXcrcNqK!Y%$F zLbJ*j_C(OAjAgOot@^D}Q}ZK~$=s!h@IfR{9P4_wX2ll&<0E`lDBGc=G5*3%8kwq= zK03IG_1|~zb=!$!;zCB()9-~c^Z{GifGs|{O+NGI_vpyAx1G7}Lg?b#Ug%1B9{_H> zXPCcrLo9;>*Un=E5yPlFe_?IpKJiri`VOdZ>t@~SuRtTT5(;qb-}MMio+f-gjQ+MP zoSv}ev4_eN1V=qF%-ER$!fdoBR4>G4Dp(8KU(rKi;lyj9J)v+`Y|Da4E%Ux}`cc$m zggic5V$<+&+$T3&#~I&ca_4)S;nuHF=zXJJ!gWOLF+09D*7fGt5FM0d)r2LWV}g}V zP0hDA);#%z^+?+tBbe|Zg++u%&25!RZ}=N?aU9Y-u*^1R{Zkd_biUabrgKr-dZ8Vg zqB|^5hvThqH#3SX?`Gcz;!Eo7(BVg>$-jo`r+0QQ-MdRgHFY}Y|Kz!T@~aS4w>iWx<$Wz;hYk1mRDmBw{rI>!h4s~QxjH4Tl*D8yUw;vdUlgDD5)8qwY zdmg&oa|=80O?I{lJsvX zEjgMWNH_;DD$@S&1z?p?ibpF2|M6{WwbA`j!2T<;ax%VfVn8~ymQ+mQ60nkYw5rfe zLG)K`gCN~?P3az}WjaHy0BYpnDe205cV)t~wUe^54c)QYN zhcSbXBf9JW{SoIJV6%b|>L*1Z&s3maM4<1?T3kNOW32A~RtooX!>+OqzyEP~Vz z?h9l?Q}uhq-+k|Q{Cnz}q76Eb`8HNv%Jb6eu@&DAS*e73#cyg*lDxC>!SWh^@9~?_NKC0mX~=7? zYMvLx`}SU|;=-N6G8n+@s=g2o%S+)~*3z2Kj=yEfh+S`3Bl~iwIj(L~ngHX03^core$Ky3Br)_|cpVDNexUsYT z%PAN|*DG^dH(#8t>M`Y-X!GR<{|gjlZ7Q18E0XH`E!ir#Y+RvHaMGSsT_{PbJI>#> ze?fj(N*#vA1xlhxZ-eMX@T z77tS*N+XVKO-?sejkU@DZbNia-_bX%eZU?amOQ)$ZF)FKcgGo*yvSpy=A#xfh`C@u zgyypLW=lBd``^&w-%k5_FpYfxVy*A%c!PoK^zx)#V6fb z!yaFbdrvoRYmfodY<JIxUL15K<$1eX zmRMU2F1;7Jov?IcL2@j1TyRV7GL2JiK2>~exYQ_F;#T?)9B#C@_L_x6z^CMm57@_Y zVPkZ6E0YvWa2fC1?i>@Ji7mRqI>~V)_(X`Ew#eu%DLkui=)E)Xpm|>`UW|XgZ#;6a z0+}nfrhDo=YnI!j{I@UM;UfAumYm2nJ~svnv@!i;WR_#OevCw!;o=sWnVk>Z8j;Ft ztkm-!ub3DxzbttJq7l}YC~$1i9fM;*ohefiiB7i%WO9Y2g5mFXbY!5=xM#cjr~jd8 zUE{4ppxaglGrQ1?Nf=+lNxwgkaX z))Y^_Mh+B>Eud_qx|1DquXK@rA<8LCUT=O?L@d(^W{I;BS*R?gU@J4}611-%;SVx- z0-G3)(&zp0^gpP=;k~mJ|AAFHPy_MsH47Fx_lGHcM=$$Iwh@4n9(^24O-pt=36NZ^ zUFqvtk3y=z;prA|wI6-@mRGl!VIPr>il^$tXzDo_MB!@KP5Ze60VHDAFl6q$Bp!bk zn)Rd!aOQu)2mYnxSX^pxfT=MHFBMWXJ|-2$56r zw281?uKMJqrp#%CuAGL=Q)@Hs6kA}K(s)_Ve*bw){E9`$frFgOi$jCRS0hw8 zxU~K9vSf8sgG1uqkDZcyzM#;u#}BgRGxKIDfi_HYFDN!RRYtgIria9HV}3@z)VC#n zr;jaz7*q|uJPJfg51+6#{=9DgJM#ZS;)gR@(fqsVMf?5vHdX%kA$9n90Ihz{<>(il zrTGH;#W2i>c% z|NLqY#~&l@`wg3Ymm?vGlf~#5K}&ZE&MH?D>Mq-`gzDlwR3ekSy5LfSlun|m@E{oR zucO}hohWiwmT2qV9;UvuFav(ywhbv7ts1{%G2h z)NKtKiy_{p2>axHo5P&i;U&|JcZB15Ye5;+<5CJ_5{7qu;no9RxKPx`l$fg*h#snm zFlPs+)%piyg1No)X`0l@?-%%i0eX>iy}L{(y}+fPDm)f~u>ES8^k!e4zHRJYVIgUl z&Ys_LB9>@&#V?_K|4P(sb85^rg!=qZwkw;FKGKx9)t;~ps=k}mgDIWe=bc=1j>71G zL%>lIWs4^a!?sa^rv?0!b%>dQ?#Zsk-ogAb2NAz!E;;Xi@HI1ElON;Jn z4M8qhI?W4PL)=72=?5^kZS`Ga&iVjHvSDu`KJWOP6H}fh9t?tSGmA8_{3(Y=!d20t zRV10LG9q&vsh5f<`RXwT5`m+;?D0h$gLg2(2$V-ERT#>)9~xKtoJ~9mbbi;Rokc#& z;5ezuPSv@btJ>o|)XgoE71}_h@<4qKEJEe-vD;o{ZCj~Ru;Zx!w(Z^D_#gY#R|{eg z)+goqY~f}6K1BX2i}_E2q=On8q9(^Ngk4+j_57d#%o=lg*C?0OH#r#(A5YboU)8^t zDF@R|_raJYD=pE?$*wt1uhC5S#~D|(eQ&rV0v_7oBf_Wn6G_f5^~k`cF+WVZApGT( z4Ht;>b}e#k*YtH%D*DXcYp1t#9_v~HHM`0TX3~D~9fOW7ufvdMZWzGK2k((IUkur& zUVc>L8|&Y30Z%!!iJK|3Nqt<)-`=XSwm0P(aA_5&isu+SpcENp+S)g%?Wk8*h%G^V zp=aZyv)MAV1;Q*CG6}SedkE?ed|V4Jx06K^`|Yy z{!&FZr448Z291;3o7x$_%;;CJ#Bf<0dd>5)d#k~&hlOLm5L5OM4NhSprKnBQj;ag4y8p@ikc4S zf>AQ~1Awc+gpp!pup59TLH7;5zAV^enSm#HkBsr@nzjD?_G(crwBqsIJ(JU& z%Eki@ahe#OZo5eqzPo>qOZqkgdC6|I3fY6b4dEZTO}R5jT22suql39zAkHf@5A5MX z?Ki7Gj{=FOY;^U%A#r1u?mT@A?*f5tgq-NCimjgbkelHK2zW6Uh}y+bBn$v-h9xm; zdT_v*F~*E{XW1*}9mxyHyIJp|_XHuX-Weu{Uw!QL;*H(0G-R;hGz}qdK}-YiKQj;i z&OtIoeUk|>^DVm8k@YoCeu-fNSTz)_O4qSr{u-#9)UZw%lz32~I+rpO!h^XU_ShdNT3va(5A| z?f1aN&mb0u(b2))o~9ZUz>~N)UbE_AVw0BEHz}WL*zhdfZ?RFEW#yzhWbL66icCGK z%;lfi3}(aGXB@1m({@$45GVk4w$l;@Buuu+rk{R>XVsRNcUQ&V!nhyU`FLk~n5v^X8{hiyjVp^P8qiMeG>~RK}*g5Xm-Bb_=d`iJZ-FqtkCpLyBROX!V;w_kvM73~TF z(T*vKgz_8w<#tEfX;{YIEb zl-&02R#_Ed)IbjBPm4WN7LPiX{2pt19-a^U@$#HHRD*<@uEo>?yZi@|4k=reNV(&& z5s-T7@!Uv%cwZvXBE^S8;EWR=xKwJ;)#dz7#}+C*K8XZ42X{6X&bTop^?tE^bh(?U z;F++fAuU5JcG1%m<5+>Vs&FKzH(_R!RHKgf^G9`40~xT(#p?H@S%}Aq&xu%JsB!@^ zS-p6wjjMj;==3Ay27kk_KoUeeN$%7-a79%4g`WA#oQ(s!`0SW(RK3TWXo^7eIWyG$ zJbC7TD|C`#kqfK?n%|9TO$!)I4;8CDUPD>~tl9x>=~CZR+3gwHDsQAeK;w9q5{s*| zSSfh9^l3bl#X*@-tnHyPi=BKea4LU`I6x!zD)?&x{9otBJ_R;n;8UDZRr$o&tCUPk zf-y}eYEX)_98%)T(&V^R91?wRIvQLA2mQ@vQ4PgRUsArEjt_PWhYmgMRs@T;`eHp_ zeim=ns~Y`VvM!P3&aJ@I9bmHby7fOG!4G~!6xnroH8}Sy1++N5DZ9!8ZbT(2iAM(v7zOe@6fx)TU@NN6YuxU ziR7YuOQ?iOfx#1swkX8)$96Si@MBS8TVK?d=nHtG2A|{P#&0^x>tqdzwYw8cq4+WV z`S=b2ULS{#S-nMd;?>$4>NHDxu|1A)I8np6lijQ+VKEU#^EJ))puz`E-`#>w-#hv4 znmS3Yb)4zpT~ol{R3|?>G(St82nhR`8zq{O7MgJyD?;3k$$B*MIf|)F-28Krnly@( zE5`|)MH859w5~tTljrH{$0<7cKQ&HbISD0D@xm#e>0yK@h^Z)GWW{YzXGzyf4se#2 zN$*pD^f`KkU0d6$GJ{d&j5Zorw*0vKQ`{LZ@Q$c5^Qh7eXoP-r2n98#tIL(v&391{ zOCKZ*Vzx+m4$%p2IY02cr*5_>;59dVbCnIdIk|04wX34D;0u#8+;MprN=g&qT&cA) z@ur2-Fv7|+Bz$-@O={NETKm|=dK5yHk#JCy5{5ckiH5I`;!4NxoL&Og3N;o*mfwJH zjtq{Et!a@|AU=*qOe-2KZq+a2t~wN)4~gE}8zFc}5dsy8g8`=6C$FVA-rSYl#V_6z zNc9v3i&8+AoC*mQ9e_1Loy$10&nv~Wwui6I(=sw1wdLC3-RQ;E(o`$FEBIUZT@xe? zM>+FV!`JY1%GssdJ!4?4g><~;O!*VD*f~D73Ge3ihi(@PFcIp$b&fjUef=b=hX>p^Yp8I3Xzynw-c-5)T;RNj$3{{`o-(W>>J2~@D zg!r#x_-|~bUl~&J8soe$k(uzX1Z?`DJU@zdSqdJ5c8~9dATu2!*&57+-PpXP9|yx|4pkDoK7mE`vATaB z3GtZ*np<2H-1&VF01f6$~LA`ckKu$wddQEv{r+zv%+K8pA9j^PCsi z^a%)D+%a$E-mP|+sKy=I8SbQtKUx`K1})ZMiKELtV)S%$ro08OaJ|notq}2=BRc0q z$jpf!9%Gi2!CMYIsOTjAnNlU!*Na_TdFt-~AdzwKjaHpTIJerJmTDI6@-hV+zf0w(VyeIlHwXyGl z4GkROsIF9<-D)|pG#52%+BC;1W-Rac9$t#zR!|I$5y!e9@GMol-Uf~++RFbiXLd=I zzwmtaT5APK*wQ^C&|`y}gCki1iEN*H`$3KBy`0AiJUfr>FFpljk^o{i4e@>F$Sf1n zookH($W7#e8C;pmdxw0ORMs<49lGdZBPYit^w zHe+@}zk*-zo`_Ei3~$-^u-qGN2T@s*Y3OC(8{K?q+xSjEM^P@%USg8JZ^%|*=*h}# z3S@^rS2#N|{Xk_bdRsv78__yYpq4>+~D%`G*Wl=8rT=p2c-(CV0SYx0F%bwfNiG zopXIk_2E7B0n^h*d4rrHN0{~}ERr;hTmw`X>Rg<8}*S z>LadBjeL_YAEHxnf34)L7bDkF{7CtY4`E4 z$-o(Zl-FypaTI9BqlRYjKX_XX5SQPJG75CFFjN0=T444KPi5+B{_@)2PR{F9fio3C zyVLLdp4l^_9xIwz_j}OpnSp(siP)Gi!~L1Cyp~Qt6BpjR>JMU$FV)*8HKOJ=Qp~^a z=JIM{%vq0eoF_Q=eTX!8?N!&dZVs^$P~)UtjdPyV_m~dA%%f^RF>q43QX=uL~W` z$G2(xPtz0>wY}i`_YNi5oAlFm`n_s>1m?R}?^UH#vCY`^3Ik*+8oT=)op5e|_44x7 zjOn%S)MY~{Mmf(`V=XB>r2*JhSD5BQ#X+K(D{%-!24>_kYVf{F4L2l3Y)jbuy?7B3 z{DT8k`Z6s$#}HNEYF8)4aUm%*&t`a+g}ogiMTJ1XyT)Y3^{l#o6>V=6CIvEphEd0u zB1^5}@_D)bijY@2-(~3#lt^>9&y9b|Pt!35y0+8z8<0Ts#>M;$aL_(>6<7E|sa9LN z!s+5=GC1ZJC-YmqNHK;B2vx7=&|pZ!fi9D&QndNQ#TnCT+lK%4uSaE*6J&D{5awnBx}B_1_6+^T7= z>Yk2JPP;l_&cvds7>7XDEKhs&j8wMOb!ajrEN`*yG*S=(PV3>_nKe^>{Sf3^$enXo z!yUT8EchZP>cTZ;!=6ARlbPVue1Hq9KvtOiR*M;V!uw#)%BtC;^`J12CCoOAS~vp5jhjr%wKl z(^Ui8bxefjnT91(W6uK=d_WWT0sTOd$%T5Q>1tC3siRL>r05O{1I(+WiY+TEDX>Lk zS(g?HN3u+kt#xp)t#{ns-_FRA6$72=uo&-G`_?$~#;3YMVe^Hx|2jg+uMBY+UzG7m zVo&hFIX1u!F7xZNgc>J}MsH#Zo$nUJkj4V|nJDzijh}eU%ztWccJ?5ADI-xuT<)v8 zQY3hm-3>T0&Ooio*$3!k|RmifDIdi#Qzq1;8$9@&dMD01#P&)A2^=Z*(I_lBc#H9e4 z`a7i`{K30Jk;E09BBTts%`lCSML|2edTATk5l8zgcs?{{!D)vhC5OtgRihNc1ptWc zzB@G|T&WxGKKkTLHvmRiAh(ckLw(Xp?*$-!NvPunKGx+ni$v8NOuG>aY!gCy^CLl= z^Uj!0h0K&dcN&@@p!N_Fg(!w~%Xaj5XLBp5-k+t>tmPCo-@xbLtrgr`A7AxP_|&&h>)c% z3tL0nO$JW5g!t+2fsR6Q>>x&?g5uj#1Pd!SZ=)8*!P)i;_IX~WA;I)6yLJ&hlFhKX z!Ox!{=08dli^z<`_dYKnZdaK?!E^|EJLBFB!zTB1b&>-T@YX>@JOrd!s@!HEepS~g zT#u;w61M*9c!gt%(K;kXn$g674!Xr~M0%Cd)}OSd?TG=+BYX(L?Ilp%SRr8*`cqLi z=X1p7LpQLds@IW8mMk4I(6h-@E4nm>;T>yVV$^hGwi7>E3-*l%-u)rHn)6zxcp_@H z)~)|vDG~c_%0>OA%|IS0o-mJ3J1dv(M69Ym(dUMlm*jAN{lM-+@Lk z9s9YR_x>gqi3F3!ASz2(fGk|!lSuBZBHp)rD+K{>vlj!KkW#7U-MG+?Dn&eUW>B0H zejB?`qeU-he0@Tq+@!b3G5*SK0Tfe@8gt{k}wnjJ5Sy^XJRpiW)IZ!TEG24I5{8M-b_ipZ>6D$&a| z9J5?3W!L_yoZr`BTjn4xwScg-qselJJ=-ydQj1;5&t2{Ya~mo9JC#2-M#)r1F3r`8 zO&q-j5!Yj~9n`GC@MABD|NM~6uIV{;3>v;o?xdU`57I%u|A`Ts{{08FXDz?sX*CYFadPtxQ5~Sg>hh+e2MJh2w5nB*4V(SMSy#=mokfn<2r0l#d zspSA4(8?aZIiApn|GqnKv-I~Ku;eN5#)y(>Sp%rJF_>qSC#GEMhob^F_s&u;#O70$ zLAHOQ_-k6DzY^W^dW~7oAT&cYu3GWeS1BT+)UW( z=G>3c*l3x=R;wNzRQ-)Q z_!p`9Qx5vrE%KZ@8j;qxrczO#MxbV%_;_pk-1OC!!m9StGejn;cJU!ihHKqI8!P}B z8W4XR5nOG9``Ji6EU3L$nst-0S1V3w*k_vVj@_z$l6`bb=4+|Mr2Rq`JvK&gOXT-U z#s5@0&)+od(%FtXlP+`-MCvZL^4*_G>mTZgX8^H)u-)|?Utb1lkvhS``M0`ygQQbE z2FPfd#Q7%Pc6S(*N9y{yFHHUP?O!pOFV9A!RZC>!*B{=-ID+C;*zP6{m^5enmZueTHm4 z-u+o;6TBO=8h6o}bHDp;Rj+e=Nhj3zc_!>$mZwlCT*_8%s1Xu+?Gpps4IhoSMNNBc z0JAzF4!6*_iNAZ(*BqgZhD==2+It|%6vf+Ek-4E%G7LXqozKik!UAAtCowja=Z?0C@05Q7C zSv!TAVW`EHn7kRynocm^?$Wr0V2wKg65MGlxYL9*8r&MU;1=B78g~eb z1cDRXg1bAn*V$*EbH8tA-5>W4MZH}VZ&lA{${6Drles_S$|;ZF9%;h%Dy`3utzYYI zyLA=5#qzcD@;0gO2IKltLDjMOF|yZ=cQSDcBvt(Es)pD`q4v?AiR`!(1b_D2T#R z6*7`#LfKS8{QD|c{=)Guc&g%ABcEk#O8QQ1v{9SY1c5@YW2LT=BgH%IN#YNMhG?B) zFPM&;1sFW=45f)jdIs`WGSr4JF7r#|3hnL?cAe>?USbm#8t+7#Y82pi*b>@9v6D)f zNqP5`#o(vZl58IPmrN+=JPod|b_J?-HFb3UU$T<_y|4Yh1o0cZ*k!%kcpP%{E?R|J z2X*@|-czUP!AiG&H)IYMJ-b5%?0c4mzH0)oYnfO^|0HO>Zlw7;V{M@Rqn=EUMQCZg z!{2V6W$c4J@*8dhadvt4KY-(FQ4BMdLVAW(fAakBLn#*^3m%H;jAVFv6xmEn%I`eS zRTnv}GhRp76p%enpfW3J5v;+ehG*4*E!ao$WA{E<6l+Be8i510sIx=y-C9@?b~wi< zM&3uhx#eHaF2_TO*DI2Pf2DKWSVg^IyJfN9YS*E1D92AtuNE1#u>~hqK zp{7pCyd|j}{G;aUHb+nX150KiFlHd$$E?e+l&nA?rTx(*DM<#EoUAkV9aU!G+7Ib`# zWoCjQF@Y7!G`gUKcHdqm3XzG6AV{<@v+E`0Gc3VS{?oJHCgdzcI2&uD88^BVOTR%Kj>-qqxS%BEbY!94}gUUSR3-;2hgtQ{&sa9@2&Ewr^VtwWfdx){4Q zjsCXB%_^5m8LvuwQ{lDIC-X|IkSlbA%BoaSW#fH|0OD{4ATE5<54EB$;2<0hL*DT- z=tPKm7vkC~sXN)~tK?=0(hJsKLi z{QR=5M+QqWyfI#r9N3vvmr|dRy@24LqRvQn4)6M9Y^HYVsvN?s*ZHG^? zb&AT&j#7V^$2-jXBf)~eQ;Di($bZzIv=~z#ukAPM3x#JO4bQ){GUCQO9mfs2E!1`2 z8_7-bMiv}-miAnb`B3B8R)2oxDN1|PLKCMSg^{$&&#<1X7ssEPoMt1^CVqU&aT*p? zGx#al_E5-qKl+)1>y?`AbLC#q73Q`>L4zUhORLA?^W%RV3kc;akQaXv3cpF0-`+Lr z#rPw_6nalMn(f`y!iRCX4;Y?D?N`clENZ#-9#E*(#lc~7%#}(?lSitr$)>mGblQ|% zL&a`uWt+?x`A;vEpGft`^5m8s-uS5~UGM!0q?+V+<_%=zQuyj2l(*xirqAj-pK{$z^x*81yBg9~-P3g_ z?Rp|kA<;;}kjJpv;9lW571=JV%z>A*U}wEE^cg>6Gg}cuB~F4oaOctZ$*Gyu%ci_L zyFu6&No@};zSC3RQ~d7fJ#vJj8$_g}$tP+dLG`xd(F()!wH1GozfAH*(iKq1B54)? zx9q%gOOW81BR4tLq0xdQ%v+G4YNYBH3T9iieUQlVK2iD2y{{`Z@N|DvVEM~)C$JZKC;n*s+krJV^EjgO zZGc|Xd6qC{-2OMtpE&VTj1Q-s5i3{QLbrSxz!6>vHCN`-v9F@( z0nYt0OM8lV|3(8asgma`#b`!j$jvP*G&k;q^o*gwI?)67+-!5i<$nVGzgKr7;a@+Z z_{@=VkCCN<9EAE3qBhS$i6(YObtgb-g(`v7ZatqqceJHUp9j;p-+td2FE)Gcj=^2w z`q4K0PC7fgG-bM)r!8W^7n9;Ke(${p_a8P-tw8Aj5?v)D^{iPH;2O1s0ZWa{w!P~m zHI-if=k^bC2Zg5|Gpk&oFk=V!cjfzjU*f7(=~}nSx0q?Na!b^*Ig}etE!9O zWrn$$!yvhw+V$}|_CyhFy=#?++pPqzsc@8nwSww2Rkw|(GF|OtHHkb`pItt_ z{j;eIcg6En6WcwnHA#}x;8^vt?sV0fy<5XginE!~sP+^hVTE!Z>0C%igBq!r*S2pI zXQK=?+S+&KTbrqyp45lDaGNQQX~UMG`m0!X4g3jAA11dww6(5GKKD;5J%d;M!g`;c z*paVKp0Fp{)j{Xt{nGgAe!zS{kE+!)ewe+7F`d(+dU=a8mxO9}h3~`5`kY3Y*nD#V z-p-$eZOBry_a{}rAaHF9;~;N01`ZNEYGv@k>S%w6wB)Dp_n>c%R5dqOizMf+ijM;x zE`~rF0H-XDGqPM)acds{%he`QWXv7x1dpMpf?EZ&Ne1Gy8Rx z{fy|#uL#p`o2q7R;i;M z>G)PR4fSCGMf}6&0@b4V!}aB`s5{1nPwnuAivlG=ac_`V@wT??-i6(AEPG$u1ElLf z(5>9ms}lI{Z2CVz_P@XQccb)trmC#=SXZ@Uif3LT?yt343WFWxNm!#CaeidhpJP%M z-OO#MrrQkewI7q6mXmys4K14DPnipnKIjr6V4UjMl`1=Iq+PlSnoSm()K6)h&h;Cp zAX;y}Q0jN%B%$%6=M4OhWx-`#< zdOVrbl$3p zuf9t~i8)eOoh8my5E1W3*XMe-+Uoo2X~g2h$xs4c9XYxq9oTYL)n{0X#JZV7r~CAm z0>&ITCkh(%yyAtxM5#YY$vqyNWu0tdnI2Wgf2_yA1w!RY0sansBTpivzbtOmYBb|{ zAguHXC}gKr!zNRu2Nkq#eeS%^8YbCi#KC*R=H1Bb7$ZrO-Rd!Bkj{cWRu+uugmvY# zdV`lRnRYOhra)%8_$f1GouC`9~#~ipl$+ z+?=}ub3I-B{vp=kS1aIpz1e*I%6{(Dv00@dSPU*hu(I-XWCokuEliBZYlMQRs;Nw$ zxbh!2jsG38(qH{NWICG$(ZMfVO88OgS<|Kj_3rsxGHFpNg>ZLQ9>VY@b5~4L4g?oS8%U%U@QQseYAiEij7P@VJ`d)ALd z`iHG}s!`8gG)#Sii2l<;4m*{v)Karqb#L>pq#}47e3xM>9|1oLoQz2?7UErqq+bdT zYYHDx0XC9^WSNu`2Pml(Iu0t&jYW`Z_SeT5O9I`MUqE)stn%e3>GsI__I6$$)^Hjy z*C^bMQgJdyeeohBHb8$go!BDlwz~an5&CW8BgjZfH`y2RF86t5V(Qu3C_lGZ$nUBl zt$857SqI)kA()198uJ*P@M^$0(z7j9V>z|- z3iqhKm5frYJuQ+wLqWBQ5Ny+`2N<3Utw@R4rdUN%xVVy>X?B2_}<1xb%nx!w< z?iQX(>ZdfHFOI#)jQ~DE6U4Aqn;024Z#vhBeA>1K!&&SahQ6k;A4lDW&xTaBq!KbU zHSM}Drc&R4=_01JZEik8f8NM^gfmitFo7#Udp)V{<J>V!9I% zOokDg?%lfiV(IK`1x&3D#&)_WeTJ<~Iq*}+qei?>3AlqU9T)yS@-Dhu?KlUDdEpsY zG9}O88lj6Kj{PCeXfV1#zBe%YH+7T3r+eVK=E3T$2$Ue?%n5T)4CEQ@O)m($T%XvK ze+bE0TdJftUKPV#c_}I#&w5PF0ujpujIE6UZx>wDq2t(+4uiC?(LV*q|186PjqLx- zWrQ_}5Y6S5)jU|5<5PXV#b+VrN$b9b6t)Ic`JeVd$I~#2lON%@@Uh%k69rYfRGA&1cPT4q>!>EO=~&X2?{Jum-|4vXdd0_JSZ;aPvkqkmWU zl?KMIVdRT%JCdTGXDix{v7%2hricHg45cZhV9?~9ss2!ew{lT0;SLMmDyXKpPrt5v z`T?}uSONUTOto^iQt?N%p^c9vQ9=G z&--uS>mU}5i-Po{p}-Y&+g^t0-kWQOrni(v2_~lC<!QTV|w)oQ-lNTd=XMxc|ZW;yzZX_5~PiG{zX#AGU#8Ybj4 z(-)9+tU=h3Py&70#={^Q{wzNjg=5?EIREXb(Bj$Ph)*AT!39%<;&&>at)}-xQvxCL zJ)LBPKXvfdQ01QuOF*iw><$K+%03P+_B;6_{E3EeFF3yTkM!D0hL~8hIs{<3Hl63h`W?0!Wm{-P*G$sJ)ZD&K3b##f z?1<@gFC-|NkiC*EV?QfnT=?(%+>w&!~#$aeoh9BgB-cNHQ~9;$fj( zn$e}T)THOOHuKid)HJlKaL0F6&RnowHo*O#57*C`y7zVF&8d- z!xrQsLZVvxB5Z^X&AD{>BdVwDXP_dEF-1I-CCU8?=Mui{Z5CbdElflkddEo<Sf-)BW7j75?&`U$zCI&-$Og$?bWVjE6#s9}HSzjwnwf$t}yvD#gR)eX@Ht4VLsxPzq`0hMl8HId>d>tv+T`0h`Z79ym3{oZPmx)p($|#SA zXj3}k(e_o7=`(Bg!e>jr67~K{Y2ppg|Ndb&Wj5TIXHx?!R3FlX{SY7+^7_fNTb(jS z68S&NxuRcyz7g7gKL6JfaUta9w^s$^QgvAqq2>$xFwRBTMt0*naSD>R5g-c71Q!}@ z*~IgaUg<@9dPoLk)7^}^`#8?kz*)RqCzy&;%&cSkk|qLkM}AussUm#)Gib;YFsTPZ zJxF%y)Hu{I@*C%GAX84hes64F!ffKg?fPks!o{GlR+zQg3sfZKW#^HyiZ!N(#ZY4( zNU(yF?jI|_PGj>TXE2^**7&k#%vAla;}PF(H)wPe*Ye9jHSlQ>t?6X=S)PnNc2M#U zz)FDOL9GBfw=upx36 z1sSG*wfq=?A~31|xuer1>N9Lp&i#89N)wvk4st1-IVC(zl5)1-28=vPrYaX1E#nN*`woTZT>B4Q2Z^PtoN9ll|p&HbeG7``Gjyxf}OVG!@9svW#S_&h8_abom2mzZ2s4J=9m7j(USGP zi-#~T?C|q`zPQ;StChy{;yiN}&FaQNX4rwSn}IEjX%w%@m;2s3xi3Wi!@xB8D(7$PP|ruw z>=$pfpq9ZPUYCc3Gwi@5+wa#u)0aBf$9_e8PdeY(9jPUX*B}$SCRfADaeFExSdq{V z2Stx60~xkG={%qd6zj06In<)DpzB$yXJbd!c>eAasr)mVv7?kX+vA9k*!1b|%|wnKGwD2Wzsogu{zy=Q2LwSeFACuAjh zEjT^DdPBSyZz<)94aoj$_L6%CI16F^Y5MEK4~cR^4Twl^*Ks+rukBuwY^7krDnQ@ zNsII{eX;8xiAsgxbb17j<(sNy1je(4vhQi58_X2iQ2efm9yri#p(7In)cr!4UKiZr zyI&>pG3^>=zm7D#`NO~bijRb8%ciy}^oQ2-zb5gSB#En`=L2eEdpq$1QBI^y{aHMr z{d+qU7a6-V?fyX#`YfMBk{*YPLh7pwoAuP+P4{4&hMZ?yj{q&ckO8+iUNLD|p}<{% zbeu^IQY|fSQCTl#q!e&+`El1f0P?r;AM6PiF0(~VS<7*qUS$dmqra4WB}=2IS!0hx zr`LHl^mpT5QgIX}?1(QD;Ktx>>g3U`u?$p0M{2P5YO#oAhA*sffgyt`am*CF9Mq;d z@a1Tt!^ju-dhj_=_GXMP11*K(TTHh{MNmE`K33u`)?fz#RC(&B;$@rzbr_FX&NUzi z)vYSb_oh!?+8O);xjO+({rYejXy3`uo!7d?u8SRadbde7z1iJvhrb|#8~6-Nw~PO> z+iJ=WEJ1bH&3ayMN6yf)Lz!@_*&xbqZTcnLmhu`)hITuOLswA!Ox!EIrOlG7f#icN za*tDcGA|~)O6#2L+*M%_&A01DJ6mXU5ya&)qfv8G{@M`(K1dHgG~*%G6wT_;xO+C5 z05ZsKuOBAnr5j6_wO`HUzA94ucoSEOM~ny+f@69LW-GZuzSkt8 z{$~2a;Qf+LRg}$Im7X*H*&jar8|CfVlaMeZXjR!ys7&%6hhvz|S$~I_d2!W8dS0{Y zzvD2l2+A}J6{8+E>aYEA^q?&Q+kWF#sj#_OaOz69LHMF3={#Fp`%_Wo>w;}%EW9e` zqF_+<(f;bk>~5@~7iFrc?b;%sq0K`B%C-`CqXkitr=*& zNb%DJ`KCLj)n!Du;9E?qT?%L7DuILc1h1&O5}TOav%_iJ{>=85bU(fD#oDl%ro8E~ z`yJ@!}3|omO9#I0{a3`VJ*9?$BFMn0S{yurXn92Pi9j=VE`7cA8=XGZHlrR|8Qn z={Ry!Rn^B*LDIFmoi2AtMFbE${D8N9f3S{h|4szc_Xcn)UVu(}4x0?omHVnf>xW(G z1#$vR#+%{j{(Ky%Uw;ojMtp!PVR;EQS)|d9^F$tX_j`$fgWF(_!!M0Cr}|udx4UhN zUk@AO;xYq7LF<=2HsF|Qq>CzZgenTq-~9kml#=@8P71KRI_o_MG$kWg=2iUxRjTJ+_%- zkllyRSiw|UukB>l`{`n73sp|Uq@K`ptB%Yj@pKGbE`lq1lQ9Vkh^tcGB!^Lf zxt1rk&atyOm5s`VfqL3;4(Mv8E3`Gw%`6>_g67R z3PFoYanv#lHLQd-9Y2TwOEn`xVxEjpkO!o|Wg9e?Y_j%CADzht(>D?+f*Q>zPZ;+4 zODaNtUWBnkD|G7* zel57|!H5);6})bod;j_Mn)?C>6A37I^Hw|*M4AzIy;13-kr8Y+oQcWQnFz?$#8Upw zTFTJ^_#%L-hkn<}fGhCf9Woq?3*a_VOFPfzeNtnLi_?p9Sgut8P>i3#sggQO^1%IzI?|-$Qvq9@KD_Prj+{aLmXT%^9j{&=Nobms1%QJR zDF8ok(%dH9Xc5MY&0ia-kHEE-_J&2lA{P%49bS0%I!I0=5;76Kx12AGRj&a(MwqtP zW{3pze^+bwI0Sgf^=6gq|Nh{=;eP*_SP8C$53MTHUl!K+Z5CS4N?y_Jz6X;qMpt~Y zg_f-Z=q;PeRC$wFK_=M^SQ_jarQUmKc#cv8#5NSQ{X#cQt`u0l8>LQ8leGhu9@fVk zN}Jrt( zIx^VU<`m>B>BSb5Krf6AE-Qxzq<17tL>J&#Z;?^W?3<$Ma@L?MZdMFidRx>&EHW_wmOClk|nVrC>Y=rz<( zHQCtv5DqAtsr-dA_QVa0`VGX~TDp$(_>G5Lz9-sjTnkDf$l~CO$ck`nSdp0d085Jt zOVY2`r;GaW+b5Ceym`uF8|#chDaYDR=>7Q$=?wHdUGYbA^gmBJD@<|{TL5V;s|9Ng zfe^uW1SGOZQf*&e=jvn{e2Op0g=-s{okUCZR|*x8S>9oDu&J@T7Srh?S|(>JE)VDQ za3}7@X?iG`>T3dIqi%!Zj_k4K37W--(E!*UEKgRUfp$|GE5*v<53H*M12Vy+c5V;! zpGNU2LZqr9>6U13woHH*UwLSPrBa2veA2ey^O+enI^@B@ISca0GaF(95-pH6COHPx z59)gy3{vm-W*6zn--!NvR-3JI3(1DCgh8+dE4pg}+gzh1vz=_ubk$#VIOQ{d|3$2XW-gPpKzV-_NP&C?%7hjr# z&6%yn--of69FCM8c*DjY$Ypb^?R$kyXZW{w@#tqxqnt%tmQa;eu#U8M7K^e|(nQ?^n$?U41cv&h?-slJXzw?Lh6fF*=)6_?>OdLWy-_Qf}k z56i@qw5YD}zT=Fk^T|KBZO8n_=t8$-^bOr>vKi}-LUfyscJ+p&%18e@)BHzjLOj2= zd4*0Bj{VRDv&x*)KjCSY+bGqH3}h#0C^Vs=u;8#Z;P@U!R#S%Q%<~+lfw`mPLrgmR z$<>qgTIVhGtTw^?93*c_RRBylVrz_cxA_v4vFly540a_a3ndR~|da;b)EF;A?05ejbgSSyxa&4@M)k zRL%WFsB{|N!F_WdP5tmOe{2544^NyDyWa6`NqPwW+>^0(^%MVGat!G74HD z$bsg}* zxsw`ae2)4iMOTUKqWQ(J35GY&Xa&xpjHRlw0p47fq@F0Zc-@C~b@<2H+u0S0d6c4G zb8>1|6Zd0W?+dO=DQOg=9eq^l6}@LKWZE>mhLti?vHA_`q$k_?|0N?~*th7vdGnRq zKs8jKy3-D!fB*rq<(964b@g|s@h+iZZfoQ&qs=bK5jBS91`x8oER5gKH_VK9|cAt zaJySG%lWR7X$0Bd_=NXaa+JZOU$tuszC1jnFb# znj13_N+s34Ii&^)Z(jxrTchl1;21;QyKGkw`kdIqupyiScJE1 zN=-7==7yoFsS!ab#5_=PWRJ{l)=jWPK)9sO-`D;CsuT%oVDBnn{{ycijiIV4`~|J^ zpI)-f66JPpsO49NL9@dH z87c=1G1#qgk_ON3_vVriIn*YrkiP0KWu23Ql;#*3fY+>o$cB6V$tEHcIoNb%kD z)Nl>2fxUV@ISU-(S>0>GM5?+ZFZ@tc=2>xi6V0T%I$iYxgmqTlcu8x1r}*{AL%%dV z7RZF#*XcfqK0(jcL@wYTcbO)BQV|46)V}&XYY8ft>qjZE_2w01bvqSyiRw>s=<``M ze=Qf+`cYrXz*xCd*%T7QDZ4r8v%XQ@Gm8>*Krh=;uw6@$-H9AMS>$XCRbVQ|V)(sd%?pF-QhgwgEybGLJ{D9xZt|S%FpYOO19-mMrNSRp? z+|1t{e(r9lvRde^e|q4Se-efJou{&1&CtmUb-`h8)v*j`o<5i{3%dTR_^mGP+eP-Q zP*RGSVYqJ$FK5#~edmZm+O~vpz8G5I(h3-DxllqROe^!9&qbJKCyqS>t@vlrC1QHU|7-jA|f$@PLsa6BqHm+#{dJhSS(V9 z6Vc~E4MNmqXW9DYn&Q&92LspMFhQC>^^^yQ$h@0-3KIEKPX9Zt@8BQP2JMO5Mu`Yt zH&0e9vF?(U5e~L0?~@aTV<8zXmU1vQ=j|OQ4+Pgt4n_Bw_l!O*0WHl}&u7uP4_D-S zUg%JgNA><*+`~`jUIx7-5~2#5x#u9VR9P2IPqzxkbM{yIA{ z3@mPFfeF@HM}a0bVZlME_i>}NrWDO@KATUGuXRz*N|`FHezPi8j``~p9jnvXd_Huo z%Xuc$tQ0qNY~}HyMy%p#?u0j0zHFy+EvM&qb-PvoLTJ=|vE%453V*r%;jW2G;iu;> zm{A`2G9DK4QK^4WQQKqTuEtfS){ewPs%a1=`bVksWk>wWipzb&Yo}WdA8SYZ?keo^)Qjijwx2U@%UznIm zsVp_chd3Wb1csA;yq!frB_6vSRDz$$yNDjoBfv?+{Uh#H39yM1zEl^ws_r!?V#RpB z@5C2T(WZrkgAfYomCC!;ZwG>!{4UdA)78$z3!UPjUoT+#70l!=1iB7y)-8rvk_&PHbdR1hr5Bfn9{jg$2~8xoX~0r zHSF-wE^Plgadu*}z2|L&G}dz#iYG;6I87*gX_0}Pq4u!il-r9qS{}W~gj_+X=Eo7O z9;PFb7F|$^wrm4ywQJ$`B=o-WJXxxl#y~rekQYnkfpZ?8@6N#s*rpar8HUbDB6#@W z&20A7ais$4JBO$0zX={PWNoMxOadj#7XqH>0&@E{!?AxPrlor|gAy^)oeZDa36|yy>iCiSkEFLH%MXRQhF{h-9zj8Fu|0l4)2)=K5v=mDQI-0H| zVU?PuL$+MT(v~OST+EYYlBvJ_tTf_JE)b4)zH(DIs*m~){oLQEph|*7&R(hqnrSbo z>9TD5zr6sa2Mnq$eqH0}=zcdw-Ia*wDY&ha+X^^H)2y-1AsV58L^Ysp%4n(DO?~Jf zKKPVAA@U1mpIBPZFQr9bx71K9FA$PA8xtLq8)CVn*|f94uxUP(%0E79e_1wv4kG!( zkn=hNF`4&<9Si@%%XZmG(qOyIP{D?7R~l#QAH3K5QaZe4YaOKfYZ8?UPU?qCSafy(O4P|(cg2`Aa#*H( z*zyDeDTQ$XVt-vBUaduC77?_D5>fO;*8vqJG+f(9{G4BWu6Tx)oB7*pZoD46Is@13 zs_>o&X?QtgO&2+P`#}yp&Mxe|9}N`Bf6?MBXkii4P=)wasn4!6zOz42b{Pq-3kCXZ zyX8Y@^0g!@!XHFne$pAO7_Z%$yn%-c76Y_=_N^>SI|%6V;xETKruW2d>#g{|F32-R zKdxKu_N}iWz@i0gc!!ZilRl8fW02;fqZ41Qs?~2-bUOh8J?VXY>l^)^4#ye@wT!?4 zcV)$t5A17^G$zwZSGal6x1OoBXgX%?575_9?#4@iMj~3l_M)-$ z;8wFd!b%H;6Z_`v)ejfB;2UM7OyCLop0LZe1up2G8;$>Wj+lgQ9#%B94zc94t9w_N zb<^>F+G)aHn;aZYMpN!8`BN#qrB3{QrZMT;XE^ zSdOnmby z9Ml#PW{UfA3w0tv(!UBj`iVRB15GM{lkxL;lkAp0x`vnXA(z9O>@dN1Kp~G1g@tWV z7u{Xj4Ji45Le6>^%1t>Oip_{5^8%8%U}C1R+9yb5DhiA<=gg#do8pbuMj`Ut zY({}46xA^{abO~9EES6ZC$8k`y2E^wf*qS)jiLS8 z<|T2tkb`nv`EMW785PV2y2Re@9>>oIEGZ8y2cZ)U#;Mj+T7IPc2noHIj3d5s>qYY= zajsvec(>+;c<0KH%_Wx;`|wdCg9c-91M@g%M#N|pF&cu&!M9V+oJQ!}t(h>1sWJx~ z7a4-jZ_XV`@ZL?L+uI$BP*5P0V_yuwWq1z)2e-dglpxij)-t_ug(va)nxAT^#!Y6E z9Yt_21j}-t5UP)Hq|muaA`$8CkUwcI{yX~}Ptbmq(xN^KkQ0gU@O)=zt45$fV?HI= z2Uyc%!wl7DN89mn-c3Iaf9_Jaop;2}nn3r&>H&VaFScbfVr*AjS9m821lp^(7r5f@w$${z*0EE{gt% z>R}GOxjxt9ffZfVdDd`{c3c^H9@r-1mxM>9m;&Uyb;)B>Brj>>a@l zVyzLE-v_QDEG>pF+(bFN7#d9TX64bHW4BkYt|Yd9`J+S!xl_h4pv?>sBR{B@HKjk= zz=cDMR)ZOLg-A2`(Nd|4CviUHVyd?n9jIG7dFNE<`E&0r->_kxxuEkK$=I>Na0Xa&!$yma&ERWmJf$3G0YZgWpaOtIoKft%4X-UfJr7C+ zjNS@pefDY)sT67w8s<+2rmi4m*u?mRU|w;Zvbnhj6P1UYtVsbW79YR}nu(ZrCL%o~ zaE}3gd-%YF!4-&AnSH#(hkMR>=AXlD%Aye$TfP?u)?NQ zoBR+nTwf0qVZ6?9nA<@ANVoLkOwPXIO51*-)s(@mtZ6XC14JhbSN_f?_ARc#vnY8! zx5q{4_+u=DFi?IGrP3K%bwRbnw`Y2m6a&(I@>_>~e*6fZ>_H-WiocEQsmoQj?$Ntl z!*HpZcKqDRzCpRf@3J1?o~aUnGrOpwMEguhwT`}WvJ!z$AHScJ=P(SlQul6dMMLs@ z3MLv7g>ytfW}L`9Q~oG_%A7H6|evP19<$oRe#pmV1doNSK%#Oh_`SR zM^GPV$q{?)XVQR}y`JS1#hSGTbTQ1X>5jGR);30^^Z#BB|kpG;5N zNUY^ou!*1c_#&RP9c2V3dJ&PTDz7Bp0Oqv9$zXH53P88JTA48;dhc6_f`dUD)oORe_%8R|`^x3uHo~`yCKM=y>K0^%7O1AFyy)}f2Bm<)1cwWk3tv-5w zga7N1JE-t)u3qfa-Jphgnb7|yFX)C|KKD~xFc*jl298}~g?1ONP@OE5(0__aG*Ae`*Z>pD@duxM0{+1*KK^kse@j_YPa* zy=H$kV8|9pny^V9kAPxQDi<(gD=CMKb=JD65rOBK!lr{~kA%~p0H0bD4G+mI=FTpR zi7t?(AWvubNtXy%*=@5XA$ZYHC|^QE+vlStzqh_93(F1gY1wwQLtUf(!y7$A;IAH} ze~9t_iu5*q6kfr+^`FG!&c{3@sh#h06AIHeBBQ0VV?1$o8Rx4{I5cY7w$8hA6bQbZ zk~}>g^_P?&q?U*D#@M7>{Lsce+VDx4*rlYSe8*v7fUi*DZA<0ZL~#-hl1T@pa=FS} z=sBOUDuZal$2tYZ9dR!b1?p}rhg=O>dHXEW((1Ca{P0W6DTIH#tH<@|@Nxr#|?N8)PFo|cy0dwIwrc&^~O4p0sFdWsu4Zjfa}xZO6>=*gBG3f)@*Y)W6*=KN;ExNBuQJk`=I87Uo!$Cee)hmQ-b z5S)B8?R6GdxEsaK>TZULkzJ@pfe#+V!?CGY2R@-K*uDrj)ZzGS@SL^y!nr~Sp!&A) zshnK~n`$u-)Y8A;f@gr+Xe2O&JfwbOX1sQFz}@NgX+jwOEe@*1a0)SMtTD`tKOh6) zM5+@!GNe_Ih@6j6>z zB(HFm|( z?Z;M$H)~40Yd~)PW?*QV8nBh-e;xVrekb>W%SNTJT zo~fAY7kdcZM~?pLj`U9FXU}hfGn~sbx8iO`A?K!T&xS|~e*U#{k?uxb13oHw4v96u zKmuoiO5{#>Y*;>tCZfx@mOktSsy3+uiOccHP2Z_3vVp5QW@UmuK@z99KLQ5jv|fhx z#ny*AmB}FIf|_iQ7i{KNl)dt2F)`S~pu#_DHaRH;lv}h~e5bnMnFErK29i zT@tk^XczNnH66enn zD4CjP<>TJF9i@<4BQc<+nf}pR()4U*0oEe9?j8^5yIdp9?^$Spx{LK><`Dey5@2xI zN-Jh|TR1dczqz=-ZqHKQY5h0{OyzU%CN7Fb8$(~cfWG6R=xNY;K2q|u$pBh3ieHKt z%`AAkJ)-nak%M?%C5S}$m+fjN;0S#|h@kJg)%VXisoAK-tuZzB^XA#c4b=!Y{uE-ZvC3e&FG7{kW@W<_&W-(U zueFjFo|);@phTB|n5twk{=h*1U#B2Yia;BLjqjh&;YuPyYzR+YxK*nP<(B~tzy~Sz zB;Pq4T3y2ANl7dGbdZwmCWow#9JAqD8g&W+1n7W`Z$yd!=5<+R{vG#eoaMCHVR6azN0i z!qOJJ-5epW!)XSLm1#YePv7Sbs3WfGl}-gl5`*iwVWbzI>*Ku+$z(FjD8i;p{KJpL z7EGVRi?lHb-XNKw#~RGW)GxL6CE{*AQ>4tcRopFb`9v49BKe1Cx^8RTf~ecYO9R_g zP=sCUWBYRQjE?@d^_HovU`9vD3%mH%1?N``Vpa+To0f)8B@gnyM;HeO<>N1BPyEfeyE3MNvwc?0km!@R<@JcmII+S}1Fr^tv#tQxPgEEQCzfYt*% zo+PlivU^6X#x{mOd`Ynh!Vd? zpv2w0@b~`#ZT#y3*RdjkwOH_UUD5EkJfLO}wCg|s!Lmz1T$E@uJqUHRf37pR%xC$G z?ZC1%$e7*Pp-QX)x8%nwC&jCr(a=bu%-34@5O0G0GV((>G<~&tN61t*udu~q^VTCw zIn@H6?{-wnHK9VUb_A|WcEZ-@$b}MJW{FV#d;z=x(wryTXF!q38H;0@ewvpjDN{2Uk8JDh{JrJLQo*^i>esTw91w|?j;kxNMhoVlCi~3 zLboAQP~db}`VQgVU(Og;k99%B-KdVOT;$W&Hl`I`;Qb#*5t=Se9L-ZkeqfGDS*z3U zx!?l=1GXwfiP_x}m~Qas|3lbYMz#5FZ^Fe&fC9mxxE7}rE$&jB;#%AxI28Bb1&Uj7 zcMI-NihJ<@#Y?c__U5-{&irTA={fI5o_rx$_xuYCy!4V2=9>`%>m9!dCKP@K)IP~svMk%;VQxd%suSXW6~(h?V2)O zo_7qje!U0x{0ENuWUEkHDs5h%ku@dohyUV!Ly@}N^Zpp{$GJ;l z5NhoI(ERVO>h2m;8ZChFCcw_jNPVMw-`C-ViT#&J5?Q_N57FMTq$e0|${HI7azMht z8W~0mG}c|I;~})6JS|nBDX^_3;|jOD6uG5C$NOGEq-~=Lc-Ua|ghF^5r9Bso5u1Ox z9cpN^+F0}{@+=wFs|R6)2whq?+yg#TF^7DSo&V!&Ia`w?%TAvaSh<|Sa9 zTQ?syF3L#7z1Q;K%h94eUQ4r)FaOk4PKcbND%~DOYFpC^RdJ_<0p)yAGd}Y`)do5O zo0P*LfF!q5js|o4tyY288o7Gl{$$wHq=ZBSx*boDxz|s*UAdlVt>Jx9_fI#L7s7R) z=m+XR9bpxfg8osushu9nbU7M_-{QXTz}Z;OH(kG~v);+mF0*54t18i*<@U&5Pd|Pz z?7kLIih^tZX8}YQd+~%7KeUPeNoR{|;E%BH#;?vPpUQ`P7_6V@NS5K)Z}Md`Iq)3t zUv;4Dy>(AV=I@3+xkECwcD3A3pJOnA_RF(-r0_aUu@Q5-MTPd=-^I9+kiUWArXbX_ z`YXAItT1v`36D?NPp2nb)8!T^>86>tc{k)o7198THV#r)kFpRoY}v5ptG+`TECh6= zaM51IRncx)!teTWLUyo5R+KNU-eXoO68iZylP_RZcoj7ROt5kq_Y{uw}_8{howbBcx+5__1-H5;;tkE)UPse8Z~l#mU^Qc@zK5g*Jc(exv1tA=yHBf z{_dQqzuJ*2D;QOJV-U~Uhsd$fhU9s3gLpW6W>@h@lPj)Gb+Kkc?H@HlgkQ0fG3UL_ zavLhv7HZ-15X#7Et5Fj+-+y_lckCjcPA~+jE@&gLM&T}}VJy&O9;#PJBMn}Rd#WX{ zNY(MKms!Kr)}f4dB_?lBv#dD7HCK?&?2Gr6qdhSWwe0v*`mRN07y1Nowr!&y_0sHk zBn*AS8PE6kyU0;Z4ee)X;)*UWw-t@ur!PMncfBY+7!dvUD=7LIj)*+%2bkti{RQ=d ze*yT;yiFMvp=#G%=w$RY9J5{ZScp2T+G@t9%VG}ily|c}xTy*VHyvI16(*odVK|1Spdgf-$1yKzh&Rr*cS65XS zxc=8QxH;=A)M*`Vg|7$n;0cP0#*twcU(Lz=IbYaCRIvC57%p616<-_mRb8FBwk(r( z0PS;5e8&2L1MxGvB}-5>RT_lat`EIE^?n zO&!}w{~(p)5XqBl4IgQwdY!ag3~le}@y4|e+!zB5T3bHpoUiAFe zb!;i4SWSm|6tNXzA*zMWoxrwaxd9q(7?mon*}G_Qh7-7R3VJ6W4 zBsFvUb#&(*ntvc|XPQx}CnQROzVdeaqS~WO`ZkyYt^c3-Fd~U)PC|~-30lUPbZ=ZUXP7{!~i2^P=Zd9 zWmFSF-^{Tnp52hdHoEPmK;n@V-NRL`)Lhv0m1~XAmr*iZTjiSTnZvs0?TI(2pdjqMpmUS=abj#fUs+ zQ>>!8f|H+18{OUWiW=(vMJ_o|$7c+2(Raw;B1LMCov6KfzYzH6x~KCh)C)3U=lOs* ztflXs`Z>P%EM`h>%$P}6Nbt|Rc8d-y};zU{Fm+fL9RTdme zj1?P-3V1)}R8aa238%7cK0HjK4nVVNhq9s}R*_fT@R~WJ$<3d@u_6C>K3Pk;E=`3U z-8Q!ca}I~-IEI!z>TS7Tb(9SX3hE%(=7PVNHO0``))v07yzstwl38>6+jOc*^VWq| zv5eq9YeAshX@Ly-LM!)_=YLj2Nf1mg*a5GcUv+{)*o1tz52xp7*kqLUEJ!0pY|KVJlW$4DycB<3sH6dB($VNbOtqrh)^dpvvfE~N zos8d&XWcPGj_G_#)Nmn+(>l6)T#(dAH9*z=G@OEnTuV*H!4igPWvCsGAOY>WrA^MD zVq&0I1JKN?aRm}qZh}g( zlC8F~K80j%iAj+yU2)=;M9HhmfH1Cg3G8GRU_iLScc6ca^0AMf*+SbuGv9)XUY-Jl4i#wOegy)BkD&ok6R! zo$RE8bq#D6?J4VN304j-RFWo#0^t5`D|Zu=Jh0}wECsfpq;tmUfXm8XXNt|BaX3mB z6pWhNe?~f~2`>5Dx%-#nhbTLu-mgiCAQiS(<*s0VO0rLyKg((K35ICw0hY0(4*Nvp z7Sx~T9V{Z2gIZ19n+S;58;ED^XSa%ggUd`E96>i)dm{6B3&_o3j2dN*Em}(1OWj4v zO=$)5Pw*dbvzj0>@d)39rqE3kQ-}G@opA$^K1>R!dFOw zWAdCR)YVtlKJ8E`A2ki)qk-+>Wp4nd1qDsx_s}vqtg|9G{g&XM6>ccR>sMVClN0^| zqE46UB$4N)eGVUu-53T+!GGR^)k1y*kAELHNQ9dwp?Z$6B0FVd<>SDuXtJ!Hc2Q_4 zcv*(`RW!0~G=)|dk;IReeiYC+GnAX^m^YV})+GWLf^~S>NE~|)6K@-pgxt-^0X}!W zk@lf@4CPa(AZ|9Y;S){qKm%LRgi<30I;&r9?S3hBy$lWYd6w_PxKa8n zXzbqtG}32*0@^O{>If?nfRW-U0%-l>Jgd*LI9a(uZ6BW=jfi#d;(AJcYejE2XvXMC z%auR&qaryrO)f_}wlM>Y8a^Vg27EuA=$R&3^n8H;;c{w)=!=NCXbg}g;;c&8+tE%l zx1mWKA;<=Q7_O*B{5)=bu3%3riak65gf`C zZ+6L)UfA#cL12P&y-jarA}7+E7PRkYH_oeCTg)1W6tHM~JB@9%w$|ZH4s~nsAa1F$ zUgCOl#F^*94PAjb5*k)r|AK9#Y{z%lPG5@0Ss>)CwKh3f_i=829|A%;NTXlgM@ z8q|RT-c|iam{nqgpj}g`&vlFSik$%`29U`XGX8N8&dvP-P7Fx{u}ZRhA5L|AA*+85 z(x@5|t_L;!)Wht$sJp33H5Nkt6Yuz$h+yX^_HEPDd6WF7>2Qw={7Cyp)!)uZF_~qa z=lWa|(IGKsuMJ3LzPCS`@t61`qt4%MK|Pyud!_&UTEzYR?1`qo`6A}WB?ke&tlbZ? zI=%p^tA+T141t=o+JfX;4!B}@C4e_K(SN!Oa(F}EvfR7^g2nb!5q1w2UrT+Mzlrda zqEYigw4bD8Zkt!mq)nh)t|GjKTSX)zA6LoH9kc5po&6ECa4z+<><%?liBYh1az8!XxuAz(w2q&tRhXBy9;g91CbF!VT*yPe-_7P&2j31 zt_}#n8r|)ZhoU!5kZ%cvn)H7}CXK>axF{$N^DFwiNF(8T(%;(Aua;4P4HbeOE}P{) z#=pE;@cC_bp%}IKK7H6d3AJMXrVKY7;CUSRvFJo-)u(RbZv0ccT@(JJmx#rR-Z)q3 zZOBIEHj~4si67x42`S=!IHsUNMTLJksl&z&+!ko%AGTJQtK;j*4Uyk}>Bj$666j91 zsa5xV(Q#NqE3kH#?5KR%(5&X!2>bLD_C~kSXg%ifE!<-U2TGSLk(;coqMS?}XZzZ5 z{JN=u!?wgYRfO3wdBDw{hmK@cG}~zzLr2aN`!`3b25%jrVgv-Ns`Hn~?heuz%@4N? zqX?-rv<-xFYEKhgA9or5vYLVpD?`1n#^N6pz5V~w<*h*p5;7nZ+1#_lCNjQL&SXoCG9wxke z$k?T8e*WVAFA`uwYzUG$DlXR@yxF%Y&-8&aU;J)=ed#LVs4Hur&sY9Znx9iPQ*61T zl{Apx)X7kXuxY%z>bkfzqf<2bGmAcdB4vI1K2g}Uw(+%c7Vuh}F*U?msiHaFVqg&# z_??>>PSL`36DsF#j54Ra&?Ygd#aYv`CJ6GWTH{uuTj5xro=o8Wi2qg}gb>$p#^uZ~ z$$NjwLBSO~f-V_zz>G1B>$g`E%ThJVFHXjfxdNwa+&?Kv5d7&Azb-|3>t&c3^ec>@ zy;&`ve*w<&dID58i5aY7@WLjE;7eO?$W}Q(sLS3=^Lml!u5$q0HU7Xk*v9^B*(H8# z#BWP9*p4UXeHkVwk(|0dRT&)RI^VnOb2Y`kFy#E)u9U=ljhHJU)E|?{W&qOsWyJKy zJEaqeQzUe8t2Yv1#6%=+zN#uo0DM0Ss=3-UJ?d-9dpSAm5}4X%U*X03^2}kI7o34A zeeEnHy7~J{V2Fo3Zs>i1)O8us7VVi*Zgvy@`aiZ}+M^;&ZXzT_Z~p7l%#JLC&x5~$ zi+Zu`!O9+lZhV&0C*dqY(1BHrjiQ)bZvmqOLDyoqd6IX_WnZ z`U3Jm{h>g`MV5?Pbn*gKgx)mgSzIsJ%@1rny*Tat@i(uAN%hP1vl~Va5)!^?e2w|- zcf&fXKf3>R-XrvkkpA}kpN&8b7J_@8-`z3g4pWx({QG8aqJa=O$bPrK(Yx#F_|M<4 z%F`EX!d?)KkTMrzDq8$8VmU&piR;<_2yKsn2nJXpQQh%xvAApARsG6MD`}=UoYAr8g znicXon`XcaI7M*RG`hf$sy36?)D(I$&~n@U)_0*?Hn5?kAAa3?%ilF1nebH)xB`JYdKFao>uA)+9m%CB*d|p* zc6_~XLrR?Y4Jw65!6S9({BhyHkq;|X@O409DySk1pgloO2CB%`cB>e4x|5Uw;MgD_ zZbR33l8|>Ke1DJWs|n$Q!{J67Zx=JT5aP;Nnze+bl*(u9lycFgHGyxn!7n7{zje+- zs)kY>%BQw`s{0-@gguF~*&=({7L@1HgJLdz?|d9`=55+G+g>nmz`j#d$QAj-JM8@Q z-r(X+sEni+q4~v|6(N`psl3&leHo!2u)&r_*X;-Eh3`e1rr3&o)Qh?5pR8-L-R}(T zwC&%;trR(0J6nqYUf1N6cRf zQtzrATaP_xi&yPH2Kypz24rY+n&#BBkOptzm0UFjFIW$3H=5_z#2iP?_*Tc3(vr(d zJH(UyXARuqvaKk}iOr!Tcx~pM__u64ho0!fE0TOr-o}b1MGnT_&iH>l`>%o~LUKvq zZSXK}FWYnwQaP&CV71vs*BmFW!x}AbdF&n>N7}tgruI98y6HE7ip)=pQgc#H!od#Y z;B8xN2hKiDf}A2P2cG14LrCf8&NX1^+LxQ((lQSV0eR9L)g_Wc?0(a{Sa+dvH{VxF zS%Fn*!-l^+emNQ^?pI0|+sT`2zqUHHBHJ4IUYNcmu8}T`O{9+rK*u}mi41ZfqVl?Y z+`8kushD5h2C@hMsfehkxHW&V#2iVys+ed%$a>pZQDS5tgq+TpCFej^5P*r*Rtj)J z+-`@P23bwGpg|#|tF6rI-UQ4~Z&0NgCm0MUOVgZ$?UJDTCHaok9OO_FC1a{9UN8e+ z-w5$UTLvRtcD(ciEaJ1#+bdpUe&ZArhb(C&lJ3fg-1lW`Jr(YHS6R=2Rwi_8Oa+!NdKcdDG) z_6F`t;*w~83GH|RfOb>x{GSlW|4HBZSE@&kQ?A(c4%dwTd0=Dq&o_g>z`^3h*TaLm zA}z}$$0pO2D7fj|YkzD6Z*~HA9i^Kzy02&fCYjXC?fg>q`pVbR0JuscgfpLOS3>M1 zCz}Dq!3#U)oPrsFinLj8UMDKG0%YYcbNTLkVpKnV#fszP2qBra$kTv!8rN#eCUS!H zt2gevR7b~DU>}#7u?L;J8Rfy7zL2?hG!2BkXbl=Oz%m9WEPO1h&mzwo34?C6tSQ`~ zArzqlx4{xUzyWt5t(qXy?!4gjDR@nI760J^`07skj?Ar>vGK~md9NzbO_qyHp$07b zMw70wV@|AI*?K&4D1A1Z%K_xF_`N04thToM>S}|?ZdxE=hPe)^!wS;Jyz--Q`Nc zXO-Wad&_XcM+5%DjGoI4AD8Yz7|(}S%8MpKqT$gwcfjk{WG%PWRQe9KcK+p- z#}ljIduEJ=YQvdS)2k@MU$hY1OENt7plVLgS=LCB@v7SEbNeKO4lD7>6B=To7Wy6^0o!Rdksj0b#YujNEKccP`A*~?AV8iF!@9!Zfo-N%yMxA1d!H!KA=vc3bTb5M@Cfeo~ zdaFF%4;1@F%Ag3woufY=GC^=9$*}60k%l*bWC2w)@Pv_$9Qdk!88{MN7vQlV{%K0& z2AGHH5I`b&@9b zao&0vC3jz3bVzLcPO}lp-6B#>%fSg{(?+~uS8t@B^?+u0@5IX!FG z)0JA#p1~~%^M3}$UL_wHO|rA!$hSmH%mRfAq%GDwE?6CwZ|fS#SLh>UuhFk&3H}C*Sz>+lR&i>EfGjvF|OTX;!2VT~BPBO&MQCy`Kua_-QXG&K7lt0&23* zVcZ{iuh=%PN54}Hpz^oCB4c62uQB&&H?6ek_oOBfXOn8Smu=A>E74;tWn874Sd+B2 zGkcHms+j3}*#1HufQY@-rXwEuG^45g_xC@V&`6p$iWeC_NNgfo@C`Plh*Zw{sLGrV z%9lgxIQS$rl5w^@JlYqAVbLFsN&CBK{9j+?|EVS&uaR|HJ8m;o;DK;XiW2=d*H2S` zmd{Q`$uNZy8RQ^D19YK}O4x<>ru~5;MbTOq*#z4yHNAPx8!zhE@I*dnJHA6~@ZeV= zCH&7Q7FkS=<_Qvu`RS3U2bn39>6fTGVHV z{3(ZPa`^{2)cXJy=R2MN#7$0AIuTd&(N5G>#xP_i!2uuG^DE!Eed*lzE?t24^EzL1 zCAPxqu+JIo^SQQNiyHlU*eDa8rn=R6bWlG_vw5N|bSnd9wmYVag|MfU^gFT3S^1$K zZfb%)aHm7voLIL(l;a;BwLd!t6+>ru`$e-O5el5@^2yJZ! zoB5Hl)Wh$Hb~KMo3LtJiLx=X@<9!U*=@-LKGq|g^Rq|YehN^F#Gz?aNSohYs{%%%s z7pYA>c7ghi`0^hiLvY`VP1e)u!YkhxJ7(&qB0La%DG+WvfL7z9kK6t{Pp5*po{B#N zaC%G#wM~seMG!Q!K}8Nd8EJ^1@3`ZMlo&W(>vTjuM_Lxziyvq$z(o`+DOAjdP&RDo zv@NQ4yw1T$)dpO&V{N_W0Co}`Zkh|KkO2&nfc0$KNj%hD{L6>o z!!3F{`Z%)*5j+>Z%_gD5^$IKca0vvh%I~!oP%VX2*i6yUud_GL(j7|})Z7#t^wEpN zK@QCUs9DyuknU(g*3@#g6Zu)EC(iZ{%kd`(`@dep>1Irv{Hs?Ib3z7gSpsx!n4Tc^ z(b{YeAs>BgKg@`gcj>3Slvaty;iO`V|25u#E-qin)fvitd)?=4T)nr%dX4J-{3#oD)S$E0h79o&CNeH z9>zDmYa9E~U&Gcuavonl_y!PR$0Ir#`EgrhLeWO`nu7$wZQ&i?=qW*aT z_c?lmlHs?ZHg1-&xq`ffHXh<5cP;N$iUrw_t6!l9(q3pm^>YQQbOi(gq@jNfzG_E9 zbv^=5cQFMb`sTkzvXx!P-ZKwl+aL6$rzCH)(M@{o#ib?lB4_1Yo)F7UyKSS$iz|}i z197p*jtBjhW~oebP=*c7R|{2sv;Jn(H(*}~ZZdVcM-w>$Nt|W&t-zPuKQC%%F5A0( zOqW?-GvH*4P$9%k{&wB*s^jycTHpo;V}v?atliiBj6VS@Alzo~bsM(m@xSsK?n>k~ zF!Uye0QDmlx@=&0uzJ*Wai1i4aDJ(lOO{4M#MkhOrhtol9Z(V$hhCRRQ7XQboCb1l5jbJN;UvMurS^5?&-`vG4f2 zmYG_KbcSj~zqf2Jo$DB``$-hbSgO=eN@w80MpWHOw$cn$&Fndhsi-EAdRG7Ul=L9& zoE{eH(#Mj3yEn9!zB`l6i14QHTW!i>Vff#>Fe=<`SIK|+trR3d5$y*+cam$fe5VjDC>1A(uvc zyO=X_e*DT0GQf0r6sB%*q6ckw%TiT#xe_dr8er^J;d(CZjulh$UdH({5gJ%IfIIp@ z$`eHJG=mw=xRCp_McppBzLABY*0?!#5+i6v&BfbMbrAT{newzNo^9jj#yhM%-IHFn zRiq$|j_Zr3o!c%i?2`}Z*YA>k-1vTj@P`H?asuozq~WtUqvt=fITGJ$q<1EYm%sa_ z?tqmvasE$yGTS5&)o#PvckYD9LxV%knUR(|N9)9IW!?-xMj|}NgoOx|>j_VCyT0A? zNw=_Ss74_os6_uNXrJ*nts#k?v>|eAszUAl0vokb#0e|8T)h1M>C6CLjq`E^g8uL+|b(!W`!!0G@MP7})#)KU{CFVj$YN8jd-+-fP? z&m5;HHzOF>E9Hvdun#s49TTq9M>CbiZVM~4q+b;JICuo;e4QGGIC>|0qYik=%K+?U zX1o8=OhGl@oYIl*`h3{x2|{8ne$2^P+1TmNFHKo2{_7nH(oPSI|FJdRE*hr4`DTc| z+4e$T#%o>;TU>iK1ShXdr*od}!;QoEjQ8$-ponw>(T9T{N{JnwAKNCx?4x;?zF9YS zIJV1A><0@v+F#MoUYWXtVb@t5p827vWSJn_oE`1aqnPOAeS?z)QO5_WShkvkUeCms zw$jjshE*IrW^To$TPMozo9i-uXn6!izKQb!l_(iFz8$v*Pxo;_1<=n-9{%)780Ok} zyV3KTiXPmgQ?LphlTL>xSy*4;W))D;wv9IwP0c73p9>m_&*FbD9RseaXUlVq{iGO( zwE^54dFTL5+A2K2{XhO_lhQ#p_&+5rj18JP-3r%f+2d7w#*IUh7XaqJj1VDu|H|r@ z&my&+BKAJlq3DOrS8agmLBS`n%2ptCzq~fDAEP?w?k%4*&K(J4p5j@LmH(^WJl>{O zv>lr1r3B7uH}!#E$f_-q{a~xl5>C&19``>A@*7?D2575;K(YR+l-@!{0TO-uG37LN zYCtF|YErq-s;pu?I>(ARZq>^c`F`)^ZjXi4f;s~Imo@%*8)ta!wFNfLXO*Bg%A+wT zoFJ7)3i&m=uZ*jOv|M}G?XfvYK%f5i)IwESxIa}W6mW8xdpRrE#I_GyHleFF?#nca zgK@Dm>v5_;tIBTJ_r>(XPX&32 zFu{2z41}0E&IGA0jG34^CYczRat1JZY4_0-X0`7aP;JCdk;88Xet(8VLaFzpNwP*y zHsM5*TVB}rg|XSp-Y*Mdt&S6Fl5}>2mx6PaC`}YYL;RrOB2~WaPo(Cd9jn*?pT`Lg zXO`g%2b8oHC6j8M|Il3do}e`CO=Qnd-thuf2?GDoUCWZE&aT=L>&X~8QRZhQL~o89*ZU+@ zEL?IPZ4gb^Gtcv?j=7m2&u$)Rz$ANI&%*nOEGCah$|-~P%L@YkFv3VaS)k0qTciHcaZLZgqtHVgX#royS;d%r`d2D41~B0!0oau zz6Km4m4FmeLok$tBFBu0EI!_h#plQh;so_Ipqc>^T+i4L;Z*B>BD1??Wt||X$2wf` zIVdb_7Z_lxNX%~yt4WUQ2WoBm7vk|5wtZxar$-*b%5fjO&?erj#M!N0jj__m{fOK= z@3Wmx2jk5^M9_5drNhdis86uM#;@$|ybXWGtVyqlMdX(NxX^1#g32`Jo5wL2oV|IaY~uRm_qF>EPCd`qo# z(R|;a?KjC0OK-1I+U3G;Hnu&QKI>JL&h zQrCk?z$oF@Q&ldrg1BK`JpM=JjpFvBhucfw9{G3Zd(sRBjg)ulKj0obq4`+2Tr;KS z>lnSZjg~nhb^Ai~vvmIE_gGJ?8!xmC!3NvFv|HdaCKkF+fon?oW8n%b3Z$}`qWiNY zX!dDi$LV1}!|56tl{wJ$cJ&aAg!)@9Zj`eu%l;f`SVIX?&d5g)uLaGAoNubr)x>t8 z9TWq;l6{!+09Zag-@OhcRo&38$Vc;^YgiFKGq;S1J0B_PAAckAvBEO=6fJx)*jo{h z^IJ{COKp|PaYiHVe@AB1_&jMA;>2K(c{*+o-BHMxPX8`4b-0&{`9@?PntZmYWsf|o z1QPT2(l11g%f+9c(93eJ2%Ckuq9V_bz!h{VtG0apj0=seC=suqW0~vs3Uw4~xI}1* zd2M`Opvb)^3UPlPWzHrk3A)crhhja(Gk5PcEoaBE2l|^ve1wtr3@Y{`A+x^;ahudJ z@5disXLO^Z>8XU~SqeXyCK3&9bxfEk@LgdTHyj!N;zF4A`6AUvC{^xOI4;AHWz~t= zLeFg(j9=p?+|GYu^`IV7c=iB-A}Yxm3M|M#WyrTK6F9y+%)9>~?Jx8?d!59ZQj@+h z%`8lGs3aJrn_ifLIH`K+#T5K)L6S4%SHIvE^t~n`PJrX-(sx8u>fawPk0^Jl6S43~ zpba!JRyOuSstFf?LpKS^460(AW=V}Qd#78=gEJUGPN+|q*30R-B9Qnet$!Z?{%64e zZI|wezn0)vK(Hf2QJ0h{{b3sX_zx6zqCk~DC?veM`d)kVQbs4*->tOhbRHR{iC|j_ z7r4FE?p#`wmh#;1Tf`I|NfW8X$|X8}`D)uG`qn6V%Mk3&^a~Rry5H}B%BcE7WtQ+B znh!MNWdvLaM05?0R4=OhrA~Oc1OBNg{<$H;6|NsHn* zrY!q|PaAy6rGkNb?%D56rCLk?q=o>Z&Sp#bCgGH~SCKSJ$ODkbcD{PU2w%c+eRf9z zToG0JPSifAuUexyTb8<^{CX?H<^g=*pFEJ1(MhTC^Ey`qpQlKIQ9bpVxir_L^FbBA zMBcFXlY4~5oX3iK>AIcife)zY9J`zh%u#FhoLCS|cJg_CieNRFa9lk`^1ojZ;l`Gb zf#l9G&6Uuy;%Z48ZQ!&cvv-4*FO{){WS9I4?rfHTfnAnaAC7ad(3D}X-*)hL6NZ*6 zK!2iDUT%Pe!oimosL~AzeByE#EsM(*z%ebbs-nJ#400v%YSCpLG7;4e-$1)6%s$ZX z_LvAWP&HhaH@t@hb`S9bvaPp9NiooIuo+}JR~##dy?)2OW~%}t%rmp+X2x3e4-1B- zl0D7OCWf!r-@!DUix;BWVQc=JPIp%p@_adb*J1$W@eo?fGq<~5B?zSyJ#x!|@8((? zD+0_3HLfS2m7k~pUy(3c@^nnq^h1u%sd0TvX& zSLFJEt#)lo=kdaX7YDv?f+MA)*&B&zFX9mU^zAb*N)bX^WK=O^4IbVVoC>4w(U9|R zov)#LX!+W6De)O#{_Xngpmm5bEU-)}BmArM{5z;6M2oxNfFn z32{Yutxd>EVh8XkgU8(PkiUiWRTI-rjD>}Vc{0;xxkKk{7FoGzWS{SJ(Lk#l zf~!0FP+I{tGP=XUcz?7@r}BU#!W8{zKBxiIK1(-#;mM`x@ZqGOv3@+(>6Ae|YwL1;W2=sN9^Q>K5k zS~sijaYh~P`n;vpet=Y0Y$>&VJA}bbfmd;m1A#0^f?^1gV!qbw?6Vb|E)V;S9*x0+ z0Ih|kn*27leyYOv8}VT$qTC^r}~SDg4ykVrYlT zslv)Qt<7E9m%n?)fx6j6;rWF#gr@|yA%&%_$GraN{e13gEQJ3;&Xg?9MDh>mR9$n2 zx~Xf*uAO6wyW||RjY>W!60>(l(0D4l9SP-C8W&-@0g|j9JbRS^JA|4X)DO=Oxz^3H z*5KF15|6eXj|I&O7&>DYojIOtmSihY|FEy~L0HHWy<$%ji12)!h9 zJOF!Hh2raaW~%HvxX|>!7Mm8So)Wp_VBLLx3nsdVRc?7CY$6aD}Z%c5JeJuGs410IXmsRN#8wU8EXY` zHsm(9uEFM3Y`6X|*yq1N$+9)(LxcOrX!z2(43KhsaNG4ws-5;WRWVAi_b|iH zQENPsR3+^G%+T)DV|Atu;CE0+1b&gG;UGlNu^| z>Of}U#uHGlm-fGP1E><{nq36Z2Uil?yd)68UKe3pj#2!;KkumF!B^JSc)zs)5?V0h zwzFX5s(2`~9(&$AaF@D_0aLs<;Ou6~zo26v9mz@+Jst&(bC{~w!bOp{R+KO=wv9xaqRf1Z^K8W$AX|wm*`s2T3yLf2(0UK#(DGS>^CS_@n zgA~TQFnUihOp^0X0hfNP>{} zv|VuX+>oJ&h4;frQ^S^L6UJIww9BfGZsj*5_WCoyMSA#7^T#4oD6<_Dgf9p5Y7lP8Ug>nFYasEYqS@_Qx)F0LoyjBa%XhujKx zm-LTCo@Zl3fBhf;L`Avb*H*x(z*&LUo_xKh%?Bn!%keq(H7i{mV6a~0TU7g1tJ_RW zs)?e}N<)#Mn20b?$KX%5w81NWEBiVCT~yEv=HeLj?7;$Ldut6jtPS|59$*(J5hEiP z?L6zXnD_U84W;Qb=6^0QzAVUX?7VWr2efbiFxNQ^QzavgE~=3GSZjWf9O8Y<03^n0l zZV^4B;-)WG=r_NTMx<4#s=AGKDL(BBJ!r05vS&&6f25KM`xb|lK$s{X1aT~;SpBh* zBTr#b^Qgmkqp;S+OtH#NVghlSIp7sD{ctWY@T~wJ2fWK@jYsBrczEzoke}h>b!*wr z>J9^&h(PiLT@!wOvZj7N*bW-2#1elYU-FZi%)?J$;a%hoeK_V>J`!~W(wPegk!F5^ zOwEXnL3xd|RFM+b8*r7X9P^X|)v*}Vs%w6bgL8bPqdrK3*w75ssnx$W4+CdJQlK-= zXjcyn-v8qFqx(o|5_H3|ye*hwMhiUfr^G zn8-!z2^(^+HT{JJ@KyK`wzv*G-G{#7Y%&z4rbVV)ebN1KFCH9b zUO6?VyVaBD-bfSc!{oiw*lKJF>f=Q-AYKjWw?jAeqYOSgZ$WMAz*^@u#QQu+!5K{p zx&8<`DiU-%dj|kxNO2f|W(~=Ab+x#$gO3o!haDK|!r-rA;Ej66;g&#=tK`Vc<&5)< z-E4!~(j5ZgI)9EVtLm4}zAEKVX1g3xzZJPA9(aRz#0pYvqUhVhL5?Ql?oZaL<(Rf< zC^CO?k?%=pCwJU(tKfkV&=SjL{^zp=|K#)-_fuZS3sWu9iP|h}^WN@nPEQ+Py}S?$ zXR&Q83>=n?XpojUO!(HqO%@K_%QlJ$61sp#(l zbFanO*C$Vf6Ualp6P~2Ac1*q{=Y0NtJj=6)pA3&*AP-kyTTwN*Pu$Q>BcJ~(Of0A( zhSIcg`>{~4^l#i2c;gtf;fIKC)I@PN11OUKwX+(xj{M?ngxzD7r|o&llKajnS2E6G zFX0}`c8_S_pV&!J|4#D^U~qI!A8;~G!m6B%O{BMreLH^w`;wzj0{sC&L!(Ua@3q5~ zSOiMd4kgs7=%lQO-e=3x6tTREs}#11M-PMC<`ZvomC-tHcZ6DvaVdRPhK(-Hu|~61 za!K$-oorcQWm+i0z^3mT#Kzuf8B&JT_AV z)bPen^xmH9<%)6WYjz2xTVsJQh#&VAGrL@+XW~z=4)`)KSOISg`F{z>MJ2dK&<7&f z1yBVIg@1GuyVi`-Q#n31zU(tj%i?mV{yu~~(8pVOHY=^FWXz~O#6v?q4!p|O3z#+B zKDRY6agr#+l%b8Q)IWYa`WXOOITs<})2#e)W(uG0Eg;-RpFZ>D>L1&J<7uUlJaOdZ z4+@OIA;^qAcS6p#gjfd8xc(?0HYM`M83V~(0ey%})@{XjN(UEejP-mG>07*N7bJNd z{D4^=o(?a=?oyr>w4@ewP}>d({@txXJ5;cVYfC>}J#02&Y%_dF zxv!x@8qRVF9aawIx<*WrBkcAj8L_}&(r?YRRL!)w?nKgWB8Z-rV)xUcDOebkg`j__qe|r5cX+)bby!*74 zoz;qv_aM}Z??cIiuzJGXvRGAuqe{k<0x?=8s{>WLOElxyd)BMei%y2qr+)6OtX5K9 z*D%(UG>2hc{4u@39Z=~p??ndwQT->%=u0%@de#Z6?M|GHqaIp5S}4y%GpMBKl#eW& z)&cz_RuwtvL|gS?&I(8IKTan@^Z$K1i3xmx$CS~1E2Ve!q~{A>8nTZ7;FvbE1N42I zj7fR{=?wEL0oI8;`$8)#{HjphX5WynJ=vUa49qR(XHr?;!_Zleh=Ixs{VufvZ-T zu@W_XA(q9|ZWBbjp?TDhyuJ&~#5dQ!>}c|efSB#%8))lX~gCq0;iqf{?1 zCn+XYoEiz=B`_d!R)qL{8FIy4?Rqe`W`I@x#Co$>9s|4_m{63yf5Vn@Lb_(Pw;0)U zOj}1-O%RQ!Cop}Ejq4R7$WG>h1da?r$_4kVY4&WjhB!7^T`8zy2YK<~`Z21qvk{e% zu`K+iFWA$L1DY{IS2m%>*xN#YERfuh(^86A!_Np4b8WqDRP{*Z3o8n3)Zzd zXG44x8h1DHf!nBvEixqoA-h~<$Qhr#>e7mEmMhd52awq8Y@E)#$!+25E-_Vta7kfR zVi&v=caoxDy__4o1G9f(ls@^#*Gd;SY=C4@X4R2dh-k<_k(iYkMKTV_W(u0RI2#At$Z&-Gav&$9bi!F16Pn6PU}awVpMv?@_^(5%H@tiye4P0Z zr&>j)bL)Zxs%@8}> zW?8*eOWcvaH=xgg5kQ zHyCN4$}Fdxc&o2Pony8b?+&xbP-3~ZqmDtX!SI}X%ovfTym^m-+t zhn|kO$9sbJD_aQZW#|-|NFU6^#Zas&-JgHZNZv^LEVla-XQ zaET2WtE6PG*$_*& zNgcS0RwTldci;QIK@AsD6|8Vc8@t}v=Qsr#O~irX8M-uGXiunaz6bjSHm-xDe7ej4 zrT)=IwGeH4o{3H-G+9r6P!8Ak;fN0`pMdu<0hnG)z^EK{#EFN4LD1c>6D&dS0n{Gy)?=$p(xX^Mc;A z$$CSF2iU-!{jZR=EBc35MC5juuq}l9`I0AZvSv#;QF`Rfy>6>dX7a2mLjiZtWx}H8 zlk1XZ>_!T`6Y{9L28f8afR@C}xGPBRd2!7uUL}JEtmEKZ)Q;l-wENJb7y--yd`#h8kPy1R zrOVs{iG?$a=)De9Zz#VoLikFJCa`|U)pgwt@Zfv(C;w>;nSXU7lpmxy+I5s`!okl4aupI8!=b|@T#U-LQbcuLIZhZ?X@v$~L zNKeK*k}PM!Tx#esv48F3<7&xV8u4*+&*eOER3y-c3|~OFr$pg;cU-Q0%kM$UW;hva zKY!P1hPByK&xRtjcA&z;$vL0B?wc*m`~?u+qQ&w|!n#i4%^&#ikH}qTI~;@#ndlB8 zYa#msv9lpiv=_`~6H)!#>#o3X7OJjqu?;~MNiIT6q}{FUhk8&3l;eyf@#wCGK1;9(`-WY8KGv{F+BYO7*xHK(D(&IC$V+Q zJ)Zra(ppDUaUi~I$ZDq|o}P-ct*mIrytb$eM+Q%6{4%s=9xd|Ib=JMpT0g1kDM(rp zn9Z{d!<|XUAUP)(f)0z!m@5#PvDVI7{E%Ca!#6P#6B6@8PaL(FueR}UPkuJpF1ikH znL!FN%hulwIehryga)fy%x>0gDa9v23o7`;=)FoVW3qrA^}R6#+?NraI)t4?AF~Df zUG+aVwL9IQK<}ehw0im}T|AQ54JV z5QILDW~EQmRCL$`;3F&i`EpAi8h28#Dmi{Ix}E!IvbeYYr(8=K+RK7gleAem22Re2w!*xMUS1TqcGE ztsHivB6fW!~;pVR+$?3e{xo<2FlyXn%8F3xL7 z-pUWl^%nZzZuUFKxw6ErI+QBbz)D?sT)@3E9+Q8-MJ0Vm`8}|txpE}S5?Jg@l``~% z)l^it7zLsCZF0zcA+eufYb9NG4(48W;pkB*U^@_qm14O|ypGF$vnbv+I7$0#A>E!K zbX(sA#28-q%f=U|{Vf2+*7$g?7P0o9jxp(Sy>c4T70ZUWP8D%C(o)pWS~8U?@w7yp(YB@C8x>7MTf)-@Y*_z~a|p}ys%TgO!^0t?3JS*_U2 z;Pq~$~PV)g2Lm!4*ylYqb=yE2(GLq5kT zTwYY1G$R*J%yfOA*6u; zt`4`&o=kt@F>U)KBoq>0PHp$ue&j?Dgp$5^$e@L&z|1W#Yhydo;wSx7?QHDp7`J zfp5uX85BbDBd}lE$rkb6c|oU+C_4HOeOZu+bvhf1->=(kH;Q7@X1H4#&C9zHogtuS z9|EIK4*q*FW&Nv|POod_{<)Z>phi(}XJ<1P?VJ)hz@p-mF4|um4N@FI)N$Mu6su;R zFF(z}9)Ag_v{m7!wbVfKSfxlz;5WeQn6ifZzCyQ;>BgZz1)Xl7B^^I$5b$v?RhC~- zTNcHkT7KfU@l<~I3dh6yAVM|-4@2V94s^|4fln4=?hrO5<3N_c`0Zx=u`9q>PpHd z!#&y7f&TJL9cD57nB-K5GM&lZF%k2II`nt*0y_f|^F2NqL1%ZAFS2%lsMZFMWIpHF z+~Ro2AFCr1F&NJI%_(M5G?!&v^t7vZR_ln7`-FJxaXOZlQ}2hLUYQ zIW(Ntu^EvQy>kyuy*P#HJCcID77JYS$>x9#pw@RI0Km^*ugOd!rE6Xm!Se&2m)}*~ zKElfjE&SKU4;l@T^A_3VDKUSDq-}mqL;!BYq#fP!5IxI|qX`qe(YJ8nH^d*HFF2U< z{Q82^fIxU8I4Dw|QE*+bMOxeOebe2LAKjLP1NOj$QbaGK5V14TqzFlPuyT(t@E8>&&%QQJhDW^5uZ4u8J2O+yBqUhB^@RBP!+>1+>G#}%7)2@ttb>Zfrj z>Z*N^DQHR%gzjCN%`r7~^=L)grSX0I<{$=_9pLS`h^<%)fSIifDZ_^_7vd}h1 zFj|(EUbhpnu<>sI++2&Nf6${h>NWTv2l2pJfGZ1c@`~bNowa(fgY+(#eNh&*b71?K zKD6~+l=TX0KzW*DOaJ~C*-Mu6dKOaG*nIr!sBDZDjxr}d>y~Bua<{=iJI8P5xDvs~>L>>UbHfylQl~^MRDvyrD%E63Ft@ei< zCB@__v=wAZXjJvoK%4ylbIi|Q=j8s*kCQRPH`4;E$6ZS!{(QgY>}`@JmI_I6h1Kn? z6ogsIp-Y1M7QF6W0>?J(|>(to7rHlL+A=cxBoN;%^O9}Z@>1sT!G0+@>%u!i3&Eh!FtXH8V#ZYT<&rl+WGaq0)^m@+u!`%HYY0(Y5 zPmiya9F_m@lWPc{|za}RSsMJB2iayOZLfoIC7yPD48*|$pGC^ zn3e7qoI^Y2Mt&vDSjf<^=y$mx*00SroF&rt+I?}8Qz(7Bn<2T zxDI;%^BckpXGj1wfw#m z3_Z#m)n)y&7d^j^P~ZC#zH$1d?eUxNCMQkYZSGolcbYUqOW7Pp5R7-b))VGJn!OLf zBvMIy6Pbs zKAI$WvZMvTxllb7q-}?f+mch3)F*_wW03A#kq`cS_+82~%qTUb!+K+|$p z)bW$6&}ZK&Sv>vpzGuPB7iQVsRHLQ{u!#F!+J9mO{CW3j(@rNt?A=y`9ZKGgZ#>qtSE$=5b7Xcp_@xLZH!Uq1b2ysc(co(?}Thc;3n@=H3#BFMDIvl^2MAE{kf zuqq{PF#0U{3AG8EQLherlnUS;O2HgrZZ$YX&g9vtvcMn7qD#LqppUsE!4FRF#nR7R zIj6~HWHca-jhGm?9LUs8xJc>;dBG~sZ0~2GHR2kGH9EGnIjpZJ4zdP6U_`Qzg|sO} zAy<2Y`NK-j`J$GfE-5OfdP3?L?+oi?Nwg}QmXopKty*C@8kM!RX~yNRUt(PC-3<#= zLBS{n+SLx^kv!G{DkciD(&Hg{DdA%BP#=f3Nl^O^3XCI39{I3*rtlmHh}!)H8vQ67 zQ}Ki-;zB*YBEWrDIygLRdWT1`D^2b3}WAOpwfV-ZMB@UWgt7C9X~w57G`N8iZa8@dJL+anuAC zf7nGAAvx*u&AS43#6Q9G>!Mtn3!sB=r_0WGb{-Q_t~}npEUOC(gOiq~sAo`BP*Tll zW1p58Yg3jD3|is)g)1rIAVWS@EEI^=VPv~@NOD%+bM_sCAjQfkj4CCs;tDv$+s$0` zGRyO#OR3(l!v5JX0Nia;NY|_F`aInIq6sN4L%G(A!FAbPW}`Ukn+VvaK7Z<2`2Dhl3j9MFgs6c`^d6UQ6Q1@llHN&f)kDuV2@$(p z(r$T>Eo=U_oHHx9aQFi%{47VhN^5rbzGVBJOD;#gCaVFE1;ti?FXqv#=Gryd&4uLe z*f&yn{#~lj{@31rDl<qZY?aB>MG(@Nn;n3AW$M~()fCR}llSTrX zxH8H09eIBfWFmoh^Ydl9UnZrHvwhN?6l{ad>9Sc zCIu=-sE!}(ar2VL(F*oBtYIHH$_oPE!h>t6UcH- z>?dQ3hpdq+g%ohHkecXH_V{V{ng2eAP1m<$sZj*g5Yg*JM8=vdM0}wvRt72pUYg*% zw`5Tdb&l;c(a5Y5D?HWsYi|j@-9j?45%2PY(xde}`n9fB`Z_ja7MO$a;gS|h??Y8Or|5O|4nWLA^IV4>P3IV&KKUL%zPDHvDJD>Py^*Z3t zTNnh{m2R>HzvL1WI48$mEDX0X=ryg~ni1Q^} zABuJ7_zYcvr-(ruLKpqk@eT$o#~==|>Mb;5)NOek#grGe6T2z#8lFY&M2Tx?oAHBN z;)iP(a!vn8XdcU*k+L-5FEkw(w2DOX!<&Ncthb(9!v0Aq=q}Om6Zn5UaTQ=Sk;*yn1*dp6Viz)Wx<$3>RaUvq zd=_}ucMy2p{v``DSpxVzw*RtOZYMUK=0pYTe4(fr>vQq2vT2EVFpI|-c}lYl(qV{7 ztHL9Wq)~|Q9+*4Kuo5Ywys~VN%SBSH4u$gg?pza-Cy=B-7FfvA;+cchOUv3@f~!DR zgjN3;Od;(y6#j(|5}MOpx4rml(jep}Sv-k8{K_g1bKQ&X1JDCOUCF zIE2LNB>!U<`}eY9JWeW~27V{esB+G;t zc`ylIChKGqk8$|iI#!gDi|H9$X*`9L(P$2AG4tMxfXzNjLNklcOfkIH5cLIhlpagR z4AOfX(!K=Gq!B!VHKz`nbaTv)7=Z&_b83z%&!Gbt zkpx!+i5?6O{W8#}D1utX<(k9a>X#5P!?0UYe|K@>m*HYAp*@tmK+^CY&Z!17Tspw< z?==DiBnR)h*ukzWjrwgCVJAc|Bbm%$pxr100WfG$<`q>@SQcm18c^ZW?iIYGu;VNeFmK` z2G$T$JrY~S?2<1ns?2uIMr3^)oRBYNc1W>53fx70;d+wmgUoRA377gw%5=j*Wra}# z*-G`XFt1(gBfdT+OBxYN8u>LmRAN1wkw2fYB7gprdH;S*d(!w1bxtLp_utAZrT0D- zf@$UqDFPx0zt|JgTT zU;S=1URfWbzaBDqDK+$5C8-~;P)fZWT$p=&65XAhj`7-&H26a4OykuZu!Z-8V+3s_ zA8^U=t=Q95jUV9RRzXkHSLjbi{mWImhmLnmbq>IGJ4wb_(;nR3^`Oe%)o!=R>DV9S zikB_GZlYwzx)uz<-=h}};w5iJqOJ#Iyzof0D8;LY5Up1;6z&Q#(&Ad74Yc;zYcwO= zQIQL-(PL>S3+7H&J%;qITXd2yNU8d_uto+&NuQPWcx&Lbj{I{2bUvfMK!Ud$I0Z(( zadYiLEN%W=6iEOehJn&BWU5c*STuyAgrJ4~{l(RX+#gj#nR^A>a_>QR^G3+4?D*K( zzZaa|VcsF&f+T+^5||nhYuW7gb{9ROGQV7pP@Hu`U!F-jJHK!;j`w)3tvwacVv<@n zeE-w>(OMs4N7d+X+cN7<`k|k~Kx%J~A3Cy0&7hY3sNrq@CQfH-LcZl3w8u71`r5;H zSjlwTvMP;_yj?=S-LY{u`#BcF)Uwc(`o}z*?88ItYuzMhQfaqHZFnif9ibr@rZS~VXy{G@`!2$_r zOhTG6$SpWT@&61+=Vu1ra-2`J2~kH3bymdoCorS`*a^ZvxMytOL2n*b$JacRd7rMs zq+zkaIPJC=R9PJfXK4M@A7OEsbKOaT;W`WKu+#w*l`y1%N1qi?=t_Tf3LoAn>mPYC zLLGP$?9m;3EPm7ECs14qqB;>2x4_ZUZcoN1>Cfm?>0eRlxS|6bAaR%@JR>emz1T)s5>g+p+mph|94Vw=0dp zG%~K+HEp08zkjtGt~AJeuV%~53&wm~4{MG3u40K(;p57B2uRN{8;_oZSMm$$s3sc| z!yfupijuae^%+V%{X)d1Cv?ySufbI6aHRW1iwOTKN_NcYSpAMt;Dvx{F{^k=v@|m$ z8xq-cLLIg4M5La0pbwg03aW#97ki?(`eB87XT67!_GEhk?eZ8f}Jh~z0=*QgF1@|6FbAo_pnuD4Y(KvGXdmMHN9iHSh`4>1XnaRL8WF<^1? zrBM)!o|62xP1&PgWiioJ`(a%D+WcUbxU|Uz<`9PLjuBz{!^Y~B@xh8dZ~Q7QD#Mzs z4z#6HvB@eZJ_s;Raj>V$oeVBDXB{aHW)ZZGGSJI7m!7UpJNIQ4T-m{hAF=lT>p{#C zS@@Cdr#61y{a!Eq&ot8L0|iT)Pr@J<&#oMo`(YIUFWRX~Z&6>`-qwYfWj;_vyp(hgeQH|2G*Ic$^P09XWF95n-wrT&P z48ke08>9iBY{1N13kR^SKoLZ~c6gERu-7u-$mX#k-+g$Nw}LKcYE&!s4%R*kF&W&_ zBazG2_LOuQ?EFV|bk1~(YK|4`wDsZT){vt2di+P13a4W3bB=d6YL23-LjkJl1Himq zVUy=}-aF0Pq~&xw$~VmJPUcU2?SH7$dp)AW1K#`~iw2I(%Doe&S&7A3%Y9;siNeTt zsiYsy38ao?ED@lJR$r5fEP6-d)Lgs6Fz(vbODV?OMjZ&p9TUY-*H@T0ag+x;M|6b7 zu}Nz1bN8!q^7OX&Lx!yv4aCQRQZma4n`@Qw*>Xp&ARm>wvT9C(cgC|`jSHe+593H& zleky^BxgMvwgfcRz;N6@9e_)oCGDZN;a@HV#2DpCL2@%C9u%ETel6~I`sTTr0*}8U z#TO-osZE9-@fg=7d3*NR4$v&rEs+dtxw9^rXG6h!Lcxb0<%FnhcX7rY4YA+mU;7MY z_gg!BqL%?9z@)C7xnrJ@?S(@9GqVVt%bqRf!G0E1tO3r_7srT2X>lD|%@>(6!Wn@l)y*L+waoP(pB z0nh6&eP=bVMt!vWnN%F3?}hMcx||ek-^v~^Ci?I7{6`KSb|e@aKgSUuI(8J`>C;}G z^xz0GbU^xNkByI)lNq(H-nzGy9n76Z;rLHkzHLF36AV^b5t#b^T*vb<)mqS>{ z)}O3S*L4H(M1?s@i_?&D_afj{rhcF`@Hk3YRy>O(Wg52i{HQ7vyZ<%D>xq6g_~p8T zD`;u2$b0y63vl1+`yiJ9vb8ftO*2GT;>MUu`m}hicwD%U1oBt>3ZcL9g60J=!{WW~ ztxF&J<%ef^{|P|(_f$hzED-mAk}IxudT?hQD#x0oQwkXdqi~r_^Hb_s(*#;^e%cUz zu>H2$gplTuZMJ1SD)g0Jbp^64H*s&<;;@exULEvslU;hI*RQQL%Erdu1H3`^D!P=_ zpF_;jk>V8Y^^l98Xg8qYFmE*&PSn(*>p5d*hhx(qOcGy~GxE1^4?W=e-$#P!z3d=)9UPEp*?G?@zsE<&Etf3S4hcKZd^@ z*X|VyKM}^qJqJH8^kk<%5*x0J-?YbL4>+dKA!+QLMqZwm#n0E9zRaq{pqI+qagP-7 z=drS@bbn*9-y3Z*shs||bY8CGD2(rE{tzp?K?kQVjOM-FpCiqR?~hsgv-tZ{?HV-g z(EGEVPaV!4qMi@e?)yK1i!vuapX{@}g89M>8r#6$E!DW=s;?t&5(nn!9QU4zTCYle zm$3Y}hJ7t>-A-T)YX3hy&-H*+0O*Uz&}PO558+y0QY25!c{!KTbJ$Cf? z@ygo&66ZU~G{yDqSnVsyJ#vK*m96{p_vgz}A8l=e?&qFY4fGa^KUHtLJqdiaVUKdu zUJ|c=#9oRrI%@6V&qnWmiew&(Ol(epoiZrhQvbirSFcmN_{nx{F{w(82xfJh_b#<2 zbhe}(imeq>hfoEYoRN zhM{PIEn|v914pldG1>MVLiA6T2tlUTL9t7QhpS!t(qz>gGOQdn%}Sdi*G<+yz5=>1Rc5@>7Fyn2#*w4r*)`l>&d?@DvH}N#+q&&Qy2WO)hk8Vgylm zj4&V&b=$!6kAXA07F%coJXNQCqXNYxBu{`lq&Ik=CM4hOk1%z=(t`D{Go?i{{Zt6k zDbFR_f_0KEf`?DvCi-9kddqhH*LeRMMNrg1N+?q`uHC--R>L-ew_Zq1`2hT6Th*Xf ze=~*BY@5GSkoVCwvzZ>~W^8Z8OgYp#sMn+(q|hj>BDMAo+GQiZ98W>MFIcu|GeLp7u+p*p(~%Z&Odj2AfL*IK z91e2VIyTAM$0{C7t;Wr4JZu9$PI6rC-&o#1^)cDwvA%m7YJ~d*pAe;H$Ob^%IQbJz{DZH4o;ZnE2bht{sUMm!)seugzg%T~{`qd`)4M1j9a zoGE=yMf{b_0C%irg8#W>34P zU+H#Ow$I*2!l}uCDQwU@9mSVer5sw^A1&m6D*QtxsfB2RL}jki6mRXslH~H99o``E z;`B7{MvU=A%4O9<>Y4-xz5_M6-V-7G6=x#byLeuXSH~N$Ea~~f2FY+!u(*Ed&r@!J1D#A zdG#R2$DFf@8)G4(AK!e%d2AFbKM|XlzRq>K8Q}im8cOWdl3PQZJSva!-1v5Lctwd4 zZ{y>S|8PFrSB>&kv-w1W{7LUuiDJFh$&Xl+S{-$Fz5Yt}rIbcEa`MMqf>{0-RTo(7xN=4S>3?xk`a z4p}9)M-QV8Pwx$g=T!b0n z9m`03`WKrq!9zxlx}h;|mw6d*Q5(bKvwoOVo>-BmB zFN-5y-fH~%?fbAz{tTlK2QHQ57^J}_gTsO9MPk{bERSg)RCO;A1Vk0$NJ<4H=*;XS zwrmBS9q|M@er+-Xp>Q~CjiXr`YP%uGBs{1_>r+%71*n@BO>gWZrt1#io;=!l$zQ1t6UQV>7Wp8 zZh0mw)QrfDrYH^F;sznrc)!Z*34`2P9PwBQHB<#e&9#NeU6m^NyzYd(}+C>Kzs`rjx+cJ4ovM7TJeB2rf3YugM@e%?wPw5Cq3wq)wuvCv8IrRIG-W zt+U5`2y%KT4vi877iWrSvQ_%sU$dl3=Pk4|rxtyC(ztVpDI@$Vc_r;%-pzR3WelG4 z5+O$ZIwk++y3~1Mt~nSO*G%*u@85ra=oE(WsolB15Ro!DKywhBE2LnAZ^50v^2Hqb zj@#V8PHyxeBS&FQH;uLV!A81T1`xN;;Aw4I1fYX&g&xL7apgENU^rnT1c@mi4ah&dE zdu9bXPw(sG)5Y#t>vxUVQ{6(_N=naDe@>t;M(G{ z%K4?{Te7wfgFo=8{CWLV&gFLJ^RY~g4hno&&uwkO@VHO9Slz`kl2Yl*r#{ns+|UUn(C3ENdw)_@WHcstlvm9&x zCm^-u(X4m^8Kyv(pb`37HU@B+7t@{R+B*c8F6gBPg?gU*rL9Vq7n3;NwD6a9jyjkT zaZg)fy@`M%pfNwA17Q~4U?bMBu%tb&bU;L0u(A9j5h!t|XMu#qqL_lBDA}P2fwgkE zKF+)&EW)jE_oC4PFQi<)^CFA<<1@;dDr$(nfEGmGPBde5-e#`Rek?*wb{5!SUceBR zRjn@|iw($)xkuJH)v1R#N#sYb|dv zdV(EKHE5c}W0t{|*0;>@TG_*X(f7Vg{2Btem7kDb+X0Kor?7h$r zNU_^2NhpnvT~=ersKf>=b$&Ke9`x5#&WL+6aY_lGIw(8ktKrM!hke_Qw?m+^cirCN z4<2ciKBD{9p{ja!)_NWL?36pwz1{QvSWBNns|wzZc8enqK2P%AgtAo+DeTSvZbO*A zf?MR?N5ye$K59^<9+^g*83q|s5&E+@-e*H^w`RlBmJYibQb*N6{$U&(k=*V$z*=`J zsjm}oE#gvX?R6lR(0H>2yzZF3-mGB=tS88V!=?}qfI^0gP<~1qJDs@pYh&O_{f?bq zQ4@4ujqjCq?sJ$;KP6!6d-&27-|Wd_KVv8C*zDm%2wE zU^Px@H0Ky}M%LbW;N0mCM|F~f9MEx-B|BK32MS1(FHkaF(K%NK|FBQk5YU8B;pV-HIm)_#H$6~x4H~|!7C1!Wp-j2POyHh?7(HP9toeB}F zKh1q{(FGQ~K3Qt#2Vfqh0YR=O=r^q-z5Y++-bnBtn|8b6vo5V|>ME*6L0wf3*}G?v zdccy?qUiCn3qo`4V%;oN9d~^jJ*mkSAYcBmPE&XZv2pM4)nKe(yK@?C>IoC_ye!t$ zbf|2Tv@NIPNWjp2dTEWH%s+r#qNyJCRYH(FL5w9rUh!- zTJ?(K9w%S9%T|Aq7xq~E^e6s?b3Mg{D1Ax%LzFv-XyPgX*FxKG)-gV%SiV`+|9Sx^ zDxe^T#p2VNbw&CC#|esgnn=VK<=YEmr}udum3lKeZ$I;Ae&O&bf`2(g8ZH?sjulGi z@nPv(whA%`#{%)dL`O2&&m>R1X9p%cxha2xnVM)j;F|ZvFeOXppBr;lcpCt^OyK9f zcSR1am&cl_j2G8k+~OdZ{;qEFDbU7ue2GJIpr0MOIRX@Bg#>MV%_|y@i6aK{vkZH( z)yR=)1ixLy8hrLCfnSO*uT8$9j#y6l?mY!V7K^Z=of7;#()M(nc%h9cRuzy%&vKFj zcom=!>-u5ajec1fin zRhWEi2>U|i*z8I$@S!&7Gbict1Ilkj;6>Lrg8;@;I5WrQ{HGdtqbHY*5XxA)59_uL z&QOjb#L6E(g1I@sAM?{kRq=0!e>nhqXgi5KPWPQ7HLG3Muj>dtV^!=w-_9)Q7uF(X zBV7}cT;s#i0@Z2^R5R{q`a7rK!rW9ck)6%aubp=GkY;EX8KGFWZJdqbBE}u`;B+ab z6$zJ>)jt10-c)@4oU?c(YMoik12kj1%Jm~9Yu`gR76?bm`@A!}CFcDut`K|g+KZC4 zX{RQK1>xjw`sbbg58XOkzxIKHi`<`OLiYEQd6#$jLehF6Pwni3?ncn=OCr%{@&1X6 zRD#5X;3}wA(e^MQIAEmSOa_ZdT8r$?eSYUvXueFTDduG&H(*!n~Fko#y{8Nk)o zABLYI3wczFvM`;5gADJdH9HVxeEd*wi9xP75_dwm3Ee}f?yJ%M0iCp!>`Gre>-J&V zPwwhHC9z6-u1G}{kq7L`VJZSXUHJhvBOw^aa z!UstGB(-UU^%~4tQh!@+w=OlsFDq(~JdD(~R7kwai#YH~m8(`_^Uga`GF2F! z{E?OzgSqGaPR`p6%4_*9(k`(XJug$=7XS9zQ@D-K6gpIBvfBAj?C19x7jt~>U7~S2 zypuzjs{?b;>%?gctV8Dv!5t$cz!6T70CjU{YhH~jj#mzAYf3O>Zw`Gn^DR<8b>eO>0-~?cm`eP^VAIcy-k^-vZ^vSn7!~D7%&9d$h#GCE1PUfBk%@uPP+c zZ<&HOIU;dM%EnwZVHE=Re-IgxmiS;R>1J1rPSXmPWC;3G zMG(j`h4-ShKx|c&p0IT9Kw5xqW*Y3Wd;zQc-Bk#b#cA(#2;>r~->D@N#Up$@!bM zc>XLKmr|CGB=A!+hCfsmvcwjT1B(%Al|S?k7A412!Jl>nhesMd+3naMY5G2m{+|q{ z8na2iAfc?k@QV&7NsAO&Yx%TZJ%$ZKcB_%1z(9C+LFQdiX4XL*vd(bHGK}}jV2O02 zC0nD)9roTH=Y8ERJ=r4>v=&_LAk12G=v|RY-fK$suQ3`>OBCnY&PsQm#Zv2k2Inef z+<#7B?|`~(0}jW92Rs(&cOr%%avTq}?nHC>g&j+ZD2Z$J(@v=ywZf$TL29##?vsTW zuH=f?v-R4y963GA(~Js4mrfsNZHeG4WGiX7UbKJEyMOo9Oa>7}%P{~KV&*fQN(Z-E z``+Us$$En@HHLTD2jMl1&W}_@Y2$S@!cL|$m_u}^t23N+_evR0>=BY4FnuQCi~C}& zn*W4!?u7PV``&-IK^Xw}-)aHAs|W#Z8D=TcMg!YlE%f^P!-pGf1W-gn#qUWY``{$9pWlM4?Y@J3fNihPBpd&(9=Do z$hOlDuwDHv?Qa55UtHKY-v3kxH;6xCmS72w zo4d_YtKgsd^Fd24-TKkvNC`K=?eg^<_MqDZ**%N?I1FMYrEQji1uQTz_0Z~dEEa%i zHHE9eoc}iHcl``h&2z}MfDI{KUyGmUIKmeJK2|Zxs(otYDT&Ud?l=DJ$_G@ttPjVczaA{t^xD~bZNFg?B;nE>fd~CvW0^r1Yd0bscHDX z4Mp$Z2a`3M#|??$cP0GarJ@LCGkKZKXMn-`@2qpw!}VaQ5DIBMnet#3K5k3eSYrjH zCctD}i7G^)5Z&t(d=VgXcpt7e7sRIKQZ&wQ7odBrO`+$UZRxtJ#J969aQpqs`z7p!~rVjn2-1D;$2g?seS7%^VzLkezcrrPn^l^$%V%TKHY>ue%6XRezt<-#;mdDg(WP0n|MXf$W9s< z^`m`sB;90oyC38hV!oH_z29^~3XkWG-H=WH&;MRH4MpBea-{nnnZs9{a6DRJ=l4h2 zc69$3HUG|!Gt09`gR~T>Ag;R^4vvJg(lLve?`*!r8@kuASp}e_uw7XTN*D=kAXyU$U~Y=9ptX!ovKNQQqeN~t#h6TIGm*&MI~Lag}-@eyKXE611F0v?sBU~rVacm zN*y5Or1765EH0^)4_G-5@S@&4rRZC^=;T>Dt7IFTuG^yq(ZGg@w8^w+mzDl)Mg6D! zf3P&fcaLC9C(T-P1ig6Li=-*!~_WCG@RwZ>S1?o+LYy3=fcE@lchZxOS;H)}ZlS^crYumXDx`>%_SgbG0IlFjGTH&)8F3~1y2-i!3 zyOD?JcBH;KGC5I+aRNuIQfZMMDQ->X-oL#2H(|zSoak-g4aY;IncppfbN>=+zQRaf z;@x@jGXdmjUac(qdLaL6O)G_U#m~2C=cSJ0;pCWEtIdrc$DKTP^mIalj{Ul&-Olc{ z##KVT$42G??;r8vBJ@L&Mk2|B zluxRJ8wYA$fO(&ciXa7fMseBJIx+rJUP6hY3(KoqZ_XMci&?Gdhps-!xQT9%m~_^o zx}y=*Bn7FiJQFtE0Zv1%e$V;-BTozE`2wJRl$M=3M?$W0HXohW%lf|F*Hhd;<|rlu z{Z5oQKZ3CZ=(x#~7Ps#x5;A(L|MUZ~{sA3~19`(uroilq5|4-9mvD#nAL<&$f=j8@ zxA>5XIYv1w$froz3P8y9EH8YhvGm_Uk%9ap3L!cR ze^{_!Vy<8DKBc?DiG0X~Cvep{yx@!+S^T3Y6NPJzoC(RrJL|L_Z>T;s8(H-26> zfU+pO8SuI#kPlDspRJW)5Ix?kGgObk*PA+PidVN833piaKvi! zbBO#_p{7ye|XoFp>_=$ zS-@ypuUp|Qo1F1{DR&h zH^~PrYPmBSlW=&u*z00QGV}C+-i3C^Vs$vJQcH11YxLCUd6cLHP?m$6q|JIPLCvpl zOSt{6vb*I`Z;;KZGtf@rAFthiS4qi?LU}Pkqa7qjQ|?rzYZY(!6bzVA^cm#Zhuj@! z-954r&LxVm=Tdg8ks+}_G6!@0P?LM^9s~Z=G1T!6k~8XvP)o5Ot~q9X;B-jyruoi1NCZQi|Y(1H%hc2d!#S-HP4iwI;oeiO0vbsT+LJf5yr!Q6z6 zd!+i+w|xo9xwrq9h*3#IHsbDEsrmR$bO{v(={An(hS^|a6a_ze6c&ZtyBdv8onl>Y zc&QO49s5x>iO5V{dRmMu1mUXHLtpiKzR<|XkW-JpaZxJ3<5o4&>%xZwUW34i1YNBn)jM8A)_xjLe{qZ~0FIRJ4Q~(trxl%Ll!fG%0k!QHthGq?R`GQ1DPB-=^ z2n1~@39-ddHLxmN&E#wP7Fpnt$w1S-^tIJljh!8+GZ*MpM>;_Y0}-~HLz^(dmXNr( z&_%XjvlkKZC&TwwbD_d+XlKjus~Pzim3b$9+*`&gVZSw=e7^6t}}b{)qv0EH-j#Q|a562}Irq z?WfgZdm2{fMijP{<9me^Nai?>6E6;Y*^LcnycLsG9_wr}(lAp1WnkZWCm)J*6`n0Y zgj|hc-r5#6jc|GqUU?9ZG-dta0|)T0U44`Ol}5Q??*681r&8th$e*leD8X!61VmHxyocE>5w~~?gsvLN zvRn9fi-Q8e=m5RR*(|>+sjpoVt-IwT>#@IvS8lLdb#)8<}}c_UE``WI{>;5aEfGA( z*V#{JTGZA^De6z<8!*}K8$8m$|E6bA%2(XS+Ptg{pS~T`0bCkwXg%GwXs9}F$Qarl z=Xnj?Ok@3sADVAys-?2gy%r`%LOg8pK5}Hmbl}AMd5y7yNoYiz{1_^`_sxv5)Wb~$ z7wn`AKM4(I@kMuIGFy$_EHpS*aQyQ{{%=?20T{qs3KFt3t3w=ej&wmSx5BhdKd($h zt@}w?^A#8sQaJ|raCk#-)%-gicMg8O5JKfuUsy2?d5%qYJ($lR?y0V<1jI2QE%R_% zoBu@8b1U2Gm9It~Aj2@AJDNqQ>5hQYRpU49^WWgX~Z`9Q>{Fo(bZ zO_}aj7zQ1bl^Y>N6B>HuU{QUExHP(Y zxe`<8YjlS*!9%<83&nvyjNWgJU1aChhx1nsGi%9muDMO)XQTbH9h!dOTX$D#n|@7? zx2=2)pcGg#^24kXuVuz04d5UOjUkF#0)p9Y+qk<&y{HUUq z;IGQ9^>r$7Y)NADl|e3^0?U8?*L<*ufdq6%uX@-gL)KV!8C*u#0s0o8<3JI^34f|= zpZzmKePR^t4w)w)KC>2MG~-A0mqFT`e)Legj@#hRoy+Q^QumUnG`VD5UL)|)V58_Z zBqR)sI%j;$TsaMJ3WbmLOszZ-E7ZhHs1GuE!ap+}`P<+!&wl@Tpzrf@Z}2_V6!sfRN%? zMtxdJzHB+}nrFmlBOZIV%Oify*)XQTY5zS#0SAd>1wUvm9O~B5^pjaD0M*~wU7DNm9C|4Ni49}`Xv(8Bd`Pb9ECn1afzWm6QX`% zy@V&X1PnD`p|18I)w6bpk|TMCS355^E%o~L5SSOkCH__v%uVFHXn{dFe97fJ~_I3HZ80{1i%S}2`L|4o+sPjwgrA|NhKe~2LtzJDMU;njr+TIh$Q-^&(mF~qLI zSkVdOmx1`v5fdGFmr&(hYh5aPY~a`la#@;X{QJ&St7>@jDyIa(syU4Y^dvYJ;)FwO zlgPonfKJ76nzEQ2Zk0tqVqzm!X#YU6V1xtCK9@UC?t$|cV@%EzGv`VTGF=h`Ek@A> zlRBVZLZ@kW<=_78cF*qE&fWs&w^$Z3&J!rl=DMHhpJ6ozul;uk#L+fmR)_1-$E*4Z zo_4(7+1UFgau$@8Yw99Nct9m5ZSUGZ)%bf2t#hN#CNv9W1>0K~p2mGEFB>eaM3mq+ zrloUc7AVF8ix;~E8&wk?^#!k0P)vQUi963WF~0}ruY`&SXV^e)f)=j+d>6(UZ6B@c z3JVX89Uh8mHDZw){H?5nF!(xk%0V71rzVzZTZDOS`yG#II(a$jrr)$;L65nlLF0qu zHNbS_2UEt0&$-T5_uO||V^ux5^7o|2_9=R)B%DFzb|**Vhv=doM{}WM5V@&LI>1Z$@y~w=v@~caKl*N^!W#1) zz3bgu5=V?od4tPu|D1%2q9n2W5Wm~5s^P8awUgC*!M=Z~DCgU8aOo3i!Fo=|+@~ia zW@gNxYD5088sYAwO9tPf`t}XD^0mc&#%SQ(#si;kS<}og@2N@)jsl0Z3nZr6Zi1=*c7& zawEq!=(b6|RXBm`ysidsTl!n~E=Pvm(ChzVSHq)%N0$_x{GBIFHclFJ0Rm#tP=zDGq)T zp{b8WbQf>pXSCxhEGDvX$G5c}i5HQdU9*Qc5IiK2X-uvgResjhEj1Tfs-_H^WOR@(0l`*^{v5|@rQ@Eq zRh9%FHnzXaZ`!}@z|<5cBg@!y@4F`?!*|>M<^Q|&6oUv#r%y?eVZ4@%(|4=cK3)c>AO28lk7^K`s^^w zRu$rMuza_fPyo0a6ByFr$NmN%9N>x7HBga&>0N^D$lQ26C!{O;AlV*-$(zIW!vr#; zg>`he@r$H0a8*)ZwUeYDTi1ACz)m;6R1p-fkwoK6+UmI~>0RklNw6a9Oswts9-n^&~3-WAcHWYjpycWxqS(c2){j>tvI63w$ zwSuw=@s!`4j)Jl9m3x3MolY+*VlH@aF zasj5|ZA9N`!lqGIs=qr7N5}t~kv37Uv{v;HbXTiu#4ForpWd+np~TG)I7EX{GE;H1 zlnq_i9jRJ(cGe(KB@Iqwo0%;U$pLLL?py& z*J|HSpybyX7c+woy{zMlul%R21bE%}yOkw9HO}d`+y35nM#PoNAw`}ma7Kj~2}F7| zx5O(3j_AigcR(jwRu~>{@nJGPp6aneE=7*J9?!KctO*@7jPR=u?`n$}|0t0Y{D>_W zklmT{)h_hi3{K&B`aV2E^8j%X5Hu&u{@jY7~^cBSQlfNAmCD%G7HXfnc z3TpYb(A-xCjtqj?`&NGiT{lD;Exii4>fyKO(P&sp%mI zs3L}S)0HJWtk!+Z*B{C^)(3>V56*G!Z54cW5I&fusZw#Pkn%*h`Ovqwxvchi*6>zn z(TNl&=7AMv<-sQRW=Yy(k&)8k=9Rwsn|eJdCn^{1o}I6Wq6&rcyVuqenYcsCw)kSO zP&aDciTyCDZ;w^2_!1eF!qvn0?RA{KPDED*7z2No25WHjx<`F5jg8f+(iwg#-lom#g2{9Cy92sEMz-J-`?vjJok zJl^U$g=S1;tz-9X92@1@Aa%Cnb@E^oyfDKiN(JQ4>mA%8$tq=HU>ZDhw!QpV@K!g` zTPXJd)i(hbUW4B;2|BTjy8*Yy?};jXF{IG$sI6lVR_<$r6$Y7mJ~jh4Aj9S|7OV2x zHO9$Fc`{q=if7YYD|+q73gDL4nNIDCrHth}7gus@sH%MLx`Zdh8bUFkvillBV!CRgkI zn|S+w7`xCJ44|aLpe%ZkIQk#MA2tJs^C>09%v&D(N}Q+fw^tL{`dP-q1y`wZu$3&^zk-#G4=aPX>rg;1u>F5IaevV9U!WKi;{*FddBr`Rs<`KCzn;$vETzSzA zd!T!Q`LW%BOXBYPKh#9|MDf?@IGX)47J5de*4xC9`bucfFAY>n6fn;8K>%z)yz~jY z{GX*j)q43yAwYG+Z4pkr7UFx~{Ll8S_aCCFXw1JzpiN#+{ob(tOLJ&9$0Z_G$>d-$ zqw*)EKSR77)7EuG!`5#U3OnuTXQ1kJeH#eJ}w+YohcLMI9JKzkwanlu}I zT_G^oHlNLp31xBsPSdCyNUPf#k(&IdDk^uq3=+E4QN;ulb$UD5GicU}joIt_IBiz5 zA)cW`5WiXLu#}3TD2haj(g#mm_XD;$%au0f7iJocNbQt`_&KK1%^!7Ii(5lfj42~> zrjDuF*Ko|3c=PBVj&8qaLTkl9n#+VU$-MM#UGXSv zD9HI~8EmM!J3bxc9tp&+zTRSNYGOtYc+-O7iEZ`olTm~iY_Jx`-KesX8X_#WEM_Ls z!+VbxV95wv8%72^)h(b>A(>f`JnN;1fd|p9@g3AbF4m-Wux7UX1ChSojpe1NW7k}5 zF86ETFvh;ad0qtdzQ9&aC2)>i;_^Ib*hxz*{}vOUn_(f9t(p|vNxg0V8oSGeFMm?!Bg4i~KZPZ{ zd3Mcn>XLqpR_*Ygr5Csg^*R)>9m|UfhDZIl79E{bjS`Xf1K>NfcB?NJ5TJtU99EWU z-%RnT;c=Kd0$qkFI(&psVC@dxu5IMXBiIDQE~qn+Nau zGhR*ciWhNVqj_xDx9Gzx^R)ln)+Ykx*_)BnP4^@7P34dkWamca8r3W>jeNw@K5Ntu z#CpTI{NmsEQSD{OJp)E4#P@|~;`Nn_} zRe1x`Ya658nY2So*n8Ef2XiE;C-!p z`RsAMyT~H*#J_#3x-g^FdW`Ice)vCfEKeMG$53^mNx}@hv4icqLIy)DEd+gZlA8wo z%nu>DNj4OpiBm+#DA9yvt9VfVsQzSKwbhtWB>$bk)&C{kDz4DK(ycVj*Eg7syAiNB z8uH^f{z>94pYU|PNY5y@s zW;&bn6|(zYe<=8qLq`Wq6Z+h@ET2m)->T&H=WbhQyog>hBl-fZSYEwE-akwAK7z#i zcA^pfv@ZnS(sx;*x-2e%k-k6@KTVnUl7{jb^El{(Nx-NP$irh-t4`@HBn_! zwuCdU@+RKe&06{AvsHfuJh<^N%tM^xm*js|lHzryNk>o4_qJGu+fjUyN}gWLSrQiL zF?}u}#fNr=%c>E+3S2Fz%Fs+t$;B`7|L6mt66F$?+T}Eu2q0>QbBk(G+FNLQ+%=5H zBIsVhxbo!2HpLj7{^W>u&UoSq>j|+8lNV{OoW`L*>W>Un8t?QLe4=sB4-DJLlSby& z%`en`Kt&qwu~_^XN+zSMlfp%>4zvyu{3PnQw~z@PINkE{1S4ZbG~fGB*J@pK{9v$C z#RE3Q*`9U6BAmQ089j;E6$`Gi{6!9Q)o%AGGt~RO2vuOsmn?+5I?ScA#hFt~zeQM$ z=ZnXaSap)K-QZJb+`DyFkteora`>ywHgU3x8iLcRDK09pK$uQC6dDU;R*mHO!!Wys zfkthVH#Sd>AT{eg-;)g;)%boxu;EG6r3y@2NH0x!>lXF-QCwZb0Ik5K>u>p9%D4MK zE{w~2;!gKTNrym(d`(9VXw$cwiow8d`#UWJV*(mLx+52H3tiuRI?5u#y163-=;*!& z=bW$tsb=nlNM1fQ*%eFB$ARXLT{yW3=0%t9KMt_6opS%?Hln+oPw)Kz$c6Lp#!Wr&`&dGr60jcQJNp1^Tk12>l7MqF|I{sM zGtnW@r!*(Wq+79onE)oW9)qp|{92uoVl%fgp|RpShEnVg9FY{o@XGWR9E~xG>#Qd$ z=xMLd8j#*P+SwoI)djn26u3JghqQe?Wk7}|wl88xIuP!A;-7gAyN!x3W&Bqb^#7H0 zdaO!A08M5iH85W2)p_#0{`ksG^xa_lo$+kx6Y}%^5y$8C@>g=THu~?wW3#ZzGSojQ4U(r7=@9pFm=5k|M3KaC)RZwVF^#QfeV*zw?&+xur6Fay^2PG~oNiEVJNd zE7DsPMVK?`_DFfPE`8s|tiM|TI9Y;}3U;}V`uXM2e|+oAyfY#j&2(LR9{S zlefVVrDp9aGdAx1ngT;NyJ(q7+-%<;uPRUMH(5glkTi0O7 z7>4`_EJmF!wA+(`(K=Es+?s$0On*H+%e<2PsG5!SVB5=Y|221h$EN|9Y_i8&~dqBb8M$kf;=MKuijQGhw3HQmKr9-X7ndrdx~`uir-^%<#$ig@Cos& zet8XJ>mMyux9?bb&ZggpM5bEkNI4NW9Pg$^up24QTW1pt!L2{&AHt`pkO9QDCV(Ec zI#;I3EUI90MSr9@Rf42JTaKRB%(+q&f7RJv2(uf1`f8l{sYWo8#^!LA(TsX!xoll;++s*R}lr_ORZ&o)mH^4;>V7{A4zLhHih7m zxy{#2S@KNtke%ijK-H57i}jOPC?q(uPxG%Txt!~uOPF&VK4reXUW=jhqVc7@0>LUp zHN9Jpc$-i80xeWsJ4b}@0DvWg&uCMh<4bc+?tJclndXt3nY2M3KK1l$)scAV%)M%r z=N@bwyY?MM(>5s(hyyrTP`5x5LDYA{x=rH>Z=D(SX#P9}-TwTssXFk~;tqAgj`{b* z-Jm!9)XsJc??F|nQ@@jBcM-8R}^OOt`C0_F4w*nuH_NZ0a5nuo#UPVBonY zcHw^ZCLz$3j=%TFVzTmF%XFyftoX9?{`0%R;vHpKJw$tJ!)C%T-CUai&-6#ZvDUyp zRk}(XzO426kJ0fm?o^HrrYXbXG!7p>uXAhLGFdEJhRTZZEF~vV#`#hFOT>~4!cbSY zncXUdZ|n)d?WOoKehjE{Vi<2P2b1e8FR_igf{611Wf51=(x=xjd2Qzlmy|ZTI#d+8nHFIlok}8%eY~0o|OxH9w3ZeOvd)@`-;zU1II279HZB z7dv1#0|6mO{EE;u(BbxQ^$ij;U6k}&@pyzpwxns28wg`t5D3dCXME9a`jqU=?ObD5 zYZ0IWYw!zX@-mHKCTgLrP+VvzW~gpDS1K5@m7aIb41ZX4Qu#&t-G+%UW{B8C^bD%m z$g9j3&!|*!F#&V_bM9pv_dHLrGs)-Nm^gE8NfU)h%(44XGWSB2Ik`oUW!{%Gz0{<} z+wD$&6GxV*=CrOa`$OzuN?w^ziPni|z)#F#9r*XztgOxX+%kw?ee9U^(caB)8cdHv zUv{oUn)m$YW5xX;+bCPI9oA)qWYwli$>!A_PN z@yBta%q;S8_QRo(x;wBZ`1(~l?y2fY&fhT>sq1>T7VPQ2Z^5?la9xh7k{vo)@G%z{ zT_eN*SDjMS=s#zaGv_a>S#k$Y%z=f6M8_WvXmYr6k&!&mAH*}M_1xGZQv@`$*W{CceZQaR zI1TF>cgrZRfovtw3bOL@)qo`wEgHcJloC(P90l)5Ox~m>fS$A?Z{485{SUXB)TvPg$EZ79A^y9{M(Ft74WUkQ*^ z+uQdnaEpd!U7YR{^2El=OhQ>a-j>bKg7RAtOd#c9IkyV)3bK<2{2WbcF(#`yV&pI4vUxTJ49i zmZqh&YQ;3-0Fb}oIs4^ut$XteUk*@K&pGbz+uAlGTOT>~#0`s>PCSw?3FMb^JnJ{z zcJU=%t0z7)la8kf+Ffu|zU9u>iUO`V4Dp(@N(GdV)QZZA1e1P@!=rAE&er-pwAVo- z6~kN3_2;jJxk;#-g!BW;ooWUdi<{d|>P}bo8I7_d8owhSY~FHB@L*}@85j8eFNA4+ zEJ8H-HeWQRPe{w+LYrsv#$QH}b@wK3miM=MCbW|pCMA^iK&0cMv_7eE=h7?>b}&&K z$Q*^xUr5gXA93{G*p3gOb;L1P*i7cyNE52PYB@YsACL34$~SQ+>5-2(S@}+bKVr?b zc43floRa>aUeSk#H=H^^zqmf9cKVXS6DX7E?brYwUdE{&?;<62I7yGLu^UR}iKDHE zBcqjHy}C3?w)ODM4c=phbSt>C8$Xk@J}{)vg`09<5a8Q+F*#_!Sj&^=v18n7!HCCe zmHfs0^U$RAzeTSyKyo5LrFF;3V={R#mgZ3`=>7KZmy7X~2+Bg2WkUVQ0(Jbuj)<0E zznF@T$Qv6BzIU=WIleUvk8TrI@mR7>?lKHjo`*BK=Io{cfhgI}#WT4!FXi6vI+_4N z2XuSpl=iqxNI%FwxS`ZW$~v`MmA?2f;FwKKOVBXb+NcBV8#L)F z649c^!%;9Y-0g!Um?tZOSO(H>D)b`+t5<(!-1H27P-4bh zA!_f1H@FS?vkCIEIqqf{7mlFi0(2L0FgqKE1wK-^)=6LUvRwMpjSBj~LuFlU}IB2+d-LAEL*-wpe6m7@Gib80|YO+INDGUqbF zt@N2?)clsZ5P~eX`ev0|RJaSdG+>HDn1W2W{i64!X{p=IV)tpE(=3wr^yJNo;$pGd z1}kT1ysOI_s+81MpdHTK%XG3KWKucs^p1dv!}rv04x}5Fw3^aG?&(#LFIQjQInjXR z-uCE17e;Igy$8gx{?hWApnp2bhtvD=*NR4F%C|Ma`JSY`Mhoa$rlF6qDDU=AgKW8_ z6@Fo6koO4BMIm7q^a=Qfx)r3^mJmq{6Fsru(nk+%aI%)Do#n_FY5{S~WcJF^x`;vRmu<>o{>_?N9^`h#3DF^&F z@j|v9U_oM#i1j$}NKgtdKT|e;I;X&|O`8ME#f66}fK7`R3yG8yLMz^z|LtiC=w>jl zjNpQ)#nG+Hn(qUs-~C&ZLF2oJ-9-jx(G)~~7lLASPFnu-&L__hZy%pUU6DadI(;;H5GS18LhujV~FuQKg>RU>i7GcT#np9_zTorI6?Dq+7P*l>d({F0ImRWb}MVICyC9Fg%-X$cz9R;e* z2V6okbJau0N*Xq0QzR?`9yhso{sXYX!lyWr2tw$s?l{-k)i$q?Z12b5T=6hxF&@xP zaHtgM2EJzl+te$?oRwzTHeEaml5w;Nlna`+K9kQa(1o@DP52rjUC!BCf%upo*<;DM z`y2siI5aH!qDsKG8EPT}XRAkNm_H~y>aTV10Xt$bR#l=fX-Coh@uRG6WX&6LQ4c{Y zZyG>L{S@6d({f+|P4$3|YyEPEM$2NYY#1!x*r7c6gGcvV)LFUKoQQ9sZ+y1so?Z_< zIu{72fn0+-ba;X=g`)nxH&$xbPpxuF@SQ12{A5F>n9QO2iZ7-pH3}Bt#8N@i;lU@S zhxCRL6U#hJ$xHyeWw6L;C<3+V{lZ?HM)?y661n;Y@4~CXElmj8yD3ds^H8wt_fgY) ze%_-~(`rH9bY@tbSy2%P70-IjBwOLJ5%0xD761gwn)xn43iah9>I$!UGZ!&?bE80tV5h5)!CjG)q@H21V4nrBkKb) zao`%P`|-5sAB;QpbNxFOEs_|MoZDsdDhjw)7++(H4bs*F7KUjkm#T zt^Hq5%%bOxe&bPNSg-mhP&r}h{>il8@;+cE`?4MV<1CH4_gDrdljz_C8Tq~cYWph{ zPMDLn*)t9m_!dEKO|Gv@+Z2YBTD!qsD8aR{VnL%l3Fg-(|7H8~hziz#GWfQePt2Wh z8QOvMI%J*|FMfWtQ+Ae)GlL1|I*U9cL*>`tDt?{iS$`0gt!t_JTI<~rBr+rE%clBD z$0aR-I9vMxQBKFXZ@V+j_m}U%AL~_$`t>hLZiVvHNWoO?8cNmq%5+F~?3&s6qb#Al zG+3v;IZJfqzk^5!-?}#kh2yl~Amesnc9e$mo_Mt$04ds*!d!n#%Qq+9da9FC3WyE- z+7W7HuFIZB0Sp3K2m1`Ki8#aRNNO;R!0K5D0ycO*n%L3ZG2Z&OU<=FHQ>~C|l*PL2 zTNK|X@}$^b^>%rt)pF6xC&p@rOjgfl+h&3Kf0S}`N^=*tw}nkt=;&gPb2AKsF*;5F zuKd_OlOREW?i}AA%c2kZ)sc+*V1iOWU)zQ&^0%!H{4b~1^uODDgp73}UYCx`uZzf( zYM-D_Uixh}5!*W`)doSzq1!NC;y3DNYFM7s0ISL}8 zYn@}bJDxgTgl4p!Z$#Ejx+Ysay%U-M*6i+-=FimyMX1an+u6c z!c`k7&E09P(mu=nh> zj^!Q2koGB9HsX{lfC}Q_0p<$WW9H0ud#zt97;uYf+=c=)bL2&+vfW|sTKFVCi3!;t zbH@zp2)4rrgm_etXeF1|mmZmOj*nzRhKZlrunr)Kc@k zrjDd{ja!?y)@N1tPXFuMf3M1(zYE#P5TqeS;m)fZfd1+@Kd(-{Z!of0R^EyKd-<<1 z;w_3a3ktkV%f=eiY-2L|Xa$-N6jX7j1~YG7}pn803kKL)w$A>)2F&RjbN zi^T(F@c7}j#U1*<4b{V^KUo^fS3*d0sHdy)GKgg=Ic9>F(9HwXVGIHc52AFkOx*`O zQn2Gjz9dqYySQ?_sUI!vKBdprB2D*e`CN~mE&6GeLxZ6+|nDo zn;B|{B6+1-y@$4HmjnE&iJ*S8-PUS55gkqp2}ed$MydGOkx`j$0#^f_iPSkxqyV@C zCB$@k=7wm5s>bR{h=`&&=-e9|pvt@u*Yv7&?+;k<(3H*B6u1=$nuUNlf|SMs`G+jt zlkKe9bQffs@hucZJwrFr^plip#>(iX2q}~j;zi+fS3*c>kY)&c6HOmcPZ4i2N68CQ z)0SY0aDx0eDuv-Oia7V}RJ>pLm@UzUyKlWomec&8-PfMc08lax93o z;%ofYr_PGCV?d2_bwYrJ>=RJMgmP;+4#_DSi-oyzR3P#JveS}9Yi10wxAYqs1h?>J z;ABV18BJaXYv8rQ1a3i9vC$1)uB7l(SaEDuRMM%!{=5(g39Cr0qoraLy399yw_PvI zY@9*AL){_r|q8eR*R)Q88&#uXD780?1oKi z`x>UOdz&%P2Z*c?eic2r_gnp<<8p>){IpY!8To&zbjsSG$VX_L$cFsxBcTF)Zpsaq z>#I||>{W_kbUDYLVKPKd? zw-$cCJ=1DT(MPwl=XPe3S|i>%S?d{ih!Hzpj#-0T_RzmmxLzdH-O=@Hc7MbgmCEZ*Elo$=1 zc%AOjUoaBMh)G!0-i+sZ_`4pbwR)j5Jiip=sQhr+S1wD2@QpRBl?Jr~%KMH%Hl{N1 z?O&o)gc+?ePNU&x3=!!1xjvbsQ8aIFrQ?_1qg(k|9va10t_xDyZN@1QsPH4RQ)>SO z;1GqzB?c!uG@ZEt47W*9-KXmF{UQxT5Bm^UA>7-$owQy&w<}7BhL_{Y^Q!XDf*+%z zCVItaPVcu^^3VtVyvf?22q3aYj2KQECQq;UG^bH6^=|Yd!>TpLJGAs z7kR3$h*nD{Zk_lminva6*R&RR_+Lhxs&zPOTtATBHqlYZ-x+hgI;lcs_O4$1lv33< zQR?Gj;2PiIVYs;2JnFBR&ycT^GdHhf%M{AF=!2{&?a|30G8-BC`4&0y>O{sfCe|e4 z>VThP#E!rMaQ+)~ii01)vwY5Mp<{LL<1Kj8pqY~s} zJLGIJBYeME-{z1ysg2HC(o6dmymEQfnyn}zCd?_zHN>lp;fix9g)qd-^znFxUY2VG zgVllfY&AZCtpN=!o_W-rP_iV3Go$Oyw2vzFIC%_v+rh2Im~`X0w@!&%oA}l+XKN4u z`S++GX^O%6_R%L0;@AAIYY_h7StbYF2u_O(jJ$nc*2i{FhVT9xL{^vb7^%saRp z+R^ch5}q-&n(-bmC!TeO$Jp}+@}_jYG4pk+Pr z9iJ~lmo{APSb5SIw0YJ{QTtfr&{LYf(!O%sh`cTX_Q(fB2gwBjguI<&3t5+E1?7p% zE{rZ$2y;*tlId^m?3{oBxjxqe3%>K;Cc1VY{8}C_ElY3N9I#Dmubr>qmAW}V@m<4SC zDSPTTM}$vK?LOGfriD+UPGaFSz%$9ro}KmU{p{4GP+`y$!EyDNs>|U)VtjlMde*2aj&W)RRIRzlt`LiE-C(h*} zz`NS&A_)@e^86{$-~M*`>4pY;ugJP|VHq&z7I^cH=&^!z-$wN9C{M*5)jx7op4tHuz}At!kk<1 zU;QBMT(V5xn~wZTPrS6GTwCQkEz2u-XLn-ORd~GQ+R8}KeYbL=Wl-vB#oM~MN9gB2>Z&YD8sGoX9$N-I;9(FL}37F z5RmSc5imgmO(0c*9!V7SD8>}gG-iOA|cA0kvRblyOjX} zV+?bFoMr=wYGz5;e5VabW&(VlTIxSpkU(6%s#Z02Uo;sOZb5(Ta1btYL6?cqs?QT| zIy+fFZ@R4XsRMd&r0-rLGN~j*Ew=6c;}eaEy?PnbaSgjTqI43k!!V{<24WbP zvF-WRBa?@gLEU1EGNJ~*_4@XsOf#(4Uj$sFU@tT&Cd?#3Ny(U84mn`^KIx$_hSgI2 zVCou0Z1MDlv!xU?0}<@zUx(N0@eDrG+pFX0!t;vqsDUon z{8+l=$o9Z@FVwWKi}+RGWhzS-3*#Yk)6gFks{eZ){~r?kUw@*a4Q(K(3q^#-;ywBJ zvrQrxF?4kM*$%1YU-nTvSh84X9q6-q`nul^wFx%>^cK7Ll|I_w$Nmx4q7UD#Bx5W; zqILOo$+q1UKNr=vJ+5On?ko2pm>?b2?T%a%i~1b;@|=)=CB`vB_i+fQxG|duin;{j zIqwfIR$cqosV%>{n6Xa1T#!(dg~ZcXw6!Wc#1X_jrTbgr^_71O>OK5BBJ)2jm}@7B z?iR+@&M0TeO24e$`p$(Y> z0XXFI%(d$?D&=^+f(N5tqgOtfAl!VTz!>bJ7^iH$xtjNOy5nx;39621FpE|EQTat4KYLO<|dGQ*DO@dx}0*9{=wU-)7%AP=6<=4pTO`ANWS zO!}OQWQb#bnd9-!n4vVFL1f=qeAXUXe%{VZhGKui>1o6A&}*@$OoSiJHKx?lNEv=u z;;8Hwms5x?%k3u5fiu&E8XZXD)4a!pzn88qUuAc~XjHXxEZA+d$}n;yDRXVgh1VwG z#hvGSCb0It>wT<0wh>VkE6XNAAlf;Q@#ZnL6M0mT2mFceJd<8B0pW&`^wpr7(!S2aKRC|eE~gV3UN8Fnu+n{f*T>d{lN;8z zWc7R}jvuVoUV4$B);T-*u<3qR!QvBv)eLfx^69~S91{qF_iUG1Mr9>y=9vc+4}EP3 z5nl5oO3~Di@N*evZ; zpM+7mcv%^VQhb&!){Ksk#!{zp`Qc1b5t2r-^3_G5*|>6nebX4E-?A-2hm%G-#j}gZghz*aZ6dhCB3dq zF=mzI&fA3oPtPc5H^V;_-2XLwy&%Im;FbAL3nG2x4SdC=wOzb#hY)?~zTp@olxg__ z{JXgh9V%>cKC@OUzu6+a&WV?~MBR2cF@B~{Ks-_gYbG^?te>`P?=hi9AXnIDy`aya zJ4T3y!cM=G8DE~?;Uif#nUC%LoHEl?StSGbT$@4S_0+EhWq9YEF$)IBmV-8zYY15- z9;S)M7E=R(*x+CQ@k=1r4zeTb#^I25-yN;Pg-c}K$N!x>9o6Re9= zMH)j&#>L@hRxnODHjOZ>pTuPbIRAoubjoGalR^e=pX6y}9Zn#6AwN_w@IVqbUBaA5 zDFrvaL#HNIwWgMUL7hAazVg*eB*Bg^Zqu=lRM5mhoe?1$HMdr^dxF++IvFB(NGRqb-Xmf@tTgr)+riMRaz3ert4xCk`eWzj#a^xV> zAJ>sm5M8I`8%5rImxg)NV26e6_yX&r>5xgB#69D(hw;pQxpL8xC`0tRzRk!K^QLF` zN8**PMMn>h`b!mdmX_WI4~@$a4{!4AE;$L%%JiHs-COTB-Ov6kv0XG#=vO3BTuX5p zEO)9EfkcVb^h}<=MyX{Mt{Wvby~G{59bX7ce%he^Y9zd&!1Ag;35eM)t`0Zzrw1@A zX5XJJRA6Lm|IXLK)xkW^zn$ld^l-2l<$ChEhQAuJ^8w?$_Ki!!#S(S$pFO{08RboV zDh$_jPItU9v8lMM>F^W8#~RUQBjWX&i$Ti)d}fedu%rR$UZ_<5%w>=)&=*t+f;{jv zty^)1Psz}!5d7K|ca~%n;#EXBls(-`+Q*;OS>03Fs)JHLbdfAbzofn5cs#*6=|>Uv z61G_$@_F_w>??Or<+&s*Z7OQ9)h7Y$aQ~X|9J*M}hp7zGY9;wMKg}IDh!pe(jlbG+ z@0LGuXY_0k^DG&(SHt`;U+mxpq70?SPbH1oi?japXS3xB9t7G&hJ0un!C%sWPy%xrvw2Ua@Q;1yd>vT^wDA~}L3*$f^ zlg|l(l5P+4k(DgsR3AM-pPM8boI%)Zz#XeP*J%6S-W1u8-8x(pw?F7O8}kkAJN$_m zPU8Rn!*@n1*)BQTjGyCt-T+O%Ws=Nv7h7LWIv!7&FxAh2YVK?}pc%_`?e~0kGw)p>PUpNdoc354SWtjCuw@Z7aaubO5U zLO4>+Q$)FP`vh&0o0ZstPGc4rI8zgV&$+l3Lz!w-n(kLuC+8SLn|{fF{&ok%6ROM{ zKqca*vW7<*-*$s5lVQKm))&UntCQPx2*wAF9Z@8XA= zzx8hW?Z{ZUCq#3iFXl!eIZvQV4>;i2$KA%j*q68A%HQ%MtL&Bhe@=gPD#(#ei4(1M zsJ-+1IbZ13;8~zYgq7N?p2F=Af}WrUmdw`DDNM(V%vbZGZ&OIMiIH6U4ZzcC-8ZAR zcszG={6_qaI~bldT7WCR1=aN3z*dS7Q2({2)>^U*&XO(dP2mwwd0%B8yo4$3+2H0; zDbmQ^-s{t4^pIByle=5d%+#iJ`^cwh4KhsF*0Y`5Me=C6Ht+DDq?3{)W+il9B+Ilb z!`$Xy{RZ&~1Knw!7^6kg_L_ahNNIAT3tRd2#AmO^zprwStk=FtzGYYk6XiF*y8Z6< z)hm>QV29&6+YbYncuiNDuEhzL9v`rU^pZpe9a_p6_E6!YTwgr_MQVk2_#aHj0^lI+A- zgk|13$|lRWYlcP>)8$0_1hl%g|7OL*CqB)$ojBk5?ET*k=)X>_X95ZI(3Uk+SX(;J zyT8NHKK9t(?^15?fan{GaUot=C^H$-$CWm&v#}igN1T+k0T{31ingo(|2TjLuIv0Y z@VKb{mxU;I8rFsGRD9|&-Cp=GPx$i+y1S*y*k*f7n^aB2d_}Iv?~h_=$2B6X&ShPR zn;Ct#P@dOD5W}Llt}CqZs{`s+OrefrjX+sj_MAjA4yQI!pBNoq{rzzmz_T+#G4v@@cbpJ4Z@Z8UVaLqcJ6~AQEAo!N* ztk1?blHV#a7BYla@8Z+6z+-28H+Gjn8=b+s*CTpGl#*{Fv9!lq4?hV%Q{V{E#p+Rf zxgVo;ERC?$x|@sY#MfKAxOK;-^HpMtm8HNzfE2>pp2Q>P75qclG2y+NKPkn&+#+Q9 zUbsTMX$TlO>+%Bq3-<1&p`)^_N;jRaX{^o0@c@JX^E6aunLP60EUVqIyiXs_DKtJe zyu{-&QBQ-`NYp95&8}LTnz`Yp4m|GC;>fnjl;^1%8PMjB zY}NdXBffO^be|lVh7p?mq>&)922np%;*8!z--RmM<5HVAnm~@1T3^ZQ=`Q+wL(jY#tNC)3Mp{EXaQn?^U)(V){(|WjnDcT>RA*nqTx2?hjjib2+>^O1Eh09~U z5A|8kO5rKbufg|F`E44oehwWx!!CVPKiJKi-YUk;;Vr%F3jJ~kFCPUXx2ObW|)?_{VA>`MN4$F5&P8r_KsU4ls1q(Qwoas1^ zoiQD9g?bdX6iJ7X75%C+p`+JkZN-c}c}ad?O!xrhC`in;Zwm7mZpVV9NctHbw(AhJ zyA9WE&^Xt!cZ(sQGjrx9BfTk-c4)nh)r6aDCC#mC?m$;LCU8wh!bdw_;vo$py$~|eGHNZIm#)==TR&1~Je9DjYds%D)YfF}@ zD}@0$b5iH9=WmC3uwyoCM}}ADXDzpF5l#xL)&Uz=o$5RUsxukqJLOv1Z!K)M75WZG zPX*!4!m5A#`-Jn@L5*45sV<~*Ylw1QI|&HjVfnY)f^UMf)yK{7FNyA7_sC}!%;kBS zQLMbCNG4MS;#PVUPf11)jaAq_2GqXg#Mm#hWlo+=DTA(0M$u4+G@Ucdj4L1@Q&78caP*LotpWqFttH^4gf5MEH z(A$qyJX5l$cC?NOai}TuFQW4r4IB&9f)c4FXA^o$W;QQBbrebNYI5q@Au`Jn2d9ToyjUEkzDf%j zl$GAV`gmtqIdxwut2_iD1WMhzZ}{o93A1lOSUc|3E8OI#u@8Qql>6TA!GZq{sty;O zSAr`$ejdz#sCnhOaJN*iu71+l|IrP%bt&I9B>Kummz`Sg_;>~xo z?6ULD!szIv0iRn$Uc}pgsQn2K6=*lwiAkY}Koa-IZMfeZ3!(?iHt@5d@8F5C%oHm| zMm3#eGk%XzBgHwjt>H-oLw`4#W!d9YV!gJ|wnlZ4_dxx?1}>K8<<#Grmr@!wL*zbd$c zswb`1$|Af$!Y{x>ParK|Iz2@qW&;lb-x4t*MP#p= zjvqP4KQa~LjG3JbIu*EE^NG#MuVnE&j)ncX2UPukK+?!CzgWBT1S?o<0;oNr`B;{? zU-b$&8NQIa4^hX)vg|z5@Pq4Sxv*CyPo#=S#h1(k)kL1qE@lJv|ENJ>iYI!y9y}Fz z880Lom1{Pc%;MQx=i*B54NXie&v#g)^M3iVqv`i}$(N?aF%yftq`=UYsU+iUBoj@? z9PVnCRziLZn}i@GbYH7Q0rcXgEcD{kdN)MImN9%p#@CG(uL_x`UFDbIw=bDe!rnsy zdB`MUbz`lYM~7-{!&%!9=MG`5`2yBowL>bmDOT|-Ec(Q{nFb)`)c&Kv-En)RXCv8fKe60*R?|8S?b@mzr9M%2%}-; z6EhEj;7*3DLS1O)A~#OTqq=;veYGdTDm{0Q9OqY*hh5iT<2QMaczj}oc+5^$n|5Mt z9f(?uKDXxHtj~e#=ccVMi^=+Xe&oB-x1xK|LB+9t_We&zF|RMT1ZIV=)y| zm~Io)wogqTwn~tW%<3^e(A$5bmZ!aUWu-cmuzM*=Anqe8#GTk+Y|`YVK+9K^Cn_?s8TZv>cTT=nYJeDXux$2Z5)(H{)X@tg|J{04+(Boxqk zeL3Hn)n&te3hlSx){sm5GV#cy}KR{0N2qOx!KcS$t7iWGb%b)~7V8ATWV za8s{^T~e*R|wpoy>1SS>t&H< zU;;bbe!1itZnAZE;aC2^V3_bYJ_Uc(6!&j&=(`_@c2R2Z*JuCpeZJ)xlDtT!_KJb@ zI}?<*__)jw6GO(Q$srbfA-Uz<0UUy^gR2}V>lw3gSH$S2R~sYq9raI)$~8D9BsgP4 zCv(r|G`3n?oD`_&h}b!nG!-bSHcl8Y8#n};;W-a%ODJwLP7YUpTj12b?S z5VV3#cWW{1{9Ii~r%l|~-%=ulQ=SPY!5(m-jP6V=1bk+LohcK`33Mj=mX>Fk4zI$i zuuW6`xeGvMLUfn`;ETRu9i$z0d_d+vuvp~=f44;Dc1mK_GRQFdVg!6br(aI9B0KH| zL}Ex$4pw0T^dCCSm-{jCr}}1mq%t8pY4^WIstVJ_&$9r1X<5W10Ry)&M*|Nptc5-VX%)>MH{<&jVWW^T1H;8$Dkq3Nt_B-?fLo1hPz&)nhzSXIh#VWbEvDRt+pHNpJz{K2iaeO9m* ztNX-o7O2vs5qZ}&HTTyrVU?3kYgJ>_wD{{uexlK?R+Gl72gdfK1P^}hSTqYOo*UU4 zX|j6Ts>v@v(z=*Oqj;Z_dHl77r1*mKnQPWY`IQCO>TgBxe;4YtYQBSCz&WHywK1?y zO!LmhDIoovc&Yvz7aPqu98J*9{eDq?wMy@qa|x=={4S|8yH`7YD`=9Byuj8x-sWW) z8E05B(#!DxwpARoF!{#80QbZkeG_@4QrDT@VI8nnH7-3Y^nij3pdSGfBo6wD0i8#3 zHto-zpw=VD^<6z>hC_=|RI!BI&)w6C)jgZs{l3~yvp6Ce zMgs-Y*6R)Vd;(cv>_|bJvOGGz_t0(X!^|*`hr|12fK4F*U91ci+V_I|yctZgA~G;G zV{Zerpwrch!UMjSMqxy>7mH1OZ4=;grg;boa6&%bc`dQyjGZ`{)bjF+`CK^sT@v*d zZs^19ZV`J%v$3a;1i{vF(gO#N2W5gE@*#iC$;|Mk#s6t+)B*MQL=n+ zRXBs&pT0J8NT)UG`kn`EQ#+CR89Bi^8De74=n;ZMoiU1VqvdNv$3}iZW)a%d^mGwW zGJ6NWIe^u!WLaHw-A8Z1ko(MEd{SG_<}w`zaK5CHD0iKw;IyXiIeG>|ImiFxaUcdu zKYuX#=Dsbx+Va=CotLjaiECx$n-*+9{fg^?g4}tgRP) zZx9vyMuv#KDw(iXu&BkfbjM#w=xq_rMaaI0`xX4cG5v}0XEcy->+ByYi8oKK@UjVj zFU>exx)C3uVgl|6;f;siJv8tB;CCeqRg-=@tAGGm(7TV=rXrc=6{F8ZWAfG~*OKm; zfs8YglbN_osC3CEW!0S^BM9+)7(nb|DWR-wiI(iYlVvkV^dunM?fNx>A3cQ!Bg5!3 z|C^T^nQFN$OYxnehPh;T71Q(!pHMRzlrI7n(|>xctd7yTF41#|Pi#pA)edMkWpzx&)){@vf6|07;4wL#LaxJ?^bcERGBn|XAS z9N9Kk#`#`bc?z!V@e7>RNW*I(XKYVR${T>Lum+7xHdcxa$&RMFB^Bb-j_;F=O%^k& zmuopBDpL?sY4^m~+Q0hCv#l)4MGq+T9CIcIkONm(*;V=GI^we*MADyFD*P}N$W25> zb8gxg;a!B1{_;@U)2p2|w!;NEV*tDAiB+FAU|XcCN|hr3V&ZcLJcHe@ zx*_ z+}QU3)vuo=U-&`c;3~s>{V+q;%^7nr1Qz72&ZY~csxd$>CFzGs`asn zln=JKRP2J6xn>#hBsDyJmuuz%SN5!zX}wKXdFUI%4jKDuLMykPJAUUqNpV|JFYJ}X zKM<>#BYxLitwcT<3N6fK+PN~Jg?hNxBFOO>X2Gh(D{N@pfkxa*^LgBfTyi#Ky-KQ2 zkjOaE#y2^?Aq|Eg)fH;y=*VC91-&yHVvjWKDw=8*$j@&Wn1z?{pXp*z?Z)b(4m7M} z02VVap_XP9`T_c(%Ny$lc%mgk(IgaC9(2U&s4_roiV5HN9FlrX)P+ezl!yj+8=G_+ z?prRmal`)r&N2GB2olK?_LtONJ^Pa$@b`~@&-&b9#V<-frPzz&y?M4zyTX0W;=c1V zcFE6OO&KY!tQVBk^})%WT--<7m^t-wN!+N>Rb}wg?FCK9LQ8SzCk&fw;Ey{GfdZnV zDch4crT4I*Z4qsuqfgHvfqozR-4dfl2~8K(MMG;q6SlJV-w=bMz*t#7f!b{RuWxK2@1R65{Gk~!*Z*8j}X7_q`oa&e}Sb6--M(Z3jD1A z{Z|obUxy3V6EAcGrG1QyI{tJQi}M2g6&|8#8Ny%s@?ady zW;nP;^ic&=4NG8osPt1eoGaFb1&W(z5cuYjvTcL*v8@_1-f1ueU%S>&upPojm+0qm ziD>i1E!hkA=pul;B-0wVfZHI4DfOraw1W?aosL=^oKmHt|3QOR5|WFJ2>3V)brfCrrv(apf^M> z-T?b%>0lSm6IV0AJq5TC*Q@`&0VTT{8@R~eW|U( zHaI%)Z8b}^Hb5o4h91&jTL_s2jkXKxgvB7y6W*LpxH;lCVm|^NAKwIO6<6MoI_m!E zbQ~g~l#t88>lu1=_XzR{aa0f3&&$AjqC7L~n?BO!mtF)A} zrHNYU*D;ttakVQ_Bfoz*KI^hZ?cv@sy>R|olx=|SK5t<|hWL8{r@fgZSmcLL^RdUf z%ZrvAJGv4b)?ST|job20t-Rp<0zW3f!F ze#*VIs8%2AnkbegL@LpCA4+-fEyM7H26(q6uF4#LC^!+I#InWtjPe3laXu0#IX&*dkdPK}yP}5YP zi0ehJ)*4YG$HA{^O3?dIp6si#H$Kvre(TGOG^T9u!c^y$>_K5tHcEOuQ^iU%dhaiZ zThLTF{rZ?`gjN!^RzqaJe#YX_FSbuptbbke9M523{T@sf~*qrawg`c2Jw=@zi-^7#j6-_<9EZbu(2aCwM;z6qx7_WZ27YK=w&EK5 z2N`oFfI8?$+Ku#_(gn|!F7AQW{QK$qF_NZK=*D~mVI2yLW|Nj*H~}Pg1?K>DPvUwc z1>|ncXw>M9Pn1)iZv&jxLQ@QHio&Kt;`L5hNR>tv9EVD`IULp&?i!LOu5;SAAVydlz0^*yLd|;#i@WYel{*} zSSVa}DODtDAtr6OlPk?LZh(OrgNE8clp4=`0%c29?tB9(MxB8=C)ui{c-c(y%&Cgg zx>-k0lk3IFoT_EaLiNu}pG9KWGV|+~rE9|?cop!^=rUg$J$B`#{L`G| zSsn__hdnpSW$Y98&-GPy2%;f)(89~Lag0H@OawkOIP?HBlf|{nJaQ5i(#8d^h#I?re7rd&6WVf<8aKk_tKe*2iZrpPzFEaifB+>8U9zoN;<>vHd zyEVMsXW=8Q9?4FSM8e_Ysg}P|QkE*dzV}2lAA+rV{>laYcN;vVL;vx<=2`PK+dGMi z@+$Ac8dfa6CmCm5=?aI`kXXhgc(fe`!KgyE+48d|MmQT<%t}?eZOxj!IOsHBj>6pa z2vbEObz2Ggl9KFn{8_8D@8l$3vpP)Ad(kze5^Kt^c&eWh?c2mbsBw}S35p6pDHBYo zV2Q^QJ<*s7o$T`IkwY=!aD7Exu^~cKWxLb}!PObeCISaM4#4H&Og^W*eAYu|%3b2Q zJ4x$#jf^}Q3H(GaJ+`3;&}ZPfKL2JQ%nD4Srp_)iQC;!>{856l? z9NMNRAmFD2tf2i?GDee(2#9I@X5DEjDi;fouWX=Hn;@9LHLA}_vGncQA$3~!{to;a`VpUl`s_{`DR%I-!_o-kBMqgjO$uV z2Qt4z{v-_N;;F;)IU64gsB1Qa6hlZOG+q5*>0C|~X`HpfQMkoB&oRh~kRfKwz1|L> zgwSgDIW#k&<(bry`V8%npZB@Q+M`np-n>rqgxyvldc=c_cD*2F%a2(@lxk<>sARFI zY6Z<&pj9e$;~yz$w9zvOY1V((D{su>LiVAm+m|uew`rp6_Eb^$>fBNXiV)s)t|18w z2rdE^utw*Jj}yg+-G}*myQ7J|>|sea)8`9Hgs#v$DIHNA+76Uw*)S|!qNvVLD&-pE zcQD2t%c0gajJ@r$FuHL~nCIa;e|&zwIz;VAD8n=BckA1PUVGpor46u;TP{h?>AZ_= zZ71DayxH!XCkEHb3tqA%sc{(8abAcag0fsr*)sdE;Z#w*6Y8GR@PImky+BMiV<`5o zvowG7VkXU+_sOvIsqjAWCx|$PB<`GR_Lq(UgI@bfwS{^(tf~p4xx*q%^=f~DWv-k< z1s4;(CEO|ERQ{=4K?Y09&k_Kezqp3&UM2`jbdP{DXKb|4=U}+G>w$Diy(ug+X{yvW z^Z#Hj4S)OD{XOOVGY|UmX9DXx#A2z2tvVZ@_%?Y$i?|BP2pJCI7ASFToY&#q zZ#FWn4?^%aEz)=~2S?-LkBdv%D*#K}@ahP0s@6)tIftA5>`KZ=BO^X_nb+Lth0xN| zRB|4UiRS5+sFGUSKw$A1vg{;i6ig^}1pm7amFd#za+7@p;6s#VlLn~}%?2dr-; z3!Rt7@$WC(OpJqUA^Q8XfA;;{I47-j)tG~>-03$uBA~pdXjY_}4Lch+l7#9y1V5@B zJA+rznGKreo#I)rs6L=$B$46GVed5GOZ@JN8ien9EcjM#4+r=!yY-ROSTIVXSl!$= zBOK1xxZ05kuGZlqV%5`GlV|Em?>o~gfyqF5zvf<*A zxn1bdx)gBg`A^4kBU#Ti;pIIbmf#`aEBZn8W`dN`!de&@zHp8C{{ z&c5cn`QwDj;-2Fo7;AU=f^?G^s}~-gC-5kPcM+Y02JPoZN=xHZ2!=S@p!yGO_ZcxP zQfyBDCG;PNbvJk&3*62~RyDiS2R4jJT2@f`$UZ@UoM{9+%tz!j_W?>Ee!vP}%r?|8 zHF-1H^3d8OG7>7E77u-Ph%@Rscc^HwkZb7Ln&4g z>{D=o2+{)7W+EqMQ{>I3%m|VMkKv~?^+H48_}Dt)@Sf)0%456RVP_s{lP}Phsx{9P zfJuUDCe*;{T-$AQ-&21(w}?Y;3Pa!H7e>f$k*RZqldely)ywqnY=5J91xM|_bivzW zLZ|_NRoSp42HdN0EAs2^McM_DJw^a3e)fn&hCQXlxEH;Xn9(&V(6%b& z49#HD+5CPhW$kO?S=O72+Kb&n>d~MU2|LrqTx>5Egb(?U;j=flE1n8y9N%&679+yl z_lswaVzoq_UkUj$Ql2bl3WT56Cvk{wgy1zf3T<9z6&=4OzbpaB^Yy+csNB zJND;Ur~krbA0m;`vSLcsx8ekHE@jDhb)n;{-N*(QX}4Ejy?(_P1&)q`7I&b<8%NqB zxTTDwYaXY7)!NGc&^W;~`Dy!xAGFy`2cy-bIJ{&_!Ds0&v}~j1Nreil{k$|JOV^n+ z7}Qv)+#?@K-z@4o;|YUYDgA=&3H7ewy9svj>;JVw`1}a7`-S?>`E^gXkd-|DI!T$6 zQ@>a4C&|BLch6lMlme=F&{YMRzT4oYcutNgu&Yb&H2t6hpW76x|G}91VdG~L=uj+1Fo)+Vv=uCP#RG+X*&3n*V@;GW0I0cKX!Ru^aWJo|y8-G67jn z^E1Kfnn^Vvd>jbp836$V{(hAud=hIrQ0bfj;kphYR?@X6l;DW(S)zR$pjwoe2Jn&B z!Jc|0b}Trhkn-Le6TvqvP>itq?TuM-7HM)FgTV~w&3M@kfpSb`7YsB2(W?2K%8@&+pi7X*>)>WP;?>x8Gd-)tCNW>w( z0;=nvhre5!y)dkjxSVh|EZHP``XyOht=I4{S#xV;Ol;>Z)9DEx^ghpTJ@XS(eL`f` z4y3AiNA5dAv{T;SRX3xxlcBZ4wYK7STD`u6%4~Ca-~cf#f!3C-B+>|&<vE+tMVKfVuMkG3vnm6%Zv9T zZBie=E1Nd9vD`+BSmjLN#h9y<^F!Kos4#ycRakoJDW6SoxbA^)0k$7K$h8qJ>GcgU z2Gl+7$#^yLr%wBRFBg@fs=d$!j2B@}Y<%8X%*g0J@}oxl)W4kME{+rx+cQxzk9fPE zMiC{~O{SJcK8Cm{x(Iw{>h}Fd*Hb5r7>A-Koxa!Vv(4wZ$5XVBZIl3|%J6-9 zu-0ko>3HyBPZ^pp?aIA-H2%w0q6UWs%}A=~KDzt&7i(d9B< zyl}{D;M}m>0t%0t5)0Q}6l|y)Z7WK-k_MiUM9R@&*a@5Vwp`(xPq+Klcn-A~_td=+ zmv8mtkTS7TqezhB1r1#B4<&y5?u>k<#Fua!#^v{G4VE#^-<9oZubFYc?i+|M|oAq<@t^3Z@*Vpskv&?|JMM=w?j%P2Mrhoi} zD!-6tmprIi8*2d(U1@(?xb;85M=X)5q;ppD-f9z5U6q(de~-FX`hxaS~Bvqa3@M?U`mGdlEqMw=d@(L=BV98 zdqIkkW5nSI=?p4Oi)l~716Dxm3sdnE>d}NtVkB!kx^L0cs;$YhH~h z9z!1k>9O^9v9kN-fTtV1vHk12(IFtm;`6zK(jsB&XLLe=4U(LK6|G8`gpGg_Esf-|Fr$JQh%ifyxMJo@1Cz_w#cv zu;a~}X|yef1?^iuGxn*BVm;Tacj3WEY}^fA=EC@DfQOP~A&K$I_YxdByj;@3)kKLV z^>QgH4y_ENdkHtj0;GfW3VZK9v@TvV?nW%91s5Jl+3Ml{jEuXKCrTa28vbJfZ^XBE zSCoYnram)Di6{*tkbMB#dCg)Qfx+f^zpbmJk{$$iDzC@W${rtL=nYc^&_O-u=a3Tq zda@O9Hh52}j;P!#4o&W$miD$OFZI&aLIH=k5*Nok(c$^vH(U~__VPp*&91x5<@k%g z>z31NrHv&=duJ2I`56$fm9mATR=KIv$#+jU1T~)ncMCP^`nB0JWKDnKW(3QtZbji+;?c4B~bb9ve zUH$K-GhF8Br8HmUzT=9~J!{1NoubKyCc=Ny#{M(=wW0`HjP-*}**@eAb*45M)~cs< z>=35rknZM1*qSF^j+28c-s)&-{0>bcG6}Ttp5G)4Ke4j=hHisqyes~Id)8u4bvCZ$&qzX21wh<3V8?dyuxa#SkWPbv%04pWBLbw0F34y++~vf;k2ttaDNk!ShZre12ir{I&rD z&|^^_N#3g_g*9O2Jf_e?XPbT7S_b@%{!M^!(N%yg zkGt&l&9<3-fCy*oW&C*egM|ASK>S?OinVbVv=v`<0!c0{uO%~k?+2Zr#3qVB*F#o2 zlG5pXq&|jfy=7Of!&~p{_jmVPZz%yO$Sig9M8ZlF$AVjtDP2Y-(%h(&P0! zg_M$1I01UVW%N`QYL$7g^q@?Q*Jpfh$#QW|7ge=j#3^|rclZG%l95<-&I#Ovk=QTJ zZ*NVOv0h*}qV_nbO2`pSiSLsx_=dn6i*v@qFo(sZbDT;CA#Dtn(KT7) zo-Fe9!_i|C4y26+pW|cdpOde8eDR<8a%-D_6OM~J-2Vu>Xfv{ZZIyncGi#cF>J)>j zH6~Ba$ebPMf-UfvB7V*%Zv*MF{2}y%BmcnNq^l8N`ill-my7tQJ0-(rSKMBIg^9}l zg~7JMA%Hkr^7o$#w_D3$_Xi%jg|7t5aIfPXN~L3C;Y{UHzsLS90%|W$1i#!AK=0SV zOUh;$ejMTc9{MpF$%V@|Dkn%bu^f8k$sXjDlScT1%8pLtP1ID77jep3%}59Y8aIBf zFV7x-oL%qGKfqE>i#}?15hd!Px2@D;$uu#)uXwpfAfV$PYBR&6hmruI>p~zZAv`Hh7W6=p6TU! zj+3bf+RPuoY zRF-}VYd$02%T);4oH{P4A0|^y<@OD81m1x-8J}c>EqM&k8G4Pt{|cNlL*Q%!MfnoM;q>RJeYPMlEcEzi5_+2Z*tyU}7u;oER2| zuM;tzc~F`eRT*LN#hD!=uo+38r)9tAjHEg!F!I5(uW|@YxgRyUV+L!y-aWl7vzw(2 z@z@k|q$fuGyn%0H-is-?vyfC9NTco7JUcHV73%@ms(|L_D8X|!j2Xk13Va{bu6 zbBG$Qv}@P&{e}~8Mth&<#9i8he3~-rN<+kMLm*nMM-E1TiZ7-0PkldGWRc2rBcDIRiT>k3=#f1;gcSn<86IvifDi26j^ z^r1Tk<;i#+wjQ!-Q26m27N<8oQQ5!0%xrmwKcDo!X;aKGAqm|ZLMaW$nTsx9h z3NE-z6&3eOX9z3a%zhk08q=?$*shTS}w3)`T3n3wU>DLA5ChiqRM{|8=0E_-vx z<+9Ws>2dU9bV<&1FRW`ea;1Yf2lcN}as-%8j?x=*B%R@YAv0{EY&nGoJT)MSer%&a zpYm7dD#-Y23ivyk_GT<0%1LV|4&DyYbyGh&~Mm;K^>9anp{WFC% zc9ilT0=v#b=JDd~m#U82YEpHMC3@YF60buxO$>4!R^Dem2jtk3rUR1$Or@Nty8Zp@ zE4D)>#Y_~0@(#T}XV8~YOuPME4Qxx~&v~q0bAb1le<%rQ@@w_YqH7F5S@v>!ce7Ug z3stPb&rM-B$zQtmQcwNW+7r~!OA01G)!ms(%XCeceET}$v7OPmB$CK^1OF7hRJ4gc zH{rAPm1Gb!6}~BkmKYUE;+6&5I0sV3U)`Xjz@kUfW>14!DqczhtNP`Q`~j^wj8V;3vvqq3#{7 z#e!b0y*NwwM|tzcDECGs$GrjcpGKOIosZ#3&`js^J5tMl9sap@q=_aK)9CN=c)P9P z?ilY32wR%4RSP1vt9D*PR(-25M5~u?v+?MQP39juP9WWv7D+`>>qM6u2B@$ltLDzP z=F^OJ{oN7csv5N>&V?pbQaMXO;DyoRnjGbnYTghoebL0>ueJB3A6VvU=D(P3I@h?PJ4) zCBC(ovccTbSXyZqNoEQRZ{AKmC#aw7rkjh2EiMgfdD#2jjnO%s6Qloe9S}m{6Q+m! zW)^E0udwW~DSDjS-kIB2yJ3?sQ+13Cjs>*;u$AHGg>QPn%P;JpH_%GSSfjNwlkEp1 zUY=2R@!IxoZc~ZaWi|WWR9$o>ZQR+K+ujEf&qsKx@tawp`|lR7X~W1Mn&oeUhYvsH zv{{~ddz5ZEP|Q1UB7c^EZnwx5Zn7X$u{Jojz(dqt`Mmr7))V0;x^t`*Jh1QhAg*}|39)UX7Jj5rLJ_UT37H*e2L^6LxVr?m;_j2@ zIcH|hyr=hhKV>B=AO2ZcdtZD1>{=w(NMoPmz0+J($0KeFeC-P~z{9fOc?*|F!NLr) z=z@rr|FmX{wA+MItRanCL3`g(+0=sP?bCjW%>$-=NA?o}pG$sr_BH`2AtHsB(4cx*~An`?mwA57-(P+8U$OiHI-VK_=ln2yo0ZcC-V?`9u1b+m{~H=eMal(=Dn4`k&MoB9ihWT>Sco29w;0 zOdvvC5W#22J%xW3p|Wp{SB5u5`AsJblYL@tT8?|qV!)vEXGl9-HAW|J(rf($D{8T7 z8udr?bl?}ScqFRV>P(D_5BM106*Xt7$HLkVY1@oEnyiHtH*_}EQS#J6js{71%w~`b z+v;<*&QZ_Av*l(iienzI^jQo+`c0xk?{b{v*=*1rM{;<9UGE;i2}Rb|(j~kS=?Prl zWJQ{fEOpLK_N{R`Q<(~UyRaQ$!2?f(ee{%fK@aIpzcJk%_>N$yqg&~VLFDT}f55?PhM z&dS&70-S^?c6iG(GlOtrenkzaP?SsItEaYSw&qNa)^0Q?f=*v@~EDf-+2o=FLB-3|8+I^53+r{1d0PI33ybL zp)B60ZCCzr-G(plR)DB_hychzrV=zRVSE4Q5Su>siZTJn?{umPNF*y|=Cnh`KqrA@@Ltz2DqG%uCG8%=cT%wbkq9d?KI*}8nwg)vS=ZjPmk)5j`i zo)@kxzZv;AC+K0#panVRShX}xeCfAXs}jLr_JL;E#(D|1+Vq@ z_RFlIQ&__GoHcWjo0YIRC?XENvwqptDZ53bEya>;y!dT*ZG91q1DHu`pg#C!JQCQ{ zbSMh&YL(RR*4!or6cuXkA}>37Z?z2D@?RVEHe9f0>rShElUZn(=zs?Hf#2(OxHQc; z0p2cz`4QzAgw#r0@qEv621pop4rxQwvRg@yE|gblu8X)QMU5%Q8?pbS0F}Az`BY}U;9++Ck>6p+lC1L(Cs4iJ-V1=hCmU6gpmEqtv z#Akitk~G|HhUuo0O@`YrW3X2v>1rU=%VVg~s26nr zat}nRD+({St%;%b5T@KoOdL`GjE)?}a8CcA#$kHJ#xB!*bnZ4gkLRF7S&xDL!_wZX zv&O&7J@A@;VbD~r(*Sg2uPJlI&2HSLEJ_f-HMwSY=2h^%%k5_VQ`=#i{T54@42k%1 zS+xY6ee*D_hBLDhLXH+q_l1Ae9%5#>rCTw`MgO1H(=J2e0GgpCS4}K48zg1~PP5KMnK6xQyj@`FN9y z*_$WZnfau5_4dxAKgjKCl*63P6UAbTO(K3IvHhu2NO?eBKpl}ErUZgl^RF1;rdaVn#CU*>*L+`6P0 zC9}z~`{! zrLK7Y1oI;d{eo$TmSMqxGbBjm^_z%-R*Fh@bcF!Gn*8JEjn05eMRsO{jJ_Y#@1PF z*FI*l%w>ealib0s!CAZ)u1QG*Pieq1+=Y+IZj%`{*%pmj;6ku}V{Z#t zYk^(zX@GM}kqjFuI*PLT!SSmVCBR4v9l~n=E%>^U0_dLpVuq$L6-_BuDx6RZ<7X7xjy z@cE^00{t1}%e-u443e#0{fG=l^=4S7jF}3Y6z-VY9>nBV8G%q^A5^BKGVDpvY@SRHuRW||W_2@=yVn(>a3`H&4qw2JUuatd_p2ar1hYYw z9ViV~kSw+}Pr_=WYJ|tU&$gCFqdJ_a;o{sBD@GF+%iqiK4@#6D8f;qE(f?}ZgG0`i zEG58s{PrgaoWIp#t+En4OFKfBNn>~9%0QuJji zxV#l%pW$422I9pd8(c%buDL-aT$#V4oFrS`p#VyiN!iwo%9C8{x~W5IBs;4!4cX*y zj=l^_T$RhyS@A1!utSpfwDKU_G|a730bV z+cgyA(3IzKyHkq8Na;2zDTOcysiTbV7n+fOPW?q%StPnkM~QZLXVu&33rptThXB4J zGQM0-wV+l{$Y0kC3R`0J2#Rpe8(IPqJ|x!z-#B_7*Xk4FGe98CS)(N$4xwuBvyZVtix_+r9Zi0))IK6SkXRBORIHNJgl>XyMb9BbDjdGe`pCY7Vxd zwsb_4eQ~$2WgOUj<>wV~1CNSO!~EGk!-mA|Sr^?{gxk2f$gk(K7CTlBae`@k*5Ygv z14D@1KU}Edn1DA2%cPeI24Ysf%V&5rL=du1duShV(>Rpi*H1xC%}9W7M3C_yOV!s2 zT$V#&-H8kRp#)n=O}&#%naK78{PgNZ+uo(WhKFk9pcYsa28dQWU1x)FraGwV$I3bh z_@*VUihgb618iFN{3B7-l$y|H&Sn&|WqF`#*4-LJ7r_=eEgkFmT7X}?omt4Z%PAE6 zDp2IKJ{G3yft`oXcFP-Yz?x>z#fVq? zDd5as_Yi#E#gId(S7&S4sZg2oJZmahQ{x9#Q9g) z9u57K%2nf8K3&dTv=qxzsBH44>}ElCTyp4QBVXn}CGCs^e!W03sS3EZek17C=9wjZ zVvKZS2D|MCDfbr#of?TNm={k){PRhPtQfv@Y{F<(n`jZcAs2SAsx-Ml@p}^Gw zBiXmh1&pm$k%;*BKGb2>{L{rbB8Sn(qNx~9!Tq_0j>M;Z*g&=&a7;kgOWl_S9Rl&* zZ{uG#xM>m!5G4VylhOH1=?{0|rxi}5QG?IDs#@oz{#xti^1(sUMH707LvdYy104Uo z3k4^#6FNG6#&CWp`*GlB`f@I=E5|1aLy4ti>7f* zw{QPUXp#?A+)>MrW|pmdmddBzgMuIZuAB&rb&)9{UT)jHnbK`)#4MhTR81FVGIE*C z0Y1^LLYQNP7D%Ny{BKv5Qnq(K9dza3?>%vQxwE^!dy!(}_(@W_>H4eLjX0KiW|4Mx z+A|_}&FGiEX*r247MgW`-k;FH4iCZwIHZT!qIRR0`vLgQ z{&)w~I6s)%W7-znZjpv_9_WaPw-r>pBfNj$(*9y?Y<m~0Hj&^*y8j+zt*&?GN#Sd4*O}j$@!N}~HvT4JPkjYh5uie46?tEVP z<$qy^ONk-jWq@v99{8>aPQ)VWflpzENwT!ZgIg&@<>E&f=v;xIA@D>vMj!<|GZ2uh}6yF4g{%TcQ`lo_h6I3UIEh#8R8Q z-xfAoxPzuuD&MwUk{+l!o8szrhx`_pu&uK{Y<`0dEzNDxSj1X`Qs|?Je>|obG)52Y zDscZw@RVyk1_alXcT2Vjk(&;_jMx_lFnp@`8DQXg#O8mIK+0oMR@Mbkp1@w;i4|C) zsG8wI16c1^(7g0}>cL0*)|A(vUO9<_neCE2GoxlFDL~Fu0ZAgRV*I3R_Qs8(je2(< z2B;O)%Yg-G8^Qf{R!|Uo5x1Q07Em*}?*%BL2%PG8GL=ZC0gZ8>0&2aMmq0LvW_eSf zD*%c?2XlB%B|p>=@QKmF3Su;l_@(y-m|Ks=XkpO20}v@5==Lmm2n0dsjTPIYpmE=C zs6Jvb@?~1@-0gDB;vz42DX}BSr8vlvft>GRxm>ohyKY>K6l5vV^x;}Oz3~N1)?}GhTWhpIsfOH~x8s&6 zy%QJGs7@8nU6Mh=Lw08`rEe}+&+hX&e`@%A+0qfSr{V$J7oEmBoJ}$1DQDRn2Y|In zQkMRKyWFeRv5kyE3XN@4hZl`_O(SFtwDROl@c}^Lb8@i4TR7SM zmv_b&YbHZq3EW>+>9uNeOdU{8eSq7a-UcarHI64wHVckIz~DF=Uv+QFu1t`w~+rEUi;VbEdByH4Rqg;XBh=R94wi1 z!Yy|+%a94(&gF#MoA;=vtN6jw(wOnMN98D|vkotFlM~7{1l-~>9P?ifw3-i5ACkS> zxD(2z*#6!b9OAVTZaSO%ve-zb0cyFE*}o<2-pI;TR$0qMECN_1Ue)$8I!Nu6i4=~y zUkj3{PFX{MiQI<@Nw|Cebm{qnt?*~MGo6Vw8n7fx9PJ+eF8Y0qhFe0HZ(2^5{7+Mk zcoz0q7uiL}@}m?%LBOrom=9>^3yPU#ZaFB&FyQjVwIy&Ua*j{!y9AG71eEM;TDWQ9JkJ-aYgL z>6raM!%afbneIOl;1K^AImIWj`D)O8_V#nDG~RU;olSQ32kr-q1#FwYB_7_-?#ENn zbt033FV8I66Q_$Rl8CPk;*AmHa+>lre!UCNi3VAFRjHB!bU31Q_obPYG*@H2SUe9E zkcJ&o+1eE83~ZN`6Y7Yfv=tP=%^Y#9m-AQM`DtaWF!y}BEEp;!MYRpI`dO0xwuax6 zK|IfyI%S6XqTgnw4VXtv@ zC$H^)&YpZI+2f}3Pdr;Q33b~E21*NyV;}K#jI8BE`kiuB&K7`^T(YG~4Y`fI2-^D{HDfTZbGzrqw&n36?=M^A+}z_?Tkt0fl{gYSK5PK`71omRB=_& z%a(k3d>Jr>3Oe?)g3HB#_Y6jMyk%vgi<3CTi0pJCRPT)ATWWjzF%SvzU{Z@dy5%<* zjpF~&;gc;&rjb_IzQ@kCfDBi$9g^KXFjs)$eDWp35*c3cq$Ape*s+OC{qX+vD%4K#3CHyQ=Th~sxAJo< z%=2ysN=r`2KMhcH-(G03q7nr+uE%ilyT@zz;LybqRk~lr4ySH&HCQR^Wm-p3xbR5mo@Q7l+Y1}tC_9P!4N<_C~PQUfJo$_8*!4@_D=f}ByBIrV)HW> zheYl96G22YO$1Fr#%tiBlx>lyIkwpXGHPlyrx_%@jEiZ^#qONGoZX_T4N=ZrUhyHE7hv1-9!VFLkcaIdnG%)X7Dhn zmM?pB?e1>t&LRpy$@ zdIP@mf__r>NPH|-*ztwYZGjb6>9~p8{NG#}B16DE+J+{B?0klgC7v~nFmr8d^5J#U z0%o2!swbUG-=wU0eN=WrIy1)t`*j>XnGHfp8tC{qjVG~k|HV1wwMS3zpquUpjz%w= zH_eW@G~A>K7tr3v>YOdYo((eG7cce8YOfU@)CDCH1{zY`U&U@cVt5}-wJT4jb>-Ko z?)_C(^M4pW@8@WI4Sid$`9XEw3L6n!Tsd`Mu3wSYA|C$gW|d^8VO-@LdJ0FMhia9{ z)leaOC^o8pUTkRg2ZP;HyH9C6;?OY5Y6np*`g`|WhcE<46DGY-9tl0=!7~cM`a}12 zhekRdeH_Yr^?pE^jBOf`H`~>18;%;HnXlAy0s=**Rgg*IS?)YZp>QF8^tCQg4E$zE zK%J;iIrGOomNA5hr_*0)J-$s+N`3Uv)TA!_U;n(tkLoh|Uw&vBB|2$*40BEBJ&s1tvXQ zKPIlu#!PTmUydo~t(jVVu3BUVvS|E#UDtAZCHajJGiE&!K>M`|8IWF0gIn{||xPd`z)EBB4>eGTc-3fYRiIwg+;Js&n z>doHc@$ZS<l*c0D~@o*}jDB zarfR(TP?B!29L`yfnr{e4R)kIyHQ#hK;wCEBz|?o5*bjff9-J&*~|S(z?vU8dIdl;es=Ve_}5JUQqi0&i0$33t4!%&mS!F8x?`hjR|Hb3+mi(hWmT(tNWu-2Y;)df*XJ2=|6QM_~~7h}{^ ztxj&|%_kZyLib3I?;x6v4{Mv#4PxpRps<;C*;b^u*}QyU;m*0I!;0C@uTkCmXCUie z6u-9v+NyWar|rGh;AA&*N%G~c&)>HsabTowIlmtQl|gZ={^{$PQZZEc@bAh4hGJn5 zB6>|641=&P881{Dep5CSH}9rww{uk2^KTg0{*VA)GkTqvPp&4}8nt1*jRAt?=C2iv z2B@&JEcq`7(}Nb;LmK@PLK@uyvR5#aVnmSAV=nJs*K+7Y3oJ3|=zP|{m_KHUovpw| z8IY{Xdbva*$O!1nDDAtI zFa@$;Y9+{N&cAQ3N|$5eJniSr8R&c8e*Li2O9nwR0_wuat{C#6jJfu3QIJJd>n%?6 zz^j*n*kr74s{(#LZ99@so&Jaujy)}TSNFofH{k748bsDsyM3X+CMZ=1!=||T;mv3b zXG&!)d(asJAfd?MHcRXuO-pirN61=`UJAS+t_IvOvzyaeeAMN^HVV zOjcxw9#xdWLEM~)yma#2?OiZ8FjZ}6i`La~Nn;(gt&csW2xI3K@?h1HX_S>_3gUb5 z0`&(?gNb!kz=}Y&{=LnW5$=nKgDjdf>!0`U>sYS_;;nuhUO`u!=)_>t2lMY|o5poq zmZ!O1yt5lMuKzulu~+3VT8enX842^h{_s+IFZLxQ#O4|kVFdkCK&4ZYIlPI<6j^_r z)Zo5XjeT&6TrLawUUmC`WdW0YO!+qt#a)+L7jqkkWd4hq6m(-8S6}C)8lsf*CpwLP zOlUZBy;5!RYGk@F+Ba|6oGk^m=H=SNFl3s#97I=F9qioz*3G*^VTWCFil%}hW1MHs zVVutQGdvFnsoC(yiy#A(ST*DUdSWpW`F@@I(ahX+IOHN5SUV9mP9#mHX1?&2ZI-UKZ zqlP-DgjQ@&N;WcZvaQTcKfdPr#X!8}YwBDbb7G>#TN?bj@(?5$CK zIu{y?`PK2Es2)7pBRDj$R-IA$v_W-zlxy(#jFxx0;gPTDapP}~4ryRdfX(CoivK?~ z{-34(e_w)^(b480r_E=z?X^AeCInK3x53$ISXvHa{Xurx{Xh+NIU6aTUp$X@4CQNr zjI+gu{Z0XSa8{Kwi5oqPR(2&p?0!j0vwrgFW#x9`9@n&1wezwkbwEE|iH4Z&!wn2S+gx{YvT0@I{45`)?9Wk5gB{Ys7l&6T= zcLP%uhv)!rm`9!X3(t@*+JIwRHdXOAys)Iexu8ng% zT+(}?FZTQSCBjZ6Q4!;mVepx;uQNoK6gLB7ijMMI8V)4rm=m*xV_w3s8j3lKqk1REKuS z>A0>br2o$x+x4vBkHUL~Ej^D4<=f-&fla^#0d92Fs#a!D<-S}_7Z0FyGV434d7Yc+L?)h)-M)N{h!*mH_ols|?3=@uZZ_s|=R38HqVae5`PVAULl zVlJS7b*Z`-v_WlHb1}Liv=X>POxuTslxE9V^$on6-uKJL(Y=(!m{aL-x;_ilWT9}` zu?u3AuChE$Uge3&sDN)CYn7K$yxFIbm70gTh{YWi`*iA;Z7i97IK6?4`W(O}+mB_X zcRVx4*k%h-9#X9+)*=k=-MA?T{m=Jv3^-$-zFlHdyg7aa*eQd??|s|93PzX&9k)N4 zdIn3W&oA+ko&{94UiJOG2mEJ$fKzBODmw8IAgB||R4jfuVk@Xy{xW5i6__~^YivBy zBIMjlRC;TWz9o~jGOJKP>!#P^+A5zdR7!+8|E>lxaU@yxt40S>PHVM6Sr(R-j2DRz zqFJ(F1C~@^!OL|>&N#OWUaai7wQ++dF5>cC&KG3(Do*;>@9HYEt>s&E;@U!DasU6mqvS?&l}Fr+j&Qp3yL`MLEtGG^X0cHBI;8|*+0u^2o(3KEpjsBvx8 zX4|jCDgHx!`b@|ZHB;~*?QrI5tBoe}^@f^XtcVBT8Xp@Q&yGGI>T4+Ro~T!+=rDa< zx}phYwawQ8TI*JwJpeUlR_!xr<08DJ!teHFawxh5FhTpgYuJ#ur2h7dXiwD7p`^}< z$Nat&4;n`*=T5uu&az)9>{4{rIc{jBn_0jyPJ7`t?A%D)(m2A)pt8{`0BE(BgUOef zyk&^P)7jl~Q+~+GVb8@Kp})BpV77-CobmGg-Bf*u5W-2FmU?rVY#1iwnjahNlGGoZ z(yLxCj-NK0*R$7tZ0((es-y;pWrZYs-0&>Z%)^y-D3JM-FZO}UDpAWGl1nxb>F4P0 zZvD&*YyMTL2_#&*qYuL8q~}8V`1KbXIQ`84NxynSe)m^Dh+&pE8}#$BkJ-6fM9iV) zP$Bq-0>MMJyedO=33Y0mOjhdt>Z`!P8bB%v>w``>cFNFd>fCaLCmvzxLU3b!H2QQS z0&#_V^iA5lqYfyYO(y*wSavKKN5v;;fc`U<R1)Q`Rd*i+?bN2WLqKMCc0eVJ z?SbiOeR_!D<;0JLEDQx6dx={qteo?+_mZaz0uj;Kq4Qjwbl?{*>4n9M0uvmj-uhdC z2fZJ&P*F8r#T}z+#(z88-&L28| zj-{Q3kT`vB$qkRbG`P}ezRGW}$63ZG@2E} zEa`7%2EoO?aja|x^b}zWnb`#4jEcnzHTC`ddCB70Q~ca)A%9~IS8E&k84`*^>SU-|@+vrAQ2u7l7H`_6UdSxfCY2?Ox!<`mx=kT1p886#tp z&8wbNo;1)>I<>4XevZ)@7jt!EKXGU8>8lsii@UdZ`1`=(RRs!vy!5@K&tXXW=Xu67 z9pu=1FC(8lX|G0(Rn-n_+Bkgsds5X0{ct;3xye$DkUd9yuZg{! z7R$*?DaVlDH+3XjF>db&%-6T)vO4PC%99R4HS%z-2+hyAOZDoP#^A=+$$L{x_xEkx zT@PG_EmNPsN1MU#v`G`q9$lsX%(OesIG!m$(@Y;?5P=yRSE2+Lo%t=g*dyr$nlg-z z(@cP|*B{3=F3}o(|4Dm7POw6piH=FCM`2pkKnpc_)J!>_5YP11Pc?4?iEF7Ic6EQJ zx1Q4iR`0hzYz0(&OEwg}55vBiwa`zJ!V<*_SbnyD4Hf zzbb&fKC~vpw$(Tgws+>?YEKF(L&$3sS(1(lu0_TOjS_D%rjlz4BGZEYY!`P8OUG4$ zY#6*}zl!vOJ+b{zGw5fnj(d@_ehnK;2VV;A+#x2sV3$_5U{}mrz1>DJpx|1j%|zNu z5p<&iByWQZeAKuTvK6Y{wG+(5i|nh*(|9Oj;BC3Iq~$>r?0XB7D#=fhs&EHwZ<=ff zmD^VmHCP|e)k78sFKwq&MwsVkO;GLsNhD=8kj6+YLhle6 z_qr#u^-!@QR^l)zE8H{Wa(K|=BjmOJNn7GtecY=HlDFD)vB(hK&=1Mn2Lk{D>ZNbg zv2Ng7OhQf=KL@|+hP-b~S2V@-#jBYclm}c7`loDeNcXoYH2rdsP*|4xw3pb~QWJk~ za=nI4f{TTry@0iTYETrlrc%Tw&c~@9d_Pr>2vD|r~;=b7& z?40HnhaT{c9RPn~sdZ~Ae&C;aS{n-*YN|=VeME=jMW6FS^9xkWlA~(lZZz}5puVT# zw-~S<9gHijp}JUxb~0%CX!`r}^oVluVqFn{$?{3ui^CZ`_wBQpW1)@aNAi?*#d4fW zNnqP~6hdDkJLZcc|NbLKFB7z|E!dh~K3RoDR(zq8(%7XcQ&f!ZL11?1Sl+9VI zdPH9?0Az9Da!qV!ov2}+@T@^`L8AvwqtC2#}Sv0A^EY;u~)n(F+K(*i$o-}6c1Ngjg7VVcJPw}3K4 zL{R|BKbaOVT$Ja`DWY-?N!dBIhz^kFzGYtGBeJr-l~WArtp?!2vCYbJA9T6~b{D^w4VTn9tl3_&<~F!}5%Y9#l{goQzBTT>B?c&R zM(1ld6y0gxao)G5Thm`~Z)olD0e;X49<%l+v%E*>aPk>A)s1Or9+b0^vH>~z7gP4M z4XAkFrYZFG86)*MPPF7s6s~;Bw}r%_c~|C>bLhZYYhsadA{4+)o~0$NpXA zZG7#|v+GcyYjK>nsL5hY^Cu}9hM?jt7Dx+ry=w%}r!n~ryjnQZ;}&nB*6&G9RVHik zQ2qM92L8d-2ky&x3!m^WT#XIOQw@*bZ`XnDjpVdXRFaoyo6U!Nr?kAD!g_Tij&J3? zCe%ZrTyZT}(R=G$OpL<%+K!en*LMUIMmd8SE^2)Fhu zQYHIc``UL!8EIB8JYlZvvalp0+&}HWYQ244&9mI_B7TjJ`&XN(1BPG- zy&q$C3_dl>&7r2H-qk=%-P}rQ{I^v;Gd(LqCb*RJ;(bS|O*~l*obA_a3q0u|D2HLz zZH^mmDNEmjvB@&(+pImnlO}-jire4IVTU`K5byr*cKW1SQj6JaLf#sNkB7wRY6nOPXy+41v|#VE^!07)i4VwHHi6)y_Xk2RFEYfm ziMI~io>HT-E?^HIV}gUf#E3!DL5}B*I!V{&T-;sxNevctqFp-^>M{qUfAaJj3>m4t zU_DvR^K>{<(@9LQat%f)pjfLDeiD_3m4hF$C1U5xb~N?Z(FJj!4G*4czFfFTZ2a#F z!M}kHc$N+nc_~w-*n~EZ(cx$*qf@fYA)$^Gnp-yEfE*Wq zoHnP>$g!9;-r>W-T(EhJJQm~h=fitH_*d;Im(|=lx;a}s=ez0=TmMKT8}(%hbE+^k zmuyR%jQFGeC8cp2`-fbZbT|h0{8EET^0!}KQ83ynPuV_D?rbu56ih-jLC|?m$L|Xs z2pG|OTWNFs$<3w*tkoUTZ(Gj%ksU4jsg`1VRKi&#Q-}mC_;j=C zhdBPHYd5$jpuT|w3p%s@a|h{?yu1L_Y^Uilv1!7`v~GjnXP_U3ge2MgMyFM?24#Y{ zu+#V5^6(&QGLBBi5AhhX<)Mnr_pAe~#Gg}(%kF|-aT%bii3N(H2^qtGKs)^;*IO4V zZixi%MmWL8OB7JG3Yr`dn`5?iAu)Ih+PlD6Cb%y2#3l?o%Cp7sIBJnph}Cz|E(b%j zJE}oXsW@K@5WOrPT4qHNpRw|T8tao&qt$Vpr~qwVC=JB)-J6^Mm#&>H(ily;W|O>3gn3vtmMs2lv18scu+XrpZ8XU`CbPjTChdArkYT#x*|r0*ingLGWo<1EiT z65F~LL^MbIkJZnIjg1+pjjuAe{6^b6q}l%0w(@5Zn+6%6yjw)5$U{@4etV|IX-M=mKW{Ie9Uqeh+k~@_#qyh z(jMfmuX`Fff%?V=(i6$?JL|-NXtQ8}NnOgE(XgZCKEvv%Aj6<)$El#mj4K3Kgm-8w za@gOf1E~D#L&F!#peS`-hmGe?-2MaH|F7fBzjyy&u@b0RYCm{j5~aG&wvtFu$D=|A zRd)^}r{;LrzyFQ2c4P9MC)(w7k>g>ko$ekgU@2_t0p16R(SG^zo<^#=vPnQbzRCZ= zHRC4YaY!ZDZa)4G7S!R1hJS!Op{yfUs*v*K(ShpLj8(&=_ziP!YNte^#zw_-#p(J> z)mNF(vhVPE=PWA3Bx&tZ4U@H{j&O(k$vTK?PG^=3muPlVuxUZ#btJ(iRhqvDBLccf z-?}5B4F#WlDDk--uJQka-qLE8Oz;jA_1xGO>#=oeavDi3GDfVHA&aeBsi$|D{8)CRI|9}ANmF*V=e&QL_ZrsR{3 zFsdqYI*^9T_Pl45Lzn=QJJ8Nq5RRr?e$bcF8-T!e(r09s6E?oUUon@?-))Y4%?uC* z9>1#E0cy8xRA^ESs7;Ff!IYCxCY9xYl;cfP(gk;I_^BnvY0Wj5Nb2gl{3WneBTc|p z0PZNXbYj=xs3Wgt-`3LxHID#-o&Uh1hG%p#Uyo3yNwwsL2?thR!$3~0DJ7bkTj zsfW&E9S_!Vq?fJ`Q`PM@?9-IJ#H%wrW=?R|Mhvy}^i2M+HLl!1fBK_jbNp!xnjUbU zLTn1~Nk5lJ(B%Fg?a1?pB)5k#ebot#nu#VhnGS8wVMSo7I>9 znEghhq9ZSv))3@14aoYGf;&`cXY%bQL*s~rya&KyQcpWp_tf`Q&_raC$j^IYYkjuW z>yN*8UVuJWepE91L+NU7(Bqa(XpBZVHRI)eIKJ{qFA$}Kn_w7133V_#6cVo*Yy12d zZ-Z&ir2Fe?{ic&v(718zLu0LF2-=*~Fuq{OCP#_zup~3U^aT_zuGv7bvKeGoi$C$< z6cO~7a~_hyfso!e_I-a}{ZBs*G4za4acZ;G@XvD|7bLr<-r+)y(c1PbONb3Npbemu zcmM7*+rG&2c*0PAYiGR7k$)nft?fu+diMm7^X=);?rzy~Lw3*2SM&JBOa6?LtRNKl zTN3yTlFe(_d?od)NgWBkjyHb6kMW`h<*esv*KWiG^-1@dbbtQKvwT zx6jBoVss3xb~8s_wrnIdL|0HR^{ZutXE2RW$&=Gz)ay~rbIw7O-i|8NJnJ2;Mjffd zI4Uvk9Xux?)%ncR98Vn`xxo+z)~E@<^^NCO)m>nKPG&z5^NSI2GUlvx4}i|PZpC2; z_dgse0IZ_ET>{oF^vEfNfr9C&tv}yG5wt@!peScHkeX_u^2D44KimluE(xsr9Fw|_ z@+R4E2VRn8L3CQpZ$1cZUVYd@29#=l&)oZoD!R0Tqz=D^hsf@H3L>PWh6#bkb%0yO zlJ>_-u||yC4MQ^W5@qxRa7V7oxvazLgB`=^>+gXX06?G7*IScn8+7Rwfa&0?%0^BG zPT04&9wSi$uBG!zv<3qa-yuE93)@MO==kh4x4Of8NOr{vaqgi}jE=YiKYo2tNxK;3=M^t6eIVrd-dk)q zK<;2v`M{9ldsEggl)JtcqVXEanXV*B*7^%?ov#HHuRgMIw_-#6%#N=64#?g6Z7JLh zfqD{hXS6pH%+YPATgBG+KwN^FL2KiQMrYM$o=KrzR!^tRoZTU&gR5V&SDMS;pLYes zMBL5R1lhwF3WNWk?UX{f2kR2k0*4d!F1i2Y^2-V%oS3uf?J|26U8A*url&>IGQo!F zhvGYPZr8NSFO0^qpmql+FXG$GMA~o+Fa2o`_K;|j<+qy9j_D5#LD;#?gQ5`Es-J}c zH|8Nr_|mLpqA`qnVho-vLNV%HKO8Ou zp8nvWuRIJ!?f#@X!LUL4WMicZureYepe3@r+h~f9_eq>hSw{y0rU$K&Hvj9>2RCa0 zG3*Y9PIndmN*n%bJMfYY&h9Dm+|YgGhW(tpKWU#ie+A_CTN#y8{x-R~F8c7zS+BHZ z>^t~LU&qBwT&(aERRy~xNzORYDhKBxBNT1T2(Z{1&eJPLed(4VjODt8dU{{q*N*m{L1@NSwZ>bQ3q zm~JD=v~3Qa_vOh(ssgCYs-tDUQ)qzV$r!eJ6mpK`tuDSIk||ft(j`0~BRW_McuC`c zA=F9W0NzKzgBsDL1%9)aQ_B74pH{yhG{nN@_u>07_w+z(R=~#%{IuofZ<;SU(WA%+ z!oPl^gp5Ku=txPw%$#7jaAsM09&dL?{#0-{9SXFF9zkjiwtR>)+)M@gkF%Yy=*yDjB*5S17tVPRQOhU?bd1Luy&>C z#C!7ojA7S6VxnK}No?!4+%|-k31*krk0i3T%xqLL90z5e1vF^2J}vfMP@-av^B(q; zBHJlf&KLSICf56VId`19X(I3K=eh_!*PX17!i_-VMJ-qzuyuK8Kb~zXuN=TG&SHq@ zV83i#cHJTYBya4Mg_fV+j)gf157{Y_%)H)7!ii(1@tqx;%d}noY7-vnx}ZRe?dLwG z0l7`C+)AS*pkb0YvAZ$tafa;7wiSN$hpf&^qSj53c91__`hR{NgF}z&)}Nm_t&RkK z;sghEc%8!x5IYYu7`1=6P$k0yZVId(?8g|h{fSHSY^V~D?qJ{I0tJR42 z^+z4Kk-O7PdGzxV@dNXJIoe(IhGx;`+l^*@fa8h?pM`?#n+nra1XqB#Eue>^{ai)t zZlG%FCzjn_(1h>Z(-En=iT$7}AMfAS_gptEFVy*E{@ok)e;>EU@)fdf9J1IWA-?LI z!5Vkb-ybXRos@*ifXhvU5DJorURe>Vr}DgpqNu|)qqkAYz|e(NgPUn4kbx8s2N;qM?9?NF1%a-M!s_H0IYLfwP|v;)<-l$^3AXn2jY#;goF0=$9Tm9^jh0z4{7gcA@Odk0!4@pxF{ z8175?JdwDgq}R335$#MQpjz}08-{I_s=$8>m1pO$i7F4d?aJyriaC$V_(b#lx>%{E7!Zdr%7lB2BgrgKMnhMmTY|Ko(c}wA$#Eo zY>xyuSJ*yx@2-CgSgr2xEpz{yL5_S~an!sJAH-74qTCCsTAL2oMp9?3*ez%EHu}THNF4Pd!Bdj;ahsnP~|09b!(KhT`}#?(BKjSPCVU-9qqtd3R=w z_WfRgVsW|J5ll_d#XH&aao^(Po18gug34+J^ZEydZj^n--B9PBmy}bq*i{MaJbwAy6X;s-6Uo1x&Rydz3nzmd zqM}A`m&%J!YVJNXXVdU&D$>ik>qDU-GX$9l=cB#!O=HYtC9oWpb17EqPE5x-!%hVb zdbt+F6VA|a-_3+H0$&ErlvFjNzl@JRHo+EYjMJ$AOb=4hA2wT<0t_V)b22At_m5p$ zUAJlISMNctNB*Hpz4h|f=9mJcid(DvG~vi)TH0HN#Q{D6hnWK={1}n*ObO$Mbb5zN zjwwM`@Y!z7K+RvPG>K++b=3k0>+@LeJ4SbWpQUEz`xw)|>%ooTv1a-6Rfv1;yTyOI zivA@yX!JuEeAPrNqKsIT&8pT0-`u6{3i)n--99Z9AF#rWFgW#n{Fj!n_Xy;e^gu65 zcigdm$slGM?JeB<8Knf;LMBpPdBOA~QBI_Ox$xS1a9WMb?F6 z8u#6;z(15qP|rbr_vXJ$KUfF7`uFfebSTy`Kqe*V4()w(YyKbAN6}-@3f4=Vryn}y zQ8g*f3@8sQlRAaQ6re-8PcRYdG+0-Vz;lm2&+#%)jHFK%!QEILTR7sjlB&8YbsJpR z8{IIldN-%+2^P_<*|1Amia!7oJ$kZc=QiA9sNdg85mPysw2!svk@fMbKRHFs19l?CluP^dHg4vcziVPU$|~rPx4=&lZfqpg!`J)1Ej?lS z&~3wH9cN3tzs|^kHNWUtIs6aCxE3Gq`^i=MolqL?LVK&ewM>PN`ti)dm^OtbrZ? z=1GeMjV~W)@mHUwJd|KPsI#$A2*doJzX>`oX)Dz&Q23o~Lr?>}@W`SqnxRwok-eMZ zS46L5zoaX^8J*+aYmVv9l%p|mB%TXBZ!`Z9lDl}?wAMSUNkaTf*$FSE607boo^f3` z`yELje+{<5;mYMZ_dopK;K+oT{4%dy7-=`^nWqPHRUu2v15|^*Tq7^~@{|${#$9VL ztVhG%HrvN|5r}OY3vAw{Z(ZjZZl%#zcrCSb34tpcOBTQM=#w5dFfVqY$7of*el_e# z@R}{X_CH2fL~d{ce5a6QX*dUu*gA`^H0Zf8SPLmJ`?URY0P6edy(<0vp$})FDsh9~ zJKF5Wh$->F8+{25$L~Vxh+{!!%8eMaEf2R89T!H6iVUYevRYm3g#nuaE{EY&VZp)Y zM-^ksjVO*_D=!J88BVzi@^tj`YGUBhwdOypzW+LSgsi>;ZAlP{lofisW-#8CC^PZ+ zd4{i80Q|1ZB-vhi$yN1SPUmuZ&1C2-7iYBkam!-5RZ^W(=&sJoJK2JdAoCkrp5n#p?v$Egsd~KkPbro|SN1(>G_3!lI=#Kxo z>HmQup`L&kirbBm^qYwL+0x{S zI+#v`$Mlj7vBcC4rz?%vyryW5#>}G1bHx`+A(|EVaB0MNGJvxHx>MU(fn~oGd(?1R zaF9ysKA}=YML-(PNOR|cngocISosd^%te(Oec*}FaiVqC|I}CkUTu7X^q^cWB28y_ z7!{_lD>N1~22m(ph$_Ma{&ozccC85Dg0^l3IO)8+_ff6HhTthCM;_wBe0AxwxS}Tz_7$Yvx<>>2X zcej)!qOA5VU<6M~ptH3H=@^pYMZ|7z6nA_Mk}$1!`hR)>bZY?amd9=YnR5@`S2i6v z1Qr5GAje8|0N*msH;?UXA~V#&z&cI(1qYo?r3c?(mss&*yGSyKBTV7J zjb95##OEDgXtn8@njqpB`sa_459m$r{}K;1h(&}GzDDvjA815`ic|jnEnnul=R5S) zaIW_DqkBh%@mX8}hZ-$J7co*c@q%8XLAL4J+~iZMQ~2EsU+2CSePh1S?UFn``uC&W ztn*1`u;bTvc1Pl49wIJ*WBi!LeiiDwU;N)~=jn-Ur{5<8lgmtke;TH~bHDBBLT|+0 znKQWF%eS12-ILynEzm^XhfCp_@BGsd|NrpMdx(^@;=ylv3MD2EDC5o&pu3fqMh<6nfv)m#LPtD82LxnDXzd~5GvEhLc8E-HW$fj&wZ9$IEurAU{WZ z>d|7N*35bD2Vw}DV(eFn*voU*AIpDq;>)Q?Tk-uzNE?|+p!H?vAC=30{;YBHkkRHk zeKdx24o3&m1~qOp%~6N?^veH^oRPb-bboS$@TN6bym#cg_CQa6yn9O`+nN3M99|x< zGO1tYILRXg=ad-cAFCqgua6W0D#LIfE&H4aMr&Qf}Y#gYH481fptAaAH$O-i6_<{3O z^1E;nvA^(X?oJ*83nGp&Ku_%(ks$7qb?2zPR@ZBJrq=l6VPofsddb}RpNnbkxkvDo zJg?3#1ytBZLx0X;z0Z)+9!eyP+a51tlI3wkBndw!Nan^Kfs&I59qv1m^ieG)ubN7* zmc&et?ooSf$?0q)wG(Y7bxwax)D#6q?M!0RkF!X5SMKPp+bzX>*mC$r$Ije`$~)gB z9k9qDsK+l-Y$M>tH^0)UvgHR`_fF(ve(TH7b*_hk`IKb?T^pt%Tf%~Nu%~V#Z+n&i z$aKjF%*8W(>d0JNlBafH1-F*Y>@UK6X>HMGAwvgAbrb?gMo-0UP^>;Om2;ct{)JlZ zY<8x~)W!rT%9AL1_xjYc%FxP?kSNGgs3EHHiZ@S@=riBA3>VzWVrdCKkm^*S*>`RE zI|@(XG}Pv-^BVHL|3s33VmQ|h{nE5BxtmQ)2l z&QlF*cUl-OlC^@#grhR}oPN}NWzb$`9RF*2(L+Vfp!{M@R0GBurD(rz9%WFv{vNaK zBdSBgJ#D|Shyn2$v8B5W^+e24uZr&L3RK*Ok8nFHg8y`2qs`#DdJXD+zhlc^D4-cX zGxW_h{UUBTAG)_>6iIq@VpL|_E(dLZR2?6erWXm;;HNgBRDu~5kj>1uF`~!YOgdYu z03VA%x$pn(4UJizoo)y$p|Lkxy=gA4wIY`onUw&3^ZnW5l&{B!YY$$Ta6bYRZLLJ_ z68U9OK*3pj#XzULJnk)VP(`=}@)bftv3jeEHEXWAfo!(hJ=D?xMiVMy`C!`#)L#0K ziNiQh2g05il#_=|D9BGRRMaX zyUw5c0jv*JuWC$v&r~FuH^LX_DHSRbJMO6Ur990(6tC7F1-3lvL^QfBPy8*}2*Wti zqzZP0Ju-i*f{*ZK13?_4Pu_;5A7WSv=^+4C!{(z8w~6qht7M<&z)tsc2l>sc>&hgx zbjA!@`qj%4uM8QS0NwDZRnPaVnqg0{DVgezZhsC^S2aBCA(1r@z+!P>cAc*_QKp<} zB4O7qS+kh`=x;TVBbXN%$i4v2u+c-teUlTX;IDTLs(_Eb`}^=&9hK4LJ|3U{`d6=K z@Br}o4Zaw6U*KCZcWMh`GnO6Bmsm7HtTeFDc&3~-Mc_Ck)aYW>lWc^O^A~f;g6ea- zi>BWMBq?@$2pk2+?&&q}Z`jpsjE3ORRD3Bo$Gjb{k0xlBt>#A2OiKhpVK>m8mvmx# zY)h;-F_dKe&Tk&nNa7zf%aZz##Fn4tqx^I>r4Y;tsTg;{`4PzOJgOX<`#5XMF_Q; z_TET|Fx~5wcNt}3wdQrTl_*1bq29PG4VJWXGY{fj%HjG*gKxEZ!0X1tn`(FSf_`VSnW*c90Orl$LJ#f}1!FWO5|2^ZlS9;#>*|C*w9E;+qVgVX!0 zLn4J#Qs!VJwnw2+awL`^e}^fg%!Y==eY0`Wi}?<&yb+Gx$axHL7%$5BVQh z#$Rr(@mc*3TF<M;37?3YfB`uiBuH=172uF*4z1gv8Vetj(T-B&E81ho{UnhJQatnLTa`3px|>^H0_SD8Fr2dCK9w;1#66oBv5ON*d>=uhEuIv1m?^DrzLj z4rqWB>TRVO(D}GY25^MvO3#p4q{a!mq`d^Z1_V)V;!|n3UmHbQN=gnCO86Y%`2e;c>VteAuk9n9w5=MM7p%5LWL|C5=2|wP zRFPxhU-(({0;vf)lZ4%7B8dZnMU4$=Y{@cI1Lq3eH~OQ~DcaAp1AKfkHtXaDS49J- zMm=NdHv^D*KFOQe77mqPCV!y~dt_BZ(*sERmZ`Y|4m*3`o73?CYMf2T)0RM)f&A=OW))#2e@^&$II47o+>H8%xZp{gTs8c3DeRAAzyMxiV*D9* zO<$w!0f!H%PEFR!&gR|T4<+4z^{*j%LRC6ZvxaKqin6O(tUtatK8|G?F=aY!1j0bA z4VzmtGf2;ckB_s9Uxv{w!Qz~XN-XSkO9FVtcBbuxCSf`{(f z^p&BMXnDrSr+)ky_56{~UPcb~nE`Xs9$0%f0;UM6SrbIbd9iWI_b}l2=d%YqL9mWT z0Ml>Z&SIXCA|3F4*gjlyaFjbK$f*s?CZdxBIhVN}`K~q5V4VF8UVpKmkJ$Y-(+<%@h=#d{<4fmqxCh!S&k0 zw1yBE7PY(2F+tfz$xS2GKyC4t5vqm~rMP^-!-RiL>{|OT-82Uv4URcVUA4zSlVWSb zG_`(y?=B8Qr^TfE9rRp|7;Z*ydSHqqOWz9kAr{j!nJMI$7M4D0;Sz3x>5$hxnVRg1vmm6fVY z40($FNYvx`x-0op8vFah1!toj^TrP`NhfAC_Dgp`?m3*>Ep=xn8f#mwJmA{VH@2kS zJB$*rH1>MtpCi;P-ouz+VczkXpnFeHhqzlH{VNJUayMLeMB*UtTWAJ*C}DW-y8qKc z!K!b#gp-Yy3vEs9WBcOWYbh9RVW|NgWO)>j;+XMe^{;AK;OPOdm$B9R_z@~!ow>ti zPX2cMe7qn4^(g)CAv5x~rF2(~!MR(CPw+zmppL?ihFBRlOmRquE=J=tIGJ6rCd^CmX{HpU=6m)Dg(_K-X^naR>k8~vk!D66bWBg>4_*= z8}$bt+>WmFCJuHunIM+;1L@ks_q>W@pkUGVCnRT2d@5`P?Sx%J?F0jfiDv9I$K<+& zsXJE$vzSIXNr_(2&(0|DcGro?_#b$FAA!TCzE-g_I3*)pD=XL}{yKX78wN!c=u_O* zGFrqO^WTM}xe*ANd%@$sAF%NsC!l}=N_*)bz=LhO+4^nMvRyk;(~|2aVXCWtIm~L_ z0G=Qw2V)xV_)HvB+!qd(?xOA}I;BzZ)Vzvaic4LY04dDm41OBR@u#p4iR~Cc7InYV|4$5v(#R`bx8_FiO03=4*nQ~pYawf5zfn^S)&C%c7zt-R7M?Tw=)Cu5Knfv z4DD}E4009n=Pxi}$MT(|4xB3~7gIdA5&9cz*bG-(alls3xO(gL&!7WIJ!B1hUM}3f z>ZZMjSI@`_k%;#keHLfN_qB9a=CiYT@#lEx zn=gUMcEbX6_$E?`klq4A%rK#f=RyQwtY7@IvBtmmZ=Wk>XlW8NWqFc0FfvFVZM=>G zjhOA5#%@L@PjO`lZ7wrY`4}_GoZ6X(s@m1VB>O?s_A?0-UBg&gKOWBTXXOyyiXnB! zR-~AA7xiUvTVK7^P$8^7!DBTOhm#1diBm2_XWNRA+Uu>AX1tpLWmlrRzM&s@+Ph%w zwD#ro0OGE=07f|#?`5$p{IU>)pBeYG2Y%1jUsCZ|O5gJ4D|~iRtWsLn^gM2RrTnk_ z8<0#R`z>hnJGn=h><>i#b#N-VyQc`qU1RwM3@}k)tq-P@1(v&ms_pk}eK=;b8-NhHIdp7jI5;JkekIkn43NU2fs%IVpO&mSCN;3ihQ#{`Uhbi?=? ziNb*BS{DI{>Hotv^28H zI$;#3j^#FVwv6AvkzSmO?FA;R)K=o&Mx4e_lrzWoBn-PTa%|DN)vdreF-vO6&;C?U zn-y1b%C;%H2paaMZWCwjs&h>9{uZ)~USk5|?v?raFf1^dIDwuKL?o^QuSdd@i{7u4)^$`VvkyT7%&U_$aV)3yZ624t){ zl@rA^o!ty*rOpT6OaxUN--u;=sB5CJ+cF$78A2I3+ceGErdNh-bhHOLW;W*gSEL7z zor{Q$YvuDZ*p()Xef&oP{lCKU$I)1_#0JmX%g)ZgMP&am8U^ zmNGi}!ru5;kxnq8Hz9pw>bszUPw%Nn_Q=|UvKz;ro{wb@zVruk3~cWXeAyXbE&4DU z$D+yqKunue9+D49ib*Qc{X3G;9W-XL-npAA7^vI)u zqu%<4p)&@~+?s(Owz0>A*9I`rE{~tk1`IQ17>QtW8ZaP-l&GH^K34r`1Z?nye0#m~ z^QRjOy%^`^=l7;rd6X)edx*|HgziC6Lha_sP_hr~B{wcY7`7@^zA| zmz>9cTyEDU_z8V=WEX2Rl%-kjF-7kkIA>cHd`vy-=dJ>MeN9pb`Rz!Lr<+!}6C&4x z6$4)B^>Vd(`?=6{|Xyd{cg6S_6_o0YK)=5 zn*f(?<&KN7fy>M)8{6`t`SfH?vs2&)_H*w^ahRS~I_eaIN8oo=9J4=kP}X!$-ve!FNqI z@D4E4;yMP6=||I3C@w=7@rL7)+#hH;_DMZoQZ+;jC@(EN>>Vt4?rRbtpv=wl3rW(1 zhvV!owqoKol9)Rb5U}1v%b(%%R5z~fj#gnLa&6B+-N5~gDP9wS2zTlQFEkT68B%i| zw5AC#jI`LulxQhUzoo{9K2L9t!^$GI7ZspbFBT3C_$AUKrBJ`vF^jMZQzmrRrLr$W zUAm}KWv`4$EfE4rJNrVq{#ewd2#c}sfnN32ltMV#yDx!Qu)#9yXga7a(&dLFnOgB& zM$}qwq?4TpkVZF584@;gOu?-Th>J(MHlVbwnIux;gQ%^I^}saX>mRo>kcD*cYjcJq z>`va#M*N7-I87A3M)w4cf=P@JnQfb7_ff~A`m#;j^PMNeaUK%KkaLhFF{)YNR>`ak zIx+DcMGPGj%7#}nHY-#3^#GbLg(KX|(B{^Kp}8TJtf-G)XwM}2cKL#n#>nZ|`g8(% zy%MJ|B5+t%3ldc%PQ83sTAwva!XxrTYD zAZc^x7hJG%+O9f@_>_$}eih?ofARpcnJvX3Vd$(wXoS2aGlu&)(DHQ`Ek#d z9QmMsLad(aTS-w-THVc_EfI*4SuUb* zCph=Dz4+y7EhKHYh8gnJLyZ13mbTMqTPDd)&0ZSf%P6DDYqj(!?abx_)$Tvm%G`ap zFo}U>FS%R$|1O39Vy1&D92s0pU;g|Lz5KVrKtUmzVLQ#or50CV{Gc;GCcD)oCVjO& zxect=0}OmDj=+cJP!bwYWq>`BS|WK|&@;{LdZ_*}f=(vHdh%K?_1l2)zg<=3o97ne zrT3$%MPoJpVtJZY`Kn=?*B*7t@6_po$s?(+s^;z+kXMe({tJUU0L!;0?u^8X9`!m& zKWP*jRYS1X*PC^%p;PA16?n(LBJa8Nk<1p&g+2gpP$cD-!5LCxd@`r#Mm}aC4$0S4 zUm$BMlaf(Ct{A1ojkkXYXbH#Hdz+B?^G{|5d@4qs)F)N>jr(!B+b^BdRW^osrU7zF zo9=%J>i0FmdF4Z$jvutpo*vcC#BLl$Nb@aEdj6qEA^JQ#7{SXWNMhl}BEvDx-?sly zHAnMe%7+&d;=nVnr|IX>Oz~1|Fc;At0@iV9yap`wwD^Y@VoIw{Sr7bhe2f@q@IX06 z^{4S`C)bW#@e6p1LL4Wm>0xDD*E)r}nDGUfYh`CW zAhX8Afz*|@HAQj=%IZO__i&hBVGB0fbr4=G7DqD>kCn&K+M4=he~+ z4^j&UOpU}m)KE`?!d%kM+ZhkmL4nsG7H$JSe8bFbVwQjDgk8)4EYY4IZ@%h-RLh>@ zyS4y6ax++7uU)mbZuzs%m&P=a%&CZsmvBLeAahufSh26)zaMJA~)&4o#~N# z?~jt869ZVuW+8+`W7Tg3G_>)8tX^v(QRKijtDK`r6527j8`V9MZF3ju$5l#DT|?01 z{(uVOWPP*3rJ-N%X3T)#?LYQ06cNc8!_4)wpi%ks$t2FpRgW{A@Z{))`}fRfB|ee| z4|}YABb!g5nBE4_$Mg>R8vN?Fk?YcJjSc6`<`i}NSfWJZ zjxo60rmq)(ASmUt2@0_Aa0I<)Vz=Cx^gSiha(&JDlV|t3naHpy>WLC1B?+}}8WjeF zWoVzc;a@Q9K z4|Vb~xk5iTM!)+fg6km5muhSJ{B_(G`aR3?y$s;yd`)J|t2;62NUo~JW8?-Le=}e?V=VL31 zhguyWx_$mk22QSf@c(ZE(`D&Xx`!p=9bJv8W3(k^04Kcsx78gWZZJlj^vCs&r+Kf+ zps)L6FSMboX8`q>PWXt#JV7BsCmtt=9=)gfZuN^AyF{H>EBZ{Z!}7Y^q;{4)@T}^& z!6D4bzry$Wdbai~-!k2>wsq1m`%aAXzQ|ZjNO7}8p0F}_pGuC(XTH{mf>%ps{7b&` zukuT%2iq*H&RLqI(JCZR&v`r)mdolJFKfdYU2IHRkN?%{_BNZEfFiOy8#lSM`IsyJ zQvgzdVm;Ii4z24AItZ?1SRQL3Ij+S7#%nHK_FzlYYv086a8)1l zS7L^ETnGyXhb0a7xO{yQg<@pxv{@z~;ef{DOmE0L%0j9N{Qd9y9~9bT%9t*mrHmQx z#2Y{__qIlB2&>zAi0kjeSzBQ^JNzxH+7iUOAWSzE?mW?0%I0=L?LyD%9J0R%l2otR zCj>^Ln>hYBcA5&ZAgFo$zK~QG2cH~ZoZ!O;70aF<&h z$v(k?uk6xa#lsk!zkHC-#s&+dc)B z3v?C)=vUOuYCeow@B?0DWeAb}-rxN(BVFaU53`fj230*07;X8bG%4Ho;aCrbJ%P9vRs8z=@x27BA)XCSk06((7q1gMv^Bkv% zY3IQMrVBPW4zP|wO&KfI1Ii?VhqPc-@W5{T`Z4q^4HaBugR_N&!6_@W8t|`J0j>n4 zLNZo04zv_7Eu(C*sG_ZGNs+ug`m74LDvaf!1IgKze$H~Bu%|!uW&4l?kcBu1%B+yC z=*fTNYXa7hK(L1dk<&8J`_a>Hx!cAqy}+zGP!k4U{NWzIOQX}-?#P*QJnx-m{Pkox z;pfQXmnr2)zQ|9JbC#pIk?3a`N}@iWRqS`hJ%5!at-XBwVfNH0v8-}!acyU~{Y~Mp zbq4vYN3^AkoX?dhtbr?MpP$a2s5N3Wl_~q92A%qzN&ivf`&pT?&RY`__;Q;oN__SR z{ro=2zGyux@??t&{LIDMJ@{z4y*=7u@sRC8W?$YF-R`x=EmfHL$^-b+ z58k}Nda)Szi$VR31E#by0@S^t8PHyybMip;fM-BaYBu z^Ce`(SU^UarH}658=mBOz_Np?+V^j4qYZu)?B50ldSp62_h;H32*htPIN_%aD-XQy z594AkeAzlk3ZdEJ9w1K$7RgH5wttlReZg|KXKL2_MgiccIuMZ@r}WRA+nQP^+;c8a zQrSTw{~cfmu%X0U{{I}bn_12{C$|P~mT!ee%(yiAC(fHYa~13PbJ*jLI^%%~`Rv(P zt+e-B$KaluZ9(y-yp0z=N155!@qm-%t~ecJ6Nedzs|OZ8_#(hkJ>5)zBnOKvCUDIm z{bYj~uefM)Z)NQbPsE~Se4e2NQh1d_E0)2R9@BtG%(M>Y#PmpKwLUtMf%C~8?$%eG zazHmG++}p#m8t(xtR#~A;O92H;(<)Tt&>bec8-0nOJBsS4xYW?)4T-jvY=D?uFI;x zIhACf>l@8X^)BVIhJe2ZTH{*QXD9kc@O$E%F&e2{=Dy5}8Tuvb9p0K@84ClJpVa|? zYO%>C*0yZUvg*_8?EKy*F3U?RxUNo2^{;K06O*l7C7}x@%O&6yO~AK;WY=n(GXhk- zg2k`vEPPlh;v-l|91RxxTi)SLTqG{zR5Gb8+$k%q1F+nlrCt6Q#bY|FUhNwExz5d* zvVzyd)ZMnCkI!rFT6_+C$*IgEI!vl{I=}a0;2pu40d@n{vtEkY!~6NX0#XVKV?++t zRIjv0R?@ZoeR1y8?P&zIAT{%CJpg_Y9NaVq`O+3~VD+$?2PFCOZLF8hXL1HBV7S)} z9hmHh7xV2er$g|)|_*kAZ7uDC@*i}IV5KDW$o?t)MK6>|V z6Em@LhZWDHs}_(2>1rt3b!B|9#y{jGP*+mUA**-tvqr^g+*ry)rc3w3y~Za7Vw_@a z1{+AyjA;IuaEK*0L0RZ#c_&!0?p6s6BIs5FnRa&6AcMBacsL~`=8}!?CcoW*15k6o zaS&uK6<=;w(!v{9msWQ1&_iH=`2EJqv(&uGr(qV5oRNVEueF9TwbKAi8y&|I{^voG@^E3^gcatf&w^q5Gr*6;mncYJ&)bO}q z-q}1;yH&DbS}#zxm4kZ8GTw%yYauyQ#%1bti1~!m=Yz6zNf3q?Thz$#FK`PzrbA>M zn3PFWZvK@saTtq0q>N_ZSxbc+XBmO);r%{gP#pxMm^Qp!91|~?h?~X#4t6&4Gbi&X zY2O4y>V2B?*fHds^ahnoZ6g z8<}ggUQKm%0Hus7!x)Gn4PY(+j*XrZ&Mny55k)aC|WAC*UiV;p&jv?j_#;M(+vuX5vJ^`fa z%1(T_GWN@_Rm%fXZ$u@Hd!wd1e;>Z16K;$zGIx8a1W(QI`5w@mwzd9&p20ckUO!(2 z*kH`RmdqX}BzY}ERs}(S8K}cvqldhg7~dJVWlL49EXn#knf$bm&StRREOC`0ng?zS zNvyfUrqa;N=oZoi!z4K*FG$i3xR~|j{NF;Obhfu>eDAjZ`80>-{FfZ{FtMkjP|1C$E3l)+hmMUZp-4-v}MV$jEKU|190p7S=ZMitf2mbVfz9f#bE+!+> z4x2h?>N6+lv}a<0um4yz9J)Ia?@Y*TY+1H}l+xcfxEZo3xL~QM0F{W3Z|VVC6tpH9 zwVT;@*kzz@QE2i-zJK}pQFdzcC+esK2hVb)w(r`if} zKO6*v8SP0qn%OO8E9?5oar;(=e>x-=Rd4tKOd@8V9Wu{Xy$p?S9HH2|?_2+;Q?IURSIvdEirjA{K+3(hNsx>`9GO zV4%U^vyet;DI}kgB1-xwXIy6cYs=b7j$YTVWE*JR?{6P55hlUP-urSzj(fmJ@|qMO zfC1^)r@`GcdXb5(nBQ3ed`dZPEG^oU)C(y~a^eY7d$@#SZ@8@Eo@Za!#Q)Pt5?iG;5RGPwP^-nbSsMquo?ak%YOSS3!Y<01NYM?gaBKJ zsu$x9NaRB5HTsBs2N{kf64IoEG1J~w_UP6-v_0*yUM^~yeaVHvswWs8gPb#P9-!p# zhu>_0(>S^5BhC(Rh5$p2n_F^E4M&?HsgzLT(51>Ww)aBe0OT+c%dErAD7t=@i)uL_ z`|EIqrqY9qr<}6~-5#xm{mS}bZUuGJZ!94i8UH)r<~n+;)Xv))ILLcUC) zlxyFLq_-6FULnuBav6&JebRc`44@7WHFf*`w>2LqJCT4&_ZOCNuz&|bf( zaiKjskO>|;bht!8UZX27I9CJrLXz@7QyLV&ns#`WV2&Gp! z=wcj3TmNOHF|3;De_>H^C;QK<{XaqNlcfrUER$Hel9CeVvz3ZjC4NpBE9v^FL0i7+ zK{luIGL#9xR%go}XPPPt6nD%9PiDuXO_Wy$v~xqYWLij$9>ky%w(^wPJ#VJRs|9;J zBUA0H9oUsDvbnrp0sHF;#q^(GpWn9N!E>_iFHqsOg$z z;U;&rB^MSxCao-t6q(df8g7M5%P_whe<4u#_}y@wfa-=w)Rar;E}dkSaLG64NME+W z!2#tBGeU}T+;m^aXQ6=^nZB=ukF67KEMNgaq3Cm@x-r*r5kpB9W8)-LAJKqvG9ZZY zcrDKUfr^src<&$gzmp^#7O#uLg(~Ft!0!Kk%Dds`6!hE~UJd(kZuS1ZSTZ>{)-tg{ zIeDs*f{HYY%^n3Y4eOrO_wgUcwMJ(ZL=q}wU!KP{FS$3iwclb71vAa_Ip0d&ZRGmR zOnQmg&R{360Xx>JFkL&oUyHM>KFA%NBR9Vyr?1<23_Hv7(mokKL5*?Za-vWi=+;tE z=*Rb4s261i%pJ5Aw31!3GN+aRKTm+NFQ7>T=IblvA>M{QTYDjK@yiYDck@+n4AT;X z{-Ix~p`TlBPH1RYGM`(l`wLr}sJRUZ30Vbe#%uUuDn|!x~gaXbSx1lvtqP zXkG+hCCigS|NP~`zRI3hR*lK6@`3)l9!{0C0~No4+eW)pj*lt^9s|!ZG%vr&Mkf9O zSn%Q_Ip$si^CSUHTMQ;FMPvC+S4Kk)ft4`=6z^>{NAae20*2N_JH3f0_%Q9lK0nwe ztZYysZr@N8CICc7OCF-tzkTd6ycmojy`pMRtp%`P1{Xr)P4}9}k9=C8C~D zOGZsL$gP|KP51Fk4k#L4U|Mr_*1yjut*ptmvD^e`>|&!JuA%h5dNwm6V4;b>N;#lH z92BkubcAy=hLxpQ${srm&$fO_c`>0XJja`&^_udlU8D2M91cUqf5m;IFygd$Bq7%R zyk_G0Xg+ktYs@&nQDC<#d4zN5s#>3|-NGIOntX!MkowQaPB>G3N1VJ9qy7<<$t zR+Q{gofRA8bs&~~22bs-VAxcqNC)i&q98eoZGb=0ykFQ$^hm*IfBANh?KFS0k6V30 zUx&l%G{&chlwVn5f`0CS>Z2w!8P*t#)O)jC%6P)dqNTuYnDoRRAGT`R*M#~E%Vfsw znR3GWi4P{4GRR9JNMk`{~0Irz8wk1y^#e(xBToqk)8fzbyb}$aKX0L z=)37wzVl7~0m;52kx&9pOAcn#bW1sdR=wj<{w$WR^iGJKf7H^WKNKDNuKQvW4g1FxjkPec!%l3}eYK8QQPf}y|B zp82oz%WS=dVZ%%u+_(n%1*N@Z9@5g<9i!25N-`1l>9a1F=DOF(#%Dh?!D+iuJi#}? z>YYc11d5i{2pq5?MuAQEAe(h!F!}FszQUrr$RWu zi}+6&(YpO2$t!6=5{bleU%PwTerMU+-zfnKrf~(eU^sRi=ZANB30?T^7Xo)2W#)}A zt6oYJh2YVJJXy4Y;19#FKoR-#uQU8+zW{I1A*QpQ547U*eAP)^trX;Ib9tyAq!z<@ zLx8@aT#JqO^gwJ{r$E%Pj3EoVL~dr__P9f90-Hrn(2UYw`Cf*UH84x3$D45H)jws- zjTBvkjC5@mhps^R1x|hrSqd@vou+o zV@JD@G?g!`T_eX+Xc^A$wWmY?NPrsPau+lbrXt z?RgnBU+|9MlO5|T=bgmia}A|j0fLky)zcMi<} zD$)o7ilj(0G((q2cf(Kv2qQ5=hvduWobUJj;XTiD{(-sfYwqjbd+oK>UYi{}3bHJv zuZe?uKRWtIfn#b>ltHSC!A)iS(<~QW8IuH(JZ2cE^{gVV$aCgCjJvR@IU#O0N`Er~ z@<&oSiPG2UE=VTFDk^GzTmNZdKB@@1FOd#KG?9U;OIy~r4Yc`^u1MNn*PI5K$vStp zesAcqnu!Ja3bh7ug}&xvQdZUH-t%E;90rX4P$c+sUpq-;?l}3i#e|+onMIB7NQuqu zg=+o<2z|NdpVtzS%9gv$Jx+r)l1` zB@$5ME}&h1itxCD3Qz7Ii9f=_#Y!sdQr2zD{gNNHsyQbv>muUs-bb}IUu_2tKIgly z-c!42d}KV(fWG8a`zewhVe65J@8TKq5i{8H_jIS%0Y0}*;@UqSpv{Q(27K5N2kix6Zq*zly=_-Y zE@3X-<&5V?K_#ez#3?t|mmZ75QIB1#%cBDrwPlF(J^D?j#9WAEzVA!4zX|}wI5%KZJU*Y5P*WM_?Rhr?W<3w*0tno zjEa&uWgfK0#6_&y?E<3qXd}hLfJcN zV~)NO=lX@Q7Zi+`JRVohvF?cjwL2J-fs9XZCREW5>z9Pu|*> ztAU%jP;~3<-uA-0D^?|St`Zr2hl>)er{D?XG&9w_`?l;3zXDqP0%I`7XI}V`O zsc~QmpFoH)k8gc_SB)qxBVWlvCKP_ymO&Hx0MYrja4e1hui zp{0b*^Mh|B6}CQe&s{qy+rNfs^lO+@2v`=3A%8X`rI@$&z4F|^+s^Q+>)%gj7}zdK zpZ3#SJNv@U_QUNqhhDA7rN{vA!}Mw&1ApCrVr~q#d(`k@xN&BOi99QdwZ1ZQK^Mpt zN%Mes4l?cDlm+z8CDW1VWemAWwhfENeUcN{dxR_5=RvWb&K-P@hE3(#jO)&AQ8dGn zy4Oo6bnupMhkCX~&Lru*{z~ubC`Ktl^te%6RqDw3sf};b0r64ITze^Oapc6mZ-7Xa>$+nsGw)y)8C`0(sp?Ej z->wM}pUZSQ@}DkNqKMC!2D~kYpNcO!>@P{x+t4@}@Z$@0UDSEHWC4u`A>A!fui#TF z6apsitxm>cBbyLMXc5#SnJ4AxfH*2z>(p>7qm8Q*ovJDzlJd)OGpPYYN9tL9#qV~}tumHJ!v1iPTE)agEv!YuaD(3+jsXh>dbe! zcnPr5wZs6NH`Pn=Pj2A0-P&l#?1IHrxzBO!a_rWDi9-pKP+7~#GQxV`5HjFuxt%)# zrH#Xaplz|3#8-0ayUNan*sb|C?orpw+P!pLiGWP4<`cd?SfBzitwU^Q9UOJ-W>@3r3;=)uYR{hNhvutmr)HR` zuGY44+~d1)*;@91q1gFLMA8b859bUd&?P4W=j3gPzYN%%^mVdY&kdum#xhG^LMKOE z`Fe~XhE>Yc(j~|GS>1T}bFUU+Q$&ETD;$tZkJ6n<>F=|G9>=L)Of{n7ZuBP2beC(H zTnQ$M<1N;>80cST)X#R6Tg4DuI0O<%ODo^NK8OG~*QR*Ca-kHTzZ{os@z}z>9=fIQ{})c*{wwWu<$w^x2daO@+yC8T z0bzO5W9b&@jJrl`Hp`NBaBU%2c=$a322X0%+wx$DhN6A}C!T?lr;N8LxR@t2)h$ zZ-WEyQ}K(xv=9C*qmi^O*CQFE>PpzcTj#nkcwQqGq5Q*?XnR_5tK-2UYwHI1)%tmI z)}t_h10 z|GBqzH;w2S`FVFSEeD1x3xE@~ z=0Ghatjgj^g7g=LgnVE@cYA23Gf(*n&~kkf<_C^V*G*mz{uw3+Qd%5CZZm{RyhXU$ z9SptMfS1fF#!4Q2gbA)~^&C5zP@?j>dwiCA3aS#KFssjWv5Y1Hvb`uO>F z<}ZcQ<4>5U7qVxe%Q`z{UBpIw70180Ya%`CH}>B$%CPEq1HlsM;g3wMnPad((4<7n zd@0$W$77URphU;o`upS6^0>8)CK~Rwp_*N?XrYWs{rOkvMz$sdURCr7H~<#wqUr57PBL+mk?v7bnfz#P=NFrjDs)7%2d@=kdA1 zCgFEy{|%)C%9SSrW^FOsJKNJ|vjfw6dlM;Zt$EAu_SH{YPX%%T;)%lIYmj;JL0NO2 zY&~wZ$0o?@L569UN}qK1^`*?|T~T0IIQ_8Elo@I1=8CO+&a;_Tic+YqlDV9m=nlk%w;)=Q%J?_@Gozu>TQZ8$ zs`Cp&laa`gQqP-+Bz2Zo#+Zy?7JR2x&|OoqJA98JBl=;E)y-+Z7*BXzki6<<-fxav zTA5QH);Ww20e*kp^rC~`bs-_ciGAkGQ4EdxzfaWvDFMGoJ}=l3y`aRnRCB}*oO;bu z26=g8Fu%P91WAOMa`WEWBR02RD#fbDxyOiWm#Qanfgu$o-2or|G--54+(JU}*y3WU zI%8mw`x3JQDF=rputxMFnpkh0u!O45Jq3B^OYCIb@cr=PF=RKy$7svS%DhMjI&bfU z*E_|MT@D2!s9rcYg)7?W&=kuD7e~9(L|74^iz`eoURKF1F9Ba~kbV)UxVQV>9QFNI zfqgf}_q#Wqcf$f}bkr<&d7z{bALd!T%O>%}==HK3~Q6~0u!YV*XA6n!+n z42U!k3w3laAEGHJ@znv$*o*w-uaRHfr{rj`LH_^;mq36W-g51=`eT%vLZJ)zpBNoH5 zlVDrXip7R_!;Gz|U0=6WnQIW)L&Hn5!S8`lOG2nNO0c#-=LxtE00$ zme#o!u+IbMaA|t;e!#-y!}<3Io(RBYic<3hlJRlR0L`9d)D@lfj>gu*+`t;UYd=oC~Ol9XzC_#gZE8D^nYPIK^y7r8w@BaFd=N;)f%n<@E2T(D^?Yq3hoQ5&RK#ZNbF7 zFpBYeh3_WZ-AZJ3sY^siFf{3fsJoY(An2P$-1ip3u)^PSzX!fD}$bxBL9 z)=X^(DaT9Hti5v-f0N36il@CylrSKZMc@?EO3?bPNHyd`$qor$x`N9-v#bS%VyA-U zv}7`R{FTBgk9+fa%dJKvIj&fKCb^KEH zjlxv!!rbE8nbmgI;(x25ySX5bEG_iy7;bP-mlL<0i7x#OmYY1OJxz+AxxQnmA0%fW zoqH}9lnJucP*GTF81@;sQ%JzZ+;~YFhp%eU~_+J=$wit6Q?&})# zVcx(^8oG;Fo*;P63k_aPVy@bcSc$8&Qr7r2+6t0SnNt9kob8%;b)v0rIINHCk9$ac zX-$hoNY0bBh8s8`@}%kl8|S z1IP=gX|20cn!dKjZ8eN!&?H|wi5axsx^>| zq-f9g+`WK&iHYVy(A3C~of!-xMcC9S+$N9fLUskK*O=d>+d@M>eb?+>w4PtSNFu`j z@y(=YmmW~~mR>bWzCuUhoTt+V6X7}w5cI^El1_dHhv967C*02t~UztGLGm1Zb zK6&E;ouB^UzPo+s$?-3-FN_TVvB_QZipZ1OZ%i=U+FUUWNpi`DkjKi<#=vc$crk`{js9wk6qHfYB^#((qHEp^gc z1V4fAj$}36D0Taw86P(>O?+YX*~1nuWvZixstfEpsU}yC>Gk_pt9#tCT7nO4@FSn` z)uHdmE$^(-mlEkhJxB?D#NwdXPu%-CX2C5BrnmB^ag@exX;Tq>`+;B{srw-vuvAs|%c6mNLAk=~ca$mq#)-uk_62iVx6R3q$b z(^jJxRz4k;hYhqm`U+cv4EUJknhL3DJ-Q*-iam9OCCo4s00ljVn0;M;%X;*ysG6N} zeWV+zwSbKfkl#|2R{N^uaTJpArS2YC@EYeOcBNAhaHX77NYL>kLfEn4~S|CGL<@=RcMi}UgJWh~kB;)7$Nw$HIxpi!O>sw~m>m=(tTLXj50}Sr+ z^uluHW3FF=J=5y0CmG4=sp+YJ4J2T(Fx4Wm+t3CbhVv3!z73T3rs9s!*D@2TDg(p&FsaZZ$k zWAMxjBR#Qi!h|uhmrx2oDW>t7)V9=O#K$6&ftGDr%=pG{dd0eFnVrpz9PGSm&S%#E zImbh7ne{z;`Y$-@zsR~hE8T2>rR52<}?drdog1ft0sgDrnHomFhThCO~44KoX z*M(bHRN3oX{#qG-8j_Xg2#0JA2s{>Hzmj?RL4@#p zD;bTh!45A~v%2kq3;bk3;bwv6F_HW|^P$!KFTeQmBnhK=p0*`^*PS zkwKUNL4UE!apVRe+uj+o<bDJnGAVLhVP3SefMCWUbJ%->&dgKe zIO6o5+07R;XrYmRsj~kqlm&W{_=4My2T~lHw%S!Np^M}#B!S2LDC!-P@So&vgKf(| za@IjXmn6LBpPLX{3=|$;E$Ly+MwHg1WmO;z{5V=be%TtY+?_vSbBS+sL(V7Kya3#= zE644N)sJQ&8gafVc3v@2Td6=1M9ARc;<-C&dzN5FMfVA<+pB>jrAMPV{ReP)f-u%9 z>7P8Fz86g)HS&9JNiPQaBky5)$t^QO)f)e7`4X(=!ztDUrJp7A_^J#ewBX$yh1=A# z?=%o-9s*;p-Jc2`RGeCSs)x~Sl^~3K&vpO9g*0nGp0soKTV7V_qcIMLrdoqn(hnd? z72U7!ZSDph;0hV}Cj2&9X&dF8_s|hih$ORD@0^8zQa+}&gB$b$T_WqDo`)XQEkjiI zUVLhF%*pGmG6&%6N8-%hOp8vihiR@RS~*)^o#GX&?)@3HHvDGn4z+NeqGNw{hqu9v zq4(HhJq331K-JWhwf0V_7i_A5G>QKZYX}f`r!NB#BPwbC&>1Cd_b?j~>wnb;GI`g% ztcff)w@kabdr#VPujSk6Ltls+k8Uy1k}u^jyeqY}egAlpOxpXE+;xg!A{6Vs;Ls-M zS+aN}Z^HZnRkrb1cXO0HI@Fn2GbMa~t7kD!;+g?7-R0=M&v_3JSG{ito-LrzCjS9p zOUyPwQh#A2^}lWgtzHJUb>R#1==R}29-Pv=*IsgQAbdG@7w5$!n}Frjuy!8H;;44B zva64dG9YxE_t4|f?Z?X!3$KsIz5lQ^fN=*0oPss~;Vf1>yyTymWpONv!3QP>{UDTf z0Ysb>y1d|-dZM?ifzuhThdWa51AN~lh2!XzRTWt(i9+ielCosJSk#{1+h!zaoN5W# zn>Z~!ugo$zu8Ng>th$&u(fJYOauqJuk2f;C$`1_}b1K4tP8WljEGHJCw6SwS$6(xUY1D7ZTIsG6t%TXvAAdY(*{SyF;%c8w-8tvG zkq+KtX!OGQcI5-RY=DW3XG6EkM9xO1U*r%o{HAY;^~!2Jk~88SInT@8I4EDzS%h4{ zrho35-A?OHsOk_aW{%uHoyoA$@0i4rnu6rzWs`K9Z!hF?;7#Q>mlQZkrD>nEa_9LM zAID--Vz*gq8IJ7C4=W`2&8*BKPE1b6g4X2D;tc=N@atVPk_YbyaY`3Ge4@%M6x!!4 zpSx*{NVX+Yxae&`p6*17m<1_VCS?x|U$K7INmBmh-CpBSS-w(Nbh4xZ!E4XxVCMY% z&n4i$weZ^rhf!6DXFs(&su}dgn$Dqw|Dh1Fpq)yd-%+S zuNAQ9sLg?iUo4{aVnt$el^LW@n~jS8CUEs3GF(7{_@njR9%@#)9K#4FgF1lev3tQB zb)*f!V50`Y)Y1LZ@hTX1*QcGkp)c`FfUCidBReH{;t;h}~yP^rOz+_Bv4>z|0p-IBNY;?wgwzeTB4 z|A{ZfKZDP2G`HYXEAicf|H7*OXg63W1BKrmrT`9apvqBfZ2j$d&}?|(1grts_jm?=vt!k(Z6@r^zZLhj@K6p`BV#%U#~W>m z$6t07x9AWR_Bz*-TR5l<9{upDQ@|_=Zr(E_OWrVdIjRR~^3B^d#8r9J7~Fd|9y4L_ z3=k%2Q*h^TucXc)9FDBJp%r=b?c}nYpFqE0Z~+wU;Vd1lT_vt?8ACCNAwK#;kdRR* z?qU;k^obDxQb|KzCA*FvjybZVi;SJz``Y`>;#>z`aT0~5k3gK}N<~QB0~nCOITIRS z%T0AZF5EcRv~*pW+YO`8k+-J>Ae4c!>V0MH*hFx`r5}?F-}KAT7q=SH{JL8Xjc}^* z-wN6?i%QUNYVvmctE(^7qI@h>G|n+2Ou%!FPT3CLu|%?+Xuk*Zl>L?@T99!+Mwy3?5lMUS>t9 zIA(FcKd@zJ09tKjhWkE$J)Nx>5X5jaUI0ftSVK;pfKvUL)9*D@gT@&`MLS_%%N*&l zZWiu>z11I%RHDe-aO5riN>_76j#}~|vj{rw-*fdlOKm`I$bv&W@KefyLpgd$-~|Ks z{0oLfyg;qn(=Ma>6(vUr;n*0zzUJY`@Veh;jBC71h`D~GMv^7Zliv09y@-V`Zq&W{ z7nRT7UdErcZEIKDb93bX2*ud&4s=bxD*?q?rX%pm4*@0jq-jI^VzLE6l{Sl3NRR5d zI3GuEy@KN#p)(&*|8USbmey*0*qlg8q(Z8_#)Ps@;M#~*?{>k8+{`inK>LkhFuRO zeH33Qgm#G__m(OGTfFbJ&v;$}B21cXDO``S+P~dNAaX~E%4%f7P695c68QA}?#L*^ z!+b=hX5L7KqgtS7x6G91YpFh&qeMC9*mMU!LKmn`7l zC)QiyuM*Ty<^vv5)N4~?f$QrQGifGmvg|UxY>NkI9w%{>)zEX58m&Dt09Q{Pz27t| z|D0~Y!{E8cZd9jVw`I#@BWhWOOx`X^%rODpo@dVolvGlQi{i3Z;ZB0KpU!m&esmvc zh#>F_c#f;drNTsr8&D*L1_zpP3Ivy%1n4OT#w7QM*V#xZW5Sx^ALaSi`|{ezv}sv? zSIussh>N;fq1f;QU2#J2v`0tv)~QzZq7Cf3*2+P#qAK&#V)A-A@D#H{vc4#E=!+&% zg%D+BeAcMTi2=HKj{j$ojHb+A-Z5x?LznC((WFLMZR9n~SfbBS0!zua=Yaj0*H<}e zTTuCuGkrqle!a&hDuID(D zcaUrh4j7Z4Jx|j__O2jkVJ%z>&IIWv8M1F$xD~5?7T)ymk!0Huv?B-sRe?(=rL)6| zCa2Egh|=KWPL{{QzSno#$4sN`wkZ<~h;((+fQKmvC2zp9q?EEz ztx;^o@8`GE|JryA5Xjazc4aez)V^~9%1fzPZ#alu9$2^4NoWXGOMjQWe?j1!F?2me zDAQ?v_A^v-+lAFwqGv@I0W!r8Woa^=|HMe61k`;#aO)W{ixUFT7|>`2`f~pYaA(Dt z4+K)kk7s<45X|lRo8+S>7q}_4r$gNV4l}Y9c!A2v<_sTW*u0ECbogNu6WXog*r+7==>&35W-V%xUSi5UCmUb)hhZPF}=ymj^l|(Sy zCMO9qJh~xgjpN9L9_H*t{2wHNm>w;tm8oa$l(f@`8CX}3Xw=bzS*NB+O-Kp!xcM(9 zbu%;G@mEk?&F$eppq{7eZ-$s%zxKPn-p^KoA6Uq)wbucT&%_pjMp$ZT+TEf6MMq)l zDM~@UzqsnobKJ1sY4HON7hjOZ&{vGWa@;lwRVFQJPuv-2_jeza7=Y>6TxbO=jk8BD z=d2)waF^N+O2JBxzk?pTLvC5Rd$any%=p?ED;vb^`syY*OTTG`TM@tnJKb1b#=s&?7GedUV*Wz9@v zwKHGe!zwXc6=Aw%Q%|U=K|GbLSM%xa?Q$_ea1R}Hp!%jGg#M5t%`W(o8+V`!z)kvx zc|&i{)%#1w;lf#Ez$KV88>(lM<+FcuoiB(&eu_v+*-#%tGaq$4y$d#1J%~0Iy6Hxl z*ct_lsRa*`MUxT|lQf^7-|Q`)cY8ynhh?r8#(J;iD0Zq?5?%Ka#9vA*pB;dPAGTO9 z9jAuv7cai1t530g&)@THWvbURlIUusH0Q@-Jo>*^D~(0`1WYf~z3smyrcCw|&=*R} zIdhe<#ikJ0K`JaEHEtSf&3vwQnAUSYCb5BMbN(5pMpjw6ZAlaWaSr~lGQHYeJkw!{o&yqj zsTuLu&OrHj&@*?3E4P*-Wo%*t><87drcy`ZLdAfu45Y4RJX!=~#*0}6iVf*)s)cYj+4=DcT-b*X27k)k~C}>TEjY_Pe23dngk( zx2-X8&F}5y_sUqNJtYba)nOl$);Dy|H+|!sN!5NLxLaPFPoYyjD|u$JMa}p*9D#>~ z4{rrLUy~EqQspzlOZK+EJ9aUY2Dc|z7$4@}Wni5&dK)DI%qplvtfVeDKjuCOpKY*{v5pHSBl+pvXA+;ev19IO6YFuw}iQYW^ z@}#WBqrm!XnQ%D0W-95hL&Ceb^~hHBFMoHB^G+`qiHy zcWW%6ATf^~x8-j^@p;nuAco&MYvd(^n{~$HH3BM>>UZB^f!sAxjf|)2AdA(ttT=PN z>`b&u{F_fnxUs6kik%MpZq97wgze9?*;95`lCKu=&Pld@D>OQalvQ{#S$e}Jw`pBI z-vdfJX1=(+jgkUSm1_7HHw`Wm0;aJDY- zVlxud-~4FNZ9HmK&%Z7n&PG9`#z%gJ-5~^g`!eisp6jhIbDQD#6tQI6vGbEi4Z2;w zA*XvTwVt@@KR~ekZB#WTFm=HJShN;z(e<7-!nT$e_6GKbc2q6^hyps{8TFX7;hV+R z=`p>tG7Aq&HdJ)j=XQ;GmqctCrL^cW)@|EBh(9$h81`jVnUzadfUR7)7QUnh!51Sj z=|W7UIE%{dwPsp9oOI3ffs0dZMv^gV=L^)nj+~%Z5Im1G(vg!DXYx zs$XYrtBF@0&X-4e7#Il$j?7E)Hs zRUUe8D+LW600t4zkPYop5C7EdrY6IfN<9VE-zfK%jq_A7oL6ug?GK-;45dA%&eLEj zYxH=kqvJk6rgWwvYmn{y>+r~FWlrDkC*LgycEvPoL>+Khd-Aon7ri;7ahgw zUyJh_TOBvky!$GT$#}D_p0k z|Bz1o7w7taoBI<$B9wXO?guLLa~@|?D@H^X)Vk9dHb8y}i;DbgRN{q`%nN9ktt;%V zMiBkfQA8hB|LU=NA>CL=+0)(H)GyZ}oRtC{NenuPj5z(zcO#XiY?B_nF13VoRO1x+ zz9hHqyg=JK<5>ygalO#Hno`XF(Q2L_6jVD|H1{U@^I6RLZNMMWu5U)Rhp9K>>+Q$--nX2H8TH( zsGo%LK;TcT$j(z1Y&5cW+SkkPyAh<*>TmBF@~cj7&I|OP@%thnl-TC?iU11|5?$e^ zeeb6;2J+dI53xwkQtYd{%{q8*ihxxwyRkWrsf${YGwwm9S}tkjF}G=Tsm>-%bFbzY zwh4Y<@#MEI&oV?p6JK!}qqC-+m84|mH|8+Mu;AVHSNQ2%Jtk!1e2<^(X-QZj2yZR` zS&fQ*%M8;_$VOTUe^ce%eZ1)wii(&+#kudLlOB5-)}mkpQK;8QOq2byJ>@kkY2_-j zwst{+a6l3X?-sBuW3Y7sEIK4~nPwR&H~>^X4nCURQ>6X%vYJXtlX)#PgA49dtby(3 z7SIB`YXXDX;~?YJIAo0%H~1lJ)#jrF=`VU<#73L+ALmlkS&bYaz1y$BtagbbNezmk*|L=njRcm|*~vcLw(;oCawLF=PNzxv&Ut!P-5SGL@#bLh0&~78Sgq9K`rh?)J6Yxmh#Br`{!ek12^iN~cv*3fuv+-|2F2 z!(dmJ-2ZAOzP5OxLP9z2pU(F!4vMW@4Cb$El(Vo9#znT!Stz6eY7y1cbtob7g;Bf$ zd%2T)aYzfU?Sxr2lS%>@1Z^aq)KngF{dqSNuoKx|ghw|c$t8?KaZ?u>hQ7%A2{Xos zsViY`m$iy%HFt*VHU28Dy^F?h9QCAM8~iueb1mDa9c6( zL9rO@?9o8!vV(EDBk973_cFhbGLaI8@V1MF*0)%>6TFm*t!IDVWDNt)Z0Y#B)N;Ct zQ%h?@bHVF+`uc`bmzZ;R4y@bL+vsuV`TSNG-`gyc?z%A^S(f@W& z|6*Y|(Q=h`130y+{%yV0d`PUneU6$-^&~A|mm6Nz_n(y)TrE8o#dq`KO|*p(?Qn^h z`FVh2K*Zk~tJ3lKN87cAAw81ekUG1D+C8R+brB^sE08eOi(6}vqQa2aB;NE#$*|`I z3j5TKzrMD5E|YKLX?tgKAL|0TDR!ERWD_!FK zC+YBK!!i3E$R45_AkOtmaWN-F(c-xFIihseD%yd@dBuX$kNH(d64-3W>aI#p!M-}{ zLk);^z9klLzI!hC$Kk5&H4WW7%kuu@YeFMzc$ zKsZYJ7Jc`k7Cy)rk70*!u&c-o>rx7&fc( zNije@XD?*HE9RfuA!`~4Sa_9_Q8~KwZ%BF}2Sh^HyYg7h>uO6FEa#i-)N3fjiRQbY%4au6P1+NGKu_J2$nmBLO5p`en?kkQ-jdrH`Gwb5)NB zf6JW)St_WWYPI$~qZS7|=u3uxacmVHyMOVf_g5IX;J)A4>kDYJB|vt`O->gVAqSds z{fPoSCYvST5AyBRrpGkIqsRO6(>Gl&9SNNfDH^ zH9#AMbUzf!9Fo;R_l$N4AvTLF{F4=$mJi^`{pWm2d>TH78f~w$TjvOYaRELpk|5ra z#rmn*Uc7p;36ZKO!Lt!qwiBJ$5sH<2tNuiQJCq<)nN0UFmH1;_{ln*c;81dk z{;&FRxtM5hVwf4kg)8M9-)~Pg3v17;;wno5Oa2ZT5QxAIq?wleDu0d7^c3(#D)|B; zcUia=b?T}EvT`vgdh;R7Qoj!5^xJNr@{Vb2IdCx8eA4O!6ulvINVe7qPvGo9MHS~C z<$|rB`Il7O~ed2RU<% z&I|bUTr(~j=?()S8nW)&&q4?U<`%&S=`~(qK1I*o?*x1Tw{8_he}TEvcXHo;k$7|v z*riel}qobDcQYiv}eAJ(joW?>rnmU#uG7%nkx?Pzq*aQ44d1?h~WdaC=t1oFnlw^3+# z!EI%rU}wVL&H-0#O=~aK{nxW-rRPpIlw{uV*&+S5br1nJ+OTwI|3CI64XzD5B3*24 z7IXIU1A||jCoC={210g;UzDxb&G$=^JWeN1nD2Hy@Ws`Wgf*S?D;WXl7fiBI`N#1l zQOmm}laIa}LDX5ZU6)h25pj}|tJzSApp^eC#NvFGD_t=^GI?CAL-cCPxNYq(P_h&| z2Y_Z1@VabGd75Xu!AQ$Yv2U$>{SY4EMca9p)n%YQee4)2X?qKxz44U_ws62sX)=;? z&2QpWF@$`4pVF3WV<1S{cvwDBx;OOE{~{8$LJ1C9z~w>UGKO8sKk7e+t&s5tqEVUTK*K zP%!;1EeEEOYM}@YBCe;cKZ5dlSPqR6A%0h?hoyawmo2z{3pd)e!k7`8k)|hUGVk%N zX@hjUMZQ3%0pUsIdKNYkwZy zLXmzP?NX1^FA07-jED+vPz=5YEp;cmQe~Fi#hF6Qu%qSW-xB-#JY;73b7phQVwnrI zv&}f)#meBmtc@N&v^9aPV&fGm5$6fGn!B|YVlDnJ^h**!gtsvj^z(iYytjOYt1)F? z-)ljd5`KGjiF(}*W^$IKb^+@PKf0&X=a>2}61w9hg7>F}`Yx)D%IybTq(3pQC3;j$ zHUGlacf|{-tZKK6GdVtEAFHAA6U`_qdL5H&tuDy$k8IRg+<&ZZut3mH+&^tx{AU8% z)8fevT}~%-2&G@}qkA#^nlPLEW)RRI3dNwx=PAoR!Cr~Cn@R*Bi3lEd`q0fFd8-^# z`#+)qCvzj_QfhTF$Q0+7p8m4s zN}V0nSz@FOE1vwpLRsg2Wz~9<_ze-ps2*3#@MRc=>}1_on9_Mdgx$UQ%Jxy?KROQ7 z=A3B+wb;5w8{y8ko%P4F*7Q9+UL&o?^b=-sx47z;L*Hp361$2-4+Bn`tzw}e+6zzL z;`N;3&162X9bk-RaAPPnZtj1AxK?I0&fG@oSAnG;G^l&To?;2=(^tQTUZqJuKQ6zR zXJh+T6%`K=jfSjf%Z-Gq{U$Uvt}2oAoc+{UGtPg37W)IXBuubn(cSgYaJYU2F6D~; zEMnnO^LbJftQn^K#!~)K_If`13#vlY=X=)&xA*$sEE?Y}#QV*(j-o)R^m_`z~~Xc>AHJMK=w<&j@6<>+5aF=uOS7P7oJ4 zi+Ivea^S!nE7U114he(Stnfs`uKu3n9Ii0aau2g88`hM%ipWf)F`2n9#D?}O+=g_`4gv(Dq`4vO$Ee)QZmhT9HL%;lz}wJnI2)Xk2@g$OOLP8W=E5~VZTOHVeh z|5-($;2Q3FJ?%l{V|3EjhCB+hPU#tIgqJw@^<~Ig+BfWgyOo+4L&GwDritC54oyNJ= z&966>wI0`;_moe|X53|NA|>Q6Yu0@sz1!E~+M7#P`L%mHIp|*>v~)}izvlim`nD-k zgy&oLdgzgKy=%>f+IW!BaK*5h>$xK1*&^-lB92G)VMNFB!r1o~#uou0w~rTI-ge8I z^Pkayk;!i#fIONgun!zzxT6&|!9EcZic+q=ZrOCfE$kNK&D)j&^7w|uLehGd8m;Bc zTn;{CYqAp78VB^!*O9AOGQc071bO8V-{qO_zgZVm757uAk|zPvBozo+%M#2B^H@g~ zpP{3{*&(qBth^X6z@Ys4ut4?2LpKNz{=iI3KG%;m&yl1YNfV7*n4x5}5u($}JZSX_kbydZ;_D2=%WH*b zo3J_DW1sX&No{HY-G`(c?2B-I^~TImfi*-McB>x0EMYSB7R3LW4`9iwHr15C8D@v* zqn$!IBFWRnv$)_8IO4R#!%bxGu!7nduScjp+4J$KNi3-!Ykj-^lg2#3H;CL<%B(f$ z+g~j4Qy(r0w(rg@jarWp`!Za6o(lA`I=`CAVk){Z$wS6TqSH#S_veA})--T-bV?-6 zz?8Xj`MX+P_g3`P0*|GsnH~@1$ej(otZ$I4vZLOEAeoF8Pmb#X!EUFZ6GP&RVbNCJ zp4DG1_vxW!0PQS$7{5=W%5Y)Ls6hiuC z3Q(?G)6Pvo^O%;kP%wCu?NE2#z>=nRtCP6qMP_9uZ3mj z#XHMGZ_Of8cr5t}H+&R3x+mBZ?2V1gg0*Yy9d`Sl&JasGii;6gZ9G(kK{zk?YOOW+ z6gPu#VLswflq<~0Rwvj!0xZSyh8!bm7jCsu~=o2EeuMGAPTCq^rbchcOy zqyW!BqZZdcH&=pEbsmF%kbI1_nH??IUB-{e+co*9v8EYu@34RZdfrN#@-wm;p7fSQ zYCbai_d!tX7cNUSGQc*jrU$@Ji!XZU0;`L;b3f9(sksQ?`F5Qex*SYAlUN3iV zQg-@ft(josRGA8Q_A-&=DkLEOHVWRxyk>Z5#!Vpj?u)OEM%jq4Pk;b|6xk!FACC7_ z^>x^r|S^oSS~k3!o&sU-Dvz+awpOLsa&?n#r<~g??JJp z-bXt4PrCvwz8~wXjy91CpWN-izaKMDva;hO-SIrocCCRF5MvB6zm+6>J3NSx1Rr~P zlsfwU!t>l%;@p$9hUy1Mq^+0LBInDDRy49b(X&U1-YJT$BTtNEj0@a5hRcE$2$!yA zzWF@Fpo&$^YwltPR!n%|$f~={C})XVeuUrB?!(%EnY>;@zjFw#2UrKRlN7_44^&aW za@SeLn@*WEwjaD6Q!jxwb@>~+eox$Ws%BxE^-tsxm%mK+Zr+#M#w=M}wVODCz|N`L zrDa_rQ7PW5RP$j_Cjm9)4{-Gh_u&l9O~t*OreVp00BE&kQFt|W_xjWFpriEC^{?CI zG6k~~ZZ$Oq)rngQu};u*D&iWqczlO2w=Y@rRY9i@3$KkQQmeK;7Bz( zbZCTKrIp;q+;pK}Ew2}el)A0GFPCTg8LV1a6{L?&GG*T$YfPJT_G>}JE?i2k`$CS| zk>sc%%>Uu*y`$lLzkdH2jNYR})CAE9qW4H5h~7Iv^xpg6lSuUFy?4Xty+#ndj?rfj z#Ecp(jB+-gQS_T*qBic0Aow8Ae{0Sk;3w zW{EDJS-P%Eoo-95r#mDC6s4z9D8K(++R^u!JFUcfWiBoCD(fXMO)`o=S8Afle~8-V zpnz|xMl%P$$$ zMx8H`TdXnI3M}Bo&x0+d3iJk;AwF*-d?@BQS|?tH_>fKr-Icy}%A%2`C%E9{A*P7s z^qiXzBz+DLg2OhD&};seZ_Deuj&x=`B;8fAK?_y(H-cqQ8Vj>^j~d&3E~phYyHf04 zesLI2ncJHc!AHmK@Y%VKPC7@3c{^Y&ma7@PEDx~sQqD>KO)1WN6*FVGVHnp48?v_2 z?eXIUW$p8J%>?Y`<+pQB1}xXU2r^LP1vST_1+^PgC+1vneqgN!t}TDQ8!*fVMzOBC zi{x=H>cA@%2ZL$$QSNmN&U$zU)9v{x1^Nfoys~Ly=T^f>CvyFzurnzs-I$n~p$}_n z8EB~QhBm@9gFLWs&rp#p3KM9w?Zi`MfsrNs8uU4 z<@1X&0d~X+6IPt}hy&a0y|I66dpVfxI57wU{f(kJ{&N$onCrzKC}w5>|MWjEx%?{} z{QHp#5Ha2N(HzW+-9q|UA9KS9>}gJ{#!zxsIPj8u;TAi8e8qWi@&O}@Awv-0CvJa zq1k|jH^ypVe|8t@9FwuFJWCGx<7$ZTq<|uis1?|2H14R#FU_OA{Q4s1egbiOKD>-8 z596fHWbgt7*@MN&3g$>FlB=A&?Tkqt?~0RQMY@(A47jk$7tt|l+uSnU{3$u#`bW%M zi$(cSq8s5CPXG(A8d6ao{A75g`?OgQSo}wF4jjoRaBqjFV=dCg zgzZ3zoEC?%3nwFS!0S|*a13ZPUaM*WNaB@q1y%3Z+W!LkvhYqIY`T}V-8$cY`pw6= zMy6>B>~(7Q*sist9=B3Fz-yc|Dt<4SI;cGz{BcmscA(U9^QXqLfhJRCrbN#U{59?> zn{660map5$uVp&=JAB{SeM#Y=>y6*(j|)8#DBsf`A=IUK4oM=+4S?>mWq6f`_Ro6)C-+~zIMJm6U5t0oK2F}!DIg=Q zD09ZBn6mgA_S^Q=AHH4jAkw7oCAk~;@Z!H%0JV?K-_EK$2vB^Svcctc*xp#M&7DA# zH=3JYyT8&yRvk@%nPM_W@$~~8JZ-;^)dBP61lrFkl!3~G&#;!oBuJQ-1y5=TcD~95 zO%?fQY%CB#qOqa564IWX*33o_d0LR#2+V*K4}%cFev;tM5D#}hJPY8KcUuj&UM(zk z@S32hc`EKewo9O7U7+VDJbjlgYhRzl_`8EoK*`sn>5AhcxVnDTWm`LF^O5Swtul5e zP0RfwEmi836gRoAd#m3P_(Y=6M*NuxU6xld<$PhDy`}DH>LbctPp9`CdgyK6u()m9 z(Dq$l8c}~GzD8W7H|kuPnrn(| z3W>e}c|cq_IU9%g(vz?GM$G(M5lS4~k!+ejk?tmOTaK*C)ROMt34oN{Ul|wwzjWy3 zD^!~v{^-H_ZJLN{KtIm#j{ox+C!sgJZiJrBp_{g2ln~L|PZlQhz2z>@+eu^hjC3CV z@$;lk&we4CdJ$Lu)&5aZ7Nj|NfdO%p#u4h+e93p(H!7NB7@OW#;x*No_v_gtS^b1( z_W2(-NWhjpda-e>gLJ=Rn?5z#~DLa~ZAPr^R}sZ{g^{aAI8wRdUJN{q%CI;**m^emV2%m zqI;39Ov@~XTQ@<_;xpZXzmis9vhG%T4{`SMds*qbhHA6*tfizi3DIDzIuC855AjgW z&tdQc`g)ksut1$&eN7gL&-y6kQthD;27jr1inGhDP?aWC!BhTWVaD5isJmE)QIz!m zl$Hm(Q6r&~|BrGmgAhbzm}uv&e`v&tT+}|6mjLh_rvj}=%n9>gV9)sNQs8U6515~y zr4w!TFRtb>YSbhz^nty$bSOfwV+K7ebBMsF*Rv5ZEsUNnML+lK~Ppf zHq%matggp?};bk>y$NYT35)NR z+N~W-+mK!kk0#~yQuW~;YXP6^%KEj5ieQ-h3dSG2Al7_^{qu*O>KrzGDi)8{F6unQ z-o$yUx8Sc(pZaP1c;*#Y2g@2qcnHsLeNk?*i6UHa=yq8BuzK~+g&YsL6#==xK`d~! zC@76BTmVo&s+tyIs}iIs|Gk4lM*cZ&;7f_$>SayiSm0oM3j9q8KgtYW3X?(72xBt- zgdB}12ZEfsS|>5767`SF)-}bD9lwn#NxP?$HRoUHYZ~@=B75ZRauZ@K0Jx`=i&9!z z$i#@q<)5$QOxt<{g5%Nr;#^rKg2Mro-pt zHa_FQ%cmY#kJsre^Ppi<*noX9}`cpu?pDvD*XAWI02Q1s@ z{LA)5x6{MAo-mn=9bMm*^f9Dh@{f?WJ#F2F%!3ZX<>haE^p3mZ#P6OTAq9a;xDT9_ z+$te=ZvkceG(e81F^SJ5E&5QZZA=K<1MACGd(epc-0ap-6-h>JTs!riv&@{daPiF@RbV%nguQ+B zh2~*N0%0^9bngncxK(mmy7s~g5@vfsCml8v7UV?Bw<`u+T7G9N=zrB^7IfZv<9i>0 z2nDaE->uj7oX5tCICep}-mKR@375?g4x6GS7$T8V~@Wmwl$v;fxd z{sah~7zQ_V0G(dj7--6a`g^c1;j<`tiH-iVa;C_o!d8?{dw9;YIC@$sHHgX@7BuXS zB#f=zq|=8|XyC3|dv~$_^sT@yCuR@(78w zhHXjxD#P*aB9xCJy^{E0{EsDQ0IPQhrE$7v(}|PAu29{#_yG}-pyRD=a-@F(H`Hm(+htjaP)z@Z?lGKO(XL>*Jo%xX*@U z%ofD-{k?W{QrgI%-jkmMO$A<48Sh`q#imyo*9uU+OkxrL&Zj@J@sh7VR8g)zUEA|j ziymV=WsndPTe$&LNs}jJ+4t+!pr8_SvZXb1KDjhKyL=7D_PhwuH0pLcHpIxqx`Fw6 zH7e%%2dsR3O`Q?Yd}h%p%zrDxmGcQES8^M4PqtEyLdk@iy}ea%q(zntd9cclwnc_` zv|UdUVENT0ZB1dT+Qq=9k3)N_30OA!tJ806m6Gcj&Xu@kvWFhcovoCkV~*}J9r%h9 zxF=3fpH$4>p5;>SUbVG=O(vas%Kw@Jk&d}X9lsg2JI_0s?)nmB|^X#j^{wI3TfJ1)n^#RnVjTlMLmspUUm*9aPd}ABI3J&z3XTZVAeqV{r zU1ip>jtf)-1#g41Ro)!Bp0|mjR*5%5vz4+d`C&Yw5)9N{YX*`EX&0pm$NMOyE=H`S zl+v)*z|C=f1+kAi@xea|g5qq&oK+e4!ru_lPQ@DD>dTfKS!{(f`I49|*Sa6-qy<{a z=mApl{-%5(6KYJSwT-oC2EB9dp>wypfq9tf#B@;LkZ)jB#jwkEjYV3Higgz|fE9JX z;E0dzaz2{hLNK8nwFwa8Y4kEj{Zlqhg=wDS?GlxdnKWd@U$g+wMB**5i$hMiKsTPp zxWE39bg^%nwM(gN^0L;Til2z7|{$NG!Is3$K|xVj&`srmYx(iYu4}WwB$fg; zhd~GIj}+1!Em>>jurzW$AX|JT-b@&u8=HI#-{r+kig*7Mm*(bc0KiL7J%G6#-}!lr zgrpH4M>rmxV%`PY&}tr7(*Q$!EdDpuWKJHljZ68 z%*Xsbh3G{fI4*#70yvUAE5vZ!_B9&7{G5q-%VYcQz7TPmf*`!o2f_Rx7%Hd?5<=%U z-Oxrsz&d^#l)H8p-DwY-WhB6*G}?C2p9Cgj4XgJ`k*>{mEb%Kajk{Dm0V6v_K#NMs z3%ydedp!NtZT2W1yqUtjEA7r}vV9i+`Vkad_H!dTAe8>O`gj4x@G@SGgZvy&)+%H1gH%Irx;=#nQ+LYzT|T!28h#g&Z9@(skN>%RO%pSk}1;WN0!J>%g%+h9gW zYjfgi5m8(7!9-ZYP*i?P7D)B#z$~$CAldy@FJ{i-?}IDFZs+pBHBW?F*vF+zFPEX1 zwpB@IDan5-8Ob*PxjtvUukM@|g@k?yS+^Fs$M%HHrL?kNEG=!IslL*RU4On)^uq-MlIm{qFtMJqk6N3~r^=A2MmX ztIxc7@V?EC?eH-GSGe(Cz&#Fdonx|Vl7{?~GDcQ=$BdEv!Gym%2ai%C=+j zsZA@#*Nk&t$N!K0avYE=C(DIvKYsW{B29UyC}Ig3z-E`b`kD-AL}Fck8eENM1j_Nmt^hY z;=jY_PsLu`F01IKvX#yvrurg`^>HCwKm&=F)jijvNyW_Nzl3;b&GO4E-W8ZLRs0#& zWN2ekaI8)KVQngx88}363hKR^Hz-J|9b8I9^Cb!Mp;MZ4pDli|J^N0JF)@5rmv%?c zsX}ShJ~A7Hdc(roS5B!|HT7Lp*_!=k{(Ph@W*hf6Z?RlpVxWiywi$|yR=iqh)nY7I zVdAWC$Uwk9tewGL&4)0F9ccdk{#%Yc?6O!?2?l5lIU{kBihDGz&W^2@B=6Q5uZU0> z@`1TgZUk0aduyg%_awYQgdB|($vJfu;rMW!P2o2&lJvo(&!)#7bLi2z*eG7(w@|;A zDO=&o3So{pOeMe%3kuonaP@hhytb4EX%l=`0~;SILAC^H?cyAs&kY`Ar1NT6u5W}& zE9`Xa%)6BzF(O35(t{eVFPgfRwlu%8EK^-D9t zYo><|TL{VH=P}JL+6g=C=hr!#XFoeaf?R&n&ynsnFRriXI;WTE**&c^S^9czbkvh~ zQS5-r)y+uAkMt5UgD7HW^e}fj2`!0!$a6CYhReQ`MxP7$?}p<%G1QZPV5}-5fsrjK z*%73p*o?t}HZLM@v7;w+b9BA{S_HED!gn6+zWfqCdCbl-x6v2m}eZ839A`Yb zrKk%G_Sd6Z-)7@nY!14l*Y8AGVNql;1RkvQdcD524ypK_`2ZbB54WE(iMKwIBYbS6 zk75eMWpw02_|oCMHkBxED>3=FY5xVoLwI2TaN6!1tzF(vbPo63@u-0CW)4;ZEbxUK zwxn>*a{5n1zq{1C?|j)N4byGEE|IIAJo~;KTk7>W#x*wr%en{c`MsIQV%3yZJ$vS1 zF_$H};XC#-vUKVMa*{|Q;ev-|kGj#*cSDzzggzE{SWP_QbDpRDWk5-5&e zO%p$WV8btOTR;4eEDlM;K3uL*PE^BCW7(8pyn_#MBy&_+47>7>2}`79LwE>PEVsAS zPC}>nc^-X0$SiKZnB#rHgDuJ+L+SUCVs zRog_CYK>NGn2MA4Hyysq{J)P2&_X?4W6YjWKzL`<#6iy(kj`!wKWgi7!4jj_73q)k z-llicb*6Ogk66tUBZJA-?)%`v7!_QnQJ`cW~h8M zoQzjB(a>>5WlZ)af~4mT!AsVapDQsn+h)BR?n)$Iy3LN~pFLzN_Cac`7*}q|yl1hT z2xn;f%IuvNqGGKM7Px-DrG{QTj9o1i;PcyPvIWGWMWwCQZd@yfN9kx~SRxWwyja}g$6WgKf1JtrG>?oRprZfN?OS~G0&&>sy926>hu z5~Mcnjk$-%`^@e!z4ma#%>$M87hRNF`NsTuJ%%sIn}L5tagc4Q&3HZRIs>-eo3u+2 z-h2vnR@N1C16tNFI3enp;`<}*ctn|xiu33sFM4)IWS+pCcl`AYSx1CWS2d_IS-*Zz z&ut-Xe|@Cqh6V)x1U5e{e8HleiV1 zH)pz;toF3IILIKB9P*&1%WsdG_>B6|kJpEf1C=|IZc{IG0yWYCK!dkVn3+ohBcQVl{4GN9D-7?C1RHclh`1deBmeB%x z5X;=4+G99g=kW#H{jsat^vWo^ZCp0V**X2`!88H?;9>?TZ_CYdib6vw%7YbE;_rb_gh(dcXQ7&P0~yZH@kRmj%S ze4z(Bv+C^2F_De`_%Vb8X z5-1$Z8&-!aGSn$Mg?c@Sm93SZ*m1{kNxU$*Gn&)E_}N|62j`dFx|!UXspk7}g{LUi zU+>pBd&r{gooya}hlqdqWn7(Mbdzz^@9;?8_gx}{(wo|9=|?~d#KQAzB8CZ$3mdA$I4QJ)8l#=_<@e~#r5QEFgmC#l<0y^q#2ccR0n|<_nzvd4}t8X zE=dxbco!aHAG?%Z*8eKNMvp=>odb#`ELBO=lV{^NOHwrjG?#O)TM`-ry0VPtG|VG z+cCC!5lXWcSazg#G;r)(N#^=D*QYJ zyHqkm{oU6^LzZ--E=-)WCtHtNo#}+5(REB{bh-r~ROsOjRJQZZ6jQF+*&ur1L?ZT)My)=@5{%Kr*8+UP>$2O?dPi}N{9ok6g?GDO$O7mkeFMe#B zf;P>52~??mj&_8Kp}5qY*8R^bWEW1%XyCAGbMM=K{zVf9?-HI}d}t}KEAH*1ydJ*C z(r^?nc~DGS>Vp1O3;#z!M7eA9tog4Fz>4h-wUT8Hh8Vu_rgkgRube<}7hY0Eqmj2aTmKew2n9Pvl8Lv#wgc z%N$eKSA`RYyW8I~p#Y^;##X8}Z5h8FwXmIZ{RB)l*2_TmNVM!(y$|!v@3(+yHXK)g zccn$gf|YUm1M>^h*Q&i@+NzA;DlpLyxC=F^pZT!D3*xhoP=@tz}@`g#FmXD$ADC#yD6|=&sA=H z_iUD=DWO(cB7!j97A!T}v-lIZG8)vxV0vVM=S@Hi7|(8-7D7UW>=cq1&4I>Ba_T#3 zgS}72E$TDnsc{EAgyIfE!Cs@XQ0)CYM$mJUYt7BGs=lwcN1ma>M0=c~nn1t18ejW>)B z((xRLNi**r32eQK>FkL?k20)&q|TOYp!!N(QfEfER^`0&*R}eYQ3MP8<>lUZs-QW~ zpcMc3CBv$fPqLjc<#B7XFn?CI{{@6*S=d696Rq(DU4v$g;nA$>r|5@U~xPbeW+vX`txX2XT>jU zZ<%}W!Z9Vphe5kDH86M!Ew_Pb<0Mb8^NK+BdPv8Z_=W8BEiUBd8(yq4R-0h59;T1* z*@;IrBo@SWgqw3g?=F|Dcc^TY&z`2PE`O@ltjSgFF5d^bpd@`3K{q4@278hU*<^sA zm4rh*dtz5ZT&DePVr|-@};u%gJDrOi&z|IvaCFLD8rauTBzSA)lny-eddYb z6-QZ7KCkm3q6|{~TKn^2bj-4dVSC%!NmJXnw~10enBDbumpc2k1*C&N*xV`C<1rKSQINb;AyyJt-THc7Jo*lwfEZ6JHGc z!5Sc@a>dWIRdrR&P>OInx;3HoYdXoIQXy$g%u4aqrVDNgT!q&at$8$la$VtFc*Al8 zAH;CA?ptCQAbD-Fa*XTPf9&=+mBOe_-{j164?vXs|vMoVe>%Wh(nc~Q*bhO zu|~Y?Y}9F0)X~~Zo1yx4g$}&4bwj4$b*1ukcVms;Z>P;W7XP$6u3Pi-py{q_B~#|w zBzHl6)tsXu-mcNNyFExxU{!?vUUI_1saQSbdqRDK``+vGX7l5(Y2}35u%p_J zo!V%mu|iA4&3qVyExn;F--3m13>Jn07}r=ktCB-K4(xl?zv^SK-aKL}w{6Fqp`G5p zemh{+NjEoBT4dtTHxJC`l%)rF^hxO!l`WhTy`jJLzdz8p_PyLm#N%SowXD%OfTakI z-345BuXATT*0H+L9<5PX-Rax+*0VB4Txli@`A~M0t?D99T3co$)zyRLeBp()gWr|n zVs`O(9zo&rD~mD(h`0L|6SO9Kmm3R%=fo{)dkt+66i;Y6r+KYU>57Aqb17OS!3z29 zhsm3{Sr@$8kTwe#U8}bbV(W9Hu2dhHi@7szZ42$^lWr&^VF9JI?;4cZmux$lqe@gEI@ zS~M&iggvG%$-(t^4)h^HIe@+U=v)w?;o56jaN_WnSf-DNpYMW;Q$eN}PgHWX6DAK5&04Ze5dldx*Aon&gRb(4!p;UvPS#b?{-scQXWGu>k1!_B!#kgZb& zN=e$Z$F+CS#HOh+JpJsXbTKbWbmCW8N>k&NnREI;k6^=RzePo*6|Aqe52Rt+&{r#q z=oI87N$wJHH}8j6Yg@^iHOkpzW4-SGmu< zey!^_;J|VtW)kuIotS>LZRau7@MNRa`yr1{7};jawIGVRPVeGO7IP}FVt5IbnGbJ- zeI{--;v;Pn2ASI z9=^i+TdJ8qnbcRX_-8fJD_y-%xARL7>^b`CPd;8t?)N~vpA5H?vrdS*Dm|#tg(Uo& zJc`3IU{dGkYqx&8bGIIpzbgB5rTx@e*AZRgdGf^R<~XeGVbupQ7RbD(pdd0REHok9 zN&2P5dibbjW6i?QIFYNsWw+4nvr7Q(_9{QsM0I+%P258ze2M8zz~X+eZa|LKCgZJo z>5;pE`i|Q!@3ZU{Z8*(%7fBMhIknY*ybg#QI<+0c@I)4Ds7uROe1R5v?^f zeG4dSS+i(@kjpx#<3+hIvN+tleEK!%Lb(0Scx#r*bQ)~pw<*R#~ zxkfrbY*_ltV!Nuy8l7C*02V#ta|N2~@HNR)ya=CZXBvC`+kO-)$5J7|sGg7-urYD5H3^O|Is_^}ZJwdE5w?YL#<3bGz zUFnVNcBrDyzd#Z4EIA&je&bN+9wSkdI=171Xui^8<&+ngCRkgi-W~d*krb}2C zx4@w{lp531>63%Fb7{YOX>nt=ot$dQnnlC1ii0qtyGX78%%AVbApch!%lP+QmWPq4 zUv&P@gzZ1d#CaovNx9stzWo1k_o*cr9z40)xqY;-nSfxorAQPZZw20mN(DxB?2AkjgPQYf4L*aPhh{Qi z?IK+_G-ypJkUMqXMg*<+@MZ5`16l}6ol@k(W}^M=wI6>*;Z3o>VFOm`+FL4s$aGG- zXa91~)n6^ZAFGqQ_R<h~uFU5x8ktE43!*K#}&vjNe_`xtZjgg=<^pGA*LcF@;Dt zUEO3#8Ch&%a2?~M+3h=knUo8WAIfy1y@tzFX5t*HviG?rc};h^p%dM;jS1^hNWzO7 zD&C}JS=Rq@X3^5O2_KCZB1kj6h}QW!?&WMHL6}#XO4GyPV1e~QxP*z-e-K}u01Rk` z9{5tIdl`m%U5bA}LuNIY%{opTp&v`)tro zs#3V{b&fAXsVsn3nv|SlaLvU;MdzDU2(F=}MAtNI^kV{#-v?Xai(9 zAVmf(rKmPIj-R*ZnEF~^3G9!E)LQl-Y>Ide^|~b}c_U->y1GPGS3i>WqtdPdlGkV% zCTd5Bo7BNu1@XlQ0cA7oa{*RdOD*g1eO8BfKzXMMAv-eh0wNu_^ts;6O5K=eLpjoxO9wDcCcA!loqR2m*(B-;Wu8|5eG4rv!L$fL5V$oG~*0s{H zY=@uu%sjO_cv-t5`FZy9jv$MvZ!vnVVj9lg~@?YkTq&)bL05}C)CBU)g!IVKiO(k%X(+%Lj)^|}Jc{QoZz zJjH~sg&0p};}!V(En%m5e?xjq6BV1LbXs^#Y-Ut;a|>}XV+3i6dXpc>gN(#{*}v%9 zzC0ZCZX)>JV+WTO>r_=POzR<3XaH`J5nPLO04B6wOOB$K~%f~_=Qi&WBO6<`tnu^8KkBEl~-)pi7C8yC?KKV1aIt*j26Jd z$85;93Gz3nt9#PQbSS)g9u06bkYj1q{nCC%X=7JDE2=(cPP0@2Td~7qtCM z^!(>)r$dd!?GSg_WXtDsuqzJtnJ2MJ#YFq~vInR*gK)J5uZUuPg@k++)TI;8?hur_ z43?Af3v<smQbJw_vLh{o2st>`9|$)W=W^&9VUITa;=^fFO? zG|<2gr!avYWg0Z$SaVL@&8y3ctqf9v=DKaF4y>_vV0fT^4dFy7hjP zOK1n0+nSSNIcyJ9iTXOy$Qw>*b!S@gj*b_Q$Ck+Yx^!NXpUEgaUcgtMt#r!TxzT}#NB0i1+ zK>fZc?KvgrA`oRzxBy=F?0$`|DRXt!tPuM+}YY>|c+&#oF%!yqFTPhQDw#5H4WX`T%O=R}2UB zU(^(swAQ|W{sQ8haQTb4qod_bQ+G4(G(qXRL7=g&(a*h4{^zCozb?}M^~|#-DJK&BaS*u4d0k>~kf2SJ=5iFNE?F*VCff z!FQEuhb}`=CC}(z@IRS`7T%ozKudM;vVN7*LxfO4&1xY)3~5=Zg}yaYExp<8G20;< zk>flZBE-!DGv=CdBxZ@BtN|OwMg5jr;JGTeR?7X(M_n0YZtH;W^7<>B3yp;uwU5az z4vy%t+{I3%_!~#2-CS|LKKXWR53O9ZgAQN*>7#+MShM!Lrbi-PWwXt);o3v zKgp*Kl8sly_dZ5p1t-Ki4XcE@Knl>r2nohnm&I|9={H?S-RD?lj{b?|P2}>Gf4ZI_ z7cumIbUXg-ejt?tz`d`!|5JXH7GV16kT|kZz(h`d0m&A+q%m>G&cC?c43V4cT4rn& zk^MXqnsAvvCDa!^Tiw9sl;x*`YfSemr0u~k$VXxU#Cs%=9hauGqN5Zh<6Is1xBH*# z2`U)98$At*VC}A0vU!r^55ecuKfKtr-=dv?b++Ke5W8-j6h<8PuL)xs!-wH)0R8jv zEB%4;4}W6q`|?oUH3TmyUatgQb&X}oE_ct5TYTm|Q0n7R1zWlr4%c@tcvzPE6fqdo zI*>P8tv!GAqe}GLtkG z0zs-*S{p)*!@VAJK-}GIQp^%lmVe9fiVm#nX5LV1mjAk+;?n_+b?Nh|l1Ye9$f?q0 zCsX057E~R3r}VjcGZCss0mtn6DZF^o2i4w*Y@W2+V5R|mbPndT3Vu=Lz` zLUd%>$Z$u%j2*4PXEJrwuc|$%`yysusq`OZ*6%uzra`9$nJg+n?qdpRxJdMSBe(&e zp{mv^iq&?07Ml+3MMXNG}-XuTdbY0dKZ`KyA7X0KwfdAKep zrs|*qCCQnAep^?r&6=*f1nB5MHRQde9bCb)ZK}MYZ1Z zE53hJi$=d-ishXbqZw(OL0LBU)@iauB^G3M9I3UVL zEsMpXzj+z=@iG`Uk{z0{*8OpVF1Y&ogY|mlt$K!QH~?lGk#3%GPzH4D+yG5pl?sI+ zJaV(vdq;d)hOwv|BipO-Rj|efl{UV)0P2t4a5w+al7I09j3{l==sS4#SqNH<{lZ{$ zs4ykaM~#a9{2$IM?!F&5us`E4x6w)}0VB7Tqlfvob+%lyoOV{~lY`P^0LpLNwaM^H zhbQ2j+f7;=qJfb|`KA1H21cz2E88%h@?5Of5A5c@k-=`5W_;UW+j8&g?83dw9po;B zDu2Y+@5bA`!LfkB+^={J13$KlJqImoWLaIAq{p-%)0RAEwYBSZvecoAmLZs)Cw1l) ze@s;+bbDb7C?7ZD-G#8Al5s;?8#nXjeb{N*W}czq_lS5X)52P0gsbC?O}XVt-F7e z3{hS_Cw}smg7WQo^3o`C2oA@k941v{I$nj+=iO5G>Ce`M=vk+OQG?Y}2@xxyuk43X zUxe1A*N2MVVZg5+X9l$%te5wCj=U^6lnosd*awch2=o`=f;Zi_$e7e5UnWRi7+{|n z3)+^6xcI{-{1^~+ELCYrZu*s-wqPi zYt~?2rIj@1z$@uXw~$zL0mEFUm7EO^IPJ0+|7Tp;VQ0j>ri+#Y9+-y5T6@M@>jmzj@aS{GQ#U2f}|XGd$B!oapJYF^Ir{lgr>wKQyw z`d-GMqsM=xesb8y5wjUGc%I5nzaMndYS%T<_#$rQinnR7)v5q53G}HnT=iD$p=BJ- z9Eq0)zdNxzk@#!fmUkoQPZqm?U3wH&+B>m%ju`HkX}Nbuh1a7Nx2)rDHE+88%?}=< zf>z(gk%8vvuy*!}1zA5bO;)>6fZkt>rwC~e=(ZOL&G_=dA5$=k^^`PJHNNouHR{w; zmr%jq(<#}Ub4`RU7UVoo!~_|>T{%mKcePmE4jgcj{dk;>>1+h_eC`G`p7joKH;{e? zxsR(Vw=kFRfm={=ynPK3cn@c|?(BW>Njlg`TfU0ElWhcwk*Ua*A1fstb9;%Uqm^0O z)u#)rLgB&}m5Snf?CEaruMe*iD-&Qdi$9yVVqM%PDw+6V!;Q~Nt2JKTlFp;$7jtUQ zzx%i8I*C-DFXw}eT~s54#!gH&meZ?hQN&H}(QQL8_~ykxSuJ#+#-LXQHsjl@RbUH{ zuBk|Y{FNndfi;)9tVVeXj#qzdx2+Y3cgjV)dKE%(t@a{SvY~cjb6Z`I zZd$sqo@2DVr_-O_B|o~wpgqwg*+as>qDy^cFy!+|C8rIL_i!XR66e>ruh`uS*<{;lQ9QRZ( zZc7+ktKfY($Y(7t^I$ivUaj{LnUdt%j(P&gvo~$fWL~*U=W#A~F_k*Y?H~B5G4qmC z)E!frgGFGP7=e3Bv|U^oK};oGFi1#KF!?F~2Dc!q-s;0m)#<+NCw0|Hdyw7zOi?fa zk`VH{GlQOK4uROH_=xCCNlu^a!vaSy+;ntX!ZIW1II+FyKSKo8p06u;$P7*!YGH2n z=Puxq8NUNWv^I+Z2`k-hIa3{%h(m|%u^sITC{>oVYSE}Mh_~c)vD>YB z%%2Gwq^$#buL})KW!qIoV;j>0LTL`zMBF~H+b2W_3iUNa8a*jccbUkQdsU#Fpx4#) zi6w!M2nhAEBPDH7HX6ejQAXm=ou6rPKxpF!PrOb={U7%be0%vFMwxxulpoxqa0@o zXUrl7)w@Jl`4x3Z@@r4m9CZJVE)~Go4<&8bUJ`@=OL6CDi{47f63$Z85dxgocy!#p}gqIAGZHE3!kB zZkb9MF#d2P!8v?a(^4)|Wpe&OVE~6{icwx7{W_XNUTpqk+~k&gRF58H3|#qK?kgon z?IyG-9ZFVOByLS0e}wBb?S?1+0@c@_)ynwmApRR#y$9bXt~tdct`LhMZ&fW9EwPY} zZ}SYfw;mTYG1K{@5L)b&Qg#a%i*PJ`MjxC1VdFJckE=z9VJNQ`=QW|IFTBPQX?D*9 zix{mIIhCf!7(GAGwO6IK(~2c6n`qP=o#Gs+dhdUc$X+(toCvPQQE9%4Av-#;cE}6W zX+OP5YmcY&N;67&jV-}~WVkH)YIx>1+B~n2PmIs3&o-0giamA?%gk z!|H>=?kzCMD#ZE*T@bQFVV|8k9av4!t)dg=CSNNeq+c5_>;vs`I;fZ?wKJNxl=44Z zs11tTMbMPz%!ow)^Vnl8ki&UR-IJ(b&N#_rzSe)RS%JGvyU3;F&_+>sijPXL&HQ|Q z`et1~GwJ?EAb)-{-pW0AbUzReDeA$%sD>!a=;W#GfmBkqHicr^eF{+Kz1Moxd95$C ze}vp}=qn|~`lf`L^kRyfL93g&7wc`YLI<|H`P>B?+_iD#e(O%{^hyOj5(iPa38u*sM-2h;1cFcH6AwK3s=#!40 z6xU}&TQGduDfeY^f`e$ADBQ=m%+Az#yz`a{wHd>6wBFl%e<%5uNE`i&wJg;i#%L_L@VfSFL{=4) z7L+gWE$6xU))fZveJ-hr^PQ33QL#E%6$WJ}!VQ82*^~0YmUJmYCP6ui8pXRG z6WRV1hGk4Q?u{-Xyo6VuJihI0pK)q-nno3HsitL z@c>QdEXFB=0&d37;b+_3tppZVGh@{G8)IPs4d0s|NU2L@Jn%GYKzX7S`>g}J77CiX z`+rtKmEJLx4v!!1`eqRY(|?PcNIh%+D&8I?__lJ4XAf7MPZ=xXHAHB&_-a9h6|<%k z00Ah-NNc9+Z^uQD5j~46J|K<=M$IR@ZeY3yFl{<1yHKd*Hx{7MkXl;9YGoT4A_oPr!s*+Gd1d%i@^B?~QV(k3Bce@qogX{Mmo}LDjzzenf<5i1W3MHe_ zjwfP#Moe{WZWpRr@fv)59a(2$l z^=_bBe?XAELECM*g@XyV)mc=$bANei;3k9>`>FWsrNKbfv`(oOLR42UFa*na0r)M4 zs^HIn|9MLt9w}pKD;;Ede(C|QEA}jH!jxT1N(L&HAPdL)8Si5rEZFh$l5nBli@(b1 zba%VEq&>4rHYt^i-L{Z>?Lt8!unR>VAVpdlte+RoBC`4u4AVCg3Qw&om3gwG`_7jy z^jBpPFo1Se#9w&cbKQ$jH8a7-*$V?8dXCYp4r~*@cSn`06DQ#Nq*r|(3YBa71X5hA zd%Qn#7y+){7qe@#?f5kZE+-v)^i>C9W(UI6Zjl}(lztAK7)#%%k=FZJ!!Ywf3&*g=Ic5e;v9Zc_O+f z*V3ihI{BP9z@kHmAm#g(hULVmdCcK%HZ?H*pe!h#v8R5723A91)45GJSeo{U;)7ZV z5m;8K5a>1Kar17~I9_x8+!NB3s#ak%AmZ>8i*K^>hkh&zPr!Xj7hZ1B7eYP*>C1Z* z<@SrLN@Xi%7XwzRSrYF)(Za^tn>ho{^j2rh2I`h;>7O`~71Wsx{g>o{$vD$lde_Qd zuC*eZdZo5V>w$Nu3tzWSTR58M4JF5nuUV#6tQ~@2^_hldT`aL%z)=O8JfB)eRnD3} zg<;@Ppe)dZ?Ib>6N3=f9YK9dTHQjog=#H3QuHSGhiTyyAoXRaP`SiM~0hd`vfftkA z{Va(Fv|p3Hb)Z{9jwgf*F=$(pC}tIrB_FjzC*5sQ*rf{HrL7mc-wpy5*wGJ=F+b=P zYPZ2bJHm$yTn%TcIk+(FQZ0e#+7=Uo+BdQj^(_sNLcTDrW>oQ3vc8}vzUdDAgS+lb zJ%)Cds}9E!`xT)9@p^Jc^;1n~z0b^;+{i}Jw)hp$u2}7x?mpvE(x0SRU>)OuWvxOw?lFM z^E3aS?p_irbJUGrf(9E%-MJIQC#YFBjBa<=R`qq6ODx8%F(ecFCB zD^3A{$sflNbB<0p(wx-p-309*b9wL~I1S+_~pE3%c$3Vo`TwP&Y?xW^vc_@cNy&6y#ss0jkN4|zPv zwgIu5AvBomHE3fK4$nPI%TduYcfDkM{-qO_w&>SwZ>v@q!!beyh`uD!zYu@!QXjq%YXSSN@`CBCKa#;x0=BUX;aT!!nsWRkASfV$(s~S zfK-NILM!`!Y3|@spKx6`m1!`|fX)slnMGruPzGYSn&PSBzCNQ1sa`2wV@s>PK<`dGy-W zjBs#Fw^c8T!J&#E?@b$(nT$h3&g2Kqf{EMQjolSzX8W@kBP30%DWUXj(eFcZdT z0Z+z#$1jPH3jt|_LU!E(+_{Lo;+Ms(1+0EUIC)u#S)!vB_64Mz-k;z9qxPqk5c_Z0 zqFUrP+~S{5#$*r^PQfdbzE-VIsxZT7P1pA$Ksy+D{9rY|T`DR4c2s*S|JME0kUY?F zYEyoLQg9MMW}UF2=0zP%Z=q`Cssr)@iIM$!R5N+Mql?-SE35TQG?6_JK~$C|&@{ZK z1{7pTu3S~gz>*i^hsFi@QT$-h)g{fIv^gaf

ADe}AZ>5mLiHSP8rAl&Eev zzg)ZiVE6*=ch8F(F*gr?19lwICj-{9?7sgB0w;w%KC5&L*gMjZGc6g;M4WKCK7ysl zfU-8rOlWk+-jcnL*P>?IhQRh>(%X+=gPx`OX4jfZy#8~QxOGH}meniPO1raleCOOE zp2@iY(`O3-_H&(Lye-=-O1!%EBFqcrzZ#+?T6}tB zsz}4u+fU1`2|N#pMpFUoNU-US$oCVC^2M8lUD`*aEulA-@7})A{cJDJ83hxl+V#M5cGW*y=VB~Za1*}G69Wr zzavh%Clk(M{uDmF#Ckfq)iErk-}Nh5TadioWBO6?{U6+;R0SgNpJD%kL~C)Ow%#j; zr<&rVoz?5z5mor)?DtgpRMmqnj<%X^2tGu-o6pN*U#PZ;QxNg$iDV$|ox0o@P$Swh z2jPJTRK&?#q#G4CFd3`49Pr1Yd z7cB|VeNlgaQi!H?9C#EFSAPJ@!{^)1*0jYQ#F5|_CHR=DBJhhQ;g~P~_T}_rfWxc# z(o0QQ#D`+m)VE4()uS_fx!dCA0wf8AM_=x)oUf z##^skqATZf0U^$9=a~iHI;O@B1npIsWjnYw`Dzp=sS^6CG|=Hy5j`P{u0^KN^D}pY z>eXymgWUV>Pn3YRx#?t%-2yAOPlH}75#;IE0c2jq#3ADT+#(ax>b|R^mn+4?>l%U= z;6Z(Pl2nAMrU4g5O5?9P!e5&z0ZQmp0}-n+!6ssDBnA3Un9)0%vV^&S2gQEF5O4C6 zx72w0csUQsnRH%Uy?`fGD75H=m#0lQF##;;dC1F{?24F^ zDN`Te>1-s#F#aCuD3XJbyWa~k-$J?=gz4kBiIoU`lWDuO&;QN!Q7HZ;cX<)vEUm*U z>B@pD96}Qi4I7@kv}AwUNJ9nv4x~n_L%7r)P!eR-Igmf7ytigpeGmL9Y+=L+o#PZ9 zTRdpdkU;0a;AGF=s^*J#svTWq73{TUNQ!EM0@0;W}nB9_tL5Q8H%YD;Km@|&>L3s_8hFMzbXWOFYlNVUt}*Zle)UC zl(sOwGJ0cRc=uVA^mVSz8M#pvEFB**MY>^(yPo8_L^81)Z6)(0(w63q9flz&8$#g< zScSq^HMo$fASf;kX6d}X%OQ_axqa!z`(pz-)sLNEJY}p!_JYU0JI%hg@f5U2I`Aq= z*F;g%7cy^abcu}66a|l0>wFIM(M>KHTLd%1czIh+RGB&;*F2N2?^?u7D9@8J#3wM4 zp301ekyZ8jJC9$1h&fC1R4WS78rm;QvquevYxE88AI;arzSC4r-if!)C0pouR!crL z`yA-6K&Ho07>dv@%+9Y@=Ca9;b>k2TU4BYtIG~u9r84wWaF^lXD}+hI9=&tialmTq z7emRt484ai?G3^Sq{kRnadO)t+efDpl;jibb>o9xJC=C-E6o7VCJ=dMK-mSUx`UZ#@3HZ<+f&|cn|#k-EY8;e@?HeMC~6N z(DdO_rjveMvAsa?woh1VCHYn2^!caDfhIASwpw5=zkrBq#fE~=SFx5AvN%iKpnwY4 z(^%6!B{iwaOQVp=jO8A%sppjC3&E3%SW`9p2Ro;19)ptT-)Qa3BJ}6nyVM)qfu~8< z0tg4`I#iujrkB?N_@;a)G2c<~CKo^U9@l}jJX3ZaC@i?~^}u2d2_4&f{N=&#ZyFCK zRlcS?nX5!OdaUf}hf6jRTuwgE$iHm}|D+)w`{LlD6?Q%L@3r*H7*=bb-qX1$ zJMgdUpqYMW!d<%0S`&HQIxAu6N_zY~Q>|!nYWk-sjnJ9qO_)g`v&YUwKkCH|Eu2yT zJ>N#;-tpN&YpQ;u4Cyll9WWHPFj@&P6M=2a#uBZUc&uMRY!%RRbyJmKV{xKl{ey6P zJ|Pg>WvFVt)~M_T&IPwUZT~MyFEum#)sEwS9V3W>$KDq4k0QT4M_}T3VjIZmi~5Ex zFDHP=ns4l_$GLfvA+wwr!;=C?|FZ&15Sr)-pwsjOO?-H7t}cC+0ZC=|_;iuuQ z+6NI?Fw`Vmq^-N<)s_fEc1A$rCfYWToWhTlBk2^~Y93_U_83_m#lhrg1-c#DZt960!orS25 zB8N$+5WT#k!hr|x52(yjNU*n+Gq&R$$U%(M5TALR01`AAD^9}g zt$dOu+3Q;6boY^vNMJ~*Bf(bfBrkuswzS1WFZ@a^e9M8rtDc%|D&`Hd-AaNhq#it~ zAnQ3y#=Kyw{LVbbUTKE=sSHVg61m@FNFDUlELQedS4?pi*cOJdFW%QwYQoCsRR2_# z(()0|MVM#4jm^HBHH2Sk)$&$S{Y5|^$I_naN-o*B|1OQ`p2Z@VBC-s8 zFwJL3i!JBUV7VK~ILlxcsQ!B=;ro6c*0i@@n>$e06Tg%VqNdp7!_J;tfL!_bw{gY% zq_<|VtcKIpR2oX&W=byQsEd8=g3HpzXU01@EsHN35wd^Oh9FU-ro}$W>7pB&*15_} zJ|`%S(ey(_461T!kNyo24EHWvY0Tkqwg^D|x3=^j1uO>Ig)z_VDp~z5o3pZSYOBT- zyR#4PkvdFK$ zDS2XF$X^wd6L36ipzc4m{Esy6pX2MDzdjgf(go4qXIdYV>{I9e>+=7*2k-x#!L2&6 z9tQ9F!)TWZT22fld%`%6=*~y79L}D<0a)D>>6BbVWa@9cpWH1qe!qV$AdO>EvG>ek zPXOO;>X?h`<@?e^_e=@8!g;Fr)g1kW`ZWntz9zc4_ma^`U07PFCSE0s!DW62I zrz2f?t`^7LAOS6YjdW*ZV>bPwU3YpSe-PO(TG*5d`81Xigj+;i>Y=JE2BfYjYgST~ z4GI3{Gs3`I+=$vJ(;b?BJ0@gB{EY2MwrNlU6g(l2aA|Kc5Qo+H)89G;OjT@HK2F9V zcJgOMzZu%zj4=pK5or6$oT#xiSdQDOedx-+7|7@J?2M&1d1BMJ!Jl5cSg|LZPEbJ; zjBnRD(|^sFv4}?D=iPrZazYuMD?xv6(BRq(tYa)@>kZIX0n{J# z7Jl#BpMAp|{y`U23olx0Qs(BB9A<7Ojk)z)9D~Rq0CC#OcEYwWRaovOeh@xMAdx2F z$H&4RbH(7M)l8)x7Pg5s6k7qi?oHy{AgCESKP1sakO0$4w`4QA~60@Dl_=@^u?yxfsj)AP7C*kVa-q3HFg;XdlUkPMt0 zyisnDw90z3;IjBKku7gR=sGp6YS4x2h=z*HFH!7srnfPxB~STeY!a`pTarb?N{+T; zk1m5O|sS=T_?0(Soz+Shrdk?CJO1_xBL{SWA!_TaU2yvM7Ml z8`2U@>mv>+g}2(He=02oE|(t4>dk$?Z(gTNzTGV-EUd_2BojiIa+4c!7nhZJx;OBL z1k%yb{kc3rmo}r~e>Cl&6#=!*N6mRb2^UuxnySQ zd~W8(CHFrlwv}!i9@kBv%YH-&AaNIKc>HhYmDf2_1gKU4`gM^zMJSk)O%s_|Kf!`6 zkHXbc>6(yMB+NggV2o?b2tbjV&*$be_fhiwO%ILHtq-BeMUzq==hE%hEyp|=%5~`? zxpe_PJ+t3{d7gDI+HP@&o83ES1-A)Q6srh=Y!jODvTKIuR|WO|01uY2qI}vha4d?v z?Z;o-VD13MB;o5Z9OC%te>M64wEgX_+dyd!2@C*Y<(o6k9<5Ev`(!6h6TvXpK)26G z0@1*oRFtW7PZ$5Cxio+3C&<;F`yaLyijj~>Fz(^&)WC?@0&X&-X z%MrjYB5{?|J=*&ppU6u8=5LYplN+yu>8fMA3uIuKoc%!9-4(IjgOZQA;AVoCtb{%| zU*9H=PN0{A0vxc6WkvJ=Xc}G-N9>ari*!grGqY}xC>i05x`FAMjp^^`_Bb<^1)nDj ztBH`AFe)rL0=5BMAV2iQW7S~D$#qwiHkTM=mL*N7mplbyZ9bxd%1%@Yg zRAcjfWu>6dBQ`A&BK*E#7&0>)W0SG2ju@^;kNH`+I-8I>`yzUON6C_x1rQ!jsO=z> zO3eI8J%6d~-P(a8qrFn67w#esd7fAxp|MAJ8Nu>e9n0z@*FjUH+O6!+RN>+sPm12c%GT1kVHNFF?uG+^>S?7XWNkVYXGtW%?gl*Z zWM!3?)DNJQYpp6nfOlBfxcIR2*3rVRs78F1oyf=_r62?7 z7(GcS?c0-#REqE~vi*94v!1VuC%hZ)xcBXs_f1f6B$4%UV(a_bKyW+9(0p_2QYIR)>L+iUVoOm;Kiy$2`3--EN5iiMM%{5-3-Q;WIN=cgvK!BV%cntf(%9xn(spXtDA;*fL+XQvEp zb)lXC?*x?&u%K16z;2eWAfQ1T=!5@-eKAC@_T4&F{>w4vkG@^j5$Gm{E)q@yjCgA1 z0598r|70)i&~IWhQ?{QLOr`iq2Od7;h;?wtv-4WQn4OI@K_x1+*SE`-4_}U9D+kB@ zY}nivNvP$E{XDWe8_c#;z~}g1KCFFuZ%D_h3jB`}0oV0Q4X!=QUh714Uczj1 z{r&y-2Y$E{wno_3i>?d&=f1V`5C+ies0y<7l~+f%&wclVSEEvChl>oytEwO1D&hI$ znhaoubMho$I}Rl!akjf6CMilc1C49x9?zQCx}!NxN%C7GsmOkolC68nr?KE$fX9h+ zQ@}f_W2~A#akoxZLFU+XMN;Ytq0fnQ^D0P@ShnUzpev(9W3k!k5?top0_QUTH8Tz~;dSr;GqGe= zlJ>tk*?&6Pe;r#*NUTj;P2i{VZ28~?*Ml!u&?EIMzwkWx4p;mobdw*O1Ng+Oyonm7 z1F%Dvj+C6~E6w@poNkBCz_j7s6r`i>hX{?Y5$}@T8+{XOJ&e>CocQV=>VIt9WDRoq z#{8k#onZw{v7K{1f8L#m*~8^RTWtV@3V4<#EhYX8B7j-@9I+?~s<@3>p{G{>=w&WG z^<{DCdqib{)}O)^adkJ;oJ#x;-ct9DRveL7biC*{TRyJ38|(PtUr^_f?PPH8VII(K z9MY&ijjc(4RoaY`Xi}t3%A?nZ-D0WCMDO=1=o!~^v$UTqsdQZsScwOKrvw~y7EKq; ziC2o_&z}4QGQYiknop%ysc>O(@b@+ZjAYL4)Do(GcAHy-m+s78z}?mO00`Iq9Q81X zhR6Du03TVTI{eXE^3DB8VW)wLK!bX|elC?&CFX*d^;&K#9&xy6A>t+N6;UG9DL;Tr z*h{9E$x7K!^5m@sJ^5-W$cmocKpH^=L!^JXe)hiQFoIb*-p~l=%6CRh zYe^ZR{E+$M&Fl|aNK<<=mZ&*YI$j2O<-Q)+TdjBtt)n-8 z;_dKD#Z%U~7rp#yi9+?fzc9x0VYv>prYgw*!E;AMP5`Yrw_T*jr=sUs^>a=mp44K$ zn{_Ky^^KT>0%hQHEH(NNYm6#FY<)VZD7`zisnE~oHX7UYLW0NV(-l>~x?xCI-D_23 zJ0^gmkQ_i1_SxjMb0YdZE6aMI*S#m6UF;}hT5OwbdJ3Ljq9pakcGK$Z>X8jipKYCk zLfSW?u3!I*=ubz9XUbwzpUeO@tZ)mtos^(sah5JEDnMrAFY3>?aIw)q4-(TW_8plP zy@fE`wp!8?6bvl_53kZJsrqX#=qqS*T6TezNfRl$xh5LW0c!UKO!Q7iJ(SuGsFm?w zC@tjtP_>&|_vQH%NQRNv3VJ46H0UJg?_SE{9z14OuHvFFZaQ=1<`vl7Yb^7OnatX? zr+A+~EL1plN>!L^g_#jVE|G+8#hbJfaVP&k@ZT>;(>siEXvG z;$eFju50>uIe0#RX=1%r8tVj?KNx5g zi9`OFMrSQGx)1OfH(NimIxJlq!Zc<7`(a8-QV^KChXbu*js6VL-`v*ttYuTh z>h+*6mes5zqv;2f8d*tq8K#*(X{d2byO}^w`ccy;i|z_fLAc2I;Yl05GG%j-WUC)= zGGU&(6QI^6N)D{y+qQbKj-!akyY=L370AeLb(gC+EUQ>fW?rU<=D}B85FRnR}Il=FC@2@*Pi$iE4iNS+h?^v!T>f=#bg6i2?8tFm2JqYvZ z1A^Z~@y!~5(%3W)Rvv{m+l;5I4EfzNnsTjmGhEwp=qB$efiuUZsPrsHW%u!({D2<> z-3n%-wL!O21eb?q?Lra(v3qAO%ZGRW-FJPh`nU21a#;1(P5kX&oh2|RYtIQ{W%)l` zwtp@pf`{ZiKvZy)|3pu^SUEzws2cW6MV->Vyc;hif_Z*JThP8XH>JA??kt^Wgu!QL zwppfY9Uoxr+}lqwzxQ(nxHyRfG=hll3r{p2MI|;6_G*{Uv!T<-4BJ-&<#8N12Xalm z?#9~*)@OP<^~|eP4hbM%cPY2O36l23maGmqc`)$QT8c_zKiG8sJulWBgZ*^l$xNcT zC{?la0l)e;`znXYOS04v^RfsB?wGG2-2$}|VSJX$elx1jPN#N_nPnzUlHsy|UQ)0R}TKV?_n z5jgz0K6Hwm@E}YhoM(bRSS!P@Iut?zdgWjOeA7&c?KWsaiCvzyEZ1Z-d>ocKYzU4Q zS%Al7nJ&;68r6p~lLg65CJy8?B?c}wbmMc3^G$Ai9%m^F+ZbAC?v^r>H)nY0pK7-16OPG4F+0Jb9eO@o=r5RLhnLOTzjIo$^)4I8S19F zh`ID)J1lyR8co_&n{qdnWHjZ6;1q6vP5ebhdxm0=%dt10np<|hbo%$XgYg%TTQ*(y zZ5c#$(i*EHd3R7iIz+HJU>XFh2=W8Ix4(YQJS*X_R4ztNLpVuSI0Y)^NHD+} z!F(yx6kc{IxvNgC1G~L*C429e5mECrnUfzvd1x zO8@301>@%eudUe^47Ns;^~h~$ay0{fD0i&>Jn%wi<-=kg&M23|eqpuG5qv;7hRqLR z7SDlR4n$eich@h2OdWBnyGM^lYe?EcSKjp2AXjRE)e9l!YVETZooY{LoJx3oyX>wo zYTmbuauq2s&QyWa6-HBTh&>)Xzr5f>M9oIdhLZH+n!!=8KBwsxMtcar%b1iPoSMJh zc$O#@2xbms*5SJ9w~^Xnc~A+d#cpibzyi)h#^9Vl;bm;;Xo4LrD8gy7>^Sk;E%hF~+QV zdZ1~@|8%MPVhhYBv`~Krht}OREnywnKk^QR7)W3pG3>lS!wr)}Wfyj~@MGly|b%HtI^KD3>EUn@X;}_sNxY-lto@EH2EX3wn%(hNr$@ zx;4^K76bPYTd04&xfls_z=*)B6yn%Vs3(L>{0>5bp(E*HTk!dSk#pMrLG1h&66|Nf zq+p6YNRVxzn2E@9xV^M^(W)7lLj_Z2-LPo29u~(IY$L?6a4QtBCKG zfvFl*8pvg+SHUz-sz5b3HVBP~^fBu6$M`mowyJKo@5_w16^cgMq(};AItXcMAgHV$ zGS$Q4H5G%@e@&Zw>7m8(@JZqgpe|JJG%F{9tL%dH32)!4^g{kjXYjWr6u0(D0ohuE zq=ynSaxSy6G`qT8@S*!2_838dI|7+^EcjJs_==bZg7 z8zxRkLUH@kkIVfEBf$9g_V>S^WxOK6B#$q)txM`5s{aHxB}$+0F8HhvU7i`nlJQ0_ zYSi}kJ)@^ruX4(@k(_$YM?e8c)STX>FLH-kU1cEOCow68X{eI$3|_xP-WQd5tU}0%?yh+=^PX6cs!)nTw3~F#BQ0A^qh;a zC%!y+K6EBm_{p8z^d*y*CyquQI~9{!O&F7e@x8%r=bCR^23Lm*d>oHlZ?Z9LhNgDH z6vy^d>v5h%X$@eKv1V|iilh!pkrF^jIvb6RL+Nx?{U$t2;WVzvY4H%Ggy-X>SQq}bp5c>Q({3iXhM!X3os6S+zLG8DbNIsU=p63Ml9O*SYUEMA@?D*a+0Fr z-76#JMKq*ez0=>2MX3eL0^aS$xilRsC7iVj93LOh7aGRZuzTl)S``1>^?w?KI~oRJ8CAH|3jA*#B-zWqAv{**HaDq=$XVcPLEY-c@%BHP4gCdj*( z2b|^!H{|UZ%HwwmAI12{d-taFg{8XK*>G#@$3)M3@#}y~REcc4UWB75cPNv^s&8IZ zW5ZjKyDrfV?XK)M{kY(9qw2+Ntb)-b`cy-3FM6p9~`IBM;vj*p^(CweL z*_O7uGCq07oR(9dRo*atZ9g_Qa<_&AdGk5YgMf_kJpZ(7Xd1|@UwUF>Uop*p+eIe? zY$2N!+P!J3z%{u^@%-Afp*?1zjQ@-t(sW^!NcW;IW#yL8cHyLO>F(O3B*V&FaEJ2E zLXWG~sKeABS?wy*WUUcBJ0p6PtucuheH=l>C1WP(vYLW{JXJ%-#rm#ehcW3`LN;z% zqNow2m({yB^BvzYx0JoVrg=mY zG^;%XsGJfb8xjA%gY`;2Zm32YgOZqf^B!eqbW!IJcHQ zTM2{Rpjr7Qcc1M>b%S=DTLkBUtQvZtCUrx*tqL^oWd(=X5ieAl#yLh4Z5284 zkMdO(3CjXb&8FrFm=}&eaF*U3JoHj(D~%2J`9~Y_ubi`Ua$gMad-q8Nw$g%udfU%7 zflML$Ee-Q~z5loqJbG}77Se|I6+5NH_w!y|I$hDEj$k`KfXx7AEBNuc8tH#BZ+7E>s?LcWuD z`2C@CzZ9^+xV|0L6N<5vr{w)Mr?qS`u8{EBDMD@CrIZ=@cB1QW z7n29jnq;wdNAqgb#Q+21Tdf5kaRFwU@YHYe^}+*y5oS*ds4}w7@dgtsLX-3;xYo3I z*inkRxStgU#o`K9XB#BjA9FWdfbA6Nv{@>wns>M_`L@!R?9(sQW*Qu(SLn(aQsxpLT{^6ml+KeZ?xI|9nmKTf_-dckw# z>(Zx`Ncw~|R}cFuDU58;xad40pxpza#UVdW_$)ov9?xr*L>#okDasm;nFbXz;}v!& zM~5gzLDn{Lmi4uvRwRuUH%)qn1k&t=cj_fA{tbZ#h-U=ZdIzF&zx#e*1d?jJ1T;XO zx^-+CFLueV;;FVGj%)WzvxI|h5R{dql#>v909~~$1{T4l;mp;e1qye#oXXMSq45SO zy$HTQgRFde-voYRQ}5!{LC$N5|Bxcsk8vqp)x^I3ZD)zc6^r64YQVAP($Xr#@1X*W zD<;oiGbkXbgmqU7#E9Vqoyh?h@dm{t)WikV7^BboPK?0%CtYGxa81EfEq#X+8^E~7 zpYY5-NT1iV0kL5TS+pU%*cr7foaJh_QDxEH79JGWGIG^v@c_U~i@|u)l=JOSUQ_J?OB`*-RQEvwG z$+-;E=ik^!O?t%kj#H9p<9y%I%f%7wKVoRsVMgIx=FCUAs$~~shrR4#gzK7Q;6<#P zeD%ug(r;4d0k244?gn^CGIaZv1*T2v44V2)5#BHtyud?fo$7O8q-kwZD{jxNWi&#+ z=s=lz2P*f-vH*TK60cI?b={0+v*Kg*ZU+~C{jAP=_T#aQ1S~}XG#Xn#rG%_q9|+uF zPx5S1-PhlG!PNAg+=k*AUEpDz$DPStRAq*R5o-}?by1_OiO<5k=k+q(%9U;YWy;3f z{Il>JBK5MseM}I;X@g5%Y}HJb?egncv28 z3`*e~@t#IKe(%Q%&TC^%YmYv<3D*|%FCdxIA6Kr2^WG$%ICFNn&b3KPVtt?6Ul#gq zp@h#Pf=%Tnc0&H>!43Sm zhzBo9FepcN#Dgus{7)!11;XgP0%*lTy^2>zx?*RincrMX3!Gq%PpzxG93M$+oN#or zQnT9;jGOWm6@_*qyq5ZR;p@N7Mn2%uyLn$VDs^*N*G5%*TTTtKVWFJIgjN%~FuXNS z(A8eoIYB*3f!sYPFYa!SKh`pTPn@lFN4#zxWMzTL@XCz}dH>1Hs7W&O`wA;6Q+p*_ z$tCYrm$LmO*%UhsclP7yles2~Yl-aJt#XEwSqHLbMJ9$_Q;u986Mi&Zzst_P`NX(H z6AUrp94+E+46neAu#(Tx_HRRd_V7QCe<&UE06ktWqsCCpJ}-y$iEVp9Tgtb+bqa@b zl$|5fN^!Azts+0MkZD}Hj`eKP%P53Mn~ zq)iivRv4w~s20U|5#7UU{bmF%MJC8qYc0~Abu{e%dxqM+|6gt|Fmw6z>hHhexrPs{ zP3S)c^ZY+Z<=z&2l&DdzsET74)T`*uOL)Du8utBcuth#XC?9yV*9XEGXOIzdg-_vS zPeELt1#iK8px-DKaxN1a+`F*x@o?^Iis@_Q{mz?Nu8Xg<#L`lTsa>DVxP<{bp1v&@ zCm_`_ly)58#O)(!pF*_Z+=&tYsdk{)0)Kkmc1D=wubt0>*1RpTHMGbOCw1e5CNMjT|4yuxxF_os@DYgjur{Xsrqs<)K&YzcaW9+si1 z$#2VE+h{;5ATW`RA}}n+SAOkmO=o?B{TO;xG$Mbl$2FAnu(~- zA=~Q1=z4>dC&ZL9ot8oR7NF<3zlzqIou2ETvfd^XahoD4JHpgOjR2;-&*M{ySG#8O zK<|{EYMO2-K&E5t`g{meI8^q-L54w=rDE7ccxhS;AjPe66CR54Ld4g#FXo=@JfnH0 zW98UPi{AqU0>a>{OPKJ=QHKoE2MC3MW0}>h6W3@$(7fJlJ^Q%^v|5y!&*h`SgMRZM zkD4}WSinr{l@ovak2Uw}-n|#3gQ8d=`UtapZLx?8{dhB-Hv6@NILoAq{U+LJ)hVL0 zKq#h^1LgXs%Mg#^L);^1qJ5u4p$YFqRbOahf8=iQdLHD0&7|~+MqaB3u(noqx##UX zcFVcNEq*B2bD|8!NWc>g)8ryWz&=w?vDf$IxSd?rbL9K9_Z>=<=S|f5zC@n2GfL%GJtQC+aH$adPTFJXbE}j zQcO7~-@Ag|!(p!|W`Y$aj6kr#ECwuSU)_>xe~o%May4U`rDjom-O#e$M7#MiODn5A zJ?lo@&dV$`u+igY47GfL4frEd%)Tab2H&}v+JgBtojO+0Ew#bLo`J93|6ISiyL@O> zP-ni?GrfPyKO|x9U{V$7C{=O!R;sGe-BFlBa}RqXCEj%b@D80TEahG+cwqNOxH`^? zXQqqeZHFgvAuy`)4^BRZL9oC_gsOBSfn`bH1@AIZN=c$;sNn+E@>7on&kx}?Rd7p_ z*U}yd!O$jU;2`&C^os0ANZj;<%Y4xmue-(Z?Zox89P4Ji112k{VcR;TaLWmIPYC2? ztD02yvqiCp7Nl!SRqB*Eh!vZ^KRn~VTko5XVt@JxwwC`+@%cEs>?9`TBTT>WFi#;q z_SalecVOSV&z2l-)I)=ou-y3TEYe>W&*wMM|FQ_vAaRv5#J4rA(J0iPU=lAWj<3vf zS7+bO42HO!RbIUccK7hmx3xtBk2$9iaj3&K*HuLSaaV#6Vj^tBU_vwg^az8 zLy$qLYXD;c-4h1#e8m-&9o_Gq8z%sK5(tVC1s#?Io7X5TQX_9u#cvG1xXaT{b9NQ&8D6cf zYeEYrBFH(O<|7OWm%82SZ;qn+$C=`F1mu7-0fkuQn5& z=-$WyN1eT-Ui~zfUyHr1;LoGenLGejn>Zo684`MRR1dAmPF@o+UayGT@3jPs`Tr!t zkB4*1({S`^7uBPVG?<{APUwdVv*!QCv+kGtZQiBOR=ss2zN;6f5Y&aFU*Pe7`hWcA zxh}?0fJ4c1bi4)jFi7=1+zG6P_xe-2{-&PLqggG(3-S~aRX1dcB=tsR2!N5BR+C-h z8P=u%=P!>;De2H{<)l6XX;6PH61Z3KwSCk_^F212i&XEV4wzu6mY;wPNpR(Z!AkO+VkN=vBTqJVV$5g?~4~t2!TTo3=dc)feIDnP90dwknYHVIKP&h%L-Qos9PzWpZCvq4<1K0Yb zS9zHw^83B72u6Q9Q$Nr4p_lJ<{t#pn1`9K47`v_VV~LP)rvgkBc5yjjcfA!m*y&8c zbHLv$bDmN3+EsOSnF@1f$Xp_!QDPnoa*r`928zUR0RM@RT!d9%Rnk%SBa&e^$?n-2p|t`C|G zsa?dc$IKGbCmFk$4gq8B@rni0v%_HK--h;vyTdv0^=|!#cVPsAUb(Q6i91LBW*+`S zgZXEmA@Jx})4?}bpOKT{!Z>kZQN^8DCYXtGK`-*cetwi_jB|DVj$JuPx>+)R>{eum z8k|DkOx0;@SMiK|+%miUQ}Da4rRn}=us$!e95LNM}4fXOndigELar$w(6OB1d z0-tl?3Z3L!!KQeF4VBLNgv1@;#sA!){<8jdym0-o!lGXzjsawjM@5CRu%0I&RU|IG zE1dl|pjfspVTJKUCOczw#^tuw)?(v}LM-Yo1W@sT<<+JCr^H=F2OA#_Il6Fe&&m() zH?!Cu#zEpHLTGZOH!$88j71qGgp7UB+Zjcx#DPC-X6obT$kIOlWqP&eHZy{&o3_&Q zFiH$!xs@A$c2cd6@y(vw8f)+6-qMBPjx06*>#0!&NhTsDCY7$ITKOW98MWG*P@l3p zh3c)l@jsmt?uy{;J(50udCnv8vpxtrcMnVe64`h({rmW(Mww2rh&GqrMfng7at!Yi zWyZv;CfDf)y^OE0?N`c)bF-%0a#iHZG1{4Q(A2AIu?RNl7S zkix6$Hz3#9^`9ObCZlq~j}fB*Wt$A$v?ZY*q4ZU`rXzg@#ZdS;e)Y@gSZ#jy9Q!S{ z3A-S1RNMDzUjow2h_vm^n$l=q|L~3$6oR9X3L0AW;EkF4%QwKfSmNtHwCRrdPX%DS zlQe5YPvT9$+>t)<*^%2tPE@JDWjLI6;{o^aA{Qv{k@&zwqOr`l~Aw z?KE0{OTd55a={veJXCyMCwy zsv%Y48{+mIVqT%^eIHz5567lxqNbaD0k7s`$uVbJ?rO`lDx(brrSS`CW=D#Xb@q_{%E8KhYXAMvrppuLbkqD# zqGZl0>O|da3#dVgw4Y2($3w{H*BCJRx_?3M)h-UbjpUQ2Y3y+C#F<4YRmQaA8~C{wTK5fDq67y{$xQ+c&U8 z_3=ZFN&4*!R@8L8-?w}x;`*^aM0EnGkJ2ldM9=o5lBEw${4Fu_xL0wTTc32;R}6wf zc9iDal&OE#zgj%H%LCOLfG_6fukd8Ek@(#3fG=FzNj zF;YmPDG9ySc^JbSt2czrY2}wsl;qgePo!*^Xy&3*1x-CtMSJ6^wZKOZwecsi12Xzm zR8d8HEz=obqJK)}F;TH$RmVS+8VIOHBUxfm=N19WVnw}ybgOao_xCp$^Pt8{O{&ta zRyaxfa!)ulQfKm>8|mxY4CiTh#KBwZxMh{I+z@rgw9CQ6WgrT`4MPf!21m+GZ zYxJu}1kZ{76oAV~Uz*E6iVgtZRalCGS-I5x2w5T^t|$1{N(9 zn9rjc#JLgc++)?9F4heGCDgI;ho9h~s96&g9-OZJkZv}v!iGb6#M6twR*woYwM4XE z!QPr4gwQHFSqV9_VC)$^%h5%^cz7^cpWQbSVr_C%i{uDSsYV>m)@ANzp-a5kZI0yz z-`9qVo$nk8JDSZOOe;#Jn+Kx%+>D;=#upS}#^!H7KK~_N?0sRJ7W=VO!NP@oiOR9~ zpgz^%>`~c)2a82&r1#6dfd{{{*cXl81Q`!|MlaeGhyHR6Xal8-S{E$;C~;1oXV0W? z43&kbTnfp>?dvtO72N!{Cb9pb97@Y_%SJ2d`0%bXn~zYl((}Zc2marytP--~sIF@z zuV!utHI)46(|k5E+72G04%9v=sMAor-=Ia5uQeS;&L5!S^pm|8rz{SFGl>1okbkhy zvZsZ%j_qA3*L(E0f?D!CNeuCV-%0Y7m9E{}&+`g#dadL<+Mf`{%_O2Vqrqiq=XZHQ zS#Rpc7K!Z*2HJJTP7faIca(7IcwQIso9e%JnmzK7C$;#S2fg1~-6O$-Lq3mtPaF4e zFqS7hsiUN<4X|Fke*9ny%&5KpY+1~F)+X2J$F;-!(c0HU>SUqa6aQ^Z(}T5Dg~HFG z6Az?;pHu+fOBf+wJjIEF>*uE}pg zpx*>Dt#VRP=Oy!%_<%g$-LF(+jndD1+0x>S0*Ck!9qR#J=Xq%&J}gM-}ls_qUnGYA&I(VBC26BJ+_!YRE!*&}Ly}sYEX+ zSK&slL$FXOrA>~mbectU{MZ16#CIrbk`x)$cuE{cF)sisg>Df$CEnp7@}H)eApcI4 zKSr+!b2R-iQt&W9W!k!Vn{LD&(h6#t!)!EpS~s_+nV@YCa>4Lap@j5@l!LKe%@Ga( z$dIu;u*h1CREu#%T7WTS5S!IyXT%^m6sCaEo?|La|4N@WgEJCBk6F+!Wdi;@xKHUk z`I(mpzhUhBF{{_=k@btYdY`_}4m2DVUu-zv-eeI^dpIReeR;SmQf2K3%I2F^S)E)< zhfC|#Bs*}+eInf5q$!`h0IyJe=cBS_eUA_T)r8k3Oso!LPm|3{&qjcunpI-Cv~hS4 z&w|G7kaa$#S~TBRGoXb9SB`}bAI=1W`u8`IyXvJ~>Xku#j^u}G9V?u?eic3DB*2k4*kJSnNRWz&oF z&Wllgj^u2ac|ySzui&fxkuTHj?CsB{t7-o`;s}{N=5j>ASv=CaK7C3|*SZsV#9}ad zEUAZat>Ucq)MUU(P`hsIlh&pPsnrflIh&GycTaCmhs=nO>u~Pk@^*m2a3%54#7BKU za|GVpGdpS+b~w#IoIuK+rtIw#u>oOK0-5Y5LqJ+1#TLud^^F?-GQgY@>tyGP++R#Q z-gx9!Q(hP4&oJ(4LlM(Gv$wD%C7n zBoC)6dXiiA(Ychm*T7~axOd;HtC`6OnqXoXc?Ad7Vg z=qB}1TQ`w17E5sb5EPG{w07yJXTAB{2unIb*pxFZsza@@kujx&FJWG*iTAc24Ou13 z96bmHlX*}EeATM3Vn_sMTicp-?~By>e|_#b%3fKN`s!&tw=iGtL=yR+=;6AYA{Dzac@y{3;m(=*K~VwDvS-yt3Gms5Re}|0ywy%t%h!k^8d=SZ z7Xi414zlB`XMWH3Sxu;Eg?4LGxZ=LG)3aug)Kni)F6idqchF(1@@zaFF}6r)FuV3; zz8zMR7}CN4F*;mtsNp%vdF!*;ic?jXxD}wjyVB4Rtsh>b5K}mxILMe@zHl%i@9hcX&M-owPmV zOK&eA&FtuuUrH`D_5cYc0ExBVoUHqurbiBL(h#IRP*!xwV>J%ad&B*GuBOFf3f}Gt z8R0r=XfX&ZY@-VN#cTG2;vJ7I{7FemA7wF(C*dEv;Wf@{NLRInKw`?Qym2Ttt*q}X zKUPU6iQugDaoq*MgbJliEsZ*$)VSus-)49w%YlvKo;6VbXJJ8*-}&*$yZH+c0NddO zT^N8R?M(pOXKh=(p*k(gcNtTKqrZXsFF3|VwY1bv_T0hfVr2Ug@5J5^0eyP^W!!Wc zyy4cPCEtN*;C8Qk} z&n~32Cn3e*WKFA`B63%xnma`V%4+^E*F~lyXq-ic#aMKIGv`B#LnrryRak2GdT_Pv z+}jNnoC|256KRe#epaW_6^(bzYG0~Vqge2Ia4MgH1t27M!MTcc&gG@CdBVn3blmuW;)GX+#9mybPxC~0;ljAk8AXX+}*Qf;|rfQckM|% z%d`ZrPRGWOVvqXdxN5nJd0{+eESSfa=Ct%#xSo0OoV>{2>%0%^a1s@H@Rtbx8&TeL z+v%I`FQw2-CoX_yddZyDroK~UU3-xVd1asd+T%z2d*4wNj(n}Y_GSU4k02Yc416=AuZT=`EFE=INPVd=*A+`qPI`d%B;AKtnbi8 z8GXSqn^nyAw*p)_QjY&^F=+mg8h?42O++`J`tE-3zrX#AU3_~6EPlWH^oy*$ zR-anj&)8L?&2rg<0iz0%GsS!Ss@1Mg`OVDqfZToo>3f*~Ye}?`$L(+7a0eqoX4{#X zHv2UoY|&HTUdiLbnMJ?{K**e_weQ7iZ5PZ<2o5Y zw%LP?O(g7QwOiYi?1IakXV>(ZUhV)*`7=f?YgDPBDEbZlXXekafhKKEkD50n&A-$0 z`oEo3g*tyAjM8+P4V_yT>V>SUatk)Ey1xQ|UdlBlLmfZpF)k8sr<$PeQ<*GG>U@z= zW=|9%;&Ka7tY%4*7plqNXi3zue#BFx-NpU{WcRwIRaB*qAi&JbrDp-f;`Cd@+)N>U z>T?LCdXA7c6@I}e1>XN}8OOHcH4oab+`KP_rPV47$aO zmNMsF)h+mOitsTv`n#9Y(s`t3jsvo%40DZj*Y17e@Kq*%&`+2$?A@Bhh&IrJ z23SFu2$2!HuUO-M488NOFJ=Pr)E|~CY+h2yl0gQ3o7$s4-mrK}7nE#S zZO*LFsz{znotAL?u^WW9Y)9^nFLlK;&0C;Xr@3$i)S#vXg2oUL+^{$}DuV}6Cl~4J zN5a>sd2MZyQbY=@ayswAi}$+mi8m=9>PZ*~Bj_7nQw5xBXNP;Gu8kouhv!*(@gbu2 zM0I5=nm_n`e;yT>amP45Q$-n?FBYN%;Mi&TznJR7xPZ#J5B0^~)7WAqL{wL%hRPe4 z9j*RB;bJ$ebID>H3*1jhIGARv4(>hmosz9iYcaggq1dU7lsA3*Nhd&#GO%D`Z{$YklAySh&SSXG~+jsX>-0K?&qjJ-DFYQJ#1ovb{}BT zOjz~y>U#L(&(N=*A@kGS)>Bt~Hlknuy)yypTMPw$)06b0L;+&&Z~ z#k1A&w$;Z8aR#axvL?a_3 z-*dmkc62BWi$q5+`-vi^s=w)yxc|?G;8brSTbZoBnvf2?rJxZ-y0WsXS8_ftyZgZX z)ccCcg1Y{f(ocFPW=wJ>S1x1B?}Zb8F|OU7iyQQ58m)AcF(EEy5#*6Nm$fx3%lWmgFX7rib?y zLpoUFuJmIFWPc_IE=_D*T}@ z?msI@ZfaK5|Mv>3(;ApACyCwrWJ&=s^jOee7zZa)v8%BLtS!!X`w;xDAC()2U_S%2imU&Q6W_1P(j$Bj;eG+=t zpA3S@LM9dcsKWp1E*$`l@Mb(u;Utxq82%WYn!Lavl!(bp*FQT>T+Qo)TJ18V)*41! zz2}}u1wZ!z=K%4Qqnti31Tv3oO=-?EkjT*8U-@%(wWaro-ue01&e&a^rQ$YkRDnX& z{Z>-h05P^ZYp>bDrzfS0pPY^Ef6ar=dtW?LoyccS>hdzZI1V{cRylY}g@$4NS}pMb zA~XObgx(C9mXPnjtLJYC42bdFzDBbVHGnuC24w#{Xf$PuoU*VU65&NLzNk2Dd@x3T zUffr#m=DN;7dV(e1(1>PPMJV}k?sT8d)fYOAcR)hvFlcDo9_+shLB7yIB=P`3;T^S zNGX%!7$V&PfCu5rPB&1h6k$74|Y zAtp!oF%OXRjS^TsoX8(tS5k^poOZeHgw{S{)w}a}4_l@6UNm(!12C$+y$ed!|VZ@wmo$Pj3_G0Vlpj_wTjwFgLIdR zZ2G`IbAMnP-JV^2332dU@R;jV2C8mH0v{y(=8ydmzL0M_#}itSlQMM9A{|y-e8ar( z>ojXL|A^Uh`KD8K9Rf7}Ozu?ejdxP(e4qGHxg`9cp@_2Ay3*~JszoOj#TGB-@89Xgph;tHt1e5nQ%qp$k)GazmD3eqTEZvbIO|>8Q#uHN%-uiWTUd` z`@vtt-CnMhfxknb%=q|+MupXn&fc%;g<{`$oZu<06K|qwQPaiytz{iU(qNu}Ds{>iiq*+uh9kNzkHjT z5V?_3Nux-k4)V%5gtGy@uOmi}>%d}>lss}SqHg&i)b`7++G~DLB^#InvT8vStbKBGwuZ?vcVSkCm1u7UNp(s5=kTh``95VU}}yR5)=j znCen4Ux19OK&Y5}#V+ZPP7i0|F8wrk<8D4ecZjn4v}3i+9gDZF2)1P0ChE#e&68%y zY_B6R6W1f#&C$$cFc|`-ru$!P8MG=Ph40%fwu=OX1FKWRN#oKCJErJKH5 ztsId;`qjNmi>q_p)Q3)hy-NV)SXeX%uh?dlRdO#O{uvJqcdZ!U;~2lO!uvI^(&t+W zAh4*iyI$+PMu3QHkZt#$;)TFY*h9vv(W4>yCz;9t}rJ*yg**@&oySbv@jO22jyLe~b%XX`7uKJ$f#~M(5 zIi%2$9bXerHe#q1&Zs+qmKAP`6CZY$*}9lnRgq~`0rb6qWmgxpP%ez)M~%bO0tuFf zFJl%c5ibDyhwE3u@$_7URc+MV-C~(b1xU7X?!+gfICdAhws|-@gng@tt{W$Qg?Ur> zA%%lOHYSTBr5WG3&qQYB);+6TjWYAqppiD{=Esca03q5MEJSJMUM=9rK_>e@Fs zAcun23BcX}Pb(riId%!AShi_ZWT7ql$sAn7_YBzM3?Pq5U;rQYms##*fE`!AvZ?ry zgsBR^#CZ{a;#8RSjv}adVy=gVZ|>G7eR|-FJ^zn9zjXUzH&%E<>zf5Nysu>`!W8>F zPri#W@pUMneM2Wx=Sk=1loK}KDPv)oyN;m{CWm@sHbHYo_BiW;5fH&NUNh~?(q9~C zo13KAa5>YZPl{W#7v0FaUWz>U=S!b+p7jY@)tBTUhl(RUKy2k*mg$JI8oM~x+dFSN z=f14uxuf6nvsDn2NXUrCkptS$uid(3@(9`Ldd*9%sZx@03j?2@M7v}@UkZI;5^|TG zj6Hr(^fUv_T#nx9XE~Z-U3m4$=NaC>2=U&1nA+IX+1_o!Y5V*YufHD$)#ux08jcZ0 z1-sGwJNKn$yqlFE#MVil(t&7bko~kzv&tOyv$OR+`5e`>ocHiNY?C0Uh>hSwtlD0~ zC9ke*4VoG4Qlh+yu3rUrE9s?c*LN3cruH|VnVA1hx+xv>xz0o(8;U?3azwQCGh6g~ z874Mb9BZD0(IpQxotGSLTaHbK{cJIuTv{H-uUx;#VQ{#_|E$w9CUbxVyINZX@Q zILTxin0Bku2yYz6-&vO7+0?o2Bt!H>-Qzin^Aknd?bjbyE`_+0j-UDkz9H@`QVZc7 zuT?tUy#~`jB;{VFTYUDh0nMp4Qb&zFn5my6aLe4(cHHC$`bH(J zDx+>K)K@KKnt29XABAJKszAS_l8c^y31cWM>FQi5P&8W^I5-^@-w8!$G!1=jtMHAH zLKM4|1=o53-kj>I;88?LxL!)@hJq>`V;vYH?VqTSvt(}2o`RGeDnIO98fcz`Szl%K z=TH1TsQC3cj^W^0)3zATOoMd_`@ojOd9eF8#zDmXz3EE}z0q@RbD62{JTIIk`lZc_ z-U@!PJn|Jvg~8&W7-)46Lez;`P@o`YM7?upq&)IlMUw#bJ&*g4*IdNf$rrZ|s=MWQ z9DM)&e*Wv#g$SH3mW=;zuET#`&De-$nyxKvmacV}LU8I>j-ojLBnd`K5l^u?b>n#KY$CeD95{;acygKf=s?Rkwta44^#CGx*tG-)96C z1zp9ai_I{P=l}viAeYkwmtS>5#jJ+hY<~T!r3AG1^V!L&f22uFwvr+f*mO4ST;r(; z$Tc+swsiox>an&1GUNbv2{$585pX46fm{7aNk}P~WRH=&UuT^_^0pDI5EagF7w%LX zBrX+!+*W-bKCm>gMx2mUv(qMC?71cE;VX_%sMXy}0*6#*s7|}DDVrHtoIm6|I7_%8 zUX>nUs#Su^pQR9x(yMnb?X$cvn!Hfr1+G^&UaHMwFTw>3MfW2>G*j|dKD@>bbY4f6 z6HXVW@@T&6Ey&EKInsp4t{XU{mj%u@GVDwhmw`SoIn9?BZpk0DbRD>NJm|KNvN9*~ zOQ|p(@os@n=|LsFrKoNJyg6liv86N(=xye#@VaX1aKPV)Gb~PPl0S7qe8iqDH6aBv zgCm}KYNXfnKJ~qDydm+|?pi9fR(1cV^~ zZ4-47ScYPp7#!SJfRsP~=ImfjfxUX4GrZH1T4jc|EW*2uLJ8_{5{RZY^PXZOU8&b8j-^La4AwAkBy(Xmj5&04Jbb$(`@pYel zKG^Kr%s!5_K)tj_d*g=Zn9rXPuTQTh1h`k|+~uWa4**T7<|m7&`%UAI)qzm{7n5H; zl}<5~_;oKi*pTPz4pdxH1=e5AKQSu*O^$+Re(lg$G`M$bzjUs`)Yzl)?@{qAi_Iu&}9dBA-!%`O9gvZi&UG*P*x(;pl{cdTxQQoKb zqWE@jI+Ow`4rX$W*LRP8i5IwQ?Ba3F*`pn+=8Y&va&D~k8_flG;#8Cu0!=M8EDnB| zvYJe~6|Q0$oUBr5a;m=Y3)=_+U&ru;CfeHt5F!Zh#b2bcm6pgX`0a6ih|B>k5a#=> z>p4|Skq8)G?)fJ-c!Xz^Ju8_)z9S=`%{8PxS-e;`#hiNh#ZP?i5nMOz+@1Hp{HtW8e(+odJ8t-;93}&wc^pkMqVj6E9$N-L>i$*eS>+s>cM4IxC zmGSbUk!yC^0nbxuro%u{q=6OrxcWF9grxC9Y6+A}c3IQ`HP&M?MURwqHZeQww_Gq? z!(*@Y>v?$RVB=W1edwKl{my02wEV(q3u%gHzLWKsL$@raFGD5Jd6aAX;*$1sU_{&? z6^jpvji0b-WaEKWs1fMNboWU5m-&q#FDx*5E#~T~7lP55D*~irD|T7UaM}VVFh<=4ySL|{>*dF1zZ`$d0&jo z%f`+By=p?LuRHKX@4@G{bp7-bhmF(ro=k$Y!hSSDsscCavIl*&IG`37qL>smpvkp2irmxQfBK9 zS$_EHXlF2CR^z~ZBNKQ8#`Rj9>PQRKu+z@Rl9cwMkC#>2Bwo=wF9>*ip>Ze9l4-&V zcXseK>|_DJnpmk)1mr%aTZO?UN|J(wiUB4mU3?mAZdbfK6v-^+sm~q4BMFZ>{9qRy2l;vKfex$Af(r%;&GZBBTM`az&VyE796Z23 ziOd8y2SA5oE;Uk!$TgoI~H%~$=JH{OF#AqvGl(A z5$SLeUQy9-I6FP!nNU`St}flw?^rowa=Wq4-y2|w=U7H0@A+wKr`0`+NGez^p2irD z2g1+?0UkcGYD4QLhsJMtkFOIRv)CJ&=9jq5ArYtA7=!nyq`OMBqkh9aEgO3pFvqCOSf_`frQ#Ub*uh zMUNcR{k_p0Q={fQX&_mvcWW|Zz*g^U*_Eg!+4&@9-E0hFlF-Y-7bk;7Xa2}(ix1sA z+g~tAV=7o)yJsiE0YR1wXR#ZfHBDxeb|((0SCV@wSv$-8kg8BqH0*CZW)PFy0&KMx zf`nAB1}?y-avsQ83B(->pP!su%g1zk0sP09Vn^fKC4b<(>Fuf|0Y7@8hIY*p-iLlG zxFpvEN9@jRC7k(v4x8@Y>$FmA#T5;BD4M^?z3jFrGjiI~0yjC%z*!Xm{^2pan6Exb zDouSPZ@suiMP{3CR?7{vp5z2_1sSY-gw4_eI7WAJoOq)Ubhay6v5b@DkYU0ADrGO5 zaRHgGHzJ62&Vlk=n&#zKcdT*BT{(bi=5Toa$tutQ%!A4*)CX51ziT(2Q?aJHkEx-z zciUQ|%>1S>bwT$w0SVP9%+O+T)<4U9sDM^O#=jhtH$6RJvd_B#W2>5o<7Ibq8f?Id z=~%P9(+y#77-!d~g|MF)SYIuto%gJ$T0Pw7yiB-Y-Fri}M95fpcF}O^(}Os2QGqIh zAES^-;*U2Qj@9pCiXx^~b*urNrhpogQJA=}LQQ05mtZsb#5WCneyVb=C#cm*x^z>s zjIzOoxOd6W0AWWT2_G&f@azvs1iY}Vx=xq{XJX6I-BT0}G|x8&nrmK$?aY*oh{CJa zH~?ek1YEp#9%eZ_gs-Bc@}Wk7u{>OD9eIB-Eq^jXbQJ?_bD|Qte!D)P$&r`_wWykP zWJwTNZrV4()3)UGSe#QkA1{;d8$k1R-$^Aj)x>j6f>`AbGXt_eIKx-$Q8T?@Ea9eX zARHmwgW4RJq3S-B?0lHMWc#R-%zTo{mnC0M&1I-S)??HjQq}WV`vhq%*7*&&_nLVj zT?MfqF}-?5zwiE0)iq2iFoXV%h{4Q&qxE9TRPov)w|&!%Xd!LnQ?16dr@1cW{RU*f zgl#`*sJ!2B8b=$7F709>lSH~7<_*au(#`VMe)<07LxG!}zrSlLu(&hLb(`|t9VH{! z!9@(_l?ZUwH9m5}QgjOuG*FwH>xJSOpzjP_pna{Anz;?#jLG87Uby9H?8jQ);#lD`LvZE^Xty=^CoUJ^@lyGD6Q(T?&Prc z!(c@ktP@0OG^#jb@tk+;X0B|gz0H5f>2kLjlk0rdmF9KAfHvy9F&Vp9^RK?cgFQGV z4(6}Ep>{8<(h|4!!%0ksd{FJX4JQ<-Rr4sBf!*M;+h_GS=|-$iT&op>jq6?^-^|)X z8tyEO>S26wR8(@O!3*=CefLYwdZ4581k)+6hhv!v2jieE!R&#qQ7cNv7lYED@a8z?k)~!$@qZ!RGJoZO3F`sgP>e0-<>v z64cA<5t-|L+Mcg`e`eK}h~;hB3UA^{DM6o`hQ5ez&y3H2%*GE6WS}oEifoFDl+uL_^nv;9~MIX1kviDfpM&@zp6V)H400O*!q1 zs$gviTU!B{@1vbO72`J}*-oa3oP9;(ga;VXcZybaTSR^>KD3 zeW+#aW?`a?ji5hSN5{PU{j|d*SUhK%%JsXsSuqh77tI~n^nA=;Il1)Bao4w`Tq0&h zHiiNHGAb37XD?QyL+Nbzd*zj$_AT`njR9#V@$LjV=P^?}&wlptSzLb-BM%j7N^0}J zGyU)rVv&E>66;2smKtC5{E+1AN4nc*;h>|Jgk|(af~TSnGg1V`C38+h&6c~g>2O_7 z9sH|^vl2n#n!phdohH7@;Q?0FaEsIb=&LujRN!o8Ub)! zSD-yfx*ow>QS00au7U~GjIrtEsc$S5%JL#>8<#O6+b5#~VG(&9r7Uj;Co&>gmfUoM zs(I4k;W496UgaJLa^N%GUTuk;3*0zpkQ@DKqG#JeIH1Tov(pbQG;s}~9yaP9cI~+y z+U&Fa-Ot@?Fa6o)+}|M0oI{wH=dFTF)1pf&8D7}*eOYS4y%Isw+iiA4*dEDhnE@+kkh zU0h?IsW<<>>WT~<&@tQ_CsBma`(OCnlZnliyN!m{b<5r%7UYOAz6)2J&I3;)Q+0{(0WSxHzX0DpNs!ij^St)A za0GAL6V-3+XlXr5Ke|t*lC!8{09dEtI_D&FcM^H-yM*umQkuucpTr&^nxA$DBW`b1 ziRBd!6Lk4df5L2GkJCxr#ep1jjN_Xgm)~S}TbyR9KX~U4fs)XE``FU`M^FrKgIz7) zBM35e+8p{55e>UYReEv$EW~SB^LHH3FOr25LV#_vMWZDJ zV8a+NL{}X|QkH@UZvR4|uw7r43Yi zEe^l=X#${G7`ZHSmkn*A_jw6%k(B#z9e~6u7>y2>NgUNEIelo48m0mHJ-X)_Cd+8< ze)d=H`f{nTy(Ep3wIV1r`zde!d-Cz|5E?$YV?FY-cML5wR&Vg)toeirh&zL_osDUm z4k&+_oP+K?zE`XJLGo149yKTx!LM0fL~7fFy;Njfl6P6oWqC%`S|#JgID)H*qM)LM zg;c~ydgN5{Dq5!GX*1sgms;Ho%g@1&BgGni3L2zv-QTLoQf3@5^`%*%0{fp`w;D+i zer-6Bwhr0=I#Tt)` z();zhEwaoNX<>~HV&-@A^Tt#0)Z=A^9THpN9SeUKf0 zH~S`aY5A0wTFKG&Ao~yH)ibK5t3IcULL*W4M|%na2F!5_1k=sE%U>->8>Zb>gPNt| zm=9%NvtO26>$p;>d&v0?W7YSZMH?(E12JxQuFho2#+?Vo0sG54Z*67&gFCk{WT}+%Z!dsF{(X05 z7P5iPFY;zT>Hj&B{GSp<;V<}cAhG%>fmlpn@5ODCS<-96e~6J$7d`KG|JJJ#$cK_g zP=5~*hX?378>$feToHKo4Gn!$HMd1mvOd-R&1tP-e7Hs}SILFDNhYv_GB9!U48P~B zO=y(ByzpZ&wV=#&W)i+mOwVxwSF(d7Wr+ z*;^;`wo0^uG##FkRz76p#3DZ%y%|ZUik>sOSg`LumQ?dG)U{`^8N9(7OYZ4x&0%B< zGNQryG50XuJQj88%lt(9#rUB^#r}0yyQ`r(!^=o{Pa&+-G0(r=E3yLK3=#i(viq-^=7p^{ft;qFPPSZGfBNAP z$+%O01zNs+j9I?Idv~98x@ZAVX-@+W43z3FpDjB7WXZgluBeWoWZ(aRM=Y~z@_Xrw zW(6KjkBAd8UuJ(98bsx-*|7usOerE%&Q*0>vu@@V@Vtn?@7t!)b)n5A<2e&otNWb{ zFyi#!4&H+SB22ONp(x-=Y(;Mt4~j@1zMPvtz8^z8s+|j4+a8Rqqhcs=8l}d(SnqT; zA|w1T*>OH22hjnlY(pDT}BH!>G4a_~^?Iu`q`%=@o*ulf(g`q{Db zK?ardU$Uaivt$sXk@Pp;Ib1Ta3-!#pg1TE?3m9UTh#)S)mda;wGW&+$@3dPDkjF8Z zhBxPXo-RS=Q{)TN1|cBEJHW`BdfQd;Ks2$rXR+^$I=vV$eFL!-g;zcR;y{uKYhZRy z7q$2Ep>4tzU~mj|_qNEF8oDz|iHEhhQphT5UoPSsn%L(AIpXS8oy5aeY zy@EcVqSXm}T*pZ8Iana&Wq?}Hoj_bVI)EM}5I1y5HMM_H6)$11eMcYq&WxXRvQ}EH zys%E`n3L~~Uew0dtF4>A8Z)SKOeic{oRwjH)W>KQW-g7KVZJ~!2Hms zuJAcNr@Ro(*Ha(vmSQV>RS+~n50t=ifwRDi&EA6cg3NMAS@G#UnR6ICxe#~!rarKZ zu^m+Uy`!!YMX?@GG+sLYcl~wWMNgNrI@d$KdHeUr*RPY@pw#U~O!a4nc6eFp18kI$ zYz4eT`H;gK?7d zW!X>AI@$#o6k$Djq&Ih6^!xz(Q;^b1U!uXxbB_jFER@KiXg()%_xT)QuRONy}Qt}tG z0rZh;Ho-P&x&sblGR}7rTcw6eH;tPp^xXM@bo}Ve{VuWH>DymTXa9?@uZ(Id(7H?l z6o(dvVlD2)-P=;!-CctRid%7aC{A$*?oNvoDXzi2xNDH%eKT+7y_xsTIzR4Bva;4) zcio(Ga`xGK-#q^;Fo5UgBfdU5d4`c>ONRSiOBi+?EgNbbQ1}iB;dw7Szx>a)JQ8AS z8zb{latTU7H-gOQcBr0ae>e`F18UN-I|)bE(3jL)%!?cdJciG(= zJ-qtZ3~FGlx9OhUw?;f>=eGYkv6|<}nse4m?>Xj0T6oiMp5(-)`?7GNG@6xg8DN^6 za~j1IX7&XoKUa!IaRQ!!m&mP{3r@KK1~e{sR{Hpz3dN9z@cb)RCfg1DAKm36O2`xP zOLzG{Fjs3x2K!CaUv{mSei2cn67lN-dNm-cT-FprLaAcZ$qVNkU_BjWra3$nKV& zJDX*G8W*^M9&xIghG);Mz*?QkP6cd|;X3F(W<%{M_R;v0_odb&IUP%R8 zoews_rS7Z3PSjO*OBKB%)x{cSICpNfjPS#qA&1V?pRPI?7|2Wu&%x5f{6E+^dK!Hx zpT3Y`W&CHX^D7#Q*hk&}-KYPb-uqwA!jTF_7=yLl@h*f3yh{LpHEr|U6xEifDIF{~ zYz#`u?$iry>aUbethgTAarRFWWb@M@S>E6889 zdys=t3!b;E5#*`+>mFPY$jCBe_fpP-d>oKPwCyqJZy1=G4#&~PIW@Lh0v>UC%NYuae~W%#q#ys92IMKNwvPapS}D;p-q6) z_H%&>Yh=G&T9yD%WP!w3b(!aKk|Dh&(R#}6JqyD0h(oZ6rJ-!flll1h>R}Rdg-+8# zEw0~xgEC#s9`%?WWu$OR!;u*$I~;)W}!_ zbTk$9_4aTA^+um{6i1HJEv4YjPn9Ma(kW;~j}K+(N%lxQXjaKMtT#zo)n>3KM=;O^ zre~+T(LABx{cZEd38Q~sAR!p`KrM#6tp;pxbMve9?tY$_WI>fgO3ir>@ifHmLh?T>i=M{)}<_mpkqFEO}6{v{6J3n?0 zV4>iiZaS9w>je7et1kir_PSzLgDM{{5D(PmfKx9j$1V~*vUYRHHrQQR^STf`#KMtB z)m@;-)RV|DlS(;R<(EI|l$H0tTV7Lm4<8&fe?Blkn$Je7_8iQYo+8tUE))^WtgTpE zh@^WU4O0q^7WJiCC|d&qmsQ>o(EDtwIWM(f&%YbZsUcusN#qh6Zw{)XZ~u}>)0kPEi~hzEowxneMj0cK3EF*)|&4yyA{G}T-kTh^*UYAN}At< z`6Zhx)3G>pv1Z-%x>5|etR8yoo*XZHJKhX_u>|RQ_1b0If=Pdq5A)5bCST)#jm1dA zL&aSsh5>0B%38LD)bkI*nFZ3ZbW*oczbA<8^P^uYJf^2*@vO(e=edlq>I@Ov@ad@Q zni>lmmY?kdb$Fz+Ma=wj)=tuNAyqlKqTtm~o?WNYK?pdr7?7`z3Bqm`#jIiPKyK)S zGnGs2H4vPtk6kjO;eaX5gw#$~h$3U82jYdXe$BEi`r90lB<9<45B%8Ou*T&pFQ9B% zd5EROj|`d7eOy?rKK5X0Sou70jWFz#J;TLT^NMC!45@ju*H^PgKKYAE$;@>J*drlH zZ7h3?C#l*jLh)t5d^_6hti_o{!z$68e-i`{;8^G|BGSzs#Yo zo-M}vqsI8|9Z}?oYDqFjS^to-_mu=0&0;c}ayT9CS_-7>)aN%gRf)!x>L=}4cfQ6j zMd{7aKs_oE+3BgYt_1HGgc5bXk{|38nh}3Ee5-i42;_700yM|~Nk>7BxJNn^$g$iS z=y(<1Dpo?74@%@kxD?ddJ%W?(pwER0=vw7Yyfn@)0-{C@U*2L(Z}@pHy|8}b;SY0> zRVs;O58VUZWtO9sOZGAx8b#k+E;WrDzelkSI7DYy3(3RrjEk8EbXO7VyeK3?KmBV$ z{pYzM6Z$FP_FUd2Z16_3e)!>!K5BHEWwDp+gK{RcY2ku!{-H%Rw8CW!ZT&@P;J=CP zDSKe%#wQSH3xjp@FKeQ4E8^>dr~x!Q`uJt-g|uXk*4zph$O36r1E2$J@QX-5u6#57 zE>2^i=0*93bRCIwZ5eAK`|6RT((f)jF+kGav3Lv#L)=NZ^TB0+jKqGvnhaUE2gC%)-c@Yg^!~{tS}jDJ+hIR!Z7bPfUtd-uc_j;3s&Nuydy=yre}g8t9Ii^HrLP(Rr;9$WvG}Nr(vg90 z_j9~3EpQs*hwT&Az+u%c#}| zwNF#iispyA{bspXg}c&yyV>@;X%b{^*BoNdB8LkA1;0sI`Fk`05Dn+|>vBbq9h~qU zPx>*zcRu)sdw_&^2yQX|yj6*gNCJq9)LO7RocxpnpGb+ZN@bq z{|!g;QI54w9DPh_!IUIp964pT!*5w7NtkzqtkgORcS(!7H?q)xX=kKT^6218{6{S;l z&zknaxS1j}c}eD=U@RpDvS!P(L192XBLAM2!uRkY@b;4KPp70TRDlRZ@1$vEM;x%M zJf8?)e(2G19u)RgR+?}|FVqpcJ)ORR+>2td>+fS!RkpZxswuk{!~QEo6{m+#=2XSB zKWPps_KntmN+_FdJ&F&QP2Tc&p}q)}cpH3+a2oM2Z4xD`EqyaaUBb9?AF1Tuu&p~H zaxY(7)@_(14SqjH5cqwgMGqac3McFt2V72kC;XFQw>7V~;R)D!W$~wC*|^0#1A5~b zId;$m`{kRuVONw-pXGPW&Gf@W2c&{uow4pY#v@yLy(Ro7g*Dq50z)ZWemIw(Mfc{f z{(Gk-#=srxX?GMtHhbN_ij&0SrsTTjcG+gTN>O1TgN_(H18QmA@F>g=yuH+YZGQED zKU;snb~NhnL=oeHgTkxkwI@^>THkp`Q4x-aOAK>>-AO76O+dCJnex__#3UkMG`2o% z;FHH4*54UC9|Ff>Bu{XyJcieozPHFSO9FJ6A}(TOCJas}k4h%pvW2W5V%-h?HGyX3 zyUexrG3B|71~}Ha}Q>- zPot$FooI`o<5>5RI57n|C#QXs)+$IScdsU&Hcu2A^!iSG5z7JBeYM1b;qP7;$6}-{ z)4*Vt;opRa16sdE!ojgC?H;hBDr@xg+e&|bg@+7QZo3lu$$rDZ_3tFWe}8pauif3V zFjm=uUD%zb9F7PrjNvIl<70YGd-0F?Sk4|%47DOHAo0`UmOg*Uf^`_C@6MsyTyABE ziOD`cA;vZR0mKKmlo{`|wU>T9B~Y^>*%Tf#(sK_f#pbiKA?p*fsXILflBfs+8~U}{ z*+;V(hd_1LG)>cuhz`DdTxN#JzEMZm3lBHSkd&7x#OiNXDXb{Pq7WCw1n5}tZ8 zhsd32-o`h6PFYs~=fWgipygC73cmgwu1V?S#=+%Ba7o(`7RZsRe}ePYB_b+3`803m zOp+mBF;Ru9bN)q^OVK(9aimE4?_>>obm>ldz2CW)fZcG-Nf6j43S-;gTHn-E`!Gd1 zV7q?(l=%N4uKwe8o#Ho&ou?jYens|~6HtIXpLadUQWe-Enm0b)`Q3~iRT=6F1h(e= zUxb@~$>iOM4nThIVMG8*pn8b8xjHLwR6yv(4KG<-CjfzgPxlK@r^l7bUzI!q-?2bb zJ{#Cw6J{MC_>bJ3dC)P(_Cq3rz99Jo!2y-zO&C+0MvTMV#Z^x3cZH+{8^ruvvGVjv z4EUL#eU$(p(KBhDLyRym_9XWMo!GW=N^9YPa<5Mh0?~_QU*|I!vWHY1rgzb4Lo8u3Rxf7fTvQZbQYtR&&TH=CvJoMNW;7 z8i{G4J8hw3P zfVxaT2GA*<5e|B-`p#tyfjONaN^?2Jp;*Y1{y4o-dKP@k{=h%!jZHPSyr3RTXFM!B5~%`>Zmd9RDr7ion{?$phsbYTUt0J{D4Wg(VhmC)A% zA?|u801IhrfBIvF^oR-W-#IvLFU;hZb{xof~ zaFaqcZ$_k~74RugZ?63it*=sLm_6wnQsJU)56TSA;6sK!*N1N>R+FQ1z4%yOkE-oR=7vM*AK7mts^U7hPp89aybNs9rkEuuQ6 zW;!bUeL$msp|6OouHMPnu_2}(7Pg&V^J=RIV#TEIh{)phR5**mxF1r;-h+OK&G8j_ z(Ad=h%b-AOC17ULltMs@!fDi8Y35Ijfm0fM#&MaLE637gk9s#)4f%Vjw%ZlD#u7Xv z8SA8RscyAeXwCj^A87^s7!$-y>N!^C_R#HIVoAuBSzSBrj+|sNjM}|O;~$k+;W;R9 z09^4Q{bW;>)#hi5S@kfUD3SD4<0g}Tsj2jR8OHL!wP&s_8QQsu$q6-a|Cb2SDdi6E z*Y(Ae42~%hckfk!>!%)1e`Q_eLWiYmFg90|KX~zyne5a1cze(y-nWH1IzHmzGo)io;9o~($WWcY$Ka#*JrZ*enwZ0k=i4{ z6i>Mpyy;Wy`MFM=og2DpaWy>KHuU1Wm(7k4k)$QyZ_jOxB2)panDJ7_M-|#9sJfOu zF%j0(#c56%n@6BS=DI}j>tFIq@SkOS)$m>2KUd98f99Ii@j(0sA~9O?j8`I_RKff^ zdQI^Z;$u@!y5Fev=I4a{;?Pjye>RH$>8O~9By%h4DLuU%QHM3U(!VtA=@enl*8bwbAJ_5E7iM}4DaKmCu~MFkqA8qF0nIY76q>Vy5u^3or>1ki@%>yL~% z>AA9ZZ*XU3EA-wN3`M1uHp5C8(TtM2f}E1HP(9Cb?u4Ste;1>~*+jh&?3J0i0X29y z4A*+gcxW&-s+5~|Xi1BD9V9rJm(CS2Hr}h5%Cwgv#>sS-oC);1k(RKir1pBLjNyMM zf3gxqOeiaWuMcy($-beksA-6TP5XX$A_b<{WHCRcR*mpu( z))?RnTR)sj)9*`(+YHGeM0)4Td`MMI_@pQ^z?Yt6OMHV9c3^5o>i(!9Auu!J@!H9o zdxwq2^r)+U{3qXn*g?qZf4qTpNS)+jEPfY^AMMS=j?CeEFQP*8imOky@Wto5F_ycF zJ8O~Ny#HdT{-^I7s-qY9R|sA!>oOBY9Je32xNYjPf_e#b9lUssI`4&-$zLnoo^z(W zq4ZbL4gdrUUM(N~=f=iB2eaYpOXZM@{;-D`whASV_uL*ts;bvB@{N7dTCTLX) z*f2AJ7YgZu&uKBzDy`*1jy*22Ey{pd&Qm*ld<$3WCt+YplnwS2o9=h~r^9BbY-nb0 z^XW6blE-Q7Dz49yWBU?mu>@_{68xP#K*=mzUFf67W!2mC&j9E$%*GUcjHcht(@4)d zI!_N;QL8sYC?b$rR|g6lDd0n!D1tEsSyZ2R&0Zl@_%F zJLM0AI{FZjp?a)8AV!aOS~NP>6@Jl$a$cfdCE3VRA1LY82piOJ>-mXMRC8HqM6P=t z7nNvcQqlImSzCxD*WlT!sw;ym@{`%V=keH>X}W9O&<@oPtjGqs*84u6W7z!ZjwV0M z^9l*H`t3}S1Iw_pT)05ops)VSi;~o0?ov7Q~EI}vV>)+PhAd#Y6O{%`^{rD}3@)*Kt8PSq*Y z1bsx5SYB7{kgcZ`ls+RjGQGa}-9`ZQ!?*Gz%{{mvpx7blePRd7zL6Pt1MYhPk+L$# za$~TcvR(FPyHiF)sJ=>z@eP%MnvT4YS`kMf>LzPSqNxIVRh936)v0``11gB;*@)Tt zk+u9bllGcOusuD*Z?XFeF}YvYwBDi(iAF@6HlBDHZZIt&qs&Ie9yg9Oiq=J`{n#^< zbB?S@!dWX%s@ZfJ)3X1APCxByvaMKyU)CJB=&~e*G=9wnvT86OKxsnz(%>~WcF%SU zU-3HCFZbO;){s{T1HCQ-y{3>-b`Bfu=xS<(R_S6gmVe52klBw*v7dH!SsK7Uow*z` zyyvWRgwp7HuPSz^)*s#T4;=_r*gQE;U8s!hDBz8qGzT8tKy7|<@;qgTO6U_b+$fXA zf^wf<*zbon&1eQ0r_o)tf+x@Yo>KDz@ME#AV3i`P7m!dO24S`mv4TeW%h6ADjbUF% zmM}=GZLC$9a<;$&Wb4xI#|644us5jTG1~av5~;uJyHr<|`?YNdEyn;MrlXtlS*44G z#;{Ok(PJk}5sgF5(c;e`64uV_s4m)cWGz>Kx3Jh~rJmPaJ2E|%&0qFW`Zs?|2VYT3 zDEr~|#e|2~9)0zpmHACh|Jzw}do448MB4YEU`se*O$PhJRu)HbuF|yN|94#S)tNch zsUR?6IAOfjf3=*NFU8CfL?@d?wi9lat|{Tv=*Wa*No@NZffqeS@AO?nxTYdgH#z@| zLOl1ps?bz#rtZ5>CZC401P!zRg&tC<(J2_GL%bNxePIIkrR{h5b?oC)A+9GV&G!ns)-N?CK}SsZ9}HKhG~>C1O4O5r?6LyUZ4SkA~+ z_{<+*E%-9yEM z$UdD%R1P*nG<7_LVRtyF)V>7|DyEyuv-vKn&!B0J=?eO!JQg$;n>#7SHgv#Ul1xS4 zF4IoWj&{`w#W}ApDIa_+GEh>E?jKvPku?mAH^Uq!>RB0nEx?w5}G;Nd|&#F-|L78$mmm241}`we%)7 zZ=HtE5GSc|zam=gGUm|RVlj4m^gD6pHZ=!dNT4NjH&xD-xz)!UqnfgSTT$+6Q_$9=J3}eR zccrQ11upPkNR6~z=e>&olC1Z-ifN`XL2(h$4Y5jZ{~v2{G*#R zP{^As&{z*L^Oi=S=ux)&&qc}7%3HOK$eg0*c{ahe)eW{v$4&s4*H~6fdo!{`UL#)L zkGAZ$3W-MCRNh&mo)v}z7%V!+_0Z1G7)ky^M|=PhXN=0QD0`>H*8_4yOW$^t=+#dDx#8I^O3Am%{@kT?acJZ9;InE3uTj2^M(io^RWXR{s z$dZ;oPm(J<*H?Q+P7*nFJLV|Y?phWQU27I9QfXf?>7eEP3c*R1Be?;yXijT|y{pv2gTI%D^9cG#Ga` z-JS{?P`{DA{>qa8E&+$yH2DlTCl)W>9<9EJrYe$?4(?;COBuN=c8Y54p7}gwD|0%e z&zLm9UVH{GF6m~&TPu=mxo7ejnQnQ;I(TA->`s!={Tm!~ABal({m+Nmx z2IZ8t|F-l$YD7>xMg%go>H0&`-)EPrPwk$Phj<<_uMB>rn8Fj)z5YlmW0mjrlys86 z?*N1t$Rk8{!nhKy}0mD|N9kvJ623LkIfX4o7!f3>PP(x*9qpgx8RQqdQG zy>Tc>(Qga0y(^AG3y+S`iw|dYMeBr2XVh;w%8Pm1s%kz<@1$+W3-axD_NJohl_NrK zsVTy-L$ie{^Ic4u**OCGT4>e41I67es~JS5ud?NhbZH8v=C!iuIwAfU7e9^7Abe!F zQ(wRL_i-uo-$XqKW{+S|RQFH)HuO6sTQ+vgWSvHMw37s%ohS&X5I^5*Y9{ zFuWY_$wo=mcD}h|GJ?@~MKDKqaiEPfE!ni$>3~=HJwmss_T1ht|4NG6n$w}!wJuU6 z)hQQ27GyJ;Zj~~JBd`8fxiUVDZ@BrznT3n7w07y}NjG}!m!4jz@nv?0V29_CAcW6t z=n%2bSGGA#Af>u#KqUQf;irKb-ve8$@Jmn*L8wx#?14yvB8X+5r$Y|o)2o?<4zo87 zZ)+Er#cZ&0L#vSw_?vBi3$)N2I9(&a-q0%x)r=i%@t)L=^jeEq3{=o>_ZyFPyn9P( zVo_i!>``Qn@{>MCc5P{-_Rn;IW#Ha@z+h)9<_8=d zxdA=A-WQ{8AbrW!O1oVpIr?vVURD6tao3{Ie(OX9jce)?+Gjb=zExBXsT} zGVT$-nt_N<=LW5=w&V=NEepN&{WMfyYrv7gBdUC_=7$(mhQerY7MQ;FDUkO;0x2^J zNd>F0GNR*s=@=uQlu|8-3s3Kd7#Y&>Um|bMc#$kJlK!h`k-}$b=yms7i;Dt{MY{Q~EwL|X_*{KbT&$l}V~R`O8-V_9 z=d`2kt=-N&A1`9qXu}&rQK`3=;0ez2x3146M)PIISln5|76ad$-rI;r5P?@wXQe8rkdPBrVzNII6H zE|vNiq>Degqa9%fRv4cco3@87g|x*|ydsNm#_jh;=u-6CN6-H$C{K`7Az8L^*RaT} zDB+(VnH21`|5plVRTuKK3#4}uij17o<$~8F9slqG=oV|IxYR$j%t0T?77`EA^ZlD+ zmb*5&1#57*1-$a*-rg5CM>r7Y0-rKAk&!`OQ96W;wP?X2TPA^d6?w z%P;~@#*e63*rSDQ?N2aO$hi5nCNf9}HN$Y*( z7zwTLt_PNfK2nH{F!aZoCeqvUk@Krd`?vp}k+Z55@U8*TKww1s43ZpRLKc@C%aocK+fufwTI4vrL_OHd0IcLs|bTPIZ!4KyjZRu_jK5jrYe*3XA@$1yr(AJ2?tUU0cc-~lQ-%kE;X$b~ z%*?C)GG(6SCpy^A8P7G zS|nsR4bwCh)sgA;D+%hp!q-Uyn&TtB+PMP_dE`o@YJ4-MXTzYP?YB!~<-Oh{RH{GM ziZTDP##bmM;zh#=qaoIEoO2%~1p4b;Hi>`Dx-S@Pnu_p*I8c&Ns5d6H{Es z^mmxq28`&8(UootAs0AtJZKP8>KXEqnVK#Nu=$Kco%}aNKWYXvXc%q^-nP~?d|8j_ zqcpQz6ouzJrFYz|K4g6CXoOemAB)L#C+a}v?|$u$gc|G@!S;g@jW8MMm$L<|n{EYH z%IM}NY3krSQ(gzPS?%7DUA@*06E+$8tG2t>$F=(Ku`7mc1^w2U&NCeCb>xOchtDT0 zf)73R@LOsGN|CmqqREy1KX1Wqs$HJzw*P`k{43Vn{m3E~vgOlM=Cpi6d6?N-%4o^(iMX!KpQ(U)5x&r1 zd?V>74%%StmR^ohO1)#bT%)_+-tM|pQzAl0ya03>jILSz7NGvD(zJ&^Jk6W$c-sP@0Q!54 z6p|qQtjN3|Z38LI#|69Oqit&=x#I;yZN5uFqvD4v2V4r|w2$4Xib0)f%u^j2&n-2f z-&hK@1ZNXoWEMgw6Q?ed!5>w!KeceE#0}p42+APb`d&7bjohuyFm6nHe1kDP+B}bRz3UsihAN&6Vn3S2tQ)IL%i4GE{D&6)Zb7=VKN6+g zQXnQ$9~Lt`t}H}7eO#EnKghmWkxA_HYGHE(N7*xZ^Rec3Q}=pZ=5vl=gVn?;hiY{)F^>#KKr@Uktr-578)X4G1VP{ z<>6V?rgJ||kFDtNvco4v`sv-kM2+tJKhC~{c>fw z)=Cad=2__+%s)8cw#|wgk>#Hr(E%YG`R)*z?Y3EfkdeC9K>)sePxQm3-4bn)XO8?D%%8-6nz9S#>|S!~zB_)+o4ZAC zb_$)jdl;)YT4B@qHKS_$Y1L?X@qUHhQ!2bh5920!xU#NsQB?LrcxdR1)5)2n+x4Jd zwNukGNdQ|>w+x)d*6&_w^nS&ACyD#l4(zj7D6{`i%-mOpQ!?I)-X;q{jFY~);)>)O zcif5|;+<>aj^e%t_E*w}&G$un^EG|2zjg5*t3N%s*tAWv{A~n{2WKWBM#%n)2R_!* zyd<)0VwyE8?#G@;#Xkvf5A1wt#pQZ`_HZ2cw^jUh|IE7vBM;1__m}Sa=6xw#T=+O6 z&o|#=F~)0TfPOy15pz=Y-W0D|W7RR(ee5Fh@7!%@8&&Z@AivRc;I9I~h=Np!;#^zGq6rR99?P_uhFM4{BDZ|HKDTJ|sfZ>X-v~HQZ){|1PlTu9 z-QS#v9$7-gHVrdFO6_|tB-+Fk9Rbe((*`!9@|YK8sCXD17X>%Z0x8#A?+2#0=(&0^ z)EEtAMZp4Zx<_x9e-Jc)V4v*SFQi>K-!~V0q9Y3{wRywbE;FjclmT_+WHgjuD|E6l z^r?fkls9w_TuJXP`+Ut|6G&;>Cu!2=0oDG|=Oh#S``)mBN`uX_?k9WT_?zbH)0Ob` z1N;NQXZ#~sa>-g)Ooov*s9z=&ALtEvB)pipX<4bo_Ik~~6BT4Vn0PFE{vz8Vz-Cs( z|7*jumc%E0%x7=d6}wsi*rMa5BCIdCH)ob%Rq_{>G*F|Hl@3~JS&`c=;58L3pbYqb zgGmweFoaQ>gIL7OwSdL5|NbIsa@Iy4U3!_^6+@k>CB?^B|9c_#36vM&WxF zILuJQAhmos^hoeQ|8eYT`sqkCl-(i@=N;1*%Kt0GaQGmMceI$3f7t@s^V5gKuHVR# z)I}{em@3{(LBQ!;4%Jg~!}VD_J`DB$9Uyu;Fr4ImwjbB8 zt2!%hR<`Les1p!|=$iN8E%mH)M}aT*Dt0o(8w*cM z#o7qDE>d~oxOO*x7oKXtH0bZ;YZ<;vw7ECCrk|3UM2`Jq|PRPmmWB~ z{3cCJYhnQTT&!)`XJ!?<$%fZ&Wx;+P0a3uqJ~GRSW{20bvS_9|i6m1p1;|huDToCUW{tx-oYj>LTBN&*@+Watzg&KbQO5UFm|< z6w(s`o|muZI$GS(0SfckeV#HAH+uf4Y6@88CtSF1S>rd!XzvrKV%~3F*oVzDl6qEtqn@4DX@q%{hfn(@*j|Dv zO3KF}v~~^GE6Vl`D^tJuz6-D!moGOEp9)-5@&GNO`=~2Y+vbfHw|=WRUax&!R2}4$ zGIBf?NA{547FqM`WqW>jSW~}Zqdac61TXz1HovM4mY(cXVvr>3t9QxtLQ* zrd|uRg%@>0=d|lxv-)&>hg^+2!)82YO|)#ByeHGJkG|azq-@~(*BfrM@Jv!SS_gyz zrTSf3LT5TV*^Ihp>V7v@{V?YRAd1ZA@QXkGyk!}$t9v~64r>y&J%c;w_PQ`|nm3{* z8IlH?!}CM`EU_I0d+1P#cGoS;H0d}En`(fl7aeg=inn<|wZeZ7aT~zbKPR)gF6N*C zXN1^CHMQB|kkYSaIS3AQLI)(qVn+e|-W3U%97tv0j?IhNW;1BALiUFMqbW7MX7uVK z*Ub9w#@{<~Oh1Snl0A^c&6XOXr+HnUb;*KMPr)CaI#=sm#zbuN5fka1o@|=51IQf5 zZC5W#{NOW9PI-@&`fO9P3~!N5JBW1Ymf>&RET*W)$2TP)Y*)?qf)|I1ceIP3v>OXm zBOItMUn&-$P9NU*v*Iit^1g}yF!(aq4@#%z-LpY{QPS2JX|T-R)E^V3cZ9-z%V+No z^T8N5dKq?J%B%z4in+p^H=ETGXek@_Z;Ed()=5P}eZ15wljHICbg(V2XaEEE+_lOW z{8t&B<) z2{@jG?SzQvdH_#?3cT7E{HXBF#H?q1VZq6oO2f<3y-1d?K0;>G@4!vKz;n&AS>G$`vL*cV}XmUxMl zw(U)q*ez;agKu=Tvi<{aRPB`aEGmk+_Tz9{7POJIt1Xe|u0@q0{s=XYZO z<3^YCQa_DX8lbk;r$#^N4|-eLhNV|j!@47YhlY-DZhgo1gyL8vj`K?Q;h z?qX_?*Br&?p%^q{VKx1=_#C?pIpUh?pPLFV zk`;=+k*ffw3UCt@YJrb4!73-nh=t~R^JfV;nwyvWqk2sKP$K_^di^%?MWd#m+&+WF zi`l&R1vECU66~|DrDPhqDeJVz0~*~83etzz`O-|WR{6ib=iOeM?Sd;y zcYv$ysFb`jUWEdj=M0+{asupof9Oxl$>hrKx1{G%8h?1RCDzl3RUS-|ek*S!)%1Ic z*0;_`)D*1DNucCnol`6oJ)iQKHB%e%6d$7TfL+vfsZ{PV2swjM*_mx*NK%=57k&NkJ`(_R`MlR zshd^*X5_IX>QQ17iqa??+kEiP6Ny$0pGxQO^^< zx|62^$Swmu77{ArzlGi)4xi`c-(>H5$!z#^DPN%JVO;G~t$eBSV!$Vk_TnOcOi*4;w*yY@03$pF<`d;;n@9IalOMHiJB!H&-iM%v zMhA}s`e|gT>BgwxP2-gpI;Zg)aJJ7r1=RK4XBffPdeM;rglyMJ*xv#LtbdN<0PN6u zU%NA7TNMFt?RHb>guc7O3ep@is@i78pa+F?vVjjYADo@M-WM}tL~4`1mGSSF!S9ECXZ*ElW@4UNRma?K zZ<%ba@R^fvV}yO>eJ0<=FZ^md@UTDZ@O!$2Ls$Cr1gHEOG4L|2xN)}-HksPUg793H zu{=)ETva37$vEYtVWbbj1!hFoDp0-dpkzCX-9Aqp{R zET(79{X@RvILB(qtd4(}@3}nT48$5+>{RJMLH;4h*xa$Izb(7TYT4&EVTeA^vac2J zvKQbT(|iJEbOs$dREcm`?~(o9zeXlLks$hZllRRx_1d5sV=U-J^PQlFTu`Ooj(yys zdDRpUjcIEioR_jo4|wF*C1-C8Nj{JVz+lt>=ze&QM#bu~;$pTVRCLY3a&E=Do>~n&VA>;nUxe1D3>)9CZEXxT zh0c^`B92YkFS6t`zUmb1U%s=Q?SZ$2-uzmi^RwDS(R(M=j8YATtBN`gv>HRi)kG5K!vpT6zh?F)z zqChu+k&CdTOzEawGTB^E(MX_m@QJX`R0DJ8Y%0g&@0x1(Z{V|m+`P4j!I-_zlnt>$ z83^2W{VtcqmcK=SNW)*NBXwIzQ~PF-mMz7U^59#rq(bmuryxcVke(qKNv*|k2FD)2E3!mBfyUWB?9&3?U&pY!mCGCNmHp^ zym{U;vIO9|sk!WXWo?H<-^u%0DUZcLCve;2$c3w%{HYYY)>yp}HzRZCdfP*Ph-RcZ z`OHO#EOoK7o!_CpN`9f63#&oBOC#3goBWX`zV578n^s?^dP4vWPu0EiN(><0+RL6w zc3%T*J1H2#^&a)FyARcxcFlO}DC*lU_qwVWwZa3&xx!ds*JQ5Isq_v3k{LTJkVUOy z5<6MBV~+cDBFExIAIPf_WFU!Y_dAn-8O&Tt6YDrw6rg5MKK`LUIa}OFV!>p>fiDOe zgU(fMUGA0SCBG<=0VOU+ z1y<>b30gsOtb|LLzTRXWX%guABBuK|J0o4r=9?j?#e?*^3rjOSY_0evjR*I=8WnU4^nWpCyPnH?Ge z*{T7W%w**Ja)2Gxj$u)}7yU9}Ir!6y-wH#e`)a4FKFkv9a#%{L(5Jg;$eofAe3_gW z6Si5epCPoCwL?6hms$idH-tPwf5KvIvprWk*B|5JxpvJ9suHs0wI|t?6$h9G+V#ii z*p7-}jg0F1YOqrmg@Jwn>x_njH%z7~n$*#p6V}#>N97$d=T7uY6Il!|GoDY6(PesL zH#*4zokw7G*pyr0Q30kKo6B-~j^r&a_3M{VitiRYImOy-+uKYF^nKX;s{V`rA7vkd z7N}h>Sr*^j==WeNsI#17=HVMUSIqbL$H31>3to4``u{8{Cu5p4VA*C zWpNd?SERU=gm2_uBF`zqpKZG9ap(LNI-jb@tC13Qpqta=@HPkEKjbSZ?Qx32XArNg zi)SP35%~iwTcte5II(|=?g2f}=71)K9L2>2>G2)}0@40~!Gs_D=y(vT?!kM>Nek_( zq??lXUMwP<1NojKjfs_PdS$-+T3pvR+on#1SHF0B>N)|(UpEu#+ue@~ z_60sIuuNXD>>71tn^msTs&M^t6Z9Oo^mot{P(GQ6qoKd9*mY_3aT4yzf$H5oA}ts1S^weeAA}zDU)r zih61@ta*>oJk!hVuy8BQYj@q*GOP_KALzG|Tw|ztBmq>dEVE&+pc8WYJ!aYe#Yd^Y zpofYKmi_>)mF;L)&k@9Gv5xEf+Fz`|m~X>KgJS$R`=V-bjT37_j>=3WM)CPzB;mk1 zW%qtXll$)89f)gAeVRj>q<}yqd}iV7y>8=<7j9g0`S8>b($!=IB^Kj65QwB2oWaU> z1)LcBCpTcB1E`QH!xgC{0iBYx(TWo*+tgH+1OX_03i~6)eDm~Z4|!yg8ZT~p0AjTE zavs2DzhJgBY(l(dmGu>1-$>_LFx-*VO^3aBc35d!T|-Q^V|R->4}JL;(bl14#0@xr z=BnPoP{WT)gCBA5%#s;*j??$ZiBrpltX{wm6JUOR4fs3FZH4$z&@z}4QJ3nzpC=G2 zO#k|dEzdbcnP?@~3StdOp){J5GhdkNJa*V_RO=D@=Ljj}D2`e&%$DWx1=vO)0zL>#&%f^A<;Nvp4c?6$8QdGx zd&NbLFhmMK+z|<16|;z}svBu&((;%RZ zzGS>`#{?z5K8dmP)zdU_bw@{T?D;5&8FCps>p5I}xPhwp4#+=94Sw2c3};A93^C6KqPG;9J|kY=!foyQ5hlmgzAl4f%;W#Q$R zprK_#rUmb3C8AN#r8{iU*)scEQD4_bFStmu%WAytyVbcD9IYXy=d-&9ms-`u{4mGE zre*QxB9$H7m+hQnL|;939!+ax2gZ?=Cp0o)F;M_#ip25|Xl=@Axz4SaY#u+WfLJY$ z`}x#R59tVk?;+p}W3ODquS~6M)%O`6sI^vTdWj^;+B?TMO)n6 z-5o+{p%gFf6n76U!HZi7ZpGc9_?v#8-*et`o^$5@V@D>FOzxSr_gdFyUCUP~*1w&C z8R=C^J)-URGS7~|M|Rmt*h*iUu`CubDKsoMeZQM498?NE|DeHmz;lmNw*1|S%1`K} zReC~+-fc(9+D_Y?%)E+zliqyYDcDBtI#J>ozkS-WL1LF?C)vN$f!_BZjbeY@rlYtR z>rzvSIh=ozKC)2MkGF~!KIF;<141S4CD+>lYJB%2iqqv-s*MA*)@N$<51!g1?YBia zr!Z#G@)pceh~eo7nP%GY|YSsJq!fejOSwy zg`!$hzIio~n1Fk3I}xx^z_g+rXq(z7#>T&3r3Jd3RZ}VZI)iLG&0@&K(*R9vx#rEg zKKgps&OOZ^_1l=TpFWzVE?F$+NIcxrGB98l&4+!DoK85qUspcWIO=@=KI+=%bIZ*# zh9AS^Grz6Lim0}(1vOriLfdJ|%i(iM>-j>q*xd>1vrhMaHZk}HYajxlZu5y(^uJ_- zsB+Xl@tzwD*Iqa>dARd;WBdoB$R;V#*E^F2)n*C2Lm5%O20SjoYAfuGEPF7ePLSk!N0nK_O8c7tUWN=S`+#gdXwS8v9Oqr){(?(XWJAS5 ztsvAYVAOeXOB3j*wz{Hdfl^8(7dE5Yr%*}eXp@M1Ai&F+F9rQTPAS@C@D7C=wUD*m zGr|l&f%O%ErRiQ7r{mKs%-TJ)IAH{}69`|oH`0Hu5ojgHDH$oSx*enE-AeIetrZ$z zwx59)t%yqph?C!shshGUkV7ps`ElHbJvkuaf+d^|Ia<0pUbXuupM@t-SqgY2CJcNJ zRY9u6Dl8BQ;o6#t3#&8M6jaDZ=Or96$HLdRl^gQCTTgQ~w4(v!X!;T69A=sRm(ElT z_7eGmgKPvpNmue)VR%{lE&%;e`W~Q|)F3~jp|)W2Pc11!ZAi+Yp~~`hX&x@YMPJa( zkm~L_T8jm1NL^}cKua5*7Z#*6V>Aagc;+?zg5Ewk#gJ52RHBG|agZ zm-YiPdvLK*OZ7wt(?fl!`MB_URGSZWOcir;BzjLqEP&-p5j=#L8ln~jLok_Z&{T$J zkPSLiJxe>#9?h7zP#t@?DL7>k{<9nvY6j_ocq z(zSYKV?97tbW?Ul+0-^bG-tIhI^6c&ucHQmN`?>|UGqBGR7}w0{KZ~}ef8FT^_8ZX zG+%F$hxGNff0%Z!4oSW}8w1%-xk`VFL|u!HqeL^A6he56E?H$}uA>r-4{k7%#Ivt}*2HLLAxBgvonSMml z-K*_;J&NA;gB+zc(}M_*i7lP&8Jpa+030!E+#iKS`j8+rs+D%7%(35Ekm*aNd?9b6 z<5DB65vdap#oK|jh;F<8E9tHVuZ(!e;u&?YG=M8#3sQ0 zHf;0FN5FKkorTs9#g>P|&u&57qSAOxF2)Cd9@9Ob>Ys9zfk}75WoK&%d1|dgKd!CK zhRep#rU#7}aSQR=`y&|2beZToLkQI2-IPyA3rAkE;v&gAe7mcZil zi07}4ZCBYKH8U_FjB-Z5uRL+tQAIEpoGV)b`~zb=T^7Iho!p`KKt0pdvaa)*WjF4L zYlVj)2c0g|^Hb2=sW=@K$F*@Gagy48?R(7KVtQe{>KHRLPPH3DSE*qC!PtEodJ9jr zMOFS-kb#k`_C%e59Z7Fa@Ku}@sXyf)aWtRvtw1B6AO z9#ECh-)rCr@w2_h;oQd5OwHsP4O(JGb}H2Y7cqZQxa#IGT#qUK|Np}Fd)n4Sh zn)xlB_ejx|G9`9G({iQ96A?aMv^DSKqRS13yD9~rGn7H%(>mWb*=b~@-aoPaxgUS; z$-l0KY6fK*z0-jNDS_L+-;X5=LBD?i|N499O4PX;Lcjp^kc5_lh8dxA^u?!+Ii_2H z5X^V;s55G|CH5QUkk(2VG3Sdx88-N5cx5c0;MvpE$+sXkB16I0pf{`OIiXdp&fMGr zEOT|5!)byaV5V=FF20fW~*I852$^Y6Mxz#BHm#_xM^HLWtcb^sEO zPgJNK_ZoC);9r*wTf-k0&Z3^O3EQk4-H8}xkds~el-?)PXzi#5n0KtY&p(k^`V3xw z=R^#dqe3Xu?jPEv-txw-rL1qVxqw3=w*q>ffrGi^$+H9NOGC)`F`?{}RXxH`@GzOXNQKVPl8@}@XQSJ@~&iV9xCorxh=DcO2WD)xCB!YfN zO4BV&+CR!@Eqr@YOET)PN;6AKu~OL z9bCl8zMBuoKdvjB(t1f0bfPB$Dj@mFXWZbLMiMdYS>QB`MxSu^r-F#> z-KEB8aPm1ZwYC6VgjsR*ls*rH4PInV^r}HT+cU<1_wb8#h9ijcZf! zdloPY(0uwg04)iTIW;{5RKQ=wX{qLQ3%>x@`u>%{o#InRTk~gdiy7Q!_BW`b zZTyslsc8FY9bPQL4vnF!&3y5Z`!(pNIT+KL_SZrzwiT&hqJ$tlVBP#3j)%@2C9wV}n+72Cio`;_kDC7eMUR31qz?5)=Dg0(z@{E!eB zi5&6}Znqb#O)-+z!FYOJm*j$&@HY%MISe zyq$*gb>=x>Q`vd877vledK)PnTG~(&B@q_1i75}0gd?J|i`Hy6bVM9_pv^5+>i)9n zLq@R`k1hItQ+IS{37*`Xu-x8+)cjSI{&oFPlZ6abv>xGaPrWGvm)abu@4YdYa=|9i zwsoSDC7&@fm9t5y?*=A+V4RpynBA(HB!-7^j)qqjHQ-IQ-u{I&dB9O)%ue7b=@D{3* zN3fM}u)`Q_S7J6s*Sl?E7E6$&>D|L^!jZGZ-bpKR7WI%GHSK1i%fCb6hJ)phV?zCg zfTBDB*A+UVF9&Uua&^5|9rB$E2}^cff{dL50)Dw4l)nUUjY^N}M9!T5C5< z%pn#f=gIeG&%U|Y`z+zytQ~rfz3DR}Q|eJqmBMKM#oi+%e3-!@U|A1V{y06T)S-;^ zq?FNYj!`qL_fvGU{=BIf0AiA|U2wgrq)8%;4QzSOgo`t)_i!Ve#DjxtNL8A)DRnp( zA$B<-29*`ic?a!#X5Wwq-7YBey^D?qMnniOI}5I83GO6#IGk+0;4=M9OOOd-^(Gjz zviqLX&kc!-%}=44H9<;O>n~|LEq52XpPGu7rqC(40K0&fBr=m7pH%>a-wxCa7m>|O zVyO9&>WAUZx#ET8%lPIf-#*JurfaO1e%ySRd&JD@V1ROrn-T+Ss6|E_fSZ!rHMYBy zN%BBTD)aJI7lEC(S0}~ufP@iwO{Mr-J~M#XO~Vb&=X4AWo!U5yV0(YTSph&ISEB^( z?x>1GmzZ}()vtPf_1ntu6le*+yd+ZMC-bJz6IR>r{kqTu!U9d##nzvwh8ddrecb*1 z+c<|{Xx_Lnw0J~axc@g@v!RFN!y2#`W4m7j*3UDv$(7hcq?6h-3Um!FQim7?el}uq zgdcy4AaU^HZZrqjpe05A8o}1I797EaK0(F=av$H@a(vkG z?`5Wi(MATI+isnWVB1uVNj6I(oQUZFq1Uj2q;?U0z_fY(X#M^G zj=FE7GxgN_t_4VWUTei~*Wg$($QWyQqvVWk80rhlhHDjoxdW;x%NYHYqr$GAU+l)w zotq7?FuQRGo+no@yJt}?Qha#XRU_nF3m_yUOr!OB*X-W_3Mw7|bm=>-WmRiNDBT0=a zMbCiy)%-dX@{L+cEL1HtCD@c*`wDjHi|1c?0rGZej;}5?xcd0IwC5YohzV`%N-Yk_ z7^?QI`{8r!=-Opvf5|uIDhabUxKyvdmDepyS!n@3$|m=cfU|mwGFpwhSu*7S?V$7~e+_RB zw5Rb@KT-if+aYv7T{XN#lh16*fgnEvoXHTTS)AM`bw942&X;>YoubWK(0IP=iuNpJ zZfe33Iiz~;S8E0UhC$262ymbmbqa5jx2-d-Fk5(vT?@MPK30g3Gf|`XPKYGJn8{gw z7Tx6O0Y6beS;mUN0}R_Q4VNcoRj5lf-mE>d_$uih$vfH++33ljX9jpzuQN^2{GAs5 zvrV<)Oks5A1!6?MWChN8IiXTMN}y79W0xj4N;Fgdv^C`uqCex=$NxI#|J5Sknli{V zsPAp@kKw+XM<;A*u@WGGAQ(`%P=PR?QP4#q+i7%-m?6u+# z%k7r!_wD}eAL|TLX>R!Bs)emiqk_+mp=8^>oqPj)lCvEc) z!%)kOI9ilzvi!R^Ek2|kfq-9IJpnbj+JBL(de-4%nqL2cu&OI?5{3(r5DmEmsHWJi z9sc39fDn7i7;2MWbMP{!c+T5pe(^dI^I*sCGU(gP)cUaB4#SWRLPTWJzjYt+Hget< z#eQNJt}^-ULI6$P1-@q({?+OV$0bo)J*$Wtl36yIKYNeBDdAhe4}AL-sk%_otZzrp z|FH@K%*GjgW3@vA@M1DId$Pq0hI&4TL9KzSMDcn6p}9yEB*GwGDK=MoZC*Jml*3}9 zG-!W*`#d4dhOTBB(K=&S)r#~O8F|6w!l>=6=Uf2`0LU@In_gr5#E4d6Ub_Dz9P>x< zo5kx9;~eDpeM^%{#C1ALV)VC07{G9WrwI zthp~lfm52av$1_Qyi*R~6`z(f!LagHD*VIK?hcHVI!K-R@5Vk}l6mJlgjjq1?bOuB zb4dS!!PC?5ie$5qhtlh&D86+|ULPxvGn0~wxLCEvu@_&~>Y3@bHz)&!lgYh^9PT~s zr4Fw)p5#l*vYi?R;L}jdjM+o`IIZSi56EYrpYhPni&hROrsEX4XP9{&#Vw0Q4WZWw z&=#PYl*4PQmZnM^+=Ay$``4(WX+i7koAy9U8slMOb zv!e$b8{L*UOw_l^Vm*(3}N|v z`)eE8d4xMU`T`{@Q5p0LMH1Fpd3yw$ep;(U*|9>UbeKcsI@&ZJj^}RYDU348MU!s#ACCa`*r+OrovpS0U^Mfr5FX8sDitXT@UZu)%eAa^2*G+;(Qu zrxNUDLE3ZQg^HGRy}5gdQ83k=EVAPL-rm@KI*=M(`(8CLv)Km(#12qx+c9vwGOOJ9 z8o?Ko*7Gur-l|CEd>Q7D$}thRl|NL<-S^&My2&~{qIs2%L!0)iBeI-^aC1as9 zMF;do+~3UL(ed&PNaH)|pFFR;yxGf$kcUq7X3cJ0DZIfDnYrE&sg7Lr%i%<<#Sv_^e-;wc;VSPzYz-f9?za zevT&%V!&>5!h?gd#jZd~Ed z#60&uRpn1%3b@f`%OEnmq6d7dmp5Fz3m4=}XRCLrY@AXbOTy&7L&=>In56HjK(RCu z9aKt<=%5;-l5)r+KzFcs?*e6~?IlV}prnp%2O+1kEVNh>uR-HULPFgk!aaBZ<;b@L zKiwACGMxc(s4|s)XG&$H_|Eo1@@{}&%3qj{e-^NyRZ@oGQ4PUsM~`eaz*3do)f^-O zhg2SakI-BcjJIr(fV02GSKh`K?BYe;_;>wEMm%w`A?b@?pCJqp8vWWBqvIB95c0zI z^Wn+|{uE?aN@h@s8rA1~3F zGf<{kw6VyX84Xz>dAN=Xbnp@ad*MOyk|br=Pp-}Gz|Tc6YIxCafjWUdh&QzYd4O^c zFomWkPF7j=N|>NqVPg(sFKa|ICf}S_&v3}b zT!)@dlZ&|GOWEZ%JGAGgCu?8_h+bIdfGdn-5irRKH5cS!HrP^ja{orM4CNudz5rN2 z)=yIB1=PwZXf+58i8sx_Q}*{}6C)jE0vb+sp1p!PM+2XFDx3o{9oo_fnhypSSopPT zZCiUd#6V=%FSAsZ&~_yhKgc1HTQAokq1^6)t!f;f`~*qH^hT5yu>8f3539+sSp7{{ zutzksY6;Y5w*0v((^og2&?40wz`CS))1&?-Wqh|bTP15C=mosxWL0zsy!p;gL~Xoz znv}BV{*a0TU)3nZEXIbVbaN{MxUX|fg5UKHe573Uah+7(hLHM?o$y6ayFi7>>UFSs zuhHo~p?KxrW{~~*wZl7JkiN(ckYg_3M8c-|o~QEN5DbaR2}5UMgyb=4y>Cuv1oqwy z!YCXa(~Q@y-L19!gWPNb#+xu|V>0%xdquySjMzGRW?oeOT;;-R(1FaN8VZwwM;d)c z7tx|G4_-AOsNNIje#tWdLmks7Q(q0i4jpCEMV4Q`Z}k<%pk>`s3Di5QA=ShUf%j@b zJ%Ojadq?gC%6_8>*KKT=3x4PpQ(?Xiq)I1LLmxBOO!ZN22D}ZHQ zNK6Jpj$tD4e9DLfM7JvAeMPwm*A{&$mN|JnEKcPfOUPhDx|+xJN69;b(tAyBIRI=# zS?Mnc_02{L<9axSUvqFNoS;zebSEXnavkrEM13ez`_wxU{(aGjBPz zT+7>P7Lc369#deLg4tU{uLB*CHXARIGVV~L{SZ(5C$TEDh)-gf0p1>C`$ScS+iZ_ApU{ z9SCU4w0PmgLPyizK*(v&1oX<|Y5tY#bV(_`9!CG_He;`TZp?|RtPh;&(OemE!vXX5eMdE_a83S!$o(PlYuv&;+fQZ!#Z#YCqmI*EtRJIW+~l z{7csPcVX|}YXdif@}Y>{tagl1zw-dcov+&7QTwTxl38t(Uy>Vq^uBnLz7J`5V+4rw7 zSziFk5(pFlR^LywaY4=`_&SPPyj_?<;{eG?nIH>R&eGx;8>}bVQmL( zVRpYvP~!S+gU#plK3IkBfBk z8bh=L#?xCLX+pTovq1lGx1T2EvvH`VX2unwC;78R@ZJkqyRsJI72A>)*HiE= z(b+Or0-N2<84B=(3%5RNgAsvn(G(%PtS!{`VC<2=t`RZTtSU+$q`=AtP|rcL0p@|) zh#J&wVYkt|ZwPx|1GB^H#gYM;PR7n5OfPPQHB&7Y((3Ldm0ADD?qeuE`wmzV7_hgny}(gre_PI-ttA-PmDFc{cRToRv@KtGrSfaYdd8Y=)a*WNrPmY?zJ@M9-GX7H(TY+!`>M;M(xzy%Hz#8VH=1g`$ijw zI)bg&WNVTfCN@x~T41ydZg2P!T@LN2KV8whk-nSQBacncE?H6q=Xl{iqVnzvH z8vQ>S3S(JR3)BNtCpT>`yHAt#g9M6qY!4!wy2NW0 z6D)@5`ASrVly>fr?slB;2S?d7vwAN>uNImw;a7El62lOke%sYM&`(ylDT_~Z(=0*t6L?~dlVG8Wh&SS&6HA||Zb8EiatHWopvwoefwx+27ha6(#$2O$4 zeZOs!Fih4>Q55gqcj|x}ac6k7WW3ZAB00O+5LsA#>ME%}i&tUFGy8$^xdU95rAUyKX*s zpvqNeWexq*js1Vz3f<&?q@1&+kQ!Wq1_B0r0L*_%=iuydP-EZcaaXvFl;*b0&tcGF zA8&fimtW@FpW%le`5!xknm)Y9BK!%f({6M7&ZgNqk_dk2p_vn&r>?6AI1mng1$78m_z^5a1ACIiM}INSw=r_N^VE%FDT0R^6o&H&Z=@>llXEX}eQJqCtc8 zAM!3%UUPk?r+fH3dKbw%Vz3D*0HOPW2L>8{u$wtrTQ;6hdjxO$9|(@z1~n1nv?JRo z>wsCN*KFk%UQkUfF6I#s=eNCgc<}j-kGRn#oAGIk--r=23&Bb&D_q3rz3J+{>E6s) zE|uu)+)A{I*oy3@^Di~vHRB(FCdgiwY7g2c1Y?f1WQMdWlomf0ue>J+v8$cYxfV&K z0aFq5p&f3s5r?J(go5HJ^{;?g13%u$CtDfs$3TPSZNYutVq7b8FS{E`C?GMbp{O)A z;R3B+VR7O4=$$k@G>8 z{BPe)TtYikxtC1s6#}mbY3;D-syFptJ204ZUdz%N0A`WoQP9%pi9VUVL&NBxu#Aq0 zxqtW1kI;{)d>p`=NH9^K-wt5=O-6_Wj@_7VzvD8?&mHr2JU?4!BErf5bjU4+KnbuI z_C{(`k?fc2GyK(39dymYKezbmO;{I z3j3SS;x2N=iD=L?U}Z9zt*&tktf-r(@eS=dM&;z0FB=?JnDzvl8UOtGXibaNuoR1s z9Qu}6F!QFcOx7GiSnOyYIaa)@wGU6thxK^rSF=)9pVO6ZNBZhq=G|&^WXRMv3A>di zGe9|Cas(#OGu2QvaWefht%D@t8l92^DQZw49`UHImSKBYV?&F4Vt(VfV~4j13n|CFeWhDisjdfX!%4l`bR z*@7kfdc@yYd7R*6h^WhMz>|e>e$%XlLe^P-UohC^cbE@gl!TzKJx*2~s!sWQD#~g# z;Y6tam6LmX4$E@b;W6ggH%PtnX^XP+iswel?jR#%zQnNf%OZKf5cZKXSC=K#$-<=e zJ`E=3B%bBoEoJOk(Ll|S+chEv`=3tRe;-!=b0zb`cnL4%8O)~J2B#3qkfgskIR{gM z<@fqn%*LLFyFtb~MpS9%Hou^%>A4Zw)`qppBts0VTQTl-Y3J&3jRqj2+KD2b+d2;i4G`$zx7 zpv$R?J zUj9ui-NXr>%_h)b5mnm+Jmn?mnDDX>zcu+CHfH`lcQkz`y2*8} z^k@GFq?uv#h2LF!70$Q!+01_IGvQk-dH)pBo;7e8CmG6q7BPot%~R#?+4<7QjH&%{ zXbfj{Av(lMJ(5ZiZW&ZHBeljTcsw*{yZAX)s={oGm6xkOAvw%AKSCNKEnTk-SKa{e zJLi^wmdUVI2Er>!vos@gEJ6Y#`v`drFFSb(_>M*?9_^9qD_BxEBD0kUuN6q?u2jML zynWyC=NVQ~(kK0mm(?K0;v1qdlFPa0lfU-}`-p)IPM%zlkM5=#pt!i$@HB&^IdM<7 zT`4R8?yDvdv)@#@^RC#S@Nc109kr@QB0;)9v4ZWEThk+o}w@( zpFAWEjburU<^PeT&P~1QuD&7gm{L}jJKRkTzg)CqNh2t-g3$=jc)X6WU@4-DlNg2dPjRV zX~x#CeU1aVDax?|=z%4~5Lg>&rxbL-XDhNpdwp&FpYd zFC89E+j~2(Z$usO`Y91@u$B6XiUXlxMdq^NCZD1#I<0`Vw>GofF@PO}bl8q| zhZYSgFD?9S50oBfw4Zd2H(0_V+SjJ{7g&_E>NQ_U(XGnNpy){9+0rXuCEV6a*q^GV zK51?Z(lJ^)5WT*%JbzuvxA0WWg($h9hV~R+nGdhac5niQBu^@ZmfJ<0e-kxab{jp8 zJhfC0`Q8OE(H+CM<=7BWW0WSlLzcfM8H5yv|pp_$c_q zH>Y}rHDla4p)@V@)30-r8U`5|+5NhUD}y}W`{Hf*0^V*7cs~rrpaPu1bYx+Y2an~i z=#)7M*8=A?k41Su9tow2t^SOx9qAHw^dDU=`J4$yq5h zO-YN@kUU}aHbA+xQlLaU>d$zbH|}V8MD#i)EUv-Y_3a=};=!^eI*j$*zLrQ^!XdPs zlK7AC@=x~GhsfY|U%fkFfelyNZ;KhgiZ1DP+KEFYP#nSYF7Uai_EfgJiyrPN7-9KEBi2GBnzVlcK?%|6@6V&Abc#y zj}pyxBZ6jUqvsVA(Y`~SsDXFcULM3bbGW?e0s$;my?=(p1+F6T<|K)2g#q%yOBW8f zrIOPr2`>2T-t55G*e`o8kGWl55J))wQ`Y?7+eTtCDk!UN^V7l=*Z}6>aGk&)K2KBc zAbykOw>HVl$?M=JzK1*4HA)E+Z{-)wmr|U)dR)tl!pN$#>!f!uXtjOdSgs1Hzbj&c zw2vhU?Hdjs&DCxCgKxim`Cs@Ji#|w_Jn8K-RSrp|w zW_QX9FNp(#T>%1##_&z=n?3^U|PvoB1^)AjJgLwq~j+ery;B`qYK*i*o6I?=9^%jAyfA(?jt z{{uh<(R$#uw{-vBuWHwSuUBd8kqF%3hJ)5rmOyoU;V!Sx%Apt zP!0vHF(LGQ|LsfL6sD*tRk95QO-ZVEESqW`*x3Q1^D2DWf)~0ki>Lu}GLRAVbN|;d z`Ia4S{;d9MY7YFvi8&uyXgC?SHdjn2q1n71UB57)TC5%Z3dMQS4nfxhG*C0Q@g`LH z`7fntAa^NAk?_}%8&FXXvH8cjy+v7ldPo@*rrpr{bS-?U7tBP7%{t~v?`2v3?T04q zp6F1?j|q%JQg|+|G5S|Z8qdXSVt%GA4rwZ~Xp=>iAU*KFGr03`CYf^aYFfS^3C%*V z1o0NCP3cM6*u~cdQYy*{AM;$Rtuu29aV&gqEl~8-!j|5P{Dq&usk0%#&|E?v`X%Z- z40?_JQY~x!57i;gDfgu35__wlO<7>v*k=(7Boj&@}SV<)kYea;!m;{lhNZ!tupoh9~v45jLswTWuo zi7aIflD-{OYGzd{z}V_$9n}X~hz={RR`r+P5R9q0$lJwVyAyxnv$l0b;03Co)ec8? zbwpj2gQb)C1_uk#V2ofriyR~SX^{Peo=DQ9^~Bj&z+h#LCAs1RzXd&jZO?yY9`hJ+ zrW)*!OnD~1Ma0|T9d=d#Xnu;D^B_WOQlBPsVXIJoF=8q{0Zyn8?7dalZ_t;Xk3TJ5 z>zlY~Ne$UMFI*0~$yW#<&2I;)c;#O2Jjg^99QY>fjSGa_oIP;%iK`l1Z1Ji#vOI79 zv3#`1vKrnch>oEvNYL>8Vl8p4d21lq3)wyPSMMk$D7=>{4~W#?tr}1G(iIN2HmWkb?vMmCe(T_WhG7Z#IRM zR%0YBtb{6D3shTVSc?HApe=*cq7s%&OTYY+Fk{j4cK)Z3HT|}_WQFeGeq{z>2w=Rr zklndqCJt)x0WB*aT2akie)?Yh{LZSf>6Kw!_7Dji(NU}y8g5qdra2m5u*jD>qf!FZ z0NNAhew@~7uOGP>!@C7x*|hA|e}Q{!IONf$GHveY)$M zm3Aa*aMoeEDb@!8iw$PQVKaN9f-;RChLFTe zkACU}z$w|u@?1B1+Z2YcB#{?y0400)ElN6C4Ct5+;( z-d)uG+b3MWNo!gx`-kvZk9IZkP4nAngFkjYD_4(sc4^|FG&4T?tL^t0c3q_0vR*#N zZDu=)n&AJ2$Nx(RJ&%R^9JNk6i0{)ASlZk7@_hv!aw|TS;?wAD1ggY1o86hiODBDo zi?lOIY+??x?54ceVnauLfbSEk1OLM4Ga7*0?m^w~# z5o_oh=a$-Q4A7g!wzY(B6&%D=G%d9a(A!DJ#k>&&j#MX%s|8=Z*9WDQt)tW`B$=B1 zDM%aeY@|0h(72k4yfhorASZVyF^gUPEYuVBUTdUzfy z1s8f4#vsnu_?t#q4WG{@;&|E4!UAk)kKf!bgFMO#C2e)c+!@5lA!r%H3dtC;GuHgu zmpIZI+_J_nmJE=9j}%--(p(WzyK7TO-&IUDV5&RvvXhsY_Yc!}9Ui{ZbrwZQi1k|6 zkV9I{4AMJ^a0P7|f7bQTbG+#izFu%Gh$$U`+NSDW8PcyN+vc{h{rJpC z&gd7b=Ik`R2x$#7J~(i zp8nwo>Vj%=H4gvwgEC8VkD_L2dGXkp14yZBpODC-G`ym(?|xQN74}ra z5Yh2HjX0r9$zD^ilt{3G5S$*vhqOlemDL7m3aZM(bYzC-01A!si7olfI?yOL_6w$I zIRwSGuTKu2Lz7a;uoSs2Liw_yMk&b>hSeSjvAHnv`y zhT>qygQHxU%(XS5KdmB7>Rg6c`LeZ(Gl!ZxsONP`%`2^nM)2luJ#z=TtvP!1g|hZa zOKRtaXMN1T>=?Ngyvj1NPkk>iRu;ZW{Mz5ZaMX&FGyY60%<0m{sK~3+okp@!&u8@z z_C+v#xp6eq1qrQcJjy(`VLKQoZe@99?i1XWVnbwgcQirw%J%V9k+(%+;H z{T)SofgsR)|Mh#;(Po3vq7Sn~DC{e1meiw>dHmXU6QkczKr18K`>ZhIfe5^VTA8cC zZ>dB2-nNFqUBey7-l|{Yy6g4UIkQ+%jBHQFII>FNyJrb1<{Z8ht(n8~#g% z=rZDB*9pSGJ->!0)Uok3I)Hcng=PF{b{XIvyHRb$@FgsD?Zf=rvg5OM*hJ*kHA&J& z=@J7V)lBodjmptsA}f{*HytC|y85Sxda-_^GH~yFuMTx32_WwPb@$~Oy#S(UZ^J-) z-V+c@q7;2HUPM8`Zf;3}XF&;K|;>*T;BQKa^FKcPFx@nM6o zm&aKI&p*D6sSo{4z_t79^wBuH(KaBTU-!(xnnlGa9rnxS`42Q=+Z7@Cs(&Yr? zoTkrJ9iVya+ODMV;rqTW;ic6oyRc*K>uosFJc3MIsi3o`?M3sv$B*3l^K zTF~Rcx|#>6Y`rc^Dryzy5)wa%!r~v=5I&u;H>IpQvxVu_8OTGcjDcW9zdx@5ha#26 zDeQ96ZdCr1MUt%csKk<+$NENYufVSAXe#cg&w5^t;#bwp!|v7X&DfUuZ-M06q61Cm zmT(nmaAfz6$8R%{5j+#i$pWQE&L#}Mzft@Slio3Jx6cKD{|rNb;NK7iJWp=Lc;EHr z;zuS<=%S(@L%WnN&0Z?(cJ8p<;GSBkeb7%Kd7$mFJpO**z-3!HepIZN^`QpAwWAq- zWcIyfUEsQUhq--I6Xu(jZf#H|Uxc0*A5m)Xn?E<{?;+)Dq;#OtMb58)>KDsPS4wbGpP$~5 zG|xUjU__k75PfQV&1t1{OR;EFKDA=*U+U*5<*}q>Fi>Fx#-EK)m;aBgvyN(W?Y4aq zTnYt>72SA|A}#K_NGS!{LU9SM!CeCsXp6f`aY}Fq4xtoxmk^-Ey-4uDWq;q1d+t3W zVZ;Q^eo70^#%6-v#LA3f=(olBB?e}L&^?V(EvUR-+ zYIu*3ywT7+WC%5YmRLzsKBXZSJv8I-Bv1P=VQ zL!a!ZqGrvr`S_sZDiASrF;hV26l*@&`El`dZ8Ftm-*xU$_7-{gtN*NMJjl2A*HH#n zR^KJh;#VGBAIILZ6LbEf8ac<4ug-N-#$AqN-8poOFI3*NKX9NqJB)#Sj&)7}UYVGl zqcz>vEm*inC&-mmSl6rrvS6hVLT%@)+ZREz0lV3+y4x?s+;s*$WV4?6tygu}#u87S z7i!#>p*S%r|BD6^`kGncF`WXJ1^Qv>41(9ilc8<6N}@5!g+p+ajWwR2*iN^KSY4_7 zTFapL{C4uTYqt&oEAPK)Gwu_<`JFHx(>m=DI0B9b6MW%c!2wr+ZO#M`jj5 z)SO`uQ^DXWl=6cNp z3;U#kPx+Q!Slhg-Tmo627i=ZX8&cf5n=%hw3K}hpeeWUmc>bCaqGD;kKJ>=7T7@@2 z9(=0IpRc(9FLyo-Xpj&+0toh@uEb-ZM%SwhcvCT-7IbkpT>!QQI~wodM0gi^eQvQC z2-#3}aqs%&JH1>ALBPT*lvfDeLTb7O0nZ(^=PTT4+UDcooJ9O@%5K7L&>53FS+$uQ4{*$-3I)1%f1@?vdq>StZ9bXWq> znVR!eEq-zG<0b5=z;%8J} z!R=6&in`h%O^@h53Ve=qFMYY1*dH;?igf^Zc6~lv3a2o}#E;%*a#S))bRUak8RShj z5Ud^}3-EaWI!LZ1D)UY10(2!2=e-&nr{u+0)_F6A8hUZ|bYz)FXE=ai&YB!2TvgbI zHoSLLG)boM9s+zZ1}$D@C`k}3=H)@qh{K{(B-lO1nkAx&gD!UR&_OvT~9TS_5$%%ybk7OyHHq{u$}L6G;7xU&mnU zZ=@w+isbu@yqt7 z4Le0upNa6dk^qhtv8xG(>@U8x&(>hV-eXc|XIHtyHWcu`2?RuVQytTXe^YdMKp<5E)@DZQ;a>6TZsL{^CRXBNdgCUT_FYK?o?Q2!M z5=99fLBbOG4K{0>xtaWqCx-J~4ThSe*lL;W0^@dqA#38S$HP7Y{%IoAU{9V^<1=k+ z1;$R#0;C5vk*E0AWa=yYCkLMNi<9ly&!3HRqN)Og6*q!3-R_(l4?> za`WJ(wR_m<-Qk#d{9-?sYF?-FSo!??{)Y1$gOLy#I1x75IEBpo7G9WddJy2==G+yovm!o~IUU-Scv; z&4N$qa&aZ=%eQyMKZKwYEU-L+HJGlo9)P|yx+?)nD2~V#T9pu^e*6lHtj=DuCvaP@ z=={a(&GIqZZ;{^LLxRFxT63P=2Pr*1a_XenNRjSq5#L|+7WGc;ciQ$Ksb>p64Nu+p zWn$uQ`KTjUG#Vkdg;+U$z=SNnUo}5%g1-Dnpy;E&NB{7gXY>;&@(aZ_firr-Z0L2aD{J9-T^ zsQEysaEa@&&KuJG>ZMnhqzetAgNz-h0gM=!U=4rIuTSV!3N1NcO8XKfqGo2;ZYSX< zrcK z8&a5C4!jPrsAe~GLsNs6S~SCn4)P7~t$o#4;ijdb+iqALbm ztoct*cNeI>JoMXcBPalumyX<+@R)f`YsVe-oFApHYOhpiGoJ2Da1Tf&*7ZZH>+jlQ z9nM-R11L-PO@=+;X?dufM}<9w8~^g@-{dq3dHUk7zI4&bdE81|wC;8`kNh-S9sZn= zsfZ|Y?U%R-gs27}GltA~bASb~O%kXxgf6Pzw@Cu)fG74MS)(zn%FqpL|LBXZkRcx>)w%apdCx^4hxv26)Y=O+BalFi-fGR8+puPB0MZ5| zlr6a8aO`1~L>K4j0N+j-%NQ}}G89{lvD`Qq)7wlLO9xbM6!gm-?U5Cdk7?pCeg-6n zB0d;|yTE_G;Fk2Ov3x>v>xyiDP&}>Cj}ifnn@GQ6E0po5NTSp~ZM5}s|_uyX2&=``wR0Gk=t!x?M*pF%xP~Y1MIIx)pDa^WpO5Mj;lm4^KF(gNQ$@xEwI{Dz4C_wSOPK_=~*yx3HZHaZy+F{1X;^C0Y$W%w=LdU`4=zdx>T58{2&Ew%) zANN`G*yITkn7;|fjRSz7Pp`AdI#z;~q*rNa(_gcZCtQHy4+AmWvVlg~Zg;1o77byv zxSX#^&wsSR#hd-O(e0!-3Eu*MzC2>qy8a^NWCVf5sk5S|Bu!qwh8+ID@!KZ_3O`nFG5n%0||PH z;28{ZM2z;}!)Q}`h9lJ;wSS-;ci>kwNqt0$aa2TT4Yn9*JW?BZ?D3$;u`1IM|k9m|Oa@`}yRUJp=OdUiZiM62|Nye_Hqy_6SeX zg<`G+zvU(`<#PZUChb!vN_d(I9G*@x5WV9;LeHAMm#jne2YCcZNUdl!)g`>6gMl`~ zqt$!C;y`f;U8>c}vp#w&Tykw%9F|Q0J8^DNvjq+W~&Ds0Fz9~0oX?2hX1M)~#cyaaA!dOU$F*AGs z2-lfYRN*gT>LZk!2Tn*tu6A)VuM>sovV{&4=M83KW^J9X6oSv!hMEZ$O1{><0vKyTVMpCI z`a6irn0PJT{L$w_ErVLXx^-otmprCWN?jqB#Xeoa#{~SH}`mmazIi^#Yx|8%^6C?;INkgq|i1z?yME$(Xn(?kq z5n_(TJ7ej?Q6xOXy5!MboWM>LdHmjgX{xHJ=II>BI&N?4jq*Eh3bjSbUh}yTMFdNzkkFau+|`8o3<2Itf@_ke`cUeMh-E%| zxNiKj#db{WtMg}a4pU}*ZXv{+ULKTMfdm=Tx3Ez%IkgWC^vZ<2w^b+ zelo0!K79KeD?k3sMFI*Uq2@#ioK^J)i4h@`DWK2Y11vI4tS-z0qIhY%X7q<*ES`%~ z5ee;)vn{NzZx3wgmBz1@RLG%JTa>}F3`>#(aW~K8{6HgwpT4u^XCC5vvlW*r$MC}UO_6^ zOu)u~_(LdGogMGFK8-#e8@1$6@|QgJ<`DLDtq;!6GrTft9B5 z(bqYR-hE6ipF=9(A_UbWM39GEp8g4&+p0Nqyx(Y4(-oxY2YRVW(m>GeC`GgY0tw3@ zKIUDKt@mHOpr2zAochZ5R4(U>v@h4}{^B_vlIru_$>b+t{ka|E!{eu%U_I;MY>hme zLncAyk9$qCos@!n2pGhy$mkXH(-@%eDy!-fjQ>nTUGYL zH-K)(>&$?q#2e25ha$czgYK`

0w@O-JX4xBX9p&ZvvQcpDUW&X3#l8@F=!mm4;z z?*&JLiD0%16te+w9fz*Fk`dj1ftQs+zSRGS@nfiV_)X(oXI|4_5_HtLs5@^?2mUMj z^4di2OS21u(sC5TZ>t^CN|&m3pv`>lp1`RH{|caP2iyLBKi2ag+r)P@iu`Q@g)xTH z6H~B)_9~>eqV9giq5SjQNd~UI{HzzYXrUIVy^vO9kH1?3(w;S%t+`MQ>Z4aTxX6FY z6fApYzk;Ui0&}F*%@P_)Ql{w1!QQl3)E2H$lt(U`4@pTTdg((OTS(*1NahMwt`hYq zO?FYba>(DvZnz*=%MvAJ=z}{`c(szY!|n zX|`43?Cn6F>}?uT>58*Fb1Fazb2;emH^5)_{fRjb;*5F(g#DTF!V~O8yi?ejsij<< zSpg+Nd+))cP}fX=Sp3YV1TXE~*-pT5XZt2{0rwllYLP~6%Dr76JF-iVesygTqYdmg zb58(S5kk0)OowYMQ~<*vA@4!Fo;M)hXqxP(klq$`eVq7gcb0IO;-~a4q;W|txEN|3 zvIvFre=e^=XX^1b2-tW$SInG1af%mfmWCH^#di*Rv@&-b#+iJ`p*kuX)ox0l)LKSC zt15oKxRZqxnE?p-c8kZpu^LcvN5_+MBbmUEMpz)_CFRfGk&HG!zg#8ZB-d5Rybc-s z7V*RnLJ**Jy42J0{0jZpUuU?%OmH+eXy@Bm;__RGqF%kziBz?k-%SL?$H`CDKXwVI zjD~ZM*MTwzsebzK6kc;3jL9{EDD8jy0K`H6jBuMP20muQ*lEU&DPZ5HGLba1HOUG^j!R*VrAM=#LA~ z=&pDN6Z06deypuB@#&XOsFU^BrSWAv@OP}o$&yUd&m=SSgf?RrPR##!v+p7Cs!PJ9 ztKn4~54@hEk6aV(Tq0VNE2;KgubAgGD)V@ zgyAPefj`z{lo?*D{0Si|!li#(Jf<;kLuo2&e=lTC)zrxN?!mRP2IiiwQO`*hLkqn1 znkM5qmpaYcQ)<_599>H%X9D3r8iKqGs>me@)-wkTnw_ED5*k9@{?xWV1z4&Z#6d%W ze3F1h#yuzPBhOS{Z4fwSw*9q&-+GBTg&Pzwm%-{f9jrE=ZfzfX^Acf~hO^$kwiQHp zs(HaSqFh6&>2?3venB7AqmpG9VrIckRrT1%i9L_n@dA4QGWcz(}^@9=Q^j zh833$-FWN)I2_9U^&oooNPSI@h)1{&YXFy6jdF=*g(AdbS+lLLMsf1PK5Bz6boUp` zRw*S9(^&~vf4Faw$!Sx7;UX?UpRI-)b@w}}W&4)Q&Eya`fbW8H0l{DpWzF2OJU14- z_8%bnuQ>BP{h3x>(XnsE zOc+*sj(+*86C6_G_y)Je6eH~STt7VZ6_m_#0%sN*F`jZdl!ps*D;NX&uz@yH_0;Hq za}PX&!28*tr!Vn)ph~+I?mx&4ngeHg|3p#WDEum_@x*kC9!Qwe)ZNkAgXE;|u4EhO zOzv#8RwV5r@>h99ooDyvNvl0aIP7yem-KZUs#6rpE7n#;*Ml}lG8@o`TktG(VPnjh z;}W7yGfy9{9TW(8d`Y=#yH>AvH&yVLsxK4u_&Gexj*An zvd%|$VArbw8eOG$1!K<&!ZP#+ikE}xE`o4UCp6wRniWlm2A^Mf;m?>mYBup)*PyU} z7c=X(pAvm4FACA@PZy+C=o^y67hN!ULZc(wyB+N193K5-6?y~SAF?nQ%^wInjBWG! zUsRueM<*U^!_{w?fgR_~GVeX3GS8(pOz{*_b$2Ekn;l&JQ&k(cul9BRCgmOf<`AOy z_~B6F0PE4H7hxq=QammWHGdK30Dl5aK}WR<+q$os1dLJlf!r0CxWgwlafT)&+2p$m zziRKk@*2lVEYp31Q{0}aaxPc)fj#sg>EE5R{kDG6h&eV8uCOx(ydUg+h6y=)%-9qI z{!BYnz86vSLdhg;J5oF&>YFF;IX~WHaoz$s!XVh-MUgszU)X-#8_GfrggpSE?DZ^?Q$41S0IhtRe z>AL)Ru)PMOC)85}dnf&;u|cN0v4xH{qKT#N+i7_bpi!UNW)Z@yr)Y#HkxB@_d#9IY zpaF_>gRr?OSDSI9aHs-|l2wD@`c$SpA6p%fDR*y9+5QX9{y&e82nA4=-CbN(slf36 z{NSIfiMWS}ajH^5x^iVS2eATU1^O?^+C@R0eAg7a3(Ko3O9<^u)yPolg@HVkKF83S2OC{Qr<5Hl2htqCOYIqcRGk^ z%B7f82|tf02YRl4-z`0G*E6Nv@wTA3Xlcd>Th>V!KTnqL;9KGM)X_tDJk`bL+K%^k z@%BpLj{3|#Z_WzeV6F-fDSgozm55FxMt&tm#WstOPFe>2{+V07I_tzvYXZv32CebR z%GDKpiPFH`d})ar07>~$djP8gxG@ywv4`J%uaPW2i{teO%&_9C-s~v{nhkd`()&yw z(V4V~euu6W`=~O!-E#|;k+=vj1kIuvuD@bvo zfi&oag-&$&gGp$=pNwqHuOob-R-L=TcTt{QaCPKl!l=Ii9BmL}N47dP7@w`Z++3kv z5pS71E9-;e z(tCyn7jlo$wcc2A#>#^M%3TG58D>ZVJnEJe5Z+OWZqJwR7gpy4NO&f0g}=BFSmO9U z0l$ByU#%VlJOvidv{#9ANzw5ck720#sDs5{RPQfQ0)X+sh!S6`FGUx>RU%2*<`nL& zoX2(VUXJnuyx6s(}ZZl! z?3*$-3X#Q>V--5?Zf|mt#i_ftvI>!WNN@u1LZVcAb#Z}*-eM2DAeC5_W7+2gWRVWN zP1jNQ9jgs;q!df@=b01NVWo|OmVi1c1Au+SHPt_aRse&)-zT00YbMsM2cAAZ#jXuW=&bjVDTkdtwH1kFSbv7yKMVPc7;c ziRKJ$*_pL!R(3xTKH>2hHtMP2JcXMWiMMq{(U;v$mJE9qT|5MVfYs${AvH82y4LZt z^iK-*4`Z)|1@j-pypSQW(Sc2uH)aG}BQ1>gp3WSDuA?^jROfB%<*y9wkMH>7ZtARf zL|lwDuFxfIKU%gb>Rl<+y|VUv=hmI^hFU+aRK~*OVcFQ3HuJX`=r&AlMcbC9J@l&e z^V>me|I%sbh#&1Mm)VDvJl5B>Z^7ZdWg*@AN1{yn-Qm;~VE}@IqubQ?0h_aXnitVA z8uwsxlLsy^U{CqMI=xW$FuGDZsoy%80Ir?{(x}oi~n=ed2=!PMlkP(z>5|n zNmDoSfpqggV&`bP9;4Sn%Kss$F{+nA=SQQ?67c4>Eet^k%N1IGef|+Ww$h-UX4?k~ zSbVZ=&I>>a3M@44Vb)jGgZMiPFtPPU?Wdl(y*_a!G6Q;5*h4TZFav%j08At%E+Qk7 zX#bT-C6>g5(1xfQ4wHNFtN@}1bzXLw zL*KN<0+Kg$oN-;`>f4BH4FJg94;zzgB`QH)d4}4($am5?@q>wOmRXg1C~gyDa|&mj zD;agZI+yK0SO72Uvc~1&nb5+Am7L}09&OR}Sxol&8UZ^rZMk*<9;s(LM|gLyS;HINPbO(XmDK4FQ$CC3+Ih;MaLdO4U zXR3eBvhaH#@m}W31@*zQ;*{rbBq`M46*q zx$6Gs?VY8&KAFa=nvwas07vCBkV9vE;~JWi#NC2X3eaSa?SiO&|x4!)biNr6;G!9*~Nalq}6w zXJU&4_Cr4s#j0s+ky%pyOmG7eazz<;C!YM|jT{}>?n6(HOjGC0YP_7W%$&Zm?fe_8 za#k~`v)yKw^TwH$Mh6m!(w0|@Z_ZV5SI%KG0R~dCCCL>$jjWp_zkCnXx5pOa#tD^5DVfZK1@aQj8c04hQl1P*4-91kIx-k$;w^D|!{C%>UUF|LlRq6$waZ@9 zp!w(sIVE6zdb}x85KY=GB80jo0-Pr`H{3tg?*)q$9qpv%ZWq-`nM`VQ z4Y8r?0@6u%kh32Es}X|i7shHI)pj$V4RxkZ1@U#tnuzAEbSl*rVXjWlgP+>|^B@l< z-|mfZD}Ug!>60p}kH^Z=Z#|0=`Uh<>%8sB`{a<`D@*vqK1ZRWZl#>7gErI@q`g-F2 zpg19VE$+}Hx*~E`IP%@RCr&n<1dWS_WDUR7%~C#BX5?;(ENwc9I1zd7>frYtvOZ{i z;w%7j>W};K?PIU%!r*1W-FL!QGiVV3rSDM*TQx~!)1n)HU>|nhD+%#AGA0g|s-wjc zh@;$9kXN_5!(Y~K=65u1$hYiWE$Gqbg8eGaw_jJED?X%FlCqcVkLQU%WkzsXpoi;oV~EC=PJ9=fg7 z5}95GiNVR0#`XWeSjag+t5qa?Y7em;+&S!*1`jNcntOGCgx=bP&ohMl)P9&_oMV=F z?!>l#?dq*zL~}*!quIXqZ-jov;_!YCoL0UXRBY|Q7|GGE_>ew&v8lh;n~DPScl&RN zGEKVS>{K zFm9W0`$)TN+ zssboBXJv`_VFqJ}*2-f->i`}o9j}X@@EKy2rr;mw0Q`S!&-3pX*V4=7%$wsp9R^gc zX87^Co#&6L%wj1e`M@TEy1s>8Gx3@~zK$46hO^Ht2<|l$NhMPN z6k|wRB2b8UoZ>}JCfyA` z#@7Tdwlld^PFgE;2n$BPFJNd7^uo!5jN)y4FX(^yM zdCZ*Y^Z018Ax*N=xA^G(rAu5w;xtWGCO}u&_aP36Kor9-0;0Tgtye&R%E+{CF~x%$ zi&cCaDk^}s19lxSpYd)mAIGw09qq};OWF+b{!Q*%0MDsKPw(&RmE?wN9C;RY| zZ%(i#hfrau<1CNP`|`VylbCT;f$(t7oW@>Z1K6q51#WhzMA^|aS!Ym+Sd5^7Nh96S zgmz%ebeUU;o^R*wP;=}~Q@42CKpNcjD zJH`m0$zJ}xmJG5t4?5c##TUGr>8z)#p7vxy*(ZC_IOJl3+njND*?GXJ^VST0aLyQi z*8L;xnRPZ_Ok$(UG?wm=->a#t9uY@Ooru*HuKku2q3(?UTl=dE{3w&|<$NeRK|j?h z4-`p0%)gkTX}_j*h`NqH(>-Rtm^=9hw58xMz*{2p&cqUp41h=EcD?(+&mQdVJelNH zCI@eG`zkDg)k+B1p6KZTvY*9OXdP4ph!4NR^L_J)#(-?-2po~4@XN{1eC-3uzC z8MF^sUq4=vZ_i%h+%r_&F}*I(!9yM0e1X$%@2+S+e{`oX5OgnMCarcCtzgcbn>-2B975wuR2^aOP}3-1d@^a>abhc8k%u#eDV$@Ag$Nf(6*&+O zPQ?_$I?z&_3snJc1~mg#QtSQPZZ-q?Go$~4^wH@g3M1`!d?q&*IW*jO_c-@hfh3$2 zH_flLnbJA~#Gc50Uh@TGKfxvcCyD#|^=br+i9edT|0k=1Es^HM>uap20l6 zr!B7gD-3R>9?Z44S5gWl@8XS5hAzEXVKag`*k1=uv~(m|cCu)I^>zveuT2wO1Rx~L znLz6ovkTndmvr0Asz3%+5X-KlvBZlr;;<(QJlS$GSAP}LnoXi*9w0_CyTA3Uzj;)< zbW8^5YwUk=A0==x!t#XvUhl&giV->6X-pj7xO@wGcXG;x3Ui+|%S(PfZ%upcy1}!P zWw~EJUruhN_Y5ZuzgDy!w7d^o{~j*PG@jTbLxwEZFwjcQ^Tx=@PeX_K#mOY6C1S+hx!Gwo?Y6@PuvKng#btogD4Q z%a*v-%!bdc(e6t-EitH`Z8f%ork2LsP?lJ9$a-u?sa) zJ5B$qIv}mWcC>rWxNMHk`rkV<{(HPhQ;hvLcFi}OWE!&Y=WhHLn~$i`y(ueIF3rEX zE7)1-EZb#v8bi>HLI~nE??EH`N@Hp_l4Dxv{iTs zbPp$zweFVYDlLsFBO(6&DZ&7SVa|ow+G)IZ+Qdd1N1b)XSwwbvEw!$sryt%Y*L@y+ zG^)k1_AT=-=@w6FUf*m`W&dm0H)TE(-1h8d^~B3$Zm**1jqrfAE&+sZCt83AGMMw5 zc7?=4z_4E*#_gSXMTiN3P)X*^i|IVS8A?ML%80KUfp$L^R~Z|NQI z@2_8`8BFc^1S!C0XE^;zqa?1B%v}=utEwlz>mq={3>qg+r%v@rE}vbVSZ8}*;Gu7- zAe!&@aN5?lqF@peZ$N>8$lj}UiK)|#hfMCz;|8yhh0v1W;N#S0VIM1y;bxY#FPodS!r#VobB5sbrI(cBUq94*I0^rovMZ5`@@(4QTva z)fawv#bt_>_JtxT3+B!{#D(k@2TofF-;2oXg9 z^`3E}#HzaKT`_7G2!4}M=K3E+KeE*W{FKGZ)5cpFzIRTWD#AXIEGnrUi#qXF%rfVP7Q(erwxu}Te?&*Yi_T*h%kil_yj+oe%6-Op)mGfU{jIW;u&N`nMIE_oD)6IGv&al2U%WOLDyEj|QxpxC3X+oBW zWE;;9j(h~7%R6uA5Uo5g%MB%az5250d)aM$w%e55U z{W=i|>;OlMTWLPWZK)j1c>9>Wy&&>g0i!_#7G&dAA7_!=J>0(CbnxZ?K4pbqN1sk`<>0f4>?a@9 zk5UKMPzA38RS<@OBYaTTp0;m!)NQ0hIf*W ztyZ)ehI}LPof;Qi7d+cf3er>2*-kHRQiiu|rLMFLs^FtGQJruNbna2f^4%@XQJ-&J zR@i?yIJ|$d|9LeVEBkpYN7=t6toxq@W!~8AaqUxEM`ijIji-d4HDLyy8nav~qOLm67dlY? zEn`fz7u!a=1*-;G?9dpXZ`}FSI6@9g>O3iUGba>GYN?T&J%|-pG)QLcXn9NUCn=%S zA9L9wR{kjqO+f_XOf&zOYnEkjrb0VTiK@r48%ol_YB$;z!hgykqTdL7XAH3m%xjGG z1y~E^=EImg`%p&PIf+7}uH{#um@&;>Hr`ErpdF>55Kg1Q8AIf^6lgf1!{(aV^1vY* zb7=xHq>K~bSqNYw)K&WPi>0q;xV4!6}euc2V*{Rs&H=15qfgqR6P{Vn#fw68+7O} z%T9)?FDG7h>s{IUiH8wo-xJC>;VYbhtdmih3U(h7?{kbwdjD1mXp$XLu0Fg(X737< z*a=HVZS4}bJ8FC0kL=DND^WU)-wZ4^kt2ugS5hCVP@`L@{3~Fq{YKowj%Md-L5rL2 zf12fg9$@c(j77f&o+bU8FzX%n&_FBy*Ae-j8JK@w9~x-G4*#nN*V0Chtcr@VZ%1%D z6!OO_#STxbNV-W{iXjWh8WpP>i}vCCmhVxir^6OTo*a#_#Wz}unvTc|Ys7bV6?{Dp zLYv3V__#{ItNNsGNpC zVfCfN-4I}ZFGtxO;ohlCt(_Bfl9L)@J>_~O;rM!FuyDiaJn?M9(n74Go9#~t{U%Cf znPJ~0Cw0xgg;99m8)KheG8v*={S71>bKZZsfM)|g8tmbw$z$x#GbxJjE>I}kEY>5S zfDnum>VzV2z1{{wE7yY-bI?w@dd)*?0TvtkmJo9L#k0?NJpmD=PZn z2;rV826+jLqpiF1i8>SGTuozURy%5j$9w8jaCVjuLO z_|ht$fO#2jmd2zq>RCQDb|>g`{aQ0-x9&xqn1_58)n|%|NR}r)m0{#?pDIp8xv=0e zFM0vOM8Rf-w|&>ER_0G>Vsalji}a@~*LEnirmRZ^|35J$CY1-5|!OE*QWG~s!+i3k#Ie5AoTyL2*a^G^HW~3hPpq%Aim1^j zlz%DRpID;2bAF7ql?Qm2WsNQH_W4sr64y@>9s*xhJI;MF)()S)GnalWAva^Q)B98J zH{k;!)U#Pfkq4eZu*wCkWaOY;1Q%n*k+2wk10UjMUclj7nnSkAnNiLgBe zHin8TveP4)t!#1JxXuh3^R`pzc{@{SoYH;73_+|LiPw+se&6*Se)3d5=^GJvGHg65 z9%3tpa8|sYy9s7|Epc=@M=WJ6rbh6N507W+c4le3IpSF4ZWHx$VHYGPL*sy2FyAzb zJd5%a8C=6e5Y0>Af*HPyc#@rnod`o*Mx7a{m*9UaxGomN`Nq9Rpj8+idHBsMkvc9N@D^VS^)jY;3Md zlwE+-*froZ1>WZPh)3jynV-L$z^7#=xZWC3XEiQO&kcCFqE>P`CR}_GbA_C}{ml>i zQfmofKO{+`4d+w^hyXPV!8ML86BE^Te56=P^UEFtgx+=umP=!5$) z2~X#vr=|&OE9i^6@&?DNFT%rFP8FFZzPwc^HU;+mO9`b~$kbsslC z=im?hnFKG|G11ZbO5cjvc&vb!G$2rl=uB!gRqSYo=Y6JapeJ+&a+2(d)c?pZRv z$KI7wG1gB{$Rpw%@?~v8Tl?11MWVNY{`J=yTlkOu?NT!(@-6ni_I-fY$!TJ;M>~yc zz3lDdZ7@@{^g#8Tp8&$yOxT8obSj*7E%K}MG=9qs!LJZ^>d+(nhO7gsX|r@ zRH}pWLd)QEl2@i~!*ev3NkwLgS2pXOW9umfR&xB?M(D|HF0Dgn$td6EeQJ8crM z8I0`dgx|ieZe;(lcP|R;FMeA$ukr=}isX&l#+;O0)23P3NwsfNSnR3D)q#c|X2`_D z=}xPE>3N0gZ)S&hi3?ECabb7QBmwP;M8D}KJXQzxF6dtOCw0x%o8VMi_ik4PFn)J0 zrZ(-|3mV!^E+nO@)|9~C*%H#~J zdQJo_1LxGbI9D{~Ks(H%UqSVO?!vh>w! zz)#UHnPyT`HFnF=VGMJzJ7#}OCUXa^F)OgGN9-F~CL4)=FSexTR@g5s1kMJ)IxAbP+DgpQtW{?IjP4yL zU+&nL@UL<^$rUHKtXus|2dv6pvcX613f7ULXAHT~lU%rijlM%(1ze$Fm4%_U2RZBv zi3z&Abwww*n7_6N0aUaemg8D4O=Fv~B-VMpyC&rBr+yPR@2mi?du(Bn9_hR<49G=< zt(f?3cac6v7~GP57BJUlO}Zk})(mFE&X@N1#Pv(ZX=1Y9vys=}Xy5T`I4{(q zZ#1?-(z1gG40S?X<_o{g-HW~er0rb#W-8w@>N$zp59rGL>bqakpLMK}0VV)U8iM}N zc>$XAb~p9OptgH%PGZbjyp^Idz7(9mEQ&)@JKUdIAehy8S%!77>I=un- z6f8tiL==WX>Fp`(i^>az;=o#z*W-Q#rlP(!^$wP7EQj$7QHkN`O8F zdP;0-t3Ou-5D_kw+8nnuYtD7S(3L$diP=R-iRnonZW7Lbmh#0-S^lT{Cbu49LjC~nt%U*s(SoAD@5tVuOx~QgXm!wF^Ra*@?Z7*RIAxX z93Ca3bvC$tc$Ye`8c%7_tqnHpagn=2n4&~1pBN6oC9!#R>WGC@mdsu<*=Oo^6>90( zVuD5Ls)vBEP_37KWucg!H%t5~#W$t~$>x!A6?UP1TJ_iBat%l9Bp>^{*ugsD9d9#p zAKKpDids60M~nqaS~MSlrM*xK9FP~FSr>PGl?8Y4pmXfais8vVB|=L7B7~s-AkTZm zFIZdSu<^B=!8IkF$29ho(A#lAxfRJ4tGeZjpx|MvuHLsdW*Hyj@aBCQo^N7Ly}$wl z8EG3LcFACKf(|ugqne=Q-Uv@!c^`@rP>7LLaw1+`j#51@6Kd_?=Uj&cxu`Sr_B21@1ILa4XalIy><_})$YVxMbp4PJ@|I(ln_OH*qw;W?pMHzI%MQ6Dvp&oOch7QUEzfYmsg62I^6?Tv+R0Hq=svF;<|MC1o; z7#y1-y2|+NN#Q9~o;KKMCN%~b99hrL5HzU*nobUQEnxz5uVZ88A6W1ZrxJWR^qj7% zo;y2cHe39o+iPD&_j&YLgr3`l>w?>1;ZCXsj$m)hdpWB>eb>=5iXq2ZnUnx?7*wWN z?2#n(o`ln=%#ogWA11%MxK%uy{xO*rbin|9$u;LadUi_vtYeIIoOVRnxMJ`E&N7Jy0Qzz4;%Hyx6rALbTbRj%aES%`(&g_3Te zq$ZssIqv;)9_wfJ*ZiJzuNWWe|BX?{Xnlk6-{D#0@=lJeV9cdN1lEa-8&7}Hdn><0 z6TC748ZzK1y;&FNS>^bh7`*P4O?SxuK-Ube1g{c3)iT+Q{J7^&`&!4}K=h6>|@z0Ur_il8v_*XXzIRX4}J z+k)<_b|y_D7|X}ZmvuetxvNTg_M_R1iNEfBdM5R&@D7<~e!eT^ITfHrUvm~4Dq?qX zh6By96mF0F)xNd5cYT5m+{f76iWZU=EP_VJj8S(_qpovm$l_q;b2!P4qcJj%HM$&? zSdM#$WjqQo@m@tnlB*+3`Q@&D{REr2Mx<*7s1Y}q`^nRaU__*-de>E3QB5f|LTa>y zMQypBiy{D#_2?UK7{38ZSR6M#KL4Oq~_Sgf?aym`t=&Prn z^h^!Ubx`6J5xeonUTaG5f@DVrw^emoXq~Wqz(o80;|EyGQ-7WthC&@w(B!T$xI~S3 z@akD(@Q)P_vM8tPbwdCrFL9-QHH`7C!pVd^61hzBlBd@d1<)46UHkg=F)-e@m6UQ< zGwRQ_UM`zS1p1zOL(CzGX3!rc-)A+4T3ZSJ1tH+C(=ArC-2N17tn}&F?}rOZv4a7u z=)I31$xec@W5)7;_thcGo6L)M`ac<&<%{eya^;|H=K}YS6rxY~Ao7HWC&9rtJkY6& z{>Co+fzf=VOyDz9PJP_I2Uzz$V_#E?-Y|QBWvPx)7q?tyV}I_5F6sj4Lx!M;^^>3r$1wM zq9+hYaNmFN9=?}GMk9QHZ;m8s09%So%io?|mL1mSfui-j2Duc19Ta?s!6XJ2Nbg)N)p z3XK{$)G;K>n&8W7o-r#>n#!!TAi$aFGP}7uQex`I!Fk*FaI^SGD7z(C!7KPJ_`$|r!4zWK#HcjEqX(#tPy4{fhVw|O{L zBslh6BGFknSf1Zm)3i6w@@Hw1S|CC($x2(#8_K*fe@Q56! z25e>sMs0*O0NnC(J)k_obO^c@BZI6YK{3BKtYjzK zu>+|@y?-5)f4@iqn8y9DLXT^8);NCaTlE+bdfPa-Iz^oM8z?yTDVLd|OY0*ncDUeZ z=NFUZoJfc)`|F}dSQR_!s>+t{#~YrD(dvPHxFxoPz+D8_2A%YV;Aw~K16;d7zvmAO zKmrpY!v+Ay2g>$>=9c&L_yvbnpN@O(zr&KKYuP=*ej{enoq(uuU^zSgE?-LhJ8zQ} z&ZbFzR)bLCHN;mh(i9o;z&xia8|j<#b4=9>s(_$LJiX%0F5s?MO|N(x0D_b9!^DAt zaM1zUidT033_emR(`CNA7|u^%$tKETM*bm!5nkuMi>au4A}x@2Xfd5vJhYb1txXN-Y|yj};zs~z;yrA-VN-knnR?zn zuDmaalLtPtYV{=O#2ZKP`w}7718$Rp5f&5MI8WiKYH=|m%JJ}N(J%mHx>XShfrNsn zZDJTb#1-IqRWSQ#57FY}wbwO439*B~a_jvs_mT6oj{}Z+r*dwKZ${?Da+>HJbR;uH zTQlB=QM={$+~dw|W%o=;9uk^Wu;fq8l9A47%CPHF+!=Y+R3s#_UN`AZ#U}tzS#SjB z(Dw&Z@R`+RRP$&3IRWBk>EJE{hA;5rVC22&dm-VuYCm5E#<$5|ES7l+VZE{h+B2nZ zk3PVCxT^VoZ zIGC4zPI6Tpz?&j0Le5z);FpGKQ;r25(E)7lv(1$B^kHIgdw#@y5z9Ga-NZ{SX&j`C zX4s@L+{=gRouvR>XEg?sRVgmxIpt zZ!&^qYW{Lqw9XF#1k@Z)_wOcB`{)2<4Nd`I*zD4|>j97Rsk>?0;bNs(_Dw1kn`*T? z?_GX9<{whgX~*;AobKQ*vtIhfGPIori!;c4Pqg`R_ijryg@xfeyp}49(9+Lkn|6*s z%T@4zIx|pub8e*dp1#li!hTsjH>a>3CGeTSj1!DH+}n%(53&injNv(qg+aH?1ml5O zVA`97-nZI3Fl&htl9V1yGqZPIk+R)uDkmwTs)Er$oezA4W^CFz)aBaa+{U6{EgrweKPo!1#jYh8moly6rK_{V4Vg~R@>3^jby!v z#+vHoR0Dek$f28S7sfeadxWC ze={>bpxICBpru~e^C-y>OqJN0RL$CpBdW9d`V_;L{+K%3+ZT5;Yp3WhE%WumqTDVB zzt^Q5=!Y7hVKjr34_eZMa$wU=#p0#ENaFEm8S&xda~=pXyqA*H$Xr$a);+)ZsKv|B z8tnFJ)O1YDiaoP^ls2p=KX;Qp51x%_>4N@OK;wp~OGCp8x!8|2%u82HKuk z-x8uR_}^j!{oEe^zByl#`SG?nmc_B__jyi1(9}BeW5q@Ai_bEgA`_Qb$fR*iIBl56 zeBA3gi|8Ckc~EgM=*7ak8liooRliI9x8Ln+S3f3>zPv3u@=g$t#^E)5=uw4HV@d^7 zLo4gpR!blv0+9k)k`SFM3tEkOW#SEkCs6$(dwHTa7vghGNhDdPO%5NN6HnXX#TP5i z2481~+t!BQ^zmBFCt=^iZ&Uyngb+{KTGsZ?a=aD3>jI&IH~fCS3}l4IjMkzYLS9C6 zBZNtGnjaB)lc*tHniWwYkhk6`>bpWdQ7w35EDoj{3qHo$g5P(CrVrCC?W+!OLQ1{BF^gDQJnbj;Hh9j1_rtj*B z7PZrW&qYD~!D)#v1|;EOTK%2hezvu5DOWyuGKFM?@;l`f*lq0Fb-onJVzH(yrskOBjzVbZDx(=m z6j$l}h~=_Ybt7!;4DB9cYUa4l5WhaKX$Cg2I6M^%S-2&w3YjI5me|yqpY;}_X9)a4M`fm-DJUkTaGRzE zQmqUaS*ctyZ!Qf*6`B*}mq|?G*%2`dPt=N~`k5%s-+Ptr#HI1_%DzI1tAB%J3< zIUCSDM#qhj8f>~gR#j~g5H-^t^&CECG1V=7qYL=c3-HL)pmi%j`-#luJ9-JvltHih z*rnT0kE*vSW}VKcdff{Z4xR4+>$TuH8ja7R{%4%GEaysk`R>n^5?0BA>MQ?ITOGfQO1Dm2pySUzoc}kKi?61{C<}1 z;D*9yKHGr2z@L8_7J7uMH}79qq|)LT32u92E4zr5F1EkYe%;`+esIS$;K?1!_o@DN z=ishZ$h|EMe?kLWPvQ)NOzy#vXvU|kJz{m1n`~8x5VUpL{c=!OQ@4Rx1$zEG5zFO9 z%J-cmjf+sVzZW_4OBu1BLL(WZp4?%odPz@|VH5{)mO4`Fs{RXwv0^`9@-^cPvafG5 z)i+F@o~8d{?f*fL)QvyY@uTJb{koWA3!IkWS$FWPcH~-B5ftZgyAk94q`GFli7Gp8 z@wWLLvOXc;;g*+?{ADuNRjZ-tO}YYImOR}Kiz@2ORc*arOj9BFcg0t(1oTs9)0>yk zN~ZA!HtX>>Q@M8?@ol)Z2S$m-H;7?at-#!w89L(nB}ht2vhsxozH){r|LaG8iQnXj za8hj<9;mVc^Vk17Ab-Ubws-Z&k@;$*Xn6E&GxV0o&z@nFU{td_!-E4)(D`Vc^KZOeI4NHh&Wp3-Ff;lIwk<d#$`I_Wa`sC3Oly`8St+z3*N~V zX40Cw^Z1fnSytv2FKgei3peIlM`1h-zHy#h1?K}RQ z0}A^(@@gy;X_INXt+P*@MO+5IiO#8>czYAWaBf^t`}~-= z#7gKfx&Nd9Ap7I+U|ZMnevi)GDg&Fhhchcdcb>5|+iC$US%LU=>gdq1*k(5Q*|(6_ zRy{6miNMdNng~YAxGyqGNrS2*;I-OH3QIEv*$KZB!tH&foR8mHu<@#a%qAMI0)X#$ zvT<4c`v;QSy`ir}a&p_0ajnrZ%T?10HpcSCtKTlWf39N@fjh4k6a_RJq_4C%J07vw zzQ?GYIxa^tiww_PY+(QoZq=Zn4reL8`y`27;+*gG!?v5+v8RMTUO&k4TO>X{^xZ7; zctFeN`XK9uR+Vm8hg<^944MCSS<$SfTFA#u4gMoqBMAJd$+wbXRTm4UmP*B{viZyf zt`Y;!g+{G*f4adJECc+CLR<{9O4sG?-w2h43_e!n95;f-HQn#IHuJ5kl~*atV2m&( z#``MjkHb07c24@xxBAD3hT*(cqMP`qSdcguP%^VQoG4}?_4zLJd%>^>rIsu20nJjV z;}dGb49)Ks0C{jxV(wI;XJ3rtH&qY5ibqqnkO!y4fM?MW!2)kpx!LYHj~Y^0%=|> z;lbfUztIm+BcSGp?7l^5@Orxts$sYzPdv6$(IIdq5Xz-%@GMwEfD~Xv=;V+*y5RJD zv|F&s%tU$+6K*6%eWt5t1m5~g8tOjzYuo0tVWmPAXu2Z!A#jpFl#A`LSl81(5h*|& znZZAcYV@f`+&lFBz$ky))l|yZ(N$@9P~2Ne&!3z3mo04{kXi0ws27~hZJ!TDo|riS zhk)Ku-$VkA>btfTgRx>hi`ih&h%(os-QODlAWPpg;&L;a#B$v%ZwD+Vh-b6BUDaxu(zIt*sEoOz*mcQ{ghwDqKVv`2HE)8K2B5r z4lyV0z?Ke~95p0bu7{Y|qiYdYzm_%~=g$J>7tGf?=dav&IERcKy6X+g)xsYA%dy1% zLQ7{Om21pa`ZT0=_<<>al@OvMdynG{Y-8V|B(rb;Zrw z_6GiRllqCW2uYEpsu2bz12y8;q7tx?OeTrkPxKcLsLQH)Z`bdLiq4j_ZXr5rXURrU z3(mM@bUMf- zW>&?I34eR!aZX`ahXB*Ij>W!`;M23{kepOvisEl=6N#G7)= zQIpB_#r`m|UoDJ&wg{GfnsAmXhF@Y`Z>X~K!n&0=c+=i^saBG$Uf%k6J#kR*jcMKf zbqab^OsYi8rzBt^CabZn!voKZC*t{lLQW|muH{|Qi1jT*_aLb=&MIgb4s>*MtX&^W zzN*ye>WyV;FmA>((Xic5^3P5aa!issy?2moFYdAS5j|InOaI(8$=Y*csCqbEgl$Gk zD49kunP=_{jVf=w*=UZ7Ii0ih&yrjYsGc`}H_aR-5&w6UTm*C)wA&stDDb}_`G=j< zA9KYVhr-GGvFq=OmR&6K`{K_|X}_I&!YdUWWmdVL z3F*r%85DDO(Q3~f^p;xYkXDcDm<>+zzry~+t&;7ys^bEScanX$kc1s)=Tk_lBQ@iA z@mXlxUP1Tk)~CGV#$v&eofyejf!kPtA<6~M?=>gdYl4;aQ=%o_9xMurv8keqF^a`y zYOuVPoyHjU@-HYKkh2pXcyX5=5lZSJuG|G(e9R;0x zwo^|k&uDN^rs;QX4Bj;8Tn^x{=}lM4x2^G3TeaU3F2OIu_HWLJh@AN{fwSzU-87+0 zn%mXeRTFJC-R=O=?RMNTMckYRK*f9CnyiN>5iM5bbP9u2Kv?J*DgLreT~DNY=)9mz zJ;F9*B?d0&ldZrtYqhtAh@G>1a<3v6ZUv1H8v~%# z97N(o)eORql!n1!)fUx15DFHrxKx2ouhTgS-x%D~QJHy_Wr$k^^Kqno&yRi10kYyG z`yv^hSOt2m!VVg`UmPm-0LVcXhsjhh0_4=;FaSzaT~3VFp~Ui)nZBpVZr%FSEkl=>BhVTuT;qt!_WRvzZh~ z(3O-Ov5hFqhnXy5TN?60dD95cbOutO-y>YOhTrWlRQa-W&&RnZOgQ)J_Xyd6*3NQxhH>X7>A!aP*7Wk|#^;tdc1yb{xajhoQC}llEotjs?YKqKF z0&I^Tsniiewaa?qSA_rrM!MuP^}_~3lDK9PzuY%w6TX^<*u-tRuEdp2YOf2#UDsBfuk+d^_Xpld`2JeEXt$lbbvkMD0l>#AHZHdk<-`*Ou@` zx(U@$=M!9#h&?X_8|9!6TsNYt$+XQ+O#gBJJlQ@XFq@`73PYru?9naTRclw)dx%5( zI>OeLmurU@o0A4B_u{kfEn$pH#(Cfu(NTu%R%ew`VSP%-SP~olcfe{1y!K|pPMkW- zdEIJeKmx*&gXq6I39WLx``v&z^bVz+KO0L`TIfAXgV(DN>i+b(yXSO*VVxh#U|9u8 z0}%un(Z{A&_YeHXV)!Km={9+)-PLaTutL1{Z++R*Puno}x17^H0xkB;h;cjo|@gaHk{6GC4_e-&B zT*-;QpM@B@no1wEkw|*ozWoB-$lr4{AmB2P4jl-c+i9e(q zQaHk#CNQEHT6$CssNV{lw|kY)RRlMMoL-&Vw&fy0F47oJA-5$uMBYZkGGgk}}gt5?XKHt5~GSxkF3XCgx zDDf{U^zy?*_@BestQQZGF!RVDJqv<}e^3hEViE_>r17M@X#eF#{BQRcOaMSTE7|Om z{p7V5?SBhzLp%g*={wqQi2y2@W)odpHvz}bGNgClUks<)4X?mS&c+Q?ReKLa{0Nr) zZ)#=4tK@g}XZOG+tEaP3WNT%IQh za3!lTh9Ca6TZoUB@8p0q>~S;A|8jnnz}PUM#cLRJ6R8a_(Zwk?Z0L9>0M6kAeND<$ zkaPMXUI|jF0DSGo%OHj^!pUFb`u(t9NxMVqPuVyD2+(HFQVUiTVA*sdfK}(M{jq3a^r5;69S0WK?B>jOwxMU) z-ANcHcL8d)%2ev8aoUYzpaRqj`h%p0TEdeq$0eV|2+N{;{Xq=2+5O4qoR)wxhu%p6 zh5j!$5ol&ZO+)HE(6Isz>(O5b^vD7&DoNk|ADO}uC3vh>crTC z21VlEZN5tw>+d)BKjWGei!-&$8D`wcYBE8z!AehuQx0;FXXl~lU{cMXsoLgqmrb$2 z)J?=7qRX=#&?(o;7P<@*{oRDdk_=dFH5JQs?rgCZ#xNrW-s;W&`7N^V<}3epHN%H# zj`)X9c6;bE-X$}}+0}?_!=>(8?2|uTOD|5G+GPQ;D{Z_|Rq`wV#KcB2a7Ef4BfXVa zMXMjR0M3(sY6t)JJ1&4$uDmBh1nAp}Cvfl9t z3>86m|48&>=Bcpfo^i8JIk$dI7j_@jm@ayMwz@Obp|u0!*Sbue1i_dQ{A3fMH~0^x z90v);|J}Bv1Rc$)Wh>jHmvzFoLV2q0vPq^G?ly7jJbY@U0E?}Q;DEomqytov2N(p4 z+^bp=b@nRRd7sb1fMRGkcr7?mEr zmQgSSUvfI6-z#l%x;Ihppo{@M)H%;gc7%lmD*o>n?cewUW|k!@6M}j~NcE4x`0a0D zWF(OAqWT|&@xMx>7RVa#ZA6j~Be?i!zblQhk%B%99HBq&8x>Jn%oYlJ*CR!&2crT| z1FH71Bm;wkxm~mYbPC_~IbpK4=A+I9g9U_j2|9eR@_l zGB;N##7v!!{V6pKT{`#sR*k(6ptWZ%H^p+Isvl)xEyuYJuNpHm^Vza+m6h_260Xbz zTDP=WQfR<2LSbJ?XMky-wP@^`1T7qZ>FTT$H)!b7q{212|6RRsKf~NoK0qJ*61|T- zCN2Z4F=@p%69m6jwITw|uXIr`0hB-HDlNS9`tzEr+CyNxGMX|P@Gf$%E$+}^gXtE@ zV1_e=`;``-tT+HP&VZZ4-h+&YR)N0|gBwZ8-BePreGS4&0L*eDp;*Md%wqi-a(m_R zj`+5zrAr{Q`7f3H*WT4-BYJzQk)$C>eCH*?{^NDvGBRhTvndU%$$^Kq;?7Fc7Kp?$ zFw98?-|U!cv-$J!a3d{8zsA;~|0lsQ_6>t1*5pZxQmO2|-Re1;U0~*Dc*Kbk^piyh zgQ})P)o@RA?|O{j7cAq#5%9au8QnSQkNMB{7kc zcsR#`?6;84O8QT}oZ2H>cv|aESxc-6cLKmV~YxzYQ(_B z9$=L`p)&~H!YR`E1J&c_COOCk@8BQBOPA&auI<13MVAxUCH55Xgr|JoE|@*tw+$nN zlVIV78&yl+qb~C>bJjmM+J7q-j`!@h^4?!iH+?N&>*AWJY`Z&@NZseX(8qqCgUy@4 zDJSm(zoj%&%&0#nNN}&zxLLZG-Q8Ct(0qdoYcHi7lkc4~TFra&^R3>L$KgQ$LN!w%?sMo$Ol)Se zOl{lXk^wE*@6WNJn_Q19G34iolSbi?K}Q5`r*rTAXnDG2lc_?@9*DjyyJEYC_82e& zs**DqCQLHKGD8)l9#d}iT?zG#x&EVi!Htt3Jp)ocdwPZ~e${AHgX?P?1O7pddKe7Z z3PncXFtUVk$rI66C3tkt?4#oK1*+ICUk?dzMK;s7o}@9ovakkEH2fF^j12PCNN zJKq!g++2EJouORO6ZuMiqLVL%mKiWM&X|7o|9yQh0Ik!_0ES@t2aUJP2F~ZfcgCl) zY4_l2PPJRdEGGpRppkV3Rr6+9$K3i8b21lx7PV4#8PgIeZD;B3W%tWcR&@s)H>#9r z@1Ib!d?oqVlAPk|J>V>pWIqq{djqc?gnTn-xI}B++WGs;xpXygV~9S)5IpgZDWq(~?$LD)$X!s9V z$A35CED4Z;PLj*RFa5kze^J;U%@oVKg8%ES{^!;MS%5$p0WxP^q9D+ygW(|VWBh1j z(=UH0Bj-GcX=A`#Md+xwFAFd#t!cE=AqpXLlAJ;Hv+;ZF2q`Ggywk?%iP zE=ma@jv-o*cA@9l$qP@hv&Ay$lKt-6i|hBS3(%NV_4B)sj+pQKz*W0j??-WMSBcUe zZi!TG6;DBVl#~_maQ|^GKzdoPMOk^Wj?+7^*Au5~KxelD`zCp{MdYdGP{xNHsM~J6 zpx)x2ero)n;&&_`RRBT06l3{tUqujM#q3_`$QyjEtsHx?6+@!v?Bb_=E2bSB6zDjIC4r={`5N|}bI$1R}I1t3_@wpbC)f@&`qWu;8E&P?xn+8Y2#6`CB7Y<$T%PZEJV)lA4~4d0Krx zyCK>|FB?qH;4*+D-$g4!?tx4y-_BiN!@V*~gTzCLK73P!7N%!B6hVkl&|LdaLUd#* zExftiptMoYj7+zH$c;+XjM_E{RJ--?+h8V4MY_J1z)w`@vFA~M%%nfBHXac$o7@oW zDdbu_RK2@AVe#3r>rc3iY7e4QKYcX7Y{$Pr@Q2H1Lu=?9>4lbv;>}o|9|F4{y_;K9 zi>ENM6?Uu^d@e7V)?G*nWeE6DDI$IRUI_lj_+q~$5q!(Q<-XtKj}XG3o{+rV zGUW@CEc%^ zp<$*cmT7cT$qAPf;eXf1;3})Sk!Q%yZ2Tjx#WpI_=g4YNVIt4YVs(Sw>)BVR^!tXUJ^tNadH3_hzJeylQE-c}yGe|Zm zFyT;1TY+oLOy-IY^Hn5b()Ij6Dbpkrm-8Um7u}5MwmFGuOL?Q_M4#f*<#5~*i90(g zu?^oCuRF3Y_#bU|_L9PNrV}&2f5=Bj0qoK?7bvf_Ao^gJdwRNXQ*b=y(1AByak6`v zHBnun@)+lSe-g$^BKQ?(yo6^-CW888(m^eFTxUK^)j-@m%m@D&D`L^YT$2KzcSayH zmN)lX7c*IC1ioN7G(BQN$8q7h21{=$@sYo8&|#K#&1+Qd zSSaFU=gvO>E9`e{9F%_&I$)E-) zdgQT74`MFukOOeh&A6OapYLBzSyVS66v#CO=tNp~)(7N$^oh1q%Ib*K?z$MBt{k(O zmw{VE2-qL(1dzvNWXy4bDlq8k$jK0Hn zPgb#p(t81pN0iHa(kF5^$Ya14f;B$|x4C@5k(awU)2UG6?(dPSpNty;d|tM9m!NMI z0gU?J zKI?InR@E(Pto&(_s$Lin!;LG++-xCe`QuCnuz>V=RM2v5&lV0TK*qUOjrVUiB>;UE zPpJL`P$`#f+EMDvIZTLA4DM0s$%EGtGeoYjq2~m%4xti3J;wDc2-LwyO6^n(t?T%6 zNG`y{vKq9?_eLdoR6ocgTh7z`RYcefL(+SYoi68@=_k_8Dvl&zn8jd*>xRzC%jsT< z_ka<@maDhvfUPp|vca?jl9CAi8MEaxM|YfEs51Jhdx#EsuaCxU7XUwK% z4xN&f`}2s&(5Bp9ZXWF@8+G!(w=5yxPHkMzjbmqZpXqYoKmxS-isU&?3aC$rnoq>K zdL)fzoMOYdo@#hKMaNkPb^9TYHY~vVvvO8XBQ#Oha^(Wa>&_tdkqI{Yv!t;6XuNXa z3m0bMNT7r;{0o{je{T7(Zy83NYxzS7BawbiUt)KG0(-Tgm6)B=Q$Bu}a!^ff7Me{$>91r&^b!a+2 zpNDKK*UN>oe=LS!Y*_S`z!lSJtngAm1C`jqud10Azj~^>i#t2rkQ=6_qf#DUaixfMTYaTYN5lRT49GaNiYDqWxLxi@LH!r_Tum`klv^Eu+%? zGKSyIyrYA-4^K_t{RyU5GblVbe|JYc#&@l=`cpUT6XYS-xcy&@U&%EJJ9~TnoA(LW zAvd>yi@XXJpM!VH8#0Dt+>q zu-}*ZVm}V=?*=vKXd`I|W{bS$f3MU;vCChc;$=EShoI(6Bob~wrQ)?*OAiP>>P<`8 zKn=o-!4{Z+u=)uV#mapcu+9FBd>D3pIpm{D)UT`j*9<^QI*%pI2Zbj?Tk65^32#cX z*G-D9?Q{}vd&VJGI|;eM46Oh50{C(@D~8h?(+UZ;h8WgEsxXn1W@s9jMS|Y7vbw1w zXcHfj`svaKF5bJ#yZQ+$P#eb<=iqzxEqiaL|G}IlV#hGinKUKMZ86(!cyJPc(dd7? z=Ra2Zf6t#kz89b&JAkcuyArgH3+VmT+!u|d#|Cj`f+L6UWR4d1RfnH@M%7DS zgufNBg7A7uqO;pqdvh4msmo#fyMSPdhZ6hybukR6GI=7X??WN1mkf8Sq2~FA31ypo zjJRlmH-;j={P=K^AQ+zV{W?{^>sE{f#zHSEE&=~KHKE+@g-v?*i?E!hldN%EIZXqN z!kpQnd|Q2VSpn>ySVPb?iKId6>klwNDpYPJPni}hgO`Ft?G{iA8U}Zg(#)b3AFa5>ou5}O%UunjRp3dh4e$y;1OY9QdLk(Z_%-0t{ zWD^W307(O!jK>cGpSk8sG<4xMu#vD~$?wE)j#EHV_qoG86?l7Dp^RlY<7u|V9ZuqH zgmE?`Myd5IfDM5dtr*VxNKsX*4*z?UMJkzt-ER{HyA$v|InX)@J+KFSx%B!3MXc*I zEItOnXWb?PO_;eIbi`rZu#8O3WGH_G#tE&|^GU?m=2Sm+|8B1AlVnM+;L!+rWKtD# zj9;YD>rKp;o8sbKc0Xq6!5Bv){yR=)t|O1R&dUd;l0d$rpr16Tx1F(iVeI#Z0bB&N zE!N)4%{P2uJY!bI`o+F*yZ4NtSV7e;NpivFMQ{560>2u zn?aHsp;`OVZLV`MF##1YcFVPWl=+AHDg6?I4INI<_x-!B@7}yC^UtU+yjkA%tOYf` zy_bX39(~fwT(A@bqA72ru^&-RKWP6b0Q%DV!B?;A)Vd1&N=d|{++$y;&_f}*YiB0a zO(t|OZ1H_)QoYAb-$Tc7gW9*=6}B5~Z$FujeNF;Bxm`i$Xs$piuGO!F{lEL3cawgf|AlMb44_FXWO34NfnwXJi>tv$;o!rrs&Qy^xN8=5?ii?gl71q(jWo;dd1-5sIfT;6F__Q=5?HX?|h z$Aw{*E*Kfh?)>35+$I`6lKt~c(_k|ydRn1|2N*eL|K0Tx^1S_m^efZ#oN!L2xm90( zwdeUJ!rQG-sB#tn_5Cb##5g7z_{BS0)G8Bunv7X5n)UM2MZNW~4gF04>fQZNlB%XM}1@P7gTEdw;ZvkDt7X!`D+ot_>QHn$|9 z*F)Xf|9trKcBVc!8I98PHLY)Y`~@5L3$qQyG0{C6C_wZ(fspn4Y+w0T4>kHY?rnr! z#eEuz7Ik0gqWkhie{;{a{S4ESAJ--?y?XoAywe5OhcKy}`(}wVM`5sT>2}wlA!_Cu z##ba!T9B9Fo%(?u%WE8(}?Fnwm5QWMJH+S}$F-Ixg| z$c&S%=T#ZVL4KO_+c;HfkTneZq47LSo%tRf_+JM`qc9q#P&c&2Am-|iP%uumb2H{^ zqyGCn|NRL+bE?5;Fn+Bca%zbL`R$GRci0g zeVncYatK|qUUD00N~i%SH3?W=&3)7j8F=@{Ds+js3>lV?#+=@(VW8twH!gE^zTY<+ zLFm|P8_LYQ;JD0FmL-vDUOG05St*nI(N|GVAj{&ROy4)>1Am39r;#GP zL;$GTnT{vu_xCai~z?B&cm%4Nv^%1U5gvYwNlY=Q*3E;(#`gDLh&H(Lt zs6FiL_Ld5}w2vAbiO198j?boLrYiQc1TaS1!Mv|y2o9^Iy1CVsAmGpt_NjmDb~XfD z{;AD{HH(F;i(Tkd z?14Eh0r&pA`OVGjm~B|j{EHsJn)6f&S27A!ajsU;xO)roU6K1Seak8P+8eSJ!0-l6 z7U#Z$c2gPa%@dMH25As4>pqtHTw4lC#$FhZ(p4|7NWWqa@|k>*ZG$w=VOfPzkO5FvKe+$EXqZ^CBy(-jAHl zCDNRDG1d{@^XcxD+@@BkplQZB0C%!`UShMK`_X4 zpnO?;v(3z2Vc@8aW5BAe4viQh4dGN>VLU&I%P#f7$3R2YUevm;7-o5ujZ=J+qI2lq z96{(Bu_Ue{`Wsdjj8)~Cv4A{hXCgV-*KPW9qdtBP*sOLZQO5+d-oM&cyPFn&&39N7 z6^`r5o^!CZ`InL+G^}r=X8fYFQ{=dt||1Ax+ zdSL>dt{yq)wkaE_p$IR*gB&pyZY@pJ2k!1KmStsY5!0-34_r%BqIXH^StYzgbh9Wo z?oGI+#F}0A^!lV0Dq>GMT-h+q2FD5GG@3UgT!1O^oaV?TOD`JW}^zZVpE zQ~?AznGjYBt+vLsD;=p79l5*;N~O2Q^<)lyAE5yF@v8L_CIqnM`{2^5J8F1UQLbMF zAT*%?~)Gi8d(QTZ+xS=cYt|il{8N-VPZ3zTmwRSATcm`{UN6v{^#sH)-f< zrE&+!b~%aT-I{MIr^y0R&E=a}E>2_73iDk3d8e$eQO1NrK0=PMYOHqGSQVgdoT;gf zIFRG@jG@>CH)$`wt%*(+Ze8WmiU7nP9gglgT>zr+aM-y~%d)%dJpLraH5e==JzP># zw#F3Eh=ce0HDKClh*eP5Pkv6Po57mRs@JE0X)Ae}=CWPO#ITnE|@sF&*B1sWuXl0XOJrm~${o0l~wTQQf0YUu}K1sP5ZNGdPR z&FNGnfgGKhVBQRNJ6F(S z&)r1S?)Yenb0(*0Ma~1UYDUz4uTb8TL~!Vlq|(N?ZCK}oA(S6b4FojPIrmBd2*p+j zJ~GI3%4)QC;6Ykr($U1rs=zAFMbH2?k+x5;^%8E)Dg2we7C`!LU{5r7-?LQCcZXz4 zM*=HO%cb67X2D^pGyi-mj%0?j|L}AyJ4}AlgP7X``b__{Ab-<-bwXi=M+%1Z;nz&# zzUY!ig4Ayf>aWjETFC$ro?$u2byv4Cqjs@3rH&e5ilRVPC1L%hyUu3AL`s2~BI3<& ztM?>>F5cjTuVy)VRAe+DfrjYK7>^yo4{nspvtL&gcr(lbPvQq_N6y2OhozSSdY|0B zS{6VN;HYhst<4|L&0MTpL6EE-jhQ%c&AVzz%ByLqwU4moOFJxlMf{kQD(dtJ^0K4X zvue!hQ^SdSn6cGWg=FPJ*LP(}870^I(!&`%`7?S4K@8 z2l@5s^9Q@q@&+RcVOCgjkP!g|=7Aq>oa3@s3LH(+*XV^|kL-iRL$(~GsR8-<_=N7@ zLd4mA)iHw93vmPP2-Nn)__qux7^;^@4J1BcUjIIVXgMJ9y!bWtTlgL z7;ln-g%;;84e4TbWwVDDYzTj=$~zbPp9~H5Bbv)d zIYj@#r;*MR2Hm)Q&)%Eym)R$QFWelkMk)#V_Ga4`67nS(PZ?XZ!}^Uk)h=0F<=k4C z4Z?;=rjO$=|AHhDTu39ukaK@1pDf22y^z;lG*b zySFo4Dr`@o@XW8W#%}!4)xtr@raBtKb6sdXJ6l}U{_oQ9j}p=Hlprsf#n%`o?G$&U z*3`ty4<^r3r^}0Ahzh`Pr|DZ?YtAi3iMBhm$<_)%3hlGtT1tBRpGbtxM*p-etDo|N zi@a@hZ-#%)R=mjd$oEdx7q%jrj_UDbz<60gEYWE!fT4AW-pC8H@_3%hZ0R8eJDJbO z`-62ZqMwK=O;1Af^*_-RKV@!sIq}M}75V1*%fxvqT@*jhrsY^`^!i|!&M7ovvSNg& z=O%_f^wju{5SgEsc0LB<;*C>Q=YfkO9=SQSuEcb(zSz@v-sM2<#?=SwFm@DVGko0b zy^N4W;b%drm;qvn|9$JDMSrVibXLli&flu(@qg%g&!8sPZhtrl1P~Mu1eC5wk*?B< z1REl~_o9f@K?YPnpczbAPy! zS=U;>w!kO75C6vl|16*9aZCc(KSjcr7F2b56MWtz>n+kYy{T{Ug;40RP}aqnJmn+1 zpG@iGu33*#JK25g54zp?+QDBvyOoo4+poB`zpwM90DSYzRsTx zPVR`Qn^hx!W-fC@oe+Ss9+m5cV&yW{t$akL;lhm$vHob)u-vLr( z%g=J?m%kZ*;bh+Qo2)&+O%e`cKYIDNWB>Uy9S!*_eOOZ|y~I~ta{`YLs6WYV9ydct z^*st3o0pWbkX8brkVRT5EQ|2D2Qk4_U@HDMyDBYpvb(FVXMoA1Q7}n$_nH40 zDij54S=lz_gLuyN2;Y2mYwAiD{di_it-i^e!4d<_cqa7;@uI}psMBHuTqgz5Ytl=x|>WDO`%XGfXCj=xDCxa>bh#oRn9*>5xnE?Y_!lE-XCjS zF<3tAfLtQ?O_2EwnS76DEpI^nCO}f?;L(CIbzFXQ_KQpzf6j@qBDXt5Gdfs~kKSE| zgchUgg$YTk2)tLo}yu@Cy^Y138bGs*da$=g)-(i=v;3?DA# zI4jv_3C}Gmt9O4jI#CGRz@QW}%Sr(Xo3QQ0XcI`{wCD0?VGIzgv)(K+9ZDk%a6l&H zOt^lsb`PtS>-TeFZ`GKhy?#ipAX4F7Hm`SPU`D>5qqH*b^MI=pL*#lJgK*cOr||SCO(j5q~RBjBnN^O3P@Nv2{L-gFUT5bbh?S`~0uu_6Sxp~dH}E;CuUdZJEK1?~}Kt zk>Bwg@FgDUxl2m6iw*fPz_{H05z=$~U`mOoWHLV+hnGA2%w)YCqsS#|JoWf--1BM2 z%++#0nMDFI1xRf;V9i4N)8F3W;B^+X2w}<-Tjs$}T7pM=c}?c9<T&*8wH_O5QKQ=9aEZ#e)Qg7>h{qe ztU5hdoob7qJiXHI%EIZ_{blOBB}ZOfo-jLG;^HG6!l*yhn@f<2Vfkm__}3u6JI=I5 z+)x8d{_z^Kuvgs_q@2uAgBG4PbsD_<6IKBD#_DvBmTSF9)y=asJ99`u|4ZfIDn;U* znDQWq$R{ncn|c_-vw+9g%M7Cd1=jguF-G&=<5qQ5CWon2Ug}l@DbCGq=85O1lFutj zhuS(#f7DI_a?QTGhzS@GhI5}q#rA?RmydP7K9MZ_kRUpQA+M0uS=9KTuc`38q z12p=id#ZKC*+6hhnrL!)ck{`o4+Rj11IZgX0j~JNuBL|&%iJQK;^GsXH z5)9-T`BFe=AZ3|Wbd~M5$3IJ}md@XE{O85avL|8M^PdFiAzO^e^4`q<1J?NOo>bu( zIPE5ydqPl#AQA@;o!CLPUhCgb9EeFl3KX>eRRfshgP68`&rrt%`u8mPN>T_J)_Rn=gfLv1H?dA8)LV~d?#G9BT!Ae z+(QeQ!P^5bPA8w?P7bZOpwIUy75cyZApH$b?sfl;9J}^LlAasamk(t`Rk4Q)$visF z0jaa)Nx&x-+h{=*{&X&Q(|7ku4NWU2fbf@7ud-OU`cB;joG00Z=<9VBZZ^}~-!|4% zX8K`nm)z1Ea(kX9oeuTGiKYS_1LP|1(lMOR4VMYUa2EebqdQT7sJ#n~zaJXZGatj( zu@~^l;^#$kSF}gog8;vjplrZRI!hf`Z+8pi8=%e6ihf--=8L8q%zIxLHAI7o0Ax>I z>Ywo%d$${N&$7|3%q~O~hu%;wrv7|gB3-^x5Kz$d09ay&+bP#%**2G85}@zYdDiz$ zr9sNwNLRKtrbr8wzO61~?NM>3ZrD$ZOl2qq6e!mU7FJvalr{}+)Kiec_xKPXv9B6W z9^z@YMcwcJ{xqg+9`=I<@N|@)$pP@x3Df`9rEW-pw82F2uo9O6XrEN8S1iADVvkh< zk5LSb1s93}7PFj5UO^FcYu2?snua1XB7lC4sE#9la%j_Zm=7D|~P}9v@lew&S z12*?^rF7Bqr(Zmo@}+xV-9%&E!-}Ijgakd{s*GYSI*ACjlOUzHNXU$EO zkrOl7_5;FO*qL*F`2mjWpvYb+CVQc{tWcqufCtuyxjoRA!tfB5R%bAxG;kjs_`JqB zmx&p^z=p2?)iRrS?eAA_{b<~o1xLE3R$+lmLd1kmnOQ2?f2v4yxu&k-&+9>as!6gpUJ z&xU9PdX9)+>vsOq&fkI>^B+keS4n(?@;)>UoxEBPO%oseOm4n` zH>Q|etcQD@nXCLgu5&emu$>11NB5F4rCldU8zSKi4HIws;TOU1<0rlPp;n*&>fleUTt&txr^y*vb1jM^OtY zZ5nuxiuK^phzg>4YRks;kT1PZ3sE`V=_83MIo`kT8U~*<6o+^8C07`Q!p-dsBqV+u z*I1S(DLXM;Ojx3OeUw_J@E^DR_Qk7$*Uw+PG(yz z2r5~AZ$OHB%DnR5&HulFQ~n>2s8}V{S{p0UwX-Y1M3r~!P|QX6*Ld&`X!XBNBLc+4 z%_19TAm<9|ppI!-nSakc!O1=>=$@O56)uBq%b8Jn`(2&!|EeLSp7k~w6%TTVtk-aC zHRlCCSbr4q`4diWEP(hD5f`EcVd5FP9H*8U7i9G27j>zl1!_MCyX=ZYS(qO`cR4L+ znm@Lb!%lR{hx&jwuIjV85uAV>s#ICqvOa%Ywl277xNq7=@Ye_4gy=hrQ&wOGmj`FnujD(a!X7#Zz<{{@k3((rNsVR+(I5lg7u?dQC-_s#TyY zuS0Y3&E-Vk?nh}Xx}3`5EEFAA?l9rpoQ{iEKa7B6Ju9M@xs~SxnlUf3fbjC z;Ko2HpyzqY6~O2P=Qlu-+~%kLb+^eb4!>nnIh#t_cgtBSZ6#zkD1+?sOQzohT$%p? zYTIU}*%b8k9Qf(I{X9~r)bDwaH+2+lYVMr+#J!R?^FlNt8f?%TcAQxPUnSYZICp1% z`ngfgPv2vn$E36_>{hbBN_0R~{gO!)F2Na2QOu_`jV^j-I-l5%&8~(t;_r`g?ixV4 zI6*&F%2$nHO$LCWT91nf`tMo7vzzpl;c@Dk-FUOIoXQ*Y57QB-a%E%+Q~TX6GtBwn zyI`bL0~sdiP=X#;|Kk>l);IHyMcA-lt|H2ka=Zvox0sQ&eTI?WiKXuNsVHeu)5e}N zvln0RHf`p|FTj+pnf><} zwE2pl(8R@?W@%FBArj8_BDc5bR9NZhb!{`iSWRZVkno&6Q&xb`G>uT|6wENkwtyE` zx*8da+x|l7vrh^fyaMxPymCv<&7Wh_a-z3&=`Yg#V5AKte7N*nl`U&Z?X1?$b0&<< z`6_*`KkWH|2%6%8r{29zjO0dVa$WYnuhi&isq$M8%$w9X?n9n)jUdO7XFNex3zG(~ z<1|r|E4FO#>$?rM-KJR%K&RNC1-c`xP$;IGY^>RuDW*2GR%S|SPy5^&oyv03=h@NN zpL;hg0Zs5Q)&&VIAAVK2ln-#&Jh@uo_1&;M%;}1yC(9H&;&*AmcXSkk-P3>Jp!4^I zV0(+trjWVqT<3_~2ezLjB_s~Fba49Q$c_7Mi%*9i$o>h&&-etz!N=BHUaHX`Cmhtg zz|+*tE$+)Ntt>`Q_j|>T-td}dxZ7h`+LiuqGdn(W=Dghxvd}CvN!)6|ITi5P}yKwG!|}dA7-fsEG_1VGw3g^lY{5&SwxppyL)vS zQzRxiKgi9uRty=@pDKvyn7+mGkhkqyG0O^n)%{nKV~-@PCb}Zta^x|Imk=G#rEI^f zs3vR#T)f{S{?JGcL~hV+Q6-Jm$E|2G$Syl(S?*S>T)p+7T-?woFQip-%BlZb;=~;x9;#@n!o#sHESfbLX`*Bd z7-lf&s@YL731(l6To%W)5@#AUJ!+>E*POrtQD+k&lz+k_@63DP_r&11@l|geDkcml z?>%;vw6k>WtIdYoZn{_HOto$Xb6F)tmIq^5XW5Jyw)|v?o14 zgP1hX{jdoN!S2OtCKaDRmN$~_h#&XdQZu|vivX*)JXRt!`4``Bi;k3iu3RY-QpgW5 zSZ`iq!c#~*pe8~7hjYTR40uRs)E8G847sfV_DFJ7N6^1770ifUfbP-BMCmLc{` z?(D*;Cnt@!bpWO7&i!7OXeIIJ1w^*VWXY9hOOo$AmtcEa_5a*5Im# zi3#k?t!TFVmhFWvEaw_yxGuDWs^{K+8ZLb?*JBqG*A+p~xO{xs=+o+?VRq3$9VjgY z3iLi8N*NFOsM8#=0h}^ZTV7}5zbhG=(KTsqb8JCSB4U?atZzkY7Rp;~>W+h*XCS>k zr1D7CY3x(*`&NZp|7$ip?sD|Yq|3B5m(RVB%jnvWV-?(8ofp0P*Tx_}wY=3OubUXOnbH`cSdLtyMhvKs)i*6~@hiRLd%oXSANDY$J*z(#dDE+i)lUpOS!o z&CaNKYJHbryO*#M+2*R8^hCYKRnF-tiRUTz@3tk{rr<4+Sq%f0B$dEOThl(oiS32p zN?gebd({<5SN_p@S>8co5mkD|jz+y}&ArRXHtJ0$0Z$G{elAP$h8_n)sC|^teDUW{ z)7me47zQj7^@COE`nQdD8;|f`pb}_Qefr9E-L?vleXV7vOFZIW5jt2XX2%9CA==MD zS}i5U;P*%3|7h&iC1<$R8?HtycN?GO*^H6g**||XBg#{#JIhWnf1TdxyYbD!;%+C~ zHHGO#j|#t*&OP(i)6(@X?cRQaH?5Wp}UyAe}dtzho zKhy)hoJ=40_Ez`c&vKuHt(pNZilhF8r*VVv5;7n50e zaWjjuZtp6=RFk*x@-9C^oaB8pE(znGE7F^jJ5s5zMWlKU&7yu9Ndv{+fbiQUlzA6N z(WK|mDGI7X@M6^D0t>I@&C6;=yO+%<5ls)FmyNNDL7*g=t2b@iaY)$Be9DhMQm-y? zklJBn85!%cJTLvL@%MG?!8#qAW-@&aQ+5S6!tydw7i1!z*s)kAg+HMXbk}nZyd|I> zCk)WC92L$GE|a+II_BVUT&$ z!)|VFK=<(>Q=Gt>KKIs=;n(f;*l;Iug`A0y0H=PpDgg1u+RxL&W2UvQ0A|$u;XsiI zeczVI`(tXKsW(?Lgya=s>VrtZQ4ozt%WhAGn?f?ap?B;I80`h?Mh|4X7n2ZmCgAXgPKE%U{Ogz4y6^R&oB$NWH?$xW^g zJ{-V_Vj&O7LSsQMduF`E>n0y&>G_mc^wp9u-W4QRVL1)yD@G&33hR^<63ylocf7mG zZqFGftGN5hJDcVXpSEfYD!hbg=}l-e-prGOuh(*Q@}^k}AC;c&Zn6a9Qll>;Y?l(X zJ~!)frjViT_-tnn&|lAh{YyB{ET;F>S$+Q|ivQwND|XY`isui&gmF7|y+Dul&+Om>%|qHNS_`bB zL_L>EVMcW;$gevw3oQXk%{GaIARfE@9wyHvc4khlP~r5rINcdn4<+( zoK(r2c(@~*74cp2ifHwIv=9kDp6w6%=G4hm>epKizfxobzI|-zQZjYSXwoGKqtQk@ zUQ8%lE;sI-l!o2su&X-gd@)&?<+$C75vDqW%VK% zX=u$;ZeWCzb$wFb?7|S22`!mp|GRw5fA|t=JrUNk)SU=DKiT`+HRY2)>~jtFP%$8i z)H;p|TJgOw6h-t*h8(PD5IRH7RX(^bKQ0++SA2VMetKa#C1PGb@aXc~gAzt^v-+!8 zlv*`<{=h_s{Ab|)?+>mj5J&{vb;h2>Jt5BtvU_N6XuZO*O9BxN8vSQu}RO%HCqAEkgF*bW(pmoN{I&;vm18L*(y%5mT4eV)(fB6%UuZ zb|(JIu`E~qhNF#ov*}A47~IzK<)^u&vP|B9VVT5)*7fvCzBBVT7E+IXyN6nrcUC{C zx5wfPjE>Hqt_FNu{ileFGxu-q?W86;Y!spPFS4>`cY6WIScQ@pX%>u;#dwA#S*maaE=v_|bda42izy zEl2pVs|q~zb!>TsjCnN&yW5+ltk$_1yD8XgZf2jKgeAP;1$Elqt$WmRH;;SJGGJbu zV%3yjL|B`8hcr;k>1*rH`-hz6at&j$Desvw*%x(opOgcPUcOqqDpck_L&hO!7keyH zUVTawxdZuD!wUGE7ken-l0NFheO4?`{O+~9KugybcV@EZ;8~l6mF-}|nTHt<= z)Vpa8#(He>%W{D%2`tuAZ)REPKDyy26J=c7TNC}9fVb>yC+2{}p<&_jnPAp1LTFB5 zYxh0Fwwu)UgT(z^L;x9_Nce)E+!-<&%mlE=e#@E69Mydvn5RaUak?TnU~{A};$*YJ z@Ng9TKm$Fns@91c?Mb;8NmN5+fTX>IO_u08e?3~-f7z@t)N^Wb#2mz`SwV{`GI!)q zaPgo*i23q6?X_Kk`84Q2j+#~xF1ryyDqxE&P;%>)t4G6C9mVQYnFMH&*vJihkjDlQbDp>aiw9F#g?t?MV<{HL$xb`Ylrlm}dNba;FA7+ek6!&!M z2URHet0rHc6j_X<1p!-RCoZRKaAm51Me9T|1*&~eo}z`Ry=E=^!h*i(^EOAgduLwk z{463OWp|>66kp?gF<7E-sHEa!x5^u1;T_{zEb>3u@W~Cc~A7X(E3Zjix$SU~=e6-Db3$$Io)d`|y`eGwrv?q;_u_ zc1gCXBR{n4>pA@lYZZ5o@^U6TIVM!hUHFS;*Jhf9IQ?p9Wm^@lydSd11xrbD-jbio zj}D7%vU*5&WcW$JWk2(An2&q0h(^x3s!|3EXpD0Ke?_!b@T_mh&$C?VkWli9RaO0D zHqoY3zFW-FMkI1Njvu?Kx*( zmh(fODL7|a)cN-ClPPA_2K-)7D){Nj)LW>Ze7$Ii`B1rl!usv!iX{OtIM-hs3SvhT;C4Wgh|F52QJtu#)LIyC5U)I;&8g*fhD~ z#r8tI)(!id(y!(5IphE0rJ6&j1z<{jUoh<6ww&GBwtmb{aWPf2J;AxaO242|qXe@S zG>3gUCSW!);r+5pWw@`N_yfSU&~kz8fO+c@5iCB=Cm!-kzGm>IK;t2JMzwn}yZYCAQ8`c-{&bl1a`p z8)Zo<`Bvn=Gf6uZVa)Q+0|^4Z4s31}t&|`^9yu`};K(D68JqI|1q-3{bn+O#i}lP< zFGWcDl=~r457Eq1N|966I6`?q98cd=7d{io~HBYN&dPw4I>*5<-Hzp3!@6lwp2@LJ!mNKwnUCXFcRlKe;XieoB4FN z^!SI9<89iSrVC#?t|LvkdAV%(^kd?3{UX8*ZZWGeL&hL`3QFWRu$zAK=c!tlF#&MW zWX68R04~lZIaW{&R*l`xQ#(}$G#?$Y^~?or%?8snF{hI5D!}x#!hR zS=7E3^HrpxlDWz1IrkJ*4BT0R4 zq%}N%zS51{6cT0utS}06N6{sBB2di;@gXn(DA~$QX(;=E4cHh1b=t# zc%L|Xv7j_j(HHXSTB6SN#h%k!%C)WXUx}r^@IUAzhgyCP{)&r5oZHaa%(Q z%)UAKrt_D}K7|{;6G5uTK1~zp41rsHbd@i}u<=CjeF^Y``4j>N^8R$66rJ{A=<~K( zY8Ubv13>=98X;xJ2f8XQ`iY8xq3^^s4m3v~4{!8{62r<{089Mbug&1jgS;AAOOC#~ zB$n$fW(1~FHaiC0FDaV4ZMx#6s=i1l-hP4%+bKG{40sT7`E9B`m#FxovDTFIy~#WK z%%lA1$-2j-)Q76RNYkQQFrwRy>>{68Kf8fK1C`K{{tu*GUBYz}gZV0$8O^vYQZ{6B zSw|g3iO_e$SSQSj#>Y*Qn7^*vZTrMPeI9z_~Yh@mT-A+F_19n7UomK}Swcn8LaFN@jbPT8>QVRM@`56%1Msx58Y z|FoUDkCo&k{OY|JonpwE7y&IDI5QEx8yU#EZPR9xNbac!eT77kTLj(0oZOHVcrO+<_V2TvFnFt?BeB;%6#FN!n98xTVM1Wg<+3TuzscfNgD{rU9A+#QjWf7K z{F^r=7|hRxyqb26)KU6BFn^8-xRsRMW`}UqsbAKxZ>`L!$nx;S5US9B zhjMyQiRd#n-q?mFW0m9CQG-Amn&ErL)m%}Li0maD**X0$3sP*8`e9fL<^B1u)XX1rijAuGxwsN z2B9kh+-gmsjXSNy$Sv(u>a1VMqvb(iO(1C`Z^hEtZ#Y;v;>>yeJ(NXr#5H+~0V0oH zE6ckx(-XVp2@q?`9=eUeHf^{Ht!l~k%`VA!MnP(y1Jobb=&Y>$0Hl|@H7Yc$#%v{? zk~hih(@<$-j$zWuQvL&?Q^Je5b~%K|pqB!6`_QmpG_KZ&Wdy!B~# zy8{VqQSg1L;t^wwZGQ3AffNPYfjZKNco>w=NpP zXpWCCX#CL+#6;(QBhkrvj4rHo9X$fRHQ%&n^j7#_XS`b`M$>Gq6T4c-yi25of#xOC z!3uJ=ITL>hwo#eeXT%nCbgL_PTXYH}tjN0N8Bw63bPzNPoDu~FD(()VR$p`L)aMNS zg4FiTb)gI`Fz&4MHwq8rvYfO;>pwi|j!E?P47?)R(_uGJpObis`Vw&Y<{k?Et2EJ$ zEDzKfTzS;r(qjvxQcQ2TGxdDCPHzP^+dxbHd!s4Dy<{R8AzwEv5Bj6Kt^5h%5i}dS z@24BD_b|8b4e4wx{xSq~Z6wZ{12M56L4{ts7co_9xd_qw@*Z({55s|8&Ea81ePqW$ zeM7Oq0d*Jk1C32(kA694CS-AgUx>`t3rUaO>7S%+KWkGMS6XHyggP|X)o6LvO;8gC zh>d~vO-!lI6&*)PwPkc659jeFBn?67dbE}4W3Eig;NeEZrQ1M^C)dfJj0Ul!=dsl( zUaqE{$4b{sFIC|;1cd5+PD(-?&O{z8@*@$X-NX-&WM?eO)zV6+A1O{q;0)^v)(0C! zA+91NmbEc6T*j+kEDUwD7Z?RvFk(}9u5(82T~Br=tIEZCRoYWW3@JppdR6O~_xQyX zh0RAkKe<4H%f`6=+Vf*UhKlhj-`ZOE{6P=iNepSz2=D-}8a6tCgmO`CiselMR%&Nb z2d*^_lr!rN=L3o~RbgS*=Qn{dXMK5)-wHB1Xj&vgR@yX$jUr4(iRya^<{q+EA+HqC z3Sv4+jf|+8H1s;O+&VamA+7}(s^HkG3?B%8`E?+|5ie_ZTtUTL($`1Z!vI%e)m9yHp`n95qkcDq)Z1oqO z9E%;DD{-{c>Mby-ooQW`JDKu2?2kw!K0DS5ke?H}kPhhk$+gydPw%8Gr03Zx?0bdN zr@Y1P#M2q2_6H;0AI8n~X}~!`;Nb6Atb0B)jO#zu_&Km|!I!j%cB?tT(rAvLkap?4 z!UAhGSIV5wM1E@F>^(}9F$*D;yS+K|BunPfPLP>M0JGZBiC30MNcHNuVzou9Ic}qm zV*_%~D7SR(6})*$*mNbUO|C^F{Rc7La{;EivJ$!2VgHUXi5!0Dk#JYCZ4dFi!q}F zu|6=BZ*uEdlSKhFuTvJf)6{D=>4gq-o$SZm&lwTPjKa{hKs&CHO}T;QcTBz#;&sJA z%aGEHovp8886MVV2}=G9gLi_s7cyBIutQmOKb$n?o!{M;n%ANXn<(m_PJ+YXcM-+C z;Cqy2*cSdq?Af5q3%3zt+*@^ZsHBLAoyA5kjivS6Q0oK z#(6|q?1tm8|u*O|3x+3m>#oCfAqE0lJ@ z(M$xL_|u^!*B<)o$?sgJIl^ORyIxP#V$BOnrxtxo^?nRK_?Cle?abp@O@xluUN+S3 znu6KohA(lzD^RXoe%6kNc|a`M(F`fIIXbJI8iVhc`Rr5*B0lF;{Z|&i9I`*)-~O;1 zMzEaRN$HO{HZki@dwVRq-#Tu;BeC`t;1eOq|BED^Yv1rSztx`OLkt|s4h_tm_I*#w zmkDp3xvilxk`j(ynYLU^l&hP2fW$}#<^T9B|9N&5ci=?^%`Hc>tp!*?kvl<_VRj-O z7A3tKql3j^DMr$fMO(m!v4#kg&yxt-};>=cbcQ_jL2Txj%v z21!NOuO>)ls2)1%$Y<gk0Cr_@<9lTW zF2Mn4qLg~J?Jt;(0pN*P=XB-4jPU$?j@hFI>hQlzQ(W&>`n;a0?^@aRI`Zf4%6FL2 zN^24|z9rq>Q>%7VeA@1d^_Q@zQf%>`&9*nFr21%P{t@?@P-jjDsO>x+o4Wv}q!=Rm z0~Hw8W%OB5v7hBzL1et<((4dH2Kl$ok!A7~IpAW2T?d2@m<~{M+J5df4dE&T^zgYO zMw;E?LlXue5rEe1ze>EBu8RAB(dsXAAE88X>LUWMS8b-{*b4H{uT5TGZL2Pzk|6C) z#8Ie7-s7;3h3ea@wN`m%8VO?NxotHWHyI$l#{=0@*b|5R&I7<3mcL4@S;3to)^VEIC5YGVSw0y&*@W|ofCkT-I@(t7SuECvJBsx5}ENjUazTSrUmU4Xg@!{4YxVKe5_tQPAfKFwWRi{k}KSXoefQ%tn&FRw#)wy3wgT^+m- zLiPJ)*{oM=tY#$+m&N}=h9bkQNG#DZD>^p)AbwF$h+8JT`aQJBWd95)Z8M>$Y#jDo zMXC19I@{a&ay}13Gyl)%yTIqPQ=)ST%9wCyEKF13aPd()#Oi~`aunPn-$PIu1b(x| z%Js0@xnHna@6hU<6A7`UMkS$yEqFj^el#%Sk?nC`^QP&Dm;3ek=CNlcQyU&i$#T>i zi>xmk9-1_!GyQln-)+7v@6MaT_WbhB$G~a%z~`&;FtsT^Nc_C11p!cUMZ7$SU`t_* zWhV^8jBZ4PlhXxttfuZzJ-WO%7Y%Z}CT=KR+W+Z_eRj)@=;OHwr|ioJ=cX_mx!pEt zx##7kU^?X0uPHQY1m#>$HKM1$Il20FU zymt4++U#*a%)5(2G4d6!os&dK=xH+3l@%xM1!cOp?a+5+|}e6cBnnwjo} zg!mBV@<$0zpxJ@)bj2lQ=u7V{GnL*`e-Sp4JvhSdVD5b9pxNj2{?ZOk5woG+6jVxa zgy>Bj-F`AfX`d{_i2hrIc}HmX2?rOtbJ}vrgRS##)jPTl^eA^pThp8U<&bB2!X-r+ z(V_&}TpG-_8X|A+c5zk~v=ZX_T~ovAO!8=CZlyEZ=dM!Dc}u>{sLf#2MW=34&?WTf zkySaSc50<}^zCJWYtfm{NIq9>@5WYnh}qsn_dRW4x7M4vf>-T0L%)(EXp;<^xQ#F% z4~3V8C9<5r8h;USNrzj7H00W>hq^WmrwS2eio9z2@xMw|DBb6E(Ei_JQk=}%nonh_ zC}Qw_-UpJ1K@<=h^he7F>Ee`UrFq z;kZODtA9(hxWN)RvzM*6dj2DgP_Uw26B`ovU(h7!2O-n3-ZW-!{`zk-MwFm-$6z%f zYu;$yM7f4#iI3)KWXXgDiaT+8`y<8XARzE-_jWUt`6xn>May*O8s9{&_I3J)8=WDm z*6Qiq#4<#_%%82AGHe7uSu)7UvT-(JMZ_l!K^R{h`=o}eHoO55cgCVWx&cmpd({O- zdpvyMK>l6#?apDQ(-_$e32g_|Uah!eR>X*6ZphjwHMYpqVU&&hZBzxdwt?7g8Kvpf zk**+NF;Es0K#2zOGC6-V_&V(N>T`{;LU(xSs`@qV3}Lck#%9X6l$@1m@3`OQwm{e& zW!`mgXm6kiV&&q}g6i&%JbGn)5P3t+^z#0clZhF~hl%P==V%G^Xezf(F5)_q{^Pih zl$BY)s$a>&Wai{RY6-Yr!f!U%Il1Ny2^wu&zps1Y=!1sE;17Cb)@^E%Q)-#ALe;_d z`yc6h6KwxKRe-L$to3Z{a zoES$hyFnOIL8t}wXAqFFQS_1$wvr0v%uA$NoY@ru1yha0=ilisYHb>dxDBrkmfiQ^p^T)wBLn+1<)q6XUBgSb4f`NA7%AArxT5*&Ko zUk4iGuk%+*STKJLIJxDk2E+VfJ`=bpmEIEsG$J1+>O!g^W(v_Ht08X`U8eZM*!$d6UIS2s2G@*X>| zfVsQ__XC_Pk680|9zAN-pp2FI zzokCDZGUTgSkB2i7yj<{{%tuz#WAhj{sN_@GP~Jd|CJi6|6_%{_?d_N%Zv@pDge)w zZaTdVrH;UE^gdEPnS?bp;3#i}^7Ne0Xe+9=4q|nO1QQ&;cr0 zeCfwWj)}4*{5M%D`hi0&Tnt{ob9|odA94Un(tOwaYEQzWU{-6PpvgVC~x<@nRB1elzMMVaPTAbkYrvfR^hJ}z_g*~E;Xy6(^NNJ0t6Cy z%gNNy-JqZmDb{axVS;FY77aWo%_EbeEuUx<_AERT_=Yj8=$c5%9}4G`N2rcZ?8eK! z3YE!3T|6NJ9Up4Pf$d`pJ$DxJd0R88I7(hxyygFlmW=!bub4((e*?rq@y3AXfsoI+ zhD=DA0j)B|$u7^PC#ofz=nfaN8AfMU7?URXOO` zCxwApY3x5esHEq_f;5*l1ObNqRN=3R`uoZ(wydDV91Wa@VvuD^qp4sT>m16@4zi8o zX?tZs)HOL&1H7+7uOLEhfV=cv#;F;GTgKZ+kg!K-K+)X8BznlM^Cc)Q6K0ic^Ta>G zqdf1y^f#JnW|LyuKzX-TzY+59MPUX zF!G>lCw!!5&b&_4+*@car0+h=!b7%~7vXn7o!}+B3+S{2{vr#K;`()cy-r^&&XZpp zhMr(7#Jls|b0;QuXw(#%(7Fi z;PS(WJ*KH?TlRm*5&t{W=syA=&T`OQcHt0wtyiw>YYs|;nY5~&P*f?CN^BoMuOrBx z#P+|F6MP9MX-BXcX@#0b{#n+45pE{iD6jTirE2P1`u*|B#GCo&G5#Dm<_6s_l4}N5 z)o+UJKmBkm>Qa0xIqFT8zRl2PZ{)0#vq?8dGPoRZXd3E5gXV~0auUwH`?+I0gWG~@ zc(ndIf>)=U|IM2@?}SrO^p!+~fmarz7#UWq2NQ!-iWWx&=+h>3)UX@su5STdl!rs0 z4%VHOY&gw)_~$4oS?2wE&I6BV7IhC_QmB$0F*<~#;Q7BkBg>nAE9GBl+Oog+r!kb& z2b1{Yxm7WLuD>Z}ls40g^yY2iezSb3*X9NNNAHr_oi4~^3^BK%H=S?y;Lgc6x3R}; zh;t^ki+yIFkmM((pf^UXZcGpxrG6+0fs1dUg;LG>COp&LNkd-5j#YVbC{svNNy)50 zHbnrhlqDjV!uUmmq)mG>q+#RG%KamuvAw+=SWt_^_**Tg1$L8{oB>|>x}dZ1(J2ev z>ym-&WXVTO4p=MSis!eA#y^8Z1g^0KJMzj3Frc*gWbz-= zcjgNgq=syAYu{l}0~>26O!UI`vur+@i{qP(xH=fv5sQ-6y|(~gp>G4~{6jxVS3K|- z(ilZMky|LJo7D35z895o(-i{V#sK9U@a~1tKgt0P9Y=57ySA(b6igc4q2ddLr;VS z6aw9ox!87Dw6eKuk*Pd{m)E2s2fz-3HZIjo|O?rzRw;JAUsf#n|Thok=5?U zoAU{(7CJ0bu4(nA5cfRn#nESph2WgE>DXoebI$Mjcq>JAgzY8#03wJfx8#WsX5<#8 zH(0%kv5Wy+L*!_CoY9(MS?WYeO57=Y{^@iXC0 zWzPh^?u`f9Ib&XE4g}iG7-SUcygoqlW2F^m1&yd-Fv+>jDw${ zLU;dK8$MS)E8clD^I@POG|^=;nz4R+E8G$IEOL0&Z@~1Hot{#x*Rc-pI9aU$grN?t zZCNeuG%hM*HgGYoTvSVhQ+1hhS3Zl~P~s`LYHdzonu2w9IDc~{JK-<(wG)DW)?Z-m z%>f@J>Z4@*rTVidjhlL=u8){9can+3DT(^DiBsjD{aVS=bySvVo;ts(fxVuXrKwC<&7I!GcEx1c)3lw(? zUZ6lI?tYkO?RC!ko;}yT#hIbd%3B4BSq zIEMVd%x?9KMhJ+=ATm}K1%PvipNFhNsp54nAy0W z3C&48?W-l!lUGbM*Cz78V{<&YHFANK4$-SnAy&Yi>`U|Eo(sZ z)Bo4m<+%NCJAym^Fp^K-oe4CpFIp03dfr0&9tW`gE?d;BOUWYr_GZ>hbIw{O z0-ht;pU~|PUw#L>G+LCKpG2V;G-GXhf;^gcCErN2HU4sLiHk0Q}>%84e*oo`-H$`FOhT-ToStln!9;^Ng>$ zMu7mo4%KA73)Xf&sxKnj=;=oR@^YBN@Vi2ZqvTQQMzh+GSuTSEA(!Ax1bR3FtT=F! zOoeU4*rBk&O?(lxUmqmnZ#||7{G>9{A`d9^z6dgez0ZSB8v{~cf&>TX$~VXR`|QHtVxbyWO-m(gTnN=i$2t4pF!djvO6&=+7(dyqqj|f?F{s z^KOPg;UG=LK*A0Z`-!e0gSXGwG_yf((Dgyf>5KNoJOBh*QgY(v_}-b2=?96O#pHe8 zj2x`8wqW)M&{oy205GNkZh_SC{N5hM2v<<-D(UrtDCZzGPe;K~e+K}H-oB{uSz!BE zgx{ZBWcpNe>ottAFOkzmM!X6R8`-3gkX0gc;m>@iZL1h4oQ`lQ$s96#YOf6VL@#)+ zLDq#Ui7Q%mf&23c1%kb(u9ii@=dlnQUV7ftp5>cst__}KIm0bzd8!(JerB0&f+fOs zU&%vU%+MW_^+!^%g!<(m_kk*{*x?DnXSiD|&#a}gR`Wf!+3kRGQ+j}T3u7+*T$E|~ z8M4BZnI4Q^i(91qU_HxbV7%HCUb3NGzR8YX1A64_qPRQ4Nqz2!o^1MDIa5N4siO{9 zL0&jNl-es5_sD6FNKP1KvywB8N+NXkxgXArVOeVy*SLTC(YPXf z05{RjrZ3#qGaZ_i;7P}|s;RXqPDEiW{e25}PJBAxm&9M(4W1FK60CzQWjgGOPRgFY ze72UDIn4L`_OD%Zx}P>$U{+TlLaGwK1e0Gl{i^98^+yI>RS2!dX;Dk$Y=M`X42?$b zL=P~ww0T{dsGT;eE1c4Az9z&7@)*cHudA52m#ShA%3Tj$U^%SlOcfhlLf7(Lv(>&Q z6U~+;$EhO0=F(sOjR8PBPI}3$5KNI{|9NNic~{}0g?Q$15b;PAbG9w_$#TH$ z8|dO11Wf-Q{oDH@Sg%=Nx)YnZooduu*Kph9xr%6XJF3ue>ZQuNh02Jx)l%f@*Z%6? zHz;QWoq#7|j0$L3dlS@AMDf}g`mVL_Js3B?nhr0pQ5vOh> zmK;JpCZ${x`$~iAoR9*fMP|wa}Zw8qOVN@`d0E^^DSvewlz0+#dtJB;*S$|He7n8q8ljnfe5Qeb*kRq%HwTGG{Yv#gno#v;`@DRZ-9 zrK|hwizlO^nL_HBDvfrKw07R~C9f;VPwyajtHg`rIlc~N%>#eS#J@9uf`h_>#V=gE zW*dUehAk$OGb=sLE$4Uv7L6SZLFM0HULemMD)nU%)v+>Yt`6R;C^Jo+^$afjqW-Ar zEyC5`fWrE1n=Et>ZFKv@=t||k0Pm@-iRl+`_rG_vt+1@-OFIk6)}itA)-=@rA+_Yg zO`)CtjNxpb%VBl~!NdB=omJ9x*G?)b(`c(nPxysqC8*78SDUJv_+;w-iF#Yd?|ZZH zH?^>sy>N%kL(xYpucyXlS?MYfDMmP9aDrKvtnIltp&rhd<`^8L`#J_hTI5;7C2YX& zg)_Ll#RsxPIf#>yYqSipL*%Skv#75*1pSStWdARc-ngePR%{~Zw=i3jM-6R#6GlBO3!bhmeY*J0b>DjZDnvsI z^#GxytQUxP2m8m}d-$Z}kGs4uvwj9O-s?Z$A4t_^%C;+kSF^Tn@*kH~`}n=ITiB-w zt?iwC7*G~1(8vC<+aqt8$IusBwHI3dTO=eZsFx}nt2DI5B@d+$+!BJe^g7}h{QjFM z*PGjo*_^kp2C+EUVY zhUo4VKt=R6M zqMU^2^snxpYi#`PM^N0de1>Nv0~WMZV(g06MX4ngn*Z* zVS*--lW#Qd?Uy0SOKJ2`{)!#>ledTp>G=E_b*@?Iq_P zCrjMVn@2%m#&p-eU2n~4i-!Y>94)q}0xc1tePXvuI)AYc$a`m2yi4y5u zc7~Mn@LM5S7wLfIt^nrl%?b9^M@|J$p|0~Ay6@;_9e(7giR<&l?n<|>eGmDH%)6gu zuAQ6=8@v5xp8lBdx=(scd13T0&YzHKGr`ARFBjs~yA%sqc~f7fm0~C*9BX0U0q)ex z)GX*&VMlR*`H89`$$f+aG{%T7?!9JhOukqU2KuCmHkM*Z5g6t`lY}|vG6%!srW_v> zT|V2t`Jv69FH@#%)Hsgo>3?@!rZ<9ir%42Gm1~d6%R$SXSxH_LNmk0H$B_u4(0oRr z9!AX`zUiJP{kqR9Q z{$6_vuo^((mKa@X?TNEh|9cslF$3}}{^j@_?BM>I>SZ}3mWM`V(}SG@C9=@PgvH#v z-dea7G|K$^p31cOeLPTS#Awa6pubkcT@xdu!#iO)2>UZy%}&2m`k>2S2$F?Dy1P_~ zQD{ho34DhyZ^8zS#_lDBog;N>R|Pat_qg_n)bDOoF~=xd%D@oYT)^K<-{qBR=ZbLj z*_UKR86)OvQUC)*two#Kvd<*?1If_NN#nHJ_b=FT3IZfPlMpjl@>}DNh za!*q0T+l~x5i=NHVf?0UvNgH&lpNg74@RiGH>Ic3TCN*F-&{v<_ z&IAk;gDy64Z>rxNES*x{AQdc1E~C7BSkD{YI# zu1I$3)#1v)E{t9Aa0z~US=r}T{5bBp5~yZ^7r5wWmF(ExrrX~SZ~^N`fMp&DY8d6KqtwM72`q5?EZgn}quag>JOj+=AWVy_S>_x^%=@&QRln zuYvucL&JI-${c%BoiK~;raxVIF6D)wysTCgH%s}ffN27nhi3DK(H!ckRN+9lDZ}{E zi^$aI=F4-r#^y7}{mE`)dSklDiFS>D4b1!>8RCB(kDWS@;pAWa(Er0>`@gq>EUTsh z5RrB4w<1ctJLZf(&M(H3)~LVgeD*@bDEF{xQgwx)B?YKr>uOb3nTNCI_zZ%ovLROo zMr3vQ8DG8E`1RN({msO)@N+ga>=n{`7b6D6#JV;^o(q@)u`hjPM8t9-q@D`ETI|bH zA@7ZILnR9!?}G&I=JW?~;59yA68D}#fsyy~G z&EPR!mYdg1@agKnXi^WI3c!T!2;m3%*&hnJxWq+rlUU2y->3!DN)*cgf8=NxqU zO3d)Z1{GQEOv%YVsQD+dZ60Ff`LKkma?+?Zz=G+*(kKWdLXFh>?TPWO9ld}<#Gt!A z5YAANy4z>UyBpRUoI~w8y`>DuyY_J=hvsGjTXFe68@qH&e2gwMk;GU4(YH>rK+2mg zCIsspumI?YU94kfycwHECZ@8(17=@IGj8H#Zh(87 ze&*9lHb5iDAi#UUMe4dK?8dJ4$gP$>V~~a4&uE$w=r870>0~E=3Uv@4Y1x9&?^xk4 zp1bYJ2NHZrqn*R`?icGVohjl*RQi&+HiXibe4u~PGK(urYwT052TA;h7+QzbiZ^A= zZcLjSx1Pydbe9_zqPoPl0Au~NT-#3<8v!3qSP!A0bG|s5dtveW5BKVpn30Ycd#IB< z04J#!&wAb;7gPtR)xB2@-F$E@KyoV(v(GnW^jR*8A3WJyYOU6Ye*6X#+ZYg&05DN zT)MoVwI2zN;QOYD#=s~RV^meUo%=^-)|gJ_@OYM!bWmJ_)WJ$e;@7sTp)cb}V3lOT zRVV57K40T77hFyb!rSDP+=FF*dzFEkduN>e0(rRxJdbM|x{gR+f1;1Hat)cwH4_Ow zV2hUUc~tW8{MS>PkEAV;A|1y9ySJ+%&_kP!>yEN`iPs*z!OuH)T(Ar_!>=ny&I+v- zx(rV~s)>$N{Y|Ba7`!WcQkK6Eyqjv<|2N%RerX;SwZ`}Jw2QpS){uq!G=QOLT*IyXpg5IRJCBAfW37`DN z3o}sT#}=RV=Uo3`bbt>>;vgzyjj;?j#WP>kPBXUQScyQr-e37w!H{MEqwczx=aeJW z(idZ*n5*YE6;ERyaP##R?lT@i4>_yGE%Vw*dDAqu$@Q9EM5jx{4#N6h49>o!3Mc#P z-nrMB0 z`-fzYjVB8;Z#67>sm@^P;xWD}Y~T!;XSr(A*lXHv{0gW6e3eC9u*V9J8=RC4 zBfbygk1Zo_K1Y1NK<*nFth8Ic#P=_U5Jt~Drnz=fbk*il4n_}TKoTa7Tp0_SytOdp z?}LbX+1ygdAFH4aqi22{zqF|zS2~ugR?P;j)`baoJ#3=ulIVB#dcY=gE~yU%25WOT z4G+3WJk{z2uy&gkTG7cD#L$xn6@iMb3ckx#P2P!Ez6Ii76SqekrG+--!e(&v%UraY zl~5bsHUF6kdih74y_aaazMEZLYS3ylR)}{n9&aFt6b@&1VWJ`-M7kafP<~)o^}eU7 zZK?~EYiIGjIbz;dACyl1az_Oy%afC933!(Dvr*VF_&4@0kQ+T8`@AEVA1+kCRg(zB z2TJ3eo`~L%XO(}GPEe`~04}o0deQeeA;vcTp5`;D$|`s|k_hrlVhUa3NawD%Al^Qi zO%*6!q64Y!G8_{C6?W2js8iH8K%7m;*ibzCKmg2B0_nB4O=aAH6v2!;p{e)^ahd{A(^Ky3;}!b9PINgG*{y+G{SQ5lYwmwC%=yx#^4aI*e0f%^LSG z8=(i4hHc#npcOcZaNBV0wB}CXz8&_#d9qKxOS;J8VM^xhI7#lIR#B(xI*9wDjBN+U zBp}Q_(NKl+5p$*jH?4lHxqmora$~aKHKug&>0Z9?=N5Ia$)X8RDX)Z?IT(WI0J2d!@ET zSADKnjSH^}3)jv1OUH=ZpA%~ynnND;`x;{%~aD9 zGeJeyeWJv~)vLMjadG6z&GW1KZ6a0^$PT6P`T}8M@JvEm`=x@O{7F%DN{NC9d4-sX_o-1$}Cem{rexrTL=jY+Clm-=%MB zuBFtG*36F}B0cZ+{rhn`lHV5!pI6cvsw=BVBV9cV#_JZH-Qay|;IRDvp>*f}o`jHo z;bgPtNj}Dis*o>M0lS@UHJIoVu+Fg}he02i6YU~B%Q%aSWRn-00wsVc?1mDvN;}Z! z8W6$pz48s61Q5MrwaVGxaI^+M`Dp_|3297OehXSy-GBkBpJ5n0M>C1fFgDvUM6J4< z-jE5Wt42Nsgw>fN;TWqV{oK5ihXEC+@D35_yU=|#AuP+WOF-Sc!SAfyeud$Ef77huRu$o`|83EXh~nJDb)DuwERG1rMCEaP4fD?^ zU+v#-BwQJs^aHZ_twQi$;nhmj_N{Gow&Ks(LMj)XR2VUxA?dP9(oZ{*|9?X!|EC|j z>cN39Pxz1Lwi;O=d-dB7zt3@^V>IBrZOG@maozd1G{@MvnY`v}k5kdr`6%ff4w!tL z#hEfjS`F5R#^=~I7uN!-Ibe9A5oX?aZE!>^LQFkzzC(W`@cm9)_QAS(klSSw;z#fx zk_(){A1!wHBhY<_SWs%F!Am$q@Oc!t+Un2;UXXW`B(3%+yw;h=#~E<0&^ zm;|;NeT#)FR?Gc`_-oV^z!FZCpv7aT6xRUYP1eDx&>xfd=Lg#UvRW0JOt`aV(GnNM^DeeD zmW(=NDp(gb1t;-H)r4U*%NP)cCK zt2S1ByaJzBFAStVkfhma8^ksZ62O)xF{tTeN?PH5CBGYm)v+mZ7&k3SmD>`84%nv$ zB^ZBE%HIX7IYx(BP5LPZ(k~Nhd-}{75@Y|!Frk05rgvtuv0gzx{LAcW4gZNAk|FUK z?|84SX<{;u;qI_NswkRX6IiWAZpB&y1w8Ty4+wZd<54^m-u$qdke= z$=GW8teo@fEf?Kg_cdGSQqnwls=+}j@1{83aJM@fSGV2%E=6+F@i3sFVNcVF$e!iF zyYE+uZQK1$pwn_k&-HN7BC42&&_H4yQ|5?tS0=Q@G?D*L!|-m5Y-gu6dYJIR)s#$? ziA-nrF>lS0Hp${#rWxBHWnKzh5(oaEnk3ug;w&RaTT4cumuTe+z0;EQ5<7p#)T z3de;PYF$=!83qVal`;<8g>JO}$vXZ3uLW>(>1LZJahxV=OZdcf9d;sZg4v4VRf(Mg zHQHwI?BWb}kC!N6n*ep&7F12&-2%8Wr)KLcm4h%WhtL=J^)-WWHH65m)Y?Rg3%5Vjf+vb`TNb z{H^KA{pVhr(Mv}6m{QObXy&(8mra6YqY!htzEK*<$P)QI4lVXSShExDnE99PaIF7q zw66(xjeT~sLBnJP+{ThTieN%6v=jWvoR?~O(Yp3ZyKziof3UIa>z#!x!7&3W0{{I{mZN;iRH#%2aD3 zy09d`GpJOxokP8Mg}#zf?0J{h6S)DL5y9mJqK5Sg(@5s!SuT70}-D=&y0jS zbI`eUH!TES@xlOlfc5aCz@w+#xL3(MMqqa*t!iV7wmgv}NKk*ig|?z=nv>x#KItGH zoIFC_tj`%=HO-bt}sRbyx|Fiup}9f^lSQcHaO2yP)-pLD~U-I8b%JCr@+tH8(NdcA*PkrV; z-kMgi&oO7P$OsI!-~Q=isUwm4nr=f!w**D2t*la(J&73 zDpE=I6)!b7+Xuz6F8t}Z2}a;x7dOVNIn$_4107FpgMU|&nccQwuB1%B$Y}PbhcK7I z8F9Y`$ay+$JCQR-&pd5iFowmzL&nhsc zVL)S_u%ev)ig8^E^F{ecWrWyki4maBK;CJLWo0?zVa)IZ^lGOSUmht!b(U3eTedpp z5N@+6(p~R*)5r|zgw5JdI5)_{_IRaswZj@TRvdUj6gVWL1&z;yu7cG;%U|1LLHn<- zY)WQWh6ouewgOj*X$W3vrY*OPe@q<`TnlT;gw?w2a@C>AQ}m3K=w-q@{Ve67k>bu3 ze)8aI*O2MdN>YUW!RWUcKQ1^9DSs<>Kv7ROJ%VSLGl>?_@3j5-`s2y@|X~4Oz4vRH315CSz?3>HX_r z%=P&Cz#wGsc--wh(I6Fo-;p60S;qX=nqN7`oaf3HvxO>1X34B=BXLf}Hh0=0qy}zqtNQ?x+d1)-CeN|;jdW(?i)Sje9 zDNE_MGaS3!GF9p-E5VfEYRQJBpk~3ZeB5*|A=nCUsvafI*=pg0UtDYDydrQL+makES(;3I2Mabc+ zQVmfFO<5QHyN`}t=9Bfg+E!%qUou{UAzY8UXSrq@)QS6#x|>rl_A$Drq_3)(f2?N| z%W_*K$+pg0ObIL>m#oE&2}73xu2>BYW&hdW{<5K5n0Wu-F#qjDo6RLPLCO=VKc$2C z2W`W6s4g*hA=$u9h6NA@g}YtR`s)_Fo_PVPf@F|+S#3aKA|~c=EMW{QfK|e|U{iCS zh|q9Sa4eHrp?V)9?z?s$aHB1rlZbWxVO&Vqema_}f_7866(BJyoxsjjy}}h^Scb(J zI#Wx<>ZfPKHrm3zmkmm75_NUk!(^wt{4vW_yb4pS^J=Pi$WC?zHoXzwYAtk1JXvb| zqC$~!Q|dbJWhpHHCNj_yhyKXSHoh&eDdr&|@mW+>I5*mHr~I%Rn>bP9seLzSTNsQh zYK*TQRd7zl!OQSVZ2m@R@8uUW35PczYvo5J1KyPC(=;${Wew7qC39kG!8rd^T)r*| z4fTu~B+7v?L;;9*F%(4e*UXGW@0)H-#UvmXmC)*@ADZG;{Jcuq9X!6yqm7&vmm2%w zRC=i(Cq6#ZN0WnY9s3-pMoJIj6}1E@2ok4lm(?KROTWxTiNhf;u`tuSvwb}lt6(ej z&YRlZ?BlmAi>ZJgNB74KfZFUfb}byqAl*+cvlp-HF9X=VsPjoIF;aL(MC4L4WZWC% zLvU52uepK3AVn>sv$FwT$%<>C{|5T04!`dsQdtH;F{1BPfyUcwnv+)+N-$C33VkrfTNum3ph=G0zvCbL59m3 zFHZ{lgdyuqs`olS!fKB#1Z&=xZz?A*o;X8U0va(UY|($&l2_Dz>vKD8SJPv{HD0c1 zCy`YNe6Y2XbPQ0l9cw%i3T4Kl>vFwf>2eU0D`YC|Co8Pi8dTQ)#rkKtqMtIw=ZfmGG+Z+;flt6~4yA_vfz&icp6b*3V5 zb_}+N%U60>u8D?}WM1!}YzobfJOt0}&?7-=69-@LXMJ<|lw1~$BEmTd#0%2np4HoJ zZ<6-b2NU&pcI*vQAThaZwE0Jvw8}}F%RB&i^D)f89!utS>srkx_QDv3dNTl-FdLwC zdNc8v;fUTHBxipe7ge`*+K&YplH%s#SvyjCySEzM!F?&`eQQ)RCz% zM&b)k+2MIV$I0@!)W2KEdNt(F&da^&K>mP7q8{6SO<`xVNGRj8Fm-O7b?W6bZrx#E zJEkdDqYdXOp95(G<&w|RCB~qB&dl=u2 zUK%sMdzVP{z3V>(<4cFI`?4owl7#=!4&V99Mic;^;t=9daA40^7^ZBx02Lg>=_|I> zE^4NV!IYR0^RqOAj)!ChGu?*6{#i7_WrN-NNy7=xBT_A6HDGN(?@5wtL5v^UbjNe#9<14WNQ#lCre?_`GL$u>?mLa+bouoU!27x8w~Z$-|$mN z*ss#>@0BgwjC_Av66k&)q1ICFHq*L@^d|1H2(svSAsZzI%KYca-7zCuC|I(i-k4E22gt8;&V0AtdeQzvtnEAG@k~f zm=h6wdfv-@v|v-7GvI#1dwDV(Tc4g4qY$*W%|9o(zH*|56Mv^^k-xXx zyQI{PCf$hsSh1`$?ylC!tI=uruzP%xWZkhBI;|e}RFV3tDhK_{)X3$=3%LfzQG*T* z|1H;$x|0{aDq;RH%W~^f)I&n_3%zP7T9gklIVDkqalYKMi6j^er08S6bFYi41|fqe zDP!@_fsa8l4F=tj?=)r(-p=a%n+0H~{>j;lm@7|o;6k2@VGC}$rv3ajUcj`fXD(_ui*N1+ygPTpG5)6*la1g9D%V3arAA`a0IHnv zlmDH~Boi@mV(XK$z=;0H?iF!$inKRG;hF@wV2LwK7y<2!BfW z4zk6=l}o%a-mV0$wUDJ>ag@_sIe(+|l9_2r>`O1+>ntPwmPdeVp^dht$kP0GCaV&z z;Hj<1al0fssTV+-^w;~(fVIn^V!h6d*E=@yYE=57mHik(J8pyBroL*#8x~b2D$CET z+Cx9KZpY`ZL>(o^xXmV~{!x*&jC7qZmKL1p$bwj|*e2)%lXCjg|AVc*IFSW7($(V8 zs*C)?&wszNx*q^IoLHO1#U^7ibka}4h;#APlO9#LKj!SCK^S17FhUw$HQfa}l;ZiKaaUHA@wgEa@lvp=L zAa0X=s2RZH{f&aGb#xL|TW!|Vc`Yj)`}P+XVjqqVuLlxZ3P+6?VQq~5B_FOWc$STy zv3qLEzJ4aMd~ZQy{RJ<{@boQ~!4DSnfjnO0!Gd4gtCVG>Fh6amw~o7_U}!Ry>5o{D z)MQ?ak&icv8q7n`AtjvJdwqk%8)T0a)3lXXAlOYaNygMCQIbA{Lscq_&?;4Y&KtkX zB#qXdB>UAco}+lm;kir|_h;vLB9l4#Gc8p6Ws6}VN`cES128?FxWR)_i_ISsMvUXC z^c12cM~=UkBk>u%!+ohh(Gm2g%|*7Q{&}8PDoD=XjO3DN*eo6PVU~vl;LHX29HE-V zMLNP_x+ExlnI4f7;ymS8n1f2u2lUHgj43uujjmr8GIsFXOE|~Ke|^4~TZK7z(c4jv z7tY<(!Pa2C)_`u0Cngeu~((SZ`N9arPi0|cACFqOyQ!6_h!8{h-8a{F`on# z7ug9d-_^*g%%EuKnOwu__Yam@<6fRsLH+{B-Lf7U2~n5l)fhm^hXWS*wVSy*9-Q>! zK0FsO1|M|zk5h4UJd%=DUP;-D5UZ02!|Srbm-w|Ohf6DKI-OJ=x0l}(*N0}>6yY$M z9)&kl-N>gp*1CY?cV|Kd`LCsPiW?hTq3~TW>tU7Niql=redBa&wsTLwV1`u_GZ^|oR~3wBaC0Wp z1RTt*DW|*dt~yxUaAdnUWpvwuAZl2!{jmQ(v@tQb0MZ?=*2XK%c6s zTnEFu)CzGfeY&s{! zn}(ax2ZK9mX8Y+2fyVx~q0SPF8bbx&ruZ6o;AJePVXP7%wA0mwl|#dBtv)Y3BOvGSg2=U$&!iV=$-LXS6eh zwU6H1tMJW6=xP{Qjy|mFj4rF5^zcmG<-ILcacnzQuOPM}X?4i;&!-fGLMnN$Fv{kl z(6Zw(-6gD)h(v$s|8>BdBv{NB(E>4o$iO-rU-E+N{kQG0URdQRg{#?esQ~0b79)yL zrv-88;ZajzpjFZCl-8o0{cetS^0R%3F*FMIaQZh0eYT&yZPLz>Pff4L4z+;1N3Mk@ zW|cPIePUCfYOwWr^nFQiCz)*K)7Ce~(V+z~C|;LI@aA5`DAWCOAts;$P}iY})2JFe z>spLl-dMQ!LzjccHi^7iCllJHO~`fW#|v-wW97x13-lmbWHRr?DkM}v#Ox>A&(Cs& zJjf82w6@kyQsq}0^@ndFz5c>Sv~vJP(34AcB0~q8a+LzvHF*Yj$-)M)$Y0yrclq1R=i3@0R9hLXJ!YUj0?ijIfr@{?i zf~rqBdlqC48;@M`rS_EPrq9l-Z(>M_F^b=W+r?UaEr`WE?cP(8oqLe^AuuhBk`bbxC-afLn;h%j;dti2c>QX zT>(*nguvi;;6VMwiyK$D|JncJm$POK}&weKqUc(rBkd1%ErVuco3Eo97 zQf6Zw*p(ZM_&9p+OoUBvYe4QnO)C)cw0%&4}s!)A$u0efD~Rze9R;sk|=5 zf6TKaIcY_GP*dMM%cR6dCeX1T~JxB3+o8Gz)DpgU7c!*htH&nGOu*3HB*sRScdnp9!RuDhoDe za|(T`tffjlb;0~$$TEy2#&MDW*3+*DoUpoHWt&bK{-QfQi$B5Ni~{V%3dU(Ubwxm^ z>g_%?58gdN3K*D=C9LaCjna3xC5|9aru>)bkcC&>u+r0~K8dnGhZ6@wQbm z%rrhoj~Bj;yLqE0RUcGr;D4D` zRj)FO@#?8>k*-KU;vXVaW4v%HLi4=z@=4`x;Wta#fUM`R`J@1&>1d~$YsNkG`@&9- zr?fE^0djT7#_!?t=Xm_zH`?sW5HjI;Fued4@Io3}#8kA2R)byQl-l4uskPHf7UlyZh*4?qtl(|q?|5RSW#XXWX_Nkv`Ms}(;cu^M zEyLg9cwzX{K6B@wbB}}eV=AWu@+ekQ&yO8i7yu`EQz|}tw4XoPe`nz8+d<-)F~YXZ zGLj;#x(Alud$h7J?@D`@fV=sQJ=#yCfL5vAcLm=DE@H#e@~1kye$;4%GAZId z6wT7~7?u8MWvC^|ez|G@@X0wCPc&TfI|C}$M|fJwA56`T^0KRc{36?vD+G|1?!;WOtE}(()bi)CUX`giQ1^U zHjJ)tMM^q7Ggx2&vaCw@{n`>s#fz30+rq?R^qNO5h*%dXl_#qji8jR~`dmaPRr<4!L-#;pqK zTU<5uFt2r!pX*zRNg5+U!>`Sb85hjV#dvmZw`^KcNPW-BKm~UQ(kOx*RI|tRJ^tS= z^iVc%)nbos6wkIa_1fUSD)=|OXx9?axpgS$l+115CLA4{=%aB$*N~+p7SEH!)4#pR zdLR?sk#=7?r_gfFSAA+fl^ash%9GIqPs+-TFW#v)@RV+%YC4&;a@OF8$t>mWB~LtR z`$4z2U`B_LV8x_DosiPO_d7Kvn~lzjajIBa&P^v^sSmdli?!By=WWB(?s`m8As!%qY`Y@lD6wKyH>oEgWjP9>Rq93s_gfF$fw#xgBO2Ola_$tSjgcHyAQ+QVITG zDcd{r^YEdedd`WP=3@^g&PYZdBQrF6rkKgz3Qpa5xljW4boCgtxl*=zV&nA)7V?v> zE_hX=ikiV6a?H{OuoD=^uvb>q8KbP2aWO)AG+PScZ@UgMce;ML`BOp%H|%7ft!HRH zTo{HNZm&bCTvDK|c7pFQl6G?x$7j_!JQ zh2313%R57%P|%t1`h~vF?%_s)R&QVa)~lcGUVDOGd_@trM`v2A&$|DpU5x9YD4`U-=sDX z&Necncdt8RM>%HN26R3*(Uhs(RMp3evxgaVY24-51p|8T_?8w*KK_2L15BWbf6#d zRT@Ti-A9Lrrc!5nyt4C(usnryURnM_ZjM0P3FD10*c()4(U~7OqdbAVAM$;rnX4oJj^?>qbFK`YtIi4QU(KENX_g>o7?mRU}-r12( z`J-`B^M_p~pv|ciK4IfGc;?%AYBI6KXPRKvGUA&tD@ea5@H!PMAGJHhR-aPLe zM-t8cHt@Ady(r9<#6c^O zD@)vxi|UgrnZaMxJ!)!b)px&nyyW#UwX~Ysn0q9GgJldE4+B>-;6|>O_Jgdvr8eIW zD*S)@U#QDHE~YrPquJnAx+2wEsN(FhkHyLatdKTBx99oSiw#}JiCddUu{@4Hkv3NB zmR-6ON}vl|c!Z`{CjBqI-aD$Pwp;rpQ~?2{DIJww1OY(^RRpB>UX|XF-a_+H1Zh%& z)PQtChtR7??_D}dPk=}XfrRqqdEW0m`#pQ_bH@53Ypk)7F|smp&wF0;n!ovRB+s$X zHn{sEyA`mp%YRjrEycx7k@`m@rfk+~)#)JWx$Hw$w$cbU*M;d0gVZMsw8bF&qr0E( zZ@i!|Q+TFdAKFG;LCX!yAl`UlBTK|}Q3_j|KE4s8AG0X02oC?IrG8HhM)8G_hpdtC zi-q))A}xR)A?v6`vA>3qPV7rgEw;vsh-rRq(yyiGc_%Su+%DBPb307Fz0}u#fS6Q| zx_K~YAiAhKxGHoa{{>JrlFi$PWU#SS%Rw>xH*m-&*c);ZR)`<$pbF{WJ62NNl@3d= zc|(+$?UKyqZ@6&M=jKxxOAhRH+X!RE>{u;yng|ziXWm`Q$@KS2+IDtd~&>x(uOVKKRo0zx6hG;8c zo-|l|+nMFB9`DyI~hdJ;14 z?Vn`9vBghQMPRv1QB`M5kCiDNIORw`;ev;y#~#i%F=r-5ybB@@dRm?s|4krIbiuv# zoFJ0m7d6MnmjrzE1Ltsz<=%?cxT&x-x*f9cah(HQl{;cmQwiN6^NJ7bBwb7X<|geQ zn_HrogMd57;-h|Z@t$hYh>@4J{W1(U`pwd6Mfk;eI?`!eM25{JgmDYZ`rbxlO)1#8 zz>dWeSD7ByEzfx|Qb)dE@@CW3`3Pu6FW#}ezyZZ;t4N&&w2(t8PS-`<;?{U(U|!jQjynhf8GG;W0S;nD5a)KR*-3+<@ur{3re zyD-Gm+I+vDk(_77cFAaCSUnpIvwvD?zb+mLGrtP32{0S?%!4AkzL6@3n|2ZLS9lBy zj39ts7Xe{*g3;(!j63woJjo?iTWTgp4OuVEs#A)zq?PB3$CJh}iA|4M%YCV!yy0@38qx;PX{uQ!Y*QQ#8!p-rSjKIcLe@ zorg2FAU1TrW3F=|+_TWU9NAx9ZRnXYh$F|M4Cu(S{#C9;caThXHeqjG1qb412b3Ve ztn>HlNdBqqtmsqaN%KN)R?S*+Zu~1lyK3b$s|`E7AxNQHP5tYvY!GP5DLl9{ZTBU1 z2DBe+k6lKWNgD-q&TWF6ttO1ety{*Afy`|y#lvDJw$5f|j;%Qd9UbBP$jD*T(X8_F z4DuCte*jb-mj3l72=q`6{}U>P2xi&p&k1kG1+%YUPH@Zr_J_V(f;Zi?Ae6A@H&I5yV?ACefZ&j5T?uNNBs9 z>z1HXb3o)_3SO#To)Np?=0n`xE}ePCSK0yjR&TsL!mMXRRTf30U$V5*@25KO0?*x< z|0CHUX$}b)th~QxgI~$m-u~t0>F77_S@p7t(s{_u|XW|IAA)sj0lm#SH{B#g9G-Jt;ftQfr#IM#Hmb z&<3sTbMIyXo?K3&d`(ToH8(@2{lIRMmLcpua#i2 z%r6n|aW2oJD3YaFQ~&b=Q`h+euilRo_4$g?jRB8WKJA}A4gwE`r{aU5))`C zX>gxAnMM*pjraJWog*pph{j^4oskU%z9`U3m7qU2+B7n)X)7o#;e-y63$EbCmPTX5 zK3yd-k=6NSDy{sw$9uf}Q7uU0eQ#+Uot)ew|FT8EmquHXio-ztrciL2H+Ol1DTN-v zG3@mhdZiasWX*|J6w+dUVyexx^4=A0eB41*n^mwU9hrsAQ;#?iAG}68^9vh10Ots= zdZ##=7Usn?#(NHHXMVBj98v#p&+vQlEHz8<^z~{MI8E)%NA}P412jAuX$9?!`xqq` z#rDJZOVKkj)MF+bhLcnH*ZyX4WbR}p>PjNipIk<{!Z+%dwnLKnh!&l;2%l?wAlwSX z8z|#)HU5iJp6Iy+@gFpt6>6`SD@G*MEL9m5^4o_y#XUlSRoXt3vF3BrLZ?R}aC;JV=sC z*!P^q`65e2&sd5>Vz5@o%~w6=LtASaZZ-fa#g8JLbgNk;ER(8phZxie2aZ(P{f7(tMTIuR8J|!6GEId(*)YqM za~OgM3j3WRoR1PlY){hj%xN?{D9An9<+NC+|AdS_#zU#r3wW&9eKBw=SkxPydqs2@ zX~p7-Sfp2L-Ymhk@hfM58~0@HBYxhWIQhvNg~L++tU&v~4sT4*ha3MiSsQrGKcbo#a=>mdstP zRmqqZB&ttI72vx)TYwH^&O|f^VRcrIlse%~Y^zNl# z%~#)%rOC9sWSL@x_J2D9g543v?^lWa+urTTW)VG4_-(%bUpL1jSNPF0|K)ky8zz8HC>J&E$shDF;f7DBkns%TkGYuAZe^5?fzvfwclk;! z4MW3=XQF>4@ylmpuq<3pA!IhZG`u2_)%Wa>w||C08J>KZ>KgpZ?wMM?ESDTx)fc=dXTCySz1#o<7ug4%9}W6fc>G6ojU;&)j#2db(#uPX3yji#aBlg}Jx4H-Dyt3`0ur!eZ|edH)c`qb z^9Pb~h@5A2`$_EKYTJV2N40t?R_UJ(W103sT~~D&h?IX^C}zLp{1kG#Qh&3jB3Gwd zpj_1VT?Cgq{J)Ql$P>KxrLqLi-q&;o;aTFz+&yqce{a^{@HF^@BP$j#AOC3-zPw-{(|B2V`EfqZu?BSsBtZYAR^ccW8&tLfQK} zU)s_Ip9C^)H^=1;O*^u2e4wp}9Da;hJ6@|Kr8T!?3EzAg`rU2R4)8weL&8y7xE5#2 z9pb%*aXcEagk562-O_HTehyWnD22w8TM4!o;}JyUAK3)fLIh} zopv@=H^?HXA87NxP#T!yu%KiE6F5n+K=~eJw(*g=d}$R_YxU40j^DYnM=&X~%CU~D z<}+NIMa39__yL@{#YDy1ok~RkV0pDvpP*cTaS#bXVKE#Ab8W|#>Ai_pmiS7+l4Id| zR6F8GG$pPoV~hqzd3m@41&SRe@c&jRpo7F83kqmQ7jK6)aFvV zi>CCcg&(qKGF)zl(7FJ|SW-E8RCk#xuZsH+x5m4r$RU>$K9OBK4rWb|SjH$``0G9? z588HM01Ph>P|WLQZrM41zGzD-%>Q7S9Z+6+&F3_X_{GY-p|-P8XRxEzLe$qE zfew-2hQorzze!4!B!=%^F5mt#yHl4)m`e~okae3RtwP4(GnlLJTVqvni)Zx@0a#aafAG{JNJ?KWv? zS|doo)x`{Ri#!CT0a^ic^86gCPF>5TFD8UCs85lt*!{HLY|)CL_Qu19lc< zJKs!8qeti}d?9SPu7G<##H9?j58QDungph=zxB)HdBP~sSEn7mT-AQdv>LMDX;d+B z_{8Z5(=S0d??HdJ?YlRQa^KaWUWqGmx-LGc@fWhGtO}8+U@yY(Bf@Zo*O}Qby+VpA zCOI2R!PkwC^BTO4vtjVo2G%vl(gpl;SvRA0-|>r;Vd;%~I^skg05?(%yKf)7gGp+@ zq4@4YR2ErNp3m7=>~ez7md9p>jH}3r(*N0|1&k4BYHG@qx8>*Ot6VDX{BPyvwc`jh zXfkmq`dFg>c$Q5Mhq-ezP8F7kPnGHWu9!p z5B|{SrP(^W^$dtbxa}kK|!UfRhSr^R&V1Z_m(x)sdK4v;rT zNanj1xY_42*n@h@;De^F>&m}p@V@CrhTmc>cd#4psp6>4d)aO#(|~QG7O+ijxyzWt z<)#UvDhp@p%VFd4^N(X2kwWtX51v}ogkMC5*UZn4M;fIIS&N6U8}>F>yM6AR`p->t zFdQ(u``+)o`}&M2y0PE>9=Ce{%70h1BIiUY-O*;5M3doQEBh=4pnrAMaEQQvsg`+d z-2D(6Iuv&PrMPE-)`hbYl(U#RlEkl`%FB-)=+rdJto|n8ynVGFR{cOigdH-k&YFM> zf@zSzBW+lclw|1b7fh2)GxAdNPW11}?M$lI64tmK$W6|B zJN~yT)_x{S=7a=5gc>1amJt_d1m5sc}K;FNQJel=b1?cKFMr)sX$NmbF#xm6X?pOt79q_mRSYO-5-gvE2Lla zI5l>yvG#(pZ&XFJoVTx<-apuOX_Bv5sg8&xo3B*_CQlPG<<+Te_1`=V{p*A)l8;cB z=M!O{YBV9;=tkrtZYqC|n7kRMe%nlju+qgZtN83hbNA&%LR#2|VuoZuX60oKPl!Uw z>^0u!=>R_IiTkH%C#bWl*GH))936#boAKgvTsIRb1c&p*%3klrt^ln0k?`@fm` zy-VbxMd!=^GYCzbbILivpt*3F$2{IvY$EH!qQC^`=SysM;tg8fy3XSjHlXSf> z;5Ye+B)ZIX-Qer%{;o%ua3}aX2~N=t9C3v)akEP(*9LxflY;~fghaKYLdoV6(Ba>t zh?ez)(2r`|CGMiE680m_3E$q&Q>qyk>+ta>?e0I*`c$;SOff0S{=p(|$)Hbsz5`O) zH-5g`TMl_d3yGZ4ElB*$7traVQ~$%>b#wopuc6lLHAXbA&a!Ts=f-dpN?w!Y zv31A?eU*b)S;DDpqP-mykYA`-T-2_4TO|R8QJpE8{g$WCQjeNsb#-2AGLuNXSQ>cgIGZwX*;P9PC;TdQexD*p(I3 zwa3CD%Ah1EGnD04g1vZZpChem)uDZ<8TD9Vdw6>>y$TC#vIq~6?4${?nG2sNUY03+ zDK~*yRx`Sm@X&^rj%LlL7Z;(5mn{#gwX{#yg+_2?g^vAQJ*r#(gE8Ca*1z-jqbd^C zottAgvc3(muM_f*Cf(h;8##=2+xA2Z`?l+~Bp$-A3s<$3v97p?Uvl{{6Z4Ebu8-8$ z-ePiKk@{i|FqpZ&J>zL6I@5-qz9r@x)h`T3YX^t5w*6T>HhRX zEr=?xP4e{$|`U;j+>elwG)>0**WtN&$ zaP2CYrR35ZcVgx~jnH=cC=?son*TlPzHkm6%#g3?%y3GZQ*OpFEusP5_N|)rwthmRh(StW8%P(5;5=vTO%g7G*g(K^?y##9cgSG_Gd?yL;kH9Dd~1nfk*tQ&V)a$ys{;dv_EHHQyB;4g_Hu4%fT7)bqeX z&uy0YjjJA#)d{pEzm@aah(M~;`t0ctz44p5@Rkg{sM)Q0?$etg>Ugj>g-h{zqKLo~ z2&zsSsx*>D_hqhk8By`flG9w8nZ6SfETzm}!Ezqffxn2%o4xW;h=mEQT)fX1`~v8E)K`h@z8cSyUeyeZ5329x(qg_k832_lEs z5uAbLYXXCMXzxzi%wP zr6-zbU|HJ~{=3W9syA!zJNrvS|j9QvH>-D);hfI>hNfg zl9JdJKY6Io&BEs= zO8`Vp^y*E7`c~G#$3pYO|FDeFnMk|nc}oLevgW{zqrIURzG9pw6e81!?tg@8&mZ0& z_y+1uzNt#%FM{tQ*O*#dtDo6Q;?<1Hc;MsDwHbiOr?L&nq5yX~x8TL)wGNj~#dxoH zGZ=05tyY`#n*C_voO}Jonu|+=&u2(B{1YRV#k@)!;sYMN%bV5F9Vn8yc)d#|%>s+`~8-65MNkBo@P>NSK2w3 zeKyBIFE$?0gg8TWNZ98q=@EJnEt|~|*t+fnr+(2DN@(gxnI&->k$G}wdG4*8`2wZR zLV2-_<}mef+51+?8A4OUH9=tlH2}>oA6R}i+96l0;9NkRW>zLTQ2O&^z1=a;EGJLM^wX5zs0ePCd8&CmH!T9z& zl5)h?Peve3ji~AiKc3L5)@{gDu{}z5!WsXs?|{q7k7?tO#_!|tvMn!H>l`aC4{r^z zRkMRZPk4=V>`z;*O|FL@2y&PDZ_KRMxia0bbB3%Ep!1*Q55myawFiHm%_FCt4|(^9 zM8ZB7nzYTCp~H51idH3`N2gL9r9Q6gw59z6W&)ae9f~P5QryTuh+bMWNQ0r*^D>JBO`5bTkQ|K8*wPT*)`eJBn;Jl8}6w-*wh~sC{4VE@@J3!?! zgg5dH>bfw(a9*Za@Om7c1UX5aN$#>p-R*CkZVx-$wbYi`4h>@tTQ7~PE(te3y+%5# zcFb1Z-EK{}vMSMQmpxQJF-|?T)%nYx0OK;3-k^BtNKDHeeT?;v^)$MUJr>bgaE#VAoprrQ^!N31} z{P;1~jwe?9lJ>3+);jJ(yMx57LS_^FT?6AoJeCnv2OGZid6P5FoW1A{JE;lYBMif@m}E_j^N90 zFz1&Y^Y7c-@-P7+nXhYRs|L$MsEG8qj=r&(T>I5xSn*8u$wy1s<~;A;oGURHt^Qb( zi(TR`uA{65LmntzK8OH!MKernU@V*oN(R2&-Yt|A?WPpPx6PF(7(K_^oeG0KXG-Hl+d#iUg@7Fdv`s>KFSih1V0>{O}CN zF369pQZcY9bEJdnj$16Tk0%iR%gn?7_IcuT$5K|Cx;UF0nQmlgSanEA`0lE;3EAOE_wT1> z8(7za(|4rPhHE5GN=t?ZQ^4A%B_m?4t#Yec>4%pFp1YYd783BTFW(x5;~5c$e#QsT zG^1Kzh3p3F2J6n{sPm-g2s@)oc(=YDnE%ca&z+CFp;cYzk$dj@Cpl#pz~{5@167K0 zZi2J1Ww|P=f_#Bv+{ow!nW1%^9M|iyD9lY>zJT2a9bLvpF zJ9p>bagVFb0*m}zRQx45m^Agf9@uB>U$2lVWJyg7y8j#D?S?Qx4azjKdNQ!^Jc~u>`)= zc$_KzCTIx-hk#{5(Kc#n`I;z4O&YORtrMF$2*LTbXYgv@hy~tLpxk@ML~iu62#@#Y$mO?< zZ$nQk0QH)*HNh`s%ZR>11&^%&EvH7L<>{EGvHQ{i!6ZN8ndVq~`tYrulrhi<)9*ig z)(uT{NB+EnNtO!WxHq>$053-}$OlH-m|h7RA`cc5xsk~(FN`8wLa!obgG%-p1)Bn) zRrhO7eyxIaL+XRW>BX;Z60-K_N;N-dR-1cl*o>50E*jYTFAUJTija2#y`$W8ACqKn z>yMCOn@p93ogFOR&s7KNk~MU-Z=c@h7p}RyUN^bkoG{bc{u?M`xq#*bd_Y5=-5)dr z^tIHcRD`X0kEj8fZ@<&YwkWG_(qC6C<`UNWpAzauI0IYP<*wY5nM8HVVKKq({(E;u zUNbaV_X@Dsw#bEZ^7>CMjP5E>aO@Q$Scl!-#n)G9Ax@AUxH^5f?fj^|ZI@bet;X&7 zoJ3kRcZkPT))6;nr5p|+(&@YuPiaTJ8?AMgT2n4Aw^Ld!lNmL2rTr zq&I9`}@E}K_j<4U_EUfKKA z=jjxKU!~(bDKO)=u71?Kr1KIoI*aHZQpk?s0Bd`wYIUf#;jXQ{ zW2A<|(|&On1hVe{?tTwi87-fv1zWq$y_37xb=YM&|9gRzwqUjST{j%C^WOIym#`_c zX8yAHJ2rUiL`D;rt~S3bAn{@b`j0X)GD=phn`28iqCNFR0&lFhKOJU^WLWS?SU1HXk`#ky*;Vsxg!{Itvrhs^}JFTT52m3z) z^t$7P_A^P_2_l9ya_yBOAxgaLV8WN^a$X&Ll(`Ke5T~M7bIALNsZTOSvP2EF8pLOF z&}raip@m4=AQC7fGR0LU9j)Yh7HK5dmzhFBI+c_9?*hsR!-QeoPi#jE;~3cPRpfk6 zDxFbg9F!}T<#Uj!7SGWpOjVaxfkx@vVZGn|P!OabX-L^!y@W^RD5y>=Np#d7A|7O; zCZ@kD5t?oNB!$s2RD-=R7wXR>-v8KUxd-3+IDSID8t(zV9;H8xFiGnxQra}+^>(A` zeKB3I;nERRicRMEd!vfim!f9pFhUKPd7iz%fI6BB>O6z6le~;~qBN)_MVFZ%OK6)r zS?jy!OS&l|Juh8U6c&E_@Iuur=LkSY4ot)M>iBCDAe=QYg+JR)me?_TNg}B=cST+k zq?$1YGc@FQ&_7)38!_+KZ$@}JcWcOa)NAIa(`mo@UJ~E6T$Ifl@28*_44ei?*&nr^^pIoO9ZzSd*2=H4` zc~6p=AJ@{!hs6sT5Wh3Yx;>#0`ZoOJrt}5n@FtwiE|iB^MxO&K=xLjOzP-EN5Ek(TCg z6VDV54)ZnOj!XS6VL4QjRk#=_lhWF(O4^umXcY3**T&*~UuihO;?d=+Zn(ke(A`uk zsj1nrpNAZI;?@@z6viJ0#YSN(zqtjCfa;h|mDP4a0e<0Tt#y7uM4_xXLZ*zMDXeZB zj_Z)8^+a96wp3kLE3msp#StC!pxxbFS&tS|^@`hRSk?EXtHt2el;cV@^x`$o;mMHt zY7?Y$L^lc9<&@UF)HnYUuZXMF%kOJ$c4VmGi~Mz43?8eaO58CfBZ&jL&iuD{5F9!7 zWmdCJF&dHc<#6c!%eB9kM4?Fn0^&WO;J@%b-2_LGlX5o^)*D{I|5e%T!Cj5wy6^7p z(y$|AUg>vBS&3|%hViqsr@Z+l7r!~#WWK0E>wNcd!7~8+NgO7KMOKiYJdQ}I$_g6x zt{>Q5-TAemF ztk3?4k>%|DZLul7Lm~5+V@}u;J#&(~%uWQT|7aok8?liT%zjoOVhwW%`$mee{WkoU zgfsA;V^Qy_)aH)DJ~)diveqh0T*Yihni3L9be%$%qnrViaMqCF$3z;mXplpZsAYEz z+1FC@^K3<(+HUStr`qgp4g=y%^<4qkpuyO9$Qgzui|!(mArFtTOs^j+rw)H0Mz5KX zPNA{**dpTnqBxh9kLL(QJFkRw@*<*=tG>%1uQ zqhM{>)-8bAR6DmorkHhS2Z?0|-a5CRP!7;R+;jj3vP0q-v$DQ0s5HQylhMnRpg-6g zIL$RBhjeya)A5sq#s*ZTBYkCx%ntG-+9KTpJSQ*rS*Jqo5G{M;Fdeo&h2YK4&|5wc zO8hNQPGawa$50C1Oi0Cumk%+gV6mgR_4hY@JJO8Y+Z`i3w;jWko0~{Sf8WqM9pFhJ z6RI>^8l+Y5T5=`EnpBW%w8o^$Ofz}s5N-Sxv1jtt55wpmA{Ly*CmQkRgv+{Ge+x{6 z_DXk9aR+%vhZ7Yqb4bx@BYDDGnx83dmRwv#f-v)_uW|{oE?Z zWhsTpDIJJpr`5C->EN0|HnhEPt55Tx{Z~q|-B$0-XXE7!q3{W~qrhwk>O6Ac_oDuM zyZd{~o9nLS?e&|VReKTxFpBDBSiC1(V#R#E9R+fgX&Crg`qz5E|1XB|A6-9zM9o;p z^z=O5e?JYfbP2h#2Hwuxx~7Mn+Pbc_l+kFYlZ)6tUr_Ti(f#?4w2&EU_y zJsVTf=q8OA4#O1c25Qp1fvrC>RCXF1xOiz4q?2Rc4p6+N=MgVC5dgRjkV%So@WkzY8u5J=28}m2(Q&K1?k^(#%;l?l&nJg4^}R1ld;eP373+&1Zd|buUWg zh#m?tg~h~76vY33ejC;oRC_|hBjC4!b#p zEYCrW54VO=#64D;#j=EEPDc5jxy+WHgOFXforgbbdxGXq^-Q3gm+Z!aAKuhRiu{Pw$xgzDGWs7%T`qgO)e5gyH5)XodxaQ z$G?^SF8sRfj^|ie-`-riAN;Hp4>+>#n0+h5VA-;9=0|`LZW&uxao$qU*+0@kka1l;jTWaGv4v3{Y499M2%y zU#ywspe=XZ06H1sZuCPK5l~-m${EQ!PymC^N;}YNA^v-lj|c=<#NEDPX{@~~`6ytU zg655Z(@QW{SKQQcw!yQ5`iZ4D22nofUQL3w-Uqv-gm;#SY;s443F*_gv24kig_-cd z0od<$J$utC8Z?4NTl!6bN}^W%n)eW>0fhBePt=V3`p@qv!q2-Ed^cI%2D>+=21W6_aQCuOaES)NRJiTT&XXpHyLg2-eZnxVKty<3@MQ z)>+@2r2E5XdAlfa&M5B?@UZRGCFHgRYO%?71YEn{uHEc^0fV}d1dcr4T?2-kSHNH} z2Bdf{mKzc}&83~Y64qWr{g2c2KdDk)*1__VTED*UQU*5wtaE7$d=$DsM3JO!9TZvRu)!7)>p3>9T_4p{fRAVS(&*~Z8S9>sGDPB?%? z;c-%#?bU&Ek)H}VGSXt-7y+1yCe}Qu8V3b}zrTN~lhoi7wd1nbo`$bUl$bw#$AlYY zB+)Gr-CuaoY8;M5M`4};n70YR@(#TD+RQxp=9upnF)B8z>rM#;5?!X)LR?oUq z8;A|;5OiW9ujL=ZCkbIZUw`zN^QQU_d~^P6s1+#~5m`bhrNf|U{ZfC zqGOO83nyEhgzcO^KWyYkLSY8NcYB`E5Dvc6axBCw7M`{;_1A8d+_Ap@N|Bw)T{2|$ z_ly*`&6d!#?^Ph0gg)L7LZjB9_eSYjY^@r>`{&eyQ8w}GwlyeSJm}3g1OBvmCvm=d zq?BDO{<|MC-LR0~+5%zNk|;~pi@T`ztB9gkSi5glqt^j}CF_vx#;NZx{Bad2Hzwp_ z;OWftDJ^on+|k7Ez8jIodiZN^A^f>wwIO36GU0V09ewl*wP6gUSCLt2W>Fnt5=%P{ z)kS`yVM!$Yqs7n>uG73Nb6?OTW$Sp?wRScZmHlVbAS-G-u%G2ZWz2sQnamvJy;y_F_=i!wNSaaBh^!@87T`vt*!Hbv?KC^QL4LtA2lM3E zvPd;%p-{}D`$&IxpJDB(A>tbRWjH{2feYwUOBX=??TC|~HFf&W9hg2KV3F5Hpy`Cu zHRZ8jq$>|@Ml1$=%#vmtvfNl%9W-k~*5?kXsK2V)t@JofuP9un;p}N%eMUQ%h03k8 zK91rt{`z*Yq^nX@Cahqcc%eih=Q*R8I<%yHVdfk|R!Qr!|AZvW-MMqB3HpqT#O1!h zo4TG^y*&EY>CcGxz`yx78NNiA8hEXyx&@(B$`i#iqz$f1-3UgeZxeLq0p=+E;u^sX zfgmR;8qVNwQ1;oica<{(t>gZLF)E^4;~Rp;PDGU}=)xMS&WM#y6IaZH$35>%n+h}{ ze%RzzwFedNlUl%u=CccPNoSu+82%T-<-hEg8=dD7H`AdnD)RGtdK9}&^JMVt^$Gk5 z{){3LH-k=h7Ngn1_H1x^_ME&s*&05L(fqq#--(0>BFK{3+?N|0f=Sk=DQtinU9@)z zlL@RU#W$4h^1zmOZnbaEv?0D9cuH!`n%0egl#1n+wk?sU9X%Ezs{J z`TG_TwuYcRu!|F6^ZC*XDcZKS1--HIwPiAxy#mU0e7vrcVSdUiZ)71c`{hoSC+4_V zD`$yxQ1i4%eOv~mp9#j~9Ku8Jl1>KXZfbkKI){N*%tP_?0HQc5o>5EN2AEv&XQCt+{A{Y{EHG90|S}nLQTX9^7ilo!FC5JiW{dlbRiDU63=BMC|H!d8;EJ?#r}*WGtS znjw`Qa0`Tx(qctkRdPfgSbLt+<}H;rAMj1y(Lm{ILgUK{l$XDw^=jdB4!Jgt4~T~e|;(vGnpa8 zD(i77w@k~*xqOUucZqPgCQ&CkJ|!$xpF!G58tU+i*3M8pzH=8fdeM#n6*3~k)r8kK ze&JSdJZr%$-aFuowMg6NOd+AKueJ1kaSF35&rRwRJ7=b_s$n~Mhq~5nlxb%t(uA0QCG6ky3$t*i zyWsCL1s2%Oi`eW4fmGb}!@;!HkpiHdfa%=|y;eM}RA<0z(((H%Ovu%*7ce7GQH`_#hp8ds_{(TE;Boh^P& z9G3AXeJQ;>IE2h_^GVy%=Q#_7G6p{jrjlKkzi3T}3gDM7{TM$rvWP3E1*KxFb>fUx zztE=H%1|AzX$vF|!!*Bg%9~6gK3b0?Zp%NV91MC6awuI=76spl1P9c;;)cp6%to&HUj3VZp!V6&|tAgFON&BTq*c- zU)bTGm5z=A)XdoS=Uvxb68LDBt!fRv{ifHaz^3MmFPu2s+vmeVpT{P2Ht*~U3a!W{7oT|15|jRa-9~#yZ=O4Q2aS2 zZC}NY(-dPhcWWY}@MwGgbBREZ={I`vLf7&XS#|hqVCoe2-7ED>0+>Aym{gyai8tpN zqe9F50#gl(@)%G@wQ<|yVcKmV462vkXh0lK&RE}m{)E zHRD76e}$6&a>V@m?CX?ZBSalH1~ zh&7#$KU(#6IjDpM%QJbhzKNR#RWwxjk{(Xw#`V8qq>?e#ibJI3-Ct&({ai#%`M(@w&9rAKrq>$_~AO{R4(l%evX)Adhm2ui146C5H`d{ICUh-M;v6GjmD+0F`&OvfMA3_jSzY zWBJpKXUMw|K6o~HZ*yhmfbip?Qh*3mu6NjG0Gn_eTNeMn)c4FI((;`pS-2pPQ>X?Wj}w^^IusuDlI zoG|gnXbp(Xyh{mM88%-4ro%J&*|E69NF=iMahqsnYghVXK!v8fzOI_TC4&O7pE~5Y zt1ni{iuAdOqy(-zXymnF4Uhfu@0&v-DNqBH&jTm}bghlZ&IjvuE4PV!i^zK|FOV)l zjPC$GanYYtYVh*IZ&1B_v39#i(M^f!Pl>j~g^3E_8PI8X>Q|4DGwsl`<%y7Xkfx zM8$iQQst)xA(k0nn>6SHLPdWf=OHR+X9ivp&kX(yCJv=8d5zdYh14;bX); zwu{fkBxkTpt24vY54$gqV6TiY3s?1eVkw;y(U3lV)~|bH#j-#**>GwZt?NE2rZCky z-rK*2fy-{@wlHz6`%vg${QEYiC`bvHwI_a6qy-bpP1X`UmhiZjT^-_L@V@G}N$_T) z%hg&U6eRCbJp;ZNpd;7$Ow{Op#S`hyJqCMNN`LQ$Yo{Elm-w8P`p)?83~(|4{kHuu zr;x*@Vq^X_UdQ`N=?6Sg>a`4h2&agn7E;j$PY1{5n+t%#k9ukzh@;DdL;be(Jg{Vp zpqV<*IOr+-5-OfsqTJK_8FiEmmTwQMMsy|w>`S-Ya!t@{Ky3s%)*ttLtZh7;Jc4Hi zcM5htljZoZQg$i`@Uup_-Y0K}Kf4htQmJRtj#%`rF~KT~BQO_1h?~r|mEV;oPxz?y zs>d{PZ5NeXD{6kB4;PVvO33fu`xkyVsnfkgcCLi3>bofF_C$83x&-^*kd3I5c5oQZ zHh;G15eGeZrKdCT`nb{K(r5lL&-{AUs*_D9st#5`W6*X�yQRjnobwslsWovj>U3 z1!vb^iVWAMcumZ4oLOvUj3j2v(q*A3%;tmWKzqK6{vF zEL-;YDGe!pr^vnCdr&aG>GFW<9>2q5^;rkR*Sw@!$?Z2xGkmKpYtbb`N}vk-pWxQP zf*Z#PTSo55hxpes&#;ki_o*DNgaYd8j`nPiKIuPMaHu;`swj3^w8e!+Iad_LS zT4b;#^JJmFX{RSnSL)Y);lU((U_qM9=}ZNFT*))7+VcA(l(+wx3H|5!3KgzJES4(z zxtLf3WbapRG*G^ZSBHG%BLE7}O{V&w*8NJru*ANjPPUPs;vsV4=3qh~Wr}UNNjPti zwwXojb@-ojiqd*-pyqAj?FAJHd8A7CMK7Dw%8+cg9G)`VyvZZSeW_t6|JBLrunt;d z;&40Ny72=!ov*2Qo^tm_*JW0hF3HP-jrBvkkc;1cCm*imu(cz}B6x!sUEqn(?TOH` zg1U|EQoLv6eOHD*r(L8R%Expj=WXlP9wFl0Kae0~$ob#5&Ckwm#nwkao!O~uo^=95 zi197!>|zv?@j4^gA1*vGeC$!(+)w7^ME0o|eRn~$Lx}G)-YEX%+lEB{!v5%9jngiA%ZJvi+%!(_SoGH(Cx1xqM6YNWFjsDPXy}t5chN(2;E9WS zES)=xgcp_et_xM}Bwt()y9^oKZ=hWm=sDsY^s1>IVte%=V(~09kp(zkJyRWq;*Yka zVv*X~&G_EF0@;7Q8*KD_8E2_&===O&`R~BDygiam!E@`MJFzU8sKfQ&`t5MIEhE-X zM7^n@^KjRwXM|6k^B9Sil2heAM>&(@`{;agr|~tF#G)$Uheu)EaaR|F-{NU$?>)Oy zt?N&FW>(}d@b2@iTnO@#W591U9Fm)1iASh__iDUtP?9^!2A@}tBdt^(>$q5@cllv> z`^5Z~W`ZgDgnzmG|M2yeQEh(Rx_1&1v_NrdftKP>ph$~{wz#`Xi%XE=9-xIH1&S68 zF2N?0xpx@5qPTcRnTe7%OwFYhKrH7MAwJ6(L~iIcyQ{ zqZvV8U5$Q9W8*$`_?8mTh8P=bt_jgMqiXIwcNjcVI&ZCaLbNT6x$a-iSCDV+xnpT< zv#yIr3znEW=-;iRIs_+cW)Dv%m& z{)s{IWdJUv=__Vxcf+^{>=)NA>&Yu6@G>EaemhGy>xBewqNB?l^p5T>1BFujiyFF! z1PuyzxwDHlB(rS%U*A@vY|72OEVOP#8y&>!XV004+td>#GhT1NnwrZ(jfpL!K*cb% z$Pa%Ls}dqos}roPAY`!2SkrTy696uH`0x#{GON2{O&xa+!MM4M7voSa2%vudNJuIz z-v)nvCLHs9<-4@nUNQH2Pf@|{V)AJ5snN?jOrQr4Hdb4=#S-9HM#9pTUGbs}6Ixp~ zBP~5T`5G)GT)6&{+_o7+;%RUJTZGQNVnGmOd7un3dTh=#L20PHpFr(UwmcIdP_YLd zl9?JWi}EuCE@h%k(8S@QJ1N1#od(a=Q8qa{y$orBcH6^bG!_WELSQz39H>?d*^93C zPp|z{0S;uCFI^?&^79v29X+lQ4X15UFweBX@@xzHt#i5xR?()5R9let_qpDXp8+|mk9<-4O!;sh&JA=3-dpW9v5UcRyN^|1<7lZ$ z3aChsRBQU%JW76J8LSIi5PcjyCAmYI&hcBebB(ike`D=p0N4}X)>w(#qvg-;>D*%U2F>c)1-;!fNkQ@ow#?(rc5cP{_T zyCBOjbfa1Kd;5iKj~VIQ0T!tSe@Y3MFfi+uA#kSL@uD|`qFL{!F(PUM^;bMCPoa6i zu261J*NhvMeIG$}^I~1U_|z)|g`NrDXrfOGrfwBW3||k8`RW5Rp37q`n)(ZdHv5zX z)A6T%4mRulKjee|-0c1LHE|BkdnttXm&;n*9O*B0G|~pGo{Y6&M88gS1GA#S*EO(& zzT|=E_-7C-LvC{9dX!m|)H?p|62G3Bb95k2(hA~5M#EO^D$rpQuioa>^7;30^z_I9 z3}_I}SvspCXhc0{U&Wo)($LT3Mfr+0bEqzk?e(+kcPno(-n3S3{Zb!3nRVNl3ana= z^FOljdU!|Bb%-*BR#D@6EGVS6x?T)MiY-*D-&J zgl4dj9O-~%Hc?Q{J9f3m75k+&|FYuZUqtj`n|5Ums@a<43PHEepDolgl-Jis*7=PABda41neX!a5s6g2hAofrHVlMEeC(x_e z1XC45CbrI+-NRpSp_}1r>HP}6o7rJT(p2A)jxUe;d9J-VReKt}F%j&3UH5D8z8@2_ z>h%OoD%e_)CA%T65wl#Ob};<$oaHE|O1oJ8bL2**?~u0Lq2~YNp^E+i!l(86Jf*So z1hb4{4IU}fb}3SoeSV#dJ$_smtthR+xVyDFIXnHEGr~Ht$AG^{gXfElo*!T;4E*q0uSx)?Mba}SGob@F^5=r{U zEAvP#Z1arvZG9CeXQkq^=_2h8GMT&i;6mfAO16pSn=8-w<(aneAQ;pnUpz+f1kZxx#(H2VKyO`JPrzFW zM3|WnIL#o%1cDHHRUn|^>_M{_9N#Mo{l3zy_@Z*~4^qIJSVj3;3hHXR1&*_A{D$1| z3h-vLQwT$S$rxHBite0!nPn@mWPnoH!~xS70NsPk3hXN3(H#vJpE^>3B2IvM-^n_h zO)bjFiDA2A^$fF(pdSnO0W}RDFieSI`dvGAPwOVm;4V+Uyt zHuqtgm0B&joo~RJgj1KG*DP>+sP=Ux|5rx-c^T;G{8^Qzm0% zUc|Uf8xC&hPFt+kQ-gh+?Q{m_85K5<@AVP(>MAsnz{|4?L@V{LOEmEXy@DGy`=wfS z@XNIcwdZ7I3Dh!Tp)IN3I+Wo^(-JKA_R(7=2^Y0<7L-2=*7TA5L{1Y*V`=2j5jf6>C9i9?AgUfLOWCaV*j3g)7r^%lVh{Gn}V1!nFArab)|#f)mLT6W*7Hy z!NBTS)^>A2jh8%`@uRzVsKWBpAno3l->z}gY*I~dx>qU_h=Rd&VO4T$a z6^XZe0ju!xzO0HAyt6CYou54q1+YVG5hv%!P;{Zn^^ZivIiOu(yC-{)WHs4#?fTABUCwi(;^l#jp(77ea0Y6u{O`U(Q=d99V^-Z$8x!4T-3gbgur>7Jz;_yRjvF zj;P5Qw{fEQ*~c|fl)5f6bqNr}I{vjEtamNR4A;SLXw5J35PdZN#ovOS#^Dw+_V=5V zQHNeUH0MbFXS0Ep46g>Fr){efP&K5_$;&N;J5?3cF`lk<_nEsl@KWl9W+P*AI<&CV zYVoct!k1$ttvpyrThZdJ?Kk@k{{$4q}9Q2VbnLUc0v`m~Ax_qkGkP zKbO-IXTY6=H?{||GSkX0)wnwOfy=C0~L`FGL+=?3!xn><^s494JN8;zNvmp~vW@KG-JBnTTRiwQG)lpf> zv>6U(ml`MG7{PIC)!`fBUrr~CeZj28t$>1Oh1KbQd|w6Cq7yLc@kA=@jdCnsg_em- zDY|8%R3U!C(7~U?=5D(#w`Nqs4Nv)yO+s&{wmyp(bBbVVrPv}sH%74YqN~_Ooqobs z^v-(FK&9(>Zpaj*L~AOT1irppka(KWP5d$9fkp+(uO5QZAiJsKPRj5yps-?s|qp^#@Zi%_FZMPEm zv8O)}6w6fH-HlVX)#lOhq6uNS#;MguAS$|OI(xLZmiFBd71k-TWJjid*0=+)zg??e zWI576lzAWdCOs|(2(p*__0)CJlrFxnb2Nqtx-!(yb`9%|;oO((D?ygse;8=T-Os4gj4QUPH#s}C(?k^>sOdwc?c6@LZf@rDibgHZb7z7`DnHvVAB;|J3m z(kp)EA*jq(IdwT_b8k51g1+gFouPc5dp`@Z?@_AN*`;HSeW1pTL?Lx`?qV+PbWHuQ zElC$ltHIuXPmC%5`y%}Ja>N|2=}!P=_S@MDlU$$cFaheITG6u8UJ3K3wTACf!UVFj zO7(0(_^E>rMl8ajfNaG6f{W*h3!{8A0R66VACqnY>X@wI zZ&dyWKsBI>i~XZ}%QVA!!h11o5ns6@`@qal`Inl|~1u@hIu ztLX3xc%m!Fo~*$mUCX{lUu9L(ufUY=f9j2NWSnl$#YPSNf27}*fdzHzzq2_5BxVyXmwaBDx|C!29kzK*q_A!&TF_-I#k zqO9t@fa+C^&df;EwCqG>LcOL0qf|uJE7VQN$ z{wS9nT+Gq&C}^vtL(fbfZeZnr-Q;GY1Lf{0ee)3X0?6l>52VR9(%~;y{J@sP2OTnN z!0seS&7f<@#fW`jbVwU4PlJ_iMk|F(9xB8*%VSh^iJ!fLqZK47ZsK~ zTP`&r&>~o0j()%RLlE zgH9`L&?|4f;~3+|WWfgbEZEPL2-gjuMtzvXn8mY)pJ-NyVXB(Ofx;fC=wf)BP6o!J zbSgnBE-gBhk0+pyx2{h6duMdt4uNidRc@YbLY+mAL|v3;`9vimhKe(QpEHHy^0qny zdFZ~?og%eVCZZkHc{PZU)4uvZV^2S}{IL`b@yAZ;3r4!$Ux*zPhP3y0e+{eq=|NdK z6`5FBHx$u8Hejxm8I=nDCS+P`nM_L4RTuBQ4ue&r#s>q2h2hl&Q(Uy426_NrC-r%Q zYtIHgeAF{;R2Jl(8_wam+H7Ygu%n{1yO6CX$h@t3J&EFR50>L-e+ z!)g(7Fw!_nG1pk6coyZu4c|Jkq^t;RUX2Dv_B6@l_$-(fa8GzB%VmAYC2_|SOV>O| zB6otN&FTxT($eei!xr=6V1)VGyWjStgKdqKeWT14LO>zvr5Tssx)8TR?fXcIsWSID z-8Att?x5|XF7;hicnw~WpcYE~bp4Ls7Q>RV<6IAIuP>{!nRyA3*?#nh%#EkAy6ETy zC!~%;_3(hc6Uk9Q#m)0+`q4Qyxnd#SU$F{#{yM+EY7Qn##=`!|>jm5&PG?rO%(c%4 zEQr~Pt$S4J+J^m>QYCL&DFlIXo~2pVQ3a}t&f4d4olg3Ru3KX-PX@BD*Zpmz{dH@1 z7FKvz_t?MKbUW^u4!*@%8E-H z@#JLWjt{DQ`-HjA_63E%6UmPJLpESEO}T5EHDpc(1mnLVI`bX&_lo}hiT8q_o6;`kfWEcY`;ttD`2uow6jKjM+lc9)${l&K9VPMLGON5glo>3 z8FoKY%gyIWK5-~~6y|~o{zWSv|JO3==P_rr%d>gujfTLVg;v3+Mg62bkm1Z*miF>K=xeySux?aD3N#NpMCqCT0|}gtXxhIq>2e z5;9Ar(w6i(kkgMeEfnbi3KR4U^#yC~ARl9LJpqT2rVZaYyfyNTF%|~}9+MUu-HNjS zGd0y{_CW%@6t;)%r#A6!>6%fNR}wQCw}DOVePw`J0E!y@vp<=qLTQu}QDHQqWXt>p zb>frsy?@j)EIzx zUUc};J$RL4HSjFVrY88^3BPthVDQNiD-o+gE$Ee#;D9e3(P?ONQ5oXhEu}>M(ylNk zE&w)ja;TgUX0+f|f=S8WXAbSpSX>%!J2g0wjz9P6qxkcF_Io*xxT?pIQNIcmp!f{b zn~;oeR*@F`q&gD3OZxUn;#9+7*8Wpkk=ds}iLZ=2VGbp`dIuH)mTg(1B%rsREF87$ z%j~^EAQomb3)K%|6A_zdBJ6(+WMB!)MC^LAw#8ZWGYi-UG`!qE`VCe5w_+}^Ly>m% zg`H>99{~@gV*ye!V@X@B!FsgdHg%82><1jol40-F_wsiBkmWTLPjx&o2sBYEpJZ5= z$Rt>{$?hJM(Q=zgpw^AqGfxGoGzH z$#r*;Of+YrNsP+%+SmxTLr`vtXj$Us%$Hn~??QiBh?UXm#cNZzhd!5L9`p4p2)Sfp{Zzs;y zvY)A5Nq%|{@o5sN9k+yi4$gWC@nnC+MJqJ9@}vnT9j4@3%hTwzYhaI|7jYxgn5lIw zws2aD*6?weDW$bEyzd7aGqIu1{tQ2eU zED?rdrK!E^h6vU-h8g~b3A|xEEQoOnRKEp-tyu8cHoko;Jm1Vict2nO5`Jvv(uey}7Sk@SdA z?snL2?GxQcQinQHSqXoDsg~=Z_5by#{eR!p&tiF>B5LKrg#z_f#aaM%bQ6-jA3f@) zt3yKd245+EW{;du)pI;a=B=VVumca^Cm5jZBd@YeN~*xiTBrLjSe60ybZR95lnEA^ zu}OHZjJcD5IIYx9&ZmZ5%W;U|Ez(z$S{`=L%A&!zMLa`F+GaDOk9c%JFq;0)TC%5? z-K87SDdP+m$v(}JD5tJl8LaX4K8s-F%lNL#DwlMn7k$rSI#7L)#2V&t?<^0(=SSTa zj5X^0a=TxgHZ>iRDbv1e08d4qhi`vi8Xp#l3lJ)v8pV0^Kked|>%5@t&lw)kiMSPE z#lXYaEZVS6$7>nmzv%Y zUS(;GL9mi;F&1zh-Q9oe?IGR(KPajg<3n$+Io+nHo$Q~)=nUB-TGI8++U85pQ8vHl z#!V-oGeMCh|JW|4>r(3=n+*;$!17l6^@rScWgX2K`uORiCi@<%SBiwxjpgOfZ>P78 zRE$G1bT)+?mhVnC0`E2!t<;YeQ1;0f($3NoukyhV9OBuSz#EL{_x})e?dU%XHbl+p zehu19qSO_D+_7PBDIRP&D-_p1(F&@hyi?V4yG@~QS^Hf*UQ2=1;a-# zqk-b&z$mo7Iyvx0Z%b9Rx$5d6ijN<04DRb$mnHTE8N0pR-C0jc6YF`RwVVUO4>7Jx z2b7eK2QdXHmu)W{143+m^Y(KaF)ur0!3$tX*5XnGtqzTsstOru4_W$t&9%!Z=w?Y< z4Xyg{#FANd^;04DwUKo~uPckQN~qW8$sMq2Q|hKeSXUl%)YPN7y`GcS7Lg@_BP~wn zZtOct!#tjyYC6jZI3o9_&K!$OHeAL?^Ywy7z@uI8;vyty*M(5`^+zx~>*HnR)$=^Z zEo43Z&1V)oMo2C5L0(?N4De$`TKrB}7A3hep2|C`^O_#8a3<2EJ_1#Kp#UK8hMVTo zqusTI51(WtEBx43y+c^+%6Hbn%fR0O=PFdwIP(NHqK-Z}WPucUUj4=B5&hboFeWa2xdTB6(X6YdVA0Ol;AB>WeQUIHc$(OW;J zaB>?1R0Q<|T81#qHhY(jhmquZ6pFuQ)JnUfcSiklo~wU)%yAEB$b7}-MFI6lNQf)+ zy^ezP_^MHQ^McC2e`SsliRcq0sVW@f$0DiK7O=G`@_^FR-lP|`Pu!fq_f?} zr@JTlR&-0Mb@JS_>w4t+>Q!Gn`>fd{Rny};N;M+(t9GXvY4UxW_ExKsy5B-N?yX}C ze~L><8HIu_nmo%F5?S7TZm5XXNM+KAR9VjxhdeAjVNf{E=O6gjHbV_#5nI+RLhc>oQAc_sJ-j zsPSFyDXpbgJ-HxIar+P($AMs@>8y2Bo8jss57<3()Lq|-EiTWQdpH)he_A?PV1y}Y zQdvZL#MCzWvn`pNqgnF0=IX-#V;bmx$+$lKSXEai2DBc{_}u1302JG3chvC0>s(2& z4S(TX3$0Yz;=VeqYrn2iD7FpSzy4&O!*syn<4JpO4tZU^B+Wz0&BV4_A}DMLY>z8N+DL&h6ydM8Ie@^>&tHLT6jb z$Hd*de80Z;AVC}}9%gs@e6joyuzgi`{3|!Vj!WkYDnRlJFbL~G4L8cIx*>`MZs8&{|Bbjb#-V?nGNL^If2R6Tlri~RSZ=-x1W~#g1831MxH*2g( z!N~g6X`c^DA=vD(S{S#61)#tf@_B3(B=)wezKAFVW6droX-0uFUFX#HcCiRskZ$fLCrLs?a)=e8?!6eCV8el2=)h{36z=#_j_g-2JckPOlPFt5Z z8~ZkUNJ2+1K2x~^3;b8Vyfm6Qzdc{7*ERJ&omG0L{C`|N|I7W;5%LGv`3`a`1WMMt6h973p=1ZzcLKC<wN+q;`Ba+ke?Wy5q5T+7I@c{c`x|tyb7qirdJd8&s$7> zTnrEQt3l!3N!eC#Qpz84vsOm5CEkR_j7O70vl&BD*O$mG46Pv$|20SxNbI!EvRf5Z z%9s%~>+13YN8}#AI=0d0y-5EBW{}Us19#cW!4Qt;nSyi7u`dL*EbS9Qokp6cWb1 zBps(Uy)`(Pe}eiN9)~ze$@HJcI9cWLxpOmpOPz%8$k0H!ZF%Xgo-owa>dw z`tg0fux`7_LIV&~^>r^BQ<2(hRkrTDqSZQ*2lbv(yvvx1F+IPQZE-ELbgzi8exCTD zG@7Y(KmXFF;qBKtMz46Ywo)wtJ9^(=28;LYO{TSa0SnDK$K#wyB)|1*mgVhlHI}F* zl>9rnP1~G=#mO*&L2c?xzY$@B<*Mw|qG8E8PuZI))a9`x?vQ`q2c+VCk7-@WRe*zV z!?8%?-mvo4vBmKjsvJ9wOwBmgI;7NWwRA|x%yx~cr7xM(fCNIXrDm|g zBFMZtu~sFqi`BU-!S2*3o?EkiU3BW5p|a1{t&;s0z&d>`7z6#y;nTe>1=SJ1T)o-+^`6Lh+4SE6dJry_!OhkB+0z&nmb&>5cEBJnnQ+Z^c__2HXP& z6=$nx7f@ICi?)!iLESQK_g{!t2~mxS6VI5w36P%9j#1_CgU{6#?;1nog8>c_2%&WW&7oqAHQs6wG!>zeJM`!#iDW8 z9$7Bf$;@Gcdqfm9xtB5wa0a5U_P53gja}R^ra4a}$J7GP6!r#X)~&}Zh6NdAPi}UT zVx75FX+QfTj#<%Rd$Mfu_wVJqT>lfR=IzQK2T%Sl@7r>nPgXCW7TY^gBmRrPHzV+K ztP2t)T3a*WNvAj1!I1+*Uskvj0%&=o6IoffvmWpec6V!hll~&~i2xr^8|<*(VEZA1 z*6JrV`D5;0vUL^gYMQpmDil5~EHTICZv619^F-8SYtAr8POaw$XM35{Np{l6CCuP9 zX?cTxp&X29aeD8h5z+Gf@H|FKB8_pMZ%44p0V&#|hI9{?i8zxwkf{(=Lyr}Qu!0B- zUY=wVnM|0)%&Hf;6kYz56VSIy!mb4BN55$~CfTWP<+_u$CB{n-U5zqjrSuZ#yQ#Oe$nxdaTft(58FFla(SL;{-GBCzhbF?n$L$;J5Y0_|37K1b9Nl@Hxa> zY6|+h_ERS9l6G@`QN%1@gY>`ve*`cFA3Gdco9}53vYWsTo6+2-dYv-s>gQG%t-GsjR)Mt^T&he)(7>_#D|GEM zB=Z_|^f_yylPSMV>%zc)>sIHDt4t_*W(p^QJeZp1MLGp?wG4nnE#nZMo1Zh zV9{2WLdO41#j6yATy_!8aiMb=YewSNtutzf72P?Oy521u_oyVTRA{Z`Q`0d_7hG$H za5`Iq?X3%+*S>aIG*!HQE~hS(dqwFUGpv$oF6h%{d;5CH7L4IumV3>7FlW&(&HCBW zRwGh#vqxyBXo}ZH@vtPEPA${Mo;xtZQ_Co zxDoX@C)$4-hb76L%|7iiH31uw-stA;&&|t8t)_D259jIYh^m{3bOwVR>75>$m3YYg zUWM^y2F6=2hZ6Z>#SL=&768If3Dw*kuspM0Y+)bD+K%0nE!-Z^h^IYFP{?BK`KH8} zg~z0(`Zu%Zym&7V-uORm9RC9Vz(9P_kbZR@q2U0D<+j`5#{LHLtvF5nT<)b0En_rD zF!ndZLvp9vwz$X7;)@J^c?d7&!)G+O;u$>@a({2*r?$fX1zH<+>u+t{HDSNCbRa?W zMIBc}nZm}nQJai{mWdx){Vf!_srvIN3rlW{slOFbWgV`ScA&grdrURWD*cHRRx4<9 z)TtuiDNopI>NFdehGaQFhh@sL`t?=puOAv6)xh-^>U220aRD}`$Co~6nT6}bEWK`7 z1^>m9KWYndmCyalC1yCTtsZ;V@*tec9vE?(s07UdGxv1v6?*NGZ|zUY zfHM5y#$Seb$Yvn_9!&C|3mHcKO~EG-Et%S#$vQJIZqY&&$qUy zdAN#tRhI~6u=AMbhQDgG?P#7nk%()61SZdu#%JCwh-`(1h7og`sp{yTLkK9Ro7}fc zrGqvCuf`BNiMl4m2JTsC%!ChpOVP_!hWkxeNY>&%Xuw4s6Q3>SYfRT~-FY=t(B9$l zXkEL1$H8(IyWE7^T_lltXQ~3hZPf6a!bQEyxaz&McH%6t#6?F;qR(JjVMlLsN4ckw zmJ`Om7b+Pv^%6E~fyv{y7fhCcKx+40g@A_oIkizpo1=q+oBMX3JuzwpbByptam*R0 zq9fwYHV~Y+s{h~(IB|ZBeQ30@-Gpoi(>(_1X|EAVwbWL((~I?-~@)r0_1u%Pnh>B(5-#TuJKy->ZL8p zv9EPoh`c(K`h<&rzu4>tcwIfBv!C2cG7q*9By;`a0AGX)Ul9}x%0C%=ZdzF|40(_U zB5PXN79ez1MNshIfiN5wP?Q1W!AN(L9j5D@{CW-=HhskWio;N{zq%%gqcXb-U&C|I z>8hcm%U%32kTDVw-@PI>*W+EiL#+>cjPbNZK#|GyEVQJ9F!3d2jXh zsl3k{1s~M4PHud`F(m0DblpzX>;&Y195|trP*Gqiw4M5Q^zihv+r|9Fp7VN0_A@$# z`)fH0r;`Nm)oRoFqO)6!Gyy^rv|dv1h~Smz``{br*Su+c+Kh?<8yKq`dK!uBh>UKm&~{KbuRvBR9LKy)prl(Z=RP3rZB-YEvr|b$ELq zrTUUgpQ$m~=82#?czdpXLtE)d{JeRCY{#)r=A5De|XTcPKeR%R?NnFuIzoz+}z=7d;O8Za(!jawdVEKzG3s2pJ;)1XHZlh0Ghe4Q0eSn6EDDlaMPjl>%iL$5q z*Gc8a{^eZu{@|a;rKxxGg!>)?$pI^O;Es}Cpa&H8z&b}(x7H|j^en}ahowV%A!v^v`gB`+sefk*Tt2%Pv7OrSYW+)6yM>UxU7JRX zG|kh+y4h+~zv1^%bxO4lbv0M^cfP zs}IF+Ms-eRh)7K^dSHzd)o)t4Q^_}ZUc9g1UU$8OD)W6wFe=7F;k8gwIN&;cghtmp z8N)5Caom#2HZE&U4@?3{9)%-iRGhidY#k`nD$91hN12Bb)E1+e1Y$ptdxwiYvVkqrNq@Ve)3JeG3?=fx-%FGa8XZ}U(_^`xtSVccHJ zqi^5*#5gxa$KM%iX`hs?IzJD}{{OEMpK&w*b-%IuSSqlJ0zw|(4W`9-bPs8nxQ+Hxre$wf`y@&Wym9v#-cny15 ziahW785qS}N!I;}#(LDEJxaIfRQFy2$~_lkR2)|F3pp(9r`>7BCU z(tKGnVe}k>a@v3B8KLA7>R1rF)#C=2RSu2QMOpIqnsXQqV2OJ5us|hs!+95#Y3)BQ ziJ^31^M7KRR!jGH=iCr8AWXdmOeEb!32>e))wlhc@mzgjp~bt#De!pk<>Q|a9Y`f) z2=W_j_4B|G|LJuE{oVF2zCUTKnO@%m?yr|bH!uDf4%Mjxf1~vl_-M&%l~59sjS;pQ zpf3m?Z8nBp3(G@IJI`aaH^Mb$k8Eqkra%r{Xg;3TYt}G)xLJOtvJq&Oi|CF&XEs+lIQcT0nx(57*Ni=qvX{{jFDi9 z5a=U8LhAS7e;6^`+JJJ8n|jxUFVO^Yl(GLHaLcc*Pp3>pUqvnvpld-|{@1(pj~gHd zcXRExhF$E%@!LYQ(9^TsxyR*&gCjb2_V$FY7``Rihc%tmWoIcH7VendtWX7*W&YsrZ$u&O#q>YM13$f3yp##5^T}>WNo6ds zNoUmY8!MfeNh$r3vfPNXyZsYkyXbbg$%e!CICG(5DGw@jifn$qeQ`h(h91r1%9Y3P zq*>KCuVY^$={i<6i+}lMDU92y3gR{xGcgZ8Q)8a-BD)VdRX1VQTN@RguwP;$-8!*4GYvYIn^Q0Bu$fkpDJ zt)vfF)Ta8^Qd#AqwqaRE8h?L<3dn|8wkWn4T*Z|S+lHsYkiD(0Fj9cfyQGpa5payc zcZ&&g)lt^*?5w?j*!MDAD1EJV*rd4?raihLvB;cgyuhPm=I{)qk7ETvcPMpTDV?r*wY^qDA18gC^>DmXu)p0IB+~a;#Tu6N_CJ4BsrPd1gGS1i{GY~R-aqki zCZ1yx*AHy^l>CQ|f6+eWZ^e-*X*tNy>WI>C8OD#>!L&#-Onz2D=I22!{CBC~xWVo<}1K}pd_BBg7m|{YgZsOT~yjj;X8@?c$Q~HZ=_fJA!!AXx`T#bHaEI#gGfo9WaK7h3?A9B3##t8$ zJn9{!=fJikSw9MSpMU!E38JO6d19bw_$yvJKsj7GFD3+U4De-Z)y*qD0Pjk4OVnzI z45Iho8^v_eq3U`z!w;YV^J$x2cm6m|+}eZ}Sgn-Y0ma0?bIQC*Kzki!r?85EHzo8) zKyaJU+^fiwOV{}pxr!Cl2bBxLk{G zYgJcB`G@3%hC1V)<>Mg&?=GKr*5=8cx?%JpA{P+$(RWp7BXy6M$Bv$;kCs#nnh@ae zmc72dl(pw$H9MRcqoQEp>>%#85RU|uI!)mVKrC#pjFJf|C@kfq%<0E)NyvE_iu369&DK&sPy3F0PSliX;6lJrJi?1r(>rx&5o>M5 zdbZ;q2Fz7esR0V9yp?ozXdQTFeI|K-EE%9Ai6Pyz1eD@*D1Q-SJ6PoQ$X(58y*&t% zbkn;z*%~W7j)`Wz$+ zpdxhY4pyhZa^H-3c?Da!)GMqWb5mEcGSgXSsxWCOJxFPDR!dL}*ShBdVM%VY9 z))_fv14_7afmX}1*v>UrVYmt;Z%dfFy(a_j8Y#dPYmkwgw}v-YubV`;6zTuv3#z(K zr^wrzs=m}jDLQp59I1aXo2D)91v##Hme>2`H!Tw?l%${rK4>tni3}0ge4&tgz5JR` zApj$;NJUhAdc6IBt*M@yUxRn_<5rS(4ZFid*5hgBS8x&Au){N+IL3&+41T)vf&6g0 z?J9qb4)Vq~X4oux9H7iqk)<>5>hhO&+n%tG$h;(a3tpQBEKfd;_^n2=*ic~kCD&%+ z^~BCDi9!w7UZA86oRRJjYqxQ%Q0(i4)YH#&`mWSjWB&MA6!6Xs5rGCjBAyU|OBvfA zE}pInbEP)DJsm&K3@O-%hf+ZTyza`uVBjI3SHYM^qEv!^VnUMaV=(D@IM(Ac>sy?( zL4P42D;<~{^vXQuLHsZn{2|1Q@)8eOpYVD$bkNE)Lt%7&CotHzpos7!k;jmn5oHQ} z??dlcH*hV?;C!_x82)ry4^{lfy*xO=pu|{*ZY$haV+n$+s1**gb1!ealTlKd72cJG zJ(W@Z2IVRqlnHh;?yURJ4rtP#iI!2WDhep?N?X!wUf>3}hmP!-13t8ciSonUq>_>F z41Ic7FHPE_~R1)Vh41CXNW4BnR$W8H@b>|+f)yV{iKz> zoDJg?p`=;Y7S)Jqen_!hGEs+oSCbJ*{Tpq(dC~8OO8mMt#`hl8`e-Vm7Et|%7{JwR@;k>9;(f50kTn4 zN~HqGKny&|Ysm^T7WZk=3m2;$3(g#t#s0r`wR01W?y7w&1VN=``)z-hw7r$-)qmWY zpwWUFUtpIz=Ti%!QVHA)_d|BzWh?r}@B9%L(SiiikhtAw3JCQ}{|mb9mb52j8pz2)*sAM4J7Y0$jz0Y8^UoluTHj@&2%+>xjnSVZ>Q3N3E-52#{Uz9EiVh{7q?|)G&l~UMrd!9-k{~o9g)$tM?w%W?UvM%fv4Yd0%ktr-CgVs5lm~C<4a(F1)-ws**u1ZKV78> z_we0PpQt)Z0NxkB+)!Q3ozz|DBl_+!Ir35oq+6stL;aX|O%s8=+L;*^F1uy_H~*xn zP8ME!^0H{StKTe;n0VAz-#~J)M)FEyyyp)Y!d5%aV+Ft$pZvmw#-H=#Q&GjI@VKge zhTF+&i|QDnsb?-eiAg86e8r}Nn<7i%wvp%(Ob;uuML^=seX?r#K(|S)9vS~eTC`i^ z+gbG-oiu0-o?F_9Eb}r9uTA;7fI8BgOYT#bMh&y_Q{!bcb4 zO=SLgE(j;U&fpyF#Uu(Z9w=3kq1h*PX-xW*9!f{Vp#n)C$!Rz^Dc_ z8Yoo#r#ax-D4SQqW+BE$5fTy-$>es-QeZ;6 zDJ8o(0#PnpiP&qmo`L6{M+(>t;goq%EnO z=@KnO*a5b-k?<8y^ph`V$1ekqUzYo{nD-Fv^8yPj#rq~|1&4&4J)I2w{abHO3MEnX zi$47mK8MQqYFI5i`qe)Z^MNHH|Ek3pqjN8o7y{fpiS;H_4o32+t$EyUnFaEe+JTFZ z14X>@EJTU^p)u{vp_I(%`}~tFSyO3{=|04#+D-J&1P%6E@pl?R^6`SReFJRW&67*VnZV>b&(u90|j&WbaFFG7D-ms$1zF0#P?_I~K6xR*Ipa4_hd@ACC;i#@j zPt3@B4(crJ4x0c|dBG~t>))q_oT#>WFDdILY=bERd^?9tAG}%5GA&pQ6YE6^7w#rE zen9t!V?=E!eDdjrx|-V`=+QN5`_s}Z=9<77h5)CoX2^-H&guAzNFBTfBo9Aa6~Qas zY@wt3F-b3NPqL1W@3Twl;)1VdcY4GbLDT)<;?vaZog$jf1#n(tAM-)x&6BbV;&aNY+~3h z{-FVwa2h{?fBkxjCpT@{`K+U&XqhBy)KC7>)19qtjF`=d^jq+#&8kFDRnr}ixv_A6 z>T<=z&}t+uIUY(8{pWKu-Wz65%<*)uXl-(9`f!SGBAc{V`Gebbd&#;+MeyD)-zmG| zYxhbz-{k=k;8D>8+D}aEE#syF>Pb)%JJ^4f6j}IillG;9dW}}>Sg*Vtj$JaFaCEW) zcc&)2Qr69QoZ|rRz+-Y&AEyXDg#i7vR?oLZ_cMSw6rA13|5AIUjr4)AwZbgf`CCHbq z|61?2#(wuc$#amCj637G#+>t>*YDyyOoCKQ@{`Ha2(O&#SBn@I8i}ZP09iG`-9@X| zC3Rx^#!)bs9a;qAV4a_c&XdqZb3e3c?B)!{o*BGj%yn%8mOWn@pTuOo)uFxQ9~W5p zOTRa09t4Zq7f$a7(2Ii*E}(ryZo!#L+KFCc^{c~@x5(|J;F%A_%kSK1b=$~r=x4+d zA-@hw^D)c7*pX3u15#!3v`SD143z%Wwl4c^o(Qe1n~hb5F)fi9JZRX=TwUP#)2Z?zEV?t0fse`%&ck2VnMMqG{=mz`T@jBFk82f((a?^Q!3Yu; zF5(z|8LQx58lCM?SrOz&UP6uBc`h}Er*D0Z`thLA+E%@-ifu2t6s191mRHnSd$739 z8GP;vva`$M%c5c-aT6oDNZ}i@Wg2xYbj}R-&HVU=G?Og??#OP`3bmLt4Q%kMaCk#U zHKv|}T?WmM(NMOd$NZexxQn(CPpm$jGJMt<1k%6EaGZ2;iLoom}oH=d! zKw;;X+752XELA`}mq1)r^ZiB#CgHE2F$|zmi&9z_2P*7z9M!&@FQCEhRIc~A^foMs z!{FZ&i*eq(WWp7{iKy1nUCqlsmhKxp|B@stwi(9<{(hGFXhUd@aN1{(zlEPk4%%Te z8>(j(+F$ZJv0Zy82a7ZYW4)ak@H{r-KMFVIRkwFqKyF?gAUhmB##rU3(~9{TD@3D5 zbM(uNs>nEPsuzrM5kJS_ek)_D^y;dr7lW%;WbzaPZlzwl)jH(= z^!>5}q9ZPJl37whRZ9FTCK@9Qk z(Lrr=0_1^)rnbQyWhMe9;Kl%op1mPqCN#sfg{Rg-n|?@-oWUB05$UOP6K8W3XPC97 zT0QDaeenv9c*2!t!sNK>miRBh3+aKeEuyO1rfkk|u;1c%)4I`k%!W-|zG`bq0o$dz z4;`V;G}oU(g>xpGUAyLq=4yY1y<5ete2YK75KXFs8dP!(nq&(`uFt-wycie1ms5C* zZ@@a#faQ>$(%)A6dN@rTdA;#Z4-hjO|y8mSr(z=vncsYFH zZUW=?a(ro8fosiUjYt0#+tTVC<>ZI%&+_xR2VuHaJ{1j_L3&!8Kt9<>au-=>a3(d8 z2Zk%^s8Y}uW-xhy4}c4oUvN=$)2d23`cYe#33S%v#g4i1^9 z^Vz0+r6m}A-=?TKc8N{W8VrHrLGR>;wIj3ii>@X_nP-a zgYwS|dYG}|;`hAF&U3C`ak!IJ(FBLL-(xe}#i+RH1Z>APq$MK_ZMk4}Iq#p{zxoYU zw|FTn*G^`7oF9)K>TgzT5BcI7Kl_qKRAJU;Zm`u;p^qE8heJf5GX1!lKqln+rt`N! zzM|P`UM6+YXST~{^I2nh86OEAoz{ZNAcc|WGIkd&@$<=ceB`O>&H5xbi6Drdv_A$jLNdn#$?edz5FT*c(jne zT2(gq6;+|$|5P?CrNw{{2&e;n=LvrE0)#r#VbwE*J^0O{Pqil1DgY`O5{CPw__F)) z)GVrR1R(=G;ZWWRRV7#vx-YaG z?-6EnrkwR;S=%xO8?%g$MMM@SOEFkfwCeZw9z$`JX`2KI6GYfmXFT7rJ6b;qdd^<+ zJ`P9B?p%i*0n(DV2RCTu{<{JTr8zE1Bm8J|?NhF}oI&)HR z%I$@Z#sa#$#|*ZPfBR5yd~-Q)Psly~v>XhqT%!7>jnTJ^NB4PlAVyda9_>{(hr{G+ zRQknD%=0tbZ%TzfXZp5-nxZq z)w4(hG_C{HYW=NB3-D0RJR{Mld7Ch!jSU#`xX*@DIw7VL;-~T^-WYQhNfMB+cRJmL z9x>V0OiU&{ouje7@1CtSMe|QcJ3^Uwnm=)E)q1U1_+{E2w?sF1 zo`0M#OfzCqjIgV@ZDZvKEIQgh&x$3`_G09vSL4pbLZ%o7_7UVY`PCc2gS#KOS=&JN z%Zy`Q{WGYqQyGQ6NZgr9IMH~@Wbs{_usrm(pwT8V8h%Oi?+%ZNuD;7u)|snomrgW6 zK+PE21$j0Kh0C%Y&rlwZa_%DI-Q3_Rc^yq8J@dWjI;-QZn%nv+al9u=sS>TT1_FBl zUPP-Db?3AV_RC6aMH!~cH(#}Mwr0(i&j4!WrB%2T`SrziPx#%%!hWF&DS-3(y*O8; zd{sD@Av|Lqx#ADO!4BteBL>WxyK9w(tAId8m0+Mp$JVV3FoJmu?kDaCg+{j=itW3D z5j*b12=aZd<*B*Rk4g^_-`r6%TqRVem{VqI!7WT%#x2qN9EUsGZL^_9@dGG0Et^lI z6iE-Jvd9(oYX_&E6zvK%uCk&fRp`D{?&EG&hn8un7=q;wD84%kh*v7__xLO@5XRpP zv@e{;_3oSMWJ>pybtpyw=#Nsmh6|L_X6zhaBcqtRYcojtZ!CNuPay}rQB)#|M;mWA zTTg&+d07On*u?gwGi1yX;J zP#X2OThw6|Lu&Qf!?mPvQ0b%P#BUwrv-2Q2403awyY}A>HuLSuHo|$Z9gTLoNyd%< zg+)aR1ir$fzl4cib+GFVO-_E&R)?;_CA7>gTrNN)fyXX(Q+ZF8`|h2hXBIn4E|^X} zmNL{lFp{P^>YBZ7f@vi`8eXo>9@N-+?b>$4Fnjz>BVcgDOWsvRUg)8PtJtjAmzsdZ zpTJ?P)!$NVBcc7RMSXs*IIh4-XyOxUgKn8zCH5<|z@c%k%CEM(k(}1)9O~I5l8bX| z*hLL{gz#a#k0_alY>PAD7)pZSX!26|{gt{IFVncE`!c2AJBuMvp1J3jn($BgsotEH zZ`!3rMy4@H#U6F{(%MoXX&_~w>5C}s9;M*^U&WFQMFTvs&*NB4UZ*I{`RIWy(|O^V zf~Bh$?+?w_*FTB|eZ|IznS2;!D<913@Cb&djL|B>4bi6F?QvRy-e^kXaX z{_OFTJMB-3F27AqJ-2=-BRKMGWX89(%V6a$kV6CZ@v<#j2B>-B!n@=)JfuE+k^ z7cK6XxXz%Mt3)6=2y;b#|A;$#JoQaqQTLew9y8Gz@qT!gTAGFBj)()~M{DzkgubN& zQ7=|yujmM1TZ@Mpf%cqw?(~FbSeZybtF$(53*Nt%iW?)u)O2;d$Yzg5bvY>Vn$9of z?7ra&6lRgT$)h_?@-HuEZvSN~+tNb8`3~&hm=EL-`_oc44UXxvJTrZk_su-XGr(a| zwJaE7eakaH!*(moBf}5sIcT&m=$F#SEcHBCB5fvcP11EosKn_joS;fPrp-DfjGwAu z(1uiLzs62iHDF}Hx|4p4E}NFsy3C{QED1R}0pd3GjLBm;7m*M+Hryp0uRcu{V0+m; zy!Jlv2|#n{Ox}>@1)#u&BIS2>(7XP)h;$3kxiY3`ropS9Fen?5Ect%q(T z>(VO@Ti1W^oT+y382GAp}fs2`|Yb|y<7M)ICLPtZ%RLJmEuEb}V9FrjY-L zsutm-WL;N^B?j7#D-E=SO;2E@=9RKO_$y^aaG8-s%D&VZO^{J+yg5xj$1>eISi^oR zphS|1s>JVG#gz9HqPf1_<^ym`THbSg$qr`8Pa)MBnERHOb_#4)!KT!wQ4o!KX;=}s zEBwv(uvtQkk7g+{%dm?&lB;v*K`A6vRdLm|iIK|ONn)XSQtw-A zY^VrW3U^YB^85^1CiegQ2F01(wu#vm1XIXgx^mppqZQX&vA$JoCX+wBPChbJ3aHxR z9jdsCj+Wl{4ln~drGfa*?egC(s&;+Wn0Wg{P~7hgk%axM+t7Qqqty>7U){_hC6ViNz|mrYuRm=RR!Q=RzD1LIlkYe!{If7auz<*IJ(j!l`?^_B3xgMGMl5I~o ziTFGC08>b2H-RPeq`$*M`^Fr5$zsKl}7c#%iDN3Hf+^fgk9Q{gS9fWw1SJ0_qK4_h8y0O>^)2J-WaT9 zP7$dZ&o-T-rV!W>kaffGN$Cr^jPmL;n;J?aSuXM6JxxtWC_9WmS>i`wZcN-5IUz&V zJRP9$itd~u5FnzV6MGhq-(zsAr4<49)wWde$R@ZB?|r3c+iM?#%1(7s(lP59tqO6- z!}=^mmy*trdo!WcB~UCk`$z^W>@U@hyJ*778OEp72(~Bc6cF@oykwDTNcrtSNi>C8 zqeLH;^^tw*y8ZcG3zJyqWkzP-g-Gt%xtNbx_T+!W*?|`Ge%GPm&xP_Iy5nj7_tHpn zRHyMe@O}Z8RMh#{*X($u?P}ljalyoAD*?Yo&`ri4>+v^1pjwt(7e;ZRs%!?s|1|gi z@>O+)90rMl`Nm29ap*!PQ7GL18R+BjkO>APqgsB$8h2PB z!nOo_UwZyh9+(QlJ+YtugbFaPKSxnWqlA_Rzxu$^2Uq&D3(tIob|Q12%T?K30*W~t z$FI4U9~b3S6X_r96uLZ3Ic4)?)tk$^+k=+IXVDmFev{>Mw{b0(x2d0`E}#*eJop6+ zLr^PF7X}c-3yjgIT6!j}Tc+Vr{+6VtuV6{;es2)0j42LWISdDu@YIOVv)8okx0U*{ zzI!QNb&5h1-Mn78e>--}Hf&&8>6X2ga#Q4w z-cMC#MJt(b)i`?jXZLWN4X7?w<^7lxYKhdZwUlb#xQ)!6(}qfDZ8!Tv#Y_EK09>n~Rz(Jj_ZmwSidpeeWK)$(H{^ z-Zn|Hy`$p9ZV~wFlS-uyL40K3f>~k(m_9~VDBmhxzOP0z{}5sWiU67e?T3`dp0o!H zGj#n5Hb}XXtP;CEAD-Od5Dd((!!EH1j$RR9jOnioTy@3187hiy>M6||4OsX9HvM}| zOxZQi*;hA|*{~fG5Qn8_9Q zJIR?=pYuC#T_E!)4{5mA!_9`nZI;hAg&)$;Sk}}uk2L(OBU*hgON^UxcDyMNnaJRR z4%0^GZ1DZ%?BB#P5x4EgnBoGnN+!(5V2mSaf=>^ph?Bg}(*MNy{@qJQNJRDatm zJu^8+Z+)dFAA0pIq&9M58A}nW57mvL+MJePg9^L?)>XV3`jyjJ5g9gKiOs(Fz6B2E z$6q7*2Kq5cHb@TGK)v(KR$|=AJ0@dJbOqQ_2tKpHo-D8}w-Js~SI5Wh&#V|PaRDSq z`R>uf^oL5snGqWJyLngp9_U4Aa0{v}0NRioIvHp zx${G92j7eAB=X|BgKxh<4oEr%>9}XIiGghyr-*k^eW^qomd1bGw){0tEp;P>ZR7f6 z07sjE)>~4P5VC-=DYIG`hNU(HIkBs3vjo&25iAu|L!G?5Eaz_{x!KM$w20eG@9Co1 z{tw&HUq0B{%DLq49O89|Ia+OPP<2wrjM-7~q_CL*+GHjfd3UEvmEn4jffPU3iWBx} ziL$lBeIj+X!%MTIR_hg;ckx(GAqnrdx}Vv`GpuhV3Do|47^wjj+HQ*<#i{ocM(dSA z^IWZk1-(Ko7)R>X=i^ATL_dToSp0l-wBtMQ9B3(2jA0q{?ApOG%#y@ER{tgvLkRGW zVzFetQx>g{WzN58$3DBO z&l--kn`tF+(;v~6;X6L6OtX+^vG2_s;We{H8}WIa)s0f&?U!z-g|BM7N9gWSFE4}F z)Y+?k6S{Y)d>9`gUg?n1bLlod$s0$dp>Q$Y6(3HTEz;BauF~El3ia<3RGF!9bfPwI``=UdI#*@$*_a*vU=Xn zZ}eF{4(S2ZUTsJY>nWP@&~rgK5hdC*YHbNUAltT;s#?mp2*r*jfa8WD+0r%ZvjMlQ zYQ~BLx<7$!Z%KIvPUY*++BD1~aiIbjS~wrsprjUt({@4jZU#vcpMoM${(Wr!@dP#M ziW0*6juUuE_9#S8)_f&97?3)))I#gMo1a7t1@D(P{7w3NV3nXm&!F==;5MwY)xO>P zzZ})SG^+hdGIl;OEiE#?bVOLvZ{;i%O(?Ni7nn?n#D-&QM6fA*6_`6b1+d!OlS)h> zOD?aPx+6Fb#YM+g)@dcvkNi_)KZOhe5R^m5s|)2Cww4?<>00k!+y}g3bqeTPz`y0y zkQ~OUf0+zr{=)vag^=fZ6Q(702OZo&ea*e$#Xw%0T+&i9hY5Qdj7lu|+cpgKjTR+_ zgjGI$<*8*928Z}_Onz;*Y5R?diP?7&>$1T1KgcU)~D!i*fcYI$2L+%@*sybyTH5>m7J5)AOEV_=x9wN8HqHC&xdRJKK(@|6$<`N4jD$IRF*zOs+zJQ{!BA5_UoxFoZ%-r1|@5E#zXX>dLpH3&sdmc<4Cb| zU|_jH;Av(ZmKGcThV^L!y9r{E!Ma3CgZ*;b`h*##I6L%hLPKSRrOFt&G#stgWE3;| zCixvA=n9;cbwCMbi%x zA~a{G+6j#+*be3z+1q(oN2QL&^^8k^Cl2|{C*YFA#+0xiYP}1A_6aEwnaoCE%Xbds zWi8P(`SBAO&d>$xdVu|!{;hyGR6u5pY^{S)zTfMK^`V+4nK*Inv7D6El2tX4RSTsf z+gb^um^E67@vXzqo*##yU^h12%fJ<=(X>;V#BDiD!9tMNJ^m8BE;3VAjZK1GEXdr; z#9;$J5uCB=QB0%~9j5(@R63ha6K@)bI{SPmc8;7(Dp{~lfFCVsDHy(ZR#^nudmQ%z zW)ef{%gB@t7weEs%wI+sl1vs!B{=G>U8D7t6ykAWCZ^dBG37Lp3&_f)L?Dv}4Unm~ zAyqGAqigdOli)IPdCCb+7`Wulc&Lc}7csBZ7Lnm1B{iMZT=G&;QY;nVBnUZ#;RbBzFhj{vh?yuM!L|e-i+i{V=0-h(& z}ttenb)n=d0=vT{I^2(#sv2Kglzv4s%UetI@fyx)20) zoTVJ<8IT?WZGg7&eG$H=EuY0L;M(&T5RP{Na1RRe_ie|DyA*z1$@U4NmqdZJ>$VW#!P` zyt#fy5zSvllN6gLA?Ijb6SR%mf+g&hoFqwr+aJA#2hm^=F){xYvfV@e+$OUnj2=s}Y^c4_oWU&!DB57=l2qAF)x^%DuLSfmn74SA# z?@UL3E z^QF|R*$$V`BMG`5I;PK%zd)X^Cx-DFI_OL96I8AwBRQ_iK**(I94U|HYb)IDy+j=G z+ETfBZEppnh_p9g8AY94D@pytU)*bcCD^X0wnLgn4pThU<>KfCi=N+jv5WYZ^=R%G zz8@<%GitjM=S;YyycL;YE0t$j$0_9KIK+|OYOQjpvp|FA3`Tp9l)PN4u(+fXr7JlC zC$^cDBUe}US2&hS1Xr__=@g4S2Wk?D<`y4QbPoo}oLYF~8?dpz`t_jLsFli76f9vD zxmO9GIn=qoG(`Js=~3B_%gi!=HRTnvb{J`*PS>W#IycQk&&@I$hWoRA&6rkESu7k**mciKbtvOiN%SJznl;@gq>%Z+Q3ChdTuS=BKH zh3){hhJ+oQ&&q)$ie;bwS zwuy0?XYX!WE4@+5&>oZ9`gQRew2%GHf;~Yve+M0HPVUm{t;wDXMif?@PaUo8@v%o0 zX|hfnnQvgi-M5J*7mxRA1{oM;X7q-px^QY6ifM=AwE^6g|Hh2`SqmDMSm~nq{jZT1 z%|+*>?BbDG&(DewjOGoXrQ>#;S&$aZ|07p;sfYH9BFcyZS6Rpp?C%!zD_wJ_@gAZ2 zcyb5`e+|=q+s-qZ{d+XM(rbtP?_*x4za_qDyJ;`{y-SB!zXMp(YA;~FoI85`84@Z! zWJ>slH9=C!hC>6HO_)rDwO;bF!}3QoaoIQ1$4rtjcT{opt09j2&la*ORmcufXRbjX zyDqi3B?<3k*TFOzHa%G~d`tFloLzG8uJC*3QOKf5*n2Eca=lzhZ0@j@s&2MLy{l4q z!bB0Aq5s3q!bfAL`y|&~7%RC9J(WU~IGeEZN;b^^EyPw&)eLR|bcf+;>j#SvZ<-}_ zJWXbM1oc0`oXoL%m}`%2!n{h(F$a5psXu&@rxSduHj)<^hX)EHz-H$_Yy1|lrva1V ztswe1S#~L&+d|*=qNH$P6c5jvG}JJ91A0{mNwD7gp4VEbvfE01sP`*3cVur{IPVV^ z$G0qE<$>cqEx|FVwMHqzg{EkS3UeuA1hKL~(Hd1yGWOut>k6B!*mk>l>5?OFNh!sf z*QxE3LE}X#5T>0UOrCEK1CE6WK2-lE`YsR*>cli|s2zJ}jA9k7$8JVR;vl_ySppi% zb6m7!ino+O?nAzGMHk2%K^4Y~|2IP%W^Ww1<=!#Z(<)PlxAGQ#n1* z%lH3Kj1|}`w5!Rgu!9-^FHdJ;X#B6&SogSW+UH-@b-PZT{_>kKd_(g2R29V zv1+zFfim;2V_EGlSJ_bSx72eJ_IxaV;tq}a{R}@)ykw@HZd1J;I@oN|VJi}Ne5|=< zJW{8&X?Bv)mIsB*Uuz#A9Barqz3seIuidZHQ`~kQIPXQz)2$xjdA*L(Q$(y(RIe{A zz5E2dfg$r`zz?#J`A8s#woBk~Qi6uO92Adglha;ZJ0tkXM7`6sKKCc&tS1k)FV zVOZThbu~9K&2GH$&R$8~ew(*by&s4&y8H-4#8<=c%Oz_sOB+(9?~=&^Nss7m*lTGK zJY&BrS8G7<<(4~-hwp(_LISG z_MAIa_Ls1;J#vnm>v+Z}H)1}Mf%M#7cAVjfcnT{;*e!ZgQ0himFBkvk(jI(%ui{^# zQ6pm34>iVpymZexwH>Rj4Qg(ILPM-r;STg2^!lDupGt309#re_>wG1AMJdSbh^g)9 zIp8HxtkP~Y3X~CA885LlTljiQ&4n1NUsL+=h8g(viH1ezSL<?V#I5SCMWwV3c$)w@y6j$Q2vyE>wwxcF2aj#(S>cU)Xoop-HbDVwou8_d8?nE?cNMdloUQ*6{6mNT z$UT~5DLmJnB%a?)=cH6ecqUKem~C?;Xb$%q33D1s%|Tp?DKh_2H!bfxpDI;^c%!dGYUznGC&h&FspXH zd-m;#1771@DBBKi^2Oe5nTKm&Pw2ShxOoc}l)M*#+5Z}}Z@c9V~=JT14TSlTP_jkD5Jwj{%ktY1tL{v&5>9gKn z9H@ST<+`B5TvwIth5w9a?I zLaIEP0%xZI4;^`xde>kY!Iz7i*4IC=04DHiGqZXeZ`I}ocg`8cXvIdJzT$HpZ_h^r z#YHJIx0E~f1?UcUB+NEu&zDozaH;r(O0)cl(?gkPSB?7EbB<`P41qsQTy|dbzo(Mbt;y5kiSpQDt9PfZQ@A%}7 zc*lGWF1Uwhvixz+q( zHRb7!Sg7S2_egMHZOJu-{g8k|rArq{WGO`MUov$=>}>>;b`83p%5r8(Vk5Fa;u4bu zPZ6H{SOv(iaU1B0IzyX?@sPS?q^t)cF}cEk`{PUGf}M|CjiyPp5jsDIu{crk>u zRXNz~SjYGy|3m&*@W;!{rk25m6cnUhS~`~p)ot<$P6f^P>1`lZd8s9u53>PfTCBEjw57!$ia+aEv)C-A-Sr|B9E^4(C(#T+Rs9 zA96&Ctj~N>p}%8%F_B#uZ0vPOuQa(ipP8`Rm()|qS=hBDr1uNul{{K%Q)Ap}?6}S7Zcq0Xz90G!u zPR9L9*#50=|9ad>5?jwQBo~^H^oJ1pE#H2!ul?GYTJN9aP*Kk2B-?(do@JMM*WYl@ z!dp3ptx0GLI%4MiI%wN1UrOu+s<`kf|IB`(NLcDHcK-2JBxZua*CWs>Ai<)~s*-ha z$wK=FG-vdzDA%B&X<++X8ALxwIM~{*9_!~N-x&#)u7O#aaJpt#cY;nhN#QW|rQr&o znw*3ckmvAv$P<))|E<=nuLCQN(F#_Yy>ae^dvhcj5BS=wTp6~W>VO`gl}fOo)Uvvk zJ1W|JQ&9U}mig%&Ay9>_@5()q+C`MOin0!V+nxTn%?@lfRQiSj2nW;~kS)BV0>FAd zs^N$yuIvifr2wNiYgYQ>F+u8gWE~6-T zE!`JO9267Tu`md#t5CM4O-fhb-_z~SFN#`SheysgD{p^1>|saBpat}AY#=+)ckBFD zZvU!=%<|a2J&w+tH)T4_-d-azT8@bYkD32xOJ)9F}JX8$9Epz-sUrr z&4AmRd(|5+R*%WM;auM=8bO{x?Hh3zh7jSM^5Q!X`y`}1&v^=0Toq2dV2`0!FqDkH zg+PULdx%eRZV=9*P6vFq1^9Xk?XsH|{qH2tpQ7*bWYT-- z-E$>oKL-((CA<(ceA!j+^VFzRRAHO7^vouy_X}GjS{lkLbF#fa5b|cW{?4@mHeR++ z*z+#o%yUl+hr;lFuv^G*GGH%SSg`l$?#O&HwmG)}pHE&Ms*(|@+TAw>vUL?*L#aBW zMMwPl)Ts>vLt3|{{8j5ng~bFidO;UuK70~1OScSrrIKcj#lFj#%PDGo<-J28=rVyQ zj&1b7>h@TaXrP1sZN2ER*Xcsdh^=a-?P4l2KjWWrJE%}ID02)^c+T$YR2LI-T@m5r zfo2pGTL=i${@-@fy^bQ13B`+*|7p*qLi8(jYbwUXx7D`g zaK=4rWzX%(JdVG$l7lI&eWKo0e?IL%&u_KP*y622>9#yffW$tHu_Pf$x2<>%15xHb zL#pgvon^ykQY{Z_k5(6(QK8Y~;C%W$(_v^xN6emRxa91HyGo}rBdachXS1llK?!7q zsGhAbKR;xBA@QzK=>oeRw)tbQO5QMadNt3?y*#lj%XTl7XQ<(3|BD7hexkW)sM|Vy zJKU8Vz1e>G+_7kFMlfE>)`)g*wymGq?OFU7HQpg2X!yY7z_hsMc1U`8;joG@R1Ijk zcXQTuuu?=@LDH+pdB+!@=)f=z71J7}C7>|{e;ikTv?bT8SpTVw&P~!-$!e<@@QSDq z809dWNg>>~AQ`s6x;a@7s?t_tp||^v<3KwHXe8OXD%zRIHm;M!yT6}QMsH&TtQ0kG zsrcd3@AX?5TW}gAp}81|JFo?N9Tv3{5%O9UCzy{m`3hu6U^CnZQ+SJ0ei}M27Z@@5 z9%Lu@Y6If)RCd8n3Tl=tj1dKBL?s$zsYzY`z5_8MzapxNK*?#V&hZ1a;;o+Ds>`30 z4Hili@`iErGrTg&N_Zi=F%++vhDrp|`Ppij9_-Q!n%p&5(_$joq z62Zyi?jpCkvf2^feA5O2vN-SfZnL(SNq>nDSl4Twg3baHCSJG+2mUgmE+6A_1-oRB z5Yr_&;O&lwU9roJNak+kM)0F4R?8Mvs7o$JTB{UC`AyB$`#XUM=cQ z9zPv{6i-`>J%?A)Fg1FNo)L~t7AE{b>`tuiRn;~CYy}9*;P+9(->lk{p zi^J7JK>)|E+$?dkLp@tetl9YBPJgs#I@$9@P?@eOZNQAAVLb_Mg+Vov>k+G-rD)!IWv^-K>7 z%}B(E;OZwVqMQ4+)beBRHu*x}%>E99 zuaT&3-hMbxjlaA0ZQEmdZ0V`rCm`4G$O<5sMm!ADc+y%qH4j#unnwMwp-!GH(oGnc z^Af1+7vG+@ZP-s91;fnwvaH>Fsj2G5bH#NRZL}Bs%{-_Azii!VSB|h51dLptb}zko z3&=(i_{X53mA(i>>aiS^p}D^t&%K&v5ZW%bft=boUgW;r??(UA*C8+K)JoS3wz##g6Z3&~aDx_$VU0>J6*Kt-=N z4STx5CJCw*+^=%Z>>a=Kke27#8A)?Fgmz@rt`+ripY2zGQ)MS#hc9HmrPWT7v>RmS z;>9gyM)}e7g*fK7? zN*^p?=lSp^JE{#;%CwS)jMOdlmAgV;TpY7^y{X?xf7XIodD%obH9H*zGPT8K_K5q_ zXyRhN>eDPFGA&k)!5m;fO>Rz4QPE6~Z%7Db=L+b-^P>gyw`l8gSu#n^?!Sxvu(x<$LvQjQlA6>b{k z20A7x?Gh60J3Ibr}_?Ar;nj6GIdKEs>E=rZX^NaI*rq_#dsa!?i`_D!=X8Owq`cPF@k zb(>sqD#Ff(W7So@N>!zNXDrlp8e^>4DoakvIHXzV2nM@AH-jyuD0#`&%CHz%JNd&d zdsRAPT>|PQFjjnLnhDjk;~orHjE`zI;D3{P%XKtq%yUIa6bw3eXmXgvH9RT63 zTGp^TtfEBH^O)7x&Roxom}D7?_kL+^^QywV$Roo#IydlIL~PA=-Eu~ zaiIJa0V#e2$yz{Nh3y0X1vA60Rhvh@B||32tW8$B<@H0#QCf9t+kGiQ z=0az>v))*gVH>Xf-RZb67_Rwq9$PEWi}Gexno3X)3;$)!lMdlwlPe8tumP zrr70eQFnelDq!hklJgB()E4z?4W`W&nG>(oB#Ou$XtYiKx{FW%@^yXQKg&ptiX*#MJJ{<^s1dQyKrvb zM(nHkYMa2cVDmp?)eA!)qOFQdvUF#aYfu+)9VR|oaFkUows2LnVLLRAK$gU}VhymP zp_t>8!piEiI}{(7S{6p%l`8jrG;v20PCR! z2ksWNu>fQY^UnCX8Kq!td#`_ZuQNa0^Bn^Y)RLY^?a$kzxXv#tUnOpnpXCYRz1-6D zxoi7SEVFV|U#4_8g+O2UK$i9zGm2ve!7rFoZ!@9pFhy|qWtbNgs_%&Frc-x5Egq$8 zM+|V*ix?BHIU-Sss}{Rd1d0iMF3~xxVXdt7Ok3F*i}VkCFi2BfWOr(>+2-ebv8v@~ zG`92paObYkf3Z7yXlTYRz-Y{&F6+MSpLtB@R57Gi(+_cfwJVy|w`fa87uX8--9@S; z7jgzYY<9A68~cAsr)FViJPSY|^WWon+6WNVS_xiKTXaFl?}yCmH(T#WlSTgwVk%jC zKU}J%b4$p8aM3}xdKswr(>r}cjr16ugSQ2aBP?p3%k^2PLUV&n^=Fq`6Bp{HtHxr&8>`kkr0*ovs(^8E0L+EcFo7!HdWWC1FR_<0!iKU9}r!T0BR#E zq0Fms31OzZ%HKIA73;WOL>BjM(j=m4IxJ=69I7J z^f3Y02PE zbG3zE4RS0LTz~q=epC_BsM;*?c(!q-d%4>9fOn(_LJ}RfDvCV$Ts!CxqC>s6FFP+V z|JWVX##sVWDr)X`FMv9~WeI?1u3yEcVfCZJ(QW}$D}Yf`5V0SnB+cydR$9%n!4g`S zqag#d!A~2%32=jcHI(f)_W$wqm0@je+tx`SNN_K16yy)9=wGcZR! z>}W3DX(<7NemTE)P~jn~qRst6cKd6aC?XwwqdGmsv6}(0Q-NVz_)Ob3lHezdEYI-0 zfRmcUE(dBbZ;3R!@-=qr@~YEot@*25^2x8{v$feuWQC#leGHv=QdaH&)Fj2YE?VYu z&2Q}%PREG8t}z{h75#;lLu_8Z^wt&OaJxn27P_bFfs0!ga8jsMZ)+ES>DxgY+e2S} zjoWqvrBV!IE+nOg8P+=o6>&OO1a3A8Md(V&djE}4K)dh?dGakeWkwhUFEn{Eo|S}% z3DjfFaYh!0mVjc!b6f3`RxDEKOaYk->0ZGm}XpOWq;2 zp-;qR<*}i?#-1Ds#HkO&Vwhgtso}wYu&NQCq4e2xp{)uKhO7xtsARptAH5;fG=tX1C&`A|EczL@UkE1y1$?(5WU zgoiBmLj}{G{+zn+@>r_@-gq5_Sw||4ab9d{s>KVHv_-r&MWRjCtvb=OfhY9#sy?p6 zALqIScy+y7y)FS*gkOR*yl1JoOeARxysp`6b_18@HxH7V@cmdq#-BDv+5JjNW3LNj zob|j}gdJZPX11kA>;!fFu6}fvTru80e+^dO5iLnF>2SNPg=msY8fGRVRK_}0e|AsR z?qq#M`Tg}LSx#!)2yCJWq^xwgM}B|z9mMnO*G>yi2w2Q$Ori+oXS1c0ys!KC9xAo! zkA`x+meU13NzOTuqoCmVmGK$&`Q~s{&e+mwS{%%aTMD*nCr67loVadqTNz|glHge( z^h2i2j3s=D5UZ8;S%U&Svl&~YNxSu6#-M}a5q9it5+62|ZaCG57 z1IL?;0TDbO2(+Aj`y{IkU;M|oz!u1}rKp4ii_SH??jYVYO zG>fYzt^I5pZpzlH`bbW#g|l9~k?=;VvW3lQq+06Y)~{i4#m0s}hNuBlL1L3s$^C`` zg*V=rJ-Kb=ym*z7!VTRZ^T>)}uRTtja&3TQn>$l~z|%Cd@oIMeez}5d{(x>p!**>D zrsG#W`}cFnNN}W{39MZwGM&od zHQQuN3USC(rKCj9+qRXy&{A$LS z-M|z?BU+GMvz^++Jb4bpz<{+chH<5w`;8O{8x&UJ86>?z;ZCE*?ad)Vtm`Kk%-0)1 z5FcCn9OF*n;QX0H_7(?iAMaO3i6JM<=G{1d zx9S8|Hgv21enPs`+DPh9zApI!(foZr5{J!s^fQ4<%@2TKX7I%#pN7>kUI>8q{8Tk% zJLk4!8a{I35gpBi$!90J`&g4`+PwY0*KYU6xw3z2 zfQi?hL${YZB0I2rSkrY)O-)!h5gi`btXEj)e0Ls2_2+b$^IDVV=pF3f72^2h(DA`^ zi4KV+e{+FapEL;h$xzKkLN* zc-ZfFo&C9M7jC_}AN_ubsa_Lr;PYp&ZeTBM=>zDhYxA!S z;vc)sUzz|@A;|aSy5lj0h|9fe<%ZLt*6E#-S)_}~kfoBPxcg!JPMO}Ft| zNoj!3uGN0e5aui=n}8MpzQVtkZhkniz+F`B>LVNJf^DpZ$lio-@L)8;PBd?LH~d+2 zdB~k#AZG4Bu)67!mZ^Ly45JJG``3t*pajJ~0>nUcbI^3~hx~Nirlt`jUIXr9RjW8l zAZ0+Z@W>9XZl@0~b#tsduGIj{`io(sFR4~cTa4|yXo=}2_jT-)3EvjZAZ;mtIo`KL z{IEnN{%pI~J}QMLRu#3kLndH&L@8&sv9u~G{AHADc7`l(sNULodcESvrtCo+@L){k zw`(y?w%WdmbZtf5c+1l8nk>yvjx*v1TFr4Gv?$4S`8gcQSjMHk5M%i1=s72C)E~PD zgP0qAkj2ltewm&`-Hh(<*ac>jP_ZvH9v0EX5054002DRxBuh1^I;rUtl$+co$k;cf zS#elWt1IDJVENu0z%#MF4E?rTiRfXHx9B6w5604I$-Uaq=uzIgV7Y^{Ps$f4#}rdpTSvNIr;F z`&ItMWh2Z!2y1KW?uIb>>g_W9#Z1Xc=HM&H%>+CB!) zU{&vRJDrs>tTPcLV)->h_0K5cbh5DhUc5xloqipi!>w)Ro^MV<~W)yD- zu+ru4$Exgv1JxVU#y>xm*7X`C2As5Hu*I+Akm2QyPbRkZs0)W$0VC5-KGx~9hNwDc zlGInMNmWnL6BWA=LY+i*06#ei?hEJ*pJ&gU+02|1_)?=K!z=*#^H3>_J5~bjZAq{u zR~e~d1qK`wGx`3(2pbbRwnD1IZvMb)7DwhR6+4l#FEo)@Ux4fJJEmS?tWN88!#9bD zEws;FeMnrnKYqJ@pc`VVQrHS{S&L>LRi?TgmamvG+a2_*=*`Kl$ZO+9A2-Ce%(sbs zxU9a&0(diJ?<`JLHnO^#eCO6Z&C3KzKI5Khg0`1$F)u%Ld7>T< zK&wTRf1#*#9f)i&PEDDq`n$omb|V6@jY)K=PU~sj!k3FOHHq7#HN2_2KK&PgDTxXh zD?T;O>Rk4|a<4sabTOytrgPs;veISj4eErG;z(BAzOJju6cxrk!S7pB;aZW-VQZ80 z^tYb!D!-sB4!Ly12YP}W*Iv)+>b7O;^tc0yP3G#U=H=}*vjT9MJGE4GG#33F!FZ(*|A{*iuW(;fguHwO zZquHdE*c(Cx}#;6Rm_QR>t~qsYHkaw|JcD@wk^{;F4YLoK{1R~KeR@5PuCjlyf*T! zctVnlJI+(FMU&Sr>8}Vt4#iYT^p{r6l)EOx{+xaD6ftgFmuuwkH(cdG^v}wzH3G1;xT9$^1Mq{SOq>;M=TNxd zJn)cus^wYRw`%rfNyb?J4^DymG+f*Q21IK3cRg=#;Y{#Z35hEIGI5qvIj3OqP4X{} zEj$;Y`NaOJt18*egt$@ujGv*#2(BHbPbY79KbAfQ39WEsi@!|hx2Nf=BEdS+?RHZY z#p2|GbI?po&djl|FqY@TOnq*i%>o8l!T#{C&!NU0A^Af)w33lnN{c0|&MtOpTTsYD(0YYkqWHJ>cuRk7<+CeL~~59<01^_4nX-{czpv7K6N6`Uyr zssTRjfb9-1KjXXm!7-;|$Yyz~tkWqtYvM+=Z~!Ha^p+=5*)DIPl9ic|N3j1o&%!J> zArIC}Kuc;l4Hf}h`vXC+VrI#@kVF)1B9~eQ%{y#CuKwDJoo57zEwmXvlF=%IEfUFI z4jwa09N!ci4XV4PPE|KrBM;N;{Db25c(bw>>#k)w7M8Auo%iBynRc%?vy^(?R>-zh z2)6q-{Vn7)Q6Q;b(hjpV@@BJF)T#{%gCB)Cj~-OSVm;Hpl8&uIE=vDbXog+#@qE|w zw-~x-V~k#`zprBPUbN4JR#_Lv0-O!e(x!~(*+{;^D}dP2)EM;jeUW|ArhRYF%Leu$ zzWg9bs+&N^csAI|ufRWj-M{8L7iq0d9FsM84^l>>R$4_|lJB@SaaKZk+@%p|&~2n$ zu{FjU+51E1&{E~C3smvj3!;6(2Ie83`=#x-E`O!(e>-ENK=ShPUho}L^ZSG)boC;tNCEvjJYA&Kyl>#VR7PQOQtjxNpL5^?Oc%vQ#=T7M^N0p7i9 zv)+;1NFHWk)#CP$97=I6iN@vqK2zcURp$Gh9!+7>q6X{Q_R``t*v9mAsus%E40=p_ z-_7P&2Yz=b1|HTIoRRUQ6Q%0%9zn-Z8dxZ}v}dRq!n)F!3fN+B36pgiYRV~IztXX< zYkTWnOuxVT@=jeI8%1yxt~o*MyAovp0y;_bps|EvMBLXNukQq}!qp$vP=_Z}`mx#` zMl7Vd|FrR$KJ?`RB{CHcL&zrfV8D%?U?3gzu@p)%YbsN3;2Z}1L+$<_DS*g?nKI%Bu)Wd~oc5rKWM`S)H*Y!L_ z7;EC#>AaFI4Y1}7*i3vI$ov4OlSrQCPSB2jX6@9JyQ@i5sYzYQz#t%7oFFW9wMuaFKK{Io*?$&_nb|?ZbUJ7+0Kf!HcKWfMLkU363tz1&ho>-_u)UA$5C?q z53Dn+9yj(i3rd7%zBR3LKOs2f7OI=KSGZM5Sru?Dop%I-hCkwaH}x>lb7<6UQaWEd zOR}-vV6#~TlFAQziEio}ZEhuRZ+HM7<;g8`!fBysf0kzu`?*WSD8U<#6E&+JA>;m7 z4IW14*`)$$*Rjl_Hq_Va4imE8iR0kLLlWE$kNu2SdGg?48fQsCfu&9lrol(ll0S5q z-_tyvI{D%BXklIA5fJ~#S85{gH--&=_U3MTMLFwjO87%#lJK+`v+-Dyh{@qwranGz z;qg3lOIC{2!9UTtYc)Wv@6~JIYC@trrTTC zjW8!eideC9XTn8O7%3%dSn7)r2k6Com=u0mhKj@MHW$0k9j}vJ(aj_#__>P+++Whn zW76}UJ}cQftC9bM?xFBcn!EPqP4*JrA9r35b=YQ6)c;2CeR5(YF+;hCYRR~{3F`<0#=CaMyAiMN}UjdTeW#ad~>85mTcPp2af*r|?&=EjDcCxOfyg*~LmIGw2L;RHfTIUCHAv^@#S_v}j zaGoNYXCc+_HeYo}l-}(-6id}zU%b>^KLw)r5uju%cIFUdGG1iWxj<}={|;wBfN>6^ZG32c`%LJ^n(p5fS@5*Zb?5NxfE$SF`uC`)HrJPt)^_jD)vYczLFx3 zk1N^5WIcGCk{u_BZ%Q*V2{&d_a{Dmxp`rbgYW_Uc+Ph-UMX-HR=`T5So$@`Yymj=} zbAV*|Y>RUMUe6-v{q@D8Q%Zml&i?0l7cf5$c{iJuV$N!TWw4lpbD>;N&7}*@d8bt8 zlXL>#oB>rhDoKJPA;i0YU+95>V&PUR?S3e4A()Dl@oz z9j{4vyIM)^Em4oNf&BBVrYK9dYI#9rOIo&Ppn8`o5?+%5Q2s$9ZWBa@uJ9}TMx|8) zgPLm4-KoBq>_~umO0D+g>j;1w>uZGIXg6@c+H>dTgTAg5zsXD**N9}dYJ}dNYpbAM z3<=ZDZrV>@=S;V;MCTbxnbx~gHemPO9Ys>2Xkgi;iWRAq2Dxv`#(8|0Yk}U- zi(zFc4c;hw`qOdeyP0N$h0a8=t1<(Wq!gRejLZ3Ms zb~1~rK`U%kQh$(~PCx1>((=8D;oIk5k{YNm@P4$EMnn5W%kbxW!&8VxRibzI+S|HM zx%}kz!I)+k`Z!~9?=d`!ZT?|frOpdSG-4~IZ2NRMuFra8Z};?Dv%(dXW9_YAYu1(O zhNQZJE;{?~GL3)Afd4s4v$G%s))Om*44Ud~HFeXR6|T-P45i~MZ^3Bod%-1A**w_e z@!%0yYXV`xt+a~nOINC6h@L=11&V5L>m{T3OFO!4ifgDcYZ%}{0D}p^DNX1X zcUzNFN?cC)TM`_L*|h|Q zmBAXl+GZzJSVHVP=@Xf23<2ULJb9M_Y)!FGW5=$0Qb^U%*2io(MeZyPiJEfTJ9FIS z-7U5PtmxX8ru@j|P^^SJP0aoEm4=Px!-x9^ zDQY2tc2^T+fV1GHbjW9fdo)z-E}77$3tdQ>K^!8=I{OFND}}-kogJc?X{8Numn!uw zmM$T~n>XMVGGoq-ShfaR6=iucFw)KJ+fOY6e6H&|i7g^KO@~*{5#OD{I5hV1Q-f`- za{Mv}e&|GN`@j9*&OSa?(75HLnYs3&Vh3T@gz!NvZ0k6X9y5AboZjS;{=(cq9GtFy zWjODh!fTywRU2+GK}5{1ji=eIfjk>C|8|o8Qy8BG^S0@+=1zzk7fMC=BUMO|6#JsJ zwU(>t4Cp^xQ9zZ|TL@cx92{73m z7MCQ7QSvy+{py?h+kjuCIAHcZG*#T@1q{bxLO!|py2Q|7?5wCe+#>{hk|>dK2UA`; zydE@-a34aL-wj%UQMP@V7?%Gxj{5JR%=s#K%OsEU@aI+8f4`PHWgx=Twn?KP;!|u4 z-=^LQy5(yF+O#OObgn-+G>qQJQfGoYaX%*UI`r6NmjLt5eua&&VZC_9&USXNs_rq8 z>?&CbQ7r?aqr(-G$d!Tf9AW|kU{SJ2IS9z&$6=atG|$*TOgR9s{xrn2Q}z&v`vvEb z7}p^i+}?z6Ls9^&mr>c4J?Bm;xzRU+oL2>s0BOcN07w=#7u^EIuZgRreYE5cv~-5z zLaf0%SO?QVD$;7aBAfgJ&a5@Vrq&^2HAX_N7g}#Y4{^V*5ZGY^M>Ka+77`FVS!ByI z&vi_sk7;&e#+2fD3*a(rljE+9L_ByWF(88;;QT`4Q#L|$HbM%#d5oWzV7LCbFSY6C z<~1!j6NK+j5We6+h(J=4!v_F;?JJF(5Ly>*=GT;P)O+cg z&&>#pbU#3ID9@3fto79qd8$3|YEe-E&vN|Q^zCw(nm6|=#!6Q>Z-U(%Q_1f86@>jO&fm*Dkm_UX>5ewhk_Qv%jVs92& z0_Uu0ZKBfxDKXv%+aZWu=&@UkL=T1ux+DGhQU<9iEyz_LbC`|2Z?G^o|H0r!AMf{T z6*s&#UP)DRA8LW6^Uc>!!bA9ggI{T89yH1xqr_swuq&a*Z{v!N9& zQ^lyBw9&!RoQ&}MJbb%aX3G(j#}f{>He3RwKN9hh;`TazoERZrL-L=LDqW}4`$pzn zTHBw`za7D99VG*5B1gaG=PZY}_8|>f&+)5KprUydW-p)}cw+IA$drHS`ualtj#_x& z`~Q{e{-ZqA_FXGGH#A4Sn{0dQ;m0EuKuJP((YF4)7Q6(mzxF}Ze)p`Ti+atpe;tr# zOJ2IJCaTarT^KO=M|jfk({2i%;RdUO+;W%|xRd92UCVBcOAlUlV9WAo&6ZflybfeJ zaX+SbyE>*YLHU@lPW$>(7X4sc?3jOKn_;b{8@V)Hw;Jc3!(v&1+$UDG)287#3zFlY zzq9lI8of{7fk?+=lXy2V%l}ec`=?{OqX60?1+xi$fm`sAw>*20z2I9?n%U8I5Pnqk z{m1tY=2|Z#YXS~rbXt&j_m8TtzST(OEU%e_g`l6qi|gWo#!vc{UzBkDq&2PU~#u zSn0F>tWM%m*SQGQ16t7s5GpbhlHs}~P$zWx5p5L7SzcU`TMB^YoA9!|y4e~uybNkY zt#!WGi#-AQf;OqkIIMN|=Z)G>c9=uHfYuf3^!DnSwE;zt20;`B6YqDe{fxHh*|2^~ zy>6-gV4Zk2$C64H2U-LNK(BOkj5QHD-+njPM-_ly+vvYt&z^DhRdugD=`?_uvk<*W z6l`rWIHcTG1@g{|Ym8$v0B>z{I0W~apdVgPT<^f1O$|+7I+I=hXagnYMZy3h_r;O@ zArUPVZj7eQn*)L9KD$YY+>2Pyr-%giEF)fhAauV#uQk*%ONpo}amM0sMvNv8i;%;vwQKZL$5&cGr3LOu zejCylo=B(QHdo_25+5$F*x#(-pJI+5y?O>fcxDpmSSs)T62k1L$|?Y&S5<~EW>eQ} z!%n~k1E|^gp;4{tZgTTpBnQwTUmLnfWqoU|<~nbEOOWafoA1Z!^;Qs1a{lZ&>s2>8 zwHMPC5KGT32N6V%VLqvTonN_3$BM}#bZFZ~4yz?SJntO$2}&CYT^K$MI?x7`I|#?u z-|9BpVj-p9-u0eV%k+Gfl3d*X6~1IIfbE40S=7|L8#U5SG-+O6973$!Otla1o+F}Y zugCv*$8boiSBfHUPE9O3D2BzP5jofDcK3=c7VO|{Zt3ApU1oAe+LIJb{MI*iLrwhV zc?Czt(!4Hp6{F3XDitqMXDGZ!FFJ?kUAhzETK)&mR3Lv;iR+p5F3K8BT6;hJcJ+UU zV_TGVHZOxkXXfXukNP#@bWOiur}Q+sWdBV@@m zG9Ny2vDk{U!?YuJt5ipe5hOb{T;h0|E$@Z^pVcPUm-cWq(oWZ`uS^S9#mzx8^2BA=Wsst!xqzfCXq!d|17?iAmu1TtE4sDgP zgq>r3zd-G{t;Z?rz&#s};}>l@xT$wh+kMX`Lqy+8+XA^lYL%@OW&n&5YBk&T=!Jf!@p>f>?&+J3Nk z7j7I!opr0PQ;%Mc-Vk=dQxGA1mg8U!uwBdaJAu-6j;>DM_>1bjzCaJ8Y4|^t!tIn* zC)^hsGGSb4lz2F0tQh|R&JI2pc_Y(q1wMz#4a)7aPuU87!5r^e5kC8<;jhxVYGC)o zKe*Q5iHX81AhmAur{mmRWx2F00+`AJFGg7|tJQ{&Hx7Rgx1csTPSdmXz*v*Ak^ox0 zq(JMTYLM;^t|Hc{JoitSuu;+7d-IjAMD|PJ>%H^aA?>UwMLm5w0j}O%9^$h7%@0QO zhkp2A(Kb`r8f__@FO$=S5Tk0UE8XkS;=2e#toAvaQ!JRIf6tZ1g&Y#A_jo@oiD@LO zX@jnQDkF1O;JM6e1Ro_ll$hI!SAYj35Y=t1OC{iL@NhZ)zW6nh#4J}6L^h`T9Pa@R zgQ4_u!`=Blq)aqd*b`a7OBIO!<-r2^<~D`Z^mma45kkCfaQ%0$G^SzxvvN0#xwjJI z?72p{D61v=yN(oHHe^>H>xw;@GGtHhLwu-*hzaua9c$L zM^DV`ZwWqVd-a}h-a+!=vb+cV>q|@!$?4K%oR(uw-2d*&`X^BB47R0y#!yIw=+m>X zwFApHoBQRii-?}G>1jWAE`?8Az8Bl#JcK+qN2`Rqrds2QQ>>l%tq!d3>#!UBmWb7N zly=M0yNbc5ARE8q+Klrx$WsS+40p!wl=V?VOLdbA#UU3H4UNXdi?f|y)9%&kUnWgw zIhVyS_^mNHEE}%ta$sn=AM{?=Nqm=(nZ3m}ll{x${`Tw+5VEhE_kZQ}!B~~@w`()c zeEyaDG8f0UEGiABPoV850j%NT@QmQb(XFlV@9m%X%!A0T7*}bi;zT%1+7kp$0E)(EUT5bss#?7pwl=dv%Ua;2eLN8&iff-CHCh``wCC&_!|VKtZ`2d5?bfE<76yM6+~fl) zs1ESgP=@zX%Z`dHAK3cfexnR)4~H!OGK2Tie8uMyKP&HX2|2VB1r@9u`2NmAz3K)- z-5ZQ%ZTD<@W&{Z4;Ckh>f>5ewBl9+`>~ZiZ<$J-UATpaj?_G|Ddjg9V?Yx_mfXwXn zPZmN;d1*SH(oX_I)QRER@OMnYYXF|e!EHLbScbN_G}V$FV4Li3pCzCu4%dN9Z%HH!5I~-=821IhY(J}E=zMtMP8fq>TutJQ5*sO~L>4P((%`&}!= zO`pC6H>X4|K6w5vhPxUi(I9y6@MnUIxnkhssZyvd5GBjHvO-dahT7T;xxZ?=qh+I4 zRmmGP>=kme#>bCw^&5IELg1+FN5~glq8kk;8dPO{sWV)~dXjcPJgZGU--oG2;HKY0 ztsAeU3Ayl`;(Ks!AS=S=o~&HH7a?=miMH-JEc-NC9EyiamqpL0l5SjWm$K)_SAn~- z*;MO)R;?u!aQ8872p4jma?50E_inm#S-gyYTQAJ<_p#tL6Cl;fL*W(29W5fS*8&J) zFs%{!Ys1z}?b3>@C&XNa=P%tFZg*QF{esRV&;~F&U`Rix94qzwkKEATU*w9HmbU*e z$)&8j{zMw{MNjYbNO8;_;lqi~0T;bv(=PALs*Jc(e75B|t5Js6J}S{JF& zJY@JJ@#C_kyRQ~&bFxGUHz8@8K!kNKiCLCB1W%Qe`DZ?bmt##+IBn^5r$BF2TqkJ! zOCL=>clMI?=Keg6 zqzxmUu7UcuDJTDXqF?u^U^ZEUFdObB|C8yq@RW|76xMJ`WQ759lQ}kTna91Wod_Aj zL|jY-+oU4#@f--(4%Ppx?mk*;a{r8@LAC#S#CFN`6%B#5I$jBnHIRUt>Fn;(6?*&Z zIsLe_9Z;YM5S|M735b;*vp{#x-M6Gv)g%thgPJfeweUA;osV z>eM%EnTsYHKa^>_tMKC0k=d!5^sM5_OFm?Qrb$8j?&`l3x=$59fmC`pNmNXX< z=w5+&-mddtqeDW#YAX6tLSV8vVoq@^l^*x~T(F#y>xeXVEJMg2;OcEDHf4k0D+q*Y zde}qa)IKw~`Tj%z^&O*OE?Q)+hnKnC(?X1Q{ zG(5O||I*+iHPzaAeYAMwi|zgjsxbN~FVz)dqW{GqcrW(z{BWw%R%w{o z0&;IURxXH;2Jw?~%J6t{=gdiwNZ0*7t*Z6yQQSJM3EH<$&RF08=edL5A`x>>%g?Ic z*HT{3?_94hMPgg*(|Eg~eVa~;2j_{)EJTskzvD0W_7AbOr6&UW|7RT~a0HUK#gYEv z5NL2W>B)Z5=XJlILZy{j&{u|KTBg{%S9p7-;)vjsdg1iQ2@uO<@^O=M3bwQN;xF(kjTB!<6vv7D1PbWDl{ytu{#})cd;@h-q4Rf6o3e zXX&OlD7-I!-7%kL(eT!8EidaSSORSpiO$^Fov-aqbA$)&eZ>*JrA?D*Zw z?CM5I<{~u%f=6?wE#SNEyJZ2nfT&{`!0x)mP#wH{8I43T0T3>O(X`|rG;n%)ZCn~C z_sf8mpd0FJVWN*Tr!DR%ddY?YO@gZ=#qIif9bv97TZiMTu zvn@z|JMHyu4QkyY4fi4^S;|M_X|$6Q&diFpPi65T*IXf-9Q^4$kbwF%Z=_zcJkOxv z^SN$IbS6yNht*23Hg}YJHU+=(b^lw&#TDic58&_c?!{(&RQv$Rrk9|3MsE80%O56+ zf@F4NZ@QiUA{zt>47Qx#tKm0kvq+lZ8*`EOsZeM85Y9F;+jL^XM zJ6O@u5bE=tnD+XNmUsc?+$Db%=&+nSQeK&%h|y}Gfx3Pc1$iCBz`<5 zZ4#jiyIXGrjwyeXyZj9IQyM3#23DD0+SzfWFPEri%5lI#xB4!wKp3Z5 z=aWONtk?Qm!$23*bcb>sUVUC4cv3%AhjTmoY5Ffw(^ZHS)d^KsmimSq4g5Iy?s%8~ zW3>MQgZsqsmKCMe+yy46t2||okd#*p`Mu2mw#g5#%vvM9YuGchy1Y1dOS#*6%DTLm zb#t#~_bQ;szo%7%IIlT#>c7@^u}uuZyd!;|qe4{D3ZFBm4h9?WKP!?Vb<21YtL#&9 z3EHhSqduf&Ae(|cn@H1%w=J|QUlc)Fy%7~k>Hmrdt^nB{j;AV>pY%KEgj#}Z%b5X< zuCd8>NH!y>30n;%q4I?8&1Bu^m$p$FLrrx;TzgMtM&0aunCq&%ycYWI3MDB_a!Dr*Fy^^gx) z1IRi~HzpyO3uJ<*wH20P9+hI5Ef>7Rm1?R-9lWi?;Uz>imyyt_5Vga{8V-5znjEd47h|f^@L*-$ zn<&1tqQ+9jc~+L~AwpAX_-G;$SDyUNhr(LHYNwiDbw%`q^SxOnT_=fW>Qe(~>16)$ zxUqFv!X%@H$qQ zWfCnmL}tm&iI-C}co(CB3q&r&YV6eZq^Y8& zHFp!K)}@_B0r;Q`_}UwMZ2<`w*J*vArwo+wHBrublHg3{y+0ou9}5^Y#?#qli?VK@ z9BSU2?%4>CFV$6iiZa}J4{pe8Nob$w&|X^Q!jB(Ayx&xqjEh)7@;*D?(%((1P>NUO zzy1z2I?grZ_YCT;t?*QcPI;w2k_4k2uRtvg3LCWRf(BWWanU^1rO9o)$vN9(dMv4c zib=_mh_QLz##CxOaWK9mu(n}6!mY=e(X4BR zI_y5dt<^e7Q-7k@tk7Hi!80wr98p%v&3yg7rQ;BvKz?fm2w?32_6$FXTz-ZDXN=x@ zWD-qT+yjjjh(uPqp=EdkU=C2HtO@Ro7%&%yqTP$#mcFb)nfifb*pl$Q`6bSg9j^~H zLQk4tFqZ&L$(B^wvY#@iRyk4T{ti4@oj*>Zz4RnG!YOx z=JtpUzO#>JsQ|v_1|9`PHqs2Iy?QDbN#gUWr&wuuQRX%p{N$z{SXb^kKLH~B&(ZSt zq%%+tVqI39!KzpQsCo;gbQnlR3GknM;y>tNYk0P#W;71l-KnMil`b*^QaP4XQU#E zI=%^QOZp_5f+vm^cVfEbz*hbVw^RKVE+F<^caoTRnXvB5(Hos^_3fDgm)n+gIA8sL zV!5z_$r@g*tBH!hV};C<#Ql?|f5{kRud#dsKNWS}TPng|Cx1Bz66baI1*LdKK%iv_9lX+e@s{a%*)&F@W}&P}Db~h8pp& z`-xgwU)rZqAIZe4O5khVKisZsp;2i#Bm7z}iZixSH&=-<3T456v4$pNM`!PbXGJ9F zT;cIRpv@@+5nQsG=nfIMrf#r6+;^YKI8iIY{X1F=BiiI2J(9kLEF9owzln{33`}2Z z(894Yg01a@m^Q;|7m8;3&*(SpWvR||a(&08U^SpGXKeQ7fsLJc#RTO?id0bFhhZHJ zI`7E)E>fn55GB&RyqyU^{8s?mLW6gxlmWv}m7w>fChfpvPlCKA*ZW}wa{7+JQLc;B zQAGecu)eE&9ntW-hH0yn56*d+=kS`o zfVzOQ;0C=1vwFb*>5H8ezlVML=aAvd7N&BVBhTD>ol?=j8LKJ+V9!!hVlAjhyssE> zq6}SoM_XDHU`di!j?>#;ORZJ9XAn?G7aYIpeIYpYdGwn{QNiP&BAC=Lhz{{!>E09C zs3MqUQ2E@`OjLy~=)A9C09&LcGVhM~*6>`ib-1(E6SW|*&LK08QS+g3-!**^+xeL; zB#L*d&>x?=TPKv{(ze3M>pwA3e^-)tZ_zw^&zxfTG13(AeI1IOTAg*)*DVpvE74K( zP@wpnwY|#Vy}EhqdvFBzdqvw_rBxFz`i}I@2DVkWI9{=fYs_=o0iD$~gI{ss><(Aj zMEF%_pJOhCmE&uoe##w)WR_GKG6qfQ$*eAm56OHI=8>9+eWNDfl{evS&g)^zcd*D0 zFz6tS@DeNL3L#_R%o^JSwJx=FXhRzOE_%YBhNM z>c8acYG9@!=CrAa;l6(YJi&z;Kz!S*M%8r^&p-bP^jn>5koVXRf6-7YvOrtXnjI+8 z!A}9gTu90wdjOc0j1qZ=OCf4MPqC#VPY3bvfT~zMnnNmt^~Lapa`DULNe1~81?_6< zHNPwZfV}+AjU}eJK_?T~Nx@nM%`puj96qt(2Ok6hqut{5x+~Uzl zM2OhzUby=fV1&n+CYa^9>92bsP}@Y9Q`V*MIpag;6G_Yhfh?cZ(_|n#HFj?ZVd^oj zHY*$hCl&1tQ3P<;+BRlSwj2#^<4~t;=YlhfSl9*{m?Tq8@8B5Fz+NV<%uWGjEpY}j zd+3u7z#jz`J9#}ojuD2s&RIXN9LWhp;w^^K$9c7)+fv+DL^yN_0)1XmvK1gSR;Ac7X@%dNzG#l(Ci`aKf{0e2I9`9FjRTU;f%{s zY|u};OJ=%sEV%P(wrz9mQc2y%QSJ*r|8QMjAMw52AiDwZ3N^kGWlPQ*J5;A`@co6} zwWy}60@n2h6T4tu_>g&m77UYvsL^%GB;N==)*&Tsq=if}fQ$N?M`y^!X~AxUowQeF zck#Ej>40y5`o{=BQVk{z=4qF>`v!=b4+2Sa%yi(s12)9vFSzQUzaFdkpnVcj73E%N zOoDv1Q(!L1j>c>B(76Z8pHzL8*J);zn>;j$?z7lFw5G#s64vOWch@Xc@|-|v-Sjat z&7r(VRDY(z%&C~Jl^}Up-l%(2b|XLu^mT>Dx#r%d zu@oc*54~y+PVSeTqiHLAeEU+K{~blJS+pB%ID|-ri^~Q2_Uer&Wws&UXDutNU-c?5 zyOGCLYbrni@qDccu8iLv3gO^;<%Qp#xOpGhdz5=de64fc{eMtgSQD9-=$mFOvw42o zUyJLA*lPIp4=qz-WR3r;D87euJwlnqE-u|M-)3@W5McAbI;|4gVO|kqWY^IhiCSR; zT6>W%+N(AOT*>EQMO|viy5}{LIkebO9y=53_K4&zLI-=pt-wL=d|FbA_Y&_j5Ia+n z&dldx0%fk+5wwEidd=Ot+^W&&@Fu>nqDr?~?JV%VEro9PJV$grUQ9KF1+OMq$pQSV zVICJc;B8_y3F^Ksi0EX>UC(i=xzf(1WN2Ljo!+rqMZlL%HW+O_>4heUa{PEesO=j0=hBh=&l&JIjfazo7i(uE6< z`1Jm-xzlBDKX*-9rja?|^|X#b9s792tP1SW55NqTtzU?xglV7lErpF%#bls0dayRc zujlwzy^eAkoVB_5^=RJV3cZ%Uzjy+=+ZWP6n6UD??w&KA<ljqWPyyp zD+nO5uS>s@V&d6IyoSU+bA8eI)@wQe_E4f3NZtk6Xdv$H)`mMlZC#nJwyuZVUVM4d znV|zUl)lhkXP7hOyuYIH)l@8=jXa%Aw>%{9MlUKFrK1ulANa+IZ(%?fo~}CtN^vcB zLAhNOKVnmroKl+lJ@#n+ETui@`SI#IhE&(DfN#IYNH!q4pInx%6AbY2JoKAEBr*mz z{Q^YqeDuaz69cv);i;7_a^zSFy6EYQzb^AU?Jk)=g|g)GZxxDuX;*U7|Q zUXEBY!J`yLUf$Dm!*XOOlO_#Yr$=nV7SCDlysaIn-ye&MjR+LcNZhLA1&(7+Csr2}*1-Bbc@q8#Yy?Dg)BDrHc*ylncRMI!i(jl& zMTLOjgpOibse!qE;+h~sFfkGO-m`v$kD$2ALPHkZfW)50+jsdabQmuq6>hlT2H}xG z35LF$z+ZzQ0m8FprUAw#JEdEh&37CK#i`s&vc=?dX|C>%=r}jS4K8ZeK182EbB1Rx z4o>;*Q>;lMt%_+=v>5qKW5GoCbGy7@ZWGFDExcNF6?Y?vocqZ^=inf^GmQU~4e zzf?}hl^h2T^G^w}{ec1EU)bxZ9d!R6G5@Knbot zp~Q>SG0pCOffBlZLWxB?4FBIy7259=z)KueTr067;aq2=QFG~>L-RN~(dPVHaeRx} zsJvG!V?o4I(}IXWr?AHSZS<6LK3K>0@<OPGgVbe|WEv*W)jF6niNS1k4;oAIJ8PiR`UDRH(cr#v;GP-^7G+3b2lo_JsM%bWtnU_Mh1*Q_w6rsj` z(oj%!x4M(-UCiCn*+lDFZyevUUU)U_yiMG%t&sV3trRv{aMYcBWq6cvT%ZaCZl>(r zt~Ep8G{Iep8t*hTHCwA>>Y+L_^xHT)nqL#QtCQ|F(*ZXhCetsqM`Xq$&@{#-~|4H?(S+^ zg8bGrkfBt0H*Dq>5FRykYbyexsm5o2zBbWQD@qNVcj0HvM;>acIYU-DVF)=5=J$)` zK}ra1F9Moy`fV_?vcgBHVov^V`}Canbda*b%5T@N6&m7C3z^bw%CiEFmc_R86w0tE z_T(}f*!31Ycgkfji z4En%i9M_ZrdbboM@cb$!xj;8REbCPD#kGC7V?hM?T)EX|ZnJ~1}*O~(32CsCnYLd8+{^*b;sTR+{g z^a(aVw6cme#y4oe8%L*Mf^CpGburH&_S*M1;S*`eNNS2-{jGzty~9WKdEb`bJ7vqGR$+lpFW`4XSEyy<~Od<$r23_VFDb zw7iUhDFXCkE}xAvJVze~b7|yKt3}PdDS2`#g)r?u&7g6amB94Oen81Y@s~M3d2OcX z{APPzOX`%rz65`D1}b1V18qdL0+U;D_}Pp%(g;-Qe`v)VIsbS}TE}hI4GZ z-(ZiH^^uC{@MJKKHso~Y>Wa&B0CfglR`Rn0RJ-$lP6CKbjaNJZ#BB3ot@C!|vVOMO z1)pEQlu>P>i#D>J3WP8IT8SBoL*k*w+ga8@I-#`>N9T`U(5toRxakW z8skUy8#IXwg?RED=+noBth8Ps#F1-d@Ql_U_%-AkoB*mzOQ#u;KK4vu395nNy96Xs z3h;+QNBBNK3qC&w4jL@fRYfoZ8o%cS(Z@94OUI7x$RIcxgGc&>_hd`soO;F@9SGYrG@ht@Gwc3j(xmA@$?7p}N{MAg6de_jSwKlfC>}n8j02 z3r>b-+C8rf1M4J)=Mdrv%QsQSx;YOVdv02S&7MZcAM#v*F0UG{poL&#O%HHUvG!g) zs1|PA`#QoUIpfmdQarxk{DW}Do@F;oYG-9^S#9E&a7IK)XPXV`OGWY&ZDzO{4ROsS z+jx<^Va_`+zxdgk?F-SeIBencLCEhLzPKzp3fv;O*0(_>-PRrKvig#L21S@-a9sN! zk5#pnzB7-ofOJY zI;<Nll;Z5U=~#gL6qd=W9Ln-G}wb+`|>aUSO1U~$IrO(0-AMR{IVjzbyk2{>#_5HEb5zatx#E)&DfhvE9h5h z%>B`o6c>)KV`tIfYW`N7GBn9(eJ0Q@wwLM$g@(&!gFe(JT7Mm`MwgjNP=!AQ3R2_a za8NzhPc?sO5Yyp2{?v}w1E*Swc+94Rs9)wF>(x-18!|W<@Ww57MZYL-!PtUASEzPe zGit?Ij;GXO*4yCk*Ixdw0Af`K`js%6%uP@Ht57h!dppEgFk2k`_f}${3XY!R@v@O^ z<7_y%9kixER248) zNCLcmm6N2sPrw)?Kge-*dV+hOt1{%_%r{FjunfOAR*t1J(Lwv~V*qYC(}~5W6tPY^ zwM2(kARW6!l}woWvq-3{ZeEf(U6;Q!f%SH!!Zt0?s~27PeKgIpgCIiwVEA463-e1g zs!7j{dcvIDMd>p?dMhl~o@`Q*d8h$%^(0d9u=BDF^a%cD{dx|!jrp#))HHDJr!*s7 zl3J(h)FcBSr-iJ!Z@4D!35jIOMz^ya8@XlRK5L4tgZ#!Tz;caI&&8253x9it&EPVY zZP?AL7E=zJD2Or5wm>lBZ%p#+fp*Fk9cY7ol|$o`m+)A)fdhwQSt15005gRyuY(Fr z)GOGYU}eV+`oYUCnzf#LwAMF#&^)y1Y!i8|zMum8ttJCKwRdQRSnfFBd}aTGO4tZ( zY%qA{bKw%9ZNu!*5^lo#oOAHe_BFTl*0>Kq#d?!K<#9`pjrEokgByb@ZA<;I>Kxkg z)LZvBe%y&~4th7Q0M10@Bo$z|(b#KAt$ELe{dFJa;$4Z2Vo}2;)VAOu&3T%FJ8ap2;&bt5Kr8$VVn0 z3?O)#%<48#5IPo@ou5^CUZJZ;ziBz*Z5+s+6XLa#GExR?lyWaoC7>MbyEU=1lt_m( zm1;nrwT@?Co7TYl?txa1vgZmNI;Kpj+K^erV-Ho9$C;(9&0EjPh+4l5lco?X)xR3J zq-7-UBH;2AIzbDZgixJZAg7;{T`e=$>V%`dvBYkFH&izpNzI5jGgm!hya~YwgruAb z%1J+;Kt;#_WQlJYWmS(Y$DF%t6!=?)f0hM?=yu%tU~yB-hTNHkIyO; zc{#klZmOgG!||vwRjMoAX3?#}y7uckG&$@W&-b9iI-v(q!T1t(q0BXTIsIAdyVSG} z4&#s4W9YhOjH{^_8T#h=Z1xpSeKVq3`aTANTsg;WHKJ=(5Y?t^x&&st^!nJp?)jx3pF97~X*QGV8zO!veRlwzDq=4$v2+tr}vvZLG3%cFlX9HaP4?>rOD8+NB^f8V}%j!{f% zOeV`NRoI-WNjD!dS{^M*GB0`qijm@F1H|djrM>rRW7Cy?rnWqh^sWfuO=dIfP|fh) zMtVS0=G2DdS7M{qkJtzDSChjdVJUgv1U%WG=F?MKm5+|_cuA6U7CIKnz&uGtof&s@ z2i1iTPYqz|ir$DhFWFBWi{Dr0nc*4#n9M~ za3iVnjZeKFw{Syg;E6Vw3$Z@3_TGKGu;+w#=~W>{c(G<$a(6?!c)?|0r~dt6Yw@bM z#!wxrkQF3gjdTobO`^jxfQj=Qra%{;l9LdaFEPs9^Um#8O~ST-_dd1ytfZgj+2x{27V*MgLEsTV0UT^#?%(-UuMI zhc$j;s%-{Wi3&mv?ckIW3|R-{_sebvR>Zi0Z_tsd3hGRtNEhEw#0QWR0e>I^!C_n>ZibvF$#U^2>1@`;FfWIaa!Xn8vZwYt?(cM|;io@_r zPJe%QBZ1iU;w14Wd`1tBCD5ac&r+A9L=sn3D>zyVt96X#Fd%YEHNtPAQ<@WEk(d$X+QDTZ5Bj>1#OPVCsZ#;!$Ni-idk>uZ`le-E zlFv-OrYTQq4Lb+4;#peN4#x_3^TEcm2I4$h#bYD|*f9<7b-vsexTT_x`ivRgy^J~+{^NuOy zrP!4xhe5Z7I*c1+X18g0f)pG6j0wY~YZaC&9qb)ESuS4F>^cu`E!SxUajRy%|Ci$U z&l%i*2=64pog9uu&tyfie19smBG_h6Cg-x|4T({z#wzMsbJPEjd=~XMOs7!}fY_ zNL!WUNCjv5w^IiPi``?TAb9Ar{-Ve4It_t!yVK7)GYWKL+jhx~R=vDZAZxRqBk2ex z3toW`k^@4vj|PNV^RGGNWfC}^R@{HVqI=yON$y~xa9b2jJQ&g}lqM&BF8kNk>Xriv z&%BGK_JYvI|2yx&;G;9{s#lPUrFeDY-$Mx@rBF)a8uW@YBmHV~tPiT*1?V*QDcN%K zqS6ooXfyq};kC;{r&fQzf4Op~EXcI7?i{P!H88gW7to@hI;rC|!Vlc8%wK8aPy21br=!Muxa7g z5Uy@_n7AoMXn`FsI`iD0D!7iAdL~?&zq?`mi&8gR#I3=P zPK;|K9FYhE?e0Ia>^sO_7q2LTn#@J2Mp0*SifxV-$}K#F&v@eoL211x*-gLFJuL>C z7eEdMlH>AUCr4V4`cf@WF4N}+G{sikD;&iqD+!?ejo)RT$e_`_YR;>GmjoI~c?VwO zZ;{E>4j+jOLbi5faQAtma~vVvU4WU{@P@56?IgHmIiLuV}b20ou(%Qjh#~_fSg+SEeuyYP2)gx`jKGOPm;9xI2K7Vp@JQar(DJE8`D7vpBm` zrQZKFI`kCbB{{vgA>&Q&ToQy?t^O0u_8)Exb3~>pP&1k;ov#7frAJx()p@UMW@6^i z7Ac2kzMwui3&)(v^)gY@!v{>aC1>)lY1QlG9s(P8>!+3A85KGq^%;cE66J>%QsgEd z>6TmzpEsvm3(I5M9)uCwFz$0=QYiX0COF>A;G93WU!0pjai1acysIouH)LSf%pkWU zb{g!Cw`f-p`R;aVBQi@pLn8^+F+*MUjzhGl&N~F@-|Km82pMvI480a&T{knhRyyaI zbhzv7tkh*~aG?AF)Q4N>e;Jc{;&5Z`j&e`-UFiEj4m}}1?^2fk|9FzPGGbQ+%1oCh zKW|TI?!Y?=viNfH{+9OG+osHpop~`-gvtR<8lQ@?yjNre12u4K<~S5`pIW;|Qfc3B zCOV16=h;pH(~8PoM;T^4A}}VH6F=jwF8oEHP_}&1!ogJPxAS4*Ip`_8r_+s!9#brI zG(_>11mP&K&Bg2QOa|Y?1yNnNHntGFy*nO^XEaAK3YAf!sL50;yt*h4!O>|7g?0^G zI1tt0p8yPF?{=772KDGx!{^si6iK8@V)oV^9-CSjsbir6Dr$G(h@B!7se+H!0yyO$ z0rw17-x+YYQfWVIRkQmRZ(HSl#3CQQDdk2f8(a&Mh3491P6sJ+6^D@1+bk9igtZuc zuzT)HbO1FR_BfkyP5CJTZf0;i2uuU1eV0+EHIHI#CwHXnpBUd|8x-V{*u<@B*=xJ+ zSE~)8j{xlf5Bo0eQ7_$OxD)3~gtfxe6wx}AI^;`|x!RNGzM2_N!ldDs$5eSkstYFg z*VA`5rlUFG<*n44AV4{5PkQ0PlRhNY3;@I&(N96x37y_gg*GX+;vb6v1+dYdl@ zYFskFPz5_qp#N+Ym$55b#@2YPm&bh9y|*NAgToo5C*%{>K>zZdv$ae8P)u8~@Oo3S zgkM;W*P9|S){vZ=XBV>EB))4|6}LNhuL`wDBjz`XmL6v|UUlqeIS#l{T)qK#n_I5u z@Po7NJlw%L_;sFmlDmD@-3$@o1Djk@Gj zp?c4R+!)rwpBpTC3tc1QZYJ*pn5^8mQ+Z!@?NBe(M;iyIf>r3}rxwf-MFqYM$zcP2VKq%wM%aeB|W@z4B@5tRGJp^7K z&}=;ws>B%dyOI@@!HC(!OGEV+}$YD`51=7Da?H z-du&(({m-lFJ0f)QfTs&oj!Py6(A~Uok0@orfc^v>H$NfkGdH+sv$Tm-or`To>@s?u!ejyw09kN{{yM&-h6vBZjVSM;2^OA7#c=O&&`0;rMz)Za~-e{4_X2^j4$uQ zKY!cK&r7}m{PaAG4ifjlZhm=x@g^Z}1@E+Oua%ndlsRK1uEp2v{zh9|R#s~`&rfBo z9=B~z{Ej0y1+jOW$a)x!d@3``Apu0u$u~5oQOFnju!NQW&~zEkYZa;q{)r8FtXnK} z@SGucwe8c5mS+v~OUTn9QbV9cFZ{_8LVRdixe4HT65Tv!tuZco_^mZry}~@mCps%1 zme7PCwmkxE3dWyl;LT`Wwjir1f{QLd7zcRF`Mm4vTA&PWNm?1t4@j+3|WM8t*IXxQ*t9h0A>_AUkRRrsRB zV&54fOR7cZO`yhgY%*4ja{|M>Yu`vYNG73gZuypy5nFUdXs8=Q0wS^Jj< zhQpejqZ|0_=5GkH5x4(m-5%d*s>G2f|0jBR?ZIOSg8b12WLSiI9J=S%~AACamx#9h+ zkK~w{4Q}JqcEd++lEHcSUbEmFN<`h6?BDiUy*Va3J>gy7BNORt*U6NAu$|LD;zlE-MUPm9g z(tdlA3uTf8l&%`ywthnPze*z02}(KbiVNP(Cj;;QGu0X)tpPk@>gp9}$*tn`#NkC* zCHz%|`$uclB8IO7K%J-p(&OYCcmx6{feiyhYf4M@$ym^aiY{(4tR2lnd$Z}6a4qa5 zECsAB&))L2kA9-1k3vYt8@wx-pHgr&*&HmjOyz7p9J%Yn3^Q&Wv8UIR%j**Bmy5w{ zXbjt6p+sl~C0;>ERX~TxTY&hokX>k`_uH23B1~k?ZW$EPI!}a?qzmZdgh%hKu(cra zAnZuSo=?RI7rGgj-}TabJCR`<7zsDhD{=~uQYM3Ay3;b)M~22h2xa`%lL`F*Vp zdJndLCB%_R8a$3BsD>eQw|_SWS+vZH_}1%gPj7Etyh6;xa;)d zqI_=#tQyiJJC)Kp2x5No?zG;!ofvgI9f$bZ!r|JLMX~X=JTF7Lb#)UEm``^7`g<#M z3jA$5G|{XYgOLUoRgU~#7@#s+-CV(#64T~Z8p7Y3V|kJhiR5nl#$uCQG?d>^=EwCV z3gp=MC{0;nsMVNC*HVBH?5@8&{uP}zSPz-Ux226c+NAA!U^wx!VgR9rFrx$%vFYfK zH=+7~)}mEG{NYBN<709J)?GSR;N-rJmUgd-d&~g!s%EbWS#>B?RrA+28Qut)wW`K+ z`pQEUz#q7ckNGLSRuvP5XhdYZp2lA0#gFVi!J+A1jrUiwWR1L z)sg(wKx3n}L%Xc<9dy`Hue-77^X1E0+WEAjA7qEEn@#**zqGvps4n)>CzY9dcSxn# zvY|r+-5bY-G@B({8z>`U6xv~iWR8`nzaMnI>%svL4vjQVEVXl%zujNf@bKO%`V+#OnUBC7N+Aj1|2w2&**r5LL~)(6x9b z7gFezYRAp17*br;jD9I<*=_6ZBtOf|I1qR-ag)cgMV=f1usRpb?fkLsEk*e+#36HS z$fvMhfn^7FcQH&<%(J|oV;-TWVV&){)v=t*&zr1D2$N2mr{=!!`&Ednm<( zq<}ixP;_1jk~j_3Uw>qoelicsquesqoB)P)0zUcIDU4M`=xPp zG60=kbg`cG6ZAx@&V(hoK-2R*a%LuFWkg@Ln8073Yv0J z*@ilN%w}oluV`zm@Yf{j23(gSDGEL)_B0fN%Z}SiJ_q~X;;oRpz5VjQoYm}hy$|_SJv;1y^=LU-EK#!gp~M=|$6#h@ z>-m=R4!)LxP|OpmLu}J53BkVJ&x}dw;Zf9*6%*oE{BO9o-zs`gp%sruNULsYo8$y{ zzYfOVoG04gaS(ak*AlrRZ(ze%Tq2=r%wNHu_HsJk?|fEZuv76oXn7Mz1op9VmI{(W z)JuKE_Ttm=kIw|4Jckp#wSl@`^<(4{M)8TYhm@_0Yx`N|E|j)~pHBn3m826E!4Tw- ztOCLo={GAkdAKoI9CD$GJN%HNR9fvRjnN6KI2Qv zh$SwZRF_VV3Z+nR;trktc-{Vz?3eGuAH-TMc!e}$=hd94H&--U(RdZbol^>K6SX6g z76TVa&pb}>@;PA^&fR{Z4hs}NmD^Jq63c5i7%d++4uH(FB{9V^2d$QQ0_$Iy|RbERE9M)_MVxLj$)zn~;B z7lH*0^q-m41^QPTTpVe|HsOCyisgQ*KN0;O)VMjKQx}kDXcGiEhm^$?bQZMG78wUek?Xa{BnV1N=3G zM*Pd?qFr)>1bzN>0|lSw$RY3-XASmqNxkYy-su)iYclaftjb|h4Rt2>43e~3_4A3c zu!rZHrGhCxC^W%a|13HF2fl#0(1&;7|A!E|=R15MmwIzMNZ>PAP9>;mTNcD;08}hv zs?Rxa%7(u0^lWbbTxN0MPFN-W?f%lEWQ8z<1|S{@uO3dOnp6~ME>x|zp7B&nzM<1< z{_!aMa+&-lrHH?VMW6|C4?|9FwrhBPMd1J~?2~fPzAZF0p$i0;|8PEDlJ?Z9>UtjobwRIvu zBFeSV;nozW#cdYe8iV)@1`WTeJ(dn6xdpJFuGo$l;O4T~ zUE|9O-|D&W7}*6{CnIG%QAxdaAa5UYuOdiOd+wa7y>Y6U+ucM_3~3;cL?OY_q6N(x z)@k(`dzBF7HuG?7WhGr9?bh~tth%)tdQ21y))=ot5+|o(tbm`8Sc0N>8Y#V%dBx!~ zAEb?gORvliLPs5$IIx6VsDdH$gYPdk9-t+FF-K>wc4)k|rBf7)+jLr(KI)3Sl@&fgu-!_@<*4!;FZ$B%pjPWH>TH1L$D zqb|p1!H(I3UP(*vCQV@HfQ97!4?^E5f{hmP+!-gHj?X%fqU`qLQFB=~?At zzSlzp{8iT`l^qzj8y>iEj$6ll@Qc2?8xZ`?t4WfESc~{2sooVnep~}E1s?*%fkH9% zs;hvn7u$5Zmeil5LfcH}1AMg0@=<>RwDrF;jF)DMk=7URG$|F*9Wa<@_vbBYT^vc@IYj8`*&ZzZwHZ48(N1H6R!dQv=7+a?9lt;2Tf279Z^SH3S z;d0C7yQRbV;{qCipYFgQ`?DEj7oqM9ewrKWy!%z?ZQXBW&B2n8 zja?p{6bf(wRYOiH+I;$z8=nh<@<@hlBA#n~r`|PR)JC5}a%$FdZ&9YBU&&%^+MmOGJ!+l z*&lAzTU&+Oj~O(&AuV*hByY=qv!C05eiwP$oy_dgp5#wF>~7t0{4?-R9?$^89N)Hy6u&6wGV)BOmlJQFP_KniQlWV3gt)X>(Nx zB<#%mJ$%XSj>uL_rt@3|$JaIYHM`XsAa->D%UmWD8-wOzumL(;x@z6L{=QH_pD3wY zCMWU2{@r9e-prHwB3_;K9EC<wE`M{9^+2#S(; zc}126WTAaI!Vq}X`brFRx+Oez(-PdNH38!X@I3M+ChFJq7M~~tCAcfG{N#4WDa-B; z!GmU4Uq(qhnkz45E>V!@ipl%Q+c-KBd?E?&t%-O zCxo{`gPGvcle`lyuA9B0rIsUJY9q(G6&RD?c zy;0|e$L?Mq+|wPJ3VpRC>WkY5AMO7XS-4oMP-upz?6240@RYkT8032?)i14*{e^xH z1f${Su}_1xOpq-3YyJ?qoypoCw;Py6YRa!3?Xj~SfUi}J9OlaSteNS!wjYu?#v6#f zCi?-tt|*nV_*qzTf4R$0L=)f@L1d_cvw9mPeu-6ye@*pQ?&%0-;>P?Zm`WAu3Ln># z(x1oQV7&fnBO^n@Af&%h73=if)vk@grEEOMd-5%3q>U(%VFB@*W^jI)_A_$7BENnH zG7@#k-MQ(4qTS}%T99>eQp9x;q|JG`gMsy_A@{0^4{+w*j8PwCml|@l%Rr_xQfrqo zOP$pFNlJOsQCL-ztE8awF|bRGj`%rW!K9MK&U8#Jh;W)oz+v%GKZw~JQp11d@aeBp zGAOq~Kp1x*55v5ZX7%kmh&i|-|6<_eJmIx`zs(3}&;NV1iD+xBc{Q?w;h(c6|GgRl z8s!xMum&hwhrZsiA1649O;8IP%9-KyJGc1+J#WOzi~2s~!yP%NdroTG@Cv1EIGJOwFAh91543FO5n#wuI5zpi19bQFr>bL76?{v5%`O`SiM(P9FoNVwxv`m@d0se$}OdV+4(^L zsytpCbZ@&bu1;+FEnX6>(BwWYJkxsklV!}nWmA?kT395+)yywo;38#YLVy z6Pal3_#jst0EAC#A_3|x+e`h_3rU2u*nE-4{AiXMDi)m`%j+A2J+${Tq*7)t=?C6r ziMb(cFcv3(2#7vVgj;pjjfucAR>n^kzBVc?R?RDSaEvtrf|-ndu`$vEo==~e6^(=q zgX-@cy_6OBQ!qqOl7A65Y?TNqXmWk>o$sN08KnCRTn5>tf(bX?N}uLeA2MDZ;d0Tz z9o&9T+aF==@x0Y~r^@va9k5B&eQATe{QKuGG0`RI?w0pQi}0wnvCv1iZ)!6#-S6_| z8??w|SkQrgYOuR?nX5_quF;w~0RY7gUG=<_{aX9WVH*vo@QyR|gjEA1s9S&cQ9<21 zBW}LAnn8H@6SWNlwnE}rx_*pT{*tV!CN{+rUj-e0zP0CXp2bCL2;&S1*gWc$%22 z*EAftQSDY1g|g6eE`b3NO_yJ`$_j>Pe4@*69qE(hJ*)bO^jGPxY!&g&p6r;r&%8=3 zt^56#AWBrD9(+4PS{kqD;PU7cG0%-JXg=66 z+2_c(QJlK-{z>cCa*&R_(~!3z#jD48(DQI3VT>AtJWmH@V$_XGx;+qkc+?>^2I#lp zuw$klkB0RM14{I>Z`P@Jf3ibFE5h2nL#+6Lox`QN3Z$fica-#~bsm8?$k}b;vSM9w ze%vN~9i{vr_mGH1q_wW(07tR(YZ!^fmso5H66Hq@(g!Q?9pb-vw}x<(*|@YwvXbGU z{lMXS_StBm^PY#o?2vcS4w*<@Ptr$K24#Mi$=hpo%q6}+Z-xJ$pO3hD{A|&5XbN4qSH5DE;ewN*0tvR?b2|c zatYC->b6epqSvd4>!8^#<`?{#vfYZXOCII{-IgS+txyadkC1Bp&-G(o&GlGH@xwE? z@p{fZ8G3I%jWm^ZJRL5b^B3!bv< z4J?X?)$?t((A=6bhYG4tGCI9qS5g$pm>6rvVw~ro$EJwW#4wU z@~Gs{XE3(yQyAsDrbGVvh%Tj@xTx26_n(f9<^XdhL1(<+Zy%yKYyfW>{laE$PDgBM zV4Roz1q+78Kkvqjx6i3Qt5iPvril1-*9SPrx+Y_%o{yBjT-~?m3u)4TzQAK7fDRW* z#=)WhvjSLe-G8}1gFz3Hv-c{eI{P9eu6HRX^Mzca4TzgeA^^+fzitz9lab(wjz4vj z*u?cy(NBt2d2YYUy0drombc&=5gpVHzwX>Sjdnk753N_uiY))s#(Dc)&Dt$I-wo+} zRW*h&1E6of-$0n*A7XyIJ2~&e{+<^9_pld2UXtl}=B@;ix8 zXYYmmANC>@f{S)7&I}P%(8x|u`RmwaxiIv^hZBa z!k(G>Q)xrMPH;Adw{lNPkTJjZ%ZZ8BgAuE?|Gv2PuP(KgTMCTekhJtr`o){>hM1|- zh~{0di|Vj?Sf$|qSt|Y4)z#EWxcxgV5uQ^^V1GGikmJ$>El}PPXs6ZCG6_l<{!kIP z``IoPCUT#-xOVMrtsyv`50s6IooG{`M~O4D+ucO?v2v^?Tg-e%9$u&^k_yPu&|d#J zl03L7-0_cFU0n-&e2A$Wx{E&h@4H2WBkR~i?_+rz#KYn~Nr@1kfb?ryygb_C@ zm%P1Ln-7$*?7s7d8d-@DEnNJiMF2Kg0jVjtQ#X#$r4AbNDRz0an19?_7ZSo1|M;-V zDDm!OaK91|+(DqJnm?HcjQxSKgW|Gfp^AHV_<#9FDet4k+? zx_|yA&ujlxSb;vIG|XgCG1GJwYJ=dIE`r!KdGe&3AA@JNSUH$mGD9{lI?l;NC%krV ztrl-uF76RT$;YV+V5+iOV78igb8j2&MZBc%Yy8$OGal6AI_uK06|wIBImVEj8f-qX zK5SGMf)`p|6*`Mg{O2PjEItnP;hNEtH4!iy+Z^WDSD3riJPhUq)ht^0)gP*8*!P4? z$&!v5Ei}D(CndISD9xFzf@uMfzjoA%71pZDpePj7JI`nJJzUN;e!lI-UPE`b*-CvmEch!`-9&+$ zLLyB~Xn&)(y4f$Lzxuq0B~{?>wT^oI$^Bb#)iz5XZjz^_FPi?q$+dMWt@mQOBlv%M3e zkWB2Z1x`0=W8OS5$%!>N0*!d=Y>$8^QrgTyC<}&#ECe@-sXwo)|BEP%WuIUQ*qNK-!)lT3bf3&vS9>mg}!RttYBiWa&XDk4#goH-xCR$)64Z$~D|u zIf)zwNRLVTHCbCWes2_{L&Y1^^Qu5bf$HG$SFjCkE@L0Scw&2z9=h@Sr~ICef`wvA z?%7^P8YKHz`SlCSFxJL=H+bTnV09%lQHTgvWcE4M02#)sW16I@cHB(iA%5GJki||h zVDDTzPxH$FM`}Nyv)Y6%+6ey**(-h=0|*)8BCJW<7UO?ZIxWhJOQ$#D*nG;&WIP&_hgdN}nQziM$EAOgAM z0qlXe4^hiLEA#X*ih)$IBkq^D2rR0 z?w`Sj9ltJ!0g>sSka$ove(%JUWJZZ8&_w?AZ(FIWVJFu*y1@SE51Kp2-V+QCMz2A-=0lK)N>H2t##&f@}%bTGg~qto4|Vsn ztq5(xoU#%_T@DqB+(%e(#Du;5spC7_7X7@LBUD(rm+x5M1^*{HAN znH-=K&wDC#=)Im#`j4mB!Q3Q}c(Frwi9cWTaThUy0YE7)z*q0e+Y67qgo6O44d-$U zrQ~<~&`d2_blwll#p&~@S`qj~KgP5@s!n8HOk=VYwu}?=^srz^_2=ggr;)9bkwWf< z_!6~=45?T>)KYoX&CBwH$1q>)yW>d82#Fx^kF0a(X-aOAxP#g}c)^d;maZ1g8*(<$ zK)O}YJJb2lg)&>GJo&SzaCgTXH(2i;dK)Zf!IzD#l&sHBlqlve5t3gFuyHs+pAZsy zXc6H(znIpbIHt~)WPJYT8##HNd!X3mlyC6kOdml^c}{-6L-T3LID5_jp|9Hz#!z

Ytrj(xSgDuW zal9(wAwHvu6Ar6k&g~7cs+8yL>z~*>&uQG=FgVTHgXW87##@~~0vJZH5*3u>A{2T@ z?fFE_eovp)wXRX#G_gCFw9dS_(l#;gzuL*scV2QO?LZ=bl&I&enrj_jJP`8Iez(51 zJIBv=``A}tpFYm+(rM!ons*==oo!#(m)m>q0_QF@oL9_a;mJ7Hv7XIj;so5%l(wRf zknvBWsZ!F#*x zHJuW!FR|x^O_ZHJ8}ow9m$FnU@%OVZi{S@`kfct=1duB0rDe0j` zW~PlL*qNkz9kpsGO&_!DUZu#Q1|m*O%Dw@i$p94ZF z9r}(nIJf=MJ=v8YJOVVSHORb(fa(%faUKH3cq1;^s27L7b!21gnR3(e<~h)2`pm$& zn!14328(xmOSR|oG+CCLYD+I(-`xM4?A!jtx@@@3uVQfwQaNx(#Zp7!s1f9hzcgU( z_C^1I2eh#vN$~d*K{yu;Ae+OTy4De#Zdtgjm@V7!t|0?1u;By)K9I0e@Cs1}67Q{y zf~eYTCvlkUWgi1@6^J61_#R;e;(x>{ip*Ty}9)H!? zfy)dj7#?VNsBS&9clNy>BxZM)yDQ|5FOYuAo91v>zoGpnepoJmqJ$)5qewjdVZxRT-s5C(kEOaSK z2Wd8>BUL(zbVBbnfP$zr={<;aLMLUJNse!DiPZ^|_*QKUNS(+;~wNX3%Vw55$)NG^o3tP%pK z-|`91QZ5^Nvm)MTdZtS*^@`Fe@sxL_M0yvnkNa%R>TRbA<)hvo zO*tQylYY^FgcXa>R3d04>->$waL?QtG8-Sd56MnvnZWg4tqv-;5wl6376gH0Evjy<9G1}0m*?5@$xUGO)1sf zZd_;xE(n{OT^wb()$eZgmdsWBF!8t$ptC(wrOP*Fh;0zg@(!t;8qb?coZ;t^z&vT= z1xOQFx z7`<8DTGFsqZRy&~U|SOCFb@ed#LWcmfw$)G{D+g$|8AA)VW7KfApin=IEQrgZFsk2G9S8D6Oxf5PZw8 zF^a+V)(srytTF3eGzi74TKWXYO&V%i?8eH$u&hPtVceO6sPOfr_@Q>^?f7vwp_gJP zXkf-Zo*&+@(o7?f}A95FBnO6pY@Qrn}Twqt>1A?Segq){8i@=P2$nU1vtl8Hmb z42GkH}u#;(WAc^EaqFt5>hj4 zmA=)vBwL4Dgq z0!Vp1ySDE*mhlR)~pjB(hhgw9BVfFF&p^=-Ncx^<4anJ~y2O1ND)RN>kk8@nU0 z@bk9IiWt%cXz*rUK-g85nmKd&76ag>MDvtXx^G~F+}j(&l>hWj~2~bBEg1(c?AF92<>*U!qcjB+^jMbe-Y$K3vHf*2Nzj`t{5x>2`3!24%Ciz^28m`Q=1+^6g=6P}7!}L= zyrfC}6^U3@2Ywm!!6tL8o^Up7x&i`c02)zd7>|yDp$#G~C8o|RGwFjk!S%213GNPV z&NI#5ab6s4D6X*u%zaNG^+n%>vvRJdMXZi%ng01x-rDA4OV)!AHYdN!^jiIthEB1; zIAJ1Tju$i9L=JS<)zX~KVeC|MZtc>H96Rviy6698TSe8fg))2b;i;f}EU&x5_+trJ;m{<3qpMb^eGH`%Q@ zW5b!Dk~;kE#}iJj^@D$~S?UTv-fo2Rf%6%6|5>Xb{r~CXkOSq=t1JHeE0(UAw6v?a^1gv@e4s{xt z-qu(*>}rJdJL=(wP3InYRXMKnC52v_bS|I%1!J1N!vc*>rljX^MA48guK1}s&Fj>n zAdtNtH+(!L*2ETQ|KOE~VmAsa1zJnSM*lI3zi+eTOP?=CcbaIXck=IMBv?LjXF7Wq zzZ8^yeb9}}`~i(2nf=TU2iyZe7iq7`9gO_i2$wSYZ`Ft2WC85@6e?H1L#qyr&D<4? z_2N>0gErc#Zw=OPLQTLQ`2N84kvk1-muF3^YwZ87JUX#~qwmW4ZRws$qmRp)cHci$ z3-gw6rl1@e4#NIR@sHW*=HD7;!~3FDMc7JMHUd~q^AmXkJ1w%0t@BcAZ6i@1brYC#l)WGt=3+8J z5wzoX`9omjBrRiMyVLkk7y!_ffA2x*`TXl$yU;5!+rfE%;(*e-jzs;oeH=71q~nqd zK)1Bjr{5K+sQL|30*b3_jAQZ`!(*yPo|@Q!59Xg3O|}sUlaN-|Ivbu$gxrm3h~sCt zt!093W^mW9CPGTMN(`TF^gyui;Uzo$CXn zX%?~EpzkBF?h7g0T_WwRtrry^ErdeY57y;+{qLs(*%bb;n0jxpljj=qWQtq$;abK+ z-BkpuixiieePfhPKg@6bT9b+%a)XtEQ$4I-04RhrfXtk4>)2o$A#ng_x@wE44hS@` zN&Cht=~vY)4(Tv`6HE2za<>AMD^uui@(<*p>$>F#YUWaY4$Y7U5ndkpN~+Jhtf4|q z$(5Ob4Xf`KA8fq6R*9`YFzNQ3_9-hWlutj92P5SCe7nswq-1xE9iOe`C1~F9KjVNp z?@C$s$M^$=o}LteT-gQ7C^AN3NEB-W%p-VJeS&6{L$m^oCh|UbeX+rIDrPYNh8~YL z5QGlWC`H+&uK= zJE_+K>DDdKO@!?|YOistNg}Gv+Ics|8N6yM5?_J<4TMYBC~Qw`Sl1AR?yY#GVhq$t zhMzYv)B(kdO<6=mQT)4$8TlhL;am&Ug-)?$uPr z;XfLztA@EzsiNa*Q&>J;4hVi~j9^Haz_1Z4j@eHDyjsH60SH-n)Rwd-T8}ICPhj_DC>{L8Pi@HCy*m}fIw4F3{F}t&a z!|@GCqfC)PLW)_R765$@e5u(Tz+AU^$>~?mOc_WQ4lBd-rGhxpeOCGUTG(j!4wB&@ zxsyey4c*&vbsQ2C;ExyCdDRgl^{r&- zStN(?_fYL4OYmI&rNhO;+XlcA6>R2@_|dC?s0tY4<3O~V=GLUh)NDmC3BWGU{!Mj2 zLeAled=RR#kf_R&?|vReu9nuY6$@MPS@9gTVZ)r9W27M^na?poJ3SQe^2FppUbd{2 zlHXlN{aZ;jBVFmDnR1z6^0}X=rZN+2t_D2}W79QgxDHg&Y*P2hxkwztZI6e=#}{$ME$0#(K4#X<_OVMf7Blt3d50XJKAeh>(v5W#R z${2LP{^8}JLD1z9;V6w>$OiK&MO?%D{n}Np^IKhRBqvwMHJ1v9pWJ&pQT3Ef4^RcU zzh6TnJpS(doK65Fx}p8ka4KlDqY-h39 zbUHk3>pGc<`6Dl}8xOwYEpTscc2s;N&Uxs_!Nj|vd)af3cc|+!@B*!)Fs`rAgef>? zU7Thn9+`(S#nc#HSQvxsO1Ps<>*DD?j9x1d(Ad>6!G{J$$caMWsgp9``OW$QNmuKE zM8?kuNVVRSF`NoIWg{3g)7z33BM?(`wu^uM^2fQ@3QT(#zLNwi?VlF4kwy;(93MhK*@o~vtnf!a?B2@7sG_szg z^4g8Uv0;DM7HYXPtBwro5K++A;u}J~r1OmSm()0G-8BW;TH3rdIdkV2$s}i_cF)q# zK%CgTzK&n25AE*w*sy)QzCS40xagEFca&j%BcMRrbro&~)>Sbd6S>8&h{cP@lNCr8R=H~^jn9&L#BXJj{%9Jee*U+0O{V67{ z0ivux@?^B~cKeBT(+_dQvPpltvqo+NCn;t!CSJa~H$)$h%_<%Oc^(=7G_>RPxag=a zN?n30T(+uo(~gh;X^5i3rDBW&_PR}UFp8WR3rkKWk(BfrxI1LlU)e{Jc3wuzj$EG@ zKE+yL-irc;qa?@ke{Q`bc)9MzuZy&&?Y53fClN|V7k<=TmW^W1BassZPm(dr3R~CY ztI)VxW!2=Rzwxyrja_^@0TH5zqgw1s7%F;2Xz7&l+gHngkn6ng7^9mNwbP9lzL9Mf zL>z@^DXiAajfEFP#6&zE?MQOSmTob}cjoiq7r9i&#!A~x`R;0O+ri=4Zo|Q{`Dg+` zJ(&PcYFqoXn(di+v=U%^l~ukMB(%JBgl}x~p37pN3k+Ox#dz`JJmp_yA1xh1nE1Hc z?ndEkGY?^$O-1onTw6h95}u!0-2PeJOhQ23_Jn%u^uKW_hF=0tV(c#gv7+|xry7X_ z;-S5I`gAs82SKN5=8^~8A2blj@so+$#&VXB^zq3?#OauXB~kd)^e0uvOBXYtUGrJL@WK@ujv|12LJtS60zcP-7E(XZFcJQh%yF(INM@BU{t9X~b>^ z&*CiIwnWdWT(9rbiIb-u$A9n;uk~ZYJE9p!u-oa5;6{n70p)Mxq zAb60~x!Q9J?V7dxI(rCTuky43mQ%(w^y}<@)vXZYw;?&tBW8cqo)!P2ore?wh-Mfp zQa7`_N?7gdHWymRf52I%sRMi$8?)VRN}%&6X>Byj``t`uYo?1^6lA~(nYVCU1{nXn zx?Z{K7^r*v4s)%%rgogxAxHe=j9k!r8Itc&dgbU)5|*F(fZvw|kLFzpQo`#uWG$)@ z;cSfpiz@v4Xn@Uf4@(_nULX6?7&nb`J6!uUPPzVE%WrR{oO#0X| zAvbacN4WaA&Vlu6WmWZZr)e${RF?T1w?h~yNEAQUTdGqGlJ$l$RC@lth)|UQHc1|0 za>S=OC2Ta}JJK)Wv*ZAj z@2X`Pg5UulaxxAPP=0*ZS}yK-=>q7GL8Qp}#}M%v7FkAF7VkV`zK`Jfg?G3^S}LMF7bJ_%IUHQ;P+#OwAxX|xz-(O&ZOE6~75mT1F} zRqXdKT@&>>;Yfun8n>7&W;&=~ouOixAG-qSav>ut#5Z3k!AEYf6@qLU)!9$XTy28f z2#lwgV3DUJg=DF64)Qkq<%6m14oRv;{Gm0R_7VCs`adjyeJ&QFmEytNqPhO=N3Dbh z5BJbX#R9Ra^`LIR$pgWvg`71XK{DM3`|8n42QD&-4##$0n)TsI7NcYOnrW5@h#n$> zi#pa}XMUkb{TnnR#47!wV)9FJ)0&%Sws%?eYvC-ez={ja^p>+*!#th_m`d~y=zDO_ z-&WGr>|EGl{L^cq5ynS-`^H^tMk;cwMhF)#x)It!_%2onSM$y5ZnIB3Bm7$Y#PxQ) z=$k{m;FHML-BsBiZV1qnkGxQT?8+IdSwQxnr6xf#vlnmh=l7$plPxrjYOMq)7i2e3?R)pzTRz=v@ZXWV1F$U_`b4Vq0n%Gr7PIybm|yZwBr=3Z$WDIl zzt923hX1hLCzfZ8&;0K~238x`(3+H5BXH92=cJZoEF8CiQ&->(`4svNHJ+p?SyD+G z^|CNne~ESTE7h0^0RA1Id&1yg@)Ca{ZPpj6kJ2pi1V>*J=6!>&r*bJZ-&N&4mh3m? zMcQ)tKRj8PpjLmxP^Oaw&AQ=O6TRruQPsd4qCydk>NwYrqWQ2(4>^ox;7qUTN)atX z1comjop+fal*+`jx7#G8`ER~pQJ#7H2KD>%AOB*GB;6L=8InExiW0>w z-pGP8ZbLQy|^eU3%rt& zF8>dVNm$IaDZ10Z6K{l_$NRC#OACp!r?pG+zsaujt6>39g_nhZI~VR zl9nxu-_QN3x2qqCuH%DgSk;->)h((}ekf8rPZwqgar+X#X81;tIKs==b4G@k$b+aV zC9-*cSm+^AgbdcPG8AnU?0n{e?3)~B$OzTu z3+o=SBg;ejx46GKSUFh%;s#8ynL>w~wL0{XOjirQGYU^j6ry3w=t6x4q|z2dLQ;MPWBIn3SqouJPrOarulzi{)AiD+efX5N zg}o?nMEhH}ft3`F@eha;@*Xo=#wf=VKf1aFJ@|kT!VounU)!y9bAt85$SGVf3_f>I zyCN4avpH)`w`QN@vdd13^xWUw+o+~^JvIB2tsqkuIV;>+!t~VPAd~&HOm@!Hm3y2; zp}yy1d}3Fi*v0QEc4OC@bKVJhK9_xx9j*3y8E($`x~Pv2ny=S9;h$9@0Dz$IJQeQYt1BYr2FUwEOAyaILmV^^%G zM*furr~2DoA0w8qzXuIVCD^;8DHMSLn?!meZ&R_)=$0|2OM&flzQ zC{h83(s0OfN|e>QDXv&Z|AGuAFp~U}tv3K&AHiXonET{bjVc}`QQRpPa#fj-J;)uk zQpvvCplGR*VLW`Xk>Tl-5RaT;y&P~m2>y@CloQ=GNo{;o%mBueg1{245UCj!SmDH3 z&d;Bb2?=D5(q{%ixkGN6zJD5CL_SF~!?@n-I#$-JiR;=ZDDRM965n9(MAJ}Uq1MQWgHZ4;yOdlri^lM%X4>lhwoldGY6VBx)3{A#*WhADBQxK9) z#V5h53L3_76`1YLL%2|;83N;{C;<}Z- z@tho*|1gDdeavefRDTM8!~;9{4&r0Dd*Mbh>Nc==*j6vBR??Qq^fXcGK1K5_vc{{- zLDmrWj%jY~`DZVcuD~>xaxk* zeuo*)PbJVp+rMr8iPG0H3e}PqQ>CezdBWlHK+SUG5#BWTmW!9X0;W(|!NXF!!Hc!t zVF*3w|HAjr#v8$sP{V?%`6^i4DxE(&#pCEA1#EGM$rM}!>-;PKbo}?ViBcO zLLEyaWb~2uYrmK4x7K9(npZNZ8E{wroyU(;-_!prK?F)`xn_-JEn1{w#4OCEDjh&! zFhpJXf>PjFY2bree#LmYZ%SKFZ;o9PKags;($+X_N56Q|HZ`xMn zuwY=C2CeDCs09S@0dB5)0ws4ETKy^pq-QUM^vzC;ur0dRs4)s+LgKl=$nmoI;)yvY zM{S+7K5ec03CWZ-RA4I|TRq2o_;3$oWzQbvPwuBDTVp2J z5NiKmN>|WQk7ejJ9-j75S9vWL;+y^C;^Qrx-S$U%lmW%@opr*uP|yGDg`W1J*f;oD z;82Y8{AlNS_u8pin5}U!S)ON+gZ)xsqBS3P*stR{Nqh9WHBi2KFlc8o`0bDmO(m&D z7GktOb1Ecpu5QhNjX67r0LBecfaVia22O|O@R^RO!EN~3V2iEKQaadn3Br{0u=IMJ zS(SOF4&@iCsm8saz986S&ue${^FZU4wR!=Z&|IBWLG)zR9zM6mjBvRw(P{;6IgaX? zIr%*B6TtC9S=M9_(h z=j(>n5vG5ahW>d~uM55%5Y6rCFw-<6EiO>!u8?nIc4c|(%44E?ft%C>*altd7aR4+{oF86EbxDP|Xm@B&=X0bvr8=szpglxZh6Q&lrTY{0b20 zs%^ITwIc}BlqN|Ytv^-wxW{o{(>;qr3pjj29~|Rk8dttE<-N)Fs_p~uR(*Mzg64c7 zS+jk_luX~Rmm{Jjji4u9r5h%5fsvDA&yV_z<|D*k&9_Gp>+3#JOY64b7(pi*YK4nS z6L~>KC9{h9LIt z44cxCy*1sNRT-$0jDJp9k!~W%D{-w&fG?x7Gc%_+6wDOcLLJ?0`UREFuI{P9X45Y4 z6K1=KF@AHE<#kjnA#_46nbw~Tw4y=wr|yHAKZ%MOj&Y%6!%fBR-U z+`(gJRO#tf_F+HIy8YO)s-Rr^I%t5w#FHSrIVj>v&;mdPRfjj%izXYxSw$w;l+2`h zKtHD)on=Q+<$@3M}0I%*C4C#F~7}B^LdHUGND4e8bkCtl9=v((2 zAH~*_OYe<}Rdq_YwXOpRwX8W$cF^{HI727a6ITb{AL05dLkm_SCIP_6(D4Xh?HHOwA~Tkl$^_pJ!^_ z7X3NCRJKB*-uPoKYE8fSyL$a0T@Zikab``xX)R$$TD-hsGh*7mG3oyw5n&EIX=9zR zVsn^7RF_{y2A&deN#c%zZP@nOGP<1KlL{AiX$&6R|MOeScKW`0zEbu#q#Z9gW`Dl^VUCJ4*_tmt9BSfh0@NNS+Aj`H&sCZWdfop9VOXY-;$*SEboFvz~Q`k@VmPDy)M zePLd(q&FvzR%a`lpvdMHcs13xeP*6wJcPeu9-8ttxXhBRUmqWB3_ekEo{(S+789UN zIr8-12*wiO7710+w=3Bv>dZ`Rgyb5~K`SpeRrro^jV-4`S!#Wg@;0TA(X<*K(uuh% z^f<&`_7nmg!1v16?X~VlY@EM^R6p;m)!;;w`D5p1&!6K~z-QwI67mP0xLxl$On*7v z$%q^3x$?CQv+>1$&jq&{x=_$bv+$fY_yc0~2k^i#^g5fl#oTp5%aoYEX)(8;<3n8bp_d)@L z`b=Yj=5|!(Tt&Hf=RWH6SmHm}P8xY`$jeyWT+b^WSDt5Tq4fCsbx4m@VyT-wZ|A1M zk+pJ2JB~ORl)^~7RQR5lq{mbrrmR^c8DumN=am7Tz6}o21?KQ6NWY2gR!;*7mb~Pc zUz8<+14MOAPu!sc+s?~fd=YVUkNSPTMb>|1tYsr@7PN3&8oa1NJ`BV|irh{}Pc&`8wY1MUsvYt5S^PqgQNT^iMn$WmerehTYTpg!yb{K^!BC>5p^= z%FHcDO?;8rrJ@v|OwpZ34casx6$+}lj{8x9Vi;Jt$livSOpgOC#3^6{M8Ao*0gAWCIeU3L?s3UF-z%}S`f?zEgfgZba=&9iw#!BDL(`y%YRFMWjNKa8h9zI zIdi|l1{;)imT=vpsKA_yciU$iEuVm;%)X^MhCthnO~1uE#gMBnT}vs#_rlc|o8~*; ze4Sw(T}n!1Ou5XF4BNZOH9!XKa4XT&_$6!YM`YlJ=W9wGwj2`Y*GP#xoqjM@C;UFZ zEnVPS)(5>Z<q(+^Z=etdcMc0U~ z4b(elW5xJD7qj0tmjFQx>>gQa(KpYpsEB-D`3m*CF^hltf4dp}k3O=HUn|eN+;1DI z7rk9w6OdpfXRyKaH9JcMmzeTakgA?vYSg}8h%?F)kd%L=8d4pq4jx>6Tm;fsIp=$; zKVJ;cZ&6{5k7@ZCq&cq_sDBrvjY{Tzo%~$8xV@>UOOupY{bFHZ|4Wbv~@TL_%UsI@ZW{l{~iI6J2`+i9$A7v6@0GR?xjQ`?^zeOm3m~pGeW`ZFYta0B@|Z; zuate6=sREV!GlnPfsq_#a;i4lctS$^50DDsU@pbqY>>$Ma{LrGgpWDhl9zdxeN|BF zb?)72-{IhW7*D`|J&g)(5Dh67BA_3L7cZL>2 zxIOoVe3jG6+zG1O^H+_pb@LD&BabK#TIwd3mcw@o8S}5roiwv}SszfH{r0A}RZYXg z+vdne^Tn_>(7+Y;p!mj1Yolt!iLk=XK33oSf0d8_e$*i{CP0$jEucsRSo3>aje$=Pd@d}So7`Aqy4_I$eWsUk!XP#XFQFyN;~w>qCB zdB5H34P90uHXR`850xh0M|FPigi_Yl!sSDl-B2ywE}?OBWD~bL|2|jTki7O}APP8Q zwcKmJ?NDRgO?=kBB5krcA;gz|d`IuTcmV!JR*3S9r?a^`Kl0pg4gOyLLfFgN%k&ID zYF>_G&wSSR(+I~1aP>twJ z+qGQ;+Pr+Ro?L2qF(j=x8y@KVxOU_Y|I*ax^u0q)Uew!h}D0Pk> zJA>9Kt~v0xcILO(QR7GSSKeBK ~g%8->kT>tL>aJ4;3*2*PnTrYL`d3hDoCXn>c zJ?4mnXCV6!`;$Q_rW&&f_%4GP*uprZ?ho|}%lO~w{|aH%W{~vSPJx>=jA&#QgNxkf zuQd?J!mm%_9=7Aq?U^R;DeTEsst~kIxiOETtz9YRE`(P0SSzqEnoY)WsVfS7&!EVN zMaHXCz_9A0{Gaow{Ch=r`SnXHuBX%SaT*rUp1c0rkDImxuk`ni3Dsv@T3#k=fom;$ zV&b6>Ketp$u;5y65tGNw-`Dc-1$Lm%njp!*`=d0MavrR|1;FDt9NypYJqIE8osJ*SD z#!bDMM$fI1V&jwDe}p#Ob)l^%ohI4^oKS1B29CHKw7vpy*o|5bVa;N&rC2@Ira{*F9_d5Lx$r0#pu z{3xAq$CkX$<4aUY`KJ>ZnuxBhSE`4Lss`jF{5mwJ=9C>HV*dctcd3cXuPV0pr0iC{i))g%-g@20LQwRDm2kSAXc#rGlo67+l5v^h7=eP0 zOA_w25>@WLH5iWp{_c_tjLf4hh`v!@DNj7c*WrR;q=&g4TI6e_)3ylqKyD~9Q&hzk zmxf9wXaG!XxN23v;XJX+!_gKBSs+C@>CV8k+W3i81usRFDr9Jpm2AZKXBj3^h@1pv zdxZ9Ug5QqV+YI8j^9j7W7(fJ{L+HVNx&+<&n$a`u0*f4f7N0Q6Nb)0q&G-O z8dLdqSqE!PRx<=hi}Ai*bAhkqp43j}C{%RRYwp@FNXxpO$kN3oo;rbgmN1~KT8oMU zk=qXei6jX{d9UxTx)AoJ`YQ{8G<55|-ZQ}@d1W%7&NdpYqkB3ZQ3wi!tud-!d3Rw~ z+W2>84^OVco?dI$F@6-1=?&zA>czrfK}EgxEgVXl=)(DOSDiNYSbJ|aRb#;IWZ6H6^Cgbq3YZljtA_2w2xY!h5W!<* zviSg2SyUNWRXg@)gM^WH-f2ejISg*PNvnLajYb)UQ{)@t?8*3p9+zk;MbW+^9}3YE zs>v+Dm=g7Kg-(NpT7G>Iim2kSnDs0c$b^{%Y$@f`A0I(G9z5>|(~KCIl1C`MC>}j2 z_66ZYdz5_7NOOdv8qFvkfE>ETNQoS{)g-`CBS~Vcq-&LE$#PDTAHu?Q%oU81b?kdL zmD#h{wFv?Tv%1nfB6%dw$_9$L+qlbWiB0aD?{_+i6rqkB`#Fq5YC-nksV76S83P8b zQ_{P&Di$A%x#ZUVS@!(UL`_~h6b|UphWFeyLs``2EvH64^C6^E-v{W((pr4t6!vMh zJ0lTmh;6|3Fjwyo=%5|NRDN|H(H6CINCfjJT< zpYYZeYrfP`%(pKXMM9sn7lN!&1`j7rp6ujv2DR;rnda|3+wsEevi2QMJwLL4{*(P- zk@^Ty=hhkke#kSd^dl(7e)NhN`4_7>M*Bl9A>dA4lUk}CW03r(E7mZ?_!YSOd!Tp) ztaqpxYoV)=s6JmBo!SZ~e#q*%9qJ;L)#FOPn;&&YYda&;4D?X z6;fUuJW0a_HZi^soX`)$^(s02AD@rwI^$03gFA-=0nZnjNc|c}&uiE9hlr%q(#!cT z>xR6ji(8b1;fW&b27V0bF!g9!c;$G$F$kGih9l27N`r$RVD{??SRU?-KP;F zW$FNakCc+pRwiTD)$dv_*bs%Vj}QEx`*v(4q;iDVKahlR?2}n3UhpUT2#T?g>>SUn+jBJH$CoknBcWxok ze*VB-&5r6gbpF065!4Evn_HJ>oBsUbO>vZ=0`Y}8>{8n@1CautJd~q;5%@DtAn+su zX~v5LW#(mWiSW?A?75C{o2PX_D=9(kQ+vrSKhr4-X=$D%>B@%K#{*<|d%ZreK7XM& z9BoC=4}E4!9b3hfbc!H#1)qKrQ79)FxN8;c7@=LsJG%Le2#@B&7`@8GKlhwac-gL( z&Ct(hu5O$J{!Pa7lyFl{ntfy!flM6yvb`nufOb927RZy?n6VY_*PU?Ph)H?d@q0o; z6XYnL$8oTMLfIr_Qi`{6<(`o8(D{(rj`74fu@X9+*HoHnUKHXg-P%QINxrf~6}XL@!Y z0%C;NG*c!T;BOS3x{hf@eEJn;PLn+6&zZpRdE%=;j+@D1gYoP|5YOOz0Npara1EK3 zu>~|@v6FeSsb3$7@q=KAMUxP4+PoQ_*(ZgsLu^`#nycUl=ad22C}))jQ66&kc6dv> zPq#9$LXs#_X@Iv1pru$7aKE?~ULmmOcc{MQT{J~uKMrH?9OLlsc<0A_-4=!zj}uvK z@)iyBiXumUy!`sBGm`GPs_IvPacr%_c=%&WEv9lFmX*!Q>rz-#qY|IKdZZWvp5eQ# zjE)I$CSIjslNN10=(?jgypt+o6FTOUp6+XXbd}>~TkO`KEibQ@E-paUGt=PCEzex- z_PPi?@XsFfU$5vtTGI8vyEncWC4qJx2WsWW2Js}gfibzb4o~XPl2di8z9?Dxytas9 zG0Dh?wH*f^B$6b_x=r;5mg3kHlC~*x^SD@izSFx_hQd$JxBzN~zvB5}U2=?Vv81R) zR#uq|=mSd8dL31UUrL95BK?XciUX3ot@7F%h|`iLvNEFu^_2**H zmT@)Yu@l>)?#s%PJOxG9S(ul4X4v7Nd^e>a{+B_{MVY+pjv7~d8}87c%r7so{ZD>l zzPQ0L>=9ADP~+MKiYgRG$zgi>f4AHJyw#AYP9l5n)6;{g(l>cp##|i51*N95g?BvaGJ6?}d(J7%@h8GWf5Wg|(%VvX9HMBm z-^s?mrU(nVP3p6=Ueb?JG!@jMAowGjsrDN4i-KU5po_>)Ij%jiF0?!R&V;$PUO&*f zUm`VGbygSeT(068ivVLbS@7PLh|Kinaf9r;_J^UmGygUt{MVmuT>$cqDaR$w)sqZo zl_9Gp?g2BU)~6*b05iisk+LBue`ON^YZBlO=L0!S>BzQ<@PB zkwe!s@l=A|Mv!wT5-nwhp%nhICs>X%EH}v0Ln?MGDp7F>0@6sJX*CddbZN`q4fFal za*oFew?ZjK(hSsg;DJAAT5kew6bQX|d^1)@Sis%~4l5J~dVP7&WpzdBRt5V+Q`MOB zTOHsI@&mRmUT3PjcD>0Vro5mRYwx5jLVQ=F02=hOVj}L%Dvz*w2o*)y@W}(zt9CJa z^PD(&$cn!#T>=$=slMP>BvESBaz74=?y6!_yo0Q?bi9uR#?g%l#Mr(-2Pwtmnx2iE0YQKy%Jv|YS1 zNWUdYm(DnOHF7lo2@y1y+6_k&-H;&qNbaWF@Fq`BSPfGvP1)Fx(;T?ubOy~5YtjA-ORWdMxV|EOfBCyo$*Y$clPS`B=LGLo^#%WqTEy%xa!(dxE=H+^VuZ$I=6t~ z1j9SMBr|T5X*qe*f^%LU24RiCZawDEp?|+L{5bXd_V8ck*K?DtLpf0B7Z3UrnR7w< zI~vx*O`s;?_Ek@i)=z5IYr}V?j#OFXJ<);ZZvx=J%QuS7g%e#H$mp%2A%{nhrRGQY zAByE0(f7tES#^#ALKn$c9#jYnerP%iGO5;E*>aj^zqH+~z?AEDTcsD#u5gpkeZJl_ zKFQh?B{3m8$WA1AQKitS*bZbQ_ryF?_O2=qy@<)n@7jI68mXBOcX!#hr!4bj`O zBzH2p8}5wK|N0g2T)|V~px@DkN$BI@6oIo7OjYsN{+aFDtKTlZ9d~k?gO!>-v>bdL z6A`@TnSY*=__$b_$GFa2i+(>^s&yM!iV~$;KMm8uw6R3z`lsT-4@u63l{O#kd^>wdWSjTip}1DNg2AJYbiQ%8 zG2;vXe!iLcygl>3`l5O1r!KqGQtx{&OrZmrG7@U3zxIG?!du+g0k;rNGhehuR$#~< ztc8hHrH=da?eE$_o6|IEU#c-0i1)w02)ix6P7hv=Q~E_4eBR*_V&TGzx@P{h4qnsA zi1PzAv*l)1nS{((eLMeb5Ze>L=fSJQglyD(~PqBL@t-qhES znrr&s*-PUH?1|GDMGfB8#V8~*P%s;QE545cf-b#*Zbc7cXmT9m$qR2v$S-OWMq98Bu zz-C?FOBQ!M4aF9t{2=<1KwdcJksf1U4YjdDY>vWI%wz$p(#n~UE$rLcxxg#4n}rjX zIRQNefpMLnH%Eb=^eqNG*)~)NqhPBC01z$hRX&L|JLFK z4inAF&5~*svw!!8#rQ(aL!CTj_tBL3uZ?|HgPrOcpBJ85chu~t_&NmNbNo#MBV5W; z5H2)Z8TkfyOyvs43Ht^ed&wAyssPKC>V*Z%U>0j#^MmA=kMg)$`RV|0T5H7x?%hiL zX_KLFgu+5a4*u8B=A9iGwn^?a+koQROQoJp_O!(l?5(Gn`?bHvH~$7WWdEm$pq&Ur zR7zx=IS~Nbe{G25*^dax-8@J4{6S>-N~7|oIXN*;6yU2z(Fe0GPsfeIdu%5|Wxjx( z0-oPzTR7#v#Z%d@pp&2BIj00A>-u{$ z2~K7pDLzToDf8er2nggE%rB0sH0~4q;KTF!Pt5U~cGA7pX_C*iT$>iJ6p1MfK;_~< zJ)o6}r)M`7(MkNKa|00Tamjt5rs695j##79+TzK4(KSn%Nj7aqwf%vB9)Ll?fgX@Q z|Jfde!8{>n%yf0;vwK+ zV%|EB*qK(&FjMx-4 zQhTp2_x-%j{XNh3et+iQ{I2V`&ht2rv9MNO2kHl078GR`qi^mu7>GPdC^WR{;TELiXSALXNMKwWd_Ze^mp!l+fxUPp_B}} z4@-Ol2m;J?Z`x!u3Y|*9b>?AHTq`WOO-_nV81v#31_?cgH|S$z*CthlaUv1Lwxz zV~O^Gaw}joE5-95L3ZK3pp)v6&c{#oK0HJym}5RCP(l|{H)SIZhfFc3?=mEu<1mh6 zMiH47W4JdSn}Mo4@dH9vEuR80$tY`8gDGTwjSGzbD76cPuI671s*jz2AKdoGBs-3z z#@p*}#*!t#dITY#TUz=ZF|C%!iPG?$v}m22{CuYZyO4v})!zP=ux-lagR$v(gDOe_ zA=}v`yuY!Vfj{se7tN803T~(vX0aidSyx?n5K9U2ICi+@|6SH)Bq>XzRq!H`V}<== z7foeEhimq~_n5f5x4y|U3nJ*CG&6ESpG4C-X$T7$Vau4TKOFZ?T;D>$OSJS5<(N&R zmQuh-I}i1f@}DGLjYjO{)))o6R$PGDm6s4U!Sy2^ieLaDFCA%yRS6&}N#1A~EgM$6 zV*52X&)}L0H#(cMC28CqI5&5?d8BLNjIu&?@S^(V!&1^Qky;|D?%>d{pUOq8_nZGR+QH_-oc$6j(On+wTn1Eu83I3UA}bk`H!qzcsT>5DYoQ5N*Z!0D zMF{>yiC%P@9@=_P9zrR1Nk@^2l-{&uSbiaF+IJ)`3LGvw!}HzOKYp@ZA)q1ScsgrB zh)3%3tfj|GFpG>{>{|J>tG?>BW04;KHrjp*Qn{!jCPUwTC`bhA){HsY;8-$Wn4#*Y z&moPwJ{F9^>p+c8;HpiFyC(`Xqo0MGn^eM_QJ05r4Kil$NKSUS>9kU}U+WuJPKA9< zCTSDN)XKOJ(#WnYy!jP&A%EH*7F;A#@S-nA#PrPMzcBxA9s8Tr@6ZofcX+asDIk<~{ zm4iE9?QKeDczL%5DzccP?WqrL@p?|i9^TEL^$SOGR^@vV#*Yo~gMhSpLwuk>8X|mE z7fZ0)z3RNL_jWa2oO;l;vX_{VguF1fs6V3wje)$?e2yN<1DGwjj^%Pm(vsldncGwB z_KVei8uhJQ*Mb2_i^Sm^x}QXR%r&nM2pD;wubaHffW2A_;re>@%Cc0c}?kRS#U;H-w;K$Y|b^Z)&E~;DC*0x7^trk4v|<89aKxkzYO? zCRp$Y7~`uCU>H0bM}^*zJFFGrk5)m^fOs>I3?{Q;BE#ECy%n63iPy103&7`X>$@SM8q~0ERW}SLj z5VChi%x7V+yawq*sVmqE#IN{X+=*f$Vn+t>6kuJ1edhMYN||-trMqf%fqUN%>=r5F+hEWTJDR@#gaYcRpN0b zFyV0CAB!tbsDRkD#V|Abl$>PinRVB?!t)a|^LFM1vbWY!(tU~?J0$N}DK^$+zof3u zjE)ZhKQb9g=CnRl=1EK`#8~2`LgJ(S{}^)T#WL$U&}s$*@L$vex-gnS3n$*^N{yGt zapcNwXh^evp8?Nl#=Cb2Gq5pa zmzXDo%zBp+>dPk!#Q-r3caY#@c-MT_s$h*PM6F1+pHPYZd;)!~?qmXC3D zh2{oCem>|=t|dnpu)%TYle1AXiu^L?Keyh0-JC|eSP{cjU)b%7WccD!yZO^%&Fm?mPwWV4DQh)QB0bUwT2xS=&nt=iR&Bf|b5hR@(AC%&rmH$;zOZj{R& z`5jq-WB9Z}{eYxN$Ue>l2?~ zY0#qYksNufe@k<2;z~P{p$3%_@*UEX??$$RIvnI7qfyR8EFG$}IF+`>I^D9Gwf+J% z9Q-pzi|`G&<8qbCGH|HoY{u%Wt$ML|f$T9alqq1Ix&7d%df6qa+t1|YVmERF|4qMu zJ9!6cna%Ft^2Sm2>|}{eK<1A2&1%i=ofzTb>qU(vE7=gL$O-lHiKMzXsT;GY^Nq*Y zW3TfbSxU;_m8vrRkowwnt8<9|pE<$TRs7`wJH<=G9cRfC;XkehJLr_HRs~q6TG@jm z*_F>0FHhQ;LU%eWsoKxpIY>+Q)t+0_qg&hp{G<57m3jN{@Fm~Vk>mmF1o$-G- z9=T+~-wbqjGUY$R0*pLd33^E~3J*l0tk4M>Ts~W(4^7s+DJJTR#yp{8)LH9By*pa9 z6L2ybTlG5q1%tkJ5+Xxx)s_8R>5_u|nG=a0%0X_Q@X3S)Xuj4^9^2-Ju*oO;azUUo z6MH@FWh*xl$9tx(_X=eUiXKoQIy1M8^MSFxdG-M~^sZG`rIXPZRwIuAbjj;iz4Q6^ycD z9F*Qh;y%ecQ6cXya{=lu5U#F)CKm&^TtMj26c@vD!g4b$X=Xfd= z;k73p`T^$f#u6pYE}tAjxqCcYv6&7tU>>e{Y0%LZ#S$sEjmsBAzSPkJM>)qJ$&EX+ zgDPF@XoUN%cwdQCal9`YjT`n7;U6PE}v zL`%XY^vmj)Lzza0eC`gJf4>rwjxaostBa*jcvlnda|ELX2(Fs*P*GLWz7fp}4|+PI z52-6oGg(X;hcK7k;LqSqm^ab>MytvsuII99OB+M+%L*-EU5`_}G)umbZ952k0V1@up|ttazE{s}Ri&(4Y`#qV zaI<@3-B5Qe(!bwJwI>hBP@L1aVmFYRZ#DtCg#i~+gw^DY@Pt~A@_(e7mhs)NL5SZ5 zN(h`u@iycuF2Su9P5Z@D_mz{=Hzfz&6}-32PUle>W#w`V2oQ1lvZ^Y36}66eZ#KiU^nF zWau1VXHECykH`u3-rfv28KrsacPGRZkSbyS;fm0%(^W(&b9h}B3NA6ybudH}pQm_J z)*!nYlMfkT8d1^VA_vezDZjtwW48dxJF=ltu9zh07VS?@Z4l)Ykgp#Yk?|GnnKic! zNn>R$Bv5*nS>0B>YrYH(qkNVEc4Y$mdA2#_EEK#ADU(3GPf$P zAk4`c@eixZ(lKt-0}l;GX-`#ijiE?kxtB*LWmlXOAc{Z4Jz}CyStb?Amo9LpyyW0K~z3{mcM83mcS67upu% zMxv=9-(S*8S3m)y_3L0$uT7l^_Pa5dZ*tC=gFe144`T?uau4AOE)Rb60&{v{^S$I~ zX+ipCxTjxSYblXqtLJ^#x$9|+yLm&sIIYoVd9!E=TeMr zB!aIRW_s!(+`8xXnd3f?N8JpTFYN?7bE>oQFWp?NTjBEjs*roznVp|aQ1HjyvX?qm zl*3p34G%YVZu*z2mzWnQ7A`aS6vwi1ULWmTvP)f*-#m3iyyCq)7L1g==%rqAsdZNJ z{MTmo9~gfKrX*_rCh3-Lwq7RT1|E7I1rg2h`-7?R-6*TKv0G6hbhW!E(2Er@VoknG zl!!>KlJF)k1J^l6I)Zyf)ygR>&5L;!P%-9fKnWCSPaj}JTewqXKEX$OEQIAY;N{uw zyspgn?SebL(hlkn5*n)QyUFI^AQD(}c#=j?eKjS`>_ms#*w*)(+W8{6MMXT4kY^GT z974{f?_j7-5t$e-KnW+}#WyQVhxBVL_Z8jH8y_?*f4RaQX08Q`k<`6A`(tMe4{DyT z$xs-~&0w?%XF6-)B0Ck=&KPuKpuGfa)(zwcGtAN0!JEyr;w#2!J|Eg}^}TDh{Pe8l zZ8(qXL(hIedldw#@$?mGJ5_;0S#^pzTJ~TIujmH@#WM9ctItl~T4cHeauYD_Y%VVd zEvw-JJR?Cp2=yW*X$Q>m{;q$V*l%AcLE6Z?F8lH`%Le;i7H68@1(A%j6>Ck#miB5F&8N|i(jCvxO4bunQ8Re& zj=UQ^1hWT>n}C0l$go7b<2h+UOdBWa*8xnxQ8BCjz`3ymy3O7qs^W(f&ST6-HOUM$ zNnJUM(|}E#gFQPbvKBYYeB6_<$v!D8cE5QiJue^)$kaJPExUuKVvS08>dP!i6#O;izT(A=)lia!MH%b?y%J z)njAfpq%BbvQBY6TwCr~ZRAN6!P?_$-)uO_KKsbRC{;@zJ)#0>$G=0zlquYp;up={ znWB@Se5Ji>zwym7$p!9mB=Cxq%yJa%HRxC1N7V0(FyaZ{PLfAmbAjGbjF5GK9gPm!JBBd9==NYOrt>v8cTL?jC zp0@t)xqO$=pM8hWaKTd4D!nX53va}^%m+emU`4!OJ_baj=-jD?zx0YWUI_YedgZQO zXPc>LbErgc(vM+*3b7pVu*^cQ(tC?cC;Xg4vS>Kxzjuc7wwyn<%NQmkzoQ!T8t}ZU zPC^;=j7ZhU3?){wrM6p+5l1VRcb(wZm$51139N+}pm!_eOn_BQ=M~txeLpRxv13*u#h1Ob| zC9mu+_bJ>M$=2PY744wzHV7-P_n+Hu)1`e~5aQb+@+5L$dOy`>>w^`MAg^xyq5kP+ zqM*g+%;&-PE?2D_gs>Yi!;ykl@5xg*>F8c}oUS;M)u6-gQeFj~9t&Qnoo=2uk%2y4 zZMxrhK0h+&Gkq*^i50tMmkPR>gi4y9tggGZ&vy)75Wh*cbVih_%Xtlxn>z+vUROyo zg?0?FhF;CM^w3AIx`<$+wM_>Mw?0m!7g=38oc`>XWokdn#9y)zyzVkNtE+U&I_vJZ zOz5dY&hIk*GrS*aQQi^vEO2L?B4e)I=LehJF{ut#)L+|8)y7tOTZtfjro}z>XWmjW%06Vl9z#gq`_BNr@M>@$@HQ861-gwol zWg;Zx>&E-Xsju%@tLw2`et2nVd1B@}XOgP8*qf)KyNBmC+6IQvn87M5dv~o~GzuG> zD~1SVfo{*$7x5DYGdhC!O0VPZpY~FqgMh0{YV)saW$zbnE(Q=^Ktq^YpNZ zKhMw|6`2w>_gW3(VOHb1{*2~izM`w+x|RVYnf)lby! zI|ep-vQeq@*W&}e8a4@gFDN%v5B3M2V#%{dESn}1BLiyKOKe$#rxzRy5!(42IjoobY@+!YYzJCh5tvV2f- zyhjC3B&7@$e-XNq?Ary!j-D(F+6U)#e((OWajcy{u+S@hhn~qnu(nI($w)N0lc$~4 zu|Sa@OyIdfQ+{1gV#t$dCAhrUwuNR{7f7MWnCbL>_@e=pW5-R@c77#^8vU`~0H>Y@ zQs#w$g$US9{j)JVhh*E;sA^FD1z3mI9}SXgB243KqujD6hNKVjS@ho9`Jm?Pzc6zA z*4ZU6lM9MA+$y)R&=@{$-bvDtK}j8M>23;S@KRpHYhQ4N+;3aMX4_|l-g20k?ta+T z?>6H&un)!bKUXuKj!M?K&I9;L)bJE4) zGMbz)2ikDA^ha=&%oIUT?7WbHx6Tyb1{^?dywGgbVU~zE<_Hq zFFO`H669tX`{I|m*#ga%s?(}RqCUIG@k5J))l!EY7c2Hh-9v%Jm^Y=zMyAFNa>6r} z#Y4c3u=ky{y|1Ec7?eom-r7@P)m%t<*$O%kqpW&%of3TJ6hBBB4cVdy@CWZ|usF6` zV&ejHp6V2Bx{vH6D@nrZ$9DY0vya)gdPZKIa0p)cE;Tf<_|2>2%By*eh2pQ0bR!?3 z=u1(Z?DN4{n3UuysN!^;J5-}35m-OLDJTECxE@rhCAZG-ToeyJ?EvVzt7-y6GEv$| zXs~HL4&<@aNN#=4;Vxp2bOS7VZq~u@iGwNcH2G<2Yg+qo9Y^+KHnhSC_6xYMUpK@wsTybvE9%Cw!KUWaF)U=-_whmvBx$9Smrvn*Yq8@ zgrc#_!LSQ#^~bbYQD%nhvxpmp>+K6hM9W?rPMH}NV)R@sc%$>oikNUxBy!=(Dw}vA zaO1@c=Z~xNju4T{6WhtCUY+YV{9$`X{7ac}HMdIs#J^fe&qh4@oh?*7WbveWO4M^w z+LZbIs@|1*XT17|dWV|N0mJT>o!2`zZFiTZtu9;A16}QH;a_h04sJ|N`xSXLl10df zUL$G|Ns+P$8Rv$hVl>;NZa?3?Sk|9fi*wuaI+Re|{S~+`(2OC5`@Ln9q$Y2cz_T>gzsREiWgRqB? zt%JjWdDOp9KrG{%dE9PT;E7-{#o#i3|5*|m&)q%^MajZ_At3rkhQkd zY&glHFM`}~Kv&QuGP$yF%A15<8PDy5voMTfUYaJFj-4dFp6r(a0R{1<>slxe8(0Y~ zGo8vqY&8(-W^4w!%|s^~{!Xxu@C6gVcr*oVlJW)?)!i0{XOwqZDo2^*hu0URCF;zn zJ0Yx0wT{bk)*Q+vv%x$2#R9^=`z4anTqSUnd$UEwKU}XDzWE*Z~c|&RTauqupHw}vm_AztsbylM|grl=MJQ;zgEl4|T6MPK>Sm3Vx#y&v)^ zwCWwf;IPa&3iSCL|f_u-VEkO{mFa+PIC;eD;$+ zGf%NHpjSlUj89(M7FOJG$0U+}nn-LpZVj5ibG{)ot&a8e;)?Nk(eUbgpRCMi#8f8( zG=blxoiThRGimsAYkmx6f10*M2@@r2CoJ)?Bg*+?b1^-BYfG4RN;2xr)18F)iT0`b z!c7(V+RoY{>y}miN~>6wNHa7K@l+e_rTa|T)h9y-aM|X(ku@d&!;`Mee)D>>PJRue zuu+g#9=OJe(1%TV`=^%ba&7jOc;P(phZ;fX%CGrNC9Ayh>?c3gH-A8%IR{`*Hto;a_wCP)bX&A= zKMv7b+hVm=2A(>pno#AY;g&jiHtlg*xnncRu-_xOHq*kPwnp27Vhd!1l&oaMIyn^U zLs>4+)KQVysi`!s+R_v)oGdYTw~Np>wrtqoNF>Mo{)3s>%9P_g0z}Cy*V&VWiQ+%- z_TTUsRLJbsX}iD3#iRehPPr)7#VhfjtT{|hF4Vhy7jD;;=F-S)6HC+vWYVv(BeOLR zhZ0xac*uh2OK zaTpc8U1)`VwN?^R$itdj`bzeV4%@}fzW(9&(opj?HWuq^^T$>xI+A9N+NFN1(8!yA zzQS?~2RM=ZIYctCA?~DY3mx|j2Zbd&z)+rMi$p>ggqmZ!uQ12G!8GuI@NPvw;apAT zUg2TqSnlbjpX(&KrbaEXe))$CpZwLgPhz=;XX~n1QSkhp$2Y%qdLA#F=6n45;!JRp zx3tn7DP;XEo(Vu}eSC4z{Wy!(;R)=IIFrlfRQv{C&ra}->M1A7L@_dg(c0I)J?^5r zk1Y)Q4%xojQr_Ly5!Aa}4>rhlkPLT{tb;FY$WF7!Tqjac!@i2XNsg1DHqJU<^~hKm zZf-9hFs_B-^|#|k-)IjmZ&u~g%Tiw-A|1QgvSe;+TX70PV5FTLihn8~{$=x>>o`Zz zzwaBry;$R$BmZ>*YD{O6{vb&?9sg6w^{BuYUn;6#N~o}3ux{_*D(^a)93w7vf`}fIS?t`tGT`K=;Y_{P`Sq zrD8RG|Gsm9x^ow!egcblLg$@uh1fbk0(9uI=+1bG4R1Pr5fM!Br;zUP_&)AFXimRY zRzEA`yc5MJSA5I@_9dzu(1KOMEDRqd8a^(3UMd%F^GVWTg)R4W`TmRd#1n~mC4gp^ zxnr~A{5nuAc+8WH5dMnk?D}56Q_G~sB7S}29EO$MpA&`Pl}^4;hU@8vuC`|AV<(J$ zP>+u%`o6SUoxgipo^WwEax8$=I?=x)S`R|U_J>z^50idmk$Up_?SUq(=%@TYC22_N zFQLP~$sAJku_fPMOsdO90jVlM(Js3Y7R(Bt0>V=A!&|%s!^(p2VLmG^)1Gi`$%#De z?-E_LrHG{E|f7c=8nyb`UR$R}ovQ4Vf(C!td1gdthj{GE4hwu!+6op|&ZY zbKtQ7+!N*N?cxKuB^eHExlHk$vJKZ!xCwa;x`Pr}Ayp^qsX>GQwYzt!q1Uj!@kaH^ElnLA#MJc0VfEge$MyJmmN4m&9(odQkXw+RsKf+BPs^eu)m}3wMnysP8;VI zNlTUyCVzpDU;Wb*brSt9>nWU9eATesZ?-F_DHEsX3;Ij3a!P#M^AGrRLf3$8eZ>ZS zza4EmF?e-0qd5j31V0Oectg|t!d|*T33cEc-_(!rhj`V=Z;p#{R~ti{AyJ8qq(xmW z(DY6NT#){Cz6M0)*GXdO&__!-=m!4G&-oG=YRve(4i(MsCNRDf{F;9J{!y+ml5Gc# znQN=_*Iy2gh*}5v7mOFc!w`gavLAZ{jb)y+p+{xEz!)NTV(82DVdgiC6i+J5>_~0I zG4W}ANsUYT=nCc0qkgg{Dfb=)P;gG{4=u&GP9C>R&s6@<`VZM=1CsFEGbqr<{2=C& zHSYRwYv-&UhcTUwOTf^CW{Yyd(Xexinfp-?@K{mD=>*DQjAKKv>ahMcBxH zokn?F&Y9N06_xXAGfic%T76jiSAUDH{)RWiy3~jTIjgjZ!tnH8@_ZLos-!&aAf7K} zUd{MQ<^=d656pNnEu%`uEXzEKJUNMa*v&#kelBI5^1F6EWSQM0N(CXYTc>veJ2p-` zz*>;7XEeL-B6di?@>1nikGPwTb#Wcga*A=Fu94Y;M`iGxw%J$g3fQmbmFz?a1kvnm zm>C%$`Ft^I=e0*;A(S-OeKX{t8m<|Kr z#`pO$eKPZe`1GECfubT-ufL7%Fjpyjs=n0%-UV2q`2ZX@p8To}zL!Z(r9suNR>{Tl z>o(Oruv&z}Uh>)Y;?Tt4$}e!iU4KP+E)vlMOu0-kpiS~K_j9eT2cQ{%CI{@(LtLfJ zgDL5TzYHj8tA~2WArHTR3`_%hxQMETGNFe`1FLpT2wPt#l;9lIy0=@-w~EQoL~eo=?ItzmXBJDbN@9%q_M; zDYykiAO@{;-*}TvV|B{voGI<8oT^p_NdaLl0G;r!WV2bHAF4`AwjC%;%xi?W$lH;MBqZw}Yf=L4fe0zO!ulu~7sNc<6x4Cm@$L%6CNQ%E_WQrMj zb&zfFi7-jZ2VQz$X?c+$ZC>_qPNEj}xe)21=-l^JmjNs_aK`r`n@XMJ_4onMsS)-O z3A#_(8kWqpdzo0JC;8bAsmOo)2c3+k{i*7s%(h_O*9yPp+ZP{SLKAfC+Swy*z+%mR z&vtP0DLhHPKV(~zRSg`CII&C5w*(IJxV6iG&?@7$8JAu-9%1*_80G+$IGZ17k8-U# zLXjIN?IP$QqX;CaFtvLd=YTQ0Yl%FW9qQpMbpCA8pF^MjGf^}SO2sp%qJlk4xLIad`VYgBDMn6wqwCXM7_DhN3^yyA z6p-ZI#a)s2hG>$?>Z?iF8Ul{96Yj2JZJX3x(~p&oxLy?Hs76#0$J?{#P%+l2Y<7kl z6;hP96W~F<&n}u{ZP_=btyDrZ&)bsRi$m!KaiK43TZRO{&rDQ017w?p50iTO*})cD z#WvD}jsu=T#+xm5?xlkgG{1L#wh~Ab{9v*c&=W;bTbf<*Vv8f3sF_{ZP*$ZOKaSCB zcXR#$>VFA9O7>w5aVFwDj(^**H-MqYW{WsN&k|#K#Wrve`jsktZPcKH?)<7a%sz@% zHh2bsc^`JQV>N73?>2VR5_V_V0p!G26w>$)Y4I=n`ajko_7j*7@0gyT|AdUA&LbvK zmQzQW8j8!;X0D(zC^l_aYR${}?I4W|ztl)SSA~Brp_}prn zZ(?B2L3uO}tCxJ%DC<{2zi8KIbIw9eI2VOGE3<{dnaS)%zpa+AeJv$@EcXF$%OIZi zM+Wn|XkZB@)-Q+)HV>0jpyB59m?C@1c&a(m%k%zuH>v=;#e-EVr0i8;UJaI&+o?aP zc?eh!r?Id<)#Od4tINeTemA=HPyrNp=1G1%0}EMbmFlz4?e5vhofS~=*=xm&UlYsk zan-N~+PgyV;cZr{gy!k{eXD9_Q;2VA{H6Gp~n0iHz#mtFUAUUIOTk9ZulmyLQ) zx_)}S^JbB)t3#ii^LMW`<~`+FG$|6*o;gl@va9nk}b_ogz$X3X5a9ne!_=>@btwN zTPj#cB*a+0R;kDJePnG1=mX(+4{IcO{UrN=T(yO;+~VO-957mz?RKA0@FM-DOhjUA*1A33hWfvC zAwwEaINSnbDyV_~+(KsjW1IP2cA;MaDBd^s*0W!UWJGsGR=WJ6mi_+#$pasKlvC)m z2OF9RaqU>!pH;mFej?F+xTl>PM^GjdPj5v>lSY)rUm`}b3;vB-?6X`G5RCo%Zuk2K zUieG!IEkl4TcD5s=~5KAXYxI)hFm_=QusPMnHA5EAq?e(!Wtl}Ke#B5=gp9_tc(vuv)10>6Hbn5?)nD$awhA+S@b_h{gat3k@JOIf$XR6DR zv)rODb1eaqp{+Q^_MDm1&y|OAz#j|4)C0Z=O3?LNLC%DdPR1tQQ{s<)y;Wyks-=lH zXUskm9S)_D+Ws$zbf>g6>+bQZ4ywQA-T6Pj8?YaqV+n0p8(b>z<=E)6PQMtK_A=n0(u%uK#cEo*JNHkc${Z8M7dqeH)wT%EY8 zSK9^l*$gefh>0$k!YxwovmWo0G*Pyzma$~r&!4JGq$anye+o)=NH!BaK&_4(iI-kIBJr1~XKSo`g!cu% zkR;`!1k|n0OGzI2LXf4qIslVCPy>se6$|e@23F*amjK>+-~PSz^rGBW&$-|<`lTxd zZ#Q2K=Z_~^OZF^Cx<8DFeGWc1YoejbTfEIv4QcCamyq*56391%`bb6jF}NV7=-qWdF9P>VSa=lQ|wPsJ{oB}v`i+EREh zdyw;K)wEtLNX7O-pe>VX`?*Z@n2f|T0<>t}?TUk-6Cs0&K|-VN#RM2j+YS{I?T+Gq ztq*fc@U93#=2KpuX|MI6$n~7Cd4Q>Wb(4sgSk(0u@%2|GX6BygTO8ecD}7pO$1Mw2 zZrMMI!rkcwex=6|e>9TQ&ptWnxViET_*>49`87za2wLye}B`W>q)Bi~Xy?u%9_ZYR@(pdQHBP-5Gk-w~e0^j1JM zVOS3tP4XHvELuY5zb$VKr8rr_rKt2?=oHSm{SEK(rO(!T*2crBs@KsZYBtz%Uw_a` zLaIfdv!7Pvsr1wy(_j~Nr!sk60d?7?|mR2h0*p54A&5MjyyL8al+AOo|pi%NeLMHhqhB3S9H$F*J7OWx~D%J3@_0T{pP-wMqDvlymQdI$>yP8rUO#F09$ zf34)(Sv^z5(%WMY3E!gdl+}R79>0TR?|Q)4Uqcj~M-1V#uI#I3?3t{e)oQoXK#*`y z&C7#zvSsp5H;32Er@7*y1O8@(4b=e9Rxcmfv!Ta7lFtoDBs~?{POQ+O%47zwqJl>4q2i$S_F4rq{ zg)YL+`l5DFvlttJkE7?L?e!N}BzpkQTHbg|v&?osTIOnK$HKh;dl!v_oIh4C7o6o# zh6%J&cAWt_ENzBl1y2Wj=rh-H&CY8pu^acRn+>xIOUi@ZU)+n$bS`VwW0xlR`Rc3Z z=0_vw88M9Z38@rwre2J_RS`rogz+Lmb zEg9F*@1I(CG&GKkr=@@n#L*%{jr6;|rmF58);?vR?Sg1#BZo~i&dfq^-=&k#pfuUj zS0^p|8S~TP6WQdm_i*m+=1I(PoxP9hc(0!NHg0UPvyMJoqjY5tuUE?Y!Bg^wKlau4 z{An+xk?pk;27g#{1BPJ=(Ygsnn2pcxSuJ%5Mh)z=DJ;(AZW=aRw$2l4oNogvIIC+q zeK=Olz~t&pqN+c$76)(;tIIFL__pTZBrKd|fUI4g&U#=RMBB1R-sZPy`&Bx3@mbsZ zUqaAU(ngB~)E)3lZ~)w1e&OgcndI&N(710|{|blc#-Ln!DlJ-D4rcppF!wra@tY>X zD=1|zt4qiJEOc~OloFCN(WLO3(-l0-%-jz>ne*$(mJV{&%Wg1qWeYj{>HDtMpjaFA zq^S&=UC+j=cd`)H0dEaxm+|?(9Lv9V8Mkc0imPV;t1nnBL~N5|OTZwA7%%C; zIp*WJOf~m6L+sH+TsH_cQwG%}uw!DV>sx$|axWWv27SAks%yD9_Du_bhM zzouWsDXhj*iUZO|zt!c$YT|kFhT%cVMUrtWl)4Mt~?$5B?2M*99UY0*|rYZCB^elhK-?I@v zL*ytVP=p%djd6zNo8Ol+0_wr}9)?I6sM%X!P9MWQrpi!HjbKZn9Z4zud|NkuJ}i+vf)ykHmd`z!%C<#WDuwexc=4>ry zicu6&PVNQAEt7US@>t(yxLt?{m?XL_d@i{Q&8wJv`Kr6(p+6Wkf?qIW>nm2)p14*k z2y&;v_j+rclz2gmnI5xkIs6cmN;s-QC~SoO`J&bj(8?p{+^>%?+@u|U7}W1=q0+S% zFei2kIS#wS3=)FL>kS$`8{M!?PNt0$(Gocprg?X>Y>Tjz}JwuaCK z)g~W&OLF3u+LjJ&3Cb24ePHZxl(`v(@GGY-;+KDLf}X${3Nm%_`g`CX)g{?4T)iTa zoHX!@1zI}5!qeqC^cN~aLU-oVcwAlNz`*KoJLku1>`+AbQ>b2j7HAQIUDQr`RlKBw zYn`5{;nB#2vpi7+_M={1dSBLwC>b&?hZ8goWKABpfjd88|T_pVR+TwPBI}KE+XJiJYfI+ z-dyTiad)DgH+yb+eFb!UokqLJB#1~dW;_1wxTB9|JaXR{1M0|^(BLaE&cT@jOxrP& zIHqgs*XH~Gy}H1MNAcbQcl$5=97u2j6(wyLf@G6`9k_~l_o0fLaD6zs7%CSM*p2M*6ykyM>ou{>t^XZHLD(5)y7YPr9mONKZJO!{dY!Gr1v z#?IP@X^G>vEEqVRJvLNSiN&jDAWUYHGf@s^&!PWZxW@R^2e?50ieX^SV1i;xG0IOf?lo+wtOtHDthY6Yl+bOOjIcHj(y-!6#of zpZVch_L6Mw26iL)>Ol>LJJzCbr&+!uxu2Mj99iamX0XwCZ{-&H$-+Ca;R5K zn=6Cd;En^SGd)LOd*<@%q!uH}9fW0xt@A+3U4fA9O-1l&dW}ez{o6y+fDDCk7z6ht zlmB~R*XLKB!{|qW4b%IU(X~6IuXy`7qSN8>KMrMphAx2B#!|V@C0F~dk41dZTNaO` znPvS+Y5gkU=^DbaD}Jjpf6PWv^?U1g^B%q!_jY4wuG7d`C1Qgawbx+Ttuz_qH{+i<5E=$}|hE6Zj_U zOJ^XvblDmtG_II`rdQP0 zC=Vw1=^Ook<>tZV2rXiwzT{lRv|r`-$}f)hhbpBybwYWd36g4XC0hLFleNC*$&vq^ z2l!h9M=9;Kdy8H@pT!I5!Mn`x-r8cNA}ZJEFlncLSAPBO4F!{E^Gkf%WLAHSk#oy3 zpU%pE`u6{gDS-)unj0FsMDaER{h)L}T@sKEzrS7q#XST`_i;GUJtH?{2;X#~;1yb~ zQMRU&<3s-+y52gf&2Y;ehTty6U5iU`cXxL!F2SX^7k77PvEUZmrMMIb60~T6V#SK~ z*hH9Dd)|K#Kme)bzyx``ujG^ z86lHr+LQv-tBMeQuQoUe234ZW=v_(k{`K+?N)!`?3jo6h55H{H9r}4kXF3DGI6Zb_ z=~=qm)Rs1qvkvx9cqY8;%~96_BYgQOQ+|N>Jk*pJo*rKj^mM$cVS5}k;!KxQ zB>cmM-9an8$;Zd185~Z{IthR=WNr!nsHv{gD0iZ;=`%i#jj+T_wZ~(Tg-3*dGGl=F zEF^~3j2+VxeSk`ns;KL;`-_!G8oL&c4Pg;wwGI<+LaY5nzfaYZH3ZHVUx8*k6FI3h z``I|rOREmg*|!4#%W`SMICzcff>Yw(h$`Z;Ssy_RP~5c+i7>Wv7hkL8tu;Yd`=;FYrR;5-$s^0wVc`hh1Thyo> z&CTh}xwByas$2$LpZwxKgHs{ro82*lTR{K=!0pv5d>myI(1hPGJ0UJwk@zMd4Bjv> zuB*MUp8`oaFH~yu7yMy1YDBhKLqOBjY`z2JYohgo@EQYryjMMHsSC9WK_VL!?x>T^ z1F?L_VN}Ktn?=of(GJ=g?Q1Sae|tjjv!&WuK$q&e9^6E5Sdiw0VyxvedK;~(=uaKT zRcA^t7AQ1ipfg>uSMT-|d%y&=R3FSVQn3b;^+zT2xDA|DEB|^Wq9DRR3#TGiWO}Sh zWeKt!4~<&XOH#kx60cvT#J}Z3MQ`lYnxD{&vbm zt$L{@&)yG%bym&Qo3Cxt?L_1*$wS=WjxxdpkxgnlEV+g!TML;_;5i<*bV;^|t-lBh z{1s-coO*aDO{^XF)5j;@0niQU%Q=2yaSj}V(8OI3PXr3rwuf-^ys62=FYO3MlAGkX+z*zp-l1CeQ-xdhO`YQK%j zhcg{71Qh7#1v-T}paN2v_XT0BVf9ewSNqG^RC_x$RmnJD$&^A`5&k$j=$(JfXeU|7 ze(fbxpyo+E@2zt z0Pf#(Y7WhEnw{bQ`HlkHZKau@!^_X7?)rINl%x_S*yVJ;uWv9yJwP+5@I|9y-#_7r zMc?2jtNBT>mj#QYP8C5yj)g=w)0|7$xkz(-0J22LL^^fv^F_1&mo$B7XHwU%@7VJE zr8HB0I{HC?HRRr;Lcz+bxw%^s}3HOvQ zQ78hN;>`o=yA+#)$UHAWA_;SaCt-SWBJlKz4eNwpbeFTx>GLW!mYCOIG2Zwm6?-dK zQki?bo1SjR8f^%UhhU#FTNDR(vgOCXtOx{B2`^mH9)h>qo4nqK%F;(R=@blT4fwLz zcFcM7Qb#D2YUfIXwm7gh& z7jg*f4QD@lA(%Ivdwq4T1cj^pFUE-fSp(QnCz?_gSS==WyB(w?j((Ox4~<*6invKU zn+=7&i|&TVSAwYS@^!Q8Rj8iIDJRb+q9OVp7h=rDI9?7D%?IO`^j-WT3g zHdi&sJ9YzN5SFTgVmz%w>yjdT)&MUBwnE8)*{)9^4{6xO#yhyA#2ochY zmD+e}G^mw{E5Ue4I)d{YcO+)N;sL$?ue(zcAUe=6vc8V1MC*Pn+&hHAJY{DIu;CDc zJRAO+y#MjPp_~mkFa@@R;{Z!x3*aNd5D7LKHm!hGi6Q1Vs*lbQJ9!uMn}ne%MtxmN z-EYVv1)uhL+Aiy*G_1sON$#Zm%(5j1=SP^rn{d6K`+W0+^g2vO6+!FA9qimtKJpoO ztPCtT*kw49fIme3(sxlPi&DeCo;1qI73=|TZsoV7313V_AwvS*k}-<}0N5~C95@3!fI$kT{2ySRL{;!nfp0u#iTQV*ylz~HMv`B^gtywvihU{~>Y!oBdRaAr| zjMewEU0C+O0;ahN(?8NXj+vv!1G(_LWt;iPUN9ZU<9V>NWhYe@nDv5a@UU+0c5EbS zmkjQKp#Ix^zkwDQn}J>}i;U*daF#Drd^;Z^{L4hTgS^)tm;p)246c&a%q;+Rufn&! z%`t()2~ZWlOk~|B&1wqBl|)YuDYrXSme@JIsNjLKnl`soz~NmYaE*DdT6g zZps54K=L`og!f$Cd@IbVkMvxIxun^^5n`cQ0bI|M%gP-sf}P~waGq$-kdsHCGotn8 z$PUg%v{hx4z@C_d*1+_J&X`AoTwS^&D@?s)TMID}ECfntb0-qYx?SPpZ}0>hVyeX^ zV$pR6*#izMhL=?RUKkAsZ`HMp$kqJbYgQ_Z8o@D;S&0MOb?2&Pg%=irIkfU@#I^Dg zxwAgE{s5ytL)7ry7%p|FIO8_-iBNGIRuE^Y=g@%p!!;x$a1s!Ut#9uqCwxz?SEpMN zVBfJ8;&J*eZBtJW%9DQ4_JbpjNz?nKr((vw^ZRwg{6k<&Z-@ z23bSY<>4FOPctIwj{1796S98djJPp zN%B0mWcl`u5X4|zW12GxJ4KE+|9h-k?@`1zlfgcig;ctOvr-Z=ji>IIWFhXsCV<6L z)7-`q7IR6Phnf`JONv%C{zd@q-^ss1h)&MzwojUriFcJu2aUf`sxZcWZOBZe2O*Q_ z`4Km&zsDB(j%h!p8c^|E4eJ3%nY8c-Ja}8eHQK-8biCB0v0x{^%;$14Gyq0`24FPX zNJy&!J0FX^*#Fm*d}zQXURdA;QzS6KMyFdv6bcB zmvqP*PIL~f3c)7^d{kMpCPL;lgR6m^ZB3~lh~Ro&1z?DT(HLB~P_=h&o11Z5#-lvu zJeXk@z24(3g9gA8KvO9JN%EJe1FUC@(QH7ira}eYUfRLl@HLB_fq=J>3I+Vv9WGho zEKd-H?NJ1*&tIr;ABb{5thf4!GNw-9jD#@eiLhR~9Hpxy@a@bRB{lDV`b`|c2E&K* zesSp%CglydWq}t8476bB^G(h3f^x*Qha?iXYEp_S%*1WVs$o+j7ayo4L z9Zskl6}%4Ndai{rf}nlIbru%1&hcJ$;Qr4sy!iYLy*ZPH6;cUa7{2=gnKCoNXjd@a zj_{tDg!XgE_cdR;K{R)vDfsG%ByF*`rgS9vs$Yn37}Mcgt!jhR(LT28UN<4r8NXh? ze<&0PjoXW>?Sa;xh~cEzF*l6e8y8i4ta-YewM1N2Y~n*EJO7eJu%ejfem4Nka6}kKd#1xbF?p8Rmj!+ zMmdpJUEyQ-hdn1Fp%E!Xpo*)p#N-pf&0O)~w}Hj6OV#bRbuzMa+ywaDz& zsZ0`VgexH`w%@Ks(hTFZ>W7t02%YpR2yK8r*V>jrgpG-(_z<y|N$7a{L1##05?=#ionsE{&bwz%=?=xw9|VoF;{s0s{fbVhB)VoO_p2C|iif}RM6OaXcG zUn_&pv{h=$bU>&DTWBjwmsDnbn2%#pJBn*Z4F;-C2~9;mv;MrUmc4@iZ^7!nxXnKp ziPs_Q?V7HHfn=i%SXrqq!%sU0i11OoA}Cz4^s2ySTN7lkwa`}p`3xjAiTT}o#TS3do&SpaXf>jWXs@qj<${S`? zj?AAlw;Yqd%rJd9VKG>JjxwLTaW{K>z&d_sv!e?d`$9r``y;^pmo-$Lfz+bLr+P%9 z29XV|weIdmru@n=ZbU;^u^^3bur@`O5!+dZ<_Q?xpX`8;oJBidpA_!}n3p_4yWY7Q z&{pLAe=)`r*kJmtExq@vN4wjvFY^}=akMMjQ1G)`CRg2?gTd&(6#&eqQ*?Yv#iVQR zqKIifW~_&d6)tHS*g#IJ2^NcU%1`ZsyrIyCzEhQRsGDZ1=~}j>xt{h#Tw3&1n>d0|2I8Won2#v9{n0B84JFV zaaM?aw3jQ7kfAD&0Dr>%l9j4%0N4AoQ{j%G_ZPGhh^t{q-pz-dAqKB%CokK?R~6qT z0K$@CF^nU4LoCpDN<>4#rp|J?w^U**SaRiT8B)3d;4$+2Yv?F6h^9=52Kxtb8tGsd zGGJ8JO8}=5i$*yK!vYJAHm>CsuYGaw*<3y>4~-Ztp389d?yn6Y|8r^qtUiAd^UMW} zqP8}F9~@Jg0WjaZ47WoskZwl>DspZ9d|a%)JFV{5EFLqQYH?c0@>CCj&++uay2N{> zQ;@!NNr9TyIjjq^a6wiggd>*d*kWW-8t_QUn~k&En+p6-rbQ=m0+2-#Y{L?!&S zDl>O$PvVkP28s9V45(r<*i})qLW@t{Wq#mC$eIou_|5007dNcL9~j8D+j-~btX}kZ zviO}Wd56DnFX&f$G^?7Ch4<$~3Jz~*uSCGn>7U-{yNk6g8j6&((;{zbHM7Z=Z{T_- zZ7ROEDbvaRC9kPUTrmCxoD3rse$>5xNg$}VBamNsw=pm`gD^GHV?+W@N-fV>dXZMQAsv+|c4O8Nk9%MEHCZ7yt2zOWJBtgqsQ<8|;3NPuxTvwXDKp`blHiEUrA zaVo!erbLcMF*tB*%R^}q^K8^$3quP*5CALl2} zMYye*a3?N!gY=4^jM*&JCve zyyLXHBJoiFG@EqhLT{^2T%-FR1rGE4qHe2~DK~Y+le$ z8gSvO9WW=V=hOyj`kc&ye9&>7c0gIi&`G|$cAJ}s)I)Cm7fic>CBRt>q8nhbnzB4J zua;?EDHXqww@?sglB7T-C_7vn!mg(|tXj3`Gm1W&!LhU~8zNsavkbKZH^CS|saZoF zBKRVv?Hd$ZLma!6ouk(=KHA{&#+=AL$}Jt1+&bo`9B|hDZuR~v9&iDnA4|1y%A=PE zoj0n9TqdD{b!;~1I^62>HM6UGpnl=J7Pd7zv|O$|KMClrk!`AumsL%Hmw<_+YE`g) z--?J_!idfHBuqeYOiT<4Rf!o9v>p=On3e%w`rjyza1&YMU#&tU1?>Mav!RZ06Vv z{_2qdeq{CZdzyYWDa5ye&7V5;9)12wyMrtMND8Ip+{Y%GoD(gkr1UV?7pImV!1;+7 zR^RW|#UzlyFKzwu8uIj18W%wc8n#SN;nu;HzAsmWtb6$qwRZ@py%O zbp%c75BP82qdlP_0e-#9poQ084>jkNB1sZ8E`Q(rO}Q3d)qvVLZIfr3Gf8XUllMEH*A0<0lWA^)#!4y{k3kMr<-?Q_N*Ya?W!`^Gz(x<-1dEcvG_4mk$Ml0){n7 z%da}*t<3^E6MAgBbDS9h?svcCvHNCmnpp=Q1myDZ#??ziXRX5#m{UGcy$_GArJ90j z!9yoiQy<-&yS*i;mDEZCQv95{YSjUU6%vHN-}y8635gAm^y1~%{li8^CK-g}htVb3eox{S>J zzE8(aAm4lGa;1aZSzkRaqy^y?}nUnT1K|O%-EhN)~pG>P-*keRT5p6Q~b(ncQcm~Bbvx%E`>Nd zo4;X0819WNNSgP*~v!6NxhntlY=|YbXnr8w(>mzNHY0Z6?#z3-3KJMBcM-@ z+Soo(Z>v5_L*_{*KF}$G@1T!Z4Gbp|#NvB#vIX<5ozrQVReli14gNDDVD9w!4_e*@ zsi0OQD*sy~a)T!eqmGjJA-vC@(mQn;v=tn3X=dKDPN*Ebvg#vgA)f!+?x}NV4u&Or z-LgHm?O+=j(%kt`ejUtL7bc=#0X$sc7S{q7k@G)gqW(>LK&b9}zbqUW)~R;x1X2Ts zEX{4sd3*Hh*3X96$GRvE-NJ=v711BJ1}*pGwSBPD{@`Kja9$yB@llbB<2C&7g(CgK z(RSHGZAGm15GS63ps|iU@5T4B2Z`X~d_Y~Ghh&-ItoS?on(8Wkc5xkb+KDNuOb=Cs zp2G+CwM#6K=vVKUj9rKJ=K{XZ(G`L&nb^8cI6}4nILcT9`|h(=I@ z2bt&Vehcc863~~cuIGxpzxbtmx>u0{LV-^WiB$PPU-_kEWe&^y#9o>0k{7hpaNi7s zej}(AIfym9r?*I)MyOUNyAKNl;Xjn%hD3dAfCNW|2-v;GIW3!NPhUH-v%RzHl*{V} zxmeX>(0CQOY7vWZHz*UIxM3iO>>Cg3PxZ`d9jvOYosh+_86sKtag6#r+MfF91G^^; z(8Ds@vGhI=&RmNhH1k+t=wl6bLF_1|Xa4bwVc#D0Cv@bEZeR5VHOM_=%&lk454o5! zq`>~E>FwPeMVb!+bGnQm#eQU+(BdgU~MX=%**(GP@~!t zxanue%bn4~$MUXPj_g96L!nxhTycVfZecaQkR~&is_%SvExjB-0-Gt|qyGi6#1`x8 zz}k2L4USBzLLL>tq~AK79sf3)!W>F?<*VDS7$-wV-#d7M9U?iuwmSw7wh`-gGaRDj z9%gi7dR-&I^!$N8KiNSG6vurO)+ub_?JyAvp<^o{_{2?=C^$q@%YC@J1*0@!v*~cA z#I5C3d1}AjrRqe2ECn<7HSP)xx@-~cj*6$?m1>mbR%F4XI)s3w8 z(p#0-U`PM`4Z1ReQ>-xs6xRE7+!gt0y~oJrfU^N{wQLayD9Ucs@Y|z{@|`=%u1@dw zN_>u^0c;%0#j6I-SbCc1QaZ(aeJEEc`Vi1^DSwOAV{=KRtgwWhj!og?a`YFA&9sCa z>A*0nFq<3i;*3}tm7SRDKXM$UpLwyl>W8Qn`=TA)yqbjoa!P9TGq2 zw``5p{IH1SlBH0zZ09{fN2zt4)yOCfgyNN^q=Q_r=v!gO$NaSSJ=+J)#P_@3e4{3` z61^+W<}wD&H3AaCX!bt4ik)c9@s;@3G~sFirAkXRwnUq`?=$yll$?n#;yYr#oH2v$ z*w={`R1qFB$Ckd;iErqA+j%Me-1yS|D3*v}`9i9d4!N##hOY~{JxW>bf3Hil4cMvCs6 zjN1zmUB1>)TNz*CuSO*s1raKd$<^240A)LLL%$M7Y6AXh1nR!^oa2tKUaMyd@~WaK z!Uhk2`sTO+x_r_3h%=r9FMc`ZiuW3KyW7$COQPh@i@|f2(@pNeZ9iQ6Z*n$o(;5!# zR?Yrirc5nUcvllkwn}!YBuY8mJN2F&QDbL6u1sDhRq)Qfsg|B&aS^jP@W-|o`WVGXZ8=TmA%$!W!=(X!>u+Gh~I|7xVO&|bSx{eVZ?HK{&m!uZVu?K+W;`n_N7p~fY6RRm2L zH?`Wl4WpliPUTtWCNt63-`ue1skG`N+dk5vzY@6!&H zqWCWLxsaO=`E@@E&_dIw&JzNqpFwQOuXM6D?A*MeRoPdrE^GI0q?>&0naz6im+kKJ`d7qsgQviUP0Plg zYmx^}oeKX0%kLnI5W^Q}JXyQle---I3;)FU{{Zaj@L^2a6(Xs7kCNnXAr*x>6gOMY zy&*boTmc87S@2Cj@IXdQ%S?Gv&t&k2bCQu@`$e3JG3|_=V(VtrN72$2e_PD>-xrq{ z4aNkH0Gc6Q8)SJuFp|jLO{XC!lXcd*?Q@JaJloB|G^qWd5Su!57SAyL*IL)zap<_2 zRu#~mJ73lyh!@G$q#q9ePZLgoIEDOe^|B(IQjSEBH_pPQI%b8#n&#~Ir7)S5%YZ5; zf+}`ccfsHYm%Lc7&sm1?x+@)nJoB69n6$x5T(~fvO4vu|+}d?b?fv?*+@%1J>I*c>vd{wxeGvfOsf@1JYo25;-XuxfOiO&m5%21TIoMF&xD+fyw_1di- zU21lM*qeMTu0iiJW1%}Pgnoy>b6~Cf2U1?s#CLK(Z33ks`LOEyGtkxdG__0zT(9ci zMUx%*Pg>~cEP5Wx^tLm>C&;BGkQki7yQ3k=iJRa_0 zn?LHtpqeSiuSC05pBueH@3`1Y$=DbiY9qS7osP*5vEAR+(9QX&ZHH%(T^31d{^+NG z%Fd(3rUW)gPAi`R()@m9Le~Z)3Rs!hrwO^p%IqVJ-Nj}I55WIkaF1SKzG!^*rWQTT zb!KkU@`|<5H4Yc`qudkCxu-{v<6|Pq*crE^Eq8H}41Gkzqcc%>`I^ZSv5#G0&6P2K z?dc9rLd$8JFn;ha)PU{|9~c}MV{RcX`Y%}dwgZTzsU}ZyGoJ|{7R#3ik>OMnfA$oQBmmao0~_z+=?U!+&W3|Bc`OGkDLT0#L@-lbk`` zr8Mz-D_pEo)Ie*)RHgdR@|EGErb*9iIP8L7YGWV1B^cSB5la5E91lfvk9$6}N(us}%P+k0D~w5hZd z4{cDF?%GWnz8kHQwUOovy0EFig8GsJ$Q<5cK4sj--L?h5A@eNFMv!S_Bd%VF#Ur8u zq@^n)uC827Sn@X*Gbo{YhhSGC2YUpIwVwBjny;H~bL~uN0^;GT3 z{m~p;uik^O?a3@m7!%wPJWk^GWSC9UQ0mA$p@78U{N~BGUWfcQ`$1V(rg5WiSTyWK z3x1VB(-g0MJqvxvLaZoi_&XHr-?iiY*msNP;))=Xb;0eMWf!72l|5kJ#J9LzY|2axfK5!vheG$l zIYsy_0Ur-!8p!9(zaZG}h!;klaBWgwu(d~n3=Q7;DeJ$fBY(T~`Q@a{hpXRP>xmtV z`Z1nG787yIwf`jGuYv(U(IC=9yL9!=DK$=<5M;C+)(kSwB>!7++QNjM-y$=)H6^vU zQT`)L=NF4sW_#$QhoAWcDWHdRdr3Iz^<*YodW=BEn60MF7sLDpEIg9fT}n?*GyptXk|R`%~)Az@jB?S;lNzcAv?&~PSf0rTiB|6((XMY#}_ z_0g$_Lw8}AWH4*r1=}UpbHO|ZvZ2|Q1#kGesto*5xwwtm{zGmdD3rtbw^yCI=B{X6 zy!ASdO~u)T{lLmUt)W!4mlhij-n*HUHn`Q%1TkkG2=?uN@&{O zt~>k#&bM==X_De#AcdL)kiqu|BC8l*o+@q?Fcp3fL9^6?7jsHfHZz4VVQFvnR_g;Z zqE7MtP_t66Nl-|WH`fX&mkg)P$W=5YTw>R2R7UL^z5mYp)6=9;&UE2^&!Z-sKf^ot z9F-;02wy^zCS#P>^>7E1cFqjly|=zeL1dYMe0Y-IzW;lLK#2GG={*S+dy%uw{`dCO z@?OIg%M5@f6{i!rG7yQf*VOgLgH-5F83B%bwsXtVHu9+!cT9YFj=$Eu-7$rqwFPof zmzPmoJqlJ=;`9@z)Sx=pwgZ;j5hE zYQt#}VJTi(-}wWwpY9wwZBy1*PEsDZlgON`QPhCbNh`^b>O}T^6hhb7oFn=m(O-_j z4-3U0$gF)mu1WGq(bDRa0GFpF@(fA5nu9YdXxM@n*PhBQ<5niuI=(5o<)1=Om0p<@YIp-k}@60BeOgC^+Tg*@S3#E?eu>#2j5sIkxN z#SZY~^CDPw+qFe!YjB^(S%Ce^&zOBGU}uoL3lm8y9ak+fcV;M9SK*qg>27f1C9&A_ zfG@Cj4tWVc$#MK(%tbpU)k+W3+_g{P#0+5YtCSvu2LLneNfmUY-RR@!{a@(;oPKSm z!fGCPv?VS5k9O83;aUz60U%@zw%cL-H<0~L0qcKWcGl#mBxOd&#(H3{ou_e*xy3}3 zi@v=&4jE_I*VW$X);pZnQhjGeS>J0DFuIPSxkXG%_%H^mi2sF2E?fi0Hl2^uA@fBD ziMBkg0DX-9no@VW0&E$ps5?8W|Ma1eaT*!y_%%nE^(}a4eYPU3Zfl(&`_PyFj?Uf*)77G)={qXVK z#Pdl5@hk1K4_JIgu+*i7?(?%Z7kP~oV1L;vypKs_z>dC|!&4{4kv5nY56Ce0Q)fo} zzc|PLn=%j~0YJ>tl9yvveEyH10ZR?zht43VW~pO+kWshubMz|VnkZA*b4x01G^|Dl zTz;1tMIwKh>=-Tdthx2N|EG=?CzsZ2Oc^`eYkJqS2~(300A!F=?bnEi2rbwc&Qi$K zv(B;f*pLU%VV!lhNS2SkyWb zF2eVYAI42-Is}vTz@)ZO#o|AmW=~a*=mGet(v@@h3q;9=a8uKfVI;lpJgiLie%EzU z4h<-Dz&Bwpg>uz5hm%g|6Ynj?_7*0i;;2q$U|aqA#DN@we96{}tw3!rS%QYzD&g&O zxJ$P0{yh@LT}@*YZX4=nk*$z9X8%B*GsVV}~NC zdd657j7r$kUv*4|4K{~2%zIOfYUKHOjn3EfA;LYSt|GlIP1Zvn8Ww zS%U)zAqF^li{Ck9%|xwF-M{-YIhsw*y%x=0ZhY9ByY{>!VeVvGmfQ`d3=F|tq-%cb0DM|^{IY~yK zz%|xink&I; zjdY5CZ>r;cNZ$6@MU0Q=|;QoO!&>|kjI>K3kLj%nG=k4$H;E%(r#*LyYu`I1{wZwyxE1k$ zaY!dWdH*re=*)`LGwKib!I*+liv7WrA9MsnV8d%f+gClE{M+^JW7TS!*Xnq)tRa-7 zUM$^!D1p};_n7-NySttHP<49PzMo!cF%Sb-QZ^MxxyVh+bMa-wfsa2<9mV{VG1JOL zex(Mbczu}UdY!*v)x7dO&x+NEQA``1XFlgzOt-358OC!ada>(4&vTGgOCrZ)|ND>( z`iKaVc=N^%_Hv=VlmCsL%=^Oc=lcF12;?o9kV4{KLK3U1wnzzPXK)GM356B{plcj) zxq=*h$z;+*5WwhNlh14A-lH2ixsTB-R zlN18PHJ06RdJxx<2X+D>V_xN^lMSfoIlVHD_4-_bHzR$sqV{IMNCsLE<*rGL%g=A) zv!XyVOpiuJt1Fq1L=G|e)!Z?Rt-QJvkW&v)QF8SWbC2(Nn*c# zStW)ADIi9KP}7-iHT-N(wwgtu{@#UOM5hTyixigWc$djZeAdvm8+X4wZPeS9g;7?L z4EtAKH}Nj+ElL`jB#W<%fs?oq^X4$~QH#dD&50!Q=K=vD#LiD`vo=Fsx9SF8BGF2h zXK+3lz5ld{XZrb@4Wu{y5{ z@hok}hL^!dRu0puW3u*Jmr3-53Up$jbjrAqmZ&@tm+~|b5yjs1`hik71xw@S zv=QRvaLw`H4FJ->{4D3a!qTlzeAnmpA*jod3R5q#*wwFJa2&z)0a})&cUe}dRU$GQ zBS8%^>?mC97e)q6$-rl(MqWspruvzu2*3<97Yu5Zp%?i8aCI5|>;1nU*`Lt?ybyyl z=;VrRA5NBBQl`3GaCcnbr^C}>yDBDynWVJ~_vITnqra!gg4}@u*a9G@S9!LphY#k% z&0Ck+9;`EPx>N>Vq?DyNPQ>U5c(H6=29pIH6CP-RTS`Yxw%5%<;xpVtpv<$OfkT?w zm`3Wm%;xLNUfJ`*jkhMX;=SYcRETS(JWSP^BN##5~7 zDy=7x{s`msGwikaqlsz5jPzCR>0pyVm*D@n<8}8@@sSdcb-FU>lIr=ewj}&A$ zxPkaHv97mimwh2WT(3Q^WUHwRx$M`Kk+O*$Vji>UU3CrGD}ko0m$1X`IDhZzie>2f zX{V`fTvx!hR`nO;I<_o#VoZ8dp04|5Dblc9j;6b|C#kFbiBBYzPMN z)zyO(G(WeUzScJQ*!r1wx;pmL969kwvCewnM7ZUhatO~U7rQU)3it%HVPm6Mt~O(6 zRhV*!w~&2@w~85ejQoB+3m<*+n@wxixc;J}4^mYs8LW#f+YcOF#?F`R(|Ygt^lBnX zNJSr|jS(Zs!@gj*@!UdoDe>85T*MkDgsjcpG$vh^f_C_R)0mpw-*nbb@K{z#6}!2E z5-p$VKw`-}3TrZIgL^aYN2jh0@s)K~_Tqy!P07}L=c^X!(zjf=L`7v(^ca?Q7DlNx z$Q&is@f)L~jGrd0n4PgU1aQ=!t}#Kj^CZQ>+?*s)@{Ftk!**s@7yLW)YJW`1q^inR z@OcD1HDOaN;+D7>!|S}FMr6c6PY<{&%pCTu0HW__aNleZLPL?(+OPyw zoF2*KIO`3p!l$Za$HA|+2O!-391jwdyQP|ta*=Jidqzp5y-t9qTzOI1I}0EuTs+4@ z?ja$(&E}ac(-x9X{rMFagYNvoEapLOr+=Bfx%?Y*2AAv*Pn7AFGEslMBjxAE+6)V} z7!_2c3cwVfD2ED8zBP%n07J&6s)`)E9wB zVuXXbb6-YJ;T(@+cRJ)^89%)SzGcJs`WzGus31lbYg7GcU3EpKiAzA?+16`lr*?%D zF8}d!8K#KJ$OaP4JrsOu!W9xeGV!XoINXSDtRp}74Z;y!A?q|0@oqa(>C`>okhl6| zo#GuJWf^64zp%O?dVe#bL@TC^HPfCB7d?&!b;RTx?VRIG-N-e(L|MXq?Ni8pZ}heh zBvgZT6c>BK=g#=jnmwY+M05Xi?@KQEPNln3cVFfA25av#b1OqewB?m!D;owJUAw9A-|k zFFQm_PLutHb>%&CNtvYe%V0Q<`3yjpSf>ECTuU*pT&Hc+j6tFErYDSA9aX|mq0zIF*Xa;f~8s;`;v**puRZa*z2ErVMx z;gamut1?gV@&zi$jgPXQBqaZ%=ml2Z;u5>V_oB#*<;Ls-{LIl<^p5PUw#QpJ%xC1E zE6Mr)O051r1$iPI$R^#(SoGyb2zZ4IacQ)8BLxvA#k23`s;fNoe=vNMVh@N~G6W8n zmW!W?D+@>A1v3lR*Hqeg&n(J791Yo;Op<3HcR-e;6Xvii4~V#lj*kjUO}H$%6LehGfqbvmyWivoz1k^1^AH52=xOwUVHo43^`^))KEz6ATFkfB^FlJrGMY#`^AsQj= zylwG;HX|GHY>CEcxK27D?va(GG_mJTRe#5T8y*+NvNVbDD|sy&$a-2-=qXO?NwFi3 z;K$&~uNVm2IpLH!ZR8j~pixOdiOVvP%~YslFB_iX%x(uauh@t2c<}%l)U+cto@(MD z8i=gT-J_prf4baHuF)L?T`9H|t&>KO*{07HDuP`Va0&lHIWfySHB!~RjWFE9xqcUB z4YN{gEW6aeu&v|O^oNIXSvy}M(|vhamB*g!h)qGnr)Bx(;@d8;{`cn3HXb zJi%XuM1B2`Y4#X!OgmTha6!`)^F*8RsjO5|yYwB268z_`F`u+bRp|gbg|b;{zcj_Y z$YR=Q9=_So*qsw7W}Kzmdk1QRqO6VUw1s&TNy<`CLFr}ae4pbZ9XZNKD)&5afWCd% zV--f_CrYO{iA`vU@Icg_Y$GA1k|BRGQ;%MnaOi+-8nfzP=+p!&%aQhzfdif!){ zL)#sDR|hu~by=>g`Z&^NI;-4NkvJQzh#s~nLw{oYAUX2{a-TwAE79t#P#;%!D(kPD z%_AQwE@eEJLhdg{b|W}AEP4|^tK~s6>&V7bc1e~mD})9x({;!!xDt#S%qsa^-Sxx1J$Z~&d|(d zk9IrcOE=1k+K-HY0L#%;5w>8)H3_PXOkD@iig$!G`UqY@NPu*xU-2dYgvW0hWx?@Ji|h!;bB>1ymWRhvjddsO zd`KaS#+~f{6p-&eca5g;UxwxHi~)IIly(5&tkNJX$diD6A3j>WcPHtX{2zwyU(K1j zfUX2aP+PN$1RQ-g0ID_qF|kfIO-jzE(kg4vaBdi9)?8=pI}Kqy#Yr;nLUO9QTJm6-*I#P42< zG6bJVXBi+)U0wmH6XU53XeVAaon;O z2sU|P&>E$>`Ex(heb_*rlax-e!&XG!ukQL`Th;N$aUlOA3%GB=wsUm<=n}2_s}jY} z4AkZ_{V}U9wSt>rCyPUO5v|TcXIrmBE>w<=n`mG@k$rcVXJN2?=a4L)XK_kpNPb1< zYZL#F2GWZMT_PsZ6)yZ~O1n_FA~$$`nO{ags@Ie5WncM!7bG!e#8v2WWY|u2lsbl# ze0iVNSgbFTk!yqLnvZjTR@>TxVq0jgU)vRdSVm`CJ??wXgv^&bdg6Wu4xcSlA0ipl zC2bhY{F+hQ(9?KZa@XIgc}#QAa`H7wXSM8c$3!$)FLU(DY5wYyVguXe#v8M;%kp+r zQk{m?!-R^ZeA)P!!dW)H+o?Y@aq8eduH<912C3t+L;B4>KrA@_T9Wcnk%OO$E8jeV z8|U9Y`cBFPvqwfRbqjSQ4xTJ|*N>|kdl@xwEk>JH-4)nsmaBg)mL`1&-jdq#<@Ic` zNPGO6_b#zAalYus=f(w5oo|jUjv@zsE86!>Un{=qGfx;6Fj&Zw7v3mLo_SVSiGE*_ z(cLpAeWa_Id4WwsjrwXv4KhHxSINo>e=q7ye#%}8P@x{X>$1k@tRrjUj|8I5c(QJ4EfcFcD$ zQ7=GSn(PCI;x5;oo}c^z(ZK6FBUa}^su`o_FMAXh7F~^EahtYmJ=*44sQ5nA{NheK z8qU+SlU}VUS}cA2 zmhffZ*p5xt!qvV2{>~WRV^*-k9QUiwMWRb@%CwM@gX7@pEdp${Zuhw)`2I&&c7Kof z>P>pF7FDL*{|GkYObB7(j{l$V?7xHLZ#i8k!d`R-+!45?-go~4yK!FuicbpGQ=m)q zj?(6if66l$_Ne;kZ^!b6kkfjUS^8^lv@E$=!BL@ibec?f0-c7CI>NX5MUZZTx!FwB zDu0|^a_eg_^{Y{RQ_NXsRdC4@!=8)fZ)(15u40y7p@tC-Bo>e_lB2voT3u429G*(0ZkcrKVedCMP)gzKJlnV7l}DhqwE(I zZ!U|fWrDpqBSeCJXSg@=1ndt3Fgupd(tHUrEU!~-4=NV$Dv6}ryQz$tqq5CaXO6Sh zqpjU<%Ccfn@rdLu*3NUZme3>TZLNGKTyRH16T(1Jc27Q48d9ecs12>ZO4W(6E*hIW zHz{RR{7z8I4u0E56o*mnE*pyZ#`=dkshRbB%8NAu2ufspM5@DMnuoXQZ*9G5CVD6X zJtf1wHH#ws)qXl(Q1g{btk&xE+bgLW{y^M&$*DT(sm4qVl_4zYo_FV^37-7Bi?DEK zicMZ2ytHIn;_Fx7YwIa9uPlq(_}HgzLi(!Yy_k`|n=9-umZMu42w}jJ|K2#=ayi2s zfK?!vmhb_TyWVEr*1{T{N!>(3nLhA#M@&*b5VFX9wW7pFnj98YzFj*(y|T8 zKNE=ef#R&Y^{w%zCNsrWhR%6t9e*PC{?q|o=fYz8VpmwP^7}<=e>7OSOr)|O($Y8^ z9&T$~dayPD7zhYA?YTHFb-2vbMN&JNnP((xgVfQC67zkC!m7T+)!Rzb$}Val-x3Vb z+*2(`2GWHxWtH`+t96^b5RL92F*8t?l)`)eTu57IB`>4Wrr3f7^yZ8$I(jRX(iAkP;oJ~w8V`C{;i?~;CX1pUi%TKyV+=RCLM(+Lm;D2pgRKQJ|7 z`Kw*1{h8UIx9*Yr|xJoAH7laQtyX!LVV5$TGSDND;feL2Cp8#30U-=(o8zUqF7Q= z@?fx#|A2@IYfXW+QDHJ2fmpe!L0%v z?E|NoE<)Q<^aVRd!5oxQlZs4klv@GE=y%eG&6BGwQS}Q@RF@Rh?@z~BWvv!a8xF|k zwdLv_g`f$%I@YAl1``^ZJmH(&Cv9RF5w{yZwjsWuLxKyA3i?sni&FT?=Ok&rDtUb$ zMceX=gGoWiL!qZn)0~`Dd=Jb1EG^l`3BLkG+R<%cOz0M0`c(5=yk&$O$^Ck3lTe>} zz~Tjlc#shJ&Fgv1YND`ZY^2Z@=JOgNr)Vg#2F#7>X%sKV<$GhyRmbXW2pR=9PQ}2J z%1~2`v8*AmXn#d!3my_&0CILs21o?{Ks3@St&_-Ks#fZqHnyR&gd2a+r}hMeb9mM~ zE{nqF|B62q@W;;apA z?w&E;O2x=;%Z>Vw!1Xe4lkH^!-|jom&vw?c-=1DP zZ7Yd9%WH239URoYq>N2azlh|WJ< zBs(!bU3jG6Mg9DbzVkmwmd~#c?p*$FU=-Hgn7~C~7}gZMmnuGdgbw@d#VVkOHtt1s za$h#TH!XUkKD>zzrk@_uW-(hbFokHO-xKPB5k;8S$NbFCEPVQ97`lw^?YUA392br| z-)7tyf4U>SgyNk8(qH?C_p-%$E6aSPLYQF04x-8-DEx?+fX=!rdH+JLPGpgY5x@qd zjxa70Cw0}66j z*jyaFcgKgt10|p@(j`|qkB$P268SH((C9CvO0lpTKJ+=<$!4d;Nu^AITgD8_xf@UJ zp&Rv;G#icTjxWY5rCnuG{7(DJJKl7v!ry;2#9TE!`x8QrJEn9#&fYkGuT?Vx|kCvNbvNIt#Zt&_KoJJ5(AiCn=N}S_`yP37So(a(XW<8T1x? z4S$OD3F}sHWI>u-G- z&(vCNv&a8Leica)zayNg8+0+;vgXx;hH*y!JpY#`@!vuAe;SEYgZ;a(^XzYd5C7=- zf1;6poTB$39}_mF;28%pgkVEs*uHk}tly6C4LMNGcaVU3Ngk7r^|1ER#=)wsvpyLs zS1dn)>&CGJMPmum2L32okSk%Ma>W%@B{)pdjEC8HIH62vqt8};8zf%kAsS_m27ea_CM2> z`1HbjiX?$o??wP+AgF{Yz7cN*!DmFpk+)P_GY`O53ZwIAxvWfabm2fdR5a@B&6OiE zyZ~c|dle*&;#HBw^6#m55Rq7y4W>(pcS<0o*#J@X)e!$Yd>hB7z$;nX52xlv42grB z@sd-w1?N4Z0ivFQDOZYL3Y@Sl6?f8fz}pnG;dM#oyl(&v^W&i`6;JrY+bDY}E9EqX z5^uccw=3`$zk05oWpp|i{p#Y3%bn39w8w98fIwUA6(_TsXvCvzMesmO4b!~avWZ{r z5kX8Fir_UJrfQ?lR}vptF>;xf``*lXgid%$Bg#j=+R?%qguM#9Nj)l?|Jj+oAoZV{oAaI3u7{4h|EmG<7j`jI z12ICGxf2H&zCpQI!??pn@htXt(XG&oK`O2(TXc)~-04eY`@khbF-=9e(!p;Eo};>u zzQn8`PqR4NnyR*fL=7RNoGX-Aw@c0#!>bk6t;gw_(>0Dx8RBgj#azvBlrQrpZ6yo) z+>aeqPwVzMEVPCX?jxNJ6gU3G-_kSwSGQxF8bMU-iA>*F%nWunK*T#8|2|CpGj<75 zQ(5>5x=@u(9p7@Oi$2d&4Awi0;lMlz_+u@1f&evJ3z@)t{p$%+9Rysy4*d10OOWAc z@`iqIMp`m>YH?B|)ph?6HtUiYYZrD9s|BKc5v7I#LhcZ`K{XWaebSium+5c&zF@7ArSJ|?6B#dF;Eo~@a^w0UTksp zkO|kh0J`<{$kHlN;0O5Aqu%4O$CSzR!N!aM*#mNpdQK zLoqDoZsRRzbS~oH<)kK20Xn@)L`}UK>T>*)2M&xA2+U9dyQz zYaZp;l)*=tKK9;?#Cbm2_DlHLWJr zfdF#ssphA8fNiTW1e?f`d_PYlO^MnlrIO|_JY#mitSQC}U?+d-=__is(mgh4DehBK zGtfft=cp6t)mXk{nZ2o88&S8G9r@i{{%8tS&8Pps7UoXo-k9sEVvo3e^f;kz{EaSh z(ge?&Uv^cNZvG?DvOUdd0G|1Dl%Ez(nCthOJz~(-Wnt5kRy)?N7vh*mY9=e~&th-d zh9#vZ3#d+^RL_rD9#6^9>pzdo1kiu&TX68x1Fual|9_6z{egF|P<^arIS(5-k&yiE zr$W~zP3OQ%)6IAu6{A9(NwBHo%4MO!(7_dqOq?hy64C(FuHo`X5|AG#7om$8ZV-HW zl97CBZ@X|I*Ks>Qj}uXc`EVz zt@A^&RRs+s$%Hhl#srHfN|CK>DUQRa(I%Tn*v9nuwVf&dMPM zi0x1T&Ub=8)dppj2-AW{B^YA{@$ll8z?-nf90d(R$-Dhxd3w27jqA-_RMmiY#zSK- z7K3J(5|?J}&3hiV2qM3^-OYFl((vjekW$r0WqtYdtq?lfpy-oXern#BV4>CiDtCjP z{780B@LfzR!#(&+2}M*@HQ0xVe%7gE?n|qiz;1b-c|S@1ed;KQ3D4_Xaps^)4)d?E zD!5_Ier;*PnF2AhHhar60(^aReH75=h*jJoOB-LtG9N;hu)G*BT;eAvgb*JNYUCpFfn;bYUU z+A(}#{2>|H`_n(Fpst5o&nnmdPiBLqgdXC*gc!eusgue?j)g7`fmKJHUOm7Sx2;o3 zAQ9v;1*!oWi9};imOf)ad?WMC@J{(8oM*{WwKn&*)_n-6$WG`uslH*q< zw*jLxwHYpeI%$l#MxnFwhu`gYg`(?KB!y2?e%ogY=J^IL_BR7D`s#ih+mfAHLN}tX z`IS_Y9ZvoJeU5&$NOkS>XSK;6(;otan*h7Xx&JF}{S}3W9Z)#pnrT^VWzlD;V(&+Z z;%wmji&VZb12&DNPVQWJJOM6p7@V%<=gGrW0WDlhTn%D~Q%lLAvSXZ)=uL zktQrdY__Iqk_24Q0J6mfO3XRdCQA|Es_;C9RUwESQSh~)rr zNcJ{{oY5fis}n#zV_ICfl~NFfzn0?wfc3CfA}&h1aMWTGr%a+>1(Y{n88vTZ18iCg zK~LJx>`7y8DN6=dwSXp&N!I+WLJAvB`oIUZZL>s!1Rs^JhojDq8;4tAaFeH0hgU%# zmvz%3uat1tFl(tyh1FlP@9A|^;arj=UfV`>8B)QuT=un;$3);9V*DItAjQZ(ZT}1% zc5g7WUDcHyyi7lqC%559T7sJW^M+2^!d~+sdFWJ0_&}=J8+nk zDUJOoSu%d4_4^_8bIcInZ6?KIZYoAd6sA;cBJjOYNF(h5VY7Nf<@~O2;8MV*b`I(* z@T}X(kII2IhlH^XB0w*?P64?z43S_Yk;T0iN!~pO(-9H6Oy%N@&f+jvAxBQUG01Pl z;(MkERy$82N}3b$2fqSfW`{R>_8sQny!x*K3C2gT4eUz=RaPtztABt&Fdw?UgmZ!RZ3il;(2@tw6@=Sb>TEMB@7Gc8DmpJ9){rG zSpJshpudR+)TRJR`F7sM=o-!J`_VMjQU+-clp=K}+4^ukNRqWF7Z<$Ac@@r%7+1xT z6Q*0dDY;?^t3AiCY5yV|Fz;Z8lVG5n8` z=|5*={^9(xL+bCWn?K9`B$6*e8RmS(u8J%ilW`bn7>n?J`f4>rM?cUebbZC*nMAT| zKsnl2zCiT4&kx-p4v?s35nAl7nV&ZdntoEDeTT#|LkbT^x{6aGn@^%GcmV-`5XE4u zV&j@3Iu~@Opqk07o5MO5TMrklje#=BcCb%@G#BCCm_nEz`OI;_GYh; z9XpyJwLu@}3Da`U$pqwj4K6S5S~_NS{fGzjoMt*tr@{GZevsD+$x7Fq|^ni~fWT>sxHGQ?vEl`nvl^1WqE%X7c za-MBpfI+olW;<`j_DE3JrjweL*+}N@t_l=u@KVLZNAFgZ=xW<!8;2NrDRVCOG6(O=%cKVQq=HZwtTM~`B*UDYm2D8zn!_} zpvC@0SoU8c$2dI#h^b)13sU%q1*>9s%cgLICnCQGl}Sw>_*rb`Gg$Zt{`LR>^}^sj z+3jC8d-QAq_3zoK+4SipaR$SE_9UgYc#`i#b6E1Gi7yn{|8`3cwZW^Q zk5DOe4C#!4?1cT2%7%y0lY!WXr9H}`reao+!Qj$bo9*vo$ipr21q(v_O)}L%0mP~Y zDngoi9UAy}5!8?$pBH;HR$%(Uau6wD{zwz^HBy5Nbl8-;(}9tBS>N{L_3Qi+y0l1P zGg8!QDxlO(?3?33pFM`3E-&f65s%JxP!Kq{`InG>!KoZ**>)pGeN$t8{ zf6MC90h%VEjyAI|giCnsRT4$dGKQKaQTy7y0bpyw#fD}mYm362`N>(>ehzyV8$KY7 zEtJadx;0kvn<@6MeyayEs1zsu*4v-D4jmoO?bw9jCg(Pmi&b7XxdCI+;Xg`G9X#v$ z218Z-XWa|DL=`g05y1Dg5_g?Bg2gp_*6Hg4)0JLdHn=q}wr*m6s~A*+;YgGe5+zJ; zAf%=6HiCwnNe(O@5(3<42jNTxLaWXfOiDMF9Y5-}M3-kxye@X)flXIulxS=uBPCmY zEUaR_P?)II-S~>RZ6RetkfWJD{RY`GxfiK8c^r>X!^9`9*0%Vec*nobE;BiLnu=Hc zQ6yt3rO4V4?U2^pamBRw`UDrW?quu0|$abZx1s;%38qmKw7-ERQH_VFWB_9ZxJ zTf1&MapIzHm2t6qz3-8+$;UtzhV>(!$wt;Y8$WHU=4>}{F~#Fc?8B8TEL z0~&5je>bV)o1Kq|J`8h4P?cTn(reZa@@#&hbaubQdREVglfNZY&lC{8Fw&?9;_!f=I}YR7ju$-s`|U9>*D?h>-%K+hi|_7moLhH=+^%5(!bht zr9rY;Pt^H8PlAW~pQTJQq<&I@O(zSreCgvRH0jnTC}6XIss)eV@?zgoOXtY79W|*| zry0q)N<&*c-+)=LLpyHYzVSt(n@J1N*0cziK~w=GTD@y#!% z~vD3ZJ_9g zhtRNCunxK$hZ!QN*mGcLEXmeEFw#<^9oiM$SI8o0Jkp}Xv#_CBE=pEpo4yi8ylfNA z6Cs-mI|QV7n?%2*fCE@b)F)NQiD^!aV8O#p!BM(;g08C|#X*yI< zR)@iFqq*3uno3PfUFD@GNUtn3x9t`3`&BJVWYl;JHwiXrW!XKe7+z>uSsi4x5nERx~r z!FJ+g-m3ivo|Pm;#3fo|+vf5b>98G#tzlK|pO*9UILZpC8f=g5b4o|!p*%cMy2c@+ zC#BDx^u_!ECka5By+vHM#`lNn$`n>@X@0MS&(E_vIa<_)q`m)rQa+0!pq@>f`bY;>c+4rN^%7 zccfz9iN$Wbi@FMKaB!UHWC}RO!RDtAfB#}}+tCZKD!X{R%q|&YWhbT9(tN}(EiBgr zk&aW%hy{}p_+pSkK1!0aW2i}=O~c?-m~lKYIv7qU10P+*j}?PuzQep5BlM_v=c?TEj@FS~f*h!+BE)0XoeWk_D>6hy zl7bnl_Hu_921$4Z#2~f|Lv2`Gyc^=jLU!lUaz3ML^FwoL6=~~I!?^{nD+h}HkkYQG zpJn~|8psLk&|RX?UipJ%{Hhv>RZ2BG+VBIkUVr|rXe-#?&4KrUaJP+#t2XvMeskOy zCFwto%rnps?C#68+=aZkD~)es+u7{E7ZIFJZ%x^F`|Kl93`*l>Ot{J zuJmw9y?*{ut)=i#Y4ehzBs@|{f!e*}Vv8Iy6=XsGC8#Qs9zO0d1y#rDLB%-atw9!N zgXdLZ26&)P)C{2fFQ&_{!;Y(;cJJ0M# z;K2XRre;S{@7kQ=B>WL^o%})JbqQreH_V3Pc9<74oJFB?u5+^40P@(aQ`%{HZNRd? z_9hd*$5kX=hOaA_szZeGy`HFD>G|jQ%xi4OHSe;ENa&RpSj{o|_ibnDKFd5uT%hqr zTF&0=GaZ<&ZFg3A`scMnvG8;D4n3RE=KFLox^P~?va*Z*4v_#VQp1u)#8k4P%ag@4ClRx;ycazdu!=1 zjbO+Ve)WYZ8Bm1xgHofu$MZ9~<#Q3} zEdA)I7vjmQI#jE8md5=0*N>8qxwg%q^@DF;@X)^1MpEax3R!-Rjj27e^n$9QDu8Yr z(?9dUv`m;;VAp5ot@M&r?GIAldZLYJx)DjiA63O62=SJn*j#hK?cQgAX%UrHqRko! z_E6y_6%zDY`(mkWDhq@I#h*0H1uD}~PZiO;Zia5o!NmeD^ZYc@aHl~dfr953eKLla zb6Yl}gsn+kmrD|gun~l_5>uaKH`-05cisc;P_($xdI1iLj7)Y|^So4)psi<49DHq& zimzY+QD)ZGyN?=!nDRhfKwkZeO#IS;8i!-E3bmtIo)7$E1l^dko69{9D-{&OqK9vU zki+6*_Nj=oaAPZDJ@SBajQvddM3ldku*NM zNFD{QbXbPv{xOxOTw}VHCJ~XSHo>XP3pG-u-1;y)zHHfDtL z-zB^`n)P8aroCp8Fb1vSs?U{(J_?oH6C;S3zU`C2!Dgr)=@Z7&lzmgZUQiiqs8{cx z4~Fgho@ukU)T0}S$mWh}GL@S5ywr_48X9~7zIj;&=3!04{a7?!z}XYYM1OHebaLU=-`pr=QU$R z+(tRu-1wCrYRTtwuMy%m&5hfzJ$e#Yc+~l_hFz|sDdB_Z^?gpRTl&R!%F9ezqhayygx=R|%CDXD?3_y3^iRaFSR1D$&lo$HL4;h+1e9K@)d=EZo=J-j|^!`q_n ztj!u{LHFwkA$0Q62(W#c==Zb4?Npa2W?d4GP4acZh>{VLov~V;v5)n^JxHO8o93e# z4hv*HtD@rw+zqcnon2ZdOQmV|5}H5%i^}zv(D9iJL5XU~biio$Ppa{650Vl(0w^ip zdPhp|E4RAngAbH4RQ!HT%pB(k0pN%K4nyQf7vnCeE4@VGt97CRt50YIuC^}x z;*clZzXFUI@K)OC0w%)9@~JKt152~bZYy0s^zbmIS2kkpO%Q@+nSSTckglW* z>#pz_{F&J15p#)pYgZIZMQL8*209$O17MC-i8&Vl>Pn6fmqXl=c|4ZAvJg+BGlfL_ zZUPtC4@aW_(bN?ic#4vDtKr?%DjT)A+r7js_Mqcecd$f47y`(5x_ip=6sMYbkK`#) zN^Zzar`MNX;>07Zu4}xuv$5CWA1b_tRjf+l42B{$F0jf8Wgi{4(1BAkLwoDs&zcGmBTCz5T*$ z`?&_+*5V24jHLrV>DwiWKmIz?va%<+DA%}yB-Bh-;sY<8wl!D0QR=;^pWjP_ilr!C zRlA0lqR;MPxNdu-=ceotLrx_lYU}c!W`W4ZReqfuHpun&l9w+v9I*^q36PhW^1%d3DVh7&K0wupg+&z@ z@)n2c>&17lr>*Bv=jZ0Ph?-;v75$QThghiti^+G9LwKe2%f7hly(J*=F+yzU8;@8u zoc)rl=dzF-dXm|ZF&}Kpja-*TF^R8`y<^$55DkZJIdpxf1!m~Sud?78Vro4b?3H~J zZC=gP_KhVieA7wVv)wfXwxg0~P+eaEX@xD#fUF9qx0_m6(>=GPWk*!m$6#k?EzA$a z_ULEFwX92}zffiz$H}T2*Uyr{eP7b#!^zTgqMcuHM5i*~tW1%;mf&;Ys;yI>kP%$u zA2B&!iWu~Gr-Wn&4ynD!*8n%e2vV_O3C8&`Gu4H1I?#I9uPbqQ29R!nlZ<>pn?a@;1PzTAxbynD2P5hn+TwJD{B>dwCcJ_y}AJqW<@cE%G;>Ht|^b$vOs3w9g z91PlevYvhH_ZhSZ_~yx-P{Rs%v+-?!Qy?UQ(<3y>tlPxu;}d5eBRqUR0MyV|KqBRm z?kow`RGZJhQy~88PhuljqZL`KR7ao#mq91S5U{mtP2;kB=PUIF(vp5A&-0Xw+L<82 z9&{7l+7aVt!57ScV=I_6*yj49MNauQZA5mPh~I2;v9CkMTqkT(EjJ1A31iz6H*Okr z5n9!2*>PsZITRc_j0Egmx2up?qi?6})HuMB9j9s6_D?n*TH{{^+bLyxmq47TC#qL( zAdYcZ&(!Kyex>)nY%nDc+v(W<1U~;$3j3$W*>4`257%#?!1uDf{d$C^s@uucHMr+- z9#AzOeAGmNDWkIte5ZI?JhL~PgH4>0fiPDS>5*AJ`L9Kg99ntn;$f0dt@D;gUK}PJ zkXVDigfBk-+!CH8e{6MCN+(%Ci}GkV-FDwX9~om1HQY4v&WpjM3L5G6Y}#99sH+4- zcsuj&aP*ju{$4c}9)`76a{ik@MuY&Oc$IL<))=P1+%u`c7^^?k3dmSHK6=W+029G% z!L~KdGKX8(=sg5~r-pz*rlMgI+4!kEIPM%OfvK;M2q)~3$n*0+FG6W&DrA4M+j#5J z>cxJea`8Qv1*oq2tiLJ%(+n`ZhxI0js3?PpM_UOjz=-%I(EOY?pFOkp1DEH)b|N#D zTFF>w#v#`F%JoZitK`o)TNz*? zNW3K2k_wPK)*B5AJF?2-fa(o5ZBvD=^*m;FNU>Yp{_0Sth8WUt}2EJoG&IDn_wa`2g2Uj9*}+6dM zwz+bo#N+Om*(_cqbn!vdE%AEU1<9Nm5+5Rrq`T-~K85icxKL!UQmoAmq$+i|9` z@Lnw7BeBe7dYI}BrYa`Z`DH}8SRukOQM?Lca7fyfU%o{T!b=6K-klx+|kwsxVINd0woeEI|E}8LOYyZ4k2XM18lL@P2u%r8*-;tOq1d+xJ9ykM8#n7>*P8HVjualcEcLbBG3T!GZcEyQNN#0Q6 z6t?!E2Qi-TPjAa1xxeu>5Ukvx8D#RT)7|IxqwlEc>M0f#i@~u^&ppXo3TvHnXwCEs z;)2>A-TMCDRX3S`5eob^D|L6A;f4J*S5EzIMqF0`B?R2C>2YSnG4FP*nVI(~X6P#s zs*2)XgDTyyD2J-Gv96JsB&+YskaFoLR6vp0ZrGtzl1<4Ja>-tjtlB$F8jvN-<)Iwou04S0$~)!-ek-2$2q{?j<$;0L zhwtIkKbuYSc|#PXha_G$>WjFoIAZ?*BdcAVAhv{11!iKX9=yjM9)NoT_X#sODhv$K z{BR{P0_w`!GThMY@`{2QFi1%1Z7Xe-HGil87@u>HiU*T)w`7ch)#c0J#60-`$~z&09aj2d$p|#Lv984{}!Qhs}&+Y9azY3Rc}? z;lG7esrtMGF4#joT(TARQA=J1>=XPvFXO>1J#zf;7OP*@Rt6lU;YVamHITdXF^tzl zAu*qn|B(siHZw7aR$31{DZnEqti;;eHj~E#&ND6s;5AGaB&}rm0X;0(MmCU47!dsu zx_Heca(U_OqC&o0Z!Y#I4pJ(YByxF5QYW7#1tM+~6fzaqtA(+a#i%9sz5r>H-0GW6 z586xW-PVq&_&d{1$`58t@N&?p$45<%h@wS>sk4heKlNbE*0q*m^Yhc=slK(xb?+BG_O@AdfFz-TGT@1Bk_1FBq}xz$_F> zHMG()*>t6XV<%94(z_Oe=M0GDlc=n~aXAtgw#eN0pCO&UuSY}>WYHS7(c{Paal%*T zf8Zlqy5vJkipPEZR=p|t3@*i;>~BJ;3gU_r5Q__+N)>@>snY>A@*?NWmMS;wRE-_k zT=m`Q8h();V%wy$(z--}+HrZB&U)S~G(2uM;={3IGUrt@)N9OJ%BAF$9h^tVIdj{q z&TKJWvgQk}!uPoh^3+2Ybc08r3} zYdhC5g)g*Qum4pkW{Znseo)K67oZOf)bUU7Fbpo~ClNrzDH7NYM|*r5rCzuXv6h_v z%+IxlkFd3ZRX6>h62ihT*>NF{tq2zX!lSsUfJCl`yC-*4WZwyZKa*4HM!+rn+n4VCT=K3+~2assy0J*S#Hk81e``Fk-t@6m#6BK~LLYZ=-a=il5&Di3(;Fj%yg zn`Rg0i9QyF;k>7S0`|tzv>U&t-o#%VQgMCId^{e$md{^!T_=EIk^Tdhs)JbII&8oS zIE9v$j8BAcS3a9eb?B!J_fURg+Rdwi{vW)$-cMYejSFdfV zgN=jA=da);-M@!U@ZtxS{TTKxA%fpM8#tt%A<`);`*oMkdll73tCIU35WaLmngLnz za;HbheeISAqSqe9P}mk|l36=|u~V`#+0ha#;JOVe(a9PQeG%3w2Qp-80P>(}(x`k4 znBJ8FqvET=OrJgXFU)6gDG-xR_3k8mCT}c`RI+yB?%taYoO(vZ5(OB(R{zCIvE<4) zFCEf#+50v#6|FoR=un`ColxI3*58MP?T*hth$<%))CR~ts2nGuR1tYuydI_eOIJIU z{H%5$%i-&9!c=aY8Dwg03Fp{Azy&GSemqyn_UB<+X`Zd6>}|QTXf|4dkouh&`#6ga zii(3;;~eTs1$S1ox*u?i+`A%n5Kllya!YXfhI9cg>79DbsN+KXUY&efV-pl$Js-lj zgb&>ri1e{1$a0seC%?Q8+cYb84*vFp{qsvDEQApi&LSzCMz7UDu~&W==h`N`S`AN% zqcX233Gsf%HU6b8x8-FD=T3m zfMQej4-4jEz(Y{gg6}z5Y~jv`*t;qUV_qSG4t>J0@3!7+;lVk;y~veA$97sYN1|Y$ zg2R~v$F?dLXdoXVcJIJkq4U4oIY}(q9$6=KFOPXW)1zSmPq!_rL#L(ia+13Iojp54 zN|y^tU(W-o_ImY^6cR$jdO(#vBJq~XN~SnUN;@2K^N=0MFSr& zVw1a~RwCeHZ_Bt~eytdS$>L&oF-EpC`(@GAt3#zt$-0h7SB#)Hr}V}d*p}=Wm5Ile zbYr-wNpMl&Xe7iKywd7C{pj2QDs~L++qE5?KHa%RZ|+59esNOAjLAyO;T7RunCAC= zC;7yS`7*m;?h|#Thpwr6BDJ%l)%tU5_i!n)778K?R99cr)wgGJ!n$Cux!Fq zzdmV)6x}`jUV=M2e;d zuISK+3PFoZbj$0k$ZXxpCCdy`WE4@2p9Z@(s%vx($U_%F?>dVuVtxnz7IL!;5wjab zC5Ooq>j|mUtc%$e{T;a%8wUG}i7><`P_b2?c3|NuEe6&wg!d+FyMo9{PWhB9v1w+V zSn?xUhWr8s@ps_k_y!TJ*<#bw9=ROl8NA>mr+p$@FyvZD0)0$KHnO_&TjdRW7suxdtOH1y?ndIc;Eo z-3hW8CeV5NPozD<0Z}i!JKL<6iK~(fER$}BmgYBvW z_}!bo`M-2DnyEQRmAK?Hr4I>`AIt}$9)KJDg}LlQ%`ynuIm^n(Z_XAqEUBesHRckw zlndx4etl6#Pizq)SHCrFq$qs?!GGVb*zQ$=X)~`|g-{S9fKy*6UO6iLxFtn*hwenn zYHQ?|c$#eynGWBxtm;4T($xvk{mVPuF5B z+GhefwIF2n_@7O|ltWbSW50{YRuH-@byt&@mRl`D*rG=5O>0*_ML?{CibYUbf@g6k4Rkp=g2NE-mgJ9D;lC;ske>;BLj;9fG@ii!y{1s=GWfxmv;_ z@bi^4>N5CDc3#HRe593bHD8O^nFV29dBBYLl5_$&SXI}iP#&f^(r#v^i`?_)=<^$} z>SAKd$tcRl9((}9(M=@B-95U#Frwv1QDg{zFQjAqPxE10N@QQEkaa==7_*04J)F=W z)ss1*$>m+2o`hOVx8_0jUFt<+PAl1;)U2voy+1*83;9wU3gepXQcqbMMC3wp-(U21 zZZU@gK2~Gs-3GDjr@&42$0{UdA=^P8zyAvxex-0YhERt_GjdxClza?MDOggEv@6LR;j^`%uj$Z}mRRn@U3EV4^6t*K=4@efE7 zHJdP{aM*o(-B~uzF7Y$`tTnV(*qW; zS@HvJdjH&)ol>P#33svNs|X-OJYFfR{5UfNj~eF5do2m;0UO?C%pb#Cgm`Cjk~0Gn zeI{dmD~BNmF)(ivV5wu9eoYkK{ypz{1^*-xCy}f%KwLD1jsma^!-bblwDlf#kZ;EQ z^-}DKtcuSAR?Ug^S!^z*gNrP+D%6V452NznjPys14`W*UT&f#VPr*=UR0IuQH-SgD z!1yDmbcWKmc)+a|dK%Ob6v6zuPJPTBzDSs!wpgwTWYl4r@jDiQ#&}B#NeogrbTQQF zT`Gcx^)ha*G(Snytx|fW;)-CaSgZ9&TD`Y~^t?j`OBM6Yx05X`Eq2$yMOS#Py4X`- zV+~L^x&ho@k&JJ*u2SY`MT~Q8Ux;c@-V_Ysr&iB*;-_G)Rj++>q0bFnC*YR;{FWlF zO))aF{6yFa!HRfu#syTpYB+HN68IXqiLP4~-c>6U=2X(v?L7?~B2NzbK`yx{ zp0j^X{lTk6k1i1zSyT}*e`0L=xh)rgzGQc!Zy6RX9-5Cv*y~1Ov5S-buMiYja zYR=a}1LD+b4TMNi*{D-Vp*IBam2e0(N%pD*2Xgu0y;0Y%;!O&0owkav#bFmEU61=% zm#m;HT7Om%t(~8JxOmj zLKYz!ln%6f_%?K^WlkbzRbyU!!yt@CM!1&F!(Nyp5vOoB{R4owKz~k4cdPGNXc~5d zL%zCc(!+q-l!AzLZk4B%(JZuL2x(RQWF88uI>;u!1R;#RDvLZY#}m&>aoTV*ev`oB zXn;-Vn1yLoz~b+zIM)R3OhazmUE9za+ID7^8PzJdCA?sH`sYQ&zeI)4-Y*HGKK}<6 z7NA9x81WaeHeIk@NtZ{n+_l>Ks0l~iPL3Th$*kLG`Aw%)YGy|uPHc?9ZQ60c!}OQ0 z^wfPBplyPC*t5mE7Xe*)ku>2naO=b1&%1fYpN!w&0M&CIUI_TvMRrXBP|>k+G>KIJ zL0$v(05ci=OKy!;84k<^TV91=dw~8 zp4I@Atz=|&ry#{+c@XMDudnDDG@``16m(;Zi(`7iogbP2DjexN0Ik_8PE z4ww@ilY1}0MT8#ko`ZD28bO$;|K4Anc^;xjXii@C>@OU-}@P&sUxDb)H9ZgyC?@9OS!1^G< zU|;MHm~Y=0;p1t>gQ}kBxN?~o505Wm|h{Pv< zI-fN3SA`Fgr9Y$wJZ$eoOoz10CO*t%hrEw}Wd6Q{_{T44aviael~X(9H$o>Xn!OJD z-%=^C1xmUCp8`23{H3sBwiYnN@2-@1b8D_k_qwSPY(Q(++#RI(kO8T3!5j5UA%*+A z;P|O<`A)F`okL=m8X%JjrnSYh;)#6UI~zOXejP%yqg!hrrWFMoBfHF3ih0apybQ4< zv160V2ElM8DMy3j^i@jtR3P7KHhX8g!ngKilQNieHYYN4E+AL(KVB*S{b&amqQiS* zV@LqA1O^|7xS%p10eKTKE`JjnwWBvi$!6XRajFVP8aZdp^m$*S!7%q2>=ZFipVII% z=}`BZC{g30Z<`HO-ebZyQarKAMJAJLCDS*{Q34rG8H*^%fYnZr3X}13Nkom6;$tXrdT$Szr=;{5hJ5MAU3v-l1W{K(N zg7aSJxdPcWaPE0ryn^I1^^XZ&^Fxtgxj9ZW)L9}O~&V-)yjfV1|Fopf z7p=`NGiw2|aC>DJ-?fe+^kT%VC=*g?ZEatV z=?O{hdCEygDmt6{(F|%ZT7O3^qpF@1MK7I>*dBMn%y8TF#Qsf=t;Of|?N~_(3sY6H zjnhRO*H|Su7$R4TXj;2LP}sPSm`o>ro9=^3pdx}4tQN?n9LpTF=Qz1sd~6}1i)@9r zGmIGjzW$^sa&Yz4G#(XpY9)IIXs?Xq8~f?1Qg_HXG51~@m<%^9fhF7h&hY_R`g0O_ zTy^GqBc&reDpem%lTKjfaCf!sksag*#Iae;c#A{L0po3r_ehIvSOd|K*d2D6{$e<0 z1X1Z2WOfcZXDPUL8#8l#8gZvMQf{+&D|Sdhc`6+bF2L=a4e)#G>x&fl-GJ~m$2Wp2 z0h;RSbc?^yz)!|%Y|H1T*Xs-mC1M^$QHBa$`n-Vt=u(>kwl+@-jXdTYyiHaKLu!5q zNLOL)QDyA#85MjHCRNh*-Y*VP++BpH~zfi<@XscKywKAqLyX_^Seof$w`!vd*c<%YsxiNCy6D)0FGi>Z+CS2Zhp+uo} zgXw|<|A8@n4f1%T6Q@63lx%Xv`N0gaF^8nco7GbmGAg@eekp-o(-yef{|DXgfM^v7 zC!s~H(f}s(X2;l(-fCci@g(5`C*B04oN-e&Xrz!?8>)RK8%gzles^9*4~u9)HM*(; zmhl{pZ5hUCQ1Rqx8f&zbMMB*(9v z-y-S^^#QXZW0BvsyuK~t$kXVe{Ez2C065Iw7w`WXHD8KlI|;D|mX-lfky=wi$NXIn z!R5xcz4&h)8we!iu)e&a(%}$G6xA_5wnr9;<7dgjfWPF*TyKK%(XWer3jp$a`MMSt z$W|OGyy^Ze$5!QP2c)QQGu=_=J&Klhyvg`oSOa~I)T2#s{Q0NLC=;dxVOk*R%iocD z`sC8F;5(pNwx6Cretom;7PRIS>G8MU8`0 ztbOB^RPJr&Li2 z)HwSN1IyOha=!yocPNb8tF|XHh?oE1@K#cnrpxI??biKLxL{%^*Lxwa5wr48m!SM5 zJ-0EUsNr5x@lgx$C(Wft)m?S@Bv$MESU_-vtGTkc3bFCA02`KQqEH4L@7;tpTA?Q_ z3*!V6oO51Cmx0%xQN&W-mb<~H*r+al*R=_UM2kFo*cG7TR!y=J+wrsp|9 zk6~=XLSiegW%c3rw&iYjSFsc}vo3KElf~{s*mL#X&TbDUCB^6ys~AFqx~_O(Eam68 z`(lxv5&pP4E3XsYCPUPTpd`*y?Q(mHFSX*=x`|~T-)i#7BaH>&BO<@rMEqGmOhq(*LBkb9B;Ufs_P~|K$ z77n0s3_{iJK6DqbxG-Um!?GC%sl>FTI902c-l9};vk$3^b2S*CiZ}PA4-%+9v&G_< zprVj#N}7xM79^`%ft+k12}dZKj2~DI5t1508pm0SOq)BGo4;8alMJw%Bu1r6i<{pg zKV!1ga0saP5?91-EsLGR`);;mwStNDqmYK+!#lBr8P^DyVTur6wm;GIl5*426TnrUTx$1F5} zo+#C!*8on=W`r&84}^12>WG=Z`@|9p<3B|+g480WJFb+oMBnj~rKkAqEuUEO>`-`Z z=z{z^+>-_GN9P!r)l5qZLc8@5*WmJ8i-A7el2L61B43FCcP~SR2`T(YLGvZ4II~e` zQg6}C*J`)YXiv3vM)9WH!^V*iEAEWKIL~b~YqT&Y1l58NrrS1zIr8e6g(>y>+>_R_ zz3`^5$Gyqww7!a0KQ;G zA(!NcV#5Nb=q_fA8Buej3D8R9ksAD_F%hsnv`b2Hbf3{wfmLLCi3YyR07N|b@j6sH z7dv~}Lzev*)n}UkdgX$=eoRzYHd}gGi-GfVPX#@)#=%=_DKVAxrM|RT{Dp@rOaiMY zmeV=)Ac?xeGM_#rDs>nKmLO%X6YUj5t-{x<>R|YO)By&|?=ZAdJY8|~L$$oPrx3&R zxma_yf2I#~5*BlOzPK=Gx+bHToclQQwoc=^1fxzvG|*F8Hmiyvb2aC_@ySeJ++x+f z?NZOOel;u@P`a2;ht`_xRZd?$+d0Q?e2~KNP~^&dc+ZU;B(dTu7I8)^2J$cH^qMrt z-7_;!%eB~LKMDDoFhz=TJjYit{7JAxK=ST=!27^&S{>a|^Dv0fyE4z!< zu1iTtarpI(Vz8Kzr!z-zhJPY~TCOjK^p07r?xV$2Fr{=%aE{-CeNzOpE|jU`ws>xn zu{$gBQnOkQ#p~)firdN6>)`3HoQxg!$L&UAxJW5~3wi%nlTOc#ls`B%axbZ{QPKoN zMBAAUZf_!*jc2fKGrY~}IYGug6-|-6OlFr$C|FGt4z3fEbFMJ8Ckm6H>*LS}>0>kj zO6eUv+RjKv$TRV@|>C16K1FA)rTDORhm@-~Wp`>vlMY0(@*LgJ|;kASl2CC!%$bw*_ zAS4`-PbkvY0=nONKJR%g*wX#ts#J#y9BJH(mGh{b1q#ChlhNUVYfI?1|;mIUe!s*$OXQfes|&=k6lR8 z-mbKq?BcV;XAZcEfRlie8r;0QKHtkoRlwq~yGx#`%Edz9lu%UKi^Xq87Yh3=>yHf2 z80TPZQ)lEvGl4acpPTqeZ~iCIVt=F)qAv>K%-|MaCESMh-R8ktEEUbecF)S<5?R24 zw3@@MFRNa5_*Fk|5OS5w3ppAA@uxz-99;v}-DQ_>#T!u6v#jAG9N}!Q_pxNE>1wj7 z!Kx@}>JAje>ee&_5Z`n;b8Sxl7(lfc($L$&Q<&SkK z%sb9_7&<3E)j`~mpG*4M>>tNX?9$=?ylpCsGnjhfT zX(ZAwr``=4kl3;XrZ ziNb*nml`ZC6R8PNu2ws}e2|;s%>C|Ux_RH!R%m*yp&$zcDlf0WmY4fqa-I8WbG&E7 z%a4BjLT-0EfnKCscZ0X2Znnst34|X1%%0vI$|`RBJF475gTFnUp^koWhz>R{l$i}} zR*xM1^?@uA-LztQW?5Im zkKlBIRs8o8wc&!>zY|s(xji>~?)ffE@kwLa~3Mf%bbpR^8YLt2HgRo5&L>*us z38kDKt;`ni0TuoCIMtJ{C@@2jz;9% z&DZh8pVDDWQ5gH$`Snc}ls07>_FP!Xi~ve3es31|^#T zX6|&$OvaYZ&)i?4xIVs1la1}~3GR#HIUZkoyv`>lwUZA!hZ zrrZ$Jo8eA2MpD#yWf^4hC?-SL_0nU`-DZ%5gQTivUBqK|@snc)T)(<=pnJHJ4JwT#@O0H7gFt?Nh zqv%IBTb%FCb7oA|#a*N9jre1WRF~2-G^hZGIb7na@KQz;M^`@vLT6K(Nt#eDV|(}q z!~|(E2liuSzJ0Hyktm}nW<0nq)U&42g$z=1#>_V)%A&U5-vM)~oy7id<=e&vRv|ad z4GJB4X;7@w>ccM^lcW=5@>Ks&4f=#~yEW()AnEiNAJMZj(&>hv^Au8{}} zm!0+;yU)ti5?6iikJw(W_|th!LnL5xv)u}6=?aTs*Uzc21LnOQ?+h+m)XB+7crli3 zgRzxn`yBdt4aJ?0|E3EOj3dYSSL>RvM{(kHaRM8OGT8lO!L~Q-Oz9;L#FCCsW72I+ zs=I?=((BULFs_66uJgLn{v*PX%S%`U$rxTWbjJv9m%H@ zGbut$*RqCK3N!hKe_73?l;{~AmPXDb``bU&P4I#dg1fbD4Ql^civ8!YCh9dJdBAEQ zk5`pgb|*z&Wy;Cw)POb+#Z8Mg&@eprG*UxA89|G$EuKR40b3Eu09vmDOz{4kfzvLA z?>ZVxR=MXYR+8_lHT>)S^;9YMXnRNtRmduwM;HnctX_tiYYJOC!au}>+E~>i$5@+Z z;N1xwMD#j67iEMQRS78lYzF@hJnjXplCFaCR6lzU;>WuXNy;8+&|ErwHDu>VqhH9+ z2JW5`r*eFUlB9jFRn(rxBSG*8y$_2Z4gNv=LV%wW#)!sIw*>p6;sT252@V&R86`P#8(fpk&2MbQkmdMNC&i(fxz(zpz(A_%_(qlE$MO^giZm<%Yac9waH*6zW z$$@xVD~U^`nKOBrhVrpEBx zCZKyyq{jI9$!Q8r+jZ*=EZ?6k=_Lx^hVI#Uhm`+LNCy4F4-bQ$ z{=on|en@PqhJk<`jLqTecP?uU4(P~@{Jz8pK7;29QUeOKU1%>aJ8NR@zC)=VxfO$yzflq-6>8tHc-2BeJ;_P%b~y_er5yc~fsOR4VuE zWK(AiLz?Jp9}=XV$M0i`2L#iafhX>OMHqkfonlfDr^>W*6(i%KYtz10nOmg}flt>d zqN>Y`M*f0n!O5{o{k&VWnSxXYCw6tVk58y`#`SD_AW|Sc(#?O3y8$qAhxMKSWlZxo z3|MB`HV1QMMi;x2ua5AQGnq%ACmk1xX~Kp+Rlf^jg;2YnA5Z#&-e4feHep(wtaXzq z_YIQIRO=M25!Bl*LqnFE?Zb|bkNr8|4q9+f{QnV8{#WG#NkCl)9Ev_gcLJt5BFsTS z2pfCA6>17%KB%96OReM06?KRktb@I}S+nI7yIJu-bi?(*x{+EiVOk7P{cU|YqztP` z^X)maSzS$sPg$69MIGTtrHvPUfl!ZJ~Ro`2#e2D6jIlH{#q^PTR0rGh%(LN109O_)Fq&A@)9R*r>o~CH1oeq zA#VfyV`DK8V(kCcZuCCz|I&?n(xlBGHn$!6if@vfq%fLA5#aQFHrO7{Pf*v`B!$i8jrFd?jXHVD2m4 zjp%j2c^vP`n~L{i?iElAX};_A5$@Kg-WZSgWo4V?Cq^U^I=BEYhjpj%Z=@>l+Xh&i za56H5r9z7=^SwVBd#&siwM+qIzjO+%AlP|GS|T8oy0RT0jfc2v7JZ0X_DbM@dKF31 zTM9=kWcYM^Gi^v)g`||2rTQ7B=verE;D~sf85&MF7mIc>%1m4V#exG1P|)HE-|L-F zYM`~=kHQOjf*=#zI4p`dEF1WEbVpsI(oWeT>_HvMDHbb@EWFPgUh(lh3Zxg(Wq0q) z<`SL00*=q}u6z%Uz-10$KjiVR_&qh2F|Ip@mGtw;){&z?h{~$J_^K@QZQoec(#aO= zGMxJ`Gs{TwMXVF;GHG~~xQcAw8WG;-IWTkTRHO#Z8_(iXliYw%2o=K{%e%g8g#j3{ zf8#i(=oCh@-6=^vVH!JiZq^u5=3PC-l|RIXzvqJ(AiW^rhkKW z8+uY#-4#yjPyV)I4*-xw1tEqd3k~m-uSo^Am}Ff<^nqM9MZuOO9A|SELvlUUlNEJE z);Nw&v4W&2`cHe~Fo+G~a1CXn$6}`-M^9rpyU^-GQOt*o<~=vU1AVRKv$!ucDCWAQ zZtvpNRrF#rRy9c_3qyk|5D5#0{=~#TU3M4IwF3t5h@-y|UVca*S`2fL^7)w@lUFfz z8TZG;$VakHW13$ynrhTviyv&O(IaX0^fO*)`rEgVSO<*N4Q0`dPL|&q&$J2eWmuJK zLK68GukZTd1xNH!8h&8E$MXA3m;!!U+#54Re&{YqZSY6tDv?DG@3O-30qn zL1W@I6nch+Rpe&GuYV`4*$@zwqa@$qi<{wNTZ=-N-*dw!Q!QB|+F=-{vg~lZbAjO+$p zDBfE?FpCY$r-9f92^mUSp7bLArEYY+Ncv@*FuarQfp1W?ed%ZDcU-w|1rEX~^a;VgQT zO+s)@Cw%i3S~RvZZ5@9{I&ex$0P2;+l||w4)OC1A-*5s&F}=-M8|CERE6OFi8q&m} zd#*~JvV$qNFLadseAA(0vZ*=}LU4E&BYNM%d*pbfqgpLit7Re%OLZy@)rFOZ!#Lj?aeGV9PlflAE$X^P z<&9xN7&sj;l8i}NXwdW66cTsG-G{}&p+^{LS*Df0Z)Bd^5B zE-A?GF{5cP-oI`T^j+OCsPW$KdZ9)OL?-GbCl=gtRi(!gu{S#(I)jh3PGc5HCwU`r z9+J{O7(c)FXYy~gWo^d_WWg`Yk57#%q7HK0!Xl)U=y)X_P&enUd8E8XP5sk8B(E$@ zgY~KPWtK{sv1@Z@2{w}*qu8vWAF{va=?=||ej=$Y$~^vTM$?0ipzl^RUb z-T0kVHzD0x&|+p~#C)s?uz2n*URqVUsDnu_u`Hu{?iT8=yh`e!RA+{y*~Otft}`t3 zcLGuk^GBywvjIOS?UN<2yUn%ah#YCD>FpSLa;k%jz#kg_lbiLn_`Q(hfYcj@0N^Y^(KC~m2 zmH*$kM<)q?uxP^A%7XnX%IOF?-MBBeg33`cUnU}{+M^DaS`r>;HiqU)D(JRIr9_g~ zUgr=<8LJf>SiP=G8gRLr#5BMfxxM+QNT zrmd3BzIw~oSpD3IA9{2~FPAo+5o2E*@vIE) zeNI+d+sL^44-aWsqnPO6uB*yf&lpzdwV{#+E6hPcTscsHaj%v)!eX*wPF8VyCZTLs z>?SVZOUshnXSyqoW2X_hjJ5G?Jd$NZO1 z6=3JBIi~zVcUBO`$lbeDj7xoBJB66G|kU`B=n6y2BW1~6wT{s;5+gS=y&xu3YU(d z591=Iu#mjBRPX(K;nESf9fiwyc{m5)VC=OWpTo~uUev@QPS%ijrdUq zPJ|7Ry%y#_!F?}*P;FTAq-%>{ODXCU$F34L&-F10r1GaoklHi@Xay_{_;q1q>i3A8Q=q;1!ctkka-(zg{nNk+t+*=6+qwyH_A z@T--tSV&JbujQ;m#DC&#o~9~*Ty;Q4wK}Vv>iT=>1=j&gT~oD6(&IzZ@}9mDHAsd^ zwYAd1u()>IW=?yJn{tjQ9-6A0A=8w|^yL#4#B#Z*d#>)|l7?t%hj?vv4`^&gKvo@z z$%ZUO+-UG`nf60?2-2}1IO`eSH~HMVQr3uNSD6ob0&B02R}PnoIy(jRy1aS4@AgaX zJo47w2mLs#!6C?gS8ua)=a8=n|D`NfL4vSP0G?j7~+%5sX4e`L>x>-ZZ!NG757zi_#l6eac zUL;?-F&b5User08Tfj9e+uL3PAF-wGFx3y&yajWqp^&ad?O z5NbaVL$Hm(&|Esi0S&Af2Dal?hlAN3lD)A#I0+5fxT4Iw8z&~i3h(M!huK)9J)&q| zV$VXxn6IU}#O{%}<*xnND{p0aN)f@93kcC`%*q=u)H>}gpv>smOp{%-)2N?1-6HB9 zijHE`Wqj#7!)-MqrF<nWIS$cOewy z+4L%>pDYkwIUoobtxnXcDgX{?L~iclRMRo@`Zb$o6&4~5ebW#jDmpo(+cFC8f~5P# zE#V~8Q4TWB&7dXHi}7Ajn@vRPJ`^bZ(gMKApe4S@I-|(**3nXO zNXx!BQtKB%Dzj>^XaUx8rB92KBs@#W4_ja)=H5uD?~b=&-*KEYdZ)Nv$5m)cnT~!MY+%llZJ-Inq>1e>af=c9 zOD@7myd_M&;xhx9#GxJDcXdS7vi{f1Vf0q9P(PxXwdd5Y{j$R1UaofCQzc!<>N?cK zz^X9Zks_h!`Y-Q`q6AA_GN)|utZ?brA4+>Tu<=VtWmjx@c@&Z2YI_i_-QOs0hLj4; zCYUI9w?;;t5^KsVU4g7Gt%a(ge)qJzy}e(##p4(}_!vM(DY4PHUrbei^#|ND*M6gq zLn3%3C%k{7TYarXtz{xAMX8oQh!2YUyo?!987_6hg_q{2$lu});alhrK#SV}YhBM?iW6&I1y{%0&LJ$miUJx~N9ZjH?s%8dOKE^Tt ztLT=hwd(qCEtPVFNIwX;V=8B^w7c26UnpD_{JUG0%@gbMgNO+}iXY{G{7Z(LnHQvi zXtuU4nwV(oS2GjY*Jz}dIZ#Dr_-2JwNfbhaA-{6|UQdlfDIqo)Co%>X1&bfYL~Je< zB?xhhkRm(bPh_xAoW+?YoB5g6V!^i2Mu4{vw2~bK%C0whgR`yoU_VzwU>~SF?v7`| z|1t^n-qPU|*rg50{C>zxJ&IaoOA#Zz9kDnVA}CeY$Z72&Q@3D-{dhZyerCoF{xIaA zrAdI=BX5DRBLR+Dn#5^M;WgEZ7Kdq3nKVa{>9>}iwWeSy>YDk%d+m@WEm$S%#0O2m=@ZY5aD?|jG z;Q$2XCXopMazo3y*iP{DV>90U+fS?~hDoXh+;iYT_MJ!a;;&)YZy2;A(TtJNjj&Ma z?N!mGq1W;+o~)e>yjJx>ykylly(;<4hR4M^;XRW+@MP=u2=uY3I*cij5|44LXUTS% zarofEWUSPt8SZJb0h&&UKU`HjoZQOmxWtA3uB4vVRWdU@;Gl9>zGkzhiQiN+`6T%m zX&XIhRw7rSxq8oRTupF{#W!W7rZg`+P3*NLWy<;&=|=_hb7zq0WX#7PO(Fz~*}KnU z_K0|p?U#;?ta;Tp!sml_MXMQwL%N#w@JRauAL1LQ!fKC9cpp+DwK%j~mfed|Kx$jxsTSqwO@IIu1}N(u6jW$!yeeCG%Dqu3OA&|s6b60Y_~Myh$+ zZ+wAnROIzm^r{%)u&DYap*v+NVou^-jR3XH<`9=1C=10h)4fZx1h9q^WUw5xGQiR| z)2y5zu!h6R-*X;qun&}m+-7aHx*rfs`Hx?1_bqEm8Dx{~BS*0#f=cGJEJ|0_^+nd+ zbd_L!wzk4_Q<+H_bGCuyyB=n2;MH7Fm61kbTB&4Y@#$X9e)bIunWLdk)=2aBIk)-MNfjTUD|x#^Ri#rVV@jZ=Ai2&I3r2N%P~2 ze)E%t`U|q`LEO~-SKiRioe>EhYJDLu$nikfIp;M;r*%=k;qJLIR6Xu1eZc$s0-JKv zck!+SO&Uqy>j!$O1j~uV&ZRq};|rNkcB1HO>A;I$+(j^=!y-Vp^5ZIk(c%(OU-~O? zTKc@$giTxQR{TLD$^{*^bzHAz9DX^U-!Fg(d=EsnJs>kI%zGV~FsyQUIe1TWV&ln0 zL+Ae8Q_saPZI$kvq~4aA08c&Dp&1*(r?V>Bn<0~8Z4;^Vu-_29u7<#c_lMBKZ{{3_ zN)NjKIJAI6GR97DzzB-jXjSq&ITUWrRav$EgtsgspYUguOndlV_#`q(Q)e11c;67a zT66PL*8rS32HpBpWBd^M`+2$cMt{lAC%HdIYmj~LY;eeG2lrlWS%XB=*5J1xiQ#Y6 zN>-uw%l9>`(AZ?&h*CSk0Dia7Yu&saYc{c?53xuF-Cwd3d_+<_6hXSd@fPJs1%Bzl=2m@CdAjH#|qej_w!f_++@IwEHHCY1!fyU2c3=*EG}c5HP&%Mg?EC zd$knvo&TOPS=A<}l3`7P44XM;oJ-fLBnqC{s*xT3(fFHiG;JK5hHPV5g}TXGh7t|) zF0|-jOB?Fs!@eI>1q6K~LvP4kI1EbJ-weH)SnL0R;6icB7OZoiV(veJFU2{h%MOfmp)^r%gYZ*n8Fu}wTlh4O- ze6Dp3pWACvKE$2uz{Soo=8pc9G+LusL$?T9vPzscDIQWqyVRloPIEhS8aueltid|c z#C7Zyz9cD8k zMBTwf$1PTh5C(e(vpB!YIqfFA3bPW&{f21!bsb&dYz z;X=twuL;J(o3zw)%OKlk((}FSBOOiNYH#@uzf3X$6KvOHd6Uo6v0AdXPaBWs!Y|aI z(#)!%NX_D)It33h{%WzWgJBVMOF(RmP@!c`4d@T4N23f|i>dsvj}?{896L$og4;U$ zHBjx9Pwr){$-1{TiUxRdZlo@_WeZ~?msOdCZc?=^Ndm)8H0!0gw-BPNVicY*PKC5j z+UQ$3;z_Ug5(ISzq;>H{^I{Kvzo5*ItC+_HJf&`S`WmxANxhe6Nf zSDyOi%)?IZ?lJ7bO@$NlKAdUQag}%-iJ=1zp$d|ZL>hT|ufZBI-s&2N6|62t+~P@X zhHzwN-_bFJicY_xKh2Un_>@M>zk?C4MldK>Tq5u`n)+xM*{sUn(lR z6lHAY%-$11ql8-m7u|C9D4%&`R}}|tmyDZlXf@7Q;HKjdGsE%s_q0i`9OFWs1*J8K zB-5MXYko#FtwNvotRG ziu!h=K6k$d?|d0*4;?e&Po)fgB}x~gWARc96?{g=BI`SJh!VO?IC#VBQVg?h`uI|@ z(klH#@A!*7UITA{niKY|xGM}=WGrJ_Q8p7XU~L6$+-nS?3tFsF{+ud=VP5K`D&1sn z^cB+4Xg1*ipHXy|s~3|cOoOMA71Smwix>Yr0qGlST#~5I+oQrN#+Xs*-EOK}Wsb0x zV_W^yk+$YJF0pcTFWYcZ<&+!k>)`d;dHY~Ctv)1Ks_*$|} zr?_9S$fe@*uku+6VXrr2{WlM?P-SOZWBf|w=9^-qD0xmAs|(bRE+e7=0g(iZfou`y z;fUlNK}oN0EA(HT^np4!=XG)IB^l6#QLOLGajj`r76q3Gf%pl^cecX%RY`m8$Dxo- z`4Z=&j($3aIP$)1%dfZ@^TV{+sz|i?3t!0-6C}hDRqxY#_$w3+PJBTQrAZ#l4o;1$ zhwKsOOo1%xyIf_jY576JfX=~!wVOqF*PK*-mjQ@TI*w0stE8suV%kyA607d zLUGVMggf`In3lVeV|`*ONQU^HmbfCmakTH%r&*e*bY(4aX+WJaxvrP0XFM0wV=QRL zn(kFdgzt9aCWfrxzRl(f5{O<|j`4Y%o3^+r__W(BRBGo{RZWi63P1iyG%_9@HKL+UCiEN?L)f+$9WtE>vJg`J%I5Jb60|LnJ&oc~zSX8KB z!E2aBn#j#uQn@C)Q*(x#jF4)bPsy5N^(IbM2?QS0@*p_2{!B_Q#B0R`uM|%}c`r{*6|4WCbMk5Eqp^NgGOY3iMJ)uxD zNatU8q-Q*$r_)RJCgRzafegQ)v+AJY}K{Ai~hIlBZQU4r;ZM2E=Nwn z6D>bJMbNkcXR8M?+*cCkD?VNyk@1XP^{Fg$m!5lGZT-ma$j{t#y8U5nxS+mlu)3IP zNP_TFycnRhC=|&?AjuHzwp}T`b9fcawsDTc>%2o1hDhHh%YKqrb~mU$v~DpJ&M3_( z+X&vM&om>lU<-arvNbsCS0xwYjP$W!4z|SMoX*G{Mu#VW#(>FHEQ`ifuu?cd)cmXi zM)=6|nS%7@C&KH^DJ97py(Ae1RBT||-o{9`Bb?~7HMaIBL^}>R{oz-4(}@M7mUwlP zr%t#~gQJm5b*YUT+zH(mqJn61;}LMg<;l0(xX`%j<9)9<0!?Qu>HB>Wj`=v>2?l(uz2zyM+js8X8`76M2imV zrAnD8M$u=ZgO<_=944uDneH-wVbxYi<`jQX7!5T6gwFRPJ>~?i6UKx1(V+nriTa)- zqnO@>6mad4oorTjyrb}nPhWr(VAXyo8Mt!aEdOPt+Jxe50fb!zMel&G~BFV)scuy6au1Bzb4x1sAWk1sS5> zf7t2ExiCa{R`jm9FYl_=KB0^Z`N+^6nHovw#Sn~tsV?iwQ799_C7M=cSY%-q6iA7O zTShJ*CwBG~;d0>wHN5ME39-Ax6Q}-0a30M010HmCIqt!LXR$32KV2{aRYBC1BY^*| zu@+w_L4+dK3Kd*v|GcS>h=iff*sYot@q1%%5A@x{QAMd;o1N8AqRD0Fh}Fs?cP(Y` zE`Fin`Ru330^Y^bjz)ip-s|Dys*dPOE9RWq^R{fK)T;wSDa%_oslKzxerw68kv=(#4|rIhN$C=BOL!`*5AhZ=A6I^M5TE}0 z0XM?Hgdq$lRzA?ig-;h)Lbzo?RZQtoh9%pC??)`54J^l0xirX278H7ak-mQKkM-85 z=>|ogM5&iOJ5P|r!~|G`-u+m|;LBAH@p;+r{yYxSm5*~ zj3zfXvw6Q${ts3jU6x?smc*9;y-%>{Ho5c#1WIyvvWLDTr07%B^pu_#C)(`o7O0ij zpRT=@qGAK3+xg}&YVY%6r(Nn)iN7~Tns^NMACzQKA@Smnmi~`FJe}7;h3Lcz8ydau zKnn81Zo0}ZtIi`p3HbW-B=n#Wjs1yLp&f~rwygQr9F=S8Y`j!NYFH{Z+ZSwa5z>u< z>X{-MO66I;G*ZrLXVrM0P@vR&cd26?OF%T5(D{Pegd)ZsTzd>P%2Zco*0q?nIsoaT ziT$XJAu*<%r)M!pJakbFcyuco^X@c(1bH`5vMFM%-+P?}cU7PgWVAEkOsXV?eld`1 zXXq1-sH=&DYd39zEs4T9&OPvQQ&`mIzU)QH7q~LA%%7F##G9&YOSq(Pm9;lrk5oGW zR=IY#YV2BJR}BDi*L^R;_vE*_Vgak2IePhq9S$7L;&iO125|5>B8}9H3Nj1mfOvRQ zIpjO4n%rs0td!5fFu54f4Pp?Dj~pi@`>T6Pz^K?uIH#I>H|RV}iQ^V;M<>Z_9-mh3 zcbNCIG+xgf7_ZAe(1aw z%;WKGoKz_gysOGa>I|#=e|&vqP@HSFZXggENC=I)G!PsD1b1lM8+U>R3-0dj?oM!T z+(U2=uEB!`ceuUxIcM(7ojLbcSMgO>0rkD>U28qI0@|#-{}S&d*}@UVVCiM4S=*uY z)bg!z;8Hd`beZypR`j>^!U?sHAUcX-o#o~^c#NMWSGc+d1F12^E~0Cvzvt5~C2>ss z0zgut4LfP6gAQ=}$=sCM7?CP-EE5mrx_K2^SlbSNcSzYlzx|)zkP=abjn%^`B?%1a z-zX)+4Cbz{uWf(FVyORaI?NY~fDexka&p{uaq(|RRYW!*MyaRyn(rG?TdDXflT}R0 zUj^p@f;7H7J3Cv?{NUo^!iI`)iCd_vz2QUTXBQ5mPsRKsJWTm3+Wv2_GLi~G@_cDF zJ)zy%B@kRUFJC&P_w!fq+FnjZoI&&%Yy1%m$hiGVwiv%MI{1x%B40idDNiq=9?75a zlY(U9?dqS9n)jU_=CIAbq39Dm*k*?i{H!~ka1N5X3XeR6eHUw>(T#WiG?y2XctDqH4K`p_16uw*1Z8Jt-? z@=*edh1RL;8B%d9+RaG3Y^Ed~BmPWvD~gg1ZP4gqNeHD;+4*+;9m=paVvo>I1TWQh z?y7Fh6k)gn~P=OvSp52x;A<(v=wSH6nW1X1#)I%Pt136lba z{7fh*ggS)17>#`n1?h(|ipA-B%i|2CT7?YJ63xUCq0g>qlIhZ44Z;ab*uythQ0inP z!1k+W+7#n&u(~Hs)Yofx`=Sn-XxhkT|C=cKaIty&>~O`s|0jj~)OjDts)l$jP9N>Lztevb>%`sis89-@m z_xDwAfB>*sEn7!dJ?aTS+JIR;c11njZ-kJ(fsd$3W1D>*B>MrQjW=y%xva5(j)oGb zMa|5v|wogEqnE#v8q9hQ59PDI9J!IsE(1o`MU6Hh1uH#$^bl9 z4G4GAcem!R*QG&9Oajt!YM`wy)NMkHp1DxyEe~p4W&gK-k)Yz018#3`H98#hY$Sf| z2n%%q{(T+x-~EBXDA_BPR|7Sfi^zLS;5UiGwqGys&uO+rc8^|%B_B4^&=L{9Hq+Ch^t^6A7FCM&8P=rVOlM{#;D6hGB9|fjAN8)-rzugc`xE_b8 zxATFvK3jm&ezkR(U!iM9GD7+?>r`7O35K3Rikv^4*_A2AjdOG z2h7SMm{4%gHj@cw3VYdp4DhTolaFUx&gu9Ly$?m1-{|6+sJX|er@n_RPY5YgMHC}~ zMgk60YhIsCF1C{|)1NrS-xRv|m z$Qp(ik=QKn7Z0ly11Y%pB_*t{a>rNQg6=n!jR-8N!^d`GRV%^MYUq;U8FI_6kYmo` zK*UQ%AQWlFQb}R*t`|Sfa3Ti)WIcI&Lt9d|iYwJvwy#S?{G|S&KEKLc#;L2KTO_(# z-9xe)uTaVVL6KAN_U@N;!CKrjFsbQI^w0)FO4Rj~Ru*Bo@L9C74LWrhWcN@lwwbha zc;lnSFVMij*Lhc3I{!vRxZghxYo|MfJnwe7{RbA4;YA}l0k|yK%0t*odME2k_XxQV zo8KfG^rGdYD%+*^)o%q0}1ulv;Ra6%hn}8W*OjGSC3yQ4+mA-hVSP)JI zj3V~s>zs`s{y?Q(2hY)Zm;TuO)}KYQoU6r@!;|RZk^K^WF66~lC zmX$d#YGJ(Ku9i{f70b7dQz&3hP?fMvPbUH8Xkl%9VqpU7*5VWB8y=0S=&}d*HWM9b zP+iqJ-Qvm#W3QtIszya&nIeCM5|a`S{HpJh{GvhGHD;N8%q##If9Ka&v#G;l?OMq5 zn^WHZBWhl7G##yK{ z;y5#7X`PAv65qeRc@#tt@{@}>%z}JH3%*@Ty?f*z0~M^u{kjAF+S;1HwGsTQop>Ah zs!Q*Gtr)+=$b@p~!Pt_uvC=4@3a>ey2ZjNgGkgj5Kmnrf{0W)u{3A(MkhY;hZ7&jL z4$JI8$*VLlYM$`8t%IxRpBjc~dvAJ;yg_tp%5;&iYxArXuEL^00$k<^c^3dK#Fq^+ zq0xuZm`pDo-8n&u% zU4rO|pKZQSprgt{sXUN15uO&~Af#j#%^2NK2?zWm?g@Ovny{Ixi`l0rS%t2la8<4h zGbd9D{RMxQv_;+>8kwZ%9=O2smW`EBm`vxjv96?Zg!%!&rE3-@Z4)5BBoSM^q!U3) z@%aZE`B&HVaZ=Uy%9dDXj1pFF?d(I%5Wf8(lG&*>{V*z*rAFCqyO-_{2K%?IKf5S z2WL`^&~Y~HWPGZBAab~XOqQ9uRGGB|)+AKcI$}wNHberNQF@UH$fTZ`|AZeVSf(q# z7qhobvg3d?0%|AlQ%8Z?K>_KMpT7W+v4WN~FD^Qi+6c{<0mxUg^+^JGT%9jE$RM&H z@fkJF4$|pt!Nbl2-SUsb6Wp6ioNUDh^b}qR_vsBcr!6m;-=5#(A5X3J%FnZ@MHZ4j zTw0B)cyzgkWW&)i7kpf&aoVVN7niJJNsNEf6JQotTYTA?&FgW4K|<4S)b4nhTM%gVUydPB}N z187XI&-Rte*t?2*HFoZfVc!f>iFeDyyaLau>(8D}#OG;5@EixBdih|=rm<4BpY;1r(pEL)8RJ|$0?%<9IB0tKy@sLohnx9W8s#9Tp%4ZyT zG^MCr`guExdgcrylpT912y`f> z{?sW;w23mNh)3vwM`k^^OHfemjabF?Bpc7il2m*R{>3{lx^lK$LBi!?_hAN5?)WE% zTQXNG3RwglNGmc7JR|(k^$eb-M$>*sK6w|Y`1$>vOw$OU8r@1QVp8;t4M2Yne1Ja) zzGcPMhR0=+ltNb(tqV9@SZJrf^cdvxZqY@FqIP^yb^eTU85)s%s5w=;;!_^s&kLCT zg1?>l<7||?LKrQfh|R;AL_PfDNPX}y!*GlVg+`#e@JUcjKidcoFa@(L6n*-u4jrkI z7ryFRk>wG-0&y!-_CA58PTf+dAWnn5dU)TSm?(9PgMKA&KG+4!`s86= zfFs5#@BR`_xIPy6nPOIc5Y9rP6WRR72VV7=TAIW+Yl!{vd870oyYx0mlpYY>1QyQW zI-2nC6>^=G`r)6dU@~DWOLnUJ9mE4t_C?+|aEb0%$IpCfIYIgS#*mYiL_1^=*_2ru z5m^)YkH0iHFx)=s9v(Pg^LMbH>bLVsy_<$-hmn+$y5+s(t*R?HK}>L2)yQn2%`m2b zd=B|CD{iq@ShLpO8rCg%zf$FI!7sNlFD34b2tZ+5u@0Oft=*Eb@yt?z(s1Fr)kf=# zV_xCoGlK__GLu%%ySk*2mfZ<;p~nArCjV0h>ek?kqNPROK0I1uxv8oUS&qPH?R_?xK|#G-qs{qVwOQ8 zaoI=)`ZO?ZN;Dc<1nTuhBA2P+@~t1M7DArhHSBrHdGq|=g5kRV7a1z$8EE^8$WJN2 z*ZbYSaHanCcdG^I$_Swiq=g=jD|A=LS^un}5abCrGdfp@O!--)Lk03j>v(z&JR#RCrjiRhGDYS~-|;=5Xe zFz3#ts#4I4=#sVS@+YE#CvIles#4&m@S1Op_Gfg^9cczK6D{R@-33Y$n>f7Xn44sp zA4k6D?8mQ`XT=dilXbMei_^xQ9IrMl#oJ0+L_{6MEWsUo`}v7e#Kz&nr9F|g*Qz~G zd2hEf_}MgX+@q8c)7xA$=Vc97^Y- z0CUnXdYs#t;{pS3IIyHYuFZDGc<}99$s*|Hn{CD28>+-GLoFeKeY}5M+2Cc;=z6}1 z>Sm)4zxhmL$OzVs?K$Mx`^WgI+wN>wUfY^vkDnV4qxV0B;A*X&u0&3h|=?Fg~6 zUuOZu$6)l|_{v10PS<7sFb#)M3!(1sneRP3%FSd&`xaC{0|ZJq?3#~)lmKkEMl)XU z5-Bu#{Z^KUlVpI^wWQG5LO|{#jUfOpWa}mt<8aZn)Z0=6Ub(XV45219mjw_CjbX3S zqUpBh^G?fAP453AyU`eb%OT<}N0(p?WqtIEbW?PW)&DWr#)Gs)eycaqwZ9O;B1Zy< z&eIz`PFJ<*Z*69>jv>&OQcmPj1FK2S#2tFZ1un7b-R4z0Ztpiob(_md&A*Yt^RbBg z6U73e7&~aC+twu1pLQx9HVf_Nr(8LIHc*NbWZ6|uR9^f>wp+J0I8154V5D1h%EpAd zzGcSH|K8;v@o;`XvOh9crwYp0q@+Fs4W9b2f`ms4fB)k@6ESslb(u$s1ZJJ&GdPY* zuYG{L5`b)Rp;)8$NIB0LTQS_E4QPS}dR3#=ro1Yv(G+D9EJ0hbFq|i?pv+9SNOt!1 zW?5E@b*yTdvf$jZ^PHt1nxCtdmO}96rt9)Ih?^&;6}I}djR+fgPBI!G|q1ew{dquqVs zpg916do~G*_Fz=1tvpwx+?gymRx381e5ztGE~}4@ISXs3WJ^|HUE{92Vndtf3FR`LJ^pBkcf?(u?qkU$F8{&DKBpjy@fDI49AEQ zDC_=gd2){?wExpf#6LhoqxaK|JolXmiehMl8iDt|NEjvGjGd_U{8MO@wxb}}Hvs&Q z;2+E*77WV-BJ!g~mjq=Dv{=v=Z4xQARjtzm%`ji?sFSLQ%2L76 zr@6h7wX9S;l3K@2;tve=x@>#nL4j@d6hgnLXyoCH)kuc$>3RZb+$A>NI46VsT1%_t z50F6Clkut+Bb;S&?NtI_UV&InzeUkXvG;{Mfx-aIZXCsS##r!{Gh=8eXKHXq3T~ z9aVbZWrae=2h9)b*R;lOJ%43MDVJHk1sXEmn3rec-qX#vJd{?Uxrw!VI6;>Z$!y~R zuXnUU+Ys;!+rLjyUl30V#Qls2MI)kIAd}#$J~z z-XVxfsUuKei@M9#75k-yS#`Ixu8J>4%j{+f-o(l32Z?g_u-hna`r1U=1wMRL(nRKs zGtlwa)r)BYCe^2jY(Z2%C@Q)1-nqLU7DnM0cwUw~dfZg4 z;aaZTL=B@7ix=TALY8;`qF8TX{Dloas>Xd#toa{^-J)r1O?HrR7xH~YJ?w}u*;L&koaNQ_en_URYp=Y`ykY6e$!cWn|K6EobYzfZ z$c(0sl>-KhT}QNXBa5kf7He?BRRJxa*~d^; zKm*^x4-FNQOji0iiww^Jj@=%%Uc97n>#MD$5<(+_$Q%QMo(lt`x9MYv%;8|C3M2h~ zF~%?=-WXO2KZnUei97SR8l%nEta*(h&Fr#e3}zx#o?1Np$yYjjRYWhTdjaQfB~}Sf zVm?@!Q~;;VW}@7x=pIttZEK^vH0QAJ&B_?wH`sLg6gen5D=|5EHQH;C1oiJI>9^tD z1?YNIm2oUxrCd*Sy784aR2|qoH92H<&Sr)>EU`3xJQyf##`rI!SsaG;pRE2YSJ+uK z`=suO;B;|z2K3zxfq`w^sj!4J!+%*!|Lw={%S5>Q&7yvCgje_>z64JjY?318@Hz7fGRlTNn+0635+H7~5@}%L-8}Y^8@f z;zrM%v?&+xxWlH$5@DfJHuR+}6vVhO;C{^MnEwah!_`w&5x=6b@;p9}Q$1**K@4Hq z2WDCmED#oFL@pxIEn$)jNL>UyufF@2|v5fCY7l^yROED!A-PY>Al0hK>=ZtLgtwW`ef-hn0^I9 z@62AlDQy+Bbe6Jy(w~*y*Olm)xyg$roDUnk>zJV{WSSbl!b-V*(giC3V@OedibW+G zP@Ftg{<+JS_`y+zwlU^|8K?db-~5>&U9tulbc6v6QMY2fTC3UOB$uk>%v?&rj?B4k z1z?rX7!?|=hyP*BM6qYI_>Kpc!bfv8`N{G9@=+-lXIv-E)l^ZZw1W8g@kIObA9aP#u+yG>$U zQd7!9$oc*GGsDe#zunXPnOp8vBFh3t<4u#r&dvI_Tb;F(9Yeathjv^)WKjHOPR6zL z+v<3onZd%PalExcS%G(vM)8t(kRAT0QkKR5g{WGxUCOlp6b-3ClP!S?qtSRfuySN& zW`fb#e4)zcL(>-%fV*d9WVX%dq98lhBVmW>a|j_9_&!VSF%$ z=Z#2-Zpt-v={ZAGhaq|mJ}(ye&GpNfPw>Ny7=yD1ykGvijTX8Jje(mQjgzj*Tb*q6 z<)+)O>dR^ZjqNKw>*tMA4LURR&Ef}Y73FFZ&&%Ok_tmF94C`?g50Te)WzX{)Ie(j{ z;aLMF8`UiJCA9fF1pXW3eoc|9-}Da_;6HpI|J0Ews;l4B>6GbzgYB{-O%W8aJ3Uzn zqpxMHfN{Nb80)x7A=as^dek!`Oj#4d=3E<=pJRE+c5*}M+0~+K36a^dZuJVYlC?57 z?viUAi{b*wdPGc7bOzG1$eGNOmn;iHRX{_bv59EjX{yRdWw6P{l3<>YgnviK6MtB- z7*QEeLDPpa?zk(2CXcR3FXRqd*c^TKlZmg>)hV^-((57IUpOHuA%`wB-^&4iICeF^ zq%fztTMPNs8Uqn^26+(pA6>QKNKgV5y&xl>QJraywY-255*KK*y7<-1$fBV=u|G}2 zvB0qdnsJf$nk(m0Xaqnd#w@{f%^4?-@(lq-b9Fz1 zxo0`0Hah6Hk*_Nu^0;g=>X}%uw3qsKu?_^nFDYnSO2obpO zKP4mHHxGVNrP5_WBJdbgeNHwJ-S;W7D@C0;1&F(}`+kxc`^8Hws35HOrYTDz@nvAF zDO|+shP{cF7#PX4du5|qG@qFgK>@6AKiT{=e~?fJ|M-;SbcE#mbjBD5gJH!?oGP>i zG&=(J*Je?TXptnSssT3M1Q;ltG;{Vb5*C!#lYu<7r6>hj2yBr)sSFiSl8@CI1(4-+e_d}tN6+t`B5-2K%Zr1W)uA{70H6BquZ@iVnGq5^L za#Y7iZNKLUo&9>aNO;s_y~N0DJkuPr{9nKv7==XPX76wO*RM9_RhRw0JpI3xNq@O3 z-|nDy*Luqo$|vrUxvpXE1J6;=EVPp;2t{Ga_(~PY?@mBg3zJwb6cHq2vdpJcqAld! z6U{hZ6f=)CR*vVYHK5^lE$%0=7j6$lx*$;c*C(1jHyJSm3v{WnN|E9x(3Z||*&tpN z-TI?RBYPGm4$N%z_->mAz#5Tk*&%`o5Le6<6Vbii#HUdGK}CNoK#hg4s5eG7CLJ~@ zsR2sEN5&~KmbtySE#B}9YSTSw6T@JXE1Pr@( znvJ7Qa!z{KfpaB3Q4Abb1>9EKhU@m+lbGmsg1lzNcR- ze%`bviyI^lM~>M9H9JC=#-cyp2=8yw#@VG9voaKFb#BS1g-lkR-kLz~Krp`oqdabaa;G;2-%X5gf5;%sw=)C+Ug4M72z(fe6h;I#Uu8V>ShV!m6mb{k*A!KMhSV8yE zeX%uC#)(IV8z9CpCB3JQV7e3ieJ`)>QbAM$c_QPQdSswBMv>sP$-CV5TKF>v%;x^wEv{Ha&mG^#zOAVJPLewZHyEm=i=Q`QLZz0 zHKC;svQ{iaPlv2GY_MW~khAyF{R6?6E+s};afI3N{j?wR8|Uk;vhGm1Q<+kAN@~P+ zg#~m-tQWSjsr~FW3wF+e=t3$hyYBpg2_R=BjqFhgY&8p}dh_}3YPl?>5BVH?ujx62m z&hhg{!jC1h9MQTA@|9G@`6HD@l0Roh&F;v|Nbu-~)eXD8Ws4D4x^rfsWerHsPgDQ^ zCyy9jC2Fl6UA@^2TD2phN702u9l>$$PMQ$Ch)=7%p0QeGPEn^qj?q@J>@Bl2HjpzF z=6PI%I>YRIn6CIOi-S5aI7*r!uhIRZC{$z!1B48agb2sK9xO%3iCgduM8gX&aFT0n z{0g<{joE&abTJhyG2>+ZKrpA-g{??gZE>)i7)gF|OBuM&6BAm$aVb#x!y9w6`2BfH z>Gw-+&|bJLJ>sH38Sc}_3~va~-sM?diW(=R!9oJ??agm$#fI578I_kWdmNBTtWhN_ zoKntGCcCu3A??D3i2M7Nf~N_NMgdI4MRyfs${F?FoqZqVK>SElqmt+;Zp=dD&&An` zW-Mz+V!uqJ5$NLwU)wWIAj3tb-aE;_!zrZ+NvwLv-1d096VCYeX6Wy|`(KCYuWUi} z1mc0l$`oYZSCYNU!8We%(WBRVm(Q{tZ2G*Eejh)-dJ4YCf61H709}#=VdZg6^Lr#@ ztR?(eLYuq*1X7-22u%qCOuFqCNqXIDESBo8eNX!NEqdef(TKA3EXpmM}Y zIinW^F6X;9gOlZ!Cr-SRo9|ZR(m;l4&wZ%nPVQSep@D{6G1Z4f4q7*G94@^t;-HVW z?G-Rb_zaEk&aFdDOZK`SbEibkXSu~%HoMZ|4tY8KAHwH9Y-wPP;~xxI*BF%j&$#Ws zUU;tX2vss+m?EeMnvLjmEDS~!rV-@2bTzuc4ou{t9i>CUlfDZmdCoN$;ij={rsEUh z4w2W=Vb^)!5%!1zA%Uy&r}xDf&kPt9AmfN!t3XXGNRuasT?i=OFNaktC0(6GoVGID z@g))bHvF*3R80L~@k;fB5a+PAk8GeGKz0{_oJvqRG-x3LX6rozC0%TByo*$^SC~mT zdG550QaMXD`-VisgBJEt27?8Aw(k{w`nBj+5q4ZdH7YTH5|_Czvh)BK+b>H0VXKZZ zt0EmIPNvdXJ281GPVlP?8VCcWI)B&x7aMc7L zBm4_yf&xNqD;t}Zu1z288UelNakI&`H|~<^VA=D(q&18bu{<$S!Vb|y#h zLG1W1y+(Gl$5Q2G#+>p2H8kTQOO=FM>2nTpZ~(HAa_i!II;X>hPJzPL>r&R2xr2=$ z@2XV-|LeBFO9C0WB}Fpog83HJMQRCyrrOe*ln@8f ztS<3@{^~>I3R`bc(53eW8_|*Ld|zkxpOrj@v8Y{$F1=WXLf9mBmS^md+xyzAjR!*ig|F51~Tan~tt@iC~Nwd28X4uO++9Eo2N?`5P5HGak z31czfnw_i9#-fDgnu_?dq-cA^ztN5U@dAq?z|A=;=CMy#%xzW)<-IY2TzM11S>w2%|%IqCbX6ZFT(ws<9=hJKq;4aA=W>F!=l1`yBpHBDum<6W&|Kz zJ}K{xQxs-t7t23b@~gvnZ6$J4Dw{{mB>bmex?3i~*Bj0q=3M!Yz=&N@4t5OiDG1wX&lmdEykH`$vrady)Js z4vFBekvC-;ax9xsan1@Rbvp(+r*89bVa69Z^>HMdzsD!U9McFk(_O^G(=xJNkLRFE zS;WljUUS$S_w9p}!tkVxZB>w5yrzi#+TBV|*do?rMg0IAToRFPV;>c$9r;hr@}1Ja zL9hGsa@s~_PKDETTcxf-ZK+sQMHU;2SqW3R(jE#PzC@EizP&?lDML2l#rh1RMSfH} z_SHa~I$Vjf7~g$h!`*EZp%JKE&J-bYO*3SWZsFUT4LsihUFm`w9qn`Y593aE4Opb2 zY0X4=eE4n(w5p~AjL8y>$xp&FdB2!1Jsw^950h@M{dC9S`-{cWjc!1~Tg~!{J)o38 z`_x<~EuVY0jA-%=+t5C$(*c*nwiuw+;}gzS;;e`?ahq5_wjBv8wcjg%fY3)@RDuB0 zh6pPflL@G{80(XSi#22hjdO=A#QmyWl{|By=uD!l-Wt*ngx7kJv+728jEvhqOau9f zM_=y`c;LW&EN;p5s>IUw87cyg2D##sujcFVY93Sg3WfAjAPP!`_#|Tl?jE*`i0ItO zXIGPta7fQkVQcJ0G+W7_#rE7r)bWg4jy{Zvd76C{VQH*JP;$PRC$I8W-JvqTd06JP(Iw){ zUu-5y>VSekJy_MtHLjoMQ93~pmu1%vmcsF8ytqmDkV#M~L0bIe%Vvli4LSI+k5QCp zi94qB^W2WvcCv2+XwSHPvox25pL1#I`d>Sl|M051mZB?mFEQ;b((hr<$RDx?Dazz7 zc4B}v<{C&W#_Ed|2?s{Tg*S-~KNm8H6slgDi6^^IXkDd3U z{(If@AO193guqvC_Ld5wYoGph4o6PQ*|x1S-zEd)7%}b((nD*&VhD99D*QozjL9G=#d%z! zgi>NqB!VA6Om8rU=8dSV*jK!D@76lwAvk%?% zgjR5U`nB?^GXRW^_@a7p`c}TG3UhiD#>~&u%_}KD}8F}>f?ie{$efZ0jH`m=?DT^u#V(20=`gb zV|C#9ZN(3Qu0-S@jhUVunm63+!n=%p8va+^%uXN|_8=wtrL=^u8#wLONzphBeRa_?9~`$tIkyMPoZV$UZUlAooOc>B@vNtrUm`iHSNc56Y4( zjQ#rl(HNX6u1QFe5Fn;=6`j=dU`+>o58iz0$)v1^YUSL&9e?EeQ~~}$emU(7C{cHO zkw2N>Cf~BpvE%u5hh@i33?Yn25HGcvy04Jr)IuYrkw}-W9Mm$43AB}%_>4LfI!Hqp zu41)K&mG>^D5l0rygY}R()`Fsp@#@G_<2UGnFKt?N5kB@lGRJDBN=Y4e0-Ptf!3eI zO?ht$M{8LtnM?FIZ>{99E5gd<%+(T+KLyrqd;*hYt>z$?M>;{Yg@&-j7KP;y;D4hJ zIqf_U{7kWs=glIX&}sn~jjP1HQ!35kli4Z`eTFT6Fl?n9KwC@2FIdyKFWRtcFqBhO zOBYq_q+ySKXPs~pgSNY|Ixaj2P#@XlRp{ub9ZL9I<_ThDUCZ4j3t>BuGdR=eS$L5^RN10zsT8HD`+#mK4;Y2s=t`D5h!a&$id@C6QSW!}8L zo&YJFh`w?AMpSaFWc=LwP67kLT0p`j_%fV`G>WKvkJt>HjhKymA}F6WYb>7t5hcGx z6&AeZrFcIcUvRn6vb{R3Vr_ZFlH>6oua*D8@x(-5Eh{7mX{+WXiCD`F%|RNXS?$6X zIW}D@jf5Zjip6VM<25`!zbg_iez%~gfjO-uqgLEO3PR8fEr;<7y~~cm*gGV6`p*mt zN*0|!!cvxpK|8DgjX#Qqc+lt3^Kk5~jCbVm5M}{yzhe_kbf+h-XMPSojnca((R+?#wf(}5Hp=NBa_n$uUM>ej4?JV^uZ;F5%}jLaGiHD}ls7l9aF9ZVou z_B<@)edmh7kD%bRvMn$EruUbTIb}eH@;ky~Om?sEP@$58yfw-7WUGeCQnqtWh@6GC z#&xnJcle|Cy)Soc-_GgwW9$Oil^!lKDs&MClG!voOaRwJBiN)^HNgN5#a(0;n(HY3 za#^B>MoHk6lk^}ZVUP;4RqvC1&bDpev_Yk=8AQGdWE74e)A76q%V}rY7hcwvtS!&GbU^Q?UyM~l%l}m|29TiOo z2P$bj@-xV8b5(i6GPY`+4rmoz3i|yPQ4$MUyb()G*NXQ6QPSH6sN>?^k@^cX6U0qd zjEA^~;3q0T9_x3wp zNmw@nVpX6W9u-sMai-CF^pG#AaNK)jQ5H8EZ8=s3v&|!@Y+3o{5QktyU>t!c ztA$z6NAqAyT^(z=STiNw@8-FeJDLrLZo022Iwgf`tveS_5@ya6&DmIibhdaK6H<)W zNGP;dPGaL#Hq5}@^@a4P7kf0x2Osq4ZwC%nFT{29Xb4{rR}# zMcVwKqBgX0hO$UuV1L4((sh%+`(`<4O{l6Yew?PzvQO@%%;8{}JuZlRY+UWR7Ub)P z1YA=3#y=H^Fz2+f6b*0?f8035nLZ>dS}5ia4z53Y1+P3nHhVP71^=DU&vC;x6qB8k zWiC_$iF{3^0tr9aQQV@+z6>S7kukI;lCzRhvV!<7o*x^iS9tF!M1R_7AhYV0YTl& zNNpu|-Mg0kpd!a@b*S*TMPwQR^(0@9YtFT&^(l?{dK39(DyK`!*!!^OkL#S$C9+2^ z0)0@@H_AUdgx3{<3h$`L_2N|}++KjC9J$4vdl}-;OTKH#Q*jF!tvcZwHS1UcPybLi zEA$xlHIilFU$E+Yj<770C84 zDQ4<@?gkg`vHi!fQGIeqlK>aPIod$mirDH_k9*g4mZAmub!o1ZwO@@c#R5)mC8D`` zq4-D};wcw;{ngtKMAU9(ySf37B}sasGeHCHx1T@dpVQd;IMiO68m) zxD3jkNVfrX$DjK^HXxFk^-)v!rr}!rS8K2oJyij;QJ9Pc{$#DaOsB3~XR>v(RI~-* zkjB${{vOasGUq97xf*TYTM;EROeI94Z50`-q^a=vDX^W09%0TxNMsHT>K)*Il=}@r zeN35jif&;ZU=^W+uzsm{Z0dDhcHeuSXC)^E-r#~XRvFqDMzQXDQg+?5YQo`2{fgmN zUFcVM))pv!`Dwjl@@`f@O97tRA4C0h*0n^<4gq_wwebl5e=b9RM$sp$wf@(8_0I~r zFW}V~6y4C|4-_y2OK3haY&IEjiJZnWT7--@D!z&2h;8e(J4b)Qf6cP}>RdG;vKiTg zX~xu~8}LQip|UiHbi$4n@<-U_&SMX)^T(~kY!GJs7xPjuxP)9v?uj}`tS(KA)bG`pg06hfHq zC0&x1gQ+O;P2ny&kUT-foT3MLmw~0u>i%qf$JZG-OKtIgya4K_R2dgUcv>bxyX|J% zAi+-_0152LS36O5t=@yDd`k0Dv0MtIRyqn#Qw6*t)9hPyV;tY8c3ZpzKXoE;aXp`t zqNttOWnY-0vhkB5hltYt{J=dW256;k3=9;dC~FE}Uz=}w5PQ^9j*|;vH=Fi}vpnWk zD1+L~iIjdgq+mBf1;Dp7DxbQkQjhc;VWM1nR%M=+&6?`XC?AA2Y9^`qh+c)6;hIe$ zx#Mj-p#=Qd1F*rypA;<7vUGae@z<36v^&-`>)y|w<&8Grwb zC>Y$5<+4jqOzL6SU8B%7;IjH9=bGHFQOuT)TXay#r_WKy%;Wn1Og~mQ^se{`+)ivq zKnbvN7rh=JtTV}N5BCb=qx!61%>O!r(lWn}UA)o^uO-n*V(wIOfvlD{o~$q(R5>S2 zqLfnEZk$nUGU-!Ss$sycVvWQ*C9lh7;niOinO>D{XvtEyh#s0MpwazlX`+?ZL_C6Q zG}(zby8iil>$__yD|#)Y@C4Is9!>4u=K^-YHZv_{kbyS1wlU74a=1?5iyvUIZX9_E z$MCDNzqPjH!bb7v;57}F6>{f7$#YNtuAyV*A zN($ih<*57jee}`;uoZz(!{VKBFe*Vt)PROqdNSj;1C$W+mCF>_bT2YKXYbFHdCj}J zz_|ZI*HuPE*>8J66p$RcW9SwXq&o(X?(Q5qrMtV4j-fjSkd~HIVt_#jDQN}iy5oD| zo^#K+pJuUUKCqtWAAA35OAC&WuHT^p(!Jv1Tl)kgfm*}o)R8n1`k3eFga$0c*!&XD z_3FT96hxiw03-dTnC2DrsffO#$19`{}z5o|YeftK28}J2oh+sF^QHO4%}zHaTp>L*oO3DeB(WFUcf9?;lCSw7e~; zJ^~W1*SnFWZ878c5371RE=rRF?@xM=DYnLci@8$|nN6X)p5M|QUD<&A$~?NR(+=6? zOrP(}4j-He;nI(3gUUA2qjjfJHPPu}BXA`J%ar}Zk9I%kXr6s- zzqotjD!(h;&9ks*Z-fsC6$*mFi%-)v$XiQ!lB5JXje;6@cN6U2-vrzr4VUGH*Ug*F zP>gSBq;w)Hqzy279@cXKzK;Zb5x7q%Az*3F0em#h(m=PO=_}G-X{s4MNIKN6K+G|6 zb2u|zr1*)1);agvp=kQuH3aS6Z5MA4OgC>RAaBPmi*#Ww zmxino$e;g}@cTFpj~Zd`*q~~6^*p)S7XGV4hz(GX(sjj4KB-B%jJJ+ryb^D*eox- zsM0lBO4n!o?AsS#70Gjhp|y_hb;)f(nYUv;c{3xnZ>0c~HvIu>7aiK?7_mQG=a`mb z9QI^czCTo71XYOR86>#TDQD>)XZA%j-p5r`48dEaxIUxL3oF|_%rS8n zsw}RhqP4lgYjK(EvFTo-oCu*H(Ke2ky)sg`m}w>6nXmLN_paX3;GWhd`1;IY)jIy$^%LrUUmxZ{8{~Z(K1jF`0N&CAvelf58t#Oxj)^ zKH-;n4^w5}Pi$6+aJW{^_cC6qn)$Ds>7P#Fw{-B@?h<)?d=&?u@Jbd|F-6Uq_L+O) z$F;UPqg}_AC zVc!Q7B7`UOQRu&*>TeD?35>Dq2?8h@)6(gM>`k$F_Bx>QZM3|+0Cp_Q4xHbJK$fJR zwA2UevK-|l^XwWM-HTj4HH=EBaZ$F3}F z=riVS#j4zTHOW3`%qKNYq4Ua@(NRWEmCk-NmOFh@aMI_MC2ZO|tKyqAj85Y?+_9=5 zPu*h^&fV;lwLnZta9!~7`GLjmOPu`GL04uR>evYk;yf{2t=I-3^GJo#txUlzufygb+&w zKt=k{XOAAJoAJe4?xqss(gBMBm44NJ%%=_{3^vFDAV`bc7EMvxqygvPJ*G&7+w&09 z6$FwXD%gmrI6XxeDQot|^=QB## z`~-EfiLh@nzIE4$wq!WPSQPbRPAed)ZL;CX|7ze~TbYgrm`eXGC?qM^D|H<)+O72g zD{bCc^GNy$c5;k?yklolE4lgUInl~j$sX6Xo8h&uM{Kfq%8Q=Pe^K5=bji}SN#0wP zBWu4MwIV%qw;PQ+<^qiD^$M8ia{? zBV8$EbP{KbS=|~(ex;(leCb#)0@#-GA;)V7e7LuRpW%C$>FJ4M_-%qYC#^X--RK3sbWKWE!km+|w|E-*U zflz~u^!IY?hn@g{tpOt6ZtemRgk-ZF%>4P|lY-9_Tih8Iq@hFzk|! z2cge5MNZOggE03l67HPh_U&_z^LJ{7TvL2#uXA#SRvv-KzjnzQ8W5h^;#?dX^*n)d z`N<>xfD%KDPeh&@UQ_KDuW{T9XkCxq9hAFz8HAVJe@#+wCd6izZw$sCIprRgAr7u*!8lpJ$7IzQo-_%__FfiGn>|-OW1a zeakWI>b1PnXk1JChhJbk0WLF;l^ zpZCs(Q#gY3)$i$(PY7Zxk{)@`Gr&JRn$u$B2j$Q|&&QvwfR|l)feF718ZU0mX)fNz zZoAz$4$P#ft6bNa8{9${?{8&BYbj8qZ#ODWczGOFA2MUI^ua$w3oa+T4ZME} z*c*pZap$c?GKK6?)AFTc8b7l2&O@y_vtsGE`vlR}oW_HU!mxBUdMyLg^>ERM+6fWH z#)A8cMb|)Yi`~|>t?`(iw4od{iAW`RK5DZ0$EGTM11L-vuT6_?zS^kulgpPmvydmA z@pmvM!Xjt?%271vKJo3ARZqHy&cRCsHIf>S$Cz`rXPrx=q{%{iBpyFOywg?eX))n!jDc zecLN4isD@D{pUTA0oS#+A{(zH{_)FyG<>$epDWJ1bZegvSMe|RQ!v#xM-mSBx9BQ$O$kZ^XTK;^#PTC;X!;2${sN3$6L^V31psA;%RfH_I)om}*ln zC7looM-!%K373G*vH{tos6L{Lho`w5nKSZgH)F0C#ZSkTDPXvwY_{dSm<@4!-`M#} zMcpwsH^VMQmcv{PL8zYXMs-4lh%06`aWq-OZl$=~>}$mFS;##_kE``)D7%M+$CGZOvQLL{I(bRNVv zDOCaYKZphOoNW0a#s^^CiLCudKiw=S*v<(^dnJ_zr8jwuKb|mUKy$`jSDnfbbQBD- zZM47eMd(9{kdXHdA8^Zj?PD>>s5kSrY4hPLG`rM7>C==iT$@J>qpOmQ4n`}j7fQ!j zw;kr!IYv%xVa!pVb4X2XRi;Aa*n>O#XH zR?UuE9%nD-I_qDM{hj?fIH_PSF!j7;YlE@^*JYwD&L4yO4Zd4xepq(R$ql)}3y;uQ zXV%-c1=l+-j4T8_W4$Q%jZ{)SHOx;J!rzVuII#c zsNS;n#A`UUtq&}odGpV{r3fjG?{_hxw(++S{a^G}m<{Fhq{EH({b`2| z&azM9O22vO{qbXUpW66I-+t3^-Z#H3@I_Zl^VB}eXjN5U->5gJMPKKjSwQ~2hR&IZb+O!%^1XJ54`4v6Yk|u^7U1|`aPV`dOX;BqX^g#7 zbjM_X0ancAGlwb^BFL#uYv$;Q^i5>|20=V!`jhh79o*n-s4QGm(uYNZz@B#AtszJ! zc)lW~1ADQpZJx7VFeQ_jgY=Wmy_rbNIT1{ZU-r>~GMpDT;K$6nwpBqIQJh)0S zZz`zsm-C}i$UWz7aLv~~@|#A{1^Z?w`)T1ZYcX4uQymVW*2*HZjqQ8gv-ONRP9;ME zs+yoAC{@1L<{(|buE4{+H=xh1M%_pBpnbue!TFrzTEMvPSpC$cUfYxN zELN7}aI3h`ZNxwSvZmmmflBP}I^>^jz<$H{zT&?#T_IV-NcZCx|5?f=_WzW95&=(1 zUeJFTFRTHvRhjxR0K-g!H^<%oay=rkZ=mzQ6f*Vhot-sAUE}|D*NthuRk`pflkI|) zuGwCcu@vZY{_)Q7T0pK?zd-u8RltdQY29`UkG8hweB#1xlb}_&%(M0b-*Ly0xACP6 zO*2t43XUA+ub->LmwG9$z(3gc)}QrEpHCOMBv;f&8A{y_vY7G99Ziq5_>j|HiAR3& zE(c2uInqt*&pQY_N&m5YFYpADNw_8;;}mRoTL1)2@F%iz#P&o5@|H+{y_09mRR;Lr zpYsuzc?abe9@tY_t)UfB#|*t%NX#}rj7W6zGBv0o8S$jfc9-b%9|5tsZjkw1s1Xmn zN0yPu!V+;>SfEzFR7=0(GqKt}fOkgQin*q?)mc;Q31$ zIA3fl!aIc)uQKZ8C1j;J@p>sq@lCFVbST=eJ35Q@c=Vl7#U_+bYkU}MfZFB-V%F$u z42|6vD|^cv@GBq)lvBBXIer>5fJb6{S(eo$`b?!2Ge`D!X% z)SvhG&KIpnGDv1xnO{$7o*!B5w|P|F{QgM27#z7L7cvJt;rwbkzJ$(jOqAhLN3lt6 zkD4|wyXsD}T_93>RBALLHv$o5bHTyAeN}iH-+Lri>mj$S+ z@5l6tt;#09g}Gq~ci}o}e9NTVn=R?ZUPQpglq17(M|JPUU6k+0)}I9S72R&Lk3bO)wtL0>BR^~Ro4fw@@f zrz7vL0H7ATbgu=PXb8VOM**%BX65(d{@uqQG>p?Lu~NOkK(W0fa!E*YO)fawEMX2AK#Z+q&B;B)Znb_ zi(dYcQRcTI8l4n{+kNBMb$zn^O@)3%cjZLAd;42{l0XE^@rdL^ootXj>4J2oGx313 zA-#^vgl=9RzxFMsLAu>JSo7SaR(410x;A>?{CAT#|NVf)8g>js(JC{E{ey5HJ4fHP z2IW?R$ds1TQG^V+(-pjcAZjq)w#o0UEIOg2l) z`FnTB>?7Pnns_y@$zM~S<84{P5!yC~rJemhq$Dp}0sSA>(4kVdRDLIa$tvHO z;jXvyw@EGUPhRMdS&P~KrrzyLbj3BG89kJX%iq)%sGKkIH4N3V0RBY8tmFOFpP#DR^y%ivtjKD_H=6VHM7x~r z`))=r>rKJif&03~X=+r*QHfb6nL2VYfXw1-HUYTHJ~QG&dO24>-BRp-`j+KL zzDfn9^a+yOmmz$>a9O|2^GDgrB1X3D?%Eg3sIfh3)`aV_KDR=QHu2HwMaKbx0iWZC zWXl)AlCk4(heaaaVtmZ7-J^5^+Gdu|-~b2>NY82ANJ)uzpKDTPIROM@1L)lZESaP0 z&*_<}#7ihl_*Q8JS@@-KiJ+o5*2qB| zlOk-yVER>{q;+Y$CieiC?1Z?~)LW;v6dS4Sg3#!f@})7xsTF2tc=kH&Pl*NygJ zlD8!*mYEIt=f%ux0E?+YsdVtOQ*dok%_^OZk(GYO2CZq*?p)s8;$~ayv^zd0B3*0hT3Mk6Wdzi&g@f{q_r%KW7y$jI z;f|tev*E6J8EneQk9~?M<*+0A!_TtDLO%8=$(4TIJ?rK< z_3GU~V7V9yqWZe=-+p&}uhW~>K*4r9HLjg>sZ~C2bnmEV($;XY;LbH3*wXvCmzAij z=UG_&iB(10cDL+yz4SeObn6rzSeC)0MR;)gDhw6p`TPQRCGj7rct{p<$VR8Ar~-rp zcJl{m`I|&Rg)-#%nbQ49{LLEwRAAXw|G+nn=|20;zO0^`HH~dwE5%;z%iQgnhEl^@ zJI9aF*SCZKQ8@)qPY7&H)=ZI`0lul415`QQ@wC>0r?<+!7N**a2ru{>i; zH)9`=*dG{jQCasprvH;=W3XBCV|29Q+C1Pq?F`B*M#TrPP1y3)1i!n{{rM6mO87F% zJ*Cbit_1?@a&UhKaZegmLdStxGl-b@?dT@?l8t*Vx%t&$RGMsGJDwYUlKaryP+-8i zb}OiJ+RSlXGip3^FMDqXo0bizrfwJ;Yzh_ez-6w2k^v@-vv5+!hI~1-zOOVnnY&+BN4RAVRU4nQ#f5m7Tvy9|cM%Ht|s| z)8hU3xT7PXxYv@>^Jp**~5Kr=@E4wK-F6%B4qq6&6NCs*0#g!U?6Rw#l8)1F=L1N zwySOOAX>nI!liRnZ|9v6(}9<4pZ-Bw9vU+pQxe*HqR|%xdl&~up9aehoXh7-IDS%0nu(H;5ZAwFKK{v#1X=E1=Pe>S6KS*^ZHx}k?Y z)yeRYX*+F>G|{GXOPu(GIeMTx*((~?Kz?sMNzaj>WKa5D6yQUAe%5+KU5=K5eMh6- zxT6rt6Fo$x`i_}(H^bLKN7^7%KD$%My%Mh2AJuB&S$i)InUH@k9p|1)ur$XNbkfds zo`ps%D2XSZeG2eAR|47}-_63|Yz+hpa}9-&G=#nyuR}EBS_*dP$LXh{aP3QXzWs(v z104Il_zS*HIO=Jk=VF9!*-j*ZzT;B@;((1( zPj){&|9iFrdxa&Fpx;{w-E*PgdCgmPfuCu(c1;FNqto03JiKn>^ujpv=V^#f3 zkbcXuCF4qLH3H1LxOb{KN4Y_*FN_^0B|+^9DS|ZBu-=uAA0KdpZN*-+E}6!{*`@V0 zDzz|l#lMS%;+7m@gb;O+?aD_WNV@Dm$&qamiM#3;USWrFe`SLi%_P`;I?g#AX@N*SW&#J4cAgzwEC5WJy!zhKk#pt7Kozo>zZpE>vXY%A=P?mIN2uPQ}hIU?GbB8vjzvU=B4{Ly@YIfSldK#6V9ulyYJL{Z1 z%Xs=@1?WB2QGf3u75Og3e6IIIdVgL75d$LqSz;;^wDEPq$VB8l-Lu5!HLIUcXay7@ zVF-yKYcr!Xd+%H#n0WGvn%G$tEUheZl?Fm+>}=C%rs{o6V#6>9KNt+vDcZzqg7t{& zbf{&7$+>{c*aXQMGC4d<2?|EZ4R5t@n=vfm-_t_|C>BSmN+SreqA@WpvKAuAf%&r) zq(?jaDTyaPw4@BXc!gq^`>G^j8!5`Cr_>h8q)9nrw&LwAV(M3GB0Hbrek_l9$1oZv zp_Lp#6J;?@B2fA@Q~&q%peOM~1LE2Jd<_8UsIYYeKeW;AB*{r=zi7l=p-siMHopt& zUSZ6DMF2k>-!~)lx-mvTY-P3OVY1uSe3q!DI7%X1n#3%+tQ}m9^t$In;xgn~`Xizv z@23-mGdFFYSDjxS_cm}5F3z*4A5Y4-0q1yqD*9b9EW~n9YlRPd4Xlqhb$T*Hvt>I)9yiIyGpC2_QPnTQ z6{md6l0dajmEOEwb-LArz>`h`)`04#%222p4My~$P#>oJI6=z8jKiCX-i~eO{lawn z_5emg11Qun5M=R4G;lFolM~hXy{e|@`CS`-D+Y^!%4;)BvtpX%Bnq~LkrWc%u|7p>8`3l&hn}_Zq%E2-U%m$Q zKTDQ`@t)RW6WwAR#L8l4HXm1}*caS9|8ib^Xf%$$%=OW5LLV1mNH;%@eX$aT%cK;A z{na}(T`}K3oxKnFfRLxI>Z{}=LnDm=@^*>8ke%#E#|ixY-yEhh&yY;0gE$*+We^gk zbpe|70;Cxvk3PtMGjt*r%3IuiRgO@H(0R8rd&c(Y^=!-JHzgROFNfAXT))kE#m>d! zsU2M-lPyDwr3HTl6r)I3h$P^0Br=`S0%0zJ`QOAd{Qm{$KDvU-v9fvN^drH%JY#at}KEo#Mp!wNg zp>lG!(7X^pfTG5#DO8)+n54*8CA#3QeOYA8;nh4gd2}E>@?~wbs;AtTgxGOxahKvU zEYN#ArEHjBHzt@*l34nvjo-Rex6O|4+Zn$BzvR0xdVeLz0V)vi*ZpM^6+DkQ`~IGm zGZIa3nPTvJ?Y5UgBY6sfIM?qOc?AkYVZ}9rmJKZ_8KZ^8|H?U_gM7VTvres#uK$(L z7x_EsQRXa)VjWazT%=Ib#MP!Ka^9)ApLC*`9G>)Ahl5D(!ZOgNaZ>3)=mQrjUUE*2 zz{Y$1VOxNCJLvs+=7@SvQaZ$ypYnmBrhOXL&8=m@QBBMoQ_6}harwF7bk0xaY zIqCFf?NPTp$4!^8x6~h7lUHDO?S3ft!3p&+pxZz=2)4+(VLnQK4xmuFmx!nu;_ir5 z;s3)bAZjKaw6A?^D-+yxSd=Lu7D3u2J;0SD`5MtMwI={;}>xh zD1Q;(ifnx=+0q8d96gXrI`+E0I+G!DQ{ccRr5?<1CyF5%^t(|Vs^`IKQDd6? z0h0#rZ^b=+wM6;CfHtumdnOCc_xXkyX|Zr(;#xH!>HUa9%&N=Cv5%wem+Xs+Z}R9m z+Hp^&srT4+`=^x*k{1|1sJLOuSL*yR0EE~i^;1q&kP%P5$CRYEpxnB?ZAWeA$ibPd z5&@n#rHo(4CfDapS?FWvNT>;wyq?Gw)^i-%aI7AK*kH#(gx-WNs|OlA)Q{K$Bo)p5 zBRk^E4-xD}o~S7j?!fPB`@xY~DuYu#_zTor71h_&r3az-7FCI(TAi>96~8uAtS2j6ox(Ve&b%&W5F*ckNa0qk^@Ls(3J)1@Yc<_+VHMx zd=}B}oLwk)se_X;{}|;J_vUP6z4hqp3JB#RrrEH8`lb<$`RVAuPo^n5CbBG58mzG) z+|Vfrrwel>HsC_+Vcd5|-P@29ds?KZ2(X8L!ww7e3VE17wA^*>_I;cR!}~Bg300cU zWW`?oG7H)#ZzoT-DaJW`ZMe&Qt^eP+?i(sRRrqw1U*2YY0xxx0LHH_trr?$ZeV7F5TVHPtGv`YzSY-=Kp$Kady^YK z5=yeQEu!nMQPhU#XC@uLVfj*k{sKjpZaK%7<3Jh3Z;`2@d+s8~z#giDwpg{Z=2fxEC3wY*_ ziTl?dRm5aa$|bgZnBm;zW$x1+_EuOnHIpgSE?qcQ5i9e6nDidocH$a;UYW0|Zv;3#_$ z@j5Q{$RnjyP$*tirtha=G+RW(KDxzLe}5BsmQqHe9GDiGngrV5=gKZ&!=@N7vk{?G z3o>}?x*;f_Qt6zW#Z?GM@#@wz%F^HSaLC<<%pMbD^>Kv8k68dK$~T3ibu$JBCS~6H z^D1LwRX;`64~}0kk3Ewa8~(HrnrB*#P0x|(oQ)MP9o$m+1NXW+HyybqPAC14YDtFL-wQm&nY=$LZc z6esBTyL>6JrO4r1g0AUPTFam?pk(=b)?iEU1j55?daJJ>3bF5w*yf)!JAa)EycyG0{73 zjBJo6{4brzKfN7)b6|azdvc5RabEdwrmeK4nYI!8moUB;6WzT1vv8<#bVco&h=2Bx z?vmqG@+fj~;^)q&Q`4s#?-rh`L)9cJ1L*y%&zDRu)4(m-Z)8J0gIWQhT-BYGU(Efi zi~uFNoVDsh^ zq6Va%0@!M*3N5lF+u11=%c~PUFRI$~d$&h?Ms{PL$Q}|JNU}=<0gkh)Cxp*#_0e8Q zXX^C|;QKSSTMcU!JY^Y-OuKCX^yzI{y_7@ZDq^g$!7^73zPh2WsxvxzH%Eb@ZWARE z=lTIn>(mdlizugc!{;PDLf>M1(qft`Ho2uqkA2;xV5xtD6+?*pqB;_Cv7la_pPTR~ ze1iKywZ2YA!MIa?h!GH{8WGkJd-((#+sz4tDw8-6Q<@Q;h7{gFpO@&^j^`S)UtX_# zp5&*dl-!{@lfhG$VtwvSl2tRq9}03^oG)m|C$8E9K{eNI)2w6f7*Gk0wRXSso?)*$ zgl}Q|B6fK_O@H$jxmrMtJb9Tv`r3GG!~Qf+xUclicYCs^oh3gt_KGaQS)Ct;Sg5&E zl1+#9B>tJtVKBk1sw3aF-jvWHk-c&bd&IGd(8eQy16#GM>-+~$&^ijMUjaj-xx@C3CC!8Mt_B5vzl{A zkTsXot=18Iru}}!nCh(C$|;nSuTD+I0OdFe6Tj8LKud9XJ9n1}`9YQCgsJB4A_$!Q z8A|A;oHjeS(-)(8TQ+iQkYImsyA;zy`|U&3hf~Lec6AiJDYMEiXU4jPG{}#qm%@dw zDxe(cFC#(FNk5^!`RrA*Gh5450#g2ZA1=#beKSvqU6+fq-*!&$Q@O=_5 zb@cn%{HBKW&QFPU)Vb7P%~3C_j9DBX)=>%%Bp(fC&6*L9lLJ;~~jrd;Izi{kFL*(a%hwZBB9)ZXbT< zz}lv7V?~+4tFu8ty9o)8`{rQX$b!%Y|2pP#bI6E!Oln`z(XmDW}iM+K+rDQRI7YGX0& zZHdZ|ETWd)!f~4Ms4P|JybAD0Hx;zYWC9u*!edu&a9TD)#tLL_06x1hsUVQ+M^>l` z$}&Fihxk17#GXr;!Uo$@N66bJIyrO9Z)>ra5S>gW_D9qpRB+NKy*=9*5rXECm(D zC+uR!VX{TQA`S?3H`o#s$(Rg4@-JEe&?CN-Q)mL2Vk%n8UN~BL@}mu>CQYbU0vn|f zlp#fHb6$YVy*!TnYxyrVi)qYl+Mgf&r|2rjFER+agTd0{FS$gM@yRW!Mb*w^I(@2a zOwrC~Kv4eW;)h_1Y~*AQ7C6r%CGH+k^G05o;DdJkX6L?Lx^$wFdU68z^<%7bE?T*Z zGV2zuEicpX^@P%%2&cYtqK)gEiF;!#ldGlluucfg_s*nK!#4TqcHr2B>JGD`B`5p> zr)r_E;!@*VUz^u8)r&02eG%`sA34I$4~IWSLpPZsIxYfIqRZa;zs)w`*Rwx8ic5SF z;&$~rP5rz(#TIP2+&uGYrk6r+j!h5|-rM)=LEA;2x1{d$SG{VUxX+p;wX+(9?lSzt z>I%eN_E`IUqyd|OiL&QK%9V(2;AKkPohbWgPQXd&c%TYzO^feT^P02OAivZ{R}!hu z3+HO3U&B|QMV{~@OJRm=bc-IlZ{Yf6_Xz!+j<^S^?)07-RQR8;+P)}n21s_MA8|I9 zM{!UJ6)YoxZ@q+f_ka^_#u6InxH)K$6`9Ji+EcH9hKxbuF?}MiB$qxSea;LcmHo&f zp+4D)8hkE!p9Cyr#6YQi=+>sIX>JS=83onAStVSczh4{>3Bm?$K%76rAH&!#U29<+ ziX~Y3vQ6P+90&AC3q-ID1-~Y}rWyDPFyo~bP8Jlp6I}|B**hS%7zptj)wiJEf#vjX!%-h zUioe3c*lmm7DlVP#(y9$Kbz@PcM!x2bprh4U^7AWmj&Xfyd(g8ZlM3q)A6MtZihz| zV&=9BQjxrvNx-?*HR+o-$h<5UZ*k;6_#|90a$p$GZG#{6Q!P8SPotNqiBy*D|(2a`DHmMG?3hMT0x+*@O%O|7oz$2rELPwNM zW1q^Fz$(5m`HVfs-KqIetQ9vJ$S3X_lV%fnB`$k}7)U#Q&4~hPXn}|_0h4563V@P$ z)y>IvGz}#RD<+aXPQ$r8E(ZdV%T9|~+P!_|J=2@)jr;j;e}7ri7QBkn^dPn_Nq^{n z`-@0RPf@VZK|_0a`&tf5r@C1QAF^CV&U204jA!4Bt0(g$hHZ6vzxD2e`lFT%Ow_N# zMvLcabeJ`%si4n`LgVTdGKE_|O>y|XufaLFnrCse#vlxnTMlb1+S z-4NpUR69StR1bGXfzZZnDWIq-R@+yGJW5M*i#`--sO8`^4tx}!pYuJo94`y zEyIR=&hc<77%bNRZt*0?t{cPQAImFDIVg5X%WhAv_ zlGkPpo}~MXJnbC6S@}i5j!%A>kB6xm%k>*9Iq>H4EsVocvz=@(4OvDSS-LjRL>kr? zn4(;c;H?^>1+EN+55G1Um7>wuk$UneDwag9en?B z=UaEf=G~ktInklND9p3K9+Y=5Zi9O0vUuLn@J|;=h)r<+x2|SHTIp+)w1y;{kGcCs ze_)|Vv>(KYf0xw#2~zs&*&`4`PPBI@O(Whnmuo?9q&DIxxC!-H5{DP&V@tujD$t;X z%>}`l+Xdm};0z^W1{Hy|9~3_0*QtHJ4nMD}`v2v)V1$5$BF3bgJ$lHzubxy3?816!V_YsCFq=&C9jPd7eYo8WnJdYk6^B4KUnhX-z z_I`4SoL!zPr3`K!GGx+G68dO=itfa1sLyLUSowHL%^FuDrs~HP9=^ZXW3V6ByGa2w z&ycy>`6`aq<2D|BroH3mW$cNa`3uFty7hoc8UK?`z@Bh*=l1+x&5bc83Qeu?B0rWH zc=j!M-@vxUQ@YLeS$qmKl~|X~x0}BaWDR#4wDM=NvZAY3>GrN6T9xi}Joa{u<5YOGTPiEKJmX$rTI72Eq0 z+0w80m;Clk>ZGFoTKC@HYaA{dWb2vme%m;z58vwUrkQ=lPXrV)Ev9@;L`+QTeG~6b z?aEfI7s;zfnUwQLWE+X1kvSY>$NG}hn-;`-!|8<-MiKE5d{$zt)II_uWDH)I>a;DA z#@nRKh~0fn@KB0XE0DO5L*=;cyPacL=>9u|V}{`%5w}*Jq9Wymu=%}dLK6}0X9t;q zjn$y*!U1QXiQm3#a3Az^!}fZGZS=~3ZB(I3c69v1ep|2MxuMCeihg~A;C

Xgsa z$X6KGzKTK2*FJx3L1PD$E3F^@33x}MBhT?mE^PlJ3!p;L^&cj<{&gCcOKs3??}UQe zZ7;Epg7orooK*V0A;}w-v`kg&3@M3;>el2$?Kt)b9%?}53?+sHy{`LP65EpZkdHTv0=F_T>3e2%qUum%&)iO^{z*9K!Z9PVwq>NaNdvVq>ft>( z=u$vsy_|L++p)wXL+ouvi{5#IDBkseryjQ&D-_AoB#%5=H<5aTHl;ImQ>Gqz{YY|> zRc$gbjn;_kUNJSb-{O`U{8_8;`%aM7%GM7gB$Cvn$%y?f0@Lf@nFxHK-q8>F-_;F} zhc1w+>}d&C*35ii!1oi^X*UrS`^5kelWHL&K=ITcl(SnE4dRX}6Pf;{wBIWJ84=26Mg z1}X)acaaR3=S|yj(8$(#f`L{mznGx-CZfYs3zQ=^7zhOyeZ-)7JiY>kBXA&TtA~W7 z1+gic91SwYsb2`$W+1ZA5{0mz8Y67lAqz2x>WlHS1CV+hCAYH=NzKKx0Q0Uk)2+YO zPl$xH>D{}8pub%NB;V1lZmw|qKCX)V%UREljF?GK-L+(?i!&%YKt?N}CPN_qB9B<$ zIm-g50tnBMkOMm}1=BP(8%HNp$XX{P8(?gBt#gp+qSvGAW%AjJ?MdYs2t^vj^2);{ zSDNISBIUaDN;P{c1IzI`5Fsn;;!m{9xLr?ML!iU%2rb3m5x@RMiT=g8sHwV(M8r(K zeq348t-kw}zWW)ej-XZE|NRI5lnGfVAwu5LqG~z9df|Y7R`30B@xhoUds`2yUV489v-C$4raWbpS(%TiW#fRkwzwoA@g#GOD(`36OhZ*Ud2gQr& z5VEz%q*u$bD7796mRu~SdXOV>`?+JTgdgB%qbk!w2GQD;KeT7g>jkI*cCfJz{M3km zXLZ_Xg3XHT0h4U#5CtGUoArE^2B@7xPq>7rVlyNtUZ;vCZGWaYiSRg%*3^{&+Vf5s z6`+sd+9f%)pnZ3Zh&JQ*%}cg-=@~!nUI2|kp+x`7jqVq50gNq26Z1g)F3%U63 za9s>DYMZ*QH_!X>$>x9-go~Y}HwKi>^OGr^r^!Z|}$h)s8=dPx%TcS=~ zA4ad=A`y>K(>fc^>el}fRM_S#A;8#Zc`xXI$QXN8wK+ZO5Ak(zl3GSaeS-`Vj~&)K zf@P!{oZE30P10|S`Rp)mCrrM{r}UZbSgks!sZ&Qu%<&%ZOjt4f!puGTqZ?pIyTHTr zGLZ*Xsyyv4{YcVR_Iiebf{QAY>@As>0+ms0dpLQuLE+P2o=FdfwTZKnYV`sLOCuBv~BlmGS?FA5bi zsK`Nz=ye+kyOydp-F)-&tJ@x2IdDMiIR|XMeB8L0w`=T%3h6ntcMM(vuy3kk9=6k; z%c(r?nP4iMCbO&C^Wvy~rF@W^CD^K64dqMmq2nVY@V2_-&LszDquf~>9Zh>;Y!S6lswhj({Hbw zoBp~b2Ju2x7Ny|VWzpGv;f?o&>Mw+;2fmi6d*8oIbEX+A@%7c9hgoz<^8a?{>%ByU zO+Euc11;$b#9#kJC|z>(MZs% zuMAdl(@LaE+O>h{j?cO<}Xs_B3D`p$4T*RJgl zi9y1Q9wo*gI*DFFlo>|vM2j9hS`fX*AcQE#_Ve!j zKKuQC&2ilGW898wUF%%uI@h_DQEi!m5lHfd3x*Xs7eUUkXWIamO-j*#H-ku>bPh0p zL4y{<6rp3EnP)B4UGA)=`oQ``@3(bFqm-oq!mWMqvn+K%O^DtpgIzL}2lh&s2h4_4 z7Q09l-!3$kk1koN?(MkhMa>Hy`-Nhl^Q#7O`5&1J1nAlg6kJ33N4fCOeZ@EB8#z;1 zUqm_&Q1_ns>AT?&yRj(X2w^W#yHdIV>K_zuZjcrieUN!$HT;9~p0w^E(0wYIH(a3C zM-q1s{o#jRqDvH6+A&Gj#*gK)5#48#B$_=B`M4D8A?b4UG%Z8ZWg3R+iRxd&MPP3C z9j#wL0C;Z1D@O1NwzUCU9^3gCwq@++aPs?gzH;HOp>8=_Z~4b15d$JC_rBGCZXLY{ zi?doqQ8A*v&*H~=DQ*w&nOpNywEZeqic0$iz>-0PHUPH#YdQANd=^Qv+~I zmF6bI?%4+eE%n2LO-pWgQGSUz=GYb`47Tnb+dPz^<4lVTPNgAuUnexN56JiY8EPy# z@Vv|0uo1aO@`3~@cyrj^9=oNg{<9b>4lzNXn6Y1_J8FTyDG6pcadWREU@Dxvl(&sT zhPy9#f}Nm4-iHAZOT_;EiVapa^jpdCmtx}2nj#e-lH*}BueX=@C-V8t*7DM0WoLey zFH(f3epeqsOJTpS^s`;yi7%7jgcA(&OOPyN!s)KdZZ!}*%3&bL{618x6FaN@&M1IQ zIu+ZYN9>g7WDN|OF=E?P^5?r14j&GqCc+vmLqLh6o3crtwB1%YV^y~aaNap7u(jkG z(B`dpyJn$`JvWp&tZAJXhuVHt!Mg{ewG)RoXnf(E^Mo!Z5K#!CkR?xo+_1H4!kFkn5s{w^Ji`@B*-yNeuGsWHxK3YzIJ$_3xQ8*mV zNdK#Cy-vq^_LqsPr3>0)g%y+tEtgR_6^yG>7bidG&T$n7>L@_cd8f_&;H;sHzm2M% zk)_yo8o(5@tSlQ|dRJ^wb`Jc3ZyEtoPcxA1`X~!QCuo8KY>Mv4(yqpJ?cH*!-1zq5 z!~je{@}B-!!b?G2*)u54$*(xyU+(O=d=D~B%L|9_!#bikc3!b-W-D@1gp0@qft|US zoI^x2kH6i}zRxBb7>Iu5)Rb~czSxN})!>s~ZD6LGRQF2E4Hd22G>DVJXM3Ja?V_jp z;){aF;gG|~R{6RhRYD*rFSDBBkx0KnjAXX1J#iq=u}DodAV2q_8D6J3rh*?ppm8Br z+!F+ZJleTu(?y`s$pYT~?u#&Y+)z6{Ny6OLPr3mrY+RUZd>fJrvUd+a>1kDl!3`00 zwIqqp;G=9OVpSg5o&$&f_wD}B7X%KZ6`~Iw`K)U3Lk&-vWpVaZ*|l_G&sEQ(;7HO% z;)`K~hS%J7@dUm;Yp941Y{ei?L`|qdkNoKPdzEsnU*v||gc$4KM2KUIJ71Z7Z+%ZK zgv_cS;)b%J{(5(Bmso0or~Zs`>3dQt!w<86zPRG|y@`q>ifMfBe%{Z%IR zq@Q@@?oIyv>kXL>f;waMvzULK|Bk5F9V+Z0iqS$jwau4@${raFkZyMdOV>NMkL@)# z$n;sI6=ALQPh3HM>2>hpT88H`@8Eew78NLqfQO_*fs9g{B<8!@ENe`$F}runMg`(t zAfRikxU}!|+^9Tb4rC_jgorB4RqY^YbEvG(e_NDYHK6?)= z4T_q$Y~jUEOv@;4>G2VOPoCOg|9guBz*mJXx2z24zNq_##d|ARfVzh|RXzEOCMW%KEBvPq|Nbrtk(6#6HJ7}@e$KD`A7#5ysy=4uW~b_c{c ztI*f4IMxfwa75P9@G|xeFb=Je0IE^}$~|?5$P=8hBxERPwb2@f72peAZxk^&`%Wjn zk6W}X7cAoJPz*02mo2K-C&dwrq*i{CyJiY+ll#IF8#16z3m%WWkrrcnzsHssQo2UV zgvyQAOm(r&9~jEn!npXn-7^u^q{Itg0pq` z20-Dobd6z69Z?Uo$M%`O{-HenheP!5TXW-P&`a49?ulZ6x%`cmqM8HmF=$a^%osX- zBJVpJNK-$=GFtJ6rhblYUwPJ~LB=*!si0jepV63SUgQ4V%L+lBZJKegw}*%Gbrha+ z#^VAHzp@t41~ANM`i*jQlKUsUT7NiL(!mo?imsr;ovg0>)m{uH_W*Ctg8S|$_tr-K(R zrb++>NXt?lwnS`U5-%$y4~J0YWnDcfs*C(JYC`jYPtz-!bAj}poSZMndzAMYBohwL zj0U!79Ey!7Sk86xn4@r1vRM$<-VuY&1JA{EtOgc4Kl6{~FR1DUU-rCu= zQTf)O4bPgHEG`_lKNC0e$SDtfV&{QrbpE*V#V~H>`)jpoCOpM@*W`yRh6tm0DEI@z zBrgK>vBbLRsx60D)Vhg|8x`Y*e=#_R@d8-0t;sy~ah7){Qu|%H>}!H!d2H?n#u5gR ze_swh7Sg@zy{z$M{MFzY8FqyBS!;Ul=)GvYSWOeE&FVJ=UkK>>avZRx+$DmUx|(9; zRl4lM#%9D;o5*0Php#^vh&#Suv1Vu~AVKUGHR0USA$pu6`qt~Fc}4mZuA72!bv8*Y z#ps?*^mMbX(dDzuCj@d;*DbSDy+2`H-kn>`G~WIyEp2xqyl#;49)B65RwXT(7=FaC z#MQ86%v6^@)&-U3%*9p2E9!Wn@$Q2e`otFgCCc?{RN6HMYkSJ2-Yp@`G_@zco_Ljy zwcNIAMuCvkQ4l}hz4q6Y(8ixv*&Pg{y~OL8YjNaqOPR@~Nx$1)JJNsY+5T~6 z?ik{MKgvk9m$&F1NssDzq^g{sa&T_b#k=8OA7Np#tDNM_J9}E5rMRx333T{>P!=KYZtJ z;K3Li$cZA3qureQcj*1`v1o-=D%+Wyh;Qutu{Yfm!3aK3MgMF{P;LmXTqcUMD@Pz} zIJyNjgEB^+6Z*@^$t0d2;Bt_8nchv|ArQNJS%Ra+ESW}cKM$niq)2;GFHdtjiuT$8 zS+>95Rb+ak-1?i`g zKEw=~K^RPuBkHk2^b0!8tYQUKfXhxeL@MIWtq5u>>2y+O&Bg%n(2yt3`@-x}Bu|z{ zheW?Op`PC?uGWOVi4uMq@lt)IxY-A^0Q4e&$stC<{ed{jks*iB=HLW@xMj_geI0v!mvba{1#ffR4m!<**OxJ)9P=KUA^lKH)Zs4o;|J=oTZ&59M>54n zjnnAHl*@3AZ_^Ee^NQ~f&S59Ug6miRNYHp7lPt<@E;c}Rm+RHZ&+7FY1xH-5m}c?RBlt6 zcd1zw#`fl2S034!GNY8>v14n;S>WaP>+t{Tl9p9uH=9ufJc$`kwW zp)#m(7z6sDRvq%Wo}AUD6`1zak^Z6ipn3FR^+Y`=p5+_iPw`W{h0!qK^(&;8ZCcKDTKg>~=e8p|l`*132(v>D@)RtT<5 zo!$G_eII0vxIyTSMRU?yCAK5NjM=-d&HOIq85MH`m9x@5I1VmmmLn=}PU}Lm{K@2A zA!^;;e?BWOoNp+%Tz$IrYBY4L3(K*@Q5S*U@RUl0Iu-}uXeq>N=CgrU3mwo@12fXjRmjcZILOk@0=IMe2pYr zyf{Y#a|m6cY!*e#w=e2jGRkVLBjG9#VOb7Exo!##o7I*z?EHA+Jwj%L8j5%e zs47`MihF-Btsfs_mDDWnHU*-I?5g|oT~IdB(gkkdpw8`EF)v)7f0E{k8B4}#V)*`? zGolK};vg9-pb-rQ2d}X*jSr8ePZm|hJ!)Yd9x}3#zN2hgA4SKxE9cj`` z#?xoKa$oRpB#zeuRaV)k3Wh))sZ5O2vbA+55g&`2_*Byz8|CWw>F4G%^j6T$3SI zF$w0%*C2Zs^!baR?(P47{dogt;;8n^^A)XIFZlV`r5B$%nH$H~iI)vc8<71_FU^Y{ z#gO&shgkT%T@2Q%dnPA&tE(n3zoD+TH+6#_?$SKLWdizqdL-GBT6PSIeG3jJ^tS0d z6ZzmS%JhZwh`74cA(%4ztp{0UY@K}Yce6fz*D4H=*VI4DU1Mdjpj&n~$vgb7hy0c3 zZ{B6lYvJSgzj6*c9=SVO+m_^-v}?Fx)4*YsX&N?z|k=1(zFssene?r|lI%VDcly2Plh*ZBWkD1gCs)5TY z+6zUC8z9lwS*@qO<;zpm68!8MwYjInVEETaUGmjDP6^}IuPsjB{e7;L15%z?K^{CX zmsFNl>yF+{-bi}6D$&|-gPX3EOd%!xc*g4>`v&eJj+aym|I_qV$p-@F|`gbJ{(BlQP7xA;6x_SuCs$fKmoKiPOYH2ySG|R- zryMGeGZ|@JmN0+sT-Oe8V*soL5po-ISl|R3d;vzJ-Jn5v=IqjU>mMxPP38IAs-l!B zf5#pj*!ti7*8f2qLF=?QYk9kNZK|C|T}RVinY%Ild2)s0AIaR*=vrR#j4-KAJYEsm z`7|;1p~LCRBT#8Y1OJS9a+<+MgTsW>_sx(r^i$TgXY1U0^w4aH*2!|p0-N1;qR=&B z#v5ehlziOoy8Gh~{XeeZ3VIG2A+h^7%i*21aiSxnzhXqC*!NQHcAhqL4*mgl{)&%& zXO34`yOo@iIN4pQziY>-32|6a#9@(p|8_Bc#SB1nyd%B>cI7bSZDDi(1;h~vht=9D zPx?d2;ru{9Xub;2(IX&^faA1;&aM=@C3Cq$QEOKz*5G7oR%z~3+-+sqkrrk-XAVt4 zjoDfbPhld{DWU2+kAYn-?5U|-xw@K((Iv32aU^^6erU54aq|8{L% zZ2=dzZjlcc%&gIDUA*oOHMvwt63Hi9X}f;u&WW?H-@lSvcOvtM?FTD-5V)gXs)1`z zw%zEOCcz{ZBimV}ub|41e+3sf%~ic9NPqmtoZDD zGv|egRKWIFL|-lKv#f5v9=)559Gc>DR;vRG5B;xxxtK!ALRlOb~4i*sqhbM7GL{D zseVfK5$p;d^RlfCCs$m$F3Q4AJX@cL3Tb}#XRDM!ss(C`n~3BNNC$L{4nCS|XBW$V zELS}4(UBALQ3Rdy>=7D|;OR0UphaXYC0JtE2QSY?U=f=I+}m4FBt<4e-3E2?eib15 zB5aY1vF*Jtk>S-;WS@3`817{MN?|YWbNaabuUy~XA=1cg+$je@huqh%hLEgb)MM(% znpm|0`}@Sz95}nlN?pCMY|S+s!S~5m+Um_b*yhd{pUI@3(Yh@IboN#?J&5bbfOuDE z!n7wP>d<%T{6iBlD$Wav`{mJkL0}-3?>wn~d-6?pXhCjk#FuVIHi+u+)fv-B#Uqn~ z!+(~aKlb^3yr)|V<{^KrbTbuylkwW~LKU@tyo{OzE&=I4W`&FxCaS;O#41s%sH)f9 z={X;EcMH50La(1`0Vxs6wQP29M8w*{PCA!5hQl_{YzoiWEEzw-xrKR1rKt|Ki*7>h zvXhDsRxPQ;`TBpcb5AESa~~HfswyAyQbP!!o16+Y!x!*VC@GBkMPhB$II4xanL%#K zKRt2~P++P$n#@d)?n>IS5M`1HuXXu!kwSNCi9j0s}QUv55lilxqQ zUk$%~Fov$gM2|lIdG)?2DU~Yi3|1j}7hY4`LMQw^RT!F|K^+WGmJ8h40pwVm_D|CL zY7@S8F>5Km_JQ{W)O+6ywzZhuB*gZP(jTmItIasfA5@?Ck z2gz*)INL1GeHOCRU`jr*q|g`r(qjuDlm+TCNSfz5>6#)C@nAw4pp{GM2kxVD4)O2G z_z}8W+xY?&xYJbY4Qp3;Iv3v_09p$B2G}1(q8z%>d0n)ul3URhCQK2&cts*4Rl0rx zD>jI(!iBB-a!eBg5#NIKZIs8#+A7vxzOZ1N81?v1FY=$z{|FVXumFDkVx|zuNl9?; zkYTPU)6fp+<6W8J{yLZ7ZE?I=i_Br%9rk|uLp7k&2t#~v$=c$xuj%vCvagNP%lNpm zH}56pRWnFOesNUQkZ6YOTRnSYTFOr@^;YUtYR#J|V?Y~zPSWz1OAwHf_L(hF&@SL{ z|6_*3FiOO3&tvN|0DAHL|0I{cp#N93fQ`Y4e}qi`dXo_XT%ZLH!jxT5Y+?xwi!=6I z=^u;LhdzakbAQq2!pFUbZV{K28NX{aDb82Dzzf>+ZtZWRo?~o9H$8cN^YU_-ep6%x z6}`8*F{PQMBJTk|*Il6||8>4YOBGMxxw{vwMCMtu}F|-wI%LOn} z%FkW`3K|m@gRhO=@Etl-9dsq^K`w-9Vy?Z-rHJOYZvtCw(p?C%w185LgLawmv;KFuKPPdz$(UC}-q zD?v~x6&N&emyoAYmSa_3)~NYG_bTo9X~e8(q1wiirs}*?W3eM+MA{QRO>twT3)PDSU6mfb=bIIXwmPLA4})$RzLq$pf6)EWbKgcjBDywD3cdj<#ExgcoS zKP&L;ob%y-NM0yDoc|oeJWN-A#j!K~NPCpR5>g&86T@=sSY>65uwMWuSk6f8M}B*N z`S$6FRhSp9H{uJeMu5pzno+O0n+fZi>of`pc{dn0YcWvvN%>utEpW}+%qy^r(X$82 zYMW=%7KCgER&6}U`7t}|sNf&~zh0>$p2NuUA3~iC;MrzDmW-!o!?P;PeVT-;*n@Aq z(X^iqRtI?cz1B7yjzq{4) zVf9Os*=Zp3BG)Kt&xT=T)SSpm`p{#_>#Y9U-#2+SS7_P$CCQi*ch|M_>Cnsm{~)|fV?3nyd+^m!Ca8160S;8FBaXFu6sa`FQ#uRzK5Q4q4GU%266mvH zQoJ$LtC!LQdgp2-Balug9A6;;}NBECF*) z^2i{0sczD(vB3VDIaCEi07(uZ<|~Ayufa<8MbRzYNWMCW1S?pjB2=7}Pz-OG(yR%`Ky4)J2=qwXmv zbiYq17@yt;8H4RM?lIoFzv!p1NH9b4E$ejWr%5%$t891^*^&)h(xJ~F=*8Ow+43z;( zotQeufj)0B6?(~X{~_`vG8|^&D`&MVf!WpD+w$D9o#M)Vnf6Kq!W@maR@Pqb7VbZO zb&YwC>$I@Nx>;YuMMo3;wB&-ty0iaGn(#!fRHERb{>NeLM$P7-8UvxUMPOsL;FFp> zWs`r?OOMHMJ@@ACdhXiXd8pj%eQb9LKe$LCWyMr7C55})oe)q?$hTP-<1Be+wISX; za$}N9k%xQEz=%LYUfJGbl#c;2jea`Cw9$=bPVG`C(h{oe3}|CLRTMqZaWeo+@>q2_ z41L3~GS5s;Lx&L1_bYa!7jTaQ>1uE|BFYQSZacziv0`7ERxw* z%gOq|;#3TpA;JHBM6CF;VSHwYpX{r!MlN&xKx%>1S|cb=y6_7lkcJ|ZA+OO_t9LPY zmltow4uUzyctVwR=0<)PbV16gUPBoQj6I>bZKEvX{n(_GifD98+E9dKxl0BTn$^XG z)Gy1J4fCI+!PQ;)6#OzmT+h^gmt?6RxHar$MseU>g}tuNnzPSd7ynQjavDv{R6ffR zGqhArfS(F_MAN=MXz{I{uZy*G`tT7GxpfwWnmx{>C=z;c$&BQ^8o4@Had`^Sbb>#4R19r;U20wITtcZn~e$QA;FY0q4gmSb-&SV1aK z{>!s0auZ=9>BEH!G79xa7pI)W@5%vfMY2z+t8`6Ag~zCh-;_QzWSYo)a=$A^O;&U9 zPOr4tL|5t6;IS+wU}FlBN3iBha-0^RwGLD7~Z@Yiyqrs`j%C4H*tB9FKX z>m8(?4I5TU?b)%U$Es#Nww+n-drRWfdaRo)>h}B$^P}P>a>jnL{1)oP3>y6g8nwl3 zX`pSd(e3EuL;`eGOBXSloN96_U>`RKy6XE=Sp6R?mPvx=Kj}+_)L3gxwq|;C+D$hq z68Bu=LzuwePc~W-OD#k_*Ovg7H`?T8)0rZ$bHR4KxxT9^<@QTEw+g&>H8eSjL{v$O z0eG+S%E`1nx3yz~LfSrqf0+vhbG^A6UJfWrdl8rd1Y5z3ltJ&LE z-)$q1$JL%iD%-#IfPV~!#eVl5f)g3Y8nsG@Kj>e~Y8I0*`mn&aRe+uN=XeGE{2+qv zW(sR|uV)!A^-p}(KshaqdSle{?jmqcbEsxU(Sb`eb#A|`TvrM>c+5Sn0^E0>%%w3E zp&FCQH3dAUz4depF&32maP*r7X+Ly3l1PsxVqmyO^k(&Bsq$?ZMi=KuY=I+>FRCV0 z8}?B=pb5=f%|WCJ$~R~!S@>q*j3ayb+JUQ!I)-I)yK_w}IGbFg0XEPC@i!jjpK>z? z+d33t&Ln>aSTnH0tMt^;pK92ME4OG~ga3%9WpwVy137jfV|4w#6pb!8Z6w#BRVWNk z`_6Tl6D*3*|8P3WbqlBt4!?Jpmr(5KGI*OkPZ!>d_bhp`__SnLKikvMGd%bn0#)+M zr6(zt*mtZy&+Y#FB%~iLe*fc!u=-Swms7R2TOMv2a#0LSPqw7j*M%p#DV>?;M3p1F z^NaTfJ^%pHh|Va?({Y#2Y5SmgjT3ekljAS6({}Is>r-VHi5KT=_*G!_FwA-Fj~cpt znSpYRZWE007PUYxVn&j(=!D3uLULbvOl3|uF$GY6<6e^g{o^y19Zl~ir8mgrZ^t;w zH52CS*y<)f{(MI!6)#%_29!vSMAK6|{*j1Y~AUbM#n%Qa^* z{F?%z4!prUktXbfmh7+mG5Nz!Qij8>PAgxTAWg`TMu;`CCM@~lWX37H$?dpY7qb{b z()Hx(Cy7=!tcX%(r=tCe=Ea;QLq1-d$9S<$eXIMA3Fk}9@zJ-$@#%!?cmV*CvNf-|*gd`1F(c^ga9I+gI7mgd8Z0LX7*^!u{enY@Q#zV5-B?AZo=2iOt6o!xU0Ekla$V#9!WBbuKycXz9I87dMtsEEh z2Fhs;z>|Zh$aV!4LdM0k@^-|Ta5s4&ONf5A_E193f%;ekSYHMrR~5nEwevL6(% zvk}FUe79LWEZ>z5N0-&=z#>))QqiP{}zVIgT(;pUP-e4>e~;@1D^<#BeKoFltQT}aLuZP_dId@R8O%#CjB!5P6@aRxYD`AB0-MeD zB?oh~IIhLbIK#3LlV-8E_{e1Yn1E;_S&N8Dl6Sjrj41v1rleBG-N-syV3Bn|r@R|R zN!`f=Jq8Cu`Uw%(Z7fn#LAw;Kb zeg51_dI1b{*lB1}^1o2=Mg3%y=Cd-}y^CvS_Dx2h%mmSYJDjMFJ&C1$0z^#yKiW4 zvKSZyE&YSBezxGhKLs#}5Zn92N+|R~E-*b~sD??LUjR)S3I;3;* z#oxgY^hk;_OdSU!WW$TBt%svK+b?z48|yb44l*U#a(AvYH7)GP_n^3h2|4wl_d!-~ zf3dl`hTRCmJc|O<9U4~GgaYz_`MVktMyO1kZ6r$1GZXwp_}T_PvMTyP9e26cTsk@F zPAkgFx|hO`-*zi9y$?UW_T^mTPuaNV?sxc7u%qBZY10JeYcee(h0^&o;WTBi;qdC6 zU%H1yaQYT`&)rK`GH$>6%#rujB@zX&ZOUmX`9IFW{x)Ow^~~nBe;gwPo9PQBR;wX^=8^p6=Ud0`qnU3p0o-8KORd66mQS_mpkD;*oCZK3GF)5q zrEP$1Y%nkTen`TnWxhB&Sfz@lXf&RWdO!+~=Oo<@M(v)560r}SJtFOhCwx{P*Jv@c zz+ZxAftu7$SV@6xkl~6EnvTZuH{ELKv*-qPc(2+P5!6?rP0P5x_ePX1f{s&j|Lt|Z zG=t(jrZV<`_lTMw3su>qE{EZA>4JjZ#^DyymxUl>;g4yFTTTlAjmz6Bi!Y!0*?F$R z^=!7O9p?{Wa7~gJP9d5i!SY$UEYHwz=tY73vp(sl|}%?U62X6{(81~zKK6Y*$;2vB$(~Zx7uIC z(20@~(}|d_U|rZabxWzUpAwxP4^XrgB&tVWo(5cdic^I%p&T2W7Rsm8m$yY$^hO7H z+kHt0JkYhaa)qsa7xhKMGD$2hDhGOFZ|-d^=tzeChKb{eFG{?8XZ{-pYTyB?2TpNM zcy6(&G&mLKq~d%Rb_?OPD_@^1Ni7)jrNx=FOyYxZxJdz3H3G-mkfwe~$rfE6J-%Zf zl-qw`VuMOT4Yu%dl5W)2HTO~p$QuGat@PcO^_%Yd#dsKrmwoeQ%vhs5{EmM@?0Mav zf%Hc=hx~Wa`XDu3@!QdoBr>oK_n&$3Kg2rwp-f;{6Z1hS$i_zumM>u+S-Z4WcNHm{ zFU{>Z&&O1sTulGk30_NPCBP~s7@CkdCMMtO4ChN2V>h&YPrIA+#awS_yB!fIn z)o5*}A^8Y1W*k%WBfjY`#-8{DbqOx-cfro>G-nBLE&KbfW|UGZ*@KUBRmD@8zsIq6 z$WVrD7vExO$APkpvzKe6F%B<^+=!rn)Mz{4BiUWMVw*hiJYJ3L+PdnC7AbGm$UOtG;@yZ~=ck3Ihq)(xmb@jbswqnFnCNpX){OG#9NMWz(<(d>?upw2kq1BLPvqU`|W=09&DPAQr5i)lmWfab! zy!Uhu10F|rv>c(syc5UR>C=kZ?4Q{aYcs-T9#+7J?c-st#vddXr6l(Jgpn^YV=hU* z+Z{`|e&_F#Q0=-$V%6&Ilw!9X=x?~Stc#Dy4l6zlP6BRnZp^61u3uu!tMNNvn!9r+ z3$W13SzO-P21!exhwZ|9QA7`JM#YtI#w=}D%BLo^=~H(&rw~zz$M~L#coO+yu>YfS zKPKkhyDLs|xfS#0DeRwQo(k11>r|X@4rV?nk`7A8qg9wZdMqQxeD)?@0wMZf`vo50 zyU0w$Wp&-73Ien7wSd|R8tM^1o!AK**`Yx|aSWM&o{lEtd9k;y3Av3enFnY6=eK3C zCz{si(=_yekQd-t-y^MJ?l27cZ!x_n4JsV=y!dq%lDn~vr81=WLDbXei7%X9`hU%O zf7ibWMBaGq3ulTofnl8sTXJNGaFPJuSaMA9f67`CGr5{>JZD&;n4mt+(>BnnX!R0g zM(Htz$uQe8O9R7ff)D6>-C!H4_yLs|L04~CFXR=?OvCf_n+SDzPlZi2X3G9-V8E$= z5!)i<{W}y7`N?S{&PZi};?U}y13kVa0;~BF2owF;1X?INJ+YWAtvKp4dCnh zx86Q2yfV7Xz$7$c1lcFb6U}$7N+S(>tdaH{HVVK_k)ia8Hjnsd@lrLaq6s>HdwJ0W zhbh5nSNRO-_o|RwXGsmydofbkQ~e%S=$#wPvp5fOTad~ zJ5|N>@e1?flhKgpKhOnV!MLgG)utYJa4X`NCgI>n^O?^2u36HV_Jt}@My-;>=-!hW z5>iKkLf!t7WqX_n8UphfQ$gARIb#h}UK`$A2%aIl4NnJcx6!t^XUdo=Nqa%V@yaic zQO*t9){MS!m+^M+Yj~|0y41+_&BAtr&2(h`-JpCqJ{7hi>E4}jO?XkX5QS?2Y7M(b z)@?L}N#CkmKa~2;5u}598jtT`sEP3>05|VvuO*CC(f5dbt#p!SlxDZB3=k!rjOZgs~ykyo5`?2+ZbLf-C!tbl!wn7+rfj=s}p0Av5t_Z&vn8K_L~)T1_w< z>I2@$>Q}T+kxoT)(rqk!uRbVP(s8PN8&NqY9+&H{f>>!hz~j^!D@!`>p3o z+pa11901i@r(fcnf-(2#E}RjvoxV+oEVfmwp!vW80o2=!2RjdwPrsa-(f>x-gAQ4E z4X?c0OUpJs@aSO#GwpD(laoM(1kF1*l5Io|&5rAYzE;_#ZBc#w@*)W{(tvrpbFSGA zE4KPl4fEP;P|BL$5R@Na?U@R-<0`HM+84m}b=`?#Ux|k@O7qjG#J&tTS6j;WQOzdB zO$wx1Cc{806E93HX-5yX9P{n6z>|E#!HryZo|3A*7pya2no`aBAZE8Mq-J$+M!a703rzq z39~ETTVjQ+ypyH+WLH1m22dAKT0cP9{}bxk?*Kg1^HAtSS6)i-^;`w15YEteAYBOM zSkp8t?+?NA4F)MgT3?^%bknh}tDt_-k3ANYaqQL&zbrku2>NjfW_4BB!Y|TuSHn1F zY5&T(OJ|W8Ur!28GLH3jf<$y}L-LO#iL&XQ^n0!arv@SD53nt;owd|*CnaiQColIj( z#YY-y1yE*dk2Iwyk2=#=Axg3~=NZ0L$o)>8^p5M}4M|(5?#$$3R-;`D9R+lL>FP9N z*jnM;Br!Uu^wGDrE?&wcl}t9o+c&5>a!M+3X>`H!Z5^LUI1Hn65EZ zB_>E}ro`Vp6GID9AIou~wZ|H-j*7Bx9uTkk6`$Jw!Dn1+oA?>+>Bb2O`^SdRC3tfB z@rKR_LgIN0DAfirT>O!>Y;A;{@3#Fbv(AbU7V{^ix^YBLpDEm2~PgM}HYA}xI8A)Q>5h|f_ z>f#Oj`GQ5Jh#SnBanWT>3!zhK5p@@#W7EA+y(vIfZp&r9D;X=wbT}|+{Dm7&f}#_% z+RSb2DyVW<34mQ!s<%rF$WG@rvoWB~O(1`18ryC&z7^{Gui5oiGC*ntmlNsYvs_FX zM@kak^WpS+YhO~xFc=K#*7VG1Xa+0U>m7?wp^m9G(Vzt#r zFXpK3$9s3t$-nUr|0-_oFm}vRNE;D!^!^$VnSPR75Qkf$vY4$-^fMvQV@eJ@WPWPK z5aA{2V9jgFKPHTiryD$bAfbWVZ&t%EQ#R~}iV!mu4^>xkHr~RYdA~rnEWDHJk7_~1 zdwecW#Jw1o6qkR>=A6lbsQcI3{5y7D#Q^uuf39yl_tBycTBpNlyYWwV!M_J2_BVi1 zOXR_@y%Zo}5t(~UYHq0x-iOoDTPaZc6?hyMW4+97CzyBqtMg$h;{Dw7je46gpgNXrruSA>o>LCeMb(;iiCI5#1b z`dMy9{s);jz#b?0aOZ49c~;QMt7wHoN-n{!6y*Fls(8oh7-vYewLo@j?9A?&5@K-t z35ST>Y_?eO6J^$M|6aup3bXv&o89-LI|Z&dI#sBdzD{DP(a4&@q=%}8&1gFA_-F0w7LsDg5n_WRGjd~HzWHvU+6`my&xLPTo$-;mrNyu_>5z>b(+BMLW}SR1Rp zM>5<lxsU%eAkw?d-}2K6xmR2?lgO z{=U~Rm?_^-?~RxpoMkLRMV&ubqc|V(r6dyKwomKt@->VQ7!=kp&y4M+>Ef1l{4k94Zd4F>vhpq*!8CWEV9l>`8|CKWNPs;n_AvF^o;zhJ~A;=U~97|flo-FN8 zky4xKd=LgE(3c&sdS2OUL(=5-j?U-Na6AJa5LChVOr{$aae}1{l<(C&LvIK>-?mvW zFZO({9=;JN45G)%;LM2$iD$?#n2 zCKFy&L>aAY)eb=?2VLm;^ud2cfW_Bzi5p)~n?b$Sf{z(3)m+OFn;Z_~47q$#0Gjke)>!+@xo-#ts^ z1I{yP?SP~gMRRzjHUiRkdCKhD1`HM)ZLXHf##t|-N-y$@dWZo?>954M@*n1wZsCAL z6cw0q07gX3aVG(JSMF(0-Boe~x;cGCy%{%tL#{y_BKsY-K6NMyrm|NGn$9Q0-33Li$~PTF{@Z4_Pcxb{cIN1wY|BAkW(4qhEiNH;>H#bdtVP+BOBM z88njTEAiet;HqbB=@(yf@A&M_HdT67U?lvavT?C8@A`4h;ej8%d^TN%eAS;+ z7`kWMR)~kRx(Yj|a}b{@t(14UlV`w$+7foNrVD>r?DX1(u#R^EP%~AOP=MEYlY1>B z`7M{_gnR_}`0Hux&`%fR{z~RGkki0?!FgxD&`$S#YE-?!+~K3TG!NH#C3OB=ZHN$m z0b=g6bCu@h+y9ABY`&Jdi4D?MH^E+%zx%TefDQXUZIJ#+IHDpIXfq5MV1`#%#cesr z%#ruh)AFG|$PmTlGQT;Z-eGjhog%CPSX`2`r>i70ht&1INsn5}VrIR;v@A0aNIqK1 zOd4J^>Z(GQz}TEAX)~kJgoo5Bn(s}4M4FcN<=}kh!kZbQJ(Q%Q0q4$_gS;%0g>$)A zNb|5;m$c2_#*~XG>4r5bB2njq(bPm&`5k(UPxOPhA!6KyoZ*4fu7Ix+otrrBGK1G; z=}geb@_Y)2Mh>}h+l-(&`Kc;<#_U*oh4--YjO!G_e*UYw2~c5O%3~KOhwamCfB81! z#e1%2mGcIXYq}wsW54^(b^ayD6J71rtqt<#f#>bj?k=;@3x9V7Wk#s_D~xcjlstbR zYSA550u@PRa%fro;`*$+Y~VWl`LjW6u+jyw-VZ{#Gk0%7nC8cQO!g|LsueCfgoi4G z_xMrIS(V&naBkA*cCFGQ1{#KmYM>q;{fv=l_bsMz2I(&uxuyWaD<8PY^}?n5gQ=c# z3y+M#HptEeU+7`qp^us)3$-lzaAZ4nB4DE5P*aCw?06n(JDEO(T1a-w!jwE5$HdlN z{|K_m?o!>y#h75ei4K-#sfj-s@=L&Az$}JX=<=4h;C-*`P>ZD|i+W}{UW$*OaC4=w6fjG^M}>t5pX$V` zAd3={LB*aocL_>^Wv%tXxr-fJ=?1EOi0@6;uU7V-eg2oeNsSSAtUygzhFh`v;&hLs z;^RSN-S>;Vc9Mn{ws*4emJ*@QNRd+v{%4FIFHdIVLM2>R;2T@-ZZ?e8+K#>X)ok-E zx6*nf^M*oF^%Z8a9eqVF?h%?Md?pXaj?NI>NhBIiNk7Uw|iHRwL-ap22 zrKOzH2mfIE|Cj~v5I>AN9PELo6ZR`Yf>_W1UW?}(W-2aXW@2_f{4hn9>5|b9=pnu> zp;=ZpRX%mK-Ebn!7WNX8hG<}+_|DPJT9_^a(RMZch> zpCP|lzGTqX$v?OI{za(#pOh;XaRaGFZp4@_l^M40z2plo#Tvt2PSyof7PGzYgp-Kk zvKYX5fXv`(>;PnCv_={q_>0VKP>xVe0^xaXid_{77WvX_d;+LZ@Zr9l8v5GUjRKdd zA-vJmO{jM;4(z_$Ak-7RXT^1OHqn+ONVraq;yGxeB%QVp&a?l`nU%s$l}Thh!#a2N zY^tUjL@LGMlyew80@DrZ`5f<8aSMmX7v8ywp`M;30e362L$3}Ic!)X;%toj{zPx&Z z%B$$P^}L*{tYAr*RJ0|r9B~;TU8yz5OKnG-0bT&CT}w z2@v3#A=`vMWRkkx2gi`!@V4uHRNf$%*hF3jAFx0(~0VV{PYGg1{6zp9;byYTokz878@%r|s(!?Y1Q zownsh&S|J?ZDvZG4rdwfvtIDg$@o9KZ|cOMQ$RXesA)Dt#b-fs-5WzD zU^QT;UGjfk@UOOdTzA9z(orh9+zNaZf{&*Bw`)N#)Ca>@4ch@-<+ zllX7ly4y8WR8>jzP0v55pWQeiuMk`kKH$J1;%+HLd|*hFSbl68qTG=8IuTGehGqy; z10=&$kg0-X7qj07u{_q}O5nBff$_Z5ccvL=xzvktScAFx<^}e{o!j>a0hBMsV&kw__Abee&XdP-{ZJt0_Y5^r;}gV@UR*&N^}5pwmV)KHGcK2 z9O~oTGEV60TW5259HIRzv-&Gd0UN9JE_a)D!^xHpCO_WlgC5aUOc`@QW%+GJlEjIm z6j5AFN2O1l3eBukMsf(Xm~v^dpN9c0W6GUPmCa%JmJ&a@; zk4`Pte}o3-s$Zw6gp_#&erAN*>ubbnyez)E>pU|0*8HOF{-MQ_X8}i3G#Rz@fUi$C z=!P6iH>!<0Q*tl%q()zV+NK;;e8k=o`v*OEvE;GF+i2$M(--yIuk9Jy=k?J4(WZ^0 zsBbQoy0fUn1s*(-hAAQvwP`J);7w&mSNwIxr zDUVq}(Sxa?{}otoFJ};JtAt0CFqzg#J9AE=n`SAa$Sb931o6X_h1kF0^rf z)%i%G@IhhO=9#_72rEcJQ*}q|u$%jr!itcUfv4Pa_5del6PofV$t_wC%Z?78!=>Oi z_-eHa!)l)*tE7(jlGe279F<<7>N-mK!C5jFCwu$Bw+?kj0bMYilG&7AJUajQdH|3d=JgMIr+?Iinhr3Hec_nw zGbkRfwn5*g9cL47IXRjAl(uKK=&yP8Zfw*V%ntU{+_xsL+V5xAChJ>U9M}s#9uc%% zP;FPqW?UfvHK44LSOOdGMRZ}WNU5&eQP@c`u9AVnFjrW!1X;MQ!EkXF!YP{$V_6&` zpUIMZ0@kpfxJ#{X10K5t*$9_AlEYj74Zd=M8MN^j^Iv+PC!Bo(woKm;$&Ed-(5v1fvT0{nVb!C`F14CQmi%ch;HKpchRmoU;U3JN z;*B(}Qp>(gD?;)3WySYu=!cn8;#COa-48T&xm(OZ7^K?lN;m!|b9ZsVPwP%y&f@WE z6q7-QI4}t#kqncE7qVnN*Hs5ZM9EtfCl$@P%zPh;a6oCvh}#94t>iO%mKNgWPhSIQ#ZDW`fH&iLP&|2?p(23+TC;uK{aQ{5lq{GXG3XSGtzAW`K`jVc^Xf z@$0uNYTbx@=yVTi58sHz%5epb^^`tpp)>tbn~2dDPUP=cR>~n%Kbhs`>CB6(PHlr9 z!AEriroxX(d{3M}b(}pud*VN~AU}LnWXAVQn;cTiY8TnW&;ycVSk7Bb8w|m4!^%Ok zF3=DYO%W@?Cfmf^- zUV~{`s=Odz7vAXmcvI_kq@~_OR(iW7>!l4gAkukw_~oG0YG8Y6@HOmz|KYonBD#Sv z-$xuu_*hYw2S}0K`RD5oiovl}(gMp~GS9?}cNH1R*rJ9B<#_lLupi#2c0 zprnqpG#`3Tsea>XFhHsfffpL-Zt1HVOsE1^G}}pEOJ>ztaU}IShxd-@PASjB>BcN3JfYYy(|%F@A86{q_NzQ8LX}} zlK;wh|C_!$a3Ax}tq0w)?cEZVr#$BOh3tPH)Bj}{_XJ}G@G=&^rJgV%ked&$SNlSX z(v)Ch5zV%v8{I2Gi`E}+p$eB$pr6gOZ{(L2^1~}AdY^5W(wI6P0k@A`vd}%MM{nQ= zPBmy~yecb4ErtS&2AeX zCso25ey#$zzobU>9&=-9h05D!?I5X8mx+G-Oo)=&mU56BE`n)CqUbHWmASB+Ou4(> zAals{x$e2?sYA3MaCpBN!t~?tT?|~RH33%FLRVG}SvUY)?lCd9I5#PHlJveAkd$=^ z=-*T_oj;cRx(|G3rrR>cwULpdl<7;xHVNg1>S8=yEvGk}qws76Mif)C@q}82L>Ul%@TRv7J#qAeavvRVdTQE0Ur04~? z&Z_aZzK5H`aK#Lne*=h!EGzfpa3doK{8)@-J99cYyRM2ULsccdgLkkhE#pVgO3 zX+UpwmODAedaX+9mWL(>`jsHa+lw35^CgO7G+27f6^}-nR*gk*La$TcK`@8KyFUaC}mV>dMD-?hcvhwjk1t}VKk4-%u z>3SPgkE`w`8AkG4Fk0$Cg7BaXd3S*pFfUIvKfpv>Q`7)h;S3cUPP@?ON{r#){vE;+ zZEhaZPa-7<32_A!>9Z5eV_8X@rp>C^9rxd($YMDh5^eJ(5m&ES4N#J@jB1iZ-VojIvZLH<-mF*?D~5G7gW!X&g*PuVnY)Xdi+q3-Almo{Z=J-Sg!F6 z%NiFjTnhdU1Pi+66w0w%(aEF~h0snWcO5&Ib#{ zn6$x_e!h!%6Ss|mZaZjPHJ(wM=Q~z9x3_)hYP*>EfwSVpuXb7JPIneL=vZ?6I$2}y zd5o3eskRG#ZetGtFZzu>)d{1kcq4K)$ETDVF^Fbl<=dB5d6Kbbyw4`^-?>lj)DQB@NR|Tun~@)pO})X%v8IF^r{?J!nFZsz=o)TYI-2!>hb-c4j5_8-Cnjat?e<6+dS$rt@@4X2SQig z?()}~dM9yE@N@_BhU{_I=#pK&v(k972*Zn{$S9#g#fMBWh_vxHS%QCRWe#S8BrjC= zO4p|`8fehr?L${3N*VaQ_bUUO5teVLLUNoLX}?M~GbP@SbkSsoge;g3*;6#LJ`pGr zTu5J_@?Z(|@}$T-#>u^}u)ha9ers#px6hYep@*>>pX8|d@OU9mVMTRYOl|~l$=rS6 z>9+uQ9~g;E6Qf$iwWt{3$Ge?IJ_YmJ=VcrIy)&qYQC^3C;CD?xN;`ZvH(8LGgYKs} zJXJIv<-I>?-{j#m>$9-#6N9T*3oqN*Fc`)x6Zv=55aWJseonW?`t&g5LKQWIw2Tvze)Mp<+<_L@r0Pb?Epzn{MeT@L`wYk}$wrt*^-(P(%D9wX^FgAiN1g0`+G&qCFJAM`?CN zq3rQvJc^YAEJnzJr3(xp(hGQXskRLa7R8HKd${RjC|N3~1L0P)FSc^4%{{MGoXmEN zuIhpLN<0S%*#KBT{>BXFT4Gpa?(gkz8l1D{HAJpva4K(O~ zfJ9biVH0cq&t~ETHvscmwqnuRd(yi4zJX?i+?5&*b50Q3l{zBds%(m>+mLQ4wg9jg zm@bnAZsna{e3TtaP!V1TL-W~of!yP|Fn(Bf$!^68hb*e57n%kV$^$Tv_09O`&%A#5 zBM*e;B;JUY6z3*@tSS0+)pIm`GHwey;gZ{ZWLzaOU+V6q#>F`Hi&(S}_OsL04$dca zo$=cX_mb>b<$4Ze-5pVSQoSm~!SFIc-^9RDt@w|bdvrLE`822p)802aTUO73Y(ZlS z+6M|K3jI%%>aQXbC==Kn6}JWOd-LC({%NV!*f5x|>`DzoL;CLwRvd{!IZoH7+IA9N z3$C?f4mZXVL|7~4{=n{sr*0kcM4Q6tmFLKHMVHhIk0LDO;5APV-3*jptO4+ud9od( zYAhCo*e>iW&jvrd6A=3tKcXvn5dDw}H>}wo$sYlcKL&bb)`i|RYK7=H^=0HMD`CWc zUgil6zbvmvV9%#m;luQCvNv?>$K%=>ZE>VAn4nO^{y_`&RxHZX;?w_?2^+w8fF~}; zSSbZZhGh|?0ZoeuY6#YNQ~-XTbqe!hl{Blnbbi~tiE)5`sW`p_AWNd|u}+j#a5jqK zqc181@czhnh4mg3CMW{I2>UF?=x`TxT9Z4uj{c*uFSoDdqahtT&e~)BvfrsJcq89c z<{?79P-v$4SJjPMZ1Qzatk`-cb>i(?z6yisuP1z8Afr?Re=m5NaGq@0>Kju90k93% z69GD?s!h!enU#zy2ea$YU7Q(jHYI~*#bm<{)L0fcbEdT5lg4U%{T?tUU7n{e5)uUT zeJ=-?Dw#o4zmzYT@ICbOQWT-yfdh;`@Xg1Y)K- z#y8{)wftI((hmIqr;+oXLJ&XV;>%$gVPC+T=cf5GoAj#(khf{Uu`5j>{KQXb-b#&%}11M!r^8@{`6}?=RJ9^I|y$MsY&BS3r zWT+=)(WshcN_YjpLx}F2)?~XzMkS4bYi5!E;6Xwi*7aXR`Tz84{Bw;dL}GU=qBMyJ zD2?Z*Dll_wXhj`QiT|M8&})2nrFpClOLB6rBZM9V8M85J&A*?K;MXO{soH4s zP`#ZvHqHLGVfml4u!#b6P|o{5Ynvwvk_jbSLbgRMw+qXF?hT$xK(%cbNBTmn?z(rb z{BIWrKF5~OFLdv5L9>=rA@wmXB%zkr22y>P~1xYMcrBtKK7BOAl_ul!z_EsT9 zSp47({aGi^3oBaid52T?8ca7N;LEAA9nPM;1>$}Qb}*N8G^8&WZL_<>-@^=1IfSlW zZwFyXky%{0nZR>2VWBP3GzI{ZQCWwIMuz3B+V9p|MonGBlv^RKqmrORG2@CZn~eon zGW@bqutkV@xC-GWeK=vz*4pQpioC#Wti@k}h~>5H1Zc)Adz7NUCrFr{xZ3xM&1O`rUVE zJjJ35yY6gYe(X_#dXAcZ`~XfsJryA)s2L97NY3{nXU#no`KkXmz)=HJR@+Csqx?56 z_K#opQ0*A-bMRH+5ed`on|5T~W@X<2((=NB5Fg18)6&>y?C`AY`@E!Gvl|1uSrYu` znsih0!&DBqd?~-n_z`~cvmy#)-_6o7)|dJk>ts#;pb-DGD^$J!vAABIf=iB<$iw>Z z&NrTHlJF&-aAU}s4N`8nz_w#TejomTGUtE0-?rDj6d1$Lk(*DEIO2ny;1;meHoK5C zfaQFmPqm3lKNnHvV=l(5vTXm>Exd?H+{)lN^RP*k3D<>H@Y0noG&hk6sFYAPJ>iIj zUk#vRLq!f^EHWCZ9(7Y#X#VXNq48OnV)*b>0xr+GQJ#9Q&;VDpk!ApsYu~~JxMOr~ zrdu~W05?3;MC@pYH$RupL~Ude63okrFCzhsK18Y`pcfygn(OxrePQPH+hPDI#c}N4 zDF*W6dJYWC%vq^UB1;}9)K369JgaWQ?+o;bI&C z%!c_!=Pg-MbTyHULK2+#atGB?-rtw}FEHyLsIcHDlBmj8N&$o`*7{vSxl}&*5-v&~ zvn$iz6{TE=k+wAHL{}IVod_NF8igbyWTdjM{S(YgfCLgUyH!hU29RuRX09kHOMO^`iSrcK1K<_n$(j zC?;zB@jn2uzxw}cnS4ehlT8)`UD6w7xW&WY3aNM(!B3nY^pkhCei@4xBN(84wGde; zj1V;>4tY%u6Odpfl?mKgs+htneW_2rV)vF{B~*yU`@%zHqlIvLglXLrY9cT{j6`;2 zOQT1WPQ?>%O(A{2EW|0Dm&Db(^a&OSuN>!rM zOHS_yr-it}$-Aj#Hk8eKI7STpiWq0qRSy`gDpZ@Yq_72EWb zx_2rQy@)w#5XFY-Ekh=rsf`_nGoOwXa9^ic;mN?B!qaSWg&~x+#~5IiM&lsgym6J5 zEhBK5T>R>y-KvtNI7H(zMb<=&^i#$VB`h)TEq4!GJ#T7_7|g zH=SNVQ+`n*1egQ+2%OQE_0lwn#X38G9_smZ#4uv@60~ z6Q?sINm=IYnmbIt@mD871$7h$kVXVxL0!KqbQ;A`daJ2&wDU^42_pOmzb$6~Xq)XZ zTRk+jz`Q zpKhU}+Td~L2U{l-1AVGyX{3=kXW+e4P)N7@^PT;zx-DNQF=M(F4EJhynCwF+2%>8zco;_$cO8-h3EM3|{iqm8j3D@KRV-xojx0 z|L@;FvAC$Q>VJPk8fb<4I=YLHi^FpZZPI-jax&w1(#+6BO06u!c5jO+i6x-5CXuXNKQv?;;;pF`^xht-Z4%aS{GKqP*$Ty-(n*7u zj^Vo_yU^~u(byu#AC=Au+I|(1T5Ma76X^6l%pbQ-R^D1)WF!^=aIq+yjaMNzRj69Z z#fcQ6AzK`b+&uTxXCI|N`mTd*I&p}H_dOxsjVuzwk!I3RwsgtoUm={pP`6XJ8V#Gc z2;AVHP1)h_u9KwzW#LmH)ZP3CkG*qQs|_Co6ra}x*fe<1{3X3wL7e0}5!X+HU-ywp z?;bYX44;kL-Nn2#91TSvc0+fQD(&E#NK7At$;#YG)gmO_MOkeZ`GBAN2#lI-YCOZgm+a{902pA1(KQBd>A1#*%u948%WIa*j9hEivdwf|7 z1j}8O!_{Y-Sq#dB?Oo`e<>eM+o8Yi@oR}!$rEmb^?h+fFtii1K8%&<$5b@ zO;pNnzm&>BgJL4xWK^b=+@+*JG5{NXK=xNcbsHO|wrYb84g5yKVJW(wRfsXt(B4~m z81{`IpQUg+BT^zxX z3z2Q@)UuP2I(DT+r;hAC!vBlEbuI|yWxsOApzf@n@0dQz95jpVmaRKBZ-=nL>CR_s zQ;p9@G?F$PAo)gdX@6_Yj0!NZ*|=(Ijikv!1QkKpYuI@&fRi0W{kq4#RT*4mbUA71 zHH>h2^D)b#Dx$O;aav9$u|0bzv#;nl)45F1Kl@5cYUWtGWKr@$)&UV_j+xr?X93_-P$ey;jPU1 zeBLHmxNX#XpTI0dTJBJKFW#h^?;45Kew+9DDKn=HwX{Z+0vlRh1ubGz^ROg3K`1*1 zXAyEyg;3?-EU6K>^7@2`or#hMXG0%vzdf`z`R;n^?(5wd{}((Vmk}zP-~CsB@b8rw zs*h@G-(5UDAcreaN-4Zf%KcFLc7=IQgiGx*Oy~(h=2dOq(6{Vxb4zh1(v_OWv(ma# zzU0n)ul}`4&+oFnW~cml;HMRL55w3+J(RVwqs+;l%AYJ)?hYoR;dA`XrzOH1=bGf_ z;!BH`4l!;~VGWfbidh2Vm=1?=keEACeFF(-LMk60uZFGdccFF1mCs*=2=-=$;}YHU zxCFgWuFL_?$_rPA<9tgaZVCb+Te9qF;nS!{L|}-6xAKZFG?Xj|<<+ld3)Kndm(EP# zz@HXvh!BvNb1U=Zg&ot)MkbcD7${RLFr1y4ot&`z%)WfZkfgDskb++jdT3tZ97*_R z+j(^rU4Xk#_fCYX*kQF2qPV0o;Y*2f{ez-DSYmkQC-K(lz%6G)#0nFh0d)a(Ye!}Y zBY%^|c{F=arhzj)W)E#dym`WK<$dK!uOQgVQ2Mvj@~CU!OIFuWYft$r8CcS{MX6RV z#+CaqjX-h75YKNfnphQM5pcb1{W^vKvPa$BiYKvhqqueSCUvi!GSiCsr>+2nQNFbH zkjF1#R_pupMOpXuH~!Rar)uz?FK_t{$U|&g;sYC9#c1}xg>Bu02BpJuH(P(s{1?>m zzb2x%2uAPapE+o{QWd>D{Fk0QQq~nsjVh6fh*7U%g(rO~0z(=oDpuk_s^wB%Zo_(v zC8v`+8V^iE1sgA2u&5HdMz6^VkeZ#B+5~SXb$aE>#|qXdWtyRSnB|9)?yJ+=qa6oV z*@oK_vTNU#y)K*o_@uzWpKpV$V$X-`eP&RAEDRhsIpxsrj_DKR#+xPwHV} zz~e=fb+a0M`K>hJIUBVv4uUWYEc1RSKMxD9bnZ$2WmD`h*91Srn~wZ7TA!#(Og@yw zZsp;to;h_cL3k4iG@u?J-?WIhJfNB)N|$0ncTJww4d70+*P5$Na8R@_cISrLsso+W z`c+dde2LUY13A|<{bQ+VA8i>UC=HdP<@k(@HS)edDz_b#_U@S`b-vd6ygRi25uf0w zsjY9jYT%c3RWvXjon!k!kCX*RDfQSqMxt!~nPw%>3-OaNyPqascHW#WgG?8*#foQ( zR>{e}PE4)sLQ{!#{$NW+U8-q?31iiDoX9*R$nB&h$1-eK<8m=pkEn^1*1w4bWzYCT zhfKh4ML2Sn2a&1Eweu4geFs$|wxLE(i!rZ?Cz{5QyDLSRqjk*tes_h=_(#N}&sE9# zj5!CZ-D3kpyt-|_Eu5DI|vj)}N93K3z2^!&Quf8rA+Ramu-` z%egm>xC@R&0YI<20XAFT$Azw_YsK+*>^=4A1XP$zD{zlyLcA%C(l8#^A3^A))QE2? z_qItWU@TQBHD7T(=GN>{4ONZ@T6cctc9n-;0Chgvt5ev;d@9ABjq>8yY;%JZ#O^I^k=U+W@c??52lo1^u!}S$ZV|#V_cE$8gUZH>!-i} zLMF(ZU1*n)Twy-%(1ZxDuT~hcoGtKTF>&D%2RsOIclM_mD==byq<6K3TSH~$v3R8K z*F%B~dN3|-#Eq1msX<4*@yRrpBi(6Vxvy57$Z1~vJrR7W%lgI$&#W}Mr@lbP`i`9> zU#IT-CRclhh$nAWB)IY?xksJusd;yWE_>DxFWf-=;~7E6F)rt1?v^Y6hmKN4cV!C}Ql@+Yr;)@DfY*L&xps z)7$6Rv_$!(;H$I^>bdgdp0*td&_-~oD$up%;v}rXoquF(vnMXU*?Az(mvQN1aWn5 zD-ag!k5ERB5XHjm>RP_JfWt;$vXZp*BSj8&S{bOq9(;Z)UWHT)IOqpbScV~FQx-8< zd1@W;y84Az8dValSad@bd?6FQ_v;#0-1Tq1SUw8?gn*MM)@85B@7!N`^+#qb6xcf8 zw8}~qmO2eBEZO;vFhEly4_Sg$X8FEyh0jzRp+FDC& z`&fOVw=+Xs3>U~VEe~#etT_UJRo2wiXIrGNw(F^|J-R=n$%QIsZP6!ST|NYm&a5y3 zQo}U_k7DTN`ea*I-zQ^H!NrJ0zMqdsrVdN9?LF5v+}8W-$%U|R1%Un_n?&LPo3K{5 zM~20}VS6jWD^g0QN+q35I7=L zW9w&DlvCdRFLot@w~Kael61YB@lJZxiB<2(RjAvEwRegC<0G=*~Z5IpF;aw$GM(Z^jbo zN{b%1v-@rv6hl=I-WFjFVng@#&r~lbMZO+;Mn>@djU5%n+G0sPu;=B9K_D1t6*>_GbxEV1`y;_^waacPx z&5e6fWo5VHOKb}CN%8MF-?>k_EkYu@-8d~Jxz}yNf!9||fAgOFjs};zaGTw=optfc zljt9i&l@Tg`r)<5>UXpEw?|o(gQM@X-8NJ#>T5o!rwP|qh4BvvkFcV;3qeNPj+>ig zGq!7gFut-&&Ixh57edd<^;>x&-ccN?8Ovbb7$C0yz%5E3){TS^RvqC+6{tpqy)lzH zQ-)(*Gc+&yebtg?DT~EapNf+CnoF(Vm~NIVh}NC*P*|Nt#ZL0ZtELqTsceT9Im$aI z+DHFNx3G?4PT64sA;h}jixRF4gl3uD$PHE**Dj;a^fVM=PH#4DKruX2z>kiwZLz^v z&hRaD1EU_H@>EG5O&;bNbUKCv5rjoewN=tYy-pTlFyqEvBzhLyKJf z^y|2vzT`XcV3gjMiew;aGELq;&+hsL$B5=1o^RgtCP=RnCvkU zp{_?zbw@Sedt`dVbd@a6ZM+!NdVqlKJi zkM3310L<~p!8T>f-ovl7Xk9Witke@j<e%OI|S^cOPXh7Bb{etsC;kyck13Zjm?DNQi`G5Ff__n^PIde2@sM$GxkGstHPI=o+ajb zYQ2A}tVH`1{U$f|()NX{e`T0URDqjXjOa$UX$~S`P#F}GlU#xN6t5x{U|$4k4T+L zj&CO@d}(#7oi*a(F@U z_)S9|d8{WLqpFNzGWyWUT+_)yEO1*@=3?M=%5LKCyt%{}Pr|-AIY>oivPIPdCa6+w zJE7oLBxsbER8g0AW8jLoMt{0R>P6wA_=!#j6+~Iqo|tDpARp~kgaR8#zzG)8W@_NY zPCdiw{KGRd5A4RFu(o3%KC$={ZtWTtFc~M}(Y#9X$LRKNH3cn7^nRT#m~ol-;yP8f zHj|CgiI_)d<<^!iTb$n_R_W;Nn6tWDHG%@Oc33~3qI=f-HQ@UMUZA^sE-J&IZq%ZU z*bgLDeGTgjD;w$F)4tU)7WlxN@>b$KYT0->`f!!^`WZEtU9-pWY&N+t?R0|~4Ga)@ zc=QI}G3W%=cO0J#%Sgb<9WpnVL3w57Lnc}_CrmwCVYVs)(^*jQ5L9d75c;IhWa8tk zY9H^*wAxAx5Sw0<0kG>g;2id{l%nee@SwgOBBi)W9D@Z>4UxMkjTuM8=O~@dbsR>s z8(oEOXIN3Qlir4yIPnZ>tn4)dAI6`}J0n`@mW!Sf9;MFTA@$2!OhlZRo!#8ifB(x8 zXcGx7E?}}0BQA!*`A@p$ri~b-Kb8=%>Dn9mCjW)l0)3BA|6mpFP(ckbzk2HPMygkV z*BAx#qjhUYRaLfK_FWKLnmLV0?G^3OcTQa_mC|vs&=#~e`n+@q9GGn08gR+o@DRWq( z-bSDRL5(p?9Vu6XW-(mdk&0@^`cSuPw4a|bqvTmC8VRS0?LZZccynBpCNmy9v^%L~Bmc-Sfv zl(Ss$l6J7DyzFt-K+*gH6mXPX<4$VFo$@Yb|Mn9DIqIYC1Z=5{E`?CA^AuA`wbg0z zVc$!aCH-1Mi)V~f$~(>FBNx9`#!{qzKxx>4A5UvZOZN^8i!XwPJ~0@VCJ9LEjyyN4 zKxYypgaC8n!XAnViZtrn^?kMP+)`@m<$%3Em-URd`Qpq?AwF_#ha^;!4o{SMD@=pz z4@i6{bnv?2P<6TWqc>e-tffM}cj*iwo{qThUeqBQp%sTwfrwHw49c<1LaEn_i4M{p zm01OrOOIHQCh3BcWR&ej@?qseRs{$gq)8L=Mq$qI*-xi}f0S`NNIRc=$~uwB9&irz zKjrP>M%+s8Qh3eMi1AJD&^S@UT`?uqgu*_n-h3@`-2LREeE%}pi$!b#k7f54hi82N z_%P#OF-@+rI9sOG^4L|()4FcmZ(vmcY{6pfdl`v|Z!^_k9vg@m&=bdTx-B0)Gw!VU z>;asUVR9>|9)enpr|009JF9#!Bvl(7p5xx;AF$&sg!^AQ7 zuPYoo;p@(;m?soutt%G1psEC8s$ubX3v}th!^ryPy3t*8uj=i^+dCe%ja>`!fV0c; z8ScCyFAT`&g&kod@i)dXT849d?t{z{VV`2@{JB--8SE=`o(=jHjGl?~8HR}ah^%@? ztW5>C7<-ZivNHT9W*cGpX@sG=G4**LCD(5c5GyjpEiHWA1YL}b6!ncB)ojWW7dc>nF;NrkaF!}d@ORnThT zi_zlGk7t0Jc0G3CcABenOgoHE1ZtXfi+iP)2AVk^P?zTS;y(H}Dm{z~;ukFk6x0e$ zE*n#?;&0ji&KV5~(Wl=}wSC)gVwC#V>-iVHLLBnzPWS&p*L4l*{*3yp)H5lUL;~6i zL5}Q?yf|lkAADrs(WxbXKtWPr6MW_Ld($^~GK#l-)P$5HzurV61)0Off1Tc(X_p}` zCCr&f=D~Y?1VR~BG;*IRughqwV0`xEfNcmII^p_vPqYz!17rD7Sed z+7%@A2Z8wZOe!UKQ}9)S+F=7$fuiPIln)?#Oa~(Zzu;TatP*uvCu>32@|TbYl0%D< zMifS!TRuj!k0v6w9W>F?%*FCirdd|d^V95QuSu`{uzy!m^g{_@yJs=cxWqz4JY{D7 zN81;A5d2VpeF!-;YYUkL?hRHBB^^BDK}{_+a8ABgslpbA|JpTlBb8$LVs@!-NBRrGkUjE zg7_JL%b98w9Da`=yr%!vT(r=00C&7e@HS zT{#oNv1M;N1@tj042QQNc9FHc<;yQ#%i9-DaZV4fojsm3r<&5~=TN7kMbXbsCX<0DLa=m2+?|5#O>{RZ8MFb4BfkB0-kb*t2OJ zRryoE`~V+uyY&jLovOSlr`5FZ^&E>td7B_udbNWoOdS}U*olBpg0gf&9@ZC$V@9`S zx!)fMkM^J$UMF4V%Mo`?=&|e(+nI6qTWl`fY6hvv`&8F8+Z7cWQW>?)FxYrLjLpqc z-*kBp#TKZ@wtOZ=C$1K#xpv;L{Nv#Ag@dqUoK1BB_qa;@vd3JjoSbZk?k<# z%6ZhW{p##VaXC+9TYq%a>zKh}Zlq3-{{ul|RV%vIG3-;hpMKm&@8NfM0&T|>rv{H& ztoDSgGv>h-8i={q(Dphox*BMt}!0r=RF@hpFCmVh8WeZd(0-jypGdmjy>~KsMb&U z59%_e!d=5uWfWSE? zDUR;3f-8l2jMF5#KTNi&S znh>-qR(dK?95Y_zDt=INJVo|Y$VknD4=+iUD9Bla)H<{3Gq)!?wG}*>=Xml=kT-p< z&^s47J+fOf9`}{r6Ty>pT-5L%@S12UdlSJ8(GB`(*1XgFn#$ntN@k3Dm^GFxrj-mB zu^;perq5YnGT4uPzL)RfMe@qf|1!&7&9_swLBej~W=Uwum!a!Kw{qV?Nv_;oPz?jp z>d3+~+24Bap^@Xtmtx-3k*q)KQ9XwBTVvm;;>=vBoSej`uTqFzwUplEmYf4r9PNqZ8^)96LtyomTXJ{SxpfbT7WaOG52`+ezK*a~Dp#j|Q` zY8y7~=9b>xg3c!*dwv%pTebqEzqg$3qRdZuw{Gl#)rKHvEN!RPEoYB(YBV#GXX7Km zy*;nPVPPmmI~nmRnV7klugZIet3F+MQRL2eq7a5i$ws`N-yz8Q_6E=*Y8@dUJaSea zJys&mTyK{b;BqqHG3!pWaIN$uej(0jg}x6*=|gQ?%?nQ@#I!7o)aNoD*Gww391(cP zUVt{)ooq9QuwI8c7@VN0`jv#GYMqXI?z;6WONmL(E2HTqPk>y){7~Z)Eg4>2)9HbC zEoUpjz4dmxWyrfNW(4nNRvl8f=&Qk|pQ5+6=<$}#zS#sHvjs9|Isl1pB5MRG5A{OH@p%e2Y=0VWE-mc;mGa@ToA%Kh-g~(Am$ewhJ6R@a$$;t-(#JDkz(v& zC8$$pYSPMmD82jfLO5OeV)ZgTJ6VJS*m195OPmI^`A`cK{(KPbKRNVJo#-Bh@l_$A zN_lGh{M)(nQq8%Ko0Udk z=)9Adt#h(XEZ;SjRAU*jZ#9JMKCK)IS8wSs&`WudNQ8Hj>_^!}nxmv-O#Ji`ovs#0 z6srmPbI!P{#~CC>0noievOpR=kD*;AdHYYx74 zrbcw~vWPXM^1L6Xo!)4uD@(c{JhA#haY6wuOsumbiDDn^ofSJBC6J?j8xA3sS%|Y& zE!bizjz>UlE(_N{B~kp!6ZyRwM%Q}COsexGdIz*2*=Bi<+^Q1Xt%HxkS60J+r*%J4cfvip;9GU)qB0j;BX^p6lz?&kSW7PTF=e9u z9hk8OWjxQ-7qiM`=Tlol&PoQhz7UIg?EYx_i>qQqq#pT*#{E;JLTcv6NCulX`iAt1 z)GDRxNuNHv)8u~?E@3&x*Fv#irKBend0B6z9)t;*0u0d7KtHOsv-i2oIVKC#m;J#&i%X)Vtb*J+91QMVg z^)RcY$SZnWIqeGAjU4atdvNi2NQv}b@;GV{Cpp}ax6|C2Fj|pmM7Z18x32^S`JI`* zspCTBM`;BOBfE&PXVD>x-(}1DuHVFFY5XJ3TqoRi$qoX5==&cqff-kz#l2S&{c^|7 zi@xb1ZN*;cTCWjX$0XSI0UcK!o!Dj=O!{(S@bd7C)2ncCCc_hw%(3! zbENSQpNx4BKXlDT5ST?SyBlw`@;7%07*Xb81!~7UD5jqS6EY6e4`Xn*MpseaI{8IVLI3Bek)J8W!RaHB}e%jIw+drZjS;+Q#W9nUSh|JCw#qBeP_X z_)Y8Tou)5PIfbeDOPd3ps?mGxZ@Q*Wp_;c-t8rvMl6rsV`rRmF0USqC8IiRvq5q67`7|r(q|JGFltVrUwz*&kOnEte0wW%{VFeRUq9!um z_PT^#G?RT|qVw*4#awTJ2#4im z7ke!At5FJ}cMzZ2!~~HUA5And@H`+gO=GIp-rn<(yhh9@C+0YU2{>tt;^>*lv#X7S zO6by#=W$r8m+w&uhtD9|n}VJFyes;^d*?=Xp8WRYRMlqXCadaZkRn)Cr2Ghx5t92; zKXIs3O9V+KUWq+=17}@ZvX-xizgER8pR(^AlW)A!F~;dgMin?LfvmS7`rP?KZ`8iu zU~3u4Ie4fsK8oX#K<%ueI%5{j6s17Z_50UtAODH7F}J24A+j|rpLAbh(He>AIk2*ub@FqM#fIwiOcrB6=YIi{L0N+xjr zK^SyZit6ddsl3+hu#WP$e4d+&4ppSY5X55FY$+%vOg&03RG4v*Ty4St74)4>jE8t7-> zg4N%KnuBwE$iW}T<@=;E6Lt4XA3V__raXc=Q@lu1iHfmN2F%X=Y}bc`uf+;=s=QgoMML zkeI76sFR8)&`OK@%n9T2`P#xvT0T8DTwm1-7imnHK0TwNPmXXJk{ENzx_WKY=~(%4 zaJ5%@g&8)RT=||LW&~DAQrg7GyPmIUIUaMYGhMBfdF@%7ca9S&pwW7iJ0vZ9Xc?g* z3^DuaO5U&Aan82mM*rEM>at_6WmF`RRW`rxT1T10A$4TTYHQ1AH|n6x(_4Me_7Ca% z=G`TEr}mJ6pMObd9KGpQBhadYD}-plTZ^n!*N zD74EfFCa2HEm7VR`i)C8z@cT@bPan)8Wp>z5rMz0&d?VtsG?yHeB*D)CDHpTwO_3W zJwGIq%lR~F&oxvb`IW1WD?!-R@W-USY@cUZSRC{{XDF3k2ES2n?%|7Ap|J;kk0lkJ z8iX(bZjg#uze>h#Ym|P}*k|RT)KLs}BlK$mFM{smYf19nG#}-KEca@GQkQy}pk@6* z_$s`Lhn=DOw*NQ!$}h>e^!J~r#m6C|(i6T{#c6vp?Rf`Iv7U_83mF=^&C{nj8xby! zJn-K`DLQ7p$3I7TGpQM8rSc@3XiOqXSawD))S zynrtpwC`7@yoFm2cl_K+tHUc39BQHkhGx328h$jb$gxNG=V7&xu}_7 zRz-4c-ahONybskQA@7QdB+y`>h^Xr zLd%EhcUm2^MgHx{r}b}c8(94Li-_IQp5>;f-z18Za4k#PwascX&F*jsf!~SzMUX-8 zkU_DCu8+9sD2Mj?ygqCwX@3X@DXy~gsCM$Re;|O!NCGx@-(!qTTEH`bFxKbE8-wWMK{K4_eea(zy0-8@A`9K$}@3eYykATd#kW zQweSCfV8eRa3gi7ZG&TvszQrqXZEn? z{f+Sge1G@^yfPzl<9y&AUp*5}pU%5PRr%GIrY(kFu~$TWw`G839wr!?S&KvlaB~dGzvbl2esw#|Yc8Ac1^8ga^aVqkobs@b>RMc-VI}4NVC2*R z-RJu*(TTOC zS`>p%TEgo3#K*-v8$E1&Ra3VPZ&v$(Io=_)%!V++=^EkP3IPZF+>6(q>AuX4+uuZk zopvGX%khThnrd$m2AQ)TpiYsh%QGmkMp1ojh$Y7wiRf=v!LdoV1d+PmBHk`(Par>0 zcb~Y@rjR!xLOcJeo+&0BP>qF)stT`Q>JcZV&q23^)q5<@ZwAoext4)$50n=STKb7V zG+}k^l)6rUg~&Um5-OQs&4N6_;W|4rO|FBr%o)fOdyWVF95?k3xdxsL{?%;#)na58 z#e$E-mXRA+HAONL9tjfqAP@(<{-a~9z~PiiYy8Wfh)l=^Lf$_BGARAvJ$ZRCbvIwR zOnY(aC`}&Wc9TQSRPq^2LU5r?up5du4y)?pe>zS|H79Xy?kC;{VWG*df;!%-*wnqO zbMfg=@Xf}_huk_pbQo4XJV7HY*^c{QQcR$=o{V|W)xdis)^f3`qIS-`^GiZR_S+a_(n;O@?9)yGfsVX+c^b+NF)m>gCD<<|#mHN4qb<9{7E_Nhvi=wxNlu zl2p-n^kQcHITjh+Mgj7tLl*{&Y=lGVVqHv4P$ZpXkIu7kUd>X!Oa_Ugd7L`A@B!{r z?KZ^)aCvY!Y;5|?ZtP5gGof~vr;5FaTD1iRvjg}7Y?c;n!vxmLDlWxiT1)s?{4=9J zhuQc1k9JWB?2jt;SM%Vp_J?xa-aUrb?Y|a&{#eOJC*@@w2}s+mO_^x*RY6(CdjC9@ zXT$%<8aYUHSDE6c$YLZ{D5_4vJJ*O|ZxVCeS$W`$%a@EyPMkusq0-l4B^n=R?Xx|* zrHfj@8C5E&s!DcVE~FXYX6WL)lObPenp$rX>XatDpXl>6gFO(3hc6XE7U0Q8;J?)K z#T#e0M7fIa%X`jSEsJ=(uRJ-lQTX%xt|+O)e?I=-bCJHmi`WQBbe9n zXgm*2Ch8Mr2p?-+A%qS$?s+6?SfKIKmSzf~56{#Od$t)khj7H2D~c(HPexl*C%t%? zs+}L@=?Ve#6Pxs7rrm5vX17I|Af(6~NrBSp;L=kH-`x`R$88cXb_sw5B%IaZBb(Q% zw0&~_lZ1R!WLZXU>7afx-U&*L)%SB6QchoYLElz|1NT^rj2W1%aI%?uooY(#FB zAY#V2WUten$Rcy!znX(;Tz_qkBd3QKq0O*$8?`bEj&G1>ED>)SmUiIyT8_zPp-Q3i zkMcn3%mlM%1mh|{2U0)z`pqCLhF;xrfM;2^M}fY-J_GN6ie5cJ)&0_=y^DRfwScVa zG{vvrl3?Bb8YytN0XEhTrE*8R;tpX@xEvv9RGsA97Qj?^n$DUr6wTpA-sG3?O8fb& z%<624@r2PE20|Gez8(J`Wt}Vz33;sBin!Kf-#0O0oe+Z6JAtbc2n&LUcJI%-s5UpZ32(^`iw*3QFk{k}xI_^mIBca+5yrN+{I#wc zd$GdiE6L_o{|uA=|5<;H0uUytZvxjG|A)Hq@5lgxxk(&YNS{t<2?U0Tc_|HiOONOs zvW3+}Sgk97UiI2U|EOS>p3qB5d@mPh0H~q8^VYliiCyEGp7y$jwU2HY zRi|*wn6aLjS*+!^g4TG4gneKi4YDJgsB#R+aqswkuN;C^BhZfwDDymaVin#u|dOioYSj9K(fOSrpJ||El0?#58M0C)0yqsi0uTASX_vfQY1>N zM9`2m9ES)};ax^x#AaQSOw<51Z=TKXbhXelGpLvLiN8tBMH%YglM=%vaMzr5OWway zQ6&1I2U4U0H$so^i(@+Px^9ypqExR>>HANc#ldqO+1iiB@P-wUaUXWZA1=ro+f=63 z0_W~wf)BuW>$!;IWpS&$Yzl5&?s5(y$kEJq-3StuFwqZwQ5tWx zqrke_5hq)cud$AhKg!qL{Xz8*r=s?9Ok5z>*YyKC%1KV-m}go~Q%N$w5J@ai!|9ZxfU76=!*Wlvi%} zLCU@7>^BFl8Cxb?kJP3D;H&aOlJIl?bB_fggc9RRBm~V>yccc{qN|wT6Knr;{*9cw!ggt1rM2y^Vv87gKY>7x_s55fbs2TNWIF)xbYmzlC)j+y&+wa!O%14zmS8|f zj3+4O<7Lp$F;K4IO#`uAcqIwmVw-m)seNc?`3lB4wsSaQV{MEcD~1XpN54E}Y@U}Xp{%dLFIcu$g%MuQhZR&{ z7Lzmdr3JnqTA#WShiMq$Gr7;!AIm2eqlThTe~(aayXcLg+`?cP3LGVA?bKQ`Id5v& zzN;eAT1J+?=u*KJ@Ssy3eE}M?G(D6pEISexo*3p2Zmd=)*iF=vG=H8~qiXtmx#Q*7h)?i;XD3 z1zTL-x)xS39$FVx>#QaEUvevd2TZ_AQlzl-7)vY@O;^I*^HK6S>*!tUjOIoxW*%ZY zsnJx0xLAcpvhQxN-Q$#}J=npopq}hJyIsn;{rpVUUuBGagZV|Lhe9sPC8t;QQTD6} zbCWPluF{Jk}GaS2AxTv$1M?|fiboby&2Xnn{DO89CM6YgpeD<*uX{F$S# z%GUr)i0io<;m=rG%rGdfDPfVcr-@VYBUeG5Y>#;~@+Fl_*<>Kz#-1JE73K!{hMEwXT!VMf`zD#vm!!Qr$*It@!;fHYZ zmM`VDW)PfM8PWPo=&@?_5)M~Mk-HMfi|Z7Wy2`vkYUO`4-YdaY<2nA)!g~KLYVDTt zWyM_2GvVyEg#$;{QsKYu8e(pg0_O4KB9@!9I-18_LlsJiJ4ndN&D%EVt{U+6$U?1I zU~WQHu;sp;7A@tt+P%7>K3&67w#bxYD-n9uDw}F4QC>JXS*67E!DD(Vm?wBuZvCZT z)1KD|1|*KtK0%@a*S_#!IVm3%z2h4QqiU_P(=k(h!cTokkK18Vd!zD(er}9hyu@&S z@w1EW)VJ0KzHq+Z(9>1y*kC96R!$MgSgm_w@2x8k(&O8Oo%Wz^VzY^2VAu~*os>{h zA#8zq0NddssLV*ar}%d$2a=CB$+}p5(c>TRY*$x)^c$BU=SrErBStx8}Vp$b)T<6m_JE&e|}8*xjx+Yj!cVz1zVT)<3=jGre)bDX-obM_FnWx-PMws z=+b40PN{Dwr!0NCSvv$B6U>$72fb@N{dHC zt_1q2Og)OG>AN9(gOx$~Z2aN!d=c@C6%5m+Pdzoi5ZG{ka8_~^0$3H!rg3TE0prt# z%PeJ;ZC?w`b2#%|`h;I&FZo8OwQxR89hA>=iUj&~XYyy4rmZjpv?g1VRz{YPT1e)1 z@GSI_XYp)CDPt{NjCQ(r&XfuX;ldCoTGbXY6n}B;rZ|QD6pmtnM0? zltdV%6}%&3zZyU)GNXD5?EmpV<14X}2ho2-sQKrJC3{Ieg4H zx0xUmrkfTsCYlPDA~Q+KBJm>w>E*^PI)pJbhV_B+BGD~nK{l&E7Sl~A^^^kgS!XxYcd2Ukgxr@85(Xfm{t>YO7w4(uxI zLyB{>^NBaJXf?m&eZ*I*)xfiC5`3A-c#oQN8Der+`-(D3z4=Oc`tq@d~ z)|#WSa!@4Zo!H3EHaf^U3Rk64EY~OQhIQwK)_7?1mRW;_4-*zO#(8gO=L^|B1M2Ur zN6&NBw&huP6hwX2C^!}|$u}tZM<9Ho+SQyXTY#_zy|o3`K@59F$O--sg*V zBAhT)9|l#FeI^+4IYt+HgEF2ix<225aEY7I{HXNbT?hV?0Moi&O(0nufBnv4#`m=< zT&pwpx)Y8))Axc)i7+2(;WIkK>Zj5x93+GgI4-Y-A42I=W-P3LXL2BizarwD?PU|1 zi%^h39pD{S?t6HY(Bqt)m6xXd-A)#aHOeUKn_i%*mMImVOjunS4p`;NVu`p$g#B86747{q1^2^LuHGNFnSqZ%{txaL{E60R@T4SBu7!T*wa6A6Q&~A49)jy-H z8=YzcF=Ks__wtXS3qCTE58=oEE0H5n*GPDDD`NsTaBtz{wa*5$Hd`uP&vX@ptmSm0 z%7}%qPUfNx1$d{KZ?kV1hjeuz4f;&iZrHi=l13=(+g*&uBLIV4mJyh7fj_v+%9x@9 z7^^SBbZ5aF`h*6x6}=@`3MonR5pvCIfX9I5jv#q-6$)4)SldOwbVCi#(5{JzK}?bX zQI9jC6BJ~noFm!e$(AkH;Ma1w5w98Yf;20}M&c2jNq;(f^o|9oqHkAH4rZ=dF064f z-iN)b4n$8Oi4*EZ7Y}(vWG`)Nn$RnX(mrgAh`{U$!+^&NN0X+3Iy&LUvtKgo_iyaB z7hEASQbZ2eA1$5tvQH&~$yAvV7yY+XOhliusGT*S8W zCOP)ug{hs(QP|DQTj20_@#U-iNnf@7WR<-B(x*Xlf(ufWJ{tFF2@XmTVT{~o5Yc+| zzImJnj)Ja-<9olKDk-9!Oo*iq>lGj;4B_M^vzBMrrrD>JFtvgKFxjyKBAkQ!+#S>5 zECpYiOJ>w{;vr4v@v)r>k^51^Ba2lUfUkVAu`gX5JlvfP^?8+UeE z#t@7M)#<9Q3=r)LUD~;$dj+49hBY@OuYwb)PySJ6(HeT3mwMc+%?yc#VGRp5G#o@u z!!;0r8A1u*JnK=yX=Ni6h7Mx@I2Y>LYHvmL4mH3;x1fZ&PR*wA(0DvE_5o9uJCm)Q zV*5dlv5Dr2IpSKFW0#7IR0cOPi{#5s#Aa^7J!?u1%nsl|<$6 zMoK+%#-9)Yb%;OHcrVa$JIlV1n8^spN5ne=aprNAy+EYWPt+U=q1Y4&C8XF>$Z=;OP>g2kJOs~$d**M0<_M6JtW=cB$*2)18tn;I)fIsj+dh{Pm{{q(k0T&|PUu}IYnH=hit`O(U zOk^?Qt2L+uwgCkT+h?YqC7-5mrQ>c0SZ61UpN9Jz4;!RhQ{fPED0)ImU}tzrE0D6Y z0q-IDM?+)dbn^z?Vfv-MKkA|5^4sONf*WM#|5yd7*kho-oe zZBpacYj7g14%V$lW|D&j2vTh{fjjUqt;gw$CB0Wz3rIGV?jrJA>JPyL?_LOga<~|Z z&hyjbOk1G1z}cUdZ8tG6X4@Dz-Vyf_GNwy!x|I~;5H>XAVTB^u>ZE@Um%EpaO0}2 z73Mg*)d!XL<{Xos#Kd5urU!v| zCoRY;O;}4fPf7Ms@w}k6;Ip=Y#nktC%*-x!1fDO^0M09p;Ab6pQ3nVeP)7`A0)i!; z%%kBgAbDI)52F=r>N}woiONI)X?@sUn|VV=AjUMS83oS(h|Q%s zxRJDYjf^}9{^Dym@za;5ELfyuD7q|m>g};k4afN*@)XFCuLZd(EuMdEu3aw1hQ_*O zFjw~d{25sj-xg(Mm_0MM$399 z?|t^c`s^akA;+i8ab=fNIzM{sf}+INjN~zk$T{)%r|4t)_I_s|wA9cOsV(cAswYDmTlch6i3kf5DDVO!Ldot2@BUaMA^+lg@Yp zTi?>hr`f$*^j7sMXV~GMz+K>ho}tGl?#5Sd*;1ZX*orO;b4RwY= zannt*TMAS;vZ(uX;j4>fZ|kRq8p)L~NaQBM5Cf$ zwtS;K#5aMv$a?0S7!n32^=exi%I-1SWQJOdS`*4x^V_mEC0Jiugz-FWR$c0&3bBi} zpdZ`TA|Y~j7}ShYOa*VJ-0IB1+KGV}5ms;V&E4&sfn@V%4X}qK*5Zi)_gWbC{qT?{ z6s63iIqP#`r;63Ix?ZJhq@RvmC6xWd&c9_1|6rY7(k_F8&aL`wHc8&|g;!TnAHOL3 zD1m23_Bi>smQjWAg%dpG3v(``)omL#NO~Q7r|K~EfKzK;J&u%DOU-JLPj)d`I;LWj zqK}3n=|dtZ2drb2Hft|H3nkISKU4|}5n?)e$t*n)vEo)?IiEJEjwyCB_(3j8pV9e6 z7uSdVHFVo`uh-Q@0;uq6cCs1p;uC%=I9Iu*Zw8TUQ^v+-Mf@0(h;(mF_61MZbXiUO zxO$jinGRF>^J!_!ILZyEuSf$Tm0xU8ZF&aiHkK8`d=+JZtggNyt zswN!{?vkZL6-sdC0dVVMsWInA_c%OX%8>=mOTQCpln(Qbx zDlIB^u+OLvXs7O9tcr3&aB6Qdnm_76nyRoj+4GHJ`9F0!9n%3Fu9X#;HA$y<-F>P$ z-$pcp=16f99<;lzoCgP8Bu*?0%jaQy&^OZ#iNOLwSnN{94N^g>fy$qmISbJ1LOb^l zKS&`fwmrnnOlXMK>KANk)&Ekymy4^zpq_v5Zko$n{%jWI-Kn<&Gco|}d`nEXo1(VI zTqqD(>EMlzlm9wPY04tkSfaln^i<|GXe%VOM`EjPl3|a!z6CT&%Wym`Jxz+R1)&H1 z5?TB0;8A`ep{hsm4UgpI9)6;WY)j^`%#(mn-Cx($AB_&$P&dm5Vxp$bpDjB#@TxLL zN?oIu+t>8zJiyalG3kVxJ(tar)?r*Z#YLUs*{kD&PRUwl2SzMZh3!!LJ-~hnH<@b~ z8ftOE%afIozC&5rr#zDXzLR$OUVB6f?wC4c<^J9O?k2uS zNRYNN$Q$a>=EtgDT%Th|SJ-45@wCpF9N|zHUqd?RPF@BZ%V3bf`=i#_XaWDh&h?W; z2`yEViu?6<-|$PH%6MBtH_!3a?M*cfqfe^P{CstXN!N(=LEH1Z(#&11Yf>Ijki2fD z9%xfGxk*~LG8GDyUtDnWYfIVUcidK!B~Nx0)YR|I8q9>9+OM1A*GH&dZ8SpbHChVC z3-7!;G>->eh9m0qb_Gbq*BkkU%+COZx^2P=77p0AgwX~-IgvXZZQDwuPt5Dp!D3(k zP>f$si$e&@B!;lRb%pe-obIMrJ+c+NL#CPXT28MM>L}-9G4qZ1+3p7r14f-TNs{l~ zRs1ro`#OK_@g?HdS=%E=fmdyt@Mk2cO&Ocp`_+e`H&!C(WaS5ym+jnaL6SUm9E#K2 z<1Lzn4IOLA*wg6412}=~+v>QNeixtn38FloUk^rHr(u4Oy4Y3I$Iz!!b~qH-{`uAs z1I86G*P!rj%>Bn$@&sb(EPGg4{n)+Mr3+>!IDSQ2X;YGzJ+tk#W7{%2&02QMmyHjz zIS~8{&=24NG<#iDhP?2M`X=3o5j(p#NY`g}D)-0sGCEaG73IH(asQZ%pH$joA9do zJyj>>|Di;3Kxd^4`DCDh$2H z643}BEEuz4Z8iZtmWyn$`U?H_7XyafaPo-L!x@y)nf@1!7XA9@^t1bw>Ml~V7+Moo zY$SnaX@{U+G47* zaLxeaybs_!2+36SxgSlic{)z0z1Vn9=~n5b-!<7}&XS>Eunvj6H2dafg3iPf>Lll> zY%ltO3w(c+@12Dhcnw#v#T@7Lv&aNjwE8SkGFHqxh(0_ ze(|>mELyLS$@#)1oM~^T^_Y$a`4wM!CyN)J>-)v`)$+o#FWLJ52Wq1y^sK|y8K~)A zGkSej4yjw4pKxswDJ&LF@TewXFPv}6W2#w5b2SsqDB@a}8Hlzvc=2T#_IfGF-!(-$ zvnLS?3O75B?`~E-&|qJk$);HQnpa+NonZ*DZfFrrl=&PtNu))*tusHP$fyjB`U~lyky^|HgL>2~fP9 zw;?>8Cm!OnqBmVJ%~p`$!Kj=zO?cd0O(ceT#1e@bS^|cp6E!g%q}2nc zqIjK##baNZGDN%&#PvQwe&LBmsLzhnwRCpYgEk~>qZH&_5Tkg6^ZndJ&==+>V8rW! z>#iWI+DM(lF8}KMITiH<4hX;FmQE8G_PczK=@TKLwEd*IhbJp@Yx5#?PgUi2+>T7D zUU&U7Y1%~D9RTR$U2wCxkHh0gT~O0n+nIsS%g<5s&<_1SzY|@m7*=wu6OH=^gf1Ar zS!Gz<%G?L&3OY~rYm~D0iLi`KpH53VsC}V}@D1^W%Ol?}E0mteeM#zMKg|DT&w645 zFj;@o?^d>OVxa0m_}wk(MlIF>y3K^@e&mQvS@3(A^W7H%gm^VNUyE_TD;m>Ter1P# zl3p@U)sfaKEN2ZLx0`*}LcQ4IVpLy~OJrPIUh~)57ZvoQq48=JU3LG`0ssL%Z+`-Z zHPoA=hBYwp1P{4RB@rv-DlpMe-6QuILapuedrc+P-$^Ajw5t{G)Zi$x%=#5Gk*0I7 z3Z7>Y;i03Iwn$b&6F=Ffba7G69DDr}u``bPg9gR%Y}Ik^|3}rgULx(TI+cV7V+v+0 zSKSnO6nao6}4Nd2PZynt1r6vaP@xmCCDfz#4_Xv6_ir$ z_oi>W0))j9r(*cbc2-QP^95bua>(Y-YYJR7F#HR;~ z(Zcl8@KkC>$%4<{pE1<*+jk|(9kePi6Q<6EeF50&CPH(4zv+PjB50z-DLEOR`3`Z> zw?`1z6p)o3VH1Npce12A1H@2R69V@RTxBGJGEi!?vfygTP92kPj8!B%sZ^upbgtyl zI!E0#@3*95KdOWfelI1&rK|K43>vqGZ7~R26}A+kVu5a?2?&kwh|a(~c>JW(gy7A_ zUcA^(RGufkZK6OrddFr`pCh8~unIdl%q zi_ch^u+NSsZ4vO`X-PN~9$8*x9Gk9g&w_(>rwk=7Unei=JST~fo#U0k)y=<|rXgIV zFD5LKX642npl}|@P@w8R>2HX9nwW&q408l81)>HQ3_ABonuM9M_)vkr;T`?Z5zn~4 zZcN~y-P;Sr6!1BUS{lxOfJ%7-oYBaR6HY^Fi^l-*6Yx$Uz3-InN@Rong&FD3J=n6y z+@IB{g}U;?v7H4R^2r;Qb@ z3e)*pTiO+#$;BORx>fCv)!pYYsm@j!LCFq;PU(l>y4jM;Tx~(6OPS^1d_!8{8hPu^ zIL|?_Zi1bo;kjWP5=;2>K(tp~GAY8d7wW|MDV#Z%Hyn!c3B)j-11nMJTf7y~! zm*=J^8D-mD4###)0dUxiuW~$nP7rqXw(&2c-NY; zX^qv5=%$aSxpfkW@Zx73$Re6bj(O0Anm{wp!sueMmF!JL^5%v z^2B@S;ic{}V}5T~hm2`V{5ptj*G7^cihK!H#dNJNLE3ydfpY5H79fQ^!UJOkolDGr z@%sG^McqlTNghL`S!A+q>Ww_zRn$OcyTYHh`?BOl643|o)6iyqJvrD2uLbIdn^4^6 z`*$4tHh`A+2hP$~vA=#;^5DO%7gf}z@A4AvEKRQqBP$`S?4!<3gKOdC7xGzAC0F=35?@!PMY_9=CsW4N`Hj3fRNXf+jzUG z9^?v4$iotze7Wes;3u2v>FdwbF`#I7qUV&SF^%=qSxiwZQ?#SLup=ohp(qMsx&y(R zv7~G)<9qSc(k;Y+6Qvsd`dDkrv&bEOq$5MAq0k-m$FMjS%w{q-4SS`i4)sSjyJQB& zl|`v7kXn0#w*YO&|Kt3S2gFL{xF{yuvC3TXlea6zLa_v)2bBBCMqGZyu4*6VGH_j? zi4OJ8K>Tyrm z6%VSXoE7xObjo&XoIav`A?~ryx?n8)z4qURI*1;TAECGJ^KZ!XKa){6F0nWY`>dWI*RXch5J=MKkv0LS@Qr6fO zA`m*jN8qDGn6Q^-P|}vpmkL7MrUW{dx2p&g_&k4cum=@VraSsX9O2knAO+CvrYdX; zk~o_9AU9sT7^%=|Z8ebCPxR8OyuA6N*X8KgSz?+|S580rNr$M_Ye5D;ks4b0b83RR zU#1}mKebDq;O_mv8Fp~X@a=z2gsXK7AYzovn(`daCnBmG2eT%SRKbN?ozx5VV!PkZ z+8m%F81da$L1F@45X^kI7L^11!!V?QZrj|LCh%snfOG36GxfP1x#{yA3i!fw>@he^ zdpVp3pAFs1VO+e0k;p}#v4 zwXi$w@{T*@X_Qg@@8}cc2%-;kWtDo85~y2Ya2$pPlxS`u2n>x+CtKt#VwFO^t9>G$t}I(x|&wu_w6@4Q|93sDgnr)di5kdtx341rp~bu zC|g{r_R^s+pp1A`bx-u14ecTjebuSC%(S}jZC26KrQCNo1xuw8Sm)Cd8dU zUx|5RQDSlT7`^&-E~#f%7ml)pm1l>z`_e zS`Je_yOw!pHI%L@0P})n885YD_?l@A*!$;aV9q8JvW);JuVxxCPh?U&{!*_MbspSf zB0wvlqT@fMDD|>@c(_w{HC7S*b_%oPjP)BmXMEU5iI~V_+sBjN@4k(Vxc~7r=J=pc zl^F6Ry=)jGv;3yHLWL5fO?q3eeHv5>*XEZI7~WMkYc%>s*tr3nqc zi%>pY4{O~p$H&{4hnp^vlUk*VeOHCp%MUF$()@|us-e7vAD}D>NSa{HpH{c#6q^{> zRK7RN2!+!{KI&l%Y1Q>m8+-lQc}NaYzjoM&popg5tp&w#J)>cP33{jV&rr|YwLC@c z>l&itw_mA=E`oROdS+Q|P^;ga9H`)W%<(YH4BV!lB8v?ux?{(fiC9s@rb4;l`*anjHV%M+@;SiXt!SfKwV;$oRdm z_`J;?q|KLe_^U($Jc)Mq4CCH1gejA$WhwP?kTa_e)dB2N`&<1M4kRtNHrq?46la89 zoN4d0PrS(b{#9};Q6%n#11zICQMroR)wotkx`E9^wD05_R6f-$QcYud!kZ{sUuHPh zpfvROKLl_8u4hn@%egN8@A9mB1AXJG4$4x{ayi0PB_^02$Z1VG6H#r3qI)PsVU40T z9IrxwI+tvOfeXSAzkHAN0n8O}9)ZWww?c=CUJ7`p!V#E4%48;TO|d$L^^|@>-QJ>) zazE!rCSi^WH6Jfjmn>+rlCrj@DjoOKSo23w< zQ)f`1h^zDK8MZh{B0^TS%muOWz&Vv9g#%J9wCr!&{n+5jx+$;U`NgH6(h&;j?SL+p z!3wrScl!z6TI3NBCsn?b23UUB;mVkZ`vk(`C=hvBX^T79QrS+Q%kYS=BsB|VlIp{y zhzSps8uHnE>+)%_)N1@}_xMTer7Z4{E+8yaJA>{|u}uWq5FuFu7E||g)$-{Z^#mf8 zdun(0sC}#4Z2ZEiN^u5Czw~DzWF+E`p5dOA`(8OWKG9M20eg5R4Z&2?6J3MdytQZ* zS(8BrXb3e7qlbmnn(jKp#ClbB&#m^j}a zODsQH(2DcE?Ecg-V?fFj#td~SCx55?sDv2(znMj>Fe}nYfu(jjE8nl^Yqst6@Mj3n zAS~=H?_QhQXH59Kl>PF)&^2mPAa19BK{2&W2*(y*LHF`uh{)Z5m^wPUFS*pIcym0@~h{Ku^SAE$4E0lAi& zBw#+Q4m2r|WwCYl<%uh|7>9wmni_(x_M%T`*E2=$lO3$LT$-u48I)C&&8-yF!^Fde zJQ$?KrPX^k=R{t#izmHj1P>IJ8CNCCRgocSEQi|AM0$k|cZs4lzFa(~U}P2M zEdIcQ797Y|`EQ;0|Ky64U+8?f?|Vcg9RIUp`TK#{d(g!`0{9EX69{ErhyAu;G$SX= zP3k~rCeFkFP*_432;;UR-U1~=z*F@oOT}d3=94G^NiP!T=z^0iX4znt^sTxjm_d_R z0hbXx=&*c3W%_UD4(M1?Q~~5z6so;%K4HP?D}>b9bP1K~Mhbx6Kb=tEtZ$&~qn_fx z>XkrvTd%!l#>)rf>#Dv|HzsT<#A-!?;?&Wh8`McI05^N>nM(vY3b?y0aHZ#&>HR5I1$2H~Iri}X{|z$L6V&X7JvrY8PJz<9-2 zJa>b;*70UxyyOtaC`}0YE9nng7nlf9ozD zp1Rm(Y74%N=B6ruDRB#F*^Sy%zx?R3G-e?*#ve%&Y=Jx>;nM%*t3K4RTYB4&JS^ob zk;!UK%SNAP@^?rLwfk(6w*S!LR7T;CS26i9&FVpBkU>)3*ZdS12@CgX@85}&HH*T4 z2)`+O`?51@(l5k3bTZ9;K>eKs9V26XNjWw* z`{z%tx(8y5UMXd!*qEja(FV@?1J9O-J~6m{T9iXiT((Wf7pn7d_Gw3jg9TGw<{i!I z6qiivl9jS4dRf0|q#f$p{+`mpKFv^o7V$HVT1=kz^@Q-)>}t_2uUP|c26MyXA7UN< z0)hVLytAwPVa@k{wlf|;+-|-o2|JY}mf^Y>eHF_d-G3CUZPKd>hz7!eN(~h<72`))3Oy%P}T72X0H`1edF5^;W1t ze=8P5)4MOSYZVZ}kvTBDsd^hhClZ|okCvM1Pnm=?|KN&zH4Tw3mhuo_o*w;TmqS_J zb9(uz$+zSd_I!C9gfS%Z30YM>1LnnoYl4n97km6h*0cI_xg`XXMb6KIyen6VkbWOe z(|&BiC@9;_)G@m3u?&^y56+mYbi+{)gdNn?cX7Opir&sfj~{`a7!4cW>3}7l6qse9 zqL7=!wSEv~xcF6-|EbM8GB&a2t%?8e#x-W>_hdN#pNW=O7{$rkS<(lUF{e(-ErI%} zT1ianB_zb{sAN`3Ymu)TlB+61=~6B7Q}DuMMrv4tQkwF^PaDlyZaNCMS)1D{4fP9W z`l%x7^$WI#ZQpN3*yO~KcFJi_jYvtjUtNpSEM7_Iv(&O}rRXVrcap?Hp9!dvh!OD7 zuNOdY%80G!!U|N2rTRgc$3!(+3-cWegc=vlgI6V$@VZ;n{(ar)#ePUbjEc_WphREj z)&8<%%p2@sEIM$x#la-GM>ObLT5AV^!P^=lQ2eG*4II_yU}4gCX1serN*(ycDddtoeGk{N4xQqtVk6{-bJY#o|S zj%~Ay4hY8Unw)s&*YFme%Jz4BB+IkGC+}3s8af8-5&q#P8ItwE{$*wvjn&oD|Bq&3 zix4TSa#1%5T;|#JQ#F?*NcWPkrQr~sj$B5b%^^CSYthFSZ1E6sO}eY2?1qr z#Gi8L|44LyhZ=QQw`43OSkpR2511kb_Rknz3#*F=98yKEQlTid8X}JR{vkXJ1fW@D zXU2!$1x9Sdj1*^(wT$NCc~HF(qi4Xhh_Pd)r+C`gZeE<{F&|;9T1YXKNrc}AJ%)T! z3IZ~0lS-G=T$I(Zkq&*Evn_Dp#OAeqQoG;)a{szR#mGe5Qg@}uqd#9c8WJ~_` zcx$d({X_=xRrL#X0lw;0zG6a|RXVoMx3tw2{axjWLRNC#N5-b7n+LI7dl@B@EQbFd zUvI${*S2ko21_7lK`7h`5`w#Ha7l1?cXxMp_W;4&3wL*S4ek)!;niAuuYJxv_uTgn z=A7Rgqxar=6a5J?M^7rGYl}32)z>GDN<_Iof$#E}ZC}*Qf`JtLQGO~O;vWNeK-=|l zo92|28x%XMij|1UZFt5l*X`?v$p(3#ElM6qc(%k3w+?PT`@C{&*LHtTV%Siy(@TPK zqB2>JVS@!T`-2QVVqkVQ?4j&-j-Sg!TWJjf8r7f464Y=Ikyx_xNJP zr*OPH)~A`mqnR4PAa>Q4!}*QDUqW9rZz%}%Djst}Kr~_1RBXn9%CjpI_^h8*73OC$ zJIs7DfIY}uCQxhDnk2FvL@J zfM3(>F%u2932ypkc7y^R$7WOpt&|jj)MsncF1JaiFi)je0%)uS_yUaUOpjN}b%XN5 z6fX*vwkh3>rD&E*$ro0RcpLwZ-w^UQ(i_1KN-YF4k9dTRb!D)rYfBCoo+_Lfj(MUH zhRJX#+Do7rlgvUgD5Vhl)V7Fol@8JC38TDaGX;bK#U54cnb2eh)T`V|XzDOmj7eVf z3b#^@7ri)Vl}>)a?xmV|e`oyW7y$bV_=I0C=eL(_(X*7{_d|A*zB0z7dTDGns8)0D zC}m@sCA~C$t=zY8_tyKLVf_CX-!nf$KOX(R=M@Y>D6s-mgDO_KsXIZv=-}NIWpL`N4EWO~@xBJ|aI1thVo@I_sT@n(L|w@r?t{Ymtdx8{CPR}pzp!P`imGbl zHTJ?eqIbyWOQ4mjY&e%=k@kENK+ZipeSO3L#XSi$oV{EChg_^~pcZTFH*4B6p{8K_ zJA89<WKz1ECihX#lxHZ`H( zsOkYlf}OaI$m3RDm}ZZ9$ej~20zyeRqQ%FP%hdh6wU*ISFLwUhH1_8;A#ddHW)c39Zd+2LCteYoz5}S$)y5BB78yb(tu}BN5orTrC5w4h~&f4zz$80P=6|#cVV2%o? z^=25EKaI_FRSJciphL-pkIU$Lzj?zSYQH6G6%y@AnVEA^%bl8c08la%fUNdXQ7>mMArJy6~2$AZ^;E- zQ*+v0#oju`H(gURQX^wy)FSDtNLJhMUAGzgW$qD~QOLfh%I=AHE>&Fb#`_agR zJo80zSaQlEx9k4I+9t)?%%SwL4A%Gl?^DCST4FN5*Q?Ko+ch?t_T-M3%duo-W>H^g zH)2kCM?W*Lb+;;@`Chm06{nQydI|MThiHCy(n(G3Ho6brtCUD7X~G3q%9E5P-CtB0 zuSc-{)`)ye;1noI%-zgm6^=3`^{r=t2cuagQXS!5Iiw|;|Mj~5QPBxe}x;3@Dkyaiz=>A}kQrI+Ma~zzO zHhakDr&EOJ>4|%{QkyxbRq-HpB}_y1<9=n$L2Tv`+xt43X~e6I&A19jx2P z-GGM&(W&=25<+72+|B}lk8#4W%b$zgojXnh z!`=lyP$8wy<}!w1U6UAEk;b-G4q&P?k>H}2zxGcr`CESaE&g}^=TZ-!C&@!HK6k#W z83`j)B4o=&aPQGel_>T4X=^@OC_&?=_uYt#evxxdRt5=(Y&)h2mx%v45Vl#bR;>Di z0OzeUjoVCj>nwW6`^QdtCi*F;+2_s(EfDRC%Jf$`~@} zzB1-`hW2GVjdn=YNyb6(s4H4=TXv2bTO;?J$p>%$7X$S_gUe*N5B}V82E0A`0TEIR-X)=qs7e^lz79u1B`J%eCt>z~#;i091N$8hSy|I2-Mr6U0NYC7zbQ z=rhliNOavMEOrfzJ=Sjje1;Q z^k<^~3!aADA-=I9od{sk$fD%`#s~QC^BFU#kFhanlbJ8?dkvfD)=doFx8+%w%zzc6?RlfJ^77%Le#mX z7G}Wi7J2A6X$&eO?<_>zsd7tvXt2?Ip#y91l2>RvVP{HtL1jWA)xQI)9|fWTTyhI{ zAa{*ZZ#n>Fga@MR2mMDXGtvpedNC>&KrpeL9GS*oqabpO4so4cqi8C!KJN|^Aa&;9 z3~izHmpQ0K3v=nF>L_sGG8UeasRE-IZkK%xZ>xd=CaH#mbu0lul;5Dwm$bOv2Hyx7 z$*#CybocLR)lC2k;Q`_33_BPh6j)Kg4nj9p!r#wm%5P!^DQwrcaPVR?B3IWqcjX8A zchzsWZ|*MRUZc(qma>32A%UO%weCv!3O~5X&)KF;iHM6aI%Ca+SPeAs7Dqu9VdP)k zVRM~Idsoa%@LY2`J##_CWSZ10p@_LA@A z#q#e`!>tXkLsdpKv3${FA+4vS&(6wWVbeF1vd3+4=Kp|Vf!7Cxb4$CIk+R>3gMa%a zq(#}mdX1^Q{DC#2nPH*JHrO|KUN*Psyr4}-#XM*x-s#%n7`}-4S z9eC(S7snCA?HgI8`#pfRH6ttu7O6A;J4=CH?=KjzH~ZS}e{D#=Fhjc_iU1Ok^(jQ; zxhf+HBd>)8CtA|^GgSn%Vds-MG3K>A-KWq7XIEXL&^o zZjQ^!bt8oDlPRoT2=(U>4;TOf%i`>f=0+ffVp`+F0#2|%%A6i7>06DAKPx}dG!vU2 z1LZ&uyzQyF+uhCU#*AKcZE&z`H(G6WLdeH6{JDoTn??xz4NpzuZUkblgr`;k)Avy{}C!H*CI15P-o%7D29(fIWym#_@jBwcDqFIi|K!B{s{}fdV z{7Zj6CXr*;kEROWXlEXBO-?gwG&9u^eE(=o!zTqQnC4E$hM#ua4@+ya1yL>8#;(t45-}|A?l&+X(}*_ym~m z_pieuCyEhPEfQ-26^KwQzc1b_m^nh6ARN=5-?JN>=2^`l zejwDx!QR(8sHXXbZx})Eox1)jU+s6%#iU&%$6}od1JyzaUH#py?h}|AlAR*9rcyMh zCkCXd!HyG7vn^VAU-6*qDt~jOced$EWqK3^Lzu(XdV@Gdp(^x??WPRt!3hBf0_ys%DXM zY-%;-hcX?-P`y!Nn>YV}VUKusFnEJFhp|5v=I69e$4 zvXES02jPDlVBh@@zUge%ynK;3T}j{jv00%6du57agzTsWZj2JxD=>y#d49Y!Y7iB* z#<6aF!_r&RPo&Z>nd?65a*Y~!@}PaxmeSS?-zggYdia%8H^nlK#LvD(hF~Q$|JV1H zj)=yo>0-j=LQz+1*T_F=eaQ~9srQu8I8~B8^d&$6OL2L4@Cii+Wv#5p3+KbdvIUtM zj_&ct8po}Vc_aOPuE0g}o=#+hK)7pApOImyhXrAbB*8Hx%kr3^Z6L6d>QcB_6Gx9B zLog>FSsIU?S1=`zKit_s{;dQj?+U_Z^aE&yL!%oYBXv__$2=}J!hhYQQ6`aWGCv}>BlP1Y;ITLs>6tYv4)^ScO%Q?r<(aExES$Lr z=Jh&g1gH%9BhFS%HbCfrX0=be7_PF0(*wV`4F-r9Q)6Fi2#W@Eia~jkw)gjnYcKs-1pwVh>c>QRCt3{~FFITr>aV z(@k>@e;zoi-@{{xrhaZVAu#6z6Rx5vGprQYKj>#mD^Z`=Tuvhe%hd1T78JYBE=~43 zF$W4%?3U(RIPu7x-zy<*uUgv^CJq)l?FQQI zU3UYlsS3Ne&P0ytI%dBv($^)XB*f)ocC#5Q1M9RRxEu}g=*#7sqm--Hi#+*Ek*2mg zV$b$FgEc{#TyCoHC*`}caXiyfm!7V(teMYe4E!dqMl>6(Fk}mp)#q$i@zcq663v(8 ztN7aED{E{Eg=^2biLaYIy3ah3N`{%XO|?c%mlI50uwL$>IrUkWsuJO$MEkEjoU+!bMqsMF4{XW#wOz)9y~mrrg@-#j+)GM0e`=i-T^U=(x#=EhioXwgDCwe^^~THRa^6%t zD7?=bi=!Ai8(aA5NGR&nJvlz^yJdTtb(h$nQTbXEH+s~K&k zR`2l&1SkX4GU6GC`tS!_kY>a0`0c+(`pj?w%u>UunsEC?N3FRhnf$YdHDdE+)w`T( zZcz=X(EBf(-{f<#xvD}V+GTH7Q$M)b(2?!c)-|}2+&Yamv93pV`DN5^HjeF$C5&73 zJxd!Vv*^*}sb^wX>Y)BgVxCPmR@*9zOe~VP)Nhhif|}ZB^sKB(tUeBm{BpD{KHjIRnb$SF6uH-PCI{rF>B& zP3{8~v33dGZwbQNk99msw#af7`3r59S(W?xTa3I;Ru2Z>3FvhkdimLP{MHc}DfL&+ zzG{V2ks5vI9~mArA&gAyiMYVF1v@G{=_Q>1#z7Ch_{p=6t$OjXyzimF#vJjFVGIr8 z#nJ*oYa7_DnG~sj$;f!?D`5;tjGjheWTcL@-}p7RkcHT#on=5o&A=b-OlU;(?{Jo` zU-I~)CS4n8<;KBRs;8#`@La#w$%}rDuo>7PhGXQ37~c!(`CR)P;Y-xWB{S)}WVOO% zq%fY1N6^uSSwtMA7OxzBf;JZdBiDe}gB|`*;apWB$t=?62!D)9_f8xV7y11l;#f&j zLSJ)Q8Cj)EwqY;&v=P8;{q}*!o(9$zO%Qi>SLu8*yh7xg&er*Nt%*qZm5u=Nfx#2a2_Pqv5s&)g~@Ha||kAtb{w{(I^vf~Oa0bm6{JtSsqp&)#?# zDHJ-8_vVR%>OK~rSxm3E=&p%e>Jn(#%^m&b9p*+3J1t3WNusMk1y-e)#WhV9*0&1* z@ZHn>ZMvvk73_Ba0N6t?y#icGF0cv%!N$)4hHHw-m*tQuPyEt4^@TVsPYupXe*hOr zr`n3*xtKR4cpYL8%(<4|?pa>%ctmPk#P`M-eIbEUehUgyGNiPC!1v% zzG!pit9Pn)(`45gD@E|hx3RDfTZD~xqRUzUU3_-f-()1`JHULKT9;B38==+x)-cYj zlrvHof1APb@SviVKZXHsn^}P$+g>L4p=|?R=`)bT4{Jwlg)-;VW+1x=uJ#vy>Eymu zM4+n1kV~9!9_UB98nuvhe=xhb?#YDK1~j{{tA=*xAV)Y>=@{^{9V&3Hrc)`Lf@q~% zqaALM`E`axzG*6FUIDnHHdf;8HN^&_j*u|cEf(2RcDB6VHzpt*PH@E#pcZ6?d%OaY z)YO+Q+M?D}6yd1QK9;Xf{8i&3pdiN zrCL1YUZu7X*8QHVIS=VfJiRk=zL5d6^d_aJ7W2Aa55HsA-AT=;tcxsaMoUe4#M8#! z{;V%uDYP{8+s|=-Z|~PYzZfl47+7qT^tx2MNC23o#~JItw2GVYLYWYO5swF4)p9gReYjo5;_&~hA9aw>ba-D}9lvZ|ZR`JmexQN$ zygC{X(IMXdkLd8?;1?fWN;Rfp&@XcAv9uZez~P`m3pvViI^(g!5m#}x*juA7r8XrC zExADD)OB2!xQgHM^cDN%1Bnh!n*F-!d7bMKr%P2cx+b^BmNYc9aVT3WF$nt5~Lti1r0>gB%Y6r zzZnQ&cJN52XfagL1%i-PW5l$k`k6Qu${!_#7ZgnAlJ>Ie3M3>sM0Td2g(8k>vBHGh zIQ4Bkq9cyT)9mYcAqyx-is;1(nTI1a_e?4&Xo7awK&?%^*gaZ|124j*=gAK`lVS!; zTm7>50D7=+SwPx}age_9Ve67r;UPDM#Gi!!34xg}mbE;a{#iI)0=v zw2r~-&P|UH$)TK7WNBWK+wUuYwIXLVTQ5yVEaOSq_aI?p@EAm@g&5nO)Q$^wdQ`oQ zk=?B$A$Xt_`XrP*Jk;mzjdE!HmV727(l9qRtDIL0s*V%!)LecNC$#^i)pF|0x~`jr z;*l5{Z#iFWc*&$mQrvc}Dr*MbOnOwB!$P-A5JZtL`B~^x6#ehLi=aK)F9s#f?7Hib zq9wuEzC##D2JozE$iV&S=i-M7cP0#TX~IFo7d1~VlUAcsqp}8x-DYCx|VP}j^u zr+1l@52bR$3Spd{)LMH6Thfz(OuN50Lh`UQaGFwD z`oKjyEwM(029J(vx&cAc_omoOs0jmGEaOxQPhi_3Hk@6D8|%8uQ<0xE?R{;>ha+rK z58V`&G)oe$kymz;{q0Q=xd`&D{tO3!bnj}TmEjK zSYW8KZK)(aAOH-PME9sF4l`WS(`u{wGv3=9+VNuT(~Mn#luQ(<_M~i_!g4uhO;?Z? zJBXzimAivZ!;K+ETBkUCDQj}|-^E%3!V++`=edztC*F&*XcMx%O#|UOMybBR@21oz zbglxJ$TY|a?U;M@2FpelCTaJqPBa&;RxWJEL&wJV%9*W$AL$0zA;HAfs_^x5?fNBq zew66o!~qY~JA=y08HX{;L${hHqI?LLPK@95O{skBX*N1o;CJyldZ}7g2UobPed){0 z*>KB5CTEiUa6Zbdmm-^KyOJ=Q(+wvZ$o#|SH-5u7hbnV$&hz9yG6ok)*c9o?I}T~Z zMZ~zJ{Q>Qm52s@euwT%iA20t=r2PlrfPDLp&}V^OzsWTt=wJ$6Zk5x>x6+Qe6OPAR zMQRlDPuzIYw%>CSIq}fXrlrLrWpD<~nrY=mo~VUXXvD`Rl)GfsbN{dvi7FrN(J>2} zk7G-=Hk46~C|hj-&F*p9@bxVWRblIZQ;oL;GV)iAaSg%(d}IK|FvG@0lUqNU0AWpX z1qy=+pb1>v`CCrfpiPlSTNfWc3t5(QO1v>98R$BFUpr4$dx`+Ii zWVEb5idBBSNfpyXXKXjG_qKF&z zE2`T`&?M2L%j~n_%o=_Vo<)d#zjZDKSG?Gl*i1yJ11c`iZ>gfaOX77l4LJt$rw5`J zJvw^EZ#=Aja)K2{5N@%l7ByHtk95IMZmXzA(1+)>7q-x4=sN7dIWKq?Zfp4k11Mb_ye=p(FLnC~W~&1antJT{Fb z%#;2kF3QNCorn=wW#br9+90W`_@dn6+boclUlG0#M$M`Ha`XA7PyJGWC8R_X)L%nv zIh9t%+VXbw>io-EbW>A3LCtdcjqUhtMjC?NJ;2~$^jd=Ib6&?&bRaSYzbDArOL*5_ z8`%C6?d0L3hoYzFJ+KnQ6MM`dUX#dA=Vfwmh zkCKrDTS3ki&)sdOndQR+48yN<3etcwk|R0P$4@T)ER zZVU|CO35{=T^9p_1WBOD2uLOh$GDo_HYwAe71_bazL1?5wP#?-kkrqk&%Ch;t5uzaCZARe1l0N?W_YGlU% zlks>!Wyfpt1)J)nOtAMuvNwYF^EOeB1JDk#=Kf34z4Ksuj+k|oypsO!h14W}_N%5E zpB0&jExie!>JqGeHPbM!NxfJt=`gFRMU<14Mf4)t$OYXJFG6V2L+zIjI-#xz87_&P zckL(Y-=vmDeN#Ru*iSZa-nBcCP)!Qt(U?_#wr+7h2?SZ18SxYSSrkw--urORYq0La z0d*q3YCI3EIfMMsH;Dsh83oF*_5F#jLYgb)$?3uA!G|+$=RH{^jYkAB@7NL(eWpL2g4nj z-K0jnXRr%Eiy^>nZ0l)n2omC=xFDPT19W(swDAQe3GSe;lcKO!pOF4!I6E@=AZH7c z!G_A9rvvFl1W5^x;dbiNsVm4CxsOmFRK0<4#Zjjz8)l3{sn0Z63%%(XCP{~<1X?Jx!m#B zFR;nGa!2XfbW)DhpYFydQITUG77JhCuWE?&vd*NzDn?Pm% zlAX-?s0M?KR{fSG+W&3-Tc~`X{m0L%zGVurB(JH8hd5{6cTm9+*XHmnbk>0TN8~tv zD53R6y|$dN5e9(?4NmgQ=!hrV?PJDy^WnX;5Drmch22!kU>zB9_q9v#e6Y}aF0n30 zS*6vl=+!rXn)A@gt8>XVq?NEBLSnKh(MRN|_j0H>TDCPRdG5B#0W{-T3lFjI26W4t z=c0G(&+hDTZfcERoGizMa+aDm4D#C?X3vBXlidJ+Z+-heKsyVyH_esarHZ)>R}m^( zDg)2LmrTndNyV@dah$5x&0|zCvwiP6tAtV940zZ?Sz_&J(B9*4)qh<%@zqWhOf$4o zP6{HfDdQ--abRP~bsmrf*bAqD@9vkC1=v0LzhNYN)9Vncgl+K2Xg=P?$Iczhmy&nX}8pgm*Yf<@scBIfXs^K)ZQNzHx`gf_i zp#QLnp+4}ZcX%%9SI@<6|AX1eHRsCSCvC?jLUTz&JRyxJSl-U=2Y4%4V>lQg`w;9EadCVHfT!;%o7T~&(U%9y9D@4O2Ws0XMg%IDi}mrqVfh} z{Nn{s81oGn{0lU@F*k>qL^HFI^Mztb-*`L&XTCzS^br1~yfnbFLXddP$$9^7gBecg z{6uk%SozZMks{ITc48O^8LxF9KFDXQjPzJX0cR%RbobYg*^8RweA0%Y8yNzyLuu^ zyn41ucqMV;(^vC(Sp)wDu%rC#2puB)hD|-r{sBLew&EdIgX*uUneylNk`4lBYhD;? z4jt;6c^@kkQSr})*gKu5Z*}!eGskgrtOHjQUx(qsmkJW4+?(8OP=Fb z-<-}VdNg7Z4dsF!T}4uYr@WSEZ|EC(I8M@N|KU({k_r4?Z*%wDvm&3K^z`xot^FVh zhg{}JK|bDnNI_9qe=B|dBL=a7{elghX_@zi_joWxy5uUv-1D1B1n#404@2{jK2=o3 z4dczlTr?{4jvG!z`MLd%rSo%*7mv2rSS|TSt%}M8j-~ppoOXJz%#Fuor)zymCn+4$ z!a*@E#iauk(VA)9I?N@8H6TU8*CC_cx$28A*x@9{A=dyBt+?Xw2m?FTD(aeuNa{Lv z+^@rRfZ&P{89Pm(7~ha?Y@ft7mWm~#AXCyL?$+em_E^vRX9j@8c)+{Vq=UI~c(Ozq z>k<@z);&`$lA{4l6yB|g_6K6d@Ewdq4xk-s;PgXo>ivwwOL>B?N%hrBiC1hy)HB1j zOvPN|cELixuG+CaC%Lip<78G0!|6}&DlfbwgKW%dQY$yoXydaj3z-ft2IGppF194qID+u zYiBhm@-4H$q^D&r367@=l+a^sdDd%9k~JA(MEv)`R0cUbxhEB9KFMEYg4*nNYrx=P z))jlXadX@c5X;23{TQzcZv)SQ^{5JQ(S)(v#15ID#j$DEPtU&mxCgMq-|^TeeEOzY zO5}w!#flBUmce4yKG(R;t%Y|L4n8+6m_pmx_(!bRwsY%G$&>*|r&*njGZzPWbUkO8ysB$U+j#%Y`zAV5$8H2JYZDxV> zj;N&HgwL$U^5l=IJISz+C!9JVy?+>K4xD_KYZW^5IChhhm;MhWb<)*gipx;wfIr;a zvFr0H$GBn^9^q9+B>p=FiI?F;d*ZWo>xBr!#n0*B^b}dSLZiCF zQlIB{hU2O?JM@Har@>DmI{V-WsGr(@C;9xfQjVa4jhOw~BFZ#W>2Xy>yMk0^!B)w| zVd8M!4?QGTUs}xO-t!4@P((&$NhM{ZJ7|GIH>wvtv-?O_m>sNN^L-+vLdmcPl2LG? znsfm^LSm)cdm+w`QuIA3yK&y7<_xjGrq4L)V{*Y$IC3D)eV_FweX>(iac!> z-ADepK>oc>?<^vn)ZPo1Jr7_zfDAtq!S8n7Ma+b~ClLDc9ow74P3cL5okBK|DD;o` z7e&&^b+oP=&?IpC?&@ZzPvb{|v7rMv!W4}wr1!g$EYh>kB@k+05Cno)E-z;w6ML*S zQja(}u@Y8o+hgrUIL@+YlMx^tu%k~#iB*aCa9k!HfkrG5p>+lGmKMQUfws&t{e@R+ zpc@OB`QIldI;wi{5ffZ|qnLW^=RnnlI|oGMIu)UvnES9J=03clX$W&3<@F+i&@qJZYk*{>wNC+p{!TMP1#MP;*D4V#D2E^|LwREL&K$)@ zJeqwvX!zL;gwum5mNw_)@|hOZaeX>#7QfSwk~SCa4tX0JQlIypb6iD#u6|C}+$S=#d9=4;R;e-2}S5 zz*t`Ka{R$-BicX*nhVRV;Y{aafS8XpOPx`0KF9ev^g=9(w4B43D- za}7ZVMpw6Kn2}(2iaU7O!@dTqYQZ8D0^e>jn8c) ze9L3k{gGAC!Qd|@E&WOxnAhS`-QGp{i{O?{tJQ=|EhB?M{gHourMXNEJPZ$JXXWvF zUc`Sq-uGMqq?o`%)_g)Oxx9q`>K-7dFg~fzik4!*cfvR-Lva#ZS}_2pppW>KExCGHoH;$wHt z7Yn&7gZO|DE@{O3e-(zmp_|S|fzJII^%hNDzI9wFt>M4U9}=|Ht~W!Yd+Gl;fB&#r zA{rS7Z%KO5kyU$PqpJI3)I(}SauT6d#)sY^sm>0YrP~{BrqztOf#Bc2WIY1njY#CQpPX;@Lu>8a-Mh!aeCmg5+P1h|dVwoK^gO3nB z^^a7T$r2@wYc*rPr56+4)?A;1?7~O{A=}9~3j7gXZ)&5H=oiGzMB=URSXI*&O;GDr z6vQ6QKub}C*S$jG1+?5tKos@~6hRZeku1_pTbRCJyKN4dv|#SG=Dcw1H@>;GcqVT- z-%?Dqp`MnJ30jU7)3}mo?{J%+DYh1s6Fy#`)}}LtTeHrj!dG^4%pzPheQ~2Z{2O>- z7bFgq!6TW8tJZ=-+iK<^A$V>*%@s4g4Q{;BZ)1Y^*NOKoDs5!_;1OTb-Y5QqCYZ=E zyG}E*u=@@d)`=K~CbeH1$Z&J|Mv=$&>@JVIEG$Y4(<;_(o6fj;_D0S1QSQ=j{o)Ri zh^FX^pG2WvzAPT>#kch;XQ+A7CiUXBF;MY?gX@_zRu!|1)X7o-xZX3YKowU4pPn@4 zHFo>m<#Wt{jZlEnjQ3o89AZUO#dgbG!LP(c*zWFy5-m^LF~O$36=Y4KQ} z8?6B9lJAYW#n5TgYN;?LGDHI<_O^j%LxP9dXepH;GRR#@QopvPh{et*QD zIc4oY^99CVL?G%^y$0)rJB80N+SaR$y6y&%FVS!J(SLeS`LJ;sN=vC)ug19|%Ram(Mn|tn>k|J@KCxf7X+-E_}r!v1eOsOxf<57xU4wXhhSj7L5`z zh)1NJ-8U{GJc8IO)N8yu_N}m&KMT494tm-Bj)4x!;T73&?Cc*I7Is%(ioZh zE_IDI3xFfk8>5c;SU;rhU5sHNMG!8-p~S1f8o-8*s+%uR@o z12u;6K3(mJ|DM6Sp}kE_0%cf7K(Pqf>@R9hp;xIj_p3H+Sz#Itvf$ABnv4Y!leO8D zLE=^WoIu33kiVgyZEPrxu6+BAVEAt~q3eowI~l^POHRfx1F8`^pFV1!HMEk(9i5B3 z$2}j^_g3)U&2G@ky?zJBFT?5v%}plOb!T<>((f8+JXg>EW!XXCJo*dCleb?>YdsaZl6`iwl^p{{nkxSW9);=D{rNO0zMupEDE)qZ>a%HCh(`Z z4%%xL&jx#!E!~{)s&94t7nkhVga}y$j_qRoi>2L=mS_io_*R-pw4d>9(G}=p8-EYn zO0f|H*ufw)!{iwpiC6=0*o`X{=XGpEqxr4X%Pb9xBBwXHU)&2Yn6GQ`%!@OZkFPZFnrL!@PF|7hW%{vZ79KbXG)JiRjXPy#Vq{e1m= z@yVTYW<>2r8>0X<(kZRU=OILtv(O)9Xks|HJ92y0_v=0ZY1cGR#R(S)6CY2Kg;Oe9 za`~+^*pvca=b2`wrK5fTs#%7BgRGDgV%iP8h*0<9?bs%4i=e=Wl_&;AqM>KGD|`&t zWlqSMt3N`#azldW)j9veUi)n*<+%yQK>n?GCQYIO5Lwag8;Gsubrn|VJ~YkLbE>z zECvX*7$m}fwyzsh;Z~nYH4YDXh+tzbAloY!pQ~L9f`RR>*V-SPg&b3K>=c)hE0>kiI}DX2)5lG%iMBnHq)T+QDJjp ziLfq=$@HP)MOkayysrv6NRE7R?XzoTU4)iJLEVlTlaLc z7H#X9@`khlb~>o*O$)ThOT9DD)9UUM0YbNkhF2_lr__fs2b}EhwpxeM?#kh706#-s ze=2b~Tl1C|bkMBFgNSSMwzcB%e1$UBtLccDoNbMo)GIUu0wC%xF@7CN4aazY`QLjy;o4 zhP0|`@ELsGUMfGzwc6Y|FL##;-=*q1pDY3hdE7qTp01F;Jq?H?tkqkp6U$S}4aZM8 z%@q82Wci66MZg6l{w*-L*5YhU1YM!I!v`rO*@m#g?nS$Zt=a!|np1dU!^cv8&Ve{UYtTf>dT)$mL47_NgBFm3zh8H- zwpQ3K;JdqY9*S5U8k<3abXjBwiNJf2$4z)k8m&~O5*j7-q$yG9WxxEIKP8~E&2B>x z-uy8KA=nuuab~n{4XydrA>4j2zJp(j_Z7bM>e!G|(e!F?;A6{v=&6smKHPC0hIesd z&&6)07P%a=4&kqzf7CnG;+(o`R#azd3bNJapo*Jj!|iyvy`fQF39o!Lbi(}_pt_04 zWXH$_Dn-wrVsvGNp5)NZ9TUR2L^clvSn`zZCf z71{A;mh)c@SUP^hF+&w@AEW@Aj!A>*rOpS|jnIg7^4C`418<+U#-r_TFSzVPNYkIi zU>WxUbKRr|U8C<@&MD?{BHY~Ga2b;9+2e+$sN3J9NnW-(cqOeAf~FjP*Xk@S?ZRU@ z3zoe28@7UJhh)daD%Edz`#ZhD%CRTjMq7TZY?px5_Ah5pGUk=)G*hOM2C39o%mG?iaJOZs?&Skpx$yngTsK8rT(& zzQrf;w}XA8F7{ARJo3hKPd0g!l-I!b~EX{br(#> z98Z$<=bSRxrkP}qD#lB*U#}MU^$f3?Pw)=wDFtISytVc{D-3x0Xcy6iPGT5` z}IrrgF%kMM>3>E^CNP#}E#Qif!NN9uKT=Z#O}F@)hq)jI08E=mY2z3N^X# zpVK^kIknsie(3gcuMt;}=B%gl-!qyHBZ&`_s&xZ?rU8g}@ZGqo^0jluoi3N~X&we> z&I&@`7+DZ#F3d(W@FVj5vmRD!3rtTH2Y>ts*rStwuh1qqPN2j!tcU0;zw`TklP~zX z^zBM@?c379jPFJf-}$0DQcX!)yrd5zhiHMI_TC-)lBOc9EcufD3W;0r|3}w%hr_vc zUx$#4XfwJX7`-#mMeiYqXrq@I61|HuTJ$uhY4-a5K!s_vXjXjKO@hhm0FR%l`*lF zpR(G!n3sjlvVYTG_*A9FxaP0MeE+j##WLw5o7<|DYjwFHMp=eIIkzV-vD;3x3Vc?_ zx4tjW_>@XyIhWtq36IrnG&?^#^>v{c-b%kP;4&$km ze2G_nwRcLfzggt^sxqpE;XukIQze-b2d$65yEs}U^f+94r8S7y-klJ#ekNRxX6zil zbYP@geUMDJUKWDPlTj9Pjy%s-g{Igb4gjihSeI9sX<_TOkBHZ^N-Z3EIxqfm;S+r$ z#k?Q>OOPp)F*nKby1H#t@Tg$Z;%r4Ao67#DGXgrhcW%cdmid@v=;~{IK@9&z*Dp|> z*)lQ1l(~oH3ecSGG-Rlko-{y^T`GHiURW|2z3S}Ytm|+vDI3DF6lpb>O0Pm3MSevG zrz^4MJ~>%f{H3dOK|p_?RE_KkjItqi65Yz{0b){obzr-@V+Z07!;Ch&tglXw!@;+>Mu496 z8TZP})O>vagMywg%kykmv80d!0D^pndYg#64Pb2=WHo7On} zhr?3!%QsnlvMNJ-V49y#<NN5d}3Z9dF5^lfQ1M= z7h7Ik@a8XHr2z*pxk}J1rf1@2mpQ#wOq>TFT&d2xt&(|aAC%B4W3WwpTHS3e9%?LU zfM1*K8F;BL1E1?1DZuf5!I z-0iX_kPdNcMFPP?GDPL{bkg%%bJ0|2c!}DXG`y(1BjNGKYWm_1{UPaunfF2Y{FQ!```XQ3UkpdhGE)v`H8Ar#D?Eyz`175FH+ zHNY$OkE`*g=|?Tv!XGsQy!d%C?@eF%=t(e`N-zggvfKf`}j{4_HDwgnzZ@PmM; z?}SHiw0XFvQf)@p7XoOvQZ4xHN~_4P7^Jq{4m&(oJEXN~z%Z1RsziRM!1wXxpb`Pb z{r8f2pz0!NYESU@VM@5B4f5spjW;(W{fy<_OtW{{6rpwu-7g^1VBJwDS!3hZ>4APJ z1rPZ6W9JykEtjhLYZ7{aLtt7mrwgoB3?*;U8L7nK)El}R3Ft<8^&2iez1JDcA>(tK z?HXO#;>&koGDnF4P`q2cIINK=&Jz6vHbT}#Lo#nJ6wcdy-Rvqe8hohC61?Av>?MmB zb9t6`Km`Vza5n$(;dv=RNLf8mKi=+FVC{GjRMyJ7!5KFH&Yk-AaojY7ajM@2SgC~C z^F79o6ld-oRjm)a7JHTlhKCC+uia!CZrzN^5wtO^l_)i2pq=G>o$7&bn`bg3;wNs* z!|b?Xd4||JB{Pym$Bg?gtmBJn5<#K_C$Ti+ny8@+KN(G@brBk$C30^Z$ zW1am>*()0RSfe8rZJCbU6BcFAJq5tfmeXaJAyD*_5sE*I=iY5C@h3YOuP=SP)9Qfk z7mRm=6Mdg1!OqU%i9B0b;(X%pfp#{p0z{2vR80Emc^uw3GGx4eTj-lUN|2he@EPH& zm@M&WyUULKCi?Gf<<*<@6G16|W@%0aUb}@_sh&BPt&!kaWA8F)pI|HlrFvX4&PsZ8 zZ7s3C*f6S8!=YP_XJ`=Q0w7T098BRMq-N;aFEeO($Os*&iXD2J?;wrspUW9!u<+4! zNAo==+{@b=+HZBk#PEF~=Jub1T>Sxe(xw3tmvZi6fmM7S@9&feDt7-u?#n7;O(|*A zvnuuJM4H;HKV-<-l(jf#!B|6O!Z6$g)Zy#zYKCZK%CDujhLc&vrnQE(>9OKJC-=KW zIW0u_EcyMn6$c(JMm8-inW6Cx39l!$=W(!x88Z9fwSPW7*Iv7^0Pp#mFEsPg+m_E~2s(6Xa+JBbz}Q7z}&e z6NTvq&#^lz8ghUmAkET2RjRcVznt#)A7i44o)s>j0559teqUazm5ivjFjn<2TZ^>{ zSjKwk>^Zu$v5LomLInsIK1y8{DM<)Z>CK2e?$O1XIql6Na@Dsk{VW62q?=rOi7@(R z3Ym=fwg`j}w0(ch+|G{cDEfnn;fQ~PuvVJv)5XmTce1fo)zEhSUu&B}kF!kib{>2` zri8x^Hhhd3ZmHl_WIZ3WqoTd0U_$ zjmk)21HaIpEfTsI4`2HkOH8JV0H1yCIC zyLCyR?3c2V&O)(Sbl!)=_**0?3tBOQQjPJ4TrDgF|k-lz?LWjyIaV)wTFjB8yMn!PW zZEt42%H-yI%GaCgUl%TDLdERn*Ux0>^C8Msaz`$D&P=6xRbXUvvV~&o zqdG4(MVfydzd!Q#pWj0`u)~ou1t5{Cbaku@F_cM8WSbPYa)7W*APW#u!uGW=m87g@vJ(G6H6etF9K zGPgPH<#Iu3|gqFoTT8IcrmjJvxx~-DR2vKVYY* z$b~ov=WOd%`)FOduK--wV|UaKatr~9axID-HGxI#F*pG^?@CGCv*>6JNGhKrk(F8( z(m$kjpNTK09XFe|hSj83KaxoYvlObZ)Tq_EK06FXJ(^S;p&n}1lUTS^cV+*I1D%1y z<=C*vmSb~zvM#HA)0tcyYZ@B_6lkK@s*@6j>VRea&T?H>Y>LR-@{-~Y8?0e{MruMS_^^Z*| zY@4Dy<76+%pWlcXR+CTL{sR2}K63%#vHrt6;hb;jMu_~~!}nQ+Moq6KKB|bG)D73A zafQ<5-I+X zYf5>aukf?;9tOWeacBAZ3clXY)M-jR|8oPo6wx3qG&arT_nPzfq5f7yG~%VLOz4pF zAI~NYJjSbgm2hs>zX^n+l+T{ zEVO6b_K4?AQ_6;qm;;08wz;uJ=vvAlC5xU5Egl@%4!!D?(xXJoad@;Yi3sx&3^?W8iteg(PR-R0d>ziIKRX?wDCV^Q`@vcRTI4`)hP8~@W zSp5X477^oNauIZec`n4Q+&<;};<7NXRW9bSC5oh)`!Am89?-wE*=fDU@nE6OaVv&; z?5A%TY`R$c>w2stcT2r-yDy^AZ7-SYjJU>f_yMiYv9w;jEp(li(l+fytLF>95~h6n z>gAi^)nCy!a(hQ+Oz8d~0n)bLQl&}LeWCW%SF~aP4~^J(ts+3P1 zk*`mWWVRPFGMT?=AxX*>mDHTc!dfKyjkVX$g0ao_OH|@VVdW9SLPh7DZ8F z1yZQKRs>05vPkzSz}`Ce#c%PdBCJ05r&gYSPLBxz!a+m7Oj;co^E(44$ z^53T2yk=E+WJpkqJB@J;^(#Fa=1dxA6Y1p^|N z`Vx%W%zz7}bPCdHe;=5A{3P|T&+X69@$nAUBlIW{-2k^?Bd=<<1fs)O>UFELe1Ly# z2%yC@;3fp0E|ylZz8aQEQ_QJt*jAbYEhe`r5S8}u-@UHBGqXP448&qrt=-r9yuBKOHGv%>GR4zbwjm>Pg=aEhn^yIa4dKL0-B z$A-UUa4w&=$kE)p)O}#l69Q3<8r?s{Q~y<%|Me#p#29^eNHbb%d`eOXR?vMVV8$B~ zX=cbEMd3Q~rF_OSq$O@ZvZ`64L#R|ll9L4c1J~4cx#ZVMK(GbtUG+D>cS^Hn5qPt( zoUYf;LLis#_g#`fWSC{}(O|=33CY+#-bW3s2@Gl_JUeU3X74!ds7$nJr+B_5_dRHN z4G$BO&`{Z}P_w1(f!griUjTWGMkV`Iyy-FA9En44z@Or+Jc^J^T$2Hg- zR`-D4d?Zr%2is%BFk<%nfO zXrzLMCePd-aeiA^)XgXF?`G6|oc2UDh4BRrCCqA9B>si@$Qpe-Fe)m1_-v{td5~Ew@4pmOz7!&3O zcqkiaNSIDn!#$LldZ6yYNZqT}_lr<7FcIR{>4Up9P-SSYdZYT*AVQtLR~x&hPK`ax z-t-ZP*&ztfdUtwP_fx^K$#2 zNBR5EAFqKr=J4qi(z@c~U~rP2j_3pO_q=&pVJtA8e9k<5%E>EO=I5f3x&tyuP`HoT z{K#g6#v3`xuU6svQj&Dj@lN$f>ON6-bwm>z5zBS3)ZHc-%6ZPaM&)0B`mV7`%`~M5 zjZ>Ood_7fS&L4`yFpB4<)}`eRZQQ|Uu$rqnRg@8SDRPEXnjhR^^pk(*98zFv|z?pw^BdgT@%R%-d!*oN!>eJcAe7vfb#B2A~IPS^uit0!5 zJla!#xb7^*t5*9bD9#~g%?LuO^lgKNbMGw&2CBvX0WyWrN>*GI#|c*LZv7Z);c%ybY% zR^^#GAiMF$Tse^_$qbo%2l?s~$fFEaCnC3hwDte?5pWolSdMfGjPZWhd%g3F+wGy~*R;d0_v;6sZ z3jTMn^s4KKxb*je=*i1er#sMsK40cQYwxvt!W;?>jDc%>C-(BxaSHWOu|@> z`ogfSqmp7;>g(ykJWkdn5LedEs&Gp$Y>E^@-(~5Zjy^w{_}~$XU!hXCab&rUV%W|D zGY#^Wz<0WNv94cJrymJL3gdplbUp}oNGh|pV=kZ2jgy-gm(*`xm`}3W1rujL4!(9s zxzD+N3=b>bFHtD?+@~(s(q#gZy?sn7Km#1-Z@ZrSwS;or@}jRLL7Ohx`#z5KTgm;! zx!jmI2o3tKS^2r{(8jP~aaX0VeKZD#%UCqmyxGC&x@5lT?iK*lIWdo5C?4SXWBZwD zH63yUQ883?nJ#HnwbDCq+2j=($mP22P>3Q84occ<6{Ys8_UgmOdIl*ddq!feBh%`x!%N}&wX7p= z2BxykZ$;bRY^lV~ovy2Xkw4lEl7d~|2qlNFtP}!mXZKp4(nY(3CIy@qh1H8Wo5*9L z*O|nTa55N*CcOzpekNTz7YKRgf@X#v1#zv*e_%d{L2r_ zxp14=K1pM4$vcw06L7Wb^V)ISq1g5W=4JQN6C+Z$iq1KePlq+b>Ze5)9(I;q27h4; zzhMis%e~zW-VWf5!ZA`o)R)$0Y>DD(1lKhSGvHDzgTmt5*oO4U`cnB{UZLR zaLN1MX#TI?dgw7NkxBG3Q4?wVZ+Vwwyf&y8OT$r7(k-9vndIO8g)ZkCAc;yCchcnNSv0 z^#!ZrJjtcWkbAr>8%Qts@!?v8Tk#7r7r+PbWRJk29HVA-Urr%soiLRgpaIFeKW7Hg zb@gHFp7@ILJvPfl!dwOJ9Ix4s>6H28+P(=Q8~bKNi5_1rNi^&ExlA6|A>A4zMd zDYi|`*^H1N?fQLLl=el^8~Cj%4{qFH7t+^}>A|!?346`i?~zP`<#VZzi6S)M2WQl@ z(AD%$r-(m#0n89g$?Z&^pD<=JZq1%a#R>ZIjH(M$#SnyU3}TBQys_oQQH_U;=rMFA zGGtM&|Fwz+!OacF-4?hwoT~Rz$rdL-LGJbo&BaN32&(tipjZA2wW|GjME7M=obULH zwL|sa+qr zeJ|ciDzo4L_4-i(+!6888l)}@i7V_78!aG+c=FcOp-WKWqZzDuUklY;u+H)z}5D$14L@XOn^8m~0TmVq}_IBVKbpB3SgEi9^ zDtD1h?o_&;HTzCOe!L!h!9VfsELws}#Z*oimE?2kJc&2sg(V*a(S1gxbl4Al^|#8z zvklVkDHnO<7@}$`sAps~CrPj~6<`}WouL7{i4$R%8S?z6?Bh2~iBiFWeg0G8@Sp9U zzp@ZohJNQmopemnxOVp(Ou?Sg8dpT{6;*sGh>vf{n(AvR2u9yKz^Z0M@fyn7^;05B z{~)=(w!Wx;|28Mz$uQ=|LoPnAO%3N80DxXSrm#38(H$7_GD@yCMj%Zp6kPRkFunjb@wAS zpY1_wG$%1g;~4_s`JbP^O91y`@AXy-%sfSR&j4yxSM`n7C1LI0i9QTEBT-Nwvwz5_u`snmBn}B1@}D<}v8iY>x0fV$A`-CS6Tpfc#R@U0N$U&R(dU6jM$UGXLo61;M>| z71vFc0RBEs_4Mt#E+pNsNhA*q&qC6apVS@bD8E4JZ@Y!Li#Qf{=0E$Qp{v@{b9#c;8p#bQ2F8Qq&;Uq~ z5K|=CWypwEUH({DsRn2>-@`0S1fu6RZ|}12_xVh+$-_?wswE-fu_J0!n|y>XM)c`r zTDoVc%es7&R0$=mt>T04kV~+aJZGV(P z1`h0b-kwnic7(T=a;cE@76&W7M?#@O^&i?3OUipqBfhA{j#Dw%()XG>=JV8R*-5y~ zj?S8;_8gbhft>6NuH@A1S9*|QZv#P@Q8h>_dfUnnsYRd?*21SBc%Tl)35oGi`iXcZ z@~$ToahXw7@vR4&+N4B}X1POLH$Il(Hz>5)E#)}CT$!%s*UF2{-7f1Pt!G(8lyIv< z+EJo!`k?T&`it+W{8_H&8i1|Xjhp%^gHbK)aPzGz`XLkR(MWjb3tB2nHuoC9V-=nB z0Ljc>I^9W=zf~aHk9yG4kBqs)X{A0tU00*(!A^30Qn(w`@3zR}?U$}!G4mXiUH{7q zKx=vITBOdQqximfK+Wd}{HBU!Fva}Qw;bIk7yXSlb*eL;KyvMpJEfN+Mv7-!Rx|o5jrcW9a_}S`#UNF z)l>~_={3znb6@=-Xw5g_{-P8@T? zhjk`iP6YKRsXoX($`NzbAA46D#8d*QN>*)#Rj_z-ryZQ#MB3!9J7(OW7l!4IZc9*& z>s&rxVqM$ajnGp`o_EK&q(i)qHLIZJ&0ic-uby2Y@+1|Hvt?c4Kw0+HDSRU{BSm}y z6ilcK?z9?{qn`FC`V*JauX8OP3|XppqcD24lAk_Y2m%>$1oDbnp0@RMoiNda$_@S8 zdB8r&>GPOBtkI3=P%F+c7=M&+Phu6X%00SVXsx{bnW$Qm0^+Q>j)v8Mi}k>Ec5v6n z+@$yY1%OJmV)8hId#bMNWSWhp-Hzv64 zHSHysHz`W)ca6+TkCthi;CLD!H2%H|VL;Z^`ZEF2ld>5f+sb?Tmn+>8^x#6SAr&)O z{>8jawaV4SjIa^ejJngeEYBVJ1{GA0e0?=cb91Jdug<7p>YGgb9&vPA6WlKRsPK-) zAsSN_O%t#SF)F)>3RbZBvGQts^aPAb@_d9%SnKT{9nS~Rsc#YjMxe0CYLT1SZ249aB?1G?(HiA*?j0Wth?+o6xA#+rEi!c)Q9QbN17On)Ih@9uzPgdLtlm$048BMEyg;w->63LL z%G2m4s$|d}&l;ib*;_#|JXQloQEnoMH=pU#fYYcrCN!Crz6MS+J0gN49{rp^ zy-^A=z;x;x=gs_~Q}JRau@4F_LADVm;jC15IcM3{Z}@Oer)7Vvs0+%snQAyFP(hcE zyUQHQ_X4XkFw4}1cgv<5H?Y#U0USGM9~Q2H8cHX(vt(rR#g=W^=jYW%;%#i1@+#;l zAk135O?}MFFC-CN;CV5!y{vajvhngB-57D+!S*qS;WwkLpHGnc4e}{$2!7|XzWQxy z;r9=BbYKRq$L+41Y;-GfF&<2x@N%_)aqzdpB z=?ra=%DV=x9*#8x$;P|s#kRYgpR2b`&xVuQ{k{v)Vwd`8^G1vf3KeBBbBw&bT@t*q zlqD~Xna8PDp4><%2 zd?^I6?~B?Z%F0Sy5SJ#=p`<^~n+o`J&<&ZUHuz(_@S#3|Chv>#uSR10uY}Of3L>w1 z5AS+lF^gg*xcT~^86&eEydAQ?yLcUp(*ATG=Qj&^rX&_GkrQn+HEuWygDi<7JL&V7 zKXwgo7D!Aj#cFwsuCTh8tvnNYg%paWDS9G-je3ZfYCmh3dhgwF?Nc>nm2L^kPs4OG56E$~%CGvV@48P~V!W4$ zFVC#G9F#Z}8?vK3(P^y8V)D+M!J(e^H8)n=AhNTmx?@K@`CobOJ&W2A=u1k;12zUOg~v16v=7~!Gp8AH ztw4hWCF|~LJ+DTD>gx#0k&KtrJn*7e2-?Bt%*%CYfeu`Qw@T4HPrDZwR}Xv31+}Up zh_-z~0Tuw@wPEw%?Hl&GbP4kbj!t2El3Cpui4(xn+!T)^w~wU5@C}P$3L`;^_vy^A znW;j4DZ-^{PUh*RD6k$bgmmNL)B#`ZG%#lBxV{o0pm;|r!yBZnRCq8nWyytqY!LuZ zL8@B~evnopPEM;=oAM`VSw}D&r3Pf%dI9A_;yqPlejT^NLZh=@(7X9f3Jua-7^hwB z8_Q&^%`czo78}TsDD^6~^Fu~7hM#yFx96v?>a=+I+Mk7txqL_KPbDxxaCE5y`b1#DFwvsKxm zL~cGzWfoFui+bv1M@@rT&7&Ka2MSfHSPOP!T9~apf%0KKltOJ}oy|rf9NjCoac8NY z7PMw&88eyfUu&mn&kj^Qn`fk3NdCf_GP?V#)f9s_8NC+p4fWB!vu|bdqSIl-g4M^` zGH)`=J@Iy<%e&3kaylX>pS2es_fwdcx#|w;4K`r?7y^DJL)^{0N`~AJ-PwphvADLl zKbThsSj&+ud)*-ktK3efHlXX|90?LmSZfUDRy!tBDp)ef5So5wr*was&(&)nA(~vA zH+!g6K`~`zs?R!-Ah>+sp|}S>7EL6!)gVT9$fOSef(kLMR6kL&o95ugnQIde4%Y;^ z7gUgEw(1L^ytT`n6=bxVm~rf$YFZU3Vlq>&`(=OM5KF$%ftSWER2@OjYJ3JY z)vu{i5J9K;L#BI%qA7)0Uh~f7p3l-ga~csG}< z#6E!YvkL7{h;xVK=6sWyw%)AdOhbc1UjC-ThFEuJdS2*OFWTX%OxM-7L9JZu@V(og za;DTFz_%lzHnFo|!}U3*C)ty|32l>CNE`XwDcuhUy|m~AkvCyn6B5nE)jr`>Ci6IF zG}`+Gh89~x<$XfwhKh+%q@%lZ_9t?CfkaIEnI{H@K+T*xd(~o5aZGMUj=8q%^ z!8d9aZ!)}RtPsQPfU7I*6@PF%YM8i4`78|~r$Fc22v!sWI=eOU;n9yp=BMbeu^1 zXutvtXR=-do5b9X_ZBLlYC4|TkPnj_!qcIy|~oVgQxb`OS)68pJ;Y;@TaeD@iC0Ws4ymv2k2 z*c1b@4l`cq&RbFCp7d~mTV5i<02RR1boO~yF-))37s z)&!b8=*Ob!>1vf^Setzr2NM#?Bwi{Vc~@twAJ4LVrI~gC z2a%?8&dFZh%qIEr4c*I6$g}X{huG+~NsqjlAhtIa?0l)Y>FJXAegIcdzLMmF`=U2J zrwRF*;w{lc$}M7>?>uWHPao33ZsW6bt*msvy;6!`W^!?Y>)fxvqXC7FX}SMlq#->~ z6v(;VjTX8RHO=+}$1s??F8zE5zr{|MlERNsX^7l29=|d^8g>aCjvMJK5~g_20C3;S zr&20Z!D6Ckjp&)x2{84#(PKW(wc$2S!>NjFnTm2{K+(F6L!G$|8RPAoth?DjTh9z^ zsEiVwVF)%aHnVD48s2J&u7Bv*e5)0qwGu0h|7paHTt_JW(3#q(rm*Lq$uRIHN^C+?}D@_L#G z&jnEnvrvWz81uk6YpCFbKt&KDoURjVC#m)ENCxR62d~FVmtw<0t#`tkQ|iZfe)7wm zy(mBd)7l5r%e=IQ_bNX(7JCwAwO=;*7={8l5`@a4HoZzEVp8_%?5W=uiJxZ7Os)tQ z>OA4F#h94=t3ply1bCiqdioD2yS5{Weq$>>5Sn-E{#YTw{$FA|^endtRx)?elW%EWLyCUW6pg&UT)TL7b1_e}90Sz1?e?`2bS}Tdehf_1zkoM?B z7ACRYQX}y1`C`9HK-1u`? zRg;X8F_Q=*k+az?0PEvM8Jb?+5umw!!Te-)5LH7nI|y2vZYO%wygHy|#b|hF5yu^u zG!r4!C4O{3=K1P<@^BMkK(?L_^0dj6V$ak0fv0EWbY^qApkb)fSzmWXI)55rlv z?;p_xh6iozx#(y>2H6?xL<@2#gDHku&N{5VS(V&G=6rCL z^)%b|MZ1$}-l#q&Lzrv66zwbTm-0D3TS|Y< zzmiyWU_vjkDZT%Y3NHLn1bxy{a{#KFKToQD9H!;OrV$SQsRR9j&7jfN{`w;6fP5*x zUdUu+FIvFG*rWW_iZavzz5M20XKCOdQ}q`jtz_HUp|~me`p4;t^c@tZY;%}Zltigf zKY@Wq$sHQOPa9cc1(3mESgbde%x7)Ws9zjFr!;H8$8 zGG&8WHH}n{=%9CaY7JE4rRjX@F8l_a#uceifNTpNq6{bpIR`l@2M2_|$V%-8Q*(fe zD1Xquv-dLkmW{36B44{HQ%j1&9Oh5iA_vyq6z*ET%txyEWGa)ZSL_ZYQxnjkg7QnI zYwt7PuBxyS-q)aXvt%O9nVb9+Hya-P?D|waH9|~~q8{qFBd1h2of5ZV07_Y7u8V&Endyv@B}NVRdDHCSOsiq> z`8NvinridwyvZ`J>pxzfst=Z9jF_JirHs6wVYNcD|H}18$=4>Xs&e}KHwo1rZdmUt z>wUO`Pktsr87}qXo4!)r0F0KK&A&Tp{ya;ySH5`TiA+e$b-SkJ{T(f%^rR2xX!p4D zE!lRDd|Ux^XFetgL(t97;>fs!e0lw9mHl-Ol}JZ#d93`Vwkclgg`)NthQN9E57J|B z`9iTrL0|Q@RA)z^JyKcMOEHy?qx;}!0l{?rCAfwizrelm0uGsMc&qsm02+ z=iAo$d684kdl8Qw=xi(JKSJS#Rn&*csR$-jN6WIvP@?1x#TXYo!C0~%@jE}cTJ5wf z*`QYLPo%K*-WZ8IeR>;bX_+Qj%3i%Z;6$W2AZ&0D-yd{FGJW(PWc511?pY4X^y2w4`>eh@|Tne`|rAv;;QIV<(yGDi+ z*`>cU9#$$s+L7kCe^es+DKxP~$)u6)6@%{e~JKq-v%_jn0t4=<^f1&sS5rLB6OtiYcppUfX#~d8||}S)L&Oo#}^f8mnjCKL0fmZTq8uyL|Gb zuzNmTL5IevU}CEkN>L>5AC5GY`&HRDr*~APKFHUBcAwzlU5oq%^6)*5l73<&;xPa6 z?s^_KJR4U`YebGTE~)-%vG7~Uu4=E&;0zW+Ektm6)zy%3CA}4qP7Gp9aM0On43h^U zDl!fX3fjfv6xY%vK~M!bN ztSOJiM;mvZE#DQl0q0ZTyq2rOJIZae)O9ybWuAxveB8RM zxc{X{DMcA-l-+NAvcxsb?jKfsp}TbRuDaM>R}dfDiZ#u|=Cl61s#O~~$k~iphntj* zqI1s>{$%-A{R%l4m$(x1c}~3Rms$E;E9qjy>5-Pb8FPBB1wP}p?R~%Zmn5ozrZirJ z@n88fJ_MR7sXrzv%Q&G>LF@G-{B-Qv0pqWu^;(=Qn8%2MuyXgOPZe(Paph~7WN|3X zlF|g@Gu>NPYLKDhTm)H233~B~^1Xq^;*Ume)T|i|lVoqfKU7+R1;@;w?5~z+s3n~& zOT~o9zH>G6bUv)+@|xSno`B|wAw98MF~pYu4PN*3-b+?JX{POrUQyl6=L|9$S%-z& z+jSf5NR;L~^g81$Q^oVxl>gsn?2jP;Q(*uvA$U=zpWi0UDvhB_!txt*bEsC&ap|j8 zY4uPfogpq;qGu9IHOQKv3-gDJFkDUcp2%WhDdqrlQ-42gU6}&cMH1^NM<+#;M#ezGu36HR#>f*I!Zd7EWwnYy^#{#kzg z!B#`DGBm_29ilyk{>)MT%~_ioGWcUWR7B6xPm5Od{LoL9NffAlUv!7UmM^Aej<2*q z14~aO;;v+f9bP*{LqDfM7)I&d$fcYvY&M(?66dVqsEr#D}!N6V)!E<%;Z|D$1aXC;z{ERxH55? z?4rUCy7jjJFqHj?Ut{*;nJn>A?_BT6SL3Y_>8nF&%M>q;Ucm#!k4xXbIV^#Pyy?K) zIqOR)iwi5dz4p_*HUd{=;Naw{iNELCYG_SyT5tO3OW!?vqhq(-v?tECHennm@0CRu zT>cEa$G(^eMikmlr+i=;)!ow29W`OkMZL}VtYPs$2xS|EbQREg;rr)&OMirvD2wJU zwMb}qSj0NikSU&Hen^aMLVY6bZgN0|>Mks>ZV1$6_W~^^F{E@D4=->4U}S#D!+ga_ zhU0oh5N&0z#L_-^NiMhYrX3fAP_n>Vltj8@WPEG8`do9g=OvlfkpJEf&ulmbNIDhx zc~`pyTKM9ti=s@tLIkq(w?Lafhd)}I$-b_r<{RJ_I?fX#4u+`8OkQC8d-^hu1ptPU+<`G?-tCeG}I5| zOFG|<)s0tuB4wGJfOH$UN>tBV!);J*O7#Ln_Hx+`h&q+U?K;%w|D6*1dq#$t91|9j zgkV^nc_N#(J|?Qos4u3F=H6#J97#81#T$AOzX^V1)5&0ch?!35&bY}51bm1G;H0Ql zOt;Zc6G{S%qn6#h((Y>(?pc_sv9XSo?YmeG!i_^jX!n~;yPiyJ)Xax?i7$>nJXIUo z(ZX1PjO)PXLw$yPNL=E9jw1JbIo%8nUw4dN{kF*c7d-k40d-)>b#Q$rJ}$UK{!gFw zPa;cS>drcA-n8VPhH9c2UQhinAEmZP;ra(#m$p&Oq#B#~0*T!*!5&ykmK~Qu;>%Q> zX%WvU^84P+`UAveuOsj(C^YE@sh~Pn6i!Y9z0X&}njqGbiVMB{?c<7d-YVVL z3q9NTo;oTc+itRIw+oL{0?nFWxlFk(^q@&U%ED+lwAuqeGr+1?zu?w#NrX?gj1<7H z=90S)Xxei(u&jHcs)t8ZU1Ib;gDi~jJ(CgD6zv&hS9269`9rznM4{4{Q}0#Uv2?-5 zuJ+NqFYhkdexb|yj7$tKxfnY%Vf7O|uDlg`1uWcfJlQqb+LC@v@rvC1>@+OnPE;zt zyj=YH>gLv|=dR6${7LP9%d=6 zgMby_;nTwHys>+q1jyL@@1+?L-k`gRA@i(9#*meGZQ1Veoc-Fb+qAW<>{yWA!78pQ z6KtU>I^v6?5CEIjk%L(Kg&%C~o9Q!-)ZjQvp|-5Ex0JM79wMStGGuzKs|QBvEdw!c z33kvwd1S`uN%a_wp3s{gkb!K3R6_Nsz-d0?Wpu+?3)y#TSR>2e)kcjnm)%jomKlzO z3Nn-x_P5GX)PNc-8EY38r323E0DJ$0FK-VDVl}gOOb5>2o~=Kp=4X6Gwvap}-~tJQ zZ7GO6k)ak;RHW&OBawCxsxCtbW6lgs&OK6T&nmu_@u_pTXZ9TBx`*u54pwn)|pJSp7-Z}jcv)oF&|6<^OCYb*|rWQ09 z`^K>XEO*}#f9I@!g1J&{I{5f|>873@hnQXOriP_8dULg@ZueoBQG6314~yiDQiQ3M zT;K6i&rQ*u1QId!bqj*vrbd~f4T`O_Y%}|Nss%c7=8j`a&$NI|5-!STRWCM^4@p(Z znX=*bp;2|(GYj1r#ILIf$CIV(C#^O_J~+qxq}`0M{pwpp@Hgx6pXnqZhEButu4|rs z#neA?O%Da8kiF~NS432F!~bLJE5qXGmaPLJNPyt(gS!OC;O_1&3GVI;?(QzZ-5Cfr zkU($=?hHYb!GZ_>$SdbO_q+G!O!trO-LJcBu6yvqW z<|}g)q+@!wv&6j5wJ|4!xYQBrD&(orsy8ZDOlx*z{hH_-Z}0o%6>qwkvSZBWW183C z4hnPk)!+KO1<>iG1kuB3Au@Tw>$|5){>!+FykVlVw2^iy03>O7s-VXJK#LRXHzWf{ zv%XvDP^4s<#Eg%P=qR#UT?s;{w{QRctdobJuVi@6B3z+#s_+ciT73 zerX)QjAP>!KnQ=LA61BO=)Efg=nY7g%WQQvmYhXue<``qs7N!L_()sI{&e?^W{cVC zt&L|cyxzE0p;usv92h7Cm~nU(@erSsZt!|K2&_}=ofVBukJG+32b< zsabSQX#HVyG7z_mz;=WT7UC-9 zne*Wa1NL!5?EyB&E1@?jN3jc9)0ukT@Mwvp@T00nP3`rhzWp5knGE6+xx)I{nB<73 zIdH*x0m5&dfRyqKtZreByo2wo$8s#&E;)GD=F$D*F#?4@epDqqY3w@4ckud+2W9P8 z$fF989f{SLYeImH+S6~?Xs0{4&CbK{ppr{2TX*!vb-cc2G@Gr4zj&olIPHa4dEq{P z9s1}jMwjxcmI<1UQUA3~38}^CjjcckwZ(AM+n`?#1`s#Knfp3}D~0T45mAqTPvV=A z6#BofvS_TI2l(~|{?jUwReQdzaRCXaLO{(<@>)RpOW^8H383dqgmER28~aV{uwz`u)M=~0ERwkX zmts0!1#AWObJ$a~u@<>)97NZ~6KLrsyM{E}hJZ8>Q2oM5(7;Dn$_<#~HKGE*dmB~Z z#_E$0*-vVf5aqB30;VPEi7Z(@TE1X(W8C$sILzES#Bi@qI|ct$#Cs0t9LZST&lAhv zwnayC85dF%GI-4l{W-HMLDiR8_@Bh;e_E>*v7b+YWvY$u5+OJf=kq8?S8=isI&th^VJ6#G3QKRE({Iy9#vn|iNdi=fR80+S|J%7q#xuL3MZj zG6)3+zHI-thVqBLI6;Lc=%$gWr?tB{PEK8J_MkEpUeCUguk&p%@LbNPFhKMjo8+9~ z9Vzww^$susskx5ah(^c)-o$UDx{`--bR5X=eUtPtNmaY*YmiuDGcvom{U=FjbdpcC z@y?u}>0smC0|(^H<&C!*yz;OO7n!O^TXZe6c*85GYEqQ@aT2^n9wRly1J5n3o+mi# zC510Oe{M5^%mvau-!+$xJo@q@L)g<4ViHR{PSj%|A+5qzhQ@MmG&6m-YGCC$rI$ z9isxQV=v6|2vFodeD>DNP-JA09+)SN8;9e%?t^UGn?QZ5`=csuA6i?q@^45Kpi)1e zOf|%<#Y)4C;d9l@qaQCE`r8vh1Xa4ilOx^z1R0={WkUJj<~>u!p|X`dgE3eOs(q#X zDT)Q{jan&8!sW}HQ}^h>SlpMW(LK=f1Wp_XwVOdlrnap2PBU&(4XW@B;E4|b9)p}A z*XuJVsu|~@RRB%tLf~*acN|%9;pWt4mis!aVDmreAVqit@BQo^73e$vZEpCJ72d+^ z+Yhy%y9*zgvz|I9n~fZV3k#|wr#BI0Jm+Sw<1E4q+EAY7$7~{Sw!h8_S6U2#b0dRZ zXe(Pt)hn9k(I&CbtaPmeB#Xc3Lh^dslzBt3%tO615n-h9JhY8N-R3f0Sb|1%@VxlY zaVm7tY}wSK5IHLYS=cVvl1F&YUeZx{B{7GhRj~&F(A0E54ltcphV#OIl_$aEe(pbO z59U$W(XV42ro0~E<-F`kvI#P}G43Bawt#^O%tCJ9=qh#J&n`y7l>YoS8GiO8krSpDLwD%95uyr_tUzAjM}-_Ko6M=;H_j zPh{`^qbiE32vG@pXDsk7R#W8XsfO9AH4roJbkkzMnTrI|b}CUzYyMO&5i`N}ZlYeM z(6oe{_oJ|p-7`px)*wfEaThi?&EJ24ve!jwf{(ZVyZf+p5dl`ItRUbd~6hrE41 zw*YQadX{Vlum2F)-p;~5FwoO@x60Lr&xmLdaIzDpQo3y=+eDjsXqN9co!uedy9ypu zt*N!oVAUS{DO+k$%xI8icbre0;cHeP(hTB`ip5(y}I}3ZB#jd`* z)>rZ>dKF55C5u3j97*-|a>Tl3U$7pU8m(*!`ss~43M1HKpBkt@l}SfG@yp-r04WC} z$F+n%Pj?+K)~fPbuxUEA5j@A=wLUiW(k>k?-80>Pq4v~B zvCyI(+W*8b*mw=6NY!f0_s;tvUl_GXi^z`E?npD4AFuC=YzscAMSSy8{epmrFM_%5 zPqv?%C(Qx$#m&jiBlodyWIf~OSDQDGP_Dy}#|7C(9qo40j({w>Y}obgY4Ht~`!m?) zuaIV&nBsz^AXyJ`y~Oy-N^z#lf>fJlesh!?%_2R|+E@t&(zyDfftJ~TUgJDJs!iqj zz6Km|WBlH__08!NK{QGFAJ0P_@|eaSp{H|y%aVaWsct2F%wYr3z^ z5j#u^?@y}19vnubeZ?4dh&6TT(r$H`4`DI%)M`thw;*_^LZqarnlDTlHy4001G3^=46?_8uPRX<(cU&L|@agA;Op#2&YnbT8>y7OXt&Y z^r(f=8&7H1=zabaQRZ~x%Ax~s%Sl(`Yl*O7b%IIfwkesIi4!li>~ z#1;A6AmDR5zWf0C7-w%z(dCJA=DYa7Ux38J#AbIinWU{l4L4g@$0=$7kAkp)WcUg^kM*he@%Ej{)dp#`NlwGOpYR;%j>p6@ci=2xi0GJr1=eRaVj%OZcSo=m&Q}qKCBxzScmXTbSRy z662Gt6u2Z`^e+}}lL_YKwjw@2kiO6`01mS17oEzcdyeD;aIu^dC+FEsoQ447s6XU} zG1K?jUgCeRi1*b5o<3*WvSQ$nMQ(iyNc1Mdzlu|&EbmG!qr;hIV)-)gabU?h740m?X$okQ zuw95_A)l|Dy(V2HLvmw+>EKdJJ|tYI!GBxTZ5Hoy?4EDY+m;m+REc8IbG{*>KnM`hL<)yqvA+k9I~36%lkZnObAD9iV!ENg0&f2 zd&9&G^{PwchV06o_RI~nn?iIU3Zq^+hU}Lm(vP#xVrt+R$<-s*GR2YpJ^h3+u;;^w zZ`_0T@}Zv1icnpLseyPhUYD)Ey&bg81%Hdt)D&ajYJM`8ff7%=Zs9F8Ct`pr!b=Dp zJoD#gqQ+>)g75|J4S1Mq(W^VZz9E^_KBVHzDeiq56g0xTZ|J82^1Y$T!1664}&d%IhmEIhkz0By@_|L0gV16q3NUg!+Y=E(O$r=}g z#Q`rQ))%%?(07>o_uKyYOS%u4F!(gM@BT(=!o~l>s`~=|*0H2JOS-a|RLECnL(_MOzj79xyt7Wa2kt8ll^Gh+ z#QL?2frcGNZwe;Dm(V}y)MMV5XPiQSxr1^jRw&gr%wq%D06R!aZn<W?QwI))jv) zcwjM?!h1b5%VMsWo74Nc>NB|>x;a^V5dADpvqP!)ang4;FHNS*7BL#%6)`MW)^~{z zMzyW39OJT07kyzM7NBNstqyW+m^+rdWu#g3l*v?xesifijFA+gW@|RCl0vx!dAJTn zmz+_}mU?KT8D?TQEu%J%JS<{AJm zRP=o68tR)dykh4W)@&q>$2lx8RM4UK?%B@%th`E89~l-cJL$yJg9!^Z&7>8kn$KdQ ztN3M4$Z(gbUUz87w;cM8{@#|o#mh$(^{?K@InDl|$X{-a#q%?~cut7(BX9ZNgpX^L ze$ii__g*|!l#2nUYcj*wAA5t9Bi4neK79=qu`_mOWuC5I$*b0LQ|~!ZdAQ8#FAxHJ zsdhF5(`vP{tJ_pgE0pm>fiv;U%>`-pcrT<3^25^Syws{Oz{?%5mNv?MTVcXca86+9 zLt%NVv!2Lje3`Gi>do``P`o178n4^;Sn)*ddqHx&b@~JAcXn+RcTV?4z90F=FDOLS z7d#7xp+KH9!>n_l5E1^Q_@l{tSS9834SvT+)r%p zttHxZx<6fL*|^?c;Q@8Mp=Q-h>U|lh_EjoA2#b&!p&WJ#G}jN2=O_BhIsEx(8U(?= zj~Ci)nxe1QJ5!Ei{V^o~786Kab|}h*5EoRR45yW!&5P^3rI&p&lBgsNm~Y?MTSz&y z#ff>g>kr}WzxTpN#Eem^neQZ(G}u!=-%A($vh5LcocYgIgrokZfUq~7ooYETvu2G= z*N~|4B@#yU`~Vy@@Wz}J*!Wh&|9P0d_U=gZvz`Z_k`Fyv6gd9I$Ku}7Q=ro`vFPsh zH(9hFuQ3+GpvUbZA3jm33Q1`;EJE|W8fs`1V8$M#A(=y>L){Reh}wZ0-qZoemG@yO z79^11i5I7ns>9#q(cH&x7=<|qYzzhcUz_fU1U(LSZ@DHsMAL7j4ACT!oX!@(yU{vP&4W=69TQ+_pDf$XCaSMoe5fv6Fb=$(Dk{GnW@A{A zRubBS&pFp*e`J}5K2p&lchZW93xYW20gL54t7rEgKC8!HuOri_Q#{DKmB*G{{9fAD z)s^i9ysjt%jK-Lv2J_Nw3B%ADBa+VEu7BnHeP=-S{q;-%3<0}YCN6=v3&A0HLv z=`Z|JB(}N1JY-yKyO!1oC7Ucod| z*`@8@Jwe5v-n z5#*882eMZnvfcUn zuBQ>#>2zzMZ@*Id>usbzhQD}P7Xj-eSlmK%>9CnT^J?42m}1qbm$)$uwcqv?mTue{ zYqRWwe%He45IVk*maUvx(#=4A1;{5^tM%tM^ii@y3dPYgT~Z{sXuV~-{dDXV=l!0N zV2Mw;d&)WQgqRpHrR%P>8K!KHH6MQL+v;5ZtVe>VcQ#y3s@QqSvg(v-B3Hv?#*>kU z;gUVN%LH6`1woYaD_=glR_(DT*{N~i>vg1@IgOKK?~BzxTwUiVFaSqpz)Tff3$Gl@x3 z6j?qmNK%XxIy|_LQ`GpOPxjI zNh_Qh!OVbfG7Ww#H(WG)tRPmMRP%&T6fByp)a3H7d3X6+yh_9floJnHeJ`A|nz~((0l*=rEEaIrX)&j|$Q02j-1%9q;P&{yH<(f?UU0rGhJ{a_nI0>#AH5ez2 z9wX@;fwH3tyP!%xhfU7BCC6)xlZshjt(D$MYz(HaCd0b|Qf5Xcs&1D9WiRQUgn9$r zqKlr2_ipE%c3LZaTL@^L^&O~9z6u1WzS>ht@+#BMT{ueHs@WsY5z&f$WFn|d^_Pf{X?2Rr$>wx;cgTW9u_Gfck@gf6S^6&;q(JY4en*PsigtrT}U zD+8JVdQn>r^(ti>YrV>VWN(vHhRa(-_**VEY&*SXKdo8I7=LEcrdQ&Xw5q>0L}32< z)%Y-jWFFQz-~VBQa#+tcNCRpl&v>@MP)v>@PfVK0zY+F-oaH~}Du)%MiNcr~H7AyX zH;uQe7uDb(&MM!UWWUckQf;sPL8*o}^21Wnu&HRI=If%+PO&dqT{}|-q&utJCJu7x zwJ9+M%7DO8N~6F)0IjCARB09_3iepSyJqTOw4Y)@!88I4Hva0VHN1iZ(w@I+F`}0E z6?r;r_YD|(ei>Iz(+)Q=Oo-D_yydMzs-g2b_}(wkrvmlprjl&djR|)<<~#Go<-yv{N%C& z77d7@i|VpY@cg1*9$@bnafP&<(0Oid*`KJ9ro1&pvIzWH&)CcjaZlP0gkSTj7k=xR zv~vt&k{0+rP;nP=#vIJ64O_?H(|Ub1UIH_NTE8hbW!UEV?k~%M=5Sfmy8~7D*}IpE zcfJgpHv4xLfYhEMkU_lul?f>xLBlA50emXvw^!IK@;xhvw0nhsmFEVWf+^Rr3z0VOt9z6QWM)b@%N;{|7! zA(u$d+RM*c?7yEVS794qm`fST!97q-vesj!O6NtkM5F>UEbP_b?6W@%I?6fsrHtwl z3Ird?V%mgy*MO<9J?BdN0F=&3s-LCwD?UubY>PMMb24XMn*6T~@a%^PP4xntH4TiPYQjrR1^a7j)cy z#aQ*Q!-+s1RF9%}VyM^kd+sPtutm+HWovVvTk`M4dysnP@^{P`&|nQx)l$3g>PH8G zBqyV)X@Q!XNL9vje!}B$GTyzD8PyM0iypmXfQ4f+{ht|p8{gT_`~rKBE17vqX||jy zA+R133Jps1f)Q^OPNZ~o_SaqGRGD>neBMf)l7=l3VshM>2!6w0-;+fdDMAQ)qzt?r zZn)l_fmAv^(l@TmBXR*ZovJ0o=_}aHJ*iY@Je0*DomakVms}~}t3R9Ur<_ROe;LZZ zk?wDAXj+S93X1w@Ay$BAbLFZKesz1TUq9nJUxK}}ObZB0IcECAc0gYGv&DSK78$%| zHZ8yWDa}N-qDAYCIMdZobXFg&rmYmfvIj|k-fWhT`LNWm%T2*f8cm-Bm`>xd;%;nL zumW#Xw=GtA1P#+la>x~2sLuPJp7<}rd+wm*02y9S=D+>pw*P1T;g5GT|CRx#v$KSa z*ZC+mU8E3Zjn=(OjOdvB7JG3dlM2W@9pgvgvGRzWV+uV`x``nrx_htXcpN{^!Yy7YjdqnAN>NvfEqj3$d;Cph5WJ2|T{YHT?=}&tn$dtv z44>@Qu_48fbM|OZze!xVHJOKF5%Qae#*lGxD-4O~qN82nwDoZ-PSSgjUjDoXN? z{i>G$6pG<7tud~Tkm}o&Cs_&G@3#Nb zT+1=xF)2fTF;VkKTcgd2Ma{cV4T=vklUu=0bS-FZ}(1S>hS zR@vrZG9$Sg*ZQiJ>Wc$NqjXWc+rk9-CE&ZZx&kUTYiXFWZB$Df%GZU;8sEd^;lg1# zglN_0rec7)318s|vtZ&ejk-%(_|EGN8H7yA&kHf`LU}ZiVX%rP<0*MyVfxG=LR^bfl{_VjoBJFJ&PzDRJdxOB{__nK`+2hhWWXujC+2fAMzFFR&? z^@}B&ctsB|x!Oz!z;ZScsgqzh&tgTstXgp{9fNIm>uHd?9#V9vMx$miS*61D z0kRI}s$X0ZMTzO9%p-Jb_!PjdZWmx-`g^LKEi|ASc6IAMPvJDa9HB&7Jw98kDBDhW zio*i@h=n)rJ6vu=7S)1R?6Ay^*TE)Pv$hAA4t$X>YCKDh*3_S%)+*)($nENnw6d>v zR%?@LI><*^f%=oy$gNETO>KEdpFO38Bb^15c9#x(>AtID0N5;s7x(@_J^#x#f2!!X}K8w{1qzLC2Gf%Keu!Nv=R#ENmJOz9k+LD6DuB$ zF*+gttO?eZI&Bj|>%t4(Z+x#j7bc`>R%t#qi`-(AArz)(RKv3o6J*;oNWEwqvY$eD z7qRd9qDz;4Mj!V;mo~YgPS}lMzmZ&PxHa5*m1>Dir)yST>Et<=_1|sxPgIEegHGdu zt{wibi|~LT7u;QK*%ZR`WlGTjV;{ooP)cUA=BsQ<=4}?l74hP-TFtLqSiCqNkg&`d zRnm5rYLw76zlw?EUAy#UQa}19GrRQdf7+g754|qIE|p~bx`6r0arj4oQ7Mi(!(t32 z7F|( zHD|_Fxj&Y_!CKQenXE2YHNkD-~vO(4V;)7xwtHD~H4QSC-CFE6YI1T4exJMTVg zA|teVMTuoMYi}TT&XT5{MKHi?GMPBRIa#z)?P#UGw+4J0gNr^bAUg;Iaur$xC0p7GX*J_+&t2Au0p<@c$95f6Pmf0YMk1Q(e<~YUf2mBIZUUA>&_nCE)p09?4zlR*9pYbP{dOp;MW!tkD7}a@n zAae}o;0!H!=JxepRy`2uXod(3tl%|y5?tw?A=BujIlDF7Fa95h!#5}ha{IraN){9H z8bnB3c{dfU@%78^2yz$Th`uV#+jY=msRYr%?E~U6zZRO@;1*%*Ai?CXl|{m&Z9$X7 zCLW*nEdzZD+~&JlX#{%Un1kwfLBIKb`3CZ>1LOJ+ZDjml1R*T@q%Fxge!B-d&oX?l zEIZp8Hu+|;V%7m&zJ*M;m7mYY`e>l`z;6mi)l3Q3;Xr{S#$sGE-Ti==QqtpfAk^`;1{N!rxPXyJbcPepiY z2W^V$0W=Q0a9}PK7(38uXO7_ekkMLMc1Z*kDI5G|8M$X^xe&Cco=&C;q~xmEm32lV zLB_3#B25ZSikqVZ>6xDXZx~I#zg3jDL{hERE{G5eeEifd%lz{$wF&|l!up4N6J{dM z4fW!GR&zs(DsSPg!0G@C-%6d_R$sr*q2F))s&2D>V_ALe4|F||=9`;``hQcY1|3&0 zl1(o;+Dy_Sb*okjee^1g`kGw&#vs;T?+6=3$F^sF>M>Ln{A6@;grgFHbgsatj{xUB zpFw&k`6gi=wew{Dyh^yu$~uJ2t=%cGaHK5PB<|W8?CB?8!)p~DhjSNEFe|9nF!(8L zZymwqeHYD;I_>UGSn4*VYweyDLrfIVpiJv%#^*61g~QwcH{A!qa9b>{z$9r@;J}yA zwgco>aFa@NTRa-Kh`c3nJGB(94-)XmwMT7gcbjYv3EBU6^@Wfh&fVP*f+D`2VRvh3 zof+J!@QdW^LvB5WWd2*7G#tx1kkn?TYpvZ*cqrYSs;GJqqJ)roI-uFoc zdw;p*(rNse-UZ7=_h}kaoCFjqjUpY2{mljXKFI`Kt6jIt+>u? z@7~L;`mVU9A382^H8EbDIs_Q-QZi08FW|HlG3%eBNa@%4rI~&@%6-3z3SYMnMQQ!v z)i0>YZ#+*6n7Ua~ zZbEIDl(-f9yhK>-TZOMMS(qj%Q*Z5~rlXpZfAGw|cgFR-1vvjqL9_+i3&`|A@k9Dg<$3cYyUav` zn5frmb$4c(v?rf~nmbJ@0&gIB)tV9+^Yi7#7pV#~am~}bLxc$P*Pop3QTko=!dQ0n ztJ4D(#4O98KBR}7EFaPmJm)>T6z59pE65iufDWUV+e$=($*a*<%1n!tkm_9AdhgL(p<_)~gcSyo>xW_NB~Hxj z#TxB$UG5%K(z=Xk-!BqFVEu6ibD=?4)h#-%2o}h#8mx6#HXf;(>lfXsL^T)zj9X(> zpBY54lWq;bwk-t}k32!^!6H^6Lz^=gQ`1i6EBCJQQ$49DSo?`(G~$j7tKt12)zuHS z=L%wj(SFgI#8a-P#8^&J3&E)ToPx3a2)l6;p>H}lP*ApOE%P3C%hPx=xaoAqfkxR#VcR;%*3RR zCjq7legX9AM=H(gM;SWm;ui287<}E~T*uK)ZscC+5;XE1^T+K{*6TUdUXO{IPrHG# z4j{slw|g(g(I?5|Y``m~!t_>Ms;%@tkQ~r?tWskf9_F1c$x{qmDgvq({jLE@itm#r9-|2#2W(xe+7@%2SIJGr}57YmZ_h+uD>9= z*}%5k8R=)TpkAq_*(ckkG4O0AO5?k&r}OxdT98*c_mpM4L;eKwKttOa!H{7@Y%qSb>}xII&L$>VBGbVnQNBP1$~1ggX|L9`jH+ku?&g&D)wo5qgXS~mj}~` z>!WvE5sEXr;&d>s`o}99W|M8m?kazY!L5b{&2HQFhQ;ATLBK2Jrl|+?6gFu7bcMgr z@qDXP?zBqGEYcwM(8OgWg||5m=>E)u;>XrsUby_2pM%XmoppNN^2hO9V$c}k>Tn8%|4$N!%o8pj2{ zd{vg4=7f6X9}Aw`o@Kc$vP-6Tm9zbut5iJioKHIY_u3ZCsGHJIzGoNghrZT(GYum9 z2(@_rn=DlSyMe{xv*w;uaix6qdbfth&be)7j$5iOxqEhqo(7WAWD{=L{rOUneo5jbLCOyPt@va#=;Sq}?2^ zwY2-;NWRUo&gfEXYyNG=)Jl^5;?ifk%l3pm+xt0h?UjqC-y?&3dP)JuE_q(J6uTYD zZ#;6F58WsEk$R6j1L{d$G2qS@W891f{tM6*`4Aj8x`|G9M}W@HMd85;80Vj&FfROU zv)kVpum9$lO{*z4kv~X?{N%_Hw`WEyFN(bsd?(OsxeqOJENH;s_=?VeFKrZGl}6&M zMvpPZzAJs_+o9sg>p1Q#As8xS#J)J%avDI|+dn8I0o@Cnn| zED@(0Z3GWdi6}Mt18H)chhV&5-ucO}XpGBMJ%HTI*%7%3)BIq;$FN^jfgI5gZH3GV z#P@?BoT)19ig|@szCB3{sVpNHXNE=kwiu|8W_Qb*o{F|s#2x#Ym_8VlULCYE76*FA zM%dEcn-PsI=!RUUo`J6PTdag-$0GJ@)G}E zVbe7#)k40cYFfLNmi*z!`SQ$=u$7jCGe_ay`=JNoE4Q|E zKyhcOs>eAECAuyL|&!1re^4NYFP}6(&GWC@H1b;FH@OfdJ z&wc9krT1{NuTDt}$vIZuX=kdYlK8Uuvi_xm7w}e#q*Vtq`F7`#@<5sCgTwW2zC^x; zyVSiKE)jbxsJOTlt3Q(wojuci6+zo6d!{*E&!^q*DEE$iC&z1S zO5S>9;?UZ*xR%%Ec2~DJXrPA05y-eX>}+9Cf){t=VaEf(BM{mH6a0z44gxd|DX_9etzLd--ai zYGsmSPP$@liLBA`qH$l?v*#@Tul1yk?b9@(IGJ6U^hRgkYgSpR`v;ZiQhj@3J$3Hd zQw1F~WHy}DA$$f4cGwu+n)q_LFCsn#G({-yh|@mTIhn} zRY`*V`;C_n#Abn^>eddafzQ zijwT;t#ncQZMV=IzvR1sU}`tkH)_R58Z;gEIm8klu^@wC6$=|5(0BBX`64`sOc2x} zC=Ibozh~>yZ+Yp+PSmb5H0srksp9)N2%N4_akMa^{|067R8X!>D$Lu%x7w_upk3OB zQm)qrPw0uQi_hb`@*D)N1(w6u^vj_G&6;**6k=(3bJ-AV!5rv8kl$?PUb}HX!?eiY zLaA#{Uim~JGCVn}mnY^Q*u^XIrywS$A3@j3X z0;XOi(4XjJHv7wK$;s|n1dVb2h8j+p+Ia)>ufmkeXLBwswr!h|<(hsra-al@_ z)DO?lm!n_owudtL?OS?{$P^ykG|OkZ$%9 z$}jF!+=z%~4;}(>v99mbeRr-aHyx7npIhX*S&ApEt2aF_AuDILbgi52&Qg;vDXne(}^WVXv^cTcsU&fVB80f*e}vk*m;CiA{vRAbJ|k6dbjry@N2 zbC=&~@-CJA7Xb^7c*$Pzvg0)`x3g3>4;S;2 zBhUQXx;D&(t=d7sBdwz3zd1gBBUU+alkXI!o^XP|*E!e=W~k_}_g}O!eqJqEwbEu(Qt#Er=(chTp0$K62REqm z09o9qgj;7X{n(8JGnz}$93!-J*FUNlhp9#=fvb%x=!)lz+^c8KZ!VxZWt==by5Uct zy1~pEJ=k79i5ac0B3o4pFfgKoM$)_sKQ5MV5s^y^j^@*o>U>* z;yJ7c`U{_=fm%!N8&zCIa8=AE#1C-Ae#~Es*b&R)(J0#&g_{a|dHA8{78iNh%@=#? zG?GO*2QM@g3C3QC^o~f1r}~0_<`!5NNg-rE5e>#$$W?8chv2NS!b7S=)Jy6zJhUt4 zAPIW=caQGXtpPmw`vd1WWgfB*0k#1$tyPZ;D<6&`p~7~eaa4mvRx7pF7nw!11rBrb zADa^2BUrMbmc8;NG1w`%TTl|w%dG6KOyu3hpLKb@QXOTpSZ|#Qr{{I9u z2P4)ecSaex{IpQN2(j<7JPM;l+mn4b5llp5Ut+U`q+6Zi6F?W4ENHg0y^J_|&B5uM zTGRMni^b^+SJVpc%j*U;SJS9ONXA`_@?%Bii2#z@@? z*n^9%2~FBV4;JpEm##O8^$sv~w)!e%iM$DK!EaT^FTZOoE=PSMiP)ThTs`{c?*pMNJU3rC3*9PYs6|Vi(pK>6hLuqXQ>Aq45(8MxI!H-7 zQ=>%1%ENc!BnFzCG#_QXs)h>L4XLzNOk#dF>#Gg3hhcRK|JwgdQ|O~kzHy`NsoQ57 zs3n=0`N*F~pQ3pM>!Cm|9KQR#!9#0qPjJIfx0V)vXoYc=q1feF0kA*lV9}=y8LJ}n z1D*cHx!!p74@x-1s$|cX$Ae?2kN(x}>{HF#n_?RUrk1k>t#PQ`0{vvG8(kIGPPbK~ z$#{J(U9N#0?*_Gml1kRk;-5?JEoqPkjx<{dwVm*54TkA0$%+p1p6kRa@$VOp=Zp@% zoOydNk*C>N#%K-^AcTJYIbE|!DYk>BhkthFGl3`BNk)1hajsBrqfov6VNK;j^od1uIUR`hu^0_xP1)G{KY;h0X3=xA7XkSvX$&e3TLiF zE8|CBA&e%lT{|WXc=+_I+yI0O($p<|$Hc4c?vXL?Dzu{-L8#9z`9s!pM7<5cLmZtA z{XXZOf1;sspxN5tkcN6Vxu(qGmaC(JO0^C1zRW{8 zD{S^de2Y8t`lli&A$0orcv%jQ^yfdBk(fU?q_C*t^(f;%wK!7*X_DZ^7MIjBhtI;o zVmVIekW>DARpYcVD8mg9^3jH}UWygQEhQfih?7RB8);XBJN6D@A4HmY;sB}ICRJx? z69Ku&U>fg=eC!_15Q~XFaUWIHfqqw0_GntKGE~Z?6?5OYlZWo%kwCvXywbdVN##+b zDNQBz8g96=C{12qtpp{G1lH2T=F-$E-063VV#5vL52v+L)_02v>Rltc)KYbPPY9mtcl)kq)=vA3L`Fo*9Wu+7X6XV15JW6BVxXQjv3)j}cA?J97Armz zx3gqt!;Lwq~iO13)+;$NJzT-2B8*!j2Q7o04;xi^vfy^we2C?WEyTUeO9Amr|k zPh)?sKC}@q+>!rJ_+pwzx%tCdLIjyq`ecL>FQTL!RxyB&i9(Kvq8k} zVF(Nbfl8+op2iBUC_L{K7b(|2REFV_$b<9tw@It?5xt1gn2GY2DSMP-k>~l}V28s# z#IZ^08p)I~6${Fcdq~Ehhp_m8_b(2SMGK7RUbUAUG@p`CGhgWg{3&jja16Un0HHqM zqxm%ahDnQtJcY~8Qm)f4vKtGyPO|`J?>pA6Eo!w@_zMGz3 zB-m8+M#Ajaaj>H&^NRo+t~{O9jc?vMgWd-vCc~70#p_+uLJ>(2OBu+MR=m!WGR(}E z_um@vcg)JfKx!E=KL7CI{d@1;IcPu<)J}*P9R^De(WH4bQ`y#={8BycqNi6R8|LI# zu9|MrL{&!c#6$~kvPsHkz|{EMMVM|In#cH#J;ZCRyZq5wm|@1{azmu_cKeA}Fg^B) zFpIu&;&$#)F^QIPa5Nsk-LTmGU6*bFe`v^4Xb-R=j$+Xh1$KQ?+Xf-sSyS!U&6zbt zxH$9@` zU3F^XG)DfR7M&E%qyCCa7K{s^aW4&lhOTkd{T(4C+rSd=LHiwtW_I$Y4G7d*;CL{Q z0c{1ecfH6qYa}9&b+x!TTOn{6zHKvE28q^QN4{D6Q<{nTi&)^Cz9)tQ{x|V}4gUb8 zQKBrP?^X%yF$_M*qkQt1KY2u?s5Y)ruLoa~W;#_~xm0P?dz^GA8;_i@Q7l>vq58c~ zGNamwNn55GkI9QxQlnWDUM^uuQ>L0S?wxB89_z$1-)NFWK*!NNFNpFcM6H!3XXiFw%q4b!TLp$8}58UBi9__ z@5(n+E`mzd8UIeIlmg-QcMn}hR5{mW|GZ(WMMx>id__NQxBv5v%V|Iw%4RVRpNK(n zB6=Id__`-`>4kmUf#a<~AsN-N{mvKPLz#KTt8f?7)=2YjfwN(;98qkFR@mB@Vw_`z zZ0Df*o{-2j=7E6|{sDWEch<}^9d@Sx0Z=;+*jO#9X0x>1f$JdsaTo60Kc?)lJW^4q znPzjbt?-|k$`)USYC-JB(P2Ek)AG?hUTFpH?cAk9jt-vUeMkd1OKqL}iQUYt)($6{ z=a4>g_I}0ISueSA27nA?+voEH#xTJ+|sZlJh)(A8jJbSwo=p4I?A`ZDDpK);KywTM$Gd0dsEL}>L ztrs{*!QB^|&K zZLQ!w7Bx>OyDgdiO^JX}+ySg&$5MZFy;5d>y<64U*&ct>?f+^<1O;{mzHkV0Aq?ou z;C1Zr3V5KGc4C*uUBHo~M@{{z6)0wFW|EL^g!4V2f2K)N1QWGn0w;9HMRy>2;*O?4 z{!I1wPHF+rAjauF}P_^wVJhHrC|&{G}pAh*yP>Ids&ERMEA68%~&4$#6e4+$a^-1Txr|}yq zp~}+{gHNOS!Z-31vB1ycwx?vtwj>C`1Vr1Y5}7=cbC)!GZNQuS7nQBETOvoWO%tVB zZqt)$rcRjG9T-WEgBD%LQ5`H&)O(_HiT8W3-CY$I!N zHt3#AGTC;nXh?hE*0y7{eAe4B-0W}Qz3Q@dJeLo-34t!X8zP=!Ry_GMYpy~ts znw%3=l2abfvs18nmHeW2$U$M|0=F1!qO8I9;FeIIb~nIvEyUg*}^w_>^;vMF?9bxmb135-MTsABj()^$^?Ol85i6q){HG92L72LKzp2lhA z?#l7IZI%UORiEj#Kdr-ZQ}VHFPyN-XF&ZGpaFK7Pfi*SM27B-fm1gIqW3wywDM$Jl zbUn50NloSSt;4r?8oqE3D!cTJzi%=t>9QQAfPAr3knJ6F7xSjVutQj5>Yl42qc>;X z*L=h5>;7e=Z$9+2fAE5k@7)!l+|m7)N5$lq?@#uE4UbQ7`%)%$29qRnR(A(a)0D^& z&%B5>Y^Qzwlpz~y^8@buMKsMM1x8sNe)H2YYW|dEC-gZ4y)YseSUb|85 z{)Y;F4(FTBZ8z|>EOq~K)w``VaHiKIwEh@Y;CE^Pb65!fhpJwL0v{E{bspbNqdsQg zAk2)Z9?U#-yI4C1XvG)-~Edb7o>Bsd&<$6DB@d^go;qT-8PmF{x%uX8;5z~Zs$ z@fYrE4#;tP>$S-dE5Gv6`KNMJFS)Gj$?gDcj1C#s^GP9`$-Ma=?^@6HDt#FI)@c!< zzTMV79ObL!zWdyQGvy{-*Ndb4bo?NVBo|HvhWsPi=NcN`YdHEYxiMEMKM`4e@Myal zMd0O@5x6X!AbHlubKVqE11c-LH{F+bKYfo|l`rVUS~vkb%!?RT#~G!dsy9265*VcKXDmvGC0%xh<5Q3)Lu>M%ycv{b)54j-!@O8Net}OGl+-LtLyv+&4qi%j$6ew^~lD=CN$5MM{ z`#Wbsk^pSwj zwk`K&mH+P)GFl9dtniX=$9~Ie=?`_LhYX&2GOELDo%GLv{!J?LRS`3~c8G2HlZwY9 zKLMH;#tkeyqw5S6PG`=-3DrQ*$dff*^oc9zo6nVnNH4DauvZaZqySF1U@MT$OrNKM z(mZGiZ7OBfF5F|(_clCgMD<;lo(+ocsqhkPY!t!d&f-NcYC;-8GLlmj%U4pda)kxl zIu;jPvEkwXYOD*YwV*uxO6P5OVyW2g{V5~BAFk6CUasZ4PKmz8oD zKS-oL(fx2dG1uXyPTx@gb{MdyezwDa^yzo2+$atP4W7t6d=mKQoFt7tBI)t607DX{ zZt9*pDT(V$2RgkfjWNR|=9<*Cvmgn-C=1Pcg};%}28b?*aO^oqsL;njBcz}&7NKv? zgq7#`3*uz;xBp6AA{gNfl*qLCm(qSaea2rzK}O{(fxr8Z{|QC<7E8h9S;f4UfXy~= zA<@JLGG`Bm*(uGl_k1XYKC4CuhLdJOEZlb4T8yv%W@wv=^rrH@JVZ2HTv6Q+aG8fH z^?KhWjZ2@tk)^2aow0F5vR_6YBaECc-mF53pc8B5FTbGQur$I!s+n#$&`rw9;|Q24 zz7#YLS5hGfTrLL^3?I7clthQg-%?t3DcSRBvi$+{*+f@^mTk-~!@KDMCnZ1vi5vRR z``*%rOR;CMF<%U#VI4-6Qc(r5?)g2pSWEPXJe0fA`_gtQkPQ>CTDo-?2G8ASY|c^v z^F|CQLC3eb@6&Dd2lHq6Is|`e%YuSmO3WpuvsYghKgR4lTe)K%b<4tI!1RQ*9|i*6 z!)u2CbZsWRhc$+u0H#R?q5}NqlPR{QUQ|y>btJ7m#o|n$Uo^@-W`*S%CEi&;AE*cH-?;1tnit_scDEvLZe z@!5Bjvix8))0uMJtd;`8#f5)H+~@Z zI_;;~g{5YhLtn%3i?5bFScX$MisN^l{_UOrKDZ(zcnmqyEZG{8sVgV>PSpe*8Immx zFNW{#?w!uDDm3n*ruamQWkJfYx%5`S*!R`keowMNWnOQuS}LumCl$|&j~@Ot$7}ql zGk>~S&b3w0Am+#NFY=TyOf8~Xcd`1_YwhjE<1x`CgDcVQB!rO}?LQ!_ymq^6p-ic<_7IBb%VSK%~y*}1X|r^laO zd%Xw4Q0yzi;@iC*SEtblAl{)k)YeOOMjT~&c25jcBnI0Ghqalh?=YT4fdwHZ+k2W_ z;IwgSzH~Tt$UM?&P>-E!)6cd~@I{HFKi==Sii&wzi5I9D34pTzw=H`qq+h zh4F;iJ#iC9lk9N?oIuhNY>l9a6dDY*(*a!q2+Fd+&$ra2G(;a%V6jFeU zHNE{N!B-AmT01BQYydW)xyq~8-SaP73g30eSXQA7orFK)6wCmo4LpJXI9+YaxAq8l zrCj`EmbJMC9WGK}r2~>TtbL!*_7J4L)JmN{2m*t}FuwwJ!sK_j@ zB);$24($AT(!Tv+ol7mQ*b5uuP^-vyO(^Ta=Ou6OSuP*19?wRFlrSt?CQdebClVPn z$1A-rAZVqRoBi>-`O$*o`?jkM=Uaox6GD$?LdAxUH!~JTdUQ>E{~5GlRk|}qi3Fpm zelZpVuZ9t&93e1~4gy0Btc@u98mzJT*(y*Rew0mLp&MrkaHNwOM|3^KV%nTeoxj4r)&2}xikCR>b?&Yz-=wFos) z0L{I&QUrV7RXQRZI7)%uK$SKVk4;K6H}}V6fiKkgeDXYCTVY0q<%_1g<>T_aHHi#y z<|JiZfC?Ki%3{oW4hN+1?bk(rOq6KD+2E5zk$^&fnQl(p6 z2wYxSP-p1~CO7h`C}IST7=X0Z@D~K zY{ruAJDIMqmCbVFh-98IfTeLk6gb@m+m~2OvQ04-D&=0gg~{VlBz#x_i~fk{JpzKa z2er!J$mjRH|HY>ErSlaYzIuMia495As@^%PTrzbN)dww*y)0BNrfQrwkXIZM3&X*pL=UNh!AD=3`qK_e z-M8#;QgzUtW0Fb4x04>9_t7n5WXx9xPMT^ok|DinP0BV@8YRsZ;&?K>mf?$A0JqrkbdjW%VUTM-S<@A^vYPt-nBfe-E8i|(Eso1X5~4q=l{`(Qfnw~@&Z zXm0dk+HK)Q>K}`iUIyJ9pv1b94_ex)$R#GN;y=auEZlx8ogz|}n8zr4%)nh}IqWmy z%iE4ueW@+ougMaU0Wr(DR)yM{@uyoptS4yL4GvHwr)FnPE^g@wl6`AB!X7@XFo-CJ zsdISmh!s(FhN`)US-6&NTW+%9fA~!#lYfT*_*&mzbC~P>b1m2&g#gdNN``hY4wft? z8+dE4B%sUCj^NcCwj8&oYm|NSF;0Bgt9+op-w=8*Zg$*J6fMOQJuKI47#~i*Ug~Od zIhyp@Pc-m2ga*7hYI58e4}5_yO5zGzplUzt^KVE0dwx3w9tj~=Lmo6~F;_J;q@RFj zaKKe7QS!CO$u4fz9+tb&AYM?LRXx7cOIE;btJ+mEKH69 zr)1?Ks@zL=b#Z;^<{0zrsnwSvms5s?b&^pdwDp-n)ap|#9LPK?)sM$0n0j380I}AH zladn|k&3hi9(B71JKnCVIf_(9f2<{Q@c1|d=3BpyPnlQ3?>yAJdY%|XfC6tatB^!r zj&A3Lh-sb^{W${Ih55r2B`TSThc^;M8IHeO?pu{E7k@%8?L965fMo^nKunfLEO+wv zKebTK^K()lS9Y{Da+wY9m=Szkb{0w>&a**KWlCDQQR)zAjOi%Zl%B|0z**&`YppK&r3Zd8xkO_B(dTgu-o3*P5NVeuEi| zYI^tljOWBi23_xwQ~`)g^k}^B=GY=$IcMwce0M@5Pa-j?CdRHQ2&OUvGZ|(i=+&08 z_wjPu6EBP8v+Dmovv|}e({A34iS*MZT97zV8z%MnFyZQg8fH$jhfPpP;u16bay6RF z4^3x;}Gv5CQfiNi*12QsR#-%ve|1lZ_b2Ott*;fBbhW;br zfmOp|`~kRqbebcGzDZtQ$fAYyG~~v0$G#^9-Qg&O9DS({C{Y0_w{dEQV!ImR=tq25 z7BIoLw!4|sG6p}nH#BiCx+gtADOSbWjuJ@joY)@Kt%k|;N7oyr?hnPl{4yLjo_M7_ zF(1t8P0N;TT4K%8`8!a_%rn8_%c7ehUzNsD)-;5tIM;x0!g*~^o3>ltzFy7yD{^UL zlqktrI5QuA;4M5c_{P^dep@eiZF3w<%Q<_~f-VmBk~<5dg3VFOLJ=P_{JkS2?M8^@ zp$V1{k~Ard0I?_BdmVQjbY2%7>@pe%6TiZbM#v9w3^e!;PKeK&z;2VI!==i(__i1Z z6F2(c4u(SGm4X}HSX;Xl7$Jaz+qgZSX^p?n>wEI0{g8LK8n#NircnZtfx{W{Hdll{ zo5hPnbR7HSGFeW_g=?U{L=4V7sWOYp-@c~(;btGuD4ON<;6@kCjeNQf%U(86r)GU_ zihX2VkLiB|w7Y3m8e}eeo+*5d84xJ|bj0B?YO#hOw0v0W0!%Kqd-186!B$29jzI01 zcq|$}XrSn8mwQtXlEMWS05v|Pv? zWv6Srr7roBi-Wi;?gf@G3j7GV-(0c0>)Je6QzL_c-4g-;C>VXJo?HB*cBz5v%VH`?n;V)SSf8i3ScM>@B*fQ#e7aG5>BVn&` zt{!uqO51V1nvJ{6S>(muOgd}?hlVQFP>O1|P0xn{>zH}ixZqBw?Yd*ft;e-PX?N4R zbkIuCm#{Cbyi})224V7x8`_vc+0)}EYmoSw&^e!w;Gr;* zvcri982apqP8d|ABG3qO<^HvWP)`Nd!t_nf)t$}nErgR-}2J1By)8R{BldyFZTiC^};n>bcbS(RYI+1ifpN1o5v^zFiSfMHhQCr&vr)tB*{1D zQt3Z;MlyB5&$R0u^PcwA9=`f?UxLlfcv}D0%v&d=*y@d`*GKausTCwnODJ*e zPJAv268^IV#OCRd^zX{7c<^__+98TI99%Cwdk$KYdM;%5RZwIRxSn?8QQxdC`TR)} ze#3zgB)^;y-+`AI?3>RiewbS=te%eifinIvt$#d{uq530i_JOR8TPk$rtEYTA_Xrw zbw=#=2)pOks53nAnbAQKfes`px#3?4#p)-&iH2&R*hEuVqh2j1to-x@8^F3s2>NOk zqr_O%+xrK~-)xnl5T@#9BxtqE^qhZyr5k`jYOo&XyO4xwK&Uhux0;2Wq1_B|=0<9* z#K^UbtOL9>y77hLo?quBW|2#5mlqa!b z$Q8;qKB~uzypUnt6fjeztp0^R!0ghK5r{P<)h>;DY&E&d-Ht(zd+|0_fJ_R_D{X|` z@aCq07tjl45}_UL)`>MyNl9e4fr@|w&you*4_!)I?ULs2$cJUnP_v@V+dtnuWw%85 zsYU{l%R_>XC2+>r_OTv9oJ-JL>+JXRdkMw)4b8xo-hBFg%kxsizy>mrl#2t} zFVn6aueVtcjdgi^xZfSgQE(!K@$)cz)Cx=(LeH@oL&BAYY0pyCO-Lk`r54bV0Q;&K|y&BBu%!~iFXEW9Xc zi;|AlJO1rgr<@{v^zs~dL_me<)oK3fLspOm^BMn_vHrdUBVynu@+O72w%n4b7zMyk z5TVU-F%f(BO8ofz1odPuUZpH9eI0FrLi(H%Y(R$r+q?sx?P=Fn^FrP;)r-BAu(}$$ z5CH8&P!~+M@ldTlgxNt3>SCs$(BeunMN=r-@=g`JeA58hz!fY;FS-ekCXX?6kKJaH zokHW)7>)aE*u8!8sjsffrc4#;B1PSmQ|Ee}z}5E+)b!8z!Ut7Hl+xy6XiF_{AKg`= z%8LS9_Dg8x6~ZQv z0nG0C_~v?ppF{D42kygs8g??AVOK*e&1}FBM90D{P#Z2C&!j=18B}|qC0H;SrE>fE z5`DucJ2f#Y^cgZ9qWJKdXd7Pe5N?;^&qAG%6wTM!VE~FfBDBzGIn0DD@V3S+D6Qfe z5C#pDhBWCAV_OFr_cb(Y4BMWPFa6O8%)<1|mx@M%1S+JT-~U8F#+`*hLsI>Pa>+#! z;dn1go~2$MKO7y8&()eufYoofdffCaVEVtiWRE9g{%RgK=evMSw36@G|K=e61Q=nF zPn>0f%7*q z(@wHvJ+}s2ZCaYf0-*?>lSWxEEv+Kk=<7!;YP&}8L>zHh>w%3G)8EI>2~_?gC1)l0 zrD$a02`2poa*2_{gx!=&cz$>Je`o7|dKtexRA+*?FlGsn#GG;pt~Qr+E$uha5*wtt zTpqb5)(RmCc&Rf**AqS>-Q9zLJI>{6z?i7;)@yC<<1Fa8=iX#~B zqQfy36f{y9*7sa+G}-DPwfIVXPuAXn4epwxN!W|RhhWqGh(h}$xwya@B#yP&oc2k? z2Vyj|e3ZHP*HI2jgqW82`*V5Jo>xWEJ%&Ou3dw(g!gPMZUTfyy58@k{&01jv4q6bd zj*O=|5xCD0d}ePEpMgJe_=&f1IRKSGxtvN$R(&-NOpb_1op<+6T1CujpG60SPUmmn(lPtkTDP5lyJ&c#y zfMG*ExsJ-K={B!fi0a|j@TW7L1@{EUM2Z5oU!3LshZveDXTlq%s{rv9cj&|Iob2Y| zY&8XD8ySdMS0>O2wlN{!M0b2+V7ZOq>(8RL8W!h&o0j}jd>Lg>bn0bY;KFB#WCb$5 zu)mD*D~FXAmz)U}Ei-=8R>MdzdR9Zp;n-zLhw;+|MfnqC`YN?w$X77!*<*|MB@S( zn@^rStC-EQOpI=lO4l0Hg9{dlwVDWvY(jHniS>urxC_aZb4}W}WYS5!vmPQDC6$HN zD<5E}G-LCd`X;zQskWJ2Z8ulxN@ffaCw7Df4)Q(xKqw2i_wVO9?N0-giNxnc>zE33 zd1bgZ7r+=F=(vri^N+V6u;2olG|N)6cj4(2)>!H=^uO8;4o{W1z~%ufe&>(()5cIJnu@J#!x2(00bTOwsl0kcw(2)ZNz zD(y_Pl`dd{n?22NwJg3;ts^uc1 z>fMOx=7FEuK53N~<)TG_dMzKcsi*5@>OoNFe;b>FiN$s9Z(tvWHvI~({7kT3ry!F< zh1vfZNksvGvm+Y_ZJ>l2A6RL;@%)K@nt1w^oF} z-&5w*qrW%-LpcR(Q>%Qi z;c67qz$7RT44}8^AXIJ%#T4p^b)S#}65F7F9K1mHL5=K+Am~B=ZLqvIjew@oxKomyfJt7f09C;>1-kGNIV&D^TxPEVgu=e_uUtG6-D04StEt z4VmYtgC&;4m9;8i0vq@ZNK4TEGIX$Yvz= z^!~~p0ECgACTn5yhSHZ<}ejKF=d0+b& z#}OzeX~GgRbw6Hq)NU%bx|qq*w&Y4PfGkVaWHBpY&CZ@;ujF&XI1?Vy%%v{$sMOrCO$NizrE}OC zVxO8ox3&_*m0@gC57GPG#`9g}ZXGpaH!{R(4M*~j^2!HUV|TgkwP?_ro2kC&+@-t_ zJD&|CE1vNhWm>UGOu9*EWP-$DjS1Q{CNfg}Nsj;1Lv|1Sq(NV`)E|V8 zpwn0Lb|pR%{bEiu>+47_;b<+jwQw~?>5D1J3ul98G~j^2Ptbzgs!t{!7oyggH8pH5eb8CCFSgC%9$vS@s(=-k zpp`(F+4{d(D{KG*X3R9Z{ufmdod{B$c#p^ra7$kQliR{($|;eIQVucRnfBRWn-Fzb zzjFLi^1>c(gW1DID8wYkYq&i#_ym9BjpSmiVy5(9xGFBC4uOa1UF7S8VSHy^-lvnV z;#Ei~njI^#_RRKO!hbp3Fl%;i8oG|aW@3an)myPAwlUc7dmnWr_EM=p=)30+YLJzw z^hatw!W=l)lK`BH_P)j)Q2wJPWxnN%QVqoyFU(SO>Cb2vYbSB%9>VIZOnP)u(cxX& zE*J$pa#xG!e{SPdFA{C1#2X(T*ALapb7|=6x)R_enwlLgu+jHrP7~G1-5G&w+@p(j z2>N3o_kCBYLnSK@^mT`Ea=DU4Q(v19KKcHnVC>t+m~nW0-CByiY#0OLYu^P-7@u6!9ROwsaM2&?qs!mt=YJEcC#hc9QKB=birvZbFWGg<)fr-t0c zqW$#dt1gzXE83ZL@i2cHM;_^FgeZd2?z_}TH}E)IaW7yt%=|?u-c%fq=3~kWvF?|> za>UAmCuM5G-+0lIH@~vYK-a`~Vdn_T>*3UA^9Wnq&b9&lXwSM~xZ1?&*5Jf16kq1D z<1YD;Jx7ZQabMG5%m+Ua(4Y4%T=wu+rr>j5`N%mh5txKv@7jyjhM!Q5e!Y{8 z_~gp)VaxD&!q&|TmR1bY|LrG{0y+tU)yz2DG+Yv3vl)E`xNd>`gWj(XJW$b1b;g~_ z?LMH-rM)YE=0R2(Bd3iBtC#19XjHrE2O4j+R(#bM(@paS(r@aDzN9o6^k?-EDv=YS z<3F8mD%yeAbh@izSVHkMkf1sSB@-ENbBHzVh|6AbeMzzL{GK`% zIVWT!jFzBBpTJq=0O%ZVwri*IrHyL+=_X>m0S!Su0+fqqA7zf09eH%}WLrK2vd$GY4cr9bK(3?uYA@QO1Lp?R z%JIfD6kVrW+TuCUAxhrKPt)p7zT_&2NLWVFm3m67V$uz2+CA(i05%PHN^>YY5NE6! z*y4JDFN0BDP_AmHc`yemw{V4YS){7DL7lNkLrOvd*301QapG(aI9G^D5hN8l%xgA& z_Q{Zs2c?Dg)f@~ycdIAvbq;}|d}CJ#n73%|x!@YBry~+y1fjDtqoptQ{!)HHj_!n| z3hz)Sw#ptB9Z>|&L(b2#ANeOOlrnhOawcJn?D2HFc7lPLtuDUf)_B3|&3Gr+M#MgP z6CE{cpObJyROO7Nb}x?vI&gjAK?DKE_<~&riTLt(#s6SC5peLcCsp^DMj9?{X>g4Q z*;6-)vk>u=ms@jI1IG7nDgA??Erx2K!kGEW5PH6BYo0QR$;k3J0W1BtR}7n=X>;m) zQ6}HpkAl!?Z$QmEM3Xz;(CH6H%kt1HDT!~~eqg$9harW4xjpXAH-;@^*K#1yuSw2h zXlpJiJB{mayqr7hxhw^_e#O<_WfL_V3{T94L+{sr4X=vhy9a9lKW!f@qD3RE{}X-x z6jzs%zgh13no2{H0{3zx8kv$piaM{wW5ROOk;VBT*%zT+}(K7J%%_e*}U~M zeSuYmx+x*RHPV7y1GG=7!7%YCoxLduju62UW1@@zj1GJVx7U!|6hR(`-_%B`4YY<6 z`B)ZrV_=g29wFpKk<;hO;w)HJW^197n0G7J_$U$~K_7WnQ8%EpOBi2EMx z&5RoG^kS53*ks-yIS8+n`jvB?8aG`nC=%7&-?;X3+m+aFe1MA zrvF!&kL(NgnaQP%;ay?Es}DFWPFz#Lg2TsaC#-P16Y6}+Lz~jXN0;=SU(I<11T9-* z%#Z;!PWoB5s~4`G(eSpOz`_|QUHSMGr!3Ks8>i-ruk#E$LAIFdMcc|#X_>%kOTiEz zgJQzaS;{T(Ep=fyjfWH|b6bGcz#YyI@+o_V1?Ge0Me}N`FtZ#l$`gby@I38 zuaMp&0AoHaM1q(+Cg0;Tjl156jSD=>&auvZ$P}tzRLBsjJeGlmvmb`xGLVjgYyY!X z-iad#w-CNPMg#+UZ^RI)BxzT-xDnu;BLx$ zNe$VwQ_Z^w-jrX&?-jx}MIFwGp-f&D!=AeK{&RJ((CyC0pHoVY#s(cc=b_dDh%{5^ zdGjYxey4)T<1>#8h^G@eI#6w{iWfAeDbPzzChhn=3c~=u4_@mF)^-W{uMeWyg!5;u zFHkq!MkWa7^vaV@BeYwLBN2(HtrQU)tVZ}ePW!{H-u!71{bj2NBEJf>O_8Q_Nh3ds z)yjhZBadJuM);88NEp$%*c3Uwy(+gsUF-)Zn=!vm8Kue}G|EFG#3ve5>#15X-EK@& z=ovpMs98uVHgv03uV2xG*Fsy8cjcI#>Hg-E+PrPLhx1W#w5j_`v*AT0Ty!9PWWql2 zIGV>P>84fJy2)g(b2eO}ok_1z zDZ;R!Q{AJDz8%Bc)aHunbp)odOFG4>(IeHxsEg-Cl4?eQQX9i>!gbdh2C@u`PnBV!4i+TX8EEv z`Wm!XitLEt_5t-Rzjggm7Lm-4KJzuqKg9D_ii#MY4Qhwt)NE?eNY|}+r}(K2>g@$) zn>uSHNV=zrN0pQJF~_Dcgh4T^I1Njtt!$kRAZo`dWSZ7$RpG;IN?;dcrAeizaN=-6 zS-v$?_dDHZ*mK3MO@1F)X>`CZr35e?Aqp&(w$5^AG1$DTeY^hGb9D=^B8|7F5X8o~ zjJEy70FIg&h!QpBv%S+Xw*7LDTMuzl9L?8CWkjY6c^?)y*kL-fT-zmL1y44t49%98 z73yrV9f{H6(r94pMH3xjYQCLePTsGQF)8ET3FOh`EK~EE!v+LwR}qy`+d0F$rWBQR zOU+4VrQA$Dl@cC>o{nVmxi4+Ts9Q7^s91ggaugEsrG5CudP^jA&lO6Y1+qEbA5G{* z51l+B`cXsyqve)NN!2ScXclZ-W_IQe7jC%MagZVq<90vvRnV?f7%bh`EgNTTe0;TH z-28n|(rEHG9ehGOPdIk|FLGlS=LWrKU#M zG6{s?m#1aZ@^2`0%C+Be7+Y;U%T4lt4qm}qBPHkZL%e2hf&ioJT=3=kGdjv8n+DT} ztC6&ZZRay{lQ>-JUg3M`b+w9JY*tt?f!f)r*1K8lzE%RYRgsn-BodR^YM=L*`~f>E9=YWc4KPE6%jg;ZKWsA4 znC&?bzb>@E8KHOcdU})gBBnAjz4xaR_`Z|tJN}lFj45x~In6__OjFUlKM$!@mzk6E z(4(3|O?FYa61|2OhuJ`|fyd`X*Q=s>-D>qW6|5Lypxu&@FdTR{7e<^J(jdZ~{X$@5 z8Ge(~g$BVLZXki!gH5dYC`n?ht|A-;Ghj^A(+g|-`1EBeB|L%e_jO>r8ps5)d>bfqG{mD&J;E-~d-7S@5_X`9&i~{@OUWixSS4-!Sb>F%VNdX+*-!C1j zLJu##8(u7Wl*058khaIp+P^MJ6{se>_S0(NDpX+EaXnjO379(uK^^2|)6NQ>$zgjb zXcoHDa_SP(BT{BCb_gX}+U&+`>%T}gbznl!oqAbQ4(}zNgB4OyjBbL(?AT*Ky=@}K zi#r~y?MUcQE)A6!tBn zl3odKg0`1^kPkVoD!TXGxqegI+-G;_NU}$-`l`oSaPXXCFJFPV+bKpUZy~9bmh9!O z(lW$l0&ORjz~l6K*-Ft#lGn!b7&^~z!8fOZ7RF*soLw3z<=Niz@fF^NYBnEF9czfa zsb@Z#Sbl3gZAFI))B>^@kN=X(MlzhIqki0rS2_9kP@^9GcO;ER@hxo@%v#}%-&6UU zG0m!o+4sb6s_FDI7MVrH#M}EZw})+Vf5*e0iWZ5{qXehUY(DuvY>Guyib}C@>GE-G zYNF%yq}-tGyJWN*P!GJP(C2z)idXf$YNFL=e`@WMukJcu&LfHPsioy!U_+B9pKm2)iO|~50$t7`W#g+T!^Ne!ve1f~-?`UY|;^KM|1=ko{Tp;PH3Hx(@`CLWc zPT&}#pb{DSo?pUggIOlA{g{#EmE;{*0Bvw~-8E)~JxT`JZih3e3EW0E;UkG#tyjbX z_Y!w^%WDTTE(h?_E6DbVY3h|Lao~f{>zQR<>*M`>Ss)2#h6%OQR!9+)uDs|M~dRP=9+#+MU%n z*8Ukmel-@M#%`3W1xSbwru%KDAt#9|r5Z5JdCAveEmo!*3P%Y7rBA?JJSp zOv6Ryn-hu4<@++{Tkqc&@9Rsg%s^-fl?5t3C{}823P^O9Tt2W)Nm1$@1{K(=dVSv@ z&V4ty{7Q2*^Oo!6c<8+j zRPFmqV(C@NpvZbx!K{!jV+hQFC=g1TKsJbXHNwS{_fuNcA*u=lq4vddR`MF+L8dF( z<)aEYGf3U=v-Of^O9s`V;iZ}Koe1(dqsXYIcOQ2;?!6vVT0H(7q?~-ty;sw{oubDB z7I!HNDdHfW60$MFwjIU*yQV5}WdF3L@v*9RBvVzPv?qce8?qGUB>QoXfJCcScdfW3 z$=K;T;;6({DYZG(%8ltf~KN6owJlpi;*&&4nYMD*B(w_6aC5U>Po0^b&ilu2ZciUVFX_#(|3RUROFOT z$SIw|*grgPgnjOgT9H$PvoWf2>XihgUB4jwy0yPQq`{p?k}NdXtiBKApQ`br`#;KL zsfjB`ojCE29W;6C_BggGS&{k8tw$2FS#r}LtPb>{X=<6_|q$?eiL5JR|#`wH&vGJidvpdlppX|xg#=v57x0K=)l^ID@ zmlDWUhM|4X-A)f=HitO>s2eMZGTbj1(-kXeU}s&`F5do9yVJ&MbtyF_O?9yb65HN-f!>R*0d^)$4ieKWZOje`RXSXDHQyjW`q6G zzC6@@P%AO8FoNl~{rrB&?m?e+dOf!D9mEv=JanTfu$(Lsj*aw7+RP8H}-LnHAbp1bmkT@!m;=*~EW- z80nw1oxJ(q@56qK-xYp>-5p=Vx^@&^o4|?h^sEWB2}>s^rT)-8Ql%wh(BDCv=!=IF z3nl+sJ5?ucgfv|jxq=)d8Q0$0s(j3mX(TROFDCJ>=!If+c8z3n%U6|O3hP`LQ98es z-+&t)WK0dDcjudk6ED9P&$V1_2pW?QmbN8|vmhU@VhPR!3l3!OiV7O%?umY?b+$vY z4Ed0JB>Ua-vLz6_bn(}12s5IVMSB5rw{P9_#5OPW5uF)>3B@H2lbm(~@-!D^rUWHy zRv5_{#u(A=>7-6RlG>PH0&L-$g}Tzkrlma6Y6dGmx)ER6Id1acN?MDO%2BTVHU`fBff0$Hp{*`9JT@-RPj($tn0<8vPmCp*nJHm}?qc zI)`6UiDpuR|9-*#2|Z7+wVdGbCYj&76Z+dZ{Oj_J0LXxMHuwz#v;uMmVX@6fhI6W< zzBFU+)9(mG%1^^S)791*_i{jg8*hlpH>(5Zbg7>Sg(=UZO)PSVd7 z&$U}MLw-7%S003K4=)pze-;Knu^zpza_4^v@%jwE5A}5|vYZ$RaR6kbk=d@Gnxj*jd`mz^kSn&Vo`VOcjv##qAQD8tJARR#h zC`gmuix`R^AkvGpfOILLccc@lRH;e}9Rh^jOX$6YE&>6m(xv}#)cM|-_x;ymWjzUN zrQGM^9DIbb)}+2Xf-{sid_P z0XG^!kLOG6>7%cwC=AN+RLznRa^-rUm(&0ogdt>7AD=-M;mQX>*i2Ed@+>?yWolr5 zH&=iQ(N?33u|>4Rifw6c^)&G;V$JVAceVWsXa2;VzaOI>62-`y>atiUyOa@wZ>^6n3T45BTXzCTOUz zW{Jkh;5O;HQ1dGKL(>J>Rj`$WMtbaj&7hWNcv>&dUy3%F|Ks~#N`L+$?I!Nz?%UEE z&;8EHt$_HQ;)l^Nz6kL2{=U#DVp4(C-;;aUXEoj%u?n$APkz{+OW(CU<_VnHhdHFp zkr6IEwYD@cSv@OorImK5hfMHvF-a^kfK6r_YsF`@HlIul?Y)?FIIRP~HC8F%Q#@@g z`TG>*3T+Q`K&9}m5XH1D3-S}hHZ_BVw+xH!#tyUY#%J2}9^dg{7&XUalS9G|m*Yq3dCi&(6O+c-=J5CMU_PaE~_~VWBBC z{GPudy3u<17R_=Ei2mrqGZy%sm@DmbfPSHu#QPe}^t{uBjPFuXaj`>LOi`Epmcbn_ z!WzimyBpZe3ZYC>JBaLu7L31TXCl;pu7 z37xTLm!Dm-N5Ax-SWCB{s4CIGTZ#7UiEF3vz2oDZ4|JR+^5=$x@VVos>VmCSg-`Nd z%)!9-Ra|m5R|R_?*Q;~%s+Nq8%$2@wf>{`h^QjK$Iqes@j&0}hoCN;q&aj6$oS-$z z{H@uC;Cb2UBu?|fiUV(s`NqN%ZrFn|Q|Q$;1Oq$Q6|nq;CB%#Jtg^DGI0gS6TmbVf z4W!XWbZbU$fW^7lxME2$W;w53D826uMX5jP>OK0N$pO@z@l7#r7g1L~A20RV3S`i} z_~h_-e{T!uxO%^7dm)J$FUDHc0Md9@*Tmad0i?T=)o&AO0jGCXkSY?&sii)uNJuMb z8Szxb;<6%^ij=-azGsBF? zA=3?f%kzD(6YL)m%J3~#Y%I6CkeH;Qr>AIfzK2O>;R~cXcSaAhbT86k`XVkK@}$Ic z#2OXB(h=yudHyD5XKSBFhQrz@$JOJ5#zUv~YRm*~Id+5DH}u9=1w-b{1C(G4w&uCT z2m+VvZdgM)#pQDJS&DFSP@Hff{RT{qgXC02cCc^AO$qDy8ISq%+ha!T>!mU>quu+Y z7IMf{dg&qKp37)5&VX)VQCkHz`_nk!i2ShowkKI5u)9U_LJ)7nH-Lg3DLEW!_`s*t zRF5OZhG{FCJ;fkSaK(TTH-;kMoah^|-|W^|kKpHOnb0;)*SGi9?Sk z-b@o+;=5POrG-r44|VR3+#sD6n5R}g`dUi2HU^>mmEXI*!8$t0fNjK|r~-|*-SN_i zY$6qK-KBy3Z{>ArwCPS6)kO$5Y%lxrg+H3tJvuts-&)!+X99CdXBTtQC|Gzf6upK8 z#!6h-6d2~Sr$#=&)~{xYYOEPd!XOegoA&^6*32%l@5cFdLEs>^Zo>?nG@HRPLU+Vm!>1Slrl`y?YuY=bu724? z&>sG9D*k|HvcJIXwi@7dbq)(VF6Zv7&9pc#2AcXc0iWa@ELU7r9Zo#j82lNu9q-)G z$3FV6(RV@$0_9P#!boi{v7 zt)q#=g?Ei%g!u{7Hvu=VzekjldgfIbtd#hUW-_e6zNHY-)I7lr|D1d#&Ta+~Sh!xa z*e|gTZ;TU0dm4*Uq>b&~@bE$vi+arikQx_}FK$Y%o!!rS>~(b}c0)eCgN3HlzF_gq zezLpZ8>n`!25p(}8c&`Up;hlUl$^a%Rj;gW_ka_+QHderJYKkOb&h#PXi9~{?RRv? zSM)tSw1{x~l~cGU`rbk`o=?}V{$ibeM*eRd*jrDxLSs&EV}>-CsHN;fM9$;vTess0 z`bcT(S551YW5;|ALVdVVBa~0C#VAu&)rEjvz)Y_>-iKLY@9=@iEXh_B4yUc}@CoK{ z1yM*1R)?ANxZ~!OEZ7$}%BWER#<`?u-^6nIOffpzYPyp>=qsfW_d>7Tyn%h|Vrfdg zw)Y%S^3*gd)50?mCsqwEB!0W!&B*H5dJ{Y1lmFUm`kzZeFE+r$1+*{KRFRa0Q!vXR zT`G@m3t4gw>q2E8lI_fh323Q;V4ci%;>`%H`}}5viTttBv9M+ZL}t6j>&h9G!z+`oe94{@>l3lgNIED_kqF4ghHuvmJig9F`c6SsbdYYIw1f(JFj0U7 zC1GHD+_r_>UDY7oW9pG4+d!nx5x&Q(0j;v|$ zouN_m(vcd&onT@H1j}DkV&N2X^pDT@PWn&&g_z`<$QkiXA7mF72gRQ_Z<2LcJT^LZ z+C8Bqve;_qh#jk|VgnLmqs(u_#fbr8B}>hzn*JmYq1d7s3($<@S?eFqD=>&jBqez0A6xOqQoL|xhwQ%qmMD+kmtq~{ zN^(dkE|92_dBeKc+}OxYrdyaJ2HOKT1}yV>ziwQ}xPmHs4b;Ko*N|(VR0=-2*?x?W z0Tp_^FROT^hB%zTA@;F;HKi70_V{eOh`EM%Onl@ZgK|`qRBlC=W9^zKVPp%fUXM_BIWt;2dSQpzuEN1uW7vF!>@VO?PubjCa zQ@h?iaVr|Oxoq%!JVC{(&Uww4Gr;+{w=+zQ?o2iEk5P7;v<6Q5&8f55t2E&CTh$e< zh*m~(#qgf0Ev1Q)seOvw-Vbq@6IPCvq7G06fHs*p?f7C1mi?_k%>&fSwizn>Vx$Nm zDUlTcrBlHIZ+Q{rP5BoJhdR>O`F1Pt7n!>3Z)q~UQ*#Ll=koa3EggR`P(OB|E~d|? zdtFSc4u7vIFNMzuG>g5bHVDgSk!#c=?A4n{_u5Qx!U?C!SRm!K!1Ut4)UGs34WPKRP>2#A#%w7+1{-tIu(Y zdfPRbC}kK+gjzqq<@&Zt64f-s^gI0n;EqoXS}tXDq-Q&w&GPca*j>D&JB18xR%Hlq z!Z3{m22~750}<|*{%StwDrD)-s=C@B<`L@%yW=FJnH8V7E~gPYz+%dJ{Pc~`Z8sM6 zvF3iMX^5<9F>dwONSdM|tALs?9cmS`>RrW&sz$edW?34(WgHrZCqb#4T*nQrz=;_t zw!ZHch;-HwUVyz-=5+Z-lBMl+E$>JmN<;Acf$`2a^Fdhne`gne?KA}3H_(Psk{;Rz zf|_lUm?GV;Hq)qrPt!`nVF#MnIsL`!S9L2#46tqb6jMsV0m-OZZ!H|u!k zrJa%97X)*EE&Kl??r)H|Dq%NdeirgO0ss5vV(#+CLfi-Gjhvr~0`dV)8Y89*ZLBe7 z0DdANf+e;QbHD^Jn@O1FM9hZj{U`7FNw!QTEXp?A00d(=inuo*L<4!nT#Z5X3o*?8 z%%&7@i>{nlHv>>58*aBaA}hJ%`zLR8kA)W&V}o-NXHH)VkrhswmET4pUV>^Yr#6X4 z$(xlCN@&TiYoqi)lQARP5Od3Cfg#LKTn z$3a7W{*2}Rwbb30_ln6L(S+|+;&Rz6#6Lz#jkDme(3dm<%~(u7ebKwLB|k~n^qgdK z0AbW)^?<5WhBEedI;j;8noJ2u0B^T*!l^ja@+zHl1u~?Gv z(?NSxIjsp0vVNJ(m6`hd1$9hUScnF2Ae*_8K67=N+CUDJte|^r(u%~ki48e!J8=U@ zvJ|e@zE0tPE?RybVeP4zJ%1L^!3;9n%;DutE%1^^_$Hf4`M!w(gCEzVZwqz(-akH{ z5=9GEh+kP0=(`zYju&dLXg5=VQ?+=%d+Hp0gY~LBzZ|$W4?kdt2<4r0yCi3MEnduA zy4dU2^Syo7YeBQKCg>o)^q@^4Vol1hz63C}= zB?{NSt3KKH9OtfVWk^K=_#V<1-hH6rRYXb}u=gNklx>QG?FzJ_+flW}R8y>dPrIYC zukr!$r)|^*8&cX@B(p(Qd`|(PCeI|Hh8lx&Z6&h$fMMfnD`f|7 zjqS09dl0q|8H740TUxHS_Elh3IbfiID2>#^7_-NAU{)tYnyJ;-jWM;YrzH5M#DA?! zjJCmG_|)dwD|hF=87p5r6D$f3K=(q^!}eMbjAAnMF_eo!gn|$hGnq1I`C5r`ejbb`U@RqAnF)o6i1&oU#Xj!)T~-Nue~MdagQ%?-5l3c=N(iA zv{y8sTY0|PSU+>_9N8OWm?TadPqyq4m1B8A2<+9me4<8&>_nW z?1C-{g^4Q{RbzMtjpS$C%&Xb!5kWa{I)~yB7lLx>>+wyV5nayoXk7KSnKygJiTq*$ zP_ER>5gCw9W`Z1BN+&}10Hr))UP!LrlhJwveSDrK{z$r{^QpX`=5)zk=R@g6b;eos zFz$u}_19xqiV=7W?!}oXar?F#;WWsO+!V7=n`a9BWlQYp6^0IJHQmga z5;aO`A{j?&eCFWbj2f07D&^ln>_0BZ;{M>55K1Zz<@VTgh?MN3As&P<0NGDxgzvBP zT3F85OCIo^Q_rTLhsxkw!S5hkXmHZ3Nu9rM}AVMF* zmJ(ji{YP+XDwv>|qMdXP!I7z*IT)!RSs#r@kJWY)&-|IMZ?pzhs2oMwEFqBi19l|{ zm^GklAU}aQrjBcS(7LkApC4dZ8%b_+Y>m%;Y@>ttEFwK#tnc~SONDoV==6dqhno2P zpa>TVBWq%pN@U*s+tB6_3bk12j6P0VjcP=i!{{vnQ+YEd*rx zLoedMZO?eqPuadj@Vz3q94En#`|awq1D0wJ9zmp`B~4p+9%onyHT4uzOL2%V^bc1# zumQ4pE0|{bm_;10X#8Izj>$T{J;CbFid(P1eZ!PH0HID?0cjqv;#z$$`@qW8<#$ki zzkJUc71Q}iur%i)hh%!8-K$@gtMplb&b@5Zz2}1T&yqy)tu9m7MVvL!u6C6{H-+|- z$O}6Cha^?pH&uN|c&1MixFiB@8pYd0E{pHD{2yBTt!S*XV0p)G=128K@cn-2?|8k= zipAZQtAo@EAGO9cFfAIyh+M@?xA9t8ka`{PxK>pMaLb(~Hsm%iRl0$4tu9K8x=n4z zpnDwmE)j`Z$QuAzwCtaI)(TGx`5&MM0CrZQ`rt!YQiBnU?n(!Fc3<1okDhK>B4+08 z4;FhgSpPY9l_qC%>?oDA?#Gcq3NYyciK<&Yy_f2;Nbn4p<@nYN|HX>oxcP+vo%T7<7O~#hX_M_Lx3XQ&^cU z`()v5Qqtksj@(Yxb(eNd^I|EUq@jfHq83V02M=gF?WZFY$`yOX^UFygPXxC+-6%rA zqYl%TWwIO{4GJZ25uZ^B>yjlkMTh{dCB8pk;3VKOp|%90;k92g=rTv5G;(#$|ExE+ z?)5i-$IF}R=D*mal6oxy-W(iSEdJNDp)kitV0RC@b_ahkVE-YQvN7Kv7^ZiQ-y>yU zlhNF^YM_MZ2b5C4+i_XJjv*%M=^j}$K%MwIT-;e{1e5H)??roLG(-gTA~z{O^_-pSbRjZg-XV~e@cuZbu!u8oq zVo~5?9rbV`3>ERO-dY+x$kah_!j9?H25BmJVJ|a#buLXe8{P5b64k~m(W@!ng?TM=zCu)wAA`n4XNA3 zp*C?tnIWmt#*?A|6QV(Wpe{{00=pVDU4{T6BQ_ldXN4!ty1n`-g?@3w|2bJ$ZfI}* zFQNCNGG0JUeBdElT~}qkP2F^bq&Ptix37#)c&_T>T(1=?x~{rry{M4#0U`XuX&tu# z`_KnaBLtJT4N13*J^-1}IP+W=jP6&5U%a@I5w#-0#&WDt!!bXYSg{G&D zdo`J(-YLyIjVL-`{`Pf#6aKz_B#w=@8IibwXaYI$p>f9Co1bo7Ag_xtwfo;c zAgOdjYuvpG^xZ{d`8t^oB4#E3#J;SB{4|to7uTMkW+3}GYPWmvoz@IBP-h#8(r{B> zfhYp?BKr`)7Ixqq@ts868o(`qMO78EKrPL-*&11oK`2F{@N>ANs{dp;o$(*DsgR8S znKHJtk+jILaJ^A<#8`OxoeaTa;`o+)2HYjJ(XvcJhJ{tT3ZZw*th15{TXv8QILG`G z_pxSJ&1l80{S4zeE^Z&(sRZcKL2m0&2jC(O=~|1>_v{J!QeXISjCJ!baOK{`B9*JPC4g;gx zDvitrf@bojB>s(uTCmB2FjOFZ`v~HfTjJ;BlvF1Aj!v<4&rDO8?`GGnGM|T&(YQFh zpJvDYWN89#!8CB|Dh^yO@{hE-_zcWXnCGFJ8o1nE8_3Dc+ku`txv&+Xj~`~OsK$d} zD$!QNaZ)9B?eo*6Z`ms^XxemP4it9=dFwrklNZmgtxoMa`XIdARK;zNe|xaI&sbw4 z0s<;w%4ojut3-);9M2G2LDI?Q&j+tw-; zoeVtYfxo!=BD*Rs|kqc48|XH!$F{BkikTp8t(NQANk zFoJbKRjH7RvTjc0;YK0oD}j`{kq?Itd3}rltXQ-BH`v=|wSjtMP33~IT#t9F;I<;R z z+?2MNX27v7@7&wz5e-7iNwtI?=wMgQ1V@xR9;=)`l(Io7xjmO5kT*^b5(+Wi?XSH& zC!v1G{~Dbsm)f3mnp9@nt5hYw>$z!yuSm>$kUpd!k&k+6A3cRqUArfvDsiA>Ex&p! zxP)^&k<>MsO5qVu$rM&B9;JgdXW^wkW~o|i-Ujb-OBS!rEBr9_K}hqw7AcLk{qCu2 z>h$7TFzVdAWlL@HUi7TD-eBLZijdjvZye|^^LaE!K}Q_cyyumKVVQ&J3hFWcd-4dQ z!L4em*O<&V>A1;WQg z<+a^DLA>9V$h&mh{2KgX(CEcvYvTIc5KgUQk1gKGlq9A)>o_a0xXJfD2>(~G(G(&a_O13GwLn`*!;&rZCl8Z3$J2_34|r9jp_55(CC}$nJcjEdL0K zv0%^Lu2mkp^8+&Z`8|4R&>UR=?cxb`nyvifn|Hx+W9-H=x+7X@X^zl!rVUxaeXURS z-TDzdD=3D{YJKr}53dbtE`0tW3(*|*%<7WG^iG7%6RJo(qMmX)E?{+J&Yvoep~BM? zR`Q-SO1dHY4vNOJB06fle8?h5s14!|d&})fUW7fHA|a>37}b#p0#xW zc;o7F(#@=*{w$Tetaj3V{nFP3?s^j8MK+b-vJ@5=w^~=wvZuzxRkBbr8j{-Gf&;K| zmdiQe?=8^dD>^?EP1^`2BkCJd9q-KNt~HRY)AT_0v!Pc8^MDKDPGX4>nW6$_AaB(t z9D76ypy^oU0iGjf<-t#1EEEkkVlDBK^WnEtJc5mj?|GS1 zd)2+aIkwxFn$KE)SCI~mP7iYwKnT5S1b#+P�|PSrQF9*mog`v2Kt+ip_O7@h`Bt zagK}doLe-K%YKN_dzkuEI?_R1f;L98=eXR`Og?!hiF1#GlD{#&a5S%%vppB)5sWEV zoUobx%#qeBr8nR;p~?Hm@pY!o*)cEPlC?+Mr0kzhi>>ocR^`ZaRhl22MTmt9F%qj- zJjacMdvJF-Zd+Gn#0)cZ#&MI2QBHo_2K-9V4-&Zo5dThci$94vc%qp!oqpx`L! zkKm(k;Q3{#e?ej~`OlwQ{l#YXJ8hl0nAxvl$#(EMlXNY&ww|pWy&61Uc=S?y^NDe9 z+qCz>LVXB|l|e*GJA4}td~WKN*W|`D0g9C7h2lO8J-NlE4jpyS>>S2%W|QTY3s>e)O5y!>^QlajObVKOHCe8h@mn< zC`$A8EZXr$2s7WPab64JTVZ;*1!gy*N(cFQ&}xVC$+!({NQSyCOCiVBB^)g6Oh=*! z1Yk-=T*bzvkwXQxue}oDpBOspHo^cfAY%Ff4^uzI@MfO{sTo9C7S`n_?&3q4NN;3M zwoO#(L4oSIXuTLo`7dIHm#?AA+#H;y4F)<`t6q7Y9NTNFt`@rEDay0WK0dbmo&X)M z^Qx;m_Lyw(hdV@6Vy8LO>onf^m-~TM>SIHxYIXysc=mTlW59BOoh6gKbs&kJP1_bou}LmY}sGsKK@~SIpn_qbPaqdiF7>XE|{XQ=PCiC-uP%r-3TZL+)v1>5HJG{ zeGzgC1jQB#WLx&_u%$`|_;MxYRqQj-$Urt4U}p5`t3oH0A1MGk_b13bCZcXMb6p+t zk9V1iHQbx#2%vc)>>;Nze)q)ly6|;r4|N@O8^wt>QHxo@E$SnCyg!f8 z%H@oljfoQowkik#GW={iA5ZH{L2pLRr^3a*^I{@Wv;0|{=0XIQuGhvUQPlC4?Bui^ zut-mg6^Cxa_Bq?Wi60n*>8f97ZzD8?g-JYAJjenckJ`LnCPE4cCR;>&;!E#rB>>;g zq^x+r*F?^CPg;xhhK&wOYxyyIEBlBx#j~!1%Bc#iC)32o`wd+2e@zK|taOP@Xw*pB z6F(um?kmlM-2%zBZQrGXhyVGPvk#_fKVE1 z<*^Q&5ZPq^Fd-J~eq5Um5~DP#q>=NJmRHZIPk-YlOv*egjVqr4M+zHU9#0M~{^_E5IkX- zp3m%LNARJF8;yx>-Lr0pll#)OvBTJVIE$pfs;>v7QJ1wR=Tm(d5wmaqVie2luzAx9 zqP&s|E`4CPtcWEnW2k(tEy)UAUwAL2SB&$)U+(Vbs~HI0e>Y;;5~maU*&C$ z#WM?h71w_+pP#wd?chI-oA);9lZ)YfpAVt6cd?86Hy?a7Rmud^i1Sd;#2De|q^AYv ztR_EVd8dQt!WwCM1*Edd9zh0m(-82tdn+FTsMoperM~wz=z?-cA5lanSX|-6@T8lsyuY}Ao7A2P`TE+=ANO*dERT#{dAs<>@~56nHX^JeD9`Yf6R6RhKhu{w5wdWfa?6Pplb@X!l_heaSV)Lw zEq9~#rEc~w!R#R96CLp^=Nc} z{?bx0#xlX%LI+OrA_j!A450Zu=Vr>b?z8LwSYzsUY7QeiudgP8*LhW}8=}jJ0^R@> z%}v!sb5Gb6p}g_la&aMC?1>IIwcqtaX9P{DB+YDi2H*>@=8b2gcM7PNGdB#pul=Go zXxLfL$ts3bYW8gFQ*C~ENclLa)T|!oh&32)qO0%h!0>eYCYGL8DGo1RW&jEqK9b0k|U&+K^&^Hc&npQ@Kk!M=;ATDGWpzW=kUK(uNEGn zveiq<&!-yym>cVSSgI5|sc6QRMnxO@Y88e}>`ql~@X~6rvk!MITr8}y^DVM->**#XkU|Q&){f|h19`a3ikv&H{ zcNeJoy(^z=@<10;sd|&+AO7arj6+{MGJ8rAx&i(++MkEsc@pFi`vh<$c9*x;5rSEV zvgjzPPA?;u%Qb0lpBC<$M0A;S+^a4%dv<9FLnXECql!?Z6V8kvlX(X!h=NYa->j>tQ|1OdM;hL;B7A09_k0O1so zw=8Clky!0#-H#ftPonmUQoYB?m9yn9qKlthnZI2%d&aUW zY{Er%7cU-J#<$kM(=kcIQ)L=secn{7FYBFSS)=I`cPIq64vi!VpL zdNfffQK&ZiL14b_4iNNtp8b;rT#3%>b@IqZXbl~20_ zwIZzQO5L~pJzYL*eik}}ygH!pkG2a5Pm4ckN%&X=AW1yBDY$uxRqgy;n7z&Jhl^!_T3fjZhR zY1ybLL~OBkf8J{U16_gCN0HPukGu-@fwQZFM(=RmDY4a)uUv0k0KZnkIzHCwu91Ob zZWc*8-R>V>R55G1iRq&N%fFVp-&1-_J$bDbzEccNPTz-Jh7G7wu^!r+*+6H zU~v0LXNY;Bp#4cHgkT4hV5&^r4fA0`+D3Uo%{Gs5tM%GnLO(5j-NUs;(Q|F5Xt(sQ zdCSLaa?toRc+@wJCoAT=pTe}0{Pxmz2^V1sVb(4(Qa`P5nE0{|{-Hh`7(YAf{vO^O zXrWtxVZ1Q8^eUHn>4|~Q6oW$nk^@leyr~dYRmY=F?Xtdmna@_l4rTCYEXAMpropJf zNr*;$xoyC=Eo@vmQUZjkjP$VL~KA^{+#LN!klxFIa#rf zCDX{&DJ8#bAaVQz2knxP1Q_y{cvqOQM&$G?*yG z+c(!<@Nm>xJhHavbf@)=+_)}{`_jv8+Dz?^ikY&TVvmiQ8J8Htc^{rXtYSd3-liY1 zr9d_)wP*Hcw4&=KHH+S7iK7jZ6hdo(UL5lM=AzL$K;3}qY_KuKqT&GN8Fw>P_ zI|Q5(cm$Y`KY5u@jBAnt1}>t_s|SuIW4*rhW3LR;$rscTS)FZ#y>+tEH0E3p6D3}P29TUNb-x@H{8P;mlaoQPRFn-61Jcqp$_;=;#Z`_%qk1cHw;ARlQrZ1A+dVZWiqJDy=PcZ^_?aeHL#4yJtUxv6#s#yrF}N= z^W+=}zF@v?Cbt$y?$uuD zA8(kgJO9#NTb7BEWDbMx+u_P;o~Bh_h_QKWEY+lHCM)C3>VwDny2_92X{*E|`ILRz zS+Ns4bEi;Po2b|f8-KWMDi|@-pW4!k5GrTO)CTqghfNFuii_K9aB#%|U|`$0ct8bx zdc1kUnwUbE5Wc$aXH`)S{M9M1qNPapLXFkdR&A(M^@NfVqtWOi5_f2W^^pqzfgEE$SrM zylQD!`#?_6J@Pa^$(Cy3L_c#D&~)9;WqN}n<}Gab4AU7N@~TdmT4{D)IABf`o%lVC z`QWDMPEM-8e3)C_{yB-MrFUYl`tC0{{P7y^NtYTj=5kT@WcbI_Ka2HU2>*ArASR8U zEY`)zDcphmFgYc)pO^GU!hIX2Av8!#BLq>|;FG0X&-*}4Q4KSD>+x2;!S;4XRa+rW zeOQ+03E$B^0e^TF+!ol)-$ftI-jycfYd}uuRgv|$+cc|ivC_WKkR6pdmer)WeaT-W)uGT0%noykv$gX} z*Oh#9W6KG(Zro=)rQJ4VXHs}_&a&m!uqohS?VMU+%Woa*gt}*n6E2tLPRP0YMN}Xl z!rkCernzhZ(Ll*Go5;X3AQEgv5K{!I2ouy5_%dyW*-mqv$Its=mBB(-XJOm-sNaLS zTW_|0`^b3K>vC)4G0PbT$2M^7yC*M%&Nb!B{d?i zTE4%(U*5BeXm0=1if+;0AySdBb+?E*Id-xk7X2Z~%9w*0{Ymf*?Q>mSeNO{jH`B=c zI988|Paf@*dULb-N^M`b7kM>3Qq6vOc)XNa4!(L_-T159!Of?Vwu1jbCHy78`%8WP z(XlSBL|jFp+IkwOyL1y>@TdUHg)ciRMNj~X6U*gJ4zHdv<{xuA%$yG+Gj$kG_uPDe zow22okRbc%LtVZwnMSo(X+F;rdj1Hr$EsMb!X|N__l!Nu6x(}pzk)NR1VTH65p=HX zln>a#W8$pRhG?eh$cAhuU{c&~(Pk!}tfVP7(kErIQMqiV$kp_19Y%GK(Pwr)y{v1g5Cb9^4#M6cxdnl}fb)6tR{Oij+neF@|&THi^K(BB+~ zSuRGA2HNWKpMfd5hNEiG>s=8e*LwvHEWbhFBdnuS2I2`z6{-DQAz?=Cq$L~Q1**G6Hd?vm~CmlVkCmc|qNP5R? zUqdMFBes{}fwfx)34zsaT#O?i9pDVGAi^u9yGrx{*`-5fHAkaiCp9^z&R%)Wfgrlc zft_xxvrR9fR;KcG@0!Ox*D7Nrm2#8R4C z{ij2`%Svu)q-&IQyzLAKkG6f&IJ@csjrPPRm2TBz3ma;JPax;AYv})!RmcoAJCkkF+)#?YZB;c#)zj&UI0wVZg31eb&R;L3=u^HG$JxbkeXT&dBMBh3!6#IovS=4`IjTIYESYP zZv?F5Zy&HLP3w51*6-DS+{HTJhij$-_oSOLKcn54rO{(^(Hi)2j(rU;c@ne4Y}OJJ zye4|r^qRSP*EGu!wDcvL4%DLMAhbZ_pCD0*+ht5 z|0D{Z_ys<)TkVbK6`Ibd;v0(Myg2FP!+1g+AKhr2{>_2@c9;LXl+w*7|5d+a`5S#Jk&kj+;5(nj)Fy0R(V@1W4?2 zi46o*=Y8aQx+*Fkn4j;Y%5MmvIw{ zPu6xNrRS69UflrPH*8U zT$y@z#%oO1rQ=uFNKcaV+P1#1DLU>w?Rb4hG!fwU4BB$skkbj#DRbVpnxMQn`lzJd z$~HQrM*LV?-i*-HXgfFU>rSgdwutRD|9q1--+ZHeu~o`Wkl2kIO;rb{$0JG6CrH=Q z@J4;_-rBc)_%zz6B!jS^$|&j3v)2?xMvP8{qNfdo@F(0?P{_jbc(IOi@PR%>r&|`3 z8q&!2X41o;(y!i0`BV|Qc0iO~=m80|C+bp(lzv}qH2{20I=I_jE1}2fWyEFHpt|{# zXE*Cqa3@r0^*q0dv1D(MiiJwG#C%b-lpRynHpK2kxay)$=Kh$E53xAv)I~x<;#L1g z-lk#wQw%#d6w&f=Fe)liKK1WB0V?M!&}xl*W+!ld33Vyl*VjmXxiHi#bpGm1iB*nX zt;WJyZ<8LU2hB=Qh=whA`^${oD0SxIMFN?MnbV+xu4uD`tmvbec|9l=N(N%!7)>{k z3MOH0dQ)|18rAd-LB%fXU*~zZ@`dposjj8Iq>2vU9RqH(E zW2Wz}cDG%=d#YbpcyqTW1rhMnwF+4i?y{Ydc+W{03ZxOMH4wKr`PnY#vo_vbQ#vMv z%_W+6r3vzneqcM>qz1V4rTfw4sY@&~ne0S$#bOSr2w^U1ivA1gs$In~+Y8=!X$$W} z*GbOAT?2Wn)z>wiwzEThd#|csni&Y6>nnYmx>=hafj3IgFnX=)QeY3>{E*{k;rp;G z_RJzlpFN$^?uYt!^VZ*IKEy@7fz1K9NJkz5JQd<5&AjCa)rj8d9J#$vF@7#w+F^K^ zt~t7qcJ82`czK8_=@Tas==y5~8Ggpynw?4m2l$;om^1nj(p+z2e2(HbMHel9#r>Zo zaH9<75OueBrys6Jzo!hoNrxLB)C?ptMmu*^>zp|zW=4w(FJ+Cl6V$d?t2=% zn~f?5hrtRP>v!w2x{rb-FkO1=3Y^oQw^K*KV(#O-nyKMoIh1y(o-gM4OGx>#OZvec zU<$EdzG&TFIS~f=CQGu*R$@$mgXkm@U^zz>;^}kHa%V!-3&=Z=PNz!ozRX5G@0TM| z|9y75-*pqeo$9y4eKkP@D4d<0pXU32SfyGdiE2dBr7NZea~;3Nfhx@oPPeHqXD6vc9 z5oDs3uzdYci7tvBS(=ME7Ekp(IMPvC<@w}WC5t1Znhh#_)ZKAD^8uHNDw_ZD-9{iQM4 za_d>cyzW0(-=Fbk-2kh)6n0jCak^(ThKFdxmLCN+vmqVRGEUFP52mqYiZ#xVz6h1@xkD>n^<}fch;`!l)%1q1kpA+Sia(#Wk z6i|U{X|JUaH$yb`*pgjOqlB7~c&G5+RdWOc7;_8`I?Gx1SIYGJmj9SI9~W`QS>_wK z88RCkgk?W9K7MMmmpgC!dS`2B*zSk#;})lDkIWyCT@CqIl6>)pOUe?mW1A*Rzl9X7 zKd_Dhv$WOf&x^O>z(Sa=vugKUH6tH4sjZxo;D3wbC^dcskmJaBfob<~M|x#O{>zN} z{A8SlO^CfLLdkfRFGb33Zne?=;O|-duM0GTuUIi^Jb_he^Du6Z>EZoskk8kXGHdI; zd!G`<2btEs!~Xq)lO*W?m=0f2;xAk}e}&EeeABb^KW=YRCo-n0uV@EUXbzbG*i|e5 zJ_N7c`&J2m(?5=k@M|t8pR&fY+|0sQ3~v$ncXW9DrpYlqxPEK`OHnJ8Umg)p^!nk% zumlCYLdXJVNefAPj;+L`PyM4Ur8h{U45fUS^6uEPk5~bwq?tzknv0xpT{IiXFu_m=nr%G(5e!TeJIfuoRw2jtKpTXqLMoVaPhJoEVi*IYf=! z&+U0iychp&VlLnONt{Qus<035#LT;hnt&STZ4EqY(njaYyLi@4b8Mikrn@!L|2O)7 zWr)9?K54-YdC@2H_}vT(p5>#I22ax`-o}X?lNxCFyNLb&bzk5AxUZ-1I@SO2hQt2Y zI}4V;|Hs^WhBehSZNq|5K?M{$T@eszN=LfVq$(gSG(|e0w-5rx0$2bGRjSfK0s;vo zgdz$On$$o-DAI%wIz;-n@e(ifzMtcJj`#iZ{_)uA&R%6^&UMbLHM8279~rgV1IbC3 zDJ@MWAIC&`EzTHHpazy7PMmeqZg;#qhi5*WvM$_xDk55cw~PglL3^A=2ALhXC38#G zO3!fw85Oc5<-eL6b#XIz?tvlamNbuQG5dFMsA5J_mVUm_t!qz=Ffwnud%|3dwVqYF z=Czh)m8dn;?34$&grGU+4sht{VBiS6KFO!JdYz0Lt_$rnwt`2`4C1TnbOPo-o$7?mVqH7($hswko@XgJo4Z6 z7y*UriWm@{Ox}$;KWaZ@@wl(AP?l!5w`Ls!%(f-bJSzFY8zY>ZOu^slyoYKKJttI( zj~#nLC!tpnXv&!Nda2Jog$IO6n%udD9ynV%xpTjR*;_(qmR`f6)25EI9v*3-wY}oD z9TT*eFqj@KFjmsIdo1lIT3Wl)X@71Nf05R1lvX6m(6sf?s~mB>>q%YIxvmI_`(|aw z-_H*Pa`yw}tJ*$mRQMgYn9m>MOEr2hbDU{R~NMpx)h?Exb7w~m{s zqzWC)Z*{W@{v5tH@cN6nT=;ByiRFh^?Y4c_zRN@DQYtu)F0!3)yI0LRQ}N?#H7}X7 zMS?^^PnH6iKoE*VCSb7Fa<0s|=2pkLFLgZTf#|i#xokM)qB1%M9d5q;Xr_{DYhLX8 z^jwcC?uBzBYrAyAkKwL8tC>g%9$@aA8NkY7SH~0RJp_Q9FSC4@gXUq1pKJQsN_cNp zLacs8PS_RZuAV1q2{RWv5;`|PY7OT*pduO3WPLN<7uPdXd;CA1#IJp8Sc#BVl(D+} zk600(&ZcUVEDtk{O@GbGwK_DV7At_u1)}N$`dtKx9EH`=#A<21*-f)EtjUV6ogj|> z;)k4xA5o5*jH(4B7C~5b@eBfxczqI5VV}Pgc;lvp5oI75~HX1nD;!k zuU^0jcx<%F&ZZ|wxn&+YC7iAmE6nqDgprA2#? z8(mhsAJ1_vIhvbTqCIO@c;TqiV;=yNXhQ(F+(uKu`8%fU zB0XH)I^g0{n-jK@Pwn08XoTLQrwwFFKCtYsies^JGM}gndANA z1>X&WGb??~vY@6;(NTe1H~7L+gn9Ga=n;76PsJBJN25BM%@myexkORhv1ObM;-meP z(gV@zuBGynZECd3$uS1J)f-xSWaz9l7|gFs2~f>dHOB}=bjH1XaDmqS!*Ce-{IKUJ zgUrjX$1eNc20F~cbMIdC=Mt4Lp__M%kyKI13%1t^9@C+_ zis9=kOEZ|+ix<{z^oM?rF0pHQeL$s9?wB%#gz@8^61-!|+-}c5nA}rKaICcs=HT}09-)IUvgwFOH$g)T-1sB#ZhNi~Qp++uVw>-+$21^t9 zr7yrkvue%ah}TUIpw^0#)DH7aP+%ziZ_Kyi@g9zB);BIXQGGz=RB=`)bGxPn_*H1? z$;%LVl1~0C3GFf<-T|*|fXkGD!Dr)(bw}X@~_P zyb09whh<*nTfPEctbOM8(z`0Qj${RqsrpOy8)g9diVfPm_Y89=jzgKv1vY zvZ8LX+?nN#ajEL~TUEDGDXC$;jd$AhVN-He@9(Ha4VNe33A2mu|HMHhPG%)RTs>^> zO_QThjJXfW*8RGI`((JU=7Uh@KYn^}@lq>j7?=gToa{qPx1_vzJCf(I9mii}{knl||GcfM-vWmNIg z1#RPB@r5}-(3D}yD(@|6w!URG;KG6$gF{F2jDtDu6IHryWo7Z*^LjC#_ue+%)ZkXe zh48aTixW4U$(KE>-h|6PRhnQXh*te5S&HB}UufGDQ*yg5#CvsN%zr9cz}QTWk1!2( z!**YpNDVVd3@?C2EF*Id#-t5x7@pX#h6cQqjQow;9eEf(n(-o@S4} zcl_*R`ndJ}TQ{D1rR6+6(rx6~bJFX^9fPM0E>fy`ssyQ@`$0P^xf1i-#&@Nl>6sGx zr5#`M_X@wi`E?4CpqP#PrWOAKp_<5>sp>?D-PRpNWL7WCW0M3g;cgdN3B|JF5lsULL<99Y!9=775QjjzYZT(wc5Dn)pb*rR(V*D%z5NU%~)S{Y| z_ZPp_T?K;k@6EkbHBoP=M!ur`p`0Z<`7HmeC_vu0dL#$DckBQXxLbA>QTcLsb^huH z7=TNz=Yc8?Qb8A8e+628ixUWfXoOmI{CGs4TcabwikVx_MPX%=sUuI+v=r``NoGv| zDq5wOk#yjd(G4vPfwK+IGo9z?gt_7Hr8ZNOb#t@v&|fhixuXbxH^9Hw8B#W{S3&M- z2jfEl#&y6ftn^|x-}ahHcln&ykuUcfKKfouxek+fhB7(Me^%%Yp~AvjHuF5cG~lxVm`g(XRjq)`{TAIQ?_z$k#}5ys;xD$H@PSOp@uKu zCwMU2qyWiL;4x{g*&5;Zk=47k01$g0!yLe{bxjrG3hT^3cXhB%fMFkS!odhmv2EJ? z*Vp+6C71XS^^7O5b(hbe`LUc2v(b8(jnA&Ncg?Pjobaqs`xSSB#$dmfF=vrJyU3WA)K78f6DR_t-%vf(I6r8fl<%1nHCbEUyRzF z!B@dT*`xl3cP3xhLSH!$0B)OFO*akK_}Irc$$zJhe~SbZ3&=0EBdyi5x}{$WWz1-7 zYz#Abni{iU-15J^7u6|XiN%G+}UY?oTxAkTCW(g9TvRNfAeWI%_ED_;e z$vyx)mjrDF=yF?B1UDrVtztbfE9Dz-;Bfg*1xGu`x5t21L`qU`RQ&jR@#z*E? zJz*LLB#yq3I@g5;LQ-)JsXnL?xbjArztSZSJw9 zRQ0j!@>as;=@bAVy_pc@Z>toX$w!J@`-(DdF`P3&uXqF3n=XAiW%bb7w;_Vv(c5r4 zM*OjJI`bP}K=U8VoogN_^GM@)Id)|t{e*y#iba&Fd($GokMS3^;sJtN*qJw}K?fcA z_;;dfKxj?Wxm>jp(~m`dJYmt0%jfc1!|dzId$B~ZRnGy|4}HW{(%EsTt2YEfmHHL4 z>0MQ_*_{&^bizJ8Ec?yBsM&KA!1F!u-kDtb!_qWHF8{qOI)E~jCr_Pq=5}GIlW4!g z{o3VZwVJpo?sgQmgN| zC)p*aD%n7L-Qc7t@A<dj;BhVmqBMr`f^klr3`gRvr&f!^Y8I$(dB)^IrY< z`fw;`CUr1a@nPX$&^qJ_0PyBuj4ve2%F2KE(Nb*n2G7*@5zg(nt<{umnT4ITRa`-V z!Nn309YBg5>abSr#cTU(=(xvyyleFa`Dt>Mg{Xna|z*&Zoy<7X-sXD0#=nO@(TO7A%5opZg zUuFP2z*UAeR@rFmW-W!pea*ya1wVUJq~<{2gxthU+vM>8sO8eNb00Ma z$!0f=E}ntfukcvGgkS|c4xh(*OJfbhPd}^MaOH9sjibBlHNz7W?x_R)SX_+r90)sS z|JeR$qaFR0QTLX@l=rkN|?yyMWk{{z)p$~_c2PHaC#W^u?l82Z7t?dNFIUg#a zl<#Mr3Qnz^e}1yp@$$gKlX72`#y<1$lgfJeu-;P1aeEGlvC=?Jj!GVdTyrPVImo6F z(XFUU#bF*z_jTdb7gbZ=CCHDon7Lg$u^!=E+~!rjl3iYzH9sJMuUhh|B2!i~-cfnK zID>HJpRSgy=M_|GS_S9(VR@r9-j4-tt}UMp5t`Tr{yX-rogxPeLPi+srT_h6@yd%BNH4YNaDswj29NhK;ZZyv-33%`k#mpvkwJ(>> z&Yp6#Ly{Ie?lYHdj128H!rRlksT&~e&v)wtb@y9&StS^tR%XrOy%1LGe!0%jirc@G zL;iXyd}IC{JAezt%uAcVzdzkMZTKmjT=Bb)$dLee$C2Sv@dslZPPQ3d=Nx#ZUeapF zqw-2e_#CeCxXj9E$ctQ4Yqou9+U5?p&o|We=_uH*R`xZ>n6949CwrLEet9`)_-eN0 z1I8TPf2Bd}^%Wd9&Wrw`X>u%sYohwFkph14qe*ATctBNYxmAc|E7=hz*%rAnKH|VR z!89uIp|*_ocHTwhJz7_n%m^nTaXzi~Z^-ALHLKo>^SUdj_APhgoNKrZ{jG9)BTFwg z0X&xYJ^1WxJR?Cmj9ffdb z^02saSB0y_ozMlJC&U1^1h^>*Qi~VD?yN^{e~)zZj(XHDMt>9X*a_^(e~lF&$VNq#Qiv+vol`R#NbZu| z;zS7Lb2lDwTP&NPPbbbS7@chOYm$+C%^L}CYUN9?s=Pjx?2mDlo#2tGw$-SPQm_H%)AJd( z&hgMYlbN=yWa{Lp-f-&y9uQzZx7(o7w^-Xtp0vh4a0HbI4MRp9k$fOj3nr0mvAdc2 z>{+_wN=26IlwE6xn~g-eTS`^XWvio5wuSI66_xlphbF$rugw!GPM99tY?MZ@)zhL|2M8o>5m`@}SSsmvEVj%tIiN6=;D*U(; z#A6wnXqC@sw|9I^HBTvW>dw6+>?8SBkhkTKt35JiBR;WDJ+i`5GuD%`99yYxSGAdj z^RX0Z`_`Nlw18V)s4T}tJC7xYCdGE2 ze6v^1pJ+HR%sQZ%**e+A(zr zw$>28KNitvsjf)X^RoRHZIzVTFI*JDX$kN=JIB25#dV-2elBI*;N?)yUlID>yz7{# zno+D_%0?atWtF+M-WI#mPFg9P%D1u)^>>!^19$cV0l3}B#m@)CzVnxg?)j8o{Ez?n z`68Wmf8*OeFmub-(-kZqZk2=3gId-=j;do;^RJ#*AEDP!(n^nXqJsJJ2VXSMosv)} z3*gNN#{frGWio694!n4_{-ZeWuL1u1FegW+J;cXR32Qv!Sjgi5|!FRClGz_WJ0-u_9;c7MFYKm$S<05ow~CpNi) zn|dPO_^nys*neThOCo@~xK1th`LhP`uB2jK?g8%CsMc^a2lDK%J<&|aG^p@5rzO^P zbWipEn$sE(3+*yAoC%q1WJuU6V+Pc7{XVS0BGna3e2C(vbN%mWmOH9veUwgHOIubx z{^iH?y(vdd$m-A51T!4oZT_cr0*9u7ik{mk?_qylE%8$@g>j%TP1yCfeZ`@pSny59 zQ1xZ;F%qk>A)z|mdt&^K0?Fs65dd6T6!93ircQP$ zvx&gbpUt_Re;y)j8BjrH zrGjuABFe=D8O8+t>>w^|M~wHpHhl;`@Q`|XchHwmN-X5>%T&xvkF1t8W!8@b@nhKQ zZ18xtw5_X#z+wmv$OqytDFMqTf1k>qQ&yO4I+O1>% zzFYW(me%Ri+8jaVFL78C_X$U(_7Maq;5wxkP-o`y%t%|brK6RgTb3~aB2{|z#}5i! zXeac19!yks`l!#&7;*T&1N9Evq;=NXL&J_ zm?A(6dUX*^v6@UMp0K5 zwBwX0azyRZv9&bu@ym?wEKF(C{_Z6p^ot143ty;4on^o=?SxdbT`Bz|+ml9sYJT7_ zll}XbE(tZOrCMKHCK+a?6L`WhKpnhsl0rV!D|vujXc1Q=jZ}WNWnw(EL?)%m8O;Qy zl4nE^sM3BTJK0{JQ*GW4thNh5JzEu^o}fbVz~UD2mI(leFCu)eP1XmHuvE|&4mnYCuJGWEwe`9h!n5i;?b#(0 z7+lAI{tpuex}hcfyr@7RHblNySVon!xugx|{G@vB_nsggoj7s>QyAJ4v1_lpq60Ha z2j{X@E{z~%4FWQBxG1$?&8J-%T$|nYnA*p&r6o>>c2(Qe%=*%|{d~cApDfEsqk%<4 zhYcmIjvV#wQg~414O(Y6*qbF2a6l0@Z^=QRa3wQGNaJsLn>Zkvc;Y0LQIqkvSPrI_ z4)}?5{A)Nsj}iM$0v)Y6fJgq_QK_?+E?_&V7F_UHqks%-KnB^h+^6hiF<%I_Ba_Qw zcev9iC0b{_jZE@DKsmTP3)C|JV5ZhyuIu(VpQgUjikV({5^gV`r4j3)Bb$Fi2rw&O zKpoFr0`8trBB*G(&wp}tpM1+ZWu$ue_x@N9g8-B1Ka0Bdm#)<49Ez%WRqNIvKzWRm z^-d=g&Zl1*&V?(_C>91x0nJ@A@7cB&JJ8Anamwmb#iOu5(*%j%|%Z2xLFla87 z!1Ff-X(Bp~QBpxIz71BVS2SVY(ZdH@D{ls5Y&!BwSBsAwY}rNTUR#8D8(VM{c_1(c z)T7YhLzNSE;Ywe+OH2X^e|1=EOy>BGZFSf{=nJENZ~7m-i!mRX>PBkWKmxKY33k%?CQVY>T*Sj_QAX1d+ge0gLWV?JKpKtpICsK>+pKLzxZg zVHi*dGRP}A@^g{1ZUZ6BXZ9CtI-NFw4W?Q6Q4ioGTJtkJ@6x}MU)3zH;>XMR_2cs^ zsdW~9WOf)jejx65+x;=O){*;OS{c$hE#Dx@GSUlInFq}1%wlPsV3+p)Zf=FrJJUL0 zLOAyKw6Y*}>>4cMN6OcJ;K75Ym{8y zfNUX#p6*n_gHYoJ)?G9B0TGBtM*(BH6xtE-H)7yVXdEKiYr2(8MpL>3#%>0rSoGn2 z8V1D7rx#@FlAYQrp``A(UGt3-K}YB1m7ZS;;Q6ct_dek?gub{+!_Nt|5J5pMCmHMkO1_0Q$!&h0p>!NQqCrA~7?sor1)6!Fk7cta(8Iy%R$XWzI29{PcktSwKz9nuZ!?=m8#^dOgBp9mVVZFZ z5hcR=MVhAJS38ZM(MXX#noW>`K+`dyoV-R;h;E9el;p1|*Wm+(VnYRt-#L?+HX+td__- z`rH%J0pWg^&zM&{*H_hY&(7mGpeRLkH;Ym2_!S(PYBU(prz{E!3DFUcJS+To5;8)|l zj0k1N{&b62TX^X}IRQoyD)k|~Vb(~9B9|I8U}i?0NkcWobu~nmFz|i%W7e>_Id_HN z-zNR1I@?|23McT+sxzVR9Fd?iv1$`j*OVwIg+DrP-3oB`)>WF4)xRwHEQt9MEEi;b z-hX9@wXUI?Iqv=T(4{c6l?nU}sI_WSZ4!kBK`Omm&-xDP?oP+})l_k7Q4Mb1f~}(c zoyh(d$7T!}~Mdyeh@aG(UEoBNSB3%)R%NH)c`cN1#wyMDEhIPe_8s zUyI_snmdQ0a^3CI^(yx5yUyyjZ>&m1E5F_(G2 zVXd6k$B*4)QO>C?qA%PrY&WcpkNTjD)%dCo{IIKx{(UzscM65d0IQH+OBNl(O;!Fu zp#IS%UsY@OWld;`zHn^KO-CRJD@qk<&aYssIa0lgO#e_^!Ni?KK{nkt>@Nc-RGQ3x zw{kUsivVg&mNzx47Jjr0xW$%D711}9R1eM{lLK=+STf102?$}z)#3i9*%-@I!%DWq z4GTPgm^U@uP!ct0P^)ig|CK-x{|RwDtkF0ugOyF%kk(DB4P8129>5s~NF z^B+QpIAmUB$WkbTEsWXUneGF+odx_7z6y_(8HiHh9}xHK5W z7^NH?E!)8gp^w8-kVsii7oS<-n0jN)I)MjLmV%4y{GEeh(^&M9FL;8OrQv*{>DO0w zhhu#Zk!KtdKA0@9D(*NWXzK?BxG(4B@I8ESlCJ-uQG^4B{4^+keWX_z;n&(tOI4nP zqR9tZdH3A8aWL9>Xu1kXH0DruwF~M0WnjbgeN$vEiP4$1Z`- z*0wixltj1PF11xjCxTxmp*cL|S zjn|eo#1T!$+5mSHNbp%el8*IQ|7K4umbiKbJ>|%$$j5{iE`FEj6`g3Hg^e>@tYO3pZFk=GPWY=rO=jb@o6g;l9TW6ZU%+#F}y6#m!3^J!u(R z@Xr(9@0Tr3Fk*Ehaz#3gpyICnffiyH0AjPB>50xCw4R1m_T2NDUJx!5-^U^iK1ehe zN>=LiYmMc42x^T8SSMH&9cFeYEYig^8EZX<#IPP1jtD6Z+9gg(_iBvxX%_H5SV~+V z6aZ*AR#5`_h>W_|%W^sa_wyFo_aZ61C~dP60Y|l2k1-K_Yr3V$T&~+2!>by zB5mOkUnI%-1x?cMNbM&9I)8K;APUh+xWSit`mh9G;2as$KEhr~j_dOYP>}8`wN?JT zG$_`*jI*UtIaTn=9jb_`E!{leY^jgC;T)uo(_hREugy2ete7Y0{NYd_cmyC?2%;*(HZ;1avV%+bL=5FpgccvOOzg|b4@gd zE`B+JCF;!idy@E%rV1yxhyl1o&HFCSpFS`22e?fZbY9vupxq8y%kJwagwVfcPjBd3 zl7j^;neTOKa<9i1RS8u(HjQX}$u6lMkGFkXvI0ag zG^3nJ*uOC05dRI_ci|Y(a!^ymeLKuzMkNx|715Bf;mT*Qa85tdgmqkq$ek~6=>k*> z)g=Ly#_N2#B;Oa)kP5S*B)-?R?7HyT_X|s~)*e@uyLls8sSwr_JUN0QrkjVoVXP$abiy$(4U4z3L%VUytmjPg+(u}Psp19zZpF-Yh zjtj_HF5lqCuRJgxiA{FH>`hCCfWJU4YV~jt7ZL~D$Z4RVh-7DL*Kr~2*B&K{XaRNg zx?_LZaokzN=z%Wv#s_6rE^289eSGwi(*wCAmr&Cu+TWdQugB0pF-$AXKU(mz^aks9 zO^$0UOPa|l`Z$4%@O>`_Uv&j6vRA#Haa(wTOWUtUjcxYGMxg6L2KGB{2q~i)pW-}n z5$H6f{&x2ksi}gxKS89w1fY=#nZ;bcx`bFgR_1^6O>clY#Fp)2*+7?b18VgEeJ)#u zq_D{P$YKE2anmDy!meZV3X)o78XUYfS_Fn}qX%;E@gGLhznbJm+oyhvVN4;DMXQh) z-L_HvZ{`N^d@IZES-M`^?zc@67@3b(>}*OCOqhQlTB%>(J@Kxf6^!O?>q|zvBuT!u zY=|9BmrSIvwHaihXy=<>&G)6e%bUSxSHKw}bjDVX8!~~pktZgLo5dXs=dMhXd`;-U zq3Sf1547E|eRd|lY3@)xZBC^b$Aiv-f+Q(rWX@3UMM;i}^2DARJg79r@jBGCuI{qb z7MQh{A5YGy40tbo-+&d{D6gW(v&Zjr7?_pFFW=uoZQN9u5zv1lHnMP9qW;xAQZ#5f zW`dUnqlQwXvPoVgc-ivz>z>)}nSn$SBTu!2zKLVkPg@4@u%hSqe zy-mTT@iSwr3>BmEC30!bQdMwY!US4TJ=ko!I2qcLP+k4*_76T)c!BomMgm`O;LTS3 zb>(|C;Q;#|(D>~AFWEYD-hE-TaUUUpLX75CkMLfnX5F86y7)I4Hc|-$Bh?`kMDYQt> zr%b1*D9svw*yySL7xP?~2~~I(S8uzd;j1f&a2BKh;=L`;^0NC{cM?~XCv>)KQlpS5Dw&Gd|&0iW9Ihp8RwSew6Kg4SXx`;cMBjl?rX4(u+I{ z5AGh|`6e0Jf2!+A<*fMyDociX|I(8LwP2j5(4Nv;xy@_GtP}9%FiqT4LDVV0bEhiA z^GS)+!ID8=BhdU*C7(WRk-D_?@&54ey&mM17)RUqym&=2F3{FX6gd!S3qO;R)&5=7 zu*1O|+WIZsLv^BEG(#r+S$=0xRd;OO2%)HM*)~};Q`ibGZRy7<6&P7L;XuMd3;OCs z^9S^MN?Eb?q}QTyEK)(+?ExXt*FzKf2gKSa5&q(~20Q!}Feft>GP2FGbv^tCq8RHXdH9 z3w*Tq0M2XS2yWJIfd%IVZPB|hf`N}+CiIkbQ-z3f;?od&(xB)yNVb%1IN`a4Mj1f=oHkCy@>KU%YcpENGFWA4@g0ej+IM< zNV=NmKPzBBMsFm>v~{wW2po#~g!61n$#wK(ufxma)vqe1QFF;{S$Whev#<7$db^hG zNNLMP1&1RWx=;B`eJR4WN{$7#R;GE3*s778N18^51De8~-04WTG4%tgxu98(BMAhDI(Y z6DwcMCg9^^8b(XrW$C!py&_ljO;WHuf>sCl4KyraNS#`!**)Dio(Es3M;<_y+^%JN>YxXgqSF)U8OlF=-B^y93B3mWW2!%O9m)P2opM@)Izq3@Ottb&1PLG1o^=0G!+ zBjD>dycZlbtrs=fjqB~r&kR^u#SV|!-Yo-tWn|uyG`t|kCcG-r*p^d3Er<_7i4$U= zF9$pIWrNq-&bUTO4*DvVw)Kn#tdEG9vsScj#YExzkXv$R2GMZOz|3WXD(f&II(r5uRDRzin$33euhui8*PX z;S7r4kiWvHi?#d8tL`H4YFe!+_G?y@Btxm)Qessw?(>>rr<3WeBwK0M?*S#+@owh+ z@@5LN<-YM-mC%NF_CDs8Jw8#Xor1=tGURV#++*ZDBsMj*pc3Yktcnv;P6yKoX|+8fvYQ8 zvqt;Ab3^17xq};$Ln*H<>Apu8u3)~KfWu~p+V_C>daJjM3A>|0U-nbRHgR6?L6>V_ zW9%`(^;-bCWKFyD#CE}2DPP?UgUy0h2R7uM>DxSe1X@_0|VJ%%b7<_ zdN$^Pi1dBG%z06fA7r&x2(dh)%DZ9DzpX_zkq9Ps;gTFBjTd zikGkCgsz^f8=85SInEcpsICS*lLieXpJ@e$HqmB4ON-dk#DcuH3aHT5Tq%|mMGFp#V%MM}Wg^ zs)uJoYobgicDV;k0@6JysFf4)uU<72v?a?Ed9rLmGJ@l)Xv}!KGjQ6AOR^< zPJ5En2knt;p6Kr?QI@-d)7g>R`Po{YV77jfACHP1QKSpiU^$YhFjeC zA}pa(C_XS(s!RMpJqk)J+meHr2PZcUH!j~@ZS>e&E}5i2J>jnk`0&x|N>IP_P(xRGclx2N??jfL37Z>&NPIiDxWYL(T%7OKa-X<#C#FEc zPD0VqsM#|DrLG%#m@frt<#>rwdw}zFiqxjpdxnWbr#k3NwQH79LJ-ywGO)5mZb@)N zwyo(Wu-c(zvbM~d*I53`W3gv``(UPIB3W#8M5j)ShW4p25Cd$FEZqFLnE`0gmtz`_ z@E{%`tqKa7SbxHDwtOPlwvQrAgEB^%PZn(#8@+MJV!SHqV2}grqy3_f*!=v0+oE;W z=shg!nY|;nu~KINE~%w%YmeqRaeZM`*LqSEo-!sYw~o=CD4P|-+siHriOM=J!_jiU z-;2j&dYS^N-7yd+WF86S>p%iM0Yu6GGQOvbt=Urw;JUgL>bmLqCf zHVRrfmynAm+dx`79@rLWrDc$pc;{cI<#&YL4KeL*(EVUPyP>*2LO}^YXdMF;@cOwi z`+xQk5MA>dz}jWx93HWS+aGKH|9%vIe;*+O1W;u|547<9Mv4C3 z<^BJk91xw0B@k^K&u_l|$E5w;+^>km|MYMv6d2aTTx#Fl|2ughS`Gp6yvk^q|J?Td zpGEv%dOLmt7#16cc!Ku--M9*&zz%FesVLw7H1Pl3oY$}&e3vNe=XxoW2zm8uXeYIY zzR!>NL~=k!U+g1_Vg3sE{;vi+jk?F-L|vo%!7u+e0{Uy)iLTCQFch4Hm#0eodx8_1gS|Zpkc4_xi*i=qd1<@5l z31R(aGWgtq*RITaQlBcay&yt*e2lLm&$yrmQZW_-u80gApuQlW7)IYt<)K;2;LYW=O?U&lpG&|<6%{+{BU1JKj@0SH zQ=+R3-+@%X&6zaL&2O3b+x1DudZ*^+Nm#E2JHDR8w%f>jAc4`!2S`IEZ;k40-R*Q6 zF>!nC)MmdVi=7Cy#!GUgOl&AvRr0A4J*%0wt|dY!^Bq>2Hokwf2(MTqG>MfGim~u1T_JhYzi}6 zS?M+cI2qsI)T!$4szQ8gbD@g;WA82R^!SvgY;RGk2a^=w0oqaI$C2=98`3o z{y2t~s&&#VvfK(sh2s)m>0ODB(|O{(9JCnV;~(dPVr&xk>5t1#{y~J2gVrWz0P^`G*t;xj~yD_vsy7JljY<2Z|n&MiTWG>jQIOO>vd2ov|SRpmwpBA3G zeB@Q^JkX6`IV6MNeN*D8HsAzOO3l{x%HS4pyEG63iTs5=b~>s10y5@wDr69M_wy7B zo)G2bYy@dOAKS|$S2&C_Kzswz{raRG3R>dsKZ>;qDqkNupm-r0{igYg*TH+9)1J?~ z`VHup)M!u<%ZJ%($Sydb0tVnHldF-4LLqD6;t7*1>$CB3v+!A1L2(EYn1LYt=Az=Z zSBuJQTin&2XFzB%T2+N|O;XHsx_B9(JjHyLM)gI@PH zACD?o%uo~>JXF2f;^wsPV)c7?JeIZfGITuN-syI8ahn1-(jx-KQdrhb^&?Id1aFc_ zK*lb?vS;EQ_al~^!SPwlW! z9c-ixmOoQIrEzvCne)B{=^p4=y6Byt0x(YIx!hB8#fd6^?7d1!x8%|_*)M?SOgiT7 zhK2RO=5-y@FIHn#UGx+@Ba@sbPlC_`E2}M*dY`0eVSI(%gM`sn=`@(pj3t8wLUQod z>g(!&?{mXf9B(dw(RX4(9_XuGTHWjmPMYm7EYw@wTfAl3-R&h59>-fCMSb@EwP4K4Bv%zP?ggP2%B{oiJ3) zNaU|hpaMW9{UxdI#s#AUp}leT#rZK(dYD;odGcfm1}0N^tJnpGJN$|>NiA&vs0tCF zn6OX{0_pJDT`MqOuLe9HXMazN7}%!x*LJ2OwrHoAHV4HqAC@(pI#MZ+2k*~ZeEF0A zklO0A<`BcqE)N??1+0^ePR`z_^`$^zFG@;O(bFI^-(qoxEu26>|0NM_s#c3@UyJ9~ z`Pr{-TjOp$-o4M~S4rBW_NWJga4PA{t(TMU-BGbX-=DuF^Y-fRuoWw9t%Qks>G^f=Usk2ZGdK z0i*^g0Rki{odA(e2<6PP?-J0J{ky)``Et(7r{^NcJTrHjd+zcZlXC;S0t389d6{_G z>ki5h6FF(0HY=&r_eFwCCZ$zkT()Int`!<#vYdcfQdpzd56c)Oo zsw@U>zs{{MA%;oe$tg9xuP4N#YN`_Uwcvb4Tyih`enT*nZ(*w4xkcYaa@MqOZU&!M zqK6CGwa)j;PSgw3uyw76JDvQ%? z9y044$)udr7;5NM4eUgsP%~wi(c`eSF+Tq+g~j!VpPMNUs&JQLPHEu+2eF@4SW9as zEnuc@M7HqdEC7WW->VqS@HFN)pRBq+fx|~KXVQbn{+ATHn7Um^+mKp4=~5VEM|tGX zTg%uajn{OXLd|R87DNc?0ve~1fw5O{b=~lcI%KP0lJV4K>yz?VJ1&Ql+Q?=K&Xewz zg;QmSq@5q!v%SazHNEf=Z`e-VXq=mGGR@N_KdJ7uuXX*x0=0I;Vb77wX$F(|v|3wf z)!lnyP;=DO(TySm2Q1u>ki;?nESq$jo0g^Y=&d#&#q?Y$h~m~gwTo*#w7_dD!m(KNqwysK&)?7lkVl`bVBCUQ8! znW`N-X7*Gh=x)|_zAX?R)|Q!9Y&SxUFmsp!5IhyRbFVvj`%J4THe;C3h%Fpyu}PaP z&1*5wi#(%zNQ09aawn%}aBdNYuMYxf2J7M4Vyp}`AckM*BEGq($TaA%wh;Di{uFN7 zFx_b9r6;7Cj&eC}SGM>OaPC!MuMzN3++ zI1+P8B5&MESe)Trv`XU%EhQw!Azl^&=VEZ?B9|)q@K*pQ7!*Xa$*ajCrZy4l!g1HC z6Etq;df(Z(FB~@=sx5CMDL9^Uy?qh`Lq@<6)qglMSs2-#yRptVvll@Yc5-JlE-PR> z@3SzD$_U^|FbG<3zbyJfQcY^N@g2Wo;Xb`$s1#Oq9lkl>LtHZBPb=ePHX(Q+r@W%s zGITspxE}G8c`oh~mo2>myI(K1q2`Uu4C?NNngDoiu(L@tUz# zeqp97_sU!E-8o#a9XwGEH%?VLL(JQMdTm^2kZuQ@8lI*8NdVo9J2m#%#MeCi@FLl7 zKBLobX6xe9b+MBiC#@j6XI}lP&d2RI$sR}DqAk1`t%qJXv$w_3lXZTS-3GQRvNtZM ztv4%USezjGsnG%k_a&g}x863#S3R|jBQ*UCTm7Iq;ud&w zs-IILu>vw5WLRod>Z<_uJYuek+@tI0CZuXU6-#qpOpn_f85idp3Ot6a81bmkiL4-> zR2XAp&6luU=qb61)R0uF%X4pK8>RF)7Dr4V2Y6w6UL1g&m_2~1#?JU-U%Uyl%i){u zntz}AYE5}4bLZ8yPfP|jJXI-K4D}0pyzY1762gIq=Gd{j zzabN$_M@h|q&oV6lRrdp(+9gXnTn0!znC! zcSh{8%+oJ-&T$Q#47g=ug^>5M`$z+>AmxNrHr$9oK+>hiB~>TliL2(+v*u%)cpHmZ z4`C*~h-OId3=9RYoBmu{RlY`pYl zcgfe=BiOm{#dF8QCXWB%Y!e$eY4do>H_khK{PdVi@pOe%avo1WUAHv#^A1z7HXc2! z>vRCdO2&6R%s6jUhW8T@(dTGuS3AswAdtvIH%UWA(jge@HYQA09>%P_EZa{zwk7SH z2d&QV)?C=IuzxLL03iMBlk9C_*b$MYmYA2A(A}J{rr6Cqtxja$1edbBX45e@`Ow=UT9vl103eO@yp!5=o#~=KLwBBWy=9>4=%Nez_g9CGS zPf^snVWZ(W;{-3rfm1dX^_8D_vo+A`XvZaY6Gb!8jw74Q8wOi}3uk;YcZav6(Z@^A zUH4+q3;r5G?1Iv%bYurPQJ$Jz{c^ zk-mMg?)bNFF5XG9OwK)2LZq?hp&2SKYki(Z@PSNhmj_O6I#p7gGaTnCN)0upyqi2l z4ZVT$cD6NRu#c`H%J+@uJ<0hf^~i-!;MS4X#)`xGxAHJU`a#+zME7ZY;5#S&SPCBh zwWhWRW#uDgUxrfGAW5Fbp~5LA1}#;gT#z!dlDNn|hWICiUQ&7JArH*~~l%##-*a+O&l(w-1BfS5gP zgHAzPYRx@+o=68vUw{zwDvBL=w;q(c4B_c;p@M75ceQMun@Cnjn@k&%!|z>Rtx6QUngMlWFJ9@~~1f!jCB6<~@_=Is1hiJZu>|Oehb@{OP$p zkqIH%3Z$Vt>^wYe^JAiq(=K7ueAL{-Liy;j=kRDYGs`iTRuZXp4sSzM;r7X)fzz?M zm&@EM?CdMSlS0&DXX?eY-+P{Q=Q%c~I(OXBc{DxLhMw#JF$=tM%k`H<7U+H2N7u3| z2zlz7Ncd~MG9;uQjxNi0=__FLKcYAc;wR_vmPg`Ds4B|IpIEwDVZIApS=_1N zIi5o+X(shn!eeqeO-u_jCl#iUi0S)i~KCwUU6l7 zp0=vIS}}VpNYJh?5&xi#@VuzaTNzcG>*U#tdu5UVcE@yI6^Z1g-S)0`zJ#xP*HEgp zcjF8Q@`Rpk={}Et^jx8O#s@#8Fg+lIoxAEoB~F*P@7oi#y$=2K+(_Y0+34&nagE6G zL>KM27S+m%)5JQ_xpkXQ%^g;>{0Oy;&u+)~(F2p44hG|~=H8=aQBP5^YRWi(bnakt zO2LXZ?RMh1GspHN@bE|CqW-N1|5%_Y#gI2NCwBoAr`tgA2z0GO?sKp! zVZ2%s=QWalB?t{CD@;7y8ern4Y2#tsM@imCnKZsTjI;8XVubL!uce5G-)Kwl4&prn zlhe~kqWCS+x;_PXCp%+KjaN>36iYJjUDP_Mv}l-|`E;^1ThgS*3^LD_g^Y7r&f(od z>AWWoq?HZ3Wjxu)1~Na>LrysY(Q2i|le(rZ3NF=>>bJADf*{qTFUMgOFg6`xF)@V3>A!@IEiG_GXEr(a2FI&@I#b*8#IH7vmFozG z#D(|r4d)nD{pMnRI8o&Kgq8%wF1a=ecc628ILrvGzV*X!A%73sJh7f2i(V*dw?U7b zr!n(~D(=nJQo2v&I;~jGJ^ztgpm9VyHzQwLP42m`+I#|IlbQolw^;gQYt5@LgK*Cb zgNk89K%jH451U<_DwESoM8#fx%HZz z<`@(;bokl~&cKp!ho6X-04Z-_KmtT^bH!mp8_fE2_teH6G=Ns;PTnX+jRy1nww zg&NZ<7V(MD>%nXN`s49gO@U0Jq_Uo8u&fGCyW%ow?>PFEF0a z@>zFAL;a-`%=2QLhI(R%n^S7u;lN46u|fqw)Msz4aZe|{3K7&~Si?*2jT6SRJwkqM ziR*9b@>fZHZM6!@RVB-3xTm&Dg)(`kw^+A7dSq8S3O0}~oQ*s-lzp=Dy&v%Q25BIc z@?|&nbqYM6cx4D>5p5h9iQy04m`|N|B)yYbC$-T2h>&GYLCdjc7IRW;IU6@Pwu~B4 zW@bm?Mht7JI|3(*_N9Dwn~eq6Kj%J4)BL2wrAl>LB(#*^6O)k>??cPa5S-__y&PM> zK@m|*G)FRCc%4TyZ#1$n@pW!&G*hrP9ZBAq5Q=jG_jmOTY^D7txPfNi=n3z1YlnB? z1`(*r+K!#cJ3b9GCX-pZW#T2SPTkk`enu;oEII3i&%&Q+h_=?8e>>2l7ti#xwYJf^%io$f6ZYmC$j8%1$+j$13+Sfxt`dwsz! z(y$q*EN|@Lh^hLgN)9#`Fn6qUgnG*ypT`AM5fy!RI9X`J#Vsn!f2G8%hM}B@lJ4nTsY(jduWqab4*vIb974vN#Jz~O_ z(WqLxF?N^v&$1?RtusT?&vSNL%zE~zml*BcY;p)a8n_p1F$0ht?A)LCZrKhVyA0|W+%2e8 z2KdgoiHOju-IevHw`nVAve`@}=YG~_Kf%tO;#hBPJDZ}DJm_c?6ek_kS|{&(Ep-uq z1>4P)ZwQGSnit+_;*Uu25M{TF9T;wv#(!+3J;pUFuL&JUIdL0dN6nr5Jw`yw8hdf- zh>~njz&Tn`5|30GoVUIpQI|`j*Jk6LhL`a<;|y5~AW-JjDXA8uI#34VLpBZ6=5SV! z1tMJg9$7`P-JoIDrgVq!6eV~Lj($PVWVSZSPh|zUdJg?z-}lER1`vnqLV>Cwj&fBnj8KhJ50KdnQ)JzqI4mA^ebx?QdrvGLFIfgEQS^U%Xm zWvX>P1&*qWo=<~UCw>{>o_oWc+J8FZ>U|%aRgVlZYV0ov7<(tCa=hh(F#0?bRg&Z_wol^l zX)n>9IrYRok9jI;F`t5FXiDEC>4NK%HStpJkv}1~qgM(Bjy)H*G>zcOQZushKYNNs zcRZge_D25|^jO22=O<+D-0Vnz+hFwhizTNx@J2!r58A%&?E(c5uUgFQO65?^VGCck zE@rA1C(N-W1pYn}9^u-V14Wo^oBnfBxaxxv_Y9JXk)Dh8sySq%6f`C2HXO${%@Ddp zNR&gZryxC6iFaN#6m2ABXk3}2=*-!wss9?{rlD2%k2 z`?5Q?qKe`>UFa~yANhNl#9&xH#^%mPe>RWE_*eSD!Ky|{#acZ23h%s&C-;D(B$O;0 zI~^1@uTq}-xGFyFZaXY}ama95!p8l?lg5(qSV|La66d!tF2NCnvv!agRYs9_)!D@D zIz6ESLd@qpGxc~zv?YsZ4Ak`bKEH)NMA6lbbCrB2bE8$MzD_4W{J_dYutK^|u>?y) zV&DaR)EytLILf&uF!k>;&U;My$|l>nHQu>Vj#32T8>l%gAZ}DEiA|JGhPbz9TjyXi zvBaWgSsWFF23yFKiHW9TTXNZsmpo(iynEeyETq!fV3oLJP(GQOkr%*25{VDZ0eTKS})&fwS_A)ggq^QY5i4`w>)yf*fP@(47h%K&TGIKY%=i(P^`eez;rv7%?`8WVgKTZJRb~(fm`K=6ChFOiTKWOLi6Y@y2;v&^)LM*Rs*PFpe8{#59lf z+D-Y$_(n_m>AE@9rA29?t!wc1hchxH{&;i;BM&^9^w_6G7hs1x7pQ$?igkK*l+n8? z2_pr{t+q2Z1n=@wNBcShDd<}K6_>bN?N6B}r-~5Np74t4$DXmYYB%g-#KoFt*|6f7 zFaBM18|8twofd0oE>d{sVn?u7bjWyWkM+flXA!yc;~+~h24yLTR?awjjDzhoPviwv zI{|(Ht-J~7RtU3KGwq@hJ1uDe&fd>yd49CKD>f>|EDmotjFR+@sA=C%J8%|zHxA>A zD?A>ouh3{^%`iM01ci6Guon&Ip-AdG6Zn~zK@h+oe*51r# zW6GZ=EQRL+m(l~Y$pyn=s0U(jPMqwx2K=z_@ygkTas6}0I&<{bl9~Oc{@|(Lmy18N zRvOhcaWUFl^jurv;w6r7;zv=xjp`xU8i=FXYQMo>y$C0&nK0!r_F+g5q@OFe?Em_9 zlTedYEbg-Uz?vg`Ojgudttxj#gotzBndoG#30niEsCk<}90J=|Qs_Q;d6*DQjU`G9 za(W)bHp;w}4m}P}3+WS!e&FV*RV88IP%PWhV)yt+CFPoRM76?5THjb^=Y(<|E#5l` zLe&o%e|weGG?J?mDT0Ex*h>IGWJ?;764js-&_lwJ|sv9nf>GLiI zsaGlYhH9g8h?j8eM=1()cn|c$>S*=I%2;dx!2w*FiFp&EL*4f)oIIuenmGyn!KV#n9=n_f3OAiE(EgY80?fQfvtf_LLIZh6_NJC{XydY9{^3>9R;-1Z%cuKARc8tp0z4)d;@MuO}E z^R5>WH1i0Hl(N<_-2}2<;Lbhy=4z$h#}K?82P-BZpxSAW2glaUuJ`-8KKGeXIj@UM z!0`!OxcA+@8Fe&oFl8iRsskgOp;M`VtsV$~q55>k{Gh`q#d~kj>C1DAGa$iIn^i|a zY{EE`$BMid8uy_leUp9MDa0&7tviSgM-$o?&3%)hT8Ea12fL`ln%gEFGK%r@(~AqF z7E=Y=LKjMGw|jvoe+OzoBHxQ#CRMZ{Ogpiy`*sgHmfYuCHEBSOEuS>9C>KVy?E>Ll zhhp+qZ|)q*EpRKBMd;SJ;ySNG+XU>kLy|^~pu^ZqJ7uYxY-re~ySaCYWwsI>f;y#zSE!B}@ovr}S*A3x1peYzs`M8K21TGVkk0wtb*k6TBlj3N;4<=b+`Z`9vnD54k( zyBzX~qVIgP1wB8@l%>x3T%0HLy`!O)g~*Ww$H%x2j+bDXKzfzXk1Y~L=S-s$MUob& z3X6R+Tyn`4H};uaKL|21He)wK7ejZ2yZPy=Oe_d{`Q@sbpBjugat}7$7m8hzayR>Q zpAlu3@agNNAsb()<|nKtI?;D^{xC&R;f985qBLF|<9OPj0akkCw&-mM)fC|fb72$2 zSS4@88&CF@V-<%y$j5;X>OqBJ``UJX5P?6roDH%TL!8g;Fh>P6pu=956+u4KNJaFh zPG-xwK-WOOa}JdX?~GKA9mv)Aj0ue4wTqNzWlk*KeW^Ic%vt@ace`y-ElB~tAz|_D z?vnb%HMbJnQpIA_0$X17M|dU_@4Qrj;cUSiW+x#c>ufPTfnxI5>S0N&U#iZBSRxsHcBQ9qAQ@f;{t7rRehe##VpK%T?Rbv==9|9d4r^ zy8GZ;L^Ld6Ok(LkwaaMtt-2#AZv^%V!?J z`5T3{%}9~#sB?Mm9X+`7+Pq3$i*QU4h;>gFs7>X$eXszdokp~MC|TpUvwnlJ(Qsff z-MYieoY5`UeMm+e#FuT|3pug__-IoNo$)GMo)O3%}@tJ`sUYdW~%DN#N@_8Gg#G3O|uDbDBU(CD>>0yyIbe?dV!((vQ zj_D_zuOcU&dL7u#f+n+TYFWW{*^N|&Yt~s*{NY(ejvhZF;(~p28K2TOZU;;R#C^xqWVIUy7(i+-~XFO%PfxIMXnvs z$y&jz(r24Y^RFfrdW>F}25~QZ1*ml`d{~H>w1+BHM>0}VuK`ea@)U(cf{JWqJic1` z?34F?9cXgxAnkiV=D%L#&kK#B+0~G!?qH*RdHB&mmBE8+Fe|%)S8_K(55T;k5f>eI|A>*`n}!-dw){O! zhgp}tQ2KBa01!dj;_wwVH!evQ%B0!0181+r+#!fdjsPJ3;NRntF3lfQR*zNuQ>Axi z5yNqCD(B#*jI_Hfi4W3f`=9&LV`yK}huNuM4zk|fF%+eOi0TZ;$UYcVuNirLm(OV)}VJZE=FQis7E z)Cdj>Qv5(B9=hVFy*6*!d?g{58zC~gAuPZ-#!T9eKUcI=hV<+MGLxPD1^?rh2O1Jb zz{R$mW&XRTukr5uK*O6jyY}Hw6e#XJ0-~e<%#0ZG-NgTTE1QYp!psG33$Im#im_MI zl2ioGTmh>MOfx7~9ya%xY?j-aG#q_6Q?mUrJaiQ`FV$lHz)jkqM?SQg#$XNru{@3l zTh`UfoJfEr{pJ0Aa!poVXg0h}`CF8jc^xY!deg_6Z>xD7BMWXcFrlQeu zj!uvi?k(qOkAjDNJ0zV8rTPH6()koFo6gDa9!j;KZVO4;wnTw-e}KY+Z6z5U#hgHG07<4?e1LA?gO2<}-9luxDnH~pw9c=rhb z^eZFTY9+qtH*XSl0A7df@V!>9C9)#(cb@PZ>+XP$2f#eGPY)>59Ho~gv$VYbkVk_z z^PXp=|7-0=PCb`pvaWbNsJ|$KT(;L0__!sPjd|!sX{4BK0l3v;OOKgER730lg(!BL`hA|<18KzPfB1GFm!7*% z2vcM%Y4iXt^AM)=_NLvKc>(Nb^G?a!x_~gRK?iqI(=5#RD9sy`?gt#<(EipiED4G_ z51{`BmTa@tO#UYR_`3uq`PXg%OKh{WWpsP?w!Y4BU@Fa(ecF>Yk@{nk@4r9up=Vk3 zYn=ZT3}MrfmVT#^m!a-t2hE3cKZhstX(_l4wgA*}xW60lm)=i{@%ayE#L@)lE3uSf z0+7OeSMpZig)J@YPbOPFf--mwA9IB0JeLu?3?iMJenCb)H?4`&Msi0m{(l=7dPCvi z%o@-4)kTy-S6@%=Rc4>b0gRBlQkxfD#`qtWicV>@vk*loUSeN=*wu%$W;N`U{Q}-@ zEk_!b{Ei@hNeq7ypi7x-`V<|b+VU=ASbjr|D22untEK|g7Jo=1o=iiAYK|?Lh7}))bnzGpDP5SSxevtJ zP~c&nBwNa*)&onc`i3Gy>7}B5+l9tUC?iI_oj(*Iz^=ND`&!$YO_s0O0q`;>E`L&V z2Y$H#rAf!4(5qIu1XxiAbLYu2tyBe?;E%T!(Czbq+hS$6&v$DoJ+PtwsoSH#Bp{JI$@iC4lk_Ywe7N+>?-ZgY0oa~MS$^2E?a^mq;g*+u6^-d?&#j@A zbiF2i_nW^aCnosU_+q}s1lb>Q3c#m60pq8;gMuOhxBPbe$C{41#QsGkaR4v4sA^p$DblXzbSGV>6%`b_HcyNy5-STlrwg#uz4& z;Q@9twmOOSm4mJnGPmD9m1?CnNiXlb{Mm}fa52DFHgO+?JfM+TNO;JB&Icup(=|!y zwybwB>J5k=ilZX+08Q(e3!m}l-qs+G0`7aL@_TK5FI9)&mgpyK3hoy=9y+7c?08#p>h!Hkh78%a1a8S)U$Z@S`;}vX)=&A8%K12R z%8`$DlzlwE>%r3_w{J3uu^jbh3DrGwdfV?iZt&l~zHP?^Lxwdq%1Xbx7gUgPM`BxI zM}mC^dWuJIX^8o{;^`O{+M#eSAse6?WXne^+5@cgR;y53IMAwBLT zfxi}x_fiPU7cCYxyvwF*g%~3_*}l%tu;`@x@KcBex)F++K0ZD!w*x9K(|I}@UkU&x z9>q?cT+gX1VT<*hpM@^Af64AFwYJl>#jNq(_JdV+FSmR{M7r=zoLfT*nr_%c&Y=(Z z?SrDwZeb<35Vll{j?5oVX1ueNJI>z0iz46}V-oZH_9}|}c;k$3k*?CJP!KQJkVM*q zU#{tzF8Z$c!WhXMGu7K>($#@*v3za6IJ)`A@d!EoPqb04lHXWXaV;5{A0Os9W5O1# zyey*&{KAf{nc|n~*Vn9Vcyac5!B^(acGnp7n9$WI@2Fim-PPWhMd#THIs0=~FT~d%32KE0%IW8t zGfxa$#p_MF?l&U>ijG_7_dV??Ma#$%GZIq5-zc!P}(LH=ks zyIN4s-ASZ(PvL5Gd3+EE&DExy)!xlmpaaI1$>pRw#s zO^>ZzjV3-?VA?pdtGx6*t=^0QAXaKTR}L6Vf9E=LLIkttTRkmUv;XK47QixRnXe^X zr&P%3=vb`yAKh9&4~n)Mnq2i&W>=8V(ggWb>2A`Rwd)dG$@s+DXh~<2r|U7B_lh6g z)KLC&fwg!+}vEd_xHBjwPv1=CFOH1Z<626{y4CAc??%@?hQbTzF2im~J#U(GsS5s4Sue`U%)Ppl=tBKmRWI)J5o%XD6*1SRwkyDI} z_U)lru}cDRU2!*aH~PJD=_J6ps5IZQWsBM6gvYbf6x1CS&ZuHWyTV(B$t$M1Vquv# z8A`ZGCyzF-`ZB?tz@CU2b1pmomn?X=_Ep&ibQXEKASlJt*)4T<91i{g5(uA$B!G#W zT5q{^i3s#5{^y~84YX_gR-i;TPAlf}rvZ(;u9)V>xV(YO@o#(6;t>EW0KBt6;#4W@ zM((J+qhk$5LP8?3x3||Z0X*<=ofvG%uz30ERkh8t03P_n_V!g}XD^T)^DPFIdsbsK zWjk1~TuVbFeQFa7Napt}zyR%zxv%rRs4WprMi5vC&b)KW5vK3fepEXiQ)FXI-{0xO z|GS45EWiK{zSdHvN61!l`<7<*W6qyR0`eMpIqJWhkM3PCjH@Z{^Hp{6pQo`j`J<5i z)HqT5bKhQ#35+k5z%bJ3p=VbWupd)nX&OrdKjs52z>yf6pZoTz9BkeXhT&|fJi3ao zeJ3JIZ~xnPRlBym5ZI|DwyYPwGBM;J@D0)e$rm60%CA?bwDm^jrmzjk>?{8D3nMDJ zGBY>nSGbC*{g=xUWn#3izSX$evG8xy{IQJ1O9Qj|s!N*v@PE0iNZ`U)zqR}R;?SR* z#*(2w!wH6Iz7qRz1*q(=Oe|gl*bC9m;`DI+szUZ-vivol|CybClJNhToqrI`|Cycd zcbAo>;D2UkB{i0;-x9_CKWld4(`(#T_+I_zkieN09Jc8DCo{8(g;peO2cZ^HKECep zUk=5YM-1CzYTuzpiLI@MsRrHGx6nhP-&6P>wu2Ya$f(&_gpQwHr?uEp6@cR8rtbZO zISrU&YcR~27&ACH*kJB8`~)1S!YMm41rF_6WtRWs>maX!y|?;o2w0e6IJ5xL>}Ij zoyd&ubuDmz9QZ4{_QVme#S*Vc#BTb_3dX->QhS%>c@O3n^t7*MPL8DiIoaCdpXRs< z%@l8MYSNpT*-Q_|#P8nNjcjX}24O+i+Fa^@irLaT{{>$`XX`#N=Za%9^F+mwJdyK#2=!mihWC;!c0f62gvCLlnGXJ@R$63bK=$BgW{K6y%QZJzpY zeczHMEy>J3s0sa}{++;O^Z6nwqx#oW6|FaPpC>d#+8=kgrA!Z){fi<0E?FBH$F{+_ zUP>szSxv>2e&NuiCkT!tJ-h3_E*(jld4lg=8qewl6P@-(xi3-qA2#&AeWYl+iMh!Q z^J0N6#?ps^@7l{Wowoj^;no9dAagzO*wTv>`!;l=@f(}m;!@)1ulV@~BnBK1+TF6~ zg{<+L7}b<=5Tk{@)MM>OdyZhbHMvtv|o!B!MN=?q4vMt|X74Q{uX) zBfp|?OC$n@v-uzxMy{pm^b%)(al9&UN)%YD`Ta%X|5D44gCLHQ7Uy7{@Qc$};;%J0 zC59oBHmq3bFN~--2^}_ZxuWNOMIx8BB@nL_g?1{%#vi9Ce|lo^2KH{A$Y&t+gls#I$mF473l!}&1~<3J@-%2 zm2tutcw!2qtL2(k9_KlA8W-*5;LuT0Qj-2!y4Gu$>fZexpJozH^jn-yt&Bf;vjNymJ#u!a<+ysMD)A^V1}hmh9;J900AH2!1H$6|hI-fM%39Uu~RR zVW%5>Qm+nkm)rM*CQ4qeOz5}atO~9+2M{J7_~I|56(m1XYYIw0tNR)C=Z z>O9xEg=ja9^Z0UK98So&RwvbW(e=RP_DFT_OZUg`D_$|FHgoTM^2HS(S0^3MD9SEt zZGY*^@oyCJPc@$4M1sOLHeTB2t$rss{pLo?WC9$?-i z69|h~rl9$7GdClD;o-9?oKF@eEfyuB*SOb2hHpaWMWJo?Y`>5|0nw1g1sek352nm$7$EZu zK&X`%fx(i6S+c;C`*hW!LM^FN#cf4R1?vIf^KWlmI0WiZwaECi<9PK=fI|Zie&|4w z;}m{wt)f4tpYi=RTXjFUZ9{^pz)NtI##$qqw-uv4H=yPO)U!Uoz3`d40fMiqiQ#a) zfN6(V?Kv6iYf<9uISY_sbSGRY4~Hj!R$4x@D`l($&Wlsd=&Ln>(LyL%M_)FaTJZ0E zo&I))wd*#Y2JBM@IMIIo8(v&}(!g$)CrQ;a#~vp!*%zKOAkn$0w?DIj%PT=gTTX(5 zOvrU;e875~ux~m_!l7Y~I&Ly#l7H12>WDXStTwnn0|?8}X@MM)f}EF2*HF`k<^d2D zA0EQ`NdQ3T;>}G>yV{|{R!yW>W3z#UnIjEqdf)YiSZ4@<*rNN7l)HmwucR5hc<=jT zobDZuj%dY1I9K8QS(*O59YQmV&e3k~HjfYl_Uzi+Xhq&YrhW5EN3W&;b_cJk+g}W; z`c%+QV=tJI1Oh?aXLjNOzlge{Ni`^!ZYRWC&~L|tSyzX#Mt3RwJ~yUS;z%r#5-`d> zWWp(CqcICWK%yyx-gz+;wFcJi=B5N{6lMWvoc^_)p9uiAdNKs@I~E!Kc5bn;^6FpMvk5!z zsmAx#MjH%4y|OxHW@cH$olHnV+!=R`B_1Om?A)W^>;1sej$fo}2;?439*WpJjuo@j z!oUGcwKjwlE8QKels&ng+glh8h!H&tiYHHF1a9Q1?7Q-Z|NQol;@&8S27$beklNqc z@k5!OHz6$&!btf$W}(;K&UI*E%LS14UxueR&`n|MZZO$zlK%HhxpcU`zCIX&^Sbct z6}Um?1n0H-lWF#@i!T-7)6p^qmdvgq)5iCyXkL`}RaiE2bh=^Iv$CTGX~s{&1kIx0 zGTT{?a6E4` z5ltMyE-su}9A-_@h&(rBT#Rr%tG~jJ`B|qWUlF8r3;N=`8h|-K!S_no>q-wKgpPH} z&6bR7f0^$8%DG&GqS(j$N9n{vvAUzu(2WKUuI^$JFxE{)0fcoUsjZ*NtDXnR8xIA& z1^b##s&=fjM00a9E18qe6m-GZRfZfk3XM4FPROz2v?DrolID6P%Sr$Me$LV3KWns? zT}C)yYFocv!AFfW$D0%Xj8;s`pS=OPvSd<(im)E^uH$p2Q-v;EJ(D)_tKe6AZ>y~JUez)Q0G z*MI8W8G+V@Ltg06t@WGt1PcOlp=)4ZaP~KbTvk`9&Gf)mJTKF_EqfmW&m_i?IiqMy z4U$cL-;w;j2C##m$C!4Tp6O_6X=+NaYfQ3kmxL1@$ALET)VW!5Mly42`Bl7;t*eKA z+hO&v1U{Xli^E+wXv63Uw=hqoIPSOU0EPVS?ih~YW&i4D#YfRN=h9hY;6xtTj_axD zJ~vOB&N#u2Bs?fQw2t1$QyI`??K0)SA%A+sWnD`%ZD&e&l$ zHxW<3e^>y#D&c`wiX7Q_?|#Y`sTpr}pq>bTk9JNr$YK@&q~U>T##lk;d`Xu!@KiqR zqet1UD{f`hh!B2yV({(lO`uRjrvMUrxK%}<=IAlADS-7a4jOhG8v*iKBc%Ax%t|zX z+brb!$*bl8q}yzh&qy+qB6P&|yw;B!Zi6Bhwqi#RWjvZ^Ff&i_vlx0{u{rx^=}upq zB8lyilam&7K-hE(+%hI1N9_)^YsWZkkFcv7!ewbYn`nyoS!+GVTMi z*O@6~Q6kIH$16X2UmTQbuvrC0npk=9JC{&x9<LYVZs3x&lvi(GC5qJ%fySm>f&VF33IYgT8KVxDNourM5>&2l^`st>THZ>7vZ<*qx$*C}$ z2ujbbF-db^90kryF9RN+=DK|NZrq7;ub$Oje#=aU`o1?##@(H)RPB(`v0Sk7~i>G1l980(;+aJn( zxpz=a60Q@NIS4^~JHk4OYHu)Y%4WCEOX;ZOu(!8wfB|st*h6VOQvFm4m zajGTRaak!#>i2wZ@#-OWEUoieJ3NmSj!!p>wyBGWB!WwVE|`$tf@^>ppPll5z<$Mq zLisRjmmC`gv_w0haHQb4!<2#Fq`?390@hUiLAn0phiv~Bf zywx?xn{OXBF!xM66>S(P<30jwH+J>_=G?^jn1fuMm`!{@@Cb0c(ef9P2p+lS7s`py zHA*QM3}#Z0yiBv7m67-_OtM=nM#De+M&BzCzkiCb3glyv0p<@lYwKCbaMq~~7u*EM z(G<11HmLHl_DpS5y0h8Nxy3DS>mFgtLYhpr3uxsU0B}vJVD8q1wdnuMIN@0jZ|sROBzT|JNopHW_3-UK{M7T~}PDttVq za5I2IG6^8PLJ6P4(f8fz<4^M0Byoyd_~QvG@-y9R{;XYAwAc+Q#mI|m>Xu3~>$iX~ zT5Ql9h>s*Gu}CGn|b5$xT8t;NaM9Py{{?h;ja!C z!r5#POiqK*De%(|@y1MpFI_*CuNYNm(-828?cUsAO0mHD%G6{6hYJb}xxT1naZjiF z;@?T}>TcV%7nV@+aJF|N7wRd!#tc-_{TWv-EcH9M_&?Z?)jztX z3-WKzFAUO+_G5wI^_Rw{i=N}jR5!}67y@J+)H$*F)N(NNnjz2DkL6zGBbRyiexp;X zX#U^6aaRYs6W`gyIbp+WzOlFBa zH!uJ83)&rz1EyiBO`Y<7X@i$L7<|`ZTo?}i9)zz}p#HaIuC%)KdJGbilasY(&H27d zqcSpgGt}1Brc8hs=3`I^5LNuF$fh=G7lk&2)}G>`sgYIMe!&}L-gZ7x61R}qOk-_!&u_}= zMmtxp%^l@Zw+qJP#0*c|r7!hgJk+lNYRF=lLT~2C9B1&n4ntH`kH%rgEa9mpD@FPr z4dlwV#)3S4JNtuKy4mUvR^&<30IdwMbg z!FO#;?pluzC$7KbU^3uqy+o*gV^~U|Mv}Z_EAFKqDTZ(AKHr9?q}{}r$!}XvU&p_A zK%@bQ$SBUyTB^oA)5+jjiYdUo?f(@yPy;mtw@&U^yEMa<T927EX1e{yI* z>)xv0{@oHlLk)m{yBbKu4#c?Bse)tx!%tOaf$n-RzX#R*hv@Trsrd;cxHLDh@t<~e zb+!9ciX6DLQ|r?7E?;nAQxZs{B!J**l8%Sj2y=0AO7vLW%D*d%gT&P}roEM?^Tl zrl5)|B;iCK2wDmV80Q?-j1)a=2Cjr_vwiSVR`n)5OS~!>nYZG2vd^!Yy`CP5CtY>04K9S&%i8Kv(Wn;l#qpz5#kp=1ba7X6|3-c`CH6gC;-Xp zyn#Lyh1s)vL52#^|MYGHbkhNRY8lz3ejZRn#?5aQ3NeGLdTe$NmUB1{=^+UKx`TD` z`si9k>KORZesIG!s#?FxrD7l%6{j4yFOo9#Qzs7)^tV6mdus)r0BAXQ&#w;>RyB2w zAe22Y*<#^5&JPMO^lsVR4?YH8!UA~y7F2=ZZHk#QY%2QjZJV%V|l(-G%T$j0qWw6`YowrJ!LR@E)f~G^Rgcy zi`EG4Bc6+$Xlta4=%d^;hH)&zxf6|cm7Yhs^|bbZv252htX>{OF?Z@ z2vh+%bUGRaR~u$hhpNlip5KqOwSp1w)u3iipV4`%RClmAK$uCb0^RkIAPZ@siv@)Z z=NQcaL}~1WUIKGNZ}q#a3NgOaYKz`i)>vGm7_Wd~X8a`uP*gAh?u1FEjT8!{ndXaf zy0xA!D~XWGOB?4qc9gEr*ZhHfQxKzkn74x}puo+bArkkv13=2jr=Vx=VlP1 zWf%y$0lENp0D!L5y-L_^?(y_!xXwKhP>ZJ^6jd$)E=Q@kFdu)DMQs{fmHNQwniaUy zh>Ds3@Ug74`6cZL1MphA-|Yd{VE<&{PQ7t_`sBKmX|QWSrO+KB`}#ksaBGf!LI4Du2XJTX zyxRjKD=Xy|4@*BNwfMxqT=lqq>S=JB=e4s!8~xb?j^V*YQZuP@3p7eHNHL|#I(59h zHq~ak+f{ATpLZnRvTxt3V%NF#n97O&hrRC%YiirtwIK>B7K&0uw}mDmQUn5S6hWki zP^3zv6N>a2(Tylox6-R1y-5o-fFhvuUJ^P&XaON1lmNLi`)*OcbGD!7-oM9R>k&fM znrqH6-tmrij3Fvpogc&@DH;bf8oFgf)#lNa3{`L5A^Y0HmSDpVtW$f0q`+S7I6IH) zU?ph5N-%sJ1ijk_dIo@<>}mk>uUzsp2WuO)oHyH*sd8kX;L_M!Q{HC}#)X0YVxIV7 zZB~efQ?LABH8s&sZI4hY6M}b~YX0OyGy|!w88BEz31shG9V*kSon|_-^ETyOO(+L1 z#6G8{@RAHY{ixYyFbL>O-8DIJ7a+Wx0XfJfY1g8@DKIsvNg95LQ%an}>&rVl?2f9; zmeU5_4jDu33|Ry;e)Gc0UZPK3aw4coodn16$_ZLGYPX-_;5N`hL}y&MgC{MvbDW?N zbT0gIQv{H6UC|4js5ehUg+%nN{EBUAUj=jpd051?e#cQVop}pd_Osj@0DXzmsM$Tx zHH$epP}lsdJ!@WJt3=j;%v&E(SJl`Ua58J7k98{!4E<%48XnK(vpaXI&pbUFVzKna z9hDp`L(6Voqtf!0iF@><9luZr_l+k-4zFIRfce7wYHX4dzmwhUM|N1s_CjEX?LJvH39AQOLRlRfyyag43Lw^kb~6bZD4Dyu2nCqHhS_<>!(ip?8AX~ zq8Qc*=Mxf@MpAb)Dc504Dk(n7uK5#^JO@nwsAcPJ(asW zkZz)tbzt(=>CtdKTT8KvF>UjV-W))rH88KKe#x9C?w)JoD;_+jaO=>{lB?;0C6`(A z*aQPcczJ;)9eok!7pd-CA9M=w#4@=R=(RQeu!KX*;#(p*J!eXBc7pvLWPFL;iPYdz z`gbDokXv&zdLtWJGpX6^h41Q>^44+wy-XNMv+ls9!yFPx_Qx%WlD+t~;! zv8{*#c@@<`Fy0&OGzCbgZq{hC&gc#5eUUo%{F!XF1V0o06c;(#bAII#)feu*fBkRU z@ca)@_~inX$p0=G?4fX)^LnWN0AVsCExs|3$1EZ^D%#|kf6v)zs$9uzPJNN8m~ULd z0YUD?MxmE5%Jx>^3Ip$lW)-RqFwlysbyOc7cn|tKVelrKYfJcr!kzwliC}(lg6#TUX62IK*rRMGu?o?*&LHr=L@}1E|T7O2>)}0E(HN-XazE8O8u*;wxr;o#)=? zpSJC$D*6Jq^FZ1@o3h3oS6UK!%8oDz1ZS#(Xre|{G#Uv8-B}-V+ghY-gJBq+i>BN? zksZ!JhAuE$QgR%j7$NQ=-DG1!0YZ={YESR^%87=@KsO@D*aJ5k>BR@eA$dQX9QR6Y zeR7zslR8?7r2_8hwf*ce;*UpX-{`R(35jw~9s(t%UBFfw6hVK2;lBOoC;)TY3_G~? z05P%V&Fz-~$qjqw0dyGH0CM0!+FCesYNP=V!$|#&393!&$i4cMs%|hLhcJTDj`AX^ zrL<~(v+^R;>%M$6-YITV%C-C~$BuV($^_1vbPc&$F~YPuZM3}{QRqqR#5jbK+bElD zb*Vt1E>X8d`uO5(;a>tW0d%{t6pA}&n z{$woAYm!fHR8(5565v9)EdX-)w$>VCF7<*Q^fA)FjhK#gM=q^UP4uH|N!CmOi4Eu& zm>Vw_*v16Lo2zz#7ZC>=0Oq+1?4DTA_9#3j4}cH|kaMQA1TF)|6LW^09mn>10CeXw zzc-lYvyvqjw@J7YYu6kpY4YKz$p{eE7P^2{u+CTBX~^|clm&_lWA3huOlDT0<@^wUb5q z^k2e4{imoVB+c#l@Qxj5ccmFjKq2vq;K^;w2PG3+FqT-&Ag;P+o*Ozztr1nklx+Qt z3$lqq)_7V0BxMzuKx3)b#|9)#9!qsBT~x78u-I^xn8(SKPnvf_N#R^`T4Ox zKWqu+G+{L*fww4pju(|g6>>|z90cNGxnREBQj?fACeSZRcTFs>2&myJ`^UX|vvql7 z^K&8&^iSQfR1iniEfIh>6makO)~xe?od55v<}P0Zm4fHa|F*iA;#8vP1F{0{$NNSBYA@Gj$W|x`SvQ3A|}^Qoh_jPL`X)YVPHEa z3_$@4_76j>RpD9enGdYHN4y3~9pHC?X<^bW%3T)?r%HS9Al9%X%?}K0fOi(*0vZ`+ zvpb*N$0po#YmEd{u&wV))I%nzz7({hY;DAn3xUUUqDnSlRg|s8H1Uvci7;@mQ54#7 zTFd1D0~tb8TaiJ={kc~>P#4~}<>)9a_^n=QSYqI_K3Ny3cx1>n|Eij2vrt2kUS6_f zFHjv10|1va@aaN={wvA57Z0CvEy~&0fi6G0kXE>!NP7Io{CDT5ZjU^Nv!=rzR9S5d zo)@VjBU=D=V%;_fMRG5K0Y_JRdbTJ6-j)ie^zO`~!Z&~lI=_E|3Ou|g`HXmaB?~Xx zkJh+Ucml|P88Y9QMRC22u2cpw!?nAY&9;;qD-OV{<}CDXA(FCPHw(;hzyot;6Mism zLP>1M2^wfi6n-x)J>yo>L?>*5@# zTIudmZA(Mtp>W<1c?b^D3!P!g$%(Ma37|XkNdJTV3*$X6o=YCw_Yo3xd#V7pA#Lq9 z5Ow=J_rco==SL>Z+kARG;#abZtE{oPYXj9=-`4c+bI5MxYcNtJ(=8nHP5-;gck8$~ zzNpIt42W2dXixAa4J@-X$p^C*H$V5?vvrP9 zBc!S&2Fe!(xV^p0myhpyVAj^ww5}WlBp^ff|HHC+6}?yo2KJHCv$FWO&gxaqq{cMP z?G3)>ICbc%|E6S8<wdH1-@T0Q&Cy?$ zN?aU-%8wljztL^JH_l%MvRgM$Vt7*bT*jgoPIA({P_ckoli?1rzHcN|4I;C%^xZzx zt+r}vWIW<+Qzk3|YJ$#Wxx|3U+IquFA+8@rC|e_3%Yyj~M_Bkl6_AIgr@v-L3q$;N zC9(6)-WIAvwh?i7hkNNWsv*p@$f}Erq@hJ0F9R1i3Ta*rYiBQ z;)lh-pC{1e43MZLo8ba_DDrbJbaAnBKRyqYUtKMe;A%V=bV_)C^o?QA(O=(?YxA)f zRjTh!1`QvWIXE~bSA1`|1mUqI4A~2Po+P_)K)HF%gQ^1p7aay00=z{a0*k`ukVWkB zGBVs-93y|WWM7G0Zse&x!j87-0X5Zs8Z$9Wy+|{zi-r!93c;D$8VfSSi9-u;u79hSPvXy&x2Ce zspcplp#0_D(%ShFLnJs@l8f&^r5*fq;>K$R?~5d@EfKajnCaFit&C2UyRHZpbexO; ze`O+6hO-15(B}wp#h;MH1Yp)D$EZUf4&?^KINz6vC72^g=95BH$#@wA6jm;Wj@*~= zBCsHrSUl$`VCL>=q@mzq5mzw38X3?x1yakdJ~-DiWQVw+2qr$Q55O0(0OBS9RAqos z3JSf2aGjkS{n^F5vT@r3ZBI7K@4^64z*Fj#cU`}~0`pyckP{_j>_d@m>iK@B*V z{+d4JfF#?`z*9n*&D-YPp0e9ct&FGH1uCHI=8rw#IN1)$iU3h_Pain0y)j4Dwjhh{ zs`r1#*nE0aIiOD=Ah+8rY;1z4e{2a<M7QYD09dTl5IV96m?HOWyJz#esFj^(=?8u0;aq|7 z@$mrnNse0^kuip$=)8~;G#*jbRat7{T>-mBz)sh71i{{I@F&c zM?a`0@AnUo0mEkN0Nczw(GVI+4TvdXy*{e;87G(*4pe~dvcUqBwGPONnuvDs&!<17Wk~r=ZwGIJbIx!^@H6Me$|uGXC;hy#F#n^b?@3qG*k2eXfGq zfrV%D#U-ZpiK1+dwTHz%m`f9)9+a;1kVd?R`Q0Q3|J+h&4fP==mxAKIWgNw(8M&25 z5kcamrOB?>wdzP}0-)5QiEq)2vhXpS`itviP_f1f0RP&r&96iGM~bI1t*$7+CjlPkDvl;{z-69kwyj~@ljoxzWK<#O&4U=KW#R6QT>gt^13IALJeEN>@CUh)EL{w5}39&!ztk} ziy^+N@8W>?Z3!Uy57a8eHBf;qRmx{)PPz5*eUY|-bIo;koRo6SjUJ7l|Ln~&P|(^v3kcl-cz=^%H9H`B)wQ)*@OZqtoPm+; z$m37@|NfITzaMmy9+JdbyG!He=Ys+TtHkrpdk&aJ)KRT8E#XiMtgK~E!VJqvzx2-$j<_jZ$64W_ThE>yRw0Aot&s<@%ayCF@(n#KDO@k<=S=!*OvPI zJU!d8a{vmCQ?c>UMQVNyaCXA?V|F?^eYLP(F~g6z;z$Dc%CSO`e-O>G%kKfsCzwi{ z=2k8@F2g`N!st>7Ei1@EsaE0GKbE4w z!wIHvM}i&=*@kF|yLa!JfD&(q9+-9=sLSU7+)@MWldNBA&1BCA-Y}<}SZ&6RXZ_)# zCSOp^z<|TfhY&;P%SVdj{0lDZR-unP$9HN>8VVocx+c~a zD!w~k|NIayPOF_f$TG(I-I||t-&1Fj@1-6V=0l$z^m=F4d?`QRChcv|&+$v$bZWRk zhi%CHSLb)Ka{tD>|I73K>+guOf-YB~^%di>-`(QB{@VZkKS!djPjbh6y7}E&uoqqT z^z;O|JmGv`rM*I^+5cAYi6Cx*srNvs%{36XC8?dy7?hivTS)<-PEbHVKxN$4 zCloawWeBY2^x|UZlP6Ea!NG)gr>jp2?G27F^0~%^1QWt>j#9uU=L71jQc`k(+RR0o zM8ad?pzeK=5HE_j8|$+%vlkjRQs~%L56Xw?Kn=PH@csE7E3@g@+0AW}Nr9IFK!1$o zwJpSaD0aKml>Mt&sk!94<=(pNRo-NsSS(|^7mnU38~E9lI&FWSx}w=7$=|}}{u`sz z6hbAyW>`4cbC?jnR#8o^I0miylYQ)%8M&?lypy$WG%9VmvLQO*a{A{B%sY4X-l`y#kSY>HQJ zP6|$d20VdO;lPcJf-svZsv(`7n``o>A`dRm-Gma}b`8V>YuM^7>;t3TkFD!~pa=w+ zABKUdys#~2Y#;l^&fXv~_K)EB0r!9>^&zGWdv|{D(+hEpJvZ*$<;X~u_#*>z@q zj!Q~0&OV^?&a>bVuv(Q{h9E59nz0sc}CsWgkL|SOW@y9S7mGL)9XXm2_0fRHRbjz>WGN zB@Ys9Yg_K^^fs!A-$T0*JGt(ZHs)K7=dt@GfU=`bm{U+V`Jl58)aRlL2r0D+zXPWiN#`LND&U1V-fJqjKGcBJ{n~f?KYLtuheaj)N?wQ&km6&PXNdEqJ9Y zQM2kJVv-3qQNUJQYmdn>-&*wOt2iU6f@+S3NJ@jSZxRnI)ABVOo8h`JK=V?Yl0enr zbqAF94}vyG?&pSWZkQF6oL=CAeufv2d`l0{&+P;&#*5T=S1Ms)i}3oFN)BrBK`O*E zd*pZA&X^f{CA#}Pa0<0algz4ap_v@$9de($TO;FDb%ENvX@o)eD$o^xPC2R64FFHn zm?safR{aIk9H?Z3QoK~zygCSnbf-nLuEh9R71F(>_5!F}8xPC}JuD(G-;;Hpw+{xJ z!#%V`_o=gS8DQmTqbFB^O5uTLX?^T9$1{?7@9w~0u+2{io42c|5`-ndg&$tv?DP?^ zy>Y%^C(CmA=4i2$xw|x^6t}@GRztV*et&x5Z(wYmkx}b&^84BWj@RH>xmG7Z7-qlK{I3gpxrn{CJ;%tAV?>{aO0YW?}GZK*lo+Z)W282P?>( zBO^w`SJlc;mG@*=0aH~QBC%LWXv9`jR5am-*z)QSt7X|cHhuoMn?Rdxyeh*&nNLI& z^G|Ph$H)DfjXbn3pw5I{>9n(PH2=eBSO&EbMHLTyml+JHi^?YK>B#ewrnz7r`pH6EH5WVKzehG0XL_6Kx~do zOp}SrL(beW3ZUh5OO$dUOtnms7JBRo-K8n(Z4u;9%?WCH7UY{uKaK(PdsjMwO4Nh= zkklLGm&SMHhvRR=e8u0!s=klC0cP>AZ(H#k-u*qU{mG+<4}zSxzh3|5w+%%)V-LE5 zg?r|fO+~MP*!8$>X;DhbIUiDCLLD_lX6K2;4l|?C==FZEb*e)EB`je_2?hMpawCeG zKOn5yOi&GxS4g%u$dj4bt^es~57LgqIv}aXISg`(juW6!%SSR8zmlxOUv*{qOO+|i z5m=^8or3$f?=!VC3mUT9xi{&V^kl-;eJP_1+o!>1Be@@^oj|o6bl$=DpZCxbz`MIT zZanDq`KK^sX9>iY!2`<=4<6eQP=Q**zX-8~(&X$Huw)K^%s{Ytp4xN($hmF|Q*)b> zzJ2_=$Z4QS;yMcS`>H38P_`?F1_#;y$`+RtdQ;Qua!4fdJ^=|xM?Sl@M`cv1oZ5+L za+h^)l%IZKU(KI11`N1AIp&&$+k!GPGsS^$t!eidn6z9^^|jg%qHj5R_K*EL5EDG! zUmg}mi@QGLHTY`}=;jR)#711M^560f&*>`(af?Af9Vkj=pCC7FK|7GjMP)k=DChI7 zL7cQUCxO}_PhD46*RUFF;qkp(YuBr`Qv|(v!Uc^unV6WC>I8j+3Nr6i2vW5j{(rb@ z=j!Wng7ok65|HGA4U4valzqFmX_Qoh0yX@VQ7|-zZ4?+6xLGT+S<7jFT=Pes1n4U- z33P%PjEJMu6nOSvww&4eM|Oznn(c%0i;h~Py_e!uU85HAu(^ZL zN{$?fSg@|%n&Asm%m*n8FALGd>lQ9Z5`+<6P-DY9zl8@a<<>xNqEkadL;0YQQsuHO zb8`zXfph+W_|Oqj?EKg71$Y-Y5)}+#<_FH>f86oTb_5qS`TTFQ-t^>qF}3cXhle!> zj)KlT(l**u_YE?15;laz#Sb&I-plRwTJK08SQ;D|B3)asFUJ#VcIGsm)gQH&20oZ$ zjEghk>m7anUH@_$qZb)3vispazxr--oqV?kyz#SZv-&$T$!qe@kFFDSD&VcZmi>B1 zWhr7~(BJ)4@m;>d!^6wd+uPd@^me31fYap$e|hD*c62{=774*mNT+xf2l?c%@7^6kWZvjhkJyDa}*mS49~|J^PB-7Wv!E&t!1 z$hYyBxAUctA6*u^iSKrNyan=}9MR@33px(3uKvO&D^n*()>)a+dM>$NT!ObNtG}Po zFB)^h%c<)>bgA~2K@>-uY{vD&4i;b7=Yc|mkVKl2)12=RQHxJ=g9YV~c6OSYT5@tQ zXGZJUq%$-Y=!CTpF}x`oa6uzV$2b<*3wpabm;Cl=X!dR%~SgT%Zq#wI1HJ#FlmNU~}! zO}D`a@aui#?bAwg&`VA>hY{y3p5;5{J(JD6|7+R&Z(w8eBNcmJvzGtLApP}%ec?Ax z)3>5>^x4royV$mlrL0&d=M_Tol(4AznU98O4n(b?a!>y=$=YJwk0^I9&0x+LS_Bl0 znI5i`zB?tun2ekVU@Xeva6z*DhqzRnxTgV@h(e^pq-5=h?PZ9WE@L}}w0nMV~;*`6RXFX?> zjeKko2&Ok210@4Je{D=(xa?{AUXVnR%h!=Q-@2+0|M@SOgmR21UV*chI(S!&*+P}c zQqssrT9M5#Xt+10W2B1%(Zo_0lAU%3cSDLPPrK77{V8e5D(Lcs2ty$qU z##L@*?L?B*;G4MD7}?E>EaJw#K=-uiFC85d!{BBl zN!{)YYR5MP8B#%94~+Gdipp zprlmCFvMBK9y`}JZ*5DmRf$NV+#wrk*avagR~yF8eCi+^Rkzxy3~w|%#~5=FCinSXtDcEp}|^$?Wg zxYvQB;)XO5T7toyjypbfl;wn{r#!6K}A46UFgjbn&IFI%G6XAJ!hGm2bV7r&|@@+>1~(}9A<~-xtTN0 zu_k*qJ+Uw%IXT%Ga>Zpa%iGC&5}loI_4FT9?~&r>jk$1Z6704YvIPTc<(hRUZW<^0 zz7)kG%NaM@+eL!jyqT)e%o8IU3;p(eVkU&WGC>FX2H7R)GBgo#+;lLQy72m|v+qVv z{%2+&gbhUb6H{6hJN=LUdeo2mHlRHNA-M3=n)$Adc-ef(c!Q%8xnAa+sraEPfx9ft zO*e8{B8<3J(&A2RK@24K7G*`;GWT#sxbb9OPztFhjJ$eGat%x3gO%e|nC!3`I;b?$ zJ9IrEpKhSS+&_x?AsSQwB0+S&;VoebgLPDmN2RmMT&p+ohSK%;2#XG{4n?}*?d0|7 zx>HSx)zx7qdM3`7-TTiT?g0B4@N_r!S+f2JRz&xK0~Q#%K{3ptB59#px8s!|Q$_${ays8#90eRX zEIB6>!_$=vWZUx7!zdmP4QK2^u%J8{Sa<+D=(RwuTJU@2<)Mshn&v z+v2vCO(jlJXsMiX7NdPBB*4U!5wP;&ys+l~^z?m+2I2=J;nxR$*vg|i$3F6Ya;d76 zkTB9=ib0aLG%ZOVPD0FW8EXWOc7E*z0DV|Y>W=ELpIUbp!SL_X^Ke&olCG8tSV>MA z3lh8eI#m=Fk`vM>Gy#w!FOA{c89Kj@yxk;o%5Cm}%`o0ktrQ{em#WXIG~_u8T2QWF zb8}dSpUVtLm&DIc<1GTKh?y#3Rt*LYH*6Gfg-h{v4If6lkeRU+zxlb)ces&gW=dzo zzXFqAJed0oUxJfqJzn^u&WxRJ4;q1m!Z0`j-CMn>A&xDcHHn1)*Ed#g>(b-B9vC{K z@KNg>FL*zYiflrnPpCXg4b1J=4Mt^Lqp^-j0+c$fK7H|uyPGITdnd>i>%JAGZ%lo6 z3=Qe%8;TQ4F5qPtB&G7AL@va!La=&CuQDghCfLm4Sg;8mS9v?vvda;KbJ}&i@{7U2 zGA+D4RWcI2xdA4bkFb{~vf+OH?mOv|@0`Im{P;gXuecRe(}K~P`0(RluFsNgAxQzf3cx+J2_&oR-^<#LtqPWIp#~8ngC*SX_LITv#0t z-@=w8Zf2vY8hadeLbdU6p}N9uEMua8sAx*x^z=}^H*VV-?fw+CG#{8$T_Wk{+IREK zdPC*XY`^GYe7e%>;SLu4_{<4gU_!#>oANkAWWC{1jUY#&6n>=tBb<>NyDNQq>@d9knNKrHjq4+z7rlV|Q;Hl<{sFCQK^$19uSRrpQ zj7MJ&VyRXmx}y08&Lkh&l!Ya^Mx3GVG3&9d5UV`Nx<*95FxB8MXlYXs`aiYIZ@~fH{c_-2F(virhcNjLYu*O$Sfh}x zD28iOopF}cq`A&cnY-7dJJ!vL=v;(9)WRI7F18iay`b>>!0`0kLVyY4h7&@U3!N45 z`CVW38N^H-j9f{K4;RRIKOa7=BYpHO2Mg?_3P$&xsGkkFYspF_DoXn;LY#0oe`2N~ ziI2voP_Yy~yoR!HANQ@y#II!ybFeLJ-YROQxNC}rhGO^=`P3>@J>&kfXC$3FtyUq( z;r+v>?3L)9vUNEnHRJ}uX{w(tXx?+_Jj@j!Yiio9B+gA^HkVxFVtVtgodT0J8Cut$ zErHNdM$M&&s9TN1Z)nSrg;j5^POaQv4@j=RIn+!@irfSAHYisBa29j)x}+eJ`4EAa zE>ep-4OUjdkbt!f3CNsHSGhMVyOo|UL)f$+uWU{$!XBK3Wpa4Go9tN5GP;?6Pcx`` z3X(r2Z`4M{Ku?!~xL9hRe!Tv-T+uJtW#278(+}D4?)@Q59o0Ghk+*9>r|M#!O2yVp z1AH7i!WPDpes`dqv4=+u8^znE1*m|jH?QGMfBS<65-NJIiXuu{fs0VwaW@w@nz~_`0Pp#M0j$J0%gW)VPM+YS~<)# zP(A8VeqF~**i@#%@BNMSsf^tak^Qs?vLZ(Zdb_UQ1xeRqh%VGXh-#Y>KBUXw=Pgvn zMmq0g5$I5dbtf^C)vb@KVH4-&Jn7c7L`BjZIpSI;f|koHf5{(xd-S(RKmw6*_Zff0 z#W(hF2xbR`z}jv#335G+KB4+d9jDJ&XJSeXIUrb3Dj~#}NvH1kJ|p7_0!g$PIGTdf zz6k(WtiN^Pog9d>R(G6eg?_LoeTPelO+aUWk-X(f=k($NtJa$WcUea0nh1TrRtRSV zCT}0Ynk~t`afaui-ctmFmrQ--N@}I7t?AvocrQg_m;_X0$aLMkyG5Lj)vV=N2reS+ z@)y(mu?9-?qnfld-r~8=YYE5wFdO$m5odpRivN8nYC)f{Ba0?{KYWZmc~6S7;7x3S zTt^gdt4m7a{F^zMlT18CcSS%?3S)s=tG&S){5CLfM;92nNT}kQxY=|0_}5&(@p8|q zsP&>tRtIn`!ZA=m>>N|)*4 z(`X*y(Rg7n~C+b95rFTnLtw z6OgGXfby9CP-%sbGZo4sMf=$l?y_!ZG*4Q2;h8iR8%R*ImXp&8M4j=_P(V4ZcS&Y- zkeqN4hUx|$j;Dw0!m z*&Qa*@k(~Da&g_>ir6X*o1s#nyON&Ck&pIUCRwnGjn&|0Y=WI*N^bGQDM_nJBpjTf zr-;Dv9!Pj+REwbv5rYd|_sRIaKhF!YCw>}r?{e_yLI%vpsGl6nk0AMma79iTud?+c zS698NoCIA);tiC~@Mz%&3LDz{%E!VSKQ7ogAQu*WSPywE6qYIqc^k4tM<=_#5YbXy@ClB3^bA1$xD9POg1YPe&-c?&nZf z;KptbS4~Sg+$EyyLWhe4Iz1Lc!Yh;Tt%>-Jt0U#p9!q8YOHJgYC9zz9D+5i)o3zl# z)eA2)iq&1~*Jq9uo|!doxlKsH6`}C%#hCJ_!eqjvh>%AsTe9gp!KA-n>IJ&)@WqfF zJ!{Rw>ob0^nW&X!U)V2Ed5?i!hxVSuaiAPY5n=d%cfBN?UfWVh>MixyXn#A}3nNTy z`{NqQIdqRZPiqK!Sn0bBObVG>b+v~;dKL56UU*L6=zH(GH$cK|;_u{DU1^Qhtd<{n zemHKn9f2+4lsLaKe1_=tj@Ix!qV1Z^E!DMuexmx zg7GSLE-GRiXH)@UPSI}|jxf2Z9Dj_Z(sF@wwRE)(jAmugf zhA*<)&bK>8dVNxM=%RVKDxrF)U^vELE|7l0m&_ShT}}`)qyEa@^3I&$(|Ejq(3DuNQnfHN;!!~wE)0Hl16qv1q+h#dMXmQT&%Cd9 zx8jPQzII(CAM_lS(F);BykP0=d@9Kdb4uM6GoQ{bc8xct0e5c@#EwK8HwOzEOm-&1 z6vjBK(J$JPP)vr3mk9cq6e<=0Qfpu=*&3hOzda36SBG+~;0#!kFS*?*YQEDJ1RUp< z@g45jHm@6)5((DU$P!@+j=nw?Yy|()1xBJkieq-8Y}JKW4QcJ2 z;`QBoQiwPB_Y_Oh?wGoNyzm_ZF`2(>ept;IlY&vUOn*w6oyDMY2ZX{XWAvgbwmKrE zyWsrP(0Z}Xt38=qgL>DC9TA(;Y%))AlB-v4V`5tfHPsZTl;`$}LstsapF_vP!N&_9 z(r`A5vAiV3dFB1uLIg`)>>N^TN@Ym?O>v2d9kg-^&%}{(??!E&=u&STz0tugv$JCL z?^AMwCS$efc^6}gO{Ni^#W%vp3@+8n`^ZDn!M)B+oe)Fj&;$>MN=i9>)7VIS{Fcmm z{MIXj)&97Ax2YV5-gI=F@nWlDQ--JbV;BQPZ*YrPkH*LHzAvsXXjlj_C`ywo7g=`xEi_IE~fS*oEg?wT)?J~Y1`HY_~mZc306877uPgmx~U{0CKIdn&A9u)PnY`7}FS|5>IfS4MvcLGU&zzzEgys@ z#_6F)g}q<+^l6+*Q4Ta23()4RU{3o`XD7wenDPGV<)$$ePEBr?p@|U=*3_IhcE?N7 zGN~%*A-T?3Cd0{rNh@Le_vgkdCRz_JhR(HF4m*FWEEZYVoaI>BOx$JNWyw-M>1cKf z>db?ZuD&uQQ+`>r>PlNVjca|IYPraz5y+tmZ}m+mUDrZ@Av1i#FQQ>xSoLZ2FiNM; zMqLVJ+{3qp&i~Si4CJAV?G-AXJxXiNmR4WqsG)GrE^3mW9dmW%^q%k(og#bu(FzfI z=)>{YvIc#cZXTTB$qPM;8}ngz`|1h<`(8b6$7gYca!FmeW?SXd_75|-|j(xdQWA$@=KX%N}-MF$>Ts_JnG1y=IhXr=#VZ;DRDf zo2v(^8ros!BX5IV9ZAM#$ah_1hDMS$8K0ypPNYq>i4V7zH*M4eENj zba;n;@GH?5`t6kjC5^D(&+hBH5p8W55{bI*)Q}O42EEwrw7OBv)eJCR-QtOf{p$Q<g06$sIzfjs=kFrFprgJEIWb9)t%rBC>Ec{c)#j-L zWcQoPHh_BX2$x0aUPPHKe^OTpGbfkd=_niT%lF=XtnS!2<33tkoh*Q1mO2j4t>Vc% zAIk`{9LeUjVg9q_QK3PbU}$Hf5$-ki-TfY@x!mh2?s6?b<=1|wcLp6L$>+)o`sn!@ zu~`L|5zPH@62h}>o^ax-O9QFFhgF$JJZ2@hY`MPQqpgIHiDIg2Ouy6Pl+{&Mly=$< zNhhZnd?)MyRLzyhb}6t$hU}x62Fz)>P&G=Hj^;akxn@YzbIQ|02Y;ZqzlCYvN3p&T z8kay#{IwcDwT8rA=gEqfcuOQ5E_&i^wDRFbhjhEJhstK_=w*BjiP0Usmb%)|{6OeY z@X}hIXt?KDjfbvdxoQia@7S^)PaP6@d;U`8Y^TA!q1Lc`5BpN5hH;%WMGG+vbZ_8@ zq_|Rrb$EL8acx79eT_jhw=DtQdSWM;GC`6T8cGlQAHW?u7pda@b#q49>5u_6t)HiY*+Pf2UftlK`O0W7bj0#Nv**rO{;KYLei$> zniwcN7>#L>EMv*H-K|oB^++Nm#DQz$rA5`us!1moQs}(TJmpzOAM)aCYx!jwlO~4I zy%nDqb+5ZNRC3{V4*^bABc0S~Q90U@p@ARjD6}*%7d?cCP%barB3 zKm(6o(=YUbJahr{c8Co?>(!Op_GG$3UIX4`h8tFMhe1I#TY-)Vh-e1J8 z6<+)7#P~P?y7CAu5L{?N`W*c#V#xgssnyrb@ft*bnEX}Wmua!jWqDTAq1mDN$(`nXf73HT3ceb287KkW z=Z5sj=l#sGq8sFgK=u>lsL_CxSA(*EtW;cPH*->vnWa;X0TbTRtLvOyU_gNEyxV3~ zdX$4!ciL$iyG#-W?P6NgQXz@jn$GH2YCiFT#;)*O)=53elP35$Wo5u(8!r=N5?$hH zni&w2IpiW!BHo$z0%RI-^^vZ~tLG^*BOm?<-KrLv;@}Fw6biB)x0v=>Dzi{+;LkBU z-g81F=!qr*wH-V1w3ikcnK^D7()c8`QUgx{Im1pu)ACWo>v9P?7>IO@j6Cv!XEohj zH8E-U0h*4kf@}B^A&A~}SxAEr4 zg0`pLnNprzISxyg#j$!NuS!&+ynChJqb+srDGn0J6m$2@{Pv4EcTZXyUF$HRGgq>W zJ#HSa85AsNW~8Kdf?hjpJYa@gMceS+*$gMXmgS;+*ncki{D;ZDSVqEvZYjYnW~yPR zE&fzr!a$LcQqLsO&90d|9M=)Mrxn`t`=|j_scbE7iCpV#?0UH4Z&^)CfO}YP zt^k{fJYBbnR|9R5RW~(bFO7+iiSsv}Q<0IRh(s(L7EHN}ckGCMUywZFb%QQdOU`mK z52}GW8q894j;=>U-Q%9zyxZps9Tf#pJ<%5`&x6-~ z5fX7EzCM;?iv@iy@3-EKnpJpnkC?ov1VzZMj2jd0Bb8a7aI(j;74tGpz9I7*k1^RK zvq}N|vH=joepU)|Z!(;!FkhRb~hG|)d3VKSLZfp%V%8~-uMQ$Q z9)(Q?7q&)4m6*rN@cC?;WZn!9ucMDw_QhOtC+lo?RgJ&UmOeWGyIS`-pY}lU2+Ihw zau=>1YbFp_5iupSsN+asFln*m`uKR59G?ojjExI_rDI3yy*TUfR%DgH`2=T&X7pv4 zjMg)k5igIwh~^rF8I;-J?>(5M!&0MZ~t z$NFGl$8umr(%7KbEmOK7vmOgcK!fBjuEl`_q2jA>fgqA!^+kWnz+iaN((+ugZR=pV zLc{D3@d4e3OQE?vcaq7IxTK9Yr#kEhRwU&-%L(fq(3Jj=tMXbos53MUs|Wg9#V^dZ zR!gCVmO?w0y3blF-QcXxiF+Yi&0@p>GQb@VD67B~K-9zblsl)n$^%EGzW)B;3>diH zcNI{iTF*pbnK{=StcYm@jQwg!k|ZnxulW0j1|7CnNy75CA=9sR5bT6qduEU;$};Dn zr_hKT^#Q4|U8JkO(8<8w>%hF?I}WbCIo^Xo2YhphlN3$3F|ISRDqW_1c+S(IKQmuX zG(~WNfnWC{>2yRe8>Z2dG#{`LUc&L|mgP#@#Nsvy=Lxyrl5IWFo0Ym)j0_EFh2>ie z@j2^}(>`3w)7+aI<)NbVtWJ`zFIVqqmIUqt5d$Gf(}@vpnY&8l%o?QiVyi^LrMC!f z#_ur8_2aj3c=^&|sm+IMXoF*2dF9fn{97g3nG{t0kgGIPHF6@0_N8^IA?ISHz4fN? zb}qr7WNm59)+6PElF~IiF%p71Eabg8$F?AC%=nk;3gj$ke`}9=lqwm{{}f0WFVOyozvpu{WS`AAzq^{S*eJNz;Lq)?*`J65FK!Jc8I8z%58YL1e{!%aI-6HF#yA; ziNCvLL)VlW)EOYh^U=eEu5MU@Wt}pNpuN|0 zK_3>Uo!XMw)q4|3SDqTre?NvaXIOYO4(hS%&b^WB0GpMW!sPWQp*M}pJ)NYs6nx?> z3!FGJG!RI;BI&hS3-{5Gla4)haw|af(|0E;Su*a1i?_(j)X!s27nJ8Zi&Kt%DK8e9 zdM_pqt)m2#i?+CFb>p-Q)kww_~c4bf+ zbozPbp+o#DCoV9sg>y`g^pQz9dZdmaHu}YNTYgM2y*5_0OqwMBVrS8YT{>V{*-M7{-#Re21kz~@ee);KgEy&9lP0hYr4)bq_(WQhQDWL*{ zyC3G@h0pNi=LkDhqN~fz)eRhcu!Ul7vyGv!AXEnvLW7-Ji@REZ7z!ozWy`{S+Fxj+ zkqw&eV&!D#b*=iz`)oq8$L7I~uwJqRCdsyuIK4O*j1R=&m^pYD^%WYnC<7IQ(el<0 ziu1Tjb`7%pzx}@FHwAm6I@l`(0RhEX+Z7XMEIvgF?ul7=75AnrS4ZP>E* z_U*FN3mLb~%TJisn>N_4o+);*ny7lT?^)Qc15|(WVVUH**s^{};b`JTt7L`eQm(Vd z?8-o=jm~elKv4?U{!+{4(4n`= zcQ4!S&*HR z3l6p|*zBvsHv2E8%+BGOM_UEYw|lvK*|;QRGa^zl;4;~Yx4$cMNYb5FHBQzH7q~{e zI33sKRxOjGlTR;LT|^67OuOQhu=9?5m@g`;PPhkHJHxsssU!EmECRcHOt9O!s^}Tv z&d1>d0r8RE+Uh*9YFE2A^m*IFhJM0h%7F^ztgwkLxnlqSyIiC1j(uElIo;*VIU&!G z7`t2NLi|mumGhqkR|~j$Wt|)#F-Fjj!i+JCb00o;xT>#=GrYfSv(Zg!&zt!IgAzNG zFy)CAS{chzM=k0-wHx$1bE6>TMbkxnDX#s;Oy){zL!xN1Xzb?c=n!dYpCb#!y4>_w z=MQp;4VMg@oa0YNa~6r*(;N2k8{5D3e<=IzuqN}RZE2Abxn%_G#?ys*xRI6ilf`8gd@ef zK}GypzQ2l&kE$Y2pqCqOkThk^UrX*@Cs*ot2;aJsKo1WTtxO=`N5MWHZ~Sn7l%7s= zvFK6PO|wMTniZ*- zxu0ufh=r7X-==k@6z&870eJ9I# zHYXV6c%n7AHMx;L2iKotea;nJzU;h(Sl+lRXc+d5J;@Jfqxp_7({vCrc+}AphlZa# zm~u7>6Z_-Yo9oYZD#WV><&v?eY-xj}r!H&6RI^52?bk7%DrX>9F-~T5vb~sEh?JVw z7v{&B8`RmxD$!W(n|$>#U)PQqp7|wqD)3>D&BOL}sPDL`E2#$rrW)OH zX~`SLnB!=~`}L$pMx)!){gRK z799(sak$9^S*Hp8<(+N${4r(854^7l*PBlCaQ?gNlT8P5S%nS_ez3gYV=HjU@)^Pz z9R4=y#zm#&?UEaq8{3F}SeAQONNz)EGd?WjgR-Qhh3OZ}cWaDI2zcyx?v;iQTQaT?LByp|4_i+O)WO>a3YQv z-AZs;>wErmDe1&?lTN&h@TbM9)2YQ2F=Th0jjuxOq)v;i!`8^^n{wDGXfsVpXG`Of~xLph;_l*qAKjA!h~_@)UV>G;qQtda}^eF;}Ol z*Ot)4)a{pQR+;IB*VyEi6~YHAjMFJeyj7`ERs}(aMuAr|K6u6uxscIr-|cREey@3S zru@pb1hxg*8W7!=$N3Cz+eoc)vSZ-mmxef15a{7{+O$ z%u|a(JfS(4s5z?Ds8cNz-<7e>Z%><<9w*Hj`c}j|9rG-Fud8^uRYU3>7YzNhZT)D& zA=@#iB?oU_^IoNHNU1a<=%QCCkDWr9`|e%;hr9lilYVv&HSpP!BiqB73u=b8>#OrY z9{k5L4FxCCFWZ?9a-)|nu)v_?MxWqS7zpmfn_39-@I@8$?(D+@=Jnn#U0He4#Ll_v zxkKJ{_Z9EwiC9^Avqw23#Pu*EIj><#VG%bXo82~sjj-}wC^=A zE)L1n^Pa#%gy6QyN%MgTjL``xa#^!X{1BEQDrzOn}|DXj8(@C`Z{#-EPaS-5Kc3cvu37{{ zk2rXVetlpmxbHZcR}u!Mxv(6FSSy@!2yUFCSf_txx*g$wqj^F5^KIL4Zw@ z$hh0$lX~AD$)eqGk51d@RW#n88J)n+GMtIqmuoX}oewyn8!w7o=dorc&Dme7$<*-> z3Rn=u2fu8R7fxob%ZzSf=;vN9RhsP7Qtlts45KTE4cKh%C$w8~qT>~m`2b=blLcb+9%!al^sE2)=UC?jV+1kJe&$Z`_8J$fQHcvYwiE@>-;@uJz z^aA)j8cS@ZRrg}lH+#yfLN>1?*G-rO1bfW%IeviP2tt#4SdWFXB7SCG&)Io61CLAv zCl}UuA;TLilgmvbU+cWO4bm~u6Eo&7*r&#)eB<~d=RTt`ZZ*+y8@ssH&p1|Kd)9{k zb%%u#GySDe9^l2){|OBBeCnDgfjLk?ga68@qMfkl7LxL3ltZsy)!X=P-QSS6IAwbM zD}{(3`OvQXlZnqijP-eOg@^&eLpg+}Vx9IwRh>T3MjL+)by8a!30D zpeKNkRJHI(ABRUE9Lr0|FeIsJ=f=s|!=5t*q*w5jWk$l=kLWun9!zBK2W;N;xWsx5 zxa;$TH0(=gR|?twU78}AOQ) ztHa}A9UI(a%KJC4pw)?axjWMZmTwT3_il&k7Gk@|Zs|C)+irmeVR6V0V>YbU1PUuT z>l~C=F*A){ycS6PRnqT#`k|j4>sfFH*+Dc`brJCe6f*a2k{h2tpWZv;I63{xvDGr; zNvV>OG46stXz_-C&e(MCDao%ph#O?_(3%;Es*~rogzKb%O*=k;L)yYkX;Rb18Edyp zcWMFko$H6&v8w1SSblku4-)&L@aUXjW#4|tf+7-rG~QHJrCB%m)M4 zw0~jrlE-N{YT(r&;e`5S_XG-`*qX=31h@5BOd7v^@6v9N8Sq^@RD*@YvN%AKZcwWf zu_-IJ`RIgh1Q&-iH5SIB&TfX~0Es5@m*L6JYIFgyvu~=JONzD2Q{v9vk70RuQz+y$ zzbWgXh~j-?X~rB|zwXtg%B8zMp~|jVCaGwFGlIX?$ZTuuJyou&6##3v||L8;6xOxdhL! zmOFlRR1SS^r3j0+^JsctS2cmr9xe<&-9I?Rn^xSM5OFB)t$CypTo&h|5bYMYIE23e zI)DG7W*`r9>W#e&A14hJ$umKi3GZ%yIU`K=L6@U4iy=cD=vBjfXQo%MVNTZl2G#B5 zkDzh~jXE+X^o{%Tj*ufU+N_S(w=a@5zxk?3`ZN)%)JOZqYxsM9QS762Y5L*?>+@gk zzY$;9H&SA|6<&Pt@X_+qFTlz0H#`PYlN_s@uAuxTYJVPM&8aAbfBTcO4@f$y+f8f`T-l{|LR zdqV09*7=jN1YJm3Thl)FmM!SZ(;{z8>7h1mnI(atxKYYGVCo*zk%FS6w50*Tr=g2P zGgVjQ*S!OYsUtoP)bnlevT{2AQ<(~a$d5LMtm_@Rq%U8a^FUXchlxEc#0)Kn*I+;1 ze8ZUJ?b^l(oVC`y&+3!ZUpL<`7?&fZcef3Ic9DJzkP&Is9sR1g*ALkjJci}Y%3bDC z5-N957z^lcDKY0mJ2IK>vzNe=I`3Ho-mb@goWYjkA*wM&;!Q~{5qp|Jj~?rc!+4=n zx<%YANngc8^L7tD9{=^KO>48$Qy;78_b;lBM43Mi&#fd!dnaZU{`Ymu-dk$M!EkeJ ze%YwH3!VxuvaW^S`sBOHfXO|$c<5=z#8MvuvJ%DdUMgElD|T(g-5~1?d{#T!RM&}{ zI$$^1&Tfs1OM2>fPILF%!V^xNiT6sRl2y>Oy9DP+2Tf$)Uq-B z(by~?N$r16U6g=Np8$t+PUphkVsid zRiEL{EtzOcjT&8&*t&c}a1%I~g~MhC7(%|W=m;s@CLUZ#KA8LHh+cTe*Fk&5otwk( zbLJxS8F?tpGhZD%5c~k+NZrO9V%=_2{IzxLG)s@$^9C=A@G5W>nQ1J-V==^sa>gYx*lq94^mnUqwCm=7__#QeoRvhI)GiNM) z8AXPotxXkDTw_}%BDWYo?b};5*d@GTD9_{_jrP*1y#?rOPq<{r=TpUI)T_UpwJZpd z*bWeGm*YMhW=&Puk;$f0LXjroQT=q*Z&=fPBrF?aEN_5+>yppl8qI$Pf1A;-59Wz5 z(cI?Ho!r-Z2hN&)J3s7jswQ+fccgI0=O^tX?pGm1#_*w}CazQo_F$&hu7~%d_jX>C zKtY)~QRGKT{`ZW!Ih_;xY3Z9|+4OfxPgquul{ErygJ_g}H1O+r&nmhHLHhdkDZ6{! z-+;{~kM9WiUw7J8$now|mg%_aU93ju?V(we-i^2?9LwQTDYM9V-B^gH>8LSymx@!u z0Rrt?OB={NcE5e-zV`UHBgV4+7{n*etBZvpVH0MIcdDS=5wV5D4;*{))J)tmhw^cn zz8x&g%(27@%CGXot&aL?bEGtLpFyx8o_(ipPi~C<$ZCD1m!ep-rFD#Sszy=M;6BD) z2fdnL92m}02{`kpgEil_f2-$38l)=)HJ{Mb-wzXYq^{|h)Gl_Wc`p!=Dvhb^So!J+G0UPp^1j>^X zFVeOvnvnJkGMN179caav?o&yj3KL?5Kui&=k7<)VVR`}5HI#z)>J5nZYv=vMlhm=) z4vD~0%>?cw;kOHNLN~c4joTh9*>q}PZHmNX{%ikQaepZ3T2p-uc-$OzCLVGbzWqJA zOc1kS#oM76eP`n-S@;a1v$n`TH?#^hc2)k}Z+Cexe<`#AGEwym@BOpe=0={s3Pj1$ zRQO>zQ5bu03IBbO7JrKYdCam415Ksxiyy_XUrDnuIFHMGE6g<7jJ zlAkdj7=Ds@X~VXgpHRWo&+axF^QEX4J3{NK@Xe*Q)_ON+POabjVVBA@>|UjzG~B!F zG%DwOB7K@vlfYa1_8D*3+qfx2e!#wikOB?%4s@%f%xGuagJAm{1wwyuzA}=_?Mco% zmVS`F%m!~b%R;MA44?0v+H&c#(NChDYO8EbMzh{t86_i7w@z68W1oTCJ9zEezm!BGskhBnS4E_1T zd6NATq|soVDlJCZk|MP7oRfH}I2ae&!83ULMfQ6=3>CgDt~+A`*4-IAsLHA1k$k`I z;@cLCZ`N&vT0kPnlkJ9=CUYW6CeJP}8`M4aX#lyPA^uk)#Gzk{W?X}t3vSAEB`nxncQAOc*f9ZE3z zyv;?r1cu`oO{4%PUzHl0KrCg+42*;7TW*#?+b37N+f?rcS|PE41AXutCB&bs#u|N! zm^@=(Qtj_|>ue-A3{-lJ`0p|54}1QP!B?{)sVf5#Z_)hvHwQFNaCBO&Ua6hs6T`4R z_a3u8XIiS`jH0C)GoL8B5-;`Yir1Vxp(3o)KUe4b>LN(UzFU%}iphG93JgydnhK^~ znqABP%6jZ0kZ#9@r;Ye(v8TTE@y#inIvgx+wjH{94^E`V7*rLcLP7kOQ==@C6U!WM zq*Y$?CeGFOiNHnG1%8bww(x~y>!=RTGRM|RJ5voVtRIDJ;1`$UJrCy|Oza+zn-^mOW4Q#}2Y0&%5K z{N`ME*Bwmjx)-Gh@E*GQZiuGo1zehuqN?Y6XH!|a{cPd5$eL6j-|?bA>66}Gn|qdG z^)*25N30!mUoZTmul7toKS^FOFV5z{oHPi3w$NjwCi4Y2-2eD~2} z`Z+3m`2K=nx7fTK#^{R7;oDc;Mth;AOd~kGl;D+L-|-Uj17Gvk>^MBuJh-Sl&jj)% zN98#-N0yX~=#*{kear63G8rAjHMIeV5`+q9ePa@KWLU6?chym@o>vZOf*;OelfD6S z#e~lcN}+rGNu7X!TsRQ!+AyPm_6lL2Q$T)zM$9q40~K63x^~WgO>gJR7_L1t{OgTG zT+2>+c7Hd73rbneI%tgT+CBaS*d}J;_HItEOf*YM&v!!}I=CY;RYl7H2+7&JVC~5A z@+0zHdYLx9?(7q_Jl&+HV;^^IqWN#MNSEE+^JnK1C^Ps`N=ys0t@FH6f<Y~C>Sq5=V;?2_K9TUjvUn6e&o~WX*Kh9U1A5Qlx&M_ zW*V#A>^*&xcQsGJzB^Hq0N;`a=!b7Lq21+(I1!A~QSR2M4n=ZJ-#y;yH&r5i?Un<- z!PUJ^C6R^jE}Wk+yP+tDeKzA=Qb_} zeYllgJY%&5!90;#4D3e8S~1i) zFhtkNiky6OmB_sq-?<>p4sW8-7Cza)%3>USdgD?MSmJAo&Xmi-z)!(Ig06B?b-A<0 z!eM&(%c_UkBjK0z=`%|zKDKS6w#h6ym0eH3DXUJpl;()g*jUuZ-Ef}OH4|gGV$0K2 z#kTvZ-+n48n`wSw^SsClaPi4p`tIY#Fj*h#L2Y@vb-3Yt7{ON$KTm(jnIeDn8cUQ37_I&%kB&8#p598 zhkefH>w19JIG8uj%LM6Df*-B=<61@`#v$uUk4Ft#t?KwUN^ZQhf+n|K0R%X@wKW=E zCbDc~dy$y9M9Rs(JUp2_X=#T;zsT4Sw8+l*#ZS+Q(u8NXzFpAsp6!De!|PRPWfgd= z1AN(O@aeKo|A9q$;SaiSdW7~VdQ6ZN{?O&mVw1u@0zK)Z@(d~p9;U%!?iVlAk|>kE z2r1aT=Ghx+JJZ(L4*xYzkEC6F_B*j*zpe8xVZhDZx$PG#{pS1Aa4FJ}(&LhmR{v2J zfF@_WQt>@ar9LAGFiNdYq)m4en}eTMzR5C;?W*msYnVrG>kDsv;P8sd?oPqfh5qiN6AWo&=}u*AyMuO--JOzW08YX2?A zuV^?o)oX7w7ip)>hgPiGrM0f$EYT0?rNM%+rGbvfA9Z`KLB?E9ll(&fGBox6R-(4* z5v$QQXr#Gg8{l1ldiQs(n=8v17w^m&M#Kyfxd;y~w~CGLrHS9X3ohwSL3wWpytok_ zDKQo#8k)20JRkH1gttoAd*IiZt=YonE%;fINWkzd8dDpug!0bnO8hy5OT#rl5E=A)4>ob!&$T^KdOymfVq-&!QOfJCyw+VF8@tbSVtYMpZdhV znBwlLJAx+14@&_CN|B(Ul42Gx-ut2(lIab@0}FiH;$q4;mZseP%}qp#Vuui_jTn(< zQ0c6U@|Ct;)jb|Zo;j#nFpP^D9&AjXhP(m;t!zntF*Cy+kVvCT$Lq`V1*I+KZ#hb)2M;RS-Z_D8IdLZd0b_L&)dqt zl3El{k}m>`fDj!L+)-0iDUPqW_@?ZgU>kc|sK?UziX=4!$F;9Q*J!MeMnrS7)rF0l|ACfILecxkZU8=qj6$d%T+nrDqs|@f=@F% z(f|*f8dj1pQhQz^{+C&&TR0#veFo~hWjpc1`?u~d-#9K5Ox6tAN{@tPB8N@>FMa(} zbY?G~|HQWbApV{wwEFZy!~pi#)b4eVmLCt$pky>YGy*o}JR2`(x=A36j&rr(@gDtViMXae^1j=Rme&@vYHG2cV`t-6 zCk65ST>YW+h3+c0vd2`!B!|D!X#pNRPt{9N+-T@Py+k_eT46hH4mFO#*i-q7;6U zfRJu@5ERHsWP4-Ng^f+ja%@y^>)RRC50Jz}cmM(&b4+gh#32Rfsbpwt+Gq13!AQxH zxX5K=Y|XGE>Hp+~=88yOmu;B#57zrfLG~t+A=;s~&#q|Omt;s8KIuZZp%aS@Nfj=+ ziy>n-r^D|LfsqO-S?(8O9~3q+SJgAtFo~zwtGSU`PtIO@u7k?jCY*9!=)@oi6+g9)=ysz`At?_m9aJIR&1F+QR043s z&CXtdorSHd4_ijAAVuBwaJ4K5c#Vh+wtNh3OOM^PI{)o5S^f0`;nhjCGDI`&VX)p8_ zeM}kUw;KnHFqT~@Zd>eUO1}wDRv>E=(}Wl3k~QPXbv#6BJ%3>&q!aSEXc~SdK>|Wl zWu3Yfaj+-9cz${~m<5q0mN}4@F#VJQ=r6hD-=yh3i`^)r=Aii}rpI5A_|CiXtXuO- zfBl!hNV;3E2Wiv~>NK}~vka2QDZhcRdOXd0lbR!=idrLLLvv#Ip%hK!^rGldkczmV5`HQrFaQn1E45P}NU3>O9RFRbV~$7H$?N)CD+pE_n_Tw_d!3A4yanWg3$JTJ?`fpC zBqH6k)%-2>_vao2`!B=wi^H(k-ny3U_0kTkCu+#S>?7@%V?w-)O#*7`sb^p6`R){z zjc`!05;5gsM7BW_)3nU1`D3~2*No6ptgVG!l#Frhk)0HI?s-& zLb2|jZM8q0NW~CZs}-Ivjw&<=$iCvz>+ph)Y=0T zPeoGJsrp6ZeXFbg?I%}Jqp`p;OeVt%fw6?w3W9R+p;2xPvDgymEgZeZ*n5321@$aY z9VNm0=k>ES;y-_ZZ0a0I*o$fQzjU_wAu>cANU`l{5F-w6T+HPzU26tc=Vv(HcKane z)8_My$(}3!r__Li!1md zt`SD?YukYf(a%(%BL)8IG;!LWL#vW6@$Lsb&RgU{_Pd{u z8Kk@{tp~&>YwB^I=iT37plHBLS5t!6RA*~2^aEaaDk4>T^p|trYxr;ELwxV6Yj7}2 zPRqZ;Sm5Y5QMJ9km5}^#1s|C|M-G0sP2efo>e6V_OU+u+^|s1LoES?qzCP-K`4*aK z>Gbq1#VbFY^Dm|H5Aot3xqtl*rJOjp!{i$J_j{^Kv`U-6JkVDTb#QhMr%NtF4rk&< z8eJ~EL?v$zJnFP@acuT~OfIoUIq*9;`ChagucAz*_1xdRx$#L;FQvQnzSwl4wf55^ zeGoJ!td&&I!9Ek{@6Q;^)WJT{kVm%Sy2jt@Npy>@3pF5pwYPA(EunN(6a~l^| z9n#O^D z=JzbfEwk=Z1|?wrZo9u3`Jd!crNOUj|5EIYheWkL3BRifPiY$GL_1rdohFKS_r6<_ zj9nG@Tr?!D_$gDGpCI&zOHc1Js}Z21)ELv8uUFz_;B=1}C75>!dAzKI0$f>piRuj`hSP7x!yb;Fmjlz`A|Zh#x7% z^-@J@hrz2Iddf^aMfsR69RZw-LQt@KdubOHe13U51I+-68|X5-jNByPVWApKlg(+uXnd;K_5VPhk*qS8L>{-h8Q}7nwvgC*$|2 zAz;BW_@|B+B!ccQ= zi%7XMY{iMMEy{gvWYXe$;*_H#nT;CYLW4HO=}q0uXwItvsoja1%jKRSu`udQ>Bh8J zVBD}Rd-Pxzezfy4X}U5NIl4s;sGY79$Wz%F$ThovCY!l#T4P5M1@M{C)g-6XSu!Hu1n!R1+faT2`_O#rAh>z9oOrw@n&YyR(zK9%_V4Wit{CZuBI^b8K4zVHMF`gIwf_fua z((%~DpgHNgTu&1H`0%eCFSU#4KhtMhuB7KOf@Q#+5n7PL1$WnN3Yx}URtnn#|01eZ_E+PJ8`SMA4GaSU zmLQ(H$~r9xJf1&6{FYJVuGP#fNMxH!J5VbaEVUAONIgbZ%IDhBC_Dx&8)TM|%@s9=&n%vA&Kp z*ka3(wRh%W$rKfl7T!_T>R9JSX(@ulPF=HFWJB%^S{;GzeWN{$GJKrWNOfk*Cn;~M zHL+QaxjboDcAzIn^5?Z!`yHm6AvJj1j;a%$OTNe436z3$=P zmjbiSkkh|i!k|h*^(s9by*1{1+2FR@fpaxjP)~~+6GfjsW1IeTnuEC~&F;&v)i@g< zin6$1tbXLC&mVjdRhg(%s{Ozr6Q}32OZy(LnfO|P!eYCBmjDdg?m>zzdBIP^1aBFD zr+t!S2)#=?IL~hGW@LifZ^(b@-E5gQ7kl+yB96zj7aO!-3L{Fja+jPs>3${b?LxFqAa=KkIml76_3)G9>gs@{Hs zkV8yY=3HJHJOD0OH+X%?&NI29lzkSAgn35v^+TOwT}=`5pR}$pO94VMt?3C&8K7AE zOStSuZtht|<_Y=X=kL7?U9Sz}Tv4KeLpuRP^kTL+M0(LzU=02`PhAA@F|%e%5PV)d7*unzCoEA zSqN>ZvDZ_cH+17CJAczX2YO~u#i!?WH(q$`Jn*B5ZA+=6+_&yL;jU)y7Dd`E-ib4C z@5-M$dYg~qjw0GMHae zV6`sDGg18rj!iGlP$4FYQ^A9-$JxY1yw3S$nyvRO|p+vSwx8TP;RjCfw-MsR_?70e+v(D(08!=BGC{qrmSCj&Ni%G1Z z0^ghMHi<*dUW#CayTIh@Eu#jrq%)i}V)Ti008EHO-^(A`l@|4uezW6)VOp02)Jr9% zfvb;UImp}6Q}~_<``q0teA;w*$D7p(DPj0f zu%ph36&|h&=Fx^G_UJXS5T3yzpHVtU?TA-7nZT(4mvri-4T%1m`Tdf+?q9(nSQHRtt<8TE*cP{lRo-GY@QU2~n6$MWDSL9dueGGKi#YaCukt!=lg)z)da zs5q)OrBqXc!$CA79swuQj(L1 z=SbRx^vHg2xtV3e{t|=PmC4YT&lsc3-)xGXMAILJrY-AC4t8%L#BYgFntL)@xIbpJ z|98gn!}Ggl)2T&n6wrmR{tMUi^q}(&v$|TPWOq2IZH$`g7=O)_-t=WnZtv@5J0R8s z%UlU=OVviJzv)>!d9JAO;N9wx1k28|=18aSK2vcXqbQzz-~$TEHgKZ#JS^|`3OQvb zRtQSX?rh}-Gt7+zqty0r98&6CF;i-E=(RKA=XURu%Nq2U0|tBTFNckQa;V@~d= z%GHVywoQf>4B=Ns(&Y|4jybO2yH!y~iDi=yFGZM=HkXH`ke>bw-V<)fX_59d;g8iU z&H#6Vl#!V9EmMF|`S!051;f H5`yj#OnowfNbnTY5s%`;P+*XQDNG4KB~hmEOkH zXI@Y8ctSD($JLlKO2t*BY!EQTp3cSpsv-ZG3IC;lQvXDqY@EN7dR*YQ@i(AeeWbNC zdgrc;HNps&+|=(8r#<>^^A7!9!ik}J6g>?l+?}yz(jV=l$5!Hc_h99WZ8s_9{j>{m zPF_SQA*k3n!r*D(Ynrx6=wm?0f@LbhlxnJkdeUquso6*dUV{#vhPp@Ad2rY)K0g}sFE7k6#X*NF@^&|&$z3H&zEl}o?o zXZPAr5t!;gW(Y~MYF3Pg+@RV7D&(RR7+Ven5@pO)CZHIpXuBjku=`Ty^7@i|$d!v-q1-TRs|EGD!J*a%1^Jvl&-7Qtn%T|GwxJ@xBliy&za#8Sh=U(3N zVxC?r3#n4MpOfu>h{h5CGQegINojwY%y*n)qqneXdVX z?>o^N@PH2A%hGbgMrYx<4K6q>5l*a5GCo-A>j08&6A>-F$y?hERGVS z*ZV?GH8pLuBN5bRQ^hI6g!L5d8BRELDTZtf?0iP1{c!lvcS~w9mVAJ`co1eL2~&6h zY5O1*jRTg6bVr2C&ACWh(Ya0-nz;rvTf7zF18WfQkBE_80Ke0LlXOfxq(r&tlzVNF z(Und-)X*^iPZG?P2_%JP*vnd%F6(tvCce#qtbjo}+*WFHPzV>RX8 z`-u{BKGG8J732*&Rztl{7E5{lgL8k7Bwmp2rRFiXPQf66HM+o@UWJZK zaioGY;VlcHf~r6E+qvip@e%ouhNHA7Q`c_mNa?ES?_Oj^NGL;#=ZUXfNq<3EsJrU9 zx>ryQb+g9lGhy(`A=k*k%DG{Ek8OpAzeo?*EXWO4+5n@*W9h(AjK zRMTL*Il;rXfM%(uk0n}@3>~7u&<%z-WNjwY5Y)X zj8+AMs@~q!VoR7~Qt;2)J`uD%wQT1n>)RaShYuf~A2C^uDl5Ral$~~@nS9(n{-SOC zp)O3XMsw;(--VyN^=~gJk%?XGH&*2Qy9gGTL0jK*?roTr1dw&zQT#Wcb32zp{`<6n zrMSe%CQ_c`@BS)e@XJC(E*>BgT%tM_LS%%>=kH^0zUh>E(BKA)_3uSqTiN5~L;GBW zHrI9&W9>48-dN#JvFN;@EIL~_QzXM2pKP2rCm*y+RmqPY^tqC>9y{w{5a+yXO4#p9 zMue@&M&5ywq=Ca02wmjC z_U`&a^0RebxT}o0&7um)dXGygS)$)F?W}a{b+H=N2h#QdtFu9(EG99r zOv~%Kik|!c*B~4>PVTbnJ$!MA{g*V#-vZlX+;}Q;jx8hp7F$2~&YuweSHMQ;8UKO1 z><#`__@8iBo#SfZ$mh;+f$46(5m0YuEfT``200K(}kDyx#0qe%LEPLc&%Q3K9Uhm&TeLF{dBEa1xM>?xy3FEFt&DEy5YUByS)wts3A$Q;W zUpUe;%HoQ~Gn;xD_+Lr92lo5Y)EMm9_p8(0409vis(!1qZ^@2ESy@^x+XdLCP)Jkf zT5bHW{Fg40V`<%!WTtqTQb$lTfRE|^9Sr3}g~nyR_dAWWJI>`#c0?G-g$oq}7QkMy zynU8&C*aFIhs#+|h4n5mnOuUs(-5U*Ji{;bX#NnO+d2Kj=T|Y&sI+R^`OT+W^Sr9+%zS>^>MO3!^`?Fkx z*J8dQqLvBYlsz*`YE5NYK3kSor_PMbpVDa@eunn6Fi~uwm%7pL`041(vC51MC$^Gn zu=Qm^N5RNCuYsp+Xbf`|u=VW-zWIY^wO9D$7q(}Tq(Sg~kxqT##w9QPuyskmFYkG> zr1f6-|4QP0c#FaPiqZq%oP|3rMRk}qF?sIm_Sm3e_Rx^GAi598pWLu3Ntr{9BKTW! zwo;~;Gc_pN(m&{GwJf!m5O^;UomprE+$^t}YVp zZ;2D%{9OC|UD5vMw3*WU`~qba_xXPBa_wJp1kLK4M*04^VZvESw2d)Z&G=&d4wV-p z>T1E$>92ynV&Wm=S+6ML2n$2loZ*VR;74|yR`rmh?xO;pC*%qS{J=giYGLe#XyuVJ zTF-5-Mv)q=r*PVaiG~JNkVe{&B0Tj&Js`&s@TQo=w^X}+fieNTCVT0l(^Ud-M6dmJ zVWcSPZvS=Q(_w6{KLhygY0|kd<~QziSX-J8#J*-JLYbJvayueZt92Vao}W%NIzX8& z({huG@X_C*Ec2m)nKhRw2S&~hq0$)g-(DFmDq`xOUgU-j$(VN$vkzuWk z_;FPDf`T%5wS!0nwk2nsr%cE=oFW!JV)%~kt=8zrNJQu5BAU|{VR;#+WOT})@b|4I zaA_qVLa++xChf;!_{9=+&?TgfdJI?}ZFp2gI%JXMEop19B+P(7UC_gW0 zRIhplAX|{dN*QvD(w<|yT;xKE1T*&+YX0P%)skjkv$SQc(kIb9mjgM^VwO#O?0A+( z>6^i&Lfw9aHFiwV6$=Z}4vFp|B;20b0gia9DfO0thN1HzO{L+ zRZvBz@nP7fHu5uSq;&(I}rl@fRsim%)! z<29nGWWcxF1eOymb&n4>meNX=6<~Q+z{FMuh)}0KIuR>2=zT*o*j6)MN2O*5h4uWEK+(pG4F~bAINA%FqzzEwgqZsq!jfo zOmt3qv|K35;%W5V5z@go0Gmu=scgFaK@a0a5eH=ip(~+V0{8<}g@Z+lq*ljQM1?5E zB(l6`_V@-+zEDFeDzW+ZIjHe^aO`#dp7~8>JPoG6LiQ0$|60L&w)Ig1u=_)Di#^ys z|MH^mvNPaizwkg%_&>L&C<1`KLP<~t0%auMVnKXymw+jXfd$Q2uv>~_z*t%bHkTMMOHijHrvsJ z_L9(!OwWg&e6V}(`i_t(gLcY;R^^j(rweFaCw!$sdmbIjJnjqa+;xX`jc&i0{(WY1 zAgdjoU{G3+Ivo7@{PYDv&`5(4h=I)_i%8&_VB{(n`WDaSy=P-rH9c3^?y*L+ z6kuO3HAg!G6`qbh!63=|s`q9+2%Df_1Z6U3M5bFM=IN-hec*5;6>^|@d1Iz@{7j>x z9KEY4x^-AQH`!LK@LQ@EdcvQY=3Y%x^k zGT0h)#b*XHOG4%;x9a0wM>b{PNLFR8xm{M9ykNmgsKml*e~h2W-yHETTKIbhR z67Ke*lpU(`l#+SCw>U$aLThub*IeJ zG~XKNZu zA!f^&2<*>33z0+&H`TKzFbv7x>fVN6oS5oUADY9mgxr#5EVeX=k;wJoiV-yI7BU|S z33&FC%Kj4;|Nk!q3@DsOuy@VM^rsDl-#ti~B7H%rJe@TY^c|T98BgMkWiL?{qs|zt zD+%a5o-uvwJ;00y-mO*gLz<`MW!kEdFPs^h*RSzTun`A91I|55hUVR1J> zn!$oYaF;-^;O>&(Fu1$BI|R4j5MYqOgS)%COK_J#L(qZXc6_^cyUFg}?d^Zl&pf{# z>FTPt-g>(_$rPD!2&J#yyKCYH6bwAdQqUO)cV%mrzgzKa!sP6lU0=iT7&WK+TkybZ zJ1@+(85o<(M3jZotvmD(xlZaI){+z`1VvlrS@v0|KX=_SLpNuUisDad*M_zqGk*=R z#I$k2V7*p%4#s&FonfEnJS{_&p$3^-E^UT0JkjAArw0ksUK@rWcJzrbrUzekoS;mb z_np@bG;TtRHcpGn5;(@@zMjzHLci(tSyZvJ_}1ZRGJ zEUGs40mi+x$r>4GbLvdH3YF{=`(j3{03z&5UGBkmsYGL%_fwx|dG7zen*#+(Dv5OQ zLTLStTFw8%?)1a?$+ z#(i2_N}E7oMh^k+|J*h+^0qVj_T9KDfEK11x2mqc+sSy@y8o9(u5BOMD;bOTN>Tf5 zJ`GStG&C9?$+O77BQF?z)GMYkOX+wQP>_NwpGIW@{ywp)##S6y% zrODsbPp3*HipDK?>D85Z_~T_~TU?2V=5X}(mo*y+|6u1pcz~g`^~VfI5F9dTHOQv* z)$WE2R;FCDhE$KNbbSxV;$LMBw+UvnI$eS&%i8j=5qo7|_8%a2#>N8$is^cx#{$2& zin54TezbiqcDyBzcJpNw1QG88) z#XZ`N*n}vWr5EAbYk$ZO?*m~mzX;1*@m)T72&zIGnGj);zN|COm#91(*G-)fw|a~` zGzmBP(@_8uO{mM!F_0!I*Pt~4RLIIa^1z=<2r6T#?&(Rg>b#CYJ5oB$(82tEaOuz$ z9@joHQ=&lIur1@rst+^0cI=-#zhcDq!E_*?|L#%YNUXvGgP$=9-^F=&u!VZQJti*1 zsH;kdE)++kg`&!V1qNU$Ld$INVQp=Sks#I87Dpr0Pk$HEJS5N!{t8_YJ;bNb2U72b zl)%TPm`M4ZhU=y$q0+nyNJ=&z{JPkZveN#~^CEvv_J1F2-Fz0oC0fY+=TmP#K-UV~ zo#@fN`>bx&rB?Taz`Z|*O3E=Ic}kQ#;C|7d*XDs&+BeeJrPgaYnri7{2>tsmwg1<& z%||-@l~s$%+Fuc5MzWy}k@U|`C(2&eH$(bU<^TKlyncm11qyOH4T{;=44#npNHt*a zCsRi4z=gOVvr<&86F6sW#$r5&7v3z`R- zwyZCYUf+=f%^cI<#uEwo!7rr2-wwniFb`-P#x-1zISSq4WHjzPR{r9WuAk)DgCFfh z6OOCr>=CzG{+Nd9Va2wGeq{L#){)xjxM17k8lKbQuR=K7%}{4cp{uVzQ28xK59)ITn+$KBvoN*a zxkP8ZHZDGki3+M)^I-gItrt^7LMSStOcaMHn3Z`pl55;KFvA&tQ0$C4jW+TU_@H(t zCHFXa;j7Pm0$MfHz5{R!KWz{lopsejO>h1+)Bk%A$Sh&_@kWelm3dG#P93_5Ffd&g z>e09k$5!YLVgg{qC7b`#!&2`uLHgYm}V&>cGS2i(uxT7Tv9ei7)5{Yf>>p zr`u>`_`92G1PHB{hV0=C>MQ^D*DgHOO=rB6{6G*WJv@y;WWrrkBq5V!i&NK-96 zAhb-5jWMq$;QckL!&{an=p0KMR*c1MuL^Hcc=dJJL-)>jduBVPJN^8XdlHEdkyPkY z7Q7Y$k^r}x#o=(wiu~Eg^KMhEbQA#iN-TAaNYESUGze1@_&%gA3feY=qC6^N=Hmj3 z9T&eBp;#XN!>t}Urg4*KI7=wk!zm*|joCb&PTmbB|r>XEd)ek4h!@d&G|>QIJUh+lGK1@27gpiUqhT)o)TZr91?#2 zC$5z~T!o2|eeFsDD3T0acHqjlKMbsrKM+#s9-VzAvZYJJ)cNv}Z7ijS+XR;lmqE0+ zC@P@{Z-bx?9MVe-am~1DuNwuVl}cq0jFn;>o_!bbeoH`q7DVY>h5+^5DVl5}@wh80 zqI827d29Rlg;rcEs<$${T3wy#7>@qkAOp)=U3`cYnOJUatA5jv`F0%%3o75jVWy1)YJ z7#%4x(C0b4pT3I9jGAD)W!S;uVCu~}pb}@~eZy~q0GBP8$mW~=dx~7$EAPI+jG-3kVZC=%5*R(=>C+-(&GNtu=l?sRV4{jya`C=$f=JY zIwsr9=Xm>8X)s437biZsx1tYDhaqZ5hADZQWrS;VXWXEU7wumo*#EE9=^=h-$C0b- z(93?)ak`oraO4j6&VD(p@HQi!_h>OdC3}Y|U>_#Nl5Zx28mUB&+s@ez|KZv(sEN*R zjzO-a_cYHEc(kK%(o^Y4;@big2}m3Y!{Wazg9I0G<+V2>P}RvQb=DJ75m3xpx2VHn z();O_UDuQRTX6p;7#_)z3#_4Im`W;;iJnz*Z{_|5HJ!?yo2ob$WDpc$|B|`vNlXl* zoi!Q)sS^<#hw-x$p_u`^?-@J`rzBG8iovI7`AWS>_h94`ge-w^6KdtU8I3hKJgR`7 z=K<12W9?3h0@cz&e`1MoPytbj9t0;W2EzFvKQ` zeLk9N7{(ZE3mub@CZM`fBQT<|%#cyvE=237QZjIEcd4Jwn}7LjRUlg#hL881o*ZmA zj)%(d9_O`3pPbfwhiQy-TvA`Jq>!<(1Alom!}D$`wpNs{s~F)vE7z=l2hdJMHh`|9 zS4qq~B&Z#|4s86(HYBL-(Mj>c0VHf;gIkJFR|u?((qlj|~p&ZlE%30r8Bn=1J8~zr(WzH){?KCP%%rNMhPh1tlZT zg)7=XI`#@b(`VO6eXhu#=Tow`{L3f)H-@&h4?AblGC}sH<8d`v#1?nJ&hzeypJ-bn zGc0YMiVoHmOA~vwXJjTL%ts~pnQ!iD@Jm)h>)XW2A45lxc&i?D`i9#`m78UZi9dFz z#tfWB@^a{JX_j=O7W;pZF6J`YiT;W1w(hYUc~}i#N5MPrtgg+mbPX0tj%OM%hM-e} zXpslHLSSK!1|Rw~$CVxbp`$Qs;UY#U#*s8qmeq*G&}+9i1g_>z?oRHb|jRHy3S&>~!iM zR8bU(3y!0xYyz|DSdBQbb4Nt~#swW%MD6t!cscmomq zZT&1T0(+gcs;NFTK7CljrtwKSGAi(a$2Rgf-to}lPEH^)T zLemp&RR8p4jxmUu6hEAeum>m7STm_i*0=D7Vo2+XBY8>i%;W2|!*)JN4klte3$7aZ zV%SivbL}~7E;f6wWc+Ec|B?~>KR|pTWsdxqoX1#wpUf6AMk>qOE=6|k<(?kuV=E&H zjHnZX;7AmE*oPl83+ViU0jH)P?a0#N>dEO-u8%H81AKs7V7NDC8o8~eCR5?b3+uC=h2mO$3XVt2YVa!^TovbS{$#qW% zmstK@bjQ>3b0-dB8vdyX%>K3JdCqP)iAv0Z=NC5<-q$rGbX?X8Dd`p9N?g}4J6L)5 zw11{8AYjzv>NU}#y}_fpq2XSlk*>8nBiQGZC5u$5X(cS^-A}JgqYV+LMm4wIMR7ANhhfmO4g~M)YYbw+Pu0F3l=#x z)e7}ftt=MF;%<1lZ3K@WfmeI){PGPfdnaKg5WdNQx37vp1KwY<5}#FgGwnXhhB+cp zdL)yHYdE2gFDu`Tpz$L=N0v%Yg_R6#xHi^Pc5CprVHbp^2$7i1&KPI$8(?G)tjRtf zz74y396T*^c-6$E_>l6C;Jr*7bnSV?jp^FGaKCk^J5;39zC}r+xWZMHplD% zbKq)9pyCm$*q~%IOOzvm5%okYnaKscnY;?^)O~WrPP@O~dwhbNr8J9OReBe~OcID5Cn=e6lCCmct1NZE;8G!2Y+f(%CXj3m-f{8ZVhtu&z_Z9V?B6U(1Cgi^ROep&;_Rb z^_+qYY2GM<=e`C-ulF7cFHa&}gDBY(uGJ;|RwirI%^5?GEqzQ$SLgDj*QU<9e4Zml zI#Ewggk|~#BjAjy|2mq?6oEwl6@Xq=T37@c8g?XqBx{ z;yJomm)_sy%DD#z!Fx*!- zVebo_z2Ci_0NZ-A_?_9$Y|N%~TzzW}t-ZPdQhQY4e+4(^Qq}Pr@$nXd1RL8Gcz|_v zj0rS%&CR?_r%6I@WcRYd@;e{GS%N4`eXkr#4Wm>0RlPGWVEeprqog9>gq*hFN2-JF zlz<$SsZdkhlo(`YT_SnXwyJpb)1$_09{a+k)|$rpX0VE-2uUsU;?o<4mrG>J_Immb zH%~Cx(QOO5!efGmJMJ@O|9QV^)Mx(eZj~E$C^{utI^mZoot-Df zPgh7pZedKk?hYi4UEra_O>d;V6oeLEIsWtQh0KwM1WR0Rn@?%Qe!uAi>PtVg@Yi7c zEbfyP-u!x<51V-#61`cibak$>R9nbVGuvM1Eb*2mEpBjE`|bzYZM?_r{I0JQ>5{no z3quCx)MwuLMTL)0a~;o^jVE0pQ0OJA9Wd@rI9d1Cl(biaFWD!S>{j#a+7~)4q1e z)msPf_cMW*U%&&eZJ^Oy$0NHff5NQ>-b=^}+UBzz!DwFv5ATNupWDHgi>?BJV3ApG zW^f9>f;Q^GZja5jmb#u}w|Kucixll+U0@M)W2M|EP)8TYW=1aq#qGl)B)_kOd(`?Q%d?Rs zJKdP%{EI-7h%|z#fAmK>T{HgP)+LLIAv5E^rD274_(f?p#pe){bKJ=Kb;l3$$%oa+ zQjP3vF+Lxit7dycR?o*thRcM`9K!?x}N6DBH=+!xv*&d{l1qN=INE=#HMNvpU-6d;XU-< z&x~qx_jwcSxwX+T!;2T6*akO+Z>sDG0CHlYA+4T_Rs#j$7tgr1k4TfRR|rt{`A6F9 zv52Smj%Q5G>Hh};CN?0N>^+PQ+7<0^uNmkihd!b~{Vg6aJ|TWW*x02&TDk=1P`wc( zgUqCDVg^CKwC($DO0=grx!2)pBsm-Oto{nwKJ=fPSstZHcoqeGkiZ#$^~ShUaZ?51d%eM{84(~AS$Y=O?xE>+_&B%Yw?SEBAXS&j_bzl8NW&mBp zM-ydNMAOtq$959pOi!lb>U(A3xu?v_>hl;L`=#4>86y*Z8S z9ZqzGB!#nU_+$P)-$cM_u`$hWUzEu>0Nm~SG^$0A!6|4VUJ-YINhSi>rz-Xu^e)1@ue;h%5}06ok(JN z5EpBz^WF8?oL9O`A$vY!g4{7}o^_V4`YlE7wgE zEM`IB9}nx;YJ_8;X>ye)jR-O7^2Y(c(KBuGG~1vjZ}EEtGaU~U115$!u`hNS%LYcyy5+9?PMYH-oUx(-<~Yt3Ta|DMgz+dG(00M?Gu|vG*zHx-sV8O4 z4Q?=HtiB-S!%*?zJmZ-JuCj|*&_Dco6Ux5)@-5zFeO4CCWRi|;R3Ii_+qHfWeQPtN zZHOP~bG+{xuITQjl6qdz9(8CI;SjdZ0!w(KXiRGSr8@e6&tjJP(7!*YhX5uvkNQ@kWW#a$VW^E}89D;l(gVe`I}M2o=Zm=tl@v!Q&~- z`O-Shc}uUQa5XA(yEYk~j81hGZvZ1=iO2T4x;>qG35%y=m=BN!Y@feRvn+r-?|hgbLj;_N8%!uW9VR#FHp8({9Mp0=Pal1$8*k{k{n1?ixpZ}&agrv+bT24cTZ31F43&RN@Y6R2M zic8DGWqAdRr#B|300^%k?ZRI&GJ=dI#S<*vfBQv7^T15x3*Tt$J=V`oZq=#4GNDme zdBe$WbzMLbeuJW8uohv_*%Y5``aPEd{fb)46LzBLUa{e$LYfJIgo#P%m-UE26{y?9 zDLJy(#t)|&h{;pgKszFv?Wjl6;vr;*q0<(Qoy*0oLkD}VDYND-f~v=+lmum)by*#y zJAa{=nGp0JH888OH1~sLyX;Tc*@vIwf{lb`r@C&@?u(9fxZRJBrTml|Aw-_Nk33H>ZViyL;I2Nr30{w5G!NmXK*;yk+{R-bk#(HL(!xvZgR$xd<50t^|J z3CU>Ir_siZ1mz{(yKqHA91z!Wl%Y!i8?@%P#V&7g7bF_Qc-8Uov3BWq*jbhKx3K!c?bl3>SE=d$Fgy zl1I*PGGUU-P+%mPFo@=BC>Ev}dg~BbOS=u3@kbiXVX%R;QV}dr(N#w+lDEI;Q6hxJ zEv5stJ?>=0?zCLDC{(Q3w{lg9_BxzZJg*qLuuRD6{Mth%v%WUnKr`qEw^hS-4wSKc zF~sY|o9xx7Ni%s7N4RqBVahPd-~17Z=BYWADTrzXxIlHy*cKFmA#0+{qZ}?NAH+X1 z@?ZA`y0UM5w>>KSO~2jn532q*Dl(8+o}3szG=<(`)V~u)OMUPs8eTEfi@NoQF;i_S z%21p-*GnT{WZ}E%(QvjB-8!Fc#$b6N@hhLyugz3!ENfix*L43a$WIj$NwlT`|KOI{ zN~ZkwW5d1$xorlH^Q0uof4A?8Ol~65L0WfGtSf{{&WTcIcZ7bpez>^%rmJpXxL$dE z@@l8X{}Pr*j!#SG)Q3=EgPyJ8xK^pv8ab=oC0D}2rj!;W5RH|>t>PLpQeVp*hq=@B zklm!%7M*d6K zKSr_VBfRJMaDDBV#6X7vI07d^KcvQDGsF@k5Kfe|&of%@yv~%(Yd?DoyYxnTauBE- zFW$C-)mwlbH)y79HCFhubx$Xb8q2+6THe)J{u>ZnNaA#;wQ&dejh-DjQh%|K9u~9t z_?}doBfeAh!F{_^HWZkk5=!=br?S*sPwdx~&1Tmg_jOiG*^XkKi+Z+@*nfGS#l0OX z(Qz+trY(C=#R;UoaggNDkNz?tI${Go>t*A;i={8V7lOUwY#UTI>yItDgXvjL?f84E z{{Y|eQyoV4ofT^p5l=2M|f)l2QM?8|clXLyG$xbF#( z3^OiHZVye)lNf`d5}!n|xqbJN0EH)?zuB3pIvEily}_NW%cwDWMYuq08Aha3tEjTA z1XpcpORbC1*3(0vtd{VKxKavTbp(ZU1x3Q>!DOgAPu~;JYVi#Wak}ztlBv!Chl&xi z@)tQqz)@`}8EkT8@a))`ISg@zY$RS46lm6XDAPKi?w|n?7uFhIM`Yup#qcXv{G9H6XbUn^eX~t-1>5 zNZA{A)%;7j%hv>EIDWKPAB6Ww97Ih72EdaUsiKPx!E}%X}|2U+>uqEDuF>7-m zL^nP`ZyKT9Wz1-bybp-UGu#JDw#2o@bJ)dNn@DuE4alLFXn)GlyS7Zz+^_Y=7S21u=Mm7HJ z>i;up|7-ne1L+Vgl}~`0u0@#5R0xg>19qypC&hx;c)XWoikxP(zveH8ON&2Kh;n!V z%diVZ#MgZ_8wxq@l#KK0ZH+g7IDS?G_z-&QDXVaLg|(-vhlVAx(f@Ge5igBiJY6#& zQV@nKU39{{vS0*+%8jPG^Kvoo?H48Wk%g#PGMVN4l`mVf)eJK#iBSIcZAy|GfA zxMB0Ap)H-d>7b(9qw9tv>lw|z^9J>HN8xxncw(ec=4@3UK0Ix0?8x8JMOg%`i6hr6 z^6pa-y8N6p@9#^8YwjGn_f5*beSdC5cxT$yR};oj--Xnii5v^9)X{${CgGV6L59Gz z67pIJdO6xsI_3-A-JKuq?;aG$(pRQtcuQkx+%*ziNn&2q#l#pa^N2r)d)|T(dm79( z18)UUat`BJKe%(rNwv;o=s{?ocMlzQRu%k_{Vys? z?twgIoge>nzq-iKd3F={MF{+UjW~E7ow3nyvhh8?m0znYogg!W`YaJ(uMMG@9G*%I zMeq}#%uJh}=3F+&uDOb+lNZSAa`DbQ!R8xXS*HwAJ~nHgUPZWT_RbN=RAV-kfxe`0 za`??`!|j0F2aPK9S6MCq8)D`Q^&BvH`Pr8nm1HpQhe2j2irA9^=Lob131)3`ju*= zPIeI*S-s$zrO`L-itf{}TR=%0ZNV{-F{YM3_ZlFefewa7rrD@T`ZQ@yms`b&>Q0?$ zRi@upaLcnSj5isF$|q740_1ACyl5c-X4?}R&Bas?}9!-pKN3t7*z z6_&}y{FC2avP(i{RvY~2vtjFi!K>YUN((jAd+SY!;wt#SI!1hfW`1hF5Avl|f9Vaf zqXGzql)g5k^4JV!C&GPZX872{chcwLciB|-#sB_XpDO@F?Tezya+Dj+qW2$8TiVn5+0acW2A-n zj1~1^O<(`LfEGbVJM>LRbHy@)2WUqobH}zCWze95y>}gqKf54`--o4HRP| z@AhavGIfaQ%F8uOh)w*8=-)IYGc#GiWEk;6b<4<`Ts}Y;zGH49Q*oC;yVa%o!5g2P z){y%}S=-4vc#j?r8faV)lA!EH_t9%=h02R(Ehtg2`*$x3;Z85(MH!_3UdYy0)YO<7 zn?1slUB!P@fx7<}zU=JJhWn&6>@l(Z((MwW$&Ut;-g5}QU>%_Ym)J3EC5w!vGOwW- zvU+|8kB#EodOsCsKppcj!^2h|q`csG&$!ey=k^NVAs?{IdAffV?krbY|Nid)>NROb zMGU!TrVs>zSXRa$)f3pqoL_IG=Nz6l#Z<3bVf8F_@Iv*>aL#aLMtV z_)u>uQ^Yi8i|p=xkGOwLX_L5vBP!UzigRwXE!xPbe)PR3ip(N(crj$uM>O1{8hyFC zA1~-q^KDSp&UuKXYXq-ZrwA0(GL_zzMN8Mu9=vH0uepB9t9b*_`4(QE0K32Qku?D+ z`J28VwwJ{?r!d8saR`jb^+0^S7E(XOk+#{RJxy<)lrS;%y!K_Ylw1f!)o|U(Ot$Cu z=3g9SIf?*8iX!qxc5lM+_xd!6u6lQP;BDSq3x(dpHW4n=YTCvXpkE%KHU_Ph6x3Qc z6(qWEzfn|QaD>5APw(^=Huf~M$W>_8TyiV-RR?vZ!FryKx0N;6T7b;r9^Jl-o?cQ< ziP}&tzB`d^1Z(9mq8puUBVK)pGP6ARb40?Fxdp}qSnciifE3B)-zUHhbH&eUceRcp zmsel@xAM#dqngLvjmI3Qy7F`B9hKH-1I66vlGcp%>dCtDz16TzJI_ZXngWyPh);Ml=_e?znAMvyQ&CsHg2B?)a+@wQY!EPnMK$20Et(u4elUmdQ|sUC8Tpfm{r3C{0HfV3C+)5NhbX)aZS3UOF({c zXaoaG$)6~2T9LI835~Xb!OFbL2K}0m-}mDbJ5idW4k%WfFpUalsUs0!xc1~cdQf;e z4vdtJ8}?ZR)nV1~#DmD;mIv1*hUJJ&^rN1tOUVL~_(?3mH)0rQw9rIQH&S+hLPpb< zMSGFTPN) z02xFLfJ$9VYjGAFq?3wDkC%w>$TvcVE&^?hxp#1iwiOR?9@92shzyO+{)8b3{p zJ$?a8^L5j2Ss>O$luqKw{8?f|_=6$1@x`|U`g-C^7Z(13!ru2pnsxaopu@y7H~%A{ z_FI49sFliy*W(oRm1?i<;NLN`P-sB@BpT=OfYcZ(?8@sUw%zu-l@FK#UT_k*t`=q{ z?U~B71M|PVaHwluYsf)>2vmdoagauT{rsCX(Ck(wFW#rZ*;vvN(aEPcR;$J^XBsM1 zBv9gv71dsHM6m>FLFz}g3d)gU7rdblN?8j1%VJI%8*mzYiN@l@TP+i#{2*eMvCrJc z!L!oA5*2N3bf6YIZ_ikbQEo=vKZXn zTx$M1sMb}wg{&}_a#R;b(^*C@o=faZr6U$VKZ z)`IrKH>)AuAz$MXnbIXg?hN)JL&kj1SIL7S4}HVptA6x4aglbrJb#G|b%~HDR9vf9 z+}>WgIq98*`8%vx6vAsM6R$(_E043`B z?28*=5S5tyPU(wkej4=Y`1hX9$m7?qS?Gp;WT$|^TR1%kXHcfyIW}%za>!bS?r!-; z0OXS7=XwKbPo=xqTkmOu8+`@>Mn8Suzl^$>4;l?GDlk2dDK?HIY-dIaAwiQKIcM0I z^IcDJRqf9Rf*l#_3KVF%X~G(HA!JBZN3Ay1kmLDM_%M%V)E{gAZ#>NZ70dZhLnGF3 z#p^$S&=y8t0p#NbB7keo?`*xrkHoTMHOw=Xrj%P%w+l<}Ah~ZEvBN1r0^hD@k`hAC zpdVG*?0Tow)_1(P`@XY4VQW+Hxy{7vfQ|RIhvv4ANeray-A=k{u$hG|k-q5%b7GUt z#a8c-n~vZDekvKm|4RKSt#zSc%|=1&AmIaCAU*K>TrBG-pUM2TCgi3v*5$nK1io5N zxiWugPg9+NNGM!W=jIcC`Si|GjW;db8k5$33esZp5*A{EtyZCik_JneHGmI;=7Odw z9IIuV(+s;W8;v*_{qxh$ec#c0VhZn#Vmwn{T)JylZ)e7E-@EiEd(xH{;-pag_>$41u4F&; zT>zxu;7V*lL&UmLPuA%r8;wGbX@)y6K5R(Vi%maE7v;=ig()X$(Z({_8aw|SVHjuk&Wl98559YwvwEMQP<5<_d#FJoALo-sLdW)D z^*@xOtqZ_5i9=Vq{KOh=Qt5gRJ#_(Dy2;F$p>RNfnKU?#@{o2QRDZMqP~Na0giw@B z!PF(HIeSno=r;ns-Q*p+N#G$u?OlG7U^rAyBRCG6otMxGx;Q~uU^XNZC)^jhiB{$T zV8u3)v17W_KQQDn0@^1=Lsz>(5W5AE0gR(Pl|hGVr@@)82i^t)F9 zb#q%l<$MS>+SW~=TqxgaLg74f5)@>UopoN8YA{b z7eqWIGg_{iw5w%cZhJfiXa4TH6uoo<#mFcH!$p%dlOs&Ly_~XL_>Ko@-+)Cf&{h!wI@ z@AIS$x?h+(vy#cGEmoWx)I3}@h+4vdPV&&8a`)fY@UG16HO-9YfRSv>JDf&Rb?lsQ zqZhpLJRKkbQuYm3_&_VHpT%X4?ulB7qh?jo4!BGSkaaccqqsbyv-zAq)G4m=zn;}U zUYig3CHax*%y>wMBLeEeTPS%FSBYH7r?{Bm z)4Kh}#I!rd(8HOIcIYAGW1WUtZ=oX5Ai}R3XNoR?k6>lh@q_*+8@={|^I5W=d0SDA zSni}GeI__5*+srDA=UO%xNntbsCLQK%hBe@(7S~PY7#@XJ+Dq-s=%Q)+Mbi|l7*jP zY_EriMu$ZQ?iq_yQ$o8Qhx7^?+2k_1#whxrI%dO~OV`8@^RrWD7P_3< zKPT^YPq?li%-T^_$O%+3>n|fO*19=bsO;`}{y@5jf=;v}!LSl{G|Me_c{GWoQISZe zs8shSKr7Ia+||4egp^90yTvX*Q~7DiF7#n6Z&3{G5D**5^p@M!@c`AFzV3Kp*hOi1 z+;w1E$QnQ4v3i_SV424$HWsLQSAv`wwWJJa$F#9+6P@jZy1rfOvDoF@zvuDR5AFNS zB8zE7B@Vb}az4qmW&-s6t`Y1q8fBWbFCgCGR2t+&Y;d+?KY-{Kfj;8~*=R%QBA30h z-)g81K~n+ZlyJw**eqj)#H#2g*RxT-mE@Q2P0B<*%T2;R5KMe{Pc{5-qD7uijc0#7 z+vW%-fxgh2C4_9XcFm|8F}*KwJDT#|WnK?oNY&;;?6}$d`D?X8S4`q7$dD++;vOJI zyCkpUg8rqkD;kUyQ+=x&UKuas@%~=;`OWNU^J<2M|6j5lAHKlz^LOpKM4dcu6naop z4E~<-zu+zY#76cXpt;+=eL3{`nf~;}KHoz0As4ojHgGB)?@nCdu1#2I?Q;#AP~4Zv zdrqh2GwXYBsBa6uCnvlgm=D#VGY#7mD+ZO@t}izBHdIR4ii$=#9doc#Fr(Zql8h5a zLm7ETP1!&hQ*FW?37GJWklxpGZ@?pQq%)!;<0)qG8B8E>nTYoTJ3XBpGHtQ$4E>Xo zxUiZ)WbE`cJ6Ovep)xX+-#9W*OIl-Xf5ADn#F*u3a5XuaGQk=#b6AMnlYY4(?r<>u)v zQ?&Hy3S6e>d05oGIpm;I0^dpuN>m@5j1dIIiq`S~)LDF!p7oy-%ZrHqW};+7s29>r zSCC1ZK<@MuCV8~Y2N*K7`uS5?4!aZY2BhPe*i%(M9#(=k>|v*}27iKmrLw!m^Jl+$ zGn3nGD2GfonLO?qnQmUvQc!e_n)(&ay#3{oW`dO|Q>E)LV`bKZP0!eOTaR0yfS0J6 zhc`*?Z0n!<@tDeHYjWHi`iMg_L2AsEiDh76xCLmIxp4`I5lk$1;8Ir#(DTz^sEI;00b0Nx8AtrgkCUX8C0~ryju|5%ugL< zmOt={y{veQ?7w6s8P~p|(lmIEz3Z9bw~D=JBbc?#6g}&?3%9C2!FHw_Ps7$hYG#XV zqMhG16(qJ2jVza0obCB?5KwSrxLyM?c6jv07%(c@V-e2VJ^D`me!He!UjQDjKw~jO zoH}WtUet@r*)uNQq9Q|pdN!G5z`dX2EQ=F8ep0KGT{}vwnWg-7*5J(i?t?DQB ztCOf^DtoRdz2z+1Rqq~z*-U`gY(FUPzaX@Dw%c!^!lqQ0mRK%a00_aqG8!u5pLWeB zozP1`O3$7d#(P{MwC!~f_9Atg$izQA6MN!k1}|5*VO8L2`dkZqBP>w4pwYqjbA_?t#pyd#5o zEl6WyKjfTQIr34;vPXnp|8_Oo_@R^9_1ZPBpafU=rA>^FQK1*OY&?mVVm!4GA}2k? z9-Ybjh^qgkh&1!b*V17{+FdwZs)#CHlB_tK#B<$$b?Ntf1mYn$mgE-Q{|57yPV0}w z+1Si3KQv))lOU~)6=4za$Fu}J`pWJQA$o6FC0_3NgMPx~8+2J!%Gwl}%m^_4f z(!TCWJ1K+qgv2Yx(FVrh;^JIIP%KNLJ;{P}y2@n9Lox8iZ`Dr<6$5r}}Z|qt2A)ef9Sv?WgrlPQZbXWcI4nYHz@c28>8*OsP0g6Vb&}EBj zyXjTwp?Mt(qLrTK{%qncFv(NmSH|I-N-H$ltO?UIXJ0nmt^?g?+2iXV7|~olVj-`mtg3pKAfgg!Xg4>#17aKX+q{Jp zh5ox46r)vyh#kDtYa8bDP$%)tZxngX)u7q2#Ry2PZbqc8;@UJcUmU zN*&L$4?ok}V7)9&CmCd$vH0~jMp4PKG=Ed}4P847&fd7~+Wo}A@A zoMnqr9<<3?Z9EbB8?Tmh*<|@3?QP5nmI%tNzbrRadBzM7L7u|e=q9<+-d6OOw_ktG zF#AOpX>+~~|5W0=y-HQAyDK^dN$t0LpjY}b(oX$SinP?mzmow{Z!fltbg!JRR2H~; zf``~DXPgXWE$a81&2D5a-{zg!?k573+i^Va(bd6Z&wC<17Q*fw*Dl90CB{sjpP7|D z{^%uq9NEXk2Pcz-eo3#fJHkdOnXzBiOI$rKZ{D2T>VL5O9rml;2LamapRm~n;8cxs1(ds6!0d)WulM(I~kf1J8&%Zmn^|jiGP?I zH}o=Gji2*-!g#BS`Sh*ua{5|kID~^+zXe(NLTN!H3|_$GUQ2~?jL05)1KgQm;eMbNO=qiW4usP4Lrf}Ld0+l&MjP2c zw+^#X??hONbh8jOR$L|z>}K!Yu&!D#_+HOZiNf9Dpqt+XZRkudJQl6S0IvED>-1>9 z4lLO-80G|dTAXgO;NM$aN~uSQTow%zmbk9v2l!v5{7DIQ&%x^I$HwqxHS+jz&zZxmS!iRo|;ZRqOGj&3dSvJCdGaKtu+{aoiW#~`Mhbsqm{A|ku; zOb;x&RzlQe4Ak*U7Q1RU))hQNw zV4ri7!}p80`{2$;BQ_L8Q^Q_E`TR>(d4knNi|H=)ApvI#R4h=E_A)6qET9(Em-1m} zWgyA(LMN^#8^C!YcCyjARKu%Q%#<-VKIg#p@w{TF3{sbJM9mvrk|fJ55J7f#1R1Sr z{NCabBw8Me{ya}qvlpL+*fW2?zM4QlmFPfS>6ojQ?uJPv7#b*7^IKAww%4Z0-Xy{1 zQ5zHI?w5)2(SDUsWa)Oq&csk9roi=qaL`MpMzx*CrP*QvZyTn;U9hB5Jbw#!C&RR} z$)UPNYuL$*offnT|g%oNzi~UBvv?l zwrn2bS9Ox%kkI8i((u_Gr*>w=TRqWB)cC#4P)kd^>`s;8vDiQGdi$%!N*fde(Vt@X zqD1CC;Or`5`B`s4u97}(|6yvjP-aS6vS&`L1HY^(is|=STK=PTl%~wYH$?T``~!a3 z8F?gA8PR#Colo{aRk>icU2AfZAxCCjwp#@*!NLu%y7Z;cBe=T`mTpXRsNb3;6$TAC zl^ebpkDjbcnrHKB{;-#(BZa`65>QAGM~7m}ck{K#j-k26u7@H57%0V272LCt`Ccro z(9c9%Ahj<`Y{VhI+R&HkV4`6j;fd&c%n*r(WI8wRg;eru5r@92HxeNjJ$fXwdYw$d zzWMoM>~!b$Ib*9i;nt$L%xJBOaiqfKmM6h|J&VIb+c2IC=DsL~vLf5x@X%Ma{WPBw zo+ffSYCci5xjI0)-1jWM$w;Wkn0x+x0z2u z`wpPtLgSmp8YB)!MWrB>8cH=Fv;9)(xY23-v&qO>;3 zc)v+xQLooD@PU0r^{b2GKvgX@WySM1S89hE;xvuF1{f@qnxg@1&gYXsn%IoE4XN-(?TJM#a>Gs71A5RgTJWckZIkHFhbC8^}uu19ck(rlX!mIO9J*)Nnu#q-B zF=25%(Xn9?e_qhXK2pd$ej-4oc-xPj0~(cW>E^U+*niJ(M~iNr!fYa!4ta4i%V*xj zVHlrO{s$-9GL4Jv{J1qX1EV~w%nOfYnWj#q#r`0xg8cRSYWAaddw#TDFY)+a0F+4I zDzW=T$RO`>iZE4ImXb{T%+a}%#ivMyZw4*&M6$*Y)SImE%b!fVxLyUaw;Lpb*_ohm z180we_u#9&lQz1J>s%d%0_wAWtB99J`LYlW=MG1_WF1k&=h1Xi`MGg_dt}wcnK?2& zPXnnBc@^A`NYbNIXh##?4Y7!LNoZ^?+f{(IMtk>AT9-%NADsJs+$QIOn&Si_5TFqmGweBbXkdc|U^gpDQmNJe;J`?R;(LeFFh!r*FtD%V+Dt;Q{-HmuB{l zs$4hcU!DYGU@TkHnY;GUi$ zdp??oU!pm<|3pVZQL;k63?YQy-8-+ndCj*g^n@Sr(su|}y=F2r@D={hzs^B2QJD+3 zR%GC^l;xYlFgH0RFmXUFv)kwh&uM8&Q5_MSn2`Ri#ro7H&aObkg;Fs>>K5NgHIsKu z+?gZWSWIW%{oZS#pX(-S?lGHSUjS*z*vc0^e^bs5zUBFuXVSS%{a)jEl2q9SB6b~& z*QK?Ni7JQ994!$$tm++GSv!a379@8kV~sgs_k|ittL?f`$__gB{b>PP_4=YVYo0+@ z8m)y(e~D+Cw(vyaajh2HlI)g9Ok0W-3RU9mtn%8GhZ~D1O#e)iBzh(9nRJC;r6|qFmkVcN|D?SEy- zM5={*G%-|)2NIj+rEHxFWHr!|;Ag~>;h5KQ{cfeIc9M>{y=NRaHWPXU@ga%i$Jay^h4y4L$;ve)dRID5QsFRaSK(C%p(L6h_ zaUsk#7e=Tfdd(D&{LDbHBjMHWKLPJ|Jr!LR*an-D3@Qq|yVg!caX4~xR^E`L1%%6( zCG%qGtYGd`-1F&VUuU@&4NH(JU^Ii2y9+jl9BP!CSgWDtcC zEcsZcGEe)D%TuPr&arYJel8qT6nFmE6SmkD{3r}9ru^x!ABqkTY&@{i9XdV$$U@M) zSB}+Tgq{eCsDWBZ)*)+cCnWTimsw{iDWI(&5X#DQcM35C6z2>z`&yRyA!W3S4YuMGd1Il}n#DOwPI+9XeyXl->v znRFkBl(n+t9_-dn)cKv5%pqEsX9@=IxMkcskUBQ|MCMNnUVMH(IiL58mS8BDOR62vDhzep+AJ6~aPxFMwYH22pXPjO3boXOJYg|)5) z!Hy9+zg7X`yPNp!Mu&3yZGLn&43+rHKYpx>XX(J)2`A$RGUzrotLgkVaqhc7jz3W0 zN0Rd)b7xOExQ!KO%UkZAPry+B)eE4@Yu)m%%pQCx6V_keXvmn+=*bFEw$tvFb$+<&gleY#1(KjDF8p`1-8*#x$$+j$&qm#=F2;G5s7?B2Eu z5?v^;(f5m_1xmDF@-A~gpNa(izRcd)|9DU^=i{m#qJoYAdGP^E3!i3Y*ppg@DBo7o zNuxDGCLWc-23d}o=YEpQvPoL4=`vz+@ur7#Nt$oi>ofpyn{#BR;f3i$O1Y;o*A}%f zTm;joh6prfh9(zj;9I3&^*k7;*_t^;SiN0^Va>z`o zT4-s#Pd0yjQ`RhxU=rf)Si-HDG$C=3O@fZP{~6XY8ZN^1D;4mn7;!YQG&r*9=6-8H zRJKb^%ldnfX{>S?HjY!JQ62U+j7~1!E?c1uF)%8-@O*OO0auWmvTC8;8b>-o#sG5d z_-ROy%aSYp_oJ<9c7fLPq>Khz7CFgx;M`*y)b_d>jZ*1&$89QWfL^x)6-&%vT?KS! zlR2x;swOb*+_lNBqd8=`mHyQ#PDgHjR%~YS^RO74iv{3`W6**;f~ZO&YizoQ*!Yd} zDQlCUhkDk0^z6UdRUvm(8M}M5{Pk(U15GI#LTt2~dCwd4t9#bDMC4Ls*;L{`Dh7Xn ze3+s#%>O{Jp2qZ)C+Nw~*{!qMXnqLoPPB=5Y`q@gzaz*0->Kiidm{b`EI^`DuxNO9 zI7RUFC2rIGI^W%TIyj%NF+lj8+8in?8as;~RiwvUxZj2?51eSQ zCRp=-oHi@{aj`I^%pvi?bce>W>O1Z>#0>R*E>7X)Q-{lUANcAmlI)77_JT6WF8eM^ zzU7)q^``lAmXw4ibeXCu?Oq%jR!fl7tSxeiALlr7D93yg_2MZ~C zG$QS)nGjvp0rA*ZEhyR*r8R``Y<>RncLYdBbW0OI*O8imi_B;2v=o(bN1 zM!n*bO5B7@u};}VLxw&WI9x&WnChOc%%ah~CD)v^0JZXV@+QPVvYEc(V{-3AwR{bY z@=Edx<@DrDBL%QvS4HZMC4NP+0AUWmQN}o8LJqlg3x9GGQH<_wqlC2;Ov5eXXYy7Kt4{asGbrT>brs8*Pii?W0 zX#Zi#(5I|V8~B2j%k4qGtTlH`+i+9Th9*~*g(C4s1DYu}N0vVQcIB8pOxnbr-bz*W z{zF7Gf1ls?pMoyl-S2My0v-O9d-cEG0m~s+&N3xB&flUJtCkK(CY0J`@rvL2VXn-7 z|FZw(@M~JAbZ5dN?u^P;yM!Ko&Lx#Myp{%-!xDr;`w*b+s=h}igG{$8?}lE>)#DpA zwG{%@!^wa0F^!@?3VWAh9BICL?^le9xt?L+IdFF#+)SsJ=Vw7??;M3Em3F?MKEH`l zj+7@~NOs^K-CQ))6@!p7)p5-|t$b$7x`cyf^m6hizTZ-)Zl9ahn-TxXgjmjr10pxt z_{lUx1sTwVVo{8btY6s`Z{Y@4EHm&4CB6-yC|k?>!_ie7xt^b2ylACl>RKE0;gmVl zD`QdT&SRd!f!%GiVENswJc6A=urD{GmMhVitzare`)zRmc}}+j=~rxY-oK|RJq&Rd zpZ1Vzz*wBJ%_cg`q2e~eZj3WWo-x=_Om;C+xy$K`-lIGZMyB0NeAZzN>n%ij!vfU~ zcU4KMLY*?Mpj~r<4i}yo(|p7qrJq0$(`-uTr%p*6cHyYE=FuAdIZ%gRwb`>@DWH!e zbGV1pUBjHhsvU~dOS9BS&fjv&&f=1MEu8#_e7Lp7tJtEECg*@hESjAkW2IRicXw>u zlorAj5bo8W;Y?Kh)LRffEPFU3yEdDLq_qmJ&i|x?sk=@Zx*nElp%t>zV{f))R?VJ=lzK~lx1+q?7^v_p5V_-`9^xl6D zV$4vhJ8hbO$gJ7dXTv8G&OE;oK9TrljmI>P&uWXk0EMgR`C2?Rb}aI*1#-)ySeDA0 z0|UYY)BR06gFTkZv_*!HI9VC}D5!FJ*4QCq-v=rKGb=ZBQvGVH%*Y~W4J8eD&`F9e64@` z{?SW0Y!Y!9nUY&t`ezvc-{@Y{An{|pczQBUWDuNI^Yi@T5x!BP){Tb}5 z*J4iB{&lNN&rpCMbGzWbm;-=T@k}VXZ;J`K-YeF0bN+Euwo9MLl%3y`^s{vk^VIh0 zHSW%WyOvz;(~OO0@phO_Y@+*;!I{b4T`K7smdgTz^_UDd*b6h~8X?0k!@8W}II5e^ z0|-9{n=96-P?rXPeNCGyi@;N z!=S+SmkF?}Ji(lM%0!kQb(+(PDdLBrs_CRWNSEWY==q{yWX!^i=b9~{f}f&UC&429 zokKW^6R&PZ-((XlIfmyPW(xC;FaGYp(cxp;|u}>@F*N_Na zaQ>|3RUCdjCGlv~Y^FMkL%gTwKnkTqM zImO)P8Nng9_jTumot{}gvsT^rLIQAJk}i8kM6!pIBK9@*c^a2?YixQqYBr{oMUxdu zH3ZYs-8hqp1y?oh68!tM>?NPLHuuJ~uB>=@w-)F`k|N^sWHgq&p+MY+SYdQjO+y3w z_r?(Xk+ zB*kID5istvqQDXX`{|;PVlaW~KMbpk4P=^*LaKxz1F+gNW6=O<=SKn-eubt9# zF|Fs!jVSBKpXxuCQ^0rS3)Le}2~vSmF$?npYEC};hAG&v3R@Gr52B9WT7#^jt8QYn zD;FKfY>1ZnL#qr&HmU%6>re67KXteb)Z$3t_8q}Eg`E3lJgi`5WU#bBs+Y}$FuRRT zE*XMZ;>Lo~0t2PROu(51(X*DbWCL2CH5;wuJ#1b~H=L@8ONKIY; z1EV^MjA0=O?O@%yo66?k>p$VKFuQv^6q~8T5`N?xoDp7(DX*+rTaxj;PrFS+4Vupx zD^Pla)N|_=40`?w(%jJflYH?Lleu-tWrr;fzmaCjxR+4O$~s^KMR`sObSGJ};=wnJ zABs{N0Uey8OZf5>(gxZtLUOAB2COMglj-)5KsAgzdY){&9Xa}E0co(HN+nA zMiVRS5vF1vthxs%ZLUFbzEJG@IL9fAg|f1~E4ea3$gR8wF}&%9jg|RZlHsTioiwC8 zEeyh0q8@o#CcH47A@v{zK?Bnh{X!UY5rP+^fn~F7zaCF@} z+rvhFQGE>i@I$7VT)p@-VTIwXf&cTN{`>KV^J&5_vExQV?}kGq{nOpMmb^gQuYfrA zJFm2>rYO=v@t0M?PMjM~eKp3=3yfE9zW&1`k;&_(d?bsHo2k~SlDKdtE3xn5O< z8U@-4RzKeL6W_tB)tmUOQ0(&BfRBi;M*-9$mU>|(_v3;>3jw?$Q>;*?3IjwZXG(bu zb>n!rDy>6okbR;^?CiQ;Mp&fK$>^#+&w=Ae3SG+YU)A9o*F3AM7SXGhHrxj`xsv-E zWo<%thocU!<*Vg8t`2q0xHTy;y;-4xDz=w_Mu`O}6+KyJITVt2&-ppWxninI-k8)p zdus(uTuc) z+0RasAof7c1lU)%=R@j}pZi085d^HH=)YY3QMeqXDLwkJEFi_kn8o1{cb&)c7(0v3 z8jc~?dI_S@fQbd;yH|9Cnr7;x1uhsr~lJ{Id5fFm{1+sfPC8hQ+`R#vyX$rj8=t6siL6uO$IsyJ1+)e#vSX-9m%ETy^XqH<3+I|a)HK+6*&=pB`MQ5dqz8i*_YpuZdMps%5r*k_ zeMO2aRm8C+Lp_%~4PUUkdSbKm6EE!9;sLdD&2Jb5fE?>^aNzVR{|^5h1%&H|Hj#NS z_!CVmEcCNGtd4`$f=W|T?Pq^p4;RmOVy#H`vYDgOVk~EQmCm}T*OzcJ71c%?Vh!Q| zoI+e!b&laP$|&$ek^GLsuVPBZW(?C;aTTPCvJ~3DNtIv5=bq}s2cUQ4mByXg4ZWm* z>Sp&orOQiwK-?&|2?IcXWscMI*P8HsZ-NRzu%=UK$xLr-AnCF#sB0VN+VRgkTZ*$j z^`heVDtQhRX5Sood1nlfmSd4n2;Yj6W{+N=STxmG#ZB!_8g}HrCt-1PuQ&0ROE8G0zn26Xf2T<6!(Zl^KXFEArd&BU>N?NG{&P$B zG8iVpy{u~PZsu)H7@D5^o|V$(G3q8A-^{flgdI13z8&jC`sQsx#78A);jNPrfSFQ1 z%%tO1e+bBDJ(l%g^b4>MWEoxeg?n~P|7@59ym%^DwscrqU~lhq)J4MuP@I)I=7Gpk z%|AhIjgK-IZg}7KZz^+E3lj5ZbU+tb)i6p`3&>TAlQS9&^KfMbyY6K?ouGLt06EsB z#R}+l^%f9H*vP}}=vovopaCoJI(^Cufz=a0S+swNy0}t7e_f7At6}y>e+FdkcF5>M z5ZHcjOJe={ONC5`Zt|ef3#-GQ==Q$da4!MX%3xA}+=hFoEg5w~wEv8VFJWGy^r9fn zaLm2{n2tM{@4mde#`g3zWyOLH1{py7O9%*taDYK*Iv`eT#cI`mP8B}IKzN$NfQ_FR z4G=4S?b8e|Yel-<+@Tdljsj3uDAkv2Y(|oeJh1kLv<{)Tz^e}1DJVmOF-o%lSf5}$ zsbu;QVn0StbRcU^5?6_eTmwe_e6K;`lqdpV`w@y-#5h-}E*pFk{1g@xDFUS8_(E=K z*PDR^@u<%|gp6>`I%1dJn!&6Ehi2E@bga<04HVawa#`Ko8{;2UcPOhQ%!hikSfFY4 z9rvc6_{@-KUTolNsquXdVbIs1r_#+duzf{iG9x-5U~KJkhLxl?w=q5A{*!8#hGXu^ z#+jz?7%~6mLQ#@BrnT2h;FM|(0x&|xvE01-K;Y22HYQqkeq6T3Er}7E@)=i7O0)*~ zPKb(0*nO1GXjrKI9GU2{0-K?uXWJK zWPi!R=|kt?Mjh2_YTh^p>MO;@bpxNWuf1S)pf|Hp zM^Rhv_&{kw7&1JfQ=x41t124>&^LoZAfZ|+IK=BJS?%6 zYr-}-YxARkeH+b~vS-5>_m2R}&oLrqh>091E?)}8@P&K%QT zMGx6^h0mVtTdwBqmrA^b;7m1Ul#v5Si-Pe5#smIw2QL)e;QC;xmZ8f;**!UmykEK+ z5LZm-POf(Vkml(F^m}D75&O>Vvv=ZFC;{BnQfONZQbGMl=m5e!lt=Q$8Rn)$sC=75 zusf(iS>abcodTPGa!|qu>a4@?#e^*B1UrC+N{3IO6*-GFoe2(lET-j0hx(}ZnGVdN zbK9^8!I{lmLfU}`@-v&PkzbsbeUAi9F>ILg<*zPO)$O@!* z^7~rk{4Y<#^8M3}h6hfCK!>t9XS}(i*O*PyF-!9$uT#oh9-zFH??Lb=j3@#|g`pAM z^TmJjs^{q#`a(1%wnnxe4@uUm@L_sFZysZ-a1v>KrJ}-Q2b+)4y4vl7%)4hFPJK+% zCqkS$K6$-dFk%p~@jB%#gu~lVXh9v4+Ugd*6NU#~K6V&NlmbCB?DC2JEe`#kp89`r z=F6|~{nM*|t~g#hH+T}S1n!q|@mqjsi?eF9K2K0Id6PGR<9^>x%3W4|k3LLVI{Nb8 zkeVG65golJ`sI>RVa)hiRN%!*w*Ic* zYW*)lV*R}?j-k&YfP;A(m5%EoU~$x>9jywE)+}V|-DI~J%H4|; zSug{F8fOs@9z(b>_-;IJw+bV6Hp8gd6uTO{@eC|+b&3)JPo(NOHv~+tj$31UJj%4= z`&@r9e+|IbtgbY8$!A#mJ&(}#EpZ&ahpC$@g-k}DijG16xr;%F7RU<&`Dhf;Dcj&0 z-{VF`0ObMfi3E`Am5IA$I+^=74K2>lM}kI$c(Lra-#w43hV*XAaj|0ngxiOGrg1u3 zqO?!@M97wU$a1+lYaX!8kGs4?xYb!1j>0)r5Rb2oSmn%ymCu2K`WP3(PQDxlBZrmi^W)6EeV1*_7v+ zx1eR)60fwUq4jtF_8#~vEC0`rf%hp4V8kAYp2S$m8q=LC)a|`>L5vi_E+%w+vR9|`1e^RVSEzy0bXN#B$Xp(dh%mEQH*RCS z_7jydrx==@?~e~Kd;d9X1X^Db!}eED|9>D!whiFT0GXY#GYfoL2 zu_oG~`|ZZ~1{uNm-lCjgx$Y~ykQpi6)4)!i!=Zl~kDwNM6%GE@0tC(hVtvQEI1VRv zJRy%Mz5J>IX?X~-tJ=FtC&U!Q57sKhmUr~M^g}~EOFYW&RM1xytA}pT2Xs2<~ zqTW)T4>=`$yb#!{wqWR>0H`9eO(z0RV|2})!Jon9wtOA|cku!8RV(&AqfXe&}EODd6gv3DKBOW2=D03Fe zvYP&^_Zx;EmoHlavnx^@Ua|O%KlKAh9`M4e1Zg zadCB_*c-)=^Blr8-VAJy5&mq_8HCWuY%EV*HQjgvMX^_Sbl?@70$4((?b$Rl>D^>8 zG6a|*I>RYiB1bxHOs!TQ*7c#WdtjL*T5_S%pE-Iye0Z#rIcz}m*8k>4p$zr4*91_W zy#1?>n*UOUn73c#kQLr2(fkim@bAtTTE!K|+&f$NSSTFq#HkrELC*J--zmzXdt03c zT=OJcmO_y9o^v!htU2mJ%CRd?MFVS+)S|B&7V(m7@qvwhlp7vcq9yj?H#<1<8A%6^ z8D_FESj>~XTNjPp$ykc}{_UFiU;O?5yb`<>;0qsQZR#r1iSC^dzKx6NKbs`7xu8|- zXxgyNt>|lZCGr9K5%;qzuAx>$kcq4;`N`#aw6SD85r}m({UgOxi6~66`;Tq zMWqbRv;!(wnD!(H?v8PXGTW_auJQw8$hZrsZuMGkeU;{(qg4GCL%iJ%O?#U)d*>>VbiPu!HhgvW7!htC? zO2z_U<*BNeQ=e;p9PR@cp%uwrG35DZ@%Bm^pjJP>9?=CBZ8DZEx^_nkNy6Q^s+|CK zt{=2Tf%q8Seq)%$fe-py$mbjA=H`j&h+s2ok|={p%<(|%xu?d-6G{PH9NeXtrMsA) zSvo=}3q(vpF`dXzmA^_AS%G{fKX^4596$b;QMUHHxv^T>cm%F3Ln`YG46 zD}e237f`s%RY+usGd9!SHRWSz!v`1vW7Il`Q6Jt3YYH#?*jb`HigYWPSq_8cT68u` zBS?0)TSq5VIkuV^Xdw1mkIF8SvGS7*orr!sSGaqyXDwhsAGlMbQ{zGLb{9j5&$e*_ zb6;=__Hz6=sJpDaF#Yu;+uWQ}Vp1xrC$W^1UC#QQaG-5^Ll2;dN2krJj>0vU;;mtK zvTkKP8u9nX)ml0kUC@))>R%56AK@t7x4WRqp-c3xWH|{tfIWi*4O{fVSZn?!eMfxvV<% zKJmD@*1xL)NL^#}ijE^qB&aDI<1#l8eC5#?P5a%UIk!xQnyy9x^{uP_A=EM3Xyw!$ zEP8}jSudTDU#k7P{B&!z;Q>EqT0t~@*9=9mTc<;i{K8}KzKAu6vvx9f6xCaRj%623C8#UK zaQIXTqKX$@9BfzhcE99yt8}Cv>=cB+%CV1=&OK~qW=U(H2bku7Vf9OS&xw4f z^p>ZZqgn$aV8~Sii6hf&G~K~{=1M!gVJ;Xf>M?$>BZ$O zUN8<(>a~IFbuD0)=<78xa$|u%n+xE%HJ-Bj2X#k*jx1<8po4O;bhwO-5IfMPol9pK z^@vL%^Z@d(Em*%M5*3cWSMqOYXS)VNcHO?`PIq4K$yf-xcK7d+mUlvwG^{BlwM-dR z`CDM!qfAq+bzP6*=hXMpTg?5qxG`u-R90u9cxK{ zF&fXf%igzkREK|#Okrs#wKW;&eL>h+!xsSZHeX`W z<)Olh&0wct%ks~C6iun*(3~^l7Q^qlx+qrs;pbW}mQGjg0=y0GST(L#{z-W9Dg4cJ zo~*SkT$TYMzxR!8V71;G0p>SE6rz`&jApu}@~n5ifGhn$ea{lQm{>|l732OXsJqDa z-cLL5Ac8QccIeaRppjn;e&1PJM-3^{gyI#GO&7O*cdcJBMgFPkte41|J@~bcrMrN! zm;VK=M|tuEj+F|D+a}Vn!5q#%e!Ly&xL!poQcOxl0=qk-bck@WYso1x;n`0`9Znp8 zUZ$8Lh359Btj&{!Kt2oP(t5K+iqwHKJ2NgLV#{vX4)|J|Pqk79N1Ss|Bkl*>Thcv_ zg8gbx${Hz+xnPD|7;0)W<^dL#HL7D(Hx*$XkcVl-!C~6cvlq*`q%unl!L`VW6_KgW zl7J*XNm%K`woN~=KYsDUU)by9j@$}#S!mg+FL@-nMo#0f7<+b8Dw7q6Hvk6>9n85aR|)2eNk zyWQ%5-`!SmM_V{yn~LEbWNh5UGF-9`Hv?uGcx&Th1C1~;X*^mU%&cpHVXLK_;FWuN z{2q})ZG#O>lST#Pve~G1LshlM6h_Wr^z@P)NZPo7+SI?ck)Ya(;o*J(|2Ar6h@;YnMez;L^@#jA^rpp{; zR#(2aK_Kg+WxoHiB;CJTQnTiFwSQO=uI=AMq5?lj1dz)g8qvO5>Tmtd397^p989*qxd4LiP^m(`KfgY2Ke{DVCMXw0 zL+pyPpDqR?g}XXPJmERd-d<>t({0tA1pgNMfrX_kq|O{RF+m(e0~oFiqX0J;$~hOE zh^lKc0Nz;S1}1osAuD8&!#8_#K=Zy=@zdYN)>M_UH|u-SoH!VZGoAcgKel;hzGlJMK#??qzQu;f?e4r&oC20YqKT81=C^VK~@^=V(=I6!W##f8x3Ao zOw;k`p|7AKq#kGnKdsoV{^DkWRE8^#GOjD7ynr5M4(w8##3p23?%CAgk{3ZIJ3?tO zEo6e-U7LAS?F_MUR54aqq4C<;TeRFodhhbngQs{#ZI{QG+;cOJHV3#38DO(mP^X02 zo(Z$}YB>tjbn-ciN z1Rlr&h(CU>%i~9)YnbV9EF#H9ZIKYjpF8yJzj&g5Bt>|uz(rE);+Ywil1=U_2zQsP zKtd5Emd9}qGNPX>#1*{T!d7Cp=B|B-?;0;Or+}Z9mBu(~f%!>5NHqR+MKEVi`s>v9 zN={+wWR8OxG1`d71czpse59`lKaOaaOR~*t$<|Dw_8Kv|!Puj{0vjSTlwrx7spSvG zD}?xIB;1qQwe(~SuZ^bq!2hX6$BMs<*Yu9fYNy3Tu*Af7``rV<-OwF(&>jfQk+o6n zC7h#4vTzJ{!n9HATW^iItfaPh|2m`9=*0u~e@0)8g=nK|EKJ>Lmm&|_$lJPlr(Yhp z7!teR7kw7+JsYw7HVANw-=skS)Mabon@9Dv{`rODqL*^sQ;W&*wBN4oXNhjbU2YK& zb833d;v0^);c=N%^&(F!h|REhjA-Alk5kvBw)A;Mh50}!K(`lJZovk#1A^IJjOlGC zbGPe;}~8@POe%s)k?84y!5 zrs|0z*Fz|VvKqv9A%;&*BAjez9Tf%kfnSs z?-8|CHJEk0cN= zZPSLF@ui9#ub~mkWp{n==G)c@xn>@z7L4=Szp0Kx-ecEy$-1VMx#J7v_g=qSR@|o8 z`W&YP+9ZSa|M3d=h4Dq+iV3&8t2kDI)58A9=!QmTir{XZ#}m>mTUhL<&da#yLi3Uh z9RnYR>G0;j0^M?VgZ5L+-~p1br0nQ71iQZ(0tY{NeYK<@fW8I+Xhjd;1u*p`Gdfpx-$iti4lM4q-=~OP7 zBGdwyk&R?@Z(Iv+lM7t?N&{{vhA=q{;GiqfWvBD@kBL7E#$NQcmcB8meTKALUa}i# zU@=>3qr51Z6}KhnV;U`OZgUCgx#diIcVe|{r@LMVLUKY8Sv{9-zM#rzzR%pE6Py-S z$+CR+l1Ls{r8Ka!YA{yNvEB@a|H*jqvZJdmV$nL%01km$R8aNl(5h7ct10?;fB&q$ zIB3niwcSXxu0WOn%CPgnCgOvqb(BuNop}{wi8zcpj1`$}81k*9C+*kIh#LD`vsmug zO$AJOs9vq+fw)`=9Ppvm&Btj9AKo-q2oQNhUP1YlpBf1ke zaY_sPgZA{1ar5aw zNxL={@!Vq=au6~SmnY#9?z-o0mgb^4FUo_2{yoQ zb~RJyPqE11jle0Z%J7P}DjaVM&{cCp@7O~aKeBh{8H zk@nDP&#g$C+rXY?y@osEXSZ1$MV^M~QjI3g>+1-FmW!EXe~8Z+TJEb`*8Xe~Ho&~7 zYO6`xQ}&;dCutzi9{Izzm7_+(y~q%8-BVSUbLV=2HVL#@E=S^5|C;?9d5Qis{QSua z!_!q~3GdeBHW8e!WB=kY`GY9pG_B1n=iS^~d)gF6Q$eEU_=DXXoLwNzLscmD%#Pjb z^rmGBWQTz1{1MZcU228>r9oMbXoLBo+ia<3f^Pe{26Vykc3aBYHJ~ke10c5GO3Hr_ zH?s!Gz%VEW=*?iDr><9KpUCp>hM5{H%Ylu%({=cF`3W{$3oDSPb$>&$6yp{t{$#K= zS<}_+s#|*AFFQbPS0<44$eK=a2d>@=9u_K|Mh~&KY|wllwv58EZr>YjV0Sv!_hE*+ zxH)=LNM#~vq^`k-GW)zr5R|-_3?SI#h$jM~te$F0Oqoit;WlXbzOKq>NKy=lbF+=6 zzkM`qg+Wcxq-C|(h_zFJ%8MYY7!Yj;@43e?;losx z(XH}q?e6E(ERx7Ch1GG5)&ZN^7M)z@@BolS%@+9%S9!#?S1(460kH>Xac%?1$5S5` zxS^h7@bYuhmqm2W7`5(Rm0P(}e&8p(m`1lzi7R8f7#W2~$|~R|KxUIa(VK;2;1xNe zgYFM7vbw^ze_hQxPcZhJ;DTGve8cPNgB+VA@2MJ5Jzc1hX&ur4;>rxKS*H3%A;bao z+HF8R;vT_&b8avV6I+}uf~IUe@{~(gL~R}emPS(aZ(&<6)C_??@-kV8_8Z9EH9XlS z1ZP^tOW{?aXae|`@y%x|9q0N|ZE*TMXnQ`^=|@Gj-mOxmP33d(%je*C_f$GAtGK+) z>1Wy}$&wgH_C%~@d>_xrY+?=R=ODo9ncs~9MRR)RCU-7EO*=f2E(rVO)UuSy+CdgB z9^y@b2*J7>kLKl+#O*_BWVx}TTT=z39Xs^72X>{lMk|I|2=U1x<04CGDSW()+K;TV zMdbHUz@*>H-G4KRv~Ud97(yvIY{$GErkcB&$q@R8PzndqvDj=0N};>7J)*LATuZts zrP;xT7EU>&gAXmp?m`F+0Is3~kv5p01<7Mcl#@r3(Z_0nmD>$L;ZY{uJ^7+YMRwg> z`Oh)1W-bIJ-g+PwWE&ucPDs4%&MFQ6jp?@v%#m;z8S`hZ*8%KhiF+aCKz}AHVXIGg z8`3{2y`QQ?LPYnh!-W6M>-=}}^8Zu_{$+enF?{76MQ0pxp45(++P_(GZ}B@91fPl4 z>B~9C)trXSNaCPvvi1C_-%e))VwL=cdoMJh(&mJeFuj#!jRlMof;z({Y(J~_DcqnzLD`MceG(Vx{xH+`~ymIil0(Y8m z^586KaW-iiy|?r32xp#kb?{kh|jR=g(g46Hh$lXQso`pDJ|zh+DaO z#CWy&z+km&x~$HTz@A@FI1vo2CO_;q*LgNXccu4q^uOD(y7+=vXH+) zkGEL~t~53&)k%c$=vlk=65tWF*lypO!kk6>!Pzf(mCdum)LZ~>(CAqvf%FHYk?8uP z>Z&U$Nld`f>oI-*pAUH3HB~EYh=E5KU!SgSs#=#d0?^ZgB&c|5ED~$n%X8*Ca;qT#ztTt-Bej|LnXIWFM7tW} zn9&b!MPco_TU-reaqzKf4*DX<&+*Km1okT|uZsUm;6v)Z21-zo%e~=di21fg^LTq9 z2iEEUGp5)lnPac)uN#P1FJO}X6($5&D>iiBT!RnIstAqIq&DaaK-U#5 zUaggTf)<%64|wPQQTCouP4L^ccM=FlFH%%MP*kLg^bRVb(nWd|q_+^7^Z-%>L6F{i zhtMJPAWeGjEtJrE3!OL5-p@Y!+%evJ&c0tW@+~9lzt&v8IoESr!UIb5Z&OL=YO3`gp+h!ph$5+(TN1;I-ZnVH&*n}3O2 zyA}qh^%X-M#pCvc=lnM_th}*xu5*9a8kZa-x<^VsE1F#+AoDhk4$P&O03A(A z+%#((4-O`jtN0`O#3w-e4cvc8gyus?_c=LD4t{P9M1Ml7s2_BAhb8;*IWy<8HA(U) z1|vBqk5er`E=?4j6JB7>zUwbbbJY@v0uf5fk@N-aul_(PFhY@N)Hvxyh$`%n+?Y2r z8m0msAk3hi6r7qlp_{NfeuRV4s6UV{?3m-d>LjU(U?3u`&SW9g>cb(^>gka*O^j<0vo& zZ?wq!MpWu2oz1J=iaA=Fm7P@0Wy~#@G?T6<=(U;(YW+#bZ5JcXT_vRre&i2W(sv3b zMc8j#QZ=Zw0_OsGk~vYJMCI&^WSu%!tx;xnb#qX~24MwJE=-3OCl&PL#Db08MEFk4Y`cPo-gMgc?0T}VaNp)l zUSyd7v{i{>G=GTN_a@aJrrsLI1y%_?()g226S%y+(TY8XxbY!vdV-^aLE(hN^Xx68 zr5Jzsoj{c)HLR#f2S~qIpCs-*ja&JXVPRml{kq78U|-0#u6UNaX^r_(vb39fb zpRQVqdVyi1z$|cfj9mb+Ck}9~oqc(l_@s7F9Ymg+XrF~8(vaK7Ph`e&zokv#F34Sb zWq7vm5v*pSBHkTQYRZJwe1Fn!52c7P=9T*DyI@r&p&GbJuwbW0R7wQ57vkz{AR~n? zijibQ%oazptV`EZaCJyC3iHMGd`z82BuAOiPi>opHPxtg>PBld#4V8h)s&!`J{iK7 zMy9oIs@*Ou*_kvn#PP|A2)~dFkc%;XTLY!g{^oCggEqTklOf$e%X=B|uPguqHvyEL z=z*90rYqA`@tS9zZ0CNBL27jB8cF*`CG}0tmYt|~NYzwW3fWRan)YJiesq86aZ2Wt&Tk8&D1(KP>5NuBZ<`n3c^G!!|C)yL5#F%F zcO#XHdse(L_+aq~RRU@r?C*ve}ehIx^N-`_<7@O6%Eyw;N?Lnr+2as?l58uR`*K1Ncsa zlM=gmuO*pq5$c1FnG9ShJSk8wJERMQ0+u;!6?sA?!C9M0y7`10?_zn?x@y&TKjBDK zif13H+RSdJ$t;Z(GL}x0komW7pl{?IQ!2_v0wLiK%1r6M&^DaPewKEH*UXeN&{}_j zR|4`+lp1`Ot?Ny+X`speR~)E`SGV8$D_UBdam}W?dT5I&FoS>*kQ(;x>0P-Rj_NwI z=9ByKL_)%c{Yrkz*5H1`IXnzNcZcKN?b(hMw%y}U@r=YGYcJQ?Yi6E=6#~zS@&$b= z{3G#;Ae%@tH12(n=rbU9@#U+0TbPmvv3#(;8ChWaZMv6mp+srx5<9IA2#^qX3DgCO z2}*W}87^Kwm*T**#=zOfa7ryak;^tcA21L0rOt8W+0P7Hml2Ize0R@?=&hx|zG5VT zlh0KdbTx3K3Z3hehMOzUpXV=B4DF_B)wwD`FJg-DJpe{Kd*!HLcT2l`{o0|Wwt|~> zS^L}QWezF3AJr;HFIT6++gov-8pgR5MK)Q?Li69F~ z(n%)?O3+jC?9HyjV>${fUi=6UHn0X*DHHh4A-Fa%&+eNE%^UV0yuA3m)G#1~CNJVk|5k$( zMN4G^Hz+2`+a!-qX6X9BpS0`(UK6=4M=1`tnl3|+l=i*zZn4Wd*`#EZ< z|LngYg4A(^XM8wCYZhK?ke$8B(FLKC@+-lO3yg~xsQsoRB$O4dba9y*JI%%TJ?r-+ z?i%*w<;3s6e8%p=EVH0EMDueEZESJc2=;s3D*$N^2EhH(fjtz3tASr5Wa&IsCbLX) zSVJqUCf~hQC{Xfsp|%5TX_J%|rD@p|q~B9UJvS^;D76nt`Ru4)R@Kcqj@F$#er-?S z)^@B!my;(S(X{4PH%Vm@QRT6NFW~m2do8PD8?b) zf$&f(=fLQpd-i&c3a`$cw{F;C z62FY3)?UyZsEv+vZvU&u{ND$_f1)hc1ybh=OSZ>$!GaHtidQul*my@S>_%5l*05!5 zYm3{vNoOZ&<|y+Y5E*F|raSr2%L`5Y{B|hC>d|givJR){#QB;wd&$B-{eOnef7$b< z#*U6G6WQ@g2dwrK2YQkP{N?g=wd|hCU@twD)7e1s#(XV&tS0JbHY@t^V_9jEd(zdp zRD~2bp3`QR%h|C0oXvx(BN?h*2n6(?gV5k%5I;=^zHN5M#9_{+mNh9Lo*?|;eul1| zy*Jy-)yKBk#zL-P$s*SLi4V&H7`U~l$&FKvw1Z0gFQFH`%g3K8PI%^oene90MRlszO_tTVtw+mSg_pF4X_&A&uvy5=TY~BGAKE z9y|zoIa5uGBWg$scxzu%9^)LMZU1&h;;ya%Gkxt6{BBF5fW(Ew$?~@o6w!)~t}okL zz%y_xmu3e)&Hl1Hor?X^RcMU$8$EsgMLTs!!1Gdat)qT=ZW3e0W}WA6e8jSJXU{F*XC!ug4*_LR3wc zlVrO#V4RRT9RaUP7BwHtTL1L=a**ilL&u3K{lzqpYtWir#2^&!4E_S(?gXUsgbli; z?aacP+0pIP97l(eOZwvXXTLjiN|xo3hMsw?C<4Z8XKjU1Awz;Ugn={PKi>E1-YLSv zEI-G&BK{MB%`uLY>eIMg8sPMyv*YBl^0hpR1fGc{WuaR-1k$Ed~qqnsTR9(;IFJ?b_a5Q_^ZL=U1=r(t%Vf;K=7fVmW^-=QP}U z)?e&$1Nc6wS6LUUap{;5z$Q^lJ!dC#DGjq0ccoUQ=0kqieJC89roDYP#-~F`F4Eda z#8eE^0whu@>-d`CND(#sB7axtPJS3=7_+a>^d4qg`%J^Oc8E<$$iUZ5t&{)VN=Q95 zG-__@w^*#zj8jh2?d+V`qTz1Sy@_|o-kIJnpNue z@Pk+O3IAw?4OH37o^dcm-Sd%@f296guX(H)HlbZxCD?Y)q*JSXC|kkl_=nUsg~XN? zY%OqqTk6(FEkulH#mBsAL&2xd`OuBa@O88pkGAd^heTa;*qbiGLTeq{=#Ut#t&nE$ zV7KX07~;J^L07V*sRdlid6IIPDF7jCX~3|g+e6X%=1M{Grb(vfyDB@(1WdWbKWJ5E zL|{~X$83t;{wHjK%4WsBS>vO@}n_scFju64THZ zoqd2Ek6uDP^%f2^yt>LZV*<9hJo5MENZnKl%E#ryu?v*g^jyDtka8hggx(mZR@N0U zFjfuiSRT=7&ei|@&yw`N+U~&eiH&DsEkriAEEoiv#jbeJYF1?T-d;Sf4#U;Jn};9=ig?EZC2$Q20e*>{XYE> zM=%>;b+`80rEzfkOwF&HflSqvU3UNnsx^>sE_Xr6TM ziU6%TzTYVM`QQd!3TObd>9ko`U5Ui0FT-bVJRta?_x)x+R!xfIeU(Q+q59T9FsG_h`oUj7VDC~!aAYx4a5zM^ieHDb$)o95E37`YU-S5^ zri*kCV92&U8dHSxAkRn_IpLcDyt5>I5msU8VMOL>w0M)l!bwxghL8#?^d*h4$$8yN zVK}|io{1Pr`OQFUqt+Svk(#SoZbP;8_W;e7Rqsv`R(OWNeXUp|@!{D@2l?W_mtnEu zh7p?-^!1j7TAeM!h%NiD;YnZHP_iF8Yk^o+f&){^4#e&BRSS_x@j?grCgV00 zK?}l>u#%0Zg~f~b-j*7-o8Dwcg{NTQG27DMvBaW>u(uBOC=E&6l_8 zoYJ{(BRjl8g3Z=b4lFy}+&3ZEJ?LGelU|8#^)ws6Y3rfKa02b}x1O(YBoJQz{e+wP z#c6=qdEfD^=DEyXu0iU1!O`2Tp7o=XH77;mar>s5WCR_?=vji>L+p$c=Lufj#Dpys zZ+YYQd+jxL=%}S#2|gdkqkQ7;^8vkZG$Psav^uFd%F5O|=JG|Oq@al4^@a7xI|nrf zfqW7n7!P%;l4A-9olRTYhu|M_lHi{iL@+ymEOn26NSpr^XHAsA*k=b~f&MZ=qfhN2 zRApZtn8yfd_g5DDgvy{7p#WWWwl3|;^q&h=+y1=cXQLCGudORlqTYr$d(E~}DMaJ@ z*o*9^*OJvToZbSt;l}6gIubGb<7;$%fm;Ix25?<3t}yhuy=S7}g-2%7fK2Lcu88Wi zVZfz?f$I6qz{HJf^MA*L|EFmX5lfAT^kzx8*%RW;&rR}v=9;}>eKWIQJ?>P270ae; zwHU7w*k7b#c`j#(Ig-5)c1SV^E!jJfoO1HhWw`$z6-1D7MSZ$X#Z~g(lVJ%VAOE86 z#)jM{0REpQ{ltc0lSl+|6+r2L=n24RaM`bjbxteH6z|k~lf$Hi#|YDsnG8;UTO=YN zlr9AU;VeFhHsv{_&mlJ7--cJv7A_G98KLteiAIGUhex-cTH+b;Gwb6;vhazE_v%t#W19P8YiEtDZr{4t0s8Wx}ukf#&VaBw@qMY5luUpG!E zq_fJEQr26|()C&r691ui-qQbw1yT$iHR*j+#w1d;b6;*a^>uEt}S?n5KfH{6$cJ>m_gjfmLR}JWZAPb7va4DMlLiJ6Z^f7NN9D)mVG)WFVAx`4)EjF=bFBNgvNVr?ppy$zd<58a-gCKFC^@>Y|9f)W*QTt1A zvNRPRXE_M|+vdAMp^K216$krPXZDN=NA@J)FW=F0oZ5r7`O%5i<#xF;uwIabL#KUy zwo=`#>#FJQ$3ROm3s8{ASaloaaAGY}>@p#JI9>BCw+%}6w(ITu`S-E63l)TElih0T zC%>_~;=blGpNZS<&4=DoAF&(9)}xXsA)6oD?i28(EZtC@1mMrC4ZAvO&HB2IkL~Jm zi_Xu^#DI4`f+6g8JfdnP8}=9^{=pV;_|KGgb6k(y3L{sv=xqYYcFJyQ3pkR@nuKONW-Qwc|&7c~fb133HX=!jF zRqJMR<}==CF}hLh&tKv~zt$CD%)Eh8m%eJnb4HvCSK@Tc)tkmRQJ@32IpzWF<=0BF zNt*d{|F5=~3ar8}_DBBn#MY$>-0T^8jcxD5v+ky1I{E!AoZF=^bx&QiW}16okI>^j z;gj3#ftzjb^Op}!|BB2(trAAv-dhlxNH%DUbhgdjmOtv}TR35Eo`X(IET{e*s?#I} zek&_y8x|Uh^h&|nh2Qvy?WWh>^ayyZ-r1j*6{s}JCE4-p!IN}LTvo(dJZZR;_cR{@ z1aP)BfPZS%F}FH}-p942%3hQj1q8_Ei0i$-vUNDsCd{jJQcHueYfQfov$5_IAxD9V zu!k%K)&d-s1J3mL)?#*kLQY|+5>)+NjTO5G8URusWrw>0m0Nop-JdkYkw#+)eJhe7 zqP9Onx3BD#o3P;zjWYSfq*<_79z_j*Bdk^Jhl$$<6sh+0%7j9{bgj~SH5JkBT{WS+ zu%9{)@TY???G=HX&1)x=S05Hv3i@KAD$K3wWk5QSFaXF93<6Y6N00$u$$sc7^8Az0 z39xkMjxq%JWVBw>*tFTJox6(&DcwTkLLb=EP)8AdA}4sT3l?eoO5Zs(0xL-5&~707 z>d3mEZ>Wt8ziLQesLFGyZGYXE4nMnRvwU$$$Es>Wk!Dp1I;*Zru}9&J%QOPYoC)5Y z4CfE%XC5ms+2JL#TiHk0RGBYYhQUgaUC&+!OOaH*=+^{M`8e3#4d-&3IfD>HH4-wh zBY`!OD88JR8eFK18JOQT)u8BC|0ZsNYXU$qA5M4}%zXJSVtc0VM+SOuF97w0!3ZxAA+eql0ixMo zt?@NN5`?)RdEy`r7Qz3+U_ARB>p!^H&cK|yTir5eH;uwC!55tL!Q8fV3?!EZ%k zU%x#I9QkHMCLmgNgR0Ds+^fd4O(0fa256bISsR^X=YN; z8nFEh?!G$xI`cDw-c{i?RqMwuOSfnN6&iA{Gq{AuR!dH`6V^hz(N@K#bAAH*i*#aw zx~u0VFwLUc6l0Nzazt<`Rpi?+uQN{}B^tm%#POA}b;#iu71$CjKvJoFyTTp=!VNuJz$H4xT2jiN44Nb-S=jF-HH`Z3 z+WaJ0)+`j!xjApfa&2JSirm(vi!V;e=yln!8+j#)UCrp9-Xvbp)k+L)?T+HMS%5ID zG!^7R8+I%fs+%~Jl_cHI?0(BJs#N4XNeL@%f4E5pAe=jNgn8JY%G-|@x)rMdDMP2R zoc%rDiS7VuCJ^;Fo#VM1ETzbOLx7RZ{oNc2m~>R>y?3KnxY838OL~k_zho`lfri8s zwN4c_VqH0)y_%9t4)Rw-wne_0?){n`P_pZ^J#-WtSLkUfOsAzDTHNSZxAKKubMdVO zf-G=O0#5(3&;}R<4x;AJ@#E#&t*Sl-@}lO>jys<+`8x9poW|lz&izI}qQH(Xm~^)! zbf77E2oU*WtAkFdE{*%D3~5D%Q!0nRk8RjH$sntZMBwWdIGY%0 z9)U;C1>NI|0XPFfSkA-lq$s2*opc0RXKXc)FwUDr+TDsnx(N$4GbY&NaKJ2d2}K)N zli6y?Ei-aPkxsOF01v2hF@JukoGNwdsMFuKpcXsPjWSmk@v z3J}flnX`USzWd?S4Q|TYAy6P*Oc+fwmN(lpnFfr{dDWW zX?CCD*O)tWi}EF-|N5K$FH+}!Mf&Xv6ZTS_B{hwI%`xd#i0ScCsh#TA zu)RGBv)l{0ICeELbGyN!Y$HuyB{Wbye>udtRXy!ys1ZKc*1b*;!Dp4bTNxT3lls0X zex=q6km|z1?a({z_dlTwAWz4HXWze`FHj7G^r>WSRW&S@ z!14IS77MRVIGKU8dJMlrRzN34GoZD41z}gw57~bJOB8=-g<-IPJIES6;8~&fc01}x zBHXI{)7C4RUJYgDYg#cvspsUX#yu&&!8B>kXRQW{SyMQpv1N6EsW{W^@oFI}`R-?# z$;gT|j!+`C>!XFT5Ad$4imxZ-B+b_&FREiCFKRg4O4==sKMl$ep@;x*EJQ0gsR(e0^8Jj3)xrfG8c$6hPkfMO@W8VROx#B)NEq%l1k-Gsfz3$ zYN__4)aLZ>MOU+>#+Eu{Ih(yT{v=3VJqknol|HBir6AwYN*Xwmcmil}=sX3*a#ixJ z9rcWZ`ceo@j$b?2c}xtzEDdn-;GgGzUqc%&(#>iaBL&Vn3})8do9~F2L86N1>-+?? zZO;4ze_zp)i`6fy$>B5$pax^I2U;1Iw||Hx%qA8JSQ9bTrXBMwlstw7m4-ioxRhnQ zM2yp%!Jo!kMayYu!(;bo_nl8?E$Du;YQHE-JMBJ=j%g_q1ncTx+StdXypI6We)S84 z7}ieA7pgxRw$?IC#?m{BGOj+ACW8W$SAEy=fRljQPhJB&AOW7*nL~F4)b~RIc{1f6 zyUl?6tBJFA;F>52hZHMlL}H)gHE*=UmWxcvf`eOM=jb%yyMLti?8<>x*(CbDoQW4U zFQ+sO6Y5Ihzp@tFVSLn+TJk+>4JgjI9`8e<);&NRT>D5l7w&l~L4y=I8Fuhpk^vTv z3`8$%X##%rIi~=##1&?}rYr>|O%;^9#gPTU#zfZekM|x_9vG*7Ba!}Sa48#OQAljx z448T6scJ1npKeBljiO+Dg%pf%rkYy&wdYmn$Vx<8EbBmt$&rnTQ_SY{Zw~MQ|zj>F$-5LSR*e*3M1$pds=mj zf^5(bj(m@q@WQk6B$NlXO|lrFU&?+iWANSe)%FvNUOVcT`PYn%P|1ybG9o{3O-RY$ zJ(wz%=6F^@DY7&7=(yu+&3~ptpi`8(p&~X&*Un@scZFzf@NRn4{Sq5M>SeLTQkJ{^$aW zX*16Z2ECD`^Kco64&@)77G~9t=noTJb0s7v*d4}S~5xziL6p@ix-%{zj~)<+zH7RuVguvtoZ6bCVBJHGmq42t0I?d zW<6JEyc}WBuX2J|QispuKA+L^wLr_uF>u~)8{n@izsTxfXDS_BurVH?m(5Q4Xxfe^ z$E{~f&>`S@2vb8nDRjD$nN?6Fv+m5V?mEN|)4W09XM?WfIm|oH9kZX(a`10uk?5*s z%IdR9Bd+y?n)T0!kG~9g9`DN`_CU3SG>Bl);zP-+$DuE1A%*ci_hOn$3#<@mpKD>?RY09KG9y@n2A zLA@=B(8S$Z0Hv%AJI>hyvQyC1k|qZtBeIFGBif4$1-d-o2HG=nYzz~4gwz);-l6wC z<6Vtv!m0FxrpokEw^j)~IsQKQLOWo2e^@%C|6xaHwPXWft|a&y;Fo5_STK&~(>3jj zIPLnr!%mDAp=?#R&4A|3%Q~P*R3!4C4E52tIV1E>O26Rz71Q-^J~(btCXC*1io_3nLBK zQ?_i$7`_JoRz$7F7?)PeKW2WQ@Mu5PrS)ec9Dyay@H<6{&_j;MbN_ zE5{JS9FVdt*2&vqlDPh)sdn=7-MO{bGn@+-x%B5cTtez}!~0k1p@JV;1lOo^WU{^3 z6bZ_p&QGkrW?HAGio8A6>8}=feM+7SsJ3ink}kw9%nKhkaqBl41!q@k4L%>Zw=5=y zn->Sg+L;2pu(6H&h1ykJ>Q`HmW;lrtx{qe1bf4hswaZ4fy@w9;ReV+qGm8b;ySyoi zpt>r&_x^xj+CrYdBGJqvim2>;kWx+u?fl)6=gk)nC3ri?JEIQGnJJUWO7i2L5f6LM z(h{y;3Tb#V?-KcVkpG)n{-42S4=~m9>qI+?G&H}uAf2=Dt^8rkmP$N#x8#VM*+-4q zxkLuD!j#0S#R*-Xl5$Yd{{CO$mH{=EU~IEl&#KxXvjeYSO{EwW9@|w1J3&GF= z@G;eTLT5`g;F}h1X~+>6Ibo}htl&@{IKh0xqn(HmkQ1x{(@xcy)($Lnw-vbfc(eAi zuoVKXpr2K|?33D$5V8b_KAbh0w~C9lqNY>4vYIPicLHZ9;A+~}nI)QF()5B{DenMG z!}}Xb4(-roD{4|rY6OgMi4*ljwBe{TJtfBbMp7HpsURJueaziC`fA}R9ga2!cN+Ir zcpm<{+0#z|Toz2D)dnXZg9E}1aC-(hlZVe+pI68hK~}@5?Mp~l3Em=3SmmlY$}^Cf zwHUa2+yM_)@$p24J93uQv?ZfY2^{xHu_EBE2BgHsz_@Hy41Lx9)n>7Pk|ez4q|(8= z%U@l}52Y*IPxzzQWcSl31kTGbPbS3~{u=j>a+Dt@tUy=)wWS6A<2hV_wD(*1N0te2 zZola-_o?sVLZcECvyL_=&K=h(=8q&Q7+C=?2AQ?1?LW9&`~m@DjyOn5tLei}_PIi| zX>?`>X^>M88|vLEUjkgR8QK$j-1O#m8bNosA0v)3F@J(VRAUA^zz-wtS81>UJsUb2NzyeYt@ zIyw=K!c)voN-p@bS>j=gOh>!1km$ll&U97khJ3{o)k1sfyLGNa|O8oLH_jd zodI4kvn0{C4XSokIxe{3&c|&x5b*&#X$`OQi54vvY*bT{`IKXpFNuFaP-svg<#A2I zJ+a!fzLyHc>0uk*tvwi%3n3@PlJbC&lDY27!)vZBPj(aWy{k`Y<-g3*U6XPKc3fu_ z_7eV`AMig9l9+EG-gB3xja!K$ne!gk=H~`lN%5;1P22?zIZn|tHx0}3(-#fpf}+^0 zvH2o}zgEbCe`WAtPlsF{MPti|tOFbuF>+z{(X09}ia&1W!Zv#zs*kli{$1uUiP*7V zaod$Fn-HrNahpG`&H4Z(SDl!RDj*!SmnzD+0iLS?Is0;!zb3m-`9l8D(j--K~!+6)m9JM{k4fyk5 zHmWy?;(>G@|?HVI|ZE0T@VSD`d_L5Vev9OFq?_VFB8H{ezU9`n=EVZX{3VkGjIx6^`7G5)P$WaKNhIJx=NHG&_OBk+~7^2Nq`8LGg2;dj46_ zR3FV>Qw-zFJf|AgxF*Qt4j#ncvE4Q|HOuWg=BuAlptKgbM+sO|*{?S2(T}|lXR-68 zZFlbhJTF!LO6D4|n-s~C+S_oxT6`p`1R$29^^xl_|K|P)j1us%u=gu|4^^>oKM#zd zh7jEe*Q>@8idMXW0pm{+T0>-THQ30j=oSr$PXE*QhCw)RToCUb90 zp9p15SOVg~qgC@s-@NipyfS}KJSXExKdaDE*3ezP5M&%ulG_*O||o&pdVZ?UwV- zLu`(X>Q0gjJ+1OiKKHM!!{eK_{FtrB+ST(*mw+z&l&uUxg*$h!su$BKZf6ek5=K*91D|?;j3VB(JBRN= znxU!=1AwM{ZY5`{(dzgl0~LZMs5SzJ}VMk^95h}1+O zp|TCP5-%lO*{QSyu4p?z`rAGE&~c4IbD;>F$wB3lI`g=#`l(W*_TW9!&L)k&_Tf-) z4x7Zz`ue-2Vpd7eLW`riVG$;%axU~RYlcptRrHR%w?EL3Z!&a)L7LHsvRzy%%7pjcbeXfkS$9P$NFQk5e zH72NjJuM$*Vsl$ixk|&t!4}o6H1Z;z(x}j<15K$gl(~2M)qcQ@!{uzf2>f!%RjX|c z_vw~87adrN{o=BdeOv?X^_rp9z1ZiPZLVb{h#1FGY^=;65n9M*hu<(}Uuy>#mi;`L z&-C$qO!5u{v7$G4+nJ`xnmZZfB0kUg8~J1`pY(MZIVZ`U94MOcY4%?5N%we%>Phuw z-A1|;Fn5_^H+v&NOd=U>)-i+r<pk3{d8!Xeh>IcKXT~t#A|?ovU8*eMKYX`>5cM zN7W^x`0bO;dwRLtST!D?FUaMKEz}+u!Ys16e9Nxoamk3oW%PYJ_nk7OCwp%Wvz!#4 z*=cv={;Op~%B!JHcV-4g(+To+wzv5dz&LoFPjzE|e_~1y(a?w;6V{c=ivjnmSL&mz z&j!oHR^$w)pF4IdjuRlFi3+<3)P?cig^qZ)0*PK+MwkRM4ZkZt+DyII_O{XFf3?%V>IuEm168SITp5XdabLSQFAd) zGaO`MaoNo7K8pL)B7GPf{%#eQdCALE4ty!iItXF>;0{p!vLda0JtUhtoJTZqIo;YM zZy^Cv9|~h-7phxTx2xFxWy{Wx8F@`%sGrQu$`n|jmI&i^rwpfF8!_+HK_+1e!&w-z z?B6K7jP&kIq**kjHW0CRrs=RRVaQ^bhlD-qMg?24kB^)`1ZbtLqx5F2V|C;u>fKwH zj$|n{)sVZ%TTz*-=oNhZAb_>;;bh;DR8i{F3A~dxTlyairzO(R`QI!eGl^}D4hYh; zg~$5!jD{>>V0ufZ;_fo_iPuTs-@4^&_R-OFoVIlWsYgGKY(4hHD`eKEYVQe7Pkr|* zhHubM%+iPHz}ui4cM**nCLuq)er)YLt8f*X(cj?5;j5D4PGs|=A#GZ6aT=d#t$8?`cl@=W(N-jpHvRAvh3HK@J37ynb5q0ge9ssY5 z;W-F%OP)GR4QGM3bMzy%HpwAeQg1M-*qj;@CrLVt70k8uRP6i^n37GA&H$B-kt;*m z>fzL_8rzK2v1!u9*n5z|I9-2NWr)FeIF!}VS_57ziNWj6*fcD0ee*o!f-kZY=I9i$eNW-FZritZ^36c>7p|fAeaaqL#(5pe?~}4jBsMPi z$S#lg#IYn|!I@*-v!6=2Tk!|Z|27r=*B0XcXOczAE2qT(?~~~#znkY9`0A8B&3dcp z+BqXJb$U}5bYHe%uktz-Cl%`^N)_1Y3XqK)yA@4@FVMZxFg*92C7~7FgHmYXF z_Yj|KW21q)DjxlQCPJ;uVi7ewk*I+o<7)))ceec=Q zBNftKxaP~diu(Rq0DAKV<~~xrVqZx4WOBAKY?~^r^$MB()8=eDB0T%P%it+P$&j>n zIy&}5c|wLAhlytWOyVo56{X3K8Mvk1O_77$S7O-#{VoKklX~IG6G@4Qpn^z(jQRfWVT9SSN>BGI?!B z0;??eMB?C#6aT(L1?E%kJTn(?I{ipvsG*p85Iq9S zsd8Our_g+nPlB_;m9B{NH}79snME1W8EP|RB3n-Yk(;GtRP+WyNdoShxme^O_3Sn1 z7IXon@v$TWhymR1RHN_tsc!t@7aBWVWU40@@>vFEgx1wSiUNJ2eG)~hwZ({Hrwh#} zJGJSDh$miby>2zx8uYEp?z+}l{0g=;XB>*rfYgbT{&0UPJRTY7duaoEf$Ij4k`)}H zdnbnTDMGKlwVTD9@%;$#pv{xq@soNO2gC@ciTp=|-Y|}bJ1}+{J&p$x2Z)J^MB6Q3 zsX1?pVkC;%XB9kl8R$VHu+-Wsx;{{uuW9|oO!oUw8dUsu|n8z%l*>Jg1ND? z`0KeZf64q8&O$+*yNYG!vY#Mav%3bG2bO0u5^q>``9^= zbln#bHIs&!{Pw)VmkJDsZPoU-;=k*nxzVA*7701i=Z?uacePSi^XFu37*1ACJgW^`eBBZ~lK(NX zsTLqVwdeX$h>I@}zNr;3vPNpQtMLykzbzPD9rCcL9qO@Q*btSi6Py3E$M3dUuuJS` z{%Loi?68>b5vgE%cLVL8bbekJub`o2&{h<#H>i)0GW0%*MW{EYlj=AA5=joUo#ee# zv99F_X|~4$$JaPFci>O^$0ew6I@`1oLZ_#<`|%|!=*yf%af104>Pa+b-fvg_I;6JR z)G`xpJQ^GykKVFbB)hpAa$hQIT)bMnc@XbrsLs(-M4^U1A;(^R=aY%Um=J3*%N*RT z67F29GN&y$tO_W{3PSuB> z7?(c>)wxazd?)(FK)2)~wOtex~;+A9%(kChX`IMXhS=z0Ali`K@M|cl5MLO;P5Tv2ZP8-HuWXoLm_JhuSZX}0+6s5NJf~Y zmU_5YVK9SQRPviqqrvor(Kgd2i5=<9rMG(e?C{to;21QJRepUmDzG!6B_ z15a&1BTKq;VZ3t|P6_e+o!Yn^!xYW?XbG$73us9t}Y7i4VAbK8M$v#R>RrzU4g))3CZtdguy* zoii>X(`wBl>7$nV4A62}B2Brax`s=q@@Nhap}io(3#>zYXH)v)e*OfoXI5l7HZN1?%Wmya@5Zql>n0SKHXVt?zQjw!L}vwCJF^;HRnPNi zG~fsuXUKWzR4Nc>rojXWXYT_9?VR!(YQz0&^#DDiPiyqkRJXK^pLOaSzo?$tWT5fB zaM*0=l1Hwc9MPPhuyM?sAq%%PFlwQA;7~^rQOkS)uPxx$C4A|l`+RvwEPn1(ckS|` zh}zAt&w|dAK^jew8tW=*B^+j)4?RQGUymJs3B&qFJ#xl3D! zV~8UoDoAvV@wx|i0pD+Z<9_MZMu{C zY?>UYeOQSl0E?}vH7SBT_D3Til4^)kJo8uyUxUP$Q%wgAC4@#WGy*Nr2Thh8<&0Cu zp`pFC|H{VD=-ft*?~~PpAZF3Lk%M3I81ouTN@QUwOIs( zYSE@JsMVvWp$GihaEzYD<=kZ6?3?kz6j_UrH=Czo|9Cq2J{lG*srrOqxxV~QQUhN+ zqlP#9t|=V4S|rrBvw^AwdsI+&PG0kO{y>Q(ibh(h`-7R$XQng<8bk+U)_9opCtTU| z2Qsy{DJJ*cJZ&)e6QR%Z`Q5hQ>ur^}=6M4um$+G*=t}vyoUd=nD!VCkESVnqCXx@l zY2zvo6hczIHVq1qYg=@|T#MTev5YsFC`~rw(RFd_birRXymr)yN7%0{8~AI!FAH6I z-mVZF|C^<#RZo4yk|PTtAhaT3?ro0#c5|P*|GwSjH|M4JoNSOt&5fP}r&6e3_kW00 z`RvI9uSr-H7A{0>>U=CNT-p-U$oj%+#QTzVONN@BsVZfP410zOCa*Vj6w1z|e$l%- zmAYmR&PfCvC;dZXU+@QQ_&F7vUjMfObE$dWF`aX`Y@>u3GQU8e>BhFU{^lv4l}KGA zCn>TR_{GKrfiQausqd;SB4v}!5)(Iqiu*TxZ;%tOavi0MsIOmu4136)l!@f?s<}8z zmwxURanjEX^2NK+xG!-bEweRNp}Vzm{K?6d5M8MU(XG?#SQwkuQ#y0% zv*i6Q!M%YSou0*Etp*RSAt2U?Z;y{VGQ4Y-ZQU__+He;pRXb!FbBsG#{uo6q#mkJS zD4y#!@HTD7*^@L^8l5$YKN02ht1qILBZ(8zuIX9wO8WS;Ok|I7KDO5?S3FT+zX;G@ zO3C0C8Iz>tM2RjHVHEnbpCP4+PV6ja0wagcj=e(El^-HUAo5jHQ6mD=H2gp=PywE- zPHp6`jE799aQwl=mE8C9r6!=;KPOfA@6;fL@MP)TLiY)v+JL49_Ob!I?ydr&|C#bm zk5HSl!@BPB+D}}-V+nU;#`qLsWT~Uj>b-W}jbe^eIW zp~ZPuSOm@DWVn5-OQf~g1qg`ZO0FVivay|0WPi9|SKG%8KC2@}>Nb3R+y3xFdtp-R zz(P$7?E-1+8<-qSe%j#nJO84H`(?=urOULtGb5tfWJkbiu9{Ye0YR6i4sWaV?*VD* zbVRs(yEut3E;5{6jizh$9PBEKac%&m^=Zg&!5sxf0s1%HSy$b2 zA5U0%K769qCLl#e_;R2;X&JYD{ZjD=G47vbCGT3W)hbA}Aj~u0bidkvuOOkkOZ-** z`)5^E%FiKr3m&)f<-x3d^Yil(*(dJkzovmX))#6A7Ia~WKQBA;_7tfXj$Cp812&C1 z&z})7HadJW$%q*p~nq>B(bQKW`IKza=z zq98>PkPgyIXd$6P=t?Nk0)*b1^xkjYnfdO_UH8s!efMuzD_JbgInOT7-dbNZR194Q zFaDJUFzF&u@>aVImAJz~AH1MrpzYd5)|qrKr=Oco=B>fioS!!-*01^Eg~e!Vbl?6- z=v+@S;HB$)7_D&{35fwBZ+cu~xKDA7946C7`8r*4r;*4!>(!0_Y`|rubIP;$dCl@F zJE)Y6p0>0o5Lpr7aqF$XRpVC%rs~vCQs>VlB%t)5+ovexFp3rmAJ~xqeu0lx;lor~ z3|>2E%0GF1V>VmV^O{%eV5^RlxV_USC+S~zd(_^tacFxo>&dw}_=;8N?Ph@LZK#?x zC)awiP=&@c)~NDgectvjxBd=De*2*7vb$ncLXm)x0|G zpQ%JyE-U{cclnoo=IxB0=8UAMYcGXe|M97O(#3Wt2XAGOOM7wJju)SFU-z$*w}f-S z##llnHxxVg*T()ey#*$u<6Z-4^s*yl^A_jl?|XisTLPM9iq{~9DG?6DvU=i*S;|mM z&xT!xi|pq@j~dys!kFsL&^jU=Z4$$G^;_Rgd_xsdrt%<_DST%hI%P_c2VHtYvh-ND5iK**V9WlhKvg@D}DSty#a^2@~q zSnz-~>52k8E-!3u8<6x}gb7f8EXo5iA6TfrZZNXw>*dHacrM63Bv>K#YaTTTp-71zDL=PS{V{8$@>F6f08 zeKD~$oy$v_*bz(FaDLi`5(j;I6j;2vJUMRdeq7YXtPS`*sXOqC>{0#N>1P;Y?lVX9 zb_$FM?)eq$+TiuMXInW8l~PqETz<4;r>w<&3;MyZ7!A?7H^y87%Cb`On~zr)SkhOv z2fQvxrNZUA+jLe!!Q$zkauGRps-#Jr&pC)eJlgeiFsEe7@+NL=n(8nzGnuF(B`Mw$8$Fs{20b3#FKvUc5p7}u- z$1Vc3E}>ToNSXt6P$SqL-Oxa=agcM0m?hzA;x>zL2^Moj6@+q_}d~`f(Jp zy~w!cd2ALA989Vi=GCFR<`C!|s?CXQj-3Qd)A=5ktqgQw9#lyx1$ia^0kg)Tci51kgbFWF?2F(oS? zM=&j|PUD_eho#R?xATTd7Bid9-}rI2MLz`Q30rBFpl5UHVv%+=_@D6hUFh%lmBdZbiDxQtoo=1 zm*4-mH&3nc8mK_iA`aPB>YaPhyP;8DjEy8t-D?5J@$(_#M#SaEVvU^hCLiymW*mM5 zRIS=+(LOsf>jN-d$G2W#_09Dyp+{nsa2QrZE@J&BV~IsML7xrf$EPghZ2U>xHYV9h zliMNg=*dI!vY<(|C*%2#VUd3C`WB^P`~(!C-09cUVW~jZX6Nfp&(JJIcPdaCz5}UM z^Q^&@gLg3tlewEfg8y51l6k4*WXMes2_ZhkCO>R-{W_bWL1cAU%BR3tc{q>%E8iPy zii4EgRaxf|VS3*_@r~Nu*!3L%w8=(n%eii#XF;d?mQ>Z6PBqN2RErdn5*USGGw3UBi$U zai|VsyG1}i$M)Or4UTm0Q@mnt{%jCg0W>U%>veZE#;Iz2;np+k_~cMz8lS6=;0Krm{+CXP%bSsJQY zJ4p@a-}i@ClPHw?7NwoBXanFc!MPWSIbA=k;#Q2UM(-)7JA^N2)vjNK{uxvL3CMLp zC>&cQPSC3AnUoP`lb9K*^f}ILx;xw$xgj`y?pz@wDjoIQ06RtU*Zjzvhr+eN;(tTt zNMWlSzg(|hdq9^d{fjl)@OvLb(K;_-Y2@ow%3!2n|pPX z`dv+eB%qgWd{!C|OTwFtk4>0Cv?5y28K&g7&W#aqW%NxYQqp`@MDez>)Hhl7Q=&dmBnxeeDQUnnf|FEd3; zt|~NRTxdE7#|BMSRePgKcfG2GUs5WS+8)T`%P-|4twLgyE0fWGgMot_04p?oC7TsE?&G*Ew5KSZ*PY9&S)!uyLDeW7;W-F z4?#Ul8E*{aT;9X2M9S``WNwOQ17_NBy#U_p!taemY_QYCIh(hVZ5Cz?W_)y5_4Cvp zoI6Vyomd-xC&nj3NE0f4*~j6GI7E*!IB0pBi$bywphzvm^5vs4@8`!LQM^z1Tcx(BAe%r-iD=am;hW8o z*Sz!Yv|SO*Lj_srrxA(7w`dDr{6{9ivfC75ebX`+wbRY1OCPp*g-rMy4WSc9ig1|a zoSqcTi=G@v{1if74x45prnB(Uo@D@)1{L3qITqMCRx+(mWOdq% z!jcNMwc$SYO9SgG74AvBN@LW>aMNulHibCcXb%3d_0ygbBD*KYbbDF=&_Y{21d` z1TSdpJ8+${9LkW7s#TciH5}MJcxYfWwMfz!Ka+x@PhzL3<)4A~rW02m4V|)-Tx?_4 zjygJb^TwX9$r>nR?nVyhcpv-yCu3NQi-B;TZ)oLe`_r3e-i#hwLw_glr_R$X33nwf zv-ER`KZ+1o{nAa({sHBUL~$iu_?|d?GqKbMJ6cTE9pL&_nDr6FB9W~j2?#v*E-NKchhqG8REqh zJ+(Q>7_cy!=^v%EdK6<()nz;kugu975DFE~OK)b0M$G?xC@?cEbJVu^WXX2{4tlkI zw$gZI%VN9vLE)cm1zbRo+5+@+W(o{|c&X<8Jrjn3o4fy1*CUSTd)x_7!pm_{VgaDGrHU-oHObZ20&ZaEUenC8k_EKahdR5hYjM9q zstCI%N>75O3^qU^pz-!xCCIfz>FQ!)g6nvrYuDSRT+4ga;lh-T>ywUUeAw zDbma43cn>DvcK%WmAW6Z0Jg@-{ZoXh7+DxSOidyjC&rMl&1v3n@siHpURd*=_1i{| zF>--#`e>J%9m^Z>e1eN3JgAtYVxm|Oo8Yv=7Xe4#Z9~Ltgn>h;VPsvrc8GgNNO(m) znz{q@hg!!R)6I{|oRR!748BGySVndpm~|BbZ7n-GT4Gv16((`HkK~}Z(2EfiK6|ZP@Ynn~OS@d(H@5jZm z6(wlXBJV6My}k?CI}Iy0lr=|HCdbZ`W%4fRH0NdhL%m#90f>_26^GR%UsXOUJ39$HQz zSsbcb?Gen63mzI+x+XZWyCD^J+8AWrS3|>iLSs|?5pAkyCkd6jV%Bk4fHy)CCtv) zI7XWD7vWRPO`dxQxCF(kvywoSM~E*tlgAaZihhsof1Ay|Rz<)zFQxI3M$lW^cOUI7 zb;aoPt^J>(0RHFCxOJgLjWmP{GwUMX{5ZP{Z?6s#(M2>bF*QDXy7L9n;W-979J0Mo z8|=$-wAiGUr>FiS(DcvHD}NQdhfCbBtCLjRaOViflx0P66>6YTEn`c%<9oCv1B`qA z+uDFG#*J4iyY(9OM=^LkwvEx0*g~wM8{N(I?Pc^Lh8|g*s#Qjr=k7zDX2kpAsA!o= zbMh6x%1%wK(G-_4rsIw#g#?o2-aJbmxz294V7$*gvvHTy?on}E9UjaBckg*psFQOA zV2cY4w(xt)V}n#z3$k81TTTP;HN3L|blh=F$`+_+0-i4Pf@P(4B_DG5Iy&GVSHC=FzmtBVz#)NjVHj6p@O!Q<d&;`2H+*Z? zD60nCfoZQg`rTyn|Hg}d5HErb{JCYpaWo97q?WG&SI~^h8iX+wKQgJL{NehhKhmV4 znu~cDNZ&*xJWv@`1M@zB3oh|*hAqC57<{FTsBPsJ*HkB z=$$?l;bS{GI!b%UyYHbo+w$!3BVM+A%rn>!w_`=I7h0%#xA zb@7{Sx;W^6Yk6a&+;R8YkyffU*i-wnB~*N$rF$GE$PdP*U7a9F8_U3<;;C@h14_+vUoy)zg zpG&k=jMdyF6l#yHqO|c6#5k;7(GCNO2eqb~JBOLkUE6ml#_gsnKN1y<-uvsx8A*uh zV?3h!4-7W?frP<4tvZN`1niz?N9Ta8N#?#?N}|@?-gi4gg!O+(`Pz`cwPA*iW>TWA z;sl41jgQ_ETwAXM>93)i{OU9_Ecuao%kY0RdJp;C?2t>k25 zB-n)D*mUk9tc9f^vo)-79_0_ZJ*o=i>miZUAP!L4H)FzPd5_WsACO?h+s!zLtLj>adr4DG}kQ4J8)VGfmYz zNszh$=~c%S60y*PXD)gQAq)jLBN@W11{>Fzgy=FaI@W{%Y0zVUE9`Sn?uXSQQGCuV z6U^9WY!>vw1q6B)Z)zMVP?;>c+C6HNq=!*fg7gRh`gj0%69ECkAw0J>mlDS$#Ew30r4j~RO z!Tq}(+MXjViot-fD^kQCFaacb*rz)V$R?G!WCqv>-V9yuCy=P&^o@X{#;)RIt;F>6 z_4BgPZ=9}x&p1q1oMDZVo!}pQ8;t@pl5{d85V4O>sJ*85~NxF+Lj^{uI@-x zdU#-Qo*fIU8^~I#^;+Ofwt@WlamCQx5fL@y_o@}FwSjO!F?tG@y1U-c_(oX(UtMiF zukLWEx?GGeepiDNOL;Fu>C1+UNep7}W6pmti;l`rrk<96&S*YJ;sA2;L7Zt?0}ONB zCp|Y`Ox=FjD*9%Nf#TzX)Kj8MxPf7uG+#52up#bAMOXGEE=Czko5w(5cngY%Cq2!* z_gc3-g!-Z4Rh_J5iL={BbYz2~(3igZpx_ZfOXbwexZ5A2oFnVc0xk+E$)e^*Ap)D+ zLNKiHZVif1ZqE>ms?a#99NE;6pDGjBy2-e8wD%xvz4qU$fC^kP4%~Cfv;4dz?bXgN zN07uUIb)+B?nQH&l?sj=Q6`G;P;Ik78_mFRGy&>V^3Ox;2X0@zPdbPd*%3P$?taE; zIT4*X{|MuYNiWPWUVi_@ia=LyzG$o+SQ%VjIW8Wx@XtLsix1QWU&d0N9FNn8+TUXC zeKmyv^f5V_TBXvb+dYf63;w=t3KgpA!xoK0M-S-mGd1~lqeUCssXTh+BwY^fYhgQt zgn@qF#MaRc8`_uE5~xX5Zest?dxJXv(Vg(PS^Mz!4}`HQs!Hf)qj5d9EWuMdlJc$+ zdbVR^3KvN(UzEDIJ1<<8Y*sr_URY_AV0pjrLL^Wh_KL})|C4(wE~@XE4qvpTMyNlY z4A;58_b57Bb`PRgG4l+dCwoe2eM;-OJhu%Bwioo6@#i+EhINAx;O3%Vx=bvO?9<>6 z)8W1=p075a06HVRNP-Xd7=|LgUmo%B6tEo8Tdwq~g7udkRG8bw*ZPv|N(J!?WsL0r zn>Ai)X0@}x2hgNap~}6UB0#?*-~WZ%ya|1bW$Q6KqzHZootA~A5Fl5BPo3sUx(aVeqFf`mDH37Gz*Q+#QpOq4(U-JQUd z?C?HC1E?#8tT9@AkK_YZED#4Jmh4s;J^QyGTUexVNF#6?Q`*UKZsaHw3hT9Ny|&Tb z?e&`27|@ua6tyNncI*Cc3B*K=8`h3N=Q7WAvFnzc%ZJTB1^6;a5fg0J!Qatc6j$F+ zWUtcYI0fZ#aZyfBkCx9uEaZt#znh{P*t(o_^n2#fYaNoh;Lg@pR+HDmDp?SGb#mhs zoxent*O*`vr3|~HE}vxHPKtl=l%qA^IW)Sbki>uT{j=rgOgzu;I#Y+jUevpnsiu0L zeE0zs35GowFN&#^%D>}GFtf_;mz7@Hw-p6$q^%7!ULM}Y+7H$KK1rzvIRsLiZ2z;S zRa5`1YD>*-W_u;(RsWjv|6Mcuzdp;Pi9FCoH?Zu4Zt%h^N4MdTcIJBjQ?FGxSD{0u zSblpH+OMvBMX^0m@w04u!Bk~6NL(N2k(ZKhLx_SAAEZ+bz0T>#4=5^+su5BkJYV3e zz0h4##}8?-t=CFM{llXEW9B1W6yq5OE4GUA@@KB@dmj#da*3>-Bey>k&<_m~EaXZe zg8q^-w=3&DMC2+rvWQ|pf(1Q)gWiH~P$x377&b{%H2W@JqmMSc#a_B|UW=>1r3Bq% z%lbPW5|>mv;v)TL(Yd*+r-MTez=h9HlpQYgy^eF9|2u4Olf@`T_Mpy^!|A-E;(co; z1VLAiXcj}pf!?`k+?#I7AmbVgVIHvmUU^RP>&5l#$L@9%dCo>l=#~%-8a(^UN!v`0 zZL;|35BiG!xG=8suD9$q1K+{(-N09i^Za2Z1*Wff;^4Npzmi&pF3-;AnLil|J#JWz zy08PeG6A7LSIeDeV^h?0nJ)1XZUjosh(C7~PqRnwDEC}ntwp}y$e5abr#`u`cH@Xi zRTl`lJ^<)X?Py|JmRudB!%<(Oo@e8_ymFL&p>Pf5^IM}7rWE~vuE_mdaBJC%c>2LC zMMk8L^+2MLGUB9f-szj=%aEcJYVOHBZ@}GIh5*1fAw<^{+IBs>>7Jx?-+{9bVtu5I zfr;z*&rc4gxmV$6p`{4~cQ_N71T(TYR3>LGElG&t=6Af4^T=%PC6L*4F9{vJ3-GImvxwiEpz7&%F8gG%2Ob|m zd}$bm#e#<427d=5;(|>aNv+7-aDyJqU;?nByJzDa8v_1b;F^=TBi45KyG^HWjiAkf z{mUt}e!Cj~a=lpX#!Ajg;q_Cs!H1)kKWs1J;+*!9Y)PD2PpSfplAhC{LPoiqP0G=? z^!)sAs&cF{@M0}%0UuAPBRS!^{O2}ps4^~(b$80)vaP)j!Nm*d8KiecMtaYKK zp05b8J*X0zXrmKLAQdXcjuhLiOX7uYKi9~Z40Rz`TshS@A;nEIyjC9Wa@8V@HcRL^ z`1^nJUNH;&eAXRML=t*qBqOle?tZAAh|Wk%<2H7PphP$`JmoALk0D-qo^Gud<6QNS z=1l*1_@=!`%OP41qG~j~CHdt>_gAXD23;&M_fTX1jp|7@-4wYhe?^rCQI@Ybw|Wsa zi5g*V#}fvR4MgucYGp9U?7HOBt_}7j8&yl&B|%50JM$L+f9z`sy3g@5Dk%=kU-*W3 ztJCA0<%I?w)IXs-V)=1k@_;5pnvzdl(XP|A3rMnFTJjI$*>%iqEg2`f=RVy6Uhm8( zK+wJT=B?VpQTdgWx#B|VCzmS!x0py=|`**n3;B}=~`DztQLF4&Tkd^rV5ZbTH3&hSs47J-r@QQ z3lcR!sm$=nND$xy`jB&;;e0g$p%SqOQUjePCcg?4%ZxSol?H5R<1p~D5h9N^V_~-g z4?owGwl|ZF1EAFnD3=DJEs)9#+?nGrCo^DJ+x3G^wP{^$vZjjHOlj>3nClv~v;eoO zTbq6dJ`AOY{lm!&J(sWVvir=TPZ^emy#xfkxA~-pF5m#uMvPoj%dijdGsJkXerPt6 zYx>3*>|tb`F0)pHOC$MWD+PlP@@=5u9F1~ocaaHDzV9P?ZAhF6HBONgn3Rxg?WSej zOIa|@UNja$v;KL$<8d-F_UFXZ(vdcBy4(pc$%2%;k=GY8`SLjV8EFJrdPHMtFE?4* ztXg4FY@5~pINhAxX`wuM6Zm5OLcn?B{g^qp)7%<+F2Qzu6t9d4aFE#ujvV%Xd(H(I zT4hSKD?wTJ|T%4a=cx>dJG>@aX}Iv90ak8AW}4Fgtwz zw@V!ehGO$HET!uCIeMTqy(#e~N)yM8N}vFlBmR62sCOyL$7>OUOy7$wd9-~$561WvlWcu8_x z2{FS z0KlxCDtuR)dYq^3Mct}<=w!5V!65+biKE;w+TMT{j&GjkWP67imi{XDeNy)%NzA)J z)%Qts*MY0Kvx+U>w8+DkpW?kd9+JE`@_HJ00N3p(3Z2_@4=2r@us`WawX?(YUwz9) zk@owVlI_;lt^&>58wJkq98F-s!xzDrdfp{DER*xoZU7?4YhgPt^8Tycx^~Qq+ zh;Iv8uS{F4+qg)UG5&t!8xsw-JF}^G7j?&9G4y_5`2?RaWSr7aOg(=7G%bmW`o1K` zE#O6J&hNPaJMXu3M*lIq{GT^nNxve>bMwl%QlV=96($Y=?HYJpd1j>)|FEqeYNS)V zrh&Mw_)iPxfGTkFtEgX3NM$Rd=R0tJ=fo_#?8h67nl_xjq&*$^MmF}Z;_Gq`dGvOW`lOoV3 zV`;BH)UNiubFHI;=-aO$f6s{@=`S;G=lw7+=E^y@gc3(9c5CzzE!|F&mem+J6m2P+ z#X4pupVdZHwr0tr9$X?DR;Td0ZEXg@1 z==Waw?@(`%vq)Ck*vSbOZ3}U?k3qT-6p;kIj=F3rM2#!_mAOs*<{h0CU7R*x%nFd= z+D{*e`JSJ&2=sFF50(w=-Zahl`zDdw6iPjCt*70F9+3(FFM@_PPQJEToUu>OjB#56 z-VU`BjF5~IRhOVH`&&|@R{(b+`%Z|rprei?SV-r)JPi1aXYjDV89Z(c`V#!U1V>ZN zHdXZlP>yT=inM;r3=(imRuxw^W4B(P-n;<>K;Wa^NE>eY$3K;OANHVM-ZJgumwse3 z+z4H;wD{l_RHbeazy$sK_an0)%@2EN#(2cHZn$4tIc|zL^{c_caCc3qOpyEem_UF7 z*{OfK_jIiH=hOG(karUdaFKcj_BUY1n}BW}?kIL)c+G`uz?n9jK!2gfMsa8J@~pB# zC~zEK;kSj`Br{DVJL&P*H%xVZH(!L`kDrn4b^|>i3upz53nh6_s#>>b8tP;=Fgy=f zm3aSit)EL1mb-GOTchPR)H6&-$@jMyEM`+EDW9oz0C^3R%NqDW6k>Wm?{V{r)_d>s z#~Zt#;8P6>I21qc4-C1q>)vu-saa$N4U?cK5dAU;Hs!mk$#8#ce4Kr8%4f4aeK&-L zStAnbk{lcP67)vjT#b~%yql&3KtNE&Rw^ngGnwneLn6jE>Z|+4xZU~rx%*uE)mjqr zvJF32|IPU0_kZx64K!?i@jdSp&PO=41EuBVxGvCYH}aKCOH$F^yrD}%NzX-ct6{^i zl+}JTX#SCJg`;c#u5`V>iL-5##1So7zz0twiP99xS7Q2qiww2W1%oqKwlk(jL`#m0 z$B<@r+;U5|7G1T*kcwkN@dlw1$r}v`)3+y z8JT0r!b+~aE1d6CyahXBgefP22zI}nwDleD95!%7f{tUcExh_`bY?0=V0VJ09e~Nq#UDG91j<%vs$m6YTkAa)JIhCC(FJC*|<&3LmX*SA_S^TuL9}g zoZ~n|sB+*w3!zOy=r1ug+ef!yUi(qqNIN{PVj;%D_#&Ij5bHy>UQM>_(-bynhEH0Q zY<%~1+;TzEhpJR>dbLwixl9A7su@`XXk>ToXAevto1*RDN=9#(RvsNljK6aJ6!D8^ zS}$eI`TUFZOtjAij9ovFL%#(DL*`i+Rk(z#U z>`&RHKVw^;JXTfiz-3j=<-%W~*VxzQn1wVM%$Z6g&K zf_yiwY+%qvp?d55`nDX<8_C%R>S|XYmeV zf1jr6hDh+}v~hzAUCM85{B|dvX&Q4FunQs7?7HFm;>OD*n8>oDlAGZ-OYb)N zN^G6W!$vQt_s?w0d9GEQLXOL!;uvO(Dl^c+_WXrqgR2;Hd) za>^Jxf0=&CnM4J7dG=%*%*BaYgX)h?GqauwJM$qWj}21>!H1%v*Od7e^i3*CNcc(V zdu3svz+m>ei?yB!shU&V2pFYpvopGH>2njj$Ej2EKHMYJzwlZ~+e4iqiin`Yx^6I5aPxACQL)}O$VVv`AR{yz= zL6vvY4ygwX%xsojG@ITjBd|jkouDvm9Cy9tAu@`OB)+`3nHRCosmc(g&84a!vi6^^ z@c+m9;s0YbQCuysqPNGeoL>sejdC);Eg<;u|U6x!td zAbbpVS{U#FwwOB2es6dLJQO@?zduuY> z50E6-YSFp2X2@y#^SSa**9`~=@X5Wws_&kzW=ix9ctvAr0vH!XlHzUPbV%V2uEOzpHl{R6@{}>~J6VfHjWRCexOaa3 z#7}1bFAh3jmz?$Nl%Q*=Z^M8FnKTyn;D+U$KX=BMxcsBsqwL9xYq3x;jx4&&)K-L3 z%4CoXNB!xGJFplqPN%&7WXEh%qSjkdI_jBOVfEs31Xcn3y%q=!g58vUge%A82IcYu zUU$jk0L(7dkn9nF0>747`U_dRE5r^DXGNwL#D|n{VDc{WFOw_W&)u1pY?hJ?&ZeCT z|4_t;D1pw*ZEu;H@4^D@v^mOc_uT~o_A{&k?a}z*=zIq-H!%m|f0NyE%-na3LaF5a znS@L0-}l#ENKX0n83S!Hv2@7;*pH_Lh?(z<6!a#=g<~!>H2_7X(YnjOjWor*!IX-n z86wUOOwzA3T|7AZC=sTIwsgbBK&(q?1VWWib469^G4|F0gZ^!%!tZ2*cND}-qiz+q z(aKtn$vv1;LT8<54d+km`>kfzdQ?-saQVP^t?;{SPfuE}$4kwA`oAP%lpbje|JH(f zrK+l$$0PXqGC=}n0I*k`f7*j7iTZYseTGp(WULN1Y)&IDwo2kFu`XEdk32mvR*rX5Bc1daNlpb6iy(YjZz@Q}Vsq(@lGXfDKq)TTjNhtrO zOspHX->R6JUu^@nXM2%tNI`$0y>Pg)7BpZ4++2}K>5Z)HW@J$XDr|AyQ?Rct zI~dCA@m^a)sjn4Rq9$b*oGKJiu%BM`yZ`#@kTAjkWAwlz$wI~Y`(deWKRsg@)#^Dk zgKl<}MYsdsnoX6RmZG2>pK*s zxVB*BOuid9Xj@K}kaVpvP{-A^Qv!I_u(1 zav@-JyDX{!vvky~8%}6^i$&a%1y-fD9MT5DO-YA|DJT7ZJ6E!V_y8EW z02XfJ7TpoTM#T}%$!c1rg|nEQ8x6wtXk5?xL1{xz5#Q5*+ufqJQr@+#Y| z_DO2hLw6CJWEJ@1!PiUr!_R&}E`1PpHlJp^VK`F-! zjT8b{6)g&`+Bq)D$E+#_7kKrnnIustzUW^*%ZvxF+D~|%YOn3)3v3k$h%#>HvuCMJ zne^6ZOPVc3@$XQAs^&RxL z${(W87oVQx3k*o&JL__9ilkjldwdE94GUXXX}X}g#eUF`Mf{q<`Z80R)l233PY(ZN z?|oWdX_w}0acA-~y7jYtF0t$N>1W06r9(P1NxcODa zU#544^uqV%fmT}Tba&a{P8AKh3dR;C8l70NE6Gu0XGaCq%c5IZEfoS7lnUTiDT*%#+=8G2otdq^S&?&J#qLx2 zE@YM)QxE)5oRT?7FU+jO-PbV+`w;op^?4K!e@C{e!;aE5mMi1Ed~^=GEstz98+X2J z3}ilRxoRm zZDLFYYEoRE=kChGQPcPNA;dd$Xa-FqZalVLdH_TomyiCG3C9-(e94{_EF5omlWmzc zr*Y~hi?1|sSBU&6C2X=ld?vu5ru^GgNruQW^XnGCc-&54n3VRpG|{^78YxC>?NI#s zSFJfv^5+*Ioca#tv}&aDI-}|kY29V{-nqjlt0}~0fC*|^9Xy-EY9V_|5o>!-xCoHd z{|b4h*u=#aHD1TU4w);guPO!UcM8GY0U%>&**?3Nn0&yWZYN`T8!FLtD>O}LU{a&W zANh7KsZN)xE?OubpPv)?x;pve0of*eqT6DT+k?!2iX~E=u+ZuvF^+fn!3*|?*){>^ z32;r@ZOPT9CQQV8xAmfDFn2`VAm%O4f`~I6;Mo}B)U1Z0V{8q;v>Zu;f|~lwoHAX` z`T~?_mnwd&34%(o$F_XkRZL*xv8$$dCgBj7CI1=YPfq<>--@T2`5G(aq0g+_yzwZa z!!zOo$lPo^7bUk`tvAi#tET6QKD~+N{>+u5>rJEX%i&Wl^jAN4v!3((usoV?+WIZD zK~XgEC46jWS|`b@+3i_UXrwPwabV6+62ip%%J+UN4h1#`0{vys>FOO}n8lbVz>B&+ z=ae6xouCcxTxF%`>7Kn2ZweTnAHRKmo5nUwVQ z67$lR)XZGVpNL!B&KD4kx%ngP==D^LOTfXY&58K~?~UvwK0lH)g4tbhN!+w!kC>DJ z-7WB-zk#B4X3`$NW@BeWreiBJPvf;Wt~sf)Cr?K;Y^X5M0$@FEbxQ`tEsSz7P=*F- zQEc*(`HEiqJbSWJuQ(f}D6pLmC|WgpI}>o+c!TeTI&jc{y#Iz0IYO!f@aUy|?%OMY zHBI?(EN6Q*q!q1{bA|vN-dD-@w+Np!e{s%!KmhC|xKjPXo)Hgyr#>zTl{Y8wG$j}e zF(YC7eR|TvnEu4LlJ@Gj$7(EH_xh1M6+*OkF7cvXC{CwwPcS))ax&TRr>0M3ObyxZ)j%q9ScB|)UL8af%wZV(ei>P=J zg^Mz5wwLefe%>?2Dc z-8{w-eLba~=W%rgPhHMJ*87@iLmASNvn)4CHdg5@<9&^65E}Igklbd(pYy)t;gHKj z7TqQqLAk&{gv6)`YxRwX5X!SU5}DkMIA1)xait*gP)fvTdp;hxBLI|HL~y`I>-hGB zh9AcPnN$( zmBN%A24oEq)BS%QUxGFYYUrM4+fJx|2UFM6?0KC(`orf~IKbTo3DghP6B_PHoRGel z_4K{iQV|Ll;UF`eKH3K^e(6QaQ*JRHNi>g2Es_>0ZTiwXep=c3Z4CpPBLX8ckJX)Z zIH^ulgMR4mY8I!NEdQa?31@@(E_`|Bf?f~~)2Bxkpol?wLRw8P6G2(Val3)ALg31C z>4aNQm>{X^w8HKbW^i&oMQ(fNd$r%d%e0G57r+gw*R^fR{2-{X)Yv$oK5ncO9~P=q z_LV-sy1RC2*5A;6bo0`ZP4)=5VQce5V(wh8#+|c+xNBOA4zh$d*q9;m;1dU8biFFb z+Z_%^TT1{;xFEel64EAvsqXO}XoK_Q@Cp)UX1E;cy;$?j`a{m-sA3JY zS*^QK12sTXL#Fqi4YjC3ZL1}uyt zcx&syb<6mTsdldeMZIa`MN65^oX^0JA}hN*rIz&Dk|11x^mQBo@d*>ybS*T*5OTF& zAq!}8^Zj?Pw7%Myz1PW2%E}qp^sBi@3KuGzNZCj1ET@SzAVo0vLxsbrrI1LS8k^IY zsI%ecrdR!~oDQFkRMXDx%sy!QQ3tv2sLK)N)%?1Q)>%DkiIhr;%P{b|brdYlnr^&O z!1CF>vs#jt|K|fQTfaOJTCH!A(=sU(5Q`Lb#ao&!NPdjo-atZ8dy=>s?}QI%B$uDg z*A$ggV8)rIxRJ2E^Y5X$EkS{6C%y*s>DYkR-WqdoNW-JvxowmkR9o;fv{AMLu z^DkG*bByDQ>Dkh7N_{nHh~Qwot*slsa7P+()ClgaL=sF7BJ2DOCnB%Z=;U)7lc$RVQKey z+TWh-a2gj9Tp&C2zH1xvp2^LrxjKyQT$z0D8-=Uc)p0q>Pw}!uXEktfL~ly-t$kQ{ zTrt}70Hkbasv@_jE0wYyC?mi;(!2k@%69^5J16ithA|T3e3{~OV387TFg;k~LWvaC zDgxAP59v*H?wS>U65&+C0nL2bEXYy;1@-QWNxW2%e|Baiq`MrkM{9o8@!E#8Pg&jO z_Nb-#CqPJfnhw!OjLQjkVh@cLwn>v^M5c;zDexhp7&F;o?UECVRT%W*j5bn36hp15 z2RnhcIxK`bR4<`@8y+`o=jhyk6YZx{+Qf+=%Ui{vU2*jW6dyJ9zO=Y(lQ0=OR3nEn zcVy!#)g=&TJ}cYh=>OZr#2h;dId$0b2(Z)~Ge;LvTAly^oM5%5k6$|s^TXQi3HdaP zeQZ3tk6X9%v(&_otEeDnPUay+RK=K4uW*#t0L=_%$)AQh!^2zoJh;0uc(Tn!CLvOn zA1AK<*e4(izaHRZ1qHiJ#=l>Aox68IHzgPJqCl=oG%e?mc8xqEDdSgyZ0-@>*@HEQ zKp?1L_|LVt3Zr+<7Nr`?X5MTF^|wc`Y4%|4j5OUivL2YF3Flrbj>8B|6z|6Ne^y0?{?gBSxNI~)df;EdWuYtx7UWMF8fKY(> zSlEwqn6tlph z2!PGLV1eJ_y;zO2DdMTluHA=BL%yLBEH*mV+ib8nAa!ZLC(A&6rO)P@F_|C1d3-|qws82I^RJ*=)hC%{s_!P|DQW$Ph58o$b zAFpYyEtFIiWB-!YOm-tv1Cq-N)E8o(!$_;Ll-IA*es#P&unVeV>2G7j}{u#xJ`rs z3St9&lnXsjzLmY5g7MCQ{G_zRy&6H1_L4aEjq5I=nm<|T4VWD!P=(Zzlt2-0j>E;J zwLAOrTv=_#(5hq_Y`?8;{=nS-anQDDl5;gz6KdR)A4`*q+G}${;Oo!2g~8k#z60e| znQw=!t_!DZpDQX8l$xm+@9U|NW^_gM#eI>K%gptv0qUCiD@8_GdI1Rb9D(}rcl&eU zojnxa7)`@X;`YgCMgBG^98e(crvax&Fw_wA*G0b8?+G)DqbBJlY4g+hj@xGWrS zJDdOJiKYO;z{-Nn{2^PGFw(fGa6mZYneT1GBvAKv5 zG71(z2E{CR3JYWUpKjOF>}06L6p%P%B9@QB477 zQWl!dHzOBgdLeK$+wP@oo$M&J{!s47zaNCOZtB(hYuONPb@JjCB-q3cJN+njhadcH)V-=;X4jZ+*@ekrVa|)kP#tdjp*?R^|+3D zY=Up~aX5S{{1D|!gY>wO|4X7sn`vpBH`yILVl8LA3;e$tJIkOr-Yt#8;2J_8K>`dG z+zBp&yCk@~L$Kg(!GeSY0t6=+2r}3(Fu?{7E`z(fC%EnW_wL==Z+H94`=Pt4-|Fhq zr|X>G^BgJ2!;V2wQfI7mwO}R>;V$10ojN-}sW=UREG2 zBA!U!jUFvKXrqm1zjgs=i~pLW!j##)aBFpUPJvygyaxk+i3@3xmP5haWxvK}_qXAJwHarG*SuK9Q{ipyrAU zp#gg_B5mUfvD)=#XK;M*Gly1iIa2j8CRw-i)nA;k*X22U(F=c-ZO5G_*Xwt18K*!~dQ^AE)?kLtvf$m>`AgS7ugX$R{1cz{!W zWlc5`cTIgj(>JKaD`_f8&#%~Rx2AOX^z3^8a@XGug@sS8>!4~GnawCMs;0RB&+0$^ z1>*$;7b|;}Y7Hoec+98Fh9O{o(@9sz&7jz+_!rgY<@vvZ>%T$L1OVSrEgmZYr}&Kf zW?`rRaE3p*(k@44paIyAyH4YPZP&n=|s#P(gj^(%@mgOSpsr3FSn z!)GK3<<7zNFB*NNXm0*IzcedqEk=KUYQEG#(O7p~0eTF5R_7gKn0e_Lh$3UU0<#Fr zdZ!E7MA(?7OwlD~G_8DCLDX)hqrpN@oZPApO{2)%wOpd~RhXv$q+?<(l(L{cCc1SMEGRYlCowPA&tC8%-D)uX>zx24ALQ}Px`p}g?EAvnX( z{M@Vm>2YKGq&eqXfLS=TALraDxZ9!iW~({RE)+cg>j59~?p;Hiw|(b?xW}|tWN|~Q zbNBpXo^Nr$>+Y`}5*c!y_>rUMY`DPUPe3(~141Upb%yFmX5vwfP5lhz#=>p?9@P=u&>J^7t{Ws97Lg zpb~nD54nUtUv154=MqKsc)~C?yap+OW)ZL~dx#v(=Mo3a2ra4WuA7^*<1$niaUSM2 z-|9nn^T}j5D=I3=_n3BhACUdtJ$1v}l&o4_)e3>a6y{JeppL+H*lu7;3izZlXHd85 ztc-gy;Bc-UCqQpGi8`1k4RaA({myXp@qc)qK}RfWBGry?J}TRiL1>3y7Xy`}!U$aq zC?Tq|E)HvGzqm1DJ09CEexNI%tNG1Ogv7{CUTu7x$-)4Q_&SZ_7XjArRT)$F&+XL4 z!mXAuv)&c{=SAsN7BCqpIg3zN4Z;uO(7?LaRr6LFE)ApvPqk3sm1zVEB)qpo%k*v4 z=>ISD@?@}_;VFUPd-5=0cHzL5y)rZ5K)8PE*t{YShxrVtq(tBD=b7ctrRyrR zpQktRB~-9>_*VN+cR-O|fvJ*xx>c?55FK&FZJHKS9<4c)7!&Ds4*oo3QALdMK>YU; zVZy@1l2l{E`ub|#{{Hu;9`xNOw8om-F8jz|f?;C$+~qdE#7cWbB)SQ{#unB11+I1{p{NP#6Z*fOVepg{H!WCTh%|klFCr3`dp7Zyo3t#5Qw12T?hJ`4qYxc<6? z&Y9zO?IYgi39iIIDniBU2xGN?omV$8+ET`Kl1^dX<+B$7iD!$z3DZ;VAk zMW^RzfP9E0$K5^5E;4eoF4hyBVME2<#})H=I1mB#Rf06^2(%HHD8W+D(4a%Lr8bc6 z#*#TR*m83!S_1$ByL5l5+x?~q?HKb?w4wSFpz2LSh-UvW5&2|6fDV5uhYCd%0OGT@MTM~N_u zs24?H!aM+1ck}|HDlOAQ1^9I6g9-nZ4*|v$3@2PZxqRBe09CxvRfo_@NL18fXA<#Z77WsHF9&z0krR9hXRK_%)mPk;x$Y3 znfes|;NV+udfZozuHRH~CHI?tl}^Yb%%VxHW=hrAc8Rj-25j(CWs{d3G8vh2qpqKn zmGb#LvsdQvb%+!hPxJ@+wJPj~+`1n9aD8SJ%lRh_OS8~D*FMQ`n6+S<25~n|C7(#~ zLH}a7n8W+Bg^ytiWn39X-(^%LN|}E2=5d$I3(~Z34gGG9%YiMHTk|`J*zAvumyrZYNMtoxUNRiqCd0=5}q*rZWEgyRE_UFCWAFJKAPNHjllDpAM?H z@Y|t;9Wp7jX0doON<==Y84)p~j7B~1CWd$!V2eCQw=cE{h)>?x^Dr+W95o~Bcg~gC z`(jS0sg<3vdBm3Li`F2!;J`5$E5Ch37;XmUy587g8P&ypp%%v;TJQ$q-0Qcs0W#_E zQ61LV6c^sr)p7`wO8LBzJ$un{DeG{>nY+m$T(!b}{`7=rSO;3{v_F9Ol4~X19fg24 zMi`qopiyjo(1ZK;tD=KjF0=)Tri2FGExH(mtNC1{I5t6(UU1`7wBYR|5U?d1pBmg6 zS{&+*JQN?gqq#u3WX_`_n+rUH=?Z*16IUfN5vJi5oe{dj`RR!p-(s46mLbN3k0f2k zj6oJMgj0Zu3MV{KpQf>^NRJTBIhSAva)EvC z)<>0C#aE@pvBHApw$S4fd<|6?K=<-lR9oJ6gxWHr9R=1g-i~Gmj&e5=xCi#F>EH}` zFcYhs;0)J3<&7D#81(TNczC>k)c;J_AZ!!Z21Hh~$FVY~h8JW^M|Xc$oz}IU3ReE% z+{T=qpPHXW_bFI?GZ8}fHeI)t-o>p*QMjnQ2?D8ztH#G?@Dlwx{O4~@kJZbS@RyP} zOAx9c@NI6Z1k!uUZMTF@bd>RW-hg*794dYK8v)!yxdg36R6lO5I4 zDpNuOR9L5s$9c&<$dq9*@;efdhp2?NTa^;WzJC(C+LWd|R1$$lx1bZ3Do zmVr3=`jdGhEWN<=mS$6ug9y0L_9a>(B*DSx1HGn<@9QBZ&y??a+wLEell$S`+vXez zwo}LwEj1UHa^~i$ z41)7lf&!VM&#I*)+oIcF>lwM-1k*y6WvlZfw+KFX#|OX4l&j|QC2kiSmR@lj)KKS-(uRuNTf1m|TnXrVDRkcs2HInLnx(5h9BR$nmGEs4Oe&XE z>ZApUai-0(q#S^mcxvcJ)P@-^4!@FZi)Y^YFPwa^HHXfR9v41FU8f|iu2<82piV+e z^Io;KBw0SjCAoWqQ|~c_Hp2M$==o-?6EdZ;Kh(;HKTKR*`5VEKlHNW-UFnj9)Sqf& zlA$Yr{=>pf7{ROJQv+@~7aB~da|jh#oQ*}A&U(V$G?gYb2~+jXcK=W~Ht9Qe9xyo! z;c)sk#|}tO2#1iBPOy|5>SHFzqo!Z|=7+PAO>tAam%*z!_%Pt0eWPU_rZKD5CfqSEaRDV9DlotzA9=q` z?{Q8F-Jxfe49@yLdQv$qD)XUjC$D{fMs@WHys{^D8+qD?E^G(m`fMu=BEe zA2TasFZVeIt4##Sp58RphWy{o@+HE^op4-oM-`@-1P87BjPHirUA;viX)2qiArsNyeMQTNX7&#fCu1$+uB~W}KzMALp;ZOOLmNX7nPP;m$ zcJEzj=h8h7ZXGZHz~VlAS?K@a^qdwKrhAIjH{~MzYHK(h)Sl4~&9MFGAnU`Q%Jv;@ zW=nyom-a*ESAEyq3q;~b3akR9OpzJ9mjjVnse$~TlQ?kEN7zL&t$woAu(MC?d}g98 zJ&`7$Z7iTe@Sa{gc7(ioL;thlAhYl7`_H%V^ez@5{imeWS*!3*^|Ee%Ly zkW2YpB>pg3XMR{cJ`_|YNWaK6EK;j6>grona($tLN4y@2lDDe)GZ!+tYSkODPkoSc zcSjYx15wZgj$3VAReAYmof1th{|BaNei1zrhi`z>(vp(ivTb9Gd!?s_+qhr${9=!5 zg1%0RMpG9DG08oTyv05n&lT`(J06Bic+&dRn)^lXd$;YbUWkg{5V>;nve?{UORecz z4c^@Kj95!W1jL+Af>eK)(Jte>Be4}e@JOc|Rs^NRyscc+j%&p|-ZqWGG$(a}$CLCG zGo8PpTz^g)M477CfwD&FFxYNiad(LvnPhi*CkR+`JsZV4)p}5UJMXJJOc0#!bVUrw zZ;rjky&Px>gXD9A-G0igFk?ySG;+g6Vou;{$i`-p0xjJ#cX`qCFE*cW!WggeqL<4( z#23XCOwP4BfA7E<3UFBg3E}@?(G>$69hX7q2JX2=W|a=t#*X(4dK{>Ga>?_^ZtvTAE zL~|r06Kd`HoMWKAyW7(aF7blLq4Q2esc*5W=3mmmVdBOLdNN^Sei(%H{r!u#L!u&IcqvvG0@8%7_U&gI#RiMrKlheYyorAYja(=C4u zB-yf9vozI)Y_9o%XBAGy@TX6*6BmVPFjNTo>+nc!y`CJaV6zuuvqJ{U0qCE~-k93v z-|mH_QNww5m+FAHLds^8^L>8T9a)OVz}_6)`?^9w`|SD zfYz8&Ek-1WTwnBt9=DKD+xeb~X(foeyvRC5e(QoRlA_sUzU0< zTcdz$!fAx-@|#q>LNpRS?8|;YW&ov7WAe zJe^I#SiLg4L6_2@KwW2!4WmitvT<$!EmQ8W%BF#vB^2v+QTXyVnXbyZ-xnqv)Vax5 zX+L^;dN|o4`=L+^c5Sw=Gk?A*OqviSAQWW_bRTw|?|vsmH(i`B0(qsmSKGEu&G=@R zVx-ctI@&c-Tn5 zWmNt3ZZoc!t&~DyDuOci@ERBz1HX`=gSZW+p14XmhY?W+(2Tg6LqyAZGS^3QnN25IW9KlUbcwocQk+j z{86suJ#qA!36pc{9H?hL3?M(vP2$nk{)lf?DE+&|-XE=NpbkOqtE<5bCzaQ9*J;_9 z4J83d{_T?hT>IcME%Z!*tkP9sK18h8-`{Xg;mAx7hDLbtDjD%kB)CL z9wDS&Ok9f0{?x=>vS!WR6gcuBLW@ZGsU+fQi059<$Jqw^)a1*T2ye7i-yNa2Uf@xL zt_xc3gs_p1CHh$Uh7D;t)+S{cB+^lBt)8+o^XL5YkGJhS3cTF50(qWl!nkk@R?H|| zo1RtyCCxR;MS^PPYzmyMP-$hj60eXp&hg@7>xR3j{HwgzzZ>yd1&$xpy_noS>(b!Q ze#dfLl#ER&{uJ}n=U)z{&k~>J9i}s?^-tJy zqS(I9e^lfL9t|gj;#iAM1gVjo41kD4sKce=)}Cocf9PM}#69@T!>g(Csy(x}$$muU zJJIl`4`2C|F+}01?i8OhHP`Um4g*%RotXHW?rhmh8|5F*Zp9NrH#&~r#_t(x)J$q~ zgiRO1VEX=z)n7(r(_6u>n!{qMzi??#QBmbM&8B5F{{W~Ju~ zXk$_`n($)#%Z|HH-gq~%dEZQcumDOD%hlp+%3(%OS#AtZQh)-nR8_mZKy%MK zDa*appw42e691tf-rV;4g~lkKvFRTvh--rN!H*PT_rTpb?;FdC*l!r^ZM=Wmvas_9 zNu523Y1F@C36NtvGJEbZrn(&S+%UZ~T-{LIF5T?KyvvQ+;GfFlHpeJI8#y3vtRJJ? c2xIz_uxYc`e#oRLPc+n_qM#`ck+qEcFGm4CwEzGB literal 0 HcmV?d00001 diff --git a/resources/img/tensorboard_advanced_4.png b/resources/img/tensorboard_advanced_4.png new file mode 100644 index 0000000000000000000000000000000000000000..7bf6204e8640bb81e18a3d738212281dfb8314b3 GIT binary patch literal 607057 zcmeFZS5#APw=NFSL_|;ol&&<9D!qjwDj-dIuhM%j2}MLfL3-~fz4uNC9qB!R0EzTY zC?S!WKj(~d#`yov-Zy*i@AA9Iy2x1T9qXO*d1rm*`u;&(k^COrJv=--a%H7AT6lO5 zR`KwNyY3R-{_|q`Cln9wKDC2_!Utsq1(pwzsQ8Yd+NE;rR@f{h(=RNJ}QF zgDCh^RUL#1L@u3sZDtAK(m{vT#CSHwzOJsKWq0IaSj?X=u8*jyzPC*P;y-wSho@*U zviZ$3=-02F6@CFY&J?js_^kk9%lrMTdJU(m(<=Rt1dr|Bs!r9nM1td2c4#qO5Il6VeWfGM>jHfJ@4-B11hN~Pc-}i9O zS_=NedJ^VD`~)xW(+Gwl%z(ryT%3DcG3H?5bt!*?BJ1DtXSJSLFSDq5V`FLV*0J_% zc<5Oly?%GBm-B+fc9tWW`N^GE6~br(%XiM7e{jO}x?*EL8IZ2>Y3iw_EnzJ!!_E6j zqCYV3t~lR6UKF;pXc#4)8GRVN`E|j4e!A6y*P`O;$Gk;4f68wv_V8HGKPOiN)(irC z8kC${;oZ9@(J7ZSTegZr2ep+Q`+4>Yk{=`961~dr)IYTh9fIP6l7=2cCrq62iOIgy z8z5Jd7e6An`9#`RQ~24Ch3ZI(gpT+R|AG>W1;H~V5@`!w*bg9^k|N>JQ-XWlxs=DB z>_;c`C5H)!KHzLrxj{x@!aMe%v#ey&U&Tpa17LRwX$Bd?nuWEhmc-%_MqMCNO#dJF#{>AAw8I01|p+QK^8M}iG+m5@Kc-tYgo)LFR^ zd#Y>@k{fGMZC$#~W*0AsvAr6o2>!8(U8eJ}WX9yPH{FqnZ2V|f6J;O-#Y>xasCc;7 zrT+fNfL%IXuV>qNO3T$y;6cdjK+|{=5_3x7Rljkn0#!+x$V}uve{yBJF;0ClBXp83 z<1V)ON`a6elT;OCEJcgQ25NWS^Y4v2T)4Y7EoN#)hUd!uA^k>eQ1s59K5e{hROikQ zvtCbgvaTV%7y|>A8Re#ra-VQdhmXo9?*O~*ru7EyhYB6x@lfKumS_7$q-;TKa(q9I zKw^UUwENMA`|rDrY98u*cCp}`CwuUTH4k4nOz4kv%l)=4a(4>oTV{WJ;%+H{A1rrC ze?IemLpG(TO3B^FK=2w9gcf%O`;Wb6`3VB&O#gS zy8d7~3e8#_ukj*WOVdY(R^%<`uc{IXy6}NtGgIEe6ufUzf0j?(Yam{Zh34B$j%<@R zkOYN3u*B2udyG%g>9{n9qanDchXi5E*?4g^UHh-VV}G!Nyze-h#B_+;Lj>lBPr0t}Gq+5Ud zW)%Gu*&aQ8BlL^xiZAh7dS5`FZXa)-LLZHl*zqfmZ*QJE#8)Wg6evB)OwxGC;lgpi znayF!eikniM?G|54f14>NawlT;mFYC@8aM3LleJy@4dXoaqsY6{5_Met@oTB`*I0$ znP_fnKPf8F8hw$IWbmHDhP3drXPjq@XYR>6P0gtfQ)(WO&&@q*t-h{W_LD_|BbiGb zbyUijedu&mP1TkaTz(>ccK)2QFA}EVO;6DF=Ori98~D^j)Y{at)J?;$lV2uNy=>MA*V!raEsHOcC@a^& zmYV4VRlU;^(k1(_R>rHnSn8{H@QN{?e(HLg;X>Toe7X~G8F{3M|818ni78ZX z7?;k@mSmJvXaly%x3#uq`b_LT*nWjJ0Hy=!(ds*AdoyQX^!WunT4N8_J!^3xP@hgc zD!Q2$8Y5p5I&TpnU)){uGu<-FGJfS*FUh;dtor%F`Q+~^--Qia3?xQx(Zil1MKlFG zM1}+wtfyU15VNDg-QKMZVLM(zYW0-$uj?i2FE@p~+)kxW=}z}(-jL}>@kH^`n5L8z zCfbDCByUi2&kip{x;6Hff$ms-UL@@Pl0n*BQ3{oT5zio_cQj*7LDOv`{n!9Qj-jK7-=MgB|AJDJl7>$eL4=_ z_+{)jES1jfMAb*tPoC#_L859^B2^kkc=df?FJQV=v`cqgJ6)reGL~i9q}sRInLThL zy>Ql%kGX@V%C?FAiN0#UeTVHK%+kujT0i>zG5m0D&E?M+rs_DjPteN4u-$MXkS9}iw? zG#z|@^}e~@Ed=#)&YT4;btXx(~3m9qml>tL?0HvO@Kz4}M=WT1#Fl?uQLP zG>$Z0sB9}|rM0jednWikk}Z9ep2U#bAr)Zn*fkrieOV--#+~Eq)wY0^nBgCv%!voA zt*dUN@24wj-{m6Zy3~@XXI&84|2Z2syE`i%!)!;jMU>jV+;y-Oa5C&aF)ckaJe^Yv zDyr5%O&pECn0WH%@|gR)S};tg*`{+Maul8@l8_x2^dcfFF~RF~9>?!T4P(=pB6fOy zRl2Sot{OQY#6X9uOe+kC2Z+Hzwv*<7WCJ@vhd%7}_= z^=d^Mx_)_rS~j*q-FUfmAYcsI*w^^EQ4AJPy=Yuyq-TKfzg|B(qfeuMFWRvz?zPtM zIe6ld`69C~6Zo=+n@)`0&8+?0%jVri^h;!!!kpwBcRA8+#kI99x$*3e=(0=UqKwOy z8wAX1uNV~VpmwglI{rCZ!>=FZi0eN~g)hU~Rs2F0Sm$Tz#eiaY|I2IYv4MFU~kmn)qm~t!HQ_X^*H!XYok`W)l_#-8rsD*P!W@ z*l~^=FhaDj^3-I$m28V(BG(1$ARE+u`ug-OHj0z4)y%PFKd`m4w{-0*Kz%#dhfl+h2Y{X2~o+26a4&x(&XX^P_o+_R|p>EM9Z zfgr%+CCy=M!^?XybZDj}Z;gG+bq7Z}a%iXJL2N-m_Nk>G?@O=Hu-?2XOHq%RCpyLk zf41AQV(4wsrUif0+pl6zdLrWE#oF-VGVuOBXL$M)At-oaL(2vt`II8Pkgf6jjejT7 zl~vv}h`&4bM5CYIMOgoi=g4iIK#K zzw-QxuyzZyC;tZ8e@z1a2HJlEeE$a8f8EOc8)*Lq+JA1E{_Q&d8}9VquJgYh&ispk z#zKUD=Pt`9y#MJ%&M4#S~8iI%$XVWlcmjujpPI zKSM0i_bsaav0t=L`mb#7|JC|!0(GF=<=Dg)G_8M)r${xLmh}0PiO?8Y#_=gQ>RTE;c8FQq&*GmoF>?-grQ10TZLvZ$ zP=ze_Ctmj<&H6H|D$A2Y!#VBNj)<|Tw=r5>fL z>!BFCEUN8gSQDm~FeLNSQcn*7o6mMYLe6#cJkJ21SDVrvd1~JY$g}ToD7lu85cs~! zk&b6aL6-korddv~I8eQBw^XE_>3_QAEke~J`_r!>7=kBGXP*~;L(>9g6wP<}M|frm z4!G_%x5z@y2$I$Qc+4oGl7n+0#sqE}P-&I_a|}<(;cHLYb~bOcbcq(or>!z_R$6bxTg;#$pZV3QulDaGAXnDtTOmMVrR4{*tVgRr+GgZOFcz6j| z#k`>9-t)Sn{!WX;^aTRylhEFd3eh~12oZhqpC8Hph}r-AzBaWkCXwG1FVyZaoI&~d zF;{KYRsL@LRDNggLZn8ep%2#j(|t3<+s9~}ez(kH7V@Lz%~{ap26&(_q!Uy z=M!=QeqscqjYmCf5+tgY3cc8|le(*we)&<{YRDJ7`b;G$A&)>?K|MRA>9i{Hu^Tw-s49yF5XiiaIKWj@1qly-#5j?s+}rIYqaOcsL5KBl@-TR z-4o9C3Yd#sgSu(ZR5xmbsC~H{{DL@UNbZF}NoA*8Da# zky}`ro`{}uGMu6k|GIx<)zqatA|=J8ncehkx$yw=Nd%Helraq# zbb_q9x#b{7Wk4^|kX!P=2XnuWUl#&%m!oCsS2j)RbAw$)U8j+GGWB!L_8MgFS?C7; ziE92rBoP6+t=`57)h4={QMncSwX@@$7vH@3#4~91ViUw+GsE%70!*6(L+XQ5XBK8F zeC~}(9EOt{wMQ`Tg-eWKBLbM~D&crQr`~!^N_@hL9J$mbjkTXecxar|6)?813jcm39W)HUOTYk!cgy4)m5bdC&e zG@b070#+}U$&u%$Z?;)x(q-}`Dg71*5}ZDES_GScQa@b|ceJ6h!6Of*y$2etqO8QqKK^C=!b6@Tq?wP2)N$AU2Ck7~A!f1EN- z<<~zg+Ki-=meT^|yuhrly}6*^O@aN9Ubrf1aqE0>K3GMC8f%7|sDJ2!TgF+dx2q~P zzKbtf0^oQLhc|BAD{&PMSsNQW+`cB(H;0&FE!D&k@u4x!5r!Hw=fL`5b zdh?lkrR9Z3!yi}qK(A^|(n{qC#}R|y>MxJ!*6X9bPY0hxDEB*VNi-E;q~wiNmy9b`65nPtCe$dm(%{l~cskUY z*UUw@jf-P1zla_-nS10A<0fvdM^-E6B6s3vb&;m=Zv|VT3E@Gij+1ILt@>Y$GeO&S ztDWMq4Al%u6VpiX)5U@z>A%B>E2QN^yXxLEe;IxT3X-KP|4isMa_<6gUDCo2FolHP zBR8>^gao3bvA?VPOws-=EyMUuf8DQx#S8x$I@9UobZqc)bsSM}N&6L7fm9@Nf0}Vw zkna}{PV87k&o8^RXlbeSA2=`MAq`d7WN{jByq(pIMYD4$O5XWHudmyqz}+N?Q;0~3 zT}y6eh-=K_oE9OY=N>Hx%(vpL@J*p(%smgJQP~zF7It4ehL00Bd#2_8ZUm~lqi`l9 z$#pJ7mpuk|NGia}8s{piQtyZrh;udTc8Hl#!m{f5IuvEs+!XCB{prbcaxzoJ2Iq*S zp^ka5z17Z6IS4;)&bCJLqA#kS4-n;#e|6SITODM_U2KrD{t76uBqEDbmsyvnhV0u!?dlFJ}VGw~eoo)(w%d91s%q(y8x%g#bB6$nNkoEA8^$_8K} zE6cUn=xGPAo*aZMM5CdwRflXWl!DMPlK*bT;Mn*+H=>qohba^%vGQdm6E{ z4numhKi1h?G%$y5qTcE@G;0j*zpP`;4r4ZsX*+*eH%{p>?KfSr1wn=tF1xN12=6z*(mzuP_J$yY{Cf;VA08p zFk}Ckie4&?1`XaOAqg^_R|}(vtNwYBH_h3cX^g1#>xN(1Hxm+yN>jANZ6{f+y}61m z46I%ke*Nz$&_f-SduWEg<k)_|pAC6h~+#>X$_(R1Pt@+Y%ly6S1?e*l3qG z|EQnAAK72b=azqVCnIx%?(qiM&QAOHemN(>Rbk`Z9@cl*Qxv#VLip7*a?y=1%Ux=( zIqc!iD!%HiW-CqeF_vOMUrf9@f~pRcu4k&jB6BUH+IlaywADA%3o?0Q~J2F8{BkbGcY!L z&!$=FFm#W;tZzp(y%HbX+v6jttuZ;Q1Gzs)MKapxKuPF)k1$z@icBnJ;pc%s)M4?W z*^ZG>p-qz}cVR{u4+gD$Z2@1pmYs`inv?IvY~P-DJ?;TQe9l6fw0m372l>~spp#lU zC4j!M?$z6|d&1q$9hj_7$VBmK^Psp{gXEthF>qz0qku7!>r32ZL?p&eNPK?@rIG zH9?Q<9o%EtL5`pPym}ttI*aTvhq|Hjx?N7favxk6)YTBHOA3uc2WZDD$4AM`-|-{ zR8E7+mcEW~5E{RVc(v(lw0Rtp_BCDZnNzWY)t>wN*qN)3aJ)V*its&Bl3ydQa%-Ek z-fsWWh@#UsDZzout+MgBBh$1Y^d zTBX|M^Mvg?dM6L_PW=q@sj=jWG3%0 z2Om~6bu!F~Uj4|Rme^U{RXwsg$|0nmjL|QDd}VLzaC*(l3uBVfoOp}VCisAsZWr8< zNk85Banq`Se9;y*gA#6xqiSmmxa@GS7KK z{^kX1@Vua%+AG;U8KI(Ms8d1e7Ooa~6mOoo9YLbXQ}VW8-A#{E5S$B0bAvNe>dZmH zzgZUt>(|p0aUy{U1-84$VCE#~VrLRVUIo@|_aIlqo%132(OkO>T>~(Fopv;Si8mE` ze?QvQe77sMFXdoAKy@*+l5{aq@=q^0$*Nw}jB{)&|Azoc+t1gT;}<>>EK>9CUee^5 zFSt&dv6pPJI~+qdZ|NWWrvCh>!__HtTf&WC(aJ_88k1H>x^ zj3McAYhy?rW!t}L=7RPEpR~WK%c+voIKnoQ9Tul=xQ|;tzA)JB@!AAV&?o5hpcq{O ze;qB2B}k&5L((Lgw1p6bxtGR?wlkj=$`4$WVI=l>^N%G#y%jJZV4uxvlXDh3T`y z-Dp64k9t)44P+y z*yuwV`y2VA+I`_{hnukJp;yq0z=XDq3mn#;*r5z3& z{w!KUF!X+k#Ie@U%?_^rW%Gr6ET;(_H@6{pz@!lN3$74d`kNUo_)#N^_i5#uRPe?k zz)1(e9WKe8N1c?dOk(d=M=@ufpoeJUbfS=9R2uKQ?qON7@TGD_Y}#p&8M8K~He*ey zkQDKGFDnCG4~xHN!iMLlde$_FV-rT(JAV$Mx2y)eYsQasL5hzK7^}t#z}_UK<(=)H zH;&s}h$OvKeMeH9P(OkNyTMb$lnpTWY4a;SO7ZDsBZ#SdK&fNW`0Dlw~MHgNtT z=WhIU%|^bf;;yc|4<979+Ghf6uxt~1?MVj+9EQnLo2;+RC5pv@HeL8H1-^@E+m60|F|;il8tt+gTz7LUu%)vbRpGjdCmo`Wlc zyZ|dB3E-XSa5@D?7{|I%!t%>7^3m2DiLG*_oOB4|P)2_khlb#4Mpsv`h3_zer zVbdSypGwvLeF{Y`Y*O63HFCRYAg)Ag@WWBM#;8Ue4%rry5n8hJ3rcS8sKFdb-( z<@6P*xj#(yw$M}!$lnsYjs&lzbmnI-$0TirTt+!x_k$8qBEp4*$gjo|Z+7o(6F>q;S{=-JkJtFZ1SgJk&l2 z2se-Yb(Rr%X+#}W7w)7WHXxFqb(xLyWpOPC6xjX&1+oK8_bW?}K_6b)A2ig9KKcbi zxUe}5G8Uc_0CRc$ajNIgXgI;)_3DOk-$DRC7**d6Nt#7tFUV_cLa-|juGO1rxl|7; zDK7Ml{8HbNG_|C^8zyRZZh2?1_PAUm&INzJx>@7()VI!RXY5fIT9V= z3-i&g5j6X!KagRu5ZMC{De%R(IMi}3x%HpGr4l5r_l%IXCLp`LvyBphr^f?A(GJFajoIBUbFHdKTin0?OhSN{?6O{6qA$-B&<;sw}2a=}91`Q{HjqTl_ zIg+=B<*~bAYq1V-WikyKN;`hn#RV9EY}RU}IfrqGe+A;pTnOsCsGulUGuVJCWqM13 zf4Ou7Tr$R@<&{?ydlOlJnes`a;igKs++9m(zbtPXO{FT*vy*5?Rb5^^t@JKUjvga8 z9WD@?KACXZt7^VZm9Z!_bt>Ko6LTo60ra+#@gsRF_097tp=@+Etd3wX5W*4ZK1(W9Vl8`HPOs7NHp=nybB z_`OQp2dLA4hNL~rxy;v$!bAidHaWxkzja)fVJ^~ZDc$@hrrCVZyqihIepUeT*P@p z6iq@t>%CNC#5@EOUk4cQ0rv8PKYY*qiQm#1VqflC%@?iv%MT;2q*dQ2W~Ej%ZRR*I z1b13fyAj8o0r0h-l~u}_fcDn~y2`&Yn8g;)Os$njeUHMW$qK&i}soF(+BGcRNDP- zuW_ET3)q70$(Wi|VD@cdpHxD;aEBxrnWUK6#6Cla);*17Q$E%lXtkCxl|K;eifWD=ZJ|8NJj3=*3uv&hWwh-%O69%C3x_Dlf9O znPbwFfo6X3#2p9w6B!vkKZ&b(XT5$C)9IEp)=8d2@4D=JUX)srV*91eq_@HiP=3&^ zV7e?UJEYm{w1T6Oysv@pGVL4YLSfhR&VtmL+>^0r^G zf?eOYiYPC-9zuGUH#jXYX!ykb5GX zh=_#`t_>qgABVE8RbJueZW{W{49O-{Mm>1srV_R83hBX-(SD#)Vh-pO{qt)pmcvGD z2s#q`@&W8z)(dV^%d&J^9A$3}F|8&KrjGR5Ie)XcUcGxwR7vu4NLX-HjK_0iqkktJ zHxrS=Ox80ddnE+sN9WIP4IylP2xkYW)vj>Vo1-8`doB!SX6Dub-4=VOiP+^3LF7-& zc>DW{0=G->S%&9$!N)U@5h_#~s3KrI=j0d;B`sBo$;mWiedoR1&Ly(OGqfCF)Bk8K zzw{|JIjWB{;$zWqnZR9r0(aw^qi5uw1J$`JlIZ>2F|zU*>fBWm478=avPFYqx+{WD zwi)jzLk1Za1ntU1lQ18yLBP^d#paNyWLTRmX;FOhu{L|qENv8)=?p6nv`sYKhF;AW zLG1?*j7Lj)@EHW~4`mS;w5v{;9X4A1a#(s_O{%3Bs7?=s4Xk|jn#cgn4SZ40;Y=&^ zo4Xbsmw@|M19#RBCGA|XTT^EZop7AerZ7MQwgwlbOv)$)&%X)L9zD%V&wZidcocXz zb>gGtXDXJg|C{n3wff0RqG`^cF%+TTyq4^whkEU}UK|XePxZxkVb1}SIz>MsrHy@L z*Sx@p5SHCWuW&ciHbTMyT^UTi*$&0JL=jDP?6#R%%LDdd-?-sIi=te6`s>>e5jxK{o#nn^D-wA(}@!^=;bxqgx? z*T~UQB<0-qJ=SXO?KkXocDU$6Hm>$z6T{rcWcH%9L8Gb;H|uK&l6l?qV`@_N{iO}U z=0|=(H6gA%Peg|8KU-wVUZ)Y)r5LOIP#5bhC5TN7&J9wB9LemvscfJvmY`76Od0Ru zo1NY`ow&@SGDXGBqzUDj^XBhDhTLy5GR>5gjlIRX`^R!$@y@BKyo^t?bKs5(raUA~ z)kQTs^tUGtF;#V!P!Ac?mwGd1Lm4j}Qv%>2N~eQ!Ha)K{a&G6t6q$L(m@~Q4h0xu4 z za^oNgnh)z5_9FNo$T4W>Z15}tmafNm-@ruaIKuPnO&{ zsJ=Lmm*XVJ$rOpTA{0L-i&muUXW>dN#=P|_X9LNFr}M0601Z3|(ZVwxf0pZZ)>;GVN8?&KjBt z&u*t^>K8K&{326xrApBtu7||Rs<|O`f5$&(HEy-5WVPoj0SCr zof;PQ?pG{ttY3GW&^2YHwYPOk!P0>_6eJ3XBI&n1W3U?+W5@ZQ!})yTMAh@V%crdA zo~STiqzjBBVd=F?THIEujGk4B1@rLOIrKVV{_d>!D-c{T?p6is(!c%d=kpR-T*oT(6R@!TU8!Bx1ojN!XDODUBGjYI8jd^!!hjU02WKJwg1#|MPj5LRR6 z6+X+~@@xvtG-RpBdPJzmH0)W~=o%Q=W-|F5q^Q0v!8}x1!+jp`d+jeIHuAlXdwxo6 zNDkTEnibZI57}?tFjPgIWP@#A@K@e^pv@u6@WPw>|K6gR36ENtJ0!{*-yKzo#dpe;}W=X=hY0K~1sRomNjnjaVt>5>&!l zIlk5JEJ2iaC$V1w&WQIlg}g(A?~1+hxr##aj5D<->QP3^cKPW8;+H!hr~O z64qFgbrJUuWg?S&=OP-URk3%^*i2}gF_L{&F+7b323?wx%(3Kzwdjmu%< zvd*IC<+Bu)Xo+R?s-lh$A0Fw+F!9Z!KWPc2 zGf566%lMt&hM_;HedtxklNKzMZ#&0Y27&tGOyrUjLaYR}OhFCP_2%;8f&j^FKA;@M z{Gage!5Tjh0LfPEUelB1xl&$>&8o-Sqpuc{7pFGs&|*BePsGm%yKCV}){Yr^>duOS zq3zI`4WvQ6U_KV}uJTQwodJ|7B<&rg_=-5e;s-fy4=GUgL)TB2)5|^}?s5JO z0FTqSj4iZp|_^Fohva>~JvIw`B>7F5Fc7UM6LJbs08 z9!3uK02;zI?nbmb%T=#+_^6CT35ALTiR{!1NlmAPwyy^R%%E(jX!vICOOlGAu7~ij z&F%F%4l!}Nx$fEEEr+k+jE<>l89gG#3HJg-2rC$e2Y)r|qH4E~Gm=J873Hu1L=o&- z*6EMBa=&EO*1nIecy+T!<2qA1RgC_F^}{*Ihw?0?#EpfRK)(v5U6#uHhfB?qEayuE zvaxEhF)iFFkTH!P1EQdj#5x?_se-^cyx z?w2<^GV<2jAhPM@##9crac^z+7I8F-aK~c<1cpZf!OB0v9{P zQu7o}zwP6f6XkanM`i^tGS7-8*H*`D$6dZGcU%!fx7?OiCu%ph} zx!dIWpqprp`fBCSM){|p+0O1f+%6yY9M!iC8pS0TO`j+CRjs0S=FT3#pwQynXFI)+0&4Tih|?mBga$~xrFeHlG-!JVPIq%`*=mtj z^Y?)%;(B%WCiC9#_&QZ%DgqETbmO#{hT4{|q|S!z5~Ko4)hBCcTLVno9xBi5AGg1s zF%;WYYyGQGwgg71zpUb2^}X(toWb&9bm&dJ7B2M>(UMB7b zwe3IIk=qjZevNv=qRCja(E=OQ{p1j~^G%(@VDO>PZNA3@&~&0ou1-DSUozHwE@NbH zlSVuhLnc%itoA*$Jy^q)j>jsYG|ug%VfzU4V^B|gaE{Q~3rMO&NC7==ulB%xnE@H~ zBg4_H^%r2$-wn(LjWHThPZT=8<(C3=xVwSJCz6|;;^s!?9eFQvf7~`2LUzoQohaNn zB-(THs7c(WDdf&DQi&APclsB9943JJcf~5&K4`@t+P)Ln@#d6we{|cqPB^`q4$d>5 z!Oh*j-w+-{k!s|B%=~?k=CJ_&`2zi!|NX;U7sM0QU9^*apq~$r)j$q**ZU-GKO@>c zkZZHWHy4*V526K)*KsoPp8R2^f+QxUIBL8I?!0!On>6-cSt+oxGz;!NNJziQS#7~Y zI>qJ`U?WH59WCXFfjV7u70J`u>F(KIX%8PnBoZ&#kgb8?)CmNx37gRybCUYssHbNn z$yvxi%F~G2d;GG6<~L83lkezcaiJ1=yeRZW=V~?GW->hOOqZYh(Kk5zF@M=n-4cHt z;b7522r1qzs2sm3WiUw;&h9|)#)Yf`l_7hOt7u_zT7%w*z1AaRau@GhX?3HKEk!-b zKIkN(DgOgo-11x`I!NR^wBdTV`|Ru1jhUbR61!2Y#U3y|SmDZZ{EGrQ;3$`B>zKZA z%Enk>U>9p+G_7b>0!|7Rt97>GfH{BXvF0FqoC0`)VRG7)o=?P7Ep%y)Bp*1%u>?eu zw!&LfhS4pC?{678#&ZNt0%IK+;e01iu5<6~)z9h=0O}80v5q10S9Z}^bPs6JozbMv zt5S`s=00JA3oqy@jd!UlrN)GEk6GTn>?AVmc5;*@Z{jVysto8C5ScPH#}S= znN>&V(|1js7K$b$9~J{<%(%ygE-%=%FSBnO4HStQ6PyxYdfU#2I4A(>|@j za%hTmB!(9*gAzhcV*0T80ywh;N!;Z8{tsuxJEgXjr(T%_4 z1MTJ(?Q)e*!i~8&VkJ~wD#gxaYQTj~8uxNyRzx9Ng7$}NX#4(KNGQ{7yEnKgUmjPq zg^Vds&*j{H-@YGAx5dC1K$gy_oX5`H8WHkrXTkia*r=D&k#W$_h+i{g?fr!H8x-gI zAo0_VCAx68#rk){P=O|D5j^|8u_zELSlU*y9&9ZrH#fZ z(X>$hSbgTyCD~3kUWlY>Q?Os1^Y!uJYL$_pArFHXe%DU##1Gt|T4A)r!CJD&$;tTfbFQl+vN7WFiF zBZ?r@Br2`ht}+k^jLStk#!htwR66f-RvPS7`75I|D&`=C{nB8?Rg!a#F=wDb454fb zy&h+Bujepq3Ull%+nsU7Q3Cu@27>nxnn8PGDv4~ z|9Oe7Ookf;BXbOJQ%EPDq#-Y^*|qYWjDL5D9)gkTfl$o>VMTZR``xyuh($Lj^2veP z0*;9%h`H&k6J}%pzNNrPk0<5#XzJhgzOKi;&4H?74fAouq$#h!LVj=Sh*KvNJDT;q z`=3*#u5^}Rr^#B~jV?@)kGJryd4(Zr1qbiDjO(SFWHIVPTd!+h7shw`XBn^-eQ96C z@qPJdDqsOb<#<%Q0U^CpW#@><5Ta2v;r$^5%Z=4AB;^df`#Ok=4o_PoKNzx(x>Qbc zIS^FXFD3c17Jc9S|I`}W(GD&xts;H)GFy_$%x5c12g|c`lG4z)mP_Dbw*!K{a8GC1 zlk;TR#+U8l`~=GP%FrjV`NoC(hxraJeQzp;0|dM7F>=V5;X@g92J(e5pzwrv6Ny z0|N^q*BGymaLby42Y)TZn~`0C=YqncsPCcUS!8;y<_%aEP_b2Z&afbPv>!(LXB4W~ zV|;3|{oPMu!lkdV-;w3MrOvx?WDSQ)`|jgCGh&g=uBPJS|DYPPe3H}5tAkDUB(yUA z3MeLAAr>!EoJ%neQI%SLAsb6tq}#d4-1*Biw9aKeOCjFalsof0JzXaDzG0IB?Py-C_6 zAupTaZ+Yf)(g7Z;-8s3q8qDf3rw4`yBWGc&%7|7TTE@<_fmb&VFU>sHedJNP$ZP5Y zj{=hg7|#qHO~|BMY_?6^$I_ZVP4oT&S_TbWr~nw9*?#N5`fd9Afa9{7>!45s4vHHR z4y`yJd}K5|;bbQsa-&h}_at9zo`NEE4NF zrJ;}ost$4!qQb-Pz=oUW7ru+Fn`d+LdMI>TsjCeXN&v4t;^HZ(nzsPDxVCP-A-B%i zYHl(XVSW0KxhXjc()GjhpVsQmuS0Zq2^|&9zt-$iLHpgQCJnHrXC7?lJp+4ZM z^Kt>q7U;1&WKBNTRh0KRi8#82-{GyW+My?T@UWvBSNhHKU^jcX(4Uk3Y!l;y1(2iE zEr+;&VQb%Ap+lc(Dl*$<@QSKA5@6+5`yNo3VRk5pn#+vYOW6Z?H@8GJ4n95j8Q#En z*PbHk>svU%`%=YccEiWOACSk3ubk!uYSI2jGvT`MKXC5nLYW_J9HYgxW5_wvbG*ZH zlE>@-K1YMs4&ypA+N&KkCIS}Z@MlkWI$fPvk9f@54%13ovSr?*V&~p3s3l}#i|1#@ zgZ=tx27ZfD-KNE@m7?&)zweyqpr3xi@BRrlFspp164Kt&P#TT*Tze87>nA?mHh};S*`$WEo}5UkeHI;M$?cji zrvcDzL5!u;6#JFEQqJY=kVa+V0{}pokdkZ_Zrc&x=RI_DWoKOAxR&(dIY6D5EFI_w z?;{quoVzV5pCdZ7G50O+&B>grw-aR(<`OxTmri4CaEY9ovB$;fZ3fv^or$Ng;-(DU zY-ofM#hO{WLIV2A91?N4V*B6V*%(Uz9J&z*;`MgKTg0#6W8 zbSosSFaPp6Y-L_8WJk1#XN{r4DjArgoXUraZp2@^G3k?SCm$i{6V^_Y4S`{-;0t;Z zva4Sa4A+A6d|E(~&->Miex=|YJojKHyccr&dZ+_F-MsgQk@G^2{wOd8<0&MP9QbyE z$6o@o+TSYttb>OBsZcufK0G$7#~KW|{i2`D7#vH2E_3p{Dr*I-$U}`4^?ZSHq=0Qc zU{$JF8ZQZdv8H_RFSyEx--QbBjR#&I1FuNngW8>!-HMK-ePY)Jb#qzMxK=otcz|3+ zqL0RZ<+zNDTG|Kk2dqEMFZ(Z8jga?Nf)>=|^~{}^yjm|8A>K<2Hy;C8Uwd#uTg{7M zAkL=*|1SnGhV|6w66YlIBrTU9x_jZ!f-Maj{>j0M3iNn!tUd$Y{gS0*6s$rH_ZqtG z|2|VtnaU#@7c7eFDsO=m^tXA{@L46DnjUMR#&aT!)yV)ZtERnvk1{1cLBT>r1xBE9 zi*$Ia>A9d!yY}>Z6{hBbuVomWjIOgjL+Whc)y*um496w1CgiS#rhjLc72iKC=D9EV zelqpRsX<47$`^~IDEofDA@{&U#szNem|FICB5tRN ziWJe=NaqvFj4j;2Hb*oziH?ChG_H7tlvqivA`ZW)w2i%*k=1Y5DFeBm?A?<^o923e z9?PA<8vo=<@3+n<+ZgMxTl6s~Gp*9VX@6B3#SFS&HPnMo!5()(JdzyGf*AT4l zQ=JPA^sA-L)!CL@TqgP23b$lT9M-S$HWfa(LxWLs;U}8OD#s~R##f$tZQj@(tjxU2V7}?R_@M&6^kN)l*S`NKSoj55qk)U1qb<(n z)$g+7pLTBihF>nl;aQnwr%u$8^!Y2dGKZL`#>d)k*&Z{WWtI*Rdh6cRU(9KsoAPn;@m~y$*N1<0a$n={j`fUf zuwI~+Gjl)s$(|?jHO*+pq4`FmGpN_Rp5Ic-I?{K%K-F@V3H0=75ooq!tm}*gKEGuY z>qiXj;+A-}^9CGSL%QA7-&|OHd~9qQ?A0~5_GbLuv#FS83N2RZdm`dmduZ4T$_KAa zua&mt*7XM8u~B;7W=Jvh_g#?6&vs#t^JBGcLpSz(B8Fxb)k>1SnVw^2bARFWR98QR zohzsa^Sh!!hJOMb6`;MMs3z5J`|kvJz+~4wX=ZyiD8|&4(8Jx zbpnkodKJ5y@$6LGpa$k*;%Kyz`c?aZH4-$mD6^2!a_Pnd6T+m z0rtLJ00B`9^r4Ou(4x})>z-hVGHRbO=(qEm#`A#GCf+sM*fh}aQ}{}dJ-J5T%>i2& zY%e3F3W=-B=NMNCJ0fg(RI2pIvVAhuIl0EQ>(FpYKI$yXq-4(EWDdF?i|g?<*2o#F z801K@M2DRB_y{0v0)HQDzN=XXS#qXN13q@FlYUz;K0#~ZC$qI~OSadLHR;^CFoiF= zFX?@_xQIsc-p9z(+avutrU`m;{Kn=yS4+}s@71Gm;$y1Ccr8~7xFZ=IQ1 z^7kjrhYXlVt&~|+e)F#@=islt4f$Cs2UXmTaXWsN2>bmVa^sv&fJ{lgjMFD(P`bYD zsMX9BO{LOrTTP)y(pMh~1_z`Mjdy#6k2!{=>f)V#{Wgc?KH>T-HYPoAF}W#W3uB4s zxwG1^QukxyqE$E`-Y+4r)om6sIvmamZ@^w7O#|ZKB&wZhQ;12x%gC~-k5cddqw{nZ zJYDJ*um>+!?qxOmR(b!s1y=mDbnp5~C~fTVfwuy-8ho z9VK^b%OgaZ_sX2OwM%yxv8q(VVYt=UHz@qgA!@tb~Qt9`{a=+SjUSGsn?{PZkCJ}?C#NU9qO zoA~xNmT5W7;u1``sKJ`=aO)uHYk7q6RF;9)H1ZJi(tg2dKOT##q#m8c4CS=J+#A z?Jdm`rz=u3T66MUN0v$O70oK%fE2WbwK(U0Xf7Kd4Y#so(`-Eb?qnM72pymfG@d6k zEHj;ad6tskI6MMYlhs!f5!v8tYU-Xya3Gz^w+Xwyl|Yrcd?~Zqu;p;zq|=Y#sE0E9 zrbYfP`dyS6XvDSo$2MJ9!YBMcRt?I&CNJ}R&4wy^=MNoxObh`S32FarOSKL8_^(WT zM>j-f)SbV~JtSx(j5u+wH>iTsYI}6is#d!i^}5w0MRh3(#`q9CvlFG7-%o6YiD6Vz z^)|`d2g}+@W+eOIrWfP<6;DIc-WKKxH?`2qhQdTpy{}={NBnXmx>USuJ?A5^fjJqd zH&Z+oztdV+y+&t69oXg0p zN$3qQ>3V1QlBY*;P>B5t%Np7BXF|X>tLoSFbnmpE9Lhj_D&$$Yno_jXqhGSJ1cPM7 z#UEq7E75fg>w>>stfWRF@W$el`8)G5r6@;Y)0_2)fi6k4y^DQJ8TItToDV`><)^m9 za)lx7UciBc#|tVrmZVyqB+zxc0@Ggb74isMlegs|7uNZZeri9s{(3f$zw%;f05IGb z=Tbfa;{H*0^~1?g_~0-|*~;C!{W7(EuGtV@kxuJgDbMGgTQHW22!Q-~zbZ(34%S4N z^8AHiZoH3AkzVKb8AhmG5}y~XkPj6OOu(SOXJUhxv|m@W=tIinUA!r(Y&ImGS3KOu zG49+ttlSNf0UzfkL3h}BM;w%#xi4w+RT=wooFE)K)U4{OL9Cy`7oMrG5FKCU7>Dq_ zJ|TX6VlgzB02MUcRqu-0bZ+@q8}^3aVfPOUlm5kcho7?u%v^-5B* zdb9D8vZ-Zn@y0Z`X4R7#_!!m@ed=d!GX)U5^L64TYY!b7-GJ8wrI5<5QHsuefhs1t=7BP>{XsnOOxSN0EsN;7D4lCgJCAoG zHvXw7Fhg8T&kCtR#yFGam78G4mFjiN$3paT5^np>RYVl*s{!biGf|v)>B{`5?qv&g zP2-{63cX5gBcm^u|HE|tP5y%OdETJWmE=i)7Ffma5nn!}Cx$6ijR@+WW`qJHBp4$) zYof!NmEcHhU%$bu^4r8*L-x`UZ1ySfecK|6T59}awiz*{m1Hb@y1M%863Ty>SJi!M zEeVQczy2Zn1P7^`md?HSxEBG+JWiM@SAOfPG1%AhN!Hi3aVhQCKRtxR^$J*`k$O29NB!oeKjs?@XPD5Q7Rh)XApDqK z^z&6jBfXgq&HM2Pd{2AE;Ejyg35yXiizgQgwH+q}?CtTPp%1HX8+}xy%p2A5OM%S4 zjrY9O^YZ?MIK!#D=sCLZ^LffKNKC(#@6N75Id-SYmrf(TlCBVBvfMq7IYq&roC|eH zAErnS5*2a4*{3EFaP4g9#3`LKHfQtek1IDQcrE`|kpC(7oXc)ZGQVvKt* zMw08U-?YNptvH(wMkm@g$nnTdzw6!Re#wv><32!mncQnBUDU?};o)?l^Y@&8+}yg3 zC}GQRme?Enc#)OA48yufsOJw!;?;?4jk2cp`Yfx2{a_9^mI)slVee*L<2$Ll0i|dv zj8}E>*0hAH)b==g7`|lQeXx~+6eqLP%6Dz>K2FD}t7DA`Jm#;Gc-JG#J27J+HJLZF z0af0T<6%CCV1uKq;G;9)&$naZ9#VVkzFFr^+od!*#`zpICCxO{;5@5ke=Sc_)^)lL6+H6iCIKy`Y=xzk^96yPcAi*@4QF9W3#fn81CDm%EZxYTM!`ocw%z*8O` z>hW8574}IECw*rvo2+#~3~MkALW25smL+ zIph8%QgKh@^IEQGg*nBCnB&_o?L`XifHbv-o)VMIHjck2+FjoXI(56Bnhy#E=XP?u z&TDKV>K!bD)^&9st3+(B{!@>cu25?++%nYV1%I$_l8zN?Y7e$;}xoBuzK z-#im5QuzfcK57;Pc~8wY%lXV2sl6o{=$r0;Rrx~uC=<4vG{t4N{YB4>jw+ZE_=Vlv ziwFxS+!ft2JN4RHHvN91-eNUr?xpVc@#Btd%g2uPcMbXBYd^l-f&C}9<#l}@T}c2} z%D_!#Rvu}&q6Is-rO*r7P75`4KBXHpYh~@I7V^*wiBH&xu~!SP=4^rJx4Vrk%$Q>l7UQUb zWESl;$(n_A)KN?ll=!BqHu^?Nxs~G!O51p?KBVfq_2ISN-PE%M&xf@&6sL#?6lo ztwXrqwh)_d7n4VKFDGNg2NoDBHiHnIp;pz%57$9PF{xuo|Hw57Zua|nd~VoiDT~*T zY|1|0yqvMKu2bF=?6%)KCFA9{!r|U3>P6oDo5C7nD3%~9q4@nEcMM{hvEo_r=T4Nt zt<9Hbya*O+lP>4-(QRk!KsPy(8^U#Vs3T(}iQ*T0UrUHHnSSRPIYOIcOCFxOz*_|i z#I<+NA-NxYADUZ$3u`DR`jO+Mc@|8w8x>|h)kNYV--IpZ!>in2+t?^frv=iyRUZ4d zUYYc2z5B?aN2qaiHtz8~+hqD)hPCi~s)<`mIdYuWmm_r|g72y%TZgl~=++9uz#K;B z=ZwC-3Gz#mRYmdKKl5-MTw_qebw}{g3$z9D{#^V7o$rQQ@PR@hgzkAV&Cz_3)!(^o zr29csq3x(@RbkL_JrP-Tf6#n=9eTf&ML$u0xySnTGDd|1PZ=NJ zc+}eah|kQ+uKfT7%`wezGx47R%G8_+ws*7%6bGhcp@UpR-`+4|lgU}*`b=};o1?wM$s%=PbY?6CCxkyB=y7377&Get7 z?>Qwi`%8X=Yw2@aC)$>-k~Jcz^Y#lC>);l<1)Tfy`Io!OdYfpfOM&QG&W}BddMmx1 ziH$c9k#Wt?8AjznlJ-b&p+BDqPwh*-ayFegNJrpq&+vbWtycGJb|tLsOQE};G~fbh zNeTzwO$W`!bd_eV6t+-ie!i3?44z`-MVc&b7;D{{ZUJ-m%34Pp*G?(nuX`pl=2VXm zfl4W|o#>Oq1%{{Il}a-!pZPV;Y!6(?Gk(vzTT|yo71w66PQ9%jWiN_~z&uY~nhu#> z@=zVqpltS=YhOrjmR6vui;mc!0AwTe=E5r+vk4dq1wD`sF!vLW)dGKu^6$|Dz#G5m3|_>ZOv%+QY)*57)IuNiV=&FG_8;E38DOEyU9Y`+%3h zp4EZ#dR&C`sqeS8&Yex7YK82c`B!YfN&GQbHRRBX`wrOIN6=i(^~=utLw1dI5vR21 z@9@G5OE8L*c5ZW^%T7I9FLqSba-8a^23GX_)FAJ{_R4$hVCv~D{JlX< z6fF9dGxBPSYx$WIg%TUV6M5iIa2IH$4^?dU}x9ISrN>KT^E&^Cv#c zgQ;+T=iD;9&Ty=VUTiwHvrs&*IFIxCWo`|A-Ztao@2;_zt!2Lwpb6UYSw>LY%cHwO zm*kUG4rb|i5aCMnVQFyxH>Gq9~$qUD{F+ zQm?6<{QI{i4xup`H3F{_@9A&GrN!2C+ybCxBz-F_g^ilr!3`Z6wP&iH>)Za9%Ls-N z3DwF%o!I+W2^7UjuRIZY8e%f}eWy>lw(4u{?@*^@Njv)XL#I5Bm^pGvbNqlA_unqA zpkN&OrS0^|@=oB-N~fPwE!kiR(6+VfU*Gn}thm2fQYzzRB$l{7(J{TG=x|IOeJ^F~ zXbBiAz4SN46WQQLUf4{&h2>>>g{&(~;?o2B9OHLgByyDNkD7$%(}~t0hDwfN%rtY| zLapODtEOkq@Uky!tRr2kHFZ9`wlZo~Ij@t?dh?MtMmj1eO{e6!b-cd_!WvYlwNMXT&Y7;Do1icL0b zvr-121uCU&>rYeW;&gcal?j;Xcf`BlSu^{0$>b(`lj$L$GOlNSGIQYZ?BKYuRPG1! z(;TZEHU@gXde+=ANRMPMh-?XNi5Ar3yBUsmFWP@0%-EY*MaIgUB)-9>h5Fi{vx#2=kU} zCawz{uf}^eoF6ncDVb|yo3~#v86$1mSDRts4i%rSVIASIE%C`Me}+qCXO(t2*8?^X zg~!T2OS9ekJSNf35P=E|!XmPeG@ABaZ~%?J$>3%WWN$#w-;s_*jLw%A-Yq@s{de#A z875GQ>UeaxkWSv8AB=RBoiJJXp(Fq0%Fz2cp7AqEcLIT7Z}3t#e;+u1@yNJ+`wafYKSj9@-d&T zKH_y47A9(66}@uVO=sly0O`E_%mDhCM?2yqZRzZ-DY>(1Hm#NOVQP5k%ulqeoxYw} zX^nGJTB%a=UFM@t&;)<7bqYZJ2vD)POZs+g$jxSo7aVi^wODOgZLCc&s7s(d;|VyY zI0%{R-73h^yxdYI*pMDzbisc~@-|y(m5!cufc}xS>sVO7Vog-apwn-|XP1*(Rtf47L-OzpWn<9K$@O(ZULLd#-N8_+acUy$T? zzQ)&&ZWEs|wzunD2%Xpq0fCs^Hb?d?dvmT&x9q<0yFO<`!9D}MuG8E#t9g7KRHW+4 z@EhPcO<`jGRVkK;xxKB`6MMSs#Kv!RWlv{D#?p@4akFr>!s05>_km4MysmVFd_W~% zdAm|~+De7T4m@^fBK9h2(gCz<8cxs7vSv^Rc%{|eF2;DrS1l|`G#sniBA7=jCIhm5 zMw|>|ole^~`bL)MnUCeq&bY0<*kTNIE#*n?e(UsoG;VeLp4m4oawyj)dgAfy-o@!w zEZ~pR6N~M4iJN`To|wzp3`ZCw`7P3ZTi7|wX*5Kw3~!$6!WP{6%HtTg^sZ&|qEPwY zENo8NYxy5=V-w*ZWJzw{Hoy|r!r^XTDP>5Hb?c}ux$P83X4OBXyWgwTiJ}aKAG14P zNs;@(&WQcO{=PLIxa=25pzf2sopREeHfr(bt`@&<`6}1^)xB)#uDR=qVt3@(8UjMb zz$(~N=#K)OS+znO9-dVKiJ4mekz@Xj^iZ=`o@q0o#GodWHDewptNq7dw|H0JwW*YRJ#3h_ zsJpwx_vgX}K8d~n5~BGPrrFmt`zp`FAA=|OUFbCX(~yNM4@qTMq23r6$yWHUa7H59 z+|Bs<1^42#S~>Q~s>k625=`M#Um>tN%{{0Rw=Q_&w2NoJZpyA z?b9g@a{a`3pq!@;t#NqMBJ<4JMY0`?HT=1jajQw*4L9wo>hC2vF2Vv`kxW`O+r(^X zpTNdDCg7l$N>t`yrFWK@W9^vPlYE)Q&v_*ntR~t1(Tg{{6CRjPI#x*_U!_BW7pIdR zT*xg{XDv-#<-zo9_?^9|hP~9&GtIK?j2VuSY6`J0Bb$G}tpwKz-ql~=lz?vpi|~nu z*VnRq{T5_dqZzUr|7~=Yl=GdyBE)FZ>x^TPWFphc)?QG0QCloVUfwQk5pb%u&{Jjk z$95pYnjSv$I~b^G&Q^3!Vf6WykZ)RumgW#1vrvzS%BPP`YskduFbm9-UAIy8tn-Qd zuyEE*6|lSZV~!`G4DML3`J&8cGbeqmzBtHlL?$RD@VPJ2%jI6jdfvho?TE z9@19vZWX3@ZKa2naF*@mGU$O? z=I`sg6pbN|s-P(i@aD-cz&y8#T0_{|1`qI1ymJo|664}${R63Wy288n1O8iF+xk7! z?Nw>0%BqWf?oWikqBiEaYGBq)Pw(*dh2Kl{%#<0QWXO^0zesnVhFU=lCzt?{dmCFH zSff6^`(}KA;KGrCoyB$dcvUdNG?SG*n;YZu5>~&mmr`S)#UhIM$LvZGDF;rJ22sI$Ai5mVmbG&i{Cu3#fkl40X;D*Yu ztkHOKt6X5QB1{XPy7~!YVLqJc{oypMP9}t1dB=`q>uS~U5H;`UNuNfZCfi*MS0FrH znBFMPT2L=hmuZu+qKAn-Zj`O3(Q1ND?;u9a#2u`!T4n`Wu8HWGT3-uP}jZ~zb;YZV&$hJ70dAS)@ghy&-GqA8#<(#EAG zgE`|hxQ)F@TpY_CTK&>XTRF(y{-3v(br!?^ardlN(IY_ZrP!eXA5>8_*tsJzx-IW8 zXzvhd{QbS3rS|uYEia*O->dye*3m!n-RMjSf1NqYbm0oW>brkkq_I9D&i*Z9mI;3& zS&%I9^>`=9nY&dAV*Mmm6m2#g+(O~DKOJZeJxT1dKh3C9xI+c+opaxtwYUuGm8LMyXBBvyiYUHodj?ZE1j32GU1R4MCWlh5cs7kZz@m8v8BDiFh4rR< zI&b{=pZ9a&%I4#U_K*%BwA{VkU^@(bK+gX5gHey`hh&@7hk)RO$&W)z(Gdf_1&N1V z3zh-J_+o~aze7SR7VCtNV#yCIg*k0{b=dyyFU(%&?-e74MEXIJN+B(!yQsyr4q&x* zjr`zJ+x#}kDm6j~-!m2=6FiC9wyJbbKi6%c+@zx7-*KMob+Q&QLsNK(CoGtJ@WHV@ z<|{5odTviU{TW!Vqk6jEe$EpDSjiQY2=4HTR+?(jmAunaAGi?KF>WF;0k`PbcukuB z8dV7|-d!mLb*zfD9m-?j z&_UE+sLC52m$#HtK(b%9rgU^`T-K{;TU=8(vSPOLRV~9i_;ja zqT~?UNXpNArKmjV?dvAC{80O7ZfLaQT>>!;GE3wQ{3kqtj-jM6Z$NWOgXAERCG~%j zWV{bdwg>$2$H^0-6qP=EH;5^$CZ#?&CBszx{36ZM|?SZatpX z`^cilNRedmn8_fTiG?S!dX%~3^uWhS6NOrd=u16K@s!-lgO4L

Njk6PIV25Px>Qf!yDC{TxM2JpJE9|4fQn0v!+&ye}lpc zHBx7$x2?R)knsrL7hmWPYb|gw zmrQ0LrT%Oz6zuKl4Vl!?qbamX!QDI&b|d}WgV4iu6xfYp(5tlfN-V9P!Z&1+|gFwry3B*XyWw z6wdm~_gC(<(0S{xlno9RrnuMv2vLPP-(j2CeVeW099LLPF>`|=b@W$LVS9cP5_t2w zRd`(|G;$%CqQ`hvrR{O>!{zkV@*ysT)$f9L8}y0jI6q>M5+VB1sRMr$UE&t)v?0Dm z6tnO$bU7`D2L{;(XD7+zOCPidEa~yUHiQaGardlmBdp4|ecJo1NsLEaQ z!B1ao3<^4FS9{u;7^xKNg8~QO$ly{9G?H$zJ$+JqBM&!uQ+$v!R1O6U9^||!@4^1l zh|hGy)o-9aMTBv5_av#FFmU^vAHs@>PY~BsW(k`Wx{rAU+(Rb(&HNc>$MHBm(Rv&y^*ou|caR6at1#R~554+PM6f99WWa8YdaIxA)}}WVVtM=xdA4`eF6JWC|4TPY7f?FlPI zRg=8U%4|6)=PcPGM!U(oQBE@jRftCr|s&7!!R{?twT300+;;EF0Oa$z`DF; z>G#X4%w_J40qlhy60OdND_w1TNX4hT>Tia2=NReKgDwY{63!!yl0bb`y4Ii61t1a@ zxDFai-6yYvLsp?O-lN{m6(JwXg35#U;F|;F0ZvA>yE@J@nMz$-_8JbpD~gR?C#AZO z&%4{@Po>v$YW9rIM`-Ek)&%wfolQv>J<^ZmEiAaD%`aTeA$^><%bDx<5c3r{?g(F2 za@jO9DVn(pu|6hEG?NJ_G?FPM4UEAn`&0A$WdAIo^tPpq9ryusDuj%!9cJ`HfZgZ6 z9{Txy@Nv_mDYMN7x|FWQP5CR6x5!GY9e8_Jj)hd_VG1eD;_jal6GFIhP-FEDujU5+ z#Zx5+;wu#jgo%V8m&resgu&}4Vivo5GSqKMMb4%pD(+>1~j{@`<8H;3xN7a!FRn%mj@W%SsQfj!S#6}$;1(J(o$RzL+7l}x{FmP+Ri)bV!7bByRpVW;E7*JZrH1soD$ex^- z!O1j3xN;uEW2SU(<1L)4TqZp2>%#RL_%LoLQ2(j17}b-0Ie_@_Qx8EU-zMiLYP@yB z*-yW+D@BkN{5cMNhoaYEcP_E0U7>^PqLAb2Mo~l@5()y=(pr+5q=HVn>@SSxRQwY4 z_Wl2EqW=pH+swtTjnt9masFUD>8hsIGzoJcpB9ji$7T$LJDOedK+`PA&k7LjAHkzYc zdh}#O>Xyp=&noRky1_nbbCSKK4ORf*!KoTXHo*u6NsFx91#HH@c3S|1DzjPn`69BY zWP5kpNvBClv_8qYN4}ys4HgRUCqQI$-0qaDi?jL5>&9?Eq8Z4qRN8K6z&b;|^%-68 z)#($am2KCPuZS!(kQPa7;>b{E2_+qqGWRdA=Kb%qu9&M8Ak3tWf$;!Bj;0Quo2r)D zsbJiju0g4h^P()$;&f+rN9OWY#q;R)VPUC%mOIB2hk(75kTCNVRZP7_;a+Zf*ixkr zD0#?ZS>m7nsVQx)H+q3*w;4l_HE|l;{*Uk``MaJ~3!<3#oF#1)X#_|@-l6mslh1MG zrGb3A5PK)qaCd8Zc5$@_ohrik>^^BBHVAZe|MOnc(09P-v?qhFC9Y7G!#-SE`Lr|o z$)iH4WsU1GkNWL3x#w78$BGo!vL-%(1~a}yVPO;66XH`sr=9+X>Sky)xKxbeCWjS$ z)((U^<7Ii=)p+3re(H*4CG2mG&h$aw-g3|YG2Va;Wf(#0Dsojp4g50fKl3RcX{ERa z-@>ll?f@1JZagOzY8eMFY*&!yVqe)0h+I0}34F%gNGc*yHQrS*j!1XcOgyz5h-qm%n->;^M(c}vz!jS!`?u?Nq0Y~2K84b#O1bgp&ali|8lU3no zWh2bP4Bc(3<|hHWgnkFN*l*)$U`~L}>24iwP6~iz{U$Ga@9o=Cru!FEB>q&<^pG5D z96Npz_aS@0>Bqpf2aH;Gj)hzbs?T=5>iwc9K0`3!z&gLG-ygJzSGW)w)KWK5#DV=2 zJ<;$qhDQxvxl5oXM6v#y;M(~TG4ms_h|VB;(*ga_+qSb~%}YSh4!BjQ6ZfAXr*2N~ zTZokWl54kc@DM|P-p0jQ5u-Zy!d$_wuFr>E0nPPbN3{hwN@Q%L4yjYccimIr53mp5 zVS&gPYs*7?btTQqyC%)R6-=o;H2Edzg}R*+ph-}vJL&VX)5LGGLLp<0Hr1cnTvTQR zV`zoHqLJ=&pDb3hI&|WWuh)x*KR+&ck~d66_x4@qK~WX=i(o&oT&pC6y$qx#KEmEO zMKu&!y$bFrFoRJ7mEC9W+LF54z#Yo29N1(EBpE>Jos<-8(IB?;AOqfJoiyi(vHn_P zd-kQm31+Y9QJQtGMoSN5McpW#Lq1#>=IT;VVTn(NAPoA>Aor}S4QZdvZ9@YO(!Gl4 z(%c9EN!R@2>-Qf#@$WDz0b>N>K7#Um?6_N3f8NPjkl=(4;1z?1Ty{;WLv?qPdOlrh znO9P2$QY|xz;)m4tiV=%YrLTKPz;egJF>vgw|rW9>-GjLf;Ie0dU*l1g8=StY%@pP zdR=D!x?HSy3xQWjH60k(9Hy9SB7H(#*bh0?-GS$J=utSrQ( z(F@H3_%{P;vcEB@RG3?yriz{tW8Vhw_(tA<({{9 zTV>xWUk`nHypso%=TYg1M>U#T{kl(YxWZLi(s{nh7dNvNGZ8TFb$DLR(@K4Dhxy-G z3S%Aq(pYxu!;iE~Y|pmHCmYX&s<=av3mvZF;lNBuIB{ktBU-mAt14oRB|OaAWfG6_ zh*%kFr$|u~*HCt%%FtK2({U&JedAT#y%6*%*V#grKJcjz08G;q%nW<(dc}FjGIy+m zj~11jq9{?a`Ow@U>|0QR9@74|qNJHFn;6DoHDPdoc4*s8q$3R#CR z8*k_}n0Ng#$8vXlb6|)gck^~@U>wYw;(u(v3F9h+7XO`7_F4m{@V7}~B z*Vjy4nTcdQOgDlJM#P5FKmRzK+`p6+Z4NiN)rbS)?jcM;f15Pl)hiPnWN7tn1Q_nd zqibg4^dT%PK0gxcqHh+_4FF$r^Pme2>)Q=_gZSw0%TBl7_O^eWSynn^xatm}(-Uyt z#j315xX&NjKUn+JU1w=q2ill)*`yHSR$7IXvs^a%7W4F8h0S)msj{!GZl@@-wBBc< z+0qy?UT&N)V3V(dd8i4ud9V&$X>U%lV`vg5rghSQP-d_lmO7w>*dW3moAkb);S>|q=`FzhT$m6cLy$YXH(7H;j%-w&8?I`pYPj4=SP%##~ zyv=)h#L+6|Qwk<6v8$Tj^Mh*(v~|9*<9VbBO#=9fHr}-zEe34b*gr++3}%?J2=UIJ znTa|gec*TJo03NNH&y%1zzs}{`00lbS(~354vomi9fnlVeut@J@?^ z4$j!=5)fuV5W=&Iev*klPvsNA8lZiXKK!w4D!vaDf#_-Q;He)r2mTwH4ew0Ldhw*j z%fFhZBAOBQbsFRYsfH7VEwdQKcv~r)L2+_dZc6rK2$5Spm+dz_4_jgW|McZA0lf8N)zaA!%FacODi7>t6<`$w%Pc#T!20t zw?w;5;CQ|}>rZ@2xfYfsR8jvour2HZ@Izbl`iXBHUK3-?BXfSZ!`)k#o})-|5w=(x zP271Rn|G|UW*t9eOoCF^${L|FG6CMh>G}*aplcS|^DR0Xk*QucKp!1#)V?YQUl}tD z^A6vh358A64Ye!8wBYsu1$Il8VNQMNB<)FR+KE6hzA>@jLcd&n51_@5{?Bu2$FE_e zWd`gH+~`DX5)9MRLiihowSr+Mez%UlQi3I4-_?#Z$^DA_OvHui&!>Ioc6Eq@`>oAD z`hhl{x^WHFo7e!7L;K)*UE%h8(+!db*BZxLuVK!s$2#*$chVSVj#t+UqA26Zu-!a{ zPu))!%1+RURDeg7NtpTWD0qB@A9&BW);-g2$*}I3qpqEG zZ~=YXI-MB?m9}#oF3~+ktgG%T@@1q5H@O$kemri&cFJ9HG_YBeVZAkWKJ4P8p<&=J6vCeArvwfF+Bn54G^n(vA2|s&4Yiwt3;x?woV)9Z;9zU4`U)k{7^K-~$^M!D&VFoon zUMQm6(jGe&#;s%4*iH|QvY~5-jYcBADZZ@qFu4u`WUrFSwGO%Pe8Y^##E*;hhHk{m z8-1M|kPMHb53~&K!KF=&vVvI#QCFu)Ci(LM(v!8R zK##ufwX0=%sJwfyCpTK8IDJHTf(oV?e>MsUzRfgH;o@AzLG~tNQfX{QJ~xFSSz|Sh z@b?H)MV#F{bNVy5hG`%Fe^~$tr#%!DbRlT9wU3*x`S_De<9xaSN*b%&+FuEsI2}4a z504(oB%3*^5STUlCi^dhbQez)9sZXhQmPx4322 zp9;NYItW!nxWR5atsppFRq(>9FubmSqzBP^w9@28A^pQ)Y{U=cvBz`h!PGDnsJ04x)V zQa{(d>NCBW19bWPWT&@12m)~DXd<2b^tF}P<}cDBAnN;o$I|uyh}Klpyr&{ zm>fG*VtTn$;C0q;nX?I27JEsow7bw|(i4#3@!ud53x79rO3DvR%oWcnz_jarviRrH zN<{@tJDS_=-ZNX7rZSS0IR-(#&5#}UPBd=Ry?e|JWF9EeN@TsGbsFYyOKRjw0 zU)wXtG?U^k=eP2z`MtEOCQp_%jPzq!Q!oc z5h*WqKxjkc^3iD<`hBJ+*qHa;Tmb+ShPdX3E1;tOgmPy=Jhzo7N==mZM-^PI8Qd3- z=_$a}Q<1O4cKe9k`1JV4bQOm4)%v?-f+=vAx;!?6d6@iqI}D555Qf`79dtclf<}(u zJhMPh&_4!TEkRRZA5T1+)#H+8)X8%v-2}q*IgYqbL!viJ^gjLaCGItq;CscQy?zOuKNBVM@K;a@9x?fv*@?(n>$hl{Q|gW~71Ib)!YTH@<7 zvL(GtB|@K+cGR(K6ty=2bRzu_d9dLuae};&sF&;F9KI;O3Gh-G1%hm2Pu5DR_Z)vT zKqK=eEf^WhVt*24{0X#Cf~NNEEO~j+rj;#)545TY_>i=|L&{x07tE`1{kpE8pX~#5 zB?isimDE(iMHXfy$Ce&1iYh20zct=%CjOT3pPzeb-}&C-xgZ$GBKB02#Rc^OE>5=F z4)4D@WKp3ZTZHp9r9!xR^1-N+=PW)!AqT=NKK4I+c>fQ3-x=0)w(UJ4Y7|8x(mOaJ zARy8^7QjMPnp6Rin$UX_L_{E9qevH|NbiJ>v>+fs5JCwciS*t(ygPG`j`LjSoDsjg z&wb9fK$4xk*Is@7*81~o%ud}JA{ly7<}As{RL9`I^&I`U@1St94~g`P!@SkZj&u1_ z^AcCSFW&ylZ97!Rs6q4=yYRQ>pgA`s(M`kXUb-xY_#-^)SKHpk7Iv)7EsL+l?>2r~ zi!vC+-q|g^IJN*UtsESIxbkdZ4~WJ)*ZR@3VJN$tj__`(N>aa} z+B;)AQ|Cez)#{0GgcaMSgHreErn}PG5Qs`$#4nb%sCT2sW?rnd_Irz{&sD&Z<9p7Q zXm-o}lmz|x;hOYgS0nS*nnF*%LoXb*W4lKC`V&a8M)8|j8mU;}(hKyLN)xq4U1mic z268$&;w!u*g0i74^`hO*)856)Z@KThJly7GO&jI>5ovFkS2Zgc7v^TL{YX{a=HPh` z;CYVO=2$y2li;#R)u+zaV0 z+ul4{vR{rS7?T=p-u8l_*QJwFYmFnh%P7>F*Hbw(n0(8=%h1(X@!74?=QDCI$}Clr zOZUdCr>Qa7*9XM8*nWX%#E{mzK7EOY@N+vIvUmfh6FL`(nt-b!LtC+ULKCo z-s{7oao79BxxUY!|HDi9jvB2tK9hRR!R{yh;iaC~mh2}a-Qk~Z?M=ih930z`pY$A+ zPMIADRSH9tM7L*mQw~J=XK$I%BG)tC7N?25-co9)^+n!(ZAJIpl7BJ4|71rs!DIn8 z6YY-G&96HmLVJg_OS?S)P0&>@+H2U5gs*U&PI@Nb_ z4-wmnFQko=|HXy>?w9_V9Mu$(KyXT1EbYIx%x^wQr^)JP>XQdXM7`3)KXA$J zp7&|?2ifo=aC%GV5^!}cq||)=-9Tx_)XTQjr1kG2M^2pBulhxhLNc;?n2*t^Uv&5m zT+$u;4;J)4dT>6JL}Bqe)284lV~W2Pj)7v32FY@3lwby_%3HO`y1&$k{+TEI*TZCf zAd;NQENJVi*jtVL>s$X`tNp-1mAUDY5{TrExbW;37pl=dr;-z(^H#09cA%u?7aeV4 zZ9uR%$?_HY+kegd{`@908;BGy>8jgeaQyGG$$ud6KXC&BNB_9SNM^tOFGhYpznAZC z=PozZ_{7u{7lharv&jE$_Sd)eMI)X$SSR(uPaNp`4Si*f+;9?sL$(+@l5*v*|Jvt@ z2|VqcU0q>nwOQ`ezead6W);Zt^2*BVUtLD#uAdAh!1gb#Ec;sX9?&tV2Yyjc0UhIu z9+t`D@x`s9cf-9Ecd-7`%_TT|?w*4=% z@BKJC^Z%o zW-D?naLQWbn~lb|P@P$4P7>>rKNP6K@dv z(HEAPGBo)JhT!3*3j%{eq@aM5m+HgtwHV?MSJPwz3yzTf2pWGXxKkYmRK|A@Ke6;T4U@}B zWb#{yt~^Z{w{>}0S@WHSEM_?QW9V0o@#*?q<*+%OwK4MeXQ|<%%36BS#rj|0{FyV= zAc+}WKKmrH!fBddAtND{6qJ7&`CJPkXbo(ehL%>lh@wpRXV(3C=)0$gS@G?Wu4MJG z#A+E|Z=5VLQl^3hLD^-~5{M+%d6@i_dOu~5U+|K>dVFz?%yRD4wxSl>x9x$(wh;wg6?xfvm&tb%*2bbs`SkTLFN@jqmnI(4mw=Nm0pRH`mDT>8gK z;5*Y73HNy>3Meok#ajH;f!Dfu2!}8)d}ghJxkXy8xQ}HW9>^HC5wFTmhm zWVnDJl=%XzJAYmf{zAZ5F5r%qqU6xuao;yh^b;>qKsnW!b^#W(ziayXEdWIVRmZiS zahm;I)7Nj+2!Lx>Vyja8W~P5|7ysaR4%xsh9b=@E{Awuow+vaN9JqG#`V#YB>)VijwTE^Of`cW_%nO~*6OMoQfO zQ-?=x5Shlg-Y**o*SV#ssp+;9Jrr{v9Vhu#yvSy_cDXZtS72pjB^g%Sd$w}ggg5%r ziq6mP(;>U&gfLdc&&IMy`!+ijxAse(vhhR#>%ofUj)*D&M`vf@5AhrTPr&3^=!(97 z1HJO%EAQNGguJv)!KVxHNeBq2?a|QFD;zv{(0psAB))s6bg`MM(>yg+!%uB%YpZi> zXm7W0p^;8GO*zlnV5<+)v9mT5&lz^34QNjEs}e>BV3@%(moNtK}InE253XzME1 zvfa(8&a=^07vDxdah$E%nr#p}DY@M4u)8sa?vZ|amZGuwXQe3Dp9JeZS2+>P-#3W&`)y@q<%Izk3S03_n+f`Fysmas z*LlEk;&%L|ny0<{Y^kYRSBAbi6BD{-dt<4yd~hXuGtssoj$BE^uY93j zvoIe03K7pKogvz_MasWRi~k!X%df{AXav|Y6;?7J zRYCi3kcmvZdC}1(J{J?W5NxV2+09e><+($rqHr?dW^Q3DOa&a?2y`; z?krSf`#0qOo3}vuqu;t(67VRjfmKz|`QZs6YRiR?*pfzK+Xx=LsH){;-ed^R)vkQD z5RpXgO&ZgfXRPz{_3To7l@p5A7Ufu3D!rFD_P+k1-POX6a&$#{PG5qVcIO|6{={~w+e(@Ho#9Iki%VL=9MBLnb%7tCsr$qr)!gFqT4TKUlm)_}oK(^!MO zP0VQu+qE9!P@LyzBnfZ4YYTVD={Y{M=vYj6kag9@D2H#&V+JR!mW`2m5d*^KBa#$L zY}sYKjH=QB&qKLS~sL0u1Ku70Zpr)HIl`yc9Y4$v^nYtaf_%cyG zes?)FXfoH>aSkQDYs)7-Xd35&d%|(K(6|WMb2GDA4%CQL z*cD`6h{#;rTE!T}fj(+$Od`=NsrYoekRRQe_||m6Z3!WG#X_S~2H!p^FmIX^cNZh7 z6|P*l@<{F;J5GFxzbK3E5?16lzlP`FTW>IhQM}lRMqFa5})T3uO7XsJ}Pn~yKFMO$a+k( zlaW`y0=bJU!-t4#bZVqH66tfbtpjxgzX{>$eH)fjsZTlbD7}I5Fn>devIm`V= zCAR15%yTMNZ{&E4a0SWYaOZ)TnwPkg%~fqpUc%@(_5NNt>zKoJxo~EdjLPH**M>mx z3uP(MhGk^rRX>NMe0VS5F(ABc;yLtLLvg^mOuUHSzUL`XTdn#*2s8{@4hvT;AdCjF zrS_<4WvivMq!0^jWnw?$YTQ_1r&0!*BwDMx;ojt#pN|9*E-~l7dKNo1H^-X-FPli2 zXuon2re!pGBKz4T?6vEAJ9FOFt^&KOCDnq8)|wx=e93NxpXUhYK3Jvbto!boAA5-C zXr#bLvbkd#mO}rd<1WY&y`X5MsEtiLDD*yhVrE7$$7>~H%5rCJZ+A{DOJfR;IB4sG zNYkgAiQk=w?|2cn{HC!t$)BxVuNZ=sYhMHUNjVx;e5cO^kLcu&-)MDcpwJX}^iFzr z0ZdhR_ihm`Yha|^8@G~aNzKN?nAF{@ zk;DFpf$;4-a%mKUv##o)lFUWAxm4bY`P$+s{)b!#Pu=H{L7rz>~yaXENoB8@EE)19ZZ# zF_y-?r#H~+*V3Cpr{S00l$f&)enP(77ca@@}Ae!j5n_264RJjwUqFr zMPJOw-*sAJKrb)Eq~h978%{z_93G( zqRG&<+U8rBvGy*n3S`>;e8e26%Cyjkg9Z0L+#r;BTRyU`Z(^!BE?G7GQWWsiQVZQEUDuXjNFYO#<9UnIsXCrY7+nAFk(>*W{ zD}=SFOG&pJAp+j23N$}b*jhiYilE_Pr6b|yYXi>8M;IJbN_+g^Y;T-;O(H0|Fo||H zR>qYp-CV2uwP(wLD)Z~6x)jzEYNq;#+p0}^ZyQH_m3hWscf{b9-}UBV&aKnk-Wx3j zfq^Kf$CE&7)zTSJw5yV;=xlmMHYaCEC5|HJoG`}09kBT6OW``!m4ZO;8Oyexk*K>E zqHesk6u%dfU9s3~)G_>+X|N+%g30JLu*|Ho*OK)}UgWddFH3?En8492p!NcH6zw$V zI%b62rf6x%3)~4@5|hAt1TU>Ty+V53#H7q_;|X@q5u?{o>PRrJWB1f{gB<5d$KobF zKRn6P*3!5lYqqmiz30it(FVp`Q_JopO%Cb|ZFV;{1TAqAT_%@wW_R=2T}MgtYz|So z1$yawEHub^D~SyI;kMz^x-SJ!V$y`Dvls3$j@|Ox!0S#`)ep{tR{6$9mjJAY7T5=70Iy5Nl zFk_3HAS$g1IaA!si6XwwWgdDuL>uvclS3#s4LK+S~eOkpQ!e;p}TN@{H}!8&{Evmr-M1tTfOK% zoWGRHCxHbEQKz`m`x^1p74tVceOu&lj{N#`Ne%9iE~FM1oMiJ6>GgNZuh1STT4&;z z;x|8~Qr0cF-7Xfc7(QVRlqZM0`~ailCsokXn{Q!_Ct4VEnZ=eiyomr_nd~~!)(da5 zAtjvBV2N@tSWn2a_W$V&=v}u%tkTYav#DYOJi7Bxj5=RDQc5&m6s!nx2GE^>o(j6F z=XZ>io&e>z1=SC*(e}dwehd{bZHr-Tr!jm2Q4}b?m`nGrqS5iZc3~P)nYfF!sh`@{ z>^M>5QS#wjNUdxTCj#om(93#(io5nWn+|p_dT9H8<#GGg(D`u?cLl|duK{m2f)F7u zX2VeU-Bh4q(jHt>GYyJQTc-3WxE2*!^2of@8tNBhbDc&2;V9rQpy3p6#IPW*S0jzpY1B$@Pu3jBvoz?$4=A)oqY1ZrI)oye^vT&0Ce*_H@*r1GYMU4j)^yf zqH^;ZR`KlGc&SDgzca=|ExDeh3L+@w7?K z`1t#XoI@WmY0q6}iZDWhMJ^`-G0$9-5KC{>sllW;IWhf&;IStcbD6k2T66|GmaA-K=|n$2I2gsF(P6Q%hz)XuuJY>O zw>n+W9U}TXw+9LSNy8yyT;-J346s0sZos?@Ec%|#+eKBUuE*8P;=k)xyJeDfb$ z@~^oB%XpbD=UC;*xfes8jhVONDERNs=Lqry&>3c=+0ndighUEz6d+XY6sAd0QjjNU z8~SCkw!4kLe%yW1*pID6rLSpTqg3TQ`EUtD=<09$M6`&0&Im4RwllQv{LM5CJRLXj zOnX|#3zx}k7XbAr8&`bSql%k;7_x3rHxZA)$&@c_F3leVY)2tFLN_8L$!lc9F9MBEL87J zg_P>qygSt3Ce&w^l-O*eW{8+9gVrJ@QxU^2jo$ei*v2SJu6$zEDgjBT)9T9j9$7PS zqAEigmZ3Bv+Aja3M;}`Zb{AO-XCwp$X5vtg;;wTlQRYQxp0gYAD`9f`4TMHEk5MJW^1j|#qKpOfNmxA?;0s!ac6Z`D{4vJ9R;qIc zpK%?-9~*AlHXcA8feIine##y=@`fq93WI>=ypK3rMl|0?oFjXjJbMRjXE;z>SLCiN zOg0RQm%LGdqEQCfv?`&v206WLLtW>5vae~Xf9y|X+7hW=y>QGUoZHS_RoD8<+Ate` z2}nAofUv1p*P#)c4JL#^48yq7S%mzJTbSWA?LoQPejG-nX$e~X)Xf&iytu}duqke9 zw&hje4pwpWXtdF}(it}A^CI5Wh`gmmwQK?1Da}RX0~Pb!>L1e238(ZyK^z)4SufCB zDoxX(X~{~HMdo8mB0wfn_H%ZHvR@Oc%C^DYih-<_Akix>^7@&{$s0MYA1t*s9_#gb z2Ik)NGf4vPr3Z$;3xjaTSJ$mlRjRC@M-Q4Q&;`d zS;dH1au$W$+!N`UV4`x#6Jlki6cdafRft+s0gjgemP~+UrGenRSqqG*wMJ}D0|G)=B-AgPyumbz9X@1lILncJU7qNQ&hC( znHRR6g?ikp)tq{AEd5x}<*%~UkY0q2bv3Y|WBhdj0c59e*25lX%v7ZT~}T^=kfi z=t}4g^g6wsfV`6(X@y{lGZ;O33?EZu1m)tIj<~Az2HSIHCLM#TJ)jd z4U|OE`?PmdciD0Aa}(9)%|MjG4$L*K)K`Ub6TL%lr#s07-Dp{extQOuWUPjdGt=op zS>advVl5XW^rSSB%AAcMB^BPf(~?H ziq82KvFQ-oJhG8b&!oZIsy=d&3Z$gS8T$~e3BF#t`w?dZS@1GUtdxS_cdS}jG<7|s})mh5J! zFcz#98e~<;g}HU;UznK)_S?O->xVw)2=x@rsTctH;Ffo_{FnRoq}|sJ+?T&IcXC_t zHM@*Vz%y1^BI0Cc$TcdTcH*}kIkX3c-QGzjJQzGefxLB9CYkyTwd##?iM~Wctm-ll zNoGkuO6I~s>FR|ldvsjPfaI9fb*Hr@DfFlUNcp`&w~F5J_dxTOK-lEg&!+3hsgDqn z7P9MtzE0vZg`MZOTKWaIXj!J|)9$#>6zMRWon1m6!Rh>#elR=>_VQtX;pr|P9ZTrN z&>OWy>4GRBJ=@sw&E4R&6;MpkjIkrdW5o85REgG=a~UqY-N%YGLl}J8v5Q3?mKZ!? zGyJTf(HT>X9xGK#?p(8vknxs9+g0gB*+nLof-j?y9>9iQ1^)yjKQ3*B(u`iRwMZP6rsN_@dWI1$fNVPDb zHY5PYB5`N3D_8qY8%UkCfbB>0MUDBv|Ja~@t68cYn+ExV zu!2gvveoMDx#85Z){8BtFJXc6mXzj4Ygh~Elgl9e+~7fqYuro=iN1n>I>az=ANh@q zSi)(#N8_a7;+OXgW!UPj5OppbJzE<6@H1*QFT0{5h=+r03#zA8R2|;0eJ&@n7fz{^1_&CW(;B-dv+%%TY1Yg6uMBE}*oJVSdior&+rgm$IAUQ>^G$?|k>cC5rA;Tdw{AYKn_ty3-;HT8Bk_-yhCG;%b zCI?Qe8xJ^QxKrnl_>j}yB`}cZAIWciQqU%rVOQMXFtPxWeIyh4Ej%}e)*FSjm*4X* z9J-*XYb(33Ca?A^)MA?Ur=2LW@fo5@p@3*!UB9ytV0Zb=Fh??B%@@HBH2hGB$HqUs zy&d#{tzBGPn^-PMmYI(;vIxU!qiG!!mi^PQr7Flz^w_$3$BCvb!@1RM)A38p zVlgxge&+P6P_AtALYEJnu6kLRit0&6Xa#~9UYO<|b5N^mM@%ffE}E~+mxQNh)TY!3 zMk-a@yMYgb_94PcxbBNBgj!*yEBnLNOr?rN26SnVS-0bbVA_GX7CJ2G3QuC~BqKZG z$ixOu+J4!vA(zkw5z)`lB@>w#NWZ@Q2SM52M2=Xq#nAd>rPHv_9%2<9MPxirxzanl7y|M&!5I3bG`s1Kg}X!I#f)x>1rg_79Y z1~>kHm$&^I?fvyL2_?sto0(_Xr52vBo}=+;9E)?$Mmj;|g6`^?{GO);H7t64BPLCUSvL%RiXsF z8j^4q4jnIF8+2c?O09JI5@Yw2TXP1soM1Er%?^=qGFd@(H$G9Z%L?b}XedLL%z_je zp=Q`M+4tHc>|HJ7q}+H(QTV}7+v@Jld=ug;<%y18#GF@lUWIbEiThrKmEMmAUlCU> z-lSE330AeTRITF)k>O))knRhbhnIf#W+#>2o~LB#R}|_!BYRi4_T9qo3?R34@|K#V zb&fidvRWCU8cizt<>~2518K-D5Jw~F&xVW0m&cPr zQ$T!PbQ~i$E>KI{#hv~|DMo}(a;cS{<494suHAe8D->OLA#o;_UNh)w@G(n-zqoe) z+?A7(YJN7x6$eR05?ay|yZgN5+c?q#<$~tT{AMRlFpl8^?mGEF*)E70)dy4{Ut?0^ zy~>V@kA7Z7fMG#e*nB z$lJXbmUexJFjMjcc=$L&&cg59(25T%z3b0Io70Q3Vog@YT5Eg`b^>c0PYMdh0>D@q z6|DP)uD^vt-9Q>!$8_W3t8uF8{>DlznpH;r`wD^a&8t9_vo{QZhkDpt8o}9#g_vTI zw&tLOeN7l^b0u#()O*`FFkX8mkSjZK#GYF!h)ZWhR5P1@AJ99u=QWvcKgBsL!{{wR zR8k32c@vAGc<61~@C*a!*fzTJ>HSmzFMdx6=| z=PeXm86`96?N`BNK9Gp4Sk5!kH0a z;N@D^^{l{=e+{lV6u;4fdy};3XjAjVnIr2#_d-QeA73COKU{H3PlYP^0nP8yGE1TV1CvmAg%3ywM8Yr zdw2wGXQX;aoT%_4&be9JfS7zMFmMv1Yp>W_;+DYQCX%$C7#XIa{nkxgv>K=@it0MK z-?5XFD6;Ch?KST`=Rn?9Pr|DmQp&8i;pD@gML=#m%o8VpmRM?CZltq*lUAcYK&fr^ zdU5SpkJKK|?E$pJKJ2eK!Ty#L94rhgr@x#QU+r(AHaecqSaoUCHQ1HoVpub2xUCHa zO87^)xP(ViFE?N4X9oe&~MRNW~%D$LE3gFl)X7OKW*ZR`{5Zh z?S!nd3YHF<*lYNhA<%s!$Y9%u@9OkgcKK|&pl)=_qU1a%k)bekNCqVgHkemw_KLv1B~?EbiBCAk&kE~?ppSi78s ze*gA%YIyv!1)5uT4v9)F*;5Nw`MuV#oRPfME4&J&D6lMYVH!bqxD6#0HRf5hoigL{ zqf@_*m#21aE4z=NSKQuVoa%4PFHKZ&R2@=}%pwRxQZr=DEHhB3pKrH!$81AY%| z--6CQ8WcwF`RLQt?|fmGzC(PL?HZ-egEY!s?WZiY@X}jGex*iLMU&5CZTXT|Vemar z*9!dY4!toPO4bkXF+sg)DK4O#MsX>yLdw_E8|1?}k|XrBiEmmSnztKj#x!$Qt1e7G zVUIg^IV-1X(}Dm%2wY%QqvTGn(O|qiBJx4O`AZDWEtp<3zXwKqg{NvAck${5NKPgL z%NkGcYcpu-gAn0bMo$VsDUq`O@tQUGnf#0J7-K)OByB7(J6dW!LCh9#RZ+p(+>eB^ zwSdETyOCRc0MvYaez;*-QFuU&*dXwg*k03`j9+|GzHZ@-Iqni{KGGZS6@1~Q-`&Mc z$ei5ogY;1$4-~UW`vzx|j_C(8%z)z0O@b?(e*B>PZ{wQPo9c!%J_8gQTCXcvbTa3f z9B0ROUe)#4%HUC<%C^>5Ti|>Q2QnaS_c0d&&;e$)XnAVEL2KwI7+0a5)qJ)0)u+;$ zJ3(t70e(rvFBRuWyQV{bL=yGisUR|jKaIuNtkqo>pD>dbsb5@WT}hGLuP78c+X35 z;i`h~-gA^~h8&j#0&JZxf@e zw|D4`N2;fyXwX7`bwK|~Y9oQ2WZOWrA`NL6jIm4b!M zMdi*&ei1B6kI~0JtzN{W`DJ3?JvBANgD;>$u18wh%$e~9_*ieW_7+=sQ~#(GoszF^ zP8bIXme@pVN$&JaIWxi9c<6L+yiN0^dXcfTG)lu(NKz#LrkGsIiCgHO>U?G&*c@w% zg^I;pFuW`VoTN_Re&`(bXEUSnsTUd5=B`a0rhHw0As`-7CwG_Uh>&qGh|4py7>%z+ z+5*$xU;_=nS`~cIK3)lMV-%><##f)NJvd03CjREsr9qnz_s;;Od9KZ5>4PvYCr6_a zZg(Bve{_jW@G-0^)F`SJdmG`+1|evQ)tjuU%}Lp?^76SVaY^7}$|eY(XN`&4N$fBq zreX`|G_zb4b6(yJ-V*k$TZjuimJDE!7Pi}u>Fuu3$OSH8LG3zdieeS|Ib?=4m^NFq zkPb6OVz7Q$FO^Xv8eV2jfZzsN_(1iuJPaC`Ri6mX4wD0MQ4Dt$-0OQqM(c+ z@y$E(j&o@t)MFZzbI7V;=%o(Ggj|jI>_(3@cJCu@8%J`07WN10RU-!%Xs8X>)8<_^ zCsL{6587q}fB~HEY78D=e(nq&48y}g#4h_lXm%%!o<8hp9nWCrN7i;qpGj?Xl0PQndFFKnhHt~Sff%5eSGiW-UfSF1Ln|uvl`dv94$1e5T*)O#!FR3X3pN6Uv zIEFz9Rx6tlOg^!YVp;Wq$pkCnl0+kG;=@V4Mt&7Wcd|F5Pyw8kw)CobLYE&xNrU*< zp3)c&LY`%T2))$3JV*8Y`Hl*JH=fI3mN}J9L3JTh6c|CGqC8KL zM&)4g1A{h$0P_|&?=*NJ{qWfQJwJ*Ju9gqNLmmwa^^%mJ_JyRsH0q|^BR9{V4J#Ch zfKX__nYyM~1?a|3ALxb1jZPNj@nrAG(ZSTvv)ao?>vUi9b9y&ME|9Nj`a#(IXjwk% znhRRf!%H{qu+Cjm_SG)1Q8DMn9h%`SQ)$vjc?kdPjEl9b;Fsn#FSV2Uu%fOT7?C-V zJ(rVm5b4=i$Q_hNC3kJMA=U6CHSa0|?4v9dw{Ny>?4tXb*=T~RHTXKnJwMAn(4ED% z3fhAn?(FgQNbv;PUs;t;G`*e ztwP@Z&{BoOsIlvash2*6N`J}pUd3qq`t+0d_48dn4#fY19xS-cyy*LkHz-(bdwEMF zW@WCvXIdg%s%#EoAZNqn%MNXNk{*~T~0mh|!!96FYwPw7~a z$A><;GtaTtHbIT1IfL$J+sn_QA&#<5qQ0pMRq4lvNA0sc5GNbJy5kZhxKI)OC+1 z=oGnH>Ir^=kx z2sZM5CL9#3vX;tgI@XIe+zJU&QEuoJnTD9ju$So={R*uWuVNLV-h<|NV()y zc)aX_+D(Xe61(uH)z3!&$E0Gz${isC(+M--I+?i{!-a9YCniN!){*#eihiu)*%m+1 zOVuYrUX$iftJ%zB!Fh9#8XHbwx1zB$HId>ti{C1zl02?ol$P zz^!nV?{SLvTzy|KV(DjVY_N~KBxt&+iQxckgE6jp^y*p=Hj#YW zuRrP3EJz;d5o421t*rK28(0QtM7W}hZy3v-ga)`H%VeJxvvpZ66_=iZGfJ<9VMa{! ze;QWwhY}b)?&EbkMd6jGDScoPb9=Fj9}xk=$x5v?z|T1h&wY2;Jczsa+QB2u5t>_( z?CnuCAZ;Jp7{U;(?=t)-N)Nz6^9q3xqdYJW#n7kK6FpQw0Q5oC;y`yz2Y?J={C(9W z?|jAu@hSlfp{DIIh7-dYVrm-wde)UI9UvytDJ{b&p<_1Y3`E(cnL1U&#P<}Ug}4=C z#RMCuU}it>z&-fQrPLNb=j7D?7acSM6wz&Gj z&%PunkYm~T&79`r?XORSnjRz~z6JGbnahBDk)!4r)Kt}TTj|nNUWkw_$SSEaCBW{M zKiyWQ@P6$zanrye9OWb_!(5Bx%FMl-s+ozn09UJ>;bOreKlvghKl?ie>|^&b&kRr3 zJFBpFWid2 zJ>^tYDE;-h%|E?r!X40DcYnm{=j*V4VYc>+Lx)$x3&cU?-M!_l$jLwMtljhw0XDPN zYd@F4KA2-rUF9~}7X9;?U9!lBhwMf#$znykga=- zto>IN<1=`MtlCemr8;tTZb-TzbYC60kpo1cV^8f*9yDD$&@26waxz2UVuMFj;M>nP zK^mFuLnI#?cV7LehNk`Vr>WgRfD?o3jg1T$Szrt3C(c*=wusnSvg!c2T%pG{T&YJ$>9r2q7`yIH^f1Eqku-;p zt(VA<_KsqjLGMw}Mhuvf;E4IQZHIE1U0FdV`pykHW1}(+vXcIj?WFs#=sx<~PYX^g zL_I``;#EqO$VxPa4u4Zf7sepps>XYKDNu?8g8uE7q^TD$b6x- z%Q%jet6~{lveah}h%T!5)?w=qEi=NK<(dk7`%Bnnvh3gBl#4g^Aw&-0GHpU%YG97P z3G-6b&z!f#f1_F<7hw6(yRZ@7%)IkQz-hE+H6`^jmydkq-2W}}fB&C^>pn5&0Uwqe zNO7Z>3P8H+_x?1Q0_-Xbjffi9`4(x>r_tWeU~t~zKc{E>Gj;RFPk&-3qw&|(IzL9U zn86J9^mqHF;v5oXwl79;4UQTpa2YH)44TYFk5zu7=C?EdgBMX0X>Hu~BCdD5J{XhW zK;rD16b*1b_2$b0TsRVTFkMuV0j@Bif+qoWx8% zfY%-D-^VorXC*|&F^&a2_JP>8!0(&+@DaU{ut|jxo%~FH{OgbX{U!ddA8uzS-duZ9 z;PCt#4fgqh7EDZlktsOu$E^LiU!N+%z%1eHf`WZC=YGF)GZio!?b3yTedvhqskrG6 zoR%SETif$L_xSz$-&Em$c#&`kI4y_YIsB`K^gn$1f8Eko0UDU*`F@-2PwY`7iVAM<@TGW&f`- zPfbmgZPa&GOJ)qxDq`0nbY#+dQxTS3@^|)Z`RV~Q${nX}-}=2Z{a!1{m_pJ5`e_0i zzyIextmC*NAzd>2T#V)4O80*>l|F4?Qs>NZS>x|5)8AUqzu)w-n2%58Y;er?Ei40^ zHXvJ<3{rJx6+zAo1{$i(K{aG%e%Ck?fF{m@u^RH`O;1?}eU20Jk$QqPph*2(-*qJH z6G$F_hIsL0*U?w08BQj;KR#sSLt+W0H4hKcG6sfkW7YmjOo!LN1);*x{yA$&#+?W> zI~qhuxY|K+!SfTK!8>Xc6z*1qxG>TypKhDi`W-a~6B%yYPE}GQl%TZlJ(F!-8Q@z0 zIK1-+8vc}?E&$N#<+y}BDZPDxfzX^;il7(0_(z9u&4UB5xHb^Ac7mFeF0pTfw#C~* zFi)qXx4J+>A5`@w0NC?d5O0-B9{`Y-g9oDze<47XMYh4`D+-kAM1IIQ;~IdmEbZN; z20KPxKW4l^01I>i2#aQB0B-|fqD?2EbDoec4Uhfs&FTN^o%-;c0$0tdRrjacpCPPLclQ1?}0<&y-Q9>pDd%X?cDit+|WPBQ9}^4Z)S+h&*ZgC99T zs}@`iV7OG5M(o(L6t12V-5pjeB#fOHpsbGt@GKqC#5TQc(Epzbvd$M7+^6#zYM?41 zpPk$fjI>D}OZwps6YRll=FE(5tXJ#lgVukC_De^S>EOuMLZ47K2~$6oyK=k)SLTMQ zyQDP!dbDo}46yV3nKJ&d!Yb4bh6(;gRD4i2W{?2 z7#e|l%1SL^@hMGoWrdTWUhni=e9ztqa^Z2S z08k=eSnGGEZHzFh$>i0cA9K}PqPS3D=MlI5rG@qUjyaDp@d+?phFb%-gu*G#Agrdam|zhfD5l zFO9g7|Bx2UG6`J7hn*S{zN5cPf|pO4S#(|D!iQM`?4S=oygPL5KXgqp6NV9}{_gle z5OaRBURnozD0mw)(+A9A#t(1q0dpX?RlO7$( zXB;SXlc)5aHjsCnWpAK8I}6P_kD)`>Xy4dCqP;jjlr$t^(PQa@rWR%rWN$-LL!~y;M5-t>b@Q*S3C zN+=M9g6YqM{Z%`ElVXQ5pr%t_&_4b4zKk)Es2`n9gYYg24KM+keitker{8rNfF9e1 zbV#F|C9j`5D37P;IS&+#>r92F)s_P!N|$z+K4^kj1`~hsAgLP8grPZ|V0M*yC9=W8 z?=D&dAM60o>qS~|ri6Mes|Nwe_(k?aWz}-WO%t*;0vb8!?)*!R+Z}|$m|r;CmkI*T z64;_Fdg&Cdhe(XRJB(J)LansQj`eaL=u4dIICb;9YALd7qGt`PsWR;ju&ZVa9sa(5_duCMUGl@7;N&>+pvO^9|nKzNkE&dc~op}xOm*T zU+8jQ3W!YE>>Nm6c6BMU|JWQdw`a=i-4`zWzVs2xP8Oz%26A9mL+CI`qt)R_iwroH zj&DS8g2H%{@6LjSoM5tR zV#)HiF%A})<1X$=Op~HpLKFk|c^>v2XxHaVHD>kAd3g8)XSi%lDNwK^rmc~xnL*No z$z2*?xX|vL_nv#;v3j)ho=0nOKj#|59^_m)M6;w>s%xtCPkr&7kCDI&agmzca2fHe zY7?6}z2|)oSN~*!Fc57dow5_a-|TZX5JI#d=dJ}I95vmn^D9j zo+3>B(t-am=}a+BqI4Z41v<*FZDeghM|Z%mA&kYfGWrAyR_}-2T}A~z7Sg3ZgmiE` znmKNQ&>qlme_@X!985Ekd)yxXP2B$aLf{NAMPw0ef38?2_n9E$m$O8V)B2#NvdiJ& zv6Y}@Vj?k!hcg^Z|LC=A;SN%nI?o`jPiEK;L5w;9{gfKN{e|5efT6em97>q8o-l{1 z^yb1z%@pN${nl#G`p&_*G`b4LRPlL&ktj+4p=PaW?gR+UTbr5n$Tn5sRfo|ZhC#NW zk5EYIm#4r&>P9J1(06@uePDu>uEx|(qi~gK@Idz}(82m;py=8G<_Gic zy*uXJ{DCls#i5lC_2v+jZmyOPkS11SG@9SH9Z0uT?-21f58^K`1b9DhxNycL10^>k zxeUgw6sNtU^TBRQR98Wl|1Lv|01xpKg5M|kQ%Ln#$<&RUL7$7=^g4>IYj1uuTnB!g z{H(&c&1qmr%wIpEys;4a(xNCW%^i$LI-S9Kb(7FP{nnt`tH41L7r@p5sH=INK(NkE zWobQyJ6sijxw)5F-w5k97{d-j3@U}OwiR$e1*fkXe@hbKA! z4ovI3H)v(imW;R~v^{jFdgFt2;9HSug4bCDZ&(DK(f7N!IS-;-<#m9U+6gb8(HQvM z6AaZkcclUJCMZo@yeSV55Z_7Xw?EBGRi3MrkTls)9&w(rbpXfFMCf z=@0~@mq-mYp`%pkB_t?SYUm{)?(5`sG20o)m^{sN~j!h!32Fa>BahUw_#v)SJC!5`XI)wiSPD#g?;Sz>m6mqn$ zG3XC@;JL>>sgc_2;rM|?knz3?8|$F)IT}g_!+wE+33{-=@D8;x9#Dk^6&nTGSZ>=y z%YIv^{R1>D*QGER`+`v3I4O$>F_$`fDyNX{`)auOVs1ZK~TMSSJ!VN(H~w{?m8&&d}~Bu zi<`r#mSGaqhSxxwnC}9eDwLp`zNup|k+lPJT(j`f>I3|*eC5|l+QK>u&p_Gr11GJI zAw?j!k7yc>WVO0kawog6=Q=cHliLqcmx!VP>&y$TQ(Za%PwD5*Hwko35gVBp7F)Xg zLG3wz0St0z=h$18c1!}Q0aYG^hba+|TN!Ye5tk=5&ybc>T!*P&-R&PTF<{Kn{?T+2drE57m4v;L1=XU3V6m z;$=O5@0T)eAuzgW9uHOib#5RxIvX+ebbWPG&jbgmm5K-LfRgLQ14Wc!Uu&&tuwYMO z12ovhyu=YzGL?}T)B9ZEj2d06iQ{>^nkjD#IXpn-tO6zbEq*IP?4W12-*u)h zmv`%~hRy7Io_N)p3@ALawqFw9seq|C4R+5ah00%t+K^XFmv*Z9*9H2V5Apz%-z2GH zX#vb#oVEibJg4g+VvWUvs!flnYP2e#H7C9?Fog zy6U!h@6QMpDL|nvfXh#>|Mb=8YY=;-zEd4jXQ8UK2p4aWe#(!S-~DLGGrZ73jf0gTvsN?Aj$- z$)L?jke>skXZ7UU%;!I^>8k}iM31nKiKqM)CFsyiLj}J)DA`%ADN=YFO zKHP^nWsAMi9>`Z`O0BbnIZA*s@_~`^8K_Ef0k8tgv$j;jNs!FVXi~6CN{C?(Q@^j2Z+s0!5t7?h`O%2 zA<%8BFl`-PmclU@s6CD{!In^~lnL0DMTwDxYOO+f$2S?mp9$9o#=QexMN=CdL%#By z2?Lv|a+iwR(V!A7@(y+YX8l|7EZ-y*kg4UsvV+@Y><%pDgOmSWX1&+P?tziz9H~Aa zlK4}o`3#LAcdYFTTg^~7Hx&qgfV}Jj6f-iH0#YLBY(QNo7;X`>$;$5l2xary z)MaM1)KOwDP;JQUm5`|#@KdFrk@W6UktB$z5mYM4byMt|Ij(aIws5{`046F2+nJup zn8Smdg*U(%z>!_{g(HpABw+Z`D+GeVz=wRRjb_eCY%Yr5K#JS~mzG+Br2u0FHEkIQjdjbF5*mNkKDU z@?v^G+#N!tK>>&gx?Tjq&%r{amN+wD2sBsq)*;-NTN$njkw;G9#R(XE@61ukJKj|Y zij`QQgYm6N01ydsl*6gOLw_Nk9kcHIuEj+D2Vm%D>snf%-VcZqoCloDN6XlyT`Yr* zBvz5*2Da8aT&6$b%sBw6rGi|r6m*eCh<7CNUBtV+xXNsGuQQKu>7d}N;nXPRlRarl zq-0@VUOUkJYs6d~VwUM*J+5bC$Jp⪚qUMZdX{FuN;>HnN>@=5TBVV2B6CEWGaWp zG&hNhe{f?*n z@2uwAuZ)BNLbOu9`t48u_C>+->|3ogbKCJaUU8L>-~0lh0~D2cq6k22q1~!fw*?ZoP;3^;xUuXyUSrnrR{Jf!Ov;(v2H9)}0NssNk?x6{D6fW&(Czc2Y>aW$7I}W2LOm+aohw zQzFE54AMZyk`}PH7zy!RnEC;eQbYm{sx`ns zv7}n$xsyf)4K|bpkZO~w2t$JpbVNfNpoSIcKy3^_bX@M|vthRb@ARI;Q`RQdj6CWB>Joezt?%vXUljBwn@L)Yh{NVKtU7itbpSp7bn(h_!6owO^JXH z2(fwzi5*NfZ14piK71;G~? zi3Tnc^cC-U0BtpzdOos-2-%rC8SH(h(o;L2gC1OfXp(`|qVzanu^_{-8Yr+D^BJp! zW|fXMID;cCm>SLl%(bz*E*HYF^|(Ia(rnoTf6D`@p<6K8^FG*;*8n3sIz%vZ6>o~Y zOe^(9k7>dM4A@TCXQRcZdZrYq?DMbpTzmrTA^{*L{KB1gZ4GAo*qo|VmsqRn_FJk^UT1;>^2or1f1}PW@qEJ2F-;- zaydHO6q5uJGDnU|9^sGYd40;BP?pjf_@_m_rrCd`Z~!`JqwWQSmGNGB%V10fIeKvX z{!TbxG|+c1)qA34?++*+5u zZCam2cHIyj0a~ z2afz1OH3QYT6zPh$HqPn6yRY^sp@h1>be>(wb@<*-v?+;d?4@Q@NAvl@NoXJGI`Wj z=JbbkOf11@f?&HHN1B=aK_TrJE{1d0rRN_2w#?he2wq}SZQWwKt)(gCPf^jq6npMn`XMe{n+hNTjB^Gu&KMHq01ozemf9gk%R>HJ71J)UJ2C{s$#YoP z$tmkHkyhT+*jqV-oTs2ENe*iJ5OiAuSy*^O-r$3#$iLwt6~aIR|W%O+v`<&4oJvTATzYEciqN zVvO7oKo52SA{VgZydr9EM{9X{r)SwV6qigz(Kb&xE{*i#DEo@HZdBHIk5@V<0);Ey zst5n6t#?{k^gtTb3EFOqI4!j>MxfcO19U3I76ExLgTTZ*-3mBJPn~6}>W2vT^oqa% zOUV!yMJx!KtiO0Fsv z@f*z8fQ=S)zzwLYZyu)IcxPe$Q%xrz%I%oTVRN-{SLE1!r*DkkU@xmLa82tNVT~kv zoY&1+YK)r8rl3{d*vBdE>$r1etngc4{V5=bU4V|{$Y-Ac-o`i!3-(=t??7O-;VNkE za_#^SNYe&T;$#rhQX?q<%!x*TAPe;Zx{OCzq@*3WfdjcNSK<`EEV70D^< z;aDP|am7O%)i_yh3qpV*2B_JPK=oZykR?9%J@751Lq z+oLOARS^1c&f=#oQmh2p13=}JGPjjd@(^>%_XPtocK8=ysIeJNYwEqN>Ksgwl1`=~ zwIFrIwkMRjkiI}e>wl!DcG;jDmFRqST=W)YiyUm78;c6Ze^Vc`D)uLzW*m*4bU9sE+TX8Gun1dp?dGA;41S4%iQrdVo=^ZC@5`T3jhj9|yZUr$50fvE&8J-8LLDB6-ADVkB?CS12O5Akkv?8^Z~SYIPxb9*K~qhU!<&CJd%6AO|Nkq` zJf**kMN%g7yhfLUk!Hg0mdXCVzIns=yZFM9y_aHNai3+CRnt;UQsiRw+9!v*v!;l8 z*cnnlb9~=h1GwEWLUxqsMZZ0-;GchYA$4ZA-}qV?|kql z16`RL`ejE0)<1bHe?H)7va18^-~W@p-eG+t7W!Ho<^}V=yo~nQ8zzr!itg=V)V;Ij zoJ+)pDmj>4sd+k4n}=$PDYhv*|1ZSm7`Zp&Odx6SNx(>dYwUez0a^FE1zoesMfq|) z6ib!m@qxnT;3a&QeoulZ=T>7}?i_6H>G268R$Y^V6SwBt-O3NXp`Wf{LxiNMXyjvf zq77K;t9uLB*E5JRrXv{JXRU^m&(|CZm(jPiw$M`Z3ABag6T+N9Oqn`#E)G*62XE1% zHg3>#Qo&FEUqRuE7HE|yhJm^MkpvAKLuQ$?M%ih5qk{ALt1s1Od{(Q9eLYWpC>CJ$ zBWIYt&Yg&Ib!yl>O@o&e4v438gN6*UgSw84YNCN5>UA4PcD*Q+&|YWF)24V`k6 zU#u(k-1tZ`eGA(l-G`AI#)xm844)I@311E7nWgHp{>p;W2;A_l6bxN@qNy$*aOe76 zi<#ae2FscLBNFHdxdjAWIgilRyQz09^BmscIq+1)e$h5MY=v}>Ts$9^^Fz<^={f#` zpHk$NW%kFk(6rH6F0$^}+Wq?hglP{@1B1J>G(OfIuXCMR_d3;tshECBe4INIJ#B_B z)~=a-3Lnh6^=n?LS=*0o8Z|XM^MwxfvTv(U3P;!MxGV=G`^?r!#Dsm^Ii}Y^%(hEQT zP;Cf}@)%RJ`OGZSM{UC5f8X;zkr93kM9|A#0&(r=$$tN-LZ0Tpz0UWw*po*#)N=i5 z>MdbbC!+f$1q+Re&oQG@Tz?_KEsCy?@PYMnYP-$GbLKqGi_2G>NST>_%A498+|LuL z`Wx*t$~{($sDiQIrUa_4`sTRkXiD7d!n`|IPz#3rGuz?Lkr|ZWQ|Ix5+Exqky4DNv zf#H#;*J20U=O!&f1!vRmJDf4Cw_&M$)+(4w+zLpnkZAE8IG#I`cUmMV$0Y)Ro6Vi% zi7{Qumr2T(u=sFHbX@RR5$X;4@kNyuXY*gpn!;D#p;T=U5j}(G$xj}ZHRD|hz2{cq z%T5e13qLzv$5$(t-csMU-WA?gBD11Ou~E0fyBQl4Fk@d0-J_67(#OW>)vS=D5*+ zgD>&blwEf8ayYzdC%f>>16vkeJCnj|zIMzEVfy7|qS zExSDZ7b*k@Gf&JV$WFCdjDS5J_Wdp5g2CD*7{11&m;G?Hg=7PJxR?RWuJAFGUWN75 z10F)p*yf7m2h`hErk-_6_*~F&k3cofIs{>n6w9r9Bacz_TF$bW|&tKo-?zXX@1d zR#j(LLa8ra@HhN;;i?Nu;Z<8blLNG=AMCNDTl5k`Mch3C7!mY3&j02j#{o>15eL6K zNA75}MqMFGD}QVY#?AgV2UmrYqp@g2ZO0^CL$q4WVNJ>yL&RVyU(PytnO)w)%$C)D z4JYO_#BwU!Z*<9N1>3Oy18s@-g^Ev6S{aIysUBNy4@V9F;0XO*IRGbD|mbn!B-gA=dl*zw3Oj56CQ6;u^8MwpcQ?2 zqi~U|MmNYQ;b-oA-7gDd(Qk70SMA(>_)BhlhdVu&yVsg$b&~S z4zFW-Yn6p5j#;o=kCfv4r2vzxW7UEeEQOyqI$JzZ&vJTjx0`|E@}=U(_>$hZ+~H_` z{s70OXu0*rq4l#0gSXwz%&PMld|svDf8?mrLMU6v^Jp7mx>THeCU?A(3oeFjQfIH7 zn{gKk-mCVI!&skV8o?#_ap3s$tsAJff*T*SZw9Hdw1ykIZ_T%^&!0UxcqwxN?QLPW z>Y)4c`xn@HVV`2t{qC>LGiYUHU&-1As+nv!j?vpE7>FCYMAgckI(!==Cv=P zRxAYL4_j~zX&Hg_zAa%Bwk&uwTk+|f_4P%)l@^Bv6sCkve&vOEyI#48@}OTsw3bw% zRL=TLhMucCkvMhuX4cR^!3BBD)Yj&P@FwLxe0{1|3aDE}Q zu3YP|`vfK{u-p7oZxJ1iHFHj5BvmShIF+3{THEpKG{x^~!CC}dc}7ey(8giI2lU)! zqK`-z7CBydIr#K+?;tDx%Wf2b%bq=VcT)oOtP;98{mYl}#v@rf@dN#UKa0Tqi3c@M7*h4^|?z zefdKOR5vmILL`o~+_l1JcadbG;XSzSAS86KR%fogQ%-+xSL5tK3mId9ZV{v3FXcrD z@M*oUgL^t)_|P41d2$3EiNNrj73pSX&dxz2DcM%nk6Ksv#P+dw#;|p6+$zp9tEtE( zCRfL?j!d=T8C5j6%8ez?ZDsU?7cU}p4?Q|trrR(TZ_KJ&!K>gk<1>mrdGeDD=I#0L zW_EUD&glmU{TgvD&OXchmv_H@EJN!X$AQ9E$TF+4dAUu5-x})SR|u=FSPD8Et)X5u zFeXt~-{in~Xg^9>`M`M;ttzt5{!Xfxy;>D+cs)ckadt6gxCfV>Xmk6RYrkM-(6q|$ zo+E9`sD^+Dy5^*bRD(p(fqgy!xVu?3cm!|m$7+QjJy&J)?KXEph#Hr5@ZOs7(go}q zbAVNeZ&k^td4q&`Rh=Iqm_HI^pj-dSAm&xrpZ2JEF$8!UQy;Z z3ux)nLR%j>b`#>VO@aeZ!u%BqL2~~l-}9ehNNWu$=DinlZ~(sIn&#kNZ<{`m-xiS( z%G_EcfWDAJ`JnBjO)Mpr9#lFUNQ*3pnZ_*0BwJ?)vfytV_{6+V1KHvnUU!%v0L}~5 zfsqrc8dKtOx3I}mZv|P54Mgrq5pWSo>9n@}l{IeIoHF#$-v!IW35#vDi!eh3rL<7s zk%8@bySoDXk6^((%5w#VF0Nw>Z6=02#e(67o;+((SDNluxs^TSpPiCUZzts7^aII= zR~?%tnRB*v1w{X(yKVUvDuabZNs@iZglknjtq7muQjfg1FRQEQ!V8Uv?KUdF)5R=H zlM-#rtTaDe@g5}Ms=QOYaip4Cm2Um6y05g~3KlrPlVLtqpYD8~4rUvP5oS_vfz$cD zK3Xjp8}hO(7-r%_SiVA1iK|m_`@2D7hp8!w9niXab?_B3w(NvHPbj%q#t+Rqpjp`S z5Eq>~dg}G=PwyvmaK~6EuLW~Tt#1p&_OgnmBtOV-6&)@Q4(FJ8o9=6|IaQK(V*K|5 z7aOZ?vIzLP*SZr}C5dA>{uyPHmKwcx9xg8y)h(&ejCqrjym_s?K>a)*;cHR7PtFmr`^TUT-&E-Gi!!MP^fdlB* zmjgoZxSMW%7Y!X(-*%MNjSD(eoz6FFY4m>~;}>Ue)rQDNx84eWjLaUypkO9WZx-WP z;S{lB?>Hr~OP9Q5dku1;5E=)5Ao%1S6~GrS^lRC9EU(?(SYO?Nn*K%L=xfSWdF5zY z^$B|$pBYV5eR(Naw$)-Tcx`j#PNb`XT9Yu!*u~dyaX!pxsK-BFPNt`U#o>6?73^}O zr3@w1bkbWWaIYvo4R2EeYu?a??Pgk5;^c9pJ^O=_&DRUvON-ieG>NgvZ^z!4CiUo@ zBb^|a%*ed$G>{@lIXbO5I}O2ZbGrMY=ruWdJY#8LsOPi(YKi6x9eT&;>gx@cB6J!` zEw+9RZ+YMEUSCSO&h%u+b3@T}q}5amn=s;TFMRMbVzoX}q@LtZ|J&pArC)J~>Jk3( z6n-&LmipsK3R?*hoU#Mm#t1(@A^WW+I$hg|pZ{`g^BqCnIFcqftJ7xO_M=wmEPHa^ z=dCdP;UEoUkOA^Gb9T+;C>-YLij8hHDE!y*?OF($WMAs_M2YC&8*JSkT<3M+Kt(l6y4pz z)ixAKmQtOPj>__z14}jD zW=%r{SKCkoL43|wqKcWDlW$$;Ww&U9iK{BoFJVHdEup-Zb7 z)ZE~nH{gyCUTk2A75JMnKkSU1Sv z;7dt!lQYl!JQF{na)$$U`@cgFfU-Qrr58Tf8;U9Jaw@Zh=io(%LMT@wuXw;V0u_ z0~htf98EFIe_({ZH2p!mB&pS7evT$MHJC|Tb7nCl`1*?>8p=jOBe^Mal37`*GOl&C zZ~0cjY_sG@SG8Zi4FzfHoXcu|`3i9;7b8}`S67H5^1heQv~Esd$m1JJgDRS@H-6Ch z(061hsI5S5j;Nk4z4#n27c-0}FkeD59At?Tag@@&BD6vX?53R8!@2!3;qpOy<8b$& zY(>|D2BX~ZC`8A?+ltlHcoo8_f7Kj(B2WfF@PDH9BT5oaTpu2Iakr_}0wGv*jQ?>o z4lU;vR@gjSWaM{svbl%JX{lEt3{JBqj`KADc2TM%KvKBgt#Z>unD@b+`C*6y%!i*HVgSM{Ewn1)y6EkLV_6pdZiXV9-+HXgx{33j5pM-ahNZ z>ES$>w}q^P?fNZ#?|-3Duch})Wtv4PTZV-!4da4O%?+M&#Dtg>3+4E@SQ#z^9K0nr z5E!SC{glb3sX9G)>uiuzlVJpYld?~`fmlo?5Q5AdB9DkH+3`M)I)&y{^{l~cnGSB% z_*AcVUfm%mo2Bu~uNAL(ei&ogpMRlGYhB-tV&v6mEMHZ6mMpEe=hkd-_(UNK$I zKl1({^19iZ>vieT^U-a{KsED=L&;ni>FZ+8xb-RXL6LEY{>npMrgbY3bt%C)fsc{a zfvqO?*Q+LO)q0K^vYPc5>NW(y*6ONMVur`*&^!Y|WzwxOa@h&)7P3A=y{6>VDjrJ5 z!`y|(boDb*k5A0MV~G1Vpd6#oYwO>NN%b4oQkre+?A|wEj?DJ@oYkSzNuKR1I5Vee z!j0-Fo)m6l^D6Z;7PzLZqI+ncITq$&GPqF_7=Ezr*#6;^9y?h6`JAbe^E$T2_N%VG z$t9#+*c#+F-4IJYkhNRF8m?l$p=FDnNvg{zDB1AM6<~|CiC`-c(Jq?hJ@}Y*d5D5k z*&6TB^W6w(m+vbu>wK8LIQ^NtI)i^H1c&uG7fgr}Raar=_{R}SBN^D>yTd(A;a6>N zTcj}k=0F>amyL_he0`s!Wto-TfuS71!m~3i=$W2kulBdk9S(R6t%mB>g~2KkB?Cg} z2DN>{AET4dTU#CLje=#vm$@*=zI)^h;gol4@9aK_HH{oOIr!pS>khDFN8bB22m2o% z6t#cq(rL2hQ4`84NO&tcZgbfvfjuB+YqHhPmlf02R>~WJM9f8{>P4NY$|6ir)bG<73^Ku zDANqgA70OPTFH(NHPAHguP!ogYOS`v&=n@Y7SxvMA%ziLE_pRBTA*!qbV6cyhE4D^Vg0%Q z#%Hvzgc+))gtVWRaHoVh zbv{nEcvXg^(i1=>YJIy5T1kp(78VvotSo+SdItM*McrJLRIO&7%E@V()~5@$d;FlS zB~z3do!6N%n%SYgQ4wy*nXJ(eJe$jt@&>kgMWbdpvi-rwZgX!p!n3$C6=$2csNbu| z2E#Q3(&sx@vHecdLOa5l=DHa%hxs7_n@=Q#s^7KVnlI%STivmmmKdUn#zZDUQm>cr> z=n(&OMIh@-X8`;s|2P>M`PXJWS+^T%eN?H9fB!EnP2=>p!xoLP$bhk>lfgM$4@U0{ z?u~Z-RA03@Syg+(;6ANRR-E|iD}8l)jw~PVUz0G!^7fmf_3IybkRkn@_peNUTz`yj zBQF^}JUQqx#0c_p8pJ^ksrG{To~k<*cQ;>zWUQ+iIX6e%{z2L!VSIUp#*52msNCy1 zu88?}RDk3aCIvZ_@6Ks_yKGcm!k>>mE&DeyebbAtF>>}>A7^u|rXM=t>U!%QYPcWG zdDGk4SFqYzY(JJVT++{i5IwdmfXJgVWA+#0SQ_h(Oir2OH|Yclv_7tu6fY6^M_N;Q zB5?!sSd=`A++~4*VIo~K+k^Ta|4mV%_&R!zR&91ls!=biM3z3Mibc3!X_*{?jk7wg zOduRtdQ!hBy8raw^gG`QRV(HXTNq%rt6<=0a|jCXE0z~0x3%PEW=eRKv6|KsFe3QX z0F~kLy{C%t+0zWafyJ0hPYPzxr&I z7s1v#=g*PjfBDYe-)ip$6lDe+ZT)-z7hpXRs`Dooz`xM}+FOeBYbUBM{BslhvJ`2w zVB2oWqg7|S{|Pt}FIbt)V8ewI|9iE*=e8xof#^e%Q|}6G%M8x#ztxIsXt!0P#$)wA z7KFVVY)Dnb?ChvfJn|XK9XK8G_r=Euy4u`L6-+X>C(s=G_Dn^e;VQ~K{0};j;Cq4e z%s%l(?*1w%|B)N7VFbj8V^7LIxFD?|Sk=nxf*t*UuT@_Y#)iT<{)x+A)Bx{0ET_r8 zO}gWFWzTuOVNM1MiNBp$`X}+62=rc_!9{)bMwL#2ABMV*_uH-(8$W#i_gl;SbAh@v zbU5uu4E%5IJugQ(g~drtxw3pu#l8as?a+kE1tIW|q572cS<3PqS?!``Le26nYdh zIW6@D{e8;5|9KDpk&8faJUp(CZx26yj6C{IX~;na{yr+|=vV#x%O8%>0o`7>abe%r zzwqZ{`aG0hc3s!Lf5(IW58K`G)s4g8g}dipWp6j=SKdAH84P^NsTMr>+tJY9OcA{A zF^~Nz+e`n^gX#11l&WSMC$dw*;qS+`b1A^nM9K>7(2sM`l|MNfvqT|$UrY|j=rd0I zeP9<@2+$ov1^Jgd^l^EQH5)7Ud2;(ur|>@bb3JMZVkBk3giQ zr$4oIAY3s{Dg4_lEs9K_1x#n52IaCXJxs7-in~cySGRHzhTwi&$z=PhzxvdU&Cs*v zht}6_n=?ChSO3x45L#y}QB39^Ts9D>RKWh!ZS=?An&sW<@@VF#)rw85b?RHc zzkSij*Wj9r%T@Yc&rtZmzee!+H72M0$us0QVXirY`cIxlB(xY zwnYYq`P({ZbiIL{H}XD}SMp2F4j1^<;-C+zw*F{b*_qW{*!pIuC6ssCJBRqUCL=sgsb_NmX?CSAf@s-S7b1%d`z%m{O6W0twyxhPGIwe zt$8$_Zp~G^n4E+UTzg4p#ZG0M>DS7IQy)+k?p+!&e&wpYvefLs-wBKeanM}3OfMO} zS6W(n7-ERr1ZSDbhNoKDGwPjS*=((#JTNIaw@xEJ?{8^k6;;V!1Tn*Cg(`~Y@%~yS zoLD4IT>EKQr|l)`)_2!Y3#q6U$5oh)PjwjEmg}~P(Vw`Hy{oXh)N%UE4Z&TPin|xz zgoe6~R~MJ8z4;>SGjPS6Blh-9+6~l9#s8_$hj_l>ft?rKxwigOfEzeFXTq*f`^-Gx zrpS0?VBWUrp=U4jEXLd6%b$H*XR-90?nt}Vlm@g&)YrFx@>|w_FW{dcZAKZ&9p0@w zHj>YlP)i@qgabcS>xIy{d?&E74XpLa2HO4X{JZcm4UI3)L1#*B+VASeBhqXT(fiV$ zHsd2;sH$aZvu6$diYeX>qi6;D?VM=mD*_eFPzJr)UWmdlorFrDRhf?6;XbnaJsddc zG~TlV4R{OlS0#2)2Hv;4XjxVsYQprxXauQF6yHeU9KQnL*9sF&_i-aoPH*xP$k7n<;y>fpDeYAd-Gu}Vs5)}=E z-}G-r=LDkhW-e|iRMQ%EsA#B3uvXoER_zndXD8LW9j8xGSo@bVPT4cbx~c5QVz=+7 zsC{r0&4ZZKWk6*N?P1o~{$^+H^!-=KIbg45WOUC<^9NMQne)DKBcg9_Qd2Y$zUP^N z1~0*_nCj(};-6bl35Wg*pwXBErpdh>_tTE}+b)CNiPHL%v~*->GkT8iHp+ba`<;rU zSa@I*!-6FJ@;$&^V5OG0uI|@}P!iisEchb2;yp zS2#sA&ogYx{aeTPaUP1a#wJNSvVsr0q71JNzSrcAX}JQMnERBOl1BU`cnKU9cas66 zn*ShT0UfgN{hRz+3C^jW#Ix}}W$RZJ_ti5WkBo|V`rrQZ>)iuDxw$sq&$^>Ne8l{2 zbxuXW_SKf~)$1cYqw*V=qBD8EAju*o7<|9)E5qm9!R z@Ex4T--#tH-63$n8u$}ry$ z+cm7n-C2Se;XS1H`DOia_R|s{BMs0-2_`w_ezZ)b%PYwu(@E&u>cU0M4etp9&UMyC z5*@aUMNefqQ#jttGp4F=oVk3cQSIO}fEer?_+^Lgql3(?F&=|6FKEXKao=jv*Ogo^2w=gI^$e zwx2KLB|g9imm0%4uxJ(yTR|I5wD-9swKd#}Ih#McLqmpQ&GPZdfDmc!&hFzYEt-iE zj~$&847aoGZ)FIoj}k(#vYF;Rq8HEN`D|+GEd8;gXRnC+10f-n}f@6O+%RZpMXNjUi2Gaz@d5 zeiNR-`Ie){=xWZo;zaO57x&OJ?=5r7=j(SSuxpuzooY~Q!>g3(X487VQL>UkCux|` zX@tXk3A8fuf6>v8ulJtEc_)c$*R{B~U!5K0Wx&h^%7bjmt!o>-_(rqgT>jTm(b9Qj zvQ>4zkNKdb)Z}_pdDQpA9r(+DpKmaeW#4Q`&uVrUx=EIMtBBQ6yO6uGV(I-^PV#@i&C@-nnm zSAqPrm_&nBntdS`c#@xXak{~*=FWZzci$Qqto%~G=Ex@M)o>%ndspY2OY>*32Nm>m z4}DU)(6HBmp_=3I)O?(J_gy4020LtIp?%;J?O+kw$qQHtX}BnKbD_9730?H*dHuEH zNgb=>3h)@plyA}_N1EtviFkI4txxfFt`UNKD>9ENh)BIr*z`W)zbW7YcxMqEp2EEcEo zTZ(t853+k4fB%z)(=p~qt5Bq$lhF(>8?j5GsIJ5znoW(jvea{=v~S)e0Y7uliC{qp zp`+lEUm)B2B_&!t)iiykC;fkzO9_d&6*eHU%hwxKKZ4=IS+);1hTXBK*cglyGhc4N zN9@XXw$Kim7jH;(FE0@o#Ij)e5px^M2kT<&$#__Zt0=#AhM>6AXk0dhoO`nS5ecz% z({}duQD?RQS}B|Nk42{2k@bo{7jyUcSU5Ms+BOqH9w<-tXOWG0B?o{14Y3rUyUfww z6y?P1v4$b#N3MCB6B^JteG5y13!BI?)5T2nP>EP4{VqB`Lq#&LMg7oL49mz%3Smtt z?y5-xe7!Cw=O!{$)n>-St>N9YZ`p8^9q*)EqcY=#H*jH;f=r*oO+#3Flt_u~s|eVN zs;wV!<8vRDo`+IZeu3~ZxZ6fUX4==uob*I*!}4}O6^`77I_c)1vgVsBUnDCdm!jlA zOtp=9b*WJ_l|?C-C7yis6$_@drEL1~M3Z#iA8FSJ{|(KT@(U-)4bs^Dmwdx1C+zKv zTj3VEi~)3n6=d1_J|!pT45Z1UdhQc%q^utNXTQlq|Wh^ z;>)Fx3U5>$E}o3cbtP0rtz`Drm2j1uyPlbKp|1+zeX@u0hF(u-dG?nHKMrS>cr8hh z^#N9bk+eiO@^~6;`2-I|P#k0C*Q`JF{G2*=vxUIZH|;f(fGuni6$# zf_C&ZA=zTMiLxBB+ltjWTnPR$7*jzUjw1L8q04gw21&vc!MUgIwJWF!o3}>3&7|J+ zXhPE3vtyJy<7JB~)5^=Cx)locq|B?kF~TIbdI`*Ovd0t2XDS((-pyfI#MF4MsQLQd)2@|ET(zf0Pyr9G zOt^ABw8txZWpmCPSHo^ke(*t4R%s9S`(dOU~hM9KyTR?@s5NdoGwS*duQk^h=wU zZbss?do_I`x6^!sgXiUi+e&S4JttDc6W{>H-=36rU)sH3y3isa)#}q*rW9{{IYD~n zvl^e#`yO#EX`_X~uJq(0UkWKb;pCucD4|Y{E^JrT3Q|(=Y7oC|faFm1Fa6^khNoC> z)ft`~{4M0m5uuXjcSX%rY;aw;er+(Yba}UO#biLJwe=~i#G5Ci7g5j3zAy40{JBp& zZ_%!5A$QyQnh{smKhk-y`;$udew%4*S6`0Hdur+EUXl@ZG1r-_Qw4(#j>C<)-5RG` zLpRL*u`O*Zw*q*;&c0yXaeMsR^kRj36R4Qs1*6BJYEG^F*^eJ`FG*L3?fn6a$W&-3 zlkT_y4|(~zeRB?xiW7ZH5;jnA_8 zw1ut-$rn0~t_1#6P-n%%JhYrU@>ID#ZM0OLv;UUQ!-i4K@w!;Uz6nda$3uw8t(B0+ z?pyEP&-AUDmd74^JAp2BYsg8Gnt0La{0C{kvtUK}K=|-Ec=r%VgQt`Z|WhRK?L}4_;JJqC2s4zvbr8dNj zb5=AWh1bIIvm&nQo3rs4vbO@dFjQM`iv7cU2%b%#LuEBNvc0|T-&lS7Nm%+#j(*r7n zrQ)xZj{u#eoN6e_Oe6h(UgxX#qzaYk@=L`R5>aJc62_i7vD$}v(Db2{tfQwmjupso zdS@La#!<#agR5{aBxXY+*4&g$h*LT1+HOO4<7^HjyuUl)91w%a&&eVZ(w(Lx(V52U z?}@{Gf)0kS=vssTy))4WwXSGo+acAvUK(qP;OK54lF-vIb^$Ki@_Uc zTm{V|U~Bj0kgV8VCsL9_n*%)Lr)X;Sdd$Lq=FYtM+TF0imC4Oq>E;Xi(Ole zU9MjXIoA)17=C}-L5`;$d14NswNONVK1YMrnP#q2QlT~KHrMoG*n>`Xal+H!i3q*} zEIiJQB~G7gdKv~&o!b&+&@25iiGB`NId&0-=ittQ`0myp_s2?^>GPh$W0{1Nh4f@} z!m#+fjHaZakE8j>z)(z)z-a?peXf;4;RYhClpDR&H2i*89i&EeEnM*?8Knm0zvo?@ zL=l!k@r?XEa-trqPxbtUjxQ`UG1qln`dsd3-R#*}vVo*?sbH5u*}=K8NlLb9j>xGnb0VOjMC33l# zMx=rGUZgOqZ|(!)un2kd=sMXvxzfQ`m9xSu)@OJ*;WBxd%}FJr!+Xr6LdKYqksy&3 zFDs_*J)7tOBT7?Xat!XitB5|4g0&yHx_jZw9~3^GyzmF?8K8ZF1)Uu)d)k_nZRz6R zpx<0W$a$l<^z-&-tgT_G3bC9$C2X$BjXq{5E|onr1r5*2U&_e1x8c!6hMh z(~vx7aqO~e*#=E0i+8K<^zg}Uo0wbkZuVr&Trn^wqQH4XUC6C*wtP6NczuSe7N_vO zA(BQ|tZpw$;bE)skNXe4BFCiq5v`o=my(OSd|M7};WHyQ`ZwjoSkb4Upy zpEBN>%{ic`QQO$|<~D}!vHj3SN~GxYnwgh#fAy`eNoUP%e}XAH?H4q=bRf+x@+jHcT^aXEceDltuzGH6Cx@6? zz;ZxSjfl1xl~UK=iQ(NFx*w4;Cw=cY!_Fio2vIJEjhugbk?I1N>D;ljo{5YbUl7mb zb-HeI052bhtYJ0UEo>%~h)i{Fp0*IwfBswX?y9bAh3ndmZBhzTCSJ08Z_t!=(>-!+ zVE138#W4J1+pyEZVBd(Ha0kCV!bfbPmtLfE|S;SCz>=X8~Q}(3)L)n|hL%sg*P=`XZiDZ14lFPgAtpDm6y{1Ai=5+E3v(T}?geP^Yi zGUhC;>ZQFE?~eUFsXw-!snB{kF>N$JEDLi{F{?E?LZSq$VeDh=G1lA=%e^N6`92Gf z?@0L&x2a5(05yA?-$uFjHlN8|f7_#0WbSj|HhhwLqk9f3v3jyrKAgBOr6I4f1#vOE z0hwEysVej!CylWfjjn#$EEX*599|P)z|=V0*leaIx@@iwz!Zs6LDvpeNof%~dvJh` zX%`j^EMMxc?OwT6(5LPB^x}?_9XF?qoQuRjLCNn4b;!@QZ_~sbtzn~y0*?&NlOF6v zkJT(rbgqu^M)!=(So!O(PJ=o8oSFI`^L(mxD`Qcx1jj*xzAJJ0`Z%iQU#K@xlBdV2 zTctfCu=VZf;)xhe7V?P4*rP|z$ISOtajW7^Be!SX>at~fF4aY;JQ5uIH6ju z-IuH3oe6q?dQVLyyYnZWVV1tTFn#l7L_;SvK(o_DTSq?KX6LuP73b>&rXD~~=PJrl z6ia4plPbMUt7G~IaLRI8@$!duLU@U_x@Xfd_ggx1Pt*{jPZtBVHxY%W@Qf=TK>;+n z{}k0#JbF*1%x>r-tK|g5p^`(PJA&H#96P$|Q1k$48mnGca)}`U4(F{cl5xGi-I_17 zYI(87fF2cNBH~?`QQj|BYYBgBdHX9s?$%UE{lVpKT$w#V4k z7PKEl(bQfgy{QcRyB!|1OX`J@p!>0uXUVl!Cn{SeG5{Z5k_&LqLyR>FKq`bT{cH>U z`fz5YtUc6pLC@%-E&eFv4PRc<4^MrNeC$8W@_)d+6K{Zvu0p4ZR9d2WTjaO7W|T3N ziXcVd1)Ic8_h5oRWv8BOsP=^{*^=;KHhBuv6GaZ+!Rf?iA{x_?sFk;C;#li(CMx41ydYweN#+htdG+WFnQ zO#0ZNGQxh56lvH+d7DB?Eyhkx_d`=+1(shNtrM)XkdxC8GTnV zymp=xL{Bhz<8_ypYlXM}WG(SpE;TZtYL#gv4$WA-;nF;|u>62UhF6bvoeD?eA3_Y9 zJ*!6}qpOFA4WF@6ZkZXi60pZ}bQcqIrkc@)8GGj|2xA!)0+I_%|MHKtCv>a~5tpBQl0@Ez>R8YmpYes1rom&Ha zP5kMp>I@|I<_?EWf8TOW47z3zZ26$hz|Xe#U8MxnI>1A^Df#a7vP9oA&O&1r?nRum zu0OFRc!1gau4sG^5vE%1rLT!&Ep^BJ#xV-$m^iIq){q9ABhdxYK@>a??3*Y=*jT~u zx?WR=EY9hDI=(!zyf~XTpQY^f_qErDL4ZVOnSO^o_WVZaGJwJ8y#l2`BZQ-hyTK1< zwxy-w*88mMtOQWO1bvZw!AEu|HjU9fskz1-f6uNwOf%?HKyztAZ{`uW{pysA%Ka|y z1H!OJR_ug?+5~N=>w(N{F%#D-uRHb#j($IsU>)F2Rk+|g*#ka3;>XlGg16Lhc5ea~ zC|dQrn{5TJ#E52U)>?KJV2Ec|NC&FL!oJ0G%uJWmYHA;j74RyAd6^(&ZE8>s^dUx- zZN$(lbo=K|*=#c7dvx7iflfFf^71@!dvN2!{$*^DTPK^63GgK9B@e#9Q6sq@=XXS3 zW+n+&s|mB^Y>+JX1B(y+p`Z`KZIJN;&adPX{fJ$82I5i zTGdHbEHAYE+j+Kb1%R_DGd%vsGtT_W=@zr~$7^A=)1`&FZGFN*MRJ4ZhQVw4KIvL&4$uL2Z+Y;sXy2`-v4EfvZcLfPiwQ z+??&TY{Q4F*hB;++npt)eQ+*U?D3afbGB_w-*g>IBSEKCR3F@Yx3rlg`M|^W^~HeQ zoidTh%2x)cVdt&gZ(I4pq%V}u>zfQ;#7bYar0Qgvgb3Vh*RuA`IUoR0_cKkkdQG0W zDV6Q!0URxjy6i`o5t_ib%*OBuz4`2B9GOw2m4TPWGiEqpAW&Qu1(3cBJ`1HkOQEgm z)ub%gUvsBZtjzFRNY?Vsme3a&#^tu;@WpPO040=*NeBB|?qT!ZWnWm0k`CL!pBk^g z_wK2scHcGD(~K$El(8A-i%~amprt?mxY%;;Z*vX>NirXDCWhEqIrncY2W24-RD+|E zt-MGF8k#wqzTM9V68*IV`5BOcj>)!GqLm&^iQbTe_tW2&fF}G#{zn_+x9^@Xn1d8` zGU`)^7boY~xw3Yt%$)ZVf5bZ_<-n#+FD9O7iq&R3GdXPTtPZ9nxi2?OoIY#zJ%W-j zR5Lv_^z3~dd;e(pL(^2snX{A7xhVX2H|n^G3)!7&vU-v*At zyrSvWz?If5rmFLCZQi}q@gA&J=Ihe{Ks!Ah86nlM|2?-p@~!t(z*dS`=M3O3H~wI& zM@_J;+mfw&_UDR!TEPki4PCug*D3X&&Yizp46@v0qWUHe{r4EIrvc;(?W@D`u}97~ zzzZ&GWug%n%npqLRL*$7A_?`T3 z9|ZkC^ZfGShm&=4G*}-@U0s*YX92x3hj!-xJKu$DMaXZX3rnA}F5c|;c}VlGs36K7 zD8^`SuL?8;n3U(>PalnW=CUk~bB*th`E78`-flv8dSdls*kTZh;Qh6DWhwB3UsY+?QS|;KCaup6S%-o z{D>2JUBjZ-w)I4XukrWqs+Ukw0ka55zH=BG*#gK6|1A8ISP3Uo`BZYNq<8)Pf_HFK zJM|1u4dM!AE6?-^Wa#pB-{1nQs}zBmzc0CWgCVOa2CecPnx2X>ggbu!-gy~(F~AOo z%S66Y&&)X?(a>zZP3^yL#?)5G;Z-S;i&^LsId`wn?es zyi@CE5ms>s6xI#{SUfWb(K3M#pr>i(URW1xN15g;?Bn0G)(gKmO{?<6A-Ro(~^CbL1k5-!ka8cvZU_7SWAG zhmX{F+ZBc$eXgv;H1DH97ko_mY?KaNRa!oacGNCZMw?`4lLK1DNIGxe6#5*$ciT1< zA;Gv7bhZY>jG5Ko`hF$YsW!{s+S2<+X5TiOUswOv^7(7Y_3}-n*O9sid^eOkTYKf|+)bLqj*; z@KOHNj()Ci)$ya(M=SD^&bLG=j0wi^LtM}3q@O3^JN^b1oXwgbLl1&7=W_PID9{`-*)#z(t(8F5;zgZL1f*RnY$c_TfP4RW(8-7bH`B0hRg9#jCro z*4}XtR%)_L7~Jl8SvrOKrzn^xm|On4eA{KN@9zj`3DZ9f?Aq{K!be}IlU)2XqF`Rx zyp+xosHxE54k+%ZZw16JwNT`z-~IZ~|L4B1G<0*XVsdu7sbf$B(g0#+B(9JY;d$iO z53c({)l=ZrpZ=lGlkiGc;fuodptO_yaHteTa102V##9MJO% zp!}izxT;048waPiXsJ8GEO-~mZD8c78Fo1?OqqS&y4^uH-Ui^ zqkH;*7gUMa|80S~T}vwFBxN9En8C?z`Kc0xlh)|ybzj_ImTN$}YM z4u0?ODh&!_?|oA4+iv%wt=D##$t}18R)i-QUtYmabCU7|}*7 z)zBDJIc=@!HDUEZ_P>K^P^0y$rT}v@p?6J^klXK?d}fp0LXqV%A+nAB*uUQNzh=tn z1`o?%@{tvARfNgD?h;3n)z1p67glL>He_``f_?w6PM~J(5aVW4NpMxf@z%yl7YYvU zy0&eneHXLl!U7ifQA{@vIN{T@kg(Z2>CGSlxY&L7U~RorriH~M57zAc-qsqt8J^27 zC2QcWyj19b*LHXFVs%>Y@3EHJsIKU?dMVqP1nYSo|7Xz~T;x|1g#kA=(2TJHEx^c5 z%{)~tKfiPKvdRMEuh;DN5O_NVtLa5^PUVr!^M}9lRP$S z_XsNAz_i&>v5VWkw9B`ufW}c`kw<0kJKiJ}+`*O~c$wx=*O;5jnoQOCKa2b`xw*wZ zj=$v?jytKKl9>$q=(qG^x&ldTE7J(D8cv@NObf?l`!VnRSjb}#9O_U7HPMn_2PbJFJ)6$1C$(QpzbgCKi-#n4z`%?#W{4AGjiILY zMGa@*kY7HvF(@RgZ4Juy(gAI_<|x7Nm8D_qX408nE7x1+2IE^WW^ufc$tshdT$Go} z%kM0Ra+`!3`<+r4xB20zA+jZR?0bZoQ#DZEj>F@HePJG>d+4SXSZZa3nTALPki0iRX+>NAtNOW`~z?y5fNMr<*a zFu^!jc&hTC&9u^g$8{hdMEnkHxqR6lAotT;f*uNwPxoqF?T_cJm&Y`_oh1&cm?nhi zB{fY89UCzKkXtZDDwpMv#?8XQlISe_TX?0*(U-4@4qp4qZc*pToOi?GDi{LoUbEvyp zqv}qdlM7qm$;`f;p&cL1)#&w!0IZ9+Z2^_~Ht`nq4tc zZj4xJHC#EafmjEo8|?_wwC&pI1_0t_}zgS7mq-;wgn*P&Gqc?vqn4VOp)0T~F| z=a}E()-BEZ>bB&#k#Uf_nX<^&<^?opjOcJ2onQYX%fUz5x92pvIcn2T-0s16;n>Nu5h2FV9@#W!+cT%}azolvXT%!N;00b9FnuTQg z`V2|B1@aufV|k7Oi(@Idh&nr)t_crJJ0vbyJ}H~yO=<+jlrJk%3(~ISgK^t?-Tj&e zEcBXc!^*oVOrky}BKIhT5$!4(07nPhO{ryR#-Cdyu_?$v57=t)CpJ$0kDu#VWgvSU z$O%d%`?5@#HDG!9N9IZ{#Hq_#8lpzKCR^y?d0m8Q>dp_SbG6%N6DnQu2j^+ z6ZUS(7KKp7rCL_BXY0Yd-k}FzRmg}1HVE9Yp0-Zg6K)UvS@IMO7b@r zs^Jb$lzXlDXs0M2E85L090D3ccIb$G>ses_f-jk@ozmThO9eAkf4U%uA5_R1zs;Zp291 z>fwKX2k@kXKi1;4yC`V=qUfNAW7ma5J4C7@QT3A?)^1W16q*{sY2-Y*)pklw&y^{N zO%vN3pHM8TE3bEkTPi=m8ykh0r&=X#kA6Gl6eU=RgpCS)sd`%4h(gSIC-4@ zlbd_w+p@jwV>wddw6?@#-?lfH3M{~5stV81NR z{{9A@H;7m8j;Nj%S~fZEY86VX@N&uk@vWb!eFx{n7j(r^2vR_Ig8$VqS_qFu`uc19 zTx;L~maHp%!-7C-z<}B9K%CHat;6aVBwFuv@%_PcYCP zmdY1&_7+GAB3UIbFI;7;CSN9DD0XQEl{Pu$IlJNiele-4OS=4Bt~?uFkYMr36DxO~ zr_(2UMK%_gTCt z1~qPi_%!jvlkpGn*SUurPap^e?%VK^-o`wv`~`;(FuBS7)pGSj$gQ4jaCPBBBS+t` z4bhc(D=$YlbKR!TjkSvcQ+ND#a+g$0;}JbiQi-?~-!^RJQ{s7Uk}Wx97KN)j<4P%T zP~(d3x8_&_y!NE)YuM)R&R zM1DPlRbAeHa1Zo|Y075eiM$?c{Z%_A@!Kv*kJqMWl`NcY&HL^XX^qr`lzT59O)Dtm z#?vUg(rtN}uoq(yb;fc~rLEu;e4Ff4Dxbo(Q~A~{368n{V@QF(kR~caksF3ov+u2+ z$Y-(3u{C;gyyOR2zKV&|#%mm}U1JWC@bdBl6(Ah6#6>|bMhv=#+iErQ01-kML&iLH z@XDTgBFajlhG``1anC=Prj2D3b3F&_UN~?KS?=Pfy4U(I3*USNuSu8pCnDeLRjf43 zZ-S7`7G2E^MmW?ht@O(=14PPa+N^RisV~0f3b*u=_#o)pG|Ce%x5quJ0n51Y#~F$I zK%HPrNw%>)V0ou&%*VfTMfZDvN!qh3dV}v-z0k##CGv(BZ=v-nE}P?zCf5{DGkBw} z2|qr8>%Z+KuQs_S)1p2K1H~OH4u&XMO|Y$6h1C=PI~47tN(Sx!9GNOw=m7U}G#mZW zxC(z>XG$0@_fcx8pGBoe^qtS0zQx590XFKSWZ25#f%5gvR@I1nD_{m4Y@(Ibt!6tf z&hCELG2w$}^9@AGoAWr>{jA^Y7;kiGJLJ8mDuP*%aPTz zAhbA+5l?tr(ClTLWTKD9rxe<6ZY*mE7@`plSm)g2b`r%_6u{U(zI<3eiR^LJ60b67 z6CBA4l&xqm5rW4yzkt{Q0krj`O&Aax?B`7_!j#X=50AR5tiRB&p;&p>M}ypwEA}ms zh4$wg&ZwqC2VO$^I`j^rXJ;Q)e>xa5*Ou=4;IGl1)@T=ef7tN~8c`E>`jBc{qt6^r zA{g9L9Sj!>Ff!)GF!OsmY!-9R=&8&9IbeVjOsncu%rtlGB3f?;D}|--hBv&O^t~}% z3C=v!1REfovr#CFkHS2`h{Z^3_}YdU1#A!u!%PEJ3-yU*xamSxJrYfJs%vw&qPkpS zf%Z7;l_Wt}nsZ2mPH^*nNyQGHfV9q>WwyG{x}^&ny%O7ld&J3Z1ot4vginlpn;M%d zB|)-U!Z~Jkis(N)x^SaW2|`LQQg~y1D-#U6yVn!2xJ( zB--5I(OJ-B!6?fJkr^xd9M|kNg7uGNZg{y3D-Og4jVrqPBr#8=)Zcz4@}P(NPu?fW zTts8bPCfv?2kf#&@3_AxP!ga(`XrSt*rA%6Z8qIx5JgS+=-F)9$6WE3k>nVjSbG1X zx+y1LjK)0b9GlluAR|K~xm6v*v2{iaekoFWh* ztGGzuJc6ObF8}bujys+8HK*2J)@#ImeH(Q`_{iCwCDc}EKE-gmYeFV^Djc%;#yhr- ze8fwx5hs$T2LSX0fR~ncOX(r*uL2t|iDfNa+eNC=WHoI4mxq&?yZ z%QE@aI+;z8g!n3O)b4&1ke(e5cidCa7>%04Fy0u@hIi_4%_Gu+Kq&ksI`(n>6IL!k z^4Ezdiyj@}1A7vbUT6TFDtGwZn3~Itpc$07-E~!s{!S=v)(1uqdw=Px>s|olDNBHUIAv_IeZ%2lFf~NdtCJcILyPCUI86l z_e6Q5uEo6r0Ez|tqyb^ZgKj>_v^OAG-TZFGF7&`P%z$sR3wJ<8deiD@*!1E<9?SSP4+G0ZT$lGWzkY5wo+m#0804c_*Dmx_a>}+-#Exuf+_l`Fm+1cL zAwWs{|1VDFgvNyqjI?F-`58*X4}CAjQShi#j-cl?@o>4^Uv%68m4*j&;fgj=2#u{xw(1H*mA1$|M85U zgPb`4V1?Wl-5b2HYH256%vQH<@nFEjf*tNkZYf4?xTQ01Nj(#$D*>FeUMo&~RFNEf-^n!Rw;j)$z(F-_dC;wdUeOSZ) z$i-_0*lRgfy}T4;*ERi(v z56eQX953fyG3<9T*w;`-dxAOcZ5eK+mvWwT7^vqiq;EpY5Z1>xYi02+a+mW&`3QxEK+3o>-+2(W$l_?Ie+?vSx znyVE{RDOQ&^8{g~=PCaqycqFVxD<;;XIf*9UPK%0!-DKTH9#Qc2QsZJS{}tMX^z!Q zeLJhldmHF$pEfcdw_2a6U!!(V3D6)W@GxT6V?@xL06-uBQRdVM8Qy2Tr5$g9d}q&A z^4Ay_I(qg?$JII#^$!i}yG>n%O?-u!7N7}sYM-k5y5?d1r`F610EORnsq)@14!;#Y z-9sDhJ!6h9J8ahSOusM#I)bf?bP_V5y^4X$tUK$kh@>h6aA)hci9;LS;g0avZI>Gj zK>et#9=Irs0^%~POe@WTD6>}Q$wpw4;yBPxrV~7^eWfoxk*$?QdcjXchPhc(RqU>h z&dwmyhLa&O1}EVFZ<|!njF7r~a!O{BeQ$qtb6Qr-&6}_gg|6J+NnS=q&YBKpDtOWMIH5z*HcQKv3Vo*UkqP;v~i-QB=R)kYSQfPkeqLC$&tJ`p1U(*=U>q1bc#U z^!47rfmdzY`B4O!cIssEG!F&{`@&+8I1?x0iQbOgeGA3MAh+4BPo3LRh;b3ovx%hx z4)>Tm_?FjP7N{Rkb%dNyamY149!r6UuSvx;`a(zrNgi;zi0Ql2fvOQPa!9 zOJ|j0Qm_@p-zsX>4uSj6AGx;-_(?83UHxxm?O%aXL+{ls79k9Lt7ih`(gosIZCo95 z$}gDB5M<+Q0ai;~uw&U)Nxa6JaSNWx1r_SvOWvAX6Jy_ioBG4@T>tH0Y*;E2qMt=O zb1kUhDx7r2RXe5bcH6K!w`{aVDLlcn`}C!r{b_tbX-8V+WMnoI18*<7LW|n$&YN01 zzGXK5W^HBZJY_z$*|UgUv|8`O&EF=RaL7#>LpM^nZ9Y|#2ttX#q5U2A@KcX1FDPf{GNF#WarPIH+*U^Rgg|Hu~1FFot?nXtK9n2{bwZv3x8-Ha#ut6fWER|!<4 z8q}yhyL(V&itJ`^q#-Vwq?*z`WbxJmhSz|Vdp^pOZ>#5$IyduJ0#j)zgxX*NnCn4S zmu`J7wLfnf`-qcu|N9)ScpSlQmoQwgA4erOcR^$2H&uklo*;^R2Ai9gfw?j*KX!g3 zle{aO;UB-GjNJ!e)t_6W`B!YR zK0f(7ujI}X3#bxCF=B3xXTmg|vjxpBmv|Rj+TG&8lBcqrA$W?NFbU`^Pi<|rW0D-e z(@!efMK>(LMpv$?5ppswnF0gjD~O~2o(dv@K z>1sAJ?GTM7y90l*=;0Vtqe5l8WFOSb7& z_HFZQeDLPnm$K&B2hWYQu(rsWXX}2paRxU9%?XZ=;(^p-H&FjIGsZruT(rVk)s+(4 z3bwSDBGsfGBLs+i6?44LT~(H-y%wx-jxFnTWL4BYC#5h^XOWN1so=Ag=LV4uW5j}) zjLoFTHl#xxsqJK*?`)-nnf!%@+ndOE#zTOLlG;c`Z5XYh7|(RWjTU#4GGeEax+)$e zG*3dN$`BBp5jXU-cg8LeM=&7Ov z^_fR7B=1P)QlbDQ1_5PjNPU{~5z20A!DK(SzXcS6p7q?Y2f!==&8@UD%h3(_n|e41 z=5zWBI!p7lA=x39c(s&cb^T)-3q`qeCpID)$Sn!Ya&eqFv216WT3=s*!*#CA@y(;H z$WwX+gh=yXfV;Gce581XuXohny9J?|-v}CLP|+BI9U-S)RH);oIs@t{fL}81(}LEn z`!(#juI0Jj_y%_NB;xiD1+sf%hA+e%9%WXU!5e)-4C;s7DV?`!^Md-=vf20owz*;U zjV&z-dsK>>v@qS`6{z88oCxZDSri6W=EKO#fDVh4$z_Q}W?EP$dwyBL$W*Iq*v8c znU{UE_{0>_!`VpLvKdHB2Pfrq6sFxBoyO%NgabkBe0E8ysO!;5${R)dmY{^sv1Gi5 zF21us-e@5XJB!5@tLo*Nz@h^;81~Qd>aT}(pc|wCVm9`yr=xCDfN-81_Aj1A*h!J{ zx*GRl^4oA8Ch}+Q(#`=@`bRMZ1qa8+=khs(OC`kzDwfDShgGA+fc|&N5fC>|FpSX^ zN_MHsdh3D5HTj{P)%7VE_1)kOuSW1~kEn9Z#(;SjKo@)3x?wP$JIe3^;^s|wW2)i? zlySV(A(W^hED~pG--?nGbB9-+DuZCngxC_1Cknf~m}`s#OwO+Hu_mfKo;Qvw#x>$; zZg}u9dkUQPiO5hJ!vUhFL~{8bH{P6)y`=|wf>FZi;D)0N)KsInd(i{{FVc`JBQEQ? ziK-T~9|KmhA_5vm8DW9aFC+Dtne&gq#OEa%v}>@&X?=is?K00wCw7Fm8|0FJPKg-Y z$e4@JaHnRBZps|dbhMVJf}{K;%u=7F`loD@$=}@{kDZx4s1%Hpv?tn`HZbl!pVQ(# zm7q1i=2e-xL1iN*)hZ7(U1e9y?TgxcTTzojLiUQP9O;M`$v2iNi7lBNi|NXSy_?Kn zU*&jT*5+<=hm(xBNeXA4^8l!#z@Zz(JJ@omU{A@;k=0=>FNLuhbEvi^ZrDKxUj8u= zIw;cFj5skjLRf6G=B8-e3QGihX)o@G@w(o=>f^4D0M#L{IGMLfXn_+u?!8E8^Gx^_ z;=YztR3D$i@x+>z4aKV}_nbA?Cq-3r0o92|0m4@L3p(GY*5Zwa1MJ&A00Y?@mru@n zAj^Tv4akCVGhnTlZ8FDrZ^i$wKrgcv=*vLH@jo+ZRf~WV04cLdY1MgFcHbQ^s7ZjJ zh+iTgvRaTB0At3axs}|6ykpyV@kY&-<~(bgy@8f|x3Y;VdONTaXhToelDvg-0c}BM z%Y!&|k@T2;5sX-*2W}dbRj!ga#ZzVk;fAgrS9$I->@_v&QU|tlv1qCi;VYc_07k2= z)NDDdj)v7@C?UpxdwiG){hvC$sZ)H_j+}{L#`v?{(oG}o=3-$?#EfeV{`}Pw8>DwR z=0MD>(G$VT?IPxx%PgDZvGmDP@_G5I8DzJ{Ct2U5RS^!?;W-+3&)-ghw$wH@wh^K> zb2H4u?LqsiBO|el-97PpFf zR6kP_LM+?8vo2%osv9b)^fVxGzM{U&z7>epPrZXv55^Fg^wsGaSFy-YarJ${S91oR z7<;QlVHW1QYVfji;E3p&T*S~##(0C|-twVQ3`3s@YIQVf|0Km;ymDAQ9YOE=TD ztESY?x*!FA&}L;=i!p*goLjJFD!ES)nXYUh6eu4uK@94a~ndg$dVttnZS8$0B!4?NWdx+@^x>X8&nx8 zFtvcExz(AlbCCz_pF`a*?do&TCwUlh>P*}~6|yY^uC zzG_6cT8rUE@?#d(3}j^oPxlO6(Oe86IGkiAW0qQM>fOdEC4Alf_{BPAhT!Ms@#&7> zgMnimjAMn;K~L(v7tx660)Cq0f|b^DsnI}9lo-l=F$&@T;}dQuT_LrRwrb3J$|n(K ziSRrkzS2-P802brp-G?f1@GPH+IQIU(HPUB-TnJB3_XgkW*{;qVCaHBBY_X1>33ct zv~Sge#0m*Lk+Jl($h$p7M_@(4=)3Uu=Foo>ECicZAni1XKLa3s7VnP>AC3#r2O9fJ zWO8Qajgd+VIgqPx7RBo_K%)Y!y^$&Vpc^<;Jnx^yYEj&5F6YuKQ2i4U=w*XUUJn(f zaW}#@$FmyH35ckhQveEPKAc`JWwzaYtc(ma8OT}ODscek?6k5zPZHp(@#|x~q*|<; zvOSI~4eXCsTZoW|CbE~Y8l~b(X)7pgJK8>?JS&{x@U8a}g}G89wk zVN3PdOSU7hhsRdRw?6Ch(SI5Rr6QR6LrA#Qioee6D#a$F?|wpq=@-{PFWEmV@^!1o8tYZKU5*_){)gp!W9AE) zUTPq5)XEzITbcZ1)mcpS0(s2UkdhQOM4DfJvg+1OD?MPM-02FbBA?03QOh9~J@6(K z=W*nztkmE@Az4^rxq6^`yk@@Nl}%tAzwB|KO~bj4i44d@-#TCF-~ zA9S)Mx@MqHOMC?}IDwPXNn@2vOO!9jEv@*U68B%cL|rKo9lA?)%ZO~6`Wxw1@t#z) zJ}1Vy(Sv{-$tzft5NUZ|GBmBG!Smx9KU&1zoHPP?X&T3o>)CBytUj4J4lZa-b(P9g>EUWI647o(Lb-pht9HGia*WORSNKyB195mD$9&OYJ> zAVYKdGxJIhf>Nln@Is9dx{@O;0lhkXQ2THO>)O7aodd>5+Upg(P=tW!4ddcyN4fbaXI+c92+ck7soi3)%5fk|sc^+AJa8Fiq6dJs1 zu+!_3(1%lG528b=)SU%K2GF@kSa4YSWk>D?U_PU{RX#?N##nrBa=6E?m$`r&L{;Kl z=axi73pEc)RIGp)?Qnls@ybh+x-Rj`(@2DuPuam}u>j?#CAmY<;(gv-7Y-waoisHQ zipUQ9ew>EVBKWlT;Q-|=iL#s%DtwJh@s**_p~XiN;bl@tawWaM&GqS>N-t&c<=U5O z3uzvPY;seapDib7i+g`)`luBy>x7O_CF{;%b&uc0S)Fht?NoY3kFd@{j%p2p)w@5M ze9&jF4_nG%$7%n?Xcbs(5rX47IIcQ3F_D9fi7T))7;Z+ooglYvNI7~#ACEPivL1|w3z zb$c0;s&jCKz4}^hIn95GK#K^)2i&yc#2Pm)-LYPyu0=DFo0&|YDfn$K1ya5VlTHhX{H1|_f!3F}3{hE)eBW@90zYy7HTP-ZD{ zJ6BKf7~KJMee$ z-f}6w!O@fjS8a{1+Wt^Tm}nu}Mf3WF7_2)Ae3ix_rdDy-g+yPEW+dNCFGncruQYV)gu% z(80@<84-V;2~LS7f0f@fWL0orsoQIJB6WU)(D)bZ1!zoQ$Ad8d@hIx0gQKkJWFyR? zOmsxdc8!kCsYIBf!0jO(Q`F}~Wh`j4ft#*O_uiweW$DQ`p(q05lEeyHbaVIWH#mD4uqv6FSJ7!O$aQZ0Y34!xad&~k(7_BdeuCPoYr<2WH z%l9+-f%KbFsO^C$p98yU0NA%3yhK z_dBUY_puUJ?xmL(&vy(19oLyQRcE#)LZAu%ewhp*RnrG_7)cK#@8!(yWKE!^n z=xldzsfmB?f&W5+&PTbOI0Z<>)DWXaA;*uBLZqaUqr2*6WZ`vN`bg=2kiAEeBqRc7 zG9Z+EE!T=8WhVY3pBC$z`%*#&KXI5OL=kL8Sm%&sg-3L4VZfs-qH8)6#DoMeI#g>5 z%UQ|q$B3xj`KZsLxO4)&r`FN?U)}-WRsb<<@9rMhG1+CuXPi~^vqy?c(P-o>LS9#f z;^{P0Js%(;I@@Jn7;i|9A{i+2vf-rrM&FlU!SWV(*xywYq>Sz-Bu zL-Ggnnia=s-4(f1KunD9jHJ9GT6>SsfSThhn%`WmfKI53bRQ(vsj3Vu+pzDDek?mb zc%6Xr<#nGnAsxr=H}JeMXt|fSzUS=ssK_MT|EXJXX1cjHoQ4Ps3|KbgcG5w)>kYN|*R^zQD=g zn0r&T6ZVqxtx}LkR$m6|UOQwUuB)~g&ZCY4ldm*|`j9B)K-Kn!w5gAOFW8BgFFijzOv-ApJM<^fPx6f{=WVK>-T-}UT z8$w-4{?pY7+6VX?;{6#w=+&T~CoN2=)^&nNnyUaazQm-aW;pyS#p-{@1MtPlfOxY7 zZH9Qk8{zTN^ft2)K25uT_L|swT}XT+4bL zC;>L7s03q<-y=HI5zpbs@idH?P(F3^t5O`l=;6(dHCj+M|{O@eVL0(z{v zY&sPQ&?D{n=yZw2o+(ujYJnCZN=f}hiY%H|WidH5=Bf*|csO2a!3MkJ2^}i4S+r5P z`29IRXK(Nil9$jkZpy`3dYlBLSzo-DMjV4mvEI(uHaUjb`-+^#s z8N$L&xdn+G%C2c5k4{wt0yXCIHF|vd+rC#~g=te+)7*AKY20|#tWI|VSR0 zHx{rwZO^5~Qli1q*DRi3M4II4Vr~D|ZePP_U4*ak{5Q?^5c2U;=XUjL!^T?{ClFqW z5d2cDn%gwK+wvTBVWHN`wUQw%j2N`!bEW0yb*Qnu18W=UeE9pXm#` zM$Khw%n||}=R^y!6LLlHISYstRwwX8Nexq6PNM`$>x!-kLL=crlTgXV zkMAY3@4_~*JK$Eh073~wr>n2Tk#%p!Kj9^zR2OvNqiB{V4yf#L#fQO9-Lnz|IVH0Q zPDB2Kc@wr;o~*UX#BU#p4-}0}UhN`v#EmyAT+;elbJfQshQKXjDa!E}?fUM(k3Nu5 zFbaonlTms|Obd*wCjuhc+fZvw{kXbVNyrc1(=FIp2h6wTXt=^MH#!9n(gJ+y97e0d z!MtZqZIOqshq<6Jm{VAxK||Qh)(&K79bg7}SIkY28i)FAIPWk6VAp4?LT#WhE~<-i z4ZOLs$Z}ut1MY4Q#DeXzO6Meo=(R)9u3~}I*3iL0khec6=PBjw3UcDl#LRejiI5thX>$G!f!=ACHwo7{3+&9 zGGhlCt!+#~Prun|pH@UlN}`!)9EKIf4aR0)=ic9?`Uf>01sZXbM{r+X=eZ<$3yS&C z(JMJ%RMmPs-)|69>3%YB*nnA6!C4GaLsjhRZV+I!CTNpcJiU z?-S-Y`1@=Y!CvRKaRE~T&;P98o?rSQu~=NU2ItE#cS%ceky$dx}>GiM}Ylv$h%w5Z_hh3DWw{3tl#l8&FZtW=A1@zG zlX$K{@ir^ay7<*Q5fNQN-_b(N!z~RYwAX&7bZ2a{SLGq72K3u9a`KBMr^Lwy>W=`MrgvOA{l0=sMF#E

48MD|Zao#}LgP&PS{x|pfW)rfE?a#^>Y#?U8BsGhGE~cZuG@@L>J1LKfo1NBz zoCwqpNadYJisg^=|FBI74{C1?l=fc$nI1fZ0H#d34EN4Nd`W&=dQFrkf{;PY(Ync4 z&!-b7!|HVtdK;*fS5W<#Z~D2x)~H!-l>}jB_*|ZZjd*7W$FAQM0aFr4JYg?XVsv+B zf~s6U@^L-w6L!zQ$T`QY1V0;(pYp#^3T_wcfAlw<(my5ro~M%6`~}HC9@EoD0fVXb zraez%6WG;F*Y%wp+y#um>)0%M6D$A{T?a3mxn!^cEa2&DHo6G{Q?cD}Smji3t~~4( zs83T)J(gk?^@HKjE$GWmoNDlKeuU*0dq)%^YTpmu&&-@w(5dHTPyAfpquZG4@0=RA zzavld*$g4<SJ=c!_wp6(DY38QXG z)mn~NnRLw#`^}~ExP@y*H?#JsCB&I-(o-_wWXMI+y{8BtSumN>QCy%D)czjs-(T+8 z2M?76uf<0DxJ-_AIyi;Gk*4%FNcYh4VUGt;awpWotM8hh^1}E|?FD)~j!YsIeUT4p zsJU=tbUUQPZ7rl^esgsy12eCN8|{BlreHV171A@0x!K)FuIh_JJ;?3$z?G^dJlLyP z@RR1V5I8KY{XJ{_7o+Xx7r+&vpI@5hG57yJq!fAXb&F3pHLLd5ufsXh0}#^~i1`el zm-w<<=XSguaUzWMTSgkl_-C|q7i+<}k8&V(k@85bymV|#VM+wHYUhOY0$BEbS1~8O0A-^7>vrhnnGgzd^N7iOW zefI9n*PvM-i9$mOS1^pZ!;Asq8zxXj7tyz z`Rcf%!Eho7y544!v+6F5&wcT3wfJ+?U2k6*F63Bc=DGgj#RWx-6w?@ zrkLY1NGQ_R%3fC8Rk5>;H39uABykc;qK6irKc~M-Jw-b*}8+)%%y?c zW@Ml$^78+Hawsuhz`GM!Yx_cXz$n^EGSrqKx;n$!n6SOQ<)cM3#(vQuPF|n%mbls# zVG0ng`vyKh7st&$YVVgYxd^yqQp@+;wp0u=B3-^2&+m&hKB{t{QbHv0vfrL*UV3E1 zE%<=XH_f2ALG={dtjI~>Rq_7PC?5M5~Hj8`(qz=_jY#%nv3QFQ^~0%&gk zOb_}dU57X2c`6I5^&Vz~vbs;Xx$w(lE9aC7AJz(9PQ?!~6!pby^L_IxF;}_>Q4_!D zYHJp6uM&zav(uf^x&ybmGkIKt_m8TgS(&;+pR#)#-u1AOb3JI%l1hpg^-RpT_h47@ zz{p6hoXV{zK&4=J!d^R->tT2w>YWTHms(TnI~7dS`DE9(NiGl<8Y3ebJmq0Nmr9zh zhvX(=z~RP&o1D2&`e_x^yOCVvlF0Mx)*V5Ykc|QlhXwj0{f#2 zU`H?az8}~63j-JA25%^LRgPFMU#$G>VA$VZwP9RKs8?JWQeDc*jg!lk@~`WYTcK*#G#IJA}FMr0GV1&%tSM`#(F6J|_25 z4;O-WH+oOen|Hn;8dsQW%2DV8P3S-5C=8)_be%d|_Tayh=Rf&_Kfkcw!`kZywcu;G zyR^bqIx+-do|k7}j8QOev|`ekS?s<4Aw(xDqVR5usU_*?>qSLM9)y$lHHQ^Pe0fz-d6Fd z?}%2sNbpIO#8kfGi->PIg)GcCLsFf9nmQ^hv}4p2a#y-T=|ubfe|NREWsV*;?Kp^T z{Ry|#u``&Q`tyNd{=9p3Lg zG#^Si>{~{d;I^+bT0VX3lYP+`KqAJ-=&YGF=Pkimi5~5BgOelGu#9EGHEhR^X0@i# zQjG3;lghfGjk&IclelEm#(5!s-CMT_X+ieX`#h0T!))1QvJTcgxX^(#(olxSo@S{1K zLUwBGnB=5$nG$F=kk^%=jMpWCQ0){?X(qp}C(%O4oKAG|1Ur7oGK}CJ4#r!TmmT)9 zN*S%FzA2D~W|J{a-9_lp3=AhNK4kqGmB{taM?F2ve0~P=Cc*k&dtNVM9b!1CzUmM3z>)5GoSFMq_Zv9-|M%K15$d~fz*rIM(b!1fAjHc#!lZGsx3e(|$ zKEj2JZ)4JP&HEel<3dzhPhfXg?Lw$76*9)_8A&!<3oz0d7|xiv&bMD1))B4ER0 z_Tvhb7g(bA(K*6b_1zOyF>A0_HOS7bN8#4xh~~QgoU+ie!jhen|0(I81>n?0CLd`v zM4WWA7~G>Xxn^RAF@GYqUE7smG6r1Xw|<9Szk0R?TgJw5g=fZ_Y5IKL3mQLu@uF-zI$qu zo<%U83^Hsnyrmaay`yTs)!$y@f9U7MvG_~Hu3d!Wk458lsz)}^4q%V)=A~K6S3eYY zKh%BQXVbJ3+TnLN(XV-5s|C6{=8xQ^w}MOJl7h*z^@|a=PVYSxa{Qff{)~CblIa}( z&0uSRUedp?%f?Y=pz(hiaBd%EK5J6^T%D;F%wRf;on%aJV4#$;Yq<81s0Q#?ZI&3e z494V_+7H^?@mUU^e`@H&tBAsEuYs^lhhPzw z`1;X<{&29c=Hz^R5Tk4Ql#%SOOw{a$tgY1Tlc9T+uz*UpX2d<(^kBcFY#ej({QVmX z0e%BbPtQLfx3Ms+1l*jQ9T?OGUTY?+-pv?hH7O?8(70&gL@jNCuEcQ@qLRs8z=lgd z)(ZNO11vsk5O0TQigVFq4Fqgd7&GDZKV;3?zS!w+Z$cwZ#{I1AEVP!R;6iUZ=>J{M zbduvJ?_+kH%!Rda-9d+L00cNt!{P?$ALfv=zfhs|DT~Y6Q0noUtjBfub<>k7HK;ti zL$(_Gb)>z|Y|-n`K)Ajf=NDdQ$EpSkdkKHQjGM$ibc)jtSx<-6rg^2J>b)B*J$JPS z$YG8`6L=S`>T1EV9DI@yUaE|^w z#Jn}=uegze=0gvgg)Lgw^M>7pxQW~Yd^2uJV1OX`+_@%@H{2saAqPc#^BtL86$mWt z=s%G1Ptlj(MsvP;`O&$Iy=j^oXy^MK>tB zmU!Ht6ZDYk(0!7;#tgmBgo|%k{GqL_{+%`~56Vks+rJbC22hvLCQSAW9Ue9-(ce*~ zcvA$M=6{#vxreJG7Oez`zHJ_;V9Q(nOd(A6CA&qSPV16Er;_v;anxGIX0(NcL;)hY zWz?63A7VCY`Cp&=IG_gXa;VWY$gNW*2B2bXarO|Z?JM9d8B)cGnEfp6%Wj;B?GiSP zDSGu%%!&{>j(1THP%;7fQTEab^a3JD=%_+=G7{YP(nujK7(m}oZ{0yhxRb-f|*FcFRUd<7G& zGp+W@SI?aH3L=jen41zUqngiX-e_7SuSBb~PD*IM!^aoK9xOOPAMb3;U6D^>2}xE` zWGDavMJCeTk4!(~@)*3pfIm$%SdQ^tP1hbM0WiMt0@(*K9^t(RCfGE}l7&IT*Zx#Y z>GGZUvh(Ok9(|BuT@{k}C4^yLi15Gxi{<;dezVpzA?b z&C$MZQ!LCnZ^h1jJs0y4am`|m)F4L_Cvp3MUQCn@&>R(i+)|RR4y>HDEJZiU@>WX; zIUbV6Ro^^g&HP`V&&e&h$HqJOH0`zP5@6LpB5;!fUGI70vB3_Dk6WkSScN`k-$VM2 z3_PG8*`;Z_#IO+#G~{+r63e0;mrpg;+1g~o3n{qVG8!L<;WX(azOp#sZT83OU8f+B zF1CcZNs)2SvP=m=3?CGNi=zJWsDpZ;*tE*k=wStK^B-pcSx{9%1Ir?{>NH=Crd7GEmi=y|L{{N>e zkk!6XTo)Br<$k9-6y|mF(__uzj@kR z{E(iO!TBT1?s2>bnQs!Tg@ukVMPSyi9~TE?^W?Ww!%{&*eC4~0`39SgOa0EqWFgQDQhpy2@#e>Vrg>EKlHJ*2FOKDxdKtNkc1&)0 zi)R6(TxDf7QByY)#xGVxv^<4{paQonVLT=`XS81Bc&F#a2(y_O`f+?CpyT+SRtQ^k zT|TQa9-`;vJH@J7V4utP%uikL^E{gW#3zx>ZEs3V@%~)^xM@qNK$V($tcd&Z;H@k% z-4A{yYMX4wNagQul@{*~+`@TJS_cMwTk|Kd(Gta`&ch^ybBx z;QYOOw``Ty|J;-$+~#AOwi z0*>sP1;+u5i76K{j!W#&vlr{Mn%hnK@?P0uE9!$y7S6CfDG(WIFYP5z_ylF<1u>S6 zrh%;-n8W)u@4HPV;}>qjaF1>o9FTSERK|pP*5ieV-d!;xoNi2_We=GeeLM*XCH2Fb zg-Qc|IC-v@1ZNOf9~h3PdH!s#!A?hVk3rgak%A=hhd%!i#q*<<* z-Qj~i`y%PrySvaqWBCc+xq(s-8X~+*+nrwhxs^{vp>sIA#0rL7LYBWO(Y%Ul^t7-J zw-d|IcH^Lb-h9on#ucaPk^vo>*MHRi-Y+zFEqPX9y~nrcFxdV3)&=&t>m=<61pe3X zw+`vj)lXXy^%(Sgb#Ge2+=OV^I&(w+L)vS%x$1PUJt@`ateGuV!r%A9$DfQYtc>4d zs!8;ba-w-NiTYC6?|I7C@1Q!Ze}7vT*JURo*~Lsgn>TFbsZbg#S-|sBQDx~D4&e{& ztT)aHmO7rTSZ>Bvz&UF?l`bXcP1O(K@`DNzyF>I)Zu57CEbflI;Z|nTwwq9ljlUd| z@2g?1HTm#?p!ICNC?N1L%BAV4kqV5ttleC=votyt)uFKTgYAkbK)IGB(C&*j43ZWx zedmqZ2fZ(ir)2>O)Kj#=Dqj=8dCk+(W|h2c95UJGbW4xp-@oH+^PS`F?RwYK#cXun zj{WOd(C!UKt;lOa-X0$J?!U{HDe{M!JUzCLfjb?u-FT-oi={-fRT9VE6FA$=Vtz@r zwhR8@F4CK( zD6c;}@;&7ds_~mjt;OYq-n#1W)wF&L-?dSF)#Pb@B`;Q{b>+ddId@Z9W=!0WTB3X+ zz3V<)t7%%&+ z5ve}QAsu+Gj+ApW^ueezI`~DVwFDM($~N0P8C;p;@Ve~*OE`TO7XyoaAIJG)2($lq zlxHeCNR3wgh$QgONUJDtG(0eAIXuj23ceip*cshdIBI=2NT}&@be;89?MO|$Ln1-< zL2>FnTSnI~KhY1_xDPYRK#>bB|r_1^Iy zncmYQU%Y)MmK5I^${IMjX0EUsd^d8jzh1O1er{*>>@#bK)b zM0v(=*Jk&T5cB-L56aTyn8z8R@1gT{sCPMN(Ug^?+vh#6%Zahy(`DMZ=Ab4p;G^b^ z><|9K@=oq3x$@g=pMS6WeKYmyXH7Nj(4~6o_cdy9*OdMYg&Hv}cWPdo;;d52Ol7q{ zeiYYJOdLe-V%%Ds{BP&k7@2t_u>GnK-Wf0O?cI4@VXJ+Vraf=chT0`Z z*9RNAQFb{Ab*w;$g5Qm0iIXj#-U&FKUYw5+dId!PmFo9map=*&RaX)3tDQN=I;s6Zn%-IQ7bz@J{yGws#B8Q zTRCYiV-SGNEv1q0GIu0);D#=fH13?dI5r=sZiNm{7 zSuH_gW!_t0xL2E~>MIAv$kTE*>l#mvW`PQJG(@IbV917FYTnRPF9M4-$A^8JTS%a?82&WC!*`xpeWk%dO` zPamyL{hLhn&)l`enaSVZAHMkYa=t}@Nwv1q9+7i!eYO+lf0hHZOkcKce$$t$%EN@S z-L{U{v}R?@Yzx>}9MD2io{pkz-6!({!@QYrX)O)oo#hEG1ks=ir*s*H-d*W@Om>Ls zMOP_j{S6hN31@ZPOv5T1G^XNAPIwT7Rpqj=-kBPIcmTr;ENIPOsRv$`SbR&S;B0b` z@S7}-&?IQ17=5qQL1mDPcjGr3RR7PK%kjDBC5Vw-zR~5+GzGL`?BLFI2v)Uc6iRD?;fH2^U+$Z9kCV$gCW8}U@>VxixVYJ@^UUyk81Y|9(dh|bcDH?;li ziObK6!x3|n*KbM-E{|J0=6XI01Io_G4c}dQRC^mB zM`=Dhh-&$1dbnXaSd7BFF+r;3V0Lf>p!TG0AyHX;IqH9iB?gQ--?CRDMx zr9f*?pV03CO!#mutoCP|JHRL~N>6dw2G;H1%c#WvwkRB9{xfJYD}J$He0>kXgwukl zWaREuHtXqLDD!yA)LMT}B0!X{o_=PDJj$1Q&t3DH`@ia!e^oJCqL)Yl*^zope1o=| zPE2+w%%wBpQDT5VUmZiKSwBWRz zzMGx;)<1D#8djw)ogFNaHw9$9e@hcjb)v6aAA?*m=BvvOVlaPIl|`cO-92k)apG+B zVrnFuKdHSQxcnqnSX)}7&Ts=S`OV=D$U*3&lz6PnKwu2XWChqcVAyfYi|~G0Jbm&- zn)&4s?N@O}IM++^>WqLZCc`jv?1!x5jrqB}KXxbnS6}6yF>3nZrDwgdQJ;eHqi+A` zN)>y*-4c0Bu(c$Rwua;Ml67p%?91*t)!DT1!_ds=y1mfa2=-of^Uu~(OM4-#2e3Sw z4piWuC*D0VrZLQImg4@p)uH6Fgw*XxZDzaiujO&VJ(fe03&fqF_k2OWfMyIMpJ9eI zxit598O$wUz*JbRZGw9o8>#}y2PMk{d2^V=->I|RGnck-A{;MZ*cn&gdFX{&%^^5x zTv4AJyTDfr+4Z;b{;$Q$XsSlFDNZe1wJ=nQ5w{XRWvWqiq+FxJSyY%*&OP=n;iWIg z{rgWBb7-FnYcBtFrE5er?oQroayomoR{{+ifU6_x^cM+hsIo38rIQqU~*Lp$7 zeEq#0fr5A9x&WT%_FW#dcl`Y%IahZ5P4oG)MoQNO zCso%4_+9=;)|{@VXmBh@=E*SOBPUEGP@Ew#feuKoYja&bZulEjTyY2hO+g@5xrAf53W(BpSts*E|LX4vJ-qegeYFBH- zNHj&YwOV^`v8k=JQF}zFy|)@sd~-ebbN`;}dYwGI+{@*uC zm-889O4I{9waJF~WHWr0njBmX)T5Q8j#>^u05MAN_Qp@kEHDT!zrDyAXNx2J`r}vv z8pS_@gb67L^yZ%ekZux;irV$tyB>Mm{Z5P+NV;HpZ{Fz~;LbPS7p9g5$dxSHL=#yQ z478{mFM0FibzaFSEiw*XH&f%e{mxZWwn6gkSdGGfjrzksUx7?4gb65K&7^TS;4vMu zT;F?lHb$O;Z!lA_i=Vv4M}d=_Qr9e;rO3_vBFChlC2>rJ2lLuMZ=UJ6$K#(1WC;51 z%RvoQfPFJ*xFp-E3zJpde7b=(;rL@nf*6RVFEvatWb1cUb<8tc^CLH;u3;3VXq6jG zxE=ezP0p7zpNpKv$`rX?joXhm-G8N`pY;3&R+svb!fi{V_4#rRE3qz5R*+lo4aW7K z@IIY1Df;?&)ns{I|3E75;jL%Xac1}AycY-yB<+i%*Si{ zS((ZSG-2uD=V$#MNi` zr|?@f@qYeXBo@J+sW;BiLiWGIEMSMF`7&6K60ZIrjPwmJAN#A^5VTB{izs4{C}RY# zqnuad?BWHdn@5IMDEg>RxGjsS2H)_7F`zjgv$B61u8OI=2gGQ?v}_FA_TMOU@bV#k z$=9fOn!)S9YwiK)(f5e)37HCjA;#H$VYv=?YdJ_LsyOEkP01T zp?rvuizy0J$J@ZLe-gQWg15haO(s*IH2t~kMc9SYu>7R3d~7jBf(>FU0*$ zr$h`;2Ce+DLM^lRm09p`mTo0CV9Rzcu&Z`|pH4js)m63RT6c(!;C`cHO)Qc6ptdZf zG9jL{d5q`SxaYx*$>xq-y&SerS>aMA#`58Vin`QU*Gla9Zl36atr`DK^)0`yM*H7> zHUmKJ0lMI`to;S<*mvH8E2X6Rt)jy$4t^-^(PxhT)x(N_Yfinu#MF3iZCZKQg|97l zH-?&@8?hIMR?Ix%v}F+Q@7CfeIWh~Y0Vuh_-g>CP`?HMC%{zTj4f_3Q%#rE{@Ww$l zm-i6gZ`e&5;Nb(INWcY@VUh-nbf-N<=D<>j@G>ofdWmaaDs9os*TP^64!E^0HKs6_ zauq9-ZX!R##V4z3oWY#w%6K(oY3dYYa_5BxVl+Hs_|4keXFi!$__lsN%L@e+Km&biZ23RPevo_y2q_7Y6F9>idiACB3D|& z22oxH}(_<;&chEq&lPS(^G z<@#JmQDLvVfRc|U){Esliqx*UWFv4d@?MqR#$qKLRUqw03RuRfnhIp(Syu?}&8K zJe4jz+nonwX04NzbdGE3Wa5)Eexc@xadZWcRvE@z4r@&BEa%=`E=4IOQQK-}*p
    snsUAWB8bxXHWYPT7 zg;v-#)to#jup@qo5hDY@aNI0&hL7sNf1>jFuN5w)C<2;;J(CaV9K#1}k1-J=1EBRJj7*kpE zp>?ne=SbSe#w^ih-b3R*n5gR3u#g_|2wbDEdL7rKu$pqB?tP)HJD!i0qda-}kNcfx z%hnr^gp^e|?8{yslAm$J$vRsP%jLnZnE?m%X2CccX9KQ&jDu_uqsn7G`)I2lqE7V;>3>7k>yoTA* ziHsUP+UaYHxpn&kpf#}k;iNrsy`F3)X3u{;_>l@&WGSfO?HDohC2aW}4|UOzcYtxm zQus?fT9emAk@QVV3+P70o9aPbjOBmR?|)-{$!vW1mpFCl+Zry1o5ZB4(@mf1xW~r+ zvvAY)4^|KY*#75at;fnxo>p{w)t{6r+H?D7%NU)-A(^;|4hU@$No#^}Z%hfY-@-|f z1@@3%-1mlT-}dPl=wlU&X>bN$kXu=#mRhE+pup`&tMN1P`!nv~5B3i?NvVqpXIipw z)T<;=FJ<+A4=gR6E}dxawPg$u(r_=6>x7m6+8hh^pmr0^{B=sz=5&2jsy6BdD8eT8 z`z2!v9Lig~WOlL)Reja1Kwpoay?r*RKm0O%J;-O6^3}AV7~}vPrq`z^u&WO_G3|&a zw*OO&|3V>3L+0$nz87u7$%SwP`!cLX#awzP`0TmF#Nzkt6{?Q}1;Y?Wb;N)i+u8Ac z@m8(mM$LPC8eugCCx{uz)sOs+*vJ^Jb0QL@CKQ ze9DUlr<+}@YO&H-v!>LejM&V+vp5+=#t9Ckf1d@g7 zUOOzAW-#P-4pfw z&kvu)zYNn$ja{xj1+!mhS=}Dmd8nN^O6D2tBOqN<@k*Gt2G^2OxVTOJz&|3_jjg=d z$=fMxc9-iDl&X>H*l0J3XdK+&ar@KEFvVAb+JJ2TNZC_f*N$spAR`eQf4!yxu(KN=Yd}ZR-5x zrc0L)6Y(|KyQSeX(=YPsXOp$trhbj&PV#bnmGjNnT~>MT-$6H*)KIHTuT12n`({lx zbhsh>2%^9;O^D0J;Ovyhl(?So|6wQoWF(5$A%lzsp>sjwFZj3(yH)b3{? zq~8S{V=yA5=rIl2g{)(v-X_91f>L$@F`+gv&)V6T*N40>{t^wPo)IIXeNlO48 zZU(q-w(B!DtcC2CU2m>NzmpX9SNB`aXvX5tY`4_Sc!(@g>`y|;iTH4zzrLbO3NLle zDc$c}T_8hTQkO74DXubIM(r_-`fD^#w9MDGaWbR%3x(nj5#(=;c$Vl5 z&q<>QaSb=}bsg_h25(ccR~G$pso%hf?rx+?`46P{Ox@OS1073~mNNz}D!-2qRErFn z-p>{r&YnslOYy=N>02EY%41R`x&H9NRUcTtrZ!qhrWb6-@fmrIeM6MTRXnp%r@qU# zE0~T44nuJ9$ah0Gp18Qg($~RJ{;Y&F(iiUnKN_6ZxOL>GD0B@s_WA2Z-Hp~Mf+Jax z|D!)K*opR1!{99;JFJG_9lt6nBf(1q`BxKsczJUp4B1%04Rtyj^NU7R_xWF>XqJCN zxVi0ayq5tb**sB4O35Ff>-@sMW=wyaUK-D7D9Uh0FZ~Ca6<8WL*%v}?CUrSWX~Jp{ zgI&QhGY<>GWC@-#)_${V!PBXWuTKUM=N&ToZ>yzq4$mk1V&!X)iGwRs!7qH2&H7}2 z(v=jN$21>l`zc*4r_}?wRLUez9rmZG`vmO0n7A*Nau!#wa-$sQ!uB_i`Q-}m%U4S^ z+Q)|E*gPY6N?D*lv1rDr-L-H6n)502$GbW%2M5>{B|AcL02jhM;l;!Bm02F1h$qIe z5KHF|?l6{_LTz|9yt`w^uGFmGEw&M;LBWV-0m+NR=DjAoSO^w3rRhtJD*9=Jld~4* zX`O6JZoA^XIokjQ-34h-#xdhuk6OQG!%o)$7CD>zVn$-uu%0Ndf@cierC2l%)Ae%#FNUKmik8PpyPj~FQdi2R4z>N!s2Go&9)j!w~vN4!c zIYkW^rEg-wnrhX&e?YGOfiZ$Pel1hM8~@W*Uph>62Xjh3kacE%Dfnz@r9F}^TIle{ zXStOPm*Dz`Pr=K)7G}PO_0uU$WYDcZB0o1^|H?mK*nq{QUj^^0+xtd3&$uutM zGaKnMZrYiN?az1gdWeGePHH*<5-uNq%f7Frp^KJP);JJX=6l_ma?S&DOV7I!-cB}3 zl-iU+J$U6JgRLkUd_^5!0WKXS}N&M665ZxC8w%c=qOnh>P+`pO-4s%OJ~VH5R&a0a}R z_&W$ONj8dchBn4j2(X>FgTR9>%!xZAAZ-2LP{hAWD_Mjfms2`LpFhiK9w1)qQj&uj zlk|rSvtUbR3-19t7e93p`g|p45WCVqTF&Gfed{yos0+NZY|`7FsiR@5{g_(;)(4v%W1GqPIz71-?gyJQ zqjCK`XHv}am8*VNyY5guSODJ8aQo#l$@xUE;3z^lP6Flarl6V7MDbaKy#Z~PPtMD3 zsc}F;@>yp4YpJIJOB_8+kc+ve=8KOd_P+$PBEzGYfe>|ZA$e=vDi)+i|I+g8DPVIX#^7!4ySg-wl>V7Ux#bT2?yJG=$@O~+7tdRfausBh2gFy}Ity|RQ*W5&*;101?u}3x-^%teP zh2HY9pEnLY0=Tp(UFFUfXh+J|M&rpNDY)~yyReSQi75p=;rT}BxIpqZ210Ko){!BP5`$28I0u%cbwbI@ z{$!-ONhEOkFwjiGdtD#i&IR5K`2!PG8(+|;YEGoOlR2F3iM5V%22S@lGg50>Lc4_2 zSS<-A~9>uc~z@a}}hdJO|PwsNmBn2mb$@`9g#Yf#T@ zgoVnM5(Td$bF1=tq1xS2a7by}Ud-~&WbRND(q^uxl~^WK10ONb6~*TSR<>ow=qF0~ zTG+n(F4o>Y(R-H|9cEf~n?DNbZ6kP>QPyg8RZ3ngZH}3}W@tL3sBS2dOlP1M{;{N% z3XxR`GlrN=a%XE!2O7&BX4DwBOBLwJ5L5Y@}& znnnz;`Uy(qEeAvmQn)RXjt;GvDa?distAAaED+K_@a@9BEnq=tFK0ft_)MPO6$sgX z#6(uEUP4B6rT`Y(qN>J{gx0TC1>jq7II+>Hzj$-a{5i8pb;%Mtv$X@1eWb(fO2n;5 zuZ0)5xt~QK1CdRrsWn_|B=GJ>xG*%N_`o1nk_{Wg!1+V|Y08!n{TgI~OPS*23Z-by znu2*|pEzMS)3v)bGHU4wMmzdIWv^}gY7P%`xhpFDtVVS$^yX=N0R4q$QMq^eo94UR zHmtsm!2`2nCDO9IJ~&pFJV-344XVEXm)rF(3_E5@;jI+Yg?m@>qdUd-pJ_S!;_d zj#p`WvHS^>Adep!g2Qd;UnM09-o^^dPVVt>tO_11NWYRD|IzlY*h&y`n<`0+T5WYN z`X`XU^L(F`TK`@9t9U>_n;KUVEZw*rC*X}@WMDd6ZapUJ!OsrdPPq~BD@I$2iKa&< z6JdvUUF84|%XcbPUb~uf+FEE()UfBHr7VpbZLfYt;`_*+sYDfD{zbtpzEV}X4hAfMqvmjYwg9Y$~&yeaUCbWL%%hSgvpR zC*}+X_|pc_jo!B*Z^M~LyDG`I(xh&eO#iMe=s<1rnI~!Iy39wmi0IhtzTmm$r`MbA zORhV=v$a0(pH5^wZuiOQ`#r)vXv-#3XJ^RWL?$a&%q+-%B)3TiE}KJH~J61IHg7e2{EiG9~oRu1;|AI5Qcwp3lL3CnxMtaJi!OH{GZrpbC=vax+Wz8Ozp7*WXh%j$KfOIS@2N_QIJ zZW#rrkEW=$vT#AYB9N>uLZ@w_)3@{0-f|45HyvTx?ia)_GyxCkLD16?wG_AEZnRA+S~v2vm@7+)wSVIF&j)4g18;vY%2be;p&}2dFu!shR;F= zDJkWFy>~Gt%qRV*N9`|ulWEM42o9{EsEboRycLbADUSH|7NT=Eu8geeMlp9t*drp_}9S;vQ+tZW59p^ zSc(R84=g_1lRAIYxK)f8v`zgk5VSV^+u3iFd#ZXRy%#^}2pOYl+Z3gZ#)sYKjtdj(WJcPpzJBqlGh=UY_it&s4&F93zeVk_F^0-gc2QSux-m717daqoq1adX2;}=MaA;Y; zJ1aB1QHzTpG~IB$(IM_KuH%f5y7b#gZ*#;bxn%fKdLSTtrDXpDa-^IkvEA~$RljqY zMPk_zYkHwc&4Z@IA!uNP6pO#l2R6}dyTo3HAe>g>P6npOfM@KV%JaVn|6<{b1{KL~ zIjhg-n*(_Q$tflY^M`$BrlfCk=di$^MRUvgCdyLw)WwdB_$&{GUvCw*w7kE4%Ut;| z?s?dF*m01W_WQBlN+eLd>y2ou($O7Pm1mruBJgjkr;*I`d z$l5zpoz{BfK)&y~ew@A@)}XK4hVt$KwfpmlOc60FVak^$MKKsDCc9l)*3r^1f0)o8Vs^@DXU zC!@3a!DeaJ^#uUL4asI?V}g429r$%bTEE-fa}iFTn#Y#D^j21Yhlz@sn)ST(oE5jq zN=@H$g%P)w61AGA*mxXZ%fC-U&?vi3V_lFo;d74=7sca@-9Y&tZfb9gQcDkSsQU4Hg_#;+X)19OE9jz82TN2F z=+RG$;|^LoU73;jCN+KTk*3V%*JWwZGULzo^t$~f8?VQCeUA1a3-Loq-=oRDKDjU7 z)y*r_G^9@eMX1NUz0S!&ur4gXmO~sYFN;!nXbe_Ez&koFMly^y+|u(TxIQK`nlG!f zXBCGEUYRb4xi8x1c0Kby;D1q^o{X7y4V`-n)<7C$u`Olhokk3-O9l%-D1PqK&FblH z*m4ESuOc~TO&nVzQOL8gXnxV#>uIVmewqX2_bQ=8$dHX1Go7!GNZF?1tLGH885l(d zM3%(XP0Fe4%c!F9x37dsKJKsU^lv^U8-HC7d!^IpEUM>w?+d1?FiQZ%SH%}^r!<|4 z)w&cTRRTYTtiaFks zd?TKx_B(Y5S_b5+KK`wl5$l$cYZO$X(CpYC#$-@5bNIkRD01&(-SK>pHqP*DK-ytC z$)dS`)U*V2#Y915+W#-@W(bqqopn>O=XS9UWV)b*Vx&zvwVC56$tJjxyWocB7=xC|)=3_ndq)t37l9!$R_XbLfT=%IQc@6eEq9*0kt`EJ_lMN@>E zH2yZgZwrWyD|SaW+MFHrI+n`~AeuQwHk1kp4t^Q?-Wv);p;{ag*Oca&yYjoFH`}hrSd$Y2xZ4+-a>ktF z)!uJ$HwDv2`jxRIZe@v@d6ib{YPdoiV9@|QrhCZJVIlFpgGu8NxPi3uTBmG#N|S`Q zBkN^)1G|krMh>TzA}NDFdsYjp?3nhgc#I$}b+7&tYi#dVj(S`RJ~{SSryn^uVeU9x z*5~Dzv@9m()YWR5a$8#xH}NGhWCnzDVG(9LMh2GKseIVYpoyqb-Q=j|Md~!<8hgZ? ztso9PBy$`jix1tZ_Oe7bzV{TF%B7e!isJS7>y3{Ga8<#7^WBW7k>$jy3U^g95Zh2P z_c!8wH@Qa{M0&jv7aROYN$t~q*#6f(av>mb==_jKsZ=mkQqMu0&)boHNIKpg=N{NP z?SZq*V{lauS&$4gDTOIfw3(B5ckiqbu(6hC>^k?Ylu(gAu%M(Jl8k+eWJ z$VX=OPuF(%{X^p?%sQ@~%C#p>3|zJ2*u9W$T*kO=Bl+e~c!Fa#f$pYJod~pMuwSgB zl0(z!<_cK;QDS5kMXT-?hZPm>_HjpmEdZ zCtPURbTljZoycYOZFUuDKu6FXsFns^vI)y^1<}&wy`^ zN9g%FC(lo})RwG!=)@M>5=+xtDs)b{)vWgZ_>Ea-GS3kQ(RQtnD+wNYS(R#wVrEJV0T0>8_dG z5=>h1*yCnwF5{A|mdM(kHVKVmPL$Z$dWxw(?K1Pk(8>|9N_1Y*%7 zR?^GztOx2f_c5{3WZYERK6#?Lvn@54E|ayH(0;dYD@;x*+$uw`nd9f8d*DQ6gh3HQ zV&2|C%EeR9)y_s9q_&cLZJ*n;IFvN*BvWqD_)XmG#AYMOm?O`$pjaxXx65luI=yft zL^Y2Zm+T4Du~3?A5d3CvW>0pr1_CP6OlWfEepdn6e(WqgkWNeKvkx%dv#meHo8yM~ zPph`1l}+P^w~{w)&+Z-vx(7OkBgR)L!bvB;S}zNN7p*VpJt5bif|rYT$ zOSXm~*p6GnQLZ1Ri09jEIr{`QsPL&KX`JmOZ$I_$JlgZTx-YvnG3{yW8Qyk)u4^^y zX$~b@O-}0n0=vKBPCz}A zWCJP6ed~2vL3D1LB2Wb5XK>?Whj^JcFTI2y^U7qcj~}myGKt6+9uPg>rCv#xKDTCE zm(+)s0L*%E>i*)KAdm>rd+7mIC{Ui9Hdz5D;NaQv`+Oms7;(Li3Q?E7;Xa`#;=pR! z2ARFg1<%CTI4-3OOy5|<^xm^XjVoX z_|rO@#33!`hmXX!&LW%0bSxAZR zg!0Wp<+SN7{!C{xqA2lqma5~p;Tja#`~lCORAt&bY9jB-P;B&=B`bA(k^RzeYasi84%;cQ zelI=7LC$j$wVvO!{93V0YP`No%vrxxh}X?^K=hG^O~xHCEy(5O8o06CKgKSx0W!64 z>%<|G4{pikJ=AdheiHtqROg`?MEN-g+V5Ld(&HUvN1ttB%k@g=mF>9EOw@&2WjB8N z*g0+Z!^;GGRfCleCR6fwm!u-oA{k@4BegmB_o|IfPmuPe-?v60JI^fsi(Vrh_1-ohMu9lWlMLZ>L9dZ2TY(Ex%ch=EPL}QOU|y zfi1r~fvatZ!IR*X{s$A0`{U|?0)gJGV-+&HpZC9P&|OU?pAzsFvIydO04#_1KB$^lf0aCkH z9?#)WXdn7mQ%$%KFqz2X<$3nha^b8cNHAt^QY#N(ZOZJh*gbf}CwC;F@jGAIaE8Bi zl5ao|>yUH9EfJmRlN7C);m8gVOrI=POq}Z%|DFd^5p?kUKv#@Lo+T_L4!73=+?<=g z6)6lZ1SpSACdNGrKEPF49gNu3dp5ye%g2+$Uf!V{C@(HUQ}2-8=RaezC|rMDEvvXy z{Ek%b-D`0z#Cgh6Wnlg(1FZx2a8t9=7P4`0h4k#_(al#T zDZT8-Pxtp{43LaM|L9=&7fa4VG4fqnI$l*$Nr12Ax#8onJSl|@DS_PFP+;PXaOd5kL#!mt&;33Ej*Fkeq+=A;3>Wy3GX)SdjA+zap?^DeDukgF%1Eibr2w&UqdcUC%!jEM989Z3mZr|nCQ5o|1N zxPp1g{=($$fYcrCTq%3f8{H-gEkn+LVJeWXRsmYyr*EotwteB-*GX2`+7Kh6Id;3_0I z@SUWthj6Y>gE|tri`vX=XyZcJ?D-Esj z3{vqtdKyTMfntg>U#K^@C%loIE!V$;4K%oLA6~KHYj8v3&{ku%X^l2iklsRM>w)b1 z0;zOUiqZ89HquDv`?5szJ6riB(6Lzh4j5bg0+hF9x^jKT1%7d{0TpJ{5A8$$qif~= zGvI&m;x28s3?OzjOaDPT^(JVitZU@ah?LCn+DchkvN21`T13!Vndjz0#>$I{qyv14 zsx4Q!l>3R3^R{Db)}gAMA9=8eogbM^;90gNo@O+^XFcgx>wMexVU);SR)Qlt2eT4m z*SPRg4}C~Z6O#V0@0(@E)wKn&w5Yi~qsJ9OlZC z|H?!>fipSW71sJhUe;Li`u$>xLVyPy)_I|Q0P!%_?vn@zDa!p7$%V72@@K~S!JE;( z)@eE2gRtuJc1p*=T((xDU)V!=&-~nr$xv0Vu(M;UUY$Bv@3p%C&#!CzvK!>&xzozF zBSW<3Z8-DdO?fYE_?I?e?tx2PIqIf5@C$X!kz$131yHv-*jEk8r2@Bhv(P&E!h%3` zS~IQLYRi2yHA$ND<=Y<+~sq$ftxrLHfMp zcW4ayGQYtxkVzcpE(*r2H-gt7KN~Y22+kuyCqvQ9gw$`Y7TkSR9DL3K&kdY>(G%v? z)Yy{M*hlyJS5dLNc^a-COsbE8bvQhXqaBnPhrsI8qDOPev?r0?Hq{i-UVbCg&@oJPRH(e7+}3C{Sknq)?*@Wic7K zjF)BS(|xF;FVCra;7-o`x>2_*hRE4>h5Qyn_%u}sFCyS3p#xsiSA9w#nol-HPdX?@ zz$Of~d=Pk-ZYT~gQm~9$+!JS6?>Pqa7Fg-Q^}bjLh*HhSp$ZyFKgZEQ^EYn&y5TF9 z+!*Cr?FAoP!uDA+wOH)f9R2&ujKAK-e|^T$UW81LA>A86Z_)J`3nubuwhGbO2a5?2 zMMk(a{dP@noc^b`q(pp{0?y6Jlg8T_{=VGfFq+8x4Bg&Pc9hxKqA;rXf)GcOXjcZf zZ*Wxpgp=ZvWW@g%>t`llG8ZCdfKKdMgB&HK$`9S;;G?n z<{efABlv2OKOEPPfdwSyHX`Fqq=G?3V(=D&N6ez8RUFE~TbF@5;cpW292_4i%n*E_`9ycpf@&C|3@7u4u6?^Z~ll5 z59Xk9RpJo8FcZ+ucV>&R4H2|~ng4tf;sI1sNaiHc@ZxK$&KBR4YbS|_GoyOJwS)TApNju-|0K@5H=7Bd(qs9 z;4DN&H7>SY-5Uf`b0bwQmjEyYieeOXTv5&_Pq)pw9*X8Lx-QG4q06tnz0-kf-}$)N zq?@baIV>VSB&7!dLdT&u#nnH)-Yb6jvVHYlBe)n~SMWG-siLG0X$CQojxNfrUfhcB zewRB8L(JHP4c@>Tu}^lmDKp6$i&ME}OMR{m>?O^SI5DYopDExbi#A@s^{y#4LdcJ>AxmfPf!pK&}O4Ivzq zB64h8KN(E+-N?m6f2SEVLZBqyZaiwQUcKz^RN=}?V-0m=T;D0LaK!fLcWWSXdR|5G zxz>#Xx`P-GcN`cN){R}p?BqTrAqy=27^$ep?`9wfMc-dNrrCkM+I5bir0aL0pT~4m zm!IxVuGZo&O*;uMvOJ?Fm5+wQ^k$8mnl`B4;G=;QZEih#C1((K7tqn-Wz)S|V;@(b z(ulFQa$`m(&zYrFUjLo~ltJhIIokH$Cd4li;5T6d|My*c`z2ZTrAx}~d9M59bNpaT zN+^|q{BwiQ2+GT9w@GxIkokO8TBZEn{4=RYRsR#(g}NgTMeX+~n1k%0t8USQSV&Fy zoo5B7)exzwmA-Y=rDjs1J?Wr+!2Ezd(%9B2NqRT@z4Grn=^MO|304(`wBuO&+|$;T zICcrIqG#znVsbG=Ih=Sq{ncScX3$M8k@ zP!(wn>8K2P4e+f0V>N9M`eK)yXp@{}Y{d;)ZLiph_wB7(;5k0F87XIUf9!#(*Kf_{ z8PA(|gVloMi1%WoEVe2r-c;&W7wkVTf7fA(N*)^yrmAaNtCpGPUJWV->nX#cLIhv9 zMuI%`&K+t^FbhM-C;GBzd2XmuvSbm7W86IH@^~R<=sLiS15!--Vm*~eL|^8X>*kky zL~WqZu9-gH(-e-BbaE*#l0=$a5?B`>@-E_CF9H~~z5BRP$?iEU-jCdCjTtjo+o^Fz zo@VHl*4SfVkjX;O zi_e;*oUMj?nNEZAJ55od0YNL2Pwwv(SXs(o9<2{&(WaTKkA4eTd;8shMuK5Z?k|$X z|FS2A6sdhl%`rNqJt6&VFEHnzvb%g%Dfh~dnG4v$9w&!Vfg6MI@**J6O9_+*XackX z&Vj7{6c{qGX%q-n%QPH0;6`&gbwqOh`Uot-px*G8*_FUVX>RZy4(XlUOHq$S1g5X3 zu{4O_T3U<-9(PF|^kuU|Y@&+L+K;E*&1GFB;uU?)#;8|X#hTLR<_7+_o=E;bYkaQ+ z(@m&s9EX0eR8N7dgiiZ!uHdE41?8dLvtz&oaARemYwV{)GO}fO=MB;bVTkkqEr%=W z$<6@fR{b8HJ-`;jplGtKk!#>)wWDg{&DB-bu18#W`T3Z~KF=U)YDG%9k}>|Qm&mU0 zrD!|7t_;Xe8bq~YBe1#|a%pGX)Pi#n^)|Xqmm4hS-Km+H{)D6QyNz!eOYQ#YC(=-K zU1i7Ts2k*ftkTL!=$tENzzt@RAcfZo=o_M;%yT8J!oo$y4erBRX4d6>#Ajt;R{bt9 zA$0Us{nZ!|H5Pt^dAFLOZ=ojrwA(kmz5fuhCv&HM6ybXhB2@OH6t6P@uT1D?g8gg3 z-rM@|nl>mB`RpOu*3Qy2Ew%VbVl|JMuB#K#fi3#kA}i-e!iKS|F(ec+Wz(;`n)@V= zoq%)lLc?4BNVr~_6+P45J^wLbvshKGMGpqx^8nq58Y`PXSLZXd0q6 zv=qI17+Ff_nu?~m^`uZ*t02o#ebM8id`(9-^YQRv(nj<6K{O4F5KwUeM)*VH)_rVBoQBCPnw7$d;2;@ z?CB!{BV%)JRRBCF!Hf}hdRRLPNt$VbX~ok_m1fYBYJ~wB?3(`1I?v<+aqiiTe2~|| zJ$Hl8n`f`<%%bK$RchNkol?ZuQEywF*@~R+!AImvlf;S#S32o#?R>B`inx(J1zcD? z;W%(t)Epx6%_A>2#oHG^rZ_>Ha1ECfEtv8OA>v#dL$kymSRZ;z&lhGkb1iI78|Bt3 zK5ayDj(&8|ahs1hJ-64m_5L;PqS@puH;-oKp`hD|RvVF-z_vBXIJbnfs9W#dCU)Md zClf9fOez0OWBZ@F^0N;3*gCo&?5{rduDlzE{SE_1-_wkEK;bWdL5(9`wV}v)ZTBEG z>S11Gp;MrVPonV)>$O&e&~66F`_b~@-<1y*6@FoVcFwx4>px0>=_@K#G@s&41UKFKjgMZ^x@n(bD+CbUpF`YbJIgWlR19E2Ij=? z?+lC-6%gbTb@S+#CYvpL@*l2>UwBb&-5h>*nV<`9hL7KHFQqr^Hm{}xa51ASxq_2f z6=5v7>f{{Q4UlV_n^Fh35z^GYV|S-)QL*56SI?5Wrh_isZ?pnk$7h)22F%k4wDgXr z4qTze^5&VI(H_zze-L#)GC4^;+c$XS*hL}8D3AGRUM!G2o@uyhpX;E5J%7{U-g_)S zpRh2X|0&v>VW;>2oc^}ibvD71{0s6F;}d-^6)Q#joX6Ge+4fp^RgCHaYZa>{Ua&u> z330qxHG-UAw7+h*K+meY2hiM86H4=BvZ6=N=ndhDI2HDbBM&z$Ny*@H1)jsSWaupSTfPQM-(F9_fs z5rsV;8Ipj1{gB71$6dIZ+~H#ZQ=WO5P`$eR&^w3^qaLF|biLE-;j~n!$Sb}JeLcDZ z%RFCCP6%sRc)zuKiwVI`vN2mL@539-@09g=RVvCR7YI%N`VUP*X#$mbLFa>4Qasn< z$_vb8@}gD1JMU1nWB4*OzXNitudJi=@E}JIRu}DAwAk}+d@Xh#V)%kwi`#jVq%G{$+ZbzRvU8I-!MCs8cfAtFSb* zz;DLcXwj5dqR5<97k{*&(&i%S3{D!>Ds}T-Sc+?? z2zv8^Ccn}|-N+8L4tLw2JREtM6ydIw5>RDTN}&%hqoWq8;OgfE^#u(3SCyA?Tqt6c zodsFcgVCJouS1ARL*nrTjWKE!acEg(1LxN7k@v>L@K3pb*W=`xs_9<%dSh0^C`;TH zBAeA|d%&JGD~E3+<2m312X%o9UJzV^%yb^A@C0TLz@v~*6)UE`4K&*=Zmeb-V9=J| z5L-7&|<4~0Lxu?K@-*`M`V58}oKwPrY&NoLoPhm@r0QeLR*JOgaxu-x)v{p(;D<7cLC8F6>z>b7#Xfn&NoL zo=@m=WnBhLBJ>7M>|9!Gn2X?VQu#3kNAW%`Gr4C_1>|+;ZE6jz`5OT*^bbfoc_xJJ zT6$nz2Jxi9?zTd{X;>RceU_}MyT|KUs1?rT@8*|Nu-1%~-4;k%Y!1JD%~gj>%>HU3 z!d#kspT}*ULyhl$ z9iuJ^?<`(FezOyGMOVw7+AQOmXufDov7PG~sXz@_=&oQ)6}2OqFnIYztUl?{sCl^0 zxX*VFTJ<*7pD88nCvKLBo|+3!2|nqpJ>8a)6*~U4ii+byvBw^h*a>{HWGp zClhC;O>MZK$nbB-AImfwnaH}+>!n0B##uyvgvENS^D(nmi2%2oP9s&C@oYH4q@XsU z-TY3q+m;LUtj~Ef_)#bUZw9@**pgIicN+QlhXk_OdT*s@^us+ElQS3j1w6|Hj}kJm z>+IJfO|YsYy|jp*a$`~2F=X&7e&2UFDy^?_*q?1?x~%T-WIL|Hlk9)fP5qCf6^s4I z|Cq67v>rc`^{?@_rc4lgeJIBz0qb`gyu;0}+#HA_(@>O$Tq+v}W{YmcoffAnMu6)L zBZq5(shfJNOs;$8v!A*5hz;5N389#L5f?Xy#M&I*I2uODH}M96$``MM`w8Hu&(bBd zQ|bhi!V=g&!Q#?448IV4+Zqu0Q@lm_Y;x%5WGMI~_Q{L3Ddx(lD7JD{f|>)-LhR0g zTIij7PC;1_8EQ(6z)to89X!&~t2_7+sG|EP@z2d=%9im*9r!5)Sn6h=HJ(n!%xdXl zxuky?@?A8vHID-&-;$&1%#b|nx*72PncPsnN1qxQ4E*lsL^+=k;KlJ9{Yk$gU&EM^ zJ31EPxHiN3T=7R@0GhIjw%C@o`cbX`wz-_0w42`U;QK|oKijX5)@_~w=GV~vaPC?t zo`J@-YcSTKmaWFXlOHJ>gcJQBB*X^=|-T_Y#uu*^l2pB(e<({;tytL?b6h?^j}iAf;w(zWu6Z7p&y22JCbz1l7Df%J2tWv1sxCqT`3}7`F~|kv|(?nzJw`4 zjvcyRrH3`{Q4ExP%&?m$bp>3%)k`}~e5F(m%52wTRGFqgi`j8cSWtCTOwNi>$r%t7 z9~3skM_fHj30WUvvvJ_t&Gs>MT?BU&@J=H7$cqKUEXT)+`J;j-uA*dXwAg40#9i?H98^2ZZOyF_67t2SRa3FPvFL}#c1f*z`3QT1F;zlRv8|7MPbm@GSXq<}O=#_2)a_u|Gn6KDX z3Ro8Bb$VdyR@u+fVD~fiG8&CdYG&+et2DNiGGkJ17pRrKz0Rm}${EFT7JF1;KouE2 zWy@8^_fo+6BmQn2M+~_aQWc6r?DB^NVaL_k0UVKgwvxxwixQO+>61$K`aOT~Kvv-y z=u@=b4qn|C3NSn`%H8Z6nuMXdr>nG_;;Z1r2k(*_mbj;@V^D!H*R62Po`=!Mai1SVH_JDP5 zH{Ag@ulWSDZR}o7LgL_5{6o-7g(07#j4@}j&lHvG*YI%212nYOX<&iAQ-Gk*PPAR^ z*fUl7(^h~}{q$-L;z}WVD#uqh2%I{^-OlUsJLG+WWADSgz0(?`a=(%t47}yLXr#CP zXkUqrCfwkYEo!!^14#_HSnv^>CiP!$VG|@ zqF!djp9SeC@@|g;`Dug+n6$s5qPdG3gWw!+Bw;c^aEI&<6yRY?d1!eUgg?g@{Q38& z>VYBu`VEUOyJ|T(5*culNUe$y3(LhDI;NRgXrmEn6TQNX!vXtFDRUwwWXYg<*|1A- z)|b7Od-oJof!Ps|oP47~A}ynNd*j53TnE>cR7qiH$q0rta_q0d^i5mC4sZQ@mOfZ zowDD^;bBmcSasusK9Bu|^Y5)Z7Qrj&lu4~uqV)O~*+#_Rh!Uq{xAMCYFCj|&Y0R>= z`frB54}~PB`SzhLf)QKXZnzY2h##I;{&_q9DE6Fbw8WiwAz`pR)=H*(LTnMM%p-u+ z*%FD1?EDQ$QKBTZNkc8B{M8DDzq*CPrfA}aSgY-%_3@ZF{B^kUJ=W^4fn>735XLjU zO1meDY)0ZAteksidytOBS_eCd`vz6y!1brD2)h-RpB(GIIvy};6eC}UzK+Sfs(EV3 zr^s^wJZ@vAOzF~td_@ZpR9_yvTrkuHsUlNCd9H&q7=;%h}hT|{vd zYvyW-MMiD)(p!YUnV66IuIGN3=!%^8FIx8s_fg-y8Y)>FPFWINcz?q{bIfaX`T_Nx zaW`5Yu|(YAbzwO+1l)9`70ZY{ z^uw#DghD^5m4uUamIpXvwZ2i4&cy>Mx1zENZH;&f%XSI`>LSu&`K6I_(NEONi~jj4 zV~Zi`;ls4;n1}b%q;Mi1X6}`Rin^}1I67x}lY*u+M79a7>|?iY{xJ#8wSU{g4t;8W z<=|1Nf6jZ=X4cU7#OV9Ph0b_!7W$F4o;|KCw5A-z&Ksoa!F#k=tyMo|>XDpk zvn_dz*4tRo;D(_-;Uz<4&UrG>NeMzZX#>VC-djuUbz`0LRS$)Kyfr#DpOTsMCD=c? z@i6A{RXFx;=Xvi?$>rWWmCnYuk_vSh+(B8kdd{rx2kZD%wKMyLlZ3%?s=2^+H(=P$ ze6JSrO)B20Ce^$h(_yLgF8V4PYwDf7 zU;k#wSN9B%URMcT(0|MMs>}#nx4z2vdU8U`$Mz^#k_L&d+0aXZ7A8ba<&Qq(uM2Yh zKz>q__Gh=tS5;3}P(XyU@0mMm_1nMdl0ApQLC!y!g5d(hL3F5yAvz=NC1aGY9EIw) zX>&M-+-A6H>mV29WQJ9WP1Z+wy!sx`Jp|zexgxb5ehy~>RI)7->{cFS*y=QCIEGi? z0X8YzH}bFIz2{;p0!7=!sp)1j0?3Y;zkH;f&rj_HDJMZ#mGUUh^!q<>W&8wDv;FQn zkc<(iKX;uc($lFnW%U7T1a@w2)zhdLWg%0f;mat%vw(Xuk4~2LId(8eWIj z&rCH`gc?kImp|rSYV!%syf0h5lh8nM5GuZ$>*S-Z!hDX5L!8;edaD}J7uin?*P-vO zwKiTPo8rkXQQr<$A(Ev_&r;386bdhAJrHrsjW08F=&U%s&CDB&t&GA34GO+1ZDs(M zU%o&)G%ne#yo6LZ_tF7IDFmZ>JZ|*B_kpA@N2=`;qD!7BGpIu_F1N@yAUd;G(b{Dh ztmh6`r zE7LTk)zGakeHE<-BYkO4Xp*=aw?1tzgm(C=BY%D0QYyeR|WoUAuHaX-OH#wM0 zjkOvdm9c&Ga+mxs(=+gEdfFH_GEH0BVu1PkJbRUtpUWQ&#cT+_B>0cRk|Q8kyqGQ% zR-$7&-&GVDT^XueH39D68F%8I*Bd z*zj$08A(>OMA@xK^M?ha-S|TxE6s6#(szK9`>DFl$lr-+>u)9BNxy9XuFB<$KBf5l zG}Mh?)1_ch4?NP9h9dP+HOh(|ZSS8OQ}N-~Q2Emz>wP{~cgg=?f#uqFNv8Sxv0Ysj zw-=^(va=pU-cZsNI89rdKcn@Mv4Cu1F>3p!R;07^bR=@!D}!3{Z|xpGeg#$|+E~*8 z)w3SknK`D0+OzASCnY{mrM(X`Ru?W6SfRmeo%K#Pc#_O)C(H|uk~f=*J)aipP?xla zgblGk6Kx9nC(G$$2lSq*UC~E=M%Z*ddi64xVfgkkdC%i&0h-Rmj&XnAE;qyHKW0)K z1fJKXe7^jK$ScageN8^TG%`z19DiaMZ)90*e6&w;@95_parRrdk+xcrL!RH;n9E?-M*Kt;xD$r?P|=b=m1m7b~yZ&KYFRN|epH3h&*( zpe1qJD4V1jQ^Srk&)sx#+VmE&K0NOFAGo8UhQC8fA9)xIX z=KT>r=v=I-hF5@@Rgk>6WJU8fc-TBm`~++4)bo2*+yu$dP`uww1|Cj5T+jt|zx}z5 z;TwRdWb<~W%%AZMpcjl$`MGd9T;4DxAaI;kqfV;$f!s934_bsniVt98rKDa$>9p=L z!G^tqY`)0925F@<5gAZJ*`F(Q;>WFT4g>uTd|J6FN(LIuxlQ@VmW_tdm#MQlx%Q5K z6I!{FZH!|;-iKw9R^RR8!FbCQ@4{jCcL^dtk$&KlKPHnUM`40eeCJ?WzvIjGKtzFT8v<_X3^>3n< zth962;siPnSqF!x`9Ob`hz0@~i@COXTB3ml{I^~}8Mbc4=1ym0`5LC_Tssu6j`da7axCoVnhRu;oJZ&; z9?rOU&*H8#X$P&CaaeN;>nr0q8CDhwO3DGvg&0|aV7i$i3S}aaFU3(5i{<>ENyS+9 zq>h%h*VhX?Fk!PNrN<^*u297{if=9TEG>QAPP%-KXKcxFUK&t6U+np|D7XOeE3L4D zh%ttIogp6Ky5jiix`M+Id+=bg(WMkq4S0M@v5AQOCfn{D>u;yA{Fk(f+rb6xfYbHDjU zMTwAxwOe`UpY;q-8%zNiikIw0D>o@0!az5dE>G~HTGeCA9~{_sjd_P+tjmdAgAGG< zuzu0x?ald{Taqzy*?Fr~iDB7^7U?fej@7VMx+#ml{a;r_{@{(;W=}0hT~mBxfZi%& zYFSCwRgmTU?x0v@87^*G0B|XU&G)^50_l0Q@!)heRY8DjLKg8Sgl7n{hFq3bzD}R%4cbMMnP_ow z3HsO1uf~v>ijrp)FI>{dnNBl8H#Ppg;{RB$fB*2h5m;n@d8)NHrc}-_*Np}h5GWI= zg3?$DK(O_(bnQe@MaD#(mW+olhjc-OE&aiCJM5`6{lPBK$}KXa7e|e)I z2SUm(pf+afuwt=BfAg&5KH>5EyPJYnv)KtJ7UhZ~3S~V)5tp8oxebVdx}m&+iatgw z@eu$`{No0Z`)AXFx^gD|mCapmS*G}>?2H5j#M-KI3#SBj_^KlNOiR!`P8I(iza|g}Ase=?WOdS!0?QeL9o-RoiAe-y zNvZiQDUt0-sKEA;xtzJe19O?c`CM*adD4bU}cN!@IRc(gL^>-Ml- z+SPHQU|v^j()HhL z#R%GTc}nZ&ERHvlW71o`yj6}4<$^>1{2KKq*li1m+=8(Yu-to?xDBUYnvKp2W0eRi^yW z04%6}R7;6U#cjv=#~)&NaWxSZ8Vf9k3;Oe)AD2gF^A-z4$Pj8IZ%L44h~oS5lb}!2 za?ojbgXfu|o068pDEsuw?ZKkR_mY6Y5*g?)UPfb06jo~PeNjPBZOEwQ=)(HD-e
    cQ>`sdJ*tPG%=y zrEd+N9>Y$oOLLI!%Rd4Z_AEpqZrbJlbR`|#NU{6!L1PvF{I}SFve?=&XC6&eahsb4a-QJt9K$uL6?l}?J z&es7;=bQ6>i4{-+w{2)nbQ(cg1(Z|vgda58jK#904&ZBQlAu0UXH~?49TU+XQn#Aa zzFT*NQchjIt|(BGM0y;RrmSuodsYvL%dXKev6`#jlL^k3WPijv^_u9ZXx2(Qr+?Cz zwMYYnTX#X?_#dZOP5aCN(V2UQZQ@alH!#@j=?EIR#JB+4OW?>dxn)jJpUaE$#1$zG z=!u&y?UT3}RgKRXZPW)}(4_&8ME)97oN_~YS2xBqs$W+QaTOZ1HFwPmXGQ;q zMflrw)f;)i@HZEhABivTZ7eS8{rmOwnt&DvH_{LS-&a9O{ua2}Y&tA{PfpPCKm-*TMhfhPR|@i7gFq&yM!=6mI2{xKFt;DbLMjA*longd#|a93 zP#aTERFHC!8>W%Mf|8V`_9|H}+iO0fU@<-#KJp0gKA0u%R}H0w#38F7>i&02wR?Zh;ItTOq+H8{IRBy=v(R^z9@nF_R7B3tRk^(vL^lSR!C-3R8^zimo@4O+NjH)!$?$V48Q`nNl+i28;<2 z%oU8YYNie2R$rc?gpk8%L4nWrHuuOM>9(9$sHU1e+rdAbwxFW(=opp}Ti zQK2#bCyFP^v2#Hj(5v{|7_kRm+O_VSxg43Zu_|N)IsyV8#f`b;yG%zR=^aXkc*z-; z^OfG+=!{gxBN6;-frmq=`uNfi*NfGH@^Iy%wA%|PMibf5(t?lTVP!COTVjL&Xj-w! z=uWK;Xa-MLkTFVS>*QP5D62W0V>9&R!5g#GpvBNt@CR6N`3KVk7&>{|cb~84zp(Z>@Ch z0LPNMiRk?ZDQ}V>9{!qKQDFQxAL|GE_z`7T6NM68qNl>h%R{J&Eo38Pq0Q)i#aC;m zE5i;S;guoVAnh9_DquJT;H0MQW0=xcDrNA)9DCNAi$mLgDD@Vq{E;Qm@UP0*W1!5s zqh4`pz+5(lzm5NC!5^y5M{PNbBrY5;6mCpHhZHD6<*_&Kxl58|xUj8*FdQ`kQI?Cw zsjyT=tcoQJS1!OzLWz+FCjkc{R%9kz*4dPM_ z?k|neM#y{0sQH_KHj+o;Ls)x-xX>HQnK&_mYNX4NanS3^z>|1_&vbW%q-5;w97Y7ld=cV zC=I_$Q}GYYUA?=IXR+9gMCNfbH9C56k#D zq0in1s|PkvE@)Y>rZa`g`bR6VOTP6EW(5qk1TlMTC8q7TUJ?oAcWm9MA#&$o`-V=P z))lj^J9XlxixPBZTi9wVj7>keQS3Y1x;3nf2K0$`S64r*dp&=+Al+g|KO-?w!(=z1 zY|R8R33fAz5^$-+N>7G{=bgO_{32cu>DA-%wwm}`R+yXFO_S=uVj753Fv^&qHb7kA zP3Txf2;8gRK8M`QVW?aT>7+X%67ixEKWs3D)QzVRDm)#d*bSLoF8yi68PEXS&gjMY z7n@(3&rTkK<|i~5lEpioM-_GaV9_f6FE?uuHQs#VK6NSL*K*T>piNogp9#gk?7iaS zjh1owkhj5~0RnQjuRQ#mLtJ-uC>B`ao<=8K?}bIRz94c<2;`0(3x~HwvH61{I|Pn~ zpS}4pedJ{Y!jE)k3+p^wqwRM@au8BJG1<9GX{z|-CIhH#vu4MV4rc_tVU-phP!nRT z#y;)|ZJT&BM*4GG<(?koTZLM7j{(vEQlpR2LM57?_(H3J7=<69)|G%A)t8dL54`4n zw^D|07<_?)je>vG=+`7tdd>Pi!yr5}<&{IIC_Xt|MY0(`gguorW9l*|g;M?CBm99Z zQA?jrD2qBUfPNamu@xA35V?5)1fzh|KKje>h}P=+j0F3&qHCWPuMy;xKtE@s8^RzT z;|XjFFKLi3DR9(J!bn112Sl@x!GIkx(F_`}adM^NY{6p%?p_a`=%B$jvE<#&p$+OD zk5lt;{yTF2$&mlPzJB$7H&UPbXFfYyCA>8yN!i;%r|ow5zFNb-guM#bR>eeg2awAm z0B|JSbzdm-yjGri72nEc0?tBKxyZ|t`dQNz1S50g-Cg!NgOjKsD`SYrDwUcMcyH=sL$`{rxn*p~YRlD|Non86d`IuUoK-(9RSlbn>%kUsW`aISUeD#QU8z)cH8{bPV-isGR$nM^IWu{)THmdkx zuY2ru&&iFAU>OdoDVw0hs_HV5@s=_0TMsji&wOpYr2z?AbBf3;&u>w$X(pVF?d$ zx#{5rZ5hbz-rB5JE1Y?;

    hXK7A%zUCU2*H2`kT?{;&Ch;%gkh03hzW^v!zjQ9(fA3I#c})TxTS zwhD27rhfub4MN97Zw^DqGRqmIPHRWTll!Vj!Bos^-sy*^^D-EH!aDpgmZG?X!4qA{~FyY#>CyVdg!ad zky#P}7tJcce8{O7SUl#S&dD)s@ZQ(HOqc2@$Y%~K62~5|IM6`7=)5;zyX7huz1xV& zt9NR44xAFzE!NOWQ>)c~w{Ef`tt-=4>GY?1$K%OKnutedVjO4h#$b|@(W>Ge~)@a%qe&?h%g z_uQ)^7l}29hJR*TSbexhKaOqy({#Pnt^KoD#A=m_pqT*NyrGnMQ?8v&NtOBr$~2dY zL{2s9BC^Sq$VFtp;an6QoSWBkzZ29}0~}I7;gzTGVgc6TZM8sJ)6jcbbl~l}x?}_W zeyZV448xF^B?qhJjaS*50gl6i;$h9-b0TYN;2d?)lRef zh?;%mEyijY!gD_Vk8#3c-eT}2d^&b!#?NA}zOmX40cTO^FRjffu$wS7r`9Vzav=0< z!gCnXOtNqG^$21@!K~{&cmLk(H0uod5`7d3#hcjjKhBSIg#Cb59E_<+*WYd z_dGGgw|-6;XFfbt8$g`%1)UhDKNX!g(TO~}F}P_jWaOsl?V#-Uo9ch^jH;cqP-jU+ zG_S0H zjkVRI7tGwNYoZqG$_3cgIh-8fHNdfe)(qp+?H1WiX+22QbVJx&<#8T%sP61xTMcD^ z0bbTk?a1-=OL+PD{kDUFB|Jm&lu|z)6a0=-v)>NhJ}%>kvZjj5v#zt)tCFAT9Nt@; zHg>`@Aj>-F?2d$Wxlur*;7kufeMt$Q`@D19TGP46^y!7!4al5#NQC$h1v}5gfA(C# z7(44;l-#YizJFo9Ncw29cGdYmgpU94xJiml$x)KwMV;dR?iajhZZaW|o{XQQAWORo z%v>PK)Je99aTY(&S|+pt*v2n-#U^|s*VuJW|B1HArqUB*2t|y{1Hyg>njNypglfM#|0Gu9&9L8dmwDsBFM6Z? z(LJjKK_Dx>D7~ZJpr`m`p&A^H_!Yc)yd27Ls}R)_@`AQGpDFL~?0)m(7Gua2VBAeF zA1~ufwdWAq%CjoKR6o#Vdxub-pa$&N1rbUWn`@)9Twgj3kTE=&t`b679w?%)}31MUbW`)zU1 zn@|8E3plD%v1EQgLMXu0-1070LbH4so^Qic{gTl8yowVFHtb6rW%Y_TIs+Q* z!HYaXh%FJ(lUo83g=`Jc^Pes1dv!4kpLZ5r1sGz4ZI`49@{X5l4SBY*3W_H>YaWnE zc19xBA5^i7#BPM{NyL)!4pH zi%I>;_FYPIS2~P>Gyu`LE=jL;=)U<9+z=LwGD=caq+QV1_4^XRkkD#Ib z?MRoc*86pjYon`{Nh(Nr0DeH+y|tM?0MEn)d0^%^R?%W&XS)>SI#%y36TDgERUi!F z^>^-PJ9jF#zf(Cb zR#4IE8YUqic4b^lwlZ*fwbT6RgT&hyW3l-;-I3-qJNDrCT8Z!N9$+sZYw}^=FDjuA zE}J3FBed?+T*B)E{dQxR_8sdH&5*h|YKNF!D)n&n@*Sa6CZEy|{4bcV7T;_$<=z*c zqu&s?)3bQ4Kxt7-kdeRWttD;W!`@-jX^@GnFH3;U*4a94h?Q1Un1(jn`54Xgo>qwd zKkz}6I+h`4D9sZFj%pA#i0YtQ%^v3S00h+wYKe z>pijGt@r`CPtI+x~Bpq=)3qk5XRiNBcDL6UM}4&QMect=em6d#}O&i1Y4%mZ8!{e9o`Vn z+O?Xo!zKR;Y<_!@Bb9?i2n%9goFx_Rjh((H;rS!O2Y!C{m#0k$8l7k$Ak!*yAyDC@ z26{|hNb6i=*E8;@H8X*k391OcPGt7#M-}|j9TySZ^gU@(mt^omv%dEA=FP5-#K%<^ z_x#X8_*bB5-oHm#WAa4C4c;C`1$SF4S-DtT*SWr%a|L}N>tr=~(V?bV|Fi|59Kj#8 zq&&oh;wfrYA>7M%^<-hvF@@|sCd04_Bi(YAInW*Eo8LPqRTkbqd!YKw9XI7Xzv-_7 z5OfVrUN8GV@e5g=1Y+P*1uK4AtqDOm?)5-=1-Ge99-ZDl>32gMn@dM+lxoOG7>smVrHCoK zpR4_dB$M}XobyLAlZ6tVl0_Y;LEui@5qz&O-Zc$lZy=o2WvwTAce;p?=MBO42rqrxRFpNOEqn_D zoGHj;Jr&bfxL7q1OI@z}3q;fr^p|fpE^?9&)}K)0jKLP=V)M|Kb{EBi#mdvez|FJ3 zP}dkXp$H8p^|js@%k#cc-k9^Q2O#xGEcm@;!Ktxc-Wp${V{1{q+OZ{RhCs(}@<3ka z;j{R-;;yuM6Mgg7dfp z!@dh~8XTUOEMJ?v3|p0_CjVE6@Y;vKVWw_;x`3(UJl0`aooKAzT6~(cu$>6smzr#j z>~w1b?m6rdAhX)u5o)&0#$4{Pv*bv!Mi_d-*gw>lS)`1h@ zPJm6tdiUDxr8hcFmpwkNr$+>}m8RZe;K6a3N!Z)PX9O7DICk$D?zNq9s$C;*Mv|h;pn^Yh22=#kvBkWZ@E96+iSw-7w7A!oD-yeczxZ{ph znCrH}w=u(N^)RU{Mt-#q2e>0B8E3Hi%5IIl&zyKO@FLtYG9GRtB_j!MU-)$N4g|ltiVnkbZ^tLNebP@FTi3#(uc@z2@R*q7(4^cv5 zC2{KTW;6LDu=y{lEa~v4GYh>t+vcLg*aw}8Dop#YS-q0ily_aek^B*788t4K&{1m* zE-{c1F5z$FNd0|c70;w(6{V^h0mB79vwpj+l1XmFoJiXt&=(dw_t~RoDnq)HtX9Hp zOQ={UMfgQRZJGGu(%6&TSNb7XAJou87n2b{@XlRKnsH~Oxei^Hx7F{0mj}YG6Iw9L z#YXo|(g5#noUUUh*jx5ZV~%Yk2$_K)Yv-Q=pqvyNclE|>g#z7?645JG(bNqdwk~>cqrev zfxgp|j%h&;^_zV~K#T2dTa8Vt5HcGu7{%s3g_s1T2yD?{y zatZs;MIF=!AQOzqV8v-3F4T;9VFut0wG^AeowV`kd|V7}xSS#@TGdv(uwZM^nGGLB z5M{-9_HS8bplA6w52p)?#UxMkwsu_W`AtGQQX9&R9ML@!Xz{?wf9}Jk0s+$7#UP60 zA3O8fjkO4N=?))5?$3Rj>lj}pEl~Mj8n}O#EQfBvCc1m<7pVZuSbJ<^2R+wr3pGYq zPvhw`TDHofmgjzmjaq!J`94Nby1Vy%p=e#O!Pvcs^>^mk!DcD9sn~!WHYW2mzSN;m zruJrB3!ZHU&z3m7;_O>iO*{to{mM)r)Z})c$QLHISwU(J7T9CY?bN(>jlUgM3S2HL zdT*v~_Y4uva?kUeNcz_$w5sWm37>Z2JBSX#RpU#-yx4@PxA3orRHkY}6SeBVr)c^% z&d@85<##-qd*uu8U{#|XEvE*l(N}aQ_R%CH4dT1XCKXH3=M0yRT!Gg~EtL0*)xAmd z0Oi^#YT%_gZz`{4p}^~KG4Ky7@zEywvOBW*;cwFfF9jDzWrQN#EtY}#g(=OkMyfIy z%SM#n$7>WSN{_}1ldSsVU+qlc8(z~vO?*M$Q|$F5CMyz#XVqu>dQ0i-D?Q0qd>&$F zf|$H2d;t+X6a4Etp8rh9`ERjZNw?1bt6YL_;g}Qur&EKH;;~BJQss4eESDch^%*66 zmynE($9(g&P$HwbIGV}>)bBi=a9sq7Sn#LRa^kRS<57u_Sd25>ybE0A*Pz-YX8a@$ z5x&pv-3x_Ijqw}Sl9ETJ#8$-;Rae&Z=LFSmm|nhdn~UnKm5*$c=+jbGTu=I7lkh7E zc(+b5{YJ2}ZG-1lNq2}%$cwMmcQh{U`k8E=cUQNM@^RUKpmTPLWyqf(OyHboH#91{ z-$u71!66-sFO8jB6{EimYGWQkus>u(q7NRtqXV7`*JjvmS|+VeShhh@_#y9U_5`Y- zwUkX7>mf+D0U5wqCcSGL?)>!7r7*#&D+GRDKA*9lCHF<83xSSL@POB=2-8vx^Ade8 zZ@?T;*U7!=2)L_lQvSdmX%L!63F5;QK&1~@eY+Gyy{&jS4{v%z_6oB7!raWmqut=3 zQ}PAdGv)hJz09@MY_~e*^Az2X*%V>Aoko-riT}6Z^Uss>gvgFjR^y4WSh_zWT?!M^ z6C6)BmAIJVW3^8ffg(ZCEq8(MP@$5PLG)79V195Fp6m?d;P%p@wGepyS)6@+<(jqhsFw6oFRkylUo{H~0Nhvo{_Ph#w>}Bp-_9eV3o_>7fkx&U z3HL3(6kj!}lJl=$Am;!nEZx^vCYTwP1BR*>ZI^h!G`dxf?%JdCZf!0vmf=wPd5e zwa84HqEj!9^cBVKr`ADtEoW3r?Gn=ZxVD^r1c{JE#QQ#SeryW=MaeW6C-JXnS=dKr!x zSheT7+}VLUJ`HmI1=ymg3$dh$)!KSz`Un-U-~4+zIen(s{ib*KmFoDgOKbG8T2Qh2 z)H7yJT%pNQC{mocIb_c5$PiJ*d&INfo|&wVfG6CHk-cTgl)UbuD6=(3gV7m2E25cG zls+~Vob|!O_HyARxh}&Ak5H{hmSp>WF6IVKi}cdbwraJ-A(^W&h}9x@VBeSORE-lQ zkHf?g^ucPa?Do8OHq7?FR&4`rd}^92ZF6QT;Q)S0V~Ika?g@zFEaX)YALw=ZIdxvI}d z`MIWE&tpJxJX7)TErlo^*ZUD@r$B^&WEJ!=q0(~e9Bq$*9|zL*;CF<)3g75d8S=Bb z#f)?t=f|axvQTC5_00W5-8Lh8_U+~AmI_Y)g73}>ozt030(WY=gXtNk>~%(GQ&_x; z_^VE)tejBLHHE|O`bFn{`?Myv6?2PH#kM-{2oSE0$-81-YPZm}d21ii=L&x9ztz1s zBd}@Ok@%|RyZLKEaT45jZ+q-d?Kt}wwZ4k)ozC}trFwJ?CQr7qdoN@mNm8&csirN=aNlEh$3YV*I*r~lTEBj>zjKK&O~ z?tc}}jh@a`_?OJDrp8^MqPa_Z&VUfZsjmm5sd7wr;5JRKxCv@YT7fr|?)k%RLmaI; zuZ1*J!5m$|L}m+*a1VH0dmWjAJ2tO1zgpaqlSn0+I~4}I=#eRLg|dZo$Q3cBP9L;; z&tQG6eA{Xa<&myz`ssuDk}jDWVCB_K(1vDe-QyYunq7$+ho&;Y5BbK`V+gx%@R49^ z05t4Z8qLPBmiutT>6L%#R&q8r2l)wspdw?{ zokJST`|#FjpnrWilLxVnt&hZ3l;$`Mt}cJlq4V)$LC`Fv85^79p$pGtyT^`=g&F~o ze?8b2F|$&v@(iTCZy-BcIM1q^lU*c)o3@of)Yij^GFD52!s{M$++;P(Yi;0DDQ zOptp?cMku?0n^lf*oD7uRT1AUxG?@u^fcb#{YpaP^CpIyaL{jt9TeVOH^x`SK2{6D@3?^n8-j~@HD|4gkbW^1Wt{BaT8 zQ42ZopSdCGoVZ_gI*i2HZZAG*A29&B#4pxionKCvod-Y~V(NIqq};_z143s&+sjN2 zFN}fS1?QV@){C)j{F6^!5ouE`J{lnPb9qg!Bndd^s3*GCO9?)n8S*vD^OO;xV$n~z zcfMK`_YS>U8S+v7cOG`#;1jIvSVFbH)hF=4yTZN~C%MJ&`2urM(m?K`tHBs9%TQZ! zRM9t8Cz$unkV$I(O>`m0j>qeU(mCBTliaDD2hX-2Tuh|wNKcmny=}`A9%O}lc81ww ztVFb5RK!$nRZnXRYlq^@-bu-M?FgAf0RS7(&2W z0(Z8p^%HC(Z258TG^bz4Z5B_YY0RGKEDAZyJ()*aL})B~T|Or)xHkJQSJh3nA`XH_ zJ&hR+|ECAlbUUyqoN;oo39&>3uq{835KIX+@siiFQKEg0X;<%O?_p=^&%f0=rB_W)S9=$;eQNIR z^^=bnyjJ{Z;0MJuXCDA9@) zR8ds5#8$giR8iEXXiE@#s~sz z&N=se?sK0T)o_DP)Pve>lpOaK`6XRr;a=JMvk!(!_6g<=s^D8uWWJO9yat{q`|uXM z1y%FGf=E&>Tdm~TW4ODx^Q5+*$!}1$F-*|qF|2G#a1^^tnA3dp1 zvK*nS9>KgH!K)<-h<{VCNt=jKx|j4Kjqe;-FHqQD)16yO`G+8^-mo_DzdFlLM4tQMiYiC9qpeDIZ|0Q*41;6v(as!3~q{M z+9k*{r;Xg`<>7#HP&c4tYgmT!Ao?|Nbdcd8{^zr}CqoRGDuQ$M@)Oe@hkf@=fRf{% zmvz)iUQNzZzB6Z}KYRK;aj^?DG=PHRG&@RhqaIaEEHjJtjaUx5o{?B3dNI>?6pTgX z`JVg2HyYj`yl0usa(2sXwj%iBFlZ`$*EW<*;&ooJ9pB}8+a@vU6W$x-!W}2EKlVNK zJ)P3-PkruoigHfD>9%3m@{<~ek8lfB`wSi4uu5@055R`8-uls0IBKDkXBBFLIjJf@ zmVFLgZfS5{b)6aWv|xo5{#Y#NK6xlFNnS_sh_CnE?eciSS(~s#`|K`( zSL#-bFRs;Wh=hDE?;6P$82;uuGqL8X!?gI?F#lQ)q4D_PUtR#vb#2hWpb%ZXcpH{2 zUmVj}6|y7@*Yvj9y;oOY-toZnR8hb6)cmjph}L;u0I`gF5CI8ClT22Pkt zLhy8}-BiL3GcyZddd&Au%C z;%+7KH$OIoLguWU@%G&chaHsxGV|I3(aa#01w~81lQ*fX7)=J>pFxaxWp|AfE*S;| zz2XKLB7gp1>92tl;@BBFv~1p5bFltm{s^?t%CNx$Xd-!keF<(EVNYce$8?o_g!8^m zI99vY&Utm#YxF{R!_pzGSEMJ9n`#LOT+Vb6>QOI9+(#cCPja>uAY%F1r{r=)73xFN zwcL&T1n3;&v*zPg5jR6+GHc0lqHd@NRWWTRCm^{VM}!?bxaXRrIv&&%aot#*@Oo(6 zB+Jhh7VhN2y_)cJ8X#T8VPqVFoDjk{xJ>F-%L98DM|L^nb$p5^MDj0sUc|>qdCDT_ zv|wkv4cj^KTG#S6X+x(XHPZOE+@JL*Hs&E-1VnVO1ZqYTTu@lB+s3FK&mz622za+v%FE<7!OfYS+BCOwFiEm#1Qp7x0d^o$$e&brfy|{h& z{<()dEHU()rovGhx zh3J#r>_19J3UUIW(%U^SlFTH=`$WEnZUwRHLwLfjfbCDR7Y1`_m#l3WLHrD^)9RS! z%pRPsG{w*WV#e`MF~DH2+foxvX>#%Gwun83vjLlkoZjvcuGb zeA2a8GqmqsGeOq-=grUNhM5gUk|xk!|}{m2Jm2 z>*{O^&1us}-^0bRZz%9Q;zW0K#^+H&7pb8bAZoY_sXl%?Yuf0gBmZ5;^U+l$ae<=1 zr6f!4YJvH<<2n1%#@->`Bbz*iM4yiNaYySk=Hl%}@{9(>I*y%q0wd#4#Uy2>4gt+n zAbSTiH*?Uy^Gs!_OpJ=UiuxxlL)T%l5F&V+--!{4ok#Yxg=iJ=DXPXdW7= zJfRcLY$?#_OLbae)8fiZvZz^of4NSttk*{PQQNIjzF=vE=ZMxz><*zbN@$AImWd$B z&E`R$Cy5v&HJam)JooN-eHKrg>koor-yD^su=)e$`dwNI1|2bvZPy-)IuC0lU61J9 z+qtQyXC85@{dHaorec`+Qx*x(qG+avVYEg3ZqZeqH9W=py9D;>X1>BlCb{v3NHFxn z@pd=F-2FGddmkkS$LC}#o&T5_U)^*YFpkp_AdtVZD=|J zbIZfSx5PG(xt&k+iK9=z3Rl=$g}H)Qhk#_xUkb0cEo5j+4MxY!x%EC2^U`aqn^VM# z4P_aaAhRFHlh1(2S=)Pcq$&X17vAO4wZB>3E z6*!l^-T+&#)1~J`lc8$0=|6vTt-T={mNC*KcLgW=Ptmgzp%>rc_tEuP)?>p2A-GSh zm?5m!Pm8*z=l0`+c?NyEZZISvGdUD;-Uj`^a>ll*kTiloxgrI$r&V-Io4#7aW!hS? znj8sc&CPt<>_+_hb;GIC73#GQnOeQ|suOCkKW6}LmYxZvtSB5fr2GLQo?n+CqS$A! zM-md7sP3$8MXJ21`TXfbhrV_bI|{P+GWYqDX`p&ASh=-GH7j*E9sz&maHYHH@%_lg z*^spx*ejk{t@M|r3{4G|KFVc0tDSG(MX~l4cURfo`K+TbF4;R!%U~bBR_`?h7Vc45 z61J&$sUc=xTMNsS7`NE1ezmH5KTv6}x6-c5)wI;{-N+JsX?fYsWp1ij7(%EHeW=W37Q=dd2RGg8xBoH8=wP1b#5toI&KepS(Ybv&3+PT#0MN);vdVh@k}o zu}QijDcre9{napKq-;wls%Kt4PTPEJRF(da)7Q>=2h^x9&O|79l`UU^Zzh0nFz}0~ zIQ=)SZR@mo?axd6XwvYJN6gD)+h#NdnOBMjfy$Z$t(7o0CzqI)ja4 zEyj!2CjeF;g5xg!DYsr{Q9}&un+$Q36VF}#&9~f%@P{1!T9Y5ac_ncz$)1SnI zk7szjok8g79f)D|zjGC%qJSg`eFpck`A?J`a~}0dW>a1>V$I|mx8@eVu9l{|%Ql-y zY}~=uY)M?pe$cqM9Vlp4quKfrs+`2mCB<*yQL_{~0ECQ#((J-0wFopZ@>#v|Y*)bi z@)nFglAR0#-yZZFZ?^EGt5~EJNbJ-zLZyW8Q{WDZ_hmE&{366qYm43=v|g5F^RxP* zWGjnZp>?;EE*}vnu}C=tA+`$q9l1N?u1Es6U-9#)Ik98p-u4teUrpUG3p1kz(o28V zZK(vf!>ey>TZcG-7FNK4KZU?m(F341UW}7*Na;JzNpe0u@V;$yBb+a(Xs-sj8&jJT zdarD01>8b@vfhu{Y0eZ8+dfyJKhE_mlasW{+;}D#e*4_cx;1b2T{x_n_tJT*Ua`oJE>r#5{Z<>x03wMh!m#TpCSXgIsHj$)coeR8Kw37`A^2}GU|pniM{jocZAN!{3{%z zo)MLVyb=A-8%U^7eJu`|EE~mO@^pK_l!0)24S}&UWrbEeH-%#X{JUr)@(>z&v9d_1rwHnm!JXOsJ^OJHGYepJJS}_6QNkmvLp_r2@rB=xOLn}8d7KK zS8L3N$_2CN8CR2Xam$>!m%l5X*qS)@oz4UEZK6JH(gaPAdd+&D3XVaxl=*G#S{C-c zrPP+H?zEZdo$z8}9dWBT9Wx-e<;LWJ4Q=I=98u~`1=4bGFF<&)14wLxvCV+R^9?L0 z3U77_=&dB3%%~kpgnI?7g>Q~HonaU!^z!(2m0KxPN|8*ayalAz@oNcDlr6j0DH0nk zE%n*L<@9I2=%T8+nAS|f8+L~!icQ}L=TkS>q4-<#lgcnbgB)#n4=tbaii|wY@7goV z3(K3Se`equjaOs)dPTisi$Hbo7 z;y$K6-~fF1q_y4hFCECgvE^ILEL%p!G!#_*2LGx5xCW@+YCM3>iETGiFTNigOO30s zYrPi-;hon4t4wW{?PoPNNxz>Tn ztNuu;ZKsIn(>~E0=KdJ_kzA-yCmKKc(%phK8G#gVwMin)UczS@gKc}B$Oe7q_#_h| zDV&w^Fp?oeS=OuaW7vfGl#>&HDxvmZ;YjYD>Z z`0|eQwUd(O4&Uga=6lM!EkB0=M>O(bQx&se>TRi|;bqs5#=-EA&&A+62OU$kVYTFp z=tHDTp8*V-%9WGe(L(Y)$wG&0MMh3W7>iL{rFM5;aN+l0$fAqp&*3?5V$H#{mA+IE zewnfsXEONM^zq_np^LmHb?Z&<^g*UW{G`KXlY`raC4G!N#w}$@Dwxc|FG>uorveHo z0=!KvMTz`_Jr#Y5xDsbdgu4N5f8BGF?^BR3>r3HOF|EaTu8l=u67}=Xp0ru5BLXz0 z3l;^bT^xU&++@H+m1EAlty=-*vTHuE<(R?macm{#0){ge4~2q0w#L^NPHc0mNo74U zHwAbj#nDZ3XiM4t(T`LrVi#(>?kLw=&v?ag(#tfO7_F9q!q(jT0!ZF_9gKUVdRMcD865KqNAOS05voSItqA9pjt7Lc+a(=V^P1>GQ#&YxJZC&! z^SbgohtHHT)P+5cw|U8H-`k^wH4pRBPukU$=Uf#|=jD2qe8a3d6SYBoFyLv@OvmLl z2=4&%(|R7MXhW_Yj26~Dc10<}-WxXAsP1zLievS7i^Axgq6#|><#mT$y?eidVxwa+ z1+NZ4M_cYD7*i}gY)py5WGxJss^Fdu1Ude&5fk;itR^>ien>F>;YvjGMdu;)@$F<@ z%M{^M4y$7(Upg$=7br8|3J)!pfwG-W6?Ew5N z&hEVKm2Q1Jj2qSZZ9b51UCuZBucPEfb*v92mf~*Bk7FuR^&^Aq=uAeu@roujFrPxq z{1ukMw`^%Db4mWx1U-~c!(XWq#vE-K+#txE#hH&_q+QL-~C)EiZ5IWn88pvEicH>erVRU5t&9&(+% za0EH@@!SK~D;)gEyU7`wc&{-Q))PgMkjQ!2%+0$etwJ7G-LY8VYAEoKE+c)bhK9@RL2_o zv&wZI-SdxkgJZl}YhRuiT6wDJO7y$CUw@ zQ}05PLTmjE5$-X;xq0BB+nn(3HNGxv;ZA--WlwZgTIr6?Eem=lSCm+V?h(d3sXu>E zxo1UkJ+Ms`e z7q8h>4?EQyl-OGnIo$R4=~g`% zkFxJCo}KDBBh_CYbI4R)$Kd-2Je>>tPBpPQr^7v1d7~V_a|ul||Ja~R^VwoCi^j6e z^$ML7w@hr zW%SI;W`_pB>y5#L7Q{91_z;8=n+l2hn3{{rS+sveTqvbh+TeLKCA0m6*KGXbIB_wr zmjwF34ywcVs$6-irR&|FA8u%>%GbTh5T!-2bi}ZZWFu*i+yKYU<07>eBlmyO$QvaB z>87jt!uK5u)j4Hrpdu_i$#zxQJvt=;GOrvnpV1SQ54m!k?vv}!dOXgrTSegL4V_L5 za!h8ZR#>nKQc3ooBK9Pax}_-i9a3}^PTLd_779cCfTsQ6bMgqvkF?ylRm3>*uc6^z z`|5|pKCs0$U{7OsQ!nK#?KaN@#TmKuXTmwR6GX*(LmCSe-@_*_NG%zwD`ZQzmZ)~| zP5>`Yibxh`O(&%kG7F*Gg-1?MFl4dlSYLM9kG?;el@w0Rg~fyUQ()xdis2c%mO?JR zG=s$kim0{S6us@|?z6Cg*bA$NBp|($n_~s9I0GtW1ivnbW-{?@{IFa?ZW4K_)~oNL zlAp)S3W&6Yw3HTya!8qLx$@V){xJm?;59KPm@TJv$9c*FqrQWiirs z2iSwwUQQqBQz1>F?ZYrAGA4vAK3x2FC;9i`@;MEx-=Lr4YqqF2HLS}uL(o&Opj^n< zU1IsA^{K>!^@KbC)lg~OBZ?!kwDI3P{cU78hKp15V?msv5lfYvzk5yC)rdxX#QFvB ze*9_FS<|#B^f$h@wE;?j4T*L{g=_#GK#U+{@}-t6S|`;J5Bxok^P ziq`Qf+s~W-&g5y5yr-qBF|?mu{kpTnSa*pzf8@D-D6tb4sQP{4>S)2h|37?L4`3s3 z1$?X_*k_Hq5S>Sg8bB+l`ftKu^)*8n|A#G6_I;XN%}pkZYT)lO$cV~ zDJh&(gwz|Q7wPhTl-+f3r;paVyQ*eF;^i z$;zBk;~p=FkU=VY*1gWMlayyDhf2mAI7Gisu9dz&f$aW_7vr7=oH%5gSV5!~z=f?3 zr=hS%7Ijlc=h5W)VUOYUTqF9cjZboV#_+A)EA|h{$bDxQgez}sdw(9bgZjf$HXa53 zXa%^3r(ZUrVn_CD)+z&)Gag#6JV0KH+cjG)v^tGW5j~8mjMdRXXAXNg)xgHrM@5}! zbm=Q=GV{Uy{t)O*Z{`&g-Y-zML~uIp^DL>g)8nZ`3I5(%km(#LrT0 z&7V6H(W^I#)`u&U=$>DVco6rte2|+AX-Tc5GWx+z(_)Jl;2fMnpA=95g1r{5-41iH z-kt%}`cl`+3f|v1;}Ft*vYRUUO_iM0baraM3hn9(4u!CuFgH;ckO zvi0Tqs}K5LjQ_mrd1|RE@G2f{NJz&M*)dApbg)^lk6G?3D?h)R4!gX)TeAC5M)c4P zHi{aN7!Rx&@F05Kh1In(+*sG!@a!lnvpzLHQ2Y}+`70m)BbXTZ)+_PVJmQ>a8KYJeQ%l24MN-BohC%7|(c1E|4({hK zf9OUQ|C(hr)4Jrgdv1cg6vb?$-Xd3ULx=mx{#Qm>VXGg+y;-&xB0&b^RHiWxmOT%&`GiW_Eb^>(v<0N5@z9z&P*rxd@74xTfL?`7lg zrdOcI$0|VR3)|Efytnr11yv~>wlKzWq>6?lUyG*CSdQE@N67K=x1Ms81~B8@ROrtMAdP{jtYs6yze01Ptin zb{!d_L-a>;R99YYWmE5&#>gktCJ3)4m?URwDaET38Y`@h)1{J{eZnwW^2iUxzNLL^ z3_9qh>ptRF(Ruzk#=va7+2{tvqt!%`13fw9ucUdXxUyw=Cc#5VDzCFO%6T#j#mW zIN>`(m;*KOFL<fG)%}3pcIBaT72^;1_&&7my_H65kJ%D7pIlV0)!7aZT%2(NEfOWR!;iE`UOWy z@Io;jvzx1%WZM1J`NJFAZuEV!z`3vc=acp=CyY!@N^59wxpGh4#iZjYRbIDGBhw74 zqBm9ORU!(HhOt_5I}VFr=$nQJD7S0MK-_zH!o&NXO1fLlNf0l_Uq3hF=-A$`P#;8_ zeC+Df>uR5{TsL_#I#`*bFZ!yB!Rd1gK_@uL+#RX%I9};&e!w`6|=KC%bM>Sp~BMH%&qJq>fno&t(WT<{UyEm?1$?TJt6oByU?7Zt# zEg#m|(5WG(7w%7ndX2$peGlk*?el(S>cdsZMC%YamDVpaHdRsD^g+)cV~ z_UhWlZBDEOxi3IZ_LiGhtX)c$ZmVhS6Ke>G?G~hgxt|dpstN-mWMuQOW-%5|gTb@7 zYEyK6=lmZmzY{gVtZDIruQ}{^9~~ck(9+JPbe5gM=-U5M-lo$v#q-|oB!g+s{kyOq zDX=Wn)P&@0RIE3dGa%-yi6 z)Nd;CG;gyV`zv*LNg0*ku5+GVy=s{L$J2BE!<4W+=*D#SV!-mNGeH6l7iDt699iv9 z29iGBM5V^?VpaA;t>JLeSp`3#wm?Z7IRozcTut9CHK4R!av>+XX;UwiG5m}0tJ${M zk;k}5=jw9zqb#pTmA_@GI8lOjEo(CtomH9wsSQbt=hM}71{Ohh${|M1M1QB98KQSG7 z!-_|D(sX4@5a#&dW`G5A$9AJye#B39BtAGR$~Md8hN@E`<)NJTHLpHKT4jZ&mmBnx z%t_G&nyUM==cZ5wO%~iyf`3JV&6mO;DEB*|I-j&%a#h8N6^Cicw*ggd(!&{u@7z8( z#kAG0&6S881?3*`OfeB&V_t{>QB^kmJ(Gu6KT|;d+HQ%tgVtYOB>$mqls9sQ*v_|x z<2ns5><%|o=#w^Tp`vF9kJtus7clHLDl`q+Kc^sXTzgL zP$q=0?qnz*H+6APt}5(1;G_5@^=yFyoukE}_P94mc%WuYe{&VdNW+JHLv(PJV7xUV zg3T*nT>c4MG41tB*`F=f6q3ro27cT@LKJE!*)l5$5^v=BRk>O->P~)ojg(;Aq;e_e zLu-)f2q1K>+3%E>HY9{w)+8Da<#U>Z2?N7}?YQ9^8;OqzJ_JS;tGPhl z5nmz@oj0LWkuQd*u72<*`~+#fN#5c5)TC3TM|P4Y{5JXX@3t|No6jOcAE*n+;i;bT zN2&c=gEH*WsA0RAu5>jl6PntGa@9a=I&b-N9Nx?AWuyap6$4OM3!;ZReL*ncvja2( zcOxP7yc;aP z&1IpO2a?`ijaeQ`0;wyMb3K_^sa{oRmu-{7sl(cG(7eRydhsNO#5T!B{79?*s);hw z@lS=S!d`d&bOv^FK3i#DWv_UHSU&PoVxnT8dE)O3&#A zgVN73`?L1Jg(+AXl@(YvjwE{knfOF0ayA)ROeN@f>w^q%zXbjDJNS-;?F#|}VYb<( zN5f*z11JRsjuO`CWN6c)EUHAYbs(gIli)Ld#x9R^L={}q&03zND-SYBxu!ydu93of z9s5@ToP*kK`G5*})f@$?tYPjCB6qU?eX#tm`}pn0*(PyNh&#rn8oI^b&O7)S$vUa7 zDn68lDiYRznk0E~G_NL=8%jvM@9=ASLJgFdg(nq;wQ04H0wF+lxc#5H% zrZNKoaZ*I%5tm01&NePh^dkP%0RP*E1F07dvaR7)&+$dQ4;hdSEUM{Wgt4NUaud5H z#EWuco+<)WvkuF>b=QU*=72f_qqng49A&<98|e4hnEE|)u@NjB%#0y~Sqay|rQFq5 z1w?Q0^(@6a4$tol4CjB``oc6KUOi18PtsgWPdoY%-pOZVsH|lwn!pzEVcFZPUA8`~ zK2jO*{%G0Rp8W5|bH9(@->=L^`S(06wb2;fr_+b{`fsbc&$^2N|MUv}11IYTUQv-D z6sBd#Xn`vtg;~N{jEUP=O9Mh^pYZg^v!z)Z^#YONn$@9XW+bd_pdK1x2xR3UFNw`v zr^~pbhXdN8XRMeEnC+ZHxtp?C-aCg?s|4tD4&Y~@)nW(UK2e*4jOI4MHcy|u0@WPt zRcqEMc01g2he<>|JAnIR6JlRZ2csjEHhkE<=Q zZq|j2eL^xxPSq^(*u)E%{D3sVkmo(lwZ-mxeG2Kblbd+Y<*yW;-_fqS<_d50{3}NC z4L4ExQ7)<^S8?>yN3BrrHQi~9eyvBP`*sTm9cSS7gZ0kly~>J$EeiZ~&_b9SHa>K7 z1iN)!kFkQfVl$UFX_C{sXeeqSEa%1*aJgpor*CuxV`ULJ5{2)+tpn?xS&7C{I$yCt zDV8%-m!A~tuzQ$g777-ExvQ*g7Zbeq=Z-0VO&uL*RpF zY*%Xm)Q+*IS8#q3(VCVsSghGxp0^dd`#Sc>{X4cOx}8Q}t8vGcam}8@E4WIRp#yTG zCGA+x=AR$-zuxpZJ<-%bS$Qz3YBE{m+j2bBlIJ3~q0S*DsL8m5NrbJ(DtWJwluwqs z&z8w;`RmjtM0s?o)-8kP@~&VWa@7)`S-G;R#PJ&d95_2wY2PM2K?_c$_t~eEy2l;4 zezeLCV|0BjR#9Q?VkK8d7RloYoNshJl-Apx+cQ0U=3*~b9PlVYu>`+#4&kn(S0#As zCXlbsdX}DZc2mmxkL9{g4*S`QqCK;W6+V>*_F6@`;d z^O1|I(z@}5tX@#2uSG4wzvoi#^COdP!AD8jAa2}^IH|~I0<#Xy%P6 zMWP_4(~AeDob2Ho(ndr4cYzLt3K_aYRdJ!( zG-2sdw%`}Hfz~KDS0>HN-nA%lco-_NVtco(lxv2J9&)gA_NSQUuh{Lx2{J>Ubq|%NS+Vq-*5zWe^N;itCSZDT?Vn>!s&$| z9Xt?Ssy^#Nn}K)%3*R`Y@9r!8-z18xE%BqKMnuDqD?$<=A zele$K6J`FDRw_TjA2@Tt^TtaH*%KKcjXYw@98yxHVAiyA@?js!ifJkvwt6YA7xg2U?2Ec>4(Pw6jumn=OnHOwJSX$I3&F4 zgq85V@TktfvY)LCWbsf|FbOr<{}u8_yMMb=w?}?QU%VV!WKzrX&x@3IRJd&FuxW2t zE`UtVNy)P5IUV#SAIl)P+gRk-$*^pYxlfd1Qx#Owt+s&%JU z&(^ylKJOfiNKWe?A1O3CeSdpZCfK=Ctw%vEHfnCAm7^%c zZ5Y5HYX-b&v&$iH*C#bE&c39VhLGTZ0`+wKkcr7R;UX{~J)h9SGpei|;Eb5GMpFW++s!v?2 zo0D{U9{JcZVe-tfER6M*W0&Z7tm$uEG(O4L&Ld#JPRIp#h>U~ZEUIHBvGkZRO_#)+ zm8FECGX+s0+o2yr8Fz0KyfqhR*tZltR|(wtG>VRRJA9@kRPZKBsg>@nwIF#|H(N`1 zgvw?TrOY&L;X||FghT>;K@lt2iiXPJhgghkNyi)V1rhqyKBVPQw5brhW~%H=5rc^KkuhOH5P*SYj-8;V&jV#D$_BjaVr(|s=jfv z45mTxhNz7Aw|1M|Y4id!QcKG>X|Lh*(`%l7dbH;9eA93ja#5UmZgto|;q~D8n2)w&}Q;%d4#1-l1Kl7q#A#YX%^ot*eFe?o#)t*kRqpT3uCn z-TB5E*{mQwJm9Y3zY?|z8~<_ybhhv*z%tlNDf!-3!?wB^z2Yi!LQwV_(+-Ka@2|i! z*@6XNB@NOxEyyYtX39lB}AveolOe2 zLhBoLtQMxtML>w4RmSJJamvLV>nCaNdMa&Xrflv}B$laQ_ZAuCxR=O)n^yW8Ag8XN zqXgpr-?SY~hsR;)qa~Mj_(E^}22nF)bE8=aziBC5^2U={)r$pHr%l&hcUprj{I>0B zN5hl;IBD49F@1SH9ijM@G~D8j#Fh_*gW=`L2->-No?$ZotRugx zr0OjZj*kGutG9e{6meOBc3c}y2*BI3RGuD`*VV}Tqg#K`{Ege|FP_ZwC<`Z2s=XqY zUq6ZP`O1D<#tD*Jh6xI4+x}MSC$z#nE?HgKs!Su^hCTsja2v0Z*{Q)B(b1INbu$xqJ8J z6`6B+)EFq|hXE7|vi8hXyuxV{_+M|)P^6kxKo!d`c3+~NchH1BzLNzyWqK9A|NX5U zkPuWG)-sU*arqak`A&S*ThfQ4rV;y_3sZZULXJ!)zk;O0PS19G6-p*f3nhBr5rs+l zeSl6|%h7KGY2pS+EnDCbEinUul=hlLaJ#AUmk1^F5gamYLRikrV$H$An`nTXotr^F zwwjG6#^i3g$VP@Eik?bvyYbS$Y2+a5_;y+-Ps}+WOU&VX&Ytx7KeW&lNK%si2G++77?NTyT2Z!vC=_0tp2dNZ`)K3ai zzJ6^bDcoGwthD;y)fk=6A`lNF#a@*?!bc`=2(y#34Tlq{gxlWR!#OrN-%%Km~T_9Tmri0^g@ zsGX3*d3Fo4Sdcs2mXA`N=w&V|c#m?zMsF1J3TGUE)!-&7rB}f$3x< zt3uVL=cfC%6*;iOie++h4b%8(vgPl#tM>J)keZ@#NyaCH!frE)zJyfda&`dD4h?X` z!fgjB6HR>fvwrqQi9zLApP}%%Epva}v?$4qwhmc_2i^UG&-C-$1YlS4UF8-bLs8nxue5mnz60Nia^!km!tXAj1c zy^U3JvKNd3w(yNs;09!rJvE8dcH%{D=sj&qdP@}L>S{G;GtfxOvRvEwLVPr+bk;V@ z!uvv9l{z zmgM?s3Q%b)v2vOA&1}WlkAW1c|}5u`9L^5H4EcG z{L8`K*-|1T`%))M6JB<8*U#T%AMtK}U?eyQ)z<-nwc+j-ec6i2fa1CE6*~S47 z#*?QjkMmuX0q`-Fql1lc`)eow8F2cOOFQE2A&-q~=N;R(?k6i*=@-OaGsMFs`C2}R z?Gf+u5p=%CBKM^3^vUq!%&z0e2oJVZ*ZB7=xI2ggk89GSYgEbA8!#I*R(gUaSQRn+ zT$NyWYDb4sjmO4PGnNoH9}KpWB}FXk`LHUkUw8|*8iOuPKF8xxx{TTlN?=D(r?_dj zO7aHk@~{ovH>MCuNekyq)BhGJTLosHEznjimQOQGTcZW$%A#d4Wc>HBR4vc;phE3W z-|z1PK$psLK3M{MP$<&E2Yt_VwD7L;1)WuI1m<4%O1vC-MLWu;&VjFAEJ^s`G}yow z6+jw3s#|=o6W0zY2mqCBBa6-DPp{fQ9u4l&tC-X;JpRPOOpun{A&QN@ZrW4 zQj3CB#aA8syNocXgJ$CD^H|TK+UQ`am&yIM;QwLk{Ckj*5Kz#!d#+fWE z9oktXDM>%?j7-G#2X%ib4(kpe1^uj&2_j$VT~-4}kKH`lS?rvQ7tp-*y=oWR+lGd> ziaF(4rv?4tnNAO(gy;%p$tvc&%|N^n-R??;7BahzRZ|(~3Z{f9bFrt80a5-XlfcSV zN;!}kFM)!81sk3v^&%!_n=|u6&y4I>N{SqNAZN306Y}bVCq9$$tX+?s42bK1UQ}8F zXT0F+8qaX02v-g~DaK4=Y!l)<_PjL2DeI(uA4Wzu(S|D$MkYq!NPY zL9_r6se~7&1+fy9L0O)D4>bp=WqS}P$xhrLNgL#K`p)F=oBvBRJbn)Q5igRlMZkv&j0rrq<38w^|QI+p6u z_GHs1YSxr}p5gahOSq-VG}w_aY$yF<@B_jz0EP$#uHHy&|N6M&33nR7Jlzt0oF}-R zi{Ru|pG}bscPo#IesP)}xR`Qj4xx#({q?>>N0kv~r(u<@@FK_$)MLtS*YxIot#_te zy|OONm9`^~Y3RSB>OVngg(_f+@zg%`R1W}4b(stPp`7@RwLu47y{efXayTVhQsc1l zk`MJQ{Nc)SUphZ+8Gcx7y-3)RyV(=ZKQ$eExJXecw9z>F-4>%~{X)h`EL%EcLCWQE zQ&mMnS1^IC@G3iAw_b^Wvo|v+xriFEDnV`6{Xn1~a=rit!MPdf=6hck=*Yvq7UaLX zUucc6i9nW;Ec78}M5y?#Q{V^1ogQY}%)SV%7Yrzq`@mgGQwXD(R!JQxE7R}Yf3ay= zK#Gb}X7D3LnVRZGG#;-WORyQx0r^u&&U+vPkA0_RV!GE(8veIbyB?25&wT0vYOk;s z*iepwq$dRJHhyPs>y!_ItUg|shu+?YT>`zbvU&y@MW;52%c<|CpkuMZbB0cLrrJx? z?bin@su#K^6F{?eLiWA5lj^rfxqWAW!!&;9q7-bjW$ie29aDNIUM1A?Xg0{%u3ag@ zQ|NHc`lP5Hy-~L=XYI)7#M8$4cPU)&DQ6_-t2^;w;pjo_x8&2sqYtdI<#ya)UR$bu z$+He07hZ;s+K+rClETP4DiPD~5Y~$&w~!z^x;v@vD&(?eNpx`%E&}grhra^gD|E&` zmTsqfS0gOwZmx^TW-eW;jVo0bMpsZoC}rrjDOcBXb3LL6R>$*!b(?-Q4 z9NCnMtsal@i%+LP;fe z;5^ep_NyTRT7}$=df+OJRv%LLBy^)IH|+iSy7ujwxDy1q8bhlFqhRBd7gK>b<0A_EtzX_ckvtHYY`=k9#P8dEv>ll@#xb;}3I#Kh(FqmQQOpPpf*p z4vM#tThysXvWH4_drE4ThL`(>_#!7H_J==NuwpsF$`y3f!sk`^n~vR^>QR58cA zFK3N7OvY0ZVMG&Cfu0ujS?I&eyy4h@wKbk4dSVIj%iGM@0Sb%P*Rr# zW&j0Rr+S^N+^d~u*j$!4YM^K(eda`DUOp>m=53(8^XcKh?+%3v+;y%w7+3DKR$k?O zf%V4W|1PrJSH8tnrJj$aZg_2jy!oGBp7mZx7TX%jQVc~r4|fQwPPWn4#_*m-_@C(-j3^+Y4@q!6x@Ekx2I_Hpz!B6vgX4o zbF~tO6ZY^6(z-3uNqT%03hiYYh38kG!rpZ5{@gu5IoEJIHK>e-+oh!QdCZdP_k^GS z#;MMiC;69m^j+Z;u^x;U2R4_2t6s-(-j$ZuaB9N4rraYf0tmt@J=eN|MsKM-KoZP+ zpsu$%>8v~J#KmYCtW4pdN$dH^PX&qoS?GtT2Aj(RoBRgrvdceOeN30zOz8=|cRVZG zW-b?^RGsP$-RnRw?*j{OT2nhd?at~wA@5zGNm9OQ%EQ_7O>b|T{+?~ox?NGACcTFX z?g%+^>hDgBT7M?N$1=K@vb&xv>0OJ?Q}pvFZ;z5L`O6DnJLzJg_gwFAP+hfqZAs*> zFHkh}N{=-3j5)JKc=>bEHT(t(jxSr1^?d*=pp-(TE<0lC4*F!ezs{5VZI*YM=A2P8 zBXqO~eMxpcP6y|8gfam4U%?3FJmelEz6)cJ){~>4V~iUZpzYVT)T`VJ0Vg`#S7bNY zU<aHbu~<} zsy{}NefRkY!cj)=K3*hRm1}RSv8Ggrld*5E0yye$K=euM;d2m zh6rl`4H4q3p)i>fT(Boj+hYiI*{-2h7^Z2~Fb) zE00vTdyG2ZYPBhz=eby;+}I(HZ5AE%s8f`j+TB40J58Bd`0H7{ zyNqNy2CDoau)0YgTNd+~0x1HShpeMnYj4!Po={~Ys@!@c+sxjO-sGs&XJorlL_+>_ z!ftCR%>B8ZP^tyIR0f(Yj}Q$^9ddW5uy?U^*`S;gtagp>k_@lM$r|0C4%!ZpAoXA@ zUKlfVM=8Iq_@d$`ux?ia6p5tEo38oGud8F-$@@u0} z6{U=$7u%c=@pNjLy*@pTVIL9ALXC#7nv1P-fxwn1N7JRD6SwgS#*W;|L5lb|OUM5S zf^3=aVaM$B#PFjf)oCfN@#xNR5X|JhNn@A5b6 zbTS$C&VtyKs05sQD&#dUWMVNtRvX%}cbQXnlCwOHVK!el!qZ2SHF#mfhS$c3S02pZ z5d3@>L)ii0g|(z?@b%{zP@6KpJuCM11xcRGQvTRkeABBE zI|hp`j#*r}x|Djck{XB**l2exgHjejmOaMDr-lXj#Ck2tA4^Rnl6(~jc8^=@sw#<( zvpJ}un`gS1Ws)|ULOSC)38bHU!)}{JiSqyP^`2o(t=-nJB5DxD1O!xSLX!?EkWdu@ zhzN*CQF@bJL+@aageuacD@8(0lKEi@l%ceCIsp-RHX2kE~qzk(J!{ zJ?9*Aj4`zyt-|lvuJYExA>SBh#~#>myJ~~JpU+5~rFv6Yk8y~QJi3KIrT%7LWMIj&ZheJW1WLjTBO5qlz3X4=&E)J^+2olj_D`OO*(;~|1*T}+eL`Fz-y+Yrzj9pSz8^P#?%s44)mgH) z)V`)u<-EUk{_I^hqS^CoPAkh@V*4;GVEM_yXvmX}(>c`fMf6zAvGKI1Bt&bf!cGao=DYB|Tmf@CN zR`e(nG7im2o3xUuRQlHc{MlYi+C%a^Pwc}T>wxFBkYm&w9I(f6KBlUF151UOBe@1c z-W_+^cs+V2-Pg_i@bMOmoS&{9o-#Cm)(CwTndYD3`|3J`M_Ck`_S~U;O`{~QQkMBB z)VZ2T(RK5X_k4@j?xeJ=hC9W`N%!NcpC>>=Y5xN}nlcCEMt{#Bq@XDTElJiv7}F zBZUe4Nga*xz_vUu)+Z0v0ib!=VqIkRXi@G~ID=2B0Ys^^cC8#%Axmi7|~xWEe$yIaVGIaJf50KM4to9HD_0EB7#aGh0+UskVU z7kYCZg`1BM^biN$9hygIx6T)dM29K-{Occ|{6qpSiN=(hgtotV<~=dRhi-|+wY%a< z;f(B`07g=)*@HGIjAlacjXE~@!v z+1!Klil_4YE+A=sk52Uk!eHSO*+5o}oI^$1g#uGV+j1Wp4*j6L=s>siBTmz~YXUf%@U z*W6M$No|-o_^Gq)J4vMWmOyO;iKQSB+}EE8e_z*y^0(UcZ)#68Y2#qJ%@sO!4(8oK zDv`vcskTaBI^#SZtlu^|mnkW@wnRd0!*ete~=b-q_Gr z^T;^6aokYpezz;I=jezE9ux;(0a+Liw(Qvs1@On&hkB-EbjPhgLssg-^Mfe(scEi+ z+rRlpUUfx~VktqC1G<)tF5@6=xT5{QAa`lMw+r z>tRa7ny*H@H67|&Ttam@Z|;7Pr&EWZmR03SLC#VQL+Mn|N<)sPnjhb!twmh%+9G>~xgjWI9b~m^PfCPfEW(XyZwBVk&pp?%R%zm}vY_9#SjBl< z4>*N)lepXA(S}|_O)thob#mmrN_Cw_I8-tcD|dc5J1%k#a?j72jV5`tCjf`_|G4xX zfqzCDA0NCc@2!=gC_f@GVwvstPFx(&w6V(Dx+tbwR2@cyLR1E*6WsSqqxwF9-#c|%oLvWcZ!Y0&?E@F(+~&im zhs5te42$a#$tOlfD<%>&22K7tZ3Acc?ZOhBk!79z%ANNT2k));d95LL&ezt@tgAmt z^Oci-W#ILi0QyeSx>oZ9z4SpGsR6E#85vWzXHhqB<1vK&kqsVZkPr!0V0yw@&jlON zJxbR#&w`qIY{yR|0EOCqTELxKpFw3Hox>|WIC*nCqoR!H(KK}(92W(854jXk&LFU5 zXT-H}McIrpdO%gYJbAZbK~Tyx2>uy&@>y0vv`#bz*-r3+EzDLfZEFIK1g>59O2}JR zYt|5nai@Z9`3fF@$ZI8s+;05!g*A6m-FsKP_b(*EYI_LzK$QI=Dw<#Ih-iyxGwix( z(2kFXr7z{BCGd~4ezmHZ7aFyd8qEmvm}P?gFz&a=$t!c^$UvCh6>? zP^UWseA&g>)(8^*^XKBpoE2nc{D18Ld^xd3zR~^6Q;}_=tW$Y-&?MCqH|K8h$89i{ zdebB-4^#R!MS|4Mj$GUS$8}w2L{fiS^GyV9X_sJr@a;9%49bM^3;z%9zokmg{5nKWc zzowS|vW%&W%HL<@6C;8sXp$u^_goHMx2)}MSPYz?wo6E?95w}!BHWbAZlHr6slXrm z_eOao7n3PA%nT_1$O$uyYCAmb9q0~K-l5zrxgC)G$A)Sw`p8_!KDQFWVsNszIQ%_^ zuIetYhz5bb?^{x{PDsi6fDK)}fud{2W^H}J3x)04oi|H^7KYV5hd;yzwVQ0pv+a|b z=OrCZjl~_*kMHlPXhaqYi`NZjid2`jfCj8I(cKPhH4|SlhcTbU1%IT!q7?3P8xxEf zR$py!r@&_ue>*&keWx40>vd2Gga9hGpY=Io>v4@1RSq>^(^VaHTgj7<@=<3;F0YQ` zZ-j4zy{geOt!^NT@-@RZkE~-{-?$z+?^eSG4YKsYpReHi=M)?irTv(9ofum7?!o5B zc5I%V-3k@cf{nn(Q@mx{$;L$wYcx7?%8cC|q()em793($Ht?zqb6Mhd?@NlQ zhG%?43{;L4wps-^&ZxJUjtJ`v%~u9P>k;sm3m+3|w^Q1n|{lab7S9wd!xeGC1Gxtg{yeXA|959j8bxu@wjh-G?2{XYs z6TN1LE`hCg6Md~}%tFG_rn^iy!vh=>#Vd$?BZkIQqbgTyi|k*p?iO zksL<7nWFB6P1R~CTEo6zp>SrkavsFVwp$ZtA-U1E)WuAURmJ1!sC~J5Tp+!n8N6Pb zL5l#uS!l9L6-@u4SYwzxB(f!2ltuu0m&N>H_Z?;1iuL^oZ|^%4{D;Q8?r(C0qr`e% zK{O)3LC(_Uj+c;S`{V95M?hgW(S@ zTh`atsggPEHLm?xI;u+Q#cY2z)8teplclCV zG3WR5B5Jhy_`OBCTja3RQxAdNk@P99=twh1<`3a2%yBV*I!0vwCeRIPZ1k0roaDZq z7g9>IDoa&IAAYdg(x@^hKYX?(HhhO)2)01}wG{bB`g@h9tmWeGsUY3r=dWp|M=@#c zT%oAP+S2_|y;Rk_0^FM4vrKQE)&ySa(AiE>HX2J>TfIqUY=3`w>>zt3;TvTq_U!k_ z5AO-v?0M6(lZwZ`YfLPP;eVcl8eJg?jYnWZO(>_J$k7;K)SwWsxE6?)gw)3J4tF<1 zxUUVar)*hRXCiiN%L)52wC2+j1ykQsd-0sNGvc+NxJpj~Q*lU|=UEwV-NEykaM8k` z)68PC#=M@Zs~;Rcgi2H#51Tma4Dn;^ceB2WrEah72n|RGEV8Ye(NG#B=Rr9(QfuZ! z)s1sz?v%wJHtt3qf0Ykf?yyDOBOr?ULS(FB-_FTi=lUTc%DXS}UL_V2Q(zzcunl3% z46}#F{(Q&s+e^13naT52n@O72_5N)=TyuJ#@q}0I0c}s{*53ADQsl!fxxV(~k%gIu z_|t9pnHdqQZ3($vfdbX3(|sPJYQd$h z39p1$WhD{@tJ^elEGnLF=OcK>*#;l39`ZIV??fCMSynO(qwF=e^m^p@t#V5m{HDY0 zK5SzS9oh^0W44pZ1snh0aNDdGIkHwtWOrORoyA_yGrtobA$%llBC zeR4bI>DREaft?T=4<@qP4B`n(ytjZPMW*hAz#H&;tD@V}x-WAz>9E6OsiyU_a)!e* zQ8r~^LVSZ!F1BcX@;uA`;IHid3Cil`-#)=>cwyI!(1*-{^cM>h;=QX98o~3 zo<&F;Mu6*JmNJ$Iah;K#8@Iv!>Sq9IFHx<>D>^TF1u5(`8!k(gU8Zy6z>wkTBNF~4 z$GgzooM`#i_5^9~iOF)B75<0T4048AT3T#Qf9CP~i3?H+p-&12}v z(SMiR(ehu;I;%#HPws;wt(5%Ns}& zY|~&fSYtd51-B}XE)6E3gquwUvpCyWgOnZ z`jSB35`VOd(?XWS1y9Pz(3uo5Q8yjcup_OZ^qbyJJx=L8A#JGKs({4)w z{WESl&D-Pu%HszPsKO*g@C7ywJbbnM)1xMD~m% zgzCqrYx{nBtjX#6_hp8QRr%MYPJP+(Wj~qPs&c7Id-4i{hHd#wbC~R#oI1V60-{{V*R6~2i)65Lj9R-Nj>>NRp|nh^ zZm%#_$_aTnu!o_a0Ez}K7w(hPNQq|_r`FQwR9?59pg+{L8~%a@TS_N5C8W`yb&VM< zgNRf1>IYDF5DTmwhVuldHPl(9JZ+d~3dOqQeslh7(v(n@F!vtH+)vH|>E__fL5B&? z{q);j8}Few)?xmAhNo-W$>%3Ko)i0?q&zoYj=0(|mIQqg_a>210Lk!S0wWFtA^1S5h ze}cDwsF`8d=uO}tTAuc1>lak-rH4M$blVCK;vt=bgp^_&5TOb^X4uz3rZFLp&xD>K zlcg_t9HjyNuq!MB(&h4xwP{<`KFXjgDTd1=wLA0b+-)#6?ihQ>bhIaDhpN$7ZHioQ zWOy0MhI1g14_)*>3e5jmZwf+vGe??Dj45KoG@JUl5*pRS3tiG-eFdg0dn@<)8 zEG_ZfCPufLfmyPnV#b>eZQaj(2vYBDbkWgyJ}hA65{f zT%)UOYwTE0F`bpIC6^gT@Hh;|vN^p*0APKWsLX_9L~4u`3xvh7;I;IR2wX!@Nfo)V zcCN>GG!y+=X$3h-XPN&{-0gT|B1wD`tAy5wJwEz*rScrBJ<7 zi=F*OfkD~cAarG=#5R-U7;ISjyZr12%5Oj^=P+Vu%``na7KjnOvoyC*F$-;z&dyp6 zG+bI3b{Lh)Zb!}kRd4*){{N2`oQ%KqC}c76Mq1IIa4sSFPO^}B15P$S?v#;L5f$M_ z84D#QuWoA|46GKKS$utttBwR7XRk0v@NHUQ#`h(xY1z)#T6Y#0Pj6baa#hl>KBv{= zxfSPgwp+DF2Br4nr#7f=xQ6E-8u-`m6gM#gU;zs|mK7dl3Rhpz|RR=5_gWmfk`>)s<@t7>^ z1GhnKXYL_d<*l@xRwb{(&bc8;#>2vxn(WT_fv4&o@(%b$wdEX1)uh4Qq=S=wH0BUE z1amS-*Yf&IY=4pDJiR=jTXzstcek4>{C(YCNnW~*=dva=X};(XknM`p06lcYzJFt9 zz5*--1Qty4F1tAECJZ8z=Tp%x#(LT`_gsj==}~&Bgs(-Ryxt|-3Mkt;<1_~+qAiH+*kta^{FheX`pA`A7Owz?8JS7JIH z7#lJLrGL=fuv3~fEyvf(_3;|$tSmyCqFz#0x!PnAm!3uxrnl{J9}AO`4fG}Sr_6x7 zjNO{agyE=%iRVYue`2qnu@jf;(?o%V_Tm^)M(G8$hn5PkO%;21AH$vo@H&UCK8x?GaglEgjzdAHU|`XeAzZ5x`<%Zgjg16#cp2we8?2l+rkIr5%Q48Tq0ypMt>D zz5tL-e&xZK$95+ZQGWqz%8qJ|3FSv7&y)O&dgoy)SzA=AYHF37sNdvoP@11(aW$S-*P0yTF>vPyTuz z(>M4k>NcHlpBfalWN-=uG_ED5#wx2BN2i~T8!OO2Szrvedx2VvV|a^E&NZGH#@r7O z8q`ztaY2|T7mVbhDQM^h=rhWj9&OqFy~X2`E|E!5w=JE{6KIR)pO|AzgHX!Mu0(dh zFZWM|BKK}nhnT9L#WW}(Z@onK1M{G4aZ6|OL65={qV6y8QH9Dak$*K(xNzu~^X_$K zA`IBCtIPO&FNF;4EjIJ4T^hKMVy^5jYlXio=W$n0|9NL5+9} z^W7cRiPGwJC6jdY+{};p)Nps(lH7qevOe|&nW>e-7@XC>8ZvLM09|15TT6P(v}tF< zMrMkP{CQ6y)XaEdrs5(a{};ggzg_^i0k~n0;Pl=l`YOkpkFQ*z^*Q1XLQ)9U;bJt( zoFo8z+g-B7Txu&_rXXT8@$Tt!tS-%vv3r3}y5+Q6K-WObDgV!C+t603 zdxzT-N9%8mr|Ao9@B%Cys{xEcvhAxjBlM?WIib)3*TV2TL8)K4b7&29W8}EBo>n_} zU*@sSh#Xs?^EZ;uKL33EY+}pLM3;}|a{QH{NTKEEQwHQneAhC>cQbc38g6Ht9;1x7 zhjlYwOJ{*(89X+A?Ig^LnsBHbt~uAyoHAN!KSPVG0Zv7}{jKJ?nVc_JBo|0^HvF)? zZ%x;NLt2i)W$EPrs|dv^=i9nk3M-~u6r1Z89qSfPKvo{>MNs!*xsdulN8UZVAg+J~ zn9h>a3~G0pcNtmPKCP$Wib8x6ieDzmt>(U_hq-(w1ski50QKoU%Nfz+Aws zD81=^i;cOzp}@rF{ih?z4SdAQ!f%)B70A8a7i|dlLioKocwc-XJwYuI<6LdGG%OlZ}1J6rf(Th7fdIRzwby9vzYf=F`+q_L4g!2N}}- zQHx(NOtOL$Si?U>fhwT*#>=;UStBD`{6=m0I72P$XcbK&3Ev?(dNHMz$hhyJE?G68 zG5}rpr=_U@=qW+w#uL%EsuAumSo^noL(Q!br;jaXI&Nqls10=(a|WYL)gcZIe6`Yh z+lAkak+^g+C#9iL_qRLwA>TROjnpwBFOj3HDI0qRJ#D1awONf2V<8is9%p*<6O+^e zJb3N-bX7K5?9qs1k<}wo7wiwsEo@Sd*kj9N@+EUJ+awlZH00WQWf20Bbd7Iw_vJ_t4%_$qdU*?1Ps+#{{(&?3?7WQEEU~>lAm!@bX zk$Gmmjk)obLRqI-x}YNhl!3XtUUz=xFk)4f&P}27gz>pF=CDn@w$Q0;AqfS+q!NyOMNhgh%Z~p_c|>qt6@v4otoROhC3M zjd5Qxb>aa$)9fry7BOIFMSxEH@!Bvjm9JP6R|LO9$(9OUQk!Q^8Gj|qj*G_F5pWce z+J|gVYh;Ut3UW9-r9tMlKxRMTp3b<;4oyVo8GiB|Gw`1@K|y9}{5)_&%5O-eVp zc8t47>UQk<`LEJdNpD?&`L5@m&-FKMJU zh4Air@pR-j3$t#Eol)Co%kT_#Z>{|J#_9^I5N1fI>#CmkKP`XY{qj%5aRGIo+^)}R z{*sK~lEG@zh`>po8_>>ctlKb|=UC3&610BB1;>8XG}bhd&r8ud5zKct6iSZhOcc~N zYL=B+_uW0K%V@kr!|jKqdyK1HO3jldzHXwJel~ukz{fcC>Ya?({JQQ(^UTNR4=G6R z8&KcU=O(@DBwbqBrDi)qIjsiaA!Q6S<^)8#LEddb->4?iQZ+OUAb^m@Id=rg2pN*L_J%t^GS z^`ctr5rsu58lsDl zO@#aUCl|!TMWEL8kjy^0peXda??{|)OF%K{6YAB|l&fOpr+Xv4w@-fFPP6ftp;^3O z>C);3nns=}B??o9yM@sr$o+bG){{?KlY`wmJYDvyCK9~>`l)z|C`tmn4ttlAvN%|G zwrho(k{hgxc9qwro_9`25HZ4y$^ItmFN*sQbGCdgQWc0I02mI-jQrUuRg3 z_&M1jhX_b~vDhlh6UV4ba}@Yxr1!KloT6|e&V(_ABY#k)Z!8I&CpTg%$K)}}%1e(% z-B&aeWrP0dKScKMK$6ry?P!rHF2@yf&*# zGlp6_cwi;K*Vsx`)6`mk`SH|F`lNL1vT5=DRr(C++0g+S>IC82*0ZSYjn>R{7Vsse z16BI{N1;-B3Oc#+r{en@OOZ$E$*Nkp@p1o0Vg!Jwmxv1Q*yN&JZf|lEsgS3%Hj?`Z zq07@uL}%_nTePyz%-g^?7?GIgkpt`oCy_GRt8Q1buHPvrfA3;Hr6#l_cyC+()4a57f~`Hl?_VVa9fd=Q@* z3*t|=#Siq{=0zs%5jAq$m075AULVILu(67<3twVlF+gx0`omT5ExD~LSN<~7S)o_0O1NItFr$NrUl9q#=_4SW&$ zv--xbyqdk?or~0M(<67c z*k4bk`Sc_e1m14mE#n>`XNPevxSC7<5GMP@<1VeZAm7pM3VKcH4>i+_HdR+NxRN5g zJVe@Kjm?h_W0{r4xgX)ki!`gtm0y#V5`>#dBxlQW0n-IPpo{>jF^IJ+M#Mh1PTgIz z5qVzwlq-x$w{9R0rn8k=BV7%QjEMQCMP}(ngeufjr*Xj;01_gNm#;op)?5i&D8mRk z4v*AG1es%b+70|X9}byjR1O3fWYMG;#eAOmbpivpeSS)ttU>?XTb*JFFaLTLx zv2DY=u%-6Ho_xkBe?*8tWyq`iA{nG7+WC z9RlM4|N3PhRY(;L1TVLtwy3m{NZg=olczEq|eqA1|D#dM{!@hBu(O32Vv6*k+j? zI_9}+V{QWp()r_~CkIDBGGzPfOzl#Pbqajg z;b=it;(m&1u8HWcU<+9d?7=;LKwv^nq(j^SC~GCCU@OB^Yt3N{VJgV7(K2g*QfGz1 z9DhW1f_`8RDid}9$b~EAn0)9nt|XWZH&u5SUw1Fu2g0Co-Hs0n?9K^KXXFu`jWbJk z3`%=Y*V_|WDeWT<7zOttjpzKKL|ViF8B`4BAmd{eK z$D4$>hd%HaUMwB4mbdXf2F~#P35FhDeHH&f5aFVdkzuwV9{P8hoIgj*jJsvRL|kt5 zi9(r)pSuef{6SSKdE|?NC^C-(frXI8aN10=E_OKQlm^rFG+V)^!SZwn;$1SWysgmQ zaN$*Gw&)Qu_&HJw8FIkWxl#l!tD#gsd>WGdCBten&EzIq2{@a>&l(whs{pWu!&PFO zqPjoy4zJZ@=H#ovO?%wZ-Rwz_p0$YTeum5Pz9ZMCxsJlMDraFH~dBn}Pjw|iPpl7OS zQ|#%(&TnhJIk(>P*HG(zf^l>_A5x#P9yj5)p#%ok@JX)Z=QY*PjAwaFICP!Pj@{V* zc-(Z#%)4e*Fw-Qq7EPEj?TyY1SuK+vmsS5ib3cF3&K*~kXxEHgvmyP8&x4{^!uhUPPAg3h)kD@-hx4hPt$nnro ziOdJt*~rfsWQm}kGrr6#IbI-2`|CLkdLKF*)vGI!&=6Rp0mt?m_G3{bE?Fvi@M!3Q z<3FNDzK+i(u{IfV-bOP`p?g<&l{zyGGo8meGZ7q`2Zp!5Im{Fl;nfb=sR|M*se`?2QK)V!E) zyxKC@r0(QN-06=d)#1~)vNY~)Hy!+j;^~NP2H*o%)l%2T0G*4}CezfRa(&lhLb>*7 zaClXc_-*4Ca~0{YP;md@Lk~)-!mptnnqGXW+3}SI0ANOLL%B{CCiT-@`Ecb~8O_1|?=6pPdgh)}J*5ldzX= z_be8Z`$?kx)gNjv2|ysFfl*kQb~OqEMf8s}f-S6)TiHL)LGopLX~JW#&UQ0Y`jS?g zBV^36M#hEZ#%hSrxJx=;Q&GX_{7j(2pDXcyeG^MEqU^~q!%oqxUW@J@r^sI{VX9|@ zolj1gp=a+x`j1wnlj-eD8JltqEgS*!yK#R?QIRd4An$Z;#WWbA+Q{_Z;NnvsEtCuU zZw?LAxgG~L(S#$K686oJ0k)Txi(5}y?1G-r+h3Ey$B)cBR-?tWq^@({=MBw_Z;zQF zhCAI87x<^j(-%t&#(v0$R6SPKF@FEN(0Sh|9`?)%Ur}HdkssgPnprXAL{5x;-n1Z0z^*>Phyv`$K`Y%1{AMmU=s6Y{Ffkvv$@Kq*Fo9eYbr0j$pOH zI;sA57o352VLzeJa-=<_r>Ap?tcxW7A+I{K39*T=0*!UegxIe~5#4x{;vf#*;5 zy4`}s9DG8Y_B={MwsH*;(?7@0l?~s?4qA15@RPoZ0b1EFHmL(1L!c|%)P_)9r%y4V zlhHG8*~YI&3FH0bZ@61q1=vzU@?e*jWzCdy9cev7pHHYB#53K<;^}@>a=q8P;JozE zZSH^ZWB+^m@`9~@b?btyZy`Ae$cf$oJ}Bam7k(3IBQ|xOMlWE?XPpx#3a~cb>G657 zbyp^W_}>K0zmIthmdofoxGLj!g)N>>5y(fJCUl6WXjC|HG29{lhMNn+l##EGQ@5A- zJQ{VIOjbnoLoac?1c|L(+_ONLVbR^1K0`|P1Vr%-_td)8`px%A!jhwN9hoZ&ee3D5 zV^Y5i>6+XrJ_}!V?SphuT`xJT-s{Fe>mls01-0-sda){y^AOngM)=M7gXXa5Uxh+OJZoPQ$5P zzu#~LS!W%Tu1WQc*tN9HlW$K)O4lth3D!FAh*5fSeRbarB=#nl zd=n~YPh5r1`0jl6BKQ5mOVVBee1HFKr^dK^5`%!{)#3Pedt}s{c?5L>UH@FU%4t;= zs!?VRixQH;7{=IbuIU`7p9UlEDOOU%o6~9Ib-0)FX}WS+>lgK#HXcxMMm`Y2Rghwx zSVp-G#-D;*9H+kG=$(q_!@jZC>U8a@`dU$KQx#Z22u@5(O~=A;^S!BI;`gZ?0rXI}2lS05EZfBLUquywqP8782jUfd3!Bqd5AD_oG$ad zsq^PS`t8KtR<`GIa0m9hISPV^$oZ`il{`+OP~r6Cem<1uKj3G+0*pR2G&FFcx5%=p zVmNbi;94woWuZUsx>X+r5i#BvdwShdtfVAmrtV@_ZGR z6SFz3vGZ_Ex8w>c8Di3m=zf9#)=gvWjq)gUt13j@K&tj)P&ZSGD^*I)wEmveYu#E> z``HP+q3m4s*t~u1qq`0NMUMI3+f2_8{4u?|l~|C+_X43tmtBxSkCxQy?rZLwW6;Ne z>ea9@RJYieCBxqV@b75+;yvKr^5&Or7awZ4^f@NzBb6)>?R8sv*wizJ%RSaqORPwA zwy7fe($Z@-3Irco>Jx3`A1VYxOXh=whu|TOEjG!v&2bZoR(`b z?y_4=?H}e#vN$Vjq-#_XkZ^?%j4ey;t)86PWSH)Rry&8!cdwHshhkNPOcQcBVLjLO z&$LK3DqFNxx2j89Y|D0>gG^U@K35-y{dxt@r(|Vtvy<(pk)10{J+a{%rc3+(ow@vM zV)aqbbV|+-YM1#C@(tnskSa46R8|-xgs&@zG9<7*O;VTe+6fOW2^~_NJNw_0dXYqq%Josm#gIlwK?QkY*$t-|EmyiJ zYhZ$vXA+4%VL|Lo2bGBT?B*SV{xwRpk7ntcl_JO9=?qcQRm8scNZ0k~*%Dc#za0WPxWB)Uy2b zLxg87y>{HO#Azg481KAWBZ*kuJ^gsBZ#`?~>+?!bG}kydz!NbT3HuFts`ycv8bf&g!wdpYV$zsWTRnmvzbBZcyCo6;6Z3t0;u$#sfK{O zX!%{o|35?gc?9Dt2iHraOnyiOXKgZgKkvGhO%?im&*cCj0B44NBU}{=O0tP~7F}tg zI2(12rGHY7NEc?CV*CR75)Kffnwb!cx30m%AH*ztg>$z;FUc~)Tv-v(9b#L0!l(P* z92rrh_Rl}PwA-i0+Ly$bLZ%ErREE3|n{4Y^0V@D$+Rd%uPhgcSZ}G4bd}goq+hY!! zB5yQS=&!e*MqEX{(Z%4-k+L#}J<1@_(jjUO$<;LJ5x&yMacX~a_`{P(%HyJlm!j(> zTw5QEl>@pZb2LThMtJ9*P^m(yDa_P+IL&bZg2PvcCO%1YaPHdbY0YDgf9-cYPN=Mm zc}th|HiH}S*8e9}07orN!{!h8E5|MK#IGnSN89Q@wQvh#6V9WLJ~8VRrKkCIJ$rEV zMZ|+lVQ0t0^7{xU0_y02;v4(lZDiUBD!H!tgQ6b}8;;xi3cb~?)X3_3vt0AOj}mXRw-?z z{ty$FS=j0xPBM^5Qd=z5ZJ^SX^oFaM>>bUOWc<_J?2^6T|Qk{ zhDpweh~quvZO8jvm?iNFY{Rq;yV?jhh-2oKbA zA#Yvyi>Vi?jls7SzM(~{bpp&|ea$+v{@xA2Yh zC<)Xi;%RUY#u?~4b{%qk5AOCjeQ|TYt=9yUQTRIXrU-TI_ujii$BpPp@(r@aD)ODJ zd#U0O20@oLGV8%+k+qf0Ok_H% zF6O=T^G=TRD?@SqN2{Q~%ff391`_jluBOA5Dsm*$Bqs~=P4EY zV}k@6^>5DS+D;pc7Y|0yfAwr8;ZA!0hey}OVPK#$RL_-1OA{0|rE@$}KEq7ynCr*j z5kOpI&poytNto@mED+o&80C*%%6ehfbLcexYWcsIofq54))<5yTW-6#stn}EJdges z{(#V>EhXkgnk%FDl7goR;fb`Gf!U!PLO-XS4*hb+lU@1iw>LxehSW3N?+U6~ zW{OG}GnDqjV{Vm3#I&>c1&fLqCKi^8l`_H(2TV-ETeDkOAw=3@+hbzg1<1;RFGlJ( z&8?K&q#3S-9CviB;puO-n#BwaqAdEtC{-gh%sO$MVo4iB6S$~+1RODx+p{td1E zeW2M^fQn1=+68=$FDhc+TZ&JpJpW@i`gTfA0?{eCEq&Z&CS;1(7hHzeb6J9V%`~Fm z)MjCLs`)^0@6y&i{DHv!PW_qof*@WPl;#U}>!AD$W|LupeV?`5q(Ofr-scrogU`s_& z-a)*D%KA?Krz7w2CISlrPaUL#z(-y!G@*Jd!o-p~FeLH~WP|+ly%kr8h;T4vz=GJK zh+!>-e;MTxhxbpDjTQ>>Mia=ni+eKfi!YoGxw^}wgE|u|IjI_sfj8K4rWsXfarJM^ znRP>@f)LSD8c0v8nb4OfJS|it)1AZRQ-?Eg2PDbD!O@l|%aM`|MQG4#NUHv__;G!x6u8`?{m16SAl72J@}3-4+rJJ5IMYrQiPIwD_o! z%gW+jKM#G;Ea7=Yb!pJiP9Fb^3p|;pN|-gG1;xIL;ykau4_c`Bu^&5MMBkjg*%59q zfe39_n~%I{rOYew^$>2N5}lm2z_qlAQ_h%jZ~Qys2Bc-56B89I6={N=;A;d2BE^7W z#MhYo^9Rp_b+PXAh+3c_fTH?r~ldLjEFnq%jcVif6Xz zQ>MPUxIDs4=do&T7A94(a2V5YmQs1HQ`6X3%s`a{KYxV_CClotXRW@ivd3Gnmb9h} z=Svx0Pnoc0m^C}utcLSC&D^!9PHajwOW5AMIj=Ks+oREu*u{!ZpSlyx*`Fyn zc{ga{y6)b9VWE(eCab(pL>y5 z@URjEXqC1eMKOex&sS!Ma%}Q#+_epB-{v!r4{e5d6%+bGU$;-+`^sy-o0=3eGhDXX z__R~v0>RL0p!hskr{0y+57Sd*%_kx%4a>U}7Y}5omK=bWx%H2c)u8_ls^M3Nzf~IC z>$(cEX0Z%fD2G#Qe!4S%MU9h9&SSXNG$aP$rixCLp`1?QU8;4F%PxgWPomSdZ5Lh^ z4+B$Vo}UG)>fR*?DQ2>TTPi4EAIm=;F=I&pY)nIW81)7O_MO>N*4D=2N z>iF7iTm5oQdm+des-C+9~d<{r*4Rsuoovs2No3U9}Rc zt;T58uDxniji{JKX|+ZOQCkUGdvCEv?NQW@peptVMNyP~({t`~KIgvg-#NeklIx1= zlDyxq=ll75JRifMDs{eO7%pn&dX`XKVEfs-esNr8pu}qRa=|hYUtvyAI>kLH|K#7^ zgeK0hemjd-zc)Ue9!_NaR&4$tpnDA?xNR&^{qm2|HRHm3y?#yC!y_lTjB^`helD{Jbm3d|)=NZ_?z`Rg6m@14GnRR_j7>&@k2q;hf@ML zm`Gqafw?h(wX5~I*SPIPjiGypqD$S)H&bCYjQA^Tzj{f7-D z%`Ro==y5^cj)u9tpAcr4zDgT0TTQs)l@nvmdew(vwh;yH2Tp+7-41UXPOim9CztiC z3U74Z&tpHR-mSMsw(O-#IDI#8>8%%Et~u}usCK;FYL7zipMAbR_p(-7jxW08+`oCU zz`wzCtIlkaWipJfTp$SGAMb?95XU{g9&!M2nAbc&uhw&D3+=2-EaDuFnmDv^KBqf@ zfepuTxR|Mus?X-m&84u~m5Pk`hBH0O)rr9j&uIM48;QiY6Q3{LH*N+-Z~7iVzCPI4 z|3K#6N6Gd~zTl#lQ-;P7xPWZ+pL1`qED82@uEQmhXGW{Xf6QlX3A{Hnvi>stwLRf- z`|)jI-9kt>bh!83*VmmG_wV6G)!-YS?kBSikpYXYRfvcO3Fs?dm>L&IB8L~X1uMBp zDPtQA+T{#w)r`aL#hf^Xc=%?o-3l%pDUxdH+C@CAaZcm`bD-rIXOwM2evNk!Q^IJL)4SAy% zh=?$$(MK_AS>k~nA@6i(kYf<@*jvcH9^-|4Dht}$kes;cr?F%K{7^{GJY@C4A%s2| zosC;0M(M6r$V9sXd;2+hioru#R?vv|0iWK;=%%MNH4vQG`{DSF{ka~Y18B;7_JGQf z7WQb{i7}s$zx+J^UVpUZ&?hGbG;v*Z#m3vUE+k*(v+o(9S!OcR>2*lx%1odem+>g9 zJ-w4Ig>hqS;OSp!Le;F9V_(0$OZbx4C1@{8liFBV^&Ys&(@>^t?y>{@)p=M{?=t;T zz&eL05hS+TszDaWk8-xv7+iU(%@}dY!97Q&B@T1%u_Lon+q9}gVw&0NW z{YS0GJ#=V0rFs#PJ3C?Q9vEox)8DimO3q6|ftD?Y10z>ji!6eI}Q7-Du#YptWuZU2o?{ z7Nr2)TSH7XVVBgPq$=aWWF;{4z(*2QllT5g=&~#BTZQ6o zXX6q*{7AvWlPI^-yg*j|LrefrnFp7i?xS!J3Al2MYKKQGVjUccHtyt*1aLUBoZ=%V zCWn1%w6-sW37z!BAWOhuA>4+9B6%&Dg8|`!$vMVNyPS<1?&rFSn3bDq}3NYmRo@ zMt@y(koQ)piy~FPz~SpAF~%|xa5Z&f52F-_+nUgYO${P9k;3kxTWOZ|NQ1V; zh>Hbt<@HUQ9?{o0z=G(pbrrj~c>7Ga{rKSWG+*%!(50I^zQa3mE+sJR^zuOY5u^Wp zuhO0er*9xO*?s^8mpj8OdUjhK`SjCE{cSUqPwK*1#?$nh_8inlYRC028Oy&v7<8Ss ztsuWiNBd2s-)+#0Ny$)a2m&K#SZz+~Ox{4$uWUFcoPSAwQ%vA27;!ppcDofT37}S4 zy?)V15XeB}e_-!zsG&5$qJo9?z@iMxou(Ryb?rs7EoAlj9E@vyv~ ziZ}A|ekgtR;hRr*;j=fVla|+iTz+0Ym+eN(Y~sI+qZ!a)^BgD9bKl*oruvm{wWr|SrDL`)Jqo+&;FUrxhVW>v>yK{A0e>^ zZf2&Gopk32SXQ#-i0`Re*hraCnO7WCJIHLt-J&_MYx%#k`p-LzE#(z^l+1U+{*vZn z82Mvy9ABz1$aLDUqAWByvA3?*UV@u;#QRi?U4X`{*Bmgn{ka|Kp#9*Ea3h#t{Jyg6 z3c25DVcBKY4nD3%2xh+lY>Ra-zwPOQk8){%b`l&H2F@+Tua?Wm&SxdJ?NJ27Oy-%K zVP@29FlTWNT&UVst10l-P)B0%PX1(QBo*VSD8@3lcJ7u-U;n0vh)GCSeVGgA;GyG$ z{l_`2hz9Ft6FwX=xF0Qc3N5l;mi>iP;C=(|iZmmP31hGbL9CKyN}|1V0DMj5?3OA^ zqoM;xj>wU8_35p{I`(AWm5PPVSxDZ_twhv5+FetH%-Y^JG$>A?5{wlIfU2~ zT0ssuritAm5qbCe^cQIUaTP5%@aK<5VqU+_&jc$nZ@1%M?$o1&rvM73JTAz!1Me-( z!AQ&U#m6^3IFiS?X^jpz03%1wH$(To0>iRYsa4H*zsgyFF4JBn`bp8DR3WWr_FVHBngTNbiaT6ZZU6~k;#O(!Qs z+ueo^tiG$Hx@#R^qH&l*wt%k+s@FR^y9A3dQxKH>h|=M5i^^%gO4ddSn`(4(A=#yb z+QlVr3EBP)g?{*Wo@*`p>ZZu$tQ*D7Ax59d%Bhv?7%bB&{nSlntu{95z6fhk1U`bO z?FI*q%hlGC2KhuX(LatI=)-O-Z|_Zc;_sSYi-@I1jVmH3$s)fNj%9UEDrv~--5X=CMwv&47f zNrO$fssIl*($)uO&16oP-ym!%x-Eh3f_R9cOP95NM|!Bmt(^{4LzS92g$-z6LC=Mg zz$kBPBOr#8AUq=>Ub#LggI(y~E!WyEitxA9vKNc%VZ%LGceZ;mn0h$TkwytlqY? z-1IuC8M1*179BLere@$rtPT5w6>+Im*ECnN4W9u#s{Xs(D z16Ccif>t$Z?Nm|0Q4)a5G~)xn^@~BJ&|0^9b!;Gu;D*MoYt~}oFTKi)bAU11iDwb{ zMSHYR4=d)d-Ue1ufKl9&(K|=gZm0ddO$F4}xxfz1D&YQ*?H%gx7U z?(QSaimjXN)LE});J0d~-I7h@u#kX&Og?rJCzeWI|6dEuZ%2n-vq!0Y65E`f3 zhrqjQ+Ld%LxNLZhr*_n%_74*LbC6CuFtfx>`|RT{?%{;ywQ4~f0T)4N^G5!}O5&xc zkDmGShk3Spd%kU=2_DVh@N*9nn9*r?i;Y%&;L=z=J9l(K58|}|(gUY5zx3PFf;dS$ zjJtEzvT_Q>tTp);N9q~2#%=n_6DvSq_$FH9yt=^43l~_XmfkXR94)j+{m~90n$b2p zYo3xeNK&v|>pm!~`L&zvS<|Tgw;#mAk`&%1wY6x?wpz<`=(;t=U;P~9?HcaL-7o6> zY-@pTvKy4ZRXK9G;_-4KsB`(GJTA<_JOBUyoXU8Uu^)MFM?9@l%v=J^cF7clNSr#& zTOeW)bt7TtXLSe1;`hxg&sGMYLvz2&MB51$?Jq5s)S>LA3L&GF?|QKQlQe+M<7qwa z-Dp|8pYo+R!)XUI3sIzywZ_ss5-w9L`Q#Gn<>my@?b*tiX^2oMD(^RW6C3>@brtYS zb%HCY3vQ-&YHL}@e(c3D0&~sq&uRQ%QFqwET$B3Z?ab1i!liHsz)U3fjyIu-1Pp}5r@Z01*S_0Cq}>seqs?p-g&WIzx3nh zQCmtF9SXoyA}`S&yY68WoMNJT@rV1PoTxp?$G>Vyc<0qa4|!+ z;bheqf)f7%77OtBq?+T0ZcUrnfBsVa;Hg>wwMXM~b5Hc;{9^)KN$jjFk9{hivZQqp z@9uf=SRtuq_@!^?etl_;KS$n%2#!(Y72=nNl7SOj6s;FqAitai-Nkg?tx;@V+QYAU z6j-Ief=+Yd?f$q5P`43M*LpV7+C05oP&V~1M#)zpu5Z81s*U;*e%zX*58C39%3)D~ znnNzbgq3G0O`Bilk)|>4V%9QP={$0S4A*;ZMl6o_hPwmSY)rn0eI!t;j4JYy@fsfH ziANTJfz}l{N;b2sbBPIOyxUu_dEN|esLriQR|5!uG+)DCM#YmLD74d(aYhvtJ6MI# zXM^qYqtx>Y1xoX-Pag#Djjuj=@GVvC`-|`4;YpfyA?MoxbQ{~q0OHg+6s{mLq?ox* zS6~d9J4dB)jS@7lGeCvxTXSUSPvbP1R_M=r>;mF+QbHih(S`tD+Nyn;Ke!EbC*;6^MbD-1S zp|r|W@E;>PC5e7*w6paC(C8s89{)2im)xNuAvR2r!3-kf9$bh01elC1v_LPG!v{*m z-UuB|m8aPbBqZ1Nw8vGeR5v=SVVXx(9be4>8|zqITM37rgOcg&$G@U^UhV81dCyVs?8GR?d%MnhN%?+;8*?XqXAg3WG9K%*xR;l$v+5WfEYxR`o z0=suKYF%>9tiruDEYxbz&RgkaPg=${9Hc$4>M-kQ81CPF6w&|VC!6=l!?~hS5rOZ4mNlb7-LrI0N$*LuOEx}p037*C;Q9%>m; zSG=ZebJ^}%5SW9j(_nm3y?CqaX%SKUl&_)pvN%xPbL!%mK-$@-`Mh^;rqmwI66Igv zFjX_0#|>Ehf{AY4RnM^yO1dS`5N~X8XZ@BohsHimd7Q0Ab~suUv~p~s@(f%EQ&{I~ zudh`DS+BQyD#8y-oamRMiqNsrKf|Poo?407V#hbCt{4uog3uI=1K6JL6-J(=LIQ`!3B&T!gHCe>p8;x-ur=bJHTeT z?xT4jQdcwB>q{2SEn-X_)wM3qfma?%%d+yv+bi)rG0GzG3q(4im zCgx}&IvBGa&)CX?6VvsS2o{#ceo64FEDEJPx`y#$sh0NcP<}W5-#Nnn`oZf&NK5^= zw(UQxX#J^eX(O%l0H9Q%wP_O@XYN<8I%Kgxt}Z+eUZkZ~cE1oIaN(nf{57smkp^dd z9SaBskOhQl;UP3VbAwLhdc_k2(L8P9mj;swZ7O$-7G2nimNZpO>dTE?tdr%|GJ2dp zvnX?Ua;^S=87>ZZJa!;ktErq_M1Gmbl2%n|;HTZr>qPlS0r{hh zbdlevPN0(9!eKk^ThohEYE@!+D1yWe_gSVpM?N_PsT16xvxsEjH$KJS0u`a~+#;$E zgs-#H;{v0uG`iw=^Vkjj1Qlwsp7e)r_UZ76!7%TY&r}EF|9{R5H-iM}%V~Z3@64tB z>FBwzl7Lwk4&E>`sb$g5V3t7xva!;?xH92<%3zgw0fd+C<51zR?45n*E{)SwK!c0sL zpXCd;#7m~aqbkM14wBme8HgTtL&^#<6wHtBgdART03f&;5&wF$>$td9pFr#TOrJed z(1@%iJWFouU(e9Llg8Re4b>mtIQ?NLsu!NjXyyyeO@Ns_A(X?p3#o{$r=!g$}Wq2xOmX zkmMbO@$$tyA$JF8>#1eVAp9co^(S+_0DCS&@CPwISotbnuULf@EH5RHWU6RX8Y^+KB-@8_wbpUdQP4Jmn=Ly zRy>^;&XNQgQz2AZT&9UP{DK%zwtm#F;nVc?vNCl(D1Y|{i!2c`ewKc8TdLV}dJduL zO;Cj=(H?BWxqpK>|Ff!#B;B!+*$Ne?#d6tKrKcvao3jyn)D>uJ36j)(D7WiMhigk2 zIlM15j{WRKcw(a_2F_TFp2C=Ul^6M(c5RDUi{mUAB_gv=qHwYi>GDS<;<@$DUP4wr zkG&4H<}}D?W;gxhmbx`rLCaUN_TFWqsz+hEj&b_SX+vn}Ax@1GYc~BqDXPC$U~%-H zXD#QOY2kSZcvrH(QuNOSBzF$>EJ69EZdq@dP$hUa5!^OB6 zJ^s||7YE(~8WT@{k!hX>BJu!7Iukhpp4vTv ze&G5vcaPLU#b*wwb983|pklNtIsbB1JwU5aNA{{RS~yS8LLOj8w$%~7QH|oosnq#z z$CLKqoBH&)!z3};>K%UVjGFgG2-Fl=*Lh`ugG+9?tJ%X60MBXePs{Zal8Gvfg`CUv zBA~4Y#WP0PUpO-7gf3WldNTbtZ6ER4nG;Bf+x&zlhTf)INNf>rzWr`jd4laFHEhIg ziUMP5CpIbou9Z0ht4s}hZe)*et3dAP+#X*AjubP#iiAEgpAG>`(n-|DBpY3)#|)&D zm(XYd?-K>T&Ro4zr}>Qh59t;TO%QJ@aKUel7CV2^etm!#XsQk=Vb~S5?bC`Llp^g! zyh+GJW28qCk3SopNz|?h<3_dDFP#c|pJNAdhvhH)?PC3vVf2dB?(W~bPKbCawq7=e zUVC6f6(tEK6RdPDUK{Q|bLZF~$h~Dnd(jS7Hr!U8^)1133jZ4Tpss5-lZ~lpxE-iq zw!|z|wwAwejmLq#WPARzrLGek3Tk%*L+*bcDExVUM04t%vVi>X;ZhcpW?t(rQ)1>7 zgK1{nMhZ@#bZG(1>nd43mZBW+w%ZI;#wnX`Qj#{n#qN3uW0wO9sI{CG2Eftl$qqLY)SE`U^Kfe%hdzV> z6im`xuIk>j!Fl%a-k++=e^fyk(Tj{ZonNA8nP$K-Fy4l)7G|UYAG8!#9u*fBAP@T5 zuf%~-35OeGgfROfyu#fy;^!)j?V-~ucg(h0u&iv|PCVHJ=gq5dlh3=K3Z#y{8ja5S zv`X(L2G7wq=ALX39+$xyQIL65bCs)H*`anfn>HOj^ata`(o%7xPZ1(Xpuy=9Q)eR` z56w_|#OC%SKOC5fZ{K=)Dhatj~KL6BO-C%$+!?`6Mp4cg}%PqWIY)V zT#z{*$QgDWjH;10rI217G0vNlQdI|c9{5*V#LLB6K;G1l%)viy*H;PYds^(lw{h8m zAnJi>nA^|ZSW*yP;^^YX(%@Pmk+m?L9+I>wlPP}#*Y%F-gP$y}l(cbt)#EeVm{RSQ zk_I%ad<+|V)N%d4UDk(RBLHusZf;E@oFPi*!7lTa9=1h^X}|@?H)%S=kh-FZ&pcj4 zGG{<%eIDw`jITv69Y7cW2OQgQcM_kd?5Cm!(T=(g!_90jwxxA+36fR5bIu09x4}tH zWbDIM8$h!c)orbHLWguF~ee7!q1CvAT?-f<3yHRXA zkVJ`>M*pcO`O8@*zQv+RMLOruM{`E7f6Zn8Xse3&+k}3BJHA%GenUll7t97Tf=)pw zTsz)f)-3^ssTZ%^_K}k(>IH&nV-n5*c)Sk7!ID&!^lYfRBHDF>*vaAbXtaprio_;p8r;vgu2CpcTg(&Tax`7`m}K-60XE-^}z{$w_c^7NI8`>LI@ zbJ*t-m`M4o%9KK?q%QWnG-KvCf}S2@^%FJ+Oc32i#$9KW3mcNzy`)Plb~C}MpjKJ@3t?Xf+n@{fo&6!+Udu5^wS^j!T_EE26x zJEQ`woe(;U>2+T6WDx%4%t_3!xt8`wN)Ln&tuxdTyFVlbZvR}nAp^c8s3K+F*#e!< zp(z|yEyz>9G`Jfj+8IS@(jEQeuqhL}VX6((Jl^1!y&9GW`q@b#oVkHnRp~71E#|^1 zx(N(!-5&RrVpt!_Bkr>t-At7&RJc%Q6og?kIe$4Oh{lxU;QX4F2#0-b^KH7dL*!#w zXk#2{qx9=bsHYPwC0Bh8STV;yOOZm3DB3a=TL znD=hwTiyfS>Zo8C`rJeyrlLH@mOdj%<$0dVDjh2H#v`PN;Rlyiv8NEncIV`&8%mRj zS{dl=e5`m!Xu)O~=2I_yktp2&V~h({QNBml=s=aUjkvyHbNceyUVcWTlK>aDy+^Fg z%c12-=fuov2Ct z2Lb?OJ8KYbNTR)k8KmJmrq4R$`gzgVh^5MG)ZLo6UM4GMbYx-#&%n)8!5V69y3b;X zw84kO;2}koAwo1U(smHoBsK`FD|BtW8xVWlJSa03?Zqi8B??6Z%RPT6Y4(e=mnW}2{I_G#X7KO=v6&OS0TE||c)M-Q z0j3QP>sX0Q1|MRWhj3Zu!?%xZi$*H9B!$QvFuNZ)pz zQS}}x&)LxGqKtIO8+>iazF$hpB|c0I15YZU?tRjDSy{8e-1scsAXS`6oK19?0(n!F z!OEp(B=4l?Oz(eLsSxY*O#_n3_P-5CZCA-@&kRSL*hs5qe28l$2~j-J&)^QqfI*A5 z_|>q`_XUy6TYGSh_IC)V-32-VsJ|rm)hh2wFF0p4?VyKC(fnI%#0(n zPM3C)_-?G;0s;6jU2nEj?IID?Y}BR*o8|0~MZ@VIpSs|1JRVG1r&~H~3wH48YC1s^ z4M9I?und`ly%wOkqH0M3LpI-o?pd#AO?*{dj;&*s+zfG{QApDPxL zW!!@Z2f6eNA~|eQcS6XGWQ=edEbF`Goszc%m7mp26^enZPecyCmS5bDOc-AaV!0aZ z`+U62rB3mH-TQI60|=I?z~ot^?L6 zG@7H`S+9A0eX<#J6k4S}(@vK3-~y&7LN!v-==vsTR1{@7eNlwck00q9_IiFRxQi_A z*RflFxUf?8$;18n<^5=SW3Mknib*F$3ArEq*1$YdSG`DZQw|MC^Ba56CDZR!(g*Lv zZ6W`JqyJLAf4^;8=A*SSo^H_*u1w}R6n9w^G3|r9R9aQ^Afhz4UT4m$T1eY|xHo!S zd52e7eIvAlk}cWg2_2rg`jx%-*0*WuTvlK5>A922cLSN}DRh7&@_MaJWSdl1bIDC6 zc>$U6Nz_cmAjY>I?H90Ik)O*^{=8fH729jep|C@oNS){~$FL*T4Ki$Ikqi< zI=3cHjSuErZq{s#-*=Iu)-Dt2uH2Ma_8rF9tOPZKDT7j-?yXeak>Be1bkVH*zZI*0 zpc>}ZM3SiG0^8SxKXovVJLs^X<%ZO>9?;SRdeR|!gd?0e)|RLa?#ZMbSkGW5hlRfm ze{)5PetaTb9rNQURLTEhaD-E{SZ9&~nPEzVs|k3);ky}d0@u)D{%LreAD z0_(X8Ly2`dp`b^MHR0*CfN+mfcLB-|+bwS*_R3{Z_u7yGrzvbW?-F$*-M3^B_*XhU z=M`aNuLK6NDc?L#XC4E1NyZNB>2nquAbq7PtiOli84N-5s!P~9bOr}Elq6vl?cQPEtQds zz?SI*2g*WNFk|l>d*!nS<&8&=w*fJtx*mW-KK22W%FB`A?MtR;y~C06xB+Gknde(} zCmEVD9(q^}$LIhJf>Di*)%P{d&6qYP0>-PN>t~<}=87ga#j~PM%~v?vv8C;=w^#v|1{NRk7YM9=<&m^z* zi2T^O^GrIM;Sqm>7>N}+VkjR-LyaQ35e3J^nhUd{71!wbDPBNCI$@B~uuGXYKUFCn z_o|F7SRNdyzi(W}XaPJ+oQ4)9LEzn(+E$v ztGLosMKz>8OQ>EU-B2_pv_bS-)ruQ6=P%PutBzj|cR6-t1{}DfZY1qQRuV;-T@*!L z-`ficiE(0aHa+XepBq0Ch-u2FLj7SwRweYz^LN1>x@4<2yC;k*+AdgW)xBQ$b{#^j zKke64oEHCU6) zUlwU-Q>*+I8Fe*v=#C7`MS(f8d{gIH9tdwEZ&P!BYfVHaTvM^%o_k)1O@~d|$i6b{ z(9Z1H8k>A99g>Ua6bxZG=p*{*=T=;k>#Z(V%VpaCs%6dVK-9P6as#uJT)}+5! zb_iokgY4&yF$EY z0m?WPw8%W4!CC|jM?@pFxW#kx_rRhD?Lm@`u6^zi=cZA+!9*5 z)c0ksSL{oNVT-1WSEnz+*THV5`#ik)iWwiqPG?;Y)#bPoq7 zEsgl1@f%kr+moo+Ko4Dxe%~d*W1ryA>sg!bjc>L>2%~ZM~(|vA7)s&cX4cxq?zs1pcK+x~7@^{6)s8I26tw(z+lK$MS z&xF`bMV;DwN|KZd>y=u4I~=~>hrH*+=`k8{Yfy6(IIUIUno=afzyeyYOI%@1+0T9k zrO{e$O7(O3s82fL`~3EQg>T#5LGLiC{0$xjQ^)l*(8)cB(RDnolcKIEuwtpt~;F=XFw>?#$$H*~KL>yAln| zL|-5T?S8yw0v>*El==KrN+yx1(S@{=YZMmJsM3Z+7|DEgg|yr7(}Pt=A3U;J%I6ne zR$-|Utrhuf?9zC>-G8f+s0Th5wV8L>H!vZ8m3Ppk6j72C;KEy`m~pWLd!4&fZ>xMP z(UyRmi|;QVNvuR^8hDL_4S#rQbyzLxJr_HC3Ie$jTCh%8FTeC0ELTNT92+BP%G_On zw)(l?eh`yyp?kc|O279s=33~8nvB_s%ZA`JK6uo40Vl|D3;!rI0O%TDfC>rv?+^aR z7b$vQ|1~?oEteMTiAXrnR-ghLRFq8&!CBjNUlHe>l9lMHk=}lW_S$qD)dK_@;6j~X zY3jnF>Bw!@&?0ew791SgD*?iL&%on2!Y;XHCrzByRNjUOpM0%;nQe<(yUjpt#c6zV+|0(O6| z?{{zliwkPAp)*n%K!=JhGs#)-P@%<}SC%D7+tC%k>>o`uWkTM>YvK#DAQPpEPb+Na z(l~w^*CR-vXL3D6KdH8}K`Np5pt3mscOOuD#J`UHwq6?k3nXh>ar{bj^3J zvANof@XE(KUbFvk&3Y*opOx##I`;^9$!y8@>4w|n#5*+=jyK*yS(-nGdpsj0r0=en zK5;tq{mRTgRTR(#9+WOhcYk7wPA_F5lkE<8>Uj;t)|$D<{7ax&eVRY`b<>4RS|hDE+2-w7XN5Y+Vnu5 z&)6E`hfW{fo+|fGTc>?-#^;tomrQdC&HzlL9Gt}D&i7+m0VDk!>FGEQ67#KPXhz8u z`)5l>l^fi0unN(~mpBvK80C2=W}b`a6jA|(*VE^gDh;*(F&p-D}Ep^FG%O8 zeRxo4(iF?peUASkb>4f=v`Gh}!`SU|XwAlMXcq1#+}Ua^Jds}Q^3EmGDU}B()*YOm ze15YF#F`*Y$^78`KxXLu*I!~YB3n{7`7yT>XC88n_VASllaJFkFm>m8cGmVQSm{@6 zV(?a5D6Vm~hrqmlc>#1y7AnXX_4jj_xm3pxv}Bt5x)=^1JbDC-(GKaYQRfQBxitLtVltt|Rqq|BO zhdbv5p2w4Rdj=Q3sU*$J{&TM#h~<8^;EGqYMX@?Rs*P)?Fxi-pKUb7YlCiOH*B% zqurDl@!~FXZ*K8evIrkad-#}xJYE2BZNgW2sH2x(1`6j9CZ-wt1pz(WE_8fAl3)2L z>WI$C9Useuqiu%g!cOo1T1mpryIs)(HeR zunUf~#zkcHxD0D}n{P;oP1lVp41EeT=NE8$E5x3et=I+^JAtRkWo)Tme{&_%J<6UF ze!s?(%m{v7oY& z$ykxi=UpD}uPj4`qV||!t+@f;qf707J^`|Jqjw`Bxo|DZ=2+SN&%&|&Hi6Xrbvexu z?rB0c&xeveq&DLc#I`(yR&%nL`8j*QX>g6r?R2)rky6O6YfJSe=0p0ps4xM`S@O#T z)}K5FNB?2?9~5{0!pEuev(ub+{rIRjNbRazAc^&n-1+u4F^#VrLGdXpKtYL{wh`o+ z@^@(EF~TyLeeWXd{tIA^LR+@zds_2DJXCfUU%U zai`t^Ynf@i%n>iE_~C; z<2ci8Tn}sf%Dt~TKU5EPCPBWsQ?bdhE9!l9SW-zXB8}F}Y6&@q!P3-lx@^4~w0VoA z>JU>ub@X*XNl+hJZAFjE65QwPe9fk+2W*#*`?PmBWkB7Eu~bPFDUVzpRP9FZ8#S_c zMX`vw7R!;u7QOeDUclX8rxhK33u`M{p2cDmhwW|7X_~7Wx|dYEeQR2CSBlunOvS zO3!jXFYhmqq#6DdpAEMa!o?p_y%B_^HawO0iUjWvZ`4MQPKxyp^gS1Z6hPsN zGd$-f6N;*K(qd$ohL=MBM_TZ2K}fH2wO4m%cRcl`5|{4@gW)ZD43H z;Q3Y$VORt)d)#SP@4@E=Bt;Y|toHUdOrtu?qSqSS6}Stb2(Lb$uEwQs@Q3{Cm!Lk0 zS!AGUw;wt!^Wc}`xGGk8e;M>AzNP#yZey!5|IA|ii?L{1U}uAuOgd-J7(tBhX2Pp; zk>%TZC#P?+R$lNaiSsqz!9O!Qywrz+sys*ByWbaPOZSq}AaUQ9Q;T2I*FU1<5(R9} zz)mf;yCPWP=|kR^MsVZIYic=soVGOw@sftFN&7;Ja+#V1;{NB`Nlm3eMTAef=;2D8 zB;Qo{4-vccLuE!Q)!{o@<}~4(BKLha7EJ{=$L)JEW-&JhU>=;(hWqB6%jG&L5}Y9) zV;`nFH?T>PdYE{)?w%l6p7)j&P%n?lnEGs{ZXajvy-<+p=rSfy46P-}duT4pt+VZ9 z*J(}2dd0_OG@WZ%RKvhh1j_k7a-eT1Gk0m)f}H(Ns(;Z2gH)6+gyj)se!0hpwmNmO zzC1XP6!UC8eY<`&{rkASc1?4VxK(-}(VzULh1eyWW zxj(G^JCtJl@c6K{HLZncH?Hj)rMUlY_+gTT_w>I%)3s{c^^D2jK-mp+K4f;J4_;8=;)V@GsBvPmDqo+4(&+rCUM9;IL+PDz&*#(1_0c2mKD zmo`rp@=MMF*n5s^B@e@0{B;zUf38qTFa{Vuk1W+>OIVe7ntQIR?LVk()two+`W~rp4txq>M`$CwR+>r}K%{?dYN8 zIP>I)LvyvCDj(jzgG_1GqjJS&YGJ?n@hflt71n6^(*gcXsB|h zqw^yBW_N2PR7Y>dO)2-;sCV(PyVMOmr>2bCYv?!WloeZ=ct!1=G@Q_c5EvB4RsP$E9bRJ%rG@wcpHfHK|_@qG!pa@r&2 zp!z>kRYdGb!1zJ4c3qVr=vdbdaaoPSx)L+93hhY&9xN?^=eLhJ9Sw0_Wzm?qmYh;E z@9fy>A7LrM117?%CYE1ObU&abiS6J&_6Mr#0lt0P9M1UJT4-E_p5D45ftaNh&i` z6=ckZRu^1_7Gd|xqiBSp2``4@rZHL!7H24sN~bZwzw(_nFpO6A3S@|kk@8Xg(IVXi zPVWa6K3!+QPbhcAu6Y8N$>X~u`!(1Oqu$pNW>AvTvWyCk8b?C|a$1K0gY>6G!Cf({ z5Gc5KwRok4YM=|D`=EGiojiVVJ$(BAMt6rdhXnT&@KU_D0 z^fezpe~$HD*FWQVetzTeX`K~05&6vHzDNnz?s1ry`g4v&7+1NU*W#>4@`9U_u9?MRTx7e-vBN7F4lyG?0yw^GiWTit!cW)_Tq!-`ig)l6-3dZ4OZ2{rwtP@nuBSaNiGrSOk=5lkq z83>W05}pz8dI9#4ZSGa__wAjujl%qGOV^C(5G6HZ|%pt68cG_nv{7 z3#OzbQ3VJ=HScjTKADf#|0{0tPg40~5{=;B)6-J;o+GjE0ZLvp+4&m)^A$@m?k-ap z3TTxf!MTyfROxVXl^(j^Duja~64v2h*0`Ow{ccK`krSuO=8r_50a4(POQ-G(=ID6Z zYar7{!Jmn;azt$)e&G?9@E;dh4@=KgJE}rF(k1yzSS9JFeS^Gs(+wqt8H7Kac zTSan{-EB7V$JtP4{`ebH;P(=h<<8d;HBN)iy-knjG>CBmv|aAgh{uAzPNrHpPJf5k ztUGG^o6>T{iiWHAr^>)0e!9>=>qJ3w1LCbsgkQGAkc4%~jN^xL&%Zqe-4autJv%+( z2Yri8K0O|O8Mq`~zZ6^Tf3*Gm$6|CIVWyTpbC>#Y+;2fXSneWeEwZQ}x`c$hrhk1~|qfIZY3aZS))7tIGH4N@<&QpDTi9mG%djnCl(NM8qi(VidGoW1|1 zxRPIlEr#NZ zOHORe#w;MNEAv!Q*LWMK%Mw`Y4n_ttmlUhYB+V`|#9o6PKOXC3qy8KyYgYPlvDkK# z-CTs#MBf6`t#i%!?oe2DmFbQ`HZPzr&a>8_H~|y-%-)NqKc-Srw5uoWjple0&$zx+ z3$E?Kg1>6QCegw0x*qY3n$Ryi*qho#i=%lQ17~jepu$s;ob!Ez z@?1-5*Dv@<<Ar2(XCh$^oC#LawsYT2f1IS>!j$NgL2Pw5r z8wYn!zu{C8cDg}K?0Dui&z{h7GOx58;$~cc zM=ZRO*=HLzt*9cp1F6NVZ)CFfB0}C)%0y~pE^5%MZpjBpoGiUynH|Vx1ITFGu8lU8 zR78>LYa6}e>ZGHC4hH#5j=;2>0YUE{H3xssu)(}e*48R7?mM-#sn^FU-Lz~oM|E}Y zm#{ZTyP(C8-j(fc=vFMl){Rxy>3Oki*yHhL{%)W5OX6y+M(wJ~NrG)t-99W`Im69Lf1k~h&x=~X`AFBD<{PXOVZap+1Ffwl43Zk)=hZcA~fXTLPn z>hd#g+@kTB`CKyG0oEsWl+n#qXRAQ}196*-heu##GuFVqAva#^OaXEar_72j<`27v z;hWPNi?ZI}yO;NTov_4*z;URG<7p>e>}cz%ENwFcS(0F5a>NG~5o`zMb)3nXQ5->?bL!{ z^Iw1y#m59QHv`Nr^gb56tdTz_5os+%tvTN{|LEmY$}5+^bJj6u$>rRh0kt8v)aiQn zDoXvpkryQSfF80-3*9m}pz3y6?6a!oRm&maup;qX<X|DxcZkTL=x+HD8mw8B1l+p}h+dEAS z79CgCGUnv8+#&UXu}VTD7Wdowk3(tOmBeW}G^iE@5fAqQ%aV2t{G5##__9(7cy`7L zO9bu4FJBSg9ugmQ=|$=O;wk~RDwu#z=X*SBmbleS#GR}T!}q9~_J!T1XH|HizzrMg z&L~0Ix3DlY`%JDI#r;rA94<00ntpw=Ud`(>kv1G2iPVVMxy9RfZ-(N2CKN35n z-dMMOeWysTQqpXbD9IyGi3VG<7F@dyufCN=uOflR+Uh29#g-u?#**JQc2p>d(>C>W zwAoSSr?GXM5%LRv7nYuy4YrZmv-8|H);Ek;z_zO2H@4yR*G|*OC^KFao7b&bPnzwh z56v6YEt6vV+~)70Umu*Pj}Hg?T2Jp=&>CT6e4_eX*YX^@1+Cvov@7WMcK&@wJ(C9& z`*_QX8Hxjs(qH}2qv2X!5zP=toz6g~aNN`f(u~ir?I%MYNV7sdnh7IarUG+@Lvjv0 z`{d{23=%NHZhEHpyFOU&0gKZ*Sg@G8xZywFh)xK;>5w0rNlgjzV+bEdI|HOtI)hqZ z()Ei(ONDjz-P+lYWQ#6O&rPI>O*H)m9{WgSavvY3KQ5d}}EZ#nFmP1Zz&P6HXkj7}IUGw+pQ$t!2LM(8OXz>$kj{20{(f;-ID2kEy8bY3=M%8~ z0vlS&3G8#)xnCcS@_qc0$_aCy1O~U>mDY@fLmBX~*0OYHAsnfd$rIbvjGKzC58l&gu#i!Si9F_DvrCX$d*_m896Y$z6L^Bit$ahPe}Vi^nnqArc< z2}cIA@!DPk+zx%XpTt#6OwE=)nffM4NG5c;JlIjNQTeJq$8sLOi5W4vu&v**66KUS zwj{!~)#GYD+p$8uDYTvCW0uXyKm9D7f3V8gapEggr4eQYV=8V)v=`$YN<9(>?REU> z+7fS2#UIdxqO8vd{u??hnFQd(k)TbLHhvAHijw5<)(3hW36$%jz*e;!z)K~1h5lS8 zo^ttYA9cdgV@t*Pm}{wd?X!f+7oaiRl~R3ru=*{=KDD5to08gpr(hMj&ZQ)^mY4jpgkOK_NqJ6)2mcUN$SE_-tEvB+!Z>wfB{MP_H>WGDWO zmuB&$4X0jE-4wtbw5QV|cm^#L&L0?Ad}TW*x5w}tg`SlO$9L9*Hb(Qj&@tMjFF5{^ z$#bkoxja;^E}q4FaRszw=I3N9CEG+Vi=XE7ES~<>=O??TL-P>o zR0x&z+>)ukwU1aw zw#%bq7uQOVvIoVq(9kYIcbek+uXtW#q zYQ!aU+rN3oafou+@t>%noNK z%Y9fvZ$R)`rIjn_F8p|?C$U*gInr=^uOF~Q;&pk6VSpnN(-&=_B2alj{$&TR&wt?k*X{VH1ii; zv>yA+4h-?%D>e^OdekWTrLbT+p|n2WI}s13`KbeQ-?oSE8}K(M)yHV!dTz&}DFl!5 z)eu$6&WKCrcq1MsOYEVAH2JHxf(bFt}_yDpz(# zVA)xBRPe-`Y2ZHp(=CR9%CQ$!oxP@eVLQ}G_PkVPPfB(k_`;(S6zM;fEQ;Sf+&9gJ~GQ7IW~Gw5#=sxqz15IY%NkLefc_4)WA zn9gIicx1TYP4u!cTe#TXTTAK`#fC9d_Mnw<+da`PAygX3GV3XJAq-&B(OdI0UxbE5S_Vev#K7m0d zvsDw%^dA;BI{Tp3MKoo5VjEEHqSo!a)sN$JJL!qr_uUMqQ0qv3t*X?9$&y;v-roTw z9KLwoNV7Pg6gCr<9pG3ab=npj2 zrzr**Vau%M=Hlq}{i?)Q@!cq+mQ$m|D_DPWFzSHu54V`-HiEMq_?tZmKBQ=2;xJkf z*34bzfqq*?bI&1jH%+(%${iu52j3UTcbv>>@&##oA+)j*0d;VD(6|#X?8%(r(!0r( zT@8s7Nb|+_rU&O>k2#i=L}xYE6P2v}4XTA2oaH}~o8kq+fJ>o;=^Io%zsbgy|i_GCn8z9g2}wWW>+mBgfnFNN6Bg&V2UJWN-LcI#!X>QU!kjwUtb zXv>tomA9JG$F`sAIl~9QCDt7o}6WdTv?tYRl7sM7jO(J5l=2pB(M1Q%MCamTtS>-u5UGh&eg= z+NXY9`ux;;aXEHtas;w0L>kmLFKF-n`%Gs%#l_?l5<1bFlAS`X4j-f+zk_cqtv~-E zrCsiWJ-ms$Tg~m+srZ5xGaE#&{6hrlbW(i#)84?hx3(rq>Bs;IS~Sr5OS;RL&e_}y zF3c<>AeNPtHRBiu4@wtfN6o@3ptrVn+V^ntkH=<0M&p25yP)9@%)lpHXwz_yyL~16 zY2Ng)Hv@fK0nN@0^U?Ma8!DLRXotB7DY|;R&@y1>3TQ6ZCp5dkTSa!|_2jbz%It|V zxb0jJ7y~U=9Z}fKvoMIxP5aioR@dlPD{kJv^?Rv}K75i7SFP{9wkKToVbsSW`Ga`X zm(;Aj9kTg>mwhfVZRdrwg5ytRU9F>4u{r6H?X4Wf%-wTy^b9$2ChzEBT zyf=eYXw!&7&OQ5{s(#zpow&YuwrIf8hsZUMjWd`sd9145i}ace7Vi&h+*V}T@opVj zY3Y90?%WZ$fan~F(_ef5u$QT-@Zu1TiHAHS&~(0VYQ*b-($?`%`c0NT|727@A%0u4 zGWL{aMVFu{0qQHBwCvBHQtN?V-Bv8u5PaAK{l`>IJt| z9M;^%0XVgHzKu5^KWy6?+R~?mg>(b9JNCgvU0jN?1)dav)%GXzHReJwfdlRa4kgiL z=^Uf%Q%@=xpKyQ$a-9T_Q{GN89(L*sbI>-I^822bbF?NPBD|#$iP*9%2xa(7zU;t+ zw`YC#vnTv6z(=k*4tGO1#1=7?G&deP-3990Yn@~88)sh#<%v7yZ37;~C6%y`EKz&X zu-&t}q3SSnnUz`Sx|4GaFmnQdOyEc@xrBI2O3S;}SW25-YMmMH3}|PXhiuLMrO}ej z>p#Zv2-V%?xc;2NNFYIhZJxlQF4AkZTphX8u#FTrGO}>%=5m*Fx#hNAyU41LFET9I ze zhVz5f&XlKpjwV-kySYg(ZVngd|3fj&b@b2@yPqu~1|2J9ZmIa8u{)r0+M)tE0hF<$ zLcYD_X97=xODC)BjWKNk4H2n_<=L5Kec~cN3M(N0IR5qF0vpCvqRG$&^kZ-%ee*ajnQjSJ@Hq-XXBttZpS?`RGVe!NjqXq=I4Usr;s zcx8B6LSJ3cIKHwwUg?%diVL2^X{|;I_N#{9+PUtxoFIv_&x3VGy`4f#O|)_GZl{sj zDzUCk3>`6d1;En{Tl86vrJHG^-&PSg1tSqp-8aI{t5My5O&0w#AnYD|d1 z4l`7|=P00q`nS#KflMNGm>)lL%YQTsu{zcYMG8R1e-F}7HDB);rigk(m4rFnrN~iF6|c5-Hc1*Q*$1W z+gj&v4+NPu?#2Os#t&8}D%pp(1rB_L! zYF&%GhVlk@Q+Gmdamq56!xq_%Z$;QDF0vc#gt&bEjVk&JKbQq1QPhB0V*}ZxVJDWe z;((4fLsO;{{rfcX2p->kSwBAfCe;}rRrIf+XyOVJj0N~|T1z}hJNbL@VmSDM#p-~$ z{+zhzJWu(1+h?~z)9G<4 zuBTyNxr`wMvLbd#P_DN1+uf_&XTq5k?C!afbDl%7l_Vc{ z^96+T>mIa#M7SK~gbv{xO~J&S@8Mo;G9UA{vm+ettJNvsFy#fAcv|Hzy}U&=Ut0rh z_FpP=e>8i6A7%q`Q#RAbZQc?dh1vej1ElBQNwf73)1X^%V*SvO`~=+84B$T!xqyDA zCF0$Y927KPDz?)?!NcV&wrQo3w4PG~@PIYB;6a*O`NO`^^>eao-W%W~QXhspUZFL!!xdw}+kT_^q>wWUELqrDI9m}l*k zGz?n)3;9`b2LS+<=}09@Ljo861S$@~e0ne1^#}#2&UD4q+sABt+F+NyOkKNKg)V`= zfPDf_xRIIPG`p3TH+8;v_XRQ-VLSzWuzdnO^5wvtvY$V$O}Sy5otZav5K(?oKa%0p zebRkCD?4CwKFQc`ecVbfN9wa$0HqDom%#M8bV^hUt--#GWJ>tW0YUlh+H}Q3pA~w; z1iNZii$N8?>a8zTnWP4O(uvVu>EzPRI^(j|x4U0WuKS*Bt}Yp$eA~CHJvm6+AH&+q zbC2z;x*V{d`Hlku9e8jD{kmF|8+-jM4}q&j(SK%m0q^*9`PqV+gF*FH_jce znm+W?!nHFHFl;qsH%ENb8`q8Ovvr;h7wPN~h6~(J?mdVp_qolL5 zrGmx#+i4;>FH@8P6hC;)A@|3Z(^68;zKrzUYtHQH*{*$1f8^)dt7%THQ=^r$Wp5>2 z4R@-u>h`M^7dygqu5SK2=6(c4rEVt+d{5UVUl4#hcFaN$2w8 z=@+arRO+$kwt>Yq)2{kn>j>5l4R$9TFjRKpN86;rMz%@pB<*UU%0%MUc$wIwYd81O z#r@+1_fG;)=DTQ)V9U~^^JRenKWi7q zFT)Tz$@NMS*n-B}+(m>aiQQ?DOfAg)moIWpkKAU>(7~mBtQMr@AyJk!2i*|j8Nwbd zH2D53eEc;C_;c+a2!3fO8cs1cFnu%`(1E*7pRuX0-Z@#Wb6bul3Ncmy=Ix$aE3luB2V zo_Qka+8(kwZ5}fhr5@!Wcoa1Zg6X}&#qV!B+u|1NE9OS?>^`wiy5MeDh3!*iJft#? z5#Nm=UA`oCwzlol&=daYxYh;T_!V#D>^OS>CSWgL*Wl^w)xAW+P%YqXSi2WjWQiuy z@>-!b4l@JyHf-0AWQJL+XK7|cl&>Ca%hT7KO$g>Ue7J71eSY)}J8*+a3F$c`ao%UON8O9Wlwj@T z($4c%FCCBm*~CXax=_N|EAGUSt5YwLK58AV4gwpJo9YLY4<1BkleA}$t?ou;s2NMw zv?jm<;8d&`gn*3un&>u{Z-<3@ohyb<*D zLCF4o<4$Gec~~0Eczkr?s4I|hHIY=}qg)vbmCCxUp#7ZF8lCzzOWTOH-PKRg%d-7z zbp;$|JQQ54FD!xmjhl09KiwFUv%kT%muu}H<+Z9(&}9}5RjLync`A%F+FNpI#W?ZR zOz$uG#TGseO)ozpwUe;Tm>QUWX+$MZhAA9COku-OSuvSe8?poI=c{$yk1^xJQ0UaH zKf6GJ_b$?eIj~$^X7G^}%GiD)NXt;mnRiq+C$T6y7fb~3X-r{E8A-G8(24`hB?w!-9Q z&%d!S`U!*v*S2mcB~V6Q!+HXh5&+hLp(oPj>hrw>+M$FAvbAZvz8HqsG>mYQf+JFB zBDl=hhbVA^aRn3qIdu4EhV*QfaE0q7YL?(mbHP~_|3f&M_UNmsi`P|1wSm-j{}r@i zf=4f6{M6gxW}W+ph3V*4^4^0!|LqL)dHdGQ+|+7D9HtTb)N81Al~DqS(9A6C7(wj) z&qw04cW2u6`x?L7tV#h<9a{>2{d(p8uwvrfVs&emcMVtEY?X|U_cvG7-F?Mu zqSo(WmK#I4rUay>+eW(A{J>ATWwMA{@#_$($`u%FvyFUK)!3&jd#msd5c;p)^Ji8{ z8GWI7)W~eUjk5Pu)TH#i9exvr&f>Uv*L1Uu*^+me8{_1gTI|Cw>Hy+L(9Q!R+x>1I zcIlSQ?iWVgBu0T`?sragti8zD{!YjB^Adl&-_L-O0V&t9x8jRe;AT$N32XxO5=)zy zG9A93ywT&R(o<*XW*)x@=KLg=6>Kw8Kq-ske3#ZR|SEo%*PPk#jGvFH4;r zh37kO*hzT13ior44AQ2Y<_C2vC#n|jZ-#%U&g&9GZt>R9JUKd_QitwbdvB+YdY&uK zaDQ%zddjtZpHdyUjC08j-MaM0#`i}dFGx;+(^~!SCWkFN;uFnnl{jR#E@&zJ5~3&K zc1N>YE6eKwLm{M05a8(gSVR8p%~SUSp(qcst%0}zvwmOBVe*Jexf=3teH6XpmOt*c zJB0mwT@>T=JpEtwZvOTfC=)MK+O?E=L_OPt{XHRRfNANY_gsrpT!t&KtUdA~)+$ga+q+R5X?Cg<{k4NnG*Qx9BS0 zv;Vnk-jOx&5b$u}=_ex`I3k^&KFsG>BwZ$!+fhQdFL-Jlvd%g{S}uU+6+R}# zu8Sx`zQ*u^h#F=zAk8^*d0k)$X@{sx%pHElyg%(SbNV`%w%UnnMq@vkDoQq*{Rc&t?oD5yUuYea# z*)6WwE(Sf9XlH3qJNjM{=lr!zr!-Hy{+{hZ)FnZSKO^%$PySyoU;Gq*Bhy!~)EHw6 zY1%Zzs$`W5<^yjC;gFrYI3Ru(c1aB+W3(6;oN$8H?GfVM@>{kX9Hw)zl#dnW;#?>%8}!^ua0o!)1u&AQ zkCSa!b7<{ZRQO_MJoqxU+2Gdg?u$b>tM*+%De`S_X=VyPcupq0DE-2nmd*K3(O$Q+ z(%Kpi9#-hmML6Y|3zMcO26p^UyRr1TiBVtOs}R4`q`j9&;9f4Wn@)~=nM zw0-RRk3^06T4!NPSWkZt}zA05LMo%H80;ebl= zF7PL54Qlo?o4YMlf(!tZuzSB=1#i4uThf3g#FF(q!{Z${=)!`xh5(TK(;%Bc@;D6n znss7u5WO75G$wb6J_1k{vyKAddvJQd=M@(b>A913}v&2xxSylrlUK*v)yd*H{!;(`N?tHf)dh2AO zvwnj7){`V8tdk>JK$1g=ezl7r((6L z&=UeONjiiou8N)=?^&(;+hl)@7*g~2gA1T+J&9hi$YUg1xIo#?ZJKr@JE_r0(`!9* za!>57_+je$IK|0l_j3(Bzi9WJj=-b=S3N?i%UHX5aU5oPDtc-U6SjFXVyuF>e)Cq* zlkj@1nblrhp@I$a!O~!__)mu*_<^uE*AIkdX(+)t>7fexOUGPP$Z7hX{Gn)f;v7mrIxt9ndk6Ctqw=Nt^n6B(93qK(Z}K12aa)m zv7}jITIAo~2x=n%!%G}d*mm_Xm+8daxGxlQ2GSG+?O8Gz`V1Ugp2_Ly#*F&uxwvUXyplI zZ0o7q=V^TyjXP4 zZ#$rt>M~aeX0SnuoN>PujMP_5*=h#B`A|?bdRL-EW*@Y11q&^UKu=eVpV*%VRb`MK zv=6+;PNf;SZH)~0oz45j6ne~-q0XSjidEAtQeMkTGzW*<21lLyD{+Mgf+FMNu=Nk5 z%Vnp;Nhq{bQ!PvVcGP%d0k4s8F;ZyU z+Y!hl(m0}W$7y+?i=Nz=MW()T?-udfSY0idZs5fk7x_F^K+`Mu9TyoT0kz2o3fnNI zTrun0Kxn!{Iu&c@yBCzcikhm2*R+tA zS`X5S*^iTwJZFH%S5|LKLJN~!oIj`1$ixaLG&h^b`@rphG~}v?lN4 z^^_HDot1`Gx;O?luet|kQKuVhiY!_=JcY)ogm{Ta8iH{$^eN8vJ!8%(Q=5N=20!oY zFAGZci|Iq;LzFuO8&feGlLzqurKRlItn{63&cRmemtfBohv(1$5o%u{=-V5pftw?S zw2P~EE}Y4B25wW5z}P^nT1avO8rRDF1Ij%%oTq8A*Z>*i^0;KnH7O>A!| zxxU44>=y@t=9SY9HG&<4!gmynQr>K1o+PVnpK)jJ*ZK+K_Lml`kCehDnU<1|_>MRB z8yb(FkJs+7qBd)-G*u=d3L7!VTPO`6JW4x#PC2_(`@0_j7{Tn-F&{{csOJoX3p^Lm zs3ie!K|{ZFB-w<~IlH}rd;QVXp?npIqgHAZvBYWR%{mD;=h`#-Nt%-}x~=ndLpCwL ztVf#bJ*f`D12?zN$JkL=i-g(Dq-`bbl*XiKsO$wIQh*xzEZa*Y)F>9vq z`*^e+;o#nve5I?|Q0R&LQUE6O?kHP+SpK>3p5}@yN`7?soj} zi^WU?8Wv*pH)h)uq6nZRVP?y*^`F4uoHS@5gie<)9YPU2k;&<*F!b$PhGFt=_ktr8 zn@N=JGabP|a$!8Vr7e>oY<@dCkIdulk=wk2xBDp{Lj{@SEftd-9?gkdgED0qmI&9jz6SpW!G8(SxKPpRNXW_z$qVt; z2PO&NhPR@4;QS6D7I3 zZfR=q+YYl6JF1Vt*E)K`h^h4}zzcvno-|xh7w|>ZYOV{^=YhphjHS#V5S6|N?q;}S zagJg>H9`Z(I-E5|JBFAdDa-RKEP>&Vzi*abe$v!T9xp9fT};7s($Cyh!0oM?>$}pP zZJ>b5PE%e@J)vq)PIyjog`%jMg?PeKv7Bbm7eJ_G>wyDLU(^TG!`%ADO?&o=Q@}Rr1TZv`eG;x{ruxa5B)0&NL+T&Oo-VjzIu38x!(xuFX$#8G>3Tlc}#VsL~ zT8?_)hPF}C*7!+Qx#CD)f|6L3mDuGQ@*YP~t3IfZx}bIL8ve=sOdHe{H>hG=n`;UN zU+`}K_rw@pMw}Z~2oRzCBLNCxepwxR1LH^lX2@s0%7ugoQU%IsA3!dCv&O}ww+xF7 zfk0L34iJ5sP_06QiO0u94UO5B@+YbYGfMX}-`h-AHbsr3 z3vnc)lnCCkgBPsx;E{D0Tj@T-B*v9ORq?LA-Ut_y#s6+S9IOeS(YFtW=}?+*x;f#O z!=IN#MVUCtua(%uSyqjDw3`cLCkJrjvjtRJhm*w-!(f?VZgAHsU z8f8W#(k%E(U(_$O3B}+{Ywgkyf9%L6a`2;leVSmhAsK5L1l&y|-~KlF^lL{`TF#xG=#35yu;)Q6@_61fMQUT1!sG{5o0s-h`>o(;a#0q3VxmLJ8IYN)}jAi z8k|`GJqkd~P?m5skCDw;_siLibExS;s%cPfu7mX#S)YHANzR*BkenzkHltu=Xi^$& z)(kJ!Zl1JukzOnFFo#TLdc#%Cy1e{Q;eS(Z{<#BxE{G(XBq!!u^jfMJICPl623&zF zRY68aWmYSsz7GrYF-wySZM3j?3GrU`p2;@=Tu1{LpK`mlP3 zR8o-l&OOaIA1VfqAv+TYcnZL?S%!6AOb8e_=X8BwLnXJ>RE*QFgnlGV*9XsnOT#{1 zv{tya2jRO|L)Ezo*Vxvk*22ohV+W5GJqC$_GBR0O!Vn2(cd~G_;h7mq+NtXE(L?Hk z6Y$8qj+%%c3ht}GWW6E#Ob>~EX4zjd`ajOv&rb?0_GB(3xe+)rCqg$zE)5LxOB%4; z15ZA#S&k;C9+bsC=Dg3Qf1K-j5?x%zIFs^Lx_SUFesom+o|%7}&ZkIz=^1K}^9>3r z{o4O+;a>eF*NZF7l)T$1PavG~J z%diXvD5nH{R<>I(V@V!BpTNWX7b@mENk-eqP92$r>v`r0youx9^mka13oNq`2 zl$3CHZ7Und*Qk_~e>;Txvz{*a0uOK-(c=o0RvRN5LFMp0J1Uvd=SrIvi`!N@1(l76bcaI{saM}2D;}8#78ttCCZ1gX{}fwI2s<8Z zy9kpD?ym`CTQ67s0JT+%29$Tx`P>OJK8fY??Tm4*U3g6BaRsy|e&T^{C~64ZEO)@C zo6kxbIIeQN)bp2P8H;z3CeVSec_(Q50YmJNQQpBwU8>v-SHbJj_y$6>F)Nh@NdcL& z4jPA$UTKXwZ0Dz12@-|7j=^Lvzo-Snfj)sbq@^IzJO9^nw30w`Rq_uZD3fY zFVG-39&y*b0KXRU)v!U&GuTbD&5_iITuGbx_D(`u?}*Kxe&Vlt_xE+X)P?t)AV=M& zk-ssOn2bD7V@!{c1I%+wOQ|kAkC3bfJOz~L?mESkcbEfklytP7B)Zs>l)Kvu$*@El zYv`_N$K4MN(}{B=wGz+tI!`rN{_hQuABX!l#%By$uilFtTnew>p|!Hy#PC1hdNuwz z-0$h!czDJc=_gSogX46KA|JiAGtzb78Gi|&t-PHIZpyqE;lSB!IJ4~P zV20^1_7T|xKbq)B%s@N;rM6LgOgJOR$7S(>z!34Q(vicOCTIX33`jtM(50*;7{9U$ z^0^LzY7$Hta2K>?l@m)kDajK}6lpdip#yvJ+gSNfMTkL<@hS{@?Xz5F}PLFo94+(5c>cW%Y z9*WdommuF-FN?o#Wd}`lRZ{oclL9SZYP))i>@^JeX>R@h&ziVD-buxL2K65H!IW=f zHte&@%Ytp$AKPsgs_0a{ihq0vI z#xxUUx+oL1AmZkh=K~^ZbLRr{^%wnLKFU5vl+^LSg5d6Qt{li}eN9MRSRnV%Wqm*o z6sw0;tJDwESBwX&HfZq>v}>AV@&3tQg7%;G>X(IoAQQ0NB?knIP1{ji_8pM(R|g4V~{uTxESHV~aSZ zBxOvWh%_#=ydX#;J*dCw$kFT%ZxIX)<3(NQ$6sgxR9e$7leto5GZq{hclQ%qw715u z8~T`>oQw>AnVRQCPfcemtMa)%ED}xNECM!w)LAKry_(?o4h^hj7)Y^=lm2Ag?!5M2 zgF8gbBf#ywA!e}Sq^N*Y`HZ!JCXwB-VREspgk#}_kx>0Z=Z!f6%OS0pn(cSC@6G)c zY}X-U%z8}cE^h1ZPsPoW6rPzO3n8y%=Hv6AA-5j`aDlXldV$nY;nw? z$+q|-JoFzIAwd?5qeH28Kh;O{YXrTE6ApWQ|KCm#|8&yIEEijfD3l=M?<3lJIg@fW zCM(wNQRYBs5XJnI@zE*z{f=MWAUi5Sy>Fnv1I+-pn9s^@Y>rM@$--J{ufXHG=b}r4 ztue**-CRa_6O}u6R<8VUYX4Tk`+d9Wkl~f?rj6zJZZYT7o_8bg@Rpq(kmI3CMs%OU z9_YYG{``AEMgl;KH@}+uqFtmJSwGbA{_Vj&;$(l*ML%RrdoY0oLqJt@*)AYs8uFk* z-$x6<3FNS_;Si_p=7-qDjO)s>w-FsxkPha=3rLxL)*Nq#d1i7Po!r4xwj`7A2tkqO z1qm*Ki$bYGqUYml`wRWq-_}18*n9TeX)-5bsBEbcQmFw1n6gJSI~f6efbEL*Plx-r z|Ddt^m(S<_h3t6Cy%83lIEF|&$hYV*npFdw5bx`k8}kpFbvB0K7TD4liWABh4uq=O zn@t+*L;hX>(o`0Wey5yb6A%@to_yl7Fc6Fu?UqN941@Z zno1yB8iu)kl9e=UsaHmXzl4k*G>Db-EH32U$YTwxl@0*p$AVpxENdBhnFs9HSQEJ- zT&Yd$181r@qtvO39{zgY|FnJnydh1;)#)n#=omE_5I)1Uy`g=j3-E%-mdP={u-ce< z{td0xrzKG7VjB*?tP7G?YDRk!qFS%rdW{+7qeKvqTx)F(^Z|y4nZFn4RI43aQ`(>W z5%jbQjxHw1`gDwRX@C9eT*$vTSlJ&_YpNG&#(z9`);mO}2`Lzr#=Bv-2`uVc3cb=$ z4%x!m5~tVC=B#_|?8;#lJUP!dXzQeiA-~m% zEg*qCsUC^Sttc6yPQ1%3yh*l7uH;T1;amZbw-dOG@iw9&@4QVDsn0^jUiYtoj6fMT zz!T69OZ@$A2bf$BrQAZuAoHAE_z0_N@+}LUBKi8IyvSwOe-BzN&L9+Q-i-9cc<-_} z>=FVaJ(P0_f`|edw5v+`Sn469F_WXy2+1RAi{?0sLuc&Nm-4<~u+tmkczO%ucuRKk zd7bj;oz-^1ZtdH8%Lo{* zaHHx2)O28ow5hb%HPh~qxWbv=*R@}OLPBUcst5up>eQu}qa ze4k*YLaPj9cyc#K@^msdHi>a9Ho@0kxr;_;m!s ziVZo4oi3ZxyUUBeati%%cmf&k>p*OX1OjFG0qTSlf?@*ZO1q8GjACbj^kFsRBq;-D zr6YbN!$1rtjlLIiKRc0T>D%Y>a2Ru8Po>L&`9;Mxog98RtX=1(`k_adredhR#EHjk z>|D0ub^^Ao0olYaJ2IpLw*hU)=q|^5Cc4%52=#)|YVEGdSAB13x0`$4` zK_rSEG(>k|ozf3-lL;EjVr9i9suTWW>A3jqkMRb%S~@gv@LcZPS+DwAIGTLN-)+cG zi1=VpG{E_qG6a~>33;VoA}OIP=93&&9(R1d9|@_`Q&tcJOVbnDg2u!vr112mGXQ#F znrMf?&k0vLIZO@1G}-YU1*%6Tzs>EB;aSKhy~0fS0((|@YpCD8bxp$+yz)ja?qi)z zFH(uOt=u#F!;VxC)vA-YvSR9^Fp88)ucO`?V?^?*WHg$rTbhNT$6dfM;?fVdd8cAK@LCM{F{7lzwkW0bE~{7tE4@90xTx5Q3zsL7X)7smky z;x|@v5s8*pF-{|&S(KUd+HUH`VAb2eZ}+Jm zNK07E8GFs4hTu(kZ^RyvsX=no_hXaCSVD6+&HwTB9Y9U3?beDYa8M+mpwdHAs#uVY zBE5?A5~_4U?^SvrRB0*!6i|Ba9qBzHHH0EH^bP?+;NPBe@14(c&iBvkaS~vhWbfyF z>U!3S=uQfLtm-H&(V`%Zh`q(f*dEPHF#{+Dy~l%ewqqa$26tG(zlODlzS=7$P32@waO6?J0*%(^1;h;a~eonO6smd~|QG z_`cd6_kI!g(Nl*(?OnIe>jGi!cRbs0aA#s{bySI7FKCe>F1Z`ousyNnW*spdAXtMXu30VPsno`SE5QVhK>TT<5dYvKuJoD|V%o%x^})eEuq zgiRvJzsI0|w3{rh1AuMh`|L^T|3Ik&F^^Q1kv3xSdT?7T;14=kksSLV&~wizTLx0{*y2iIi5BJ)L^=MFLoB|Pd4p-+@0$vXI~($pGd z0(GHQXzbVMe%GRRvhKCuRsJED_#@G9*EXxQ0%x@!R2H-P5>%_v76CjNQ)Qd17YY2~ z5ih@sxNyC^A|)TE$%+&JfwZQ zyaG9I5bnOI&X2{ka{o7L9i%0gXfA~$cd(bb$6J&(G^&_;3lQj$SeQD>1DvFYeA()! zq*^8y(vdVrB8PG0&%iTzN+V47rn5s>tb)jI(+H4i(~mUtf39V1)2Q9>op1q{MDIUMUdj@YBY5<-LR{Jb}<^jug$D1Z$3pXXTmcyr-c| z3nqf7H9C9S4qL!;GGWf)pX$p{yjtoIsN9g^@OPaDg@QJ(ZSSVNWEzgLDSY`JeR~c3 zL!;dQj1~IOVoSIe_Ix12HM62yyt8)PW|unZJ&w)d0~=e!?H;$}ZJV zYukGVkRNbxzvsa4-~1jT2~Z6K18~M}PE{zY-m@X=;T+pp^52|m=tKu$^J?ll(T!lRjs{IDMkOQmu>mnO$9e-*2F zrUAV*^);%&w6!N#mH@}_U4TeUG^_5mlbtSMZj|*Y9{L!0t?Q$7f*$wyNwp`5Yxqu| z^g&?xxW%<7_E+65ONiTnQ?M+!fR+hFvffrwz9kpXv0IjS4$8OTZwy_22Mz?NiNh;| z@5(*HfEM_dHb5QEpf3|<1Y)IvVGe)TfMs`@wCJ|Y5M9=LrA(UR|DPpScA}Fv{ z)CUF6fCsPF3#jUYM#}7i6T+`jzx3fu;dccXUT>0kaKAImGM%OMVKZcF2-?V0a$0N}a^wGYQtuzmHtH)hB&r2tgW_~IW zke4pCQ_+*WYEY@^Go~nXPbtg9A&pH%r5IlX)W%q#lEhqm3D zLxS5U&#qW{{4d;4|J%u@3VdGb&l`V-3Qb+*uxUeBMPb9SXjPS54r85{ldJ{SSc5<} z5+wzXpcDWu4b}M4IKPRd^Nu=@^eTC~b%2vcOKjgnP3Lp?I@^fzuo8H2k4#Y;t63|a zv-?~nc3hyp!T*DNlmF<4bPLTq#2{$EQL7>aTA%}?3Dn=HZtyGZak3Bfi&Em3Y@c>o zOIffbwVO=#iyrfCMHUkM)&u*!Bo}x}_=fc#)p`IW%iBzl1h7dAwnFZ{MLcHdR3KJy z1b^E#y_iYREF!gt(mqK)5)}kR*=YC+8Zo|BiU8J&FSoCqOWtn56Lw`xJ3ap+oB8m; zpcKUi@RLoe(F<#rFBw}rCQNoag8f17rGj4vmzO{NW^Aq==Nz6@Z{o^40wt_}$ZSH; zFrNAtC4Mc!qwZVN3|yAFv}ifjs2b=$!adz~K0xJ@Km3v?1AVtiIdl~nzwFt#@Lhwp zLP8r(nJh*$DYQltR$_No*+?KZq%C>P@humbks58Ex^iljY{Lpp{i`i zuz%6`qT6q=;XgusHp68q*@PSQf2eYiw)Sv@Mv*Ms$v_iZXA!k2hL7LQ zk)oRTZ10d0qRMivFd1io9`v&1vZX+#AsFY*24wvhweZp4OEXt9O*73Vcw{8ZHJaT- z=J5OoTcm=_vMswkSB#qJ#IliaQzEikZIqIgPF_YG7o-$n z9DfvLPRg?gPn~Ba&Z#?Mhxs46hEE9Hw|@sq(!e|3_qspC6DXE(?Ps_o4fyiUPr>n5 z@o_oWmGg&rH#jd;+Pb(elyID6!>K9_n{HOo5^KPczw7N6$)gM}3GA znbE!@vOI`7+PKe)M=#th8A=XPaUw*!MppU__F27#lXG=`SbVsA@ZicbYg3@{}hx13+=@Fm<_m9_dJPt-mDm^DG zd=pk2bp<2pZT^`D`<1Y6$52?SeL9O=K$DQHPtLX&F0s-ljEtjp1MSvD?r{uzQxM6I z8d7fW8LUSe`X$dl8KbMEz5J#3>CNi(K@$Q0xm)J!kM~@rf*ZK~!*ZxC97vhWseiHHA{7`zH@lG1UGf`0OCw&JWD zs6?ykO^=yFuZs;w<~F6q{G4%qCAMHpYQRDYpZ<#SZ+w`!Ha-JgQ7~E1NINZXRIl#T zp}b_1mN{9tGDz)E@kH8wAvW z4UFCw-TBzQ%fz3#qn}fZD!bcRhxiFn>@tgbZlD|}S{*O(@xTuJv(i=H5go!}jYd?AP0CoF>YKlfWxw zvyNQZC390z#x-8ksr|~2@GV2Gf=QGEO+&8XRd8pW*TfAZgj5oSJQnBrv`dwA6Q3j9 z*~uVFXB|y+#Tf3jIqgtNP4|a17F~QxP$7%alMkYZUXSs|X{qg#O@j7`<#!gG8p!wa zkjt`Ty?30e`R%gHP#{}A>H1E7c0)`?f2-MyC!FtYCT+ht=1~#NH9017f`7S67y{UJ zStz_KGe&xef7s4$APsFj7t<5zy!GaF(Ts0QJKhEZ9lvKsM3UjUvX07<9liyAr2(en zMojV~PT2+)?RG^S6f)rLUgRk`7(gS4;hdc*F@;UT@*^;1*@(fFOTw$S@t z)p`jHzdiW>JPQ->w+&2x_an-r0bMbUgkC2ubAgCZe#&SGX{`63blL|vDQWDP|m*-dqsj6v0C z*@XwuFwR%$;&Ruc9LD0H*mWs_b+$pNEY8xR%oQge4OLg(O$JD^-{z)CtsTwMIi1v# zqLn%dj2GEty!-ai!#Sg*TLerc@hy(-_w(;ka!)9~ZfHAs_6d0@pH&W?Mb6_SlH}B`~jq-t9Kbn5nv&^y4U*gluU+)&CPg&PU zyC^hM%=E>lR2nQr?=7jP?BBub!-C%GD*BSNg;L&xqZ!%lK;he1R{e zfBqApTctN$h2t7<%4ux~!i*Va59xL#-5i06sk(zK0E!5PC0!+j)@!EOVkX$x)=FfJ zrRtW1xm6u_?Vh4*dQ0HZ6H}#$ra^$E+Lk95*RGR%=BZMnY8T0WgfcIEF~PQd?}Upc&E&Zckuhy&4BDcXQl@Bl5maT|n^h<9y5xxOY;Fk;tMeC%w6!8>;^ zk73!GCJs|jTC+=v&VOu9!#SQ2rS82^SX4aiqnH|mhFZ<`=dBsH(QlOk;bmakK83%5 znp@n~=jplM{v?<1hG156CTj^=w5Z`;?bfcjUn0X;+PO_Z+uCFNUl~t>E`bc*z9YZ! zW8@JB$%Zw~E0MBW``4*==qD-gzD2*^WA61gC%`N7b9o?zcl?Dt{u>d)#zq&&vA%8S z#QA)49Hav3qqspW!{R3;tq3=z2xowWUwivF&OdlL%uUJ^v(7A^SBJ}8s?0m%#A9``YW6}Use5fJgh;yXHjy0701u!S=di@3qRp}Fi|wMUZKC1Tz*xMKGteP zKXJIIv@`1!4ZS|_k@WB(nJM)jFJM<(A?Zv;-3C()3qlE>*B?FGgV4=vPArWQjZequ z;WaLnvmWj5c=+mXSwZ6WL>ZP?8Xpi&`r08MAyvJ_Fqrq=21qWXly?rA`7GHhHvO|8duB)6yOCcRgcJG%)}dWBxA5##l%W{>eIj9Hr{<*QNYOlbVD zV9<=>s`codObgN>#web0KX+Bc+toWgPF3qU)b%S@8hUy%X*;YNHJ9f-3AJfhsF9$%)GdSvZ%Zy?^3#&nv`ley7C$e`fVqP90Y?oV{ zHLlzO*2$RMdKN#hQkU%FA6^D${y;dkkVr$NtD<#lX|6H-9^15D)tuApy62}0s9;{{ zTx^SG7w$%C*moOfq1BDxDvq+Y9=j7bhcn$?UNOh4O+N?ne1NUQ zhu$Mv){pI|c4K-{y?(%anspG>=azElME~ba*)azRWaqs0+-}yvKGFK+HOok7Klsvm zKb3gYq`+v)_=wqU3r;aOQ$I>rX@Qhu@z!e9L5aEA#&NrH2a{`2F`B9>WX#Xt)sGyg z(l_9GCPWx*SMq3MaVZC33fANhjzn?arS-N0(q=RCN>7u*2jBlG;TmWI2DXim<#%~y z(6;ql=*{=13VQQ)u5NvLRhef}U67-bN6=*a;;{#14lZ)3E=|x}{{%PCv;`fZsjNyIO59YIuUVHJ4NujmT$+Drypi3*?ID`OAEh zg01uGez@JmLh_7AO2IV&65K#xm4-(Z>)a9nJ=GXeCpsebjE|ekwt*sfxZ!S8m}!mKRNV4b&(kn?`do?mSw|ga=)&Bah4v9t2%Z5I_L3xT{S)%6x}>NX zQ&R^CNf#?P&$`3Ect$>LEj5nZ{cLSqFTIiIBg!h5)ETrwMzNxNb4xM@xMOHK7>GVD8g^BEnj#IdCWUP!Sj-A6ZYaZNM;?H`u=a0z%sE?)CxkU(-V%RrFvYn5a&d zvsDgx*I#?b13(wCE4051p0(62botNhqv_A?oTk!Pm6Rdr~T<+{}zUSdt1UAK?nbxVNxnD#^hrB9`T?M&ixW3sHL;^y5!=K?1jnOl=mkr>44v?;_nCB?2QkU{5OGsW}oRbZ929;@RV!H$_6$mDA z?p$S+3lxXsd#|I1V>*%`Fa^Q@_L#76;!6y^F&~wT)-%s#%uK15y!>@q6{3dro@fTw zDd@7}w3V&n31@BVS-}sU1r$lv-DXY3029mnjmXT6cjH&<5*8Li_x1i;2j|ZNcJ8?ah!+XmGZCjspFHWKKObiZWSTv?iOlJ0inJEsmK#9xR~eqd*7|B@H61 z-H-{c6=9AbHwM+xBBk3GCBGM%ZYN_nW((50K`HIde?`6j`zrtOf<2{04V8-NuBMn4 zYUSn;EVD92cb-E?&r(&z2=oU*69BXEVuUufS1;-czhiZrC_;+YtlXBsaJ1GdliNTJ zIDM?B;_w6o95RlKXF@866PI(fzOQlPcyQbx<7eDeKG&_;tGxyhbfa#sQR;x>2>e)8 z_^#MyoNx2pZ%xbpY;yhe`ezLVaK~$AKJ;_`X3fgrfUbOv3wA{ChDa7Zq|o?uLx!Ta zmyej8x%j-eQEN9jh@4C&&%6sP%+^y1%c&Dsk7X&NtE$hWfRfLFej=9ng${;D%kID}LSwooFp~Qr zT0eFfKVdvfa0gxWNFtd>X`4>u&Mp?Il;XHxNBX|= z3DVw)g=-bg!Ue2(?ORy3+W+cWc7%vaWLYJq%J#kDdR4vjP~TE{;|ib)WmhQiRlzkA zwNPGh&XlZ-p9bC*PrXmum$#$=FzF;-n}{5>mMZ!bW6Vrj?o(A|JG-xh3TqRvx$f z8rUUKR*BA<#4=|<5TQkV;qobHx3B^?#aVZYfTFGvBdv^S?|a1=*BClnzaXjEpT9AK zCc24`YBj$__p((X5k=)t^B3VwbHEx${%M13ll&Puo* z8Db>z;5Uo=%}{?jkQni{pW_wbQe``DsOQOuRYAkAqoZnU8)1%S$f_tbAp1=yCyF|o zY)vc^0{gJqE4U(aXmL0lRBN%my|aa?+{R+^n|<^<-yK*;4binDSvajV&6`O-WAp=l zWr0%1$ek*Cr55zQckSk<1ZSSh4}17uS6dNGdzQW&-7Te>rZq#RH*DK-svu+thj)rX zBv0rhT32J-xcC2;3t&uo-nzF(0Vp;9vsv`F*8&=ZZ|HuNqrdAqd|U0-3m2twm`lI-v4 zvE7K}=)-OMVe8wfn+~}@47&Z%kk)f_(825Hc${c>l2|)X-HhLyE50}_{<0K5U6}2E zj=VV8v1vZCIlb3>E}G_fwwivv>aac2m?zY5IHuQTf4*!_rl+So0FfV%miqmr9Z@@*+(*?tIS;VinogS^{nAn{umUX zB9mW)#}8$T(@TTN7@b6;Nr*D^7;Xkt(G9+=qldfD&8hDvzbXrNWu4Sehmc0G@~4H~ ze6>8Ng<%dxP^|I?6t&kXeBZ z_dc!Xe9U&9OW6wNO+D{--qZYo`;ex+eVd%ugx&W;AGUFde1A`vsM**B#50c;vhyAc zZ>aCiX4t5tLa!O{9@%(Km{!)UdPG;{d-;96V}Tm<-7-Y_7LN{B8jUOc!)N^ELXug? zamNY-iu=G@Q@Q7wsDA_z0Tu!k`tBiFWu73~NRGNft;4QT*+KS%IUg2w7Wg&CKfVPJ z1049|o|XOpwlN{jc}N>%GRk}?(_WvvcuO`IQXCP}7QRLay3}6_Xu}u3a)SRk4bF{jf|0=iAVes z2Tk}RbQ=SHh~Wmz)gDkJB!^)84A09prTR@<$G&==wep^|?f^RS9TGF!J{aL2*Q-3q z?>+-e`6nx}Q9VG=_eJT#eQ{W(Oso9+yoIm`>gDRy2)CWj+*phU+SL7rCeafE@dl!ow|aKo7~<9fpvAn8ywd_g zR;mbVQaqWPn;DPIkoLS>f2@gOpyjXL&+2e3j;1x8xQFlY9Jz|qEEFz5M@;F}4!28F zx;m`7y8de&&_W}3sor^G!G5uDKq{mqmz}ZC{2zZvOK}+yDM za5O0KqH^*=l1@{d-D_*q{o~`%kea-hOd=DAU?@4nz2=-UKfJU~d|vM~(GqE*dz!3m z?^2bZ0bHs(>i{L$#ug8X-3zq#~EN6@v$^eG6c4@k)@P^a3xr(_vZRFH~r zH?6>FifwD2VvcV&`3m`ekY&>eY74%8(5j#Dhu$Y|4T)(QxA%98nwg{LH#!Rsr@~IB zsLC&wKJsk~wh5~gv{bnjnniw}z)SiJu`={NJ(B}%6QfN{Bq7r`YEdoI>-y$ud)0YC z-c%Q{uQxr|nYQJ)K`qSkChJ6@sP8D>p?Y?umLF~HQ62NY%tbw}5^l{j3X6CKKDW7Z zKH7Y$)rLIlM;_k+Z3GeB3~k2MH`Anv`C2pCk~oIHQK)lT>fM1iAH&6FHgAIL6tZwM z&AT*hfCM$hx*3md7CoC|IG@{D8OjXkt|4~3yMB&0?=#s*$D`6+r;GjZ>sR7D#ZOl< z=N4kblSo!RC_0x7umZgmA2EW7R2^FiWA3EKyaJx}s4DG;LxH-t9rfcB8D`Wp+N)XR z6O0VzPpzotP0}@dDcs70zS;*@b3J*GBIxE2Tq~Cidz+Ts1O9nvBUUot)`%K>?B_k? zY-MaOT1@%^8t#EACUH{mL1Q)Sl^7MpVL?_3jbZ?v&P+5K)9{h>kQ!a)xAbh6?{Lq4 za*!tF_mb_ebjRZ<5Y7CbA|A^V2e;${7H0Zy7DgMOeV8uVDtuIc;K_~xia5?==C4vB z?ph#g$KdB8T0=UHD=o$(GJk}-(Q#iQVy2bnnCDhjBt9UA%@@451!%)@K>hc{D@k9x zcbCdgmx`uiQ9k7JPE}b%8B6BZF>k%B@Ww-=?#5?BIa)!SHOZhml46HsYeu4a$mav3bd^EK1Zp!Ow zS7Go|e!IPrbW^=Dpj)hETSluEx*NGbG1_}HnX$oPm~L!8QBY#R*C`r~U8P3rRLA*8 z>txFjZ?Lp%-#g{Dn8zZb4qEwVqK@{hs7D4(bT916c{)=&DoJ+>H=FR!=>4hqadZ1j zDUU9;G;ov1j5!Kw)0bNdnyG8E5$lAYccBm&x+r zS16zk_q{m6!0lf(ZW_!g_17aKI}vcCMV1i-k4mb}B6Nkww2Zb{@QG+|6F z*k~WJj7gPvZE_3RuDnmHLOn4Q{=A|KW9w_6$}CvbacnBPk4rSxn0Kz*O zGI39;!%wD9J^1RyViBrb-5O7f@8E>ya-tSgJt;9I=0O4Pbc!HG$e@6t+@fqL&=`Q& z^ex;x$KHKIO@L&7HE7^16+BZ+B-RXb!dNzl^a*v5kh14eOr(tpzX&`ZYSk088o-V2 z=Gi7*VQ}^>C zJ#&02AF@;}j=7mxbS~@Tjn$U#J=Oe&GXom#v{wj9V;F)`Vjui^LvmSX?jJpvpX4O> zPtEjJ4aJZ3xH!YWBJ41jHzr;)wC%EqGJ=GBem+)`ZVji+E_v-a41NW~iK7H1i_pHO z4X4N^fI5ABPaZ$7Udlv1eVBJ=VP@0#hi3R^OfYd`PAw!yuM23Z+|uE&zK>v|eGqSX z#o~#~a_84`nE}%+$~d}lzJ^WnO^MYve30cgr92XvQfZqK(>f*amczKc`h_<(_;>!J z<1R9);i!b=6Fay3S|oUy=Q|zGssT@XJ&#PR$DuSjuQ>`g$5#^?a;cWYz1A+=#k+}l zYmb&Sq@|Di)sxt87-Nu_u)Md}0*`Nx_;h$4BGbH!L+nC8ytYA5dBBVmlYi(Y@X9LF zr?lF1Y`%fSYkWOX)*WS6$T0H(4v^e^WY|_&&cSmrjvbf3)E>}S&?21674{s!<9w%< z%bjS4J@7)h^-dB&a?N6ss}tM9y?Vq5>fr(7zh{OBZ2|`Wv)!F%{`=3;E(TJbamOw# z<{RSknkZIrsw#j&E6iehHkrF1dmB)iZj13hTbRT8Utq(Wi35-1l6i~UJ)*DG`}M51E973ykhP@*Yh_?dg>2Md>7p4UFqwq8UgE zQ9_Jt^7Cx-zgLCJ+x6Lrm|V1)yGR|a|MFC~FP|%=Lfi9gqsc}!`!Sm7s%ZOqIuEMl zJ(QbHby%H#7Z+J=KjyIrI!bBGJ%VsFoxXwuRrT5tPU3RX}i^3{jW$4b?zm=J(KvM}cPWo5VB>k)-;qdeK&sp=W= z6$rH&E|6vG-J<1zwEZ?kigsm~Njm!k>ly(zg1D7JW&76lkqANx8J>6?=aXndt>alY zF1cU0+h{1BJ>{Ia5w~pl`N-J7=k$+&D-`#cn(~SpAM=0Q)~ZrHTv5Sbi)qWx=#z%W z(r{E?su86d6J}in%PUWXKg@|(XBRqM(i8f$3Q(q&o9Qf#HYE=ct z2;x^msbYw05&KQX#RH^lNjp?Dr89Dsuz9ZTFL>!xia&~%)Ur1|3sre|4A~X+3C9{X zCvA&hxcBja>WK!(=B!Hd3dr)XhUw&5UA|QPk--=HF9U!;7{NgDQQ7U`&(To!;?tRD zG!HuxN}u=-fRU#{1J zV7k>Gk^R2*Z*xdms>@&tT4^t!Cv~)S&4QUQ(~10EWghm4tYw5Vxv~VsJpXP1BJ_AJ z-LQ9wWsw1!-eXKGbBRJ+lX+0m$Xkv;?_EeuZzfr@%0f->O2x%@al5%UF2oZ0H>KjXTuP>sr$aDL|Lq69;NyJ?_4WGOgzp~rM+rhKW9j?zmq zR|$(K;{xWsv9WWKC*%jjEDyTAy+Wnx;{66Pp|OaX`v#I6Hv>xgJ~>4!e#-(eFuoSQ z#2|P(Qc`BwOTI)H?%t!uLNy3t#|n)$@RWuFVn}>c%2wqL?vM&$D9pi(9F=`Vn z@$&|I3(nR^&nMKU3YUJeU+uj+v^Z%++Lg7;5*idw*W^IH;K39M#f%K29aXC`$+oSr zkI`^tfWYQ2u&2*IZeKKb3^k`0(u6Nye zcs=i3^wglKmhYpU95LT1WWKV^s@Tbb#W(z6Q_sR(MWLsJ%g8?GqMD2))d6JlQD4Kr za$i}Vp=**^V)9>rftERPkJE$ImL!)l;;C9@;Q`XDAY#Ynoe*U2T!^Of*Jy72bVrEywkAuYT_#I?2+z>yB7Z<#sxo{T8I=2C(9D{Bo4iL8-9C9%< zipT|k0^dn!Lw$Yx(KWX3CTgoIcOkD@b^Ygr+0g<3xGb0DCe6d8iRULp-2@4=J9w3& zfjMjH`A7D)jT57##rvX^DK#6}@fpkSq-|TuP6kPtoqmmj9lS^mq?of`Y^OjdiV@6m z#PVhr6*=w+QMYL9pbgsx1Sy*!{M7c7^J4@@;S5Lm2Teg-hbLWq!(OiPw_Z&xj13e5 z+16Vd)Jq+2LXNu$j+(~M4Nni-Y1r>ijIa9TD{tB~bez~7EtWCcD9-Z6NYR{s(H^i7 z(-efi9vqK|Li8?rt=TGQS9k{b3gXUL6_%KG7DFc?R@cw$x2V# z$x|`!M<@G#5GQa~rg4WBG&F|Ja{0$qc^`f_`>PX1J`vMW6(}j<*MM^m#7}RwB5A%*ZLfr9IS$P~Hd{O<0{EL??Q_JdE3}lT$H50pS zd>N37w*?G~I;#vrh5jNVC!e}YF}E*&0?%i(e16I9lY?L0$~C7Er`dbM zoDs~HQWbx20_)kIp<9_3BYn3p?Y<@PNy}Wj8!p%+sS2vQ2GdvYJS)_F&xG^(Jv&_^&W|6q60(V5W6PK z#dW*LeC*<7+sKb?9Uzdyc*$=5W%$#Sh@Z%P*8*BEdp5+M4;ldE2#7RN==I0-pC7n+ksEKqe|X|HtHbPl7HFKy#m^VS>Cb`D zfmMy``j`WDYkF=?1Lc%({0xW3&Ze(x%xE?3CH^^gz|$G8wkzQWBp0XTk@@hK2BW#Y zl>=ggGm-={=gMx9`lyc5m>Af1@_7zpr1}SH(st#Zz`(OGA^x}7WuB(P9b`S!Cs5Se zzGZIj9Tx9n7#%`La=1gf=)8e3%j-S)nl<`7m-i<60(W7ix595Cu#q|;P|EAB$8IVR zbMmXB^DAnJ6X2USmoGZ?CLG>x=r;8pCugAo^R6?$Lh2uV5jhY4#?ahZkoW#lKV$F#B&dfq?o?9x`LtjB)h{gA+c=ae?3 zzO36#2RY0)o7k`4wfVT+SB*Z=JB>kfvM$JsLEXw%9da}Rhf*2*PqRYi#asXS=&5T? zw!CFja9IfvLt-7)d zV*iRdi4{n{jDvq(agSWaX(lyJTFU?MrdB^P|Dtb|*n)L!&Lvpwon(>o;?&E!=$G4} zu+r+R?`G>(O+0?K@vtiJ3V4LBnx&un^A^DW_I-JF%L{#SCym&(QYqb!C4f^kV06D< z$7hwMv`9OOf=eZNaf;+|m5u$41?uw;=wlg=Q<)9L#aX2Q9#t9xjn-X731gW(NTlFn zy~Tkr5o&?YiP~C&Ru*-Z?kk>{^WN|KFk4q>AlJ3T0`9Acqqg9PS}rjsIU-^gg#*{%$XNb_rzya!BBTW znm*jW8nqHHNpqUxyeEd+cQ8L2vBGOO8m`7EvFZvhLMn@|B!$!SXYN*hSS)dIU7{7}>9Lgb5tfsUxKDv${Ss%Iv6?nQ$O)kt zf9CUpCu7)#oj9JgKQ*Mzrpu%hP?MD|y|o4|!0%#H9JC}Xz)S+K3+}`aI~n^PLQ4}C zs9UG8O*wWIY4%FjW*`XR={U9Qn98oZ8v-otQ<77S{uXIUO1^{I^o(zT@qvIlQzul_ zBZZOra&br<_0m~roBpV~ALX7VJ^qO?FO>DCM!MMeE-l_f&@cLLwc20P^BHY|*^@!{ z(B`wg=7U2>v!Ac_AD6ag$LCGUzkOjk{&v&vq0hSBY}xlCU>3;gh_yGdPFJ*ZbZGi{ThZFlF=PeB1th0_f3rxXW9v+5OBiP zKo@jBy+dLrs6V~=CTBaIeqKUiZN{8VKQ+`Et_uTwF3QA=mBFo_^+-oQ)Pi%8VeNX3 zI6ed5h|~6_!BR2gM7xepT*!49ltnhf8GdI&94`)?YP!F`)ylnFc(k7pw0IbDqcL_t zn(p+gFXdJ*f5Z*3|1_1~;Vfj&8IN|aJeN-dOokE|jGg{#KXB;|iwvXZwMu1>upNLc ztavSm?RJFa)JDJJFvi>mbhX&&X(MD!V?yCCDF~1>+f_d2Wp(~iW>QnKEZX3=C9UFd z23k*N3z$jFErx8G%PMU;n5-27^`(BWM%cl>!X30!$sG13U*_0gd4lVRwJMbuE`}fa zY+u-5Z#q8FFV^2CfJiNE4r(vdGgwlQ2JpLx_g)CK<&m$jBZ*AMINKYlWMw)`y28(Z-`g~C-< zvQFv^{22RdD$hmi2k5$o{hIbiwTde% zoypTat!^{JX-CQ%%@Z4U*I%Q(<0O{R&`(vf%)O)foFBL9WUH&QZ&o_f6*2!ei}^W1 z0rNc@nK{+>Aa2+t1h@Mx*gNt({H-K6-~(7R(|SG=XM-g}R2J{N;hVdkO<#zJSpc;v zfn&j!I#9%2U`=c`H)ez>kmpbvKZ{?v17Q8+S@y`|CXMByMFr?m$0XaY(3XkeN=XU? zH9?*)z)JuwP69&WmK^6gFcjx-8{{6Jj;N9bAA*12&)xgcO-3flEYrJXi29;lodn$J zAg(QEK-v7`TTIJi2kcl$WEx!7-Qzp0#IrDAK1_!<-`vw>_}NJ;W+YHwv>ImX=~pSL z^&RdEDQ?zGK9ZacX^dIjen&ffdLrUL_8ZXVe~*v>Q2?h_k@j_k#mRd2ZL*84S$w_V zBj^C;*95d_X1%`o2X4Dr9MBs-Yv0-JVi0!PY(9B*@iosg#uA(Cx0>t|SwkiBbqzNQ z7*_olwgXNbR_wFH>0DpzcbMO1c-CR4U4u)%*!MTcS7-(%vf|DBn!gfJG4{(FXqZW< zv3HzkxLJB{(EvD+_>Bb<55F_2xkt-IKN;Y$O3Ih4KwGK7;Dc2LJXit-^oBq(1p|6RxIP@2>vwYdAL(-Kw%+(HMal3#AQA_AbB1H=Y)tGWvbptb3 zIZg|)lEb6;xc#9l+!ITh*}}1}v&!TbGv|_6QpXCf4%O>Jm0MBF2Vinv9BErl;Z)&{ z)V`<-Atc<1?ghZ{Kv)MCthzk&sTtOJc&1FF8aTeyVVArv`$KYUbl)IQ&zEgdFdE98 zZV62AzhOe00PG{(P@rjW{eWswBIt8QGW*(=-D3P-yAS`D#r*k#SLx?3v_{yk2mGrN zKK}-CsICrA2tQfecq`zhc1dAGKV#1C0yk?&<85POa||fe^O5&dQTSf`g7@r*pLbn2 zXdWt!OwFF8&g#G&aJ&Ep#3eN%L(dhNE-rqy4!9}qOGSR=z`Cl39Dzzb)JK6|YvapA zIcr@9kC0GV;YM}?X^E97O>qdjAkW+bGp;~c2}^hswYMORSyh{LjK{%pSYeFKcEzd8 zA$FHp3Y$3Hlq>sEnoWYDtlpW5GActzM;Wt0KU zE^n+vDeL%@*_ObIqS!aJDi-o{|awY)fX$Uu$W9Wg)-)%MKK$^dUfSdKULQ zxW;}SGVt`b&Q!}f+fG(EbaNyyJ~kEh<*)5$wn%6B? z%)xbqX}~7l;0LU|(WHs|oi|!WVY7rc`q5NXL9PmWKA5qjsOO)opL0GIpB(Fsa%zHq zgqJ!G+U?jzo=v_WgW;$L69f%UljN?vRxk>3j)gw>7Fsf#b2zNYdC$HJ=kasg{p(6B zs*Nw09q-j|xAG;Q*G!g=mRcBI#mQjK%>+>fUxU3*323QU40eD|HHVpCZzI*ldgQnW z?LJU4srBJ^tOBg=?baxK;Q5bw3Qe>hW0rk{qgC3y8!|085sm_A>cF1;SjI!N9vO`J zGEXoW?_HsFYI8p9dF~gskhQ4V0e&nmB3}nx1!g>s409{>HYQqGyNMOfh0n;=+?83b zMRzgX@f(S6sUul2iFD@U0rM}kYnKQmetKK_QSZg)TX9taqlrD53~Q2W1?@Y<*>x?( z+CQgo|EpUyPLO?6&3g5=i)2U5H0XB8c-^DOCp3TV_#)A{a|UuCL()UHls65LuY+1t z6^E_qZ8;g6qGyHS0cCrh7LMFRtynbZ!bH#GNrlwl_koQ7jq4zR31oU3SHzv*8P63o zE|TW8-)j{87y5iOAHXpf4qXcYHVDRxz8sN`xgQJ1^>q&;HYBRd@JSu1V}MKvrKfA^ z{obsO{8jBtb;CwzthxNei3qVyCa2j3R6aYy&C0}uI48DFKG(Oa`!{&xxoB4e_LggQ zbNavID-Y{wp1W|CtiqUYGLcbc-x0WGHAb@SYw|+jaQ2d*SiIxk)x`qplO_ox->QS| zGn%iEZAqQcEzFFP7e*-N*_-+azz1^st+}NcYiZ#ZZZiDzwSEedkY`L^aBa=kiBz`% z3#Q(fj#hxP)ktHKhCHVh!pe-bpw>#}6f7K=chQ4{5_Tb$$)8`R9?Y-fQz3JG7pdMa1Hv)nXZl=+GVN?R87A^&GifPn7am^j}-G?(zDSbZYA!#ZI*` zQwwc%ST+PxsW8L}jBy%EMP02@0^N5t>c{R<^)pH_T);2B6Gz1>*utde5vsUueNaZx z`oSlm#)}*UTCBV5%Y;S@J-h(k3;igCh{Km++T!wSn1DKlm_9fc*bbf$knQ`uVeH?6*@ zCyx>~JV2--OInYlP#=gl$vDvF+SsIQ{t9%Y%Ug9IhbZi!ef+$8wHNdQqh#%iq(cD1 z01w4#?Y2$^7fTk`3cWg0Cpg#uD2m>XOY!M7Sk$rZjqw^P$sAcX z#y9<5z!afdhLo1UP?3S9)REvU8g8jGl^wvX3afJQ5l5*5&@bx-%at!4SN4=s zMmR-TQ4%~6{4)mAaZ!G5d6sT7T0@=lMRFL~1d|Uk=Uj=D))NlCG<+s}5gwX4^s&^E zp{0uDEe?S}#jGPIKj%v-E$J)y-A*^MFm4-q?koy6HY0kC=F+M}-KNTOAwkWrVL?&9 zC7^zbpZ{pN>K8a4ozp7*&MN7+*vGwSG|>XFR0#iU4e%L-X+zQu! zR5~adI+!~YiWyw!17~=H^AnJzr6H6 zr=&QXDkyoS7^EI&zpen$s~QL2)09%9>*%?wD)qFes*d&c<`yN1n;xeEa(J7QnRRk1 zx;&m=TPc$A9T!45XxXXf<5;L(e32%=Ia1@q$q~|AAGWbRqqGrqKLQA3?w?c(h^y+C zHf3<6*C`K`Ooo2}6?PzQdp-{uXp?wjX#{p*D?SyGrk!ejCO+U@qq*Rn^?Z1z> zU7kcu4Hs!!iYPf;+?3Au&NZ_zUFRK|q6*%K9aV_j0lh7&_B4fk6=42*tw%n`MYR5^ z0B04>v0{O$rD`W*rpc;mb|XJ`W%T`kqGD$wWc%IM1HEGQ0xQM#VFS$U=eZ(aC|^qk z757|A!e=@`m!t7fr{+-hwsqxEah+0=y*?DvJ#zjO6s02%^I8{lCVMC6U`!1rxU&4F zn&Ksl2GfVGf<+eU{K)$(K@bo3omr@eHTbS( zP~MG)x<%!VnbO|*Vf8ZA8TKr?KZy;x95Q#K>}%S^&w1zKnnyQu2*uR>qmZ1cuPJ+* zrjR)j*tqF4OLEC6f3;7)<(mG}y7u$$$cI;TKSz4)i0}OLCQt#Te(#uutP80=?NlHY z@U|2E-TNxjM!LQIZN~`y5N&|QIR`##_W5x?XJxR{QROyn{(-Y#!yJ_g2j|(VUW&5W zQ(8C`v8CI^R|HfFL+eNIZ&V4-hc-qzt9m4~@3tFc&3UC}KOU6~b(2yFT3DqgGfF~| z1f&Qz&T`0w@{E>2!k(ud=e|jF9=?m;$+GtJY?H{fX5lMYE=6b5C@JD3g!~JV4+D3G zOmVKToO>1f;||G90%IJ{XEti@!{txu1n#H7N0y~hM|itf2RBSVw?zK$zk{g#bmKO9$L6tX;;-_MeCGlw=w$BBM?A2d?TTKLd4YPL4S19>C zk{fmV%W?YMDqs^%kYoOdAR~PHnbygu%T(mQN8>OCw1Wu??;v$e5b*BZL3(U^$S?Ck z-DZm+10|w4VN6GnVdvpohXV-Fh}`+N1E8`GiAy2u-oA-yBA_7kU*IuIk2)BMkUgnT z6Vs{v3YACsO5$_*=Ui(Fr(Qd`l~hxE0XAOTTZNOlC4CvCrsKEiXL61drIz}(grC9DF zP6j^@dED+I+aNV@lqd;6vN?r(p70B*>>o8$r!!~Eor|Q?8;#v~&l=q0BLr|} z`JlZWYpqIng2y)#SLUZ*aSg>kA}b-0KKoRYCgdfk8zQw$qB_pK*?AfTd0YFOA$u}s zuhCW+Uo#zd2>DpUwJ<*z^VF^QDN`U1kTh+NM)LPh+f=o;REg}J28aqzexVLOdap}a zb!vdye?wfPQ*?vY)t;33y0MqWx*$=*fLLLb*-XDQ`NV+61Y|R~>l|I4lUuzk+!s)D zh$Q6>VKj+TNFD5=^JY8=GmpiY2qrJ4_YK`p5$(<14n-qwq)_rBwp9WU@d#P%IsADFSn{ec65`0w73-7MoiG9UgoY zWRc?3U6h!%)NA^{pCGN}OrA&4Yy95m`vktFHTOB1cWsG$?MS1e}m^gi2m zmsPP;79l`*vG-trz!t8a2UZ^sG6|{0m^|?gUpYvhk2+L^GQSGA;Hd7DyRfyZaeev0 zL$!o3&;0V;m3VI`ZUp3)pSEkE&Fkq%fUn_`6j0<)6PlbMz3n|RE0$Rpa{_en{FZl}TU(hr+z} zO6t7FmTwtXoi48KY9<@fF)L7k?vUIqc86;9?c|9-elL>}sq)d@>t|IYr0T23Sd2@Ev&Uao)Q29 z(y3o5OPA_I@`|#Y$UExR8;BZUb(Bk6dBA!6)(PPgef%5LY z>OvoWI~ubEaIA(!FSvaV{3+89ATg8z~=77KeVi9l_LGUL0$Erx>}U4 zB#63GTr{RFayvN8%4pk>F|G+_o*;#09el<%28KB-%1337WZ6_Ppvj(8U2!7UY9p^k z4gJFC!9-H%MKn#16)W%=9x2PzL5txbS-o1nPAUWNMD5iife%CX;<;dt#F`8m?y8$G zeJk)^&}Xw$;K8Gj&#jBUIY(W%Wb|k-5A^0#9_Duc$eyvD%-E^sNv{`@X%|ke>KkXH z?*tRdxy*gb++)WST*WZ%OcTpZHTjVKh;K{)G1zSSt;iGjZp*08Rmc0r6Fye^H|&(E z?|mP(q@8gO$4?$em3746L(qd~FdJT0xx|eO{GM^0_fZkErnPSJm)#9LhCZ36y;$54 zGl4kr#y%k*(Q3qST6-CFGbS8bFuk@Vnk5Id$Icu+f^2PNABp3)bS<>f~_IAKDUDs*KBD@+XcHFgzmJ|sDI9;qynR%%gN_9p@GO32c z_cArLKqEC)Lu`cSWZ)(GQPEQKEng%VNl#Vo_~|IgzngZ-xOQl0+K~!)>12|M+^YnD z9aG2k{AZp}Y{dryzwvU;Or8%YV#+MMnNlFHNfiNSvyw+x4ynz8tM>Ic~ zBv*zpv!~;fBWpAd3%;0V%Ox)YYH5h?bZ8!pPSL$)c+%VJ!MfvFlBP>6Tw>blUnhKr zwdAdR7j{p}L%{dt@Nkovia24=%#bdpDX6cEo;1{7u;74IKT&-pp_Xu{rJ4}A?XrUm zkBjlJA;EjZWqH+y{Ws=62;Z8AuSZAqFFUhj-n7N$dU@vBX~nIyzc6dC-Z6XFznn79 zsXv;ZH-FuUx={{wc>K^aHr{b;#cY2ox=+^b43Zsch?pRGT_2zEVpNjyI!OV)ZaPzU z+fD3AS@S@3xmb^?;BUY+{1jWu3R;o4XMv{%YZ}M~a$J!fbbkN4|0qE78H-9#tA)20 zt;$D%efD#UVFPnT(5EFOHacj9ML92$qP`LOmw+P0uRP{;X|)Vvp9g!e9$O|*&$*Pv zwwVDLnc4D*Q@xGL4Nw>FVz?*jE~2K)D-;GDZSx!nlV+sw9SvQ&+q_o2_l7+qyrVS~ zRfoA`w|uD%@)SZewP69Ah&_y!ZK{)7y2epo8VVV_1t?4c9MXt;A83Z|8_C&d@@*Dc zH^WADRy%Q$EgFWgtq=9X&pQ^r~LlGnG)jx0HU`rE=L{CbbmNBy3BrJ zbHXPf%zXs*P;Tbq?)pmuHlf0>^4b1gUD6V3;X_cD%Gl8Nb{{6FtVXWPoP6HQAVK16 zmzQIdCd*z@J4jE?=dAl_GU;?zmnqteS(_lN5HipvPTT^rbO(G6?Us^ZW^!o+nZ&cRVldfqhbQ1bH5oA$=b-v z^;7-i?l_dL!ee75<@`PwDoVC4nJ)-Te45SAKTxUb+$CLuO5%KCdv) zA3nC15-W?;zrV<1T6*s*xPweX*U=twxB(PR^;%)z*aG*$@3e0+3b(|4~eOK+Eh zUF0Hn-}>5&z@25uN#oSH2eX)b{3uAp<#rQ+wK=9gVu}vLe^Q#N89)`oOo7Evg-Ey&~wQ66G9EMehI*EPk z(dI50;UyOx@RkxTTX#0nnXZ0Yi8cIQG9ObFx;icYc}ym`!gZ!V8tSW;oT+1)+rRf= zrP2{J(q%@-aCu>0^IP7!UVy`IeBqj+Steyas`4xOC?Y!SB_has&&1($nOtA3;i5Ba zQy!^>%wohi)x90rc8gb(`u57thd#DqDh283y$l0Jd;$~qM4h+tVIEY@sIgu*m)cj| zF`g=}*SWVGGae#QUwCHGSA0ZH`BObhBa+ab9V;`?2J^9(F(t zI!alW+R>0CL$tvHpHuP@IJ$R^l8CJC2 z@_L){jCj)!I`*c_orN^-LVLWn%uHOoyKc3ZdC|IipYTAw<@S}xgV8tq_4#a;g?lN9 z`(9U43M)zYT#ov)i91Gzyx(6agVXeH3@d2wedWyf!q#pLwmR-5A)l<6Ey{Y;jyznU z=;z5DG!$?#8LCjwdty2!J0RVtHp1}U);k^W zjA!Y{z-%Ty|ByxT&HJ1(H|IipFwKam45;@y+>DTlaPgFSsPpu8bSBMxIsDZ34|&Bk zT5dX6DAOUPs61{zHGkn-5nsQ-N^Ubmqz#UNDcJOnHZK}Mdb52JI|nzXt_bc&-t!}s zB%Q*0*2XRwtd-QC$~xG7m>siFTD|)KK5A%o5eJnT{zThMg2esq_o>_-kE?|%xPPm4 zW52UAw|;@o=_;Y01$Q;}*AmY@;2jU>PXWt#DV_T#p%~P>uh}Ppg6PETUCv+p(ul<` z%YCvea@pgACpp6H4_7rN&${bo@NtS#YhA5_tyvbc8a+XDBla!__h3GGx^Xsvi`I9V z=;}{u5?oidhoTPCmJAUZ-(_bZ)Aupt=G6(;H>a4bh)aL#%z597tRt##MnWW@d*pXK3gSU3HgV`)kBXQ*2tN&YFkR&{C=NM*_UeD z6A-x{-k(zorRku|Alf-~92RkLjFvifA8`j6vz4^wQ0?6GP!(dG0Mw?`wv>+Fr!m8OYeGg{@4*{7bInHP`?~Ga1 zbnJz4xLhVb(p_rdCF3n-vzaBRK>~C>5vAw7zL5AByWcxfXfL&Wr#ZhiW~l`pf$mk= z=6iK}WWh7Tt}oh*gjveP84~s!Yr@W;m>o~F{--yyK84)dlcGn)*(x>2O#3ap0%E4z zMo}ccJ=Y@qQ|Lji{Ai4ixQ)rV6JzsjQTfQQBwOy%Cnbr zEwSp9XD{~m`@ByJN_>!{`c#PHY5rn3D-klP02WDGN5xS=3~vA1r&SaZn;ithPVUR^vy)j)rRWYS{`!Cw(e3tFZ zdavSzc53e2&sVxv^L>KmvqNYTCS*JmdoSJhET?`$-6D0)llcK;Q~w;tN()pJM{vfl^{-fA`iy@0MnXY; z&)@uQI73eA^d0@>aY0@bmjFzpt3XvJP=iNH?(WnhZW0GQ4HuOy8Mi=yG9pEHjeT{q z-kcIk*VdA|Ii7^HxOm!cwuF#8EAc<-T;}8grw^!$|@o zxSX{-h)(e}eTY+MkSodU>xATNsnM37qmBCo;>(Qh>*WjBgAs80cs{OIaYjWp+bZvd*QR`S176%HE(c0*bsRM zt8CtIeEgQ|BmM1W{pUq7mD_!-f460k$S~!?QqkB8KvuewF;5v4&dD&_idhHEp^!m~ z7df!hsi()C0;Z^PQ{mCRO6R_{9r4g1*+yBEfe}FkYGWX=Fg?Sjju0gLdu$XN`QQfC ze%>xdJo6fLE|N~euXspl%|Yz1i)cIpX`*_a#|RNGxE%A@z)oi@8{^vdc~_yOXJimJ zNqK;?qN8$OUwM^@sN}n>=XeYN&SS6D_#0lN+M7Z0^9FfK>6l3?Q@z!W$I9oTMD~^} zukp>y)~@saQ%a~BMOpNU3;l%`!Po3*{o%d#fT73XR`T4$Vtx?S3`DCsG|HiYIX zU9!rIKJ0@TEmn}kI_mI?-eyH{qKXyB0Ysbsd*AU+h{$1O+F6`xy=@BGUu*%~5tG zhnG)^Z4Wz)iC|D|_DmUK^N9ew|DQzX6xN9Jz z;c88d6r>yDOpok7gSgAk>45V^9WI^7hJ2HX8Z>s1X7kYvM zWvdB^9Mi%sb8RPqcXdsO{N%9hCf&Hn>g8}D@6lwg`TW5_&0LM`p^aMoAgD+Iv1T&s zmm!QX)KgAnV9CShIE(G&_@`HKfeq&N;6^Dm4^Nf+rgAEH^a_~hXO$_6EOeT*5SDQe zczl{fs1iKrUVM~M!`jWgOnm3VzggkJ+wCLlk4kKMK?Cx=9xMM`%t*>7+f)jkgZ+zi z|DMlJ;~7^H1U$AlWrZ+@z-=#!^OfEwI>I>b|E zSdSUtWYF}!X%eQnTvin%oPCen%C@L{;^eJ_E+5Ih!?qa*z=xrvoH2`mefRF8wo1w9 zd`3CrUJggoC_q9+qr2jKt}!jpv$ z2Zl~4f{97_pbsH6e89q}=8DD5@V>$$^wFR>DY_>16WbhN3co$pSGdt@E%IF-HZyo6 zOdiV0b2|gJGD9C4t zN+G>o6dJX3`p)v{-g3H~?KWPG`Jx@U zTT4YX778YVX4d;Tud6x`vv}1(4l6zT)S>5k!@_o?!;&%^OF}vxju|}CKNNXgd8M2n zA|mgztaMY`RwA;C_QLF|S76NjO`oC4mG@6+@e9r z=?~u*e&7D6nrl`KzF7RsstZ zL~MUe@EMTMlQzP{z`Pk*cNU=OmaOG+TbS_ydq(f437Y=^L__vv^c1i5W$95$H~v&L zuFvNFm{k!;SvjQ?Yzb)LNSX}XIA^%a?wn6l{HU&n6i=o9I32?jXu zV?32x;I+%x?8?!cWx>L-eE9(kt&EASs5@*BlPl#Pof*3kmwjB9)A)pNnm^n_O8PCL z)iv2$K8Z=^MpEv!^U|!+VleM|VxgMcjk99W-=2HmwmQqD9$iJ_w2z9UO(Ap%?Y$ZZ zFPNh?95@Y`s{)qS7V4kY@qaqsuw^1dy zKgz&0cjb8CYkTv(N~W?VM4GrgJJm(N@@G+#(Rhr221y>E;?Co^lmkymR^n{kh49v{pw zdAi%I9ck4l&sR4yCUi|w)>~_@`|W33TGkLj)MyIBg33HML{?xa6g2@=rk`U1p$nGA+lnWvMQ zA(lB*skw8e-gC>e_@@CB812m%MDeWGK^ z z9Q10fwr4l~V>zj;*i^+syLSa)Ap`vEezqG1^`Q)#mS*O6F(u&B#{ zsiqo7!_DgPR0Qv>z15Ge3t`hMaX6*j`SY?HM2teOsI~MS=y_|!-c~t$@?5`gC2q*M z23T(Ah=EnN6}spl6ZSurLt=sDcSg)CKgo_>_@5oxr+43+?OdH!HFL5bix-g9pro>$Q2zdS>xq>Y^GM8L!iw&+LT_pm z4Z?{ z5nAK(%aaLBuWLFlJ+Z1Z8|t#DmDQ{H;!$$_u}c4n==bjV3Qf|Mc=IX@7C2yUik6S?0Jj ztOYmI`fqMHwWYi0%boQnLu57*OzHk%zNsV$cNS#vqR`|g|o4&$qKW+c?g!{DFRbqv!>7)rT z#9_`s?y;hAK+VCoX4=})Pf5TJIApA4H{K`z@G8ly|9gs_&Lvg1_t)Gi$g}~=t=Yjf z=!qoh;V-I_rLqi_II{iHR{V%;V3a;l$7@FSnPgjH8DuD%B4ZQX*e|^PMK$%vgdZ!? z-z($mfvY=pS~KGiAG>9mq`7R-#=oHk#_VhJu<8-De0LR5I&DGd#@dT zteU1K;~%vVBr$a0zu-equ*1lVJwm%=*{U0w^*CB)X}%x0ah7izXLA_)h$-W3=I+JJ z-Mu}ap0Fo-5E(`mDV}!ERbOuT1MPigk z_s(32APEi)sK{J4z?}}Pucf5hUwQxBh#+c(Z}*HtL&mC49Ue{A=&y*MhgG-Fd+9vi z@3!C9k(bfda>L%RNuCs)=|E%?h^O9XPnU7P+#*PvHE8EQbWcAtP=-n1q+r$KLS zO!$`8p>Sc@eBXB+UDD0_+={Z1oYC94(`rhD&7yzSYp!l367%E>vU;Pne|T{u1fS-l+COH z2><}&%Y2QS3dd%NY87ze&^3D#7IJp-272D!+x&yix>_SQU=_U=FIrre4gt?E#UWE| zZnfSqXW=`&RheJi8sVnH@w=BJFLqMu+RiuO9zLrjU)_n2_{Q_n zUeT#v_LE373l%E~kq=mMt+Kas?%?AkJO{f5vwL@d(295&upwq}?KEHJl~_5ZENMt3 z4!z~L%dFQ0of|@qcO{y|Kdsm@IzGTK?dfjyF)bM;_SL$j$;P@58{i1%bn2vC?dA)b zGrL@p@;ld#2>i2E6{9{L@}iP$5dZd2c7u!GGS*Uc+c)lrz1Y%&GC_xpH>Rt}B+(r|b#|-ZfstSuv5u zLg)jpu6pF178M!N>CU}1RIa}`b|d2g6ldG*Q?SEuEH^O0W5PDE-iF}G9YD{h_G zalxn1(@iS&&r7kzvY)!tEg7Tt{3*4S+5ekG4-23*4tgB<5;%yOXBz`dpiyV zxRVmyOqafqbA#Aa2@A$N-o$p{zXe*Hq}^nAUmX zM}1O^nvZ9`s56FrOL$S|sDn0eLgl!-_xrt_#?xgO7wBccjA^YTa8ZZ~Jo~@nA7=VsS?oW*g zg7B0=UC7ho*}-)8YZ>FxJF=>b%#O?g&*6R0z3pSaCAfbSQ@JS;VB^Wu62LUJ zf^+{R2pK<3ZCu5T`L8+%s83-a<8*(E$Y}iE=YRZ*T1wNh2=IG4x6Sz@m-EtTh3sj- zAg9~!`TMB@IT^oxX$|sHIq{AQKw(5#Tvp{PB^cce2xA|0oFu5ZJm!=>0RZ##C8o`I z`W=z3L(9DPI>pO_T@51iUK#~dQaJ;rB|jGgRKymzMu_fuN8WbJ+oN({x?0u;{uM*u zuW`)puOOKH^=a7KHtwSpNxHpz?1RJ<;2!%BNIYKcTRW5QUBq(bjn(CQshCv2CA zHofKvHTFzV6WR1zLh@JA?-Z}zWAr`o=yz@SQ+WA@MlT?%|MH=a>#20oPpd*U><|6; z%kb0p1E%OA>;PZoqschEzYK$ch$yn}ujGr2pljxA`*b094}y?*-{nqJX28#X*qgHK z#qgtqJ0XA-PNWq)qUm7SYY43*Qz=vS%wM$A88DANoNlR-Ru+CZ8m$wjR8?aNjrg5) z!JMz_~Q4Z24 z-ngMfdWj<{-gvMm%`pI#k-9p8R17#Fbx9q>YT|2sQr3zAyo`tDVJXLB-bj3l>GeNx0Q9#%}*FjQDfPdzJnN{3Nxz z?T;@L{K4BG=l~HF|DOUlA`_G@_M_9KcaR&K9rEh1ckfO==1pIEujAlHank3688h{6 zXSm)LQ;czpsH9{6<7oD-)Bu#gyLw5dN^0&wv1d@P=n`oW_$mG=|)Wz_)>xb=2%7XMC()b_SPU9iu4Wobr;s=G7 ziO>0ZOsFH4os{mH1XScj%F<9`%Gf4E!7yaJrwA4yz`hl!DRO8j9i;~i;J8l zsS9r&l~cKvnAvt8O7yi-{+WOKKSak*$EZOKOSN2E0lZGq)UvTl2{si$&#h?Lc}f!p z?CE88N5VsbbR(QB(;nD=wrA3gH<`mqMnnzj1Kl_in0{00l1bEwt;uNeG_x>=C>UU_w=t2W_-r`T#c;17 zIf{i>`hKx}&nE20R%KsIrf$RSLX1?B7OW9wStq*XOQxxV9P{ELJzs=`RUve2j-)B+I8!r-^ zRXKo@O0v;@U_Uz0GWhkepz+c5znx9jQ6y(-#Hx-?^s$yfsht z$O1HWNs^X)J0K;>#YFEAHnbyIn*Q(sQ>v4cnYo}#DIcsdeUL#>YC$q1M~BxYodTRL z26DRq-{N{|Um+2m)}PcCkH(}}JC%=tfu|yY@0!JqY%6;om5El`NeQdhfOb%nRa9@1 zXK&{vNKs5OML2!soEO99@671Zl~XqIeiPAx@CCzEQU#1_Oex!SKC%E9xktk!n`%~H z)kXd!(|?rjAJ_Gt0k_}o8=p;hj3}OSVN2j|YYTOU&ATHHH_f}%nyNMa{#gBi%K7Yr`n$uYzAUud3_v<$^LUah&w>R1tlD)xNV-l^xp15Bv)>_@hw7 zGIxZ7V)yG)79&9wLK9@>^;$7J_MZ;jZ%5COGD+bIFL)bdF&1O%_2HRg44vD1Z!a%| zyDrR`e$qIgLJKQJG4et))0@bv5GrzI-^2d7!L6*Og3(Zjn&$D1e-PIHNv$=R({)+m zk*4*}N3!U)-~E!sktfGOP$O_?V6eZl*EZSE1(PFP@Op52|Sef_O)*RdXUSBne4M^ zhalI&*&|W2e(!mpzxa|#* z%A*Bj>4`;frr0Dc7NIv_rUWrE$EBoe6K0;pVytU2`TrI&#UDaOv0(crA^XK&_3rR> zWXQmh8RAY9;9GIKLXwhVaahm^diP1=i$uXTrHi560F}kf!uu!tO$9Yn9)?#$2bF?= ziQ4P|6`>Ih?c#mM=Tq{UM~%i5)j^V4PnX~3w0rK|$9PAXq;3iB*()Ym0khQ~YfNP} zVY}1z>521KbHpZxWfjh+h)$CB%iB9eLM?=2_j4&smaQLgljrIxwE~}(2f1-+ra1I{ z{uf&NA1dTFY_&?2heh2YR*uU^f|>K~TaA%cpKi6futfBgaSpOhU-{G*=waXmgxb+zD2wvD=m(!rVc5ssGSKEQX= zpnw*}%+yT!KF&U+C!wzZVA7=}#z4-!aevn>Wh0>oD0Vip@{)*NTOWOE+15ZH#iS|X ztlKT5FlAmT{dIIOjOPKE+%%@^tr*kmjS#9jOY@k;SYwLRh|$S2QmP8)8S)Nt-=X82 zU#|u&d-aH9i87E1L?1fI0QGS2$bSugYcMY=dw+d@J+iOJvm7tP}@$Iuc~!O;|LB5OZFKbaI8#E7X_9GQ|Dd=>mc=Dus_ z5^zm)+u43TQnJE0ns^B=NMSN)&8Jkynw+EQ@Klu-u1^ngf zeD4E0ujz6)fS61k5o$EKOM43iL^%DBZ*eUr1%MGZW-E~)+@o)$Qx}W(eM@a?hqscm z@>HZIT)%j`2&dDPINZy^*eaS-+)rn*Fd+kMA$d|oty1UdMJ7Jd)0icU5KhY_Df5$$r{y@#~oa?wulQ<1bY1zkx@S`ZI(kaK0B zV0~f>^et)gZCc&|a+Cqs4ifD_y7_HvSUhUCoR(IZWj)kEv@k%_R)2P+|D=x=#r%VU zg!XTy8rnw=RH3X(%qL6+?*s5T4xTP%34k`qc(OOL_{@O85*1x6WlrThhRO>RXHj{a^xXVx*b--ZU zEK;zUZc8$tf<97NLBI)bBM%I}Xtkx7nMef{Q_VV;ONV}tb&e$S!?%Ga0^|zVN25}BAT3AoHl}<(YplO&2LDqWSA}Z-a;AY~<+jVN@=>ey-qbZE zS0kyj0-Vd6o1NDtxfKJ9>7~dRvN5k*fYEeQ?^^&W^e6o`6*0%!f%oV&9;Ed2+^pU3 z%|f+F@?1@16Pyb2sMtEMIMgvrJ{!w8Y#U2}&0-7o8lAW@)^C!%zwmCJ2!~ zpKM3<3Zbt_<+7so@f?<4?MAzVJ0Q3PkXyA)Q!o}#tw`po6;Z6F?_1ZnxmGZ5=fOK! z!>?5^4>_;l$o!fjMZ$_`*}>HXsjd^Fcw6{S5g^%Bb>gPVQ@s{SrVgKxvL7hEE`OX( zWrMOSY!k(uiQEhgJ8L!NlO;V=!OnQ#LRodu{cEX`{j!Cx)Fn}^7U9r;3SNHm2mLLT z$qtR>c73(+Pg@3xp`@Fn*MjP(1YPCvd8X%!3=}sMeQ5+cFp2gD6te21nhdjVt-ct11~l|!bPXf~HQCLZ5< zc)T;#$1$jj?M|W~6qPI>WohZRzHU@;6K&-C0{ zpj&uuKu+UgHkIl_)qDT-w2|-5^xDaD&StBpR*66PmE%w7+<7~>$n9s$s*6%D0*|Sk zMG;D%OIn&1{l`HFAr)5Ot0V>Lvx!cW4Yt<#$`rSsCQx4&4Fr1J{l2@9Y^B!RbOA6| z|3OH)1rU=X`(CY7hV!l0TsIiWD`=%8Xu~<8qS&jHXY{%%3NkX%Vril%?7gEaH6KPv z4MaUvN;jsg(sO-w(Ikl!rWfd+W)Ye~KcUQR!AZ&h-EyIZR^pu1gWLhk5Mk8|Cp9cM zs?<9jTDe?Kr#=s;F5PJu=3QMP^x`|AvF~8f|L7x<83XXorZmXvr0>O4hQ4BX@vY`y zGLzX!$dqb8-dnD!{HBVufC_W?+YSMp22-{K)11RR$y&vxwt<_Twm7a2@k>!<=J;fW z$rp#}L2%m-FFejqh`uy#s&11=bzqq5>O1F_>vyYy^$wNk`+vNPUqe$A4>|pS@^&?? z`00H>c8bWJh4A4+fd^g3E7uATPcy^AaE;Uxh#P`asGo^uhsqj3n>m_ zIHjU?4@KUr!7wsz`d}f z(A%*#Ro7$$g5BOvjq<6jWYsWN!)u=2;#@Lirdw2|oK&MnOg)RyawRoYStM_PlBE8{ zTL+3z*xzq$4&d4b4`KlWN!@oW(y(vegeC%6+=E=8FW+shy|UP8_DMpol*&lZZkRLC zrd2i6 zY-y@&3g^-?2dHtTR<8{eM{u-&0Z@fKFo3Z;60VXS4EkQdhx{Zz<6bwSTGVKs%|LoX zrYsHP#MK0}JQ+6ikYlrE$=dly<;wH@aP}5Ym$T&hBH%g1zVVJwAnUFdUney8{;k&- zZ8@<{DA9>g!*WhI186NSuQnm8i#OVBxb~(b4!J+e;Qjyj`p&2(*KKP>R1l>LDop|i zNE1;==n9BR6A=+<(xinN0#YMLFF~YBfGvn1CG^mXv>+vsnovUz5Q<1oC|}&?-us=i z_r7COg^qn6BD# z*aKsjojvNwi!8nyHSQ4$S8P}FbBl*?Uf?hNu4NLWt47@%doG*rC&2%S7W^CTLMfVVXAsXa;BKhe zV@GW^m52*(fe8_(NdO$CZmq5|=*jK%`RozDg913KhMWKiuFiYiV|mz#b+sUNySc&a z*Z$~4kk#7E!_bOnz$e3s*zZ{p1b4P90J6!ikUQEN9s$ZGz7{m2_XHWu<2F0RSY=83 zz3G$rLK@ksihi=%!17$xJ)g@fzu?TafE7`@#$Rs!g^Vs*Uz9!=F2ATY_A~?7ewMky z;#oLo#D+(0ffEw0LWjSKtJn7DjpVAmgx(k1>L1}Lc8<(XsruYD79X;r@xR_N>T25- z*`}NIg1ZA1tbG8K5KG$K_FTud#V}@06QCD}w&ru`*TR@$h{o=qwu3^aGWtsje&jBC zzZsUd-QsmK{a1(%Xtig4jQvC4JU%_=m=?^Qrp>WsxasfN%pL8_h46k(8F<&M6_R0q zJwM{W6zgGk65~6jHJRx3ayX@rxk6`6Fndpy;@}+Z^i})WeCgnbhiPF^4qd1r<2SFh zlm%0#+F;nn|GS+(MfGL;dprM@{(6hXsqaB=E^A)reu(J;(eX>(Z?jLx-}#jN=?k9e zya21ND3FeSI#)`8l9$7}!=kTx^mO~$JH{DJptyrlwGeRaDFn~&A(d@9(t)8web!QL zz3x4oGT0X}*B1tul{EoA4z-s%TdzLUVJyZ|((3b=^sSa8BAh9W4zJMYG;0;4t#d2H zUO`tdCqG_&#Csn8*cawzbM4M+X=oQm`j}4xL@(Uo>x=3O-sx{syg4mh`^E(|^O>`x zLptU6wO4QT@td&kw92a_%+Nm1=>7PDQ(p>h;j2t8lDxblneooYvr;w|=L1aSpIuw5 z&i4*y8k+o%bp3A?2mIV=a8!g_S!7-ptBESxm-$o-Qx`M@OXi50Ika~$-Vo5Q?RdkM#LOfFV%}b1} zyb#lU&fhDPB6B%=f}u{XX30Os)b>rjMZ}wNS_U>@G49%V?jiMh(VfI8H}N_%E?MCba^$Zn`hZy;R;;bj$NC@l&m#!zAgk5` z#~!u4*80GSfxsx=+C8`9IpgJNNEQ~EbkHC$E8!F2i+!%YZfa3gEe*|t5_|8Nq?JSj zLyjA~GKe)woF%yb?1}&9e5dg&se+z7Y7t`fR#CUgq^4*$T{NELS_Q&dr}MnlVD zD@Lbmo!28W7}GZj$kyj|(t*3?WOZ7z(T2+9neB=b?<>bdZ1?Nrl-Iipl`<)&xS_ja z58{h7NpxJ>Rp~OQp4A-n)nTYsqS@$!(TmI!d#RCtm;fW_)qZc;9IZ#!I~skO(EgJ- z-QMCMb%v!q3d32NwIjLHsFBNMt~_~lWqi?nNzD$GbuLr+gEggR3!D0SOno&;J3anY z7VuB8$NXeNi~qAt17_3O^m+a@7X_(ZzrJauep#r%S)Ojj^i#&6Fz7-okTJuMi%|(^ zUb>|ss`zN$UR{aiF}I=H)7G@5W8sdOa9Q%){JkSJ&@GCv+uB@D9{VdyG#b)Yv79wRVS4aIHn8^Ry0Fy+ zuu#_ykFE4BybMIDCf)>0AignO@wRE zNk?9M`;gNvI@%~Mj@esyh-J;AB%Mw#{mkVpbB^$cQmw>yzU?5IXY`M9#CL%`MH5## z3*|VT5BBZ;oVpdd#bGt+XL||3XZvZrb+R`Dxy;kOR1dktm38M@L)`NFWK)VFz+gy( zb!d+Jvz_!WMe@*%RWZZP?~F5&fJvz4<&E7PhBm#! zq1fAA_N!PtqLAlhfF`-bQ{RiiQ2B_`eW{x|@?FB_l)**TvuWjq?t2I`J-JR%L}xU5GaeUD?P5rnF6X>t!Q~rq5=BYYr?k zWny;f=uLNK+y4^7T4;68oi^tDr%12Z2FjP`m|1<>#(>cPGO=7fWtVoE2ml$qw@QJ5 zNYgrr{q9b1HZ1j!rITw5tXYFK&vq;AJkqC{q1Edf@) z&?1YFuR1^@1*x%xil5q9(g;fC@)kRBXxKOVDc>+1nxPshlDBLxBCeoA;?)G&EgRc? zHs@{uIKEr4pM!+W*+4IvaDJ6T4`RO10Mm9rG;WAKt9LYGj(!$|vHDH0SLx@>E-`7C zit>tV0d?o_|CSf2)GIr`tn73f2mA+zACD}TdMtHH=8PqiFpZ>FHnV)eT1+<{{BVf4 zn8-h5cv70erc~)9*0^_KH+D@ft#-8ccDJP0k4nG0rv<%k7jr7SVAD+Ak|qzyf}K8B zrX*Ly!cft!6S-AxQmp&B_@`|T9>UEGvB8Ksdz0r=q0(h$)CC$W5zRcyM>p0^@6{Ov zV<%b_Qg+9T15V77J<1jq6$9q7XVQ^49vP{LSf~!Ar6TnxQZ?R`{8y*|7mO zdetnEm*V|yCC#@lHd73pJS-V#utH%?-u2uJW^MYpoGXCdV!-6R1(r=o_!BBvtdURI z|Mop9zrMv`aFE^aX2H+bUqNSQ5XspSwf@glgvSgMBO~sV0e#Zl%I*>UN9Mt@{JocE zBoC~7HchkIBF4&Wl#MPE6*H?!*X|ViJ)5Z%vJ>%WI*+4wFvRXza#!i8+!!C|cX%`& zqAG{ptL^lBq)sbA?gH+lC9v ztB1C!zI=}Rn{luri?g_s%<{TA`G(C-uD0L)EUU_gMA$L$XF}GY`k3^Q?#n@<@?PzX zlLh5t;9CENiW$`&H!h6taQb}PyYm&X<1SMTAnL~P!w$CtkJ$3j9q)cx?N@yCuc8zB z{MrLs?GpNqnX}6?_)zkzE<*wX9UN>ZkKz9CKJl`J_s`95g6^9x(s6|^Cf;&O?DEdY z_`0opm<2vp6MgA4u9)wJ=zORKo9eFZrXzqL9?c2!&TpOi`Q^-+|K0@<5%aRgve~La zoo9>i;qqXehWM<(`C|-!y`%c_rPHpLSHmKV7H%x%PE^^B>ickh;>FY+^Y8$7Q-A7e-O@tPlGtr0@t*K5(cY{K@U(}9crcCtK4g{Mr*DSq= zRHOK3?JEy{0Jxv@p^C6}ct^vO2|&qCe~FaqEmXQ|%)w4$=^LIAp!cN=J2PZo?SFtf ze*!S`Ou(dWqWrA%ofoIztF~hWoU0A%O8+FCE0>q&c^+X=9EtK8T-4{@! zrq@pg78+VeGp&7|Ev;@Yu<}?@$pikxyCR~_^dVsQkv>!)c*C0hIp3paeYLa=ECDEq zMRxZ4%wl(m?-aPERuMUR>M1|`vf{l2?uT%HXWs7QKkn9CsbFGs0uuQ@n`=W7r0#?n zq#@4vZMsDG`1MO-Y!Tj2hQWPL1{#Ukw%$nJXco#?!>HI3L~;QeyRNyq1AVv{;!}AM zStMagNjG3*Sb4O1!I9vWIXW@J9;WueX9{D^3L?bgG_0IS}7Amt)2smgF@Db90VStg7Haosb=q`5t_!uxtb}!DC zxK#UP$*8j;a1kx-{1o)-O5R4*ET zzBJ)6DWl}aC36%uUCZc+eLr^vIXOyk?XEKHE55ieKuyCd*ukN90<=EJ?DRD7qR^F8XWoG9es)MHIIc`wRc#DFIrp-+AsV#M-95*S+60 z5L3QsGa_wHT4ICc#FUs;#n(K*gE%pB3blc9b_$>!yq8j=EaIM zh4(wXwZ`Kv$Ytq&Rp~M`M|*2UBSLKWk1a^Bg^3=Q5>+*IL&RH#n!*NXlizUhF7Nod z|D~k&yZ#n38Ti@ks;Qf7RL0bf9o1l{hwqI$ho!JD_lwpUbqwI9o}N%ql1y*N8J4+- zU@qNTE79*$Hft#=av@YV3>8`x+-n-Mj|j%pQ;avLDNE;*B>WYZ#?Yj8{y1ReaU$33 zgl<0NiPfZ;`g7nbK)AvYwTt^1?+}SK0VWuhxzKrjom1Qz;Gi*IxvCao9yvM3l)W6o z>f^30s>{t_D|r)LDw?f2eeugXto%kSpi9^Pyc=B2Eb(iO-r9I%&luSZ$WOcdytA3! zmvj3>Ac>h6CXn!a7<6N%wB1hh*H68hR)nWU8JRWli= z>$8%upvxM${pX7I}DqpNVZ}KKlt`d|ppp!OSgD@_FRk>NW*3bALsqtsst4e4KWk_sJm31*Bi=0x(*u$VGI)II z-ooqbZiA?WF2XL7dgE)v5QK<_;|kxBPB0%YVeG3?B+afVpB!L&E^?e0a`1>WACpP? zi;d(3DcIG8E!OaWIWEE#bKm9M8LBTs*17gN?~N&*2qcB-7}c>8QQiZsoyt1=o^_Gx zNwxd-J*WPyt>d2NLoM~rjN*17HelNS1OgnsVL#&-MDKE9U>N7PsXct6RK)7ZJ$Gq6OfnEPAq||}uUkiz_S=0 zxmhJi6mO0*QgAfQ9y7mt&dpx0x<;938RF{hIn;o-~5`d8O-n=l=zV~KM;6S+#@OQS)^qWWgt z$^-Ok+F5V-8<#&6hm61PpLT5F1jgp+v zrw{PQA{$H_4;n7|HZ;g_%VTeYHq-(vJhUx1d8|Zu(t;D7BKp`2$)8a6H*i&R&C%um zRVK7}(Up!80>KYZ8BVp>?*gS_<2cz{c;%)`57(`zFQ_=Uq+W96f2e|{RT%_6!)hAAW z;#*#c{Xil1Q)3d=E2Iv{YxbOWvd2hkJB&wjNXXYEwt%Z2s^_3Sxu&?o+}Oj++sCvu2xH{Kixf%SqTZK)-l@XBahm?$>}!;~;49KbaFR#v0_*EPdn z@)DZ+@$w%H;)Ei}bChtZ-iSen8SY4yQ$!%^lEBXhPI05D$K2RASC^ysdZQnd5SX#l_$=rO!1G@@B0Hm%TyVJG(aHN1#mo zod2E>)Kx=Om(&UTlB7Sq&Ug5YPyIm4f;`~1G1WNp+rleC{q4I<0#0>~Pni18SHuaB zZP?_&bDzyUqo1(G3s}7be-&XX{S;ps4SAKW7Eu^cBT)tt^753$ssS}V=nUVV2%c4i zeF=b`=S-t)fR9JM5gxXN71-(A(F1?h*R%Nnv7IXi=DG4ld3>)oz+ydjVy+5w+h65l z(JZUZv}_xcKgXh#h;@928i36!=0rPQ;`g-7L^i2&*_wCq(><%zI@-88F0rN1?nUuR z@t~IB({!x%D4OEp6Vr;JCg+@7yR6BWNqb$KlyGSbbVP!$%x7&D?;jSula8GonS1xZpA}#{0Ree>(_yc)A8^E!aLv zXBV#@JgLRHu?R%Kv$N{V``_fvsl6J%KY8EnTn!Zl%}^kDq16-FuqnFqq1P$&;S#*-7jh{d5p-{Y1TU(Gvj>* z+}F5md4M8+YnFZs;8?D!hP=$ieu!|4-oG1KdGu@G(Ryp>%~ldu{V(b;JD*)dqebAs z!bou2ZQ@4bo_U0;yZsj_Tz0HTeSq&=tellZgj<#h%!S{D3BADfXgYKhUG_EfO~r!_ zjirdwmsp%9|F{kKqMWj(*Z~JCMqars5P$n^?8P#l<%+)7Npl8$(L6DM_#b8IQz_1q z%l2%liG4xueVH6E=hC-A-ag+r)9ON7fe0^sE<41IFL1fB)TcZe=U!E`3Au09X+5~W zW3!G>>8N#ID=P6JRorV_6#VE4>jTgrg9)hywyS@q%>Ja+&>!Be&7psl`SepylZ*NN z&d}e03C2jOtO&#wnwq?XiXgd7FM-Jeuau>b_Rfz?`isusDsm~og$x9IoYs^Ikd<~#hYl1I$@4`7)JjMgm0)og3K zH0In5fD0i_@4DK2R<)srD2@`2d?>U;X17lGTrT>*&({t8h8&*%(K-9!^?h4L2j z9$U7WT5nieQ-OisPSIAsnPS5{h{~XSs&epttTO1+9+$81vAM|V<{FIdP5rDb0XYuH(7ZD2sa!UW}@+;=gNV5;=AAvfG-b*hCIH2MlOfV^uGg) zbhta{eooIei-snQ#rJyO{4EPrA0Dp1w`~J>0H!YU{lYheQvm2Ar;=ty{5rl%yjluv z{T|cB<`-k`COFC)s*x)|kW~p=6Tple1RCF*rHV~5s#+gu6J%JRpMVgZVn*uDbJ^E6 zVMT>L{(6Q_W&~@0C<|}fBFCmyILVrRMtK`;wfStLyl|`S!FwQ#^DO8>NfGWp>1XOS ziKY!C4`zjRC6T3I2R;108lLsywo0?3;Y@bv?;0uV^=;vNfk6MgdP0)9{TAT_Y5L9l zIV)gXSt8S-$sZI_W9m(H{vnoAgAOnKD^81quFDjZ><2De&5xx{nKsu#B6sMTNvZ-Ohs|$?Ttt zAly7fsL2``lGbSE*EWvrfbtY5xz28!(obHy<00JzS^cU`?-EXy=aHhJi0_*<90<8S zUvBk_)sa2hXvw)SIqatsF!t#C9D@8b^Cj02`(@x5=}NbylhDS>%PeFDmM&nnBDm(o z7Wx6as<*k(-^);L1koIsq`WfT?cn#k2`{xeP#5-3w~re_gNozZI={3)ZJ&z-UKLKt z3nKp=_xla~-J$_>eqd|&(Y%YvObfB~o}`f;6| z#S@yrVO;%Sc;PT|y=U2;6_0$9;!R`easeu3AqH4yUMbM#T8@=URt!X?Y|7AGtML-RB)z-K*wgeURDIgsYvaQ)0t4B{ zOu3(SR%w)P#%u2!=6$HybCk~1=IfkdTjlhjg?N-JJ788^UG8=k{1nixQF#o62(*Amlh>9 zw9xo8WZ{jG`wfMXE_PtzW?&}>3M>%K4h5`Yrs6wHT%`0I^0Ox*70=c+R5!kt

    m zYcb19EYk{at$%o5v9Jz*&oTAcqr)w8U{4sMKT|onFk@QfmKi`kURM2r672!Wa-*YU zo<~?E_{lGk#xv*FM z5qLtP*61@-TW~(*qh&o!1s&M7ChU&?h97}JdfjiMQI1*Sw`6@fm8EOx6TVD(g}IDF z0VL}Ieq307$;$gvjKo<=x7yXT8yNz@9lM``BFytZ{4PhzubQ;Rz`tf1C*N7V>%CiS z9qzRXl`V)aPqz}boh*u`YsNom^BNm}TIhV2a$7fC1WZ7YO;Jya<=X3u(gmEq8sZf! z@^<&R+SjFh=IMpsXAN^Y%$bFyHmBt}vyF?K9Q_zqHvu_krWLx5$>VnPxd1bj2e)B_ zyyU#DWs~vRKzwx1K@G4eOW;HyyzWH>5?7)`jJ*3gOUQ>Hg-e^Y=`#W_L8PvY?>Xbv z7JKV#5oUjxjG`tRYR9*p@+bW-D|}rmd;KI)q5)R#Lt_# zde{|FiSesh2P_L=PmHYjv_41Y3bi~~fp4^uM+euOgGiViq4+IigSkw^3od;_~=cZ_dbJ6P>X z19M!xzo8|cGNa!4jVRJhE~+YDq3$Qpl|9|Wh1+g~(tY(4lWQJ(DqOZw6gtR3*jrXi zA{+#mC32EAG9A0_xj|m~`37Kk%O#+K9DF0W)zp#B z>t=`V*RDwA=%fvEykW8+kUj(NsNl+r8+53IcD4BM@sLG@GLY)*LId|W>Q5=enc6lm%{T9JMpW#qy6x$9pb2@9Xou-EeH$>viJW6}ZI;8J6_DbWf4 za_bK>Y&h`&&k@VnLA#t#Q9UMH6T%;E({lu*O;x&gV-mWXQhB86!`aeemI^JS}{~1etSEeOvCk&lraTK%?1S zWX<-;no)DIN6fF=xq%~frIt_Aw>-VD&r@)soM0UNy0o6LW-oH97G%AC+y`RQ`W7CZvA*ldtn(WDIc z^^v3#*^jcB2Z{L6qlF0uQn$}oUAO6>3EKvj0q@|mOmE{uuVYH{#$cRnSkqeS?9cM* zd)KqJ0+mk?2|XLchVdF6Z)s}P9b};PcH^b%qPHXOUK7Fo#C6B~Mstk$UbAEQMi+$o+SqSyhkY)X26AAlPbWyvXqz z+(EaRqzzn-W zPwH}#b(h4IXpVdf5jjhZ6sI?uJ)l7E?~QQvuh3>EkZrG0`4N zM4Cl|7p1!{541^%IStKDy(k^^4BVo30!u zHSycVL)f2lk+7d*^s%;#wz+YL$dwn|U){v5FfWiEiz0o!eJq?A!@~u>(p6$f3?*Xd zhFnxtmD7lmjF)Wg8&6T(c)=o4K-YHXV|Kl8(t%wJlKfhq@3K8n*(uE=im`&B5O8xm zH(SZcT2bxF4~Y0+`=NVZIebaeBGWbosI2k_^CObfo(X>Wb$8!W!j#fj;!Vz9+=Fih zP~TH+45yk?!LnJpY9YE+gY^6@loo*zrps?&v&~|&&s6dwP*25e&m~G9C>h>#B)DA6 zI^b@*`x>55L5`?VQ~9%W1TNBMD*4G?_wI*Qw74Iw)us;)i25r^7M1w1_ z;6s|$-acISZsw9StGnZWyp@$jddw3EQvflqeOM5U!K#)SE$0fAxyw$cugXh5=*`gueA^F5^~r{BEdKH@ z%d&FKu$og;(o!n4a>^~Ti{nX_&9uZ@;%4{u2f}^9729IgT9_*t2ERen(z~adZav@e znZb*1ULCQk#*1{L6`i^C^Abil2xrI<$t-t?At1-@+aNYksjd7*XpUsMVFY>L3WUh5 zeU-;4OT=#S9HfxxWIRbJjnv0sjN~z_3v5goF;C_?V&Hp@d2gi^^}5M|b6|kdqg)JI z$RT@3<*L;NjE!~lT%(E=Dlk``ofEH3**|{ngnXA)u2|oJ(A;~P1?w%|ff^xkz>in= z*dJ+MDEXzUS~bon$CCQ}3dw96@Mem4t;YwZ7UN$^3=JA(l^%LW^I1y6AGkyJ@`H$5BazFlSH0zZD zE%S`BuV8&p0{v=mOS)@QZL~fqoC-Z>MmkYPMyTo?@twbFuJb}#kQpJn(RvY@3nCvP zRX$F82De>=OGjLBmzRfGrV0Xign$UQTUOk#-2V05wtPtU;f#N7bT4K8UfRwF0C9j9 zFuKrceT-uJ8VQZIM{%w{s5BYFY1BFAK$6R;`X1_VsTFmN#MFWP73C$s`YbowtVQ2d zoFS4TW=kI|)ukV`lwVro54{4dyyN}dw^fPt>fO*Uz1uMyho)WfGI^^xRw?{F{feMcYlQ@Uonz*G&s0QhZ{LM&G+LD7getKL^x zOvc-J(5_Oimdi?BaGTge)=vo_d!^`1*D%{Ro#GOIQ?c7OmMW>Tk+$(!GG#EXudeTx z?NNo~R_sXp*6vX`=AfUSnbGaAT1}_1;^j#|!;36pMLej@ziHS{i7;-54pmi#WxAeZ zmL7w1CaQOKD%XV8h^BtV;ppF@=oWK2#9*gciWJEkmS?T;s`>a*o!8pf+a)QD3i{JC z;=wOiX-wcrNl7!*CH*{shjV=B=8CL?UOSs}gBvq zVQ;-!I=`IE!gV&b(>eS39`V;1Dx65`9+ZIzV0@lP5m@|cbB-m`;$A~(nV7{_n#t7Z z#6#6Tddm*6ry$J1SMNq-RMlF#g-W1?x(qwLUQ(wiSxqzIC#!aQs5&~(2+o1AT@Y`V zra-nu_OTzhVC5GzR!z1%)+*XQj-dmK#;g-M>fl!_HjpO);;wI+Ngn3J8*bO=epYF9 zH%Pe}a-NZ+ZjQmaj2HJD_x##D8oB%0Z1IjQXbb7*L?U@TT_^T>EcPsp7C9Tka6F1Z zhdlP1g!e_*%e__MZ*H5&Vp1B3Tzg(U`a*8!?unnQ84s3w=fFmy*FY}yKJbl_@Ax9Z zy5jnug?e7nDMA)_fM`Qg&tGbBGBu{$#6{+{rjj$TyG!_O`b5B8{x zahCDf2-Zii()b#tehHcZ`tZ*XkDI>!763e{Use$f(m!$w`6e1UxxkW+6!|Cto1cqQ zrgMFyo;B$*W|MNXYbQt>#bRs(0uY${ll^-kkb?tr6~(geeocP|G8KsQ;SlnK!$a#Sop$;>?wueyJXLJr*}$g*(P{p3FVS0k<~IQ$V@v{idw`{bBt19Uat?b#jm;Www|g ztQXdNLTP3aUUUs0nVeyK^qHM*R%8!*a)@n{Yd+{>{&9RrRNTkK5# zo5&U|D!ZDpO{xpD@V*_yraK)vhR$MPG4g6;$5@db6u*si){%8(%~Ato4!rFXAFKQh zNH;EUwrYD!?`|@SIFoMD>|jfpZk4A2!)uadN1(+D$QO1n+jE%$s3S~giAI?@@6LRJyKL^58Z0sdg5+xqTPvm)(8R)VY<7C%}F_c5e52NJ^`($yW$6giI z+`NB#NdJ0l_gZ${o9U?Eg_GVE75z2%-f)YcWgb_C0r&u*tFx~XgBxzPYcR{3H-fbH z1TX3czmK5%Z4TKiPhwuM>facH_vCu#9*}zdBCrS_KDiM;4|7;Bc+jK^((&>>o<4XYM|pVuAYeH3XGL*W=$!UjdoFHu-6>1Q5^cB^ zv6LCNBGUrWed%tFsOu)yqn3{lzqXJb>d_IPA^4fA8xUa5GQvAefEcwUuLn%*TNKxa zIh~|7%O~rbM@*z^dAt0>TO}3MvPINz3!mQIoI~qGP{^bbrjjP|%FljHK8^XY=A-fE z3==>+-SH~+SbT+JaGIQSnJ2BP*Wk}`&0K#0kf>%E(b}VTv)jzAJ`a0s+AR6GN2cu%`VnU zpU-k$ZBL|hRhP|VFLsQC=_uXUj^ot5?p(T>SHtA647pVAFm|sj_PM_Em|+{HuAxbN zTE}l?^xCEok-vRdu{X9@78!r^jC0pw!J$w{kK)p}j_8x4*k7QU8KY z{>|g;_WR6Gf1$oG+5hyySP^0BD37IQx zAa!g6p})bE%ltD+hLp|sZkbU4VgCw&0V*<>!5R+6Zn;%DINi%8ECIr!?&pyK3B- zARI2rnXTP2ZSwcoUomRF77wnN;UesJ{(Q%G_-cQr1jb`1+A8F@ZgIR|(Y=~ONOvO7 zayDPdjmb_As6ECtAMY@qva=vkpYnP2x3QPYd@4XfyDz}{c;_;ACz-CuOE=3HoCLH0 zpLOwHuRzRYXCLiuGLMO&;c(~2UfYk-4dC0~ZZvruz2#VUh!Z@0e4|W{%N|)V=+S1k zbDSaSHK0+phKRkpT2P*1CAa2Wb+d$RL9${{mdm;Ec}5=Iqo=`H;K6FMWwQ{@E<#|n zYU?DK_d9XnpXb1OSxN)^8N>Cc?=76=w6X9%2(alxn2mG+RG+6-%lnL)gBeU=`u!dP ztt7#CNnLgpAp>1Lx`*-W9qT9Enmn8fpVixqm+z^?6oM1+(64h3%{Md_)z|vE8qaLKD8rGB@Sw` z)XpkMji33xiL73G6A$Y8Xi#ZTR*D2?x-{1_^Osj|ILm{q26=1^3ZVS>j-}A+DaYXH zhg>Y~-^(H_P|SNzc;_8zy9%cI43CP>ecF>8e5$Z^cD2}duPrRfxpGXG2(B1~XLwl( zJlN69H&?H@YbUM{|K%2EnTwc=3h|@Z$`v(Zs6)a+#REPt3c6l;Rtm#rUCwk=i~Oe- zKea`dj#|F6y5_!`~86nA3M#&0A`)H zywV;T!C%S5OWrrr=+`MjYSg7GK9XBgIL+O;JAJ3qcTw5m*`062O@;eS-PRa~q7iQ> z8^LaSc;9$?%Gm6{{Fxt=d?BwCgq8+{b$AOiTBUCfrp_kHZm4rGzV^B{NB=jjza?I% zlr4T0bQE|%%@eK^bMdB0*_+;Xf{uh*&L!dV%1R#ZbLzWheP54W+C4^hcy+u^q;tY{ znMY3&)4K8PWtaL<1!b0ODN4IRUU?T!mn>c{`;?tP&kd!~V}V0fm5VC|7oXvsScWqP z``b|&N9;4sW2VGz5=GlGE@Ou;N99P~L=2i%dSX=(tClrf!$?d_Vv_FyWy8y=Bvu?v zE<~BA3`=qKaMz9C+VLmM0rMDpb|Bj8K($_+0{2UXEF6jz)* z>4tH%PE*r;v3w`&VX70Z$@WrZA)_$Llbhi@l594?-B2+10_gh{CjVioJ%Q-%~cJ zUJu68xvS0R$>!?Ij*H6Wwe!MtcmxK0`n(@Ol%`s*b65QCom6Qo3x|pK_;QbQwbp8U zSXOHfybDWnx-%nn*eG^*87I&x0{_I;E_N4_IR?&6kBL37$JLC%tO8e2Ad8`tEz~1x z^AfDFg6uRBW@3a7gl62B>X7V(lAyQqBq=s#eRE@@x@Pz&QeCaAbD7tQqmmCHPFZlp zxbPpNz@Njl+^1h1>fc3uY?m+R~xE zvpiAAcCnnx5{_%(?PxSP?Z8)U%j3Rs(Xs@QbDN3yb;YJ*?-#;=lYrfMc)oNs;r_%@ za}$>R2r{(-P6|k>sJHFSfJ(a-=k$`v?ef+V+uz@j3MLfXq;C+jl}p$rSLCZ#+kiVl z9psg}sZN^>7Fy~r$-8-{cqDc zbT1-Ed;hvX|NT>!C1`KT&(&gmSB45mtP|;p*S(g}pXe+l7&Rim3nJ}=0C#S{S7iWh z_TpPMmTc4PGl=FFo!Y}Wr{nEzAQIV&@4~4X4T$_Jf8if%J^UjJ*VKnI1m!11cC032 z3b<<3>wE%P+w0=ina-fYo9S7Ea&r z;U5bAKi6+f8VH(kcOlKGo9xU#`x!$Oximn$C5PW)0{z|;KUsYLL~bgceJSjP(FT3% zGV^LTORUrH9TwoYQ&}kjztWbK*Tzl!9%Ry?g+TXn4fLUIFIU&dRMv|HS;9KDHO0*H9}@Uq zzN&nIC?{^#P_K9>T3jo?UZzZv#*8OwTVAnF_){t4NFX;GscFj+v_gKDo;|rsGs&uC z6d&<>td4s#rBPlL>k$mQ;K@RcdNdaY#W&rv7%6(cJM_JY{uff^nLQL7g!`K9p(&@0h|5uM|^7jOi4-z0(FhWf2x z_xl!25U~2AG#Om|aOyF6{AxwYagk6-KUOUuOV)`*scpzS;-b!q^|DVf$<)R=U$817|bs#!@lX}+mf8mrf`_@EXM0_@5zkx+>L zPZjHhZ&42~;mk)qEwx4s-1=37B$)zCXh+%#U3=G`ZfHb6BLDEyLx*oLwiOfl#+<$? zol!@z&Sz2)Vj0%Cu=n*Byywiaxv~6b8@Kx*bdma6iE-YVx#A;ACr0InEHD3OLoQ43f-#zZ@U6xsv1v$1*&6w)oJ_=H0tGQQlG9jT{3HM8b<8|iQcBEO%f&C9m zE9R3+h1}A9zcwFl-XGIrA`9}G9e7FS+FAPm1-0BBi^vE!a}SmFI3ZNb2s+xIJ8^!h z6iL!(ls54I75>m&E|152)nlvNe5#=bPgZ~1p9&JYF}CB->>WUxw3Q{sCDr5YV%p7m zbYtN|q&p`=SI&I^WAWFW4H>J35v8qWS38v!f@WqD4fZ0}XF*7) zFgw&9ei-gkbbi(g-y1ZlSeOfRp#;F07J?v1qZkWr){EM6IEqip7LCNpjbr3XPuRwT zDZeS`i;72k0l;wpEK)XiVgqYnRrTUEh8cmGZTUIFP4dTGf-0xRD`S#quqC-dR-tv!$S@@_nAjutZ>RxRMEK48W>2`k%&GiiOI%A=9 zmQxz^l*{=>GYyoA{$;lRvS|3oNsfRI^9bE^P-t!uzA4Cy?koz;ne^EGED%`|Y%zJ| zeF46a`)jRp{(Dr4_;m$*a!G2G>1nPG({F^;CE?Wc+4y(ntogedot|pg53v&n}c1@ngy^5(yGdU?$N&3Hz3TzG> z72|a9Z01j0%7DZoCzW=-aY;VYZ_1TQ#we6g%F~e+ethq9NW<-yl_0kC`em6rAHhA` zYT_W>Xv62{+it(-Yn&xt(s|EE(0z63kx7+cgl=JBJ;0@Ldw&xGObioq72`pCKWmoOf2KBVU0`QJg^qp;FHKP6Zx&CuQs||ShQ?< zzZKKz9cqT$A-8{i7p3VN;<_WbdF;Ut)G(%IF6&kNkM9JE-vpEdpd}q_&kgY5IR0X{ zROF4ja@8eLHZK~L=*6STl4I_H4G%2wp|~bDf!*iWVhKtTU74|fZxs<);N8yUOU+MS z$mJPl)WqAz*_ts8lLfrLJ57B-`Ji?-Kk8|CG>G=@G4Aq69V)M-H`EHr{ zVTc3odm}#6(y6qp#Tzp|Wz1Ut6-E9(Y!YyR!_0R3{Ds}^zpF-ItqhvFT6U8tdB7F& z8b$)t`BR6ogl~~zB|%^%$i-j8xbWIaZ8{0Tp@ZIo?+;3GBO1eXzG%j=Ir=w(ObN4LhyX;U3wXC)2Ixe`J zC_~QzACn<6{jjy|0p7&qFP9$YDSpvaSV-$RQ3H!lAfXBXN-l%M>Qq zKjN5wdLDoquSR+vKLdgnpLOm^becdle1zV?J6#y)W*8*l6?`inPA`-`nDTga0% zJA*=ScL5aC*-l2$lbOk&Y5!x_)=L=Jh{6&0=rK;ZI0n%3p$`qai#uR#bF`03QD5{P z7>0TbU!mM67?c=4EsAp?9Z6a7@DLG>L~WwH=js|$a(EUNQ0D4i++Lf;g&^-*X^oF4 z_eZ|U=$dwP?3Z}<9&+W(O zhP#u2pK|`5V#E^obMJ73=hnNZD%5|%Vt;EA?}wI)<_cbHOXgF3n4ITPGt8Hr6+HC@wkBfwT~4{v=rX#c;B2a^b{M7NiRgrzNohNjE|e^&TAbs zyL{XeQYdg)F8cK0Bw?%QU^#_rv1i8-HtIW6XZP^IA2m>aXQQ9i0j%=UI!6ECJji2$ z;w^tSu}0C~6l0wrj~JsA$~to8?fa{UVw^fvl*py1A^M2k3)+F0}1CL#|3x zYDTRIzf}g3$+GW~WQ9=uc58t0+JFYCQc+}n^Pc7w+7rH#*mHQbjX9W>r((2~nK zvff?WmUqHpB46mF?#~ocl$|bfB3yGgK%ftSIu>N*f!kpg$Fyjuagvleh(IU7qD9oP3yE3gV zA>%ee1piz=_=Ge)S;fWt4C7Vn+Cmyy%lEP>bGPM0gYBFJO z<%VVOEGEPwNe?cneV@Ngi61v~5usCi2$8gB>t!z~K-DAo`s4LzHXIZj5;4o%&}pHV z;|lo73b>DDgiuWvE4peGQu`}3h&ktvtOc9XKolMb$D(9K6VYHtq60J9Shgg zWqiwux)8sIwW$%hb(K?SVG30|C)Ua}BsCd<%2$L_HFJ(s!t5T1{ILuq2=aM zk%7kXxl0Y`>!NS$3as0-J!g*dNZ)$s_WA)+Bg4g4Sk|#kA3l4oj_zEBI-qQ$5_Ltl zG?1P~13%K?|Mdb&3tq|pOEcjcX~@@hYmim!ma~^Rkta%au1qk9To=>c*UERgCmHiq zr@ne>j3Vm30grELHW?}BHYV+9KE(T$i;n1=A+!fMTSYOQkgojYO{i6K=?yQw#<922 zVcAe4jliHUfz_Wm-|uXL%nPETCwbDemUZU?1Eu4=vLcBCw-O}Dp!ZjY1%tZ##5M|2 zxUu?4{m7>z1H_Tt=4{}`M9oy~g;j<|rz`2HQ6EJe2f86J&+bP3$N`NHkGL1iA3YM` zBz-CuV}=d6@!n{ChSnw5ucsPcFD2*=hi{Bt z-z~U1+)*6V4Dp&-x6Q}bz;HvUOUYgJsdq+s zgmr>iqK7^3MVCL`3OEJdc?~<#j`2>HWI54++K>1eCNEQ5JhfFc&wOTts#Q?kv|CEAHhr+YP7fx@1BUS%46E7OD=5%0yiUuJ( z*SscJ-7>oU-p_?gg&y9Z*lmWsCe@si^Lx5@6WX}OQkCBgZy3o7+fj(hPUT__W4k^5zE$ESY`C#GCxyuuh<=jXp&mx8o$1V;`adgzcZZkBV^j!r^eD-Kf% zw*$_MxAh3yqFtyLQ5qx0@>*sPZ&`w;qs^H`QopAor==gIixd71Z|O&eJ4EX@RXof? zzk}BcI=!s2in?z<+>vhg?C@Nl4Zc<7!rk8JgzZ%SnI7QBqW9fi8l|nAamGP5oh{F`* z`3njx^-^FA77Stk=&2W5+WZ^wKDnns$PoeK94ViY>&2+)rmB1UlXBHJDc0DVtF#lt zXA>uaW84Xf?}}QeGR$UxFox`?k-<;|^b?;(yyjQTfRV$bWWZH9M;AHo(``o$7V`B@ z{ab|9+U@if69%M${4s1EOj>Q2V(kZh#D#^3x#A>d1M^t8^k zCVb~0_`i7E4VPJ8QNV}$A3j0hP?SkjF%HG58qxVEl0^ajWXmnF@?hf!!f?5$j9ifi zSoP)#Tw76$VF+~ASxZ^mFv{0UXF#s%7=R%dk^2z7D_a{l%xV8>7^(VOXr~I)a-R8B zWVN%&h)pUxPd0ULZzHs3yxE%(we9(;*0XWY|L8?Y!dQ094%)dq^uxHeaBmE{tYgzW z%_rzur2PKq2!P19D{Q!ybT_LuUX(UuX||X^ivi{hEz9pWc4^4WLB9(K8X0la`E>UKgd@M;qDR4olA#Hj9ddj)qeNogD z-KOXEU${RYR^)L zodVq{*VMLacY6g+3r!<8NSJ(^CW5|eK3C{`(;bgNj7rhhQEA-oDP%nH%V1CF+++mC zf!gJodkSx%-8|Fh)yZ4|(n6#;@aLa{ADc87qO7%knnts<< z((2IYRbxWerJ|P>7uX9eu}ukvZ7sN#+p$k22YW|>5Xi-meSco$TXre8+8D{aU@e!y zPjB@u6AxBlKf_ip@9H16;KU@46jIzS!y4C;ZuL_mOp}KMS^c}hZ7q%6Ne-R9fcpmH zwk)9{|I%CeXMj)g)x@jm={3rgu}0g zNOi8HaIvP%M(<~Qzssd|5yGL0-(7dzBo4f<^BhO=$VKxLFooz&b0v)}W8)Zmn(+ir z+WImO)a<%Q(=ewe-q|vQSvp9C&^__rn9y7R=HmjRvtGV@Zf1lQ9hlPhkVGqyX&U^or zHV@MA3m+ZRQ(95=DcAw|)B;l}j)IFPqgGsIjK%j z6VC~6bdOYu_LXm~uCt!q|L%a9K+dyxN7+*Dn@e56ys}fo$rTw}82a$_?r zJmu>bSKPhq=4mWN+P(#-TJ-7(vowD=czCI6l!>&-vOJfs)oh(DlQ#>siswY&ZjL0& zl;z&-4t1xItL-|>cm=o8MXb*%JlhL|AwzY&jC1I@v7v~Anhay!&XSiqyIp0`J)A2- zZBc`r!v)oKj@#4M(@#@MtYMD-I#V3i*8NtcN#8oIYpyx$;Uq@K^J^sFBqwN^fnZz@6>~=Ul*p^PaJ|xm2pWI;hRdxhJC^v3h^(yjie=p5 zR?1e2cuc(83U(Qrs$#ZJQtJs_V67dlRjI+P^)f5XRbzy#gE`S z`|l4>7J-q5&XhAmh~@q16s8f`{`TlKn_yWYg0B6TB&J&HjQzt%@NhA4oHwZLZWJ^( zz-dvgfmsGBxJX*f+Zw~`K9CO&`80oyhSYwE{Ppa%AQnzWj$ckOVIRo^TL}k$VSAN& z&xce3LpGeUIJj1;&Fe(?AS+EJR-Nd5-7Ui0{((c|lN?Qr!QdBPm(lXzyy~Z#L(=u- zP=Y1=-UV{f2eqYCuX=kv@@J{6bUnMD5&*NCL9z8U-&vdG;<7*5?TM%!K@Y6py)Yf1sQNV*%5gou4bK&Dz>eINUY$VP04z)j2Z;3lOWQ>_@>; zj8OcG7i?ud_6>xUa7AA>TwzHomD%P!=@iIxqz^u7pH`tvtnQB6Oqqjy7^Cw(zqCTu zXH~yD>|a8;wa+#eYSjx zc+cLYU@CbtUPK|4TdPSPF^Yj;r6^iz&x#N_W3j$4`Smc00W%>vIC&`$?LNVuW>^=v z+FsYb$Hxc?ZjX0#+we6}LNwdUj2Sk4f8o!>!BF~4HF#Wclo{i#tT{1X++kXIM8{BG$1xZ&eNJik@%iD zRw{q>oHXMPmtEJjdr+f$bi4OULXz8Ehk4`rc+1SXVfZN6O>JV5_bRKS6Dx!VyxrWnRu2t=rZZr>#+^i87>~m?pEk}v<0tWJ>)L29H_P0k| z3T5)6+%-PhFZQMXO#Qw2r1o8Rhx9h=an)uMhj<5NPSlr3+%v$CuV&1swdSe=}1YH#YH zz_HxrhTn|oudNO+XAa{>9OBz``A?Wo@v#2OnbKSGf)y{M~NFArel=ob%zsCL0AG7(Y_3DNs%9@ zNCB}w#u>#TT53%GmVY`=EJeMtCGjtzu0W{4UJ{LNEEFJNJ zz~TWo3LW=B6>_CR6r}jVqqD+KuhJ|16jaG=_00_s9Aht-6XPL>=!br0I_!I_82&b9 z#WI`{(nl$p#`d}*1o5tNcbSnj2bdA!rrEGE?zXq!ZeZ)w91%=bgQK$B+xP3+91h=7 zhr}#xiOk+=+F)_#)p__Xok7B!BBR>|xB8T}G+u?Ah71{0g=dz-`_XzbWjxl%k(RT~A_??+Yesd0_suA7~eX;kzzDp&E6>;-5WuK-q z2i>g)xXT_FT;dTAh(di|ThtHtu#=GuD!ryUwAM@PY~OX=Yc`&ON~QhjGm!@S<+l11+C z1->Azxk2EAwR_FG@LMuE>-P@eNcJL%)DFBy5?M;%Rdigid$@0m7~}W0dc&u(3~d(% zwj9my%;wP#*_c-YRhJvu@{VS?5yZ?-CTjQGWIh$9IOwZ0bCU{^C|xr|>VPhw#!O`8UL>W@msH%Z zx$kY4p&S2^Bvq7W)S@uot-tVkI#VkAb}BrI0bWes47r;+xrm5w@wp!DqJM4En_*Ti z+a)%t#G;mKQGuSJocDPjd1}s`=B(`wuaUvIU(#$%E^9SnffW%*B4~D_Y2QZ_hTV?3-W({{ zi+>p}uT3ZShBmLc+!9AMAdq8o)LS?LUb*f(m1oQI5taa7mM$%Jqqd1%BtPl8(G#`_ zS#m%LBbU=ss%6r(da@FFDlE%&MrcGKvDtK%nMjlPuszvT(<AxAnXnjWD>VaCM!5eJFae7t3aeC5^@86JRR(D`29t* z-}5%4S6a%k7DdB-UsFT8a~|lRNq;!Tg88d4$i46i%uO;}SaIB#MnjCncol8f+;n$$ zt(vDGD11=u2EtVTDEo*MH=%iieq+?>H{Z8pf;y?|eQ+1+y4phqO9ls9f?)Qd$;AcpHYe!srxdV5jtl92 ze7gDqM}3bQgWaqJCK{q0W$@=du6N>E4<=sjQzuunFg9UpcbYRj7*EH+*;$Uw=7{zk=ua z5G~K4+m1?2?g^tM(QtrEzOu_4U%7X8{hi4Y650Z53@ksUaFKkajkKLMoOSCn1KCMe zd5=i1H4R&LVvEAn6N@}M-L^Rk9yI}2kDKTXx3?D;xH6`wZ|!_nC%HnfP~jOqu1QG2p9 zxK+4?h;qDC3!Hzm#_E9~3ip7%^Kqc*@+8T822j?;&GHacxs4tYjl|DAlpS8JIJRL@ zgH{r}OebG;>P?$Ylk9yw8tp!CFYF0ECFg8gFx_)o^lF_Apv(f=1K+hW=H5sm?`R+tgI~a`l6_Fw)HEQ^uBkL19>qTBMVTu}_`gixXzLvG}g_9xXpcR)Vm;O(XXvi zPsrz=Ff_XNNThvjlV{3<9gz{wI9$bQY?jN7p#*Q9(&_H z{Yb&wJb%j2I{jz(g`>!iphOt@OUV%G8i6AK^Zz8-2AHeM1r0=V9;Y(3-fV7JdKyO# zulCP{fH#`Y7$i=l{EWV;Bh|^bZiau`2qO%KjK4wv#;(j%2xg@uiLXsmt~#cm(HPMk z!XUWK_(>y@K`;c4WsP+5Rkr5?d?rg+h({>JZVm~lZ<+C>CAJ>=Z$~Bj=3iTn*lni{4se%}PyMfid- z!oXC}mZrK$UlMx8faF^pnu^kL20q)$!?MHmxv?i9-^TU|Z5EM+e@p`POVACO`xrmSbX=rtTkn)s|*p}U;^Q*3g;HbH{=j85}i{I6K zxY0b%eZXAgt8G8$6Q{NU&DuVY>44~ z+GJr1b`1fl>H&M<=A)0?Y&O?|0vLR8JhR?bSsB}VK#G@Z#GMpaZahyPf9Ew4 zQ+I>w9UVbv_@*`^zsAm1G{$ z#@-E%rB$<1-gb!Wc;dk4gzi`JXh>FiQ(?aXM7myOLTsoUBZVFqp=o}#MAacZ5;iwGPHT)@{NYt zj`xOJ`i&2u_Ef9X#SS=ar2AJ@C@&g&b?|12N9wj^XSA{JF#N4>xv?XY<;1lU<frClJsRLAq5pC#IR-QpxgF@IOY#RzGLk$+n{YtnJ^ z^I+qT&JTB1=?!qXH-6r%2En?GAyUUv1G{dO(r}%yuEQzw0$se1WM)GG1l+oPKy7(< za;Ptb6`bePIwMplX2~t#wlrVuBygFC?=Zt_G+yKunZxC1?#@Sk8EUiPQS>m|J(+|-ai+>E4-?t!#*rs zw4xGidAUB1!O`(F1H@4L;s&-w`1#4V_xRau;?XKRTkJ9}`6~0;YAooJt#H7Db1|ZA z@4S6T%+(F;rG(dWAw`NnXi!u&$$+M~SDdSuUzz?1PW(XQGRAJ?iIDMTUmgj}ZO42s z&%(HK6q31~XD?+{)~DyA@Yu|^j2L5+-*vn>@*-fN9UU3knoc&0@P!f02up5d`+Za` zwi>G2a2j{8uTuCyvj2WL$q?&>c+2pkkiQ@=KbHo<=gk6}D5ToA(j~C+;-O&9G>__P zumD@-TgC}Zqn8!iqjfCIYv{x2xOc)3P9xuWEwc?JKxd*T3Ggb7g5SX52y>dTIBo&3dF7D&Zh4V{rTH_i zd|iUQ`gx*$Jz3c9x5&QS&ck90Nr@XQkWaF(7TPxjwxOGEqwaDi+pc&*e}4DBzEsIf zan}yfluwozK@=s?qgDxYSVu3eRX`~xf>!VCZniOHM{kx9A4RCJdp(>FOdbOa-l?aA zZ^w6^jz?j3V(e?Yc|(F}gHB3E+!nuxDgPj%_P_1nE4 zRv@F)L$@t}BU&7N>AofH4jJ#xh%xP$8@61`bp^#O>2CEaRwsOE)zmN3O)`>N%-Xd? zK5k7f7;z8Ce_SKLq4?NV#gZ*l>9OQ>-tt1XvT(R{;n&vuofA~izpGn-8Ua7tZa@@{ z{CrvX@3ujJ6{pkI2Vl$)xx-jxagfFxLK#C;Fz$`Ba@xq@s4Fmsp+EMx{PJ4!+BuNJ zPV?EuVZ3=`z?5Y4{t*9Q20Za=^ePpUz{uZMVr7%-$6h*(5TiJ(guvCkOE*!Uoa|o5 zYKsMJw6VLNtuoY=_zlun@qtUe71r|_R|61^e0TLHDlC{IIhvsxd6xm|k|4%Eoh;UEg=PQc!J!(D1XN*H`Y&4+WA7 zAscrTAfbYuB%kSg+mUq?ef5L!PY*6P|A&g?e=BM}X}K;eL(By)(l6RISn_-6xOw!&e0YOAe1a6K~tS zdlOxm@TF5XZfCX}y|ob*sXMzUUZxpa2II3c-iGb?f`3!C91g0kv*(R|S|}FJw0)y( z?$t`pWxQ|K&7uHqUWYt2M1|Cv`7AUi@v)YBBs>?VyUW6nfbZGg4@(>uAWg^%43$3EBtG zF*Zioq73xORxaK1y5dxZw1h6t)bY&T8Y;XlF)FS+sXbc-WG(&>@Kf8AQy`u`tMNe4_|=Ng*bB#~1BFwj=g7o!s8Q934rWNzwt1U~zKyGVhHz=L)!j5UAs({+)X2UwQr$^n_Nv9n{M!ZYYYjQ= zF09OwMw^dxHdonRSlKgyt#!qaqW6(L+o5*cmpNO7Us3%fq;f&xUfWNnoFMvygEo6& z`dMo~`d>MVNRReMBw0Pq%}1RoWK2akdM)N$RtLd7J+x2MS}(MDBx?J##oC`gs$_g5 z+M(mOzIQe>J?(gW`mr7%dGCik385$4uIfG;kkrMyhN$Pl)#r~bL&)vh-P|;Whc#^_ zTR(oej}+W~yW@604wE0Xd#UA#ip+0;9q_ZiR`Hg{ znrIK+RDWu5fJ)fv$|^US1o!k8ow@g!cOu&m6MwRRnIplXWm~1QJe1)-1ZhVY5Lpr( zYz!Vnl)J-bTTj2xK-xTLBA`UXAKI&Z7@Znz{(Pr;7EE3zN6Uzpr#@q zzCV)|SzQ#Gn`}21%lO!Hx>3G(Zx^=l;JPt){= zJB4bX8g5B#=ZmrjV?@Z(`IGURlFsjr-y^_fYC$I4H9oB#@xA##?@I^f! z1i$*BKK|Eb7E3%ARf)q;j5#Ph^ETO#AFi_$O4X7|iLE`JJ~1DA%>ed>G#d7*Q_OWf ziCRVd5y1Oj|8WWjp!cl*MTCDY_@f(|qbd&w{lQVV4QggSYb`W{g`$*|PyMWW{5<(O z<8hopiKN|h?D0-cCz=)Pog4^j}r*19>C}oL&8;k zv_=8fiUKn;!ciA9G!*`;m;QIt29N^2Kcs-~P`}mBeY~9E=c448(eX_^g9#~{{E95# zFl^TnD3}Em3W}4e)}6V}I;B1Cxq4OF&y4fgh_Kjx%m3VJfJR;co|i@U6Gg+}iZ%_4 zQKjAQ%DOPcv7(r^7dLmDIz67TljYzU)jBTlSg$j{MZV8?Dg7nX|8lBR8g6IzY@0 z5SFDCRgjl@Z-;mOzdq=%A6iA7+qK6}RbhCA*BORC@tY(}>982v%>YyG$7A#WCkSp17ZgElt4W3Hep3c zcqxWgX0+`AYe^=l#@aJnLa@%JQ>P8pVg{Bc8SOgqr#MPwb10?tqW;31t!Sb_HleXc3Y%m02;lJ>bi9moNpu*ai5fv@N-%xcqQ%{@M$^i_+)s^o;jTFR0 zY~W~6mEpN1p2F|d&>~C&5qKS4&Cy%ynz?UrIY%Z2zj zT>h_}5(t=OH)CGg*xdQ{%Ps^1_{54M6f{}sQ_#DD z0F+z2av8nFv`LNfo587{xA~Vh0B*f0r>0||xR#w)o%hg=_)d^rv9FXDL`7Z8U(J$i zePuWfXv5rfMqn)!jfUG(Sz5LI5!iCDyZYx}X9xY=?D?_&5q&3j|0DYT_x){fp-5zF zW}uj6DH?F7Oi$UAnhp%)q9J1Gl(ow}hKW)$S6jM2<9uK`rUT9F=v!NWHf>|cf+*hO z@2)=eKG`wv*FeQoTTWDQ2dvo>yGnh%XVg+{9DD!CLHA^o9o5q9$pPJ5+P$}k-hAkS z2~bEO+n_8uW7J$j!%xdT4o~X=jcl;mX?Y~-WvJ@U{SuAp8;;a73n5hR5^w9IoYn9gn z#0b<6{j-y8U51QAZ^*w*K1aSxNsc`|I=kk5F8iU(a7mJV$P;!fNk9Pt%}`J)93j?d z&Mmh5l7XJON}ky#~`C7b-zujH|39q3@$+_yH5rkPX@mOgHXFP zwrbaX9rqVR>OWdxm0nd`eR>28K6l+uo;*m_+4=!D+*AR8e@<_}5Pi3%k$y|apl<)u zt=sY1hWfnzz~E(~L1zFP+fHl@z<}JZojj_Y-n5j06x@S15}L4`Z$_){JnJN8UL6!j zsRt%fJrAtJeel#{M?7J?uLOopKR{u29+dmr7OPvvx9Zq4uZ7n*r*!@Mm#9pU|46_P zvWWnNUJ#8RT0#Ta75=kJp!r~`n6R81J%81+M2b`nWH&6Y^L!M?ymyQSj1EWIib7H< z($GEj&iSP?kfG<{**m`n(e-D@-}<9aQqP7%43iYXt2pm?l0u*HZye z((*d^+F`EOx<~ACNsEjBn~DHTNRRDrJ>S2-$IE#Kl1YATnzE6$Q$DI~1mK&8a_TlB z9j}1k$R;ws?=J!b&wzQHWub6?0ArWL^kiAra>j-_=Nn>c;mfq+*{);okFjb^@jPA- z6!5%;aQ0^;bVtsK==r)A#;9@o9jCVG7l0EG?@RB!8P!}}1uJ0G5`Fny2W>w>2Gs2c zJO6hM!`=9iu=(2Z{ShGN2W5cz&2qEt8@~vpZuxVZ(kaW`r01pu*cMq+)(W%sgTG%D zwJ5bHCe6{pVeuxy#2hyiDv*YB~bvS>~P5-%_lBc{+v9?lJ4~!RYVoF$W*mx)s zSdn@>$2sckTrr}IdO{D9~#7IH8Q`4J%D z4?2f_f@n(KH+tnp3tfxC_v3;nhF&vT9(_<9$t#9!i9YugN3?4XU5K3IUvL!<|F3q~ zHozL|)({|041l}0hyByzp_hI(Zo~e8Ve8`=gu%Y!4M>oiz)+MnlCr-QTdB|FI0F9@ z3m{mC@;qk-C*|^lk5yvb!KB6B2uoUg$WB^d4#A; zbkT}*`g~S;v)pDEyP;WL&m*|Dx)*jIx$P~*XE8g!vDZ2Kum$d3%=17vnHWJaZ6h-7 zha4ZrtT-2dJWG=gzm2th%iY#qCG^3XlEoInezuVQG@SK_&Nmlw7s$&UEvfr^O`5y7 zkLBtXlKz10-?i%^t>FH40-y~;fI1EOp4}fu5?tsr&r^JX9}1Ycwf<+PPU|L~H^2mA zrY8fYk?7wc(MKyB$<~32iSKKebsH*dvbnADE+4=G0W``t7-c>ZX;@(9B|=nb4SC62 z=kCQZ_r~`M+u=$S72hluslt?_8C=-=|7-kOAb^Ag7y)_F_zFMZjG5UJ;;n0i;~3k2 zmTyy4pdxxc3j>L1|LfawXS{*Mhn>%#ZDYH`FHlC^humQP+VUwxI%V7{Rjc^cOz)BV za@6@Srf+%_{kDvJ#w2hXiq#Ju70oaZBG(C7b@b4EE%Gk&T5Vvvc3YAGW)RvLDv0Qj z&q$gX@vdMm0x-_q*-GRee_pGE7?oFoqK~Y1RhVqtjq~ZExLaF3Dl6g(x;#zeD--uS zdEgPc%3x1$&42aVQ$o*SzEYwqEJeg_pqj85P8%~}!7XmS{=ke!g{X0-VQY~KE;0T9 zHn^-$csi9m#pW>Xe$t$vE%YIbyiobh=n1xG&3cD`{R+z`e z{{}^T)=n{o4+|oLWgm67JrGAdk};OU;8OnK>HMkfRLq)wAorTnGb$xxmV9 zO2)DHr@C!nMUTox*Rw|vBjb#%W4=f)1kN%t?=E6bn{K|)UruH`IyZlrf}B-aEv5kR zTa+;gqY}#7bj8y{3R@3Cv~UGt?)p7M25%*++^oy5{#r0ZX|k>{X2l3rSN29pO^_aI0AqfzEC`R|83^_x7U z49*l^CTcu|4oj3*Cy&2O%9(=WajJVRsYj^KD!{DOrwL9chdahI!@4G(?17bJjN&22 z4iv+biLtxBmGD^wkSoRVM73M)YTjrC955@U5{WbH6Zb@(8^~2mcd#b$dfH8HOcV;k z?W(xo6f2vr6!1OKmZpebO~^m__8)9;tJ#?T0ol*-4UX?dgp8$^^T0v4@q}bYMNvkH z_;d;a4Mezn_RXvYbLDyNI(ZLes82MG;#~1c=_Pn{$$okWLSG#pm&;%^?549QWYQ~Y ztm9=!q^1Q?Ek*=Z6$|lWhB^Z-!HWZ{m}OFk-C9`OMW01BSH%)yR5R$89!0$p!L7Rc zL0pQJ`P;syKcyouZYaTCo!^Cn+%?}BzQh1q!Srh_*k9hqXV8@E|KsbsqncXNuPrD= zr6V966r^_uU8N~V??R{o(i2JmX(A=mAkrm(N>h3-2~7x~lpsCy-UA4s*Dv0gJ2Uq; zbHBB+Sce6Fz&Y=E-~H@oKYJ%$W!wBCGJCt}bn92HU;uY?ZY}R!dQ@e=Da+X`VOw9| zLFHVx#lPy@8-&3H7aUcw$wN z>6wjWhavN4)1+KEIvEjL42)vjg3*?t$>{lFXM;YO+g~{1f2|ArMc7uT6yetQ`#2DII||}^PsNU0>KMG-7Z0c7x}SLv zr>7eMrs;&$QI@;vS{hx0$!yme(4?r@ns`TeJb>H*3$RZ;=NKq{yGXvi-at{$9=_Bd zWdzU<-lCIP!|UDfqPI#Atz{Ui5h3!HZ zs;lG*mI%qe<3a>6C}Mh*Zd|oDIcWspuM=F+gx#}qvX`9V92(xL9M7o1=JA$0$<4GX zp-m#Z6uK%zv=xieTYme3U+$A6#=IN>iuaTOuEf3c@X1~8T9%G^nZ?%>L5<~prU9mNV8xh8b z2Ym}C)zbhNTT^3~X1zh8y0rJtSOoQI0TcVnzk%F7-v|zeHjy$R{r+84>oA(L(8)E({b_?Jg7pTn=Hw@445M>-X=+9PlxoC1Z@WzWk zL1zR$bW3zKZetF3ZntcXy{@w(dNYZT&wQwjYkP*FqmZW!#$cPl1V6wG;65dHHaqwI z9s2+xA;!oDk_Z-P;d4eUiwPDZH~;Hb86I6>bEOJ*lmjq*y1B%CNxP&^t#z5QTC+tU zWL_`vyvo-Lk8~ZRG-hY7qwqn!T@KK8(xzO5x=xAEo z?bX%{8SikG%cIV=T=kUjxB#pTFt$gAhl90i2>>P63YOQZ=CE7lM*8A7`&*7oO-;e? zf_4{wfKCrK=Q?5;UUD=XW;%;i^a^nz4}bHy4FuOPK5eADc+;*D4zkaTFB2Mb4i`^r z_m&OhRE(W#8qW>4C&-s77HfD*u-ShrpW^%#gGc^BDxfVp{4=&A!YCg1m^o&B`Rl%O zeSD!r0% z*d@*)IU&GD5|v3MQ}y1CF!*U9lQ4V#ARTBD!JDrRyQizpFh;&s;V00U)UJt$@S>=g zN6{d;ia?WZ>Csl$vX4$;KgKGFzKDlF1Yl0J#T%XCHqfcmjgyPFz`Og8MwM)!mbCE# zIN9M;>`e-fRXB!T%3C3s$ou^x@7wXNH0$m&voSSLkYrdxw7kKhpE>)?ddmy2)c$nS{Uni)l^sYD1D&%lX611<*E=|2z=XYXw7wq0^MmA67cMRBwQnfFp^O)RZr*Arn4}k6ff3`2fUa5f>3G)xb zKT5YF0O4%O!#V`Wm|WQ<;r!*UV0+rHcSY`A0|K+_%%!0b^(xh}TP#Yb2q%wDGe#rR zK)+P|X;dBe;*xoG0HC11sgJkVe=OK&VZI^RdZxzwZwhit&Dmiii#u!rGNXYTrZe{f zagsrz_x$$@Ets~Z67DEp{lphsLx8IKO@oN}??hYNzPjn%gv*%22e`rzzY(ocW%E&V zGvEwQZ>dK((Qjy7ENXo|+pxGi0UeewtK>goxF$2jUSZqx8#P~-x#z#C$a1gguyMPc zw4us(vwCzKUBCEypD<^=+PDH`8bgg8-<`OB02)B)L=EzW1uO~xf6D|;&*N#ZKJ?wWpG*7S26*R^21sb%{&Pz{8@*D8UClKj37g6AK} z-z81|*YPG1dgW~wX70cYz!Z+rk#P?-#*Or;0p;hi++U^-V8bi!s^&~6u;c}uDnREN zSqimiVqAHbSxbE;M>MbLa9w>UQ^b6KM48;IykA~DWK3wf)onXMtBz1eQ@$pmFY!Q523rkSzb^Usx>Pvsf8vdnG}d2feg1Yw7l+LXvVmtlN45CeGQbJd%=9$# zABorfrf!Ymf1HEU0f$qV`h(Jo_0p5Pa^&W30!)>D{spAh*2qk$Dd>DOXbRGpdvzfY zpW_!*gN-_MYCdpklf+YM#mQ27b zv0VUx#Aa7F$$u%sC~yyA9C+LDQJ;Ue29|#xrOR$tujBo@pS3Wz6>al&^_!0kgkn^>|Lv!U4~ zDeOuFu9kq0RsaEr{ve$Z{M8-d!`lD79wIqFns2KVPAe4{F7P-W>~k3)$69o&wE5!X zcfsXQL$%3r_KgMQiVq)~BSR*Or~{j1{kJ@_qBEQ;=WkgYwf?Fu4d4%LN|ak-^CH21 zI90_brdNU$j(=#W0JlD;y9k=q37z57%v_1yWdCD_C%wvS8L??90j9s-eUE(peLJTb z#S^n@Po{_pl=e=v&&x!u6&vpRt?{tA%GPPu{0wMSo51Uelxn!EhV51SWK-RQ3-H995-Y_ReQ;AXe6PQLHo026$hJ{%8h z89cZf$nk4boU-h}>sTSV#J`;HsVyFqK74Z=T1P99mjLYSqY$AKpcBQKE_78K?zH%- zP4f!ih;^hrjS$KK45Mr_ddGq)Qy%1?DzoO3*;AI*v{NZ>lzNB@hm)_cgyk8nSXKgFZ~sxcnzjxFxj zbcsaT6s)m-FWw~Iu~vx9fQq}02F9cDI@vEXMw8vf-0ahrJ@_Hr|0Hw%b>-3i#ScroQfA|^#y-)Hlq zyoB`>N#!HQFTAOZr|G?x?}x5Zd~%TM;2nRQBKBl(qf{O5#;V011502mzK`aYSsj`3 z7*jsH$k44t@4Iywmz#pd=s8owY@S+0sp+LMumeN$5eES#G#M&>UZ#MwrSHHP+&xX} zNj=ICt;4Fe&EBl!idP+S`5~uw@*jrQ_ax0bDV0qaEg1JyIxX2gS&p3Y5+}on$GdWk z!~)+V_rq+`;iGj2exblh@pu;!pCzKZGCQucbFNhDd9RFC-USG-&*L)|HmEyGkfK-1 z)@T1cz4_ng>@Ru}PS$v33KpBgG*pHWFn}bT6f;G}$Hc zxN?BYWep=NMINtWY%CBRo6!KPDpxJ45zgzd>G7dk9<+MNuVov2-%h?OJJWsXw53f< zrhiK*@VEtisYJNr4$QJ(MrPTUdhX2>?*3XrVv7St{ma;jq~7ADpG?-*)gJt4!k8U5 za^NUL?~V%CEPoHQ%_Ki$M{k{0X9NzXh)ey8T>PIGMfh;aivRVh<255I_ZrMVN8iR4 zzY;|3gua$aomC2{jAPW!!*J88QdK|xNyq*e6)!Cz%-VAH2(8UX(i;!;Dy3Xy=WBXx zLs!~_05C{{FUYp2gSVy-9r-@TTzOe__w2L(`FpaAF8X0SAID<$w~YuqV%ihxg0@9$GFb6Il(DXu;PTq`w(Bf+7IF)BEESlQ=q0{$ zRm8vV_zC6TX=EX#lF-|~6K^-Z@cI)cq@j%<^wUb22agIJV~u7zW)E$Ew8Wg1^^IP? z{`Ep@$CbpZbFy60dh7xx%TaRNI1 zuc^AjY=FX2pB)&-#Jr^|<}i1WbhCgkkP+_3*zL<|O1nY<<%l<4nN)}5hp!2$vl)d2 zJmwRjHgxm*=hya6tH#7-t}T<#ITru8KmQ&-OYD$<6we`&+y%G}Uso+LdBOPkHnhw+ zU(ES46FO8qU~orp`8%Yl-}t}Yhkxw>Wj_eUuk+Z7CI&bqp39)}FofIP2eAR$a4ZV* zUa(o$`DMjrtWlMWYlKA>>7dNzjL_MU!UCW7*iLd^W9``flU~j`lze3D9Y0$QUWL32 zHzStnALJd+8!ZoSgG;PC)^0Rn$ZKso`@rOr9q1+$MnmfQep?CCyy?05qYoiCbiLh9 zuzlmJ*v-W2!EPumF*)DfgeCq){ks7V2dj;ZzC#t{+fq+6_bbRHeO37;xNEjboCUUq z_qR8E-z=15_&Kx%=q|VxG1f--rF1xMmdzBP|Eom&&ssr~=&Grh*7vYo5j>Ll_YY=! z$R9cLcH+sN4~!Oz#(Eh56-b1#F>gMH(wD)2$me}QUjVOX85JLeatXqB7Kp&={a6~+ zWFB+|IX}C!f9O#e3FL=c)GHcj=EQcAB3HjAAy*Qwbx4~5iF@gHVs#nz+bJ{_O%gFI zC++Uz{m<~jR;YSC5)aXiwC|5WR{Gn)G_vayc@{Y*H~#}c{a&!w4KWhaeMTo zo2`G>X216&=I~zhAycPgvB%zcdkIUl2b}m-Qrw{*%b?2bR}7o#uM5tjSdDDETg}Gj z?>{AimIwAQ#*sx!3z)4@A^lrwl5xRLq`Q~Ij3F{;k#%wN5w|&g<2d8W0l>ZSPeo?8 zn*S&2#Ba@5&Nob?NM=YEZg>mT##4S6_^T3sKQzN!$V_Pt1mqIIpUG;tcQJir46T_> zad*IRVA7iX^5oZ+WDDP3%PyAr`LL`B@5TVnT}6E7&oVx@un$NDc6Y3VF{OF ztNh$j(ye$qqs&?diKT_v4dK76o&Q`%_<4NBt0p49uEq66L~%$ z(kvJ79blVRF6!1hw$N<>uN?HczarkGo_)iblHu`SfT@&yXc+wL^Aw;&?c-^D#VR&* z^|+CRX6}JFHuFVT7lj8uBZLZLpFiacj}T=!#JxB6<{a9*(ZKdAiGvRI3P7<*`-QCk z%k-@|RE{6>%KazO@c53g+x^_zN#q;JJ}4{x?pL)uzcOb7I?>4ZW%>U2wHUoR2AF^0 z%@;g2Omi>u%Cm&A^9_!0&jFHreqmP96~5dyu=?!@7-NptufWd&1Gtl~BT!av<$#Sg zIuorA(%H(--WI4f@7%<^wler0I{Wu;cP z;-)OCm7EnYn3|XPNI2RW(3o$lxw*@uy>FVaKnhcGiC-!710&GbUvJ8~^E$8J)=QjV ze*17@wViz=7Rrnd6CQ3O9h9hb}S3J-yVpJS_qCYwtL*&3x$I2mBdC~x6d0h}f*jsRs z{J~_3)#dQ^1686^T_U7J&2y%V2yf1gI}Bgf3xSCn!owRib%Fbh{ngwDx=B>?jk={a z$mN1)D6gTZqg!;Wr%PXI1zV??i~q7-K#&%yZc?yng}J}(?{{_laB}z?{=ocY-4msM z?{WBbI-5vtX`~5{5Mfn|;X1LnmnXAL#^DvQ3|V~i9-q`IGy5Yr`zDO5cDi?(q7N<$ zS{+Na>8pXfyYp$wcC(3hXI@ze>2BES5H1B}m~*exMnDuDx%w+Y)*IPJG=g2Jwz`NP zWaJ@SJ3lNoY(M?j{Ygm19@WYAv$2VMwIaI&?RVC$wPkKNQ@=$gD;``@qDA#!Lsrs( zc5cePLh1oP_S-v~te@F&c9RPRIi}WA%>m|X<@m7YwsCV0*6(-hSs5e5Bw$WG-@eH) z-1>QI(}$3py6uhcL^{*K-(g1tUBw5xwu7p_YbCDvu(f?ngbmr#QYmMvH3~g+8J=l z>m-}FD5%$Z?Heb_5pNi#vCQL1=EjhqzMT*-RfKlb1!5_g{d{Boo{bFb!VeW4-{n&| z8aQCmJ|{A%>VS(ic3R!l6P(=aN^g;5-u)tv9PjNk!6X{-MT>{t@m8K>r_VX5LoGKzyBh)^LT!d5ea!kjh}RWRW2 z`!`ihnIo%vnh=eARP4SgITlZ6?LwPh$Uz7?u{3nnk-dZy}^oxHG?p! zsepdQraD4*SV_~K-i#+f$kjF1hSU&QRsWLFQ|^o*!)jZNo*yr87uif~9XvKMYf3er zOQceZ*PGqVI$mbjVkuYPJRwQ{lPbhVJSJTst4`RI!SZ3a%OY(QzWt(t@IM&Xdh}c_ zDThMXJgNAwh#-(-7PF8BeK$r^7Gg7s^|r9(8uF;{FpRhT1*@v#Wo{FA&}6mEKJwV4 zLl#5Mpo2+*^wuF+Q8h)Rb3DTcSNhhGLVO6jonmgQa&pb_q2^CO&2_v<(YiY z*Qg~wxMxD+US#0arww^|t@mzM^>6^n%LVcxxBls*5%+4W6JEAky>U9^NfhEF%?y8f zX^XU7PU9G)aB`ks+C77>)q`egoA$CN3NCm!P#;nSC$tT5E=`R2ZmNhr!Pq35!wpgf zS6D8Ta#7m5S8w$F!nXKbr>%__@}s$G9d9E4=9Z?mq;Xgl{!BwI%3J+MJrZ2`eD+{- z!b$3UW3;r?7M>I$n|$~jp!waGoyiQcsNV*wjxYrmwLHELr`)rV)MSv|VP3gH%IKIW z$@=$9i$|VaC2m$|2=35N0qbgWKhv zc*&?~oS}DjsaKeHyHUEM#C?3gY@>HaWcgxCad9|ve}EGgZs zd_}ci1O*Qz%cAQk3S%I)naZXRb%6y(`H}Lk!Rb%Cn@l$Oakv2i4p%O#s;gW?#amkhxA0kuBxpjS_20jd!U4Ap|2eBTNO8-qf8bLbN2PRGFEW$CNTjx}OqFk`*twhJB-tB?mJs?`ljnfo`s z_^cUr*S(D7_~bEk6cY^O??_~Nt^B3fXo#<`{4=pK+fWB7z!m+zPCfrdEUy3?Ey z)%53w`%X2R9ezG>`z2qG-g(0084fu|%a|~9r~VpvG3N{9^{(uxUE|vswyKPUXbb00 zQzwr2lu~yWmvi3bj~X&5h)ffO{kijc9)dz?J9NCnn1p~+dhHMK$_~4QJET1p;~oKR zDm@Ja-8hF$h44cfYIw2#V9=*E65*-p_VKZqavm_<&BcdVlgvn@)F+@Vxe^g=} z;lFn8dV$oAHJlMLkDDieTAGet%Ff(MIW2LTq{3(Irerqwp`WKN^@$w2&jezNHo7xy zNe_RUo#H>iw%5*LzsckE7pf0H&NZdk0;tX3_}x+wZ+n6KMBCB*mu`9R0xM_*ol$+; z4}LS{namd$K!NqGx@zJnWzqX(Rb!Of*^6$nall)0OmEVYK;tI&81}i;=YmC>tQ1aI zK3*LV)rwj}L56L*k2oY_7@_#{=$^e{&?odWN3-@qqLB1A;_&cAv;C)UrwFBCye%mi zen!}7(?Fq8Dh|w||9S}mtVxuR<>fua^JLka=&?I0$BDUIWVFSqk$@DWk`DB+>!1({ zt>+%mxavlMgV82+n(#r=w0rzg{g%F5C5%}YqZCH7><|P7+d;Z%xnR|K5#|F6AL>3XYLa*DRX)WsfG69AlFB`vlYlyy2pd(`jdSHp?zx#q3@$3| zDb(3y@QL>+q!jH#_0)ox#epAmn^&{ecAMT}nE96p`Y ze86(3*2onxzwotLbypL%yd>{-SUR3`@J27hxy}Bc?beoZ3ZJ82bH9#uK7=uL*1RBt z6Tk+yk~{$T0d*g#f4|E+Bk>aU4wgBH>(5iPOXk#gWf9PGosJ8u*Z9t?>db zX)%`XKf_|>FBw33ZPJm^SEHbP>nDdhnfu(HE;e{F#p~%*TruB=ME~bpNFjx5zZjH4 zx29uH>-t2c4;!EC0fk-lRkB2amfH>(&xb1ad2QOCn8=)e7Hquhq|fs~TP>inT9?`O zwbkQdw`VE1?79ZBkYL2GyAN&Q-U{|ioXfmRUN`N=R<+UMrIpDq`h4va(V|3}*18|X zUBBHQ|K*!I-}Pyy(*EncStZdQ~qm)3)-J4lt8+&8_4mX@Z$?fl^{4=w*FM zWu`xI;YOF7mfwpc3DnMEzUN-yKO2BWNmnkz@zW#mE`v_FuOT;Oe)|bLUKRcAC$NNn zm~wJU5&p0L)kXMyfe$2y6Dbn6>M=r(_SIVDGD#G62t42sM_>~BJAveFCfB~%No%m8 z6X`q~+&Ka{{ML;4+q0)QQ9BFv0x=$szI16evyJd0H(>P}&RESCVTdYZQ;M-swg~Nt zAoJXj&R}_=Ti(KG3*5hVhZ$LlM$@jm!Dk)71(8^M?@zBP`Z_%n0-AO2d!oQfYHN^+ z)GcFJ>1}-3SxMjKWrvY_qnK(GcH!Q;UVrEZc8&0|&1CJEb5b8=>EAl$Tkv?~1#BYn zH^@SBR}zz^S>fTd!2-zAB!cbunCX-AS(&`-+{!O$P>}sViZ$M&%SJcHPhP$g^zrNF zLmTD*SB>i%qhHu~4CS(H!Cx}nt6h+3BYpMcv)n*blUEU)Ucd$h|3;n}hJyB9@K_kA zBzSHa8(bE75ym2`s`TP}k(^q2eTQq739*b>Pp{3185&JDyOHVzVyLz{rJY%7C(N#$ z2ehs{4hq0|Kz#T21vPlh(R$^wnodQi`ZLpETIqk7`GATD&}%c7DFSOU{~W7tQ6a~? zIz6%(^Deh#Z(5ky!g#9?Q%9CZLNlI0M4dllK9qeLfDt0(3>3z#+#2(%+Sb@x_E(Jc zjBB24O7R`iL7r_DoBw3+O$y7X9QU-}R6C_W+9#{VoF^b&WKwys)NU6#dJK_Ai8Z>p zkD4qyLNL}gwoF70-G0Q$UYg1;^wShZf_sx3M;OS`Sq+3D&L+bzJpGKa)X;!S!D5Woe{?jWkSj`fj2=eML6aZfP$sg=ATH8$KSkXI{1M+Bh?1Pb9Cj0>B zI!_7vRD1<_iJh5pu_Lp>NpFzo%S7_Eo7vSJ_h%&#u&Hfhx9d%^7Y_>EuIp>QW# zTY)LUtMLsLHK_#vy#E#$ATY~Hb8Y$s)I;B?@HJyWYvglMEq@rQTt$xN0df4|-CJWQ1WYL=*FQveX9&qQBg@-VC$K`+V<$t~CRI<$8T)=eUMZKIT z&9}w3@S3ctE{MzzSNSd#v?6i2TbKr19)l>mN&zK591-rvmEp(n$({zlewv6H#kIA%VBb_67ug7ErYk_8{cc1@2%k2rg=B-=;4&AoeeF*Bgh!K#65}c zJ3dYvCNW<0M=g3}#2X^XZ}D>Wy+N7Kt~_EHAEwJo+dkD+0m9;4sG$qyDHc5jogZQ= zt(i~8>x;}B|2K8c82O0Er99RqgvoXgWhllIn~?xDvcBGS^JEsrtitX05JD3Aeu!#M z2(c4%cK+Uc|6WbH0ZnyGhEJLd1Y^*UeLbDKyet0;@0v1$^Kk`(&gl54f8y6nad%56 zFQMSfD~B?j9-$$w8xo!v8{36wnXniKhBawJQsOtd_0GJp(SNwFzaw-RVDHzt&$3dFWmULUm9RmEOaXWaM_ zvLq{IFP5uSW0W%cv?otkdNH#8&JZtA!tBhZ&$%+;5U)8;GCEaf?`*7pl3i}dEg zZmAH{CFR%cuy*0R-wM+I`Oy^rty_7FkeZj-q`L8RD33NHai(& zoWf*5kuZPeF@6nA!S-?Z*z4+Ls7ak?^{?(JFPb>KSrKB0%{e@SRA;-9?{3?yP*`?K zohCQBw?5J$N7O(XG&RxK)`p#|m()${-lE8{`ndO`tWbn;LSXI83oD2j4rrfbqYKQ* zczC}@S&Z3Qdp=jpC2KJ475FOr^(TUnS)!3rZ-~VKP50XJkO5OQ=)%EP4QCR8(~^!D zo?>bJ_QlO;@K%0VAe=Ehb=O9OZ0VW(&n|+Gm(CrL#854G&66u|;g2F3&yykk z)@u=DB?5WA099pW7rnuLD0_y?r<(LGR(^~1SJ{DcSc8cUynVC}s!CsG_g9RUZk}=n zh9}yiz^lh}6zzc8qO^VYIV8_XI>jNzjCS+rI~k#f%BW|2>Bjf+hjIHu$6O>2yK%X% z7oOPC$Sx49lXQaxZIpx%s2DHK>xye7_IyMUP zw2c0{p;XsR>UksQI+g}K=lA|;i`ygzY|e@p=HuP(wB6npGgc>kK>{=O&I<%67;0X~uF`W58KPXZse zeTnai-eyyv;}P?z0$z=D*YVfo`+E-46Ah9c`M~vq=YvKr*&5Gizd108c5>QHHtu%X zQf+DaCG&L%(UfyKMsVmX$oGvSCBV$oKl4WRS7%Hrzvr~7_vNEBF$ z+rFU#^$XCaknR<@Sidn2bkOaZIPzjdxR}jSo4m?o00RYO0iAY!dcJ0#n_M!r559VR zt7Uun9_D@E7Es?olDGo+AXB1PV_@bp5Pm7Duszxlsr1#<+{47w)GQ;R#&e(!yd5vk zFed~VYkc~01`>xI{<-weLj%-GY?;s@fiK1Tp3GvLE&3|x{;1M}@7!mNM*JUkt;K{5 zuUI4YU6*~U2?rku`@LJBueIr^sp&3W|VwrM9M~#W$RfCso-n%0-19E)K*H!H3Po?u`A`T;}Uu zsJL}xIRB0F*-5>6r!aWulLn+(8H24s4?}asws1|fmSk|s8g+Pr9l(DL$I2v@-ZnZ} zUx@vT)bAj?PHN_#33_QE6&!kw9p0+XWM8!tyr8fl7!|%36pB#9Srar z*X#VdTknqEMWw8EgBID*V3(OKdDk>hl|UA>IdIwRWk(-H7+P za+^E!K)9Z<*PZgGv4z4$aRyzLzLDyeB8EoU0Jx=BfOMSq}?*}EA;mZOZnBL z&WWSCs*BSezV)s#e9REtQJ@FaVgI#CtLg4Ics>@Dd7EYP>1xFONN~jZSuX#Adwz$0 zl!L_p4-Zc1bRg?wRW;j~aa0ze)ZoKiY@H$HQ`PrvLu#npq0@sLNzjSrz;3vomDMx~_dR*tl>6yl$03ZszV>d60Cd0kW){Yfdt z0IR3dzbkA<{p~R^jb#vf!lq5h$U6jO#Y1!g`@VWQ#lcxsyt0Ys4`Q{63Zh(_om_#_ zKI;!Zo5yFD!CzmN&^^BMt@@RkFRZxcfyu;>=tn+9Pqdv^*{Xh5mzo;XIN?MY7D%8h z<&ByCOmuOgMUxoYzFP7@L}BTH$!M3hj(*>>G0Hm52T3;VX4JMU?bXkERH5%n?+j5> zd5wV(LsjJujZRxDs_f5t@4T>qUo%T2qNCO}M~Bqi$UQF2&>hn0qo-19{yv1D9F{L2 zWfrqxlvi+!a7v}`B&7WASK^jxORtyWFdy=yW^=;UgynEw^B*=RjG0%SOvcs(GhXOV z4*U~{NWfa&D?2WXtlapGJhGWtxc~G5_)}>6yTKLT5;+!jqn{lux|AX+wf5bF5Xv0M zl5e_A%Hefp9R8FOrMwT7$oz8Ic8T{5I1DP}f2&K7XtZoS;8f~3X>{yRa$Ae>(*-LE z!^YXD5Sm(rck;$|l@kYZlj`L2gES8x7%r{0V(lw18WLfM2X^-Jpqm^jd;vd85M~pz zen$T7o+d@nZJ2H%K*gx(%l${Bn82`wL$Yj0RWmEy!)GW(!;EIQF!h_jje` zj|^8B+w+C)mIQtUt{H7)yJdKcl#F!FJi9w_!??yPQ9QK!-AQM4)c#;jL@=$(nyS?1 z-IeM|u918@Huskr)te9-X$9E3~PEG zF^)zs>*#mW>)%+i5a-D@zEf%8V0Kay7-Nv1tr(-;++a`V(WVHv+U`ru!$+GO#*nsq zOpccCa%WLxxyQp-8(ZPYSK!i3B&-jXkIGznuv!T0w$ywUTm%I1LTvFxJtY;G;RoWlM3_fE7Wo>Xe)1L0|rQ_dR^Xv_B%xd4u zCL`9Gc68_UtG<@^9ITydU)L!TJ3@caq;u@=wUI{h`vb3(Tvedz7f z{m%7qyXG(5?#qR1*Sp%xsa!nHu$`W2svl#NjA+;Rx_#>9fDa2*^*3_c;~(yu)O3aS z{GvZX>fMYePj>-~WIsm1os6-@U9}zy3sN|-r(m%ajZ6gAbi**#pcAuF`iOqpwsBp| zlil{<`KXij{p|N{s+o3|3Vz$;HqRg#_9{b-1;|1{{mS&bBDVX3IkWT=Ol9LQxxcp%)DUfWnX@%fq=?&{c%@3flV`Y&O!eM;sC`uGc8k7+y28s12ic+d0(M)go`h=a#?cs z!d>s2074-HkvxTfdVZ6VVjb0bPb-3Rz)Go#?VaVQiyA9h>We=N-8xxYf-WKBH{D~L zz<-8oVZ`yKom=BfJAr>GcV=%;*1a}B^PRpUcbea8wJ<+wBO!!ck#tR7tp@bY`cntd zjj&jMUno9xpPbtktCdd$J{YVD4bh75q<9Z`GDW>LKN*7SD|AEYmqY0$d*2=j6Cm*8 z235&=EezIE{5m%#w>-;H@!%^x~11Me*e!OxwPSahT?Op zAGf1i_V>(cmR4=gtvvWoS7gyyv5$7K%^Q`jgE3=}p^Kg!J{yVRO~fXFr9Vb)?b0bw zJLtj${#9_{XtXpw0RE578!j8%owD!uG2tIQIW#%O_q0wadoTgIg40zRFF#}(iTz$R^dlKj80j=iH)0Wguy{l$UwIN7cn*!1vs>v0HfUZP3F>+6V)|m$TxLUT)GC++y-x&;geqe zN{asbTTlf7^&Ls}aqE<}Ee+Urs#^~Qvr$)~ie)(#tXcB;+v_dPJIu<|6tLKx0d;fG zkH`8idNl%?9{qY*YAtEE(4NT{g55S9_z+vYO>6z4+-=?zoJ9;Va!EJ|rSsOCF-$p6 z%O?u9Z?)Je+*+Wc@Z}!VI!y8F{wB*9QR477vFiU?Dqh!W(3#Tif`7g-f0n2ZuM$)c zJ){`vU|15oL)EGTxA19Ga8YV)Ut5fy4rkHi;hDJr(YdarEb3t|f-Z%xzi4Tj(v)ZK%yYoZIpNx-lez#0cJ%Hpt(W?pWT`yG0bP z_%T}hu!NnCb;uwsAOjkzhe_z8E;m+9xtCYO@!`?r0?+;8)qEu2yq<|kk}@`VQP7|eWKtr+pFdAR#? zB8c8k^%4#a*PfL9w7(9<{kb*#o~CF46InuGwEB+Z60X>3x_9NxR#cs>k&OxR5$-f= z;<}iAL9zv;1@FqSN3T%oooL1hzX*4u&r{reKNP=D5PP5EsE@bCqe*E~e6WLMdTqzA z9C+8Mf2x!;yM?wG9VX$ZPxF2n9{LTOGfnu?%yvj!GwayMv{{8UNB%4caqrP$N%D~O zO2nBR*L^C9DaQRw36T2NOZEE?{I5?D#k=8Qyv4$QzGp22keDl4r zHJP+k=Z-|9Kbo?ylak2`I*;?Lp-}SvDvXxSOe0Tqx7f#e#zQ(w7||-X;%zwKcVFevtYOB<2pc-N{~t%@fBk|;nzGKF$SGAzlG@dO7y^QqY))D~&68#? zdn^VZEEs*nM#i&H^2uT=EINwsC3MUuuLZiP77-ir1bFPto9-N%2T=K#A}x+q&@}+m$(L z@$k-%vf zrvbe*8^pI*t*Xhru%ijhE!}Wt4&U-$6SmtD8a}KwdF^U6xy3Y7Ip>Tzl(%vOEOXMhZl6IPWC90`i-XNE^xEv`O8)*5E~uNnNLRtMVI_c2_Y zW`r>7_K#S{cpAn@7NK{Gm);mzvgWAl2XCcV?5eUmhhyqx$F5$L^LJY?93wzrrFLBE zb6>V;3>?>K%zWycvuJfhyM|9Szo@mm_TCYn~s?!u5nBqv{!mIq@f@J0rFTSE3w9cXKDq;Z< z!)3ZLpF|V~rl1=FWDY$&b?6=xaC3qt)I#yLh2nhQfwS0_jSgT#zRWbJUXR^Y9Pc5W zRa_N9KVcKDC)Ed|J72F(Zf-H=V$k8icd?Sf7ED7awf(vuCpc+;kU3*BrH{M)wA)#> zS$yy_e;WCu1gvmjR;ieMMtFpmKT~5^Y>#>2)NV$J&Ff`}R!K{}75r~UA?c$}Z0k@i zT@x#p`Ru;dzaAryk_p#cB>#AAk&hm9a9x);oQL z&thuD8yM(E1>uhQK;6cuR24Y@} z;YV73H8)oZi%)v&j{guJbMpG3vi5%o)&Kc>D>D~~FOa$6b2EU{mZaygDx3}D+WR{Z zIgkA9>7PO#0_lREOjmyf}K0_!my=_J826yBx}L>7EgSrD>U?F zoP=jOv^RaIE4Z6`=%aiylw#pI@ATA z8)If!xQ{X71&`tN&o{dskRm`@eSeJCNRw2GUYl9pP5yB{Em4Yh>ya0u`Wyx;Ywzkh zQ$3&RoL3zM^oKteS853LRj5tvx93<{GDe+OLxCwv6MnLXSBXK!Xx=_YdqwMGf6LrR+N{3`Az+Km-p6~E zXM3Q%SN6Y?ZOqeisX(N?^M_Cx$lR3P_%runF~k-&c3G{+R+h56;TC~Bpy z34K`^Wy3RKe|J5rl6b!WJ8H3JB;;lGeI%ygQ=M`#y53J#tqK*vlAvO|PKfnH4%En% z`Yh9&AT>dcdiBcP@dJOjJTH4}EWLEin3)@XC#V+s;e1M-HY{>(n71 zXnK2eRLRUsTsj=?_0X-%o9*60!50;Wl@lQD^OV952E6Z%-p$+~eRcJOUA?SuZ-t%A zyia|1Dm0TXL;!CE*(tUj7WO*`a5}pG0Q@xm60r!9GbJ~#x7@e$Jc|WC??VREd#{kN zF%Vo;TbZpQ^gS)icTYO6SP`okB7)ytlj(N^h_d2+yF}s#Zr_;~Xu~Fas8L#6s$}4_ zlsM63d6lZZ5kN&B-F|Y(e(d$V1o!5)AFYw6JN76@Bp)c;)f`@q5^obvUT^+P{&J|FlA(s5e|w&5w31NE&|UqyR$c9hXA{E;sbkk265jY%xr> zRc*?r?>jOa>}?{oAWs%Pixrr2`%S^wJq0jz_Vd^(_{X&mFxYoSc8i=+=iJg}5B@9l zS5t@7+LRH!u*eGoj>`K2OSYzKsNSc{(DcEp41(-ONSmQWRTgIohrPN!>4}v%@Hm`N z+9Dn%ias(!4qbh1F*y~9ChkFcOw8Gvr9Nb%+SS?KAI1;w6uWpYEPe3)YcH#*B;ELU8FTT?^N{;<7~!QC$(*24ZP!eJtsYc;*FA zKm7@>msg&6&v1WVSE{#ZH63>Ac2Fg6rg%#$r>&2SGoIWn{&e&Ss(-0ovy8;ogfD!#&*WB*&PGH$mb9Q;$Qj1I zAB@a;s`&1nJl3#;%6@7GvS_|{)m{ecc2jL_zkV3)zs*eG+!Xu06FmM38Rxa3&w6+1 z5w9OQWpv{}hbKJr>f!0N0q13)-Q27`+GDZjAzBv*8E9S^LuB?~csrYJ(Z@n7WmvO4 zqmoQ_?4XO__2+KZ=~qyq)jRrK08#dOVF?}p#(t3y0p8BT!A!p!Px7i*j_Nud>A{Gm zyOcat%`?cY;hJ*tBJy_QmZ=DQ9ktefni=UjS8LYE@@OKzboJ72g!U6H#bgSh5K~t^ zBNH2S-IQ|P%(+P?uC*jz6VNxkQCZMm}RD?|nv=3ckq z-#_m6%}Us44Q=w&uO#0IR4Hd*++T;$POL1Rv?scG6(2HAO5n)9OHHbI;Re(uf2Xn^j$z2ny0JR%-O8Tq;rr~-6iqyv?2-TO&& zRrCFdXu(`5u2mcrnjGuSFKE97t1?<|V66|2;U+51JCA<=qU92J%)nYzb5e3LcPH<291*)^@xl;)k2}euy>YMvTBN|eXoSKQ|+ffnfDQ9 z8EJPYp$A_V}7^|o4s%B0 zji8a6YoR3GOG~9--{87)UO$>=%QVcOHU9BZ970f#_6b{EdaZaJ15bTWZ9gJnGp45u ztCEH?GdEq;6Tf*flwAEV%%OUE;+A7*{qyIY6C7iCdR<*BACK^OkY%+%9ZDKJIbmA`&+BXEg&EAe^>bQKLk6{);^pq(tt$05CLu|%K#$M|>@0iYV z*jj4wSG9HYtu1L~^0PmZZ&L6VA&X&?xy2?o+VU-p7~YzXX(EP)iaU|nhqS6;P?o=5 z1rg>cczZXa4zVJY+pfiUWC} zlvzRurLa2XJyu!^dRB>kgD%%z5%=O9KctCl1F&ovVWp_K(`cPf(e+k`6?=D2=!@tj$a#gIeqz-QMBkA{~`urz~W_lQV>N4 zdTQyPC>%fPJXtkz1Fg<)xb{eHOvKV@gkcAlvkIs0YzD3#dG^4f51!0m zdY6$&$)XLiwh|-bIoBWk>1(8oxb&Vx_p?(5%QQHWPpDDoqDAexljURIjNdL-_MW66 zWZGK$-*N{1oae8(9DU3|?T66nuu%aR-5FaFVsEe$8C719484ivU*J0K@2F#m5lMG8yB9IMCtoCnN#Bvx_sHj%UveXz}+KORg;499vn784Qpx zieAO7;w*`lNEa!&m(7|Wsx@ushQWJHz1sdi0VOMHik~w(u!>H?d0n@G-*?j^S}pu| zKv$J+C;PZv)Zyqbh?a?&p`|tJ+zhYCrhW3UBjQ#X5&rnJZ~hG*<|XUw_=-|Pr6=uL z@S9hLF|$T46-(OUFYK4x1SPpgbfUQk)oC&{=^=ssf zhFuCakA#l+C%66VR6Y-qaIR0|!B5Jol$P!7j9c~o!!4)^0xJa@t-^zD2{`%IxtZ(Nc5g%m>Ob?pX+qmm_%5=9Ys-~Qauz0}c&fo?42G2hOU)!(+kK`37V+F<4FiXn zOsPEX{G6*axYTz%#aPS?&IUNyd1L3^tkj!bbz7nKaBY&~9dwb{*Y0;j9K|$NqK*}{ zZbX4*G267yrFm`DWK)6UoH=lX(NMiak|0dGF(00BX+r!E_3NRSEN4*#t3<732nN3z-VlWL3?61(57CLFE|asD>)(sN z294R}zSM?Bna#dGte=?JT4~C$PyE>A@n0PmV9Q0p*^V+H%8)EITEDP)=#vu8x>o=o z4Ter4fuhcnjzgEPt$+1fyY=C~KF)|qOVuErW!@|(N74vFxYPi=KUX$z@v~$adKgml zQjYgH{+_pEESLnE%i(VM2w+?III^Qt?4p5R&*=zej^FP$kmY8?*OH6hSkhOqew6x| z!bYWFmsw3>XqUOo^bK5vA;sIfm_Deombs$o2+L7OYT=QHX|vSa zK`8Vyj&oB@2EEd~F6(3Ju@yx5MFb;qc+TgQ_fgj%gbz{i0zWI=yIT8x$cC>}3QB52 zkY4McH>z3a`WJw4?^aE-HJZ0FI?jeUg^FL^Yzz#Y|jXj1p?l7?=&0m!T!9BcTK#j z+q-6Bn6!H!x>YPEUb4dK{>|ZwJ5#nFu~Gd&rL4s^g#B z(*Ca(`&Y>SSKU#m@YwM2pCRe5KVfvpLb)YqUUI(dd?G(z6Pj~NM~6N5v3Gs0YVFv~ z=DqsNMnQTV9j>1B*A!?`6z%l!bc9!yA6O8RO_j^zNp*MKG5s*i!_K;tB7`>cX^`ab z%Zm03X;OrurQZ>zwik9fQaQIz4J58r)O;CT>i&w#s~<28s2h zMv8zi?s7tlv}Ke-zggsJT7zeF6!=yKH1FsZhN48Z&@6s7(o?vQU45(QNxuP_Arv*{{|nsw-GcL?o4p&z)n-i4Ry^t;jVX|Q z77$Z)j>e!ey0vZ*-N)}ZVt;LnEf=~yl61+tR$j6UYm3;3uS8yKpg#&l-e1PvS=9Vo z>hjr(C<}#L)WY~oT6PC(YtJ-V$o@zyL~o>Mijju5lPv&-LSFjj;qKo+&KdC2a>|L< zrp}3}O$?KKC#30yy3s9IyD#BWrDV0edF4_Cm6tvVLmR6Xlf|pIU@SL6;`l{^{mcE$ z`3iO~1Xlig+TuT&GJn}V;oN!C0-srK->d(XMYx+>OrO2yMYc-kHZB(x)G7?{Rd#S% zntxMCoB03`6`Jyth3dRzx9~lsdWy$kl1H5M(SV}z=k{6jm!Tw8FrvREt&IU&*i@># z0THG!;0K=7`=Kj0{!WTkz~}C;(kBzHwT;YYj03wcrQxN#{1!ysTeXLelhpOGd{@j_ zY_aV$C^k9+cqkRGVd69RJsJgig5jLtTWlaGVU+k5gyPR6vDjlvX^h~F)m|-l|KxIP zrI-U>x3%VxQ=3^>4|Kpa^p##mAwu;ADfhfVM*DT~MRRXM?%qAVHK^gt$*SUrC!%Us zjUt2>QC;s@h^|i&Coh?h>#x)n55eb(&E(}f3=J$BBr9gP4~ECx8)dI{(b3*N-d)Dz zg$Iu$X}a`++jyhCvJ4K+33OEi5R0o1*OpU)Cm6} zRE5f8FJ*qZ^2@|iR;5!@)9sg^-FDHhRNS?0UZ`{~m1VJwNiGxNtZ&+hB}7$(?KFrJ z6$!f-Mw<2>EynDWfJ zVxwL-q#U;+e#Mih4RgvWdd))*RKIAEC;;CKER(TO?{GD832!n7+u5%l@uPNe)ia*1 z&L2kX>IQ;YpjJiEHdX{;by2TB*jy2nN;f|xR(kjmXz%7H)5(@>%DbLlS?y)MM=b~` z@f&Z+$SO8IQko1FcA)~lp`OeGZ>{HhVv6J8J5@sujWj0ky>$A56HgU};dPmr6TbN+ zpgis+NLmX5p7Y!bbrF5CNn(1poG}y>5G=6JC_j(;rh^j1ec#GW*k5A@qw8wkb=k4w z{_+4^#N)HgcRS#v=83r5Kiga^dCJw$q-_O~+*RzGBHh13Rk-)9A?*IXobba0l3bJG zvV`|X-}V-O`>xT;(-k1c+eM+lF$?CLm;kXzAnV+R_<&!^8jxG^QwrkK;M?JRD)9Lj zJfxo;S<3Y|fy+Rqt8ni)1z)?oMu*2IoiI!AJh{;*khyVzQqN;*qBEZM#irk&4;+5u zCNTWVJw{;>+a=Iq*`S1TN!axe-8qre>`-gk6~`u;hbsE!LEndC*IK_iwZm-sJDp7w zwU~K}qNSA@j6ytUA>sh6yA79PB|u=F*$zH;?kz}_H$+v7k9M6Cu!^C!mH4bFy&%c+ zaCi4tc{&8Eoetrl$GjrDC(8p1h;=m7>i?k)i@8b5Kcf|D(|f>_X?h~AInpOweEN!? zvz4nm%F8o2(d#g{7MZ?t5-&fAs8lNKJ`+Sj!nwxuxL@>dXTgM4SyT-MJ~13dw(D)g zZYK=I5Y0joWhJQhl&-03CaZuAZkzTy$v|h~ZPqJXjkAagDWH3ePa7CQMW4c~^_o>y zDY#M&bkZ;;XAh&1xspBLB~cHHBR+f8RIed_^P1Rm8VjeFydEq|>?=RZN|)ZS817^b zbTVabag0#%G($X466e{8j#t`-SpHtQkwnkP47D!L<2>h z%-$g^2pvn2N!@KTr4!Tj*M=3GC0Z$*F4|XQbz5}z*3vW$Mn}anDs#SUIw|idgmWdT z^|)CZTlB))rmDUAVrIqhdc%eF)&6)d<2|Dn)OR~K#}1V=Tb8qb6`g3oUACmyTF_fp z&HHEtITc6uZk`vy)sII0NNrPIqPLk|(VkHWzfcc+nBnQe(z6 zpZc&_&b#{1LhZ-`b5d|UACp=jPw{zL!5QP^gNW2HHaeF@RYkl+Ou9xh(K=9D-p z1Yt()I%GJ{1nSiM>Mw-aWGubTj7yw@3b(W{x1aT55@e#(KUuP>hj|sulOLJYOb*`4 zFZn9k`O0T>U&3X*ZTTqwYVl~qK~IYQvS`P#qR7i?mgriYpiR2G_2_}Hh}Kz z_F;orJ?nCw(PIWdU$*X0|C#;-Mz=B^p8Pt6jWZ%NW|=7(-6zLH2;UTbr|?#Aif+1% zzv1{Kw3C2k>7`+ve;$-|Jz9o>$)hBeMm#7yp6lToyxMUf{Q%ycy=&FtxHUgCub8&3 znoxT(mpBw9ZyuCqP^7N^?j0w7qwpCVCX|VP3+lJy9lZ(X9xa~7Y89~roY{Rm;9oi@ z$eiNstm|E=4+Vkp%s2{u*6A1d_Y)IGqzfxwHiVa{>%YL$tlAmVzTx4NZ8?|(4or!%(RTlKHWn$~7o3VS}-*iD|SkqtO*{Nvv5 z0Y$go%}!g>8J^z7$-_OEnFY@Wgad>j7{xlYkFGX4YqFflaQdUhnXRHU)4y1H>^AIB zy1w`Co1>J3_b|W+wT~Mi7XCt^gRQ&wu*_>vS#kVEY7SFkb3DXj_Ab*||`J6Rj~?C_&**gzr})?zDhw7XzKsK`S9Mf2A$bN&=t z#whl|DSVD&eD=8nkzCSpi)@(Y9+zTMR~@1x4;8k0`}2lO*c-hUPvg%g(HDzl zR^Gqw6rZohOkQUGRD~obHbvvW#1_l-qe(cD*%sbiFD)lpvGKIA(w!Gnd}B3SGt5H$ z;^cJ|blA%qfQYK3rYWAFTF(axU!buko+aB!ww}Z&JrkZq0o%QiCMs05x5-r=F>_O3 z!>Y)_-+s`4z!|ut)@f1}Dt(XEq8vh>Ohv9_WKq3c3nLW{GA?b``BOKaa0 z^wA=~zpLFKi5ZqD=Z583`pLOTg3M zH|EupZ#qL-cwRuOlcRrj_#jji8B1_g1L%K@cl}{{|9%gx7bVFm*rfhGCf4domvo1W z)#DszCXX8u#k(996tj2-@Rs6TMBFu<^AwRsJuFp-jG%?#Kw5C;W$O*m%#}n?WTWyh zY-x{~Zvc@y%@en@w z6W$M|s0j7BOCn%JekpoE;g9sg?Q2>E`E>Fi7L3Q3>v;Gw_&w{Cd*e{-H4lx2+5me|6#*CH&s8})<`ixocZP8H zdSF^7mxbMc1^Je8DCeRsQG2A;_lA7*M9J^%(*N*||GNG`Va2DocuUMZsP^@RyFL>9 zQ5ZVmjvkBti2FR(-eL}9VQx(RyC@NQmsFvv-{JFvRXeuPKg4^hwHwPy6OHCA2yaYc zn#|gi3qY|{)ggk1D2K+PXJZWVX67Pe$@1nUB0Uvh%2gJJd+U3RrR_Pl+{FY8@l;jW zgYTU-lR2zhr5z>kSH`$E%!zC0WJaiGfBUfn`M&oHLDogYP|Q)O|3c#7V9TxpXo0Of z>3F8`bKGS&wztoB5i&M;47%>X|CO_4- zk|&qZgI$@!Ej^Dk_EKzH<>4&v(IHiI!0W3P-xV7a$D*G)iV zCFflEUY&G}if3)xm6We(51%($HJYgagOfpR}c^vgUF-ZCFr zA+g)Kb|VzJ9*Jw6eS3bIwJe#25?ON}Mk5e2Sm2Ix^1KE&dGie%r(mM$EBQRr-n(Pd z_PA;p#ApxG`-L=g6Hv-sT(c|!lunhNaAOuJmQEQ|V{S_P22d%vXFnqt<6*Rzn+Wqv zFG1U&W&cYY`#;}%VA}20i2Xsk5E?obrdX@;${uC$Z_SK8LQPh**2~^%K8DT?p?9@= z1ykhpldr~nq^e?k_=G~kwxK;870Wp=F0n`F+orHi%ON&7nS@Y)snS;0+z>yxQLZHE zof~nAUDc?Km?a5!B{M$R3`%41pVA9zS!{X+v@Bo(S2>jcU-YggO6dyM-X4@utWC|F z;Kme#NyDx}r9xLxT=NEJD_M2&_cTgmU9__S zHZO=QXjK?@1@+!~ioyT?lqLP0AI37;?uP#qW8zUdm&JeTd)cfxiPIXAUv2<6h zO90*hrs_1S12~=c?s9BT$a5soNL>SzATy>vZ{PUn-xu;Gce8fcH0# zT#9B5VrW+8{kRiFJnl!U46n8dhh-gpTFNd363m|n7Mk>mVI6p)=XlmDPWODCI3q9}d-FYNLS&0~6Rf4|n+nCn zngKs0ke;s<>I|bnm^JEZJ0E0|9WKjt;d6@|KOQBzg@2vl=kfAQ&sNAY({WJc!e^t8 ztc7pj_7|XqzSj1|^3(a@;GM?MSE~w^d+JgyjX|+_%>}uB9o~`V{q{lX;qKNcHdw*$JN=e+Jow<74-0K#viN#!te0E)q^^*Zo6vk8l%6>AP`hEqnz{rzJyD8zn_Sw@ z>;#5+em{N+BT3=?ui)k0MQK(1>hA-&f!Mn=(Moi@J-wiL6`_t3f)?$A`HO+{loB?R zXt$drp7d~_qgiAA%Q|s!3UN_(w``RUg&u42=6&x!4Xt+4sdC_L5=2pK(|G~LoMwQ2 z`N;Yng({Z4dYqy}w`_`6UwOV}5t)ZvC_VNw0?^!`35(WzDj{NBHT&eJUwKA!TE-<;!*-r&03t9m0CE2 zS+A>eU)K_@WePS+6&brTp#VfUM0rc6N+9y5{}BrRNPcDsbgjzFJ{2}QY__jbn)8qY zzI9)mQ^vft*a^yjI~LCOJWBNLzLAMtV49y!#|9!hn?Z@~)2Nfyl8}>UD)CONI87^) z$(J!L)>gUegcxDu1mh4gwD@6+S!J)0n92Q^@}tnHLVI4NrV(XRLWSkZ_|Ogm(zyJK z%cuqVs6M+EUVZ`8&?jrZ)onE9+&T}~&-HX$>JLu!F~!EHEto7~N8u7oXr_%zw%Ey~ zrf6hyGav3GSG)SW$_w1}y>i2eFgUv3YkSB2e}=&af%aUtd(;1sF%TXEx=Y@7vt?h3 zd@wil1{6BID+l{U;--(J5ACNd<)~|X1x<2ovYcLwz6Z~%4LuFL{7JO{w0Pfg?!yNW z#V~S8@Di&o*J{aCFsshKeg-199=>P6KW6UL$l>V9t|{JAebbkt(`wqn)bHe}KXH|OQO@9{ zgMMYnlDo5_&ANnNOz?U!gDBk_xizpa@A1GiJaC*Z;{y{K0YHo)%HKsNy~`vn|07lY z!#96AUo7Jb_SG59E6e{T_Kx*Ghj28-7ktYmLhf%NV6Z^Z0Z8k*qkdmm-X5(p57JP88jHdy{GG+=2LMgbZVHo$M zKd96#Lz<*NaB_g^@XQbzUSCnjC?{*Yb9B zb|RrupH(cbn*x0%XA7jAOMA)_+Q19tjtg!UO1ljOPQ+;@b(svW(PvtmXz+9K-twHo zN_IR|1#X#XIik5puPahQ>~I(|iq9)`5q4E*nG$mPA03h3IVkh(X9C{#vrqjf|8{KI z$DNZm3-SP(1StdpDBRN@KZ|ImvPD4KwjK*|^q?$HmL(%X!W|EPmT;2Y+a}d)W|Wd1 zWOYTVpA0@pan62;dd^v5GdlD^au}C=mXfXDs&vxil3gsN0(NEy zK}Js??h~N^Iak*Kc;_kDC)~DoIfHXm)~&WTLTCdWMsO4 zo4Hpyzy9Qj!J!L6%EOXV|qkhXrlGi)hj;J!f#ueHH$MOb;4=#i=Q(N z%QP_tsQLM`2n1^Ehac}ip_;YoGm`cMcth5X;aG79kHdyT9rXhpxI}EF6Xd(-g4rId z<)xlNKU^M!HC?KYC!vAA)E)d7@2FsEvF7&A}$XuRynK0X^|A63iA9R7T zq1G9M$(}w^Pky8iC(?}T+S$}s1%7uH8ae&w5_z44Zs&*LWd0hxPrj1DOe4P&5@K?T zx;%}ud+8Yj-&4%iaLcUhC>?ex$MUZxpu;b2Wu- z{lNCsM4X7qvG~c-1giAovW4KtFl4oV)yScLbDn7iSJ%GNE@+&Uc>IKXTqFmin@4n) z&8QdhaEsr8WKR+D_PiRUW6%))<~4h1xlO;)yvV4e&5ysR#))adh4uiaG10YO810AF zZo{QfYioEwDxt#^_0X-v)T?;FxqW{6v#m#2ju(&Ltww~G^qiG_7- zwlm4Ugn+EdDpjukUoKtNe({AU_;0}#D0ETwU@?xqZn$Rp^%@D^@3|9qF${FG?m8pQ z)N0Hk_02GbTPqEf_B+rF#;*_rh7GF#l93}<`#)u&d*Fyap(W5w!;4YQ*)k2p!&=mpqgm61bq{aCLJDNaGE392~M1yF|*`zhQ`BbWC z*6?+k__lF!`^KUOw31fiI|xu|YpOSJBh-ODz54Ud<%Zj}3(w+gw>P9&Q3Y|jVZhes zd*A$vHPV`)zm{wBHs9SVEe70N+b60vq-!(uTPtBqJDOq4DrG#|^m@}lt&VQCcqdXu zYd=3=BaQyXvC8bz1XJEn0^ScZm$WLD&>Y}?J$KEZ7dEp}O0+dkHR=r!(TiR(qXNy* zbOI4wPfOwk#c__fUg9YNvUV%dG&-z$B+!2OlcLm4Jo{a=*D_}D_$D?hE;#D$&k!xJ zl82VpROyrg!q(KAe{C4@)H3*fn_n?VY?-RLIKxmCC52bLYG*4g9X}YW*|`{MDzv z4};pEpRRZ z8AZP)tPSK4KV!Bb-aS>K=|YR*E8rcihlnn-trU++7|e!l+-;dHdh) zZrD!THZ8H|Tn~4E-jVy+knN9F3^?32uPjo~FvHsm-B}sC0=)}YX-QG(`j#VEkj2$x zXgeBR<)C52sIc~{ozM;L>aK7r!uF|vCbiSV$GTrHO>NsP6z+LgW|^V(yli1<5zXJq zFtD}yZ_>{l(vq?&tfEU~#uhGpBIIIsB!lmF%rHJG=^G;I;3wEJ(*qll?xTB3uQ3 zon-8W_kLpk%+&$cMp#QkjuefA!TeOY@|d~OaKeSyi(mK9p9{wqg!(0wm{6(5cl)oG zy9(Hwo6&30YYD|XmUML)e*z?Tc3em9dONI3 zWV+OymlXmo+6I~Tcs@?$+*}L0qcibTD>WLX+2=s;Wi0Ec0eefnof~eYol#5esTKr$ zUwNhD&ojIVnQbVHDh012a7y5;X1KflS79ZHa(>zhBhC+}rmDuC9>*WTrL$^zm|rA` z(9aZF-(4c}Xx62~UEoysX_nn?1ibU}L@_lzdquCM)I@GM56nk?ISNuB>^S(;JG4Bz z_WqFUcI15>K~RrGuKmMRwiPYaq+1(L=e)V}=gg{nn$9PiMkqub3IV{kFU@q zVi5a#i1xYp!q>F9RN32;c$@wu`9>!CqZNC02IxvA^f-;fC4z$aop9Z$><4l(%iJv5 zS&2Ddmq=ZsEeqrPG$n~CzQrm(&m=|5iLMPd$&T(HWn5`c3Qz9s}a&@VwF zJjVkMf66eZ*?Oi8wTDTePVV3FwNpMf;zeQYv>e5?7Lw@~+N zDVTJSI98h#Yy=wjUiR$&ZZtI9xAYP`RvdE!Hm;2*v^;i^ha6{Iu6MBYJali;@3hF$ zE6!D~`cZGHHW}W#;90eK&&bZ%JIzR+LDeJ8%rq^F7-Gphwo-Wami@w&+`%e#xn7y; zT5k%*EVl;}(ND}`CLn@WIUcHx+*lgfQ8Sh1!qcHM`WGyQh1ZXSP*Q(v_5_M(yn!oh zBwSy-HgnZ7p0i5;$8dAHb14m<+y7XiQ7@OjpMSJsGp1~Cl1D(NXo5sTh8;U zWuf%f0yMMIyK}^6ELwg>zJ85cco`vAZz}{4x8pRY^bY%?&N^`UWx&!NuwHIGVc@tt zX{C|wOpAJh;p;~6en^<4QtNe<%ZQ*P@>o|H`8<|+tC8~l_jrvj;L*|U7X4C^+JxNL zh+>0e&2#Zh{Kn$y7x0_Q{zPe_6mC2sw3ouG5LiPARyPmz^*D4`zi&A#60$%uz;()E=ry!9?|Tgj?M%p_66)Nk^la7zZ}LQX-`;Sh#b;1UJ#4JvMT z4KhDv)r0SP{qo%H7*!Ke^>M!PI zU$1qFp07Bm1y!7RPM`Ws-h#2iNxe_4kjH)jKI(f!mjY@>OC)%x_D`}5`{>rawL=-q zUs%X&2c`aIFMY-s{oi)c3AY0ajc}QbtW)!ciSDY&i$!m{V^%7G7e3HLbJ*Pt=hspL zPJU=Pv~!TdYcIF#8%sR0>zm_LsvCQH5i8tJyM}i;B0Pz8Rj-}!PwI21 zqa;ps)W7EDdK-lJ@!DP_{iLzES;Ks{muOGlB;Y6Y=@XWnEcTO^zjox#c8nL7S`Ng|mVZvRoJ<~$ zsI(Y)uV%%_-fG@&+Pt0-Ab9jl)2(r<>u1dAfZQ!Y5?m3H0sieb0h}zJ%{0-B@;Pa5 z&^{9h<@qlTZjN_uPD`%Ul$lvJ-gd9Vx);I-1%!1Brd-OJItt-we^sxp@m%Ss&_oK2 z@;+#2Uzkw_I4jB#LU|B0LJM5oS$LbuuL=1hS2}OVMO@BD(5;GVp z6pmv#dNBd}c98A>#_$~YS+(<@V z%_MgeI4gLul8#3GG0VJhN5?BUg9FAwvCB&)-&r!v!@$N3l)i=TaQ~2SaHj?t_PO?e z-@;+&3e-|X(zQ0nkNw@po}w2*ujT>7AE52vjLt~ z6UfirvJtBEo}5lL<}m-oAZf;5iS@P_k*Yd)k8s_V9mLz!Z@$4U4KCBVj0`@-isi&HNpA*NY5S%Q_#)u>vRc zRjbjig1bGoFk;eC#L2^F>vgY2BUVR*HYU5ZiH~ifKjLKi>hc7@wrQbN^z_AD!(po? z#4knc?ZYRK5~Y0r%lX+gwmd#1PK#mLafWxEfH6AOM?qAQG5+4Vfu7MZ1%n|6BH>D= zawPJf+sYkOd$wDWZr^%MZ2gQm$HG`qjn%TKkrM$_{K+8PDKAXe>Bzu9ns5BufiX{O zWqIU$*=ov>?1A(ixK|ivN<_=TOh+aD9oK=(WZLP{%s>P&qBWunda{3ZI-~GlGVbw* z28~b`+ErI|_JCs#r^7h8gE-QH=fjiTTsc%GAOa{8B5!!TBX`v*JR@>&TRc4bLk(iz<9>!pnyBJ06ho}DPsw$j6&{9RXX>=CxyQWhMA z8(b;0Q)ZZMpAJTFbri3ak8WqjnW+#sP&QYGB@3<|fmo$PrPgCt5EeYh;5vYkyG2SGsVRs^=jeeT z3P|TbN=gtI-K{jpknV0q*MPC{o6qy<=lTBr8!zrZw)?){*E#1p=eo{|t;$uo;|Se7 zG3sYDoyj64t;O+(b)nVElo|iD8|j(kfoB)eFTc;Pn`SHx*Ox9l@tex9gr25$HT+qn zzf@ZMX1?qm6X`NG!b)nye!yf8s);Uj^T7J6wP8q%kP&kWc8BnP4GjHR-4QXC)xt9g z?u3Wt^SJW$s>+GFt`WQ|27bb;W5Wd@*AR!H zEom3-d7JwxlY!@`r$Tsb^ZRd&L))#3wUkc6aM@#xn9e>sfWIX(Oz@@movyYCXV~qO z=Ij)~%_j;ZtCUS3{AKyC#8vpR7J-srN&Mw&9B#Ie~&?%-@~EJI2$KF zwunjVe-UOBo}1m=d@Vv7$3**5K z)EqlCbL$1XHKvJ$>EG<4guCFWoRI|)R0axfKVdp~Q_Y^08h3~3JlC)P3A#Nj)8hFI zAD!?}v`&yC@tMF2lSlCOHzvlFp7p#9kDqiW-D?YdNBeHW>k zHhk!3QmLy=emZZC;p18F*B2LP{t?Bync7;z&-!k=VqL&DAioW0TIK}yC$N$(eq5tJ zjV%9z)`1fSY7qJRx@;Yu1t?3DmOM56L?Dd!{tB#u4dJHU*T>hK&<}5s z0zp4GlVL>97&G$rVu`wzM}vQz=8`fQM14MQ9O8C)XclbJQE8dMT$smgT{x_=_PH-J z@2GekJ5N?zwT)e43{W^vV5mO>{&(iw>cNh$0;1kRI8GeV`adPU8bQz4mSNg#*~C#+)jXH( z8>6K4BGasAomq$SWJDuK!4<%Rco1jQ>pqp>@7@_JB0G&@e)ey3w#&|V+D-pl+Jp6W zS7La0eR)k}HRqbV1k-2x9;LY#1d!ya1)FIkfmBnP3 ztF%e*s<{=3%x>Dk2RiCv?Fje%^22j(f08x~ym{R#%ALi;P9}hC0)G`h?a8hVhKMFe?9h1KY@(V8 zYqbyw@rG4${e$eo;)gg6#w4BN`hE=7w&I_8gAscha-*oUkN9UVp1B~-OgHH0{=pIU z#_z)|WT42OQm#7=0WgooVVjsw~6( zxU9KoVRhry5hW6H@$u|%6H|$MK!m}oy{Gg3Xp!U@Ct~54e4_&;!gW!${{^XF_YbteLOhbZjtA z>(N8hUN4+^=Ip4LIPf`GwM-eUyZle*=g;91FJ4@%&pd@RF?PQkR$qB`FgrxE;_yEc zt?qcs_pe=VtL8JMu>i`LDD$ES(nML7Ru3<%+fP8(FIgY4X78|`B93BRD#g=RiWP&6 ziEzjD4mYhZdsx_*)L)wov|M*$C9)F^Q@O zwz_5>U;8{>@>*z~mDDR1?_8bk(9L^Ow;|#l&|((u60r{z^~tYjX)YpG;jdoW=>%#A za7>830lxq}{575I=vUO=V*593M^Q3dnd^%|%k(0{Qljg%)+^{zyq0vB8(x*-SM<6_ z>(p6b+uk_O)XRiW9Ta}R0e*XqO0>KzHX82}VJqC)T$(ryb;2R;u+0Xw!<~Ut5rt6H z;`jHVuHijNimg^p=0)bcnAcSbd{Jk+t}6KBR!pp2!^QOb}ZeU()G4k zX5S}kB>y4bkx&Jy1|Zkw`Ag~W0MTx=OM{Fd)20`xDo(ohC9FTaEe99UE*o#3M>v~z z*1=%o$PHN93I)XIG3i=bV`LGU&sLIS=0)nD%}y5lAs7s*H^lrRaT}s9=RbaJ_Hbfi zVA{4-13dXx?^tYjc^N`5DczzV(r$uXY$+;$Tk|8G@|o8EG#U@G3DjN1se!Z0ZNMnv zOa1lo;Iu7(oKz4zLi8eaflwt^b$ReXAd%Z$SqW=M&&g>-fl{bf1}I5U0R=Ms%Pe^o2F&#_JG(@c0IY&M9# zXN+s{RGHwpy|o(T93uvhIR5IR#QUa&D$_id%f*p@tDZZ!d**vXC!WW0SZesIIJKLQp}5(zw8`kcHn+;x5Mp|wfHUg= zVZXB}>vpH>Gbih6b@k4!U@zhB;q#G9jQjaPJ8{SYs(FEiD16gMKHs|zk7Kz2z=>MJuZ3|}xI9)?0 z$*mQ|q$9+?I?Kk50KuZHhbotRs|kk~(EWC33pGmwU;8XPR|V2W;?DP$L_5$&EA(Q9arg4qY8;MoR4{Q3ic4! z+L1Za&grTs<(!rR#yYlAPc#GSOT==Vr8opjaOcXSi`DQ zNXt@3F-tQ~G$@Is><*p=953f+sh!J#(YU=Q+<6ojL49~{rayWr zOX1?)x&Z0iwy&*6iT{%Q$M^A3>!Nv1^mKYid z7&r)gO|=$gn|K*z>}cn1`DUWL#r%c!&Bq!`|8qjiG#9bqsncf-M+kJ$XIN8nwrs#O zhFN#QtYdqJD@*t?=W>eGN-t57PJWgQf;=23QaPlE|BA>7SNn&hfUD?h0ft$n9u#^% zAb<^P@m)rPO)lWKai(P0r)0Xd`NTVto;|U*7os#N|3E4r;&9jSbx~Kz^IK@^o9{}0 zVDpQ>bDE)~R^s%<@hJNVDfs6B^Uv6&1dctPoj-}{vxx2Tb|0fuqS7l;i}-5&!==Sa z*5g}he<)9Hk7WujSXVZ*v^W((piHLrGv(Ho+oAj2X|Ta?4kv)5$qAcO4F!{nJtrE&tQkU^`gGA{u6S(&r8KTvf_p9m^|S=pkhPb(u2@ zF1_Eu_t?va<6He8%+V_|%^GxUM~*1j2U1`_**HCRSL@dveWn3XsF&O2{IyAN`GmRt zgBu)*;0QxCI&E`ZNwtLW)YfbnXNvMYFV$8$j%k7)GhY4Tz+20ah&~rvrXxGDb>*r+ zZsyTW98PA7XKfNvn1UU4L~^)Pw|`$tmt#PAh~dILY$$ZhpfpjcXSKCwABMndzYkJu z!i<*@SQGMf{pf5ow0WL(PvvY1P&>k*5gpA81_gIu!>yv`-tG@rZhw#=$4-p#%Wzx z%Z4Qb)2EYFu}T-36TS~mA(_VQU8jQtusG9XgKI8Cp`m&TQs?o~VpIFK)}#k^9YQry z6v}H2b(0sTgIyg1mW!8^wrXxE%!9ov3&Pq5|CZG`;L%C>Jjm{M*ezMmz(wvO*6i8S z085ynfYZ|H;?h>kXn*Sv=Cl`0mBCupOSr(_Ir$%_{!s^n zP*Siz%3k|z#x6p5)wFAwWqesm+_I-%_3;mdo#Lj|p+DLbM-rAQ zs!vj9RriSH>?TFaEnjVnl=BQbhuRhKl%4Jl5@)=B+BG>NA7j$*ps^EecZ>w1#W_Qs zdfp9v(>>;8@A>*wan6?xW3|)_+aM>0aCHdvF)gX?sQ314Ui7vx&8@L6G1R!t2~SOz zg{7VqEHkkiFTJQ9x^2*aR}sWy%UG^)7o@&)-*HUoffL zTvMmaxlBu*mf)LGW4tr4mhr1JDte&fi+IgRuYk6yzKBN`Q`a%GxRUe?g~mnN^Z%UY z2DbV4sG+fP#TUO%6!)lP7Z&Lyfn@-sqLf4`r zuUvEk^rD}_82$C#M`=nEeIwk4=d}8=J|E%t4?p-Z$ocCb%ddwLE<5i+p9I&3x`7#z zIs>=j##uNK>bYEjj}Qfp>iPK%9bFO9Lc=^k(_14T@<5(gkPFe~8PK=E_(KQK`waFf z<~g(|gLQmgsR5~*>uZQAj54akVfS;*-E3G+UY?B{0^4;go)&Qhh^|L>(}w!GylL|e z_5}u*?U&?>i>q&=^qk}}Sq>b!JM}NV?eU$mV0zGcxK2myQI4z>zlpb(`-F??52Bxn`p(HE)8B^4W^=*BS44U+mi^rGZP9?CGzaI-f_NDQGx=3|5cP%j?h(t?T`3_H2jDPTvi?-(^GK zvF6K>)-@kCi0}127KQgS*V?DICXl`LsD?DV6#9HEc%x})X8c9P!YeoAuGspQdbf0c z({Y~To%W`L$DlKljEy=gNI(W8s?vJK#y5!f&y5+_w?g&?sGzwr@O*st9=7`QTsQDy z#)7cKGb|!MY!;YL`ma=>i2yhMvIZ01aziOMJwq}fI<_eSV9At#b#3a=m7fiD|Kw~C zaBvSl*8vgolp<>{f5?(;_`aMyS(|MM)3E*%Gp22(8*laieoVN^427U?71B(#6Ee5E+N;xzJ0jd8J4d`x%tUY&Yx_1hF&g z`V?=#el_4)?DpA7KICqGE znGYh8P^_nVQJ+px5|7t(LIu9Ql<}JAou(fbskZtLnf|{5(FU|FlID~8W|b!%uf?%g zR)X;R5MrzRskmVdBDuZ60L(F96Ej_ko-P*2HY16;prx=~ebTjP1l>5P2P4R*oY(ru zQc8KdzF)+%YkidP+*3R;gRA~-22?tod-JGY9ov&mN?@&lEX|3iRE6PJ{y)e+?NZUO z3mnlyaO3MwC&R&eOSk@y|Saq2r+xj+J8OH|5Wg# z(FRVK7k%f}bbL1Gu8Bnle@(|Kz7Z@uq!9LO|GV@3ry2k&Wlqb@S{+T1N}41G!f5Ze z58*TN;>(*V7TeM|&FabC5MyKd2}x3X5IDG2+>=#>b*GBR4S(mB1n^y+)O#ob>|lz} zkb6rO*@aQSE0l^~xJs=5l7t~<;baZsjdcR2kHCnRCD&J`u@G>2$#35J%}wkgj#Msb zrCI}9g*fLurSNchB#50UDR#$}7K}~n4eoO;kcRcowFp)p0gD;w?~PiF2?m`CQtTX` zGXQ%ZZ4K{J(?`LmL!KKBPW4Dd<+wKcSk?hh1-?VxqBW<8y1&{SV}Gl2385*LkY$kH z9rQn|T-v`zw=UpShFa^Up)L_VE~&%5`^_R(i}*4GSv=bKl&N9DB*Fqj9l@i(p|i8y z84Xi4nvGnYbDWNuDAK&p;mC$Irhi%jN z>Pg}11hsuZtdeHt^+Zq}7N5cGoe&X6-g(OYR<-3ciKkboxi))Dk33&BMSU zT%T-xKOmG9x#W1(+|k0WKHuY6dASw%;KHZeN=qJUC4*} z@qOT5vk3n|#i}p_c}^iK-b$cym7u4+1E{aj0yL8i1qDBylTxulM$lCh}4>5oa53Hp%0{! zV5F(;wAB$1YMMtOeYyjdk&h~&lB4Xp5lbu^%!V>!(To`Vg$Mf|AqEreVj-PPszdev zh1>r^g&ip@$Rjc2m$=xDpO0g5ZX_Z%(xoCDFhZd#c4jY5k&5S!$OR>ku2SL z5y}AI-6v;bA{!RQ=YTCTxf0qkHbOg-2WHvuIKZaCE631}Kt50aj6f9xVWzar^KLK< zt_Nx}-f9@#wOGCsYhd9#{*44cSe^0@SWfrELdM`o=HWf%)fKJwiF+H>iSfqZNBt;| z4^JCIgUHfv2RbTGG;^|xhl70!{~h7}_2aQ3c9QeRh4V;2(|iM%D|-;r7=YaWd;=OT z4?vBT`U{op&v`C9^j|adxBAT@Gr8{mK`?vFiSh#Vp}}=Y1@(%Z&A!OJgWsS*JjAQ* zq-t1d{;&meTB?%bvT)udA`eiCJ%ju5N-je>i_xAlH)vr&9$A}N@fvxb4iqX#4-LwhU0TsncNG8_YJe5GC z`Ngl0+G(TF->@~xNt{fg0-)FRnhwfNX8jhr03g4&7E^nNGmU zAymH1QZ%LJXhlCY9I{>cRUjnyNK;oMjVsGB^fhqz8MoTWxcienG9Zl`%v1(w)c#!u zyoC4RW`h5Kf^47UcKFONcR4hKWb^BDWHUWE@NqgIe?Dd!$E+kEjLA9arKj)$ z%buc148I6=ahYIH$i<5_O_ zbEvdvsMHeG_~Avq51S$xex8)_PW^bGJk z*Sg-4`A{ePp~9XTXRo#uy*q`u?v=S<2i`|o%b=!;F_))^_S%+H7+~+sP0QBbV=yuo zy;|9v8*H|FGAQe7d-A2Eo~GQ0>{JLJs73Os9g8yxH)tPTwPTaiT7s9P-7S&~9f>?H z3C#eB6`|(E!xHn`=ZeDcyA@o+_`dvS-5XEsY7jX;6NLv-j0|e}i@QHk2ff(Inr_`G zAo$cS{0Z`;Z*-!;sB%&*A8=%GJU*n@_PMb(vuoDMe4Vm9IwaYUbS=53yuYFpJJ7r+ zE^@52^l?qQjBp;|DYKE3l212#*GY7VhJK$ZPA}SHXlQ8h$3R% z#s_E;DCbyc`7^MsKkvXD{y9a6|0N>B6?(a8tP)rG{^ZXQ5W&_Wah!n8~U^k;XJ_F@`JDM zH%!o9Y1^Ccs|W%I3yAx=H&w1(Ge56T|Lp>=cZ5w(a7Ag*D_C|V1fQ7hUaiwY6a+id zMAM$gRl&?n0s(euR=@yM(6r5(($*4+T!*KjZUKl|f38cCwX-iRp)O+5R|oCM(CT@A z)b9PthCb}Qv?h>DN>73^N+&8E#o0GgfF%+vyo8Iq}qsN^gliFfha*m-NM)nE?Y(SkQ zsWry-GqDM}_8<2R9vzR>Gu!epnU6DCXZWX^GH;YtN<98=NdL$43_O;_?l?GhhC(9Q zoLdNbaR2Jm;48~8kKtoyZUX(7Qq~j_rT)OLJw_nu;xEVGV?d&*PEy&WUCJ@{ZutRz z7GV7uWs#pELn@QmHk43Wlm)0wX3dlKX}CO8I4$qxGu~Hj8^%vE35OmdSE4$;Ejb6@ zsQRQlrAvDW)`f#sQbAi?wo<_`q$5QjFZTE+yDg9c*_pP3_okQz_DpKN^LExVj>2{d zH)|B<^N4^>4(%>ny2`H4=Mvy%*pAN9U3{UnpIRv%Fp8*G5q5BE&SR<{s@${_+moj4 ze;fbW!T@z1yVGqh3GTG9JFHW^^AO}*jyes5yG2Tn)AI>7EWe{fhS`@lJ%}x+o3v6G zX+Kytyh~V-l?iAz?!wc zYK3`VUHbm*685k14SOeP|CeP+ShM0_fMFD8s4jcj%hk=G zl{=b`M0*dHo{2Lf&sHkJuuukz;;*tHsCEU>9TrS z9bj0H96#Hc$znaN`78^xpE^4Dq4L4;V--=ZrK^rC0RqR&r>knKxBF;|4wt4g2uP zR|=zH0|vs9_N~OyGE=8WhngyzV`MOoP0n0mi;eF9RAap#i13~SaUf44#5C_T7Vk}1 zDvpi``$H+rU5SM#JWdXW`GIRt6zv(fIu9bO)b=Vw{MUo~4Y7iF&X1ONuSI_yoCVkrs_6}?9oAd}T!;P9 z>%GOb`;~2orDoTA^qpfrX(PN=Fx7^vNr|7&J~M4Sdm^D6%@@D7G|df7a*w=dj@L66;tf8Pz9}Y6+v`j_@;xLOtEwwnewji1*H-Nxm~(zT&h)L z!tF@qUDQ`>0JZ{0H-P2rp1cpdRFb`Gd!OG2_yu_Z^GTzyVUTSr2>i%E5Yu4G57Pk^ zRvb~MP#LD4^0B1&cyKAs8JQvm$zR};}tS<&B%!E`nOQY<=%MgypDwv+E zGQ89LxGrLaQU7GDemajtF6N|(yAZdaPVMt#incb!I?EPGlk|pe4eMZ5O(xR z&U_R3`01SC?snx2Aq&%6d<)0)FR0D;W7N3rWAtz5a>d&6Rj0X(T>E(P1>c!1^)T2q z1JZ61CNr?&dUGD&`?9%hw~)k~V7g~e5mqzrzce!J8pJ%>C>)m+4KI0hH#MDmu(g3LuJUaks7SZ^*i>$$|R-S*~ zWXk@wXreiDz2Zz^R{-~ikGDn)1^7(!mS1^DpZ{)tg$#bmoPri4{%>#BpEt5iL**U5 z?(_ynCb_h9JY9-=sjlBIKQqMj?)6)Lt=H$uTXr&!h=p<^Wb~xnQ5ucW39iFy0REZ~ z7s+k6PEXqau2vU!`h0X|!lCc>Q8PK!S%y3NLIFA}XC9rqrXYoU#s;#Db>r$zLVsgA zHmHBv+T%^|b|}s)30Algy;d8zl)p<_;ZQwPJ>w{T9+aXsi1M=QdW>V_4TG&OieElt zzZzEk|2}rSK+kvYBrE>rB2u}<`lrEZ9}5|nyFEppD}n47mZdL-0~Ha`{uR?#=lRy$ zo6ehevzW-ZT$MK_M1T+U6+t~eN}tRt5q{5Y8B$yZFp#*MQ21%e%m0QEo< zqJwQYQ~eVEexHGn`kqdJpO_gQ#6E6QS4=Rd_4byb)i&O{tZwT6?+alCXyXf!i3@qQ zoc+U7AM@x6&T^3>ywJZN;WR4Eb&)51u6=P6pyy z7m!P#@@0VnU3NI#Vtr{2ZQj-0l^)RUa+xWzIl2toE%H2TIn_SltH3#jP5;oma zS~H#*{pXr$=2osSFcB*JNZNv{6<6@GD9mH{QNYqU%v^NtFkjar`f zg&hs0p)VTwPJF4PS}CMroBnTH!2ZWs1rUQT#PJvKaAqO^TpjDIs#tKk7v}YP0eHc- z+w0-2#xOE|#l4qdz+Xe}xd;h76$JyESt-Yue~;yR?WjML&F=C9G#S8q(?WD zwhZ`u_fIcYV@T+mwi*~OH?&VqE~+OA;)N(RvAu)g7EadY%ℑfGKod`FyW#xqV?~ z2afK2sNRLDD6x9^0;*P;&X(nYWaKl8g>Xb8E6);;x@(Z@i^_Kuyj=>`5o%^fpS7mg z%3xiVWXJxjEOFxDtqX4U_tMRIsa#8_8-uz@@M;=mbngBIeg6R`-K!^Imm1boeLzplK>R1EjLu0PrzXNBUZa~Z^!nGeZY1MT`7`kjT@37G!V3&5?h z&wumPg+@qb)joDd92ycBwqLN!mDtvzcU6$kpZSbtt&mFur*`7>wv7xvm}R>*s{(|1 zF;Sj(S0Ak@+46B0_h&j9)~t zQEh;uLSol+b^Vv`EgrxV)o+g{$0&q**$n65W)V0Ggi*-nuRpC1N_}vzn*5d5=~A>1 z$c$Kt5`FIJj(%O8kz<;%onVwMKchLgrf3X&9-D5AdslNr3ZZC5(6BEz3L^!Z>|LV7 z%a?jyHK=60RNuSXo4lPDWGLTrr5>%I`A#%6@h^_fws|c5o*IUT{JOewK><< zXg5NmZ2G|$1@!?t-B@rlwC44k2GG+kdUq4dv~L%=z<6t=)Ytj@s)K8WG@0o-gTNU8 z^=&^7Xf%!>GZIGVIltI%rOH=%3oX;8O;&p0>%KEpZ+?_vxR{6(@__`Hln7c{+D%8( zh1{yNrg~q|4VJTd*Jd!t@sgMZuGU)J$Q5V-doQ@vV3YJsvR~yz!=9RFqTK-9b%ZpX z9;8j+ujl$78G`2xoTnvrSIOs}55rdSe}v$AkF%IvJU9h(j7_vA4eE&H&Y=2+Dp#m7 zBd;XR%slAdTnxwPr-b~FijjFB^ZG)*muo73IWr!e{Zo$EXs3n!R5I;gLgA-W0wslx z6MaBCwYT$|f$p_=>D6%YmXTb~=o@t)fUjiChUWQ3{SGLv7y=U9I45t^96OAj?;a%B zs-1q*%$f7P*}V0RjO7J)h*Nyx^2%y@c@J88yW%K3DQcUh_OXb2RaC{0|G_3;_^X8{ zqDFx78Tu+CH`b^53*XwSaN(_n`i;dU@pSjVELXZviEYchVWohd)r0ZUlVF+)mTnK7 zzsZjNJfE_Fx?2R;$!qf24sOQ(hQnDZJoO-b1^|w@lB@4!?kzIlOW7DA#h zvHaN*A(l6N3iAj&57}*qnZ@M1;Cp6fuiPN#vi6pv`E5Cy%@_iV&lKTsgc|>^ zo7k0&H-tcxyETO7-!v>}Myb>wM#Q~xf-s{(n8#`&L_2@ya{+3U{bj_BCYQ_L`Ns)- zl(gX2qm3N58}BZ5r^^^x5RY4@s5b1dnz>D#@=i$4+>XntO|13T-@lk=ne-&spv5`e zqCUlB9QfX>hLyn2Gq$S^=acP?46&N*sGh3s+Wkn|_>z%qcnnLpZ)&$ry*+cyeDUnY zGW-Ne;`SK~n_n1lc=v{2=p&}Nazg9Y3$x}-gua3Fv>H849P&#wxO%WgxgMF{PZ_@# zvMLl!oj+l1QZur)lk~9_s7N5gQ?L;loIj z3^~ZOSxc`38Gc+JHSZTz_iT=3I&LKu%TFWmS|m`HNxRslfxvtoiKXRBIM3T-Du2b- zo1_kRI!XkZmc4g(CAbPv0C3`IBxxy{ZgX_8q3@@}(3enW_es*^3w< z&52h8UC!AWw@z$%ax?^|Dq3q^D@lIcr_3J=M}^Y4#?8cPtPLq=4)4~_6wc0U`EDLl zd;i~Op)FEz@f{Ma41XN|KLvx_;vK*_q7{SSDyuMtoJ}c`G?RpHJg(6GHT0Q*@@W(q zB@T`YChK!s)^YBgl5eDX`O>E-49he*Aum6pI}_qllUmtu!)kL&jtXET+JC#~T75yY z0ln8hwm!qBNQbeyo3uPSo06thM&c2WJK_e`rww|aIX=rZl%4D)gcBSmESL|2py6S=G>4>6fD zuwOGVT3}qU4P^5|C0NC)^-R!vUw9q~Nl7L7MKo0#hiI<$h@o$r$EQy-OpLiB=MARFp;P~p;vVDdLM>;klu)3kw zJuteD6x(r=1gGBSllCJ{a&7}e)I3?GtM}##AM+J9o@Q-G&j=SPEG!GFtj>7X*TB@U zL%~9~FQ$%l@a=O(H^xq4b%!BL8g5^0!Q#V)d^6Hxn;+*(x6f$u-1|p7p)5)KuH~>U7@JfEjVBj*$2Qy1rtNpaZxU0UBEgi^H*={~e zh@baBY28KmwRQF7kyDMxZTfHY{9$~0m~Kvq(h%+C<6Ouv#{$uU%N41@#eSO!v0&Ov=Z`wy%gsi?Le#`b5}s z10pgXKjK^Yx{0Dl@~XCpJr+3_8AnXzAu30iKFqoy6{)_PpyCTPY?kI)_s(eiIazt6 z=i*XgSid2$Td3FU`cn`YJGf>WUyMLKut3-jz-_)jibi%4BRurPm0`m>yMBA>jj#MS zb58Rqy2@=QYVRki2~Sh$8us?0^z32V9#5aaPHNJNg(G@O=(iacu*M>8%SJT;w?zYFIkXV62E*z1q_U?WVy)3zomecWQj%eWhx)C{7z+^?Hq_ z84+8-tU_5RZ~O|DA2GR%F_Gc&Y<7GdF|>Oo@C$Ue601<{JstKD5ANT1sxqdP4{4hNa*Pz`QrCzkY`p=ToQ4E*wRO$q^4~|jN0eIcSo3GzT z++R=T{o?|JqHIRPUl4rn4(MCz%`#t2YTM(Ol>}bI>B= zR(LnT^sq%_`Y~S0STEKTcK7sD)P+K@6t|zY?Cf*t5RtmJ?41U{8_*rkGUWUScbDR$ zf`^cxs>zWnXZ7bll}J1^h|>1?YR=1>K@m7vaXD!ZX>%YOps#tZ<3@`%K_&B^J6wqx zvt}{g;iJAu->S?=ap&nNu4idxPB_0pvKR#BZ%RFX%?`CsBPbu8+OGXlZiTAw?@1!> z^L4UTKQ*}>e`tP$I?I^vn`8~m+2C|rzJ;U6T3Pa0$h(_F+u~j;%6AV5ncCcaZ9-G{ zsBVBB_~Rv4>7gfBDx?<>?Yhf6OK()%_% z`m9Q)i$Gh9rLNV9#oLJs$8AK22FM1XdGeVEvRx2qK_+@M~I4m=Yrhp_|vYCw?ug$g| zSy2Q|96RLBdHC=h+hG@KT=|_y=qW3k4DTu~^30pG-r{{vy?=VqY`VYyP;B49Y>s$( ze!~qiSP`J5+PV93?Mcb|xC`M~pP6Oo#8A(6Y%#523XRQ>30 z$d}$;DM%?iw?Wi zHT&kf8}!!&Fwg|Sl{X76@2XL(!GuzWSxI6%?x(aOKi%MIMyv~!)(ps_DM)0{5J^sq8?;*NaZH5Z=|8JI34E4~cjHIL8ZytxC@Mry_gx)Z z$9t5Up-?$v-|@@y$=5Q@Ag<6pxz}U_U(aXa1A%dt*I=#AA z(kI$_R4Y)1s4t?I`BdBOQYs87b||?4aA)NIwAnaBDulobO_BjMU&`&P&U?Bs-H=;x zB48Oo4K%p<_Q@?eFn{5xo5ScmosPC<-9II#T|?f%Q5a?i6gGRFU}AsD@isxzwjui9 z;9-HR8YsHO&dNN!OjRfKz3TK+hd$w{8JN3Pxjw;MRwP#L6eE;BzQ^nTO?s@9AuGpl zyvqQ1e4o`ma$NY$%~!QQeQA}n3KPe4g&z&NAr-HN$6tU?!2vW*9vMM6y`TDv+oH_rV$PqJn5BFu#Q>@c_`nv7S!`B>iW4= zi^{TPf_-E}1Nhnebn*L#ajj6>ffWhPHUIlRGPfs{9O>7K+dXH#MWr8o&q$ORYbX4G z@_POXXokoN9sQ6<|B^LjLf6h6ZhU#!1aA=@?|3gFWw_pnbY7oaC&YnwH1dXc8Sbv? zXP%C}KL2VB5 zU!;F>BJ(#3Ozrb9X*Om%kH2mr%kTPwP>^}OX;hO?RzbsjcL~zZkJlbC?oxfRZ%j|v zPz*LS0dq83ncSg?h3+5zQp-;gp%-z;m4>3{} z?RH%0wC&(T;@P9BHTQ_VS~EncuPx|ubYb@1fwGW)7c`HpS1~VVGJm@I8D)eVPyoZN- z)!cN4z9;<#SNJ{Lf1GMLywy{Uzmy3j3 zVy>=j^M8``;A)fT@UD)Vd%O$RN+7e*@BPlgtHBp@n{j~TwOeR$e{Ta`hN>*lp zIDAKj)KkL75b4&eu)Lc)cL%`i6%lVY_zX|RrF60gPak+g|GWNPxBWEnPdG}IOgazA z-RMR$J4IL5)jNXMf9Q7lDPF|y@^&92;*M9*Vz~-2+2;2xT4KY}DqhW=N!igc;#uiV zV?n48-_vHApu?|K_uk}6_eUiP2-Iq@>E6RN{MdHXjA^(`yicoe69<3lWcn&2b4}qQ z?bfAvBFv4*+ot;Xr)Ry)hMebtYl5yF*7A@fOu*v=YsrZ4J|*&q`lBm_4WMm2%I|dj z8CL$mzO@a=E^=P2_+5)3P~#>qIQ&g}%Fh?ni$@;;UZ|elkfxjVA{}Np%#ccPvm{{j zWUE~WK4|iIl3OD=@EV?aMFhXDLzdXKp-Y23h=Go*& zsP^ms{()bT04^kDo;d_;k^hl z7S%q;3j)z`{#p%enU-~Ap}ZZ(X{gmtk5E?*EXW~q6seJ9%nKLT+Xt$h3D(|(smUHu$$mD4rvB-IY;sEPBB_v(IvmJ zDBEDUcPZ+qfdBuY6J!CR@7j7VyLU;U(Zn)Tikhf?Q2~RNCw`FC{o}C zdG-M=PzCs{SMlzs=^}f2*z5E*6Y;YZgH&|mONE7RGP{4%GW#!%n^aDAe&8?FOHdmj zLxxx`(Hsyr9K*18d`148{GD+@&jc z^9MM~*70MF80#+aM#isyPH1w^U#;_{QseN`*NnJoz(0JsD|WoHaLD@9IOvgShiPUL z7w)=9=6(r9+`c7`eE#NTg26T$$6YmCO9sT*jgnKwFAN?PY*I@yigUP_`);_Kb#3m? zLSBkYC19J(GoGMYU799qRM;&r z=MyLSTdz~R5DY?Huy;aynu2ys`$mnJ94vTY?nFVbP$za z$*aiZyEx{3T<1kIXR}fsr>}OTdFvg(Gy1%!N%j8?6#v<_b|S^0Q-7g2a0C&^AV~6G zq-){1YEs=$Xd@m;Sa6Fa@}jM!Zr=#o*OgXASsaQ<$AN0DvUG{2puy1$*Pu(FoJ~XES%A7Q7lW!=U9~X|}~+z`K{mawd-JD?F_k zCp<~+X<#eY%-@NM2NQOQDUW{u%!U=GGHr@e0OM;c!x(6Qww_O`@lHe2Btq-cyk@j6 z&9Eiz07;<0LO>407FgP$5m~9F=Wg@&O&K4Z~lGzIIqI$82UkqC&1HYLp+A_jcsWd zlFipMtp!z_{_EcN$$W*k_vR%c@@^D(Y~#uqkpYyRCrtZg5(M;#W4-7oa}w(zd@5O7QAd9)c# zfI(Yo|BtWtj%U03-^WYS(qUCwRa=dgqNS)6RBP0#sx3yX+O0xfJz|9T<-XtV`}1D+@B3#SkJmpr=RC)`p4WAqG3sv#n}9ek6Lq8Lbu}FC zf5P$qw#c*kT<5m83r<|-ADL2z?TF9GDA}GJ#=49qxK6!ZrO2V;lQ0eKl?d=o6!C_> zo4aj(-}&(Q_WlI_Wxr3NBt;Oq=--rcSa$7`q!+zvK5i!He7*uj_R)U7wcruI$P)ls=;kAC?tU}b}o63Ac>#4NjVsz?{kIZplUt|FaS#D;TQ`*<9u41>GmRvukXL;=0E zjH1uFX}(@pMW*bPdkOgYoVA?dNQf$o-r(8TzI_dOP`_5M0S!JguaGc58HF#lYwtaU z#CvpeEj@X~hpr>7U>*GdZy=lbzV{PRT2|LN^Ws!PD-Dn$@r&=U7neu zxEMbU2A(BOiEgr0iTjeK-{|=Xf7G^J(&V!GPXA!!Qnmhzu?`B$@{+MH0RVq4{@j>m ze6EW7Y-$hfbzO|vyKx314m;e_h%D3|-YRJH?j3_HxBth;zmlCo_2XgxR^nWHQ1j7C z(ybG>9VXW;9XV(iX}ipPZoM}Aj6RS=k%IySRhiUXYTRjk zJ908#3?N}yK&}30@45>0&DmSigs%_#+ja|q-IcHD{4D)0{j+2LJCaaQ#JOzb-xRcN z&U5yj{Soy5GkT|Q`yr~;01m}-A7uR*uUYF?@|*ih#LT7Qp2b~n)7d1yPGBpem~y1c zM}3}BC}qf>c98Sj5B^?qnqB8}9g?T{o6r`d+)$kwN zfH8o-B4OQe@g{y~F@ugK82_LYVMC-RBb9Q^Q=_(IX* z6yor@xCh)|y~p*rw_x3^;8KEAM%0D3+bGL{Ywy3Cc~!cxEsWr4HGWTj?rl?Ee#ws( z4~94D%z&AwsbvmPWe3&DQmMCw6;o;n2UO3f3LnNwJ=`okcyhtA!FYWE<0!_M@ylwR z=RnVINYAD;uahV&i4l_dAT)5`1;WX&;Sq9^tv6p&&UeAS1 z?+6@!{XZl9UuA_+sTJtPelaZP6^TYQg#egy6w0|rs>@g)bEczQ7d+RgOb@4HH9S|C zWxv4pTYmGmYs&;6N>j>lviAo`kBJ`JX=9^AePlqB3Kc}NeM_<@$4=gCAPsYp+q%O5 zBQ~)Nk>vY^C2%#^1Q>C!JN0Nzd{V9#=HsdfLUD zE1gQUMPj~9*$KPe2y+nJ^Sb@fY-}=Y0@(ae%&gY1VNpE|GKZXY7GU~04y#HAe=u!0 zZ9z8Q!AnQ!0e?GG1rQcrr;MDU_^u5flt1k8UPQW1+=AHKne+ZPa`f*fR`Mz5Oo1do zc=}(9^G9pedXJlIok0BFC~4F=-+6hfq4N9iJRb0z>ix1K4UY^|mVaxxklxKaY{2&# z2}cZ~k28q;R2D$<`NlOt(Mb)98xeZ;Vfm5gnzB-xl!iV$AnsQ|qFW=~q^sV7_huaW z@1anSE*icJeb9Ts56=HVV2yu6jlo)T{DvF6t5I6Yw|#u1!GI_DUUQ*$@J2dgG!|d9 z@^Wc!CVnzXZX@;NjnhIWun?E)=j+56D&vNjOnE=ot?@BMAvgFNXM*t1XA}5~34J7X z&rBF5OniSJVUWaj&bumW=Y@YS;WBqS`=u|WOwP;ZJZa0%&y9|h1iu%sGou5?jpxH@ zf)fTaQkg(MyGFyFlJZ*acyTSYmS%5`rhTuL7##DWc=ABWT-q<@GJrQI45zY{I_4S2 z(yJMCKVfK5rNkR~;zp#KB^8!TMof%F4@wyn`R1UM2Vh zHj}uhGl>OWK@e5U=NV%C+1N&Cb}|FC%2{EUQ0yE$J<=NFgMp7)3FA>Q1)#!tAn;xz z)mN^@f|4xJ>6>-|MCsUWdoy_d{>j-}I(AS-9}BUAZ< zR-Hewg-lM$qh^`5jKX{7WmxVBd)~r3L#~JD$e92N`ydm|)o4RRnK1us>3#-BUbV-c4b1lcnXCyIn zT}B4yNn1K+hxcHU`e3BhVEt)vSP;v)TLxPkRj%v>)(hkb4qyDmE{l4g{%+t^L)De! z0Za-nDslm9d~AGc0H3$DEp5#tMXz{K!+Jkj8A`0-63k@c9)SW8SLecZXE>+YTb^}0RCq;Hsu09 z9sZ6KG@AWGfGSwMtE{X%7!QR(a*NLJD&sUk%5ksAm_IP^PK{)#E`w@|^MNBVuimyh z_UEQj&*C*T1a)H^8LDSut^Y(&ZIrc{=9%V~Rx^0ttu+pU93*LLjWc)N-1|1X7WwJQ zCBp^N92ZP)g{J$`K6}d2FWuAY=3Y3K3sG(#Dc>Hj-Fb+UUo`?UiUvErM)h(VV|J5xF)_XoT6FmC2uBBVvwfWCZF1^%w!bd zr{iN`5a?B8ti4WA>(4CHA8PBwd@)sS0`B~Y*JCRKQ_ob9)9KZ6>hD_seg0*^XBrc;;)WJCKKnW8F;5@0Rh;FJNLs5q>Y^8vIm@XWr*?4 z;(JXM(+6K}ynv;z*K9gmy>aUIar)0rq&4Es*51nS=q~Ow=oLRIx#`iV<34zrH@9Yt z^DPav>4(6RngJmAK&&&x7|4F|^)PVDYgYaTPVnsTcFV}waf_qNy7{=^NtsI?a;4mC z_DY3l!%6MonEJr?MxU1EC|?%(L6p^*>07$}X@BUswcbTy-pu($svlwP2i->c&I>Z@ zDtqNMbynuRLo*t+72C@=DNWLE4`M``x)747led?r9ccCnG$eaXmu+qfinl1?^=$&K z3~|5>bN0e2W_%nvwIh74-#B(2tsb>8Zb!9z#uOr;EjvT-j!Ip`TF?o1$MH>%IU`KW=W^W__=@2?@< zJ)WU<-kB_c#uR3c34CNTy zlGP(}^i+O_IjYtJ6@3(ko|(NLiw%Tn4=r|zQZ{lf6NddIwY?fQYWN#@&Xy$aj62%( z-OglGVd7t{OtA({UV4(&_04lrz96X}c{fjPeyqE#N3t~IYj3m0!aA*0aG!%SHy-XM zyUKr}l@AJ^Tt*G7piG^4V>l+*kfYSMMeiB!E-$=Unyr?T>`A}U=Ab6Y5_FlbdGx$LDku*OT+z&Y#I7@E_zkknyVY%)RW{Af(zp;k%)%! z(u7W@ANt9cbzMDvA!{mbPCmjoXBja{2#PxjWrn$5X?%G7Elb#RAuikBZIK04mU_q?&=R4D-c~5c&iVwW^ zGdjoNc~|(Dw1vWz99a+QCJe_0U{Nj;QtF$0@P3afakO!%*{-nPXv#Woiz*OfIN1`O zwG5ZL8fsx|sPETtBx~4zN;^^NmJDBl_QEH$`ira=$fWM&@oagqWAF%vW|-HsYk;B7 z`0uab78P6xR6+l75*$_D1yaQZcA$DEX6ZWlKhm+9-zPoq@}>7Q2M&iYEp|e_guU7U zF}FyiaG2IZe7T0@RPiqi)5!0Le4 z&4E6XaY<%J;U$|rc5El!6~L9|^8vE&Ps0WLXm-x;7=6cSXU%A)Vg0()j@N1f-98~i zB_}u{l!qNxKOy`#u%9xJ0>hE8$}wzKM_lG}%<*OPj>=f`;nRcb;s$kkH8;nWw9si> zCZ}=3Nnrz8JYCgh;{NxYuCSC9-&GN$eC}*01AeF@CW3Vu5ckoJ_13IRs{R}c?SAyU zosuFE5#?l}@7%TBNgJqz{wxS_%rxQvE17%4r8gETQ++s)Ki5g%2nC7EOfjYvHQ*Y- z`(WL}`SR=+Inu?FXym?Fl6jei9&#^kU{T47*z6!QJ$OH(*W)Z9cE6NiqFgK%R* z4Ed>f)x*rH4>qxMpR9@)X|eA0>pNAqyO)8#hh+yOgDqzg?bbg`Mvew zw4(zK?S+_b>&V$c*xd1>4pTdt-lsCHMgt!T=ll(_y>3^{7XD27FL=kE|uRyN-#BSn!tI z+yR$j)adz}l4K?Ra?nTa`JdgSL9ISYCRO}c+p_Jkf&8p^%He< zRd@1NBI~HAc*#WI)9J!=xw6JHxE6TVvF>L@L3b$K^Yd$6j+u+5J)2FL2Pf_EXzW!5 zgm|_^<@a08!iP@xha$Ib+nh*}K5^`}4UFda*ruT61QNEfFD7r-+-r6@Jp-zi#qJN) z#ZD+^;FKrC>O`1Z?0J2*Co?XFq_Wys@mfW%25FoPBS(2_f?)c7mzrL)G-^_xxpB-$ zEPwGoWs^wWnAybMD#w&-^_Xxh$wZgI!aQW$FEvs!FUiWj);7Dq+gl##&ytbM=X%@3 zv{!ZCNunIY<}J-Z|C968#C%d<-NE*Y1izizZN3kLOJXrN6Bu0UI7D-JyD_YhV22pi z5^<6Hg^NK{k1&ag8--olJP+W@(grTBW}oEKZ$WG~Yb(AqIEK{r54)p{UV`Q87f#=s zVV;b+(p>x6*7$Hu-*3G7bxm}sgkiwAMZ$kWG_9J7oKL4R-1$Xg|D)D$Zqd>o9^|ymgx7?v+PUb+3K#PP*%Rz>xCjGjX)}60%(ZiIq>8IYMt<}}5!{O&{DvV!*y;wR+ zUJ~W8%SY-Y>96n4RH+wuhq7wGlTEYDON(s2IgQBKUP*pKVHP!Zp35Jj_3k~lNEDs) zEw{C&cq}2}y~(`uWtgt>{k9-WajD4>>YtiLlDUSVQ)9tV<$3-YR?I6LZHdimQbmcK z>+^l{54AYzv!+Zwj&FFtVq`2WE(7hQQ6df!?8BM%yv{gIOls9Ov%!AyF{)I+YV5FU zvfn<--c3d?$}>ZF<%5LQ`j!RAKu^6saAWxRbFXSw4_`VGVQd!jkQNW$ez4+II(bz> zN)G_&MTHogHe&Y8M5phTIj*xw~M-Uj8`e ztz(M}me(`ltBe*p$#tCW(W5j(uYKU%K<94R8jcV)y%HmdN_ zFP(N}jfHtq;E zi6=npkegf+KZ}QIVx3`AW7B}Vvyqt%TvSDE>J_U=cIHE?lJ2{xnXo?2b(CdkQm^s> z#q*EWw+EpYUB0&V%22=+y$8RlNX(#b% zD_D`33z2~F5_+Mr_Fgp`gTsWoQs*A|k@aDGuKMo0qju9=Yy1(qRerVb=8J#!q^%DD z*>YfbBbiO}kQk430sQ7@nLdy#Bg@BTcc|ZogZ-{r{g(_S^D3qMalBR>lIGIRm-F_= zv4Mc&t&o|gUClJT0Y#3SkLSyWJ=3o8Pk4V3wtki9MWvJSPLRP$%05NiKDIbAjyM+4 zNdL04q%lr6-r(K!wwJ>J%d6PmcVBTUSkilNEdw_Izaze?Z*}6k8l(12t>hQ;%~fIP zOn&j!EyU-$4vKbxRf=-7I5}yOicQgmg6?aAh z%va2|?pQB+G+19y-1#Ffw%RbHhTJ@CE_u|eu75P853w~`(E}o~RC1smIWP}>C_BW{ z;RGH15F?|V)4ME|a@z`CW4c)lUk70bk9l?4xm89=K+yo)@009OZJ>(M<-f*dmCQk8FLnjik`q>NKVt4%d*IPx3e)Ot zH-C<)s(cUmGgA&q{&fx!?_x=@r8&M{i~K?rN{DToBb+ajxXhtt0N|y zo!Z@Xs{2SsE*HIyc5@kXzD{sgrp&jsxs zb~>c*+KoRu);qHSV(wyUoJ!31BbPscj@j`(8fNn#12d1n>60bGs>hf=Zody==a-bS zK5pI<>LRk``c6rD-!&wD*EKfVVJ7T2N$bx&_){7_d(7awxBP20AHs>Io6E{pJel`7 zCfIG0*CF$KPzCOYwMXZ_Cq=Wh42Nqq16p7TaWDffX1Mv;RDG4M8`s=5C!Q}>Lncq( z=`z;qoBPJeCD16o;9C;NRX<7}rY~$%tMO`FTh+yk-@zZh0!?G9ph&rrCq?p&x|$kgU9;X0vn#XV^7niwtZqml25fXa+(1EZu_SoE8i??< zEvggT+=%XOpv!bTF>~daf&Sizi`crVW8k%<>GJI%PZ8;k|LO9yUHbk1#3}#pDgmHu zIaw4D8IgH9b*1gbBInx*>de|eRt}!;;|9L$i^%uZO$h_nu#bVGt}TYn&~3Z0?H^|m zQ1TE76#BI+(bMkthE~SjO*$#-cXHSj+11c6_M3hNyEZ>f`LaNHA$)aG(^x-x?2GbvE@MnE;p5#eI`9N>vaBr&lr7k&< zj*WRi?1-sDB{mT6$M(Hw>E%;XoW;Ix+*_^<_m9H6)4nt5P1Zormz+m)3Zj%F)Q{Sg z|Cb!`Cn4mmFyKkK9FvC5WlH{tRyAVhw%1PUH-pBHhLK^{&N!V9JMPU#nil#ObCJ5w zujJ;y_AaLBL6sAYySb_b!oEveSInFw2+XV7Wa~;VYiCWa>)2M7Gqwn$Bell8PCu~E z9Ga5GNVz|e-YW-;&dy^kY;R~88_+giR~-!nvGp2ej*dK_M0c*0C$GFKy-#4}Ci{9R z84FRYGXJyAf1RtxmgE#JKbMhGLIxeSwCJfB4zgM^75{TeMHU%82XIPMUV?Gm#bpPZ z`=*}`ZyGDmv?*iDgjQ_1XLW6E+~pM(uovmN!Y#l9>dG^XozZ=vE0PiLQDc1>IZ*Jf zGMk`SgXk4J3+e3Q2i$-JwYQjSD!)H63wj6_BYj_G~syyMfbC!-n!Z zHM>VH^ZK%;bL2azQ0>2^?YuQ%IPBJjx|^gVdyz+WZ@*E`4x1`{gG6XIC#oB1$GdJ* z06zup&@&u};+OJ`i3W>JvT=lyCiN^WQ=#AZEu9{w2Jp4b1t_gm*J$j=%*2S}t*U*UpNHzlNTVlW!gqa@#xxvxHP zUri`%fXJJ#%c+}mCxVPdTENrIwN#Oh?_YbksLWI8SJxn2|55i{gTM5xr%QfyN42_8 zsUrigMDH7{7b#9LSorX}-oekA7Q(@(i`~iRzLqlX5x!;}S_D^axucwOE;WVZM+#9z zdtDRRw=|bvH7u0<7jcpLZ*oU3mxX&tcmW|o{tc-)TDB<8H7B6K7_v1#dc1k&wwoxT zQp)Hi$du+Y4+~i1En{twDe6I`Pja69MnDqx3VF;DqZeHt#ouVLBGUbe^-EVZ`aWcuq-f2u*>t0bLq$93&5;2Sy{UA&6=bUJ3lNO^_wQaa z$57P50B?(rb`2gTfX$o1NL7y?FCb<2{LNQK3#a&%bma?iM-ROD0}?%|;s5N8|L!mS z*;BA7b23!>94^_d{8jA#5pJ{A4DZ5KAJ7LWUlujOUmv0gQq0U>En3W3>k-}F<$lqo z&A4*RX^FPhQT^H|t(ZJ)#bSg=d>zlYP|MQ+Ye~Mu+@=SJ8)fxJ`I)sDP3Cl=#;RL- z*hb*B?Y>bm@u9Il@$p@ardjIc^u_1Zc7HE~rZakY`q5q&Ok3)>L|4mbSmbF@fO$yW zb6e4gDtPAob_a!REg8}2(|DiUr)W}ZKW&=Hdh*V_u5PyNm%cw-rPTn9`6*EFpEt;V zAA57Y^`UR&M=|JJ*PekK^=?hfVZdj3qf4&PYU6pk??r^eys;Sh^(bqOd9E`YGi1v8 zOXZTzcaYH0YQ2UMk3*OdzIrV|ahd#29gWud}K_!zR{?L)GqBY~5t!I4suP zWGU#qCW`aNZ?TTB?#(Na*juzjt0J`|ZSQ#39`i^MLQ@sIXp`M|n6vju`e^tRHeTCC zwVvLCZdKcV6rB(1LSW#Ub^S+Q4Xo6$P*Y2C8TgtvP&|*zn!G1M^qe@(^oL6-X1xA~ zgygUM`QMv7%g4o8_2z%;!|Zo@PHG%)zDhh7m*H3<=Eg#`eq3Lg;$wdJh{izzHU0RH zKX@u~Y*QQn%CzfEyf|s!3a#l1)^96vs>sHhE3;}%%U{iz6b%LAy?wM_zTh0w*r?#F ze<;#n?ILaI!+itHP7BMR>`LA>9`Py7%vDrK#cDSfy&{;(1fh2l6lXtJm+J-@>1tDL zmn*wd*0P>l+)kb?&o)uUoO%a$#+<}$O!>eXR8|+?n|oe5N#E;W<;fHJe+6jfWy&}s zy#JsY;&6K_@DIAxQq|e(K;Af!u$=M$$Alz~pA9G0Jz?_wz`7x4XFgK0Fdf>&zRx2+ zk2ItN+19<;?ZdZ`zFbyEl%(iFVqn_q;u8ofG1azGCGQXCn#g-A+SaoGiuY{W=dzww z9tU}RtK?8Ngk^^q>>EX0upzv8UAZVmT1ERW6_3gDpR||zndwfS#eBff#;4lJAOn^f z>6UZuQL=0@5;_B_UH#c$^}UlOJ(8cooG{)mI%Wy&R~2OG5~ zy!!jP@vn2;OhLIVjG}n6at-jGg|d}KK_Tc7J<}_^s ziC~D5@jK{F5t@xAhz%*0)2*P1%@4dRZXCi2L&X$BGPpL!o1)K&>@^A>3@MrpF$bB* z(O0ih?VJv?e3h9f21cd*H(d1Z_Dq$RDURCL83RwMPjq&yw&~>pUL1a>%Yvf{ zSEfi(PBd7|0#l6>w{j_pSJ@$kYLbO36}W@0s4l>ZuW00~envrGn%5UnhNzU&#>tRdy^dA7Jn1o z0EVsZOu+jo-E$Ga&sdkgoTJJL&$tdCyNHl?e8ijc>-rl7h9HIcz4tsO~;U# zt#%b~%BW{sK`eSEq*G(I!s0HY&5ajJt<~%=LZiHK{&WxJgG0Q7e+ay|DwRKzty?+# zj(7=KmPC10==nMK;ZhqBAx!2)?I#Ex5Sv$Nd6Z&vMJHyVd+^Vl$3LHgDf9qZ42I9P zUL^?*e$)k<_yIhlNg$ugq}y%A?+1hMgVU2NFq=WNF{FOEEC5+N;B1%7bvv=kd9z&9 zifz!&d=QiDEp3u)fZXAaEslwU!x#>&2)bCj_y*#S@P3ng)`t>3%3pKL z*j);7dII>qh$BEhBYT1?z+fBZLpY$P6Ikv2I5urn;%tgn^ zq?nn7-3sDMz_eS{VEpOCrm4hu5pt>vKP2?pe`Tb;J^R=+8wD||{lfHLk^b)i5v-t+ z3$_kX`Jc)_PWtbBI~mUms;>KZn)6!-biL1wI`B~o;)j=N97u30SPbe`ZhGtc*uHyl zA-umKya_YxEb|%B)hwpttvEy8nv0+G9xw-4e##Q`)d;d?SpremESNTvaq;ViidwfTt08ZLe`1 z`3+ImuPU0zQ0VHu8{?7Oh9|zgR63RVR4}uf%E((-aM?jc#aZ_Z39s?QK|kSv)%s)K z4o%I_Ut&O%-Q~l?Y`tWf9Bv)L-ir^mDGFlyh~|{E&W9$r2D)GWeFOiCCg19;puqoo zl0PN~z;OfkiE5|wE%!zV`S}9VL*4;$o`02c+Jmz_2A&ov(beij*vs>g zG*8l(2{26jigcVO)$NHr1x!rWffn$`PWw%J^m|bEdf*R^3DxE@Chnsz^XfY_Lv3CT z73Dz^Rho`3k2vESJh#?w+&i4dN;!U4M=&GQO;#?pzBzoija|JBoLM?Qv%PN2#O68S z(_^l%<=ps_$)jlyRfd8pShYiBKgFMRo)&}0A{v_Lf-*4;foqr#eFs;Mb`r87Wt`<@ zJps0HJ)6=g@eRURL-IpD$lBdWRNv>04Fay(bVFLjSuJA!fApmPDkFd(mUjxWdXlqz z*8d0l{Ie4Tacw2!1`Dbl9L=3IGCnnBq!ed>cHva~#}3+J4a%@Y6=!b%BDZs;VPS;K zC%8v80h|cRtr0s9eOxl8s2Ar6&NUoBM;^nby*y`#7IazY1Yfq?S8cC#g(){L!elpl z87%ZEw0iKKfMb#K5v-=QSGgVd>DB;DY*(I?p5p;BIS~Uk=i@AArt@;GaHA)N;q+7zX7>h1_VR+MvcxP z3?Zw0f?M?tJFE7^z6AU}u5`s3pP^cAGWZbJNnO7;zuYosMN`N-wz&yUY5e?w6~K-s z%es+MRm`pRre#j1Qg;a;P`k z2!C2bT(ARC1=M$GZU}K8Jmi|)Gj{~@gXsqm|Jv(_9T>CVQn*Vf=P6&_e*6r zm<21r7eYtCE+g=4ut9m-SZTz+%^9yGf}fbSC;EBmu2&rDNU|yTba- zdIF8#kWgXPaE$UZ4S+N4oyX8--?8KQSzl%CgxqU+y7J>G8RN4ke?fSo_xGZ9!5Q1Gwtcx zC0Y9hbAF8K)a~UE*jfxc?lc)DJ@Yf!XI%6v4dBPQe9Tw>xxd$)pZS{pF&98XHp~gt z5;j2l^=0ZaGA*^uQhI{Sd1H&f1CWH&ieAv+t9Nc9Q<^sD>TvWm>gg;6!? ze_M?jX*c;f_;~O;E6eLg&%xey`l$*`8{bV#utKI9gne0`yqcurrh)p-%n$Rzb{6CSWo&srw`VCyV$qu5IGbhdmToV48v4#bHS zKop$hLpto9n&(X71~QIKCx~l$Ei{2mS`StjYarBg7XED$ClLriji9G$uRjxmBx5&{m z24BM3#7DuE+CPJNIQV`I4H+HzXDm~mnadrH_tMFS+o^IeJAqjUYOs`y4_*wY{=f%r=^xpj0Urgzo!B(pPP!_uVcaz)m0; z+pVo7>w>4rPje8dW@7WZu~)SS^WH7%Tl1F>)MluM?eJHlRyuiyJ6{r+4ywlllj+PW zUbLI{rrg4L7>VNd;XCJJQA0^i9(AFl7OZ$^;VI*wq? z2AkNl?dk^2yhc+gA6+JAFr+LR)d$C|_y0Y=ecrV1>yaVhNcg?1uODm&%HvFdNfi$tLLjVHnEQWq-K{G&(%y8rh1S zuqn2@Y$m6PAKGxT?Sx#&5HAW~0!KPCHHg&v<{9doqO?pb1Pr^e6^O zPU0@-V2?S|fPu!ue0c~dsznVX5B=HH)alaGgFazNmwUd$VM*eCiAH&6m7gllA?3QK zXY|yu;AV{d<_8$Y-4#M2l>XXegJ}DEpKY%aH*avHTn7k;|B*+8AES%^Bu!PGr|_fs zA8AazAoWxsf3jjOi1GfD!FM4Bdv~^(a`4AxqpM`pYW9v6^^y8L z`?0i1jk%wHv-VJuQo<}BgOBF9`!E+RbXM#0?LCIUCA$HSws}zgQAPC|htbRI9u~JK zEZxN)$F2P$JffzOUy)lKzVv33sZC?=PJNC7-At9rP=gwB5BKQ`Wl(@IMUc(#h0o|5 zwi_?<2k6I0t6?_{7ZiB9sb7_H^6r%C@;$^Fr<`v*zU>{};+#cWCOx{Pxn*Zg2l`>(Y#?a|)kHuOLZm?}u#Q5$y5{7Fc*AO}A5|h2}w1k;eQR zwJwu1Xgbw8{NV*eE;{fQWhi0(#TnOiMeQ2eFG^m}`vON?GNTfhOk$sRXKWc&M=7ZW zxEM*0sv^nGlzeh@7F*(#frmP@yvd1N7L%AatsQXxDQ>VZo{m=st@aWk2_;9u>wQ*2 zn3?Al{=Vo756$#no$q420xd4`St{RYZtc=g>h&0 zz&s=N9jiA<7W_OGmL4!VlRb5Gt+pi$!3I_~sxsV|HdnBth;Akiu^0XK|Ll%jV_NkB z_y*2MzPoA7mIMB(Y``k7qfzVLq6*aKeoruwdR99iR9lCRSvJlLA6PJO68C+lWRfZ} zP{-N~aUeJ6ePq8LBJ`wcvbc&?R)c|<*b=+?;Tp;yJnh-ZBYWL_GKn@R7M{}2FSDAM zHB{%SfB3LdfT$RnkYo<#J=<^`YIrNmLa^$sGDc19%;_W9*Tx~m$0|)TiHEv^IlZ7z z;gXX&nd4FIqpwmBa~NHgf1M!!bi;SSAGA|}*-Sv|(0eV9 z{D#?1AIZn!PrnBnmkx9s19iNNaqb2_@J|L=N3R<8%AmzGSq>tn38M#jHZuE>;{r|U zGQ2IlewG0@FBXW+8^Ekilh?flVpaxrY_JXMUW>bXLRpc0D_-+vUSLa*hn#KT1avHY zP^f9#gdq2*!_Sj;Kk|=p6DFe7DCgnsM|_PHsqZs#p~fZ2j-$xt6pQfSAf+XeZ?SBY zn81NpWM4}YSYV=h^WJETG_iiq)^EaiX}Njm;2t}@-wkM7!Rg4KwVm8~O?d&fVE^Nu zNp@{LoW`C`V`^Ro$51!62AO@A@AASzkdr5)fz#&vw3=P@0N%7*qHo<&{yB4AgRFCr zDkC?FcjjWPThd@MwK8}it_OhEm6w`?uri<&UGSxwYL_Vy%t71@?n8Urqp=iMwxM1k zaV1ot41L2P866Q9LY7Mp1Ws2^%_-g(id-X+RFgCDpTh4z!h8X#D;~v^tpXN(UcnuQ zQL9qN+|5{S z8-+L7-sqff3o)f)&+oD}YvNzGBU^+3bdO9=Up!kqH0Su()$~SCo)gu$(=L@l7ZpHY z9y+*o37sB@Yj__`Q0VB^?j-^Oosi~*0rO#!!k)1!yDaqHG5P4<(0uHNA5%qg<|P~L zPi((LaI}tZ3{QTaT9jL`jM`g2s6K&t4V2Mfx=K+_IS1#6rF$vh!aWDpn}Nj|fkdAq zFvtU0cE|nLqDD^Su6d-^Crf)E$#DfQiq zfbjIYmfaE1)HruFNUd3gO;utMWcyOYA21!aJ5gO83%@LpzISVd=6s*9Cv4N`$^|hX z`r|TQl*0O0j zPERn)s(nD27ejE)X+B|_JtGY*Jx%(45(6I|V5?{p;z;m88s$Jd^mkCD&`G=ehdHR-h5kg z+c|``y;4v!2vT1=?brEhSnlF_SD++(sDeh`Ew;A^!95|9rH+KKBxQ!iFd1}dD`Hx^5tCzn>wW$*RvZIGwb;|DMXH0e%};x!EJuXIvlzSCdrD!)K>kGH}!sqyL=l zMoH|j!__CVP-ZwboOG_Psw-ag31i;0|h|59$cQKc?lg{HoUQ}w`l z4;U92L21SQjq@-=Ntqc=JZU(&K<0YW(31rQGz#h|r)T=cV^GSlCr5ZOKGCP!M;pEG z`fZmkRaWP81yMFi$Gvh>kttz=E&;^(_BTvj>x}cQBYm+&u~nzio6jm(H10ysIc}=a zy>oe&zjB2IT%JP*DHGR$&K4@>(lwR6fYb-d12|EL8Xsj|d(q`K4x!@jr~X;h&HBMg z*UN`iHR*P&Mp^7VOnm4+cHEcR_5A?c-7@{!DgA3@_ZM!mkg_Y4BgdmFH7HUpw;Vmq zcWOYAb&{^oUin3n(XTbUxoG?1%gwR&x02#PX|@%BjgzVrooAf)jq=I9EHx5#oGd?=*^ehN4*)XRqJegS(W-92!0`65I&xN zg_#{U*_wVrEX(Y#94Sc|lz0%E5gxhu^zBXN?E*?ivMaLKH<4s=#pb1KX971UP7I|y zKQU_u=gu&F+OC+v@09fCnLxx`X? zroKO??q=esQZ?~4-ZrqdwOs9ZKJW=2GwM?eP}zb)%F((HKC-F0<_?S9RB<}ZK$W@I^rL#QMCwzIXT zew%yG*fp@DA>FavRe zm5${({$Wcd&9H}ftf*YE7* z=-%4DIw3&NACP%#2IpIRwjtj&ys|c%&tRLN+-ZhFxD~mWU$U^<=W30>hk2JepI&W7 z7vz+yKNvYi(&-CoVJb4um2{pS>kyH{^42NUOqa*@FEw&6eavrqsEEiZR3Fs)V-$f7 zImdtPZG|ui@XH@;?*E(is;=tQ?}oyZ71V@a8#2%2rqhxF^@{%6u^rD#z0*E$T|%;y zL)UhTGOC!YB%lv-0^jjLC(ivVaUZ+nz zQ0PZkkKj&yG-76^l;!?4PaNmoT8?MGMLw&4)_ci|f4Hjcvb~v$@kQV>$g>0vWs;lx zb=sE4s0H#6jftTc>-cl75|VeYxn=izc*N$L2vp~Udm0fDdp)NRQZOw!X>-j5vlT+? zCxVg3c&_oYt@Fyn(dD?fTA6vIcPg4m#3tES!MR6}g>;7g$lJa3&s$Cg^$=CQ<9O)n z)jq6LPRXHvHP?ys&FHPiE7jRE5>}qeo-YHne9mxd*q!6!Uap*C^cQjgTq5ZSPupN^ zkb46?z4|MkXjbj&*c?Pyxyr99~TbULD3ed{{^stfeD8SS?FUT`UWtk*|jz;x1B{X!Iuj(%Z^2m9~+Q=H=zq|OR{ z#m%my)e%(~f+A+Vtt3QPu*KGO+0}GX`U~I5S#K%Zv01vJ>>)5^YxD>I@t={^uWUg= zwDxD)+x|JDsb`^{x_nk2(9d%8{WL3Hgm@SpU~Cs&?_+H{1G!e!7r*E+-$sjtNO|0C=>pqk2_{{f?b1q4*2NKpX+0TqGJ zK|usGs0aZe6e*EjLl0F!rAvuGC@VtfodBVOv{0q@UK5%?=#YQd{cdx2fB$oGa^8FA zo#fuRGoLAU?##tEW4U`;EM-@`Zoua&q5>;)%HZFW9G&+!hs01*P*dIDclxoT=y{15 z`|qyN>Pq7WFZDAy>l3=A#0GW_26sN_l^6=y=$9WA@f|qmN)#quaTt+VopTfJTT$+| ze8wYEZOuQ;sENNi@&fORc*GMt=Sm}z z%j|Cb&?yVfn|s{yC%BXZ)NHJ6jj-@W|Nd-L8gPXzy_`}`leD~l-KJWlryw;~CS~@Q z$rATEbH8<@1j~Jc^IjK}RS#sHjkvrwwzqTKT>W4`t&5FP`f(X|2F`H^dQnqS8F45< zFKZq5xK;UdwY4m=bJP3%h*}q9Kp7*WxRdO;pPQgpdYmrkIlIOPG}ze_&VIg|2ZF>w z&vs3}ggmY-`fU${befTdL2qd;&$vR8L&BOCp3tshqrK6;V8k3}DOyU?ra%WCJ# zk+7CWH`r|0-ws+j=_feGSzN46UJmCTDGc(=jkO0`>|b~_Uq=*3JW6B>bxj*UyW<1l zt+5R&thxiOp970bt8I!WdXcW7?*#6e5vb4S78*AC?!%p*??w{Kj8qREPnHPN%=liG zfU_=)gb!PS{NO_)wAx0NbP?*3q|m5|p6w7my(%E`=T zQ=(LJxbhLd3V~o@{rUb;?7-C?2kVDTs0-VFASEeugb>otsUqtAc>PaHmUDVMeI&l& zw`}zPaW^U0z$R51n@e__zsl{srwe)r!pwb>eV?X(XkNT0L@ZJJ3_18ZM0;RXC}DDi z0#9lxqUx~2>0@*OSN*W_iFH#O7ezfr#v<(20~kmFg#1$2>Gm+npL^}kzg93>4Z^?&IgC~qgM7=+@Cmm3;x70X%(ejs*pD4c? z>C=b$Q9|>Mn(;Be&evLy0{kOpdChE~Oo6Rv_XVx%Wb2p1GE#XE&#j)m2-!W^&Gi6$ zg%FeEZ`f#ko&rir+*bY~c3^KNsW#DVRyC3x{HfEZZO+7Rz^5dT%quy!j&j2Z%7p(NeL)O9nIGz8+l!(7 zzd!m7awa=C*LxCiTfO`$QXD(O3-mF$7e+{UT!!(e z%WA=7>)GdiV5eGWsQd(Ha2VmmNZuTSN<2k)qnzWl#0DaSc9bzvLkE?iMUAwYv=!s0 zIbEA&iaM}7YY)cfzCsKme^;r$D~7*9A8AmfCRDmnULKu0hk|y~r1(LpjKuR(f$tC2 zkKG6@JZ@Ab|4i!m=P#}IpQ!6>d6xRWPnUzL!U`aIR^rveIzj}IK$y>`LN$E?MQ)~- zAU)jI-)g_F2(#xzeT>Yg=;QK3?$?G6a~-&&mh8`xza2%zr{N25vtjQhU=*Qi)!VQ; zx6UKA*x%|#L}+LB52|$+XJJ1LP?uX%#u1L3E58K&-?<1RgAjIIkT&!Z^#EHIC z4_$tZI8|r>9(;pmt~a{;X2$-eT2ClwbD2Ehn24LhD%Ht!4Y0gVv5D1NyyU~EpRG9o zFZQatEy|8B(rQi@evbITu@?J{?|SCT#R`X5L3pB!84r$c#|(qaX(jl9)+$4`{O0RZ zij;aG`1@nm=vOH-+n;p9;`-0}l;AIX5xhATpt}ZX%D89x^BDe4>q*TUU2Gs;ebLzw z_l*w|Jj>9|R`<-i&C`EVcK>>#OG&cM^(b*n!v8ZK{@{^Jd2bZaM&TPgr6xHFR0QXs zxHWLDC0F8TJ-ay*qgg#pYq61D5dRgg4pZ!S_qCNU`CKx607W?7sxiY{XJEYR9FWHU zWf_Jv&iihL;ih5d#QE|o=unjeP+=6GB)rF5bb@2oId(II-cI~~(nCob(y|mYQ;{1p zIE7J>;{`C^|CvH?;$8fyh+O>vc%SMk)# z{u23b(iWVg(y;J@1{q~=?KA$zrPtGPo_1)IJ;n&%CCp3=$v)kgD|E6aRkw(@NgzZx za$+AmsLJ^^s2fXTwpMZaF?uurgq`~~+FqeaDo9B>qvMt$3F;G&rfB~3_W}Gl8rv^a zhZ~4$-J`uk;vTN*P@}246(IyBwFU0fBrYc&31=evD90v=BAsr2Lj9TTn`>aVD9atV7xOvn?LSfOcWVC5a@H&) z2*p@r{|IHIKe4)iTqHq}PAQKn`T@VqEicby`qPp6Re*mNSfCh1XM(hjU8Nv= zvt8`ryPD*}eUIb4g@-$x$IG3X7wr&BLc|Rrus)p7htogG^b%<1^Nv3U;Glm^V>(&#fco(++cy_c&;;HjR zCrRy{s;Z9s{;F}pDAV=ZsKw!J=gf9>P`3%v^NZlhKs@dxBs_H?J=AQhCmn-~9#Jl{ z2)7gCo;n+GeTiNR&4H)4M5iWxz({v4VNUg5`VTI3_N!q(o?>U?D)Brb3}g}y6N!a9 zb=2r6T+wh-D0}YlB<-;Q0oT_b@Nkb*$fb}^{>e$3dtJ}n^(kB;anq}6f2&6g{43{P zCIzD%rfXz(DTy_+I-q&Nsbxo%RoOirqA@|qYfFj$eTCN#Mz5*6$0yyBWtd9l!%xZ? zv4nVgWU@f;JF?aaqX>!1EcVv#OT`WwP27Ix%b&+i@_IBF% z7|~6m%vkI27!yw_Id;m-xvHydRUv6f`{rD%ez@J!=67%T>#XTuO)Z%CJjrke@pl70 zW`0F+J+O1#f3(y1^WT5PqB%P6Cz27C&_I$DA4!5azd!hx{Y@GDl~(r9meAyN*u*v7 zDn5mNjPcq>d13aK)V$2&z4nBNb2uV&4dQ-NGRL7k9=>KOHdOK0vLz`*VeIxnP1E}1 zc$K})L*NWc?olOfhvx{dACWyy*B_(3E_XK@PY1w61*`&g*J^oNe z>RN82xJOIkfHR<(qjnolVUMlvN~cgi^eWOKL{3@?J0;er4+Xi3&)wlCFU{IrO7Oho zen;a!8oiQwN&|T*(4mQ!Tsti&xYxf>1266M)IsJJQR|zBS=~` zExL;T`*;l~coazOx)~_e%%0W&(2r_-N_8HZDgN{_kGw%n()S^C3cER5$LH14NQB&| zpTU|ei+_OgOzin5b6<5SV7a4LO#uk{B-M3ZM%0pNSx3J87r`v*-m#v`D7*~o{Orn0 z$m#Xf7sFeQV}p|NWplpm*d+`Ar+4KBg=6v)2+t3vxo)1AK!o!L-+|Y^Qu8;O{_&l6 z3UEx)i{cbU)R?3AiM@FAbzf7`)=5vAgUX6Nk)Tf-$}Ss1HZ1A9dVb<))%kKy>ZBN# zuc>m0_1G)w1QMWwZ(Q8@o~&et6_7{Y1eGgRsgYW-(;NYCdf$N)11X14BCyTt2Qi4kg2(p_^=HW0DgnY37oCH7qnPt;5ObbE^qEb;VpX1% zpxXJ5@%NY0`P2tQZlJ72OZ-YJZ~mQ8|LBe)ee)4#v$ zV|9^(EVAYqYY5dhuA1H@1By_`3{w3~l_WtF<1INckk_2T;CXC2l0yTikC@>GYkYYQ z`P9Fv0KuxFa5A2GIw;&cb~AEHUcOC*-}22{H2_YK!cJVoS>|$5T!-_gyXzlsAOe20lmKQvh0PdyKRG#U8E}G1yPLAa&8hAbKjJs9 z8UJMo5xcY2iH!vOfXPAl`OT1neLKN8Z|dc%vXA!VpnAvI&Yu=F$A-C+G^U26CO>OVVnd;jIS zW|fXa&z;o|U}KUHI#N&)qgEI9rRo4A#QEz-Ptd2yUSb-c#d_IJp&~;a5+9;Q7UF)k zd+tT7V@=)EhX_X$1J4v?Xn$+{xv zuRlh3kJ#I?Pml>5Yi-bYC> z5-8>Z^5W^=ww~jIO1MUQzg#XTkIqY>zu%U!V`lowMvcoBJdqB!&7rV|7DRL$pj+%2 zk^C&UtfktHkHH#@NSBtc|J4e;<|g?_6Hk8W_Y=A?lk0qDIm`H03X-ltTs~0ykee3~ zJx}U?nIFD$ka+yXGR1W(?IL-kgz`<0U!_=5J!N~rY)lZ~GTM$PmDgIg;wimK2e+q%0nCQE6GT{&0>-1F4Xo-i&_bpy3d zR`Ao6@HYQ8J2E_l(+w(chap|j2&t>n(3J{)r2^&}$@2FRu{kodL)RSZTHz-I`kQ(n z-TGX+QL|L2q`X#ju=;2kDRIz~czoY@!eu6Ss(3(8hgs?3s{Ubv{&{wf_1g9A5t|YQ zbFYIEFZbQ!ooe>l(zVJRJ-~+G1;-O{9i{u+YAtbtim`k*C9r;($?03+y0411TK=X3 z%q;uq<(RxoYzJ#?8P_5jS(=HK(kP)F<+@r@^2>{=XDb<>;!53=%wx^lN#5v0;s5-ZL|z19sY{gPH!Rcvt zetqp%r>t&L*pvybyI0X0ZSU;p#G^@B3Xztj_H!RSgR8GH5=d4GO`_NFp6$W5RYthX z@$L-OH^qfu7O{e%GV0GYISh-Z?1Wm5{RN`lULXwBnQAc~IlLnivPQbN&P<7F=KDJj zYUW2>amQs1ztFj8vjx_%*wQFBABM)YYuu{WO|)7s_k@!CJpEo9GRLgI!bPZ4;6r@T4BRv3)r1zBc-|cROf&sJ>|!d|{;x zP#b5TuRdNgu>mGKk|PWGMSvxpb|1sFfQ6H#pF-{eob)`5u8v1;a)EB!Ya&-HXnZ-u zpCUM#DbIFS?zG$)5Rt0T8D4aD^Q1s7=An|Hf+@wIXMlJ11=bhZ;uT}u^l%FaY&(vS zgzP4uX>VQur~xW&DHeb zHf|qT>=d=MP>4b^flOT8)__W>)%%1pl9BRvrX<};KAD7)!UXHAquqE+-{aVrid%|K*%Eg){Xa zoOrYocl#bm3~ib(ft`3MypyNdNq>OytLbT)Q~U%rp==0Az3+Yi8M`=ecxh9% zX`C4WYP+Dr3BwNQZEiY!S*6c>$hYp+_MyHtNUR|DH7(boQ%m$KeS0mdgv~47{E~d= z-ij}kv=tf?T31*sQu8u5o;VscN~F#awy#R9{UF-2qZ5=fF6 z@mHAq{hDe}X0_==d~ee&-p+@b>5K>d{k@v9>)sA@Axc31(KMc93b~GedLwahd)@?x zZq73cVI*+$!7w!s`NHO!BSW|=3)|Yo0;Y;fRM$B`f=G?zkQzTW6`5yXUq%RX3kn44XkBHTGUnzdW^Lcyicy-NR_IT#R zAZ9;a52$xvt7L@dR_&Vu+CBGL_1TVpuoLh4S`bO+2b^{bWe*D95Nl~Y?~GRK>zVtX z{}5p<{(Ps_kFvh{u(kTU{%#$kzu1I_!!F6;Wae4FzDcTpRTANlE=kf*ND2y4^+;V& z$@p-{`H4a7xEmBU)n|Y$zZDvp2iuXy02W`LS{uP^NbOV4H-H3(mIF!A+(GqDi`PWm zJB!>w(URqhY1OAEGT>~Y8ji}OUaa61GqtB>X{yne&=Zj{%oEw1 zO-)ja#akFp$0E3+#Bjx8)4YYdwa;qeKc#{@FAiYIKGA+Ktw9EbbFUKZW?PGb~yggtZea-ea# zo}b+fGH_z`^!?}+4Q^IUw-0}OUB>1Wlv&9!h?})K4Ue^#@OxLUWhT_|T0`(!ll^%M z)rI0-IwKLDm4D$G=tnYC*F0}A$vXw#D0MRTBk5hHaW{PSRC1`y#|R5j?2BlxY?(Tt z;YHl>k{zbXnd{XBHB>sUjjw~f2;g53>Nd)NLsCp}BIw>s)zOL#EpeTec+scaYg5dt z@TMy9SI*1wB1tFmI)xd-02HW4vv(XCuve+}aZClBy73w_E|YT4377Y<<&35uTi~_q z;mo`elaKb`&h2!Mcq>PhXi>|_ikdsi{ss7m5;wENP@L+~Pzi|WY8~oDiLE}}RBL-% za-~-Rc^C+}Z}nwn!Me9355;zctW7w+)21&}T8rJ!CAEHAr8?(!)2P%8Z{pmm0VwBu z;wz&b^`;;gXl2amCm#6iHrRV*=EA#kq#Nj4@2v2D(9ae_-8ScVKTuBhwDfpsSa`MNZcfqla5+B{OIYw1=&(`#d z+_)o##R?(NnoX_?8LP73#C~zqoNyOtU~XtcJGS!k@3jEbyR2Jg_7+b?Vt%v~G!+O~uvdLaNNrg{FUTj7jwnX1wFo^&ZdT!|H96Ag&)QP?m z$QTZ;e0kLEw%F57LdE<|DWi5zY~kS0gAw5j^mxdtm8vG0BQeXe z#kR7%HS^Udr+syhIFI(itHRsYR_;%7>%c_f)8km-ebJ-y>gE6DS}k8BxmC`KrY^iB z-qPW`Q|?7P)?AL!(GHIKU5%aSETj$v$pw*LKC;k17%nrh9Tt@#qKL51#2%t1=ojgw z27S26XZV5_o9Wu&_#H&y_e0`tE>Go`zCF9G$*-gQV&+LX6vEV9?SXP23S!;e4@qOU z2Ut-bZS`uy-zPnp1K=Lz<$!wTUh0eMH}GcCrRkhXbX%sJr7&W>J#=x zOpcLk<>DMaPm9n=!!aXJ`J`TG1ZXy1Hlm3k#AGRqk;be~Jl7e-5(+yqrn*mg7Y=<} zuhG6oqa2XKt8N0;p>z9?A9u80dhAXG{DO$E ztzLg8l=U4)3ZD4*QMGw(_!;hg_hOl6KR)EhXB-xDKfu?ouaXE>O^~cV6h4B_=@WA~ z`w0)AW4;@C!iJkUjI|NknlgAioGGR%B;jedJi3onh{aqyo^ize!eVfbTe=ZgV5Pld zczKI-?Al36L_$^#;3Asun%cgVWIyQ6SWpFE@dy_7U z#l#-M=?6N~Co_Btnt(Y#=PmKQaZ#S=Wi3+gbks@`ya&M9+t(e3-Xr#QZi z%;OuB$?WQ^se9WQU^+E|wQ*bZU1kS>Zo@!mZfeUxVm#efRfC)yx9-*JwkJxCokj-9 zR!j0=%(Xg1n5+d^Qfz^EKc|CxG|*zbQ|>cgFAUc)Ww+{d34tt}WJcb@g#d+<+@36{ zrxMdIz}{*B?@xQAtT9xn)0`jA<{<+Q0YzRbdVV#^5{|EbL(9|VX+69Zq{Qx)k>m8q zCL47Mk+3y=*uW54ujx!6Ebt}Ax+*!`kKeMO{ReKZBLN}wH(QmuQ;x}a$FZOc-2JJL*mB^`4-lejwsa1>z=dan(Z11G>Z)(dREKg z{!*5&%$wNC1+C1X!E)owNs=%Xn+wYVsIAxaO{=fw3fq7v(lQ)4wK;xWIJ)KV_L`_w zMqfgVQ+r;VDZ6N|ofnvA5q;)+bkY=`(9x!;*Vr>~oU;_P1GQbmxS-c_WwbHXa(=Nc zbri1&g(TGuMNYUGnJFBm zOG-1_u5;dTR;xt;k}{-1?Jx90nD^-Um#4bq#=vz(a6%3K6z$sDC~6`DoF9z4(A)=$ESaIijHR#r@i_@ z1_SB&(tH#;2|+g7-k&+1dDB>!-vEYO+qKMzH~w;JfhS?Y_WiVY!}IicRg+?k z7<&pK`@VGL5RHbtb4SGjY%j3pXHlrz{5&GiThx}xZLw;qzWmgl;q?a36DaTRlVWCk z4xM(OKzB9$JP?mI4pS$e8@XUScjnt}mstO0w+9Z*>!#|Bku|t`GV{q-G4~=iuIkWu z%6^oJeH|@P)!Q0tbXLNMeG6#yBuOp`*m+j%xpU|PYS`si)-U&arG}H~uV=&WzR~vV zW$UEDfqI~KMVC&TtrG(zre>xolu{7YqiC?OtcN<62)3Y*d}_e5ZRb0Lx9Q@+fc1*5 z3Ypw|&)~jpM*VG!yST_kZE+Ud7OY;F@>*#nC$9wC&+OEnBdr&t3_&L?G7y^y#s&!X z(*CcQy#0}NW=;{M8vEkD1{sZzYQJW5tf6yY&h8=jhP>WPI z)SSh#UNEck<*=6+HhciZpL1Kt@%Cv3a9N(;{O2N*r+o)i#U@r#14FGzxw$)Y^~+lA zAv!PvNh|y*$1xkUQstm(+}MSDQrql?Xw8*8PQM|+f#Kk(`6jlH+PMQhPeoVCOx|46 zClCr&m{iyr9uGPSE9t0L>qX^@M2jTJGPt9jcs+&1r=S;Y@U$Y3azN|g9Qvw0jDTr% zEC>a&I4uS@N>mp174_#G`e?YW+CavgI z{{g-m@INn+HL*sx(ttSiaL-dbrsgimo%B5ahK;lZVVgtMd~f-u5Rk1NRix~B8KQMb z%EBJF+w581B9cSrbYBN?&kLM{3$~ZAZdX0Rco>vkLhfVnR_nKkA0-e*4*(0%WMf@@ ziK&4zSmhL|-pWh^$nmYiDIA~em5&UtT-rkZVI$qwVJ1jFTe8W}?J2FVZUDRrx1YGX z5}Wu+8`bCYC^lB7*|vu@8{_cP^B)}m_>ZnLDKf+HdV=J>U&|ixvM)W~tnEul8pAzq z6ej8EhVkj*|DNr%OD|IHprQ`g`P@o%?pSMdB+(9JY$2EuX;M z(cwEQam~3bJ@_R;D5=tZ4*L@Woc7hw#UY#Pox7fGTKd}o580wt#bZCg;{HeRiYF9@ zpdO-FYb$cZ;kO`p7ClgXqGb?}KeUQ>He0qH*n05@HADA430!&0;WOGsDC;sy+Cs{Q z(a=2j>l;zmWUvh6mAnB0mvrF$;YytiEDfLxPs>aPiuZXbkFJd&{EzY`Oye3k5l3e) zB@E=*B^-Gi7mf}LX`|IWv4K)2>elIF4fl4wuB~TPI=hK(2>U{1Nbgul@LZwirI_B< zLDfMTBC!7a-kGr9(E z$rqocKRQe{Jic4|1@qYcag5?5Uu_0pg}x$#H6W$Kj92e-oEPH8#gc?CI-uS@GmPCw zOX;TwaWy{0q}2tx3~NDWGhX0kp8n~v9Auhmo?wWyrKm5=Zn`#IsY4cgsmNogkG|rz z>D1_Dn@NPxrwt&C(+?&-w)MSI@U!csG}UK?#O5+b9jA;>G*HT2t*0_N0anxWon0pJ zn!!CoQO!admUGHg=I4|OZ3UCBm9;6svnzuZ0G6}VvsUEwQjJIK{XDn;fuW<{fb7rj zybBa!HshoI%=q8p^Mmw0R)bB>jiPKmr3K$#M>Hn*Ta7HuQXAZ0FV*Y`x6Cw4?Bvkv zgBqR^s72eU2*C%JGuvLoh+3jmZhED;LisT@t zGhXD7zpKA+I<^0jLcQN3Ss-1sK~E+9XGv1M;P@@Gd{rL|0}pP)()QJQlm(Yo$$U!A zwv-5C+4=qkL&^nq-_Js0IW*GdGdIup!U+9`+L0FV-jH3Xmo>J4`}hQ2eF+UQ)~HW8 zo5J$m5gBEeqVOJVDF>kcQU-ZR!_aL!ScOb5#Bj@@TSR&0`kj&x!$aM<^TSsA8;)

    lDqhR($ZPUREF7%ck>(4(Sx zyPv5A2L+Y+7)SWUHg(3ZoVD3o3L$SC0dw+I^AOOf*H!BW~xh- z1EyyKn*sjj1^d36c)?#h#E+Z!KmYH{wdJ*+ATBv`_}LnAxypp&hDh)Jsd z6mU6Tb5XUt00dRrkCbXI)eud1!txH|p&e4qY?BPMZHn>RZA-0^4eb6stLZ5T&{5KHvLQ$AZ?f18s$IyU{ponk#Ns z^6mCp?xWZ}0FGiV$iY3%qFrX7-F2dA z4ZP%j`vbn4BPkDQ?jl@Tt{}(!1fw%v$er7xnAVgSeQW}(N5^+>Lq8i*QB)IW^-Hlr zl9!JC%9&@`3wpgODWcJ-$*j#vKMeSHDgTCsn+63B{mG3?6FQ?2$_aF zR^Yxb1E2U--e+fOSUXpO5DNxF9MSn8Af90*VIsf06k`IEzZ>zY<%h><*9DHM8~KH~aoqq(i2-9$Q+1S)S@ktucZUJRH)l zS=q0QSGgvBiPa1W0W{|Ka;_-*4%PY9VK$x_4CU$zLOU)h1)rS}S_u8agDHNYo*n-k39PhrX|$@{eswgAqCJ z`v8K9vipq)fFCSW9)AD!DfRGGnq>trGGJk8qCxq`-}irWX0X`itv5)`E@b|JQuB=l z%-IEh37~wu4_>yEsrsNcUe>cHj8#HwJu`1~@%u!5(UXZAYg=HP;_>d*iVN_PRaFI$ z016NVg#mhoHqo+6-vX6a_3sObCl8NP3=Itxxz61~JFiSO*2)wz{nwpSoV4DssM?t8 zQN7^({rZ~44KH@ge>)0)7ZSc1fps~>6c~`&CC0yib`ee~DJjrTEK&i6!89LKsD!f= z0rbsqr}q$A&;EksllQwIp^XL>@~nNgrCZT`VelfCN|?}nI7os4ah)E@+5~+O5fM$! zov{_r>D5-j)3`Vx*MGS$HSPCa#HE>WT)G5B$FBr#MURtok7+T4nJ_pADBPkceLmx! zc=p>G8m^^%x%%4cUFqt~b07n;AFb2{i@lsA;~u@W<&nujO~pw!M8Q(qef&>o`ok6e z@bCZU!}EW0_PM~e%+fNW|qBFc;^y)b1N2 zA}qY?NL_ppL?cs38~ceZ)9nraWP{_Mdx z!@@LEt!(qnCvOr+xg?>oP`N}YkG~}EfA_ThjOTZXD0ChR7%X3s8v&;|30J{)O@_~y z+F`i(HMeGbtQde_t_~{@a&|KfEaxpybkkhlGVIz0m?;Vj^a1_K?8%{_4Ygdk10aj@ zS_(Sz{8(PeBs7$X|AWIwsj$cQZ;*?G%j(fzz!0-Tgpg(1IjN;`97L7mwE|>v-ynQ< zZdbZ3&QlhSCV?Ps!K*>2q~}t3-f?X(1D7Zb%eiV=3%Pgi-UkD;Wq0;{p7YYK*2y8A zcUC};)o*KfOiUD?ehYwOwT10I}WmeR9TGp$g>lcmd-hj5tk(J!sPcY9K~Z zX8}w)g9I3xsXTPY>L3iPDRn?sA#wo|6->k0Qh`tA+cC)n;Y-!evT_~jxV*t4-Pb%FV=4e@lY`*((XldbxgJoWoFRQ_b0H)Uqy$8g(cqaag|ni%ACBhIEy z^aC8HGF)P3b)y)lkj2RY^|hH2w_e+P7M*oR;GxABW5lpIy5=Ptgs)k)$T-wm2)A`7}PwfDUz0`ZvCzKwn4nrFH z7>)qjun$0P`jR=WtW3Q}r3rfF{ji-o5><1Bq8_;yXL__`+CApCi`7blonguF{Nl*q znm`9QP4PT(Dfg3;T%BIj{H~Cna8yh_#$CoQYR$+SjHD$f3xez;x?{=sfJzjmJ zy+?5>O2#`WCayB)MQvtr(HfXJwfT%=q4eA6(^pp7GBh@-p9j;~Utr~*Axd&(COk3? zWL8duM-Br3oW=$YjF$CFWfEa7;%Dmvj&1en@QDLK$961+8S=z`Tlm?r<_&eaPvWp* zr8a$t^RpGV<=lxm@V2J}uWA^fU)XKo!Tm*_)v2%Cvd94oz$-MVDJ_cr65#)@K<5Ab zEO-^Nhlzw$FPCbBXjG8c)76}mIh2nrY!HB|TGD0@VI%Q>v(z+#px zZ1Ixel4JLW7qh#daKK$3a+-MFY^>l>VD^44$u4Uhq`|$)J+4R=S$U8#x78ZH)>ld% zW)ZV}oD7R_%LSxa7Ro(A-lx(aoiPK%s&5CmG2&$A!kEA7&Smn zVX}AglAUU|Gj_pnqf2EHkT317fLhHBpb%`*u3rqC1cP>9dTGoi9-klzUTyGkn*vBK zx1EPk;0o}(Xo=(Az98G2274gkW8yi@D1{3XN!*8{l0dE|46G6=IO|#EzPz*|&(|i_ z+cFO~F0pxPSa;}QnR%pKKlZ+1&4b*K7}s1d>~}(!fG%a?H#~RWM^Gb4y04mJV_!o< zJ%FU+-j63_WZ8-0=K^Tnggy_;`>nfGir*_wvhgCwhTEXe(vn5q70AnX-mKKU_x&tvz}Tv8-Gg?6kVmZbiL5-nM9T?g0f%b7E*I$;_N(;`ukX8Z)Q@G~ zcy?0j=%ailq;CFbzRD%NXk$(uOS@--d5TRObcL7p9tQ@8)71C&m|KNN77GZKeKI+2r<%V0< zsW!yZ;8$YUEg%^CCBJNpcY`n>VJ!A;8vyWF7@6d-XHp_7lB2lklxPYV7iSmYzbaQ3xtXHFr9A5)U zG49YR;H{?DyVN>{i`5(gkI^J<_%ju!7LCH!XCDd9z)J(83~BmDi(jIT#crg`)W#e8 z7}VDvwP9K6#g*9)b>o*dS77D^_)a?1F>J|E5vN6bvR9U=m8rjLk`gGGBnfQ+FXO74 z>;U+jh+I7`;s6LMc%5Fi@t)y2u-SaMc0Da)@Ntsuk|1k2>Mg2PxuOGs-rIhUKRG4q zz67~%Y(TP6{tiYgzj`mt+AX^Q^pv2x6SubBqm9owi$u@>iJdynJK;k{kpuq`-~4AV zq_{-|G6w{ZH@^kQ+}{Cf6@{wZQ3IkH>~lStkvh40?F*SvuS(!^K=Xc0Hp2b5`vYMf zp5~!yhY@jf-r-qZ-5g0m4Rek@O2F{8-Uz^nvf?9s??$iBfJ$0|Jj-m~-ol-HRwk4~{l zINHG#AKBMjxn#70i-4O?z4< zfJ+le03t;K##L@n*8`8e(90VuG!Lm8eGvKcWaH1YgU%B4fz&Sq92J4kFxh6gH9V)! zxP%T)C}}Csdt5u0k2$b9lU8$ zEb0!Japq*PH3sGK#3!FmY|H5&_uV>ske3p21MM1tUA5%~1yK$dz6O!tWiXRiR=LND znayx--QKayL-pbBe`NOJYS;gC+)~-1{{ggdF+)#PY5u|y=%+wp$54Rc6 zb;rC*)Qajdu3O10EYQBR^`kKy@t+1D_PKGm0Uy6zy14@jcb`0H34Xe1Q9N1E;fF4g*Oo=CW4+D2Y9u8;>l-)eizj+P{6Zm*CX&lIN*=_c|M6TJdR{_PV9wJUnE(|WF{plrY0enBsS$#R%B^`3H>$x{9kaI^9&`GaL9@-sC$IA z&w>;UeE-qJub{RuZ%-CXPn`aI)mqt%#@Cii17d_=fVtHGC6)CqPEdhoZ!aQ6f`O%; zY6viFAUz;>4+)W)!&acHLaiC~X>c6~1yj&;V;fyV@>uwWdzRdcOwR3l1PY@r8{KOo z6StQ;pLly$&!H~%1GhIltk0+H2cW2B4N4K+(dx+3zwYQJW@3D>F<|QDB(96%@Y{8^ zsv&MA(wI;Kj>?(W1tcGuz`ri`DQTqJ5alN2-O7#ekhiVPCrjr0a$|;+@=F~@=b_6$ za4`OmpN2-*;Ih)L=-S_NQ)P}@9RA_@{uUJd`Gfa2!IsBR zL01x|K=;TSz zFlsx1ON^^4JCs(PFURk^GUN z;Y=UW=Fa1Iy{Ns35RTutxax!A0lDUYL*aU&-@GvkK+ZIFDJ;A!-dRm>s=bEqwU7zR zq1{X@7TzFd{Q7;7!E?c+ro zv+E%tS3puGf!ateRX8F?`;qz|C7}g$&uK#M3g1FdfEF%pwme2nF&)w#aXcXei2|ix z>poMN-n`?T^Iw5J2QZsL38NMJ)WmG}|MXO=08`#Y9d;iO77L7ez zq=E~3EeKx1&>0PbV(ZsZ!N(wCQ+E1p{kGL)k`z3$c1qKd)k5RLhNm+alS+8k0nudL zkslRN!n+6vQXVojCxtln0c^GX=0Hx#PA9?UxQ#ZzY795}2V~q9zKE&#@vU!>(Wxx( zINSwTl&ONqg(UPC7d)cWVMMxitocM4VL@(xw;E|uQz?6}yT<(iHBjJpus?I5i`Wb9 zE=dWzrozDmq&iZl#b|yeH<~jdNMTI!#0;j2|=*{gkA*&xi{MK+c$Fn`8y@juhwR` zVh~us(m)1P3}*ejO$^)`uK+{C|IKspVUgYgIdIB+HcN7g=T)leFcF9KY_kmOtCfOJ zy$0!hY%)t&uc8a^oyvSh1T@O>cr(xy;}+3gOuJ|~s(+*j)Y1PR%Dy@*s&#!^6a`Tf z5JW;skQ5}PK|%zirAxZIq(hM|W#|$FB!(V3M7mSDksKO^8sdBBoU?nMeSY6{@yDXp zto6q8K5^go1M$6v!CS+uRoSmzPFVIaU**$COnkhVU;H7L=hkq}CzJtR%Ybu~fO`Rmn;wf<2E>toyuaV# zlbj$uDvHyk>OcDMuy?e-;YRg#&9|{%oQb3KVmGVoUBG2sByls7h;O| z8}?E;FojB08cmHF4|&G2D1qag3sd3ijVQjMLmD&N?c3?WmDsvdVdLLNU@J4mg~k z&drH57hia3MO>*{VV#^qTCg4OH&q&C-d2kn$l(jT%>;UQp?v>et1W*&5+b@3Tn7Q| znEQD;e_NJT}XuA6C$SEW(XLpSo3qc%I+FM10pcNVbx z2oJ~Ros-XrFlux}Pfpuy81o%~L$U<=?R9ROb!mB@fAm$+%cA^OOd~&dl~E=CX@h>$ zs4HUj0}l`G?sBN|{m+JU((-k>3uhKVSJS-~xm@e*r1M?_Y;X z_zjS#NN?C10DdIBYJt{RpPIL;0m$+jlbtTXfICSDPwJ%EjEM_fs-_3e1kQQ9hD3)ZR8i_~{ajgWyDrP`$>YYWAap2B$!AkhBpNca29lPyu;q*cC^ zl*^}?375!s8Xw{EhC7+)wskrAWiTGRWsNt(b3cw8GU|)69QWd$_Q)w!(Wi;vnOrR4 zL$VcDtd+WWAC5id5lZ79@{iVDd=HAB*7oI-w!|vMaT?y|e3wa0f+u>L*WRG|DaM0VG}Ee%QfQYRa`t`b?L zzlWP+;BNp|mOF?mWoNFbxO>n2li2Ce}lKFmb=K;z8!eT_D`3Bx(13`iu zs^suQql^wBjLLIO@!oiN>#tQ|Dp3kvK1R038aIrIAQ1H%#*O&@WUzd9a09VZKi~KI zLQH{D|HAU72>+W=LZ`=pB;y*&RYS^!qd)XMTDG{%g!Y{LBd+6O==Rb7_t2p}JKFCG zr?C`TveMshd?;xma3uw}vj_?9r+zQ?guZ)i5M^5ThKGqRHE{vA-}_Z$4*pAd+N zqIucH$5a1h#@|DPRgBfJD~2(o#n%)Y&~N|QHvi#nbb;WB{Js|7{mXsMo+DN%ah37< zI|@AGQC|M>LVx+_-|a{FbQDD^I`Pxr?Y{T9!jAU|vPBUSPnrL_p~Rs}1o!A#b}~}= z!#oQ&WBoM56AK*+>1+PSwFhoZk&lXhe=O-2{oxi@uDW6w8vh*kzvGH&h;yS)G_dDS zzyHg(^5|EK#cglj`70N|NRon)an#b@*(wUyJbNfh7y(b>DE?MiO~p%DjbMk+uM^l} z3yi#lT^rw{@CV?l)(SzKV!6YPh5G*^?dFSj51hvO!%3Kbn0w*0ZvZ_*MU->7L1uK` zH*UXsSxBy1|)}&SN?sfd@%o+QrB}AzMUFEN@K36@Gdf zrwIu-S6|+krAd$QDDc4M`$Y?&Jjsz!%e(A*vBUPy{Rj2c@X8*6pB4saqg79)0@@`d z@do}EfW}u5hg(A=7r(cg{x2(wVgneBaFxP4HBqq(K6Q-3u?zCtoc}l|AfrbOOi*;e z4TC?93eL`xNMq&tOiG-rs|xDzdc5`@{s(48co(3Dn00i+|LxHIW>STZZyJ6Hn2p;f zwo^}1(s3|$ufLvNDYs|Udgm`|_;>qWSPyIr!WR*qe|}H8kgGi7Mm8!&cCtp>46Kj- zVSfVqhY1!asUBg{+TU2|fBJaT^W$KF?JYXb@btzXKf|{Ozg<%LW|tki=^uab$4&4r z^GJb@t}m*GL&Nz2z~8$7O3Mg1W*?)X6cn>LSAs%9K7m??)Gw%LH*49y9Km01{WLUs z!Hc&4J9PNp8D5H?=v0r}^C_1>qsG{`KI8QZ#7R0JDRxug?t`a3PfM|z*neFL!Y{El zX1#?$S1Cpw9x4cfax}mgN;^6>diT@NuSIT5*YtJ6K-NYvPX!zB)UddZe*VNCulEda znt1@88Ir)6EA1alaCPew1VS@ez8N_@%n<}Ip9!3K(h!Cqv?IMoS6iqeleL)wmeTxj z+PM`2lgIzyu>ak07A#7k55Pu2RbTR+BCFp3$Ds@K?pm~9hyU)c>6B3+PRA;3>L=Iz z`);8bT2Ej{nO{x4d;|Z0&bOBwt7ctFY28)$?auQebYZLfkcxCuOq*D zXDo|N_|d6_jV9VkwqaYiHk<7nqIZ`g(qlCFZXyNZo3H26-i!b=x092)CAaqs@yx+! zBaZMVa(kttn?;DDt;8jXTd$;cx3$LphjOgsB}rivC?iU)&%ZvI4@fmU-Wd=)Dn42Q zmfr)@(D+!bgW=a_|C{ywuh&mAy^eJ;*)Pr)t}moP_u8PGmPpE=xUKnjE6(tip@|79 zJH}jG%({0%xZOo$l4S1p9q{-DzeqlEoo`{ax9i(q!p*DR15>!y+}Yh7jofw@V^Efg zWT`(Eu1=X&XHdzfJKTK{K}7#-dRX;wEwB1W;pK{SKv!%b2jy}=z${J0ohnJtwb|-W zGXEnTNLvNf3a*LsyxocP*xOy{eE^zX>WOq$+##a4%l|fDoR)z>is5zs8t9`OwVw1?~j@&f*c_s@1tAGa;QaO}|yX2wSf* z&TO1>-y&kTd2@Gf9%IPC>PV;f?Jis1-X2#T-o9y$PDa9!@xbCV|M3m8v7HC;yW^_H zH6KESk_D9jc#sKXKNJc)9mQ*$X**n3lAj1&)~a!MK#{d4t=<4mdt; z%3)jHVWZCd=wzcw>fiJBnu39r1oI!~&=(IMoQ-~C0QUOp-}%ZD9J@BMJQkkHQNm~t zef27zX;jNSvpa4VV%oAKyjdudnCXVdwJ#yyKOEIJ03}wm(fGyj28|M%S+6xKWt|bL z`h**4eH(+47pE2>;~qP2J~C;Py|%7DxzT_$Z3|)MRUI5+2h}vyV_v4;Z}I;Q7)`@R zoe7UlbFxzy0IiOxmECsrCBXxLa0aodMz!PG!lyT&=p{HB0Ph6wLdLhzWcQmI4hE%r zU_@M&YHFd+@NV!JwBB}*IS?oX7%eTOJK0Fa(K&UL&x9Z4qr$0EZt0)oZmkNe+^x5- zv3awzvN}LA?Al=nBOxuZ$H<&rTFgiLF;W&Do|ZGu2Kyy*vw_<9S#8IBp^K-w6G@Kg zJck9^?yrQadL*G|TNxy|uO*{F^07046Mps3;L+7`VOe7zZ||(kiRrg5fO}3MOt4&H zOAvqUSL6KKI{M}6eGYJa^{Z`(^#T60BFS|*zJ=J>3*;@A0D6%v;vt_i{*WcNCHKwS zWc*`OE}$-govnv_2nU^fEw{D{v}-zJo_MS398AVzWByd~K{BSkag?~?%f^_d`(W_O zZU-&0FvU`?-fr=OvEBYM^pGL7k4Ocv_~nbJU`3TGAZ9k!h60!|fcl>3FJlZsk?LE_ zWFV!m841b+vf&mJ&FJx?P^2hotAd8M?g#r`pMBSXwy>tLH?j`wdv}{<&Z|||+1Tw1 z89O;CJ;-m|wj<0$YZIY(jm~z}!;bQGYdiPvjQn3#fG-=v9P%ikYt>NNT*EnVTrl-R zJj_hFKiwL|LIBG>p~=|DhyXFqqeFkQJBerLH8rY3Y9>I}K93L) zdCG376Jr0f9d}=?#uk%FkTPkcA_HNO*&`;HhnOTRF&Py4E^3a68heTa!6v z-(=Qx8y7cLdhy}|hxOFUtD7d|883R)T~z|hk$YNK(BNbrle)c~L#1(wudeHjhJOic zUh#||bk^Zn%UP(gsOf6P)LwFuRp?Q-rjjJ z2T+b>3=bZ6fKG*wLW)uqHd1!uP(Wc+Wo|gzdm~G*=jn!6rvd1y@~hI2FQEA{rO=Em z(VOSn#kTRfMT; zJh23%Q6b8CBkGhtE43zTRV^pV)K9gA)INSv0>EgUBW@k3PQC3dL0yYCd_qqE_`Pq- zJK)<8sF{oVA-eX{GRGPPFp@Q&-Z-?)IJCSc>7pG%jG&>N!D7cSRdCzP7cT6(pGd{S zx|A%V(4peL{aST4pRPl4$1^N_>OB9^7xfBfP#wHk;@;O$Q%Fr7lN3tY0h=KTg3S?Y zrg`xcxYMl%ayl2cp$B95l+ZnH61n!E$S~Tnia6hUGn~R4CIrZx|FF25CBpk8p}WdU{7MPn90V zmu9aK%0ew5#y#{6sfC$59;qvWr>^No$-QfVPUOpQjT3%T`h5P=lO@4YFSD1ks^9=T zIeq0q{s=!-35t|m5&w-u^w({$gOc}nKiaPw=n4SZvXCh%{)Yh~-?D`Pg9h;_yVo4@)9JkFuNma;}%y zW;18p5d{DMvGaCw+do`D$O@!jA5#;XlAkL_gTh@0sFmWrR$diu!}7P<(sdoMC;_y# zv;bvhvx;WJvwQYJ_B(-wZP)8nYme+wnHarN4I#5@`N1;dECU5njpN03AFEb;##0Qm zCpY4}ukPM-ui!?miAXsDWQ)7}!s4RCb>=#x@rD33&q!t0!>J{^+ef#K#;ziy<)mDk zhunE)*zBK8rw|}r2=+|#_UwlTit3KErjeDg71SnDDjK=XMkc4Vm&0OK2bFFfHAWKR z(yyIW3UQ+ee1CHzvWI-LTK8-c0ygWCR=6&1mk>lV}754F?T8^+mS%mUn z-xhpL&?So?RG`uz;N+~UZzXbOam#(ivwKjhm-n=O#-NA3DIsx2cPal!h5H$D|L0ue zHG9JqSxHzuDNZ=0x^8OCsd|BK!6=I$e7463WwClr6_p^U8T@M`|4YOoEOzT>^6Nm; zlsQ^*W=dmd(fwD#H2vKOp>J?+%pr5Z6RWim@1M7qO&ZQ0K8xG6faNV5M>zODtZCqZ z9G-rEd)+}mstNk^$TSUZ*PP3pH6QC&_B}M`>1g{M|26M2=3bRJxR$&swuTwbm&%pp zO_hF&oW&o-%e1u%p4K`j-d_t%TB^)a7j1j0xAjLZD~;>?GWPbt1XJgH{KQ2Wuwtvp zk-eE{Nqb}whGG9VK7s1CX0a}dLIIW>70wnG6E7=~PO^vjgcx@pcVt9_Lddt2ya3mRH_}awGd1c=Vy3`&ex5+l#Kud4A5PLTIW%r57uCf>WET z&H{2T^p#Dd&-D6`)U;eWo!O*O@87@Ia@gBSj5P3%fFe8AH%M8I{46ZdPH8GzQx#z0 zs~*!b734XC?fJocMM~+L6qSWl)h_(iTJ+kY-;iI-jf0yzgedK6@=}zn^cC+)<|B$U zjs3;)5}aDJ7K8Dh^(&Vq}@LXaDpyC z`?`v3Mv$z$`$Ze2%gVi{Nb1>92J|Z)o?GjekB1w`5!-oZ9$5k39IxK_?B_H)@DFEg znkDAjbJe*L)|`(i;$H*eP<#$R!zcm7V*C z6;|Ylbaq8KLZC(q0Y%&RTz-&xehLXHtXd{^8TEKVXt?39++8ffQy9Ph)g5pY?UPX@ zDmJ$R)6xqdydMR)e+r-pjxve_+{24pDfpqel5Yj@2Pgn@Dd}nixU;si2Ei9p$+8N7 zr#zg0?4wXaB>*4BV>{j0@*sN?!D{ixtc9T{%~Xc*D;`tcOt+f|XtBydCl z!sbg*H}}xl*P<1uUI4uYB2{%QUH5z%mF|Uk+=7BI&H#40yEOT0%fyUw6fW zL+bl50Go7V*1qdW`nE$|_5s-lMVZbgAYzdHa*GxD)4!_sx)X{<+k=pg1>~+o7Ufle z&#AP$Hcp*#j9W6$_^rQhIFLGmE+9V z$5PR`N zCtWDOpzhRy5fo*kg1x;bT7e8U$=M@9M~1SleOR+*OAIeHs-#qK>O~58T#tHIa^7tf zknz+k*drp$-wsz^xo-v;qR!R|=dhHYU5VvMwy?F0_(^^4@FY}KpEGr_gY%odmR_rg za&)ic1UyN+Fv{yOjm9PNvyKy^?PgQ=8mZr{UW8-Vlg0k$QPY~#y$PyKA1RzJ zp;*pg(QXoGY7-gYtSV<(e`0@veY!4s&Ewh3+l+hBh>N?zK(mPb`D^Snl>YM`;JNX& z^KapDNCbq*qYe#d@Vb*TU4|GbdD+;hQie;rP}l{ z-g4R^0ho!W4W*;uNw=Rb6qT!IBv3GYAS7shtWFNN{-25s+N6MozS;9!fY5Qn&DwoK zL#A6*y4WjgWGka<0)?i^FFhLI8Q&jVyfMUqpYs?%1`o#10V0qc%>;XYOvd}8GC6TM zDIidqKA4!^a}e;DX2gJ|284dc5T{00Jhi+|miCSpQ}oc! zBZkU9eR$6~Y=g0Y;65c|%5{Ggp^7?0N5SAIl6f8|>|WgN@8j`AA(i*GYPJ*7s=`_0 zRFaNj>njkqs7}=_p4A0xA|)DL?f^bq0(=lK#%pe&n5Duwn5N=A;o0S}qmN}{kXz)- z>^Nq3H&(7UsC{1C8zF??Z7K_<9&Jvukb7}VpB^p4mI<-fN1z7hiGBOGbchqq-9}tS zVi$Y&kC6v8+g{P`+pc07N&T-t=OxJ;)jkKIqnj2#aw#DUH0?HB-gh4u&NN*5AdYq( zS6PkqY)z}b07;(Cf^VIv`8NC)_wZP1?im^X;)(ncqyDb40=$EHCgI5OYBwOk6fj-p zXZG|7SYgDke=yWL2c7_kSab+;^-C0X=6j^ZuCnNNS59nw^U6$bN-A|N7A5>}Q$EFUQ1Y=n6cPZ_DEhzzGcUez!okhwdFrTy zXkWY{p?h`&5+cefFhyHX_m*+etx5fW1H;Puz|Uz5ZB!75tsgOv1rU}{xpyPHF>f># z!UV?l%^HAmgpxIY!jy)nycoe*zfPz{0WdSHaDZI1O1ZTkpmKfWP38=VcC{mGn@UB) z#c9`Tff0&*Fk99PVsO~ukE_~uv7;t@T^`oVuxmU=b%xmk9Ey11Y@L13!f%#MzZp|E zfTh3MZzjW6Hmhwocw(Yg=+Z-}N-Iy*%*haHc?N>DIHX#hI2_qsW5&7US8HQkv&nVu zcqH|#ZvF#_SRRW-izRlN1*TTh9B{9Tb1I>&^$9abP(RP|a|yWf@Rid`Pwm%7YI+3@ z&$TbXFvPp7uH>b10#Pcq{h_{R=Yx$HL)ptwnBewd%}oPJ&hP7E6jgUfoYLNTAb0T*I1WmLGH-s|X*(gNj2pb1UnVjn?sjx{b5H^}gDU_>J)X)T2m;76I)JTu+9)+$k&CzoMfnzOm4Nst zL!%6>L}AdTlk&`VG(>~z{c4H3r%aXge+2hVNHnERfV7>yNFuS}fP;mpW}ck8s#mWQ zn}xVS`1I)RIdsT%T{Ka9{uvNuLm@P5Vm+X<6*j3iKUrkH!KSZa#0DeKi5?Hw&o&}k zi)|6H0Ae^v6o3@-EeM!#m^jb~BVupOsm?!25Ii6oUN0kdMv0;Hr3Sd9G75E_)Ja@> zsJTs#tzkj1I8*1|g+kgpj=En4(WX45%mGlh{@1Z1?yB27+cC4Oz}aa;hak97C^JBj z=;!%z!v!JW49FYtc4kic-p`Heyg6zM=e=DRbKUdPMys9{5ciH`U7XTjo)hU=owi)} zH2qCV3V8VM2(@!5Yj4CoF!QYl81L?A5x3NZEXiUNUF_6*W^$a#oB1ILU z=|aElwmpLhmhyJC<7Plvs9@2&PW%mGncghn5BF=oWSev@)w%O#4Nx7VIsL`NY zj_NH=`gEe!&vIv)zp_DV_UOK=!62VKJTlQ9p;2WI3ndroIMpA7jKrD$oan^@IncE3 zoAO~*-P($ee8Bx zeer$8cb2cWn4iydWNe}?`lVi^J5RMR1_cE7A~r1-?d}a9`3`_?ujXD~`vICTIoj;i zXhjh{c>#9<_UgUuX_ci^;+& zQ}Gi7zG>}UdO%%d*S+134d~XLuxXvotsn>GuBpIJc)&~8s{C$hJsr)JuX!V-%P3ZR zxpp|XO?({7{t4&L0^Pm-Eah@Q33Jyv)hTHxo73rI1F2AXjjP0%WSr~I;oCcERU3o# zK%wF8N5`jbqXc&#D(dz7vW)F#tu=eAj|@O)Vr4vqJR&dh8kl2Wb2*0&7R|G=8JccImO9|v z2LY8Y8}*z*nCsC98wY!q-(T0t?@mbh5p@2pbO>zxIx9K$h}lqX-qvy5l_;6rO?i2gR>kjHTfRLc%$EOlt9soO!{ELvi>!#kSDsYVSm@(Q}vt!>iG z;+d3Kl`@2CJdn&8>Fpox8inn<2slb$_pLh(2QLGoU%!fQ3Xq+wu(3I>^>AMy6^F@3 zx^whbspcQ^J;+2;(WP{oR>g*~72ViDknLMvCmKSLkWVp(0o;`Ow2FWZbIYdNH#{-b z>Uv$Tzuk}AR^No`u_uvX)3ic;*Q;U93KI7UyQkyzSrwXNXXnUr4+$ zwN+fVAD^w!;A2~e2*KwR7=edC7)RS9HMe_UGj-{pY8ZdA6rc#zP%f?BMyN`j-#{21 zH_2%K&#Pm)MCz@@9eY8vunEC@yytWQ5JYyk{2*M12o zNo@V7ikY5o0=$!x+iWWnYpcOf&C3FY%;)OR_KiX-Kv%TH;%e8zGdOxSvY=(-zByB; ziBe}gdQDs~%+S>6R(1CaL6P^#65%-D`tk$)m^~0*C+1Fq=$OFMlwfBNBoj>2?8baG zy?t%mXKTO1oQ1smE?U1pJYasHB3vrIJqs!d+>J&xorMhsG0mrbIxFe6D1ykVcs6k$ z8fu|+;5CATl_eg*%4Th7c26`X99GW2o;^h8=;lAnk@z`3@1m*8C0(A|*%o}E1n}s@ zy@XGvN=H}>`1e-Nx0K1cXv?kXhXlPOP$lS`9JA#RQsunIr)QjDfFRhpK;!LA*}F*b zytdyi$w8Lz#O#7;yviZ=#3`^TKq=F>XFa{aO&B%eRN~(Z zyKHPh^jc@EitTLvmaA}?>*jC;bt~EGSqrQurJ=Wwu5N143TR{2g7#l~KyR!Agh7UY z8np*x)Zzo`1AfUIh~tt#gH=ha7zKB|GjeEgWP?CHJ-~Vf!zhW6~2gWuUN;FtYrnDqdX zZQLn-4~xB~I-x}#n-xAch1MO7Uzi$&NO{RxI|3waxJEkreG?wO7!kaA^8{%&37#`i z6e!f2TqRd_s=r(@)}^3C$82YyI>x{kR}f~bL@TI#ZL2ywQaxX23O&n*ZH-WARA^X; zPqo}fsJ<>DXvD%~D9P2k_wuUYJEr#=RhRoS9d!;S%e^u2)c4-@uCT{D^ttxA_nyq~ z#hLMZd6wBOew3HV7wA9tdvn=SDn3{n{nM=wj z{nU0&xI2#ZT6U5>mRPstecb(eur|WOi!Fm-vnVfkBJV#qM1Liv`Rlqqf^K zTS8l1Fi#NYz*n%@t&DZ7@wPBofVePW0GwtY$~qCc0(BdMJXt4c z_(?hl;jwfi6~0RS&tLs^H=CWNGAm{}#khKpK(pPHUE4{R!#Ylz27Opq*wgE)vUc<3;?>SI z$FIdkiWOh!fTW;+hJp6SBhVx3)2@9d%L+>2md!TsUPU0k0W5G%C3qtzE34Hmo(i)8 z1Sxi-+Ac)BL*dW$xC($~?1KTIqtQTRjEg8Y3Hr%j5uI1*n%LV46s2sZyey`>NNI0< z9&qHCEi+U4=Kdxh%J&}1`u>*%L-7IAR0hFXM$DW!ee-n;hK^q)XQIeTGV^nXIkXKY zsH$qg#hv526P22bl$40J_QMO%R9MYn zj1lm8?g5?*4`B{UCz?t?V0z|ZnO7%$_BThni$(HF)9xjXm+z+*tVzs+95<;%IJn6V7$c03wiEjXk?@ZG%Z^# zfK>?B1Eh|Ui54erl@Jf-<|ozp`L}@_U%?_9N;6oPG@SE@3#loS=2?k_jpRe6`&Zn6$S^skV=fa~v`{m2G{pKpRQxxsZnCH!<1euMX7D{7<++^Ph zy6cyK=S!k*JTAq-qY_=|zpObzj-FO|{g?mz^%j4*zzhSkfyP}2^kK>Ljd59lG6WLs zMYGDNKcP-wQWB|w0;<*fE_l!fR4TZ=L3!&ySrw$nbjv!^#k<`BQH-Q&)_q$*)KE=b z-Hf#B?E7AVMFHznG+=mzf~Co_;S{)Y0`gC+twD6BU?;z?Oid6^O4D`U!uWc zzqFuL?_L3qSX?k?TA9L^ciEgE$l8Y~6&zZV@H*0=vNQ%nCCeb7n8xe8l?U?f?Qdw% z07;b>fKKorG$0qVtCAu?=GuJhF)}(zk`6dsoU~Ey7zV^fGj2%8 zy{hQ5Gmo=P8}BBRMi}Wn#(!)a%F=y%E}K^T8SzZ5K&)s!dcr)dk|GH=l=CLOJ_K$Wry2yda`;$CjKG_W_vnwgn>xFW9);Qwzb8>9p#mX;H3S^=^C z_P`1VODOz|ajApjfsjBDkm|w()fn@E1JM7xg~dycI|Zd-RSBv?*a|s;N#d)f zF~FO@2l`KQ+io=vB1#w@VTNMix~|Fjpmg%Lo$jmHS+TE!m13v7P$izswb?@K8}LgRNc`|%gf6m z?<@E4^xo>}QHn(}iWf-ZN{8?{GR2}KTi(!!IN}2NtEbG^N6J-|1KUSV-)YilF%^Ntrw3PSxdU$P0`uy!7GfMPR=&eoq#>`r|g&Y>4xfY4=2l0vCwH;4xh zYa}Z?otFcxOZ`@|0~1B8!Y`g(?tVUO_k!QKB5C$SSM#gG_+m3>Rn@BWa^jEGS{tpk zC<)b#vfWs>vJT(+p8~@AiPyU)jzfH-Opcq2eSbL%+FrwIEt;yAU}XvPt!%~Vh=D+ez)|)yoJ?RstqDkff0rU( zn*eHRTB4@$$;npTjNRhRNf3xX(A$0>G9!Eo&t(my{naV65@A33zXxNsrg~qv@&nOH z9Nf&Ld$?R7bJd{Q^x>)Ml=V`znIvw-45)rI*?Mv9hamF<uBu zArlcN8;7(K~Q**!Gq z=YnM8tt&%bc}ed3)3YxO^DFxiGJ^B@I(_?{l<&Gil&zPzE$g`o{OZRZTi!9bN|0xx zhL*3k>J{%0j9EdmU;(r7JcRb^dgGtg$VrryKdsPz4`&oK?dB;HqkkU9Z2!LAusqRdvJF(3%O4USOk=c$zk3E%7I9*IWa3}gq&WVY+1 zFP)NfOtd^y(J5K6p8oET!oa}5CN?hMcHk2zkpUzB^mW4ml(29bVLv0BanUM%5)x|I zdC3An@I!p7n8MoHDO(oM7$9vva(d!kh%GM*OCOFbx$Dm;Xe%@EzjiK-miSv1+1BNi zbXcD_$6m;)#+EmgJYiIWM@&ny7pRZFaI@=f0P3dctOO2`-za5->(PWefnV(P(qV3S zGREiZ&XWccpasp7dzTxw$vjosam{q^O(On@i*&s<^-sm5yLGLI9!9no@BxgMvUj<) z`ns^k$j!53(wEl?mOm+KX1@14DRA5GBVL+FsHwr<+vM1jPpA{Kz&=cpb$aq>vMGKf zbl~)kopRlA{JHN;#rxMTjSNq0*^ra# z)0HpGa|J4+W41WG+S==}Bq5yp=V`^v)DQjQHD&UyKL{@Q6k z;vlWg``T4@oQY^Ys9;79?;bSJqA=o}tqsH=moFf6P?bqOeNm48-rE*4$%23bXUlQJ zsms$tc*tv^yKmRG1#jY4UXV<)=kV@z!iTnpDfDMty`7KBr~}-~$MXgMq~4m*7p&#(~5v`{_5RB~tgBtszeOixxUb$~f4Ut3Fk>BmPZ z5gAvQN|YxyxnM19P?R*S1^es`U3`4JRW;$st8#xFmR}ZBi@&-~v5uqmv-hjtCAJzx zKX8OZClCU}w}!i_Xhe7R`kbyre)9gS72JD`fg736{U*zwLh zt_9jt%X}5^LI27bByH_+`@ABeL+r5sg}BYvcOx$xt|z@Hh-!6f!;zV=Vjc{CUOYDT zVKla0pR!EezdIP>eCU)?&_MN3+0Rs)C&mC_BAj)4gR-WdN3p)M@d0Eh%6Rud;R-!# zdEM~HScC?wpiqa{^F4VgFAg#R=8N%xjIcN^8-uso_xD9d?3XY6h5>B%x#ZJ#8x z{u{TiSC`Ga8^C=M_fGP^pOXJMG{3!|_;BH7p-GUZKp7bui*b_MGdQTI1%v>&fz*K= zXpk*Zukt!c7^TGQ?l73h8PTymEJ51B#J&CCgSkeD(>I2*p`LdFxf3qGPNilFT~w;<)FbF;Y*LK zf)%n1eOC2RjwTLoo!RJjrzp#DIpq-5@?o#ewR0ChzS=FRjEhNF1$k%tI zTLV|Lkbb7$!k16AB1QJPUH+<_@@T^;=GYD-?_z66FB6OSY;#2=a7K%LCLk64ygM&n zP~}g4#@~EPasMVMo5(T{Ul%PSxkdYYWbnSx+&AC1+jU2_Z4X`EfVevjXoT*T^m&4F zWe(`*3rCPSt`$4zC8L%(6=fvogfL}8?zT#aLL0>idDrijtaNvGH-kR2{z`HJKW4gl zf9HD(OJfEi3*F+n?JKP$!%ahT6c&_+eEEq_Y8~)bTr1wYOsQ8P+-}8-cBCb}Z_?Mq zZ{=<3dD!VTN%x{=7wt`#25`EN-Cw;6>d~a%k5W;{scd!4V}15yD^JsFl4{OTZ8aNB zU*9p%jPw!xo7er*>PkbP-X4Vbz^N@49KbP-gq11d`tAYw%@07IvDH>jr^5PSZXRem z^Z>*)kL6NA-@g}KW;12nND4r`e`IttIxd@BGA{wBk+NlP!&?q4S3bkwB(ICu$#?4kYr2as+!l@H7cxaVI;lXQgbumnU^Il#W^duo0V5( zW&U^S-~zle)(FTw8Gvg4=2j<@b$3ZjITPCo%B@n>?Cq-h_rUqgHwBOs<`pcc^6tC+ z7U}q^JSuVviqa?)NNdg?}ffFVY6PITm@1ll^3UQR>O9jz@iFhNH@Jh7qQ_Jroa7H1&SiODrpXe-U<>` zp2zsO?9Q#N6&yUwPn%!V>DvTGd3|TAK3Ur_-*S$Vct@5VIEKT@mwb)p%?R`H^|Hz6 zCOenDDV+K37#P_;&%;%|XFQw|j~_Vqu9{<)ue>6WR!Xy4{$#=Waj;67I^$uR@fm(n z@uD!-$N%jWGT!4hB#rmazzhVCnLU*kodA`Vd(`k9hEBd^>ts*&`=P6@7aF?^0b#o*NIbZ}W~o zn%1&~qDqJu^$}Ji-H#$%JP{_)g)%4x-lIGVN%%}#-5p#LLXMheHJS#wF>0TuYV#O< zhkIFJ%*-GD_mOyqNWwV9{C9ih7ayI%_g$zDrUj$axs6ni0zyv5385N;jIanxIjvqb z!7jtV9o0E+5T^KCQbmQKP~p6I4fs76ffl7Mca{>E#C0!NmVC5~hBt){uo=%n;*{UW z+rfSw(URVeI!J%EIpjK`%Cajo!p_Dk(FxTc5xlX=N6mXuTVl$df^q7)6tlmydAh;L z{G~}a6%vW}^pDx)zqK&wa&wXPhT(#O#?-&Q!!H-B^613G(HWKj8mxpQK0X&cCiQhB zs?YnnoM72>M{~xw*20aYHZfxsxF%+_l)0E%kVi$71)DQ`Uz{u?$Zkz_d~R`-z<#)dXJ2^ST+_B)Tn*QOZ|ME`1#a+Va=yZnRSI7eX`gY)t${zWCoZjSi#m}CpB(0Y5 zfMSy@u0?8TF4>cn%f%<9Yq_!bUvy}>TbMfGE(I^zrKM}72xxt1xSR;QZ&%ETxSUKG1 z4%9U0CL1pY9hm+P)AJrAB%ge4W&K-|0F@9<@=+qy@~bn+haOt5MYf%OAV^7{uNUR_ z@4j$`d=@LAP2Dc6keOIygj{nj(s%IS6075L(0+tf&J$4RpSiffR=yYbmyTI!s zTb2b06))>0ytf0}!|RSH#x0q-V!5SG5# z40EJt`}?R^IDkrC;VM4il{_JfWTR&z0kNu33Ef?mrSkmulW4*9Mo=3YAsXixyl(Yz zf*BCDP5wp_2PHtT4~JIz%wN z49Lz@*AsQICWBtCski~{DtUJT83ZMLO9m(1r{`@stQ}+qA_8=LXL0hPL=yk|sI`*v z3GcQFPW<$@uOsF!e34Z&w7Q?7AY0%0T#aYe{81mKyGK7-SlDyX{3~|27juQe9n5(h zHbz};4cb;Slz#K9EB=C;6hc=gx~(Z!`XB4uyiH=!0>7n;a4d>R>9o&l$b`JFtHITs z^*OMbb;iW}ZJ)C#=tL64}rj}Qo4FBY??o2tiSu)8SBL68k z@ML=Yb5NX7*bJPEXJ+YOQU`96@~mEmN3zWVP4zQ^hwY7bzbp-JMMzxOTUwmX%b;Jh z0C3BmDr!;6wm)IYVZ2WP-F`~Ft+o>XIiN>jqO@(Fj;F^~edMt=>YU1uOSH#0;FsI2 z;~FXC_Q_#b{q@d8u6 zo%9i{U)OJ*&@V>a)ZnU{D2ZJA+C;SQ3)R>~=+Kvi8OHEl&l`M*a%)XGs^vYrqX~xB z(=~x=6#^q$u<#U%`&5EmUFtmaEdxp5@v+$T6Rq?^k8H z?rh@{E-i^6%YTo15sP#z2BR}BnY9@5zs}&a^PBSRQx_{fjDLMa@Dt2f;U2UVTv3VV zB!VM2W$UTk*Rje^TIr8;?Aj2xF{dwXfNX4QgZyaUOW+thPmV(b*FE(NM-Y&o7!Fd| zK0K5h`FMloREA$Zldu7t)Z_Dum%1+oKwo44F*A@@oG>k)+B#l31pt0lj&3G4_%oYX>lv@dV*J< z!HyKd+08wzm`5AYM;FF69IZ*&F>Hoi7<78hdQ5HfO6~G_VL__7`-SM+xxjoJurQNz zPXz{zlP8y4apfeGNs6n#PaJSe!;+Dnk?^Zz$g`h|zNNso`ZiCO!4ARM?r|v|Emw6*CP>P0QfsjO z!l@1Hei5Ziz zcxb@L$e^(8l#J0k<~M`ZM#*?C6r5>)3L~^rS1CNM6yxAJNy(6U!t)bmWQ-8jnwNEV zPIXh3dc0nHxE?!SjxD%Y=ig&2yqIvAUOTGra;%0V`C$RgBW8Lcf=9IJ0~TKF5=`}Q z$sg;r)Ed&C+)wH~kvN2egd+meQaBk9kmnfAap%4DH|z5cC#!H9tuUoSn^3?M+8_T^ zm=TALp8mO&mDRWI>Vk~iTr4U#St%*32a~wG;&E2@$59=7dPL#c7M8*y+7PFFMplD& zcz0=)@gLRC#H+$m%$u_r=G84?TKCSSTyxQjU>N*axW$^{t~_`UZjSm&%rL1dq_gtZ zWHv|nMhxEI-!-(lI4*Ncs6#T~HZ+jZNF^nI6)OQnw`y)`cb?G??Da7HCH|mQE!Bmt z$CC)t)IwxyA0+TZ!u^v%;X3lNh>uc~2kxeAGU(j=;0t8=bA?PhX+LR%swo%8}F8mQ|PbWMld{Z2x@z~7K?#?XXA-@Y_&b< zS7vd2$(;b|JFm%wb0#EMgo>AfhkVfsJrhEL7Th)LO{m>(#V77(L$*X@(&436sccEG z%pcFF#vV}^aU!_qoMcN0Qs3y?A`_C<-A;6DN z;-+t(IQ{I5nMwHnF?N<=ac$YU4w3-DNpOeY?k))s5}Xj+f)m``A-D#I!Y#PFySuv< zUbyRB=|0`J&))mq-TzkAQx8>ZG3S^=zgIj|;%?03i1JmlSmQMi=U{qY3KY;inunQkb$|VKF;sd08mQ!PV{EGMA{mXqDPbb!tnX zT}k}ib*dB9BRDnnCCCQ7Vd2l$UPssRVw)x}swrd4X)LtJ!sm4gk(Vn4( zxNAII*x@SK*!axoPDzGSghs=dXrDQ1aWD5uLl~iB8 zkX^!Ea!D>$hpbJ|SJVQMmWcsJfM>94Z9h{U_Y=iV*%~2i%yda+Sjnrvc-Z;E|Fx@a z^0aBpnlz{qOaN??U@i1j!}Ho1QLr=~-X`8SL!;^32SaJ=vb5eAwJd?sr?X?IWW*|m z%jW{qyjlU+Bwh@z?GjbZPJT+RjypS%TqfI`tgpH_MoWz zrDRN5n!5#xp2mkybBQ4%`vx&cvhu}+<+phl+>V;p#KzGjl}Tb|szf_aW~zi&!5C>S z{P1hy)aEFAHd|Je3k}D5qflFlF{qEvPgoNg4*u`qLR&x86Vkz5#<1_My1g=6Tw{6X zd{#<8n0(1T8i2|FO3WrQ!S60c=2Ov9NS&mm2+)EpaAex^$Vgr|EX22N*55$Ig^H2q zZq0;6TDd780NP@Ct6oebgMRJsU(_XJfdf6<6Qv&JFVYHu?$Dg*mE|fF!KnFf&1yP; z#wMlZEDWE(`M~20zey;+p}QITu{)$!!6}-7VFWApCn|xwH&3Q(H$9ex*a z)r->*gV~J!VwQ--uW924eW`o=XjSgms{2C0{nwZ>Wxgk*G($jKKB8qz%=oa>?VgAE zXh(P@;)(2o$*hnw<^spHT6xfPtkQCR<|RIofLJ|}C$Vr}QCG*p{4A|f1W%6fYR!69 zM!&P0qr=6FPH6p;>xL3~5%j+ye0Ogi@Hak_(;a&v6n#6YNE9 zcw_AbvngN|K0*bbTj<-)iDPW2!KMK58{b>V`8#V0`_-c`Xdw zK1`S)@n5Z@KLqB&F@Sj^Fi4mBt7uoADjl}@?jV9z=Wtv?>cO@%k!#-;R)X4RBaALq zufzt2Q>@r~gZxnP>HTo*q9L*1`Nl@`2uP8>p`}i> zI|mSwB-;_n$!BW0P8;RXU4#Xf#v|4teLtKMLr4sPWc{l&_W#{t#s<*V#h|pkKls7_ z3)&RxTItg%pooM9(JIpP7v2Or!T>lweSYr zxDAUsE$#!>yhmBe{EHL;q4jInV~kDRm8G|z@Lj`YUFVJ_1#!vD&}MgW=soJGd%jea zOSmzKEoB;@M%8_6WId(&Z~i-P5yql;f|(P8|H6e(OKIy-9P1`^9-2~08%Rq3rTVGa zC1@~qejh%AtYn}X?gPA8GMahMInkTF(dE4Zk3;lz!7&Th35Hz@hi{yI6=dO%jHOgU z4G*yw7EJ!1oVReVC|4UU5N#>^Y9wqL6wPNPu3I3y?!Tf<^Q3<;wxe_yn9mstckLL; zGY*r+`)x&v;C}l1B*{B8&tXRSEV^rVkD2z5_yOdF zYoa^~+#}8=*XN6*8%AtbjwGlF5yc=vxxvx!s!Qcyej1iay(nACqoM7=_lKc3PQ4j{ zc~o9(I5mIk1VjoC0{MX@Rto~LKix11Y!Y671_RJrbu;v{!YD*my$|`)o-_c@8=Rxm z)S{cA=vd^$gN7g8wPOC6CR_yH^+Ke-ERE-?T5@wtzAb$-UDN5!TD2i$>s;VR4_Pqg z`=aQw8I6awbzvMIee-=JWkK}qo-YVhy){~SzVNDiPwca_Wayeyl($D+2Y4sQ-~KjW z_ogW%CV z2cn(Q>Tk)7c^`t3&bjKB%$AFlq_rOt2g~QoXqLXu6giqtOpjr=pk!x5b;|fq`Y2S_ zJcgbATCVO4MpO&(Pv^`d{uT!;*07Umm`{9!Oen@93V(q zyg_tZfZ^?S9f|RgOyG3bykat3JkQ4QI38kvxd{L)61h@os(0WY66bn z9~jQNJxKV7kg;U}N0~n@wLhgb2$O)jA-ub=KK(=J0KA0bgL3bD-Ks7{r$}Z%0Gpa_gL|&hjC`u*^$BBu$XpdF=~( z3!S=|i3bS~u7yXodQ{P>0acBY6yEUSj`MP6q{+c90NHQ#wDV zg-kEY$f$(w+a<>ChQ^_$!!s;_gI}Q{Lc%4*xd7S4J}ppxU|+a+7$$P>@y7TZBDX+I zJ_;+9VIBovsK|1!A>JYsJ*jCi;5*eQl0S(1!cM_r*!>mV&h}<{>(ejGuQJ-JHE~tH zctV|H&a1UZ!^eij5J2i*kaRA#s|AtvCmXBY5%&IPr11Z|ZhJ2cc&fg47dEzk{x=b( z3XA2hGUz{5VTEB*2s<#1Cj4AJ(xZ|;O@+iosxn=X`zzsIU_L{4q4UVmC~X*^6BK67 zD!~QYT_-Y*=e}52-c(m82ig(CmKJcp8%t^ib1KH;Kpyp{0zWH9uH6~;_8+nYe{cc* z{n9JM3*xFD^zSD3|174HD8cVauJ{Tb_ookzHxWjMAbOGb=(-==r8`HpBB+!z8#6|T zf+fBZW*aLU!DFnC37jDr4H_Jx9>Yy+ zQq3Pj2@Y>l2;Y?e9*6%&zDv7zZ6_M%UzQ#IunccV0VkH2*^Kf}?ol8LlqP~H*#1-e zgS2_AJC*X5MF7-UMLd9q1;v|MTF)w`0i`%Wl&^I2;3I}py8s8rIuo^z%E!6BPj%mS zaoFt!e29pNQ<^DrUOwVtR3DI6!Bj?SrYx<8IaIm z@4wg|DPCF%(^u+qbp$S2F@8flk3OZX6-#U%H9&bIFF>MQw-Q4HVVST)cTH5@3;S*J zE*Hp4o{?8a2aZzr$MmH2kF#UrN@&Li7KG>O#wNkH6gGeHEr0ypZ&YY0#~js0`u}Ky z0Tu!ZDKwxKb_1w*013MQK%)l8!R(+d2-Hga%Sx}sd%?Sy#v1cx;a`N&d9{dy%DMNu z3XjW>5aTna@&~N9Ke=(%kW`e&^M*GvicIC>)aEnT5#(s?R{yX z|IE|i+Uq|r*gw7lJm?2HrM_P{|8(5Y24S~H(;`-Zp^hGm-_)VLLXJTIWOM?+Yctb@ zgZz^ufr){CXn-&quKe!U-2lXhxMt1)EY&)owiujPZB6<=&(5k0V@#Rei2;S9^$5j3X#kd>1|1&bjic`{ve z?)}1e6=^6sFa=bj$o|_?TibCELB_C5%U?Jx|2A9yaY67C2s;Je$?|divrh&M!5d)Y z5MM7lrpX2m4N0YOa{haF5ct4$5vq1|43N%S>0!aHdY}j}%~I&1_>1<0%+2R>2c`}) zi#*WFj2V8G@OHQ}*p52TB!~(Khi-XZs9fGNK6sTkQ?^hA)ax)uzx~k~{M+65Ul-mY zza#vBncHoPf7(H^cXCQf{(z{Fsw#kD=KtN~wmXr7h=-3~?C^h^yHGTtbCWAtoQoHo z>q~kxYqQX2_0X{wC7sJ3;pIvO7N+Nk72{{D> z$SEra0P+QR_q(&D#DY&Zn<%YR7XSJy3m`9y$9>6dD4IQs_op>$;XN+hTEq z>UYSe{Bh(I7FTE(d_&+_sgRhY%*&6d>-!KsoI8K;Q%Z_~d*ZwY zE1}D95|-nxc@p2B?b!c%#{aZ(-e|wedC>3ttB}2yx`}noKNly_!@Xf53XsxDF^5U5 ztU~lYla?&sUHqVTMEWN8n<$bAe@*EfT47D$sJeJ+4u{wrBh&@#&>@ouM3!vIM+8cgvDuR#0ZQOexlrU9!PPz z{LeF=6lt2*;%Zb2oBy`8{-$ymT))8idk`P1vDVdS`jX`NU4vK*%3%O06DqKXS zG5D=%+130aGeiOW5laYakP#;UUFXQ)9^3YG)>(+mV9fYozox$r!S><34Na9XK8Jez zMq(>NSIZVuo!Ol<3r&Ifc#p2rSgL_%Pt_t8E*+?O1G-zIezp5+e)~kPMLR=U!F@1o zrE-jJA4_>n&lOY+F#T(~@p~Y$pzt^vr0?=7y^hx9D z@{gnCShPtc(Zh#vAF;aUC!qy3&1po&1V8Q3x|g`PDeiZ3!q8C|Kpc|++ny7ZdrF|~ z+IG%8dG2z3cwsvQVHP4B9`2Mmd&@w_WoSNTJ?2O&@6gVFpq*NGo^>#WuKRyIj=9l{rl*+ipP1NgIYf+ySa(1X(bJd3zZDyEpeqOJvFtk zt}fYc;BIj_MJzyG(gQNEy8wsJ1|J#nJv=v;(v((Jggd9E1`EKSemzQGj*N}P9Fgn! zy>^6?(a_M(e?*tEZo6#H_gX)kl84@#YiU8Hdh9bL2)a`eId5r6@!k(b&YT~6dWO4t z+>?y(n(}+tpR->8ij;m`uoNaG9UI-x-Fgv%KZrg0x`BpKlX`*Uxzqrfco{Z=^yjAm zHtw@N?;boLwk!8|yskSKxwkqMQ`T!2KkhiHdf3p`Y6tZ2ylE5|yX+?!m7hP_iC=n~ z1V-MixDvNdxh@&aoD=TmsTN%P&h#h9!7 z7&+2pW8h5gAf(aLVLZE$szJdl7)-9x^ai$<^~c$sZ88!LRo?PhO?& z9YKn41FUxrM*Fg9RnW~mJG&Z$WO$Hvz7bWrzHF1 zSy9o@2u%Yd46i7&Qq$5R0PL751ehzTr(mqYQw4%^Zfb-+UpzJ=!CyzFr#EYzTu~{- zutCFs{-KA{{+{@0fY1Ya>+_=?_gzB9ehcm#q&heEmF&YQZXo`dd+T#pZ*#Wk>^pnZ zh8F_1F2C;Aq`*y0=m3R~y-V@78+(V^+s0_f^%yfduW?x$&}p;+rY6_Z&b2!!u(f@! zx81$)J*~saJE~2&aL9mRym}&Fq^Y{zEITI;H}jwq4y6q<{uvt3XCw2tU*sdXcE`ei z4-xLx+AVxu$2i(Hhdz7c#tMjK?^qM`l~_pP1`rg|E)m-n4<(N`Ta7A zuYe%;6OPxt&^^A5;2o4IOwVRT2BDDg4#zE_4oh7x{H z5!V5#d*341?nTj>zqLa@00XH#!0Isq0hD{g_)9HdP;{6M+RQVjynH;ku&C$)dwZPG zmGkBL6$&~lZ}p;{3XsF+=0@B$2b z_u=askCA#K=fDI=J5?yJlE7RPfN{8PE^KJP!`8G*-E>#%x!RBQjh5lnT2`kE{c0n0 ze?M57<@qdF2Mj$z_KNQ!Nqwz$G;25JjkH+(MwS~4)ZL3Bg+IXtpLsDz(9@TG>pqer zpjWyJITs1C3C)*1_9+nTj9jKcKGpUx?lRUJwdN7$3qLWNx}zOGP2c1P#Z@1vxeZai4Dp4=@v*!e)5qawUtVF^ItXB+C#^IF6H3H*RM-k$oP8q zOe@%2GxMNJA(s0iwd71k1YhSAhvC1AtCwknIQ$9H0i* z4zT&k0f1+qUPYh}3?@1>Ajo++SM|A`y5KiHRRaLU4yV8;~W}hi0@gc$0Z%0 zd*5ec2uO5sr@HLZ=q&*P2Huvfk2Akkfm!DsC!SBzmv#a%mw^gZ@P}Wf9#u8MyS-Iy z`rUxfZ~~N4pM?drhvVm`(shiKlx2sG2Eem7mF?IDR0KJ%rU1pfL3cI0T{V+$>!=vq z@8F9b_VGIroQD$RJ+70Z#&P4dt@ANhTO$_po+oT_9P!4c=JeTTr*A#cItd)p_q|j( zRv<^$M1;d|(*uzDOq*8g2+a5AfMXE=m|4Cx7XXe-&(Cw<49J>58Kv$yq&g0aassuY z^jRfWE*|1*BKWNfnAeUCcgZQiDZa_-Vg#BRYowGYILA{wX6z&%wk-w-fn2)``iQwq z$7T%sjtV+I-&=*7qA;GtjCkokMxuYp`Tfra&=P=H!uys$qxoKBRYuyGbcRtqnAq`9 zB#ea~Q9Q+tOa_mqO&S~66LscoTB&A;20A^R(RQjx07>O7 zE#qoZ2xP#D`(^*G&{WNczs;7#Le{G(5*{PiSJWwMSJRT^cHoF%SC|U_(4Loh;d0?K zGweFb1Bf#lg$AZ&%~N9ZIg+vjt`EkcIG{J|%Fgdg@EVoEmW{gb^kfMl(8X_}5?zmJ zDhcR>o?g(^<_l07>0HFn;K;>=2s+8> zR2|tynW0KFahLgHU(bX=J(0mbwF6915lU8vkC_$$Hy^PWRuttfGt)>;e^j=hVwgE;lEz8 zjpVlGNM$n$Ux#M+>2tH*8|!h~Fv( z+(+ECH#@+fX)mC@SOk(jW`~{JV1F$2e3aw~(lP=YLnLf$Dypdz-Z9$d=H#eCiktm? zd7c3nC~2v2%|ZoQY-#`g$tf^sh)deIw!hX!Vu)fl=+d>?v*6F5^l62+oz? z`CvVh<`#$Cnakuv%ovb`VhXLxPnUF)U25arH1HImR&sQmD(pmmDyq5a=X+XlL+gwy za=V%h6~JjR=7SE~7rNUdzab_tul+J2`mcP?|7&9kLz5BelS1Kp`+pd6-JMO~l&p8= ziK)vrE&m2K3&k&bl7oCDBeWbpp8;8l%8@Np|V|Mp~Tzpv<=~Jqe(9zx_JyAmCtdvo6FNIW(&vfaxC3KG~ zLopY{0U$hc&C}@tRcjHJbav?-^<;WJU{PH<^r_cjp+1<9rQTFcSeq?$SDu2kEjrM^ z*=9^v0y!FPTpsn$FZs{&)$;}Kw}Mn`qeBARH54w{xF73`LUYk8Nef{f_Kwhytvq7t z40Y9?BRM4n zcW~A{Fj=+*kC>CgC6BYDS~!Dv)NKJ*>EV8 zLGpAqprkAGmeIRNYThN)PftmgbP9 zGs%xcz&88~FRgZY|9sm%j29KDRS^^-bh8GdmmWATT673;qfY?{Ixig#=ez+8?@8;Q zFmNbqfTk74U4sK87*O#T#b8;4Te6#!_D`=%Pk*t|1SF4Acz?9k15xY6(ek2GknudD zZq2CUw4yTZ2gC1+fFfCQ$R&zQ+gl=WTEpDHdS7jLLgU*rVc)<2D(TlpTMMn8+ImLL z&M%>U4B4!<+{PW8j`>!G-q4lQbp@9e=XalBYH!v$K0^TzEqTUU3Hbcrx;Aak&e%G$ z{jj>rxYf1P#Ajb9m^)lZ*uc8;WG+LUZO?IxRXx;BDpxyBrSrLxeW{F?gM;$s+$w6n z`;2NH4cxO>-R<@Pa#VGzN)7Ql*qFic7K7^Y#2f!t`ZK1#2<_Zd^F*#+>?2WGna zp)b;yD6D{{jW^BpB5EYQfHw?{VJs)R`lXKm4V~|AYXrIhz|mK3%c%Q}0(^5Y`te&2 zuaSH)eObN*;|PxhqoHRDB3H^ThGhdb(VmkdWma6I&sz#^@1to8zA8TIJQNAo85$@l z&~TWl?wh3%ooXQbhBMoQOy~NeS|63?ZDtY2>uFe}c$pe^It(4MEy%;0Y?o+X!;ai| znx3ex+;jJb7uiuT3T{XdXVmb>te_61dB1tGqmSh2#sa z1P8QEuys=w$w0pUm{0tW;+v`l;)Rp`rOu@glu*o^Hn6nVv++x(k$HKuL2kAyr&p&Z z4=i{(K{7iT>bk56b7*SAbwBfa%hWD`@pWr}zT$V=E5<}Tt)tBc1o~I2{=Fme;gdCA zS}Yh*Y|$u_NnIw@r0(Z+=Fa;$rGym2%-0F+w{s+J*7N$FC{rBY)|4icEEPg1I!4uR zB{W#p=*7KjhAJFdBsDD3#tRlhlYYEED!~KuTQsH?we`^fGMS>j7(}0W0E+l{PKZs- zm4ZK#bqN!lQbg;!gz8kKJ%4$0Y;_h&A=(HX5kRb@)xd zFvp3>8tGq@sQ#T}{?7})kHR!>RX??feS>Jn6qZSM@jo7ilovL=R#a4+t3J|`im(k4 zIpRO(Ewaadfsa5&cx+3A;lEbAK^7CIq_gO0q`v%yST%Iw>`M$*+{Qu%6zq!`_|f3H zjgi}9PCaeKoW`;n@Sc)aAgiaxC_ng^K#k@>w}e910lfTcCO0AfviNE1WhfeH4Xw*& zUfnKB{AM|wZ9T@UmRV3j_q4;v4!)+T3DH5Yg0412S*qQN2Dha5F!kDk`1ZF`&v?Dl zok8pQF)1l$*UI)^c00OK;kH4(Kf@El%!BjFTn|j1Gi4o>Ru{JJS2}u9a!exjo7^5* zO2TXl9f>_0f_l9m&)0PUfzBJ6wq?a*w@GI>INRPQ$&aPrxtuG?LoN?NxHn>65XBN_ zeZd-tO|ko|<9?E04}*>8X@;W!E}i&EB2{B4vURT(!@W{kz(h(R%l!sD-F-8*P#8qQ zG@12A4coTbA6!YP{un`%VXOF^MuK1|VVJ}k*7Klscvg0u(~bW?t#S}8ifO@w5HI$V zkY&hG^O8uH+wP^HGds`o_FQRomdC5cunTMrW&}^S4OFMSoV=4Y5jGZwq5~q2^U@}k~*9*dSC><>wDMZNT@NwzfLx{<#|2G4049ZlxjrD)bVTsPJ0Ds{-l(t*B4Ab~Yg&6>J(v#CXm!qm|9z z1IRd^b_1@L!v|>+x%bX`%NW=YVVLZlIg~w5*p=dB+UpM25C&f_TYQgr+EJ9k{oc@& z`>ij3b|bnQ%zapu_%U-oXA#-E>M+mklCKUt$(p4pTKD{waqNvWwsAEVu6jcWq71P$ z#Z0isMuYlEdqaNR6^>E+E(kLxVPiS(3}>81bL=JKd;5-bdi{c)?bJ2=i{ONZC7ULL zRNLuzHah|>m2<8k0mCG8)k>={j0>5b7n>tr0-crXmk%gcu35HM*+vG8H59t{llIpe za*M*TzNTu|%=J*dgaN|s+LpP1!?fYD3hC5p0?+#TTNlORMBizS$9AF9A>nv@o6c)( z9oJ#IP~(rF`zx4)#Vs4?BYty~8X>OkZiO)Eej=Y_t!FH-y8ouPJ>1yWN2G1{Q=uLs z%A9Dr_Y!f7JlvJZ4U9>p2Bz<8Bs3S^Ez56g&K& zYt3Wvorc!t*zwM;R#-r##mC3z8|nM|ZbcVB0?yw`RU`EgGx*>kYVQWJ&da9IwRAe_ zEWYyo!-o_6#$2K)2?+_5hT&rM+Rf{00e^%yGMzA}E1T;HZWEt-^#)=>fCQsa-L)t2 zN1)&sGj@wNH8r(%wWvY1{gSxZSF3rcHDbVTCzTJXzFysGxw>|(gwHcTd8g%D5xIZ> z!>-Fa;Ta+sK$cE&%nDs6z5p)H<8F`Y9TnUT2D=U)++6ozMXmXwSxM8d>Eymiq>9pR z`9#B==)1#3-@3ao(IX;X^>vbZW4?+j%+Aem&gz=Xjt`wsO*G}~Yig6PJw$u1hy{A# zRE@;!8LqGoUt@sxk2Ds&Mr!(k9|(UPvbGuTX~^%5E9ZyPJ#I0`w{A|+DBD=Si?@sm zRQh}*1pPU`WYy@v9F%IMZvrL-sES1C@8^N(LTP%F8T2l}5@Hibm%HX-f%rMe>Vxv!2NV&2B&yATT6E z#KpyU^%vUug*mInllu5m7>YS74b|UvQ5j=Gx5vINW@U)(!Sn7=Bvv_U+gRXRrqr#O zDIoayY}Bvkl&MN^m)0oMc2y{jlR5pVSKam|-{~QLcC&5th0rke!>x5~;5=5e?TBsca-= zI*?%LV(0!uxKi%1kkIvOeBRsuA%301YQS})*ig+FdLC`mB>{FaVUC5oTy|P5B(2#L ze#?M%{d!QreKv*FiS=nGvm}ADb*hG})CB(l@r%YGhRxt_R=~BI@NNJg%Y`g3hc!Q8 z{WDW5DA)oh+5-ffjsa@uEI=52lfu&uTmHR;MalH&K)WQ#R~8_l2zShZcEI<>+fqWJ z0Aus0ZCo4`xo+w4zOc-(^uW7F*c%axUNlc8zwQ~;(G|#%>QPGb!U#Q-Apkr>l#(Ws z*Gis^uP7*7S&o}|1j!6-mw;p_Gf;*DYCyRd^A=oP$p9IET{MDbzdb|P;QHtT#l~yt z3wmc!70tpfGw)#jK(gxxAVr*B?tRuJk@m2t&qTI=&Pb*tqqO=(hiRIY@pE-|Z(t;k z!K`5y;jb#D@n0cr^|Wkn8kCTSBj{`mpFY~n-~71iC`S)?e}Wir={{`3z)huM;J^Jis-0;NhMbT4iC-?9 zyY!%ARRh_D0PoGdyI_E!TUJuw-&jCyJ>G)JQ06AP!Sv_^cH`O>f6-Al-|79Q-kloVQWO>8VbTkP6P@3&iw-eJSMwB8$@L`7R`xE@P?A}ck+<(W>JD6l0&-Obr9G~{ZbO}2UoSd4{Um_RnJG7JK z>b0x725Nic3d^H^U*iW9ZAV+v?H?Yk;27S?)pRms-%k$(p=3%cZ%8F`Pcf>6*|{Oc z6L#w);ye#ddM^sH+r-U_W_eTY{ajhy_ki@2tK6SH);-Tgv$$A!>Ibt~e#pfTm95d$ zh{bzbV(Te=S^b6pwq}_8BghkXj9$NFl%bX&1%d!aFg@upXL8x~9tLe74PN?IE=-y( z(x&GJj&1|8YjGLpO|6S-*a`nsj4-XrI7Yabtat!KUmy94=b`3KY=#bM%fgz&NbyQ5 z+z)jq$B6oh`|lnR3SA-&leZQ?nmZKWNpx~Z z5x~jvfZvkK)-h&4Q5hLYVZc)YVLA_`>>_C;H9F!`>oXS){>-x%A|p57T8rahpTb6m z=*7WouO%qZb#RcD`TIno1PS4dwz#!w>h;mRzp)Ceeqv$@%Qs=|*7}^yy3KGF(TUjD zdqC{Yx7^dS+D2@p5R`&?&L`ui5hA1>Y`rPVqCxZcY5{Nzk(*Eumg{Y)0EtbXP5@ew zZms`xwP=Y9%=qC0Vr^l8g$2{J=qFmW-(=L1U6B2hGDWlGt&%Sj%U&V}HKusp9wv@? zRS9k@XK+)=s232uLwu);C4R=M9c6nZPaYXd@ZhMBIPL1L3KrX~YJCwTBqrND4 zZ82}W3u!V$Po1SD&DXdOLJ#(U*wz>caByHEm@v(2HfEYS%m&6~(oub%nyI?CpHJ`F zhTBkxi~$9%2=HIOGo9iyDjb!4-*>=%6&tgC&cm*=H&8tPkL^=gC)n9I)QQ!~mG9cj ze=>eh))@)PXo33`EBGwk9Dbsrw*>5q2yr7sRKNTh?RxL{Sp3#fqx^f9D=(-qksGP| z#_L3$PSv0G0$=0s4iM~%RxX)aO=Iuz#!&J3X*?S!EET*<<^V_Ii({mtV1#p(%LKkiqJenT0I?{<7# znT-|}6F7Up@YN1aFm)%OEOE1D@akd%iLN_H5GbC$C&3N@Y z#G*HqRn5^%eNS4wgtR;VP532woV{gMceUKibhZY$bl$EUCGD~=gfpN;-Gh@pMxG%& zpHomg10|wkr+5L9>AZgG&DbV4JLO{Sepe@Eb7tR{i^jf$3r4*Hgs#~9figoB_vMsA zr!>0K15nuXdx?mJqxUfs#~UD3(G)NS=`i@Wvxv1d`OlVWAD1%*CkR-bq!YsTb-txa zwc~%f+n~D{Uao~}GAagYDA~EW)a}+E+hT+37+A(trS>hqiMXpgQ-6S+Ty8W1G+r+c zT2QI=S87X<)N7C0%xj+~Rxbo08QcQFkrG`CXJb;#<29qOEOR7BErwACWaORe{Ij%q z6L_^E`CX=IBH4xi(gMIxR?k@299oCmm4wXAWxS)ITDV{zt#vnyzCZx2eS zc7;dpwl24!2TgOpopP=IOY+#nv^`bn9q;aauv<-z{TI~Ad^o0st%k27%_o}=^x;F_ z_on?jtFBW+Is9*LS$clK(KQPNP`+TGST-8%F`7cMxrff3HR1-14q)!7} z4VFYi>FqB~Dx>*|*os>O09?E1z>D$YFP=;Xi03na@sqHbF4X1NMDcqoeCTc@N zZYNIROO-(p30LV|H5$y%nYUL> zC2IAGF+ToKz?rhZhGgjkO3V2cnLPRez&>^s z)_o+QM!Q)HTdUvfPY5FW0T+a%EJWyT_Q-A{T6*_EpUgWZO2Ab-h_AAnYVygb{AjL8 za+NMT25XHd_X47K`Ul{^f4+?DfQDtwI~hKx%egn`vergLaqL6eIW%BE-p_XPlw_9h zH!2_ylz28UcfE(pJDQ0OHeX!eZq=qt71Z>2eqVn$*C3T7Ywwm)B9dc`;j61Or|?kej8^NxmrQgV0bS9kC{5=lb4M{`uaaNCV&-cfz61yR4*(vr7@V}W{iQ*V zhAjV++0EM?K$jVR3y?sK|2{bq_3+SDxy_66QO5*+isF`{a#{QHny<1b?V#?_D95j{ zUyfj;vRZuXyYralj@tpnm~SK(QB;9Qc-#E&InBz#2723nq9OlZR{ifQ5=~fmeVKT^ z`4RUECU~(vMi*DTi*Z`cKDI$R^8Lj{A>FnST6l9dUH_Ei8Q4J#ltxAGp1axH!e_4V zC7&qP)5W}j=~{kTMNGb}Yumh6y&8_aufO**>_#_@GvIdGecX2B5=$%AeSGa)$q~uJ zEmr_y_xBNhy6cUg$oa`?bdBBmI+!&)_w{K6bz~G=c<3pc(nqxwkrelkg-4IV1$>Q- ziEF*4Ro~)sKUjNCiGk_pSF%;hHy`89 zx_|)TWJN%-&-ZdB8X!>g0i7s}=R2dy?JoU~w8pj|fR~t*2MkCkV<+C;HD9kp0~tG> zIT_D5KjO{+R8*{HKvTQs#rw()fQ!jFW1@B7)uE4CI$H=0H(ffS1fb6Ne#1)oQQqHk z!lQL9y<%^$EAT>rZ-LaPf1S~d0I^-_jwk-4QyjChSFAQ$DpM_K+Te9QD8ANjCv1)M z@oBHAzHy71m!pzSq6X?k;KppZ#XW%ka_iitzkBwRg@sT( zAebHMwe|F93odEg9u#^iSCZj=qIZ6NL{;>%$r;b9s*=*uuF;U;yL8=0q;UneJ{>5x z7me`_RuF9h46}IWTE@2mEr68ab)Hajkx!AQKCGbjeVR~JadHUzTV|L9$rdRSTW?3K zpss-)kkD8CIqImT_Y9jTj1^(Wc-YQ}g$#)aykq@V@v5sL>2k6Yy}rF|t;w+f~<<(Sv!A0#f*VBGuD+ z0KRINJ&D8P0{+%8gYsY%47pJe!z!-o9E9T0ph&V6DoGSBS?Fd$`fRr#s6k+vg-99;j$Y?zJ^~S;SF>Kz`o($ z6YF~2jWh2j4`xfbd~$#e8i(IyoUnB3l;MS)gZae9O-PsvEZQfHZ*6V-suf#~HlqTl zgJG51^St|qy{+yKP&{XSK{Yn+Q7I`5s_tZLeowm#!~+=tVbR({^_m`54ZiXMAdwBx&(_|5e_Q*Wcy$?kxLE(Dw9OXWhdp_ercAxw#sov%?Nd4!&0 zo9_0t&Ftf4T7^rRY=hRB?!h0Z_t?ZASiX`M&VJK(~U|luR2gkM4SD)9UWM zwACVgWKj^kofNQjqfJ1He*`i_94c06RH1xijyb*P72;vUQCVDh3M&1ozxayqhLtWA^PDTJCxeM$Afe3xq^YlFaF!pl7c zw`Xk?-&sdFOYoczYUBctV!^eS`@6Wa`7d_jgty&gwck~?HmhODR33jHCf*(ljsMWP zaJN4RxyBUI?A@N&6edz%;Ic-}wGooZlOh^WY?Tpc>L^rpux@ut+7bq3=^{cPM) zf1n5}fOBZfS(E=I>*H-9_z01hb=%1hXshzLKuP56f6|z;a8Zx}T@arI-J3bvUn1v9ikPWIQJpJw_M5v6i80qO_#EIR%{tln#0n#)2iy~jIV(~OV_pKqe(U|TKZ1oWH z9UdN@v#n^^0bpN*k*oni@%;cD##SBw{KM$&>ht63&-i#FEQ-qofD=;>l++c+mSR6L zp8)zNV1N+B;W6ZGw>=3ot_}iZ#&7xoGu}gYR9svaAZLD}L=Mw94aa5sk6xz*iiM)j ze#BHMc0bXx3AIv(E&-o{+IDAzqhYtrijDgL=%b}LVZ!~iKo z^K-aBZTG0qENu9%alu6%%K8(InHDpO&9cyz{7j6cubUz;-T03VHh3YdQIO=X_d8_0 z=lM@smm1TBmbBriXsT(Sf>69lmfkdjvp2>3i3yrh@&4X;Bg1}uO9=<9mM&c4c~Cz5Lcwf~po_}2uvstv%o{9jXZfwrXjF`%RDe-jK(N!%s%P&${ zeboe;Tgl0DdG@1yFn7NZ+?TO4V2Rb5?~|z1w*glYD^ImVg8SwJN_&sMn*P?_Y~h-R zwiN|i3&&T+35T9XP14aN=O`rp4SsSFXWPv$x5Gj$@HHGV&>19auA`6n^jb9^;uy6^ zG2@KpS4a~J9a>iT$-(%Tem%2Ejoq6eSK)n;j2$q zwKjmp^?*(Qq2Ux47Z?6~j(OO0Hv$!i1OP&SLh*7cKB%D(mgQAa1MMxotd^+)rUf~w z9eKQim#CgMBI5^}zkPrz;Z_fKgWWMwS}z{$SEwTqH$zfNG*o$MEL!s8%z9Ff24PO( z#IfL8FPPV&*W;#vSY2j>;+}&0X2USAwm$2u%lRY}bMJYS`1%}}h#DD$JHk3x)^dFx zZZgAa$gMK8S5i&TR9-3}yJf&l82;$%k7rc!nJR&zzxnEbxU$*wLe%<}VHG-bK1t&5 z*EPq>4RO@$?G}}4u{I>#>bE?p+e91+O3xV@l$uVp@~d@Mr1iUvQSG6)s%&7$PVQm_ z#{miiVyXGnNhA9?J?rZ$)zYZqqBXJ{%q>?*+ z@xd27$KRb@K5isNL^PVm@;89x>aTL*q)aRYZG4+f1wIOv1r_H2lT!_=~W;#RJnyz&)Mb-6fu7MT>3d8zok)PHwxt&SYcejZgf*ds>Jn<(xtWp*)=|2r!YM2w zVz(nCKFnSW4=K>)PkziedDlaE>|cgzYNBwwquiePbxV1D_eR82Z{9rqAIiQuEUI;T zSP;Yn5do1@1f&I|Tcx`jR2V=~I)-5!QIPIVK{|%+5S5OhyQE?0h9SPqx%V7_bFSz2 z{o(WMJ#6=WSI4{FwWf@O7{e59k3#QcjfIiSGQ?#^`fF zz{0A)yUjqb_a2OBRve_z@>d7f?9WDvoc!R~s)inP$2SA%*l8gC4va z`43O7?xc7GyV`1EfjihPlsl8BP_mW$dgv;eG zOFv;Jw5y?Y>i0my)zW;r4%97A+VB`Z9^#!3pDuX4r?=vAXkAPxbC9CtjG9u-96a$^ z_MIgs#bW!&5nJ6H^P7;L%@L0`|IC1k(LA7cA$g74E=7?i_moD!-)MxR$5mJ%@^>u5LUbQ z{kq9Hg@ZEwm5<`Xk94U;0}eoohoSi^+o>QEQREmSWhsqxc&eTrBQLq9cJTNRA9@R` zIC7zfcRb)@>IcTJ8}xk;qR-_F-svj>&opVgtr2xo!Ff4-vXPWOoaJnxN780TdUO2k z${ft2yegbh2jT`gwH(;U>#`BU2h!a94rO?tSNj|qJB>26vwe80w2ZsPGlxo)@q?FB zn->Y65v6AzZI!k0RpxIGg_B;-SdAwgOyghWRG=47;fK=4`?XGI#t)-<3Q9H>B5Nhg z5hCO7C%~P-{BnN8p>Ym1h~!NEc6y`LW5?Qwk8(~Yt% zq9Y}idfdQ#8egBTK8jg`{*f9e6!1(beHq$M?;AXObrCF18wTGaC}!0adroI%U)_2N zdl?1_4b_lz_@NtzP0_c!l?PF3$=pZ0N@^__fS1uJ?FX8-)@O= zxZ?g^2QMWFmCI4d+LpJ$u?n_ae?_4ec>Nl`RG%U4Rnd)X-h`I@e9Hl|D~I!U@0 z2M5Xa(Cj@df_o?*Hsui$8-%M|b`QsaR_ix(x1?K&3aW|uM5x{qT3b1bXJBs*)xr(0 zpgVzPAk|c$zX0WXzoczsnzcS~2rFxr_~dP%ba^6`&H5INYD*HK`+EmolQEzbw;&q` zUlFOWD;_IVTrRe_?+NZv6_*IbCRX*3<1g^s^5iu(xV$-N+Kp1Ff%l+l#}8Z^86ST- zF-sFD@)F+td1@e>y@WiEcVl>}cZ%TKAyj#Ox)iy#3yRyTKQtqOd1D8bJv}G8ub)46 z<2%~wu8-~3BP$06!aQ7ktrLo2Z(b5w;nf5EzC6YR)7&N(7`4S1g9saJR{*{KAe&}^ zDyyF=?4fD8n0G2<*Mo(t`^o!z31r9nl_1iodNj*6aYu^6xqY4G(OUlRhS#fKEw zEB0&l%0_Rl`x)6I<9l#06yg5rdGUT%Xs;YPJBkFw$-&W_5}V9eqoTidNh^ASEXeVs z^a|m9{SP%bZ7`kVrBD3Cs|p3ecbN-#I{TSx7&rt@yxa*T;*T#=&PvBE%g3D z*xPMUv>I$%b6*_`DREISiZ?2gqU-(p;w);jr|hyTxnWk;y#l<8SC=&~%c!}HbFfo5 z4jv7&t$W(O%dmRUk`uEhf34(jEpKq+=RhWC)|0X>M$mYyRN;6oqu))Rl;7&bT4h7* zypbqBSYn)Vn|kq+7)PAg{GjQrD8K$%P58K4Q#H9VIh0U9tZ&BJ@6TbW7?=VlT&%ab z)rolC2<#cBpS&(GAcPXoDHBCfzK>AJMt0~wGk+b6Pj)wx!&OIii$W(hmOFxl=UBXO z{u+AZBIi~i&;jRK%d1;|g8S#2FN|jW;w2=S!4P8i@SV`&wUA0SJ=vJ;fK~z1N^R7@ zb;^R0GVh^$-;Iuan#SjC+oB4hebW2&Y1mg4IcX;F|FQ}c%b4hNcqR|U3Ywj@@hIO8 zaMbV77PnYbylw0mF*kkn`d^)tgkX%u#u16!o4_n*$*;lq29Ab(3mdHrf$3~1QYT)} zW;W*RTYU?`Wz0Iv`kwm5k#Avt0d!mJ<#vVD{SO(YR|q~bas7U}@*f$Td0Zd~y!&bR z>q8!u1ePrn#FW?W;T0Uh$9p?6`75E?M9{ulwHe8Bf_QwKaw}j+ruNY?`eu;Y88GhP zrQJ9xajnEI;&u~Zg*?1vw;rYFFXVCKsnb>oM>Ma6w!d>2_rFBk;+N6TBfBT4^FI!t zAHV-TQ(d68*4EZBn&RS_KBfn_1MNPFY;+)*p@2j@(;Lua|Q+`=s-Whiof^X5xN?Jpmfy z>gdOh0111E_Dc<~um8E6zgXn&J_C;!MUss>Ncjv>|3H`XJE4`Y6Az>E^-tUh`dP+r z70+P&<~9FCal$4hz(D);t>*>)OOa55qyF%2_yV5Vxc0S>RY?;cq4ksxLlvUraq_%G z9FhiyAwPzSKZnxuilJ}YJ-oG;>g&NGssb7r%c-n=7^w^9?PU%S(gUZ4W%{XCbeAru z&feEHHlxio1vi+<)NpQG5%CL+#MkI+zhkpp|HRx$^pmsffCp5cN z>c513?jdmrv~aDXyr+Eg9AmO{QX&G5qhpd-51~c9x+!q4`YSC_DlgM;bIrn*hUpc{ z8cMB6!C@Lh-0&4?tFq`v9ezTF3i^+1jpg);tKOLV9`Qs1fPz^!lTC)-ZrZPWceJ!! zi%Nub1)YUORg?l{lKLN;Ksh)`v;~*Dc>TqOY9Lp zTD^qsUpoZm*-9sk=Hg@Ap3g>yAp|W3ohvB}dzL2jAEClJ<;npcZfLX)^`eD0JI&VPbjc2*=~GW zQbcp~_jmn5Evyiig3@MaM3Un@ptZk*Uy3y7M!Mz`|K}4!k8&Zw;yN;lIyxR?lrVU4 z@fOhw(b+F+#y*$Ob?7EG4yfd{dXsHfiGHWJI=W9SNcYNBr0{vRdxC);5BFpaWJ{pB zvALGVXx<=)+X*^Y0~J-;_X?`WKg2?(WuDkhJ|r0U%cPuFBL5|Z(BjpAKyJGge0q^{ zU+9wiia&)aiih5T)=c>WD$8hSsLXFa2Llj9)vz8BYFLbfQzzF0n2@NUK}FoXr!lxw z{6uA!C3zITF0V9p-n;P&Q-8k_zlL^fPe)~e=Y5LuUs&!^rszgi=8fukDu;3R`|>D% zVMpa<&L=c-B>CTNBEPQ@_tLYa&A}D#OQOadbhOmD4Bq$dY!zHePIpABs^1hMzU$&# z{vE(y3NFD~7hD_$&)bWD40Jn{PBmTmM>#hvjISAzDc>Dz52wm$8uw(7bGs8R$a~Aa zmY`fEf%koCh23}Qzg(INEd8I`j$ll2w%*`XY+x$tS?F#X<2Dj?+$rTVOf0R3K7T}` zrybr-pdj4hC-(VyieZEM-G~23qjLb+h650cQXwiYC>YNh(>VD3c}Cth?-ML0y^rL+ zecK-g;|y+pVfoCJxB|@Qt`295==p_;>%+dH-~$e7@&-7si~Ftd<-P_J!9^DPD)#;C z7svQi{{;cVm{$!}mgO#?E@(?}C>l;}^#U|_@WJoc9>796jSz*+;wYOcATDPh1x(+| zy7(81Cgxz@$E1o$d<^R7*^gLw!g5Yvc@wsNT@t zzFln-8^#$X^LxcbF+uTl9GfRW{=>PoQFO?l7xj)lwZf8#{<)F>b6$PYMrOG>%Ex;F z3%@k-f36b3uLT5_g^#{11{}p-z`3FY%pa9A36&}(OoqBMi(#D=7y zrnmRMRDZ60WeX0({*pbe!8GQj6V$103HPx|-VN%MUmZww-4$3@R^#duAy$Nc=Yj6+ z@tz!6zDa` zavbL`)V^PxtEvMhn~uh%vhTf4ewnk z5^VSh$=>!9pr=~g&s0O`xee(h^zDN;JpGdU?=9!%$dp{u0~fkBg+%XKu8vjZ1D#`l zg6iOnjg1fPYs#zma&4|!S~h$I9ZkZk^oM1v@g8d|+m3w*2^;Or-X$YlExqXIXb?~) zHEEyPAI`8{w8H4Zld0yYQ;6iTV>X9PnD{v&-tUtjS-!{TMO9x;u1?e)X?{}KUa2NK6^(dR zXy@(^%Hb_YdB$hQUTiT79%S%Y%jYYYEttGs*rWJ`P2NMx_FZ>MjQT2FAQpiZfZS>4Rf zh~OT=E+UXjPmmbbgASOLquRQjxUA#fOR^Kk+0Y9K9t|EWp{b#EhB|MM{np7Zyn4H_ zHTb*|;JWHV%-R0AN4F4yvZsCgIN&)Bzw)7LO%*F9+}5xcdN@U1-)y(jehrVhaOrhP zv%QwuC^v7j0?ehF0hirFstwn1n+?VHUDWfYptz_nZ;x)=4;2~n?7&<7sVkkIeu}Jv z50v{xgz4ltIw8@^+l{i2EwXKYC7rC^yJUAYOo^Twz3LlG5%VE=ARr}bsK4wi(}@jX z-sbWob6)0oQk)cz)@=@+uTs)17OT8P6eO4>j?E>6sVkjyd*^FY-R zH&iw_3$?gzfu38yN)I8Aq^KClf>Yz{kgwWjo>njDn_KeZMAJBq)3?0X^4}KkX&It% zk$Ht|YHki%Tr?r#cP=zAGSULq3*eA*+M5Qh6?v@k>Ozs_N!#XC(B$Og4d7gB(^uH# zOq7%b#||px zB*09`;AY7==ISpkWP?~|RM-W9suBTw{W0s*L7KDAJfaE+{G5o5$7TC2!Xnxjh2g$b zO#KslRX6hpvqyk;s>3ztrwLKb7Cq`N_vIgdg@nA`6$K~}Y(cLp7D ztH}CmGeG`~`qZp(WqkVDo8MfDeBceef)*-eI^KW7wj+O}y5b{;<; z^pfF3mJ(%yyLMY0j}xU}^preavG3pEQy$!dI z<-xqYtk@o3ZJx{VC-z56pxDP|GCz@>;6A}xFld<=$!9jAE@P*&H}risRvyZpDv-F% zz8dcY4;l-$&h<0*Bh-%mZbMQHf2jqT zPtT(1f~zrraR(Lcq(#Vd&Zq(I#{5gzX>Cy~;gZ`9(z0guIvU30jlwI-^_Jo{{>2?A z*8}LYQiGLs-W!okeFa_&Dc{o1!T2#*i`< zhP&q+p?PXpy}LzJX>^L+y4Z|GkdY=vm(tf|2?QjM{&#CkxQwwkJcuo1aP|CCu$^ld zUYsJ>Ph89lG}J;2oGV_3wMV-x!8^_QBRi3;QLPM{S>|xh*3ROA>;(=5ZiR-f z-ENGrFA2GI!3)?jLCdcZtvX_oLl<5oT1Lf7-ie}Iyq{|qf09vf*@@itpJ^dWkBoH?&Zga-9*hx zbp8jt`7{MOS_y3bvcRi_asbhH_B)Bse>i~>gHCceX?k`TUbe`E7M?m)Z-Nl#r{K+R znKDOU&!2OC{!W)m=*xF00?K0F*<^IfIj}Fm_RwKa2Ez1A&+!0)tG40qhDUYS*qHwX<~NLhyQ(>^%i(d3xxT#n zdZ0p^D;_qGi8&I<4@EJ_A@nHe&j&ph)&*4ZNx!R^xqkWQoeTIwVxSWXXMxLa)(@JlT`*FFr)de0yM>c{m|Q_`6xZ_66njPq$y- zr=c0xa`1da+_8zdXtHG5+E$`x^DmNniu!sIh1W zZHZ@u4P|@~eLHjW?!pHOlm3ZkxO(=_OpWD|c5(!BdJ~>mrDI<4;w*NGbjDVgvR&vb zw?M|^NRtgQu7v<7=ufjo1_x0 zd{Sn0=_RS;H3*7cC4%2N+b30#zt^vi0 z6v>Ml{`q3!=IXpzYtbvBNQedtk9_YNbX{obt+ucKBs=GR6M70Hf|8o#FGO)2W#~T# zAs)VqBm4LG%ij%?R{8yVg$a=424cNO>h2em+b71`168AA2`@{JqB3*v8n^66wOcO) zOy|9|KY%U-v|DS^g+Fw#VYq1U>yxIJUKg}acN|v#Hx*Q+1917+F-9)*9Gg>w2|;wS zG#78rzlF_8>v{M$y*r1ObF%v%8g#IohO&{nA63p<1ODd}9Rp6tw9Gu;WAmuR zvnv-B_cf5KY78E&uklohgM-0O1kgw6C#*|0 zp*b4|1`bpimK|<*s|eW`zR+>39jn5(KHg;t22Nk8<&q78j=%V$Mdayh5NR);a--1_l}^W zHh=`k^xB~xXXQkDM_wS;g4ti^yNM|L%(v_m*3sn@@J>)@+bVtSl>>(`{Zf& zat-0S6CV^h6I=&9w0Qw3eBX`W;x;LsY0?waKGK2czO$Cb@}N66KjHmY!+FITMOy7G{D&KV9CnIjiukYE0=X=O zomF_?tB=oPGfvM}P*PDd-BsYD~Bh* znU2ns(!IHlURy1%5&Sry!2ye{$G;MF&DX>v$g-`9GlqcR67d>9w=bnpsFazFcru64~ zh4Ivbv$buRMowTm7trEby+DTmP#{gGH8QKniwD3l*9EGEnhR z4{4Sc-n3Jwcj(Q4Tv-y{&KWBIuw&8MeQ@)`Zk``f5n-1Y*2j{)(}#Msk}12)Jdx{a zTC--;U?*D8iR6f1e{BA(!J*u*m}c7@hogTtRd=MmfN*=?#>mU$_ENVBji2Ewgip7o zZ6Ek3$)5*)M1=H%aAkLW6B3>pd(Tsw`jH&hQ z#tyb0+#hQwRoz^uiU4=bd6IS3OBF&LEBr|0PB<{CQhj`G>O+6pBrg{2A0{szXmI|9 zsXqYt$JTi3{>!Ec!3owJCr{SD0{phrP+cmkRcsclHOCa9B+D&4d$K{GRX+{%o>5GZ z;Z+-RoHCEx+|+BzeT9`KG$-ZD0`%Y1*$3MCX0WmghP<7f15A)re-iP05seLSszJmy zK{@L=)+bDi^H>eJ1|g)T2w+d z);uZ-HI39&618cR=wEPJY}p}dW6f-?>%DuRC^R*u`KAw6ve=lFep}8?iZ_X(UNB2v zGa8h3wAWLR7-*mzubj5&lX9hLL@#IdCMGZO)Z_jfftc1-FtI7)K@0Y2 zFJ+)*yB8u^7Me|cTdko$FQC| z9cb0A(6B?Ko?hcC4`1)HJ)jGO0V1x7Wq7_>O%L)M^Py2w<*TIHRBBSE=RjH zQjaHqE$ALq2a$=YL z9z7o|kd_Lt>#=GT=0Wy8H6|T*w$ztNcM}5yel@&;qEKy=W`Av5oz2cHmUC$z2kIXY zUv?l+G?F&4f?>WCpt5H`ir4a2))JZ82sP(b3&znxAXI%_L)aIS}<%c ztpH_}kK$c7B4Z#s4*Dy{-ppPqUoBE@lIz|p(xJE=;8hiBJ@kgA?iQ24l#i0ZI(fLb zjEpbPb$^PEqD(j=!_=Tj3Z5~cMCy8)vZ1mjv2J`|6}=$c3iz&JKw0~bQPYuHXQ9in+&h*~ zRCL}Q(nfy!CG~4R{AVnNn~^3<4g<(=T*@WkxEvCEENZcQ}MT3QlH}?F2Fu$Lmjqs`Cb0oqhkS?7KHM(jZE9RzQo!&`D)(Bf0T^=-m6Hw>R5fa z(ehX&D5o6yXD@)eznJ+;{@1miJFO|{m4tm``{=YMIU`2GqOekpda&=OS<>DyZ(F&sK`ctWU^S)qm^hncfv8N_?~{wW~UP? z)m+_hJ?N-e*;bJ0m$EW$b%FJ@=ch5yA)>nmC+uTedLPXG45ZZzLhI&pZH7)!Klq7u(pximI!$F91r#f+3jSN#D1Q zT7Zdj&aU}?0MjM;^?6ElVR^jSnYt|)$F=!)6T=o2BJKRUSk~=5-k{UduT5fP78v&k z387TGBb&L0oAL3D{JZVpr-x#>T?F9EntIYvPZe@XCtm|21yVuFLw457&|`Vvz%&t2 zu){K4Xieq5-{CG)khZMsX!-yN$K{7U^k7#XGJ_W|AX&&9C;Ibk=9tPhG8R}V;59wH z{Vl_mHL2*it7Tuu{fn-__u5`IofnDY)W76bGwrvI%T5XAwaG!MJj> z6*ye%YJ|Z!&Se^Bt`D?24;vRaen;Xo{X(m(y0N`IEK5KGcSf*$$HRL5)zmbhjYT-Q zwyoWZqfj*+x^TVY%j?clSec7LbFaQ*2gJm<;!e%K|J%_=bEa5V7|@u%b*d&;)Rck$d>PEHPR zavjY?0NUfKY?~xBLx!3@761}dF_vmyWhR{9Q$5Cf8cXARjjK(wUNrSTYsz=9baQvjN+M) z{w(><@G-0qzfpWRHjm}}qp+UajieFxkx$hke{vuk&qOb0{_i>G%Gd+2tjk}M=+DS9 z0F=-jbo}IY-%ZVYV_r3Tg*S$(-|)&Rk3S4|-9Go<|1o-^C=|Xr)qT2i_QUWborUUy)V$ifkHGF`Ac~E5!M}#nGD=BLVLuB`;X5eeIV# z|8ceOU}D>o-ZB6<)*)vsm?Oabunag?e!tFMCsIbI`K)#PfoOfZ`{A1o~rK4tfX4E6$JwbB1|LdjvrxhV_)->wpt40Fq(P6gN4P>WKKl=VvPGLEx z(oSC#dA{TnC>+)jC|Zrls<+4acee+o`xr2-%#{mfMCXV%Zw)KIj|xe`DfmBaKXxEb z`O&T<=GJ-Gbr^CFmOow7N!1}x2zv_~j(w|i{{rs*2}N7lz(yT-rRo*v95IA6uVQdO z&BhyY6?TvYaMr4(orK@1p8vjD2h?Dy7ryHK*}G z_H+6UNYUG|@05v>Tl`UV@^6GoPy_b*M(8bhqc2pSp4USE{F(&tsf_AE4bP@K&@j5R zCN}+-QN$e0u5EwYl0JvSKkSAO{M2yuk2!Ok-_5eE7J9io94mN@?VqS};kPee-qBF0 zwI!ZQ@4{pW28vA8Mu>oXCeQ>%Qc_B)Zgsc8e7u|Z>C>ke;R7QhW=kzq4wktLgLsHI zsMO9*cCm#oo<`+xsj5NuON9XVXW4 zCm!b!V?+(NzbkWZPmj2UM&!Cl7|rJW<5sFovL|z%+k>x)CANB9jz=sUG+k1V5F}J? zVNPyU;-rF3$Ip6u_2backnJEg>VFe9LG5%0B-2tb`Yb(R#^^i&WjQ%annem^S228E zb^%+I_k_Jzh*O1&DAP5%+R6XYh;dRdKA!fa|4;;nVj0G)^;|(Lh zh2UL_slk@!=0#5_TWzJnxp6aXMa9=HqKlW7i&y9isJ8^Vrlp+rPPT_u*|${O|~c8hb&KqmkT-uoAr_3W(> zf)L>Txp7L>GvUM~^ihfc@pnDpdaiFU)^3q-$-B@+Jnh9H2j~pMl&A@8%&5y{)XHdQRm%Y-!m; zHeCCWyD9ur)ZJ=W9mq>vm6d!``%)>Ea^|Ln=>74y2mGN-4X&Xx+D1U`%mm0ailg{# zMGYO?`Rz*g2LrQ;8m3s#=erm7yH{yq*|o4(Q@b})wQ`>W_ zDtrP0NeX!o0oBp{fqcYRDzthFwWGXXL~92WpEQ}@GE~l+fDLuHs!%&2T&v4eRKp}lcUT|Q>ZIdZR3qF3f4-`+DuML|M-{w* zZCb>NW$`^TSZ{5t@Kt58Z71vt&!_5y1RGd^cCQVmcMP*x2u%SQ7r3}=k1 z0h<95W3Qsl+qZM76>z`j>tLI*^B8T?;x zjsCoK4iJLhG{_k$)pkAh_AoG==Opm3XcD8JDH4%OM=DsiRXH>g=~u91${VZjBN>!l zoJ<-~29&|e#Ww~H`rPuTld?tOMbTc5t`~@ZqbCg8P>ci&bEw z-xZPnXCIOZok)jO3McBzVN&Wes^FnpM3t-M09{El+z6r2ajU1Mz!_aJDUKpUnZ@+l^{K@LhFWnFFJ$2UAyVWLuA**P-ZZ^ zxuF3nmX342G8n;oJ=*}~%wBX^gQ zSh<erG==3ox zWnt10d?M8Tj92~C;X_#m#-U~d+7Crtr&`fif2QdhG=JEGdg`g@GMEbN330gOr6$9h z_4pPW^=Af>lnE9lZg$Ew*QnFe3u;oZKRgG;;rfLr1v6{-084sW4qHy zabsVTHoforOgebLT2BjW4)ZK7c@z_-Tl`3T^ZpIJfB<$$pJEtX;1i%VMbH;sh=I@h zW9I%gRz^a9B4_xg>CCtN-_R*PX?&3kYb{p@>nY~Lq>QD^5{}}gkugaWH&gslybq~-1z)>aHkV^MJrUD)kip1m)J{T+V_9vV z*Sf#(I%&&in6Qc6!=r5u9)uId|7cgBn6EOmGKEA`CYEMbo29H5o-EPp-fz&Z4a&q` zUAQq_M=oQWp1VKWG{YG~-?9pp{D!HkgsM>+92kAMgl0lX8I?*1_I%e7l@DXPz1GuC^YP)T8HVtZ{rtVIc1xOKKP-^o+^q_KG-8Q`cAJ>DxU z(c5*k6oxu?EE^1&&s9Mr$e<^m6g#vKz5G8$3^%wwrlc^6@9YJGyg&~0V6Y_0jnQVG70EbAp9o|#nYz@sB zfYf7FHH&ikH46lgoz&V~T5-T=Kwx~O+VmjG$TPA=y}!!7-#0%)F+S4c`lV{8&r}8Z zYAK_K36*IF)f;mn*8a`bBs^ZApHP9`u0CF2GW9}n0!C*Ave?Xgf)X+(nzk?z=8TPT z#-JAcFHB4avojkL!Hn4cOcCiDA|8G-HDJz%rgmu@?HWIuSXYl}rbWJ6z+zaad#aDK z1)JRuI*Oi}2YReHw30n%Cjc{5pl4$0fq~Esp{9o6af}1*P88M*giKeDt$w^q`+v^wU`dOrhL30#x(@ zV{|GqeOEdW-YHD-gHdu}iIrx!C2H42bvJM@oV7NQTH&ZSKsufXs1ZUsjw%oGHpQrV6d!6pI+Cwk zsM38DqinVv%plyQHB$ifxWeRQnfKw*hCxbm3jcE2!z8%lCeVKD#&jJ0vkexS)N`4b zA)4HZfiY-@Jj#w$Ei#PRjk?>v7)TlMVvfX(m08;R{*&?h^S3VvLaWyeR5=@YCk>o- z9B_sT9ujlrX!lhYy;z9_$GV)voCJ7;<#^$BXg})Lo)7rRId_}8`pMQK>Eqg_J4EQ( zkBnjruB8@_e4O;xy#Z3%iGRQ*m;X_ugxLQ>iAD7eELcxp= zZXon$JpX}qqBf#v)2&+E9#1M*R1ufsO8mnD_$S*n?D&`0xR?TuiiO>(^kW=n zXf~Piuws#YV-LCeCbe{5VQW*->uC^?Fw23O1qQp?v$*zJZ!jQPrT*ZvKh+k@kKpQE z%X}P-CnK)0t)VR&9w`O~odmz-zCWBbp!{vCz2;UsQF8vnn#Tx%e4&)sr`#d2T#lKG zpnbee6`@TM4SY#Ik}R271Le+34YQC6+q}g>0x0xZ3mN8J1JCz@0aQquyCBc2SNHG?TC&UnZe4#Y#YmwQ z@Bqn-{{DTMJgqq`2>*JW1y6V22|rzb;}<{A;#!TpniEMjTf0+(Ka0=`QZfYG3GG8< zIOjkDbd*LdQ(8NUjummA5SrO|{iau~Xk z+U=SC^T=P%UbqPJes}UZrXc*qb@o*!0|FD*i7+l#f8kJz`b?`jND{p{^Hi^7?Sun$ ztJ!p2eZ!|+Llsk$taMg&rlz-7n*_YRw7xi-3mA2F*MW(=`-i+k*~mBgzdi|@dGrL{ zRWtJCf$bLPJII+vxmY))l85HPB2LlxCoDoQ2d`!c3D0)j6Eu9`bB~Qj)W?y=F!_3Y z5hK-+ez-o|SE+g^b8`L%4vlIS^KfVmi{oZpKWU+6i8GVheI+_;w9BtsYBe4wn4aA0 zNG^rElWYT|F$yQFA5l=ra&Zz2E zEf!}d-+4UWgMCb8k&Y1~{nzUL;X(*}Tixn?Yk_EkMrusxQOtd#*zJOQYH9)d-WBD` zB{eEmP$@Bg?=U*`k&4yHwT*5lnHr1xeR?Iay6)hN)nx)5RM;w*M)$tfZd$6J;~T4h zvcr(#V@+J$xcl%L72IwMWC5$|AB6ser35Cl-Z5lQUi`1+|AqDcTy#)lsK!v(SV=JC z*zvKGfn8myIOo3y!qs?mwckJnb9OY%-Mnr{Lk{Ea>1gI@fIznPS(EZ2A~RWuo0{H6 z`va?SnjD2bX`1pfH*Tgja?t?dSMs^EiHwxl02jw(&$Clq1&OZAG zy#58**XxgRCov+WI6hsjDd!47brlJE{)A@pxAGyE8zzlPe9|0=Zw~2amb+EsXGa+& zpU9%>!tix`KPe$9NPdbH=-?N&l zV>$f3k^Sk&o;cqzX=vz&1GVaKw=<{5QeMksEy$*3`e&ziN%zpVb*t`cbG>U>LyZJy z(A~zAt61|XZV>MO#w37SMO$Vr{NyeHJL>kZwucVb@WE{8YuC9rT-p)y2frxz4-g9x z-&Oy7j9j%paZtFqk4#^8DlB=rQngfCy4&|4Z?$^3E-HQATH=k5^A67X)~EDPiNTv7)p6>Vuy^=j2)JY}8aNvX8{RmB&{L)MH%z5b=RU)X zOhR6<>Wn3(%HGGtQOT&*Zll@ZHB!IXg#KfurE(+GS(mswC9XT^M9ts7 zg+|U-$?svvVKO=b#?8)qaO7> z{--(o_Etx88&f)*BLc|%F=q7fz>o2kUQdJg3li1>bmR3~nJu;ZP5TmRIf`P2?^To! z+xmVc)Fw-+wlNVH>pAK%8{HF=9Lny zhM^$?6alB_q~?%WE+V%SwW##EWwAGCb44l0A{++o0{Ut2=tCvb( z+50k6v*PZKLmEk=x*u|>FC_XcSZwNBA1`$B9zYfBN)w&xBnfU;F9r_4Usn`|1`TW` zV!cKTVXF+4)S~s*Q(`F(DYTgM+f|c&w>WfejpAWRY4w_q#eW{hTJz{&x8;3pQ6aqA zv_saT{?;I(N3Nlvbw2FK(lCUepR=ZpADG66*c*M$TfgUDz*c|^Gc-~w4KWDKCU-Kh zni}epyi%9TGZd1=_LR)Yz?jj9J=n78-96{_TY0NKo-9_P1b$4kzLCkJufN{cSYG^? zG?kax%p9AXNspP;vg=IsH^%;-*tmLIeFvT1IH2r~nMD75jc8a;TbOQDeu62zcKOaC zxlYZ!p*c#OeBqr%&I#}9%~D)7-*PneN@&%z^Viu;k#c%o1xyfj*S8DnENNmV_tr$} z7B?bmHYe8Z>ABWcYt>KGKl;`(4F3OGkue7hyR9$S?<#{O?Bmo_CAsz;%6b7(8W+?D zN9WcaNy;dEO0pUz*}c(EUJ$pP0lFb|>5A7&XD!2T;Qh~^%yooBW{X|1d~3YEPNKK0 z{;*3WA?UZef7|WK-3AZ+anCL~#u9HO?16-4GU7A3;%V^LEeRFg#qOHLV#W&w^KYUT zaP2B9n7EJ}_6^Odezd<{CN<`^KV+}2aMsV?p}A z>4_)kyT3^Y%zPtF0!F0_nx@v>GSSc~efvr}Vojnol8y8FjT@WDt);=FurPZ@DOf&{ zQiiQw2e_ARn6bDs-R6f6L4_u&LSCU^hm(fw;qRCzK?MMyyGv3%|BZgCj!-Y1 zwIL{IvUJjGQy&GlPy#`p6C(sq6k}|5KC;-%#@v_j=Q+sbn*8)UU&zKPR3B($VAbtg z(qIV%JA@v+nfejz=j(<&R3n;J&LE{NxHBKjVm1I`ZyyA&j^=Ord6V&%_j4JF-MD7E zGW3klI?@pT@fMWmX=ZBsKUsjk7$@z)M2%!0MP3sB)vfznk9MR4mtV2Y{)rRg2e7-Z z#~k9HZ)EB?93`*MFk2GT)zz`M9*SAD^>Ws)H^k!->e#9-6{FgH?{{{~2@(tP9~};W z!bzth^lQkER9A>a>i!>l?-kcn_Wgm5Z5&|~3r(tJ5a}|~OM;_-4Upc8AVpe?^b#T} z7OILA>C$_VUZYZ_h29}jLk}bb2!!NroEgR6U&opIbRX_K%;!jwbIx9S?Nz^PEqUEk zH!U&=EmXtBWXztR5bzYtAt*T&USaa?zQk<9v9SPgh58w&EW~-3h{XQ{g6isH-upSg zx5|X;0ukJ_{IObaWtL_o@e#)2p}UH|vX{ZwC|fv-?nv!GNnLp?eIjHo({HR4Yvf-~ zb%Z@&h-MWP4q#ziH@V&|mXV|HG`Q2o*umys1Zup5|32=Md!7xw6#&#sypTkkO{uLI9L%83wp{mu;?EiLZl)$jB?{iLzy z2T3egDIO-|)+l#TaI!lpG1zLZ5~FBpd@r8_6P@sAIi^5_`Z z__(+epqI-rD0y@g-ddVclMp2HYX9pd756QoIQ}ppubv&~PL(H=t<6bn;#^W2(GZ1_ zOje=tXO~VTUk-^B_~z<9Z#TG*n>&2?mOcC0yR%;ygJ0XbJ)ypCE+@d>PY}Yru`*+5 z#?N!z6aN9~3;NgBRj$csjJq02ul6~F@rDHR6%XFcXi3m7ZUu;FOVVP0zD4~)G3Ll) zw!3n+rQM7nky@F!d^Cq{!v-I`S_t;khn8vT-Gn~uILDs-*+(98Qigqp99Vu{41#RJCN?|Aly8 znCSC+ZeMtZG5|X3!n99+UPjyOUeLjHjM$5LV(8p2?Ca+@$8Jm`g$6PU)T9`}%HPMo zuQ*EJ%OUgSL>TCgKR;bR@eUL*>Igu|J>kWKb41>Zm5eO6+Nui=dVJr%f4@GRpfc8} z!accbQt7d+2E3vuBu3Vw#(nOfFW8wL=RU}fWVf7&HyhG3FbWqB{Vz0S5%=- z1H_G>VgpuNGV|mdWWW>lsv<)Uwd@>%#&P$z_qt6lF;;O%I~zF8^+;}W8iwaNuO#kG zlkzh4&77d#id*5dyULwsKV$}-sohSXyv*F|Hq#Em$BF)u=-Zr>M2N5W{Vu|6wkyqy zk!?}WIM~+=%w({<9{sz3*Rr2JUe2M`TS&`&b)KVm#2LY1pllX?-ctEjjtVUfsqB#= zyfY0KosL6*XD^vVF#GscG$AoD@ounQ@8-(FunDFx#tN%OOP`rfQd@hy=LbZ#LISQ` zz{Nxy`NFkl4)E*b#;IbH+8oS6>p47l>4Z~?6gix%v=D9&E{8;5kkkhybFG_ZBhFi+bYp7`6EyZTmVY@_o3v8Ri%sb|>ObMGZ*Agi%lzcrV3X?E4Hs&ihCA2{q5)b`%y3rpUjb;zU;z+{)v5_ z(oVB%fDOHD%Vrda%Fa{Gy29pHldRu36?m<*bH{IMih0YF?&G;bV6@^>)r1k>(P~`a zgF}!LQZ9>Z35o#$^l@22j!}U@)Q{DDcODS&xn}80QSYOor34P8_K0N$L1V%-v+WF~ zC8JRT)*0PBN+F1}qYgA`0M?Of_;5sxVF*bf#FE(5r|q%|dwdUZ1!Qy??n@p}nB^AO zN0_?wZld<&N6`&;IBKc4~m8 zmTnT>ad6&!!}IDIPfG?_*D60aO=M}M_OYbH)C3N-s&?D`u2!k+zG*NBc{mlihZkNj z(Crx8!6exT?bKh8i#(UR;&b}D{(IXhVDdqVtjG?DbB*iSdnZ==5~GUcSk=6>k@DWb z-c4K(`n_vy-c*E5SHIxCfE1O;BjpQ44i84hWY@?RLmp^O{4LzKslP?CwO$tv1Qe9X{RP*sThZ4B0DPLap~-AIi5m)%f{-; zyIlH;>o=z!-S0X`>}Cl}F|j4Rct63HRiO3~(NpQEonYLQUU+?LqN!r+EMK(hq}98t zvZth+w>&ZASE^zz@V3&%JYLK0Dpf@04B4@AjQ`RASGmdzho*nhs$l5W$o+k~I;a;8 zDYlD~kZZ2Q#o=-+`f7x_M;eE;%i@p>TVzbyq*qSpl2=!^%)VFUPIIA{gw@|uM0!^! z*FyX{D`y9YewHPI55?XN7CLbJD@8yuxC@0A~{?zTXLY>3!cVn2n9#S%;$i|q}~hMj&kX9(5y#aQ;H+FS_qQV{6N9= zErtu_wT$| z%kYJ~tySZv4y{#Z&nS}%>J9_c*w_od9N&(Peak4!NXD4!05$kn>yzq3vS-v9N<90w zS#q`!Rl1dpHxxC(csg7kBF|xJ#a*@(IQ$no3a_y_t=xoTD(UL4300~kAO@D-ZXx}9 zjaiRFC71io0)w4C08`LPz7etxDH#Gwy-TFRPGRHiAIEQF% z)aJmd&Qy^nThixMSPjda16mEGdi#*`aOe{yvEj3=kh%L3>v5bt5;5sQobZ}=x~}f< zl7fqm55X4-!)g|ahmV7*UUiDPm+8%$k0r1}>ZZ6#mz-Z9VVu$vZLjk-STgE#EsdCi zAOHxtNG9gu*qIgCiIvlRd1fJ7VxiC9R!C|}8>LwLclKY4n&u|_nXV?dNe>eZg zm*uzg9thskZAGc(SsM4XoHuq4zVq16d+?I>m*2y#xqcP(^iE;|sXuGZO6{zF zwSpU$?tlFl4aRBR4c(V7LoVsw?w)N78RQQp#_%a#2r}|37hJ4xtvq70Z>z`Z5}(o! z$Q)M)Jlo<>>6>gjmuSVcyVj5KQhWUUC-SuV&X>5@^XHOc&j_-YYstb#GJm{#ASUQ& zrI{ei{MG(jmG34@X|Qiz-AcC~+{w#dFYYhWg#g;~-PHLX!W~}6t*3LZG2Y6iG5yir z^8(Uq_v9-7N7nV*Cdn`PexJAS(}lO*UK+BEn*gu#j`-LA<(b_3zpM)-^ECvBDSf?m zpWU@wS$A$c`sah>9eM-=M2=f(f7jmn_J+p20OyVC*NmOJ_|0SWHK_~y!Oxl`%4v4W z04)jc`=UL%qf@r;e{j1tSD^0e^lyI-Sbw4i@t2nY$c)+c*5QA*&Az-@`hV@R^^vpT z8_q8S+yT1_^$qOF%s0@_CDAQ%BlaRpOj&F1^^ge_a6wyiQ?C-XTE z!RyzTgOF_U@=BW-7iZ^^BkdbEl`l*cWEON>&6`P%1Yu@QPT=C)to$`4+F7px)Y%h@ zuI#6ioOmWNo0`C4o3iUW1o|H~X)=zxeLHayYJd9A51N!eCS{y{L(`?5iV__(=IjC)?`p@N{Ade2RYPlsJ%uc!I zNF&TdPhSIirLhA0t0hC2x{09%Il(nM#Ck>GFmBjr zvnGM!%tEDs(hp}MyWE;TZGzP<@0so@#}1Ef#*)-)KVV}y{p}cOhD&S7HVen~@1ses zVh3E*@401xVEA=Nu+hDnqt&j8Rsf*_#-nPFe9+RjV+b>HzwHQyJ7N|=%Nq_zjHrzk zc}g7h6d2UMDNBq#lzB1~o>2v+@WI!HU9d_)P|Er?pYSSQ>)lv@Az8PlOcG|BQ5)Sk zWB4Ya90{YU-;oqD*>zyVJj~D(44UMJPJBYM=taJ@?9A{_H6gy!EVBE-Vt(>&+#Q?p zk{6fvr&QLu;Vmz8>*SQYd5idFW=;iqY7YKtMZ~n`3G;c2>mzHs_OsnMVw&xlR{#|N z^Ico2n?r)Si(@vFuYp7WFy!g7+Y69X%5pT$lN&i4Jciek_r!I#*`w`cz$|6O1#=7sUK5sw`FYxiC%V4#WmUVUWcG?;UqWe5I_ zD**@9v^`$lEl$cX1&;@l&9!W4e%HRCb5bsD-(JwJDZp1qt;?wtOh})I4LYqD=zstI z{j!L{S6efFrz7plZabdUf7!x4R`~RMsMWkY)k7g5VLj?<(_Tb+MX z=&C(KfB`d}|UWpQ;$z-$tk^mWIC~ zq6*kkeCu!X2nq&Lo)L-p||mw)R#5u+>>@@j5!9|#rVY5yw2%}Z3E+aO3pyDY(h@ZcC z^R^JKQsmVN7U2=4?GdA123wevody9Ci$NVJ$LYh-Qcg!L>{*MO&VDC4Ay)|uS|VQJ z6K(kX-|j~mJF6=pdX1)C`wblqxC_$JtbxhLBl+&c*$__S7bhHm{xRKF1a9~VsZBv% zUf+>QkNU(b!KUvY9uOS}(XO8B$`>Dix5j)22wqnDJ)bGX9& zNyYsZkqRrhwvmZ%74%qW?{dC=}c6-Ql z?{v9L0~ySnt_5;hd(w4-*f)bsONsI2%Pu)3Htg8RTfZ&OOm6R0o6gOEg#P6ukH~EwZa#YWpj1_8K^} zNKD5)7;EIq@dqhr*`hvEzY5a!X$MoZ)tV!F%d#@iEkB5D{ApHsGw8-4UjC~892Zym zDUsl<#h<%SG^nt_&;z>PJoOVSJFBI4J86BR7ZnpEvE@5gzF5W=<2v@#_juPTUxm5} z3ApkKMul!cva6|9ky?i!>mN(N=*Y2FX`DaML=s+`gP6K@MNY${fP>#*h|f0GpBH*5 zK(5SH2q}Cr3Iqgh6wM1tpc2R5%vBus5ahFAd;fyWf?b)*KrZ_k*MP)1vwHINr<_~U zitV6;lH98o#0iCP0!D{%3Zck%LEh1=pC>Qf-AJq09!pA^ zavro}0*Qw#&$G$bUme9&t{GLq`~-?p&)!~c;$6j&X$V`*<=-J)h@*#DWq+)fYWRJW@2nXX65QY2%SLp247Ca z(d{uL1W2ItVhIZZL0+zh_uYIv358XWECR+SrPY$K1&NK#K0ZE^<&{Uj2*7{X!8YH0 z8n^6g#p~O)&u5(ZKB@UTcYzPZW2&l4FetfrVFDqh0&B_)YY`jd?=f^4W|nrgl>w8% z^LmXux>G%)R>ML<jJF7fqsWs&@OS%|CJgOBIwTKRh`rO!)d$7>?T5=i%N0fUHV-eabr=ri)N^~-xW}f^BUL39^4x1+u!93ijUY6IEG&3M(tXEF zv$EOkV;Sw#gB}=&3kdt<(DEPiZmm3B<`m#6O?zIv{>>ndE@><}A|b_mM3Kf-7I+=P zH{U}t14L?upyjqppnD@vtG0AJa7S+!8mpq%KCU7FxW4F z+-f?T3hTBgI3T{P5*kruwUVSsLRNz+fYnZHo|p z7ExYCK?7#cS?Vd^vWf-ZOmKkyv8LN;$TG*77eV_7yslo$F{;#RAVsd= zj{tnv#g|n6*droT%_eNH(O^>t2lnNnl1EgSWMOnlYo#^oTDt5=%IXHGS8PO8;cMsn z3mZom$DN?{6jAU0FvS_g@yRFwF+TF~#i)thGW&Qj-p-mdfy-2b}u^WDrC!F3rOAHPkH(RZ}uX z3}7=DjkjjCGv-xJPF_4D`I{F$ICdf=CW9@r;5qMw6iBScVyP#LMmQ(0jz!pMwyOeD zaNZ|dJ={GOv6|+X+|8hLqSClC{tt|=Gq&Z=UHvCt!YX68yq(j>I;BqsG4z!j%?Xze zUP&v#+7?L@=sGWhG4s)uH`Mq4M=SY@Yq*Ve&M5+qw22Iw<+v2$H%*Rs*BS z1KC}Mgj;0@&C|fi4`_k`K!+Q{l!kz$=G|bkS=|meBplt?EfWl<~d-f z@-=6PfnLfWYLPBSBNFy^L`FPAO~#`YYv?q?mWO})M6~!R5Xja{yQY#^B!lE^woy|D zUEVSOX()W>{QfD_0+_&eD?&m)Gi=s&B8&mUcJT`q=(|F%>r~t8P{cn+;=b`?lUG2) z38@=Jf5AWeD+zle5?B{@ysQ5%>ht-*xz|A|I*cc$^*bKH*Ri-S8PkDt1|q)zq~p2BOKe4ZyiIz)-ZHr@!P3b)Q;$Q|3|DOLd(z&chZv zw+xsc99()46Mg(Vq{;HxNd62$M@_9`;y4(FfsLhl%A&%IH##hoV$}5^!owII^<;Tf z)ti)8FCme>z7Z58LUf9hy4W3#E#}|+2|3@+J>CRTuo>9{D0il!?^A^DrCcY~rj@4l z)|=0)0WNf^cJo|!Z}-uz^AoC4Ge5z_RSU0Wz}BxC4<|QL9*ISC7~j5qd&qHoVR;ZU zVwkXbOhroUkdzgb<@GH7EofdG)-7QPQe%GL?}8w^V>GU#KP+rFup(hXtsZ~7XU*?) zkd*DyqO7m6+_V78Y#8Pylkd#T1N2Yd82=8if zQV2E{$@=E&n)-cw^N^|J%*daZ(k_}Z1Ky{7o#Bv1q1gFqRUm11}(` zpj#KFK+5`rmnXS>((vni!dC|^^V#N^d&dP&Yj`m$Op$-Z31C;*UYlJ&@db15ftwaQ7HUT~{U_4M-vEALY>F_vN0l`0G;c7x)Pz$_Apr}cebnwkf2N1Y~5Bky=V zP`|=Zv_@TB!($gK2uK!V>c(n&(lzWl4#{1+gc(-HTB;ke3(XLoZ`^Jq3`%DFGE(ZW z6*51^lCZUtOTUsP-mAa0tze)-G4#z}`FL3xC-z-OON^7x^78sCa+XUMVZ7#KRwC~w z0proa0MSyroK@)@0FVm5K{l-Zc#v;pjogM3(g{;%wYAM(Pi)0oqB&G=+&B#E+NYe9 zrKRn>?^wHHO!I@+lMh_Kfr54X@JAJoBcc}v3)EOxi?FfxrQdBo*96oThfu}RnkNeZ zE2I-YQ}7a&&WsqijkUPBep1+TT`6y|>_PsTma0}j$!|HXn@iO$3h8B@U8>v@qs?3q zm-uXD0K{aSWZo;EPC+blVJFbn0a}E#x;V_Yib3Llb#-klN2r4Bw1U6wou6Z3e|{QT zc7f&$0Pb;`^}-g7RfT0L73cIQ>-Esd1+TSo)-ip|fM}TuE_6RmTQ>_VE!9j&yj%Mqu)axJS=?$=0g$*t3g=o(L2V#Jh))Rw0}7&wVmc zN5#qb&}}d$$Mq^==&10Wk1X)@QA`#wir8z+tqhq|1pQot6w~L2Lc`WDoSaNiNO=S2 z{@gyx*x`hNf`azW+gCtv*j)^ST9}v&Kmq@1CZvHLUpdxDi@!%|5~AjBjEa?4*2k20 z>Dsw9Wr?BFnOy?uFI;n#`AqAsc19rJYfJVArKJllLcK>P`aN7WI~l{(tNyqoIl zD;|&8SuM#cre#g- z1k@kHb!?+?gJVho+ zSq{0zY?sYQwL{VqR{vWEEO}}sL6<7j-u6nw(1bor<-eLmJ-rMG&C<-9x5vXcl zV1P4FYjbOjD&1B{QiGy}sN%Yc=*Ma5skv3=ZE=NoUKA%{nJC~Rm>w9^1^ua2fTp7hsC!B|7Z1P;R7D_|~hs$030PG3+3Ew)JgpL3v zf%!Xf$ee9#mDN%mQla+aJRz+rDGjZt9)sDadw##4x`s@t1t1m%?&y^S9ia>Z*gsLy zJAPG0(%~=jaF%JiGLSFvHqZ`bA0!{n{(`*&gb^Ez)B|rOEx0>@TJLbr(f`=A+C`Y%jU%K zZx;t_vid(B79aEsUUy<_Qw}Md(^J#L&^U#{cdJUjW#{+uNdkTmFC;=I0VE}ZW8ybqkF87(K)F0p`pF1Qw|CU^u zk!7bpDDJwXw_J>Z>f$_bwWumK04aX@=>9Ax5W1DGd#6wK;b$?94Dg@9;|)AQ?K7 zSH(fX^0N0kdZ}#KmHPder3xdrC}a|lkP*b6e{I^F?JRbAfCwwLQ_6`&h@DrKBt?Cy z3^^oiJ2>&WFcEg;3Vda;Gvi$zh<=@i1*f0aHtyJ*j>v%dgII}>*V2QFW1wCJlfGyA z6G*)!eU&oCR9wSejmv^WR}8FY6&qqqZlDAp&>30xd13C_R8_NaJYTCH%yZmEbPChs z(!~^qoXdj4@2>hU=2na8>L4EcoHFndo4|vH+3h(lzE~+S@iBpLaSgf@spGNWT_~C# z^yS#@&?Vc#v2AgiIKu*>CqLO)AQ#Ty!3s;_0$&SFkxNe<2+u=*IMVj9tG(aXGFooc9)AX&a>F70D!H>0?FVco9uN>+ z;jgLG58kl;5DdKYG?|*HY_|#3$5Y-t8fhdjQ|n3jVgMpm4R3Y6={mc(s197JB}-NSX#3*z4+ku#LPmwk*5?%-FGK>VR+o(@L=J=* z&*5~KE50PYPyYKsR3x5Ew&2UH|c!%i|CreAuAWF`$Ph+`z zooB2bplY&-_S?g9F+^6Yn4`vQ)e zUEAoNRYA$}y_Gd2^xN^=K(sjG>XR^Xh!*@-U9=__2Hk6=0pXL7|eP{=k5N?^_($&>h+2 zK&(8iIEBlNjn?9-F>o^|Us#308(v>DTVEe8}Zzqw#+fOO!Ph!*uwCXc>T9Dlc;GYwA|T|clq~9nh2oVM9($}A@r)pF52WhVy-aH6U^VF(t2t`yMF7sfyu^v~1@(wf z!IGtBnG>6H#?&J6ORcqt#qO2c=S_=;om;C8x226NFO*`%^F7K=Otof7Hw-bx#NsU- zvC$|-ag3)Oo5TE(JT#=dKC8Hhde7FK@$n(qQ=3Dt7*MQUW{k?vJ5M3%9h-hxl>jB-;m687ANm|o$IVE{TId#Fb0e`MI_P=#EaAplDi&kfq#-D&ul zd(+?DYV0u%v?zOr(^wIC28t7Ez?7B47*Z>B zYW!+1YH_0pFLWjGoP$k{vyo8pgB@i163N*Xir!oxDrU5sUukE|qpf4NzI~~@QU5w9 zj7%qVbS5hWmE*il9Fj1J1#V$_zM)jxOG=NSm9>7+Sv|FNqBg2bwnxXTaC)#< z#Su3m0OAJu>m+nehWNv+2F}<9zALbT_6;Ye;n`K(8VchJvJL0#D`wKyowSHWWGwi( zlzroB(lbH~aI!GekDQOhnbU1~F!a{LW^0CHDnU^8V`n@KQs!)Owzx+;V4mA167N9b z4O=)IUhAy?OGC17cKPB**NqvJh>j8&jr4+0Psssms)Bua1GqP67}ZBfSgQosNsiahlBX#kkDh7q!Qt@`Zds&2tt!iQb?Bc>8a7V`#^as7meH zY~O>i#_T52vcw$SfY_};&%`<6!F9B2gO zNvU)v>b+*aUbS@~k@q!-cAJBdl^5Y)ph|t*g*^!>IHNoO_i_dQ9!r7B3ehrAPYYJs z>TFjtY5qjP&(rFPVs-OyULIN+R(94%Afmm0E)vLXNDy0OGb;rm)h&yoQC2v-{6|(-4I}o$k;@1Yv8Uu zT7I#{UPkiw3L5tgOxFbi6)zLMH7OvTBm4jqa@K*E&6>pevpsm4x046;8pVBr9{<|^ zTaHO2?|E3oheyxT52ItDP|9hw?mSSR*th> zd7cd<8{7z=`Pv;~2{z{O8xI|3;|;upW}px*0EFJT7|qd*Ir(w;262Vjfqp_~sY8mV z4kuDZcoBsgl^FM@1p%5x_?D1hhMH3uutyUVg947^1T~}Bp2ZLwo1}Eji3d25M_tTg zPe&f?m*{C&)VDFdiVd-d^;oO3*DZ1B*4*r6UIdX&Hna@EU==cQVyY#Q35SA0K&rf& z1v-=e20+mAIelKjv`f2RW`{^6@O@ z5{4ZS!LK<)PWsr{hTWXz3%1J17yr%>v8gp0E6dnO{iEx?7ed%aWQkZG&*v(t}7=e2WU}!k`Fn;FYn9wGqmm$aE zR;vrj&`b}M=GaCdN6_OcDnrJnqxxP{nZ0>C_sY`fFs@R(s*xE5yenM^N)7MpcKSnG zt=`Zl+%XkUTJmaY2gEMX1Eha?>mJa_97VcXxn@?mGv0-nH_-}_1-45p`1_zKbq#8T z%clb+B(7e)${SeNjm0kWM|sT&RF79}BGt!ruLnoU+Bk!Xp*7HaB+7MmX21Rx(=Xda zz&S94IG9g+pT)TAGU3Wiaz46BB1^2qwqki>ITADStX#wq88$&Wi&}aigPPf}wAi(w z!L!+raEK2V-#7=9Vr|>J5&JpE*!frFD?@RAwU|NU|x4!!PjjYStsh;6qFaEuZ@5uda z5he0R+{M!aR{z+tX(x&PdWg5hHfmB!Ir=~5Y}%N&A5{mj(ZV5IboUrwcMmks<1qpGo@fE<_Xn;f1db?7Y`ME$c7B zwq*h?YiUe7|6Tdigx|qxa%Ldhz?zVF z@IMlzg%|8WW|Y*w$g1w-ia24gf9$@+FaQ6s`^K>n$&gdcUrqi> zrWC}zjbpVe`q<4<)g@i;Y*X@_@muuV$*A|_1mjNSi|_uA$3g)7%E2$V{KV(qfubxc zTduK3ynDs9q1T|NWxsmLH;(~d#690;z5#Y|NV6+H1QgNbL39HzHZ2AFQC= z>-NAtsy~qu_0L60pGanv3DzDE(($Ek`8!YldTrho$Ihyy<@EDA?RW+NzI17-mias$ z-5GuO`iFn-liabr0PVIrLjChf{@sO+6i_Nj1c!eGasB&YU`a~bn3n%HkMq6{;=LgP zhD@zr;Fr1NeP@)T!s$#GKzR)1f$)9CW6X}@&5Y$*W`ZlNdBZH7*MF=|P8_@Oynpb& zX%EnCbQ^C2zsvNch0Tu{2kd~{vwn~NPe1Cc^)g@Ci5(lZH{#02XtQ!rb(OLoTUB~` zp!ZuSql@hS9N>@Cd>5f5Crzi3C2FOQ98JLuk0ZP{F6k@muJt|YRAS{;$nHpU-{5D3 zU6yZYW&VK2D8sv+H%hB%Fxv<1uG;h9E-3LBkh*x}LiES3tJiMm2@06JHQKB8`uU5F zRoS`3&54PL-;{zylp=63lJkRlAmB2*DMBQWCVwPJeqnf3x%Z1q+<5)=A!|=@gp-Q) znUVJK;)n|N|5?fZ-8>UHn$!lF6P)FdUl{Y>@ItNwz?qJeQnGgxRLY6(y~?NPVwTD= zGte25=W$Ez=N}Rsp#f+P*)X~Oqx#LS5BA$^{u2!qd{D!+|7JPG1p(81t$n|l-WRfc zedlirE@yM^*X8(@Qt>YY-6L=E{@`5G$ZR9+qcGVRcH10d=c=5XVkaa5L7Dm!6Z{Xi z{`n0MnR`exzBOXbnZ^3w z%=K$Zdwg9|2I~w;sZ{IOKiZa|G4$$;5A+P73b>y z+b*madcQ`vSX(H>+3eP5nH^i)7c?auBs2doTu(OHX3xwID@S)eF1LE`&j!!jCgUHQ z%A4UceR+_QTndN!AK959x*!!YV#&^@x$A@#{V@uUW1z$9D;MD{E;v78$hcV@Cia-R zg7yko-ud+rssT#EzTC`+5~xsh*R}XTGavGmho#umqd^h=^q`EV!h`OXWmYqiE=QDi zNSq&WQJgW&uel(CAmchY6kdE%HsiT4HdWv3mS#OV3ts!aWNdcohOI{H6CRh?U_ZIF zsSf%zL#ViQ?A6{(zsUsau^8HP4~N}bIKA?Ne9dmIO%|hl&UO`QtYMU&eB9m~*N+K5s*NX#RbdLA5ScDU^1pJvUG z4PR)~$tw$H2ykINehdSF+;#G~7Fw5TykR9nE}vY+^R?U@P8a87!^xJNIpaU#{`v(6 ziPVLi%9k{~We*w8h%l{A+p{aR^G?`bDEvO~4c9*bToo?Fka;@&==`#f^(1X3fO=BdeJ%=EZ zMK;AmsS9Hq8SApePDp?--QqBwG7Ufr1Ti27Slmab8G)McuJ2qSgRZrH z#!qX7ir8&O*o!aCLuIqcVFWYND{TdEQbmd5YQ>cDrpnXVgMJCwuKo!sji6-Jlq8#P zwn`WIpt%#}t{8>9yS+-i zH4o{Gn2pP=tRr*pEPGnFmY-9gU`2(gr1;j`4)4e%ldQ)~ds?&=lps+<%mfQV7J{13 zxYjj817Si3`MK|H2XO9vdD&5FOugjlc^w@o=WHUGb0lOF_fP`4*lW5m=x3ka-8bBK zTaUwK1Y^rl+JLVYDmkacpA(>ucSsBHaSDJd3s3c2%hFjY46i&fr+wr+tcpAEgHOAD z=%bR!Ar&Uct{NgVB7{z!shkmt&4dl#w=rEkNSi+#PpoLDG*?AzWUkkrw;qtmiGL*a zmlmg9NICR(FMuzW$G6VcUwn^HIAMIO2;H0vRla3s7v*w+bl73kIVP4fEQU0bYN#*K zQIR*88@i|ToMXmeho)DleU(Y+FnK!JJW*7|8OX5m6Up+G#?73koe;NcnI8_>8Zx0% z#F$*|kC_S<7$4X6oT~eSdb66AyU|{Q0e-1^tYo?we{w3cm2bT6m1X_Kq-=qMWfg@} zC~R$GrVDW;Rhi+9O8SSunhMSy#v(uQbfNHL>U{a9m4w%*(b}nM(SbeK>weWkSWm)> zKg_YFy&YHs)CndFAy(7YYzEb}Exxh)8BE1&x&K8&O;_*c{oQ45bRvC@Qq3UG00dqa zvgCINrRsocUYc+ymE2mg!m7_FsE82k^0+qSz9!jz^qRfL@&Qg6QHa;d=<|-8{hR~v zq|v7-M#p1e7v}C58 z{+#U@ta0m$bqwzd1f0CDiJFEHM0VwOCjE#bR`T4Wn(qE|?B8 zoh-sDYNIDzVs1csEK#Y{o|Plu8oKfn_X~aAlzJi+BbkRz4mhGIu(sJ&s9mu4se>xU zp-PG`nNt|Td)IJwZmO0cA9i~&_CdrZlEc@m%Jn)+*5lAR*XiugIQ&YG*#7YPuIoX| zy-=VHybA>+${(K@(ELOa8GCUJ6a6c z8TPvUD`x$ZkKYC!V}5&KY9oB>X)xhkU!ZHG*vkVnu!XkV>DKTy)1LBEt*F&afn6OZ zHHDW;9L55dPmj+Vq>6$WQEtMpnfmdl(|Wthb#Vm{VunkN?m`rweVCm#kn?r8kOhVu zhu1S`Ce5j=D3)I1uA?i4o|~cqqjQZBC6qv2VXuZB3d8B~aKcp0x;TPMuq@5Z|IxnU zOT+!@jSrpGV-E;pS2Gf%A{A@b!`QD{6VV zwWX%_rKF!q*OPr(r&E+#yOmjr@jVWM9heHti8-R}1y_n%?IyJ_>6D&g84n*~mMxe_ zx5bb=#k8EFmRhsg3F))ps)k2;CoWbKg>kPpQe(s*Haa@m6;2Axr0MtJTh*Q#(o;xc zUHEc>Zr<2Hj_pC?lw@_YR<~{+2)n(ASiwq@Y*VSRjA)(h!VOViL!8HM=pe`3p38U6 zvEF@s=fc#XE7AWU*x_9D_G2{ECtBix!DY#PA<`R;BIUKz5P8MZ3d&8IzX~4X$ZGV_ zvqa=3{YE^>2+%;KV$TQ3(U}kTJipT`npLr z9?pnwJ*e-{*!BLSAI>P8_C}a-Iy#a;)JMG#KJt{ijse9_V~HdT8AQ(r^0R8yL}*{# z40xJ9H8B!PXa$`HA<)3+HnZo89kl)b#baCvfBRE*LliA1ryBdX3$E|Y=!ddYA=V;0 z|8740`)TKsi;NA6-8YQohmiUNAIj3*iP_1bbZC^a|Ep)+eRlRe-P9hTl2h3{=@v3X z&TM}(1*U+a*QsT&MC6)&lV=qY{H4mm0iwzg8P3x|lAA7i_PKb#0Er41$P-9w_$NGD~vU_cN-EC3}gtvULuNR4|co` z){mSW=Zh`ROM*|Xl;GndLbq(^i1C;3v`wanMOVw@j@;>XtBSZXb-Vm)PL{*#({}Iy zbKN`QtZAp}INu=CKd>zi5c2S(7&?hw${jD~ydn3SQEMM+Rr4USLs?Br=~nK-nvd*_ zSQwlALXhHIX9|w}0xgl!t}Q#;U?9w;Enga4)E_?Ca~hb;E<<-*hgOGK%_#BlCXU(g zYH-$?P>JaElBG!Px~_$^Rf9#-!2ja)x?eDn+yAP3QXg3Ee5YsCnWeO0Zr2r0P90QI zeNq)DC3R@=`d!!ZGrjZ#)G8`ImC>PjW0lli#^^w}r#a4D*Dz$^1=&>c9c4F2nNbng zHL{G8t?2!u`L=U0BI9gL1$8ByfP!*#?@xRMGXy@EA7!H>ix{8(VAw#sr?x_#YjoPc zs=|6r`n7De3N%ZjgqdYv}HQ;qE!_5%IjobMU=u-L>w$zkggK z4BzkG&wlo^pW2^~`uRMEZBO1L9P!P>{iN|dHgzgE4|i1qG1JpNEqBl`0k3VirL(-7 zKLtoAx|;LuHBTE7bpLhP{~?%kE@il$Zz7!}()9eLyhp=S0mt*m=V$hH#j-4KLyRQ6 z`2>1j&+TF=Re9V!IWVJ9%>TS$;`v%&GU*V3y5s2A$D0ojgvy&Q=J1UXEOy&XOiZ71 z@mo^f&!q{KS-#_g7|Y>laPp|_-(LG1+r3g3^bkVPwP1hs!*lUew&L-x-J0+exug;+ zt4I08I;Gal5oAnxddSd_&lwr)q#F7k;yTk9n~fewimdJB?-q%;g-(SL78JIs2EKhc zZOvlsLy_}a*5C$!8!FnnlQ=elZmvHH4>e$EE!!yNg}=M>jliV&l<^0bJhmpQ&+$xT zJVHDo!Vb3|u}tY}xUDrRV72HzWaqegfl<7t*61LIBQA`CMgW#+oaipF#MbHK$sk5C zV3vxNcWQDyC^hS4zMYAn2Up;DUxbEVIgJvG)U>LY{udBzmY~6>(%qMqA04 zx7_$!oH(M*Mw1U-m!QM#x|1m+)euV-=gBGIv!<|v2nr?~^vAqH1f`qjS$= zQ1a0sZ4c%zpDk$HS~a9sVks1!QnymlEE{vkdU`K4b4~eRuCpQ|4w3c__FysTMW(ja z_7KZtjzO8JM6#iInn%Ve9!vP!r6F$VVYBYv%5|8a?v97U(+@g9iiT|5O@o_WV$L|6 zf}Sm$lEm@l19=6mnlXXY_d7B(q5wx`*`~3c?4A`jwJk65kmSM(Ux&(UR=J> zrvAz_mR?oWqsq^wla=hyM-JUKbA>b^?M{PrOWiC%Jsi3qh8jxJ$=g#@Ah$D<8nCo9 zLasLyExO-`m;4~lRIu6KcehpNmiNjS@ z@!%@!=yaJ|{zgcO*z@tveyyG}aKLwhyRxfW5WD5mT&@2=2pmoM z{W{M(orQ+ZTDND5C*l?Y&A-v2&o5xyM=EWU7)oVs9?9KEqgUMpOAZ)_Q^LX`)w0yr zAA*zAK<;)$N(kVIEGFA~^O>2#a7-cy&%&qB@>J0qEpg07CoyA%E?*7frf(5RjJ^B< zc42`+|2<=#{-Yc0ag+q6jdIO;vrro)wYA1~J{{+b&vD>gqT(#$r{mKdiPA(=d@UnvI92#^HahF`D?K~-8DiaH?YF9iuAKuFsDp*pE`tXbu~rPObv7K zG!*ObRVuSvT7;e_|DEFXI-m1b2jhDJFK-nsL_nTA&iyuO+v}|sJ7RdLKs8q(JJs!y zMlpSA>2~L;f;$s^RRW2|r7ipM&TAg%cx19%>YD0zZ<`Dze|Z*Mnf{rzQUc2mmeb{< zXkDyiX+N_l@N`OFP9>+_ig7dPeXzNSTT-iJj*lu^%2da$yk^?dmx)pX#kjDdY?hK;yca0t5TYn>=4$}@kVDAtXf^(aP?QDj<s2~LC|ps3p%`Fj)YQ4wvoR_Pw~-< zf*B-Pw3WePfxp2wb7^!>K1R5yQR-)F%len9X^I&KA9)XwdTLXINCQj?h*f{*WQ@3Q zv9q@~KQ~a*EdmyWkddE?U1=m|a&}sS#uA^H^NXgFvQINlk1WIN8;P;5*4k=ke4=EcMmv>?8t(mmiWzR9r_6f8o!760`aFZIYVwz$&`>_nSjnMq@%vcP-EoMp0(w$ZNx&^{TU-i$t{M+A5 oFyLTXRB`iP z(b`eFUH~+T+QhFG@qUk!M3peyUf1_+f@h6`9(-d34?ck&e}n1c^eU%op-QRAY2OYF zo9*K$-moyEc*|XIiD^-Tyk1F{Rk){8?mGGR_x;1{Me%?I-#YP`=FtAy%0%%-sTGEA zX32y+vMRMG+R~kS@HgA^@DIL!SU7`gwDe~D$5(elL@!9TR5Zk*_?1XrHB}oS`W=FP zg$8z1=|$gAXXPCl%qWdB+9xNuW|-^3(3$`Qm`qtAp5IS!;(tF;Tnp!~=oIDqhnJ1k z@tn<0PFXO!1a;INrw_fY47BPZde%5pzaKu%VWNufmA(7se(hNv&SSa^oC*6|eZhQx z4(~}hn8%dGxbizbRXko?o`0l={_{A3qn@#MGUPh|#uI9607=7KRcy01Pl4D-}VXKO=Y|ez_9;-h6XTaeF`Su z{zIhh%!;FZb259V*v1^`=SZ{Q!823`1=L#?u_#|Y39Rk$`G>9d{Z~<2pumo#vmPRL z39^nTVKM<6Q}Z1H&;RVd;-`N|ZoH_5m-2l*f5`T~f3oXa?sSHur@K3`Y9Me((42t@ z5PHl&a{iPj1rC5{_EhEjz7~hN`Zpii0x?fXdhY;v&o#Dw9XL&dQJjQ{eJq3bpAnX` zGGBl4s>z@ybS{3FG9#GWYVZruK6Zm;bqIPKLSL=%P0Vn}3Iel9^c!I;MX%Oze zE6sP~k^UM3%)$n1=Tg9Td4K=aKW|}YR$v&dPf8s{O!#?lfB(Efdaz%FL`sgKa7X1Z z{R(Uok~WKfZMr-9M__N*q5RIZj?lj!%&!Lg|C-6&F6x1pd^f&e)h~BfqYoDd1<<+O z1SHduK^JKEq+ss<4uyw5{ZO>{^KDA{AIoBK>q#Z2Y1gpEMVwqi&#$-R?=}zH^IpZH z=zQ-Pa@79(LSIg$mXA2w%`Ju7m5oG*Y!WB_aN60~KbuzlZy>+YG&g0e6-H@9A3qeIOpDDE#PrKtFuWD05F zQVR0U&duT6UdkJGbXjQkNK8!J8*qmO=4K3+{ej}1N!T(}w$5smo!wKW7J2jascLVU z+t~54!xrJ1m2dCz_k`0qq~#j+`BKGilMY_WQX4iiZ%GaB$D`;k5UFt=bVG36xN&z^ z+Sowc7-q{2Bdy#SBCLzBp0SRyIMuht=1BhlrmYpbRUEw}Masky?jXZ&zR+F}v!A-j z#1o)}VfD8BZ9LVxMrl6wRFz$LW+0WIh04IDd+7Lwt!%1$>LuGl+f79>9(Ft?i~==n zv@IS7yT}~Vy(%;3iL)6K2&HNtJvGH#6Mo(uxt^MZ;cW{^(xH+_{{{8(Ge>sSk!3!5 z5R@xAx6qBe*dTKz!bbMi3STe*uo{i0TDsFTr~vTh3ud+a>rfeRKUG!j#aB#lP}mu6 z34vphy>uhRq#9gW&m7cZR?fUqda%EQf;%5GIoLGGyA&h&H3zUN@DIZQ($?@|!E753 zMtfG#Y6BB3&b@$81#J1atub_}ZP5=G**`1zwXCx(dZQIa9_+!_oRLHm2YFCL-rfw+ zY9v6$HtksGXN?fUwpO=asrI?GRV}fy;pSznivSD)o|WeWnBCH{0APRZT@nb2sVTJxH zWe4TxcvYIgdOfauHWETI?s;J7 z8aS|5^NM$E+SIi5hF6tPWavQLLZJqPc-%9hZ|dcWGf%(}IaQE_`M_GIfoZ#{s%tP+ zY39}ErRg#GHlYi2!#jrLR*1xxvoe9TH<3NJ$f%A%+#5mIYjvq5qA0+}D!m+q@opZ8 zP>V@dwo~AA#iOD`K}B=6@J3$`^<*@?Ktm*sQ-zJdMNI;02a4lZ(GYHSA=gl zz_V8fY5_n@4|Dq+cPG68e>C2X6~GFbO^C;KaO08n=2OkwVuiJKC#Y-zgcIL|sk}yq zCG4{gxZ5|1GA(j6lu~2yoFnjiSI5weZZ(-NrFbZuO{+FYXZ`YrLa`u~@QP zmp`jC_kgv8)!6YsgxK?}z*DQe^$dlT{u-7Hx~()5LFAm>*ud9HY8r<%G50Hm114cuUS|Hu91MRQl^2#^D4`yVbqyTV3I|dp1wGsUsFLkE( z+H6-6t%7+ATDyK2(Q`?Eb%{WQB*n=zWm`d*46&hHdZvZ=r)+r&E|;-Po!)fKl{lUf z2CHF*t-ViNkzV!_v^!S%$N(gh_fRZA%;IiGc(VWscl(&jAn z0aD0<>Cd!79TWfn;I|!XCKrDVhm9`#uD$kUTWt3DwLagWXnSo^bt1W>p@)EJNyQej?@8$`bfL(3FS2{WKWz?iQ}EgWk3f} zLzgitLi%(-Y-+;mpvNnq>TTB5yK^BeF0B7>b^Oo)97MG&a%&D>9C28()1a9TtUlu`$6a_isHO;j z%Qq^9OD)L348@J)cnj5B`je&X=Hy*A?S>KRdowc4qv2y#dXWxR#p4u9byN>VrvU3h zEr9-!9jLMrA-sQb8~~y2r&N^9T##Kx!iJGnG*?hChA-y;3}hU{Zr_@#5c&fMUHjZQU;aaX^e7<9K*nYzAN)o9)o%w{0`FnDg*tl=0n!Khjg3U0qUHNb$T zprGI~$IZo+4O)fhWkq>0!?NfwS>(EUTbQfIO_KIEvsoyaT$tT&TU5hkd-;<@Lp|de zn*)$6-nV=w1B5nv0b0ra`mK$nPFBnbiNMX>z}&f+eR&7IP?-<|m3Cwrbbn47lK65c zR-sMchG^=e-AQMnS1kc!ZjF5zhT)D0)S)K3L-PPyy)Qv=Zp?nJ=;Q; z{J^@W+P90W|C}#cU8Q-XFVX5dALXTYbO-Csyh|D+RkIsL(3&Yk9I~2bI#>e9>Dz<8hT3%|)IfEtzV50#!T8sb-w zZw-)#@HoUv1g;B-uQdd8(z~d!6qSl5K8;|RY29C6)Rowc4OquviyhsVYz)+sTAdqv zvIYn(dgB;vpMQL{+Q$oQEO=z-QfO+P#Z3EN?g0>>N|f2|C-%?#Wr>rdN;NU#$iz1N zzS*}GR=ANw@Q9w(Mm34jwLV_6fkk$U)%BpAuDPdQTLY?QNni&>kjeA4wREpl8PkUC zN7par!AE$_`s>-@8f7GS%<32By7Sapj-jMa#845$vr%$C(&iexh z)_N5G2Z}rl;3Ec#VP|gy#zs3?K?|_f1{|FM(kGnpLBPk4O=q(>hg^x`S}P8)$X|#8 zA4=F1@W;SS8234DViRq3X9AN{yF6Nn)5CghSI_)$k(H5fddQ~S@-D^FOEIg&W|i`S zv*KmK-i#)CT|OGNGqJaHCN1Qc6qB#X&j^pcA;ez#2GG^MF)oKGs7O12lPWJ(A&Cxi zl7vk?OeA0^a@~=_cKVY`x;?)Vd6aQi-#O=#%-6N=z#{dHcpDC)F$;A4P1`pl#y4AK zjJtF?+A&SLL>#!|sG!;(>^c@RVA;VLBY7VQOu+o8~IWYm?46?dh@x|Flbq4SX#u_wU&FJr{G zgM($=c5gQ|W=T0$aJ7!MPgiq)I@;?(uAIxzp!IM`x<=v3Y*N}(W$Gkz2fIR>MD^~b zBQ`srSl}0_C%P*AY17aK@L{ZlGT4V9!&)5g#aL4(IY#Vi69;z`0gPmX&LGa52y(^i z4>q5z4z@NF$HFGSy05V8c-(i_odGZGrbSCOA2EoI4c84^Rd?E#)lMtQ`s_h&WklbRDe@Po(1qMrB%1&kSFepCIYYEG0aV#zRD!LqbQ%zB0{A>%dwf_;ep^h;`Zw?a{5V zCbj{!X+(3DUQIm}(v)22>BG^-Yxi^{ZCkVV;J#^tL}cn}6pfU2S17lUI_zr#64*u} zHoKPWM)h>;pI=m+`elCLFl!T~hq_PGZ65C#aLZjraOIButXw3D1VjuQ)R?gG;?q@j zV?<$6JcC36pPw38m3VenuQ3`2GQNu&(Vy|f(b%_Y4!QA1o&puIXpRGFATjLdI68%F=}+B@%nTJ%jab2Q*F+ZL>GlN~_H|yZ?>8_y#+z5u zY;i5h0QQc(Ejo|Dq40TxFI5}#G_I)g;9I2&i?)>&Fs=H+j>b3cSyz+Bht{CJ3sozA zwZyrw!HdF|uTq%WBh7bb^r~vl@zN{~7Bg^{Wl&Q>H#c~9-v!JCl0}Vy8=L(ojwwJmsES0c$5WR1$?x!CUUN@Vw)Y{*H8f#3%MzGt2hN)DqT&6; zbHUa#IrB?`&g+ZCTO~^#c=jXc3#DQVf;*q^9KJ}EbA(zZ)`e9B3uVMOt&UZ@uicI< z=<4juCgoO$lIqgWvV!geLi@COj4eO;hI0ib?;7#vXxIDRisCZTYXsI({Yxk4H-qSv zO?#sy#!B#8)zP?qq|6@2KvURF2{1zz|X zaj=||&18eWOHpH@7s@+9c7&PI^?Z4DQ-84sFJy|bfK zrzom5U>}3f*JjhIl-emr-$YPR=|vRF4A^Z^h2jzK8$tWLN=b6&%v{ZNBD}&(Z6ztY zxT6F&^Zh_Hn>9Ao?C4C#d5qRat1!_=*_ySGs=5-kWfTi&*#V*q&K6c-$bcVm%iy*v zx5z4y8ls9`_&gvtxAc-`#pIL2Y?G%bWRgg153Q*SAW|FzX*n(~u8Ab5Zr9zcweEc- zYD@;eC)Rsw#CovM(k1Z^f?;z#{^JR5M%drt=}IE?MKEu7 zSaH|Rmq;zWNVFXq#I6eu*E8LKSq#c&2r4{roX@CTy&bbd7jMMU)z#(lWG=U#e^>-V z)*m6CBcL!Z{b_uiLN(**UB}ggTdScU1F>giD<=Dx4>MSn!Sj#!aduia7f;3K=*oD!`Am@-Q)XiImQ;PFpK-S=7kI;7K6*>C>6ckXwv3sVPj&;dBM0@gwT; zcgf&2dBP4B@>9kZ8)<3ojinv3Qqbi#ebZJE${x2TW3D28Fq3l}CXV}4yk-;4ru980 zUi!6CZa!(LZdi^MkfuG|wVlq4*^jXCn7ngC{NS)E8}U0YqJK_~XUIW!USNK6dzU5h z{Wu;O$=Qv@r(e&)YZXLnEDaFvyoO#HlTScdhy)G)y)YoeMjg_&7FQxX1{@;_lphc? z8_JMx1rN8b0ZFZPUCM-8Q(lhi(c7@HeVF~r>@wEDs zk(7T>!1C;Yl;}O|8>n2j0Pzz*J_PtxkXA)g-nXh5?uQM*haL~@FIvN2H_fQ%r&hY+ zv8inWGFCkrW_8|YTIHu>8b%nR!U910^UI52=n4t+QLi*v!mQcuC11x+6Hp`^iVd&v zqSE*h5<#H`PC;R=$ORLF@LQcTdhKWE!_rgYS_r3y(M;}eRZUe0arIndo4sF|p6r z6f}4_L!#?bTrU;F3Nn^=JJx7s=9_v6*}NiKY^jA`+%NoFuU}$SnD!;UcIGR;WyoJ8(Gqu7H6q%H0qi2u_VmB!i9-M%J~aSO}wd#FjwD zW@p)QS2}JsZ^^c&=t1a$_db!L)TbGu24Y{T;IE)AT_)(A+Yy;H z6V9OCzCxrTJzO$I=RoBiNb~>+oJ9z)1)#2imu1D41$hsZpxlKWjbw2TC_urk0({aM zK@yrL$S|zvoIlY)2HMMCAiZj|kkd?S0mG&aS@c8V{>itK-@b_bUbrhvx9W>H>OcR!-LjUZjCnRifXb zN5O%GP3o?h1m?71R6SM1rNI5Q`0*G9EFIV6ABDGHs=k0^RQS89b43x()=uTX*&Odu z(qNH?rX(c157cnvbKGgv$SnYb(;e8(aH3qvueA~Cq-11vw)e-+Qaj^8w<}EtEqf*_ zD?pmGUWwEZ1{>#(O+8dj zd=69X^6p@}BCn)W3+QT8q!{*PZ<|_9Dbh<=6^<)&d>j=9k z-D&P!bGb;!^{p~&Mppn^%5rx<6l>$(U}?b7QGSjHU{N^dm+-BlSSq(c}`Bm^KT2Z@$6XdVuU?>3~(P!2l>S*@{mx1hswD-hcL|G4lcRve6<@$ zRKENmLT}g|R0-`*7jAdEC9%gpAEt15-*Lke1i%2A85%)#*#*&v8JxGV7~7H%Vjd#1 z(%$7*t<`%qJqz>R2MSmT1FqC4-1vhJj<6T8+Tsvr+rQW3rvtztoVUFz4X<`5sYRK2 zP;Ozt=#-`Aa%f;-zz9+BWU($?e~evenN1XFvJ_IiFIPQnCQQutXIqkX*7B4Aq`Ow^xMB^@y8qR#UhY9yLsIkz0ojf!Dn@U_JnQ6A2-XV2=C z-byd<%MJ2E;D8Rzv<-8soAOPdlLl>fu!$hS8ss)O-bQ||114lr;m0_}fSr9pe+IWf0pzC% zEa|15i9t3SHIS>Gq=l`Xp^Tbw_{!MYapf<7wfYpI8yTMceiHN47-II@ADv##o*0=L zjt}P6B`!0N1D+4#fgrqoDnJ=Vadz}rjfGT%7yE0cwD_;B5K?@{0uZ|PP)Vuhb@U~- zU$~`ze>RA=g5@sw^x-#fJN|f(a|^0>yhWVp-_veCjmP#OXsy6vKl?kE_krOQg@blM z0eB4YcQyU#p}#1ygYO7k11&?ZgWmpLBZp{|Iq3ZfH*fzvS)OpF1m=D5rsVD4>;Ir` z;{%a;3!nb)3Iz{^+C?4r>DTi=tY{tmFdYVLBRnkocZGt5%J$%QJUsfWW5h!i(qPHW zrBx1vvQFa+`aL#2^#x%cV>v-bFGg_D=eWb>)^cf*x{9Vg+jyC}V~r$N0`v$M<#QeG z>uGniiYAAZgdoCye|?k|SQ<}{%kh>NOJ$=RCde@ zvwms+pbje)yh|KcpsXob<_x`IEqV^CWXR)mfVa`3S{nKEBh4BZOhNV-hjZ(_QtOom zn6mHqN}@0P$JRZ8p(#i4;V{QjGBkw+e7fyA#l%6P^t7d-Y3J0j-vZF%xC6h;5q8Xk zi-V4ssU*%cND+6t2%j-cdKQLkb=|jUv06=6ia3I`7TeRQe{_}ZKcL2;j~9<$NFFm& z0c6Ph(0LXU8;Q}ErU*yN=&Q%*JO!9==p1!|CnAzmzh5hw_eE3InPiSqD{gt!gsK<` zw}pi_QfHR-mEQQJRv%`7oyuE?FBY1!EzSN|K7O|A&)TBw@AIW((Ft(BTprmgSZAXI z%7q!k$#9%9%`gG5qN=TxhKHMmpWz|7t~4Z*+FT_*mbYB3I9y~#k804aZxzcx|@%O&uUmtpX1Sl!i9@B{*Y91dx{6u3J74{o=S1HC2bk?6#h z11}L8oHJ+6ID4nN&?VnJ`3V1x+QV;X#+`1D85tKqW@Iq8d^>4U!-2_Gp57T3)*v7Rzl_b6yAux_y#7SL?BhhKWt zBxTYD)_<6ZoPhPGX?`ff7v*l@y6cyYtZ;^I6;PPY?(1 zPu$`-`^N)*@1L+M__tz(=^?B&ZofTzCnxA>0HrOGN}Kh4^uO`)@<(VG!4#GS7ny%o zY(OqZ8UU>Qe_s3_mHAhp15wj>lCr$M`=^fm@dv8Z0s^W5EULQQSZ7JEfb@9AR>mJooofH@tWBb-x~@O5BN?Qvo)Ri^NA1 zp$i)W3k&8@QBg6kbK%Ha{z=j3PvdJ_RVY0D@SEEZHS6u*6E}55ilQX3jw#clbNj#d zV$pYz&8{;?Er%$kb4A51gvH;!Y9`|WCE+Isv5!vbG7yK8bRuwLBMMMx@u)bh4%8uSF?Woc=2b?zNpGv zIR8&V{Ju`q_?V|Tu*MYhT7F3LB<(WjW34AvQX4fdl?Y_@KSOx+<>0~o)^OuQz2Du} zO+)<<>p?oueIH53!O^a@1^5QF0a3RZD4y9G<|&SigWkSZm}V_}@W-Lsp2spcC$F_J zo`5%fJTmChr=2fx>dIews`v&_d_9h^nF@YDX}3A=l28HuKS@(@lT+U@><~$0#CMH z?xP5bKf^l%0t3ao`|dAOeOI2No8zxPLzAPvay`B|`N#?^YdC-SfUBJS%qKYN?c1f2 zE%h_l*oj=DptZ#MGqIobd@qee0JBeLr~J3{47ljy45N9%whz0 zr=!if6qLk4hu#$q4p}bmV;p*~B@dsC8MNi>T8m2Ui zdx@~phgX@(GI$~(1=&1RnAUP#W^YL5mKMWpRgIbH;5Emah3)&2Axw5&@{lFN5Be113@cZ8PRR30hMr z5z04@y!4mb!?~OQjOKAqXx|UV48uuuxYS`m`5Y-%o%&|(h`NGJ)w}bQ5S2V*7Qhvo z1vZF`tel+AOna;uVj<6N{hR12=>HPTopDn%2X~VL>6n-rF+QUj1;u4$eFtUG+dzZB zdoki?cJw$uOvFEY!p8?r5BeAfr}*JYJBt!L>q!k40xg~nN`MY;!E(z5Sd?%3Ln@Ar zrPE4}9{GTyFcg3F%^AWw==H(D!5lWL+K6<(NK>xf{k*L^qwo=v03Ma_07g{G_3SR2 z{oM^;u%lB}DSj!OqrX6t1h&Ho^5Te|j~t+pYI}Nl@eo2Xwci`Ua+~L3@=S(Vf!X3l zWJ8yRN}5-}*+a+3*kBJ@hP3=W7kV?I2f)38%}o~7++I!nlwL!MBIto*kyp-jplf`f zNYp&*Ln|hEt-rt4`f%0NYnmV$3o>P|%_Bv99huuqqsLfQxl= z6V%rfJ*h;D)8=m7yy>j1qtgc)plTedLpyxekG=KZpD^A4kHsm!Ns{1j0@Q67fVA;m z5#t)*rN6?!Abx>W?HZ_v`&bXzi0y7Iv4J}nH<(pCc1i(l!23e$rNu`#Y(~nRSDQFn z@0*!r+Jg%ht0S-R8bINC9$B{Al@_CrL|CTsfsChR2nlX*NoAw0fxDL1$asvoyhL6u zKQX6V=uFcP`DWhnSIPbDZl%7C`D%4-tr6t6nn(|aT^@&9J za1}Q>929VoGK=}+=e&@GP>>P!4Gx~kC>d)2{bY2s_dYE5+K-f%Qu>1lkX%?x2ql-)`zp(5m%}d%Cth z)3MOdvWOK4?xkhETzM)t*e6t#>;QlZu7Zn)eoU$P75n&hJD}y?8=NBNpXT0u{I>!- z9Ke5m3*`5ujy$L*T={S&j`aBNh=UEx&*NZYPmFwhjk1}kGCDrsXwU~n)kFG`iVCB$ zva;FUw&^z=p;SOQdV^n)iiw_H?DFNysPmm1&SC+H_WO3`a6$g^)}Q{n_rQrdO9LW( z_4RkLVWa{@EHBw?B0tsx!Wh~bf-7}9|G_{VTJFED(ug+Lj?$&JAC6~Le%47>Y~Yzs zyJ+?P4{zpG2DA1{qtp`nNfm<3eXM=`^PHtGP}j{*B-QpF%U}3; za4)`qaKcrc_pQhGrTY2V|0D+|Kj3)_?98%T{bE)>zv^e@C1(VI@h1{b%|k8yr&s+v zxc@&c@oCf55d)FUr+vFSfxe2D38p4T#@e6yjNBKOoMjOgA}HQmF}oOgEM@!4vP1=- zITLHQ3^PKJb8@~A+L$}R+pr?-eoXY|ouSglUzHy_@}ar2;*F(QETP+XpBe~yaM>UV z5EH(x!vTAwRJX*MiP$V z5h}uxlw^_5j7A0ihfQ?4Q;s^Fk;CrEUGZC681Fq~yfNmIR6paAiAY62;WokeN<%h- z^E$qe(guS6@$pVf97Pik)HF2JFJOiSsORyUwThAPQ=FhO?)CbV2vJQqEGP)lnHdzOHO{_om$Gm zR)7@Po^_qrDkD{}QB(JPLij%nx0$9bk&m+TyqB`9AvU)Xslk^d-&Gm1*JEl-Lm{eE zKG(b`HP3f{yPTwFbA}yOVqK@P7{qXCbN9VV_GBt`b`QR}P3;5^@^YR~UaCF1#Q(4f zvaf?sx)L--MM!I;9+4yEb_= zRQlz^hY!!GSg%~U)6me6xFCl0AB%(riBd{aCm!uD=4nW z$dl-^ekFKNmUsW@WAD6@jiK%o3A-iuwarrH3R=4E9H!>^AsRfHf?M~bBCZmO;rUe= zpCP!!OdzmL9?aESyYf@+(fI*4u~y)lDE8{!KB;V3WBdF2`Wy0l#^Z=Xu~yd zadBxcv@`$+F&^u4J!zNjwWZ@FjOwK93TfBj*J1_J($Y$KXP69^er}XDw%c0tN5?uB z#KUee-}|jzj@{YtEPHKD%>$4|0RkHvd%AWHcmL9IR^)!uH}h{_4Z5HXe)aYAk%j@` zf;4B@=E{P(ek~pR7p0<1aG6i3ocBM{!&!Xq;xv{`39)DFY2P(z#DWdJYM!(Uv1G8C zzQJKqcHi)4r%NSU6dn;#+1S5S&~l7Zz)9l_ zQ1yaU%MXH+j0H6dey{Wnv`Vw9cwcDeeKnh-ZEf_mF`p=)r}9&!m+_9_-P(z%UoSH8 z>k3|kJ^neV9wI}1jk|Og%j@0hv|NDyXhsY6zngU@=ox(N&HM--vz^x+^DH`c7wQH; zqr!$8&dpKcJg!w+{fwR7fxQ8H23AWUMeXD(XDP1|#tksGHmhIGiU_+pX&I6=>DR39 zAxJIl^^*(P`H-41_btWD+n;`V)Lb6^0{ecVqG#|BPMoV+RhqG>c*+hGWl5VN8dQX;5SJXshBDmaQ)?HNpmv9R)Pn+n+jei z`cLNX*H-4JW_-Bv~leeXrXg9t0N*2sMyj{j^z1vAP+*h_DdLW!|3CAf>A# zCzezHYPiwCR`2hUoSlP*iyFq zOEKd=CJugW*Qg~C&>8fy{3Beuo@i@v0lerRI$sk_F|((L!87r54& zxWY=j9~F*j_|InQm+P$ZM)V10FqyL;hw!7z%{E$-@u$sc1>L;l-WD)QS_`a96Tl|C z7PTJ!bgCY2kBLUvIxBWrf5Q4ndO$i-)NMI=wXZ+wENng2!~FM$m6w5945DX4oBJol za5Bfd+;8i-|L`rG-{7a;{G?TfYvf3WKcLt|H5VQitkT-7 z`;^tK660Lq&V{5`FobEw?{S(ZVj-0`$n92qR7_xahdpH$=3P$ zhK3d1RE@GLJ8K%0{^}*RFAeo#H8EZlZU%5{$t@thIe6$CW9dZ*pH1qmY4RVP{|G(W zaIz{Jp73&huvC(wC8A4b>O-L9JAez*2+^G0$y^LoSnbmTH9mT(0Fb4YVJ$OsBO)fY zd6%;DJX|u=D&E3EK_h|(8j!qW8*9JQh2WU0hJ%b4bHVd=F7ZL>@^18$RHnNw5n}h` zPy3Th+AZpxJ;sWsVax`cT4qA~k?%q+Ba1k-kkIW22dlRO_G^P1q}^}NhH7^wD^2FY z;_K1zkctEZV|1Wi+J7h4C(^4(R2@1lVz#|}zDKK>C}kj-g&v+h06mBX_0ZQkivz*! zd4P25%o(5b^y`=SEz+6Q_d*hI9Xh1S{6RXg%fOy|p5WRhu8-EMzt^Xua>Rd(O773u za;hF#2$k&e3e2UtH!FTRi(!~%TS9sD14?=!E0iLR#fszIPgdQtrhscRI8g|o$!rY| zkPZ{j#gf|>%CgkXQ8aKkC^|0t5M7}3r$?@*gF1i`zx3@knf>uIgih*NQ=tzKeGes0 zmZXY76arJ&go@2&TK%*aw$zR#UlY`hYL3;ud}I>58c#ZdX|=yr^kq)X`LE7t$oMWV zck9ZgsB|qV1y+Ka%M1NWY6{_LH;DZm>OWA4D7v{zXz%Yz#WAkAZ3%xgO*K*}=~EJz zi8v%w7~mf75EpN`bf^W7?MAv05@RygqyLH)`)9i0CHAuWea zHA^1-MieHt#X^4{F~Op~s~5z@f3A~*6EDPRHR>yRlA!R_t17!2623+-*_iaGhq8G~ z-t1Z}iK`dSDtM9cbYsLCs4^5b5N656#NcE8`w*N@ph`)iB%ujEN=ekfe_>!qARs2D zq2SL>Gi$hc^CpMY{KcIm*T}lk{TbgtrE4X&R%xpj2l*yG`so}XalbXRW}mt+?LA^s zk-Q%2?@0>8?uoehI@vQ@A*J)=1tnbs6z)ex(d>2J}Qc3Z}0@bE~WrwiuPxQf%(Ol>73B>X^Q1!v^D zcc;Z<=s7v%%*@PUzI?fVj)H$;x&Q=c^`#DoCj0eW-h!Fdrw%RCt?6zDioB-kAa*3! z-$`CObMBng7|}fY(qK^!o1k;KTZk>t0R!UqPD)aKNZ3n;81 zUL(FwBjp5UXJL$AD$){MY?^03_1s>fEfEQGgb=C2qotO_;;}eW45E9gXUxCZNtRi# zN|&`->>)W&;U*`^e(==#+~-z1FuyWYev){sU`tly@>|>z)_(|`Kp0=4@eV!w_0Eym zI5~-&#=Y1kv$~26@t4YqF;LptEse^^8j0 zn^d@m|uz|)6#!J;m&kUE9l?-*_*h09jBoRX`S*1-#n>ruHh42gQ zkr5DF2Hi2h44Ht)Q+2y~$#+G8d75Z%LBh^;zHV_h+h|Jl-g-as#D{_G0}45Awf+OGw@YZ8A!gh^5s}48}q|y>iF`+>*gxcIdSv{qNr0_T!Rb;)sZ4+A-6#q z76u!Hi!OR#Svrx-`A-fzT2wO^S0d{Z+qfmxp<|%G5fAS_3%u%!^;!FlYeF^@n_EN3 z7Isah5Z|2|+9})$b{QG^<#$(R`{#)V0Ng2YW*|bqGDfc6lGpX;fythxn!q?oj*Y{r zu=#x#OY?Iw_7_5a2feg@4YZ7K#y1JRe>rW%$NN4d&wc1n=RP@%46|jjO6rZWFIH#a zl|PY76R#PXE6?&+(p{ZU*f{xKz(mBsfx9u2NmQmq$0axa^76Ou$?YhFkBPk3B|V;^ z4c7KV6)9;_X9c@k?vNtA(jdh>)_TqJH*={gn{7Pf>jGP@?y(QkaS#x6vvmqK0C5}i zMOZg#u_w{zEwG3;mArk;EhlQD?8}9;FvV(z*h*_>-3IQ($;ybuBGYrlwxm*F~`AIl!Pbh_}!Tu3Wh?^IhNSp6`oPMtS~}zj~|xXMSMyl74+0 zdm74Ky)8St&Uflm5BpJ;P|%T$J5ge46dopzE4O&(Jlk!+L!-u-PqnjNZWbf;p~jM< zGDE;2Lf9(I=J795h#R4RAHTPlRx<29!-eeL&)WglOeHG^IDD=K1v%-#CE#!PCABC5|NMQWR$1kvt4IoChm)${>Hagx;7{$Ku@ znP1a&{i^{bdeosHCxiNXPW$I`5OZu41vb)ejX4fH@>jf(I`0sA7%}X2wsOt2U`s6Y ze_iJ#L$K2mMS1F>MEwTg>+7wXe49=E>y67GdPh9U`i;EgzaNhpfKL~n`{1#E z+gkBR;7 zEvy)`d9)(}cWymR|Hobbb^rhVDLxZC5u#v?!!LXPjlh9U^5GVzd9@$Emks>@pTq65 zH{#Dz^{-_EFv@4EbQf+Wzu5lzf3N8thR{tgM#)g)k-uH2e_Tu7Bm4*2Ph79y+kSrv zM|yHCgFbxtpeBi{l#0wG8vO7r)cpnm1sAgBe&E5MsxS#&yeR+q3fZ@G&IFy-dtCgcDRv6`0{!FT`FbG@fT*UhA+8aMFjEX&I}Hn z7XiW`?93L^r0gfj(A{&vZ)7;&o(eK@45=;b%#lQwwt00s84j!@?LcTtLxVV&(jml_ zvbzxiqM~cV#-pq)KG>G!iCmg73yAU;wXQphh>3{>#_s&Zd~JI~;K(pU6tiuA?fQ{c z)mW@2DSh!y-icgY>TG*b002=X)|U$-CqP&`){|{!5&rV!%aAV-=tbj?bH=34k8Jwn*GdcXIFyHV|sC=^6%)O19iElQEpMI$Z2z zX$>Haw|rjdNy{^b+jIn57upS`&-AyuO*@$mH^u{I$jIu&rCU$_qXmFz;Xn>e8b|Y( zcOz7MV%xS|`Sy+bc@^ASj1O}J-PSXl_sBPrfemst<$f=KqO024+A_u6>8f;Mtu27z zn;9hJ0E307j68%;D1x?&X^8Y-?*1c%4SpH#av=lvCG=E{c!;%wXZ74)U1g^7iQ} z+64~u*UXk<%#pIxl1XI7M)_pa?PY1UL!&brw_14x&84bZj_~Z`GBq`o<20eJrE?2axOHi* z4*4o2%JC%I+P|ui5&Fec{oSw4M;@(A+>q&q0?Z!w#*ac49K(LxKHrRt^9JQ!#h*TX z3J;1cD4AAiTOr~#L_pC{mG>I<4SV&030VgOlHZ443I58oi?8#hDwz4x$ zHx;e+VigoEHc_$7#Q@x_IwAk)+vAzu+!|Yj)v@=df!y<5Udf+@ilBq?Av84d0`kFw z2k$jpdvhKNe9gE&9w1V3V_Cj)Li74{|6(bld>fqC>m7JHLQ^BM_ZIg>+_ooZwgXpG;TjKmul%WdCwmJ^+@) zTyjVNFkCftIZ~(U^Jy~?)A9k^`Tjx--a%L91+(|*c%2F$qhtPaVWhoN$%0uEmj4J& z=>c?+960zG5sD7JJ9;Ud7%t87=aGm%$neDwKlAAz`;c~Ij6v@A$!Gu5#qoXH<69D< ztprEm(%-;iY8t@mQ^jdx5E$eKbQ0K2t6IUxhp0* zIy96(YBvb6Ds>@RtiW8%(dEY323_GUrE4BSE4kf}%Y!}WY@WpIX4HKH`+_-jl83F( zYod2~-+BB*Hf-q~Yl_%S|Afq?S!OQ%U z&;PSXdB=U@A5$L1hkkFqvE??l?9(k=AZSCGZBoroZNv?LJSGgnM{+5K3v5&C$}E3<+RX z30^CKaYsPfd*0OR$1~gBoT})PmoFR3xJ;W1P6dL|9}mc!Xc(U>D2L>Lf`wMGA@51R zS@(&_ArTQlFI>V?? z9Vk+ttg5O4$Y4I;58o{{%W9NUSBy5PO~t!6mBkDzmwsl}fZ1M_n|2d~9v{FrA8P?n z@krSC&?*sw3!N`tYqpgE2y$$RKH1N;9kz+MAZxjwN4?ZJXk3%2h9PcN?Q)tw0tA6Y zFC|EK7k+j6Qif{1r@}N<#&g-~Wl&IIiSC-od?E-|dPAy7kAc3hhCJ=3nbXWO4ye_6 z(rOk!3g^y(?7vuBPp3>|sU4virvkjfjg$?_$=nOESo7^&vt{7Rb5jY|l4cH~{~4{QgEN5{W6N zq)VqjnXm#weL%nbOa0%(_CP))m=F7yT zjuL_=X0k;RIkY(mtsRSSTl?`BaM>_JQGb=VaYXhy<8Nd!t&^MQMpup5#3tB4Igc}R z|Gs@WZ}TZRj4)+o<#9`ewTQiHAk5IqQ+2B1%Vl@(wrQN3062TG5mRPbIjP&|U39c< z9PZPnuh8bkMv01!o*w;NHNQxTK6{k^^?d82MGrc@O#>bnFI#%viuF(3o#B+E@8zSf zgsxULLExMjojVG3BKl<1cMejL=!Ug4jv6rNt)9F+k}1qvN0 z;jOuh7>ESwqchemBbgk**h~7cEZ;^2mBtZ6bzOx96FjM}L<$=x!I#OePfn&Yq_*>A zF+Kp(SLVw5CvsfoOWaze^_$W=Gw{uvDTQn8)AmTrY}s-VdDzEP7XyXIeDP8MZe%hH zn(~tEOk`F$0t|hgV`X9zcfwDEciG&{*PpO#y0vo>&;YXMcM0idPIOF|1N6+-sw#sN z%(97cou>$SO-Z&`!N1uIuwX2rTe3nHMUBd(u!qf;Eve@kpcr8~_u!kj|FrgWoru9Ge?veUgMF!@90gfQ7;x4RnxC3A z{K7fQO#nNzcf#9NrvZqgMW(5u?`xtcIsRzK<8{Cg3P*Z+$8^Li`K8N*;E{&cbSq;F zn{L&j48!RGRKG*7qv3zzh*!x6XUmt!K^s3mLa&F>Jkd( za(y<|h6QGjK-e)V+sX&4l9B9-Kr3eOTVSQ~E-pN#+u4XsaB8dn!a36=HhJm!{$mfg zr+<)SbK*_?d`GCQi zDMIiem%^D^TUgfr4gfD1@$vaJcAxG@Z=4%>AXJ;7qzmUf)VYwZYuYWChc=4)`O^$E zZ&upkKVg!6C)2c2R5uINF4={!qg|WMBPT4!TC|MNP{y z8{0h6ZXpAC%!U#d;%&_w0e)??1*~Z{r$Je`|?!_*>CCIp4<|(>HM1ZYRVIw-@5nJaNaGaOrvJ)IE}l1 zG{?&IYZrI#Yv4RuaHY;BL6UQ>dqP5@-xy+yR;x+*h(JwIQFLoy3P3n94e0{qw8~7MLMEH)jz645k>3>7V5Tp#le;XS zoL9Q1yZiDYmycN%T@g}%#3VxdEn_{Z%ML2VMRDe>bT`Y9jb(St!F^o%of`{>JW)uw zbfRe(m2wp59p&;S0s%sTj4-sU=jl7Q*}jJaYaMO;8Zh|)2)DeaDG0>T{w&;ro4i)w z-)lh%2?{3T36J*wJAIKzQ`CImv*iA8O7@y+AjNAY-YN6L#X`PZ+x2=&Q`cXk`@ebq zmu|&Vo}zOJTC-LVO>puK4(_ymx7gJMs;bEYM%m`M+!;B#NCd2^+4C=g>~1S$z&>Rg>CQ)(cOT;J1({2Y|O4Xi!SaRK8^5?V^SKln;b z8Lj)MG<~|7J;=!ve=nUeK&XJycn$*T(#Cf?*~EA3+gYmM?|(U;KCdu2BzI|X@%AMH zAtMCL5Upra?Of`MFfgiR+qIrk3+v!c_!!-o%- z3>_}G<^7Yti)82-UuK%H>$#lPJ6Yf9N!6? zIwut}{T(1>HafiB&vVno&YU_GE|^!MhT08F)kLv^5Xpo>R=6Y92!I6X<>|t(Yv#-* zlGpo7Tu{;L7_Dg%Mi4Gan@DSk zX5l%{`TzT;P5UlBTLnV@W4D);S#%<7zwh&ThRz{bEVtNH0q+F3}lOcLgv5)S| z{xJRg(eDN|U8GV9Oy3Z2JuAIpug_~>cjvXcn)pq~YYgp?l`#I)A`YjNEsb~Q!}K~- zgbu^3(jHVfnvh*Gq_Y?|ez?SSe{1D*OS@!8R3z7QKgM<~Gau!=*S%)4bJ*q+z6Wc4 zFYb>(Nb%O8R{VK!%f?VWXoQ}%zT2=<6p`5(x4i0E(oUDj%&AE0VDoXqh7RAgCQs&G zuiaSlA(ucm+{;H)hJWVSPOUBWnE9Zc8@}KV=5KoO?H&Z>0~Xxjf*Hr+r2FegTT+6^}vYA}q(e z&q&E)F!_E1uu&0%x-Jh;_FBloCv}_&u~HbvvtK!D0CTu00imM@Gu0V(2{f$o07D5f zRb8Gpg|SEH3x3gc@iJNIvQ(d|DS564lL?HDW*a%o{ASZCPTc1V?BaXfHH=LhI+GIZ z_$tE`H^fpK2gr&E74!afb3wdZTt?$lq#uyl8J?BQ#m5!{X(5UEeR+~dGhYnsj<&XW z&|M4`qnI^VSHZ+CqzIHMy1^+yU0(QR;@Ea=BI@(PM`eQ?tF}WPgsEehK1*&z^wnhi*~T)MW#sLWMNj;h zgs%+&qH1o~kD@o1- zo^+1R9G#h=q=>3_1Oc_KTvzf(OJ+5jUHWJS6#4-~J2!hT%}mF->vOj51Ul1ziXx`0 zfy7z}j6W|)JDWi?G$;Y7i@=36rJ;d=e^qFGp@YMkO`K*X1aod9ylPqZ4^KF3G|kt@ zes+DSMG9=46bs8}2meAl(vYsE=DoN|y@~=j-+x)+Fs-*^xMS=NDaBaLEet4m0VnM z;JAVL6uXE**}t=li#Z2otQjq(u}Dil2TzJOq(hzSytsut9Lj368vr@simHjdKNCSlVzBDl+kZNue-v%}+4j`p zR;sJ2BI45+uWUMK35X&W$jm_rm#4&VM2eqZ3D9WGL2~F?rH`g<8m*vmwfuzdoyq)N zKqNzZEep_ByH?s*IPZFcaT~WorU9@eQt=bx8AzjT{wDJqr1F+mc)APxe?sG=4ek#n zsswdgpPfz|S^o6t)G>K4_xyyei|a*8eU->rj^?J3}F%T^UZU{W1-@!=~g zg@1>NO{X=3O}|l`pXrU`apmnNzs|_VHi&SEqG)Srmp3p57cCmCg7DFE)QpEn@xiYz zIS^07(MSpwG;#4TOowrtz&n^E?B{Bgfs+a5TuGe^-eh*D3pzK93tDdhTp)#%jedq0 zh&guAFUZ(_Kvqy=ed<2Oto^{U)`t6)%+eJWgIod^TS9?G?Uls63@*fz4`n4o;(QwP zPGkx;J$$Be>TDnB+*z@*m2)-s|2JdyQJ|tCBM3pVbhT)-wfXVSzB?26E#SbKk|^2T zB_@82vTkz{yr!T+r8vd2&;?iP%6OdM|8O?|wA}mNQ{<2cV(YR@9L47oKF8a*NH-6CtAlR}hzFRl%4(;KF|383i$SE=2)99 z!3YWKyG|@YHUGlQAz?@%DY*J zYKGmORrAj>r98%Y>clrBZQ?xuTyHG9W)72Mv-tCe**LETwSiQ}88rrsL@4__^6Yox z{zHe1*&ieG-m7zdqiBcmEKZ#<1^j^fo!_ELS5_`P?9D``bEr28Kz{VWvvSDMp}SF}fz$@<-8gA@ zU*63jpAF<7CGWb_&`&T6|OC*@Gnlnvfqh5z7YaHIU9Bt-;&-UDT|r@O&#Xt8!w;YE?+woy5`yD z6#qLYLG9BAvXm@@USzPtATw$AccaT@(5D3iA_Z}vm=U@eK8SSx7^2|aq7X?FC-iiR zU#a96~Lg1Bw~EWHbp6@7lIzj6*68GDgafs7Sch& zb2Z5PEs^4@KxSihU%_kf!A@Hsz0J;=<~mq%WGEBo?+)tP+D99Ls@gpH0KGZIbH*5-W5NXvzR?pBtP6sPb zUT)e2LJvq~syn93(w1@=KatBAL1I2SWazd2GAkVFBs4RJ=I#H(=3CvSqmt+Q?ca}Q z$q!^k1QgRIIu4{sGE+-} z>TEM=e5S?EV0kQKToPeGAU5URTNPufBy zD~!7&e~ym1%J8F(+dPv<(PxHal6N!&7HV~`uMgt{=2R8Vwyr)5GZE13vKBA0Sl4h_ z;sk0tC`Yq1;#bg)=n)5EYzN{_Kz<#>_0^azF$D!Z-{Eq!&(p(V~~LKk#wt5Lp= z%%((avKf3*^4{kivip;RK9cjyUgG^D!-lZ8;!nODEYV)8 z=43v2#&updu>-)?jG&QJ4Lr_+Ydrm?RPBq7n)<0q(O~iG(6e}T>>!CEdTYpdIfvW0 z8-GB~(^(3$ib6bQahRRW_J8(Fb&Vw(cfgxVJCb=ZpN}Y=dHvgk9yGm4kahj z=-bu3Lj;~{;+M-AStN>3Isy3xAGJ)CH|usN`T??q8$dapl91@s!+Y8T>sxyD`t`^Q zvq1X9LXcw9N(N4`um!Eu3#LFCId6c{{Ct;m6wleCEph7ck%NthS;Ligsub~ z6R$pRv=MRP{*z+?v1UI;s~o{}kzTrEs36SzWRYO1R0 zz$87G)~?dDiR4(ybvouE*dzU&Up# z`mmfIQ~p-Xv{a_Jm}{CSfP6EC+GWZ8A%2t%lQgjQ;TgN?)Q`pC(i5f2ZzW~xhd&vt z&#Jt`3#bS3%8Aqj*tqf*$KjG2C?%$*_EV%b4S<2pg~}Sn-5wiTot&4atVIr)fesNM zrh15HLb<3}iy@K=R~vw9l~sr{fp z7lO=}$<;mGom?>jI;NdJ*CA8-czUG1eKN z{BE}UE!ycPMgGCpJx*%*)0vW}#Y}_`dcRi9WNbDM{nneD=p-Y|k;QDK0V92JjH zmJ^uxqZt6uP#-hvt0C4E{1siU9e8Os8sX{-kEtAw_Tr_N8WmYA(HBOp;JMlh8eH&0 zB4k?2#``c+kD-}YuQPgyyO1#>LuFW~^Dw4?oh7ZPw7sOfAX9`P1#>Phu6(5ppQ3ID zMi?+V=#c=0cVOosW5-dKjJ4>7Cx$oQ_7hgx4F@n*lPl)Zm%d$=*3~?J+QW`}P@{cE z1BKIQr<12zG*7)Y3l^6*#q~gFXwBleA1#s2g1Q=yufm@>Fa1!)(`4CA6D!a?@1lMT zQRQHhY8)rtJwL`ymbC`qIzT2LOSV7m>e;b&+ee77Df<9*EAcUbD&XPh;oDwTlvlSK2&)17QfSEf9#dHR%L?_O(4BLYeI zG@?%REAW4S&W43pK-%|pae8K^Te}-?oIS$M-j(5VwBrB;x$UB+7uggkd7Q^nD z?{kagbf*Q>FvJj(GK3cvzLen8*^N-e1n9oBn^s^j3N)lM#HE)gq?Zi$Znlzt20Ugz z)FtKJ8mXa(;@Y5nJkz*?wjQRE?xmK1Aq$v7nn(gkOPjed<@hp3oX=^uK~nH}{YwB% zKW6~PtujF+DhN0#$-ra7uP`0SEapV8HK0~;dd#r0)_xL7se7_Z^ya+0&aev5H&gO< zszK&>RnfnE1D|lZ-5m&GLxTW$95KbucDQ`73_3gI*^%_Ve{CLTuUFrx@iOgavI4pI znR#it4hKiEeNI@=>E*mI7k+QT{He3#VC-z|HGD(ckaCiQVE0gWD-#9rtlUAuaNlVmhbcU+23;{TD#D0^?tW9U) z+cO?v2N$TxDX7{ zkIN%$#*S5v6};qRDS$4(bG4l? zwx6qiPq&kD6AJw(bToY2(?pM!#>uV`e}7tMgL<`D+AR-dYKbS-x-Nci9_(U_crRsp zG32A!1DhkUjvMAdugtQ#As>?NpGRW*qA_9_KRPE$*SIp4-@Mf+=NJB@UH@tkD{rj1 zW_7M%z-(S9zyUkt!gNukblq>693Ni2Hh4|wA@NMMm1g%8{u)0?%N39VaIk_gDTO`` z?5kbqbrg;`Wo$S-zh7tnuvr-Ge0wquj~{c>4e~SUdF4+dc_iZEWT?`KN*2_OL(6Z* z)#Cz&1UN(SQcXmsYR?Wgrc$KM<>S~I2?>pVKAd@uw8PLm>d0^DJL6D$w3taaLMjp^5A z3zGE)()gP|ktJMnsd}s)!#cQTX*qGjW?JN!gjM|B^wX~Mf}oL}wAZtQ>}1-F54#ms zE@~Mo{Y2yHqvzIv;|Wg)?%F(T|KQyIT{epM1vp%(1E*4~p>)g_dF4E{2`zD=_GLf( zy@1mZ7$~!7;`A7K#$gUpJd4Mh6**JwJ2p@_&!0M4sfO|PK#rmJ=NpdzFW1fs3rwPo zp`c+}B)|8QUyFB1VgTxH4r5|Rhj{u-p^MS0v@gebhucVLNA6s7o;IFzZqZQJ9XUO5BfTv*W^`k2J#1QC&i-OtTV1mm;JrHj+2SKF8TI!v|&Ef)P#Chss~eoOSoknk^V72{=RRcKEHQ__ zt8=8aI0;x@gk8KTPP#KTnh-LG+k$cnM0~HLfU3)g9|B{fiWbCjSB;hdX&h6YvyYt*(cG|_=>s=Rz5$VojO5!92_zyOR_V_yC@ic4^fXs?AbK6ohKsL+886}@kzcX}_c3T2=%Cl;5my09OAk2>n;SR^eV{r_LnvPIbi5KJ2~3l3M?oOo0h4f9Kt}(j+iR$}FAJ zfkG#1jz>LQ9Szvf0~0L^>j=gcPf!y!J+1sUdl*UUOB2Y28Nile_gyF!!=sljG{V1G+A?z=6I0?etGs(M zkJo|}rGnM2b=_n5Wy#93lPq1J1D23&mN@a!=IhOty|<%9;QCyU@=}tSbmE!oT@Js2 zE=h^;%#+vmYZMDJm!1taLJ%6CK?g=F4>h?eNr3nUy#d%paHMQZdt9^L&{2JcjHxN_7 z`I`$ri#8kCvhd&r6%J$_i_j82SyQ*VD3xGXYEKiNj%M5bm49bbeP<5x1xq95*oC%f zvNKTP6us|EtVlRM(LP{B)GAh+gK0c(7OMZW_y!(yPuWN|aO+b3cgFRvcQ2|xRwTSG zJ$-)9wy*Aa!{FE$l;pAcQfi?6kg2H^eO$^6ht7OC&33X(>}+AVyS89`R69<E0ce3atP@%)bdfzzD=G3Rz)&3D6Re&|2^ zI{o`L?)BdBoCJ68i0FH`3AX_5~ym zrMLdswg>eWgtS(l?&3wu8T9@8zbx3zUACVWU&tkhhS|T6pV*-0QP&~nK`?Db{+$=rMnT_$xEl?euB!7SZ1&^Ga zDIe9<;qaV>y~L#u)RPxNP5rlYc*XpU41^1_&Lv!%V4amqgP8TnJzPU(my*d3=5G~n zlyaW zE)hv7spnzE-c(P~croL*V|*#uO3!r0Z)OT)^2A?x6HvBXx7Jr??NpTfvE+{M>a&rh z%;P*RvaRM(SvD4MMZ-J5#DW#p_Hn*(;+()&*)>; z2`1i!ow2*;goN#3kw&J86|2QI=k~aV?DE^)0GG3abrqlFs=W+w?xG8#{qq5X#YklD zOuMUSFHbmpKA=eu=64K!&q9(TYPFxSiA56AYwHu!OU6+buKV-^G(l}mEhTTc77TN2 zQ0*109+2P79*dZTz5b_&fT|3h8O?#Wch^>vXP)&L5FKQVO*}k`0b(rO;d{09;#eJb zLCYB9#{32qx3DOn9vyBNY^{tQ$QgKq(`FrbDG=^KMT`*4qEMe4_!TTaIEWrUTHYP9 zGwO6dqj>-R>RxZt85(>cj@i5bQMaOK!4+&^WF)l4nOl+EYiwI>&-EHUya8}&jRdrN zL=wE)bdB0L6lE(?aZeNICM;geAwMVM1_PLzhhP}&hp;f}#;ptexnY(|2*3QTVglQ( zb+Ec`frP=-@sR9akMkt6Z+4sSySbb*2(#{?xRX765%=a7Dj8ng{q--m7{EWawy!M* za9sWKe8laPR97=P%i)&m^WOay)>TVwF4U2XoB{KUp`+fZ*_jG6ujGC;N!xc3-{a4A zB8q%Vb~O5XYg@P6V?{}(OYbdiJU`Bszl)qylh7=^-DCdk71!RTqmK|@D$f$!9!dBf z-W5$PKQNr*rRBv9+4eo#*X}=8ckHwxpVRe0mLq@0!++dIs{x&5Jzab7owh+vo5Yq< zM~fP(R4qF#%|H*$zrCXE3)wCd95ys^^jAp$U+9suoadRveH+;yn#2`(B@R#uzFAs! zx)(p>QsZ;-+b7uY0dG7&%AWcwWBJdO{@2X}Yd(J6xYrol3J8VL&2fiC! zx=^rd;=fkt*QHY|1zxhSq4L`HHSx_jU}YU>N(c=aC8pktdTdl6W^>;1OJ1sz`mbNU z{V9WY!8Bq=8Jo}l#n8G4y|<86bh-oueTgD=yt?P7ZTG~7s&dUBPULyrFDw1uU*cPS zgzqF^C(;qN9K!fGrkOsSl=k+EVEpB|17|x^@0@IOif<@&HNkVW1ojUMWQhYywMxVVt&o0_^_9IVv7dw0N&7vVtczN@3tRqJ5h7v>I|&thXP5%@M%w|DvtUOsl;=+E9LN0(cI++}CVdq}pCr7JZ4}jP_(1J)?~HJ) znPqc}*tg~B-oP>@IXb$T`Ziq8)I%9e@j=_zOea)F zdwkw+;zQMmyl~%G8_VqJz4-EU$y`ZP!t`w2%`-@YFk?^OR{?5#R<{jSyJbsea$tF8 zD%Uzg(zMPHQ32PAul+Hk7+9x&{MbU&;^gVd#GMgpDsC!i(U}Yv zONGyA^m8$r$qn3_l>2rVOnaxZHgyXSBaR_tW?}afw_N;xKiADTNDv%1Bl|Gs2O{gU zI#}M%GdSbh-<+FE?NeJB?np)FKE7u${LJ#tkMYeoY-MB9E^X>9@BAyXzZO^0W82^WU%XwpFqb&L<=)GZrVLwP(Wp|f%iF&Sl2 zRL8aoMth^?)V+>+I38?OzWq%tmKT}dGb|Z6)gHOrKZ|m0x&1kH&x}9481KxZ>k+J8 z3-bNUrh9Dv?{1kdO{lea*^-K{X!trnGze$&hgLwVU}+6Q5LEr%neB*IL{!csO3~R>ntI68#yvH<^}sJ&0!9Wn*;Zwmou%YJ-(JZ+Pjt2vLU3gB%8#5U(<>ERIO?qu^9 zkNZ7EZ}a88dj7CBL7#DJl=An^R8eKqe(f6%zg-(~*Aph(9O_cya;OBoZbrv*ozsy)RACUWk|s7QrbKeA|-Xkmd#-G%hHBK#n)A+BA98k9wf$gM!hv@xb$;FmNAS_SkZ_pCCjTt zUxF?XVvb2`|8(!mJ4YFMINUvolRM3I7T)00Ql45;!Z^6_9`$f%l34)rlk(M2uCg7I zdZtvjnTO45s_{eS^aT&c7^HV|PjORB2Uzte&ygFp@;Uu z^WKUa45KG^lw7!hn|yZ@r#V-Ve~ARC_ZgwvW%tvbeaIu*F-(qsBC)GIlmstqJKisY zXe!8t!+BLh_eh}Ib}8SI$TE+=-t41IGRUzF{g20`s(kayCu(bHrF2Y0zjM}-lwB%Y zwY2_T-PBi-FJUc<_?VP*4HOB(%U7Gk%WP}wH~|``4Y2Qub9|daLUcT-Bq^*@)E#L9 z>5->q*xSwaSW+Oz`uei?MysusWvLWtc%Ms`#Co4K74n)3cef{j( zyQcc%2HpQE3A}|M-J-9wdJhW+MjV`c`gnEWu~D(`dho{mC+ho^wEnaZ|LxK@y;Q~y z-^S85BA*-SB0Fumv>r)2nC7iFEh`0w$9)#1skrV}B`|59U7qOFdA|yK_@BJ7b%SBZ z6f})b`#~&jFW$RXN(7>^9tJ^D@V-8gD=sc?B<+XND_J%&@OJo287kN~q{W5?lK)ic#-#K zgkzwz@*5Q&v~0sid)BY7CMy4<1;DibZAYc}h0Fgro5A{@T};sWKMDeWj*dWs9Y=LP z_Kl8K0H4E(#V&FqS8{ish8r3h&YAQB*2VJJ`21v|_`vfN+=X-D(QIz%tI~1`9gQ4 zM%?;1J#yql%d@Dl$VP0O^q3+Q{(~wrK87K3DBzR}hg=igum!GqMJ9;dk^#=<*4EZ9 zFRL{;TsS)O=(sF~xyR1NW|2Nyb|&~Vv#n+^RwG8R`m>^Q`Yqe<1799@YVu~<(I!mC z&LeNzWhn`>Sw1CFccx_DwEG%O^T#PMFczjw>B2EYztVFZi`hORfv)Au~w*g4pRV2RoLF{VZ*q&rU(jwLF&K znD>rGHRC(TsxaH!D{o?RFf4;Uk@Pg#rWv!Qe#|uXO?$8sd-C9cruDMknuSSZ z&rnE|e0O&rBl9u%%6)v@2HZLlTZ(H9MucNPv9O|eIWqYdpj$p z_%?oHQUKnY(*+X;6x*k8Ga)lSX|1}P0i~R&o^#r|y2lIzZx1j<@Izc&AF*Qoy{TSWnMoJTIxLoa{F3oA=BO{os8}EERf1(^ zOcWmWJzZzSb0a=J-MIM8M;~~931R)p`~c&}un0t$eGUSDYI1_CF)rKb^@;F_R` zPTt`9fw%Dz%st7~37tM7KYxA?!rob2TrAHWxaHZ#{ zvPfFbS8x02=Czr`Ao+KL&#CjqDGK-A)jvRFHxZhm#y4D^LZd^|{x%fyZz7hdTXzEk$Pk_r5hcy3Ih z2W8}~TD!M#$>^P!jjD~dLsIhMLqEdgQlH`D&TrsXFtlkVnGudCEadQv4@w^D4gDF3 zHeOmtl9}{5;(>SCN0?R^ce8%CA(CpGC|mB7m+5)lfv;jfHm)7LWSJ&BRs;=-bB`zB zxc9loBATulI#+TJ^}IEW9{9dIR?0f{cs2;OGBVs=AO6~>V0IwNwAExng<%|oHtW`L zKUQYO8av6NM-$_Qi%&V~&$|iruK-Lp`Hhe?Ft%8v(0)W53-s$P*&z z#O9cfK093nvd~#s)EycMsf~=(BjNGAwjyuIgL6r_zKtGgtM#+y3r-n&k9y9s&si~< zH)HzdAjgW}T33eY#p4nS;-qKRR|d+};jQ-N9?sRphZvff#}B|G0%7zXNVxINAS z>)E)O;`x=gd@l^TWIGp80ZDzDp~cR^BJ-vJNv{@LADuqg=}t+S;_Tx>Ed&PKuKxxf zI=H45QS1a>PAoheJ1OT*tM-a;`boH+C)QRoSB~6JZ`y=E%oa5Qc;y;2<1~c2f@YXJ*S@lsa7(;rH$P%Bb36*- z=JO=N40-9_3*{TQ%eECi!Ls(++hc8TC=OFdcdrhZr+i7KSt)|C*ovCMH>X-B5-n&vHN83W-GO|{s|1^`lPjj^saMF7DG zbMc72i}1)*6fZZ!`!j=@n`PfOH$@8({2Kv|`0``;Ppu9Rps`xy8N$(k#xUb$eSL>K zkjj4K$Q(p?c&cfU%D&wM%d>@$_`ZSyCoA(c43m$xoAT()xO(=gLFIa5m`yghiqX=@ zPkq-#d}JK|iClBmqvzcoEk{P*u@HS`p@G$&A#+4kxC6M281MB-tOw!&B1#f|7nG=1 z!Yf2w9kkeY&%LwBC zW9%*CqFmec;Uy^|f})`G5&;pA25ArhrInJJ0V#>0I|dO2krK&aNa^mOy99=Y8HSYZ zh9QP{Z};BMdR%Ki+yDFf@d3WT%{BLR#&I6UaXOdGzym@fh+H?9U2rqv@aD;_b|?iC zJor4x=JNBE-_JRko+-ZaluM~+TG~@5nsqK@cEili&c*{};iNR~-eOCr#*<th)<4m#hb^3;e1*BAuriZOMOzc#|Y@`MBWNv^M7ake4eDlW9h7$iT3@ zO${y*g!r}Jnma1wA&mX*hfyxyL{U)J!^V;!(*J6v36;} zJ}bCiKfNurZH|>>Ha9-q?5ETInT27TuO>z02x`xuN$Y}fu4#9uMj+-&S5?cykY0DN*=$D zd2y}0&|!#rd99h4`+8-WUuzM!coAenCQWdejePA}!ay>})_utkqC)Wif}fu3eknsQ zRq;ST8x&*?Gg>!uLl813Y)Oe5Z^#VOobOGt)$7sTJ-?I$rRaK9hI*VuH=v{DB<;IFR5%}qy>Wdo+{)3VI+A1=tg=I;Ws!SbVN z%ENW=B?tj7-P`y|U8f|3zYmxUIsfqEKip5Ax!HF;A$w+q(YM3&Mz1i&GRp&FsTSvx zp9EbK0bp5{mrg}n1(g?R;ZoBn%nk1DLqZ16Bc%&ZIxEhr(++1BK3;4ltgZBIc)aqv zw8J4lT)q&T?zaL2br=$C5X5O)e7KDG-rdI_TzReq>vL7fsXxICRbbqhOSRZGE%@sH ze++s0t^##t3X-S6Y-Fiy3&)Im%RB_V#lRl%tI1)y<^Y zW=mPm(Y{b@1L&sUzkQ>B?hEMpi<&;k>8dC zWA{2%nly+4J8l%dG;3ox3zK6@&e2UfFGDQDEI_G|PEUQitFeA8FZktf5^Fk*-{iPb zNwBpSwek02)5TIE8+V4}l!9$g^GBx@y8wo&WUIcUJG7GaIo|?UqUCx)*{d9jQPpn*L!Wry?KV2*=HWg zN%D=d8?LXdE0=VPPZhJCed%RY`F{<^{6eTck>w#*+OeZ^3G;nl_ZK~^JQmDXM=dnK z@{9z$6YY!7&TciB?Ll*jDkHfz$Ethu`OoB$m@ea6!bRXBmdmT>)fSZ}x>Et%5sXA<{)fTpIEjasl&5+x3{M#J8)V? zIQQTjHE2lRy3z`;vzyLf>3CAmdp>C#CvDLVJQ{rp~d{|MGn8_UWg!6>y zrcT2_2D8hY4ro-(u1;n9Y}v3E?iSM#LZ7j=Mk9gS2(DRNx&&Ny%vQwdNZ3m-(%`o1ld zQQIqQJVc3|Hfy0d?%E91H`dB}c<^2xlZLm*$diQ7u`NgKFJA1*bHBxa;kYa?%-0l7 zekXn%^nzugj7$;R^L_W?<2tfyrgDB{``UJ{+1rN{!Uho?{@+Uy0}vv^ zd>=i`ZbVQrSatR45^GqEjbZh9r*}>ef0Gi&41wYsN%ThM09;=MOV9_ zp6>NAwMfmMI}JgqtLoMv_lS7?V7k8l0(0-C4IU$3g2|< z6LHdd{rc|c@(+ItAjSBmF`oS(>PxEWw^~t2YY9jz)rzzB!Pe}L?Y#jXMT&1pkM zp67kDY_RV?5a5-J+sMg48RWDpqL6&Mn_f)BJ7LQ|yQH$b7gp%;Xdxg_6nY^SFZ9rx z#z{TrP>^4c6+1e4Py#wh>wPq-4f1Gw#eWpu|GjQ$Fz?&@xMh%2Y~PcuKSJ5r2PyV( zNFAtCOm$L6$`XRbWjY7_%$Rgz)6Sgesjz5eSDdE}?v6OxMSDmzZ zbu6SE*k+*Pq=H=ljK(-99s+2CzkDc7gaCZEq?3UK_gvG4XWuvZ1T5;)$Nq?8!W{1l zsj}-r3SiYkL*2A-ROu5Yc^a&T@D5T`>ZxA`($h`(Is_FsFy(bau`yV^zph@*Xru(#J*29`0<=DkvY3f;7aPZ7dkjWXNgzG z{+ss@NTd>vDQ=FB%{!Sb6HEx@E$YNR*P2K}+4;lF-p8aVW)kp*TjB`o%2-aR4_>Zs zF1;8TyXoZWG^T!37bLo;lxpMFT**B;zg zhHe?8r`f$;6DE}q)_SY5o;=DWdV;KCGM~tH^l~;|Ub*6VoX^^IJOLgh><>UK-c}Co ze@;IPnac{ljy~H#bVd?Ovh7@VTll3D_p_mT$obW&<7k2I@^Hm4jy=u=G=`Ta!L7^ai+OUrV*Avkr4K_qI+Nb-w{&j~N>0AY^_|P8@_3{pmD|d# zPdAwBM(!pd1`Y-V8`{+~)Z5y+|C!uydOI`>$w%KX%NQw4X+U(#JK#D77U1r#~QHfyZ~{68MU{~VIE_D^tkZPS`94kNP8=W#Edxifd#m`?<5 zG``mxQBm(W$>-|mo|NEnNy3@OdFjFuB2oVE|OyEGsUKh0%0UsjB^EgI>} zi(O0FrYmgi2R#7lk@KU;#*h5hx$Dguw%(GMsnOc6JvBVQ++w$jrxN#Yakq@6cZxeg#+bAcQqJy;=CI$V>AwHoa28)67Vk{^{3%h3~DR}fSk?#fWuOkHR6 z)XIpCespf1v31-r9p6|iE`I8g709O=$Z?CFAaiw(`0-Wn$q9^jb!44kw3}Q>OZT)S zMiLr$gYn3N%gS=-E3N8i>!w-rjqsLVeV}z8stY`X4n|MRtq&Q@6H$7LmcgCeVQHIS ziao|Dx+1YD!!jano|;sISnB}wzb7gG`8!u;Kgp|I$Tw3cqoo8=yU5Rhfx>fidy6|I zZUaC*aJ|U%57*k-W2?_tXu!qOsEPNBsUF2njur(3mOEwEFQ^Q?Kf1^i99A8;qy9rFsnR6*z$#bam*duI(Kb`j|xs%YWnEwiUocWQ}9KDTzM4HBLLfY{6KNtf01< zXjpFlF^rMgp_$;V&iBylw_*v-LoS|c9fG2>Yb>C}ukdB-u3iZGJZ;|2TX9(>d?w8| z@!#x+D+HywP4~n#w-(ft)~8AATDfK(bV~7e(%d@RAr{Z`kgCIB+9`-@hl+ zXk}+4!>VfzV^JmN5Q=?zpU5B#__H-XAMxj>S$7tH&cuA#A^p>F7u-F)2Ga?av=VPp z9Lf&H5EXjvy_2815v|o|&XGppORX}%KFpJ=dMj`?c;;;CX|GkVu<+rPUI5;q-~Ir? z+RbBkJ7yuEZ=a2oE^ViZ&~t6u%`%hnM&8cI=NuVr3r%bufth%Qv zns1SlRb#>L%ni@tQ1x}{PDgh7a4G3Rhu6mk*Cbp^$aXS+33vligf>5l_F*R9ge&J* zs*gY%ZYMK~l2(#+R{pRFcGv6MFTotbbF+lxmBZXyugXpyd?R~eYZ=rq^7f)z*^QqT zjsA!ltI;0SW(WAMA}XnkMwAl9Z2j@I6j7Voqol<`UWnz}1g~xu7u}OuukzX2@?wq_ zvHS-S@2@8Azn^S?1CQh>x0l)UMp7FQ|BOSB<72+YNd~%`?AGt$kb2}{!{gq-^@cVOXf%zCf=)8b{I4k~ zzQOZVN!bGnzg6`Lz6Zfev(BYNJcNlVkzVNKu0P_?(zPp#fk`jUUeE>RB7`K%4tJ&W zGslxz=pJiUA9ZCWpM10)x;4<{22CFqixlGdv&%$aeMLl1+acgl?}hBi$bB@`FxtGd za5{&OcQhg^jXXZBHl}~DAUY%0hR+1HO+d!FQe&!4!(T3^`u*3v5HMDCaZae5khJ3o zf9h%JA-#oJ7-Qs%$pT%)q2$ zO{d=#z(s7E``dhswzgKvxZ>--UrsR~2#~UBm#Ef8X^K;R-^9YEC3xPkzLg% zG2h9G04z9@ho#C;+D4i+H?>;F%@#qkrB->W%Ne*Lp zGc0PEmcuB*ThowM)OdY z$$->mwFNB?1VlMI_uzO&8Y`%O*S1+kl;BYhZV8%YvKjK%p!1h#^VeA(luE}B>mKf{ z@tf6Lzy1bmNfUk&uXiJ1G1(IaBlSHrTr4>F9!FxBl$%sOFHn8;oqbvOo5 zbz)uZ+lWBqkd_l6YRy=`jEkG%*~}}}fXf^x)qnfQfMmwek08hWtMHPd>oo#3T+&`( z7>R|sHoz{qa17i_^8IeADXGa7d8E3&S^ZzEH|NPucKT8Wk z;Zf}RM)WyX-C?lKt&B4^U{n`}_*fJ7$Ks@!ntQlr$Cr~u=zNd`{Ks;uhLvjC772lT zIP1G=uMY>mZ0rUA*a*r63@--{1PWb7X7{54TG7vHv2Y{qh$7`>t$Y0TK_6+wbgswv@-Z z&QVvHp%OV2JP{+Az_lKAw7nc<0!*$Ahfs&9x}C~Vw0BctN+L&{cS;<4`Tq_Njd2mk z!_Ruwr&lMnD# zu>O_*_+)=Q;|t)}Y%?rjrHzi$KFsWzcA#SG)zCNiQM78}MZ%eHZu~95q~P#8J~{#w zZKp#XA~4)*F~vPjlj4i=5670$vwPJ5WjstwX?$OU1g;KC1v<4~3Y+|F0jFez8LW+B8} zMG0H19qkKsL70qYvg11!%Q(Ux;H7C)$qM`yUw!(Mf7x)0CR|kGqxH-dE+5HCTV_c5 zw?jP*Z|Vr8tpGp}S7FVUFH;l6+|~fXm%3phi6e4uXv7rnM6YLUG!IbHZYO6RR@>uP zMI47kmwqgXhbr>SEe&dD9aR@SL=#l2o_20O<)Tp;cw>_@o*L75F0Vytk!^hJt`(k0 zGjV^dE+<>r!$t6=O9WfXTNw3R5kcCu5=I)A|gD<1%= zWWnpLDxVSiKD_^8r=Q+W!4U9FC?kbFVG2L~Ekv9vr2xNwimaOT%Ww=;WLH@lV)I6n zG4&?MQF*{dEh;m?V3GA2>sE(pXf>fjJqK~TOvpm6Ok~g{r_l${ItshoaD!80F}t1;L824SG#TRxqD2+eug^N@k#DSL)njMTuzSLMUqmq0g&6Y~ zh=otd{)E$|;N-Mu9kjQ0A+7`-tk6wV)PYb#u==Wsg-?Y;mL=Yxtk(4$$9-o`Oxm}~ z_PBvL_OlX`jx7mulFg)3TuDC$blS>Ldd^S`{oBp|>qGd-#Tojk#rcp< z#&iREKK_a3#xErb0CjJXZU8tUFvsr(c+43_uF?ODj<(a||yU-02lcse;?FN-Q ztjxFpH6KM_qJKT~uJxRL24C_F>)>6iUV0>OPt?}q&xW(n0X`5GPoEQ$IO|b3Q8qBb z01OQ}loJ`oE!>N5->4Ho;)?3$`j;z%>V2U;3uTv{pZ~fkmUd-6%K+mXkP+gxYKsaW zjsJB~)P$IP*F`PA@D}4j==oi@H;W#w<@UDN@GJGl@wQIrjI?u#-4k~&&dtp&wHf97 z9T;(oWPv3&{qNdNX(k`M9McB~nGCG)MCOpBPu@Bv!sdrT_-5zp>JH8V768BA1xK3G zJwAd-WqYUx(J0Du40!ssIE7k~Suq&njZ{K}t^~HGH_N80u>LW~EyU!sn!FfSBH)++ zrajznE+%H{TpeN9w9aOxysR)!K7WIbD0VJxc;@p;*MFuTj_yk|N`8SJ8OO{h97|A^ zX=hcxv=V?pn3bRrFpg%B2@Vk#QW3Ek4^JQOMnpLFUaH=EJ>+T|zHw+c3UR?Fs;mgQ z2Obq$5E%Kmtu+bScxX-*al~gcDkR&fz+2d0IZ}mnp(sIrw|2#A3>Vh@ijFaE* zzW~|b65>kgc|}jZnudM1ojp10ehqGvoC6+YOVO!>gK>vKel&sjyxZEfo;_ zUNA`oeMZQM+f-)!DT!C#xC^aCSqR^yG^KZ{PrE4Tb(?U4H;+m3RmX9DXOAY@Kg|ig z8laX#8T!u${#8!pw?PF64{Ns>^Zo(Pth|4xLGe6CN69!sZ|~X&k9S=H$?yUJQ2u-q&bG1$kGF)k5g&hTPx6=AxpsT4^A(n@>JKEG7q9Qt$Z`L0^!%o%jjyq zc|=*}Mq8)$+v4EVAIKx;vyV&v+W`8nIOoSeX-}`v{bFanjKmSqE^>OHE?p*3axW30 zz0{SAuz;AI3U;m&6W=0P za48b|+ltno0U5ACkdH?qqbE$uz6JCg=gi7_{h)LGX8Qitl*{(B(FrE=qYMSvW+W}j zv%fFn5{>F05}GhtW>ztBCT{mxRr}hNzaBEJvDFCv>Vyp!TZu@GRiQGg^t4Fkek_z$ zM0Wr1eV&u!qS zh_bjJ#{H9S>?c5SJN(NvyjM0xYq{vJYQ?kl>R-yp-XdU$k1-Xc((LV+GF)`$`$u?_3#twn??oV#hMM5B;##nus3N57mF8_{TQbza`pIJz#E;-{8Z^@OOA8C5w=bZAoGI zealE@f}>IArcfa%A(L=iNIo1{!X-3ODk0on@bD<8jJ@hx5on3w)k zjUv0YOxae~8a1EYYu*(^wgn+mbcMen<#DshThO*8mAGa4zHcPaO@z|n8(9V~kNDuu zRBqW(2RY63o9Uv8=qZ{)1yNz)qwmGn|DhWF3mW*Z_TnH)8fjgU>&f`*RR2aV-LQ1*duA5Ym?HHdG^DY7jH-tMZoK{vhMQlQN zeH^Xn4olihs`r8Kmq$(aJxF-^!$aGmT(w69Lq^o~!Dwyj9gOQ22;uL?31AofByH)@ zYt=z_|Cx3CLV#U-HeD>kPkVA6M(Wq|?+)$ZTiqx(ms6em)~aXGG3z+P>-W9+bODlw z=rnX87=2wB-NU`Q-XtPBKmsoh(lyetEIL#5k+{ z3v}P+R0ztpSXEVs{j|sdR};+1!YpfHYi+)98?T(X)}pPXcWX-30sh|6ii5JTNEz_7 z&P|*S;>Lu`=(=1|+WG%>L9#Rf-~RnybIi}+R!^9)a1WRj^}4)iA>3zv@2e5PuM10b zg5E!BS4bZ%${J!m6cQ?Pwt06@ne!4?shRXW4c;~fr_c(z!7BIQPksb^9Qn&$#wObb6xWGdj*6(Q2EPT)yZUrxECX8v zRrwv<46j}dy<70@+pDZ(AvKGHU9Ci)K4Ip<4>t_aqmgQygD&W)+isibkq!AukL#t| z+x_PY3%u9q4P7yU<0WbL*TzS3A`gcN(N;zEtn#ByUkUL$1~t5YHU2kZhS2a z&LIgTxPYc^RL{9lM!7@5?Yl;Jekw*l@C1x~mj)b4i!`vYP7ABkT1SzB4l;vVdlu>M zpNh=XiCTSV)|XO0jPn^^Q{E}5o%0Y0OdQijPY;e)rn3{bRoKtyE{#?v*Kv1l@D{M} zJr^#(KT2*svl(|)P~BChY@3YYQL=aAd*qhBh|+c$`LMo9RV*`N>ZDrvX#SU%=zm>) zZc=A%tCU#k9_L?A8nDq)QBe_H-q`k(LFyQIM1b5KN~VvGNcHwMl3lhuoLnXf8c~Pn z%gF~f1$Cd*zP93bP+GmJv&xqXQwde z`Aub7x2|*6AD(b9r6JK8+6hp$sg1Czz&=jEJu9nY{A7x-hfA)1 zwmOE-@Z&Cci(IlW1f&|jtGccbcq#+wmm$r}=&us-3V|!w4=|t8{3-DUyqmRqVf$Lq zQMY>>=tA%E8Z0LYtDz0NU`fZgTlG6FJ%;jnb;s=WXL}r=OZiA20YtvRN!U*nccO1| zrE`1q%qXx*xlZspk4!%}>6}I4c)(6eSGNG@k@bZhj#I&BPxg9>Mw^MZi-0CkAGc9f z321Wwx>O22`6M`)S}FxFt6xcQYT%&FSLrgilI332%nonl@DP8s03z zl~MehFWlZ}ZvimiQWNPnHMro;F?}{?iJWf97%P8qYiHvES8dOavFQ#aqq(@?aJ+O? z)88p2Q-|(Gs8f`5_1W;()f`r&Dhv>?3lheT;OUOR!&$Y<%Aa+}fd^Ih~Jv>e+0ZV*^)?jKV!9mPnJy)gaQdMLX1V8^H z{GL9xl?WRVBr|!>P53p%zXY8BeE$LT`oIDh{^0hf%*y@}*T`i)S3+vDBdn$^s8=e@47JJ*TQmP-7(ew9eO z^h&{mAfFrDFqIX@c50S5EIsJ(7mai@9KJ40CN;sd0=KJfCVdAz9Q6%c7g=EjrZSj! zb2}zG>K<2+texPM(9&K`s%)pq#Lpk%nvA7lP#mBzmraB<@0uY18sF-W4 z6lJddVJ;wqyg|%aoA+&^MyOaMvx-}iQ%fg4q>sFukbk1dN?+N&3@5X`+u<_nP;Xs= zNmluhRci@wna{?oL4nhkb3(9fDOW>mHMwGo2QAK=%VEM!NT!>tf>phG9dk6_fy7>m zG@caPmZ}`8W7}-?e*5mXRsKdF0Jv`Vxh2>CGJqJs$79Sl%!-c}O_W@YN>7{EK<+Ok zX8__+0Eiq)-@Ij54ExgPH5e^A_o;t1j=T4jnU@R8*_8oPvXW_Be{ul#*k$~Kg9ePGv)Ed&kj32!>NJ+UJ7EvhR|A z!X9&~t<5(ol)V!l$v+*m4NZ+d!i0?THP^xnZV~xsl^VfSFdGrSJ$I%7m!AVsTLraW zLKMYwrk#uUibw4C@_9|Z65~(UMzX2xl``0kmkTyO?8<}U;BtWdCRz{;ov|L7yq!E) zoo@rDPN|!@7>*=2EsTn~Z4Fo&>y<6xk8b7;2eMwpWv7L`tyEK04KweTM3F|>{@L@k zR@-1B;hobC@7LZoI$axe1Q+07=hW>1toHN@NTT$1>DZL-AW37Qc%4-Ht ze=o5u6uL_`6!ao(N}sTCvJPOlw@MpvfspxAl^InISGQ)YM$9NnVmoN)atiC~9sMmx8$Z_&p76}WuO^2>{rG8f)o zNAkY}i~p|N21tN26Z4b%n==0``0s*ouY%xUV(XR<5(n6BH$WQh0?Duhl;TJ0naP*W zTLTqLb3?6I05^|78iDNWDIr8~?{y!*pzF>O8cqK(_^jyn*5#DMtdGL*RaE8*{*cK-Y>y!4RXceM%q$$nDHuM;x_(mr^ST`(1$ zBGk|wMz0Il2i#W^kx#KB{H&64F4Os}$av^_m-(QI_Gg6$wYRjkkx6V?F-k&|F3OBY zC_9CU&7C$P>2MkgKAy&DNuT zxXclYA5B--E6QN0%u0`RV@ztExvII>0V-br9A~Xt($Ut@u?Ae^PJyKvw`mPFE4yNr zmzEHJ9J;Kd*rlW@(6X!>MaAAGBYbOy3h+m3ngB=pj&!oVerlZ|M5Y8V%Ci#K+~ZJ; z9v-6*ls{7Y#(%Yvfap;t0S7{-1Ah3>mHwtnG=I3ZOFhXPw}4E4wE0>0icdHLuguAW zP`R+&NQL~ORqWoKZY>6voC!9#lk?Un%hWeVvxC7+U= z{d~I~=SLkvOZ$K>$jIO2SMhG9ioWxvAgOZ~%Vi$^jWO=E8u}R`*-j$Wi6%f>Eya5; z!5`G5t(g@U$)&Go=AX3ry7{tyI?nH0RM$10fBA^h5XGs zt@C)#6x3fb%G|su4|ACJI19%+O}uz#{X0kD1)h4`xo=h3{`dh|}xpICFIthPSpDEEGgX z;ezRL*@XDM{{H?iWc!0Ux;~oB%Gi(zE@^!6KpqEH%4kfw+S@-r00jmPlFE0a^W+!# zIxBl8L3iVQ3RnJcIDV&5EMZmMPVc(;TK2{Wl5#?yGXn>5ZOIwW^4+?{K6NfNq@m3Q zQL&@qXucb0wfsUQgvrTwAD+Pbtw=;2WPJxZ-Yl5BL>An<4Kre1W zSe&!foZdA-ajh`o&t0$2w^aYU)Bgcqxw-jf*^QZjFV|toNd!kM6-sgq0UBbR2Q=3| zV1~n`6+klR6l(|gyJ~2XC9TkQ=+2SS!o6yovQ#Xorzmop-}WARInp|ZXa2}-hySTR z5`(xKvfmE#^Ya_9&}|@w9ahMF5pkOlL;l}Jr+&_W4SQCa9QfC;^!#Cp@sCQzORDPG zQ`$tugdn{9`uafAqpf;JWQjs2y#{{C#@J|+{{ewAT_Q(FE{rYV^BO1Qc+3C3|JapYTEVnnW59g7=OPwWo}!p_263a z$x!~#_iIzF2oe)ltjLl1>QF|i+3Qx;6pTp8;~xF{CNBFKDQTKEGt${$ZK3Wwxf?EQ z44%!)iSx{fUTyohZ|hMIQYik|c{)AxL6T@DV#ivI zW}e4wgcf3LEF13DFs)l zJP}KVUt9)HPY2~5pVUz;{r587|DZE@*JMNeeY>$DZqqIN@Rv&ai$7j471x)fB-v88 zeyDEML#X{`JGGn0W!w3CLKO#{)aI4SdPu>sym#s5cE9Mke|9 ztbk-;JE*j(`0f+-yUDWls=LsbXqLjA{AJ6BN_k3FaWC&#NrBZh$syGz?9dLiMb zA+S)3liSrqiJ`ynOFly7Qa+nWbe)lj&_Srt=pj!^4<#cySj?Yhs@b6zBxk)kWG6lr zTr$7uxPf@O4;}oH6A|7#3Bt`HAf+rNX+sp*W{u zo*iI6g_EUKJhzKW1uu@kt>F@X`=(Eum0k&eIW)57=@fk>wuSf0~Pa$ld?TWbTS z{LsChqYv-tpXNT9^9VYzlH$yu;-UcG%^jwF_v|q}ubMHjB#QO6qt6GppWEuZv9CY4r&cTD}01)a>3>LCKnEZ4udJfP`m+|4c!Qz zqsCwXcz=?)iS}#C^*uiy-jV4R4_e6^$O>JX!HM+=Px4+chI5OX`R`iso|0a)CJR7KAquKaQ ztXexyPWVfH?h&+}5gvj@Qs|Cbfzx9Wo4~Nl;?!u*a8K)$G;L#$tMZR(N~q8_BbkJC z@#~`0g8MRYI^mTK_pwDq)VRT!Puee%_P-Yl)g4M@tXw1q`CJx^GaC={lqJzx(NI7y zip&it--nGRL3wV6A@too*B>SY1%{=Ke&L6BLdqBllp!-7{v*@-cCTkwe;gu~|9XVt}f~8dwX|H!g;P2h; zdHys~d_MTM%i@=F=gHCJQCC{HZA8EC7KbCoEIEeEq9Ate+x^v5i3gR_d)C$SVV7US zp$7&ctLgJu3oUxHA~lxLi%Q(*i{+{^S?Bh39=PLzCj* z$0r+70jdSr4`a8%ZUqM3gBg2%N6FkXr&y`3p^V^!dB6-hHm~87du~6pyI1PmuWIUS zS@UXGD9M#ri#J}65aOVS*6%&62~Yh<48u?6-7G=uLlbwN*tT?{Jis9{!hJW_S18-2 z4-?+rF~=ObY50wOh*c?5JsD`bM`L9B_I8h^Ji_+`a&u;&&4tZV#U?b+O>2B`Ym(oA z6B8q%!+}^oOteA{o(qQH&QML$J70E&!=D}9_QHXcDR56p%4r-lGc4E^#6g5!LqX<& z^{olCsB1S6N}(R#mDD~%p3bD^qH7UB+R+G=>~V}RLk%Js`aIpo&WM0aldVd7%q-Jc zgTqv;pmJEV?&4#0XO>8_&5?M0XH3;N=uYjn^g?~o(7kk?*KwiKc0QpZD)^|=8ZkWE zIa*cYIp^Pw?9Idf@ZFd6+7kSA$$-%~!L@7G76NDsqmwT;bb#%8!lL0n+6@cwPR7yP zv1R>2#u6GLB6R>h3x8LvB+kH=bQk(?36V52_C!tK9bKN!##!jln1EVg!tBHPTr*Hl| z)U0$@W1=pp{LcBTRGZ6OV8%09G!3v%tAH41fy$`iSJ?#Fia+ZittPa?2RCrblv{mbZrCzzo8O^LQlgZ;2VdRm&s=g-&7 z-oAZJL$fxg%@1n4&>U0{-R#jh+pd}tT=5>aWb273Ab+l}U+Z2Ex_9?(R80*Hf3oz- zV^kN1_T$J}C{D>@+QbBLd=8{$7oZr;-`bF6GP#o1@nO!ZmXi20KZ8UoWh;kZ=T(L* zF{|uT$GVaZS!7=oDfMdJAj;}u(qcBFhVGgkh5Rl%)-g6{>f;B zN|O+^-jHH1CdqXFnugDD?|nU93_)B$DA@jyh*)%dh2EZflZXA_BN0tOu$5wP+xeV^ z-+5jbJUg52`qR#MyVr4J(-pl0;coGN>=nM2(3q*5?~-0z{V~@3qaq;Q7xJ{1=KBo> zC9{#0?txtpV@H?m3^OV~CAAwB8Gsf9lDVZ@rr5^vCEv-&ll`)7968$n1X9O&amE@> zRjn{pu7?PibrGu(b+^-DI-~E@X+@@Hj>b+XyH5$=B4*-wc5d;`l#dpte+zfVZWTCD%}5!Rf+6rsq`wzZvjS2(${{E^eXV~L71T6F(R+I=*?6DT=Z zuTDI`ZreIOZBDe4&?h1$&UhnJ<{l>IbpOYAseJnKyRoC0Th}C%(n#{Q?iJmogHUSM z3!kcw_s1M?H#d0Y$L#>u znzveJCTky3T|)JGpNvu&R3DuVjbe2QKtpUJmj z^j?aLeyM^yGPQ2sP$!*1C7SzShkbAHj>3Dz_+7u~E=R2CIfvxf(sZESot-Z=kE%sGINVvIuuL-wf+Az zB#Vm=bnzpRnDGwf4|6#nVO{Z(JuTzClj?sbg;^Cxfq6fak zR#%6UqZ~1`U5e?b|8(PVIJwp3-o$;mCFl|MnRkxSGy>ONl)5HIDjSP_G>jtD2~1KO z=h=@;x)GtprYveoo__KX--$d?ailo{Szqi=wbVn7-MkF{-hh6u&8ItpXP>bG=8BFk`ekHxOYY29TPt{!LLZbPntxgtaNMjDx&mJ$pIaZ>|t`5 zpPk}$zXwHXj5)#-3l(Zcg@awzJW4EXUJu-|4fPSP(&4xTXDW4hpR-_t5c~rowUhT_ z@Pia^?|Gtq-QfGm#|Evntal-or(S-aD@8M5@B*!%)@`Mhn`t-VuMMJtw#h!^b+!)C z=c#_Jp${h%WzA)x7t};%(UjW0r2=lIs;04!v9_g1hvr?OW-yrqvHwg7(Lion_~hzA zSzi^8x}WY5?2jT!Y^aNBsZZY_`azLt{_Mnq-f74FXFr~(@`!X%M3NJ8C@9dgTl+L) zOtw9`>SBZlK$PRcGFs-i|Bt=542W|5_J)OxC?JX=ptK^Ov~+_aB2o&{B^?8dbPkA0 z2-4CeAYIZ8(%mui&<#TjIn40f>~sEmgStKYIq!$}%kyn!xS6@Gb*=TgRxe8q5e1BK zM>sZYemK3$LZ$!yQR^SZdpqN|!Mb zb)gVjirynKDU$`-NwV4rC#a|pnt49d+w5yzm)b#cbt zBRDs6g&J>zmR_gYuxioIYn9x*x_>d_0cd{0p?1}WlyGhxm&|fIg-~~Bf>ezXyoW0B zVN$2K>_7#^oCL#qaJl`nLAm4Pl<4w)G)=_^r+cHr|AdQBZ@CmQ&w!_48Tu)JBxbeCNSb!ajR0xSJP%jId(;bg#@r$t2bA zSmoW#@_`HXDsRX6(x!KZViQ){uh&N)4WdPNricYZS#jgjNUkOMGZ6k>pJIKg7D`bELMhwZfh`WkJZ~OH)tHtl{n4vtz-#6|l)m^e zd(a(XcwBF;M$0>xG8hrSy?U*fO=^OxQeWnny1?#Pa{0F$zF`KCxRZ1s5C2U{LCRf@ zn^{9BDgUb<25i+AQLSM9{Z7{A5%|M1?YGp{M}{#!`cy=f6jq|GI{ymK1O?9@IFY(urk}&B61LlQsNB(fn%>1xqMD zSTHLC>BMm*#Wavdsoyc$v;|Pia6iOEtI<8K$UBl7tX6E6(E<$*S2GZX@gz)th|@vQ zn@4>+{EF0nH?${n@>$JTfJtTj!$5O4o>K1+@+-0^F`pX=g+*VMR2~u-Og1l3&Jvlp z^T$ru^K=~ZvF?+>WwImj_sg|!oHmW}Gg?RG>fz2>+$LZ`a;P~6Nh+ze@sy~oVXFMW zUbc>Ns$u^IJTl7t)|oKS&qRvAbbt5Y*84kWud7qyMcl?@fp(}!5G&~@4)`R8MjBc5 zGn8)CwQUWVXXvVvv0Nb^#4T@C6W7eAd>EjP->DnBS0ha{H%m_i?aA>>mmwH)0)Uj< zOwEM1pE%1iOQdMj=2d}Gm5{02r&Q(?A4|GU4Y0hoYI85LU}NCp^`gG~oR^tQ@mnty zCJyibICU3&>y)VcFUljpe&34Xj< zZwQ)ZZjN}HQ+CNR$0*iXl6onV$hAsbc2IZqU8YPjtn;oq+}betjwaGwTc+d9`h&aX zVCk#vQE$Ha8^N|If-W**Cg>`dkGdY@h;r%U6QmNf*fHQzaT2Ae+ec>9r^wrcMxOKs zNNb1;g?R?cIml(vbP0pjmvu#U-@K-@<{U>Qw3Hbq8xWSq4|F(ox#Ej8o5)fNnQ0c6 zs2oMZ?$xVls$HGk178tf*gy7)Ppm=fJJ#AVo5dRwvhD&BBY z;bpufwN%%h^qwqKOk0!M%Mq*(jjul0Fy9!y5)tqU!DYSAftY&#G`Bk)0p1H&+zARBvVgv5iwd(;J;`%>VBNKqJN zbg5Wyl3fg&OK?B$Z+qwKi&G!MYz*h~f67e%e&yz7R9j2!luWwdB53M$+D~^K`cn3gHKJI zZ#XLn#kHDlk>B= zG!27>9Jy`r-3)PhyhMrE=*@Jc@wk#_`Y;eqMWcW*l_3HDWa-)uML6eWiT(PQyVi83 zF+cX|_%2PeJyKCPzBEg=D#&*~^*Qh`dho1tX zdH_f4vFAdMZ6U7p6JOjZexf)U{trSQmE8qhSeOY1W3n#-&1b^~yQo8QD7bAJ795I} zyde@ZAEQh?3<;Tvxn1vAVapPk5guO!tGV+}q$C(nfGUHFlpMkyE9^pcKQ8;vA>gWJ z$C-=zHZ1gKoJl{Mmm>r%UpnNuQ)(lAVD!t>Y#~@#K59XUx_M*j1vOdurg@dLDIJ?> z*31=1QM-%>MsBqI_8yzBtx*nvs_%=Zk$RnUf}hD(9r7WhpN3voG;0NIT_`fB>y%Yo z(1%{7Qb&3!`(72{_8S|@*Aw>_o=ktoEagw^Xi#BoyZT{YI!@eC@yj-1jwWy4xN*f= z*nmMX;8J3|YtO3&%3O==I|vH5>n0Gsy|RaU{-K}St8GuHQ!KcDFUGe|#keE;_1zzL zH&^;Lb@&y7#WV9@3%NB{1yw%2V_Y@K(N`I^8H2LQgR`5fi#x@hAw}EpjM)?eqM=^! zHH}o0%DV-JCb8SKh+K**2Ptj~1F;FUGg}vmi_9uVZL&-|7JP<0@xH#j8$kb!c&u1lJ0#3NhTM15hq4_^;-lHK zj>vZro9Xjup6%Qtzj5--I}=o+LL*i?F+*j^H#QBi^;&=6vqZvwr`FS7vB#cmrm`!C zWtnQr*SR}Ra-YtL1;^9h(RhF%bjh}9EuvPoVqh`GV0ZsR*T&jXpjcIvF40YI@Vys` z{5Ef*xHDEsp2_u_-u88(;0uSoE)_YfTyu@TVq^2Xq>M*6UJh5o9V&{rAvoA6ud2vO zF%8XNBl4_|mx#B)iVN`n#0%=}{#JZ*^5i0sjQrp?(W}|KkDktC8!TJ)ZLZDUMOCqX zJJP>xqQVsFb|h|Wo3wB|9mdsHqwn`jE`B680Ly&$_DpQeb6nUk%j6%~@^4xy<(i&8 z=>VA&mOggzV`evNBGuR<9zpdd#p-o$imE}Xs}c2;nmJBAkaAbQ6ZA&+EChlG89XC*T@Se zdclg3J2!TdTW}%hVL-Y(xmkTN0)bUmP#*T=(ZW(5wx&Vt%dfBXRdAi@bLM%Vn|@`D zV`I!L)=a~QqtQC#iuF3xq})C&h(FBv8_!H-&ZBtp{D~R%+=r;Uq7J@sQU`fU=3@*5 z?f%D!wO=yz`uLN&+YyIptON-AYvj22y)Y;}3Wb{Cc2^Z>4X1oeFSGR&)|>j#t!G>W z@}%e$JTj-#Xp*&@7)9=VLW?c+l$e51=w+f zD8n4Y0e3{9#A{w9#kc41-q{gd96wQn%Bz~h82?6!0`~#G`VN{n|H4^oyT#TWh{LOX zd;O@+WG|9B$rHWM70cM!*@=T(II0Em@I6w7hmA3v3{i)pg?9^t3rVeWU?kxJFP3?X z*W(L@)7t>kLxk&dHim+xR3i1x*SrOfDmt%Ajf#RVJ4d)5wdRc;%J(d@D}JJ95JiF( z1j4VvGX)CW97IwT61B&*wv@S^JpvmwIEopPm*BkM;o_3XXwm3dzYauHC;k(gZflj{lwYA}vNtsvDuM&7?CjOV$%`Q~>M zisrll$2xi0b?I{>bq#YwArMGkqsg!`<3o@~s~VwU$D$I@tu)G2M#k?Z(Y%w!L(U0F zGSbie&WVo}Vrp0oX1mmSp+*@iN<%Vg0XgD8#2#RFLucyzqe3Ed6e7Uqv*(0DNqVJ* zX4fWyY=W@Wll+-A;i?;p~=i1I!UhLjAbym73|n4~$Ts zs&$>-+)L8)wppX53O68$b#CI~9!pVxIKUqnX0uSCon3@y2qbX1JvxpUYX=dyGm^a!CE&`y!c{c<6nlJoUAWy)2fq_8 zs%vg+$8|#$4Zz3xG&}rKbPd0A z@U`fjswZ2WdMiJDKU*iJ+Ol2cX!bnW4QHEsoEVucgpiOG_3bkKoE4=qgLFH?lnU(d^&k;mib$@U?R=ym;0zI#31BSYLC2^YR zn-uV_jrYV@WNdP2$g!oD=WE8*$I#*^9i1bS$c64HaSL-S33+Qc~~P+$ShzfJP?&qwnPQ%OwW zwY8b_6DoxPGki?s>=i@I0i_+EkMB__qj}~z>$k66arUA2pI;7Be934BUexKFWXCWS zxc0t7DIJF*T=ApLdbcr(G_9m4YDcA#Z#8(Tw(!n=YMKg!R41e$W zz2Hv6AR2h+y>0yq$$af|ah%dJgSi=Z#h=PLCygj0lQe~AKz-^)=C>zbTV6F@%?%V{ zkKlh-!nRwz<>ci1ME9^^H6DcY(^Df#wl+t1oXP(Gncr`bP`7kQo%1;7GqAC--8VO1 zXJ=<;04|2Uo)<>0Q5RG_vvYH`fyqy-7wH=2w@Q<9il07zE_0vd5un@W+rwRNlat>F zZ>P1j<&sxb4VwD&JpqLr`&v>W$0Q|v^4w@cQZDfi%*!2=?n`WIudvMedw>uDW);%8 zkvrgZKS9BhZu8?Win_u`hShhPsk^C)TI*P;dRhaT_m#x6ty?zMKaF_A5@Lpx)x7!~ zxA_|H=e_(Tu-nRj-w{12V0b3cQdAc2z(u%hOF|?Z=uqx2xl_638y^(lGN(cKlqJH3 zY4R#Q&BF<)z(PnJ*QTUo(8Lq!TO{rg z8Vm85g;G9GipsN|L7dF+I^K9CZx<<&h>M+t!!CD%KjZT|S^0kb26cT4UDS1dKobk~v0YG?;JpQ-5rrrIAtixGhx3YGU8*o`8L|E@qSR*PDhzqT2@H zTC@Qo=&vr@Aygs2%-5=v46AHJ^h$@@!7Po{x_wTF1QkSMqYB>TZQ9L)UNW-U=`3k$ z@>^_432+;J^(rU)k;0v$Znw$1z%0@Ah(*Dz@S0ZIa2HjjEVCfDlinKsg!#O@ha*5T zTiHxIw8fE3u~7aA)rcwS#-LZR;AO^3 zbfkxwgQe0PmmqUpG>DG$1M(|U(Qiv>-En)mY~HzV(QuX&vVzdI7^G*&@DttG2(heY z!NG>_?t%W|jZ+*C{GMGhzWgD=?RXX*O|XSb&d-0$)NWPR9Dg)G_h)WV=e{8IH6et` zQp^*wZsCf0oQK-By9V2lQs|cp9VP_fIF^bLmfgSq31a*zWU z5Hri;y?n+k69~pU%_#?ls4!3=hrQ;Q!J35DM{sADs@2R#aERMUNpvH8zj=*tJpImW z#R*c2(36Ekh*e3w!mg+iIg#2%C?`m(CZyVZxZGLNQs*I)Z_@9j7kigcsjwxSHYiq?M|@fI#zO$+yDWh#uXkddqFK%TvmgWN6ho~Ce1z|k<()-Ew6!s zhBq62>GyK+2T*~~{WiMg#u?hQ(7K0fjnBW5ixc2+y`{2Pq2GEpb!96~2uUalTij{& zr(kXInP;{{np|9HMpr6MRyu_mrj`Xed#>%i#^jt;Se(Ubg$J z(FA+xsd1k-W8S^P9QmeGMfiDWrTa7r`dSUEPz}7}(^H>56W!*aNw=AuurMVSPY<%J z<_uor6cXmH5yu8a>+5&jHOfhk39*L^uxF@RfafT~n;(|kZ~%QDFJlcd;twsPv7^03 zlSn0ZW-Dj8`_F5_UGOHr;$A#{56-Befti@3_g#;cjbv=+2x9`R5*?LZeU`G`adF&> zT>n@usiTu{jGokPUxqswjk`MUZ-k2+PwS|f*7cI=PnPW%JYZxD8y$@*1sdY7SGc0m zx2NJML#vQ`0-;|?Sk(eI$%Pz%(v9|NhdIUdShXDG#4#?K zw_)UqPQS%n)nKKX&0=eW|WIixst7 z23-w`8eN%6C>3F3lxgYU%+~7$W_yy%vx~*T(V++s1Ow;te28KWDsv&Z~x5=4Mepi03Q4?oYC!Bg`ReuJ_|pm%<4QEzy~i#QKl?7lWK&a9UA%0oowKuZpQWd#=hvDV?W>f6 z_bDhSB$10&UT^BVx@1!`>{z;adVvxJ1OnmRz1WUKCaw+?ErGPO^^OeQ(Tw08l{fI) zuyI}-1GlP>-d2I+Ub%7wu)Ps@HxI|l%gbY&fkC&yy^xQ^pC88`WG6nLwY0VOz2XJQ zi2^+lIsb$3MwAI(wk-=NhQ6)2Q-00rjB57?E&01AThfZsUjgcsh@K!-T zx;_AkMvjsrmb&L9tv_wwvJUl`5Mc^gxK+%S8o+~&(RhzBJLOa_OT=;KW1Lurs}B9L z+EWRM*rL)2LRlTnZNq}GO3K}x$p35Cp8M?wQx8M^6iir37(*>Wj*JO*+>unDGjq|! z3gU-*0v2KO?Z6CSMrP)Z4wP_7n+T3jhW6zJ5iYE;p&?>0E)dMp#YWCMe`gYGskDb{ zZ8^w03*E!we|*Wy%WEER8p_@h2*rg-7)L$O=Dw8yHaG9Q&lbJyA-{1;sc7#DPU{Ly zV+#BMKKlyf0Sk-lR@V4h${kgF!&Reb&w_sQxLQg}z?~Pnuz%ABJhfSU{|2da;ar2EooV}H+ihlWk-$C@< zs^12Qke37M-Qu$IQ<3=gwzh`(wzYlOl#0YrY7Xr|E)Dr|*&E22>)}VB;S~f7f3L;n zk+Tv>K>I}}mjrE;O4#Ci701hd@cZNjkJ`KpW4ISwwtS+$pAqVUQgd9(t7)m2w9VJp z=~jL&DXH)MQp_l~2JK0Bg@xg{MyL&}np=U3^Tox%MY5p1;y>iIw8Gv{PD}`OZEb$H zs3YvC1(^SXW}#2IXBtqx^))AlMF*xKato;Y27zL~nB;WxO&)W$WbHrIh90_V+xU@2 z4DQF}&>jmns=Kkkp|}gAM}tLAyriuiC-W8-_K~s3M0ohh%8H8SjScJFvW9KXiwafZ z>}oHJJGyR;*@Q<>f&x!z4oG(?Dka!Tr9i5_uGP@Z zMD_c9C)v?%5m%-)#3vrEmMn~&y1F_^(8svhrI?Eka|wl-H)YXBe7Yp}&`EsudFsy@ zbz$vGe4ptKtHc=HRzWgwsndcwuCOk6?evD(8RR}^JDvio1lYML0-JZ&Bb+*XBItPU=%=j9L-_m|FutKLzM50=pR<37Y4Z9&@Ph1blvU`nq%L2 z&R?bA*RR{Ab#&9V35t!4m3^(Gt#?SNKjfUGV+ipB1`#w>c^$qHg?m(kPyN8VySvMB zWvS2BfDnpcW{blB{mD2^Y+&2d{O*T4j;7Uq(^zi1co#@px(*7MMvC6;a5a8fPe4y` zN zA9U*+>*E^)`1?n};H0!L#*@{mULZN-NA5p!*8;^!x+gNv(7C=uXX8bH$)Zwd?2h9~ z5nScGM;}w`%erC3AjYB+s5ZPY?L}q%xPwutxMcwby90OZzHRwFVt2RbuOc-*puq8S zxVEe2dKUXLgFI6Gp$paw zcMK_5-7^PYR|gX8J)7S~oaK<8ZYV1mz;eu)xV~`a5*CfaV*;tpyyC7#`B%*3JgNUK zg5K(uDe5|0h%e(pj&)3gnn??*`(k9bd^)`~UxJ^2in2l`fOZ!&1?g%&< zj$JK7D{7-itWht^4pT)4N}^9m)VTI`YT5tj?U;8G-uKLr0B=dF&@e{NnbFzq!?YgyqGH7t}oCZ?cb% zjcS5db1Hfrv1ByZc z6dJNe@_=OSpB zS&D(I<6Cd9uB@OfREH54W1kDvuL|mUFW-$v(jdSI7QEnR}{Vcp*7-G;q zbm?n$wq~W)srL3ytCTY=2N=(=4Y?xnI-&1 z`#wUzUWg+(*O8N21|vII5U9i8?2a1UAT za$l&cHqN%%|7%GtCKv%csj1C#zgp6n&o1K<=wE%l`D4RPfInI!Fv1zFhKSw9332K; ze!Cub4Wc)TD-97Ozwn@l;$k+=>mH&ADSUnCi${KP2mj59r?05JPK#1EJ9SKdK5|ic zAk%s_wypf@#L^E;@Z($29Gp$C-@+(`&TrEX{s9I%Faa&BV2ZBb$!~Xke&zq6{I6jD zAAaXY-~~K=1VCCTo&~$q`Fw~o=k@pD|M-d;dTIszzb*en)qkM=|Ho7Q9t$GWJsGpJnn?Hb^T#`P|^ z$c-!2Cp(9b?(yHjJ@sjJMy=*~X4)2=0_Ga`4Go38N%6W$aRdLt#$?qPPABbJ%zvcz+49}X;20w!`e{`Be7nEe`8PokT1CnBcA$0%{=tX0|e;ZVzD zl{4TgWzU6vpZo0=L<1w0G0))&8$Raz{5(!>-{9apIaJh8RaLdFy3CbLkUA68x=3Bn zRRy$3F|331Lj7{Y7}-}LI5l!*=qMkqn&99YMkP74KyiGnPe*ccvf~LF*iy9-0zHQ` zio}kzWhOoD-Y;5;7T4A;r_(&O8Q#NBRO0d1eiLAGKL*_BG1~jSBWFWAqLjFtTwJuV zMc9# zV9VGtpDORROHN@-sxw%egcHpgA}No|Abwoj+|PkTgB&@Y>Epuu!s=qgj@fjb$7xc> zqxf!mHAS`e9jB~AR3jlFVF2{hnE5l8$JAqOa&l6`4dPO)D5AqSTFax+*W-Ahbf9||~1m|DNW5MIM)H_+FolIwRlV2n3&uHjAetHDz`|n5o zt?-|Dj*anDf$$mYxceJE{A-UtZ2tdk`Ty>y|NlJY@dDk@^W3%l{5yQVeX96a?8Hi24fD*p;NcI+}HCnT`U^1ZqHulD)`(5UK zb668%Xt_6H)MlVv`GkR?jh@1{gr{)_E%rI6awe@xw$s}2 zAK31fZ>dicX_licvCsapzT{LB6-wHo`cP@4bc=ihv2`sPw`KR#rS68_Auc;w0k-vhh;xTYA(5c1>waeq9q$WGWej7ejOIbGKliOZyn>in*Zt>ZmJBsVKv0YjqA6N^*$9+lk0o5DH?0Cr4Fa ze)kpnI0uHpc>Hr4|0g*4WtEEpQx9MR0|V<}^E1AzHVo82MMPG?kZ(87U7$-u0JL!= zzSV)$wHl5VX~nk2&&H<6%~mUt4`b21@8*iwa?l(O_{ENYM&$>W3G`(#KC+x8SfHl+ z-riOEFUJdm610^s3=BM&fbg`Ii=|PWYvd=M)4&YeNnBQF_v;5elAAYMJn>g!r*?;j zRqCqA&#N8(z{2hafU{wC>dj{Yya=xxH{}AJClXlF z&8|Ne%m{Nft?Fbndio;(q${2?#IZ zHLUK^Q4|&DJmu_(Ni*P&`>$xm6ET5i21dr%c(vHy`py5QUL;N_hk#$fxs3x|MV8=H z&rC$eiWOGEmmNMb2&LLg--KG8hey1PslqphtS zzk`Oat$m)EnfU;*Mon#Qenaf-wV_c+gYGz?GU162JN?}ofT!28OrvjNBK8|tn0x>l zj@C-bEpa)y>t7A`_3_zukL5e7+5EsuqBq=+QDbxW@MhW0;?7*SsELW>b3$1qKR(`P zDYMBpHlE89@bh}~bVHDPlIrT}OO9Fi_y#5yz7-V>IyS6?1R%8>rafF@>^k@so$qtF zylmA9n1)zGqKoE^D57rfj8)jfJ5;Jn)nKVk@G>sDy|8v9B?`FhPut#y7_VGn$ zyuE=mm})Y+yHFTGe0R9bQ{-NeWPYF(-K>e|=zn>|B^P@pCT0boO5S+mSX|SRqiyEou0?X#( z?-(W)7GXz6M}PKHJLL=-8XBNRa<91LBX;{vkx6NFRn@5rgOihUZk7G<&QL~Qu{k|N z$Wyju8Xr<)Ok@qIxk*gCuqhoPw+>hW3mo6%fEhtwv$B{DarnmdUcVLv0#;wYer<3` z8JatNsuRBNt26**P3VXf-~pbQ6oV#$qD0(W>tJHmF1Nc7{QUee-T5vNjMl%AdG`iD z(g~4oZl0xE%A+sU)k7cledf!I;^l7XZ)^zPXRSFdciGq>p^s4nPKF@g@llfC-mnrM z3K2r9<)M$S0G>R48{;rzsX&R<|1AqUPF+pyMh+|Vn(*#`LIU+pTPS04TH1YRD!IKY zDH{~}&XYZyRhzOzyPEtK1%+*$n*@dJt8u79b(R6pxUvrhV+C?1I21>vb__-zE~aBx z;~VmDb1$GsZgYM{je>F|fworw!?7(m?HHuG20aql+1>ZzsalpfWM41c8DM?5Wb`&i zynf_xT+4BuXnB3TbCH(K{x^7ki{!p2=2FjX}O)=fmv@IGXzATR*N;XIJ~0 z^iQinN&afrSKkBFjWT${n7{$;vHhOFiGS*DL%D#fnTiyHL-ddT^-cxU_EkY;mjQIV0=llmPMM(kX z=>ML`coswSEp`dEl?84%WPam_5ceS*N4 zxU)Za#ws|s)Vzxa(fh+JYiB!ly3Yq) z;`^^omD4*)Ks?y#W$)vEo0bK+(DeQ@YM5cvbcxUb<5=y%02 z)BLYiO%9xs{`Kzu{@4gv z+*xWzP`Vcvb1y#jC1H(=!?Pm7>LV~VHxJ#|*!WA4T9crul;gMq=N<5A@20`ouY#=Q`0`vk1huf`I4j!WEWXueEO zKFJ1_fAjSf&$ooT-Sc{s0rZ?zF512<_ii=CNP>E8=aEkD_6_K=Xay@{;QQimiOtDx z#Dgud#Fec4NiH=!M{y4r7;rCkT1!Mm{)Xj6%TF&eq9l=Xm=KuqJ%-Qvv@NcZ`Nqmh z@Zrv)l%k?yVh*31bfz49tfV`gMaX_*JSHBTh;tPmzm+k!@-E9SJJ z(YTGMno6hy!X{r^d6ruxw(ZQd{bq zY5F7;KF6YS0aJT=Lq@IkMAss57K{3+YkB`EjB(Vk&iQHm{OGAK)|u|0Oi*F+q|5JN zjWO-)6o{pi73PW!J2~VAFZ}L;2)T$$2iLuJj_&7f^4}nBpynx{?i$$quK@K90I1lw z75^ZMKXB>!x6~D&t{_&Olg4FD=My!V`_oL}5O zc`8JyJpn+yb${)b0lldcBe?AM-KY(e`w4q2)}7FX7Yg^h9WSfd5)H~HdzU5q>_sJ> zM1isrUOw(TW_;A+6K0w@81-J`xJZ%WMcI6AA?b}~0`?dz$(3qnZTe2hy~xQJR2*dQ za1A-f2d~(htf?9@%7(s;R4Gily18*C9h>Y;osTzk0e3E0U#HB%$E>HK`FTtvu=wzhunhYkG6FpzOhr z3+G-_CFk>-t45Ivv_Z`M4sD!ay4g-u|Ma~@BulDD38%aNyyc&X0DLl`DH>Q-k$Yj5 z{-4(SO9J-bV7}{!eY+9#PxSrek71Qp08DAGjnO1`e*3@tjlUQ4g%ArXQUGIu2Itp@ z`RgCMY-5k{Xknuo^HcmkJn-vp6Q#d=ncW_T-&}-NE0Fs>Qqazi!_q&Oi9vy_M`s*77`^tLpkm})?ieRi3 zaQW8IEuM`_QqnJ_YK+2450bFq($?&w|11iS?ms=3E&lubKW@Q zT}RC7LN!Igu{}d3i~!HSb~90UfFFsrOW6%J;UbyZe$!PyGG_(aDJKxQ!_=hZ0rYCd zT3V%iNA}t~XVD79NlHZeZbJ~>jB zjyraVIm))4mAd3AWKm)6l2x$LQ`cQTLm`csyBO>IlC(!FCsv@54r8cFf-*R)@+itz zH#3_%ca49HK1JG*8GoMAe*Cs&?M0(T61y?SX+01Z7{c&qMc-&C=S}4a8fn=R9^lOb zb75@Ku(2~M=p4%HU`O4>L7}bNG9Nl2Xe|$bQf76FGH&ik{d#WvbfwkGD$p0B+_=!# z2VmP5$?6!i{HQ9Z*esN7^Tqa#wDB zdrWF5jp+*1MmfYlx*k~W=Tdn~P&!u12xX^C0&^0=JtHJ2kh{+#jt*1|LPPt7nbdJM z>2$`ZV0L015idt>@3&b7eYhS%Gm?EUpwONE!G^~dhp?&9X*HX$%6UKM!^2hG2JcP= zfhF{PC5Fzlrv}UjTrOQAgr=(UOk(}MCEZv_j=xh z-PqS@xK@=@BjElb0!9VutmQ4{Sa$L0%@jdXSeVo^9n}xMvusYIkyNr`9fTHDET<#X zQs7VXtAlOJYO(7XF&6UjTIt9d8cDQ?K01RWvWFatKwpZ`Ba$1SL(7(`BRlT0lWo~@=TgH^{H>sovLG7!+z&b;x1p|X zE=(%lsCS~8noL({Yv;(C<-G5R4SCmJ6G_W^6%xOiTVoO9(#A8BqdiI1D%;!oaI04G zq5THZ5^dk@bMI5>LC^;o)~dB*&WMA#5I2oun}nL0?Y7UnZiwX2w`*k&t>!F(u(-#Z z1W%5Aw>tVmX5Zm;`vdcJSS-~8D1~O`LTX-)sFs4qw?ed27D0SqDVwzK_^qST?@+7R zJWK`Ql5bq&A^YCS+Oeaifn@-eL~#Z3eaGaf-k)^No4xAXUXUb?=wWg=Qr zW>32%=jsNqnah0asF1ZjAQ7sx->Mbv)v&7Jr8pb0<8b}+#mD~&P#47bh-l7SR-#*2 zTE}DU51seMd8fWtMXzhza1lBNT}svI1a21aUisV;u3f{9h}&{PyNkRlX+Q_i&}irH z8x?SQ97LBdFbRTXBJ{8tN#!%aVBWgX*-1eLu6&eL&=u&g)=M8%@%7F6DPqkA2nZF! z&J@G zh?4XnfR!EtkwtT;S%SJc)+BOrXgah^ zXFeH8M_zpvZqHxkR@VW4frsMDvuf#0iK&yC%@R&T`ASBomwU+MRkC8c^3^R+Zq=mj zVrKe&6B#%eV_t@5EUj_0wX3w=>zXf#mczq^u1GRg#mB<}>xB^CeZ<-bhuxTYIOqL| z(W{xv#2M4AWH~1~fG%*`D1Ij!k&Y*f+g6;6*P2t%Ex4nxmfvo47ssjC=H8>lGHaNx zJ>zt@TBo`cO46RG+VU|ile4RfVJd}gZky~tECi9ADdl&H!3B)1QBj=BTPolK{%j|x zX)L|_is_K(!UYjim-Ql(Jh-s7pBI%Y=LANT+sV9Ic_rOqu;wt&h<^PoUgJh?A=Z&} z$L+aAJ=F9nw=*Oko62piByZV}OyY*A#qwrX;n!L6v!Z!UE&QT)ssuK@Bsxbtd1{<> z)UsT+k{vZFfb;9PJbeg-3sl3p$f5W~u=z$>@uV+n^-UC#mGC__zFCgVXdD)v{fI%T z!$E?C-bx{~KlZCvPL-1H{ylmgm~Kb}ceZdag{80SJ&b{^S+JjPjlEVTrK+CE6_Ed} zJk`@97W2%Cbd|G<&o-oZ#R2a0XbsU!>lQMS)f|KST+^XLU_fCk zw%u3(b`+i|{jB8BoR%}C#-ZN({n>DP)O~K4OGucf>aaBsEc0{TKuMOjJ6gxa@bB&d zj4qicJAaKw4VN9dZLg{9Y!p>6D0`j(&rme&DK)aDKvjlz{~W)H-nj_W0g$a`^;;RU zE<4;u-OvXt$$iZV1+rXug^EjDWAF}0q;BBMo! z>rmE+Wm~IJGo|weDKLiATrofjGfZ;IW|vC|w2KDRQYFl}lZpZh=0)=@f7)3}+YZSs zXifF4aE+tbgEb?2mE&MpWc8X=dlQ976`ycLe+#eOz=KHCv&W0-Zn{qv%EZY)MO`8G zMNwSejJ3%`eRX(8+r$+%Uvzma{lU!b2M z2=5F9e$eWrm;r5R-~;Yia(ofQ#9Lrp>ZxT?%T3UcjW(r!G$9_aV8^J5h|1aIyAAaq z(TKx-QL~#3X1M}V9^2RV0;Y{fP8C&2NOY*ra%el7tC717=Y>ubk?GcD&*~=m4S@v} zz2i+ zyH0rHbNz?C{$;jJXaRse#)@&_uTQWp+gx%N4vqC2vqG#)WHN_~bM1zKW@U3FTdaDM zm@AlA?b;JdNEUX)uA!6#52n7BZlz?BYy?+w#;#6O?4C%*s|*e9Y92(Wb&XAhVmIz| z*WA(;nu|aj0X#bgv~o$0ewJf7VDoOMGVWy?irEzIiC3P4YQr8Kw9twO43iEqQ0Vk z;~;vwTUovQo{+Lkl`!O}fid^EAfIgOQua{%DO|7_->5@Py?5Mtii*yj1f-v%59Cu0 z%wA;N;xh{P2&go4#lyP&bhDC8kytHbq!H3Na40zP014AJk*8mk%E#MAEW=Y$O}W6W z^}&bedIh=EI}4p9!@T~yGl3C;haR_h_7<`%?9|2p3PyUi*Fg3jNPUPP6WGlQ!1rd% zDP`024WSd79F93GP+ApqRL)fWqjgjg%{CR0BnPNr!01!yE0lt4{>X*pQ8pS)Z2&1S?(Sexxf;X2u^tvSL3&7Qf zcH<7S*$;KwJ9PmCBWQ3Us-$Xv61mJ#GGdrX9ACBF_FivQ&g?o^v8kso4^VAE`CXT; zQ4VZyeH&5(aUSYur<4V%NKb6b@rIcXJ^e z>uvT=<0*lT5sUpWZS!^Yq8fpAbXVbEx9u6BJc~+iFSr-u4hNNG%jcX^#7Kx#oY#wV zn?9*N+%1Btlh*>%mzYoOe#xo4fo0kS05l-@z2(!m%Y zgb*SkqEr`K$};GQ?kHrcKRB}wQ!!|qZPXT|%(#?aA*M%C40D--@)8OwWC z>qmqm(57p6y^3$q)`t~=1o}^N=c*y1gHAB5A?R*6R%eI)@&2(T+}#$Zp51yQlg;}6 z7ZEGy>3Z^MR7VjgmC?=vcYXCIM=WBz^GY&ON#e=K*^)QZRiZomkH4NyPA?Gacq-Al zEV`oxG5ctw%TSxhxuVwEQ5XlU@Oe4`To9ibfXd^l8d!6H5e1q;p;fbZiyA$sXPd1W^7czbw@>(3kqf*o$RwM?^oPOPx*ogyxHYFMfK!GYU_80CfW*$S_fh1TtUy+e#R{eb zf4_OAq7TRaY;07K;=i{`mm`;SE9>}7VMFkiDTeZ5noNXtZK(T9GqYB!+d=?rHjn@Q z!h&cJqvigiZ14}gfptSmEeNbs<5W&~-Z6>kD`gVHDJ3^4U4H#1 ze-?7Ji>)bg{yJIJoycrEH-2aeGrDpsCDZ%G-E zNnacD=!aB`<1(kDb4HsdYF6QTB#9546SM7&P?fUqH89JUIP%z~{=7C$yEGGHja|;Q z8Nvw9JT?qc75fmvD6`TmMhO?30C2QX`mcyzmg2*Ox^zD;VWI)A-*N?pZ$xhY>5?Tc zK|$o?&cE=zSyTke?%lfwu^@l()NI>SRba#e=58+@{!0i}BPS@s(!;v~?aoJEHv+R# zk{Nb$yan~~@WfjVe_XZV3&_((hDeN{pJex((?ATq`_4`*YjR5FbZv_ zD@FLoqKXzPJ1?@s?+-`Yc#@h;Sr1gR|Yqg^&J}MNYz2?hwL`b#1 zUQSF{VF4*1?bR4yL5=3ieEUP`ZTi9x@l1?0Ki9r{_77Z{$NVAMaWBE0nn&)-E=31X ztzDq)xwnT^uL|!>@5XoR;fT4*l(LK$tjD$PIT-|AeS`=ST(fOfc^B_Qc(WbgRPm}O z&l`~ttAuC3XQG!19D7LWbbhDuQsMrm6d`Qb*5eGkP6Jnsy*T_OPX-w}K0P1SNKg=? zR0|PQ<-EKjm!@ai%Lfyp7};0nxV=RX1q6i{J<2!CpEXNSOGN|7wS;gi7cXYK7jSi?U-ncS~htg95{V=hXMP|S47h> zQo%%5IRqkgH*#bIT_a4?zEe#(+F^BT7rXSyTCu6;E!J7SVcBcwQ!#&~F26U(3fiMA zVtp&E#K>1vJF6CWGM$*!WyQJGuKa;+IJEr!2y1mf0c5eAp#n#S@H=)Q@fB%w-Dcg+ zD+5YLT74Iy-6_V;RDI!@zKC~!yg&Da;@>|E90D=_ zJ$E$jea0uzaG;4GL~>(M(5r3i)!uoX&X9$fVP?>V+cl+lvMY}Iv6edjj-$;)5NNrQ z&(~M|l-Ye7P0DnFaq>e2?CMN94HTRKS#%-S;73UU#6SQ<3ofqA3^qczvojhL-H)_p zE)YZ0J3=$2+&53U;}(+y93j{h1WW?gJa+EylC(^UeGYeg5^O4G`jM zO?vqp;EN-*UeaGH&wr8qtEkrBYdKK>jvU@`wtV{fTmSmizvfAM1$b5Wv!1E`Z1F#) zlp^1uw+53tL?nN2+dl;Qy=~uf&~It`e=cpO$nB1wYXN*K`~R;MrZYo^`ZxO0EqD7a zW=IilaMMU=u>4GVYnJ5wAM2L?DKLA7KOv_OGfeL>8&flemtXl+8{TZNfBIzvS$)J2 zTN}?Vza;2|{|)d{Lo-VEk^_#y8EowI|Mn|2U|{%#V>UM3&D*oJffgL}CIOoWPqWP|k0&m5r}B=|_w!i=YRYPyd(EqUGh}$~+^c zt^JRE`EcFUHP_oBHAc?UQAgRsi+_y}XB{ScuPpCuVvqRchM%Vg8~(^lp8RE1>w0*i z5sgs5U)TAHVD_8-gd9S+D~O!?#crNR1&Uq?lC$-~H!S?sr+!Gm=zQP`;v$%(e(ef= zy@bnpzyoSfNp<_R1^IdgC(?j=hxK0|eEqF2RR8J26D`0J+3Hp%Uw`Xsh5KVx@d6<( z15!b~&@V{$-IwC%fa5y){vhK|82h`Q|1&4IZeZ~k5Vq4kKc37_RxE1)OvUeQ`}->afEu8TMZPF!^-Bb7e0U|kFqxW-^nV$m4yAprMm;9duG ze|!FKf2BY}>&X|GAiI0ZT0UK4f@=DQ15#dJ{{QC>CKj7V-WH#B9{b|+obZv2l17_$ zy>hG1vm>uvNV>WZ$FZJuwE{n{*0Emk^Rj*lH>+@xOH5+8Q3l0>{HIU-75ONIzcaLy*N6KKd;17jyb8mQJOSp&k=C%m*RFvus9BNum;L`2p7>_BKCk4EJeVU3Eb#bGr}N|2^ufi& z_VYYH5{v(sw>WUA`tK*7-r#Gkz@=^F)7xO^@hv!J6PGuL+vsZ`?(l!&e$acZn_GE< zLX!*FIM-WigEDgq9L4??wXaC+_FL4xllR}E_MIM*{Vi(Ws`B4f?RztH=(kn-7Wn{7 z_uHy{qep%N$nT)aZvgof4Eal;y!{OzzvYqNzS{TB)o)+zv;X;z@cj1GzJu?Ze}_fB z)2qM3BHzOIKdF9)MZVLkzr!Nm!uLOZhef`ZncrcNZ`>K#+rPsi-^|fK95Q> z;|)_StBOPg%S?i`qli#gf9reo){YTd$I4K%Q#mTQL_wj@Q(h0=y*MB&amIb$W?D?N z)FE3K$LD!wNDnPJLU9UM7Z*;K!J?hPtotHvSa~)TWWDyn?7IYYTU$){( zT9@s0J1Sr?-dS?s_`CMbPBu_X=f-o@ZU3yL>n809T53U1`t@Sep6`I@cM+j~d^^vq zS64iz`aBB}hx=l@2LHJ}=p*X3By+4l88{?7(<3DB;60nv%< z7jTix{akkD#mO$RNZ|UCq*$b>4jg~$0ye9xKe2`DpGlbf@#ktT(;siIuY;s+Fn)on zcdC0<6j~0ghjw3nc-dH`4z?k^eQ?_DgHU zDt1i2?BM=t$^ZGrUjpGaaRh9FQRH3p_py)vem6uGAmIZ8SBo?^wh2Aw!6p<-?pXX! zoc%A+6>tN4&RJLHnEc<<`u?jG-e41)ko#xXC6Ql-HKE$E{gi67=cv}k(!qQfz$Q#g z9vyLCmuCCCQ?EAfKXEA<3Bi0v;BBhgu!z{49j@&l*$OHK&TTG z-VKpN-hvIJ=w_L=Wn0KSJ>J&VW=c#mtnchJ3g@hGSen4o956zSY@apvphXFATQIFtQvQ3 z5*C<7u~s@mB)Iem!QNky<1xU39`|!#AnkBIq{~8Mlpk&Q=%oT2{gUY=vwBNYGOO?jXC!&kwms#dpik7> zvT7c!l>4Bo*_oQg;MeJ{Gs!w7&JKd*B=|GhyT-*%uyA;-__B0tWRJ@~m&o<{RWm== z0{Eq{yVdVw)H`cu-+X%KpH%L9FE+tEQO~LI^N-HPkQbWsx+n1L>5Qf-%W5yD+sP}A zOVbH5HJ&pImp*{HV@14?E|c%P4)>il0WJ9^md7zNu9E>#(+8tOpT8QGS?Q6PWdg6d z&Q}>%$SgO?;0U02w~G#rAo)peqI)~Ddd}bn7A}EE_C%_iNn%nh(0qyz3Fh9Rjl_<& z=P^W0peY^NDYoIp%#~~lNekhGK#cU#r?R-q@J_82U1`<0nJB032i-=E@E7*aFJq|t z$jJVPje6x(Bp8;v)kfDW8N)oLx09zFcU zpWdiN)l0WxF&NOF4q7&;L^!Ouhu}Y5-8in8bfN@QZtI4G$|Tc@U_QOx2A!YmMIxfs zP1LPGMxnQ@cTkn>yZE{aPLRqY)@ke$r8yM^_ys3xo30YEyQZe5zM(;_&;_6jI`=YU z8H&8v**&N6NW|lU`X`Ys5;CA>F8;Yfz@#lsz!rHQ*_wGB_X*bd0o1ScKkrSCTc(iN zEo$t6!aApL3_x;8vI zyS%1#b+c{+E{Ol~cJDvbbsM6#Kej8m$gg$BUdCgfgXwI;B`LYAxSg#E)W_C1E-qf< zs;H*S81nhvA+Y|$%w?WK|SS3g25sNSGM{v7E z**7TQxEf2IU@7i~@-pi+V}+1PK!vO#L;n=?Wu+OT^*|uU&_DfBt{>**_cG@9flP#Gs)NeK`)PtaSU; zVfu?PZIWV=p!Dd)z~Ds)XgvGg+|rWUQ9<)zV+*Z;O5hW9#?!1^F5%h-`9w74pg1o& zI=xuhdnycTD(3=q`K-h9Kc;qzlIn=Pu-3>ol?CdcvvG01)c6CapZM`L8{SfiMitNZ zg~?yKEP^FWAKL1iesA%mpUldfY47;VFp(j_2^h!j=|)xld*$1N%#4I3W|o67^TBf; zIc0RIO@jT`EXs5C&wkJs&~c^=>*(iuBDE;y34yP zqhJ+GxLjIl%mj|X^HgvCXgi6-lBkG~XefyvYHSiyqwA#=P-!Y^eB{IK^69mj7$#bn z02wb<3!e5_D|3wVcb$$^3!H2?&$(Web@T1!!p9z-);sBs%SIoh0*2R4v2o9FF#|Ro zzTMR-u8ZyDpwd0UUHS(@B1T#=VyA`EX$>tIdjd3I3(VxX3sK4OL+^tP9d)CKC?hA( z;HqadkIW~Mkko_>?ACZHuDM_3Fp2h>G!Rek59G)md=JH5W>CDhDOmmG+2gyX#KPExXRYJJu1I6_y2IK$t4gu#y@E{-CUs(T}*W=DOZxj;UT{WQ44I3fFNdv z9-k$wK_k$|HJ$M+8g$oko_WWt=J9cHtcbs@y?rXap&zK`?xf^2FTi{8@3lq)6c-2V zYG}1;{b|qxa`P4$p`UD zCDb`D*ss^Qc+~>#eE6s4HEs8KDvM-93^%7fO4>!!A0mx*#+K#Uk=f855$aCjTb`;j z&V#b7qglq(QVG-zf!x9g8FnQz%`dh-^@oh*n`)93>EhM(lTJ&vKtb@}(XNI^Z%Psy z>|CFhBqU+|r5DUI23yUdq)vi@tt*QFCUyB^mU?iDqsavGT+Cbh+T zVtlP?DLL7#`Kc7j5kSc(u6v`PWoVqKlekCa6cngAT%99zRRVYI`eG1(*%=|gQ^?55 z-R~wyCkBj#(s10NPqO$mU7n#dFB!n8I@ZML;W{-$7+mmUg>Y|utd1obp;E<4`y~fa zdI^#)hDN>OTkgnDT|`+}TeAdmsb42c-j0wqM$a_8H?J)Mg*zam{te*t8otMAMM~=8D`^+^*VDcSjQBE8`a=SM27tzKQ&!CeG=ul*Un>OSX_w zd$yMvfUeiDYC$}$*~O^@S%)}|k}A;-d-bIb^0J{M0AqoLwC!qQ5}J{<{+1)+QSF~g zMgGj3e39pA`pb_B@*dDr$=`jIfoEd|7Ch--g2_-@j#W=)6<@NG9oNUD$twQfvvX`Kms~9EL_v#& z=cRnUVY6mY=-ljJjqBo?f@4f|Dij#Fsi>1hcwNqR%CT6?`b zyP>4&_;PIoL}@for2#FzUOnAaxBbOi7jsgoqRFMrL41aKdYoGVF~G-qQrgeSRqg`( z_$S;0@zmAu{@NOT1{r=JR1Fm4fJnc&<;AR~0CLs zQL9V+xTs>@Ew4Op0dvBCkePXr^)WYZf74s_Kd>%Qy_?i~2PUaokvaX7(zWZ2i$pt9 zDPGYcG*@rlQ}9ZGGlCWr)`@#%lXNKjP6gOg$LF_4lhaGvcs0VP3KA{hM!KR{H(EC3 zCdAYqV?n(7&*I{a)dEW@wldcfo0B69EPzq5k}KPdhY|Ab5ZSy~$3 z>d?3K@&AJ*D`Y`%ZP!EIGhLUd4P57YCpyMmMb%ycta@?@6iwD3sZk3OLWPsNb7wG#mp!Vsif5ir=Vi3^T#?x-ZM5=- zfwgFsdp6fO*GjGg`(crgHG*>c4(B?}#uZOG%p;N5MimIJh%HHHS8RebmrrEm6$v#H zagX*X>bG3x#X$detIo3wN^ZfXgT6MAHSP4_43v&3;;P>R8anhcQQ7yay>_a>cr&)*V!x(xZqU$vwIQ- zfZr_x;AY~uYkEhe2%y`$QA&Au-#hpEUbNcYX%JMwgaeffS{IE>D#cCF?kycF9QJAo zTXUsN#R}~+rk=;g9|wIRP2W8}_NcbXKjxltqw^r+P<~|7^D4?i?u9X+$6B7;spTR; z>K;YP0iQtx1=FxJ+R zaTQR@5foca&X1Zd1)bYqI5=jnSojuDUl}fDNA6sY zGCH2W7LLdKNs|5>THl-lNRz#kP`fTwcEs<_|BhXLX%zpCtY?6m9c)&x;f>YEIuv~* z3$eZtzIe2584Q;nAxCObH&zDF(Krd97EN|Ni{(VzAwJFdC>c*xoui0|KDdMQ8b__uCxec z_V@Fj4#=6j+5B`Z-0X4os;Q(VLg8%L01V`0iA_<)!Pr_*)^*A@J6D>F1{=qy3eu#JfdzF!_EV`Tss?gr-lf%`U2(kM-!(m%C( zyY_y4M9(eA-feajxehDfR(~Mm;hDbr9-)oZdjAtdoy9E0+258 zCqMtkI`voCexSGL!_I6>lWg}n2c-0!88u4_D=)T%DYbQ(cq!Glt!+@y>Vgfe-^wPO=(SPu#Vx#*DIIJS=_WOp zyoW`OV84`Exfdo+vX8ZIye?>4DO+AT*|3;^Q#+n!ypQIioi93(iU@^D8v5acH&N z>5oY#Wv+12nMA%&?fh&aeyJ2*o!b}k=}g~jpKtwpUHjSRQv~0(h5$QnUR684cywZn zI6eC4&Qrl2;YuA9%L*uKgS@CqWTxoknp`N6aBOJ0WwFLI0UB)^X|a z+?$!IXVcz77$fWX4|4UB7dl_}U)-`eM4sDRa^1>JOmefIgk-L6iJ`6DltKT;EYm^p zsY-h8b1_e}A`Tk9Ys%#FEE|m6opDtgf5D2Nd$42d7h5BJWXu^p^zr%VQNGnL4i>99GzW`0jcQHYnUNu#v zjnBpTnF{yWBN5if7;#Ip8_2!`L(op2l);W%^ZD~i*=FL+Z^G}r2y11l;kUjYZ#<^3 z>Dgr=p`nBH=X;jYsNbfx=as{iYwESsnMYBx=W0mzbZ$Ayy{DCbvYcTje;S+1*_OpK zv)kcHOCGO?WR7_5xCP-&Q>*Iel8#etcZleH>U#ZVo9aFV`NKhxdxZ*6BjVo(F+-c5OwL0H-nFgE*Nn}g)Y#QAjkH#go%?N`){M|Z zvMZ5;r5cU%91zZ_T%mc#6J|r^OBX_f?wegUNG0(L(^|%s!eDun*cSfwn%ur1^D60` zjl<7u9SM0HSr)c@r3hhoCIWWD-}u9()YirJ2?MLnfX*g1B7~l4KB{$*ywm`HimR>W zQ4W5Jjuu2+XkC&X)Phnu_Vc3x@&XRWX0u)cCx?ySSMzG1E{2H@~B$JmQ* zw{U&4J;KGNsZUx|L+9nGQWs??#nl6OMbt(=ySN^uqr4n9;ejDCCaKnH6!p3Mk{<+O=HvV>BPLKNYv-=!d<0s_qIoi4VW(Eb47=5c zW#yX>#*0zO5)=r3P`K{>-2^R0NUC(&GHIs1j$?aCk%a>(*HW3DHQoMw`Qzj?|6a3T zV&K>SG1ozkv@*IF8%Hb6&mN^~2t%ic<)sx1Rz^VeZ0H%sw+knz42l~Y<|jG)Aq2T3;%9xS7_sAKz?Z0llC+tt_c)Z^Dgkh4WEm;CMSAG>EtJRASYwj+uxm5*LxWg(Btj^`<3O65?z9dD4rd>9q~AieuqF zFy}u}EhdsXc3mbrO4>MNQyS=pn;q_k#MbTYzkax{`-)g0*~8LOViUf1xsAs&r16|h zO>&Rb>S(<5+3H06n9~4rp1iir$CEqtLPGgy{bw`x!nNOv8&*Uz=Slv2Yx@LtZX4nq*osF>;;-YmS)U-+=v_A6s>NAV3$WivfDX^zaG`0`I z*jpa^pKuL=$m`x0AJnFYLAh5M?*VuDVObh6oy?H z`3FkR@ML9K$0*h(YGylL)GRPn3E!L|F;Ns}hYJW|fRG1A^m{xDx8)U4SUU4;bmvxs zz+7ybTgTrlljkf+*f_jUWl;M~`5X24MF|T8o2klTD9Hxi&Q=tseZPPtFl*<#XuA?- zK6JIoX20=gZ{;Vu@s;&%$lE?1l+6|;UV-_3^kAs=^TMF;y>E>A_c%7^+ zf{c|;3}+ND3Zy>hHJ&p)sqkkPSKvG9DF%E_lmY#WkkvD^Dx3swAI(5&Q!??p8O$_Y z`E7kdW7}eT2N!#qQ6dgokzvrDpj^}Q1B#-C@P+fRvZmmQa4y}E0;CPeaab@OI8i1-0o@rh`wBy?yrl#t*Ky28w14H5#v~$j=OhHfom#ub9BH&0e>h zH7hR38SKobcofsyAEq~Qv@vjSMF+uc{WT6Fn*1SWvr-A2tjv7#w@yCQEp>K{tVs3Y zktSVE+1fGPqZtRwuu_+#RAlOqm`EL;-nO}1e*fN>mS_yOsIIDRC!6~%j_c8dL=r!na6Dl&<^%vC$&0qkC#pscxUqD)VGN7h@`4&dQ&Qb zv>ox?S!UP!2hDwJ!m2pN+fAocV=Lg!$s zJiAZxNJMFGt|wF#mpXdC$tpJ}&86#^Dz377{PKcf%U*I-(oH)X)ut3mMTv~1SB2{7 zpaqEyc!3ppvTQ;uCdO4yehi5`n~c620@Z`M ztoUow%2iuLzNucwUm&OZAg>417IodrpM6Yg$~SwzFiX2UhrxcPW!4fYn`337yqtN` zgtX68B{eJa(*S19pC>cls=vPUSR~iZs=k_xbgpLG(e3s0Dl(!68>D*Wb3JLbA|>LI z7wluQ7H+bc)((l|L$`_aay**|k&I zYF{FUblF8+CKmkm!WSg2YZ**$Gxq6C=+Ak<(Yzvn8B3IF6@N-t?!>q0wl1 zkjn_IPfR`qH^LQglpBbY5-qG!T=SFF96E5KMi&u|uD{3HFBm#4xCmQ7h8ck-e8tOkBCwS(okXFso(H;xQjw z!H_<_T`(c&P4zvUwS0*R+F5=!-VEnuza^!VpTZ4+B06*j~zJs;a-Xk(Y#x37N565r3H z#DK87NcPib(SCBae4jEQ*h3eBvO`(gnB|b~AM58GlETaK>e78wGtaHtTSfmdrZ>&a zH`wqBb@!NST!Hre>8@<+D7uOo6nQ#LLD9_P#FNPin~d3s_^~Nv3m<~(*uDG@{#j83 zH1`mMOk#*?RSS(7ZALl21Y-D4O8gugVnD>1|u|>skxi*TMEcV5#Jrs+I9raL-8%~uW zs}&mMMQ0Ec>*rfy&|~+N`9H=?oi&)Y<4Y1)4bSiPN;82?RU|;7uIi60*+gFO(+TyJ za(W54q^)cO2fKhHKHD3Yk6x_Gh)?8UiKw7InC>N0@WH>^-!`9bmsiu4UiTUQIr?9<9l?g z+R@b=H=CLE5gaub#yS#&o70Ai%g#c>^<}2D?~YC#4dS-#A$K4}Q!S>h@uxRNONa?h$>)n@S!=YLGPYEbdLl+30IKbhQZu+b7o= zN~@OXCY--^^`m{UA$1lJ_h#g2&U3}~(VB@{T8Z=Jt)m$QLIW&#{s)2m<9?bl7Y~o6 z96@LyQMy{ag}uF!b)8pr!|k(IPNe&UYaTL|%$qFF+3lTamM!jR7EF_y<4}BHo1Uq@ zkwW23OP5bl05aeb7d`=BVJG>(L`%AQLaq*iNX8W=3iY`A`r;_v+3J}?ty5Di2-p$K zI>OJN0f6TNS>r2>W!DzVT5q7Novh*guL^aXZ=~y0mOZtv#upcqaD38-eMq%}!p(%c zkPIhB_pPLgJwrhKMPqy8(kk&K5N%uW7I~4JTjTc&m86+@^z8glQI+GVyn2}r?G|(+ zc5|~Bvj-a?9ZseadKz@;`WvLlUd!k-BAZ=M5(gz`;Y1#TqQl+TR2xiGKpyK0m(;6Q zMbe~lty@`@{K;&D(V>tVT4is{C9{MrGY`R&dODtL2@kG`Vju`3v}Pik_wMFM zIn`-Z_7pzowA7qZ6r|(-lr{fQ~(vqiHv>dZLnRgN!hIBG%&} zg*RR|ynrN0RB51^6NUP(DSdQ^D@t$9PK$kGSDpbI6OUC-b!;XfOL$K8SFt$>nbwY! zruQ#zohX>ABvd@xsc%|Vc+#iieYoc2=&t@7&~}6{UM{bbu_hSJmnMacJ$h5yu>h-G zu(vVc`hky{Zrd{a>qk%J5C*B$xsAgd7M{KEjW^XC3DORO;#2z6JVrds%n0_1`X?*z z6eU(pr^Tm`alm6`YEH8egF1bfkw&z_VkCLCXQ{TkH6P8VcN-qN=k8ECaT zLG}+lz_T)fE%(q5GwmqcJz+Z4+@c?(N1ht0$RAxr2};kl@|QOhESGGP)73CuKlG1o zqdLGhLoB`u6}q@giwcwg&Fiif>XPe zoQpj*wil)Nv_DwIBx~3of4Ze)QLIhBXovyP-krT57Ay3E)H!J{Iqhsln_st=o%=?< z)sZcsqD%S5j*p_av}9Ipa1FRrcn&S<*c`3#Nb{<0JREf{vy&k0|M< zAXB)440I3QEOCIIcF^MSd!qn5n^bGi8~dsB6gJqEMR{A0#&rA3(qZxDnkd8ER@1(~+h$!icDiNU8eOMqhTM<8_|13nxF5SCA-LkTeDXx*r;Y!q& zWJE$z^Qdx0A8r}u03CPGiyT_;r}CJ8cZgNJXJAP?N7g75n$DWb?wP89e1}I<0?%z#O{s=uy>uNHbh`Y z)}1}P_mDsZRoM$~i3$h{uD2~LOV-LvZxX}^M|bJ&x#7ljWgw!JrTW!A3UbU1rTF@z zOJ;NYB6Ox%a`vfapP>#BnJ6WaRz;PHiw)%YBp__>r2RYS2fQst`R&+|g@k&I#bt)^fHZF7& zS(cIE^g4V=X(G$@s$AaqVAZp%!X#eRP~?S^+%(q{v`t;*-uqA5IZ*$>F6neB+@X1? z!8H<#(i}I-hRdZ46~^c|H8VvlTXm0QDKSngQO5j&*qXBPQi~vwX&U*3Hi(fLs!9}P z+}A;7#D+0INx!mE4r`dQw#)ekkXX8unZhn7Pu;-wC3Cc+15iL*p^=>pOgd@4*Q0bojs0d* z{c*&Vp`a91_()3uZ>Wegt?}v*5vs12ULroWa#j2Khlc2w@ajZ=Nk7*5{dp@*#GV*m zX7b8#@j5b=O6P%c6WL zCNJMQD4r)+Xj+vR!$)6=mAqoiypu4t@?@C6(y_>sZfbydmpLcfc0r;ODQvrF+$!W; zDDF7kx=-1^gLSnhep_Z+T?9U53aYEy)LXcd&kH(bHOh|{U0ozX9cI&P+B*%h=N3JU z#$KF=pbRdBH(sXiTbY4QU{_~nTk77cxok5wKwwC7nC2%`Rl+mujBhY10~rs~e;si6 zj2vHu?9||cL;Bho-M7%3#ue6DmUt}eboRV}vWYo|^6R95q&SlB%@3SuZ)ZCk45sCyd|5GYxb;{H zbxH383R=};NSw-wBO0;qR-d#R%yLQS9hHNOPqFnI(MDlS<>_@g4Z?3WFza`B>O**| zfBr>@E8`^iVVB1mBYaWa87M5Pf1m?b z=eY&uL>BwmhPi<2HCJ{?m<4xs<{TR{)_g7CEs(C$tQk?{qSwEHt(*JnZZfN?&A|4N z8-%O%4X?MbF{c&t1rJ*i7*s}`J;~%$n#3wFY*Mk}$Y98kgLYND$ z%U3~&hE6sviLUCT<&s5cRT`hCpS185OzruM?#ejB*WIC^-AJqEt7?vzV|OwYcIrkg zh3MV5{FbW3c;QWWJ|%K7s}dJMd5ox*w#uZ&Rk@BQ=}6{TxlBx6nd%;rSyg*4!||q9T;Ycx7s9?kftvt?JQj(-fzTeoR;jbiO~GS zbB!*Lzmz+z@MX+1b-l&x^SbA|gC*|N`C{Q0EQegHjY2$Xwefvu-6-5DcqDneYU1>Z~k-6lByx~RVmPtLu7l23(EwC#Lq zaG$QpEe&d?`5y3md2YDYX5xlO?qwmV8#OO{&w4!U+Z)gPayV!a9B3l6?ijg@21C!w z9d4T)x!+;KI!HKMlM*77g$l`=@hIx47`NYSG|a!~Vt`rM%RR+r?rT%jdFeq%h_sE? zdzEAs7aI%|G15YeoZ%{8LHO6sUWRm)>?$fN8qGiv$IPu%DdD4572(##?jfmSw(=rr z9Jj+On(UL`NZuDudLLKDzXg_c8!^i_<8m*ybfh$XuFW`g=*3N`kas)Ty;i4<(`qf5 zX(r!xhOxGy)LqVcoP6cFlyYX(cBIxdLBgniq?Ij7s-4RQ2GV-Rok|(6MKTdV2!>l* zS=FlaQL3&%Hnb*`dsTXmnw;qRfw9Sbs=x!ua^csO5d7zsJsI%zHg@tgOL214f(fYP z2r0WyA8M~uI2B24F}8}m(H9;kMAF`@8#EKr4aGRvvcm2g6GPKnl;m1!uswGM;ndrp`q##Bx8;L& zNnc%vwiFGHuj~vHcXzUzT&bZU@6WS|Jb+o~=P^~8g=o5tbUGmf%UwR1%&v0B*Nhhx z-qM3RHjzswQ^hyXG&$+0z?nPM!WnZ=9#dH0UVK@$@KkWQ*L=@a&RGH5&GDzpcN?#y zySJ$b%1XY7EE|5`>4G8UI$DW1SuJ&}w$KMJwfAOxd{THKd;EUCEBIScX~F4X$CAqK>4t!$%9@L3j*^XI7+Gj=nDbwMc$m%B%eSA=%)nsC$p4yvPBV2_kzb zUO3=Hc?zZ0TMq`eJ5RUSdFzAZm*>*Cysb*x=^?I7`ys}5gwC0h*~UUVLn?{>ZCoXV zR-Z4+t-~qyGXd!@9CIHEg{jaxd>rm0Q;5UErKntgB6^0DFuv}&cU`1C!SX<~H*)*l z0ezE`Wkt{eEn|cv+iV@NGke|reB2(K1Q~jdqDoOwm#Lsk1FTg!lb%i|nyZPk|Kw(^ zV@cb08G=lpfg2b0ij8cp;Qk2F8vaOQl0vK^&TQZ#KA>eY^5eZ5hrsp83hoiq^HiZU z_N1D1B&PkXirqLsmiPR2r~9^Uvc^=z&YjWt@v5WqK=U8F1H7B}K-?@n zVe;^m54FN1ST7Cybl-_6g7{}y|8*c%zy;9zLpzg#iNSUaNH|1qiUtvx!6wSbmZZGy zWxDM+1SINDwoHA=TU+iiPqJD)`Y-}dsqo41nECMneiRvY+wqOd%%(Ry>mIg^ujiefV=F89B1Topu$ z(Qn{vX)9>cCVAp`#C9Kcx0riq8eFYsA>NWAL%X$ky|>-K%oYGUj*;=2R6Q3=UR)(V zvUWLYwfnkt&cdF)PS4C)lfK3c)E#6D1-Z<1#nZ=cUIc2#9`e}X-l<$YG>=qxOp2yO z@eldw$6F9B0dlaLV8T3ZR}L^H9~!&Z%v9O(dY0818(CMzN`$kK*ON;B_qW0QcW|eV zNF3RG;iv$84CzstV9P{oM>>n+OL(w9mkj&h++;!cx-~Q?YHyHS z{pAK_o|MinNpEDmYj+sQE!3MUB3@lAD{*I%(L+~GBng_$FAcf~SWUC^e6HcN4t5o= z2QnBF#&jk+iK=N&gP;A%YajMhY4K@ug6I?EROv=0Ru4K>vl*&p?PGlO%7m(jL)YZ_ z6wQq#S@hOIBUg<3AS6V3K3r}v%zt77W=caIG~Jd+yKzk58c;XZ@)D~i6*TY7qKA9U zH_fmPIEPvZpeu>?D(hJ5KbF8J0F*`KAjlHo;cg4Q)6S)S>n<*H zNVcnP?fQluCi%b%=cX>VJJ?N9cK`*~J~t&bO-#pHSuBA^;e`d3!MW1DrVY$fV@-XJ zJPUjkvfYRBX~bk!^m3G28s~u9!Ze};Yqcy@{=sk9PKEem5&IE6++G9Lq3KjqrHbVi zpg+_0C`B+NW77=NfHT!-I^kye%uMt`SbDl(+eWw%RHf3!H>GS|pkwrwwaFneny-La zMz1XFqUfp4t@I7HBBG@@D?r@1N< z*FfISX0O(>2tD*1UN^YDEjt5@X0o027El(VPP+~fjoSupI?j}~P0cJ68YPyf=}W6F zq}YRhjnP&8N%oyYgTMt#j?WD9Yk%MZSUA$2rRHqcecAz@u6AvdUmbBqaWt^)0xHs_ zbds>r%sbv6zJ4Bk;Rj%lgFy|rXbWIRCF@xJifvlDI8TnYWmSKgeX5%N5c&UGc(vX2 zgx^!Xm-{sw_~F8zH9({_$MJJ;;C z(tf&nV_V=_-%{D)X|GM^5g&aADjB=aJpbP)El?4gu=UOEQ_H!R&&yhKcJ;Moy8`QX zJ=ZatW&3&}3DNb9MaSs<&X-CbI2-34y*+o{w~d?kO<8@+ZS()P>;Ek%@!J{>J~ zthhL-imQ{At-U1}m`qfvHjIw?2zIW%8Y~dlRJqX5d^2er zECU33auacFNqYdeg({jVBV%xl z-O?*~o|E?j!r~deS?GJm87u>Xca;!RGlDKF3#C&{(%%XApmGxu>8QW2b2&U7_)F5hDqu8 z^JTG8mIi5xr)RQS*KGQ1ET)76Y}h)A-hCG>iz_jyYpq;bQmc9DI1*e4)(Vb09aAa$ z;8>a!tv?9~fjV$I%&J-nKh_>iO~Z}*OX5@rnYQf_uU9!tO&Z3b=f=_E4#Jk5mu6c{ zm`p0(VwX%ZSki_usKOImw{G4bEeO9dE2GjJhWDJ@#HGDrA6iL|oYz)%oaNgr^O;9T zLa`QfY2TU#jex*?Q$~>EzE9mVa|_dJ4I)X4@?1fF1j6;#6b0*&VqEdV;6rb*EX$CZ zK$6M82$(P}#`;joNPoG)gM{zNL%j~P8K2VT8-;{Y`LtAE@YM_9IJOCzr$7)0~uyEAQaNAS>CN`SuG(^_x`431~!w2tAI$U7kj-W0$Q$t1OMDS>NS}PS(a^qm2!K+ zPPj^$R`il+5j`SX1Hayi;)q_eqwbb@f^?()f#_q>AmZ&&x@jGeB?eMzVYz^N7Qf(u zT!aR-P>lD(JchUOik?3r4w$cXRqiL;%IXB6?6vlDpEO5=heOJ+hxsbJYEc956rFG}*NIaEszr zzke$al24h+N@jV+eY4u1#JZVdzsV4C=00E*|3a7rr{Js4j{`>O+irjA)t7j&47)qa zZD@o5=18TI@u4`x1+mqy4t8|ebsTHd=W2}bXM{OkM~8Gyw#i&1@Dp$Js(c#4=MU_U zKEJad_A4+(R4_47%19_#6KI2L#6(EmDd^iCbQMIo9=#egjbH~8nk59}K#F{Dju7@O zffmHJKS<6fAW0H0aOfWXud$@Ca6ieuNg&KfE1)v;6T3)uB!W>AMoE27#H1ubBQ>)S zs>7s{98Hw7fKiKii_R9AB*jT`+(Vv?xud8~Ql<@8Q+3zC;gqELRaJt5A3peNZsr>Y z3X??o&+-}g2I%z!P=WRI*b!0#3@`}U6imGz_XoIDT&tPC4rN+wk*SzHnO1PBIp>mS z1zgu5KnH~@5ZE8ZLu9=Js^JQ`=E)8@+(x+8^rGs*sUBLr0(h|qMFvn!!}5J$2<9gX zO_Y(L8K=g?7=>$x8-hFB@|=_QlBPt*lVJaa@Wz}RnbGgvui4MkFW!%B#(mA|5-CA$ zn^Yl_TPTB`m7+{f?Ld7_lS6Gtb)O`Zh&A$J;p-z93}w7Drda@3Y^MZvC#uJNvUH&UvqGb%HRF45%1 zF12Q1JEj8&(VfvGrFI(fWhCzSnhJ)B>*5YSH-2{goU!@Mfz6bWrmU}6UX)WLSO`@4 z#js2pM(dJ_O{Yn>z+kB9Q(BR4|Ld$kK)h9@)!@1025TQ23kpjeOBkzZR4kP~6@$K6 zBV6OS%%d!+jJK>@Ag76L0fr6K!j4%W|JOK|W%=)A7mhA-GdIzCWG2@4S0{ zA-GdM{p^`HdHGtOfi=!`kRKE;S`)Nn5+PdLQ}Z*!G}|<3o2(B;3X?*+fTI9Os!}Rs z@MSPL?vP*+Z*(HbF5#JZ*05H(a2!(~Q$PKj@75htD<3W2IL4&yzDT>MS%tF(>)7QO zvzECo)W+X_*iPc|Io7vAF=n1ObSrC>?3L`H=yPU!H1e>vy}UbcCwIMav9Rl~HSth& z?cdL4=AzrKJLSjd#|C_O?YL{a$9f@t1YRCqH2I$|q)fUT^uN}5*e7xUo zUPc}%6v)4zAtjNrQMv;6LTKwtI!ZQwqZUgjmS)~?tT6ldfAd8Yw6YrS`r%pIRSPh? z9LN}o+SS`l-7Owi9PC!UQl^zZlFk0nLVE4`-2+{?lrzM3 z&JVqBM!lwH1?EO)bBld{S1VsmT}{$X;cmTNGdx$bg|IhUc1=Z(uOxGR&q)N*Mr0>{ zcN5E}9!77Nn9bs}*7B^<~5m;oJ<1rijCNP~K;*6tQIRmfX_(r#!!G ztMBY-S^BVK5^enW2Y}aef6Y29D6D(D@6hF-agzH-w}t;DL>?4BHaUViP7^bmUW2{r z=1%^AZ@NmF1=Dlqa8pUe(fMp>k>c35bS9&Rr{es)ljzjwuz}_@U&hTQ2;&~(nbuj; z{ex6v`4*~lIS2VMdEx4vinhD@^(idjgbF47_0~b}2~cByV{jw)qIdPG{%<`kod>V? zz59ED9|Uq-9qT-9y92I6Hx5~}S^Zf)^t}xD+yqWW?aywOQu}fAn`Pn)dJVZA5bGC1*=IM&0`}dpAt;|vqsGv$3 zg_xaJ3z+TSQAk{xr!TyeI5R!>V}1!pekTD)mY%!bwD(_~v!6C_SKHmoR8LVKQ;N%G z77(2OzRKoI{XV`6%BXytq|WW!;3}%THCSpzIE0+ab9l5B2KL;F-AX>j&@i_e*|nVc zwRZKD?mk^Ab(36vyQt{iaV@GHt1K_|ReLNg-_V{ixb61%@EN`z<0g0fwi(;*{R(Q( z7i?~A&f6U6x;=61S>N`Jxx~0g7f|t5`A|7Doe*aHa48vTJ+!C}a2hK3A^k|~M%?X1 z?sL=Xw5+}|cVqoBWtX=q>~w*0cKKp{LIPxQHAY0i{49O3zL&pEANnwRUts;gv-BwU zWVd%;#;xb{(Bba4%7@8$cV#%)zWqo=(jav9e)LlNuHIX0-B&N1ETrvM`!svUd3O4q zl%14h(3Hp|2yasJ)7BQOV*?V52`-nY4J@B_s`Dh_As*fa8{3T~ z4y7+lV4A>kjxxD~SJ$Q^g{UErr6=J=d4R=%LmR?%>~Ed`=PaY^1_p*r^{)q9R*mus z3`_(}Rzg(63;ZkxrUP?fd2o}iJ>3YZ5NoCHN~P9f^v8{6CYEAIO&X zpGhDMszb{UbX``dBma+1MEWmS@ct7_{(k#kSpF@5|Apnhu>4yD{w?#r*79Fi{w)Ik zmib?6`7bR07J+}u{QuEfl3`^6zsUTdP`NKPj$wT>U^((9Zn3Q4;Z&lG{|{m-T8aDt z1X_S*CT>KY=FtZ}rc-qW*+beoQdvkCft1K4HU{v@wo&`LTx7j3#s^nqJ)oKAl~zTc zpX1=)LjV8747$OvxrP{>l}mm)?q(3ioB!c;06O`qtd_0{$OPkmC zls#$WCu>kJ-Ji8#$AL=(`(F}AcHqVpy$&QChcJYmCQOv#M|eY*X;S}Gm#755-@zj0 zU+DhtXlqv{5Mq>M$SL&^6ZPwknbOnuex(k!$by9bt;D~2M{b;6 z$z;O!|Mt^9i*p;$Yon!!L;Ih($waouIBKOkz~$i#%jINQE4!kg(Khar!{y1Mt3RIQD^ZF2_Rqr)IR$~gJiWy@rAdv zr(W#wnqoS3vw$$q|BbjgdR^7+q`b`RS7UExqMnnCE>gRtyYYFghpHjdcNxx#s?Vm` zeVwdVC-cEqs-QF(k^9zb9?;axA(sXGh)@BQ|EoPaoJOoj433IN8Cm|@TaF|+9>vf; zJZ*K3CZP3KDqHOc?9!<<&ilok5mC`eu&>0a5V;KnSN=pnHw5@TAeiUne_BH(6L_Vx zaukt2-GN7+gr28dGrPA)w!>KeMw>in!WywIGHsQH?#7cq-#NSXak zkdwD;Yu<0N!Nk4++$c}0bn&@vveYrT=-VE0+0MrwOMo;E`w+IrvRFE9@O#1d%wTn> zZc?f0THMh7Iqg4Bea|+XI;Td-dv}{SXN$Wyp9-_&gMePdgPBr?*j;YHH8=G;bKmRG za;;p2qEk%ghkHjzZed^xOhtxYT6x}r??ciOo$%Eq{q0x@O0vfEFN&mUJ#pbY`z?<~-6Eq;}YF!3o% ztj71rcEl~rHv|mc3OWg0awGzJX2~brgGqkxO10?|kE<8n4OND$@q+xFA8HF5F@tL- z;`1$jSUsF|e$ShD#|lai z=5Vp%8YFg=0qktQr^=|gbrN_Z$|&FbN@~2q8rQDwf3F7qcewN*rG)BOxHcG<)Ma7 z_q&w4iRdZ>F9%v|7Og4>y5T7zTXzG5Sp=X~P0fkpToTI0^n|s~QZElkjJ;DA*d|90 zw3=MR4qjeVDxeT~r0%L9C0B}vurKg0b0>Bx`uq;gY3vMXv@~m{uDYhvu63(e-Fh}f z(GZQgG5B`@zdQ+YM~+RZ_kDNlKfeaZ#y0Qbn7p@W`cx0gUZnO@d{VrS^)awvk8HvY zg3|xxHLEV;>eq(;nu>S&ynt6y!^=&u2^mpy`D1@DBhml3f<q#x10JY8b;}hvb z7~GO1KJ;99b6L78WYVIrwdVe0q0Y-X3`tx$bF8Ra4mAwY{5Kq84m$urDcJs4$w44~ z@9G>S=VMeymcif>2IAu683lkZo_$&V6Z~ zb1;wmAW3^We65(979z}@N}#$qi{6D6dkJ+-X7Wut@!~n??vt=bE8yCYeaaiW_zR=C^+pQz#nfa- zQ>mvDVgPa|6ULM{D@~q?t_N>8K~5m3wo}8@1iqg1U@jJ^K zYjGTYN^gV5y4y}Cthj^LBmN<@d2%;=(3b7doFm;6qR;+(v3EaKQrkq*p={lI0^l5_ zvB^gH;g#ONrJdO98$meI{VpZf{pxqGlkg5PAKrV5ZuVaH-s*A})gNKSN7$pqDQacR zZDZOs*U48OGi_$sQNnmYR>Y(1c)^%cTg_#dc|-xr6YJD4*Xlg?OL!U@3;s@J;c=%f z99lykym`0T+Jx6)6d^ddfa+h$pXw$=8gb?)d_vbJT6vl^`MEAtqR{9 z6W6PhQLG)E5We=y%o@^kyTi^dl|U3KhL~&e1GHV?kj-Ob*3BPyliwe3+NC@$crIqf zjO)jgHGvrQ5}8qPyU*fmfi_ganchM)tJ8b2Cy>c%uCLdn$n*_>n<`{O7Y6| zfera4Pu%5=@LyyRHiGfxeY&AiW$#JIv`2r`YyeZ$!r_hizpblvoTTj8AJTs)J{fp? z<3lXcGG8lFd1*lf9!!zlJsN%ZW$1K%qDB#+Y_Izfq8%oU;T3rY(TLJHGte_iS%|%-?p}J7rVZeVxD2nUPtw9j0HAL)GTX#tm^k{=TADx^>%x zd$rr>gpD?GrR%es(<_orwJBAmBgQrMy(iFXKO8cg0@K&ID$hkP+I~fIe?4r}?tq1o zr;V`AgH}IyMO>M|ySz6#A{dMkdQ<3#;BGk`oP5;}W;8WU@+fHyr7rmTz^iI;LtIh$ zzKrt)MSJ_Tulsb`bLY3U3&RjkPbCguymq%MF@T78ah~r1%i)`G6iVOA^Vw$%F7MT9 zrN9|GIgg%PzP5P+gSP7@ zH_R38vyzv{6Jm|9@v_ycO^-OgSFzimSOOl3LEw)Lp%;QtzMENeku<88bJDS0(&ntU zK%L%9B&2L~`iU+XYI~4w0f%GkLTldmCM5?;Phx_L;Qixnqp`5^qX_Ln#b z0v~a=*ZhXT`S?_uLq0_Ns~pUBLlbp}Xr3!o?Eci-6ObwAic}7$x0yosJ#c3YV=Ao9 zDn%d=>FL?58|6G%FIT@?K=7aOyNW49t+`NYJ#AUV9o^W7-f9&p4>1xMmc5B7JDC%{Q;1> zRHd#=T>eQ>22>Em$~dK&s%z}e@!u!ym3vCxvd^2r;)*Nxup@)-o1J2}5SU+uc0i;S zt(CCe?b&cqX{=d;F*;Yr4<5n|D0OkISknTkN29qbj#>%y1*M7tn!TFbb;3Cx93!sW z?lLAzv9S}N(mtZwAW5J`YPQh$&wW+5Ei4rYwX9P$UA3P|hGe+AK}EvY8vzJ0q{6he zb9^nZz&P1_vMGz@)u_kx`7_DSSg2kQY9K5eI{+9Tcz?Yqztc*x59Qz0`?;h`$lZy3$L zHqWU>(l6{}6dThxX>wsKd0^Qr;ez%VY!LZ$^#ZohO1oawx4LMIu?IP+g5V$O2zJr+ioiNO!t}cazn~yGwt0e ztGCu);S2Csh7M0{v{K9|Ogc~bmGDTcXj%aS7+IbyMUK0!!?+D)cp%mU-p>Q5uD={K z-n$Uyfg)A{95SHqHAha~fSOG;i6@>KpUL}#nnHVK$)+dXuGqbs2q-Y zN^0Fn$z3d^Pfny)+}z*S5pWBrW4zDqjZn?8&MJ2~m|)nx=v5$vl9EX$qQTBP!;8J` zow6#wy3er2t!|e2FS==>UTBV@;}t7`$K?4ZQ~m~aY5@xNv)xMCO!gF>w8;`7Y zK?l)BK~Zs1q;EpZ-)1T{O+A`ig^iP5`c7Hw9O_atZ^cR^c^l)|_cWqR6_T^xo%yP! zKapCeu9jrz3K;fxW7Q$ruHF0@N_D##mKMFd*OjY@ViNJ8>8cA<@ zE~^KBm;oD$7owtRa~2;s4R{UQ5!<^-D zjE|%;6|$Qbj)nK_wSU4azT=PK(>=R|0BH13_0qi&evzIGv;?;~&`VtPv%%(u#}|VU z&~Yb7D67(rokQ=4h!^bpQHC30LPfu6Mqs2?I*`z-)|(IwkEIPfVC`_0erBrE>I@AV z132%Ed+VG%x$JcTg!oh|7Ea$f?yqT^(9u=2+-=VJgG?`iL$m{0FBZy`fhX_IjMs47 zQ8NMb5YGXw4};t$z`a$T2TSvol1puf;Yuf+i-=U3=bcAZR9}%Y<|nxL^Y&q)d~du5 z7JN*+d7E=*gRwBre54_$7#1Td!prpRXLg(1_qmvDntz6>9B|<7206$v?78*n!YHTc zpV6?$?OORtL6#OeFPgU-xLjFwAb)1FBxWwO7)`Qm3&4ipaY3WF@4U~ZxC`UKqp2<; z!jwvHT+ZEn@XE81Z>(vtx6$WZpydAi+7N}?SK(T*+`0xx050dFKh=tuPP+=|o7oV# zkXv?MogARr8(}EdDioKLq!_zU@X)UXAAr~`;$u@)$<;?AK23<7k~$ZWNA+^DOy!W~ zeScGrhZeZyLkId1`?Iw>;roS{?iUhg10^kUTrM5%KhY*=#g8WhXi6R_3+0FYSjQ+0 zT$MG7*`5TUd1KO&O6Up1Kt5{Net4-v8w8R2*UJKoUa~)2_N{OVGwc7 zl|wz~!nQ51Hb+7#BzmWwfa?VNv8v?>(IFqeGWfFRKL78fJ&2bA!(vro~&n2 zgHX#N^Ip1bPb=;?U)($x^B&!-x#@=%!m928_P;Blywbah}_Xa%;oj#xXRckah(O?)x)s*$i*NJmhP+yv4ZjNi0+{(jkNreXm|1AKv0DjL$n+M;C?zdf9aM_jbj+Z%};K z<0Qy>hKb>QL;PFmNT{`fY4#Xk^w{C1ygpKN@4&Cu&J>|~U0y0Ihv?yirKT}%!F!Da zkZXa(ZDaHcTx4B1lpXdHdZzjMgw=ZnTzCmW7Mz^CuD2$_|2}wEukf_CP_z4V9+Fr7 zV9P`UcCr!%`$+@QeBE6h-_iM>`CC5RlbWOuu;8#V0e2*6O%`RL&sVQeid|B6pwLj5 z7!BR@!u|s8!wF*HS1Hv|!O>-c&6K_R=7S0C^kKO!>BfzS-7k4AW-M98>C~;>yh4@i zb)P2?NXO+VV5$>k6^&5S^T-!a15KN+1{hPcP!kURJbBWW3pC`}XMPPv->0?D8L z^SMll+Dz;PVyRxQm`AK zrVc(PJ4f-o@|Mjhs9;O)@uPW>4@Twa#OSn?TEW91oM^kWQr400vra)Ug9Kei3=OA! zlc>7|ab^<%BLHBhFaevA3J&K`E;^c$mRrW&84LwrtxPyFFZ$lCQKm;~ySK@%P_APrC(F>#)Dg-6Bjr*qK&xf0z&dR1Mi zE10br%hTN$cZ?Hr>o5Z0;SK4&G%&Ym7`{{Ttr_p(C~2kUi-TU3QnVJk!gfcMXp8g^ zG5L3jFhkr!Xvj3)jw2HtTOg z>V!w&vr}cCtqTTsFEn4E>K>FhMo*!8QwRCdpW;=U?C$L4_aQGvdkn$hwRKqE<7({riuLYP^1AyH^iiObDeBj*0lg3r?hg`G@3| zPmF#C70Hw46VM-~vOL%#_Fihk_5R`IUwqZQ7cVwhM+7}Y^V}Yp^y$Kl(d0haO7Q4n zxj$&j??~Fm`u5s;{f-02h?&=Wq+CBGn7?mglyS0&2u;?E8*QuPV_tWY1Cu>n8$tM& z=e8MU*S%g95w3h$o6o9gyW_d+(sT6U{)e(yM>$ch_P zb9H^$958wdO-D*|m<-GRw(a>lFDX}wET4pr@bsoOp1vmaOr4Eg;L}!>HDu{Z#aQafJd(9@gd^xzDR4CvR@K zQPu7P-t=^N&fq7M@AB&Rr$;6=!Sh>x;(b-@;?%F0hV0ZT2mYWu;jOgF$KS2p(oPiX z1tv@x405b-A{fV&o^mSLaMnXNxsRsLH z2wB~-Kbl|x;zLaxtY2>Rd|#OFaH(oX_!j+#i+)@rzn-+iZPuozU}yT^aMW~ zkcsjA>hBbq2Iic1XK{ghA{v{@+FT*(%Iu$2)d#OtJ2qClOMv7$$1E>2fJ1IbaKlD< zH;$|DvbBWsR}~Tnl_up$xCh<h@aX^|#Ew6)KZ8voeyVbWzjocP9TIxoM(-3}jS( z$}c?*MGFgVq%wpnv`kva(!u>x&+H98!N{E|9!M)dP3Iz98%JE5*_;z;Gf@*{Lon^(uW`zp>O}pYNv6(d?Q4=;CH8Vk&)j>h!|S# zgdvb;n1hHw<(p@db}bWl5g7*vKPGYIrC4T%zT2AI|+vU`ed2 zbErVkIPcC5y;=T)Y+m>UM0VG@4_{*Zd=|m|#~L3{RgihDoxmkcr~>o3o>NpxUgGtN zDq+H-XIIDJc=R`}14w*6&7}yitcXWb(1vT_JSkrJ5D%?lQQw)y(kP1T$oQP)f;piS{7!dA=|=A22nHA&Ii9)q0!~M@KHJ6g@NZ-W7*nSEU;U8b zAyk2w*DDr*UDm_68=}wSMHXvEa>r(r5(cUlV!JC=np=K3zV}!sU#x3pfm{=*9g=TI z?`Bfs9F)UOft%0cu~DaC$;kWLBn)}g)ftL~t-E9rkgDMtM;L^aQ$@v&%j#V&--tEP zI@NpKP%9LH1Gip1kzuX`j^)s3+zFo0#g@(aQyXHNYRLue&Z*I|CdlV!+-b_`D4)8P zd=?dM088>YO{^D*VNrxG58V&Tc}7M&QAs(F+y9`fFc{O@dt-kRwiQdvi_?i{ z=3}<|MR1~8>b52_EN1sDKcxjkttfHA7vp3#r<*%WI$;Vj{SDP}tJQ*d_=OM;@@NYi zQ7ZA$^<*J$OXnEXsyDIbgXb*n{KZ3@ONGWY)#^X+X|-r$#wmguYt+i4_5fQvq9jp` zfjYkvdXM=_pA)3ZP+(I2wA8y*l33pH_dYV9yvD*JL!Ze0F^ZqN;F@}rComUAg3NL- zC^C1uY4q>VxD5Y#+a3HB$P^A{HS|pq&V-Smd&|+@!^+{$xdBC~=TErD9^Z=@+rdB# z*HB4w^-maS20>VWvSg@3?T>OsIEKGzfmp=?OHAA@Z6h-9ZxgFPq%QPVDwTPsF^dkz z3Lysp+sJhcNu-`)le*1jrx{ZK_KJ^F!TWR|o;Nt7tsemk{#zvbB%vE#gS*inf^{0s zzW*R~9tu*AI=KtINu?L^{RAe#WA!6Giekr{>4LWgrSV&TUS#7Qoz86y#WimbdGCDu zqu2zUhHvObce93JDDuc^;v}Z5Qa6>;w%l4dtE>G=oy${nRA*N6DrkxJXf@eG>#8*?Q3N9RexW|h4QFwmDuN9gsJ2pa$YQ<%e!goBZ zq}hZ5fzVyVNDz-=VsM^?FT8@BA9pdx4M^|OXBcx@doh)^8J7?lsj z_4W>_J;@*31iMjtCi^l~H(z&7BcsF%3XO~wS;(l9K< zIh--zWsqMx3V z;Ni=&@f>=jjZ-t>28~r_iJx8ju82U4m^WfXy{(&y73E|5zCK z(-KmeYplMz=Rln46)s876>@YoqyDpR*eD*fP3j^PJgc9P)9GejCdzE|y?au)zK9M65@}isld&{o{9s3iyz1bE~NVTF!Wh*2xph=zJhI1^-KXz+g z;!sJ?hB$Hkz$apr1QW9|eCIddI7ul8%d&HdboeWyTb~{bt&oM%u__w2zwkhI zr$^M^+7I2ZnQl-wb5|yCX3gbnzpu@;iQzE2uh(E+;}Wmpr|o(~tCp^tQmYswc`@<% zvq%LBc9gEkhEaTD>zS`rizRwV|E(rHoc15%2{-o1jgF;Tu9M=w4QK$RIKNGzuc7(h zj#(6{yF;j6o|abh{R~d4{=s{eUU$8N9MiQ^HE=;hi}cPt;ALZvpA+eoof_;zrt zh#MQ9kwuZw6XEQAcwB27R^%M~D7Kfoh(tGyKA!6}Zs(2>Cdb$8 z&KkXjMtl(3MRH~Yb6kohxcN~=5>I6eb1g;yVH5_V26b+Wl-)%avUG|-DAF1?)w4jn zQv0oeEYt9ceduq+w`~4`{g}`*MJL&tFemwY!=d4zJft+pZh{@+DYcSF^w@YJU(>tG z7I~Kmf}#bS%T%qrzauM`8%aAO+C65+zDYb-CG(ww)B)^1O!ga~HD9i-#enF}YjMVC zGYr289|3l5`IE*?>07foWfCt}jw+d9y|*8)>l&LvddP2nH5!{-y%Uv>$ME1cbHAuq zf0lb+q1%T8@uvDZd0Zb&ANd8ZR$WkOjj<-iQ2$3ghz;Bq1PmzsHu7A|EqFM+UOS z#itYi#tV@1`pUf%%G--xc`I5&XA=9_(697oCiJ{M#r%Etu0aC=AG4s6D(K2oM@IN> z#71Vv-cV1lFLG@3a1Kj@b59_i@HlsVzZ9sI7O&mwnui`7am%Iiu0WOmJ;vF!+TQq} zNJLn9Y`>ZN%ur1l+bC3{M^)pBBm9{D3k5HR;7VUc8G&p%(YlV#QPshf&zI{p9YmI* zQYMe!_yrpEjOf&k=_K^V<|opG9s-;AK5U7nM}$$*%XuXO#kLGsaFSf@@Tc$Xv5aeJ z&>2Nq&U)4jPMD^YTYsy6CS)JY!vWIwzhfaJ#f=w?gEfgN-}BU@Q9_{>qEbx{&w5{f zLQERfqh>wzI4R@7+1@`}Q+mwen3C~2B>sj-w@g(O$tQcSu&e4s-pz`g^NAY}0-@w* zW4%xK98YYkUt&9C)xU<+amk-9iH=hU?JgeU&yJ)nKFnp+7tda*0+z>i?0r_HO_W-R z3EGOBPTl-Y3JQw9Kur9so}~bw>fhulpmt$d(;VcGg;<+HX%*#TdQ4WGZ<|z+$f@-h zpn2bBM3P+_ApM87v$ z%cWi7IN1CZ0?VsQjvS2P;9##jxPm84>|H8rlvh9fDIMfnXzB76$>&v3ITpND$!|)m z{pl?(J_NVTYhhu8(eQQ0EmgFWlT)V_h5rH16#tGpG_d}bqISdkPqkPMrWG-;k;Eh4 zz?>=fG~(D+_se+N!kUyL(}DMas&Tx*JZ}w#SkdCHPSqNo+eywM*%`lLtg3A#j^=c> zb0wwqn$di;tLxwtS>cPt9-kxFsP{;E7sFuU9EGi3`t%ky-3wVV`7yl-lMgk`WSnu^ zaN~T1`@n(f;4$!5OaOJoi(Hz!5!F_f4|{@#sT}3?)o1iZEej(*$*Cq?mE^H$#6A}M z1AC9TQ!dRyKnPTr1g%E^^QsCxS2#^uz;=t6zxAe;Y}b>aPMtreQ(E*I16N){)l>X; zm^Z-6Wj8|59+E%Lne5~XB!_Spc6_k=NgtX&J$gDzqR*`|s?vuh<;Ze+I3E86Op+It zjKHI!06~zhA4^Exjp6&$9NCs2!O(;S*LtUf|M!2){E3*O{h?O5Y5h!HvK8(5n~>>6<1h&mgtRYKWidwmm>$a60*&K#?g-#0`}BJ@3bP&r1~R9i}iy zFKQDzn*p}LCD#U+@U72|g7bNWqDuJ9SVv@eS>I?o1GfTL#`#A~Nq0*P!XvkyyDE%o zxm)}v1i)^z@e+Lh^DcEW&L-k8x!lY&{A*)Q^T|Abv9kvi%t$8q~i`!apjicZYgI4v_5~Yq=IBM8o_PYx8*owg8?7Ve3 zL$M_0Zm;cVb>n3!5mqSKu*PlwV&OvZg|fb<8Xa!P;O~t{m-*)YUb>W0??;o{Z(5yU z*}*Y0i+f*AmZ!=*iVu3=9h=F15q;l?P^XdZpL!LHA2Ev9L>k0kFe+a#Gc;fC2I0F^ zn^z++du~zaKpEqNp6>L)6(6YPUCGnRkLwv{vn6>OmwvH^L?oN&TAiAk6-f0@C`-0V zX1|otPwL~>KAqPdzb-bfemMTF;^L=={u?_%HUxBBM_f8X^ z5$v@pmGU+IK@?t)nrvNPfEh0f%57Dopsohw~`+uh<gKoR5BN^x0=LX+!4K0Z0T=OSnv1^sd!*LU;8A2nMX&db%xISw^F3 zp@(&q9=_Mx%D%e>ghP+z&Kg_V$yq@(P^SCxXJJhvZZj<^!^@`T1p!UCeN#vSy#zpk zU$%%z|8yD|a7(A!vNy+ET>$W$O?i8ZMaFjQ#5hy&oLT>jVN~x{<`#C{;89ttyWUgHzPfAwPRc)u0^m8}@@$+?9+b6|mk@&( zrKh?-sNdQ8A%Ank4eyLzB94aEF1X$N2gr|*&8g;XNqtAVf6%iaH@wvPMRkc1}NQ2k>k}{@BN!w%uAJ=jHaCUwkm5V} z^NWmT-d0C9D)x)CDe@Xr_y)A8qE%SH@^GniVwMVkuaOR$+rnY8`>UX$vPfJi;8gg) zeB3}nq!oQi8#h*HZ{GRqNyK(krnDnwPn3?NA>?Jf&ALo1?a7X3Rik4V&v^UE$%9p; zVdF^V*g|cWPXb0tP=eO^dpR$RYgl39%&65a--2G1nDJxt%#5+<5Ry9 z8Sv9q-i&$*=U+wj0lpe4ZSLRx*gN^Kx2N>8bVz@n+Kh}#(A5!%q+VV99=SvuHy(QD z=z=)>Dm`04^(jSAL!sHPJC@#u-xD7BP`~kdMNZ{x90k;IW;Ak|ftQof=xsV-;XKr0 zgm@=m#>cc9_eHAG`vYU@puI|;Tv4j{M=x9NPwHJ%F)weU2R=3S44+yINnf}qtcVhw z)IYZlL~cp*L$&NK+V#d~iX&Nf80~994|+7uB}sru{nFVX7;gSweh%(qIW(|74L5LF zyJNuyE$eeVExLJJxXc_pV?CmYSE09tUGVvLt{3A-P;jJL%#`dxrqVTYj_-$x+dQ{DRZCv z>ub|=CSRS7-8r8!?CHU_3o0Sol^UCWr){ciz?;;DS7o5lZu@f30A*uy%@Mc+c&+hg zZ>k9h-V@MN1HuSXdde=l6>jKkY;*XcX9A*)XO~;G{yKwqXLy3xTJx@4WbvnELO1F! zIhy*>?N7ShOu68tlc0emlqb!~XZ%c2A{PD54;~$*F0JvtYLV_uYPBM0)6%8AQ$z5R zOk3@kIvcq~2)Oy?yn9D1EyiS)HE`3HVi&gYfnWDM8yPhH;R1Hde@BO`7JoVK@Cs}` zGdlXXr*6+R_Uqddg9UZ(Aqe+$SexTjrFFwo4dZ0hL5fY+h#85%ehnD0!qgZ0%$i&A z>5#5`X89AnCaZ#Yei+|wuHMZU74S(}d?JQMn1@Z?_e2PxxURi$$gOGe_ABf~=dO;o zxyI6&kime6~*mrTI9cd_3u9Cr5E(tKYe~0R?Kw)~%-6>8gABlP$NL>x)C8W-zLG7b$(w-Z(tQhxHH zVp-p)9lgbVZ}61h%J{77RU3Rv15F8YW9vt|A{MUwY#gIV8zs&;pdzX^s3k1VxC1QhA zFSP!*(Bsc?^Ba2E8m>Nd2Zm}qZu=v1SApN0`WBL6!>o7dWH`}X`^Km4=xj7yN5GF& z>Md{0#f7!{l~PV`$QwbPcdz#$R)=mB1`V_tWbDn?a~(B_ZImc?R(kIs#QcSFtY-A$_D*ImfF{#MtyFeth9ExXX!&lz~asjqV zCf;JIR?d!{022wj z_*{^O1;`jyK83BBuQKcSViT{0rV)L3&JbR{ZtDP_^T-gVf(7BB^rBp5J+P#A{wckW zXBy9??jZ(3^GcD}L}ftj&jDd!f~`o$#CVFVv>oMW8o+#YjK|Hhm%WLSA+lsVXdko0 z{MfcDxjx7X3~PK2Q&G>vR(KT0VqT`|`?1kW+S{#s@87Q?l85=j zoX7y;$9a$uH!f~GRc?4xjUykD{0*1)NN{*8Z|(fcRWg_Kda?oN=I-Xx;8AB0Zm5;P z=ZWv@-l4VnxVTAx;~Q0qR8aQQV^6HhK|>T=3C;uZ(S+hQ3CWSCT&lV>e3Rdt5MLh% zgz;!t_W`l^cDU>^i~BbFexevNChMH0JNRg`7Mmw4Y4yp;x7S)Oo-~AuB?3K0xVV9@aB^z!ra+ zIpWYLa&rMuZ4cpzRL_ zq~Q2))*(ic9#dFpoU14-NBW}eCIUN-l_<&cKvnx-m%sgm#@NW9AbH||zmDvC|F4>3 zUql9Y7x!T06SjM3+$@GNZdl!n!A}t5!hMvqZ}oYKwsXt3dS-AZ#&f!?OAKtIKuhkS zH8*!><5|R*17)36aV+NyPz6eEZ_W*x0STkl-LIJHFQQ0KP_4N8Xcu{!gWyEmw7Czp z&(S3c$>n$tDl(n~9w&3pVX*}`GfT6;1@{VFe7=XpZ!bRfYIARVBX>p=y&rBQ zvFMkVYCUPVEr1d}KN!~72+n`M6wx;)ldakM&bX7h9;uI=5B^QKG_9sq7%iL`_t@)L zP;H6ja!7;Y=%{N2Rfd*3jZqGpttZcYDN2SI&_v;`uBp-W`cdmjz#+_|ICekl-s@o9s&rU@kf@P>=)M5@8r>)hySjP{o$r|&PK`%=y>OeP&D9(|t|Yv_ z_5qzHh+hL%WoRTKjTdMXI489d)6@*<7(9W679w4qgDS`+#z2!~7LR3;FMxW9 zU4(7PY0;5dwb@1BW*0f>j(O~R5;c>W7wHb?dv5J|mmfdBCS-N3N+fhIXwB7Gy#T|f%Ckv`j+n;6x=tGo@L z&Bmb{@ljih8o*0o04)oUlv*j>FiR)DTdb?=HC{$ z?tK)+JLSN(TU4D$^Xl5E^RLbAHQE zRqk7VTt9kZwVcfH8LMdjJA{7bgT-T1Reeq6E!{KFV(WE!3iHgq>Ncm|osFC19t}T+ z>KQXoPlpB>5w&boLWh#$%-v55{Q_S?ckMH*GB%#M``r?QP&mt~rhi5eF*|}Z4Nz~3 zQt15;m1v&N?$!!eXJcwpVj9wy+*^`*LtYS};{sIrf`wzPJ?N{L_apSjz@Y`*la!=| z9|)*gXG}!|kw1NS1~pAAhqFMwu$*#__BHOSyN5l&F?e#E?W=Hyk9VY~oXsLotqPFK zNAQ~@=Wyi^^@sQYcp>2&ea>TnP*b4&yCC9RMKK%(Wp{idVA+ zXP1q26yGnu9ci~0=N^^@c{SEAQ2D04e@+Qc91Mx+ND(0dAbraYmun;e6s;6N?SF2J zwl=O9c(G3|>CAsPQc?dhv{s|;stN12Lrhnxwz@n(Tq}nw3XkGly#;3T&VDQ<^o=FSxj_WqGr#XfztR#1&UY<=RAzc+HC@-@i7_MXIE(EHo=@ zlVlgRy;UgUTyYOJb&a!hI)GC64cN9Bex2}sju=N?qa)l=cuHth?kBN1SA&^s5jjBQ z)skt)=nI14X~&|n8qT98IQSHR0Aa*zWvs<3*Q+_YA|&N$%3aX(Dl$U0w;-f+AnBJ+ z?G^ND!aTb96z#L_MJ7V;hivqWWKtjA3E@fNn^itP-FS&R5iz)8)mb_Rj~^>Qe3|R~`dhyF%@ibvvnS zes<2+R&D}VOb}6o@q6c+aXxZ&n%9F(zG=RJ+uHOeIVD`Blbse3=Ha<@tK4pnWBTe> zcY$7g2PncuHNhSYBHQZryU&miG9{DOz~4n&R&a(7hP5$oU&&BZUeAg-6JIR{Ya`Z% zsgI9;i1>8=UgCpwdATA^1rKPaY zf%Jms#FFse5DyMZk&Vjsr{1_MQcrUZdc zZ!pWth2zeYEd3hzcrm7VFTSACKm=A%g}q#@)A{0=vYzJ^I*{($XfZmv@_tjE7R!X4 zP8AjsnJM8t)k@%z$Q36;9$5rZGUp=3MN6c+ZKBf92W~~kF#1lMeQy=prq_SWcG}T_ z(i+(^!CICWlZD0pQcYn9>;ZG*g^*D-Ql8s&OEYR+`eZn@70=DLeFt90D;Gh4g;K>xJw+=|GYnb zTZg61uvroCn(xxfhRhdyKgE0T!s)FXsgE=!(?kKOQw@Nzw&$?c0?^|I({Q0Uw#eCZ z&jLBl3Wp{i#vj!*?&cq(jQevxCBGkN(?Bs@Al$h&0`6F@D8SNut0>cj+jIa~dIKpg zT`iN>yh`V9s0+F=9cmKIf~8eu8+3UkHvP?L$FT!KDWDfmw{f zW6__WSm;vDbIf4Sp;_T(TaGMXQmdAOpu>gKPVS7bkD3tbEVD5WdM10ZArRi;haey) z-k}`^b^nn(Xl+kPl#x7Ly7IxAv%rtuzTG0~(UiiV2yWwD1Y?}Ava_4w2dj%cf2k7s z%%6|v#{y?{bK2*;;OuMxtTg#7*S!=jX7W_(z>xFm%GmOYe&myIKlLl56Irbi^Q%4W z*+8+CH?Tr4SQPtlZ8qQaFvw9&8~?{whJt5ZcB5tEeAwzQeRuK^MZ>&42i@oTWc+M1 zZ4+qsF&o64or5SLTT@y*cG|ZIUYSO_3EVl%b)Q%zA>G%$v}E#)uSJ5$j`c2f(4BBI z$3dr%>Em>0E4dKD+%9i-CF0(G=p!I!83lfm(Mj>^Dc2i`ow<9v7!MeY*0bj>EiLqt z4wlYRCM80CMR#yL3Ib*yUO#)-(oRLw`vU%o66nNl;3~G})4)li+-0RFvVD53+dCA2 zl~~My7&vglokvsr^(xq4T3?{G5Gy3JQdNPt52aWdU|Zpqb%7t~+H{X)(zu&R;dd4L%!pNVl7tU5Nl@7ci!#oRDAXQ`+)C6)O`Kbnrq+ zc&lWsj!3R%*4}kPG^rjQx*EoBDM1yCHOCIPB+mn>LwQc&HKTdimE*DIIH(P1 zVLi{=eT$1vWt8;AU0YOWnQVaFBN+Olf~BSG(bewTeoAY%<#VmYQD?)3(Zb7ZD%CpH z53FK`Q0R6+c4>TY`lO<5fU}@>qKY;n-NgS0)H_qIYDXe#Ik_+%mubbcHzv7<&i5%e z^Ji=KLo?ca@UKbvSX`eIsrexClPa$Sfsg#qSSAq<))czDy_o zJpjJS+z<{u<{d#U(QT3uIBVAdq=Pmau z^cd#{)r!I>@|j3-!GnBydIIb2p7{i@s%xEXP<-jdcURsXIj(-)w;(K=3JPV!gOU#) ztDRMHwOgTNl^S@v%`F>Btyr2i1pqI2GjA$)1bDUmvU5N3H}CH3z=Z5K!~{JX#M_52 z5NJLIuL}ZoxHO|0JH|F`lITY>;32d^8q={K*F_G$&_){NeZ0j8wm$aM6d8S$?a z@hH&&#(pT2M)d2qOA~2QpT(id4L>V2lkRAPKeEL6{1cr05IKjLg2wj@1oNfRL3j`v z;*b8G3?9WRiEiVV?S+vlZm$4x!$wjaJkYMcy>HAl zsF2N==2Hk~*(ah&G(<-jtgg%{|JK8nt&%ZwABp_v-d4@0bg$Hc`_H=i+Ynz8UUeM!F!nLiQc{NTyqi z(MLTjm3Vz%X{{fpeWzQLCo`(C&n=>6S)NAEdZJxFrfhVEtm*H6slEk_HOW+>)*aRj z4q$O|>d6w@Kk_EC_vm1Zz8NFN;^T5op#tTMz)ET?0mpaEk{7bpNC+v7bY4?fPIZh? zoKLU?a_rw(MNx51DPk=!(`&5vdlnE~-4DcgoXTh-*N{hYh~j3arEA($!ZCU0tW6~?~p26sv2m^ixg{e6EX=rjc!qcRs z-#S~52r4sEKPCVD{FE$Ol+`bi-dYvhC0}`GYcFHUrM6#M7;aEwUOLneDWvLzYonL8eslP4VGs@N8&KDL*?0oh(%`iY=a@j( zBE8{rzU4E{tLn50B64Ijczid-KLm1dg^oVg1|f;i?5(#Xyl^eeydvD8F={su zHk>Mt%-ELi@FZ<#L`G<4B2O#51I4Vmr^+*&wpSq^Yw3$~dT$Liwik@sGjKq(G}{)o zG%Q~TbVBJBtVc7092SEp{2mnEu+Iv98M=xS{x;#SEbqhf_Hg8&gR%l1KlPLYdB|l1 z_!?KlL_+UnkBRRnSY4>3QH1iGBT1MbQ)(yYD+R;q%Ty0nu!RT&17J=*c9v>ap0Rf; zb4n8!!O|~v$8SU8rA|FzPi=8mnY+FwdwF`TD5|wD(Hr$xx7!`{vpb_7-yfBeq5S+K zW{Y2?F+9nG*e-2yK;(uy&`c>iwmAHOs&cBGYTr|Z@L+$}$@d3-sQ*pbk{6)Thm`io zo9xX91M%<1v2E+9`d-m}KL;v`mpakb-79emP~>3+T>MlJ1EmBZv;&PM&z+^$VS#DJ3X66Kz}decN#Qe((xzD6hz!P|SU9gmCti?jB~}O_ zZv*{I^Z4<{N<3BX)^*6G)du$3i&IZ=Lzu1oFohsg=Wqo#z*?}cVj2b*+P!ri2WwZF zRr#0+i>lnq9NbgO98NQvM3`SWW=IgAjlWmVZS{c0{-X3R$g@DUBROrDufVk^eFplM z-Gd*SiL3O>KS}r+9>TCD|sIkvE66MSbQfE(L)8` zCN$gO9NOnbYtu`36>pvbBtPq|Cr)@r_P@i~q^GP|5q<)#kYn9@V7(lM%mM zJcqtQ@(+OU-uiFV2?7MD9Bs25t3r$Oq`~YdbS~vy5R~~u9Pg<`9>@3y2W!L7wGMfo z^7Q*5d~!pP+r7+=6z4U_%l8yQLjR+yeS8i<*v%6iSeeFJtuAvE+7LY_5E!_j_nwq7L!#3}#cw_9mfvmlA4;}yBt>)Sil zOQplebaKUrd0b@3hpI=a{#p490&A~jZsLJ?fy;Ug-o1S5&polb`mZup&%2r8 z=onC87J8=>HSVktoDzihhk`qs8YBesK&CA5vDOA2G-+{Sqf5Nh^N@}?YCsYUINAIB z{3QxdEmz&}tR_h%q1^-1i-y1^9FhK){L@Dfk7x`Ag4jUF5v}6OnUzYfg zq1SoKttOKy)5OUZA1}PV@<pkcie#x?NK1M+E5%1jeHtizQ9XL>8{>6A3LD;*=AEitURf3s(0a__ z=w{o853A-hu^)bu-vB}8imfW-dJLrMTqjw60l3SE$>!NshK=SbpOPZ*>pmOtTyT+U z@1N#l-8fX6>IDb*(QN9Pjl)&nHO5CxITpX;sy14ipKpI^(j4S?!?^9V# zQJ5fp%Gv2XJ>QBqHnWmra(cKZ*32z964K@H27aWd+-b*sCV9OXG-$*a~B6Kd$tUi|jdqIqJ_dZqKOSNMf1n zT<$6~VJMV~cybup935Vdqs8A@F?!GfZDLrA&E@aXC`el884W-@5{!(>P~bLcin}!G z>EeO;Y!Nf_?1C<%oSBme<`1agsIwlRcgUDAC6=1AQmJaHKUPZ~@3xvqC-Gm9RD{7nJ8)&}U))fZm>0s2rbp?Adknr8Y3evfVHu zabTk~^=V$%`Wbn7(~VC8yZjH;G@lKnP-tKpo;-t!Jc_Tn(Xt*evjm%oz4pb|%3|*> z@h_kECAy=0#bLhMe!a>dSfXKth1%eyR=q1u0xO`MWMVqan*#iZ@A4YBR5({@5S{X@ zGU^*77qJ{uC=~~;G-V3stzo%A3i+m0R6D9fj=&SRRm%dxviB&r#Uq#ptb)#-pyy6nlc}x;B}Ot1lHq zbmu--Rfci}o9OzZrlky89dU%FUmm0lE$VmpUW#;y&Z_tidr@N-KfGWCxkdM z>Ge>EvHT6O0kW3Xo|=EQ6=3`c(POgDFt8j=rHAKJX&Q;JzU9vdlJm~?ir z^KpBAs#ZfhKa2JW_-U%L(k+_#n>op+$XfKdY3FY!{-Ha5;c{(&rB8r*{8tGV*#)1h zkv(H)G!+`Orr~+?hNumhgT(mECf-)F7%}?4Xinenq#60d_z{gWY01O~K%1h?@_wBF z&dh3tDT1Q z7A`W0odw|ItF%*Va@OnNulp3M-&9b@=|qJieTyOda_gBlCHMmU2`=bp5}DK_H`eJl zj`hYiuc-9zKdGkQK6~V?L^1d_4e)=zVja%%^MjDSXXz&ev95VI4H_&7&RgmyqK!R7 z-c-TRR7asucY~22gTyPI=3b(+fnCNeZ23M9R-Sw6Ih_=Oks(;CzG9~zC5x9i*9@BKzbw&L>2CSvTpun`DQoWq^;IDfy?M#m@x z!UL2m58VPD*rbm=|9H(2yNQ{UV5cN^TKZxmnPNMBzsDk;Z~Zi-#oA(f2DE7!)UOgOd(&em>cnnqGv4^6bgj0i_$XNdp=W_9>N*c_iGbtGkQx z#-tc1vUCenzA9?LT&t?ZUI%PUnl;~2?Y!3Z!p%O8^XtJjMcV`BQTfvPmUCwY@%$>7 z*a-n!-~@k11I~2OWL2fvl-?M7IS9A{c8GN$O zBh%{3Y`Q9#S(f*nnWaAyjvOcl;O20VIR}ZthJuPcp$+dEu&B)Td{`lGTu}!!wDE4& zWx)13bbvwARQX8GzVqV?_71`+0(^eg9u|fQZA@6vFS5;AhfVEfoqVO!R`U*WSl;I~c2Mz7aG;eGuy-B6)V zLa9|B>4La4CMtszYCZIw9}|}a`#a2Q_M5wKV`pAM2G_m!k8V5P*+#dNq8pmIQYjW$ zOzNNJQ-Jg$wC`SlO_*oyiQ$ZVo^7RLXoS|DQ)N$jW3Gb(334Z;I`lsEzVq$jkKo2F<8qfAZTU1PEIrBT3g-k}q> zKP8{egnDz94)JmGuwERK?_wm1O&(xXY`2q*zS5l8XC>2nE}B@m(Y~%tc^;>!`f=a? zNbbNI$O|hJ#Y*+c$sEJK-P>l&D%B11Xpg8cuwz)^&9DHL z=wxCiywPJ#K)nV$^=hG`+NrCVmTM~BqG@L21BqGz=ddC*Y~+u-Kg)-}jVa9MUI5B* zn;W|4iLuoMp>3DwEaI7;PtJRGo$d0*3)jXjSN%-Icf_AJ->)A8ulmFXip7_06_BALKT@4mrZbfCCNC zuv%vC_sct(uwd_yEwPCnLRyz!;SUd z?K_A(5aqozpROJ8(rn)*1`AiEG?!d30G1DuWODqAmso+leWZ_F8J~o^n_Fg^7VVuIDWgYJ68nzZil07h&^qrDmuL#Ozo(b~s&X>c!Sk z5sS<2*n%S!en`1}a^Mp6^53y0dSsD4Z^M3*6*2P665;AaD#pwsm-69}Rb<8$qEPVV zblMkEQPL zXTz&9N-nscCq!l6Pns!G*bCzJjjve+&Pd9)cnf)8q|$IBeb8d(_9(^wEZ-~cm}$vG z|7ONL0?!-ZO3^ms`iR@wPa_2Dgphl2M-&_Oc9h7QJxa8NE9HIbu*yV}8WMUxMetXb zP~0xRv+hKbH5}##28~N^*uaGY`;kzhf&!*J;YAfK4)s`~`&E{?cYJUOvEHV&RMms2&IDK!tHs6wUupf0P5$)D+xx^85CqxDmwgIXxZ&;~e$cPFeb#Q3H^YQnj% zQhNxNpb2FnhOwmlzR7h^A`2DQnV0nZ@o*WgG!#y zH_y234%M^3yD`3pQEoA#$Y2qwYs+!PqP;9nlR=8hwC|oSR=t6-fC!7IccRjGEF9Y}hsnckGi||=V3P+DUZ-u{ z+oA^L1B>ixx!y{V?J@7>YtP*PDVuj{8Jd{LSnurLQ01nq$lN5aC=9Pusl2=N+Vj1Q< z&Fbb9bFO*nF;E5sOW8~rQ7aqSK7)xYA)nRRjb zu`XHr0CqRmq(zY}RKgS7;c_?0s%>d%1LL=zxHtz=6^ zC~>1WYEb;Rva~-d@-g9-ere8@O6p?H{v&p+&ZDM9@fZVJydbLWGPYby^~KDVG58CA zow=K$lT+f6w{wSAT=cU3?3Fyl z6h~$T*8oU;ZNns*15!M5GNO=dt-h^tU4*xg;^Ijv=hW!mYojacg6EyxicyKd=7Jw&{yAAx&>GZ`iqe5fPalnVF6<=DcewpY5g-y_-? zVa2r-|Hr)hFP;ow8BeMKcV7hHEqGINOTpFWOf=Tkn}5BtbaQk2v0+N}2zhyHE`@S? zB93Q!YH@oi=q1BZnK*|K*9iY4LzRS!_&d|g)S4q8$E-(nRO{)7xytXpKp?44)&`-h ztBJ}0@1e220^_2ebxgt2;t#3vQhNkO&EifK_gS7t4^Cm8F5yK9OC!dVj-=ijop#!e z`2T?XAA3S(7Dn9qDG1Iv8ad*<^a|Bp@m7iggvsKpIc_UO8~h}Jme!D7NisqN?PKI0 zm-)>ps!YulRs?;y3V7R2R%ULF>3ANDqGDL>)ViLWSn z2!hS?Zlj;=_b;d}^Ou#v(={V0x;4{LGc0+3yF*%HcH5p@{>a}rOZ0BhE%Ew%Gtlb{ zWl*TGz5c;7T>Ajbf&JWauGhS#{NgGhQPy>vgBP%%5brWkds$tl6Q8&5QhMqK^_6T| zqV30dGzG-E?5VoDS}cz0+h~@*pJj6|TtQz^nbfdjg}z(n3iA}G@xRQDvcIm>esHgj zVY&~4-~*7!0-nJCi63ER1gw;>HX@@Nb7pa&k!FhLvhP9%4V1gG&ZhkuL~Nfue!sl8 zt#q8p=X6_dd>a4GBbQI9v31Y>xM#UeTxFs@^?REUb5s`vMbtDJB8cH`ar@p`1qW+oXx`{x#qRRsHbJp_3GiI(Gq1-xw=9%O5 z@_^kRtf+tH_^`Wb!c)+k@&1-i{}{ChKM5>EN#bp0_!H3j2WW*|5F{0&qR4apB)osw zA>bvMNOB_FpSNy|#kVXX4rwIO{{&K4ah~Y^2}KA` z|Am}hQ$Bt3pFj#_um$NK@c!dZWY#d=k%){5{Nqdh0h9m)GySLk3GZNXz+uJZhz|(= zCyRwYvX=iRM2Pt%B zKL7UVzwG)O5%j+?;D5F29{>{UUjm-O|2vhjDvKrAvWh0%=)IWiGN4Nj?~BV__W)OW z2LN}>rJaE@(V=C@)7PHTce6XSqT0(Y=p^#I~+k;76Y+Khu0+IA##6R z%H0cpe{;dt%eH#{ZwUAICb6(7{4s)A3GKi@bl?Q)FTf2{d1L6C^#$>dLmKnTGq}%M zOJ@`!JS28_&L94AIc^oin3$N+X_=j-mwy;aWTOZ~6#TvO#3oI@+`%IE$)kQ|bbWfb z_#Y)fGA2YKepW#y2x06mYq+}+uAn#1o7Z%i{xHfN8NT87J6r7k2{``UqQx|UQG5R! zx)JYxu)3!jCX!Bkph|%ZO#n{Gh)d=J(kCU;iM?j|TJ9#@xR)lKamRvHa(#`~OYCwmtv8^S4<5|Hb(K4;cS1mjD02asYV-_1MyHtQzhU zcqUu#M$rqVvE<1(0Rfq!JBlN?wSTR|eX$)l3QxD_IK0FOj#a^N&&P0AUhY;3FNdfV z&+qJs%g&N_y8p%;l+PCyRZ&s79pE=AO{ZU*6QLyX(2&Zs39T)f-=6XU&sdZ4${*c_ z%w1OhVsFD-ljr#FR)-tLxf`W5-ly*ia?Tgil5Y~l;!;;%rkaOO&&_4EqI++#9#pjr z$Hef8?(G!NQs$YYKDwheShL%9#24GxJ3}O0Ob?FU12$!)0{&)$|IWZ}!nSlQI&S54 zXK?D-mCZv(jVfsb)2lsJvbB;^T9m1p$OVdiqT3J@JZP0xmro0jDXALMcxbvmR>7PS zaGZ>Y_`3xWAj}=e)^wa2ALj!C-R5-@4^XXT$zN1bv{Il;PoGH1m{QVL|LUe;rovP0 zLL<@Q+81;ZIs``2wr1!uw+_$ZO8mI>XGhB`BiS6hk}q46@K_aX0r+{5eR7nqQ#fY% z7I-l1ryu&OTNW!@Ca=~w1CBp6lK52^t>gaeI5Oc z!gCl)S$^T*Xt;Yj>^J!St34YyWsvo-)!0N+(Bqvwub>V%3iAD$>Gdq4C>|+Zwn5by zfYE3X|7~|V)&JhWEDw=_mhWcKJe|F(?T80|>_lsfBP|WmS$T6vU7s>L-$RlC&I@a1 zRy__=Xt^JBBT@q=DHd&jsVXbiGm#Z!xj2tes{dh0b8jGEjWy68x2y(t`-8OF?{^v` z>YpS!x&&v*R*UR@--z6$1mHUNxrV5&&C>_1$t_F(1*!ta&q`&A)V(F%RG) zi#02jI0v{MY8fs~BI*bv+9tpaDrOLNPrjP^8Oxb`XzQYYUg*%3jRNy2LT$N^WsG{O zMaJ{F?%tU>>jTH>x?ZGz5+CEW9R#UaKI+S?MLaLC?S|8qogp_FPQUuffl1hw0?OaR z>IrYz%Wu%liqBOmj;16hj?%up+E5G8bET7RRJ@isj4vQpD%4_8F$n?Z7Kd=(CDX>B z<^F5mk6J71{$ARi!)UAscxAP<_)H+RUh)wS@pd^cxcZY z2Mh6Um=yDi9-PFX{87&+!%0W;CH8an#)Y7eMQHCJXw6qyCDol%#2HFr^s7%5IEUk5 z13Ec=W2uGbLX^wWnbd*`w1w zCl>!qkdkh92o6cfM8;r0oT+m)8}$DHwUjDR(UHk3dpym#xmp#?ORp5-K{}~?`ma2= zKz)>!3uDJ0e!k(EAw;a4Zu#bgKM&q>IA8cdeXxk<2UDGl%6D)zPUiIuwn7i)yiB7} z{h~{1p}Y!9QuqY_wB_zAvXNZu?-xV$dsjGT#;2bgL44tH=XRd5qmfd-W?wLiI-*aF zZEcgXOKUQy&zeM4aYD~qChQWuU}H@%x_nax%_%OaIM3^3aK3bD|##d-mp zWAnGj4Qq>HRQ;s_E7IA6!jkq7{iOV9!It;L3MccLt#a|u$l2l|iFkKM$Y5yCzAFp! zKjBpXPK7h+@4hfh(jVD!&}r!{mSEfZa=NHR>!agF$_L4^LVEXD($h-2Ip}V{UW+r@ z0|OBIOuV=>8vr|v9^#O@)z-ALP4n!SR$)r_QJwF<=A!`NP54M1I@JZYrR6s)Yy+T) zuuLW$Xq)ZIUQM){UsGt&B_q%L!Ap!2Er_X!^Onw3V$mDUg1*w!y0wU^L5ORP z!Y#W`Ic6hey)BH>R!L5{wkF;{FqJ|LSCR6k$3Po#F|dWHj6_ zJ8Cys=T-5U6o3L}QRyXLU!bOU#E&NB;RHo++Yt=7@NT3i5wpddU49>_Txg`zRhkXY zXgypfh1P;{DqXz1*AJE4>$eF;@E<}m?Ac`renb6%G-w~&J)`fNcT1)FTc)0NfxF;=?uyh=ZBb!jMH0VoR!V% z+WZv)QS(>w&!-W_Xhr?(eac09OZKPR+j)G>3n%l3)jK!Yy%!V2fnMJ=FPykyl&jex zUe^zI#$M)S9QAjX=k zQswk*IMslh9WyN|+aIShOD*|!xB+pcQp|<)v!=fSRISULFJn;cAJZ({i|?YIL+aeO z9D?d>HA_x+=p$tE-0N$NDb-glm_N}q&}(2bzj$o2^6`!jqmO7Z67pIttRlI}t{PQP z-0)V@zO2T=0V0#!MrYnBR$IuaXe&(c`G*PeK&QmJ{#i2riacQ=)EX|E+TMj2q-{EX z@wN~_D08uH{fe&`oxuycic|7m3Y$Y58 zMwQwD#5!%wvvZ+>+YbU)1|Lr5J5FRB1DopU)gFwK>HJQ^_)n}Ast>i$)EwM1oXTXO zhp5-Bz6O!F%uIRL9NZJ-&qCvhhi3JnLwK;ri0!!Zg<*)w<=Gmol6h8BUI6 z+r!9T%0sxP>Ttqq>ax2_hVWg!_(dO9(Ye>p)HS85FZm|Z71Yr#_9!a+s2c0pG~iV0 z&eYEeTQI3p(LDJ{Dx_F=x2GlWb%YaSH8UnN45*&w%&%3d$dYw#<9kogEl}Q`MYZOh zXl9a3vDPL`gXH@S)$hQD_TOm@aFxpLhW2&}S@uB9Opw3UN3P#fcblgmqo1EBa_ zV=+0u2{ZIW{>46=BDgL+j$2ux@jWeqiq}RB&79D0d)8wMd23j--hXrFTavyVcjV<< z8*q@jaXBn?$EDp{c3Q#TNvEH^IPhI8{Y6}-UBW*_dj2IA;9@ny#q<+t0Xr7$y!zAR z;WOXBuE43G8c1&oa;yWP+B#8QW9{dbSp!|AvQ~r4?3lW!fbD=H*e9n5%=zhe9~1*m z6X#-8TAw174tv*Wqd|6gC3WjrH4;ZoDVNQeSBh$@Sr2m>_T%^*O6eAO;aj6R(Jm4N z5R1K5QMbx}-rpHPrYl|5xA|ME_dXBqQYrDO8bI3mY*XAuHCjWLK@WKx5^72F)mb+8 zLt;%HB6)U1ETRjy`N~e9qPvNx4**Cbd>O|{DUks5Kx%L-^}u#rYk)<43r?M549Ds& zJ)B>z%g-HbA2XAA-fDz3$Ndu=H{+9AgYN8l^uFyDXRd!i-o+H)Dwpik=Mw#7S-Bv2 zK*NKyJ(=Aw3VeU2J0s{6b8Jy4RgLja3h`I-jI0a+-b75+3OM1mQF=9(Ai$b-G%bvyo=r<(%1Bvr6%!D4zv%aAYCxl>&2Ss!c~fb3x{&6X&m zwe96$G!CiHZFS-%(RA|pelbhsBnnuw==N%7+oOYD4^poD7~uoq*6Z~RB6{Cz(V8QP zZVyzx%;ozS6LX*9p#hvbw4c3K#BQcJU-c?iz3(4AuOofes!3l~rt;w1c2&Q#I!G+R z;ipp8x3Uv1qjPc!eNnC$LM|)nAj~lN+Fl;k8?yqMLQwZE1=As%Ot<3|@29#iuJ_%u zCho_kh`PQc;IOFwYBJK_RQJ(A>YQqz-dHNosX25=+eGW^<~ntYE{#Z#XUQ7Ch;AQm z-Sc5ZV0Vu2?lG@@zBq2C-g-N%y%9sZycs%tz@%H{yBuOR*uK_gXFw0~UHZAEtU^TY z)3SVF6d#tqQW~>B-iiHuE|%XlgHu4oRNs58|M_ko)o(Nl3-kC%%SBS=_CTg#*GZ7{ zJCxyQA4(Dr=%&Iqwzt0sF3e}}C{jf!=a0BF)GPyhi}^Fx#nxXA&y>ob^tJ;7Qct&O zEu@86E8I6R>8V!j5Py)%$=8W%y6kvFJ{qZg>Ev~%#Hb}kBVc03kq8-tnk1-QoA#`} zT!z(1J|!*J%XQ2lflTEP>G>DcBPIib7fM+M>O3W?qWu;{Ye7j|(sZ-dwsOu6PE{OV zR%*37H|sWV-o(ZRjqND+KJDY}+B7ZkABpktwGx$@<%EM$t#~Tc!));eTgLc7Vm{S8 zdoJzNWhQmXpF`~qr^goc*x&zE?UQf>8hha(AHdd`p@DI#HJy`3V;-eV)7a#Mb*AAh z?6aD^TT4$Vb6pHxeNPpnO?EH38L(O82W%UZsF9|e_PLp_%Iv1B?i&F07< zcg{EuRQ8QU?Le49M)>)=B~91-C*5B{ua~LPyyOAB=8tRPk6()~UDqJH5G%S8tdr;& zXISB=_iHnG=JV2~_Ii%Q6x8`=wzu$Hx)9qlTNM+S(+Ew@^CKh<6d6tsy8l5kILq)a1lkG*HEc6ius_Vl8YR}kM zzUI&O>eXGZs2cs;K9u7e2Kn}|> zz}WtaPT(a-JTF%-uM09?!pESaM>xS@>svPmIu0S-qF**5>7PBm*648KHtf!uOe;r1 z`~j5c2bR856LLG89z7zgbrO*m7K%j%ATF<95W8SlFNG72?wzUDSpzxS)brPGkKp_P z-1OOvE-JH~BggVu1^|rhd|5*Kr7qR2UAqqCuR5KZl8Gre*MoY-_V$$AV>BP-gLUv&Y#R)2~tK z)Hi-1JRGpk=KRl0%erufnvPYtinbb{dK3~di|$nHu-ynQsX%=aXMf$(>|3Rg22Hfq zk7=Fjtj|xRd#63$RxW+L;?v97I(<89J*rkz=UZUiU5w7wx%k@YylCoY-wExJ1O8YNyj zyGcGsX)kY6?FqFxx?ZYG#atT0q9RST6nl6@v)uHrs9&XuCrMvQ@|jZ z?i_n49no>iATZUXgb^qvGraAyCAM^*SqvLCzl~e7VhGa@H<%kQepE>Z z@>T!?SHw4ThS;5#E&RN5KU$Q`D65oxT&#ixeX=9yt5ttmHb(hXu+ZRpK<`!qa&0SM zRj~09Tz(G1Gt5k?;0PPD<+K8x@Y#Lg<%O&>p_A}IyP(x*`Rsd#T*jQ)Q7=<{tr>Y@ z?uD{Tw=>rky{&3iUit9`IkA|}FdLq{fh z=+m9_X9!|fT9pt(E#?>0!CYfGh*m=fHg~x4vaTAP` z9`oYN(-^~j>#UkWyE{N{*uve)u_-r@B%v$hji8HLXnD*xAr=8=SqCiAn`*g<_$)V{ zww__EZAj+wse`#8oA6|G7NFH`neRvu2(uagMeVNwtFF%PEo z&x$eJ`rvwW4OGkr$D+-P=5^!?iZpuB!f1a!af)3rU-?nNGB9R+)ElLj)5IT8_k?Ci zCXe4sc$;zG0a7lYO!#;y368sBR2-(9CUh}ydP_u$W6A_@({&*BRj~g!^o$uw)@bUY z`TS>>OmDAb4Sc}3#L7Ao`4oVzT6}3n<+*n_Xceqs!bU}I4?a*^S#nK6xF~`*_kzSZ zTAc32Zt@1S+KOq9N=LA)f=QbxiIURFB}P}LOnc{e<=+wJGGW2#V%Aw8`OG3Qq(l}> zLmho$z;Im9s*Z@7`YFugL0JSP3JQ-{wqZj(bt4uG5tRi7Ym~XnCj}PBhtpgrIn(va zQ#wfJs_1_rm;ZQ0!@!KLM`Z8BOO!K>E@~qNB?zOvu4Xk0AN@oD+g^4&gAELq3t|$h z12KE~@?PHR?zt}JTs}3?%0DW1MnNz-kHqe@OM&}iW6~JTiXZ@DL~rH0=oSzhqS0Tc zyZKaaEUyiaM>ra`V3?2nSpMZ)cG}mGvIou@>t7}1@mdLWT}`mr%Wi!Ir9q?|M^hMS zjmKz0z|Bk@Kf%1#!Rg8s)b#Pbj1Ee%7LVs8Yyj+@J|ZnoGnkT5s&1KkIu@nyB%j0w zx5wq@U*0XBJW_wjiISvRzHa6)GRQlXDh(`XLQM1X2Vr;0 zNB2LevA7{z#*NCFK~&yuHHIu741i8Xw|=m)VXHr{#}K`{_{GPXbTk3Alg%zOYD>EQ ze3UO-9(!5$PP)S62Jba_`h0V&2pPY#?-m{*Zem15r_BzCK&Hk8^5Lfnn57CzNbX2-r zrGp25iuqWig^rMX}*}`N0W&_#C>M_u>ETPnd)ivt-SoNM`yw+?cR{Ffn z+rtbvf11gz1*^i!nj8_^hi;AJFF|hCHRq`&N!Yi#wSJDN>)KuL3RB!T#cOt01=y}Q z{fX7k>Tpco{x$tzkyclICbG|7KqOV5%{g{j$!U7B1&biA^mljQ#NQ9EXCM0OjRUo~ z!_rt4z0uYtX2fmZ{@P@vsJaWKG*O-<-C0qTP`q=V>8v8Q;u`j3*rrM( zFrhFwImLxc*EAJ2#(B~{z1Nnp==Iqv!RgC}Q#$y&&y@eSBfs$0FFfMKqw@zIJt?C1 zcZWXazU=%~yj9IXn!ihPfD@F6dyUg>zQ>LErjUj{2t|w;J!~!D&DpF{z;+&Q-A^9w z>de6J2lEig3ClB|$ENK(q*QJnoo4EJ9Eo#O2okfH`z_+*OD^2E(i-_B8>)lC?&Ycr zkQN|iCui~ZvWA|#Z}C|jjS@)Rr2|y#eDx9O16P~ANoz~`2y93U#y%5t9Ju$^Gz242 zHQ^bCzG;sK=L%g9Qr8amK7y$*pRmdf&s278(QNzhX)JGwmp)1xzSS!a)0mlhm6c>z z6n#XcPtx83T<|a#(Mn>fzk?-(xdna8x;!ad`>ezNbzGn<$)fPhz`n|pdK+%xasA2~ z5^^~$hDwc-><6f%riQHic*Np(`0O|29_xQIgDt&Cd=Gw@5Q+JAKAVvBDXIY{P)>&X z-!RT6y&TtW{}mT`aEDfYp*fW-8mO<%0h&=`AUwSE^Ya!o(aICxRChD7#X3)RLi)~~ zkQdi}oMF5bNJ+2W3zYnOCD2=?&u2~1`y~@{_k^D;OR_;UE+RLK`s=w9j8XU7S)I1d zFJo-tQ)Ewsj442Gc*KHV42<0GrPM(;>Z;y>xtmuc49D7Ug`h5dDS&~*Rf-Rc>T&9} z&AXj_h9D`uMJB48+UEFYz-C__sTUbPQ;9zLWW=X0Z-+0U`7ud>U|RdlH{|{I7E$Y4 z*WErX$a9BX4Nkcr`fj>tJhAS=!RiUYnSEFPH)nM=YW*7fEpl%N&f(*16@LH5v{JB$K$seem&+To21!=P#$MA zF}72Tq2;DB?`he(qc}f|ZB0B^#nC3+y9N9dtjul8%t=ZB^VOgf;cnbF%p3y^OW*8-*LVjH!}{BE~Ms z`8FKEyG^Ckwe%p$+(BWwa=S>ynBSZVMwUyY0wr5d^%&b3%Z&wQIaM?PXK2QlygE!) zTpbQ;NJ&eVUVLK9@AT-58MF}Q;jY{}sZtV%r=?GUkqXW=6+L&mlzn%1pyCdO*>$qt zW{+q=uy}Z0Cit=VcV_{2^W1L!{n8g@j23iugv}1vCjMu_elgvwMGQmX9&sx6#>s`s z!OIC(T=hMYo^#hfqXFrlsbaUfv=?@rR&9EmA}%NDuj=l`foIFFa=A|)GY~bryiy%_ zn7QX&9?6NaCiYFWFT@5AW4xYvtpfaZ7Y`kyTyf3SxO;cJPZ<__ic3V0P$|IkiXy82 zJ0D2%C%oZgCa4&;^1`d_HcCcRuHt02h)mB-7czy<1;%|x?}^*Tg&lr{EW7l?2XvUd zSN4#tRN+ESsW)^UcN2Vc@!7}Oq=@tr?hm>u7`8Dj11A~F1KgtTV64d|cp%J}vXi)G z6TP{|lpJIaJ9~+T{4E;}@D~m3s#;?(b9*9NhSmT%ETN;1#zReX*wY<}9@T4y+@>EZ z`Y<0i)K24KMOSA zzau}IaI7V;1r0j>YscqX-eX~-F?s17hRWwwG5e)sX#f2}DUCc|YCxn!rAb~K<~WvT zG`U-xOIEdhsSTSM&d`cIw55%_PO?CnRa%V2etjplBG|MMK?m^%u7O)WWo8G~ronL_ zl7xCAlB<}0Vz+t0+k=U;{!>s0Q$0ghUWsF#bZtZSKPm$8)PKCbLjeSM*QW)aH} z_Fc#Yfs3v7abqd)eA?$hYeU36E{xnwMYG^6r3;S3a3De(kzB2wbInbRqgQzdR9pq%$1gE7IVVWdRrIDt-cR}D| zOpV6?#mJM0lJdeV$1sR{_jNB7KJvcFhE|MMkq3?p7eoA#Ov&$_Csh#kVq&uxFtuaL z+61p?gzA#pRGmS^X?~S9sN-gi_%9&G?D@qxNL^Kh4`?56He{W97>TQWj=AXzv!% zuF5XbO5dZFmY(B4rIg-9wQl)-|6xaZj3EPkCPpx=oyZhFUu1Vm(i>Wuo}9=yRty8&GrV!VbWuP&SJ^p89&(vlNYO=rWi)+r`~8V~ggn)<^z z9@t>gxIW# zPW>D^x5Su~?Dnxm+vCga`7kO_W+2FLWcs>IrU-)U79%(Mocj*a3YZo6kcX=lA}cmS zZV?*_NriQc3UZH2Kk7Rt?9@Pd)k2K^)?`pQdOtxDeiVl^*ac$!s%qDWczDiQ9tnL? zRE5%`iqR)5JNJiaM-<{@Oi~zmiVH*iOs#B6ANzB=Pt=-tjKz)kEpv5Ygj(>U5E&~g z285bq=z)rTBb7K@XpAl;iVAMr5=SFT`dT_yAbJ-2ezYFq3}bVb(kBfrgDLlXD9%9? zfRBRJ_S}z;+M#R7P;nQ}cycQ+{uRD>`M#t+)+*S58R?TJxgOXl$gM-sttpiPyKOw1 zo5<%fvmDxMkWFaRy0ih2^k2^66kc<>GJDju7>kNA;9L3RV^HU~C{Z40+rnO8JF$tx zqU47Br!L?o-u&%&(c^1?-A&TiWX2`Z1o-c#C_looHF! zP%+!fI$gTIo4$Wq1lk>^Ddm-5%!U`jUUdcA`}C!2F0lJt6m}CT+#oe8I?ulU$>PAv zUDd#VRc;MTks57{8^a#c2|<$M1X1A8x)1y{0^^6^giH;|W8d?*j;W#K$Y1G&JU@yA zK^AV^0_p)fslfj_j8{HmPb<9YX&aMVPGQ+liEEc=h%X{A^%&k@!mj3XyW%)kd`r3mKbf9JG-=uQ zyb}+OfP5=uK_8k|7^H(G#2?_VY9NnCqhd#Wcg=s-MCv7{Y!})T@ z?w&Z+EXdUYM(L;z1-?qqy5Y+9iux+Ov{yL-RQHt3YM?z_m8+cSdKMg{DpiHMNdev5 zGsO+SMU3$oAs_woZLFG9{PF%-S-TS)29h+OZmT6m9P-nzs6i`5FcQ)l)u(Z_1bfbX z_-V+jFk+PV^pkRgSjjwoH26dj+kgJv1`zGRs^EZwY)T1gy&$8cY%y5@4p%~93PTt*ZFY|G+$*R5AW!+XW4zP)^P)N`PiC9=1r6nFV}sak z#eBJ}={NbNzvbnrnoF+y^6_~m-UVA(-*6f%UUM3q-CBN_Clmt8JL{g6Uw7fy_q}(V zzIs#icjK+kKKTL3$3LO$UIHv4pOt^&XX zJId%@K>tiZVWXNPeQjYkd1tb=pnu0L*eqs+Q`0vvz|Q0=xU?3@pMC%K(paGXO+L2U z^1WUj?UrRyr-Vm20fo`Kx%cT5AHByOolEg!wWe1{l?nQEmMsPPb6o`mwPt{WKpn$cuyAMrLZ!ttqq&YB zyoX_2NjNdAW_CELhVyJc>4u$)&+Et)F7O}ob>CHMkztN2XO4y2L`zCf$nqz^)_h}! z$uV3$F76w*7^s*GX!^_eguWqphr=nReWp>AK`5nYbQ zGRevc3Z^F?i27#I{t9crd*l_Zg0Lu*Crb4`T$2op&O1eoZ9!ieK%w{J6XaDBg!gOI zl`08kpF*B#jmfF&l7If>3Ln3y=2iOBsyfOMVD4T`A3bvoXb5)Tt1cs@091tH9fF~;>GEg@7$;)( z8K?0W@AWgX+tZh2i44xM+Vbe)=UJ^|zYT*Fz0mY0!6ljr57idY)G1>E-PMpLNA6*erZS5?JMhG8x zLA~!0MP%-x)!#BXPB~;5EvDpz{47dx36Oh=g04jk?Mh;^KPXY6{&%LKoLU@gf0T<&#TEjH03Y}w+J`4=HwSQlCA;iJ6zR7tCY zWRF9$teOhBhp27?eXaV{yAEzgJd~7ins7eQ?aHy_3EKwo7)Tim=`zDFR&<>w(lV;5DCeB!}#=lk}G4|6iYqk6k?7ac6# z9bJ;GynJL*P4hR&pMS9PeV4|cw3SVvRhm^avk%wsAPEz<1f9$SHfcx7I3x3I0bvRq z8#C8Lsp)+b+vs`m@#U?Q2wr7rx^~0nPi?^k$V0M#P{sot=PCIjmyQAm`ltuA!E*$1 zPyJzMHo{y}?UN2SeUiL+k}g;0`zrh8KQL|A#!gOIHFT4Yg{nG)hsom&IY55drq0}F zN>^C$L!bN*{CzBy6?#*7g>@_HmgcfX&cn*&ag*uG+|9X4CfV?ti#Zk1Oee`w>)7r3 zd02wxfu8d??do&PjUPS3l9L@6$5#7`&d`qZLl_2=Qo(HicTMSzL*^&Dtx@QL)7rr; z2+Xe1ICrBu#H8}Y*WR1;C=h@_;EA)X@J}OMm`qAJ)F3O6R7dTwKbz9(3QQ(i1xnwt zXlhah44Gs$(J=+Y}EvZoMtp~(K%R#JYmYol@P{K1SFOveCl zXiIBtxKESZvsCW9QZDEVip}aJY3+RvlG9uCo}#nbDmw+%;V#hQb8e{Ej8YP^u*d-G zbQW~O$@=oOUMc>hBA!vFnB5xI&S{?{uMYh3N)&scOO=Q7UXQy~(sSkxj#*hdE1&a2 zPnIpXlbYW?SUno0oU767-uAGnIUTslzm9hq;-`jM$}~T%L`=6x93|r_{2w&9Q-5nY zeXeqCW_I<3(&`Nh@|!2H-AoJXlqqIXL->kSst&dGB z@6tDqlHZ;&xjSznn7Ku2x)NchWoo#UnMeKu-3~OVHlf*jJXq`A+1iqrS9dKYsA%O|nt;DDs^*F#;KRz`EPSIw{n zEjGb7OZ10toVhH)BSmkZ4InGyKWu2=7Hi_e94!_5KvRULH9F28qgLsFNt+||rB1a- zcC??jcjwlKFf2Y)cB;LOTn6?{TOnH6fcrpV^#{msa~2cI&>eM=l@`D|G{`@K1>G(ZulgX+fOtqQ)LxAICpX3$fI6saWG*{VX$ za$7lFN_50ld}R2g5>}h-n4nK2UHFwtYQt3@+r{j5?lTZ(f!5w-n&`)2`(cuJX~m4No$W3s|l95MTyv6x<>4{Xm~k=j>D*xg$F z-gfY@4+kD~pkzRNe^wn$eIMi1ScrAKZF>EMk|uq=fPY)4jbq(XR#{bNJNNlB{S7>o zMxl~AhGnL@7cToU1jk-cOr_NmCu`Rmf4tq*qIoNw<3?5!;b+&G?x(VfKaYMs{kg>< zk$94>!+hF4s?c4jiSms+io?P*4+ttKQK1)+GBhUvh9b6{+z4r zcB^Xvf1pvUtcUlXNtBajmYMp*l(c1bUtDx$adqg$s|-`v;c5Am=i=H(w^;8>p;2GQ zfFPo4UzYB&q(_e( z_Vk|CVCfPJ#2Pel?8MkVTwh#Z#b<{C&C%%#%O4}L2Rav9r!IA|ziO%eC43AIxIm*4 z;wBEKNp#w-d$%e3PSK`dci6N?zD{d=s@mH1rE5lz?eu#Q&qRX;w#gzY(kI0oKEoj` z%w7|E zOb`|=staal{lPIGCa32j zHlM$ZhPSHCk;w921~@3I!RKsOyb?cJ3aUm z#=iy5vH^}gfE&g%ezlxls7{XtES}9gKT+kJcCSTL%F;jGY2EKaVc~U6m=EeAXiHCAFr+vUSJGb&gv+={?fv z7I!2!rp~XV`zF6mQ1K&GqjcB_Z@_kLrB`=H!Ya?b`#e{7RxAdJt9u@C(Aq}a!KUSN zAqL_Y3Bg}O!nam5Y9pOtxX(Twyj14T!?dR%LN%E+w*IbUb^NC)X>Dci5jiwRks`L< zTwtOV2^saxQSVzwG3uPQY4)8wX+f#S`VCOH4G$S?=g-O3FU9e!E=wIvhi~++EO8&y zL#+bLAa#xu_caMCDwCMMJmm%N9Y>~(wFkCfuhA#XTY@Q|1HZrs6^6CchRT8Z=>=bTuioKPXj(1$gw4M1{EC0y=(-;LdrJvvv;^q4)(?0N za^bM`)8^D@@yf1!x0d%(jaf)s%@*m0%Q{`?0p)ZBk9mBh&4K2V7={JYK0gU=P)T*J zHk3Q9u*W<4csg)GUh>qM?dd-NjQ@80B7LJv-D)QZ15wz=ty*@BX5gzjJ7N>rfWTMa z4X@}L1a$!BQ_pRZ|G*J!0hK_d1z5e>EggX|K*c1ft})*sZb0n3%2PP245@|-R*GRA zsNz3x7!WoFIo`5{Rs-4sH}+H94zy9<0-0-GYP+$xUpe|L0)V@9L;xr6J;AG?2CGL5 z*>6Zs%tzlnyyE-G-9MEo(G!N3vv3c(CU{#K?X&tLOh<1jp>mw)adXi>44Auc6H|J( zWUGIjBg8)n2FG7ZiRv@8VyI7o5Uv(Z@=PS8oYoc|$><7S?ix$ey0IP+2)onxF5b@f zYa*cxy%))curMpIXVM(ebwrfAHO7o~y)l(WW!)!g%W=EHQ>>qN$M9uMI&}p&>u)2A;!-1WiuT^C?Q|riWy(WO))V)X^n1(Bso+bf=kZ6S6m8VPK&*-zN$A zh4^zvegby*YcI%!f#Znbm+K)cUibY4ADU!`aGVyEk11N}!>p}|pE~kl7a1+(oBViX zvL?7?1(Ml)=A*6|rNsrWP7PI0!DO@kkX>ki%_SApBM`iz#7SfiTJp#%AFt>9qmfhG zi^}MBSY}Ic1QSABjfa&I_5}U%ukwtN*QkEWkCCOFnR!>s!k*Y8*EC%eC@%6leNfjq z|N4)vmvZ1eLo#lAbC+>jNf+4MIMxo^?=e)zzn7Igf7K~;s-X*x+HYf>OS(AR<5Ejt za+peqTUqUrq|rcF&{gaigOGESV9r^7Bcq)FZjGpbal?7l1<6Ek&rngo7o$6`=Mp3re0rIl5Wxn#5X z)5VrNO+>mQ-OA#r-hk)Wk!_WEGF+hdJWClP(H7UUPgq0qOlk-OfQmNmEo=tox{gQw ziUIyNE;tBRYESkylM{K+q$JHOPm1@&j$eKoS9slXPHy4~VbYlZ1OW+wTINv_-cWljXe!LZRC>F^%?mCu3yOQ@imx2Y#6=D%#bzLU}A77$@`^H{Npo zeANvMQc0O|PFQrFd+BW7L-nR;7i=q3NJlf> zoplOj5X-lwXh#y=;HgX&-lpV??FVTMN|TL|rQ$rkH|I^^kug5q!X0RZ9K4ZtwD2-z z1n)6;&&su=7WFW6_+o;$2fwrob1@g&GL#)5y1I)y7X;K%q907g1=*T8deQOe5zh2OaV#7}99Sz|Ro16_5- zt7n{MaT%Mg^+ty@a%QFw!|hDck$?PkX0`kDQ6Eme=X-SDdQjJk4*2diOE@c?1#AOI zHMom&0j8L?G`w32owRf&)f<5c1E3Zo-!;qC=F0*Wk4xV^D|_~w{?aWDG3yUEPd>Vo zpmga`Bc7Dj+nd&P^X7G>!`A-1_6$Wli4rofsTm?BN(xyJY-932etK2IqoP_e^pSS= zuazyo!{?trUn)kui#vGwJuv|h9O*sx=@Hov%&T=SyGc5KcMtrmTw@bqVEq7plcGrR zSS3@#e2@sk-HOsq&{YaGS?kGw^EZmJa^kLH7{K40Rk|?UkMJt!hXy)Q#i5l4S7-Q! zp+aLI^iaV>aw=PV^E*7a-g4h-DL9%O#499mwPg=YiM;B9Y1Qi>63$2mMQ|>_3BgBR z8sD$Ado&x14hK<9>DfyMB!&??IY#&_Oy0Vy)P9E!evKTA1pRDG=g}X^&CVn{+K(~e zN1;sqq^y36!w>Y~kw%r?$T8)60@sb>#CV2Rswg?in8)J*PPi<_q>j+Oc;8N+;QFAz zfwN&hzz6j3mWs^}px1Y*e&Het4--~WYQp1h@CB$GUtqF$MH(?&e&dJ+5(Lxh{QN0- z-TW5Jx1Vj6Ng!b+>6U^nJdy@J3WQQubK|li@L|3L{4n_}sI@?A;7DHwX?p4EbqTX` z6(mc*^y;02mj{Lg+i{A8i;`}D8R89=R#@9faJFH!V2<4U3Vk?Py0+4vE}XO8ZQIrq zZUYr9Do@Ws+s4UP#+?A4yjr=8wmZF-71jae^zc4X)HXOU2 z8oI|meEitS{#qsdr6c&2sp`MMfiJyGw?6w@V26c?D>u(@4huA16usBTjbN1wtFUyH zE@tCl3iX-5^#|9MwCbAYsUpH6S#jbcp=y=sFT8isle^S&uaEsuBlQK(Cf-7)XVbdB z2*Yg|Fq%j@V7FB;;{Hn=FqO55=LkL$hyw29rIGaCK&8`x2`f?W;M(c1eDYgz8*piTJo z@L5=4490|gFYDQ;6!RH*kS_G7^NV(51F5z=tlm&qVK^6L`fW6#QMuHU^2U>NKIp>3XMiIswavaY#`EI^32PPgg z51x72FqQ4M*WpZLvNF_~dDncjFnFOpc$`HCQOfHVw-k#syR@ND0FRy3d8C-X0Regm zTfimP+}WH%S5}`~Z$a*En_RSb#Ifz0r%FPpa>!|09nNoFzXt=LNxSK%_I2JyOVqrTz9E6UTAmFTb|=9%x)Et*(Bs)i53=4zHC=YfM>Gi^QBrToB_g zx;q>tXp~XU{EaolYDm1LMLetrzyx(O{8{M`39r?E(1NBx;q2vw!n9m6(@wogIe0cj z!0QW)Z-0+Mxr5xK(94*+t)D}lH@w}Ca86^@@-nt{9V@hwulO`bdG({KRj-H5|Kels z%mTX+;d;dFXU8U_FK3q9Q-sgHe(_FHzMSDn(K&hkm2WL6cN8bYcbtuS{L4@)=%+d( z-F6reMURTbl@Fjkd$aAz0k+#)2x%80G=RUpz{;a;H zYfY1e5teI=R*_6NM5g!rah$kpr(Yf5jj&vKUogR(dZTgyyv&W0s)lp;`Y#xHEF3|} zyU@kTg73A~!p9Gkt-rp#cwl?uL?COqR_P>ChlOG7qwS`H_g>@Of?ID8C2}8xt5tnG zM7Xn84PpY_y$|o*)b{f7($IctZ2IznSE$2ao>%ZhBW!pwy&FST3do4>m8%(kgybOk zDzhC?agZ$(Al%_x_GDiDVP$I_<*1!Z^{DJK_c1q3_U_zXy&B$T@<){gN;yb+vC-1; z*3&D&$N|m%*3-#$Qzv?6`VMocA>$h^0HRhNr-DkInWjA1R3UsI*s{}tOP*rww^%?j+)QxmU zIB09r6V~(&g?bWqr;N(MLN&(wOIIxvJ+izFf~6XsRjvlJBPLRFEL_J+XvNXtxkA>* zyPW5IP?8=xjK?*YRfsy=ULbrqO1 ziDWl`pGlLLh-Dw2QihbwlXc=MP%^^#uCRfhYwN5TB=e`+?Dy{{#JKOEb8Ypb?|@9v zv#8YfmkoL-y(wc0DgZ7K07UgGv>u_iu5OzcKMDV6mQFCVv~*i=D}BlXGB@Mp|W>(iHFuq0H6XW*yBvQNQEs?ue z+qS658Aj~5lpA6Ftt;uqDstbn|K%2~@1Hq8@xt3|<1(|_)YaBV2DfOJ;+?p{cl zTK>U(G$K&S?_b*fFN?i${`k{;{}(L>7l8+h_tXO1e&~ z{jk;C#g(Amu!)vXFclKbHp1Q(xSfqm|C~lETMggjz5o2{+fAS6{n4NQ#_Z&B_FMd% zoY(M&1H^}8pt;mPCQ5vzmy@gihD6lsu%ROCGluy!^$+cxU#)92@%_u)Cva%LTDtp4vYo?~gz8 z{B~SfAt&V9%kzJK_MfFMT+0bz{2)#D``zuD)YYr^R=8w$GPZd2I*;=F-lf06lr(DC@mMC$v$IB(#^Uu6DY5P?yGzX*bSLY4<)j=KqztlD0+7eu;H`gs%A};H$n{CC2`lj_C2E_l~ zl33qAdqG#>RSSp3E`P_WCARirVaAP|Nzn(Uf1aoH{I&3WTRP<0gnqKf7H+RZ|&^`CF^T6gTSrk z|E}qe^85e6)eH{M(e!$g^Xt~H{`fzv_4f$iiW*HlPY>gsSNsJpv*kn6vV;9o59I&F zv;N;MeJcKi<29}1rW^NvoHT{j`;BnARIzbqmrJ7;ou~H)$Ck^ru)g^KnPv4s$HiX5 z?hh^D=J)^a&hxiTQ`WJbw&RMNg0nv|o-B>$@+jU9c>UBpVRn{TTtLWRBJIwJ+ZmiQ zra`rYXK82OJ+%`-==(w|M32pXoqF%1uoq%ly7r_4(UdNm5?D@uJ8t#cW6j$w(*sXj zR!d6)Exz(Q+z$4R)o=aCITos$^Tiu~+beI=x9`VDM;<4j4z7d}0<}J#2P+_-628&M z#qiZ;_5i;%^zFdsvq|xRB^wpZ8WUani{cxZHo|{qWuL9tu050-C;fcUh;U$W6VxrC z&jd9+4o;Ha4l0WF3`t=)J@l|rRRT;nnJAwX&N2uZuy}yWZlSkFR_9&2u(!72?jDMI z93{Fb4jzi>K0~cEF&W|%7+?IJb{*7{?v=ff&~jyMSrQqh8-;`5wl({y7+ zo->M(vCoQD^5z95>ic?u5LV%=3H$HTb;4$UPJYfahF7WKryGkA&1ZGinWJUaor~I^ z@L47PPSMa|AjS)S7N|TNFStbRfYd2m)Gcr zP0*#{3gNL@9d}(oK&k@f{S{bcMVXS$p-(+=um4771<2>?ZWZ(IO7b7H_wVu^eoWwk z<|f17IjQs!cg{`e4n#;^#@D0*0f#)=S8SXY9(~?B7~C`9>^&17k0l^+B)yw1OImU%dmE zF{2ZJZnlWI5LULkAJ|MkKC1Y+*!<|goqAI!Z3=6wl{u!!$)Qbt-#_wF*rIaP&gx2Y zMcK1-NjD>Bi{SZ%Wc~86Tr~O5Jf7k^d?Gaksp}o#X9%QXyk0 zWX~G;#X-yGiQeiF`XVBUGjbwH!P_x=n1*EFVN%{Jv7+v+ebU)i*s^WK@MMM5cp6JR z`#pfxz4Y3Q9kws;Yp5bVd8U*hx1fRIeKv`u1)mMXwlz}pHRQ1o8Ws*YtOtN z#iLIoXmnpkD@jSeR+bp&==F#3Y~J=Kqc4*JjRuew^Q%By1Hgl;3oTxfi#uVqjS>iI z&@DI(MfnaB#g7dSr9r$=V)m(L`*NSs1XNHj&y6jic(N<4?Irv$V%uH-LlB%um%478 z#@h}KnY8^X=;#geVMkG;0hahuBhlymiRK-W#?a}$HG%StNar~*K1nOlBD=kT(RYg~ zNyNLuSv9X*T*&}Z%+srRlXpfE6E1==>kwdfU9f*tHxrs&5Q~3SU=QpTHx_6QhP*&{ z1ri3{>@`+&`DdjD*w0%OL50^mnRR627#T9CV>TYvs>Icfy`?;>(nfjTkg9w`$S|OO zR>f^^puF2crgrXt;XQ|Qfe98bVoOc=7-%ZOk4Bl;-^=roI80ZVh(yy)V8-*ZK zY?Cq=^dG#`|2VGP|3LSJIr+q{X+LIiz(p<1(>so6wy?dK)U@fGR6pTfj$#9fg-hsI0^S!M`p9KyFq#5_2X9*U^MiwrW;~Tr)JF&%?ilzuE-u-I6zj6*! z*D&EcBScw2>`M0sO=s`nOk2Ew)WY2%5!)XzuA^gqoQ5mC=saa{9;ltFUa+V5pn<*p zExx*p@(zDteFrA6Ko7`u5QBb)UF5!MAYh=b7d|Um=-d^b7JW6Yd~5*)1w13R-1GM^ zfpVoMdCi9irLEO?iwoeZEumJzUTf>+$!WS|vv$;sjI5a492egig?pAA9fwu^{3kG` z>;3Zg2UO41fgH0~b^HKU#C$Vr|IjG%PR(+E#VWMOzjyF7M|#r7CWuH-?b+B=<3wo> zgW621AF|WLZ!&*Qz+XN5{Ehsq`T8`|ot~4?RM+bnljpiGTlk}vl#tC#y%F6b_ZOIF2?tTJO&5(4I%i6Bgm07pL;%61Dr6YCj9wt@q z=!+NX;uaY$Uvd`@$H&h6P%7Hfm-kp0O(+PVy2$f9$hdm7pt_Z2Q8!JKPW#taCIQ!& zbyOMoTQJEaOparNaU*%h2^~^$!^UM zD%?zzT?LR|kGzSIhwRqR2TdQI-uirk$*F7Wy|#*jaN zAG`pT`NOQ}ie!Bj9xu0a)G++?30eJneao%1Vu|y8`@@%T4Ixd*LTP!L=nO-IK?jbZ zf9&$ouB3VOoD=iZ;jl%2{XHbq_d8>haIY;c&Wbr`5%f7{q_*!x0~r}y zQhJ2&O9rCSjfn_0)@Ks+DU{1i{r09QMyC=wCK5znW#WMCKECLNWGBqm^MUf*w;X;r zSJP{Ep2mo+%xz3dh2t(7owW?(eRfPHsE##sQQJavh^>YkU?8>$vp4;~cHW{P8D8*Z zZ(1o*0D7ErYI1iElM!+&DD)V9=Y5FK?usU}ELqsE6!a#o=crYFns3Af6Yl}+A9eN7 za_GsRLd=q#5roM@y`cT8%ySUs0FsPbXqcaxr<~8DpWG333KKUA`bYOCbLvWA&IiPw zGUM0bFF7By0XhPjQxl+Ee{w(1>xEk%dcHlrwNP_#eSSQJl&GDtGI4>nw+k$}^^Uz) z_y-B2ee!;4k(x^q?S^%3LW24x>Bv{AqHO)|bvvXiC8xqNqq8sz=O~cwFIb8=P&?T; z>-|;x$85!uv0kO-!CG&ABpS+E=8;3DV+E=GdIxRX;#S$qvZ`7o-TN%bc_K+E7k5>G zm@k26RU$&?zNeS;&ggpu|E;ll>a{a(Z!5f>R8nskYnD}9yNnU(@+P9< zqF=Tbrlc$?S={aiIaXa29ucf`1(@HD7K)<>Ng9I7;8fZz_erZPEbm1jXkT&c%|wnH znunS4G~l7}iWPw6d|6(MpG3r?cOWW6JcUp$`Si!gd4jWm4!?Fr;l8G^luQ|Y`kxc( z^L*jf3!e8aP|}UbmT!Ku27Q^%?%s3;bC=l&oUrOKmK)l=BZ)t>wNo;RCO={gM=iCa zuAJ!~%=h9>l5N8qh1NBgqw^h>47H*(u*%I-z0Tdv0=ggj7ROSYbPlf0r+fP15W}A{ z)ZG0j(i{}M!T$|fHqOTE?sDea8+cb*_d2Jz zF*IP&lB=xL?-2~9QpI1WcdMebi)f<*87v2P^~YRrhn>d7?8gM}PZrNw&=rwb(sSwj z`@I04h-FWsS*IYComs-J9=he*z;ksW>!^4|>nRiTJ+fq2l?8T+v9e}u#dCBruQvqE zX!Iw;zt-cSiOE-6l`Jew;(oIREuU70s=poB)0hLUD5#{ZJ@M&z%Ctx6mJM=c6MsAB z=VjVTf1CQHxxAvvP1_wNk&+}emza{MJ|b$kjwnf-RM_Z9(LrzNC`uf&PuF!H%bInW zx61r9439j%NEEB$e0%>igd9|`{sMbV*|udzojYB8EYXo{G165v=n18_^1w8jbj-I+ zZ!GhpO2?E~W&-T92f9DEGmFoQiu$#0U1csIPR-KtqCR}7geh#;QI91$f~nBuupoOBux~<)}E&T_Jx(seVQUfaNQU-ZC+HE?Peg^+}vXSc=XquvX5h^4<2b z3zmUL@(;Akv#uTPx+FF1%CH`xO|=fbUG7NhJ7etnWuSr$xBf~%G{)PaC^lP8rzgIs zDcz5Hszlu%i1OR3@3!(5F_zglSl zRpwP4&5fH{R}g@X&c3}N{iH90>T%R5d-z;5 z^0}2KZl5}I=r9kd3i4dP{eYL(8c2zYYUrfDeLCh3*=YVBW$zu&_TK)FpDI;U_0&;S ztJ)f+sH(lXsl7tYsJ&N+E!Dwk)o866u{W^=iB*cCHVJ~%-kT7S)QJo1+J>;1Z(*YkQ_>if0#;zQQ3a zC;E(6X5;1S=8%P`18KmU({c|Cx&S**?LO<2dh)$@b#rOyPlhK6b~W-^>88@rV0g{- z_>yi{RqC%s7}8VS#}4FDfv#dPQ!~Z#5$~B$dkW@-3KGyXn@w{FjlEB@<2vlzl{qY2 zT}snCEXD=%-NQ!`#Nf%Lhr@611n@g1oG3Z(C111C%^r*Nof) zK4v!UyPM%m)~ydhLgMWHSD7fpoc`{x#8)6~e!|0i(e|bFy5(Q8ko|S{vIWc+VKRd` z_f&qw3<(BioNKo(6&+nvAYfivpqI|+ZKGr5T!HnugRT}1H%H)1F99D<6M9r<184-9 z^gAU&=584(f#k(EErB4y(7;Sugkg-xLD)x)s7fQ6xhgFmEXSA$GqKteUCBW{H4G&k)NfzP z8U63Np*uD17?+ILhnq-jc9R7SgEG06_u4Ki$nr$|0^tbZ3x7(3k+h7+KeobRO&Yh} zugR)u(a6680Sfo`T>;CHjQ(qetAKg>sD<9T#~adR{Iiuwl7haCGfMJBEn8hJ%JkL# zSwi2OHgq<=T5#J5OK7oTZkMc@UP;V)x&bbz>!NdGPA-@jE-UXHx6Lg{zH&}o>QX5> zbaFcGeNiSntrq#Zuayl_MzNNvqnBROg-xW8{Em3-}Zpa&IyiSZF3nKPBD7 zU*Q+@vCLF`_Y&aMz^bKdFT7CpoZu_IdnD5}S|_W`h&YVWDyo;((yLH+$NN@;o6NFh z4Zq6n?(W91eR>!wA{pWY*J6-=U3Mm=h#d?w=0nO%HxLe?qWB}&h*E= z7=Z1k^4yB54dWmdB)<^lpF;kX%sdiygmdCTRC#8Oda-C&|0 z(b+C^2P6uxbdFhBS@9}cvzlFLx%28h7yG%Gu*lB;X+HixqWtZhdw(hD7$APQIJedTUCQ z{L-}LEdNA)n<4UH1y>LW$6ie+=n1*%;bc~)`qFQ8%OSsDfa|>*mb+9&ZK3Gckevz; zZ=&&AeOdS>cI8-BHl^_4)ds8Z#z|1mjNdqM)W1$tX)sVBu@Q|PTG(fiHV2lpaLz+D z1!fCrjPFdEts;kVIsT7e|Kp9&_xB$3-U>Pht3)(Tg`T10q&>!OyuZRrpvl|F84{Zb zgk7A7F#6@tOzAwTfyF)Nlyf1P)Ru^MUCIHm(kqr4gk!47-R7O%*3NY+e$CV@+70vc z6rBu-C}LhDH#7_HGnGf9HyL6^*1=Z#SVmGQhuH~9N$k~5?pV*=l-m;dYOjH~TlV|l zD0{7>)@#R}UX^z~-us~zzQZKa+dr6@@03jbsoFbab|5-bMHey_3TIO^9$F9?P^g&; z+weJBSZ(Z0$!zFqawvCA`Ob7hZG^V9P%!G-h*>wQkdGJj;u584l%`7pkQAhrGGmHf z!eYiiFK)F+_@MGyNzGRm%)PI1FU@ui%&>b>C2K}$E;s>!-J3`?^t|m?%oyZ;1a_%d ze$g8~=YLRJqW3>ey!=OcI`=$8p)Xjk(yk9xPaDh+q^`T)Hz|i1tJ&L5*c&Y6>R&T) z8<}Yfhj!GDh3`)Qg&ux+(6M#U1nA@3k`OX+YQMesDF`j@cy_t-m%8KTu}gnCIy=jI z!?jut&!&imppG2lxE=HZb?dLJCdb|3d$|4kPSv7QaFG8L>PcJz_s1tUR6DNBYj9=7 zewai)>nO-I+P4wrj%Zr z_o+pC=il#s;TX?-E(%$!SI2!}B^mK|nEIIaDvWBbuq9@y@0O{l?pPoZWvkm?gszJt zgY5WO;#v`hQbC%Hu+;s%{cS!w0pw(j6lDtCBs{I!-KpV)u}VB}AF52U;G4=ENuC-gE;VDDzIM@Tv8f?n&MI+x@LC58T_%e>8Q&n?udtTEsNE&X8;XirF5D?G3dw5d;(8&^+|1otI;!0=T zTc?@TV(i}v|JOXwpO#_?`y;=8Kzt7VzlOif{THRD?o^c|6#bZZ`=4u^$#`LR!CWuR zRQE^W%VCSsLFY3C&M&FSzdZ93ANtS7PrM2V5ulWD#2~?fk~)-)fbP@*|NqYbUwv`! zM#z$i+}JPt?;k_{S!-^7ap^LbTzX#L+==}X5_0QDG8e0`@SmoT|MBxboY#eG{S$MT zTbOND09=KUae@06Lka;2Ql3_dC+N;k%XZ?ve_GE1EBo3yhWM`?vXMNz0^}r}#t*8g zLxu}ID436YR;Wn(!iAGj(>*MM?@_SIlYgB@`ya<}mNmb$6#e14vc8FlQz7ndenG)g z7Z;c5p3x^3_ZS{2D+@_V8f^W!3#~Xmr zicSx&R4+Sh)^YGXlwrR6T(`rDvtw%?xl=+e>1*8QZ15OW@^U_W;rz=pOR)*bZ*Onc zv%_rzr?IXz}^0t{6Uz z%)*`MAPM*B|Jc`}N%~H!<%ob;-E1q#x2)QLpjh0$Rn;9E{|C#Ro^dT)LFV(?iuSyl zCCO>GE;Us@W=Xx(gAf$5E-sNu+MB?dXO|v751Dc~toBsM*j+-nVL4nJT}P%WvI_HE z>DkKmVy-|(&996a?mBGMnqk*u!Od0PqGp_g)qweNLZ?R_KF>RY*wW{4`Wvo%b@C$r z1r3!k*aRL?+Tyv`A@kEjxgXEU9Y>v?9*A>h`|D7w5XBz_sehMuxua-s8Vywq@DU;qlArX``UJc}UQk zxd;vq|8D4DILvb7Gz)y#%RGKNut*{OxMgtZrgQkz1_M{Bwp+!TUTqlj-6tzEZe@_!=V`S9p z!3h)#jcvxQW2zx!(s?uoGjrD7o>xw8uHem^Pdz;BNYf5sF=eMif^WFbe4!f|8Cm}N z;d)_laV$)5!yLCZmem@@w3_5pqVwRvOTvZK|LhOSn!~BpAJ6jEL=3h|umg^tSMG=& zkX^^K*F}j5Ak-0LGP5hp?k0@L{6$Y0XK#g}^LwJ0{dAI!AEP6`Z0wI=F^=;p9qiL( znae#uJL*nNc0mI3z`=6l_ic)!NT3P40angxR;Y86HFew9&gsS8`*5%6B`=%zoFKU7 z=h~mY$`8<^6ei6hj(#b^vdE_&bu3Soa@)9|2?u->MXueUi~(Xq9Pf0;#oIQXPjKJS z4oW;%unGsXxLn0HlI(g@3U2Dxw*uRBT@?OY078P+n zgVn7S_5GL|G+t5IKBnmBoHI3ny(DdL@H~TFtNP}q$xz&-(NzrPfOV({;FT9*$#Di5Z&G#DPZb8~*otyANokKWp zp8{Bs;lqbn@~I>srn9$v`jGBm?^kwwFC9p!P})eSd2^y9xB1@h_AV|XX{0^=yeq{4 zy@`p5M-z*Rd1@kcj|K*gSLWf*gO=#5t*!0b-?MdfcGA@yT6IJ&@1uc+8WE!Be)o(% zQ4hNW3Mm${ld&opGvK(4Tqdp?+Z5g)Bt+0~9~xG>VJ_JPZS2QT5d?t&n!A}+cjaY_ zI$8OgUi%apG#Ic+m!K>sIhl(N>-sub9vV!SHs$3{H!5~^wn}WK893^^R~Do5DXLx8 zPluT73c`+-nOG#8YsDK~0zOv&3ajqhio@5%C-k-8-3S{!78aHqz$QGcpjx+&ICKq^ zYJq)@_i>s;R%-^3{;R+%1jOH*H@DcEB>r<`)_nC3-`yT5@42rZqJ_GbtvK<7z{6IJX(oXoq&nhUacgNGio;7OcjnjJ{ z6nhb8)-BG~K5==cde&TR+iepyY@_4@{zhqx#r>v66A-u1-PO({LKHcj;MV7Z>oBd;K1lYn=9Nhv-^p??-Wl?Khx<12 zFiI(d;Ko&eGAr`JuvAvftK6nucj7#jy6KTEQhysDcAA2nR#pw>?Ck99X}Q(a zRe-kkg;CoR)T{U7J=2m)ld;(`9l<3ns_dh%k@!XUP1`TTBp>3f9^=^jS<&Ksz+Knz zya-6Kp4Wt_hHAu}k?@1%j3#`EczGVZV-;>7Y_C7d2~a-&yB$_Zv0m)qVDJFHOYX@cPL zY1;EktxQV86qX_fw)m&A{a^6-;a;eYol?b zlpMJQj}{BfzJtf3sojsex;(@~UR>(6~hDO(Kd-RqfBA7T|_qTa+V9!nnDu%!EPbxY(x*zTV@XWp`cdTYK zt*E&8R@;{^KX}vMtngotC+JqR_lUgsjI7H9pg3oI<9+aEj~;ZfJGW1wuc*o+pxicR z-3uq&5d3j>BCyurP2PC6l~9~QU`}NpQERXBU37gJ`4gHtZT*hl^tm@rUb{5c#k8G? z4miGL$ zXDuX%Q7Y{H`t>$T#w)DpP+CcM=!bRr^{m|7c0a}7W8XBbfzVGeA3vrjdwN#vIv{MetiZB(j=$eQ@5l zM&6Z>SSB7|(Q}c@Eq+<2cmHhdlqjTy&BDZ;_ z;{5yfI6QXsy7p;(6daQFiBFC3LumxT3sKK9j;s2$-JFkZO_G~Ap=m<~N?>6Ueb=VK zL9QeBCoY4>Vcc6)dkO3CAwi6)@O7=bQx!Nxn8X z?Ydssv^jRQTJFXKEb@88n$-H?;?(%KV1Ns*E-xH5>df8N)>iS{hF!n6x7R>le`&Qo zZ+4LD#H!s(&!Uu+NAj(mblI{jW!wvK2??~3dD+kZz0L1&)8BAt?-9>Vkd(FR#qYe2 z?%x^~75)%xTr!xD@b6gwOC0R@2(~Uq@?A04W3NeW&Z4xYsz*#DMIh7UI2X-U)9zeVIx#PH%Z?;7a zPKRH`VBQ3JlJ~X!Pw*k*T_prZq6QvN5}jky{l_(&d$*=?Fnqc^VB8HJZZNn zdHxH|4?k1lUY>Od2cJFO@b2VI_!^ole&^1ENTif`&_2MfGCTVV-Oa`M`8uXFFmX0Y z3Dp=x&<6q`<8VoVl-Srm&R)e_^*n4n6Wf{qhfDJ3JGXX`!e9dHK=R&@7WDQG1xH(v zvKUfX1*0V<+S9-wUY<))G3w8@lYLp~tW?LLBMf&DKZ8bdKjrCc)g_cDegoB5afX}{ z{!Sg!`ml3D$WdvbO8oge*O|C=+F8!ifpESbA%XG^aCrG_kB22Q;BX zI~Bw?{i~7hQ0;my#$He%_qEQ!14*Ka61nA-ah>}|$*IN%YPnBMNZWaunC#a&4;K#srU#=ryziP)yt=i;m2QE2+HaIkk59zQ}M&215!LSZ<9p zDEU1Ai6-YL%U|DXuo998_RR?1iZL3T5;k@ysaKj zixfo+@Ta(JYw}@LrOh5Cr?S<;0M)Kwm%}zVSXme4W#tkkH<<*vWjr5&gC$4T z%$?K6%(EB;f#5M4P3^Iu6z`uPfdVrjQ$Jd2pzIAlLCz1-@EXFqO!1AO`P2yt*3{QR zW3pOAu$<iKK5*hl32Y6 z9F@B5tlJnrA*sr);XP~Q$4U#v#8r(6>FY%21dtbsjB9Lj=+TGgMT3q33fnHsXI+66 ze;BVzuA`Kz?93|ogaftNAq|6XpNJlRCXOF8aSh4kYN=e6ku~yoo@T)(RAuQtTx(?9 zF!61-{DF8M!1yqW|C@jVqKRb2OB_1zs-aUVJ#G~J!~vRrKwJO@XUMgOndLz$7Gx;& zl&f~rN!D~In+eD15y_#bpRr(z{Z>yYyOC};u*3{o!@sDjlha1+@ufIy9EQcygL%xH*Yp$`m0X@YT0Xo!-Y7rnQo~g`^Cc=8^Nlr}V^xXDnZy<5Ba$ zyz!!+EaL_z#KqBor~Fr8d9ar(_TPT))jyj83dudgJ|)4V^$}0r$dd_jf(f!;jEnz+ zH!lt5k1@{oyIwMwJ1VzNnc@<+x=iAhIqE|VTRdp_(2E_v5kmuxuf;iblN9p;TwNVT z1`vEMz7CawOq}4Pyofq&kQ@D(GRny@hHp0OSud}Yl!}}$4&z4R8PGxsE>)oV%nDV} zhGXM&7ZOWM9HebNWIYeE8)%_${AnFM>Z_hGZMSJO(a|9{>Eb=l zYVON(J66H=;IXl@JKBKlJP^D#n(;8~{F_a1BPYL{oXOj_Z#g5Oo17g{+-M5+r6~IX z=3l?2)<{!?V%7Q%G-i%ZEO$Dkg@kDd)A5&v8v2gnbTT~i>L_SO$)UnO_9jWJ&u4tI z7DX&w^IlxlAP~I8l&G)KKPpa{n5saYvPpaqQcyMmoo@~~-HZN>fSIBQ2^B2xXD6)j zJA?DFYv_W4K}H^YZQ$doXU@1KaVFJizd=IcFP(@n6hi18F)#om({~B?deu)o%ZbpW zQ-{Gz%)Qejseh<}&&*_wM=uYF_U_fF#jtHgv!R=Am`8utcf+|s0-QJ=}VXE;5VYF@@!#14pUg`~<7WT#V8Q*l3U8+Q^-MPu!Q4kppz_yl-<8Reyf zsnL+F!vJfZ&|58`J~E?Y+UL=7tNmWNO)n?KH*em&i;Oe?001h4r!joXgKDCTV^Z3Z z?GfyY_2^1UNZcGtp`KdzWYRyMu+ppj;-`Y=HxpT5C*qW|3@Iz!{R7a9GOPzJCuk`zRQz zrlG-EHf>FQ6U8RpUKbjrc=UB_WMZN?H1rJbjT?u_TPK2?vse2yLY7?NS!^ft0jkp#aUQlj7@%yZhVP&cK@O6nEJu zA}o0I$XOa+nMN$8Y!6m@x7JRi&*ph&rY~_bGnSk(o;Pmq&&^ru+?Q$ob+25fbfqcG zWQzT|N)MlYkF6V3iE@9X;nP88vXfh>5awxN29xg8Kwf%Vc z-ER9)@_r`>0EnZfr$?ZRWrdluv|m^3&GB+8<@6>-eoXYvYdYrGxH#TY zG`lp4+fY?a&4!!eNthDNU0q#kgjB=4tbaPR4ep->!uxzlTA$yvxlk#U)+`Ze}Ty);>t5ueCt+=l_Z;nFB3~<}q zA@7sN_OaoX>_28<_TO+(!Ea5sYF5wv5O*maGy}9zZq?y9TK2pmE;i<4;K6lW8f*3^ z@VFS!+V3x$_s8SJtl9-cMYSG3e#}ES*qB_^zX9j52~afgsB5|fx7-OiO`Bp-Va5b( zkKz_3{Mt54>8sVZva;fzkM>s*Q1~0S(%opB0rdacdLg@_7D|D~v)ts%pT1J`0q=pw ztTdF76&C$xrCB~8eUVoMW%qQos^7SVIa5mAV*OHV@jrr-Qf8bK@@S>VdpRfJNXqZY zPhmDC=h?6SFUHb-wn^?$z`{zWOHtKc(p@Y|5=FMdnCTcUl}11S!4u1nv?ZY=vrWSl zm7em(D*`Wnd|vcK+1R<-jvcP4r4{A%Dd-o>QMf=6SeeS~f9=3+j=oS9V#%L9L2HPKaQEa+ zdBgKFe55Ne_|C*~v1Hlz3H$GiJ-*_)IPPyM2SJ)DK^(F+Qd3KcTV0ak9D`Z6vz?Y* z4DA7f4XOtz$?iL}0SBw1a^Z2GK0Yrzj$BLl$a;gXThUqDPj%|cQ11ZuBOGny`tD8i zrr$J=CHph5C?z-M=(I}|E9{u{5ajn^(S!$4G;!#KU0UwI^dJIGh9z;*3N<|ZRqw&U#t))O_f} zBT6={UJRNcQH0Nqjs2J_b~A{~;BIGUqx37f5p~l=e^g`>=3zFvKdmaI7-hQV*5IHh z_IGI_C(tO94GYb99L3@|3nwOdJH^up+&JSkS#`U2>V^>#_V}qa1(SM;(|2SBvb89Ud z8=Gd?C>9+W9mOfPu*NM1E{6DgS@f7~FDNcn7ZVdZTC-Cs8Xg}0bN$+SinK>2#e67{ zp&A8bgjmBw_xJZJq9|gmbeQwiaRUdOQYa()rDq45yGm)&9uI_tg?XY>2%4#Pxk;(E zZ|^kK*xK00Fg12=-F?RKvn^0^ege0xsk*p}@wvIdLWh8FW-^Z-12bs3$OR-)cN+TB zP?O!jh&IQsWtSE&=s&62EI>X8-At1uO2|xG%l&pzHofr2?ega|O2OfJt|a}+JZ(bc z=yh8MVbtW5&>kk4q%p$2dA4fupi6WQmb@|4uGd)D2wxry0}fwj`UdzoS*GTclS&w9 z_>psHblELk?8@;cVYvrdx=+KXI$8{!bH0a^dF`_&Dy2A=X4ThE6j(a9Hi8r7{VH}R zpb*l68>yX+2}og(KLTgu5N~z_qa-G}B52f4-@u?SGxM5uoJ@B4SQ%wrmwMTRVsG0W zN&uddlXFu+VUq4aS~$q_WYLL5KHa(Z*b2f-$vLf6oIu|A)IcjX$%$()OEhkUJ&k>6 zz40z(&{Fz6P!LqFt=T|8g=3kK%Yt%(%zsSq%%3k9ZzhNBN;H>5HJbq$Cbl1p6TIY` zPh?^*&rs!Ed2lL8kfC~I04pnJr_oK%QLK}kX-e}_2xwBUCMv(g0X&-JzpGI2zz18S zThyqe#o{5iKU1>{wK565+w(d`NvF!LB=~sAlyY*}Y2tAX0I~iKIv%)G`op?`Y~R%? z>9!hc7Sk1Bn*E*39ZU7aCai4iP>NaKQ%)&b+J!t8So|kZ#Vw7nJni(xY^pDiF=sq@~RWu(M)bunydKvyzAd6se5VM;6>phOm$=7SH>LU zgFz9yH60~iz=1j2@huUf0iMMPJg~_f&ONT1#eyaX@?a$bK4yXv>{r#~Mkv1|4K##BVE&w$k7!vE{Wn1Pe)C+hRJ1Ev#Y~bZ~X*RKQy#O0}GWiv0>O9rlU>r zy4!UX2v8|#=ClHGMsspk{dWFwM5#hiG92>C-G*8W%+nwD0*@2H&g|Q(kUDaOIDS5m z{q}e2Z()Q5$micGl=9csINS|eU;i9Ed_O+xY+6~;`j&FAb6%IhO;`*Q^Mayji5^q3 zD`gm+1dDI7Iz~@*a3bHnA+?`Ne*cD$R9BWaKgwY}2^|}}dM=0ei^H_&LD7tI^*gnF znq;|@z!#fk(Xw^VS&o^ z@?VBjd=EVPnuOoRfBKA<&ch$&=l1p{OX~mwEW=ZcK3%l;n3=SzE|M+lq{K#h`roa! zUhU?l7jyXE|Aj%RI{LHFxvLW zsd^jOm?xk|BPF9?w#=F%bloRaGSzYD=5Ae@+tZ;zEV+$+F1GQf>{CNkvFuU*n!}sM-%4cBiAhc=z&E`+6RO$414) zBZB~w2?UJE31VQjiW*0OSFx^>VB~tqd`%T*+;t>uU)gZH3*~=P|FLN?t9I7JHJy9c z=q{eU(Ht#z+_!kc&XX_k>4UE~V!o{sd^viF0lj{yoz{0ZciAXk;_4;pYw}?n40)2# zi_w`{!Njy2Kn;@LW9CrqC_U4h;SQ$Jcx6ekp=#Q!I{q~@-!I{tGf}Ycc3yo@tJ=}m z!T#QuiJ@Mj^yTGs_I1o2VoIU^N?htvaq@C;(d2d{X=|8!?}$JcmsCfyahnw9Kq~Js zk2j4KHZbS@j4MA8xql<;WooL{ddCiz#iWlq`It>|(#GK=<2cE(V5P~N1Lx+-OPO?% zR=aD&sD*gaZAfYmkq`Q)8yUY8%Ak~2F|f{>>a$rAv;>d+{O3&$^DZ9WTDWr3M+a>&5Y=Je0IN z^Y6v@MXmVz$B+y@?JPqs^U~?JnqO;I4jQ99-t}_Dr{CH1TvgKTEW%_LuGK{ay6 zD@)2pJ%**jjO6`d60yK%|H~qM1MFctcdVf!2Cmy9A}hCWx{HUu_hf|_1(w8aeGi{( z-!eM?rC=m@m`iiR_xQ_)&%CXn5$6auYLNFW-|?Mi7pbO+)swZ4k{5nDK0H1W<6~OD z|Ed9;S|->Fe71#7DqCa`zM3_`mai30t&gKr0mq>WdBr{#_)lgIm)9kEe>O@=nZUn2 zx;L*G$L(nFz%u;ekY}8djKrFe{)V8QOIBr9L8bz}iYkZSXQJ3B4x>`F^4wU)47%)OWwbsBux&Lf`wU3gsDyxg;g z)sjusMl2)BBhDQv8b0vEQYy0K=d3ZAb$wW+*$(yMc*WZI75zr%NO!*4Yk7}_0k_l*3YIDP7K)<_%6(SXA}D+`u$5b_&XwgW%B*Y!1+ap zS3>ad2rBd5_1;gHNp7w^`-Ni@#mxLT$IXy4H?yiQYj;9|fJ~fjgZXg9Ri$S|m8?_g zuXV7ffNHE>D$&e!?-9!-4NpE*ee6+AE~n;IFZgH4E7XuMwaZiZO@j=>7LH%J@oCY1 zo;z||{C^19l?4Uao7O6PE(ch3P$D9IaU-gEk}{v9l-gYe5Th)OAL zLyH1*43?rCml=er#}&X8mxLSClY?uF0h~vp$2fnROJ+Huw?u|}6AV_~GS2f5ksZDP zS$7y%mv;?W!b0Ci#X8P&8`gwEcPU9bQH4UFBBEr-FTSD1XchPkHPeU9&n$-`Hb1{~ zRA{n%qbk_?H6eZp|Dg`C5}G5p2EZyv%P0Sw!8)m6ytvmy$3YlkL~M1jRD3O~GVCK2 ze1Tq(tbe8zID5xuoFsDt%dK#c-;pGB$9smB7lGURfY>Q5jWsAmRl3l;TtSzNlpHky zrr8+wH{WLMRa;@v4%gN8U=SmLh8hE`Pa(eva=Jp^hhrlxF;5FkZrq7qVK*l8vAQNd zdxoM2a7-yJ(qqe&&>=eD9N(qh7|^d!&`kY6FKXNJ4q}x77PML;OIT2T#m<`>tju6r ztr)>+I@-Z}Nz;61$Z#(?dAzk_8N|9oTk1M8iU;jYYIICoFM&89SD-IEz4~&aL`B+> z82k*q!uAuJ1Un;A`;hU5K3C%|V)rG61z4kR!&IzYM<9Q_Qgu|bs_(m-)5u-uNqp`d zAT3k9RT%AF!ESCo)TlUG4#_OhE#mfj*6A~q`4VSZc@f&x>H5@`Uzd2?H9}H&kXt0B z1Pdzy0=#no3g@wfYEXCZ!lOy)rhN!4!X%>XtM}^fNleA)KCS{r<|)_J(sI=zNVVy{ z)MUx7EOkLEce#s0t0wGlnYnO}sMM~BLC38+sI>;pZ&=J2F4Tws>FDOXg)Bxe(n<&At@Su#)x zA*CIyIeZ`^B*oM$4uAWUu_GEQ+(h^IP3-VMLuAxYxiImZJ6eGb?Glz-7=G%A7 zJu%z;lMDAI2BT&1GJe9dt}&n^W+QX9SA5pM2~LCns@yvDMX3eRZ$e|B?;64t%T;KM zZaOOJW_UyVpV-iv00kJ4sO3&rWtBD95fj3zT z$%1}{mporzHsdTb7gUTo(iJ_NNcWB^MHS0k+xvYmqvx(1CM-0D zKdo9NTvWi_9Me-cTA|rjzh*}6#7!<++`4RWis6csq@*M9XkS4#CS0`f6J<0^_UPGv zEp*6dVeRVWo5a-vuA$W12|9Yfj3h&$s`(KKf0d_*nQ-ht5BKKEZrRXNwYXGc(rJZ- zHCDJxP<*|8tDfeE0?SEal#k=oa9P@moTG>SD~Wk?$3aMCm);JgyguOuW50Ds(Z)cq z^Xn4;_$A_=Wk&GGU_Nkv?j_WP+>mphyn#g(WFC(}{GOyWl$uNI_hLJr7+@!oWUbTIPw z$-5*e;?WE*G5_Uu?-kR3vAI|0F;wG)n{(S=N__^Hb-VQ*EN_H3?)nP&kOu|50hPc` z&9_&JF26bwUH+CzY>MSFvpRMiE=H$1F|e{f@Gp05ve5}pk%HY*bL~Yt2?b~F$H92! zE1K#HL&w4=R$Aa{mK!<7y85~NQt0twaO2i%a4L~z-mqk7Xwz>S`v`il>@;%41S|#G zsjHTCG&^5|cNVVOKf~gy6w%o!Xj=BD&MIxV4;5P{M~z$adbv#Vc^1L&ik$5@!0KkW zJ&K|!s1zMx^%UQ)q&1{H{Z&sD?^v6;kFa_j&GGMTG02%n!GuPR))O4Izn$e_f`rA1I%SonyAJTXF^!d4 zkS?;Qn^0^>5*}JL*(mA*-;Vo$JLvHG#-I+B1zeKf5XpANjKc5!ZS7x?3w4WY9w!p8}TRg;lM+C z6P-zt)eTrRk@xkkzG~%s(s8K`jW40MeeS zuLy`UBl0NNSf72QMQT#}dj8vMR(pYwPzx1Fqj1G_27cNDm0=pRFe-OmOV0Z})Bvf0 ziM*v-UV6v)^GuGQFF8T5sFJa8f3IsKDbT*Mr&f5km>1zyA%&LI{DzM3fi$$X^p6c2 z^%@$Llpnj%aLjrI$jy`#F#`SM zTHr=%VsgffMV`i_D6k`un;Rtvl$d*Gw}E~|qcj5Bt&~eKjNn36f~4yX$4&O}Z+2s$ zVhICMdNuRFT$fK%2jSr`R9xaHM|t$9lM*IV%d>=IX_zQPwQ*AxRCsJ!I$y0Sdcyybky73@A7PcaDhTzM;C!TLH-k~UY?cZdTdi&g zIlei$GuiQy=X-TG*U-rBW;~hMeT1x-omWUiE-v-bHs*!r!XalgyS}1Q@P=}&mT~g_ zrNLY858gIBQ92y!K3Z2`#mg<-h5D@TBbHM7K`By@jFP5uRVT>)X@x_}$L?v>O5P9p z-UxXO9DcvX_kW4(DGA3~=086@<>+VSbAKT|pf;U!# z4dFd@$81jPHUXvjRIqW|1Tpi*q^dnYa=Rz5DrfmxPkGDN-;J#r2n~EtG@p&rv`cra zPy1=a;^LIwT?c$qvF>(-)8X2<(Rgu?Y*%8jZk+u`Z~lEqB=c?BV|gD z3~FEnA-zwaS zO89m~kS0ETefHSw84uXxG=pVL>2v!3T?wJQ`zJ$4z6~91mU&lkj{jT|SxXA$NMtyW zBL^m;CjRJWVLmfE;@&$+cY{yXVcep7ICJ&V%?2^u<5A3Q9U1Y#hQ#P?C!sW^MD+F@ z1(I%^t7$YlVj{69X!R*f)UEVd@$|CbCzJHf#KbGml;XLn0o78Wp;pMmr~@___}z{k zw6)MhpI6~w0xFa_xbY}uEnuV8qVeD`1vyLNH1*jSB*vehyu>qAWeaJh%17*3N`ekj`ix80jH{`*g?xGRp z;TDHzlPT;hf_`288cWU_C3isGrg4S8<)=aiX1!{>%4%GW?V^xW)RKpGj-5BDiJ-PM4dv~h#b=b4rT>k1lutc1sa?R9Gn;3Fj zY7nvAh6oPb8;wLU3QkRBHb+bv{uw#U+5NK;{yzwh6nOf4>vrJnv_Spdp!SQfqn$4n zXk{*ZG1ue*;k>2EY;X$o8AygM;S}9)ZHsD=juTz2sy+XkP5e+Tw7=yQOr~Ge{U*e&y zoxR{VcBrH0XLDN5Yw7!*PpJPE1xV<0dO_|>DSpryxABg_VMwO&4 z%r-?z*n}JoGF3M`q)Y`iX&Px^AS;g2GK&dBKTB6*Flo9n-s`%^Ahc)fw|HTVBJ0@C zSN1tgGOX?$Q*=N}H<;mYu{U{h8HZNJwHdGJpB5nv&)^)tDX09l3tB&Aiz%(EQ>bm+ zjkpL|m|T}E3xXOnnvVJZdHGOE`=~0_x4OPHk-EyYEo*#iNmz5(k6UQV7QwLy^zb} zX-#TOibt`kQ$^b8yk0TApa}Zm!+5Z7$#6|BmXDo@R*6HdLq7Wn*|`ZyL;L?w>|>tZ z@P4^DO>HgZ{sNUSVXC8g3*$5aa-7%$5N9AtIc}zRGWI$2dWRE8iY8hl>ry#v)v~f| zz)qCcEV?mkxtBYGyw$9=*ZY>a9Hfx)^ijymZc#l1bE8X7f?V87LlHE*-japIx^TW< z(M41kF!BL++hd_FJa%&x=HJRZ?%Qt?T~buili|2A>U^8r5*B=*YRAaTS zF6rgi)W0${V$4%g=h+aiQ2Vg$jf|>SV+I25oZMAtnFX%!v5}m@@2Co6<4f!CIOv3O zHAt_ZFeoJa%S6<2eK|=1@d+Gwi3Ns^Nj8~h2gA@i|@n`_xdwdmc(Lryb~O^`8Y zrWV|6P_Po}`|$A#3;fgKC`flD3$Put_2#;PVCd0ChyQ5}~FD zMe3@`3UgB!YsaaB9br<^O7x7hKC&TeP^g@m=t?u`CyWRCqk0W)?d4Jxo{d~QiucGxN6B&BM z=NVfwC!Ai~!)*{HR zrfaBhJLgr%#fcauo+8yg*9{IW-XYGh(NoHQglb-zbcy19V>^9JUX573vHuOhIbM*^(zU(NkIfL5%xc>0!R$=pntwK8;c1m6h%=o# z3-C2X!x>SA)Z~l=`KFe(Z9Lzr%jZ@;5=ZUi&!A>LrQbV0k9AY+y4Yq(l2)3bj#G5a z_5(Mg0>YGQ+S!y5j*Qprk-xiehp{>O{rij5B&qiIm*0xV5x%*|o1w6u$aA8dU~J)% z@N-48-b{j3VITW(+Ltoxr`JcIY(_*zeOJ>6sNG4&iU-m^w_oS)w`(MPeqe3b~|FsXGBVO zUyf^+uDvsitjo8LNw~r$;QTdL+i@mzd~mB>Oozv)1;35w*Mh)bp0L-w_<#@IUUQy_ zHYthSycN|d2?8u<61rW_O3SNQT8fP|*Bi*SwOUk^=N;OFR*3oG`Nqg}M^voQPsT2& zmB_TeDvqT1+fpuSi0P!gQlahR56e;tc381G(&SvLlBRrHgqPkF6-8g6 z+FgqIAi>vjgEZRd+s?NYs~oG5Py772cXcWjTvs+`_n$bYZ7L!am~C$+Pc;_g82)#x zOw|Rc^WE-w`t*;l^ZnJ0t7QE;351N&%Ov|FYfJN+!#1-zIkY2u?L~6InD8RJpqS(V z<3iL{bqh&2#l=gVVd$? zf1Z1EPLQ`sU_<6{fz^%RpT1M{3vqF?qZy77JC&jJ-2l|SA3DUglcH(s^2`qVie11~ z8yhz3U96LnY=g5~+oJ==Q2RZS;`JbbN`dSD9p!x`2VsoPZWQWnyfNfzP^je^3Evxe zxJ*0tG3!t6qm)d#CmL$wywsOrP-Vr}WuO&IvezruC}R;9C8z)$ntQZ0*}h|=@NIjg zn=yD_r`LgY$2W2A$wbM3OBZj_LI$_cr9iS$prBgI9dsoIE zyD?Au|CtXR2d+)qts#dPIrXC{_z2=5$KFZ~d};_4Url?^+RN+EvXzz9xmi`Eqv8J* z<{fbfhX3M4Y@K=6)LpnnTo=1rmGJbXxe@z(!XT$%JnBC@!9RU5NqM@b4O(X@Ux<_9 zu9mG;(wKj|4hUnKA-iL4ZftQhb3VT-i5{*}d%3zwl5jHbjs>fOaCVgCP_|H&Q$B~_ z2(7SvFKv29WN7nFM;xoO$jrj>LM35STja8=WzWv>Rw)gsCFbt#nDguGsyU(3*-LI8 zjf|(l+*vT$J*UQyY}XWr`zM9&s05GNe?FDL!r2`;cC4vTu@ImjB-CE11{SMN zc9P9Sl9YGb=$TYRd@9++8E-qbm+H9TGOfVEHRTD^!W|Hr!rLIbDcX{kz>MywO z-+Jv=4EZOO{P8`xUYrcgOhh#&|2et5!5qkU&*9kF8*8qflX^VIoHeqjK(XI`OL9x} zX;yaCEa6PjUbt_BJZnc*vnvW!l-;9zyauxT&Ju|2lJ@G!m{QsAopLsAlT7f2l1$W`TX?w;15dxR=p(xwgIBl zMu8K_m((5~ukljPH@%jJr^;bEG`>tN`hckj7xe`a2>vE^~h8TWAiCa zLS!<t-Ja4l3{_CmIwb+7L}-wjO9zU4XI$t{O+W@bjv^`KZPfF|q9mq%|e@EFA_yqx-}pZ;&Z zpg1Ysq+q;eN&1}~$B#`4O9_b7*2u!GET!ajb|oWCS$j`CoL`TbdW{Spw*a*B28y0A zQ}~2-cXfHz7(zL_|LX{J7~IL5h@tpCglN*v3{G-fXtoAN``U<>z@rb0I;yF&lzgTe zlvVK`@+y{m{xqri{V~KNJ-`T1qtD^~uCMtrDcewicbKx0k_1NYKYD<0aK9;>;IGsw zN3{fu{KZW>9=%Uhrd#hpOfN)aOp~Y+^A?)gJ7o4Q>KrP)1aN%qbhgvu%fB8RfBO!d zh=^8WV_e1TTXRrqlc%rrYa z{pp!wDVP(6uvz=b05vJC=y27A?*CF6YeL8dg`#M|LstGrn5TwIgVe_|>6h0{S!D}1 zLWu{2pd=dZ{iogXtvA6)$nZE?tlV60HrulqGkPM8XUg(3ntH1! zRk19b8t)_i=kfpXiN3>|WQ=z%zLSICE0THB&^MXiEeT(QWMF-ReN$OPZp5n}qdP>L zZTCu-haY4)Cl0E?I=&r`q}MiYxhzHeJhk@Sig+B6<&LBAURt~dGk)uVU=Sx9u7XL> zw-zXV=u0s48BVE+KxLm$ib zRysuQni1uEx$SGXu#n1Np?b4xRmnY0|6$t%!Y<_hxB%LfH>pu0`d=(r9~PcdY+LS( z{RJ;Sk=IUGu4#jbiOK4?7y>l*r*@$6f~YT+AHNS(Ajp9$VDeNPOY&}Ps<00TQj=U3oL-Y*W@fH=)vRqj;Msy$`7bf3Ry^;wsudG_BNVv&dDfe04-+Lkn_=o0!~X%;zjjak&j+0@6W7EIp=NPtCZyJoZE1u% zEg8feN5$~VnL?S)WMn=Di_t{yrAYo1Wqx4Or8nRV)YMoy48IdXDzghhW5hTZJGJC} zykFvce7MUC1k%vZu%xc|>C^{pErH?t3=9k=%l&z)<2qv<9b}p4W$$ncb9j6mZyBG4 zRUH^s_KZ^H8uV;Bl4<6PiH^QB?DLm)n_q5j z!k&1^xmTi`rh4;Fv`!uV6zr2(L0}RXaWv%v|DvTir?XTKAb`dLaJxM_BV+|7)UY#z zvTOJ`&_&_-S>%ovRH@AvaON((^z%LK-jS12P!@M~C;gl+ez;-{c(-(^N>Wm; zKekgouE{b*;n7=|-!{4k!LRpmKNNQA31z=ze%lbrHl!Qsr=O6Vl+RcLDoOO@D5r z#$f9HCFGqCO40H|OT4>#XXnX&KCQe`HN@?Ruo1Sol&yAG33^R~;SsgQdVA!KB+#{f z04YCFynEMMhzL*&f)OE0-2sVa*x=hjKF}sUz~y@IbvzyThgxE0Bt>{dUPZt_sda1i zkPqOX9ycv>h2BLb7+MLNeqkCA1P|aRb^dGI(+Tr??QUa}@zLj0CnhAwme(=qk%qDF ztG6<8omEf#fBFyN<^-yW?Vk0cx?3Z>r~*?xDbA4SurMjV9HE=NSIO!iu-Tmq)|0tw zOVvJ9wmB(Hy^P(A{|gumhO3ZxU|^u8$6iERbwCWm(lP73;0?4ymX{ zzgq!Rw5Y#X+qmZwe$;0zzy!+X{_kYs1NsVfjD@Z*9 z=r#4^&ecfo?XK=_FNz=wr?2Z1BP+ekU-~DVS5xWT#mDgPY|W)tR%#=$%TLb;X+W8c zjf_eu!u0b>ZNSE>_DzY8M|LLzou|VCXhc}3*{aR^sIwh!8xI+cRl3y3Eg!Z+S&^Uf zo`R8NYwG{PvTt4RU`nx5n7#R-%;Li`w+1SOm;W9Q-=)^dM6)|HSzY+0FgV}?iFEY|svs&%M>WxQ|;(Z~dw zx^$Z)YddJK=a%pP8|+i%g!*ytHvQE${eL^-t3v^|9{W z0cK`qUhp7`L5Zf3LclNUgBx`34OQm|M}@kT-W-T`%J%JvR(3KZ?Lm=1rTTcw0A~n{ z>zAyZdRLp@`FJaXRpS_2859x03`|eEtDAR9qMZe3URky4FZ>z>3ShZtu>$jM#7hCq z{1uC|>}-;3jLgog;cY{=CypS-L)-nkC-V48exksS?X$THA7YL3XPZ`7`tX|G*1B+v zshwT6duC*69r-8AHLpGnMtcRH%`0++3ibq1Cj5GF&}r)Oq&>|CbQLsvF3cv2V3pgB1tc#Ej9tqY+0zwnQ1;}e0w;dH}eXb7^~~k z$L6=3b0UC%j<7;b;uE&I5s1lfP1ThfH#)O0=6Z%!(z3GD+whFTzJ`BIgFp#l127z| zT5qVO3gOHs?P`ZtsHJ>#ygM?wTVMZToR67V>r7-+v+N^8A7-yujb+(sOSkIhssR}( z1g4qKmB6n$GqI`;hKZ?V3dZBSgV>8<&{meR!4lQ+wn1N7*ZI?oSszZKNGC>Jv3o~+ zc8iaW&8-JB4rZsPi@op+%}h;)o0?#%G=7Us_M?2Nw+2R2Ga-52j=nZ_<9c;Ic&9$r zP)|Kjr7-8rT&BI}^xMbU8T(`Nb^iRVlZ&O1&YSA`RrCHFNSzL-=1N^`eOY5$nrf5M zqrO*N$0BCwlC+p1WVO~!20AOk3gJ(}2y@D{R9b1?n_8w`ssB;R$YbRSsgw}54;28Q zz`QIDlnxkqRG_BHoJcF>9usU`Rzbl*(iOH%&13YS<#gAm>%K*37)JCr6P24T-=4)h z{dD0Sr#~-`XIjB6 z9gXp{o@1|&4WIq=a22VmQ+4TtJ;sk3hlS~LYu2p9S*n{5SW8w`7O_tddoDG{quvcq zwp&KGML0u*sU!?w#TZ2!K+8}rj@AmN%-+iG%s{rqiRr-JCjw{Qjw>T0Q(!e#IsOSy zTt@O+X*%Ntvwrqj zQ83%1l^tSpmt&Q@gSfRour?DypLX*+XpWE>P561r9JmI4?MS?Fe^{fwpt&NRApK-zpAW}#x892;= z?nbkjKkrpDKI^o!C3MGE^1kj6#)5^Dle8r1HAktma;iMBS}!dRLTdoZN@`$dh;(ag zXs{mBKVhE`J+^b(1-y>lp(_>rQ1*LHY1fpju*fDz^AV^VM69@W{Go<__sWTrEF8yh z%2ZVGgO1p@@-EJ*yM1kPYstFF2;p1_LF7xz;>`GPm)zfH0Y{)@iVLHD>}2BOU+~!6 z%_oGRbA$a7NDxE|n@|K*uz)P!qIoOC5+*ZR=8%&qV_a+$s>L2+?&Y0(XaP)=dX;O1 z+dFFeC|RuAH8j#>}=!#jvp*wh0&T_Kl;DZwIY9T@-aL*02}=q?f;K>F&YYSF;0l{w)9$cf+r@ z5HAt47WjRahYNrjN^-6(0NFl(Xp^-=88iY>Wkv~EfYMSobQ+L>5H)o~Ywd#!byyee zeYUR9zbB}S@Qmwv%^9bi9u*-w9$<`T=H?s-c^w7=@$l{fQ(8`^4XlluYck0onKDa=u2{W@%;Ja z=RvSiG-1%xr?<9eP?IL~Gc|)Gw4#}Spu+`_W4(GLmya@S)Y2+~D`5g{KD)MBFMdgn zylE1ofmetURrRe_JeRwB2_>x)P8_nXDGZ+mU@R15q|ISxP-_)NP9|HB_@Ie!nQmgvy0HGzz38iF831DaZ5 z1`WCamfQAynN5i*^aGzHXGG=zY?G0e=0^qNqeO)XJ8l~Azc=@os5Biavg!+C*Kj(I zzlPH9B87O}jnB11tgOby#wbqO+W8d$;*W=!kDq_;Kp!40EQl2C8^CibKtTyJ&b@2K zRHw2Jb_G;`2D-3=s@1!Tqus_SG|={78V(~cseNS zLkeDR=Bp`1Q|-d(jEoGw1TIuqba3z#2`p^6-3T-lu4w~Ksj)1y>29D;wIVFH!%#}f z2b9BaKApTH7r|}Fq+=Q0H(Oz>p*k>H1X9|vVNrF~6}|I7h4c*fW5i#_y1PVqR1E?p z*)tg32S%O}fk7H~!wfKS?EhK{;i3ZFU8Fu`_20oh{Q3$&Sy${v@Eh421mV2_5++Lq zd1#SZM|xS)8<4)8c(5Sf#|%S*|bY!nK*&yq(Mj&}|CEs{L)<&2!1aJ*&>b@kfV z-E_7B*CMMi4k4+eS-irlS;meB`2G8hz}yK|XfMwh4i=bzwIssKM5qqS8X3{DJ4Rch z?d-Yc-KRR)=1os(RvLE?RUNG^;lDl-PC3lp%wR_*%t%*N)y9O2Xd3i{o>;&*p?^Id znHgZ@(_ito7X4v1UYw=gDf)g5of0q`o4iXCuG z8#mFIGchrB0A{Sob1AghgQH6zk6JUvZc)GWg_V{OK|_$CAjTHe>szh=W?wO43o0mH zDvkpgT)RmFYfdP?6;k%0?M6|}j$r*f;(ratxWT~`q+VP62wSM=fTcTy+pc#O7zgMe zjo`Be;%geJ>}2@Qkk3%=CUAL+TwOFAaVeu!!Wkx$ote3eR!4g;mzy?9_FU<*J7Ec| z*d2uuy#H2In7g2695dt7f6yEuRgJqW(%@Rz)YK#)fyP#H8#bM9eSgDi+%!ao1twiM zc1l~f1jRDnDM$e=RPS*_au#kV9#xRD*HzBKrSKaGfFPK#kg(xyn+HD(O= zhsFODbNzOrT5k7kFa6Ly`P-oDgLyoU6-?OcPVhiKIKqRq8NPo9VO!?4E(XTYh{Zd<=6V%MEv# z=DFSs$RqAKf2C75)(;I?agYOpu+*w4SF8$>kfT;wT3TEM<~gKDt(!M*ZWo^_^TqN2 zo#QMw@0Q%5rgS2z^Z6HtL`DIw<)L2k?}U)w5a48EPF>WHQw-zq&jlS>#3JcI<~Ub! zWNf6Qq%>dnf#7i1v=MP*pjLGv(mQ>!L>+DX7y+u`meQ>ny-&?2R(RTOc`X)Q)_Who zq?>~pDRU_Eg4^swdQ+#$$7C#Yrz)%UNiL-3jhq7sdzDxzGCny+p?MTE1Ydx%4h4pU zeChLzuhJ&$H>*SCE}v9?d#A+9Muq^@QeZV&d zGmjPgWWPpi>?7^~K@45~=~7Fj3TPyqK;W9{%n zBwQBI1<1c zWm+BxEGRGIl<{Nt4M1$Px)vtAhSMofI()`(+2aC~Md3Zy?Tu|Di+YO7NDf{9g~y*@)ubAR`bp!0@aRQ z5b^QWfx4<&rVNlii$TxijV?C7a$v&w-(PBM5@y3U2HA^L*R;>MF~_+WKMx1!yVe*X z72tckp&rWQxpW7;SsFs{1$lXSgq~i)ex;qv8QnQ$SnjSoXC|mpPAIRg83;*(Y~HI0 z2Iz6{*!l3-`Yl64*)x~n>X@g19VbAcpN4$%OqfNaOtZ+W{?A~`&o>2fkV$zUal<1v z98}-k6xWHHdgJic)}IjvC9SHZb(x64ffD<2j-_#Qsa8S>!Crz!E48+^j!p*V!L7D4 z7?cz8V~hZ=oh4iY8s%*U@a^6R<5*^BP$$_u6bBht&B8p?o>^5|A|k9Smgwl{h^xF0 zDJA`uJ0=DwJS}ziIua!5cTuFJYZxdHz*7w880DvVJF^hx=DK6s9XARzRa8=?tMmn9 z3lAM$!Bw!H&3y9G((}a^w*L8s=*WD5^_yBxv2FbRkGvGX6^)k}M{jgc=86Y+S7$2O zC`jnSE*63cpQ<}+Y{oTY&S|KSTBrh4fCfNj8p9^$JxQq#A|};8CMq)qV(?~F)YTV# zO0Qbz=i~r(tXjQy?y9ab5Uh9&@K**Ccir-3MgW+u2>i0!jqX!y<08WPYh~E|rrG^` zHN~C`mUDJc#SaF2&rQw=>FT}DJcf+Yc_QLjL3D~?nv$d=b#wSS0m%ctq*5J)5-txCRcL-U}&1{SU1k38ToR3uQ1Z)Q1k^58&44#V?7 z7AONMrm7n><$3E$j*cZ@)lw?JqkNO|311`F2RzL-Rw-Z&YW-inBo&Q1p_J*q42^o~ zfc1>_#1)2b&ibXeSMURFA!Ss^qvW@nKnCO3lWF#d| zuB$je6Ys}$orJdaR$Sqf$`AzoIv_X(QJ#{WT_KzExpXy(20&|v$I9?opKh(%cWp6I z-DGk*qUoYY3o&bC3|?^7Q~#71sUJ1>!d=$yz23k)20ZFct#WiM!v zWI1G@9hvHv_Xz~n8>&56nq4lS8DIIfpdAbhbLn@*!_--Q4sjja4)FRmj#e?r&I68=u!Z4C~Uv1nlW!resLJeRscZG7T7gJqCzqf2COT?|KO&)=(QCf4jonTdX3dXf5M$68u>~ z?jsNn2-Vv$eIj5G+ugQr-Jed*a6VX4$8Hk2tmug4%#JWP&4iNP6tMCX{N%ra8f5RV zsF}{p%&e?>Ct2_r5N;73gg_M z(IEE^?pt&pi5aEsr}1BBVI4vS9gkuw`&M@!$jkSF#Y)4zYF~+lEBbUtYPEG>o<;4t zqs1tztaY7Kk@acsP=Jt!r&q5~KK^Hf*`xntB#C<0BmYNTI$jp1_JY=CF1ZH{v|q(v zqrWLbQoW~Lx>X_$mQrZy&xetTe>!rJ?fpTfHu9+#_9lZ#V?}eww-&&X(v@Cnt?j=^ zf?!qbyOwNZhPdrdt;8pZ3W)|bCnO}KcvfiObiRWI;fy(*b+)Jh&_{J{zOVX(xzqr%q-tU*cxhTz`LbL}~VKp&`l zJPTMl^Fla~UKG=A1PXhe&DfGG#vEQG)nl+Az%tp8vlx^RWEfWIkDMkpe{qC={P%2WjNOC1Z)H}_ z2!eglFL^E+h1ie$k4ESiuBEc*7d+Cs+%Z2u+oy6g>&zd0X}89*N{=*^UAW3|Yt~Og zL)r?&J>{ZT3G*Ju<1LFgfO?%EpjvE5SN+%x91XuX71}f6Hlr@w5@7NpV_9cNF8Qf0 zRB&>(K+>?)fnaJ_KPr{j1qZoaBhhr!fp$41r4Z?IIuC4}zy`kX9w8{%z2k53$MJrB zH7^9B85iUG3O^L#j-2}|uZM}rQ}islw_%(i!2MEguw<2LpG3Vfm~p}^_;?+E+!xx| zu}pPClTpU7C%12>#Ja7UIoX({WPjcMd|tjuVnpcZD^x^m4{*~=X33ojCRHXpbz z)W^aGz_-}P0V8Xc^vVF)WMSPZD?{caV7x1;`sak*o|NeKV5R%avhdr5eR9UQD$nuDl9E$DR(g;}4k5gw06-})& z7fWGXQ%hmkOOy~Y%+~N&t!)HSY{TknKXjIwj_fJ~u~$%Du6aU9O^~HNGc*i#ZUJt6 z)07)FIo~#KE<5DGJ&)%T13>KkC0xsQr5Ta}dtTJk1bF%Niu5KxEaQ#_{bZkD2CLZv zW}iUkj=uK2k@UPj5&Het2^CYk&9Uv8i-R(G5Af5;izJ14h4>{5Db#Xa-lY_`gAJU=$cw8kzRHT!XFAturB z+Z*M(0&Y#`r)N`~?9+{9&zx2#G8>2XzJR8&-=lO9%E0QhG9O!FLe zb!TY*5+~>|1vS8Z#m~F((h@MI_J#)h)8XmpOZQ*R{IgMi#(Babeiie>yy;e-6g$6y z6<+_>H55SomA^(SCS<$q4q;MczVHoo`LyA!>(JrN=t1 ziM6a^S&%i2_9c6ho^C6qxmr}A?y|fHwgsW()EBT*Wc-}^#X{6w5!gIOG~oTt1eg-G zSd8xu9SkosXaOXe!@e{JbH;0}Fs18?iz*kKx=;2j06ys9u_5gE>(+bu14UO=?;4^q zK|h|~6Pj0*XWqHPw=wO3M+uDZFtBdwrculLzz!P;iCVf+8+nTFaLV8P;o$*%w6Hby z((lVVX-S}F>#vdZ!)NLHU)S5X69sUVWMA8Lra{FX*! z@UX=sPH2Vxim0tq#$Ve<{~tBSrvScf@qbP4H<&?Ydff`jNMQ(8FMQKw+MDl8)C(}8 zKMVDF@SdxN__{`@koqpX%)mS!Z%x;YT z3LxSJT-0X`{n|C|Y9KiaWHy0*_%HUT7h0my%ukJFkii#kk2qSP*fe@9K_N)elx2+j zMw$+O^xt%M|FC%CaVHyWcw_{BOlE*_Al#5dW7+mRhH>vuwp@eI(?V}O*_eRn+n0^9?-W%qxcGue2G|CH!g9Vfbbl9q2|+sJ+YjU>oyHv`L(H3Jn+ zmI@D6xm9W0?(~d$SGhV;ds&-FfPvv-->G0dJbzln#&6^nt}FKsXX?^Rr)zIG%_J9v zs#?Fy7Pa+^^na9|fw?d9;Gq;EMSl56=Wrnl*Tr^EjD=I+KC&qMNvd+OQ`fdYQ+pD( zo$8b}TLDXgRr!X`vfWtTS$Qx4aG(eSt28>m)X&g@h}#JUB49hKSothFi`oOkekE}P zCm9VWt6O4L4^8)-Epm!LpA7Sj8+p5z^NckKzV!6q)d5roWGN%sZUE&0h3_fXC11So z75Zx$QvX~j(@g_vPP6Fne(C$ap&|#e;9GAoeMJyo)f?2zpWr0T&d!pUX&A4t<^x!5 zg5H&+mi(^v^LftFrKqaj8x->?IW9_eNNie$L1C|DPTyTO+rsM%m?u_h5kbb$S$p}P z(zgSuI?g5E{ZcItdQ$%8P7GmL2KnXRV?(zFUv6g@cUekPU_a7LXCFc1%}83$1_6m8}?Jr z$ALbKMn}VGsF{x$$OBJmvBSB6@b`&3kH&L*MUJQ|4qUUYQ)nkKsKll6A1_6z7n&q- zRIPFw64`zec$hrrdK|Q`S^UImjGKoKy5Pv4O{AS+$%(v^Xu->x+DR( z{SI-h=LcX7yMfdu+7k)V3_<`L7Ko-)$DG-WjEuI*oc}N|_ghXax5o@eyefdrf7OH^ zD#A5?@&rmXJ`sFJj_MOZHuJoENpdc7WoaNw295W6*=(qe7e!;HM+Z$(;}w9Kc9fDm zvF>Hq@1yN?bK6={cX*aDt8+^}O55q4#~ZcM*&rg3{m&{P+-3x9cpz;BMz;z!Sq27&71}fTjr$&YAX4io6FRfY;0{u+8G{UMBs8*Qd~$) zH5zmiEiRVcq0b#=AZ7hWK>qYQaMat+@^8UUD#RXNPt1PRt}B;L3H%KzEzP>F5vD}a zm#H*yk}_Fe$BDO=hzaLiIjC6uI-#?@=eAe*Dl(GSXp)N8;@E0rW2*Jra-XGAGcf&= zt*J3GO*nFbYcex%kyI7?06_osEvc=wS=qjqegwJCy>M=Kem|~d&H1;m?UHJtwerN1 zNtBhM`1oq#8gH#e3EWBPYKSv7zbmh_68?pgh2_;%0*=B|2zK39%2bCDi?$B2qdBqp zeyCXIu@*dea_?N>QV)B^!yudX=u}(ib>^$->Q#HK>T))a#xZl~HCmKt=EDg6Qei^Y zeJNeg)HYaXrqbV*q7aweEic5rEC{?;Ni#z@@K^SRY%;04A{B)&xTPmS(B%RZOi ziNt@1`k4MbysggxV(_(M*K4MU{efOhd`1R~+TA+;Ru(e#+|Qnk2;PX^Qyx=i3i3@u zHW~d12fmJcuF5jxq_GayvhJ>k>@1dj>x(hz$!9Qq=xJlr+C7kO8zOT3?$Gf8rQG2p zf5=g4&SDK9_rKTriEmSTS1zEFGcqm85yMj zgMSs;^Dl!_R8N5CJLPibmgo;tAOdpu;ZxW6792c4P6J0EN^1;InpD|pR052Qfb&mz zk2_lNt07W0_DcY*Gs5?tP{QPyBfe{oh96a^to6}!bRX}gI=LAnh`Q`uN)3jEW@{z0 z_YA0SFR&Kst`fm3uzs$aEjTWYdjU5YBLZcfFU+%x*i8HJSq@Y2- zrOw^v(5=!LeLh8u2k{7GWoBYh1j}CFHnVzwbK4_N#zr1_x80H2ZP=ZpTs%!5~l0BFtHl(a#_rFVO!?qV}~gIpYpW+es&ty;$nw0LYF+KlCB8dXY7m zUys{IW2*1kjqXn!=aLHH6I%Mkk9*Rs%96k3e_6ywU}E{$%tm9$;+VIv^SVh;ug+i! z#zaoe)Je{Dp!E5|;RqXFU;X?3z$7Vs&x;hecR%8tHBcryt#*3gXb!2{)>8f3DWc{U zXBwTU=ox`_$JpJ7I-^@R-DL2o>ES=i`5Q>ES`9>%J+ThL-#B zYxfvDs%aBrMLy$Tal6^E^?H;JBw+$SgY6!#)KoSvYdk6)Q(9qQ8f@N39$#k;5FRJZ z=d)Ri>$S$!8FCuAH@iZ1R3Y2)aiVWQM@c?k zs!PF`YQ`GxCqw5H_biBwhk~9SJy;!-rEOqnCe*vQx4)0_XnObV9nmAq>3YLc*XeMY zZO#sX<~xrM7Bc+rDzj{z*UAKRy+la7tZwbaNFh{*TAzPgnh$;k{cj zZJnQM58ZArHZmP--+R3=TNf}{|6%9(lgb<$(caJ9+0kOk+_=;K;?A%zCu~;0K}1-XY(~e2-RUzqhb}#i;4!`;EIhm@$dU9LUM3VIXD89|$^Lql24q0A!tL9) z2f5`Hh;JhALjU(*2Bc-G{SchJ=&) zSK_w{C^yyt|7M7H(U=&n7sq+oHLaIl$0vqVuy26mxuc+5;sZT8IH$ypHucxWz4@^T z^TVwZ-1Tr#Ehh;Mk#pvXruQBy+&rW>zZx$>T&qs^Dm zcK_V-9Yu|>#j`gEsHWf^!6gjqk-7N0Ez~F{*%VU(*PsU9)A%Fc{@B;3bpx?*mgx$R{2sW^o)d2tZ*?VUAmLLIjW)AqS3YsuA9h!?S@c z`QaQ|i`LNM`)TzE!x~XJH91LF6U%m<%G86J-||UK z6L|sKmKKGAg2Ld{&v+1xav)LR-c8wUP~qOm^zm`Nj+AN zzjmz!f7t8Xo?2RT*l(m43XWA<)NT^KwZwT(E<`dVwYY(bD~HTOqWukS5lKTdIW>#j zJv|Y_c5@=@n>$^lyjSeGAzhpw2Cc}wf_XzHc%QBg3!rKmgJzt7v#&$tFB9WkQWW=m z#~@pR8+h?GF1swIJuQuC>WZjxd^;p)Fyw?IU;P8CVx6%JCzEDR>Hk~O67IFwc8i|?0E z-TmBLF5Wuy1D-N;TJGQ3_un5U0P)+XoXqUXzrE=b#A94qbs^8mRDbn#UbDt>s-+eYV@jWiopw`j+*@R1|q&{IxwOF|ov|kXy}>8Q|EqK0h0J zDlu#_wb3=(|HZ{>S> z$Eo6Ci%}LcfqWR9A&gKl{un|&Jka0o2i5^*-6+YGgosK?N){SM$BVn+Iy*amp__Sm zjxi&r(9;w0rEmB6!z$SSlOBP+di840o2C}2p}53)ixmbsF|ir?9EZI<7PZhBdYoo% zEA8p63}FuKa(u@_|BJjbE)DHEr&*(9pGN-GbN;`ezJXXavHQuT-*_V%v#J(g0iDXd zQ6@z_&>T0Tw^NR9p&ivR_jt&`>f@(?oxu;SXI|y$A#(ZF9-RAz=B5hKo$=#};URvl zp8zCx{29d6l?dOi)JyQrsf^|WD2>YwWYx`pS6JqC=dh;7q1!R8N-eqdG5Y1La$9~v zsWDG6;Ydj1HGGDl&2U<0PaX>K<(56w$V)tTL>$^iWO}E|%Y}Kbra68Rj6X|BTJD>e zER%~SA7a^{ZzMgqM9O|)Et-r(m_@)6l7mH;1p-htF>=W`V1yKI{EcgWb@S-ty`&?;Ap1&-$ma?#vP|gl3}fX--7Lc zJg5AK#3uU-J=2l>!IPD~hb6}p<)tW?!$_548B$`!`>;Rw;p=^xBb=1bVw`>F9*H66u zKRD-cZb)o4my)DfB`lh4N=pzav0FzXU8~?`OKmR_n$l}_%bp-eVZfAR=DG;FYgg}B zj5HxhBB$3D>h~!~C~Rs~BPc%q_AN2MK_)uto7rr*a!4q(y=}mzGARA3u(S{Bw4(6M zd09cWuJR3ANFn1E0ZEkSG=pre=7OX(r3+KD=o_Ay(}nK>zwSw29FoH@yIjDojRn+w z=6;v+M)}p#=#iCETy(ozb&5g9ICAvwOCBr)a7P@&FOD8t#|6F36H&GkzZN$7&a*6Z zXJlJKUGGE@PUNEcv`6nNm*?9N{AY!ccnJ3N&%U&mKBpcNeJoz2Zt4wxT1hC4|8BWl z`#m|oJb|L<+x3?lR6--P1aT=W%t(fL@%tmq+;ot+_PBeluCA#VhH4H`Q5y1Ldc$YV z+)@r1AMxt$ihEvT5HHbx*Wv+q9*9Dg@#`E3`|pc$UvCz$-=2RsBmbKPB`iEF?8>!k zxB%@a?AGol!V`o_k0%RXOUyht$jML=RoqFG4m<=f0ojEM=|;ohuR~OsxGv#M7YZ1) zQ0>qUgtD@-_Owir7jVY;zPSqtmz9zE;_K^srnauGr=@9=ZEH4JknAtyctgDTt+G>kjb;V-=u*Fk$TM(>p9Z8IaW) z*7oe>^YPKgjx02)8&_YC8(T0oy(rr?b+&%S8dk4PTGA6ISNCav_1)lOktM%^&;lb? zj93|Lqv>+55Cy|6r{g%j#~-I;$R1LDY!xAJ>o2@)7H$_d$;$mdE`Sy)3-QI;m#X0- z_OCxB&2_&!s=LOT#kBuCdCuivCvNTWOu1j%5H;G`>Z_cznZ?6Ci^I>_^GDBUdsoVP zOR~}RBMzr38%EwFaiLHpfhX}{Ga;83x|!yiC~3L#81(s~oSX1LLu;{;<{N4|-r~+l z9EOLqBo$TE-hQ>ae9qMQjQ$@Jszm&&F2DM0n{q zs-LU%!KLRkR-vxK(&)Y1f) zbtEi$-6mf4RYD-1<0{H;1>by59Ez3b<9NTCG$$5LGzLsEHkI!_*>q~Uhj9BM2-5(1J(`%F6U$} zl1&eXytYf&DkZMvTM7|exKMlnFPT~p?}bC=XU7whoP7;sJSu{NF4SgwjSA~+z89Wc z5~3ZebfYKSJ;viav_hnQa%LR${9H-%qEGwVZ!?2~bi=#dV-r{^qX)gxr*$X4?JSvE zqaDKp23kR;a>Jx6NnUN{bUf6&%O4UXq6`TUf;DnI@0R1gfwZ|t)uycb+X<}vB@RV? z&6I7E2hQCI5*9AJYqyjkp=PFoR%Sf`F#%LJAl<17zMAFr5;*tH1!j(ur?Ji5v9r@q zdf~5c?a(*XTqO2s=DpiTR)r*{xQM3p#kwkqRnML06JzHD5p%--WJH0zxs~f)c4~J7 zo4s^>JPIYp?5JUx^mwEA?sE$T7?Bnu=W6L8REX{W`1%T{Dz~j|*>p%rcZaknofaq! zN~d&ex^okPN+S}IDuPPareV_^igb6kY#RQ>IoJDr|97wF496K~?Bjs_zH6e%rI;x^3Z(Z92n%nwAYlmX2eeD%RW!;e!XcG>*v525i@?Ix`y zkF?7ii4wT=qRtu)d3-&_w0|1qJ?@C8*`3fbM5v5NhlCAZk?_^-(*(c8JT8BxtNRA< zYV{@|dJrAf!5p+7ZnaB?H$1|6CuC(H-}Bx9{F7f~j0!JN4J_#7)YMQ!>AoFzBA-#H zK4H`Sz($goo+4Z_ZUnH18koVwWu3hQdIk(PNhhC9{HkC2iY7<-`3qN(0d0@wj|59=0gV~~o|sxZ3C^Lv zANs#!7Bo)~OzefsZnYkMLySvfM=^2Ur3sI!%2{!vry8)N)q=t^M)$F{bxP3ooQMVv zIX=p*FGSnaBxX*j984p8c`ijM6o%~{8*W;)_(DJF7FOBgst?jPzO5+Bu@lm7^7)#L zr$P<4G(LE$5t_5wvNY<;fcZg~-B`nYBA-ZQM-24>@D6ld^3nSA|HNc%-q*tHxx>Bj zvG_dfDgFF?B9|k!^7M$K9#+YOkn$thiR$pzZ!WY;Fa4mOwdUW7oR1B@5@rh;UgHl~ zdiy1bGTOY9?xCh_uI>>zmwIt^%4WN|!$#nXyfpK5xgRA%qh5?u;V&8r%1iljNvM0) zt!U@aK#J)I0m5G!jL*}q`t~POL`2_Hl(Z8e1)KwMVU|MYbT7MBfeid{3#Z0N)75K9 zx%!qh&LdNsQI2P6RWKrKTt@}G-Qff#R)ZZHwATqW^e>3x31J&XnZU+dSXfx9!E+JhH#_>L&d+@nQ-mVP z1MsFzX4A*f7dT4+CTjH(FaQ9iP2!Kf>e6`sp}t~{LJxywF0xy0_ZMv-*~L4=6i;+vzes!Bs> zvt>;u#Mgv=*iZj+`Cj-`VG=sKZ8(urH$l;bA&(7Z{{`pG@r=*$46B4?=EMm+aqk#r zlv@QMxdCTI@X8Zk&fC4E{58q-=_7tTlzW5tjy_+YMW3h4=ogV$9E0RLlKlbG= zQ7?yPUmY-@B;63UZ%s4|-#fS}eZk?q9(NWm6LBrD!BM6YwoiR8n%wv}>a2txUh7QkQBjSj-DPS}u~ zMy7pFs;~`hcrNAsXSM#LdWhnf6+`KAb`4tCTdeuqaeUUTy=~#QT9Y1t*B#f+usNaU zSoy^z#_qEC?v{wpsdVH}TVBVw`a%f4f~KTZYO${-Bregsx#m;;&tiB7ERR5XK=XL2 zhE{V=aT5NwgBb@aP~ivG{(E;mXdp4w}pVk&Xl z0%krz+KGU(tRZ9b<~}8DGV;)$cHMSeRg~@J%#+V*upM?rUjhM{tAA|+F1@+Yk98`k z(J+NJypC$@$`!aV)SwsXmPE%mmQumEb-*3}JZVegvfjqmN{=J?@$!tR5N6)lxGNfu z?~RFR#RtP;`kL z`;K_(BW9#QZIAMFT@E}E;wzdrj=F_oi3Y7bzJ(0h)7|+iFVfb*hL>T&5+NRycd*bW6K3_yIqIccRQsyxG97hH^NY`F*mQtm z$N^k&%F3O?H$Zdt7njTv+lX5aZZjlbh>>B7ByuN%y@MFQ4te>K5hS@AutsAUAVv`C z$Hu{X2b&Zyxdxzw9q0gc{B-hWXSVO^0ORVR#SGu~t!e~NP4xN&DOLOx%z=@8j(eCK?_{TNG3!Dzg z7i)Xv{k{8DB}-m>JG06IqxkT$hAZ}AiFY>No1)1AZhk*m(FZ)?WBhbzG|z(zU1hZ4&Nw`lP#nq?J_$0^kUhht<#{RBM%WU=@ot#HbL)& ze?Zit$COJIe3Vf>yJ*Zx$jr%!O`O>>KMEmswNEFV5P1*E$fl7*@=;33W5vJ^#4-Kv z!SL>JbS89kben+^vl8pKU!Q_dBFRW5=8pb9`Uz$d$LR0e+Fq>nGp>?$`{b0Befv!j2=UsFboafX2o7( zvW6og-gLmuO zFCr;X`5PK}1u7F&=me8uxXcem<{XoUZVU1B6s}Ft_f8^a@luR+UPE|qgH;j2&uHv? zj4h>s&`02gC$fGzEhXP;8RVPKJ;%|nR}Eq#nd`WW-aR@xnlG7omRGrBc&<=}@ASIj z#TJ1BCT~E9q}SG3hi?$!qUkhS4yG=G;;vb`s7?^KMSptl>|$UWeHJqO&6~V!>-m<~ znu#uq=p6i&*LPMD7{(6QIH|fO2ll!IL7HWa0Z&=G%qo^je=PA*zGxY&MGW9dquV1t z)7n1(rwXh6DE)#nq2=fN+aoDiFS9#zr&G)n4#=B_jgnnQMJNh;TLDj+cq%48PJh0i zBgpCy49+1a#Lh|hSKOdk8$cg&vqD*@jF(jR-FI%n%X;!;J1BMbXOQ;-7L zsO~RyC#tEbeSli4)*sY4&O!F~_kSL6-oA|k+O*@|%74D+K-9)bTFRjqkHQB%>rGF_gV?P}l9wjg3Hd^Jz>w&@7pWQ}^ZsFcnTk$-?% zLYYLEs16c88*lXsG1q;gp{%K3>g-j@tv-8Q5mShi)iV?Qfcw~W-Zv{xH@x>ME$R)V zTXBF#sb*JKw|+^;pm&YU_yn5GcQA;5RF+29KBGLM8?F$cnFaE86dYGRhQLldxA19EsJKA3PRaaM2 zIbW8j8jIj`th_|WCHvBt*fwJ4a0=YCUKiG%KE>_W>4oPh1kQ1&=CBwIG~-6*9L=_p z8?}nJcMsWjoiLJTvaqu+WT)7x_fsAqe5+#eva-+sTeA&>*<+Ug5nvbn`cLJe;}aBW z=LNCKa7&i#$!xR@-%0n}22`I?W9?frrZ=Du!VjHNER6~RcA`g(iis7P@AXJFLBF0FV!k#X2O zY*!oUmVdc_qETl3?`<_uj!6DxFq)_nw7V$oIP;Mr|09~LA@objyCyMiwV=2e1r<4q zpOV^l*DNH6s9$b5Y_%(hViMAMi2K)XNwJpR=~l&-=YMEn!PX;4kAaDq+jy%dRpObP zxiM1j*|VQtO&lB?Q?HojfuzlwM=j-!y_E=BTwp&lHWVbiRBa`P`OV3Wo1W}MN6&~qek<5d-FKGB+ z9SI$UOOp1VukyGKQD@-Gw(BzA`#hRuSF_a$eJY-^JG^30J}@HDCdO@aRSBv>H!wS##vBMdUX)Qs`|P z!FYPJ?V2klB?sf3yf&RT_36JtJ^jw89!l4un$v!Yx809Db(Em<6u3E2-+*Q6pO6hL zHEz{~>IMZg>h8LzJcq+mAl6Jf|6(zPzaHRaVQ)UoRn|-ofRac5b zCF}|h7%$;+{OJ8>*{xFW{N$HPJ04i-_CHKV2D^6=aP|C**DBDqIAdtSt>1e}o9Iio z@2w0HwuP!h|EMtkT@8t&U^5c`V}TMrebv&vr~FyP@}a{4dJQNC_Z4=L88dR_If+iB z8DW67G6j0M<%Y+{$NVBXgN_B*ojsQMzgS=O=!)wcQQu}d#`bu6`?S5m(xPS<|IA(jh_`uM+I#`YZ~!O z??mc1qDS9Baj@dL==@cfY8I^@QU?c{e~`i@h^V2g(d8C&$h&OS?9qzDOkCP-&nEYH zqLgpvDTtb~6XlS>*9?F8dNc`2$yj%~*PA5GN@1>Jod!3$R2OqEc=BU>FrZwCvi~vE zkgaw{J5tG`U#{o_o^`;4odB6fKZ&mz5+kmx(gM}pR~+BgUGobxt2RDRc$ngBWH@hb z6F-Et&^pEXYKGbAUiw54$%p4p6V}-MgUg1=pmjAn7&JUn5uPr&+4_07X<&VX%L5h+MRlrjc^QqpZZr<)f?jMqf53z)Zu$!Qmb2v zQz!}7rhTSxAt13Yf7@clkG$=)(i$}~BBBMf<7U^aFPFdNFSiilbZq@3pcTt9fAN;P zIPVLiosF$!)Dvb9%%i6lzrz7RVD)st;-VIAR;dbK_Pv)x| zLXY`c;ba;zF6$0-5A;0q&BbRC)JAYyj1vC!?J*#P+T0v^MWhhSfDB1t~0s4@6)3bOK### zG3fZ+!-Q7A=Ax5=#Jv_22X3Xx3F%7j@4jyAY+fN8D`zME`9nZN#~rcR>27XeNJjOXZKR!DWd*5( zE$ffmH!+t-iN&Y*-7uqrdiLoW6bIjg;BUby%$%dXkz=mf#6m`Vjni8xu~}$DI$Yf& z=lDK%!uPsvbDiP5YG3a1bvuiHxG^Ti!n}_RFq<8(p(Zq8jhFCWvLLbCZd^X(9Ck0{ z)w>Aa>fk774b_YP*+=_Er6>%}(&D=E0Gh10n*Euf?i>QYdQsl$ybzJnPV4D?MyFrk ziK-yB15rJdM9IE5)};(9=1Hofs+;#qlGF*l#(l3l94Rsbn_;^@;BrJ+W-a2r$NU8& z6lIKETdON9QI8Ed9VGKU;FZ1xjzMz(6VfegYHH#)X+ndh-|Ay^+!`-eMs@b^&_Xp7 z%b$gS3q~9J`1F(sgshFXXxAp2HV3Jh2uJdmBkImj8gg>XDp>C@6S>Bd7-t966eu*~ zkYiRJGB-DOjXwa1y0w$+2ejJ?p-X)A#}H87T3HJi@UP1+m>T`L;KZ{;5nj@aEl8Oj zHij7%%9$(qO$hurVY`%_56*z!W2cd2V17_#T=XDjAC%zy;0Bjll{%9LY{N>&xuEx) z8W5qny1Lk;q*pP7OXL7j5lKP}A_)RYm#vRsAwzYO?;3$#jJ_c~&7>@xLB)H4--Y_;_=y zG<(*}d?9HS%af{)v$6B6c)pmNB6X5_KsJv{R<>={Yz+GB83|}(eGlMp2QC&voFY1> zjSPh!M5rd&(NEb3Nl4JJg#;6#D8EN`pD3cp5yp%h)BZhAhir!#z(9 z#gaVc#$uJr%D3c0KVRRnIl~5jgE~SPUJQN?kD0%yJpX}B|DA0F)&mk`)sFjQ+`kj5 zUr&Kg7ol^mmuYN-JD?#XQBY#-z&Jfu$MWT09|;yLAMoFbK{%W4?e4~=QxND$#yW<8lXt|U%7Y@ zud2Fw5KzEEKcgwKvavDUxkGGk623PfE%9biRTwJC4%+2t9P&a2rj+24tF5h#DD`z$ zN3lie0--=zOcy^K(#J(eMTKKJpkr#vTwqwYUK_mBp?_aSD!3xY;1%bp+lCI%)m<_G zDphp&@{*^O!QuEj%B3D8tFkgu(LL|);qITp%R6k1f_SdEv>TAP5BhyvRWZ+U2Cd(* zAg%xaA>wy>H|e#$>Azy~f4g%6>Ch#7@)~gfIbjg%d_S?l^?k7;1Ih7WVSo}Ej!1|M zu+w;--$#MPN=d^ik}MmA?{xfA8R(S&Uh#Ni7bv4<*E*xWQ@whtEZ=6g;AJcI&_Y#c zWcDsP%>x;T=+fjpi-!1&HD~{B^atD|j%Fw)5=_bQtd1r1_0!oO00Nkcaz;c%Bs65n zic|UVdc*uYiUeT){EyEHF^XO4Z{ECN2)w-R!<(&G{$}g^XTXsO+(u85t@5ES`26qM z&5!I$ets+9=9MwXU6c8UG1!liUFC(iIXS;0xn%8$GkfOGA;g)J#qyex4h}m};i7<1 zB?6O?mwyG?eq(&B)NHuusj@QDi9m2Xx3pw_;0Ub!?C`DNRce}9u(#M9JNCuJ#bA3d zE=sm&<6)ozHDk7gh7blX^~l~e=JfCH@_+u58$Ic?_eZb`iK7jQMXK9+b9bW}KqC}0 zzkbEOR`}YZaO+SlX*Pm?n)7ltl|3MFCZ&0Kin&ZuG^pqp>N32l zDT0>xyhimA;10HKm;uFF)nFIsl>f|IL>w|YFvR9xaOmvruBwTMBvJeKz=!gi{Jd~meKeCwF)~J^#U9^t?ZLOUsX0t>rO?hMY zaATxgoW=?l)ESr{O`QefMeOJT(KMeTSg#{>3Ia@X_-WU*?9m)+cP!C>F*vT}JLZLj|8uxocK z2G_u%_x;PtN|6p)=Pdch`@m9$iHoZ{8*drD^}76DIqCmd`@gcIFmN5FEr>Xi{QjSK zw@|RH-rX=ajfD+^`*ndkgH{o!5uR>1X=^8uy~^ZS4AH=mpZ!?QG{$*-+}la9@bE$a zwM*vaKnVtpnV8el)8q9Z;C-{usCQZj zg-86mBK^;(pl<+}jRluDv&X-)qoNd;*$WA$`Hoz`Y zOb7Ri$V!851+?Bt|zDy>1vEocscBN78MQx(E^3-pY4WX1xcXlrb0U}kP<0;U*? zeFSilstq|{A;9xoCk#RYZl0N&WZc@uPO>olMR(w}@9I{ZGPa!h4}3&o$T@C}0*wX! z?<+1akSxY{_?nZI{#l-30X+KL+M4J=SY$t0 z*~r}`^rV;=&;>hou-}5&pnNzn)||w<8Oh1movMBJ|9qI=p;KgTcFAR!{wBvGcp-7D zxxmfSRso{(>Ep*TFJ+Nz89%?eVJB?mdl=V;b*>Du+&(QLFD)&-z>)zr2__jofvUZY z&3#+jT-4}LgB&n7iTJMd<@=oicYlAW{;+LWWfVjbD*|X$fCJ=unrqfK0r@I$Lx=HH z{h99!JHP?T1FvZ4jNGs)tYJ9jX3tL!bY;TOirH9L z_>+dz@C}78?o!~5>sCD%3%AAUK*t=0koo!f8KA;GfxwNMl+g_g%3^wo!Ih+mLO?)p zuQZou;cd7Ja2c`t+9mc^Pdu*5cQ{Wc#zp0gZ+1^tR})d)c-nlG*5t~2*&5npLG)f zg#xP+-aUT|S=Mod_DZcDe_F=sK^^S9*AqzUKf&UiXsR z?*0U(YVI}yHf~YTS7S4Smas7<9dV5^$VIXhlYf}feq}&819RwvhRj*PfwK*S!EW4>AM;d;5O{lM z#-& zYZxU3#VcT2KEFPcEBREOY-x9Qmu{mN0#s*pAc5`&AsUo%_f1VdouuXOQ=||x8&9j`(M)7kvB6ernajmX?kvC5(4JJ^LFB@+>Dn7s?KlePx6!b#Jry$# zoD?QPO|9XE=|K3 z9D`TQUBGC}BQutC(RU9`cu?iT2N`;4V$q#aQ^bt@=E)rst}292&qTDMszwrO^)}wYQRKnal4MD zHuG$u3c5VR_(P3W1s@@k)@ExPasQ+G2!eWBL`e<3#tP4sLTf?}>}G&+`||TA3Aqt2 zr~0+XpHvcX$?nRQO`WkzVVK;!&8G6y8&;=msif!Y{w3#xNYb(uWn`3WSH2qA;UVRY z4jOxyQXM6yf(4>_AN&PzB2Mq2iy1BNe5O(Wm>xg3SsjT0rZ#?EyU zK@29(QyaPOy}cFPo1Loz*#)LwDjc$gm8bgL4A9tZpBHVNg%>SxnHri8I^SJ-F;(dW zK2BM`XPNWjku!z6!77ca$v1Pq^_KcV7VI+JkM{L96RPN`9T%+v;U=R!B+-~(Iaa2S(@#014YK!P>yJ6l=bJIr&OzM6pCpS^n z2*4QVxAvzXT0$W}4A=q?kMge8*48gyVnXo$p+}Xfhaf56m|Q5MFo@IB3pLsi7c$<+ zs90E90c?bMt8g)7s*{UDcf{s3=eK4sR!cRacGI=C*1xLNUg+wWhqb@RymRU#Lmny+ zN`U%gqk>7YW=mo7#plR;TkcZc0L#4%j#9giXI0q}G*zSNl4E{&_US`&Wa&4(L~`A3 z<0&}Y^Glu{-ldp?55zlFG4K^OU%ue?C{Z~p!J+A_95#`({MQv4#eU77w;fU3vA%YQ zuazxopTg+!(K(frvS+H&OygF_n+tdom%J_>%Pyd0*&TTY44#31M9x!lql_ z-f3SnmushwGV+f^$<+a_?)A~^CUr1gDq(pTg3in&gq@o)^;$AI1rm{>yd_uv2x5j> z;vSHB$l`fHJ$kmO%hHPRAZL(K+uC$XVQusPS0fWRR9JH4e-~PF`Z(9bNpawIte6xk zKwDAyJw-p3dD{%{#!eZP#X(8p0 z{a)!KRa)YRmkl&INlaQBTj<5aZK^6*f02a^+gC@Stso++k=sA_sTNM0^#z`H1ZrB-)5<%tr$0trzL$H3b z-qxX!{?e}Qv7+9BsXg6hgXv{S>*Jb{3wETQ9(^%d`Kj(!?498k zrS=w7s;b5j7D(Bi7P=Do3FnCk3)>p%U~J}y?!!v;AyM2?C!Xu1`TBNgHjuIR${(Sg zkq+$W=$M-}M*<#wky>W%gk5f9gb=*Z<{AVd7lAJbd!7Xld;q$#{=}W_wvB+PC`*4h zUClmKSis8I10V!L%*@c&*VjwJ)vr;{$?NNFR7-}JnE#4)ACy5>*4D*4%A-_&tegW) zn6A∓B<3(O~bTt5`^8F3f;7PIEE~I_b8|vU`ge|=nU(c$0_m*rw5n}BY;nF$0;f$-R;M8G0au^Kb*XKz2dX7(9GJ$%pbqvQ&&&3s~ZvUB;w&( zaWl+;B(Aw>PSmHCIMWxyO+GZ^K-GnXr06yS`6wv~iMD}d{VvEUDa41DCtQ;qz)MoS z#ww0^01pTttv2}pOuJ^LKiEI1NU?V&{qy?!zzG}>P z?cITaMNR&G=KV-zPkKK92ROL7L(HHFH)6Bdw$dijYfSWnuu`S`#zS@UUZ|j8r(f$A zLEsOIi6Qh6nljfL?i1kSE9Bjna6#|)@Ubc3k-J6Fb1>DdQdLwTfVy%UqN<~cTloFc zz^Ef7y}vG2VMw8%$QV$YZ#rb4-GA_a(F0;_zkS)Ad9BEG%i_I;l#BvbSFH{`Bt;1T zn`Fs_4}A82@U4HNi16m|y~m-B zOQ-O01cd-Y4KN#$vA5^tHK@MJuvMytBN7C#GtouRhr#Y>z2VHY8Q3>(#iMGQDadi) z>k*>Gd?qpg3HGY@X`KO*w6k4v-S>C630PF9BVBf`$nV(JI?h0`Q6-I8FYG< znqYuGY=6e=v03SVahhqs@e^uU83$2jW5^I-l|KO%AnP5A9@5|#j>#@N{_$n-tN&~? zM5~^~YUzL5PMmi>_m)GvoIYiFCK7bVBiSesqUMMWvMV zq9Z8PWNz}=FAzhO}t1(}i#%5E9ZI#>oax7=INKim93xG&&U)z!uE>c}s= zb?X*mGCk%;ZCq8lFL|*NGnn&OB}$fr6vqdWG`aqlG)+<`B!}sjSbOM94&HGZ`AcW+fBuPw2%NfUNI_vWf^SssO9`;fz($6hg|u+ASdc#hboYjol$4`~$BzDM1&&$J zPe@{vtpR6hVB^pRxtPt?O%NN`s!s>ZL+|_g)=tTZYol~Y%xw9uj%Eg9{$B%Z1iXm~`?OHW%* z0-!i1PZwCtho0}fe+emIV@Q6+np9C+JNaT2$fd!s$Tk3!@FX00w4Kuk!_9Z_z}t!} z0ah=c5t(T_C=0Q(=w|f&jaj0mbKhN2NlDE`-cAH%KjwR=loG^Xh-&8LD6$d{0T=E% zx&r_Qr=nDNX6o#Fwg0^31CA zF$PNW>g9IZfM|4j$;e}4jnD8w!zX-eYloYm{S`q?(GW^?3m z1aXt>?-*ZY7?rLD7DkVGhu@>M^d+SEQj50Edb@_QJPxA@Ae;SWiqz;dRZJ7Z^KYk; zle~>eddl8qOZYlJGx*x4l0;>yZ;I3puT?jVGN+HAHRTBz%M)Y&ecP!bBz#I-Rf58{ z)RfaDKubkg`|i@#RwMvJBB+J?UWi(9%-TSJ7fww3bq^WzwC-_?SzUL_IvzRVlJnl> zT|YXI!2}U(=ZNtIsxmjf=SmHIE!DdjSa`H?|CyZFYyTC3!WT|3y__X|85Bzr(DrIS z+~^uwpCYkP-Np#Zv2U$xlctKt;B9h zrbx0Ls#5e^`T(l$Us`+uJk@Y1wky9*tu!ahzO*DR-u%j)&lmm?ZhZ2dJ@$?`RcMKz zykA?1biGGyM2#tQ>oxm}HZ-orcG4~kB@wreH*oDE*NckT$@-GtF#9Oyc2o2Ezkyz9 z1d1oJKhSc*r}uNcL8@DlCXggP^>o+i0o@xGU?LDSx5I@pavtm;j+OCOs71)4^`ORs zzV2q+cM)OX3Ju4S$$1Z;oQV0`d_u_>OlOX12L!no!1XEaC`(Kv2Xibw@oe;ml;1G- z@Hop-Upkr2U~YyQJTac__mJi`ti!2pQol<{HMhMK7&Abq7_K7YXeH7F#92@IgcIp= zc~H8D?{exSY#sWq?ysU{!bnl7OqBs$rFt3Yj6p&FqUF&a4tpld6sPhrtR#-15(ldD z3M#6oAaqHfez~)yOJ+XCc6g!$w;Rt9n>vJEGW!(Qxl#?+YG;?H!ToT5z->#%L$1M) z87-~m;q%TClV|&PXOB_JBj31Ke$PbXIJ4q`D;+B~+^R_9LgcL2458m_O+KF4N~X0z z={%Z3wLOuvaXE;e?B0SZrH4_dV6b~ME&Po?QXnDAC|!Ypv<{*JXWKxnzyL;Vy=Vq@ z_{P8=kE~nd0SZnM8UPm|Y05bmx2Y4KT9}KnFDxuXhM%6>0RV}vVs!ardgVdWO#^LX zhW;bY@Pv;iyE&s*=4`iZH=K0HLyGnUiKrJ=n&xS$xo|-Di03nyHw&zz;LpryLCQq{ z0Tk~aka(cK|Nd=KslPXJCIcB5m-3%@>%VT;W<`Y58X|NoQ_M{cyE**^0kqgb#bm7U zVDIr0Aw$Ck{fRbTrnTwb4vvmyYHFtP*81^h6Cu$?Mer8WJRxg0rWWp$B8T=>W7bK} zrBbhs<92tuO6JaWByrODFQg<#HIg^sp?3a{!=_!zB%96JOA=)_C(HgtpW(R~`!~VQ z#byL5p}n6fp&!eo?e5KW#Fma<-da%_&i;3?$%5j8!J$A_4Fq{Qc+$Xxu(6YjDfI)) z?K*=3tthWp;{fdCRR_vfH_X#_E+lVHPQOFPSoh_VMhe@VZOp{J^PzjBRmgC4Zk<(M ziOqNZiO1(NSBlGK0jsg?fD<%-uf%0!f6j`NUmN1gOU3u*ZU3jCp`qY%+ilPd`u_^h zN!-#0zr6td=f+0Ba4I05OI@Rn}oG5>B z4vQWL*<#jnBdYJ}9>8=5%YxP*X-=U+WuOr=ek+uO-F$r8P1c~+(KM2VIn z@Qb5uVCR-3r*y~)yu#cnbmv)~&#|(rMh)N8iqQ4f)4z}CkEXl`-Ir0o$gT4^`j(uR zMSo4<=hpJ$xt3D^AGPw{=H_Mr%RU-_0D=0mA7a^;D(mlGujvF*a_thc*FrOiuK~Iv zQ#hh$M+KP4AhzmqZ}j5zA5B{o^8%1#^vJicIYrtF&nSeqh4!Gm$>@pd+VU`D=ST2y zK*kK>Q{g$8hW>)O{l=*Nx;i9|QdCm1u3ezLrA3DBTAzi$?yzjz-*6*huQo@MUO3#PwkC~--y{&@ZelDywei#v{ddBRx?%-H*mr`iQ zBEmX=^=JAenq0M#)Jm{`nm`P9wA!tAE89VD-|;W|(Zey>xz1kK9BN{)3Y^5J&&4DL#QJPR(u}t)_=2p)TD&_b9NCf>1BMnFFuPYxxS&ebCggA z@W@mZ#K?#i_JG#MUp^dwO$6==ygV7;&HzXCnWTIOglvC)4WX);I{{GoCS{iqBpF4p z&*f0ky(*Nm=}RnRrI#fX87AWVxvr3TWqxBYS$u3-E@}%j@4>5{#Y)07@5uLHP5M;0 z1SmpeDd4iC?1_QOHc{e?ax<(%d#!o;Cl{8QkL!W}5~ycTK2uvs0Kb8H-NL=qPiD|2A*Fkyh8Bp7Pa&~lXpycN_VNgrs`DIfKJ+uzy zb&Y8c747he%m2B}M}`6*$B&B;xAUBt*Wx+wE4B4g?DM6q2Ue@?m9~7_%^TKjH}ceu zD*}ul{r+X`zYE<5oC5>K-dex*_SUqtfWV||>vKNXqu2a9*a0p_@)K6-suNVdeoZmw zMS0MVwzUJztW^~mIR%CA;RL6T(v&JSN#}#x%|G>{!i)#?Cq2a+EbLvpt8%tIZ9+sRyW1XS-PMfA(HDO&n=4 zx4qT*a{8@v>#v_>yowGQsUO@vN~3f=di03j!4?=!>(*R9Ck{n0$-7x29vQVDk_md7 zqLP#9=_jrd(SSWMS%cAIGa(p!r{6Mu!;<{bSnPbSbGx8sr1vd6kWL%2!tprGZZc8$nr#~q>%(7Y~K)&V*X;bVJGJhh3A@p>%Wb08Mvx>@KotRj5Vlu2qSSUI`CT@-AhVx71Z>FqT4IK2~`fj_Ig)$cCrxD{yHsN1`|b3YC`DVJb{(8i14MtnitSsJE-LhYS}pzy)|o)e#)1hA#H~(`OU%<0VVj;N%)OTTKFDzB z@25~kNZ6+S^%U3R5cF6BeDU!%4~ArP#Q_nth*T9dIS);+%B}Ko$@9*1JGFSInV|5* z4uXh|E($=V<)othECbpl1BY;J+xS`u-on3eEG(qkwXcpRpw~|6bzEXh__>K5aYfdH&(8zYg|GVTOCe3IoXN?onA8Ap8k8`q>?>gc}l$KK|s9GKNQ6?3tKtsyf}kpqH!BZ zkUbj9L1}n$+}T#16l6owSF=6Z9$c3r!-oz!l6K?wA=e0MU?Ax(5;^Q{>`zV)W&HE% z08;1;K@Raz6jP6Xl>#Ibz9O3K%q8fJ_G{`H~!Z6PfpP>#fI*Y9P zGUW9^?a6Xt>OK!kh-+X+D=*vZmlYpoiBE5dLUdDWCnJhd^4+__x)&XXa^G>aH%J1ZYoP@tW)6SMqPA)r&vZ&|k zc(RyxMG2y%>>n~kPgc$+0;5zgl7leEE6FrFbx~;9Mb|&klWb>iZf|dY;Q%Jmk{!VN z06yXFvEx_vu{3Q*3%~y5)#Ywco97K|Pd~RHAcZC;a~wFB0sw(hm{g;hAM+wDo9GYv zccMN;g+xi1{z~&rg;t3))%h9FUcOP6Cmi_iA;ApBm*Owl``-o^`D_RM{<8n^T(Bv0 z@2e5_8jrx6|1Qo0=Xi|MPez9sqN14ZI6N3WWL+&bO)22t$rqejrszrAWC)v_CJL7% zs988M;~A)2(TuO{&quD+xTNirAI}?r>O(}+FDV1XS%{W@KR0N_^|n(&VTes70>G697Oz zK}dk6SNv8;m&Km~b|53Eu}vo9(n{a&E#`;@wiupXun3Kb{w6c}CZDk$%wz}21s#uh zT;<u0PQWT}47&*fC4yhnkO#Oo?5f(qYBIIb<{GeXh$?uN9Oh4oR`Hw&C;#ZR&UTzPxm^F#?BCq=|B|x|@nPJlPwg!=CDqtcykr%u*T zM{2s)oTW8Ch3((rvaReeN259Qb!Zt2_St=8A@zQOV3eqV+0nthJ>qP{^pQjm?O>bV&|WDLZewCGS7#W%W)OPu->>^TZSH2jD~r}Kt+?nC`QrW_!0fN!%2E3WzX zx2Xr8V9xl?BDHhPdMCrDc6J`My=2m`&^Ev#CcEUScbhSIMQvSO?yPn3g-7gbS@A6& zg7k;)8lQ2o2vJ_0mIpNN>&?$P=-RohJ0lH!cem|nIs`T_Yx$j!cP8C-3@WYnIdUhQ z=4Nq5sVeVP)={O_4J~iB0_)`ygq+%x?N#7;E2|kJAp>q8+9(#<3)KjQjttwExevLA z?{7TfomVI?FZ*feTUYK}yw#mM>>O^W;P+X-?AgyC1{-WRN(e4m2o{`KQT_^y@Ilpi}-iU`JHqVCz{x z)wB1sV*%O`<|!A$kh5Za){tit)L%Vq_5?e2bAoDBVN3mz;oK<=Y~NJNgmD#ykYOJN zcjp`Wzi7y>72X|u{}|y>FPsk_;7dE0d)&{!{Z`bw?q`l?0th{1HTTxzN(84Y(CnS&VejHe$B`Kr420<)K#^yey4YmI& zB`s`pIQG^oqO)>F_aj_baCb6XWoYa1R8q0r!;gxQV?6vbl&?FFB+wh2_rLkp9;hkb zq#tU&!)3t{=uAsxzd(3jX!`5h)0~)fF2;!f_f#~Hq(~Kged9TDiWhWBK3f7RW|eWNum@?ezaBd+VsS-)wuhKyZiR zS}5)nq*!o@OVQ#|pg0uw;_mM5?(XjH?(S~yr*nTZcdpEQ@4Ht1NRzCFJa+ck=RA8` z{W68uI9VLkJt3)aT8c&<-f1pRld`OMmuN*l+&OreDwM)kxc9}p_d9Qac+!yqQ~^qH z_Y<^nbgB`YH;PY-MkB)AB9cl~UMp8b4y<4+`436yX)<(~Ambt?N;ty~9PtVtlRD zMttcP=jz)g*`EHMq;TQ$3TgRpv{-#^T=D!*sgBOw{i-w~k|fc2_Va|i8+zA6{(duu zrSq}7d6rB{nO8XYoVJJ`NIrd`+$G(Od(HLVbmvPdYGB^y)NQuEKa7s~k(4-_*y~6! zJ7`cO&oqhjI9GSl)*n1qW&_K!_!wW~a&<1E{PJM+Bn@@4|6>oM@b4pd=X$!;@n+@i zzZ-NZca(v2SLk`Szi$?g9~5hknPsP;J;Mv^bf{<^AL!*b^fIeh`0FuWcc@S^T2LLC(;$UY?PWfk;TgHb0vl0wJ~<+lYITzzN4jsLUE*bHbz^& z2bm;lWWfb$ji01ra-JWB5`=@H~&5sE^ zdqwabw5f@mWo2}(=V2Xo0c6?kt~a z6Msk*Ewy%5=$+ZP4_%7n-wp)tL?W^rOVLUETw!maZCjYd*6w`g;)A_@KU-0Lu)^%g8xvX z+_-|?`0{k!w`K>oXNHRF7g0{ADYe)5ghOt%k>QIb`)!=S@WNfoAKLf1k<%Z08%HJa z8f`Y-3l^PoJyt|rK2||gFRxqk4zq@dcF(QP-geny!7blwXq24Po*90Nj?KtJ8c4=S zpe_Pb0?^4@eK{WsMqywu45>i)56dyBk;hv;Ys}xH-A|pjnvACh+k3vcgGB$u0}>2O zGlY8|?LU9ucdWbVr4>_pnnMRykt%y9g;b20Z@gBsF{NVj6MIlaQ0c*_A-%Em?Sh`1KNa{qVk3>m4LBmIDfP7aBRD>9=zR@v*SDv1 zz16&IdMGai=kM~QwoQqlo5DcOAcQf5OaXR4*2*)SB=lMlW`` zgn6reu6G^6QMNuk?eqR4vD+0ikX zG|~9al_JLx2B{d#bI+c&`fa8*>c|ln-bLdR2U<82lb#MN<%=r`3Bu@*D_PyJ68{{c zzRv*_n;*(`$`cNOzjxn*iz(&4ba$+GELmP?7fFP6VXFML5Bwu3cfdNaqAA5_A)ZdC zZzaF0;qyD4DM4lASbphJCz3u6{Jw^Rlk%jm$>;R0&m^Nd0I6iZmu|G++Ws5*Zj=~zm^v*-nmP745P%yn)qM1LLh_PF9da#F{wk0T- zL%?1bv?6iK1yaSqeS4zPi{1AK#YBCgvXv?K>1n}%~NJBt!y10D}zj0N{ zY>E%ISWT#JuYOL{ax<<)Bz&Qqw8n7gTQH5I^7%+~p^atGb5wgl=9J^) zCmBZmCudOqGP3wlo-F?5S9=K`X`EO1}2ud{KwFhOQzfIhKOy3)L5GsTSx-9`H z;w$P8+Sl0zPyG&0Q5W)AhPlChZf}lG8%_O8nK_^C(PnRN=~4ITqvDuP$OB!6r})rG zJ~u%MQKj9NbdPKCr=cEpSm=FfbqF?7T6y%!nGFMHs+gSA*Lyaf%L`!M=Dz8IqG2-j z+LF48CRdFP9tBHc`4D;p{|FK-$n1d2T4kFY9G`a+ z#*{miu_?+A*WzSy(nsu?W3ZF(QQ@AvIt;6>hns}4FA*45%q(>JLb-AzZE-x!kP77z z;xps?YBgM1?mYQb@K=cNbhfpHA{35z=z1gr6X`W-Yx}U;z_E3*gDB)1UMdHiFz_H+ z8n|Acy%Ov1k>2@LgKXz^R6uW3I-A)9U8Lv$$#CID`?pAn9Pv9Byo`F>9kez3oDv zM0mt>;P-L)RDw4?tDfn_*=OlEB=KV7<67$#|95Pwt?ehnRO2=sp%!+xTt!aiuom0+ zaxL7*>e#Q)RD&h!I~vX}_L_Rc)%uXaEP5`RS>Sq}FI*y!!vgM?B@T5JivMod$^Q&{ z`492`GVEfckl2;u9dnunWZEa$gyE))<7uW!#DtY{>G08=^!7W~yJt^JyUQqpast!k zYDLk-ISpB|lgyW#@uJ7ZQEanGRT3SCt?a(_t>e~>6M;8B1Xz`r$v0bIS#%fPE9?A> z;I~wYH9TLKJ`D2N*gAakb7t~pw6f{sG>9`Y%DxgoK_iv{@_b)cThYoTHmV+hpaOyU zM?>{VOLwqgRA#wlkLpQYe4tG&#y5i_k&5ywyTLG}lSu{&u@dnZlOa3+z>?|?2vW$* zq%v{zaqP%24jkP7`hY6@y48O>W&IpPbcusney@?u2$rDggkqKBwqxY9{2E;RZnlR6 zGAI1+oBq$q{jVSPm}%hoMA*>(jI3!IkjOUig|!$dCKQmAzm7Aydar%p1o0I-S{#P+ z?N5%e`ks4Lo9=g!%Q##;)#J+#U3{Lcy@A8-lzIVx$;Ck8;Uy; zk`x+ayqI+_&TJ5sZA+dWkQ&lE4AEQ; zXy}Ea+ylSS;ZE>9AH(mweaV>}G71?fp>~wb>JhMyUrZtf|BsecFLd2Tt@++oXqcNW zh3mIGE?dkm7qTe<%R$<|q|FA@+baHf9sSGaYm{J*3Ib^I|NMYV;v3Dv?}=Zjvjsb- z4Xemor#!CW9t7=CqOb_Me!mdLc1no08syY)9I?J78WCAZC)6{aSk_bb0-$BjZ@=9# zK@R3w1Qu+GgzKP%0n6h1xa)bcl)2i2;@~`GjM`ewY0l3(q*YO_9#z1N7Qt;No7kWHk`?ha@;JD z^S`HLFCO(RC>^8<;*K-v>$c1_`h7g%;usY5B>K9Nay8o;3pKhoZ*iTF zOrCr(MylgUH-u7yL;CrwZ4lW?uQ&IW&rVF06Az>3BNEKmd0>4>dMyQDcx5r=%vpwf zvk|K;L5fWzRSxb5 zEbYO^+0VZE(b%B$f+oWL$eoS!Q!aOjZc6I`sJrgb7p#5i?)d@IJ<`VinnM1UA^4}a zOx@oQMJ=`oZK0`w_AL52JwW`aVA$vBwRGOMApm41HH5=uQ1dvltJU4fGqMQ?A@Z#V zH${`pQQlN1&!=tMaA*}$sj~95`4V=q>}~J-yL2@Veg@vUEUN(Na&^o*A=g66D1)IO zZnF<&=ye(<>(vv2FrOlj*R{zV+_eeBI9o;-Gd|Rv9uo|{7GQE zim1d=PURf!?g+fu=F=J`2V8OEB!|MA6225&BspZtfs==*Z@hH1NcnctLo}oQ3313w zydOw77M?zCp2xlx-aJvS`Yo0I(2)pL^;UFq5SO8LU767lYs-Hj{QTJW1=Rk9=-abi2Usn!|%_ouhD?uo9ST+v=GwDCJHT(e!+OTdXu!FVciT*?3SE zOIN;Q0K<}(lK;azq-7_vLjc2212TksW#j4Uu~$PWiJp5P?jkK+m(^tM_#BB1Il<3= zJY|x@yr_6mcB#cqH<(@JVo_G%GPb}*hw|5b-UUGq$xG9+e7ZFKtvLbL<%H4O7h(JZ zG$kt+2oGs!rGT;@lFOG9RCZ9Pg<(oZukoAg71)7n^M^%L%(}CMwOSG9DtT0eE)%uA z9VYB%ljDK)aj%{vTJsQ}2QqBzPn&PgRy5TY&0Q-Az3Y6ox=u{-Az(%Ro&fyQTK=DF zoI%JPayy1>*cMV4jKhpiH#J#zi)|VS>)^G)y=HDkFo{bA&|{;Zfi*OOZecg^{#;kLZ@V5;8)9Q%hrOAE$-OMznE|+ zH+5~x9u(xGHq6xZYkZ{AF5)Xvxw@&WeoRN0w{7u`y|uNld!OMZ=joMk0vQwrCWqT{ z-D4OTZCWNhD$Q;2pT)FVUCtqFy+#Wia{D@q8ue(;y2D#eD1^iH6{GT4J(#39 zOkZ>e+>k9Cv!si(gLIu#uu%W@@-@uVP+){3>~Z8$?C20(M0ZNL*69OT1oIDfJEB3} z&s)*>CJxFsm$|aOzvkuZAXw!2X&-Pew&_)ZipXK!Sw%xRB*y68FewsP<>#&6rzOL6 z%^@>nt#Hp`xk$+}{ zE7g%|W4BVSdE!IMEodkaht6C{smxS0U_S9B&1+CMd`dFj(zONIfmK$&O?qyD6s;+?yzm+uhi;Wrqo&6^TP z_N*o;KZ(r~f`4kopuZuV(tc3yQ(xeVMP=L4z2xi9hdE?ZX7#iDNL`Y`!l(i%HN?krMEjd9-Wm$sb@u11 zNJmf6PF;$HSpNPl%e_JW2GsrYTc3h~-~kvyXMYcKXB3TTW^uhfP&ihecwb`f$aAt! zHsnNVe+uE!@-U)X{{!R7IJdW2@FnyQ6@$;$W;OcJe%;@Hg<|kB?0OuAcVp`tSM8If zXIWiN90!p&_9$O)sj;LE)`jg4-AaKoVX5`D0%f85L_xl+R`*jwQ_VP)eq+yl5 zg>T=JlCIXQ`;HEmbtF5N!oh8DbG8QSQRy{b<(*GtJ2~8B54*lc;0YL;^j`N&Ej^Y}Nb@mJF@qWt*yKb^EidF>uOa1h zkyI2uy_EE_4CkUM4187@U{g+V#;XQ(trYKxP)a=9m##N=@g>voD%7&81jLvNw3OO% zywRO?qL|gWczddmEfqh6l}B@HkDZEgt)&4(^8j)t3g1><&9eL8cCE`@a{9gX_K44kZ0%8H!|Ak!3j5t> z#mdk&SsNasj!ot0FEHjUHE&gXofX_^1*)JUb9jH_-Dc0y%zZ?T^HLZro;w@)S+o*$ zs8ocj{k508lB7LrePu&0S#yG0dR^Mn`g}xlMQ}zVG3BVbHcLv*W(e%Ky0CdR&}>TeN99KQ=A(Iigi9*;l(*89B zkpT5%osLOPb)&?ceyQIS;szVlVn?-`dwU3L+&vc%d|Nu&1*xYuct>!+0&aa z=~8pzw`|tC`f|f&=krd$Cr_!Lsv#85NCi>>2LP}PP>#xI=^F|xKa`yXJ&^aqJIy?w z`9VW03eqiGI^4RC=@fQmOVk^e zJYyUZl9G;i%tQrwJOKkzV|^kHtM|ZtQ{(K}*tUBO5~4P<%JieeK|h((OaCC&_M)9( z_n@sFksS*3H~`a@&D}X-MI+Hq{8|<3FzU3046+J;jsV ztg74;wu4PU#Bj^=V8<0h??iUnMUhe67Dkzxp|${u$pf{pb%XbO;cxx!8KF3UMsmQw zaf?`9Obp@S=J=53hZkr8(F3?lbTyihgF^9PV)3WE*CxmUmAsN@d{ z%94D_eHclD>MtEp?2{R+N+Lf2Ppu`kD1e&2NH#|54r{D_499T6RoY0VeFg7Xus>>vQa@yeZAPc1C0+l|$=&bpH_!v^_6l=U!R{x@MoWW*wL(esI^BRVVgwn0 z>pDStA%kkOnx?dt>PtpTwb~#;)iAU%Uf-*ePl{>u+dwnhyV9K4KYj70qptv|@hKUY zmx%hA1&_<->e4rhIFKk#ct4HHC9^WD#nX|}(Q%|SaZ7=9YHjD7h zao%5_LguP7U^i?yW_mmy%0(@fY$Vd!e4)(>E35opkBk5QB3cxPS_WlcutVw|gSmk! zP_%x(^!(b?qZjyKyC_&K3h0!kl*pmxz%7+|+>Tg zf;;GXi(oa#*shlx;3N`|ePvYS!~}c$x<~UQ|LP^v-sbxM82W$B^ZW@_;4#cS+$(N9 z*sox8W`wA2$>@{*CI-B89AF~GXFIm7{gi58n6 z;BJP+eZ?Szg4p9oB|#|*GllbG`#mvkNzpb8oPCbBQu6;46>SKxSL zn2;&z0f&PjFX)#?g>06Ivv3s&2BDEL`q(L3vy0`WnaSQ{E@~H^8fvc8^iB2DsO6vN z%M96aDY~P=3>vxBszW$#LJe1E`Q;A1!~T(MD&~te3~S{j((JX<{00=TqxbvVo91y@%19*qk$FgW-wq`=WHG*qP4Z278FaoM`% zPcelNL`EN1X%D|sg^ur$ayqN?pnb*5>s9fa&}7w(Sn}T4oZOEC>Hl=9z)?edp@w}+ zMQ6+i+29HIMx-sNMX4~sgQy_lFMABY7B>;iKL-K05|#L6hR+kmLBSHRC}B^QD*Z$Z zOxvd*_aPG9vt-R4>tgiVW7J(IV>RfSc}2KSv(BNlKeQGERy)w-H&?^P6mhKy-@6~s zs?{rYu17%KAU=f*H2(gpV;epKNhr!F zG&IB}YHyFd%&(B-Kidf%vZvlWs8)2-Bx@P-ta%smolJAT0d|O z$nbgq+Q{o;=>i(@6B83-%}#cuRazWt&ZseT?XF&A9x+L$9(T^DLOn+x;ekZa`;sio z-U()iXh1e=18Ul!P`^zpPS$&Jw*Wri|Ihdax|f2Bsvp{Ifla9D`z95lxHGw!-};=* zYHyB(Su+*5`~!uqfbYrPLCkLPMycdXXhE1UDAHYO4UqH$q|7E!t6k*eFno~kHlndj zV3SVUZH4AiWk|=CF4f({eMf#n>L-J4k0Ze%g0_$*zSfrZr>^YUzq1L}%COo(n6FOo zS9r=Trv7_C_#X+hBw3p#Zh{vRnm1k9wI_X&WkExU;Jf3Md{KX7!RBH$k!t)|)z6Ik zK0$Qy!G+dzxBkQb?<@r9hEKWfUj?H9E4H5XPTys2IvN^}Wi#LDpF*V?%}e_LBjH5( zNBYoD`TVf7^`?WgW0muyu9J>I85LOD*8^pm?ZRoRtUFs_t)1xaLVP3~@b} zmy&qUMC=HKH1M>f;{ELB&Q?OmNO_Q(GroK(!ruX+@P7ka|Kp!f9=7B%Jl03-jQJi) zv;rL({mk)GAbr!24Cp)<(cK@RG#X7dlIlyhLn6=06@dQy&4RukLQ%e zyZSwmYLUu=fC6o0H~^LyH)wELo7pBnrWD;$Ysiq|TTK@Ysy@tL{%DLkOGxDK{T2J- zcEyhPP^t;DOALtDGzhY_r`wb^KdQ>AaX}O}wX_!)Vb=*x?Y(XJ zJUpn=na#B&kUbu9`$y}M327cYd--W+sBBJB0ncIY9pwsMs%$O>i=9y=!W z1}{K$jvNpr_&$W0#~PG+N+xACGI|HTj=v5AU!wcD=YL$I=Rt4^z+-_K+$ubf)I*o` z@!B~A8ULI9ga7z_hPt2yUoZz6&an4aVH9tula_yJwU8ufZBXh3)_;f?$V3lIarm}G;_ zfKX!>j;1q2<8p~9U=Th%Ay`L0>Seav@Q#cuW4td2QXid4K5fY`ihhm~I0`o-@cW{5 zoG(0X`8i0DYL|JD1s1y4IpF@w@c%E9;}P>7C~t~S{P_NlAMs8kjRJb$OOi&jBVSEG z074R%14gcNY7ekWt|ylw!HuM(9m^}tPMOlFJnxR<=r5H){bUj?7VG@Q##Cey^MGySYCA%ysp{18|YNnaExcfA&0;IZPpeqiobg)Rb=Pn z@FyvD3ta%-2YE$BQz0&wY`x`hRe(K0ZZ(4ov(MR?#c;cvpf=H$y8@LU4m@*dkl;6a zAcZ*Q`Hb*n1G8aIYqrk9!r~VaG86gPG7ZjVJ89__Forp?PJfF&o(UHrq(}c9kRVQ8 z#_3hG^uS`9^YQ{oW^tL!l_@5k+So=>iITQ~wpLnKoMU&}03|HTogq9^#(PY9@dOiq zMwncR(&yR#t)Ji8qV?n)+w=>Fx!9xCu#n@H&Dwle15(NE&l!KZTF_FpJnH40aJhT` zl9e4J1m(pT(<|)aZ5`cVNWH^dM%^?!4K?1rzgzRX?pttLQa2v;*gS6&xFsLnK-a93 zW+ju;9_)>gu3fu)?zP;>7?d4YqC$>5iTAQ3e_*TY`KY<2Wh{k)vYYCIc_6YJ2p;9Z z!*x5=3nEylwBUWZSOYCfU z1fr!i_R`g+e1BD|GgWsk8gyQgQ?Z3e$R(FY@dZ1+}&d1jEea1%U%QG#;sW`>VQZ8g7`i<$i}?7+IOHjY&P? zw#VsrU0Q%&EdW~*n3cNfdz^+MKYr3d@$F;~jqwJ(c+EeE>J5>lMHK^r7p_9#Kp44h z0aAu`VQ^A#WJE-jB+lz#r2wl+kuFjNF1Tx`;%iv<$A{e^R>HPP+-bnZ6VGpcxW`+e=U6|%#3v@^#hFkCjY zWzNBb&eeACS=nJawY&VFuz*VE4$?%u7R~5-D)P0REa+|}u==q=hX$J)p5EDpg{O7r zxI&rpqkMu`n#Ccfa%JP3^k*z;o+gn+40AWM$zBSl3tX?-fxC!yUw6L%#DHg(>waoq zDhOW;YLVC^a6fds{517kGi)?DCEchVR-Y<0a~QY}llcpM4eWVD(Cm-ObO1h*k$r}- zY{AmDT5WNm+4}HXdx;4G9s5rrq}l44a9Mluo3=xr+s{HJNg5Fm@iU9|=}Pl>rGfM? zyuv7EkW)OBrrUG%_o$;^Uk;CQ{K$rXRDO@5M;ZS^{IN-a)gHI+NfSX1neHcwC+6Dj z?Dd0`sRj+3ttTb)h-5*CVO!4BOc8aK3G{rB)$oMR68@2$8!V1GF!bf)byo8p8BIie z%dN;YTM|h5>RvSeIA{Om6B)oD2=lO8ezf0Wl#`K(#9r7x^02>9s@U0zjEDD<#rEmc z^4j3a?qQgY<(>8xcc%=n75|l23}wwgX-D88W|7Q^n=I6GS9|VpR%%igmys*c)gte+mzuz zfL&=jCrhY~V{oB)B!Sr>7Or6Thp9QUpN#j@3v_%|CcJ)Zj>=S_0*>EGT^TK{zVCLO zhl4rEwB9mT>w8qNe?92X(^L{er=+5!{Q2FuL9{k=u|yJC;RMf;35!i0y)H0JM$nt= zY&to5`vp*5Y4b+J`IEFtjuDWMlpb%tAo`G!Z2>C2KNGK(>+IWW-8MdyAinEvSLh1; zGd&&@WA)YseUyQuTa~Ii*(W7Myx*^|yItx5ZRW-Ik^^|Whj;TiksB?~7*ji21I5L)y zDsHYYm8qjv21~DnPUROXRzvtF=pP%TXYedHmZ?58clFEO(s!+D#CcbBDSs2=d_7(s z$<%$pdX{PlMamSee_+Sfe`+g?b|`fXN?l3yNEyJ(-57E$(n!iDOc<=@QYxI?^pCC4 z=_MoKyDqmg_K!4peqg%mzwOObs>AHHX5H}K=k0kYbj3>6IKE7S2hpN#CKnUOuFiSb zZH}AgcCx1v`o>@{oG}Z*ok%`4f zZv2@+1`#k%spHjNy6Hp#m;A5tiX#`#Iwk^7dLFb{I~AF+{73_q3KrB&plbs@&&aDn zPL}37mq2luzzRB=E<#8*e@Y08@L!dJ(GWmEjy*Da=3_T3I(PieEv66;hk~5BYmL&S zLJMm`xaZ9{%g?S4*Yrb_Xy>W#SBj@A5`jFVn*;?6jlT=R6}wN|HkBxHf+=l;2vE#T(3ZK1;ceNUoA zw*yKy#lfnt8NQ$E6Xs`jMocHZzgS!Egwxh(8_)=p#cgW^dOsDtj*&{SeoC==5TCO+AAS`cG0$;qBp|qO5=riJ+^#*zDoey(bMa)Kci|VxN)B~P@^;dkFQp*4 z;|&bI^y(Gj_)^2WBbk_;WT7K#S`jy7SAx8*k28-RB#~aTO_lsEYqbBdQS#QpQf3Z>T2b927{N2(xCDp3@!rSuozI-+8;NP36QiIG%*Zw`% z9SlSr-&{$~v!z*yS%14@)L?5C(BQ-ewJ#r*kh^x-2ZjEEkJ5$3)0kA1&#D+AeRGnB zVY7bj&EHf>iSx?3yg8(%-UMChQr25oo+q`Q*R<8pGp!F>esH@T_Y&#b>^0n-sHF33 zaIyJAat}8o@9gKd8C=I$q9;xbn$ot=Xi_CN(&!_#?5_S5{f_9T+J+oAxgBX{?GwF1 z4Z5tMgkzucx>3=ug%@%U91}c}gpaB{nKqf4k zq)hTYh1>&;42_7Wc-TlKFOyeE(3yVp_n!)6u;9|tH8c_+@0W1c$f$@7YXjUxF$N8} z_-N_0e)8&nNEAnzE9}(TP)(|_jprc#3#?(t0@*-Z){SoxiY!Fe8?Pc3Rew<+zLxbV z85v<@!?~!{u{+uBsrY%xvNhbbR#&4}l)m{u&NYQbfw58G2`;@bxsNx@Q$5v;Z)Dtg zPJz|BNTqP1<<_)S!PtMgffjoYmj}zp-}RuR@zdxhSdTpB+PqoK>vfiXT#kXo&+}}W zOKXiR>KwRt{p;cdLQOrcvmp#J>OI#V6EzsOP`$fXOPX3a> z3(vby*jVeCp0giQSZyCpWO=7=wNf(WrhN(Fc84T9?wmSqA z>2C%Y3^+>24M*AxK6I!6&kDcj!GQr%ekXKfF|=HHbUbV zb)tLVqG$s$wZ_`5YBro*{8qQK2J(uS8(BBw@=T-V(EQZz?wei+!IDO~#nQjb8U9Wd zOOm1`&?ZtMvEv31^A^5suVX=%0pRK^4#@YG{{f1nO*Qt;a2DQD^TY_Z0`rmAo=DpH zb+tJu3>ohrIE6iGn}>vF3nk9n1n#6F2owSaVEuqd-T6D(eKW2v-J20f%sL1?Bs{+v zD9Cc`I^JdIqK{x7^QCuDbmeTq4jd~_G8A^6opdrETCP=R6GT@jZ*yl{DCYj`Snn8B zgVZaYE6cZ6_K&{1opiz99C#Ddx_4Z;3rM0X$ydIvo*N!wuU%H7X9P);Aw$%PGG5yI% zG>>t?FIHnknX}M}CxnO#+B)~sj>v+}5r^F5VhCXOd-C@ER23QpJqc~Ec%hokS%;@L zc%W?I_@g&SG))K|xBF{LMlbfUC^+>euHA2~z9uq0o zZNLOH{eTmxWE2#(f$sb6Zh#nCs1#0NBhxC$%Ny$&xNxoq{J?N<9B%q5MPGxWcm3d^ z^&{D#06gwN*Eg!*mS`s?SnvS#**r6#SB0e^6_fL3JG1KI#~IV)SVP7U!{4dYe^L4b z0em}+%*oWoRwi0t$cdsI5gqi&2L#{c+=C#s<|esk(kpy9Gw|D^M4Ll5q|Roh=&7H& z6ICjr<#lT7Qd_UQMXOtuVkOb@$)vhEa5j+7FixP(2jwq`f{sU4k254_-jCcm$?cmx z4c+~Yc!J2Cb6LZ4%I`ZCt=n^D;9=u*Z@WmN)gc z?$5nH)YF~snOD?i@iu#d1g-4Cb$oT7lH59Rx9uq{W5e?Wsy8vV#TVT94n~SfzHR6$ z(P@)?2zz`q-WR7p2&4_;d4f67pb);np(VU-MOo3r`&u}}n+7-SrJ>jrIHr0^-+_iT zDWCXV)m$7g+RI5fQgF{vehH`>d`baRBSbN2wiMM5#6(5e!I_O zwyY;`xA4{o^v-|Jo6NA@FGYR0XhLVRwbYqSUb?p09ZmH$xgbPOuVhq=F$iYw_!tJZ zz^p^q6(Y4%`%~B|lkA3FNr4exa)`Ud+PvbJZAAy&;?8mYMF1i7&>~MeMD;Azgg6Z) znq9Jq(RhoVUFtmR(tt-6%?_b%2`@1$YNSlyv+DN6N7|d?$g!JN*0HK5UP1plLYmlV zRy>K&W|Y6_wSzuLT#?^sQ{94|C}+L$nN{y(fB!Z*Gn-oB`OuvHu=XuxVk9`*3i;Pp z8-iJ}YphPmnKW7n+%e5tsT)Quq9tkgsOZ56gb=a%#|~~gmt4^M2L^0JV-ie+5Wm}s zlR1-)zC}#gvS#H1{(37tjZD^G-}o~$`f;tqznBpu?Aa_THu%a0o6^N-B`SV+^BO48 z)+Y<)F0N7ewXjZH$0`#)ujGacQKkY~Tw`G(&r#VA*uvXzUJ+oGuJ}KiU;dZ}iN030 zpI|Jlt5k~<#s=a>*x|isA<*9h*vllzbSuj<$;bQb7*#IFbu22)!#2SBg6~O^+$Ez_`HyuM= zm7cF@HgLZLvs8yQf3f5EN~b8eB`0d_=e*R^VzrfEPoV4%xIGyqA9g7IMa`eI3ce~) z6L;tg4$YQ`w!3Mu^fc{Z`=%9o=ip1SMZs+6ilLvBMEg{Twz8W-b4`OIw=EWXIBN#6 z_I3B>oYmN6r=couWn=$y;!ORfHXB1*%PsaD77k}~wZzE>jQx3ovd6oh+q-#a!#@n{ zFvuB%!0|5i*L4P?)Y4k!!48+UogNvs%J))R@ zejz@tfIeh9<*PT_bE(GPrhuNKTt`NK?@Hnq^ zI(dY_MpR?XZo29HY=K-x2>>=9G_>LY-kbFU>~YDxeB8&rZ9?Ym5 z_Ea&($Exx~4yo_ZvKMmVxcplJ8?~QfJ}ZeHHy|{0wni#BiY&?l^!qm$*G!7UFOxHb ztQ3hkSK^!bWBGbBkr3D!-%-fnMUsKKVNmKX8%n%D5bCJ77|Eu;s5&3DpP>lJC} z6z<+T@g`NuAKc26jP#a7+uB8<)0Ernp5}SAU59a9?*G(NZ#*a|l;?I>j(?AF%pmOQ z>Vo3RtdpdKz1bcwJ?l_y-7B*lQUpzPVP|4zkbJR$)ViU^AOJNTHd|uS(Xea~#wu&x zsou<~^Q+jF>chk@NEZJxK}hcl#f?D&EqMg}LQOlJZQEa3+!}*c_4vKZH>AYLW9+k< z(z%iY&x6)_LHE|Wwuj5no>uDOkU=9^J&R0cd-hxftiAOZZ;5HZ+OqEu0@ST>l?Kbt zr3=w+r#jDO#OT&9uKN8HM#f&Uo7eZ|YfDmaMdUGC(LYzFZp?}Y5V7LP`}B6~m%HE4 zVh~SPN}2twZ1;Z51SF`X_lpc0c18$iRd)Q~V!DZ< z|Loag%rJw1aZ7ccMe}cIN8&&?gc*OU>vNUlOG~&x3|xf1x!T_QRXV+JZxcY$cRYpf z;%R@n5GM--Kx=)Dswe`OyO9=06&}ch;eCDyy1Rc61sPL zZr*uPD@36Lxw~V))9oH~lx7zo9L+019#Wgfm#m1kuk&*w$eR!i=-7q$2om0H$Uytt z*Uws8U)OYKe+{c&JA3kT$Vbymf)bu#Cw5Y$?P(#)E{hpUr*iDcd-m2jdMJ`^ zMJ4cTU&drUvYqdtJKWPLQn^7vV9Jja%W~d<3`-lR^B$8cFo483L^COrw8Ugm1JtlKN%f7XX!z|HenoM>!kJoZ!hls#&EzzR{Ht;dX&3xnE4vrqy}SBs z8OkJ_;Fw%MjQwo=0lPUFDtEj>LF&t>*7NH#k1%h&#%RDFiYWJXD*dLo|9Yy5H~-sbSK5Qp{>Ff+c;54a+ss<2t_LRPh5WwWjB?#3&~&T4 zT}->fXMCDWb897f$m7&v8C|i9xaUX3IUT;4^!yzfcr&HVH^yq9^L*$d@xo+PRP z&P<)cR5xyAaO68hzjk%skAXT^e&{qbPDC8Lb(dhQ5~#!zB@cFw=x}o*%<#E_u1-`B zLXq%Ux{}lFFMX+PN$Sa_h1`0Hukn=`eo1ttonU;_zLQaZtL}eHaw=ESvyyIc`D3rU z<5Bf9o1VxxvB{$e>P?>MW=!V_bO$E7k~o@qN14aN9O2#L0YmiypUt}!l^c8>E}0E= zO*+DOIhe-XoXtLA6H85UX@h{;7SGujbFb9FgZ?gD09^;7F*gv4dA5_vV9QPeJq_i? zvy4oZ6@G>E*oY@hms0NmDqv#^yIyEc^bLFxuNyHCb_4)>k{Z*+91hRG$GlQ@l@l9F z5kOQ2_u~&F;b8pidhbBA`P4p{3Ba>G3mZBgTDsF`J?6U^Un-BwpAv*=%ASr8y5Juc zvdRVWLSmd~8mJnb?7yTCd62%l9^i*}(CFX2B(hubfPjKY6OU!AE|~2e;IupG0z4`| zjaSsQ0oYF*z3h0!y~vca%Uobb>MoPnmZ37JMhqK^u%;~Sdh49k-}#-=(Qm*Nidr%j_)#`d0|*^-l9lWmL>|$-yE%FhE7MG3;PeN2L(b0-o`p^|$vno=(mD zhPRuAiX+5m+Na6=s?+XpYI5Prua;;jJJ=b!K^ z_s?QMt!@ZDRDzpbFT;0+g(9MaZj01=6 zNpDT3RS(ugFUllY&dX0E>Zu0}^(u5a-d|cB$(>V#zcRE|ZMPcSGDC%b4JXb;QU!VM zulWvCs#MF@z=~^>wnfowh8Oji&<%92^3HoAuxJdaLW@tde)jh+td+cTtHaWTO*~uI zF0P%s9HGmrbNSE{Dhfku+P9q1wIWpF4OuvI)zvC_>o=Y_M*HUu;HL;Ma25&)s;H?I z!N)i!CpX%imiVXMIo=6t7-Ab^L0U<>Jzvk7VRSad&4!`t%+$B{Prq(~#X%Hi&enL`uR>_;7RN0VHVty| zzg(bU;735+!#Me3dA5wHfmhhsJp6GW5U4S0&Y$wI3V-<}(V64IxXP{2s`)E*u60A`$-?__jHupd z;k4|PH@BmN|6}YegW?LeC{Z9d!3pjJcL?q-!3h@J3GVI^9D=(OAh^4` zHx?R);KAMH9j0ccrrw)(bF2DCS2b01pYxq9Yp=Do-sIrSY5BVoU{j}~;NBEoRNL6b zJ>eQAl5DL*<*hsO&~8%KHA{sWI%ax+F!vzH6KfLu8XT0K+yH=4T%{9=sx<(d0hw|K z1}%Cf@UvyntO!`qSyxp`rNR3!rx=duzXmgQ{`89Kk8(iBo;x#<>k=ZW^7&ErQ!Rz4 zKb;H3a}wOYY3GHug=tA8mEtVd@r+cyc#Mh2|S~9_v3L87HQIbAa(&O$v!`hy$ zMv+)a(;Zrd=p9Kai_jKIJMHKMjaqgH7IYu&<5s?CeO&r*^nwP{P??3W_4>;JeEc9_ zfbv#IV@wfYUq4k)Dq#f_Hd93!jNpoIC&tI;x3J^S;!y_}P?C~wyv(E;{-8A2d?owvSUa9nriyFEI9rM|~0N<7H0!M4^hkyx4`cz| zgJKs2(ylbklWtd1G|!!0x3V6_x^fHK(W+LB=RfBr6wA8dQ^-@Ig^oDw8IRg*avxEP zr?a?^e;Lo=!y9bl=GS>_QKm=xpx$PpS)Yv^V_%?bkoci4+Y^fdGrHk-{W2@!NR#|v zyB`ti`ez&27J^HXjKFQp4m~k_z5&DS!cbPLKu1*0;B5y~`W^>fBR)KgYYQokN(a=< zP*vDN+9g5PJFM{>0le^+C1um{CdGmF)2J)ZsFJFCvpMv1mVkp8wvh5bDb??`r~sG5 zY%#V$OR(*QP56o9psf74x}48ZDDL8_=3r`&!<*6)nA_7t(v-C5LF$9u#f4oqx*?BN z+W;_t!L~LC4s~Ad19Mf3Ie;!uk=~w6?0$C+Dl;E?fy z`A*w0fn^W#oAL82PRvwPuP2D*`(wcRoI`y3*#IhZ>+0&wYPzv40J!ARTv zpk@Wq+sE^Iez0s(QXCyXBor!mgQZ}G}fl}B{jkH z^y?+5n*A;|l5SnHp%{5q^AlkaUvBl?67=&bn3qP8c4)dbJbk>r$PsQDRWd_Vwkze0 z4iC>Y^Kw}0RJuYWU~p^D3!R&p{^1)Y5Ul(HuGEnicj;jIwMIw%Y4*Bi#hyM-M_3$p z(~*|RiF_1`EIf?cxhB06b%&L+@O1OzDB%49?DD8=DV81EtmXzdveF;AmPZwYs4Y)_d{SdKIRje^wnFR+Aj#&h+TD)ox%vQ1b7 z;w1CNg$_p3Jzyei!#wm7=&fnL8K_7fPsn@TB)3Nm$p`V*#_e=Mw{(lO|Ht4wv> zw;hd?E0LIK9|UVk-l(9o934DZ??iD8$7z@7dR))@Ef8O`zC0QlO1OJJm%*q%8U7;T zwl5TQYladmO5QO6Y@ZIRGqUQ4&^v{@{kRe9A9ir;$|wGVA>;Jfih;uE7eIe~OVH4r zwPznw`+(U+eF!UCC15cxU(T?Vv=_vLpXjlu$~cGF z32+p*x4c{4`?*>2FBB^nzTPgt5nh+ZHR*q~N4Yx)^@YM(OQRqVxL;~8E~2aeK-t;J zfn0$J>2QH1;-jUNMkSlvq&xSa|6blr2K!ux2FAf983w9#Uokjf^Msk)I<1KuzZ_r; z-CypJP5vZT!Dh|lW^O8?aHe53)0fGmL;!l5fdPRp;_VmwaZNPCRK`=lq~(bfR*Qi= z!@C$Q^cH||?f(R$qiwlh!pC;Es6NKIHRJA8IS(w*8v8Rl+0#)We@ou@Z~+z|&HfMS z!T(7Kws|uqyZK=7_`hc~BA;_>YiCxQcD%>A)0HaQEN5|UTwF7|g z@o_2wx>Bbl@GURrn=Fnw=!%GUw9>RxaQgvhHK!H}X`l%@JR%&xm(tj=>%5m-R>%>o z2u~mA`O-g1&XCA60aS8;{x>S;xaTNahlJK4|8%*js&T}9TkB(_gm`K>oEHBZyWJ)M z29w+xyQem+7EijHB%t%!{IGr($LMYQl~#C+6>W^!=ayg)u{7 zl!O8jmFw<5zc$E)m8I-;QE$FOe@zDj6pZqj#|V%GWBFkLP(=L!*Kd-JK=N;5gqpxF zFq4PJ+vn6GL0F-IM4$8nm_q4y@`MQkJd65hI5U6*6}S%QJDfwA=U`i_!V%2@Wvxa) zDD-VpSj&R+WE4ZZnX%l24;buHsVGQGPw6Dxd(7i&MA^jqNBW0(MCTma7@h|+6 zC+`mmp6c|?oA)XRqy1s@+2N~z!zT2b8mB*dMiNl7_PYSU)z<|7u)F?fRk!U#uX%km z>PaA~wPnRG7qAzHB}tr7XE5-h8Ro)656Ei;!nfOS5{P#*oQ#pb$HJN2bvC_qs{mr# zM+k00lqO-VPE|=MsU}*63jt9adi{2HWhcety|`xD&p%+OfS=9?WaH)B4HvqzVV zR%CnuTn}1N=iu@1u}WU@q)R`S52bU;tEZnWoShi{O&msfBVP|k5@zw^k#ybGJX}lC zu~7W9d9GZPrejo%63T9TZ{8>h)g$WfB&b;_{&*M)&nBT+5-JP;Jkv@#2@>2U@5yZe zY##nMc!FT4*WCgGO{9WJ7Q1+&HE^Zi4Onjk)*6Kgr}RkV0z_MB1qI1B&QOyIIz6zz z+6si>Q543lLYNc_!_^JblH>KW0dRu!*nKFyh)d*5pXyB)`5cz?q(-dA!yUQ)A*@WKGt0_2G}%C%u&@n#K;AE&ylsp=gOJF+~=4V{T) zPnOp9F*gE_prB^Du{!BM@Ac#m%nS9<(Whw5TMw9%rehx z{r|rI8V($*3h3+!&KUnNe(@>FDk7Xe->u*1OEwAJfC6;INq1N99YXyl$QHP3QrnX2 z^-${bHk|p~N1w~{nP)DF(;!NB(;YvEEWO@-;=KlN@u5P9O7V!v4De(&11JHbNN#?< zDCWmV?s<2+$3{lm&Nu4jiR$-mbTl#(fJogSFCM>{%fM6wXv^lI6UebsDhdms&FZwq zsF}tuo($jUTD*|xR0KJG9 zrQyjilBMBkXd?1f5F-#RD=J>fl0EH|Bj-HJDw=LoDs3tVZe*=~Yk#g`Y&24?&bNrn z4fG%G459oO@S_@9dYBmM)q{%1j>-<>J=s_W7wGg7ZxUG7HJN#t<*}D_Cb*ZDnMr8< zYRy`T9yInnxTTXhhL{fKi70_`(roiyN>5^mLZ~m;Vl{*5rSz&0d$eY!Z-?7!N4~kW ztBX>uQlQHe?(9z~vb1d1hZK~{!k1EJa#E=O{K^XOhaS!P!{E{*s1iI*@*_j&CT1#u5|Af0H5~#*@-T zkOto!#D!ARmda8{F#@CT!9`248cp}NC6I_NQv>-2fdx9cW*`kQ2VrTB^{%5ba0 zH@OXX2a`HFlQr6{#{kOMZ3dqYZISe>J&LbM+wx$A#gi_B>HKI8v4gw*^%y_Zk8Ui0C zy!X-m=16NwIlij3_%m;a=2LkFsG|kdd2;#}I4jP7*7ST#JY$s{jtu4JLUSD!po8Rg zJ;IGJnvKJ%#F zXRQ(q65S$Q*ukMj0HJ#h9tDMSktqk;<{ryby1(gEPAYh3J3B2ceB!h-2t(Z9s9W#G z=dbi6j^@R*!&TRdP#kup4Emb$G_-h(#KF&j;k8CMwRhi*?1Pyn)xj}m<=4Bh`|kY* zKM~iQM>(+3fq(WNUKU(X7&J`;mPGqT@VJKL5`yvj`#$OJ8a-p$6iNp-7irnAfjM=mN@hp^> zLv(JX-lV7S2k&+S2b0K&?2wH)q6z$PJ?1uH)(tv3$Y9wTHN0^-Mz;P03nK3hCvwgz zR%Ocx!ADvX>+9{cW@QK&Y2Tyc4}}1scD=$P*HZEI4=jS3_;Z}qcG;N+_V__MptT34 zUFNlzH4Q(4uBZ7A!}^zOzBfx_3!faAX0{8Dc&OC`&S{{Y-a9~LA_$sWI)2ZCJX%kH zgw_wSC>y3`L8C=byYn&QAsp#WCEKW?QSojk1l$0&fnh8fr#yq;9CP2bmg*RE;pXCOKDO)fI!t?B8ZZux z`Q~qpWO8geB&iw77%y^HkZEdRksn5!6r*()Rs&1#j({7-#e9Tn-F@C87M1V-peP-MJEU4lh z^_i9IoWb>UEqb4f0zFV(v+4HJ%#5hcJAPjl)I0~)DJ?fLahYC0X{Ae@Cwr7OY&f{t*7{PLyGMr3ob5)mw7Vj=VNCk0)3`-V{gV-;1qJnBw3y-GX zsqD1}LUeu3RH#=7xG1{s5;NPj|Cp^4JG6(=JBF%QJrK0qm?iwsK&yH62fKU${d<*e zR@7(a4y>~I4qF6h%BAxNq=Fzg!~Rm|c^6)9zP14`OCTrOD6;|QHK zEsge|bM+#H=`l76DYkOQ&1DtVHl9_K#YkCpY*z#X;i@K{5t0`reC&rZL(zu3yLpc8 z4c?*gqUhr-;e04oT;>>dI$lazcxs|NyN@kn2yiUXt@QiPQTBg++WmHVe<&TqLEjbB zpp&uh#EwjE@D4;`tpjma!u0*T25|n}IUy!llc4kPW0#vHZ%?5yWlvzuM`te+K2;Q1ZgTRL~`|?8yl$pQ2@M^2U8pf(j8MAAg^M44hb&F z%yE*WEOOIg;k4!P*h#`%_Vr(|<&zP@IOH2I9-KMfA`a!UX;0r3RrZhQ?nC|U&cnvR zX^WY(0`26k_`v(#ugsnyoP3vEcV2n1%Qyoz1Hfbqu3L94Wo|xL6-{&}9y-=o??__7 z>P36mwIli*fT|oCDxz*NY3PQ<41b|+Za#eSs>YhZQ?6~(T>I4PMMfkXO{1kS@;!>H zSZ(3zUgS>4fy5R!0alM76vaExkFZp7q1psmORPA|=k-Ni^ZAABO1hQ!c?=PJE1@Y}DZ3V5v_RAbJQt^nfdxAPXm~`i3(`Y- zTXMw79SxAbRPFj%KqO(FH2=Ev8-~yym2Rpr^G`l0-wbQlt(%TpD@7-H_$E$y%Z!2Jh6u?z>cp9kNke9-mYkFS&?2hs(a+v{bld6df8&NZ)n53s}+Y;-XOR7laSE5 zUM4uFj`uXc(9p1vCau>bJU+|0RkRZ^BsuaI0&iQ+>J95CT7Y&@v=qz0Sx5-Y#9~OF z`p>yS1M8)FgbvT^TAd`ZYKL>9M8(vQWtGfLvDtJL^fNuE_AbZGPy}BptO-iA=w)JG zG@g5&g`67XHdIN22#&v{o~JqIqdAtrWAha4YEww>RHew+%5i2?#4?tf4?Lg)=$krakdEjXzkj|q}}p@eSYY4UVV&0*KDVS_-tNK)d*i!yq4Lh{8Idu1qphy5|jem zPA=&~JXG831H7-QGj;ZzClsgM+EF?dW&$USF%pjPz~(hBmm$L@)0IK#X%cku^E?K% z@q1#wKYHSX&uBvT_ZjXFpn|ifrUM;HVCAcB{x(pnQSop~i~yCb{QNAe9W`25m7Hw< zgPVR|28B1v{x)y>)wDzZ>KR2L^;KWmOB@XyZ$MYaBUi2lBCHm^oWfdxleO}+jEOl( z_x!-AvFxTxZZn=aqn@MN?M86MlgaM_&Q7Jzao}aQqCQnn22>nvmBD*+lJ1?@5r_3-1Tc~ zPo`z_So2$W0$fZ+;FwX6lCLwWdihcy>N-P@n(FMdVk}X%bK@M&f2!($`?2o~9QYIG z;&LmuR3RGG;nBHmnP3k|RdNllf|qk#C>*cs@%x)p$X^r{qTQJqw~5giwdy=S@X-Uz z(Cm2^@ZomYP0j5qp9*&}y?d{H`mCH5s?qNb`VDE?U-#VIjvZFGDizsda_QE@NiRDz zYM34ln<*yRF*>k3?u9oeRMDiwj+?E{Hwsa9tzpn~)cRs*z=OK_r`C|1zGB{p}nt zx(}GLBF+mFa?J{q$zzNcVRXJr{DZlf|4w;2p&CpKbCn&v?M~*t_-E1T+0($zRzIiJhUXbCphUT zpTu`av+e=koTGSqJ;{!7UC)l0OYa9r-mKuM6|*Fjh8>Rh)x(`kA1fZ`g}n#{{D1xW z1&@l_1ok_-7_0P2{L2f?`Fy>X99cm*EF0AXJqm7xCB}VEI9~r{G^}R*3XT84^3Las zcg@`guaX#TqjYav>Aj)|8&1SN7LiOY1PRmE(8OiJm^`9IB5;ilkRp8z7%Kxk(>Oje zF4lr@R|Gj>$>tlRzzcxQ&^FOZWXC;qMeB~{;~y!1eQ1?IZFLG0>EX}pkP)xtgM)+TxWh7_e&Q&0Gvm3fu)yT`cvt%z`VunGd{>Oc zI2suqnzs}1eG{!ozsur+ry>x`n+a?Rs8 z`5`U8{^Mq}rvuHno{wjTmpulPYs{q;^zg2Cn#;}Fw*1muSTplsDyJqW&Ca^xSL2tH zMd%e!nfa}H$E75c1Rj0OcEYQBJDQUN>Y)p8#h>}vYSDsY?n~h>FIcC-Ki*DjqlpJmi({K-3lcSgub6I;t%7vHn z;^Fng=qjIAKIyA62YlN{g@!YDY7K3$aZJsOtvK%aP&@OfG1dj>$iBv<6cpWz$vUBxz{@(sJbvo_0xnzvAX8H&O!a zbTfk2eVPXyr(=gE^hvCEjMzo(7q7W`{C=|?@s6Hi1FiZ$S}1rOZb*bg=FGI4GbJbt zPS5#p)_h|py>VWzk#;S&-3iVjGoge%-a|H*^+8^$%w@SWmAAlXff5k(>P;-9?~JS8 zVOw9uDVl(?$sMo)TI=ZyJ8%B>g#niGy}19itdj(oYKoc(xk*=u2uyX6P#F79H5vx``g+rm zModTN3r#tM4oKA*)hTvAgp-Vp_d|hp!mo~qd!P-Sdzo_08?u`Zp<9D(wxY7uLV%GQ zs;_TyvmO~#Rw~4L*pG~iLwv}tSqcgY-NVs(;s8_}Cb8i=xbyKQOR5bM6O)Y%k;7vs zbrUnQe7p5dz;sXJkVuKKfi9r%@MLJh9Z8B_%yM9)$iuI&P*LpZmm{>t!$+c7)1Utt z%K|$4J2I%Msg5#e3W7nVQ)wgEw9b*0XhQiP&pZLF~@7-!I?MYV^Dp2%7icBGesyTyV-~(4D)vQ zzY<97sO_sgQr(?F=qDoMQaRKHY=wN=71AGAkvKH$8s_Im7JAt6k*=@b|4ChjCVigt zA#Ln67*z=3`x-Yanbf6ClZhUC9zwG5MS90DQwx?%W6Tg71=HvL&fjfk12td9Q?WvS zcD>M}2|@;aRJbWAqyFds<@=DqdbeG5dA1juY^L8V8mK09>aMEn~Zc`65g^8A}! z*a9N?*|CUHA*OHcwHZ^a7xV|PzoB7dY{i%*l}X%ume251j}2brJi*i+z9JvzOy%8?V#I(>A5eb!0ED!^dQa zn(W?NnkCetfYW#n9fTPYq+8S!Du9ZLNt6`DZH?>Qs@SAa6J;)|@>gW-TsfTSKl%;g zK8RJ6N=fHb*|q&6VpOX(Efd(AZkNd$Zg$o~>EAvScQOSm3qZ7jA<=62`(VWnXq*>iSBz%iO zOyBBPKK(B*fB@TR%(HQ)pz0$Ml0WV${F_j1T9cP~@JL8bEt)_rr~kZ^NFP0|4f&>~u@d3FB(LXil@+I%>0~Ea z&tgOD`;N>mA={BCZ2019W%4ccyrE3EWmiChq3^Rqa*E>}?szLbN#xoSL=}JC67h34 zMC@m?>V1 zSRu+WoR}n!Ju)tHreo>uoOEim75%+m_M_&C!y2gr->r~(iQI&j+T7H1F=mvXIHzyF zBr_w!3MmN_1LMfNS^aM$aJ)E4g_V7PPPJ@jcvxCPFO8ENjWCpUy}eCu3jnl$z3|&c zy0J0p|HU3t2q`|}fKXx( zi^&O4O{C~XKjM7&uvi6GWSD`Y z6dPP`nsQsWSd!dX^Pt>?09`{$pdh3a}>WGMJl{Rr2~5Smo|S2g!k{o*E`LYr1wa) z&G8|r^U^og?EoO*pK&Ch63Cl7l`fqeU(w(8*uAi&IM8rq4q~#kitzZ)q+cGmA zL&J=qqod#SYCt>XDShA3S%8bMl#?#BmZkwhl?4i9E8|6{5jhJDZb~d+6!`U0gs|)K z8$Wkzr*Y-4;^4T%Bs4Ey zq)KA&LbkVYar|w=RnP9WFd0InL%A0l)bJE3m0Eoe%s|?(50tm?pL>&1U=91IwUzXi z|2+R@b>H4a&t7`QzAXdox1C>{+79-VZwXs`+9bE!zAq9{ILSX`$m!Vk_5{oa`8Yt{ zShTa>(t1Gv+I)2axE7Xvf#yy0Pn)vf?Kw=fvr(=Wf_$kk>!nraG?kV3*Axgi$n6 zE(znE&D*oT)brFrjw~~(j|CR5lGdK59M)Ygc!hT!d6AyCX#JGgeity*e#*gekZd8N zPvxmHMw$MSagHJ)kbvE&F(32ilB2W~PmRAHUYSNFCmUY{S&d`De`QD;jV3mtLIS9v zUoq{!A8TRgR&1b%ZkxZKw~zqxSEgd7HBuYHkO^V}Jsq8OjjT3;8jWDlhQCo7V3xQ{ zZhABEJY)x4sh#>Vbo8^YQ0@9QE6vlLq1!MNE^%=v<+!wxe3vol=xP3RE($dn>_+#eY$53+gG}^bXD`IF6+gR226H|+c(s2># zaL}yMMSgv$8N-m%nE&S|hl4LIYWCR0hyl2h$~5#MaY_AG2qnKt{A{4&;^H<$MJ@m` zfsK^unj(*Kbj&EAPpQ*Q0aBYyUabc)rXae-XQ=k#x7p`)6GT;8z&p6c9kSmy9`H&u zwp0-3b`AUc`DN7zh=luh&R$^&6aF3ytp+dmAiqYU zM8F;STV(RRG55fTPUa@A`C9$CDedj~Sc$?LxMxn36-%{U>vafk;729SAa zQo3NJd0il3_5t2!Z5Q<&(Qi#?IJjkn5$D}vVV}nwv(3|-qK$(xSP_-$!7vXQEqlbG zk5Hd?Um!^4QARwSyKhi&$Uqny*ji9+>;kGRY+olAqqEKds^5a$7!4hrsF_)j0Ax2m ziAsrF06BF$B^6cUp_8Gpk6PL-CJWP=Sz){2{dlE?AJo$_3k5}=%LX9VX{L&5MN9(9 z?tjMkGfyZzW~E7YA704`9p7ncw(CU>O((wtiKeY+g7&U+CAQy5UrPuihK2dEw}9 ztL4pbVFeZV)SO0gEF@_J=MF=!_`a_G=s$ll&(6t-qmWv{6a-d8*ch_GL_DobH$3%+ zV>T*%A|vZ@{UYdRwL}NSNYQ=j@?z7w@lhhe=mU|M0dSN{N=m9xPfkx!eQ!Sev)2Uv zKsQzx=SSQ_)2LQ-qwg2hPa+$Hlmr}5rt>t#jb%tWT3RH`JrCZ8QeaC`Iv2K?P#2;- ziwOBW_(hbFzAf&qO3ho6AlXP)jy_ui}fdv%9+#i7wR=OAhQu zaP5w*Cv==}3-ThWe7xgOFJ-GDw{L`B<~o+Ty0hEl3)rhR8bI`NmnoQy`@%O>NLq#O zkb&}DVH+qi>q7Fd=t%fn$FuKx_9RcAmzu60SOtM* z6?9stm6||o+fvXPVaaA{Dve9Lt6rieM(XL_nzU+E^`IapC7Lqjm}30<5_Ka0Rbg=} z+nUF^81>RN)CTc0?g$;reOJy;Hf<+;8XO;h^56lc)NxNm<51LMc!8lmu_yKIXIIFA9%Xy!9`z6peo-;+I z5feO!TTY1iL4cR)&FvQhgWN~63O6;VpO5DU^vrp4_{=>P%x=E5{Q5m4G zk|pDytvs>NBM@Z{p2zwQFtdE(zd2oBf8eXA5t{q@lisp05)H6*t(T}VYi@_dWEj%h zbLv_ws<%Oy8sKGTpLO|hcjqoHF5W!4mvh8>x&(rI*U!i;Ud>1<2^k;2ACwDU9vk&+!xtf_sPXz3Ak(MZB-nZbW&1 zL#pp}-bZxtQx3)uMK)N0^P8I& zuxT?o*0Z?uk;#~(E8d6aRmT+(p?9is9hzq(B@WEriwKf5)xu~xjRwG=HseM z!_TSu#&9447@}(>+HBON0F(y8rgHl!BTnqq%in1nfa^7;dw0UP-kT5B>rRett2R_a z;k=VD*bl=h)IB+WcXo#&DIu{43U^izwBPW5_8wG7NlrEbyuyJ8w2sJ5E0*a75#53b z5;1|+m#fF?C08V9ymZEuyc@(Sv`lqJ6#>TRuP`{7nf7%~tdZ2xI6 zMs~<0VRl+)_74Rg*zk9$+Cn`Ak~)%lFu9>SK9yp6)SSqK6O&-A%ZD}n8kDw6#rdl~ zON#6CL;FjtGLoL_w$+t}3kvOO+t3~Hkuq9q_!%kgp;Hs=?{S!_6%D!O(d>RSSLy=q1g9mVzv9G=~5-c}fskRj5GvTm0m+zrYnWDtJ# zsQe9%!UxN}3W~8b>gg{^+GNt!PcE7zF6W??t#6h4YI|HP`>Y&@fmGcek9lt=P*W=U zOGV~A9eC2M!In;!Lu2PXrSij$Q5j&7Ru`g=pH8vTiCr}hupaPlO^UGYJiZqG{JEHe zhxiLnO6q|m7HL&%k9`xSTU`$A`;fWnU1u!;$L0-k1nc$A)oh&}Ga4g#1qIlF%BPj| zH`>Eld3|XqqO6P<`i*G^QcZ~hB8 z6bE!|ZLN}u%4ToRDMX7yn|-~HF{TW$t+1^aG?jN9*S#xA6*Yzw^GeG9g z9-Vr)k!_x0c=G($vQ7V}>RVNq**`O=IsECcV|X*t$a zrvs-7P?-MN8v{7mCo=QvJ6|_BIIjna){N_S9u>kKBXxf?tETkD6f|3bgW*C$eQ@czplPj z85JB+M0~E&xhr2;e#Zcd3m|XGxH z^G(EF>>eu{n~Eq6HSUhKaJQ%X%{OnZ9KD)mE70W`JU0B8iK3V{0z@^|A>Ip zcyBF;twJLwIk#|wWuC!vrqcXHi}%pCoY!dHa+Y1{%y}v68jYvu86rHYb#=vp;o{f)#Hi*qeNyHAxY=a#JmX?pGkhwOFM@0m{MMvwzst(HtjG)$3N+6BR>U_G?-055~7hJq=!=<}|o}dP^!RVPQ;km+WvEQ!cbvng8<|U zVn^4;o`o`qi$??vmK{Srn6dO?xNuoaqb>Vt4Yc-H@F+@5R(d9U$ z;Ru|nYD1V7pmoW$IkJ4X68#KUyfxia`{QXVyxD0D+pDVr^ooB-tT42&WY+k!ZtVWY z`e)fhOAg^+l(NeZe3m=v{E=dJMUZX(#aHuv9>1@#BSOSmOT`@Qoa{}4g)5p7tzhAcJ*)b7+7SlSBWjr`>$1_gHF9K~dGf}Nsh0cs4fO4*cKZoQ zlK=J+CXe%1mg?qRmqrA!@1$hO1P7CBs4AW2NZ6SITfA!|otgp5aF5{1M{RJ=`^eWH zp2qrZ6eRX;zOD^Z4tNy%rk4GkSTBwM6srs{A76}V&lldb zl0tbCNI5#P-5p6vnIbBA_-%Z|P+-ej7SNsLkrBBKKoUK4N#a46x>tv8bwDNHlmkl; zWPkHTxjkOqd(cT2n_lIoq#Ve8G968f_}6s&e~iz`NJB!&rURXq?B+j_E0~GBNK1DS z-12iH)fFlB9l_bTCREH5j{EgbxI3t%9uCvf%DG4w3dA{WR9vzylPS2W)x~fepYQ3S z^zW4lb)Ou4RlZDPc)FgB)lpORYc0%Ch$QcwO@1;uWo%=I|mzQ-C*#L zvhn#;!ylJW8tc|~ofO&qUMLQZs|whWIZXZKCmfYl41Uk|J2d5GQxQB(FD(_VDBGz} zQ>(Ycg;e+swPoLbervpYWf@ef=3Mq5YL4LhB<27g^h=B8frDwZrzcr+SuDu5L?M8! z?c3liv2IbSBpJY=tbqpb23@8REHVHTY~kI;q8am;4*99emXwqBGSY2 zkI=Kcf&v41JDv0v-n|e3Un2wI9ls48`0cU`A^&ZCYy(OWPOz=G`IK#5uTqS*R1Gi2wL|9ELD?p3;~Yqeou6uNjb zI}J=O)HhlM(K~Kew|J;~GP2rQyeU%_T||CQKU*%3l)kiLxg*bhDRnV--VOKp$k?ad zl6{m!*2Z~UAR+$`_)DxO)yDFx8=PKqlNp1#Yo)0A{rkfCpyjzq8znvS?5MgVq_Zx+ z2iV;;)2z=;ih2*AoBZwbrL8s_t029G5*Z3Pvr&>#`GdbGqj`CGl`rY6Xb_h+$Jygs zQ-9>&?wtcPP2f-A#eSeS(#i2@g}hMPq2kUHnZALh5icU3(@pSTc13M1nW0Q#Ds>+C z5+>}09@e(omYR9)#IW9s$}=wlrAcT_pOfdeqnCttG@0w~SP zj%~1TV&!8j>-FUGb_k!!26Fos@TAyWThu{7d`?++e^<(NjAsUd&32&H=~DUI#{WD&y->UK zZymdfUE=bRX!Vh^o0Cw{JP;k1vLW=PCLccp^@3AVd51iU`*^ZE0A2a&4befq)CSL6 zmm4wyAhcb>9eiO1UAljg9~#lL$&$ z1x8?W&(|wspxtEKaEb?bKtN}ud{~qBUnhCIJ#|BlUti617w6KKI7HuoCRaeTf9)(H zO{*Mv$hH-jx0Lz!-D%JAhm$4mfb+PIV|3pMe>`7!V#E=o8A|&*@ANVMaLsn*s8Qj= zj7ker@{>KUZDqV&p~V8z!prM9{JT~O9y)Ds42%XP#2#9^!-+e@_vI#rP` zvbLozIW4x|1NH2=vkj7f}8LOJ9r-kwELL0&MK#&6;7n2P!OXr=maOOr6--3L_2<4rjNg zCCrEOM0H;e%b_pMM#Z|s6_QX@s@f17Br?uoIhM~GuU+FEQuNof;C48kcRPCJ405H@ zj7)jAFXf4`RA$-U6s!nwJ$0E`vSLqgbaP(_&KKQ7&fbo5IxAwX3F;#J;6c4lTGxP zanapSkNsnxxgzA;mCpX+f%=~GN-l-Iiqe{r*cGLh`_wM!7rvPXXk3|1P42}@xD^ICtOE+e7+@)2-%Ms7isYVOgB*#V6WPXI8` zU;dl;%@`dRSw|w%QY9|@Vil#c@&nX8T$DuSic$vl%wy3FVAE8`pQtxvjn8fQ6r3{t zD3v24BaLWjw|AJ-3JZa1f{`;?DZQqMv;fD-#qL|Y(cVh;11YbMV#L8xOD0zg0 zMMq#S0Wb{v_|)Y-ZYlELT+#rfq;^*sX&k|QM+T3JPS3W9>@KKVIlM?3uAt)j6?ZxP zl`!{W4(MJ}7oiQ;Qy%2kJ8RDT!|T$&N&7uS)cZEA8WYPZdMbZPhaF?Q@P zFZ2H}_SI2QuHD;$C`gKww1TvB#{dd~G@^7VDcw0Vf=YKYAR-{$-AFe`cMRP%G~bQq z{jKkx_dVyV#hO264Qrlxp8MYS-q&^QYyW9T^F+4a{Gr$@u9xQE>3~Ac%b>*b2kbtG z5?YUvZC{Rfw0V5b^2JjQKY9BXBXxsVAu56~jT1|1SE11zELh@-6^uEoF`^RzsnXhI3tJKsuq|6|GK>2njZe7Q(FThx>| zBXU?I1%3MR)o1J)dnKcRy5zuV-9*2^?@VBt*oW1=WKbMA9=~l?Cknvm+k>71+l3PY zDggjFKgK^*mH9WZL}Z%KZSUs=-n{V~ z3OUq;lI=wio}U?Q+V{L&h1Pqw15neMWraO1`PP1|QGgDNFM)w2t-kWtJIAFg$mG(g zdLpBmi_2z|9uyTii=iS{=Qg>WYQ7Z;ZPeTj`1kz@7brb%h-f`7RDRj-4!}0%DV9oFY6F zY@N*@co2#8;9oH}c~YVc1Ld^65|hOA!4hrHx z{FvoTL=mj*M`xOaa9{Yitr0(?Y44BA+hUKxBcBIWd92#;v6jg(If!@V{d5nnb^5on zZ#h^B9Z{_Aq?+i7Yf?M5(*mq>W&14JtlO{al}j~1Gmt)t7!AA zL9eNnsPO$Y^5uM)gB%Y$bV<%~vjZ#mL6=&aA2LoP3GQpjld_-s#zsb?c=UV!d$msLW_jT0`Q#eEqr4ry1re zcM~4uW+#mIr@j1t-YV1yyLy$> z>FQ)XI>eS8<)f_Ioh@@=*fmlit?~$pU_PniYD-8zVe2nSAkc6Sv)D(W*Ko zaviUu&1^ZHvjhHF#Qdev4>wHotl616r`*$xgVrfW47$s0SI9E<^gZP57iO6hiG~6p ztp*5h_w-Q>rL9HgfXihg|M+5tG0R*7x>HDPw{iir@p+I`u0@tH{?5fF=2&@&A?tkc zb?C@0Muo=9N##RI0G=u6cVDhoJ~P5D7E()>C5$XEOKQ#Qtf*@1?h(5IYW8EKpOM;| z=Bw8!%r78$C9Qga%)fOOv&WKppar>kbJ$MBrwXT?565;gSLB-dXR~ai90@ey4{-GG_@O^zl9M2?(0-w4=^^g|x?H0SMRG070<>rbSLLQ}}H@`PFL} z62GV)Fq-tjJK=%AeG=S6hPRVpqK1Yv;2`*X3%k@fZkt;7BY+@4_koDVrSslwrfLhX zLDnN2n)xx*{j6p)5s$|*IKO4hzdRNqtnj$ntaOmYbQRa~&-6T&NxxTdEl!I=Gqhd< z>Md5yUjll+&@OZgU}r$W6%3}G>H+!(Yv)6<$AAvso$4cF1A|K1CXi5fggu={1TS8q z^cbW$Y>s{aJgl{R&mzo?g@n)gNq+sgDJCfo=8G3ER;?5@|BWE&wxQ6q+*)MYQ5dr1r}XVV#mx49U&0-Z^QKv2AfHg$)q@ib^lSOnxrO##1ltv^cQ0G@x0QCM zDLNbQUY{Sjj4wKKL-*>aakoEypG6Wf_+e^>71%jU*;<<8pJt7ia^9LaPg~R(Ceqt@ zf8z={p>#{$X1ua@@YAk0oK61XZu5xZtbob;ZQ}k>?YYJ8sjArk5hbfiT(#Opmb~e6 zw#9rczug~qCp-}E7m`Sx+MhRweEm&lX}#}mbj~Mqu^YL!b~7D>^bI~kbjq^~g^5VZ_nz^l$K&a9 zB)_cV)~r6 zzO)|8W?BMZVfkfsxQ$wV8&00vBZbfcy8>a=DPo+A(1L8VsF~K$fKnETn z-wsuOkicg`_569R4yQcAV76f`H8)Ga%ZCj;f?=W;WLsQZG~d_tMP~%v zh7yf*0$4c0Frz?K)vYaqWL2kr885tbz_7h|dH^QVYDY2$F$p!`(YtLAXZE{H4Yr+7 zi#|OAmP&8H)D3};=H|J~xef&MOH`tR5W0W^EB1OwM$v8i)Y*D`Cc6M5-oKFg011}% zEDv4J0T&PMo}sOU*~OhBwh^1z^QNE9`^yy1uUnt1x*Hq)I-)Duw|>Au1leE|)+{2; zB%%Ls6@ESN(!is9;88x$wZN1KPG4&Rnp9P98V9-sVdL z57iBCg=WMG21Y|;HN^E-^)~cr51HzBg<{?BR3@OpOzWF2xnE)>=OC!oy8-EE5!=z^ zd^qy!43nJu?mf-e52NYx5{Q(O;nohJTHKQAvFe`=IA*1+U!>a?6jrE59+@VyUVfel zd6ZLkHd;3DZl-hC^Q&PZw*J0Y%)Js+)K?}Fzs){%;(P3IH`%WPGaaw?qw7co;UaCN z;4hQf>E>;Ngyb-`4?dq*ERa+z0z^5{6w~#6d}tBY=uzvx#$yo32z}_e!0wX0449?F z4SbeIMU!M>-_$?yxIEo|EhQDsH!-Y`bxwy&HBU9ayBm7jV*gztI2+Lx3dTR1&(;zr z@>n1r287Uw+7#Q_VkQ?Jt3>#({s2u%gfoX$Fu?tAJ)}Fd;;MhO*u_-v3v>-|Kh1gi z^0p}d$G_6M-j*~pG(_*8PGdt3+QgC=Dzt(R-WUaj-hF6Uh3(IiAAB`57)wOGZ1TYB<(H{<`i`WTT@NV;?`%5BoqjH_6_F$VUe?WdeSSw`MsrDpjL$?E zyWXLOnS_P4)R~N-#Q6>Zny7s)cMJyG6WYKnACA*9YfosBP{YLNS(ys5PYW&y?&J}F z5!nxnJmFZ)OLfXoe$3FyLgA@)^HtBa43n6R4O7h^SyfJfqMAM={%Db|MBx^`W<`u>__p;W{vLyPYT1Lmvgv?BkSN~_D~LEK zSFa?PZS~hP&@(ZWv{o2Fn3=Kgg&mHc5LMF*yC)!=5vh0%Zf-(p-@jW(x+1nUh>)zF ziqZ`cFsX1o?gMfvmKPp(8Ivo#R^t)1K(eOpi80cP4Y!*HHiYI*&ccF>ld*ShMgcUN z%e%n->=_Ek5hQ`*%!Ol{SZJGBtQFrp;uz@ENwy7v=*j#8M?=Y(Q|L>S1Til>E9l@5>7Y8(D>D&l@O_kk+I0TQ zPbti;sNH;aP@^mccc0@2X%X(Z(vi$QD^Wd5Vtyk2m4m_M)vhP%JtzFwLeDMM3k$-N z-k{8~b7Dg|!pDM+xk-_l7+I6J-h>YUf@Zf(KuD+pcpxnNqqx*VC@_lXHI0>3fC-rd z9sy@fONEy@i9=1$|RX5OUk4DNQL$DCq3JsxU2~RPJ-<5X_o)){BlMI zf?)_;Hj9#KKnm?~IP2R0Plkgf960`6&L5TU!P)$n>!af5ptYq?kv4xU(v7c?!Euue z?=18B{QUhV`z&3L?agqN1*ryXk?je%UVICI^4;sQCp6#wioVl+_{#x(?*J$xK5!)O z&iEp_0fDS`&p z$wRGSthk=!7pn&HJR8|fyd;2Rou1a(#1irg)|=-hBPBHxoIJSD1c>uL*)o9-z$8E` zn}Q(qMkt!|6QlqRdQ<(bRzCWY9zOaMe!DgrrP(sG*yPMWRcPBvZ{j|1#IF9;xuy9Q zyDLMNh>GunHhK|*^bRmwWJXJ+7X{xr|4cCm0ZaDf#lc^it3Sk;=C6*{4q!rj*ujCd z=p41(TD@CHbnGqr&|PQ=@oaRn;tx~*Iiy*e*buH7T7%)&yDfz zi~so{+TM3kO!yhVe4%DoWq4$yehtI$oA^aVbmKE2!S{`KF5-)s*i3fW53ru≧sA z?^1ZfbZ*#a>(#4Qz-?eGCGzV;UxP)5{}}TP?VEoLiGMyohX>>^k^Ld(qkP`;ZGp;@ z!YDznC(rb0^6ro?E7^=Tt%#@l8n`a7E&kSA-&&@T!-pB9g zwMoU3*FN;Ny?8kKMm!aQhOfVXsP{gDGTI#HsZSN$Y)95BxX7pQELyZ?tptTFwMN;97xa1N#_qi%l- z2E@w*LZVvoaCVErjlj*#t@g^)c?M@@{UeYVr{+8au*)kYr3mKBo{o-2;Lo=G&Ca$R zFoBKGwfQDrTw>y%#_cM#^rucO8FGn=8X6m>Wng$TFdKBtbMnLF-`l=_3C$>~APqx$ zb%amjq`cs$T8Ygi@h>F762N8&x}<*G#vTo?k#I25jb|#5y}(Vi&SM}lM2}){<6JaY zQFj7$Ncp^%Pe^$XP@cR8V9vZ*$X#UQEk9)O0J(AAfQaN~1L|f@6U`Gji$Jf;M_Kea zr8O~;&}L~()zmFj!L@mfB5O0wkGbjs*vQ7#8AfXPVlcjs{5cJ!6FQU*OASkt3*5+* zs(u(f?~Lj`rwElGqXo4G>Gp_6SXE${e{;`H0p=Zqw6`r?^^}64T~JExc?MZ*(5_{v z^oJ;^=0Xyd$@uYO0pxJw4W11$d@`$NppW7^K()#hNDv%^;xstRnc|@nHg_AQ%Zw5pSklk+Yfb5VD@v!~Hd} zKLvmCB!bT~v}9BUV}cUS>u_AN76QS8rhyvwCvSAy{M!*wc7+ ztm2$%ISUC0@zTm$_IhM7eo;jUb{*VS1p ziFky-&|*5Ho}%GCo`z!&)7*;aVrgeQ%;RV=YcgeVr$#E3IkQRL2va#_Un}=7HkB(E+7KJ_7v>k+91A zQHz`Lcd{Ydrr%F$_m!e#ZTxZRN|bAB$Arx^z@Qv{zl}b)`%B5`MgvNkD^k>fCIBPwgPEPXZ(jeL3w|{c_Y@-r zGkZZEW|h9DbWcACx&LK8Ty$Miu$1({p@3WszYdkjERZKcd=y$@i(>x2I*H7 z%HN~k{iD6ipF<`&$m<4dTM)qAIJw(KgkN6%on4uU*_ zLAC4bJuq(Eow8oX3_AIwEKSI82~05;baQ4x+ZICv^M4iS-t=TwPJ4$s&667IZQS~7DQ1t8>OAH)&0+S!7<_rnX`eXSlO=&U+7Jg>^Gu&b~}T&@Jt*T&_0HKg+lTI|77G` z@?QPLfF?dk6m@QZ*sp~_a6k3e;XfzRjC`{uJ=fsr3ANQ)%FFsKHGjIlbTWt1#}N<- z+8s8ZZydSQtNTZCt;h7N;;%lF=j5xF08@O&n+>kXzyD){#-9&ZQ9to6u1J0pEQvB1 zcgK(9(mR=<1ucpJg{R3vE6A+KMlHiD{w3V~;^Y=oFJB^SMYG5Je(LLExU?gM#Br|^ zsEf<*EQ2uUxUsouh9OE{yM%Kl5oLZb-M3UoPAzXFBAYTUIqWp7dkgdGzD(snwX>=r z+U4(YVUWxav4+DCdmR-gi}4jYzdGg=M4aI?sXJ<$G)1<0rC6ip2329Hk7Nd#idOCMaDQ zB3qP)w=Pf^)M1B%-7Gc-BPFmVPkG|6^AyqJ6_jYI>l<6G{M9L?n;2|-y=0nmiK22jr|?`7 z?%oJHi@Rxk2Rb!vC=JnJT7z|c-eCy5digq9A2uU%G#)o4lx4zk9|P&|o8tgnIz*dq z|0QciNNG}ZPM-eFsKANfXc10d`;hiqo7I7-y@iX4r=G`J)5j?74?}e?x1Aohu&5)b zL9>PVuTJ-ZcFum!G23Wt3Bv{X+DqSE{hvP~`_2x#$6eY3AFoUmUpyp#%pl7`PhS|K zD&pLbKL7P)0fs|n!Cb_DTCx+U8hIT67TV)j3)g(ngF-C8U^QDiLCA(fAvEqGVUZo2 zuhD1h84%gswP7S^LxtrxlYDhnTUz>?kmhY%4=-;eYQuL19WZOk#F(<+;uH`q|0W}s z18{j20|z6h2KZQ!t_x@5UdO|Y*pcO+1EQr~PAnC=duuu~X?& zEAdbnlp9WR8EWw^8rYc+6Jh(i+)_d^XyVi+=`_;V#G&n$BqoA5n%P><kC zflL^|p@rYCDyQ}`=`6*9Lkn0XW{=ol{6%*|FZtUaU9&XxH4C!Q9z7wv`cP6Axl0hb z;DpfDo)Wc^t=;tuue-kgy!2O{%uO{ir^Hw#gc{z#;8{iCQ6NT7;f;)pw1}MshhI&~7AajT*=?@J z3XNpiYqecHyau=osOIk8y}K>Njql{tHXrAle-;zlR-eM3F+cwvprey%2G_~W^Uh~Z z=r=J7PL+R`#o@K^lK-CEb2xf~DX(aXL@bkHR zI*S&`AoKzD6E~$2BP#hK(J@KSFw%*L)=p#?bvT8m?c2($MzaUM6g(a_Bx-TiCTS-Y zvC`oX?1>dnOt7pjwcV#o5w2HB(yHJ-&RAc&TIC14X5;T$hZeC}0NUh)$#V6`Boa zDtA^9_BG^{4ll)Ovrry646SF|#ob!1|Krm7mA^lg*^AE47c%Y1{!b=fy z&_)sR7S*RRq{ig=`BqJqz4&e17K^+i5q$|3*go5j*^6ytjmTq)U|{#!`b$B)W6yyM zHM+46&Z^&6Vx>{Mi1DS^Kfi0{dAn$)>S9$3Ka2OlxyU8T=8ZMdwx%*ET9lbnW?`@% zFP=TIaAk4PEYVUO(aQTBL2j1543EH}=49uyRlB{Fm9~)KT0(vkUms@lgf+fh3Ve=1 z=J=4H>WZr2KcI*4vqI3i0S*)&J$XMgdm3bWv{lVVm0QzQcz~Dz^Amp2#A+c2#Qv-H zL8^fX;B7EH(Vu$GicBHuMa7>PS&ZAu?qK=-%Q#wuoM}>|+y;vxkB>gJFwaYa4D+e< zK3DO@Enn0pN<`wRZ}I47FFES1zDAm*@#bGoas^QmhQ3c~ivW^r_lbL$yGg8Rc^hpC zh3UDCWhFoa{N)74F>lN;;0@Vej^#)=pfPN7M-1)k?6jGZdL9zM19?UA2^#zmK33bK zy{ztYkLbouGIn22I}STzhL*Xil|MLSc^#%5bcXPxw4%r{sYG<-ux}^qf7vyA)1~H9 z<*Bo~p`cG35DqdR58z@-0rHeq0Jn+Kef)#)IN$XKvUh#ZXi3k&uT zyxby@S%ref{IQaf5=*rl_rHKgK)@!K0;UBa=>__MUPQRo=;8d&(fxxzPQ(`{=^0Q` zQVPO(Ks@sRVR&pJzveVK~Mfx zHW?nC?@-VWH4Ub83Qh;vV9_d9%IgCJiH+^0T*>}MZjScFyq47^92uo6T76glLmr>Y zhJtRZ@7S-JNr5q_ZX_}s@XiK?hEG{p`|F9S9g8_?`}d=FA3S(~hKXtapCVU)8A)K; zYP_%s7B)Ek#Cdng0JFMt=v7SQAU@T+9Li}04jnD<%>AmeV+yBLZ46V9I_Q%HOHyw7YL&n@X$W)N6#)tAw4S@h`DPP(w8ygj=((YUCn z@*7lX>l+c2M=u|}9*+=JYi4<< zeox#+te|qir(5~AM!v>RhbonvgE8qD=`zMVjUp{(jm-30w zuaTwD9(=lStMBBuGvbh6PkR@~_!Q$Vo zRgzs}_F?SQT^Fpn#ZF^9P*yW45fNR-?McJ>(V1#Xjm^?Uy9&#hbRg?~guB4^r3|Sb z?TMEtH30ygqu|}KfwM^qGu4jUjILr2KFC@TuOwEB%FBlUldL$=V*kjzjP8chMGRty zYbnTC+ckPPk-@*;`QIZl@8 zJ-yMwZ;@94%e$&u&4UY3dwE%TL#8TZBy`eVzfm_IuTqx15dUg(&tNL%b0`jT($n3b zi|<;TjBgGQ`Qf}UXYrUAOrX5zONLZrhu!<1`qV6h=-`#ePwcDzTiBJY%W~i8( z5rEoBu8N@7tUoTWJ&dI4pLHrb3Nm?m7H?+IS31{02h~nYzBW7C>I_Vde~+hF-bOl8 zEyS-K#riXmN6JRWyJO$2bFxYr=3?Yq7O!7O*hu#o6|s+1zwAF9czrK7)!NWoAHr;we5^I`Q^Q@ozdG4&a?H)py@(76r4~h{ z_8G74VZ>PT!Nktn<&p-um_W-0iR<%394!o-&ZN0nMmsli_m9W-J7WiF+n_x~_0GLy zRmV!Z5US;!c9sgN29B+hb=!U3iY}27ZOPM>0^7vRg_#U3NXGM|t&QmbG$NwvY(mLs z3*r%hTyv@mJeU1-g<2=x!=;RCbr1J)rXY(lG)X=I=uaWr8iHr#JoM1nLUyrqw}v>K z7HxuWB5cvIa~`KgE>mTlaL?1%I}|QevO7YOvx9bO3j(WVwf*WB0?+0=%C!16KlsIW zpND?^8V-X%rS(IHN+(0dJPzLQbZ@aD+=uU9qMn?H{ zR=}Xb05~t$p08(uX|CEr`Ozm5vbRAaSSuBbEskN;-LWCmU{)z`{}c&Pi+j%P9aNDhpXxC5Ss=>L3ql73I4QkZR&3l|G~4lO~{fgm!6^oxIl z2^tr#cUIPcQWKWzVrzsa{oE%9YAS&*IeP`V%p$vC%S2Th6rXFFTgkI0*2U||y70b! zTo+Jz^XVDwyZWmL8kuF_)?kE5vivn9goi*tMq0Y37z|7e>pbSaLLCPT%B4n!JI zBHD)^%aMVPX)%~8cyW1L*9Ve3y=;aljCj%15ahFbL>ZGxZdN%_p6eXw1~;wHzl-uP zE79u^G0oY>C-!n_4tc{-ek=IDuhCxBROLg6Lm$}UB+`wljg`d%#RV3mrqg6ZNGMd# z+LVtgGMFXqHe4HBXtaHC_>z6X4@OLp%a3k;y6?!r$=QldxB0lniIvrRtKW-yy8YPL zPR^Ll6&@H$E=X%a8<)gFVsGysSteb3Uw|u)jF@-V91xEaz$~DyPK7xnG_+#% zE+jlU+R&#H;LG^~4nUkH%cdTxV0ely^j!3gx6!jgyH)wnl97M{E|i4WMyY~fiv{l8 z8*?x~wTJhW+O8)^MrZ(s6A6siZUjHb{N0uYm*MkVRFuDCo(%gx*A5C0U{TjG8Jx>OXP_of!p~-nJ{fm>c$HHl}FW_LS};9;)(k1_v~mc6612J_qp1KK8=o132j>yFu1cUu+~7EatO=)g>ETF}}Z zb97|SZ?pJGk{7I|C?N1!pD4*JwVavMC+GaLJgGEZOqjaLjWQmBr z=EPemAy*yDmZBQk52>Lo~-1SS?TUf$E7oW08 zzlO%C&;TKk9h36PKl(TOv2E^N<`|8PHEUodj)Pm9$JAXs?p& zsf5vp2!w=h3LwHvNZ9htIG-9C$5kC**LlK#&fo#PYF{hK->!J?`-D$YryE)CeE!dK zt9o$TC;INiD0(8W^>XZ}%)qA8_NH)GlM`#ZwQ?}pHqI-~B|rsX-3kWYFfS;fwI_>@ zj~~r-u^a#L6<;FHkGd%5)Qo73H zgqhY5)-mq7mv`%b*f+r=6;364Ls5V7Sl!Fm$0leJb7G$&ou>x6wPgHy;$pS4`c3zD zR-?bk7jD-c8ZI~?o*ukp!~E-)Clrf1I+ov7JD;7L^XmIGTLejj%aJ>P*H}bc+(!x( zHT9Mb4R7@vkBwrWl9K_s%?9-2QRr+nMP} z&ooTed77S}nBHCM1ZnB4uE>FE_@Y-@MPy&Fl@@H|&_i#8TyAPd+{upqc5j0eeI5mk z9r;%%rfz709ohPFJB5NNhr!!j>!T71Lfk>Qj$Z#ToO}- zH+P2s)wl938V!29VMt+|WaGciBv753yp%v%)Hi@2(kN|u9Z=MPIpNN+1m@b&-mg{u?6rTOHLXeaWO?rv;X{ZqtikFRU`7x~o1dpTEWDV;jb@9(18l_vvZGjA zWGp-}H(+fzrQU$1nxcTcOgupJH^1}t1{|{})*LC+;V`{M<%7{_j^>5;Y7RYXaoe>F ze{BGj+Tj%S4}Q$}fW7IlWVjpTP`=9U%>>a6w!3Bg^Gy-1$EcQj`n4y0*zt(7S&~pO zk%p1fVqseCA#KaV>C86w!n?O$FM5S`dn|%nRp9N+_!w@ffDOpMpRuzKqC~n|yp1&^ zF(3WxpUizZU1p|QBZ$Qaa-R-x6#Ym|ZPdxfBQc)^Kv;SeBNGlW$1i=6P^|dd`w4bx z8}dRvM7l@E@a@myy&A1}0iOJG81@Z4r6v7T&koHH_wM-!_gb%mSl$fT!bUM?$rxA5 z1J4{1)$(M{?P<(2KEuC{Gqg+tLU+4f#TQ%mr5Fs|WmP$Ao;~<2$Z@s9^z0ifdh%DL z$c$XbZ-vA}$KmWw)*eUY9D8N)e6wM%!)m#zgH7F}m)!7by%>7$Y3|{$Xt;$I^ zk}ohb;1IFGYSb(#1f{YsgyZsb>*)+Div4S-`ruEERrw}n^x=x?Qz~7}3PfEMA{o;8 zPtMwwxw2LHS^NNtZxq9ZjX4Q=^z{4ZB?nBPf5y5QZlH(FMT^H`ckHmfX9{85P2~RHTj#Gm_0Sagn5+(~Y}g@~?#e8l zYo0^hxwv$)sqSpez-7}(wIh@ChIaKC_tm_0*%PGk_bo9(E_z1UWSvf)l@<=ql&UB5IVI?R2s`WdD}9Xb2#|Yjxi)68Z9k`+4A$ zG#oGdFiHi({d0n5MS?-lNy{0rt-&-`V(Rh2Qf+j~BZk$_ot;$A?$bP)WtU3B$%sXb z$r&$rA6t99CRWTgGsZ&`QU9T+_OwIq?4lzP{u7l}y7t0Dy?`9uU_bX_d4vY85A5kZ z*pyf5F2gn#)YMuHq|T*Xj_Oxi1xQ2XaCRd7top1WN(Pg<8J&B5h7G8$<}Lr1H~sHG zCE#5s!Mj+E$>*6TD{Po1$yfDiTrvK7Qb@hrv@BPrUUsF9M~t@Ax+IDsg*EoarAGNU zLFNXB*GgT1iu?(I{OJ1wUnlIS2~RTS3au#_|IE@@Y#ZE1grm(u_8PJ40y7d7BkoCZ zcl#$RD12|z{3j2!yh}?<(HE9MCfIe4Nb3pUm%|h>=mZ_?%>~*H`+B_nUe2M+Nq|ai=9YKVtP~SC_xqyIjZCH+L>9l3(;* zEihM^b>&2OG^;Am6g#i5y7_}3p*>PeSj7ZajrNO#ILby7H=IZ3_ME6aPT*uFYZ*#R zafgs4#!2Z;@mVD~o~+lg9{$4;<$8(a^r!+CyFNneiMmI@8s*(YFQ38IKMAX zb3VGr{M?;>xVSZACz424Gu;52 z?Cz$OHdsf+nC_;c>v)yfs;UWbfp4`N{y1Ej>{zh937X(=+L<~ynXWoG7)J2D9LnDb zB<%KOWpSX)bjyCf4L#VfMet&*EwWe0Mow7uM_29EGnb@z%F<%92*zgLl(U94C_rcR zr=?bB7&%vKw@D&%3ivtUD@A=ALsbWtOc*nh$=u(E++a7dg9oPTu-(p zY!8+@-(&jf-|ihO#<1jV^%XXLe)U_0L#lpucay`GD|TN@G~DB6UaRlVjlP4-jZ04- z92V70C*UX*?FYOFS7suaZB@HJlTIHp+_;DTs)}e-zV9?l)O9FBcIA*XkB+Pm$?iV= zZSB-!X}PGvxXZbHw~40PxKk;#p-+1xa?bA4ckPzA(*g6Uo1q0G%pne~#p=wC-eK)- zE$_|jwy3g8W_9ZUcZxboXIZ0bTc2mIV%rnGM;a`;bIOUFsE`Urjf4yr#(w_&DMk?6 zpmRwSL8PRH`6tT#IRXFMOKLoNsVU29-NBW}MCN1~+>hgCee>6Td~RJI1ea;-#qJB`vlVgZ-*19@o!LWT{Bn_N39bi}1K)wXpK;xo|6J zkI}d~jWL6|JRgpin>twTwkk2$ZWD_L*H$@aDzZz@X?8&6zsYqb9`7e78QU%kt!-&N zD4@A;80CVD-ZQ3=wncn2d()T_`&^w0F>M$6;OS>Szs{eTFh;=2MuJLFz04GNku3%m zTTJn|09cO(h;4=I^Dnw}uc>CL9U?Pfu3b75rg&C$$1!ea@L1cXdqf>_yfgTwE&4(r zmc2vJUcki(t&lDS!NfE)5+Fxl+U)wo@S*=>C=ICEZ&SrnCPl61Lsqr2TWi?c zEU=n!cH?gR9lmMMSfHO=5tM5N#6*d)`F#r(hW z|G(E7pfW^ZBuAK&Tw$)$cVcc>oF7t8)aKjR-+M~)+G*U*-iv#K^OMC6+1_vSeLBe>)PQ#Nbt^G!O z2G?sTsE%YJ`@yX11Nwg5wKE%s3YIsvV^IN=JPnr>c?V~UDU5s>#$?e)LH1+S_EiRM zs#TkwLWq%iCEnG}`YXSade=gC@C^-cScIZzH7C6QET+=5=p4{k;GIr@5fLY7^Hs$9hn z>WaksX+#>fa`2K?$2;Wx7O1>Lu~3dOI7yI?SsyYl6_8S6809?|e0To2n8Q6H2#_K( z%-V#tHQg{QN|&*@5Ai3e5wmdl#?eNYfQDse3I}P*-a@G zYR6GkLHBXHW*|O!B#gT5Zh;F2V&$!2mr%RY==s{2!MaAgt&WxY!6Zk#gRfKLL_0PC z;k|jjJSv(&g7TPy!sE+TiUMnV2F4sUEt^-{vG^2_r-AjeyMs;3Ij+)bajU4Bp~599 zDh2)f<9%a6_v1K=L%ZJMRFRjlZXZslPCvDWtZeh1L}&5TOZ%T~Oxzffu#FaK05u@o z8!@cf?l8 ztd3aupa^6LWlj91!})W=t8gOAAog;tgN`nFsNtouQU75`3{s2h*R4XIfzdTB%~yOC zTQpY@b(?~MW2)@i=mLUP%2)ekMDSzsI-%zNow|}=4kFnz=sB4BQ;FP{pYHR>2~S)W z5H8tlE3B0eT5dghjpTWAy)?&-{8L`{e?K!W9B(6BMHYdDGd+oO_t9{UqlnF4CcTKJeQf`;$&~LIIk1zEf3B`;^YQqrQOc!wwVd46hd=nc^V4(6)o+j^GL-ib7 zPw=QRsB8doG+_W*N^E!Q5DDvZmn*zRnpnaO$D^7<6;`@c`)#Cf&|?$>I()l9Q2en+_1hjOLPw!b{I{@1<#}VlO~xHiMKMTcTTglE+y(2OQSZxudj=euX8LL9rk zKR`=5ZZ7+8ItIQOia~hkL6q$@m72Myl+L%{W26|Ktvb_?CO*$D^$@C zkM@gVs>-PB_q8sb^7-kHpS86svdg}Y_*Q{-i1s_w>(DqNrRJPHL-oJsVQ4U~xi>TO zSU){Ne}hCIBI%(z!k6q)BV+pN>x{x_7^)#WVx6OLW0GKv%x=cl)_VL7vB;tX-u=;F zPiSYNtxOwa(Mh}t7V>>znJ8u}N>Bn-xY6u;CGDsc573zszIaI;DK zl*4}MFz>7Bh)Kl7q0^N1deJ6z>NJ3`?QVxRbb~ex@w=t7ayg%`H+64{i5ku~axxN% zkDvOgydT}@n%t>e!amIXHp|*@spPmd{#4g(-_K=~0GNYuZ(?9z^ty5`EqRi>AHB-k z=jR<9*sIyCHzNeyGVxvqKnK_J*s2OnI2cySgZ0eV*f{ZhS)bWsP2c9h!5u|$Q&ZC) z?St-Icj^EqP_~Nl=zv#^)(@j*2j}U(bW5;+|!p6#wqcNC&0?JSK#{d z9prD+VPRqKE#nFq3ry6i0603-(7@z1f;I)17YOp0290iiI4F$dbQ@h$R@^gC#A;u~ z737K9gkNa7`|CQEbwrGH*GPkG(y;S)(f$yXQ<+<3ll!NrM3zUALF0AJ?NKFb))yhy zTktoG6FYn9*WoS z3PS=v2+x6?1j6BdRGF2r=)}dihok1U5s2B*zzC-EHa#z&vW%?Rsg}vF-e7!dSXrEW zv-er#i!hfP^yUhY$k<8gd0EuoJ3KU$4muGtbn`5y^R)$2rp{^3t!}7 zQVMGUMBbpu=ib4P4C_}FapB|NZ{cZyEX88O3XtQdTEr~W78nJ0P=^XF?taUj$j@G)Jn7k&v+%Qh5#5xDB=^%TCxp zxu%z01YoB0F0=1-Vra6!HK7xj#@vz>`dqG-e@>rUm;jk^z&%M;+EshyN9=L6^Nv!L z!`8Ej3Io{kB&u|Tlrimuow9UEIL^OC(BzJG9l+O^0#yv&t+!iLlBtBZ#5yzhecWEe}3%Pk$pl3=cHeHcpwO zd&q4PlJvH9we10s4lCkQl}M6yB~Qz2#m3ij!M&57pxp1|fKFim5A+Iu2DBO!Q`Wa$1Iq(&cxeRlVrA>& z_CW$FyVk|9NEqpD`2Ot0iMc4G@AFh1E8{NRZF)ztw8L@>>eR~+dX{$U5DK5TO6^}B zcj~Bqpc0a5PTDy))dpGODl{{uUP&H-7k5X4WJT_F9Xls|Z_gpI zo*#u1IaAQ*Cm*(0hGQ1ov6Bngsw;4sn{CgRnj5i5Tt0uVo%2W`kw@-!HVxyI2iOg_ zCw3Ru1WcZ1-MZ=JFK&BLJHO!OXB#4YB&i<8A-!$e%@&3_{d@`sIL_QV)e{|6%ERd7 zsg04G-i2Eo1PPiw57wufG0&@5^-`l=l2yixKZj`PP$2?~!1ZdP$}Y3YMJxvo1mMp- zUmTtbWPo5k6f`Ip_iZ(8RUACM73z6+^1{9(uVR`eyVQG&{YlRl95ZPS7Ts|=U$$Q* zkLl`^%spr(Z-|PiWMt2b8BbPCqJJIj(mm5K?2ofr2^MYM}FT ze4x{Aq&|!c5nBCDY8`$0bEWHir;&|!$|1^Odr}>A%c;wadN>FourM(TwzE>9=uD<8Q`E>}dZ)s4F2=j=Vp2+dx|Ck1}gab1eR#3@E8W)Tva68tFwT0t_tV1h{p zIOH;i_B(gCr|UITiqxor30Wc!H{eNS`+Eziyq{fm@HDP)qZW#rHi8Tjl1N|(#wVXl3hG2Y$^ zNBo3Z4mnR4$)+!v6GjK>?clYgQtNk*>+Bgl*O2qM8XlxlM|~IQ)oa(-Kig+s*-jl= zd$mLapJLd#lyBonb!P{z5(yW~{W~lqVq`dPYn4F%q24r;gtqQMDe&*>4qEDz@^DDA z*!?%UIJQuSVgoR8?Ayz*{nw6y&mdQiRdKG4;ZMhZtiSsvAX|*4+9~^Zpm^~`L#?dJ z_8i_C?n((XW|aT+AZM5g)Ok4A5tQ8;Qe?u(ig;XhbqCsk9by17pb3KKo8?fmVaDp~ ztMesLCc++JXMA7`QV}C{z&U;Em{Fm-@IBOs4}_!yo=PK7C_0mpQHveQ)8~{_RAK^- z>kk34fFtI8ePQQ@2PhA?uEtMPn$!Q$zj^=RLnIivdaf{p8xY6@dRAMKdU_d)%ga&# ztoT6Kqe|)%2o!>+yAhl!(=n)F%mfqJ)l_bTPjtHUX=)NW6gL=W1} zC4voVzTY(8y+UQ^;XHs|~5%Wqu#WeCjkH zg^&D&*QPBXSJs zHsd|LqCAvztNz#+zzGV7c8^y+pQVoe7Yqj-8%g9-Wya%vfzVINS*;jN%Pod1?-Xff=r_|_GZ>|$r) zaokw&InK+HtI-Z zGKKc3jw9-ysH%vxUBr3?e@dqX-RCxoHc6Kix8E26qonoQ&n8Rguu>?{%uru@Hv_Si z1NI=mb@rCIqrCJ*#c<#gwJPoY4enw{jeh%`0nkOwK@zw(1)7o(s0S+=6p%K)7RT~1 z;fs3Hr=^vZ7-Xu>AAo?_Z%y`*&y#8}Ry3vB%LQ~D4qa0(cp7DB@oM(ENkMZ2 z8CA#(r`Vj^!^wYcH!!A2o$eSfevV&Rde2KX92>qJ86y(CRo=A#|6o%MD(!OIqxnlT?jqFP>yTV$QdhlC!22rd0X9W0-2p#K+igfl} zFHBAxcceIQdBW{n&0?FGJb-Nw?$Bt<7st^+j!wbYgOabWpKN2th9S*`1@{x z{SC+Mi0y#KxhJC?$Dee`+K&e|8V-K=+LEVStkhbBpZZ?DS&H1#5{feHoWWE&!pMFp?77tiApCDU*+07-u48(xKd#r1 zlKP-tVKK(h*>iZ?X5$0xtJrr|_DeCUZaZeno}r53p5tB@1H{Vg!O#4MB*Ut%i{e;{ zude-X;qZ`pUhK7hNjlbcbuEFdAc{j3$RsG_d^YLH&eN{T5;*lo4jUsZ3UItKykqHdl!~CL=;YnE=;!%ITmV=j*hn9Qzu;KKG_;0 z)DvHtE+g2O0sTHr>34vYc-GQmn3&> zeE_*>E973|zm=Q(noxEIKmbip-xS~9v^Jlrsl*svYe@4t%TETd)K6U%rn>4(0_OH! z@sAx=<@tf@NBnCa>C-%3dtIN;Oq{Rs0sgk>QT4&#tDf>h5%XjKvMMJW>jR)D?x78M zDUsF;s8?{7QkV&!%=$;Bq*yMeER;Ey7|bKWJ&AN$hnDeI?vHFPiCFgD+v06E%zRm&s|<<5 zX!MYvyI@vzGK{`#P?=}^LNdj#9XSv4Ypu`ei@{JnelsFLpWRVBWZczX`feT z7D|*qmOOTJ3eATKUpN+7T+>xd0w~k@N-;(XV=mq^B=5%00iqtbI`{4jTcFm>gzpPi zj9n?rw*KBj|DS-*gagq8WG3tTs4l0)U@QpjuXD+k)N$~%+_XB?9cE7G7a3VV?U}5S zC#SGa>1K0OJ+HaG%#WPy|3G#*!%@Gquz&I%D%H&FJf+vvqY} z&&b%{pZNk?3HF8(o_dVHI;ZCr2M41*c?u+vD~4-PY+d~9i$j<$a<^3~{N2m*nQK;( z)nTb*FcXF==Ix=se(~rjs71IcO%0WS+5{A1UfZ^fASZEU>+6iw(Xnw+&6a++5FHjF za|B>Vj08k~1&v@5h}BR|Xdf#VSDoumBlRkqjI5j-7$`L@0S4o5;Y<2VmF0`tcWDpCjiYZ{%derJC#+n49|w7q)608A~FI?>P7gDS0~#; z!Y=pPDdNtzYvqK`R%z6$?XpAeKYn^pXw-Op<=zQ{MS|SoJ{v`CIY2s-RQm*)9|$bS z*`q~Yj-^^6`bOTUMH|7#M@k_Fg!yjTrE6~G@21s0v51H?u9K;0G$PLpMEhO8#3Qh`iA|F=HTq`Ui|DL8l?9cDBB;_0Q7ATrL+E z2Xl6vCl6mrSh!L6)^wl8*DyzHCENEtCWr=F4Gf@28DrQL zn^5qbV;Ekx0{QflFC7q$$|&5$e9EP zGtIu2sQ}23#>_|iI{V(&5AChD*%X(_xWudHmxlLy?@1CY?<0+z@;%ouuv<}ZQeIV6 zA&b^Pd#>VqhNN+gz!FTaxC@<~?=ZQ+Sm2Q1Izg3Fp`Xjw6SO;hGh3f8vtHG7Sw@|^ zFcL_3qC2^TkK$CD;{s z!P~h6!Hz}4+{G2i2?>w1YMo+6M?Z+50#G_3HpCf39_bWe8>ji;Z6sM20cwvtsV`7NO(0Dx99M08^s>?O{ckrceMUNGCfuZdke}6s^MUL+%EX(vUbzWGO6z;*Egcf*yv|qHH^=+O= zXNEc(=)u*35bk)uv%aO%EkqZDn;vJwH30H^ip}Hx}qL+zkkRa6fIVQbf#*ab%OROtlC(K0klcuby&Kh5q|0Xh{&zT zniQY4Pmn`8vQ{;6|7bLk7@Q2y2bCAAwyoC=Aq2KM4FL!)(yATNp9R@zq9a_)(YupEa!))R%xiS8!1@M283UsbIQOh?~*dpsU z!YDa-#3HrAeJWHwM)uS5Q)(%Xy(|k;T%t5?GVebbQ{_qM-%H4R z(iP!rJ6Nt!(OSt=*dOIy^~`C9DZ(+yZb@XuP!*kS;{{i8kte=v$>~Nj`QQ1>{FzGl zAm(kQl0wn>V?O}v>jM5!*SZnpb^M?qu;oezA}DVFPvZ)=u}c&7M5jORYmXg92a8BT zsX~MM3~A+jVBBK?f*SSgB8)~;V7Y&f5$w6D9OIK=BP-9ufUWFa0251c=wf(NqCiSh=Gc)Zy370tZYpOyjCs@+07!N__9-$I_q7mV~)t#x;C8N*&2w(39wdqWQ% zT46e9tLM$Uvl)TSYy*)P-yr`W+B;gOPV*lQ3{%abB` zBg2Xz3Nz_EUpD0U5=G{|dWQFtE~qQEgX$vXJy z?X__x=sW`emzX#xp8WAt=_SYrt)u?uU9l`o%=*69$bdu|yFE3pmb0CPf%J zI5=EBh}OPqvWQF~4=6t=phfMH+?l748PJq?3xm_~9s@J;L17Rm5uR6)ky81HP61Fo zyeGW=&oB6Eoql@@$^-D@plS0#X2~qyg*{`sc*$~<*{r{RnMA1xt~&WM=E%|eCE)5B zg6YUmm%TTipYj5K6bmFkJHRL(MEBiM1YL--KL9au$6zQ&=9370FOO08{rBPF&a7`p zdQLT)58VO+Mb%7h`z!pj{(wy{Dq4ND z32JQ2#%Z2k`22Z*a4Xbl3!+|bB=eTi=rS}Q^)9-WFX$R}0=-7VC0GoB7hMIOE6~~$ zcis&t8o)dul?QRNOs0?|NZ0l80QDnYo8>!#K=9A6`t6Tb;JS}d(0=(HA^!W``_i$b z`>O9qpc-0#9PcyFHYmPP>vm}Nh>;Qc1lG}k>uF-BHYL&0T{R&n&2PXGH-L>i{RY>npGV|0b20zzda5heKCMN zZ@@(+HM`rc1rrAroyEe#~Uoi0< z?}Fo1qPwVo*D;e!^N+`UhjD_*V2&QK zoaTdO#^q2n%*X9hG-rUVdsfgp_Qg_TAP9{G2lQ#@Zq#(c_c2MKH+<>O?%@O9Bt@FP2 z>g?@}1*k-P2Pc)gMLmy4Z~4j-m<)V+Yw}L*W?PDYV`OXVvo(-J#CoNIn$kzCxB=2}uS@HG< zH|hPlvVZRYWjZkHH(@W0e?2z+qbOV+8-*XT0?!idnOIo3S%K}Nizwusr6Br9+osL2G^d$Wp@^)QeJll`HV9?V{V+w zQ|lmOxC;nUK15{OB2fa?%4#EwT_l3TO^rPD15XQ?%x~0y(@s|>y;{t3CSy%eKhT;+ zNI-ZD8}X|cQGd43_9A(QzT33t25 zb&PAkxSgjZkghNzKjSQfvlp9`@Zy^ARRc1M(i6Dth8s4gxuKnB&tWCJ$rS99461QG zzt0V`y!z&E#Nk&7ySZy=1fW5IBH85H@?gi;Prp~;U+;owx)->r@KUz=UupiL@352z zNi74bbp9KWjCNTT*bX4L`3v^dwX>8MS8G;<}ycq!gK2WNmMTBl5il} z|MJBP_TiYrmC*v15;!mRBXe%MjSPY~?&N})WbWi5ATZDhE&?g$fpL7=Sk5K=D{H~M zTtDblHTA=>ifd*YrwrQJ+YIn8<6b5ycQb!Az<+x&-(^k`XHa9KsrGx?hTfGEHx&COaMg65}sVBg6a1Qqtrx|<2Vyj1;r8V+quKret_=eBG z%K8Q8dk+#1>K_o_|MeyP@4({n&J%@a{9|>eP%9GioXw?%qqrmTke+**C@Tx;6lWD6 zwT*LOGnrK?Ges8-L6+qFLqRDTodpTI%5i&gOz>bu`(~$`z8{(r7?_0MH8Gc$+;VI2 zo32ZTe*h7yeWnEBC^#Ax4nkF)=W<&ic^A-c5U+>QBmS9GpK?DAq@~$v`-i_C*uM>A zo(Ph#Sc!;++piU1UkS?DLO(nGI}97ACcE;Y8Sq{g z71Nzl^|K>G#wH8Ok^#9eDCQ87fm3g#MlgKnmVtxawZ^>&F2N`G^OZ_b|E?NoCf*^E zI8lHE)VVprIF5M0FduFElo^u@Uhjc_KdL}p0iS_~-s_ zA$OGvQ`akmld0kokxf_20M-ha5&|yn=)A7HNKM7g{z))LhBPr#U+EiOf0}7w4U~MS zCe{Si5c}Z#;)IV^8(NKZ5p2^by622#H2PVZq zMc?$XnIHs_l^g}Kio}&8S)p$2Aco*t-{U+P$5`#Fb;Je(N^A4Wir|FUj2n*7va)_n zk;^ z9+{fDy|LQ=Sn|J~HN6@g@UmG;xmMo)>Q4z!Dj4%h%Z$WtMLS9ZV<8Gvc_|EpNP$*y zf+2xya~~>+)QC?y8tM~=RZ0k1)Qyld7a0qnfwY{G`$BXq|62v7iK79N$z-{)od27* zAGsrnG7O8?tuDs~fp>W`BI)K8jQADi7d7h?H>!i|DvUzZL zIFh6aRvlLmX(iNHN&Zgi6Ujd$3jTWdKSItk;PN#a8xF#LeFG}y^f~?=Bjr1X+S)Te zNj8Ttvq+AH7&}R%M^0KZ+P74A>~=tLRc2RC96%D2k%v*EF+M-3j4zRGD(EdC7+;v5?_MUU7%GDb z14%Gu2=R}aFJvNKngH((?Z#bN2vmijn3Wwu zkQs>3_FzLypZF%60&k+k_8rH>l`koNaITK5;%LaUS^(#-CvG8zj6^_r>aR@M(SRTG^*+cZLCiT+ zhZ?*uf>cWJSdKqDb1WT!TW+W}(9zO%0wZe(#$MS?yRIaZ=9J{TQu)R8$QS#l>>OAf zDu}6vXX+VX(`}qj0`JHzSm0~mY2qTs6do89gfDgEMU_yhlwKn$h_ocNrl#-;sa`@| zPanJquADV)XIB^Ja12N@o?B0s6)>mq**2FnSSYK70YRB>>X*F8*aK#1T-PWEJ1Jmf zK_kyLK-%d{e7*~!5n0jF;1L_ig= z^}@Gi>|k2B{DDiQr2t*ANq&X__KInYL9=V+utDQGG7OzcUI&@PEBxI%_Hbal>^Y$T z7zN4awOKuUjqq64$?N54afjg0^>o)XZj%C~{?x<77miCXoyU&SGBU*;H`_@r-Je_I z0<{!T8`R|Cs6&!=UOg0eTIPS0u`e;eu>l^pL}C_!Eajbc$xJL}EE&OJ05sn4wCjtN z{id5ZN|Wzsf3ea^H0e})Ix;O*m8K-=L&7)@_;i~)Nq7J(!7?$hwZbixpn1rLWB9;~ zrc<~9r2vaTQEV)Zq-*_^sJ{zDOPyl;;QQVBSTi+s@lMDk+|@rw76Ek*vMPnYs+Ta+ zRX^`HzPeo~tv&%pFsy!6IUTBT-jQV=q+s|WM%e^zl0q@A+%LtKzr%a*De(ii{W<$m zJfD8{7j%XT?!U`_rwP|HZ#o4)>!{o`uS7GLW1E5ZpO-K_0mEdsr`FVPUZwcrn){`{ z9ZA*`puK@HWZHxfDw>`;sLa#xL&N!Pky{IkdBO?V1l>M*x%o0450zk6V|%jr^EnA za(q-q18OZIa1a46XKaASF}B+8->A7d_Y*d! zJzI7N9rkAX>`~ypg&1R5R5%fK2wag1lCh|IsWXVe{tBvo{gj@MUSZ=|<^|19zP)mH z92vrY3P6aiZ*K&lMInHous@U`=C+85VQo62zQ?Td68y5dgdw7abC| z;_qP+GJU$(7l6%BTD9p<7aIhcPMBTXvxdr2y)YQZuppDgVmmUnmzkp+HVhH<1}vld zpCPr=ArYtnK{AX{)e%!rxX+SH6OJi1;zy>UN{?we47^*N1aUI_r;P zK~)SZYL9>7TvA`uKzAT6XFP-{4McLN6yKYI0d=3gSz|Z;5;QVmKA5a8nSTn$+t3hn z$VPh}MlQri@?s=CPDQ3fX6U%aE%QBEBt9NqK-3rr1>C2X%n=Kp*ig)< z(;I`#XRAOuyKD<;nf56oGw{QiTNR1Wz!>h!tmz9I{nagGK4C8>ep9Go^N(_Zic=4HTL-Xh8+G@lCxMFquQOc z?|bi6nOA-}Pt+LUzFpm3-LYeOdFy=Ua@%>iu*|qG)p94dy>=xz{-j%rIzVs9G|AjV#6CVy0+PMaszhS!sx@AZ)pK!dA#>c4M*-6X{FW-bcu1Ei zIR!bxNpC?Vk(WW zrwdn@<*TTDNt})VVr2%5%Nq@D`tMa#Mw-g6yo%>mzV{!@&>hf`dQX6O!~bfB@G2~H z3)kch@-))YvQ2L?&7T|gOGbe(0~DZU(z8-&RmOSQE^;+zyScb%;JW#_9T3M1CC<;T z&Ua{8SbDZ4!cR{Y!Y6(na__a`%XO6LwQO!57QUHrT_JP5JcKU+;%0e%712A&FP=9< z=(v(S0cjusCg)|*OZV5BN8KB(-n^O6cp}MaogTkYgEi8x$sqPjx)7Di!f%c`%$$K5% z*RVu6pwljXR|GfrG`X*S6NV^P6&3viq@wkCm3Z|PN7CY)Qi7vHBi;I5b2asI%(gUq zvJF67x}aMh%nsJF9p6bA#YNCRW<9R+XBvL(Wc<^aMk1W)fmz^eHQ=Cj0I<;FW2ny6 zejL&zrJTsUAHoy>SaQzi)_se@qu=A53ES=F02QMe05CEH<1Taj_RUM{=~j~M!)}ua zaG^^&Q#7ARX&mDL$<06Vi)EUKvuT?B!N{9-ur*_;`QGb}l~SvZdF?upSa{D6IP@g= z&OM9`2otX7PQDm7c0M3m44{lW<<3(n#5c&L17bNkJ+2bdTA$%7uHPMBedLs-xZghm z_aJ}w7M-h1loT$*c-B8NR|#U}ZjUEXXdaa?AiP>>s-_NC-#FeJqk-sYiUU6Wl~B4j zeZbSM&d!ereEQ>aPV!=0PuQc}lk!Is0B0$LvC1uB9Z*Ix6KM4EH@U3;EwzAyCi5If zm?^r_iUS@ZV0-tL;*6|!#%a5mHc1wG_9k+7m3%5{|DmtrG-ik-5-UQM3b1{2@fU}1 zoh}XQ`X$cvA_}*L%cBXX!`kbgVkV&bX8_2Hc3muA->J+GeF3a5#rNtWe93?&*q7b} zL;!kj<$ay;s?EyE>Hz;|qVU-DJ=mGi@j5KjQNCZ|HCr&jtka;$V=+nzd2G7a@sYz2 zb`OBP3(D$Q`z>eOcZ5LII|*a%_I-BIlG<$nL<7>(s_*HaUYODZIXlSk@@ufc)%CfQ z+vJ)1>DVsv=~t#?L63Fm1OY)9!H+XGSKIX+gQY>o$o|IX>XqFW=%!P>_5tnTaUFZH zb9iIT_SLNB?d|NLiDo2nd)P)K(+&GnQz8oJBY_w3^@Yr6pPwdLQdkS z(T=zzT)_ULWe0!w&>a*zGJD|P4YGnxl!8jkv_P$f(`C}1Y856Q>n`@Ymd%Yp732|! z<#;H(wB5HV5gbu*}`jq)$RlXe2+mTAGwMAWcrFc!2kly}-h_{DJB z8Pz&y8ZQAW%z1eMnB(}^AD>J-*{P#xyuRG&p}v%w=g_{FqnrQY!(+#K=P|mmlI+KAWAim~ zUBhA3&7CO@&6$?t73xy>Xr28h(6ZRTFBKfn2D#wya9)Ba$J9VkeZo=b=97;{aUE6B zYW!XoRqgo>FQpED4M}j z*3`6Uh5-~OGJnZ3|6%ie3G!y-ULrqHaWfA<2ICy}?;nEJrQq^S{sjci_ogee_u2YX z?bk8sMobm)7L_3!&YgyHD}31o!0a>xWQUwjdthy?6~)wB8aa)7xso<+Wg=$%B#azR z_duKgDM=M_IXW&WA`jek0S3MK3-$2Aa8T@$krlagP}QY>wcU8_bzW%U*jUCWUiJUG*E&D^Xax;woN&|4EeSdggNO=A1F5K6Wb*`SDZEa; zEgYY$pAD0SACe}60#pAkvYWpD@PUTFf7kf%K}@UY{#<<112CI~Bz^$;b@-`hugme4 z=cXumS6ah?KkB8M#ATCwOXJANw8u{w#VH0rX~~gGvr<(YERFQs{dQ~VY@;*|kSx0c z-XDthgRzdV3F}4{5(%%9xga6o8<+FJY~At~`S&j2jaS1S@^0JJD=;uKZd0+}1{#=8 zFC9;*@x|gJNCXn4dT2{ZT}Spzp67u(HH6~eK!17T2xR#j3j+mxYLN!asNTXeqs)G1%n}Y z*W!j;@KU1r_ZMT@+en1V4 zH22tcP?+&4L?Yl-)yDUZPj#1(F&fk#J#R@Ro2PD8p;YjQb1Dp85v<$$37n<3%uT=eF9C9>*ZN%EF2tOv5m8m z_01iZ(L27&%;^MUhlv?7-gHl@ru_&1blLw?z4FbW0|oK?`cG6&!e`2Py(0=tPQ!{n z5$wuChf&7bVc4@Xy^KR&f2#{Hw=vNZ#M$GR6cRaESua3*X$abrP_Iij#q5`HYq!!M z`D({xkKY80=@fEUf{`yCKDOV9jpGfKFXhgMa)zVolT=VIhD*H+!}2L2Oh7fxE23a! zmgz=uObT~;k+0F;*N4mZ;_1hD*1?4MPG+5CNlJbG}|hBG>|I_No` zY?Q5kRQE*{hWOIP4yP8chVpIgqpeg*5mGzfgH@@en)Ldx2YGrZZ(c9@8efY5(!Vn; z%@9EzvmkSuD}C01OMbxD_fZYvw_p)hTF=BoK>C^o5xw~q=o>C)fSuU61fi^?l~oA_ z+Xi;yh`2TKNaI(a!1+)>00L&FAe!CF>MdO=+{?`@*Re!K-jwb?Te1~*2JC*8TE!}A z&fv{jxG`TIgWO?iCg&1|lt+5J;`Dl~7XKs<=I(u=)6o#&NsDEGgp7n2LH0~kA>Z%y z=X%SnH*p%4g~uc7mgnZ~mOG6{$3LwLuScc}>P6U&rXkh@m2d761iwZylxd2Hz{QKi zg_au7V_g4qjpXc3wWQ;WIu)!7OyIX)=3l8Zj^s@E$v;DHQXx0zaWCBmA2pL{H;^Rp%S_ZBFzp`S%h);Yc6n}m@KEY}|HJ&chgE89c!&T>{);hKhqByj| zf|xw{N0&kzlwOe!?6>-o#EK-Xb9uB-J}w@OyEzUL(UJ0c1SWj-xO|tE8TA*XwS)8~4Eh6cD-tVLg&BzQ@EBouF3usAuoGXd;&`udqQv$aSUQz3zkeYO-t z$;lzJ?2JIXac8yZ)&8|$^{ep(m5w;!G%88whKF)OjQg#n4G;BNu*B0)QBnOcVfvw&vvWFu-eq`OL zSvw|k4Q&V|jopH&sHz&@A15IAFo3)KoSU`+fdSW=IKx3Qs?YG-v-UpM82f6S<^i|_ zs<+r}NqZoU`j@U}+;`m8TN>umS4-4?2KXKKN->a{ceL5DMC{;U)lZjl1H^NRsv=f@bt z3DoDoi*TP;}0)Y~it_UCv`i5|BOW^7_Go zbL9}8ymRu2d7ywHO~hM!m-kFf15h3xK71H*Xqudce9tEG#!=e|tBiH#Wr=KB8vOqr z-#-^y@VO6?p&URtI|xNZMoJ^YKqMxM#gJ*e5NKEnO?l!h%R){N9T^ohYTxOPtTX-Dyys?kV_Ec4g`D!vo@7P zKv)vl=XF$0=t&~iUnoo}w_y3_BQUVL`AWE6(L?z7?cNIRURK&2#pD{y40%UZ%6Tgn zRP+f_Ja7>gJ_!y-K{Gjw@aHu2YV8o}d!bHs>`C_=lZhm2f)`@dRV^z(0%J~nClzOL zZ}*Nb)Bea#T69#3FGq2y#_rdb5l$Ewx9>KJdhZ?MM2qTpV_;zI-MM2$i%R$MhVM17 z5{@rHrKF^Ul|cHL8Zo_tv$J#Ihc18rV!JO@8W%<^AHTt{Udt=8aMmzZRvvupjWyy| ze@)fX)1$9#?m+?U;p63%Jq%z!exk@XiCUk?%3R-(P5W;C*IWL>dLaH*5iI>u{=QVr znp1b0hz=$e7Ph%Fs4c12yH(CL&{(Jcz6U};Qp;=)QMaBG6$jH30j4R>|6|@rTvQb2 zy7FX=V<9F(Bd{)vyCICPzD_w!-yO5rsfV(A);yenzCIGNe>#1=o2Rm8`*yP4vmu&@JZ_=wzTPLL z>G2u*SN4QbuMgxS?9!{XD{{LWC`8@|2M@B^`t@m{yE7AO=L4#zS`j&Uy9Z()kVIZ$ zo5%6Ws-Yc*eid;WV0g9pr2vna$DTH&zH(9qC9wTo0rC?BtW-r!>i zCdMZ^87aCCSW?+*Z*NWaAjrRA0x0DLh(xrE?b9$qek&`W4Vil+M|3 zM!R)u7)k1~!OIimrY6cg7pN1}Ks}%P6e!ujDaiLAI)L!)&a&JlHqhs>%n7xbdkW;P zoPR8wU4T(3F&{o)2CwKMBllVSb@g3i#U4+Bq4hMqn&b`5)GMnH?R<1Ja>UizUxdJSGG-&hrOM3z9AAW5R;Um_{V@2l7W(TM`mgYfmfyzv7tP+VtyeOE=@qiS?kGDU zl1HGKg+-JXi-D%L4WyuewIiX<3?m_qW?<+bRbXlHGs#{;42`{UUyS3|8I~tstm9}O z?X9B}F7bM)dtC(68(6-H$7J5<8!YXD6{&_8Qa&S^ul$NG{YvK%XQe>V5_Y|K(&h$@ z7m_a?2aPoKr-g@)_+Lg+U(~(zpLmJ%6>rtTl<>WDR9m@Ge&aEX-x;mvsleW0^-&7U zMD_kTZ(x*UVcRJKdo?l<+x)Y-j6emEi;Jq6VIdcp|AS{^n>bdeti-3}L+sAfTU%^v?k3-u=R+rY0XQZtna(`KpEywCr4s+T74*dM2v3 z9x@0(>V<)lqoZAnu-pf)mQ9l3C{hxV?wk+nEmbgR+wEjg-$#!ksMD^N zc^BU(7WNeXJY0gK@YtT*Kuu=AE!kSh%9iT=suMfX%sK9Sb3sE*S2z9aydgJN(k3Ys zTm}lHdr0i8HzJagH-(wnxikJVmYGFCB6oE_W~}8Rr~<4*;9&C{LUgy~yw4Peh}&Ip zJc(81BzV=94Jv@G&TrFw@!e!gm-=ZN`X)7H=xj*wQTX*qcmeew$Sq2cn}CWEV_8aS zYDod!jQ90r!T9IXp99V=E>)P15!Cu+oN$+GAjKEk7EIKoC?`aV(CtZgwTGl-4lZuWQWp5E5$_eVCvkbtvts1Ti-9sk}Ot6@2dZ|1D0Z@|4&SVMSM{Mk? z3-m+o0)~_f&q8ExvRm-VelY-nADxcwQLZpODO&@LvZU~rGtVXyIQqB6{e8P{#{}QL zeFM1mLRl^ewXyb-#k2vZj#Chp24T#u;LWsUWXOvGFW7QsB>9(V{XceSQb8ADY`oI{ z_0x8hlVq2zkXF^ zWMovDrQ_mq7*wehXKD|)NrRYPOfHKT6Kh*$AIr1I{B#VM^`=D(O3fw-aP69+P}%2E zgkR0JLQ6_Y?($||S)_7F_QZwqn?^=PQuB$K6=4rYfyu0I_PCM*DuJsAUP~55vc~0e zTUB=5Yn&$N)TGgfduW>E@%+ObPs)_?kq+o0t|KzF@J_jc4{{W}&qoxSd5)`Ta=Dh7 z6?BS?Oo4=BUwj;P5UgX@VN^QFSCe#++NfMDCY zAP6oAhwje5IW)i`l-;HFo&LtBUQG$PbB{c>MeJ2;uAm&_lV`7^(4vtGilB%$r|F-X zVgX3>!Tx^sZPa;VC%~{0~vzJ2>XBOQ$ocOoG^er5q}W(|!WDZc5}*48=-|4#%!h}IoB!}#ZwOB!-2-R`ve zPQEhh=<$bpmGegLn8!SV4J1zT)28#U+;)7t+gfz{@q+A_IN_ zPY5-b9Ko!vt{%3LECg2hKkku@A`)8k)WO??fq4B*K z_jb{clpbFLNJ{qArWpV3%bs02$*vr-q=#-NlHnpW)4Q^>JT+1`&YuND-j(ISxgBnp zg3J2--3y>@KqHK;9DP@*q_|iao|j`$OjQ)}0S0to{O-n$0=v?V0leHRkPoiorg_4P zHQLvg!WInW{$o+neMqrD;{TnkS4oRo>Gjn>xYt8~1GqERii$l|K@4s$Gf3Y&s=C@nIiQdyd&`K&{E6Pt?WmX%2s1FubV3(_CeX4 zAxuD_Nkg+b<=*|5U>1FF?#f&RX{D)?5h(*Chmm*EMT$Kv1bfl!c-fm0sm_^;A}SPmtqPXUH|xa z0pnBZT?29ljvan>c6rpOyMz*z5|U_9pP7}im>)fQtW#8z78`5iC_w+ZNdpBp>_e|6 zO;_{t!AP1PM%LqX@>kHEPZh`So&}vgez(eEjb^oyg+Un~@}W~pOAB9{P9nAWo#KLJ zUfqMccQaV;FWrcV`MmSJM(_H)Ctq_kIxCHID83i_b$0^iFvG2eh6@tgx+9O&V@xGw zW#Z<}6XL}cg&j{eo339T``2Zd4Mi?W-wZ9SA+_WzcyzrN?i~%pnYGAJNP7yXTA`lj zP5^M^u4Ze%rJEcCE&(3+bwOHf2`d!ToMcfsxo}K6of?Nu+`6*Lk9ptU=`Gx~d&(_g zTnV6O%GrZ;*FZS=+uKm&X=a}YC6F#)d?R4VV)#ZipQ|hWy_(r#$-IMyS_TN19>Xo<-ET2oqdym zShNsjkl}v*VCw};&lqxE)w}f%G^nNWkM4Y11VMW) z*y#E^6jxwdf@Ruw`hgZ{IY-gk+w?A>qC4uY>jNvcDswN+(d)*yPtlxjfM++Frt^HjwOnF^ARCW{(Q`7u(GY&uLBM6{>cPDbc!p5?; z7M2tYq*_zRC;{4KpPnQy5|Wc!Jx;-AnB_(W^wZ2+X|WQdq?<(dsHr&L7>VGcMdMa>O6}f3om9I)bq!cdEhMa3HA;XpcKg^BWOo0v*?-<{ zA6}ri(js9;`j6x9`yLB~-koFR#>yt0+ScgGdgvSI#ZNa3Dgnl|aLyp4^_$JUK|Q8s z*aRjDb9CC(iT!>@pHAd<`orqE?@M?iGmp5J=$Bk!nYhcs!0-ahhC`)4tt!Zn;*gDKeJN2znI5wB%y7xq7pW3bWo2opOq5&| zFlz{89a>vk+4D9Fh{C!YNs%r*?hz7_jNB6GyG>8Z=klL#xJwPTt$wv_Ip_5+K2R4( z0ICD(@e4=C%BftMh!9;4WLVUg{M^8JO-ACvWOWhRlY~;#xTOYU;g=grN0!QVy2rpH z!O7LXdPZfny){8v!d3t3lzn1S=%poHc5Zb89)fK3uzz@%mVkhOu`GDKGt8u+p#h;t z^78)?_7+f4wrlq|j37vfAdQ4H2+|-RAxcY0H&W6i-Kj`-3kV_%B^}a83`j^yHz+l9 zGyLy4-}`>Y_xwK3e=XP2#bPaIp69-=*w^0sdj34|*T^T~5Atu0?hp{%2RWHlFfu+7 zT0mo#gFqliNFBYnqrDw1F=>){!4`tKeR6bE*xxmH3?QDyNdIa1 z|2ss3pNa#p`kMgcANSkn1xi3JaB`8AmVVRkf#0YFd@i7Aubdf~9{bb&1ipC1$5EU_ zK`I)Sg5R;xcPsCshB!<`drfQ|-wc}E2wi#4r62@W!R{v4 z6YyA7`60niF||S>B3+fU1n{48619rrtDoO#P?zuy;7qoiP9K8Je8tMwJov_DIGkAv~gDB^+5`uRi1_jWFD;`kKNWLOOwHF~KCx^01 zNlQytLW_qN{a(nTFbynTMTHt6K%bvKyymF;dxrcwl>AS39H9-e0arW{B>i{0oYDX( z@YMoq_$uY-!8c(B3hY@1b>MWBpU+rWTs#UVFD>_d^ypFGMidfCLo4mc$rMYxKCe@8 zJnigHp0-PQc*KCNI>JL^oT<|zcE5d@wQj%qqdyOtGW*%^M|swinaRmtyU%D6(o<6( zF%)5%T!**viBe~)avzV7hU7Fg&2kdl2uwfC2-rn7()5gsSW+(+M5h<+`CNZO0zgZ8 zgcv7@bl`r{`)@4o-*4rw7u2Y2l;+xM_G;^yy~aY#U%=Rmp1(To>?QirhKkWUOe0cP zHN~#aTjM(7;{M-2YoRXs+AIZ=h#V|Bw89i2E3*bl9blJAp|oU+Rs76F;VP@vt8+}aN~-rX3qIbfDny2a6^o6n9^IE8sXcJSzDdel^*sAR|F2bVB!PcZIV@S%eTd|HL~V6 z->Omjyuc$#dgpDPTl>QCqDcXzO*`1!u`TRjlpQ8x?h{VMt-ZB)TrfmSM1=rRZhtQY zQG!YdIWE&P{W=E;_0n-R);FP{SeM}Y-GaE^USD6QXovBbe~XETz){k2DW&`Uga7l) zenJ-buU~Tf<0bE~`OC$U#*vd)Ua5fWB&VvL*6}3OJqMr&PVd}_l$8u zAg)|gSEm&cP+UURPDC0`4)^+Qy!;OHio3%2% zY)Y`jT7&u;{(DRx^H_A&q;O^0ShiP!3pxp?DZRHH=RUOFB@jo^D4Urc8ZvUu^AEG* zhSY%#t#N5-tf1&_=ucu2RRRgT02``hf`*}1zO%ipuGA)TYiw+6qf!>!$^EBBsgaN! zW(N?ufBpQ)Sv&=T4H{E_`Ja2&?ADmps)2!lqOX6)n${-tMh~r5C%$HNu6K9!P|^Is z(29Z7d{#%Y&I(voLx*#!yP3Rn%*;N5?60N&pdE%OjQk(Q(FcfpA5eQcD3qQC%Wf27 zvdo<%CFu3Nklm^AT18B2X!~)0Qf-w)Z)j*x8>+6U8OQf^>B5+Dnpr=ye1{IR}q zw6y<^%Io)6g;f_1ddrau`j1XjpT8>FP;^X5PxVeYF&wAPVoq7kp z>jf+02pW^t*dIJlNbPv94()ew_v1##&zC+jZCZR>4{Q#uILiL~&6JI_z(lf{=&$cZ zMFVmnMx%`0Gc5PFtqTj;(2SYUQB#*lt_)AWES^?lwz0QQ1|z2)019`WGt$%dw1;4` z=R1R9hm(HD{Z#wxJN5o(X3PrZWR+(rnob)q*HX<9W4rB0fIef8{pnLTzFdvVQe-Ya zWXubUK-D@;HTGQ+pk`tq^9{0KvMrOJM5co(2C5Y~ai5<4>nZJAzW)fij*iY~nDePZ zWaz?Wg;}4L#O$wqy?^JmzmMHtK?DTZ=J$2OdKSNDbY-J0u(AeNmluAHuC~ZMp9a&G znS55-P;cpv7S@ZV!4hB%ZHev8Pv8`GK-AIEmugqAD_9Ow8c!BkJPs1WD{KyM3=OanCx=N@mP{OR)g;5fPNn&MvJ)BChtAI{E>4CAJ2lsA0 z&Obi=Z}G8}4xJR3@h3;J{XT{WD927nOe|!LAAcXl8VpGJ&guT>tS>(}RZvNB8(2KX z2}B_TsGN~}w!A1y^Zi>B((+obqP#tIKTyuOiATY`o^8lo8}O~ARaWoeC|FGZuH+7As44Z|WM)k)A2kCZD6RLGdP z7gcq2tqW`-Cn6&u1NfX`j(CZUWq=LZ#`kAX5EV;{1Wd6Sus{8yJCOSqTJ-E$NGIp7 zA2Mpe-@s`i!k#tGRUx~xxH30q5I!cM6=sYN@Xc)hkF7=@@4CIsduBNncTx4`#*%6O zc2?3x;|-^}-RwryRMvFKTG`hn})G^$`$`92|Ck3a@9}C6y2t zb5!&jG;XN}Jlp0$DTn2b|I7jZPUob`YhJ&-OE8$$%PuUGm6Mah=}@7t=Ll6A92js- z#~%kb3=I2+t?(m_@s~BDYFUg77pzlG{ulgNVvB`-JF>vy-tFBa!}sm9>?Ce-K-D^qQC zwRSNcRO^H4Jtz|rhR((RSrFH_#5O$a+J-;J<1s#_@d)7y7VPd%m@YTojeq0*1`^NW zfAs>8WuOTWMwWhl*Le@9Bus;~?kJcKIjHtzR>UMF5wkpesM+RvfWBZ~02=6`iIlO= z$mqxmIlX#)dFuGRSxmEX*(V`BK9{vPdhHB}8vREc`TMQ;*CLCQZ8(aZ*uM%H_h66@ zsP?!W8CVuvA)cKA!3q7k%E~cVmrGOk$f4$ItE<|$q_j9l(G;jCoZqOg3Msdm05;e8 z&)n#(QaFGH8GxjAN5ne@5=GqVTbPq0fgV3SJ&k_h4|`SMO%70ONKu{nD;MrjCJQom zYTAMb6*`CZc*hQl%!GuyvWkktjTk?eImwabi0f9$YZptpJ2YachhEIahLwx|KKN9_)6+#YlpT&A((d3fYgvpbGT6;u zVEp@Da?+v(H|?JF>Ijl$eN?{cCv1m_r-=E9cEAWd1$JR`tVSi|_2ua8sm8{}UGeVe z5XC#gInVxo$z+9Kg^}2M{zFKvSFqydDlFJpi5O??WI? zD9*VK;D2Ib%#{UaHE@MGO-fUY%AB~znLyP5BH^!h>dV|L3ww|PW~;Krr^*^J)l6Tz z8Snf1XHm&?iCP*N8Qq_aJgdhnzMl|*t>i?-lO-*Rn&pW9q#GSx4`AnT-w!uM?P=(v zu_2qke*M~*q7z79+g3xp+fi(xlKUqA7fKN`r(A&LR7_bsPt+Q-=G#h- z=+?!KHw5lRcdDkku|rA;+Baj63K?@Qi1%7mB@w89TTFRR{0%tSZs z1&=IT=K4lSK=2Zi;Y%Mgs)?4*z=jcOlam{W+Mf1H&2tV;H(6PhE2RL}PBeAGfWv=q zs37B;Lq&4Sow~FCec1m9iGbJBfujC3ZUDmQ{u$+$?GCQ2De+p);8z*O`nzCIDbZ?8{^U6dSk&p(Y7Glip87LW%2!6BJu+m&R zT&B^@QH+sM%6#zsHh-@C&&|P27?HRjSz`R2*K>R1CIZkv{Qp#25lB>8igQ4eDinp0I!^IvsL! zw)Sx)RJrvRqp-U}8+p=G7(&EhK#^z~Jq*W+!54h9t*enY-WLzF}t z74%_)j#s>w5ynbyqf@be5?S8a`udC_vClh6wmw=8hRLwpuDqWYqm&vlIzA4CCxHrE zq2?A*BJc)gabEj&$J_kn0S9U$wPJ=KEhbCu;?-v`TMdnG+O%yg`S_<=ECX3l5-LxA zbU~$T^afk5kd;0o@YPeehgN}57%2n_))yF4{XxB$(=(3U_lomudON*9seJd2k^8H6 zjWIJgSc2LpIaq28&E97fFhDL9Um$6t&;;ZR{VObov$^{Yi|yzv?CjO`^_UB`KR&mu zEqnVzz#QGHASR;a*Pff8Z}gWBA#dKugA<5_v%$ps<>li#g+Y>^aJXi!r@Fd&;h;i6 zezAa`pP#Ifk|v!ZXF+k1!nU>A_pUH!cf;&OXC79jWi(8Hy8Xu~{r8tnNdXyw@L;*J zn`XC{t*qdNc$bnk0ElKBI&e4o{FZ;w&33ir0uL=2F!zNO3u<|s%(|s3IIrA$ph>;) zeQi}2f+nD{vC??T@Vz3Lhn4d%N1REBZ>R4mF$kU8kaz)zB-W4v?3 zmW3E0WkEAg7SIEInMzTsFSy?npwXQ6Uo-{q;8T>WNPs(=a^t#jzV=X597qv!)_%>~ zcI(tT7zLB{i;i_MxECSdUWlRsFkZQ^g!tY)c2IkkA8~&2Do63vG`UOHBv*kD*fNc$ zp?^!5cBIT5mtNIl5{7r}zXJQ^lJXzRX>>yrT*(?RCEK2nry|d6A2n>2gQDtR_IrI`! zs)>)67c(g&=UwJ^d>mgO%te*?MTsd8z)HyYy(hckHGiSz`QBj|LiT# z+U&EZPg9hZd>#_D&e#8VLj%IT;{7&WP2R0ae4MXge084#OV|w1;o6LI_6121a5rTa zDq}OW!#zazEvh;|DV33u>I!ag+oMt+oK!^{0RkYL7WnRkIhe!~5EHLGqG;p$<6UE3 zSOW*(%NaH=k;d&YFl_3p%W>Lw?|^k(W#3kF<}S^PFr%GWim+lT$a4Z_yfC=$i72C}*Kh@RMTx*< z9=b05?3pZ>2Q1LsgD47W9RMj)4=^rJSN71ub{vk!ACmg{5_(YAQN^ zqezczoGg7L+zh5F9J5iNkatlflQR4~PO4aw-)?!Av_%}?hx!sdIdCHRZ|8x|#jt00>-6fdm0*kglqaj^Q~Eq0EvWxVE8acbstFA6*P#9;n!zb*Re?974&6H zZf&Qju}P!z-qN$eqM~>JuW*URSN;Wd=D$Z#`~fX1tNp>LCi7fDp(;f*l@MP}YSnZ( zXCyLt(4nIICV<+xAebYQ)}*mFC6xK$cUtG8JVQ(`pNCzA7Lg+mUlF$gO!f492qKFp z>-0_t#aH#byMqSRFv~XP1r~S|z+4TBBX@~gamV~X0X4$L737*DpSWgpL0JeGJ$WOG zgg-{aD58{|A3HxN&=t@DVPpkZ1TzvQv+J%6y^|5FixLI2@-Q>uqFF$r+VE$EqBesS ziHzYAugD~mb2oZ{>x%uTO4qKw^L|DIhLf{1 z?&3Nizu6auFI@gzNhrkmJE9Bv#A5m{er5I6a8mEExSye5#+DHgb6cv}2*7ldY@Dj% zWV9lh2s?+Z@d^QJNI^mL2`V&r>0}q!9HKGpFTtfXgX8a9N*+xDZdVau5b6ExD~yD*RNBB58W_`2OI)r z^rHR=5~8Nhl)-%}lh~U?)YK2b#J!20fuT?ov?{m(4v$|4+}IWre7mU8Uu2%UaVJSr zTe~o{P6}OCP4da^R9z11Fi`B~Gx{uI8^!?VS9!28M#$lM7VfL*;2-zboC$yom}$nm zw+=u8+8)O8`=PnMx?1S-`K)14R8;uag}ZgMGdX~ld{MpM*GxEkpqm~g{0VSLHQBED zE&xe$%*vo$16wGx3%cMXFshS<2!@#eTz%tl1Y8oNK>E`;NJ)9Q%I+0fvq>w=2(U2T zS6Ld=mtrO?f1%4LE)(!x4~D-z&5Y(~|1V>rC?r0ym%sI01_i@0f&#kos3^z&abvid z(c}OYbM7^8h5_^O*cYD^VVh+w9H7(S57y)1;bDggEk7c(1#gis*xwN!hvac~#O`g# zcauj2$b_YVA$5$Gp93Asbem(rls2q)>a+`CQ_Gbwn%3w*6%mOF67RxogIc`z=u+;y zxf!I4j)$@_T>}`o)!qk)A}AD%k=s#}(b@k!Dp5ySG~N2tPto3Da%N+MxD1<-cECs!;)OTJ1%&~^z%1AQ6Lv6&%@>J4sm3`7^Sd z;tg6jrumtoq_+<59G%S*MGkE#mH7>8_B;hQjzOGwdK*^^ke@bM698!FeLj-qI(lE> zyiGabRl)m+P@~(d7$zxm?d?yHdj--&)228MMJ`AI3E1tXxP&%d{MN;|A-+TJQ@ze=9yE=4SqZwU}CI@&wU#YuciQEyN9OuS65l5jKVaTl7$~S zCLwN<=hIlJL9rPWA5a{UUoO^l%RoyEIoo{%IbZkoYU0VZQR?=gmZTXLjEV5CV59K; zWMUXJ|Eo^W6OC5Wjw1Ovs&ha=0dpH%G2_%5ap}<}qwuOgFN$ztuhPGjZOz1)I$(HM z^+-RaS?aVY1PWVY8rY0J0M*LhE$;tQ&r-61a^2+dWO^z}s4}nJfQYimTYW(>?pPzggH^UVCNd_LL8gkTF0E{Wa( zhq*j3N?qad+RV9KA9aJb)K0MO%{v$->*iQax#I5S+)4AN&_&Aa0DCp-(IAy^R-S4c1U?tp^>eZzn7LD|ZjzlOiI9^^0)7mmI<25oQXFgU#4ptI|qaB)1e3?M098 z!FaJ$4YxaCQkr?xoA#sU>8vZ;$?w&ZZRhHL6$w^J)%rNW2fynr<_367ZD9z{ z4md|}e1D7ZDE0cEA2y(KO1B=tp03-FcztZmyR|jnVhFgk;*`pBpaY))X9Ld=0k$Hm zX9;G%oO$x(i7D7L0c=9@@LIF6_Fj?W73AbVBZ{We_^d}`(tmVKP3iE>tX-ZRFaU~I z7kPG9fOzmoxlchuM7T!>Dhtk=h#S{ysAdNqf5S-&asi_3;+ckVWhGB4U#FlKmf_&w zl}$aE+#BmB2=KabZ`eSc<{|u{a`d+ErS%f^nZ?j&qobXhKPf4J8fh5q+?8%Hc|3Wtz!pJH~<;Q zziN?i2{RKMYv044D(Z>Ab$He|yyf+;xP`z_EDfZ;sn4U$?dz}g(P{P`Bs=?5hEQ-w8z`l|w6Av@ytY>LnH;h;e8XF`QmOG&h*$1Ox0Wn< zR*P~D-o(|^ttU;aI7@7(w5Wk%y!bP1F!l2=mC)7wo9M_3@R-t~?4Y1vFYB{WnV~4Z zP(T#k_Q{R|4Ox!qGXCsOPlrpr0;lMc5GuJY9}xeI4k4KP$k{TqsAvqG$ou=Q54Kdt zlWMce2(B$L&yl@vVH;zZ?xiL|hqAEBvdh%95zpYx}-k#s_FMQ#5gp9;Jur+E?5W|;;SK%cxi##A0>W*Y|Ygra8S1Z zSItGDhqM-~S~ZZU{u!G_<==j6JKsFJvqc(}_*WTdg#WWI`NH1oX^3a-<4eg~K5)(b zPu@fVRA~&ZnIe0mEpm334y|pk5JYaXYzV)377PlUrm*Xod;6Q+*DBnS{p-8>bEwbt?M*D| z=d$DQ(eb0O!MxP5Q#b)zJK_fw&F zu2cGNls@4W04?2mRL@-Pv23SVht(?qZ?n$vGMG|xrR=4a#|UX?Ez5#hK&C|l?Vo+6A|H!(4jDs8J1H$lxPwzeD9B8oGrhto#OM8+3 zJDq~MAYiM-osMb`2ixVvid3$zhi-IA9tg;r#k=+iW*0x6 z-qo_sI+gw*`W@>4z@o6#_ly1&-{bbqq^X;Qcu(*qi9zvDg3Il=o0&W8Ho`AK-%w^% zW^=s}-_h3*Kc+S&|7&^>MlyH^+q^#W*t=cPgjZ4$>9q7TmDjsM8UZ&{Dq4qLUsj7f zJm<`FZK3ozcy#9Ko-cB}I%6GuKn!j) z8*g)R-v&6?f;(ykb7*XKT3T9Oaq%}!E28H4R@gjPj;l&?wPMv>7T|XD7GAl3aDYCK z`U`jd*&^QpjHChczXabpMWcpI;(i9NAQVv6ZDTqzv{Z$oQs*# zN^V?qNxHohZM>^hG*T*_H6UwO@U7ZvYiW^z13Qjf{C27v=TP8YS zD*LU3*I>hpn{&B9*!M<#NBVEQhZ;og;;F%i=!4f z{48>@dCf4b8cM8t*1ewh8NM-e4*`lOL3NZYKVhNLE8mJ5&YKA(g@P7Sg)ctYAaIv6 z7}Ad4?V5mvccx}$Fks5`5X@p?L16=p;qHLtYcxuuT3{~lOo9;;1K6bQu+QJz((1-= zI(IX`>;TS_QBL)uez2y7AL$(Syd8FGUHcj<=$Db%HoXKJo^NlGVIGjsBM@@S1tweWelEBVknjI7u< zc4T;5Z}NMaltdn~Pd77rRc~~DwD)Dcm(`arv^Xc_^O2|)Co+=v+YTK5i6Qw@;RT=v zg#J3m-Fg!g77Z zQmfdO504xs8q;(jnVGYy6GIP5Y?D9AV-KC2=-&MKX`J}W{P=5ES6LsQ@GGK&l9;7~ ze7eCOeh}(0bZ_r`c_SnL;5Yp-bty7on6>(5RDRB+(e!yIi{Gq+qxMc{5L6xL>Uw|$ zmxwN6*)k&b^WpO!VpdF9x$0xD=BMu_!Eo3WC`zsx4TS4$SX*93&DIg z6}vtgbnPey*11TAUPlGKa?Qj-n8z`EzAUg4^l{%wpl3!uRMdhHKz8`yeR^6N6pBrE z8h&H*6(%>GjYwLJID;m{d*elx>H*EG$i)Ol58(APDn#=jWJqxB*^#MQvs4OT z%l%LKdTunkhE5#6?Nib7jS}yi{Cy&gFqIyY7quAku!FLkaP$4p551utU49)?1!zAE zqx6V))M`X7Qwkcs-MFXfzLc%T`%-2Lg}u8`GbG-&Oue>>cT_fozsRb2uj0)4I@|7C z-;!{?8TFCtQp(#woI)RrMbnx>4rTop51)0es_Gap;6~D+nS2roePc7jU~?oSKIQ_z0`mJ4Q%P6==--_)!oFuTFv4w1es&J!b{j-bv%rb1*Y2 zJJT#A#Kz`4X(@0LnF=-H4b7DPNj4TRM9nI6-w5Xo*q7Y)feo$b4)jTGqMNN$MkCVO|C!%UjUGxO+-!Ue)m-4&xtDv>i2BuxP}RGdq!P(v#F_;t!p@Rz zYo|KZzvw2d@~--#BkFzd(5X(Z=-an7K8VGjeoQVCR^A6_mvv1lg&cr(YL4+>!af@M zFF(+kb`FO)>F;>+>9%MX&ynHMnqJNZV@m~dU1x%}y+R4AE(qmBcxRW@;hr8l*#5P= z;j;W+2#r4o0GOGC^GR3%#h5JEjVHb8XAZ%T0Euedyj^me~8qs}pxAf5qH?>2BnO z(P;Z9+lqu$iVm$<6R%@4NAsYf`U;aGz3c{F!!#6)3?HB7WZtg@>l45{pOuuA6DHM~ z_mjIwye%pzdA_u?RDArV*Q92=uTBYRU;+A8tO(iGZiD_+t|L=!9!0cv*%xcL7j`4# z52O*Il*&;VwC7VRVi!s}f*y4`hBl>JKRlaEnJkI2zZ|UmGTb3pmT}hC&s-sz#L7AD zEf>2~!K=k(=VNutoGC|m%0}WQ_I29##QTX@H3)%i<&dMu{)+2Xu1A{umx#Sn_NAN3 zV44#7ykvj8Iml^WE!IY_Mem8E``2pz*9m z>GkD`JIg9U*_lI6u+SzI-=K6PI>5ciqm%jGw>?r=Ko8ho6 z>DS@&8i{{qwVA&1QQy9AgD89~EEd4}CR1~*t>Q-~xQMQ{PQ#1Y0Vz~_q6Ob>UBLLM z<={-Z8kkC~uaFg%2>mPk{!P~U_fS6D{|nMK%C4;Et^u_g#22s+@&E(%b0Q|`#OP=b z;E|dRWIPJ}b!>;^8X*Urtn1{1Z-VMg3i)j=_5MC~pot>9Q&2p*fj1~nnlkyo{&Kx7 zhr?t-*8C@LY_qkzppF{b$(p%A`p~SgYW?ZdjdOPPJpPOqn?HXJG2!W3j|5e-rk}q; zPjRo_$rjmP$O>KWOZYO8k2AZ|P?Ir3RNoPOTVKD)UGcD?4tHHF7JD(jP592X5(c$O zA9BVMJf?{&CMKuBac{@ZT~7V%QGByUcP0AV#ll;VlB+tUo=SDfct43-NCWakngwcn zX4)JcdPa>Rp|a|}ij2bIuc!B|zKa9Ho{ zNs~G_w)Q@&^|Nb`+*vrKP~5}k?6wsq)6b_YTn-F?aQaX1g8wFROq$s=!Ttz{ra+r` z-|J+D?-omKRaH;3&xP7gL9k#7Nb^S}rn0khg6azhe$31ivvMXUCTgR_qlP(fpLcO1 zrGfu5O>8yAro3F8SgGG#UrjHnVr{?CUvWWl(pS0|K*tSlFi?9vDfU9+>$?Q25qZNY z7U_vAWsSUGxq}dIk43+$Z=T}yno4}hatJ1uxG&6VX+jMO0%c{IH@&^{N}HcyO9;r% zAxJ>o*RlA@c7R>XL(S<0ng!4efg+NPO;;s33BA#cgVjjA_$3g2jWE)B3>^3A+ zqK6s2O^{cadd0sI?&&6nE3GK(!H3xR1^UXU{AAy|yT747eJ!&n{IG{^aH9_%gXcpS z>bZPP#khOy^=4ITjx4?!5ARmu=p-2d8O*5S77pA!vo(TjF;Kq!n`vQ#gTaMvHOxra zK%4DG&>pv&Yw>GWyv*H2;3zfT(Np&xU^d3SNGLoMt!{o%H~>7)51JVyZeaW=f|8$hx5)kY{FDCMq{9ES}^vDqOZHf+Ad{|)AG90s!|JV zV(k&D*!hO*h?!PBNm|X*52ndj4_i)y9`=S$iLkX?wwQJ(?+xTw=!vq3`K{z7T7(yW zflurYdFG~Gk@N__i#DCB7Rih&i$9{_SAVinKx5#xtJE^a zaeo>yxVhizGQDz8F1)HVCEc(V?bbWty2ZQwbk;hFCo!{vqqnZ1DsfT}#*Qv3=sp9+ z#qVIhQnl^soAp?*>f~tXO+!u1*5!^mu~iGv6OS}J$`zkfW1YBJ60rk_G$0neZk}xN zt+O%;q~=(7y{~9xY}Rk&SXhAI-Jh?4E~bK8c=dvudm>8USL8HlWi1VlO_U1lFrb-K zkt&)hOY*NH?oYaL%bJ+y1HIQP6pZCUNtt7+k+q(g21#{kz-+u2oTVLXM3lvCIQKtt z*)-Yz@tCdjIcanC&U-JjObvaq=i!2e){AT_Cz2`8{N2_oDj~*iS_0 z2QNLMMLZ`U;bcy5udZI9Q`j(teNtyfkP7eEm|P3uTQ<&F*C&!+C!t9>H7sg7K8qgp zB3BAeuc*xJ;RNtl4EDXDUQI2w=`DU88vpz$px)*~WMP$N1C2KE zVIiGnP76b~;w>A&H`Bs$Xfd&DX922s|BKAn;8Vdr9mfSLz*tny)zohy&}%-yLMmJV z$#@lT1e}gGe$KrE?7ny~PStL7=L&+&U5qa#y=dJ;3k(WsvP-XTyX)W;0NfC+Yoznk zbiWT?n@7(G&l?J*S$~xL0)Lo8U{+@30ASkM!&`_DDMpzBS=PzkB)8Wxwia2V0^_ni z&#PfLzP`RlpU5_gJT5-ldubde&_Uyp%vl1X<AuL zSWB8ZaAeBlxW-F?ttVBV0SHX#1~1pyhR0@2v_e)AcqPPC+UJff$do76#Ktn?|;+Q0O78S!NgW7gbq7^b7Rh+ApaN=DLqoyLTnU8-5=3$5F9EOIPPdI-_#ior0QK%isPQYsCHxH;s}22 z)Bxl|)TaR$?8|#!?!Ln-3+lvrMR-}={h|1At0fi7E~0Ijad9Q(hkI!J!E?EJ>=M8$xG! zpVf%jhggS^?in}1>%qC(C=qb$mO~2+ecnK+#-yc%Fji zb7Nq>0GKk9OQj4Yhq89(=o~GTR{?z^Y;draOfl;1+ZZtU@I00imz|P^j;%RmzzgA0{Wa_Hc~vC@z6ipuOUh6TMLqoO+Py>Jw|g=PzgI`1N;M`R&vF>z}QocZ|?i3lDUh zk0ILSteUDF-BfxMo6s6yRigG-D#o3cDB(*(1MncH~q-HGE8CN_1mk*p7Pq#!4%~Xb;Pp0ClCUuLBq!%;m}F+{6g|Am!syqT=X%AZ$9jX zMRgVra_I6u7qsyO@)LW{){p(+lkO@ab?gT&>l(k^VD}VpfH?vQMOramU?+$!j3eLR z*)O>Py^BTH8j##7yw9A%qN8h%IX`*CihFc2#Fs=yEqb-11-s>E`>QZ}4RY9S30-NS}b-a8d)-*xL3+8NaqdIB>+`wPU`eg#ONq;)R z{wUFJ_k8}Y2mciq1qet*dakanxNWDDneiqNI_iqwtV`bQ1Gmik zE-n+0PiyIdK*7JaSv4qMF@gJTI%UmVGqo zIc%zm zJ@AeCSKLUM7|X@LtAO!VoXP!<_Pv3E!kaGjym;Vo%I zN6)V#toDLRQie=S$y1yx{M9R~*wkC)x%%YmxvdFzQ=BgDX*S9QO*QY#F8XBluU>>1 z8hs9a!zAOa6Z#ZmdQ$T!0p{iBE~Uu#T3&qk=GhF1n@rWj7Ksf+=qCtTlxQ%!-)_!7 z|9P8mVd5LraiF8iZ-+y~FuStyg^sT7M__rNOs0LCj?N|mfB%&(U{Ef6c$0WXs|!6R z3&3H0NH%+|Cc46Pb5Gv!R8~WgMi{NUKc;YJ3qe5xZ(EjpsK28-&FDf2&^hO+$v`Mog0}#z3+Mg(k zt=6E6IZjJX1JKL={>}nx0N|zq??ZK>$U6#MKGOVXao<0c_#S0DIXHZN09fn*8>Osw zKjuS6^Ym1hk8k#MWqsiJ^|(vsIF6GAMuDXxd!%hiKJ#F2q@>K-zFNa^<}`;#I!`u9 zrfQEjciNU@fPD5K9^DcTiz#>FdsePd6<%r6qz3|b^33x++PO7X{m=B(aKj%DP)qe0 zYIPdU2IJ2L({)Q8Xof=K>5UN1;19QK(c)zI_vSI&u^OWXV8aGbE{0q?E7yn~20wVa zyVbuwHBHQU?ZJKRQF)~x3e1xJGj{w{e6*H>Ill0CeADk>h4`&(0w+L|#$ue9nE0Y* z=HQ_0UDCDv&D}QX`j?@|F@3Pt7o!xNml)7CU<1US0?tBn;WNEyA;uKs&X!cAI`n_W2 zzdeCIoOi_Zav}TI;B;M5Ka)yp2YUXJ zZ}-^4F#9R<$p%KrbFb0@^}R7E!k=*ak%8}zn3)D~G}knvtNPNfPjF%Gr+D%4vLDuM zl5nmekLEx1->rE{Okd$yA!+aeT&MOP#ed$R-yYiFT>zi)7z&jBO*whrGAZ%57XXNA z{^?j?E26Bnc4|U$vUN^OtbEQ*bo4MFzJqlt6&YBI;+}k`KJNzO9JbKubKa|l$`M!g zrl=@9AY(u(DMyBf=^4AEutuIo*6Z9QjTCoRO*-wRm6TYjfJ$kTo0JSYen}u2#c>Nm z;R)2&T+!2x)U$9wkQZ`LWBD!0Hcx8vv#~GlmDh4=`@jh+Je9rjElXXt^q&v&cwaqU zEkd_I3;Mp^L%S;L$x=rO_)y{K?H25b^#{Z3Xv8ZvG5z`$W)tyBxFOM8(igf;c~+Ih zN?cg9Z_Hr;Bw<>TxS$S?bop{?23|KS-%j-5e@oZ@NSTc4tuLbPE3#sd{64MVb_z@Mck1?d6!x+cs*g>jxk4m!{q8CIip{u? z)klXqB@u3GyLXc~cnzBy07bKUm8*5z(1hMkY>&({U-w$9zZhCxov2=JLUEwq1ihi{ zwKkd-x^V$0W1*2yC^QbR$O{O~I32;t5XR_5$11Q*+=W2F;j;?LFz5QI-CdtXLo4qQ z(~*iQE|(#l-B`-fTM2v=$uhxv7cL4-4(3>O_5QAq^nT(D2Hlojma}WuW*1vx-SzL0 zODBkGMR)5eWO1w^*Ei_g!sGd1K=u!pRDfi`m`uPgpw9ERhrRRAAAc9LP-AVct*yZt zKc)hW2zfxnsn(_x>hJEB4vCDs(G7T^Kunhlfvp*viG=n6^)t}EcjRog4=^tX1w2g^ z=vI;&zzK=E7|cxM*QQ-GXB%}zyv^yYfJu%?=au?Z(x+aH&1n0Kzw#AJO@GO7SS+*= zxAB4+tnr~ve7BKiJ$Oq#;iI){&p97<=Z1X{g8b~F=u4g~_PLy4-Gk;;=eRq@73QJB zO_3I>aC<~_#)AnqaqHV>Z(uWKZaw|ArSXU*0aHWYNGu#C_Gwx+ZW~Scld<-hVHHNd z`GZhOVUvc)N{t!XbOHOMSGh!XLPw#Eel=20E`vU9dzTnz=I!}-92`mA+S(i@fXbVV zl&}@sSR|{hY{y57iZByJhQ6-TIf@qBm%{w&VeU*rauHV?%Y(blE2YK|^J2Wu@xo7Uyn=J+zB0Td#%LA2h5^*9Tasl#qny<V0e7 zO2)kK9fHadPH!+5*RWW^%vP3oWI4ggaWX-jC~%UQAa2_-v7AMlzVqY3B7b{tQ;C_= z+H#OngZQ*&jiJ2IMd-(#CB9l%nz}sw*Dhz=w~zUp&UZy0dF~hRRvQXS$Q+d~nQNCz zOyRb^$P??q)As=NTn z1M;`WEIFp(M4ajVtgt#{8Hr?BYuT%5@Y?|z7j$DE5J@aVQUUd)9I~g4{cKjFYvK~L3uXx7!uYvcwrU1U|$ER}2a^vYE*uo+l-f9~w|Bf3P+ zmhIp31-B}!y(){1w-+nXuQ2VFod4e5t%E;VcT{VcvjHJ{=l(9(%6J)v&3LvUcd!@R z@7^5$^7=`&JZ$kgDegL}$twW5(R_foCc1-g)i^(^?mY3QblAZ6sG*9dAE6OyYy@bCk!>?7Nlu^;~lRYo>Vp#nfJA9{9roU zA(mj1Y*FCiYla{`TW0%G^88boIfcC#9mbrmMg{!heR2ByJVihThlxory88|e+JLhK z&Aq7qYuzYXQfs1>Hd@ln%4h$xj0m|)WPufJAX9jPDWiCdZDeF*buHbV|Aro&3fCEH zsLlH{V82+TQ4$Acjg{Q_SDv^Jt@&ONqe%scD=aT#EaJ~6 zSEsHtmwuFQpsco|1vlVgy(U)=x+g#NB+T*Z#ZjmCnVH|BX?IA2Dzk3ZDG5vSLe-1c zt;cQz7+-4aGYryC40k$U$N5uU9k8_>=46_xE4{+F+cTl@+e~~t1BQ>MDKg$~QR-A@ zO~O47j6E7!94h*reR7IKIWB#Y-1L5Wi#yHf#A&;2_EEziNitB~9!$Wur)$GCYPL4W zXJkqiKKm$=Q#Bur>zeha@tMc`@U`LG1PeMh54_35d>g^Cu9@QO;>4t*j3S-DPcX4Y zcX7PS^E;{L*R1EWO_yy~ONO2wXR(ES&Yb+>d6?+uNT^7~)TVPZZ)q9h=67;~rJU)5S5$4% zA+@5b)e|9lcu4ok6?g9aYi6yR7b0X^9?jzI&TrPE9(q%A-~4+k*uP&M3hF$u!+DX# zJQZv1Z%Zp%-O#3{r!UYvn4bTC?7ekdlbZFHoPV2(*xGoxtp2BfjmS;wJ?9Y*x^)4@YrL&YlIPn%{|AyGe5LQhwy|xB#gNB0 zGu=nS2{!8qP`d69vLi?>Itfo#9fz69m#(gi41(I?$;M}doyQ3c$xjs!e_mDh`M=T{s;=Asv*x0qHgt8a(i22pMUoITsscgo6L->3k zIC{r6am3%oY+DPW#0>o)jrfN)Nt}eY|TY#v(GYThp6~hQoZZY97 z;yBNKsJw7z$fmPhO?R1LCOo*qa))H!By&aqoW1?_giQExX6uQ5h zxjbeOads$M=!U9O?WpxdT;J=vI~^P4TIr-7`S?QUMqm&B@CtwZZR`_8s?P`KhsOFe zXwJjWlpSVO#feJe;gRG}E6Gq7`@2 z3{VjrbbFsPf3YWFZCyN3aPUMh9OEa#dl#rC2{Cv}qO1F^1>X5h{fjLd8{Pa2BGVv# z`2>Mypl9+v>bio%l~Fa&;SvnWl!qK+26z)3K6-oUUivI&t^q%nll4)0*kbzwbNlj` ztQNI(b5gK)SmX{)DCt${bi|Q?iG9}l7aw(^Ir=M(3{OK4`aPaaW5w#VcKgF?R)sqF zx8QDCrv^5T5V*8PyH1p{CZ)@;SjUh%3-!c`XL!D-Q`~0>5^rU4Ps3`%ar2$ym}gkr z({pN-qh~#5)Vzj7-woZFY=fU*I3hI;r+6uBE-6A=M>HM=;zLcX%3r?vHhygJv^Rk3 z)3-VH%-|dXF_BSCcLBcD6iZS(`eps%FyLo|j$#JJM&`~0XDkr(OePw=2Aa1Ub#^7= z8Y#kuya=DJ<)>}|`a#gskK91yWuBy{}{*1>VNaNVru zBp=>U+9^2Oa)uJF=(-T2Yw0S0Vo^pi&+jbQ#Y7^Dg zvBXY1jvJ!bCr^_d_7!LAJonRt;I60p`L#5nr-SRn-)An30GIz3=13@`)-(n=dLi$ zUbjDVe>L4ErDUJvOk=y5^Ajn|MKWUkY7cpj8-4GkpGbh#n_j0%7K&ZFYy04b86#r% z2HsWh=n^|4(#Y6Yp!C$*2&kng5QdEm52vLxWP8oONOgJ)w!FQ+E~xbO9}S_f))us>QdHNA=GyTzFiRT4+twLg|5uwIlBko z`e=(=EKfB_qB9WP-3l#ScG~;utjDB!JG9U`51568omM6jApf>mRCb)z%fu2Uh3KuI zB~1_K!tz*(c#75A__(>B+1S{0+B*&geHBSq522Att8ktaJEE2UUZq~hBTCPG=oz~1 zKHswC(kN0vZ<=dIF+^vC7_%+W<$#URY#rQq*HOkR&tr42Pn0ru7EF0I?Q+bJOEX!9 zY0`0NHhk5-v~YcWmdDMu=va3>pKZBSowp-29xpVZ2vP{IsY~MdL}zcU43|^r(KaYs zJ!N)L?tHXwAHunIlF=DNLO<4nxj(<-Ff|PocY~BC4B(GURrITd%`52M*!#JKz{7~D zyR|2IyZFs-io*2^*ULeEhWJ8}F`)tWYV$kCsFkAbjd%IzoEGQ}icZJLheZGtp6lC)AnQadiA`A9zN!Xr;(15Z0l++XTCJzH_)f2zC2dC3r#0?c0TG ztbH#n{hmhN+$%P4iz!jkUr}%C=2U(<8oXLFi*bG}+bDRlJg zy)IsMO-e!<#%yiMKuVeYuIep9!j7SS`q_*)%TSrHyD@mC3r?U#DX2LTbjAXDxggjv z`&fPp!srZj+Ba@_-2Cxsx!Zv4rAwF60LL9ybZ&kzm!{Xm>`BP*SMzp(TX^>dkx1Ec zmD$yXz^M`43aPsqnUdXMJ#NOK)kjNW-YzYIglW%gr%qO*(_045;eR{)?D(kmL|i?! z=TnZFejiV(?}L+2g1EFE?d2}68u;ghHO9}2E%(q5&}J#{!jE!JQ5-e^=d>@Q1N zs{Oov)`N~aJZ@}{ZXQz_-Tlp`|MrTF76WzBSlU7ht?&J>DaUWcBvC45HE0Pcfgi0_ zE(_sgw=R~28BTiB;|q@Dwn8P=8YqDuQb>4W0_@Je$3$MMaQ0zYK`tel)wc%u6 z>!$2UAM;mX_!}7B>epJ|@82hwCchlifQ{=X4b`r=SzY&R>us(go@sD`;+fc|+C$1v z_RqSQ!{)NH(hWKcB5q=gP?7+t*AT(A#nPI_UeBb6Nm_ONm+_8#BEiW@I<7~qTN=`# zhI6^WLv4$p(b2R7?^kid=7SD9d$XIwoo~n{twZs(vokr>pOS*9x{q5pAKH+n%`R4$Oxm=7WA(`$!^6=LzAhq88al zmeL{ZKF&c|pP4&CFSJAwU;M3+?|4_mR3OP?Lh%iHTu zopp0IK%T3$HyJWl#=2(!kh48;GE?yUA>p2hT>FtNk^ z=353DZ+`K_A9xoH_zbyFuy3cLSWtU<_h+iA9iT5P;Ndd^TH&z+>6y;Q-zi!BoUzKu z{;ePB%bfgOn<*JhR)|WJ^z@v{_ebf-c`6#;KXPTkY>&jzJ=N5Ur_D(B58rk3RE?B| zEn_k;F5%f`bUS7}BD={-Y&M;iHXq*?_AHb@TmLdurku?vpGoBRlf}vsN9OLptJU^P z4~wi{$X$m`%H798Jh484w!7E+=KAg&&7CGs&!>njPu5kxfGeoD1~K z$XPAwIz`9EEA&uTYnAEF;Ey${A3uH+IbLZ2s;8@Q3d{u~jJoo)6wp5c`;bt2)fp7`71}jKM-LA1<_!@XN=zA zwNQx?G5Y@L)%BY*x(t`_{1)SyS2{rExs%s-XulxHtM1ZgTGAwXR>--t00N=zFy$7* zKDzq_@K#>yf+x=kP2V{q+h`7z6SdaLA6~yLt(7DyFNkZVvOdnd9G05CeaTt1s5kjF zld2-}27U8uh^j;`qn&N62*=v#CG1i0>pDgsdKL#aue){;jj0KWM^nBANjqPrMhbtyJ=B0PS(eeaCAB`?>y(x z9#K)}c!|EnP?*XQu13cm(%mDP}C|2&XvPM-_ z3ZO~A0L2Pk<{?1^B*!=suqn2M(I*_maTvWld_$-pI3!m8?);1CK01ZcU2NB!nS4R0 zOAd9Wkz9P-{>?`{8m2E(_N2qu*V&EWtwHVMm)==#8}GN-zG}`Sa^Law0|npIy@^-x zNgr^yMiyo@oH5=LbSFLUC-+#n^02+LLS>jGTz2iUqI6$4sxn)tPMIA-bvJ5eQ9ZayyUc{H(;lk|AzXz>vV6(G zD#l^o7<5dwSbNP~r&74V$i&JioVL50Lw9JIGWh7N@K$?&kHB~UjimJ9*H?mc2B{sd zJZjp&CIY93IuwEN#=k?0Uq|_uw?4PPDZ-RdVdte7AY=seFybIBd(4$dNu3uF2Gnbe zwr1KmJlq^(m=S4qAc@cUS_{xV`+BV0UTz72kCe&-DTsTUo8u@ysB2H|zm==Sk}sQ{ z%LU9AfW3(9W@SQDlnKg1(i1-IUndHPVN>~y2lWaS^gq44XuL7ZP7T2)!ozIn4`6JQ zbSf{bQZh90AxP|dmkPfvqdiI*Y8Pwn6x+$W72|A7*K&1{s~e9euQ#8eDBx1Ks@fK+ zP%7_i$yemT9FrE$#`j>;UN;D=NB#WzOxw|6*{+KFP_vy~6hBda#yYj@RmiP(cKMDbO2Y)Xnz1yiGhQPr1&W z!I?}&*rHj6csG)p6vOE3V5RY6FxHT4v^Fm zW1_FROb=yq20Gzr&t7KG=XrBr5G3G@n%)xH0NP5l(yEG*I#$o{9-s_a4DsZ%7sPk? z0a93E-(ptN17anB#X_rgRCDObgkE}nYpZk&4=k4WX(Hd|aD)dG3gyg!fOawYDOQT7lk4j#HlVC8?ww<)>2`)9iV$jJ4acHDB$0IXP+NHetDsZKjEC_9|iz2pUk{M*ueIXCRvum|frK*$uj)Vtekp>zJB8PI|sa z$ICldkoDcjwSGt-1I~ryC7+*};p-lN;R!%p_n>6-T4r=ki9#M(@)TtWyzAR_AuQ9g z)i2vjJl&B_>aLA5iMmN4azU$Wr_V2oGT{!dhg`lbaaU4VaHwx;d4bTBahOr?t;Il~ z!EMlXF%eXiNv&ch^1RkM zHG(PRxUvr%sB(8wv?GnKb68uo zgQs7fizmAD3E<+@3KOQeBYP72>i$rpS>PGWOcz*+mPjlVBw0-NgS$wqHI-~`PODWM7KSt zdcECuB|LFn)9-8KO30u`okVwK#pp2i{Q;+2=(G-VxF|~B-)&Rwnsof>lP?-{>mq~4 z@to#)a-G=67a>r$nnwtj*2mdikSE!Bc@8Aw zK!Ty`ZL5MHZbgds99n;#Fb1^At(VIlf2m!07E6X(^AYgaB(_MuoZ(v|{;Q^CK zzHXfk1fd~u{-IxciEeLpBp`$_A2DN&aH8*vzr-0&#oLBgEy|qXL24 zHR;(4G+E%3)s|_<;&@+Zr4~UmIWV>&_ADMou9;H+P_bOq*@%PBp2`Dgs zL#Z%)qoA{XL!hjwKD)JCHqEuTa=Og%CfYN~P--$%072t(>KMMl$1d{&t zHYP}})i7^39!#8RYM6-us;d#xHl}NOE&UxTuh&DzWs$CT=_Scp@4F9yJR+rP^FZsG zaik1>`}D)U6s3E3aH03XU>$0pS`oxhQJ^nrzeQl>^yqYBrD!a|p?2dHtF)Thy40@u zhOCd?5qlHKf&Ras(Rxx$m5j#(?q&n}?wtJqFMcFy)G(HMynCQ`3y&Sjr>@(mZRfVR zxe4U*ghn&UDKO3mzAU|ytY6GUv^q<=^wm#alMM|}pPqTD?l)aMRAkJD97E9Ng9c*C zJvnDo*U5MzID~3_t#L*&2ni`5-4FdFH;IS_I*Nn8c&q5vjU0Uey2$l7#acb)V{Wci8fOl@u*Y>lne_;R~;_v(DAawuV+Gu^G^+9ggn*pElSi!$C|s zfQEHdzr6iNSW}e?mF~S>TV-{pFwU0c7i>aDK^2OAgD5yJ5k$NTMJ7tH_M^s1>AZo7xM#0N{= z3{s??iPj?HRfH@B){-A{GfXI0R_&5*Gm)N|6&0hlWEdP`EI+E$^Jztn?o)9|njjiRN{62St#;r$@7WU^0NROGQVU+vKE1Vm*%Lw?1FYxku zcPYTk+`Q2JOX9nr(C3E;FC4Q(=Q-knv!(+;zGVn1=_wNW0l|?GprH%m*`=(caGoL9 zrs*XY6fWOd);-ez#4ohm+yg5u+Wi{BhwnU3-m42A7PL@sGVNCivMTM>94+Ob`BKqZ z!Ly)AduzBjP`7_ZO7tjX2zAY41FNXAWKiJ!b8JZ^F{Y9tDzEwl{4cU7z%9-;p_Q%+ z$ZYO4UZkwb4j>nVG{2){1ricmx<_+t+9nV5sJtsL<86KIXXR6f2@%q~9tKh->co{| zCQzTRgGf^9DZPux$7<3qA1Madke-q#vqqs0?WKZgZWZD$T3Tcu=R&eqg0G>rm$y6= zn--$uySCCb`~AK)4QM^}%zG%%Xz(a@nU0S`BNg{Z_;C*V9u7_UElcdQxIlhv;(0|L zi};6Ks{K{)MAwu&ND%qr>C==Rw?`VPkOH zi&q2JocFZc{a{^q&Fg+~w9Fh2HI@TR9@+(bezY||krTC2^I~2h|2*_hp8FS1Y6Uxg zgb1;N`AMw5mzbPs3sd-Rh}Y^Q5T6Sfo&!yDWY5C6@e%G&uW$WwfK-cE0qFp*-fZ@W zO9Bmn#KFk=C!n}6EmP1Bqxi)X5c*C_P5qRc_JI&@*8mAfR#u+|stbPkvf9B~#|+8= z!j9MLbU`4%3dGX6MX!s1Ha);z&VXifB$hNcGYhG4-H+WK6yXXEkBq#FZaXE8EXA=X z$x3NK%dO9GgIK}XNUBD0I0KSWs_Kh`o&napSQx8Txe~y5EVu#l;JoOW`d8`W{=<_+ zMbGPa`ex6}wBNpcJ9EwRz-MR;>zdGHxq_Es#8! znx3AmT+RziOJj0NE3Y9y{^AEE1HD9CcO!y>C%NcPg?*}`L`HH?kA$vVyl8#gHy=$= z)yLVNrNV>1KoG6>^2LfARMX!*Z1?#EY(EK^$Emx$dyS^89*eUi4Bs3j`wDMMn<|y} z*2Yl{Hn|N=+)yahU0U5;$P%t=A%9%X+(}fm$r05MzrNbwnsv$()qa>K3fEbRG$z6X7oe(Ikx+STs zv$2WO1TG$)_LmoL#9xIv9W7=iq4Rdz)9xkhDL6s95r9wZn#e&iad!zzpI#r}i|_K< zob8C31yqL)xr0Q6(Bjx49J}?Zb&*3L;|})?DDf2D2b2iP0Ch-w|12FLag5~Q?o7kb z*B)%|KgvcRxJUZyw;HdBG7G3ed&No;p|4yKr$|-2=|s?4TUc1wsZAn{tS>BCaN3zY z>fI9XMN5&VZuhdATFD&`sy=w&?Y8N!8~)(~%7=V7BdDWxp`@~t>k|@**1oi@CBZJc z1wYV$K_^`R55!!CZ`i=YI^Jt!d{mC3)_yZHGisSzglI&Ps{VZLq_r0(+bNV5pG*jH zm-v}`NoqPvm!uK=mM)=bB$g&cSb_w3;r78@=bZ*TcP#y=oSCr+5gPeY21Ay3*oeM8 z?Rmh>o>1u@L$+Gng@fsRuilm(e_gh-CEkJwxm=DGm3 zS&gI69xMgb6oa+G12G;qt5|#vKiDBSRw_-MRK-7&;MW31o=(|D66Ao=2AWt40-8>T zOlE;pc7{rm0Za<3h2)&aLW&={PuY0Z2yY$DlWVi|mBIOBv!x4vJwrb3(J^wV!8~;! zidzlw{5c}SLYFH(2j;O?l*ElSreXiYr%&VX0No)~Hty1q`_ZCq&K!9L6z7|SPSL{j zITwJ0vGQ8wGE3&&uMg#S5{*nuI#$ajEODX-TmT=Canl^RphGl*TmTq^}f?Zs)+ql05r`{p> z($PFk1WL6|1rK)i-y9k9+?u#<%H#^r@#PN!{-y0IXRhyZt6jr7leon{1xVY-t0%6w z&EjR1TobeoW0slG8J3Z>sY$8r?oIFwYi#l3FI9g)6%6Xcu(!rc*2Zs6R!qxl4@>@h zC*=@asEiHczwMnv_+_bK4~?nq$-m31DqS5e%M_^krXtTG;9~kLpNlzn;DstegN~HP z!s_Yupf_(qSUnFrQnx>V^tZ6MgYg-8Q^+m8kXYq_b*f(4tfvS_FY>RXY`ox~|vEnUQ3asERq%Aa9I# zo-XTl;TLw#15sy(XwwT?IaiibBhqUK1x44%2-j*uaSWkr*{nJ_4p$ZUDs~2IJ^n`3kmjd7V+?Fq^fPW`6p2)b8psL zRTPEp_EhSa^7(<>ZRq+YTmiwqQW;FNgsPhMOpeaUfqGF3eJI z+cKTT_4?P#{6O@7*$s{Oz)W2cVyP(<18Uy=G9+HR$f@B{=tSa@s(5;(>A}AF?V3{H zrLJV>sTXDR%xctp8$R1?q1@-s6ATRtJ6`E&ZDrdB^y$(N2qbA5sA0^BAo^A;fvTfq z(wK&~b6=WF>VpwrN<>cx5?t=k$U{Cu~9QJ3hJ!2%6FOU%A7-Tp-7(%Y2j*TsUfa%acKp({0_ zzPrTag7N1;8R2R%jlIHzXb*ovJq{)YEieOKmOLut>}WF}DKGWwZ~t*b*e-bID5_me zeWLI{kL$7;r7faiEJs@*N#uS)Lc(=ILb}sudJ3|#9B~7T?LOK-wXaKsJ8hL@V*pD- zUi~(c7R*xXyQe{OKnNhaK@)#p20_&KmG9)GFVH32fSIpbBv5@CSn8fAR`%Q^;9-3oh(-{us zXFgh!qT-*YK1ABbrR_Zpxp|wgJ?6-z;_J}!cwq(o&|9+R#*wrNSJvmv9p1eZgF^k| z5!rTj+|J73oiPKa^TI6(V43KF@xXA~j;U0|c3N|K6(RZShW}&6NRJ4eECEzke0e;$jK?}S}UT*F;gfG_iA6@;i zN@=J*+FAS3BmfI} zR*Fv)bgn1i`-1iTKLar6FwRNb%d3$0saZdDGKQ+Fm^#y>3sdrnhW6m4K>?iUl!%;* z_b5f&)JRB4(FH&t1AY%Eip<>`9v(g-5A=Oze-Wt0BVN1RzswG;8QPD}fS{LDj=X_EWP|>(}ywa|C*^7p^EK7o_OO22t9Y8?G)HDU4W9d5PtN znbf(A#3yW|}6Q&Rqmi#ST)vrc7JFZ^s_ z2})nk*jeeKxZl;7?=zE=dgy4@ImkDoq_D`>bsu1EdzI**`dEtN!!nD7+uA7q;Y^rf zX919S;A=L2#*WsIG)O8*eLe~jhEUrtx`;28+>V2nxjSL`pI2R~>sC=~@}BdqHg(*Y zpeeiH;e6hzCSkVreo^4B<_2u+@+@|n=~#TohqM(zQVguMdqbC~YuS#&IdQw`25 zcq(WWQ$4$7tn{in|CTHM!mY`QMn_Cc@!OLXoOXhFkACVXG1f2REA7=KY`Po+!$HHJ+$--vaogMpain zNg}F|5=A9?Jk*m01R)ortMCx57Oy^x?ar=|z*qXc4necaBPO#P^ifKM7j%4V!O!;K zTTyi#^R!}|ov_XW0DZ*e?TdEO6A)t3Mls(2?`_fEh zMx{(^{hRT54D3;`ldRW2>0skr2O}DMwdL2a{g0Lp6uZ7~e~usUeCHCNNSAhTsU#Cb z9&eVCJWSv;2~x3g=EEd;Il!~(znA6i3hiZVPdYo9@3OM-@Tesdc6rJbEZ}kCRHU&6 zT70_lKtA9RR{Ne2Py|s{Q@f|}{P1?&!7I~qY9Ip7$BE?bJOOr5?(yS*Q;z&R)u&He z6+O0Y*fN6}CPKs!Pz9v|Xnvvfv}9&2V5jd=S_P6Avj7w3$`OZBX^nhgD+rp-hg(n98_VEFl7u|U_LiG_713evG8;~4()Htpo%+JImcUL}k zhAR@qQ_KpR9x_Sdt{dDD`EV@FnuOZPl&HJCGcL0E`9?-Q&Ifx15W{fS=>nl>Nf)&p zzh9U171_x=MMT(KBo70_G9FzL^ZiKwz+Dc5IkM^fx6ggd{lf5xRxyuE`nFW)Y>vWI z6jTzp*k#u$3~BG0B z_Ph!q0v^DD8mu_No2g2RNQ77}F9U#$79+ASGD`?byvjT+e zhJYpC{lzzz6WgTYc(;uP+(N)nSfpRbjRdpQ0V^9b_Dw*z(`@cSb&q2$CTg{>Qi?D` zxEFv|uQ)>;H)Tg#1d3yIz7yJdU41a+Z~O&<)lzAie3a@);5$)c<7*J%I+fj_}AtaVpNwFk`lTTv%>o$X8XpGOOM#-970E3 z=W%+2IWA*fxS|8jvy%uu&J7R^V2=x%H2*g4zk791RUe&^k-_4bggw3Qj)9j>A>{PM zELo%10VFyVj`JME0hm8gH?ddfjk*EIBx+YQGXRv1i^5L5CLi*|e)(I@v7Bb=?Qi^(f3XV}PmT~^>8!jQ=EceMjv)&QwS_fG@P2Eh+9MF_ z8?;=_3@+rJ19h%Imh&xWaBc?xUEn)E3OEHCECYEDIU}H392vQH@$Rw=iNbif_505) zEt*}MyO%JZD%E1*`w?ugV=&%3{)YFQ4?KsM*JZ6rJDIB#ERhBh>BBMGoTBPLdH?V@1y zeI-pzCQ!|T7IS%L#|_L=k&O*7|5C%3!zDrJISY`v3jVRKI9@qZQ`6+f3C?#Wqd+8n zx>d?t=p#&KE$k6mAK>A0bCAWA>YMnsCZXOm_*^{F&3><92}3iR6(T>s z%(~9)tzHlbjbQ5i>Lc3+!k9Pf{7Ap`^|Gqr$`h?<0`b_+mCO zV2SP<1O?%FdmPk&0oTAtYd)^|6Kv}=V$u;AOFXv zVv`rZc*eVSJ^uOe?NvHE2& ztu+ddaeq3VKQ8gpPqBl*{MJ_tcHaE?y8Y>c{<;hE(J`_BJNch4!LA+9`hyGLrx*U+ zckubZ{bSARaH{-t>-_C4U{|w%C}#Py-CqPbe|PQwo9w?I+CP5!*OUEUm;3(@Cm5fM zPsJ#smif#0ILmJp`yqh3%na0>`H_*3Fy6TPIQZK)HJQhc!$BrTR$BTstD;}Q&zgLH zw^#nW%rThQGC(r`lb%V>=llvW7z}aq=1sK1rky?Bnx-|tB|;MuSz=;h02XiCHD7-Y zOe+APFI>Au`Nnbq>wnnxM5Vx1JipObuXG-S;Cx^}mA%(@0|6YB4?Nx8 zfM8?!AD89dnT+~pz?I}&7||j80N5)4gf9(>{*pYv35^!C>!6{iFnYyEkP?oYF9tlF zXkEm@LS=C1NYBg5>nQ=e7w9u0psZTA4Il@Wq}~(_@RF?EvHjHo{@vAoG1^pY*ca~M zHoNb=Q8%BRfsK^zUi_`Har8mpHUJe0;S;qY4~*~_CY-=U}W@~T5SzB0jP zr+@5;E@*+F_jX;Ea+_nV*$I8Bsw%h}7ZO5^X542tXy#k3>yrdZRn?$s)CS1UWU6F7 zj=2NgFS}rcTFWRQFrsP%Ks#pVP=(t`FU|`oEZ@2&Xz)xxb&rpkW zq!K&o>FPr3WD0aSfU|lbNC;%Qt5y{_uImiddDitC`2*2uKv$RPo;Zr`89Uh)Ju4bY z!wIYdf-7$!-nD%|mV*urYIODk7C?GY)aJW+tWt_JXlA`ivnm;quH74pt+cO4&$}r;0CKwTKBI=4>{bu;MAc59_ZrO&TsMivoh)W~QdW z0QkWBHqb)p$qZ7-?k~P#>3n|hU)Sz`L>=|l&c8Ud>bx@a4qG8nhyg^IK?KnBX`tmG zP%2!K3R@c~Qv@PMop(zVR|P>Gg^G4L%%m-pZVO~{+Q(~LjZi0tmOZ@*E?R)mKdK#+ z-@)>#j*}qz<6tGs%+Gg#{nsUi=9*Wx7GPv-UkpX-@vwlZiKL^inVrGK5Su)p(+Wu3 zVgMI9AZA44u8%!o+n?XuP=4}+db;IZ6lmlg`uN=ErJtW*i5U!#YRaIaZBdU=3qc@S zZAS*k2yfvTjocRApN-PxaTGq7^eKc6vM{i)L;?tMsM@*6Vx+W-Y(7=OpB9w$cw|+e zK*2|}K|eb}yQ;Lu+Tm|a@t>#B%M-ne^N(j8-w)L1x`Y0e0=SEv_6U+Huf%j>{OUau zpo#&qU?w;=+++w0i^25l?Di`-ey?7|P|u+-+exy=3yu7NL64RWJ5q=u-$f191#$3rG*aUXKW5Op4%Q*%qQCBYSN=y z$Bw}XY78fQKykI&JwJDILn`g(_wvHFL#-@+mzIQY!KWBDo)rPHZ)k!lPFK2GrrcKSr-n zQeIP|t>bxW-^-)pwkdJg?O1Ez8?Xv9#Xkhe58lQ)fkaRm+zy0$jC?&jK^8bB699II zLZ?mr7yw_Zs~-Viju7t(kg3%KO#=(23dg|Qc$oNx&3DDoqgiX!*+GrQQ0_^!Ne{*0 zH07BoPMp14UI%E{?PTl#I^gU+T_8I}fMNi8iu0)*fzo|T)Bo$AX2R6X0jtF+{Ww&V>Bt|R%vf@x;q%x zFageNocI=jGC2%THjSR%rEU@;Bx5H@Hd5lagjJ*KmsI)7m)M7&Zr4s;xB(?JBKPwR zWCa8_pa!x`cTR1PYKPo$l$yj6nOw5R>waVx1Dao)gCMX{R>-UoA~Dzf#e`DfitoRqPggq3<*?e(a-vw}B(IT6O?whKq|1CJ+;892<{o?yLGIeHua&~qOTKX)Iiq42&6}_lJ9_GC-dC}6)(0~>i8*BtHu>P7Y-pzgf z0u#R)66;5g9wqhXvXw#9N+a{~Sh7BRVB;-WDyWiRqJ~Bo<7uO@x`@cJg!xKECe}K@ zegdi#j*eW4IJN;AJp-C0I4no`3UITv^Ns!jM*B;*b|MGjCeJa}I#PtMx~8Tk`7|;a zAuP1aH|b5t^cl*X?(XVZfY~A!#S+0g;imG|-yZ}_3Lva9^o4V83y|GfCg~KamZ%OUW0O6-M5J?KtUdB(AqfXO-#=&KEwl%~vB@?&t z7&Q%S$mLX2!YZIspWo#9;fx{~+ewsw*oM`md!QTDw0@cIFKPn*>=XVSl%VMFXLK8l zicxp*NlBNvWL!SfuT%!)}x6Lgrd9VgS6|rT{B0Li(q$P+P>KhIytC)<8 zjIIM{5dgub+MhiBXZr-=OAn|16SG!8psvseC_wCo_hx*dBYEzwg{+P+I$)rIPfi2v zR*9Y{=U&FoV9Q1}V5ftArOvzit0VNFgu`aAn)r%?YrT&k;@l1h3&u*#R|l;MeFO82 zd(Z7uJ8yT&0kLMrC|2D_;1je8Dt88U8Y=#B7=QkO)hd{M-D6j_Y^h}LL3Hptr>3e( z-|~*q(ZbbSV&rQnsNRZSY%*_zi00`c7~4S%HAf8{*%caG1>xwMprA;ABsw3?Mx_H~ z$;EK(Du*_73W?ugCA!~35pFSB{sctJGr$!WdY*Y?&Xr7UR;|}U$9q7-fdDz_iX;RQ z>1^)oTyZ!sOtG>X|0BEe4^2odEB?x{xZz6_Gye7KcR(SCLW2h4x{ygXS?1)H)!es% z0lMJ~_g;~b^$?v%X7xLuFwz$UlyZiKY3kHYYzAKnEQgC_P#b=t(YD~M!;Xd2FggHM zQdw7*Rinh|XlK#K%(8Xfv}7J#^&H=bt}@hMS3gE%Wf35h5YvY{nWDlA&J|Z5t@}fi zEdkS zaz>YMy3I1_0-|(4sEAI8wgWEz&f0ZQ(4}5tKA;pA36j%j@nh$jaT)~jf4ApydXGV) z^58=3^`cD)@6k~$G;L7-7cRT1e;;U42tCmGZo3$qm$o)G@q33Oz7R%ab`v;4f@&B0 zF5ijXUTTntxlTdB0y@?yvRqDqz8C~u8;VYifyhR;sG^r06LmLP)bB^V&L592KaR`4 zUqW7@7qE{Tsfgd7T{hJ#FD&f4Ql(MUt7k1I=bx)yG?(p@58`xV!Z{ERX>`Ttf)n4= zJ`iPp4Y`#lV4vMTn%C3}($uun)C~sOs?VOaq3feTP1wsY;S5>vpYN@o-UqnVgGjMp zIG$f`fU!5oe#a4duiAd~+T89Dh5JHQk8w2yy>fJ>NfeOgjl;P9@JpZo>9me>~p|LVSC?Dnb%5yDiYzqT?iWe*U|6jmzb&Yi?tMlJgnR zNK*1hQnDSKInc>huwG_gC7NFM?!7N2=4>~iYy6LG$FD>@8A)BB0Zv{3OvKj(MK77wP=37mVa69pri#L@WG_3 z%l#IBU>5*1vLMg&_3Kw@U;>IuN@5z3?Nxt<&hi!CY@(MB1*jBWd7?n_JLa^%JDH%v z239n%bcaR$UuJER0XVtD&@}RY?i@Et#e&NOlimOMH-3YUE|8%y8{7OChJU`cevFw8 zx&g*sU*NZ&`^T@StkAerca3qz&nEf5{GXFHhz|$fEdIBV{mFoM*Aq}Blkx4x#{B5k z{Vx~$@w9-C%pMGu4t)mJPk#8n;h30GUS2knqfq0w|G=vL$Deo&qN~6XqOblb%KYym z@P8BhKP>G3o8W)G!u~(HgT3|g_wl94?Ghu$;2yNQ33qz8k?q693u3Xtw&c5*2{tP= zW$ER4@(=0L+vTBi7wfzNj^etp|1k*uiv=t<$JlzMD$i4Y{(ecQXRca+TcPyG6`JID z?)o;ehxHTK!)XVT)C`uq&E#Jhm;${x2;4lY`~vb1 zmt{1DxA43R3|yNDNLI#{+F-lNZd1itp#`CA^v_avaXWCMN-J#`gvV&FlTkC6adswo zQj*;j|9RKTUs*K0N9Uz`gYNMrP_xNmvp73a$V&h=!7|w{1>?~J!V35sIMSTOV_tE~ zIHs14TFuhMuEu-XX*;#=WnCl?4_&qpy5ABMAqNX%KS%SO_*NLzWlj~kPhAP7+5GoQx|(IavvU~e zXU?p*MQIpZ3F+lD916WT55Bt5CLvU0*v{*r8b#FC%VCS1dJwGHc6L5a?!n*LS z&P2O{jI*{U*ZBPnWjdz&Df1tySey6Nd)XO8v`5_8?rA7TGKUkpH-BH#IIe9VDJI_L zXA)R`U5@?68V|#fr_IcOIicB69cU&@toIZc&~=uVp0r9Sy9}F3RD9AmOktlRVk&}C zLV8>fn?-h`3xg->9D7ZjTK6uSH1Tz>SmZ|Z2g3U7@>&}BlwF6d;&0xEO;6K459Jwm zXYDLdlnq|!wmBSmfG8zne!^K~*p~b8dzbt%GjFpxl2hR{CW%{7*<=S|ZOP$5-kVff zv2G^&Ba8XeQiIlfpkJV0$Z+ji#BldTZ3Bg({(Wf30_X zeeB}`-}GRpv-^4kzX<9dN2k9y)%wd#F`=#XHf!q5WR;RUq`sq=r;-&1DQr{u2j1GTY5NZEw8o4 zi7F`ID`x3HM^2T&yrbji+(Bkpdffo`GdsKS{oHN25raWqmmF;^QR}5H<8ox>hGm) zWB57WXKegO?Mend=?QT&#mCnbASD<`r&Xl%6Qvyxy}KXFu>GbLMTdwK6qcXBc{k_u zR$Ltfk~Qzj<_*m9soqw|%X^N@Ch`#-8zEB2tMhPR-V;65uc&Iz&-$cyvaxvB*rp!$ zDaTKL{d+GEdlbFw?{-_cX{OyRvS)C4Yq&~hN(TifuA4M!AIK#;03ibxYt zY5*&!2&gm>0tyN!CG^ljY>4zx1f(Sh0@8w^hmxo?X^D|uk_Z7pO$;Rg(%v}lbIx=0 zJl`DV{qyPcV%V30KjBS_iU`>i{wP+Yo?=%0(JZ$T@oAk;s=h~;1 z>xxPGv#W!n(Ic@pGDiVjhf<0<16HprkN35#q$+fb}h2_TD) z^Uqi{&zMLI-o_!}qf(^PSJdnfBWDSOY}0|?Cz%gJkO{g`0rcTy7q zon2zBtX<7W3pVm1JYDGq+U3X-P9$v_=CR?zO>V2OC~XF*#R{i1(IOWF9jVkw;m%Yj<$!B}a9(Ww zgIgG^+An9;6e1gWS9cMF4S8W6wwRJR$9uDh>vf~;VK+A|F9QurT*KROsj*~*BQCJi zn6A)3^T@<<4HRQw;TvMg%*v!=c-(Gc=521`IrTuVNqg4Qm4v#y#^_MO()m7J~-O#m|H9`j9=8n$;YOi-z-zMd7j1jlW%3TvvEp(kU8#rlubsRUZHGGawQoxej})702L1=|Ff z-uG<2=-F~ryXs?B5d+*$h^b=s1)b1CW}VLc5Zs3(On5>w+gWxxOmfNvmIBsouVQrUq+O^YPqM8bFv#W29|rD>FH~PhHkDynsr){| z>NWK%`eTyi)SZdLS(;@&!#l!bsLO&ct5xE#md{LON~tj_V6109t5RTWP%Gi3(l;}h zvHKY|7pqGf}>led!{Gx`tK+RjSP>lgN+jv?Hw~_1xp*GmS;s%|0Gnnw4AGT>ghV;~cJ*UCFT+j8yB8f-%GJV&59k27sx9+!7@u0had#-ZZFl0`CC zFI0hIDM*bKpw2(rTU-<7{K1gYP(wR9DeWM8v23I{;2O%ppVA_X6a@R>= z55Wt4SW%*?z)SbIVJbC24<;8=eS+ebPo8iUdIK+hi)S$YmT# zS60LPEA8WEr53%UN4e#a)_#x5u{iLJD2L>57B(zJ3a4WJp{qqhP&8g_?;9uxIB=RgOR08rr~6RVLm(=D^S9+CjzY85xeL301vu$tK-5I){Dk znIG-VPo<#l(zh-8`))OZu2j<{ z19;^ba<5Gn??Xv1OA4HqZK?PoJ@$XhKK9#7a%Ez z7h24-Cb2cl$!7`T+glMgh6C_)<(9A6nJUN+g}A$pue$wc*np2?aCdi#%@jZFzszadQSbDGrJWG8cC>${*T~X`KtMmee)9-@kLJar{)qzH{MP$ z{j~KiYd3Ab-n=*lWUN_^h31mbNssCp)a#7AMw32BNWx<$lysdts<^cjF>=#vA|6Z{ z$s*Co&KmQ+K6aFm*?`QKk4tTD)hzWzy}E*fch`CYTCiOsNJbT^pZ~k5;?656=hW#w z=Z9<2QXR{Kty^AO7`i|8L5ySQu+sUy~%F zU*swa4%CK0Ac1#Bq9nq+jvA4q6I$%ZHqlnv?K_#k|SzEbyd|^ki}H3O*It;p zxJqlHie-^ZL)_My$N-bnk+dM4gACFL%F=Y8a_g00R=ad?Rk?r8v>+&J;a+ES-Q zS)J3!mS}cndNz3CO6y=Kqu;#w>n!=&6`C!>OLlAqVg|V$Wu8x|U-5_)YjI|G9^Mv` za-!Pa+@rQ=f!(IPl#@T!thHkTBjQ{t6Q#hEDTyy%q@riIJ(hPf3hu9#pSyOEoT04; zQN>g(82{c}5hec3vwC=yggkC%6TMuVW-hpHANaiU;Vi1ktG8$#W*Ai>pFj4gZTbOu3i(()q&Orv zgwlc-K$Sx$?-*23!;hMArfy|$A-Vxn|D6VzA9FNpisaz51X@^SdC04I%dzNX1wXJ? zTqN56m=IbQ7`VFh?G_*@+y%tZlT#IVqePkA@ebP!3iV(KbJVM#Z^PzCrk`t{JboQj z$w1YrRTY`?Nn!4KG|xp? zF#$O29%DFIsba<(_r7w6$qqmCG#qJof4Ra{_vWBXI<+JMND0IQJ${B5@P!)BK~e1S zR{$N+&4#Iq<2nh2P=)dsXT^8Cag*}&fF^iYsPpn260ct0frWo~^bRSPkHGOKTW^k3 ze<@a}3d><6Z1Ds$&KtDkZN|~!n;y;)9QtGu*uC+-?`mCcpfFs&dw#PVMx%P#qs9s5 zp^|1iT)1<;E5hAku9&6phzip?WM+oZ7b#fqT~!*WRFNeOileXsIZ)F=UxP*A?Ww&) z9qVo~0&dO;=M=ZI3J$RlAg(UMKw}XIzrq_oew;bqMMCMU9ZMop{KvMeyH0UKoEEga ziv7h8IXWuWge_&{eI}2Tkojp~8-c%aD0{%bBYb zYs-CkL(0CWiQdN}3#k5-(zisyyJW4AFXExHc3<^P^bvJMhO(=RGI*Wpj+w=>%x0-K z&lZyPksK4?F3w9~ueoPbM4Bnu%ODAS0*=;jEb%K0{6ZAblO>4gFPGjy>jiS|E>H`t z5Qr*sy&~OE=T;_kcf&IC%`Hx~+#CXwjBZJy^+)s{s=KG?7qdkBRhY9X?BoaX

    c_ z3Z+!HI?k~#H?CC@>Qpodw&m?Z{SFcK9$cb4-Vhg>Y}YLY?EQrved8LZ&YO;^B-QE5 zvZz%{4RA{CjlO;QeB5X(M+Hf^2FM2f2gd6k`SJ9F_xT6B&mnIBc!~hZv5+RqEBnk^ z??_&wwuJeQjODS+4ul1-K&7;{$|*t)J-zb`;_jBqJD0{o>x5gvHmc_s0y=cW;BrYfxRg9u z;Z;6)l3%r&!A<+~U(jdbr|Ku1%*3sW?NxHa6|hDXC7srw3;y=6!<9I!`r0jCwbOY; z?x&5;{kN6=s&;$0b<54;S@jQ|bpHv3z8>4h-|G-9-uiic&%kZ>jXv-3^K-yw@{-19Hrv>pJrakYQ*fDKFjn}2V3$%dEwwbJr^LHd98 zk^}ug12>K3!oHmm-4UV9AH$QOAF6pd_j(3@?pN*Un$f3Hhv2x@ncgC2e%l;=aBx4r zYrIx<(q->@R=9tD#-{ZBu4X6t+Qo6zkJ^GqF#O?ybp9?9wK0S{W|}_)P057WJB3%0s?w*T`B{_+p|m`y6;SHt-ufW~YvYrO#$ior-VptbF4j3e7= z#HUpzrok%odu_e4bL*CuhUdZ(j;w2ezjbXB>Ov*DMJgdazJ)*h;*>#5hZL8;*MZC* zXc%|BKvZ8}Kczpl?zjK^eE#MBJNLS!KJc~H2}XM_@FyZmR}%v5_-&*|%4S~LMvTth z?s)Wxzb)r>J&RiB!yic%#N#lf@{(-F7lV^tN?A((t!vpM{YQnHP zFz5FTm5`|Xy+7bsk$6vGW^WS1Rh^rIP-H!AO2H3kc8LA-k3bAG2R z_wE%l$liIdzC=20y2c4O`|NblZ+Cd-o#oeM$p`N%4}9J$_L9W!`PUg8=TC_>>_Y%U zRf_Rq`WMJ2f8dys6kO7SZu_WBhKEp4m=IFi;XuNl7z0!@51kO z%o2QVKQ|Of_5oPU!rF(DYPY;po1D;HckZnb+L8VMS~k-L!f*o5r5F49Dn%qJ zCE_MCx*EbeJWj|DAKreYK_n-_sfi|kgeV=YWs>4;UZmAVIb`Vkha+)B8$8`Wmk5UZ zZbXzwCWbA+H@8QeeSLe{3%xq|sqnYzndB~gjkhEh zsb0r&N42Yh!!7ODslwkuv*`o8&Mii8z1@-FK1YQ(?9&^;;jKI)tLZ)@kjS1*752XX1)Wb(;|9T)lYKf8}d^ZV6HMr*``PdsLl zpcvid&bRsb`5_*zzdp1F&xN!3R0ECdR+p=fV5#9Y?X;PlwpTTYHP3~IjeFJKH{QSM z@k;J)V`p$AqVMhr)%Bv<8s&$x2F}3@4`;>R?_*~64J>;vdORL9>K~HM$C{lVZE!k| zP~i_~n|xvrRcs{y(GP|fq!#6idBK)rQ5d*Uni|#5c zY{1)G*HADg$mP%x3&&=2>+_T$)Zv#3t;S<3rKD7pQswmUZhiL@x^7T`Ri$ zYv1_vC%E1}VPqa_bo)@3zNgXOhi2bN$gAyYF7QBo!AHj<2E0MwXi>F_Bq#Q= zhjB|@=}q$ zE6>px1CS6%h)bT`YJ13%}pv>Je%HwPP&RF*07 zM;tm}U_hIu*EzLaM&Uq5K)t)2f8KlAD1?>Y{{}HgcbrQgPuKAKFj#1Y?ySGB?OI#k zyZVW24sRDJNbgx+rm^jr4;wZK**el|C61tft~5nm%(aOPt(q+JFo}2+G{SC?`nCR^BG^o z4xrs_maq#Tx5@vHmEhkB&@J1Q&mM>=2lfy1GoREXxN(=(N$Kjb4}V5x+9g$+0!&@K zWL^opaiM)83=s$kJ{2IHyXNi7(Hfh(F=zNkv%R|=8c{klSRgUNyyL9B+7dPn%J91O zn}VxxWquwfH&$=?+RDUf=~oIF08YhqXtm>D!i(Fz4`*bgBNa&y)vW>01fuRjY}=*ppwzrT7uym@Qk zc}fwjnN73t%;@b01<#LMpd=saQWKiX7m z;$ZXo;xi+s)|-V6cwgESuzX#upuuPhLhm6v)6N;knD+M7L}6J~t&`Z7St=kon<6eY zna%;r{;!MuFee&=ulv}FbQbzTl-Vmb(qBu`Wk%Q{-_*eIMA4v=3(gR zYD{h#oF+ts+i1MgB8sJVjE)!I-BIJkPP}QT_|AT9d`Cp04Wmh(lrE&;YTgd^x_Enn z>rq{AidN4Ut1GV#^B6h7Z$dy+1T42!vC0lCFEQxGm1P)I>ixFCq*_91a%BPxM2(WM z)8r?)_$9xG3muhqlLVzax>=`vTL9z~qw`5(xyAgfRx7pCJhG&mOixU@8myXZQz(~T z*p*bR?Cy8C3gwp`{rdIgXS(9)fwWZqtq6Anv{VuFoCuwdV=%I*t-Q;zm*xJYrv1yJ z^z;2qHo9{C_roVcWwwvt^{VgB6;>7BS@7>26og)iR%Z-fzF4I%yCPR!SRZHCI%E~6 zkQ`>o@2{|wN79YKmH7vguRjQ?ZJbyQ^Cu=_8`7J@Ow){b++&7bQER#DYX$`OTH zZ=zc)CR|;CFUJNU0?sq;!bY@AnTgY&{$zdnKwfI4?b6qw>`+d|wa)#sxfv32q4`4| zjd`p>R#IkF==F*D=LzJ;Cwqqbu#271(21uV+zeN*TrKi>mvm1pf201cK_!dm_MFAl z_?lC@=KUG%zw_whq!hO2dQErwtM3-X)unH~Pro{Bf|tDSQRu$ipBtZjeYci%-Y;&+ z?yyRr11&BysW8)&&s!VpF_uzO8+)s=rWM#cg`_X&i-3fhA1;Y>_~PjT1RrLKW)p|$ z4;HX}(Iqv7hY)3501YqimXi<1v$sk33XT<2D; zq5j3idaJC0S4U%8@ce|Tz+Eel2rs4HObj%9l_8|i4~EaOFSDD$)fbp)Ex-HF31@f8 z>4}EIgANHbSQi`tLMg6DN7FdyK4Hu^4N&L(TaGh~ODdgV1=ViG>rRW4duo>Eh5%Z6 z7ap8$QV@HRG@IQ}Mq0wf4^;WtRpig$0#5nP!qSe3Ku5=NrfXdN<}MjFGk2&nCf#B) zrDO2))UPy}!q_*9&3L*^U3NKwqSR1#tSBoiME8xo+;Z1{>+$#ObJPtq_bCvi6RrpU zVxhj!X@R^y?~Ft(mUA_dVem)qZ8&{IalLAij78;%k+^dXT-4M}LQwnq8Oa;b?_D$J z?Al)u&fWU;;p(I8E@*R#jV%4z4rX3-qmvbHtFfO=m-*C4)zuNzAW?$k0^ zIcx0NN^VYREpr&_Lkr}9JzbYPJHs`Nuf(-Jl%pje6TUQyUJs{uoTn6jy^qkH9y&Lv zV^#NRe&X2G<$MKlQ>l{JOTXtz0+1MWcE{^i7azOZ^^5bJ^gOPCGXXDAgaMY8NX=i{ zrbCZ}ryJ<0$u=RNW6caTh*y$!fI4xFmOEu<=&qi(!)N^SRZ-_)|8O6ZB~t36fs|Sm zI4v_3M;nu|Hrh|g%g!lR+NT~J14e9JH&}dSd}|OuK@?f=UsZChF$@bgBzccS3zYA2 zkpj9Ov9L(m?xJp%o8{JMtt|_3IWwH+>FL9i3+P;O;H%lpTpjZe&!8|YYOZOlmGGzf z+oJMa{k_tg=;~?VjO4YNx4iD(QLcJYcAvRWGd?BK=_p59R{CE3tJ&!I)v|Y${{Hog z=@xO!^9LPDw#B&u({z*@f=@oR7(j)mhfQ3J%}m!RuT=;si>1+w$e)nY$X&8J$(zv2 zb_O8^j#@Rfgf;6Jgj@X+Q~;A6)>L2{V;3}0se^mVsQhe(R-huDbQnrT!5fY~UtIq> zc4$*S7A89cj_%DY`TM4M|>y*om%)3j3hYL+P*t-z3t zeg5%INZ*~e7SEPyu6Wq=VlDPVqa>T5{{aXA8&Pb3{}G-VrMoR7QFexP%_Td zaevImpo7s9wSL`W&>DB4iOO{gWcKe#Jhl5)IYM9od;z<6)-&X#PaC@0v{@!~j z{$!(Gvh_&Y&Y9}YshdPyOzq*m+^-YU9(ytH*w_3{*Uy_oYAb5)le?CgmSwrNF*B<; z&u1VIU=PbNP3TQ>3%>)mRWL5AkNKh{lNJXgu>ru7(Rh&q!gvp1&Iig>_@fT@Vt6(gw>`%e^e4~7ADD|1Pj;Ff% z35f~2+Nx%<9;>Xcx5Nj~ou8svT@^H?mribj3z^wynz{RU!z!mjyp5Mg*Mr0C{7KKv zt&;MK#1a7D17)cJkj3W%!Jtq;O|Ie7FZCV6o80ZD@1b$4lgSv@P!GdkU7@%uFF~n6 zen%Pk@IgDK4Zr7N3xr|Sy8h_-vK7JF(awKOr{`yF{u_-NeUxN7HD7u9&!_q~Qw@h@ zfs0#w^{8`*2-U@oBCE%Kd^PsoWD2fpTFvpAPD+e@ZW^dIXXvEWB%+sU`fGP7H5%wh zD`7a!vs>b)sP~IxHL1$sBZHiIrdL8Mx=y8bNXfZ)Ho(?y$s_EjETjfXy)lz^w~jFQ z3WGX0q-6DAwlqIH1XC*?{;t6qqIEriUvgbs0%J4Hy-aN~p(QiEf#%Hx?&Q8HI~ze! z8`=JFO5Vx`W|g(lIakVmNzSHz8N&v%<^Lr(?_yT`DbNmTSGAu|!&w#2v|8^!!$7@6 z(Pvu;{B6=j-UXIyPb)#Cd_YH)66+mB{-tnGLaGe@&sR(9N z_4uS<&6=79pH+#TfL*@!HQr8gb8R`i{Ik#W+?|>H8|Vzj!Ek(d-WNY}xM_K3mJRqk zIvJXpVi_(@2>DMIf7HBmn{RQ#+0O3#9DVj+; z%`quEI~HdyE~cs314F;wd3_a8pAlTGfN8mHmdl*-55`{7*6j;0vU7fX(>P{C3FEG~ ziICizk1f4SdM=pY_nhEPJpU(cAJs6s-mW|wv#+(nM_ny>Yo$EN?^NvLAr6xy*B?Id z^{FCo>1$`I2Aw~yU#p^y!x$!aIC6a(bp&jzvsTvlJ4eKGCd|Zl9CaL2n2G!4;VfT* zT@A6f@;kJi=jjdEp->Y53AEMB@OKaq__X)unm)<9KcRMC^^)fP2?`UeS8!-NP zMVre9z`44$X)OLQ46E;Nn2NP=gjC*s^_^7D&K{D=0)Ym{ zbu)&nv~ovGJ)fIYw&x#`n=i7l4!SQ>)f95(EUu-#HRMa?C+E9`LYnGi9fRr@?98^BL9~# zlweh44MZ9Qoc~^$!{hm*5%S4%itAfE?a`-Aw!KvHu)T`9dbEi@0(5L{u>9JwBt&|9 zyKU}xn1LLBT3tMUo{W*JYyK_YyQ2_3>G@Ssl;3TzO}y)mby4{hZ%>;DytMQ0GEM(Z z1tHG-`s;+dH-6{3$oJDRJCxn=!ReaoPR{=qOPTK8ebsAIq3XKs@SpI!qL9BqzX(wu zEV5oi_UCQ6J>s7xay8)6y1~(R7BS0;ueR9*OmzKemj8iQ{O4``@W*M9En8luKPWW$ z8_vnMd&#i_StEr?AmaKQ;~$qyJHS6}izRT~T=)Mqh!y`CTLXg^*C!ZN1)%91kUE&g5M~M zkU!m%=VZKvd8qYWnX{zcA7ew)y2zAT_kAK^B6;*Q|EyV)k;A4_p02t(yW2j$7Oi?o zKXNjrbl9C5BB*L+d+fbpU{~6fTdPL5xExOCfzZxVL6 z**kai4beEtYPbVQo*P0j`Kd1cIpzOr@P7I(C0g4!kpA_bKV`)K;m~!yZfW+b)uo>v z@_)FnEnm68&a}_|O(k&$UnWw_I`;Qu%L7IHvuuIEkiVyqUgQg4I$JFd|2^644qt84 zDN-u^o060wU)sxmQ26^#Y{*YF>yInTFAt0PI-+a9#PZ)0+YW5yi7mtOy47Ql}I@V(03C>?*$hc-&bAEDO(KT)8d)O&M>Pp+kw~hV+=sM>Xexi1b z7(XPa8gp1YjwXzvC2{cV^LAw)f~-!eI!O9q0o>n~49XRTNK?@a5J4o9=jB;kyE13t zxxX{z$9nDuhx_qRz5ByUw6!*ir4O(^PkNglbotfjcvxt{2xp?E=JgRYy=T;-VtCH9 zZXSt|-L7EVV}INbE0wG=A5^QO2_o**gH3nJVimvmw0_XXuX(sv9Kj5eLWC2!!J%_H zLGLiu9hi!Qx~m{#cj-Vww^Gh~N^Pa=VT2~3OZeluUt&M;TAj=?quLnntvfryg?^^>F zoTm2(C={F~`3;jF(uB7pK<}L?u5&t5CP4jc?rP`m@BjOUUh?*0p^~r4o)7I2HTM&p zZ=l&L4!|U(5@_!@`Rz$J5307TlrF0F23@Zxn?HZCBe_eR`)uj+W$X_t{*O2NksWrz|ki+eX?S<5Q|QGSrXgxC%02f&TVx^}!l4{C^NNW|Cy$nPD#bx(@c~G( zaju1cLh5OnU)|VOyNFA@nksQRo72Bdg00YtIIX6t)|+Dr4G{HUeg|EV**7@=*a-7P;$hGR^ z=FV>Zs~uSW95)bPg(=(uhQ8{V=8exPC-m}sd^!R9dR82s&%A}y2@CaWw0(VIAb1uDg7=xaRT1dFkEm6k=>i-P z-PJ7s`E{)}M;=8=kyH0{IhmpVN4+ZcUA5AX-iw9?1>^BNT!QWUOhfC^hPL{fIh+L)aQrp|Ht8CEOg z5ZLJ40@dE%pxTgO=s1^uWZIScIsCHur0`lcCsiNM@CA?FHwAV&4v1QYmzS=KI>D(a5 z2txs!gk2PX-(U({IvspqbU3k-tmIDakYWn$x6noDACiNi!L9w|nV87jw8S%NfG`vL z@J4w~BZWhU`1s8y*DNRvUrtC7(jp zp}aGCR&!=2n;Z0a2UBrD?~RhR588wXx4`1L+d(gI=yw&NzFdNaOIO@}wfUG7(v(cV zF;qci+faNsv`2u{7)PX_MbfLN(TIniy^)q37Q;DQXQi<;bKEqhr=NbugIMPJHPA|# zM_9(6d?ye3-H(W#h7BZ+C$bDFY6+zdy`ltxH0KU^Pfp5=q|13%R>$Ve+rlBGhpTX} zBrxVcc z-uMF;XFTnBT6N?#{I-caA(RFnBLg#N(W#COKHA4w_&DHS5p?@gPHrNY7=E%(HBAhp zKe*q0QfRm~XhtX3CzLn7=yP|vc;Q2>c~^8J@AEz_&f3($2~#b5S+VT4+($HNf>ggM zm#MzkTIWo^ly;5Ez3nclQ5zuMDY=(hoL=|4kNu0KZn0r58|Ee>y&d5wI%^FD-1lu= z(U@|N3#E-D&p|`ya2Xx_&CxN(=HJ+bSCu*&j|4zXRzJSWiKiKgGVAqq5Ozcf)YDuf zo4py&wl^RSNp;y_L`>aPr=i)i(~<3?Od_Y_^f0g7F**dd`Ce{)ACm zy;5*_5--)T`865q3U114of)e&#~mAyTL9_sC-c9!DEheF*dcxrM3g$Eg^067EkNa; z0&eQwOXzB=*V~4x^H=kkaf=G2PD_n-L+kFFb?O)C+ze072#|U97^EyCdP2_f{lRr- z##aY6tsWh1;#JJc?A>WVSqdvW(SR#cBS=eOQ0ml`p{F(D^=Cf?4f7hpK1okP?wy9==@{$>V2^9y@%TMBz^ZpK+)l`ma;_b!K7_#Kq!`==ht!5%rWNuo zP8<)Uy;g{Pnz(3Q?|)PsW*-vR^B4|J+4MOlet!*~pcnXyzT8W$qaA-Le65a&6UPQ4 z>1Y~`0QOn#5CqSY_6P`+)tcG>_bfMKdQ!m6=OP#QuL=|ph=`)thxfkyb@*vS(O3}% z>deEnCuqI2Td_5nJGwQzjvNK_%ndD9wG-1if-A-xT(xaDdinV*f?wVBetGv=Akn4i zMQ{IM+NCvHsA;F-)*+S?4s2)QJS+#26*WUdU9dTfV>_I{NR`HHUCv>9GN=i_TvVSB zpxmI3N3?NX9`ikbatC!xomnVP8yk|sNd9>pmwP>8riltwASmJ_1~0@gT)Cx_VXvoP zU8+_*yjI!aY_vCo%JZL(Y846)R(c+j8j!Pe%Hxg2kMzV}FgeHfY`MIrA?kCdoBm63 zQN$jO6gpmWMc*EN@~$b{+0NC2)15vxg9FO+^HNT!tI2(tyR_*G52uvd-hpN$AX(;# zp_D$OfHEJCfcefF496Gm7YIrw&O~bP37DFfpna|G(k-G*RZ9ZT1=7h%O1%aN@$9Vk z_8D0M<60q(sWy^96(GAX@+ifG(kL8H)ss}B5oI!=eiq6w*7LU|C2l4m7+E*= z{`Hu6)T<&21mVZTj0f&3;H&Z`Lm$RY}82UF%axl%QK6otO50 zMy8ggrh$hI+{24&D(Xc7tQrp^>zA%LR`A??9C8&83!FkO4V^+}-OdWMf)v;IUQ_fY zEV^7nDKP*R#F&uqy>94n_Cr_SO5}LjY|c{QhY69~A@)e#WH#d7rPb6sR~kPisbdDd z=tbsm;tmS$Gt1L#32?NR4cMG+V$+LDaPL<>x@%6if+ulsiQyI;m=$?x*{3T&Df{*T zKl^|{i{e!el=U#T>>hnbudY~q!l3W! zKyMI$oZ#9?(4##w?<~Hm_nU)i-DtIQV2@FM%$_1cR#fy|3WOo-G?>CQkPW7|)h{w~ z=#Rxi#zg#-dQ4!j6U@GqNW0|bPj$V>g#=p6GPpCLE?#yTisOMP@;1_TO7sTaUBioXw)q7WaR^M zzuhBa9QXLC7DmAy3pj;_8x!w{kG8_=PA0<*=UyMwx4c09X!6vaKcUr=CG=9Z1aknB z?&G+`Km%(ub$%O#C6a!v-K`&vtkie%I{3=|RA{iWKBn?GrFzI2daseRAL(nQBW-ug z!27C}yd!AJwJ%>aE{)UmJfV%jT`_K>>0GMU{!OCPR@_A3DPuYCLhC5@_m{93d&H5D zqJ_|-Ham_81CCc7l*CDEt8&v|TVRRggQ1r(cv&^k%|qSkPS+a0@wQks7q6uaz~__= z#w~g!+`ZhCfisXw4IxZ&r>pM?%)K^q)9d*~-||Mq7KN=i=`GJmymGaet*({lJ8&<4 zHFkJb_4@z-I<;+lf+;g(Lrh)GXnCa`M0f$LDaymmmBPhmAO$Mwp2ujqnf67TZ_~Lq zAt2#Bia_vREE+!@Iud<4SDAEvy2Mv55&LZBvf?nBLaxGv8_34`z2L>M>zBS%JJaLL z51%0o%7c#BEYvAD`rgj{*w!^-Jho;?xJpMI6i+Fjbg6+j7~*5Wt3>l5%qncw*&$*YvbGQ`b`~3Pox+(Z8^2}8YkLlrZZNvNB)|l{ zxF)lxG27kWT&O7A6c5*IYX3bWgV%`}i`M?O9GR^;cr9qo+xNAchxgiv>aVv7roHottL-Ntb~k5cB0sYuafjiK2CkijlMllXp297 zYV4N5Q8weWc1YIQ{@1job9(UD_=oRkZzCc}64tIO^Iuk9j^hsMw6(SsVRcH!z(Mb> zO{2Jv(QXjm(7B*vgY!Ys?&{-%=*zFa+IJS4poI;Up1f|oS&c@YF_Y07!x|2&zhToe zEM;Z|LjldP>#zP5UcbMQTA%1$OM_fCPVk79lP2yi$H`b1m1?0e97iV_z5xjyC>fhJ ze(E`Mv!2LfO}R&uIJF)OsR;nhu}ON)I78k6hp7-1>|6butA+079=bObFY1h&MkHz} z*-QD~V>Fw*C_j5zXW{kS8Rk=I#!elnkn5RmZU{h(O4Qvt1$_8MsRX3k#@I9$FWh_a z!t!9d;FNQmejkUpC+<|!$YNo(JZ904 zm#?WRz1?1}sP8jNpB~~PPClch3mcJ6hfhiPAHJP-Q_QMOsJDSYz-t|q(7*G!4loT_ z^F0HW60`LMKurt~XRsB6}< zNE12t^JR-Z>Q45joF|b+DO6?v;F^$vd11^2rR3ID*Z!2iA}@&dIgil5VxGX6f}TGf zcQ5K4#n=ZSv`Y_eK9H^;?3ff24GNB-DQ3 z+W2gb_a-Nl9N^OOc%ifTAuT;>=<@Q^r4y08!AW6^7#&CYhj@gcxM zh_LL-bo)_rcJE`n^~jmv`=+a2xEHkcCL4#{(TGr?*xM!1Pv z!ZbFX_L%w*BOtu1;g5_;z?x-2;QVU&AvsSfq@>7SW$md{V6$@_F_MtDs8LXRKoZw3 z_d8tP6VJAE9j#dbf!qN;ZY!+BhU`R4(iml4euB((2hAk_TL<(}%t0WM7v~i5k%q{x zHn!WVOXFQJ@2m1{spAE9&H4UO%Nm>S=?afcckDm)@?qm}_2DA1`) zL8n;t&juQUQ~85#4jADkqm2fLR*B~qt?yvzaKI3WHD zQA~{niWh*X&W#ZI(fYE-wtBlet%@z?OtN#4Z^IAl%Irb$xE!(Jxp3+*a+E8BUo`?P zq4BUMaIN1*hn#}+#RUxm9lWUkCWPH9|b(apiZ#=>hXEuE}GfIrE zJ7E(Rnj6!mr>025j-VHb1MC6Vop1tv)qa6kSABtUdUxozZc1N&l9?WSXzy?!HHuGQ zsU#Tg28TE33`U_=E?qfBeKI1}<%m%Tl%9D;)v9&kk?qD{plUI>=%+E16qXuL#{UoZ z*b9EqbwK8Yj%3W0N$=yed;1YKLr$$6W>a$hiva#R^aj}PzhxrovN_G!tJBx4?0IJ< zWY!xKzdVfk-8+`!pm4dU7rEl+7&{PR2R-*aw_#R>Nin5#LMVWX+Wmuabt)Hq~u z%_wO4{_5Atp!~2CefVi-LhL}W>9_bBADQ=g`m~+E<6VO4UcsT4l=3OS*j6Wt(=xF+zW2n47C%g$KD9K(7Au$ z0rN#sa%(qOUv!o8Boyu*KY6a)X?4!&S(W`eJvlaK?U~kjLaTKPTSK75sw?UO#c1r=!`(n3*sD3Kak2#WMBgdUI%2?Qx2gplNk|2Z?y%sl6vBj@Y;g&$~g@4eT( z_A1x4*4}5@UHi$s<6*8J1a=Nzh{wKBH`8>*m~Ew&E?JIQ4>#WW&O0^5M-o&{05mTa z-m1OH#qSn1gxtv5{Y)%a)eF#FB>1nBCmmAE?+ODf^RXPFQ)7xhc8kcR)=%XQm^<>3 z!Cr=D>|)*%>=epCA6ZWE!`?}Lb9POd`9NPJ0@=)RSV3x$YeC)S0j*Hs+*i&&#S3gT zE^#Yd7ZQ=*lmuH-vp0s|sUeFq)%XlHey`%GKri?bRGgW~r&RB_>)s4!rx(H={x*YH@J79$v%%;O~%XjmUMw z-}iQBP&4dj25_m?{h21byc*AI>G~nirf)g@cWf?=nupwRisvmJ4)$<7S!^;HR|g-o zqJ0`O3c)gb(=4k1M4K?w6U4H94OsRVK^)_@;-y~6vMT}?5H{y?(0xN4~*bo-0 z)FTSf4Srsdhdx7$Y*5UOlk!vMLDIHo@_AEkyi4y+q6?yo=qkf+Sq1R+B!KvWG2op`eTdB zMEs!R&7tKtbnsCj;=#0$jjG#wsX?nx;DBs&|4A}CqA~G*cr1DVrS~?UtLN6)Y3WJ+7Su=j8BMO z0zAprSI7c5g$GZu_%YE|@lidA3KZ2!pguT@(Di8@d)a0Rui*yz0WCY`ZTWsI10ad( zRrDnVw@ZpGW@rGU2JG*zZk48R%72{%Da`)jM#c4NGN7Cx-q!LaVKsa84Ve!`ruCQ$ z7#|-!eGeS3$5e((G_7sxLrXP*WGEK^%Z=BpobD|*Dw zG^lL4%BX5P*N9;zKI4y_YT=)>Ac!hE?T%4qlo`ON09Y$Y#QxUnPOkLVysN?e!wr(; zV2_)QvPT;xfl_*wPeDIPVDsHcg2p4D+Uh8A_EgIP{BudeH=T~P;cTYi0=V|BazmRd z!CX;4=ou#2r$2Tt4NdC3lqHUtz8M~xK^uOqA{-%$OOqqskoj2mkjEi@a}-lT)U(#(y9-&F9LkvT*=RZyRJ#1* zn`>vYc-GPQND}`9m0G<#eBpwwCR(UF?VLI(!S7*+?X1gsTg#8tA1Xw9!fO=WXa3@u z#UFhhyzj=Qs?&9#eJ_A$PRiQEc{1vgDb1G=$x^|eFH?sD4~KRfLIer%?N~g`+mHpT z6xHwSyckJ{tbOz!NWOplFP)H34I%ouDLQ8lWum(uyRPMoqUI*~jX7pDTtZ8(x1gtZ zl?n9-YuTE>jZAfAZwrH%Go+Gqh>tgNEm~pd7WdX;@-G(4aV}mgcKuf(bDi+wnj)oZ zI{zGVPegnk0(;S=xNdeqq;s|tb7g3P=3c+!H{9CA>;;F5>h8Tu`KlY`5%iAM;=l17 z+ac8=1?m>7Uvp4*u7Q|(!>W-G_R#ltr$j_cpU##nO-Tkn2`;iIzbd?U<711$e=cqK z=j?%9<7WkdK=2cMqPmuwm|oJ<&wz~A4e3q#HaJdy?)w56m_(QhFKUp_7YMt>MLx_wLTXF7(E(QD6wLLH!?kHVERC0SSh1lLp z7g)2bA%RW!jnc*Lfl_~(cDOu8|O zCHz!~f41mtru>`wuQWNq~>mN$|?}Ni` z9?J9a^Fw=;X|*V}oL>s?a9f$6m>7Ua_hIX$MnUUcrTp%Hm5T_zYpie_5Y!Um`m-CX ztgKm%U)NnBVt9WCM_Q*Dr}LaBiyiy>!2gm5|NFa&Js7INxuh5QpWfSlBV^bm#vtrR zR;Bj$y|Uj&0KEbZ3H)gk_t)G**{?EW{~{9={_nH@H^lE>Cwlih;|!qkFCBsVJKf0r z1>G<#7zu~N8Rp$2Xs3xk z`i6ck?ChrWzD`#Q)mQ(UAJa)&&XpVpx=qegraD4p<0)a>o@iC_B=K?PASNG884v&yyJ#}?^W0M#Co}@EgrB<@Uvo;^0 z{TYcCk=vzJ$NB7s6MS{j<$Z83*@RBzwMTr|R60dBDB}dQ$n9sw2SWRgNL+Z_vzETM zi$|PVZ(D=GI-j{oE(i6JCyzllqyq%__qX5H9$fM|I6pl-*B&oq@3c5KZTWsrrz0Tj zYsU10ur;|qule{bmfIiiE*$DmrjvUk{O1nW8q-h8Ty*{5!%zLgGy8th?H6fbS@i_P zDQGNL<$VFJ(;rt_T)CUuKz7YsE!I!1JFG=5!8|U%ERytm^ItNjZ*&LR!#rI!TCt-k zHGWQDdV88@w^n$^lhX2Z(=j7XNpRWsxG=>wkh4mxO+cTNuu18c@myAk_t>_m!Rk;* z&j5AH?ebHWz8KPM;NG76qT*tG`bYZiPUL|@$1go^+&Zc_;3ltZ(lWaado>3-)e}&C z_tpmMMwFd`>P!$iWPMlTTGxFg48Ahhl$x!tbAQ)6Q^G<=? zJJmS;d{|_pRS#p0e>w%m=ehz8{;2Vyv6mf4SXfx0Y@@G^{*4 z%hG?;+;LoZyN{oH9g3cgrx`p@yx^7xDKEKE+>Fdtlv@t4C)YwNB7 zh1?ZhpvXf7=9RNEB?zfC8qzUEGe1CbgB9St$4- zg_YUUUvI&wYjsjVHxTj50?@SOa`Vc70|TbeS3^*rET))@zP7xo5YP2uPomPs)fF$A z7s%{&-99%$(cShgwpNGZ6D9}gDNIL2o_JN?$&T*n+Yb>{a|GZN@qevbE9wdVEaoL39-u8t3j=re_w6Am}d^ z6uoxOHWizD6_0wBe0jEs>ID5H!Y`(L=-KwB0F6E^Ri0ez%vJ=hjJGab>Fs zStM;uSOW6UJCpijW@*Xx55gt?crX2J<5izIl)dfxQrKovBJ|CM$tt;4z1L?;&g=Cy z{a!0y(ZB+5IQ>UsT-NddM6pvn{!RDBI6B(jNQ|DXn%C+~KSOMgf{$qf)tEN(r%QRA zEcow`M_%4@TNw_Woi6gVaPfD}3*fs=(rVkWs;gys&ygsA4$v$WJxF#2vo8gc9}1S3 z(jTUp$S|b=sA*ya<;upZ)%0IaUFs0c1@(&Vd)ceY&X`jbIPRDs1(MCQ?flFV$L961 zyma^Yl+8}=RQ0dg3WMA3#d4CH0Otj&ozr9%3I{-`b%UKBE943&wLA4M+^amb+P5uQ zsdHYI50Ja45cnRGnnjgg*C^)#jTn0PP>UqZF{9;WDlT>L2{87Ims^ioL$mRiFY>2Y z9ecCv*etE6+uC9D9g!Rs%e=QY(8G`?ZxAKEn;li0+`k>oKFsCBk%4MW1+(JUr5c8R*rDP%#Z{J`hHcy664l9o}8PXxiYkjJtCR${;tQZB>$flaYOh(kwb z+tFdPxFw-LT>IHVypDss&ni#-ScNF3PE}%qRhEgg-M2yZSz}REJE;bZBgW9TpKsGl73SrF4>DUbI*H zklk-V%jiC5?vUwCk-^>%WmU-Nd&BSDRv@DY$c%2nSK6l*-!}^?gm@!yeA-Hy-=)sV z<>j|;20l{SFcbEBQBm-%UhSAs!xeU%5BK1Xw)Wg~Qq7hVN?&d?*gA$!^hURt5dE}` ze1Nbqa1=oAFp8p*c#Sr%3b)js z?~qJ%33G7KT)?g2bHlL=z1;Jj)921+yc{GtA$J98RK~x3eC2m``MT@0BqZ;lbhSfM zb*fZVLI!jL7WlTR17%FFOC9v7ZB14Qx|7|L*zKCKW5{Fxp+9hOEJ}pFM{j+f-(ryr z3j}i%#WJ;Yi|S+LAd%_vxnXWRz^f+NmcC8(oAZUtGwr#rK&crH0}I~cNhMG!pAs#P zZmc;*S`LFWd?H#X809pO-S~%p#vH5MtkY%J(V=+0*q16bcArVQTIKI$5h5~aUD#$P z-17)jVy36j{_PgRiM0%Ao#0!Z(VO5kRck1PQpzX1W*un35X)VU@_U{wdZqcAXTWPU zz~iR`qTXUt=>6@l#@s`w6Q+iG^Eajz&g*O+k;dybtPX@8j*z0{@%2_X)k%>yqCxeeyE7D%Q}p zo5L)%y6Z}CDCEaj?$wx{RUn-K?a%65a<@5p-t6uup15%2gIFqaDT`93Gi#X)_>N^& zOBJGkUUQhMl!a>**m}k?F6Y`HwNp!W6zyB+tGJN~7uJXJy*aoVOvtoW3!JPr1v)u` z`W~J2aEe}Hsr5sx0Ss%Sqlk%3&{?!+&!xpZ6bKLa=YyI|%k0pVI+M&yP z?T|T1jt>dUH-x~~B7Clkz|2T}zEUzn?TK8sLU7M%yNj0>U7Bh0H`00_iZiXc)7cSC|z7xT(m9}bN=d^?vDG=ZtBEd)%dS)9!W$>tkWT8on(%f0=fg@ftHu0 zPZ-xQu)sAqt6V14-*c_Aw~*rP*)3q-EgoAg!NJM~mB_q|eBt>@ciuNA12&LfH-i&& znR>ElaV@6(oEHIAR4AF(`w+jen?xq^%4fT;lm&${Nx8w>Kc^FR%Pqd$*r!M7Vm`<| zYGyQE_N>$MymR5P#be%HTwR+ib=!N)9pndIX^m))IgE8N=L{YZ zln3?QdtH>u)ibyVK8L@H`=AD0ocI@A-dy(Cb3m}AzrMs}BeS z`k4;V!(x4K(Sde9KR{9d+ccd2E{Y~qS;o1%lh?ncbZtX*QjZsRqbPl)4guiZie~i& zUo!5Iqw=M#=?{+|W@Zf^^pkKVKtqZ44`xsPdF20Bv|{(B@deBE#Ds(!%Fh+V#XsHu ze2H0J6VM-(8g5>04%xPIDeB)FQ*_#W0G7XL`bE0e2?(n8i@?zK4ieLtZ*28=@cPr_ zeAw<|Bi`pd?2y^q(#*iPjyqY`wu_2S6fSP__T9Y--!U>!6{@XVLu`vEo*!rguj05R zS3hFlLsYZGd%NqA$KqFK#q+2%q@))qFwLY*&g)%Th&jZPFw|x=Q7=$Lh(_Vq>NKol zxcG~F#vq3a(=yAv#K#IRScr$(*xYfiHx=8%09Z$Ava7s-cKWD5yb1D%x4|*!&Wq@@ zx7T;ym}@3(H;l~s>iwiKE$-D0XYhrx?=!4R5NUYWFQ3va@iV3|#f!d1>L_WQgmY3L4w3tK{+zzD~9pG`L0! zc&cVaxgD|{+ziY(fnPW!)$UWeLz%aB=~nbp+Ai5fNyi3pu`XLdrp@ZXDT9w4Q+F^M z2yUste!PKf!yQGdvAkWo7|YR27gEjB>6K$oRw_>L(DP+Txi#=Wt2*hThgj*&vD$^$ zS{y|XcSanKQS01WnDRAXJ>y>M}o-F-o;(kI>9>W zx)$qcTd>51LLrD^@=q7?kFEe32|l*;$AWhG0U5G9? zn}L&d8J1Mw?mph$&F%anW5r2&HL@6+bvHOZeOE`bR>BLO>Sc7csJlp%+L%=v;p@P99Ry1g0z)B zq1Jm!#J9b^zAFz0q``izbf-TFa=tHM^B!?8_nv1B+;FEv>6O2hfPkLzjT#^{NBAx9(VV2on#bn z76$2$S&(+IejfL&_?T}Rm)Y`_yuEh$kJX?t=v=koNe@7YjJ>vWEM6Sws=T|D1*5R6 zelyguXb2+TGu^(5Cno)LzDv_cpIjNA><~UGjF>I9$bcWxf0LgIWHZbn#wy{J8ZetJaK=YDSYaUFPVos&W|y9xd0!TYLLIZVv!l3eMEyt z%bt}b#CLXdxRTA1_UREBaYEzXX`^2UpHVnn?)njN7#*YG@DE}Z_4)paCHJq0D>ryL zaa5U94C8$cjY`9`ZU(kM48wf6edRfrHONnD!Z~{iVaVZ4MpzVE^uY=swo@j~PCHE`k2XAN$e5 zBnATCA^D*F?*C@Wckd=o{j=o$_!(VE2M*42X4ziar<3~&IlPqF<+h(C{sV5b(_#>S z@AM>2@6#6km0+EIydd+(w&tIOA}PpFr%k9xiRncgjIZAs~bW(9zMcA3M7~H$e(S3E#M^&~O0yM6zP;CDY{6lco6TdJ){< zo;zwsHRG~L_OWTdB5P5VIKE*{K#36okzth=6iO6-K+55?29f-QgcZ zgoRYGG#?FDk@5@HC67YY1$|uBV@N=bF8dP0%&L`U&nf@oP+u5 zmDa6qx{!0-ol&Vq)mQQ;Fsl0(J>4p)cGD4R?b>&N%dfso+bVml)aDs>gWtyRC@W`Q zMy`$~owzkZ*FRQ1 zf@T_4S#A7^$W7Vs-%ztXk+)W%YTOOOxp3>IXO&XQk*P>+ zx{&YY!uQmD3fannWuhy8t|+1+;k~-Ly3hJdGq?xC6q9(<_)11^-p0m;_vumAtK+vf zysHX9h>q|N*v2vjH%(rA+icwf5Es|;zQ}FJ$H(XUtwTE7nY7$czVzISW_J&(jPH6G z-}z#!Ay3;UA^8<6s{a$UH69Z^n^!j&up3gT^<-6c059KL5{~h3zpINJvb1kacGI~P zu%OL(<{$g!&-Q4p(A~SEvZIfGjh5RZjKu>Op@|M=rlaGL&<91ONiebu6N7#1<=e@~ zIl5~qLZ5LPMnA~f-5StM3$R0$PcOtM(?(7RPlZrcHz?H6m1|#}(I#Tt|P#K?`*kwdCw5_4UH} zta$LekEKVHjq}gU+F=Ax|F+wq;iXvs1B|$H`}EMmt|J5N+o*$5MTq#d6je1TcSSjn zLNo$y7=;JVq3rkvZl3`z**=Vmv+Z7HkUjw*>bW&f`;Pqz=5&&o75w7toYG=DyQvg8 zD+b^x%(*Dm9v%lOGMZkIei&3PL($=uuY%mZIQWX?0xYwP^_sogZ^-HD*|_3rGkyJd zs)GtFLignld{Ll#>IosSN0#;(NBAal8&g-`nu+^mU1n`bHE`2!wK=?77p;?e;$xot zr8W1f`cb2tnINTz!%o<8O`m{&eV1sUe=e-8!s%n;wcXZcDUExM9vK7ssKqokUq)eRBU8 zbnA2YSfE?%%=nHVZ{iYtU`$a^BVMLEf{)j0i)Yq7Kta@ zjHO=C+5V7oZ)aIZSliS(T>=x&2jIz}6>vFx~_BZ=#Pk5l~gKy3tSwk+NHAPgeHhlDUP{8gRq_}Ivb!b_>c)&pckrK4kuU;D{rO)cu z{FVcbKny#d60(OLF&z!)PLZm`m(-QH;rGa!wf^hYJt%UJ9`07j=SNcM?M>}fqqw`3 zvnbda!n2puK9X%hsSq4(?Xonw${3N5G42CyQA_2c7)IoUrEcTu2Ls zVwWr7+0;W<;?SvL)sk9L_bc=%j?x$xEy-#Pf1hV_SU#>N8w76k^Oc>prSz45wy0|u zfWvJ&VbF*v@35gKKR+oIxR<%i0989(rAoA<_*s_cjC4S^_URc8ggUa^YUeoL1rcB-tzIS>H@nUS@yd5qhWS$Pq#M?Iy3 z{Z$(nH$9i!fQ>G0gGz;|t@0$DbS@3^fUShe+(%wlZc&g{jN=OAzl;_0@D2Al`dKd@ z+%G46@JQ~p*o2bTo7~&B zcT4EbQzlO^+XZf~D|V)4Hku>(uC#>#5Mj&Fs&7a~NA~ID-uYyFrZ2z(fkLp!A|Ff! zsRf?`WNEQ3XlX(g8oD7A?D8c~oyccC@ZzN(&$|a=T4BV1AJ?+9)t00Ry;3akR|pMV z^_7c4y#lNdTmcEcHZ|A63jh5(=!S@ucK(rPX58E zR-%U;+2)CciSSr)`2ixw+&w*0g)wWU!|>eagY$85XP$wydn|2w zUM!BPnNzVi<_H+@RWvWqI@n9+wG3*aO^;Z|@v-v-+%zuaz1;daO8mwCc0Vk0hvs}I zm2A6fml1%gn2j@8g#6iy^`5J^S?&XGuNpD%G3e+=Er*Sd2~flk(Trb-Zdk5JK4H4NnfL; z;*S2OpZ6~m?a5y?y*7$9oS-PwqFbW% zJ}ZFU;YZrbd$LL;*Orhr&uRw?Md}soN{2 zEHe|xkh-c2h{~}^9j0*h{>whpp!~M6U?Uu_S<7_T2{p-`C9RW~hGC}nexEP%I-jc19Z+9O5tOi^CNno0jf$5k{)crZD?jwo1lUzxw*1tSqJP@ z9L-)4;?rG#2RSY%Lbu@By7?Z@7U)NReX#3s8Lw6M1NY8GZH!B_2Bi8uD>iAMzDs#Q zo;|j8jP9Dq1>aoL6d9JjzQptnmD3TD3Joqyr}{>UcQm91I=<2MNv*;sr$xyHZ{&Wz zw$B{-lDqbAzqCW`~m_clKlii(L1Ga!*PZ1{7x1-r$q<-82}y5e7o zqjrTVixI^_;JVWUp&4;C_0PFgU*<$N+tD4$0ZY=82ow}skmOQV#aOf+Vn;|oUWU(a z*I%R%2sFOh2RVGnW>Mg+JTMNm0V2QF5dhR;ZX;w=-=xh6Enty2A%nBS=?N-iJL09mWHR#sMW@$&j?cP5H0 zR-`2*xx`a+9(VEmMqD6#y@^>B38^lxY`Gs+g*y7hw7Puv-6$?Ad%{o- z?*+*KMjz4lSz>|kucL>M((_>HwYS1Y5kN;WQ{1lMQEyavl^1JJXicDqwlea^2CK=b zrYrkH{~Eus?|2rB1VS6Rp<7op`V@Vem&^vyH-`l85ew0&PqRYR_*Xb0^hZ^w7^hCMsf(S zygLjE%`Hj1WiD(gKFWu7ZeB=E8K}Rw_}<{6TE+ztn=^lRTYnlLHELgAw2H8LX7mcx z{Z8+7jL3`-go~el`1|*l`bWuJ=Js=O#nEPZdY2(rKPU=hHy^vP*k+n%ka12-QACI@w&$-J+k~IIeNMjn>RM`Wdltk_x0-E|6xCmu(Dw2NBV_~IJNyq z;lI{-Tj9v@>%GxyjW_=nLnmEekQA_ttxJ2tK>xqGu1&RV^PS2VrH^Ytg zazr-6f~~Eu@JgiHky6LHSRW)NCU!1v%260b&ke`p&$(~)YW#N2$o3u*bYbBK0)=C^ zCE2HQkr%||Tm2P7R@ut;3K-_=MF)fY^oV9ywep}Aty7_5VzIKynI!_3o9BU8)o!?D zGd+HN9>_Uucw&Sevacn2sXVW+m;~*vl=t3|m&d()zsbje+VWTIm~S|(nPHu;Xe39Vt)Z(Ig&-q89=Cu_|{$9&=bH%raC!-(c zgt!EMRtwb#!3YS|Vx-)AZWf!7lsBZLP8+6aW{_FBrTF9uXkRbqt=)J3&0nDVWM#_r zR}7FAD=R=Ppwk#pRBH15l;giOVWi$@tlO96S*UHis@GG~P=wE*kBlU-PgZioN4iMg ztal#$i`Re{8ym~NHY=i55=OslFt`KTDMu2^gsiJAt%^g=NuB7b5lXGHGvwa+2xab7<($Fk!FTJGJ3 znJ{Qcs3kflG^t|eYb%i6cok~Utl)LwzuD*ip8nuxX6H?eRsFlnj~sVWnnQSL;?>8Ii*+FAq(_qN;<> z^ijK9EWiAnFG_PhP8WHK0pGND1=PAfp+F}S_L#rg1*2V>Cd^t{VLml7>|+)vJ0fe) zz&TYh9RTTrPJeXk{#@kT2{V!2&j zoB9}BiJ>Cj@ULHA%Igqi!wjH(4Ml=iR1BWb>9wWw_v^J@zIYOzo&7ynRQ-sZVWa6S z89{>7qVuamUtQ{mU156KZ9>rbZi?;}1U*rRR=VXvL8%kbuC@B}1t76fN1E=`z$Z%- zJpGpwm49ly{uVecW|AEko#%@h?j-?z#2k^Vb#N87JOkh9FsKNruZ!uyArYx7r zIYvXA`EQ`Swzj-D+xajCJXF-4JgQZRRB-`YtNviI*L1kbqqkLN-~)ObqHCrqw0n*( z`Qh0er@`ui$@^+TQxCo!=28sSVFziYzV|#jG+20RDWhKXO75q4p>751cIo*L47Hr; z=-Zci>^1^#gU%)>0-2fi-e&MWv4oDi1jUInz_&`wOounVs}Ir* z(XeaDhki8NO_MH?BnhQGy%(o{m^oP-R{R|?7RAbZKG`zx_-1sdACu3m6qs?1a^^14 zr$rL;Ejiw}b%Zm=uz079^Hnc*o+iNFWx4c;Q0c>mLdOK&8iNuF7erWJfxB_%B&R-+ zuwLy7*X+8YqL^>B8wm6`$rB6AdzF)k-zCst;ZO?%9L$>dTG(46`ysCGBLzJ8WpdBg zENsv;eriy7=iF^{{4Z1WJGS{snuQflHN*DoU|!p)+&6U9GBeSI=J%(VW;d2M zp|vNvKpC}Y4h$rO+|)CM4+U-GerB=;1!6YR><1qk2f>qlpRC&GI<^L~NAu!`y_9ix zo?4UxlR8Ev5(>RooKlYgTwEYeQp0MN2k&QtR-seH)0%3Ni->_}>^LxWTphzvxX@6TBElcJ_bb~ z>r5*yy$ zR9u7WmfS!S_-$ybK3D^~*)@B`o zWRYl*R)#KcF5Rf8RPUY&F!afjoI<$wi1R{nRF%SBp${kD_QIE#!!6^aLchPRm6sp9 zaHFCWD#jYKV$k_ulUsbeQH;*NsvL4pAme;(oq%CU$?)vdSXMVCfN@67!B%eJFShOP zx#a<2`{BSOuUXA{uy~eZUSo@MbM1y0%@I5V<~|#%omIV6$$O%ka~jD} zj=5FqMed$GmJhU)lp$(zagW)ZCx7<62b)@s%iVcEqXv;60z*8Gsu zv|H3DPDuqm+=nl-8(xO9JN|-e+7wPU2Naxk*u^1SMmER`SAbg|WJVerV=VzvE z$1iPctE}Qr%DG^nMD#|Gt87h|i0-b^iC59y`At2=cgoq2Zo3Y`;r4bshLlsJ3#>xp z+aB!9Q%Hn}hBv18XTWS3N6~7(LBWA6K2w{VV}YRlT&&j8U47 zR9j;6di0ZD`)|v1zl?VoAN)w9PMEga+-n^pahwtwXPQ03ySL85{LJFn7&?|)=T_&# z@fwEV9ZF3xrLG0*)7N{UnTxamByhSx7E}Mvy-qLV&AFrZ&fR0RUvyg8z177yB6Hhy^p6Hn2;uV<~JYczIJetW1?JKg%X?^*WvSkfPgvst; zz65H=md^vYE20YV_GzBrA7cQQI@6!4;P}6v1#s>*RQrwYy}+E==Q=UTcDcHXpDwQk zR2c4NgD6v72Aph))#fhaIG7coO~HJ(4n%=jl@)jFMY2bYHB5qRSNSl?WlaY4tSKQ? zp6ii*Os5AV5 z11LBywW6@HpVMvbejbfb8DbyOPjh8W3K&iWtPhyuyoEvIu6$C8L%AP`Ra}S@g6OBh z%z46U5#8h(dvx&{0-SJbYZ*b;l(1SkA&nocVqNwdi>HAs^u8BgUqQ%}6qmg!oL!_4 zpFr%(OnfSIBXnH!@WFsfo59*WZv74Rzxbg! z-e=Ya@soI^g6|+u-uQ^G^S<=!xRzjSiI2STVS>OHt0Tg=ipBQxLJZ1lh1Iz)I>Py< zbWQUNdwV>K5Jowr)|k;OrNz%^+Df%oOY@~Y^^$LsJIkg(ikQ@5id}t$ep=_tI1G2Y z*2%m)J#ga4BYo4Q5j~-kD;9OFW9r+o)5CLx^<#atss~A8vV8hZzOG)2OXdSbO2zH& zWvDYFBXB{#vRw6*CuIRwCh}raAF(=11;1xdm=#XJkz>+8A?}pw?RNckEAWzxkUlB< zXVVm;(`;2V2FE9@g0o_7erea#lo;=V%B}T)gB(?Itk@6nYbDS{PdE(gGlp;>m}4@r z=&2;!4OZeQxy8e+y4h<$rIC-!LgS`(Y~#L;kTBMZx=_vTC&H$Mf($WGQckbuA__cm z6m;&bcM8IstA3@T1clQl4xPG&*4^kR4F8Bt2H0FqtN28$FD8^Kjg`8D9V&!(S={_U z@V%Yz84`bxaXeme9qB5gJ*`wI^CMPaF67fl-Qo7fsDQZ#NR*$O7K8ddmcfNn#lvFIbU1B0aZPSVbl~pJ}ak0YrXzy|V%-&El~+X)FYt{MMPsVbTgXo)?yZIpy4b zL`Z&NRs)k!LB2Zh=*i@TwzUyW&_z8CUvBXs#-XF0VC_xeKI?-Ub zDd@HWSzBe|p|FwJN(`B>`^`_3I4WG*z62k)U%D^o%wq&DB&VEqRpc-gC3ZmT%+g{48`SEbS3__w-$>+DjpMa` zN`u*i6Od&sWPmguud;%z0a$A5lfp4{)m}-OAHkQz#Xi2O8OnlvstDZgUkahpKfMUg%`Rij{39m z{Rovg8|dJLf@?jTxZhE^Na+=>;gPwM-Zvdh7-T8jigJ@qel&{Cio!y3ky^%!|LBOT z{j_7C$+o|ZJjbGy#?`eZu3O^LHq3fGL%~ny*|TTx5?^p^Pl+1asZ*T{557kOM{uE? zDa%JPu{B+VS@A>l4IH8}32rPh2x;a1-0tOLrQWdy9D-&}n_rKnTKf3yp5wZs@fca= zp`*YqU5SbIB5<61;x6DJ$NogrKgma+&Uh}$m-;iRC_+EY@TS;F$5g=NPrKEzG`%FJ zr2F5rkEMQ$b5$aR5ejOPMm(KTzf0L`Ay>22^^N`YAAb^PhYvFvC&t`-6Z{Q)AjJfz zRT!Vyg*)qWUFUT-1qV0d7YE}5?nIU2cXAWYEbJ$4Eh{__QgLe`~u-H%U9cFZn*WqyR={ z-u)fEmyut~;(tfNFtDjc0HZjj{M*cCukvuRr2QR<8AHbe!xFOsV4)#L+LTs> zWtDSs5$pKYkzDQrI>#Ja1kR&`L-HI*pdAvx>YI$U)-`4yP_!UZSUSR^BA@ zgOL{A3o51Moj15+q>3cteqP7S zsrj^-UjA-WE64tTpW)L<-EwVUEr=Z-)}xiT?9=4>y6ssHZ*K!%WWtA40@1?0W-K}R zl+p^dP%f(suIW$3R(`76Y1?>spTsA{CiFhwfvh8}ES zV7AzUN3sMHU%TK9Yf;103a*MnR^nfWGpTaPD`h{HB`TjVdv)sb{M3+P`q5Q%Xjq^xug8uG+ws5d4vG;SasHP*|YBuky=#Ek7YbTf^XERV!b2#C{hoy7~p})p!kr7i- zd}Vm*ZQIX|zWtVvkkHfM(0>Kl{*Dh}fr6KoirXVO(irUzMz$%q@m%u7WVuJv7hWs^A1 zawk7PKk5hPbCb`@;BQ)(wPvfzvTIAU;+w^=@)ouE-+n49PY@Re03Gv2Op%~qY3!LX5 z-4{@aY{iz3@;gSfh%C{sC>RRibwJws)R@EWU_n3Ol#B?UT<+pHWNuFlraa3NNyP;9 z*ZerOAHQhzl;y+Cm3bkMTUd?XWI&$tB{@jB%5=GXuamp`CfeRCktM|EsmYP_;{-rduE6?X@+C9pS$gvl!;wlLL7#R9cPIPpOlwUd0eF;1#D1xNeYfn6V9!SHvV zIgCwihbuwad*{o8gj9X#|a_>07K9!}_c;zS)pPq>B|@Yh_0TT7ACgW2e6Xre)+SNMs!~m^uIpwQQ-R zfLgJjyl8PE&kY#JB5Ui}iZ>!~as`MORjAwC4R`_XNGn;ke}Q%kORBZN-P$R6bh2Pp z9pdxr{#9zanDw7PN6p&bz+TSaU5Eivui&Z^pZk zn1<|tTDOcwE~H#Z+ZXD2+WqLm+?=Zu!tK9HFZ8wL&UdnIjl!Gz9R^3u6(L(mvCp1~Fbd@c9)Xc71H@ z^R83I1HkR+A=O~VAB|Mjo&a}nJbZFO%(Pef%yR!!{Z2s@g9kT+2o9234iZm}2(`O3 z4cYROcI|jj;3{~-XUoGnuYSH34^oK|?RKh1KI(t?Q2msT<8>$d9|cs5N~OzIA)(N8 zZguTL)@}F4l(Tx305h?v`7=b6>!E2^K|j66tfA+s$q!stWN8FFUggz+)f|9+X-&r- z_Tq401{>Q58uc>O3(&cd!`N1b-bbhaawvOw( zu$X2#qw7@IlCF|>$iuoC)VnIlRIII2xg?pV{h0a~@foBwK0H<#*iEYZBq8XL zC?X(TN(dzMP6(kUp@#F}+I#Kaz2}ax%{qVGG0s1TgOvAs%Y5g2<};ruY?8_QGtU&O z;wt9kehgt)ks|{Sewuq<9<2X^ZZj%xetsF(wlw>zwo~VoxjGyJbiVKpPNNs*4cjd{ zXx;af{fwja&TC`Xn3ENh=;<(Yj*#u~Gugb|-;fhsVqkTfHRFv&Plx)EwV z6?L8oW6^l+`q(tp8yK!(DRTvqmJk6<2B(0 zE!U=+B3*cHxaxEN@4gbZh6)%F^DbpfE>HBf%*J6Yb;z7&m4V78#}pZpfo?N8F5mVD z&rYR3qY{H&Vog-PiCQw>7xxK~_%#L?j>!0^F5tdP-^V6le-G z3t9k0Kx<4xP2ZaK@15f1t$)G5DZh^w5-)n9Xz4Dmo@CCzwDJ>Clf3>dG0lT$pzdpF zHa^|sbuahRrl3?EpFto-*Ugs3mO$o#{!D$c?JODEaCXEhkMDU5r zl`Gd&plqcR5n9@rpeh>}$yRc;IvRLrZ(K-g?6$L?`&=qmBiSrBp8|y)2d4`d^=8QS z5HzhsSnjGt&U3=h61aT%u}u7)2dag`F9-ev#p97$@2fuuBSxnU&fyt(q=Bu|rNhzf zkeXD}x$!YKO}8Voanr9PWe2uYI{I$7vfUJZeyA@;mgh_AwpKo;4a(8}$IITghSt%} zJx-U$Y>u|F&j^=r#&7Ak1U&!>DCRvjQm(8b#vz(thP=fC|5d!(zm$YaNiI&~98aM% zv>6lO{-yI@A8jM*jH}%g%Gl~DabDnC@lDXKf}BzHg4vLaS_O*oNv6X}S zJ}NOWc>yeP*Hx0{vJ3WJ9hs=5d{zJ8RFVk^jyJEXb#;bpR9=g*zHX6jSYN-e)8$?2 z+yAyF0jO-rTKdUH5*wwGdt7ajvP;hqU{!HMU;L+@{PSozBToqmTj z4GOd9Jt~&z-#N^=D1fF>wE<70Rt)2i$$~HgXc~nF4*@-1s=qp<>ag6G5WsIsb>#<8 zX#IbenYs!8KV0Spu_e1ZZ+U!X+gYxkqcg1wkV0Y?<3E1P0r-7cAI?1+M=T&jS*RNl z;9ZBL+yM5#IsDq1Tk}&8L|wgmz;ma_l_pR)y5 z5N!-Kyp;-_=g&(fFLQ99S}z9|0-TbW0D5D(s*;jEx(nF5ZeK^ezD&+Nt${blNJ!-D zwRZr-lIrDhZ0WPv_EfUlUqqz_YrqKb#!kxpKCsa#5kkYEQO*+UEaD50WcE$Le{_a@ zNDwrhyX5?My8|qd3J?+j=DTYYP%5j!q;F=-DQfx)ouhF+;g;l^QiRR zm0{;xQKd5w=Cg0V-Vh5bN&F#+G1Of(t!?TQF|=JhzeXH&i)i0U_jf_*EnnVM=WABw zSyIY-`8`P!wGbulRI8*{_mS4OQIyqJkQRQn^y$sgOlLZ_k=GKpAro>vi*ox9&P`R= zKUvX6ZEmln&IhEU9kz?3>;DD*s~i?<-{L6x{W-rYO4o^bzMh*N1Ovig+woTn+_XoJ zfKq8r#cy=Kzgcf+g&1Dcpa6WG z%{JcjJH_9Zh`111P7NEI*|<7DHeqhyy$TO>9>YecS*g1T4++2Ox^i3#7hIz}iFo46 zih?cXzn38w{h0=6o*%Ny1kIQge*c1bel{^asi8kt9V9#+hUD~}IokzbcJ0CR5@!G% zm8TrCemd#Flq#_5@wk=mPLJ~rM!kuYn0w9LydJ|zB|4N{C!Kmo7A~|7w035$(mCYw zx67M11KLz{KJ-a5YmmlCsouUXdV|W!z4(%E=q|UwF{QSS2e~J_J)17$QDfw8`)wfa z-=FEP9cp}foVx2tgZk{z4T!!=F6w71YM)E8qTYP)>q2PV&Xos z{Zj{Vj~_*|r^nrt&R)&rypF!z0ZLmdO20^14bD3N@^nFj%V#v|mM}8^zUzL=dQ9jY zSAxv)5bk9x99JXTdjs{9Sq%}=DESLo4f4XqkZVs{%80hn!)`R^a67q3+?HoJiK3k_ zMOe(=Qmd#+i{Kp{6jSQxC?Wf=n0u*zYb$?V<=$U!*HgGVu3WN*r-}i3zN(?{ZRIdy zGs&ukg^7vD2_S=!3*R2{VWDhzm+$a`AHl*3MrQY+#a=0O`Q?AK0K0ve!=_s{M&~tl5HdH!ZfpFZgHPV z{j>8tVn6laVP#iUS4;5%Z|n^ePkd4u+R@3u38QbGPsK-lzIK_jcj9pl@3SOx@#uk@ zl|SGDrrLe68Up*;aUaqhMA+iT3$rkVU2`8dDhBLz_V+ED&n+S-Fe6l?u@i;E4PaTI zTy1NMOCB*46gV^PTqqY^y190<5~qn0Na}2;MO*EkN+HjJueVr4ist>PeRmGpUY9<5 zFFVW+wF`2OwhMU?EnAeVkifzo)h5IxK3>rN=ED03{s8Vm`xCjH=J0CN95l*T$7^!V zyZ-=t#L6o8j;&aINOke6&XL6jyDkTrq1Er%P>lx_UE7uVPj|hY=7Y$OrME0q3eZve zv-3rW@ZWdW0h7?6L)ZC)`<#9*&a!zkb^L%ru6AuKf2-;OWF2&_oO$0PY5233BXDWF z#aNS>BI8Xk@%jMBW6AEVHVs>OLDVu1$`Vkb@73y047#8PJ<%r}9@P+$gK3*HEJVMx z?Hurygr&4Vh1cc{!tvnK=_~oL{*Ul38jd99a4d11J+%9-i|$>vH`1d?rwN65y&vnx z3NzT%IF|d1E_>&IWE1rBmAKg2_K%BI?37w|xcgm8OwcIP{}77VZ8v*0JuKHdtmu1; z0aIS4Nx>&=s%`EwB6su-wyHaA4k8#Zq)z7bv?Wwgx<|GXn|u9}IoRSh6jtmi%_QP@`oDo z_pIA42b2p1zdkWOT7abe#%$oHd(esP`(?n^?H*dw+x{eipjo{Vdy6i5e0(V}ZF6ijSd{4TY)6;vuZ_`D@Xq0T{N_foU z8R+K2W0&pO|CICF%v+?#7Blmz-D%Y?9t-lqc3aMuyJM=B#nyoo@^JYDV`eP;B==@7 z9lE(o27`f!>_>*(6KZNi1iL&XWs23X^d1#QU0ebSugbG1c_CBhp(lx^=-o^(*P zqGiQtgsk^Yh8ZWb8mY6RvTD*AM0iwgUqO0D$2(ul>oDG%c0V+(YCgPJ?UGt~^t+&+ znWIq4nv}8iv3&pbeUgvLXpU9ME=6hdbkfQ~4cswZBo$1{joMur#mRhZ`Z|63TibO` z-}N9`u0T^vY(Bh%;DB5WrYJ6Bh?j{ENp=athGm8ai@P6E7Nb84I$YImp#Vk^Cm z4p(B4{Fb0sn>J+tm;U8WGY4XJ< zuUb85&C1GRWA_lcdDE?099z^i`DL-vkxY&{o6l+~Kb@R%9|Lkn*Wd=|`^hWCuv zfmpGwUl0m@D)tp-WsnjSvN6E_0@`6C{q{|RNztp*X2?EFb|o@M{1Yd$t+QEDUH(cL z`n)Fy=o*G8fh&|ZKIR8gjKydG1CLQho^j#dN8bZF#GE?nPARo2L3ciak%JsaAfAJoZ!Ab6-6^ zSofd=F>=TW5NEuo=d27LR?qiep_oWo_N)4B464FJom8RA`dDGm`LdqsM)<+c82k^u z{_QPw)p@IeSdlvT!2p!-9!yVCRM{GFFyufNQ>4gd{0|~An7)Y-X3$8!d0p@Pz=f41 zy@M8`S3ExQlvxJHohIq{qXdIs1 zt%$gc&5h(wQDvY*9?192gA;H&@)Yl!ni7V=+}l12`i{Y%eZo{A?d_q``{HR*-TV3D z`)?U-AFL-qt-{3%iGD*D8|8ohqyxeI$@AfN&3d}0ZY)6b-GF?>_1MMFlc7vwISd-41vUz(kxWlPz zzseRM!>IeHde3()9UDITW~RBbj*g$n`oq`jN1R7v7{-|+P}BHL=#(=cKVxQ*oXyOF&koR(f?!!E+^}&BrCi4vyfo`GE^?hp;pc+ zdqms?TL{~{dtDBv9$u)p>$#;e_qnQid%H{R&Ty5D=40b@mtJmU(I`9b7RhnB#pBMf z*(MG9KrN4Cf3u3+oX%(c$!@b*?e!dww~{#(#UyIpk9_)t=y^><}{WQ)||AKWyx_-b;a(WZyv6Z1^i`R z7G}6x>^`k(A>}K@p{7@2en1W;sEy@{SQC9IcOxjPWd zOK~JR=US)tL{?oGi#HF2!*8dY6TH;gJ9_rdJ!k0wCp#wDvc`u>t>7_fXM;L*=mQDv zOWIpm%CI&M2`@#V)5B4Xudqkcp*Q!9JNDb3S=BP3E_mE1*KfZd^n>9C{<>=TNTNT9 zV#G`o&qRG@cM*)Q_=HJneFTn8XG|We^^Bm;0lmaxagv2@E8Ri6dPz~;`>dX0apZ|} z!K47MR_K^)%%z`aCBZVh854oFHWC`?RRp3Q45K3U^lhbw$yw%a@QZAnVP1imp6v>s zMpItE_#{6;UJnHhir2XZS3E&(x0_{C?2-37`Wq)FT9z?YSFNDiDV#E?OO=-lENd># zSH=6y*4JZFmAysfE=D~rI1RFruY79KjLs9VJlaiQUtj3pr+>)j@S=HfGTW^9C{wb# zU8>~g#>`5bev0|ax8C{Y^#RTyY-q_m+c*0zFBKzuke`X&!MR{GX;Y^DAi;dmI_Cv% z`9aeC=(r`I?t7qTTx_T{7;erno~PQuvy*KDnWB=Gb@P0p=NpM)I(p2RFUxwD-Zp!T z2l6;eHQF9)CT%nvW3n#B_=7cW9asrt-n&ueZSS{~pQsu32Rnl+uD+L9@mPW0ITeKT z1zN{9G4Ghe2--@!SENKM$-rvsA4SXQ6}RfsoUWFT-z%o*jVB$DG5$|q zmA_Oe!Ga48@YqxPgn1Yx_5kp1xV}+mXq&ISXI^^`r-n(qhl7K~J3vKGdjsloiqHY^U2c_}$Ct=yWMPHN*z))IT1SJS+vve|<_1paE zB*JKJqga*FIlE6dkcB+-RN(Y@zKv(Le+NS_??!*8WPm=z!t4Ubd!5Z6A|(3-^0r(U zKR6aBTP4(|uN9%tgaIr{yyIA%si*IOq0d&n%OYrdc`nR31=P!rWqWffxBUnAd{bQ4I7at8F@5WKTIjrc*{4}Kd!=vP$>TZ}(}@5;AFJpK0>k+&cPE1fadlr63RJT;-=a@mOAw?R z3v%nG=h3OnHVoQG{i?aPc}xaI(dz#6OgF1MMSTja6MD=Nk#p}A7teJUN#^pEk$zbF z3IugLX?yceiUL&_M#f{BQ?UUGMl(<${v;J#U$!z$Kc-<7aQ$Y;ySkd1lK}wmVk|PJ z$5gj9bviOCO8PQ;Sb6Jq3cPu6hG@oRkRqSB?{<`;#JqEf&8?fvf!Ez==WU7n$~$#T zv82(k=V*OD%NlI)YP9PRr**dMpi(uIwNrsvi?ePvYC`hsA7Q_b z!yN|1uHiHUNX-Pu1P@K_4vTT-wWjnbOOTlZ6tXc++ir67=)6UC&jI zIZCJg`cM!Ndxj7Rp8tlE{+X3V_8S=WwCMD!g_$PnJKI*wIW3Z%a+$pABnU_hXU`~5 z{iOsLG_L?qiaH=5_h5<6bKi=*wXGHExVzSYyOT;W(2=X(6VpCB3~HJV7pGdEP%mM_W?QjB}9*0{~=%P{a0Cs)+#ILJ>LT# z8P4J8QLu<_$wY~N%JTbK*U9^%-s6GY75|U>x9^=k-dEo|I}PP_WN?4o7i!%lB{!Z?iR*Yyh!xO$jlR> zSIQqm4QcBUj2S$ke4wM|S*T;j?)mZ~6TNcCT2Q{KU?faPeKCt%y*Wq=d5+Ce)Tb@E zZsuit#Qfw^7n!!q_DRxS$nyZsVhQlcd@|bM?Lwx+vXoOmx}ylE+bek=ebXGSFQe5# zW0}bb5-A#NUzl768r^qgjkFmr3 zT(^~B!Idq?8)%_d+#U0@i#@nY*V?3Y{e}4UC`X9NMQw|46&u@8YRd;CLkw9p>O9(V zF7+Gx`c~B}9S?K%8G;;Yi7qVUZF~)x?HHc1_1a>1^+8!#8R`qjT3E~5*etGELoUR| zcNH`aO7te~t>M)L&Wu#5cD<3s;iILFO&ybUluTbU&Q7j?w0m9uSnG%>f5IDFs?R0S z5a94|w3wht9%4_BSWr)If92b1mWIDm|GSh%!s3ak>`Rf_Tr_ua9{iyWn$-!oo|9%?;V>fnsrC{3 zElVO^CKj(^kSqBaKECz1X8gy1buEZzl_cb%WBLkOYnC#|skxi!p%L(T++$xN{cDP_ z_5DG@vlg?NKdH(Thg`dm=y}A3{f{&NwJ0(};yhH!8z;Y}a9GI$-hro3;94{5@ZA!u z;P8I=u@OkSRXtE-1oFbUwU#>pWyGp4V_Vlo2HsSETs|eRGB{Sm*_^5YrYv*P++R|$ z!}69K!QgdW;N^kdHPu)Qin+~m+OsIZM;BoW^y(Q*y5SDl@KHk0j`ghtKgzZtNcB5_ zurz2ccFl^j7V0EFFSZR(yKJMjNG{z1VJM%LT$ZBNe3tifN=?VZ*a8*B1s8~xvjiAi zB;@e(w6Gp*`Z8!h{sSY2-O9~kou(F5M-$y7SsQPhQCjcU^m2zA*hcg~lH@h(fPxpJ zj^lRHN27^?#ad1MJ%H3rwV3Bl`E$xl@Hl`}=%XZokdGf%I?*qufJfA3)AY3YTYI6| z!2{!9Vgb^s{@R-8lBhTB{iUuNXA#lm5eYj@X!#0#UQOT&vSXMo-2SIn&u!aaCbWO9 zUX$NYZi3(sB>(Qi1KfpWdI9ZSdGDm1mhXwm0kFW@Cn;&LvIkK3Ts|CAou|xkFzP|7 zn@H;lNLkBd=yEi4shm(RjS_&9Pmu38v;QWvMeshFu$fzwh|IYGOsTQjkl~rmsm`Y`VzG z4coC8X6?FO?-bu+x#^G90)is-;cYBD!j>fkZJG!@=2}wHiRC$l{5}AVyxz6@CemoF zbeq_sF2RbnEpGsX741kz4LPVt@&0+3eXIlMfMH=_9*bJl4ES2#jZ|0HY&w6n<_my4 zg6lKNMgqX{|M1~MzK=}LUs>-kAeWpwshnl~+gGSclEnVFDCa{Xd3ck~54(6HdJb!G4l{UZQ|!M^b%-1ArL8fpRETy&=vWM% zh#MulQTBYHa)CyNxoUN(akFB578VvY1K-9MH0wSx$zpbDjfqYiCP+Wsd>th(2>T;W z4i4R0NdkYGnVA&zdM3?{U0olFMvjI>@$Ph{NUEC^lcm)sZv8b@^r4hcAlIYcPfFf(JLwaV z^==CO?Zi8}i>xcVnYVs>hy6SM8Zve6#?7Kat%;t zH?bCe`Rw0Aq2eIXh@JOYrwe-?v0(@v$@`Qi91q)!91v~HJZ40R~o zFGFzS7C;VDAl<(~U7q&Wyo4+Y0IUq@dRfQc-+q5jXvPoVAhHgh&b;&wrtAMM@~_4F z-5>vli#(Ck-8{aG5jLsJ06IqFw$`SB2F!8i-n`s+4Cti;>?lV-eB5s)Mp4JiET}@+ zGU%VI=&OcH%+*L~K|bizwQ=8h4&vq}Ao$YeJJ*?%6&7|vc6%mP+vI9=ioumkiuX8b zSMdZ3&~(toP9uMhjLr)heT1S7WFBuZ1im}t2MvIVna{9jasIx^z4HD7z`dDEaGmSC z=;`I<=;Jd~rDbefpmF1KIH%uZAXDuDxQ^axylSZ$!NK+4Ccn5BRaYBzb#)m5#y;_) zR4QM{cZ~`&-!jETIW)68iWU5`Tre_OO?1|WyKrH_CSLEj4*RBh?U$Q(j0HSZ8nt|n zA62+{_ioh<^V15y&ueNp4w#t@4e*^RB+?Yf(4vnq$z|G3-H zstVNu9B^$b%5#umHwUzXs~D}~(-=_8IV-prB{#bh%c2bA)Z+dnV`eEMJ^61ffZx8m zTwQ1grekYkMgAlGhdYpgi3tRab-A62S9==TNlG){n_^YtwE>}lG}a=(N)kHF9Zo86 zxxE2M?K+S5ergx(*(?U@D4QBA2fEF-|BHHJtE1GL8KiJc)YL=zLPC;IrzebItL2?o zc>c?4y@`uMcu~{xW9!^jIRAR}uWw1oGp7e&LGe=Z6t#Jk_05bZ`iJK$rn}|}=yAO$ z@pjpp*#_LO&%Bw5s=8Rg?W7Y~n%iA_sJSGGid5`}2+q^|>x)Du_0%Xn2|vxW-4~|> z)^A7*k_<+T4t+X_yDNjnc?On$~lxmB79*NZN^ z+tj1^e3qGq(>)*Fy4F=*G+mjAfIEC?x*iWlt3j?m@` zblksJaBbURvTuZF9dt+PK-kek!+9A=bCTAWd_F98es)e&@B@mhS0Ejo)bOQ3z}UR; zcz1{-Hc5&UR708xk5!i8%iC9}$B;Z&UM3)i2Rq)F+>xq>Y`kkcwv3Z<@`8McS`&Xp zt;=Owa5qT$SvlJRa4B?S9%TM>W*?FQY@?W6VBo%UPwR0>t_jWI+P0Qm-HLL{&gY3F zN`i{Mje9e@{h@VSPpN-5;J~Z`SswUt-F;F?r`U6IvDpsSGLkM=NH6Z7?2ZhWt6SLR za11Pv54_r!7PWX zIvQ9rwSWO3i@l1TysP%Ms*g}>N(DR6TUxmNgKv)ZjH7ostmbB@CA8a?H6+&MBl@R+ zz^VJ&Wxs_?>C7Kkz<(f(&x@bX+Sx}qWn;K%#~G6~o; zmybQ1iGorVEjdLwOzRY)kV_xwBakajV=Q#gv})+0nCpClj+?lri1;VK+g<|Ae}RJ5 z{zCA|ys!|iM0Od>8UNANOBHSFJ=l41o3N1-1vC+6MHgHL^1$fYM&1FF`A13G5do=; z@-^h_`anlaogjyr7vxsvVld+B?z^}1s=V`f315z3OX>)Uzo__srg*2uUwQMg-%by> zMr)UeHSU$}W2BcVuKF}5f>W06Uh;DcZJA9$dSbo=q)Uf`4J30`6%%w2C9VS{8q1Yz ztXud*k4sX!iXT0u+a&ONIixyh-wyA6*e2%eJ?CJDTrIEHb^&LGeeVjD;V)q<-22N} z2jZlZu1`lWwtDnFd?(A9{=tkLo^~TKwJK944+HRj`Bf%{O}cfHFQy!b7e(#8#Z#jw z1eneSFzXLnUOUk7rSv05+cwlLp_hFE+yV33!NhtZdUV(6K?lG*#9!k1z}n?~n8kcD zT}!y#_e~nk;N=wjJxZM~Ag_PhK21G@)~U?TITZH-f}S36Mq}Sz2IWOnjO*SG3iYgH zHsf)?Ypq67MYsFosJB|iOt-3dpxyd*U(6s7Hja|6jYy41x%l7bsVurHVe5ZAXK2AQ z+}!H!{haO9msr)7Al_82z`iWr^1VnS&f*x!1K|~|`@NZFp5@DffJ}a}T$f~>!niYv zfF`rPjFp`=+g9PVR;P$T0+b$6mn8vilZxwiX>FP+0Js7wA8ZD=R6hgBqu$0DUW_%v zQo}j-8*-C(k*AohcQt*GzNiX=*jR>7gITfycP67jT{jSdke}W2Y={KzbUywK#tko* z$_LNJWYJ^98>McD?a$|<8Tn++^iHfq-FViLuaqwqi0SuB_ksl8M;}bTTj&B9G+B;l z7cK1v@PH)t*j5rQyf_ymZ%6x;>SlW*Hb?vl*(Pi{K1y}&@9$T1Eq70L`(l}IHG0zQ z#Z&N!b)m@N{jZ?45UxenlBx;AiObA?s4e^mU;A2drxl3|P1@j;{qu*c)3?UhRF#K2 z1IS`*fZus#-x;O>*knGBvoApbS?L-2=CNIQKX1G%nRizXR47Iu!X7yrC7k93<*t}4 z*^zGsP`P{mIEn_41MusA@x>JZRlk_WGu8HJHj9-q_n|60a6@tZKQc`Pu0=0^OjE3X zMWaTfyF+ubBI3cdUFkUX7&#~#POIm!?c*mJ{2#5u$2Yqtu8N`kW#_E!w=16F`Z3lJ zQQNR7?#G*uY~eYk9h=@9-*qMT)O_repZkF+E=?vUs`kSWbs0<`o1^n01c*0^fY>1X zQ$c|;(7&h3;evlO5)JVZ0`X$c)A=|KZ2IVuEMtzrG5bapC)8V6?JkKfywzZ;n&pIWISmS?@27j;X?}c~)KVAUR0ZX=}50 zGI9n?+u?jF%-4E9pUb61?PJWCZ+3&tirYs5Hv+ex%}v}laH*5ev*(7re8S^@jH!Jo z3e=w^YumNo`pp(OirEMq#a;FkPPTA&2r8BGrr45-bX&VR8x4Nnul@_b+df*6Zj^fU z5zu)HXb>P@1pNDOd3hP|_z_A<5CK2{vW>*)@8aoMhBpCg^Z$=r1XC2#X5MH(iwlsD zuYLmI8U7xCDs_%kqX1o%!9x3eud4ycb;4IkA?Kgzax8!Plft4#g<9)Sf{qJ;!yeY< zb{}x^KP}~^b5ABk{T!b78UQn#)AhNsNRJOLFx|Hm-iqYFlt_PO=or^QYwQ?x7FJkU z>;;fIS6g|rb<4UHBv-&pBbe0Fq;zHGRCggYJw-J!5EF=6chrdnA`k*n_DN1jDYIxi z`l`ute~PtUgZo?+p?&#J(BPOAu#>-xvWEcZK2QbA085qZ-H<)?Qo_Df8LL}i-|6`m zvd{K{`M9%kEzGypP?%z4WoAF*!A**@~9c5jH z<%qak%>%)LfZ?iTHPa3;BQs4`B?6wdnI`T%rDtV|U!=-bsT%;sA#;6%L2%-Z&NMMu(9RtB@DcP#KzFYCV8^n6*p6Z z%>ED&`CY|)Zj-3wz7KJZ9dxVUY_C0eB?F*iWc>;je)EUQX#jmf`>3weZbbR{`2o?y z%=WUB`&?dT<~?9T&xqSj-Tuq%t`G)LOAZU zpwga7IoMJ$%>0G}&^E>hkg+*5fbj_l>bFiO{Dt{d1_DE>aQQ+1`}N;#{BGN*q3>HdRI+ExOH=S9j3Lu$~De=m?WfI#^2mHA$v zBD(qad&NwE38oKj6rf70WF5)<|pK$5HRSldW(Oa3b;t<_!PAdpli$L5uCtAY`b#kq^h7qtzf5&%|GDcZd{@j*cGUqKimJwp_M+gG*K>*$-jRQ{C%n2&^n?XGpM_1vpiCT$Y?RG@nX5; z{`Us|`|1Ac25$hL&j?)y2P_p{q-qiWGPE_^cWGy9j(3!j?_bHRs~|7#Tb+egz4thE z<51)f9&liA0BGgMx@}dZv_l3>(0u)`$ZXEPtcYAm}+dNbTg8knBNgl zU4SdIGL8+X>cCTPxJE2PK7eE{_g@tu9-a4VeHn{9CBT$V`6^Ar>W?6Pe&k1d=0vVM znbwSFhC1{H$9tYCSpU-%22p{S}$cmNpghl&)THVRm?OX(3Kj@Nm&-|CYrMzU0` ztHZr<7O&w&{r-m-1GlUANpHf4p{othu#=|s()qV{?@x|5i#7Y8wp@Vfsm1=W31&a9 zWzjNDHa9hT#ixDi&*LKa&y|t=bS|F!AvsrkRh&(G6a3ADeDh4DvWd7;B3b0q&bh-vKXYw@5UXXE z6%i$UOoxO1^~p;9CMr)aLfHa*-^~l2hUyjx=};ODz#liB-AAp*rHbln@jy~Ilj zmrH{G(1&feFglx={#m^oJe`2Mq(lozNgL1KIVI*=YF-asQt1)t5l*L*;w`$PZB1F! zB(uwjQ&4)ojO7Id<1aH$mvegR4%l~?JL~;aT^);*g4*Q_pod}Ibsi$1`gdu^OkpOy zdGMawGcUexd(JSG?{aZ+MAjC>CT&Cp?|)#sFw3O0*dSj9&$O_iT?Zc z{hc%0AaL$Rl7f>>zL-`+uCV+JPoOpq;;g-e-+JYzFX1%r?Va4CjM>oIn1_RIY%Jd-7Rb$zUVYq=dwo9YG2_B(><>RR4TcWWns1hus62EH zovev6nNl0*o>O+u@7nJhc{1YFy~XR!^p2$msmC`;ix-;0F|#2i8CybyWw4?rJrbx6 zJIaTtR+R}dk@NaS8naKKVUL{Y{J@#O`Uh|qVs7!Haeec3I+W`BX(X3B&u(e(4?yXj zp$F2-+|keRi7Om~B6&$CUOI4&eyWbnyHz}gFRFi}s$T3aEt{ZAGsFHSKdZjA>JrGy z;7#Pcs+ifWaK*SEBMZCjxHt11YMoREVb&pgbwy&n&_ZXP(*nNtL3M`aXC6i=>;PG- zf}kzrii5XPIK}RJWY5^W*Vzo1!BhbAHw4O&iFgcb+B)+rns12>0muWVPGD4F>UQzj1x*p}Y}3=QH;YkG94WdkN5|ri z0FbQ>El$@s?cyy*X`Xpc7-6NyQzyuyD`G^3T1(kb$n4tx0Rfp5@Uf-a(vw(hhDO6` zrd5gb@6HPdaW12SotDt`Iw+^5@*L3MDB6E~tF9?tnIP7qCNFQm%9q!~8RzTVF%#z= z^hv%BB#~R8houCfiE@R=L|X;%mH?`R>e3 z2%Ypf>KI^~!WOTyIdqm|QT|h1h&>M@_C3e+MbVCD0tjq1SgJF0w3A z0NYhx0(oKKzQcAvB@_zu6RLc5d=~%QGQQuip`fANZ@&NY%N>$u-Ujlq-@P?JopLOr zt6=5v!)@uu%;5pW?){!Z;|r-bIl%j%Wp5C>oh|XBzm0qU8%=i6x^3s~Pm096dSztJ zMZ_aokMAnlgYt<#VYfaWo8>j7t{aP4`r$+m5>;@ zA(p*nfKYn&*#7-k2~p<1YqD^uY;_ zb5e@^g*HYHuqv;IFZs9M@}9}|2$z?Wnv2Sl+gi|*rjJgV+ z1U=Qe1zOnqa1BFuEA%~c5V!u-EA7Afdw<_MA`&*QPaf@8&iJIp{&7Vc{hom*cc7p2 zfWy2u!_&!8*K|@lz*+-IOXioEI*}l2wDAy*c(n4tM1Rs}+^j62BG17yK1`m_Uvq#| zDFC^t3yF2w;orX5A;VN86_})=Juc9_iZSwMeT8o*ws%M%f47POZuO zq*uT?%Q#GBoE$RD4@CB)y4{P2^&Pu(ZG8MFj^jxyMDc`QDLQ}$vzKWr`;|Menc1K? ztXUQ(6)VRnGmtqN&Qsm-DBazd5=6j95@reA%Pif&Sz@y-v$oI3wFQF(Ugidqr@eHP zCLfr#zh$0>=-%fIPS+W2_t1ABQ)-Ve6L$1a1vs>2q;-*A;IdPd+92~-;}a_5g+#%l z*AX_Eh3-73w+2d`b0vaDU)u8XbdUJ&-8;k!K}Pp|&v1&%RXkugVm?;2S!Cy?(RhmN zkQE#*zx9}I{Gth+j7Ot12a}{DgOQj`Q@01IQ=WB??ybdnJ}9bAl%5q0fe8= zALyE`+^$0vd}fO`JLbEZLwJph9dGBvXu2!5Z=Na2)3NB?`nmp)tQzi~Z*9W$^`$nf z_>&UF>1Cx0L{PIVuwFNYf3B#R0kdslWj3)hXZsX@@sK6^hYKw`9$t+0SAH9*lb ziJq#kCuC6&*nocZ@-RgU+;J_pSJCu*Y_gxy>g?yL;vEeTJ~+pTCsfX{#wGw|yrr;c zUOC!)^*!LUB3PtzK<}`1rky?HIvq9w)~qE0#I6ep9Opv--KjcVLCki1 zZhURrhl+2xhP#)e+um1Fi(@30am2B+v+L5E#yK;TUywMS6Z*V$&n)m|Y;IG*r$^MC zHbPQE>ceYp#5CD`PWdgMpB9cl7|hqswI=62Gp=;a1kYOoZ5?+1mX8t%rtGx|xl;pY z++Yhatsa$V|}((dzz2ROZD)ySYQs(MpE&68Y;Ku;k;gX{rGZe;bEH5-{rAeSYgBR34kdg*qP%z>QPBtUA;%PoXsLd)NQ>zx~(A`~U4xPMf;<3OG+!1m6$zR*Vn0L@9VsKFlP|$MnqW+t`%B zjvbNR%C-SmhTXRc<95agT3cYPF@-WmKR#~Cw0K9*Q32{2Bw|j!UrUiFj<{cZ{k`;I zO{C|;{JwAL?(HKJ#v1uH9ZoDp7ZZ6x1_2(Kee^fhX9cRs`wwM`V(cI82hIgNiS!Ea zM(kBxMOX;uuhIsdN@*xp(lW9G7`&>OvvOLd0b%JdgLh{gxv1*NfZ2D1HBWr>KXMbt9qT)&_UeaCPUwcP@ zv;RlZ!|KPycF8Z{E`uKHYuVXJO9+qs*d-w)LS^3i;Ay`xxn$eHT&j4> z{+8@ge5K#GBdTr7)){NyUZCDS>P>i!F<<$x+*fE3YlhKEukq^ezsbPZQsNy>*`g^c zT(^me#clO6pfrHf6_RccSFK(v zO9i>e#VHo|^3N(EEC3mICi0gITy}4)l44r6HU9IpTvha(vK-B^xf+X_5UH-MnZ>-P z2~Q(z*yQ+$Nxo`2atO}xPtW=IKli=x<-H?P#=t4{>9K&eVUl^7Ib1vA(`t{5uLNvT zb7@BVib1}pKFj{WaMi(99uBm!>+&O!?w!c}D=}Z+oxA?$75li#KBYz_Wg|qHx8^W} zAZT1+gB(FtFO@MJV1fw;{={DR^0L>kUc#6@_-h{vt8-!H%loWK1`p_XZZ$p(mws%W z!4)NPCvn$*u2X75to|7Hsx;QRJ9T4!TZR`e?;=0R%)_{>X(RIfl%<{RE&nH@u`*(O zEQ#W^@4eSm9-5k7mFig6*D3INa>Zq=Q$S0_bQkM+4as2c8;?uSZBMEBiD%5;@TpXG zc7Z-N7uNpbRRx>#=}d0Q-;bj`y3TTui<2%N0s2%)?9|O^PjS*^?(~~mJiGlrZmIub zm4BUqkV(4pZ&=%jk-w6*o2BCYvw5k7e>I zayo$c+M?}8!yeJ7Zur=}EwAmH9#ey+I-)cb9^SDD!6F{8lsOFyXw08@fi(vYLO=y@)Of&s zBBIYdIeKVGIB&bm6?cENF48DC9Kr6sxOr9F#X)sc+oCMZ9#+vqyT^g9V=cG$_P$-L zkm7}>&-PF?6=%y_H7rw5Vve z+dVI9W6o40zH9igEd610x${_#hJf$@WCzKXhYrg@5fB22AEzQF#%Gea3*R|4HAz|aUeaZGxQ5rYy%{;#cvdx1Tuyq^Fg|R!e|Tf& zWo#Ekb}@K375ES?0Euw16hClv0tDZxZK582R!xz}POl1lL#4Xt6%mz?kwI);nj9fP6N&OQwW}KyDH-2qh8|U2H}iQv#g!#k$&Iy z7DBybw}ri)qnJ&yTG8k#t7xB2Lo;!3zvR8ipQYLFN&eMbiw;NtBPYADe(*mhzxlySu`gyJf7WQfj0j`e`fUAB zu*-O$*KYK!V{5@xT}i!^jn76E%U%x#k~OnERhjY}!xY>4d9G0Umm>^>d=+oi@`^=Q zvDP|42jL}Y=31tp3j5t7c3o8*ATytPG3tHjJ#ix!-5(Oe8{gX)_iL*Z0tZdLdLjL+ zU$LE#T zho0TvLjgYXIkN&kh@ObAK?+B>s4%_Vr%LJQJ5C!;*?=73<+|$yx=69lKMmb^2eUX4 z+m47_obetGCla{Jg_Cpm*6IGa`+v3fo>5J9Tid82ilAblsR(vZkfKNpO;JIq0tqEF z=|!590HFwqJcxjR^b(Mogb*Qw9zYO~u0j$CU4%d=5kd$paN>UU-rpJTc*oxI{X1hE zevuJaYu#(jd9StRbVX0{yUVPaq z9&Tv~SP#Xt&+zW)NVK0_$NL+S^J4mKpjYb?I)h1>y zS-$LV538b*kXkn^uhs-$PFmtl+s#NdN8?>HIN1Qxc|K=1fXwT#yQ74@{_7r*Z*=Ps zZ6ZCxUp*SH`2CD6S+ZiqlBwUny6;vTLj6TGh?z?Y7)Bs0#PuQZmLWr{C06HdH*qNT ziH=rLjw3gq<7W!IH&cuhNx`@$ACGBN_fzF0N)VT}^hGQ$LVB{?5w z1?qkWoQy9P<0OxqYfp{2=~(VEYC#>+Lj0iSCdc;f>pR~d>I~%eFoD$)7*%kqhAU-% z!PA}!;k6M!9}#=DF3X~pXQo1;Nn*!gA3XJrl_<@=v9%Ukw{-1K0>kTaftxLnI%$d_ z)1Mw9{g({@P8K*ejVs5{H%^OJLJK({ zD6TG+$aPbN@JP*q2)ydRMMyz2TXC1KDcUhI2}uo9ccUPuwRat^#62BIl5*UvJ#aMW z+0FJFs(Ik^-;E8{1UxSxpVj!-_eGeW`&A-kTef|(U=;cdTZr2CtodG8QVg(6(n!UO zqUfbwp5#CBod%D*RUH+!?uKpjE>9Gh1+QL9hff^64(PE^bjr>C8E+$Y7d&Y{pXNXM ze5+cJcXtu(ZZLJLsqfea)%faX##8*TSmYj`Sm^ii4zb9Itb9t7jwFXvMYhf{5Y}uu z!bmeRMdFc(fojOz;*oA7z4^5(Ig_cVUM5%lc&wVWsAIf+M4f}v!hSg^6emSUR{sYp zs%}SEu`%M?%kb>{I*}5BR=lt3`!$a(1f#vpf8UT%p*fF@hwT0t=Z+;~gCWjm8OY$orgDkrRytlAhbPyT-|#^1ZhKo}qpG<4vM&BMtkFtdq#f zTxoRgy>1ZeoH{~Hd8awUIWO3VX_3!=)c~x%APL+-xQJ8$<#49=?`vS zrFih92vI&~`dGA|bL|@p;i<7U%<)M7l9S4CSW9dqVSI|SEUKJB{q*Q|yLXMQD|zOy z^!U`F-l+Vaw^v-2-#;;yUjw<7^So3YW?_Dj+51QO?Q4LUp*3cUb6q`S_d#urTUxIm zZsQNDT_{()?W^muTsxN}&z{y*rs7Zhk4(nVC|8$-kzW7BkQ0K$#bOpFU$CLZEX4j3v zvNM)?**$ULf4FMO()lV^wC?auX2Hyu#zf098l04 zrS!)r#2E>S!jy$1@keGc!1^cHLVQDpY&Kwsexm=(&xEPpL^(aXzzmtQ5M z&dLph&ph!=C+`ImJEg9Vn2&R+LQ%O`5;O7VDvb3 zkYE{k$Ik8fL^1U(LtUG)VYU@kv7sSbW|{X0hs++fGE4;v17x$A-A1TnSygvx1>4+1 ztouf|EkxFtE(CO>K=5OA8t%BYvdn4_C7Qm0&%(6-*z3N*#a?kL^&q3>dGk*m1wqif zcqAVb6&oPtN~a%l)48BH|BK)}9btmam*;y7s;uvQX^&ZQ%Fa<(E33R>`6S?hfxKSQ zT@Y|w|2>Jm&F}N$Ylh}|lhHSi<6Iupyp@IVJolI6!;#_NHl>JjaDhdxubqwD?o98f zfi`BTX`>$Uz(&|o5!-U_G({ne`z8lJvP-6I@v@9R_PW7HisVO&#u6o>?Y zUG&l-X;Y`j+<$q@P}eSLEc&UTrdU*#vGcA!Eoq!ojAu+o*3mM5?Nuj%i@k{7Vv>z5 zNvCOU0E?R1J%y4!n=<7i0!^d7!N&h z6DCQ-y@qJaA^FT$0{)C>0R$%zTk`A7i(#5EZZgTmw6pw7^MUohgw4QGO_<^Z(14in z?sTj<#H0$UetcIq$yJ;SMqY@-a`kB)pXkMMmYSk9b>7zGnz%%|^7BJ~5EMdLXL^fc z-7l7QFsWo@3E{waunyhlb(*96&vSb`kFaLv)70`#1kyr%!j)G`gm*Ef3sJa`J!7&_ zI$KKpDDVJR-&h_axe<(VnsO$OK08VJr2l|d z#@hp?v&c$hh|~-85z)&G$%lCdQ&{*%EidhKZrERcU~DTIrC`}Gp3w3sn`H5W<9 zP+z?jHl{-`sfQJ%lSAa-6)Vp+R)p;m^epa9Bp%U$sWKD$J74Z$Yif38g_+|Y#HzBA zfS<6FU}1)UAf{`u?+e!~v2FsT&(~!jzADqE(k}oz^VJh6xfM!LYC4hj8we2X$JiGB zaaR_LY^~R|gRp|-VeBW;a@nHC7FmQDyw1|V+W8R8DA#%H(>sN#MU&t^WS4J!Lp+F$jxO;;2$?3lH5G_5NpmK^IyZ&|I2R9W6nZm zUWO{);NGJ{u#ULloh-?jz-KB_p#O`mn_vFcWym{!JZ6uP!QbPwdB%BS8LGnnFD(Fr z9E<)K{2nA;K+tK=Ck4N_3!LKxn-%CfN*<%0PjQ)*K=s2?g51!5C4+?+0 z(En$MBB?AZ>7+N9O8j4dasS_4^8bIC|7|}1i!1*RBjhn}mdP{eU>UYe4_jxFkn7Mu z{*e_#5dJK_=WI7D#E`{3zvioSky`4CG6Uw9zmc2iwJh78_!wKY0K5@+XD5>3Oix_t z#wK>ENW~>>ZIlP?!BTDZg(Gi)6`mhVMIR2YsaspZ&u6Q=4m~ZOXEA>*VRusukE${8 zyED#L-ktFD(n(G@vR)KFZQ&Kkd2LJcn+n?fZr%$au59_#Vi9Z$OI_@i`Pf7fd0z7g z7UFl^1*LHEO0F_VG-}FudUh|#LjU%b zMuJ4#$-@&0Y&SCY@)}BF(NTVyiHYC0IT4=ZPI|{GLUHZG2asPi@!EuxAE3RpXf(y2 zu~n!VeSRp|%UjelbqyCO+BvsZq;q*4?Q@))0vwIwqMAUVO#qR&9@QeD)iTT6@}H zpMJZj{C?t_DfZxNH#qmOd7Q3q{;8uC7c&0F>mcWqAP90wCrG34j!H%O_VKb_G4dU!vmo|jwGZ;8jp&D9LS<>S|!O6&{PZRd--r~A(`KbH`3 zL;$af(`Zs~xGP@bCUHO|$?5&;tcTae1pGQ)KM3N@o;I;HFJ!}%PCPUG>UGcCXC{;H zt=btv&I^^9OidY2#DLI|Z4Ya=ot!?s$b68S&$|sEt`Zb}rti(#bU8B)&XOG0D=7Yz zYyuB8D}2=h6t&~>d&T<@jq0(6lN@AMrX?gCo{^M~>la3}D-eeB)~W|o+f{4qS;)#y zzpkOTfmWJzytn!<`svV-lsNN)v||s}OP>`dgLu9~+F#pzctmfIXqNDD(#iY%SxMx&+`!|?ug^wt6F)kw8JpCI7GpMavW_A# z^_n;5M`fba3<^p2?=VJs2`JuAO!qI~KW zErTIDZTL6Hb7Q4CaY;d`qoBpJ5-DnHb$1?kP29vi1l6T7Lq#)Q8vii4V45?8tpEI^ zQP2arw?J3BkfnCb}993PCIWN}&a z^%;4-l+5bt>nYrMN+FeD)yjNW^YOvAMhS{zL+>Tf-)TYOY=+iXfZ0QfkPo`3dSvBJ z<~;B1Fb7NYwy^{b#D3f7-tns0xfe4~uh<+@YtihvZ~E1h1rhJlL30?@CeCY_ax;e) zNeK5X8K_>h!ElZcqQo-4=vCdNaNT13FIAp@06bu^8`*PD)>Z{C`^))U-7R&Z%DE5o zh<$HeB3c1QQ{|j{6Lmf-+5G_Kw&ZJBD{*EESN48485t`%2J1hbm}%faRdwH)UWy>& ztHPq#iu8>@umn})f=VRA1D~Q2NZ=q<`ec}C__t=!YSwTc?$_7LJn`M~|2Ncx$>J{B zWXw3y3&sF2Z|RSu#*u{KwQ2+W>J1(DJelRnB6Q&Q2jR6U?Ty#$t7(`u@*>luv8tL!R~!O52E61tjAk zUva#>byP;vc&V4aFy@MnT7r;X-%gB@9a{=`aiX}mC;BcR-%%B(_wa{dty|X!wqJQ} zqXi&Oew9=l^i!6Zm5*itHD5-|Pp}y1$LDGP2B{cOIklsiXKq$hn}K6k$}0Sv*rtYu z5}LP|D#9I~jDPvHboMFFJ=x;+APxY04zzS?A#;KoS;9S~S8O+*x^gO7>n4vTsE8Na z*;7*@yedT9vp*l{RSGE;`@WSS&&MfR49<#m-in8TuC-o3x7z%e@K8|PQn_UbU3&@~ zp8E~S4u=h9v0nH(J{*av(XNb?;!0`(yd-$|R;^#`@WP2;tE5u& zKfyD_(Mu;?28wh6&sv7_eJaVFoTByASFH2_5s6ODsrdEL&!jWO|Kl+5pFd8CzJ0h9 zJD&FDWwXPnUnMuhOTjgVn_Q_~C}HvY*v0WhL>CB(2JZLA!S}iC+)5PHm9Aw$N#x#G z{Yyblpizy?WKzqM-sF`G7TP2xu%*-Vd43qbZJs(RIpWw-;)iev?%7T=oy$X!-jy|+ zYw*k`j3-#T$+-oDB@==}BXv$%B%1*<^s8Ow!&R&A*lZjy5A*aB%RTe|0@*Hp{TpQS zxPf1kS$$}T8uoW$>ytrLmd))4_WrsSSf>pRt+PDYeR)@5Adk$rH-eO9piHKOKyBkM zmCQrSQn;a`LI4P5?EbO)5#LquYTpa+N8V`ODk4Z^;`z&s8onMtQOs)9fjQS58`7*c z9cBW18~u_q&dwW6-9L_ec+BobQHWZD zwPxRF8O69yzSNr9c`vYd+&5lMIRGLyD=34ng6+f~fCrYw5!U<8Eo%&80f~AQ`spa3 zH5k45JdnGHD>)2S(OqtG&_#$mT4hK7_*HYFg8@fj>6PQ zYf6U6H@#=5$*I^bZqhR$NbdyMj@fgq0Bb*TIRP7Nxm52xQA0aL3SGM_m$!)8>dmT} z_8N=bX6mj|`=D}PiJhB&@Ot>ypM*CkWk0_l9euE9^evZmoIOYMm>gZfOfxKdr>>J* z6l@nTGw9lyf6RkEbg$Yav*%d7`C`Kn!fe6=01HDrKJv8$zh87KOwZdX*ry-<@+p?4 zpj#@n(NXE@K2oFScZ5ysIJ@CTa?Sx(sN9u;4pG--3AoU4!JTK_w-be|u#Hh`i@h)3 zppLYv^!d(xxnArYNl>Yixdu)WJ#R4wrD~PHmFDOo6 z@{9tHA^FVK?JdaYW~@I8<(^%3^Bb^6_Vs#b;=O<78+)xyL99%!3H2<(Ao1SSt(~Ci z6^CQy)@np0*$xhh@+piyCNy=-*ZL#ITo!$0^b)P~M-(Z!id)!bpTfQ9|@*+I~pK~gpCq58$azv3>5v{+eJOOIMnR7d7= z=Iv91)T$2Pm>7PyCt_LIwb@X9(D2vQZe@p@3^~%IJu;TK2@Z6w9d0OYEeldn>Zl=& zd##C%HcIY}u8T`#o2^6Aj#eYLEP|x+1LQ~AeD;K$N;|Ty$#x?4&pU5?p+Ny!E{l1N z_^y_&#xZl0#YBNzJ{K-ISx(P$yFEik`g?f|fxLP%{W1B+VO1Oqk&@Z3OOJ6n=)p91 zY3baE^6Yd4>T6ra(n~rzF*EY#enK0nJC2>*as`!$HKF87xSY5dIaoPS1IpLlBfVWo zoL5h+#RK&fyk1~3Uj_xy@@rLrpc(N%KSr@k9;sS`THrXZ_>Pl!h~JzqBWX#QQ-*c3 zc?1DcU)|z+n;p{{0#Z>~3kEf1Kta698dP2RjJn1^&G+E!;>G8sL*)@fv!iD?c=AAn zJf+Nap-9>blEWQff0v-oEA+2yotJ(soP7OoBg@e9w3)$iV16xa#wEwB_$n0)5*NK3 zI%!%AcYAt%GVYDjhq5vo*bwsJImIiY^IC-i_IF&{aJp z=h6x=*7nX${iIPcFj}0fd|1vxe-=?c_)J>^cXlok6Xk!HzIm=?=s5m2CL1b4`C&(rb!&Oh%bt zOryZktI={l)pQ_sGcAW%yqjH1&QaX&$hbT6Gh*wd4Dpo$&C0~=w(&VuD(Nxt3V;cG zsmKtwR)sBQ_|3+?jL?3^odR~tfY&+b{iR%QJ)@l7Q|j5ZY`Iyyixc0XH~yrL;#1rbEydceuu|Z+;*G?uh`S# zSQ6>gwlpsyO#Tz!4!#&MkK>|?^r-R#$tM~tzTe+<2sZa6NzL^ps^@0CRs1gAb^b=k z`aAmftQdIzBxtmE&i7{<^l_46f!x>Hdxbs)Yn%8;)mZ(EaJrK{qpw(ESENplw99sN>;4WJw*nV* zG^aXBS|T49d2fvD+oA=C8&RD<3E8s8Iq_^tkM;w;oG%1;EGKExJa}Q5djrD#KlUTr zKXUPn2hF22mWW*3PEk=D6t8ZH>Z88hIFHEJ*fO`;@of<{U10;55UsMU_x;v2uW(Wnf&gcuct;xkkB*&e9NeV8;qZf zcOiOaKOtw#U-r?-p{4D|_CYrL2@qzxFyU`Qp{Q+UbPpSebv?p`51x=KrRnJp;~?22){Ei<~6pD*Eh zjNBA%7(nD#p(pX*|5p8AdKPYoFRjeyG_1DAalq$zE-oq>=AyOf9=!XW?Ln`KC0wm; z%xIpa^ke zvb~#Hv~xE@9*L4i+`ClvWoL2oBW-c1hOgVCX3~=JLrY)M{%&?&2pS=J3bbw?+FZGw z%i;x$ZX_j)ZhoBMr)R~9dy61KhG7K40X=0i1lSB5{PrRmKx z(Y`G;*jIw3XYxv`89#Zhanz#H5G{Z`7Ee4S_Pvbs<7aHmJltgj~!m=wPs?f7LTPv zR7ARMSVl)!ULD;DjPZFv@o|;edQ=UEcfVX6y%UoifWWR0=H>tbz7w_QoihU+%Vjv* z^wylow9HkD;hQ18n;$Z&Nz~5Sb1H}?0H$fGDFoDut`c`0gE@O<=Il(yC?yB_jU-zk zSibpoufC~#U~KXbTawm=0O}#xPcV>ns%5l1VYCP}Q~c|iiN?TzXSX^NO;`lMTu4}r zZU`)mK;9bMY~aMrK(fGSHLz?(#-}1z4}xjqLA+zTuo0PfuDGreTib2ySoW9 z$bY!hV-3z}i{5>fo%1N_Wm1R`X>%$r!jZ3==;CLy8b*?}4m1!N_ap$ws5~A@fID*I zdYXveM@w||16{ZAZ$&0Op{1Akzpc%@4auPo*V|xSowSA~?aBV0!`hopjD*70&Y79T z zKT;q|HCDr$Zw}dbX|74S3^H*cL36r*=3vU~eif(CmH2~5P!04^EX?C=x?0Fb7PGPhMPet< z3qKB#<^lVIb?6A!z!#0oRHW_)1)l)rt4ji&51*W_Dd?%4Nf3pq$hgvzp@@-2I8VHb z9jxR=NXOJ#zdHq7`5-QZM|a`h2O$L(ufkRjmw2cd&y@ zVVQn994fp6GfyhZS*Xmug#^WKGvzCTcXY^xedG~v$~>4A0oQu~t?cjOsR4&=r7~Q~ zjLL_y3-2BA1dBPXZ94(&##%jtpVT^9P5FP$de@6zQDdeMMw2<~0r3Un^S#SDeqwa{ zF-}^JJ$F1sI9TU+*c#!k{DAYCZZ|!)35>S!PxU3MkKU@*pX=>@CVs<7_TJK zp43y{z?$C$D37Uyw%$C&#-ni|^w#^5>ynvWzA2l=H#@9P@7$;wyl0W%e8aB;Oi2xk z^9*3Vj+On6z1P#jBQMdglDakRV~BV`@2pS#P5aP6@M9Wex`3+r(gs;Xhfmf##l7d8 zcphckYTW7K+K8&vgSr=equxfcd8KzbZ5aAEndmJAO%pfc8a1~vs421oFyS7^aLGRqsj#iAdrVYiIWd{xt zyTVT9BwRfgvzi{*vt>mAZuq@GOVw4I^>42QPy$DGevP~7mh-Z*YYmzf$XD&qn3?OT zshNy`U#j`fe0M>KD|*P=)I<}M+D06ARuw`a)pgN23;;~7x0%si_2BJ+o|O8{IJp$p z_vL@M!hibMyv}LgWfAvxq*35kFK3#;>@{w*et2_9P~6j7=DI zUjJ~2T_nBvbHwL1c`|)N*NR}=N?)HAml%76oVq+gPxn@>><~+@RcC~`XR}SotlmFc zXb~%Tp7?{vaHfXq38~Q%IK_&IVT%^{kWHF7?x`(x;?hUy8+#x-NMHX4Vas>H&9F4} z{-x*54ku2>tFJhi`nz^@HmohnXp4D^)WqgJN2uuL z=Bs%fz#hiKVx^QB|89knQhFI`$`AJdyU4EmizNaP3U?)BY&>p8V{Bboy`F(oAs&>E!9i z=IO(n)^UoOcQp36$R|#7uvU5M`R5adb@;=zg?JkWX~!6fI)i@4Ak4~orGo*U=st2^ z*&kbR+MC}qJ!NFq%d))*!&ib*(-sFF>%5dbS}9kyr@i=s*HxBQL1Q9J5~|UB*rF?U zsW7WMuqM?`&&fmekH_;jaXZ(5C7F=NX>y{*mR%AUz%cBcQclxWfyqeikzaU0x{_s& zd9lvn%>}$Q#uvxL-E$spd@%c`l<}Wh0*@Apa>pKY`o_WVb-jKCs~^R z_9L5boGgV1-}pBN^-qW2yN7Bb!TZ$iC9eh;`}ZC*r-iBIiczP*lZA&TtV$KzeJtEc zoyn5SDu77f_GS+WC`Puf?`GKElG($H`gt?(s=B@B(Rcu>Vl}JUNQJ7v4O8h?3A0N4 z;XgaMajWLViB}`hJ}Y+(9U312CE&oth?R1kq+y1AW=?PskdxuslEt{@KW0($X1@bO zQDDwqS>sdRsSdjyTf1{qrIMxUB*(My5inVnm~!8n;JrE8^X4|*YdXGGJws)0%d>4N z`1IC-aCM7S!)0%?b^F|}T)G(9GI@vpJg5YzD>1-kPQ%j?N8Zq}`UpQT3jjhkM+m-q zquu!JPKtMeC*P5|IP$wGl^xR#K}1uB-PJbK^Jme~*Y=!-S9t0|wLIJ0f=z{=m7(E# zRvpAQHtZQzyx|R9ojXIJa}g>=ESWZMB%<7pg+Xi52E?}39^l^;c6r|_h3E}wJmL$Q z7#1{ENm>u&@}TRu2*8s6hUtZP zj=LRpmx6-?tpwFbdhWY;q3*!JB$VXe)g)i6&JY|< zN++Xe$E`nD#Vhj;K&mQ>J=(^(ua}+OSUKB%`J$h{Ca?a%^lsxoACzmv5GwKYgH2ww zkh`44p1X?o^&Y(2)?%TCKf5<#9*I3G(9Y8u^iaX05dc@dtb_rrN9}x7q%=hT^pS{1 zYgI^Q$;MJG=;LrI(iH^Sve(J$dwc0?fU6F*hYK$$1MPIR9sMg)_Ma>swxY1gY7HSz z!2GXtzd^pTRx1Yz?E z*+Na$JtZnK{>o??F8zy7 zRT4$83uCB@KN7Vt7r}5J+yod(B+rf( z_co27OvM(SjzdB@JUopD$TECcQ&CkExJgjfup(^8Zf#+-$Q^q3{)`lU(Rw6TR(qjV z*Vq)?wDm4%t2nK^$Zc$-Ag(D))yZPMJEC8?)K5I3mY8(XeSEuNOgUtXJ&3mBl4f3( ze0-;N=gMsFqTROD_FT+?O=cTW)op8fO>7ed8IL)z@YQW?U7*gh9Mj>;xQ7m{-AJXV zB%P7OF2NmRVG1{&p$Q(_V2SJmmw~NJ>)BNI$)3p7bclcLsp5qTjCT{}OmtL=BQ|;- zBDHfDS~qZqS_;VE|H2I0ksS@PT)W?Q^0amM+(2hCtH0^sPuY)4xDm1bQoP z<`R@1xcD0PH}NGvVTi)7SYw%KzM$^snwF?jI6lZIwHA&Kk@^RN=2a4^o$U9^WEYY0 zmgh58EEw=2_#LMxIB>c&pHU6Q`zy^y*phbgXN(nwYANY6-1cluj~7FiVD$Q=KFfKT zQm5ThOYMvgCVaZXk*m?YA@xhvs2ZAu^C zk+*#EE=?r$`Oa>roFrFyFh^Ql8(I;;Sq7Tj{q(L$h7m()$ESD+<)PI(Emx&i)PqMv zHNM}>g{x+L5WA9jVub@_z7atR6EFj4FE z1W9}>@Za5%9Nj@vdZTF2~P$05)R3=1i!c7ubby-+Nsc38i;= z%Fxy$y6M;`P@$M|$KCo>&t2~e;te@wPaCF8NIVaBIgS)1t|HW+THWMy=9Tz)q8e0B zD1?A3v4Wz&N17_72NGoVwr7>y4M)~RtayowkedWpZ>hefPYljWZv&_jku54*LY#%6 zbu5l}vfmvygXu{Xr2S?2BmC1ayNea(2Nq|x=E zhij=dPHT(yHI{jpxuWpopesTw=EyrKw=bLZV&CNLSR4;+n3HPo5y8skk9Y*vJ8_%P z(!drD(g@clnkp@prEy1@0=l;zl7Qi1XKlKoF8bv>%ywjElx+zQ*|3ZmIrjW)Q=Orn z_k_=RrHf;~nH=y?CiAK}Jr+I8vi;W`X#D(+1+Wl4Eb@XGxA2#zDM!Wj&i3!)h%3;% z(bzi7E3odx;K0}U905S*nB9a-*mV|p1iR`wM?$;R(nKLeU`xk%Cesn;`F$R1j#t^% z{Y|a8Id4hFdcp}XlUt;b*AQX3o3>kNKhOj61HXH13Q87c4zNIsh4=#QbF+HZzh-mL zyM@0WK450>r_z7+)F>39+Q4C71Q7e}k_jh#vMcd+!i`a2Q5*1oK4MP1j2Xm#?sQ1x~S|2mxko^-`@yv1n7BRsBGbUd&1 z7jum0B=-pKmJE9+6+!xnUJS7!!RZ@jP`$-&dx6A{Y)Whzg^e zUS>Wyd4xps+$~VKd}ie?K{)Db|7QI!E0f13MzD2BaH-EkSEr<9>~N%-cAfruWq~e>JDEzuu8}<8n`FFLzV{%?n9{28Aj8w8toeXPTN( zthzQyH_!*RZya-U=`ZU2wGL{m5tB@Ep+WA=W7GJQhA@=e)d(*s5TGhC^d(5JD zZq-INU>wq7I%=M~@U)|D^5rY-;?#6c5OBIiFT`23VaBOsDnsXVP)QN!ui$s_HnVvy zdvv)gCu>~Ux>RBMG5ivS5J+|624}}f)f;Z5*NsYFSMg9kh+Wf%sfPTJSU80$`7|$+ zZbUPOqD+R7udsfUphgv0XaLbal1j?8Nx-hV2@clupncK}8x>K?V~;j`%8oJ%{TG8I z>h+Z5|G`-(PquHDI6B)$AVmrOl%L{@*M=G^j++`MDr-;1o5Ahpf4QuA zwRHMV^?2?Ys;m~Cy~+`wZ~TL}jifal`+VAaPq`Cb_#;Qvq5}>Z^k&FvLq`2lS*4QW zgaAd2jg5~NDMgtciQ{VDYA7W|?*~pFRAzBSL;dSXdS(acE>LZ)cv)kCHyO(0#}}jn zY-(}`P6(X3I?cr%qERUp3IIIKjNZ;k5<9>ZCt?b=KMRcuWyBe6$S+%&a^iV;1>zbS zwK?M2j1A@Vkwii<;uIj8gVsG3GoZRVK*}FoDmA{TPm3bi!8Jh=KVQ;m{>8S;nAyc+~zagE~RCrUrW#7Mo4F5|c>wl&qH7gv}VM&&VjGkcKyQ)W?#Vj?@^nk|3ZvvPC zlJ=MKFE8mtY2TRmO5vc4DozOj2EE&NC}86$(ET__b&2f4J>St8E-LALap<@AT9YW9 z{89{J7%}3Rmm66?&w87Ki|3dqOivCviG6=bDI4P@26g{ zUc~yuZ<*Nv-WF*0A)tLcZnLfE{M>Zh6#V{L>R4$cW?-IKV}d@Xb@hwPmY(l?)7@I= z2MaZzPmGYd9C;Fl?i7|7KtYLAu6cc^=i}ZtzHO@*PfFYPxo%%A*wuLE1GNqEZvMC? z>ho=my~IZLv6)n>cdG47sqLtlh=+GfeO(yB7+rJ__dOR+K6IAjS-O&+D`H%oQ5))V zP&IV-FH&7T{GkfQ<16e&S&wD&YNqjK6f+307G1kjKDJcGSn9O(EYWjyZVD4Z`AdiBl+uUkzkQ2|EzJNbYrY{? zG8G+rVP!A2>WyghFy60YtyH(QhQK0Que+0%w^n1EtOvHpYJ|>erQ4{rfebR!G>=I* zt5ZTS_AYDM0wp`XG74LH$-NLJ?#UGH&Ga|qh$F>`#-ELW`}pGe^Npx}b0+;v!!QMy zu>IIG6zW*PcJTe}GMI`6Syei;DaZ=#U&M^lCl;q#z_46fgPeB#n}fbWkEqK$m=0$4 zI12jmcE2fqqCs-o0C8V;YYo%)H(1 z(C3UbLoZ4{2zM*1b$oOA%U%-uYi_gUXv_3|%4vg_D2MF?3jzFfLPA1bNGkBh91DVF z^62O8#2~Q@YyY$w+jzeE8lNBY3ZjaoDlRcFteOcbOzzI=`n(}eYlONDu0~d`qMJfx{Or0oMuX;t zmKq)y&gmkJ0j-T$mQw&I3aT+qV0t0&#f-?_0Q+PovU=!pjdUP^V1WbEWp+z+NmA%Ift) zk?wPP1{-|^vHM@9xWY9N%hJi7LWqfXfgz-G}ks?-oW6hVEpKK)K@9CQVw~ zJMbTrohd*0g&SYHs{HEhFjfexJXOgwHVdo`KJYU; z3fJ@-(AQCwas2j9w$VSX$$1@;`^BR?@DwX`Fel;60K&|oXvIwLM762g7D=nU8hV8< zX0=o72CKx1YgJfP))$&OW|aXmTCIUgTgX`|If3K=M9(X33QlVQU8DKwyLBxpGjCGY z)jRalyZRp*i3dK2oUu&Zns`QXbf0)J>)ob%-zB}+P}Q^QQGU}`M(E}^oPR8{F}Gs} z{UdDZe0t~%JY!de7Kq08xWyEvU_^fKhI^k#QytxQ-;+2`k~%9wD=qv@{f>ip?$d+G ziYHFTG^ zn`S>Gcgb}`^k=^Qtc{@#6E|X;r%$oKhuWX%dys>DAjVP+V7J}494v#}^4L9SMa|w-qL_PkyRgpZp+U}iz0$Ax@p)rJ zMnx^2ciQyOzI}&OAKtrTtfqf_2dy@+?F&#nk#_huAk|`IbL`I#GScT*pjhkUF1I8h z;iD4L#uNUtCl7hvBrHb_gq6r?j6(dSP3u1{nK7g4s6CUxIZE5& z?xA#DvRlHAl|h{zyseTJW;nL)VAVl=( zCGRQzAs&m5dM{@xpT5YDOk#mp-R%99|7A+HcNa<8aY*%}`%(OXVsly7OW zbrygw?4IRH5mA4>Z$BIR;S(1=+_1S5n7#nf02_xY3pepxVm}o=+D5WBKBMH*u0t(x zeCK(53^~*bynqV?#w&zFcs}H^GHpN6PF(nR8~@=K1I}&asUI}9lq52$5x4UC{*Vdm zW`z1Ba?-y?_8(gP`>VUn5zn-0P6d=V zi{223%UOQfc+x}$5r8JIcLp&#>T0Y}@v0_&*3|Fra;0qU)KV{NPvo=r-58Avf^Jwz z?t{t#%U*Z%ut?fwa6q5G{=cT}zYgwj=@C-{EBS#t6Z7mr+RZo%b)YPAIgMBb^c;M% z@>pJG-gG{v*^*6sch$0gmZe7VCr^C%_WmPXI|tap?oITp*rFlw0;RxR*Iy3<)55?u#SJX6i#E~f7dUVp*{f$cMXTE8ss;MS+`9! zh{4)0radmi8?ZJ6X{vOgF(VoO`eY5bc{hk5;HCO!wt```?Nm5;a{W@c>&c4O9JA#2A_spymZ?c>w z3-&36?7>j7SPcgE>C1=5^PHBTgEqv*4tx%(FzxyL3{#%=wal#lCN-y7lxrQWwu}sP z{3AmdzixA91JG1o&9W5}N%6S8t`$uA+I2@aOx^lBoW)?x9@ZBvz*^rRJJAyXg5h6J zQLn*pS_BzEH`?#USW(?RtI~|o`Gm_}*yp>euHNL6e`Gly#94106TS1{f(QNi`5-|g z@wLs0k*B2@Ayr{(7<+Qh8shRFti?b3at+_jf+Q86Ha`Q~$NG85QsgYVYZLMR0Nq1K A)Bpeg literal 0 HcmV?d00001 From c278cf6edafc2563c35ca35fee04a37c3618381c Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 29 Aug 2017 15:43:51 +0100 Subject: [PATCH 096/166] fix typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 050b5544..1b41f0a8 100644 --- a/README.md +++ b/README.md @@ -53,7 +53,7 @@ It is suitable for beginners who want to find clear and concise examples about T #### 6 - Multi GPU - **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. -- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. +- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. ## Dataset Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. From 0f10646e686a6d9bc3ec8315a4b0d0c15314470e Mon Sep 17 00:00:00 2001 From: Chandan Rai Date: Fri, 1 Sep 2017 19:36:28 +0530 Subject: [PATCH 097/166] corrected minor typo (#162) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1b41f0a8..4b11ce04 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,7 @@ It is suitable for beginners who want to find clear and concise examples about T - **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. ##### Unsupervised -- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-contruct it. +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. - **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/Variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/Variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. - **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. - **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. From 38a8084664ebd86390b8dc6555d20868e32fd4dd Mon Sep 17 00:00:00 2001 From: KrisRoofe <1147035757@qq.com> Date: Sun, 3 Sep 2017 07:46:25 +0800 Subject: [PATCH 098/166] fix a dead link (#166) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4b11ce04..8e0f5850 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ It is suitable for beginners who want to find clear and concise examples about T ##### Unsupervised - **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. -- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/Variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/Variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. +- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/Variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. - **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. - **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. From 651cc753b920359d40bc68d6423487dbf7cdd64c Mon Sep 17 00:00:00 2001 From: Todd Tao Date: Sat, 2 Sep 2017 18:47:06 -0500 Subject: [PATCH 099/166] A bug of notebook format (#164) It's JSON. just missing a comma. --- notebooks/4_Utils/tensorboard_basic.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/4_Utils/tensorboard_basic.ipynb b/notebooks/4_Utils/tensorboard_basic.ipynb index 05a85ca3..45c59405 100644 --- a/notebooks/4_Utils/tensorboard_basic.ipynb +++ b/notebooks/4_Utils/tensorboard_basic.ipynb @@ -176,7 +176,7 @@ }, { "cell_type": "markdown", - "metadata": {} + "metadata": {}, "source": [ "### Loss and Accuracy Visualization\n", "\n", From 0bacc592ff78776ed2224dce9062496de8c6e0a1 Mon Sep 17 00:00:00 2001 From: Todd Tao Date: Sat, 2 Sep 2017 18:49:03 -0500 Subject: [PATCH 100/166] A print function miss brackets (#165) ```python # Display logs per epoch step if (epoch+1) % display_epoch == 0: print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) ``` --- notebooks/4_Utils/tensorboard_basic.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/4_Utils/tensorboard_basic.ipynb b/notebooks/4_Utils/tensorboard_basic.ipynb index 45c59405..71a15649 100644 --- a/notebooks/4_Utils/tensorboard_basic.ipynb +++ b/notebooks/4_Utils/tensorboard_basic.ipynb @@ -161,7 +161,7 @@ " avg_cost += c / total_batch\n", " # Display logs per epoch step\n", " if (epoch+1) % display_epoch == 0:\n", - " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n", + " print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost))\n", "\n", " print(\"Optimization Finished!\")\n", "\n", From 72707e2314f38217f9977565982ea22e99830517 Mon Sep 17 00:00:00 2001 From: Suresh Alse Date: Wed, 20 Sep 2017 15:22:31 -0700 Subject: [PATCH 101/166] Fix comment in convolutional_network.py (#173) --- examples/3_NeuralNetworks/convolutional_network.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/3_NeuralNetworks/convolutional_network.py b/examples/3_NeuralNetworks/convolutional_network.py index 5cd212be..23463fc6 100644 --- a/examples/3_NeuralNetworks/convolutional_network.py +++ b/examples/3_NeuralNetworks/convolutional_network.py @@ -46,7 +46,7 @@ def conv_net(x_dict, n_classes, dropout, reuse, is_training): # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv1 = tf.layers.max_pooling2d(conv1, 2, 2) - # Convolution Layer with 32 filters and a kernel size of 5 + # Convolution Layer with 64 filters and a kernel size of 3 conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 conv2 = tf.layers.max_pooling2d(conv2, 2, 2) From 07cb125ce8b6aa1645110f686b3036d6159fac29 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E5=BF=97=E8=B1=AA?= Date: Wed, 18 Oct 2017 02:29:13 +0800 Subject: [PATCH 102/166] Simplify code in dynamic_rnn (#178) * Simplify code * Simplify code in dynamic_rnn.ipynb --- examples/3_NeuralNetworks/dynamic_rnn.py | 7 ++----- notebooks/3_NeuralNetworks/dynamic_rnn.ipynb | 7 ++----- 2 files changed, 4 insertions(+), 10 deletions(-) diff --git a/examples/3_NeuralNetworks/dynamic_rnn.py b/examples/3_NeuralNetworks/dynamic_rnn.py index 753778c3..faad368e 100644 --- a/examples/3_NeuralNetworks/dynamic_rnn.py +++ b/examples/3_NeuralNetworks/dynamic_rnn.py @@ -175,12 +175,9 @@ def dynamicRNN(x, seqlen, weights, biases): sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, seqlen: batch_seqlen}) if step % display_step == 0 or step == 1: - # Calculate batch accuracy - acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y, + # Calculate batch accuracy & loss + acc, loss = sess.run([accuracy, cost], feed_dict={x: batch_x, y: batch_y, seqlen: batch_seqlen}) - # Calculate batch loss - loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y, - seqlen: batch_seqlen}) print("Step " + str(step*batch_size) + ", Minibatch Loss= " + \ "{:.6f}".format(loss) + ", Training Accuracy= " + \ "{:.5f}".format(acc)) diff --git a/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb b/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb index 6b000566..31aa32ee 100644 --- a/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb @@ -308,12 +308,9 @@ " sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,\n", " seqlen: batch_seqlen})\n", " if step % display_step == 0 or step == 1:\n", - " # Calculate batch accuracy\n", - " acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y,\n", + " # Calculate batch accuracy & loss\n", + " acc, loss = sess.run([accuracy, cost], feed_dict={x: batch_x, y: batch_y,\n", " seqlen: batch_seqlen})\n", - " # Calculate batch loss\n", - " loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y,\n", - " seqlen: batch_seqlen})\n", " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", " \"{:.5f}\".format(acc))\n", From 404355cbd298196bad3aa4b7d6a70701339dfe54 Mon Sep 17 00:00:00 2001 From: Joe Freeman Date: Thu, 19 Oct 2017 20:08:27 +0100 Subject: [PATCH 103/166] Fix link in README to 'Variational Auto-Encoder' notebook (#177) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8e0f5850..3ae748aa 100644 --- a/README.md +++ b/README.md @@ -38,7 +38,7 @@ It is suitable for beginners who want to find clear and concise examples about T ##### Unsupervised - **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. -- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/Variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. +- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. - **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. - **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. From 69e9d5c28348b01e43815d1886c0c0540374f01e Mon Sep 17 00:00:00 2001 From: Przemek Piotrowski Date: Mon, 23 Oct 2017 19:10:10 +0200 Subject: [PATCH 104/166] Fix parameters description (#185) --- notebooks/3_NeuralNetworks/convolutional_network.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/notebooks/3_NeuralNetworks/convolutional_network.ipynb index 52c45d3e..ddbe1c27 100644 --- a/notebooks/3_NeuralNetworks/convolutional_network.ipynb +++ b/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -103,7 +103,7 @@ " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", " conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n", "\n", - " # Convolution Layer with 32 filters and a kernel size of 5\n", + " # Convolution Layer with 64 filters and a kernel size of 3\n", " conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n", " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", " conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n", From 19268838468cb8e4cd402072d2bb52bdafe870dd Mon Sep 17 00:00:00 2001 From: Peter Whidden Date: Mon, 30 Oct 2017 19:33:06 -0700 Subject: [PATCH 105/166] fixed minor typo (past tense verb) (#188) --- notebooks/3_NeuralNetworks/autoencoder.ipynb | 2 +- notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb | 2 +- notebooks/3_NeuralNetworks/convolutional_network.ipynb | 2 +- notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb | 2 +- notebooks/3_NeuralNetworks/dcgan.ipynb | 2 +- notebooks/3_NeuralNetworks/gan.ipynb | 2 +- notebooks/3_NeuralNetworks/neural_network.ipynb | 2 +- notebooks/3_NeuralNetworks/neural_network_raw.ipynb | 2 +- notebooks/3_NeuralNetworks/recurrent_network.ipynb | 2 +- notebooks/3_NeuralNetworks/variational_autoencoder.ipynb | 2 +- 10 files changed, 10 insertions(+), 10 deletions(-) diff --git a/notebooks/3_NeuralNetworks/autoencoder.ipynb b/notebooks/3_NeuralNetworks/autoencoder.ipynb index fd542252..68318441 100644 --- a/notebooks/3_NeuralNetworks/autoencoder.ipynb +++ b/notebooks/3_NeuralNetworks/autoencoder.ipynb @@ -25,7 +25,7 @@ "\n", "## MNIST Dataset Overview\n", "\n", - "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", "\n", "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", "\n", diff --git a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb index f235074f..2435b229 100644 --- a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -27,7 +27,7 @@ "\n", "## MNIST Dataset Overview\n", "\n", - "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", "\n", "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", "\n", diff --git a/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/notebooks/3_NeuralNetworks/convolutional_network.ipynb index ddbe1c27..945adb81 100644 --- a/notebooks/3_NeuralNetworks/convolutional_network.ipynb +++ b/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -25,7 +25,7 @@ "\n", "## MNIST Dataset Overview\n", "\n", - "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", "\n", "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", "\n", diff --git a/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb b/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb index 5a7dd29b..d7f2c15d 100644 --- a/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb +++ b/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb @@ -24,7 +24,7 @@ "\n", "## MNIST Dataset Overview\n", "\n", - "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", "\n", "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", "\n", diff --git a/notebooks/3_NeuralNetworks/dcgan.ipynb b/notebooks/3_NeuralNetworks/dcgan.ipynb index 2edfd785..7c3a773d 100644 --- a/notebooks/3_NeuralNetworks/dcgan.ipynb +++ b/notebooks/3_NeuralNetworks/dcgan.ipynb @@ -29,7 +29,7 @@ "\n", "## MNIST Dataset Overview\n", "\n", - "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", "\n", "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", "\n", diff --git a/notebooks/3_NeuralNetworks/gan.ipynb b/notebooks/3_NeuralNetworks/gan.ipynb index 8ab34406..0ed3086c 100644 --- a/notebooks/3_NeuralNetworks/gan.ipynb +++ b/notebooks/3_NeuralNetworks/gan.ipynb @@ -31,7 +31,7 @@ "\n", "## MNIST Dataset Overview\n", "\n", - "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", "\n", "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", "\n", diff --git a/notebooks/3_NeuralNetworks/neural_network.ipynb b/notebooks/3_NeuralNetworks/neural_network.ipynb index 33196e78..62e70727 100644 --- a/notebooks/3_NeuralNetworks/neural_network.ipynb +++ b/notebooks/3_NeuralNetworks/neural_network.ipynb @@ -24,7 +24,7 @@ "\n", "## MNIST Dataset Overview\n", "\n", - "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", "\n", "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", "\n", diff --git a/notebooks/3_NeuralNetworks/neural_network_raw.ipynb b/notebooks/3_NeuralNetworks/neural_network_raw.ipynb index d05ad4eb..6d9dbd24 100644 --- a/notebooks/3_NeuralNetworks/neural_network_raw.ipynb +++ b/notebooks/3_NeuralNetworks/neural_network_raw.ipynb @@ -24,7 +24,7 @@ "\n", "## MNIST Dataset Overview\n", "\n", - "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", "\n", "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", "\n", diff --git a/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/notebooks/3_NeuralNetworks/recurrent_network.ipynb index 81676ea3..48fe57a8 100644 --- a/notebooks/3_NeuralNetworks/recurrent_network.ipynb +++ b/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -27,7 +27,7 @@ "\n", "## MNIST Dataset Overview\n", "\n", - "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", "\n", "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", "\n", diff --git a/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb b/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb index c290e4a7..76ae0a91 100644 --- a/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb +++ b/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb @@ -31,7 +31,7 @@ "\n", "## MNIST Dataset Overview\n", "\n", - "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", "\n", "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", "\n", From 20dbad29b733683863f09c49cf59c9d076ddd35c Mon Sep 17 00:00:00 2001 From: Sean Date: Wed, 8 Nov 2017 21:49:37 +0800 Subject: [PATCH 106/166] Gan paper fix (#191) * Fixing Xavier Glorot init paper url in GAN. The original URL gives a 404. * Fixing Xavier Glorot init paper url in DCGAN. The original URL gives a 404. --- notebooks/3_NeuralNetworks/dcgan.ipynb | 2 +- notebooks/3_NeuralNetworks/gan.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/3_NeuralNetworks/dcgan.ipynb b/notebooks/3_NeuralNetworks/dcgan.ipynb index 7c3a773d..661cc74a 100644 --- a/notebooks/3_NeuralNetworks/dcgan.ipynb +++ b/notebooks/3_NeuralNetworks/dcgan.ipynb @@ -24,7 +24,7 @@ "\n", "References:\n", "- [Unsupervised representation learning with deep convolutional generative adversarial networks](https://arxiv.org/pdf/1511.06434). A Radford, L Metz, S Chintala, 2016.\n", - "- [Understanding the difficulty of training deep feedforward neural networks](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "- [Understanding the difficulty of training deep feedforward neural networks](http://proceedings.mlr.press/v9/glorot10a.html). X Glorot, Y Bengio. Aistats 9, 249-256\n", "- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167). Sergey Ioffe, Christian Szegedy. 2015.\n", "\n", "## MNIST Dataset Overview\n", diff --git a/notebooks/3_NeuralNetworks/gan.ipynb b/notebooks/3_NeuralNetworks/gan.ipynb index 0ed3086c..1bfb0bd5 100644 --- a/notebooks/3_NeuralNetworks/gan.ipynb +++ b/notebooks/3_NeuralNetworks/gan.ipynb @@ -24,7 +24,7 @@ "\n", "References:\n", "- [Generative adversarial nets](https://arxiv.org/pdf/1406.2661.pdf). I Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, Y. Bengio. Advances in neural information processing systems, 2672-2680.\n", - "- [Understanding the difficulty of training deep feedforward neural networks](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "- [Understanding the difficulty of training deep feedforward neural networks](http://proceedings.mlr.press/v9/glorot10a.html). X Glorot, Y Bengio. Aistats 9, 249-256\n", "\n", "Other tutorials:\n", "- [Generative Adversarial Networks Explained](http://kvfrans.com/generative-adversial-networks-explained/). Kevin Frans.\n", From dd2e6dcd9603d5de008d8c766453162d0204affa Mon Sep 17 00:00:00 2001 From: Jason N Date: Wed, 8 Nov 2017 21:50:05 +0800 Subject: [PATCH 107/166] Fixed the broken link to TensorFlow installation guide in README (#193) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3ae748aa..3d01ca36 100644 --- a/README.md +++ b/README.md @@ -78,7 +78,7 @@ or (if you want GPU support): pip install tensorflow_gpu ``` -For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md) +For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://www.tensorflow.org/install/) ## More Examples The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). From d3f3c83d269374e5065604b1531aa64d2dda0ea0 Mon Sep 17 00:00:00 2001 From: "Dustin R. Heart" Date: Sun, 3 Dec 2017 14:36:20 -0800 Subject: [PATCH 108/166] Support for TF 1.4 on 2_BasicModels/kmeans.py (#203) --- examples/2_BasicModels/kmeans.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/examples/2_BasicModels/kmeans.py b/examples/2_BasicModels/kmeans.py index 02498d33..68c10349 100644 --- a/examples/2_BasicModels/kmeans.py +++ b/examples/2_BasicModels/kmeans.py @@ -42,8 +42,15 @@ use_mini_batch=True) # Build KMeans graph -(all_scores, cluster_idx, scores, cluster_centers_initialized, init_op, -train_op) = kmeans.training_graph() +training_graph = kmeans.training_graph() + +if len(training_graph) > 6: # Tensorflow 1.4+ + (all_scores, cluster_idx, scores, cluster_centers_initialized, + cluster_centers_var, init_op, train_op) = training_graph +else: + (all_scores, cluster_idx, scores, cluster_centers_initialized, + init_op, train_op) = training_graph + cluster_idx = cluster_idx[0] # fix for cluster_idx being a tuple avg_distance = tf.reduce_mean(scores) From 0c4e6661de9ebb9cc314746e423285acfa5fa42b Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Tue, 12 Dec 2017 18:11:05 +0000 Subject: [PATCH 109/166] fix random forest TF 1.4 compatibility --- examples/2_BasicModels/random_forest.py | 4 ++-- notebooks/2_BasicModels/random_forest.ipynb | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/2_BasicModels/random_forest.py b/examples/2_BasicModels/random_forest.py index ef6f21f5..73c06919 100644 --- a/examples/2_BasicModels/random_forest.py +++ b/examples/2_BasicModels/random_forest.py @@ -47,7 +47,7 @@ loss_op = forest_graph.training_loss(X, Y) # Measure the accuracy -infer_op = forest_graph.inference_graph(X) +infer_op, _, _ = forest_graph.inference_graph(X) correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64)) accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) @@ -55,7 +55,7 @@ init_vars = tf.global_variables_initializer() # Start TensorFlow session -sess = tf.Session() +sess = tf.train.MonitoredSession() # Run the initializer sess.run(init_vars) diff --git a/notebooks/2_BasicModels/random_forest.ipynb b/notebooks/2_BasicModels/random_forest.ipynb index 7e45ad32..6f9cf67f 100644 --- a/notebooks/2_BasicModels/random_forest.ipynb +++ b/notebooks/2_BasicModels/random_forest.ipynb @@ -122,7 +122,7 @@ "loss_op = forest_graph.training_loss(X, Y)\n", "\n", "# Measure the accuracy\n", - "infer_op = forest_graph.inference_graph(X)\n", + "infer_op, _, _ = forest_graph.inference_graph(X)\n", "correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64))\n", "accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", "\n", @@ -158,7 +158,7 @@ ], "source": [ "# Start TensorFlow session\n", - "sess = tf.Session()\n", + "sess = tf.train.MonitoredSession()\n", "\n", "# Run the initializer\n", "sess.run(init_vars)\n", From 971c96b7b21a7f6dbbde8fa9b238f9d5fb59a147 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Tue, 12 Dec 2017 18:17:49 +0000 Subject: [PATCH 110/166] fix #205 --- examples/3_NeuralNetworks/convolutional_network.py | 2 +- notebooks/3_NeuralNetworks/convolutional_network.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/3_NeuralNetworks/convolutional_network.py b/examples/3_NeuralNetworks/convolutional_network.py index 23463fc6..e7088f1f 100644 --- a/examples/3_NeuralNetworks/convolutional_network.py +++ b/examples/3_NeuralNetworks/convolutional_network.py @@ -26,7 +26,7 @@ # Network Parameters num_input = 784 # MNIST data input (img shape: 28*28) num_classes = 10 # MNIST total classes (0-9 digits) -dropout = 0.75 # Dropout, probability to keep units +dropout = 0.25 # Dropout, probability to drop a unit # Create the neural network diff --git a/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/notebooks/3_NeuralNetworks/convolutional_network.ipynb index 945adb81..19590f46 100644 --- a/notebooks/3_NeuralNetworks/convolutional_network.ipynb +++ b/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -74,7 +74,7 @@ "# Network Parameters\n", "num_input = 784 # MNIST data input (img shape: 28*28)\n", "num_classes = 10 # MNIST total classes (0-9 digits)\n", - "dropout = 0.75 # Dropout, probability to keep units" + "dropout = 0.25 # Dropout, probability to drop a unit" ] }, { From c51413f5a8e652a923c924ef02d9145c1a5e4750 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 12 Dec 2017 18:26:49 +0000 Subject: [PATCH 111/166] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3d01ca36..c4ae069b 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update (27.08.17):** TensorFlow v1.3 is recommended. Added many new examples (kmeans, random forest, multi-gpu training, layers api, estimator api, dataset api ...). +**Update:** TensorFlow v1.4 is recommended. Added many new examples (kmeans, random forest, multi-gpu training, layers api, estimator api, dataset api ...). *If you are using older TensorFlow version (0.11 and under), please have a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* From 6e94cd9d00f29d3399753296f065f86d89429fd8 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 12 Dec 2017 18:28:27 +0000 Subject: [PATCH 112/166] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c4ae069b..a09c2826 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update:** TensorFlow v1.4 is recommended. Added many new examples (kmeans, random forest, multi-gpu training, layers api, estimator api, dataset api ...). +**Update (12/12/2017):** TensorFlow v1.4 is recommended. Added many new examples (kmeans, random forest, multi-gpu training, layers api, estimator api, dataset api ...). *If you are using older TensorFlow version (0.11 and under), please have a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* From d43c58c948cb4fec189b13a39a620e54879a3495 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Wed, 13 Dec 2017 17:55:26 +0000 Subject: [PATCH 113/166] update random_forest --- examples/2_BasicModels/random_forest.py | 8 +++++--- notebooks/2_BasicModels/random_forest.ipynb | 5 +++-- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/examples/2_BasicModels/random_forest.py b/examples/2_BasicModels/random_forest.py index 73c06919..daff4721 100644 --- a/examples/2_BasicModels/random_forest.py +++ b/examples/2_BasicModels/random_forest.py @@ -12,6 +12,7 @@ import tensorflow as tf from tensorflow.contrib.tensor_forest.python import tensor_forest +from tensorflow.python.ops import resources # Ignore all GPUs, tf random forest does not benefit from it. import os @@ -51,11 +52,12 @@ correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64)) accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) -# Initialize the variables (i.e. assign their default value) -init_vars = tf.global_variables_initializer() +# Initialize the variables (i.e. assign their default value) and forest resources +init_vars = tf.group(tf.global_variables_initializer(), + resources.initialize_resources(resources.shared_resources())) # Start TensorFlow session -sess = tf.train.MonitoredSession() +sess = tf.Session() # Run the initializer sess.run(init_vars) diff --git a/notebooks/2_BasicModels/random_forest.ipynb b/notebooks/2_BasicModels/random_forest.ipynb index 6f9cf67f..755e8b18 100644 --- a/notebooks/2_BasicModels/random_forest.ipynb +++ b/notebooks/2_BasicModels/random_forest.ipynb @@ -126,8 +126,9 @@ "correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64))\n", "accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", "\n", - "# Initialize the variables (i.e. assign their default value)\n", - "init_vars = tf.global_variables_initializer()" + "# Initialize the variables (i.e. assign their default value) and forest resources\n", + "init_vars = tf.group(tf.global_variables_initializer(),\n", + " resources.initialize_resources(resources.shared_resources()))" ] }, { From f644011425b56b293c97858f025449446cbae2a8 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sat, 13 Jan 2018 21:38:31 +0000 Subject: [PATCH 114/166] add assign_to_device fn for multi-gpu --- examples/6_MultiGPU/multigpu_cnn.py | 18 ++++++++++++++- notebooks/6_MultiGPU/multigpu_cnn.ipynb | 30 ++++++++++++++++++++++--- 2 files changed, 44 insertions(+), 4 deletions(-) diff --git a/examples/6_MultiGPU/multigpu_cnn.py b/examples/6_MultiGPU/multigpu_cnn.py index be0dae1d..be003ebd 100644 --- a/examples/6_MultiGPU/multigpu_cnn.py +++ b/examples/6_MultiGPU/multigpu_cnn.py @@ -104,6 +104,22 @@ def average_gradients(tower_grads): return average_grads +# By default, all variables will be placed on '/gpu:0' +# So we need a custom device function, to assign all variables to '/cpu:0' +# Note: If GPUs are peered, '/gpu:0' can be a faster option +PS_OPS = ['Variable', 'VariableV2', 'AutoReloadVariable'] + +def assign_to_device(device, ps_device='/cpu:0'): + def _assign(op): + node_def = op if isinstance(op, tf.NodeDef) else op.node_def + if node_def.op in PS_OPS: + return "/" + ps_device + else: + return device + + return _assign + + # Place all ops on CPU by default with tf.device('/cpu:0'): tower_grads = [] @@ -115,7 +131,7 @@ def average_gradients(tower_grads): # Loop over all GPUs and construct their own computation graph for i in range(num_gpus): - with tf.device('/gpu:%d' % i): + with tf.device(assign_to_device('/gpu:{}'.format(i), ps_device='/cpu:0')): # Split data between GPUs _x = X[i * batch_size: (i+1) * batch_size] diff --git a/notebooks/6_MultiGPU/multigpu_cnn.ipynb b/notebooks/6_MultiGPU/multigpu_cnn.ipynb index 9785d740..2d4746d2 100644 --- a/notebooks/6_MultiGPU/multigpu_cnn.ipynb +++ b/notebooks/6_MultiGPU/multigpu_cnn.ipynb @@ -167,6 +167,30 @@ { "cell_type": "code", "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# By default, all variables will be placed on '/gpu:0'\n", + "# So we need a custom device function, to assign all variables to '/cpu:0'\n", + "# Note: If GPUs are peered, '/gpu:0' can be a faster option\n", + "PS_OPS = ['Variable', 'VariableV2', 'AutoReloadVariable']\n", + "\n", + "def assign_to_device(device, ps_device='/cpu:0'):\n", + " def _assign(op):\n", + " node_def = op if isinstance(op, tf.NodeDef) else op.node_def\n", + " if node_def.op in PS_OPS:\n", + " return \"/\" + ps_device\n", + " else:\n", + " return device\n", + "\n", + " return _assign" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false @@ -214,7 +238,7 @@ "\n", " # Loop over all GPUs and construct their own computation graph\n", " for i in range(num_gpus):\n", - " with tf.device('/gpu:%d' % i):\n", + " with tf.device(assign_to_device('/gpu:{}'.format(i), ps_device='/cpu:0')):\n", "\n", " # Split data between GPUs\n", " _x = X[i * batch_size: (i+1) * batch_size]\n", @@ -289,7 +313,7 @@ "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2.0 + "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", @@ -301,4 +325,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From e98e3742c077a13170dc5a0124d4af056ebcbc64 Mon Sep 17 00:00:00 2001 From: Moulick Aggarwal Date: Mon, 5 Feb 2018 21:56:03 +0530 Subject: [PATCH 115/166] Update README.md (#226) Thanks for the fix --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a09c2826..da3cbfb3 100644 --- a/README.md +++ b/README.md @@ -52,8 +52,8 @@ It is suitable for beginners who want to find clear and concise examples about T - **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. #### 6 - Multi GPU -- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. -- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. +- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. +- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. ## Dataset Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. From 71afab1864c956cdb858f3865dd909faa50056e9 Mon Sep 17 00:00:00 2001 From: Jerry Ajay Date: Sat, 17 Feb 2018 23:55:17 -0500 Subject: [PATCH 116/166] Modified kmeans return value to accomodate to the changes made in master branch of tensorflow. (#229) --- notebooks/2_BasicModels/kmeans.ipynb | 47 +++++++++++++++++++++------- 1 file changed, 35 insertions(+), 12 deletions(-) diff --git a/notebooks/2_BasicModels/kmeans.ipynb b/notebooks/2_BasicModels/kmeans.ipynb index 83fff246..1a64ba2f 100644 --- a/notebooks/2_BasicModels/kmeans.ipynb +++ b/notebooks/2_BasicModels/kmeans.ipynb @@ -38,9 +38,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -94,8 +92,8 @@ "outputs": [], "source": [ "# Build KMeans graph\n", - "(all_scores, cluster_idx, scores, cluster_centers_initialized, init_op,\n", - "train_op) = kmeans.training_graph()\n", + "(all_scores, cluster_idx, scores, cluster_centers_initialized, \n", + " cluster_centers_vars,init_op,train_op) = kmeans.training_graph()\n", "cluster_idx = cluster_idx[0] # fix for cluster_idx being a tuple\n", "avg_distance = tf.reduce_mean(scores)\n", "\n", @@ -106,9 +104,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -142,9 +138,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -181,7 +175,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 2", "language": "python", "name": "python2" }, @@ -196,6 +190,35 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, From ef2da69b97e79e2ef13b0b4a8f1f9bdf20694795 Mon Sep 17 00:00:00 2001 From: Jerry Ajay Date: Sat, 17 Feb 2018 23:55:36 -0500 Subject: [PATCH 117/166] BugFix: Imported header file. (#230) --- notebooks/2_BasicModels/random_forest.ipynb | 44 ++++++++++++++++----- 1 file changed, 34 insertions(+), 10 deletions(-) diff --git a/notebooks/2_BasicModels/random_forest.ipynb b/notebooks/2_BasicModels/random_forest.ipynb index 755e8b18..0faad290 100644 --- a/notebooks/2_BasicModels/random_forest.ipynb +++ b/notebooks/2_BasicModels/random_forest.ipynb @@ -25,6 +25,7 @@ "from __future__ import print_function\n", "\n", "import tensorflow as tf\n", + "from tensorflow.python.ops import resources\n", "from tensorflow.contrib.tensor_forest.python import tensor_forest\n", "\n", "# Ignore all GPUs, tf random forest does not benefit from it.\n", @@ -35,9 +36,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -91,9 +90,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -134,9 +131,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -183,7 +178,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 2", "language": "python", "name": "python2" }, @@ -198,6 +193,35 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false } }, "nbformat": 4, From 4c8c201429552ee4b8aeae8c628a9eb24a63b460 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Wed, 7 Mar 2018 15:17:30 +0000 Subject: [PATCH 118/166] add eager API examples --- examples/1_Introduction/basic_eager_api.py | 68 +++++++++ .../linear_regression_eager_api.py | 70 +++++++++ .../logistic_regression_eager_api.py | 108 ++++++++++++++ .../neural_network_eager_api.py | 137 ++++++++++++++++++ 4 files changed, 383 insertions(+) create mode 100644 examples/1_Introduction/basic_eager_api.py create mode 100644 examples/2_BasicModels/linear_regression_eager_api.py create mode 100644 examples/2_BasicModels/logistic_regression_eager_api.py create mode 100644 examples/3_NeuralNetworks/neural_network_eager_api.py diff --git a/examples/1_Introduction/basic_eager_api.py b/examples/1_Introduction/basic_eager_api.py new file mode 100644 index 00000000..e00719d3 --- /dev/null +++ b/examples/1_Introduction/basic_eager_api.py @@ -0,0 +1,68 @@ +''' +Basic introduction to TensorFlow's Eager API. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ + +What is Eager API? +" Eager execution is an imperative, define-by-run interface where operations are +executed immediately as they are called from Python. This makes it easier to +get started with TensorFlow, and can make research and development more +intuitive. A vast majority of the TensorFlow API remains the same whether eager +execution is enabled or not. As a result, the exact same code that constructs +TensorFlow graphs (e.g. using the layers API) can be executed imperatively +by using eager execution. Conversely, most models written with Eager enabled +can be converted to a graph that can be further optimized and/or extracted +for deployment in production without changing code. " - Rajat Monga + +''' +from __future__ import absolute_import, division, print_function + +import numpy as np +import tensorflow as tf +import tensorflow.contrib.eager as tfe + +# Set Eager API +print("Setting Eager mode...") +tfe.enable_eager_execution() + +# Define constant tensors +print("Define constant tensors") +a = tf.constant(2) +print("a = %i" % a) +b = tf.constant(3) +print("b = %i" % b) + +# Run the operation without the need for tf.Session +print("Running operations, without tf.Session") +c = a + b +print("a + b = %i" % c) +d = a * b +print("a * b = %i" % d) + + +# Full compatibility with Numpy +print("Mixing operations with Tensors and Numpy Arrays") + +# Define constant tensors +a = tf.constant([[2., 1.], + [1., 0.]], dtype=tf.float32) +print("Tensor:\n a = %s" % a) +b = np.array([[3., 0.], + [5., 1.]], dtype=np.float32) +print("NumpyArray:\n b = %s" % b) + +# Run the operation without the need for tf.Session +print("Running operations, without tf.Session") + +c = a + b +print("a + b = %s" % c) + +d = tf.matmul(a, b) +print("a * b = %s" % d) + +print("Iterate through Tensor 'a':") +for i in range(a.shape[0]): + for j in range(a.shape[1]): + print(a[i][j]) + diff --git a/examples/2_BasicModels/linear_regression_eager_api.py b/examples/2_BasicModels/linear_regression_eager_api.py new file mode 100644 index 00000000..1da6d449 --- /dev/null +++ b/examples/2_BasicModels/linear_regression_eager_api.py @@ -0,0 +1,70 @@ +''' +A logistic regression learning algorithm example using TensorFlow library. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' +from __future__ import absolute_import, division, print_function + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf +import tensorflow.contrib.eager as tfe + +# Set Eager API +tfe.enable_eager_execution() + +# Training Data +train_X = [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, + 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1] +train_Y = [1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, + 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3] +n_samples = len(train_X) + +# Parameters +learning_rate = 0.01 +display_step = 100 +num_steps = 1000 + +# Weight and Bias +W = tfe.Variable(np.random.randn()) +b = tfe.Variable(np.random.randn()) + + +# Linear regression (Wx + b) +def linear_regression(inputs): + return inputs * W + b + + +# Mean square error +def mean_square_fn(model_fn, inputs, labels): + return tf.reduce_sum(tf.pow(model_fn(inputs) - labels, 2)) / (2 * n_samples) + + +# SGD Optimizer +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +# Compute gradients +grad = tfe.implicit_gradients(mean_square_fn) + +# Initial cost, before optimizing +print("Initial cost= {:.9f}".format( + mean_square_fn(linear_regression, train_X, train_Y)), + "W=", W.numpy(), "b=", b.numpy()) + +# Training +for step in range(num_steps): + + optimizer.apply_gradients(grad(linear_regression, train_X, train_Y)) + + if (step + 1) % display_step == 0 or step == 0: + print("Epoch:", '%04d' % (step + 1), "cost=", + "{:.9f}".format(mean_square_fn(linear_regression, train_X, train_Y)), + "W=", W.numpy(), "b=", b.numpy()) + +# Graphic display +plt.plot(train_X, train_Y, 'ro', label='Original data') +plt.plot(train_X, np.array(W * train_X + b), label='Fitted line') +plt.legend() +plt.show() diff --git a/examples/2_BasicModels/logistic_regression_eager_api.py b/examples/2_BasicModels/logistic_regression_eager_api.py new file mode 100644 index 00000000..5e244955 --- /dev/null +++ b/examples/2_BasicModels/logistic_regression_eager_api.py @@ -0,0 +1,108 @@ +''' +A logistic regression learning algorithm example using TensorFlow library. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' +from __future__ import absolute_import, division, print_function + +import tensorflow as tf +import tensorflow.contrib.eager as tfe + +# Set Eager API +tfe.enable_eager_execution() + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +# Parameters +learning_rate = 0.1 +batch_size = 128 +num_steps = 1000 +display_step = 100 + +dataset = tf.data.Dataset.from_tensor_slices( + (mnist.train.images, mnist.train.labels)).batch(batch_size) +dataset_iter = tfe.Iterator(dataset) + +# Variables +W = tfe.Variable(tf.zeros([784, 10]), name='weights') +b = tfe.Variable(tf.zeros([10]), name='bias') + + +# Logistic regression (Wx + b) +def logistic_regression(inputs): + return tf.matmul(inputs, W) + b + + +# Cross-Entropy loss function +def loss_fn(inference_fn, inputs, labels): + # Using sparse_softmax cross entropy + return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=inference_fn(inputs), labels=labels)) + + +# Calculate accuracy +def accuracy_fn(inference_fn, inputs, labels): + prediction = tf.nn.softmax(inference_fn(inputs)) + correct_pred = tf.equal(tf.argmax(prediction, 1), labels) + return tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + + +# SGD Optimizer +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +# Compute gradients +grad = tfe.implicit_gradients(loss_fn) + +# Training +average_loss = 0. +average_acc = 0. +for step in range(num_steps): + + # Iterate through the dataset + try: + d = dataset_iter.next() + except StopIteration: + # Refill queue + dataset_iter = tfe.Iterator(dataset) + d = dataset_iter.next() + + # Images + x_batch = d[0] + # Labels + y_batch = tf.cast(d[1], dtype=tf.int64) + + # Compute the batch loss + batch_loss = loss_fn(logistic_regression, x_batch, y_batch) + average_loss += batch_loss + # Compute the batch accuracy + batch_accuracy = accuracy_fn(logistic_regression, x_batch, y_batch) + average_acc += batch_accuracy + + if step == 0: + # Display the initial cost, before optimizing + print("Initial loss= {:.9f}".format(average_loss)) + + # Update the variables following gradients info + optimizer.apply_gradients(grad(logistic_regression, x_batch, y_batch)) + + # Display info + if (step + 1) % display_step == 0 or step == 0: + if step > 0: + average_loss /= display_step + average_acc /= display_step + print("Step:", '%04d' % (step + 1), " loss=", + "{:.9f}".format(average_loss), " accuracy=", + "{:.4f}".format(average_acc)) + average_loss = 0. + average_acc = 0. + +# Evaluate model on the test image set +testX = mnist.test.images +testY = mnist.test.labels + +test_acc = accuracy_fn(logistic_regression, testX, testY) +print("Testset Accuracy: {:.4f}".format(test_acc)) diff --git a/examples/3_NeuralNetworks/neural_network_eager_api.py b/examples/3_NeuralNetworks/neural_network_eager_api.py new file mode 100644 index 00000000..cf975ebd --- /dev/null +++ b/examples/3_NeuralNetworks/neural_network_eager_api.py @@ -0,0 +1,137 @@ +""" Neural Network. + +A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron) +implementation with TensorFlow. This example is using the MNIST database +of handwritten digits (http://yann.lecun.com/exdb/mnist/). + +This example is using TensorFlow layers, see 'neural_network_raw' example for +a raw implementation with variables. + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import print_function + +import tensorflow as tf +import tensorflow.contrib.eager as tfe + +# Set Eager API +tfe.enable_eager_execution() + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +# Parameters +learning_rate = 0.001 +num_steps = 1000 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) + +# Using TF Dataset to split data into batches +dataset = tf.data.Dataset.from_tensor_slices( + (mnist.train.images, mnist.train.labels)).batch(batch_size) +dataset_iter = tfe.Iterator(dataset) + + +# Define the neural network. To use eager API and tf.layers API together, +# we must instantiate a tfe.Network class as follow: +class NeuralNet(tfe.Network): + def __init__(self): + # Define each layer + super(NeuralNet, self).__init__() + # Hidden fully connected layer with 256 neurons + self.layer1 = self.track_layer( + tf.layers.Dense(n_hidden_1, activation=tf.nn.relu)) + # Hidden fully connected layer with 256 neurons + self.layer2 = self.track_layer( + tf.layers.Dense(n_hidden_2, activation=tf.nn.relu)) + # Output fully connected layer with a neuron for each class + self.out_layer = self.track_layer(tf.layers.Dense(num_classes)) + + def call(self, x): + x = self.layer1(x) + x = self.layer2(x) + return self.out_layer(x) + + +neural_net = NeuralNet() + + +# Cross-Entropy loss function +def loss_fn(inference_fn, inputs, labels): + # Using sparse_softmax cross entropy + return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=inference_fn(inputs), labels=labels)) + + +# Calculate accuracy +def accuracy_fn(inference_fn, inputs, labels): + prediction = tf.nn.softmax(inference_fn(inputs)) + correct_pred = tf.equal(tf.argmax(prediction, 1), labels) + return tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + + +# SGD Optimizer +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +# Compute gradients +grad = tfe.implicit_gradients(loss_fn) + +# Training +average_loss = 0. +average_acc = 0. +for step in range(num_steps): + + # Iterate through the dataset + try: + d = dataset_iter.next() + except StopIteration: + # Refill queue + dataset_iter = tfe.Iterator(dataset) + d = dataset_iter.next() + + # Images + x_batch = d[0] + # Labels + y_batch = tf.cast(d[1], dtype=tf.int64) + + # Compute the batch loss + batch_loss = loss_fn(neural_net, x_batch, y_batch) + average_loss += batch_loss + # Compute the batch accuracy + batch_accuracy = accuracy_fn(neural_net, x_batch, y_batch) + average_acc += batch_accuracy + + if step == 0: + # Display the initial cost, before optimizing + print("Initial loss= {:.9f}".format(average_loss)) + + # Update the variables following gradients info + optimizer.apply_gradients(grad(neural_net, x_batch, y_batch)) + + # Display info + if (step + 1) % display_step == 0 or step == 0: + if step > 0: + average_loss /= display_step + average_acc /= display_step + print("Step:", '%04d' % (step + 1), " loss=", + "{:.9f}".format(average_loss), " accuracy=", + "{:.4f}".format(average_acc)) + average_loss = 0. + average_acc = 0. + +# Evaluate model on the test image set +testX = mnist.test.images +testY = mnist.test.labels + +test_acc = accuracy_fn(neural_net, testX, testY) +print("Testset Accuracy: {:.4f}".format(test_acc)) From 79e9cd09b15bd2fa067f225bbb316a571e3823db Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sat, 17 Mar 2018 16:33:46 -0700 Subject: [PATCH 119/166] add eager API notebooks --- .../linear_regression_eager_api.py | 7 +- .../logistic_regression_eager_api.py | 5 +- .../neural_network_eager_api.py | 4 +- .../1_Introduction/basic_eager_api.ipynb | 238 ++++++++++++++ .../linear_regression_eager_api.ipynb | 181 +++++++++++ .../2_BasicModels/logistic_regression.ipynb | 14 +- .../logistic_regression_eager_api.ipynb | 268 ++++++++++++++++ .../neural_network_eager_api.ipynb | 297 ++++++++++++++++++ 8 files changed, 1005 insertions(+), 9 deletions(-) create mode 100644 notebooks/1_Introduction/basic_eager_api.ipynb create mode 100644 notebooks/2_BasicModels/linear_regression_eager_api.ipynb create mode 100644 notebooks/2_BasicModels/logistic_regression_eager_api.ipynb create mode 100644 notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb diff --git a/examples/2_BasicModels/linear_regression_eager_api.py b/examples/2_BasicModels/linear_regression_eager_api.py index 1da6d449..e08f21a8 100644 --- a/examples/2_BasicModels/linear_regression_eager_api.py +++ b/examples/2_BasicModels/linear_regression_eager_api.py @@ -1,7 +1,6 @@ -''' -A logistic regression learning algorithm example using TensorFlow library. -This example is using the MNIST database of handwritten digits -(http://yann.lecun.com/exdb/mnist/) +''' Linear Regression with Eager API. + +A linear regression learning algorithm example using TensorFlow's Eager API. Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ diff --git a/examples/2_BasicModels/logistic_regression_eager_api.py b/examples/2_BasicModels/logistic_regression_eager_api.py index 5e244955..59a8e189 100644 --- a/examples/2_BasicModels/logistic_regression_eager_api.py +++ b/examples/2_BasicModels/logistic_regression_eager_api.py @@ -1,5 +1,6 @@ -''' -A logistic regression learning algorithm example using TensorFlow library. +''' Logistic Regression with Eager API. + +A logistic regression learning algorithm example using TensorFlow's Eager API. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) diff --git a/examples/3_NeuralNetworks/neural_network_eager_api.py b/examples/3_NeuralNetworks/neural_network_eager_api.py index cf975ebd..be5addf5 100644 --- a/examples/3_NeuralNetworks/neural_network_eager_api.py +++ b/examples/3_NeuralNetworks/neural_network_eager_api.py @@ -1,7 +1,7 @@ -""" Neural Network. +""" Neural Network with Eager API. A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron) -implementation with TensorFlow. This example is using the MNIST database +implementation with TensorFlow's Eager API. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/). This example is using TensorFlow layers, see 'neural_network_raw' example for diff --git a/notebooks/1_Introduction/basic_eager_api.ipynb b/notebooks/1_Introduction/basic_eager_api.ipynb new file mode 100644 index 00000000..f2a17e51 --- /dev/null +++ b/notebooks/1_Introduction/basic_eager_api.ipynb @@ -0,0 +1,238 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic introduction to TensorFlow's Eager API\n", + "\n", + "A simple introduction to get started with TensorFlow's Eager API.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is TensorFlow's Eager API ?\n", + "\n", + "*Eager execution is an imperative, define-by-run interface where operations are\n", + "executed immediately as they are called from Python. This makes it easier to\n", + "get started with TensorFlow, and can make research and development more\n", + "intuitive. A vast majority of the TensorFlow API remains the same whether eager\n", + "execution is enabled or not. As a result, the exact same code that constructs\n", + "TensorFlow graphs (e.g. using the layers API) can be executed imperatively\n", + "by using eager execution. Conversely, most models written with Eager enabled\n", + "can be converted to a graph that can be further optimized and/or extracted\n", + "for deployment in production without changing code. - Rajat Monga*\n", + "\n", + "More info: https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import tensorflow.contrib.eager as tfe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting Eager mode...\n" + ] + } + ], + "source": [ + "# Set Eager API\n", + "print(\"Setting Eager mode...\")\n", + "tfe.enable_eager_execution()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Define constant tensors\n", + "a = 2\n", + "b = 3\n" + ] + } + ], + "source": [ + "# Define constant tensors\n", + "print(\"Define constant tensors\")\n", + "a = tf.constant(2)\n", + "print(\"a = %i\" % a)\n", + "b = tf.constant(3)\n", + "print(\"b = %i\" % b)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running operations, without tf.Session\n", + "a + b = 5\n", + "a * b = 6\n" + ] + } + ], + "source": [ + "# Run the operation without the need for tf.Session\n", + "print(\"Running operations, without tf.Session\")\n", + "c = a + b\n", + "print(\"a + b = %i\" % c)\n", + "d = a * b\n", + "print(\"a * b = %i\" % d)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mixing operations with Tensors and Numpy Arrays\n", + "Tensor:\n", + " a = tf.Tensor(\n", + "[[2. 1.]\n", + " [1. 0.]], shape=(2, 2), dtype=float32)\n", + "NumpyArray:\n", + " b = [[3. 0.]\n", + " [5. 1.]]\n" + ] + } + ], + "source": [ + "# Full compatibility with Numpy\n", + "print(\"Mixing operations with Tensors and Numpy Arrays\")\n", + "\n", + "# Define constant tensors\n", + "a = tf.constant([[2., 1.],\n", + " [1., 0.]], dtype=tf.float32)\n", + "print(\"Tensor:\\n a = %s\" % a)\n", + "b = np.array([[3., 0.],\n", + " [5., 1.]], dtype=np.float32)\n", + "print(\"NumpyArray:\\n b = %s\" % b)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running operations, without tf.Session\n", + "a + b = tf.Tensor(\n", + "[[5. 1.]\n", + " [6. 1.]], shape=(2, 2), dtype=float32)\n", + "a * b = tf.Tensor(\n", + "[[11. 1.]\n", + " [ 3. 0.]], shape=(2, 2), dtype=float32)\n" + ] + } + ], + "source": [ + "# Run the operation without the need for tf.Session\n", + "print(\"Running operations, without tf.Session\")\n", + "\n", + "c = a + b\n", + "print(\"a + b = %s\" % c)\n", + "\n", + "d = tf.matmul(a, b)\n", + "print(\"a * b = %s\" % d)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iterate through Tensor 'a':\n", + "tf.Tensor(2.0, shape=(), dtype=float32)\n", + "tf.Tensor(1.0, shape=(), dtype=float32)\n", + "tf.Tensor(1.0, shape=(), dtype=float32)\n", + "tf.Tensor(0.0, shape=(), dtype=float32)\n" + ] + } + ], + "source": [ + "print(\"Iterate through Tensor 'a':\")\n", + "for i in range(a.shape[0]):\n", + " for j in range(a.shape[1]):\n", + " print(a[i][j])" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/2_BasicModels/linear_regression_eager_api.ipynb b/notebooks/2_BasicModels/linear_regression_eager_api.ipynb new file mode 100644 index 00000000..c4ebec4e --- /dev/null +++ b/notebooks/2_BasicModels/linear_regression_eager_api.ipynb @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear Regression with Eager API\n", + "\n", + "A linear regression implemented using TensorFlow's Eager API.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import tensorflow.contrib.eager as tfe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Set Eager API\n", + "tfe.enable_eager_execution()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Data\n", + "train_X = [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,\n", + " 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1]\n", + "train_Y = [1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221,\n", + " 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3]\n", + "n_samples = len(train_X)\n", + "\n", + "# Parameters\n", + "learning_rate = 0.01\n", + "display_step = 100\n", + "num_steps = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Weight and Bias\n", + "W = tfe.Variable(np.random.randn())\n", + "b = tfe.Variable(np.random.randn())\n", + "\n", + "# Linear regression (Wx + b)\n", + "def linear_regression(inputs):\n", + " return inputs * W + b\n", + "\n", + "# Mean square error\n", + "def mean_square_fn(model_fn, inputs, labels):\n", + " return tf.reduce_sum(tf.pow(model_fn(inputs) - labels, 2)) / (2 * n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# SGD Optimizer\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Compute gradients\n", + "grad = tfe.implicit_gradients(mean_square_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost= 31.307329178 W= -0.7870768 b= -0.2507985\n", + "Epoch: 0001 cost= 9.502781868 W= -0.26173288 b= -0.17560114\n", + "Epoch: 0100 cost= 0.114994615 W= 0.36224815 b= 0.014603348\n", + "Epoch: 0200 cost= 0.106785327 W= 0.34959725 b= 0.104292504\n", + "Epoch: 0300 cost= 0.100346453 W= 0.33839324 b= 0.1837239\n", + "Epoch: 0400 cost= 0.095296182 W= 0.32847065 b= 0.25407064\n", + "Epoch: 0500 cost= 0.091335081 W= 0.3196829 b= 0.3163719\n", + "Epoch: 0600 cost= 0.088228233 W= 0.31190023 b= 0.37154746\n", + "Epoch: 0700 cost= 0.085791394 W= 0.30500764 b= 0.42041263\n", + "Epoch: 0800 cost= 0.083880097 W= 0.2989034 b= 0.46368918\n", + "Epoch: 0900 cost= 0.082380980 W= 0.2934973 b= 0.50201607\n", + "Epoch: 1000 cost= 0.081205189 W= 0.28870946 b= 0.5359594\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFkCAYAAACq4KjhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Initial cost, before optimizing\n", + "print(\"Initial cost= {:.9f}\".format(\n", + " mean_square_fn(linear_regression, train_X, train_Y)),\n", + " \"W=\", W.numpy(), \"b=\", b.numpy())\n", + "\n", + "# Training\n", + "for step in range(num_steps):\n", + "\n", + " optimizer.apply_gradients(grad(linear_regression, train_X, train_Y))\n", + "\n", + " if (step + 1) % display_step == 0 or step == 0:\n", + " print(\"Epoch:\", '%04d' % (step + 1), \"cost=\",\n", + " \"{:.9f}\".format(mean_square_fn(linear_regression, train_X, train_Y)),\n", + " \"W=\", W.numpy(), \"b=\", b.numpy())\n", + "\n", + "# Graphic display\n", + "plt.plot(train_X, train_Y, 'ro', label='Original data')\n", + "plt.plot(train_X, np.array(W * train_X + b), label='Fitted line')\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/2_BasicModels/logistic_regression.ipynb b/notebooks/2_BasicModels/logistic_regression.ipynb index d105b769..39465835 100644 --- a/notebooks/2_BasicModels/logistic_regression.ipynb +++ b/notebooks/2_BasicModels/logistic_regression.ipynb @@ -9,12 +9,24 @@ "# Logistic Regression Example\n", "\n", "A logistic regression learning algorithm example using TensorFlow library.\n", - "This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", "\n", "- Author: Aymeric Damien\n", "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, { "cell_type": "code", "execution_count": 1, diff --git a/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb b/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb new file mode 100644 index 00000000..49949e0c --- /dev/null +++ b/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb @@ -0,0 +1,268 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic Regression with Eager API\n", + "\n", + "A logistic regression implemented using TensorFlow's Eager API.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "import tensorflow.contrib.eager as tfe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Set Eager API\n", + "tfe.enable_eager_execution()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.1\n", + "batch_size = 128\n", + "num_steps = 1000\n", + "display_step = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Iterator for the dataset\n", + "dataset = tf.data.Dataset.from_tensor_slices(\n", + " (mnist.train.images, mnist.train.labels)).batch(batch_size)\n", + "dataset_iter = tfe.Iterator(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Variables\n", + "W = tfe.Variable(tf.zeros([784, 10]), name='weights')\n", + "b = tfe.Variable(tf.zeros([10]), name='bias')\n", + "\n", + "# Logistic regression (Wx + b)\n", + "def logistic_regression(inputs):\n", + " return tf.matmul(inputs, W) + b\n", + "\n", + "# Cross-Entropy loss function\n", + "def loss_fn(inference_fn, inputs, labels):\n", + " # Using sparse_softmax cross entropy\n", + " return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=inference_fn(inputs), labels=labels))\n", + "\n", + "# Calculate accuracy\n", + "def accuracy_fn(inference_fn, inputs, labels):\n", + " prediction = tf.nn.softmax(inference_fn(inputs))\n", + " correct_pred = tf.equal(tf.argmax(prediction, 1), labels)\n", + " return tf.reduce_mean(tf.cast(correct_pred, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# SGD Optimizer\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Compute gradients\n", + "grad = tfe.implicit_gradients(loss_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial loss= 2.302584887\n", + "Step: 0001 loss= 2.302584887 accuracy= 0.1172\n", + "Step: 0100 loss= 0.952338457 accuracy= 0.7955\n", + "Step: 0200 loss= 0.535867393 accuracy= 0.8712\n", + "Step: 0300 loss= 0.485415280 accuracy= 0.8757\n", + "Step: 0400 loss= 0.433947206 accuracy= 0.8843\n", + "Step: 0500 loss= 0.381990731 accuracy= 0.8971\n", + "Step: 0600 loss= 0.394154936 accuracy= 0.8947\n", + "Step: 0700 loss= 0.391497582 accuracy= 0.8905\n", + "Step: 0800 loss= 0.386373103 accuracy= 0.8945\n", + "Step: 0900 loss= 0.332039326 accuracy= 0.9096\n", + "Step: 1000 loss= 0.358993769 accuracy= 0.9002\n" + ] + } + ], + "source": [ + "# Training\n", + "average_loss = 0.\n", + "average_acc = 0.\n", + "for step in range(num_steps):\n", + "\n", + " # Iterate through the dataset\n", + " try:\n", + " d = dataset_iter.next()\n", + " except StopIteration:\n", + " # Refill queue\n", + " dataset_iter = tfe.Iterator(dataset)\n", + " d = dataset_iter.next()\n", + "\n", + " # Images\n", + " x_batch = d[0]\n", + " # Labels\n", + " y_batch = tf.cast(d[1], dtype=tf.int64)\n", + "\n", + " # Compute the batch loss\n", + " batch_loss = loss_fn(logistic_regression, x_batch, y_batch)\n", + " average_loss += batch_loss\n", + " # Compute the batch accuracy\n", + " batch_accuracy = accuracy_fn(logistic_regression, x_batch, y_batch)\n", + " average_acc += batch_accuracy\n", + "\n", + " if step == 0:\n", + " # Display the initial cost, before optimizing\n", + " print(\"Initial loss= {:.9f}\".format(average_loss))\n", + "\n", + " # Update the variables following gradients info\n", + " optimizer.apply_gradients(grad(logistic_regression, x_batch, y_batch))\n", + "\n", + " # Display info\n", + " if (step + 1) % display_step == 0 or step == 0:\n", + " if step > 0:\n", + " average_loss /= display_step\n", + " average_acc /= display_step\n", + " print(\"Step:\", '%04d' % (step + 1), \" loss=\",\n", + " \"{:.9f}\".format(average_loss), \" accuracy=\",\n", + " \"{:.4f}\".format(average_acc))\n", + " average_loss = 0.\n", + " average_acc = 0." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testset Accuracy: 0.9083\n" + ] + } + ], + "source": [ + "# Evaluate model on the test image set\n", + "testX = mnist.test.images\n", + "testY = mnist.test.labels\n", + "\n", + "test_acc = accuracy_fn(logistic_regression, testX, testY)\n", + "print(\"Testset Accuracy: {:.4f}\".format(test_acc))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb b/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb new file mode 100644 index 00000000..b99c806b --- /dev/null +++ b/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb @@ -0,0 +1,297 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network with Eager API\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow's Eager API.\n", + "\n", + "This example is using some of TensorFlow higher-level wrappers (tf.estimators, tf.layers, tf.metrics, ...), you can check 'neural_network_raw' example for a raw, and more detailed TensorFlow implementation.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "import tensorflow.contrib.eager as tfe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Set Eager API\n", + "tfe.enable_eager_execution()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 1000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of neurons\n", + "n_hidden_2 = 256 # 2nd layer number of neurons\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Using TF Dataset to split data into batches\n", + "dataset = tf.data.Dataset.from_tensor_slices(\n", + " (mnist.train.images, mnist.train.labels)).batch(batch_size)\n", + "dataset_iter = tfe.Iterator(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the neural network. To use eager API and tf.layers API together,\n", + "# we must instantiate a tfe.Network class as follow:\n", + "class NeuralNet(tfe.Network):\n", + " def __init__(self):\n", + " # Define each layer\n", + " super(NeuralNet, self).__init__()\n", + " # Hidden fully connected layer with 256 neurons\n", + " self.layer1 = self.track_layer(\n", + " tf.layers.Dense(n_hidden_1, activation=tf.nn.relu))\n", + " # Hidden fully connected layer with 256 neurons\n", + " self.layer2 = self.track_layer(\n", + " tf.layers.Dense(n_hidden_2, activation=tf.nn.relu))\n", + " # Output fully connected layer with a neuron for each class\n", + " self.out_layer = self.track_layer(tf.layers.Dense(num_classes))\n", + "\n", + " def call(self, x):\n", + " x = self.layer1(x)\n", + " x = self.layer2(x)\n", + " return self.out_layer(x)\n", + "\n", + "\n", + "neural_net = NeuralNet()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Cross-Entropy loss function\n", + "def loss_fn(inference_fn, inputs, labels):\n", + " # Using sparse_softmax cross entropy\n", + " return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=inference_fn(inputs), labels=labels))\n", + "\n", + "\n", + "# Calculate accuracy\n", + "def accuracy_fn(inference_fn, inputs, labels):\n", + " prediction = tf.nn.softmax(inference_fn(inputs))\n", + " correct_pred = tf.equal(tf.argmax(prediction, 1), labels)\n", + " return tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "\n", + "# SGD Optimizer\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Compute gradients\n", + "grad = tfe.implicit_gradients(loss_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial loss= 2.340397596\n", + "Step: 0001 loss= 2.340397596 accuracy= 0.0703\n", + "Step: 0100 loss= 0.586046159 accuracy= 0.8305\n", + "Step: 0200 loss= 0.253318846 accuracy= 0.9282\n", + "Step: 0300 loss= 0.214748293 accuracy= 0.9377\n", + "Step: 0400 loss= 0.180644721 accuracy= 0.9466\n", + "Step: 0500 loss= 0.137285724 accuracy= 0.9591\n", + "Step: 0600 loss= 0.119845696 accuracy= 0.9636\n", + "Step: 0700 loss= 0.113618039 accuracy= 0.9665\n", + "Step: 0800 loss= 0.109642141 accuracy= 0.9676\n", + "Step: 0900 loss= 0.085067607 accuracy= 0.9746\n", + "Step: 1000 loss= 0.079819344 accuracy= 0.9754\n" + ] + } + ], + "source": [ + "# Training\n", + "average_loss = 0.\n", + "average_acc = 0.\n", + "for step in range(num_steps):\n", + "\n", + " # Iterate through the dataset\n", + " try:\n", + " d = dataset_iter.next()\n", + " except StopIteration:\n", + " # Refill queue\n", + " dataset_iter = tfe.Iterator(dataset)\n", + " d = dataset_iter.next()\n", + "\n", + " # Images\n", + " x_batch = d[0]\n", + " # Labels\n", + " y_batch = tf.cast(d[1], dtype=tf.int64)\n", + "\n", + " # Compute the batch loss\n", + " batch_loss = loss_fn(neural_net, x_batch, y_batch)\n", + " average_loss += batch_loss\n", + " # Compute the batch accuracy\n", + " batch_accuracy = accuracy_fn(neural_net, x_batch, y_batch)\n", + " average_acc += batch_accuracy\n", + "\n", + " if step == 0:\n", + " # Display the initial cost, before optimizing\n", + " print(\"Initial loss= {:.9f}\".format(average_loss))\n", + "\n", + " # Update the variables following gradients info\n", + " optimizer.apply_gradients(grad(neural_net, x_batch, y_batch))\n", + "\n", + " # Display info\n", + " if (step + 1) % display_step == 0 or step == 0:\n", + " if step > 0:\n", + " average_loss /= display_step\n", + " average_acc /= display_step\n", + " print(\"Step:\", '%04d' % (step + 1), \" loss=\",\n", + " \"{:.9f}\".format(average_loss), \" accuracy=\",\n", + " \"{:.4f}\".format(average_acc))\n", + " average_loss = 0.\n", + " average_acc = 0." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testset Accuracy: 0.9719\n" + ] + } + ], + "source": [ + "# Evaluate model on the test image set\n", + "testX = mnist.test.images\n", + "testY = mnist.test.labels\n", + "\n", + "test_acc = accuracy_fn(neural_net, testX, testY)\n", + "print(\"Testset Accuracy: {:.4f}\".format(test_acc))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 821768abe6985cfcba717275279dc8836b845855 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Mon, 19 Mar 2018 15:04:18 -0700 Subject: [PATCH 120/166] add eager API --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index da3cbfb3..6458c005 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update (12/12/2017):** TensorFlow v1.4 is recommended. Added many new examples (kmeans, random forest, multi-gpu training, layers api, estimator api, dataset api ...). +**Update (03/18/2018):** TensorFlow's Eager API examples available! (TF v1.5+ recommended). *If you are using older TensorFlow version (0.11 and under), please have a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* @@ -17,10 +17,13 @@ It is suitable for beginners who want to find clear and concise examples about T #### 1 - Introduction - **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. - **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. +- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. #### 2 - Basic Models - **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. +- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. - **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. +- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. - **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. - **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. - **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. @@ -30,6 +33,7 @@ It is suitable for beginners who want to find clear and concise examples about T - **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. - **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. - **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. - **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. - **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. From e43556dfc099e26c264ec02d9707ea82b121af26 Mon Sep 17 00:00:00 2001 From: Typic Date: Wed, 28 Mar 2018 21:59:53 +0300 Subject: [PATCH 121/166] Print in hello world doesnt have () :) (#233) File "", line 2 print sess.run(hello) ^ SyntaxError: invalid syntax --- notebooks/1_Introduction/helloworld.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/1_Introduction/helloworld.ipynb b/notebooks/1_Introduction/helloworld.ipynb index a20405a5..9d7f0ace 100644 --- a/notebooks/1_Introduction/helloworld.ipynb +++ b/notebooks/1_Introduction/helloworld.ipynb @@ -59,7 +59,7 @@ ], "source": [ "# Run graph\n", - "print sess.run(hello)" + "print(sess.run(hello))" ] } ], @@ -84,4 +84,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From f6f80833e025ef22cfe9cebd9ca832e33134579f Mon Sep 17 00:00:00 2001 From: Bhavtosh Rath <33324272+BhavtoshRath@users.noreply.github.com> Date: Mon, 9 Apr 2018 18:40:53 -0500 Subject: [PATCH 122/166] Changed arg_min(to be deprecated in future tensorflow versions) to argmin (#240) --- notebooks/2_BasicModels/nearest_neighbor.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/2_BasicModels/nearest_neighbor.ipynb b/notebooks/2_BasicModels/nearest_neighbor.ipynb index 4262fb96..c8fba06f 100644 --- a/notebooks/2_BasicModels/nearest_neighbor.ipynb +++ b/notebooks/2_BasicModels/nearest_neighbor.ipynb @@ -62,7 +62,7 @@ "# Calculate L1 Distance\n", "distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)\n", "# Prediction: Get min distance index (Nearest neighbor)\n", - "pred = tf.arg_min(distance, 0)\n", + "pred = tf.argmin(distance, 0)\n", "\n", "accuracy = 0.\n", "\n", From 12ed38ed50a78897d93bbc24c90369ec70adcf76 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Thu, 26 Jul 2018 19:50:56 -0700 Subject: [PATCH 123/166] Update Examples (#261) * update examples * remove unecessary files * update eager api notebooks * add more examples * add more examples --- README.md | 6 +- .../gradient_boosted_decision_tree.py | 85 ++ .../linear_regression_eager_api.py | 4 +- .../logistic_regression_eager_api.py | 4 +- examples/2_BasicModels/word2vec.py | 195 +++++ examples/3_NeuralNetworks/neural_network.py | 2 +- .../neural_network_eager_api.py | 4 +- .../1_Introduction/basic_eager_api.ipynb | 6 +- .../gradient_boosted_decision_tree.ipynb | 266 +++++++ .../linear_regression_eager_api.ipynb | 6 +- .../logistic_regression_eager_api.ipynb | 6 +- notebooks/2_BasicModels/random_forest.ipynb | 6 +- notebooks/2_BasicModels/word2vec.ipynb | 724 ++++++++++++++++++ .../neural_network_eager_api.ipynb | 6 +- 14 files changed, 1296 insertions(+), 24 deletions(-) create mode 100644 examples/2_BasicModels/gradient_boosted_decision_tree.py create mode 100644 examples/2_BasicModels/word2vec.py create mode 100644 notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb create mode 100644 notebooks/2_BasicModels/word2vec.ipynb diff --git a/README.md b/README.md index 6458c005..787b54cd 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,9 @@ This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update (03/18/2018):** TensorFlow's Eager API examples available! (TF v1.5+ recommended). +**Update (07/25/2018):** Add new examples (GBDT, Word2Vec) + TF1.9 compatibility! (TF v1.9+ recommended). -*If you are using older TensorFlow version (0.11 and under), please have a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* +*If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* ## Tutorial index @@ -27,6 +27,8 @@ It is suitable for beginners who want to find clear and concise examples about T - **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. - **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. - **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. +- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. #### 3 - Neural Networks ##### Supervised diff --git a/examples/2_BasicModels/gradient_boosted_decision_tree.py b/examples/2_BasicModels/gradient_boosted_decision_tree.py new file mode 100644 index 00000000..00501a2b --- /dev/null +++ b/examples/2_BasicModels/gradient_boosted_decision_tree.py @@ -0,0 +1,85 @@ +""" Gradient Boosted Decision Tree (GBDT). + +Implement a Gradient Boosted Decision tree with TensorFlow to classify +handwritten digit images. This example is using the MNIST database of +handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/). + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier +from tensorflow.contrib.boosted_trees.proto import learner_pb2 as gbdt_learner + +# Ignore all GPUs (current TF GBDT does not support GPU). +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "" + +# Import MNIST data +# Set verbosity to display errors only (Remove this line for showing warnings) +tf.logging.set_verbosity(tf.logging.ERROR) +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False, + source_url='http://yann.lecun.com/exdb/mnist/') + +# Parameters +batch_size = 4096 # The number of samples per batch +num_classes = 10 # The 10 digits +num_features = 784 # Each image is 28x28 pixels +max_steps = 10000 + +# GBDT Parameters +learning_rate = 0.1 +l1_regul = 0. +l2_regul = 1. +examples_per_layer = 1000 +num_trees = 10 +max_depth = 16 + +# Fill GBDT parameters into the config proto +learner_config = gbdt_learner.LearnerConfig() +learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate +learner_config.regularization.l1 = l1_regul +learner_config.regularization.l2 = l2_regul / examples_per_layer +learner_config.constraints.max_tree_depth = max_depth +growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER +learner_config.growing_mode = growing_mode +run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300) +learner_config.multi_class_strategy = ( + gbdt_learner.LearnerConfig.DIAGONAL_HESSIAN)\ + +# Create a TensorFlor GBDT Estimator +gbdt_model = GradientBoostedDecisionTreeClassifier( + model_dir=None, # No save directory specified + learner_config=learner_config, + n_classes=num_classes, + examples_per_layer=examples_per_layer, + num_trees=num_trees, + center_bias=False, + config=run_config) + +# Display TF info logs +tf.logging.set_verbosity(tf.logging.INFO) + +# Define the input function for training +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.train.images}, y=mnist.train.labels, + batch_size=batch_size, num_epochs=None, shuffle=True) +# Train the Model +gbdt_model.fit(input_fn=input_fn, max_steps=max_steps) + +# Evaluate the Model +# Define the input function for evaluating +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.test.images}, y=mnist.test.labels, + batch_size=batch_size, shuffle=False) +# Use the Estimator 'evaluate' method +e = gbdt_model.evaluate(input_fn=input_fn) + +print("Testing Accuracy:", e['accuracy']) diff --git a/examples/2_BasicModels/linear_regression_eager_api.py b/examples/2_BasicModels/linear_regression_eager_api.py index e08f21a8..a9b2b2f7 100644 --- a/examples/2_BasicModels/linear_regression_eager_api.py +++ b/examples/2_BasicModels/linear_regression_eager_api.py @@ -10,10 +10,10 @@ import matplotlib.pyplot as plt import numpy as np import tensorflow as tf -import tensorflow.contrib.eager as tfe # Set Eager API -tfe.enable_eager_execution() +tf.enable_eager_execution() +tfe = tf.contrib.eager # Training Data train_X = [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, diff --git a/examples/2_BasicModels/logistic_regression_eager_api.py b/examples/2_BasicModels/logistic_regression_eager_api.py index 59a8e189..4ba239df 100644 --- a/examples/2_BasicModels/logistic_regression_eager_api.py +++ b/examples/2_BasicModels/logistic_regression_eager_api.py @@ -10,10 +10,10 @@ from __future__ import absolute_import, division, print_function import tensorflow as tf -import tensorflow.contrib.eager as tfe # Set Eager API -tfe.enable_eager_execution() +tf.enable_eager_execution() +tfe = tf.contrib.eager # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data diff --git a/examples/2_BasicModels/word2vec.py b/examples/2_BasicModels/word2vec.py new file mode 100644 index 00000000..45e87775 --- /dev/null +++ b/examples/2_BasicModels/word2vec.py @@ -0,0 +1,195 @@ +""" Word2Vec. + +Implement Word2Vec algorithm to compute vector representations of words. +This example is using a small chunk of Wikipedia articles to train from. + +References: + - Mikolov, Tomas et al. "Efficient Estimation of Word Representations + in Vector Space.", 2013. + +Links: + - [Word2Vec] https://arxiv.org/pdf/1301.3781.pdf + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import division, print_function, absolute_import + +import collections +import os +import random +import urllib +import zipfile + +import numpy as np +import tensorflow as tf + +# Training Parameters +learning_rate = 0.1 +batch_size = 128 +num_steps = 3000000 +display_step = 10000 +eval_step = 200000 + +# Evaluation Parameters +eval_words = ['five', 'of', 'going', 'hardware', 'american', 'britain'] + +# Word2Vec Parameters +embedding_size = 200 # Dimension of the embedding vector +max_vocabulary_size = 50000 # Total number of different words in the vocabulary +min_occurrence = 10 # Remove all words that does not appears at least n times +skip_window = 3 # How many words to consider left and right +num_skips = 2 # How many times to reuse an input to generate a label +num_sampled = 64 # Number of negative examples to sample + + +# Download a small chunk of Wikipedia articles collection +url = 'http://mattmahoney.net/dc/text8.zip' +data_path = 'text8.zip' +if not os.path.exists(data_path): + print("Downloading the dataset... (It may take some time)") + filename, _ = urllib.urlretrieve(url, data_path) + print("Done!") +# Unzip the dataset file. Text has already been processed +with zipfile.ZipFile(data_path) as f: + text_words = f.read(f.namelist()[0]).lower().split() + +# Build the dictionary and replace rare words with UNK token +count = [('UNK', -1)] +# Retrieve the most common words +count.extend(collections.Counter(text_words).most_common(max_vocabulary_size - 1)) +# Remove samples with less than 'min_occurrence' occurrences +for i in range(len(count) - 1, -1): + if count[i][1] < min_occurrence: + count.pop(i) + else: + # The collection is ordered, so stop when 'min_occurrence' is reached + break +# Compute the vocabulary size +vocabulary_size = len(count) +# Assign an id to each word +word2id = dict() +for i, (word, _)in enumerate(count): + word2id[word] = i + +data = list() +unk_count = 0 +for word in text_words: + # Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary + index = word2id.get(word, 0) + if index == 0: + unk_count += 1 + data.append(index) +count[0] = ('UNK', unk_count) +id2word = dict(zip(word2id.values(), word2id.keys())) + +print("Words count:", len(text_words)) +print("Unique words:", len(set(text_words))) +print("Vocabulary size:", vocabulary_size) +print("Most common words:", count[:10]) + +data_index = 0 +# Generate training batch for the skip-gram model +def next_batch(batch_size, num_skips, skip_window): + global data_index + assert batch_size % num_skips == 0 + assert num_skips <= 2 * skip_window + batch = np.ndarray(shape=(batch_size), dtype=np.int32) + labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) + # get window size (words left and right + current one) + span = 2 * skip_window + 1 + buffer = collections.deque(maxlen=span) + if data_index + span > len(data): + data_index = 0 + buffer.extend(data[data_index:data_index + span]) + data_index += span + for i in range(batch_size // num_skips): + context_words = [w for w in range(span) if w != skip_window] + words_to_use = random.sample(context_words, num_skips) + for j, context_word in enumerate(words_to_use): + batch[i * num_skips + j] = buffer[skip_window] + labels[i * num_skips + j, 0] = buffer[context_word] + if data_index == len(data): + buffer.extend(data[0:span]) + data_index = span + else: + buffer.append(data[data_index]) + data_index += 1 + # Backtrack a little bit to avoid skipping words in the end of a batch + data_index = (data_index + len(data) - span) % len(data) + return batch, labels + + +# Input data +X = tf.placeholder(tf.int32, shape=[None]) +# Input label +Y = tf.placeholder(tf.int32, shape=[None, 1]) + +# Ensure the following ops & var are assigned on CPU +# (some ops are not compatible on GPU) +with tf.device('/cpu:0'): + # Create the embedding variable (each row represent a word embedding vector) + embedding = tf.Variable(tf.random_normal([vocabulary_size, embedding_size])) + # Lookup the corresponding embedding vectors for each sample in X + X_embed = tf.nn.embedding_lookup(embedding, X) + + # Construct the variables for the NCE loss + nce_weights = tf.Variable(tf.random_normal([vocabulary_size, embedding_size])) + nce_biases = tf.Variable(tf.zeros([vocabulary_size])) + +# Compute the average NCE loss for the batch +loss_op = tf.reduce_mean( + tf.nn.nce_loss(weights=nce_weights, + biases=nce_biases, + labels=Y, + inputs=X_embed, + num_sampled=num_sampled, + num_classes=vocabulary_size)) + +# Define the optimizer +optimizer = tf.train.GradientDescentOptimizer(learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluation +# Compute the cosine similarity between input data embedding and every embedding vectors +X_embed_norm = X_embed / tf.sqrt(tf.reduce_sum(tf.square(X_embed))) +embedding_norm = embedding / tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keepdims=True)) +cosine_sim_op = tf.matmul(X_embed_norm, embedding_norm, transpose_b=True) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Testing data + x_test = np.array([word2id[w] for w in eval_words]) + + average_loss = 0 + for step in xrange(1, num_steps + 1): + # Get a new batch of data + batch_x, batch_y = next_batch(batch_size, num_skips, skip_window) + # Run training op + _, loss = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y}) + average_loss += loss + + if step % display_step == 0 or step == 1: + if step > 1: + average_loss /= display_step + print("Step " + str(step) + ", Average Loss= " + \ + "{:.4f}".format(average_loss)) + average_loss = 0 + + # Evaluation + if step % eval_step == 0 or step == 1: + print("Evaluation...") + sim = sess.run(cosine_sim_op, feed_dict={X: x_test}) + for i in xrange(len(eval_words)): + top_k = 8 # number of nearest neighbors + nearest = (-sim[i, :]).argsort()[1:top_k + 1] + log_str = '"%s" nearest neighbors:' % eval_words[i] + for k in xrange(top_k): + log_str = '%s %s,' % (log_str, id2word[nearest[k]]) + print(log_str) diff --git a/examples/3_NeuralNetworks/neural_network.py b/examples/3_NeuralNetworks/neural_network.py index b3bfaad4..1fff2d54 100644 --- a/examples/3_NeuralNetworks/neural_network.py +++ b/examples/3_NeuralNetworks/neural_network.py @@ -61,7 +61,7 @@ def model_fn(features, labels, mode): if mode == tf.estimator.ModeKeys.PREDICT: return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) - # Define loss and optimizer + # Define loss and optimizer loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=logits, labels=tf.cast(labels, dtype=tf.int32))) optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) diff --git a/examples/3_NeuralNetworks/neural_network_eager_api.py b/examples/3_NeuralNetworks/neural_network_eager_api.py index be5addf5..1b337bfb 100644 --- a/examples/3_NeuralNetworks/neural_network_eager_api.py +++ b/examples/3_NeuralNetworks/neural_network_eager_api.py @@ -16,10 +16,10 @@ from __future__ import print_function import tensorflow as tf -import tensorflow.contrib.eager as tfe # Set Eager API -tfe.enable_eager_execution() +tf.enable_eager_execution() +tfe = tf.contrib.eager # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data diff --git a/notebooks/1_Introduction/basic_eager_api.ipynb b/notebooks/1_Introduction/basic_eager_api.ipynb index f2a17e51..6780a3ff 100644 --- a/notebooks/1_Introduction/basic_eager_api.ipynb +++ b/notebooks/1_Introduction/basic_eager_api.ipynb @@ -42,8 +42,7 @@ "from __future__ import absolute_import, division, print_function\n", "\n", "import numpy as np\n", - "import tensorflow as tf\n", - "import tensorflow.contrib.eager as tfe" + "import tensorflow as tf" ] }, { @@ -64,7 +63,8 @@ "source": [ "# Set Eager API\n", "print(\"Setting Eager mode...\")\n", - "tfe.enable_eager_execution()" + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" ] }, { diff --git a/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb b/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb new file mode 100644 index 00000000..09e4b270 --- /dev/null +++ b/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gradient Boosted Decision Tree\n", + "\n", + "Implement a Gradient Boosted Decision tree (GBDT) with TensorFlow to classify\n", + "handwritten digit images. This example is using the MNIST database of\n", + "handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier\n", + "from tensorflow.contrib.boosted_trees.proto import learner_pb2 as gbdt_learner\n", + "\n", + "# Ignore all GPUs (current TF GBDT does not support GPU).\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "# Set verbosity to display errors only (Remove this line for showing warnings)\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False,\n", + " source_url='http://yann.lecun.com/exdb/mnist/')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "batch_size = 4096 # The number of samples per batch\n", + "num_classes = 10 # The 10 digits\n", + "num_features = 784 # Each image is 28x28 pixels\n", + "max_steps = 10000\n", + "\n", + "# GBDT Parameters\n", + "learning_rate = 0.1\n", + "l1_regul = 0.\n", + "l2_regul = 1.\n", + "examples_per_layer = 1000\n", + "num_trees = 10\n", + "max_depth = 16" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Fill GBDT parameters into the config proto\n", + "learner_config = gbdt_learner.LearnerConfig()\n", + "learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate\n", + "learner_config.regularization.l1 = l1_regul\n", + "learner_config.regularization.l2 = l2_regul / examples_per_layer\n", + "learner_config.constraints.max_tree_depth = max_depth\n", + "growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER\n", + "learner_config.growing_mode = growing_mode\n", + "run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)\n", + "learner_config.multi_class_strategy = (\n", + " gbdt_learner.LearnerConfig.DIAGONAL_HESSIAN)\\\n", + "\n", + "# Create a TensorFlor GBDT Estimator\n", + "gbdt_model = GradientBoostedDecisionTreeClassifier(\n", + " model_dir=None, # No save directory specified\n", + " learner_config=learner_config,\n", + " n_classes=num_classes,\n", + " examples_per_layer=examples_per_layer,\n", + " num_trees=num_trees,\n", + " center_bias=False,\n", + " config=run_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Active Feature Columns: ['images_0', 'images_1', 'images_2', 'images_3', 'images_4', 'images_5', 'images_6', 'images_7', 'images_8', 'images_9', 'images_10', 'images_11', 'images_12', 'images_13', 'images_14', 'images_15', 'images_16', 'images_17', 'images_18', 'images_19', 'images_20', 'images_21', 'images_22', 'images_23', 'images_24', 'images_25', 'images_26', 'images_27', 'images_28', 'images_29', 'images_30', 'images_31', 'images_32', 'images_33', 'images_34', 'images_35', 'images_36', 'images_37', 'images_38', 'images_39', 'images_40', 'images_41', 'images_42', 'images_43', 'images_44', 'images_45', 'images_46', 'images_47', 'images_48', 'images_49', 'images_50', 'images_51', 'images_52', 'images_53', 'images_54', 'images_55', 'images_56', 'images_57', 'images_58', 'images_59', 'images_60', 'images_61', 'images_62', 'images_63', 'images_64', 'images_65', 'images_66', 'images_67', 'images_68', 'images_69', 'images_70', 'images_71', 'images_72', 'images_73', 'images_74', 'images_75', 'images_76', 'images_77', 'images_78', 'images_79', 'images_80', 'images_81', 'images_82', 'images_83', 'images_84', 'images_85', 'images_86', 'images_87', 'images_88', 'images_89', 'images_90', 'images_91', 'images_92', 'images_93', 'images_94', 'images_95', 'images_96', 'images_97', 'images_98', 'images_99', 'images_100', 'images_101', 'images_102', 'images_103', 'images_104', 'images_105', 'images_106', 'images_107', 'images_108', 'images_109', 'images_110', 'images_111', 'images_112', 'images_113', 'images_114', 'images_115', 'images_116', 'images_117', 'images_118', 'images_119', 'images_120', 'images_121', 'images_122', 'images_123', 'images_124', 'images_125', 'images_126', 'images_127', 'images_128', 'images_129', 'images_130', 'images_131', 'images_132', 'images_133', 'images_134', 'images_135', 'images_136', 'images_137', 'images_138', 'images_139', 'images_140', 'images_141', 'images_142', 'images_143', 'images_144', 'images_145', 'images_146', 'images_147', 'images_148', 'images_149', 'images_150', 'images_151', 'images_152', 'images_153', 'images_154', 'images_155', 'images_156', 'images_157', 'images_158', 'images_159', 'images_160', 'images_161', 'images_162', 'images_163', 'images_164', 'images_165', 'images_166', 'images_167', 'images_168', 'images_169', 'images_170', 'images_171', 'images_172', 'images_173', 'images_174', 'images_175', 'images_176', 'images_177', 'images_178', 'images_179', 'images_180', 'images_181', 'images_182', 'images_183', 'images_184', 'images_185', 'images_186', 'images_187', 'images_188', 'images_189', 'images_190', 'images_191', 'images_192', 'images_193', 'images_194', 'images_195', 'images_196', 'images_197', 'images_198', 'images_199', 'images_200', 'images_201', 'images_202', 'images_203', 'images_204', 'images_205', 'images_206', 'images_207', 'images_208', 'images_209', 'images_210', 'images_211', 'images_212', 'images_213', 'images_214', 'images_215', 'images_216', 'images_217', 'images_218', 'images_219', 'images_220', 'images_221', 'images_222', 'images_223', 'images_224', 'images_225', 'images_226', 'images_227', 'images_228', 'images_229', 'images_230', 'images_231', 'images_232', 'images_233', 'images_234', 'images_235', 'images_236', 'images_237', 'images_238', 'images_239', 'images_240', 'images_241', 'images_242', 'images_243', 'images_244', 'images_245', 'images_246', 'images_247', 'images_248', 'images_249', 'images_250', 'images_251', 'images_252', 'images_253', 'images_254', 'images_255', 'images_256', 'images_257', 'images_258', 'images_259', 'images_260', 'images_261', 'images_262', 'images_263', 'images_264', 'images_265', 'images_266', 'images_267', 'images_268', 'images_269', 'images_270', 'images_271', 'images_272', 'images_273', 'images_274', 'images_275', 'images_276', 'images_277', 'images_278', 'images_279', 'images_280', 'images_281', 'images_282', 'images_283', 'images_284', 'images_285', 'images_286', 'images_287', 'images_288', 'images_289', 'images_290', 'images_291', 'images_292', 'images_293', 'images_294', 'images_295', 'images_296', 'images_297', 'images_298', 'images_299', 'images_300', 'images_301', 'images_302', 'images_303', 'images_304', 'images_305', 'images_306', 'images_307', 'images_308', 'images_309', 'images_310', 'images_311', 'images_312', 'images_313', 'images_314', 'images_315', 'images_316', 'images_317', 'images_318', 'images_319', 'images_320', 'images_321', 'images_322', 'images_323', 'images_324', 'images_325', 'images_326', 'images_327', 'images_328', 'images_329', 'images_330', 'images_331', 'images_332', 'images_333', 'images_334', 'images_335', 'images_336', 'images_337', 'images_338', 'images_339', 'images_340', 'images_341', 'images_342', 'images_343', 'images_344', 'images_345', 'images_346', 'images_347', 'images_348', 'images_349', 'images_350', 'images_351', 'images_352', 'images_353', 'images_354', 'images_355', 'images_356', 'images_357', 'images_358', 'images_359', 'images_360', 'images_361', 'images_362', 'images_363', 'images_364', 'images_365', 'images_366', 'images_367', 'images_368', 'images_369', 'images_370', 'images_371', 'images_372', 'images_373', 'images_374', 'images_375', 'images_376', 'images_377', 'images_378', 'images_379', 'images_380', 'images_381', 'images_382', 'images_383', 'images_384', 'images_385', 'images_386', 'images_387', 'images_388', 'images_389', 'images_390', 'images_391', 'images_392', 'images_393', 'images_394', 'images_395', 'images_396', 'images_397', 'images_398', 'images_399', 'images_400', 'images_401', 'images_402', 'images_403', 'images_404', 'images_405', 'images_406', 'images_407', 'images_408', 'images_409', 'images_410', 'images_411', 'images_412', 'images_413', 'images_414', 'images_415', 'images_416', 'images_417', 'images_418', 'images_419', 'images_420', 'images_421', 'images_422', 'images_423', 'images_424', 'images_425', 'images_426', 'images_427', 'images_428', 'images_429', 'images_430', 'images_431', 'images_432', 'images_433', 'images_434', 'images_435', 'images_436', 'images_437', 'images_438', 'images_439', 'images_440', 'images_441', 'images_442', 'images_443', 'images_444', 'images_445', 'images_446', 'images_447', 'images_448', 'images_449', 'images_450', 'images_451', 'images_452', 'images_453', 'images_454', 'images_455', 'images_456', 'images_457', 'images_458', 'images_459', 'images_460', 'images_461', 'images_462', 'images_463', 'images_464', 'images_465', 'images_466', 'images_467', 'images_468', 'images_469', 'images_470', 'images_471', 'images_472', 'images_473', 'images_474', 'images_475', 'images_476', 'images_477', 'images_478', 'images_479', 'images_480', 'images_481', 'images_482', 'images_483', 'images_484', 'images_485', 'images_486', 'images_487', 'images_488', 'images_489', 'images_490', 'images_491', 'images_492', 'images_493', 'images_494', 'images_495', 'images_496', 'images_497', 'images_498', 'images_499', 'images_500', 'images_501', 'images_502', 'images_503', 'images_504', 'images_505', 'images_506', 'images_507', 'images_508', 'images_509', 'images_510', 'images_511', 'images_512', 'images_513', 'images_514', 'images_515', 'images_516', 'images_517', 'images_518', 'images_519', 'images_520', 'images_521', 'images_522', 'images_523', 'images_524', 'images_525', 'images_526', 'images_527', 'images_528', 'images_529', 'images_530', 'images_531', 'images_532', 'images_533', 'images_534', 'images_535', 'images_536', 'images_537', 'images_538', 'images_539', 'images_540', 'images_541', 'images_542', 'images_543', 'images_544', 'images_545', 'images_546', 'images_547', 'images_548', 'images_549', 'images_550', 'images_551', 'images_552', 'images_553', 'images_554', 'images_555', 'images_556', 'images_557', 'images_558', 'images_559', 'images_560', 'images_561', 'images_562', 'images_563', 'images_564', 'images_565', 'images_566', 'images_567', 'images_568', 'images_569', 'images_570', 'images_571', 'images_572', 'images_573', 'images_574', 'images_575', 'images_576', 'images_577', 'images_578', 'images_579', 'images_580', 'images_581', 'images_582', 'images_583', 'images_584', 'images_585', 'images_586', 'images_587', 'images_588', 'images_589', 'images_590', 'images_591', 'images_592', 'images_593', 'images_594', 'images_595', 'images_596', 'images_597', 'images_598', 'images_599', 'images_600', 'images_601', 'images_602', 'images_603', 'images_604', 'images_605', 'images_606', 'images_607', 'images_608', 'images_609', 'images_610', 'images_611', 'images_612', 'images_613', 'images_614', 'images_615', 'images_616', 'images_617', 'images_618', 'images_619', 'images_620', 'images_621', 'images_622', 'images_623', 'images_624', 'images_625', 'images_626', 'images_627', 'images_628', 'images_629', 'images_630', 'images_631', 'images_632', 'images_633', 'images_634', 'images_635', 'images_636', 'images_637', 'images_638', 'images_639', 'images_640', 'images_641', 'images_642', 'images_643', 'images_644', 'images_645', 'images_646', 'images_647', 'images_648', 'images_649', 'images_650', 'images_651', 'images_652', 'images_653', 'images_654', 'images_655', 'images_656', 'images_657', 'images_658', 'images_659', 'images_660', 'images_661', 'images_662', 'images_663', 'images_664', 'images_665', 'images_666', 'images_667', 'images_668', 'images_669', 'images_670', 'images_671', 'images_672', 'images_673', 'images_674', 'images_675', 'images_676', 'images_677', 'images_678', 'images_679', 'images_680', 'images_681', 'images_682', 'images_683', 'images_684', 'images_685', 'images_686', 'images_687', 'images_688', 'images_689', 'images_690', 'images_691', 'images_692', 'images_693', 'images_694', 'images_695', 'images_696', 'images_697', 'images_698', 'images_699', 'images_700', 'images_701', 'images_702', 'images_703', 'images_704', 'images_705', 'images_706', 'images_707', 'images_708', 'images_709', 'images_710', 'images_711', 'images_712', 'images_713', 'images_714', 'images_715', 'images_716', 'images_717', 'images_718', 'images_719', 'images_720', 'images_721', 'images_722', 'images_723', 'images_724', 'images_725', 'images_726', 'images_727', 'images_728', 'images_729', 'images_730', 'images_731', 'images_732', 'images_733', 'images_734', 'images_735', 'images_736', 'images_737', 'images_738', 'images_739', 'images_740', 'images_741', 'images_742', 'images_743', 'images_744', 'images_745', 'images_746', 'images_747', 'images_748', 'images_749', 'images_750', 'images_751', 'images_752', 'images_753', 'images_754', 'images_755', 'images_756', 'images_757', 'images_758', 'images_759', 'images_760', 'images_761', 'images_762', 'images_763', 'images_764', 'images_765', 'images_766', 'images_767', 'images_768', 'images_769', 'images_770', 'images_771', 'images_772', 'images_773', 'images_774', 'images_775', 'images_776', 'images_777', 'images_778', 'images_779', 'images_780', 'images_781', 'images_782', 'images_783']\n", + "WARNING:tensorflow:From /Users/aymeric.damien/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: __new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n", + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:loss = 2.3025992, step = 1\n", + "INFO:tensorflow:Saving checkpoints for 2 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving checkpoints for 94 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:global_step/sec: 0.199624\n", + "INFO:tensorflow:loss = 0.32783023, step = 101 (500.943 sec)\n", + "INFO:tensorflow:Requesting stop since we have reached 10 trees.\n", + "INFO:tensorflow:Saving checkpoints for 161 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Loss for final step: 0.21336032.\n" + ] + }, + { + "data": { + "text/plain": [ + "GradientBoostedDecisionTreeClassifier(params={'head': , 'weight_column_name': None, 'feature_columns': None, 'center_bias': False, 'num_trees': 10, 'logits_modifier_function': None, 'use_core_libs': False, 'learner_config': num_classes: 10\n", + "regularization {\n", + " l2: 0.0010000000475\n", + "}\n", + "constraints {\n", + " max_tree_depth: 16\n", + "}\n", + "learning_rate_tuner {\n", + " fixed {\n", + " learning_rate: 0.10000000149\n", + " }\n", + "}\n", + "pruning_mode: POST_PRUNE\n", + "growing_mode: LAYER_BY_LAYER\n", + "multi_class_strategy: DIAGONAL_HESSIAN\n", + ", 'examples_per_layer': 1000})" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display TF info logs\n", + "tf.logging.set_verbosity(tf.logging.INFO)\n", + "\n", + "# Define the input function for training\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.train.images}, y=mnist.train.labels,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)\n", + "\n", + "# Train the Model\n", + "gbdt_model.fit(input_fn=input_fn, max_steps=max_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Active Feature Columns: ['images_0', 'images_1', 'images_2', 'images_3', 'images_4', 'images_5', 'images_6', 'images_7', 'images_8', 'images_9', 'images_10', 'images_11', 'images_12', 'images_13', 'images_14', 'images_15', 'images_16', 'images_17', 'images_18', 'images_19', 'images_20', 'images_21', 'images_22', 'images_23', 'images_24', 'images_25', 'images_26', 'images_27', 'images_28', 'images_29', 'images_30', 'images_31', 'images_32', 'images_33', 'images_34', 'images_35', 'images_36', 'images_37', 'images_38', 'images_39', 'images_40', 'images_41', 'images_42', 'images_43', 'images_44', 'images_45', 'images_46', 'images_47', 'images_48', 'images_49', 'images_50', 'images_51', 'images_52', 'images_53', 'images_54', 'images_55', 'images_56', 'images_57', 'images_58', 'images_59', 'images_60', 'images_61', 'images_62', 'images_63', 'images_64', 'images_65', 'images_66', 'images_67', 'images_68', 'images_69', 'images_70', 'images_71', 'images_72', 'images_73', 'images_74', 'images_75', 'images_76', 'images_77', 'images_78', 'images_79', 'images_80', 'images_81', 'images_82', 'images_83', 'images_84', 'images_85', 'images_86', 'images_87', 'images_88', 'images_89', 'images_90', 'images_91', 'images_92', 'images_93', 'images_94', 'images_95', 'images_96', 'images_97', 'images_98', 'images_99', 'images_100', 'images_101', 'images_102', 'images_103', 'images_104', 'images_105', 'images_106', 'images_107', 'images_108', 'images_109', 'images_110', 'images_111', 'images_112', 'images_113', 'images_114', 'images_115', 'images_116', 'images_117', 'images_118', 'images_119', 'images_120', 'images_121', 'images_122', 'images_123', 'images_124', 'images_125', 'images_126', 'images_127', 'images_128', 'images_129', 'images_130', 'images_131', 'images_132', 'images_133', 'images_134', 'images_135', 'images_136', 'images_137', 'images_138', 'images_139', 'images_140', 'images_141', 'images_142', 'images_143', 'images_144', 'images_145', 'images_146', 'images_147', 'images_148', 'images_149', 'images_150', 'images_151', 'images_152', 'images_153', 'images_154', 'images_155', 'images_156', 'images_157', 'images_158', 'images_159', 'images_160', 'images_161', 'images_162', 'images_163', 'images_164', 'images_165', 'images_166', 'images_167', 'images_168', 'images_169', 'images_170', 'images_171', 'images_172', 'images_173', 'images_174', 'images_175', 'images_176', 'images_177', 'images_178', 'images_179', 'images_180', 'images_181', 'images_182', 'images_183', 'images_184', 'images_185', 'images_186', 'images_187', 'images_188', 'images_189', 'images_190', 'images_191', 'images_192', 'images_193', 'images_194', 'images_195', 'images_196', 'images_197', 'images_198', 'images_199', 'images_200', 'images_201', 'images_202', 'images_203', 'images_204', 'images_205', 'images_206', 'images_207', 'images_208', 'images_209', 'images_210', 'images_211', 'images_212', 'images_213', 'images_214', 'images_215', 'images_216', 'images_217', 'images_218', 'images_219', 'images_220', 'images_221', 'images_222', 'images_223', 'images_224', 'images_225', 'images_226', 'images_227', 'images_228', 'images_229', 'images_230', 'images_231', 'images_232', 'images_233', 'images_234', 'images_235', 'images_236', 'images_237', 'images_238', 'images_239', 'images_240', 'images_241', 'images_242', 'images_243', 'images_244', 'images_245', 'images_246', 'images_247', 'images_248', 'images_249', 'images_250', 'images_251', 'images_252', 'images_253', 'images_254', 'images_255', 'images_256', 'images_257', 'images_258', 'images_259', 'images_260', 'images_261', 'images_262', 'images_263', 'images_264', 'images_265', 'images_266', 'images_267', 'images_268', 'images_269', 'images_270', 'images_271', 'images_272', 'images_273', 'images_274', 'images_275', 'images_276', 'images_277', 'images_278', 'images_279', 'images_280', 'images_281', 'images_282', 'images_283', 'images_284', 'images_285', 'images_286', 'images_287', 'images_288', 'images_289', 'images_290', 'images_291', 'images_292', 'images_293', 'images_294', 'images_295', 'images_296', 'images_297', 'images_298', 'images_299', 'images_300', 'images_301', 'images_302', 'images_303', 'images_304', 'images_305', 'images_306', 'images_307', 'images_308', 'images_309', 'images_310', 'images_311', 'images_312', 'images_313', 'images_314', 'images_315', 'images_316', 'images_317', 'images_318', 'images_319', 'images_320', 'images_321', 'images_322', 'images_323', 'images_324', 'images_325', 'images_326', 'images_327', 'images_328', 'images_329', 'images_330', 'images_331', 'images_332', 'images_333', 'images_334', 'images_335', 'images_336', 'images_337', 'images_338', 'images_339', 'images_340', 'images_341', 'images_342', 'images_343', 'images_344', 'images_345', 'images_346', 'images_347', 'images_348', 'images_349', 'images_350', 'images_351', 'images_352', 'images_353', 'images_354', 'images_355', 'images_356', 'images_357', 'images_358', 'images_359', 'images_360', 'images_361', 'images_362', 'images_363', 'images_364', 'images_365', 'images_366', 'images_367', 'images_368', 'images_369', 'images_370', 'images_371', 'images_372', 'images_373', 'images_374', 'images_375', 'images_376', 'images_377', 'images_378', 'images_379', 'images_380', 'images_381', 'images_382', 'images_383', 'images_384', 'images_385', 'images_386', 'images_387', 'images_388', 'images_389', 'images_390', 'images_391', 'images_392', 'images_393', 'images_394', 'images_395', 'images_396', 'images_397', 'images_398', 'images_399', 'images_400', 'images_401', 'images_402', 'images_403', 'images_404', 'images_405', 'images_406', 'images_407', 'images_408', 'images_409', 'images_410', 'images_411', 'images_412', 'images_413', 'images_414', 'images_415', 'images_416', 'images_417', 'images_418', 'images_419', 'images_420', 'images_421', 'images_422', 'images_423', 'images_424', 'images_425', 'images_426', 'images_427', 'images_428', 'images_429', 'images_430', 'images_431', 'images_432', 'images_433', 'images_434', 'images_435', 'images_436', 'images_437', 'images_438', 'images_439', 'images_440', 'images_441', 'images_442', 'images_443', 'images_444', 'images_445', 'images_446', 'images_447', 'images_448', 'images_449', 'images_450', 'images_451', 'images_452', 'images_453', 'images_454', 'images_455', 'images_456', 'images_457', 'images_458', 'images_459', 'images_460', 'images_461', 'images_462', 'images_463', 'images_464', 'images_465', 'images_466', 'images_467', 'images_468', 'images_469', 'images_470', 'images_471', 'images_472', 'images_473', 'images_474', 'images_475', 'images_476', 'images_477', 'images_478', 'images_479', 'images_480', 'images_481', 'images_482', 'images_483', 'images_484', 'images_485', 'images_486', 'images_487', 'images_488', 'images_489', 'images_490', 'images_491', 'images_492', 'images_493', 'images_494', 'images_495', 'images_496', 'images_497', 'images_498', 'images_499', 'images_500', 'images_501', 'images_502', 'images_503', 'images_504', 'images_505', 'images_506', 'images_507', 'images_508', 'images_509', 'images_510', 'images_511', 'images_512', 'images_513', 'images_514', 'images_515', 'images_516', 'images_517', 'images_518', 'images_519', 'images_520', 'images_521', 'images_522', 'images_523', 'images_524', 'images_525', 'images_526', 'images_527', 'images_528', 'images_529', 'images_530', 'images_531', 'images_532', 'images_533', 'images_534', 'images_535', 'images_536', 'images_537', 'images_538', 'images_539', 'images_540', 'images_541', 'images_542', 'images_543', 'images_544', 'images_545', 'images_546', 'images_547', 'images_548', 'images_549', 'images_550', 'images_551', 'images_552', 'images_553', 'images_554', 'images_555', 'images_556', 'images_557', 'images_558', 'images_559', 'images_560', 'images_561', 'images_562', 'images_563', 'images_564', 'images_565', 'images_566', 'images_567', 'images_568', 'images_569', 'images_570', 'images_571', 'images_572', 'images_573', 'images_574', 'images_575', 'images_576', 'images_577', 'images_578', 'images_579', 'images_580', 'images_581', 'images_582', 'images_583', 'images_584', 'images_585', 'images_586', 'images_587', 'images_588', 'images_589', 'images_590', 'images_591', 'images_592', 'images_593', 'images_594', 'images_595', 'images_596', 'images_597', 'images_598', 'images_599', 'images_600', 'images_601', 'images_602', 'images_603', 'images_604', 'images_605', 'images_606', 'images_607', 'images_608', 'images_609', 'images_610', 'images_611', 'images_612', 'images_613', 'images_614', 'images_615', 'images_616', 'images_617', 'images_618', 'images_619', 'images_620', 'images_621', 'images_622', 'images_623', 'images_624', 'images_625', 'images_626', 'images_627', 'images_628', 'images_629', 'images_630', 'images_631', 'images_632', 'images_633', 'images_634', 'images_635', 'images_636', 'images_637', 'images_638', 'images_639', 'images_640', 'images_641', 'images_642', 'images_643', 'images_644', 'images_645', 'images_646', 'images_647', 'images_648', 'images_649', 'images_650', 'images_651', 'images_652', 'images_653', 'images_654', 'images_655', 'images_656', 'images_657', 'images_658', 'images_659', 'images_660', 'images_661', 'images_662', 'images_663', 'images_664', 'images_665', 'images_666', 'images_667', 'images_668', 'images_669', 'images_670', 'images_671', 'images_672', 'images_673', 'images_674', 'images_675', 'images_676', 'images_677', 'images_678', 'images_679', 'images_680', 'images_681', 'images_682', 'images_683', 'images_684', 'images_685', 'images_686', 'images_687', 'images_688', 'images_689', 'images_690', 'images_691', 'images_692', 'images_693', 'images_694', 'images_695', 'images_696', 'images_697', 'images_698', 'images_699', 'images_700', 'images_701', 'images_702', 'images_703', 'images_704', 'images_705', 'images_706', 'images_707', 'images_708', 'images_709', 'images_710', 'images_711', 'images_712', 'images_713', 'images_714', 'images_715', 'images_716', 'images_717', 'images_718', 'images_719', 'images_720', 'images_721', 'images_722', 'images_723', 'images_724', 'images_725', 'images_726', 'images_727', 'images_728', 'images_729', 'images_730', 'images_731', 'images_732', 'images_733', 'images_734', 'images_735', 'images_736', 'images_737', 'images_738', 'images_739', 'images_740', 'images_741', 'images_742', 'images_743', 'images_744', 'images_745', 'images_746', 'images_747', 'images_748', 'images_749', 'images_750', 'images_751', 'images_752', 'images_753', 'images_754', 'images_755', 'images_756', 'images_757', 'images_758', 'images_759', 'images_760', 'images_761', 'images_762', 'images_763', 'images_764', 'images_765', 'images_766', 'images_767', 'images_768', 'images_769', 'images_770', 'images_771', 'images_772', 'images_773', 'images_774', 'images_775', 'images_776', 'images_777', 'images_778', 'images_779', 'images_780', 'images_781', 'images_782', 'images_783']\n", + "INFO:tensorflow:Starting evaluation at 2018-07-26-01:00:06\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt-161\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Finished evaluation at 2018-07-26-01:00:07\n", + "INFO:tensorflow:Saving dict for global step 161: accuracy = 0.9273, global_step = 161, loss = 0.23841818\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "Testing Accuracy: 0.9273\n" + ] + } + ], + "source": [ + "# Evaluate the Model\n", + "# Define the input function for evaluating\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.test.images}, y=mnist.test.labels,\n", + " batch_size=batch_size, shuffle=False)\n", + "\n", + "# Use the Estimator 'evaluate' method\n", + "e = gbdt_model.evaluate(input_fn=input_fn)\n", + "print(\"Testing Accuracy:\", e['accuracy'])" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/2_BasicModels/linear_regression_eager_api.ipynb b/notebooks/2_BasicModels/linear_regression_eager_api.ipynb index c4ebec4e..f517dc15 100644 --- a/notebooks/2_BasicModels/linear_regression_eager_api.ipynb +++ b/notebooks/2_BasicModels/linear_regression_eager_api.ipynb @@ -24,8 +24,7 @@ "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import tensorflow as tf\n", - "import tensorflow.contrib.eager as tfe" + "import tensorflow as tf" ] }, { @@ -37,7 +36,8 @@ "outputs": [], "source": [ "# Set Eager API\n", - "tfe.enable_eager_execution()" + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" ] }, { diff --git a/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb b/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb index 49949e0c..3c4eee5e 100644 --- a/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb +++ b/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb @@ -35,8 +35,7 @@ "source": [ "from __future__ import absolute_import, division, print_function\n", "\n", - "import tensorflow as tf\n", - "import tensorflow.contrib.eager as tfe" + "import tensorflow as tf" ] }, { @@ -48,7 +47,8 @@ "outputs": [], "source": [ "# Set Eager API\n", - "tfe.enable_eager_execution()" + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" ] }, { diff --git a/notebooks/2_BasicModels/random_forest.ipynb b/notebooks/2_BasicModels/random_forest.ipynb index 0faad290..4b212efc 100644 --- a/notebooks/2_BasicModels/random_forest.ipynb +++ b/notebooks/2_BasicModels/random_forest.ipynb @@ -196,9 +196,9 @@ }, "varInspector": { "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 + "lenName": 16.0, + "lenType": 16.0, + "lenVar": 40.0 }, "kernels_config": { "python": { diff --git a/notebooks/2_BasicModels/word2vec.ipynb b/notebooks/2_BasicModels/word2vec.ipynb new file mode 100644 index 00000000..9f137fcf --- /dev/null +++ b/notebooks/2_BasicModels/word2vec.ipynb @@ -0,0 +1,724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Word2Vec (Word Embedding)\n", + "\n", + "Implement Word2Vec algorithm to compute vector representations of words.\n", + "This example is using a small chunk of Wikipedia articles to train from.\n", + "\n", + "More info: [Mikolov, Tomas et al. \"Efficient Estimation of Word Representations in Vector Space.\", 2013](https://arxiv.org/pdf/1301.3781.pdf)\n", + "\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import collections\n", + "import os\n", + "import random\n", + "import urllib\n", + "import zipfile\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.1\n", + "batch_size = 128\n", + "num_steps = 3000000\n", + "display_step = 10000\n", + "eval_step = 200000\n", + "\n", + "# Evaluation Parameters\n", + "eval_words = ['five', 'of', 'going', 'hardware', 'american', 'britain']\n", + "\n", + "# Word2Vec Parameters\n", + "embedding_size = 200 # Dimension of the embedding vector\n", + "max_vocabulary_size = 50000 # Total number of different words in the vocabulary\n", + "min_occurrence = 10 # Remove all words that does not appears at least n times\n", + "skip_window = 3 # How many words to consider left and right\n", + "num_skips = 2 # How many times to reuse an input to generate a label\n", + "num_sampled = 64 # Number of negative examples to sample" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading the dataset... (It may take some time)\n", + "Done!\n" + ] + } + ], + "source": [ + "# Download a small chunk of Wikipedia articles collection\n", + "url = 'http://mattmahoney.net/dc/text8.zip'\n", + "data_path = 'text8.zip'\n", + "if not os.path.exists(data_path):\n", + " print(\"Downloading the dataset... (It may take some time)\")\n", + " filename, _ = urllib.urlretrieve(url, data_path)\n", + " print(\"Done!\")\n", + "# Unzip the dataset file. Text has already been processed\n", + "with zipfile.ZipFile(data_path) as f:\n", + " text_words = f.read(f.namelist()[0]).lower().split()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Words count: 17005207\n", + "Unique words: 253854\n", + "Vocabulary size: 50000\n", + "Most common words: [('UNK', 418391), ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764), ('in', 372201), ('a', 325873), ('to', 316376), ('zero', 264975), ('nine', 250430)]\n" + ] + } + ], + "source": [ + "# Build the dictionary and replace rare words with UNK token\n", + "count = [('UNK', -1)]\n", + "# Retrieve the most common words\n", + "count.extend(collections.Counter(text_words).most_common(max_vocabulary_size - 1))\n", + "# Remove samples with less than 'min_occurrence' occurrences\n", + "for i in range(len(count) - 1, -1):\n", + " if count[i][1] < min_occurrence:\n", + " count.pop(i)\n", + " else:\n", + " # The collection is ordered, so stop when 'min_occurrence' is reached\n", + " break\n", + "# Compute the vocabulary size\n", + "vocabulary_size = len(count)\n", + "# Assign an id to each word\n", + "word2id = dict()\n", + "for i, (word, _)in enumerate(count):\n", + " word2id[word] = i\n", + "\n", + "data = list()\n", + "unk_count = 0\n", + "for word in text_words:\n", + " # Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary\n", + " index = word2id.get(word, 0)\n", + " if index == 0:\n", + " unk_count += 1\n", + " data.append(index)\n", + "count[0] = ('UNK', unk_count)\n", + "id2word = dict(zip(word2id.values(), word2id.keys()))\n", + "\n", + "print(\"Words count:\", len(text_words))\n", + "print(\"Unique words:\", len(set(text_words)))\n", + "print(\"Vocabulary size:\", vocabulary_size)\n", + "print(\"Most common words:\", count[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data_index = 0\n", + "# Generate training batch for the skip-gram model\n", + "def next_batch(batch_size, num_skips, skip_window):\n", + " global data_index\n", + " assert batch_size % num_skips == 0\n", + " assert num_skips <= 2 * skip_window\n", + " batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n", + " labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n", + " # get window size (words left and right + current one)\n", + " span = 2 * skip_window + 1\n", + " buffer = collections.deque(maxlen=span)\n", + " if data_index + span > len(data):\n", + " data_index = 0\n", + " buffer.extend(data[data_index:data_index + span])\n", + " data_index += span\n", + " for i in range(batch_size // num_skips):\n", + " context_words = [w for w in range(span) if w != skip_window]\n", + " words_to_use = random.sample(context_words, num_skips)\n", + " for j, context_word in enumerate(words_to_use):\n", + " batch[i * num_skips + j] = buffer[skip_window]\n", + " labels[i * num_skips + j, 0] = buffer[context_word]\n", + " if data_index == len(data):\n", + " buffer.extend(data[0:span])\n", + " data_index = span\n", + " else:\n", + " buffer.append(data[data_index])\n", + " data_index += 1\n", + " # Backtrack a little bit to avoid skipping words in the end of a batch\n", + " data_index = (data_index + len(data) - span) % len(data)\n", + " return batch, labels" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Input data\n", + "X = tf.placeholder(tf.int32, shape=[None])\n", + "# Input label\n", + "Y = tf.placeholder(tf.int32, shape=[None, 1])\n", + "\n", + "# Ensure the following ops & var are assigned on CPU\n", + "# (some ops are not compatible on GPU)\n", + "with tf.device('/cpu:0'):\n", + " # Create the embedding variable (each row represent a word embedding vector)\n", + " embedding = tf.Variable(tf.random_normal([vocabulary_size, embedding_size]))\n", + " # Lookup the corresponding embedding vectors for each sample in X\n", + " X_embed = tf.nn.embedding_lookup(embedding, X)\n", + "\n", + " # Construct the variables for the NCE loss\n", + " nce_weights = tf.Variable(tf.random_normal([vocabulary_size, embedding_size]))\n", + " nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n", + "\n", + "# Compute the average NCE loss for the batch\n", + "loss_op = tf.reduce_mean(\n", + " tf.nn.nce_loss(weights=nce_weights,\n", + " biases=nce_biases,\n", + " labels=Y,\n", + " inputs=X_embed,\n", + " num_sampled=num_sampled,\n", + " num_classes=vocabulary_size))\n", + "\n", + "# Define the optimizer\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluation\n", + "# Compute the cosine similarity between input data embedding and every embedding vectors\n", + "X_embed_norm = X_embed / tf.sqrt(tf.reduce_sum(tf.square(X_embed)))\n", + "embedding_norm = embedding / tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keepdims=True))\n", + "cosine_sim_op = tf.matmul(X_embed_norm, embedding_norm, transpose_b=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Average Loss= 520.3188\n", + "Evaluation...\n", + "\"five\" nearest neighbors: brothers, swinging, dissemination, fruitful, trichloride, dll, timur, torre,\n", + "\"of\" nearest neighbors: malting, vaginal, cecil, xiaoping, arrangers, hydras, exhibits, splits,\n", + "\"going\" nearest neighbors: besht, xps, sdtv, mississippi, frequencies, tora, reciprocating, tursiops,\n", + "\"hardware\" nearest neighbors: burgh, residences, mares, attested, whirlwind, isomerism, admiration, ties,\n", + "\"american\" nearest neighbors: tensile, months, baffling, cricket, kodak, risky, nicomedia, jura,\n", + "\"britain\" nearest neighbors: superstring, interpretations, genealogical, munition, boer, occasional, psychologists, turbofan,\n", + "Step 10000, Average Loss= 202.2640\n", + "Step 20000, Average Loss= 96.5149\n", + "Step 30000, Average Loss= 67.2858\n", + "Step 40000, Average Loss= 52.5055\n", + "Step 50000, Average Loss= 42.6301\n", + "Step 60000, Average Loss= 37.3644\n", + "Step 70000, Average Loss= 33.1220\n", + "Step 80000, Average Loss= 30.5835\n", + "Step 90000, Average Loss= 28.2243\n", + "Step 100000, Average Loss= 25.5532\n", + "Step 110000, Average Loss= 24.0891\n", + "Step 120000, Average Loss= 21.8576\n", + "Step 130000, Average Loss= 21.2192\n", + "Step 140000, Average Loss= 19.8834\n", + "Step 150000, Average Loss= 19.3362\n", + "Step 160000, Average Loss= 18.3129\n", + "Step 170000, Average Loss= 17.4952\n", + "Step 180000, Average Loss= 16.8531\n", + "Step 190000, Average Loss= 15.9615\n", + "Step 200000, Average Loss= 15.0718\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, eight, six, seven, two, nine, one,\n", + "\"of\" nearest neighbors: the, is, a, was, with, in, and, on,\n", + "\"going\" nearest neighbors: time, military, called, with, used, state, most, new,\n", + "\"hardware\" nearest neighbors: deaths, system, three, at, zero, two, s, UNK,\n", + "\"american\" nearest neighbors: UNK, and, s, about, in, when, from, after,\n", + "\"britain\" nearest neighbors: years, were, from, both, of, these, is, many,\n", + "Step 210000, Average Loss= 14.9267\n", + "Step 220000, Average Loss= 15.4700\n", + "Step 230000, Average Loss= 14.0867\n", + "Step 240000, Average Loss= 14.5337\n", + "Step 250000, Average Loss= 13.2458\n", + "Step 260000, Average Loss= 13.2944\n", + "Step 270000, Average Loss= 13.0396\n", + "Step 280000, Average Loss= 12.1902\n", + "Step 290000, Average Loss= 11.7444\n", + "Step 300000, Average Loss= 11.8473\n", + "Step 310000, Average Loss= 11.1306\n", + "Step 320000, Average Loss= 11.1699\n", + "Step 330000, Average Loss= 10.8638\n", + "Step 340000, Average Loss= 10.7910\n", + "Step 350000, Average Loss= 11.0721\n", + "Step 360000, Average Loss= 10.6309\n", + "Step 370000, Average Loss= 10.4836\n", + "Step 380000, Average Loss= 10.3482\n", + "Step 390000, Average Loss= 10.0679\n", + "Step 400000, Average Loss= 10.0070\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, two, one, zero,\n", + "\"of\" nearest neighbors: and, in, the, a, for, by, is, while,\n", + "\"going\" nearest neighbors: name, called, made, military, music, people, city, was,\n", + "\"hardware\" nearest neighbors: power, a, john, the, has, see, and, system,\n", + "\"american\" nearest neighbors: s, british, UNK, john, in, during, and, from,\n", + "\"britain\" nearest neighbors: from, general, are, before, first, after, history, was,\n", + "Step 410000, Average Loss= 10.1151\n", + "Step 420000, Average Loss= 9.5719\n", + "Step 430000, Average Loss= 9.8267\n", + "Step 440000, Average Loss= 9.4704\n", + "Step 450000, Average Loss= 9.5561\n", + "Step 460000, Average Loss= 9.1479\n", + "Step 470000, Average Loss= 8.8914\n", + "Step 480000, Average Loss= 9.0281\n", + "Step 490000, Average Loss= 9.3139\n", + "Step 500000, Average Loss= 9.1559\n", + "Step 510000, Average Loss= 8.8257\n", + "Step 520000, Average Loss= 8.9081\n", + "Step 530000, Average Loss= 8.8572\n", + "Step 540000, Average Loss= 8.5835\n", + "Step 550000, Average Loss= 8.4495\n", + "Step 560000, Average Loss= 8.4193\n", + "Step 570000, Average Loss= 8.3399\n", + "Step 580000, Average Loss= 8.1633\n", + "Step 590000, Average Loss= 8.2914\n", + "Step 600000, Average Loss= 8.0268\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, two, seven, eight, one, zero,\n", + "\"of\" nearest neighbors: and, the, in, including, with, for, on, or,\n", + "\"going\" nearest neighbors: popular, king, his, music, and, time, name, being,\n", + "\"hardware\" nearest neighbors: power, over, then, than, became, at, less, for,\n", + "\"american\" nearest neighbors: english, s, german, in, french, since, john, between,\n", + "\"britain\" nearest neighbors: however, were, state, first, group, general, from, second,\n", + "Step 610000, Average Loss= 8.1733\n", + "Step 620000, Average Loss= 8.2522\n", + "Step 630000, Average Loss= 8.0434\n", + "Step 640000, Average Loss= 8.0930\n", + "Step 650000, Average Loss= 7.8770\n", + "Step 660000, Average Loss= 7.9221\n", + "Step 670000, Average Loss= 7.7645\n", + "Step 680000, Average Loss= 7.9534\n", + "Step 690000, Average Loss= 7.7507\n", + "Step 700000, Average Loss= 7.7499\n", + "Step 710000, Average Loss= 7.6629\n", + "Step 720000, Average Loss= 7.6055\n", + "Step 730000, Average Loss= 7.4779\n", + "Step 740000, Average Loss= 7.3182\n", + "Step 750000, Average Loss= 7.6399\n", + "Step 760000, Average Loss= 7.4364\n", + "Step 770000, Average Loss= 7.6509\n", + "Step 780000, Average Loss= 7.3204\n", + "Step 790000, Average Loss= 7.4101\n", + "Step 800000, Average Loss= 7.4354\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, seven, eight, two, one, nine,\n", + "\"of\" nearest neighbors: and, the, its, a, with, at, in, for,\n", + "\"going\" nearest neighbors: were, man, music, now, great, support, popular, her,\n", + "\"hardware\" nearest neighbors: power, system, then, military, high, against, since, international,\n", + "\"american\" nearest neighbors: english, british, born, b, john, french, d, german,\n", + "\"britain\" nearest neighbors: government, second, before, from, state, several, the, at,\n", + "Step 810000, Average Loss= 7.2603\n", + "Step 820000, Average Loss= 7.1646\n", + "Step 830000, Average Loss= 7.3155\n", + "Step 840000, Average Loss= 7.1274\n", + "Step 850000, Average Loss= 7.1237\n", + "Step 860000, Average Loss= 7.1528\n", + "Step 870000, Average Loss= 7.0673\n", + "Step 880000, Average Loss= 7.2167\n", + "Step 890000, Average Loss= 7.1359\n", + "Step 900000, Average Loss= 7.0940\n", + "Step 910000, Average Loss= 7.1114\n", + "Step 920000, Average Loss= 6.9328\n", + "Step 930000, Average Loss= 7.0108\n", + "Step 940000, Average Loss= 7.0630\n", + "Step 950000, Average Loss= 6.8371\n", + "Step 960000, Average Loss= 7.0466\n", + "Step 970000, Average Loss= 6.8331\n", + "Step 980000, Average Loss= 6.9670\n", + "Step 990000, Average Loss= 6.7357\n", + "Step 1000000, Average Loss= 6.6453\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, eight, seven, two, nine, zero,\n", + "\"of\" nearest neighbors: the, became, including, first, second, from, following, and,\n", + "\"going\" nearest neighbors: near, music, popular, made, while, his, works, most,\n", + "\"hardware\" nearest neighbors: power, system, before, its, using, for, thus, an,\n", + "\"american\" nearest neighbors: b, born, d, UNK, nine, john, english, seven,\n", + "\"britain\" nearest neighbors: of, following, government, home, from, state, end, several,\n", + "Step 1010000, Average Loss= 6.7193\n", + "Step 1020000, Average Loss= 6.9297\n", + "Step 1030000, Average Loss= 6.7905\n", + "Step 1040000, Average Loss= 6.7709\n", + "Step 1050000, Average Loss= 6.7337\n", + "Step 1060000, Average Loss= 6.7617\n", + "Step 1070000, Average Loss= 6.7489\n", + "Step 1080000, Average Loss= 6.6259\n", + "Step 1090000, Average Loss= 6.6415\n", + "Step 1100000, Average Loss= 6.7209\n", + "Step 1110000, Average Loss= 6.5471\n", + "Step 1120000, Average Loss= 6.6508\n", + "Step 1130000, Average Loss= 6.5184\n", + "Step 1140000, Average Loss= 6.6202\n", + "Step 1150000, Average Loss= 6.7205\n", + "Step 1160000, Average Loss= 6.5821\n", + "Step 1170000, Average Loss= 6.6200\n", + "Step 1180000, Average Loss= 6.5089\n", + "Step 1190000, Average Loss= 6.5587\n", + "Step 1200000, Average Loss= 6.4930\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, seven, eight, two, nine, zero,\n", + "\"of\" nearest neighbors: the, and, including, in, first, with, following, from,\n", + "\"going\" nearest neighbors: near, popular, works, today, large, now, when, both,\n", + "\"hardware\" nearest neighbors: power, system, computer, its, both, for, using, which,\n", + "\"american\" nearest neighbors: born, d, john, german, b, UNK, english, s,\n", + "\"britain\" nearest neighbors: state, following, government, home, became, people, were, the,\n", + "Step 1210000, Average Loss= 6.5985\n", + "Step 1220000, Average Loss= 6.4534\n", + "Step 1230000, Average Loss= 6.5083\n", + "Step 1240000, Average Loss= 6.4913\n", + "Step 1250000, Average Loss= 6.4326\n", + "Step 1260000, Average Loss= 6.3891\n", + "Step 1270000, Average Loss= 6.1601\n", + "Step 1280000, Average Loss= 6.4479\n", + "Step 1290000, Average Loss= 6.3813\n", + "Step 1300000, Average Loss= 6.5335\n", + "Step 1310000, Average Loss= 6.2971\n", + "Step 1320000, Average Loss= 6.3723\n", + "Step 1330000, Average Loss= 6.4234\n", + "Step 1340000, Average Loss= 6.3130\n", + "Step 1350000, Average Loss= 6.2867\n", + "Step 1360000, Average Loss= 6.3505\n", + "Step 1370000, Average Loss= 6.2990\n", + "Step 1380000, Average Loss= 6.3012\n", + "Step 1390000, Average Loss= 6.3112\n", + "Step 1400000, Average Loss= 6.2680\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, two, seven, eight, one, zero,\n", + "\"of\" nearest neighbors: the, its, and, including, in, with, see, for,\n", + "\"going\" nearest neighbors: near, great, like, today, began, called, an, another,\n", + "\"hardware\" nearest neighbors: power, computer, system, for, program, high, control, small,\n", + "\"american\" nearest neighbors: english, german, french, born, john, british, s, references,\n", + "\"britain\" nearest neighbors: state, great, government, people, following, became, along, home,\n", + "Step 1410000, Average Loss= 6.3157\n", + "Step 1420000, Average Loss= 6.3466\n", + "Step 1430000, Average Loss= 6.3090\n", + "Step 1440000, Average Loss= 6.3330\n", + "Step 1450000, Average Loss= 6.2072\n", + "Step 1460000, Average Loss= 6.2363\n", + "Step 1470000, Average Loss= 6.2736\n", + "Step 1480000, Average Loss= 6.1793\n", + "Step 1490000, Average Loss= 6.2977\n", + "Step 1500000, Average Loss= 6.1899\n", + "Step 1510000, Average Loss= 6.2381\n", + "Step 1520000, Average Loss= 6.1027\n", + "Step 1530000, Average Loss= 6.0046\n", + "Step 1540000, Average Loss= 6.0747\n", + "Step 1550000, Average Loss= 6.2524\n", + "Step 1560000, Average Loss= 6.1247\n", + "Step 1570000, Average Loss= 6.1937\n", + "Step 1580000, Average Loss= 6.0450\n", + "Step 1590000, Average Loss= 6.1556\n", + "Step 1600000, Average Loss= 6.1765\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, two, seven, eight, one, zero,\n", + "\"of\" nearest neighbors: the, and, its, for, from, modern, in, part,\n", + "\"going\" nearest neighbors: great, today, once, now, while, her, like, by,\n", + "\"hardware\" nearest neighbors: power, system, high, program, control, computer, typically, making,\n", + "\"american\" nearest neighbors: born, english, british, german, john, french, b, d,\n", + "\"britain\" nearest neighbors: country, state, home, government, first, following, during, from,\n", + "Step 1610000, Average Loss= 6.1029\n", + "Step 1620000, Average Loss= 6.0501\n", + "Step 1630000, Average Loss= 6.1536\n", + "Step 1640000, Average Loss= 6.0483\n", + "Step 1650000, Average Loss= 6.1197\n", + "Step 1660000, Average Loss= 6.0261\n", + "Step 1670000, Average Loss= 6.1012\n", + "Step 1680000, Average Loss= 6.1795\n", + "Step 1690000, Average Loss= 6.1224\n", + "Step 1700000, Average Loss= 6.0896\n", + "Step 1710000, Average Loss= 6.0418\n", + "Step 1720000, Average Loss= 6.0626\n", + "Step 1730000, Average Loss= 6.0214\n", + "Step 1740000, Average Loss= 6.1206\n", + "Step 1750000, Average Loss= 5.9721\n", + "Step 1760000, Average Loss= 6.0782\n", + "Step 1770000, Average Loss= 6.0291\n", + "Step 1780000, Average Loss= 6.0187\n", + "Step 1790000, Average Loss= 5.9761\n", + "Step 1800000, Average Loss= 5.7518\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, nine, two, zero,\n", + "\"of\" nearest neighbors: the, from, in, became, and, second, first, including,\n", + "\"going\" nearest neighbors: today, which, once, little, made, before, now, etc,\n", + "\"hardware\" nearest neighbors: computer, power, program, system, high, typically, current, eventually,\n", + "\"american\" nearest neighbors: b, d, born, actor, UNK, robert, william, english,\n", + "\"britain\" nearest neighbors: government, state, country, from, world, great, of, in,\n", + "Step 1810000, Average Loss= 5.9839\n", + "Step 1820000, Average Loss= 5.9931\n", + "Step 1830000, Average Loss= 6.0794\n", + "Step 1840000, Average Loss= 5.9072\n", + "Step 1850000, Average Loss= 5.9831\n", + "Step 1860000, Average Loss= 6.0023\n", + "Step 1870000, Average Loss= 5.9375\n", + "Step 1880000, Average Loss= 5.9250\n", + "Step 1890000, Average Loss= 5.9422\n", + "Step 1900000, Average Loss= 5.9339\n", + "Step 1910000, Average Loss= 5.9235\n", + "Step 1920000, Average Loss= 5.9692\n", + "Step 1930000, Average Loss= 5.9022\n", + "Step 1940000, Average Loss= 5.9599\n", + "Step 1950000, Average Loss= 6.0174\n", + "Step 1960000, Average Loss= 5.9530\n", + "Step 1970000, Average Loss= 5.9479\n", + "Step 1980000, Average Loss= 5.8870\n", + "Step 1990000, Average Loss= 5.9271\n", + "Step 2000000, Average Loss= 5.8774\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, two, nine, zero,\n", + "\"of\" nearest neighbors: and, the, from, in, within, first, including, with,\n", + "\"going\" nearest neighbors: today, before, another, little, work, etc, now, him,\n", + "\"hardware\" nearest neighbors: computer, program, system, both, making, designed, power, simple,\n", + "\"american\" nearest neighbors: actor, born, d, robert, john, b, german, writer,\n", + "\"britain\" nearest neighbors: government, state, following, great, england, became, country, from,\n", + "Step 2010000, Average Loss= 5.9373\n", + "Step 2020000, Average Loss= 5.9113\n", + "Step 2030000, Average Loss= 5.9158\n", + "Step 2040000, Average Loss= 5.9020\n", + "Step 2050000, Average Loss= 5.8608\n", + "Step 2060000, Average Loss= 5.7379\n", + "Step 2070000, Average Loss= 5.7143\n", + "Step 2080000, Average Loss= 5.9379\n", + "Step 2090000, Average Loss= 5.8201\n", + "Step 2100000, Average Loss= 5.9390\n", + "Step 2110000, Average Loss= 5.7295\n", + "Step 2120000, Average Loss= 5.8290\n", + "Step 2130000, Average Loss= 5.9042\n", + "Step 2140000, Average Loss= 5.8367\n", + "Step 2150000, Average Loss= 5.7760\n", + "Step 2160000, Average Loss= 5.8664\n", + "Step 2170000, Average Loss= 5.7974\n", + "Step 2180000, Average Loss= 5.8523\n", + "Step 2190000, Average Loss= 5.8047\n", + "Step 2200000, Average Loss= 5.8172\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, eight, two, seven, one, zero,\n", + "\"of\" nearest neighbors: the, with, group, in, its, and, from, including,\n", + "\"going\" nearest neighbors: produced, when, today, while, little, before, had, like,\n", + "\"hardware\" nearest neighbors: computer, system, power, technology, program, simple, for, designed,\n", + "\"american\" nearest neighbors: english, canadian, german, french, author, british, film, born,\n", + "\"britain\" nearest neighbors: government, great, state, established, british, england, country, army,\n", + "Step 2210000, Average Loss= 5.8847\n", + "Step 2220000, Average Loss= 5.8622\n", + "Step 2230000, Average Loss= 5.8295\n", + "Step 2240000, Average Loss= 5.8484\n", + "Step 2250000, Average Loss= 5.7917\n", + "Step 2260000, Average Loss= 5.7846\n", + "Step 2270000, Average Loss= 5.8307\n", + "Step 2280000, Average Loss= 5.7341\n", + "Step 2290000, Average Loss= 5.8519\n", + "Step 2300000, Average Loss= 5.7792\n", + "Step 2310000, Average Loss= 5.8277\n", + "Step 2320000, Average Loss= 5.7196\n", + "Step 2330000, Average Loss= 5.5469\n", + "Step 2340000, Average Loss= 5.7177\n", + "Step 2350000, Average Loss= 5.8139\n", + "Step 2360000, Average Loss= 5.7849\n", + "Step 2370000, Average Loss= 5.7022\n", + "Step 2380000, Average Loss= 5.7447\n", + "Step 2390000, Average Loss= 5.7667\n", + "Step 2400000, Average Loss= 5.7625\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, seven, two, eight, zero, nine,\n", + "\"of\" nearest neighbors: the, and, from, part, in, following, within, including,\n", + "\"going\" nearest neighbors: where, once, little, now, again, while, off, produced,\n", + "\"hardware\" nearest neighbors: system, computer, high, power, using, designed, systems, simple,\n", + "\"american\" nearest neighbors: author, actor, english, born, writer, british, b, d,\n", + "\"britain\" nearest neighbors: great, established, government, england, country, state, army, former,\n", + "Step 2410000, Average Loss= 5.6953\n", + "Step 2420000, Average Loss= 5.7413\n", + "Step 2430000, Average Loss= 5.7242\n", + "Step 2440000, Average Loss= 5.7397\n", + "Step 2450000, Average Loss= 5.7755\n", + "Step 2460000, Average Loss= 5.6881\n", + "Step 2470000, Average Loss= 5.7471\n", + "Step 2480000, Average Loss= 5.8159\n", + "Step 2490000, Average Loss= 5.7452\n", + "Step 2500000, Average Loss= 5.7547\n", + "Step 2510000, Average Loss= 5.6945\n", + "Step 2520000, Average Loss= 5.7318\n", + "Step 2530000, Average Loss= 5.6682\n", + "Step 2540000, Average Loss= 5.7660\n", + "Step 2550000, Average Loss= 5.6956\n", + "Step 2560000, Average Loss= 5.7307\n", + "Step 2570000, Average Loss= 5.7015\n", + "Step 2580000, Average Loss= 5.6932\n", + "Step 2590000, Average Loss= 5.6386\n", + "Step 2600000, Average Loss= 5.4734\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, nine, two, zero,\n", + "\"of\" nearest neighbors: the, and, in, from, became, including, for, with,\n", + "\"going\" nearest neighbors: little, again, just, a, now, where, to, for,\n", + "\"hardware\" nearest neighbors: computer, program, system, software, designed, systems, technology, current,\n", + "\"american\" nearest neighbors: actor, d, writer, b, born, singer, author, robert,\n", + "\"britain\" nearest neighbors: great, established, government, england, country, in, from, state,\n", + "Step 2610000, Average Loss= 5.7291\n", + "Step 2620000, Average Loss= 5.6412\n", + "Step 2630000, Average Loss= 5.7485\n", + "Step 2640000, Average Loss= 5.5833\n", + "Step 2650000, Average Loss= 5.6548\n", + "Step 2660000, Average Loss= 5.7159\n", + "Step 2670000, Average Loss= 5.6569\n", + "Step 2680000, Average Loss= 5.6080\n", + "Step 2690000, Average Loss= 5.7037\n", + "Step 2700000, Average Loss= 5.6360\n", + "Step 2710000, Average Loss= 5.6707\n", + "Step 2720000, Average Loss= 5.6811\n", + "Step 2730000, Average Loss= 5.6237\n", + "Step 2740000, Average Loss= 5.7050\n", + "Step 2750000, Average Loss= 5.6991\n", + "Step 2760000, Average Loss= 5.6691\n", + "Step 2770000, Average Loss= 5.7057\n", + "Step 2780000, Average Loss= 5.6162\n", + "Step 2790000, Average Loss= 5.6484\n", + "Step 2800000, Average Loss= 5.6627\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, six, three, seven, eight, nine, two, one,\n", + "\"of\" nearest neighbors: the, in, following, including, part, and, from, under,\n", + "\"going\" nearest neighbors: again, before, little, away, once, when, eventually, then,\n", + "\"hardware\" nearest neighbors: computer, system, software, program, systems, designed, for, design,\n", + "\"american\" nearest neighbors: actor, writer, singer, author, born, robert, d, john,\n", + "\"britain\" nearest neighbors: established, england, great, government, france, army, the, throughout,\n", + "Step 2810000, Average Loss= 5.5900\n", + "Step 2820000, Average Loss= 5.7053\n", + "Step 2830000, Average Loss= 5.6064\n", + "Step 2840000, Average Loss= 5.6891\n", + "Step 2850000, Average Loss= 5.5571\n", + "Step 2860000, Average Loss= 5.4490\n", + "Step 2870000, Average Loss= 5.5428\n", + "Step 2880000, Average Loss= 5.6832\n", + "Step 2890000, Average Loss= 5.5973\n", + "Step 2900000, Average Loss= 5.5816\n", + "Step 2910000, Average Loss= 5.5647\n", + "Step 2920000, Average Loss= 5.6001\n", + "Step 2930000, Average Loss= 5.6459\n", + "Step 2940000, Average Loss= 5.5622\n", + "Step 2950000, Average Loss= 5.5707\n", + "Step 2960000, Average Loss= 5.6492\n", + "Step 2970000, Average Loss= 5.5633\n", + "Step 2980000, Average Loss= 5.6323\n", + "Step 2990000, Average Loss= 5.5440\n", + "Step 3000000, Average Loss= 5.6209\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, eight, seven, two, zero, one,\n", + "\"of\" nearest neighbors: the, in, and, including, group, includes, part, from,\n", + "\"going\" nearest neighbors: once, again, when, quickly, before, eventually, little, had,\n", + "\"hardware\" nearest neighbors: computer, system, software, designed, program, simple, systems, sound,\n", + "\"american\" nearest neighbors: canadian, english, author, german, french, british, irish, australian,\n", + "\"britain\" nearest neighbors: established, england, great, government, throughout, france, british, northern,\n" + ] + } + ], + "source": [ + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()\n", + "\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " # Testing data\n", + " x_test = np.array([word2id[w] for w in eval_words])\n", + "\n", + " average_loss = 0\n", + " for step in xrange(1, num_steps + 1):\n", + " # Get a new batch of data\n", + " batch_x, batch_y = next_batch(batch_size, num_skips, skip_window)\n", + " # Run training op\n", + " _, loss = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})\n", + " average_loss += loss\n", + "\n", + " if step % display_step == 0 or step == 1:\n", + " if step > 1:\n", + " average_loss /= display_step\n", + " print(\"Step \" + str(step) + \", Average Loss= \" + \\\n", + " \"{:.4f}\".format(average_loss))\n", + " average_loss = 0\n", + "\n", + " # Evaluation\n", + " if step % eval_step == 0 or step == 1:\n", + " print(\"Evaluation...\")\n", + " sim = sess.run(cosine_sim_op, feed_dict={X: x_test})\n", + " for i in xrange(len(eval_words)):\n", + " top_k = 8 # number of nearest neighbors\n", + " nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n", + " log_str = '\"%s\" nearest neighbors:' % eval_words[i]\n", + " for k in xrange(top_k):\n", + " log_str = '%s %s,' % (log_str, id2word[nearest[k]])\n", + " print(log_str)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb b/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb index b99c806b..7c318fe0 100644 --- a/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb +++ b/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb @@ -41,8 +41,7 @@ "source": [ "from __future__ import print_function\n", "\n", - "import tensorflow as tf\n", - "import tensorflow.contrib.eager as tfe" + "import tensorflow as tf" ] }, { @@ -54,7 +53,8 @@ "outputs": [], "source": [ "# Set Eager API\n", - "tfe.enable_eager_execution()" + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" ] }, { From 7960768a3828193130c07d62da78d599046ab2d9 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sat, 15 Sep 2018 23:00:04 -0700 Subject: [PATCH 124/166] update tf dataset --- .../logistic_regression_eager_api.py | 10 ++--- .../neural_network_eager_api.py | 10 ++--- .../tensorflow_dataset_api.py | 29 +++++-------- .../logistic_regression_eager_api.ipynb | 26 ++++-------- .../neural_network_eager_api.ipynb | 28 ++++--------- .../tensorflow_dataset_api.ipynb | 42 +++++++------------ 6 files changed, 49 insertions(+), 96 deletions(-) diff --git a/examples/2_BasicModels/logistic_regression_eager_api.py b/examples/2_BasicModels/logistic_regression_eager_api.py index 4ba239df..c65205e7 100644 --- a/examples/2_BasicModels/logistic_regression_eager_api.py +++ b/examples/2_BasicModels/logistic_regression_eager_api.py @@ -26,7 +26,8 @@ display_step = 100 dataset = tf.data.Dataset.from_tensor_slices( - (mnist.train.images, mnist.train.labels)).batch(batch_size) + (mnist.train.images, mnist.train.labels)) +dataset = dataset.repeat().batch(batch_size).prefetch(batch_size) dataset_iter = tfe.Iterator(dataset) # Variables @@ -64,12 +65,7 @@ def accuracy_fn(inference_fn, inputs, labels): for step in range(num_steps): # Iterate through the dataset - try: - d = dataset_iter.next() - except StopIteration: - # Refill queue - dataset_iter = tfe.Iterator(dataset) - d = dataset_iter.next() + d = dataset_iter.next() # Images x_batch = d[0] diff --git a/examples/3_NeuralNetworks/neural_network_eager_api.py b/examples/3_NeuralNetworks/neural_network_eager_api.py index 1b337bfb..2151bba9 100644 --- a/examples/3_NeuralNetworks/neural_network_eager_api.py +++ b/examples/3_NeuralNetworks/neural_network_eager_api.py @@ -39,7 +39,8 @@ # Using TF Dataset to split data into batches dataset = tf.data.Dataset.from_tensor_slices( - (mnist.train.images, mnist.train.labels)).batch(batch_size) + (mnist.train.images, mnist.train.labels)) +dataset = dataset.repeat().batch(batch_size).prefetch(batch_size) dataset_iter = tfe.Iterator(dataset) @@ -92,12 +93,7 @@ def accuracy_fn(inference_fn, inputs, labels): for step in range(num_steps): # Iterate through the dataset - try: - d = dataset_iter.next() - except StopIteration: - # Refill queue - dataset_iter = tfe.Iterator(dataset) - d = dataset_iter.next() + d = dataset_iter.next() # Images x_batch = d[0] diff --git a/examples/5_DataManagement/tensorflow_dataset_api.py b/examples/5_DataManagement/tensorflow_dataset_api.py index 8c6a95e9..6dfd4dd1 100644 --- a/examples/5_DataManagement/tensorflow_dataset_api.py +++ b/examples/5_DataManagement/tensorflow_dataset_api.py @@ -29,21 +29,21 @@ sess = tf.Session() # Create a dataset tensor from the images and the labels -dataset = tf.contrib.data.Dataset.from_tensor_slices( +dataset = tf.data.Dataset.from_tensor_slices( (mnist.train.images, mnist.train.labels)) +# Automatically refill the data queue when empty +dataset = dataset.repeat() # Create batches of data dataset = dataset.batch(batch_size) -# Create an iterator, to go over the dataset +# Prefetch data for faster +dataset = dataset.prefetch(batch_size) + +# Create an iterator over the dataset iterator = dataset.make_initializable_iterator() -# It is better to use 2 placeholders, to avoid to load all data into memory, -# and avoid the 2Gb restriction length of a tensor. -_data = tf.placeholder(tf.float32, [None, n_input]) -_labels = tf.placeholder(tf.float32, [None, n_classes]) # Initialize the iterator -sess.run(iterator.initializer, feed_dict={_data: mnist.train.images, - _labels: mnist.train.labels}) +sess.run(iterator.initializer) -# Neural Net Input +# Neural Net Input (images, labels) X, Y = iterator.get_next() @@ -116,15 +116,8 @@ def conv_net(x, n_classes, dropout, reuse, is_training): # Training cycle for step in range(1, num_steps + 1): - try: - # Run optimization - sess.run(train_op) - except tf.errors.OutOfRangeError: - # Reload the iterator when it reaches the end of the dataset - sess.run(iterator.initializer, - feed_dict={_data: mnist.train.images, - _labels: mnist.train.labels}) - sess.run(train_op) + # Run optimization + sess.run(train_op) if step % display_step == 0 or step == 1: # Calculate batch loss and accuracy diff --git a/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb b/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb index 3c4eee5e..06aa5bca 100644 --- a/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb +++ b/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb @@ -54,9 +54,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -100,7 +98,8 @@ "source": [ "# Iterator for the dataset\n", "dataset = tf.data.Dataset.from_tensor_slices(\n", - " (mnist.train.images, mnist.train.labels)).batch(batch_size)\n", + " (mnist.train.images, mnist.train.labels))\n", + "dataset = dataset.repeat().batch(batch_size).prefetch(batch_size)\n", "dataset_iter = tfe.Iterator(dataset)" ] }, @@ -151,9 +150,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -181,12 +178,7 @@ "for step in range(num_steps):\n", "\n", " # Iterate through the dataset\n", - " try:\n", - " d = dataset_iter.next()\n", - " except StopIteration:\n", - " # Refill queue\n", - " dataset_iter = tfe.Iterator(dataset)\n", - " d = dataset_iter.next()\n", + " d = dataset_iter.next()\n", "\n", " # Images\n", " x_batch = d[0]\n", @@ -222,9 +214,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -246,7 +236,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 2", "language": "python", "name": "python2" }, @@ -260,7 +250,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.12" + "version": "2.7.14" } }, "nbformat": 4, diff --git a/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb b/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb index 7c318fe0..346f2e5d 100644 --- a/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb +++ b/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb @@ -60,9 +60,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -112,7 +110,8 @@ "source": [ "# Using TF Dataset to split data into batches\n", "dataset = tf.data.Dataset.from_tensor_slices(\n", - " (mnist.train.images, mnist.train.labels)).batch(batch_size)\n", + " (mnist.train.images, mnist.train.labels))\n", + "dataset = dataset.repeat().batch(batch_size).prefetch(batch_size)\n", "dataset_iter = tfe.Iterator(dataset)" ] }, @@ -180,9 +179,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -210,13 +207,8 @@ "for step in range(num_steps):\n", "\n", " # Iterate through the dataset\n", - " try:\n", - " d = dataset_iter.next()\n", - " except StopIteration:\n", - " # Refill queue\n", - " dataset_iter = tfe.Iterator(dataset)\n", - " d = dataset_iter.next()\n", - "\n", + " d = dataset_iter.next()\n", + " \n", " # Images\n", " x_batch = d[0]\n", " # Labels\n", @@ -251,9 +243,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -275,7 +265,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 2", "language": "python", "name": "python2" }, @@ -289,7 +279,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.12" + "version": "2.7.14" } }, "nbformat": 4, diff --git a/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb b/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb index bf9f46e8..c8af58e7 100644 --- a/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb +++ b/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb @@ -2,9 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "# TensorFlow Dataset API\n", "\n", @@ -19,9 +17,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -64,21 +60,21 @@ "sess = tf.Session()\n", "\n", "# Create a dataset tensor from the images and the labels\n", - "dataset = tf.contrib.data.Dataset.from_tensor_slices(\n", + "dataset = tf.data.Dataset.from_tensor_slices(\n", " (mnist.train.images, mnist.train.labels))\n", + "# Automatically refill the data queue when empty\n", + "dataset = dataset.repeat()\n", "# Create batches of data\n", "dataset = dataset.batch(batch_size)\n", - "# Create an iterator, to go over the dataset\n", + "# Prefetch data for faster\n", + "dataset = dataset.prefetch(batch_size)\n", + "\n", + "# Create an iterator over the dataset\n", "iterator = dataset.make_initializable_iterator()\n", - "# It is better to use 2 placeholders, to avoid to load all data into memory,\n", - "# and avoid the 2Gb restriction length of a tensor.\n", - "_data = tf.placeholder(tf.float32, [None, n_input])\n", - "_labels = tf.placeholder(tf.float32, [None, n_classes])\n", "# Initialize the iterator\n", - "sess.run(iterator.initializer, feed_dict={_data: mnist.train.images,\n", - " _labels: mnist.train.labels})\n", + "sess.run(iterator.initializer)\n", "\n", - "# Neural Net Input\n", + "# Neural Net Input (images, labels)\n", "X, Y = iterator.get_next()" ] }, @@ -155,7 +151,6 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false, "scrolled": false }, "outputs": [ @@ -188,15 +183,8 @@ "# Training cycle\n", "for step in range(1, num_steps + 1):\n", " \n", - " try:\n", - " # Run optimization\n", - " sess.run(train_op)\n", - " except tf.errors.OutOfRangeError:\n", - " # Reload the iterator when it reaches the end of the dataset\n", - " sess.run(iterator.initializer, \n", - " feed_dict={_data: mnist.train.images,\n", - " _labels: mnist.train.labels})\n", - " sess.run(train_op)\n", + " # Run optimization\n", + " sess.run(train_op)\n", " \n", " if step % display_step == 0 or step == 1:\n", " # Calculate batch loss and accuracy\n", @@ -212,7 +200,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 2", "language": "python", "name": "python2" }, @@ -226,7 +214,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.12" + "version": "2.7.14" } }, "nbformat": 4, From 84c99e3de1114c3b67c00b897eb9bbc1f7c618fc Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sat, 15 Sep 2018 23:02:57 -0700 Subject: [PATCH 125/166] fix comment --- examples/5_DataManagement/tensorflow_dataset_api.py | 2 +- notebooks/5_DataManagement/tensorflow_dataset_api.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/5_DataManagement/tensorflow_dataset_api.py b/examples/5_DataManagement/tensorflow_dataset_api.py index 6dfd4dd1..dad0132a 100644 --- a/examples/5_DataManagement/tensorflow_dataset_api.py +++ b/examples/5_DataManagement/tensorflow_dataset_api.py @@ -35,7 +35,7 @@ dataset = dataset.repeat() # Create batches of data dataset = dataset.batch(batch_size) -# Prefetch data for faster +# Prefetch data for faster consumption dataset = dataset.prefetch(batch_size) # Create an iterator over the dataset diff --git a/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb b/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb index c8af58e7..22c05e63 100644 --- a/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb +++ b/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb @@ -66,7 +66,7 @@ "dataset = dataset.repeat()\n", "# Create batches of data\n", "dataset = dataset.batch(batch_size)\n", - "# Prefetch data for faster\n", + "# Prefetch data for faster consumption\n", "dataset = dataset.prefetch(batch_size)\n", "\n", "# Create an iterator over the dataset\n", From 9e1bb504f5d0f209d000997ce2ad95bb891798ab Mon Sep 17 00:00:00 2001 From: zdc19 Date: Fri, 2 Nov 2018 16:43:37 +0800 Subject: [PATCH 126/166] Fixing a bug of 'for loop' (#280) At line#62, 'for loop' should be "range(n, -1, -1)", otherwise it would not be able to enter the loop. --- examples/2_BasicModels/word2vec.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/2_BasicModels/word2vec.py b/examples/2_BasicModels/word2vec.py index 45e87775..094fca8c 100644 --- a/examples/2_BasicModels/word2vec.py +++ b/examples/2_BasicModels/word2vec.py @@ -59,7 +59,7 @@ # Retrieve the most common words count.extend(collections.Counter(text_words).most_common(max_vocabulary_size - 1)) # Remove samples with less than 'min_occurrence' occurrences -for i in range(len(count) - 1, -1): +for i in range(len(count) - 1, -1, -1): if count[i][1] < min_occurrence: count.pop(i) else: From 796de7c65779015289c4a20012eb45fa4e852738 Mon Sep 17 00:00:00 2001 From: scyes Date: Mon, 10 Dec 2018 17:42:42 +0800 Subject: [PATCH 127/166] Bug in range(len(count) - 1, -1) (#292) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Bug in range(len(count) - 1, -1) ，need 3 arguments here, otherwise the result is empty --- notebooks/2_BasicModels/word2vec.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/2_BasicModels/word2vec.ipynb b/notebooks/2_BasicModels/word2vec.ipynb index 9f137fcf..5d9d83d4 100644 --- a/notebooks/2_BasicModels/word2vec.ipynb +++ b/notebooks/2_BasicModels/word2vec.ipynb @@ -116,7 +116,7 @@ "# Retrieve the most common words\n", "count.extend(collections.Counter(text_words).most_common(max_vocabulary_size - 1))\n", "# Remove samples with less than 'min_occurrence' occurrences\n", - "for i in range(len(count) - 1, -1):\n", + "for i in range(len(count) - 1, -1, -1):\n", " if count[i][1] < min_occurrence:\n", " count.pop(i)\n", " else:\n", From b5a9df9135701c0e55e2b2324a6ea9288f47ee91 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Micka=C3=ABl=20Schoentgen?= Date: Tue, 15 Jan 2019 20:10:46 +0100 Subject: [PATCH 128/166] Fix ResourceWarning: unclosed file (#295) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Mickaël Schoentgen --- examples/5_DataManagement/build_an_image_dataset.py | 3 ++- notebooks/5_DataManagement/build_an_image_dataset.ipynb | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/5_DataManagement/build_an_image_dataset.py b/examples/5_DataManagement/build_an_image_dataset.py index 5a19ee05..8993665b 100644 --- a/examples/5_DataManagement/build_an_image_dataset.py +++ b/examples/5_DataManagement/build_an_image_dataset.py @@ -56,7 +56,8 @@ def read_images(dataset_path, mode, batch_size): imagepaths, labels = list(), list() if mode == 'file': # Read dataset file - data = open(dataset_path, 'r').read().splitlines() + with open(dataset_path) as f: + data = f.read().splitlines() for d in data: imagepaths.append(d.split(' ')[0]) labels.append(int(d.split(' ')[1])) diff --git a/notebooks/5_DataManagement/build_an_image_dataset.ipynb b/notebooks/5_DataManagement/build_an_image_dataset.ipynb index 21b8a1d1..9df1396d 100644 --- a/notebooks/5_DataManagement/build_an_image_dataset.ipynb +++ b/notebooks/5_DataManagement/build_an_image_dataset.ipynb @@ -84,7 +84,8 @@ " imagepaths, labels = list(), list()\n", " if mode == 'file':\n", " # Read dataset file\n", - " data = open(dataset_path, 'r').read().splitlines()\n", + " with open(dataset_path) as f:\n", + " data = f.read().splitlines()\n", " for d in data:\n", " imagepaths.append(d.split(' ')[0])\n", " labels.append(int(d.split(' ')[1]))\n", From 573b050086b47b2f101526c8cb33179a6bfafd30 Mon Sep 17 00:00:00 2001 From: Jian Wei Date: Wed, 16 Jan 2019 07:13:23 +0800 Subject: [PATCH 129/166] fix kmeans comment (#296) --- examples/2_BasicModels/kmeans.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/2_BasicModels/kmeans.py b/examples/2_BasicModels/kmeans.py index 68c10349..ed4bf91b 100644 --- a/examples/2_BasicModels/kmeans.py +++ b/examples/2_BasicModels/kmeans.py @@ -16,7 +16,7 @@ import tensorflow as tf from tensorflow.contrib.factorization import KMeans -# Ignore all GPUs, tf random forest does not benefit from it. +# Ignore all GPUs, tf k-means does not benefit from it. import os os.environ["CUDA_VISIBLE_DEVICES"] = "" From 26cc8836017d539b30526c96b1d9d31e3a62ca6e Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Wed, 3 Apr 2019 23:39:39 -0700 Subject: [PATCH 130/166] Add TensorFlow v2 examples (#302) * add tf v2 examples * update README * add README * update doc --- README.md | 10 +- tensorflow_v2/README.md | 43 ++ .../0_Prerequisite/ml_introduction.ipynb | 50 ++ .../0_Prerequisite/mnist_dataset_intro.ipynb | 96 +++ .../1_Introduction/basic_operations.ipynb | 172 ++++++ .../notebooks/1_Introduction/helloworld.ipynb | 83 +++ .../2_BasicModels/linear_regression.ipynb | 204 +++++++ .../2_BasicModels/logistic_regression.ipynb | 344 +++++++++++ .../3_NeuralNetworks/autoencoder.ipynb | 338 +++++++++++ .../convolutional_network.ipynb | 400 ++++++++++++ .../convolutional_network_raw.ipynb | 429 +++++++++++++ .../notebooks/3_NeuralNetworks/dcgan.ipynb | 381 ++++++++++++ .../3_NeuralNetworks/neural_network.ipynb | 381 ++++++++++++ .../3_NeuralNetworks/neural_network_raw.ipynb | 402 ++++++++++++ .../4_Utils/build_custom_layers.ipynb | 304 ++++++++++ .../4_Utils/save_restore_model.ipynb | 573 ++++++++++++++++++ 16 files changed, 4207 insertions(+), 3 deletions(-) create mode 100644 tensorflow_v2/README.md create mode 100644 tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb create mode 100644 tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb create mode 100644 tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb create mode 100644 tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb create mode 100644 tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb create mode 100644 tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb create mode 100644 tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb create mode 100644 tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb diff --git a/README.md b/README.md index 787b54cd..72f301b4 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,10 @@ # TensorFlow Examples -This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation. +This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation for both TF v1 & v2. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update (07/25/2018):** Add new examples (GBDT, Word2Vec) + TF1.9 compatibility! (TF v1.9+ recommended). +**Update (04/03/2019):** Starting to add [TensorFlow v2 examples](tensorflow_v2)! (more coming soon). *If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* @@ -61,11 +61,15 @@ It is suitable for beginners who want to find clear and concise examples about T - **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. - **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. +## TensorFlow v2 + +The tutorial index for TF v2 is available here: [TensorFlow v2 Examples](tensorflow_v2). + ## Dataset Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. MNIST is a database of handwritten digits, for a quick description of that dataset, you can check [this notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). -Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/) +Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/). ## Installation diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md new file mode 100644 index 00000000..dcb4a2cb --- /dev/null +++ b/tensorflow_v2/README.md @@ -0,0 +1,43 @@ +## TensorFlow v2 Examples + +*** More examples to be added later... *** + +#### 0 - Prerequisite +- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb). +- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). + +#### 1 - Introduction +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow v2. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow v2 basic operations. + +#### 2 - Basic Models +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow v2. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow v2. + +#### 3 - Neural Networks +##### Supervised + +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb)). Use TensorFlow v2 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset. +- **Simple Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)). Raw implementation of a simple neural network to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow v2 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. +- **Convolutional Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)). Raw implementation of a convolutional neural network to classify MNIST digits dataset. + +##### Unsupervised +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/tensorflow_v2/dcgan.ipynb)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. + +#### 4 - Utilities +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow v2. +- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_costum_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow v2 Models. + +## Installation + +To install TensorFlow v2, simply run: +``` +pip install tensorflow==2.0.0a0 +``` + +or (if you want GPU support): +``` +pip install tensorflow_gpu==2.0.0a0 +``` diff --git a/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb b/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb new file mode 100644 index 00000000..aa271e04 --- /dev/null +++ b/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Machine Learning\n", + "\n", + "Prior to start browsing the examples, it may be useful that you get familiar with machine learning, as TensorFlow is mostly used for machine learning tasks (especially Neural Networks). You can find below a list of useful links, that can give you the basic knowledge required for this TensorFlow Tutorial.\n", + "\n", + "## Machine Learning\n", + "\n", + "- [An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples](https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer)\n", + "- [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)\n", + "- [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\n", + "- [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf)\n", + "\n", + "## Deep Learning & Neural Networks\n", + "\n", + "- [An Introduction to Neural Networks](http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html)\n", + "- [An Introduction to Image Recognition with Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721)\n", + "- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "IPython (Python 2.7)", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb new file mode 100644 index 00000000..f1813c85 --- /dev/null +++ b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb @@ -0,0 +1,96 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# MNIST Dataset Introduction\n", + "\n", + "Most examples are using MNIST dataset of handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "## Overview\n", + "\n", + "![MNIST Digits](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "## Usage\n", + "In our examples, we are using TensorFlow [input_data.py](https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/mnist/input_data.py) script to load that dataset.\n", + "It is quite useful for managing our data, and handle:\n", + "\n", + "- Dataset downloading\n", + "\n", + "- Loading the entire dataset into numpy array: \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Import MNIST\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", + "# Load data\n", + "X_train = mnist.train.images\n", + "Y_train = mnist.train.labels\n", + "X_test = mnist.test.images\n", + "Y_test = mnist.test.labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- A `next_batch` function that can iterate over the whole dataset and return only the desired fraction of the dataset samples (in order to save memory and avoid to load the entire dataset)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Get the next 64 images array and labels\n", + "batch_X, batch_Y = mnist.train.next_batch(64)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Link: http://yann.lecun.com/exdb/mnist/" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb b/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb new file mode 100644 index 00000000..769cb600 --- /dev/null +++ b/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb @@ -0,0 +1,172 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic Tensor Operations\n", + "\n", + "Basic tensor operations using TensorFlow v2.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Define tensor constants.\n", + "a = tf.constant(2)\n", + "b = tf.constant(3)\n", + "c = tf.constant(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "add = 5\n", + "sub = -1\n", + "mul = 6\n", + "div = 0.6666666666666666\n" + ] + } + ], + "source": [ + "# Various tensor operations.\n", + "# Note: Tensors also support python operators (+, *, ...)\n", + "add = tf.add(a, b)\n", + "sub = tf.subtract(a, b)\n", + "mul = tf.multiply(a, b)\n", + "div = tf.divide(a, b)\n", + "\n", + "# Access tensors value.\n", + "print(\"add =\", add.numpy())\n", + "print(\"sub =\", sub.numpy())\n", + "print(\"mul =\", mul.numpy())\n", + "print(\"div =\", div.numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean = 3\n", + "sum = 10\n" + ] + } + ], + "source": [ + "# Some more operations.\n", + "mean = tf.reduce_mean([a, b, c])\n", + "sum = tf.reduce_sum([a, b, c])\n", + "\n", + "# Access tensors value.\n", + "print(\"mean =\", mean.numpy())\n", + "print(\"sum =\", sum.numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Matrix multiplications.\n", + "matrix1 = tf.constant([[1., 2.], [3., 4.]])\n", + "matrix2 = tf.constant([[5., 6.], [7., 8.]])\n", + "\n", + "product = tf.matmul(matrix1, matrix2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display Tensor.\n", + "product" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[19., 22.],\n", + " [43., 50.]], dtype=float32)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Convert Tensor to Numpy.\n", + "product.numpy()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb b/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb new file mode 100644 index 00000000..8a9479c0 --- /dev/null +++ b/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb @@ -0,0 +1,83 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hello World\n", + "\n", + "A very simple \"hello world\" using TensorFlow v2 tensors.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(hello world, shape=(), dtype=string)\n" + ] + } + ], + "source": [ + "# Create a Tensor.\n", + "hello = tf.constant(\"hello world\")\n", + "print hello" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello world\n" + ] + } + ], + "source": [ + "# To access a Tensor value, call numpy().\n", + "print hello.numpy()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb b/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb new file mode 100644 index 00000000..17b57b8a --- /dev/null +++ b/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear Regression Example\n", + "\n", + "Linear regression implementation with TensorFlow v2 library.\n", + "\n", + "This example is using a low-level approach to better understand all mechanics behind the training process.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "rng = np.random" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Parameters.\n", + "learning_rate = 0.01\n", + "training_steps = 1000\n", + "display_step = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Training Data.\n", + "X = np.array([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n", + " 7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n", + "Y = np.array([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n", + " 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n", + "n_samples = X.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Weight and Bias, initialized randomly.\n", + "W = tf.Variable(rng.randn(), name=\"weight\")\n", + "b = tf.Variable(rng.randn(), name=\"bias\")\n", + "\n", + "# Linear regression (Wx + b).\n", + "def linear_regression(x):\n", + " return W * x + b\n", + "\n", + "# Mean square error.\n", + "def mean_square(y_pred, y_true):\n", + " return tf.reduce_sum(tf.pow(y_pred-y_true, 2)) / (2 * n_samples)\n", + "\n", + "# Stochastic Gradient Descent Optimizer.\n", + "optimizer = tf.optimizers.SGD(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization():\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " pred = linear_regression(X)\n", + " loss = mean_square(pred, Y)\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, [W, b])\n", + " \n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, [W, b]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 50, loss: 0.210631, W: 0.458940, b: -0.670898\n", + "step: 100, loss: 0.195340, W: 0.446725, b: -0.584301\n", + "step: 150, loss: 0.181797, W: 0.435230, b: -0.502807\n", + "step: 200, loss: 0.169803, W: 0.424413, b: -0.426115\n", + "step: 250, loss: 0.159181, W: 0.414232, b: -0.353942\n", + "step: 300, loss: 0.149774, W: 0.404652, b: -0.286021\n", + "step: 350, loss: 0.141443, W: 0.395636, b: -0.222102\n", + "step: 400, loss: 0.134064, W: 0.387151, b: -0.161949\n", + "step: 450, loss: 0.127530, W: 0.379167, b: -0.105341\n", + "step: 500, loss: 0.121742, W: 0.371652, b: -0.052068\n", + "step: 550, loss: 0.116617, W: 0.364581, b: -0.001933\n", + "step: 600, loss: 0.112078, W: 0.357926, b: 0.045247\n", + "step: 650, loss: 0.108058, W: 0.351663, b: 0.089647\n", + "step: 700, loss: 0.104498, W: 0.345769, b: 0.131431\n", + "step: 750, loss: 0.101345, W: 0.340223, b: 0.170753\n", + "step: 800, loss: 0.098552, W: 0.335003, b: 0.207759\n", + "step: 850, loss: 0.096079, W: 0.330091, b: 0.242583\n", + "step: 900, loss: 0.093889, W: 0.325468, b: 0.275356\n", + "step: 950, loss: 0.091949, W: 0.321118, b: 0.306198\n", + "step: 1000, loss: 0.090231, W: 0.317024, b: 0.335223\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step in range(1, training_steps + 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization()\n", + " \n", + " if step % display_step == 0:\n", + " pred = linear_regression(X)\n", + " loss = mean_square(pred, Y)\n", + " print(\"step: %i, loss: %f, W: %f, b: %f\" % (step, loss, W.numpy(), b.numpy()))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "

    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Graphic display\n", + "plt.plot(X, Y, 'ro', label='Original data')\n", + "plt.plot(X, np.array(W * X + b), label='Fitted line')\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb b/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb new file mode 100644 index 00000000..c29c42b9 --- /dev/null +++ b/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb @@ -0,0 +1,344 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic Regression Example\n", + "\n", + "Logistic regression implementation with TensorFlow v2 library.\n", + "\n", + "This example is using a low-level approach to better understand all mechanics behind the training process.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 255. \n", + "\n", + "In this example, each image will be converted to float32, normalized to [0, 1] and flattened to a 1-D array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # 0 to 9 digits\n", + "num_features = 784 # 28*28\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.01\n", + "training_steps = 1000\n", + "batch_size = 256\n", + "display_step = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Flatten images to 1-D vector of 784 features (28*28).\n", + "x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Weight of shape [784, 10], the 28*28 image features, and total number of classes.\n", + "W = tf.Variable(tf.ones([num_features, num_classes]), name=\"weight\")\n", + "# Bias of shape [10], the total number of classes.\n", + "b = tf.Variable(tf.zeros([num_classes]), name=\"bias\")\n", + "\n", + "# Logistic regression (Wx + b).\n", + "def logistic_regression(x):\n", + " # Apply softmax to normalize the logits to a probability distribution.\n", + " return tf.nn.softmax(tf.matmul(x, W) + b)\n", + "\n", + "# Cross-Entropy loss function.\n", + "def cross_entropy(y_pred, y_true):\n", + " # Encode label to a one hot vector.\n", + " y_true = tf.one_hot(y_true, depth=num_classes)\n", + " # Clip prediction values to avoid log(0) error.\n", + " y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)\n", + " # Compute cross-entropy.\n", + " return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred)))\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + "\n", + "# Stochastic gradient descent optimizer.\n", + "optimizer = tf.optimizers.SGD(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " pred = logistic_regression(x)\n", + " loss = cross_entropy(pred, y)\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, [W, b])\n", + " \n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, [W, b]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 50, loss: 608.584717, accuracy: 0.824219\n", + "step: 100, loss: 828.206482, accuracy: 0.765625\n", + "step: 150, loss: 716.329407, accuracy: 0.746094\n", + "step: 200, loss: 584.887634, accuracy: 0.820312\n", + "step: 250, loss: 472.098114, accuracy: 0.871094\n", + "step: 300, loss: 621.834595, accuracy: 0.832031\n", + "step: 350, loss: 567.288818, accuracy: 0.714844\n", + "step: 400, loss: 489.062988, accuracy: 0.847656\n", + "step: 450, loss: 496.466675, accuracy: 0.843750\n", + "step: 500, loss: 465.342224, accuracy: 0.875000\n", + "step: 550, loss: 586.347168, accuracy: 0.855469\n", + "step: 600, loss: 95.233109, accuracy: 0.906250\n", + "step: 650, loss: 88.136490, accuracy: 0.910156\n", + "step: 700, loss: 67.170349, accuracy: 0.937500\n", + "step: 750, loss: 79.673691, accuracy: 0.921875\n", + "step: 800, loss: 112.844872, accuracy: 0.914062\n", + "step: 850, loss: 92.789581, accuracy: 0.894531\n", + "step: 900, loss: 80.116165, accuracy: 0.921875\n", + "step: 950, loss: 45.706650, accuracy: 0.925781\n", + "step: 1000, loss: 72.986969, accuracy: 0.925781\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0:\n", + " pred = logistic_regression(batch_x)\n", + " loss = cross_entropy(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 0.915100\n" + ] + } + ], + "source": [ + "# Test model on validation set.\n", + "pred = logistic_regression(x_test)\n", + "print(\"Test Accuracy: %f\" % accuracy(pred, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize predictions.\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 7\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 2\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 1\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 0\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 4\n" + ] + } + ], + "source": [ + "# Predict 5 images from validation set.\n", + "n_images = 5\n", + "test_images = x_test[:n_images]\n", + "predictions = logistic_regression(test_images)\n", + "\n", + "# Display image and model prediction.\n", + "for i in range(n_images):\n", + " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n", + " plt.show()\n", + " print(\"Model prediction: %i\" % np.argmax(predictions.numpy()[i]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb new file mode 100644 index 00000000..b7c22279 --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb @@ -0,0 +1,338 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Auto-Encoder Example\n", + "\n", + "Build a 2 layers auto-encoder with TensorFlow v2 to compress images to a lower latent space and then reconstruct them.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Auto-Encoder Overview\n", + "\n", + "\"ae\"\n", + "\n", + "References:\n", + "- [Gradient-based learning applied to document recognition](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf). Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Proceedings of the IEEE, 86(11):2278-2324, November 1998.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 255. \n", + "\n", + "In this example, each image will be converted to float32, normalized to [0, 1] and flattened to a 1-D array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST Dataset parameters.\n", + "num_features = 784 # data features (img shape: 28*28).\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.01\n", + "training_steps = 20000\n", + "batch_size = 256\n", + "display_step = 1000\n", + "\n", + "# Network Parameters\n", + "num_hidden_1 = 128 # 1st layer num features.\n", + "num_hidden_2 = 64 # 2nd layer num features (the latent dim)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = x_train.astype(np.float32), x_test.astype(np.float32)\n", + "# Flatten images to 1-D vector of 784 features (28*28).\n", + "x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(10000).batch(batch_size).prefetch(1)\n", + "\n", + "test_data = tf.data.Dataset.from_tensor_slices((x_test, y_test))\n", + "test_data = test_data.repeat().batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "\n", + "# A random value generator to initialize weights.\n", + "random_normal = tf.initializers.RandomNormal()\n", + "\n", + "weights = {\n", + " 'encoder_h1': tf.Variable(random_normal([num_features, num_hidden_1])),\n", + " 'encoder_h2': tf.Variable(random_normal([num_hidden_1, num_hidden_2])),\n", + " 'decoder_h1': tf.Variable(random_normal([num_hidden_2, num_hidden_1])),\n", + " 'decoder_h2': tf.Variable(random_normal([num_hidden_1, num_features])),\n", + "}\n", + "biases = {\n", + " 'encoder_b1': tf.Variable(random_normal([num_hidden_1])),\n", + " 'encoder_b2': tf.Variable(random_normal([num_hidden_2])),\n", + " 'decoder_b1': tf.Variable(random_normal([num_hidden_1])),\n", + " 'decoder_b2': tf.Variable(random_normal([num_features])),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Building the encoder.\n", + "def encoder(x):\n", + " # Encoder Hidden layer with sigmoid activation.\n", + " layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),\n", + " biases['encoder_b1']))\n", + " # Encoder Hidden layer with sigmoid activation.\n", + " layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),\n", + " biases['encoder_b2']))\n", + " return layer_2\n", + "\n", + "\n", + "# Building the decoder.\n", + "def decoder(x):\n", + " # Decoder Hidden layer with sigmoid activation.\n", + " layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),\n", + " biases['decoder_b1']))\n", + " # Decoder Hidden layer with sigmoid activation.\n", + " layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),\n", + " biases['decoder_b2']))\n", + " return layer_2" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Mean square loss between original images and reconstructed ones.\n", + "def mean_square(reconstructed, original):\n", + " return tf.reduce_mean(tf.pow(original - reconstructed, 2))\n", + "\n", + "# Adam optimizer.\n", + "optimizer = tf.optimizers.Adam(learning_rate=learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " reconstructed_image = decoder(encoder(x))\n", + " loss = mean_square(reconstructed_image, x)\n", + "\n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = weights.values() + biases.values()\n", + " \n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " \n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))\n", + " \n", + " return loss" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 0, loss: 0.234978\n", + "step: 1000, loss: 0.014881\n", + "step: 2000, loss: 0.010402\n", + "step: 3000, loss: 0.008817\n", + "step: 4000, loss: 0.007337\n", + "step: 5000, loss: 0.006399\n", + "step: 6000, loss: 0.006039\n", + "step: 7000, loss: 0.005042\n", + "step: 8000, loss: 0.005235\n", + "step: 9000, loss: 0.004838\n", + "step: 10000, loss: 0.004552\n", + "step: 11000, loss: 0.004717\n", + "step: 12000, loss: 0.004550\n", + "step: 13000, loss: 0.004633\n", + "step: 14000, loss: 0.004469\n", + "step: 15000, loss: 0.004503\n", + "step: 16000, loss: 0.003971\n", + "step: 17000, loss: 0.004258\n", + "step: 18000, loss: 0.004012\n", + "step: 19000, loss: 0.003703\n", + "step: 20000, loss: 0.003933\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, _) in enumerate(train_data.take(training_steps + 1)):\n", + " \n", + " # Run the optimization.\n", + " loss = run_optimization(batch_x)\n", + " \n", + " if step % display_step == 0:\n", + " print(\"step: %i, loss: %f\" % (step, loss))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Testing and Visualization.\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Images\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed Images\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Encode and decode images from test set and visualize their reconstruction.\n", + "n = 4\n", + "canvas_orig = np.empty((28 * n, 28 * n))\n", + "canvas_recon = np.empty((28 * n, 28 * n))\n", + "for i, (batch_x, _) in enumerate(test_data.take(n)):\n", + " # Encode and decode the digit image.\n", + " reconstructed_images = decoder(encoder(batch_x))\n", + " # Display original images.\n", + " for j in range(n):\n", + " # Draw the generated digits.\n", + " img = batch_x[j].numpy().reshape([28, 28])\n", + " canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = img\n", + " # Display reconstructed images.\n", + " for j in range(n):\n", + " # Draw the generated digits.\n", + " reconstr_img = reconstructed_images[j].numpy().reshape([28, 28])\n", + " canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = reconstr_img\n", + "\n", + "print(\"Original Images\") \n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas_orig, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()\n", + "\n", + "print(\"Reconstructed Images\")\n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas_recon, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb new file mode 100644 index 00000000..0bf52a43 --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -0,0 +1,400 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolutional Neural Network Example\n", + "\n", + "Build a convolutional neural network with TensorFlow v2.\n", + "\n", + "This example is using a low-level approach to better understand all mechanics behind building convolutional neural networks and the training process.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CNN Overview\n", + "\n", + "![CNN](http://personal.ie.cuhk.edu.hk/~ccloy/project_target_code/images/fig3.png)\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 255. \n", + "\n", + "In this example, each image will be converted to float32 and normalized to [0, 1].\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import Model, layers\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # total classes (0-9 digits).\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.001\n", + "training_steps = 200\n", + "batch_size = 128\n", + "display_step = 10\n", + "\n", + "# Network parameters.\n", + "conv1_filters = 32 # number of filters for 1st conv layer.\n", + "conv2_filters = 64 # number of filters for 2nd conv layer.\n", + "fc1_units = 1024 # number of neurons for 1st fully-connected layer." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create TF Model.\n", + "class ConvNet(Model):\n", + " # Set layers.\n", + " def __init__(self):\n", + " super(ConvNet, self).__init__()\n", + " # Convolution Layer with 32 filters and a kernel size of 5.\n", + " self.conv1 = layers.Conv2D(32, kernel_size=5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with kernel size of 2 and strides of 2. \n", + " self.maxpool1 = layers.MaxPool2D(2, strides=2)\n", + "\n", + " # Convolution Layer with 64 filters and a kernel size of 3.\n", + " self.conv2 = layers.Conv2D(64, kernel_size=3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with kernel size of 2 and strides of 2. \n", + " self.maxpool2 = layers.MaxPool2D(2, strides=2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer.\n", + " self.flatten = layers.Flatten()\n", + "\n", + " # Fully connected layer.\n", + " self.fc1 = layers.Dense(1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied).\n", + " self.dropout = layers.Dropout(rate=0.5)\n", + "\n", + " # Output layer, class prediction.\n", + " self.out = layers.Dense(num_classes)\n", + "\n", + " # Set forward pass.\n", + " def call(self, x, is_training=False):\n", + " x = tf.reshape(x, [-1, 28, 28, 1])\n", + " x = self.conv1(x)\n", + " x = self.maxpool1(x)\n", + " x = self.conv2(x)\n", + " x = self.maxpool2(x)\n", + " x = self.flatten(x)\n", + " x = self.fc1(x)\n", + " x = self.dropout(x, training=is_training)\n", + " x = self.out(x)\n", + " if not is_training:\n", + " # tf cross entropy expect logits without softmax, so only\n", + " # apply softmax when not training.\n", + " x = tf.nn.softmax(x)\n", + " return x\n", + "\n", + "# Build neural network model.\n", + "conv_net = ConvNet()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy Loss.\n", + "# Note that this will apply 'softmax' to the logits.\n", + "def cross_entropy_loss(x, y):\n", + " # Convert labels to int 64 for tf cross-entropy function.\n", + " y = tf.cast(y, tf.int64)\n", + " # Apply softmax to logits and compute cross-entropy.\n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", + " # Average loss across the batch.\n", + " return tf.reduce_mean(loss)\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + "\n", + "# Stochastic gradient descent optimizer.\n", + "optimizer = tf.optimizers.Adam(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " # Forward pass.\n", + " pred = conv_net(x, is_training=True)\n", + " # Compute loss.\n", + " loss = cross_entropy_loss(pred, y)\n", + " \n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = conv_net.trainable_variables\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " \n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 10, loss: 1.877234, accuracy: 0.789062\n", + "step: 20, loss: 1.609390, accuracy: 0.898438\n", + "step: 30, loss: 1.555458, accuracy: 0.960938\n", + "step: 40, loss: 1.588296, accuracy: 0.921875\n", + "step: 50, loss: 1.561057, accuracy: 0.929688\n", + "step: 60, loss: 1.539851, accuracy: 0.945312\n", + "step: 70, loss: 1.527458, accuracy: 0.976562\n", + "step: 80, loss: 1.526701, accuracy: 0.945312\n", + "step: 90, loss: 1.522610, accuracy: 0.968750\n", + "step: 100, loss: 1.514970, accuracy: 0.968750\n", + "step: 110, loss: 1.489902, accuracy: 0.976562\n", + "step: 120, loss: 1.520697, accuracy: 0.953125\n", + "step: 130, loss: 1.494326, accuracy: 0.968750\n", + "step: 140, loss: 1.501781, accuracy: 0.984375\n", + "step: 150, loss: 1.506588, accuracy: 0.976562\n", + "step: 160, loss: 1.493378, accuracy: 0.984375\n", + "step: 170, loss: 1.494006, accuracy: 0.984375\n", + "step: 180, loss: 1.509782, accuracy: 0.953125\n", + "step: 190, loss: 1.516123, accuracy: 0.953125\n", + "step: 200, loss: 1.515508, accuracy: 0.953125\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0:\n", + " pred = conv_net(batch_x)\n", + " loss = cross_entropy_loss(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 0.977700\n" + ] + } + ], + "source": [ + "# Test model on validation set.\n", + "pred = conv_net(x_test)\n", + "print(\"Test Accuracy: %f\" % accuracy(pred, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize predictions.\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 7\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 2\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 1\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 0\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADXZJREFUeJzt3X+oXPWZx/HPZ00bMQ2SS0ga0uzeGmVdCW6qF1GUqhRjNlZi0UhCWLJaevtHhRb3jxUVKmpBZJvd/mMgxdAIbdqicQ219AcS1xUWyY2EmvZu2xiyTZqQH6ahiQSquU//uOfKNblzZjJzZs7c+7xfIDNznnNmHo753O85c2bm64gQgHz+pu4GANSD8ANJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSGpWL1/MNh8nBLosItzKeh2N/LZX2v6t7X22H+nkuQD0ltv9bL/tSyT9TtIdkg5J2iVpXUT8pmQbRn6gy3ox8t8gaV9E7I+Iv0j6oaTVHTwfgB7qJPyLJR2c9PhQsexjbA/bHrE90sFrAahYJ2/4TXVoccFhfURslrRZ4rAf6CedjPyHJC2Z9Pgzkg531g6AXukk/LskXWX7s7Y/KWmtpB3VtAWg29o+7I+ID20/JOnnki6RtCUifl1ZZwC6qu1LfW29GOf8QNf15EM+AKYvwg8kRfiBpAg/kBThB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kBThB5Jqe4puSbJ9QNJpSeckfRgRQ1U0hY+77rrrSuvbt29vWBscHKy4m/6xYsWK0vro6GjD2sGDB6tuZ9rpKPyF2yPiRAXPA6CHOOwHkuo0/CHpF7Z32x6uoiEAvdHpYf/NEXHY9gJJv7T9fxHxxuQVij8K/GEA+kxHI39EHC5uj0l6WdINU6yzOSKGeDMQ6C9th9/2HNtzJ+5LWiFpb1WNAeiuTg77F0p62fbE8/wgIn5WSVcAuq7t8EfEfkn/WGEvaODOO+8src+ePbtHnfSXu+++u7T+4IMPNqytXbu26namHS71AUkRfiApwg8kRfiBpAg/kBThB5Kq4lt96NCsWeX/G1atWtWjTqaX3bt3l9YffvjhhrU5c+aUbvv++++31dN0wsgPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0lxnb8P3H777aX1m266qbT+7LPPVtnOtDFv3rzS+jXXXNOwdtlll5Vuy3V+ADMW4QeSIvxAUoQfSIrwA0kRfiApwg8k5Yjo3YvZvXuxPrJs2bLS+uuvv15af++990rr119/fcPamTNnSredzprtt1tuuaVhbdGiRaXbHj9+vJ2W+kJEuJX1GPmBpAg/kBThB5Ii/EBShB9IivADSRF+IKmm3+e3vUXSFyUdi4hlxbIBST+SNCjpgKT7I+JP3Wtzenv88cdL681+Q37lypWl9Zl6LX9gYKC0fuutt5bWx8bGqmxnxmll5P+epPP/9T0i6bWIuErSa8VjANNI0/BHxBuSTp63eLWkrcX9rZLuqbgvAF3W7jn/wog4IknF7YLqWgLQC13/DT/bw5KGu/06AC5OuyP/UduLJKm4PdZoxYjYHBFDETHU5msB6IJ2w79D0obi/gZJr1TTDoBeaRp+29sk/a+kv7d9yPaXJT0j6Q7bv5d0R/EYwDTS9Jw/ItY1KH2h4l6mrfvuu6+0vmrVqtL6vn37SusjIyMX3dNM8Nhjj5XWm13HL/u+/6lTp9ppaUbhE35AUoQfSIrwA0kRfiApwg8kRfiBpJiiuwJr1qwprTebDvq5556rsp1pY3BwsLS+fv360vq5c+dK608//XTD2gcffFC6bQaM/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFNf5W3T55Zc3rN14440dPfemTZs62n66Gh4u/3W3+fPnl9ZHR0dL6zt37rzonjJh5AeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpLjO36LZs2c3rC1evLh0223btlXdzoywdOnSjrbfu3dvRZ3kxMgPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0k1vc5ve4ukL0o6FhHLimVPSPqKpOPFao9GxE+71WQ/OH36dMPanj17Sre99tprS+sDAwOl9ZMnT5bW+9mCBQsa1ppNbd7Mm2++2dH22bUy8n9P0soplv9HRCwv/pvRwQdmoqbhj4g3JE3foQfAlDo553/I9q9sb7E9r7KOAPREu+HfJGmppOWSjkj6dqMVbQ/bHrE90uZrAeiCtsIfEUcj4lxEjEn6rqQbStbdHBFDETHUbpMAqtdW+G0vmvTwS5L4ehUwzbRyqW+bpNskzbd9SNI3Jd1me7mkkHRA0le72COALmga/ohYN8Xi57vQS187e/Zsw9q7775buu29995bWn/11VdL6xs3biytd9OyZctK61dccUVpfXBwsGEtItpp6SNjY2MdbZ8dn/ADkiL8QFKEH0iK8ANJEX4gKcIPJOVOL7dc1IvZvXuxHrr66qtL608++WRp/a677iqtl/1seLedOHGitN7s30/ZNNu22+ppwty5c0vrZZdnZ7KIaGnHMvIDSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFJc5+8Dy5cvL61feeWVPerkQi+++GJH22/durVhbf369R0996xZzDA/Fa7zAyhF+IGkCD+QFOEHkiL8QFKEH0iK8ANJcaG0DzSb4rtZvZ/t37+/a8/d7GfF9+5lLpkyjPxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kFTT6/y2l0h6QdKnJY1J2hwR37E9IOlHkgYlHZB0f0T8qXutYjoq+23+Tn+3n+v4nWll5P9Q0r9GxD9IulHS12xfI+kRSa9FxFWSXiseA5gmmoY/Io5ExNvF/dOSRiUtlrRa0sTPtGyVdE+3mgRQvYs657c9KOlzkt6StDAijkjjfyAkLai6OQDd0/Jn+21/StJLkr4REX9u9XzN9rCk4fbaA9AtLY38tj+h8eB/PyK2F4uP2l5U1BdJOjbVthGxOSKGImKoioYBVKNp+D0+xD8vaTQiNk4q7ZC0obi/QdIr1bcHoFtaOey/WdI/S3rH9sR3Sx+V9IykH9v+sqQ/SFrTnRYxnZX9NHwvfzYeF2oa/oh4U1KjE/wvVNsOgF7hE35AUoQfSIrwA0kRfiApwg8kRfiBpPjpbnTVpZde2va2Z8+erbATnI+RH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeS4jo/uuqBBx5oWDt16lTptk899VTV7WASRn4gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrr/OiqXbt2Naxt3LixYU2Sdu7cWXU7mISRH0iK8ANJEX4gKcIPJEX4gaQIP5AU4QeScrM50m0vkfSCpE9LGpO0OSK+Y/sJSV+RdLxY9dGI+GmT52JCdqDLIsKtrNdK+BdJWhQRb9ueK2m3pHsk3S/pTET8e6tNEX6g+1oNf9NP+EXEEUlHivunbY9KWtxZewDqdlHn/LYHJX1O0lvFoods/8r2FtvzGmwzbHvE9khHnQKoVNPD/o9WtD8l6b8lfSsittteKOmEpJD0lMZPDR5s8hwc9gNdVtk5vyTZ/oSkn0j6eURc8G2M4ojgJxGxrMnzEH6gy1oNf9PDftuW9Lyk0cnBL94InPAlSXsvtkkA9Wnl3f5bJP2PpHc0fqlPkh6VtE7Sco0f9h+Q9NXizcGy52LkB7qs0sP+qhB+oPsqO+wHMDMRfiApwg8kRfiBpAg/kBThB5Ii/EBShB9IivADSRF+ICnCDyRF+IGkCD+QFOEHkur1FN0nJP3/pMfzi2X9qF9769e+JHprV5W9/V2rK/b0+/wXvLg9EhFDtTVQol9769e+JHprV129cdgPJEX4gaTqDv/mml+/TL/21q99SfTWrlp6q/WcH0B96h75AdSklvDbXmn7t7b32X6kjh4asX3A9ju299Q9xVgxDdox23snLRuw/Uvbvy9up5wmrabenrD9x2Lf7bG9qqbeltjeaXvU9q9tf71YXuu+K+mrlv3W88N+25dI+p2kOyQdkrRL0rqI+E1PG2nA9gFJQxFR+zVh25+XdEbSCxOzIdl+VtLJiHim+MM5LyL+rU96e0IXOXNzl3prNLP0v6jGfVfljNdVqGPkv0HSvojYHxF/kfRDSatr6KPvRcQbkk6et3i1pK3F/a0a/8fTcw166wsRcSQi3i7un5Y0MbN0rfuupK9a1BH+xZIOTnp8SP015XdI+oXt3baH625mCgsnZkYqbhfU3M/5ms7c3EvnzSzdN/uunRmvq1ZH+KeaTaSfLjncHBHXSfonSV8rDm/Rmk2Slmp8Grcjkr5dZzPFzNIvSfpGRPy5zl4mm6KvWvZbHeE/JGnJpMefkXS4hj6mFBGHi9tjkl7W+GlKPzk6MUlqcXus5n4+EhFHI+JcRIxJ+q5q3HfFzNIvSfp+RGwvFte+76bqq679Vkf4d0m6yvZnbX9S0lpJO2ro4wK25xRvxMj2HEkr1H+zD++QtKG4v0HSKzX28jH9MnNzo5mlVfO+67cZr2v5kE9xKeM/JV0iaUtEfKvnTUzB9hUaH+2l8W88/qDO3mxvk3Sbxr/1dVTSNyX9l6QfS/pbSX+QtCYiev7GW4PebtNFztzcpd4azSz9lmrcd1XOeF1JP3zCD8iJT/gBSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0jqr8DO4JozFB6IAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 4\n" + ] + } + ], + "source": [ + "# Predict 5 images from validation set.\n", + "n_images = 5\n", + "test_images = x_test[:n_images]\n", + "predictions = conv_net(test_images)\n", + "\n", + "# Display image and model prediction.\n", + "for i in range(n_images):\n", + " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n", + " plt.show()\n", + " print(\"Model prediction: %i\" % np.argmax(predictions.numpy()[i]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb new file mode 100644 index 00000000..80adb3f0 --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb @@ -0,0 +1,429 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolutional Neural Network Example\n", + "\n", + "Build a convolutional neural network with TensorFlow v2.\n", + "\n", + "This example is using a low-level approach to better understand all mechanics behind building convolutional neural networks and the training process.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CNN Overview\n", + "\n", + "![CNN](http://personal.ie.cuhk.edu.hk/~ccloy/project_target_code/images/fig3.png)\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 255. \n", + "\n", + "In this example, each image will be converted to float32 and normalized to [0, 1].\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # total classes (0-9 digits).\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.001\n", + "training_steps = 200\n", + "batch_size = 128\n", + "display_step = 10\n", + "\n", + "# Network parameters.\n", + "conv1_filters = 32 # number of filters for 1st conv layer.\n", + "conv2_filters = 64 # number of filters for 2nd conv layer.\n", + "fc1_units = 1024 # number of neurons for 1st fully-connected layer." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create some wrappers for simplicity.\n", + "def conv2d(x, W, b, strides=1):\n", + " # Conv2D wrapper, with bias and relu activation.\n", + " x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n", + " x = tf.nn.bias_add(x, b)\n", + " return tf.nn.relu(x)\n", + "\n", + "def maxpool2d(x, k=2):\n", + " # MaxPool2D wrapper.\n", + " return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "\n", + "# A random value generator to initialize weights.\n", + "random_normal = tf.initializers.RandomNormal()\n", + "\n", + "weights = {\n", + " # Conv Layer 1: 5x5 conv, 1 input, 32 filters (MNIST has 1 color channel only).\n", + " 'wc1': tf.Variable(random_normal([5, 5, 1, conv1_filters])),\n", + " # Conv Layer 2: 5x5 conv, 32 inputs, 64 filters.\n", + " 'wc2': tf.Variable(random_normal([5, 5, conv1_filters, conv2_filters])),\n", + " # FC Layer 1: 7*7*64 inputs, 1024 units.\n", + " 'wd1': tf.Variable(random_normal([7*7*64, fc1_units])),\n", + " # FC Out Layer: 1024 inputs, 10 units (total number of classes)\n", + " 'out': tf.Variable(random_normal([fc1_units, num_classes]))\n", + "}\n", + "\n", + "biases = {\n", + " 'bc1': tf.Variable(tf.zeros([conv1_filters])),\n", + " 'bc2': tf.Variable(tf.zeros([conv2_filters])),\n", + " 'bd1': tf.Variable(tf.zeros([fc1_units])),\n", + " 'out': tf.Variable(tf.zeros([num_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Create model\n", + "def conv_net(x):\n", + " \n", + " # Input shape: [-1, 28, 28, 1]. A batch of 28x28x1 (grayscale) images.\n", + " x = tf.reshape(x, [-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer. Output shape: [-1, 28, 28, 32].\n", + " conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n", + " \n", + " # Max Pooling (down-sampling). Output shape: [-1, 14, 14, 32].\n", + " conv1 = maxpool2d(conv1, k=2)\n", + "\n", + " # Convolution Layer. Output shape: [-1, 14, 14, 64].\n", + " conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n", + " \n", + " # Max Pooling (down-sampling). Output shape: [-1, 7, 7, 64].\n", + " conv2 = maxpool2d(conv2, k=2)\n", + "\n", + " # Reshape conv2 output to fit fully connected layer input, Output shape: [-1, 7*7*64].\n", + " fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n", + " \n", + " # Fully connected layer, Output shape: [-1, 1024].\n", + " fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n", + " # Apply ReLU to fc1 output for non-linearity.\n", + " fc1 = tf.nn.relu(fc1)\n", + "\n", + " # Fully connected layer, Output shape: [-1, 10].\n", + " out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n", + " # Apply softmax to normalize the logits to a probability distribution.\n", + " return tf.nn.softmax(out)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy loss function.\n", + "def cross_entropy(y_pred, y_true):\n", + " # Encode label to a one hot vector.\n", + " y_true = tf.one_hot(y_true, depth=num_classes)\n", + " # Clip prediction values to avoid log(0) error.\n", + " y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)\n", + " # Compute cross-entropy.\n", + " return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred)))\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + "\n", + "# ADAM optimizer.\n", + "optimizer = tf.optimizers.Adam(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " pred = conv_net(x)\n", + " loss = cross_entropy(pred, y)\n", + " \n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = weights.values() + biases.values()\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " \n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 10, loss: 72.370056, accuracy: 0.851562\n", + "step: 20, loss: 53.936745, accuracy: 0.882812\n", + "step: 30, loss: 29.929554, accuracy: 0.921875\n", + "step: 40, loss: 28.075102, accuracy: 0.953125\n", + "step: 50, loss: 19.366310, accuracy: 0.960938\n", + "step: 60, loss: 20.398090, accuracy: 0.945312\n", + "step: 70, loss: 29.320951, accuracy: 0.960938\n", + "step: 80, loss: 9.121045, accuracy: 0.984375\n", + "step: 90, loss: 11.680668, accuracy: 0.976562\n", + "step: 100, loss: 12.413654, accuracy: 0.976562\n", + "step: 110, loss: 6.675493, accuracy: 0.984375\n", + "step: 120, loss: 8.730624, accuracy: 0.984375\n", + "step: 130, loss: 13.608270, accuracy: 0.960938\n", + "step: 140, loss: 12.859011, accuracy: 0.968750\n", + "step: 150, loss: 9.110849, accuracy: 0.976562\n", + "step: 160, loss: 5.832032, accuracy: 0.984375\n", + "step: 170, loss: 6.996647, accuracy: 0.968750\n", + "step: 180, loss: 5.325038, accuracy: 0.992188\n", + "step: 190, loss: 8.866342, accuracy: 0.984375\n", + "step: 200, loss: 2.626245, accuracy: 1.000000\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0:\n", + " pred = conv_net(batch_x)\n", + " loss = cross_entropy(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 0.980000\n" + ] + } + ], + "source": [ + "# Test model on validation set.\n", + "pred = conv_net(x_test)\n", + "print(\"Test Accuracy: %f\" % accuracy(pred, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize predictions.\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 7\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 2\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 1\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 0\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 4\n" + ] + } + ], + "source": [ + "# Predict 5 images from validation set.\n", + "n_images = 5\n", + "test_images = x_test[:n_images]\n", + "predictions = conv_net(test_images)\n", + "\n", + "# Display image and model prediction.\n", + "for i in range(n_images):\n", + " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n", + " plt.show()\n", + " print(\"Model prediction: %i\" % np.argmax(predictions.numpy()[i]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb new file mode 100644 index 00000000..763b210f --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Deep Convolutional Generative Adversarial Network Example\n", + "\n", + "Build a deep convolutional generative adversarial network (DCGAN) to generate digit images from a noise distribution with TensorFlow v2.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DCGAN Overview\n", + "\n", + "\"dcgan\"\n", + "\n", + "References:\n", + "- [Unsupervised representation learning with deep convolutional generative adversarial networks](https://arxiv.org/pdf/1511.06434). A Radford, L Metz, S Chintala, 2016.\n", + "- [Understanding the difficulty of training deep feedforward neural networks](http://proceedings.mlr.press/v9/glorot10a.html). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167). Sergey Ioffe, Christian Szegedy. 2015.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 255. \n", + "\n", + "In this example, each image will be converted to float32 and normalized from [0, 255] to [0, 1].\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import Model, layers\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST Dataset parameters.\n", + "num_features = 784 # data features (img shape: 28*28).\n", + "\n", + "# Training parameters.\n", + "lr_generator = 0.0002\n", + "lr_discriminator = 0.0002\n", + "training_steps = 20000\n", + "batch_size = 128\n", + "display_step = 500\n", + "\n", + "# Network parameters.\n", + "noise_dim = 100 # Noise data points." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(10000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Create TF Model.\n", + "class Generator(Model):\n", + " # Set layers.\n", + " def __init__(self):\n", + " super(Generator, self).__init__()\n", + " self.fc1 = layers.Dense(7 * 7 * 128)\n", + " self.bn1 = layers.BatchNormalization()\n", + " self.conv2tr1 = layers.Conv2DTranspose(64, 5, strides=2, padding='SAME')\n", + " self.bn2 = layers.BatchNormalization()\n", + " self.conv2tr2 = layers.Conv2DTranspose(1, 5, strides=2, padding='SAME')\n", + "\n", + " # Set forward pass.\n", + " def call(self, x, is_training=False):\n", + " x = self.fc1(x)\n", + " x = self.bn1(x, training=is_training)\n", + " x = tf.nn.leaky_relu(x)\n", + " # Reshape to a 4-D array of images: (batch, height, width, channels)\n", + " # New shape: (batch, 7, 7, 128)\n", + " x = tf.reshape(x, shape=[-1, 7, 7, 128])\n", + " # Deconvolution, image shape: (batch, 14, 14, 64)\n", + " x = self.conv2tr1(x)\n", + " x = self.bn2(x, training=is_training)\n", + " x = tf.nn.leaky_relu(x)\n", + " # Deconvolution, image shape: (batch, 28, 28, 1)\n", + " x = self.conv2tr2(x)\n", + " x = tf.nn.tanh(x)\n", + " return x\n", + "\n", + "# Generator Network\n", + "# Input: Noise, Output: Image\n", + "# Note that batch normalization has different behavior at training and inference time,\n", + "# we then use a placeholder to indicates the layer if we are training or not.\n", + "class Discriminator(Model):\n", + " # Set layers.\n", + " def __init__(self):\n", + " super(Discriminator, self).__init__()\n", + " self.conv1 = layers.Conv2D(64, 5, strides=2, padding='SAME')\n", + " self.bn1 = layers.BatchNormalization()\n", + " self.conv2 = layers.Conv2D(128, 5, strides=2, padding='SAME')\n", + " self.bn2 = layers.BatchNormalization()\n", + " self.flatten = layers.Flatten()\n", + " self.fc1 = layers.Dense(1024)\n", + " self.bn3 = layers.BatchNormalization()\n", + " self.fc2 = layers.Dense(2)\n", + "\n", + " # Set forward pass.\n", + " def call(self, x, is_training=False):\n", + " x = tf.reshape(x, [-1, 28, 28, 1])\n", + " x = self.conv1(x)\n", + " x = self.bn1(x, training=is_training)\n", + " x = tf.nn.leaky_relu(x)\n", + " x = self.conv2(x)\n", + " x = self.bn2(x, training=is_training)\n", + " x = tf.nn.leaky_relu(x)\n", + " x = self.flatten(x)\n", + " x = self.fc1(x)\n", + " x = self.bn3(x, training=is_training)\n", + " x = tf.nn.leaky_relu(x)\n", + " return self.fc2(x)\n", + "\n", + "# Build neural network model.\n", + "generator = Generator()\n", + "discriminator = Discriminator()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Losses.\n", + "def generator_loss(reconstructed_image):\n", + " gen_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=reconstructed_image, labels=tf.ones([batch_size], dtype=tf.int32)))\n", + " return gen_loss\n", + "\n", + "def discriminator_loss(disc_fake, disc_real):\n", + " disc_loss_real = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=disc_real, labels=tf.ones([batch_size], dtype=tf.int32)))\n", + " disc_loss_fake = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=disc_fake, labels=tf.zeros([batch_size], dtype=tf.int32)))\n", + " return disc_loss_real + disc_loss_fake\n", + "\n", + "# Optimizers.\n", + "optimizer_gen = tf.optimizers.Adam(learning_rate=lr_generator)#, beta_1=0.5, beta_2=0.999)\n", + "optimizer_disc = tf.optimizers.Adam(learning_rate=lr_discriminator)#, beta_1=0.5, beta_2=0.999)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. Inputs: real image and noise.\n", + "def run_optimization(real_images):\n", + " \n", + " # Rescale to [-1, 1], the input range of the discriminator\n", + " real_images = real_images * 2. - 1.\n", + "\n", + " # Generate noise.\n", + " noise = np.random.normal(-1., 1., size=[batch_size, noise_dim]).astype(np.float32)\n", + " \n", + " with tf.GradientTape() as g:\n", + " \n", + " fake_images = generator(noise, is_training=True)\n", + " disc_fake = discriminator(fake_images, is_training=True)\n", + " disc_real = discriminator(real_images, is_training=True)\n", + "\n", + " disc_loss = discriminator_loss(disc_fake, disc_real)\n", + " \n", + " # Training Variables for each optimizer\n", + " gradients_disc = g.gradient(disc_loss, discriminator.trainable_variables)\n", + " optimizer_disc.apply_gradients(zip(gradients_disc, discriminator.trainable_variables))\n", + " \n", + " # Generate noise.\n", + " noise = np.random.normal(-1., 1., size=[batch_size, noise_dim]).astype(np.float32)\n", + " \n", + " with tf.GradientTape() as g:\n", + " \n", + " fake_images = generator(noise, is_training=True)\n", + " disc_fake = discriminator(fake_images, is_training=True)\n", + "\n", + " gen_loss = generator_loss(disc_fake)\n", + " \n", + " gradients_gen = g.gradient(gen_loss, generator.trainable_variables)\n", + " optimizer_gen.apply_gradients(zip(gradients_gen, generator.trainable_variables))\n", + " \n", + " return gen_loss, disc_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "initial: gen_loss: 0.694535, disc_loss: 1.403878\n", + "step: 500, gen_loss: 2.154825, disc_loss: 0.429040\n", + "step: 1000, gen_loss: 2.103464, disc_loss: 0.502638\n", + "step: 1500, gen_loss: 2.282369, disc_loss: 0.572065\n", + "step: 2000, gen_loss: 2.711531, disc_loss: 0.341791\n", + "step: 2500, gen_loss: 2.835576, disc_loss: 0.322356\n", + "step: 3000, gen_loss: 2.970127, disc_loss: 0.275483\n", + "step: 3500, gen_loss: 2.836321, disc_loss: 0.190680\n", + "step: 4000, gen_loss: 2.892855, disc_loss: 0.337681\n", + "step: 4500, gen_loss: 2.948962, disc_loss: 0.329322\n", + "step: 5000, gen_loss: 3.799061, disc_loss: 0.327288\n", + "step: 5500, gen_loss: 4.090328, disc_loss: 0.274685\n", + "step: 6000, gen_loss: 4.343777, disc_loss: 0.155223\n", + "step: 6500, gen_loss: 3.806556, disc_loss: 0.155855\n", + "step: 7000, gen_loss: 4.827947, disc_loss: 0.078372\n", + "step: 7500, gen_loss: 3.708949, disc_loss: 0.140979\n", + "step: 8000, gen_loss: 5.250406, disc_loss: 0.164736\n", + "step: 8500, gen_loss: 5.491106, disc_loss: 0.110080\n", + "step: 9000, gen_loss: 4.391072, disc_loss: 0.100240\n", + "step: 9500, gen_loss: 5.074200, disc_loss: 0.105567\n", + "step: 10000, gen_loss: 6.077592, disc_loss: 0.215981\n", + "step: 10500, gen_loss: 4.468120, disc_loss: 0.099412\n", + "step: 11000, gen_loss: 5.887744, disc_loss: 0.093534\n", + "step: 11500, gen_loss: 5.656942, disc_loss: 0.075079\n", + "step: 12000, gen_loss: 4.752551, disc_loss: 0.092505\n", + "step: 12500, gen_loss: 5.682284, disc_loss: 0.077406\n", + "step: 13000, gen_loss: 5.386811, disc_loss: 0.101259\n", + "step: 13500, gen_loss: 4.646158, disc_loss: 0.039680\n", + "step: 14000, gen_loss: 5.698145, disc_loss: 0.083461\n", + "step: 14500, gen_loss: 4.513465, disc_loss: 0.077946\n", + "step: 15000, gen_loss: 5.586041, disc_loss: 0.039668\n", + "step: 15500, gen_loss: 6.316034, disc_loss: 0.084526\n", + "step: 16000, gen_loss: 7.120735, disc_loss: 0.070267\n", + "step: 16500, gen_loss: 5.511638, disc_loss: 0.053082\n", + "step: 17000, gen_loss: 8.176781, disc_loss: 0.029212\n", + "step: 17500, gen_loss: 6.144678, disc_loss: 0.140460\n", + "step: 18000, gen_loss: 5.583275, disc_loss: 0.077311\n", + "step: 18500, gen_loss: 5.840647, disc_loss: 0.042103\n", + "step: 19000, gen_loss: 7.111396, disc_loss: 0.030435\n", + "step: 19500, gen_loss: 4.391251, disc_loss: 0.043656\n", + "step: 20000, gen_loss: 6.271616, disc_loss: 0.040568\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, _) in enumerate(train_data.take(training_steps + 1)):\n", + " \n", + " if step == 0:\n", + " # Generate noise.\n", + " noise = np.random.normal(-1., 1., size=[batch_size, noise_dim]).astype(np.float32)\n", + " gen_loss = generator_loss(discriminator(generator(noise)))\n", + " disc_loss = discriminator_loss(discriminator(batch_x), discriminator(generator(noise)))\n", + " print(\"initial: gen_loss: %f, disc_loss: %f\" % (gen_loss, disc_loss))\n", + " continue\n", + " \n", + " # Run the optimization.\n", + " gen_loss, disc_loss = run_optimization(batch_x)\n", + " \n", + " if step % display_step == 0:\n", + " print(\"step: %i, gen_loss: %f, disc_loss: %f\" % (step, gen_loss, disc_loss))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize predictions.\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Generate images from noise, using the generator network.\n", + "n = 6\n", + "canvas = np.empty((28 * n, 28 * n))\n", + "for i in range(n):\n", + " # Noise input.\n", + " z = np.random.normal(-1., 1., size=[n, noise_dim]).astype(np.float32)\n", + " # Generate image from noise.\n", + " g = generator(z).numpy()\n", + " # Rescale to original [0, 1]\n", + " g = (g + 1.) / 2\n", + " # Reverse colours for better display\n", + " g = -1 * (g - 1)\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28])\n", + "\n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb new file mode 100644 index 00000000..2bcd1860 --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Example\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow v2.\n", + "\n", + "This example is using a low-level approach to better understand all mechanics behind building neural networks and the training process.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 255. \n", + "\n", + "In this example, each image will be converted to float32, normalized to [0, 1] and flattened to a 1-D array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import Model, layers\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # total classes (0-9 digits).\n", + "num_features = 784 # data features (img shape: 28*28).\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.1\n", + "training_steps = 2000\n", + "batch_size = 256\n", + "display_step = 100\n", + "\n", + "# Network parameters.\n", + "n_hidden_1 = 128 # 1st layer number of neurons.\n", + "n_hidden_2 = 256 # 2nd layer number of neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Flatten images to 1-D vector of 784 features (28*28).\n", + "x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create TF Model.\n", + "class NeuralNet(Model):\n", + " # Set layers.\n", + " def __init__(self):\n", + " super(NeuralNet, self).__init__()\n", + " # First fully-connected hidden layer.\n", + " self.fc1 = layers.Dense(n_hidden_1, activation=tf.nn.relu)\n", + " # First fully-connected hidden layer.\n", + " self.fc2 = layers.Dense(n_hidden_2, activation=tf.nn.relu)\n", + " # Second fully-connecter hidden layer.\n", + " self.out = layers.Dense(num_classes, activation=tf.nn.softmax)\n", + "\n", + " # Set forward pass.\n", + " def call(self, x, is_training=False):\n", + " x = self.fc1(x)\n", + " x = self.out(x)\n", + " if not is_training:\n", + " # tf cross entropy expect logits without softmax, so only\n", + " # apply softmax when not training.\n", + " x = tf.nn.softmax(x)\n", + " return x\n", + "\n", + "# Build neural network model.\n", + "neural_net = NeuralNet()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy Loss.\n", + "# Note that this will apply 'softmax' to the logits.\n", + "def cross_entropy_loss(x, y):\n", + " # Convert labels to int 64 for tf cross-entropy function.\n", + " y = tf.cast(y, tf.int64)\n", + " # Apply softmax to logits and compute cross-entropy.\n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", + " # Average loss across the batch.\n", + " return tf.reduce_mean(loss)\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + "\n", + "# Stochastic gradient descent optimizer.\n", + "optimizer = tf.optimizers.SGD(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " # Forward pass.\n", + " pred = neural_net(x, is_training=True)\n", + " # Compute loss.\n", + " loss = cross_entropy_loss(pred, y)\n", + " \n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = neural_net.trainable_variables\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " \n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 100, loss: 2.031049, accuracy: 0.535156\n", + "step: 200, loss: 1.821917, accuracy: 0.722656\n", + "step: 300, loss: 1.764789, accuracy: 0.753906\n", + "step: 400, loss: 1.677593, accuracy: 0.859375\n", + "step: 500, loss: 1.643402, accuracy: 0.867188\n", + "step: 600, loss: 1.645116, accuracy: 0.859375\n", + "step: 700, loss: 1.618012, accuracy: 0.878906\n", + "step: 800, loss: 1.618097, accuracy: 0.878906\n", + "step: 900, loss: 1.616565, accuracy: 0.875000\n", + "step: 1000, loss: 1.599962, accuracy: 0.894531\n", + "step: 1100, loss: 1.593849, accuracy: 0.910156\n", + "step: 1200, loss: 1.594491, accuracy: 0.886719\n", + "step: 1300, loss: 1.622147, accuracy: 0.859375\n", + "step: 1400, loss: 1.547483, accuracy: 0.937500\n", + "step: 1500, loss: 1.581775, accuracy: 0.898438\n", + "step: 1600, loss: 1.555893, accuracy: 0.929688\n", + "step: 1700, loss: 1.578076, accuracy: 0.898438\n", + "step: 1800, loss: 1.584776, accuracy: 0.882812\n", + "step: 1900, loss: 1.563029, accuracy: 0.921875\n", + "step: 2000, loss: 1.569637, accuracy: 0.902344\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0:\n", + " pred = neural_net(batch_x, is_training=True)\n", + " loss = cross_entropy_loss(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 0.920700\n" + ] + } + ], + "source": [ + "# Test model on validation set.\n", + "pred = neural_net(x_test, is_training=False)\n", + "print(\"Test Accuracy: %f\" % accuracy(pred, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize predictions.\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 7\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 2\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 1\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 0\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 4\n" + ] + } + ], + "source": [ + "# Predict 5 images from validation set.\n", + "n_images = 5\n", + "test_images = x_test[:n_images]\n", + "predictions = neural_net(test_images)\n", + "\n", + "# Display image and model prediction.\n", + "for i in range(n_images):\n", + " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n", + " plt.show()\n", + " print(\"Model prediction: %i\" % np.argmax(predictions.numpy()[i]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb new file mode 100644 index 00000000..bbec2f13 --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb @@ -0,0 +1,402 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Example\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow v2.\n", + "\n", + "This example is using a low-level approach to better understand all mechanics behind building neural networks and the training process.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 255. \n", + "\n", + "In this example, each image will be converted to float32, normalized to [0, 1] and flattened to a 1-D array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # total classes (0-9 digits).\n", + "num_features = 784 # data features (img shape: 28*28).\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.001\n", + "training_steps = 3000\n", + "batch_size = 256\n", + "display_step = 100\n", + "\n", + "# Network parameters.\n", + "n_hidden_1 = 128 # 1st layer number of neurons.\n", + "n_hidden_2 = 256 # 2nd layer number of neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Flatten images to 1-D vector of 784 features (28*28).\n", + "x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "\n", + "# A random value generator to initialize weights.\n", + "random_normal = tf.initializers.RandomNormal()\n", + "\n", + "weights = {\n", + " 'h1': tf.Variable(random_normal([num_features, n_hidden_1])),\n", + " 'h2': tf.Variable(random_normal([n_hidden_1, n_hidden_2])),\n", + " 'out': tf.Variable(random_normal([n_hidden_2, num_classes]))\n", + "}\n", + "biases = {\n", + " 'b1': tf.Variable(tf.zeros([n_hidden_1])),\n", + " 'b2': tf.Variable(tf.zeros([n_hidden_2])),\n", + " 'out': tf.Variable(tf.zeros([num_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Create model.\n", + "def neural_net(x):\n", + " # Hidden fully connected layer with 128 neurons.\n", + " layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n", + " # Apply sigmoid to layer_1 output for non-linearity.\n", + " layer_1 = tf.nn.sigmoid(layer_1)\n", + " \n", + " # Hidden fully connected layer with 256 neurons.\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n", + " # Apply sigmoid to layer_2 output for non-linearity.\n", + " layer_2 = tf.nn.sigmoid(layer_2)\n", + " \n", + " # Output fully connected layer with a neuron for each class.\n", + " out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n", + " # Apply softmax to normalize the logits to a probability distribution.\n", + " return tf.nn.softmax(out_layer)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy loss function.\n", + "def cross_entropy(y_pred, y_true):\n", + " # Encode label to a one hot vector.\n", + " y_true = tf.one_hot(y_true, depth=num_classes)\n", + " # Clip prediction values to avoid log(0) error.\n", + " y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)\n", + " # Compute cross-entropy.\n", + " return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred)))\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + "\n", + "# Stochastic gradient descent optimizer.\n", + "optimizer = tf.optimizers.SGD(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " pred = neural_net(x)\n", + " loss = cross_entropy(pred, y)\n", + " \n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = weights.values() + biases.values()\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " \n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 100, loss: 567.292969, accuracy: 0.136719\n", + "step: 200, loss: 398.614929, accuracy: 0.562500\n", + "step: 300, loss: 226.743774, accuracy: 0.753906\n", + "step: 400, loss: 193.384521, accuracy: 0.777344\n", + "step: 500, loss: 138.649963, accuracy: 0.886719\n", + "step: 600, loss: 109.713669, accuracy: 0.898438\n", + "step: 700, loss: 90.397217, accuracy: 0.906250\n", + "step: 800, loss: 104.545380, accuracy: 0.894531\n", + "step: 900, loss: 94.204697, accuracy: 0.890625\n", + "step: 1000, loss: 81.660645, accuracy: 0.906250\n", + "step: 1100, loss: 81.237137, accuracy: 0.902344\n", + "step: 1200, loss: 65.776703, accuracy: 0.925781\n", + "step: 1300, loss: 94.195862, accuracy: 0.910156\n", + "step: 1400, loss: 79.425507, accuracy: 0.917969\n", + "step: 1500, loss: 93.508163, accuracy: 0.914062\n", + "step: 1600, loss: 88.912506, accuracy: 0.917969\n", + "step: 1700, loss: 79.033607, accuracy: 0.929688\n", + "step: 1800, loss: 65.788315, accuracy: 0.898438\n", + "step: 1900, loss: 73.462387, accuracy: 0.937500\n", + "step: 2000, loss: 59.309540, accuracy: 0.917969\n", + "step: 2100, loss: 67.014008, accuracy: 0.917969\n", + "step: 2200, loss: 48.297115, accuracy: 0.949219\n", + "step: 2300, loss: 64.523148, accuracy: 0.910156\n", + "step: 2400, loss: 72.989517, accuracy: 0.925781\n", + "step: 2500, loss: 57.588585, accuracy: 0.929688\n", + "step: 2600, loss: 44.957100, accuracy: 0.960938\n", + "step: 2700, loss: 59.788242, accuracy: 0.937500\n", + "step: 2800, loss: 63.581337, accuracy: 0.937500\n", + "step: 2900, loss: 53.471252, accuracy: 0.941406\n", + "step: 3000, loss: 43.869728, accuracy: 0.949219\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0:\n", + " pred = neural_net(batch_x)\n", + " loss = cross_entropy(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 0.936800\n" + ] + } + ], + "source": [ + "# Test model on validation set.\n", + "pred = neural_net(x_test)\n", + "print(\"Test Accuracy: %f\" % accuracy(pred, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize predictions.\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 7\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 2\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADCFJREFUeJzt3WGoXPWZx/Hvs1n7wrQvDDUarGu6RVdLxGS5iBBZXarFFSHmRaUKS2RL0xcNWNgXK76psBREtt1dfFFIaWgqrbVEs2pdbYsspguLGjVU21grcre9a8hVFGoVKSbPvrgn5VbvnLmZOTNnkuf7gTAz55kz52HI7/7PzDlz/pGZSKrnz/puQFI/DL9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFWX4paL+fJobiwhPJ5QmLDNjNc8ba+SPiOsi4lcR8UpE3D7Oa0marhj13P6IWAO8DFwLLADPADdn5i9b1nHklyZsGiP/5cArmflqZv4B+AGwbYzXkzRF44T/POC3yx4vNMv+RETsjIiDEXFwjG1J6tg4X/ittGvxod36zNwN7AZ3+6VZMs7IvwCcv+zxJ4DXxmtH0rSME/5ngAsj4pMR8RHg88DD3bQladJG3u3PzPcjYhfwY2ANsCczf9FZZ5ImauRDfSNtzM/80sRN5SQfSacuwy8VZfilogy/VJThl4oy/FJRhl8qyvBLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFWX4paIMv1SU4ZeKmuoU3arnoosuGlh76aWXWte97bbbWuv33HPPSD1piSO/VJThl4oy/FJRhl8qyvBLRRl+qSjDLxU11nH+iJgH3gaOAe9n5lwXTen0sWXLloG148ePt667sLDQdTtapouTfP42M9/o4HUkTZG7/VJR44Y/gZ9ExLMRsbOLhiRNx7i7/Vsz87WIWA/8NCJeyswDy5/Q/FHwD4M0Y8Ya+TPzteZ2EdgPXL7Cc3Zn5pxfBkqzZeTwR8TaiPjYifvAZ4EXu2pM0mSNs9t/DrA/Ik68zvcz8/FOupI0cSOHPzNfBS7rsBedhjZv3jyw9s4777Suu3///q7b0TIe6pOKMvxSUYZfKsrwS0UZfqkowy8V5aW7NZZNmza11nft2jWwdu+993bdjk6CI79UlOGXijL8UlGGXyrK8EtFGX6pKMMvFeVxfo3l4osvbq2vXbt2YO3+++/vuh2dBEd+qSjDLxVl+KWiDL9UlOGXijL8UlGGXyoqMnN6G4uY3sY0FU8//XRr/eyzzx5YG3YtgGGX9tbKMjNW8zxHfqkowy8VZfilogy/VJThl4oy/FJRhl8qaujv+SNiD3ADsJiZm5pl64D7gY3APHBTZr41uTbVl40bN7bW5+bmWusvv/zywJrH8fu1mpH/O8B1H1h2O/BEZl4IPNE8lnQKGRr+zDwAvPmBxduAvc39vcCNHfclacJG/cx/TmYeAWhu13fXkqRpmPg1/CJiJ7Bz0tuRdHJGHfmPRsQGgOZ2cdATM3N3Zs5lZvs3Q5KmatTwPwzsaO7vAB7qph1J0zI0/BFxH/A/wF9FxEJEfAG4C7g2In4NXNs8lnQKGfqZPzNvHlD6TMe9aAZdddVVY63/+uuvd9SJuuYZflJRhl8qyvBLRRl+qSjDLxVl+KWinKJbrS699NKx1r/77rs76kRdc+SXijL8UlGGXyrK8EtFGX6pKMMvFWX4paKcoru4K664orX+6KOPttbn5+db61u3bh1Ye++991rX1WicoltSK8MvFWX4paIMv1SU4ZeKMvxSUYZfKsrf8xd3zTXXtNbXrVvXWn/88cdb6x7Ln12O/FJRhl8qyvBLRRl+qSjDLxVl+KWiDL9U1NDj/BGxB7gBWMzMTc2yO4EvAifmX74jM/9zUk1qci677LLW+rDrPezbt6/LdjRFqxn5vwNct8Lyf83Mzc0/gy+dYoaGPzMPAG9OoRdJUzTOZ/5dEfHziNgTEWd11pGkqRg1/N8EPgVsBo4AXx/0xIjYGREHI+LgiNuSNAEjhT8zj2bmscw8DnwLuLzlubszcy4z50ZtUlL3Rgp/RGxY9nA78GI37UialtUc6rsPuBr4eEQsAF8Fro6IzUAC88CXJtijpAnwuv2nuXPPPbe1fujQodb6W2+91Vq/5JJLTronTZbX7ZfUyvBLRRl+qSjDLxVl+KWiDL9UlJfuPs3deuutrfX169e31h977LEOu9EsceSXijL8UlGGXyrK8EtFGX6pKMMvFWX4paI8zn+au+CCC8Zaf9hPenXqcuSXijL8UlGGXyrK8EtFGX6pKMMvFWX4paI8zn+au+GGG8Za/5FHHumoE80aR36pKMMvFWX4paIMv1SU4ZeKMvxSUYZfKmrocf6IOB/4LnAucBzYnZn/HhHrgPuBjcA8cFNm+uPvHlx55ZUDa8Om6FZdqxn53wf+MTMvAa4AvhwRnwZuB57IzAuBJ5rHkk4RQ8OfmUcy87nm/tvAYeA8YBuwt3naXuDGSTUpqXsn9Zk/IjYCW4CngHMy8wgs/YEA2ud9kjRTVn1uf0R8FHgA+Epm/i4iVrveTmDnaO1JmpRVjfwRcQZLwf9eZj7YLD4aERua+gZgcaV1M3N3Zs5l5lwXDUvqxtDwx9IQ/23gcGZ+Y1npYWBHc38H8FD37UmalNXs9m8F/h54ISIONcvuAO4CfhgRXwB+A3xuMi1qmO3btw+srVmzpnXd559/vrV+4MCBkXrS7Bsa/sz8b2DQB/zPdNuOpGnxDD+pKMMvFWX4paIMv1SU4ZeKMvxSUV66+xRw5plnttavv/76kV973759rfVjx46N/NqabY78UlGGXyrK8EtFGX6pKMMvFWX4paIMv1RUZOb0NhYxvY2dRs4444zW+pNPPjmwtri44gWW/uiWW25prb/77rutdc2ezFzVNfYc+aWiDL9UlOGXijL8UlGGXyrK8EtFGX6pKI/zS6cZj/NLamX4paIMv1SU4ZeKMvxSUYZfKsrwS0UNDX9EnB8R/xURhyPiFxFxW7P8zoj4v4g41Pwb/eLxkqZu6Ek+EbEB2JCZz0XEx4BngRuBm4DfZ+a/rHpjnuQjTdxqT/IZOmNPZh4BjjT3346Iw8B547UnqW8n9Zk/IjYCW4CnmkW7IuLnEbEnIs4asM7OiDgYEQfH6lRSp1Z9bn9EfBR4EvhaZj4YEecAbwAJ/DNLHw3+YchruNsvTdhqd/tXFf6IOAP4EfDjzPzGCvWNwI8yc9OQ1zH80oR19sOeiAjg28Dh5cFvvgg8YTvw4sk2Kak/q/m2/0rgZ8ALwPFm8R3AzcBmlnb754EvNV8Otr2WI780YZ3u9nfF8EuT5+/5JbUy/FJRhl8qyvBLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFTX0Ap4dewP432WPP94sm0Wz2tus9gX2Nqoue7tgtU+c6u/5P7TxiIOZOddbAy1mtbdZ7QvsbVR99eZuv1SU4ZeK6jv8u3vefptZ7W1W+wJ7G1UvvfX6mV9Sf/oe+SX1pJfwR8R1EfGriHglIm7vo4dBImI+Il5oZh7udYqxZhq0xYh4cdmydRHx04j4dXO74jRpPfU2EzM3t8ws3et7N2szXk99tz8i1gAvA9cCC8AzwM2Z+cupNjJARMwDc5nZ+zHhiPgb4PfAd0/MhhQRdwNvZuZdzR/OszLzn2aktzs5yZmbJ9TboJmlb6XH967LGa+70MfIfznwSma+mpl/AH4AbOuhj5mXmQeANz+weBuwt7m/l6X/PFM3oLeZkJlHMvO55v7bwImZpXt971r66kUf4T8P+O2yxwvM1pTfCfwkIp6NiJ19N7OCc07MjNTcru+5nw8aOnPzNH1gZumZee9GmfG6a32Ef6XZRGbpkMPWzPxr4O+ALze7t1qdbwKfYmkatyPA1/tspplZ+gHgK5n5uz57WW6Fvnp53/oI/wJw/rLHnwBe66GPFWXma83tIrCfpY8ps+ToiUlSm9vFnvv5o8w8mpnHMvM48C16fO+amaUfAL6XmQ82i3t/71bqq6/3rY/wPwNcGBGfjIiPAJ8HHu6hjw+JiLXNFzFExFrgs8ze7MMPAzua+zuAh3rs5U/MyszNg2aWpuf3btZmvO7lJJ/mUMa/AWuAPZn5tak3sYKI+EuWRntY+sXj9/vsLSLuA65m6VdfR4GvAv8B/BD4C+A3wOcyc+pfvA3o7WpOcubmCfU2aGbpp+jxvetyxutO+vEMP6kmz/CTijL8UlGGXyrK8EtFGX6pKMMvFWX4paIMv1TU/wNPnZK3k8+kHgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 1\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 0\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 4\n" + ] + } + ], + "source": [ + "# Predict 5 images from validation set.\n", + "n_images = 5\n", + "test_images = x_test[:n_images]\n", + "predictions = neural_net(test_images)\n", + "\n", + "# Display image and model prediction.\n", + "for i in range(n_images):\n", + " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n", + " plt.show()\n", + " print(\"Model prediction: %i\" % np.argmax(predictions.numpy()[i]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb b/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb new file mode 100644 index 00000000..760a1c9c --- /dev/null +++ b/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Build Custom Layers & Modules\n", + "\n", + "Build custom layers and modules with TensorFlow v2.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import Model, layers\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # 0 to 9 digits\n", + "num_features = 784 # 28*28\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.01\n", + "training_steps = 500\n", + "batch_size = 256\n", + "display_step = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Flatten images to 1-D vector of 784 features (28*28).\n", + "x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a custom layer\n", + "\n", + "Build a custom layer with inner-variables." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a custom layer, extending TF 'Layer' class.\n", + "# Layer compute: y = relu(W * x + b)\n", + "class CustomLayer1(layers.Layer):\n", + " \n", + " # Layer arguments.\n", + " def __init__(self, num_units, **kwargs):\n", + " # Store the number of units (neurons).\n", + " self.num_units = num_units\n", + " super(CustomLayer1, self).__init__(**kwargs)\n", + " \n", + " def build(self, input_shape):\n", + " # Note: a custom layer can also include any other TF 'layers'.\n", + " shape = tf.TensorShape((input_shape[1], self.num_units))\n", + " # Create weight variables for this layer.\n", + " self.weight = self.add_weight(name='W',\n", + " shape=shape,\n", + " initializer=tf.initializers.RandomNormal,\n", + " trainable=True)\n", + " self.bias = self.add_weight(name='b',\n", + " shape=[self.num_units])\n", + " # Make sure to call the `build` method at the end\n", + " super(CustomLayer1, self).build(input_shape)\n", + "\n", + " def call(self, inputs):\n", + " x = tf.matmul(inputs, self.weight)\n", + " x = x + self.bias\n", + " return tf.nn.relu(x)\n", + "\n", + " def get_config(self):\n", + " base_config = super(CustomLayer1, self).get_config()\n", + " base_config['num_units'] = self.num_units\n", + " return base_config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create another custom layer\n", + "\n", + "Build another custom layer with inner TF 'layers'." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a custom layer, extending TF 'Layer' class.\n", + "# Custom layer: 2 Fully-Connected layers with residual connection.\n", + "class CustomLayer2(layers.Layer):\n", + " \n", + " # Layer arguments.\n", + " def __init__(self, num_units, **kwargs):\n", + " self.num_units = num_units\n", + " super(CustomLayer2, self).__init__(**kwargs)\n", + " \n", + " def build(self, input_shape):\n", + " shape = tf.TensorShape((input_shape[1], self.num_units))\n", + " \n", + " self.inner_layer1 = layers.Dense(1)\n", + " self.inner_layer2 = layers.Dense(self.num_units)\n", + " \n", + " # Make sure to call the `build` method at the end\n", + " super(CustomLayer2, self).build(input_shape)\n", + "\n", + " def call(self, inputs):\n", + " x = self.inner_layer1(inputs)\n", + " x = tf.nn.relu(x)\n", + " x = self.inner_layer2(x)\n", + " return x + inputs\n", + "\n", + " def get_config(self):\n", + " base_config = super(CustomLayer2, self).get_config()\n", + " base_config['num_units'] = self.num_units\n", + " return base_config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build Model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Create TF Model.\n", + "class CustomNet(Model):\n", + " \n", + " def __init__(self):\n", + " super(CustomNet, self).__init__()\n", + " # Use custom layers created above.\n", + " self.layer1 = CustomLayer1(64)\n", + " self.layer2 = CustomLayer2(64)\n", + " self.out = layers.Dense(num_classes, activation=tf.nn.softmax)\n", + "\n", + " # Set forward pass.\n", + " def __call__(self, x, is_training=False):\n", + " x = self.layer1(x)\n", + " x = tf.nn.relu(x)\n", + " x = self.layer2(x)\n", + " if not is_training:\n", + " # tf cross entropy expect logits without softmax, so only\n", + " # apply softmax when not training.\n", + " x = tf.nn.softmax(x)\n", + " return x\n", + "\n", + "# Build neural network model.\n", + "custom_net = CustomNet()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy loss function.\n", + "def cross_entropy(y_pred, y_true):\n", + " y_true = tf.cast(y_true, tf.int64)\n", + " crossentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred)\n", + " return tf.reduce_mean(crossentropy)\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + "\n", + "# Adam optimizer.\n", + "optimizer = tf.optimizers.Adam(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " pred = custom_net(x, is_training=True)\n", + " loss = cross_entropy(pred, y)\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, custom_net.trainable_variables)\n", + "\n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, custom_net.trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 50, loss: 3.363096, accuracy: 0.902344\n", + "step: 100, loss: 3.344931, accuracy: 0.910156\n", + "step: 150, loss: 3.336300, accuracy: 0.914062\n", + "step: 200, loss: 3.318396, accuracy: 0.925781\n", + "step: 250, loss: 3.300045, accuracy: 0.937500\n", + "step: 300, loss: 3.335487, accuracy: 0.898438\n", + "step: 350, loss: 3.330979, accuracy: 0.914062\n", + "step: 400, loss: 3.298509, accuracy: 0.921875\n", + "step: 450, loss: 3.278253, accuracy: 0.953125\n", + "step: 500, loss: 3.285335, accuracy: 0.945312\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0:\n", + " pred = custom_net(batch_x, is_training=False)\n", + " loss = cross_entropy(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb b/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb new file mode 100644 index 00000000..6235bbfe --- /dev/null +++ b/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb @@ -0,0 +1,573 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Save & Restore a Model\n", + "\n", + "Save and Restore a model using TensorFlow v2. In this example, we will go over both low and high-level approaches: \n", + "- Low-level: TF Checkpoint.\n", + "- High-level: TF Module/Model saver.\n", + "\n", + "This example is using the MNIST database of handwritten digits as toy dataset\n", + "(http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # 0 to 9 digits\n", + "num_features = 784 # 28*28\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.01\n", + "training_steps = 1000\n", + "batch_size = 256\n", + "display_step = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Flatten images to 1-D vector of 784 features (28*28).\n", + "x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1) TF Checkpoint\n", + "\n", + "Basic logistic regression" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Weight of shape [784, 10], the 28*28 image features, and total number of classes.\n", + "W = tf.Variable(tf.random.normal([num_features, num_classes]), name=\"weight\")\n", + "# Bias of shape [10], the total number of classes.\n", + "b = tf.Variable(tf.zeros([num_classes]), name=\"bias\")\n", + "\n", + "# Logistic regression (Wx + b).\n", + "def logistic_regression(x):\n", + " # Apply softmax to normalize the logits to a probability distribution.\n", + " return tf.nn.softmax(tf.matmul(x, W) + b)\n", + "\n", + "# Cross-Entropy loss function.\n", + "def cross_entropy(y_pred, y_true):\n", + " # Encode label to a one hot vector.\n", + " y_true = tf.one_hot(y_true, depth=num_classes)\n", + " # Clip prediction values to avoid log(0) error.\n", + " y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)\n", + " # Compute cross-entropy.\n", + " return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred)))\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + "\n", + "# Adam optimizer.\n", + "optimizer = tf.optimizers.Adam(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " pred = logistic_regression(x)\n", + " loss = cross_entropy(pred, y)\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, [W, b])\n", + "\n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, [W, b]))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 50, loss: 535.380981, accuracy: 0.656250\n", + "step: 100, loss: 354.681152, accuracy: 0.765625\n", + "step: 150, loss: 225.300934, accuracy: 0.785156\n", + "step: 200, loss: 163.948761, accuracy: 0.859375\n", + "step: 250, loss: 129.653534, accuracy: 0.878906\n", + "step: 300, loss: 170.743576, accuracy: 0.859375\n", + "step: 350, loss: 97.912575, accuracy: 0.910156\n", + "step: 400, loss: 144.119141, accuracy: 0.906250\n", + "step: 450, loss: 164.991943, accuracy: 0.875000\n", + "step: 500, loss: 145.191666, accuracy: 0.871094\n", + "step: 550, loss: 82.272644, accuracy: 0.925781\n", + "step: 600, loss: 149.180237, accuracy: 0.878906\n", + "step: 650, loss: 127.171280, accuracy: 0.871094\n", + "step: 700, loss: 116.045761, accuracy: 0.910156\n", + "step: 750, loss: 92.582680, accuracy: 0.906250\n", + "step: 800, loss: 108.238007, accuracy: 0.894531\n", + "step: 850, loss: 92.755638, accuracy: 0.894531\n", + "step: 900, loss: 69.131119, accuracy: 0.902344\n", + "step: 950, loss: 67.176285, accuracy: 0.921875\n", + "step: 1000, loss: 104.205658, accuracy: 0.890625\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0:\n", + " pred = logistic_regression(batch_x)\n", + " loss = cross_entropy(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save and Load with TF Checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Save weights and optimizer variables.\n", + "# Create a dict of variables to save.\n", + "vars_to_save = {\"W\": W, \"b\": b, \"optimizer\": optimizer}\n", + "# TF Checkpoint, pass the dict as **kwargs.\n", + "checkpoint = tf.train.Checkpoint(**vars_to_save)\n", + "# TF CheckpointManager to manage saving parameters.\n", + "saver = tf.train.CheckpointManager(\n", + " checkpoint, directory=\"./tf-example\", max_to_keep=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'./tf-example/ckpt-1'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Save variables.\n", + "saver.save()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.09673191" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check weight value.\n", + "np.mean(W.numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Reset variables to test restore.\n", + "W = tf.Variable(tf.random.normal([num_features, num_classes]), name=\"weight\")\n", + "b = tf.Variable(tf.zeros([num_classes]), name=\"bias\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.0083419625" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check resetted weight value.\n", + "np.mean(W.numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set checkpoint to load data.\n", + "vars_to_load = {\"W\": W, \"b\": b, \"optimizer\": optimizer}\n", + "checkpoint = tf.train.Checkpoint(**vars_to_load)\n", + "# Restore variables from latest checkpoint.\n", + "latest_ckpt = tf.train.latest_checkpoint(\"./tf-example\")\n", + "checkpoint.restore(latest_ckpt)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.09673191" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Confirm that W has been correctly restored.\n", + "np.mean(W.numpy())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2) TF Model\n", + "\n", + "Basic neural network with TF Model" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras import Model, layers" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # 0 to 9 digits\n", + "num_features = 784 # 28*28\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.01\n", + "training_steps = 1000\n", + "batch_size = 256\n", + "display_step = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Create TF Model.\n", + "class NeuralNet(Model):\n", + " # Set layers.\n", + " def __init__(self):\n", + " super(NeuralNet, self).__init__(name=\"NeuralNet\")\n", + " # First fully-connected hidden layer.\n", + " self.fc1 = layers.Dense(64, activation=tf.nn.relu)\n", + " # Second fully-connected hidden layer.\n", + " self.fc2 = layers.Dense(128, activation=tf.nn.relu)\n", + " # Third fully-connecter hidden layer.\n", + " self.out = layers.Dense(num_classes, activation=tf.nn.softmax)\n", + "\n", + " # Set forward pass.\n", + " def __call__(self, x, is_training=False):\n", + " x = self.fc1(x)\n", + " x = self.out(x)\n", + " if not is_training:\n", + " # tf cross entropy expect logits without softmax, so only\n", + " # apply softmax when not training.\n", + " x = tf.nn.softmax(x)\n", + " return x\n", + "\n", + "# Build neural network model.\n", + "neural_net = NeuralNet()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy loss function.\n", + "def cross_entropy(y_pred, y_true):\n", + " y_true = tf.cast(y_true, tf.int64)\n", + " crossentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_true, logits=y_pred)\n", + " return tf.reduce_mean(crossentropy)\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + "\n", + "# Adam optimizer.\n", + "optimizer = tf.optimizers.Adam(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " pred = neural_net(x, is_training=True)\n", + " loss = cross_entropy(pred, y)\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, neural_net.trainable_variables)\n", + "\n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, neural_net.trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 100, loss: 2.188605, accuracy: 0.902344\n", + "step: 200, loss: 2.182990, accuracy: 0.929688\n", + "step: 300, loss: 2.180439, accuracy: 0.945312\n", + "step: 400, loss: 2.178496, accuracy: 0.957031\n", + "step: 500, loss: 2.177517, accuracy: 0.968750\n", + "step: 600, loss: 2.177163, accuracy: 0.968750\n", + "step: 700, loss: 2.177454, accuracy: 0.960938\n", + "step: 800, loss: 2.177589, accuracy: 0.960938\n", + "step: 900, loss: 2.176507, accuracy: 0.964844\n", + "step: 1000, loss: 2.177557, accuracy: 0.960938\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0:\n", + " pred = neural_net(batch_x, is_training=False)\n", + " loss = cross_entropy(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save and Load with TF Model" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Save TF model.\n", + "neural_net.save_weights(filepath=\"./tfmodel.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 0.101562\n" + ] + } + ], + "source": [ + "# Re-build neural network model with default values.\n", + "neural_net = NeuralNet()\n", + "# Test model performance.\n", + "pred = neural_net(batch_x)\n", + "print(\"accuracy: %f\" % accuracy(pred, batch_y))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load saved weights.\n", + "neural_net.load_weights(filepath=\"./tfmodel.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy: 0.960938\n" + ] + } + ], + "source": [ + "# Test that weights loaded correctly.\n", + "pred = neural_net(batch_x)\n", + "print(\"accuracy: %f\" % accuracy(pred, batch_y))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 039bfcbcac946fdda48f657c45e2eabd1d543314 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Wed, 3 Apr 2019 23:49:46 -0700 Subject: [PATCH 131/166] fix links --- tensorflow_v2/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index dcb4a2cb..d75821a4 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -24,11 +24,11 @@ ##### Unsupervised - **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. -- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/tensorflow_v2/dcgan.ipynb)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities - **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow v2. -- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_costum_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow v2 Models. +- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow v2 Models. ## Installation From e0241a313e5752fd5d0fd6dff3709fa7f084baaf Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Fri, 5 Apr 2019 14:05:49 -0700 Subject: [PATCH 132/166] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 72f301b4..e4977894 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # TensorFlow Examples -This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation for both TF v1 & v2. +This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation, for both TF v1 & v2. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). @@ -61,9 +61,9 @@ It is suitable for beginners who want to find clear and concise examples about T - **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. - **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. -## TensorFlow v2 +## TensorFlow 2.0 -The tutorial index for TF v2 is available here: [TensorFlow v2 Examples](tensorflow_v2). +The tutorial index for TF v2 is available here: [TensorFlow 2.0 Examples](tensorflow_v2). ## Dataset Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. @@ -83,7 +83,7 @@ To run them, you also need the latest version of TensorFlow. To install it: pip install tensorflow ``` -or (if you want GPU support): +or (with GPU support): ``` pip install tensorflow_gpu ``` From 26982d7f7ccc5f527baa39f59efa496861989014 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Fri, 5 Apr 2019 14:11:18 -0700 Subject: [PATCH 133/166] Update README.md --- tensorflow_v2/README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index d75821a4..63c2795a 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -1,4 +1,4 @@ -## TensorFlow v2 Examples +## TensorFlow 2.0 Examples *** More examples to be added later... *** @@ -7,19 +7,19 @@ - [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction -- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow v2. -- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow v2 basic operations. +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow 2.0. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow 2.0 basic operations. #### 2 - Basic Models -- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow v2. -- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow v2. +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. #### 3 - Neural Networks ##### Supervised -- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb)). Use TensorFlow v2 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset. +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset. - **Simple Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)). Raw implementation of a simple neural network to classify MNIST digits dataset. -- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow v2 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. - **Convolutional Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)). Raw implementation of a convolutional neural network to classify MNIST digits dataset. ##### Unsupervised @@ -27,12 +27,12 @@ - **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities -- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow v2. -- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow v2 Models. +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0. +- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models. ## Installation -To install TensorFlow v2, simply run: +To install TensorFlow 2.0, simply run: ``` pip install tensorflow==2.0.0a0 ``` From f5d759a4976cd725edd0b4b5120c1630b3f1dd65 Mon Sep 17 00:00:00 2001 From: Ryan McCormick Date: Thu, 18 Apr 2019 15:07:45 -0700 Subject: [PATCH 134/166] Change cell type from "code" to "markdown" (#304) --- tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb b/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb index aa271e04..226dd66f 100644 --- a/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb +++ b/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb @@ -1,7 +1,7 @@ { "cells": [ { - "cell_type": "code", + "cell_type": "markdown", "execution_count": null, "metadata": {}, "outputs": [], From d090625b510ad9716ad972c740e179136021b050 Mon Sep 17 00:00:00 2001 From: Sachin Prabhu Date: Mon, 3 Jun 2019 00:59:07 +0530 Subject: [PATCH 135/166] Update README.md (#312) pointed '2 - Basic Models' to tensorflow_v2 notebook --- tensorflow_v2/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index 63c2795a..f5eca6f6 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -11,8 +11,8 @@ - **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow 2.0 basic operations. #### 2 - Basic Models -- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. -- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. #### 3 - Neural Networks ##### Supervised From 042c25ce2c3d91bf5a2e0a308fea578b1e290f82 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sun, 14 Jul 2019 20:03:33 -0700 Subject: [PATCH 136/166] add TensorFlow v2 RNN examples --- README.md | 2 +- tensorflow_v2/README.md | 9 +- .../3_NeuralNetworks/bidirectional_rnn.ipynb | 243 +++++++++++++++ .../3_NeuralNetworks/dynamic_rnn.ipynb | 282 ++++++++++++++++++ .../3_NeuralNetworks/recurrent_network.ipynb | 241 +++++++++++++++ 5 files changed, 773 insertions(+), 4 deletions(-) create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb create mode 100644 tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb diff --git a/README.md b/README.md index e4977894..e1c723e6 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update (04/03/2019):** Starting to add [TensorFlow v2 examples](tensorflow_v2)! (more coming soon). +**Update (07/14/2019):** Added a few [TensorFlow v2 examples](tensorflow_v2)! (more coming soon). *If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index f5eca6f6..0f6c7436 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -1,6 +1,6 @@ ## TensorFlow 2.0 Examples -*** More examples to be added later... *** +*** More examples to be added later... *** #### 0 - Prerequisite - [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb). @@ -21,6 +21,9 @@ - **Simple Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)). Raw implementation of a simple neural network to classify MNIST digits dataset. - **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. - **Convolutional Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)). Raw implementation of a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0 'layers' and 'model' API. ##### Unsupervised - **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. @@ -34,10 +37,10 @@ To install TensorFlow 2.0, simply run: ``` -pip install tensorflow==2.0.0a0 +pip install tensorflow==2.0.0-beta1 ``` or (if you want GPU support): ``` -pip install tensorflow_gpu==2.0.0a0 +pip install tensorflow_gpu==2.0.0-beta1 ``` diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb new file mode 100644 index 00000000..19cbd07b --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -0,0 +1,243 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bi-directional Recurrent Neural Network Example\n", + "\n", + "Build a bi-directional recurrent neural network (LSTM) with TensorFlow 2.0.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BiRNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "To classify images using a recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 timesteps for every sample.\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "# Import TensorFlow v2.\n", + "import tensorflow as tf\n", + "from tensorflow.keras import Model, layers\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # total classes (0-9 digits).\n", + "num_features = 784 # data features (img shape: 28*28).\n", + "\n", + "# Training Parameters\n", + "learning_rate = 0.001\n", + "training_steps = 1000\n", + "batch_size = 32\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "# MNIST image shape is 28*28px, we will then handle 28 sequences of 28 timesteps for every sample.\n", + "num_input = 28 # number of sequences.\n", + "timesteps = 28 # timesteps.\n", + "num_units = 32 # number of neurons for the LSTM layer." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Flatten images to 1-D vector of 784 features (28*28).\n", + "x_train, x_test = x_train.reshape([-1, 28, 28]), x_test.reshape([-1, num_features])\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create LSTM Model.\n", + "class BiRNN(Model):\n", + " # Set layers.\n", + " def __init__(self):\n", + " super(BiRNN, self).__init__()\n", + " # Define 2 LSTM layers for forward and backward sequences.\n", + " lstm_fw = layers.LSTM(units=num_units)\n", + " lstm_bw = layers.LSTM(units=num_units, go_backwards=True)\n", + " # BiRNN layer.\n", + " self.bi_lstm = layers.Bidirectional(lstm_fw, backward_layer=lstm_bw)\n", + " # Output layer (num_classes).\n", + " self.out = layers.Dense(num_classes)\n", + "\n", + " # Set forward pass.\n", + " def call(self, x, is_training=False):\n", + " x = self.bi_lstm(x)\n", + " x = self.out(x)\n", + " if not is_training:\n", + " # tf cross entropy expect logits without softmax, so only\n", + " # apply softmax when not training.\n", + " x = tf.nn.softmax(x)\n", + " return x\n", + "\n", + "# Build LSTM model.\n", + "birnn_net = BiRNN()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy Loss.\n", + "# Note that this will apply 'softmax' to the logits.\n", + "def cross_entropy_loss(x, y):\n", + " # Convert labels to int 64 for tf cross-entropy function.\n", + " y = tf.cast(y, tf.int64)\n", + " # Apply softmax to logits and compute cross-entropy.\n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", + " # Average loss across the batch.\n", + " return tf.reduce_mean(loss)\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + "\n", + "# Adam optimizer.\n", + "optimizer = tf.optimizers.Adam(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " # Forward pass.\n", + " pred = birnn_net(x, is_training=True)\n", + " # Compute loss.\n", + " loss = cross_entropy_loss(pred, y)\n", + " \n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = birnn_net.trainable_variables\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " \n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 100, loss: 1.306422, accuracy: 0.625000\n", + "step: 200, loss: 0.973236, accuracy: 0.718750\n", + "step: 300, loss: 0.673558, accuracy: 0.781250\n", + "step: 400, loss: 0.439304, accuracy: 0.875000\n", + "step: 500, loss: 0.303866, accuracy: 0.906250\n", + "step: 600, loss: 0.414652, accuracy: 0.875000\n", + "step: 700, loss: 0.241098, accuracy: 0.937500\n", + "step: 800, loss: 0.204522, accuracy: 0.875000\n", + "step: 900, loss: 0.398520, accuracy: 0.843750\n", + "step: 1000, loss: 0.217469, accuracy: 0.937500\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0:\n", + " pred = birnn_net(batch_x, is_training=True)\n", + " loss = cross_entropy_loss(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb new file mode 100644 index 00000000..a3a6fa08 --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb @@ -0,0 +1,282 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dynamic Recurrent Neural Network.\n", + "\n", + "TensorFlow 2.0 implementation of a Recurrent Neural Network (LSTM) that performs dynamic computation over sequences with variable length. This example is using a toy dataset to classify linear sequences. The generated sequences have variable length.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "# Import TensorFlow v2.\n", + "import tensorflow as tf\n", + "from tensorflow.keras import Model, layers\n", + "import numpy as np\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Dataset parameters.\n", + "num_classes = 2 # linear sequence or not.\n", + "seq_max_len = 20 # Maximum sequence length.\n", + "seq_min_len = 5 # Minimum sequence length (before padding).\n", + "masking_val = -1 # -1 will represents the mask and be used to pad sequences to a common max length.\n", + "max_value = 10000 # Maximum int value.\n", + "\n", + "# Training Parameters\n", + "learning_rate = 0.001\n", + "training_steps = 2000\n", + "batch_size = 64\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "num_units = 32 # number of neurons for the LSTM layer." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# ====================\n", + "# TOY DATA GENERATOR\n", + "# ====================\n", + "\n", + "def toy_sequence_data():\n", + " \"\"\" Generate sequence of data with dynamic length.\n", + " This function generates toy samples for training:\n", + " - Class 0: linear sequences (i.e. [1, 2, 3, 4, ...])\n", + " - Class 1: random sequences (i.e. [9, 3, 10, 7,...])\n", + "\n", + " NOTICE:\n", + " We have to pad each sequence to reach 'seq_max_len' for TensorFlow\n", + " consistency (we cannot feed a numpy array with inconsistent\n", + " dimensions). The dynamic calculation will then be perform and ignore\n", + " the masked value (here -1).\n", + " \"\"\"\n", + " while True:\n", + " # Set variable sequence length.\n", + " seq_len = random.randint(seq_min_len, seq_max_len)\n", + " rand_start = random.randint(0, max_value - seq_len)\n", + " # Add a random or linear int sequence (50% prob).\n", + " if random.random() < .5:\n", + " # Generate a linear sequence.\n", + " seq = np.arange(start=rand_start, stop=rand_start+seq_len)\n", + " # Rescale values to [0., 1.].\n", + " seq = seq / max_value\n", + " # Pad sequence until the maximum length for dimension consistency.\n", + " # Masking value: -1.\n", + " seq = np.pad(seq, mode='constant', pad_width=(0, seq_max_len-seq_len), constant_values=masking_val)\n", + " label = 0\n", + " else:\n", + " # Generate a random sequence.\n", + " seq = np.random.randint(max_value, size=seq_len)\n", + " # Rescale values to [0., 1.].\n", + " seq = seq / max_value\n", + " # Pad sequence until the maximum length for dimension consistency.\n", + " # Masking value: -1.\n", + " seq = np.pad(seq, mode='constant', pad_width=(0, seq_max_len-seq_len), constant_values=masking_val)\n", + " label = 1\n", + " yield np.array(seq, dtype=np.float32), np.array(label, dtype=np.float32)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_generator(toy_sequence_data, output_types=(tf.float32, tf.float32))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create LSTM Model.\n", + "class LSTM(Model):\n", + " # Set layers.\n", + " def __init__(self):\n", + " super(LSTM, self).__init__()\n", + " # Define a Masking Layer with -1 as mask.\n", + " self.masking = layers.Masking(mask_value=masking_val)\n", + " # Define a LSTM layer to be applied over the Masking layer.\n", + " # Dynamic computation will automatically be performed to ignore -1 values.\n", + " self.lstm = layers.LSTM(units=num_units)\n", + " # Output fully connected layer (2 classes: linear or random seq).\n", + " self.out = layers.Dense(num_classes)\n", + "\n", + " # Set forward pass.\n", + " def call(self, x, is_training=False):\n", + " # A RNN Layer expects a 3-dim input (batch_size, seq_len, num_features).\n", + " x = tf.reshape(x, shape=[-1, seq_max_len, 1])\n", + " # Apply Masking layer.\n", + " x = self.masking(x)\n", + " # Apply LSTM layer.\n", + " x = self.lstm(x)\n", + " # Apply output layer.\n", + " x = self.out(x)\n", + " if not is_training:\n", + " # tf cross entropy expect logits without softmax, so only\n", + " # apply softmax when not training.\n", + " x = tf.nn.softmax(x)\n", + " return x\n", + "\n", + "# Build LSTM model.\n", + "lstm_net = LSTM()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy Loss.\n", + "# Note that this will apply 'softmax' to the logits.\n", + "def cross_entropy_loss(x, y):\n", + " # Convert labels to int 64 for tf cross-entropy function.\n", + " y = tf.cast(y, tf.int64)\n", + " # Apply softmax to logits and compute cross-entropy.\n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", + " # Average loss across the batch.\n", + " return tf.reduce_mean(loss)\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + "\n", + "# Adam optimizer.\n", + "optimizer = tf.optimizers.Adam(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " # Forward pass.\n", + " pred = lstm_net(x, is_training=True)\n", + " # Compute loss.\n", + " loss = cross_entropy_loss(pred, y)\n", + " \n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = lstm_net.trainable_variables\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " \n", + " # Update weights following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 1, loss: 0.695558, accuracy: 0.562500\n", + "step: 100, loss: 0.664089, accuracy: 0.609375\n", + "step: 200, loss: 0.408653, accuracy: 0.812500\n", + "step: 300, loss: 0.417392, accuracy: 0.828125\n", + "step: 400, loss: 0.495420, accuracy: 0.765625\n", + "step: 500, loss: 0.524736, accuracy: 0.703125\n", + "step: 600, loss: 0.401653, accuracy: 0.859375\n", + "step: 700, loss: 0.315812, accuracy: 0.906250\n", + "step: 800, loss: 0.394490, accuracy: 0.828125\n", + "step: 900, loss: 0.327425, accuracy: 0.875000\n", + "step: 1000, loss: 0.312831, accuracy: 0.843750\n", + "step: 1100, loss: 0.251562, accuracy: 0.875000\n", + "step: 1200, loss: 0.192276, accuracy: 0.906250\n", + "step: 1300, loss: 0.173289, accuracy: 0.906250\n", + "step: 1400, loss: 0.159411, accuracy: 0.937500\n", + "step: 1500, loss: 0.138854, accuracy: 0.921875\n", + "step: 1600, loss: 0.046906, accuracy: 0.984375\n", + "step: 1700, loss: 0.121232, accuracy: 0.937500\n", + "step: 1800, loss: 0.067761, accuracy: 1.000000\n", + "step: 1900, loss: 0.134532, accuracy: 0.968750\n", + "step: 2000, loss: 0.090837, accuracy: 0.953125\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0 or step == 1:\n", + " pred = lstm_net(batch_x, is_training=True)\n", + " loss = cross_entropy_loss(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb new file mode 100644 index 00000000..fe587f59 --- /dev/null +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Recurrent Neural Network Example\n", + "\n", + "Build a recurrent neural network (LSTM) with TensorFlow 2.0.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "To classify images using a recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 timesteps for every sample.\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "# Import TensorFlow v2.\n", + "import tensorflow as tf\n", + "from tensorflow.keras import Model, layers\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # total classes (0-9 digits).\n", + "num_features = 784 # data features (img shape: 28*28).\n", + "\n", + "# Training Parameters\n", + "learning_rate = 0.001\n", + "training_steps = 1000\n", + "batch_size = 32\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "# MNIST image shape is 28*28px, we will then handle 28 sequences of 28 timesteps for every sample.\n", + "num_input = 28 # number of sequences.\n", + "timesteps = 28 # timesteps.\n", + "num_units = 32 # number of neurons for the LSTM layer." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Flatten images to 1-D vector of 784 features (28*28).\n", + "x_train, x_test = x_train.reshape([-1, 28, 28]), x_test.reshape([-1, num_features])\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Create LSTM Model.\n", + "class LSTM(Model):\n", + " # Set layers.\n", + " def __init__(self):\n", + " super(LSTM, self).__init__()\n", + " # RNN (LSTM) hidden layer.\n", + " self.lstm_layer = layers.LSTM(units=num_units)\n", + " self.out = layers.Dense(num_classes)\n", + "\n", + " # Set forward pass.\n", + " def call(self, x, is_training=False):\n", + " # LSTM layer.\n", + " x = self.lstm_layer(x)\n", + " # Output layer (num_classes).\n", + " x = self.out(x)\n", + " if not is_training:\n", + " # tf cross entropy expect logits without softmax, so only\n", + " # apply softmax when not training.\n", + " x = tf.nn.softmax(x)\n", + " return x\n", + "\n", + "# Build LSTM model.\n", + "lstm_net = LSTM()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy Loss.\n", + "# Note that this will apply 'softmax' to the logits.\n", + "def cross_entropy_loss(x, y):\n", + " # Convert labels to int 64 for tf cross-entropy function.\n", + " y = tf.cast(y, tf.int64)\n", + " # Apply softmax to logits and compute cross-entropy.\n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", + " # Average loss across the batch.\n", + " return tf.reduce_mean(loss)\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + "\n", + "# Adam optimizer.\n", + "optimizer = tf.optimizers.Adam(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " # Forward pass.\n", + " pred = lstm_net(x, is_training=True)\n", + " # Compute loss.\n", + " loss = cross_entropy_loss(pred, y)\n", + " \n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = lstm_net.trainable_variables\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " \n", + " # Update weights following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 100, loss: 1.663173, accuracy: 0.531250\n", + "step: 200, loss: 1.034144, accuracy: 0.750000\n", + "step: 300, loss: 0.775579, accuracy: 0.781250\n", + "step: 400, loss: 0.840327, accuracy: 0.781250\n", + "step: 500, loss: 0.344379, accuracy: 0.937500\n", + "step: 600, loss: 0.884484, accuracy: 0.718750\n", + "step: 700, loss: 0.569674, accuracy: 0.875000\n", + "step: 800, loss: 0.401931, accuracy: 0.906250\n", + "step: 900, loss: 0.530193, accuracy: 0.812500\n", + "step: 1000, loss: 0.265871, accuracy: 0.968750\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0:\n", + " pred = lstm_net(batch_x, is_training=True)\n", + " loss = cross_entropy_loss(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 11380c8758c98ee9ebd40d91a03a4011d14cc460 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=99=88=E6=95=8F=E5=8D=8E?= Date: Mon, 5 Aug 2019 13:24:31 +0800 Subject: [PATCH 137/166] Update dcgan.py (#320) correct some misunderstanding comments --- examples/3_NeuralNetworks/dcgan.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/3_NeuralNetworks/dcgan.py b/examples/3_NeuralNetworks/dcgan.py index e7eaaaf5..2de85441 100644 --- a/examples/3_NeuralNetworks/dcgan.py +++ b/examples/3_NeuralNetworks/dcgan.py @@ -82,7 +82,7 @@ def discriminator(x, reuse=False): # Build Generator Network gen_sample = generator(noise_input) -# Build 2 Discriminator Networks (one from noise input, one from generated samples) +# Build 2 Discriminator Networks (one from real image input, one from generated samples) disc_real = discriminator(real_image_input) disc_fake = discriminator(gen_sample, reuse=True) disc_concat = tf.concat([disc_real, disc_fake], axis=0) @@ -135,7 +135,7 @@ def discriminator(x, reuse=False): z = np.random.uniform(-1., 1., size=[batch_size, noise_dim]) # Prepare Targets (Real image: 1, Fake image: 0) - # The first half of data fed to the generator are real images, + # The first half of data fed to the discriminator are real images, # the other half are fake images (coming from the generator). batch_disc_y = np.concatenate( [np.ones([batch_size]), np.zeros([batch_size])], axis=0) From bb4daed5bcf730f013acc4bd4ba5df521a5c963d Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sat, 10 Aug 2019 02:52:29 -0700 Subject: [PATCH 138/166] add new examples --- README.md | 3 + .../image_tranformation.ipynb | 418 +++++++++++++ notebooks/5_DataManagement/load_data.ipynb | 577 ++++++++++++++++++ notebooks/5_DataManagement/tfrecords.ipynb | 271 ++++++++ tensorflow_v2/README.md | 5 + .../image_transformation.ipynb | 408 +++++++++++++ .../5_DataManagement/load_data.ipynb | 530 ++++++++++++++++ .../5_DataManagement/tfrecords.ipynb | 244 ++++++++ 8 files changed, 2456 insertions(+) create mode 100644 notebooks/5_DataManagement/image_tranformation.ipynb create mode 100644 notebooks/5_DataManagement/load_data.ipynb create mode 100644 notebooks/5_DataManagement/tfrecords.ipynb create mode 100644 tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb create mode 100644 tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb create mode 100644 tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb diff --git a/README.md b/README.md index e1c723e6..bb3d777c 100644 --- a/README.md +++ b/README.md @@ -56,6 +56,9 @@ It is suitable for beginners who want to find clear and concise examples about T #### 5 - Data Management - **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. - **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. #### 6 - Multi GPU - **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. diff --git a/notebooks/5_DataManagement/image_tranformation.ipynb b/notebooks/5_DataManagement/image_tranformation.ipynb new file mode 100644 index 00000000..d55f63c2 --- /dev/null +++ b/notebooks/5_DataManagement/image_tranformation.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image Transformation (i.e. Image Augmentation)\n", + "\n", + "Learn how to apply various image augmentation techniques with TensorFlow. The transformations are meant to be applied for each image sample when training only, and each transformation will be performed with random parameters.\n", + "\n", + "**Transformations:**\n", + "- Random flip left-right\n", + "- Random contrast, brightness, saturation and hue\n", + "- Random distortion and crop\n", + "\n", + "For more information about loading data, see: [load_data.ipynb](load_data.ipynb)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "from IPython.display import Image as IImage, display\n", + "import numpy as np\n", + "import PIL\n", + "from PIL import Image\n", + "import random\n", + "import requests\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download an image.\n", + "d = requests.get(\"https://www.paristoolkit.com/Images/xeffel_view.jpg.pagespeed.ic.8XcZNqpzSj.jpg\")\n", + "with open(\"image.jpeg\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load image to numpy array.\n", + "img = PIL.Image.open('image.jpeg')\n", + "img.load()\n", + "img_array = np.array(img)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image.\n", + "PIL.Image.fromarray(img_array)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create TensorFlow session.\n", + "session = tf.Session()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly flip an image.\n", + "def random_flip_left_right(image):\n", + " return tf.image.random_flip_left_right(image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display randomly flipped image.\n", + "PIL.Image.fromarray(random_flip_left_right(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image contrast.\n", + "def random_contrast(image, minval=0.6, maxval=1.4):\n", + " r = tf.random.uniform([], minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_contrast(image, contrast_factor=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different contrast.\n", + "PIL.Image.fromarray(random_contrast(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image brightness\n", + "def random_brightness(image, minval=0., maxval=.2):\n", + " r = tf.random.uniform([], minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_brightness(image, delta=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different brightness.\n", + "PIL.Image.fromarray(random_brightness(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image saturation\n", + "def random_saturation(image, minval=0.4, maxval=2.):\n", + " r = tf.random.uniform((), minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_saturation(image, saturation_factor=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different staturation.\n", + "PIL.Image.fromarray(random_saturation(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image hue.\n", + "def random_hue(image, minval=-0.04, maxval=0.08):\n", + " r = tf.random.uniform((), minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_hue(image, delta=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different hue.\n", + "PIL.Image.fromarray(random_hue(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Distort an image by cropping it with a different aspect ratio.\n", + "def distorted_random_crop(image,\n", + " min_object_covered=0.1,\n", + " aspect_ratio_range=(3./4., 4./3.),\n", + " area_range=(0.06, 1.0),\n", + " max_attempts=100,\n", + " scope=None):\n", + "\n", + " cropbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])\n", + " sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(\n", + " tf.shape(image),\n", + " bounding_boxes=cropbox,\n", + " min_object_covered=min_object_covered,\n", + " aspect_ratio_range=aspect_ratio_range,\n", + " area_range=area_range,\n", + " max_attempts=max_attempts,\n", + " use_image_if_no_bounding_boxes=True)\n", + " bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box\n", + "\n", + " # Crop the image to the specified bounding box.\n", + " cropped_image = tf.slice(image, bbox_begin, bbox_size)\n", + " return cropped_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display cropped image.\n", + "PIL.Image.fromarray(distorted_random_crop(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Apply all transformations to an image.\n", + "# That is a common image augmentation technique for image datasets, such as ImageNet.\n", + "def transform_image(image):\n", + " image = distorted_random_crop(image)\n", + " image = random_flip_left_right(image)\n", + " image = random_contrast(image)\n", + " image = random_brightness(image)\n", + " image = random_hue(image)\n", + " image = random_saturation(image)\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display fully pre-processed image.\n", + "transformed_img = transform_image(img_array)\n", + "PIL.Image.fromarray(transformed_img.eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Resize transformed image to a 256x256px square image, ready for training.\n", + "def resize_image(image):\n", + " image = tf.image.resize(image, size=(256, 256), preserve_aspect_ratio=False)\n", + " image = tf.cast(image, tf.uint8)\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display resized image.\n", + "PIL.Image.fromarray(resize_image(transformed_img).eval(session=session))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf1", + "language": "python", + "name": "tf1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15+" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/5_DataManagement/load_data.ipynb b/notebooks/5_DataManagement/load_data.ipynb new file mode 100644 index 00000000..a6fdeec5 --- /dev/null +++ b/notebooks/5_DataManagement/load_data.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and parse data with TensorFlow\n", + "\n", + "A TensorFlow example to build input pipelines for loading data efficiently.\n", + "\n", + "\n", + "- Numpy Arrays\n", + "- Images\n", + "- CSV file\n", + "- Custom data from a Generator\n", + "\n", + "For more information about creating and loading TensorFlow's `TFRecords` data format, see: [tfrecords.ipynb](tfrecords.ipynb)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import numpy as np\n", + "import random\n", + "import requests\n", + "import string\n", + "import tarfile\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Numpy Arrays\n", + "\n", + "Build a data pipeline over numpy arrays." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a toy dataset (even and odd numbers, with respective labels of 0 and 1).\n", + "evens = np.arange(0, 100, step=2, dtype=np.int32)\n", + "evens_label = np.zeros(50, dtype=np.int32)\n", + "odds = np.arange(1, 100, step=2, dtype=np.int32)\n", + "odds_label = np.ones(50, dtype=np.int32)\n", + "# Concatenate arrays\n", + "features = np.concatenate([evens, odds])\n", + "labels = np.concatenate([evens_label, odds_label])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " # Create TF session.\n", + " sess = tf.Session()\n", + " \n", + " # Slice the numpy arrays (each row becoming a record).\n", + " data = tf.data.Dataset.from_tensor_slices((features, labels))\n", + " # Refill data indefinitely. \n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=100)\n", + " # Batch data (aggregate records together).\n", + " data = data.batch(batch_size=4)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + " \n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[82 58 80 23] [0 0 0 1]\n", + "[16 91 74 96] [0 1 0 0]\n", + "[ 4 17 32 34] [0 1 0 0]\n", + "[16 8 77 21] [0 0 1 1]\n", + "[20 99 48 18] [0 1 0 0]\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(5):\n", + " x, y = sess.run(d)\n", + " print(x, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load CSV files\n", + "\n", + "Build a data pipeline from features stored in a CSV file. For this example, Titanic dataset will be used as a toy dataset stored in CSV format." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Titanic Dataset\n", + "\n", + "\n", + "\n", + "survived|pclass|name|sex|age|sibsp|parch|ticket|fare\n", + "--------|------|----|---|---|-----|-----|------|----\n", + "1|1|\"Allen, Miss. Elisabeth Walton\"|female|29|0|0|24160|211.3375\n", + "1|1|\"Allison, Master. Hudson Trevor\"|male|0.9167|1|2|113781|151.5500\n", + "0|1|\"Allison, Miss. Helen Loraine\"|female|2|1|2|113781|151.5500\n", + "0|1|\"Allison, Mr. Hudson Joshua Creighton\"|male|30|1|2|113781|151.5500\n", + "...|...|...|...|...|...|...|...|..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Titanic dataset (in csv format).\n", + "d = requests.get(\"https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/titanic_dataset.csv\")\n", + "with open(\"titanic_dataset.csv\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Titanic dataset.\n", + "# Original features: survived,pclass,name,sex,age,sibsp,parch,ticket,fare\n", + "# Select specific columns: survived,pclass,name,sex,age,fare\n", + "column_to_use = [0, 1, 2, 3, 4, 8]\n", + "record_defaults = [tf.int32, tf.int32, tf.string, tf.string, tf.float32, tf.float32]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " # Create TF session.\n", + " sess = tf.Session()\n", + " \n", + " # Load the whole dataset file, and slice each line.\n", + " data = tf.data.experimental.CsvDataset(\"titanic_dataset.csv\", record_defaults, header=True, select_cols=column_to_use)\n", + " # Refill data indefinitely. \n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=1000)\n", + " # Batch data (aggregate records together).\n", + " data = data.batch(batch_size=2)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + " \n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0]\n", + "[3 1]\n", + "['Lam, Mr. Ali' 'Widener, Mr. Harry Elkins']\n", + "['male' 'male']\n", + "[ 0. 27.]\n", + "[ 56.4958 211.5 ]\n", + "\n", + "[0 1]\n", + "[1 1]\n", + "['Baumann, Mr. John D' 'Daly, Mr. Peter Denis ']\n", + "['male' 'male']\n", + "[ 0. 51.]\n", + "[25.925 26.55 ]\n", + "\n", + "[0 1]\n", + "[3 1]\n", + "['Assam, Mr. Ali' 'Newell, Miss. Madeleine']\n", + "['male' 'female']\n", + "[23. 31.]\n", + "[ 7.05 113.275]\n", + "\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(3):\n", + " survived, pclass, name, sex, age, fare = sess.run(d)\n", + " print(survived)\n", + " print(pclass)\n", + " print(name)\n", + " print(sex)\n", + " print(age)\n", + " print(fare)\n", + " print(\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Images\n", + "\n", + "Build a data pipeline by loading images from disk. For this example, Oxford Flowers dataset will be used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Oxford 17 flowers dataset.\n", + "d = requests.get(\"http://www.robots.ox.ac.uk/~vgg/data/flowers/17/17flowers.tgz\")\n", + "with open(\"17flowers.tgz\", \"wb\") as f:\n", + " f.write(d.content)\n", + "# Extract archive.\n", + "with tarfile.open(\"17flowers.tgz\") as t:\n", + " t.extractall()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a file to list all images path and their corresponding label.\n", + "with open('jpg/dataset.csv', 'w') as f:\n", + " c = 0\n", + " for i in range(1360):\n", + " f.write(\"jpg/image_%04i.jpg,%i\\n\" % (i+1, c))\n", + " if (i+1) % 80 == 0:\n", + " c += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " \n", + " # Load Images.\n", + " with open(\"jpg/dataset.csv\") as f:\n", + " dataset_file = f.read().splitlines()\n", + " \n", + " # Create TF session.\n", + " sess = tf.Session()\n", + "\n", + " # Load the whole dataset file, and slice each line.\n", + " data = tf.data.Dataset.from_tensor_slices(dataset_file)\n", + " # Refill data indefinitely.\n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=1000)\n", + "\n", + " # Load and pre-process images.\n", + " def load_image(path):\n", + " # Read image from path.\n", + " image = tf.io.read_file(path)\n", + " # Decode the jpeg image to array [0, 255].\n", + " image = tf.image.decode_jpeg(image)\n", + " # Resize images to a common size of 256x256.\n", + " image = tf.image.resize(image, [256, 256])\n", + " # Rescale values to [-1, 1].\n", + " image = 1. - image / 127.5\n", + " return image\n", + " # Decode each line from the dataset file.\n", + " def parse_records(line):\n", + " # File is in csv format: \"image_path,label_id\".\n", + " # TensorFlow requires a default value, but it will never be used.\n", + " image_path, image_label = tf.io.decode_csv(line, [\"\", 0])\n", + " # Apply the function to load images.\n", + " image = load_image(image_path)\n", + " return image, image_label\n", + " # Use 'map' to apply the above functions in parallel.\n", + " data = data.map(parse_records, num_parallel_calls=4)\n", + "\n", + " # Batch data (aggregate images-array together).\n", + " data = data.batch(batch_size=2)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + " \n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[[ 0.1294117 0.05098033 0.46666664]\n", + " [ 0.1368872 0.05098033 0.48909312]\n", + " [ 0.0931372 0.0068627 0.46029407]\n", + " ...\n", + " [ 0.23480386 0.0522058 0.6102941 ]\n", + " [ 0.12696075 -0.05416667 0.38063723]\n", + " [-0.10024512 -0.28848052 0.10367644]]\n", + "\n", + " [[ 0.04120708 -0.06118262 0.36256123]\n", + " [ 0.08009624 -0.02229345 0.41640145]\n", + " [ 0.06797445 -0.04132879 0.41923058]\n", + " ...\n", + " [ 0.2495715 0.06697345 0.6251221 ]\n", + " [ 0.12058818 -0.06094813 0.37577546]\n", + " [-0.05184889 -0.24009418 0.16777915]]\n", + "\n", + " [[-0.09234071 -0.22738981 0.20484066]\n", + " [-0.03100491 -0.17312062 0.2811274 ]\n", + " [ 0.01051998 -0.13237214 0.3376838 ]\n", + " ...\n", + " [ 0.27787983 0.07494056 0.64203525]\n", + " [ 0.11533964 -0.09005249 0.3869906 ]\n", + " [-0.02704227 -0.23958337 0.19454747]]\n", + "\n", + " ...\n", + "\n", + " [[ 0.07913595 -0.13069856 0.29874384]\n", + " [ 0.10140878 -0.09445572 0.35912937]\n", + " [ 0.08869672 -0.08415675 0.41446364]\n", + " ...\n", + " [ 0.25821072 0.22463232 0.69197303]\n", + " [ 0.31636214 0.25750512 0.79362744]\n", + " [ 0.09552741 0.01709598 0.57395875]]\n", + "\n", + " [[ 0.09019601 -0.12156868 0.3098039 ]\n", + " [ 0.17446858 -0.02271283 0.43218917]\n", + " [ 0.06583172 -0.10818791 0.39230233]\n", + " ...\n", + " [ 0.27021956 0.23664117 0.70269513]\n", + " [ 0.19560927 0.1385014 0.6740407 ]\n", + " [ 0.04364848 -0.03478289 0.5220798 ]]\n", + "\n", + " [[ 0.02830875 -0.18345594 0.24791664]\n", + " [ 0.12937105 -0.06781042 0.38709164]\n", + " [ 0.01120263 -0.162817 0.33767325]\n", + " ...\n", + " [ 0.25989532 0.22631687 0.69237083]\n", + " [ 0.1200884 0.06298059 0.5985198 ]\n", + " [ 0.05961001 -0.01882136 0.53804135]]]\n", + "\n", + "\n", + " [[[ 0.3333333 0.25490195 0.05882347]\n", + " [ 0.3333333 0.25490195 0.05882347]\n", + " [ 0.3340686 0.24705875 0.03039211]\n", + " ...\n", + " [-0.5215688 -0.4599266 -0.14632356]\n", + " [-0.5100491 -0.47083342 -0.03725493]\n", + " [-0.43419123 -0.39497554 0.05992639]]\n", + "\n", + " [[ 0.34117645 0.26274508 0.0666666 ]\n", + " [ 0.35646445 0.2630821 0.0744791 ]\n", + " [ 0.3632046 0.2548713 0.04384762]\n", + " ...\n", + " [-0.9210479 -0.84267783 -0.4540485 ]\n", + " [-0.9017464 -0.8390626 -0.3507018 ]\n", + " [-0.83339334 -0.7632048 -0.2534927 ]]\n", + "\n", + " [[ 0.3646446 0.2706495 0.06678915]\n", + " [ 0.37248772 0.27837008 0.07445425]\n", + " [ 0.38033658 0.27053267 0.05950326]\n", + " ...\n", + " [-0.94302344 -0.84222686 -0.30278325]\n", + " [-0.91017747 -0.8090074 -0.18615782]\n", + " [-0.83437514 -0.7402575 -0.08192408]]\n", + "\n", + " ...\n", + "\n", + " [[ 0.64705884 0.654902 0.67058825]\n", + " [ 0.6318321 0.63967526 0.65536153]\n", + " [ 0.63128924 0.6391324 0.65481865]\n", + " ...\n", + " [ 0.6313726 0.57647055 0.51372546]\n", + " [ 0.6078431 0.53725487 0.4823529 ]\n", + " [ 0.6078431 0.53725487 0.4823529 ]]\n", + "\n", + " [[ 0.654902 0.654902 0.6704657 ]\n", + " [ 0.654902 0.654902 0.6704657 ]\n", + " [ 0.64778835 0.64778835 0.6492474 ]\n", + " ...\n", + " [ 0.6392157 0.5843137 0.5215686 ]\n", + " [ 0.6393325 0.56874424 0.5138422 ]\n", + " [ 0.63106614 0.5604779 0.50557595]]\n", + "\n", + " [[ 0.654902 0.64705884 0.6313726 ]\n", + " [ 0.6548728 0.64702964 0.63134336]\n", + " [ 0.64705884 0.63210785 0.6377451 ]\n", + " ...\n", + " [ 0.63244915 0.5775472 0.5148021 ]\n", + " [ 0.6698529 0.5992647 0.5443627 ]\n", + " [ 0.6545358 0.5839475 0.5290455 ]]]] [5 9]\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(1):\n", + " batch_x, batch_y = sess.run(d)\n", + " print(batch_x, batch_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data from a Generator\n", + "\n", + "Build a data pipeline from a custom generator. For this example, a toy generator yielding random string, vector and it is used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dummy generator.\n", + "def generate_features():\n", + " # Function to generate a random string.\n", + " def random_string(length):\n", + " return ''.join(random.choice(string.ascii_letters) for m in xrange(length))\n", + " # Return a random string, a random vector, and a random int.\n", + " yield random_string(4), np.random.uniform(size=4), random.randint(0, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + "\n", + " # Create TF session.\n", + " sess = tf.Session()\n", + "\n", + " # Create TF dataset from the generator.\n", + " data = tf.data.Dataset.from_generator(generate_features, output_types=(tf.string, tf.float32, tf.int32))\n", + " # Refill data indefinitely.\n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=100)\n", + " # Batch data (aggregate records together).\n", + " data = data.batch(batch_size=4)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + "\n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['AvCS' 'kAaI' 'QwGX' 'IWOI'] [[0.6096093 0.32192084 0.26622605 0.70250475]\n", + " [0.72534287 0.7637426 0.19977213 0.74121326]\n", + " [0.6930984 0.09409562 0.4063325 0.5002103 ]\n", + " [0.05160935 0.59411395 0.276416 0.98264974]] [1 3 5 6]\n", + "['EXjS' 'brvx' 'kwNz' 'eFOb'] [[0.34355283 0.26881003 0.70575935 0.7503411 ]\n", + " [0.9584373 0.27466875 0.27802315 0.9563204 ]\n", + " [0.19129485 0.07014314 0.0932724 0.20726128]\n", + " [0.28744072 0.81736153 0.37507302 0.8984588 ]] [1 9 7 0]\n", + "['vpSa' 'UuqW' 'xaTO' 'milw'] [[0.2942028 0.8228986 0.5793326 0.16651365]\n", + " [0.28259405 0.599063 0.2922477 0.95071274]\n", + " [0.23645316 0.00258607 0.06772221 0.7291911 ]\n", + " [0.12861755 0.31435087 0.576638 0.7333119 ]] [3 5 8 4]\n", + "['UBBb' 'MUXs' 'nLJB' 'OBGl'] [[0.2677402 0.17931737 0.02607645 0.85898155]\n", + " [0.58647937 0.727203 0.13329858 0.8898983 ]\n", + " [0.13872191 0.47390288 0.7061665 0.08478573]\n", + " [0.3786016 0.22002582 0.91989636 0.45837343]] [ 5 8 0 10]\n", + "['kiiz' 'bQYG' 'WpUU' 'AuIY'] [[0.74781317 0.13744462 0.9236441 0.63558507]\n", + " [0.23649399 0.35303807 0.0951511 0.03541444]\n", + " [0.33599988 0.6906629 0.97166294 0.55850506]\n", + " [0.90997607 0.5545979 0.43635726 0.9127501 ]] [8 1 4 4]\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(5):\n", + " batch_str, batch_vector, batch_int = sess.run(d)\n", + " print(batch_str, batch_vector, batch_int)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf1", + "language": "python", + "name": "tf1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15+" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/5_DataManagement/tfrecords.ipynb b/notebooks/5_DataManagement/tfrecords.ipynb new file mode 100644 index 00000000..413b0fc2 --- /dev/null +++ b/notebooks/5_DataManagement/tfrecords.ipynb @@ -0,0 +1,271 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create and Load TFRecords\n", + "\n", + "A simple TensorFlow example to parse a dataset into TFRecord format, and then read that dataset.\n", + "\n", + "In this example, the Titanic Dataset (in CSV format) will be used as a toy dataset, for parsing all the dataset features into TFRecord format, and then building an input pipeline that can be used for training models.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Titanic Dataset\n", + "\n", + "The titanic dataset is a popular dataset for ML that provides a list of all passengers onboard the Titanic, along with various features such as their age, sex, class (1st, 2nd, 3rd)... And if the passenger survived the disaster or not.\n", + "\n", + "It can be used to see that even though some luck was involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class...\n", + "\n", + "#### Overview\n", + "survived|pclass|name|sex|age|sibsp|parch|ticket|fare\n", + "--------|------|----|---|---|-----|-----|------|----\n", + "1|1|\"Allen, Miss. Elisabeth Walton\"|female|29|0|0|24160|211.3375\n", + "1|1|\"Allison, Master. Hudson Trevor\"|male|0.9167|1|2|113781|151.5500\n", + "0|1|\"Allison, Miss. Helen Loraine\"|female|2|1|2|113781|151.5500\n", + "0|1|\"Allison, Mr. Hudson Joshua Creighton\"|male|30|1|2|113781|151.5500\n", + "...|...|...|...|...|...|...|...|...\n", + "\n", + "\n", + "#### Variable Descriptions\n", + "```\n", + "survived Survived\n", + " (0 = No; 1 = Yes)\n", + "pclass Passenger Class\n", + " (1 = 1st; 2 = 2nd; 3 = 3rd)\n", + "name Name\n", + "sex Sex\n", + "age Age\n", + "sibsp Number of Siblings/Spouses Aboard\n", + "parch Number of Parents/Children Aboard\n", + "ticket Ticket Number\n", + "fare Passenger Fare\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import csv\n", + "import requests\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Titanic dataset (in csv format).\n", + "d = requests.get(\"https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/titanic_dataset.csv\")\n", + "with open(\"titanic_dataset.csv\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create TFRecords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate Integer Features.\n", + "def build_int64_feature(data):\n", + " return tf.train.Feature(int64_list=tf.train.Int64List(value=[data]))\n", + "\n", + "# Generate Float Features.\n", + "def build_float_feature(data):\n", + " return tf.train.Feature(float_list=tf.train.FloatList(value=[data]))\n", + "\n", + "# Generate String Features.\n", + "def build_string_feature(data):\n", + " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[data]))\n", + "\n", + "# Generate a TF `Example`, parsing all features of the dataset.\n", + "def convert_to_tfexample(survived, pclass, name, sex, age, sibsp, parch, ticket, fare):\n", + " return tf.train.Example(\n", + " features=tf.train.Features(\n", + " feature={\n", + " 'survived': build_int64_feature(survived),\n", + " 'pclass': build_int64_feature(pclass),\n", + " 'name': build_string_feature(name),\n", + " 'sex': build_string_feature(sex),\n", + " 'age': build_float_feature(age),\n", + " 'sibsp': build_int64_feature(sibsp),\n", + " 'parch': build_int64_feature(parch),\n", + " 'ticket': build_string_feature(ticket),\n", + " 'fare': build_float_feature(fare),\n", + " })\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Open dataset file.\n", + "with open(\"titanic_dataset.csv\") as f:\n", + " # Output TFRecord file.\n", + " with tf.io.TFRecordWriter(\"titanic_dataset.tfrecord\") as w:\n", + " # Generate a TF Example for all row in our dataset.\n", + " # CSV reader will read and parse all rows.\n", + " reader = csv.reader(f, skipinitialspace=True)\n", + " for i, record in enumerate(reader):\n", + " # Skip header.\n", + " if i == 0:\n", + " continue\n", + " survived, pclass, name, sex, age, sibsp, parch, ticket, fare = record\n", + " # Parse each csv row to TF Example using the above functions.\n", + " example = convert_to_tfexample(int(survived), int(pclass), name, sex, float(age), int(sibsp), int(parch), ticket, float(fare))\n", + " # Serialize each TF Example to string, and write to TFRecord file.\n", + " w.write(example.SerializeToString())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load TFRecords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Build features template, with types.\n", + "features = {\n", + " 'survived': tf.io.FixedLenFeature([], tf.int64),\n", + " 'pclass': tf.io.FixedLenFeature([], tf.int64),\n", + " 'name': tf.io.FixedLenFeature([], tf.string),\n", + " 'sex': tf.io.FixedLenFeature([], tf.string),\n", + " 'age': tf.io.FixedLenFeature([], tf.float32),\n", + " 'sibsp': tf.io.FixedLenFeature([], tf.int64),\n", + " 'parch': tf.io.FixedLenFeature([], tf.int64),\n", + " 'ticket': tf.io.FixedLenFeature([], tf.string),\n", + " 'fare': tf.io.FixedLenFeature([], tf.float32),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/orus/tf1/lib/python2.7/site-packages/tensorflow/python/data/ops/dataset_ops.py:1419: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n" + ] + } + ], + "source": [ + "# Create TensorFlow session.\n", + "sess = tf.Session()\n", + "\n", + "# Load TFRecord data.\n", + "filenames = [\"titanic_dataset.tfrecord\"]\n", + "data = tf.data.TFRecordDataset(filenames)\n", + "\n", + "# Parse features, using the above template.\n", + "def parse_record(record):\n", + " return tf.io.parse_single_example(record, features=features)\n", + "# Apply the parsing to each record from the dataset.\n", + "data = data.map(parse_record)\n", + "\n", + "# Refill data indefinitely.\n", + "data = data.repeat()\n", + "# Shuffle data.\n", + "data = data.shuffle(buffer_size=1000)\n", + "# Batch data (aggregate records together).\n", + "data = data.batch(batch_size=4)\n", + "# Prefetch batch (pre-load batch for faster consumption).\n", + "data = data.prefetch(buffer_size=1)\n", + "\n", + "# Create an iterator over the dataset.\n", + "iterator = data.make_initializable_iterator()\n", + "# Initialize the iterator.\n", + "sess.run(iterator.initializer)\n", + "\n", + "# Get next data batch.\n", + "x = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'fare': array([ 35.5 , 73.5 , 133.65 , 19.2583], dtype=float32), 'name': array(['Sloper, Mr. William Thompson', 'Davies, Mr. Charles Henry',\n", + " 'Frauenthal, Dr. Henry William', 'Baclini, Miss. Marie Catherine'],\n", + " dtype=object), 'age': array([28., 18., 50., 5.], dtype=float32), 'parch': array([0, 0, 0, 1]), 'pclass': array([1, 2, 1, 3]), 'sex': array(['male', 'male', 'male', 'female'], dtype=object), 'survived': array([1, 0, 1, 1]), 'sibsp': array([0, 0, 2, 2]), 'ticket': array(['113788', 'S.O.C. 14879', 'PC 17611', '2666'], dtype=object)}\n", + "\n", + "{'fare': array([ 18.75 , 106.425, 78.85 , 90. ], dtype=float32), 'name': array(['Richards, Mrs. Sidney (Emily Hocking)', 'LeRoy, Miss. Bertha',\n", + " 'Cavendish, Mrs. Tyrell William (Julia Florence Siegel)',\n", + " 'Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)'], dtype=object), 'age': array([24., 30., 76., 35.], dtype=float32), 'parch': array([3, 0, 0, 0]), 'pclass': array([2, 1, 1, 1]), 'sex': array(['female', 'female', 'female', 'female'], dtype=object), 'survived': array([1, 1, 1, 1]), 'sibsp': array([2, 0, 1, 1]), 'ticket': array(['29106', 'PC 17761', '19877', '19943'], dtype=object)}\n", + "\n", + "{'fare': array([19.9667, 15.5 , 15.0458, 66.6 ], dtype=float32), 'name': array(['Hagland, Mr. Konrad Mathias Reiersen', 'Lennon, Miss. Mary',\n", + " 'Richard, Mr. Emile', 'Pears, Mr. Thomas Clinton'], dtype=object), 'age': array([ 0., 0., 23., 29.], dtype=float32), 'parch': array([0, 0, 0, 0]), 'pclass': array([3, 3, 2, 1]), 'sex': array(['male', 'female', 'male', 'male'], dtype=object), 'survived': array([0, 0, 0, 0]), 'sibsp': array([1, 1, 0, 1]), 'ticket': array(['65304', '370371', 'SC/PARIS 2133', '113776'], dtype=object)}\n", + "\n" + ] + } + ], + "source": [ + "# Dequeue data and display.\n", + "for i in range(3):\n", + " print(sess.run(x))\n", + " print(\"\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf1", + "language": "python", + "name": "tf1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15+" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index 0f6c7436..ba1a5999 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -33,6 +33,11 @@ - **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0. - **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models. +#### 5 - Data Management +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training. + ## Installation To install TensorFlow 2.0, simply run: diff --git a/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb b/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb new file mode 100644 index 00000000..f2b060a6 --- /dev/null +++ b/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb @@ -0,0 +1,408 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image Transformation (i.e. Image Augmentation)\n", + "\n", + "Learn how to apply various image augmentation techniques with TensorFlow 2.0. The transformations are meant to be applied for each image sample when training only, and each transformation will be performed with random parameters.\n", + "\n", + "**Transformations:**\n", + "- Random flip left-right\n", + "- Random contrast, brightness, saturation and hue\n", + "- Random distortion and crop\n", + "\n", + "For more information about loading data with TF2.0, see: [load_data.ipynb](load_data.ipynb)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "from IPython.display import Image as IImage, display\n", + "import numpy as np\n", + "import PIL\n", + "from PIL import Image\n", + "import random\n", + "import requests\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download an image.\n", + "d = requests.get(\"https://www.paristoolkit.com/Images/xeffel_view.jpg.pagespeed.ic.8XcZNqpzSj.jpg\")\n", + "with open(\"image.jpeg\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load image to numpy array.\n", + "img = PIL.Image.open('image.jpeg')\n", + "img.load()\n", + "img_array = np.array(img)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image.\n", + "PIL.Image.fromarray(img_array)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly flip an image.\n", + "def random_flip_left_right(image):\n", + " return tf.image.random_flip_left_right(image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display randomly flipped image.\n", + "PIL.Image.fromarray(random_flip_left_right(img_array).numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image contrast.\n", + "def random_contrast(image, minval=0.6, maxval=1.4):\n", + " r = tf.random.uniform([], minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_contrast(image, contrast_factor=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different contrast.\n", + "PIL.Image.fromarray(random_contrast(img_array).numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image brightness\n", + "def random_brightness(image, minval=0., maxval=.2):\n", + " r = tf.random.uniform([], minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_brightness(image, delta=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different brightness.\n", + "PIL.Image.fromarray(random_brightness(img_array).numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image saturation\n", + "def random_saturation(image, minval=0.4, maxval=2.):\n", + " r = tf.random.uniform((), minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_saturation(image, saturation_factor=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different staturation.\n", + "PIL.Image.fromarray(random_saturation(img_array).numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image hue.\n", + "def random_hue(image, minval=-0.04, maxval=0.08):\n", + " r = tf.random.uniform((), minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_hue(image, delta=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different hue.\n", + "PIL.Image.fromarray(random_hue(img_array).numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Distort an image by cropping it with a different aspect ratio.\n", + "def distorted_random_crop(image,\n", + " min_object_covered=0.1,\n", + " aspect_ratio_range=(3./4., 4./3.),\n", + " area_range=(0.06, 1.0),\n", + " max_attempts=100,\n", + " scope=None):\n", + "\n", + " cropbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])\n", + " sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(\n", + " tf.shape(image),\n", + " bounding_boxes=cropbox,\n", + " min_object_covered=min_object_covered,\n", + " aspect_ratio_range=aspect_ratio_range,\n", + " area_range=area_range,\n", + " max_attempts=max_attempts,\n", + " use_image_if_no_bounding_boxes=True)\n", + " bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box\n", + "\n", + " # Crop the image to the specified bounding box.\n", + " cropped_image = tf.slice(image, bbox_begin, bbox_size)\n", + " return cropped_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUAAAAGUCAIAAADLXWzEAAEAAElEQVR4nOz9Z4xlWXYmiq219z7uehc+IiPSu6rMqq6qru5ms2maHHI4Q2qI0WAGA+kZAdKPJwMI+iv90Q8B+i1AwOABEiRA0jN4evPEGc6QTXY3u9msduUrvYmMyPA3rjfH7b2WfpwbkZFZcbI6uiI7Td0PFzdv3Dx3n33O2Wsvvxb+8f/+QzgI1HAoWB3+fdrxKWDmIx1/bEiZ/3ObTxqOeD+P67kcFcrkdfPHsvNe1hZe5U2Tf6Mv8gOKJdOhxxMePo444u0/8jgp9weFOdqJnxNYpFzw3nWJ39xcxniFYIwZ+mFv6IexNkyR0X4U+2H0vOf1lcOYgMf4dSAsGcbaD7QmAClQgpBS2c7zntdXDmMCHuPXAmoGAkYhBIAhjoSETCb3vKf1lUOKBjXGGE8FmYGr0Mrni1nPURToXqQ9BDFmCL9hjAl4jF8HjoisvFt0K5MlG3O2NjwMwzgKQKYYXcZ4NhjvmGP8OpA8tCiGODBBX9IgY5mcDTYcboIe49lhzIHH+HXg91pxs95vr4UZUySJ1arEsgRjQD7vqX21oJ50NGGa3+nw7wUcTWRK82ul+WOP6idMBR7u90ubfZq/MQ1Mhy9cAUfzqxNah4+Tdh/S5pn2HI+I0ACgQqGFjBU4DmVtMVRqm3aux7LdM+g603lV9f3IcweDeOCLec92tdbM6NqWIR1HQ6WE5BRZ74jTTNseMHVdHf7c09bhsa23NKQ9d3E0v/2+H3vMgcd4GlyFhjFmjskgG0TtiNhRBK5kdq04JzmMgo7rFkGEGSUDGCogIYAMCkZtNLBAsABejsCJlw5jAh7jaXCliYylQTIjQySZXRjkYNDx2xAPczYNe11tooyQYTDUWjk5tFUGhWPQAibDEkBKsAn8530prybGBDzG0yA4FmwrkEKww5HSQ0FtiHZ00IIoqBU9T3qBNhb0o9BkVAaCLU/mLDdPMhsRsbA1e5AmP4/xpTEm4DGehlBHwJ4jlBBG6a6I+tFwx+itaj4bBTA3M5Px7E631w8Jo0hlXOzt2kHgOpolD0CTyIVgGyIz9i49G4wJeIynIWK0CC3QVtQV0ZbpNLqdOgU7PRFHw17Oy5w7s5TPi8D0yrbKlXLc70juKRIMGHEcAxrhaINHNVaN8StiTMBjPA2sMhArjPoibtLgYdhqBN1OGHSaW1txODSaC/mytEQc69pUJV8u0m4QsiHyg1hAHAFIkBkmA2IcJv1MMCbgMZ4GtD02kuPIDDtm0IiHjcD3wzicmTvhD/qWnTUgQz/qdQfZfAjYKXuZXjjwyYAJTGxIZlBEDAQwJuBnAiXlY641Pqq/Do9mn1ApfjZKGYboeIJ70vzMmOIvxSPm2aJMmSfbh38N8eHjHD4KYNp9SxVNj8fPmfd3bOoxr0b+fcVhGPU7w/bSybNff+20EOLTTz/96S/+wbKsOI7bvW6tViPgbrf/6Wd3N3cDq3Jm7vVvT184F6GwjuhYP6ofPjUeIfU5pviHj5hvnDrPtPOm+Htl2onlF9DXmAOP8TRQOAiCthMH2u91GmvDflsSRYPOzZt3Z2dna7VpZtntduv11v37q0KIcnnGtl0dWbMzc5OnrnqTSwKyFtoA/ed9Ka8mvioEnMZpx3g6tjbXG+u3VbABgzWlO6Vi1nGcfq+ztbrW6w5OnTp1cuk0AJw+1W80GplMZmV1PZcrlCvTxdpsaX5hoKz6sBvEjuuN7/8zwVeFgMf49eDkSpZXioJdrbHoZAvVaSGtMKZStqS1iWNtWSSldF0vk8lOT0+fPDtFsdBGsrK1O0TV0+TB0A6PKhOP8avhq0LAaRz4hauJ9YKhNne6WMwNtt3NO4GOm60hCWmyuVLc2Y7jOAyDMAyIKJ/PI4Ixutna7bVDge5QR+1Qu9PnrZIT9mLIlp/3pbyaGIfIjPE0DAN/MBi0O53+cEBEjEDALBBQT01XT55aqNaKQhKgjrW//OCO51QybjWfnXStnBLSFtJiyxOF530dryy+Khx4jF8Pg521zZVrcfOuo4cTRfvM0oznZlFYtYtTxWLxtddeY+atra04jpvNZqPR2NxcN5Gy0O9HXeUC61446Jbzi74ex0I/E4wJeIynIexsDevreRGWspiFsGSZ2ZkioOr025ub69Vq+fz588Vi3hhDRMaY7cZKNBS7W62HW/cDGe0Gu7utW/PeCYDD3WljfEngP/k/fvarHJeqKx5T/eE0P5uB56OjpvmfRUrVp6Men+onPOp8UvzPZNL8+Yc/L9bGtm1mjqJISmnbdhAEnU6n/d7/A6jjWdGJqQkLnSCIpE3t3k6/MbRtVSjkGMz58+cuv3a+UMjNzMzYxAunissP7uSsKQHyxsN7/5f/6genrv7Lhr1w+PxftCpaz6nOdqqX5IvWyZgDjwFwYAEdXEmICDqolLKuraLYb3ZavZ5vRNTpNwa7/UqlEsRRvb790SefFP4677p2qVR48/UrEzNuuVQ4v+h6tjM7O/873/nd25v9MQN+RhgT8OE4qt/4FfAz7wtZiMjMiCilLJfcasULhv7W9ka3HRqWVsa2vEyhbE3OzBCbTOCbHgeRCeLBTqP10cc3QQ6rleLp2cWp2sSp116bOffbgZW92X2+F/fKYkzAYzyGZCdKiFlKabt2FA8bzZ3t7S0m2/YKyrYyxXw3bpRrlfX1dRDKy+WFEMwcxt3yZIlQD/3+7fsPdnbqP/v03rv/uFA7/a3nfVmvLMYEfDi+ahw44brwOAErpbaabR21ep1mpI2lpNa63+86Gczl3YUTM83WDqDn+xCGYT6fV9KOiGynMgDbk2G5OrF5v/FwawCTPkD+OV/hK4oxAY8B8DkdmIgSEbofW+2WzzF7Ts5RHhNaEoue5+ZFIeeQDot5l3SgQ855bq/dyZWKQQhxhKVabmZxyZ18q3bmrVB444KzzwhjAj4cR43cegUivRK9d58VA4CUcuHc1eBGGPaMQraUnbEtx4Wsrc6eWaiWszNTxaXFM9eu3WjJzvzM9MMHy5I8QRJJVKvVE2fOn3Av74jZhh7X1HlWGBPwGI9hnwMnn0+fv7rb2GhFXWECW1qu7XA0aG5tVd6+WKvk37hy+Y2rb3XbTddWb159rd1s5AsewcTqxubc/NSJpZO7/uTOqh8XC864KOWzgfoVlbf0w1LyXY/oN07Nw3xetVhS+8oesX4vHF7nmY/o307zl2JKpWSZWl/9CwqvMwAc6HHUUuemzoWN3Q5Df/Lc0vrqsgTjaBJGVgs1MSs3Hz5cnJsZdtsPH9za3X7wn/yn/5uV29txb6tSXRxkzn+6QZQpe4LSROij1to5qgR0ZByxznZaPfDUPPOjzvOLDh+LNmM8DYiYyWQy2bwQgowhHSOT67q3b98Ow1Brbdt2Pp8/ceLE9PT0wsLC9evX/WAwMVGdnKzZlmPbrhAiDMPnfR2vLMYi9BhPA7NxvVymUApMWyE4ClgbIjTG1Gq1ZrMZx7FS6tKlS8aY9fV1AFEsFomlbdu7u01/mJW2x0I8p4C6Vx9jAn458LzcVFprZdmZfMUMtgSCIzE02o/19PSF+/fvf+973zt58iQi9vv9fD5/4cIFkCJrZxmE4zkbfV9rTzgoBOMx1ZV90dx1z30+YwIe42nQWqPn5sqTvd17/V6Lwr4SzIyNRuPs2bP1ej2Kok6nk8/n4zj+2c9+NjUzr4CFkouziwKVY2djFrHxHRwXtXsmGBPwGE8DAqCQmVIZUHQau+j3cl4mljg3NyeEkFI6jrO4uFipVJRSH3/8MUo3HHSlAs9zdMMQITMLAWMR+hlhTMAvB56Xn1kpFRkC5QjLRjI5z8249ka93e12P/300+9///v379+/ePHin//5n//2b/82Iq483OGsW6rmZmZmPlofkgGwAJHTrM1Hnf+L5m9/7vMZE/AYT4NlWX44IMPKsrL5bM224zhuNBpRFC0vLzuO8+abbxYKhR/96Ef9fn95efkHP/z5ibnqu99685QlEKWUFiMa0taLpbq+OniyLnQajrqjpNdzPtwvmppvecS82WPL20ypG3zkhrYpxx+X8eNZ+0VjGDhCK5SbEbRD8oOB0Lo2OeVmiq1OsLHZ/V/+F//597///V/88r3Fkxeu37z3tYtnB2wvr5vSvZbtZTkekLELkRe5KfW0U1jIcXGw4zMyHb5uj83f++ti7Ace42kwhoRQUlpGUxwRMyBKAGFZ8sqV1+bn5/7uR3+bL3jZbCaKgn6/e+nSpT/7sz8Lw7C+s1UuFqMosCxLyJRde4wvjTEBj/E0kBEISqCFYAEoBAtYGo1SwbvfeGtisvy33//rm7euLZ2cP7E4Oz0zsb2z+95776GAfDYLHFuWDMNQ2mMCflYY68AvN561qCaFbQyTEbbtqWzBYdb+QMecy7sALAS89tolIrp06cKZM6dR6P/6//nfR8J56+2vT09W7vZCJgUCDdFRNY+vWjrnr40xBx7jaVBKaa3jOHYcp1gsZjIZRNRad7vdnZ2dJJQSEXO53OTkZKlU8rK5b3zrm7YjJMTlQtYfdHPF/CCMnvd1vLIYc+CXG8/ajYGCALSh0JgIkCxbWJYUkgNf37p1u15v1OuNhYWFTz+9duHiudWVjYmZ2QcP7hcLeYGmkHOyWU9rbdJC/p9y3q9eOuevhzEHHuNp0CawbCEVdHvNdnuXOHI9y3FltTJ56+a9ZqP73d//R2++8fa1z2797KfvdzuDyOg79+5GUdDvdaPhIOM5QRRKaxyG9awwJuAxnoY4jmxbSYm9frfZqmsdO47lOFa/PyyVKtlsbnpq/sb1O67r2ba7vV0P/PBf/+t/XauU7ty80et1+v2+53n4RT0yx/i1oZ5ozJtuDDj8+zR/rzxqI9qUPFXmtL67afV7j7hWjqkOcHpd38Ovy6Q0/BVp7vOU+aTVf06bz1HrMOeciom6g14z57kSKza6/W4968qZyeKp02djE/zV3/0lIp48MxeH3WDQLHN5ZibXDU7cuz2cf7u8OBNttjpFpfqpdaoP//p51Ys+cn3vZ4wvNM6Nt8YxngZjTFJkx/O8bDZLREQUBEEYhidPntza2rp//34QBL1eb3p6+vLly8Jzvvc3P77x6c25qXwhi51WdzA0AT3bquhfZYyNWC83nrX7RGttSzTGZDIZ18lTsGuMCYLAdd1Op1ssli9cuOB5XrVY+Na3fqvd2v3gw/92EFCpUJ2qWp7teyprO2Vwuhi+HG6el84d9auW1ElDuuj4ZUYd4wWClDKOYymloxx/SMxsjPGH4YcffBz40W69CQBTb9SGg4AJc+V8JVfLu0WJg6yjC7nKgw4xDjwY27GeCcYc+OXGs+YYQghEjqJIh2HGpqRspVJqOPRdL9ftdqMonpqaarfbP/vZz8qlQibrbdS3Oth1VVZKadtKm8iWiC9Jg++XngO/8n6zMY4EKSVRbIzp9Xpe1jiWJYRwXZcZb9++u7Ly8A/++E+UUn/7V/9BkCmXCo2txiAc5CdPdfpmeW3HQK5ULIHtmOB5X8krii9rxMIUHMvkxvhCpN3/Y3wuxhhm7na7QRDYtp0Q8Pz8fBybTCZbqVT6/X4QhLlcwbKs9s7u1SunvvbOa50Brqx1NEaIg7jv/wbmeSx40ebzhRiL0GM8DYnGCwBhGMaxZVlZRBRShmH4ta99rVid+OCDD4bD4dmzZ4UQWutBszMzU7Y9a7cdnspUZdELHjZzljcuS/mMoAAeM/Efda/hFD8ni6MNlOY2Tt/80vzDR+xjmer2PppsQni0rTCt3jWmhgoefl1H9SejOFqFdUuGJoyRxJkz5+LebS8vYAfXHraVG//Od7/5o//TP7z30/eXlpaWFmcLNRdM8OZ3v/3//e9/WZtuzZ5YyMhcr96ruFYbXAWHnzdNNRZ0NP982jhIx6MSiiOu57TgUZZp9z8lL/qLTjT2A4/xNGitEdGyZC6XkVI2m02tdaGY8zzv/v371Wr1T/7kT86dO5fJZM6fP//Hf/zH7XY7n88pi8O463pYKRcAOYrGyQzPCmMReoynwWgGIm3iTCZjsvl+b9MYIyX2ewPN29vbdd833W53YWEqny9mM7npianqtGWkBMRCAUMlTBTaVvF5X8crizEBj/E0CGEb43e7zbJlZbO5/tCNsD8Y9KNIP7hxy7bc27dvO44zMz33wx/83ZXL54IgaA+aPeOXq+VBvyELRQbjWLbRX61grDTV79h9PGMCHuNpsC0v8DtDv1vf1bUMWZZt2w5DbFlWo9HY3Gx8+9vftm17bW0FaVgueKsPVvo6NAodRz1cWzl5cTHjOlvDoW0f0TYxxq+GsQ48xtNgWQ4iAnCr1RwMfGBl23Y+nwUQnU4viuK5uQUprbW1dWPYdTPbm2uz0zNff+vd2akT6ytbg77v2g7yV4v9wm/QHTUm4DGeBgQZxaGygFhbluPYWWZ2XOW52Uwm981v/tatm7fv3rl35cobvd7AttxmcyeXyVYr05YqdBqhjkAI4bpjQe9ZYUzAYzwNzBAEASI7jlUsFrPZrI5Ja+153r/6V/9qa2vr2rVrvV6v1WqdPn261WrNTE8i4tZ6fdAJ8rlqtTKlhDAUP+/reGXx5etCH/69SXF3pSr3xyZcHH7i9Pmn1SVOuy2HS4PiuErApJyWUsdJud7Up3q0eWrdQxDzs+e769eGnbrtRsNuHcAoO87nighy6dT0YDCIQn3m9LmTSwVp/ZPv/+iD5nBz8eLlkzMlv35tbuK1T5qmaKfVTz58lpTShzntvqUtH05LQz7i80q//2kFN4+63lL8+V+UhzzmwGM8DV4mK6XUcSgEhnHU6faVZWWzWdu219fXAaBYLNZqtZmZmUKhcPXqVcN6eqr229/+pm2JTMZdWjqxvbNeyHnP+zpeWTwr5eRFDh/9TeJlvw/9fj8KfDTx5EQVQ+g3eoSi1+ttbGz0B0673W73dgeDweuX3vR9fzgcKgszrpysZuv9ztDvZXJeMW/MVk+ldeRIwct+335jGFsXDkf6Ano+C+tZL+iniaY6HHR3vQwgcQwqky10g342m/34k1vT09OEked5J0+e/PDDDycnZL3RyHjWjc/e72pRKc08XH9YLM7r1Y6yq890/s8az+v+fyF+0xz4q5au+CrcB6MH7V0nRttzlJf3APrNTaVUo9FYXr3+x//kD5RSN27c2N1evXu3dPvu3fMnTvaaO151znGcO/fve3PFvOfERwvBfiXu228EYw58OF4WDnxc6zl1fCKgmMMgUujlc9K1OAillN1uN5PJKKXK5fJgMFhfX/dsMTk52R98urK8+tqFq6WF89fu3xn68ekFV1H8spuhn9f9/0KMjVhjPA3ZbFZJlKBNHLGQBqU2VKlUtNZCiD/8wz+8du3a+++/f/ny5Xq9LoRw7CxprpUr+Wyu0+4bVvl80URH5L9j/MoYc+AxnoZ+v6/DIJ/NVMrZ0tRUs9Nu9QZ5is+cOZMvLP3wxz+/eecGIk7V5i5durSxsRFE5htvvLG5ttnf6JbKE66X7/f8jOdFX7lYrN8QFD/hyErJtxQpjtrj6rBOdDRZQKQWUE7DcW1VR4vpTe+TfDh+Rbf8ARxeLI7oeDL4pAlzU2XHfXvz9rWpia1cf2swCBt+01Lu7GzBy1gnT821Wq219Qff/sbXT5+aqOWKn93UP7u7NnFu9ur0UtGysioihWkO2bSnnn7fjvYcpTyudZK2Ax31eR0P9uloLEKP8TQIIWIdKYmOY7W6nd1GC6WoVCpxHO/s7MaxUUqdPXt2YWHR98O333574cTS2traH/z+78zUandu3Vycmy7nbQ66z/s6Xlm8KCL0q+r3e17XdVzn7ff7rcbuXD4/PTXB8WAQamUkxPHubvP6zY04jq/fuDkYDH7rG7+Xy+Xi2HQD/823vhZwLCGen6n1mjsLs5XFauaz9nHN/9VcJ782nhUHflmKmI3xdHiZXNDv9Bq7oT/s9ofCztiZvDGm3x9ub+9YyvE8TylVrUz4fvDjH//k9v2VfD63tnI/HHbiKLh39+ZUpSDC9vO+jlcWYw78bPGyc2DLsmLfb7WbOccWrlOayHM/bO92C4VCt9PvDhuvv3b1xIkTg675xc/fc37r8p2VlYorXcX3Hy7PVa1CqXD77i2Kj2zCGnPgXxFjHXiMpyEIAqMDHfZsCfliOVuqkZDMDCw2NzcHg8H09Cwz3rx5c3t7O+PlHMfZ2FjzHLW4uLh49nw3hut3HpQmpp73dbyyeHFioY9Y9e8lCcg56n04tsbcx8X5EbMZVwVdgWyYw0iDkGfOnOn1+qVS5dS5C0KI69evG0O12qRl2dPTk3c/+ujMhfkr3/jGykN9b6Nx7u23tHKPb/6v5jr5tTHmwGM8DZ1OR6Eo5LPVajWbzQZhHAZxpVLJZrPvvPPO3NzcjRs3e73+7OysUqpQKDx8+HB6aqJcLi0vL7eHofLy9d4QrHE20rOC+lyh4KPm6x6teFcqh0nJa02DSPErpo3PKXWej5qve9S+wcfFAI6cV5xWdzqlTjIiHjwFIiaZqLEeTJ35xubdD4P6zhtn57r9u/X2+vXQ/V/8T5amZ07/13/x152Iun6c7Q8uXTwbBe1SoXLmzdc++vDe8sPW+StfP710OuOWMW6TdpRSUtkAoIkMIwCgUDKl/3N6K6XD14lI6fOMR6zvzZxWvzqtid8zTnJIqeO9/6heFCPWGC8mXMeWHHi2Ew6irfpOGIa5XM6yLESxub1VLBSGNJiYrC7UFrZWbi0tvT23dO6/+m//nda5E0unbNvOZR0Xw5LlJhsNM7+q1srnhTEBj/E0xP22kLpSKffiVrPdHgZB3rMAaX1j6+HqxoMHD6ycCHS8tbU1OTE9MTkthCjlc+WZCyBLa1u7lk3VxeJcQQghHqNhfpLnj/Hr4VXTgV9VP/NxXddRx9HDdne37jpWNl8IojiOYyIycbyx02h1OxK4Vd95/2c/3dnc8HLZX3706dbaw9OnluI4rLeb0hasfRdiKxpIKQ+e5RV4Ii8Ixhx4jKch58iVjc2cFXMcC2m5XhbAsImDMLpz597swonO6nYu656/cG5tbWNjvbdVLQXs7PZgsx1OTs+FoV5fX5+buCilZGZiYOZkFxmz32PBmAO/HHheHNjhyO+0u51WGJtSpVqbmmbmYOhni6XlB6vXPv3s/OlT/+yf/hM2dPvOPYOi3uw+WHlYrhQ4HgbDTrZUelhv7w6TRuHIzAfpdkzDXx6vGgGPcbwIh12FxpJKa+3YXj6fl1K6np3JFi3LiqJgbna6VMzfunVjMBjk8uXF0+fbve6g05ys5U8uzRdrE80Ab20P93XgBK/GrvoiYEzAYzwNFAW5XDaby/i+3+514zh2HWt2emZ9fV0o+Y/+8A+DYPjzn/+0Ui5PTk/lcjnlZvv94ebGwyuvXTx3/vRn129sNvvFmdNjcn1G+PJ1oZ8PyBy+9aTV701D2sJKX3CHV1dkOLxoDKZVYzyqP/m48q5TjpcoiCgxESceYGOMMSbK10SU32n4Fy+erxXtW9dvhFotzE/UPPMv/vSfarv045//Q7fL71xZLGbqw/ryrpMt1pbefOedRrtx98HqZHEezcAOuxcd99MehVbZlRpMSErG2rgiMuLw/OqjkjunxhEcteH14c8rjdExHrFYUIq/Om09fOHGN+bAYzzC5yXbXCY/Pz8fRUEQ9put+vbGOrBavr/+5ptvzs7P3b1zx3GcMAxty0IpcrlcxtKnT53Y2W19/NntnUbfdTKzMxP+cBdHjcgf30DSyq6P8SvjVbNCPy9RLfW8aYFnx9TJ4cjzScNhjllE1DHbCixFzdZmD3xlyXy+GPqGGMIwrNWqQd3/+jtvrqw8ODVXXlyaXTq39LNfrH5ya1t5NTdXGfjR3HS5kuVgyEISIh+k4VdArj76JaRF+P2az3e8BY4B8PhCPGiazmWySObkyQV/2KzvrJeLhWAYSeHW281mp722et+RyHFYLefbva6Ty5xZmgiDnutmzp69mslPNpvdyO9mHRaCcPRiFLx3hvHy+7IYc+DDcVw6Z2ro9wvJgeFzlByHQwSRy7oCWcexBIxirQB6/eGDBw8Q+cHyrXq9/ubrX5+ZqgyGwaDbq5QKhdq8D44fxJVKCchkHFsqECLhwLw3uNiTq19iPGsO/IXr4VUj4DG+PBJZOlki9x/cmK5MD7pDW7iVQk1rKmRzWnc367s3b9969+u/tb1zH0wUR+Hcwvzyw7uCBhvrQ6/krWxu9gOT96r36t3FxaLIVIRkRAKifSkaUUJK8sAYvyLGBPx88KzVvyNz7L0o5Sf48HC42xLCkuBZbjFfNEMYdNsAfU0mioIPPvjlldcuLy7MLy1dvHfvzsr69cnKdza26hPsUNTUw9AtVIzI3V/dnb58CpERGcUo4QdBAnNadtEYT8dYBx7jSXye5hFRWby9s+laNpPI58sz03ONRkMJ8rwsCGw26rEOf/f3vnPzxrVffvD+YOC72UqswbVlKSfjYWNqsnrh4us3bj8EABRPSoOvgBHruUP9ijfxqPf6yPmrR9Q5pUo5Pr0x7pHOm9qXNc1/yyn1olOnk1J/O+W8R9aZ9xJq93+YfNApfkuJQmsjEDKeEwchAlmu3NjYspvDpde/NmAatgZWT0ZB1y26OdGC0Aw6/jvf+Ga2lPvs1v0wVnm3NuzsuBLy2fyd2/dnJ5z/2b/4Zz5M/Dc//HRQXAKXyk5x0HGMii3biNBnsgIpj14H+3AwHT5Oaj5t6no7Wh3po9vh0sY/Wr76PsYi9KuK/YUyIuBkJaQ1IhcCpRTISdAyIzAzg6GJucX+MPAKmcUzixDjTj0uTs+fnp7rdJtnz56emZ7drK/V641yYbpayn/22ZqJom63ky9VVU7d3agbqYSlpitFAQyYaL8CQQICIgLi8ZU8+IpCvGJB/2MkeOKB8h5QwKEvqdBSQllgCbSUUEpYEgHN0rkrPT/SwbCcUVFnZ2ttpdntbnfD+YWpq1dfd91crxss31+LorhYLExMFgK/Nzc3d+HKG6HyfvrpvQfbzWKxWMwqxZFgnXAZRmCUQgg5VuC+NF6conbPBy/aPI/LXXTQQ8PMyVHMnFaKSAlkFBLRthBYKYECWUnM12adlQcm7oed3XjYdS1luZl+FF+5eqHfv7+83CqXpqYm+1pTrMPXXr/g2GpqutDpdbfbwz56GZK1SqmQUxZrGzQyMSJBktmAEkX8glmhX7r1MObAryYS+TR5CYHJS0ohkA99STRKkCWFsqRrS89Vtq2UwEEUTc/O5TLZ+tZm0O+UK8ViocxCImK/32+1Wrlc7uzZs6VSiZknJyevXr0yUS1+8vEHWutzly4RqsGgJ3ToCa2QJTLwKJIjOe/zvk8vPcYNvl9ZHHQIPfI6pAT9I7AQAgVJNChASTTGEMedZqNarvSotb3Siv3IVUUYstK9lQeb21s7YeQvP7jFjIVM2VaivtMqFEtL89ZctRBnnGzG6XQCIUmHPUeUXGGS/YNAAJBgHivAXx5fdSPWi7bRHF+EltnPTUhycZMjVarVNEREicBGG6ONYB0FoT+gbku6IiYmZVuurSxHCch59s52a21tI1eo3ltZbTRaJ+cvXDi31Ov2P7r2qfHlmYWJtU5r/f5NO7dULuQDf8OVpJClAAABQKM0BjYvmiPzuLwtx4UvXA9fdQJ+5TGKOd6jYfU5Z+wIhgUQMxIRGa2RoigKgkDosLFbDwYdlLalLEBJJkCM/WG0vr751tyJYim3U9/0g0GhUDIat3d273xyZ2qixkF/c607eaLcZNPYWZbfOqdwNAceGdV+szfiFYVSqImREICR8dfoT3s40vq7phtdjimGOb2g8JGQuvPh4fmimOofTqtXnOI3PmJeqIUZIs1khAApSCIQaTIxCZRCKiUty1JK7RfEcCBgRmJmRk1smJiRALSakEJTHESsAvJcUDrc6e9s1x98MDV/Ol9wG5GZny6t3rpna67OlH227m21M7fWzs3MzOatzMz8xmDjs/c/+Md/+qfl829EYSh6jO32IO9nvPw7X/v6//DjBxfesWG348FrsSsDObStzMA0Lcwefh9SkCqJqDT/aorbjFLytI+4raQ996NKTL+25PVIuHrK0WO8sBCShUQhQEiSUlgWOq70PMezhKvQkWAhSdZoItAhx4ExhojM49BaG2PiONZaJ5+TDwCgMmzZWN9uLs5dLObnJydO1Bvb0gosSzqOMxwOM1727Jnzw2Hw6afXBoNBp347m82tbvhDLWbn8hNVvnjx8uaWYpEjdFHYjIRgBLJksMxYAPyyUEIIpiQU9nnP5TjwzA3packkqZUfnnXMs0EkEAYFS4WWhUrZUgjU/p7kDMyGEjAxK2ZiZiImYko4MHNMMUKMRhsETUIix3EspXSquUw+0+343d1h4AQETrFSzhZkr9PK57ITE1PZfIFY+8MwGIYmiidy1Gx0W74ahMNLZ8tz55YGIFc2ee5kmSHLIiIDAFoBSA2SVPxiqcAvHL5wPSshBAMYerHccWP8iiDWxAbAMAMgC2HbjrKkyLAFe4+fiIwBY5iIQ5DMCEQAIICZgQgEIhvWRitgYgIQRKS1dhzHdmb6/fi1C+c09Rjl6vLDU2cuup7ud5rVavni5csUt3/50YeYL77++tV7H3+Yh3JdC1nIWWE2l8sh8XZz28cI1CxhRihAZgQWCMKwYCs+cuji0Vr5PC/8xjyySSw0IyK/cDfh18HLwoGPS1sxxjATAhOxMZwwWpDCs9W+csTMieRMRKQtZhRExjAQAxkh0DCDSSo2AyIKFImZKZPJCDz12Sc/qXpi6XTp3urGyvpqrXgRWZ5acjYanMnlm2tbaxv12bOVxfnFqL67fntw6ndPt9dWi2qq0e7fXV8unbpSm9WJHi6lTGqHCSHGbVZ+FXwxBx4d9CoQ71cRTAggmQEE6RhioaWUAmRSS29k9WVGRGOMEAIN7ruXEJPU39GRzCykMEk4NFBCwF5+cnL6YaO+OTVlbt+9UazOKivred4337lw9+Fwc3Mz7AdTswsC7WEvmJuapj4XsvZkxe43eg/X+tX51zuDYKomLQoVBpZEBGkAQQgClqmWp1Skc+AXawX/xtyTYv9840islxGIFqIl0AaWTFLHEMcUhSY2OnlFOj74/oTtah/MnFSlTIZN/rRte6uxNTs7LQzev7UGlC0UJpvd9vrGfceWlqV6/aEBmJiatV0vinTWy73+9VlLdmXkD1r+bp8nTr7+cKOtYttRAyl8W5IAYJYsZSxIy7Hi9mXxmA1hTMAvHRCUQCmEEmgDIJEwGo3hSMf7rzCO9l8Jucafwz4BH4SUkq2wUM6WS5Ot3fD1y98KIuz12sKJVlaX4zjO5/O25ZKBjJctFouIuHQ5f2KxtLu+nffKYGU2W+0gEKZnWyJQGAk0yIJBMgiDoNWYgL8sVKw1AEjExAFsBCX6EiImDakSr4PCozVZPy5/8tFxZKnsSEhrB8sp/yFS5kMpIh9SSp3klLxWQf3RB4FSjry+KCUhxIbDUPsxhRpjVhoQADlK5nPg6SAAQmS0sm2wrTCICbSLMfuNVv1BrTLLSq6HUY/4s1sfX3jrmzk1LVr3r9/ZHbTWG43GzZs3FxYWmDknzlQqld1VVVvsXTxZsLA8mRVh9N7M5RlVnPO2f3bmwlzgdNdp2c1dGuy2ihk5DB0l027o4R4mA9Gh3ys84npLC2hJQepzNyl+3ZRMq7TT/tq888mEfuQkoxwREFkgoIBxyPmLi6RpGHyupHNsWBsyhskAERED8N6zPQxCCE7CO/Zip40x3W7XcvM7vW6v06mUctL1es2dXtSeFINeT25vb9dqtQsXLrRaLUTMZDLVavXO8meBuDLso1KNrc0HudK8Vyitb9yeisJe37ecLCoZBaExMRFK2wJztAL3YzwBldgDYU+9ZgJgRBAICIwACAziqNvbGL8poFC4bxpBQYyGgIGD2JCByEBkjDFIQMSC2MiU2GMhhNF08E+tdaPRiAO/WJ30XBuNP1Eoa4wa7R1Vlp7nDQaDkydPZrPZer1+4cKFmZmZnZ0dHtaLPdNqBfmSDsJ284Hl9NwWdF3X3Wz1oFxCwCiKbJUSCPWF1/uCaXnPfT4iwf7+jYSCRfJCQjCjbzAFz3f2Lw6e4/3Z338TZSdRcf2I/dgEMWmDhhInMIFJ1S+SUj77zDwh4H6/X8l7p04uVqtVE/sFR9Cg2Vx7YIFutVr1ev2jjz7qdDoXL16cnZ39+7//+7/9279ttv1Wp/XZ9c/qO+1isTocNvv95vmzF3xwttoD35AxRgBlMx4iRtHh8vAYvzqeFKEFJkIU7lsy9o/8zU9ujC+EJoJ9DpyQHxEAECMRMoMhJCIAHIXapctS+ysBEZNoLcuyJNNnn322+XDZGm5XM7hWr/dbdcELSqkgCG7evDkxMXH16tX19fX333//1KlTg74aRq0bdz7wBxcvnj07PZOdWJqenT238qDRj0QOHZSGmYQAkxIt/3Sk7onPSc177oElj8gy4RhC4oHwHUpyEhCRXrC0uzES7Im9B7uiMAAQCAA52oJRAJuniwOJl3jf+GxMhIi1Wm1+YbazsluZnou3uo3des5W1dOLAFAqlSzLevPNN0+ePLm8vDwcDr/97W9LKaOBzhZcbaLl+w+Lbv7db19YunDqzupO1yh288rLKicIfY6CAJEs24ZxCOCXgzhoAkFEz1EZ1/Ic5drSVmhJSF5jPB3PS4Tep9snThqzMCSIEVAiohBKgpTp8zHG7DsOEvlZSjkzM3NveQUtZ3rhJCg1GAwcS83MzJRrE7u7u6VS6cqVK/1+/yc/+cnm5ubJkyfX1tY2th8oWcrnp0rVTHUi47kl1qrX29bSs7NlFpZApSxBOv71bs6Lpso99/k8xoEBwHEVIjKz1gwoIDaQtH0YaysvJlDy4ybopAIWExAioJCQ/BchPl4p63EQEaJI8nQRkYiEEJVK5d5nH06cn88VSq6Xl7GztrYWxsGVi3/Y7DTfeuut3d3dv/iLv2Bm3/dzuZzjOFtbdyxZLBfnF+btK2+c6bW5c2NVWlq5E8r1wigmIsdx0DAixHHsjAvbfTkoCWwLtpW0lJAI0w4BAKOINMRSaMsmYEQZ5UKtKYpNREAsCSSxNAQk5RNBPCMJ/JiycI5LRE+t83zU86bUH4bU/rQph6dt0mnBSWn+w5Fj8cB0EQBAHZjmSMpOqmHQ4QNlUYARsSXI5iDsZRyr55SdiZqY3N5euz/l7Zx7fe6DD83DrezGnfWrX7/x53/6r/7Lf/P/+t73/sbJKFtZu1u9z8y9P/tn3x34w+vX/92E17l04ndWt9qcmyhMfe3e/WuXz2WL/TAXB9N2dGO4YueXIAbmMP0OHa2ec2qFy5TrTfPDp9blPnz09HzdlF+kRsyn5p+nHL93WpVVZElhK7AtIYRQUiOiYXYUCgAhGViAAI5jKYWjbBaSWEYxDQMdmohkbv/0Y334ZUaSU6owWTJs4jiMo0CgU8pNbKxvWFZ88ezrk4Xg9md/A+TlPLdaLJ87c971WEqnWY96vcE//OyDd9752ne+ffmDn17/7NNrXT1487fOdps7u/Wt8+fnWbddT8mhcEWODQGzQgU89gN/KaiShxJZSbKEEYIUGhCoEA2BkGAZkeT8KyEZhRAShU0MvgCL2GfTetyUiHthAOnZOUfDy7IppHLUVKPpi3VdjEAja4hE1qxN7PcHfXHx3KW4t3X/+gOjuycXK5OVatC8Z2Fme21rd7NeLVSyZUdrsbR0Ulr2g7VbC5MTtUplemb+1s2tyZJbzjm3N7amq3kz1CGtFguu8nOuKISGFRqBthkTMAAcff3sf60KDiTZoQgERIAgUI48wwxGsBASEW0rQ0TGMHNMgEKx48qcctr9R7x3n3p/Exf2nHDU+aQdf9SrSj3+2AZiAAQQiCRAchwHfqfXDk/MV+vd2LZVYMz62vLsJNQmimTiB3fu29I2jnywvBzEeGKpPDkzNzm3WMrndEzSzp06dzFXYMEBxGZpaTYeanC65ZK268yRMhSgDDUZ6R5NtTmu9fC8xjn21axsJYxJ3AfEApnZMDEJAtBEzMjAQqCQkogNETMjC4mACpS0oP/oMg4G4o058Oj7tB+8aNclmBGAGVkJjo2O/H6nH3VuB0PudTKuFCjW1q53Gjs2twyV7t5bsZxMrpS5+/BO34+6/d6c41y+dOZn3/tv+ua1O8t+szHIDwbAXWkvhtFwd6ftFTvhcCBFEUhLKT3X1ZE0L5gk8rzw63PgiJhBEgJIiYi+DoUQwEjAsUnsH0YghKCBGBEtqaQQQGwYKNaIFow58NGPP+oGd1wcOE10JyQACSAQEBkpjsJBpzcMVezXvKwUrhC2HwYbW8su9fPvzu4Oop9/8PE3f+vbkzPT93/2i2JpZnqi0O/s3L59Z+LsySFmsrWSzRvra7t2aSJoCxROEFqrq33p2SxDy3aU5RCZl52Anz8H9jUYAEAJoECgFccIkhEMo44pJsMgEE1XxwLBlsqxhSWFACBD9HgLvJeFW36VkWrt3Ht0EhEZ2OjYHwL3TswsecIKg165NF0oTMbD+2G771kF4YWr2zunms1SpXz1jcuTtdk4bG2s3Gbh2rlqOTuNWvJw2GjfCborJTn31tfe1mu4suxX3/SEHYQg434Ym9h1j1aV8kUToZ87VEQYMzAAAYHArLEQ0QAbTZHmSAMAMcKQARlCaUITO5ayBSLIz4dnPWLCY1oGgBcv9C8VSAQgGBN3ExFpE3PkA9s7W20Z6sX5OTvj9Nq6EUaDHm03WvNLp1fWHjqZ8F/883/24N7G/+f//X8vFMB0EJUrZWHtwUbFMScWT6ri2cWvvW5J2ByK7Y1G9S3pZIxAj0O0RWjG8QVfDmrChUiLWJOOSRPEGSupV2gQDAoWTARMTJhlNhQZRkKthSVsCQo56zraGK0pJkMEgIIRESXS8VgX0wggre50Go7qB04fP61u8+HjH9XfeFSk8ZHU+YsUkZ6znmVZltXvdsoFd+1+x8fs0snT8zPVe7pP9tyGnNi5/RBjSSokpSvZYOP+L6ampt55/Wvt7fX11Q3Pm0CLrXg5NwwCajTb/mo7OJmjb13OWdnBL+84103p7OVaJmyajhy4sWOJ1jAqSedY7kMqUq73qDg2zp/ir0ZOqaOeMs5+ZqgCkTh8EQQgsjFJzVHYy2QQAASAoLUAEmBspVxHZWypJEjARnwgguBVEUu+miCiKIqSMCwiKhaLmUymUd+dmZzqhf78zIwHor9Dk6XzJLDb7iydPPOt3/odL6+u331AmLny+pvN9trE4iQ5yhHqzJn5zWUfwpDIZc4J3c+5tpRIIGwngyhZR1klXzhJ5GWDMihICEAAxWg4phhYMDMxshAMBCARwGGDiEpKz5KuI22lFAIgJWkrMBKbf3NP41lvFl/BzSjJRpRCEFEcx7VaLZvN3Pnww9/6zruTtWIc9NYertgQzdWq0yemtlfuLS2eXTh16cHmyvJmf2ZiYmZu0fOgE2zbOVsiDHU0WSvmyI0oX6+DCdvlwqRl6YjYcjxhBIdRxnV0/HLc5+e13r7QriRiEhFhDBAzx0kaGjCjAIEohJBKWkooK2OrjK08S9lKSkBjjB+Fw0FwMOXwYERuWpD3ceEY790YsFeVkoiS+q9xHBcKBdu2EWW/0y45oruzGYb+zPxCd+gHUfj7v/d7c/NL281Bs0/FyRPKLYShmahWVx6sCxPJsDNsPqxV8uXp2fWeXulSOOwUC55rKxOHUkpbKskkzVcuFenYCUH5McWGDSXtJBES2ZrFnj93/0+dVCEl4EgTM2sTU6yJk5Iu+1T1G6KtZ03DX7U9QgiRJCQppQSaKIqk9AB44fTpOBxur93vt3uxP6zvNlub29H52vk3r/zkZ/fXlpeNJTzPC4bB9nYAVTFfncroOI77lhkYKrdj3h7oKFuwomHFlnbMwoRSRBI8EiLSBtNqYr1geGHXg/IjNkyagAho1GRlT+9N6r2DAGAyo8RgyVIIZAZthOG9jFMY0fvYkfSSIiFgy7KYGQUmpSodW00tnggad+7e+ixbmABDt27cKUjfFXasNXA8U6vIjGp3+uh5k+W8EoOvv3n15MwkQ/SweW959X51ainjVTrdpm3ZxFpxULA8D0JpQpROGPM4UzVB2gbxhSK0Cg0RIxGZxG6lYUS9kFCyADAAAinJMQLBKAQSCENgCPf9IYgISYHDA0EdLw6O6qM+6vxfdh94IkIrpaLAtx1JREEQFL0iZL1gJ+z1G/lc4fTSaUKrs/7hoNH+5Yfva+MXLdPYXW83mvnsrG9TbDoTp6ZylUwhU8zc2zC7bUdwxoTt7sCrVQa+n6P+dKHQ9Glz6KOVYekAPCUh6QXCca3ntHFSA7HS/yP5VxGjAWDExBhFxhygXgYwzAhAtrKSVpSaAQkNgzGgCWxMBOjHTvmSL+avInCvgUMURWQ5zKy1Vkotrz+kTrtSKW5urNul3Nfe+gY17lSy+dW1uxnPDv32/RsfRtoMMsHDhyvCjgtl3O3mLLtUqZVPccEEcW/n4Xx+Msrnu0HEGJSzVjZmjiO0smBnQL8cBPzCQoXxvgMKEdDsp/jv0yQCAISJnDz60gCCVCBBkkHYC4RHAECQAIBs0uqnpfjl0uoqHxXPeqc86vFH7RObdvxROfxR/cxhJPI5jyAkiS7rGdPVO62O5Ufd/NcmT65/eOtUbWJLmt2gudnZKOQX7v1ixZa1hfnTljuzufrA6u6eWJz6H//Lf/zXf/vT95Vz9crM8pput4dFV9UHO46rwug8r9yMczvbvTLbWS9f3m4PporcT/EjcYpf/bjwvCTENH8vI+EBA/D+u0irH44jlTYRkg++xvgqQikVx/FgMEi6rvi+3+/3fd+vVOW9tTu5yYnZpUXQ/sqta6VMrj/Q01OLP/vphxsbm5cuXZiZmapUKn/2p//8+rV73W63UqloraNQZ3J5aVkmjhFg0NwolAvgVT64tszGuEiVUiHAZ1uF/yVCumh9OMQe1JOcL2WjT+cAqSc+fJy0UcZF8543mFkIkc9nMR72+/1w2M9rjdze6tbdah68fKUYXL9+08lbAp12MwS22u12pWafOXvq3Jk3f/mLTwwHUeAP+11DMDE1dfPOim2GQsDWxsbJt0/PV2Z62/bK6rW5ywL8NqlaB6IsPONIrBcM6Zz/sYI2+4c9pcNJcoz6nOz6chDMcYm4zxovyzy11pYEKaXneUGvqbUulUpzc3NrayvZfGWt3S/2w3Iln7PCzbX72eLvdzvh6VMXcrlct9f+7nd//2//+hcPHjzUZlCqGK11vV6PyNvY3FG6t7gwf27xdPXEhGF1o63BmbGV9MA0Iu3UcthNCbl9Zd2ET6Ovg9Q7EqFTVKGkiBUiCobHXsc1zTTWf1zHj3G8EEIQURiGYRh2Oh3XdWdnZ0ulUtYpVcvzllV0ssV6q12erIAIBQ4bjcb29rbjOL/7u7/73nvvaa3n5+fz+fzO1lYhl220O0M/tjPZ+s7ug5X7F8+emSqV/Giw3eyXJ5favXYmY5sQ+YgNil4BpK3z/dX+BAdOO5L2IAzwwVfaiTkFv5nL/jyOa4N41hvHcc3zuOaTBtu2kxgsIUQURfl83nGcW7duzc2eUqguLJ2YmZ6787Dh1RZml04Hg67t8ORU+Q/+4Pc/+fiz3d0GsZ6crJbKWVsyABVLpVPnzueLJTebQTKtZr3Xp8Gwa9nSzWa229tWNs/oRoP+s76uNBx1PRwXvnBW8Dj1Pv14ZlZP1r0b87wXEs+ahpN67p7nWZYVhmGhUMhmvfdv3zm5eAmj7sz0RK/VRm8yN33FiUNX5spV9923f+enP/1pq7MTBnFA3aCUmz9R9sQpgZjJF7zKhB/qy5dfn58sKIn3V1vtYX1uchJZawXkZLLZipID1F/g5/yK4PNEi3v9FZ5+vGCEg68xvpoIwzBRq3zf73a7mUxmfn6+XC7XdzcED8lv3vj0o/lTr5WmzxsqVQuz/8l/+i9/8g8/WF1dUco2hq9fv/5g5U4mK40xnuf5vr+xsaGNqdZqcRRtb2zGxhXAp09MK8l23msPQh2jGLdlSME+faZJvo+s0OIJU35KX9b0wsRfMINfcRxKeZZpebZpSDvvU4wBh35/1P7GR81PPuo8zTOO+8eMVLHlY6Cou5jhXK72i4dbp8+86ep1y/ZubG1geeHsuXP91nqvecPLVWOcLJVcpdRg0KtVy5MTJQHW9ppeOnUxX5vsbK7VpPPdNxf9oby7vjUxbXEBby/r/ER/sijMFtrVRjc7XLOylThtRmmc5/Dnkt4CN8XvmuZf5bT1dvg4qesKo2Q0RCkBkRERkYWGEB4XlRM9NDIshVAISrAUoICRDCAZOz8qWHcAAEB78xw7fscAACDSgT+M49hxHCFEEAylxGq1GkWRJTGfsVwO/OZ6v7kVhcEgjDY214qlHCJcunShkC9961vfvnz5NTI4PzMzVS3lPbdWLU+US0yRrSDrqEGvMVmpCBDhoF8syHJRsg5tOFrX+JcIjFIIJaQlpUQUkJSLJCKEpO1n8r7/UlIKAUJCUhCWATgpekO03zjhUKvTi9JzMJ1jvxzuhGetoz7r8ZMOBkk1rNAf2paxLCUAG7tNcsO43+jHTdKWPxxGod/z47t37wHodqc5vOlvbtSLxcrC/Mlez8+6jmtZkk3es7oyDAZN1yIJYae+NrtwRrE1HHROVAuOF5ggclwPMaWmzhElu/S7kzZQ2tfHsw4RFSMKSGoEMjITMABJ+agI5N6RAAAKAUVydaP/YhCEI8nu89T7SAc+0rS+PH4969wYzxoSOZfxXFsN+v1GvR6FPgNtb216GTcK/HDYduIBdDepu+MqFE5mfeOhlPLcuTPNZtNxnHarW6tNfv2dd+ubG4369qDfDvqd7e2H3dZu1lOdTj2jtA2wvba1vbkRBE3bjjwplH7pBcCnWZyJiYjJIBMiWAptRyolpEQpUQhIXsmfUpAATgInmVkza+b4gOT8ROui/ZOMOfCYAwMAGBMp5SghAxM3GnVm1xYTURAUFPajyJKqVCj0B4Neo27bQax5Y30rjvjcuQu+78cRl8s1x5Fexr5/d3N+cVpr3e1219cfdrvt6lRhe23Hqsx12rtbW7seDuq7zfJ8VCpW+6Hho9ZDfkk4sEXAzMAGkSWCLYVSSkkREzIDMxDtEyQAAI/YMhMRgdjr98yw5yp/onLz/n17UQh4jOcL0ibQATmUcWwdh+2mXy2X8467+3Az6LWnq9nqxHS3t9Ft71YqDqEQ6HXaw53txvT07Orq6unTS83W9v3lOyoKwpgsOxMYE4Rx1x/0/WEYB1v37s2fyc8tTBfdSr35ILvRsXMzisP4FU1GckViJkv6FqFlgW2BlJwQcNJ5OwmlShAbaRJOi0ijvHwAkLCX/PCEi3i/eeBvmoCPzkmOh/McV1ZQGo6LQz6v2BglBcfaREYyW1LlMjKb86hvOBwys5UtOMUai6ZlOcVi0fOcVnNoWcq2GufOnTl79iyRvn371vbO5pm5+ZgwNBzEbHmZ3XYHVh7k8o7RODFVmZw/AeHgkwcPcrvkFhVhGyBzpHm+aBw4bT4ZCUIKhVIqVhKUICFIIGiR5N4TIRFRQr1CiABtzaiNYCIGBkBKkosogj2B+eCHfafHS6+EjHEscBzHdV0i8v2hlGJqaqpcKHa7XUsBM/sRtwdRazAMIk1EvXaHyYoj2Nra3t3dvXr19WvXP7l95zNEM/Djvh91BkM/DJXrttrd5dVV5TlvvPm1E0uLGkyz1et0wLIrrpftB43nfd3PCq4lskrkXKvgOnlXerZwBCmIHQmOBFuwhWQL3n8pS9hSKTmCQCWEwsd9VIcq2uoJh6dJiS0XMiVvk464BaSnIx3+dQpHSqu3nIZ0BpnGmV+sfNRjkxRS8lEzsb8b2+VSbdB84OWdwtR8LjdRmQ5F9fXejY8uXX6XSyfrt1u5hdj1AtuyBsNWEA2/+wffOXV26Qc/+ZGTrUwtXAWWOmfPlnObhtY2B+dOn3v7t+GHf/+LT/7+43/5r/Jn3fm7q+onK9o/+4bMdiaDDTc8I7xUR3DK9R5utUZpH7wh+7dFEB9kX/vHaKBDDagS5UGj0SNjr5AwYneMQMiQeIaHXBDIEklCKCH2bM55tuvIqmgCCGDBDMxIRhARM8QQCEQbAS15cFbK9IxAg6ANxKhiYwwDMYK3Pz0+uFY1SWZmemGMWGM8X7jZTE67QsDu7m6lUgniaO3mzclaqdkeJOUp76w+OHli0QtkTTT9/kAb/uZvfWfhxLnt7dbWdndiYnp6Lnfjxs3tzQ0lpNZ6enre6CjruYY0Cmmr6c1OFFvkyUG1crrbq5tJR9qd4xKh2TyuK8KIMk0SssGAB7qFMAMj76ugj4+jEVHgvig9OsZg0k2IAUCMKsAJBMhCoARKoS1hLCTXYk8ZG0EqFwCQk1qfgAASJDMLhAPFH5NmgIyIIKRijA0CAhskBDaIhJwSQGIJAQBGfk4HPi7OcGQcTVV58br7veTodHvGkSjkoNd949z53c5gdf3GqYWZNkCukIvjcH1988z5C9FW3WJpjH7tjaunTp8ndIVgywrDWM3OT525gHevtyrVck/LITjZjHKVuHj+3HxWE0w+7CFznJX9xVppZ213CNJyurE+HgImHlWoANw/jBFRoDwopIw+IzLuxUs8kcRnIiWVUhIAtNZEBAyIgoiRR/5YSvLpUaIQBRhYUtkKLSUciUqyrUBJQGElYyKxEEwIKJmZbeT9SzjwARBtIpYGUTMAE+8z+pSIQInMjPzScuBn7VZ5Xlkdz9wdlXJdjJZjWUihRM4XSmuNLgD4wy6QjqJoe2PTkhj4/ubDh7Lgn1h499LrenOzHmtZKU8Jkdupt/KldnWiUnj9slIq0ub9j9//8z/6jiv5zTeu2IN6a+hH2UKv3cgpUbDDhm3aw25WZTvmaNebFrq4n5g4usDkT2apJOwFQhw8Xh0IYzwoS+ddaVkqqc4ZhiaKDBEhI6DNwAzMAhAkSBYChUAFoAQrJS0LpRISkZFjIiKByFKgQikEK0FIBgHVY9PfJ2ZmRgOY5PkaBAuBkQwypVipkAwjAP3KHPiolTSOjDEHfq5QTgYAQr8nBba63Yjg3LkLoKNms5mlIZOcWXxte3trc2t9IVM8ffLUR/ea66vr+VxNACuJJ+bnPdvpd3sXT8zFQXj95u3rt279+Xe/OTddW+ua5ZXVwrRfXvjm7u72fKEYhPVSMdLDppepdI44z7T1phQe1H4f6cAcH8wBOGD9YWYGBGZGQAQUKBCxZJPjgG0jM0SWiCJBBIgYo2BmbUgTazLERGCAIASIgaOIFAvbJOMCs1AYSIGWFI4iR6ItWCqUiJ+3QYw2EdACQDIikwLQQBIQAUyKzSI5O3Bq6sKLjpedAx/VrXXUcY4KP4yEMSL08xmv2xt4mUIp57YefMqkbUsqhEqxtLK+QXFkWzIIhqB1KedNTpejSOsYa5N5Y/SgP7DQrlbL0nbOnj3LFF84vfjZ3/6i1enKipwwPQ4CrFW6w0HFHWSE6LA4+nNMu2+j0Ib9gKXkzkgKD1KvSIRfRIHEwKMUFAaBQoBAwKIV2wptBVJKdiSRA6OHIgxRZEwQc6RNoE0UGyIIWLKh0BBqKZARk+hlzFpGCXQVIaNEtASgAATiA+tqtNck/mJjgBgYFKJBtAUiMwkEPjyJRQpkZjIvrQg9RoIj2yzS/JmW7ShpOXbg2kEY25Vqs7nbrG/PTk3CsEVE3W6nUix1HMdxrNUH9+vrD00ctOoPCLjfa9e371tOJp8vKqRSqXTixKLTHLhKTlaKjqVqkxO2ZQWt5Zw7NN50YFS1t332ZO7j3hCgcBy3YWTESuxPCYkmd8ZFsxf2NCoEl9QtkHspPgkVCSGklIhYsntKsSXJsqxkHABABEGaATWLmDDU0o8pik1M1DJOHJs40sZoAARCQAGMsRYgUQvQoz6BgESAZNiGR2LCgf2XNIIQzAKEJQWikBIMo+DDrfRCWIaZPm/EGuOrCSEUABuKB4NezKLgZesPlzOuV6vlNm5v9v3ecHPzxKlTG46T8zLtdru5U69MFpuNjSAeNtudtYc7s7OnyvlCLuN2u11N9ODBA3x9no3O5XKDlud5+WFruZjPsg0EpahjTlQKn/a6ANPHMn9jYhzVapRCgBAjMs7bzr4CnFCpSrytrA+6i5L/EkIUUAqBUrBCkgKFwITSBfgoFAiLhRWBiAkjDZqMiiw/jAIfo5iZBaMCRgKMtSAYZeoyG0469iLvi8RP5CcgkABgHBWaZEQBUjICH+7XlUoxs/68Dpzm7021hqXV9T2yfzJl/BQGI48rQitlmKPm96YjxQiRmsB6PP7t1OFTfpB1mQLtNzoPb342O1+uPwxmJkrDyProH/5dbW6yXC2eOVMJB73GZrP2j7+jFe9sr7GCYvXktQ8+8n207PLi/PRrp+fe+4cfgzI07GfAuvHQrDRv2MHDghjGG2trulo8fzXe2FGNn9xtP1x589J8wbtXD0gIQptRAoBEVqgls8THFjrs581GUgIKIaSApD9mcqVCkhLoWODalLONZyvHYilQUngwhmmfM4c6PpgekFCvECKvJCIKgYiUPKPkMEt4CSUzB5LIZWbBjKyFVXBIS22MOWgSU/BI396TCBQAKiP2LirhwKNrjPaqcwoAgaSQkk7UGY4PnT/RABGNeGl14DGOF1EUKRCImFSHdnPlXq/XrNeVUkaz7wfAYnV1NYnWAsBWP7C6Q2F1MhLDODx5cumtd775g7/53vmlEoJstRpnzpze3d2aUplmq533KkNFVk+GnU1LedliuTZZfLC6ka1UHTUdMxmIiVkIoQQpZMm8H140MvHA6N1VIAUKwRKFRE6csQBkC0ciWBIcBY4C12JLgkSWgvd8uYm3BkbuJSkO8pgkMQiRJTIiIJBAMdpM8UludFBt8RQSIQlpDOwbyYQQCOZQwtPwZHpg8k4pjRCSaMv9cWBvBxZSMjLAWAdOwfEZyZ5TltURj5dSurbT1zoIgji2c0oFfhAEweTERIwYRhhF1GjUZ2YnpJRRFLX8aPv6jX/6J0vzsxOXLpw+sXjy/spqLG1/QFFMnW7rzXdO/M33flibOr272+5JLM2VB2trk4XWN99+S0q59fD+e+//4s/+8XcLgkPNodEMpKSylFDAgrX1qOvZY0FUrmQhQAALaRQKkaS+M9jKEghKgIUkhbHQCCQUrMS+m3f/hQCgxH5fPoAReQAACxYHSe4R6SIhjtTW5JX4m3MSiZBZJBLbPr9VBySvg2vJT4K5HmO/wMyQIvHpWBwcZH8oBEFIjKCekTn36MM+H79r2mmfV1LB88JwOLQzqtlsAkChUHBdd6fRIaIwjNHOZDOl7a3dVquVFXGztRv5xsqUGEQUht/6+tuWZf27//g3W81urlTrDeJhEPpBEIbB9u7GJTrJaF2/fvdS5c2luanXzi1kZdjVXii8QmXCRlPyRBhrPzYMyrbBtlgBsAFlPZY9t/8hq8JE2FUIAjXulcCxkQFAokFgiUYgCWQAsmw8lAPbApM43b0HzcwEAMY8xjBh5G6CvZI6vDfIiLZzwhzcCBKzFyJIIQ93a6mE8+MeDY/kbpkSwhw8WoePubIZQCIy0pgDH46vGgd2HCebzTLz6dOnL148pbOleBiVOGptbEzX5ueXLt+8dc+yZa9dD0PfU9ba+rojQCo7iKL7Dx7sNlqTU3NWJpcvWFGkY+LVtYeMchj4tYnJT355X9mubcJyVikwjiU367sn50+Eka4VwVfgK2QA2wbbQgUgCGnEgHmfNyIiAOeUQSSJQghQAgUyACAxcoCc+HNZAqMwAhCQbSXhc3IsIlr4pDEpIQ/NB6h3RDTMe86cJ9ggAFjikZvn4DYh9wj/CWkZhdgjfjzIh9OW236uwZ7aPBqHDDEyjPOBx0hgjOn1eltbW7Oe1Wq1+l1fSikdZ6I2Wa1O5bLlOKbJyQnOw8REze+2FOLXv/7O5atX3/vJ3/u+/8bbb6Ow641WvpCJtM5kC4z2yVPnw5gnJmrFSnGyWlq7fWNnZ+dMbUHZXtjvZE9Nd6OwYhtHgKcEI1oWKMEKWALHuM+BH6tE4SIKCQpBCBBIAhCQhBjFMCfF44RExJEk7EgBh6XjiUcUk9iE9opOgYQDJJq4mhDRPJZP/2gceUDXPTi+FHhAQoZ9ohUITxB28qdJ2XI14d7B8GiOAJoIAUnwkyL0cYmOL7sI/exP8GJxYNd1TWiklDMzMw/Xb3fAunjmQrez6zgOgrWz09QxRfHw5OxEqVRq7WxevXjmG+++c2d5te2bcnmqMwgunz/R3NmMjPCylYnaTLU8a6lSENZL5cLC0gyG/aWlJSdbXF/fbPRXzi/NnDp1avnerWmMlQJHSYMghLGQFRiBYD+KMXxsQVoSETHhwBJBACUNLy1nRLEsktyAhJBIaYGIwKP0hoRBAgBhfJDTJo0p9k+2FxPCDAx7MVvJfyQ/2vMQg8DHkooe3f89FRoPxGwCABDui+Wj9KKkM3NK9putkv2FmYHwEQ2jlIjIYsyBxwAAgCiKUHO5XD59euHu/U97UeC67oNms+xox3ZDY1erE598evPCyXOlcqFWm/yjPzzxH//2ezFY+fLE2tZup7FdzmfyWTeK/VK5MAyVVF4c+ShUPp+dm58Kes2J6cVcoexmStv1m1PzC0RkF6qStRBSALNgACOBJZJCEHIUwwyPe5IQ7X3qlYAoBDIBgmtbjICIhJBQbyIiozncGgwC9zkpAABz4vLFA8akgxT4KMLr8XEOBHs8TqgCAQD3VOX9+Bk0e6M9bsgSKVuuxESCSLI1MPEPcpLeKJiF+FWNWGmHHZuxB59Pje90/pjmlz58npxyPKfEsqbOJ9V/e7T7g6l9mA8/gWXX4uYHGafdz17ZjCavnp679cufBmbYxdJnG5vZ0nRmatF52J4+8c5Hn338n/1P/+B/97/9PzfrG5cvX97aWu51OrZSNz778N133wW/PTsx5fcbEMioF1p2b762sJGR85d+f2PlXn177a1vLH123+350ULUneMWtgaFuXM9kY8AhAl10BW2iqQdxiOrDiImxl2AhA4MMDEDghQCUIISFiKAiBOpWO3roYko68E+G2dm2vsgwIakNtVjDwilfESHzCxlIgljUsLqkSi+F0BgeJSl+MSDPhgBeZB2krLW+LgCDIAQH57nbAsCAEZmcVCXBuJACMli7EYaAwAAkEmbCJFXlu/Pzc0oAa5tdXthrTS/trWZzxUlD10V1zdWZ6sZ7ccLCwunTp2K41gpNTMzE0XR9PT0+vr6n/zeu1PTkzdur+fzQlo0GPayucz8/Ey73fJ9P1P2dhstrbWXzbU63aDRnDs5E+sgRBuUDQCIEtgyeuSmQUTm0TuMKGFEGXtGHSCihMaTI2HUan7vzwPGpGO5UYdIxXvfH/xAj3u/DtLw5+fziM9/DsnO9YTCzMxMo9DRL+tG+pI/f4GRFuJ0NJ322CSUZwwEGva6TKa+s7W0eLazegdIK+CZ6sTq8h2OmmEnyKjhyr3PTtbO3bt9PwzDer1erVYty8pms1JKx3FmZmYWl+Zs2261GlK1/KAdBEPXdRdOzFz78fVg0MlnMzs7O5lsfmpmNu7Wles5jgy0H5BQosgG0EhSdhgbOXLiPGYfAgYQBh4t6BELRQSSjIgM/IiCkz8JkkfzeJID/hrP5fNEiPhI4t6PqUr+1KMDEs/wI4mAST8xyP5Qh540qQTyBPUCAGuNKPjFKSv7quKoG1zq0ce0UabNRyEMez0IfC+fzzr2Zr837PcEQmd7J+w0ojx2fD8r3V5jt5h/88G9hxMTE77vZ7NZ3/d3d3fr9Xo+n3/ttdcazR3Dend3tzu4tr29PT2R84eBZQsv40BstVqtjs8iP73baOlBD4NQ2qApDuOIZKQMoAFp0GgprS9WPZiZiAUmwqU4yIGZR8TzRDrePoGJAwT8qzyjz1Pv3odHHPLgB6JHUVlwQHN+gvF+4RyklAdH3h9fCAGA8Ktz4LQda8yBn378y8KBBRvSQTDoLEyfaDXrriWGOhz025trywXPKjqiu9N0s0UTDCyp7m1u3lzeXlhY6PV6vV7P9/18Pv/d734XEa9f/6xWOzEY+Bs7d4bD4fkz76w93LFtvbn2kHU8HA4LUzhdWbi3sgZhj/qtM+drpCA2xkTaZhZaK4sBhcTDI5CEEIm/F5J7y5xw4Njsl6c5aJxiAU/aDkZD7bHOfUvVr76SH3+mI/a+H5Wx7z0a7SXEAliIva3lcSPZoZ8/P+H9M+5L78lWxWM/8LPG8+LAR95wSYOOB71OFPi9bq+Wy/iOvbMz8AEnp4rVYqHfLnRbA62JgPvhsNlsnjp1KpfLffLJJ4j4ne98R0r5/vvvTxYMmWIUstaB6ypLZW7dfDA9nSFjLIHD4dDf3s5Nna63+3lLmJi2t1redEUIWxtWUgCCAWO5lmL/CQVy9C6EYEAcicqw38qAUqzNe7rigaGS+AeCxxnjkTjZI4ranwUDEz+iZATg0W7CkOwyCADW55JVnn7eJ3j7/p84qhzwzPzAY7xcIBP3e91Bp9vvdYBcf+DbSk5WK9Gg0+mybYlivry52vKyeS+fyxZzrVbrvffe+8Y3vpHP513XlVL+9Kc/3d7ePvG101HI3U4wOT+RyWR63XB95WGtdtb3B2jbSqnlh+u1k4PZuYWFyQpGg3v3PzlbOSVcFwgty0KMEclxpNSPtqy9oEYAABBCMADQiIBxZAQmA4eGTPLIepyM8GixGzb4OPbO9cVkfJCGE+fT5//34Gc6sIlIpP2NJtk19t8PPVfSlfLzRqyk4iy/vBU5xjheIJl2uxlGvomjXL60c+deVkSLSyd2Hi43W3XS5sTcmeEgKhSLIFAj53K53d3dzc3NP/zDP4zj+Ac/+MHOzk6lUsnnyv2ebLd6l95YKhaLD++2m42epbz79++fXlw8derUgLemp6cXz5wL++2d3cb29u5SBDJrIbC0hACFioSN8sCiP0hdhPIROR9Y9Dopagf46IUIe37dg2x2XwplfkxN3T/Fr3K7DtCbQEx8SKOY6uTbR4IyP1amy5YHuPeBeK+0E+03N4MDZDz6DQPA55IZ0oqGHRUvPSfHNCNKirk/pfzn8QVcHdtzOSg37ie1x92Vra2d03Mnz5w689ndtYbKrbX9kj9ga+obv/3O9//dv13fHcycmoGo2+oGD7tFV8wMu/eYNs+fzfzlv//JvbsrtYnprUan02gaic3+/XsPS985e/Hj7//ctrINM2FXSm//+f/85z//5ORJ5/XZKIxu5yZP3l+zFt/901gPi7CjVMEE4CFq348s5YILAPt9dZPUXBQsdfSIngUCiMTjHcURo2BiSiKjmSmJcNLWQYLB/WwhGe7fByFg//usoGTkfRkVGJiZ9roKIgMy457HXh/w3yY7R0JBKooBgBFAIDAQjspfDiiGUcKTHJ0UJABYFBCwJiBgAkGIIAUigkHmJF/C8EjsTxzT2eSkYw781cJBFpT8uW8mdRzn9OkzU5MzDzZ2bXsqf2Jm5fanr79+0RKxdHNufmLhzPm85VcqlUoh+9PVO29947e/+e1v/l//y/+uXJm9eOWNdtf3lGNZdqwj28oszJ+8d3flzJlzOa9UKJTOnTmfyxXyuVy4u9Ft94qz5eGgP2jsnDxxxsZAAduWJJ2wVzRsiAEFMyU0zIiY/Cl5398L+wakhB5Ge+UjM3MSY8z7Cb2J1J1QpGDFe+3F9ip4ICLGoA8K1Qc4ngEWB5IJRjOgVGOnhJGgjwBAMIr8YLQBgRiBxN5gBACKBAtkECMCBjaaGdAGtXctCpD2tjPUGmAsQn8F8QT17nOe5bt35+cWhO188tmnnuN6Hlqgi+WKJaVlq8LkQs/YMj9x/kx1bnbaev+D05dPX7r6zQ8+ra/tCJmzZk6eKAUUBgQcC7QvXnx9ZvrE/dWN6cmFfLa0vr557tyFzm5TMWYyeWbJBnrtxlypMFHJR+1dBVrZKgRhMQppEYNhjYSAlJiBEBgJAVkckBnxQLCEEIIRyCTuYtozCDPyKH0PAICTmERGxAjUvvSByEKwlCAECn7Ur2j/nsHIzmwEABMaYmZK6MqklHQxkHBsAAOUxG0x0J7qSwREZAyQYSLDDALtUfy2QANMREmvM0cigAQgABLCEjIJw0aIRpz/yxZ2H+PFxFOs0Pv/xcxiD63mzplzZwXAjU8+e/PNt+I4HAwGJBSy8bLF4sSMhIIPzma9dXq+YlHwnd/71iefPnyw0jt3+Tvt/kY/NmfPXQz6QN0Pi8Vytjy1sV7PeMUwNK5tNjc3v/nu7/30kzVX4skTS44r67udB8ub55eWco5qmgjYKIkGhRQSBURSaE2YhGNAUvZ1T3SEkdUKEQ/2W0CUI0syE4DYi5tEQ4lbmPftzEIgAGiSRKg1EzEASwlKoRCIRDiqrfWYXVoKSnYEQ4IJiTCxjqVlEYVGwR7XNXvKOuHI1WQMkxHGGGOIKHkQkhH2Ss2DASACZhQ6SmYhBAhJUspko7H3YjXHHHgMAADPUp7nEYGynTj0+71WHJn6bvPcYq3b7XZ6/qk33pYQfvbZj7752tzVS+d+/POP3vv7n/6T/9F/trazXW91GaKTC6fZQCbrnDy19IuPb33w2d2zF14X0jKxnpmZoLAfdHczTimbzRpBMpMF1cxnsnEcktZaazRGs9IoAcCAJTgCBhSjViQIDAZQsDZPWoz33plHMYa4947MbEbUC8l/IyKYxA4sjWFjRpU0JAgDKGVCtok4PSL1BAqJGJmFJtQGNTGxHLmLDsNAIwBwQr0MjGCAASDWBhLiTHYBkkzAzFJJZgY2zAaAUIBISv/EAEkbJ8FSokwKkgg2YpT2+Kw48EtvxHpFcfC57NuxmFkKzmRy69s7UllKYNDra4TT5y9tbm9mJDXq2/N+V5gg6vcqhSxQZWt59WtXzs5Ouz0fuj1ZLuQVhfmMM1eZOHN26d/+5d9EUdRoNCq1aqPZunjpjGfpMycmtbb7/rA31MUTM5Gdub+xNeuVotjEmqIgDo0FAgg5tJG0RMFAgAgoGFEgMhBYe6ESiZEZ9lasAmIUzEDENKrkigCoaS8J4HHbL43kWGRO/DGCWRChSATZxHZ2wC6JBAbAEBjCyIjYYNJ8jFLIJdBJRDQzswFOevEZ5ljDnk4reKQGCwCwABgIiYENCrYQAEEgCkvBvu1KAgtkgYQQ7YWEPysOPBbFny/SNlB+POooIWAAyGVcZdthTBNTM5YtA7+Xr05++7e/85f/3f9N2OgqMWhsoR7kXQwH3Qf3l+dLtdr8VOw/uHi+rEQt72Y9iCfyzukzJ2xHZrPexYuzyslFOnQcpWlgo1fKyq3dfgzUCEww1OTlnYLbbPVchIjADyJNjkGIQWt08jECIIr9iGJGREBCsU+HyfsoMAOBGQ1TQjOJnZiBBe3l3/H+PUl0YNIAwKMeSUgCQQAL0PuhXLCfZsTMTEYaRkMYEUZGRISG0DBQirciplEIJDEbJrMXcGKSChss9vwTgpM9QMRJwJYSSgpSgqUARHaEhJHWwwCEyCgIEcmMst/G/YG/WtjnRfvWLCIyxniuff/+fWI+f/7i5traxtqaZVlRpFGoTqfjWJh3ZdGzcrZqNRv9QZf8+Id/8xdZL2jU74TD1uq9e/dufrZ856Op6VqrVZ+ZnVxcXDhz9lQUhycWZ6NoWCtnLWECv5PNejHizQcPd/v+5NyJvu8bFgZVGJkgIj82/SDyY4ojiCOKQ9Ax6xh0LLQGHYtEM0z8u/vvzEh7SCRnZgYWAGCYCcAwf+5lCEY9tFEiSgQBjIkFWBjG5KUJkldEGBEERgQGA4O+FgMjfC2HER76ig1qEmFC8IQRYWggJDRgGbAMWhqkBkuzZVBpUMyGkYVCy0qaM0kpQQpWghWiRBKgBRByjKyBYgJJIA0L9USB4qPmr6YjbWs4fPzj4thpfuwkouXLI22e6SrD8dyHo6okSV7r5xGwyNke+ToG0tI4nswyNFZW4p6xXTU5OXH7k0+ifv/04hyG/R/9zV/kqLezfbdWLs/XnI8/vlmyucuFDx+Ec+drby387srdPhn0tGp0192F04sXL/zN372HstRqx4Pd5anp1nRVDnc3rF4+uHp2Ml/atts3bn4mYu+sKEJB3Fx9LyPPB6s/+dbSm5903WXtEWb8qCOF3YhDKYQSZAtwkG3JowpYmAEkwQxIyBqBAFgAdW0rcQvxqK4GMxsAiChGlIACAImRjDCGmNmTic8WhRASZOJIQkSzV12ORhmMo2AvHUaGUZOJCEKSEYuIBQGwcQCIEUbF9USiFZMdP4q1VCD3Bd3IHN5pgSgR/jkWSW8JK1nJAWoAkKPiX4TAEhCQbBkAAOLYiPUVAyKS0YC0V5wJAUCH0Y3rH565/GYYZKYmy1uDzdX7yydOn9tudQvQu3jx4u7u7kcffWRZ1tmzpxCx2+3mWA+H/sb6zsTE1PXr10+fPnnhwsX/8Jff+51vXRz4Q23ixYX5MOrv7u5ePnOpWqjduHY/HPL6zk7MYb/fCnE4PXFxu0G31z48mxt2g8DNetyJhoFmaTxXmShGEgZYAwkBSEhAMrFmAQKwREYQcq9AhdGPalDBgZxBHFW0wD13MQqBRKMmRUyjlETBgDRKSDz0thlGSiiWkTlRoRMDthlJ60kqIwFjUkPr8Ptv0rKOCBGRCYlBjBo3MSIGFCPDqGD1KAg8MY+PrutZlZVND0FKiWR6xjrz8xv/qN8fdfyjQQowxigUlhRBFEiwkWkw7MdRr7GzarloS/L7jcla8fLF0+76zoNPfy7nqxMTE3c3Wru7u9+4crZWqxljNtYbMzNTc/O4svJg6eTM73/3Oz/76fsPV3eGV5e6fTYxShQ7OzutVsfN5mJQH330wYVzV223mMtnuv69eyu3qTS7FeenqkWU8Ue3H6q5CkCGhQ0Cg6EPZBkExUAMRBwLsBAAyNKMggUwCpTIAgQmJdhj2KdbOJD9izLxQ8GeYpzEroBmmWi5SChGLipGHLUd/TwYJCXe2FHPbsZEm93r3kC0H6oBTxGYOEUiI6T9nyZbyeh5EUlAFEzAco9wESBMOpnwmAN/xSCQwRhEa+QjQWJj+t1esWB3e7sn3TPd5nYU+2++dhGYWo1GJpPZ3d2tVqsTExP9fj+KIillPp9fWb6NiHPztdpE6Y/+6I++99c/aDYGZBBRFvOZKPa319dNGExOTPUG0dbmvUFj493a792u+00f0KmAtVsoZ8qVK/MTor1x59M7Nxa9wJ5Qkq2YIR7GEm0JSMgSWQNKAQGyACFFLBERQQqQKIRMMvlRjljek+SHo1JSMDJj0T55jfKHmYEM7xv1AB5z/z6y9qFgYCBAwZLRQgA0yMhoAIAQBPPIKG2QP9fMYR+couLtGcP3ZrEnCCiZXB+AEIAsEBBRIvsm+dXnspGOD68mB35KgETamY/0/dHHPxxP0ZkRSAAhkEQWAHEcDvrdRrt14tTFMA7WNtZdqZTlrKyutzv9U1NT3a3lTqfjVedPnDjhOM79+/f7/b4Q4s6dO1Lp/9X/+r/4N//m32xt1Qd9PTExYVmWEm7YbzTbrdMXTk3NzW9s9Zcf1m1q9uN+aBRBxfXcyYnuwnxRljzU0I28IZctWcraXnuoAa2ssqMkCoOAEQXQSE4FUAIQWQmQhEKSAiERULBNKddLeNDcsGfWxr184/1fPQpuecJQn0Dint2bUQAJQEsQMxrQAGAAmdAwEwMjEoCRKTXVUp8LHjjk0WTIsiQyIzIwoyAhJLIB0JxkKY058FcNRkspQTMbspRQAvwwGAx65dr0b3/3D2/dXQGZKVerG9vd3U4wNXs6Y3eur60ppaZzE2EY9no9Y8ytW7fyPKzVKu+++42//Pd/HUfs+4Ex3O40lLIb9catG9eEUpeunDeGI8O58tTKzQ+2GzuL517r0/TyzfcVQRYG4XANVLUz1JnSHOg4I2ILYxTSAwB5IJ/nwKInZgGoCVAyclKumZHQwCPT+mO0pwWPcvpHvVGS0FH7kTvqMUI1B7KX9qkYR5HWqImYwQhhFDELw6yRmIAZDQlDrEkkAVtBinvJUIqR9WBm0qMpoTasAQSSBNSCJI7Cq3kkbn8uG+l5BWCMA0h+M2DSSioCQ4aUoxAhCsI4CGulWrMTRGydv/TmbDnX3t31qTl/5rKp/0JrXS6XC4VCq9UCgKRxro4Gf/pn//y9f/jFtWs3rl696jo5IYb1+iYZaLfbzd36lStXBMPK6urE3OVMybn2YakXxufnMtRBMlHBsXIinsjbTTOIeXhi8aQOdizOlAtZja4Kh0K5AJCk4SQJA4mPN0mnlbgXmzgyaZmDOcOIB0VfCSwACBARk4Bngciu2K/IkbyPiHUQP0bAe5keYDEzQkL2zGj2qmpFRMRIRIZYE5JhQwkRHr4OIzrcG2LQPjifR98nnJaAEZCAgARgEpsFADgOpfyqIYl/NqzBsCVkIkIbik+eufjezz8sT87OnT5BFGZy5eFqcxiYqVLp4sWLcRzbtv3GG2+cmq92u93XXnvtrfO/fffunU8++cyxM/2ef+rUmcGwWa7koihSwqpVq++88w5ZeH9z9+q7CzsNLcW8sjLSiaQ1OHtmthaHMAiM6rfDpjbDpTNzzXsbjurlbQttVENo6sSMDIYw6ftHjMxgYpaAiIBgBAMgJelIIB4R4mMftBg5ShEQkzhEEEJK1PtH7R3LiChGPcr2X6MiAWqPYe/FVMDIDmxiZiQhjQYNaAAImRijFDeh/iI+9YQQkUjPIEYFDBiQk1r2e0kX6gmOflz5wOl4xuOncfKU79O83kedZRrnp5R+v2mFvI86z7Q+xmnzt1D4cegV84PBAKKoWMj+4he/OHX5gjU9a9/Zlu06d7ERcaOlh4OWDO66mdyNlvVb//Q/f3B3DRqd77xTLU/YMv/OD77/05///ONT514n0nd36nHGe+21t9bX1+MoQKWvvHtlub5dqM1OL57LFXO37n30B9+pZExQC/TW8k8unTrjh3OZXFkIq/Hxg6/PlU7imu3Rzv3VK++eWtttxygmrRBAEEvNypAMDcdaEEFs7duZZEJhSYqPIQOQNGoABJKQuJsoVhqRJaBAlhIViiTNVkqV2I8FADIlxwBQyXvUi+hR1jyAxSO3zUFdFRE1CQKMgUmiBoiADYIhptgRApQgS4ASJJJIDGTpmUebEYnYMBETI4R+wvBF8ra3DGx+rHB8IhYAgANdRJRSjjnwVwuWZQVhHMexkCCFjKLIkpjPeFGoY8PTCzP1xjYrb22jsTg3PzldXr953XPsmcka+3r1w093doqXXz+zunHr/s3rimKMIxMHkqKNB/elCbPZrJSlCxfOZ4vVH/74p+WJmSjws55ldJS1bCnl2trqnTu3cvm870eDfvitb/32zz/6TAgYBlGpVCK0TRRmsm4vZGDDIBgkgzCMTGCSxCEA2AsI3f/AzCwEADFIAZREYEoAAJF0IEkIWCROJwREjDUAkAAUEgQKhYIFCEApzUEb0v47PV64fy8qE4gtw6wJyGASjKEJmUCikQASQACPrIaj0gB7uY0jhxYiAjJ69pNN2BJW6srHRO59wlasxgT8KiDNdpAmqUllcRDFcawESsTQ7wvkbMa7/WC5NjkxPTf/i5/fjHFw9uy5c6cWl+/+4tpHP5s8dfHkbC0HuPrzQb+z61kXBMGZE9O9cnbp5Hwch7dv+71eJ+fAidnqW29dmZic/dsf/KjX6966eW1y5sS1jz8cdHYny7aJoygKdBw+WL7XavctlVlcXCqX8lLKnd3diXJRSdjZXpf5yUym3B9GBECAhkTMEBEZRs0gYJ9D7uUDAwKAIUKRiJkioVjGhLYNACapwQRsIHEZsTGEyBJRItpSGBAgcMRiHzmBRpHYzGzwiVYvyRw41GhYGIaY2SSpwsTM6CChYIkgBEtkgQYP8PPkwSGglAIAGQTKx6JckxxPRPSseO96HxPl0CRdVn/l1iqvKo4nzOLZj592/FGNdkQEQiEiMyGT3+8J0hwHxpipqZl2u0ms/TD+5re+hTr86/9wW1ksaPDD//Bv2/Uu6mE+5zy4e7u9szNR9DxpMBpOV8oz33yr3W4KIbIKq9Xq5FSNOD5/ZunT67ev7+5e+/TjU2fOMWHgR1ffuEgc3bhxy2gSQt69fYNjOn35YhAED1eW0ejTl+cGUu30+kJlNYEhjgzExDGAZtBMNj8qN3uQQ8bIQKCQEVGCYGAhpGCKtUZEQhCAiGxGdi8SgEIIkgAEMSBIIE2IqA6M+Zg7ByQ/Aoyq7QCGSacXHJWplUlYCYArYkRO2D4AQxL2gcQHek0gokCBKBiEkpSEUibNEMXotwL3SosczKwAADGqYjuuyPGS46gcOIxNYkZmHbAxw36b48DvNl+/fK63WV95cNsSMFkp9jq7JhzmPXtoU7+13WoMw244kQscZT798JfBILuzsTY7O9vvdx52Gq5n7+xs+b5/6dJFZnP39q3tzfVhEL9+6dxWvSWEFHq4vlqfnJmcqJVd79zq6srUVBHR7XXb2WxWAHmZnG3bxUy2VsjVt3tubqbnS01gmGLDEUGUpO8B0r52f0CcBgBmEEBGCIGkCBBRclIOLnEg79scBLNhAqke87sak8RZAe91foDHCRilxZ8DjAq4S9xrm0iJAZxFBpJYZWTxaBBmICESHTh5Srz/EA/Q5/7gzAxJYYAD3yQflAiJaMyBj41DvizuK8OglCURiIRkHfR7HIex3y/n3E44kBAOe425uZkPfvrjqVol62LX+ECmViihnfO723HQXXv4IO+dqlZKlhKFfL7f7z64f69ULp4/dyWbzf7dj354+tQZZGo363fu3C4Vq14212w2bcHVWqG+vSksIYCI9ObGsu/HF86ee/jwoZUp+r6flXHkd3WklWUPu5oINHFEbBh0kpyLyHu9gpLL2VcpiZlQCmJEQcgCWTNIYFtaKFgJKQUggkiil5ktYYQAJYSQoJATBRWI97w8T3RgQSF1EmuZcN/9OluuJQEMjioTUVIfD5GtUafwZNNhSizqAISKCEZl6hJezgSImpBZEI9CNWHErUHskTo8vsYkBokMMubAx4OjcsLjEqGPeryUEgCIyLKUzeQPB2RiJUVja7XgihBMvdcMe636RoeHzW5z+8TidKve23pwx0ZHyXY+58T+cHnt7ukLJ1ut1tbWhpBQLFe+9tabROav/uqvfu+73wLkfD57tfbarTt3337r691u9wc/WJ2anZUCrl37dHJmMvAHxvBuYycMzN2bscxVrJyur2/s6u7MzMzkxMKHKyuxNUvMScUbI4ABWCAIeTCSaZ8dAQCKUds/Tiz/TAjIwBYzskBEgTLpyAksmI0rSQhQAqVCiSgAiBiI470SG08wYVs8iq8+eHelBZC4nUAjotiLxLT2S7qj0AAEwhBJZi2UMUx7Ra9MsgEhaFDMwCAIRrYuHtX6edRBYv+0iKiUGrmRjrgwxni5YVkWEZExGUspoDgMEMhzrDjol13v0/UHEuLdnTU07trqfVd13enCwvx0a60lWFSq+XKlSCZubLftrLp48eKNGzd2d3f+0R/9wfz8/Mcff1woFMvlYhQFV66+Phz4uVzOdeRr33w3joJSsRLrcH39YamcZzbFYv7iufOMVntr4+OPP86Up775tdfC+oPbN67nz+aFlYk1J6m8CfsCucfa9pTPfYvPSCeUCpCAE38wMUsEYhBaR1JKgywSa1fyeyZls0ShFNpKSQRAQlLMhvcS5Z8gYAvNI9/yHhBRyESpTrp/UlJdWABJtACAQTACjkLHkAAIBI98u0kKRELAqEf2OGSBREkyEjEziPhzbiRkgEcE/Mz9vi82UrN1U/yraUhTRFIiYo8scqc+pSOyZhkNA02u62LQr7jR4OEt6ndv3bl/omTtIFUK3s5mfXXjYxBYqU0WCxPrNzdOnDgxd2Lmxo0bhakl4ZxrhgWs8tr2ppVx508uLpxaqk3P/fu/+r4xJjSikK92OvHK+urp8xciGXca3ekZ/3d/9zt/8f/7a62j+YWZfCb/2vmLUjq5bDGbzffp6j/8zX8sZ02xOvsPq+325s43ligyw4Bdx3FMbBiE47gxmUhrpRTt5XU/CtkQAgBsJoEkbBYCCUEb0jEYEiismCHUiIaEEIiJW1WVLE3IBuMQjGCWiChRAks5QEQJEgCQhUiKWtKo0TaM+irQXnAIWURJTY+9x5rsLIhsMUKSeIgMwjAzIyECIbPFKISSFgGIJD8pA2QAiMgQaABjDAlkBjQjv3dSjFKgSBjxqD4ejSOxvmJQSgnSQoism+21G1tbW8w8VywWJ4qffvS+A2ZqZs6YuN1phmGYcezsxESj0Wg0dguFXLlcBgBm02w2+51+tTZ56dKlQqHwd3/3dzdu3LBtezAYlEsTP/zRj/76+39XmfxZoVB46+qVfn/w0QcfXrt+/fUrly9curiwMPfhhx+26pulYjQtLTuT8TzP9nK+Ua1BxCzz2UxoY6dna5KaiJmYSDIoIvm5XfUJljhSK+ERlzaw1/QMgPfz9RBDLRNmJhEFsNyLx2ISiEmJDkREYk7svUoIGMWMjCKxUDAAHgy6OLgpEzAwUlJVj4CADCMxGRilEzMzgKCkhxIIQCOSutMIMknbJ2AAg6NTJtmKzEmxTggNAOCYgFPxqtr2ksuK41i4dr3RMMYsLS29/fbba2trjX5k0RCKjuV6sis4Dov5zMrmaqfTqVar5fJiFId3796enJycnJwMA01EAz8oVaqa+PLrV4bD4dbWluNk/GEc+Pre3Qe2bZ9aOLn2cOvnP3+/Xm/YllsslH3fv3bt2mef3rSUOzs7/9qbl8vVqlOsbbYHWrqZDAwHPSap1CyDEYhMMRhQiMRs0agK5BPxhog4KsSRWHof2XGT2pTJkUj0KFh6ENmQyNWjrCwEJGRUYEtEIUBJlogoWAIDsgsEDKOEf9z7AKOCAbBvZKbELEWaAYAJgRiIQRMaJgYwxMysefR9EppCyIoTamXByAgKBAsGQM0imfQoxXAv0zBxa8HnW6uMkeBlsSofFUSklDLGaK0bjUapVKrVakopku7Zi6/f+uRnD1bWczYFgc8matV3Tpw4cePGNSEgk/Waa/V79+4JAVprZVvdbnd9c2O32bBdZ2JiotfrlauVv/vhj8MwfvfdbwwCvb6+HoZRvz+cnJi+eXe50W7ttpozM1OT07Pe8sPl+6trmzsawj/6g3/UF+61lTXLy+aU2t1a98pVy7G0AduSBkEC20ICCIUQHyDaJz7AnlbJe+WXYa8yDu6ZcfcP7mtr1CSNJQIjsgAJQDYLhSgVWoqVIKnAIAPjXrZQktiX7B8EAMjywKkpqdjOLOLRXiOSElxEYBiYhWYgFkRkSJhRrKYABgF6T09K9qa9rYEkP66QJ+/hKCpNjDnw4XhV9zUmdl2PiLT2G41GrVbr9/vf//73q9Nz+XyOtAmDYVZIx7Jq1WK/1xGsW62WHwwZ9Pb2dj6fdT270+lkM2UpcGKi1mq1TizMR1FkW0pKeePGjWK5Mj+/QGhJKYMg2NraKpVK8wsLQRTfun1HWmpyeub0mbPtznDQ94vFYqGQH/RY+362nMlYxhF0cm5ydysAHbq5HKEiMpZSOg6FGNmCP0+9T5Ax7DlXR26nvYAt2GvLEsQIB2pNCeCRVouoBSsNBKgVKs0CEYDiRxZgkRS6RJR7gm1yUmAWxjARMkPIBICMBlgQCA1EJAiSfEPWiThNMMpzFlKwgJFujZDYulAAgIYR+33cdAc6ac80diN9BZHY53zf73Q6r51cqtfrG/eXl+Zn++3dUtaazkz7/SYpmc1mdxsNMtHs3EylUsrn82HoM5jFxUUA2lnf7ja345wz6OxKjrrdLiI2m80Ts3NSyc3NDSebLZayrlI6GKBwr1x5A4C2tnYc13Ycp1AonVw6bVlOqVy5deNmJ1KKSkErrge79kzF7y8Mm5EhLnuCBEZEjMok9eeMfoJoR+8wMs/uYz+ceIRHgR8AAEaQANRMIx8SgkQEQIMGk9pTgIrAcOIiEn1IIqwFjPaEJMlpryjsiICBSCQlMoGTLUEQMjEQYWLG0syGiAg0YZJ3yIBgKOmllPRGAkZCkRjXifdbKPFBDmxGSVFf+UCO54XnJaJLKQfDoeM4pHWv16tWq4PBIJfL7a7e2l5fsSHOOmhnvYlK3nVdZTnnzixWKiXLsrJZ7+Kls9ls9urV16WUDlOn0xFC7O7urq+vDwYDpVQQBJZy/DC8dutuu9MAAHLsoN9p7G7a+UnHVWE0iEyUyWSIQNpWtTapHGu3vh3GwilKJj/qN1r1aGtjbXdNC2nVSi5aTqwBbaWZUOB+JfUn1GAhRkm/ic127/9Gif5PgJktNAAgRy2FWY2SCoBljICEKFAYQMOIBgDABwSTbBGIyPsGLaMfTYOZmUUiQiswLBIRIBGewTAzoWEwgMSQFLsFBgOAOIowI0JCYEJGQcwAmPSZGE1778rhkfNh7Af+isGyrLg/zGQy0rJ83/d9v16vdzqdTL83aNcnS7luu1sqZGrV2vZuvVStbW6uA1Cn0wmCoR8MhsP+nTu3PM+byXvFYjGKoqWlpcLZU7VardfrSSndXN5xc6/dubfdaDabzbznWsjtVr01kNlcZnPzYa/farfbCBaiJYS4v7xih73GbmeA6+XKROy3NtahR6IdZC0nGwdzCovaAGovBsK99fp5rpOYqfa8eSMRelSfPfnq8UQCAC2TwGkEiSwECiREZGmARZKlOGogSAgAIQnYa04GCCP5GVDrg1vJyIfEzAgEBkAwseCkhwMjA5ukqCUAMzElVUKAGBkkABAgETODIWBEZhztH58Djq6R8f/wH4bHtzweIaVyyHODSHHrHtc8+bgGSnlgafNPg1FeFPhZz2HSpDUI4QeRm8l1wmAyJ63uw1vv/dXtn/8d6EEmkwkinfVsy7Js20bEKIr6/X632x0Oh77vA4BSqtvtMnOlUjHGBEGgiDKZjFJKa23bdqlUymaziDhRytVqtWq1atu2EMJ13Wz2/8/efwZJlmXngeA5996nn2sPrSMjI3VmVZau6qrqLnRXCwBEAyA5ZJOgxnKX5KzZ7I7ZLpfYHVujzQyNZpgd4+wuORwOaLZkDwmAAk0A3SBal+rSInVmqAytXPvT74r98SI8o7LTMZNEVVd1oY5di3T3dH/ivnvuUd85xzEMg0KUXZhSSgiRedGklMC9oaEhAMj2Ec/zut1uFEUqV3UGpoemTltuZXt7X7BcefriTmTo7N7LVe9TAaOfpnNXSZ3ejiAO6wSgyroTKTx8CwCEAEWgSNhhJUuh6F2nyN46Rp+E/vTeCq8iVCklsgr1CnjmzFaYtSJVMsNjEqWyABXhhGWYkE8l8CeTlOBSSs6TLLLJKFUa1VGYyK++9cbOtVe9zRsq9HImcywrl2NpmnDOfd8PDylJEiFEkiSappmmWSgUEDHzS2maFocRFTKRaRAEQnQanW7GzEREWbJEmqYAYJqmZVmMsUrR0TTNtu1cLpfP513XzT4frjhUt0zTtNyCruuapgEAYyxIu0a+2opUHHn5MbrneWF3wdCrCvIfyPz0kiLucm6xA7cREJQkQ0Nm1a3wIEvxQGQqBFAEkMg7hdoPkSUAAKRPgwStT7E7CRIQiBIZdoMcCGqZFdtUBxUplQAkCgFVluakfrwm1gdFHzfL+j4Lddw3fdyCToh4UM5QKkqBqJjyME08C6LtxUsr196uGHK4UmAglUgBZL1ej+M4E7lJkgAAIYRSqusMQBECmqZTSqWUmkZt2zbtXBaOUlQjhOi6nqZp2O3mLJcQIoRQBBCRK9IN4jT1ao06HPUPH6IC8w5zXVfX9WyPsCxL13XHcaZGSpTpQRRXq9Wh0QmjUmppehdlLbr3/fab/37Lu3cl6v21AShmWEhEAERJQGUZyAKzFmuAKqtQqbLoEkPRu5ejL7Q+bQspu7eGJXu9FImUCnnWFg2RK5plMkuhuAKCMpPGIsOBfNqZ4YOiD2wf7JcUcZ+H1xnhoCEqRAoyjYN20NiPwm7ekGbaHsqbgzndRN5u7HmeJ6Xcb3V7Da/pEdJ1FoahYRiUUiGEaZqaplFKw27IiMaoJkERQnTDYLoGBGUSU03T9IPE2kwhTxV3nUKmPGfnOLwp9CLZ9jvZlqHrehzHaZpalmWCYerEtQzTtjyuilOnH/rCLxcnT/wRE9fn8z6qdS+8dOiYgl7a4UHJdkURCBBERQ45ExWQgwrskiACoMHuKoJ34EWjfYpfsb7VZoVSKOCAPyVVSqJElSiqJEgAiUglCKVk1ioNUSkp4UPrzPCpBP5oSYqUICgATdMSr9va2ezsroWdhk884tdsKjrN2na76XfbnHPOuSQ005B7tSCyhWFaOqDMF1ylVLfbNUyNMStJEl3XbdvWNC0IgjRNEVHXdUqpSHVN0xAxTVPOOSKajDFd101DStnrad0TgEzTgXNKDw5opmmSJJZlBR71ZdRtR8pTwsxTWk70sqAO9GlxJfpIPK2Pa0IeRn2PptfDIQQSAAkSCSqLISlEigggCUKWFoWKACgCoDMGRxj4Dif3q4VG7t0bCZWSoKgCRQ6L+BEEyJRlpRRyVJRIIUEiKgUpKAlKyU9t4A+IPm4SmKfpAQSesTjwt9eWOpuLImhtdzaTKI6iwOu2/U5X06mmaZxL/TC7JdOcM0JE2zZ1nRECnAvXtXM5RwjBGOESGJU8DUAltqWZpsE570aBRqjOkBBCkUqGhBClFOdE0TuJ6Ue9PoxonHNESiklhGWFmZOE6wVDcNbYjSZmzz371a8Nz52X1ApTH8i9bWCF/ZyIf1RTO3VYUgsP63ukih7U60BCs/8nigAakKnKWRszpAQOcgzYQS0rOKI/H3F+/xiRPs9XKgSCCBIUOchYQgAweNaxHAjKzDUtiFIKadaY8VMs9CeVNIrpYWq412mtryx5Gzc07sfdnThNhUIhAXSTWZbGNAGBxlSPezORyRijlBIiKKXNZjOXy128eFEp9c4778Rx7Fh2uVyyLCsIgiAIEJGYWt42hBBKqTRNlZSMImOUcy6FoJqVXVjGKj02Jqj1MuOyk2qaxhgDk4JyqGHPnH9kZP5htCvcD/O63uH3vt9+3mbswzCyJ7HVAWYrY5hYIVEMUVFFpFKEAlGICAZkzZcOUo8IAXIQ+Mm8TACHaM3soHdXez2kfk3PtIPU/V6qRKZqE4ISFSiiiERBQIBiikhQlEqlFJfyUwb+ZJJhGMhlKqVSyvf9vd1tf2fTgcjSkigIBVLNdAhBLhVICUy37cMiaYxlbJwxlee1AKDZbIyMDD/77DN7e3vf/e53dnZ2CrY1PvLUyfljGxsbr7xyfX9/f3x8fG5u7tTps81mc3t7O6sCb5omAERRJNHMLiyztA/0dilNXUdEokAIoVOGDHXKKKXE0PZ2a0ZheGTsDJLBKKQG6iz2j3ZsOEryPjUUdQQXfZSEIlnGoFJKIaUKJCoEVEoRQCASkdCD+HLWc+wIhPNIexfsE9bqJ5oVwkH9ejzsUIoEABhIQCIBJFFUZno8EECkQimFCIz0y1j949HHLAzcNzGXqPu70n7hXvygkFX9FmKf6++XtxzHIWNM12maxlMnz/3pyf/H66+//uK3vjXFl81iM4k9qkjJKllmQmlCkChqIWIcx67txHFMAOu1epIkwnCooKaW91vrU4PGD7/5xpmZOUi6f+rn/+zu7u7O1q7fDarlgauXr02MTV584KFHHz+xs1P7+r+8evXy1TBKFcGHH754/oEL27c3bty4lW0QQdiePTY+NT1eq+9222nKeaWYa7Y6hZJTqzXcXD5JkihWA3Y4Nq0NT1eIS8XWjubK0CJMOPe8X9GnN1LaZyVmSnBG6shfkpXYABCZtDxUwJlmA9yjPDen91bRTXHvByn6rB+zb9Oze98Xjw/O+7FjtE/pAyGlFOe8F4wdGRl55JFHPv/Vr4ZhqgiWB4rVgYJmAKGo67qmW5QyShnnol5v1OsN3w/SlEdRzBjpdjpJFB0/flxKefPm9Wq1+ou/+Ivb29vLy8tKqVqtNjU19fzzz3/hC194+eWXoyD45je/WalUjh07ZjvmF7/wMxOT46+//jrn/Ny5M2maXr9+tdvtVqvVL37xy3/rb/0d28q/+urry8uLjmtMz0w899xn19bWG/V2FEUT0zMXLjxYytmR180XXEI10U8B/fApkXjPIcW9RyLh3kPce8Rc3ddIBGbjUxX6p5v6Oc+QkCxsA4fFK4aHhw3D2PyPY6lY00ypKRXLhFmMURJ4ouXXMzBzkiSEEDefM21LIciUG4amEZF37UazJoSYnpoYHCl+63e/LaVMwqDdqNuG/tWf/7k/+IM/WFpcrFYrfqe9sLTx7DM/83M/9/OXrlx+7+138qW8Ar61vXnu3JlczilXSkrh//w///O/+Tf/+sjw+DPPfPYHP/jOuQuno8jzveDC+QcvXbo2OTt8bG5ifGwSc3an1TXz1TCKCWrqI+puGfXxhWl9vEhc9Glu1ucy74nZhp5N/eOfH87Dp8kM93f7H3Y46n4P1Nd5c0iZKO50upRS27ZL5eH9xq5mCI1oScKFUl6Y7u93U0gy9xVhOmNMAuEShELFE8swTYqmbriWeebUiTRNr169emxuamtry7K1KPaOzU1JlaQ8TNIgjZNWs7l+eyX3s7njc8e+851vX7t25anPPIGorl+/Krj88pe/bJj6b/7m17e2V//iX/yaECqfK37ta19DppZvL585dbFYdBH0+QdOlR3c26tPjUQlg0VpkEqim5rsA0X8sCntE6Yi/N46LO+zrvq2yFH9vOV9beaDC+jzs0/pp4N+PALZC2b0/MmapmU+G8exhobHiGaEiex0o/1Gt90NEq4MJyeEiqKEUk3TDF0301QkCWdMNwwt8HwlebFYKBWLpq6//MIPX33l5WqldOH8Wcc2n3n6qb3d7f/l6/9i7tjM8blZU2eua//iV39hf3f7n/2zfzo6MvTUk4+3Gk3D1KIoYBrVdb3b7UopKWWrq+ulcv726vIXv/hFBCa4Gh4eTdJoZnZ6ZnaCmbnbK2uy06hayvc7kmg87V8arP88fEDzTO85hMJ7DknoPUe/4yi4vyEJy8anEviDkcAfPygHAEAW1OkRIZArVaNEen5oannLGrBzukwTpqFuGEmSmJYVRREgJmkqlTINgxIpDOE4es6xJE+rlUq5VJiYqnqd5kMPPWQYRpIk//LFH6ysrAwNlJ947OEkib70/BcuX7rxzW/9QaPZfeqZp6cmJqcmx9M0mj8xd/z4sVd+9HK73T537sLe/sDK8trzzz9/5do7b731nu+lY6OzW1s7lDLPr0dJFMRSSgwb24VCVQFF3Yi6HjO0Pvf64S7jfl5l6ON86tcksP/yubds7sueh+GxT23gD4Y+bCDH/R8GEZFzLoRgjAFAmqZhmG7t7HheMjg4MjV2kko9DPc3N5Z5wsvVASnlyMjI7u4u51xxDkRIQItRp1IqurRUKEolZmenxyemDRvTNA2C4IEHHnjppZf+xt/4a6urq7quP/LIQ6ZhPPrwI6+/9k6pVJqdndtYux3F3iOPPOS45vnzF1584UdvvPFOmnDP886dO9VotAaHCqdOz1+/diuOcXhsXGMmV5FpYafT2m+E+VzR298ZmDlu29UQiELtjzAZ7vn5B5V3TfswWL/npfqoxP2yylQf5bovIuTw658y8CeTlFJZODfL6ZNSJkmilGp3O5abm5yaGypNbm/u7+13pYTpY7N7O+uWZZXLZd/3s9BUHMeUUltTsRd7XtdxLc/zNjY2TMOJ9jvV6uCVS5dDPygXS0mSPPrwI/V6vdNqj4wU1tfX/tyf+3N/5S87zbbvBX4UBchw7vhMq9ltNrqWlbNMO06iOI7PnT9LmfzSl55/643/odXkXO6apjkxPWhaGAHU6s3ZaiXo1hhyyzbagbRNmwv/I5lP7BOuk9q9OYj0CWv1swF4XwncR8L3vvDffj/pc8z3Ub9AObnPDe5+j3O/Afr+1LcC9L0/7Zt/e78dhfv0773P+8U+C6IfJZqpokiDiGosAi2O+GjZufSjH33vf/mH58+dqdVqIIVtGhsbG7ZlzMzM1DcWstTfLMs3DMM4jgGA+2khp1t6+l/+n/6mktF+vUaooZtOq75VLpfTVHTaAUHKOXTaHuf82Wemtzbatb0AKUMmFZFK0jQBg8VZ2nC2m2RmebvdLg6M5fP5jY2NTqdTr9eHhoZGR0cNw7i8uh60TUxUKR9OTM36+txlz0iGCqT94eAWDul+JfkHp3n9UZDPP4I+lcCfTFIcsgZ82Vtd15Ig3tnaYBSbzWZ9f8/QdENjtmWYmh6HftZyhXMeRVGG6Mh0b8YYZYwQkSQJo2gYVqcbtrpRpVje22+7rmvZTqvVqlYHR8aGFxaWOJeUam6+oJACpoShAhpHSqZGnMh6I5EyStM0g096ntcO+GOPPWYYBiIODw9zzldWVsbGxnKWXbRdEYZp2AGZFnOkIGGzHZiQ+0jn9WNHnzLwvan/zvrBfN732x9QmEpJQKIopYlSgOCaulfbWlu6OTI0aBm6ZeimYRBUBGSaRLvbW7aW5Z8eeG4PgMqElAdLadgtlktjo+Otzn6j2Tbt3PzUsTSIDL2FiOVycWoKGo1aGPqzxyZKlWLgt4XCOEmjONYoRUqBoG6iEIIQohFiu45lWZqmJUlSb9bW1jakhEKh1Gg0GGO5nLO9vSuQaVZXicTzu7s7GxU7l9fGvNCJP+SwyUcngf8Tj/O/1Qvdd2Hd5/nu9zgfYCDgvj7/aS8LzUAyBEKITFNKlaHDfnO/sXM7X5ScqJxr26bOk1ikCSMohGh6npTSNE3TNLP4k5TSMIx6oxH5jVMnLjJde/ONt996+729Rpvpjo7aL/zCLzBGXnjpFdPUR8cGXddxCzmJrOWFBNyBwVHUpG5SUFqaIMFm5lE7pMSPvCiKTNMuFEq1Wq3ZbBeL5Uwsu26eKBrjnlUyXGM48PxuczU/WBTm8PpHFAf+2AZrPpXA96afdgnMKNKjFTC4au9t0dgPulEaaTqjnCi/2+FRwEyToTQch3OepfJyzoMgSJKEMba5vs6ozBULtWbr8tVrzDANPf3RG++Y1KBM13R69erlrtcaHR0+eXJ+7visxs594z/8/vLKvmW7lIlipViuDOVzlUqJZIU7sqo6hmEYtmXnyl6ri4gnTpzY2trinFNKNU0rFApRO2jFGtVtSy+EQSRT38HQj/aBDt/fRNwn/fRJ4A/m9J/Sx4w0zFqMZB290Os0dtZXcjrojIokSgWlSoBIDJ1ZOgEgmmFkqfmZv7rdbmde6EK5GHlN23E63e7NhcXJqeOzcyd1q/zum6+PjIzUG/u+H84fP3X+/NlWq/XKS289/ZnHduutjc0dy8lFqSdupVwgJaap6QCQsahpHiT5u66bt9JcLve3//bfDoP4tddey+fzlmUJcfvM8eOaMRhFmBDiFgfcnKYjx3Qd2chHMp8fXwn8qQr9kXx+//d7fzo9RSUBhJRUNwBJfX9vc3XJNSjFVIGiCJSAZWicYpZHtbm5GYYhIjqOk3HXQWqhRkIfHMdxXTdNxerttYTrlerQmQungMkoSU6dOXP27Pnt7e3XX3/bdV3DcSVQCQxQQ6KZlglKQ9DTgCNimqokUUIIQpRlMcvStrfXgyBstdpLS0vb2ztbW9vXrl1jjNWefmqniT98/d1UdB+9cPzk3LHT58+dvXhqaeW+puGTT59K4E8moZJKCaUUY4wL1W436/u7Uw5QxkxdM3RGAbteu9Vo+N2uECknLCuUkyXWH3b6kXEYU0qRkjhOKdMJIXt7e0HIH7p4RikYGhoKg/i9965evXLNspyhwfEoSngqAamQKok5MiJkmsbC0imlVCJKqQiTUnIBRJE0n89funSpVqsBQJIkMzMzvu9fv3795sIt3T2by01NHXM///nH61u169evVycYwPGPemo/XsTuCkD1E8h9o2/3KSHv9zgfXNTv3vHYvkidfgCYvnHs+yvcrPrVTOr3g77F0O59nIQYQFkc+lWkkbfVWr48WLCBJFGqHE2J0N+ut/Y6ITOdfMnhQUtAhdBGnDbtnJnG2GyFg4OVOPXQI+1gbWZqcGOtPjxwWmDU9ptvX3pjbFj7q7/6V67euPFP/snXd7aCUm7M79b8bpMSj4OXgBSCKNPthL5jMNtmuXxZSuHt7xJCdNtCJITg5u5WPp9n1skfvvrKX/8bP/dP/9m/rjWNiWOTF8oT4wU1/vQvDe7zcVOZpvRNbWUnntuuXKwuXQlKaW6Ehm3JY3AG/Jib0kei33s+77v/db9E/Pv7fj/qr/D+J7rXP01m+GRS5urVCM16ely7dm11fa3t+VdvLi0ur3LFiG5HgnDUlG7nykPFYtEwDNd1NU3zfT+KIkTKqBaG4fj4eKFQCEIvTeM4jh3HeebpZweqozdvLK+vbVcqA6apd70WoNCNg8gzHhS50hhjAERK2fG8dtfTTUc3nUTwKAqjKDIMK44CTaObm9tBJB976jMtP9xtB16saL5qQjSXE0Ua1/2ko3KFsWN7tbphGDoBlSYZyCwr0PVRT/ZHSXfbwB9bY/1jQh+UL+DD7o2UpRIiRRB8d2dra2t7oFg5fmJ+YOyYjLthp1n3UrcyCpq9264VLWrKZL/WyBeZVBinCWW6BOWHwdDQiOP6pmX4fndt7fbEzOj5C+eR2Htrq1wttr32+Pg4SGvx5kIYdAFjSjXBlZSKp1IRnqniAGjnba8bSAnAhVKxrjPGGOfC0lHLm/VGZ2urcfGxRy4vfjOSdqPjnSQW8WvT+VzXT5bbsqPcqdnpjaW3T3Dp6BClHtE0xQwhU50Z8k/wuv3UBv5o6MNecIILU2OUIQF++e23i8XiyRMzI5PjA0pTifeDP/yDysj0n/4r/zvFrOuX3mEyuvXKy3JD6rrBGLNtt1oplEq5JA1sK5fLEQBgjIyODU9NTemaeXtte2N5A9nU2tamZjiTU+OGRm/dfCuK24jIuVQSMxWAEBBSSSW9JDVypchPlIyUSIvF/MTEzOrSGoqu45qE4cLi+uNPf/HEhbOFgTG+soRpyP0mcc0wDCMvUoSUqyMbG4O7u7uFYTfkacQBdRsSqWRKGO2nyX5SGbt3X6RfIuWn9KHSh/2ApZRKCaKkiMPr166eOXPGKZSvL93miphWQXcKpy48NHfyQcmcQOrXlrbzxbKTL9g5N4jjOOVOPqdbtpMrlMvVkydPp2mc8ujBB89blnHr1qKh26VitVKuNhvta9euSSmffPLxz33umZHRoazqTS6XKxRKpmFpTFcKhRCa4546++DkzMmhsWndMt2Cc+bMGccpcM4dV9c19523rzBNTU+P+15immbZBsZYm+s7XcHDbj6pmVQ4E6fWNjZtTPMsFkksiUYQVJpQon1U8/yR059o++EjpA97I3BdE5QCwddWlpVIzz9wIeFqe6+5t1e7tbhk2+7I2OSNxZVby+sDw+PFgZHd/X1A6uYKSSoUEsO0U8F105idmXfsnGFolUpJgdje2eScT03N6IZmWZabs9M0XV9fT5Jkbu74iflTPJWUsmp1cGBgyHVdXTcREQB/9hd/8cnPfv7kmYsDA2MKIOGJUqLZ6FBNK5Zc28rtbtWTsDFQNNYXF21NObmS6eYCpbeEEXHpYKDa2yPloh8Ewq/nKWcUUwGEAFV9Ks1+oqm3AO5Wof8kbFpH6aPq0/tBUb/nZRjQ8VOLwaV335qenKhWq692uuMzcxNj46/88NsEZBAEteUVAUZxeHBwuH2z0TAMo1AohWFYKrkDA0O7u5sEme8HQrVKpdL4xOiVyzc555XqkO+FQsS6QavVskIt5xaWl9cGq9aZM3MAQJAZpgNU41xyzgHQNO1uGK1v7TDDzeXLyKiuM8uyLlx48MbNHwEB1y5EbqGxv3Z86vypmcmhMXs7iKxEMJNrZi6mruGwYPe2PahEsdza3yyP6a5b7UTCJsgYSaVEvLeb/uO2nj/w9fapBP5kkh9AEkaGoa+u3J6angiCYGNja2xicmZqIvR9r9PtdrulUml+fp5z3ul0FFKmm1GS7NebYRwLpeqtZq3Z2N7etm1bKbWzsy2EKBaLmqYlCT9z9uSJk8fGx0cfe+yxL3/5y5VyNUn4ubMPMqYBgBAi8KNWq+X7vlLKMIxbCwvrm9uzc8dnjx8XQmxsrC0vL5fL5SQVzWZTSqkReumdVw0iTs1MMhVf3w3qzaYuA8exYr0AdsVr7KjtG5VKpdusUZ66ts2FQkRD1zj/kyiEM2KU/GRvvk+lfPiQm7z02/nuN0zYvyjT/R3ng9qIVZ9KqxqD4bHKuy9+26RyYHjy8q3bFx58cMA1Nq691ant6fny8PiEYZGlhXfGZ+esgVLC44Jt6wQH8sU4VDvre82GNzE7XJgePXmi3FhpvfnmdoDG8sJNDZtabiXY5bu17luXLufyxbW1fcl53pXXFy6dnRuWcdjx2swZKpRH/GAnCdtS+QtvXp56bri7d1OF0dn5J19+9Ue7HXruwjA1KkbBNB2+cRuvvdn9la95J097v/vv1gqJvrerhkaGWbD0wFjVk5o2c35fxcZCzWA7g4X95t7lPMxL22l22lVKvX5erI+ZhvWB1T6EgzT+TyXwJ5OiMA797vbOJoASIs07LiPk5s3ri4uL09PTs3PHNta3ms3m9PT06OAgVUoqMTIyJKVstVqFQiFfzBFGC/lSEASU0lqtFkVRPp8fGBjQNO3WrVuVas51jHpjb2Hx5uDw6PH5U1JpN66veF6glKpUKlNTE+MTY+VymVLq++H86bOlgYE33n7n+q2FiYkJ2zTWVxZ52CkWi5xLADAMrdFqLSzdrlQHJyfHJ2ZHmCaajV0m4rGyU6bSDP1RXTNyNhBXCKNStHTV5X7qugX+4Sb5f6zpJ83AH5Tz5n7pJ3ybPzHqd786pWHgbW9ulnJu5HXjKNQ1jEJ/fX2V6dr4xFScJlGUTE6MMRSX3vgRQaFTFvmR5IJQCAIPCRsYGs/lcpZlraysbG5uGoZx7NixsbExzrnrUk2Xk5OjYxMT88dPF0qDKdfW1puCqzTlQvI0jYPAS5IEkSqF49PHqkMTu/WWH6Zjk1OVcr5d29pZXUSm7e03kiTNldwwDC+9d0NjlkCuWYSrZHN9dXNtCeKu8hv7Szf2bt6oB416F9c3PFsXJTOkUcQotNPkJ7mWPlb0J6Uq5Sf1NvtBMi2T7W81u83G6ZPzgd+tNVvDA5VyLrdNVMf3qkODOaW1vCBot2Uadfe2HYt12s04TAYGhjhPNne2TNsdHp+K281yubymVL1ev3379sTg8NjY2PPPP887+0IFn3n6caEc3TQvX15qdbnrWIZhSSnb7bbPt3b2NjnvMEIs3eRCGZY1NjnjGGaSCsc0Ygp760vlkVPt5m2FtFiybyMsLm60u9FebdsdmKTM3qu1VpaWncKoAGOnvh/uyKkzJ4gauLlYZ641VJroRimPICHE/AnP+8eGftJAjv6M9OEy2E+7t7kf9ZtPIsXmxmqjtiPFVMfzkzABno9Cj3NuudQpFCDFvdsb3//2t1XsJa2ahZxIkSSp67qpgijxBqpD+fyg5li6rgdB0Gq1NjY2ipbjmtW5ubn2pnJti9rW1p63uLS6vr0LShsanUBglGgSlYKUMoWEkhQZM9qN/SgKjx07roQMo4TqhlJSY3Ricrrb2WK6BlxyJeuNsNONSmV3ZaNmGaYXsHpXeBKGJ0bZ8BhXbGK8mHjm1bfe1JfWH3h4qGKKjTAwCwWM7t1395NKvef+qQT+aOjD3lCkSNZuLzf291aXlhQzSwODkkc7G+sS5MTUVKFYdpj1mFu8eentq+9chrATQEtnY6EfKYlW3mY61QzLj3mBKqVUHMeu6/q+v729bbIisfPPPvssauTFN9564YVXhkfOGGYhDBNJaJJwQgiAVEpYlsm5iGNfcBW29jr1vYGhScZMnaiJydmV6+8ODg2nUhBme0HEhNB1HZS5u9M4eebYdnvH1JzR0XyqMF+uKJ0YxNaIpTESKBmAUWursBMV3XjdjxHGP9TJ/DjTp06sj4Y+bNOo3axvrN7OOTYj6FimbRmB3+20GmkaB1F4a3Gh2eoMDg6ajIbtBiShxlByrhGtXCoRioSQUqWiQFtZWdF13TTNubk5SunW1pZt27Ztr9/ebLd9r93tdDr1ZmN4dHRodMQtFpRESrU4jn2/C8gJQSEUIVrSqd9euM5AUaabbmFidi5VxHQLjuPYluN5QRT7xXLJMHKXr9woVYuJCCIeFIq5kYGKhdzgQZGInBReo7m2c7swNGzmpra32zrxckYsoo/6cf7EqfegP2XgTyZtrm/s7u7MzExdOHfm+NyswbRWo8l5cubcWU3TNra3VlZWL1++evXq1aDbtXQ2OjSYpnG5XD575rxpmkjU2MR4sVC2bZsQsrS0pGna9PT09PT0/Px8HMevvfbGytJKsVg+Pn/SC4Ou7/mhv1fbJYRlScUKpK7r2WvGmIi8lYVr7VZzZ2fn9uo6022im1yoBx58aObYHKWUK5nL5TTNvHljgYskRZ+rAFSS+E1/f8NK/AnXHNFYzrLjxBudni5WZza3a1J6+Tzr16zhTwKxuxJl7z9/8n6pH+r8/mwYChQAECQAIKgsHRdBgmJZ3yeJh31fEaB/Z/QPm/r17+2v0ffrE3vvusEGs5JUSqoiEdu27bWa5XxOQ/rmH/7B9MSxwZmJKFfxu+2RiZG11aXKwPjZL/zFkZGRhZu3wm570FXjVWNXTxGE5ZSbKZV2tWGWmhG1dFdTvNW4PebWQZHK+FBZkWPzj0Zg0fLQC9/50fHqyLrvDI6OTI6FIG5HwXa3sX+iNGGV9Ci1g6RdLNv7e1IloGuiVMIYRC5vXL72RkqcocGJwVJ+du6Bd95d+7k/T0fPful3X749kdsaKpH12/vDoyNX3nj7Sw9OvLqqau6MOz8Wbrw5SEXYWUuKY5sbHW+jzqcSbX682SDx2tbsmNtU+x2tpIHSlGBKUEAOSiAKAnq/hrx98sN5n/XJ+vho+jUx+6BMNtpn3fZWw0+zBMbsGaiDh4H3l1L/ySApgTKUUjJCpUh1pumMra6udnxvbGws57g3btxIkiSXy7VaLdtxHMeZmKgSQl5//fXvf//76+vrhBDHcbqeF4YhowhKIKLt5qIwvHL1kqnr66sraZrqut7t+q1WKwi9gYGKptHx8dF2s9Xtdo/PHpuemrpw4UKxWIzC2HFyhmFomua6LiJGYZKt5mql8rnPfe7iAw+MjY8MDQ2VKuVmt7uysjo3Nzc8PNzteI6TK5fL7XZz4dbS6PCwRiSPA8vQ7FwxQc1Psd7yVBK6lLsQsDQoVYYWdtoxmqbGGApUkigAhZ9Uh+U96e5spI/6ev43E0pUkMlXonpvJaBEkACSKIlH3n5kl/khk1ACKUrFmUZASMvQNAZX33vPsMxHHnuMEFLf3RMp39zcTNN0fGrSdd1vf/ul3d3drDXZ1taWECIIAkQqeaoTaDfrYRiPDI+Oj4/qFCulXM61R0ZGmq3W5StX/G4XpEDF0yQSPPU87/q1G0nCKaWWoVdL+ThNuBRJwienp7/6i7/8+ee/kCsW1lY3ZMpdyz5xbLZcymsEFcDg6HiscHtr33E0TaOtlqdr1uTUOGPs1q0lkIpJ4TW2NCYLQ8Opnme5AU5Mz+vokLCgpsXtkbHp3YB1Y2FKn6FkILJNXAICIKAiH8uOcx8s3S2BP+wF94GRAjzgXokgUQEBSQCIAqKyBwhEAWbjkwsgkZJTBgCSUgpS6Iwlfrp088bs3Fy1Wl29fXtibPzY1PTCzVtOzi1Vym+99Va73T5//vy5c+eSJBFCVCqVNE2pxixTH6kWUHKJUB4cGhoaKuVsGYeOaRiGsb29e/3mza2trUZtf29rPZe3t7Y2HMvqtFpxGFPA2t6uaWmjo+PFQjlO0nqzubOz02p1DMvOl4o8SUGIVmMv6naV5O1uZ2RyqjQyvrG+W6s1dYMh6PValxCoDpQYtdfXN4eLORa1NEjcYqkZKjNfNa1cjJpm2X5jP2nsAQczV+k2akOkaahEI4IAKASFJLObENVH9dx/YvTTKoERVCZmASWAJCAz5aknfjNpTEACSLzPglUf5HV+yJQpGIQQoiRFQCF3Ntb9dueJp5/a2NpstVqPPHhxamJyf2e31mhs7u6USqXTp08j4tLSkhDCNE2l1ODgYMKFodGxoYGcpWua5oXR2srt7bXlkYFyMZdzXXd8cnrm2HHXdS1dmx4fPXvmeN41pyYmH774yKn5E+OjI0PDlanJ0b1aI4hjojFKtM3tnVuLy4Efxgk3KDM1fXt9XadQzOdjLsZmjs2eOnf16s1WuzE9PZnPldfXt3Z3dxDV0ODowq2VuanR8YppQAQEt2rNJBWYxmDlcwNTcQpRu1PbWCvm8o3N5RHWNjHVQBJUAEQqlJlq+ckXwD+1NjBRkig4lL2ASuHBW4kgESTJXitJDtj7k0lIIasLJSTXKEuTaOH6taLrFEqlhYWFseGRnO2sr9z2fT8MQ6Tk/Pnze3t7t27dMgxjcHAQEbMC7kw34jDwmnvb6+udTmdre/fWws0k6JqM7GyttVotP4w450mS7O1syDgI/Q5BaLeb5UI5DOO9vb2cYxfyVsJTqZQfRs12Z31ze2tnlzCaK5YKubyta36zCSJpd5pRHBcHhmZPn9nbbTabtWKxYJl5wYlUvNmqh4G4tbBsUjw5MWDIIAo7MY9jr6PHXUiCQnXYKY3kTJvFvoWysbOm/LqOQkNOQCoACUQpBAB6/xK47zx/QN//wOmnuCZW5oImILPKxnjIpahAIaACgiB7b39q7uv+Wr1QipwnmqmnYaRZWrflX796bXxk+NqN61ESV6vVhZu3bq8slYulmcHZYqX87rvvJkly9uxZFOkr3/+PUsqB4YHmzsbo5Mz+9uba8uLWTt0wS5phcJ5Ojo6aOvnBd3/oJYwLEcWpFgTvvfMOAbjEalsdQjC3t3q72+4Mz0y292u3quAMDhUKJdOwddNEcJVSHS8wGDNytuTC0rU0DC5fXy6OTDPTGp6aNXTn7bffHhisVCvDCfi2bXa8ZugnCW9u3F46ceJEsHC7Hchy3o3bNUtFrmi51rwvcwaFsUqxvddCqntxqjsCgKSZ5YQE8GB/v+/Zv8918pGvq59WCXzoc5Z3v82sXjgcd95+MokQwpWklAqRMkLTJNnZ2hisVPcb9dnZWY2y5aWler0+MjJy/PjxxaWl7e3tycnJoaGhVqsVBAEhRCllmiZXMgiCMPBcyz558uTExISu6/mcUyjkXn755TAMz507d+rUKUrJ4q1bBJRtGZ1WO0mSMIjHx8e/+MUvRlGwfntl5tisk8+1Wi3TNJ/7mc9/8UtfGhsb87p+HMdxFA1Uy5Tiyu0lRRCZZrt5y7Jfe/1H3W53amoagMRxrOu6rpupVFsbm8OVgk5kEvnlvBM099N2fdRFi0EYxu1mq5xzgyAoDY2jXcq0rY/QW/lREWP4x8oH/qh2IEWzDrv9Op7fTQz63OYHlIf8QYUu+sV7aZ95lrE0FEbtds4xQIWv/+h7lRwzMLBM5+qNq8MjY3bZqe/eCOqhMTt+bPYM2gXXZDkmulvL3v7G6MhAJMJQ19Lm2uyJYyKIIUkefviZrYCMl0eHxNZ22+uyod/9wbVnI/3cAyfAIGPbs45hpiL3+V/62YmZ2esvfmN6eurbb196e80/O3+yvrS4sLk1OjASr676c3uPPvEz3t4uKK3bXWkkU4E1FAbBU09+LufQ/aW3JuZO5EaL0dKl9R99y5if39lbt8B1c4U9pMgmXnz17c997kEe18JW2m34kPpr7U1mVmfobULsl9++9ubVW+NVN65vRg3rF57e3ih+9r12QYrQ0lKPE80o0EAK8/767vZfDf3iw/3ykO/rtH2pLy7jEF/waVXKn27SddZse7mCwwh228393b3h4WHG2NLiwtDgyGC5UL/NGWMAQAg5ferEC6++fdnv1teKO9ubSeC3SUqpyhUs5ACEpalXKJT2a7vSGpyaGDfqbSXh7Nmzpr3/7rvvNtv7T37+S089Za1duUpUyIgkChr1ThivxO7IyOio4ZpBoqqDI7cXV4UNi4sLmxtdkQb1vU3Z3S5MdKVIbcsILCuMwmazWeh2Jkcqy2/WYKhsuKXByWNee2t4xA0wXV3fYVW8sbimUG93moqx5n59d3PNtltmacAZnJwcHyaaPlQtaoPFoF176crqwPn6SH6wHugpgMY0BkLTNPFJl8l/3GSGj9wG+GPTx+v671eSKwRKCaNIlWru762uLM+MDm/vbvlep1wut/Y2tjdv1/Z295XaaXjWq2865cHVG63LkougAyLWkaQ8SX0hFAgRcyFLleL25kZ+KlfMj5KApalgjBkGrdf349S/8PizY2NjRaoqRb4TthZu3trZrmtGxEYLuuNwFIOTM088W7x+5TrV/HarsbC7fmLI0lWYHxgBqbbWbpu5ytjYSByHXhIt31yo2BoRIZdCaKZRHV9ZXTptO5q/XyiX8hVjc7czNTVFWAt0wy5V7SRkcde2zVLBdSsDQcLz+ULBnbh26Z1L+/hoY3+0MB5Qc58bOUPT0kQh+elfn/em3n19KoE/XnS/Cy6Mw0IxJ5MYCKwuLe7tbGuSx543NDrYru0mcSCT0LQdwzACrm5fvTw6MWMySkTK/XbQbWoQ2o7umPbyViPvuiYljuM0k8g2tf39zTEm40ju7+8HoT80NDgwNEipnqZienpqbFhFW+TStfXtzb1CZaAyQpFCo1W33VKxolHN8v09akSR3wr9cHiwEPiJYxpExaYG5XIxSm09jISUkHbPnZyLQeq5os5ZyGyBVIqEo5BMT5VdGZo+dcZe3qw7lUGSr5LG0uDIoGYR29AhJKkSKTNagurjF712h3Z2NDqSSAtRIYhESqCf8Godn0rgj9f1368ElpJrGo1CnibppbfesTS9WMyXqkWkWhR2GrtbSnA7V3QK5bKuD45NWY4ddTqbK8uURznbsgyNMYJS5gsVUMrUqa7rg8Xq2NjQ1tKbJ+edWm3fNPXBwXLFZefOP3jq5LmbN66stTa67dQdf5xq+41mSyAd0kihWHadYHt79+23b2Y9TeM4LJUKhAbVanVjo20ahog6gW+1vWIQ8zRJ/G6HEvnoQ+e//+rb1cHh/Ghuf22h7oVhlLQ7dQpxfXJ6b7dZLpbevbEwOjgxNjhz66WFIIqZ6JguOJrVieL9eiMFNj7/sH/jh16zXhgetCRPk5QqBPKpBP6UfrJ0vwtOM40wDA1GN9dWNtfWjs1OX7hwJpd3Oz4nxbzf2i8UCtMnzlKz6AV+uVQo5ty3X3/tjR/9qOwYU+NjlkHCqBsmyZlzT6wsLbqWls/nzeFqqeRsxd1ioXr1rS3LsiZnR5NOa2hwDBTdWN9S3vqGRU4NU8NxqwMl3+/eun55Zrw0ODm5t71x/cplg9FcztkKgkJhRNdlkASUmX6nvbZ0k+Rq281uN+KuaSXtVkE3cg4JOo00Dk4+8OTexuKV7/87jPyCq3HOGy1/aWn1wvkTts40267OnLz2ktXuhOUCM0DlHDNN/dWdbZ0Zbq7U1EvNgA9AMqBhk6uEWpphCPEJL1j5xw0j/cQC1h9z+kAexn8CUY1kAaS3Xn19YmT41PxxwzCCICCGEyQ8DMPB0Ynzj3zm4mc+Ozx9PBUQJKI0MDg4POLmCimXbT8EprnF0tjEpATq5ovDY6Mgxc7mhsbQ0MD3w1arZZja4GAVAPb2aq1WizFSHjqu9Bzo+okzc6OjZb+529rflWGIcTRUykVBJ44CwzSZplHb3W13tja3d7a3o6BrmZTzpNPx8m5u7tgx1Ew/6OYt7drltx3HOTZ/qumnocRq2SkW8jzmkRdYDM+emo2iIOJy+sQFrnTHtE2QJAowDNu7e47GNBD28LFGovm1zSrzDYYRUA4/9VDKfs+994Wf2jjwpwQAAFnVZQT5zttvPnD+/MjIyPLK0nuX3x2bmqVMt53c4NAINSxquqmAt967sru3bzpusVRRVE8FUM1guhXFKSANw9BxnOHh4VardePmNSSCp3HOzXe73VarwTknhOVzxUqlYlqGlRvkiiWpKJTyJ07OfuYzjz504XzBtAdK+acef0wmSRxH8/Pzo5NTI+PjxeqAWyiKlBNUp0/OP/vZpx97/PH5+flyschMUwgxNTm6cONqkkbD4+NmvmjkSsNDpWI+t7+7t725ubO1NlgudDstP4hm5s4IRaWAsNup72z6zbrfaliU6SjKw1PtmNa211wV2qaWCEzT+4sh/TQSIagj0RUxJBqCGARVb1ACvQEHOT13D0R1z0FQ3nNocO9x9LxHB7J7D0ruPfqdt+8g6QcyKOX3Ne73OIjJPYcIQ0fXf/DdH5RLgxcfebLWSRTLm8UxUV9845UXqF4anzxHUnr51df393Y+89zPuCx999UX47g9PFZudPa5jBnDcj5X21zDOBibnPnR1TWrOHBirFxWga3h6LHTX3rqF6Ld5LVL7723s7ETkjDJJZCve17ciXNsSKjBKKGDBa25u5wqiFJX6M7ww89tdC1TYrFYvbzczZPhsRnDcBLP61h6bnRgyradjdq2cNTg2c81zdHy7Fy7vnLlzW9Xh4eOn3/i9krNoe7kcHVoQPe76+++9lLB1MqWWrn6w7wpKi5hTJYmZ9vmkKpO6KY2YIds/QeObE7On1rfDw2q26nniE7Jgn7zhsA/kkFQ3HP0+z4l8p7jzm8JCqIkBUFRsf4qh9ZnECnvORDJPccRkNT7Rj9Cde/xKWVk6Fqapm+++frJ06eq1ere3k4cRV/8whcWby1MTExMTE0uLy++9PLLlOFjjzw6PT1149qNra2tgYGhsZGxYrEYBkEapZblpFHHtjRUMD05eXxmKg09JWJTY4p0r9164/KVNxvNvaDTaOwux+E+gXhnYzWOutWSTWQSBV2N4cTYgG2xnZ3bod+wDMg7RESdrfXF/Z3VNOoyrlVy5fHBwW69dvPKu3HXM4GGdV9JOjI8Wc0PFAyjubnlErCYWSoPtdvdIIrtfHFq9mSuPLS5uW1T4aBfa3tarljrRInSmJ1ve7HUrO2G5yWqXq8TmVYK+b3tdYPIStHtet5H/Xw+dCIGcANTDbiuUg1SCtgbWWpeNhioew6q5D1Hv41AEXXP8QGbDj9x+rCfU7/zGkxbunWz2Ww+88xnlleXd3d3pycnL5w522k1Fm8tXL16mWrsxKkTI6OjQvKFm7fKxUoapwYz4iA2NJMCFakI/chr7s/PzlAC+7ubCzeuri1czzmmkrI8aDhFqA7nZyemSpbBw/r4sPv44+fnj02amrRNqbEEZRx5zbGR8iMPnVhdX9nb3/Q6+4r7SvqCe0HQ2dnf0kjOMXIDpWIatf3WfjlnF61c6iXVoclSddSy3JnRqRtvv93erTumDdTZq+23u1695XspFZq9u7s3OZAfJH6SpPlidavWavmJ7ToSYXxyRjPz63vNzc3NbqPmmFq3vqtxX5dxkiQf9nP5yIlZyAUoqRAQlVIpHMTNjsYzlFKI9zYn+q5ceW//tujzA9InfJKllXz86cPm4X7hJRT82tWrw8ODhmW++cbNcrk0MjS0vrLcrDfanaZVKE/MTg6NTl69fiNNU1vXxkdG87ZDJWysradhZJsWQ9LcqwVBwznhXL12reVFjAhTtU585WLK+c0bt3RTm5iY0nRbBrzeXDB19FpOHPm8vSvtPCPpQLVoMirTSHC/GSayaKGhB2kcC1EeHR4Oedxcv725a2kiV3KMgQHHhDD2O3WPKiyN5ZubbL8bPvjQQ7/9u9+9ce1yaaBqV6v1rSvVwelmN6w3OqODBa/TOHd6dq5qXt3fGM7P6hTSoG2gkp3awNCAZTohLTGtILuh5Gm7Uy/7NSW0oeqol/RDYn281tV/8vphJkmUQglKAACCEgeMp5TKFk32V4M+DNznxInq4x7T+3SR66tH3/s48gOb/w/mQB92GZd+88x9f2NlZXxi9Pqt60jx+LGZzfW1nY1VnZDPPv0Z6hSXby9vN9uU0pmZqWIh95vf+5bi3NBJ5HfSJCjYBQTR7jRci/m+30zCQrFqadxIw5xjX7r67vVrW0NDI2EYLN9cgTRN4qabt66q9JVrS2PHzymldpcujw6Uzp4/02631zdupyyXr4xVh/iykVNoHD95YfbcE521Wy99+5vdJKh73oiu65Z5benWxvLW2RPzzAIt7+x1g2ceeQDZ97793W9OzZ91K059JXEcR6G+tb2/3+jubuw/8/DJ03MjN9dWRceaHio6GJOobqcNB5zBQvFWO3INAJ1FVPcC35BhicQdGXpgfKjP5SMn5hAhFQpQSoIElSVBK6UUKAV3eLjfAsJ+Rb36dG1N+3BeP0Er4acDSXO/O+gHxfC7m1u3V5Yef+LRJInjNDKZ09zf87tesZCbP35sP0iu3rxx7MSpzz/3BQbqrVdfX1y4lsZdr11LEw94omuFJIoUD0dHT8ydfyjRS4Qxv7ZBA8klvvrWe4E/ZM1UCYSdnQ5wP2+no2Njhp3L5wfHp4/bJjs9PWpTWqxWdjbea7W98ZHputIwlQXdyjFjuDxYHJneQdW6cHZ/YzEMY40ao8MT+/W0be8XHc3fb48PD9y08oV89cKZs29dum5SOjs66pluu96wLKtacDVd3/dqC4u3Hz47O5jTV29dV8wEJct5x4LQVAmPvE6rpTpepTAoNLsTpiruVirC85pojPSZuY8oCaefJtVn/fyvrhNmUimklIBSAQAwij2mlVL2XnPU7+tCNbh3lUkl710DNO3Dp6TfjfVrcvhTUtDsg1K533zjtZztOI7dbDbbXrfsujnHrW9vW44KgmBsbEo5eSfvdLz2ys2F3/0PvyNlRCDd3FhGxTWqNCoVk6PDFd2wBkcmsDDe7LQNSIrUKFYHNLuwfGUfdTU5Vj11cpyGNA6jkSlntx5/9Zf/0tDxOb/bEq2d1196ZbsRhJ229eTpMpJ3r16uLa8OILBOa+HV14zK+t7ydSp8nStvv7V9Y7Oa33U4mSgWRbPmL12vjAwPmLnOTmeqNPxG7YWtS2/a4XHNtFaWb82OT2hESxNpWs7NlbWt/Y5lWUurV1M0fK+T11HXqNRs5ovFhVtxo1sdmKFuQURy+daNeSTjkw/Vgg9kmj++xDRUBJErSQgoAApE9QhVT5GO++xYQtxbtdb6tBFN+jDYn6zGGB8cvfv2O48/+Xiz3rixcH10eOjYsWNevS7S1HVyQojxqQl3WC6tb1y7du3We5du3rw56YQKhB/4w9WSkqmSiWPpc8dnvZjt1BpeQ7i53OzUlCvqumEPjU0E/jsvv/Li6mju0VMnxpxYt4JiXq1s1De2a6XpuVionZ3a977/o1Nnzw1VbDdXuvzK7/3o9WUhxOl8Eu6s31je0yvjNGpFST1v6LwVXn31vYXru4lSBk2S7l707R9OTx7b29pfqQzYGjXDlGHHW10WRPrtjjWNrXZ7da9pF5zI89e2dzV7NFceNIpD25urawvXvU6zOD5f0Ksn52Y62/V8dXxo7myBnlh99Tffee3FR4rTwCof9SP6cAmvLG4ffZ8xZE/w9l6E6n3OrZ6go31s1KO+2exF9tfrg2wLBe39KjtvJv8ZmgpBIggEQVEQUJhFntwfvxgAgD6d2nVIFCBklbKyTQoIAJA+IansmIiogEhEdSgyUcb3vgF57+P0qwvdt1oxakKI7CmQI1Bei6lao1mqDHEBIBWKtGAbP/jud7732/98cuY4MRzNMEdGRvxWbfXWVRV7Eeq2bZumWSwWC4VCs9lcWFjY39/XvC1KKedc07RyuVwsFh3HyefzOVeP43hsbHRubo5pBFGZppkk0dbmehD4Io11neVcK+dYTAPJRTsyC7lyHCQ3rt58+Yc/aLd3L5yf/zv/+d/81f/q1/Zvb+XArpjFSmk4YTorFn0lFVQsy2o2m4ZhUEoppa1WK01TTYCU0jAMzlPD1MLQZ4wJIbTYVzYDldhBdHF4bHK4Wjw5vhY3H/vMk7cWd/YaseYae/urjb312bHZYxMnYOKc73U45wOVskZx4ebNZrM5WC1/4ZnT13fDHZE3S8Mo08jvFIrlWtvP25aUUgnJAEXKGSEUCSM0lPd2XPfLy+0nwEg/RbDPOqGsz/rpU9e69/HdvuJ7qnaIqCl6wCcISqmDv4elXo7+6q4jHOVeAKB9JDmhve+oTH0HlEpJKQQgIkFCERSgQsgquUkO91KY+3XrU4dfVYff6d3Ova8HJABIRQCBKCXxwB/+YVtOiJitlczvcGcyCVQHB4MwAUrTJDYIJBwWFm/s7e3ptmPYBdNyLJ36nXq33RRxMHfmDKW00+msry5uUQoABNJy0bGdIU3TlFKUUsuylFL1er1er7dbe0mSDA4OXL58OYoDKbnruohqaLAShqGp6wMDFQRo7DfTNGaEGuVRwdtREFUHB/78X/oL1bLbbu/e3tj8c7/45/1G16WmiaZluV6cUNdVGiVWQQihlGKM7ezsxHFcq1lRFKFPoijSdJqmKaXYbIaUIhKa10tCp0IkTsTzpbyiJE64Uri7u3v+/Pn/+N1XaEwmR8eQR7OzsxrQqLM5OTC8trGpYsMXSig1PTffbrc3NnYK5akgMcM4AiE0QqMgNJmW+k0EShEBGQGUUgrBEyCofzBPuN/G3Y+BFb/3RqDkvQWSPFyJ7C4eyATgXcJTKUUIZKtXKXX4AgCAp6rHokd5VUp5Twncr1KgdrjDKaWkUuqgHp1CwQFRKSRICBCJiIogYCqje+4UaR/vtwSCoKQCBEUQhEIEpYD0s5npwUYgswlHRVR2/fdZgKG/86/f9xWhqAg5dEAcKAKtbkc3rFQiI0gIFovutffeXrh1zbLMSrFQLA8KJf1OfX970/e7jqEzTCqlyshgIQxDIUSapp1Ox/M8I9WklJ7nhWEYx3EYhu12m3NumURKGQTBxsbGfm1XSl4qlfJ5d2P99v7+vk61Y8eOF3L5/Z39Wq2mUWYMDpiaUavVAs8vFvLHj00kqa8bZHs7QkTLtCXnjuMoxPHx8ZGRkbXdxSRJCCGmaWqaVi3bjNDR0eMmz3e9tmEYlBJdZ41GwzA127Z1AjFRnufpfjpMbZVwWnEAil438DtdIsXczPTAYLG1t+u1u6WChu1dltMLjNOk2/Fj02CVUiGO41atcWxsPpSKJWnK05zj+GGQsyxDo1JBKiBOEsIMQEZMppAmadjnydzf8xX3ycDQpxJLv3qqvbPeg4GPMl5PkyQ9r9Hhh+rgHRxl4N7r3mHvlsB9VBGD0gPNWSkJQBAlogIgKA4qhCqJiiiJAAgE2WETjLuOT/rkV6XAAEARopSSh/evsL93XSkAoKAIHJSuzZpc9Ctt0w90e79ONZTZJSECyswRgYgA+UKh1fUq5Uocp0gUo+qF730bRfrZz352fHzccZwwDDvtpkPlUDnnWmYY1botTggJw9D3fc/zut1uEAQWaJzzbrcrhLBtO3tkuq67rqnr+uDgQBRFXa+NqIrF4tDQgJI8SdLQi0Ex1ykHtmhiJFIoFipSSkop1ZgXeg2vMzkxevr0yX/03/yG7hiC4l5z17LMnGVqTI5UCp/9zKOMsf39/Xa77TjOysrKH/zeN2ZnZ4tW5datW0HglUqlyalxz+skScJ5eubMGaJpzWYb/HST2AbRWSMfaYA6hoF0Lf3Ku+8Yhm4xq91sgZSc826nNT49E4bdpNutlgd4tzaY00k76G4sAyflXLXleRZNIAotjIPER0IRaBpxM1fmCoRA+eGX7+i73vrwhepjbPVq1t+DgX/8LSIScXBv6hBaoQ6EA7mLheCI8dyT53c2gj6yConM7F4kCkESVIoAAJrs4PgyQ5rg4WHZvVmG9WGYzHKVQBQoBXjIyQRIH/e3lACgkAgFmS5wqLR8MFUL+x1FKXFwBwiYXSwoqRRDnSHwJGEAIo1vXbm5vbb86IUzEyPDSRzstPZEmjCCeUdHqUVxGIfB/u6O53lJkmTCNuO3IAFCSGZ2Zq1PCCFCiCiKNE1jjGXatabRg/+SRNfshEjOpZJEY4amGRKlo7t7ezv1WtPNWYViTil1+frNd69c03XDtnIx5aJFgjQmKHy/2+7UL71zNZfLRVEEAK5V0KnlmPlirkJBdb1mp9MaGi6fPXvKzdlRFHQ6HcfJIaO1Wm1lZfFS3RORiKTqJPFTn33CH45KhfLytRtpwk3H2theGRqu7tY9pdTJEycajcbe3s7E6Fgm6lnS9fzQyhUnZudXNnelopTiUKVck6xQLOcrQ1LTx508ECKUiFNp9cEp3O/zpX0KCfTT4AjtU/Osjw1M5IHb924G7ic5e6ovYu87Cn7MuO+p/veUPJnx3OeCROb2BgCFBxsSIhatu39wcCGE9c7S2y8AwO/TxUxX4oBjD0pXyuxtv2ZWnCAACKUIKomYsbECxD47oviAqpgROPDeEUIYpQAgpRRCRl1/qFTpdLxqxQ158qNLl2aGBi6cnNvd3a3v73Xru3HQTZIkjuMoSaM4bbdqnU6HUprP5wkxKCNEKUS0bHKES7UkSZIk4ZyDinVdT5IkiiIpJSLrdruMkVbDj+MYkQohgyAIQi9JIo2xxE+YYkwCUdI2rWKx2O36C7dWXMijqVFHNy0dQBFGu364fHvtlXf+IEkSwzAcx0FEwzCiCMIIKHBCDST6zm7j5VdeCyM/TVMh+Lkz5yzb3drY3ak1Ez+2dFsqpJQismK+cHvxVm17O+eW6916uVi5cOHMpRtb1YGKRmi73eZJ1Gk3SoXJbqtx/frNxt728NBAo1Hb6ybLaxuOoZ8/PZ+ff1ygJhSJYwFIkRJdZ0BlXxX3fp9jH4naj4H7mZb9jsMOr/NuhfMo4x21hxk54oXGO6zIVc8whl746egndxnVpA8HZ04pPLhopJRkPti8e6CWU0BE7IWF1fvpTsg67sPAgksgSnGFJLv37G0/sGwgNaUUASkzkBhmGoeSqs/Oer/1hPt8zgjlSkmlKCG6phFCOOcpgA7EZNCKouZW3NzbXF+85TK1ev3aOzdvRV4beSCioL6368eJZjmUmbbt2g61LMt1XaUU5xwAOOfCbyEi57zn8M84maey5zwzDMOyTM/z2u2273FENDRN13XKIOUBl77JnKAZmIaes2yehvXdHV2jpm6V8iVUVBJOFdWAUEocyyaaWe/6AhkHAQKYgFptPzOGl1Y3qoWSRJMZxW7Aby1tNJtNplHDMAy25tju6u2trhciUo1SKbnOtLfefOfEsRkRR616jUdo2bbBtNffeFXTCn5DUUpb9Vqz7XNJiF4TUm7HND80mR+p6oXqF59/5srNZar4z33xZzp0SNP1VMHGzi5QmqYxQ5SCU2re87l8UPiCvjZzH292PxuY9qpS3vPKjiq9GftRdvB0AfCIH0vhIbIqY6EeI/W/gXt/flTyE0J6PGxbEhEIICGEIUHEzIrmKfRYN6PsbdjHm0fVgWgWIDLf1cEE9KnYEKGmABQSpYCCAgUyu/QPGSdCKZESlQJCUNMyRYwKwfc3d25ea7SazduLt4Jm/fbN6y6R77Uau3HIo6Bg0YLFbEt3C/lccYDqVrMd5QzXcRxKaRAEElApFcYJE4Ic9RcezlulUrFtu1AoAICQjuvajDEAKVOVJImSihCkDJVKk9QTkqRBjAJRqELOZZamES0Ugie8UM1FYZwkEU9SzbKKhdLg8IiU0t3vDNpDURTl8/lSpZymqRCiWC4NDE7UWl7HTxBZrjjAzFwcx2HoG7rLmKlrdrFkZksi6Ho6YcQwFxcXo8Ar5fJxmPoqZL5qBvWKxRduLx0/dZYQkssXy8MjXQ6nTp+v6UOnJgd0lXTD5OTFJ7ZjbW15MWGOQJ0QXYGSANmCowz76of3T/24oN/yuV8GlvLg+0zI98loppHeae64qpTi8h7BYQDg/H18m/EeAIBUSols2TNGmAaUIgBQfkdiw5E1xAySdTBiBBiVjCmdSkqpRZxMVvSCotlfw0CllBCid+oDU1kFvYvM9PmD63x/F2gplVJCSlnWDSklVzLrhSYBhRBcSpMGiJiVNRRScQlSSqEgSCUiSkIUagoJV0QoEKAcdVeQGRUSpVS/etQR6ohIACkqAKmkECmXUlrdrarjDA4NbG9sNjYa66trt5eWt7e3t7dXt7e3EdFxnHq9joitJCmXy6P5Sqk01+l0NE2TUqZpaubLvu8Xcnqr1SLAJFdp3LUsCwAI0CDFiB+YTzwKGWPMNCiljIc21R48M5mkIeeJZTmgjtfrbVAkCAJd17MupJWSffLUcd/3c4Vqo9EYnjvT6XRyuZzruvr+vnF8Bgybc04pHaxWfd+vlssEIJ/Pjw2UOp2OzUDFPgHggZ/P5wuWRg0YnRxSlHueBwzzpWKtVhsaHSPlIWKa1nCUqfSEUtMp+b5fYbEAFSioC09pKgxCFo/GASSVCgq9Mj7jCZISNjk20u12h0o28dq7W/jYY49dvXr1xttvlyFto6RCtRmxFBDgGlMYBznLDKJY13Sl+kCK+pS+SHv9QN4vmmifZre6uFtdzd7qeDdfHLzok0SUmcxKKXYXNpn3a0gt7hz36N/M8Zz5RbIzEoKEEFSgFFEHSf+gVOYVkgxp71Yz3uvxJwFERI0io4Qxph1q0e8Lhx45LxxaCEdtYEppxth33GYHnE+OageU0uyCNUa4VMAJVwCoMkApARBIetMMSlFQBBUFJQlVBBUSBSAQUCmigN5xcQEBKSALOEno67MDVJJm+o3IxIDUKALFIbdkWVZtb/sbv/NvV5dXCGCn01lZWUGiTNMMw7BWqyGibdtpmgJAGIamaQohdF3PbirbuYjGsq4LQgjOeRzHSqkkSTRNQ8QMSpF5rRhjhBCX5ZihG3ahkhumFBnTGdNnj+uAqed5WWVpTdNarZau64ZhdFpd1z0bx7Hv+5qm6bqu1IlOp6NSaDQapmkyxrKTpmlqmiaZHgUYzc4ehmEQBK7rDgwMdMPIEHrVHs8uu16vawV9enokEaGrU8+SIQjOuWWxJFGmlOVc0bIMKZ16ncVxrOusUilNTIw55cqNG+3a3obkgW0SkBEjnGI6MuBub6/fusZqO9srC0wIIZMkDRtuvsJkylBaQEFIlJQSDZQGEN37gfWhO2z9/nyBfhI4C0vexUFwGPVAOAzfHP66H2Q406KUAnYXKKhvABretzf8mMZLpJQZ2xBCGGNKCsSs6xg5LNxBEAkIoJRmC4Jzzjk/UNEpUgRKqaFRjVFKKcM7qIYfn5HezdNe/ElKpZSmaXeJ5WyxMkKzpQyHES8hhJSo64wKTIkkXHElBQEUUimRgoVKohIIioEgqLI9jxGmgEiFQmGqJAMQQASo+GAnJhKQohIKFOAf0RWRSp61FFRCSskZAZ1pjLGCYxaL+VLOfvapx83PPTs8OHjz5s1vfetbU8fmJicnX3/99TfeeCOfzxeLxbW1tUql0uh0LctQSlCKUkqlRJrGUnKv60ehTwkgIiiBICklipGcVTga6su4WikVxGkch8x8S6pESm7brmFYpWLVdqHVagGAYRjDw8MZMwshTEJyOafVamQnZYwYhoGoBsuFkmsWi8VMcvYe9Pz8dIYwM00zwwikaUoI2dvbOTc3Ydv25uamlLJQeCCLWnMpAGDQVY7jZE8tiiLLsvwUDcNIksTNmdmGVSqVNE1L0nRuamR8fDgMC3t7eyLqGASJCMuGkC4ZdJCWjPbWYpqmU1NTBZaYJErTmBCi8VBGvtRtwvT0j1eE9n1LtA8fSSGgx7TvdxIfrtjMc3S4wvs4TSk7wGKwu7xufYtkvT89+MjrrPgGEIIic/QCgaz3J6GEMKUOYtoEKaUUZMw0appG9gjjGHqwQUpQ13Vdo3rGwYcYhp6AvQsc0qOjX8hcrL3PewxMdYNznm0HmUzOdhydUk6RCCGYopKmqZCSoxSZGkwBCCgNlU5Bo0gJRilTSnFELmQqFAeSApegUmAKCVHyKA//EWSolCiCIIUSSnAGaFJmG5QgdNptkfJqsTg5OVkqFPe3tw1K5+bm5ubmLl++HIbh2NjY4OCg53nDw8O6YSEijxOepFEUxXGsuEjTVKegUUJAMUrB0HVd0zSNawxB9cRyRtl+l/gcUe3sNvZr257XdRw38KNcrqAbqtvtZlrx9PR0o9EAgE6nU7LNsbGxra0tAKCUDg4Ocs7DMHRNgxAyMTFRr9ezx1SpVDRN88PAsixELJfLvu9blpUkiWVZY6MDm5ubuq4jYmaBZ7uwzphlWYQq13WFEIVCIQgC0zRbvshiYCMjQ5ZlZTKDcw4yVeNDpVKJcz41PkQpjeM4TdOz8+PBeLlQKEwM5YIgkFLatr27frMRLcWpyBWKQaoME4y8q+lmmqb36+MggOperNFPEGZrWB2B2R0sYvE+ZGHvhegXpjqIaSK7K/5B+gWOj3zr6CG5TIgCBKpAKRAKpFQgFWiMMEYppUJgkiScS6UQgDBUhICmUcYYIaCUSBKZ3VUmyTN2I+TAJM6e0FElObvfo3G2ozOCBy6JOyji7OAmQcpolmLRU7ylpEpyBKVTTSIRUsYkkZJIDgQUAUlR6RQMygwKJiOUZA8MuJSJQqpkIiUhoJTqqgOd+SgPS+irQjOVaooCSgVSoWCU2kSZROm6HsdxkiSe5+3v78dh5Pu+aZpBENRqta2tLc/zhBBBEHie1+l0avu7SqmsTRkA6Bo1dGbozNCYbdvZtEgpOedSyiRJ4jA46vaDw+1M0zRK0TTNXC6Xpkn2OKamptbWNpQ0GUXGmJLmznbbsixES0mttt/ttONMP3fs8ubmJiHkerfFOZ/vxvv7+0qpWq02MjKSz+c3trcIIVEUTUxMtFotx3GazWYul5ucGFlaWmKMaZo2ODjo+34WKy7krcnJyWazGUVRmqbnzp1rt9vdbrdQKDqOUy6XW62WUiqLk1FKieSU0iSKKaVpmuq6bpomRaJRLOZdXdfdsREhRBZpMwzDtOKuF2vMRyQ5m7uWikgUJ4mt3dsL3Y9Q3dF7M2vr4PM+4SgECeoAHIwIvXF0Jb/vRR9sScbAiMjk3UZyP6jXwQ/giNzPqCcYe3JPCGGZVNMopRQSkBLSNOVcCqE0QwHIQ7F8UBlPKZH5wzNN+MAjpyQASMF/zEl2hznv0qsJIYLL7AWltLc9CyFMQ0NEjZHeRQIgAEkSTpFkx+WSQObfQ4kAiJQg0QkwgjojhCBFMIQQSoJAAUIqoQM5wGRmzon38zDpp0gBmCAIKAoEiQKCjCoDuCbJ7l7dtm2q68AYUsZ0PZFyr9GY53xra6vb7dq2Hcdxo9EIw7DRaESBr5QiAIbGMj8WJQgAgdcFgKymTBbspZRmYMbefGazlP3VGQlDnyAfHxu0TGy3uzxVp08d77Takqdpmrq2NTk+tr+7wzn3PG/29KkwDL1uwClXEsfHJlvNTrPZzBcruq4PjYxv79YY05xcsVQZ5Jybbl4pFQtVrA62/TBIuBclpQF7dWsvUZRp1l6zibrNOQ+CuNvtdgKn6SV7e3uMsTAMgxR3dnYIIVG3ValUcrnc/v6+lNKyrGq12u12/U6XUlqtVk3TrNfrQRAMDg6OjY2t3l42DIMxNjY21u12s+1vfn4+nyN7tUbEZYrG+SfVnFWk+RH9j1ElvSdjDhivDxvRg7JzgKAw06IBEEAe5uHfUZ4RlFL9kiIQsgIcyO7S9AS/t9eUAR7l3t5fQgBAKnXw+lChFWkaZ7L0kC2BEiRIKQUpZRRFR83mnogVQnBUCEpKmanQgqe9BZeJhQP5fLhF3bV9CJH2vtnjXimlrtHMzZN93tsOdI1KUAASECklukYV6EwjXNCedw0Rs9wGDkAoV1KhElQqQZUEQRUqBCKkRALv52Ho3+5SA0FAMZSEEkYoJUiIIjxKJOpAETAWkEqUqBHNYobzwgsvCCHW1tYYY61WyzAMXdeFEOVyOUNiZCZJD3QlkjBzI2VWaJIkuq5LwU0zB0cUvN4N6oRLJsaGixcfuoColpZWXn31dYOJn/+5z7RarZWVlXa7ffbM2PCQ0el0XnnlFcuVcydmB4btKIquXbtWHbIuWif29/c3N2uU0uFKYWywrGnazk46UHR1XQ/9llKg2cbM6JDf2A+CINbI3MRorNSNGzeiMHBsa3xsdGNjI01i17GnxycAIPYDy7I8puVtp0mZZVlBq+PYBYJMcBQClEERtJ3tWs4ten5IWWRZGEaQpHRvv5ukO34Im7v7SZJ0ApVp9Z1Op9FJLY036q2IKzNfnTqVgDCBa0T1gyn0pYzBlOoNBaoPzwHAIULrfcpz5o7NVsr7M4UAQfTtF50JXmR3Kev9vGcEESDzs6meVxgRsvjw0UvJ1LMwDPHAM47ZdWtM03UTMZBSxnEMPwb8kFIJIQQqBEXIwVRmHN6TGACQcWDvajM6enY4XJfZrzIeTuKQEgCNIoCUQgqRxbsIo0RKLiVIRYikFA3QGWNEKABQhCpApVQqUCiQUhpEgZKYeZ+lVIRgD38CUiJRh5itP+IpAmQqumIEGUHKCIXM+BBcET9KkzheXLp9+cp1Ari2cvvWwrIQYQZ+yKxfIYSmaZ7nBSjDMOyZ95mjKNunMn9eFgHKfpvxOQBk3uDepAEAJF7WuGh8pDo1NXFseqJd3zM1ePzRk4ZhvP669b3vfc+1+ZmnL1iWxYi3s9t6+pmvUkr39va2d1YMU33u4Sd0Xf/hH760trZ2dn760QdOSyn/1b/6V6PV/JNPPvnlzz/z2muvXbp06cyxyRNTo2mavvjiizQNvvj883NTo+vr65ubm6fmps7Mz9y+fXtxcbHsutPT09V8fnt7G9P0oXPnSo6ztbXlufmZmWO6riuFrVYrl8vNzs5JCdu7repQZWxsrNPpNNqBbuWLxWK1Wl1/7z3Lyg+Nloul0vpWrVgsVsx8sTLS2tlBWiq61uDEsTOnHpmaOL7vQ7vlm+4HAIdWSrF+WXcHWGA4/KuymjZ3cd3/qgqtDj5HdheWUGemOKSeloWICTkqdUnvr0gBEfEAcp9xFFFKccFiX9Eg1IjSNag4Ws61TNMQKbS6nW43kIpqpkOYnaQ8TBITJENCpdQUYVwRKlABgFQiY5ADsUkpMVDTqU5DrpQCzMpfKpJxDwV+qEGowzhwtufVQzB4anFmm6hrGjIdQAkFKuWIlCDLtj0UgkgJUjJ6aDoKISQoJASAIAlCgYgKKQNUREgueMq5FA4zOeecx9mk9VxryHR1GK8+GpeOMCGKcKlTpMiVUigFKoWUKM0wb968+Yd/+Ie+15VxrKTMOVYSpUIIUGJ7fTVN08ymze6RUirSVDPNOI5N0+SAlFJkGiKalsM51zXDDyKLMC7SgbzVbDZNx4njmFBqWVan0ymVSnZuvN3dsnK6xiDohGWrOD8+CdIvusW9vb3HH3783TffLeVKA6UBpdSD5x78/vZrDqWlij45dGzj2Wf3dzuTAyP5gjH6F/7Ur//6rzM9vvDg+SRJfj547tvf/vax47/EOZ87/gtX/2+vMS2amBx56KGHuGi98MIL86PVx84cbzabf//v//1Bmz366KP47BO/+Zu/ubi48Nxf+Kp4/Nzy8vLv//7vFy3553/pi3Ec/+vf/i3hb/2ZX/5LYfjICy+8cOXKlZPTpYfPfq7r+f/u3//b45MTx449UKuNv/DCy5bJPv/Uk9OjxYWFhVar+cVnn6+6JE3T11577cJnH00mzHajoedLpDxQOjaxEnQVQWZ7QJx7MgzrAxBKUWYyUyrZE8OAwOQdHumtRqUUYIZHkESBkgIPdWep7uhEh6yOfwQDa0rL+JjdlaCbZpBkpAcyF4BLQAQUaW/x9VRZzEIUR/bynkiUGY6BKIUKe7al5IZhGLFFaZzGgocx1QggYYzxNAYipSRSCJltEFmq0EF2zh3V4DAUlElaQFSEEEIB8QAW1psCPEqASZJIKQlIXXMZBVSYphzUHQ0cDj06lFJQQgEByAqGgZKZUM3Cy+RQV7pzIiZjikpnmfZ+J/oVSVQKBKDEgwyKTGAzSUCBEiLlXAqQEoQQUoJl0/r+3vbWVr1eQ8ENTQ+jIA78KPazLSDbC+48SE3Loim9XSP730zq9iYtC/Ye+ON1Xdf17DuapmUmYqvdME3N77T393c313Z1YlYGBpUM3nzj7eHh4fW1zfnjJ/d2awSZZVkaM0ZGRjqdTqvtG4bRarXmjs/VajUuHD+MZ2fmRkfG9/fqQ0NDhm7NzsxFYVIqlTzPu3D+wTCIz529sHBr6cL5B69dveHmWcfbGx0feuDiyTBuliqW7/t/6qvP/+P/z4ZlWbZtj46O/t7v/Z7rutVqtVqtbu3uvPbaa5ZlVSqVr3zlK1euXMnn84888sjW9s67772zv7//n/1nf7rb7SKy1159a2Zm5sKFB1dXT/7jf/yPUfq/+tf+fKPRAOHxuPXzP/+VVqNxa2V1OwJQMo0Tw3So5sh+qlOf6EwmujJ32vvMYHWAb76j0mbLmKBSgiiQKLPsdlAKADWiHTWhe7/qi6k+XOTsrmycJD2IU2HWjiGD2kmp4rT3S0KIIqAEICIgf58mdsjDiKgOK14gUaBEtuqzBWQadppGYZrGqaLMIFSTnCtEgJQAcgKEICUEUVFkd+0OScKFUKlMCSGEAKFIKUWRdYRAdQgUUaAUAUWUlEoRAAEZUCkMgVFimiYFJaXMwio9bu85ydJUZdwrpRQHu2vmVUYJEhUVoCQoUAgEQRBTRYf+c0SEzB1ACGmEUmXJzfKOvZCxnlJKCCkyKZ4IIZSUMk3SIAiazYbveXHg52zL73TjJDRNAw6x5Yyxo1sTPUKMsUwm42EIPfswS83JFkTGwBnbI6JhGIhoGYZppLOzs6Ojo0u31r3Ac3N2PlfYa9S///3vDw0Nzc/PG4bhed4Pf/jD6enpn/mZ53b2VvZrG5zzCxfO8RQ2Nzde+dGKadvFYvGll16qVCqZP3loaGhhYcH3/UKhcOLEiW63+9Zbb7muG8fx2bNnFxYWjh071mq1Tp48GQRBEARxHHPOn3rqqe3t7bGxMdu2v/KVr7Tbbc/zLMsaGxubnZ3NgGWjo6PHjx/Xdd33/anJyYsPPLi0fNPQdWdo6Myp0ws3li3TLOU16/j46ROTRAXDAy6FcHykSCmvDlVK5Vwn8MN64miaHzORgkoUNfpAIPswMD3IFz9k50MntMjyyI4AAZAgAKYyRQABElWG+cmEgGTijm189EU/7LE8TONldwU67mkCH6qpd6j3dfl+INRRIoiMAGXICCWEAEpUMok5IdSynJRjxMMkTiEFypRGURwkJEmJilIqMzlG1YGpkP0jlVSCC4kiRUTKsuWbyWwEFJmb+fBe7lxYtncAQJIkzWZiGIZrmaZpErxTAwwOOeRAlCmUUnIFXEgppVAIIFFmYl5IQCGVBBQKFYLLCGNZbCyzvTORKHUUCpREKVFmytXB5Oi6lMA5pygIIEWSufMIYY5tnT11+tr8ezeuXBZCUIY5zSXszgo6qjJkeKwMPJPFThhjmTS4o4wdxueOygo8gLJIAAjDMEeQJ/HkxFin02k2m4MD40srK4Ap5zwM40ajNTwcXr16HRHX1zer1cHXXn+5VMpHYSokHxsbMQzn9sraa6+9lisWBgcHS6XKjRu3FhYWzp07Vy6Xb91avHTpEiHkoYceiuN4a+vVd95559ixY+fPn7/87sqPXrq8vb39wAMP5PP5b/7uCysrK2NjY4ZJ1tfXX3zxxbGxMUQsl8vvvPOOlHJscoIx9uKLL1qWNTAwcPz48SRJFhcXdarlHff0iZOLN2/puk4UnD9ztlmri6QrpfzcZz/r+/6tmzcHBgZOzM/v7e3VWk3kia4h8Lhb39OcCSFBM2wO927m0C/bjIq7Vd+D9Ubet656u63iSqFEBQokIBAF2dujEKOjKm2/eLJAkjEFu6vsI8P3YUEJPYi7mLqd7fHZIujBAPphVygBnVKdoc5Qo6gRQhEIyJQLBYcpvgJ4KpEKQMFIVus5uxilFIJCJQG0w7z2DJ94WEBacokEiCJUKiKAUopEIaJBRG8qs0S8zPzMrGghhBIpgkJEaeiEEEreh+nvGZZCAleQcW8qlJQHebqoAIBkslcC8uzIqA4rPVF1kF8hhFCIiAJAKZSSHNlNEBEUEFCMUGRACZEi06CIpkMh54wMVG9eu7S6cFNyzghxbbsT+Eefa+9vpkJnYrmHY8leHzU6sugoIgoBSqk4jrOoklIqk2COY0xOj+VcZ+HWYrPVGh+fs51co7nz2quvPv300w8++OC3vvWtmZmZf/Nv/s2v/dqvLS4uLizceOihhzTNunB6vtGsz84UFxcXL158ZGB44Dd+4zd++Zd/OU35r/zKX9rZ2dnd3R0ZGXn00ceOHTv28ssvnz9/fnt7+8EHL46Pj9++vXp6/vylS5empmaUwq2tHdd1m8322NhEpi9EUfTSSy9dvHixUCg0Go3vf//7jz7x+IkTJwDgW9/6lqZpp0+fppS+/vrrtd39J5984oEHzyrgr7322sjw+MzMjOvmVldu7+zszMzMzE5P1mq1xVtv53K5B84/tl9riaBTLhTLbRG2W4475icx6qRf4F72AdWpHoPdJSoJZLkwcBgTApSAqPWcvkAoZl5vefTnd8nC/sjIA2J3ndcgB15cPIKsQMS8bfSsLM55kkAcCwSO1LhLAme/ZSAz8cgoEioJKjyI8Sou0zjmScI55wCEIGNMVyrKlAKCjIAiJCsVoCQQqaQQPZl2cJNKSBAAwDOdlzJBKUVUisDR3SvjYSklYZSnnHNOUVmW6TiOaZqZMqEOw1FwxPWVCCWl5Ae+J8kzBgeipDwEtBKhlEKiJEhESTQFmPKDe8z0YQDIGKZHvc0lTSMAAJVl6QKhoCQBAEOjmqblHLtarhzYsQBCpHDE9UAOIeJwiBXFQ2RLBrHqbUbZF7JE3DAMNU3TCQOADMzQA59yzqvF6pOPPVbMuaNjI81m/J3vfn9oeGB67kSlXKaU/v7v//7p06eFEF/4whfW1tY8z3viyccs0wlD/fLlq5NTI1Kl7U7TtkrtdntgYGBzczPDey4uLgZB0Gq1KKVDQ0P1er1Wq62urn7ta19bWlq6ceOGa2tA/L/+q3/z7/7dv/vYY49duvTa3/7Pf/XWrVvf+J3ff+qpp0ql0l/9q3/1n/yTf5Jd/9/7e3/v8rWrr7766pkzZx577LGZmZnvfve7lNLx8fGL5x5wXHtrY/PM2ZM60yzD3NzYCINocWl9eHh4cWkd0PD9aH1j79SpShCKZhCRNB0u0bLrUhlD6vOERiJlfVRo2g+Y0VPx3v8FepjndyCB4QAsbekMABCBIGYAewBAVGgY6hAE0XN59tNtAUAcOrcYvr8Yj0kFIYRSktlOvbXCaAa6AESgROgaEKSGjp3k3qpCxkoUFSIQBVJKzhNQGCWSS0gSIQVQypgCQCrF4W0gIZmsJoQchLZRCJVmvjWUPe1dykxaCqUUoUApYYxRikjvXMZRr49GDkxEQ6OWaViWlWGP1JEIc2/KDqxTqYQQqVAiM4AVAijBpQQFADJLYFBKEQAFfgJS3sFgA9DMGy+V7AUJATBD4YACxRNEiqgQyIGJLDMV2pE81SzTtk2dUd00Y4oaZcA0PPRq4hGvXi/Mlt1pphbdhdfLrGKlVM+zlX0hU7ZzuVy32x2sVoaHBjIpTQj53ve+ly8Vf/nP/uIvfeXLv/7rv/7iiy/m3MLFixcfuvjIiy++6Hvh8vLiE48/Q6n+G//8n/3a//3/XC4Xv/SlL/2rr3/jtTdf+ZVf+ZUTJ07U6/V/8f/7erlcvnjx4sTExKVLl377t/7t2TPnJyemLpx/8MUXXo7j+Ge/8vP5nKGU+vf//hvPP/8l3/cfe+yJlZVVKeHZZ5/NQNdXrlyZmppK09RxnL29Pdd1e9Y7IaRareq6vrS09Lknn7527er1G5cHKsVHH3pY08xm7Uqn1QINLzz8wOXLly/fuHL8+PEvfOV5pdSrr746ceKBPMu3m12VcqKS0GvauSplKumTXmr07xBywKiHzpTsL4WeTZvJnOxzhUJitt5QETh4gYgc752Ne5dmfoeB1YEkv/uyNJQGBVunrqllwzGYpZEk8bKRpj5AomnKcbR8vi/uDOGQ4RUoJUFyyUWGAYyiKItVZmgEAEiSRAhxWPaRYOaIUj3Ao+ScZ7GTLKgrhOBCcaESLuI0ieI0TtKEp6mQqZBcKi5V70U2lFK9GquGcUdr+PG8pQPprbL8wcMatwqVUuJQqksJR7ldKfDitBsl3Sjx4jQSKlHIkXKkR/rD3Rlw4Hg6+CfzI2a32Wk1W62W73fjMArDUPIEFSgQ2VxlW+qhEpREUdQrqdELLKkjkLXMNmaMZT/vlXQFgJ7NnMvlLMsqlUoG09IkWltbq1arjz7xJNO1IAg2NzfPnz//3HPPZZBGKeXw8PBbb731ve99RynV7fiPPvro3Nzc1WuXM1Ffq9X+0T/6R4g4MDDw1ltvfeMb3/jBD35w8uTJnZ2d3/qt3/nN3/xNzvng4OBv//Zv//Zv//a777577sxj66v1//c/+o1v/t4PHGvgycc///V/8e//x3/8L+I4fvzxx8fHx//BP/gHN2/ePHfu3PPPP99oNHZ3dz//+c8/88wziPj1r39dSnnhwoXnnnvut37rt5Ikee655yzL0nX9O9/5ThiGjz322NPPfcaLu4arP/T4xTMPnE4hubVy89GnHtE1Z2x0GoFWy6Xp8TGNKo0qP+igVPccVOE9R2+eeysnIyJF1uXvaA9AIkUaRkkU8yhMoziJYxFHaRTzKPI8z/f9MAyzp9kTKv2ot1wZTxVBQFRZyQvLzhEKmqYxloEEpVRcSmlRRyklQSmJQmCSHhimXIBSIERPjiGllCBhKFAiT0FwQGS93SVBBodQR00jzFBakqSpCAQkistEShn29h5EFFCSSgmBUgopOYDKMhuNtHtEpUQuMU4lQKJbJMtu6zlaMwmcLzpSSgICAS1d07XMWX3gsM04Ocsyzw4LmCLJqtkRKSTnIDgKQM70O9opHN4vIRB6FBFJNumSHGryzLDiOA7iiHOpAIEwRFCoXF0g1QBJKmjMVcQZFwyA0MSXSFsdeWt1x62MEgTUU85Ft13L5/MAwLlAxI7nO47jeX4u7yZJYrtOFEXM0CVAoVzyPC9JA9M0pRRIkIuo0xUKlFQ0lVQqKVEGYXdqYnJosKqUcKxpasWGq6dpur+9NzamPXJh4hd/9om9vb03Xn9pdHT0iccvViqVTqezevvW5sbqhfMnHzj3F69evlIous88/YRrF//g9144Pnfyq7/wFTefZ4xtbOxUq9X/+r/+b//7//6/GxoaWrm99LO//FUO/OXvfVeG3bjT+C//i7/zP/zT/+nta5f9oP7wI2e/8PzTL7300oMXTwl56nPPPfFbv/Vbv/Mf/+Opixft8kBpZPz3vvPisTMPnjhxIpTG3/8H/99v/Id/v7HXNfMDC2ubXsoHJsaq1cr17d16Kt5eXPkzv/ynhdKPnXn03/2bf+tz+uCjD377299GxNmZ+Ua9s762ffPGku/FDz/wYMdvQCF3/Ox5qlG7tpU3oQRQjxLbtjPJEUURpbRUKvm+nzMlIsZxbFkWISSzQQCAI03TNAgCxlhWpjejNFVSijiOs0BdtvcxxlIFPRsnFZIYRmbKmfXdLJeDUpIlXZTL5SiKTF0PgkBIbhgapTSOw5THiGgDUKYTzWaICAiUMoYZtDBTngllmbolpFRSYpocJOgokFKCAlSgpMwSfY/KokwPJBEcQCx6+t4BA8s7SUKZMMlEayQyWQdZP4ieJh7FLXVQ8lEehnyREFIquHgk6kMOI+aOgZls0TSNHCbEykN8dRaXE0IIApTeqTD548aGoelcKFBcSSmkUBKAKRQqzVzlB1rGQQReKkkIOYClE5JFtLK3h1FZBphCNlcIiDQVioLKSpkhIiqhpBISLENHxK7XjuO42WyaujY2NpbGketoABAEgaZpmT5pGMbQ0BAXaSZUs60nU20455ZpAkCWsqdpmmVZPRiWUkJnzHFs3WBxHHOeoFJJoizLyufzY2NjrVaLEDI8PBwEQavlRVF0/Phx0zRff/31DAo2Pj4+OFhduX1L03OWZbZaza3tNc/z9vb2igXrq1/96j/8h//w7NnTN25c/7/+X/4LwzD+n//Vr/3lX/3rukb+4X/3//ret79ze3PLcp0/+2d+6eFHH/n6v/7XDz300PSxY1/40pfa7fa3/vAPn3nmmTBJdNftdDqFfOlLX/rKww/XbNteXFgeGxt7863XHceJoqBcKT3zzDMpD5WSQRA88+xnlFJXL13+3ve+o2nGQw88+OSTT2SGUrVafe+997a2ts6fPz8/P69p2qVLl+QDD4yPj2qm1mw1lm9v7O41x8a9+RPnNcPJZlLX9STRpJSmDhR1h9FOp5O3bcsyV1dXCSGD09PdbjdOAgBwLJamaaflZVpSGIZxxDPu1YgNQlGQoITBKHKua6AU6LqRPZQ4jijQarWcaVVCEESl6yxJojD0I0+Mjg7rul5v7Kexb5tmmkK73R4brnQ8P0kkEwq1TG4wRilS5BQVRaCZFxhVhnOKlVAASgFXUgJmDQ2VQqXu8Ge29DP7JIUDfumxR0bxgTV74CTr2W9SaAdeXgWZYp+ZuwaJDyUtY4wwjWRqZNF4H6CUHBbB0zVJKWUaoQwJQaUAKZESkiQlhCBBKWWaSlCCMcYoqkOM19Es4mwPokpxRJKZOcCVAKmkFLy3uWSoGyE4ABg0A8IfGDhKIRzkLaXq8BpTISUIAEKI5AIUAFFSHGxtApUEBZ4XD1RK1xZu7u1sm7rmuu7QQDVN0067nqFTHcfxPK9QKMRxXCwWu16HECKECMOQUpphVA3D0PUDZGVva8vuS8iUADiOPVAtF/MFKTkBYprmmTPHkiRZXl7mnJ85c0ZK+eabb0ZRND9/MgzDMAwXFhbm5uaUUtnO+NLLPyyVc3EcCBkD8r297TRNT5486ZjWv/nX//LihZPtdv3Y9Ggadm8vXXv4wdNbqzfLpVwYRyfPX+h0Ottb66MjlcSvX79xqzowtF9rPPHkUK3eXFhc/uznfiaMksKg+8abb09PT+umcWzu+P5+fW1tbf7kie+/8P3HH3+8QsuEls/55zrd5sVHLk5MjGXJMKdPz3eandXbt+dPzM3Pz29vb7939dLw6IhuGlRj65sbk5OTE1OTjVZzbXONGaRardTqu12vHifB1ubtmZnpJPFrtVqz2QzDEACKxWKpVAKAxLSjKAo7pF6vv/baa4SQJ554Ip/Pe3HcbDYty8pUuWq1Wt9rXb58eWpyenl52XGcmZmZyclJt+B2Oh0Zt01GszQyx3FyIyMoRRx7VNc5J5TSUt7inANQQkgcR45JDc1aX1uOoihfyOXzrlKcp6Gu4e7u7UazTTWLCSVRUnrQ0YBkppSUglJEzIoTZ7KXHMSmEbJlntmOPWY7yqWIGB92cjiwIXvWecbnGbMe+loAUaOipzkfwq0VIjq6hURlFmMWaKUMCSGaTHu2a8Y3GQ+nKhVSCC6oouRI5o3kilJ6mB59EGBHIJkfqqdpq0OPtJTApZCHFrcUIgtTZ4DKHlNImXnTZHoUoS7vmENKACAqBRKIzMp9IyIQLrM0Eq4UCCkRFKOKEmVZTjFfuHn9Wn1/b272mBBpt9NK09T3/Uy6Zpm0g4ODWS5uJmCzy2aMZWJW07QkCUzTzERQ70PLsmQSmabp2GYxXzAMjSdCgWaZOiKWSqVut3vixAnXdbe3t0+cODExMWEYThRFGdZ6YGCgWCxubm4Wi8V8zhoeHnJzZhh2Xdf803/ml/K58qOPPhZ7AWNyfHz85Ml53+9evXqVUfU3/vpf7njtWif+5h9+51f+2v/+zJkz3/7m77Trmzeuvvd/+Fv/x+np6ffee2+/1hoZnfxTvzCORM8XKhcuXBgbm6CUXrt63feinZ2dWq1x+fLVqbnJM+dOWZaxs7t1/eZlL+g2WvVcwbZt2zCM537m6Vqt0f29Vq6YK5VK5UrRLeWUUrque55XrVZPnDjxu7/7uysrK3uuvrWz5rhWqVSYmJywrPbmxv7SwuXBkXHu16P2fuD7rutW3Golr8dxXN9bNU2z3mwqpT7z+AXf99v1jVZNxQlPkmTkxAkAubu7K/xGrVbbW1vQRLSzevv48eMmJs2d1SZAltW806ivr6/XarViseg3p1zXJYQoXa81g0wDbzQahUKhUCiEYVgul1dWFt9++23HsR555CFfxJ7fLRRy40MDG8vrzfpGvdFhUgIHgRwRhZQqRkGIIkJRikjgENKrOCglM6lLpAQBSimUh3GRnhe3x7ECDnBkPXhopqEa7A6yCo4seoOlRyVq779MmVmWQBCJkigTlaKAOyUUez/JNg6RCERJiETsaYxKKUUUKqVAZYEZhYwIIQgqJJg5b+WR+j4AoCSCVJkvnREAqhBJds+EHIAllVICQCglQMb8CObmqDNDISJKIFwKKUGqbNMgUhEpsmQsQRAztz8hZGxw5NatW+++/RZjbHJyfH9vZ3t7O43DKEky6yvbaDLzvt1uh1GQpSVl5XLu2i4zDJZSyjRNRLQsiwc0X3BN3VBKBF4njRMFgoAcGRnZ2dl56aWXHn/8cd/3EXF8fPwb3/jG449/JjPJ8vn8Cy+88Mgjj9Rqtcyi+853vvOlL39eCLG1tfW1r32t3fLffPOtzbXNL3/5i4SQldtrq2srTz755Geeefqll16amZvZX95y3OLK8urDDz36ueee31y91tgdTemA6bqDo6NvvvlmPp8vlUpvvPOOlcudf+BiqVS6cuXq8u31RqMhFOYKpYWlFTPH2u0mF9atW9eXVxaXlm5ev3FpdHTUcpllOn/q537BtfMCRBB5qiGCIDIdu9vtupY5UixQSm8tLa6srU4fmw2jhmFprW6zWMmVS45GMeeYhUKuWrZAmozkS6WpUqlkWZZhGI1G7I4PmaZpauC67szMTLfb3dvbE0JIgGazOTk+0Ol0Ao9Vq8XR4dKZU7NXr15jJLEMNVh1oyjyPE/XdctQD5yZc3S1YUCSJO3apgbVarXKmNhcXRwcHDQMQydioORqGtlc3WzVtjdu387Z9NzZE6YGb7z6Q9/3T5w87rX307A5PFCMQ49JhaiAS6EShYhUQ6KQABBAlAftRACV4Bk4CbJ0dgmoJMoj1TB6XHR0ER99e+DUQdkDhBzlQFN/X42CnvSmGcTsIFExCwgrpQTo+tFTU0qJUgRRZTW+hDzKRfD/Z+1Pf2XLsvwwbK09njGGO78xh8rMmqu6ulg9iE2bTbZNyqQJUzYtGvAXUrJhW4BpAwYMGIL+BAMGDH2QP/iDZIhSd7NJsQc22VN1d41dQ2bl+DLzzcMdYzzTnrc/7Ih4L7O6CMnWwcN9N+6NG3HinL32Wuu3fuu3AFiKG+IG9NoFC965BF9tk9WtdB5QjEAIESwSwoNgMcaAoPQW5UoRKYH0T5vnn/0TxXfCY4g+Ou+j8zEAUkSPIQSa3DtCEJxITqXkjBFvzR/8699/9ODh5z//Wa16q4forTM2xpgS3a7rrLVt2wJA3/eAMbnHLMt2NCznXF1OCCEpB05os7XWWgvBQYjOmeBMcD44IwQnBEej0fvvv//aa6+ldt/pdPrBBx9MJpOrq6v08M0335xMJnfv3q2qahiGs2cXdT168vjZV776pdlsdvrs3NrwX/9X/+wP/vBbb71z55/8k//j/+X/+p88O336H/6H/+hXfuVX/u//j/90engis/LLX/uF/enBD//iRyfH03fe+Xg+u1gOTznnwzC0g+qUfvjkqTHm8PDw4uqKMPbe+3dCjCEi5aIa8avLeVbIopZPnz1+8yc/Xq5nyqj54mqyVz88fUaBHp/sf+0rf+Xa9cPRdFRmZYAF8fj+Bx/8mz/4g7/5N//mz//8z3/wwQdISF3X6+F8UoyMb588vTefPavL0ec++6WbNw6NDfOr8zt37rz++uvB6a7rjo+PnXPzWTMMg5SyLPMPP/wgiYpYa/u+XS+XVxdyNpupYXCmGIahLMu9SR2cXi2uGImZoAOGUZVTDKePH9a5/OJnXw8hEEKSSvZ6vf6lr391FwbmDCgJn7l9XQhxba/KsuzwcP/Bw/tPH91v23V0nff+V37pK7dfujkd1wwoAcAQvI8WI7Rp+hBNS3kz7xsJEINJmtQnGbaYqIXJFX860Y0xIg0p3AZIgM7moMExBJ4kN14gG0S/QX0QE/Vq68nJJt0kSBKfJZVUB/uJkgmNkTEkgOg/LXO3CRBoinYRADjZ1LdjAtm31ankVDeW7zdMEURknKY8GzAgfJLpheARIiV9+MT77r7hnBtnvYnWBRfDhnfjPQL1LkAInAFjTEqeZ5wx8q0/+9Z777w7HteckbsffgQYVN8rpXiebytYQQjRNE2ySS5YEpqilCZ1O0LIarVKp5d6g621MUbvPWOsZCQER4BxLiL33sCoqm9cP0k+/NVXX10ul9evXxdCvPXWW9/4xjfu3n04mUxGo9F8Pn/jjTfefvvt27dvV1XFWfb51z//8NHd1bL7i+//+E/+5Jtf/MJX62ry+S9/7eOHp7/+z3/vK1//5af/6nf+xe/+EcvH44Ob2fiARLj74f2clpeXl0fXjh48up+VBWVZ13UxxrquQwgC6Xiy573P89waf3p6GgIsFitjjDW+bVvCyfnVxZ/82Z/86Cc/0ronghyeHAKF8aSIEd57/+3PfOZ1yvH+/bu/+It/9eDg8Nvf+2HqnUxqXlVVGWPW67Wy+vT8SZ6x5Wr2+OHqxrWbxwf7k1H1+he+tljPfvDj718tLggHQsjLn7lNCPmtf/avHj58+PnPf/71z32WUrpYLFar1fe///07d9597bXXrt28xiSbz+erdvWTn/ykqqqqmkSChDOg5P69h3fv3v2FX/iFW3vTTEhEfO+99+7evXvjxo3PfvazCTs8Pbt85513yrL8uZ/7OaPalIF//OETwdnh4eHTJ+2zpw8n4/L4aHL79q2jo6PrR+NmuWJAGCUco48RQoAQnHERt635hCJumSJ5kNtaKPoAcWPGMdLndrIL3mKCq9MglC3Zm6SU2mugEQEJ0pDi1BgB0FnxohvfMlEgUIMbzvMWOQIPAMo81xAiJDKGPgZCgL8QQ8YXInyZxF7j847q5NJTCrtjBe/iAqv1LgImjMBGk5pm4nlYHja6sCTGyLn8SzcyJnjKdVNeDSSA35xJAqQlwaQMyRgRjPz6r//61dXFycnJer1++uzxSy+9RAiRUpoNROkppWkVTiYT732EkDzwDnUHAK31arFO2S9jLBXeU0q8P8oZIZzTIpfgQ9s2VV0cHBwkqDn56tRR8Hf+zt9p2/bnfu7nqqrquu4f/sN/eH5+/rf/9t9OWpOfefmzT548+upXvp5l4jvf+e5ffP/H7dr+g3/w76/Nd956663f/zff/Ht/7+++/Mq9s7NnQOT/7v/wT/71n33v7vvvV3v17/w3/+JXf/VXr86vBh1ZlXnry2okhJjNZm3bHh8fC5kPw7B/cMQ551J0g7q4vNzbO4joKWdcsI8++ujb3/nz5XKe55kQDCD84R/94Ze/+spkvPfk9EkI7vT09F/97r9er9vPfOb13/7d3wkhzGazX//N31i3zeuvv9723eHhobHq3Y/uvHTrhGEAdNYNP3n7rWdPn37553/52rVrQojFYhFjLIpCKXV+fn7j5s3ZfJ4ajJAQLkRZVdO9vRhjEvRCxBs3bkgpnz17hoh1XZdlyTlPmrtlWb700ksvv/xyWkcf3f1YW3Prpds/9/NfSxlcVY3+6A//oKqql1+6PZlMUpH/7scfnT+7+sVvfK2u64uzJ0Z1zpDo7e2b1x89+OjDDz98/bU3GPguUTMZAwDmLAOAtLyCfx4DN5t2jF1wC8k0GQAACTECEL+BpiEioKcvJmM72yhEpSNqDQApyt2pzKYQF3Zmmaj/3mtKKQcOSHEj7xRDCJ5w2HR4AABYj+ADQKhElWAn50zcNAxHRPCIlKCg1DsI0fXaUIgIMHgJGAFYomnEaAEDABQbylsidQR0uBPfS6cIMUIMW4VZqISJAbcEuE2+DQCGWOWHwfkQhQfibCQUKGfEaWc1gZiNJwyhzCuj+n/6X/yX68XVwf4UITjnDo5OjAvFaGKtLZgmhAAw51yvFjHGxUpxzlfzgVLarNvkQgEgy7K96b7i2enpqWAUfAhKWYKvXT+Z7o0j8Yt5I2T1+OkFJUGtr25dm/zKN744OjhYLFYffXT/g/fvdJ32LnZdPx6Pj06uf/nLX/7xj988Ozu7du1a27bOuaqqSL/+W3/rr9nubLL/yv/6f/4PL54sjbO/8S9//X/8t/7uvXs/Pnv6wfzqi//7/83/6tq14+Bc21561ciMQkZvvPFqj36t2+OT/QcPHjgff+3Xfm29Xj98cO/p06dvvP4ZzpkaQrB0vlpdXcxWq8XNmwdaD4dHmZDi2fmDq8WzxWrWdm1Zl8NgrLWvvvr5+WoZyVBV9X/93/zGMAxCiD998/f/9M3ff+udtwSXRlnnwp9+yz59+voXvvDyvY/vHk72zvPS26is6QfrZ5dCsvG18f1Hd+/eu7N3lPVDp7wX4L7z/W+GQHgsjg8mqluRaN/9yduf+9znTp+cg9ey4LPl5ZPTR0dHR0mymwr03k/qUXTeG7teLF9/9TPPHj/p1o2UmXO2H4brN2+fnl8q412ggJTxPC/xxu3XLy6fEIkObVZVAbMvfOkXH3z4TpFNORNtq6wJdZ1Pp/vGGJodPb34UWBPNp0rO7NJ8utx27b6vJnxufROfOFrSk03X188XgxiX4xmX3SMu5+8mPS++PwYYwAKgYCDECNA3LKgIJBPd4Gkb3qvtw48zTBDgnTTopuaoyNaDxSIhwAQzEYRPxXMELYDho1x+JwKg0kiFxG9fw7U7TiSMca47VZIYfk2WCZCCEpNOqMIiCQm4Q2tQybrTFJtfC6LtjPf/973337n/bouAULbrhOLgBDuvev7FjAmnknCpQghQ2+WugcI1m24AUKIPM8RQeluNBrFGJvVMngnpRyPxyGEOx98NNgGge8fXHv8+FmRcx611vnx8XHbqh/98K07dz6az5ecSc7FatlcnF/1ytR1vVqtFotFogoJIdbr9Y1xaZ156da1Bw8eTKfTf/yP//F//l/9f+4/uP9bv/Wb/7O/9z89PDy8eevGzes35vOr7/7gBxRxf39/tVrNZrPDw0MpZdoHV6uVzIqzszNjTFmW4/E4zXPinAvJfvDD96+urgDdjRvXP/u5z5yfn/72b//2rL0SQlxdLsqydDauV12e53vTfeUZo6xZ9+tVl1gWlAjvPQJp21YwmVziZDp+443Xbl6/8Ru/9U/H47E2w3Q6OTjcN0bt7U2cDT/+8Y+Xy6urqytjdSJvP7j/5PDw2r/3d//9pFiUxLSXy+WTJ0+ePHkiJZ3PL9588wf7+/svvfQS5xzA9X3z9js/unnr+Ktf/UpZCS7gjc++cnQ8Xc7PHHqCbDCrvGJA3dnlo73pASFkvjrbP6ptLJt+EYk1TredAWpa1SqnOLBqNDo4Pui69vzqfP9o37ru+GSvHkn2abCHP1eNeNGQaGTb759/3ZpNTFKyiIARASIgforcsXt990LrxYs9jz8teL35Bimknj18UT0revyEAW9PA1TQ200hEgKUIUUkEWMkPmCIMXgksHGRGFG71CcQ04TxHYPKp46lCBgiIXFTGtoSMz710WKMNoYYY/CJpA1bBmw0ba+Vdc5B5EmJMFHKIuHGA9gI3o5G7OHTp3/8J3/26Ol5JQAArLU+OMCo9NC27XK5pIRxzjnnCEQrkzYpBEJ54JzHyJRS2nTWDRumZKRZJo3iajBFXt28cUsw2jQPZ6uzg/1rnHOlewBWS8gyeXJy8v/6L37z6ZNTrU3XqsX8PMuyEKDv+6//wjfeeuut9957L+2zXdd99rOf/cIXvlAT++1vf/szL//9j+9++J/+P//fv/RX/8bXv/71RTf7xl/5+tf/ys/nef706dPHDx8dHR3t7x8+efLEAjjnlsvlK6+8cnV19eTJEynljRs3Vus2wW/JPPJtqv8v/+W/vHPn/UQ8att1+lDj8ej+s8ecc2OCEPHiYj6bzUejkXNQjJgH6qxPnTYI0LU2xliWtTEuVcjv3r27mC2ttZPRhFLkvHDerFZNCA4wlmVZljibzT7++M7Z+RMkIISAiI8fP+s6/du/9ztpi7n38P5sNhuPx2+//fbp6Wk5osvl8tnp4yzLnp0+Ho1GDx48WCwWgvHr169fzB9fzB9Pp1Pv/aNnHy2XS8JwPJ5+fO/OfL5su9n5xYNXX31NSil4MZ6yDz48/+3f+U0p5fVrt/b3D7Oi+vJXv9irdtDN/tGkGn/xyZPH88XiW9/+tixZqxbYq+ezkZIlbLsDN7qN298i8zsywCcMOEBE3EwOwgiI6XcYP3k8L/NsU+Ld1/SND59w18/Pyu8Y0f7FX3nyYt34uR/eoNAYCEESCUWgEEiEXj0vGpMIW00wcM6lPkRKN8UhQgEReMY3hhoi+OeJwC7P/NRXDWEbQief7CESgNgMXfCEEBoD+uB9DBF8xMC59MEa5zNGTy9O//SP//CDjz48ONh37ZmUUmZiGPww9Np4H1yWEy5ASiIEjXGjEZtxzjm31sVoIMYsRy5SHThSSp3vCRX1KG+bhbW4tz+1St++ffv8x4+EZCF4Kbkx2hI4PNrvuvbPvvndqqpeffXVUb3v7EdXV1eU0oODg5Rvd12ntb5x48Y3vvGNk5OTpmn+1e//i//oP/pH1urZ7Krrut/7vd/9e/+Lf+8//o//b14F76zqQ/Thw48/6vqhKIqm749uXr958yYh5Pbt22+//fbR0dF0Or28vBxP9r761a9aa7/73e8mfE4p9eTJk/v37wMEpXtrzXxxicwePwAAwCNJREFU9a1v/dnDhw8ur86N9t6BNWE+WyXJPimKoTdNP9R1LYTwLgy96zurBk8pLXJ0LgQGq9Uyunh1cQkQ3njtdQCYL65u3bq1WCyWy+XJydGonn7h81/WCj/66P26rrVRp6enk/G0LEtCyN37H1JKX3nllcvLy2EYqqqaLy+V6U7vPkuauP2w7oc15zxpX073qst3H390/20hxGQyWa/XeZ4zxlzwR0dHlxez8/NzrU2WFS+99NJ4NH38+DEinp49nZyPOOePnz0o8ooQYjqKiLdv3y7Lcr1evv/h+2lkLM1NCPbwaMp2ZB3cNfjHjZHF+FwgLwmG/LSNbUfeb/8IU+z9l/dAvfi3O5PeObSftgoA8IBbQOs52Qvg+dCnn3bm6Wt6zeTYQwi9SZ5zN5ccNy2biTlFkYQ0FTGN2Aax3SB2JxO3gNmnMoLNRoAkhBB8SvsTQS1AJAQFEywEprV31nhwhAQkqMxCcOa9ikA/vPP293/wTYRuPDokxSh14fjQa2MJhfGkyvOccj0MwzCsIcaiSpo4MQRFOV5cXGqt9/f3r50cImLSuCtY3XXdpB7NZ4DES0nPTy/2D6aHR/vjcW2dPjw8XMwvi5x+4QtfODt/1vdqGHRRVDdu3HjjjTe8t7PZrCiylPm/8cYbh4eHN27cmEwm9+/f/8EPfpBl2XhcHx0d/dqv/drZs9XlvPsf/A9/5fDw8PLp6dXZ5WyxPLl+8xe+8YvPzs7f/fDDTBZf/vKXX331Vc75o0ePyrL85V/+5YuLi7t3766b7u7du9baJ0+enJ+fp37mxWJhjCqKoiiLqtofVPfDH/6g6xtEXC67LMvqepTnedu2acdsmk77FpHWNQ0BOJcAEAJQSrQynAtCWAhw7eSoyPKyzA8O9x49vd91XV3X4/H40cMnUpSUyK7VZ2dXjLHbt28vV4vZbCaEODq8fuPGS303nJ6eUorr9fLs7CyR7Rkjbbsej8eMScZYCG4YDKXIGElfs0wwxppmdXZ2iojT6TTQiAx5zjvVLRaryWTy8MnDo6Ph7OqpUqptu0511tr5ajmZjLXWzx7Oy7Js1frVV18djUaRxovZVYxxfXHKOWEZsBdXIQBAIJ8a0JI66SmBTyJYm4chRsA013fDLUQkESLipxv9t4DQrtL7/MUBNpPII8RPfXWp9pzQ6m0zMMALc7m33Vrp9fn2vQjZspIBESCN6nAhAGCImNQAECgDhhDRI4ZIaMSwYTRTb3alL0JImmOOiMY+F8178dNp4mPAxBtPUXbywEgoRJq6lwCAc0ooiRge3nsLEYZ+PSqyi/P705GX+/XQPykzYX10zlGuqxFBRMY80oEJL6L3MY0d0eg551wIFjyvaikk5gVDkgpigw8+IvWhLarxy69eo4RXIz6bnwmJX/jC5xjNzi8WdV2pYT2ZFq+88tL9j98tyzLP8xj95eX5weHel7/yhbOzMyHZ+fn5er2+ffv2q6++ulqtvvWtb11cXBwfH3/1ta+dnJx8cOe9e3cf/Mpf+3cOjm+vhua73/3Otcl+s1o0q9XJ9etSFov16umzs+vXb/7whz986623vPcPHjx49uzZ6enpbDZ7/PjxoMxisUjt+4yxx48fl2V5+/btqioeP3k4GtXT6fjR41UI4caNGyE4T5bGmDyX0+nYe7tYLBCjlJxCAQBa61SCKooihMA5ZyR47xhlyb1nuWi79eXled+3McaPP/74+OjaMOj1uq2qyaNHz956660850i81no8HlNK+743xlwtLi5mZ0DDql3MV1chhPF4HGOcTPYopV03FEVBCAkBhJAhdL3SUsqIZL5cWWuzohyG4e79B9VoZHSYTvf7XjVNS5CvV93Q2739yWq1ZjSPEYd+1bU6xuXFxXmRTwPx7334dkD/7/67/5Mbt2/94Mc/WsxXNPf1qFx1ipHnHbYBAEJ4EW1+Tk5IyhXbzG67ghFcQNhIYyZ5r83DT6XWzw14a3C45Yjsfh1jCsE/ZcabrlskuGmqTF/p7jV3kFhEROZSQAGUIqG4m44WKXURIKD1kLRtApIIkQKPSSw/lX43QUTkIeysl5AN+woRjftExrE7XAzxOXSfrlsAABIgBOM8xgic84B2vV4sVvO3f/LHaug4hXFdYDAnx8ya3qqriyuLuOnHKDLuvVeqU63KTZ5ar5xzaYRKnud5nis11KOM0tI5t1heJKALAJxeU8Kta67f2BcioywwHgDN9esvz65WwzAoZbTWo9FRlonVamWtHo9rmfHZ7GLdzm7evH79xgESmE6PDw8Pi6K4c+fOBx98kEha+/v7B4d72gzPHt87Oz87PLidZeLR2erps8dmuTo6Onn19deUjd/80z+79+ipcv70/ELbsFwurbWpt/Gjjz5CxOPj437QIYQ02VRr/fTpUynlwcHBV776hcXyylr9+MlDpYaj4wPvbdOsXnrp1unpadc1w9ClkxmP6/F43AxLAHDONU1rrXbOWGvzPK+Lsh/66ycno9Go73uteqV6ydn169dDCMOgZ7PZcrnsWgXxw6KoUp4/qCbLpRBiMV+2jfroo/vVtETEXvd5lY/3xk3T9Lp3zlUyN9oul6vU7eOc29/fh0i4qLSxxvZXVwvv/Y0bN5DEdaM9DIDChzXjhZS26x0h5PJqRRh2rR16t1istNZHRywC0cYDrjIoCGHnl2fvvv/us7OnlLG8LJQftIbT0wV7kTAMAOg3OV7ywztvjBL+2xy7wHgXG+/W+osr/qfNe/fDT0XXPqleko3Z7/4R8hzK3obNCAAEI0nwFQFGKaGbOW7ABPXR+RhDAB9cIBDTbkMBKECih5IYQyJ9IksuHmPE5D9xKzCQ3nSbMuwiCIwxxoAvDhtHoJTTGMOmb5HSddc8fPjw43t3PrrzQ2v1ay/f7tt1363RG2uGw/3x1WJOCEEiAQEwIgmEOsZD8FQrmxSwOCsIIc6Gle5X66vRaMQ511oTQtI4la7rCPX1qOiGxWRc+wBNsxiPq4ODaV2X89kaAJRSMuM3b97c398/Ojr6yle/1Pf90dHhrdvXr67OCI23bl//3OfeWK/JvXv3zs7Omqa5ceMGYyyRdRPLv8rf+Pznv/jsyeLb3/728a0bL7/8sl92r7zy0mrdffOP//jbf/HDmy9/5rWXXyWUX792rW3be/fuaa1Te8DBwYFzbnh6Op1Oy7JMVy0xUhaLxXvvvaeUOjjYe+/9R0p1gOHq6mI6HfdDRyhywWKMo3GdQK+Hjx5cu3mYmKRaDyEE7y1AyHPpnPMu3r790nRvfHl2OvQtY4xxmsa73Lhx48H9R5zLqmIffXT31q2X9vbrZ8+uspyNxvV8Pl8t14IXy2XriTs+Pk7EmCQq2HWdc850mhBijHMupPr50dHJ/v7hYN18thBCeEfn85Wzp4QQKeq+01I4o9dlWRVFOD+7LIqqWQ9SMiFySqlWAVEQIozxWnnj9Wy5ODg4Ors8++3f+92+GyjlASHPxtbqWbdmjcWYRoEmDgBqeIH5CNtaa7ZFiT+FvhrIYUvhCCGk/wHA+0/M490Z82b20k/lyJ+KSHeemYKDCBuK1sadEkT8maMreEAEQpBQCgSfe2FAQiKJnkbwGF20acVo8hx1354KxBh7ldqbIHErcUuftPx5x9KLuTrdMjFTH3LajK21FsXVajGqSmO7SmCzfu/8/NtXlx/c3BstFovl5VnHOSFACMpsXO2dvPFGuVgspczXq3a5aPKs8J4TkjfrIQ3yjDFSGhKsKqUs8+PFbEmpo5TkBTcalRo4z0SpRckopT2uggtPly2d0I9nb8/sx9Pp3qufldGVYMlnP/fa07PFogVZSVEKXogY4/7xdURsh3B22Wo9OGgdtIfXqvG45pz7cFhV1Uu3x9YM1rGHj06t9ednT7t2/fWvf725bb57563f/M3fvPXyS3/tb//yk2dP967R8bg+GE2ePGkpMzklXABjDDFQCjeuXW+aZrVYFkVRZDk7OIwxYoR50+ajiQX2+a/8/I9+9MPZ/NKTYrZ2avYk8ZNHdV0UxXq9Xi5nERCM8r4fhvZLn3upaZq7H9+/fv2WU3055fUk+/jBW8654+Njf6WttZfNXGC2bjsX4vnVWVEUozqLdK+eUMoJ5UJpL+X4s5+9defOnfPz8/39fU5Ds7pCRGPMMAzRa04DRbAatbHWRetiVU/ywredIoRU04oJvlyvM1mcXLvdtappFAAdT44Isq5tmqb13lDuKFeE+Xqac86h7WXlh0GvO8aN9FFwl7PoV1eGc4xBK2WcaxBRKzWeFBmVbLcQt4a0Q4jxRbQ4bpuIdv82DzduM/0hSdMJP2WN//8cCBR/6tjY2V92BCAICJBKRakhMiICY3R7jggRCdvwofkLmiSfiBG2IfTuYsSkNL+th794EWB73ZK7xu085BBAGVuPJlnGHn7w8enTO48fv7ucP1R6KTlHRGu8Umo6He/v7w9D/+jhk/F4AgBd13kfpZSEbECKpLwRX+CWwYa5DUIIQtAYs1wOKdwYjar1qofIpJSUSKX0ctEm+bv5rGE0r8pRIcpclEdHexcXF3/xgzf3r1373ve+17bt66+//qUvfYlzfnZ29s4770XQAHB5eTGfzxkjXCTlLfuVV44IF30/zFfLsqxffu1lwbNODa0i3/zj77ed18p3XbO3X63b80C7y7OZMUbkhLOcc26Ma/tmGPThwfUihjRoIoRgvdNad0PfnvcRUvdVBhiqqmKM1HW97i+ttUp1TauRjITEw6ORlHJa5oxD24o0gCJNPFuvGuCyqipCcOjNetVZE0JAZ6Mx7a4+AgDz+dw5d3FxodVpKrafnZ2l7us0bCnLUUpZVdWObQ4ARVHMup5Synn6MQnBG6ONMY3qrbUEWdN0XXellREiq6pRXZfz+VxrXY9K53A8qafT8WKx0NoSwqTIp9MpIU1SQWOMGaUJIYwJ70LXdV03INIsy6zr29YB+k+PVtkxCnfHtvyzM9RPGvALXjl8svj738lQf9bzCSXwM0Luv/wgJGWf6Xxwe4dc+KTn3HJExS7QeOEbSDL0n3zflJOHTww6T79BALBJygNi9CGRTJ3zPkTlQ5nLVbP++MP3Pnzv+137hEIHYWhFobWNMVprhkxrbdu2Wy6Xxvi2bZeLdZ7XVVlb6yESRHQuOBeSnH3ah5wL1nopmRA8L6T3mTHKe+ucIxStga5TIWCWBUK4ELmUsiiKbt1o5SUbNAnTcTU5GPfdlbPhvQ/uRCSfef2Nk+vXL2fz9XptrT04OHB+qKoqa7MpmTJGIviiKLRWV10LeTZYddWtLcPDo2NE+vTy7Jt/9p7S9Etf/Fo7zN//8L3xJGMZjvrC91lVVWUlOWfeBTBeGzsMw+np6Y7Hmshkm8YSGWOMITrrems1IYEykuVc1qOmaXzsvR+sJ5xzJpAw2zRGZlwp1bZrIcStW7c4z7p2GHovOACE9Up5t9qMGh5Ct54nO9zf3y+KomkaQshyucyzOnXatG0bY0xTyGezGeMhhOicz7K8KEprHSLWdZ3JGgDW63WM3rrBeRci6YdulF0XRaGIappZ1/YJ001FPoDgvAkhq6qRlJwQVhTV5WyW5zbLMiGysojGmOQJQgBE9C44F7S2xjhK0NHABThvnfUs+E/wooKHFJ9uJSU2D80nW/N3i9htQuuNE36RnPTf1uT+rcfWsyVbwi3OBS9UkT9x+C1Itk2Xtz9/cbDyC18F2YTnyc3u/HvYpbqfrGxZR1949xeuW4iEYAgxQEjdGJv+YgRCsGnni8XpenEmqK5zGhzxSBGYkIxSrpW/upx77yeTPYIsk1UIbQzgPc6u5iFERBoDBg8JYfQQIXqtLQINUWs9RCgoRSEppULrgTFSlgfGGK3sfLaklHofgtdGu0JW/dBF0AMBKVjXrawz0/39HsO1G9fTRM979+4BQF3XymigvlXrZbssy1wIPp83yCFA+NYPf/DSSy9VVaXQuX7lV7Truo8//vh8tnjjjVebfjVfzgP4QVuOZPn4vBS5B81Z7tzcKHAueodK6XZ9vt0xAyJuyCoEZcadM4yJsirabuFcMKa/uFAsQ+89oxlnCJGqYUMRP9kbUyoIha5rOJ/s7U9iIFkuJMkpFVprRGZt8D465xBdymPruk7KOEmjM3U7XF1dGWOyLEtKBlmWSSkpxa7r5rN13xnGmHcIAG2j8oJTSrWhiZGeF7mUUmZU2xiC73ullOKcl2UJAE2zOL+wUkqt1dWVuX379mKxWt6f7+/vLxZN3xspslRVLsvSGDcMA6PSe2+09j4icCmSIpqfTjJnwTFkAOQTtvYz7M5tPA8kK9q5WBs/wdzauaZPUyv/fz3C88bA53gYAMBzcflPHtuff8rw/Eb4cmutu/jCbCdRvKDLg4iO+hc/1PPXCX/5xoGepGbjJPS9ZUQTwQFA9808uM65ToKVLKesYvVR1zUhhESTZCzLczoajaz1Uvq2GRjLYsBhMM5u5ifsOpZffN+ylIieMiAEOadFkY1GRT0qHz547L3XWl9dXTHGRqNRahsOoXd+IIzxTHRD9+jp/dXMIg9f/OLn+r5fLmcffnjn9PT05ZdfznPRNMvOzRDx2fnT/f390bgyQQ0GjVHzxTBbLdKEhBjDaDRyzi3Wi+Mbhfbz2fJsUF1WFohFsITzqtdXkVApfDJdgsIE68CJgu+U+lKfM+eMUuqiMXYoysloVCxXAkA4x1er1fnjLoW1jDGjvHPRWrA29FlflllRZMPQrddrQhhn+Wg0AlqkkTplWSaX2/c9pfTGyaHW+vr16/P5vG1bQohSKsXB6XySMlZqNY0xprDAWm9Mm2wMEa1Vzqskx5EylASCeO8vZ+ssyzink8koSX8ChK7L1s2MkI1Oo1Kq67qzs0utHVJOiQgB+l4JEeq6RIxaDwzqGNFZSMKsaUSA9z4ELIqK0Zx9KmX9WaGqjfCpBR03RItPUy83YNV/R/v9twTJn0zRtynoz3h69C+4zk+QujbI1wtDlREiWPeckpn4kulh2NaZt/nCtq4Gz5uWXjzn57MqPtkL5YblxdNnb7/13eX8lBHPGHE2uAgcaAy067rlsimKbH+v8N7PZgspMqVMCEQr65yBjXoopZQTwlKMk+Y3cS6lzLt+6ZyRWQXgtDE+aMSIxE/3xokQknI5H2yIhDPOJQND1GAYoawf5qtFltU3bx3Ou2G2PAcAF5UNg3Yd4dOsZD9+8+OyzBft0kQN4rgYl1Jys9Z75UHXdVdXj4XgSKI2QyK3LbunZm6vX799tbi6/+DRzdufcTZyLhF88IZPCs6EFIQQhsQzTjNZJ1JEjHEYhr7vre0CkGpUOg9ttwS0XbceT+qqrvKCN61HxBhBKZ2A91SlX60W+wejvb0xZYkZ7hknt27duPPxU6VU4k6llZBcfYrIkoYJAKR6bwgh1bHKskzi+ADQdV3btpksGWOC59Zaa3wMnjFGCLXGURKM8ZTCRoa14pTIEBZ5XqfWSK0VYpRZdnBUX12xvu/39iZJOHQ8nlgTuq4blaO6qhFx3Sy11owR55x1xlmTePjOho0NR4wxrpYqOx6VZf58pPF2oW9Y+2lV76AsH8LzvPeFNHhHiErrG16sEv/3cewIIZ96wZ8lWB/xBSN/AWQiZLulxPSjXZlq08b4yYgbMPjtm+KLn25XNnqOcG1eJ2F4GCP4ACGkeY34+KN37t17/803v5VxR2gcjOu6ASO0lnRd55xVgwGAq6u5McZ7e3x8bbFY9Z1SSsdAOZeIPniYTKYA6JzTWsfoCKGIhFKWZVkIbDyuKcMQnLW277umWed5DoBlme3vT4pCJi9RFIWPkVB5dbG2OsCIGW33x5Jzc/fs8un5w6IoipG4Rg+Pru8RERbzy8neOMsEExQxMkFFxiNEkQnJJaXUeyulIBTyXK5WC+tMCI5QVMYYZ0MI3kWl7fnFfG/C+l7F2FOGAIFSDNFXVaXs2kUWSc4Yc7Fvh7m1lnO+L6d1XV1cnDXt3Fqbaea9Lori2vWDJPFjbSCUpeGpbev2JtOyLCeTiZDUWkuQK6Uo4YvFFWMssdn7vieEeG9CwGEYYoynp6dKqbIs00PnHGNyMpkkbvZuRidjzFqfMIvUXx1CkFLmeR6j8w53ciiIWBa0Kussn4VondNcUB+C0h1aIWQxnY6l5CFAs+68D0VejUYTSrn3UWtHCCBQxtJsEBdjNMql7cb7SClnVIQQjDFqUBARSWC4U815oWYLn6okIaalCT+NY8Ff4h7/ezxePJ8X3yL+jFEXPu7sDV7cUJwLn/zzzTecSXjBcp8/YbNDfNrRkxg+ubttnoNA0t6XfHAStUMkH7331vn546FZTk5Gq6W6uLhwJozH01bNlOpTQVXp/unT0xDc8fFx36vZbOZdNMbnWXl4eASROOem00kK/5I+c/I8IYTpdNy2rQ8WPKWUCMG7LirdA7q281VV+WAJDdYNTasGJamQFNkwKEFljEQZPVtdnT190vV2UC3jKITIC1GPCu/9fHE5nY611lkmUpS7WMyNMaNRRQnnOZ/NwnK5JDTk+XFZFYxVWrmiHt2/97goiuu3qouLs4PDk9cOX9V9o/XgLHZdb90gM1JVxXhc3rlzFxGLoiiKQms9qDUhhAu+XM7LsozgY/SpQ+v84mI8Hmf52IdAGeSFpLQoisJ7H0E552azGYCL4Djnztpnzy4Isgi+qkdJHHtQHeNESOa9n06nCUJL9pmEOCmlmcw2WkXD4JxLhj0ajfYPjlKwjS/MAKCUhhAJoVJmUkqtdd/363XjnN/fn7bdet3o6XQ8GueFoyE6LnC5WNf1uG372WzGeaYGs163o9HkatZ0XUcI5HlWj0rGwAeXrnlS/qaUj0ZlVY60Nuv1OqJnTDhn8I//4v2k953i8sZx76L13keMhEckPoILQUD0myJv8OABIGIAAOKy+CIPZGf2REMkhHAClAIlgEnMw5LnExUAIm75GBQLpVSe57u5EkqpqqpCNC8a8M6eY/jLZzJlTBqjQgic0+244EgpFWkaCiGcYllks8vLsiy11gPjjIm+7zkXEIm1VohMKUVE/skIfHMwGneqVAkISQ+tpAQo8ZEGezAulxdPfvSDb+mhOXv6vXv37mlt9vf3rfUX51cAMJnsJRkQShFJ9N5LyQFi0zRlxZPOLmOsLMskH0sIcS6s161zrll3l5dXo9Ho8PDYGLN/UCT+oMxElokYvVJKKcUF5ZwXRWGMmc1mjLG9vT0ppbHd7GrZNgaRnpwcTaaF88N4Unzw9no0zkPUSVCaoLCGQKQOzssiAa2NMS7LMsGzEMLB3sh7v1yur67mADAZ7wmRGWPKUUkpXa1WO935uq5jjEWRpSA5VbOn02ld15TSq6urHRfwRV3BLBdlUa/XvVauqqrVet62y5NrR0hUKvMURUUpdzZAGp6Khm6H1+0mnnPOBRsrpYqi6rpuPlvGiMYYSlkmq0R+TKtCa62UIoSMsknTNElCMK3D1L9ZjfhOCDlsa/6UUms2voRS5IJSStPaznKetobU+5n6PRljvbpEpF2rjA6MZk3Tz64WxriI1cHhHiIQCloPzuuqKpxzTbu4umggiqPjyfH1crpXOItXFx0XlDN5dbVkhHEgFAgFQCBJuS7EdFLRhwghBgjgN0J2ECBGJAAR8DmP+sUPlrYo42mMEa0jBBghFMAHH5wfkqcgFFLJKoTkTKJ31tqduEECCSil3m72hfhC2/C/JWHeXdytJNDmmb3tBGOcMBfReozUEx4ZxOkoJ4RImTPKvfdtqxE1pTaQjYD7LsTYbroSEGmaQhhj8BuVXIFSD70UPMvEanH50cfv3HvwbvTq7ofvFkUxGu8ZbY3RR8d7IcBsNiMohGBFUQKGYegAIKk3HJ9Mk3hV3E5C2c4fZGkB5XlIIg9Jg4ZzQMRt9qiFEElnfMSrYRiSmkee50lXzTl3cnJoTdBqobXXWhvDAEMIoHRHWs8YARRGg3fGGBc8ZCWNMUmFiwQERvARXIxRa22tLoqsLOsir/q+b5rVYr1KNdiiqGJE5wKlXEqJ6MlW+ldKWdc1IjZNw2iptR6GIYmNpG5bROy6tXe4XnVa2xTglGWdZVlZFdZaa71Spu+XRruiKKfT6aieGGO01hBJnpXpWhFCrNkAk8mWEqluOzN30yGTDCzJQSdfncQD04SAPM+zLFutr3b4VtzImKbRcHma2kUpS6Jlbbvu+/74+AgR1WAW81X6aEkyup7kIYSqquhIcpaXZR8DLpfrbgha663Ek6EUhcjKktd1yciq72yMqJW3BiBSQljbDHlOYyDMeogYSdptQqqgQgASY9hYcYgQY1KT30yCSWqOEYFg/ORA+p0Bc1o553xwIUCMzifsOoDMil3456ILSacKKSEuxrjTJU83Wym11dz4VNnmOUn7U8dONwNfoEkjYl4WQnJOWXQ2MhB5xgQFIppuxhiPMSIRIQJlDiAw7nuv0g0m23liMWK6urhVpU85UrrrzjgaIVozW8yePfngwzs/OD/7EEClcDTL5NIp64a8kLkQ/cBU7wGYsUpr3fdtUeRluT8aV2mp0e206LA9GNuI5gQPnHOl1NXVVQihrBghRGullMpyKaXkXFLaD8OglELEqqrG43Gy6hACIuFc1nXNueJ8U5wcelWUfOi7GCmjMvmQELy11roIwWY5ISikBCGYEBwJ98EhYpZlSQmz6xtnQ5Zly6ZVSiU6WrpKKZrw3pZlKQTV2joXjHEhhKbpCFRNM6Syc6pUSwmEEOuNd51SSmvPmJKSMsaMdkLSPC85D7OrhTVeCMm57HtFIN13mmUiueK0nJIGoBCZEKKua+eC975tO5JvRP/TYku1KEJIapxM5ej0KQAg+ee0vyTXkkrWzjnvIcZAKSUEuKCJxJ5GTMUY016cykKMsQSAKaUIoUWeAQbGWJYLWAbrbN/TFC0ioZRSa5x3oSzLsgBrVlqr9SoInlOKQ++Wy85Z6iwypT11GxfnvbdIfHKzaZxg9AiAQFzqicU00xQRt2s6Gnghvt2l09YGQkmWC4CA0UOAGAhlPHDqnNPaJDEqAIgRtbZ1uQlH09aeFrExJg1PirCrTuP24V/ugeO20BJCiPC8RGQdAERHLITIXEQitEHnIyVUMBljtNY7GwhQABK9o0RC9EkdKKncB++dC5I5xhjngBgJiUmpi9KYecjq6ursyZ33fvzk0burxQPAjlP38uu3L86vtO5G4zJCULphrL5+/ej02RIRjTFaD4Rglou8kEVRzOcXdHvsPH/yEikdSrIYMUZCWJZlq1WDiM4ZgJBlWZpSKETWdsu07Lz3yY2kv1qtmhBCVRdCMsbSHkcByPUbB48fnS/nAwLGCEJwmXEk0Wg/9D5GxzhQyilN2vrE9N45h0gJCVrbodcAIESWyVwrY42jhOVZEQMMw9B3A2UkywpE7LphGAatrZTSGFcVNM8zREh2vrMQFygASWleCCFG5pxfLtdN646Pj2OM63UDQI6Pjggh5+fni1lflmVd10KgMW63jydCa9/3aY55Ese21qUFlgwYEXe6wil43i3jNC2h73shqfcxRhcCcM4BCKUcXpgKoJRCEtOsqfF4fH5+3ve9c66u68lkmnCytu3yskqMCeec1jYGlJJXddYp49xG6lxQESM0zeCca5rB2ZCya+cEowOiWa/aYXAYDQBhxidRp7Tfg6O4pWQkkaotwYgkgBdCjIAEIsStTEX85JE+v3eeAgL6GLQ1KkYUNCdcBGvsMChlCKF5VmZZEWN0DlJis4vDE0JAt3Podpa5s8+fFUUnxxvSLBUMyQxCCBipNYEiSs4JohQsWIcIwSN6TpAoNcQAWVZ4H11EGUXEiGQ7hgwx0BBY4KLbnqeXgjAmnXN933ESqOfnz+48+Pit9epZP8wImsmkDCH0Q8s5H41GyQ4pIyH6sixj9MYYKUf1qKzrmjHi/Qadots5zAmY8N5z7oXIGGMxICJJ68x7X+RVP7TWekLAOdd3A6FICCuKIu19y+WyKIpkFYjYdwYRyiqL0RljCI2cSSlKIcR4XAZPVR+VMt4TISopq641gCGlmjFCiMY5xzlTne26zruY5yVjkjHo2qHvGpFnSQHDuZDnPM/Lrhv6XpXVprV4OzlgO5AZuqJio0kdt0OnAQwiMs8oYYgRIkGghKQZQOi8advO+6iVJyS2bQ9A+t54q4NH72AnEZW2g7qu+773PlhrgwdKeZZlIURKNn14u3W7yby2QXJKoRljicEKgM75ECwhVggvhGCMc74h8nnvrdXL5ZDapFM9v+uGGON4zAhhXTes12ultFLCO6QiVaRVCAEiEYILEVNByxhjrUshJGNsPbSMsRBtCMGa0DWOUOeciwGNcTEiI1Rs0BqIgMS4ZLGbMdoEkCEQgpxGHyEE8GlkZoghboZl7oz2xZBvfzxpu9n5+ePZ7Gnft5LJSX1Q19P5+olSyhqfyXIy2aurKaXcOU+KgzQqJsEGfjtqjNI0cmXTQoy7RuKf0c0QnzdWuCSOm3D/ktPgPVCkLOpe2T72XSclZ+VIKc2ZZEwgIgAZ+s45V9eJCw0xuhCM9z5sduCVR0wNfaPRKJ9MhmF99uRxv7xvjLnzwQezy2fO9KvlVZYjO95fLpeCZ0KIxWLVtn1d14zyJ0+ejEdHhFDGKJIopWSMGKNSFpdC9GTDfjvYKQV4ACClBEBrbbr9ZVkaY7wbPAZjHOdWEilFVkmR/EnbtkmSMjENVstBCDJmZZYLH3SyqK7rQ3T1KJcyb9ZqMffeO0SUMjPGK6WNcRI4InEOjHaUxsXl2lqLSI3uCWHOekQmeFEUPC2J1EabqqlCCCmyNN9CcFmVdZLUHHplbE9oRiiJMUZnndfbpGyDSBljhZAxRiF4VU+sy4IHzsi1aze0tovFSg0aACghzgWljJRy25Zku244PNznnHNOhBBaWWMcAAzDUOQctwNo06aZbD76zQ93QTghJMsy51PA7He+JFm4tZZzxjl3bjMmkjEihNjf30/uJ8U+TdM457Is61rNGEv7UQI4vDeATpsBgeZ57pxXgzHeAJCqkkKIvKDCRUIAgowRvQtcUGtCCC4EYD7S7XYIAOhCICTu5B4pRkIII4SKEGO0Hp2NLkQPkQQXkKSr4DfScJCsxVp72d07P394/+E7lxcPvVN5XtbFvhTFxexOCBEikzIv8lrKkiCPMV5746+nFs10XRJ7JpWvds71ha8/83DOpagTIKaxLynsvPfwRwBQlXlZlovZlVFD13XXjg9f/+qvzOfLPM+ramSNf/bs2cOHjxDxs5+5lj6aUiqJpKRTqkoaQkg5WxIKvby8fOfNN5v2J0M3rFeKIO+7pmt6yceruSaUEMIQed+vlosVZ1lVSe+xbZvE4wvRGaOM6RNYlYYbJULPbp7ojkC/Wq26dqCUpZVxeHio9YBIGGM+WEKYlLkQmwaVNBcvxsgY214ZUIP2nmx8L6dCcK1C2/YRXJ7nlHnnQ4hMK/De9p2hFJzX1louaIzcGq+1BbDNWjEmGGN959UwxBirqs5kDhCkzAkhq9VK66RxBYg0RhwG7b0HIFLmQmTWWsYEp8xbaM1mdiwiZYwxwiNaSnlSKQKAodfWQlVVCNw5TSlKKQnyvtMh6BACl4wSLngmRUYptdZao7U28/lcCMG5TMbWdT0hpO97hI13TSa6I1Gt1+3uhymbC5thQIRRQUkEAJJmhHgIBHawebpNybyttRAJZ9IY0zZ9CoAZY5TwqqryPE94dZ7LRDKJIPi52a15zjki7/u+a/VoikKKrBBScmfE0HutFRM+gkeCCIFZ73b+EwA8EowkQkKoPBKkSChDLiAEDBhjhOBCiBgxAAREkTYq2GLRyXn+xZ//4Xz+ZLZ4FEJTFQLD0M7nfWdluaaUU8KdYevFWfAYAoFI5vYwhJDS/b29vQRFSimVej4h5cUQ+mcd3vsU1CNuoKxUzfuLH/xBLuXx8eHeZHpx9kypwTtXFq++9fb3Hz16NB5Nb9265Vx4662fvPfee1LkDz7kCTTaVV83dxpICGG5XIYQhuZLgvjz8/M77755cHLpPYzHJSXF7OqKIMtkfXXR7B/JxWJVV6OqHA+9UcrkGR4eHD98+HA8Hu8fTGOMxqj1etn3PeO0qqp0MXeEwbQ/KmWkpEVRMCooZdba1aohhAjBGGOpXJRsHgDadgVE13WdcNS0dodhCCFwLq3tm2ZNqEPEoqgwIzHYtPsZ21vX5aXkXDYr2zTd0UnZD5vRhzGgGux63RrjBMm8w+ADAHKWAZCht117yUtdVdV0Ok1UxNQb1HVdUhFOHy2hQZgmAOqYHFQKPfI8L8syyzgXNPlSxjwhrOvWxvZZJnww1lqtLcSrsizzvAQgi8VCiCyRlmOEvlfWWgCS5+UwNM65GIe2bbt2MMalAdFJXjNFfDvzSx1R6eInjnSKI9IH2YVFG3+CGGNMrU67suLOi1gTrbVa693MgF0PUwhh6F1ivxBCAANBcnJycnp62nWdc17wgrFU1HRN08s8VLUsWe64HPpmUB0LLgROCAdA/Kd/8iEieh8hEsaYGTQSIAQoRR+M97as8rouTd9lWZY85Gq1Sk4yxkjZKK2MRAEdj8fL5fqf//N/vjj904TQALosk1nOYvTWWs7K3e0M0eW5TCMqpBgt5s31ay/96l//25955QtDB33npMw64cuy7LUZhkHIDAjVWgNhZX7wYrlllzGGqAAlYbk1flSyD+9878//6J9dPxl/8OaPyjJHApRiljNjNCFweLR//mwRPBwcHEwme/P54uLiwmgXYxRs84K74uSW1pI2EeACGSPHJwdd16RfnZ2dhQDWhL5XanCI5MaNG8ath2EYj8dlWaapQvv7+1mWrVazFCpnWZYKkkl1dX/vujHGWg0AQnIhGAB4b8c1pZTGgEoZpZTRTmtjrb1c9XkhDw4OGCPL5TzGWNVlQn211gn8TPFhWrKMkQSDJYJ0Gu1hrQ3OEEIStznFFyl4OT4+TBFHgoidc03TPHz40FlWleM8r62BZH6MgcyEj5Fxkuc8z3NCmDXRGO8sOGeEyLIsv7q6QgwRzPUbB2WVnT9plouWEjEMGhH396c+mEE1ySCLIlO6WyxmRZEdHu0zRq21qbycbn1d11mWaa0RxK7bPoWByQ7zXPa9AoAUXdfVaL1utdaEWs455zkCaZrOO6zrEQLFMKTcjRDGmAAAa5zWev9glJibiaTVdR0AFEXx2mdP+r432gHQtumXy3UIscgrxnGbQcRm3RHC0hyzakRTXwQilFU+DIP39vj4+O6Hi7bpnHPex2HQjLHkwCfHWQghRuQsq6sxpXJ2tTg9PS1HIeH2LOoOCeOEBB+tdgiEIkKIxhgkPs8ER29VU2Tc2j5JZoYQUidKnufrdZBMAgRCYwy6ay8W83NrL70LyaMKkVMGRpsInjGqlAIIUkoppQ/Wex8jVlVldBiGzhglBEcCl1dnwdObt74YdQtAJOOYk4gEEUlWhhCUbQCA8BS3uzRFMIQgBY8xem+10TOtnj15+uzZM2cWk8loGIYIQQjRNipJrlxdrm7fupWQ/dPT0+VytV6vIRLOeUiFLvc8PImbyiEwRhkn1oJStmhy51xRZDEiY9nQq64blDKMiizLOeeAcr1q1qsGgTDKKWHO+kW/LKsywTZ9r4xJhZYoZZ6GHm3ke4wxRjHGhGDLxTrVV0MImSz29ypr7WKxeHQ245ynomWel8YorYwCdXR0hNiGAAlKQUSttfcxz3kqDqcyWJphyxhDilrrJHmTgIMUUib8dpdYpns6nU6jL5wLfae9Q0pEnjHKImPEKpfX5WRSc47WWu8dQAjRlTWhBDkHITgAeE+GPhitlFKD6ij6GHETgsImAEkwR57nMU6cM2k2ytHRkRBD0zRN06Z6TJblQkiIIgRQSqVAPYGghETnAm4HwcdNnRKzTET0hECec2NcjB4J+mCkyNtln+41Ymqu4JSRguVxW5RK+yDZLo+zszNrfJpXnzwt5yzP88XyKoEOnFOxnyFSpdR8PrcuK8vSmtgPbd8PQvA8LyAyIYSQBhEBLOeUUsY4iZH3nfHexwic++CBEDaoltDAuRQiI4Sww0mBSITYfP4EsSJGQFGWOZLw8OH9e/fufeHzrw/D8NEHP3706JGU0ptVURRZlh1f+1qeS6UHY1RZccbww48e3b//k9CtnDMyE3VdWuf6vkOMVV0ktwMbIkvW6na9amOoi6KYTEeM4+Mn9+9+/OBbf/59gtnf+Bt/82u/8qvaWgJEMFTGhYhICQQkfAMnpNwpJQ+ME290xAiIgpIYkTFSVUWW87Gs+qedUir4iEiLYjyqs5TZItC2bZumGwZljGFUpJwzBXuwLeTsUtM03zQEp7Vr27bv+6ah08kBJYIQ5330LiL4vu/Pz8+PjqeI1JgN5Oica5quaZobN49SmXFXMEwt+2HDZ6AAMKjeOVPX9WhU9bZ3LkBkgmdCSCGyGFGK8tq160II72Kz7iJ450KMkTHSd0PfDSmDSGlVDBB81NpSSlOe7FyI0SDSLBMUadpKCGGIFJESQhljziXQZQOMJ0LCaDTqGqaGTg2eIGdMAgRKgXM6zBdSho4rQiOAizFGsMYOMjKIPujgvY2RWOvXK+294zRSSilBSjil1HkTYhKX7RFRKZVlglAWghl6W5ZMDSZRNerKBg/Bw9Br5xxEi4gIqViCCOhdMLrfBclN03jv87xJRbWyyqy1ZSVC45A4RqVzKsvY9nZDOnmAJB6yoYKk36YxkX3fA4DRqZIMBIUQghAGgIyTvb29vu/n8zkA1tU4Td4pisJoLwV6D12rFXUHB0WM5OJiptQGA3MuRXwmrRYEtmEBBxdCg4hIYDItq7pIMS+rBHhvaYiIVEgS0RVFUZT51dXZ/Y9+8vDhvY8+vjOfz9vlY0R8eO/D5XKZZdnHd/r1ek0I+Xf+OimK/NmzZ1ezs8985pVXXn3ZO81o7Kz3HlB7IxMuJ4XgRV627TrG2DTNMOiyLJ0LXTdo7a5d41VVNk3zR3/0Bxfny/Oz+Ze/9HMHB1OMQVJifYguMIja2qCBCs6pDCGEGCgCYZtxRwAAYH0A570UEqKVHMejghMHEDinQw9dNwCQGHjIOcbs/PxpXY0T/JtcUwwYY0yFe7oVx9lurpyLVDbzKfoVXC71qm06Soq+H4ZBxRgJTXwSPwyx6wTZzgdM2VSyh9WqSeKJCakKIQyDHgZd5HVKopJVh7B5IzvIoddSYlWNYoyXF7Nkigf7Jz7Y5XLZ922WSUoFYVAUxWw2M8aklgZKGaXcueB9zIssLURrrdYqBJNo+qMq222IKRBNn32XFiY3nkIASulysdLax0AopyGAc456pJQJXjmLy0VLqK9H+WQySkyJrlsLTiBq771zFhEJiVww1bUxeuesh+i9XzeWcchzWZY1IvZ937brFIwQQgBICMvklsfjKWNCa922vVKKkixVetNOm87TWt+s2zQUajwmSqk0QAcx7u3Xq+U6y1nfAxdESto2yjpVFFVKzUJwhEAEa10aDScSoJAQhx3MPmaTGJKxBSEE56lW3+7v76eQXgjJGFuv18vlMjFA+94452JgXEopC2f92dmVd8R7j0i5YIyxdEljjAQ4AgJuJmkjCWVVjEYjowNnHKJlQzOPMSLSPC+rstTePH16t+va07Onjx7de/zk4enpMyn5j340y7JspwA6nz+7uLgQQnz/e79dVaPVat2sO2+a+cXFxx/fNZ0jKAIYY9xy0eSFKIqMURlC9MHSzZjMlXOeUREDHXr79OlpVdV9Z64uV0PvR6Pq5NpxVWfRqbKutbYafSGKvldt35cyN55AAAJEMLEDz7z3gRofvHWWC2pU33dN8K4ZFmertZQb8V4hsmQkZVEvVrrIfcIwCKExxqQd1w5t0l5KfZvOuXTzpOSbNwrOGDcMuu+VMSaZkNZaCFGWOSFQlmVVVZSRJOzMGBuGwRiT7sRysYoxdm2vqE5juAlSsp3/lDhkUkpEkUxIawcAzoW+U8OgF4ulc05KqSMDgL5XIRDGJOc0xgiRBE8ICkqk995ZQIhGB0KAC514QmHLe00ZY99BjIiEGu2cC0LI1GHHGNniopg2ssS4CFseYtKO8N4iRueN99yYEMEgsTKj3sfkz41inCSeSbS2L6tsuleMxuXlKRNtv14pgCAki5EICXmRCcGyLAMIiUqViqIIIgbSNI3RfjweUyKcVVpb52JW5hCJMXYYdMKcGOOC0xAgiUIjpFvMptPxeFLXtRyGASAQClkmpMzaph+GrmBSSpllmXPGWJVCbi5o12jcdown4mfKONbrLiEg3pvdhU1IAaW0LMtUN9mRGtqmY0ynOT6U8uDBe08IDRjyXHIupZSMCWvcMAzW+lVrYoxplKdznm1xEzV0w6CstaxdLxIokGXZeDyOgn7v+98+O3tGaMgyWdfF+YVz3pPAeuWMU1xSFwwicknLKh/6q/nsaQzk4OBkVBVnz5598O4Hy8V6VOcA1GjjvEr4eHJiRSWkzCnh3i+HXnMOwaPR3hijhuhspESWJVGD+8lPftKs27//D/6Xx8fXuBQMkFOfyegdBN/RWJGEeDu3Kz5JznUknAlCQplnl2vTdWsCznnrfcyygnMPMDBGQtDWuYg8STqmODO1iSUSO26PHWSackVCI6EMHXStXq1Ww6C7biDIjOkZIzIr9/YmWZaF4Moqz7KMINtxUQA4IcEY471LzTdpa9iVfBOhJ7FxKaVC8vRN13UItKoqALJYLFerVeoOnc/WrBgBpmuLWllnQwSvlAFAQmjqm/Xe53lOCM2yzDsTdhTxiBAhBnDWDz5wzilFa721jnOZaj9Jm6KqKmtt0zTGGErpZDI5PiFd2y8Xfd/1ISQR36iNsTYruJAyAyTpDL0Pfa+8w61wIqYOE0KjEOTatePVsjXmPAYcjUZcUCEIobHvNKW0qiprfd8rSniWZZxlIVhjXAhKytx7n8KK1PvlffA+AGAIQWtjrQOA4MG5EIKWkue5ZIyMxlVVldYOMYAxDoEmsIoQprWOuqvruihy59G3BjDWdTEejxZMJ5eYquspDKGULuZNXdd5Lrz31jsheJ5LQnLnQgIIr65mQ6/Lsk5BddN0WS4pReecc2bdLAFACB6C5pxzTjnnWZYL4dNe0AyKUNykit5YSzhTlLS4EQF3jHDmBqWM0dYOWt99cu+d99723maZIDTO51dX89l0OqnzKoTABCeMBohSCJFJ612O5WpxZY3nNJvUK606KePJ8UQNnmAUQgpgSKLRllKa5zljkJZsXY/U4BAYxADgIZKhd0a7GAlBAUDaZrhz56Nv/sm/Pjk5efnVV+t6HCIwmVlrzy8ujqYnRVFIISDohJqmCsRl05bVHqAM3l5dXZw9ezL0bXQ6BmibDjBGCIA2yxliVHohpdySWn0iD3MmQwhFlu9IUQm/SRWC9XpJKU80g1TpyWRBCM1ynExGeZ5P90aUotI9pWht36tNhTxBwZQFDoAE+s4lqmCMEYAkBTxEmmLFFEHxyITICSHO2aoaZVk2DEor613kXIZgre0EYc4ZBBpjWK/bVOkIISQWGiFMa+uckzLPMllVowiKvtC1k2aFEkKqQj4vgVirtU4f3NhhVwXZbWej0aibQFnmjIlzP1NKCynzgiNGwFJmTEhCCHjvtbZD79Xgy4oKmaIADAGsDcv54Gwss5JQjNEPg0VEIUlZZYiRC56ilTwvvUPnvFZODQ1S532kdFNVJoTlOXfOGe22RAuMAY1z6Zo7bzjnWZEfH+/LjA2qCcHPZpdIvNYeogcgwaPRlhCG4J31WmtKEYkXgnHBx+PRaFxJPr24uEh3RykVY0yIPSWCEJaUeijDLMuESNNOIJFYR6NR36m0WSdW3GRSMcb6oaU08RRUcuwhumFwqbcMgIToKWV7+zUiOufbtvWdj4H0nTV6OZmMnA3eRfbjH//Y+8iZEEIg0qdPnz5+/Ng5IwTLCxGjz/P84OBg0S4JIQEBGUVEkWex7+aLxeys01pxzi/OLlWvQwhdu5BSrlbOOcM4kVI4p7pucM5xzrtOd13HaFaWo9GoIMitjcGvZ7NZlhVSir5TLriyqCCyZt1880/+zeHR8S8Pv3xy42bbDZPJBCl/79131c3ZrVu3JjduFBlTvV4tLrsmtln2h997+9qNV8aTw73pwf0H9548eVJmqsjIrVs3tR64YEIwHzQitO360eMHR/s3+04BJBHJwBhL5AchaFrH6ScJxdVay5yksWmpTCJFuVq2XdcdXz/YP5gKwTmngE4IIiT3rfJBb1tYXEraAQMXmHp6dxFXiiDKslytVskb931PNeGcJqxLCNn3QxJ/qesxIuXcUkrXyjjnskwwJlTokydxTjnnpKQQN5QD72KCfNq+SfXStD0l7rGUMjhjrUtcN++D1kbKTAhpHSaTjjGmOnPCsQB9XZeUcqVMWDiZ8dGoEpJrQ5wz1uq0TTgb+k53nbn18p4Usu+cd0SpnJKs74xWPexBWdZFUQzDYt0ssY0Rxnkuh0G3bcsoz7Jq03KA4H0MxlBKIZL1qnXOjcfj8WgcY5zPGmOcUmrHCMiyrChK7y1AXK1WiD7LubF9lnHvvXWaM4ngGOPeR6UMJYwQJrLMObder4Uk9Sir66IoM8QohEhAyY5llOgiZTmJIbRt75ypR3kKsJerueDFYrGQUu7t7ec5rtdtKnoZo4RkQggkRV2XiLhYzowxnKemlMG7mNgsiJRxejTdI4QoZVId3nsMHvrOxLhIKQz+n/9P/8nx8XHiDDjnvvujtx8/edi2S2O7g6Px3n41n186b4jA/f39JK6fULgExLUr45zZPxinMdAIHEFcXS6Xl1FIFoKJ4GRGkQRELzPOJW3bNhXTEPHo6CjVM54+fJRkOIu8Zkxo5ZXSMUZjXFXLk2t7L79yq+/Xs9msrseMCRO6mzduF0Uxm83X67aua4jkwYMHTjtK6WhcMUZWq1XXNUggxvjS62PBs7YdILLgse9N3+lnz86undx2zjLGEKOxihAiJQ8hjKt6l/2mGQgpsSEUg8e+s9bEGHG9Xnb9Ksvkq29Mbt68meZBpyplqhO6jYZGTNhBasdHRGd43/epwSBl12mZOktT/TCZTar1vfHGG0zo5XK5Xq+VUlmWJVX0R48eeT1O9IDEtdixxwlhIbiUqye2ZlkWWZbJwqQlmPiVaRFkWdYt21RDSvywtHnFGMupXC6Xh4eH6bRTkg8AV1dXN67fQqSXl1dqcM4FNWjnApMCIkk96IRQzrk1vus6hkIItr+/n2Wi7dZaa+/dMAxlxQ+PDiiNXd9oZZtm6FpDCa+rvR18uOs0iDHWBU9ls3QmqalYa913aZhTUVXVarXy3k8mE865dT0ibpVjAmUpFaKjCaeEc555h8tFs1y2CKwoCsZ9Yk2lfTzP8/F4zDlfrVZd18UYU5Mm5zwF0npgSqlf+Wu/9PDhfS6I91abgXN2fqmcV3v7Y8YACWQyX62a+WxdZyUipkGHIYSUlQghkEDqAweABHmmKpQ1LkQvJZUZW6/Xp88u+t5wJjgr0wVhPqyVZl3vUy1xui/OLg0O+trR5OTaYYhG9oR6YkNMHc+JoZLSTr9RNugYY4gQQujatbOotCaMERqRhBADYmqOg+BhtbzM8/zwYNz3/WKxmM/OknmcnBxorbW2EEFmtCgKrbO+741xEMlysX5IHnJOAch63S7mq5ObB++8825djxHocrmcXa0IoU3TS8YAg7XWuZhQpbIqsixTg3UWjXYAMPRuveoIYePRdLVahZCowtRv57wRQtbr9Q51TFtyylHzonQ2ItrUdJaQ3ul0cnx86L1vmmYXbHvvh2FwFtIQk1SZxO3M4WEYkoWnBtQQQoqd0rpkjCUYvKoqAOi6biw2uXRVVQlaizEeHx/f+3CZUOW0BWxWA+KO4oeIqTKZ6vadOkuGsTOPTRutiyl+Xq1Wqeqb9oIUYKenpT0okXaqqkISEaKUMsF+CdQpOIshbsF2umPpOedi9MvlkjFiXSrVBu8941mMngs+5iNX+jwv21xr5V8kge/q8Clt2RGeUp9Q2lYgbhqJ08rcomupOzY1pUVKESHpWocYkEvBOfdbmwk+Wmt98M4FIVjKsa31TdMRQvp+CAHyvAAgXdf1fZMEK0M0XOAwdN77UV7H6IdBPz2/cJEp3TOOo1FRVgUhxPuY9prd5wohpCZtIcRoXO9ih3TR0mdhjFsXrLWUQVVVN2+K5bLt2t7ZTRmVHR5nx8c1AJyenq5Wl4+ePlNmQZguqrF1XdMuKYtCCucpI5TKLN3OGKMUktS4Xg3Oma6zztlhGObzpRqsEMXx8T4AdH2jVBp1G2KMIcLxtZEQoh6zsq7KmmxvbT+e1t7JrhuG3iBxMpdcCMq8t6wohLFd36lXXn3pxo0bMeDZ2flglDX4ysuv7+3tP3709OLiaj6fWxMZsSGitTqhpoAxy7LJZNTpuRqU0R4R+k53reYcpShiVDESIQTnFF1Mmar3HgJobRFdMiq/bcrTWnsXrLXGKK0tITAajQ4O9yCS1bJJm2AikyZbZbR0FofeJargDs7dSRfsaEPJklfLJI7l0/Zf13V66NymQ6ssy6R4GmPc39+/GxeJ07uz24TJwV8i6we4bVhPW0nqUE3b0KSsU0yolLJWC8E4Z87F4IFR4Www2iHQGNAaLwXlnPZ9j0BToAAQijJ3VgCiDyGZBAAmfCG9e3o7rV1qf2OMlmVZlgwxUprEHCNnPnjsukVwG+ZMOnYfISaEHghEdM4NUSX0kVLcGXBSmElmwBgGDxES1ZFzTgkhEXzfa0pFDFwp7X1kjAWMzgUfbIyRMbaLudKN25USE6ScSoN1XbvcDcNweXUGGDOZK6Ug8hgYYUApidEjgSzLgkdrPCJhjDvnQgCtrfc+9UUTEoKPIXWvAgQfnfVphMB4nBEqQzA7orExYejVrvuAvfrK7ddee+3w8PCtt9568803CcGDg/1h6IKHy8tZ27ajUdVbwxjDiDvUNC3QsiwBQlFmhCClWBR5ng/BY1Fkq9UaEawbuMC9/TrPBRLPOc1z670PvmGMTSepY8b0fa9Uwl3ROTBm6DrHmBCSZFmWZCImk8lnP/vZL3/5y+PRFJEAYRcXFzdv3r558ybn8uOP7v3Gb/zGj3704xgXSZNdSl6PKu9tUWRZliknnTXWRme1Vh6AOgsxOCFZjKnsSXwgOxRH0CxhPLDV4kiikMYMwaMPNsk1UUYQwRgzn/ep3QSApm011WCbro8xBo/BIyEkeExEvHpU7JpgkpdLpOX9/elyuVRKE0IoRa2Hvu8pRa3JriSbIKXkOVNtLM0QSAsr/QqAbDeImN4xBD8Mw2Sf79D1ZMbOOWNMGqnBOSMkR0RKSRpn4XxkTIQA3m9YTcaYLAsnJycpTlbKRPBlWUqZ932vbQjexq1e1I7KNvQ9Yh5jtFYzTiillJI8zxHdMAwh2iwTAATiBmDDSF50vLuiQIplXnRW6bcp70gGLIRIWYD3ntIUNeAWj5SIMQTSd513LaXKGu9cQKSIhDHiPCUENyM8kCZLo5QeH59cXl72vUrpiRDoffQ+Hh1P53MwRh0cHHHOnz07W696wQtRkcm0znNOGUEg3gdrnXeRZjyxctNbUMoRAyJNm3j6OMk5p0pB13WpGIEYtNbeQQrchr7ZGPBq1q73uml9EC2RtLhx4+bl5eVsNleDHwY9DAFj6Doz3YcsKxLvoizLYdDr9frg4IBxXlUVIdEHyxjUozIEQikuLjsukAuoR/n1G9PRRDrfRXDrWZdUggDAGmWM4YwdHuw9fTpDpAQZ4zQEorVy3kqRhQjD4GSGVV1QGvt+XVXFzZu3y+rg6Ogkxkgpr8pR3w+np2fWWilpKsHXo6qqc2stYwQg5FnBqIxBzdpl3xsEBojexd4PqegqBLMbggE458CbFGem65j8MKXU+cGYoJVzzhMKQgjGibU6RFeW5aieOOcIdola5J1fLhuyEWFJhsdjRM5d0vveDVLaRiJxf/8AMa7X62EYuq5JwTZjhMssNQYkGnMahH1xcZHisV0NLOWx8YVeDsQYtpwQpVRRV8nD7ILbFMAneCzLsqSfnDwzIjqMbCuwmirASexK69Q26I1RMYY8l0KI9XrJWObZtid8GygiYqqEJ3mKelRKKRmjWZYt1+dK9ZSizDhnMs9rRCp4ZjdSaJ+YmIEbYlnKdGiS6AFAQjBGm7Za7z2ldFfAl1Imzd+w2YAcYwSRMCrVYEOw6WXTjiOECCqEGIdeLXFFCNHKOOsRCCXMaLtTaeeM9mEYemVtXlUVJXx/f392tZzP5+tVn2XZKB9NJiOZsYRchECcDc5BSqRTkI/bVlzvfaq3b8uNz/espmmE4UUhZMYSXYQxNp1Ol4s+fV72/vt3zs8v33773adPn4YQxvvjx48fOefyTHqPzXqIXhRFHexAIilk4Y0vZIEBu3UXbOhdm+VcSBJjpIwWRe4dxkBu3q4JCSFqmaHzw6CGEDVl0RovOFhj04LQ2oo6K/KKsZXWFmLI87IoJCEmyXoKKYyxWZFHMI+fPLi8OivL8tatl46OXi2Kom17xphS5rf+2b/4zne+ffPmLUrRmBghMJa2twgYfLCUpZYxtlqtnTOMIUEmJQ/RJw+c9pSwmbGaKJs8YGpJNYiYSSFFnjHfdyZ4NDoY42IM3tu+12VVADBjgtY2BBIjdJ1OAr8xxhB8CGmnTwwNLrN8t0B3npMQoo2q6pJQbLtmUG4ymTBeCbnhISd6VsJCk21rvSnLpTQ1oSxJZhkx8QcwAkkPk6eCbU1rRy8jhOScxhillGWVA4DzRqsBACLlu7EYxljGOCLxPlzNLutqFCMSCnVdj8aV0c4YwwRPuUNynjsS8nRvT+vBey+lTOMIvXfee6Ojd8Qap7XnLDjLYyAEZYxq52N3Bgyw6UxMrLBEb0h1+x3pNcH7G+V3Y1JJBjF6H7q2Z5xmWSYlr6pJ0zRKqQR6bOQrtu+SOM9JTD+lAJeXl2meOACkt0g+8/TZ1fHxsczzvlcPHz6kFPcPxpxzRE/IRq6sbTpGixAgBnTBIyICxoCEUkogYJop47Yb7qbbiVJKCArBYwzGGMZT0IR+w/3YqA6wX/3rfzXGqJQa+hXn3FVQjZCyKWPSB6uURAxCsqHrZz5IKTGCt246nkguEHGllsYoQkWeS8ZEDBQiR6Ter/I8DzF6r/th3fWBcSjLXHXWm67ruhTnOBMHNOA7ITKCSTYNCWFZxihxzoV8JLXxRcmVXp+erfI8R6SnZ48PD8+Konz06FGRV5zLu/fuWKfbblWUIkJI3XMAwViVyiSD6gCI4HIyGSNSa+LQG+91lsuwEeneLZFNKcVaNwxqGIZEXUhDdLKyHHrbd3o2W11dzpXuI1hrtZSy74ahVyloTAAyIWRvf5wK7ls01VJCZcYnk0naGhIwk6woxqiUrut6Mpmk7CsBWuPx2IdhGIbkwRLildqJmsXQ933q3UtDutfrdYwxbuVvETH1RSeZt2Hok9Hu1nqy9v29yRbpTW6ZcE6NMc5SZw2lNEYYel2VIylyRkVVifFk5Kz3PlLChcjaZu6csZ4wKhJmk3L+BEq3bdv3bQghBDebzYZhAIhKKTU4LgREYrSOHoLTIZAYnsfPO7vaWHLYyNC92IKWEPVkxsmz1XVNCOm6jlFBWeLG9klqRwjBmAgeEUgM4LynLNKN9tUgRUUIU8oMgyYkTWCI1npCmBCZc25DLKF0GLS1to511xopCu/UcrW4fftmVVVCiEavjNU+GKX6pmkySQAoIUQwvk2CkkohAyCIzhiVdnDYqsrRrS4fEgLgk/OnRIQQhr7fXRn2V//aXxmGYTabrdvL2WzW96rpLpwFNEJm8dbto+WyWTeXhWR932/nxNBr166VZamU+smdt7RWESxjiaHeG+0plS4+K0fHk3rivey6brlcexf1QIJjVOSCoeSSU24jEsgYKSCaPM/TKlHWMMo5l4REzkmISEho2xWhYbpXU8r7rr1z5/3r16+fn58lzn0I7qWXbmVZBtCHEIxRXdc5Z6zTdV3neTYMnbV+PJ5OplVRlKtl17anXa+tE3EzHW8jppdecLVqU6yIiNPp9Pj4OHXqNk0DkWRZVtdh6HUEn2XCebaLhdI6S9vzZDKhFGJExgQhJGWbidAStn2naSMDSJ00Q12PU/fP8fHxcrncSMZK6bxPfWBJpTURpA4ODprFKtH0EXF/f39/f//evXtKKQASQlr6EXAjqYWI6/U6rYwQQvp0QogQQp3z5FvSHpGaBwkhq9Z6nzY4koCPlKu37QwRtTJ9r4p8ZIxbN0trrfUxz0hC1Da7Q7DW2q7trDXpky4Wi7Zt8zwLIczn7XhcAwTnI0V0EYKPgssQmk8ZcPrq7caxp5wfAREibhGylNVnWTadTrMs67ouBuScA4a06wEERnmWZctFk26XMUpmqY86DIMejw5CCIn0uismpewJNgRyne5gKqYW+chZIITVdVkUWVnmjIOQOJKj1WqR5IMSzYOAACQbCtw2f4HtKA/vIsQYNyrLERkmLaHValVWBaVRG+2cq0qx66nY5MA/+uFHZ2dnSeH6u995d+014kbSNZMSAHLJVG+LahJa/+DBva988Utf+9rX/srXv36wtz8ejz/48P6f/umfvvPOO7MnzWRviiGMKsk5J8FxJ/q5AgBBZM2rQMOoGJ2ri8vVRVUVKHmnl9lI5iUd9IJxyjklhDOaxRgJDd4b6/TlgyAlJxYw5kjisHBVJcCEvu/ff/9950Ke53lejMdpRqMmlFf1cVnmZxfnzuu9vcmqtav2SvK9s9MHPuDxyYEd+vnqar02EGUuiQvDK6/eYMI9eHBqFVw7efnpk0vQWTRxvx7t7Y+4IOi16owxerwHzmkpoSqJ5Nly0TnriWNtY/MCD48mEfRsdpWX9PDwKJPFejWoQSPiMLRp+yckFLlwNqxWLSGkHtEYVapVUooRdNP284XhnB4d1/2Abbu+uLq7Nz0hhGhlV8sBIg8eL3XTtj0FvT/JAaK1zqkuY0ccYbCGs9wolRD4ENxisdDBVVXFyJgSqnpqlCdEUko9csbYugsJ9eGcA5XRMgAAygQ3gQbvfZFXdbV3+uzUOffSSy8BSmexbRwAj4EZEzBmwXO9CI4OQvitnLXTvTJKS8akZACAHjNSYkDXAwDZH49ijDGiQIkeKAYpGKWmc34nnJ+WefrPBCiKAjdiQ5teNCEEBH90sD8ej998880ik/fvfnz9+vVmtfylX/6FZ8+ezWazvlvlmRiG4dbNm8vlMpeV1kOjWwRCkQom8jyz2n3xC5+rquqtN9++e/f+5fnVrVsvDa3T2rk9ICis6RnN+s7EMOztHfV9P7ucF2X2mVdfHtVV9IEzIgV9+uQBJQfn50vvvVJWsEqSrOsaKSWA2EYN4NwGmWOMAPCdPcftNFkAkFKuFmpb23er+XLXwbb52//yn/7nIYSTk5Nr165FsEkUO2V94/E4NV6EEFaL9Ve/9OUv/P0vfOkLXzw+OhqPx0PbSSY/+9prL928+ejJ42/+2Z9+9NFHl8sV1BWvKu/j4PSOvp/cSJ75qhwBrJ1LFTCT4jdjnNHWWYBoE9TJOaUMEbEohTG67zUXKDNJCLRdM5tdzlapA0EQCLofrq5mbdsWeQUsliXlLBOcaG26tqUUOec26iLPo4fVYmlNcMZao3KZheg4p8MwMO/29w+djYyxsiyvFleISJkIUbfdYK0hNITgTq6/PqguRoKIQmRVRZ2hxoRFs0bkSf/g5OSkaZrlcplnVohCZgIRQ/TWWsDovNVGORvTHUICUvLUm8kYG1QC+bIQfGKG5XkJAPP5nDE2nRbGhLbRzkZrvVJqXEkA8C4YbVar5uLi6uoykW1I0vdI7J+u64yNhBBBRQrM4lY/MO3oqaCagJ+E/aR0euhN27Z7e3tlWc5mM611URSr1UoWNnho21bwXIqko+R38Ni2J36j2J4i9herWbt6O2xna+2C4eTn6VaU83kBaWvDfiuxsHHCiJxzrbvVamW3Gq6pPl8Uxfe+972iKKy1o9EoSUYmZ55IPt778bh23sxms/F4xBj70Y9+9JWvfCVFJaPRKM/zXqptaEbcdpBSKlVQSouiCME1TSMkSSdfVeXBwdHTJ22qzKcPFWNMAiBxOxxvF1P8NKi+iztgCzHuLDZd23StNgb8H/xv/3GSjCCEPDt/erAaFovF2dmZUkowfnx4JITouu4rn//iz3/ta5/5zGcwxOA8DeC1qQ+L87OPb968+fLtX7x54/js7Ozd9997/OTJYrFout45l/j0fiMFBs75oq5SZc9YZa231kNEwWXfKqMHZ8G5QCktikwipVTuHxTr9RIgllVeVVmWS6Vaxsiolmk+EaPEGBe8KfP85OSwmLLRaCRF7oOybkjV2hACo1k9klLKGBMPoZRZnxes6zom6HK5pMwdnxxZE9SQJJECISREPwydD0ZKfnR8PJ2O+77vWhuiI4SowVoTg4/eBcrQe7ter5GUZVkkHQaElNwGQkie88QDdy44p4zB1MTjGz8MJLUWKdUrZaZTQin1wTHGinxU5AERZ7PFMKisLg4ODk6fXd39+IE1vq7HlCShFkJQeIda+RhJlpVJ8yShG0r1abVJKT0JO1x9hxWnCDkxJVNbZYpInXNFUTEmxuNpnpdpwEeiavnYeO9jBMYEY0KrgEilzIfl8/RsVyTbGfaL9JJkEiFuCmO7v9ph4y96pN3i5iTQjT5qpBCdd8G5SPDw8FBrvVgsyrK8efPm06dP0+w/55walnt7e9euXXvrrbeOj4/Ho+nZ6UWiwSXKig+2KIrr168DQFJEm06naZx3epHlcnnrpZvplHBbP2+ahjFWj8qkuNR1zFp/dTXfMTTSFZ5MJomzmM5/RxBI5reLhHeb3c680/MTHWXrgclPh99strg4PDws66zrur2D8Zd//pceP3787rvvPnnyJJUrxuNxXdck4OMHj1dXy+VyuTeejOvRfD732quhvf/xR0VR8Ey+9urLl5fnH3945/zZ03wkGBN1zfO83JFph0H3Vmuts0wi8L4zxsxiRM4p58Jo55w12jOGTgQRGOE0LxhgKQSrqiLNz87z/Pj42DmRSuFSZjGgoCQEODrYE7WN0fXdHKPdm4yd9atVs1o1VNhcjCb1xLrBez8ZjeyRi4EKWQpBhSCAKEWBMWjVA8D+/rFzznvbNtp5GyuGMauKw4vzj4bBERoQo1JGKw+Rew8xeutCiJH3sOEYMREjtt06xaWMMcqEtUgdEgpCZIQwgLSgN0JwzgUAopQDMABB8AwkMiY455NJ1PoibAZw8rSsOefeRq3SEBBgjCMwiIQgYzwSAsaYGH0S6ElWarxNOTBulcbS+mCcJLtNRb60vLIsWy66qqrW63Y2WxBCtNZN052cnMicGG2G3hLUCMN8vmybXmsTt23DL6625H8SortbspuykN3GkdsF+tO+6EUDjsERZJQlsCfG4CiBTPK0VpumGY1GJycn5+fn3vuklJQAgps3b9+///Dk5Pr16zelzP/4j7+Zul8YY6ChLEshxMXFRZ7nDx488C6mfGc+XyJiKq0lAAgA0hut1+vEiQEIxpih1wC4WnZDr40xIYrEpbt16xYAfPDBByGEPM+Df26iu+u/i4ZexNvJdrbBbjdMDJwXYwFEZL/7e7+zv7+f0E4AuPPe+5eXl/PLK90PGOJqvsAQGWOruF4t11KIvu/tjZvL+fL09NQoE6hNlUnCWQT4wY9/9OTJM+/jctFyzvM85yxHcAjcGKMG49APQ8eoGI3rBMxyJqWQnNLgNUIkBAlhECkAQ6CEBEojpRhjVIOy1iYZN2+8HUwIocrqclQHG2ZXi9n5TBqrBtN1PSGsyEeMUME4RXL95Liuy3E9Wq19367KohbXj9ummzeNsYpQHkK4ulx6H6yJ6/W6JBPngtbOGGeta9eroYOLs07kOkZIwXzXDc4Zgkgo1UPvPI4nZYxxtVpBpJzLoTcRFCJ6F8uSE0JicAjUuzh0KsIG/SaEcCa4YM5VlDCtddcNhKIQJpWIUtZX17U1/tGjR81aTSYTa/x6vT6aHu4KiWVZpRXpveeCpmiTkI3pAkallImWb8eX0O38B+fcoFpCSFEUSbgnzbbeBXKpRkW2M36GYahHtWEBgHgXhkGvV23bds6GOhvtDDK9eAqGE0HtRWx5V1vGF46dAe/s/BPWG2OeZSnt2vmloigODw//1t/6H52dnT19+tR7f/369YuLi8ePH19eXv6jf/Qf7O/v//7v/z4h5Gtf+1qM8d13333llVcmk4kxKiGIhI5u3br1xS9+oe97RPKDH/ygbbs0o1ApJYRMiWS6YgnGy/NEd6GLxSyJcnFOi7wCDIjEOZ0XGx35uq4BoKqqEEJRFJzJXQpAXuhjT5nmLjDZ7aG7y7hz/ulIVFxEZKdn54PSidl77dq1ZuG7rsMIRweHWZYxQpvVOsY4iwtnLCIuZ/M7d+50Xdc37c2bNxfrpRBiPJ0oY7TW8+WirKtqvHd+8dg5HyPsNBw4F96Hk+s3ZrNZlokiHyFQpXvvYbFY1dVB8DFGJMgp4Yg0BoiRSplrba31IagNqdDGGI3tYuqk9wb9GExvh1ZZawtkzjk1hCwjRttUv6EUz86erdeF87ZpVv3QSikZxwDD3t64aZosy4yx1gYEWhQiz/t+0SGi9w62U2SbpgMgslCMIWUlpUTrflAdQU2p4JymAowxZrlcERSC531vspxSSp2zjHlKQSmXHJHqwVrDGKMMKUUAkucy+EiQOpfYP6gGSygyRjiXMiNKqWbdWRutgRg2/s3YPkSTZp9SCjJjXFClFCKLMWittd6MZUECzrnIno96oC8Mf9hIFFKafpVAlNT3M58tq6qqylESDMtksV61164fCBGkyAgRwUPwSFAIsbndOwoH2cqjpzrZriy888DJp73oadPDxLt+0aTT8crtW865tm0TPk8wFrmsq+IXfuEX3n///cPDw+SjJpNJiqKvX7957dq1r3/9G0dHR9bad9555w//8A+/972/ODw87PtWSnl0dBDB7+/v37p1azQaLZerO3fuDL0ejSbpCiSzuX79epKYTJaTZLESPzzB0Yh0Ot1P+UgmK5HJxHVLlnnt2rV0/nlWpl6XlLOwrQT6p6x3Z8AJPkgFkfS0NKtp9wQ2GAtNSyldNm2vH0bFk2bqwcGBlLLv+zRTZ77ucyHzPFfOyxBtBFlWvbFNb9166ExoutYYg5ROj0ZAqRS5c04r672y1qbww7v48MGj9XrNBVssVpTiaFSluYyr5brvlVaes0xwCpGEAN4F72NqjkWIzoU0uNcY67oYPJghzNVyaK330ZmIUXQrQymlURSi5pz2rucZZaRaLBaMEPCRE3796PorL90iBI8Opo/OrrRh1tq27aXIU2+n996FFWMMiIshRIjOG0CGRGSZJDRwzoQko3HBOEGgiKyaHIfoCEnyC2y96pumMzoQkhMSQgiUWMZi31tENMZlsowxUkqkkBH8VszBtY0hhFDKvbdKGc4pYxkidc6maQxVNRp6t5ivY8C6rrXpkCAjxFpj3RCiAfAxOs45odx7n2Y1wk7biT9HTXZ4Zoyxqqq0OHYOMz0tz1kIkVKWZbn3wTnHuQghLObLEIIxAcEAYIzIuSTIMilgy4gKW5XjhAvuwsX4XOIztd9/IoZ8Ad96HlLunn98sN91HXhXyI1cLqWUE/zP/rP/LIUqWuvvfOc78/mcEHL79u3f+mf/wlo7n89v3bqVhoaOR9OLi4vVagUQEtmjH9rHjx/fufOB1vrg4PDZs2dGu9FoEmO8detWKkQ1TQMAO+NJrnuxWHTtUkpJKU8CLOt1msIx4ZKly5jO8Pj4OAW9zm52NyllAolTYpw2MvzkkSJn7/0wDMvlMgkeCiHSzMqNAf+NX/21P//zPx/6/ujw5NatW/1CJ+nJGIKztu+6tm1jjP2gnffKGm9sWVdCSilE27brTjVNEwmnIsdABqPny9YYMy6Ec8EYm4KxLDMpIAk0eu/BwOXlZYwe8TohRA1m6PthsMEj5kmXHGLEEMJisdBqq1psHKWUEm5NXM+aoigky7XWQ6OTLCVj1BkKnAAwSgRjFHFIiQZjNLWANc26LHMhBKGxqKZrZQghi3nqQ5Cw5Sd9/ou367pmjMWIEInWWojs4OBgsZj5oKs6z3Km9f+3tj+LtSW9zgPBtf4pxj2f8Z475XAvM0mmmCRFiTZF2hA1uVCQpbYfDPi1/WAYaKD62Y+NcqPKNRjVD/aDgRa6G2io0S43bD1YtKyJMikOJplMZjLHO595zzvGf6qHtSNOnJs3jRLgDlwcnLPv3rEj/vjX/K1vFVWlEaSzECQ9IRllyKx17/38wfvvPfAeyqKmpKWSoZNgtOOcOws0RCuKwiiKnHdk8azVzorhcCiEoJ4774UQoirroppHUdTvBc6xPNNRFCFw5+DmnVtUdF0u1wx5v58OhkkQcsqvBEFgTG1MTS60c84LaBrfofWinXNJkrSuNWW/yb9Vikspi6LYbDZRFNGoh8lkorUmYncEwVAkiRc8FEKEgpOPBw2mFxoywNaKdjcooSy6YR45/IRC78ozvb/K1vRfg8FgPB5T2lwpZbwXQrz++utk3yilP51O33nnnZ2dnbquLy8vqea0s7MzHo8BnRD0LS7laRRFxujlcvnOO+8cHR2pcTgajbMsG4931qvMOVebior2iEhmFrdNFNswJIqiOE61toKrJEm4AmqbJdwE8XtGUXRxPqUrbPUaa0Bd3ZCh/b31oulOYUvT6dfr9XZZ/tv/yz95+vDBZNTTdRaFqh9Pzi8va6NLYx8eHz85Oa2N9gyNzdM0pbO3NDSMsaqcjsfjKIqMccfPTlarDTU6mpoTwl5KSYABCs98lU0mE0KlkXqmlMNgz6+WG+94XVvwYjZdxnE/TVOVBE1C1RZlnuc5TfFilZjNZs650WgkhKATxnGc7inKedKOJMdDSvns+OM4jgeDAZEhtxUI6upsbVRrlKibDJsOW+riQMT1qkiShOgsB4OBtTbLsjAMjUZiLGgfsDFmNps9fvz44OCACAQJcbG/vx9F0Ts/+4jgHM5CFMWkmHq93vnFSdqLsyyrqoKA3KPRyDpTbwyNMhNcJklCHLSMsX5vOJ/P67pO0zRJEnrwpPhJHqbTqbV2NBr5LbBROefIUyjLfLVeEGStv0tD57SzqFQoRVSW1WazKausDWVJukjC98b9MIw26zzPy/Oz6WSyG0VJoKK93V3bENNT4YTmWlRVNRgMBoNBi3Ckrtder0eZ3idPntDKHxwcSCllw/VHxTZyWa21m/X64uKC6iY0AGBb42j0gm9GCxA4fDabVVVFIxpp924rl1XVvpPaQqhxirZli2Ohh05OYutT0CahbWNMTZqu1+tRoo76KAi2BU3/Y+tilNWKtj1tCcpRSSkPD+6wzvw6imgYY0LSzDrMsoxKffQI+r0xbV1xcXGxWC2jWCohuBDvfPRDZCyIE80MyjwZ+AiFB8ZZnyCsrOGw30bT7CVqj1IySOL0/PySc1nXdRRJ5waUpqeCxDYTCyJNQ6X6SZJkWRbH8WuvvbbZbMJUr1YbY9xqmdWVOz+fciaFUEGqGtgNalMT26hSymS2KEakTcKQOofrIAiiQeKco+9qs9/Ouclk2F5MnuerZU7p3DZVwxgTAhjbunMnx7MWKgxNNp8xVldmsy4orKjKbbNhGFZ17Uxnvt5oFPf7wzTt9/vD0WhEbtJqtcqyjHOZ5yXtp806J+c5CEKmmHVaKr7ZbLJsTUx3RZFRm0GgQs5lk0DmUiJFUPREXcMj2QZOw+GQYEOuGXwTU7NYQbHuts/BOi2lBHBBYBgTxoCuq6IoKqbrypRlubO7Q4Ffi3NqNnpKvYcNj1+iVIjA2lkkJB4U8LfRNYWR9FDIwC4WC4J2UQWLMAi0uaHBNpOYEXqsKAraAN57QqqRAFR13cbS5KO2NADW2jZmNs1oNWwywNBJCHvvdYeYDptREm3Z/JMCLCVvg3ml1GazaQaUzrs7kzfMalme0QOiHdWS0T948IAugxQK2WRETFLZltbbvJfWej5bk9YQo9FESnnn1mGaqCCUhbiM4zgIo3VRqiEcmAkgr4xenM+iSMYxEaCuy9qRC1HnPssyzsTR0S1ErlQYBnEvZcMJJ63Z6hVEDMMw4DGZxCiSq3Vea7taXywWi+WTC2MMcaNLGSnFlZLeQ1nmnHNAC8gBHGPgwTgPUSzTXl9rzbmTyknj8iKzRX6xXHrvCYZOsEGS0tXCUp3TNv29VHiYTqetM9kC4q21RV5S7o2CDdtwhQuhaDcLIVbLqhE/Q9uG7lcIoWtDvfiz6bwsKrIGRENF/k+gevS9cRzHceycWy4XFxfnUlGNHijZDuC2WAURICLfkjajEEqKgPwg1zRg0J4jb/Pi4gIbNmPczjGtlsslAPFd06SPmqSRMVgsVgCs6f6jdbacsy0El3PKUVMPfVEUN/bGUsowDK31nCmSKGvNYrGAbpm3mZXFOScvphUbUrIU4Cml2n66s7Oz4+PjJE3JO22usEm50eQhAMIIkRVSSkETzJNctZneKIrIeyfHm56+cy5NkjYr3iaNWtVP1p5khs5JUuQ7832aG9lqK/rePM+J2KCuDaW4aAVcg9+2dlvSa8WS/gtxQ96Taw76M8+3Jye1Rf5LURRSbhst8A/+7b+bz2Z5vnr46IMnTx9MbgnjbJGX89WyKGsulQOoa4P1loqB1oK+frVaKdYjgaRo4ezsnDrOkRfkS9DDcw3EfJCEVPgOgiDLsvF4zDk/OTlpjInarHMpg7rWCDzPc5VE20IIRxIS5wwAKIaEsLHWxnFsrV2tVsaYVQbkrtd1PZ/P0zRN03S1WlWlbn0HSpC0LdrdtErHbmyTqO0QA1r3KExmsxmFInR3pHqt37o3vGGEb7cvlRlYA74XQmRZNpuuhRBRFA2HAwIMnJw+u7y8PLwxnkx26eRVSc00UNcm4om1NFgMlAqVDIl/tCiydggwOQt0m5R0IY0ODUM9ImpNwQKXUjpnal1674VgGjPqbSRMFW0O8sw555vNRmtNGRdqe05DNRyOlov18fGprl0QRNQGeFX96Jg1ACBoV7eCQhqTtjtjjOg4yVnVWkOjidpsNm36QKk2Ye693+aiGfMdKXINXITe1uv1bEN1Ak1lddDvt242dtr6OOf9fp+6NSlr2J6nG51iA7QgmmGt9cHBQRRFZ2dnFDA6B/Rxmu1KTihjjOJBvh0Z71tZZUy0a9WNgYkohmwJPSB6Lq2Eiz/8w29lRaYCpnVhAZ+eLMqqWi5X6/XaeeRcAmfgGdPcGCPlln41CEAIUebciVopxVmQbcrZbJ5npeCh0WVZZUpt2R5oYwHAZuVO/YJzvl6vKdcfRQenp6fTqcGtueN57pOEW8MAIFtDXmlEBDDEx41s6yOh93G8NSxhWAsh8lyXZRnFPWQMnHTGgpOCRYFMlbA88lv/yoBtuva11js7O3Y7ynQ7otE5dA4BFHjnHVrDHWLDjo8MbVVZzrm1WNfOWlCKO8fiuN/Gz9baPCvKsiSVRxgpz5rpTdYZDQiyKmtnt/V6FYidnXEYKiYqzn0QcABHAD5rvZS4mC2IvcF7ZEjTPQRjxCCn6QG3xoHMHe2ANp/USBTz3pMAe2+NrRFRKVFDQcQU1loaU6yUkJIjekQfx6H3AYUSSonRaJAEQRAEWs/W6zVDWdcGgPVSzLOsVYXtXidHo7vzWm8/yzLSkuR5JklCKq9ufMg2r7NVvo03xJqOyG2eYvt4rxLd9O1U9aFognQxBdU0UdE3g0J9pxeFmklgO9y0bqtBz4lW4/FW5FttNhtEJLFnjHmPbdxOg5RIGWldsettg40rHjwnvY362yYCaZe23g0ptbIsxfd/8J+cN2EScOWM0U5oJUP0seTcGGcqY41D9OBQiMg7UZRlllWIW/eg5nkQOOdy73C9zqx1RCOEwK32znHwIbjtmL/1UgehJvpfrbXzRkiW5WttKgGB2zZbFlpvfbksX4Ssh7hlpldKqoCmCjhv0djaeeO8KcosCAJk3jrtoAJglfa1qRxU2uZFhbXJTL2dW0tyC9vJV4m1XmtLjWauqWc455K4750BD84iAFgD3nvw3uhC1xYk07W1xnuPRPsYhpFtyL6dc3leUB8iInPOU2jcWAxXVTXnUmtbVRVuvAczHg93dsb7BzuX0yd5sfZgycbq2jhn+/3h9GSZZRljQteWEubek2UA6OAcWgEmp5TyAt57Ch0BQKnIe8JqCQBnLMEzhOMOcRtvIvooDpMklpJXlUbEXm+cJMlqtTo7OwP0ztv1er3ZZLPZzBizv7e32eSz2aIsat6Uf54TYGiUZisz0MmyEhkd2asnT57keS6Voj3QyjDdDjbeDZ3NWku9U67zde0FkOUkv52cI3L7OeeL+Zx1Bqm369bOE8ROKYtQKO2NuA5teFUVZJ+aCGs7TTIM4/v376dp+ujRo6dPn1IeXmstZdC9d76dIKFbqW4fJf3ZONjbBSRnXspAiK0WE1VVoeBZXtabvLZlGKrdSaqiABznAXMWVstllmVMMcqGkTvUhjFgfV0ZrTUA8x6liAIVWXMFaqWvJ/d9s9nU2i5XG2MMIsvysta21haQW2ONqZ0zzjnGER2gd0JiWeV0a1ygBGjIFnWeGXLdGWPOeS5UFCtjfVloznldWa21s1iVxtlca8uZ6np0XUcIm7TE1kRb2zpO0ECC2pxKUW4AvQerTeXBccEBnQdb5BXhYymO8g4FV1EUrddrhgIRKSnlHVrr6so4S+ZRAUCWZVJiGMkQ1dHNw81mw1DVlS3LqqqM1l4p0+8PrfUMBd0OIcy9Q+dMm2honDHGGLt3795sNjs+PqaMSFmW/X4/CAKtycMk3W+so63jkyRVSiLzWnt6ClVVaA07Owfn5+fUSUrRb6/Xi6KoNNvxfIeHwWfuv358fOo9Cq5ay/ac/XSdxol2a/omuYWINMBxZ2cHANbrtTaGlA4JXntrvgNBEVvuEURE1whYKwb0vWmaUvxCFSC+JScXZVF0wxwSDBIteo50nlbYWnyF74TBjDHqwSYiS6UUpQmMMVTs6Pf7BNJuIc1p0m8NfivwVVXVuuxmYaAd5mC3BJK8Yfmie6nqZmDF/v6h4167elN4ZlhVrJw3zDr0Nk3SJIy5d9lq6b22dsvDqJRC9Hmeee/v3rptrc3z0hhXVauiKISQdV1HsSjKvPVwuOhzzp2vFYsX8xXnPE1TowF8IEXKmXHeWuMRGAADL72z3gnw0jmPwBnn6JFhQEVXo5mSPV1ngkdSyqqqwAcMQ+9ypxXzwjrnrFJScpBgmUCPjCZHGUQMw+1oFfJtuq6UuxpWTlGVp3mixHIC4LXW5Bq1Gpeea78/LIqiqijml5RaMsaFYcyYqOuac0O4f8Yc59Joh8CF4N7rPM+srZBBGKpXRgc7OzuCq6dPT/I8VzLiXEgR7OxE3nvwNLo+C4LQGu+9r+pN98G3seLBwQEpXDK/NOeac845JXiJaxLRevoIUY6GYcBD9N4Sn2Rd281mc3Z21uv1yLmlWI66kb0n5jpD6f1+vz8Z7z5+9Mhdm8Z6BfxinYpu6+kEQUCbhPK3dKobN27MFwshBKUYWrQWY6zIc7KohGekYCHP8zhJWouKzdB57/3u7i4Bk+idbfxJfKDdZFVb2qEYm/7UDd9gqxl9g+5u9IgjLgFSTMRJROrg+Ph4tVqlafrSSy+tVisa0RoGcXsSEkWS5BhDql+0Akx/LuYrCp7JcWhT+lEsyVMQN2+PmBRFUfzsnbMqLxx3m81KmzKKgrPFyjs2HOyJJGFMFYWua22t05rozhMAOL9cu6YLzFjwjGVlwTmvSsdZiEBGGPIth4Ctnd+U1WgyLEAzwcU4Wn2czdxKaSYDKZQCgPl6QztAhEmxnhtTj8dj732e0Vxcapcx1lbz+SZJkl6vh2izbMm5jxQYU3hjwDnrGQfFpRScO+tjGTgui6LQVSmEUJwLjsNkuFgsymKba7Xaeusll8ZYakelbBZsPTfK/RABgPTeU0an3+8vl2vnHOeyrs1qtSEtYC0RX7EgiIqiLEuaNgaMCSF1VRlrmfc8DidVVX343vnOzk4QLJ1z4/E4joZ5SKxOgLzuDaMKOEOZm1UsovWq4Fyl6bBaGhlFzjmykGVd7wyHeZ5/+8//kjr+j46O3nrrrUF/BwCSJNHgjDGUhmxpqznnQeg45+AhCMKqKq0xvV6PCBkPDg4451mWEUnywcHBarVa1ubmzduxYdPHx3/8599N4v7No5eSpGceH3PF+70efcRaKzmnAhuJEJOSjEmSJNbaSApEfOONN5RSP/vZz07PLo2x+weT/mT/xz/+cW8wGY1GFxcXpDfRoQculSTVkBfb3okgTJIkofBECE5jVshqLqYLbWwQBNZCpVkcx8CT2XIZcBUEiXPOmEqpoImfhfPeOuRcAIrlchEEwd7e3nQ6FUJY57Wx9grRbQCgzIttLcDyKayCIKhKt7+/v1pn83l2cbH8zGc+U9fm9HQmhKhr8FB14lu/yXP6M0x7LAguZjOKegBASmPtxm8zf3pVVqRBGl0GVVWNRiOx2eQe0RgjhEoSXjsWBBHnTCkJ3mhNs4JgvV62UQT9QrpKFyXnnFpZEJx1Fr1jwApdICLnTErJOFCuxXubCD9K+8MwkRyH/cENmY5rxq1a8e1cPMaYbngPwjDs9QYEMKBHSGYtjkMpJY3YBGDWeucMOSrMbgFobV2UlEtZltQSQGakjZ2WyyVZjKIo0jQdDoebzeb8/FzFW9AcvZm8NVLhXRcREalMSsbcNWXPViVThoMuAxtKKkoaU9TdOocUB5ZFVde1rs1gMPAO1quNDg1nwnud5yX4Ks9zZ7m11ntTFBmFuP1+v6oqmsZ2cnLS6/WctsvlUil1dHTUVnE3m42IAq01YfHm83me52QQgpBwLBib2DrNGHqHiHh2ek61NGd9r9cLVFgWlZKBs77N8XjvV6vVNJxS6rXdIXmem2YSGjnDlCPgTRMvIj5+/HhnZ4fM8mw2Oz09tdYGQYBSzWYzbAoKtLzWWuZsdx+3hnEwGKzXa6J05w3GmzFm69pYJ6W0DoALyjA7586nl9Rg0D5TMptcICllmj1Azx0Azs/P2+j0WmBf67acQfhKuvf1ek36a7VaOeeo91BrTSTV3WiOft4dTyiwJ/Ay3QU5jN1sQqM7/GDQq6oqDENR13VZ1xQJFEXBlNe1tc4LA4hCSaFk2OsNdL2l0ivKyrmiAeUw7p3Xhsqg4JxAZMg4IIYheSbWWmMtETUrpVxRKc7rTV46kwTRer3OykI7aw0655lkSoacyboyRjuvUNvtOL8gCGibrlYrcnIoWG2zjmT0qCRDRT9ySGgsDRFNkc0hVJq1NgxDzpUQIs/zy8tLYgwkS+XYFiBBDAfUbsYYq/VWPlupLoqC8F6tl0glMXIXSXe0aYk2um7DLVIl2DSaGFNqbVeraRQlSoVVpYnIMs9rhpxxpVRQV04pJUQghcqLDTb5zPl8Tr8sFov98Q7d6XK5XK1WtPj0v9QHR7zH5B9uNhvrKM2DjAlEzwU6550Fa30QqDhOnXNBEMWxy7IsTdM8z4VYVpUNgiCO/WK+pqw7rTzpYhJX+l66Tar6kB9LyDyi/nrrrbeSJDk+PqY6+WKxiPuDqizrZmxVC+cQ0Pqu0AY+pGJotanqwRuIaCil82CMqWojw4hcdEqA0UXSXmqVL+DWfaVH3KJBtuwLzWNt8yam1u3HnbsqEdFn6ZpJFGlZuLjKC7SxdKuG2nxVex53vRO43e28AcaJqtTrLOOca20Xi5UIqD2OIVgAVldltnbz+YKo8Vqvva0PHQzGVMvRHiytlC6NMSwO6rr23kkpAZ0xNe2PnWgInHkBBoyPVS68HyUZ1iFT3nutNYEi7XbOtczysgn3VVXpoqi8RypaEDshdTs454xxjPHNZhMEAXG7LRaL4XD4+c9//t69e7/zO7+zt7dH4Yr3nqA5YRgiislk4r3/0Y9+9K//9b/+9re/vVqtkiSp7BYwTKEjrT450m3MzJspYRSqtQq1jaMoG9zaWOjkHtt9Q4+NHn9ZljYvnHOXl8teuomiKM80glnL0vkqDGOGQoogzzJrPWPGWCRRpBpJnue3b9/+23/7b7/99ts//O73qDIcBMHu7i7tJ6UUCyRlXFgDMtu21DFgjPj8uPfWWWa0qyvLWeAdlyIyxtSVY6iMzkibk1g2/V6chhjS3bWpgW7uh2L1NrNF7xyPx5vN5vT0lOzw7u4uAAwGg2QwjJowlS6Vgl4wuj1hdwFbpFD77SQ8SZKUVe2cA+RcyjaCJX/HdygESKtSWp5q+/R8yU5Q3gQbgEp7LxwZWc7WVpOapgDBOaJ8iihzppQiAf7kQWtIG4bWlhzpFpwDDT85bPsKGT078eabX7qcz3q9nrF2tVoNJ+PBsCcECMnKor64mE8vVpeXM0Dz6NGjy8tLwmcjGucsIiOgH+Fa6SlSXg5CCQBBoNI0VYHw3tK3msI753q9RCqeRPHNmzcO0v5777338w+f0WChxWKBCNTyUhRlmvSzLENE73C13Hjve+mAHn8YCO8QPOia5oPUYRj2k4QQHbdu3frqV7/69a9//bOf/exkMqFNQEZJCDEejxljaZqGodIaOIe7d49+4zd+49vf/vbv//7v/+mf/qljSDJG2ct2m8L1Vmxs2C3IpJOEu2ZeGaneVjd3jQb5FKRlhRBFUTjnaB6vlCFnQV05wb0UkRSRrr2xljNXu5pzGUURZ1II6b2fz5ctTuDg4ODv/t2/+4/+0T969913//v/9v/63nvvkV/NOadyqHOuzHNy83QDMq3rusWfeG+9Y96D9U5rX1XaOrdaraVUiLhYLGngbVGUBL0ucr1er7OsYigp20Sneg7jiQ2ChVxN14GOtfzYhMah66zrej9Ng2YCG+1sOrPXWzYC1xy0ngRupW90HZqLsiyLsqIasqOyMHIppauRRI51KJpbu9caQ99BUNI525P7Tv2iVRmu4Z2l1h1KhhFMeptV9uaFAkzbsvXMSTO2fmX3aDbSlpFffPOb3zy9OB8OhwReS/ujXj+RigWBDILIaK9rtAY++PDtP/qjP/qzP/uz+XzOOR/0t2Hb2aNHL7308uc///kbN27QetHeFXFojKnryhjjwSLSQhg/iVxtRlEUe0yZvKGlwoEtg3c9ShnUtSFmWWKsZ0y0QZpvGEmMMZvNZjQakefpmu5TymzfONj/tV/7ta997Wtf+MIXSKm3bja5Z2RCaccoRT1f26VJEvXrv/6rv/RLvzSbzf7p//w/ffDBB++//z61ClC+NE3T9WbRimIXEEdShJ0GTtpzbf3JNmBM+jhpB2MMPVeCBOZ5HkepUt47vl4VeVaXhUGoAerRONG1n82mAKyua8GVc76u6yCIjDFEQ/vFL37xN37jN4QQn/3sZ3/7t3/7X/yLf0GRP+Ef0zQ1xtRWQ2fGWhRF1trNZhOF1O6HxjjvLYDXta0r1x/08jwnHNtysabZQkVeVLUOw5gEQymvZEQr0Ko21ikId7MGvAES06Ok6locx8PhkJD2lDGm3SkbMq1WgQrwbTBiO91OpKDJhSax3BaZtoVTiUxUxtZ1LZWhC+gq4jZsNs2kaNIFra/e/ek6dWBvXTff7huO/rboTb43Vaq89yTArTpof6EtRFdOi0YLSD5j+43tUrgGsyC++92/XK7XcRwLJeM49iil5MbWAD6KYsFDBBFFvbKa7+/v/72/9/fG43EYhpeXlw8ePDg/P3/93qu9Xi9K0/l6ReGiJCKOajsj01o7GPYOD/cHgwFjMOzvKuRoNKvqUZSOouhyby8KwieTgVLq9PS0rVU459I0dc5EUfT06dPlcjUej6VUxphej5VlxblAZM5Z2htBEAyHo7//9/9+mqY0vC/LsgZatG06oVCWEPBRFFnrjWmbY6mZAUajdDRK/2//y//0zrsffP/733/27JluOns45w8efti2HJGf03TzLem0lKqhxiMCBtI1kD5uQzVq5KBWTcaoXVFxzqvStNWgqqoGgyEZ6ldevau1fvr0KQDMZ0sy9UIIrR3FtAQ2+NGPfvS9731vMpkcHx+3jWxHR0ej0Yieeq4rAIjjOM9zqg9RT5XggXPWewOeWM7BGKu1USqUMqD2WhrDCQB1rY3bJhGGw2EYGu9Ya9DaHF67QbsmUTbToegnAFRVRQnVuq7JDrcTbWiz0rFNGTrbbuV2Z/umNksXQL1ElGeVjAVVDQBltS0IsYZXpI1msYPZYHZb42lVAzaQct85rpJYlsgVRTeUJTGjuyNF2ULWWQO8ac/wnPBjA8snHh/CgbqmDkc7J89zxG0uQLz11ltcSinlJs+klEKlQSC9t8h8GMTOsao0gUo85nQ11F0BDSAmjMRsvZytl23wUzmT1aVAKj07IcRgMLh9+/be3h6AU3M7Go+Luqh0OTnYs97Mq+n8IP6l0VcPDw/jOE6SZDQaUe4nTVPG3WKx+Of//J9/61vfIsEmq0IRRevcmmbO/T/9p/80CILhcLi3t7e3t9f09PrFYpHn+WAwCIKAiqJHR0dBEBwfnxEx+uHhoRBiOp3S+fePbjT0xdHR0REJ22w2e/3110kwyCy3vErr9ZqUF3UmUD6MkplU6qRXqEmtLMvxeKyUury8bLHiBLRYLvIsy+jh0eDyrUKUTCk1m82iKJrP52EYHh0d3bhxYz5fPXjwgBrNACBJkvF4PB6Pf/1v/uo//If/cLVaTSYTYm9bLBZpmtZ+izH6wz/8w3/2z/7Z6enpcDgMgkDJyFpb15UQyhiwXhOPwny+7PV60+mcMkNEPQnA9vf3B4NBkeu6snXtrAOSHN6wYbWpJtrQ0JQGWsQ1mTjjPaFxKOff5mnIMmMHJrXtTyyL1kZBA8lkjBFam/KFQRAQf0Ce59CQSBZFYTw0mcKtEWs9uPacNL+mPW37ur+O/2l/oYunxDtZbHLxeIelvY3kEdHYuv1sK8NdMW5rMXQ2up6u70b+nVJbhSKCkcg2dW2cCKOy0uVqMxqNFou1tbbXcy15H2eK4rSzszMquNMzQNEjn7n1+I0xQRD0UlaWJQVgo9HTouozdv47v/M78cHaen84vAkAq2z1R9/61l/+5V+u1+uYy376ixz7gpX3v/I5esZ5njtjh0nw6p2j92/sUWMQDWhFOXz06NFoNMozE0ZJmoj5fP7s6YWuaindcmUePrqw9q02yxL0E2PMnTt3Xn755adPn+7v7z84+eloNKJBZLPZh4eHFzRalnM+n88PD58URXF6ekqP//79+2dnZ3Vdr1ab0Wh0cHDw7NmzPM8558PhsN/vr5crGtWxt7cnhKDSpXPu7t27UsoHDx7QDiC5nU6nX/7rXz84ODg/P7+4uCDz+8orrwyHwwACgLBeWgALEAKGtYF67X1dVy4L0uj99x9bVw/T5PDo9s7ObtofnZydaWuXy+V4PH705MnZxUWapr/2K1+bjHeHg1QIEUfKe8+wPxgM8jIry9KYem88jJW8uX9YbPJeHAuJVWUFove1kDgY7DLG0jAIA3ZwcDA7PVVKFUWRKkWxgM1hdbGOwmRxPrPWci59nSdqopiTgjNXgzGRRINgTE0kkgIMMLBVxpXi3pmyDJXSUQTUPBxHTnKvhPGGJSGzGPKgl/SE5/2oZ0sjuXTaWQSPgAjOewck2IILIaO4NGsRRpzzQhu3yYQQeVXHSgKCcVYqHnJelxurizTiyxqFZNZa6zSga3NL3uk8W4fBWArkzOu6yDZLKdDUDgC2Cd4O26b2TikCJvooCsMwWC4XQgiHzjMPANppEYja1pJJKSTANcqRVmExX0qMuINYRmVZOtDRiFtXFKt8d3f3/PxcxnFRFMvl0lQ6EiF44503ZSXQs9c+c68q7WKx3N+Lg7DPGLvl99oiEDmBNPiwrutX7t1olZYxZplti+nkRZDn4L132pC2qKrq5q3D23eOvvOd7/x3//0/efXlI+rtpqj98ePHRcaG/aOdwTb5AQBPnjwRQoRhuFqtrDZpmv6tv/W3fvVXf7Xf75OVGwwG//f/5//6e7/3e9QwSH4FY+zg4GAxm5MfS+FTURSkHUHJsiyd9bPpfD5bSKGyLJNCITKa7GiN07Upi4oxRhND6trUtUG03hObD0PkVIcgRU5GiSyDc47gSlRpJEd9tVptNpu9vb00TSlvJIQgLxoa9OxoNFqtiHzDG2MG6QBedMhE1qYGiYiY5/lqNl3P5++89RMVJ3/yJ39CCN6dnZ1nz54FQXDr1q3/+MffohkfpJXICZxMJh8//MAYc3R0q8grpfitWzetdfSUsemDJd9YKdXv94t8yTmnXmLKA5OaFhLCMCQSPO+9lMF4PJ5MJru7u5T65g30rw1iSekTko/icOec5IyGWSdRPO4NmOBJGA2TnpQ8jkPnzGaz8t4rJdI0UUpNV7PWrW19ZmzIMdp9SN8uGvqB1lS27isRWZHJdQ0DCQAw5L5hnCZ4dvst7bNoHWD6OmgK/sRbQp4zNLNR6CPd5m3oTADupsG2FlXI0WgkJT842Kt1HgXDKArKMjeGwJjGWm2tjZOQtIBYrzPvvTbF5fRsAhPr3XQ6vXHjBuNIvAdxIgCgLOo4jrXeugot9/cNJerOEArTcBows2XrAoA33njjF974zE9+/L0f/+itD959j+g2qfRCJY39/f1f/z/+jlKK5g9RTnt3dzeKoiSKSTLbTkZ6El/5yld+/OMfbzab+XxurZ1MJmTtb9y4QWcmpmvKgSVJ8vD4WZZlw+HQWiuU5FIYZ4MofPjwIXWQhHFU6fpyNqUn0R/2Ly5nxydnnPMoitabfLnaED2Q8xgnvfUmpwYUIYMoTh88eETukzFOKUUYifV6HUWR9/jo0ROKUYka4vj49BesbwbwsDwvZ7PZwcENY9z5s4sXCnAcxr1Bb7g7CsOw1iFKEQUBA9jb26M6Vq/Xa1n479y5c/6EyMoH4/GNolgvFovBYDAYJIAWmQtDcXl5Xlab2fxcybCF6PgGrEKuO29mfFPuQDbEdFEUedAkz9T2TKRQSZLcuHGjxTNgB4RAAI+25MYYK4rCWmvt1mvtp8nR4YFvUrtpL7p1+wYiUvGSEpCI/uDgoOt8UsWOYEiiATy2IDnGmGLYOqVdZ76ua0rg0b5tA2nOfNv6NxwOoWk77brQXR+4m8TebDbG1lEcUM5MMBRCpHHUS+K9nQnF5EKwFsCDnZLbbH7JWSCl3NnZfemll+7de+VXvv7XRuOed/z4+Phf/st/+fDhQ+dcGMlePyYIBl2VeO+d986Oz5IkquuyFwcXF+t33313fb+g1OjNmzfJC711644QYRimZ2dnRK5VFBdRFI0m6fn5+e7u7mo+I9r+qqr29/eLck2I2TiOT08e/8kfr43Of+s3f9XZoM3UUfJGSjmZTLBhoiEoKTaDXum2CZsRx3EQBJRTBYC/+Tf/5htvvEGbLwiCBw8e/PznP+dqmwulpz6bzcqy1M6ORpMwjHu93nK5TJJeEEQATAi1XK6bkiyjyXdaW+8xSfrz+UqIgHPe74+iKFVqUxR1VdXEtonIvCf4MQ/DaDzZoYparz9AxPz4ZJPljIv+YMiFXK7WFAFGcSKkAmQ0ioEeP9lP0lB5g6177tissyCNpJQiULD0cRgNe72d4YA2Gdmfuq7X6zUZwC+8+caPf/zjg4ODv/61r56cnDx79mx3d/eNN96IUlnX9eHh4XK1qKqC4qM0TZXs0calBFLVMM7k2YYSB7SqFB9WVSXkFeUVvV43BzTJ526KaDAYkNYmc0dlW6VUlhV0wrqsxsOR4BwR1+u1VDgcpVrrWudRnAThls/VgmyLDm0LtHOuKCraG206F4iERAq9JV0ISDG1gR7JP9k9kijGGGfeGENtvb1er8VjtUmj1vKTwCulqOJQ1/Xl5SUi0pN1dkvWL4TY29m9e/sOIlZVFSeKvqtVVXTcODqIo35ZaKXC/f398Xj88ssv333pppTywYPx4Y3dLF+SS7VFtoGiTL442DuUkgshvHVlXiXRgTNyMSuqqsrzXLCEtlQgR8bMDg8PP/7w2Dl348aNp4+fjEajLMs++ugj+QvxkydPlsvlrVu3Li4uOIZGb37wgx/Ecfzbv/3bX/ziF2azWVXns7mNo14YhnVdO291bZ13zstai/WapWna0LW4qqoISeeMpYwiVZgJWuS9v337dlmWn/vc53Z3Y+fghz9859mzZ0II6wiltM2IkuPqnMN1EURJv99nQsVpP47j2ri0P3z1/mst4jcIgjjtUwgQRMlgNPHIASBJEhlEcdoHJkQgojhmUqgo7DEEABkGFnwYR8ZZa622xnuvrXHgGWIYRyoMkl5KOkWFARNcBqosapM48EzXGjzrpYMwiLXWcS99oQAb67gQldZSyuFweDiZ9JRKlASAu3fvFkVBWZ979+6Rx3Fy8tFsNovjuCgKynjt7e31+/033niDWOkuL2fTy1mS9LIs45w/e3q5Wq2oCkj0GkopyvyRIiYTSotZVVUYCUQM1JZkw3sk4ekCm9pgj4y2acj08jwnv2k4HN586VYQBN66ndFYChGHURJGm80m6Ee3b9/WWl9eXtJnSQ5PLxbUvUC6hhBa1lpjHCVNiOOOQrDWf95WkhqPgPKCukNS00oyofdnsxmptn6/Tz4/Dehput9c+34qNPCGmSDLMoqkCFxEGc3ReLB/sAsAZVmGoXoucUU/y7qKo77tAQCjfU7MG1Tf5pxPJpO7d+8Sj1pZlgSnreta/PIv/7I1jiaDTSaTy4Ubj3c5V97rJOkjirq2g8HgyeNny+USPDs9OR8Oh2EQLxdrJcP1Kj8/uczuFovpaj5fTIa70/P5ce80DEyVF4O0N+z1e3GynM2PDg4nk8mzsye9QV/rKzQSYyyOt4k7qjwRqPPp06fOudnllEwExWAE9b53714URRTT7+7Gi0X19ttvv/3228PhMB70JAI4dM55hDCOQoicczIdIGKSJMPxiB7hYDTc2dk5uHFIRVEKikaT8TaCcri3f0i+FsWEo/GOlBKZJV9gPNklm0lqYtAf0R3Riwc3blIWlDG2s7MTp30hBMUdUkoVxqvVOgwjqlwMBkMpJXUOu0+4anRwJZebdVbnSvL9/f3XX36l2qzL1UpK+Tf+xt8gj5EcFsZYmqZv/u7fWq/XBMOqO9Prd/Z2iqIIg0jXMJ+tDg+Pnj45llKult8nodoWJ4To9/vL5ZL2K6F0aCoSGZA2OwjbPDNSGEWQo23eoeNnkhmg95M00nluf+bl3fFkZzLRVY3GhUoN0z4i3rzzMrEFUgWbrI33Pq/rtrBEZKvEEfvjH79FBe31ek2uBHndsZKsGaHaCrBvyjykXNqqkjHG2Zpsdets47ZkZb1niALRE7cEgOecGVMDOO/Be8s5FkVW12mSRJIL7azkzIIXDEMlnXMYKMTt7HXWoBtoCFYcxwhorSEqctsMVYrC3qDvwyDlbB2oRMnQe89QGWOpFCfefOMXVsvspz/92cXpvMpcPBm/9vorFJ1So+NsfhbFoq6MLACZiROpAizKlXVlELIsq4KQZfnCQ60CZNzGiZwvznuRMrXx1qNHyaVgAj1OL6Z5Xu7uijCUbUaK1vdgNIqiqN/vE8623+8Tqntyb7xarSgnNJ/Pe73ejRs3dnd3P3hw/p3vfOenP/3pYDA4Pz9/5513iqJ4+eWXX93bMcbURdGqVdpYgYoYY94hhXyIuDPZ44zHURAGcfsIldzmBupaK+LBbnDO9CcXQCtDiCjXtB8mUUrUObRrR6OJEIJKR71ej/7M85xuZDSaOM+owkTJJ/Kj4jgGiF4owLVzxuosy4wSCwbn5+eLs7NsNutVBYW+FEqQmiuKIgiSwWBE2gBRMsadA+cAuUjiPgBTMl7MN3u7mOfVZJLSlWDTYk6XTd44lWepFWnZUI4FwRYaxbeQXWijaNehpPKdgid5zrqZP0Yq4F/+P35vf3//M6+8+uzh4zLLOWAoZBSEr7zyuePjY2PM5eUlqQPKq6lkW9ym6vpqtZpOp6vV6uJiCg3ulYy/J+7VOKKQu0218qaHnmI02wydoCor+O2A2Da0IQE2tkbjCSnt2n5dJjfrNfnVta4Zh7LKizLr2QQBdFXZMLTWemu9tc5a9N4bjYx5h64z3Ng5JwICPtVlWWdZhujJvK2WGWOcoUQQzuJmXZCbE8UB6Snx3e9+tyrN40cnZ2cXYRh/88ufPTw8DIJgvV5zzt966628mOdFWJa19WVRLYSy1uezxUmUsJu3d8siSHtBfxAh29Fa3zjanez0F4vFpD9xDg4PD/v9YZYVl5czRD4aDc/nU2vAGC1FbYzBWASBYCjJwaC9SM7V7u7ufD4PVdACMKIo+tznPnfz5k0AOD09/Q//4T8IIe7du1cUxXw+39/fv3PnzsXFBVFgk4Ymatt+v//lv/Y3HEBVV4joGTrnaGsW64psjgdPbA/GWqeNUlFZltp661EGERWTgImyzh2ACsPAGOTcOSeUCoLAVjXtSKr0Us8jBS1kOkjfE/NrHMfOiyaaQsqjUM2ZSfZCAWYAoYpUKNHZzXJ1oc6r5UpwTjk/YhdjDSAkDENd2zAAXTulGGecAKdBwCvtpRRamyTpJUn/8ODm+dm03xsSBLcBh2jyVqIoqqstnyNh12jvKqXCcJuB59tW5GsTA3mDIiYZhhZpbAwl9gi/vVwu1yaLirUBuyrWuiq5x3VNPcCjx48fO+eIgYyccCFErrM2q0S1ADKSi8WCMTYajcjfJvHbbDaKTZrk4tbetmmntlTRJpO01oKrFhdJN07qo9Y1NHAL2+CWZUOuTDdIzhed0BqtdUWpY2u1tbrVj3AdoEaHtVapOAySLCuUUm3iDZErFVBDizFUJfacS5J2a614eHYcBMHk9ui1L98vy5KjuDyfkpbKssxbeOWlV+fzua787GIVB4vTk0sKIRbTzfe/+6M4cOv1ejMaHR8fCyG+9Mabr7/+utZaV3kcf01KmcTy+OmDzWoaBgEDfXv3YDcd0CrTnXPgqQwP2Xi5WUuJw9EAOU+D0Ut3Bio4MXneuzUibA3xOevSO+eUW/y93/nVVZYPx3tMKu3QOB9Eoducc67jmJ2enhOuq9frTSZJIPSzZ89efvnlqqpWq8vhcDifHyPieH9SV845K7k6OzsbDHveG6nQGxdK1NUqkJJzsLZWHMGZUGI8GJRlrrhXioVSWWudLsIwIUon8kKpccoYU5WmP+gBuLLK0l7svUXkzqLiGjxjklcOlODrbBOGIXJu3fPY1zZe8tYjcACOoXq2roFHALA5W3D0/X6/z+Sw14t74KxmjH34+CFBrLTWL730Ul3USRwYAKWEtV4pcffuzf/mv/k/IeI3f+1XvIfXX7v3j//xP37y5AntsN/8zd984403Xrt//3/8H//JSy+9hODSJDK68s445/JsvTZlGIZWVjyFbUAr1dqe2KC4mM0EstFg6MBt1quX7t49Pj5+9fbt3mj4n94+Plk8YRveTwc7g7FdF+MirB8tv//kLyhaoRY8IcTH7z9pFFxDQLtEAAjDsMVgmAY14b1HDwzQVbp0K+9dEocykKIWO4PAubpcr5RSlTEhH9SVc84BCzkIrDFUUU+p0lSbPDe+tiaw4IraVwaL2i83lRA2DEPuHQMfKmUb7wIkBjIUzm5mc0SGwAHY7b2XvYdxvK8RPVvPllkch5Vhs9WW4C7i4zCURblCZrWunMMoGk2ns4rpzeYZhS2oYGdnJ6/rymBRr72wYS8obZXVeRjE68UKEa3Ltwo3TQYHBwcHBwf7+/uISC5EqxVu37lR169prXVt5/P5ZDI5PT1tdV4YhqasptNpr9c7PDwyxgRBtFptEDEMOBfovKl1WVZ5rUvGodZsb3ccRwrAMnRxFFLWerWcPXr8pDfojw76KnJuvcxLGw16R3dGCne2QSCB0MB58GDtrZde/vUoZoxlWXF2cU4JjDzPCQAYRXGeF7dv3wmC4OWXX07T9N133724uHjttdcAYDqd1nV9cnJSVdWX+l+M4/6HHzwwxv30pz9F9FGshsP0c6998aOPPqKKNMWxN27cOD09rXVJlvall15KkuTJkycE6GWodnZ2pJSnp6fbfKm1t27dkqFaLpfWFXVdrVar0WisVHpxfplGQmvNuASAKE0Q0VptveefYoE/7dhsNt7qPM91XW76fe/9ajlfr9eR3DYbDQaDd9/9+SuvvEKZau4t1eTIAbu8vMzzfDwen5+fU6aQHIednZ3Dw8MbN2584xvfaFO+lJKlDRyNNlrr4TASQsRhzTkfjVSRnYwSJlzgvVehQcQ47d98eZdHZpnNmMXBbnS/f8sabyrj3KYoZgAj35SLuz/xRfgnSuS22W/e6W2q6hoRnakER845ghn0ksEwCb1Je6kOJXnISSDqugbOCrBVVed1yWsJALXVjLko4K523nFf52hKX+egIymiWGKtfRQFQjBEX1XWOa9ULwikCgRiFARRnhd5Vvb68f7+4f37989mC0Q/GKS7e5O7L902tiyKbLFYTKdP67o0tkySmMAE2lRFkddFRhRIURQtl0sKIbMsCyNpnbaurnWWF0vrytnizFob9xVjTIAQSToyls0X2Wr9SAhx88bEd1CmFK4IIaIoLMsySZKbtw4pXqI3hCI5Pz+nPlvqLKENXW4u0AMAeOu8dVabypdWm3Ga1uVGCMEZA1eXuXny5Mk777zzhkhVHGXL+cv5yig+GPcVQw0Ibuu3uAbPTX/WRstABUo4bw52x2maLBaLJ48e9fv9LWLh44/v3LmTpumXv/xlRLyYbThf3Lx5+/LysizrXo/VtSkKGum2xU48ffo0zzfDUe/wcPfNN+TZ2dnDhw8pJmeM3b179+HDh8g8VYMnk4kx5t1336UkbV2ZN998MwzDH/3ohxRErVarb3zjG/v7R9/5zl9ssoW1Js/zL33pywf7N//iL77jdV7XJumlRzduvfFmzDkTQsgwKKvq0yzwC19XKjC1J6imZKwoivd+/s577713MNmlrqy7d+/+xV/8xe/+7u++/fbbAPB3f/e/iuOYRvVQrv74+Pjtt982Wu/uTdJeTBk7xmGTrcbj8ag/uLi4SNMeIS4FstFodHh4+OjZ+4vFwhjDkR+Ox5RWKOri1n64jtd5WTLBQfIojsO0D2pWVcVSa9XvjeIbRVZOT858qTlylLJNxvKGrc53sM2tBaaj67LSCtB7pGJCCAaOoReCAxohcTIZJ/ny1rjvmx6AXq9HicalcQ+PT8/nUw0IAHVVcGdCgUe7t2/s3Xjt7lG/3z86OiIshxBC9OI4pokZVZZlWmtiRHZf/CwAi+O4rkxVEfu8D8Nwcrh/cLhLTOZBwI9u7jOG5+fni9WJ15lQ3iOUlXc5L/KL2Wx1dOfundu3Hj58OB4NpZRVWfzwB99/5eWXknFvk60YasEN51Uc8yQ1WutNOeecCyvExx895Q2JYb/f//k777XFZVpTigGiWEBDZ5emqdaaIqI0LtdZjkL2er3ecERiPBqPbbwdiimvyB+1MWZ3d0KQTK3r1Wphrd1sVoyBDJPFZrP8+QdGqaOX7oQ8BItVVvR6Y49gnXd2y8XmPQOAYRK4KpNSsjRN07TXHzIZLDdlutQI3DsMVHRxPp1NF0qGvV6PMf7o0eM//dM/Ozk5OT8/Pzy8kefF5eX0Z2+/XRXG1DoIonyTWWvWy1W/F7WFAUSkoVhhGK7Xa6l4tzhBG9daW5RZGCmlxCZbUQxzfPK0rPKqqh48+EibknO8uLi4d+/ezSN2eXmpmM3zvKyr8XgsBEMmtgiev+JRVRW47QQJwnsBwGw2m/THsxlxQcn33//QWn98fOq9p/f0ekkYhkLw0WiklPr3//7fC46/+Itf6vV6bSn45ORZVRW7u7tJkty+fZsmDL3zzju9Xu/NN99cL1e+kgQdJdab8/Pzfpj0RDKYHILgBv2zi7PForB+s1xACf1i6VUY9foT8BtdbqDSAdsprGnzvVSnIePRFc5WgAHANIQn/nq6Xvs8DEOBjKGXUnhnjKk5x4SzvX7Pe09SFzNE7xLBFfJnZWFWC9nv7ezsKByyqlLebvJlvlQKTYlmcYHZQlL8v2aIiMfHx7Q43vsoihaLxSAJYdv952jGUF3Xk/Fu5djp6amU0oM9Oz+uqoJz/uTJk/3DQVmSO2ClCIvcnZ19XBSVEOL09PTVV1/9tV/7tUePHlFR9r333tvZ/fJqtfBgGDfOFzIIZGAclIFwjAHnIN7/8CMK2wjhWGWWkDQt7IaOXj9gDTUe8Q+RGA/7EQkz8bnT4x8MBjqf0miJ4XDovWdcJUHsvTfAQQQx0crBtK7rl+4dfv7NLx8oVdd1pfXO3m6UJsAQAHtJDzwgguAIyDy1gDEGCAfj8WQwRKne+/DR+x98bOF0uSlOzi/Wq818Pl8ul1WlP/74HWvtj370k6Ojoze++OXLy8s/+IM/uLy8fPnll51zl5eXH3/8cZEtpmfzW7fucuBg3aDXK8tMl1tgI6nbzWaTN1xqm82G0PCs6TInj44GBTjnqAGL3GzG2Hq9NMakaRxGaja/RPRBEISh8qZABtZqrSvvrXO+qkvjHeCLXehPs8C11gy2252uZ2dnZ39/v9dLkiSSUu7v7+7t7ezuTvb3d51zRHaXZcXl5SXNFhqPx1/84hc//OC9lgSPc07l3ziOJRfD/iAKwtFg6L3/D8cnf/Ynf/rzd979O3/nN3/wg8s4Vq+/fiuO48GAf/azt/f29iK2G6cJl+LJyfH/+gf/5q2fvls7i4i9g52yLIPQBiLLV1m2KaHSzvK63gJX2p4b38AMXyjALRTRddp3ELEqNt57D96DN8YYXTEP6/U6qStrnFIqjhKrbBiGznrOxG4YHw13PPDB/v6Nm0exEKKuJdg129JWklNzfn4+nU6NMXO7JdZxzlGthHN+enpqq7LXTwhK8PnPf/7wcH8ymfzK179aaPH7v//7SqkbN44uLs6Hw+Grr756cHAw2QvKMpdSrlabQMUX56snj08Zhh+9/55z7gT8t//0T27duvX+u+88fvDxS7dvfdtml9OLs+Nnpq50VTA/RGfBmVBSuwUK4ywgIGdcCm1NZZx2VVFfjUujhXPPDOUYSVypU9wYM+wJyr/RwRp8KeeWCNyoscY5RwnYjx89I6KwNE2zzDgHk/3R7ZfvaTMdhKExRnCxLkt9eUaawhreKuM2YcgYK/KKc1GU6+/+4N0f/uQng+EOSnV+fvHFN17p9XqLxeILX/gCqcDpdHp0dJQOdv76X/sV59xisbh161YvHdw8uh2FCfiCsguMsTt37ownw9Vq3utHZHLTNCWHM01T4vpYb5ZE4Og7TadxHKdpAuCN0VIKY7S1Nk0TzplSIgil1ppxl6axUqrWJefoPUZRxBrAMHLqrUHr/ncbXwBaXnDbmcNa6zRNb9++ba0dJOHB4c54PP7KV77Chf/N3/rmq/fu0kecc++///6HH354//7909NTY8y9e/fqKpvNZuv1utdLhsMhxaLj8fhoPDk5OXnn7Z89fvDw/v37vTjZGY1tre/d/8Jffu+nVVWdna8/+ujH6/X61VdfvZzme6M1l4JJUdV1r6dee/WO8S4IguPZeW2LbLF+VizqUuf5RjqQUkp/BY1wnUb/FhvcPn0SVN0M8vPXJ5JyFjjLLHrKAjJkiLws64qr3PE4He2Nx9SqRVXxGMXNe59flBWPYyllXRWuKiS680qvVqvjk4vz8/Ozs7PT01OaX79pSKq8996zKEqLosjzShcFY9IYMxwOb9+6+8qrL00mk9dee02Euz/60U+/+MUvvPHG5/7Vv/pXHz/48Pz88sGDRz9/b71er+7dfwURnd2cnkzPTi+Gw53hoPfBBx8ESnz80QfnZyfO6vv3Pvfs6ePHjz8oy8JqN+yPB2lvPNzTla2qyoMhQRBSCkIR006SnJbyqo7ntx3PUmvNhai1tq5CZmudA8Am1y10kTIc2+UWnDxJwrUjIo0qj2TYpuAJ4Hb38eXx+UYlSRRFWVFIKY2zTHCq43u9hfVQ+asNkIRKqrKsy/KD995fTrN+bzdWYiDl++9/WJZlGIZVpXd29oIg6PUGSZIUtX399c/RJTHGOJdvvPEFxhjzudZ+NNwx2u1OdlQgqjqXEuPe+Jd/+Zdfe+010lPUcf7Vr361rHKqHO7s7AghqJ9JCHH79s0wVM6527dvuoamUymhlNrf39tkCyFYEAQqEKvVknNelu0IX6NNpXgkJY+iaJOVfyUBJqvjO9wat2/f3t3d7SfMe7+7u3v37t2XX7k1GU1u3T6w1j75+DFj7PLy8vj4+JVXXiFcqrV2MOxtslUK8eHhIRHEZVmmTdXv98n3c85JKe/fv3/nzp35fP7Tt56sVxwgPj0pHz5YnJ+fP/h4vtlstJ1RiXU0Ghlj0kGfrm0wGqL003yxXK+8R2dZbR0X2CrB1qi22A/4FBeaddoJoelbDFgMAIKhR2OtVVIqGRR5/ajarH/6LlXInXNRFFGJkXOel0Yb8ICV0XmeGVsKCYv1tmZGz32Z1VVlON8OMaJ2ZcHDXjo6PT0tC9OPR9aAs84ayPPKGsw2xc9+9u7tl4I7t19Ok+F0uozj/mfuf25nZ3zz6M7Tp6ff+ta/49i7d+9V59xkPL28yMIg3mSLb3zjG2QFiVcoDMPBYBCEXGtNES4iHh3dGPQOrLWr6TlV6cWmyJkUrq4450VRJCEi95yD4lyIK8oLJZM8z3u9yEMtJTALNHXWWWG05ZwLKZ313gG5HxqZQ6GddbUFsMaYvDJaaw4BhZdt3ezHb38w/o8/HPY+H8bRMtuA5Bo9SrEusjCO3SZjzYwc06HML8EU2XKnHytTol5zo0Pu6nzjh6lSam9vb7FYjMfjPM8JlIIiIjwQRSnUG1xVFYCTUp2enura3rp1i4Y8OFdTJrDf71NapSzLoij29vak4kT9Q/ikr33ta347/25JsOSjoyNq2aEm9TyvvvnNb4aRQPTL5TKKEsHDX/plnwRcyiAvC2dhPB6XVZXndVnlAH+1LDQ0aCHy3tfrdS+Noyiazh6HYahNOp2dTSaTi9nJaDRy3t6+fZuyOEEQHBwcECJVKQWQXl5eTqfTJ0+ePHjwgIrSw+FwXVjO+dnp6dtvv/3nf/ZndV2/8sorFxcXizKjvuvDw/EXf+l3qVUDAMpCGWNWi2WZ50+fPlVCruaLLMtW2SrGoBTSYMak1L7Oso3x3vqMkE/txDnWMIS9MAtNXl6DjroaCpsVlfeeofSI1ntErmtPNefl6SXB1CkLu0VfKu4NeMcFk8iZAeuEhdqlMq1NYQ2AAWOgckyDACaVFFmWMZTIxXAwuXF4a7XMGF46i0EQ7Ez2hIQnj59prRHhe9/73mDyn8Iw/N73frBaLb7ylV/8zd/6rTRNwzA4PV7/8R/9xyLzr7z82ksv3XEOAjn4+OMHUXSbEsP7+/sPHz7s9/vgbVlk81lW1yZJkiKviqLSJcznSyFE4JUUYYCReO0zR7qudVnUOg/QCr7lYa3KOm/wNIgooYeIqyLjjDldgfdUgJNxLGNCeJeBBCUQwIKAvgqttYHi9FxRMo4uDkTutQctI3AWjDMMhUe2XOfrwKHOOZeokTEGBtCnVQb1FQcSGgPec8aYQBGURnFVbWzlOUP20bzismLc364Ka/XjD99njOWLmRCCIO+bwhHGYGEMAe6z+TyKos++dv+tt946Pj7e29ur8hUDyDe5917rrLe7i4i6LLn31XJJPQARB5uvUWuwtig2ZEAk56GXH//sgzAMDw4OhEZExjE4e3gcBnFPxsIJ7/1QClMaD+bO7hG1whSqCMMwu1gKISCrQHvXYU5r417vvcArpgvsUKvEUZNsCwLroUTmNhkiHu3vKKVCK5XmrPCHwwMABHQggJd2NwzPGf93v/f/6kUxB9ys17/9f/jdzc+f/uQvvq3CkHN+fHrCBE/TtFB8s1rP53ProM5K7/27P/u5tfbs6TEAA84YCqGCKI7TtBfE0a07t6ngFEXRnbuHURRF0X0pJRV4W4B0VVWLxWK9Xp/NzuimLi4uqGXCOTedToErQju2taUtfgOM8wj+iqWIWcYYy1YrKaXjnAAYy5WjXKzEMBn0kmG/LMteMx2bvOLW7DvnnGvqyYAsUN575qxESAJVIwCAiIR0QoS8qqqTi+PBaf+Vz7z8W//1b/7kw5+v1+t79+599rOfLYri3/ybf3N2dvYP/sE/ODg4GPT6/7//z//3e9//zr1XX4rjWEWBBZjXZ4P9+HNvfvatt396fnrmjHn80ftf+oVfyPKl1jVyZ4x59d6t+SJ7+uQUmUowH493hv1Y7k4A0Don5EBrPUniy8vLx08eixs3blpTO+eYd4jI8KqzhHQV3djycta6MR2HB1b5vFWQ0Mm1rLJV22mBiM57a7xzTsUx9f2AZ4hMCKVkKKU0fIOdQezNsrqevJoE5URL1KIx9iTJnHHnwVljjDO6CoNhr9ebTCbExUH3YowZTrbYoLIsq0oAAOGBnLNSijiOlJLOWa11UeRkzYjQi5wFaoEMAgWzK7ZRyt5Twq8oqrIsAHyWbdpVyrJNHMeAzjptra31FprnPDebWkoZxUGvly6XS+9dEEophW0oflpZ3crwdkwEtLLNt5t1jYhJEkVRFMehlFwIhoiXl5eUS4MOOYb33lpd5sXl2fnZ8UmV5Qq5t05X1dv/3fuL1XK5XEZJXBl9cXGhgmBTFqKXZllWZnmZF8YY8B6dd84FgXTInHOlLur1xl1cUFL+z7795+QH8obUgtaHahmtbFObDmNMRleMFsTWFARBlmVJOthK1PXBN3m+br1Ckna6wcuLi9Y7q+uaqDnCMPRdylgAaAbwtmvYPZu1NlBx2+Sgtd6GElp7MLUQSGT/3jtrdV3nWWYvV8qYsLB8VSYeXh7ty02dP7tYMFHP1ztx7xc/8/mxjKYfP4mSxFp79uc//FK496qJnpxdPvnZD06PT1bT6UcXuT4aIrNBFHhva+NOzy4//uhxbUyxWY3H41u3bt28eTNJIu89ei+4eHJyuVgsfv7z90QQpeAsoufIkHkO2GYU2j2EiPr2snVmWuny3m+y7dxkWpe2nSgJEtHwpzjnqIOkru1qeUkAMYaCMe6VYj5ioJL4gnV6oLdCAk60ZEWMEcxwW+iPIs65ZIIz5pw1xpsajGW1MZXWZV0rrR2lOgAcQK+XtK4XaSXc8jZXcRweHOzt7e2Nx2MKtikjoLUmuiZ6tE07O7blaNvQ61aVLsvcOeO9pciCdpVS4unTJ7KZ0U4mBZr+bxrpIKU4OzslmhHOufYIHSPMW5JEuBoX0lokY4yCLXMyoYV5O37SZKaZBthKCGOsrIsgCJI4DpWUGGfrzXI+z7Ps0ePHk8kkiKOyzFarVaGrJFLoDcwXdV2jhziKAACdt9oYYzJTeIfGmEobbb0Kg16aJEkSpkmWZWVZSkRCUxdFsVitTMOWDgBEwU9CwuR2g7Xte0EQlGU5Ge5CUxxuu+G997t7Y9Zp6KdiHnB+eHhI8NUulJVmL5NAtijoFqfdlkh9M/JOa82ZIkCLlHKz2Tx69Ojhw4fz+Xw9n3IHcRxrGQRCZYtVvlyvpvPidKq1flja2YOnUsp8uYRl/sEPfnL+7W+DtspCXwRv//F/PPnxz9M05VKMaugX/sH3flJU1Y393b1X+8XN8rvf/957D94DNHEaMQbARVGa6XQByNe1FLW8XOn84xPJPQdv6qooijd/8Re/dP/N6VKLMOmDs1sMt/NVVQFwDww5Uo5hu3/qbU+zpwGVzdFXQZt14Nv58YiItqjaMil1F1ERfDrbjo2iQ4qAFPbemJPAk8z7Bp1D7Li0ptQxR9r04WxujK9KW+ZVmWeuzo0uras+fPguVVNI8beXZM2LqUw+/9nXiM2jKLPVeqssAhYwxoytkfkwUowDY8SDVe7uHDbwfUfQPzrVzu447cWU62qzdNZazrZEEHVdbzab1WpFFZp2XTlHolkIghgRhZCt4fXeO6e1cd77NE0BPTKyAcx7bgyzlodMTKfT+WIOAGma0u7PsszovJsBap9L2I85+srooizXq9X8cjqdTjer9Wg0kv0EOS+yTc1BDVIRhQbAl1dWDhE5R8mFcy6J+sa4vCpxk/uydgjGOe2dCoO8LKx3AkEoqYKg0rX1DjlDzoBmrIKn1ksASIMImxl8LYFjXdfz8/dbj0M0Y8e891qXrCFJbvFYjDERBfTxtoec/otyqC2uod2r1Hy2fQYNRYZzLlDbzhAquCwWC2rUi7gAqRTjyJzXZjmdZVl28uRpn6k8z2fTKel96lKcTacavSvrg95oNNrdZJfZ0/PxcDQejwvmB6PhUpciDXdeuT3c2zHg352frH/4Q2tNUZXOOWDMg9S1ZZzf+cwv3D66Kbg/efLIFJtxP9a1OT4+mf/5Dw4Onr77/hPhgCOgB+698eBZkLaJTb8dPw7oENgAaDeQF9FsMonrNk4DmggGAAAqiFjD6CtVGMXpYDh2zt28zZutabrG4WAck4mjfhre0HxCQ/OPDftBWZZV5W8c7VaVyTbVZpUrAZVixkjnzbjPsUN00Dj8bnqRk2fV6n568LPLk6qqXDOCnTaBlHJnZ4fYQqjTgFphNptNrzdsz99y6zDGtJHGGOe091umWErRJUnahqzIbBgJIWPnQsbYZrNZrqbIbFltGIfhKJVSFtV25ku7CLRTabhU6xO5hkzX5CvvnTGElYe6rqh2HQZX1E2u6cUDgNH+xFr75OT4+NmzxWIBAJJxL3C2Xq6KjHGutQaGIlCrIqu1HquU1s1pgx5EGxcxCYwzwWUUasC6rlf5ZlPmDGiIqQKAxWJBAkNNs5QI7OaiGGNFuWENUzRFK6SgAxG0Ikp3Sn8ydkXH3T2aqbyeyGjpqWVZNp1OieQoyzJyc6ghnnXagNsogzEmeNCm02idtzpCCuqNoTQ4gREZY2sOFVjh0YB1WlccrLVel9Kj9JjneR7kfRV642ytJePLzTJOE6WUA5guFzYQGpyKQ2wmJwJAbUytoSzLvFhN3Qfr9VqAvzx9xr12N/YlQ6Pd93/0Vpp+bK0Vy6xEZxkgF8gBHUGOEZAhdNL3Or+S0q4J9V7Diw4NFhygR7RbAlQuuWQMgTo2jDHGOgPghGCccwMSvRBeCC8YSMAtT+dquSFTKYSQUjDGUAjuA2c9k5AkqRJ93dNGl95Wzlunz9uN3jr83vvJ6Mg0E2Vb3wwR63KLu7AN9SwAUCegsbV1WpsKyHcEa50+OXnayXxcVdrKKvfNwItuFiqO0vadZBDIPeOcE6HU4eFBEKhnz559/PFH5+fneV5QWyXNp5QN0jDupXSR9HV0tcYYqGrG2GAwIG+ZQhjOuXFXmPbthUkphLi8vHTOzefzy+mURrEgojFmlPSo+mq98wyRMWqiLFgGAAyo1oraarAanS8zzVAgZ4jcIxNRwJlAzlx9bT94AA8eEKIk7kYQHqHStbVWyW2rkBCCNClpWIveee+8J4OBiBQKWZqNhAwZItvS4nPOi822E5ssIfnqtF3Jk7cNdSlVSaibdbuGAOT9CSHCICEixzYwpGSHq7dM2t57wRCddeDB2QqcRSfQoeLOYe4NchRCmsooIS3H3JtYcR7IDXeXOpdlPX966gUzDBbTmYjDVZE9e/bMOet8DRAyBlygRGQctCmXxw/y+UkgBUMfKnF2diIFM84QMDtJIhGEKTjL0XPOObJVXm33H7Ku1yGjK8rPrmBL7L9QgBm/2j0WwHlkjqFHjgEAOM8tMOsZMg/IgLFpBpw7UVRSOs63bhIAeIdNyKqF0Iwx2seYDJxlAN4z45kGhs6hAy1EqrWmNq6OIEGWrznnUomAXZGVAoDkUXsvvlOEpHiJbJe1tp1mQCFum1ZpjUNV6cbaXzGeAcB8vqzruihqIs2GJgtFdr6qqqfPHm82G8Lo5XkumOx6ItiMICiNble+a5mhqtrEgemwN6G4uql2gyqlvNVCiDiK7t+7ZxrEmHY2Bl5VFReirCtjjEMoyjIIAqK8EUJwZOi91Vsl2E8HxrhK18ZYhx48Og/eYxwE2ylzjJETQXkEumbeDFhARE7PcTugeEuIH4bh1n0118rC2NSNZNPssV157413zgHF//R+rTURbpNGWCwW1IrcfrD9rxY9YprZRYP+mGhJ2lEbFI7lxRqVaFMS1jlDQyqZ9AhFVVI07qwTQijBgbHKQykx487anAEyKI8vsl5pGWManPZu/tFqvl4xwYMoHAzTonSA1lhjrBcyDEOpAjbgxpmVdYxJWZWYbebeW8bYePfm2dlZXSkRhTGC996CdR5s2tsKZFd/O+e5Auc8Ou+3BPMNMsZcTRvpHrTQpD7pJMZ78FBbBHQAzDnuvGUeEQWiCKIUER1i5cFr3yqLQG1nzCAic5S94M65clo7563x3jn0BsExzpGD1xF6JXmE4irH6Jzj4XaKFLnuvkliWbMNR1vd1BpwCr+t1c65uvZao7U2ipX33tq6630JjlLGrbXnHTrC3d1d15D9du02xQ6z2ezWrVtlWY5GA/JMdeXa5Ao28westXUDrYHrLvRmOm3PT0JCAkYDU0lo2/SvlBKMrusanQ/iSCkFiEEQhEnMSk15pqqutTUWPDnYlfcU6uebrC4qqw0iouCbPHMOvPfIpRQKOLPWWWMqVyEipX9JO2Az/8E3U6NcgyTnnIO13ntKGtN4CgL/GM9aF7ob9HLTZclwiMgBAQEbIRQNxzp9b/ulvJlTRef0nf7+drdbay8uLoivq9frYcM67r1XQbC9Bu8J0huEISIyD5Qzo7vePghkJsDNOuNCSFdP52uPgFIUdSUDkZfF5WzWG/QN+kW+DJM48vrGus6yTVhLAPAMQwDna2O0qzecc2cwLx0g50oisErXTx59HIYheoP/5//h/93VcBSlwCd6jlEq6OR+Wu8R2dVk9O4boFF4rRHYWmZ2VRbCTp0Tm8xwm/pq9yjtQmywHK1pbS+yfadzDtA896VbsXQv7vLpOtvdS0L74tYCxOyFrzNvPvmlAKA4677Svm5d2Zr6br0NTfji62SbF77udd0o2athCIhYlwVtWRJg3nTegd36zNRSIpspExpcG0e0u7bZ1VtD3dor770AdB2sReu5GLg2i6T9xWjXXmd7AEDdJNu6m8p7H0nevtjmjb33dSnaNcTO4aV97gnS74mK2y81zeGckyK8to2bw1n23Jk/ube7d9119Fr7jIh1lbXpt9ZZc87VeXb//v2vf/3r//bf/tvpdEpJL2utk9ieExroodZasH67kt0FbxdhO+SqPURnwlL3dYfXQuKrRWS++07qNQAAdt0mt8vK+dWD6Vok2ZFYay1VDgn56L2n9AZ9nPIftjP0Ca+VCq96zbriZD8lVuf805oHXsxNhV698HXYPvj2S6/NdG3/q30DgkJABAaegefeMQcMERFe/L3evfg6PUqyP5xDm2ZnjCVRShk4yoe1pkaXBUksuYi8oc44ubykaY80crkpwCFj20osubjt97KrnXwtLcLUlhLoua3W1refO1yj+J7boNxvu5Se27VGu/b9XQHT8GJF7Kottre7u7z3eV62X9p+BBE5U1d7fvvVTecTAjLfZnDZtt5Jrhz9A9KTiDAcTtrL8J1QqxcHXLBNth4M+71+2lZV1lXR3iZ06HKLjO4XvAfGcNuu4X0YbtdZUEzvmswe2bpPCnDtrtTDlZlCZPxaWRibioWvrgl8+4ttwNKdNDMAgDVV0FFg7XmAs7qutbEAIKUUzfBFY670Dp2QSizWmCu5hasNhEy8cAPhdUVzdfgXv472U97PmxiVLqb5XuevvAzo6BRgCIge0QGCZ84hekRE5l/czYAseOHrDK80BXaSZ2HAm4yacI4bw2gKFA1vBkAmlWfWE8jBX3uI3VUlAl3KiHSfPrNXb+tegOs81+4te3H12XYzAEBebJov8q1yBwD01/dHe06ou1fYnk25Fz8XmVwpvvYGnXPgr1L07XkAgHPZSq/toCGe01NteZJotNn1AxEFfw7ytD1CFTDGNsVqvDvs9/skd2VZzjdF922tK1QUNVzXhr5hZabLFtQ22Oozzq/FtO0dGv3iqWoIjDPO0AMH6HjIld52hHUzYQDA+POPvPm/LZshdMoGAKCCUMg6CLdABcYY4Su4kO3SQKfU6RrE0nMbiH+KQHa34LXfX/huAM9evLGYE7QkvjGz9KeD69qkPTGy1so7jwhb0yvgxQIsPgUjjaKNDz0AecgewINDRM+YY+wqFvDeh9Ji4+yYZqyUtZbacp0F79A7BM/AewSompI150hPkr7IW7r2K8fPb19+PvSgNzAU0Bnh1f7kTD73zu3hruSzezYuIn/dVm/3lbx2hivxNqard1oJwQ5DZfd73YuXv3PCjrgiYpZd6c0uDoIcO389LYqISoZ1XRtXJ72oP0wJcGJc3ev12sWBzmC3T64YXNc7oi2rdIWzvdvm2WxHS3S/oLkxaC6et34FABgl8UVHOy6xFVE60qjfuW4PwMjxa7mIySEEgKIoKLXR1cTtgnb1ZfdxOvspGr27QHj1wU9pywUGL7bkHq/a3K497yuOq2uuNeDVrvH+6mWPn2KB3ad5EMx775FR7b7drFTz4Jwz5rsbq6zKrppvkXMkEa0+pF0AnR3/3E5i9pqAtffLAvmJK0REZPiC2BgAnPPdd7a/C3EV63YXVvBrsWv7KS7L/8xG725v+mDblth9EQCs0/CcabnemQykWzwhj5ELjwiIjnNkzDFGFgskV+1NkStE8bB3SAUzACBUAue8LEsvYmAIvumOBOBSOOfaYWjP6aZW0LbTKNpEXyvP7bu3iwWf4gp2LN5V7cf7IIy7C3e1gsgBERCRIcNOkQAYInIhuFDQ0XNlWRJnN9amKGvcjl9y2rjuZTjvkYjOnH3uVpsH/ymCBxbo/dsntBWzT5Ng92I9AOxKQVwT1DZ0fV6nwPPkddvr9C/+Xvspltl3UgGsE4PIziYERCqnevC1Bdv0HFsLtQVjAYBBZ4NCZwMEKoLrotX4F1fKAj4hMN2TkAHwnaRA640DbNf+k/ukrE2DGGruFDwAGEeK+6qdEAER0ZiyeYxXSgcAFFdwfT9TPkcI372p9iOSXfWfQ+d54XWKH/oFEduQ8wr0SpkI4N192IqV1sgYsw618WZT0HNwnhlnW13DERhjnqEHRJDNbV472qqYMMa0jqtpAsjntK/33nPxnIluVpwYEtBa75rZrQAg5fMC4z0gYl1fxa7YAWBabxiiEM0gdipCWLsptgikphq8PYy/hvUHaOpbQFigjkVFAAD2KTEwte/56wcAuI6rf+34xMT07dfzKwHoPnv2nHlvrs2D674ZYPuI3P8OV797tAoLEdE75hkjiDFz3l3dztXHjTNm2xXknNO1dc4zxtAzICnzJBLN6sir6erby6Tnbq6Nz92KLkJlK+iILraNu5/iWeRZ1b6zW6GQYQSdnda+3nhY2/W6MgAmhhc9RwZXnpHfpk7Be2+dB/DtFfp2SiDznzy6T+Fqtbcxs2sE2HUFWAnexhcAHsAiOkS0oIypOecOhLOO06RVFVtn8LpXT78TtPaTBzYGRmRZppqRrTRzAF4kwIxx38lRdSJb4Rqy3O6CsuuWpz2IVcs2PWK2mdHW721p8XynjuobzGMXHd2+gh2Pvc1PBMHzMRX9NNWLy0KUBPrkM2P8xRvOuRdns69MzPO+Mi331RBq2JYfri1LKzGfNpnBfYoAdJLB13xa48tWirobCxh6hPYfURcxwbtJ+vZqr+6llYemT4OC7OduwTdVDLhuVAGA4XPte9vn1U63eE6GN2XdCAbvXD5ql3V3FGMMARFQYOi32QcHW0QvPdl2J4NzvklLObYtQzLwlHd04AG8N2YLyKF7ajVF+2wRoXvBVOhmjHnvOG+nGUPtrsXeiEiXJENZFCURwiI6xpgxZRhG2noaJwIA22cE4Lx39upmuwtumtdFHAUkD1GosJ8SEo2Yh9rsNF1oO65Oa+2MFkJIIa3VzjtrLVzPQhelbnNRrKGPEkKs50VrdQVr4iLvl7N5u3BdpStFwJBx5IDAwVmwxpi61oLJbnqMxrF67y36VtS3MkU+lrxW32ujhnaWT7tH6afrAG59xwUQLOnu2nYzcXktwLs6G3Sy4p3zf1rym33CtW4e2ItfF4ySy+jhWllF8aSREmudZQAMGGOMe8vY9iFaq503gGBsbdu6N9sWStrgBPH5Ypv3DhT4a1prewT8Ksm0jeiQIaJ3DAEQAZm/drImtGlXm37pqxdPqOAubT2Ero5wuI2BgbdSCwBAjb6wfS4MPWfeMwBtCoDOWnNGvpjw/IXPEUwNALTjul9NQk7/nNsqDkQQ0ZW/AJ0cDeM+TgKttbPOew8WuIBNtizNVazePXwnJOmutTPbaxe26VCjDxCmhDVQb7geIbevt4ko8mpZ03Havt5y4rWefTex5prG7nYhCLx6fZd4aHz9rnom4W+HmFwLPBhzfnvN0JEubEA57eU15Eae0vHPyTA0nMNdgUeyS9Y8d+auWD4vvf5qi3ff9l/0wOu/b/+1N+i8A/Db1LH3zuh2HTrX/Fdk4vr0ozFBV9L4X/bGu2aNTosdYOwnv+uTz2X7v5+Sa2itaJsno1/Q0Zei9zQLiq4CqWrW7sN2PxMUvb1mZ7f4LQOOUv3gGWElkHkEzlz9SekFAGQvdqF9E2qJqiq6H9D6qvDtm3bqVj6xk7GgPCfiVex+7YONr9WueGPHTHtX3duezdbdFh9yCgBAqZAgKc5ZAN8A35yUwm07cnWrIwAgSOIX7pWiKLrf2yKTuom37rNvNdonN1C7Qf/zGwW6oe+LvuKvdHzySv7zR1sn9I0/uYW+bLOvL4jr/oscn5b1xb86VdALj+eeF1ytKj0Reg07/7v92f0Hn76enItmD9vm4nHrTGyzryTLSPU61uRccZuaZR4AEY3rJmgBtg49ABkwuBJ6AGDeS99I7HUB/rTczVUZyVynI+ZctivVVaWt5WzNEf1J6f6uq0ACXzdNEduAlqqTHqzdxhitAG/daSG6SPfWZVUKjKnrunLOcc6t3TIYNvPmPOfYWksAaIFlcF1g2mD7Oalzn1L4a2fMPieT3X353Eeek4TnxPX/XxaYkk9AW/XKAjunuw4EBcvQWXnSY257zeg+DUDyV7ycVvG1Oq4R4Gsx819VH7UHa4Z0w/UlbSxSa/Dhufc8p1gRP6WcAIxkjeSVMaSlY2xrwK6ZR0QSsK7s0VksMARkyLCjcbwnX4cRXNFf6QIQ8ip30P35afuzBSb9b8gq51UCfO4cAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display cropped image.\n", + "PIL.Image.fromarray(distorted_random_crop(img_array).numpy())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Apply all transformations to an image.\n", + "# That is a common image augmentation technique for image datasets, such as ImageNet.\n", + "def transform_image(image):\n", + " image = distorted_random_crop(image)\n", + " image = random_flip_left_right(image)\n", + " image = random_contrast(image)\n", + " image = random_brightness(image)\n", + " image = random_hue(image)\n", + " image = random_saturation(image)\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display fully pre-processed image.\n", + "transformed_img = transform_image(img_array).numpy()\n", + "PIL.Image.fromarray(transformed_img)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Resize transformed image to a 256x256px square image, ready for training.\n", + "def resize_image(image):\n", + " image = tf.image.resize(image, size=(256, 256), preserve_aspect_ratio=False)\n", + " image = tf.cast(image, tf.uint8)\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display resized image.\n", + "PIL.Image.fromarray(resize_image(transformed_img).numpy())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb b/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb new file mode 100644 index 00000000..b93fde65 --- /dev/null +++ b/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb @@ -0,0 +1,530 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and parse data with TensorFlow 2.0 (tf.data)\n", + "\n", + "A TensorFlow 2.0 example to build input pipelines for loading data efficiently.\n", + "\n", + "\n", + "- Numpy Arrays\n", + "- Images\n", + "- CSV file\n", + "- Custom data from a Generator\n", + "\n", + "For more information about creating and loading TensorFlow's `TFRecords` data format, see: [tfrecords.ipynb](tfrecords.ipynb)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import numpy as np\n", + "import random\n", + "import requests\n", + "import string\n", + "import tarfile\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Numpy Arrays\n", + "\n", + "Build a data pipeline over numpy arrays." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a toy dataset (even and odd numbers, with respective labels of 0 and 1).\n", + "evens = np.arange(0, 100, step=2, dtype=np.int32)\n", + "evens_label = np.zeros(50, dtype=np.int32)\n", + "odds = np.arange(1, 100, step=2, dtype=np.int32)\n", + "odds_label = np.ones(50, dtype=np.int32)\n", + "# Concatenate arrays\n", + "features = np.concatenate([evens, odds])\n", + "labels = np.concatenate([evens_label, odds_label])\n", + "\n", + "# Load a numpy array using tf data api with `from_tensor_slices`.\n", + "data = tf.data.Dataset.from_tensor_slices((features, labels))\n", + "# Refill data indefinitely. \n", + "data = data.repeat()\n", + "# Shuffle data.\n", + "data = data.shuffle(buffer_size=100)\n", + "# Batch data (aggregate records together).\n", + "data = data.batch(batch_size=4)\n", + "# Prefetch batch (pre-load batch for faster consumption).\n", + "data = data.prefetch(buffer_size=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor([ 9 94 29 85], shape=(4,), dtype=int32) tf.Tensor([1 0 1 1], shape=(4,), dtype=int32)\n", + "tf.Tensor([68 57 88 41], shape=(4,), dtype=int32) tf.Tensor([0 1 0 1], shape=(4,), dtype=int32)\n", + "tf.Tensor([51 19 18 56], shape=(4,), dtype=int32) tf.Tensor([1 1 0 0], shape=(4,), dtype=int32)\n", + "tf.Tensor([70 84 99 32], shape=(4,), dtype=int32) tf.Tensor([0 0 1 0], shape=(4,), dtype=int32)\n", + "tf.Tensor([40 0 25 28], shape=(4,), dtype=int32) tf.Tensor([0 0 1 0], shape=(4,), dtype=int32)\n" + ] + } + ], + "source": [ + "for batch_x, batch_y in data.take(5):\n", + " print(batch_x, batch_y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor([ 9 94 29 85], shape=(4,), dtype=int32) tf.Tensor([1 0 1 1], shape=(4,), dtype=int32)\n", + "tf.Tensor([68 57 88 41], shape=(4,), dtype=int32) tf.Tensor([0 1 0 1], shape=(4,), dtype=int32)\n", + "tf.Tensor([51 19 18 56], shape=(4,), dtype=int32) tf.Tensor([1 1 0 0], shape=(4,), dtype=int32)\n", + "tf.Tensor([70 84 99 32], shape=(4,), dtype=int32) tf.Tensor([0 0 1 0], shape=(4,), dtype=int32)\n", + "tf.Tensor([40 0 25 28], shape=(4,), dtype=int32) tf.Tensor([0 0 1 0], shape=(4,), dtype=int32)\n", + "tf.Tensor([20 38 22 79], shape=(4,), dtype=int32) tf.Tensor([0 0 0 1], shape=(4,), dtype=int32)\n", + "tf.Tensor([20 22 96 27], shape=(4,), dtype=int32) tf.Tensor([0 0 0 1], shape=(4,), dtype=int32)\n", + "tf.Tensor([34 58 86 67], shape=(4,), dtype=int32) tf.Tensor([0 0 0 1], shape=(4,), dtype=int32)\n", + "tf.Tensor([ 2 98 24 21], shape=(4,), dtype=int32) tf.Tensor([0 0 0 1], shape=(4,), dtype=int32)\n", + "tf.Tensor([16 45 18 35], shape=(4,), dtype=int32) tf.Tensor([0 1 0 1], shape=(4,), dtype=int32)\n" + ] + } + ], + "source": [ + "# Note: If you are planning on calling multiple time,\n", + "# you can user the iterator way:\n", + "ite_data = iter(data)\n", + "for i in range(5):\n", + " batch_x, batch_y = next(ite_data)\n", + " print(batch_x, batch_y)\n", + "\n", + "for i in range(5):\n", + " batch_x, batch_y = next(ite_data)\n", + " print(batch_x, batch_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load CSV files\n", + "\n", + "Build a data pipeline from features stored in a CSV file. For this example, Titanic dataset will be used as a toy dataset stored in CSV format." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Titanic Dataset\n", + "\n", + "\n", + "\n", + "survived|pclass|name|sex|age|sibsp|parch|ticket|fare\n", + "--------|------|----|---|---|-----|-----|------|----\n", + "1|1|\"Allen, Miss. Elisabeth Walton\"|female|29|0|0|24160|211.3375\n", + "1|1|\"Allison, Master. Hudson Trevor\"|male|0.9167|1|2|113781|151.5500\n", + "0|1|\"Allison, Miss. Helen Loraine\"|female|2|1|2|113781|151.5500\n", + "0|1|\"Allison, Mr. Hudson Joshua Creighton\"|male|30|1|2|113781|151.5500\n", + "...|...|...|...|...|...|...|...|..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Titanic dataset (in csv format).\n", + "d = requests.get(\"https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/titanic_dataset.csv\")\n", + "with open(\"titanic_dataset.csv\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Titanic dataset.\n", + "# Original features: survived,pclass,name,sex,age,sibsp,parch,ticket,fare\n", + "# Select specific columns: survived,pclass,name,sex,age,fare\n", + "column_to_use = [0, 1, 2, 3, 4, 8]\n", + "record_defaults = [tf.int32, tf.int32, tf.string, tf.string, tf.float32, tf.float32]\n", + "\n", + "# Load the whole dataset file, and slice each line.\n", + "data = tf.data.experimental.CsvDataset(\"titanic_dataset.csv\", record_defaults, header=True, select_cols=column_to_use)\n", + "# Refill data indefinitely.\n", + "data = data.repeat()\n", + "# Shuffle data.\n", + "data = data.shuffle(buffer_size=1000)\n", + "# Batch data (aggregate records together).\n", + "data = data.batch(batch_size=2)\n", + "# Prefetch batch (pre-load batch for faster consumption).\n", + "data = data.prefetch(buffer_size=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 1]\n", + "[2 2]\n", + "['Richards, Master. George Sibley' 'Rugg, Miss. Emily']\n", + "['male' 'female']\n", + "[ 0.8333 21. ]\n", + "[18.75 10.5 ]\n" + ] + } + ], + "source": [ + "for survived, pclass, name, sex, age, fare in data.take(1):\n", + " print(survived.numpy())\n", + " print(pclass.numpy())\n", + " print(name.numpy())\n", + " print(sex.numpy())\n", + " print(age.numpy())\n", + " print(fare.numpy())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Images\n", + "\n", + "Build a data pipeline by loading images from disk. For this example, Oxford Flowers dataset will be used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Oxford 17 flowers dataset\n", + "d = requests.get(\"http://www.robots.ox.ac.uk/~vgg/data/flowers/17/17flowers.tgz\")\n", + "with open(\"17flowers.tgz\", \"wb\") as f:\n", + " f.write(d.content)\n", + "# Extract archive.\n", + "with tarfile.open(\"17flowers.tgz\") as t:\n", + " t.extractall()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with open('jpg/dataset.csv', 'w') as f:\n", + " c = 0\n", + " for i in range(1360):\n", + " f.write(\"jpg/image_%04i.jpg,%i\\n\" % (i+1, c))\n", + " if (i+1) % 80 == 0:\n", + " c += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Images\n", + "with open(\"jpg/dataset.csv\") as f:\n", + " dataset_file = f.read().splitlines()\n", + "\n", + "# Load the whole dataset file, and slice each line.\n", + "data = tf.data.Dataset.from_tensor_slices(dataset_file)\n", + "# Refill data indefinitely.\n", + "data = data.repeat()\n", + "# Shuffle data.\n", + "data = data.shuffle(buffer_size=1000)\n", + "\n", + "# Load and pre-process images.\n", + "def load_image(path):\n", + " # Read image from path.\n", + " image = tf.io.read_file(path)\n", + " # Decode the jpeg image to array [0, 255].\n", + " image = tf.image.decode_jpeg(image)\n", + " # Resize images to a common size of 256x256.\n", + " image = tf.image.resize(image, [256, 256])\n", + " # Rescale values to [-1, 1].\n", + " image = 1. - image / 127.5\n", + " return image\n", + "# Decode each line from the dataset file.\n", + "def parse_records(line):\n", + " # File is in csv format: \"image_path,label_id\".\n", + " # TensorFlow requires a default value, but it will never be used.\n", + " image_path, image_label = tf.io.decode_csv(line, [\"\", 0])\n", + " # Apply the function to load images.\n", + " image = load_image(image_path)\n", + " return image, image_label\n", + "# Use 'map' to apply the above functions in parallel.\n", + "data = data.map(parse_records, num_parallel_calls=4)\n", + "\n", + "# Batch data (aggregate images-array together).\n", + "data = data.batch(batch_size=2)\n", + "# Prefetch batch (pre-load batch for faster consumption).\n", + "data = data.prefetch(buffer_size=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[[[-0.90260804 -0.9550551 -0.9444355 ]\n", + " [-0.9538603 -0.9715073 -0.9136642 ]\n", + " [-0.41687727 -0.37570083 -0.25462234]\n", + " ...\n", + " [ 0.4617647 0.422549 0.3754902 ]\n", + " [ 0.4934436 0.45422792 0.4071691 ]\n", + " [ 0.5530829 0.5138672 0.46680838]]\n", + "\n", + " [[-0.9301815 -0.98563874 -0.9595933 ]\n", + " [-0.9379289 -0.95557594 -0.89773285]\n", + " [-0.68581116 -0.6446346 -0.5305033 ]\n", + " ...\n", + " [ 0.46960783 0.43039215 0.38333333]\n", + " [ 0.5009191 0.46170342 0.4146446 ]\n", + " [ 0.56071925 0.52150357 0.4744447 ]]\n", + "\n", + " [[-0.9480392 -0.9862745 -0.96889937]\n", + " [-0.93367803 -0.9485103 -0.8916054 ]\n", + " [-0.9224341 -0.9033165 -0.7915518 ]\n", + " ...\n", + " [ 0.48045343 0.44123775 0.39417893]\n", + " [ 0.51623774 0.47702205 0.42996323]\n", + " [ 0.5740809 0.5348652 0.48780638]]\n", + "\n", + " ...\n", + "\n", + " [[ 0.0824219 0.37201285 0.5615885 ]\n", + " [ 0.09744179 0.3858226 0.57758886]\n", + " [ 0.1170305 0.4023859 0.59906554]\n", + " ...\n", + " [ 0.02599955 0.65661 0.7460593 ]\n", + " [-0.0751493 0.6735256 0.7022212 ]\n", + " [-0.06794965 0.73861444 0.7482958 ]]\n", + "\n", + " [[ 0.10942864 0.39136028 0.5135914 ]\n", + " [ 0.18471968 0.4658088 0.5954542 ]\n", + " [ 0.21578586 0.4813496 0.6320619 ]\n", + " ...\n", + " [ 0.22432214 0.676777 0.8324946 ]\n", + " [ 0.10089612 0.73174024 0.7959444 ]\n", + " [ 0.00907248 0.74025357 0.7495098 ]]\n", + "\n", + " [[ 0.15197992 0.43433285 0.54413676]\n", + " [ 0.20049018 0.48284316 0.60343134]\n", + " [ 0.2664752 0.5252987 0.6713772 ]\n", + " ...\n", + " [ 0.24040669 0.6644263 0.8296224 ]\n", + " [ 0.10060894 0.7192364 0.78786385]\n", + " [ 0.05363435 0.77765393 0.78206575]]]\n", + "\n", + "\n", + " [[[-0.49571514 -0.2133621 0.6807555 ]\n", + " [-0.52243936 -0.2322433 0.66971743]\n", + " [-0.5502666 -0.24438429 0.6732628 ]\n", + " ...\n", + " [-0.61084557 -0.22653186 0.7019608 ]\n", + " [-0.60784316 -0.21568632 0.65843004]\n", + " [-0.6197916 -0.22585356 0.6411722 ]]\n", + "\n", + " [[-0.5225973 -0.24024439 0.6538732 ]\n", + " [-0.54144406 -0.26501226 0.64094764]\n", + " [-0.56139374 -0.27119768 0.6341878 ]\n", + " ...\n", + " [-0.6186887 -0.22824419 0.67053366]\n", + " [-0.59662986 -0.22015929 0.6358456 ]\n", + " [-0.6119485 -0.23387194 0.6130515 ]]\n", + "\n", + " [[-0.54999995 -0.26764703 0.61539805]\n", + " [-0.56739867 -0.28504562 0.6056473 ]\n", + " [-0.58733106 -0.297135 0.5988358 ]\n", + " ...\n", + " [-0.62097263 -0.22653186 0.62466395]\n", + " [-0.60171235 -0.21739864 0.5984136 ]\n", + " [-0.614951 -0.23063731 0.579271 ]]\n", + "\n", + " ...\n", + "\n", + " [[-0.49420047 -0.25567698 -0.29812205]\n", + " [-0.5336498 -0.31243873 -0.34749448]\n", + " [-0.5600954 -0.35433567 -0.38869584]\n", + " ...\n", + " [ 0.4558211 0.22837007 0.47150737]\n", + " [ 0.49019605 0.24705881 0.4980392 ]\n", + " [ 0.5021446 0.25900733 0.5099877 ]]\n", + "\n", + " [[-0.50617576 -0.29696214 -0.31009734]\n", + " [-0.47532892 -0.28324962 -0.28901553]\n", + " [-0.45759463 -0.28628123 -0.28675795]\n", + " ...\n", + " [ 0.46366423 0.2362132 0.4793505 ]\n", + " [ 0.4980392 0.25490195 0.5058824 ]\n", + " [ 0.5099877 0.26685047 0.51783085]]\n", + "\n", + " [[-0.45882356 -0.254902 -0.26274514]\n", + " [-0.4185791 -0.23034382 -0.23034382]\n", + " [-0.37365198 -0.21194851 -0.20410538]\n", + " ...\n", + " [ 0.46366423 0.2362132 0.4793505 ]\n", + " [ 0.4980392 0.25490195 0.5058824 ]\n", + " [ 0.5099877 0.26685047 0.51783085]]]], shape=(2, 256, 256, 3), dtype=float32) tf.Tensor([8 8], shape=(2,), dtype=int32)\n" + ] + } + ], + "source": [ + "for batch_x, batch_y in data.take(1):\n", + " print(batch_x, batch_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data from a Generator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dummy generator.\n", + "def generate_features():\n", + " # Function to generate a random string.\n", + " def random_string(length):\n", + " return ''.join(random.choice(string.ascii_letters) for m in xrange(length))\n", + " # Return a random string, a random vector, and a random int.\n", + " yield random_string(4), np.random.uniform(size=4), random.randint(0, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load a numpy array using tf data api with `from_tensor_slices`.\n", + "data = tf.data.Dataset.from_generator(generate_features, output_types=(tf.string, tf.float32, tf.int32))\n", + "# Refill data indefinitely.\n", + "data = data.repeat()\n", + "# Shuffle data.\n", + "data = data.shuffle(buffer_size=100)\n", + "# Batch data (aggregate records together).\n", + "data = data.batch(batch_size=4)\n", + "# Prefetch batch (pre-load batch for faster consumption).\n", + "data = data.prefetch(buffer_size=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(['snDw' 'NvMp' 'sXsw' 'qwuk'], shape=(4,), dtype=string) tf.Tensor(\n", + "[[0.22296238 0.03515657 0.3893014 0.6875752 ]\n", + " [0.05003363 0.27605608 0.23262134 0.10671499]\n", + " [0.8992419 0.34516433 0.29739627 0.8413017 ]\n", + " [0.91913974 0.7142106 0.48333576 0.04300505]], shape=(4, 4), dtype=float32) tf.Tensor([ 2 10 4 1], shape=(4,), dtype=int32)\n", + "tf.Tensor(['vdUx' 'InFi' 'nLzy' 'oklE'], shape=(4,), dtype=string) tf.Tensor(\n", + "[[0.6512162 0.8695475 0.7012295 0.6849636 ]\n", + " [0.00812997 0.01264008 0.7774404 0.44849646]\n", + " [0.92055863 0.894824 0.3628448 0.85603875]\n", + " [0.32219294 0.9767527 0.0307372 0.12051418]], shape=(4, 4), dtype=float32) tf.Tensor([9 7 4 0], shape=(4,), dtype=int32)\n", + "tf.Tensor(['ULGI' 'dBbm' 'URgs' 'Pkpt'], shape=(4,), dtype=string) tf.Tensor(\n", + "[[0.39586228 0.7472 0.3759462 0.9277406 ]\n", + " [0.44489694 0.38694733 0.9592599 0.82675934]\n", + " [0.12597603 0.299358 0.6940909 0.34155408]\n", + " [0.3401377 0.97620344 0.6047712 0.51667166]], shape=(4, 4), dtype=float32) tf.Tensor([ 4 10 0 0], shape=(4,), dtype=int32)\n", + "tf.Tensor(['kvao' 'wWvG' 'vrzf' 'cMgG'], shape=(4,), dtype=string) tf.Tensor(\n", + "[[0.8090979 0.65837437 0.9732402 0.9298921 ]\n", + " [0.67059356 0.91655296 0.52894515 0.8964492 ]\n", + " [0.05753202 0.45829964 0.74948853 0.41164723]\n", + " [0.42602295 0.8696292 0.57220364 0.9475169 ]], shape=(4, 4), dtype=float32) tf.Tensor([6 7 6 2], shape=(4,), dtype=int32)\n", + "tf.Tensor(['kyLQ' 'kxbI' 'CkQD' 'PHlJ'], shape=(4,), dtype=string) tf.Tensor(\n", + "[[0.29089147 0.6438517 0.31005543 0.31286424]\n", + " [0.0937152 0.8887667 0.24011584 0.25746483]\n", + " [0.47577712 0.53731906 0.9178111 0.3249844 ]\n", + " [0.38328 0.39294246 0.08126572 0.5995307 ]], shape=(4, 4), dtype=float32) tf.Tensor([3 1 3 2], shape=(4,), dtype=int32)\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for batch_str, batch_vector, batch_int in data.take(5):\n", + " print(batch_str, batch_vector, batch_int)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb b/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb new file mode 100644 index 00000000..59593339 --- /dev/null +++ b/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb @@ -0,0 +1,244 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create and Load TFRecords\n", + "\n", + "A simple TensorFlow 2.0 example to parse a dataset into TFRecord format, and then read that dataset.\n", + "\n", + "In this example, the Titanic Dataset (in CSV format) will be used as a toy dataset, for parsing all the dataset features into TFRecord format, and then building an input pipeline that can be used for training models.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Titanic Dataset\n", + "\n", + "The titanic dataset is a popular dataset for ML that provides a list of all passengers onboard the Titanic, along with various features such as their age, sex, class (1st, 2nd, 3rd)... And if the passenger survived the disaster or not.\n", + "\n", + "It can be used to see that even though some luck was involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class...\n", + "\n", + "#### Overview\n", + "survived|pclass|name|sex|age|sibsp|parch|ticket|fare\n", + "--------|------|----|---|---|-----|-----|------|----\n", + "1|1|\"Allen, Miss. Elisabeth Walton\"|female|29|0|0|24160|211.3375\n", + "1|1|\"Allison, Master. Hudson Trevor\"|male|0.9167|1|2|113781|151.5500\n", + "0|1|\"Allison, Miss. Helen Loraine\"|female|2|1|2|113781|151.5500\n", + "0|1|\"Allison, Mr. Hudson Joshua Creighton\"|male|30|1|2|113781|151.5500\n", + "...|...|...|...|...|...|...|...|...\n", + "\n", + "\n", + "#### Variable Descriptions\n", + "```\n", + "survived Survived\n", + " (0 = No; 1 = Yes)\n", + "pclass Passenger Class\n", + " (1 = 1st; 2 = 2nd; 3 = 3rd)\n", + "name Name\n", + "sex Sex\n", + "age Age\n", + "sibsp Number of Siblings/Spouses Aboard\n", + "parch Number of Parents/Children Aboard\n", + "ticket Ticket Number\n", + "fare Passenger Fare\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import csv\n", + "import requests\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Titanic dataset (in csv format).\n", + "d = requests.get(\"https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/titanic_dataset.csv\")\n", + "with open(\"titanic_dataset.csv\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create TFRecords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate Integer Features.\n", + "def build_int64_feature(data):\n", + " return tf.train.Feature(int64_list=tf.train.Int64List(value=[data]))\n", + "\n", + "# Generate Float Features.\n", + "def build_float_feature(data):\n", + " return tf.train.Feature(float_list=tf.train.FloatList(value=[data]))\n", + "\n", + "# Generate String Features.\n", + "def build_string_feature(data):\n", + " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[data]))\n", + "\n", + "# Generate a TF `Example`, parsing all features of the dataset.\n", + "def convert_to_tfexample(survived, pclass, name, sex, age, sibsp, parch, ticket, fare):\n", + " return tf.train.Example(\n", + " features=tf.train.Features(\n", + " feature={\n", + " 'survived': build_int64_feature(survived),\n", + " 'pclass': build_int64_feature(pclass),\n", + " 'name': build_string_feature(name),\n", + " 'sex': build_string_feature(sex),\n", + " 'age': build_float_feature(age),\n", + " 'sibsp': build_int64_feature(sibsp),\n", + " 'parch': build_int64_feature(parch),\n", + " 'ticket': build_string_feature(ticket),\n", + " 'fare': build_float_feature(fare),\n", + " })\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Open dataset file.\n", + "with open(\"titanic_dataset.csv\") as f:\n", + " # Output TFRecord file.\n", + " with tf.io.TFRecordWriter(\"titanic_dataset.tfrecord\") as w:\n", + " # Generate a TF Example for all row in our dataset.\n", + " # CSV reader will read and parse all rows.\n", + " reader = csv.reader(f, skipinitialspace=True)\n", + " for i, record in enumerate(reader):\n", + " # Skip header.\n", + " if i == 0:\n", + " continue\n", + " survived, pclass, name, sex, age, sibsp, parch, ticket, fare = record\n", + " # Parse each csv row to TF Example using the above functions.\n", + " example = convert_to_tfexample(int(survived), int(pclass), name, sex, float(age), int(sibsp), int(parch), ticket, float(fare))\n", + " # Serialize each TF Example to string, and write to TFRecord file.\n", + " w.write(example.SerializeToString())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load TFRecords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Build features template, with types.\n", + "features = {\n", + " 'survived': tf.io.FixedLenFeature([], tf.int64),\n", + " 'pclass': tf.io.FixedLenFeature([], tf.int64),\n", + " 'name': tf.io.FixedLenFeature([], tf.string),\n", + " 'sex': tf.io.FixedLenFeature([], tf.string),\n", + " 'age': tf.io.FixedLenFeature([], tf.float32),\n", + " 'sibsp': tf.io.FixedLenFeature([], tf.int64),\n", + " 'parch': tf.io.FixedLenFeature([], tf.int64),\n", + " 'ticket': tf.io.FixedLenFeature([], tf.string),\n", + " 'fare': tf.io.FixedLenFeature([], tf.float32),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load TFRecord data.\n", + "filenames = [\"titanic_dataset.tfrecord\"]\n", + "data = tf.data.TFRecordDataset(filenames)\n", + "\n", + "# Parse features, using the above template.\n", + "def parse_record(record):\n", + " return tf.io.parse_single_example(record, features=features)\n", + "# Apply the parsing to each record from the dataset.\n", + "data = data.map(parse_record)\n", + "\n", + "# Refill data indefinitely.\n", + "data = data.repeat()\n", + "# Shuffle data.\n", + "data = data.shuffle(buffer_size=1000)\n", + "# Batch data (aggregate records together).\n", + "data = data.batch(batch_size=4)\n", + "# Prefetch batch (pre-load batch for faster consumption).\n", + "data = data.prefetch(buffer_size=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 0 0]\n", + "['Gallagher, Mr. Martin' 'Fortune, Miss. Mabel Helen'\n", + " 'Andersson, Mr. Johan Samuel' 'Jensen, Mr. Niels Peder']\n", + "[ 7.7417 263. 7.775 7.8542]\n" + ] + } + ], + "source": [ + "# Dequeue data and display.\n", + "for record in data.take(1):\n", + " print(record['survived'].numpy())\n", + " print(record['name'].numpy())\n", + " print(record['fare'].numpy())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 6cb2ad4256fda0617909af103a5b4a84c0b873f2 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sat, 10 Aug 2019 02:53:16 -0700 Subject: [PATCH 139/166] update docs --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bb3d777c..787c4ae9 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update (07/14/2019):** Added a few [TensorFlow v2 examples](tensorflow_v2)! (more coming soon). +**Update (08/09/2019):** Added new [TensorFlow v2 examples](tensorflow_v2)! (more coming soon). *If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* From f4c3435e1bb9404b4ac3cc481dfeaeb9faee97b7 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sat, 10 Aug 2019 03:00:56 -0700 Subject: [PATCH 140/166] small fixes --- ...ranformation.ipynb => image_transformation.ipynb} | 0 notebooks/5_DataManagement/tfrecords.ipynb | 12 +----------- 2 files changed, 1 insertion(+), 11 deletions(-) rename notebooks/5_DataManagement/{image_tranformation.ipynb => image_transformation.ipynb} (100%) diff --git a/notebooks/5_DataManagement/image_tranformation.ipynb b/notebooks/5_DataManagement/image_transformation.ipynb similarity index 100% rename from notebooks/5_DataManagement/image_tranformation.ipynb rename to notebooks/5_DataManagement/image_transformation.ipynb diff --git a/notebooks/5_DataManagement/tfrecords.ipynb b/notebooks/5_DataManagement/tfrecords.ipynb index 413b0fc2..24aa5000 100644 --- a/notebooks/5_DataManagement/tfrecords.ipynb +++ b/notebooks/5_DataManagement/tfrecords.ipynb @@ -173,17 +173,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /home/orus/tf1/lib/python2.7/site-packages/tensorflow/python/data/ops/dataset_ops.py:1419: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Colocations handled automatically by placer.\n" - ] - } - ], + "outputs": [], "source": [ "# Create TensorFlow session.\n", "sess = tf.Session()\n", From 0564f616b656211f4c117b30f4e37a019cac0b8e Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sat, 10 Aug 2019 03:01:46 -0700 Subject: [PATCH 141/166] fix docs --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 787c4ae9..23090f12 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update (08/09/2019):** Added new [TensorFlow v2 examples](tensorflow_v2)! (more coming soon). +**Update (08/09/2019):** Added new [TensorFlow 2.0 examples](tensorflow_v2)! (more coming soon). *If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* From 3650e199f4294aa1ea56e4ab46e56ff629263189 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sat, 17 Aug 2019 01:02:46 -0700 Subject: [PATCH 142/166] add word2vec for TF2.0 --- README.md | 2 +- tensorflow_v2/README.md | 1 + .../notebooks/2_BasicModels/word2vec.ipynb | 724 ++++++++++++++++++ 3 files changed, 726 insertions(+), 1 deletion(-) create mode 100644 tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb diff --git a/README.md b/README.md index 23090f12..50cc4fa4 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update (08/09/2019):** Added new [TensorFlow 2.0 examples](tensorflow_v2)! (more coming soon). +**Update (08/17/2019):** Added new [TensorFlow 2.0 examples](tensorflow_v2)! (more coming soon). *If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index ba1a5999..d5151917 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -13,6 +13,7 @@ #### 2 - Basic Models - **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. - **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0. #### 3 - Neural Networks ##### Supervised diff --git a/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb b/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb new file mode 100644 index 00000000..04b5b051 --- /dev/null +++ b/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb @@ -0,0 +1,724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Word2Vec (Word Embedding)\n", + "\n", + "Implement Word2Vec algorithm to compute vector representations of words, with TensorFlow 2.0. This example is using a small chunk of Wikipedia articles to train from.\n", + "\n", + "More info: [Mikolov, Tomas et al. \"Efficient Estimation of Word Representations in Vector Space.\", 2013](https://arxiv.org/pdf/1301.3781.pdf)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import collections\n", + "import os\n", + "import random\n", + "import urllib\n", + "import zipfile\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Training Parameters.\n", + "learning_rate = 0.1\n", + "batch_size = 128\n", + "num_steps = 3000000\n", + "display_step = 10000\n", + "eval_step = 200000\n", + "\n", + "# Evaluation Parameters.\n", + "eval_words = ['five', 'of', 'going', 'hardware', 'american', 'britain']\n", + "\n", + "# Word2Vec Parameters.\n", + "embedding_size = 200 # Dimension of the embedding vector.\n", + "max_vocabulary_size = 50000 # Total number of different words in the vocabulary.\n", + "min_occurrence = 10 # Remove all words that does not appears at least n times.\n", + "skip_window = 3 # How many words to consider left and right.\n", + "num_skips = 2 # How many times to reuse an input to generate a label.\n", + "num_sampled = 64 # Number of negative examples to sample." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Download a small chunk of Wikipedia articles collection.\n", + "url = 'http://mattmahoney.net/dc/text8.zip'\n", + "data_path = 'text8.zip'\n", + "if not os.path.exists(data_path):\n", + " print(\"Downloading the dataset... (It may take some time)\")\n", + " filename, _ = urllib.urlretrieve(url, data_path)\n", + " print(\"Done!\")\n", + "# Unzip the dataset file. Text has already been processed.\n", + "with zipfile.ZipFile(data_path) as f:\n", + " text_words = f.read(f.namelist()[0]).lower().split()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Words count: 17005207\n", + "Unique words: 253854\n", + "Vocabulary size: 47135\n", + "Most common words: [('UNK', 444176), ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764), ('in', 372201), ('a', 325873), ('to', 316376), ('zero', 264975), ('nine', 250430)]\n" + ] + } + ], + "source": [ + "# Build the dictionary and replace rare words with UNK token.\n", + "count = [('UNK', -1)]\n", + "# Retrieve the most common words.\n", + "count.extend(collections.Counter(text_words).most_common(max_vocabulary_size - 1))\n", + "# Remove samples with less than 'min_occurrence' occurrences.\n", + "for i in range(len(count) - 1, -1, -1):\n", + " if count[i][1] < min_occurrence:\n", + " count.pop(i)\n", + " else:\n", + " # The collection is ordered, so stop when 'min_occurrence' is reached.\n", + " break\n", + "# Compute the vocabulary size.\n", + "vocabulary_size = len(count)\n", + "# Assign an id to each word.\n", + "word2id = dict()\n", + "for i, (word, _)in enumerate(count):\n", + " word2id[word] = i\n", + "\n", + "data = list()\n", + "unk_count = 0\n", + "for word in text_words:\n", + " # Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary.\n", + " index = word2id.get(word, 0)\n", + " if index == 0:\n", + " unk_count += 1\n", + " data.append(index)\n", + "count[0] = ('UNK', unk_count)\n", + "id2word = dict(zip(word2id.values(), word2id.keys()))\n", + "\n", + "print(\"Words count:\", len(text_words))\n", + "print(\"Unique words:\", len(set(text_words)))\n", + "print(\"Vocabulary size:\", vocabulary_size)\n", + "print(\"Most common words:\", count[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "data_index = 0\n", + "# Generate training batch for the skip-gram model.\n", + "def next_batch(batch_size, num_skips, skip_window):\n", + " global data_index\n", + " assert batch_size % num_skips == 0\n", + " assert num_skips <= 2 * skip_window\n", + " batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n", + " labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n", + " # get window size (words left and right + current one).\n", + " span = 2 * skip_window + 1\n", + " buffer = collections.deque(maxlen=span)\n", + " if data_index + span > len(data):\n", + " data_index = 0\n", + " buffer.extend(data[data_index:data_index + span])\n", + " data_index += span\n", + " for i in range(batch_size // num_skips):\n", + " context_words = [w for w in range(span) if w != skip_window]\n", + " words_to_use = random.sample(context_words, num_skips)\n", + " for j, context_word in enumerate(words_to_use):\n", + " batch[i * num_skips + j] = buffer[skip_window]\n", + " labels[i * num_skips + j, 0] = buffer[context_word]\n", + " if data_index == len(data):\n", + " buffer.extend(data[0:span])\n", + " data_index = span\n", + " else:\n", + " buffer.append(data[data_index])\n", + " data_index += 1\n", + " # Backtrack a little bit to avoid skipping words in the end of a batch.\n", + " data_index = (data_index + len(data) - span) % len(data)\n", + " return batch, labels" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Ensure the following ops & var are assigned on CPU\n", + "# (some ops are not compatible on GPU).\n", + "with tf.device('/cpu:0'):\n", + " # Create the embedding variable (each row represent a word embedding vector).\n", + " embedding = tf.Variable(tf.random.normal([vocabulary_size, embedding_size]))\n", + " # Construct the variables for the NCE loss.\n", + " nce_weights = tf.Variable(tf.random.normal([vocabulary_size, embedding_size]))\n", + " nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n", + "\n", + "def get_embedding(x):\n", + " with tf.device('/cpu:0'):\n", + " # Lookup the corresponding embedding vectors for each sample in X.\n", + " x_embed = tf.nn.embedding_lookup(embedding, x)\n", + " return x_embed\n", + "\n", + "def nce_loss(x_embed, y):\n", + " with tf.device('/cpu:0'):\n", + " # Compute the average NCE loss for the batch.\n", + " y = tf.cast(y, tf.int64)\n", + " loss = tf.reduce_mean(\n", + " tf.nn.nce_loss(weights=nce_weights,\n", + " biases=nce_biases,\n", + " labels=y,\n", + " inputs=x_embed,\n", + " num_sampled=num_sampled,\n", + " num_classes=vocabulary_size))\n", + " return loss\n", + "\n", + "# Evaluation.\n", + "def evaluate(x_embed):\n", + " with tf.device('/cpu:0'):\n", + " # Compute the cosine similarity between input data embedding and every embedding vectors\n", + " x_embed = tf.cast(x_embed, tf.float32)\n", + " x_embed_norm = x_embed / tf.sqrt(tf.reduce_sum(tf.square(x_embed)))\n", + " embedding_norm = embedding / tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keepdims=True), tf.float32)\n", + " cosine_sim_op = tf.matmul(x_embed_norm, embedding_norm, transpose_b=True)\n", + " return cosine_sim_op\n", + "\n", + "# Define the optimizer.\n", + "optimizer = tf.optimizers.SGD(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " with tf.device('/cpu:0'):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " emb = get_embedding(x)\n", + " loss = nce_loss(emb, y)\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, [embedding, nce_weights, nce_biases])\n", + "\n", + " # Update W and b following gradients.\n", + " optimizer.apply_gradients(zip(gradients, [embedding, nce_weights, nce_biases]))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 1, loss: 504.444214\n", + "Evaluation...\n", + "\"five\" nearest neighbors: censure, stricken, anglicanism, stick, streetcars, shrines, horrified, sparkle,\n", + "\"of\" nearest neighbors: jolly, weary, clinicians, kerouac, economist, owls, safe, playoff,\n", + "\"going\" nearest neighbors: filament, platforms, moderately, micheal, despotic, krag, disclosed, your,\n", + "\"hardware\" nearest neighbors: occupants, paraffin, vera, reorganized, rename, declares, prima, condoned,\n", + "\"american\" nearest neighbors: portfolio, rhein, aalto, angle, lifeson, tucker, sexton, dench,\n", + "\"britain\" nearest neighbors: indivisible, disbelief, scripture, pepsi, scriptores, sighting, napalm, strike,\n", + "step: 10000, loss: 117.166962\n", + "step: 20000, loss: 65.478333\n", + "step: 30000, loss: 46.580460\n", + "step: 40000, loss: 25.563128\n", + "step: 50000, loss: 50.924446\n", + "step: 60000, loss: 51.696526\n", + "step: 70000, loss: 17.272142\n", + "step: 80000, loss: 32.579414\n", + "step: 90000, loss: 68.372032\n", + "step: 100000, loss: 36.026573\n", + "step: 110000, loss: 22.502020\n", + "step: 120000, loss: 15.788742\n", + "step: 130000, loss: 31.832420\n", + "step: 140000, loss: 25.096617\n", + "step: 150000, loss: 12.013027\n", + "step: 160000, loss: 20.574780\n", + "step: 170000, loss: 12.201975\n", + "step: 180000, loss: 20.983793\n", + "step: 190000, loss: 11.366720\n", + "step: 200000, loss: 19.431549\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, eight, six, two, seven, nine, zero,\n", + "\"of\" nearest neighbors: the, a, and, first, with, on, but, from,\n", + "\"going\" nearest neighbors: have, more, used, out, be, with, on, however,\n", + "\"hardware\" nearest neighbors: be, known, system, apollo, and, a, such, used,\n", + "\"american\" nearest neighbors: UNK, and, from, s, at, in, after, about,\n", + "\"britain\" nearest neighbors: of, and, many, the, as, used, but, such,\n", + "step: 210000, loss: 16.361233\n", + "step: 220000, loss: 17.529526\n", + "step: 230000, loss: 16.805817\n", + "step: 240000, loss: 6.365625\n", + "step: 250000, loss: 8.083097\n", + "step: 260000, loss: 11.262514\n", + "step: 270000, loss: 9.842708\n", + "step: 280000, loss: 6.363440\n", + "step: 290000, loss: 8.732617\n", + "step: 300000, loss: 10.484728\n", + "step: 310000, loss: 12.099487\n", + "step: 320000, loss: 11.496288\n", + "step: 330000, loss: 9.283813\n", + "step: 340000, loss: 10.777218\n", + "step: 350000, loss: 16.310440\n", + "step: 360000, loss: 7.495782\n", + "step: 370000, loss: 9.287696\n", + "step: 380000, loss: 6.982735\n", + "step: 390000, loss: 8.549622\n", + "step: 400000, loss: 8.388112\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, two, seven, eight, one, zero,\n", + "\"of\" nearest neighbors: the, a, with, also, for, and, which, by,\n", + "\"going\" nearest neighbors: have, are, both, called, being, a, of, had,\n", + "\"hardware\" nearest neighbors: may, de, some, have, so, which, other, also,\n", + "\"american\" nearest neighbors: s, british, UNK, from, in, including, first, see,\n", + "\"britain\" nearest neighbors: against, include, including, both, british, other, an, most,\n", + "step: 410000, loss: 8.757725\n", + "step: 420000, loss: 12.303110\n", + "step: 430000, loss: 12.325478\n", + "step: 440000, loss: 7.659882\n", + "step: 450000, loss: 6.028089\n", + "step: 460000, loss: 12.700299\n", + "step: 470000, loss: 7.063077\n", + "step: 480000, loss: 18.004183\n", + "step: 490000, loss: 7.510474\n", + "step: 500000, loss: 10.089376\n", + "step: 510000, loss: 11.404436\n", + "step: 520000, loss: 9.494527\n", + "step: 530000, loss: 7.797963\n", + "step: 540000, loss: 7.390718\n", + "step: 550000, loss: 13.911215\n", + "step: 560000, loss: 6.975731\n", + "step: 570000, loss: 6.179163\n", + "step: 580000, loss: 7.066525\n", + "step: 590000, loss: 6.487288\n", + "step: 600000, loss: 5.361528\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, six, three, seven, two, one, eight, zero,\n", + "\"of\" nearest neighbors: the, and, from, with, a, including, in, include,\n", + "\"going\" nearest neighbors: have, even, they, term, who, many, which, were,\n", + "\"hardware\" nearest neighbors: include, computer, an, which, other, each, than, may,\n", + "\"american\" nearest neighbors: english, french, s, german, from, in, film, see,\n", + "\"britain\" nearest neighbors: several, first, modern, part, government, german, was, were,\n", + "step: 610000, loss: 4.144980\n", + "step: 620000, loss: 5.865635\n", + "step: 630000, loss: 6.826498\n", + "step: 640000, loss: 8.376097\n", + "step: 650000, loss: 7.117930\n", + "step: 660000, loss: 7.639544\n", + "step: 670000, loss: 5.973255\n", + "step: 680000, loss: 4.908459\n", + "step: 690000, loss: 6.164993\n", + "step: 700000, loss: 7.360281\n", + "step: 710000, loss: 12.693079\n", + "step: 720000, loss: 6.410182\n", + "step: 730000, loss: 7.499201\n", + "step: 740000, loss: 6.509094\n", + "step: 750000, loss: 10.625893\n", + "step: 760000, loss: 7.177696\n", + "step: 770000, loss: 12.639092\n", + "step: 780000, loss: 8.441635\n", + "step: 790000, loss: 7.529139\n", + "step: 800000, loss: 6.579177\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, two, one, zero,\n", + "\"of\" nearest neighbors: and, with, in, the, its, from, by, including,\n", + "\"going\" nearest neighbors: have, they, how, include, people, however, also, their,\n", + "\"hardware\" nearest neighbors: computer, large, include, may, or, which, other, there,\n", + "\"american\" nearest neighbors: born, french, british, english, german, b, john, d,\n", + "\"britain\" nearest neighbors: country, including, include, general, part, various, several, by,\n", + "step: 810000, loss: 6.934138\n", + "step: 820000, loss: 5.686094\n", + "step: 830000, loss: 7.310243\n", + "step: 840000, loss: 5.028157\n", + "step: 850000, loss: 7.079705\n", + "step: 860000, loss: 6.768996\n", + "step: 870000, loss: 5.604030\n", + "step: 880000, loss: 8.208309\n", + "step: 890000, loss: 6.301597\n", + "step: 900000, loss: 5.733234\n", + "step: 910000, loss: 6.577081\n", + "step: 920000, loss: 6.774826\n", + "step: 930000, loss: 7.068932\n", + "step: 940000, loss: 6.694956\n", + "step: 950000, loss: 7.944673\n", + "step: 960000, loss: 5.988618\n", + "step: 970000, loss: 6.651366\n", + "step: 980000, loss: 4.595577\n", + "step: 990000, loss: 6.564834\n", + "step: 1000000, loss: 4.327858\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, seven, six, eight, two, nine, zero,\n", + "\"of\" nearest neighbors: the, first, and, became, from, under, at, with,\n", + "\"going\" nearest neighbors: others, has, then, have, how, become, had, also,\n", + "\"hardware\" nearest neighbors: computer, large, systems, these, different, either, include, using,\n", + "\"american\" nearest neighbors: b, born, d, UNK, nine, english, german, french,\n", + "\"britain\" nearest neighbors: government, island, local, country, by, including, control, within,\n", + "step: 1010000, loss: 5.841236\n", + "step: 1020000, loss: 5.805200\n", + "step: 1030000, loss: 9.962063\n", + "step: 1040000, loss: 6.281199\n", + "step: 1050000, loss: 7.147995\n", + "step: 1060000, loss: 5.721184\n", + "step: 1070000, loss: 7.080662\n", + "step: 1080000, loss: 6.638658\n", + "step: 1090000, loss: 5.814178\n", + "step: 1100000, loss: 5.195928\n", + "step: 1110000, loss: 6.724787\n", + "step: 1120000, loss: 6.503905\n", + "step: 1130000, loss: 5.762966\n", + "step: 1140000, loss: 5.790243\n", + "step: 1150000, loss: 5.958191\n", + "step: 1160000, loss: 5.997983\n", + "step: 1170000, loss: 7.065348\n", + "step: 1180000, loss: 6.073387\n", + "step: 1190000, loss: 6.644097\n", + "step: 1200000, loss: 5.934450\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, eight, seven, two, nine, zero,\n", + "\"of\" nearest neighbors: the, and, including, in, its, with, from, on,\n", + "\"going\" nearest neighbors: others, then, through, has, had, another, people, when,\n", + "\"hardware\" nearest neighbors: computer, control, systems, either, these, large, small, other,\n", + "\"american\" nearest neighbors: born, german, john, d, british, b, UNK, french,\n", + "\"britain\" nearest neighbors: local, against, british, island, country, general, including, within,\n", + "step: 1210000, loss: 5.832344\n", + "step: 1220000, loss: 6.453851\n", + "step: 1230000, loss: 6.583966\n", + "step: 1240000, loss: 5.571673\n", + "step: 1250000, loss: 5.720917\n", + "step: 1260000, loss: 7.663424\n", + "step: 1270000, loss: 6.583741\n", + "step: 1280000, loss: 8.503859\n", + "step: 1290000, loss: 5.540640\n", + "step: 1300000, loss: 6.703249\n", + "step: 1310000, loss: 5.274101\n", + "step: 1320000, loss: 5.846446\n", + "step: 1330000, loss: 5.438172\n", + "step: 1340000, loss: 6.367691\n", + "step: 1350000, loss: 6.558622\n", + "step: 1360000, loss: 9.822924\n", + "step: 1370000, loss: 4.982378\n", + "step: 1380000, loss: 6.159739\n", + "step: 1390000, loss: 5.819083\n", + "step: 1400000, loss: 7.775135\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, two, eight, one, zero,\n", + "\"of\" nearest neighbors: and, the, in, with, its, within, for, including,\n", + "\"going\" nearest neighbors: others, through, while, has, to, how, particularly, their,\n", + "\"hardware\" nearest neighbors: computer, systems, large, control, research, using, information, either,\n", + "\"american\" nearest neighbors: english, french, german, born, film, british, s, former,\n", + "\"britain\" nearest neighbors: british, country, europe, local, military, island, against, western,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 1410000, loss: 8.214248\n", + "step: 1420000, loss: 4.696859\n", + "step: 1430000, loss: 5.873761\n", + "step: 1440000, loss: 5.971557\n", + "step: 1450000, loss: 4.992722\n", + "step: 1460000, loss: 5.197714\n", + "step: 1470000, loss: 6.916918\n", + "step: 1480000, loss: 6.441984\n", + "step: 1490000, loss: 5.443647\n", + "step: 1500000, loss: 5.178482\n", + "step: 1510000, loss: 6.060414\n", + "step: 1520000, loss: 6.373306\n", + "step: 1530000, loss: 5.098322\n", + "step: 1540000, loss: 6.674916\n", + "step: 1550000, loss: 6.712685\n", + "step: 1560000, loss: 5.280202\n", + "step: 1570000, loss: 6.454964\n", + "step: 1580000, loss: 4.896697\n", + "step: 1590000, loss: 6.239226\n", + "step: 1600000, loss: 5.709726\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, two, six, seven, eight, one, zero,\n", + "\"of\" nearest neighbors: the, and, including, in, with, within, its, following,\n", + "\"going\" nearest neighbors: others, people, who, they, that, far, were, have,\n", + "\"hardware\" nearest neighbors: computer, systems, include, high, research, some, information, large,\n", + "\"american\" nearest neighbors: born, english, french, british, german, d, john, b,\n", + "\"britain\" nearest neighbors: country, military, china, europe, against, local, central, british,\n", + "step: 1610000, loss: 6.334940\n", + "step: 1620000, loss: 5.093616\n", + "step: 1630000, loss: 6.119366\n", + "step: 1640000, loss: 4.975187\n", + "step: 1650000, loss: 6.490408\n", + "step: 1660000, loss: 7.464082\n", + "step: 1670000, loss: 4.977184\n", + "step: 1680000, loss: 5.658133\n", + "step: 1690000, loss: 5.352454\n", + "step: 1700000, loss: 6.810776\n", + "step: 1710000, loss: 5.687447\n", + "step: 1720000, loss: 5.992206\n", + "step: 1730000, loss: 5.513011\n", + "step: 1740000, loss: 5.548522\n", + "step: 1750000, loss: 6.200248\n", + "step: 1760000, loss: 13.070073\n", + "step: 1770000, loss: 4.621058\n", + "step: 1780000, loss: 5.301342\n", + "step: 1790000, loss: 4.777030\n", + "step: 1800000, loss: 6.912136\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, seven, eight, two, nine, zero,\n", + "\"of\" nearest neighbors: the, in, first, from, became, and, following, under,\n", + "\"going\" nearest neighbors: others, their, through, which, therefore, open, how, that,\n", + "\"hardware\" nearest neighbors: computer, systems, include, research, standard, different, system, small,\n", + "\"american\" nearest neighbors: b, d, born, actor, UNK, english, nine, german,\n", + "\"britain\" nearest neighbors: china, country, europe, against, canada, military, island, including,\n", + "step: 1810000, loss: 5.584600\n", + "step: 1820000, loss: 5.619820\n", + "step: 1830000, loss: 6.078709\n", + "step: 1840000, loss: 5.052518\n", + "step: 1850000, loss: 5.430106\n", + "step: 1860000, loss: 7.396770\n", + "step: 1870000, loss: 5.344787\n", + "step: 1880000, loss: 5.937998\n", + "step: 1890000, loss: 5.706491\n", + "step: 1900000, loss: 5.140662\n", + "step: 1910000, loss: 5.607048\n", + "step: 1920000, loss: 5.407231\n", + "step: 1930000, loss: 6.238531\n", + "step: 1940000, loss: 5.567973\n", + "step: 1950000, loss: 4.894245\n", + "step: 1960000, loss: 6.104193\n", + "step: 1970000, loss: 5.282631\n", + "step: 1980000, loss: 6.189069\n", + "step: 1990000, loss: 6.169409\n", + "step: 2000000, loss: 6.470152\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, two, nine, zero,\n", + "\"of\" nearest neighbors: the, its, in, with, and, including, within, against,\n", + "\"going\" nearest neighbors: others, only, therefore, will, how, a, far, though,\n", + "\"hardware\" nearest neighbors: computer, systems, for, network, software, program, research, system,\n", + "\"american\" nearest neighbors: born, actor, d, italian, german, john, robert, b,\n", + "\"britain\" nearest neighbors: china, country, europe, canada, british, former, island, france,\n", + "step: 2010000, loss: 5.298714\n", + "step: 2020000, loss: 5.494207\n", + "step: 2030000, loss: 5.410875\n", + "step: 2040000, loss: 6.228232\n", + "step: 2050000, loss: 5.044596\n", + "step: 2060000, loss: 4.624638\n", + "step: 2070000, loss: 4.919327\n", + "step: 2080000, loss: 4.639625\n", + "step: 2090000, loss: 4.865627\n", + "step: 2100000, loss: 4.951073\n", + "step: 2110000, loss: 5.973768\n", + "step: 2120000, loss: 7.366824\n", + "step: 2130000, loss: 5.149571\n", + "step: 2140000, loss: 7.846234\n", + "step: 2150000, loss: 5.449315\n", + "step: 2160000, loss: 5.359211\n", + "step: 2170000, loss: 5.171029\n", + "step: 2180000, loss: 6.106437\n", + "step: 2190000, loss: 6.043995\n", + "step: 2200000, loss: 5.642351\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, two, eight, seven, zero, one,\n", + "\"of\" nearest neighbors: the, and, its, see, for, in, with, including,\n", + "\"going\" nearest neighbors: others, therefore, how, even, them, your, have, although,\n", + "\"hardware\" nearest neighbors: computer, systems, system, network, program, research, software, include,\n", + "\"american\" nearest neighbors: english, french, german, canadian, british, film, author, italian,\n", + "\"britain\" nearest neighbors: europe, china, country, germany, british, england, france, throughout,\n", + "step: 2210000, loss: 4.427110\n", + "step: 2220000, loss: 6.240989\n", + "step: 2230000, loss: 5.184978\n", + "step: 2240000, loss: 8.035570\n", + "step: 2250000, loss: 5.793781\n", + "step: 2260000, loss: 4.908427\n", + "step: 2270000, loss: 8.807668\n", + "step: 2280000, loss: 6.083229\n", + "step: 2290000, loss: 5.773360\n", + "step: 2300000, loss: 5.613671\n", + "step: 2310000, loss: 6.080076\n", + "step: 2320000, loss: 5.288568\n", + "step: 2330000, loss: 5.949232\n", + "step: 2340000, loss: 5.479994\n", + "step: 2350000, loss: 7.717686\n", + "step: 2360000, loss: 5.163609\n", + "step: 2370000, loss: 5.989407\n", + "step: 2380000, loss: 5.785729\n", + "step: 2390000, loss: 5.345478\n", + "step: 2400000, loss: 6.627133\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, two, seven, eight, zero, nine,\n", + "\"of\" nearest neighbors: the, in, and, including, from, within, its, with,\n", + "\"going\" nearest neighbors: therefore, people, they, out, only, according, your, now,\n", + "\"hardware\" nearest neighbors: computer, systems, network, program, system, software, run, design,\n", + "\"american\" nearest neighbors: author, born, actor, english, canadian, british, italian, d,\n", + "\"britain\" nearest neighbors: china, europe, country, throughout, france, canada, england, western,\n", + "step: 2410000, loss: 5.666146\n", + "step: 2420000, loss: 5.316198\n", + "step: 2430000, loss: 5.129625\n", + "step: 2440000, loss: 5.247949\n", + "step: 2450000, loss: 5.741394\n", + "step: 2460000, loss: 5.833083\n", + "step: 2470000, loss: 7.704844\n", + "step: 2480000, loss: 5.398345\n", + "step: 2490000, loss: 5.089633\n", + "step: 2500000, loss: 5.620508\n", + "step: 2510000, loss: 4.976034\n", + "step: 2520000, loss: 5.884676\n", + "step: 2530000, loss: 6.649922\n", + "step: 2540000, loss: 5.002588\n", + "step: 2550000, loss: 5.072144\n", + "step: 2560000, loss: 5.165375\n", + "step: 2570000, loss: 5.310089\n", + "step: 2580000, loss: 5.481957\n", + "step: 2590000, loss: 6.104440\n", + "step: 2600000, loss: 5.339644\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, seven, eight, nine, two, zero,\n", + "\"of\" nearest neighbors: the, first, from, with, became, in, following, and,\n", + "\"going\" nearest neighbors: how, therefore, back, will, through, always, your, make,\n", + "\"hardware\" nearest neighbors: computer, systems, system, network, program, technology, design, software,\n", + "\"american\" nearest neighbors: actor, singer, born, b, author, d, english, writer,\n", + "\"britain\" nearest neighbors: europe, china, throughout, great, england, france, country, india,\n", + "step: 2610000, loss: 7.754117\n", + "step: 2620000, loss: 5.979313\n", + "step: 2630000, loss: 5.394362\n", + "step: 2640000, loss: 4.866740\n", + "step: 2650000, loss: 5.219806\n", + "step: 2660000, loss: 6.074809\n", + "step: 2670000, loss: 6.216953\n", + "step: 2680000, loss: 5.944881\n", + "step: 2690000, loss: 5.863350\n", + "step: 2700000, loss: 6.128705\n", + "step: 2710000, loss: 5.502523\n", + "step: 2720000, loss: 5.300839\n", + "step: 2730000, loss: 6.358493\n", + "step: 2740000, loss: 6.058306\n", + "step: 2750000, loss: 4.689510\n", + "step: 2760000, loss: 6.032880\n", + "step: 2770000, loss: 5.844904\n", + "step: 2780000, loss: 5.385874\n", + "step: 2790000, loss: 5.370956\n", + "step: 2800000, loss: 4.912577\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, six, three, eight, seven, two, nine, one,\n", + "\"of\" nearest neighbors: in, the, and, from, including, following, with, under,\n", + "\"going\" nearest neighbors: your, then, through, will, how, so, back, even,\n", + "\"hardware\" nearest neighbors: computer, systems, program, network, design, standard, physical, software,\n", + "\"american\" nearest neighbors: actor, singer, born, author, writer, canadian, italian, d,\n", + "\"britain\" nearest neighbors: europe, china, england, throughout, france, india, great, germany,\n", + "step: 2810000, loss: 5.897756\n", + "step: 2820000, loss: 7.194932\n", + "step: 2830000, loss: 7.430175\n", + "step: 2840000, loss: 7.258231\n", + "step: 2850000, loss: 5.837617\n", + "step: 2860000, loss: 5.496673\n", + "step: 2870000, loss: 6.173716\n", + "step: 2880000, loss: 6.095749\n", + "step: 2890000, loss: 6.064944\n", + "step: 2900000, loss: 5.560488\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 2910000, loss: 4.966107\n", + "step: 2920000, loss: 5.789579\n", + "step: 2930000, loss: 4.525987\n", + "step: 2940000, loss: 6.704808\n", + "step: 2950000, loss: 4.506433\n", + "step: 2960000, loss: 6.251270\n", + "step: 2970000, loss: 5.588204\n", + "step: 2980000, loss: 5.423235\n", + "step: 2990000, loss: 5.613834\n", + "step: 3000000, loss: 5.137326\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, two, zero, one,\n", + "\"of\" nearest neighbors: the, including, and, with, in, its, includes, within,\n", + "\"going\" nearest neighbors: how, they, when, them, make, always, your, though,\n", + "\"hardware\" nearest neighbors: computer, systems, network, program, physical, design, technology, software,\n", + "\"american\" nearest neighbors: canadian, english, australian, british, german, film, italian, author,\n", + "\"britain\" nearest neighbors: europe, england, china, throughout, india, france, great, british,\n" + ] + } + ], + "source": [ + "# Words for testing.\n", + "x_test = np.array([word2id[w] for w in eval_words])\n", + "\n", + "# Run training for the given number of steps.\n", + "for step in xrange(1, num_steps + 1):\n", + " batch_x, batch_y = next_batch(batch_size, num_skips, skip_window)\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0 or step == 1:\n", + " loss = nce_loss(get_embedding(batch_x), batch_y)\n", + " print(\"step: %i, loss: %f\" % (step, loss))\n", + " \n", + " # Evaluation.\n", + " if step % eval_step == 0 or step == 1:\n", + " print(\"Evaluation...\")\n", + " sim = evaluate(get_embedding(x_test)).numpy()\n", + " for i in xrange(len(eval_words)):\n", + " top_k = 8 # number of nearest neighbors.\n", + " nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n", + " log_str = '\"%s\" nearest neighbors:' % eval_words[i]\n", + " for k in xrange(top_k):\n", + " log_str = '%s %s,' % (log_str, id2word[nearest[k]])\n", + " print(log_str)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 7aeb6cbf142fe5a69fd32dca2d03315846abd396 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Wed, 6 Nov 2019 22:18:35 -0800 Subject: [PATCH 143/166] Update tensorflow v2 installation --- tensorflow_v2/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index d5151917..ed23a174 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -43,10 +43,10 @@ To install TensorFlow 2.0, simply run: ``` -pip install tensorflow==2.0.0-beta1 +pip install tensorflow==2.0.0 ``` or (if you want GPU support): ``` -pip install tensorflow_gpu==2.0.0-beta1 +pip install tensorflow_gpu==2.0.0 ``` From c5772812faea39e0d0b1cc1dbd937b8ecb20091b Mon Sep 17 00:00:00 2001 From: Nikhil Kilari <36819773+kilarinikhil@users.noreply.github.com> Date: Sun, 12 Apr 2020 07:51:02 +0530 Subject: [PATCH 144/166] A minor mistake in cross entropy loss (#357) tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred),1)) or else it simply finds the sum and the reduced mean remains the sum itself. --- tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb b/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb index c29c42b9..b9b1ccc4 100644 --- a/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb +++ b/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb @@ -109,7 +109,7 @@ " # Clip prediction values to avoid log(0) error.\n", " y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)\n", " # Compute cross-entropy.\n", - " return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred)))\n", + " return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred),1))\n", "\n", "# Accuracy metric.\n", "def accuracy(y_pred, y_true):\n", From 39d9d0efa11fafc7eb1e6305a62910d72e04e0df Mon Sep 17 00:00:00 2001 From: lkdmttg7 Date: Sun, 17 May 2020 00:16:40 +0530 Subject: [PATCH 145/166] Update convolutional_network_raw.ipynb (#366) --- .../notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb index 80adb3f0..c0ffbc42 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb @@ -217,7 +217,7 @@ " loss = cross_entropy(pred, y)\n", " \n", " # Variables to update, i.e. trainable variables.\n", - " trainable_variables = weights.values() + biases.values()\n", + " trainable_variables = list(weights.values()) + list(biases.values())\n", "\n", " # Compute gradients.\n", " gradients = g.gradient(loss, trainable_variables)\n", From 2cf9bfd3609fdb03c5f8499b18a4b6bd7c43b374 Mon Sep 17 00:00:00 2001 From: Dragon-Yu <769888056@qq.com> Date: Sun, 17 May 2020 02:47:36 +0800 Subject: [PATCH 146/166] Update neural_network.ipynb (#361) Add the missing fully connected layer 2 in the RNN example --- tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb index 2bcd1860..857610ca 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb @@ -116,6 +116,7 @@ " # Set forward pass.\n", " def call(self, x, is_training=False):\n", " x = self.fc1(x)\n", + " x = self.fc2(x)\n", " x = self.out(x)\n", " if not is_training:\n", " # tf cross entropy expect logits without softmax, so only\n", From e7353b776165d124f0f560af87992311bc1984c8 Mon Sep 17 00:00:00 2001 From: Nikhil Kilari <36819773+kilarinikhil@users.noreply.github.com> Date: Sun, 17 May 2020 00:19:17 +0530 Subject: [PATCH 147/166] output layer aactivation, add fc2 in call (#358) softmax applied during training phase to output layer and fc2 layer is unused Co-authored-by: Aymeric Damien --- tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb index 857610ca..77926535 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb @@ -111,12 +111,12 @@ " # First fully-connected hidden layer.\n", " self.fc2 = layers.Dense(n_hidden_2, activation=tf.nn.relu)\n", " # Second fully-connecter hidden layer.\n", - " self.out = layers.Dense(num_classes, activation=tf.nn.softmax)\n", + " self.out = layers.Dense(num_classes)\n", "\n", " # Set forward pass.\n", " def call(self, x, is_training=False):\n", " x = self.fc1(x)\n", - " x = self.fc2(x)\n", + " x = self.fc2(x)\n" " x = self.out(x)\n", " if not is_training:\n", " # tf cross entropy expect logits without softmax, so only\n", From 922833aff43d616bd7d0cddb43366ac93215f6d7 Mon Sep 17 00:00:00 2001 From: Sebastian Stein Date: Sat, 16 May 2020 20:49:42 +0200 Subject: [PATCH 148/166] make TensorFlow 2 examples compatible with Python 3 (#339) --- tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb b/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb index 8a9479c0..a1fdad54 100644 --- a/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb +++ b/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb @@ -37,7 +37,7 @@ "source": [ "# Create a Tensor.\n", "hello = tf.constant(\"hello world\")\n", - "print hello" + "print(hello)" ] }, { @@ -55,7 +55,7 @@ ], "source": [ "# To access a Tensor value, call numpy().\n", - "print hello.numpy()" + "print(hello.numpy())" ] } ], From 3a767b1e712c386dfbb525589685cce569619953 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sat, 16 May 2020 13:14:52 -0700 Subject: [PATCH 149/166] update docs (#367) --- README.md | 129 ++-- examples/README.md | 5 + notebooks/README.md | 5 + tensorflow_v1/README.md | 93 +++ .../1_Introduction/basic_eager_api.py | 68 ++ .../1_Introduction/basic_operations.py | 75 ++ .../examples/1_Introduction/helloworld.py | 25 + .../gradient_boosted_decision_tree.py | 85 ++ .../examples/2_BasicModels/kmeans.py | 93 +++ .../2_BasicModels/linear_regression.py | 89 +++ .../linear_regression_eager_api.py | 69 ++ .../2_BasicModels/logistic_regression.py | 71 ++ .../logistic_regression_eager_api.py | 105 +++ .../2_BasicModels/nearest_neighbor.py | 55 ++ .../examples/2_BasicModels/random_forest.py | 77 ++ .../examples/2_BasicModels/word2vec.py | 195 +++++ .../examples/3_NeuralNetworks/autoencoder.py | 142 ++++ .../3_NeuralNetworks/bidirectional_rnn.py | 126 +++ .../3_NeuralNetworks/convolutional_network.py | 125 +++ .../convolutional_network_raw.py | 141 ++++ .../examples/3_NeuralNetworks/dcgan.py | 167 ++++ .../examples/3_NeuralNetworks/dynamic_rnn.py | 193 +++++ .../examples/3_NeuralNetworks/gan.py | 157 ++++ .../3_NeuralNetworks/multilayer_perceptron.py | 104 +++ .../3_NeuralNetworks/neural_network.py | 103 +++ .../neural_network_eager_api.py | 133 ++++ .../3_NeuralNetworks/neural_network_raw.py | 101 +++ .../3_NeuralNetworks/recurrent_network.py | 115 +++ .../variational_autoencoder.py | 143 ++++ .../examples/4_Utils/save_restore_model.py | 140 ++++ .../examples/4_Utils/tensorboard_advanced.py | 143 ++++ .../examples/4_Utils/tensorboard_basic.py | 97 +++ .../build_an_image_dataset.py | 212 +++++ .../tensorflow_dataset_api.py | 130 ++++ .../examples/6_MultiGPU/multigpu_basics.py | 94 +++ .../examples/6_MultiGPU/multigpu_cnn.py | 198 +++++ .../0_Prerequisite/ml_introduction.ipynb | 48 ++ .../0_Prerequisite/mnist_dataset_intro.ipynb | 94 +++ .../1_Introduction/basic_eager_api.ipynb | 238 ++++++ .../1_Introduction/basic_operations.ipynb | 220 ++++++ .../notebooks/1_Introduction/helloworld.ipynb | 87 +++ .../gradient_boosted_decision_tree.ipynb | 266 +++++++ .../notebooks/2_BasicModels/kmeans.ipynb | 226 ++++++ .../2_BasicModels/linear_regression.ipynb | 236 ++++++ .../linear_regression_eager_api.ipynb | 181 +++++ .../2_BasicModels/logistic_regression.ipynb | 174 +++++ .../logistic_regression_eager_api.ipynb | 258 +++++++ .../2_BasicModels/nearest_neighbor.ipynb | 332 ++++++++ .../2_BasicModels/random_forest.ipynb | 229 ++++++ .../notebooks/2_BasicModels/word2vec.ipynb | 724 ++++++++++++++++++ .../3_NeuralNetworks/autoencoder.ipynb | 310 ++++++++ .../3_NeuralNetworks/bidirectional_rnn.ipynb | 301 ++++++++ .../convolutional_network.ipynb | 423 ++++++++++ .../convolutional_network_raw.ipynb | 303 ++++++++ .../notebooks/3_NeuralNetworks/dcgan.ipynb | 333 ++++++++ .../3_NeuralNetworks/dynamic_rnn.ipynb | 352 +++++++++ .../notebooks/3_NeuralNetworks/gan.ipynb | 323 ++++++++ .../3_NeuralNetworks/neural_network.ipynb | 390 ++++++++++ .../neural_network_eager_api.ipynb | 287 +++++++ .../3_NeuralNetworks/neural_network_raw.ipynb | 224 ++++++ .../3_NeuralNetworks/recurrent_network.ipynb | 292 +++++++ .../variational_autoencoder.ipynb | 316 ++++++++ .../4_Utils/save_restore_model.ipynb | 252 ++++++ .../4_Utils/tensorboard_advanced.ipynb | 307 ++++++++ .../notebooks/4_Utils/tensorboard_basic.ipynb | 217 ++++++ .../build_an_image_dataset.ipynb | 291 +++++++ .../image_transformation.ipynb | 418 ++++++++++ .../5_DataManagement/load_data.ipynb | 577 ++++++++++++++ .../tensorflow_dataset_api.ipynb | 222 ++++++ .../5_DataManagement/tfrecords.ipynb | 261 +++++++ .../6_MultiGPU/multigpu_basics.ipynb | 179 +++++ .../notebooks/6_MultiGPU/multigpu_cnn.ipynb | 328 ++++++++ 72 files changed, 14174 insertions(+), 48 deletions(-) create mode 100644 examples/README.md create mode 100644 notebooks/README.md create mode 100644 tensorflow_v1/README.md create mode 100644 tensorflow_v1/examples/1_Introduction/basic_eager_api.py create mode 100644 tensorflow_v1/examples/1_Introduction/basic_operations.py create mode 100644 tensorflow_v1/examples/1_Introduction/helloworld.py create mode 100644 tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py create mode 100644 tensorflow_v1/examples/2_BasicModels/kmeans.py create mode 100644 tensorflow_v1/examples/2_BasicModels/linear_regression.py create mode 100644 tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py create mode 100644 tensorflow_v1/examples/2_BasicModels/logistic_regression.py create mode 100644 tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py create mode 100644 tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py create mode 100644 tensorflow_v1/examples/2_BasicModels/random_forest.py create mode 100644 tensorflow_v1/examples/2_BasicModels/word2vec.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/dcgan.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/gan.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/multilayer_perceptron.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/neural_network.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py create mode 100644 tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py create mode 100644 tensorflow_v1/examples/4_Utils/save_restore_model.py create mode 100644 tensorflow_v1/examples/4_Utils/tensorboard_advanced.py create mode 100644 tensorflow_v1/examples/4_Utils/tensorboard_basic.py create mode 100644 tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py create mode 100644 tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py create mode 100644 tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py create mode 100644 tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py create mode 100644 tensorflow_v1/notebooks/0_Prerequisite/ml_introduction.ipynb create mode 100644 tensorflow_v1/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb create mode 100644 tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb create mode 100644 tensorflow_v1/notebooks/1_Introduction/basic_operations.ipynb create mode 100644 tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb create mode 100644 tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_raw.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb create mode 100644 tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb create mode 100644 tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb create mode 100644 tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb create mode 100644 tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb create mode 100644 tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb create mode 100644 tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb create mode 100644 tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb create mode 100644 tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb create mode 100644 tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb create mode 100644 tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb create mode 100644 tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb diff --git a/README.md b/README.md index 50cc4fa4..00610dcf 100644 --- a/README.md +++ b/README.md @@ -4,69 +4,51 @@ This tutorial was designed for easily diving into TensorFlow, through examples. It is suitable for beginners who want to find clear and concise examples about TensorFlow. Besides the traditional 'raw' TensorFlow implementations, you can also find the latest TensorFlow API practices (such as `layers`, `estimator`, `dataset`, ...). -**Update (08/17/2019):** Added new [TensorFlow 2.0 examples](tensorflow_v2)! (more coming soon). - -*If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* +**Update (05/16/2020):** Moving all default examples to TF2. For TF v1 examples: [check here](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1). ## Tutorial index #### 0 - Prerequisite -- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb). -- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). +- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb). +- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction -- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. -- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. -- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow 2.0. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow 2.0 basic operations. #### 2 - Basic Models -- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. -- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. -- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. -- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. -- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. -- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. -- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. -- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. -- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0. #### 3 - Neural Networks ##### Supervised -- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. -- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset. +- **Simple Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)). Raw implementation of a simple neural network to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. +- **Convolutional Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)). Raw implementation of a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0 'layers' and 'model' API. ##### Unsupervised -- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. -- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. -- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. -- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities -- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. -- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. -- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0. +- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models. #### 5 - Data Management -- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. -- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. -- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). -- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. -- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training. -#### 6 - Multi GPU -- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. -- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. -## TensorFlow 2.0 +## TensorFlow v1 -The tutorial index for TF v2 is available here: [TensorFlow 2.0 Examples](tensorflow_v2). +The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tensorflow_v1). Or see below for a list of the examples. ## Dataset Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. @@ -93,11 +75,62 @@ pip install tensorflow_gpu For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://www.tensorflow.org/install/) -## More Examples -The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). -### Tutorials -- [TFLearn Quickstart](https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. +## TensorFlow v1 Examples - Index + +The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tensorflow_v1). + +#### 0 - Prerequisite +- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/ml_introduction.ipynb). +- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/mnist_dataset_intro.ipynb). + +#### 1 - Introduction +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. +- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. + +#### 2 - Basic Models +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. +- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. +- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. +- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. +- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. +- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. +- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. + +#### 3 - Neural Networks +##### Supervised + +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. + +##### Unsupervised +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. +- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. +- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. + +#### 4 - Utilities +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. +- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. +- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... + +#### 5 - Data Management +- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. +- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. + +#### 6 - Multi GPU +- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. +- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. -### Examples -- [TFLearn Examples](https://github.com/tflearn/tflearn/blob/master/examples). A large collection of examples using TFLearn. diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 00000000..196ed016 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,5 @@ +## Deprecated - Please Read + +Due to TensorFlow radically changing their API in v2, the examples index have been split between [v1](../tensorflow_v1) and [v2](../tensorflow_v2). + +The following examples are the original TF v1 examples, and will be deprecated entirely in favor of [tensorflow_v1](../tensorflow_v1) directory in a future release. diff --git a/notebooks/README.md b/notebooks/README.md new file mode 100644 index 00000000..196ed016 --- /dev/null +++ b/notebooks/README.md @@ -0,0 +1,5 @@ +## Deprecated - Please Read + +Due to TensorFlow radically changing their API in v2, the examples index have been split between [v1](../tensorflow_v1) and [v2](../tensorflow_v2). + +The following examples are the original TF v1 examples, and will be deprecated entirely in favor of [tensorflow_v1](../tensorflow_v1) directory in a future release. diff --git a/tensorflow_v1/README.md b/tensorflow_v1/README.md new file mode 100644 index 00000000..93a8c3a9 --- /dev/null +++ b/tensorflow_v1/README.md @@ -0,0 +1,93 @@ +# TensorFlow v1 Examples + +All the following examples are the original TF v1 examples. + +*If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* + +#### 0 - Prerequisite +- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/ml_introduction.ipynb). +- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/mnist_dataset_intro.ipynb). + +#### 1 - Introduction +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. +- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. + +#### 2 - Basic Models +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. +- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. +- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. +- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. +- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. +- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. +- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. + +#### 3 - Neural Networks +##### Supervised + +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. + +##### Unsupervised +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. +- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. +- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. + +#### 4 - Utilities +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. +- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. +- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... + +#### 5 - Data Management +- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. +- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. + +#### 6 - Multi GPU +- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. +- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. + +## Installation + +To download all the examples, simply clone this repository: +``` +git clone https://github.com/aymericdamien/TensorFlow-Examples +``` + +To run them, you also need the latest version of TensorFlow. To install it: +``` +pip install tensorflow==1.15.0 +``` + +or (with GPU support): +``` +pip install tensorflow_gpu==1.15.0 +``` + +For more details about TensorFlow installation, you can check [TensorFlow Installation Guide](https://www.tensorflow.org/install/) + +## Dataset +Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples. +MNIST is a database of handwritten digits, for a quick description of that dataset, you can check [this notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). + +Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/). + +## More Examples +The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). + +### Tutorials +- [TFLearn Quickstart](https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. + +### Examples +- [TFLearn Examples](https://github.com/tflearn/tflearn/blob/master/examples). A large collection of examples using TFLearn. diff --git a/tensorflow_v1/examples/1_Introduction/basic_eager_api.py b/tensorflow_v1/examples/1_Introduction/basic_eager_api.py new file mode 100644 index 00000000..e00719d3 --- /dev/null +++ b/tensorflow_v1/examples/1_Introduction/basic_eager_api.py @@ -0,0 +1,68 @@ +''' +Basic introduction to TensorFlow's Eager API. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ + +What is Eager API? +" Eager execution is an imperative, define-by-run interface where operations are +executed immediately as they are called from Python. This makes it easier to +get started with TensorFlow, and can make research and development more +intuitive. A vast majority of the TensorFlow API remains the same whether eager +execution is enabled or not. As a result, the exact same code that constructs +TensorFlow graphs (e.g. using the layers API) can be executed imperatively +by using eager execution. Conversely, most models written with Eager enabled +can be converted to a graph that can be further optimized and/or extracted +for deployment in production without changing code. " - Rajat Monga + +''' +from __future__ import absolute_import, division, print_function + +import numpy as np +import tensorflow as tf +import tensorflow.contrib.eager as tfe + +# Set Eager API +print("Setting Eager mode...") +tfe.enable_eager_execution() + +# Define constant tensors +print("Define constant tensors") +a = tf.constant(2) +print("a = %i" % a) +b = tf.constant(3) +print("b = %i" % b) + +# Run the operation without the need for tf.Session +print("Running operations, without tf.Session") +c = a + b +print("a + b = %i" % c) +d = a * b +print("a * b = %i" % d) + + +# Full compatibility with Numpy +print("Mixing operations with Tensors and Numpy Arrays") + +# Define constant tensors +a = tf.constant([[2., 1.], + [1., 0.]], dtype=tf.float32) +print("Tensor:\n a = %s" % a) +b = np.array([[3., 0.], + [5., 1.]], dtype=np.float32) +print("NumpyArray:\n b = %s" % b) + +# Run the operation without the need for tf.Session +print("Running operations, without tf.Session") + +c = a + b +print("a + b = %s" % c) + +d = tf.matmul(a, b) +print("a * b = %s" % d) + +print("Iterate through Tensor 'a':") +for i in range(a.shape[0]): + for j in range(a.shape[1]): + print(a[i][j]) + diff --git a/tensorflow_v1/examples/1_Introduction/basic_operations.py b/tensorflow_v1/examples/1_Introduction/basic_operations.py new file mode 100644 index 00000000..e1775069 --- /dev/null +++ b/tensorflow_v1/examples/1_Introduction/basic_operations.py @@ -0,0 +1,75 @@ +''' +Basic Operations example using TensorFlow library. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf + +# Basic constant operations +# The value returned by the constructor represents the output +# of the Constant op. +a = tf.constant(2) +b = tf.constant(3) + +# Launch the default graph. +with tf.Session() as sess: + print("a=2, b=3") + print("Addition with constants: %i" % sess.run(a+b)) + print("Multiplication with constants: %i" % sess.run(a*b)) + +# Basic Operations with variable as graph input +# The value returned by the constructor represents the output +# of the Variable op. (define as input when running session) +# tf Graph input +a = tf.placeholder(tf.int16) +b = tf.placeholder(tf.int16) + +# Define some operations +add = tf.add(a, b) +mul = tf.multiply(a, b) + +# Launch the default graph. +with tf.Session() as sess: + # Run every operation with variable input + print("Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3})) + print("Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3})) + + +# ---------------- +# More in details: +# Matrix Multiplication from TensorFlow official tutorial + +# Create a Constant op that produces a 1x2 matrix. The op is +# added as a node to the default graph. +# +# The value returned by the constructor represents the output +# of the Constant op. +matrix1 = tf.constant([[3., 3.]]) + +# Create another Constant that produces a 2x1 matrix. +matrix2 = tf.constant([[2.],[2.]]) + +# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs. +# The returned value, 'product', represents the result of the matrix +# multiplication. +product = tf.matmul(matrix1, matrix2) + +# To run the matmul op we call the session 'run()' method, passing 'product' +# which represents the output of the matmul op. This indicates to the call +# that we want to get the output of the matmul op back. +# +# All inputs needed by the op are run automatically by the session. They +# typically are run in parallel. +# +# The call 'run(product)' thus causes the execution of threes ops in the +# graph: the two constants and matmul. +# +# The output of the op is returned in 'result' as a numpy `ndarray` object. +with tf.Session() as sess: + result = sess.run(product) + print(result) + # ==> [[ 12.]] diff --git a/tensorflow_v1/examples/1_Introduction/helloworld.py b/tensorflow_v1/examples/1_Introduction/helloworld.py new file mode 100644 index 00000000..1c40f315 --- /dev/null +++ b/tensorflow_v1/examples/1_Introduction/helloworld.py @@ -0,0 +1,25 @@ +''' +HelloWorld example using TensorFlow library. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf + +# Simple hello world using TensorFlow + +# Create a Constant op +# The op is added as a node to the default graph. +# +# The value returned by the constructor represents the output +# of the Constant op. +hello = tf.constant('Hello, TensorFlow!') + +# Start tf session +sess = tf.Session() + +# Run the op +print(sess.run(hello)) diff --git a/tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py b/tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py new file mode 100644 index 00000000..00501a2b --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py @@ -0,0 +1,85 @@ +""" Gradient Boosted Decision Tree (GBDT). + +Implement a Gradient Boosted Decision tree with TensorFlow to classify +handwritten digit images. This example is using the MNIST database of +handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/). + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier +from tensorflow.contrib.boosted_trees.proto import learner_pb2 as gbdt_learner + +# Ignore all GPUs (current TF GBDT does not support GPU). +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "" + +# Import MNIST data +# Set verbosity to display errors only (Remove this line for showing warnings) +tf.logging.set_verbosity(tf.logging.ERROR) +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False, + source_url='http://yann.lecun.com/exdb/mnist/') + +# Parameters +batch_size = 4096 # The number of samples per batch +num_classes = 10 # The 10 digits +num_features = 784 # Each image is 28x28 pixels +max_steps = 10000 + +# GBDT Parameters +learning_rate = 0.1 +l1_regul = 0. +l2_regul = 1. +examples_per_layer = 1000 +num_trees = 10 +max_depth = 16 + +# Fill GBDT parameters into the config proto +learner_config = gbdt_learner.LearnerConfig() +learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate +learner_config.regularization.l1 = l1_regul +learner_config.regularization.l2 = l2_regul / examples_per_layer +learner_config.constraints.max_tree_depth = max_depth +growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER +learner_config.growing_mode = growing_mode +run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300) +learner_config.multi_class_strategy = ( + gbdt_learner.LearnerConfig.DIAGONAL_HESSIAN)\ + +# Create a TensorFlor GBDT Estimator +gbdt_model = GradientBoostedDecisionTreeClassifier( + model_dir=None, # No save directory specified + learner_config=learner_config, + n_classes=num_classes, + examples_per_layer=examples_per_layer, + num_trees=num_trees, + center_bias=False, + config=run_config) + +# Display TF info logs +tf.logging.set_verbosity(tf.logging.INFO) + +# Define the input function for training +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.train.images}, y=mnist.train.labels, + batch_size=batch_size, num_epochs=None, shuffle=True) +# Train the Model +gbdt_model.fit(input_fn=input_fn, max_steps=max_steps) + +# Evaluate the Model +# Define the input function for evaluating +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.test.images}, y=mnist.test.labels, + batch_size=batch_size, shuffle=False) +# Use the Estimator 'evaluate' method +e = gbdt_model.evaluate(input_fn=input_fn) + +print("Testing Accuracy:", e['accuracy']) diff --git a/tensorflow_v1/examples/2_BasicModels/kmeans.py b/tensorflow_v1/examples/2_BasicModels/kmeans.py new file mode 100644 index 00000000..ed4bf91b --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/kmeans.py @@ -0,0 +1,93 @@ +""" K-Means. + +Implement K-Means algorithm with TensorFlow, and apply it to classify +handwritten digit images. This example is using the MNIST database of +handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/). + +Note: This example requires TensorFlow v1.1.0 or over. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import numpy as np +import tensorflow as tf +from tensorflow.contrib.factorization import KMeans + +# Ignore all GPUs, tf k-means does not benefit from it. +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "" + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) +full_data_x = mnist.train.images + +# Parameters +num_steps = 50 # Total steps to train +batch_size = 1024 # The number of samples per batch +k = 25 # The number of clusters +num_classes = 10 # The 10 digits +num_features = 784 # Each image is 28x28 pixels + +# Input images +X = tf.placeholder(tf.float32, shape=[None, num_features]) +# Labels (for assigning a label to a centroid and testing) +Y = tf.placeholder(tf.float32, shape=[None, num_classes]) + +# K-Means Parameters +kmeans = KMeans(inputs=X, num_clusters=k, distance_metric='cosine', + use_mini_batch=True) + +# Build KMeans graph +training_graph = kmeans.training_graph() + +if len(training_graph) > 6: # Tensorflow 1.4+ + (all_scores, cluster_idx, scores, cluster_centers_initialized, + cluster_centers_var, init_op, train_op) = training_graph +else: + (all_scores, cluster_idx, scores, cluster_centers_initialized, + init_op, train_op) = training_graph + +cluster_idx = cluster_idx[0] # fix for cluster_idx being a tuple +avg_distance = tf.reduce_mean(scores) + +# Initialize the variables (i.e. assign their default value) +init_vars = tf.global_variables_initializer() + +# Start TensorFlow session +sess = tf.Session() + +# Run the initializer +sess.run(init_vars, feed_dict={X: full_data_x}) +sess.run(init_op, feed_dict={X: full_data_x}) + +# Training +for i in range(1, num_steps + 1): + _, d, idx = sess.run([train_op, avg_distance, cluster_idx], + feed_dict={X: full_data_x}) + if i % 10 == 0 or i == 1: + print("Step %i, Avg Distance: %f" % (i, d)) + +# Assign a label to each centroid +# Count total number of labels per centroid, using the label of each training +# sample to their closest centroid (given by 'idx') +counts = np.zeros(shape=(k, num_classes)) +for i in range(len(idx)): + counts[idx[i]] += mnist.train.labels[i] +# Assign the most frequent label to the centroid +labels_map = [np.argmax(c) for c in counts] +labels_map = tf.convert_to_tensor(labels_map) + +# Evaluation ops +# Lookup: centroid_id -> label +cluster_label = tf.nn.embedding_lookup(labels_map, cluster_idx) +# Compute accuracy +correct_prediction = tf.equal(cluster_label, tf.cast(tf.argmax(Y, 1), tf.int32)) +accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + +# Test Model +test_x, test_y = mnist.test.images, mnist.test.labels +print("Test Accuracy:", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y})) diff --git a/tensorflow_v1/examples/2_BasicModels/linear_regression.py b/tensorflow_v1/examples/2_BasicModels/linear_regression.py new file mode 100644 index 00000000..cfb1c2fa --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/linear_regression.py @@ -0,0 +1,89 @@ +''' +A linear regression learning algorithm example using TensorFlow library. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf +import numpy +import matplotlib.pyplot as plt +rng = numpy.random + +# Parameters +learning_rate = 0.01 +training_epochs = 1000 +display_step = 50 + +# Training Data +train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167, + 7.042,10.791,5.313,7.997,5.654,9.27,3.1]) +train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221, + 2.827,3.465,1.65,2.904,2.42,2.94,1.3]) +n_samples = train_X.shape[0] + +# tf Graph Input +X = tf.placeholder("float") +Y = tf.placeholder("float") + +# Set model weights +W = tf.Variable(rng.randn(), name="weight") +b = tf.Variable(rng.randn(), name="bias") + +# Construct a linear model +pred = tf.add(tf.multiply(X, W), b) + +# Mean squared error +cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples) +# Gradient descent +# Note, minimize() knows to modify W and b because Variable objects are trainable=True by default +optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Fit all training data + for epoch in range(training_epochs): + for (x, y) in zip(train_X, train_Y): + sess.run(optimizer, feed_dict={X: x, Y: y}) + + # Display logs per epoch step + if (epoch+1) % display_step == 0: + c = sess.run(cost, feed_dict={X: train_X, Y:train_Y}) + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \ + "W=", sess.run(W), "b=", sess.run(b)) + + print("Optimization Finished!") + training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) + print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n') + + # Graphic display + plt.plot(train_X, train_Y, 'ro', label='Original data') + plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line') + plt.legend() + plt.show() + + # Testing example, as requested (Issue #2) + test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1]) + test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03]) + + print("Testing... (Mean square loss Comparison)") + testing_cost = sess.run( + tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]), + feed_dict={X: test_X, Y: test_Y}) # same function as cost above + print("Testing cost=", testing_cost) + print("Absolute mean square loss difference:", abs( + training_cost - testing_cost)) + + plt.plot(test_X, test_Y, 'bo', label='Testing data') + plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line') + plt.legend() + plt.show() diff --git a/tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py b/tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py new file mode 100644 index 00000000..a9b2b2f7 --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py @@ -0,0 +1,69 @@ +''' Linear Regression with Eager API. + +A linear regression learning algorithm example using TensorFlow's Eager API. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' +from __future__ import absolute_import, division, print_function + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + +# Set Eager API +tf.enable_eager_execution() +tfe = tf.contrib.eager + +# Training Data +train_X = [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167, + 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1] +train_Y = [1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221, + 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3] +n_samples = len(train_X) + +# Parameters +learning_rate = 0.01 +display_step = 100 +num_steps = 1000 + +# Weight and Bias +W = tfe.Variable(np.random.randn()) +b = tfe.Variable(np.random.randn()) + + +# Linear regression (Wx + b) +def linear_regression(inputs): + return inputs * W + b + + +# Mean square error +def mean_square_fn(model_fn, inputs, labels): + return tf.reduce_sum(tf.pow(model_fn(inputs) - labels, 2)) / (2 * n_samples) + + +# SGD Optimizer +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +# Compute gradients +grad = tfe.implicit_gradients(mean_square_fn) + +# Initial cost, before optimizing +print("Initial cost= {:.9f}".format( + mean_square_fn(linear_regression, train_X, train_Y)), + "W=", W.numpy(), "b=", b.numpy()) + +# Training +for step in range(num_steps): + + optimizer.apply_gradients(grad(linear_regression, train_X, train_Y)) + + if (step + 1) % display_step == 0 or step == 0: + print("Epoch:", '%04d' % (step + 1), "cost=", + "{:.9f}".format(mean_square_fn(linear_regression, train_X, train_Y)), + "W=", W.numpy(), "b=", b.numpy()) + +# Graphic display +plt.plot(train_X, train_Y, 'ro', label='Original data') +plt.plot(train_X, np.array(W * train_X + b), label='Fitted line') +plt.legend() +plt.show() diff --git a/tensorflow_v1/examples/2_BasicModels/logistic_regression.py b/tensorflow_v1/examples/2_BasicModels/logistic_regression.py new file mode 100644 index 00000000..f38ea81c --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/logistic_regression.py @@ -0,0 +1,71 @@ +''' +A logistic regression learning algorithm example using TensorFlow library. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.01 +training_epochs = 25 +batch_size = 100 +display_step = 1 + +# tf Graph Input +x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784 +y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes + +# Set model weights +W = tf.Variable(tf.zeros([784, 10])) +b = tf.Variable(tf.zeros([10])) + +# Construct model +pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax + +# Minimize error using cross entropy +cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) +# Gradient Descent +optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Training cycle + for epoch in range(training_epochs): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_xs, batch_ys = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, + y: batch_ys}) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if (epoch+1) % display_step == 0: + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) + + print("Optimization Finished!") + + # Test model + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + # Calculate accuracy + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) diff --git a/tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py b/tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py new file mode 100644 index 00000000..c65205e7 --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py @@ -0,0 +1,105 @@ +''' Logistic Regression with Eager API. + +A logistic regression learning algorithm example using TensorFlow's Eager API. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' +from __future__ import absolute_import, division, print_function + +import tensorflow as tf + +# Set Eager API +tf.enable_eager_execution() +tfe = tf.contrib.eager + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +# Parameters +learning_rate = 0.1 +batch_size = 128 +num_steps = 1000 +display_step = 100 + +dataset = tf.data.Dataset.from_tensor_slices( + (mnist.train.images, mnist.train.labels)) +dataset = dataset.repeat().batch(batch_size).prefetch(batch_size) +dataset_iter = tfe.Iterator(dataset) + +# Variables +W = tfe.Variable(tf.zeros([784, 10]), name='weights') +b = tfe.Variable(tf.zeros([10]), name='bias') + + +# Logistic regression (Wx + b) +def logistic_regression(inputs): + return tf.matmul(inputs, W) + b + + +# Cross-Entropy loss function +def loss_fn(inference_fn, inputs, labels): + # Using sparse_softmax cross entropy + return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=inference_fn(inputs), labels=labels)) + + +# Calculate accuracy +def accuracy_fn(inference_fn, inputs, labels): + prediction = tf.nn.softmax(inference_fn(inputs)) + correct_pred = tf.equal(tf.argmax(prediction, 1), labels) + return tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + + +# SGD Optimizer +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +# Compute gradients +grad = tfe.implicit_gradients(loss_fn) + +# Training +average_loss = 0. +average_acc = 0. +for step in range(num_steps): + + # Iterate through the dataset + d = dataset_iter.next() + + # Images + x_batch = d[0] + # Labels + y_batch = tf.cast(d[1], dtype=tf.int64) + + # Compute the batch loss + batch_loss = loss_fn(logistic_regression, x_batch, y_batch) + average_loss += batch_loss + # Compute the batch accuracy + batch_accuracy = accuracy_fn(logistic_regression, x_batch, y_batch) + average_acc += batch_accuracy + + if step == 0: + # Display the initial cost, before optimizing + print("Initial loss= {:.9f}".format(average_loss)) + + # Update the variables following gradients info + optimizer.apply_gradients(grad(logistic_regression, x_batch, y_batch)) + + # Display info + if (step + 1) % display_step == 0 or step == 0: + if step > 0: + average_loss /= display_step + average_acc /= display_step + print("Step:", '%04d' % (step + 1), " loss=", + "{:.9f}".format(average_loss), " accuracy=", + "{:.4f}".format(average_acc)) + average_loss = 0. + average_acc = 0. + +# Evaluate model on the test image set +testX = mnist.test.images +testY = mnist.test.labels + +test_acc = accuracy_fn(logistic_regression, testX, testY) +print("Testset Accuracy: {:.4f}".format(test_acc)) diff --git a/tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py b/tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py new file mode 100644 index 00000000..ea40d68e --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py @@ -0,0 +1,55 @@ +''' +A nearest neighbor learning algorithm example using TensorFlow library. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import numpy as np +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# In this example, we limit mnist data +Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates) +Xte, Yte = mnist.test.next_batch(200) #200 for testing + +# tf Graph Input +xtr = tf.placeholder("float", [None, 784]) +xte = tf.placeholder("float", [784]) + +# Nearest Neighbor calculation using L1 Distance +# Calculate L1 Distance +distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1) +# Prediction: Get min distance index (Nearest neighbor) +pred = tf.arg_min(distance, 0) + +accuracy = 0. + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # loop over test data + for i in range(len(Xte)): + # Get nearest neighbor + nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]}) + # Get nearest neighbor class label and compare it to its true label + print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), \ + "True Class:", np.argmax(Yte[i])) + # Calculate accuracy + if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]): + accuracy += 1./len(Xte) + print("Done!") + print("Accuracy:", accuracy) diff --git a/tensorflow_v1/examples/2_BasicModels/random_forest.py b/tensorflow_v1/examples/2_BasicModels/random_forest.py new file mode 100644 index 00000000..daff4721 --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/random_forest.py @@ -0,0 +1,77 @@ +""" Random Forest. + +Implement Random Forest algorithm with TensorFlow, and apply it to classify +handwritten digit images. This example is using the MNIST database of +handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib.tensor_forest.python import tensor_forest +from tensorflow.python.ops import resources + +# Ignore all GPUs, tf random forest does not benefit from it. +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "" + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +# Parameters +num_steps = 500 # Total steps to train +batch_size = 1024 # The number of samples per batch +num_classes = 10 # The 10 digits +num_features = 784 # Each image is 28x28 pixels +num_trees = 10 +max_nodes = 1000 + +# Input and Target data +X = tf.placeholder(tf.float32, shape=[None, num_features]) +# For random forest, labels must be integers (the class id) +Y = tf.placeholder(tf.int32, shape=[None]) + +# Random Forest Parameters +hparams = tensor_forest.ForestHParams(num_classes=num_classes, + num_features=num_features, + num_trees=num_trees, + max_nodes=max_nodes).fill() + +# Build the Random Forest +forest_graph = tensor_forest.RandomForestGraphs(hparams) +# Get training graph and loss +train_op = forest_graph.training_graph(X, Y) +loss_op = forest_graph.training_loss(X, Y) + +# Measure the accuracy +infer_op, _, _ = forest_graph.inference_graph(X) +correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64)) +accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + +# Initialize the variables (i.e. assign their default value) and forest resources +init_vars = tf.group(tf.global_variables_initializer(), + resources.initialize_resources(resources.shared_resources())) + +# Start TensorFlow session +sess = tf.Session() + +# Run the initializer +sess.run(init_vars) + +# Training +for i in range(1, num_steps + 1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, batch_y = mnist.train.next_batch(batch_size) + _, l = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y}) + if i % 50 == 0 or i == 1: + acc = sess.run(accuracy_op, feed_dict={X: batch_x, Y: batch_y}) + print('Step %i, Loss: %f, Acc: %f' % (i, l, acc)) + +# Test Model +test_x, test_y = mnist.test.images, mnist.test.labels +print("Test Accuracy:", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y})) diff --git a/tensorflow_v1/examples/2_BasicModels/word2vec.py b/tensorflow_v1/examples/2_BasicModels/word2vec.py new file mode 100644 index 00000000..094fca8c --- /dev/null +++ b/tensorflow_v1/examples/2_BasicModels/word2vec.py @@ -0,0 +1,195 @@ +""" Word2Vec. + +Implement Word2Vec algorithm to compute vector representations of words. +This example is using a small chunk of Wikipedia articles to train from. + +References: + - Mikolov, Tomas et al. "Efficient Estimation of Word Representations + in Vector Space.", 2013. + +Links: + - [Word2Vec] https://arxiv.org/pdf/1301.3781.pdf + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import division, print_function, absolute_import + +import collections +import os +import random +import urllib +import zipfile + +import numpy as np +import tensorflow as tf + +# Training Parameters +learning_rate = 0.1 +batch_size = 128 +num_steps = 3000000 +display_step = 10000 +eval_step = 200000 + +# Evaluation Parameters +eval_words = ['five', 'of', 'going', 'hardware', 'american', 'britain'] + +# Word2Vec Parameters +embedding_size = 200 # Dimension of the embedding vector +max_vocabulary_size = 50000 # Total number of different words in the vocabulary +min_occurrence = 10 # Remove all words that does not appears at least n times +skip_window = 3 # How many words to consider left and right +num_skips = 2 # How many times to reuse an input to generate a label +num_sampled = 64 # Number of negative examples to sample + + +# Download a small chunk of Wikipedia articles collection +url = 'http://mattmahoney.net/dc/text8.zip' +data_path = 'text8.zip' +if not os.path.exists(data_path): + print("Downloading the dataset... (It may take some time)") + filename, _ = urllib.urlretrieve(url, data_path) + print("Done!") +# Unzip the dataset file. Text has already been processed +with zipfile.ZipFile(data_path) as f: + text_words = f.read(f.namelist()[0]).lower().split() + +# Build the dictionary and replace rare words with UNK token +count = [('UNK', -1)] +# Retrieve the most common words +count.extend(collections.Counter(text_words).most_common(max_vocabulary_size - 1)) +# Remove samples with less than 'min_occurrence' occurrences +for i in range(len(count) - 1, -1, -1): + if count[i][1] < min_occurrence: + count.pop(i) + else: + # The collection is ordered, so stop when 'min_occurrence' is reached + break +# Compute the vocabulary size +vocabulary_size = len(count) +# Assign an id to each word +word2id = dict() +for i, (word, _)in enumerate(count): + word2id[word] = i + +data = list() +unk_count = 0 +for word in text_words: + # Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary + index = word2id.get(word, 0) + if index == 0: + unk_count += 1 + data.append(index) +count[0] = ('UNK', unk_count) +id2word = dict(zip(word2id.values(), word2id.keys())) + +print("Words count:", len(text_words)) +print("Unique words:", len(set(text_words))) +print("Vocabulary size:", vocabulary_size) +print("Most common words:", count[:10]) + +data_index = 0 +# Generate training batch for the skip-gram model +def next_batch(batch_size, num_skips, skip_window): + global data_index + assert batch_size % num_skips == 0 + assert num_skips <= 2 * skip_window + batch = np.ndarray(shape=(batch_size), dtype=np.int32) + labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) + # get window size (words left and right + current one) + span = 2 * skip_window + 1 + buffer = collections.deque(maxlen=span) + if data_index + span > len(data): + data_index = 0 + buffer.extend(data[data_index:data_index + span]) + data_index += span + for i in range(batch_size // num_skips): + context_words = [w for w in range(span) if w != skip_window] + words_to_use = random.sample(context_words, num_skips) + for j, context_word in enumerate(words_to_use): + batch[i * num_skips + j] = buffer[skip_window] + labels[i * num_skips + j, 0] = buffer[context_word] + if data_index == len(data): + buffer.extend(data[0:span]) + data_index = span + else: + buffer.append(data[data_index]) + data_index += 1 + # Backtrack a little bit to avoid skipping words in the end of a batch + data_index = (data_index + len(data) - span) % len(data) + return batch, labels + + +# Input data +X = tf.placeholder(tf.int32, shape=[None]) +# Input label +Y = tf.placeholder(tf.int32, shape=[None, 1]) + +# Ensure the following ops & var are assigned on CPU +# (some ops are not compatible on GPU) +with tf.device('/cpu:0'): + # Create the embedding variable (each row represent a word embedding vector) + embedding = tf.Variable(tf.random_normal([vocabulary_size, embedding_size])) + # Lookup the corresponding embedding vectors for each sample in X + X_embed = tf.nn.embedding_lookup(embedding, X) + + # Construct the variables for the NCE loss + nce_weights = tf.Variable(tf.random_normal([vocabulary_size, embedding_size])) + nce_biases = tf.Variable(tf.zeros([vocabulary_size])) + +# Compute the average NCE loss for the batch +loss_op = tf.reduce_mean( + tf.nn.nce_loss(weights=nce_weights, + biases=nce_biases, + labels=Y, + inputs=X_embed, + num_sampled=num_sampled, + num_classes=vocabulary_size)) + +# Define the optimizer +optimizer = tf.train.GradientDescentOptimizer(learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluation +# Compute the cosine similarity between input data embedding and every embedding vectors +X_embed_norm = X_embed / tf.sqrt(tf.reduce_sum(tf.square(X_embed))) +embedding_norm = embedding / tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keepdims=True)) +cosine_sim_op = tf.matmul(X_embed_norm, embedding_norm, transpose_b=True) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Testing data + x_test = np.array([word2id[w] for w in eval_words]) + + average_loss = 0 + for step in xrange(1, num_steps + 1): + # Get a new batch of data + batch_x, batch_y = next_batch(batch_size, num_skips, skip_window) + # Run training op + _, loss = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y}) + average_loss += loss + + if step % display_step == 0 or step == 1: + if step > 1: + average_loss /= display_step + print("Step " + str(step) + ", Average Loss= " + \ + "{:.4f}".format(average_loss)) + average_loss = 0 + + # Evaluation + if step % eval_step == 0 or step == 1: + print("Evaluation...") + sim = sess.run(cosine_sim_op, feed_dict={X: x_test}) + for i in xrange(len(eval_words)): + top_k = 8 # number of nearest neighbors + nearest = (-sim[i, :]).argsort()[1:top_k + 1] + log_str = '"%s" nearest neighbors:' % eval_words[i] + for k in xrange(top_k): + log_str = '%s %s,' % (log_str, id2word[nearest[k]]) + print(log_str) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py b/tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py new file mode 100644 index 00000000..9d3ba60e --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py @@ -0,0 +1,142 @@ +""" Auto Encoder Example. + +Build a 2 layers auto-encoder with TensorFlow to compress images to a +lower latent space and then reconstruct them. + +References: + Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based + learning applied to document recognition." Proceedings of the IEEE, + 86(11):2278-2324, November 1998. + +Links: + [MNIST Dataset] http://yann.lecun.com/exdb/mnist/ + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import division, print_function, absolute_import + +import tensorflow as tf +import numpy as np +import matplotlib.pyplot as plt + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Parameters +learning_rate = 0.01 +num_steps = 30000 +batch_size = 256 + +display_step = 1000 +examples_to_show = 10 + +# Network Parameters +num_hidden_1 = 256 # 1st layer num features +num_hidden_2 = 128 # 2nd layer num features (the latent dim) +num_input = 784 # MNIST data input (img shape: 28*28) + +# tf Graph input (only pictures) +X = tf.placeholder("float", [None, num_input]) + +weights = { + 'encoder_h1': tf.Variable(tf.random_normal([num_input, num_hidden_1])), + 'encoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_hidden_2])), + 'decoder_h1': tf.Variable(tf.random_normal([num_hidden_2, num_hidden_1])), + 'decoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_input])), +} +biases = { + 'encoder_b1': tf.Variable(tf.random_normal([num_hidden_1])), + 'encoder_b2': tf.Variable(tf.random_normal([num_hidden_2])), + 'decoder_b1': tf.Variable(tf.random_normal([num_hidden_1])), + 'decoder_b2': tf.Variable(tf.random_normal([num_input])), +} + +# Building the encoder +def encoder(x): + # Encoder Hidden layer with sigmoid activation #1 + layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']), + biases['encoder_b1'])) + # Encoder Hidden layer with sigmoid activation #2 + layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']), + biases['encoder_b2'])) + return layer_2 + + +# Building the decoder +def decoder(x): + # Decoder Hidden layer with sigmoid activation #1 + layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']), + biases['decoder_b1'])) + # Decoder Hidden layer with sigmoid activation #2 + layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']), + biases['decoder_b2'])) + return layer_2 + +# Construct model +encoder_op = encoder(X) +decoder_op = decoder(encoder_op) + +# Prediction +y_pred = decoder_op +# Targets (Labels) are the input data. +y_true = X + +# Define loss and optimizer, minimize the squared error +loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) +optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start Training +# Start a new TF session +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Training + for i in range(1, num_steps+1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + + # Run optimization op (backprop) and cost op (to get loss value) + _, l = sess.run([optimizer, loss], feed_dict={X: batch_x}) + # Display logs per step + if i % display_step == 0 or i == 1: + print('Step %i: Minibatch Loss: %f' % (i, l)) + + # Testing + # Encode and decode images from test set and visualize their reconstruction. + n = 4 + canvas_orig = np.empty((28 * n, 28 * n)) + canvas_recon = np.empty((28 * n, 28 * n)) + for i in range(n): + # MNIST test set + batch_x, _ = mnist.test.next_batch(n) + # Encode and decode the digit image + g = sess.run(decoder_op, feed_dict={X: batch_x}) + + # Display original images + for j in range(n): + # Draw the original digits + canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = \ + batch_x[j].reshape([28, 28]) + # Display reconstructed images + for j in range(n): + # Draw the reconstructed digits + canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = \ + g[j].reshape([28, 28]) + + print("Original Images") + plt.figure(figsize=(n, n)) + plt.imshow(canvas_orig, origin="upper", cmap="gray") + plt.show() + + print("Reconstructed Images") + plt.figure(figsize=(n, n)) + plt.imshow(canvas_recon, origin="upper", cmap="gray") + plt.show() diff --git a/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py b/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py new file mode 100644 index 00000000..2ff862ae --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py @@ -0,0 +1,126 @@ +""" Bi-directional Recurrent Neural Network. + +A Bi-directional Recurrent Neural Network (LSTM) implementation example using +TensorFlow library. This example is using the MNIST database of handwritten +digits (http://yann.lecun.com/exdb/mnist/) + +Links: + [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib import rnn +import numpy as np + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +''' +To classify images using a bidirectional recurrent neural network, we consider +every image row as a sequence of pixels. Because MNIST image shape is 28*28px, +we will then handle 28 sequences of 28 steps for every sample. +''' + +# Training Parameters +learning_rate = 0.001 +training_steps = 10000 +batch_size = 128 +display_step = 200 + +# Network Parameters +num_input = 28 # MNIST data input (img shape: 28*28) +timesteps = 28 # timesteps +num_hidden = 128 # hidden layer num of features +num_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +X = tf.placeholder("float", [None, timesteps, num_input]) +Y = tf.placeholder("float", [None, num_classes]) + +# Define weights +weights = { + # Hidden layer weights => 2*n_hidden because of forward + backward cells + 'out': tf.Variable(tf.random_normal([2*num_hidden, num_classes])) +} +biases = { + 'out': tf.Variable(tf.random_normal([num_classes])) +} + + +def BiRNN(x, weights, biases): + + # Prepare data shape to match `rnn` function requirements + # Current data input shape: (batch_size, timesteps, n_input) + # Required shape: 'timesteps' tensors list of shape (batch_size, num_input) + + # Unstack to get a list of 'timesteps' tensors of shape (batch_size, num_input) + x = tf.unstack(x, timesteps, 1) + + # Define lstm cells with tensorflow + # Forward direction cell + lstm_fw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) + # Backward direction cell + lstm_bw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) + + # Get lstm cell output + try: + outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, + dtype=tf.float32) + except Exception: # Old TensorFlow version only returns outputs not states + outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x, + dtype=tf.float32) + + # Linear activation, using rnn inner loop last output + return tf.matmul(outputs[-1], weights['out']) + biases['out'] + +logits = BiRNN(X, weights, biases) +prediction = tf.nn.softmax(logits) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, training_steps+1): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Reshape data to get 28 seq of 28 elements + batch_x = batch_x.reshape((batch_size, timesteps, num_input)) + # Run optimization op (backprop) + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy for 128 mnist test images + test_len = 128 + test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input)) + test_label = mnist.test.labels[:test_len] + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={X: test_data, Y: test_label})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py b/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py new file mode 100644 index 00000000..e7088f1f --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py @@ -0,0 +1,125 @@ +""" Convolutional Neural Network. + +Build and train a convolutional neural network with TensorFlow. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +This example is using TensorFlow layers API, see 'convolutional_network_raw' +example for a raw implementation with variables. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import division, print_function, absolute_import + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +import tensorflow as tf + +# Training Parameters +learning_rate = 0.001 +num_steps = 2000 +batch_size = 128 + +# Network Parameters +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.25 # Dropout, probability to drop a unit + + +# Create the neural network +def conv_net(x_dict, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + # TF Estimator input is a dict, in case of multiple inputs + x = x_dict['images'] + + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer with 32 filters and a kernel size of 5 + conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv1 = tf.layers.max_pooling2d(conv1, 2, 2) + + # Convolution Layer with 64 filters and a kernel size of 3 + conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv2 = tf.layers.max_pooling2d(conv2, 2, 2) + + # Flatten the data to a 1-D vector for the fully connected layer + fc1 = tf.contrib.layers.flatten(conv2) + + # Fully connected layer (in tf contrib folder for now) + fc1 = tf.layers.dense(fc1, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) + + # Output layer, class prediction + out = tf.layers.dense(fc1, n_classes) + + return out + + +# Define the model function (following TF Estimator Template) +def model_fn(features, labels, mode): + # Build the neural network + # Because Dropout have different behavior at training and prediction time, we + # need to create 2 distinct computation graphs that still share the same weights. + logits_train = conv_net(features, num_classes, dropout, reuse=False, + is_training=True) + logits_test = conv_net(features, num_classes, dropout, reuse=True, + is_training=False) + + # Predictions + pred_classes = tf.argmax(logits_test, axis=1) + pred_probas = tf.nn.softmax(logits_test) + + # If prediction mode, early return + if mode == tf.estimator.ModeKeys.PREDICT: + return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) + + # Define loss and optimizer + loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits_train, labels=tf.cast(labels, dtype=tf.int32))) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + train_op = optimizer.minimize(loss_op, + global_step=tf.train.get_global_step()) + + # Evaluate the accuracy of the model + acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) + + # TF Estimators requires to return a EstimatorSpec, that specify + # the different ops for training, evaluating, ... + estim_specs = tf.estimator.EstimatorSpec( + mode=mode, + predictions=pred_classes, + loss=loss_op, + train_op=train_op, + eval_metric_ops={'accuracy': acc_op}) + + return estim_specs + +# Build the Estimator +model = tf.estimator.Estimator(model_fn) + +# Define the input function for training +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.train.images}, y=mnist.train.labels, + batch_size=batch_size, num_epochs=None, shuffle=True) +# Train the Model +model.train(input_fn, steps=num_steps) + +# Evaluate the Model +# Define the input function for evaluating +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.test.images}, y=mnist.test.labels, + batch_size=batch_size, shuffle=False) +# Use the Estimator 'evaluate' method +e = model.evaluate(input_fn) + +print("Testing Accuracy:", e['accuracy']) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py b/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py new file mode 100644 index 00000000..d063f21f --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py @@ -0,0 +1,141 @@ +""" Convolutional Neural Network. + +Build and train a convolutional neural network with TensorFlow. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import division, print_function, absolute_import + +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Parameters +learning_rate = 0.001 +num_steps = 200 +batch_size = 128 +display_step = 10 + +# Network Parameters +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units + +# tf Graph input +X = tf.placeholder(tf.float32, [None, num_input]) +Y = tf.placeholder(tf.float32, [None, num_classes]) +keep_prob = tf.placeholder(tf.float32) # dropout (keep probability) + + +# Create some wrappers for simplicity +def conv2d(x, W, b, strides=1): + # Conv2D wrapper, with bias and relu activation + x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') + x = tf.nn.bias_add(x, b) + return tf.nn.relu(x) + + +def maxpool2d(x, k=2): + # MaxPool2D wrapper + return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], + padding='SAME') + + +# Create model +def conv_net(x, weights, biases, dropout): + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer + conv1 = conv2d(x, weights['wc1'], biases['bc1']) + # Max Pooling (down-sampling) + conv1 = maxpool2d(conv1, k=2) + + # Convolution Layer + conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) + # Max Pooling (down-sampling) + conv2 = maxpool2d(conv2, k=2) + + # Fully connected layer + # Reshape conv2 output to fit fully connected layer input + fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) + fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) + fc1 = tf.nn.relu(fc1) + # Apply Dropout + fc1 = tf.nn.dropout(fc1, dropout) + + # Output, class prediction + out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) + return out + +# Store layers weight & bias +weights = { + # 5x5 conv, 1 input, 32 outputs + 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), + # 5x5 conv, 32 inputs, 64 outputs + 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), + # fully connected, 7*7*64 inputs, 1024 outputs + 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), + # 1024 inputs, 10 outputs (class prediction) + 'out': tf.Variable(tf.random_normal([1024, num_classes])) +} + +biases = { + 'bc1': tf.Variable(tf.random_normal([32])), + 'bc2': tf.Variable(tf.random_normal([64])), + 'bd1': tf.Variable(tf.random_normal([1024])), + 'out': tf.Variable(tf.random_normal([num_classes])) +} + +# Construct model +logits = conv_net(X, weights, biases, keep_prob) +prediction = tf.nn.softmax(logits) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + + +# Evaluate model +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, num_steps+1): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.8}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y, + keep_prob: 1.0}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy for 256 MNIST test images + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={X: mnist.test.images[:256], + Y: mnist.test.labels[:256], + keep_prob: 1.0})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/dcgan.py b/tensorflow_v1/examples/3_NeuralNetworks/dcgan.py new file mode 100644 index 00000000..2de85441 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/dcgan.py @@ -0,0 +1,167 @@ +""" Deep Convolutional Generative Adversarial Network (DCGAN). + +Using deep convolutional generative adversarial networks (DCGAN) to generate +digit images from a noise distribution. + +References: + - Unsupervised representation learning with deep convolutional generative + adversarial networks. A Radford, L Metz, S Chintala. arXiv:1511.06434. + +Links: + - [DCGAN Paper](https://arxiv.org/abs/1511.06434). + - [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import division, print_function, absolute_import + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Params +num_steps = 20000 +batch_size = 32 + +# Network Params +image_dim = 784 # 28*28 pixels * 1 channel +gen_hidden_dim = 256 +disc_hidden_dim = 256 +noise_dim = 200 # Noise data points + + +# Generator Network +# Input: Noise, Output: Image +def generator(x, reuse=False): + with tf.variable_scope('Generator', reuse=reuse): + # TensorFlow Layers automatically create variables and calculate their + # shape, based on the input. + x = tf.layers.dense(x, units=6 * 6 * 128) + x = tf.nn.tanh(x) + # Reshape to a 4-D array of images: (batch, height, width, channels) + # New shape: (batch, 6, 6, 128) + x = tf.reshape(x, shape=[-1, 6, 6, 128]) + # Deconvolution, image shape: (batch, 14, 14, 64) + x = tf.layers.conv2d_transpose(x, 64, 4, strides=2) + # Deconvolution, image shape: (batch, 28, 28, 1) + x = tf.layers.conv2d_transpose(x, 1, 2, strides=2) + # Apply sigmoid to clip values between 0 and 1 + x = tf.nn.sigmoid(x) + return x + + +# Discriminator Network +# Input: Image, Output: Prediction Real/Fake Image +def discriminator(x, reuse=False): + with tf.variable_scope('Discriminator', reuse=reuse): + # Typical convolutional neural network to classify images. + x = tf.layers.conv2d(x, 64, 5) + x = tf.nn.tanh(x) + x = tf.layers.average_pooling2d(x, 2, 2) + x = tf.layers.conv2d(x, 128, 5) + x = tf.nn.tanh(x) + x = tf.layers.average_pooling2d(x, 2, 2) + x = tf.contrib.layers.flatten(x) + x = tf.layers.dense(x, 1024) + x = tf.nn.tanh(x) + # Output 2 classes: Real and Fake images + x = tf.layers.dense(x, 2) + return x + +# Build Networks +# Network Inputs +noise_input = tf.placeholder(tf.float32, shape=[None, noise_dim]) +real_image_input = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) + +# Build Generator Network +gen_sample = generator(noise_input) + +# Build 2 Discriminator Networks (one from real image input, one from generated samples) +disc_real = discriminator(real_image_input) +disc_fake = discriminator(gen_sample, reuse=True) +disc_concat = tf.concat([disc_real, disc_fake], axis=0) + +# Build the stacked generator/discriminator +stacked_gan = discriminator(gen_sample, reuse=True) + +# Build Targets (real or fake images) +disc_target = tf.placeholder(tf.int32, shape=[None]) +gen_target = tf.placeholder(tf.int32, shape=[None]) + +# Build Loss +disc_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=disc_concat, labels=disc_target)) +gen_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=stacked_gan, labels=gen_target)) + +# Build Optimizers +optimizer_gen = tf.train.AdamOptimizer(learning_rate=0.001) +optimizer_disc = tf.train.AdamOptimizer(learning_rate=0.001) + +# Training Variables for each optimizer +# By default in TensorFlow, all variables are updated by each optimizer, so we +# need to precise for each one of them the specific variables to update. +# Generator Network Variables +gen_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Generator') +# Discriminator Network Variables +disc_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Discriminator') + +# Create training operations +train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars) +train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for i in range(1, num_steps+1): + + # Prepare Input Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + batch_x = np.reshape(batch_x, newshape=[-1, 28, 28, 1]) + # Generate noise to feed to the generator + z = np.random.uniform(-1., 1., size=[batch_size, noise_dim]) + + # Prepare Targets (Real image: 1, Fake image: 0) + # The first half of data fed to the discriminator are real images, + # the other half are fake images (coming from the generator). + batch_disc_y = np.concatenate( + [np.ones([batch_size]), np.zeros([batch_size])], axis=0) + # Generator tries to fool the discriminator, thus targets are 1. + batch_gen_y = np.ones([batch_size]) + + # Training + feed_dict = {real_image_input: batch_x, noise_input: z, + disc_target: batch_disc_y, gen_target: batch_gen_y} + _, _, gl, dl = sess.run([train_gen, train_disc, gen_loss, disc_loss], + feed_dict=feed_dict) + if i % 100 == 0 or i == 1: + print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl)) + + # Generate images from noise, using the generator network. + f, a = plt.subplots(4, 10, figsize=(10, 4)) + for i in range(10): + # Noise input. + z = np.random.uniform(-1., 1., size=[4, noise_dim]) + g = sess.run(gen_sample, feed_dict={noise_input: z}) + for j in range(4): + # Generate image from noise. Extend to 3 channels for matplot figure. + img = np.reshape(np.repeat(g[j][:, :, np.newaxis], 3, axis=2), + newshape=(28, 28, 3)) + a[j][i].imshow(img) + + f.show() + plt.draw() + plt.waitforbuttonpress() diff --git a/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py b/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py new file mode 100644 index 00000000..faad368e --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py @@ -0,0 +1,193 @@ +""" Dynamic Recurrent Neural Network. + +TensorFlow implementation of a Recurrent Neural Network (LSTM) that performs +dynamic computation over sequences with variable length. This example is using +a toy dataset to classify linear sequences. The generated sequences have +variable length. + +Links: + [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +import random + + +# ==================== +# TOY DATA GENERATOR +# ==================== +class ToySequenceData(object): + """ Generate sequence of data with dynamic length. + This class generate samples for training: + - Class 0: linear sequences (i.e. [0, 1, 2, 3,...]) + - Class 1: random sequences (i.e. [1, 3, 10, 7,...]) + + NOTICE: + We have to pad each sequence to reach 'max_seq_len' for TensorFlow + consistency (we cannot feed a numpy array with inconsistent + dimensions). The dynamic calculation will then be perform thanks to + 'seqlen' attribute that records every actual sequence length. + """ + def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3, + max_value=1000): + self.data = [] + self.labels = [] + self.seqlen = [] + for i in range(n_samples): + # Random sequence length + len = random.randint(min_seq_len, max_seq_len) + # Monitor sequence length for TensorFlow dynamic calculation + self.seqlen.append(len) + # Add a random or linear int sequence (50% prob) + if random.random() < .5: + # Generate a linear sequence + rand_start = random.randint(0, max_value - len) + s = [[float(i)/max_value] for i in + range(rand_start, rand_start + len)] + # Pad sequence for dimension consistency + s += [[0.] for i in range(max_seq_len - len)] + self.data.append(s) + self.labels.append([1., 0.]) + else: + # Generate a random sequence + s = [[float(random.randint(0, max_value))/max_value] + for i in range(len)] + # Pad sequence for dimension consistency + s += [[0.] for i in range(max_seq_len - len)] + self.data.append(s) + self.labels.append([0., 1.]) + self.batch_id = 0 + + def next(self, batch_size): + """ Return a batch of data. When dataset end is reached, start over. + """ + if self.batch_id == len(self.data): + self.batch_id = 0 + batch_data = (self.data[self.batch_id:min(self.batch_id + + batch_size, len(self.data))]) + batch_labels = (self.labels[self.batch_id:min(self.batch_id + + batch_size, len(self.data))]) + batch_seqlen = (self.seqlen[self.batch_id:min(self.batch_id + + batch_size, len(self.data))]) + self.batch_id = min(self.batch_id + batch_size, len(self.data)) + return batch_data, batch_labels, batch_seqlen + + +# ========== +# MODEL +# ========== + +# Parameters +learning_rate = 0.01 +training_steps = 10000 +batch_size = 128 +display_step = 200 + +# Network Parameters +seq_max_len = 20 # Sequence max length +n_hidden = 64 # hidden layer num of features +n_classes = 2 # linear sequence or not + +trainset = ToySequenceData(n_samples=1000, max_seq_len=seq_max_len) +testset = ToySequenceData(n_samples=500, max_seq_len=seq_max_len) + +# tf Graph input +x = tf.placeholder("float", [None, seq_max_len, 1]) +y = tf.placeholder("float", [None, n_classes]) +# A placeholder for indicating each sequence length +seqlen = tf.placeholder(tf.int32, [None]) + +# Define weights +weights = { + 'out': tf.Variable(tf.random_normal([n_hidden, n_classes])) +} +biases = { + 'out': tf.Variable(tf.random_normal([n_classes])) +} + + +def dynamicRNN(x, seqlen, weights, biases): + + # Prepare data shape to match `rnn` function requirements + # Current data input shape: (batch_size, n_steps, n_input) + # Required shape: 'n_steps' tensors list of shape (batch_size, n_input) + + # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input) + x = tf.unstack(x, seq_max_len, 1) + + # Define a lstm cell with tensorflow + lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden) + + # Get lstm cell output, providing 'sequence_length' will perform dynamic + # calculation. + outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x, dtype=tf.float32, + sequence_length=seqlen) + + # When performing dynamic calculation, we must retrieve the last + # dynamically computed output, i.e., if a sequence length is 10, we need + # to retrieve the 10th output. + # However TensorFlow doesn't support advanced indexing yet, so we build + # a custom op that for each sample in batch size, get its length and + # get the corresponding relevant output. + + # 'outputs' is a list of output at every timestep, we pack them in a Tensor + # and change back dimension to [batch_size, n_step, n_input] + outputs = tf.stack(outputs) + outputs = tf.transpose(outputs, [1, 0, 2]) + + # Hack to build the indexing and retrieve the right output. + batch_size = tf.shape(outputs)[0] + # Start indices for each sample + index = tf.range(0, batch_size) * seq_max_len + (seqlen - 1) + # Indexing + outputs = tf.gather(tf.reshape(outputs, [-1, n_hidden]), index) + + # Linear activation, using outputs computed above + return tf.matmul(outputs, weights['out']) + biases['out'] + +pred = dynamicRNN(x, seqlen, weights, biases) + +# Define loss and optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost) + +# Evaluate model +correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, training_steps + 1): + batch_x, batch_y, batch_seqlen = trainset.next(batch_size) + # Run optimization op (backprop) + sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, + seqlen: batch_seqlen}) + if step % display_step == 0 or step == 1: + # Calculate batch accuracy & loss + acc, loss = sess.run([accuracy, cost], feed_dict={x: batch_x, y: batch_y, + seqlen: batch_seqlen}) + print("Step " + str(step*batch_size) + ", Minibatch Loss= " + \ + "{:.6f}".format(loss) + ", Training Accuracy= " + \ + "{:.5f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy + test_data = testset.data + test_label = testset.labels + test_seqlen = testset.seqlen + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={x: test_data, y: test_label, + seqlen: test_seqlen})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/gan.py b/tensorflow_v1/examples/3_NeuralNetworks/gan.py new file mode 100644 index 00000000..dd5977ad --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/gan.py @@ -0,0 +1,157 @@ +""" Generative Adversarial Networks (GAN). + +Using generative adversarial networks (GAN) to generate digit images from a +noise distribution. + +References: + - Generative adversarial nets. I Goodfellow, J Pouget-Abadie, M Mirza, + B Xu, D Warde-Farley, S Ozair, Y. Bengio. Advances in neural information + processing systems, 2672-2680. + - Understanding the difficulty of training deep feedforward neural networks. + X Glorot, Y Bengio. Aistats 9, 249-256 + +Links: + - [GAN Paper](https://arxiv.org/pdf/1406.2661.pdf). + - [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + - [Xavier Glorot Init](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import division, print_function, absolute_import + +import matplotlib.pyplot as plt +import numpy as np +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Params +num_steps = 100000 +batch_size = 128 +learning_rate = 0.0002 + +# Network Params +image_dim = 784 # 28*28 pixels +gen_hidden_dim = 256 +disc_hidden_dim = 256 +noise_dim = 100 # Noise data points + +# A custom initialization (see Xavier Glorot init) +def glorot_init(shape): + return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.)) + +# Store layers weight & bias +weights = { + 'gen_hidden1': tf.Variable(glorot_init([noise_dim, gen_hidden_dim])), + 'gen_out': tf.Variable(glorot_init([gen_hidden_dim, image_dim])), + 'disc_hidden1': tf.Variable(glorot_init([image_dim, disc_hidden_dim])), + 'disc_out': tf.Variable(glorot_init([disc_hidden_dim, 1])), +} +biases = { + 'gen_hidden1': tf.Variable(tf.zeros([gen_hidden_dim])), + 'gen_out': tf.Variable(tf.zeros([image_dim])), + 'disc_hidden1': tf.Variable(tf.zeros([disc_hidden_dim])), + 'disc_out': tf.Variable(tf.zeros([1])), +} + + +# Generator +def generator(x): + hidden_layer = tf.matmul(x, weights['gen_hidden1']) + hidden_layer = tf.add(hidden_layer, biases['gen_hidden1']) + hidden_layer = tf.nn.relu(hidden_layer) + out_layer = tf.matmul(hidden_layer, weights['gen_out']) + out_layer = tf.add(out_layer, biases['gen_out']) + out_layer = tf.nn.sigmoid(out_layer) + return out_layer + + +# Discriminator +def discriminator(x): + hidden_layer = tf.matmul(x, weights['disc_hidden1']) + hidden_layer = tf.add(hidden_layer, biases['disc_hidden1']) + hidden_layer = tf.nn.relu(hidden_layer) + out_layer = tf.matmul(hidden_layer, weights['disc_out']) + out_layer = tf.add(out_layer, biases['disc_out']) + out_layer = tf.nn.sigmoid(out_layer) + return out_layer + +# Build Networks +# Network Inputs +gen_input = tf.placeholder(tf.float32, shape=[None, noise_dim], name='input_noise') +disc_input = tf.placeholder(tf.float32, shape=[None, image_dim], name='disc_input') + +# Build Generator Network +gen_sample = generator(gen_input) + +# Build 2 Discriminator Networks (one from noise input, one from generated samples) +disc_real = discriminator(disc_input) +disc_fake = discriminator(gen_sample) + +# Build Loss +gen_loss = -tf.reduce_mean(tf.log(disc_fake)) +disc_loss = -tf.reduce_mean(tf.log(disc_real) + tf.log(1. - disc_fake)) + +# Build Optimizers +optimizer_gen = tf.train.AdamOptimizer(learning_rate=learning_rate) +optimizer_disc = tf.train.AdamOptimizer(learning_rate=learning_rate) + +# Training Variables for each optimizer +# By default in TensorFlow, all variables are updated by each optimizer, so we +# need to precise for each one of them the specific variables to update. +# Generator Network Variables +gen_vars = [weights['gen_hidden1'], weights['gen_out'], + biases['gen_hidden1'], biases['gen_out']] +# Discriminator Network Variables +disc_vars = [weights['disc_hidden1'], weights['disc_out'], + biases['disc_hidden1'], biases['disc_out']] + +# Create training operations +train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars) +train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for i in range(1, num_steps+1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + # Generate noise to feed to the generator + z = np.random.uniform(-1., 1., size=[batch_size, noise_dim]) + + # Train + feed_dict = {disc_input: batch_x, gen_input: z} + _, _, gl, dl = sess.run([train_gen, train_disc, gen_loss, disc_loss], + feed_dict=feed_dict) + if i % 1000 == 0 or i == 1: + print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl)) + + # Generate images from noise, using the generator network. + f, a = plt.subplots(4, 10, figsize=(10, 4)) + for i in range(10): + # Noise input. + z = np.random.uniform(-1., 1., size=[4, noise_dim]) + g = sess.run([gen_sample], feed_dict={gen_input: z}) + g = np.reshape(g, newshape=(4, 28, 28, 1)) + # Reverse colours for better display + g = -1 * (g - 1) + for j in range(4): + # Generate image from noise. Extend to 3 channels for matplot figure. + img = np.reshape(np.repeat(g[j][:, :, np.newaxis], 3, axis=2), + newshape=(28, 28, 3)) + a[j][i].imshow(img) + + f.show() + plt.draw() + plt.waitforbuttonpress() diff --git a/tensorflow_v1/examples/3_NeuralNetworks/multilayer_perceptron.py b/tensorflow_v1/examples/3_NeuralNetworks/multilayer_perceptron.py new file mode 100644 index 00000000..cf04b015 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/multilayer_perceptron.py @@ -0,0 +1,104 @@ +""" Multilayer Perceptron. + +A Multilayer Perceptron (Neural Network) implementation example using +TensorFlow library. This example is using the MNIST database of handwritten +digits (http://yann.lecun.com/exdb/mnist/). + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +# ------------------------------------------------------------------ +# +# THIS EXAMPLE HAS BEEN RENAMED 'neural_network.py', FOR SIMPLICITY. +# +# ------------------------------------------------------------------ + + +from __future__ import print_function + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +import tensorflow as tf + +# Parameters +learning_rate = 0.001 +training_epochs = 15 +batch_size = 100 +display_step = 1 + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +X = tf.placeholder("float", [None, n_input]) +Y = tf.placeholder("float", [None, n_classes]) + +# Store layers weight & bias +weights = { + 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), + 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) +} +biases = { + 'b1': tf.Variable(tf.random_normal([n_hidden_1])), + 'b2': tf.Variable(tf.random_normal([n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_classes])) +} + + +# Create model +def multilayer_perceptron(x): + # Hidden fully connected layer with 256 neurons + layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) + # Hidden fully connected layer with 256 neurons + layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) + # Output fully connected layer with a neuron for each class + out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] + return out_layer + +# Construct model +logits = multilayer_perceptron(X) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) +# Initializing the variables +init = tf.global_variables_initializer() + +with tf.Session() as sess: + sess.run(init) + + # Training cycle + for epoch in range(training_epochs): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([train_op, loss_op], feed_dict={X: batch_x, + Y: batch_y}) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if epoch % display_step == 0: + print("Epoch:", '%04d' % (epoch+1), "cost={:.9f}".format(avg_cost)) + print("Optimization Finished!") + + # Test model + pred = tf.nn.softmax(logits) # Apply softmax to logits + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1)) + # Calculate accuracy + accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) + print("Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py b/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py new file mode 100644 index 00000000..1fff2d54 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py @@ -0,0 +1,103 @@ +""" Neural Network. + +A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron) +implementation with TensorFlow. This example is using the MNIST database +of handwritten digits (http://yann.lecun.com/exdb/mnist/). + +This example is using TensorFlow layers, see 'neural_network_raw' example for +a raw implementation with variables. + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +import tensorflow as tf + +# Parameters +learning_rate = 0.1 +num_steps = 1000 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) + + +# Define the neural network +def neural_net(x_dict): + # TF Estimator input is a dict, in case of multiple inputs + x = x_dict['images'] + # Hidden fully connected layer with 256 neurons + layer_1 = tf.layers.dense(x, n_hidden_1) + # Hidden fully connected layer with 256 neurons + layer_2 = tf.layers.dense(layer_1, n_hidden_2) + # Output fully connected layer with a neuron for each class + out_layer = tf.layers.dense(layer_2, num_classes) + return out_layer + + +# Define the model function (following TF Estimator Template) +def model_fn(features, labels, mode): + # Build the neural network + logits = neural_net(features) + + # Predictions + pred_classes = tf.argmax(logits, axis=1) + pred_probas = tf.nn.softmax(logits) + + # If prediction mode, early return + if mode == tf.estimator.ModeKeys.PREDICT: + return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) + + # Define loss and optimizer + loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits, labels=tf.cast(labels, dtype=tf.int32))) + optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) + train_op = optimizer.minimize(loss_op, + global_step=tf.train.get_global_step()) + + # Evaluate the accuracy of the model + acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes) + + # TF Estimators requires to return a EstimatorSpec, that specify + # the different ops for training, evaluating, ... + estim_specs = tf.estimator.EstimatorSpec( + mode=mode, + predictions=pred_classes, + loss=loss_op, + train_op=train_op, + eval_metric_ops={'accuracy': acc_op}) + + return estim_specs + +# Build the Estimator +model = tf.estimator.Estimator(model_fn) + +# Define the input function for training +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.train.images}, y=mnist.train.labels, + batch_size=batch_size, num_epochs=None, shuffle=True) +# Train the Model +model.train(input_fn, steps=num_steps) + +# Evaluate the Model +# Define the input function for evaluating +input_fn = tf.estimator.inputs.numpy_input_fn( + x={'images': mnist.test.images}, y=mnist.test.labels, + batch_size=batch_size, shuffle=False) +# Use the Estimator 'evaluate' method +e = model.evaluate(input_fn) + +print("Testing Accuracy:", e['accuracy']) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py b/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py new file mode 100644 index 00000000..2151bba9 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py @@ -0,0 +1,133 @@ +""" Neural Network with Eager API. + +A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron) +implementation with TensorFlow's Eager API. This example is using the MNIST database +of handwritten digits (http://yann.lecun.com/exdb/mnist/). + +This example is using TensorFlow layers, see 'neural_network_raw' example for +a raw implementation with variables. + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import print_function + +import tensorflow as tf + +# Set Eager API +tf.enable_eager_execution() +tfe = tf.contrib.eager + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=False) + +# Parameters +learning_rate = 0.001 +num_steps = 1000 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) + +# Using TF Dataset to split data into batches +dataset = tf.data.Dataset.from_tensor_slices( + (mnist.train.images, mnist.train.labels)) +dataset = dataset.repeat().batch(batch_size).prefetch(batch_size) +dataset_iter = tfe.Iterator(dataset) + + +# Define the neural network. To use eager API and tf.layers API together, +# we must instantiate a tfe.Network class as follow: +class NeuralNet(tfe.Network): + def __init__(self): + # Define each layer + super(NeuralNet, self).__init__() + # Hidden fully connected layer with 256 neurons + self.layer1 = self.track_layer( + tf.layers.Dense(n_hidden_1, activation=tf.nn.relu)) + # Hidden fully connected layer with 256 neurons + self.layer2 = self.track_layer( + tf.layers.Dense(n_hidden_2, activation=tf.nn.relu)) + # Output fully connected layer with a neuron for each class + self.out_layer = self.track_layer(tf.layers.Dense(num_classes)) + + def call(self, x): + x = self.layer1(x) + x = self.layer2(x) + return self.out_layer(x) + + +neural_net = NeuralNet() + + +# Cross-Entropy loss function +def loss_fn(inference_fn, inputs, labels): + # Using sparse_softmax cross entropy + return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=inference_fn(inputs), labels=labels)) + + +# Calculate accuracy +def accuracy_fn(inference_fn, inputs, labels): + prediction = tf.nn.softmax(inference_fn(inputs)) + correct_pred = tf.equal(tf.argmax(prediction, 1), labels) + return tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + + +# SGD Optimizer +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +# Compute gradients +grad = tfe.implicit_gradients(loss_fn) + +# Training +average_loss = 0. +average_acc = 0. +for step in range(num_steps): + + # Iterate through the dataset + d = dataset_iter.next() + + # Images + x_batch = d[0] + # Labels + y_batch = tf.cast(d[1], dtype=tf.int64) + + # Compute the batch loss + batch_loss = loss_fn(neural_net, x_batch, y_batch) + average_loss += batch_loss + # Compute the batch accuracy + batch_accuracy = accuracy_fn(neural_net, x_batch, y_batch) + average_acc += batch_accuracy + + if step == 0: + # Display the initial cost, before optimizing + print("Initial loss= {:.9f}".format(average_loss)) + + # Update the variables following gradients info + optimizer.apply_gradients(grad(neural_net, x_batch, y_batch)) + + # Display info + if (step + 1) % display_step == 0 or step == 0: + if step > 0: + average_loss /= display_step + average_acc /= display_step + print("Step:", '%04d' % (step + 1), " loss=", + "{:.9f}".format(average_loss), " accuracy=", + "{:.4f}".format(average_acc)) + average_loss = 0. + average_acc = 0. + +# Evaluate model on the test image set +testX = mnist.test.images +testY = mnist.test.labels + +test_acc = accuracy_fn(neural_net, testX, testY) +print("Testset Accuracy: {:.4f}".format(test_acc)) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py b/tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py new file mode 100644 index 00000000..9c9962ba --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py @@ -0,0 +1,101 @@ +""" Neural Network. + +A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron) +implementation with TensorFlow. This example is using the MNIST database +of handwritten digits (http://yann.lecun.com/exdb/mnist/). + +Links: + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +import tensorflow as tf + +# Parameters +learning_rate = 0.1 +num_steps = 500 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of neurons +n_hidden_2 = 256 # 2nd layer number of neurons +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +X = tf.placeholder("float", [None, num_input]) +Y = tf.placeholder("float", [None, num_classes]) + +# Store layers weight & bias +weights = { + 'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])), + 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes])) +} +biases = { + 'b1': tf.Variable(tf.random_normal([n_hidden_1])), + 'b2': tf.Variable(tf.random_normal([n_hidden_2])), + 'out': tf.Variable(tf.random_normal([num_classes])) +} + + +# Create model +def neural_net(x): + # Hidden fully connected layer with 256 neurons + layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) + # Hidden fully connected layer with 256 neurons + layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) + # Output fully connected layer with a neuron for each class + out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] + return out_layer + +# Construct model +logits = neural_net(X) +prediction = tf.nn.softmax(logits) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, num_steps+1): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy for MNIST test images + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={X: mnist.test.images, + Y: mnist.test.labels})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py b/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py new file mode 100644 index 00000000..fbc3d271 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py @@ -0,0 +1,115 @@ +""" Recurrent Neural Network. + +A Recurrent Neural Network (LSTM) implementation example using TensorFlow library. +This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/) + +Links: + [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) + [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" + +from __future__ import print_function + +import tensorflow as tf +from tensorflow.contrib import rnn + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +''' +To classify images using a recurrent neural network, we consider every image +row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then +handle 28 sequences of 28 steps for every sample. +''' + +# Training Parameters +learning_rate = 0.001 +training_steps = 10000 +batch_size = 128 +display_step = 200 + +# Network Parameters +num_input = 28 # MNIST data input (img shape: 28*28) +timesteps = 28 # timesteps +num_hidden = 128 # hidden layer num of features +num_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +X = tf.placeholder("float", [None, timesteps, num_input]) +Y = tf.placeholder("float", [None, num_classes]) + +# Define weights +weights = { + 'out': tf.Variable(tf.random_normal([num_hidden, num_classes])) +} +biases = { + 'out': tf.Variable(tf.random_normal([num_classes])) +} + + +def RNN(x, weights, biases): + + # Prepare data shape to match `rnn` function requirements + # Current data input shape: (batch_size, timesteps, n_input) + # Required shape: 'timesteps' tensors list of shape (batch_size, n_input) + + # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input) + x = tf.unstack(x, timesteps, 1) + + # Define a lstm cell with tensorflow + lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0) + + # Get lstm cell output + outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32) + + # Linear activation, using rnn inner loop last output + return tf.matmul(outputs[-1], weights['out']) + biases['out'] + +logits = RNN(X, weights, biases) +prediction = tf.nn.softmax(logits) + +# Define loss and optimizer +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits, labels=Y)) +optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for step in range(1, training_steps+1): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Reshape data to get 28 seq of 28 elements + batch_x = batch_x.reshape((batch_size, timesteps, num_input)) + # Run optimization op (backprop) + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + + print("Optimization Finished!") + + # Calculate accuracy for 128 mnist test images + test_len = 128 + test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input)) + test_label = mnist.test.labels[:test_len] + print("Testing Accuracy:", \ + sess.run(accuracy, feed_dict={X: test_data, Y: test_label})) diff --git a/tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py b/tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py new file mode 100644 index 00000000..8a8fd378 --- /dev/null +++ b/tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py @@ -0,0 +1,143 @@ +""" Variational Auto-Encoder Example. + +Using a variational auto-encoder to generate digits images from noise. +MNIST handwritten digits are used as training examples. + +References: + - Auto-Encoding Variational Bayes The International Conference on Learning + Representations (ICLR), Banff, 2014. D.P. Kingma, M. Welling + - Understanding the difficulty of training deep feedforward neural networks. + X Glorot, Y Bengio. Aistats 9, 249-256 + - Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based + learning applied to document recognition." Proceedings of the IEEE, + 86(11):2278-2324, November 1998. + +Links: + - [VAE Paper] https://arxiv.org/abs/1312.6114 + - [Xavier Glorot Init](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). + - [MNIST Dataset] http://yann.lecun.com/exdb/mnist/ + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import division, print_function, absolute_import + +import numpy as np +import matplotlib.pyplot as plt +from scipy.stats import norm +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.001 +num_steps = 30000 +batch_size = 64 + +# Network Parameters +image_dim = 784 # MNIST images are 28x28 pixels +hidden_dim = 512 +latent_dim = 2 + +# A custom initialization (see Xavier Glorot init) +def glorot_init(shape): + return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.)) + +# Variables +weights = { + 'encoder_h1': tf.Variable(glorot_init([image_dim, hidden_dim])), + 'z_mean': tf.Variable(glorot_init([hidden_dim, latent_dim])), + 'z_std': tf.Variable(glorot_init([hidden_dim, latent_dim])), + 'decoder_h1': tf.Variable(glorot_init([latent_dim, hidden_dim])), + 'decoder_out': tf.Variable(glorot_init([hidden_dim, image_dim])) +} +biases = { + 'encoder_b1': tf.Variable(glorot_init([hidden_dim])), + 'z_mean': tf.Variable(glorot_init([latent_dim])), + 'z_std': tf.Variable(glorot_init([latent_dim])), + 'decoder_b1': tf.Variable(glorot_init([hidden_dim])), + 'decoder_out': tf.Variable(glorot_init([image_dim])) +} + +# Building the encoder +input_image = tf.placeholder(tf.float32, shape=[None, image_dim]) +encoder = tf.matmul(input_image, weights['encoder_h1']) + biases['encoder_b1'] +encoder = tf.nn.tanh(encoder) +z_mean = tf.matmul(encoder, weights['z_mean']) + biases['z_mean'] +z_std = tf.matmul(encoder, weights['z_std']) + biases['z_std'] + +# Sampler: Normal (gaussian) random distribution +eps = tf.random_normal(tf.shape(z_std), dtype=tf.float32, mean=0., stddev=1.0, + name='epsilon') +z = z_mean + tf.exp(z_std / 2) * eps + +# Building the decoder (with scope to re-use these layers later) +decoder = tf.matmul(z, weights['decoder_h1']) + biases['decoder_b1'] +decoder = tf.nn.tanh(decoder) +decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out'] +decoder = tf.nn.sigmoid(decoder) + + +# Define VAE Loss +def vae_loss(x_reconstructed, x_true): + # Reconstruction loss + encode_decode_loss = x_true * tf.log(1e-10 + x_reconstructed) \ + + (1 - x_true) * tf.log(1e-10 + 1 - x_reconstructed) + encode_decode_loss = -tf.reduce_sum(encode_decode_loss, 1) + # KL Divergence loss + kl_div_loss = 1 + z_std - tf.square(z_mean) - tf.exp(z_std) + kl_div_loss = -0.5 * tf.reduce_sum(kl_div_loss, 1) + return tf.reduce_mean(encode_decode_loss + kl_div_loss) + +loss_op = vae_loss(decoder, input_image) +optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + for i in range(1, num_steps+1): + # Prepare Data + # Get the next batch of MNIST data (only images are needed, not labels) + batch_x, _ = mnist.train.next_batch(batch_size) + + # Train + feed_dict = {input_image: batch_x} + _, l = sess.run([train_op, loss_op], feed_dict=feed_dict) + if i % 1000 == 0 or i == 1: + print('Step %i, Loss: %f' % (i, l)) + + # Testing + # Generator takes noise as input + noise_input = tf.placeholder(tf.float32, shape=[None, latent_dim]) + # Rebuild the decoder to create image from noise + decoder = tf.matmul(noise_input, weights['decoder_h1']) + biases['decoder_b1'] + decoder = tf.nn.tanh(decoder) + decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out'] + decoder = tf.nn.sigmoid(decoder) + + # Building a manifold of generated digits + n = 20 + x_axis = np.linspace(-3, 3, n) + y_axis = np.linspace(-3, 3, n) + + canvas = np.empty((28 * n, 28 * n)) + for i, yi in enumerate(x_axis): + for j, xi in enumerate(y_axis): + z_mu = np.array([[xi, yi]] * batch_size) + x_mean = sess.run(decoder, feed_dict={noise_input: z_mu}) + canvas[(n - i - 1) * 28:(n - i) * 28, j * 28:(j + 1) * 28] = \ + x_mean[0].reshape(28, 28) + + plt.figure(figsize=(8, 10)) + Xi, Yi = np.meshgrid(x_axis, y_axis) + plt.imshow(canvas, origin="upper", cmap="gray") + plt.show() diff --git a/tensorflow_v1/examples/4_Utils/save_restore_model.py b/tensorflow_v1/examples/4_Utils/save_restore_model.py new file mode 100644 index 00000000..56af08b1 --- /dev/null +++ b/tensorflow_v1/examples/4_Utils/save_restore_model.py @@ -0,0 +1,140 @@ +''' +Save and Restore a model using TensorFlow. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) + +import tensorflow as tf + +# Parameters +learning_rate = 0.001 +batch_size = 100 +display_step = 1 +model_path = "/tmp/model.ckpt" + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of features +n_hidden_2 = 256 # 2nd layer number of features +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph input +x = tf.placeholder("float", [None, n_input]) +y = tf.placeholder("float", [None, n_classes]) + + +# Create model +def multilayer_perceptron(x, weights, biases): + # Hidden layer with RELU activation + layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) + layer_1 = tf.nn.relu(layer_1) + # Hidden layer with RELU activation + layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) + layer_2 = tf.nn.relu(layer_2) + # Output layer with linear activation + out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] + return out_layer + +# Store layers weight & bias +weights = { + 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), + 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) +} +biases = { + 'b1': tf.Variable(tf.random_normal([n_hidden_1])), + 'b2': tf.Variable(tf.random_normal([n_hidden_2])), + 'out': tf.Variable(tf.random_normal([n_classes])) +} + +# Construct model +pred = multilayer_perceptron(x, weights, biases) + +# Define loss and optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# 'Saver' op to save and restore all the variables +saver = tf.train.Saver() + +# Running first session +print("Starting 1st session...") +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Training cycle + for epoch in range(3): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, + y: batch_y}) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if epoch % display_step == 0: + print("Epoch:", '%04d' % (epoch+1), "cost=", \ + "{:.9f}".format(avg_cost)) + print("First Optimization Finished!") + + # Test model + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + # Calculate accuracy + accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) + print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})) + + # Save model weights to disk + save_path = saver.save(sess, model_path) + print("Model saved in file: %s" % save_path) + +# Running a new session +print("Starting 2nd session...") +with tf.Session() as sess: + # Initialize variables + sess.run(init) + + # Restore model weights from previously saved model + saver.restore(sess, model_path) + print("Model restored from file: %s" % save_path) + + # Resume training + for epoch in range(7): + avg_cost = 0. + total_batch = int(mnist.train.num_examples / batch_size) + # Loop over all batches + for i in range(total_batch): + batch_x, batch_y = mnist.train.next_batch(batch_size) + # Run optimization op (backprop) and cost op (to get loss value) + _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, + y: batch_y}) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if epoch % display_step == 0: + print("Epoch:", '%04d' % (epoch + 1), "cost=", \ + "{:.9f}".format(avg_cost)) + print("Second Optimization Finished!") + + # Test model + correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + # Calculate accuracy + accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) + print("Accuracy:", accuracy.eval( + {x: mnist.test.images, y: mnist.test.labels})) diff --git a/tensorflow_v1/examples/4_Utils/tensorboard_advanced.py b/tensorflow_v1/examples/4_Utils/tensorboard_advanced.py new file mode 100644 index 00000000..45a7f962 --- /dev/null +++ b/tensorflow_v1/examples/4_Utils/tensorboard_advanced.py @@ -0,0 +1,143 @@ +''' +Graph and Loss visualization using Tensorboard. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.01 +training_epochs = 25 +batch_size = 100 +display_step = 1 +logs_path = '/tmp/tensorflow_logs/example/' + +# Network Parameters +n_hidden_1 = 256 # 1st layer number of features +n_hidden_2 = 256 # 2nd layer number of features +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) + +# tf Graph Input +# mnist data image of shape 28*28=784 +x = tf.placeholder(tf.float32, [None, 784], name='InputData') +# 0-9 digits recognition => 10 classes +y = tf.placeholder(tf.float32, [None, 10], name='LabelData') + + +# Create model +def multilayer_perceptron(x, weights, biases): + # Hidden layer with RELU activation + layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1']) + layer_1 = tf.nn.relu(layer_1) + # Create a summary to visualize the first layer ReLU activation + tf.summary.histogram("relu1", layer_1) + # Hidden layer with RELU activation + layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2']) + layer_2 = tf.nn.relu(layer_2) + # Create another summary to visualize the second layer ReLU activation + tf.summary.histogram("relu2", layer_2) + # Output layer + out_layer = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3']) + return out_layer + +# Store layers weight & bias +weights = { + 'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='W1'), + 'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'), + 'w3': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='W3') +} +biases = { + 'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='b1'), + 'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='b2'), + 'b3': tf.Variable(tf.random_normal([n_classes]), name='b3') +} + +# Encapsulating all ops into scopes, making Tensorboard's Graph +# Visualization more convenient +with tf.name_scope('Model'): + # Build model + pred = multilayer_perceptron(x, weights, biases) + +with tf.name_scope('Loss'): + # Softmax Cross entropy (cost function) + loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y)) + +with tf.name_scope('SGD'): + # Gradient Descent + optimizer = tf.train.GradientDescentOptimizer(learning_rate) + # Op to calculate every variable gradient + grads = tf.gradients(loss, tf.trainable_variables()) + grads = list(zip(grads, tf.trainable_variables())) + # Op to update all variables according to their gradient + apply_grads = optimizer.apply_gradients(grads_and_vars=grads) + +with tf.name_scope('Accuracy'): + # Accuracy + acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + acc = tf.reduce_mean(tf.cast(acc, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Create a summary to monitor cost tensor +tf.summary.scalar("loss", loss) +# Create a summary to monitor accuracy tensor +tf.summary.scalar("accuracy", acc) +# Create summaries to visualize weights +for var in tf.trainable_variables(): + tf.summary.histogram(var.name, var) +# Summarize all gradients +for grad, var in grads: + tf.summary.histogram(var.name + '/gradient', grad) +# Merge all summaries into a single op +merged_summary_op = tf.summary.merge_all() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # op to write logs to Tensorboard + summary_writer = tf.summary.FileWriter(logs_path, + graph=tf.get_default_graph()) + + # Training cycle + for epoch in range(training_epochs): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_xs, batch_ys = mnist.train.next_batch(batch_size) + # Run optimization op (backprop), cost op (to get loss value) + # and summary nodes + _, c, summary = sess.run([apply_grads, loss, merged_summary_op], + feed_dict={x: batch_xs, y: batch_ys}) + # Write logs at every iteration + summary_writer.add_summary(summary, epoch * total_batch + i) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if (epoch+1) % display_step == 0: + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) + + print("Optimization Finished!") + + # Test model + # Calculate accuracy + print("Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels})) + + print("Run the command line:\n" \ + "--> tensorboard --logdir=/tmp/tensorflow_logs " \ + "\nThen open http://0.0.0.0:6006/ into your web browser") diff --git a/tensorflow_v1/examples/4_Utils/tensorboard_basic.py b/tensorflow_v1/examples/4_Utils/tensorboard_basic.py new file mode 100644 index 00000000..81216c0b --- /dev/null +++ b/tensorflow_v1/examples/4_Utils/tensorboard_basic.py @@ -0,0 +1,97 @@ +''' +Graph and Loss visualization using Tensorboard. +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import print_function + +import tensorflow as tf + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.01 +training_epochs = 25 +batch_size = 100 +display_epoch = 1 +logs_path = '/tmp/tensorflow_logs/example/' + +# tf Graph Input +# mnist data image of shape 28*28=784 +x = tf.placeholder(tf.float32, [None, 784], name='InputData') +# 0-9 digits recognition => 10 classes +y = tf.placeholder(tf.float32, [None, 10], name='LabelData') + +# Set model weights +W = tf.Variable(tf.zeros([784, 10]), name='Weights') +b = tf.Variable(tf.zeros([10]), name='Bias') + +# Construct model and encapsulating all ops into scopes, making +# Tensorboard's Graph visualization more convenient +with tf.name_scope('Model'): + # Model + pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax +with tf.name_scope('Loss'): + # Minimize error using cross entropy + cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) +with tf.name_scope('SGD'): + # Gradient Descent + optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) +with tf.name_scope('Accuracy'): + # Accuracy + acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) + acc = tf.reduce_mean(tf.cast(acc, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Create a summary to monitor cost tensor +tf.summary.scalar("loss", cost) +# Create a summary to monitor accuracy tensor +tf.summary.scalar("accuracy", acc) +# Merge all summaries into a single op +merged_summary_op = tf.summary.merge_all() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # op to write logs to Tensorboard + summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph()) + + # Training cycle + for epoch in range(training_epochs): + avg_cost = 0. + total_batch = int(mnist.train.num_examples/batch_size) + # Loop over all batches + for i in range(total_batch): + batch_xs, batch_ys = mnist.train.next_batch(batch_size) + # Run optimization op (backprop), cost op (to get loss value) + # and summary nodes + _, c, summary = sess.run([optimizer, cost, merged_summary_op], + feed_dict={x: batch_xs, y: batch_ys}) + # Write logs at every iteration + summary_writer.add_summary(summary, epoch * total_batch + i) + # Compute average loss + avg_cost += c / total_batch + # Display logs per epoch step + if (epoch+1) % display_epoch == 0: + print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) + + print("Optimization Finished!") + + # Test model + # Calculate accuracy + print("Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels})) + + print("Run the command line:\n" \ + "--> tensorboard --logdir=/tmp/tensorflow_logs " \ + "\nThen open http://0.0.0.0:6006/ into your web browser") diff --git a/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py b/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py new file mode 100644 index 00000000..8993665b --- /dev/null +++ b/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py @@ -0,0 +1,212 @@ +""" Build an Image Dataset in TensorFlow. + +For this example, you need to make your own set of images (JPEG). +We will show 2 different ways to build that dataset: + +- From a root folder, that will have a sub-folder containing images for each class + ``` + ROOT_FOLDER + |-------- SUBFOLDER (CLASS 0) + | | + | | ----- image1.jpg + | | ----- image2.jpg + | | ----- etc... + | + |-------- SUBFOLDER (CLASS 1) + | | + | | ----- image1.jpg + | | ----- image2.jpg + | | ----- etc... + ``` + +- From a plain text file, that will list all images with their class ID: + ``` + /path/to/image/1.jpg CLASS_ID + /path/to/image/2.jpg CLASS_ID + /path/to/image/3.jpg CLASS_ID + /path/to/image/4.jpg CLASS_ID + etc... + ``` + +Below, there are some parameters that you need to change (Marked 'CHANGE HERE'), +such as the dataset path. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import print_function + +import tensorflow as tf +import os + +# Dataset Parameters - CHANGE HERE +MODE = 'folder' # or 'file', if you choose a plain text file (see above). +DATASET_PATH = '/path/to/dataset/' # the dataset file or root folder path. + +# Image Parameters +N_CLASSES = 2 # CHANGE HERE, total number of classes +IMG_HEIGHT = 64 # CHANGE HERE, the image height to be resized to +IMG_WIDTH = 64 # CHANGE HERE, the image width to be resized to +CHANNELS = 3 # The 3 color channels, change to 1 if grayscale + + +# Reading the dataset +# 2 modes: 'file' or 'folder' +def read_images(dataset_path, mode, batch_size): + imagepaths, labels = list(), list() + if mode == 'file': + # Read dataset file + with open(dataset_path) as f: + data = f.read().splitlines() + for d in data: + imagepaths.append(d.split(' ')[0]) + labels.append(int(d.split(' ')[1])) + elif mode == 'folder': + # An ID will be affected to each sub-folders by alphabetical order + label = 0 + # List the directory + try: # Python 2 + classes = sorted(os.walk(dataset_path).next()[1]) + except Exception: # Python 3 + classes = sorted(os.walk(dataset_path).__next__()[1]) + # List each sub-directory (the classes) + for c in classes: + c_dir = os.path.join(dataset_path, c) + try: # Python 2 + walk = os.walk(c_dir).next() + except Exception: # Python 3 + walk = os.walk(c_dir).__next__() + # Add each image to the training set + for sample in walk[2]: + # Only keeps jpeg images + if sample.endswith('.jpg') or sample.endswith('.jpeg'): + imagepaths.append(os.path.join(c_dir, sample)) + labels.append(label) + label += 1 + else: + raise Exception("Unknown mode.") + + # Convert to Tensor + imagepaths = tf.convert_to_tensor(imagepaths, dtype=tf.string) + labels = tf.convert_to_tensor(labels, dtype=tf.int32) + # Build a TF Queue, shuffle data + image, label = tf.train.slice_input_producer([imagepaths, labels], + shuffle=True) + + # Read images from disk + image = tf.read_file(image) + image = tf.image.decode_jpeg(image, channels=CHANNELS) + + # Resize images to a common size + image = tf.image.resize_images(image, [IMG_HEIGHT, IMG_WIDTH]) + + # Normalize + image = image * 1.0/127.5 - 1.0 + + # Create batches + X, Y = tf.train.batch([image, label], batch_size=batch_size, + capacity=batch_size * 8, + num_threads=4) + + return X, Y + +# ----------------------------------------------- +# THIS IS A CLASSIC CNN (see examples, section 3) +# ----------------------------------------------- +# Note that a few elements have changed (usage of queues). + +# Parameters +learning_rate = 0.001 +num_steps = 10000 +batch_size = 128 +display_step = 100 + +# Network Parameters +dropout = 0.75 # Dropout, probability to keep units + +# Build the data input +X, Y = read_images(DATASET_PATH, MODE, batch_size) + + +# Create model +def conv_net(x, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + + # Convolution Layer with 32 filters and a kernel size of 5 + conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv1 = tf.layers.max_pooling2d(conv1, 2, 2) + + # Convolution Layer with 32 filters and a kernel size of 5 + conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv2 = tf.layers.max_pooling2d(conv2, 2, 2) + + # Flatten the data to a 1-D vector for the fully connected layer + fc1 = tf.contrib.layers.flatten(conv2) + + # Fully connected layer (in contrib folder for now) + fc1 = tf.layers.dense(fc1, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) + + # Output layer, class prediction + out = tf.layers.dense(fc1, n_classes) + # Because 'softmax_cross_entropy_with_logits' already apply softmax, + # we only apply softmax to testing network + out = tf.nn.softmax(out) if not is_training else out + + return out + + +# Because Dropout have different behavior at training and prediction time, we +# need to create 2 distinct computation graphs that share the same weights. + +# Create a graph for training +logits_train = conv_net(X, N_CLASSES, dropout, reuse=False, is_training=True) +# Create another graph for testing that reuse the same weights +logits_test = conv_net(X, N_CLASSES, dropout, reuse=True, is_training=False) + +# Define loss and optimizer (with train logits, for dropout to take effect) +loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( + logits=logits_train, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.cast(Y, tf.int64)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Saver object +saver = tf.train.Saver() + +# Start training +with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Start the data queue + tf.train.start_queue_runners() + + # Training cycle + for step in range(1, num_steps+1): + + if step % display_step == 0: + # Run optimization and calculate batch loss and accuracy + _, loss, acc = sess.run([train_op, loss_op, accuracy]) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + else: + # Only run the optimization op (backprop) + sess.run(train_op) + + print("Optimization Finished!") + + # Save your model + saver.save(sess, 'my_tf_model') diff --git a/tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py b/tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py new file mode 100644 index 00000000..dad0132a --- /dev/null +++ b/tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py @@ -0,0 +1,130 @@ +""" TensorFlow Dataset API. + +In this example, we will show how to load numpy array data into the new +TensorFlow 'Dataset' API. The Dataset API implements an optimized data pipeline +with queues, that make data processing and training faster (especially on GPU). + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +""" +from __future__ import print_function + +import tensorflow as tf + +# Import MNIST data (Numpy format) +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Parameters +learning_rate = 0.001 +num_steps = 2000 +batch_size = 128 +display_step = 100 + +# Network Parameters +n_input = 784 # MNIST data input (img shape: 28*28) +n_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units + +sess = tf.Session() + +# Create a dataset tensor from the images and the labels +dataset = tf.data.Dataset.from_tensor_slices( + (mnist.train.images, mnist.train.labels)) +# Automatically refill the data queue when empty +dataset = dataset.repeat() +# Create batches of data +dataset = dataset.batch(batch_size) +# Prefetch data for faster consumption +dataset = dataset.prefetch(batch_size) + +# Create an iterator over the dataset +iterator = dataset.make_initializable_iterator() +# Initialize the iterator +sess.run(iterator.initializer) + +# Neural Net Input (images, labels) +X, Y = iterator.get_next() + + +# ----------------------------------------------- +# THIS IS A CLASSIC CNN (see examples, section 3) +# ----------------------------------------------- +# Note that a few elements have changed (usage of sess run). + +# Create model +def conv_net(x, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer with 32 filters and a kernel size of 5 + conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv1 = tf.layers.max_pooling2d(conv1, 2, 2) + + # Convolution Layer with 32 filters and a kernel size of 5 + conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + conv2 = tf.layers.max_pooling2d(conv2, 2, 2) + + # Flatten the data to a 1-D vector for the fully connected layer + fc1 = tf.contrib.layers.flatten(conv2) + + # Fully connected layer (in contrib folder for now) + fc1 = tf.layers.dense(fc1, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training) + + # Output layer, class prediction + out = tf.layers.dense(fc1, n_classes) + # Because 'softmax_cross_entropy_with_logits' already apply softmax, + # we only apply softmax to testing network + out = tf.nn.softmax(out) if not is_training else out + + return out + + +# Because Dropout have different behavior at training and prediction time, we +# need to create 2 distinct computation graphs that share the same weights. + +# Create a graph for training +logits_train = conv_net(X, n_classes, dropout, reuse=False, is_training=True) +# Create another graph for testing that reuse the same weights, but has +# different behavior for 'dropout' (not applied). +logits_test = conv_net(X, n_classes, dropout, reuse=True, is_training=False) + +# Define loss and optimizer (with train logits, for dropout to take effect) +loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits_train, labels=Y)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) +train_op = optimizer.minimize(loss_op) + +# Evaluate model (with test logits, for dropout to be disabled) +correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(Y, 1)) +accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + +# Initialize the variables (i.e. assign their default value) +init = tf.global_variables_initializer() + +# Run the initializer +sess.run(init) + +# Training cycle +for step in range(1, num_steps + 1): + + # Run optimization + sess.run(train_op) + + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + # (note that this consume a new batch of data) + loss, acc = sess.run([loss_op, accuracy]) + print("Step " + str(step) + ", Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc)) + +print("Optimization Finished!") diff --git a/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py b/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py new file mode 100644 index 00000000..b31120fa --- /dev/null +++ b/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py @@ -0,0 +1,94 @@ +from __future__ import print_function +''' +Basic Multi GPU computation example using TensorFlow library. + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +''' +This tutorial requires your machine to have 2 GPUs +"/cpu:0": The CPU of your machine. +"/gpu:0": The first GPU of your machine +"/gpu:1": The second GPU of your machine +''' + + + +import numpy as np +import tensorflow as tf +import datetime + +# Processing Units logs +log_device_placement = True + +# Num of multiplications to perform +n = 10 + +''' +Example: compute A^n + B^n on 2 GPUs +Results on 8 cores with 2 GTX-980: + * Single GPU computation time: 0:00:11.277449 + * Multi GPU computation time: 0:00:07.131701 +''' +# Create random large matrix +A = np.random.rand(10000, 10000).astype('float32') +B = np.random.rand(10000, 10000).astype('float32') + +# Create a graph to store results +c1 = [] +c2 = [] + +def matpow(M, n): + if n < 1: #Abstract cases where n < 1 + return M + else: + return tf.matmul(M, matpow(M, n-1)) + +''' +Single GPU computing +''' +with tf.device('/gpu:0'): + a = tf.placeholder(tf.float32, [10000, 10000]) + b = tf.placeholder(tf.float32, [10000, 10000]) + # Compute A^n and B^n and store results in c1 + c1.append(matpow(a, n)) + c1.append(matpow(b, n)) + +with tf.device('/cpu:0'): + sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n + +t1_1 = datetime.datetime.now() +with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess: + # Run the op. + sess.run(sum, {a:A, b:B}) +t2_1 = datetime.datetime.now() + + +''' +Multi GPU computing +''' +# GPU:0 computes A^n +with tf.device('/gpu:0'): + # Compute A^n and store result in c2 + a = tf.placeholder(tf.float32, [10000, 10000]) + c2.append(matpow(a, n)) + +# GPU:1 computes B^n +with tf.device('/gpu:1'): + # Compute B^n and store result in c2 + b = tf.placeholder(tf.float32, [10000, 10000]) + c2.append(matpow(b, n)) + +with tf.device('/cpu:0'): + sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n + +t1_2 = datetime.datetime.now() +with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess: + # Run the op. + sess.run(sum, {a:A, b:B}) +t2_2 = datetime.datetime.now() + + +print("Single GPU computation time: " + str(t2_1-t1_1)) +print("Multi GPU computation time: " + str(t2_2-t1_2)) diff --git a/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py b/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py new file mode 100644 index 00000000..be003ebd --- /dev/null +++ b/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py @@ -0,0 +1,198 @@ +''' Multi-GPU Training Example. + +Train a convolutional neural network on multiple GPU with TensorFlow. + +This example is using TensorFlow layers, see 'convolutional_network_raw' example +for a raw TensorFlow implementation with variables. + +This example is using the MNIST database of handwritten digits +(http://yann.lecun.com/exdb/mnist/) + +Author: Aymeric Damien +Project: https://github.com/aymericdamien/TensorFlow-Examples/ +''' + +from __future__ import division, print_function, absolute_import + +import numpy as np +import tensorflow as tf +import time + +# Import MNIST data +from tensorflow.examples.tutorials.mnist import input_data +mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) + +# Training Parameters +num_gpus = 2 +num_steps = 200 +learning_rate = 0.001 +batch_size = 1024 +display_step = 10 + +# Network Parameters +num_input = 784 # MNIST data input (img shape: 28*28) +num_classes = 10 # MNIST total classes (0-9 digits) +dropout = 0.75 # Dropout, probability to keep units + + +# Build a convolutional neural network +def conv_net(x, n_classes, dropout, reuse, is_training): + # Define a scope for reusing the variables + with tf.variable_scope('ConvNet', reuse=reuse): + # MNIST data input is a 1-D vector of 784 features (28*28 pixels) + # Reshape to match picture format [Height x Width x Channel] + # Tensor input become 4-D: [Batch Size, Height, Width, Channel] + x = tf.reshape(x, shape=[-1, 28, 28, 1]) + + # Convolution Layer with 64 filters and a kernel size of 5 + x = tf.layers.conv2d(x, 64, 5, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + x = tf.layers.max_pooling2d(x, 2, 2) + + # Convolution Layer with 256 filters and a kernel size of 5 + x = tf.layers.conv2d(x, 256, 3, activation=tf.nn.relu) + # Convolution Layer with 512 filters and a kernel size of 5 + x = tf.layers.conv2d(x, 512, 3, activation=tf.nn.relu) + # Max Pooling (down-sampling) with strides of 2 and kernel size of 2 + x = tf.layers.max_pooling2d(x, 2, 2) + + # Flatten the data to a 1-D vector for the fully connected layer + x = tf.contrib.layers.flatten(x) + + # Fully connected layer (in contrib folder for now) + x = tf.layers.dense(x, 2048) + # Apply Dropout (if is_training is False, dropout is not applied) + x = tf.layers.dropout(x, rate=dropout, training=is_training) + + # Fully connected layer (in contrib folder for now) + x = tf.layers.dense(x, 1024) + # Apply Dropout (if is_training is False, dropout is not applied) + x = tf.layers.dropout(x, rate=dropout, training=is_training) + + # Output layer, class prediction + out = tf.layers.dense(x, n_classes) + # Because 'softmax_cross_entropy_with_logits' loss already apply + # softmax, we only apply softmax to testing network + out = tf.nn.softmax(out) if not is_training else out + + return out + + +def average_gradients(tower_grads): + average_grads = [] + for grad_and_vars in zip(*tower_grads): + # Note that each grad_and_vars looks like the following: + # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) + grads = [] + for g, _ in grad_and_vars: + # Add 0 dimension to the gradients to represent the tower. + expanded_g = tf.expand_dims(g, 0) + + # Append on a 'tower' dimension which we will average over below. + grads.append(expanded_g) + + # Average over the 'tower' dimension. + grad = tf.concat(grads, 0) + grad = tf.reduce_mean(grad, 0) + + # Keep in mind that the Variables are redundant because they are shared + # across towers. So .. we will just return the first tower's pointer to + # the Variable. + v = grad_and_vars[0][1] + grad_and_var = (grad, v) + average_grads.append(grad_and_var) + return average_grads + + +# By default, all variables will be placed on '/gpu:0' +# So we need a custom device function, to assign all variables to '/cpu:0' +# Note: If GPUs are peered, '/gpu:0' can be a faster option +PS_OPS = ['Variable', 'VariableV2', 'AutoReloadVariable'] + +def assign_to_device(device, ps_device='/cpu:0'): + def _assign(op): + node_def = op if isinstance(op, tf.NodeDef) else op.node_def + if node_def.op in PS_OPS: + return "/" + ps_device + else: + return device + + return _assign + + +# Place all ops on CPU by default +with tf.device('/cpu:0'): + tower_grads = [] + reuse_vars = False + + # tf Graph input + X = tf.placeholder(tf.float32, [None, num_input]) + Y = tf.placeholder(tf.float32, [None, num_classes]) + + # Loop over all GPUs and construct their own computation graph + for i in range(num_gpus): + with tf.device(assign_to_device('/gpu:{}'.format(i), ps_device='/cpu:0')): + + # Split data between GPUs + _x = X[i * batch_size: (i+1) * batch_size] + _y = Y[i * batch_size: (i+1) * batch_size] + + # Because Dropout have different behavior at training and prediction time, we + # need to create 2 distinct computation graphs that share the same weights. + + # Create a graph for training + logits_train = conv_net(_x, num_classes, dropout, + reuse=reuse_vars, is_training=True) + # Create another graph for testing that reuse the same weights + logits_test = conv_net(_x, num_classes, dropout, + reuse=True, is_training=False) + + # Define loss and optimizer (with train logits, for dropout to take effect) + loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=logits_train, labels=_y)) + optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) + grads = optimizer.compute_gradients(loss_op) + + # Only first GPU compute accuracy + if i == 0: + # Evaluate model (with test logits, for dropout to be disabled) + correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(_y, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) + + reuse_vars = True + tower_grads.append(grads) + + tower_grads = average_gradients(tower_grads) + train_op = optimizer.apply_gradients(tower_grads) + + # Initialize the variables (i.e. assign their default value) + init = tf.global_variables_initializer() + + # Start Training + with tf.Session() as sess: + + # Run the initializer + sess.run(init) + + # Keep training until reach max iterations + for step in range(1, num_steps + 1): + # Get a batch for each GPU + batch_x, batch_y = mnist.train.next_batch(batch_size * num_gpus) + # Run optimization op (backprop) + ts = time.time() + sess.run(train_op, feed_dict={X: batch_x, Y: batch_y}) + te = time.time() - ts + if step % display_step == 0 or step == 1: + # Calculate batch loss and accuracy + loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, + Y: batch_y}) + print("Step " + str(step) + ": Minibatch Loss= " + \ + "{:.4f}".format(loss) + ", Training Accuracy= " + \ + "{:.3f}".format(acc) + ", %i Examples/sec" % int(len(batch_x)/te)) + step += 1 + print("Optimization Finished!") + + # Calculate accuracy for MNIST test images + print("Testing Accuracy:", \ + np.mean([sess.run(accuracy, feed_dict={X: mnist.test.images[i:i+batch_size], + Y: mnist.test.labels[i:i+batch_size]}) for i in range(0, len(mnist.test.images), batch_size)])) diff --git a/tensorflow_v1/notebooks/0_Prerequisite/ml_introduction.ipynb b/tensorflow_v1/notebooks/0_Prerequisite/ml_introduction.ipynb new file mode 100644 index 00000000..fe84ef52 --- /dev/null +++ b/tensorflow_v1/notebooks/0_Prerequisite/ml_introduction.ipynb @@ -0,0 +1,48 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Machine Learning\n", + "\n", + "Prior to start browsing the examples, it may be useful that you get familiar with machine learning, as TensorFlow is mostly used for machine learning tasks (especially Neural Networks). You can find below a list of useful links, that can give you the basic knowledge required for this TensorFlow Tutorial.\n", + "\n", + "## Machine Learning\n", + "\n", + "- [An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples](https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer)\n", + "- [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)\n", + "- [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\n", + "- [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf)\n", + "\n", + "## Deep Learning & Neural Networks\n", + "\n", + "- [An Introduction to Neural Networks](http://www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html)\n", + "- [An Introduction to Image Recognition with Deep Learning](https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721)\n", + "- [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "IPython (Python 2.7)", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb b/tensorflow_v1/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb new file mode 100644 index 00000000..6b96dc0f --- /dev/null +++ b/tensorflow_v1/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb @@ -0,0 +1,94 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# MNIST Dataset Introduction\n", + "\n", + "Most examples are using MNIST dataset of handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "## Overview\n", + "\n", + "![MNIST Digits](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "## Usage\n", + "In our examples, we are using TensorFlow [input_data.py](https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/mnist/input_data.py) script to load that dataset.\n", + "It is quite useful for managing our data, and handle:\n", + "\n", + "- Dataset downloading\n", + "\n", + "- Loading the entire dataset into numpy array: \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Import MNIST\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", + "# Load data\n", + "X_train = mnist.train.images\n", + "Y_train = mnist.train.labels\n", + "X_test = mnist.test.images\n", + "Y_test = mnist.test.labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- A `next_batch` function that can iterate over the whole dataset and return only the desired fraction of the dataset samples (in order to save memory and avoid to load the entire dataset)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Get the next 64 images array and labels\n", + "batch_X, batch_Y = mnist.train.next_batch(64)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Link: http://yann.lecun.com/exdb/mnist/" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb b/tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb new file mode 100644 index 00000000..6780a3ff --- /dev/null +++ b/tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb @@ -0,0 +1,238 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic introduction to TensorFlow's Eager API\n", + "\n", + "A simple introduction to get started with TensorFlow's Eager API.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is TensorFlow's Eager API ?\n", + "\n", + "*Eager execution is an imperative, define-by-run interface where operations are\n", + "executed immediately as they are called from Python. This makes it easier to\n", + "get started with TensorFlow, and can make research and development more\n", + "intuitive. A vast majority of the TensorFlow API remains the same whether eager\n", + "execution is enabled or not. As a result, the exact same code that constructs\n", + "TensorFlow graphs (e.g. using the layers API) can be executed imperatively\n", + "by using eager execution. Conversely, most models written with Eager enabled\n", + "can be converted to a graph that can be further optimized and/or extracted\n", + "for deployment in production without changing code. - Rajat Monga*\n", + "\n", + "More info: https://research.googleblog.com/2017/10/eager-execution-imperative-define-by.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting Eager mode...\n" + ] + } + ], + "source": [ + "# Set Eager API\n", + "print(\"Setting Eager mode...\")\n", + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Define constant tensors\n", + "a = 2\n", + "b = 3\n" + ] + } + ], + "source": [ + "# Define constant tensors\n", + "print(\"Define constant tensors\")\n", + "a = tf.constant(2)\n", + "print(\"a = %i\" % a)\n", + "b = tf.constant(3)\n", + "print(\"b = %i\" % b)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running operations, without tf.Session\n", + "a + b = 5\n", + "a * b = 6\n" + ] + } + ], + "source": [ + "# Run the operation without the need for tf.Session\n", + "print(\"Running operations, without tf.Session\")\n", + "c = a + b\n", + "print(\"a + b = %i\" % c)\n", + "d = a * b\n", + "print(\"a * b = %i\" % d)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mixing operations with Tensors and Numpy Arrays\n", + "Tensor:\n", + " a = tf.Tensor(\n", + "[[2. 1.]\n", + " [1. 0.]], shape=(2, 2), dtype=float32)\n", + "NumpyArray:\n", + " b = [[3. 0.]\n", + " [5. 1.]]\n" + ] + } + ], + "source": [ + "# Full compatibility with Numpy\n", + "print(\"Mixing operations with Tensors and Numpy Arrays\")\n", + "\n", + "# Define constant tensors\n", + "a = tf.constant([[2., 1.],\n", + " [1., 0.]], dtype=tf.float32)\n", + "print(\"Tensor:\\n a = %s\" % a)\n", + "b = np.array([[3., 0.],\n", + " [5., 1.]], dtype=np.float32)\n", + "print(\"NumpyArray:\\n b = %s\" % b)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running operations, without tf.Session\n", + "a + b = tf.Tensor(\n", + "[[5. 1.]\n", + " [6. 1.]], shape=(2, 2), dtype=float32)\n", + "a * b = tf.Tensor(\n", + "[[11. 1.]\n", + " [ 3. 0.]], shape=(2, 2), dtype=float32)\n" + ] + } + ], + "source": [ + "# Run the operation without the need for tf.Session\n", + "print(\"Running operations, without tf.Session\")\n", + "\n", + "c = a + b\n", + "print(\"a + b = %s\" % c)\n", + "\n", + "d = tf.matmul(a, b)\n", + "print(\"a * b = %s\" % d)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iterate through Tensor 'a':\n", + "tf.Tensor(2.0, shape=(), dtype=float32)\n", + "tf.Tensor(1.0, shape=(), dtype=float32)\n", + "tf.Tensor(1.0, shape=(), dtype=float32)\n", + "tf.Tensor(0.0, shape=(), dtype=float32)\n" + ] + } + ], + "source": [ + "print(\"Iterate through Tensor 'a':\")\n", + "for i in range(a.shape[0]):\n", + " for j in range(a.shape[1]):\n", + " print(a[i][j])" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/1_Introduction/basic_operations.ipynb b/tensorflow_v1/notebooks/1_Introduction/basic_operations.ipynb new file mode 100644 index 00000000..9d60c1aa --- /dev/null +++ b/tensorflow_v1/notebooks/1_Introduction/basic_operations.ipynb @@ -0,0 +1,220 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Basic Operations example using TensorFlow library.\n", + "# Author: Aymeric Damien\n", + "# Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Basic constant operations\n", + "# The value returned by the constructor represents the output\n", + "# of the Constant op.\n", + "a = tf.constant(2)\n", + "b = tf.constant(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a=2, b=3\n", + "Addition with constants: 5\n", + "Multiplication with constants: 6\n" + ] + } + ], + "source": [ + "# Launch the default graph.\n", + "with tf.Session() as sess:\n", + " print \"a: %i\" % sess.run(a), \"b: %i\" % sess.run(b)\n", + " print \"Addition with constants: %i\" % sess.run(a+b)\n", + " print \"Multiplication with constants: %i\" % sess.run(a*b)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Basic Operations with variable as graph input\n", + "# The value returned by the constructor represents the output\n", + "# of the Variable op. (define as input when running session)\n", + "# tf Graph input\n", + "a = tf.placeholder(tf.int16)\n", + "b = tf.placeholder(tf.int16)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define some operations\n", + "add = tf.add(a, b)\n", + "mul = tf.multiply(a, b)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Addition with variables: 5\n", + "Multiplication with variables: 6\n" + ] + } + ], + "source": [ + "# Launch the default graph.\n", + "with tf.Session() as sess:\n", + " # Run every operation with variable input\n", + " print \"Addition with variables: %i\" % sess.run(add, feed_dict={a: 2, b: 3})\n", + " print \"Multiplication with variables: %i\" % sess.run(mul, feed_dict={a: 2, b: 3})" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ----------------\n", + "# More in details:\n", + "# Matrix Multiplication from TensorFlow official tutorial\n", + "\n", + "# Create a Constant op that produces a 1x2 matrix. The op is\n", + "# added as a node to the default graph.\n", + "#\n", + "# The value returned by the constructor represents the output\n", + "# of the Constant op.\n", + "matrix1 = tf.constant([[3., 3.]])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create another Constant that produces a 2x1 matrix.\n", + "matrix2 = tf.constant([[2.],[2.]])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.\n", + "# The returned value, 'product', represents the result of the matrix\n", + "# multiplication.\n", + "product = tf.matmul(matrix1, matrix2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 12.]]\n" + ] + } + ], + "source": [ + "# To run the matmul op we call the session 'run()' method, passing 'product'\n", + "# which represents the output of the matmul op. This indicates to the call\n", + "# that we want to get the output of the matmul op back.\n", + "#\n", + "# All inputs needed by the op are run automatically by the session. They\n", + "# typically are run in parallel.\n", + "#\n", + "# The call 'run(product)' thus causes the execution of threes ops in the\n", + "# graph: the two constants and matmul.\n", + "#\n", + "# The output of the op is returned in 'result' as a numpy `ndarray` object.\n", + "with tf.Session() as sess:\n", + " result = sess.run(product)\n", + " print result" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "IPython (Python 2.7)", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb b/tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb new file mode 100644 index 00000000..9d7f0ace --- /dev/null +++ b/tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Simple hello world using TensorFlow\n", + "\n", + "# Create a Constant op\n", + "# The op is added as a node to the default graph.\n", + "#\n", + "# The value returned by the constructor represents the output\n", + "# of the Constant op.\n", + "\n", + "hello = tf.constant('Hello, TensorFlow!')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Start tf session\n", + "sess = tf.Session()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, TensorFlow!\n" + ] + } + ], + "source": [ + "# Run graph\n", + "print(sess.run(hello))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "IPython (Python 2.7)", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb b/tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb new file mode 100644 index 00000000..09e4b270 --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gradient Boosted Decision Tree\n", + "\n", + "Implement a Gradient Boosted Decision tree (GBDT) with TensorFlow to classify\n", + "handwritten digit images. This example is using the MNIST database of\n", + "handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.contrib.boosted_trees.estimator_batch.estimator import GradientBoostedDecisionTreeClassifier\n", + "from tensorflow.contrib.boosted_trees.proto import learner_pb2 as gbdt_learner\n", + "\n", + "# Ignore all GPUs (current TF GBDT does not support GPU).\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "# Set verbosity to display errors only (Remove this line for showing warnings)\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False,\n", + " source_url='http://yann.lecun.com/exdb/mnist/')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "batch_size = 4096 # The number of samples per batch\n", + "num_classes = 10 # The 10 digits\n", + "num_features = 784 # Each image is 28x28 pixels\n", + "max_steps = 10000\n", + "\n", + "# GBDT Parameters\n", + "learning_rate = 0.1\n", + "l1_regul = 0.\n", + "l2_regul = 1.\n", + "examples_per_layer = 1000\n", + "num_trees = 10\n", + "max_depth = 16" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Fill GBDT parameters into the config proto\n", + "learner_config = gbdt_learner.LearnerConfig()\n", + "learner_config.learning_rate_tuner.fixed.learning_rate = learning_rate\n", + "learner_config.regularization.l1 = l1_regul\n", + "learner_config.regularization.l2 = l2_regul / examples_per_layer\n", + "learner_config.constraints.max_tree_depth = max_depth\n", + "growing_mode = gbdt_learner.LearnerConfig.LAYER_BY_LAYER\n", + "learner_config.growing_mode = growing_mode\n", + "run_config = tf.contrib.learn.RunConfig(save_checkpoints_secs=300)\n", + "learner_config.multi_class_strategy = (\n", + " gbdt_learner.LearnerConfig.DIAGONAL_HESSIAN)\\\n", + "\n", + "# Create a TensorFlor GBDT Estimator\n", + "gbdt_model = GradientBoostedDecisionTreeClassifier(\n", + " model_dir=None, # No save directory specified\n", + " learner_config=learner_config,\n", + " n_classes=num_classes,\n", + " examples_per_layer=examples_per_layer,\n", + " num_trees=num_trees,\n", + " center_bias=False,\n", + " config=run_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Active Feature Columns: ['images_0', 'images_1', 'images_2', 'images_3', 'images_4', 'images_5', 'images_6', 'images_7', 'images_8', 'images_9', 'images_10', 'images_11', 'images_12', 'images_13', 'images_14', 'images_15', 'images_16', 'images_17', 'images_18', 'images_19', 'images_20', 'images_21', 'images_22', 'images_23', 'images_24', 'images_25', 'images_26', 'images_27', 'images_28', 'images_29', 'images_30', 'images_31', 'images_32', 'images_33', 'images_34', 'images_35', 'images_36', 'images_37', 'images_38', 'images_39', 'images_40', 'images_41', 'images_42', 'images_43', 'images_44', 'images_45', 'images_46', 'images_47', 'images_48', 'images_49', 'images_50', 'images_51', 'images_52', 'images_53', 'images_54', 'images_55', 'images_56', 'images_57', 'images_58', 'images_59', 'images_60', 'images_61', 'images_62', 'images_63', 'images_64', 'images_65', 'images_66', 'images_67', 'images_68', 'images_69', 'images_70', 'images_71', 'images_72', 'images_73', 'images_74', 'images_75', 'images_76', 'images_77', 'images_78', 'images_79', 'images_80', 'images_81', 'images_82', 'images_83', 'images_84', 'images_85', 'images_86', 'images_87', 'images_88', 'images_89', 'images_90', 'images_91', 'images_92', 'images_93', 'images_94', 'images_95', 'images_96', 'images_97', 'images_98', 'images_99', 'images_100', 'images_101', 'images_102', 'images_103', 'images_104', 'images_105', 'images_106', 'images_107', 'images_108', 'images_109', 'images_110', 'images_111', 'images_112', 'images_113', 'images_114', 'images_115', 'images_116', 'images_117', 'images_118', 'images_119', 'images_120', 'images_121', 'images_122', 'images_123', 'images_124', 'images_125', 'images_126', 'images_127', 'images_128', 'images_129', 'images_130', 'images_131', 'images_132', 'images_133', 'images_134', 'images_135', 'images_136', 'images_137', 'images_138', 'images_139', 'images_140', 'images_141', 'images_142', 'images_143', 'images_144', 'images_145', 'images_146', 'images_147', 'images_148', 'images_149', 'images_150', 'images_151', 'images_152', 'images_153', 'images_154', 'images_155', 'images_156', 'images_157', 'images_158', 'images_159', 'images_160', 'images_161', 'images_162', 'images_163', 'images_164', 'images_165', 'images_166', 'images_167', 'images_168', 'images_169', 'images_170', 'images_171', 'images_172', 'images_173', 'images_174', 'images_175', 'images_176', 'images_177', 'images_178', 'images_179', 'images_180', 'images_181', 'images_182', 'images_183', 'images_184', 'images_185', 'images_186', 'images_187', 'images_188', 'images_189', 'images_190', 'images_191', 'images_192', 'images_193', 'images_194', 'images_195', 'images_196', 'images_197', 'images_198', 'images_199', 'images_200', 'images_201', 'images_202', 'images_203', 'images_204', 'images_205', 'images_206', 'images_207', 'images_208', 'images_209', 'images_210', 'images_211', 'images_212', 'images_213', 'images_214', 'images_215', 'images_216', 'images_217', 'images_218', 'images_219', 'images_220', 'images_221', 'images_222', 'images_223', 'images_224', 'images_225', 'images_226', 'images_227', 'images_228', 'images_229', 'images_230', 'images_231', 'images_232', 'images_233', 'images_234', 'images_235', 'images_236', 'images_237', 'images_238', 'images_239', 'images_240', 'images_241', 'images_242', 'images_243', 'images_244', 'images_245', 'images_246', 'images_247', 'images_248', 'images_249', 'images_250', 'images_251', 'images_252', 'images_253', 'images_254', 'images_255', 'images_256', 'images_257', 'images_258', 'images_259', 'images_260', 'images_261', 'images_262', 'images_263', 'images_264', 'images_265', 'images_266', 'images_267', 'images_268', 'images_269', 'images_270', 'images_271', 'images_272', 'images_273', 'images_274', 'images_275', 'images_276', 'images_277', 'images_278', 'images_279', 'images_280', 'images_281', 'images_282', 'images_283', 'images_284', 'images_285', 'images_286', 'images_287', 'images_288', 'images_289', 'images_290', 'images_291', 'images_292', 'images_293', 'images_294', 'images_295', 'images_296', 'images_297', 'images_298', 'images_299', 'images_300', 'images_301', 'images_302', 'images_303', 'images_304', 'images_305', 'images_306', 'images_307', 'images_308', 'images_309', 'images_310', 'images_311', 'images_312', 'images_313', 'images_314', 'images_315', 'images_316', 'images_317', 'images_318', 'images_319', 'images_320', 'images_321', 'images_322', 'images_323', 'images_324', 'images_325', 'images_326', 'images_327', 'images_328', 'images_329', 'images_330', 'images_331', 'images_332', 'images_333', 'images_334', 'images_335', 'images_336', 'images_337', 'images_338', 'images_339', 'images_340', 'images_341', 'images_342', 'images_343', 'images_344', 'images_345', 'images_346', 'images_347', 'images_348', 'images_349', 'images_350', 'images_351', 'images_352', 'images_353', 'images_354', 'images_355', 'images_356', 'images_357', 'images_358', 'images_359', 'images_360', 'images_361', 'images_362', 'images_363', 'images_364', 'images_365', 'images_366', 'images_367', 'images_368', 'images_369', 'images_370', 'images_371', 'images_372', 'images_373', 'images_374', 'images_375', 'images_376', 'images_377', 'images_378', 'images_379', 'images_380', 'images_381', 'images_382', 'images_383', 'images_384', 'images_385', 'images_386', 'images_387', 'images_388', 'images_389', 'images_390', 'images_391', 'images_392', 'images_393', 'images_394', 'images_395', 'images_396', 'images_397', 'images_398', 'images_399', 'images_400', 'images_401', 'images_402', 'images_403', 'images_404', 'images_405', 'images_406', 'images_407', 'images_408', 'images_409', 'images_410', 'images_411', 'images_412', 'images_413', 'images_414', 'images_415', 'images_416', 'images_417', 'images_418', 'images_419', 'images_420', 'images_421', 'images_422', 'images_423', 'images_424', 'images_425', 'images_426', 'images_427', 'images_428', 'images_429', 'images_430', 'images_431', 'images_432', 'images_433', 'images_434', 'images_435', 'images_436', 'images_437', 'images_438', 'images_439', 'images_440', 'images_441', 'images_442', 'images_443', 'images_444', 'images_445', 'images_446', 'images_447', 'images_448', 'images_449', 'images_450', 'images_451', 'images_452', 'images_453', 'images_454', 'images_455', 'images_456', 'images_457', 'images_458', 'images_459', 'images_460', 'images_461', 'images_462', 'images_463', 'images_464', 'images_465', 'images_466', 'images_467', 'images_468', 'images_469', 'images_470', 'images_471', 'images_472', 'images_473', 'images_474', 'images_475', 'images_476', 'images_477', 'images_478', 'images_479', 'images_480', 'images_481', 'images_482', 'images_483', 'images_484', 'images_485', 'images_486', 'images_487', 'images_488', 'images_489', 'images_490', 'images_491', 'images_492', 'images_493', 'images_494', 'images_495', 'images_496', 'images_497', 'images_498', 'images_499', 'images_500', 'images_501', 'images_502', 'images_503', 'images_504', 'images_505', 'images_506', 'images_507', 'images_508', 'images_509', 'images_510', 'images_511', 'images_512', 'images_513', 'images_514', 'images_515', 'images_516', 'images_517', 'images_518', 'images_519', 'images_520', 'images_521', 'images_522', 'images_523', 'images_524', 'images_525', 'images_526', 'images_527', 'images_528', 'images_529', 'images_530', 'images_531', 'images_532', 'images_533', 'images_534', 'images_535', 'images_536', 'images_537', 'images_538', 'images_539', 'images_540', 'images_541', 'images_542', 'images_543', 'images_544', 'images_545', 'images_546', 'images_547', 'images_548', 'images_549', 'images_550', 'images_551', 'images_552', 'images_553', 'images_554', 'images_555', 'images_556', 'images_557', 'images_558', 'images_559', 'images_560', 'images_561', 'images_562', 'images_563', 'images_564', 'images_565', 'images_566', 'images_567', 'images_568', 'images_569', 'images_570', 'images_571', 'images_572', 'images_573', 'images_574', 'images_575', 'images_576', 'images_577', 'images_578', 'images_579', 'images_580', 'images_581', 'images_582', 'images_583', 'images_584', 'images_585', 'images_586', 'images_587', 'images_588', 'images_589', 'images_590', 'images_591', 'images_592', 'images_593', 'images_594', 'images_595', 'images_596', 'images_597', 'images_598', 'images_599', 'images_600', 'images_601', 'images_602', 'images_603', 'images_604', 'images_605', 'images_606', 'images_607', 'images_608', 'images_609', 'images_610', 'images_611', 'images_612', 'images_613', 'images_614', 'images_615', 'images_616', 'images_617', 'images_618', 'images_619', 'images_620', 'images_621', 'images_622', 'images_623', 'images_624', 'images_625', 'images_626', 'images_627', 'images_628', 'images_629', 'images_630', 'images_631', 'images_632', 'images_633', 'images_634', 'images_635', 'images_636', 'images_637', 'images_638', 'images_639', 'images_640', 'images_641', 'images_642', 'images_643', 'images_644', 'images_645', 'images_646', 'images_647', 'images_648', 'images_649', 'images_650', 'images_651', 'images_652', 'images_653', 'images_654', 'images_655', 'images_656', 'images_657', 'images_658', 'images_659', 'images_660', 'images_661', 'images_662', 'images_663', 'images_664', 'images_665', 'images_666', 'images_667', 'images_668', 'images_669', 'images_670', 'images_671', 'images_672', 'images_673', 'images_674', 'images_675', 'images_676', 'images_677', 'images_678', 'images_679', 'images_680', 'images_681', 'images_682', 'images_683', 'images_684', 'images_685', 'images_686', 'images_687', 'images_688', 'images_689', 'images_690', 'images_691', 'images_692', 'images_693', 'images_694', 'images_695', 'images_696', 'images_697', 'images_698', 'images_699', 'images_700', 'images_701', 'images_702', 'images_703', 'images_704', 'images_705', 'images_706', 'images_707', 'images_708', 'images_709', 'images_710', 'images_711', 'images_712', 'images_713', 'images_714', 'images_715', 'images_716', 'images_717', 'images_718', 'images_719', 'images_720', 'images_721', 'images_722', 'images_723', 'images_724', 'images_725', 'images_726', 'images_727', 'images_728', 'images_729', 'images_730', 'images_731', 'images_732', 'images_733', 'images_734', 'images_735', 'images_736', 'images_737', 'images_738', 'images_739', 'images_740', 'images_741', 'images_742', 'images_743', 'images_744', 'images_745', 'images_746', 'images_747', 'images_748', 'images_749', 'images_750', 'images_751', 'images_752', 'images_753', 'images_754', 'images_755', 'images_756', 'images_757', 'images_758', 'images_759', 'images_760', 'images_761', 'images_762', 'images_763', 'images_764', 'images_765', 'images_766', 'images_767', 'images_768', 'images_769', 'images_770', 'images_771', 'images_772', 'images_773', 'images_774', 'images_775', 'images_776', 'images_777', 'images_778', 'images_779', 'images_780', 'images_781', 'images_782', 'images_783']\n", + "WARNING:tensorflow:From /Users/aymeric.damien/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: __new__ (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the `estimator_spec` method to create an equivalent one.\n", + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:loss = 2.3025992, step = 1\n", + "INFO:tensorflow:Saving checkpoints for 2 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving checkpoints for 94 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:global_step/sec: 0.199624\n", + "INFO:tensorflow:loss = 0.32783023, step = 101 (500.943 sec)\n", + "INFO:tensorflow:Requesting stop since we have reached 10 trees.\n", + "INFO:tensorflow:Saving checkpoints for 161 into /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Loss for final step: 0.21336032.\n" + ] + }, + { + "data": { + "text/plain": [ + "GradientBoostedDecisionTreeClassifier(params={'head': , 'weight_column_name': None, 'feature_columns': None, 'center_bias': False, 'num_trees': 10, 'logits_modifier_function': None, 'use_core_libs': False, 'learner_config': num_classes: 10\n", + "regularization {\n", + " l2: 0.0010000000475\n", + "}\n", + "constraints {\n", + " max_tree_depth: 16\n", + "}\n", + "learning_rate_tuner {\n", + " fixed {\n", + " learning_rate: 0.10000000149\n", + " }\n", + "}\n", + "pruning_mode: POST_PRUNE\n", + "growing_mode: LAYER_BY_LAYER\n", + "multi_class_strategy: DIAGONAL_HESSIAN\n", + ", 'examples_per_layer': 1000})" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display TF info logs\n", + "tf.logging.set_verbosity(tf.logging.INFO)\n", + "\n", + "# Define the input function for training\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.train.images}, y=mnist.train.labels,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)\n", + "\n", + "# Train the Model\n", + "gbdt_model.fit(input_fn=input_fn, max_steps=max_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Active Feature Columns: ['images_0', 'images_1', 'images_2', 'images_3', 'images_4', 'images_5', 'images_6', 'images_7', 'images_8', 'images_9', 'images_10', 'images_11', 'images_12', 'images_13', 'images_14', 'images_15', 'images_16', 'images_17', 'images_18', 'images_19', 'images_20', 'images_21', 'images_22', 'images_23', 'images_24', 'images_25', 'images_26', 'images_27', 'images_28', 'images_29', 'images_30', 'images_31', 'images_32', 'images_33', 'images_34', 'images_35', 'images_36', 'images_37', 'images_38', 'images_39', 'images_40', 'images_41', 'images_42', 'images_43', 'images_44', 'images_45', 'images_46', 'images_47', 'images_48', 'images_49', 'images_50', 'images_51', 'images_52', 'images_53', 'images_54', 'images_55', 'images_56', 'images_57', 'images_58', 'images_59', 'images_60', 'images_61', 'images_62', 'images_63', 'images_64', 'images_65', 'images_66', 'images_67', 'images_68', 'images_69', 'images_70', 'images_71', 'images_72', 'images_73', 'images_74', 'images_75', 'images_76', 'images_77', 'images_78', 'images_79', 'images_80', 'images_81', 'images_82', 'images_83', 'images_84', 'images_85', 'images_86', 'images_87', 'images_88', 'images_89', 'images_90', 'images_91', 'images_92', 'images_93', 'images_94', 'images_95', 'images_96', 'images_97', 'images_98', 'images_99', 'images_100', 'images_101', 'images_102', 'images_103', 'images_104', 'images_105', 'images_106', 'images_107', 'images_108', 'images_109', 'images_110', 'images_111', 'images_112', 'images_113', 'images_114', 'images_115', 'images_116', 'images_117', 'images_118', 'images_119', 'images_120', 'images_121', 'images_122', 'images_123', 'images_124', 'images_125', 'images_126', 'images_127', 'images_128', 'images_129', 'images_130', 'images_131', 'images_132', 'images_133', 'images_134', 'images_135', 'images_136', 'images_137', 'images_138', 'images_139', 'images_140', 'images_141', 'images_142', 'images_143', 'images_144', 'images_145', 'images_146', 'images_147', 'images_148', 'images_149', 'images_150', 'images_151', 'images_152', 'images_153', 'images_154', 'images_155', 'images_156', 'images_157', 'images_158', 'images_159', 'images_160', 'images_161', 'images_162', 'images_163', 'images_164', 'images_165', 'images_166', 'images_167', 'images_168', 'images_169', 'images_170', 'images_171', 'images_172', 'images_173', 'images_174', 'images_175', 'images_176', 'images_177', 'images_178', 'images_179', 'images_180', 'images_181', 'images_182', 'images_183', 'images_184', 'images_185', 'images_186', 'images_187', 'images_188', 'images_189', 'images_190', 'images_191', 'images_192', 'images_193', 'images_194', 'images_195', 'images_196', 'images_197', 'images_198', 'images_199', 'images_200', 'images_201', 'images_202', 'images_203', 'images_204', 'images_205', 'images_206', 'images_207', 'images_208', 'images_209', 'images_210', 'images_211', 'images_212', 'images_213', 'images_214', 'images_215', 'images_216', 'images_217', 'images_218', 'images_219', 'images_220', 'images_221', 'images_222', 'images_223', 'images_224', 'images_225', 'images_226', 'images_227', 'images_228', 'images_229', 'images_230', 'images_231', 'images_232', 'images_233', 'images_234', 'images_235', 'images_236', 'images_237', 'images_238', 'images_239', 'images_240', 'images_241', 'images_242', 'images_243', 'images_244', 'images_245', 'images_246', 'images_247', 'images_248', 'images_249', 'images_250', 'images_251', 'images_252', 'images_253', 'images_254', 'images_255', 'images_256', 'images_257', 'images_258', 'images_259', 'images_260', 'images_261', 'images_262', 'images_263', 'images_264', 'images_265', 'images_266', 'images_267', 'images_268', 'images_269', 'images_270', 'images_271', 'images_272', 'images_273', 'images_274', 'images_275', 'images_276', 'images_277', 'images_278', 'images_279', 'images_280', 'images_281', 'images_282', 'images_283', 'images_284', 'images_285', 'images_286', 'images_287', 'images_288', 'images_289', 'images_290', 'images_291', 'images_292', 'images_293', 'images_294', 'images_295', 'images_296', 'images_297', 'images_298', 'images_299', 'images_300', 'images_301', 'images_302', 'images_303', 'images_304', 'images_305', 'images_306', 'images_307', 'images_308', 'images_309', 'images_310', 'images_311', 'images_312', 'images_313', 'images_314', 'images_315', 'images_316', 'images_317', 'images_318', 'images_319', 'images_320', 'images_321', 'images_322', 'images_323', 'images_324', 'images_325', 'images_326', 'images_327', 'images_328', 'images_329', 'images_330', 'images_331', 'images_332', 'images_333', 'images_334', 'images_335', 'images_336', 'images_337', 'images_338', 'images_339', 'images_340', 'images_341', 'images_342', 'images_343', 'images_344', 'images_345', 'images_346', 'images_347', 'images_348', 'images_349', 'images_350', 'images_351', 'images_352', 'images_353', 'images_354', 'images_355', 'images_356', 'images_357', 'images_358', 'images_359', 'images_360', 'images_361', 'images_362', 'images_363', 'images_364', 'images_365', 'images_366', 'images_367', 'images_368', 'images_369', 'images_370', 'images_371', 'images_372', 'images_373', 'images_374', 'images_375', 'images_376', 'images_377', 'images_378', 'images_379', 'images_380', 'images_381', 'images_382', 'images_383', 'images_384', 'images_385', 'images_386', 'images_387', 'images_388', 'images_389', 'images_390', 'images_391', 'images_392', 'images_393', 'images_394', 'images_395', 'images_396', 'images_397', 'images_398', 'images_399', 'images_400', 'images_401', 'images_402', 'images_403', 'images_404', 'images_405', 'images_406', 'images_407', 'images_408', 'images_409', 'images_410', 'images_411', 'images_412', 'images_413', 'images_414', 'images_415', 'images_416', 'images_417', 'images_418', 'images_419', 'images_420', 'images_421', 'images_422', 'images_423', 'images_424', 'images_425', 'images_426', 'images_427', 'images_428', 'images_429', 'images_430', 'images_431', 'images_432', 'images_433', 'images_434', 'images_435', 'images_436', 'images_437', 'images_438', 'images_439', 'images_440', 'images_441', 'images_442', 'images_443', 'images_444', 'images_445', 'images_446', 'images_447', 'images_448', 'images_449', 'images_450', 'images_451', 'images_452', 'images_453', 'images_454', 'images_455', 'images_456', 'images_457', 'images_458', 'images_459', 'images_460', 'images_461', 'images_462', 'images_463', 'images_464', 'images_465', 'images_466', 'images_467', 'images_468', 'images_469', 'images_470', 'images_471', 'images_472', 'images_473', 'images_474', 'images_475', 'images_476', 'images_477', 'images_478', 'images_479', 'images_480', 'images_481', 'images_482', 'images_483', 'images_484', 'images_485', 'images_486', 'images_487', 'images_488', 'images_489', 'images_490', 'images_491', 'images_492', 'images_493', 'images_494', 'images_495', 'images_496', 'images_497', 'images_498', 'images_499', 'images_500', 'images_501', 'images_502', 'images_503', 'images_504', 'images_505', 'images_506', 'images_507', 'images_508', 'images_509', 'images_510', 'images_511', 'images_512', 'images_513', 'images_514', 'images_515', 'images_516', 'images_517', 'images_518', 'images_519', 'images_520', 'images_521', 'images_522', 'images_523', 'images_524', 'images_525', 'images_526', 'images_527', 'images_528', 'images_529', 'images_530', 'images_531', 'images_532', 'images_533', 'images_534', 'images_535', 'images_536', 'images_537', 'images_538', 'images_539', 'images_540', 'images_541', 'images_542', 'images_543', 'images_544', 'images_545', 'images_546', 'images_547', 'images_548', 'images_549', 'images_550', 'images_551', 'images_552', 'images_553', 'images_554', 'images_555', 'images_556', 'images_557', 'images_558', 'images_559', 'images_560', 'images_561', 'images_562', 'images_563', 'images_564', 'images_565', 'images_566', 'images_567', 'images_568', 'images_569', 'images_570', 'images_571', 'images_572', 'images_573', 'images_574', 'images_575', 'images_576', 'images_577', 'images_578', 'images_579', 'images_580', 'images_581', 'images_582', 'images_583', 'images_584', 'images_585', 'images_586', 'images_587', 'images_588', 'images_589', 'images_590', 'images_591', 'images_592', 'images_593', 'images_594', 'images_595', 'images_596', 'images_597', 'images_598', 'images_599', 'images_600', 'images_601', 'images_602', 'images_603', 'images_604', 'images_605', 'images_606', 'images_607', 'images_608', 'images_609', 'images_610', 'images_611', 'images_612', 'images_613', 'images_614', 'images_615', 'images_616', 'images_617', 'images_618', 'images_619', 'images_620', 'images_621', 'images_622', 'images_623', 'images_624', 'images_625', 'images_626', 'images_627', 'images_628', 'images_629', 'images_630', 'images_631', 'images_632', 'images_633', 'images_634', 'images_635', 'images_636', 'images_637', 'images_638', 'images_639', 'images_640', 'images_641', 'images_642', 'images_643', 'images_644', 'images_645', 'images_646', 'images_647', 'images_648', 'images_649', 'images_650', 'images_651', 'images_652', 'images_653', 'images_654', 'images_655', 'images_656', 'images_657', 'images_658', 'images_659', 'images_660', 'images_661', 'images_662', 'images_663', 'images_664', 'images_665', 'images_666', 'images_667', 'images_668', 'images_669', 'images_670', 'images_671', 'images_672', 'images_673', 'images_674', 'images_675', 'images_676', 'images_677', 'images_678', 'images_679', 'images_680', 'images_681', 'images_682', 'images_683', 'images_684', 'images_685', 'images_686', 'images_687', 'images_688', 'images_689', 'images_690', 'images_691', 'images_692', 'images_693', 'images_694', 'images_695', 'images_696', 'images_697', 'images_698', 'images_699', 'images_700', 'images_701', 'images_702', 'images_703', 'images_704', 'images_705', 'images_706', 'images_707', 'images_708', 'images_709', 'images_710', 'images_711', 'images_712', 'images_713', 'images_714', 'images_715', 'images_716', 'images_717', 'images_718', 'images_719', 'images_720', 'images_721', 'images_722', 'images_723', 'images_724', 'images_725', 'images_726', 'images_727', 'images_728', 'images_729', 'images_730', 'images_731', 'images_732', 'images_733', 'images_734', 'images_735', 'images_736', 'images_737', 'images_738', 'images_739', 'images_740', 'images_741', 'images_742', 'images_743', 'images_744', 'images_745', 'images_746', 'images_747', 'images_748', 'images_749', 'images_750', 'images_751', 'images_752', 'images_753', 'images_754', 'images_755', 'images_756', 'images_757', 'images_758', 'images_759', 'images_760', 'images_761', 'images_762', 'images_763', 'images_764', 'images_765', 'images_766', 'images_767', 'images_768', 'images_769', 'images_770', 'images_771', 'images_772', 'images_773', 'images_774', 'images_775', 'images_776', 'images_777', 'images_778', 'images_779', 'images_780', 'images_781', 'images_782', 'images_783']\n", + "INFO:tensorflow:Starting evaluation at 2018-07-26-01:00:06\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /var/folders/p7/9nxn5g_s5kv636j86zdg2hrr0000gr/T/tmpE_8oiQ/model.ckpt-161\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Finished evaluation at 2018-07-26-01:00:07\n", + "INFO:tensorflow:Saving dict for global step 161: accuracy = 0.9273, global_step = 161, loss = 0.23841818\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "Testing Accuracy: 0.9273\n" + ] + } + ], + "source": [ + "# Evaluate the Model\n", + "# Define the input function for evaluating\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.test.images}, y=mnist.test.labels,\n", + " batch_size=batch_size, shuffle=False)\n", + "\n", + "# Use the Estimator 'evaluate' method\n", + "e = gbdt_model.evaluate(input_fn=input_fn)\n", + "print(\"Testing Accuracy:\", e['accuracy'])" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb b/tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb new file mode 100644 index 00000000..1a64ba2f --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# K-Means Example\n", + "\n", + "Implement K-Means algorithm with TensorFlow, and apply it to classify\n", + "handwritten digit images. This example is using the MNIST database of\n", + "handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "Note: This example requires TensorFlow v1.1.0 or over.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow.contrib.factorization import KMeans\n", + "\n", + "# Ignore all GPUs, tf random forest does not benefit from it.\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "full_data_x = mnist.train.images" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "num_steps = 50 # Total steps to train\n", + "batch_size = 1024 # The number of samples per batch\n", + "k = 25 # The number of clusters\n", + "num_classes = 10 # The 10 digits\n", + "num_features = 784 # Each image is 28x28 pixels\n", + "\n", + "# Input images\n", + "X = tf.placeholder(tf.float32, shape=[None, num_features])\n", + "# Labels (for assigning a label to a centroid and testing)\n", + "Y = tf.placeholder(tf.float32, shape=[None, num_classes])\n", + "\n", + "# K-Means Parameters\n", + "kmeans = KMeans(inputs=X, num_clusters=k, distance_metric='cosine',\n", + " use_mini_batch=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build KMeans graph\n", + "(all_scores, cluster_idx, scores, cluster_centers_initialized, \n", + " cluster_centers_vars,init_op,train_op) = kmeans.training_graph()\n", + "cluster_idx = cluster_idx[0] # fix for cluster_idx being a tuple\n", + "avg_distance = tf.reduce_mean(scores)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init_vars = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Avg Distance: 0.341471\n", + "Step 10, Avg Distance: 0.221609\n", + "Step 20, Avg Distance: 0.220328\n", + "Step 30, Avg Distance: 0.219776\n", + "Step 40, Avg Distance: 0.219419\n", + "Step 50, Avg Distance: 0.219154\n" + ] + } + ], + "source": [ + "# Start TensorFlow session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init_vars, feed_dict={X: full_data_x})\n", + "sess.run(init_op, feed_dict={X: full_data_x})\n", + "\n", + "# Training\n", + "for i in range(1, num_steps + 1):\n", + " _, d, idx = sess.run([train_op, avg_distance, cluster_idx],\n", + " feed_dict={X: full_data_x})\n", + " if i % 10 == 0 or i == 1:\n", + " print(\"Step %i, Avg Distance: %f\" % (i, d))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 0.7127\n" + ] + } + ], + "source": [ + "# Assign a label to each centroid\n", + "# Count total number of labels per centroid, using the label of each training\n", + "# sample to their closest centroid (given by 'idx')\n", + "counts = np.zeros(shape=(k, num_classes))\n", + "for i in range(len(idx)):\n", + " counts[idx[i]] += mnist.train.labels[i]\n", + "# Assign the most frequent label to the centroid\n", + "labels_map = [np.argmax(c) for c in counts]\n", + "labels_map = tf.convert_to_tensor(labels_map)\n", + "\n", + "# Evaluation ops\n", + "# Lookup: centroid_id -> label\n", + "cluster_label = tf.nn.embedding_lookup(labels_map, cluster_idx)\n", + "# Compute accuracy\n", + "correct_prediction = tf.equal(cluster_label, tf.cast(tf.argmax(Y, 1), tf.int32))\n", + "accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + "\n", + "# Test Model\n", + "test_x, test_y = mnist.test.images, mnist.test.labels\n", + "print(\"Test Accuracy:\", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb b/tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb new file mode 100644 index 00000000..2c6692db --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb @@ -0,0 +1,236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Linear Regression Example\n", + "\n", + "A linear regression learning algorithm example using TensorFlow library.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "rng = numpy.random" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 1000\n", + "display_step = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Data\n", + "train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n", + " 7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n", + "train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n", + " 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n", + "n_samples = train_X.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# tf Graph Input\n", + "X = tf.placeholder(\"float\")\n", + "Y = tf.placeholder(\"float\")\n", + "\n", + "# Set model weights\n", + "W = tf.Variable(rng.randn(), name=\"weight\")\n", + "b = tf.Variable(rng.randn(), name=\"bias\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Construct a linear model\n", + "pred = tf.add(tf.multiply(X, W), b)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Mean squared error\n", + "cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)\n", + "# Gradient descent\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0050 cost= 0.195095107 W= 0.441748 b= -0.580876\n", + "Epoch: 0100 cost= 0.181448311 W= 0.430319 b= -0.498661\n", + "Epoch: 0150 cost= 0.169377610 W= 0.419571 b= -0.421336\n", + "Epoch: 0200 cost= 0.158700854 W= 0.409461 b= -0.348611\n", + "Epoch: 0250 cost= 0.149257123 W= 0.399953 b= -0.28021\n", + "Epoch: 0300 cost= 0.140904188 W= 0.391011 b= -0.215878\n", + "Epoch: 0350 cost= 0.133515999 W= 0.3826 b= -0.155372\n", + "Epoch: 0400 cost= 0.126981199 W= 0.374689 b= -0.0984639\n", + "Epoch: 0450 cost= 0.121201262 W= 0.367249 b= -0.0449408\n", + "Epoch: 0500 cost= 0.116088994 W= 0.360252 b= 0.00539905\n", + "Epoch: 0550 cost= 0.111567356 W= 0.35367 b= 0.052745\n", + "Epoch: 0600 cost= 0.107568085 W= 0.34748 b= 0.0972751\n", + "Epoch: 0650 cost= 0.104030922 W= 0.341659 b= 0.139157\n", + "Epoch: 0700 cost= 0.100902475 W= 0.336183 b= 0.178547\n", + "Epoch: 0750 cost= 0.098135538 W= 0.331033 b= 0.215595\n", + "Epoch: 0800 cost= 0.095688373 W= 0.32619 b= 0.25044\n", + "Epoch: 0850 cost= 0.093524046 W= 0.321634 b= 0.283212\n", + "Epoch: 0900 cost= 0.091609895 W= 0.317349 b= 0.314035\n", + "Epoch: 0950 cost= 0.089917004 W= 0.31332 b= 0.343025\n", + "Epoch: 1000 cost= 0.088419855 W= 0.30953 b= 0.370291\n", + "Optimization Finished!\n", + "Training cost= 0.0884199 W= 0.30953 b= 0.370291 \n", + "\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFkCAYAAACq4KjhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niEhEShJEREQkov8PMJtz3b7pz2EAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + "\n", + " # Fit all training data\n", + " for epoch in range(training_epochs):\n", + " for (x, y) in zip(train_X, train_Y):\n", + " sess.run(optimizer, feed_dict={X: x, Y: y})\n", + "\n", + " #Display logs per epoch step\n", + " if (epoch+1) % display_step == 0:\n", + " c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})\n", + " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(c), \\\n", + " \"W=\", sess.run(W), \"b=\", sess.run(b)\n", + "\n", + " print \"Optimization Finished!\"\n", + " training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})\n", + " print \"Training cost=\", training_cost, \"W=\", sess.run(W), \"b=\", sess.run(b), '\\n'\n", + "\n", + " #Graphic display\n", + " plt.plot(train_X, train_Y, 'ro', label='Original data')\n", + " plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAJYCAYAAACadoJwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbl4AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Regression result" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb b/tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb new file mode 100644 index 00000000..f517dc15 --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb @@ -0,0 +1,181 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Linear Regression with Eager API\n", + "\n", + "A linear regression implemented using TensorFlow's Eager API.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Set Eager API\n", + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Data\n", + "train_X = [3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,\n", + " 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1]\n", + "train_Y = [1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221,\n", + " 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3]\n", + "n_samples = len(train_X)\n", + "\n", + "# Parameters\n", + "learning_rate = 0.01\n", + "display_step = 100\n", + "num_steps = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Weight and Bias\n", + "W = tfe.Variable(np.random.randn())\n", + "b = tfe.Variable(np.random.randn())\n", + "\n", + "# Linear regression (Wx + b)\n", + "def linear_regression(inputs):\n", + " return inputs * W + b\n", + "\n", + "# Mean square error\n", + "def mean_square_fn(model_fn, inputs, labels):\n", + " return tf.reduce_sum(tf.pow(model_fn(inputs) - labels, 2)) / (2 * n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# SGD Optimizer\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Compute gradients\n", + "grad = tfe.implicit_gradients(mean_square_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost= 31.307329178 W= -0.7870768 b= -0.2507985\n", + "Epoch: 0001 cost= 9.502781868 W= -0.26173288 b= -0.17560114\n", + "Epoch: 0100 cost= 0.114994615 W= 0.36224815 b= 0.014603348\n", + "Epoch: 0200 cost= 0.106785327 W= 0.34959725 b= 0.104292504\n", + "Epoch: 0300 cost= 0.100346453 W= 0.33839324 b= 0.1837239\n", + "Epoch: 0400 cost= 0.095296182 W= 0.32847065 b= 0.25407064\n", + "Epoch: 0500 cost= 0.091335081 W= 0.3196829 b= 0.3163719\n", + "Epoch: 0600 cost= 0.088228233 W= 0.31190023 b= 0.37154746\n", + "Epoch: 0700 cost= 0.085791394 W= 0.30500764 b= 0.42041263\n", + "Epoch: 0800 cost= 0.083880097 W= 0.2989034 b= 0.46368918\n", + "Epoch: 0900 cost= 0.082380980 W= 0.2934973 b= 0.50201607\n", + "Epoch: 1000 cost= 0.081205189 W= 0.28870946 b= 0.5359594\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFkCAYAAACq4KjhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Initial cost, before optimizing\n", + "print(\"Initial cost= {:.9f}\".format(\n", + " mean_square_fn(linear_regression, train_X, train_Y)),\n", + " \"W=\", W.numpy(), \"b=\", b.numpy())\n", + "\n", + "# Training\n", + "for step in range(num_steps):\n", + "\n", + " optimizer.apply_gradients(grad(linear_regression, train_X, train_Y))\n", + "\n", + " if (step + 1) % display_step == 0 or step == 0:\n", + " print(\"Epoch:\", '%04d' % (step + 1), \"cost=\",\n", + " \"{:.9f}\".format(mean_square_fn(linear_regression, train_X, train_Y)),\n", + " \"W=\", W.numpy(), \"b=\", b.numpy())\n", + "\n", + "# Graphic display\n", + "plt.plot(train_X, train_Y, 'ro', label='Original data')\n", + "plt.plot(train_X, np.array(W * train_X + b), label='Fitted line')\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb b/tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb new file mode 100644 index 00000000..39465835 --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Logistic Regression Example\n", + "\n", + "A logistic regression learning algorithm example using TensorFlow library.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", + "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 25\n", + "batch_size = 100\n", + "display_step = 1\n", + "\n", + "# tf Graph Input\n", + "x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784\n", + "y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes\n", + "\n", + "# Set model weights\n", + "W = tf.Variable(tf.zeros([784, 10]))\n", + "b = tf.Variable(tf.zeros([10]))\n", + "\n", + "# Construct model\n", + "pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n", + "\n", + "# Minimize error using cross entropy\n", + "cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))\n", + "# Gradient Descent\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0001 cost= 1.182138959\n", + "Epoch: 0002 cost= 0.664778162\n", + "Epoch: 0003 cost= 0.552686284\n", + "Epoch: 0004 cost= 0.498628905\n", + "Epoch: 0005 cost= 0.465469866\n", + "Epoch: 0006 cost= 0.442537872\n", + "Epoch: 0007 cost= 0.425462044\n", + "Epoch: 0008 cost= 0.412185303\n", + "Epoch: 0009 cost= 0.401311587\n", + "Epoch: 0010 cost= 0.392326203\n", + "Epoch: 0011 cost= 0.384736038\n", + "Epoch: 0012 cost= 0.378137191\n", + "Epoch: 0013 cost= 0.372363752\n", + "Epoch: 0014 cost= 0.367308579\n", + "Epoch: 0015 cost= 0.362704660\n", + "Epoch: 0016 cost= 0.358588599\n", + "Epoch: 0017 cost= 0.354823110\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + "\n", + " # Training cycle\n", + " for epoch in range(training_epochs):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples/batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", + " # Fit training using batch data\n", + " _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,\n", + " y: batch_ys})\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if (epoch+1) % display_step == 0:\n", + " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost)\n", + "\n", + " print \"Optimization Finished!\"\n", + "\n", + " # Test model\n", + " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " # Calculate accuracy for 3000 examples\n", + " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + " print \"Accuracy:\", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]})" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb b/tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb new file mode 100644 index 00000000..06aa5bca --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Logistic Regression with Eager API\n", + "\n", + "A logistic regression implemented using TensorFlow's Eager API.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Set Eager API\n", + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.1\n", + "batch_size = 128\n", + "num_steps = 1000\n", + "display_step = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Iterator for the dataset\n", + "dataset = tf.data.Dataset.from_tensor_slices(\n", + " (mnist.train.images, mnist.train.labels))\n", + "dataset = dataset.repeat().batch(batch_size).prefetch(batch_size)\n", + "dataset_iter = tfe.Iterator(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Variables\n", + "W = tfe.Variable(tf.zeros([784, 10]), name='weights')\n", + "b = tfe.Variable(tf.zeros([10]), name='bias')\n", + "\n", + "# Logistic regression (Wx + b)\n", + "def logistic_regression(inputs):\n", + " return tf.matmul(inputs, W) + b\n", + "\n", + "# Cross-Entropy loss function\n", + "def loss_fn(inference_fn, inputs, labels):\n", + " # Using sparse_softmax cross entropy\n", + " return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=inference_fn(inputs), labels=labels))\n", + "\n", + "# Calculate accuracy\n", + "def accuracy_fn(inference_fn, inputs, labels):\n", + " prediction = tf.nn.softmax(inference_fn(inputs))\n", + " correct_pred = tf.equal(tf.argmax(prediction, 1), labels)\n", + " return tf.reduce_mean(tf.cast(correct_pred, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# SGD Optimizer\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Compute gradients\n", + "grad = tfe.implicit_gradients(loss_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial loss= 2.302584887\n", + "Step: 0001 loss= 2.302584887 accuracy= 0.1172\n", + "Step: 0100 loss= 0.952338457 accuracy= 0.7955\n", + "Step: 0200 loss= 0.535867393 accuracy= 0.8712\n", + "Step: 0300 loss= 0.485415280 accuracy= 0.8757\n", + "Step: 0400 loss= 0.433947206 accuracy= 0.8843\n", + "Step: 0500 loss= 0.381990731 accuracy= 0.8971\n", + "Step: 0600 loss= 0.394154936 accuracy= 0.8947\n", + "Step: 0700 loss= 0.391497582 accuracy= 0.8905\n", + "Step: 0800 loss= 0.386373103 accuracy= 0.8945\n", + "Step: 0900 loss= 0.332039326 accuracy= 0.9096\n", + "Step: 1000 loss= 0.358993769 accuracy= 0.9002\n" + ] + } + ], + "source": [ + "# Training\n", + "average_loss = 0.\n", + "average_acc = 0.\n", + "for step in range(num_steps):\n", + "\n", + " # Iterate through the dataset\n", + " d = dataset_iter.next()\n", + "\n", + " # Images\n", + " x_batch = d[0]\n", + " # Labels\n", + " y_batch = tf.cast(d[1], dtype=tf.int64)\n", + "\n", + " # Compute the batch loss\n", + " batch_loss = loss_fn(logistic_regression, x_batch, y_batch)\n", + " average_loss += batch_loss\n", + " # Compute the batch accuracy\n", + " batch_accuracy = accuracy_fn(logistic_regression, x_batch, y_batch)\n", + " average_acc += batch_accuracy\n", + "\n", + " if step == 0:\n", + " # Display the initial cost, before optimizing\n", + " print(\"Initial loss= {:.9f}\".format(average_loss))\n", + "\n", + " # Update the variables following gradients info\n", + " optimizer.apply_gradients(grad(logistic_regression, x_batch, y_batch))\n", + "\n", + " # Display info\n", + " if (step + 1) % display_step == 0 or step == 0:\n", + " if step > 0:\n", + " average_loss /= display_step\n", + " average_acc /= display_step\n", + " print(\"Step:\", '%04d' % (step + 1), \" loss=\",\n", + " \"{:.9f}\".format(average_loss), \" accuracy=\",\n", + " \"{:.4f}\".format(average_acc))\n", + " average_loss = 0.\n", + " average_acc = 0." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testset Accuracy: 0.9083\n" + ] + } + ], + "source": [ + "# Evaluate model on the test image set\n", + "testX = mnist.test.images\n", + "testY = mnist.test.labels\n", + "\n", + "test_acc = accuracy_fn(logistic_regression, testX, testY)\n", + "print(\"Testset Accuracy: {:.4f}\".format(test_acc))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb b/tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb new file mode 100644 index 00000000..c8fba06f --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb @@ -0,0 +1,332 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Nearest Neighbor Example\n", + "\n", + "A nearest neighbor learning algorithm example using TensorFlow library.\n", + "This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", + "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# In this example, we limit mnist data\n", + "Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)\n", + "Xte, Yte = mnist.test.next_batch(200) #200 for testing\n", + "\n", + "# tf Graph Input\n", + "xtr = tf.placeholder(\"float\", [None, 784])\n", + "xte = tf.placeholder(\"float\", [784])\n", + "\n", + "# Nearest Neighbor calculation using L1 Distance\n", + "# Calculate L1 Distance\n", + "distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)\n", + "# Prediction: Get min distance index (Nearest neighbor)\n", + "pred = tf.argmin(distance, 0)\n", + "\n", + "accuracy = 0.\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test 0 Prediction: 7 True Class: 7\n", + "Test 1 Prediction: 2 True Class: 2\n", + "Test 2 Prediction: 1 True Class: 1\n", + "Test 3 Prediction: 0 True Class: 0\n", + "Test 4 Prediction: 4 True Class: 4\n", + "Test 5 Prediction: 1 True Class: 1\n", + "Test 6 Prediction: 4 True Class: 4\n", + "Test 7 Prediction: 9 True Class: 9\n", + "Test 8 Prediction: 8 True Class: 5\n", + "Test 9 Prediction: 9 True Class: 9\n", + "Test 10 Prediction: 0 True Class: 0\n", + "Test 11 Prediction: 0 True Class: 6\n", + "Test 12 Prediction: 9 True Class: 9\n", + "Test 13 Prediction: 0 True Class: 0\n", + "Test 14 Prediction: 1 True Class: 1\n", + "Test 15 Prediction: 5 True Class: 5\n", + "Test 16 Prediction: 4 True Class: 9\n", + "Test 17 Prediction: 7 True Class: 7\n", + "Test 18 Prediction: 3 True Class: 3\n", + "Test 19 Prediction: 4 True Class: 4\n", + "Test 20 Prediction: 9 True Class: 9\n", + "Test 21 Prediction: 6 True Class: 6\n", + "Test 22 Prediction: 6 True Class: 6\n", + "Test 23 Prediction: 5 True Class: 5\n", + "Test 24 Prediction: 4 True Class: 4\n", + "Test 25 Prediction: 0 True Class: 0\n", + "Test 26 Prediction: 7 True Class: 7\n", + "Test 27 Prediction: 4 True Class: 4\n", + "Test 28 Prediction: 0 True Class: 0\n", + "Test 29 Prediction: 1 True Class: 1\n", + "Test 30 Prediction: 3 True Class: 3\n", + "Test 31 Prediction: 1 True Class: 1\n", + "Test 32 Prediction: 3 True Class: 3\n", + "Test 33 Prediction: 4 True Class: 4\n", + "Test 34 Prediction: 7 True Class: 7\n", + "Test 35 Prediction: 2 True Class: 2\n", + "Test 36 Prediction: 7 True Class: 7\n", + "Test 37 Prediction: 1 True Class: 1\n", + "Test 38 Prediction: 2 True Class: 2\n", + "Test 39 Prediction: 1 True Class: 1\n", + "Test 40 Prediction: 1 True Class: 1\n", + "Test 41 Prediction: 7 True Class: 7\n", + "Test 42 Prediction: 4 True Class: 4\n", + "Test 43 Prediction: 1 True Class: 2\n", + "Test 44 Prediction: 3 True Class: 3\n", + "Test 45 Prediction: 5 True Class: 5\n", + "Test 46 Prediction: 1 True Class: 1\n", + "Test 47 Prediction: 2 True Class: 2\n", + "Test 48 Prediction: 4 True Class: 4\n", + "Test 49 Prediction: 4 True Class: 4\n", + "Test 50 Prediction: 6 True Class: 6\n", + "Test 51 Prediction: 3 True Class: 3\n", + "Test 52 Prediction: 5 True Class: 5\n", + "Test 53 Prediction: 5 True Class: 5\n", + "Test 54 Prediction: 6 True Class: 6\n", + "Test 55 Prediction: 0 True Class: 0\n", + "Test 56 Prediction: 4 True Class: 4\n", + "Test 57 Prediction: 1 True Class: 1\n", + "Test 58 Prediction: 9 True Class: 9\n", + "Test 59 Prediction: 5 True Class: 5\n", + "Test 60 Prediction: 7 True Class: 7\n", + "Test 61 Prediction: 8 True Class: 8\n", + "Test 62 Prediction: 9 True Class: 9\n", + "Test 63 Prediction: 3 True Class: 3\n", + "Test 64 Prediction: 7 True Class: 7\n", + "Test 65 Prediction: 4 True Class: 4\n", + "Test 66 Prediction: 6 True Class: 6\n", + "Test 67 Prediction: 4 True Class: 4\n", + "Test 68 Prediction: 3 True Class: 3\n", + "Test 69 Prediction: 0 True Class: 0\n", + "Test 70 Prediction: 7 True Class: 7\n", + "Test 71 Prediction: 0 True Class: 0\n", + "Test 72 Prediction: 2 True Class: 2\n", + "Test 73 Prediction: 7 True Class: 9\n", + "Test 74 Prediction: 1 True Class: 1\n", + "Test 75 Prediction: 7 True Class: 7\n", + "Test 76 Prediction: 3 True Class: 3\n", + "Test 77 Prediction: 7 True Class: 2\n", + "Test 78 Prediction: 9 True Class: 9\n", + "Test 79 Prediction: 7 True Class: 7\n", + "Test 80 Prediction: 7 True Class: 7\n", + "Test 81 Prediction: 6 True Class: 6\n", + "Test 82 Prediction: 2 True Class: 2\n", + "Test 83 Prediction: 7 True Class: 7\n", + "Test 84 Prediction: 8 True Class: 8\n", + "Test 85 Prediction: 4 True Class: 4\n", + "Test 86 Prediction: 7 True Class: 7\n", + "Test 87 Prediction: 3 True Class: 3\n", + "Test 88 Prediction: 6 True Class: 6\n", + "Test 89 Prediction: 1 True Class: 1\n", + "Test 90 Prediction: 3 True Class: 3\n", + "Test 91 Prediction: 6 True Class: 6\n", + "Test 92 Prediction: 9 True Class: 9\n", + "Test 93 Prediction: 3 True Class: 3\n", + "Test 94 Prediction: 1 True Class: 1\n", + "Test 95 Prediction: 4 True Class: 4\n", + "Test 96 Prediction: 1 True Class: 1\n", + "Test 97 Prediction: 7 True Class: 7\n", + "Test 98 Prediction: 6 True Class: 6\n", + "Test 99 Prediction: 9 True Class: 9\n", + "Test 100 Prediction: 6 True Class: 6\n", + "Test 101 Prediction: 0 True Class: 0\n", + "Test 102 Prediction: 5 True Class: 5\n", + "Test 103 Prediction: 4 True Class: 4\n", + "Test 104 Prediction: 9 True Class: 9\n", + "Test 105 Prediction: 9 True Class: 9\n", + "Test 106 Prediction: 2 True Class: 2\n", + "Test 107 Prediction: 1 True Class: 1\n", + "Test 108 Prediction: 9 True Class: 9\n", + "Test 109 Prediction: 4 True Class: 4\n", + "Test 110 Prediction: 8 True Class: 8\n", + "Test 111 Prediction: 7 True Class: 7\n", + "Test 112 Prediction: 3 True Class: 3\n", + "Test 113 Prediction: 9 True Class: 9\n", + "Test 114 Prediction: 7 True Class: 7\n", + "Test 115 Prediction: 9 True Class: 4\n", + "Test 116 Prediction: 9 True Class: 4\n", + "Test 117 Prediction: 4 True Class: 4\n", + "Test 118 Prediction: 9 True Class: 9\n", + "Test 119 Prediction: 7 True Class: 2\n", + "Test 120 Prediction: 5 True Class: 5\n", + "Test 121 Prediction: 4 True Class: 4\n", + "Test 122 Prediction: 7 True Class: 7\n", + "Test 123 Prediction: 6 True Class: 6\n", + "Test 124 Prediction: 7 True Class: 7\n", + "Test 125 Prediction: 9 True Class: 9\n", + "Test 126 Prediction: 0 True Class: 0\n", + "Test 127 Prediction: 5 True Class: 5\n", + "Test 128 Prediction: 8 True Class: 8\n", + "Test 129 Prediction: 5 True Class: 5\n", + "Test 130 Prediction: 6 True Class: 6\n", + "Test 131 Prediction: 6 True Class: 6\n", + "Test 132 Prediction: 5 True Class: 5\n", + "Test 133 Prediction: 7 True Class: 7\n", + "Test 134 Prediction: 8 True Class: 8\n", + "Test 135 Prediction: 1 True Class: 1\n", + "Test 136 Prediction: 0 True Class: 0\n", + "Test 137 Prediction: 1 True Class: 1\n", + "Test 138 Prediction: 6 True Class: 6\n", + "Test 139 Prediction: 4 True Class: 4\n", + "Test 140 Prediction: 6 True Class: 6\n", + "Test 141 Prediction: 7 True Class: 7\n", + "Test 142 Prediction: 2 True Class: 3\n", + "Test 143 Prediction: 1 True Class: 1\n", + "Test 144 Prediction: 7 True Class: 7\n", + "Test 145 Prediction: 1 True Class: 1\n", + "Test 146 Prediction: 8 True Class: 8\n", + "Test 147 Prediction: 2 True Class: 2\n", + "Test 148 Prediction: 0 True Class: 0\n", + "Test 149 Prediction: 1 True Class: 2\n", + "Test 150 Prediction: 9 True Class: 9\n", + "Test 151 Prediction: 9 True Class: 9\n", + "Test 152 Prediction: 5 True Class: 5\n", + "Test 153 Prediction: 5 True Class: 5\n", + "Test 154 Prediction: 1 True Class: 1\n", + "Test 155 Prediction: 5 True Class: 5\n", + "Test 156 Prediction: 6 True Class: 6\n", + "Test 157 Prediction: 0 True Class: 0\n", + "Test 158 Prediction: 3 True Class: 3\n", + "Test 159 Prediction: 4 True Class: 4\n", + "Test 160 Prediction: 4 True Class: 4\n", + "Test 161 Prediction: 6 True Class: 6\n", + "Test 162 Prediction: 5 True Class: 5\n", + "Test 163 Prediction: 4 True Class: 4\n", + "Test 164 Prediction: 6 True Class: 6\n", + "Test 165 Prediction: 5 True Class: 5\n", + "Test 166 Prediction: 4 True Class: 4\n", + "Test 167 Prediction: 5 True Class: 5\n", + "Test 168 Prediction: 1 True Class: 1\n", + "Test 169 Prediction: 4 True Class: 4\n", + "Test 170 Prediction: 9 True Class: 4\n", + "Test 171 Prediction: 7 True Class: 7\n", + "Test 172 Prediction: 2 True Class: 2\n", + "Test 173 Prediction: 3 True Class: 3\n", + "Test 174 Prediction: 2 True Class: 2\n", + "Test 175 Prediction: 1 True Class: 7\n", + "Test 176 Prediction: 1 True Class: 1\n", + "Test 177 Prediction: 8 True Class: 8\n", + "Test 178 Prediction: 1 True Class: 1\n", + "Test 179 Prediction: 8 True Class: 8\n", + "Test 180 Prediction: 1 True Class: 1\n", + "Test 181 Prediction: 8 True Class: 8\n", + "Test 182 Prediction: 5 True Class: 5\n", + "Test 183 Prediction: 0 True Class: 0\n", + "Test 184 Prediction: 2 True Class: 8\n", + "Test 185 Prediction: 9 True Class: 9\n", + "Test 186 Prediction: 2 True Class: 2\n", + "Test 187 Prediction: 5 True Class: 5\n", + "Test 188 Prediction: 0 True Class: 0\n", + "Test 189 Prediction: 1 True Class: 1\n", + "Test 190 Prediction: 1 True Class: 1\n", + "Test 191 Prediction: 1 True Class: 1\n", + "Test 192 Prediction: 0 True Class: 0\n", + "Test 193 Prediction: 4 True Class: 9\n", + "Test 194 Prediction: 0 True Class: 0\n", + "Test 195 Prediction: 1 True Class: 3\n", + "Test 196 Prediction: 1 True Class: 1\n", + "Test 197 Prediction: 6 True Class: 6\n", + "Test 198 Prediction: 4 True Class: 4\n", + "Test 199 Prediction: 2 True Class: 2\n", + "Done!\n", + "Accuracy: 0.92\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + "\n", + " # loop over test data\n", + " for i in range(len(Xte)):\n", + " # Get nearest neighbor\n", + " nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})\n", + " # Get nearest neighbor class label and compare it to its true label\n", + " print \"Test\", i, \"Prediction:\", np.argmax(Ytr[nn_index]), \\\n", + " \"True Class:\", np.argmax(Yte[i])\n", + " # Calculate accuracy\n", + " if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):\n", + " accuracy += 1./len(Xte)\n", + " print \"Done!\"\n", + " print \"Accuracy:\", accuracy" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb b/tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb new file mode 100644 index 00000000..4b212efc --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb @@ -0,0 +1,229 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Random Forest Example\n", + "\n", + "Implement Random Forest algorithm with TensorFlow, and apply it to classify \n", + "handwritten digit images. This example is using the MNIST database of \n", + "handwritten digits as training samples (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.python.ops import resources\n", + "from tensorflow.contrib.tensor_forest.python import tensor_forest\n", + "\n", + "# Ignore all GPUs, tf random forest does not benefit from it.\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "num_steps = 500 # Total steps to train\n", + "batch_size = 1024 # The number of samples per batch\n", + "num_classes = 10 # The 10 digits\n", + "num_features = 784 # Each image is 28x28 pixels\n", + "num_trees = 10\n", + "max_nodes = 1000\n", + "\n", + "# Input and Target data\n", + "X = tf.placeholder(tf.float32, shape=[None, num_features])\n", + "# For random forest, labels must be integers (the class id)\n", + "Y = tf.placeholder(tf.int32, shape=[None])\n", + "\n", + "# Random Forest Parameters\n", + "hparams = tensor_forest.ForestHParams(num_classes=num_classes,\n", + " num_features=num_features,\n", + " num_trees=num_trees,\n", + " max_nodes=max_nodes).fill()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Constructing forest with params = \n", + "INFO:tensorflow:{'valid_leaf_threshold': 1, 'split_after_samples': 250, 'num_output_columns': 11, 'feature_bagging_fraction': 1.0, 'split_initializations_per_input': 3, 'bagged_features': None, 'min_split_samples': 5, 'max_nodes': 1000, 'num_features': 784, 'num_trees': 10, 'num_splits_to_consider': 784, 'base_random_seed': 0, 'num_outputs': 1, 'dominate_fraction': 0.99, 'max_fertile_nodes': 500, 'bagged_num_features': 784, 'dominate_method': 'bootstrap', 'bagging_fraction': 1.0, 'regression': False, 'num_classes': 10}\n", + "INFO:tensorflow:training graph for tree: 0\n", + "INFO:tensorflow:training graph for tree: 1\n", + "INFO:tensorflow:training graph for tree: 2\n", + "INFO:tensorflow:training graph for tree: 3\n", + "INFO:tensorflow:training graph for tree: 4\n", + "INFO:tensorflow:training graph for tree: 5\n", + "INFO:tensorflow:training graph for tree: 6\n", + "INFO:tensorflow:training graph for tree: 7\n", + "INFO:tensorflow:training graph for tree: 8\n", + "INFO:tensorflow:training graph for tree: 9\n" + ] + } + ], + "source": [ + "# Build the Random Forest\n", + "forest_graph = tensor_forest.RandomForestGraphs(hparams)\n", + "# Get training graph and loss\n", + "train_op = forest_graph.training_graph(X, Y)\n", + "loss_op = forest_graph.training_loss(X, Y)\n", + "\n", + "# Measure the accuracy\n", + "infer_op, _, _ = forest_graph.inference_graph(X)\n", + "correct_prediction = tf.equal(tf.argmax(infer_op, 1), tf.cast(Y, tf.int64))\n", + "accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value) and forest resources\n", + "init_vars = tf.group(tf.global_variables_initializer(),\n", + " resources.initialize_resources(resources.shared_resources()))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Loss: -0.000000, Acc: 0.112305\n", + "Step 50, Loss: -123.800003, Acc: 0.863281\n", + "Step 100, Loss: -274.200012, Acc: 0.863281\n", + "Step 150, Loss: -425.399994, Acc: 0.872070\n", + "Step 200, Loss: -582.799988, Acc: 0.917969\n", + "Step 250, Loss: -740.200012, Acc: 0.912109\n", + "Step 300, Loss: -895.799988, Acc: 0.939453\n", + "Step 350, Loss: -998.000000, Acc: 0.924805\n", + "Step 400, Loss: -998.000000, Acc: 0.940430\n", + "Step 450, Loss: -998.000000, Acc: 0.914062\n", + "Step 500, Loss: -998.000000, Acc: 0.927734\n", + "Test Accuracy: 0.9204\n" + ] + } + ], + "source": [ + "# Start TensorFlow session\n", + "sess = tf.train.MonitoredSession()\n", + "\n", + "# Run the initializer\n", + "sess.run(init_vars)\n", + "\n", + "# Training\n", + "for i in range(1, num_steps + 1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " _, l = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})\n", + " if i % 50 == 0 or i == 1:\n", + " acc = sess.run(accuracy_op, feed_dict={X: batch_x, Y: batch_y})\n", + " print('Step %i, Loss: %f, Acc: %f' % (i, l, acc))\n", + "\n", + "# Test Model\n", + "test_x, test_y = mnist.test.images, mnist.test.labels\n", + "print(\"Test Accuracy:\", sess.run(accuracy_op, feed_dict={X: test_x, Y: test_y}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + }, + "varInspector": { + "cols": { + "lenName": 16.0, + "lenType": 16.0, + "lenVar": 40.0 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb b/tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb new file mode 100644 index 00000000..5d9d83d4 --- /dev/null +++ b/tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb @@ -0,0 +1,724 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Word2Vec (Word Embedding)\n", + "\n", + "Implement Word2Vec algorithm to compute vector representations of words.\n", + "This example is using a small chunk of Wikipedia articles to train from.\n", + "\n", + "More info: [Mikolov, Tomas et al. \"Efficient Estimation of Word Representations in Vector Space.\", 2013](https://arxiv.org/pdf/1301.3781.pdf)\n", + "\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import collections\n", + "import os\n", + "import random\n", + "import urllib\n", + "import zipfile\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.1\n", + "batch_size = 128\n", + "num_steps = 3000000\n", + "display_step = 10000\n", + "eval_step = 200000\n", + "\n", + "# Evaluation Parameters\n", + "eval_words = ['five', 'of', 'going', 'hardware', 'american', 'britain']\n", + "\n", + "# Word2Vec Parameters\n", + "embedding_size = 200 # Dimension of the embedding vector\n", + "max_vocabulary_size = 50000 # Total number of different words in the vocabulary\n", + "min_occurrence = 10 # Remove all words that does not appears at least n times\n", + "skip_window = 3 # How many words to consider left and right\n", + "num_skips = 2 # How many times to reuse an input to generate a label\n", + "num_sampled = 64 # Number of negative examples to sample" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading the dataset... (It may take some time)\n", + "Done!\n" + ] + } + ], + "source": [ + "# Download a small chunk of Wikipedia articles collection\n", + "url = 'http://mattmahoney.net/dc/text8.zip'\n", + "data_path = 'text8.zip'\n", + "if not os.path.exists(data_path):\n", + " print(\"Downloading the dataset... (It may take some time)\")\n", + " filename, _ = urllib.urlretrieve(url, data_path)\n", + " print(\"Done!\")\n", + "# Unzip the dataset file. Text has already been processed\n", + "with zipfile.ZipFile(data_path) as f:\n", + " text_words = f.read(f.namelist()[0]).lower().split()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Words count: 17005207\n", + "Unique words: 253854\n", + "Vocabulary size: 50000\n", + "Most common words: [('UNK', 418391), ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764), ('in', 372201), ('a', 325873), ('to', 316376), ('zero', 264975), ('nine', 250430)]\n" + ] + } + ], + "source": [ + "# Build the dictionary and replace rare words with UNK token\n", + "count = [('UNK', -1)]\n", + "# Retrieve the most common words\n", + "count.extend(collections.Counter(text_words).most_common(max_vocabulary_size - 1))\n", + "# Remove samples with less than 'min_occurrence' occurrences\n", + "for i in range(len(count) - 1, -1, -1):\n", + " if count[i][1] < min_occurrence:\n", + " count.pop(i)\n", + " else:\n", + " # The collection is ordered, so stop when 'min_occurrence' is reached\n", + " break\n", + "# Compute the vocabulary size\n", + "vocabulary_size = len(count)\n", + "# Assign an id to each word\n", + "word2id = dict()\n", + "for i, (word, _)in enumerate(count):\n", + " word2id[word] = i\n", + "\n", + "data = list()\n", + "unk_count = 0\n", + "for word in text_words:\n", + " # Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary\n", + " index = word2id.get(word, 0)\n", + " if index == 0:\n", + " unk_count += 1\n", + " data.append(index)\n", + "count[0] = ('UNK', unk_count)\n", + "id2word = dict(zip(word2id.values(), word2id.keys()))\n", + "\n", + "print(\"Words count:\", len(text_words))\n", + "print(\"Unique words:\", len(set(text_words)))\n", + "print(\"Vocabulary size:\", vocabulary_size)\n", + "print(\"Most common words:\", count[:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "data_index = 0\n", + "# Generate training batch for the skip-gram model\n", + "def next_batch(batch_size, num_skips, skip_window):\n", + " global data_index\n", + " assert batch_size % num_skips == 0\n", + " assert num_skips <= 2 * skip_window\n", + " batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n", + " labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n", + " # get window size (words left and right + current one)\n", + " span = 2 * skip_window + 1\n", + " buffer = collections.deque(maxlen=span)\n", + " if data_index + span > len(data):\n", + " data_index = 0\n", + " buffer.extend(data[data_index:data_index + span])\n", + " data_index += span\n", + " for i in range(batch_size // num_skips):\n", + " context_words = [w for w in range(span) if w != skip_window]\n", + " words_to_use = random.sample(context_words, num_skips)\n", + " for j, context_word in enumerate(words_to_use):\n", + " batch[i * num_skips + j] = buffer[skip_window]\n", + " labels[i * num_skips + j, 0] = buffer[context_word]\n", + " if data_index == len(data):\n", + " buffer.extend(data[0:span])\n", + " data_index = span\n", + " else:\n", + " buffer.append(data[data_index])\n", + " data_index += 1\n", + " # Backtrack a little bit to avoid skipping words in the end of a batch\n", + " data_index = (data_index + len(data) - span) % len(data)\n", + " return batch, labels" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Input data\n", + "X = tf.placeholder(tf.int32, shape=[None])\n", + "# Input label\n", + "Y = tf.placeholder(tf.int32, shape=[None, 1])\n", + "\n", + "# Ensure the following ops & var are assigned on CPU\n", + "# (some ops are not compatible on GPU)\n", + "with tf.device('/cpu:0'):\n", + " # Create the embedding variable (each row represent a word embedding vector)\n", + " embedding = tf.Variable(tf.random_normal([vocabulary_size, embedding_size]))\n", + " # Lookup the corresponding embedding vectors for each sample in X\n", + " X_embed = tf.nn.embedding_lookup(embedding, X)\n", + "\n", + " # Construct the variables for the NCE loss\n", + " nce_weights = tf.Variable(tf.random_normal([vocabulary_size, embedding_size]))\n", + " nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n", + "\n", + "# Compute the average NCE loss for the batch\n", + "loss_op = tf.reduce_mean(\n", + " tf.nn.nce_loss(weights=nce_weights,\n", + " biases=nce_biases,\n", + " labels=Y,\n", + " inputs=X_embed,\n", + " num_sampled=num_sampled,\n", + " num_classes=vocabulary_size))\n", + "\n", + "# Define the optimizer\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluation\n", + "# Compute the cosine similarity between input data embedding and every embedding vectors\n", + "X_embed_norm = X_embed / tf.sqrt(tf.reduce_sum(tf.square(X_embed)))\n", + "embedding_norm = embedding / tf.sqrt(tf.reduce_sum(tf.square(embedding), 1, keepdims=True))\n", + "cosine_sim_op = tf.matmul(X_embed_norm, embedding_norm, transpose_b=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Average Loss= 520.3188\n", + "Evaluation...\n", + "\"five\" nearest neighbors: brothers, swinging, dissemination, fruitful, trichloride, dll, timur, torre,\n", + "\"of\" nearest neighbors: malting, vaginal, cecil, xiaoping, arrangers, hydras, exhibits, splits,\n", + "\"going\" nearest neighbors: besht, xps, sdtv, mississippi, frequencies, tora, reciprocating, tursiops,\n", + "\"hardware\" nearest neighbors: burgh, residences, mares, attested, whirlwind, isomerism, admiration, ties,\n", + "\"american\" nearest neighbors: tensile, months, baffling, cricket, kodak, risky, nicomedia, jura,\n", + "\"britain\" nearest neighbors: superstring, interpretations, genealogical, munition, boer, occasional, psychologists, turbofan,\n", + "Step 10000, Average Loss= 202.2640\n", + "Step 20000, Average Loss= 96.5149\n", + "Step 30000, Average Loss= 67.2858\n", + "Step 40000, Average Loss= 52.5055\n", + "Step 50000, Average Loss= 42.6301\n", + "Step 60000, Average Loss= 37.3644\n", + "Step 70000, Average Loss= 33.1220\n", + "Step 80000, Average Loss= 30.5835\n", + "Step 90000, Average Loss= 28.2243\n", + "Step 100000, Average Loss= 25.5532\n", + "Step 110000, Average Loss= 24.0891\n", + "Step 120000, Average Loss= 21.8576\n", + "Step 130000, Average Loss= 21.2192\n", + "Step 140000, Average Loss= 19.8834\n", + "Step 150000, Average Loss= 19.3362\n", + "Step 160000, Average Loss= 18.3129\n", + "Step 170000, Average Loss= 17.4952\n", + "Step 180000, Average Loss= 16.8531\n", + "Step 190000, Average Loss= 15.9615\n", + "Step 200000, Average Loss= 15.0718\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, eight, six, seven, two, nine, one,\n", + "\"of\" nearest neighbors: the, is, a, was, with, in, and, on,\n", + "\"going\" nearest neighbors: time, military, called, with, used, state, most, new,\n", + "\"hardware\" nearest neighbors: deaths, system, three, at, zero, two, s, UNK,\n", + "\"american\" nearest neighbors: UNK, and, s, about, in, when, from, after,\n", + "\"britain\" nearest neighbors: years, were, from, both, of, these, is, many,\n", + "Step 210000, Average Loss= 14.9267\n", + "Step 220000, Average Loss= 15.4700\n", + "Step 230000, Average Loss= 14.0867\n", + "Step 240000, Average Loss= 14.5337\n", + "Step 250000, Average Loss= 13.2458\n", + "Step 260000, Average Loss= 13.2944\n", + "Step 270000, Average Loss= 13.0396\n", + "Step 280000, Average Loss= 12.1902\n", + "Step 290000, Average Loss= 11.7444\n", + "Step 300000, Average Loss= 11.8473\n", + "Step 310000, Average Loss= 11.1306\n", + "Step 320000, Average Loss= 11.1699\n", + "Step 330000, Average Loss= 10.8638\n", + "Step 340000, Average Loss= 10.7910\n", + "Step 350000, Average Loss= 11.0721\n", + "Step 360000, Average Loss= 10.6309\n", + "Step 370000, Average Loss= 10.4836\n", + "Step 380000, Average Loss= 10.3482\n", + "Step 390000, Average Loss= 10.0679\n", + "Step 400000, Average Loss= 10.0070\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, two, one, zero,\n", + "\"of\" nearest neighbors: and, in, the, a, for, by, is, while,\n", + "\"going\" nearest neighbors: name, called, made, military, music, people, city, was,\n", + "\"hardware\" nearest neighbors: power, a, john, the, has, see, and, system,\n", + "\"american\" nearest neighbors: s, british, UNK, john, in, during, and, from,\n", + "\"britain\" nearest neighbors: from, general, are, before, first, after, history, was,\n", + "Step 410000, Average Loss= 10.1151\n", + "Step 420000, Average Loss= 9.5719\n", + "Step 430000, Average Loss= 9.8267\n", + "Step 440000, Average Loss= 9.4704\n", + "Step 450000, Average Loss= 9.5561\n", + "Step 460000, Average Loss= 9.1479\n", + "Step 470000, Average Loss= 8.8914\n", + "Step 480000, Average Loss= 9.0281\n", + "Step 490000, Average Loss= 9.3139\n", + "Step 500000, Average Loss= 9.1559\n", + "Step 510000, Average Loss= 8.8257\n", + "Step 520000, Average Loss= 8.9081\n", + "Step 530000, Average Loss= 8.8572\n", + "Step 540000, Average Loss= 8.5835\n", + "Step 550000, Average Loss= 8.4495\n", + "Step 560000, Average Loss= 8.4193\n", + "Step 570000, Average Loss= 8.3399\n", + "Step 580000, Average Loss= 8.1633\n", + "Step 590000, Average Loss= 8.2914\n", + "Step 600000, Average Loss= 8.0268\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, two, seven, eight, one, zero,\n", + "\"of\" nearest neighbors: and, the, in, including, with, for, on, or,\n", + "\"going\" nearest neighbors: popular, king, his, music, and, time, name, being,\n", + "\"hardware\" nearest neighbors: power, over, then, than, became, at, less, for,\n", + "\"american\" nearest neighbors: english, s, german, in, french, since, john, between,\n", + "\"britain\" nearest neighbors: however, were, state, first, group, general, from, second,\n", + "Step 610000, Average Loss= 8.1733\n", + "Step 620000, Average Loss= 8.2522\n", + "Step 630000, Average Loss= 8.0434\n", + "Step 640000, Average Loss= 8.0930\n", + "Step 650000, Average Loss= 7.8770\n", + "Step 660000, Average Loss= 7.9221\n", + "Step 670000, Average Loss= 7.7645\n", + "Step 680000, Average Loss= 7.9534\n", + "Step 690000, Average Loss= 7.7507\n", + "Step 700000, Average Loss= 7.7499\n", + "Step 710000, Average Loss= 7.6629\n", + "Step 720000, Average Loss= 7.6055\n", + "Step 730000, Average Loss= 7.4779\n", + "Step 740000, Average Loss= 7.3182\n", + "Step 750000, Average Loss= 7.6399\n", + "Step 760000, Average Loss= 7.4364\n", + "Step 770000, Average Loss= 7.6509\n", + "Step 780000, Average Loss= 7.3204\n", + "Step 790000, Average Loss= 7.4101\n", + "Step 800000, Average Loss= 7.4354\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, seven, eight, two, one, nine,\n", + "\"of\" nearest neighbors: and, the, its, a, with, at, in, for,\n", + "\"going\" nearest neighbors: were, man, music, now, great, support, popular, her,\n", + "\"hardware\" nearest neighbors: power, system, then, military, high, against, since, international,\n", + "\"american\" nearest neighbors: english, british, born, b, john, french, d, german,\n", + "\"britain\" nearest neighbors: government, second, before, from, state, several, the, at,\n", + "Step 810000, Average Loss= 7.2603\n", + "Step 820000, Average Loss= 7.1646\n", + "Step 830000, Average Loss= 7.3155\n", + "Step 840000, Average Loss= 7.1274\n", + "Step 850000, Average Loss= 7.1237\n", + "Step 860000, Average Loss= 7.1528\n", + "Step 870000, Average Loss= 7.0673\n", + "Step 880000, Average Loss= 7.2167\n", + "Step 890000, Average Loss= 7.1359\n", + "Step 900000, Average Loss= 7.0940\n", + "Step 910000, Average Loss= 7.1114\n", + "Step 920000, Average Loss= 6.9328\n", + "Step 930000, Average Loss= 7.0108\n", + "Step 940000, Average Loss= 7.0630\n", + "Step 950000, Average Loss= 6.8371\n", + "Step 960000, Average Loss= 7.0466\n", + "Step 970000, Average Loss= 6.8331\n", + "Step 980000, Average Loss= 6.9670\n", + "Step 990000, Average Loss= 6.7357\n", + "Step 1000000, Average Loss= 6.6453\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, eight, seven, two, nine, zero,\n", + "\"of\" nearest neighbors: the, became, including, first, second, from, following, and,\n", + "\"going\" nearest neighbors: near, music, popular, made, while, his, works, most,\n", + "\"hardware\" nearest neighbors: power, system, before, its, using, for, thus, an,\n", + "\"american\" nearest neighbors: b, born, d, UNK, nine, john, english, seven,\n", + "\"britain\" nearest neighbors: of, following, government, home, from, state, end, several,\n", + "Step 1010000, Average Loss= 6.7193\n", + "Step 1020000, Average Loss= 6.9297\n", + "Step 1030000, Average Loss= 6.7905\n", + "Step 1040000, Average Loss= 6.7709\n", + "Step 1050000, Average Loss= 6.7337\n", + "Step 1060000, Average Loss= 6.7617\n", + "Step 1070000, Average Loss= 6.7489\n", + "Step 1080000, Average Loss= 6.6259\n", + "Step 1090000, Average Loss= 6.6415\n", + "Step 1100000, Average Loss= 6.7209\n", + "Step 1110000, Average Loss= 6.5471\n", + "Step 1120000, Average Loss= 6.6508\n", + "Step 1130000, Average Loss= 6.5184\n", + "Step 1140000, Average Loss= 6.6202\n", + "Step 1150000, Average Loss= 6.7205\n", + "Step 1160000, Average Loss= 6.5821\n", + "Step 1170000, Average Loss= 6.6200\n", + "Step 1180000, Average Loss= 6.5089\n", + "Step 1190000, Average Loss= 6.5587\n", + "Step 1200000, Average Loss= 6.4930\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, seven, eight, two, nine, zero,\n", + "\"of\" nearest neighbors: the, and, including, in, first, with, following, from,\n", + "\"going\" nearest neighbors: near, popular, works, today, large, now, when, both,\n", + "\"hardware\" nearest neighbors: power, system, computer, its, both, for, using, which,\n", + "\"american\" nearest neighbors: born, d, john, german, b, UNK, english, s,\n", + "\"britain\" nearest neighbors: state, following, government, home, became, people, were, the,\n", + "Step 1210000, Average Loss= 6.5985\n", + "Step 1220000, Average Loss= 6.4534\n", + "Step 1230000, Average Loss= 6.5083\n", + "Step 1240000, Average Loss= 6.4913\n", + "Step 1250000, Average Loss= 6.4326\n", + "Step 1260000, Average Loss= 6.3891\n", + "Step 1270000, Average Loss= 6.1601\n", + "Step 1280000, Average Loss= 6.4479\n", + "Step 1290000, Average Loss= 6.3813\n", + "Step 1300000, Average Loss= 6.5335\n", + "Step 1310000, Average Loss= 6.2971\n", + "Step 1320000, Average Loss= 6.3723\n", + "Step 1330000, Average Loss= 6.4234\n", + "Step 1340000, Average Loss= 6.3130\n", + "Step 1350000, Average Loss= 6.2867\n", + "Step 1360000, Average Loss= 6.3505\n", + "Step 1370000, Average Loss= 6.2990\n", + "Step 1380000, Average Loss= 6.3012\n", + "Step 1390000, Average Loss= 6.3112\n", + "Step 1400000, Average Loss= 6.2680\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, two, seven, eight, one, zero,\n", + "\"of\" nearest neighbors: the, its, and, including, in, with, see, for,\n", + "\"going\" nearest neighbors: near, great, like, today, began, called, an, another,\n", + "\"hardware\" nearest neighbors: power, computer, system, for, program, high, control, small,\n", + "\"american\" nearest neighbors: english, german, french, born, john, british, s, references,\n", + "\"britain\" nearest neighbors: state, great, government, people, following, became, along, home,\n", + "Step 1410000, Average Loss= 6.3157\n", + "Step 1420000, Average Loss= 6.3466\n", + "Step 1430000, Average Loss= 6.3090\n", + "Step 1440000, Average Loss= 6.3330\n", + "Step 1450000, Average Loss= 6.2072\n", + "Step 1460000, Average Loss= 6.2363\n", + "Step 1470000, Average Loss= 6.2736\n", + "Step 1480000, Average Loss= 6.1793\n", + "Step 1490000, Average Loss= 6.2977\n", + "Step 1500000, Average Loss= 6.1899\n", + "Step 1510000, Average Loss= 6.2381\n", + "Step 1520000, Average Loss= 6.1027\n", + "Step 1530000, Average Loss= 6.0046\n", + "Step 1540000, Average Loss= 6.0747\n", + "Step 1550000, Average Loss= 6.2524\n", + "Step 1560000, Average Loss= 6.1247\n", + "Step 1570000, Average Loss= 6.1937\n", + "Step 1580000, Average Loss= 6.0450\n", + "Step 1590000, Average Loss= 6.1556\n", + "Step 1600000, Average Loss= 6.1765\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, two, seven, eight, one, zero,\n", + "\"of\" nearest neighbors: the, and, its, for, from, modern, in, part,\n", + "\"going\" nearest neighbors: great, today, once, now, while, her, like, by,\n", + "\"hardware\" nearest neighbors: power, system, high, program, control, computer, typically, making,\n", + "\"american\" nearest neighbors: born, english, british, german, john, french, b, d,\n", + "\"britain\" nearest neighbors: country, state, home, government, first, following, during, from,\n", + "Step 1610000, Average Loss= 6.1029\n", + "Step 1620000, Average Loss= 6.0501\n", + "Step 1630000, Average Loss= 6.1536\n", + "Step 1640000, Average Loss= 6.0483\n", + "Step 1650000, Average Loss= 6.1197\n", + "Step 1660000, Average Loss= 6.0261\n", + "Step 1670000, Average Loss= 6.1012\n", + "Step 1680000, Average Loss= 6.1795\n", + "Step 1690000, Average Loss= 6.1224\n", + "Step 1700000, Average Loss= 6.0896\n", + "Step 1710000, Average Loss= 6.0418\n", + "Step 1720000, Average Loss= 6.0626\n", + "Step 1730000, Average Loss= 6.0214\n", + "Step 1740000, Average Loss= 6.1206\n", + "Step 1750000, Average Loss= 5.9721\n", + "Step 1760000, Average Loss= 6.0782\n", + "Step 1770000, Average Loss= 6.0291\n", + "Step 1780000, Average Loss= 6.0187\n", + "Step 1790000, Average Loss= 5.9761\n", + "Step 1800000, Average Loss= 5.7518\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, nine, two, zero,\n", + "\"of\" nearest neighbors: the, from, in, became, and, second, first, including,\n", + "\"going\" nearest neighbors: today, which, once, little, made, before, now, etc,\n", + "\"hardware\" nearest neighbors: computer, power, program, system, high, typically, current, eventually,\n", + "\"american\" nearest neighbors: b, d, born, actor, UNK, robert, william, english,\n", + "\"britain\" nearest neighbors: government, state, country, from, world, great, of, in,\n", + "Step 1810000, Average Loss= 5.9839\n", + "Step 1820000, Average Loss= 5.9931\n", + "Step 1830000, Average Loss= 6.0794\n", + "Step 1840000, Average Loss= 5.9072\n", + "Step 1850000, Average Loss= 5.9831\n", + "Step 1860000, Average Loss= 6.0023\n", + "Step 1870000, Average Loss= 5.9375\n", + "Step 1880000, Average Loss= 5.9250\n", + "Step 1890000, Average Loss= 5.9422\n", + "Step 1900000, Average Loss= 5.9339\n", + "Step 1910000, Average Loss= 5.9235\n", + "Step 1920000, Average Loss= 5.9692\n", + "Step 1930000, Average Loss= 5.9022\n", + "Step 1940000, Average Loss= 5.9599\n", + "Step 1950000, Average Loss= 6.0174\n", + "Step 1960000, Average Loss= 5.9530\n", + "Step 1970000, Average Loss= 5.9479\n", + "Step 1980000, Average Loss= 5.8870\n", + "Step 1990000, Average Loss= 5.9271\n", + "Step 2000000, Average Loss= 5.8774\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, two, nine, zero,\n", + "\"of\" nearest neighbors: and, the, from, in, within, first, including, with,\n", + "\"going\" nearest neighbors: today, before, another, little, work, etc, now, him,\n", + "\"hardware\" nearest neighbors: computer, program, system, both, making, designed, power, simple,\n", + "\"american\" nearest neighbors: actor, born, d, robert, john, b, german, writer,\n", + "\"britain\" nearest neighbors: government, state, following, great, england, became, country, from,\n", + "Step 2010000, Average Loss= 5.9373\n", + "Step 2020000, Average Loss= 5.9113\n", + "Step 2030000, Average Loss= 5.9158\n", + "Step 2040000, Average Loss= 5.9020\n", + "Step 2050000, Average Loss= 5.8608\n", + "Step 2060000, Average Loss= 5.7379\n", + "Step 2070000, Average Loss= 5.7143\n", + "Step 2080000, Average Loss= 5.9379\n", + "Step 2090000, Average Loss= 5.8201\n", + "Step 2100000, Average Loss= 5.9390\n", + "Step 2110000, Average Loss= 5.7295\n", + "Step 2120000, Average Loss= 5.8290\n", + "Step 2130000, Average Loss= 5.9042\n", + "Step 2140000, Average Loss= 5.8367\n", + "Step 2150000, Average Loss= 5.7760\n", + "Step 2160000, Average Loss= 5.8664\n", + "Step 2170000, Average Loss= 5.7974\n", + "Step 2180000, Average Loss= 5.8523\n", + "Step 2190000, Average Loss= 5.8047\n", + "Step 2200000, Average Loss= 5.8172\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, eight, two, seven, one, zero,\n", + "\"of\" nearest neighbors: the, with, group, in, its, and, from, including,\n", + "\"going\" nearest neighbors: produced, when, today, while, little, before, had, like,\n", + "\"hardware\" nearest neighbors: computer, system, power, technology, program, simple, for, designed,\n", + "\"american\" nearest neighbors: english, canadian, german, french, author, british, film, born,\n", + "\"britain\" nearest neighbors: government, great, state, established, british, england, country, army,\n", + "Step 2210000, Average Loss= 5.8847\n", + "Step 2220000, Average Loss= 5.8622\n", + "Step 2230000, Average Loss= 5.8295\n", + "Step 2240000, Average Loss= 5.8484\n", + "Step 2250000, Average Loss= 5.7917\n", + "Step 2260000, Average Loss= 5.7846\n", + "Step 2270000, Average Loss= 5.8307\n", + "Step 2280000, Average Loss= 5.7341\n", + "Step 2290000, Average Loss= 5.8519\n", + "Step 2300000, Average Loss= 5.7792\n", + "Step 2310000, Average Loss= 5.8277\n", + "Step 2320000, Average Loss= 5.7196\n", + "Step 2330000, Average Loss= 5.5469\n", + "Step 2340000, Average Loss= 5.7177\n", + "Step 2350000, Average Loss= 5.8139\n", + "Step 2360000, Average Loss= 5.7849\n", + "Step 2370000, Average Loss= 5.7022\n", + "Step 2380000, Average Loss= 5.7447\n", + "Step 2390000, Average Loss= 5.7667\n", + "Step 2400000, Average Loss= 5.7625\n", + "Evaluation...\n", + "\"five\" nearest neighbors: three, four, six, seven, two, eight, zero, nine,\n", + "\"of\" nearest neighbors: the, and, from, part, in, following, within, including,\n", + "\"going\" nearest neighbors: where, once, little, now, again, while, off, produced,\n", + "\"hardware\" nearest neighbors: system, computer, high, power, using, designed, systems, simple,\n", + "\"american\" nearest neighbors: author, actor, english, born, writer, british, b, d,\n", + "\"britain\" nearest neighbors: great, established, government, england, country, state, army, former,\n", + "Step 2410000, Average Loss= 5.6953\n", + "Step 2420000, Average Loss= 5.7413\n", + "Step 2430000, Average Loss= 5.7242\n", + "Step 2440000, Average Loss= 5.7397\n", + "Step 2450000, Average Loss= 5.7755\n", + "Step 2460000, Average Loss= 5.6881\n", + "Step 2470000, Average Loss= 5.7471\n", + "Step 2480000, Average Loss= 5.8159\n", + "Step 2490000, Average Loss= 5.7452\n", + "Step 2500000, Average Loss= 5.7547\n", + "Step 2510000, Average Loss= 5.6945\n", + "Step 2520000, Average Loss= 5.7318\n", + "Step 2530000, Average Loss= 5.6682\n", + "Step 2540000, Average Loss= 5.7660\n", + "Step 2550000, Average Loss= 5.6956\n", + "Step 2560000, Average Loss= 5.7307\n", + "Step 2570000, Average Loss= 5.7015\n", + "Step 2580000, Average Loss= 5.6932\n", + "Step 2590000, Average Loss= 5.6386\n", + "Step 2600000, Average Loss= 5.4734\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, seven, eight, nine, two, zero,\n", + "\"of\" nearest neighbors: the, and, in, from, became, including, for, with,\n", + "\"going\" nearest neighbors: little, again, just, a, now, where, to, for,\n", + "\"hardware\" nearest neighbors: computer, program, system, software, designed, systems, technology, current,\n", + "\"american\" nearest neighbors: actor, d, writer, b, born, singer, author, robert,\n", + "\"britain\" nearest neighbors: great, established, government, england, country, in, from, state,\n", + "Step 2610000, Average Loss= 5.7291\n", + "Step 2620000, Average Loss= 5.6412\n", + "Step 2630000, Average Loss= 5.7485\n", + "Step 2640000, Average Loss= 5.5833\n", + "Step 2650000, Average Loss= 5.6548\n", + "Step 2660000, Average Loss= 5.7159\n", + "Step 2670000, Average Loss= 5.6569\n", + "Step 2680000, Average Loss= 5.6080\n", + "Step 2690000, Average Loss= 5.7037\n", + "Step 2700000, Average Loss= 5.6360\n", + "Step 2710000, Average Loss= 5.6707\n", + "Step 2720000, Average Loss= 5.6811\n", + "Step 2730000, Average Loss= 5.6237\n", + "Step 2740000, Average Loss= 5.7050\n", + "Step 2750000, Average Loss= 5.6991\n", + "Step 2760000, Average Loss= 5.6691\n", + "Step 2770000, Average Loss= 5.7057\n", + "Step 2780000, Average Loss= 5.6162\n", + "Step 2790000, Average Loss= 5.6484\n", + "Step 2800000, Average Loss= 5.6627\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, six, three, seven, eight, nine, two, one,\n", + "\"of\" nearest neighbors: the, in, following, including, part, and, from, under,\n", + "\"going\" nearest neighbors: again, before, little, away, once, when, eventually, then,\n", + "\"hardware\" nearest neighbors: computer, system, software, program, systems, designed, for, design,\n", + "\"american\" nearest neighbors: actor, writer, singer, author, born, robert, d, john,\n", + "\"britain\" nearest neighbors: established, england, great, government, france, army, the, throughout,\n", + "Step 2810000, Average Loss= 5.5900\n", + "Step 2820000, Average Loss= 5.7053\n", + "Step 2830000, Average Loss= 5.6064\n", + "Step 2840000, Average Loss= 5.6891\n", + "Step 2850000, Average Loss= 5.5571\n", + "Step 2860000, Average Loss= 5.4490\n", + "Step 2870000, Average Loss= 5.5428\n", + "Step 2880000, Average Loss= 5.6832\n", + "Step 2890000, Average Loss= 5.5973\n", + "Step 2900000, Average Loss= 5.5816\n", + "Step 2910000, Average Loss= 5.5647\n", + "Step 2920000, Average Loss= 5.6001\n", + "Step 2930000, Average Loss= 5.6459\n", + "Step 2940000, Average Loss= 5.5622\n", + "Step 2950000, Average Loss= 5.5707\n", + "Step 2960000, Average Loss= 5.6492\n", + "Step 2970000, Average Loss= 5.5633\n", + "Step 2980000, Average Loss= 5.6323\n", + "Step 2990000, Average Loss= 5.5440\n", + "Step 3000000, Average Loss= 5.6209\n", + "Evaluation...\n", + "\"five\" nearest neighbors: four, three, six, eight, seven, two, zero, one,\n", + "\"of\" nearest neighbors: the, in, and, including, group, includes, part, from,\n", + "\"going\" nearest neighbors: once, again, when, quickly, before, eventually, little, had,\n", + "\"hardware\" nearest neighbors: computer, system, software, designed, program, simple, systems, sound,\n", + "\"american\" nearest neighbors: canadian, english, author, german, french, british, irish, australian,\n", + "\"britain\" nearest neighbors: established, england, great, government, throughout, france, british, northern,\n" + ] + } + ], + "source": [ + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()\n", + "\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " # Testing data\n", + " x_test = np.array([word2id[w] for w in eval_words])\n", + "\n", + " average_loss = 0\n", + " for step in xrange(1, num_steps + 1):\n", + " # Get a new batch of data\n", + " batch_x, batch_y = next_batch(batch_size, num_skips, skip_window)\n", + " # Run training op\n", + " _, loss = sess.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y})\n", + " average_loss += loss\n", + "\n", + " if step % display_step == 0 or step == 1:\n", + " if step > 1:\n", + " average_loss /= display_step\n", + " print(\"Step \" + str(step) + \", Average Loss= \" + \\\n", + " \"{:.4f}\".format(average_loss))\n", + " average_loss = 0\n", + "\n", + " # Evaluation\n", + " if step % eval_step == 0 or step == 1:\n", + " print(\"Evaluation...\")\n", + " sim = sess.run(cosine_sim_op, feed_dict={X: x_test})\n", + " for i in xrange(len(eval_words)):\n", + " top_k = 8 # number of nearest neighbors\n", + " nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n", + " log_str = '\"%s\" nearest neighbors:' % eval_words[i]\n", + " for k in xrange(top_k):\n", + " log_str = '%s %s,' % (log_str, id2word[nearest[k]])\n", + " print(log_str)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb new file mode 100644 index 00000000..68318441 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Auto-Encoder Example\n", + "\n", + "Build a 2 layers auto-encoder with TensorFlow to compress images to a lower latent space and then reconstruct them.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Auto-Encoder Overview\n", + "\n", + "\"ae\"\n", + "\n", + "References:\n", + "- [Gradient-based learning applied to document recognition](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf). Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Proceedings of the IEEE, 86(11):2278-2324, November 1998.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.01\n", + "num_steps = 30000\n", + "batch_size = 256\n", + "\n", + "display_step = 1000\n", + "examples_to_show = 10\n", + "\n", + "# Network Parameters\n", + "num_hidden_1 = 256 # 1st layer num features\n", + "num_hidden_2 = 128 # 2nd layer num features (the latent dim)\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "\n", + "# tf Graph input (only pictures)\n", + "X = tf.placeholder(\"float\", [None, num_input])\n", + "\n", + "weights = {\n", + " 'encoder_h1': tf.Variable(tf.random_normal([num_input, num_hidden_1])),\n", + " 'encoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_hidden_2])),\n", + " 'decoder_h1': tf.Variable(tf.random_normal([num_hidden_2, num_hidden_1])),\n", + " 'decoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_input])),\n", + "}\n", + "biases = {\n", + " 'encoder_b1': tf.Variable(tf.random_normal([num_hidden_1])),\n", + " 'encoder_b2': tf.Variable(tf.random_normal([num_hidden_2])),\n", + " 'decoder_b1': tf.Variable(tf.random_normal([num_hidden_1])),\n", + " 'decoder_b2': tf.Variable(tf.random_normal([num_input])),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Building the encoder\n", + "def encoder(x):\n", + " # Encoder Hidden layer with sigmoid activation #1\n", + " layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),\n", + " biases['encoder_b1']))\n", + " # Encoder Hidden layer with sigmoid activation #2\n", + " layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),\n", + " biases['encoder_b2']))\n", + " return layer_2\n", + "\n", + "\n", + "# Building the decoder\n", + "def decoder(x):\n", + " # Decoder Hidden layer with sigmoid activation #1\n", + " layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),\n", + " biases['decoder_b1']))\n", + " # Decoder Hidden layer with sigmoid activation #2\n", + " layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),\n", + " biases['decoder_b2']))\n", + " return layer_2\n", + "\n", + "# Construct model\n", + "encoder_op = encoder(X)\n", + "decoder_op = decoder(encoder_op)\n", + "\n", + "# Prediction\n", + "y_pred = decoder_op\n", + "# Targets (Labels) are the input data.\n", + "y_true = X\n", + "\n", + "# Define loss and optimizer, minimize the squared error\n", + "loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2))\n", + "optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Minibatch Loss: 0.438300\n", + "Step 1000: Minibatch Loss: 0.146586\n", + "Step 2000: Minibatch Loss: 0.130722\n", + "Step 3000: Minibatch Loss: 0.117178\n", + "Step 4000: Minibatch Loss: 0.109027\n", + "Step 5000: Minibatch Loss: 0.102582\n", + "Step 6000: Minibatch Loss: 0.099183\n", + "Step 7000: Minibatch Loss: 0.095619\n", + "Step 8000: Minibatch Loss: 0.089006\n", + "Step 9000: Minibatch Loss: 0.087125\n", + "Step 10000: Minibatch Loss: 0.083930\n", + "Step 11000: Minibatch Loss: 0.077512\n", + "Step 12000: Minibatch Loss: 0.077137\n", + "Step 13000: Minibatch Loss: 0.073983\n", + "Step 14000: Minibatch Loss: 0.074218\n", + "Step 15000: Minibatch Loss: 0.074492\n", + "Step 16000: Minibatch Loss: 0.074374\n", + "Step 17000: Minibatch Loss: 0.070909\n", + "Step 18000: Minibatch Loss: 0.069438\n", + "Step 19000: Minibatch Loss: 0.068245\n", + "Step 20000: Minibatch Loss: 0.068402\n", + "Step 21000: Minibatch Loss: 0.067113\n", + "Step 22000: Minibatch Loss: 0.068241\n", + "Step 23000: Minibatch Loss: 0.062454\n", + "Step 24000: Minibatch Loss: 0.059754\n", + "Step 25000: Minibatch Loss: 0.058687\n", + "Step 26000: Minibatch Loss: 0.059107\n", + "Step 27000: Minibatch Loss: 0.055788\n", + "Step 28000: Minibatch Loss: 0.057263\n", + "Step 29000: Minibatch Loss: 0.056391\n", + "Step 30000: Minibatch Loss: 0.057672\n" + ] + } + ], + "source": [ + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + "\n", + "# Training\n", + "for i in range(1, num_steps+1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + "\n", + " # Run optimization op (backprop) and cost op (to get loss value)\n", + " _, l = sess.run([optimizer, loss], feed_dict={X: batch_x})\n", + " # Display logs per step\n", + " if i % display_step == 0 or i == 1:\n", + " print('Step %i: Minibatch Loss: %f' % (i, l))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Images\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructed Images\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Encode and decode images from test set and visualize their reconstruction.\n", + "n = 4\n", + "canvas_orig = np.empty((28 * n, 28 * n))\n", + "canvas_recon = np.empty((28 * n, 28 * n))\n", + "for i in range(n):\n", + " # MNIST test set\n", + " batch_x, _ = mnist.test.next_batch(n)\n", + " # Encode and decode the digit image\n", + " g = sess.run(decoder_op, feed_dict={X: batch_x})\n", + " \n", + " # Display original images\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = batch_x[j].reshape([28, 28])\n", + " # Display reconstructed images\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28])\n", + "\n", + "print(\"Original Images\") \n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas_orig, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()\n", + "\n", + "print(\"Reconstructed Images\")\n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas_recon, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb new file mode 100644 index 00000000..2435b229 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -0,0 +1,301 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Bi-directional Recurrent Neural Network Example\n", + "\n", + "Build a bi-directional recurrent neural network (LSTM) with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BiRNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "To classify images using a recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 timesteps for every sample.\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.contrib import rnn\n", + "import numpy as np\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.001\n", + "training_steps = 10000\n", + "batch_size = 128\n", + "display_step = 200\n", + "\n", + "# Network Parameters\n", + "num_input = 28 # MNIST data input (img shape: 28*28)\n", + "timesteps = 28 # timesteps\n", + "num_hidden = 128 # hidden layer num of features\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "X = tf.placeholder(\"float\", [None, timesteps, num_input])\n", + "Y = tf.placeholder(\"float\", [None, num_classes])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define weights\n", + "weights = {\n", + " # Hidden layer weights => 2*n_hidden because of forward + backward cells\n", + " 'out': tf.Variable(tf.random_normal([2*num_hidden, num_classes]))\n", + "}\n", + "biases = {\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def BiRNN(x, weights, biases):\n", + "\n", + " # Prepare data shape to match `rnn` function requirements\n", + " # Current data input shape: (batch_size, timesteps, n_input)\n", + " # Required shape: 'timesteps' tensors list of shape (batch_size, num_input)\n", + "\n", + " # Unstack to get a list of 'timesteps' tensors of shape (batch_size, num_input)\n", + " x = tf.unstack(x, timesteps, 1)\n", + "\n", + " # Define lstm cells with tensorflow\n", + " # Forward direction cell\n", + " lstm_fw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)\n", + " # Backward direction cell\n", + " lstm_bw_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)\n", + "\n", + " # Get lstm cell output\n", + " try:\n", + " outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", + " dtype=tf.float32)\n", + " except Exception: # Old TensorFlow version only returns outputs not states\n", + " outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,\n", + " dtype=tf.float32)\n", + "\n", + " # Linear activation, using rnn inner loop last output\n", + " return tf.matmul(outputs[-1], weights['out']) + biases['out']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "logits = BiRNN(X, weights, biases)\n", + "prediction = tf.nn.softmax(logits)\n", + "\n", + "# Define loss and optimizer\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 2.6218, Training Accuracy= 0.086\n", + "Step 200, Minibatch Loss= 2.1900, Training Accuracy= 0.211\n", + "Step 400, Minibatch Loss= 2.0144, Training Accuracy= 0.375\n", + "Step 600, Minibatch Loss= 1.8729, Training Accuracy= 0.445\n", + "Step 800, Minibatch Loss= 1.8000, Training Accuracy= 0.469\n", + "Step 1000, Minibatch Loss= 1.7244, Training Accuracy= 0.453\n", + "Step 1200, Minibatch Loss= 1.5657, Training Accuracy= 0.523\n", + "Step 1400, Minibatch Loss= 1.5473, Training Accuracy= 0.547\n", + "Step 1600, Minibatch Loss= 1.5288, Training Accuracy= 0.500\n", + "Step 1800, Minibatch Loss= 1.4203, Training Accuracy= 0.555\n", + "Step 2000, Minibatch Loss= 1.2525, Training Accuracy= 0.641\n", + "Step 2200, Minibatch Loss= 1.2696, Training Accuracy= 0.594\n", + "Step 2400, Minibatch Loss= 1.2000, Training Accuracy= 0.664\n", + "Step 2600, Minibatch Loss= 1.1017, Training Accuracy= 0.625\n", + "Step 2800, Minibatch Loss= 1.2656, Training Accuracy= 0.578\n", + "Step 3000, Minibatch Loss= 1.0830, Training Accuracy= 0.656\n", + "Step 3200, Minibatch Loss= 1.1522, Training Accuracy= 0.633\n", + "Step 3400, Minibatch Loss= 0.9484, Training Accuracy= 0.680\n", + "Step 3600, Minibatch Loss= 1.0470, Training Accuracy= 0.641\n", + "Step 3800, Minibatch Loss= 1.0609, Training Accuracy= 0.586\n", + "Step 4000, Minibatch Loss= 1.1853, Training Accuracy= 0.648\n", + "Step 4200, Minibatch Loss= 0.9438, Training Accuracy= 0.750\n", + "Step 4400, Minibatch Loss= 0.7986, Training Accuracy= 0.766\n", + "Step 4600, Minibatch Loss= 0.8070, Training Accuracy= 0.750\n", + "Step 4800, Minibatch Loss= 0.8382, Training Accuracy= 0.734\n", + "Step 5000, Minibatch Loss= 0.7397, Training Accuracy= 0.766\n", + "Step 5200, Minibatch Loss= 0.7870, Training Accuracy= 0.727\n", + "Step 5400, Minibatch Loss= 0.6380, Training Accuracy= 0.828\n", + "Step 5600, Minibatch Loss= 0.7975, Training Accuracy= 0.719\n", + "Step 5800, Minibatch Loss= 0.7934, Training Accuracy= 0.766\n", + "Step 6000, Minibatch Loss= 0.6628, Training Accuracy= 0.805\n", + "Step 6200, Minibatch Loss= 0.7958, Training Accuracy= 0.672\n", + "Step 6400, Minibatch Loss= 0.6582, Training Accuracy= 0.773\n", + "Step 6600, Minibatch Loss= 0.5908, Training Accuracy= 0.812\n", + "Step 6800, Minibatch Loss= 0.6182, Training Accuracy= 0.820\n", + "Step 7000, Minibatch Loss= 0.5513, Training Accuracy= 0.812\n", + "Step 7200, Minibatch Loss= 0.6683, Training Accuracy= 0.789\n", + "Step 7400, Minibatch Loss= 0.5337, Training Accuracy= 0.828\n", + "Step 7600, Minibatch Loss= 0.6428, Training Accuracy= 0.805\n", + "Step 7800, Minibatch Loss= 0.6708, Training Accuracy= 0.797\n", + "Step 8000, Minibatch Loss= 0.4664, Training Accuracy= 0.852\n", + "Step 8200, Minibatch Loss= 0.4249, Training Accuracy= 0.859\n", + "Step 8400, Minibatch Loss= 0.7723, Training Accuracy= 0.773\n", + "Step 8600, Minibatch Loss= 0.4706, Training Accuracy= 0.859\n", + "Step 8800, Minibatch Loss= 0.4800, Training Accuracy= 0.867\n", + "Step 9000, Minibatch Loss= 0.4636, Training Accuracy= 0.891\n", + "Step 9200, Minibatch Loss= 0.5734, Training Accuracy= 0.828\n", + "Step 9400, Minibatch Loss= 0.5548, Training Accuracy= 0.875\n", + "Step 9600, Minibatch Loss= 0.3575, Training Accuracy= 0.922\n", + "Step 9800, Minibatch Loss= 0.4566, Training Accuracy= 0.844\n", + "Step 10000, Minibatch Loss= 0.5125, Training Accuracy= 0.844\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.890625\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, training_steps+1):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Reshape data to get 28 seq of 28 elements\n", + " batch_x = batch_x.reshape((batch_size, timesteps, num_input))\n", + " # Run optimization op (backprop)\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for 128 mnist test images\n", + " test_len = 128\n", + " test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))\n", + " test_label = mnist.test.labels[:test_len]\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb new file mode 100644 index 00000000..19590f46 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Convolutional Neural Network Example\n", + "\n", + "Build a convolutional neural network with TensorFlow.\n", + "\n", + "This example is using TensorFlow layers API, see 'convolutional_network_raw' example\n", + "for a raw TensorFlow implementation with variables.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CNN Overview\n", + "\n", + "![CNN](http://personal.ie.cuhk.edu.hk/~ccloy/project_target_code/images/fig3.png)\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)\n", + "\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 2000\n", + "batch_size = 128\n", + "\n", + "# Network Parameters\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.25 # Dropout, probability to drop a unit" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create the neural network\n", + "def conv_net(x_dict, n_classes, dropout, reuse, is_training):\n", + " \n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + " # TF Estimator input is a dict, in case of multiple inputs\n", + " x = x_dict['images']\n", + "\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n", + "\n", + " # Convolution Layer with 64 filters and a kernel size of 3\n", + " conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " fc1 = tf.contrib.layers.flatten(conv2)\n", + "\n", + " # Fully connected layer (in tf contrib folder for now)\n", + " fc1 = tf.layers.dense(fc1, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)\n", + "\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(fc1, n_classes)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the model function (following TF Estimator Template)\n", + "def model_fn(features, labels, mode):\n", + " \n", + " # Build the neural network\n", + " # Because Dropout have different behavior at training and prediction time, we\n", + " # need to create 2 distinct computation graphs that still share the same weights.\n", + " logits_train = conv_net(features, num_classes, dropout, reuse=False, is_training=True)\n", + " logits_test = conv_net(features, num_classes, dropout, reuse=True, is_training=False)\n", + " \n", + " # Predictions\n", + " pred_classes = tf.argmax(logits_test, axis=1)\n", + " pred_probas = tf.nn.softmax(logits_test)\n", + " \n", + " # If prediction mode, early return\n", + " if mode == tf.estimator.ModeKeys.PREDICT:\n", + " return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) \n", + " \n", + " # Define loss and optimizer\n", + " loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=tf.cast(labels, dtype=tf.int32)))\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + " train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())\n", + " \n", + " # Evaluate the accuracy of the model\n", + " acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)\n", + " \n", + " # TF Estimators requires to return a EstimatorSpec, that specify\n", + " # the different ops for training, evaluating, ...\n", + " estim_specs = tf.estimator.EstimatorSpec(\n", + " mode=mode,\n", + " predictions=pred_classes,\n", + " loss=loss_op,\n", + " train_op=train_op,\n", + " eval_metric_ops={'accuracy': acc_op})\n", + "\n", + " return estim_specs" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpdhd6F4\n", + "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_tf_random_seed': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_save_checkpoints_steps': None, '_model_dir': '/tmp/tmpdhd6F4', '_save_summary_steps': 100}\n" + ] + } + ], + "source": [ + "# Build the Estimator\n", + "model = tf.estimator.Estimator(model_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpdhd6F4/model.ckpt.\n", + "INFO:tensorflow:loss = 2.39026, step = 1\n", + "INFO:tensorflow:global_step/sec: 238.314\n", + "INFO:tensorflow:loss = 0.237997, step = 101 (0.421 sec)\n", + "INFO:tensorflow:global_step/sec: 255.312\n", + "INFO:tensorflow:loss = 0.0954537, step = 201 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 257.194\n", + "INFO:tensorflow:loss = 0.121477, step = 301 (0.389 sec)\n", + "INFO:tensorflow:global_step/sec: 255.018\n", + "INFO:tensorflow:loss = 0.0539927, step = 401 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 254.293\n", + "INFO:tensorflow:loss = 0.0440369, step = 501 (0.393 sec)\n", + "INFO:tensorflow:global_step/sec: 256.501\n", + "INFO:tensorflow:loss = 0.0247431, step = 601 (0.390 sec)\n", + "INFO:tensorflow:global_step/sec: 252.956\n", + "INFO:tensorflow:loss = 0.0738082, step = 701 (0.395 sec)\n", + "INFO:tensorflow:global_step/sec: 253.222\n", + "INFO:tensorflow:loss = 0.134998, step = 801 (0.395 sec)\n", + "INFO:tensorflow:global_step/sec: 255.606\n", + "INFO:tensorflow:loss = 0.00438448, step = 901 (0.391 sec)\n", + "INFO:tensorflow:global_step/sec: 256.306\n", + "INFO:tensorflow:loss = 0.0471991, step = 1001 (0.390 sec)\n", + "INFO:tensorflow:global_step/sec: 255.352\n", + "INFO:tensorflow:loss = 0.0371172, step = 1101 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 253.277\n", + "INFO:tensorflow:loss = 0.0129522, step = 1201 (0.395 sec)\n", + "INFO:tensorflow:global_step/sec: 252.49\n", + "INFO:tensorflow:loss = 0.039862, step = 1301 (0.396 sec)\n", + "INFO:tensorflow:global_step/sec: 253.902\n", + "INFO:tensorflow:loss = 0.0520571, step = 1401 (0.394 sec)\n", + "INFO:tensorflow:global_step/sec: 255.572\n", + "INFO:tensorflow:loss = 0.0307549, step = 1501 (0.392 sec)\n", + "INFO:tensorflow:global_step/sec: 254.32\n", + "INFO:tensorflow:loss = 0.0108862, step = 1601 (0.393 sec)\n", + "INFO:tensorflow:global_step/sec: 255.62\n", + "INFO:tensorflow:loss = 0.0294434, step = 1701 (0.391 sec)\n", + "INFO:tensorflow:global_step/sec: 254.349\n", + "INFO:tensorflow:loss = 0.0179781, step = 1801 (0.393 sec)\n", + "INFO:tensorflow:global_step/sec: 255.508\n", + "INFO:tensorflow:loss = 0.0375271, step = 1901 (0.391 sec)\n", + "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmpdhd6F4/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 0.00440777.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define the input function for training\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.train.images}, y=mnist.train.labels,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)\n", + "# Train the Model\n", + "model.train(input_fn, steps=num_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Starting evaluation at 2017-08-21-14:25:29\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpdhd6F4/model.ckpt-2000\n", + "INFO:tensorflow:Finished evaluation at 2017-08-21-14:25:29\n", + "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.9908, global_step = 2000, loss = 0.0382241\n" + ] + }, + { + "data": { + "text/plain": [ + "{'accuracy': 0.99080002, 'global_step': 2000, 'loss': 0.038224086}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluate the Model\n", + "# Define the input function for evaluating\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.test.images}, y=mnist.test.labels,\n", + " batch_size=batch_size, shuffle=False)\n", + "# Use the Estimator 'evaluate' method\n", + "model.evaluate(input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from /tmp/tmpdhd6F4/model.ckpt-2000\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiL8QFKEH0iK8ANJEX4gKcIPJPVP82g/p9/JjhUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 7\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 2\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADCRJREFUeJzt3X/oXfV9x/Hne1n6h2n/MKvGYMV0RaclYjK+iGCYHdXi\nRND8I1UYkcnSPxqwsD8m7o8JYyCydgz/KKQ0NJXOZkSDWqdtJ8N0MKpRM383OvmWJsREUahVpDN5\n74/viXzV7z33m3vPvecm7+cDLt9zz+eee94c8srn/LrnE5mJpHr+oO8CJPXD8EtFGX6pKMMvFWX4\npaIMv1SU4ZeKMvxSUYZfKuoPp7myiPB2QmnCMjOW87mxev6IuCYifhURr0XE7eN8l6TpilHv7Y+I\nFcAB4GrgIPAUcFNmvtSyjD2/NGHT6PkvA17LzNcz8/fAj4Hrx/g+SVM0TvjPBX6z6P3BZt7HRMTW\niNgXEfvGWJekjk38hF9mbge2g7v90iwZp+c/BJy36P0XmnmSTgHjhP8p4IKI+GJEfAb4OvBQN2VJ\nmrSRd/sz88OI2Ab8FFgB7MjMFzurTNJEjXypb6SVecwvTdxUbvKRdOoy/FJRhl8qyvBLRRl+qSjD\nLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0UZfqmo\nqQ7RrXouvPDCgW2vvPJK67K33XZba/s999wzUk1aYM8vFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0WN\ndZ0/IuaBd4FjwIeZOddFUTp9bNy4cWDb8ePHW5c9ePBg1+VokS5u8vnzzHyrg++RNEXu9ktFjRv+\nBH4WEU9HxNYuCpI0HePu9m/KzEMRcTbw84h4JTP3Lv5A85+C/zFIM2asnj8zDzV/jwJ7gMuW+Mz2\nzJzzZKA0W0YOf0SsiojPnZgGvga80FVhkiZrnN3+NcCeiDjxPf+amY91UpWkiRs5/Jn5OnBph7Xo\nNLRhw4aBbe+9917rsnv27Om6HC3ipT6pKMMvFWX4paIMv1SU4ZeKMvxSUT66W2NZv359a/u2bdsG\ntt17771dl6OTYM8vFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0V5nV9jueiii1rbV61aNbBt165dXZej\nk2DPLxVl+KWiDL9UlOGXijL8UlGGXyrK8EtFRWZOb2UR01uZpuLJJ59sbT/rrLMGtg17FsCwR3tr\naZkZy/mcPb9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFTX09/wRsQO4DjiameubeauBXcA6YB64MTPf\nmVyZ6su6deta2+fm5lrbDxw4MLDN6/j9Wk7P/wPgmk/Mux14PDMvAB5v3ks6hQwNf2buBd7+xOzr\ngZ3N9E7gho7rkjRhox7zr8nMw830G8CajuqRNCVjP8MvM7Ptnv2I2ApsHXc9kro1as9/JCLWAjR/\njw76YGZuz8y5zGw/MyRpqkYN/0PAlmZ6C/BgN+VImpah4Y+I+4D/Bv4kIg5GxK3AXcDVEfEqcFXz\nXtIpZOgxf2beNKDpqx3Xohl05ZVXjrX8m2++2VEl6pp3+ElFGX6pKMMvFWX4paIMv1SU4ZeKcohu\ntbrkkkvGWv7uu+/uqBJ1zZ5fKsrwS0UZfqkowy8VZfilogy/VJThl4pyiO7iLr/88tb2Rx55pLV9\nfn6+tf2KK64Y2PbBBx+0LqvROES3pFaGXyrK8EtFGX6pKMMvFWX4paIMv1SUv+cv7qqrrmptX716\ndWv7Y4891trutfzZZc8vFWX4paIMv1SU4ZeKMvxSUYZfKsrwS0UNvc4fETuA64Cjmbm+mXcn8NfA\nifGX78jMf59UkZqcSy+9tLV92PMedu/e3WU5mqLl9Pw/AK5ZYv4/Z+aG5mXwpVPM0PBn5l7g7SnU\nImmKxjnm3xYRz0XEjog4s7OKJE3FqOH/LvAlYANwGPj2oA9GxNaI2BcR+0Zcl6QJGCn8mXkkM49l\n5nHge8BlLZ/dnplzmTk3apGSujdS+CNi7aK3m4EXuilH0rQs51LffcBXgM9HxEHg74GvRMQGIIF5\n4BsTrFHSBPjc/tPcOeec09q+f//+1vZ33nmntf3iiy8+6Zo0WT63X1Irwy8VZfilogy/VJThl4oy\n/FJRPrr7NHfLLbe0tp999tmt7Y8++miH1WiW2PNLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlFe5z/N\nnX/++WMtP+wnvTp12fNLRRl+qSjDLxVl+KWiDL9UlOGXijL8UlFe5z/NXXfddWMt//DDD3dUiWaN\nPb9UlOGXijL8UlGGXyrK8EtFGX6pKMMvFTX0On9EnAf8EFgDJLA9M/8lIlYDu4B1wDxwY2b64+8e\nbNq0aWDbsCG6Vddyev4Pgb/JzC8DlwPfjIgvA7cDj2fmBcDjzXtJp4ih4c/Mw5n5TDP9LvAycC5w\nPbCz+dhO4IZJFSmpeyd1zB8R64CNwC+BNZl5uGl6g4XDAkmniGXf2x8RnwXuB76Vmb+NiI/aMjMj\nIgcstxXYOm6hkrq1rJ4/IlayEPwfZeYDzewjEbG2aV8LHF1q2czcnplzmTnXRcGSujE0/LHQxX8f\neDkzv7Oo6SFgSzO9BXiw+/IkTcpydvuvAP4SeD4i9jfz7gDuAv4tIm4Ffg3cOJkSNczmzZsHtq1Y\nsaJ12Weffba1fe/evSPVpNk3NPyZ+V9ADGj+arflSJoW7/CTijL8UlGGXyrK8EtFGX6pKMMvFeWj\nu08BZ5xxRmv7tddeO/J37969u7X92LFjI3+3Zps9v1SU4ZeKMvxSUYZfKsrwS0UZfqkowy8VFZlL\nPn1rMisb8KgvtVu5cmVr+xNPPDGw7ejRJR+w9JGbb765tf39999vbdfsycxBP8H/GHt+qSjDLxVl\n+KWiDL9UlOGXijL8UlGGXyrK6/zSacbr/JJaGX6pKMMvFWX4paIMv1SU4ZeKMvxSUUPDHxHnRcR/\nRsRLEfFiRNzWzL8zIg5FxP7mNfrD4yVN3dCbfCJiLbA2M5+JiM8BTwM3ADcCv8vMf1r2yrzJR5q4\n5d7kM3TEnsw8DBxupt+NiJeBc8crT1LfTuqYPyLWARuBXzaztkXEcxGxIyLOHLDM1ojYFxH7xqpU\nUqeWfW9/RHwWeAL4x8x8ICLWAG8BCfwDC4cGfzXkO9ztlyZsubv9ywp/RKwEfgL8NDO/s0T7OuAn\nmbl+yPcYfmnCOvthT0QE8H3g5cXBb04EnrAZeOFki5TUn+Wc7d8E/AJ4HjjezL4DuAnYwMJu/zzw\njebkYNt32fNLE9bpbn9XDL80ef6eX1Irwy8VZfilogy/VJThl4oy/FJRhl8qyvBLRRl+qSjDLxVl\n+KWiDL9UlOGXijL8UlFDH+DZsbeAXy96//lm3iya1dpmtS6wtlF1Wdv5y/3gVH/P/6mVR+zLzLne\nCmgxq7XNal1gbaPqqzZ3+6WiDL9UVN/h397z+tvMam2zWhdY26h6qa3XY35J/em755fUk17CHxHX\nRMSvIuK1iLi9jxoGiYj5iHi+GXm41yHGmmHQjkbEC4vmrY6In0fEq83fJYdJ66m2mRi5uWVk6V63\n3ayNeD313f6IWAEcAK4GDgJPATdl5ktTLWSAiJgH5jKz92vCEfFnwO+AH54YDSki7gbezsy7mv84\nz8zMv52R2u7kJEdunlBtg0aWvoUet12XI153oY+e/zLgtcx8PTN/D/wYuL6HOmZeZu4F3v7E7OuB\nnc30Thb+8UzdgNpmQmYezsxnmul3gRMjS/e67Vrq6kUf4T8X+M2i9weZrSG/E/hZRDwdEVv7LmYJ\naxaNjPQGsKbPYpYwdOTmafrEyNIzs+1GGfG6a57w+7RNmfmnwF8A32x2b2dSLhyzzdLlmu8CX2Jh\nGLfDwLf7LKYZWfp+4FuZ+dvFbX1uuyXq6mW79RH+Q8B5i95/oZk3EzLzUPP3KLCHhcOUWXLkxCCp\nzd+jPdfzkcw8kpnHMvM48D163HbNyNL3Az/KzAea2b1vu6Xq6mu79RH+p4ALIuKLEfEZ4OvAQz3U\n8SkRsao5EUNErAK+xuyNPvwQsKWZ3gI82GMtHzMrIzcPGlmanrfdzI14nZlTfwHXsnDG/3+Bv+uj\nhgF1/THwP83rxb5rA+5jYTfw/1g4N3Ir8EfA48CrwH8Aq2eotntZGM35ORaCtran2jaxsEv/HLC/\neV3b97ZrqauX7eYdflJRnvCTijL8UlGGXyrK8EtFGX6pKMMvFWX4paIMv1TU/wNRj+er2ohshAAA\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 1\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZZUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 0\n" + ] + } + ], + "source": [ + "# Predict single images\n", + "n_images = 4\n", + "# Get images from test set\n", + "test_images = mnist.test.images[:n_images]\n", + "# Prepare the input data\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': test_images}, shuffle=False)\n", + "# Use the model to predict the images class\n", + "preds = list(model.predict(input_fn))\n", + "\n", + "# Display\n", + "for i in range(n_images):\n", + " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n", + " plt.show()\n", + " print(\"Model prediction:\", preds[i])" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb new file mode 100644 index 00000000..d7f2c15d --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Convolutional Neural Network Example\n", + "\n", + "Build a convolutional neural network with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CNN Overview\n", + "\n", + "![CNN](http://personal.ie.cuhk.edu.hk/~ccloy/project_target_code/images/fig3.png)\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import tensorflow as tf\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 500\n", + "batch_size = 128\n", + "display_step = 10\n", + "\n", + "# Network Parameters\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.75 # Dropout, probability to keep units\n", + "\n", + "# tf Graph input\n", + "X = tf.placeholder(tf.float32, [None, num_input])\n", + "Y = tf.placeholder(tf.float32, [None, num_classes])\n", + "keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create some wrappers for simplicity\n", + "def conv2d(x, W, b, strides=1):\n", + " # Conv2D wrapper, with bias and relu activation\n", + " x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')\n", + " x = tf.nn.bias_add(x, b)\n", + " return tf.nn.relu(x)\n", + "\n", + "\n", + "def maxpool2d(x, k=2):\n", + " # MaxPool2D wrapper\n", + " return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],\n", + " padding='SAME')\n", + "\n", + "\n", + "# Create model\n", + "def conv_net(x, weights, biases, dropout):\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer\n", + " conv1 = conv2d(x, weights['wc1'], biases['bc1'])\n", + " # Max Pooling (down-sampling)\n", + " conv1 = maxpool2d(conv1, k=2)\n", + "\n", + " # Convolution Layer\n", + " conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])\n", + " # Max Pooling (down-sampling)\n", + " conv2 = maxpool2d(conv2, k=2)\n", + "\n", + " # Fully connected layer\n", + " # Reshape conv2 output to fit fully connected layer input\n", + " fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])\n", + " fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])\n", + " fc1 = tf.nn.relu(fc1)\n", + " # Apply Dropout\n", + " fc1 = tf.nn.dropout(fc1, dropout)\n", + "\n", + " # Output, class prediction\n", + " out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "weights = {\n", + " # 5x5 conv, 1 input, 32 outputs\n", + " 'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),\n", + " # 5x5 conv, 32 inputs, 64 outputs\n", + " 'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),\n", + " # fully connected, 7*7*64 inputs, 1024 outputs\n", + " 'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),\n", + " # 1024 inputs, 10 outputs (class prediction)\n", + " 'out': tf.Variable(tf.random_normal([1024, num_classes]))\n", + "}\n", + "\n", + "biases = {\n", + " 'bc1': tf.Variable(tf.random_normal([32])),\n", + " 'bc2': tf.Variable(tf.random_normal([64])),\n", + " 'bd1': tf.Variable(tf.random_normal([1024])),\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", + "}\n", + "\n", + "# Construct model\n", + "logits = conv_net(X, weights, biases, keep_prob)\n", + "prediction = tf.nn.softmax(logits)\n", + "\n", + "# Define loss and optimizer\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "\n", + "# Evaluate model\n", + "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 63763.3047, Training Accuracy= 0.141\n", + "Step 10, Minibatch Loss= 26429.6680, Training Accuracy= 0.242\n", + "Step 20, Minibatch Loss= 12171.8584, Training Accuracy= 0.586\n", + "Step 30, Minibatch Loss= 6306.6318, Training Accuracy= 0.734\n", + "Step 40, Minibatch Loss= 5113.7583, Training Accuracy= 0.711\n", + "Step 50, Minibatch Loss= 4022.2131, Training Accuracy= 0.805\n", + "Step 60, Minibatch Loss= 3125.4949, Training Accuracy= 0.867\n", + "Step 70, Minibatch Loss= 2225.4875, Training Accuracy= 0.875\n", + "Step 80, Minibatch Loss= 1843.3540, Training Accuracy= 0.867\n", + "Step 90, Minibatch Loss= 1715.7744, Training Accuracy= 0.875\n", + "Step 100, Minibatch Loss= 2611.2708, Training Accuracy= 0.906\n", + "Step 110, Minibatch Loss= 4804.0913, Training Accuracy= 0.875\n", + "Step 120, Minibatch Loss= 1067.5258, Training Accuracy= 0.938\n", + "Step 130, Minibatch Loss= 2519.1514, Training Accuracy= 0.898\n", + "Step 140, Minibatch Loss= 2687.9292, Training Accuracy= 0.906\n", + "Step 150, Minibatch Loss= 1983.4077, Training Accuracy= 0.938\n", + "Step 160, Minibatch Loss= 2844.6553, Training Accuracy= 0.930\n", + "Step 170, Minibatch Loss= 3602.2524, Training Accuracy= 0.914\n", + "Step 180, Minibatch Loss= 175.3922, Training Accuracy= 0.961\n", + "Step 190, Minibatch Loss= 645.1918, Training Accuracy= 0.945\n", + "Step 200, Minibatch Loss= 1147.6567, Training Accuracy= 0.938\n", + "Step 210, Minibatch Loss= 1140.4148, Training Accuracy= 0.914\n", + "Step 220, Minibatch Loss= 1572.8756, Training Accuracy= 0.906\n", + "Step 230, Minibatch Loss= 1292.9274, Training Accuracy= 0.898\n", + "Step 240, Minibatch Loss= 1501.4623, Training Accuracy= 0.953\n", + "Step 250, Minibatch Loss= 1908.2997, Training Accuracy= 0.898\n", + "Step 260, Minibatch Loss= 2182.2380, Training Accuracy= 0.898\n", + "Step 270, Minibatch Loss= 487.5807, Training Accuracy= 0.961\n", + "Step 280, Minibatch Loss= 1284.1130, Training Accuracy= 0.945\n", + "Step 290, Minibatch Loss= 1232.4919, Training Accuracy= 0.891\n", + "Step 300, Minibatch Loss= 1198.8336, Training Accuracy= 0.945\n", + "Step 310, Minibatch Loss= 2010.5345, Training Accuracy= 0.906\n", + "Step 320, Minibatch Loss= 786.3917, Training Accuracy= 0.945\n", + "Step 330, Minibatch Loss= 1408.3556, Training Accuracy= 0.898\n", + "Step 340, Minibatch Loss= 1453.7538, Training Accuracy= 0.953\n", + "Step 350, Minibatch Loss= 999.8901, Training Accuracy= 0.906\n", + "Step 360, Minibatch Loss= 914.3958, Training Accuracy= 0.961\n", + "Step 370, Minibatch Loss= 488.0052, Training Accuracy= 0.938\n", + "Step 380, Minibatch Loss= 1070.8710, Training Accuracy= 0.922\n", + "Step 390, Minibatch Loss= 151.4658, Training Accuracy= 0.961\n", + "Step 400, Minibatch Loss= 555.3539, Training Accuracy= 0.953\n", + "Step 410, Minibatch Loss= 765.5746, Training Accuracy= 0.945\n", + "Step 420, Minibatch Loss= 326.9393, Training Accuracy= 0.969\n", + "Step 430, Minibatch Loss= 530.8968, Training Accuracy= 0.977\n", + "Step 440, Minibatch Loss= 463.3909, Training Accuracy= 0.977\n", + "Step 450, Minibatch Loss= 362.2226, Training Accuracy= 0.977\n", + "Step 460, Minibatch Loss= 414.0034, Training Accuracy= 0.953\n", + "Step 470, Minibatch Loss= 583.4587, Training Accuracy= 0.945\n", + "Step 480, Minibatch Loss= 566.1262, Training Accuracy= 0.969\n", + "Step 490, Minibatch Loss= 691.1143, Training Accuracy= 0.961\n", + "Step 500, Minibatch Loss= 282.8893, Training Accuracy= 0.984\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.976562\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, num_steps+1):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop)\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y, keep_prob: dropout})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y,\n", + " keep_prob: 1.0})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for 256 MNIST test images\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: mnist.test.images[:256],\n", + " Y: mnist.test.labels[:256],\n", + " keep_prob: 1.0}))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb new file mode 100644 index 00000000..661cc74a --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Deep Convolutional Generative Adversarial Network Example\n", + "\n", + "Build a deep convolutional generative adversarial network (DCGAN) to generate digit images from a noise distribution with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DCGAN Overview\n", + "\n", + "\"dcgan\"\n", + "\n", + "References:\n", + "- [Unsupervised representation learning with deep convolutional generative adversarial networks](https://arxiv.org/pdf/1511.06434). A Radford, L Metz, S Chintala, 2016.\n", + "- [Understanding the difficulty of training deep feedforward neural networks](http://proceedings.mlr.press/v9/glorot10a.html). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167). Sergey Ioffe, Christian Szegedy. 2015.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Params\n", + "num_steps = 10000\n", + "batch_size = 128\n", + "lr_generator = 0.002\n", + "lr_discriminator = 0.002\n", + "\n", + "# Network Params\n", + "image_dim = 784 # 28*28 pixels * 1 channel\n", + "noise_dim = 100 # Noise data points" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build Networks\n", + "# Network Inputs\n", + "noise_input = tf.placeholder(tf.float32, shape=[None, noise_dim])\n", + "real_image_input = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])\n", + "# A boolean to indicate batch normalization if it is training or inference time\n", + "is_training = tf.placeholder(tf.bool)\n", + "\n", + "#LeakyReLU activation\n", + "def leakyrelu(x, alpha=0.2):\n", + " return 0.5 * (1 + alpha) * x + 0.5 * (1 - alpha) * abs(x)\n", + "\n", + "# Generator Network\n", + "# Input: Noise, Output: Image\n", + "# Note that batch normalization has different behavior at training and inference time,\n", + "# we then use a placeholder to indicates the layer if we are training or not.\n", + "def generator(x, reuse=False):\n", + " with tf.variable_scope('Generator', reuse=reuse):\n", + " # TensorFlow Layers automatically create variables and calculate their\n", + " # shape, based on the input.\n", + " x = tf.layers.dense(x, units=7 * 7 * 128)\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = tf.nn.relu(x)\n", + " # Reshape to a 4-D array of images: (batch, height, width, channels)\n", + " # New shape: (batch, 7, 7, 128)\n", + " x = tf.reshape(x, shape=[-1, 7, 7, 128])\n", + " # Deconvolution, image shape: (batch, 14, 14, 64)\n", + " x = tf.layers.conv2d_transpose(x, 64, 5, strides=2, padding='same')\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = tf.nn.relu(x)\n", + " # Deconvolution, image shape: (batch, 28, 28, 1)\n", + " x = tf.layers.conv2d_transpose(x, 1, 5, strides=2, padding='same')\n", + " # Apply tanh for better stability - clip values to [-1, 1].\n", + " x = tf.nn.tanh(x)\n", + " return x\n", + "\n", + "\n", + "# Discriminator Network\n", + "# Input: Image, Output: Prediction Real/Fake Image\n", + "def discriminator(x, reuse=False):\n", + " with tf.variable_scope('Discriminator', reuse=reuse):\n", + " # Typical convolutional neural network to classify images.\n", + " x = tf.layers.conv2d(x, 64, 5, strides=2, padding='same')\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = leakyrelu(x)\n", + " x = tf.layers.conv2d(x, 128, 5, strides=2, padding='same')\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = leakyrelu(x)\n", + " # Flatten\n", + " x = tf.reshape(x, shape=[-1, 7*7*128])\n", + " x = tf.layers.dense(x, 1024)\n", + " x = tf.layers.batch_normalization(x, training=is_training)\n", + " x = leakyrelu(x)\n", + " # Output 2 classes: Real and Fake images\n", + " x = tf.layers.dense(x, 2)\n", + " return x\n", + "\n", + "# Build Generator Network\n", + "gen_sample = generator(noise_input)\n", + "\n", + "# Build 2 Discriminator Networks (one from noise input, one from generated samples)\n", + "disc_real = discriminator(real_image_input)\n", + "disc_fake = discriminator(gen_sample, reuse=True)\n", + "\n", + "# Build the stacked generator/discriminator\n", + "stacked_gan = discriminator(gen_sample, reuse=True)\n", + "\n", + "# Build Loss (Labels for real images: 1, for fake images: 0)\n", + "# Discriminator Loss for real and fake samples\n", + "disc_loss_real = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=disc_real, labels=tf.ones([batch_size], dtype=tf.int32)))\n", + "disc_loss_fake = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=disc_fake, labels=tf.zeros([batch_size], dtype=tf.int32)))\n", + "# Sum both loss\n", + "disc_loss = disc_loss_real + disc_loss_fake\n", + "# Generator Loss (The generator tries to fool the discriminator, thus labels are 1)\n", + "gen_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=stacked_gan, labels=tf.ones([batch_size], dtype=tf.int32)))\n", + "\n", + "# Build Optimizers\n", + "optimizer_gen = tf.train.AdamOptimizer(learning_rate=lr_generator, beta1=0.5, beta2=0.999)\n", + "optimizer_disc = tf.train.AdamOptimizer(learning_rate=lr_discriminator, beta1=0.5, beta2=0.999)\n", + "\n", + "# Training Variables for each optimizer\n", + "# By default in TensorFlow, all variables are updated by each optimizer, so we\n", + "# need to precise for each one of them the specific variables to update.\n", + "# Generator Network Variables\n", + "gen_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Generator')\n", + "# Discriminator Network Variables\n", + "disc_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Discriminator')\n", + "\n", + "# Create training operations\n", + "# TensorFlow UPDATE_OPS collection holds all batch norm operation to update the moving mean/stddev\n", + "gen_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='Generator')\n", + "# `control_dependencies` ensure that the `gen_update_ops` will be run before the `minimize` op (backprop)\n", + "with tf.control_dependencies(gen_update_ops):\n", + " train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars)\n", + "disc_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='Discriminator')\n", + "with tf.control_dependencies(disc_update_ops):\n", + " train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars)\n", + " \n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Generator Loss: 3.590350, Discriminator Loss: 1.907586\n", + "Step 500: Generator Loss: 1.254698, Discriminator Loss: 1.005236\n", + "Step 1000: Generator Loss: 1.730409, Discriminator Loss: 0.837684\n", + "Step 1500: Generator Loss: 1.962198, Discriminator Loss: 0.618827\n", + "Step 2000: Generator Loss: 2.767945, Discriminator Loss: 0.378071\n", + "Step 2500: Generator Loss: 2.370605, Discriminator Loss: 0.561247\n", + "Step 3000: Generator Loss: 3.427798, Discriminator Loss: 0.402951\n", + "Step 3500: Generator Loss: 4.904454, Discriminator Loss: 0.554856\n", + "Step 4000: Generator Loss: 4.045284, Discriminator Loss: 0.454970\n", + "Step 4500: Generator Loss: 4.577699, Discriminator Loss: 0.687195\n", + "Step 5000: Generator Loss: 3.476081, Discriminator Loss: 0.210492\n", + "Step 5500: Generator Loss: 3.898139, Discriminator Loss: 0.143352\n", + "Step 6000: Generator Loss: 4.089877, Discriminator Loss: 1.082561\n", + "Step 6500: Generator Loss: 5.911457, Discriminator Loss: 0.154059\n", + "Step 7000: Generator Loss: 3.594872, Discriminator Loss: 0.152970\n", + "Step 7500: Generator Loss: 6.067883, Discriminator Loss: 0.084864\n", + "Step 8000: Generator Loss: 6.737456, Discriminator Loss: 0.402566\n", + "Step 8500: Generator Loss: 6.630128, Discriminator Loss: 0.034838\n", + "Step 9000: Generator Loss: 6.480587, Discriminator Loss: 0.427419\n", + "Step 9500: Generator Loss: 7.200409, Discriminator Loss: 0.124268\n", + "Step 10000: Generator Loss: 5.479313, Discriminator Loss: 0.191389\n" + ] + } + ], + "source": [ + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + " \n", + "# Training\n", + "for i in range(1, num_steps+1):\n", + "\n", + " # Prepare Input Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + " batch_x = np.reshape(batch_x, newshape=[-1, 28, 28, 1])\n", + " # Rescale to [-1, 1], the input range of the discriminator\n", + " batch_x = batch_x * 2. - 1.\n", + "\n", + " # Discriminator Training\n", + " # Generate noise to feed to the generator\n", + " z = np.random.uniform(-1., 1., size=[batch_size, noise_dim])\n", + " _, dl = sess.run([train_disc, disc_loss], feed_dict={real_image_input: batch_x, noise_input: z, is_training:True})\n", + " \n", + " # Generator Training\n", + " # Generate noise to feed to the generator\n", + " z = np.random.uniform(-1., 1., size=[batch_size, noise_dim])\n", + " _, gl = sess.run([train_gen, gen_loss], feed_dict={noise_input: z, is_training:True})\n", + " \n", + " if i % 500 == 0 or i == 1:\n", + " print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAFpCAYAAACBNaNRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndxzHqXD+H+DTZ0YHtS3wAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Generate images from noise, using the generator network.\n", + "n = 6\n", + "canvas = np.empty((28 * n, 28 * n))\n", + "for i in range(n):\n", + " # Noise input.\n", + " z = np.random.uniform(-1., 1., size=[n, noise_dim])\n", + " # Generate image from noise.\n", + " g = sess.run(gen_sample, feed_dict={noise_input: z, is_training:False})\n", + " # Rescale values to the original [0, 1] (from tanh -> [-1, 1])\n", + " g = (g + 1.) / 2.\n", + " # Reverse colours for better display\n", + " g = -1 * (g - 1)\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28])\n", + "\n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb new file mode 100644 index 00000000..31aa32ee --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dynamic Recurrent Neural Network.\n", + "\n", + "TensorFlow implementation of a Recurrent Neural Network (LSTM) that performs dynamic computation over sequences with variable length. This example is using a toy dataset to classify linear sequences. The generated sequences have variable length.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ====================\n", + "# TOY DATA GENERATOR\n", + "# ====================\n", + "\n", + "class ToySequenceData(object):\n", + " \"\"\" Generate sequence of data with dynamic length.\n", + " This class generate samples for training:\n", + " - Class 0: linear sequences (i.e. [0, 1, 2, 3,...])\n", + " - Class 1: random sequences (i.e. [1, 3, 10, 7,...])\n", + "\n", + " NOTICE:\n", + " We have to pad each sequence to reach 'max_seq_len' for TensorFlow\n", + " consistency (we cannot feed a numpy array with inconsistent\n", + " dimensions). The dynamic calculation will then be perform thanks to\n", + " 'seqlen' attribute that records every actual sequence length.\n", + " \"\"\"\n", + " def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3,\n", + " max_value=1000):\n", + " self.data = []\n", + " self.labels = []\n", + " self.seqlen = []\n", + " for i in range(n_samples):\n", + " # Random sequence length\n", + " len = random.randint(min_seq_len, max_seq_len)\n", + " # Monitor sequence length for TensorFlow dynamic calculation\n", + " self.seqlen.append(len)\n", + " # Add a random or linear int sequence (50% prob)\n", + " if random.random() < .5:\n", + " # Generate a linear sequence\n", + " rand_start = random.randint(0, max_value - len)\n", + " s = [[float(i)/max_value] for i in\n", + " range(rand_start, rand_start + len)]\n", + " # Pad sequence for dimension consistency\n", + " s += [[0.] for i in range(max_seq_len - len)]\n", + " self.data.append(s)\n", + " self.labels.append([1., 0.])\n", + " else:\n", + " # Generate a random sequence\n", + " s = [[float(random.randint(0, max_value))/max_value]\n", + " for i in range(len)]\n", + " # Pad sequence for dimension consistency\n", + " s += [[0.] for i in range(max_seq_len - len)]\n", + " self.data.append(s)\n", + " self.labels.append([0., 1.])\n", + " self.batch_id = 0\n", + "\n", + " def next(self, batch_size):\n", + " \"\"\" Return a batch of data. When dataset end is reached, start over.\n", + " \"\"\"\n", + " if self.batch_id == len(self.data):\n", + " self.batch_id = 0\n", + " batch_data = (self.data[self.batch_id:min(self.batch_id +\n", + " batch_size, len(self.data))])\n", + " batch_labels = (self.labels[self.batch_id:min(self.batch_id +\n", + " batch_size, len(self.data))])\n", + " batch_seqlen = (self.seqlen[self.batch_id:min(self.batch_id +\n", + " batch_size, len(self.data))])\n", + " self.batch_id = min(self.batch_id + batch_size, len(self.data))\n", + " return batch_data, batch_labels, batch_seqlen" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ==========\n", + "# MODEL\n", + "# ==========\n", + "\n", + "# Parameters\n", + "learning_rate = 0.01\n", + "training_steps = 10000\n", + "batch_size = 128\n", + "display_step = 200\n", + "\n", + "# Network Parameters\n", + "seq_max_len = 20 # Sequence max length\n", + "n_hidden = 64 # hidden layer num of features\n", + "n_classes = 2 # linear sequence or not\n", + "\n", + "trainset = ToySequenceData(n_samples=1000, max_seq_len=seq_max_len)\n", + "testset = ToySequenceData(n_samples=500, max_seq_len=seq_max_len)\n", + "\n", + "# tf Graph input\n", + "x = tf.placeholder(\"float\", [None, seq_max_len, 1])\n", + "y = tf.placeholder(\"float\", [None, n_classes])\n", + "# A placeholder for indicating each sequence length\n", + "seqlen = tf.placeholder(tf.int32, [None])\n", + "\n", + "# Define weights\n", + "weights = {\n", + " 'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))\n", + "}\n", + "biases = {\n", + " 'out': tf.Variable(tf.random_normal([n_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def dynamicRNN(x, seqlen, weights, biases):\n", + "\n", + " # Prepare data shape to match `rnn` function requirements\n", + " # Current data input shape: (batch_size, n_steps, n_input)\n", + " # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)\n", + " \n", + " # Unstack to get a list of 'n_steps' tensors of shape (batch_size, n_input)\n", + " x = tf.unstack(x, seq_max_len, 1)\n", + "\n", + " # Define a lstm cell with tensorflow\n", + " lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden)\n", + "\n", + " # Get lstm cell output, providing 'sequence_length' will perform dynamic\n", + " # calculation.\n", + " outputs, states = tf.contrib.rnn.static_rnn(lstm_cell, x, dtype=tf.float32,\n", + " sequence_length=seqlen)\n", + "\n", + " # When performing dynamic calculation, we must retrieve the last\n", + " # dynamically computed output, i.e., if a sequence length is 10, we need\n", + " # to retrieve the 10th output.\n", + " # However TensorFlow doesn't support advanced indexing yet, so we build\n", + " # a custom op that for each sample in batch size, get its length and\n", + " # get the corresponding relevant output.\n", + "\n", + " # 'outputs' is a list of output at every timestep, we pack them in a Tensor\n", + " # and change back dimension to [batch_size, n_step, n_input]\n", + " outputs = tf.stack(outputs)\n", + " outputs = tf.transpose(outputs, [1, 0, 2])\n", + "\n", + " # Hack to build the indexing and retrieve the right output.\n", + " batch_size = tf.shape(outputs)[0]\n", + " # Start indices for each sample\n", + " index = tf.range(0, batch_size) * seq_max_len + (seqlen - 1)\n", + " # Indexing\n", + " outputs = tf.gather(tf.reshape(outputs, [-1, n_hidden]), index)\n", + "\n", + " # Linear activation, using outputs computed above\n", + " return tf.matmul(outputs, weights['out']) + biases['out']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/aymeric.damien/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gradients_impl.py:93: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", + " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" + ] + } + ], + "source": [ + "pred = dynamicRNN(x, seqlen, weights, biases)\n", + "\n", + "# Define loss and optimizer\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "\n", + "# Evaluate model\n", + "correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 0.864517, Training Accuracy= 0.42188\n", + "Step 200, Minibatch Loss= 0.686012, Training Accuracy= 0.43269\n", + "Step 400, Minibatch Loss= 0.682970, Training Accuracy= 0.48077\n", + "Step 600, Minibatch Loss= 0.679640, Training Accuracy= 0.50962\n", + "Step 800, Minibatch Loss= 0.675208, Training Accuracy= 0.53846\n", + "Step 1000, Minibatch Loss= 0.668636, Training Accuracy= 0.56731\n", + "Step 1200, Minibatch Loss= 0.657525, Training Accuracy= 0.62500\n", + "Step 1400, Minibatch Loss= 0.635423, Training Accuracy= 0.67308\n", + "Step 1600, Minibatch Loss= 0.580433, Training Accuracy= 0.75962\n", + "Step 1800, Minibatch Loss= 0.475599, Training Accuracy= 0.81731\n", + "Step 2000, Minibatch Loss= 0.434865, Training Accuracy= 0.83654\n", + "Step 2200, Minibatch Loss= 0.423690, Training Accuracy= 0.85577\n", + "Step 2400, Minibatch Loss= 0.417472, Training Accuracy= 0.85577\n", + "Step 2600, Minibatch Loss= 0.412906, Training Accuracy= 0.85577\n", + "Step 2800, Minibatch Loss= 0.409193, Training Accuracy= 0.85577\n", + "Step 3000, Minibatch Loss= 0.406035, Training Accuracy= 0.86538\n", + "Step 3200, Minibatch Loss= 0.403287, Training Accuracy= 0.87500\n", + "Step 3400, Minibatch Loss= 0.400862, Training Accuracy= 0.87500\n", + "Step 3600, Minibatch Loss= 0.398704, Training Accuracy= 0.86538\n", + "Step 3800, Minibatch Loss= 0.396768, Training Accuracy= 0.86538\n", + "Step 4000, Minibatch Loss= 0.395017, Training Accuracy= 0.86538\n", + "Step 4200, Minibatch Loss= 0.393422, Training Accuracy= 0.86538\n", + "Step 4400, Minibatch Loss= 0.391957, Training Accuracy= 0.85577\n", + "Step 4600, Minibatch Loss= 0.390600, Training Accuracy= 0.85577\n", + "Step 4800, Minibatch Loss= 0.389334, Training Accuracy= 0.86538\n", + "Step 5000, Minibatch Loss= 0.388143, Training Accuracy= 0.86538\n", + "Step 5200, Minibatch Loss= 0.387015, Training Accuracy= 0.86538\n", + "Step 5400, Minibatch Loss= 0.385940, Training Accuracy= 0.86538\n", + "Step 5600, Minibatch Loss= 0.384907, Training Accuracy= 0.86538\n", + "Step 5800, Minibatch Loss= 0.383904, Training Accuracy= 0.85577\n", + "Step 6000, Minibatch Loss= 0.382921, Training Accuracy= 0.86538\n", + "Step 6200, Minibatch Loss= 0.381941, Training Accuracy= 0.86538\n", + "Step 6400, Minibatch Loss= 0.380947, Training Accuracy= 0.86538\n", + "Step 6600, Minibatch Loss= 0.379912, Training Accuracy= 0.86538\n", + "Step 6800, Minibatch Loss= 0.378796, Training Accuracy= 0.86538\n", + "Step 7000, Minibatch Loss= 0.377540, Training Accuracy= 0.86538\n", + "Step 7200, Minibatch Loss= 0.376041, Training Accuracy= 0.86538\n", + "Step 7400, Minibatch Loss= 0.374130, Training Accuracy= 0.85577\n", + "Step 7600, Minibatch Loss= 0.371514, Training Accuracy= 0.85577\n", + "Step 7800, Minibatch Loss= 0.367723, Training Accuracy= 0.85577\n", + "Step 8000, Minibatch Loss= 0.362049, Training Accuracy= 0.85577\n", + "Step 8200, Minibatch Loss= 0.353558, Training Accuracy= 0.85577\n", + "Step 8400, Minibatch Loss= 0.341072, Training Accuracy= 0.86538\n", + "Step 8600, Minibatch Loss= 0.323062, Training Accuracy= 0.87500\n", + "Step 8800, Minibatch Loss= 0.299278, Training Accuracy= 0.89423\n", + "Step 9000, Minibatch Loss= 0.273857, Training Accuracy= 0.90385\n", + "Step 9200, Minibatch Loss= 0.248392, Training Accuracy= 0.91346\n", + "Step 9400, Minibatch Loss= 0.221348, Training Accuracy= 0.92308\n", + "Step 9600, Minibatch Loss= 0.191947, Training Accuracy= 0.92308\n", + "Step 9800, Minibatch Loss= 0.159308, Training Accuracy= 0.93269\n", + "Step 10000, Minibatch Loss= 0.136938, Training Accuracy= 0.96154\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.952\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, training_steps+1):\n", + " batch_x, batch_y, batch_seqlen = trainset.next(batch_size)\n", + " # Run optimization op (backprop)\n", + " sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,\n", + " seqlen: batch_seqlen})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch accuracy & loss\n", + " acc, loss = sess.run([accuracy, cost], feed_dict={x: batch_x, y: batch_y,\n", + " seqlen: batch_seqlen})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.6f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.5f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy\n", + " test_data = testset.data\n", + " test_label = testset.labels\n", + " test_seqlen = testset.seqlen\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={x: test_data, y: test_label,\n", + " seqlen: test_seqlen}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb new file mode 100644 index 00000000..1bfb0bd5 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb @@ -0,0 +1,323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Generative Adversarial Network Example\n", + "\n", + "Build a generative adversarial network (GAN) to generate digit images from a noise distribution with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GAN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Generative adversarial nets](https://arxiv.org/pdf/1406.2661.pdf). I Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, Y. Bengio. Advances in neural information processing systems, 2672-2680.\n", + "- [Understanding the difficulty of training deep feedforward neural networks](http://proceedings.mlr.press/v9/glorot10a.html). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "\n", + "Other tutorials:\n", + "- [Generative Adversarial Networks Explained](http://kvfrans.com/generative-adversial-networks-explained/). Kevin Frans.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Training Params\n", + "num_steps = 70000\n", + "batch_size = 128\n", + "learning_rate = 0.0002\n", + "\n", + "# Network Params\n", + "image_dim = 784 # 28*28 pixels\n", + "gen_hidden_dim = 256\n", + "disc_hidden_dim = 256\n", + "noise_dim = 100 # Noise data points\n", + "\n", + "# A custom initialization (see Xavier Glorot init)\n", + "def glorot_init(shape):\n", + " return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "weights = {\n", + " 'gen_hidden1': tf.Variable(glorot_init([noise_dim, gen_hidden_dim])),\n", + " 'gen_out': tf.Variable(glorot_init([gen_hidden_dim, image_dim])),\n", + " 'disc_hidden1': tf.Variable(glorot_init([image_dim, disc_hidden_dim])),\n", + " 'disc_out': tf.Variable(glorot_init([disc_hidden_dim, 1])),\n", + "}\n", + "biases = {\n", + " 'gen_hidden1': tf.Variable(tf.zeros([gen_hidden_dim])),\n", + " 'gen_out': tf.Variable(tf.zeros([image_dim])),\n", + " 'disc_hidden1': tf.Variable(tf.zeros([disc_hidden_dim])),\n", + " 'disc_out': tf.Variable(tf.zeros([1])),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Generator\n", + "def generator(x):\n", + " hidden_layer = tf.matmul(x, weights['gen_hidden1'])\n", + " hidden_layer = tf.add(hidden_layer, biases['gen_hidden1'])\n", + " hidden_layer = tf.nn.relu(hidden_layer)\n", + " out_layer = tf.matmul(hidden_layer, weights['gen_out'])\n", + " out_layer = tf.add(out_layer, biases['gen_out'])\n", + " out_layer = tf.nn.sigmoid(out_layer)\n", + " return out_layer\n", + "\n", + "\n", + "# Discriminator\n", + "def discriminator(x):\n", + " hidden_layer = tf.matmul(x, weights['disc_hidden1'])\n", + " hidden_layer = tf.add(hidden_layer, biases['disc_hidden1'])\n", + " hidden_layer = tf.nn.relu(hidden_layer)\n", + " out_layer = tf.matmul(hidden_layer, weights['disc_out'])\n", + " out_layer = tf.add(out_layer, biases['disc_out'])\n", + " out_layer = tf.nn.sigmoid(out_layer)\n", + " return out_layer\n", + "\n", + "# Build Networks\n", + "# Network Inputs\n", + "gen_input = tf.placeholder(tf.float32, shape=[None, noise_dim], name='input_noise')\n", + "disc_input = tf.placeholder(tf.float32, shape=[None, image_dim], name='disc_input')\n", + "\n", + "# Build Generator Network\n", + "gen_sample = generator(gen_input)\n", + "\n", + "# Build 2 Discriminator Networks (one from noise input, one from generated samples)\n", + "disc_real = discriminator(disc_input)\n", + "disc_fake = discriminator(gen_sample)\n", + "\n", + "# Build Loss\n", + "gen_loss = -tf.reduce_mean(tf.log(disc_fake))\n", + "disc_loss = -tf.reduce_mean(tf.log(disc_real) + tf.log(1. - disc_fake))\n", + "\n", + "# Build Optimizers\n", + "optimizer_gen = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "optimizer_disc = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Training Variables for each optimizer\n", + "# By default in TensorFlow, all variables are updated by each optimizer, so we\n", + "# need to precise for each one of them the specific variables to update.\n", + "# Generator Network Variables\n", + "gen_vars = [weights['gen_hidden1'], weights['gen_out'],\n", + " biases['gen_hidden1'], biases['gen_out']]\n", + "# Discriminator Network Variables\n", + "disc_vars = [weights['disc_hidden1'], weights['disc_out'],\n", + " biases['disc_hidden1'], biases['disc_out']]\n", + "\n", + "# Create training operations\n", + "train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars)\n", + "train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Generator Loss: 0.774581, Discriminator Loss: 1.300602\n", + "Step 2000: Generator Loss: 4.521158, Discriminator Loss: 0.030166\n", + "Step 4000: Generator Loss: 3.685439, Discriminator Loss: 0.125958\n", + "Step 6000: Generator Loss: 4.412449, Discriminator Loss: 0.097088\n", + "Step 8000: Generator Loss: 3.996747, Discriminator Loss: 0.150800\n", + "Step 10000: Generator Loss: 3.850827, Discriminator Loss: 0.225699\n", + "Step 12000: Generator Loss: 2.950704, Discriminator Loss: 0.279967\n", + "Step 14000: Generator Loss: 3.741951, Discriminator Loss: 0.241062\n", + "Step 16000: Generator Loss: 3.117743, Discriminator Loss: 0.432293\n", + "Step 18000: Generator Loss: 3.647199, Discriminator Loss: 0.278121\n", + "Step 20000: Generator Loss: 3.186711, Discriminator Loss: 0.313830\n", + "Step 22000: Generator Loss: 3.737114, Discriminator Loss: 0.201730\n", + "Step 24000: Generator Loss: 3.042442, Discriminator Loss: 0.454414\n", + "Step 26000: Generator Loss: 3.340376, Discriminator Loss: 0.249428\n", + "Step 28000: Generator Loss: 3.423218, Discriminator Loss: 0.369653\n", + "Step 30000: Generator Loss: 3.219242, Discriminator Loss: 0.463535\n", + "Step 32000: Generator Loss: 3.313017, Discriminator Loss: 0.276070\n", + "Step 34000: Generator Loss: 3.413397, Discriminator Loss: 0.367721\n", + "Step 36000: Generator Loss: 3.240625, Discriminator Loss: 0.446160\n", + "Step 38000: Generator Loss: 3.175355, Discriminator Loss: 0.377628\n", + "Step 40000: Generator Loss: 3.154558, Discriminator Loss: 0.478812\n", + "Step 42000: Generator Loss: 3.210753, Discriminator Loss: 0.497502\n", + "Step 44000: Generator Loss: 2.883431, Discriminator Loss: 0.395812\n", + "Step 46000: Generator Loss: 2.584176, Discriminator Loss: 0.420783\n", + "Step 48000: Generator Loss: 2.581381, Discriminator Loss: 0.469289\n", + "Step 50000: Generator Loss: 2.752729, Discriminator Loss: 0.373544\n", + "Step 52000: Generator Loss: 2.649749, Discriminator Loss: 0.463755\n", + "Step 54000: Generator Loss: 2.468188, Discriminator Loss: 0.556129\n", + "Step 56000: Generator Loss: 2.653330, Discriminator Loss: 0.377572\n", + "Step 58000: Generator Loss: 2.697943, Discriminator Loss: 0.424133\n", + "Step 60000: Generator Loss: 2.835973, Discriminator Loss: 0.413252\n", + "Step 62000: Generator Loss: 2.751346, Discriminator Loss: 0.403332\n", + "Step 64000: Generator Loss: 3.212001, Discriminator Loss: 0.534427\n", + "Step 66000: Generator Loss: 2.878227, Discriminator Loss: 0.431244\n", + "Step 68000: Generator Loss: 3.104266, Discriminator Loss: 0.426825\n", + "Step 70000: Generator Loss: 2.871485, Discriminator Loss: 0.348638\n" + ] + } + ], + "source": [ + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + "\n", + "# Training\n", + "for i in range(1, num_steps+1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + " # Generate noise to feed to the generator\n", + " z = np.random.uniform(-1., 1., size=[batch_size, noise_dim])\n", + "\n", + " # Train\n", + " feed_dict = {disc_input: batch_x, gen_input: z}\n", + " _, _, gl, dl = sess.run([train_gen, train_disc, gen_loss, disc_loss],\n", + " feed_dict=feed_dict)\n", + " if i % 2000 == 0 or i == 1:\n", + " print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAFpCAYAAACBNaNRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXm4jVX7xz/rbUQhUYl6MzVRxBGlUqGkQYOkeVa90lyi\nQeNL86/hTSlCadQsEdEsJSpFIUlKREWpDHl+f+zz3evs55zjjHvv59nuz3W5jj2ds9Zez/C973UP\nLggCDMMwjPjyr2wPwDAMw6gYdiE3DMOIOXYhNwzDiDl2ITcMw4g5diE3DMOIOXYhNwzDiDl2ITcM\nw4g5abuQO+c6O+e+ds7Ndc5dna6/YxiGsaHj0pEQ5JzbCJgNdAIWAh8DJwZBMLPS/5hhGMYGTroU\n+d7A3CAI5gVBsBp4Guiapr9lGIaxQbNxmn5vPeD7Ao8XAm2Ke3Pt2rWDnXbaKU1DMQzDiB/z589n\n6dKlrjTvTdeFvESccz2BngA77rgjU6dOzdZQDMMwIkdeXl6p35su18oPwA4FHtfPfy5JEASDgyDI\nC4Igr06dOmkahmEYRu6Trgv5x0AT51wD59ymQA/glTT9LcMwjA2atLhWgiBY65y7EBgHbAQMDYLg\ny3T8LcMwjA2dtPnIgyAYA4xJ1+83DMMwElhmp2EYRszJWtRKVPjnn38AWLNmDQAbb7xxyk8jOpxw\nwgkAzJ07F/C7+hMnTgTgueeeA6BFixZZGJ1hZA9T5IZhGDFng5Wd//nPfwAYNGhQyvNS4l999RUA\njRo1yuzAjCTr1q0DYJdddgHgm2++AUBlJaZPn57yuGXLlgDUqFEDgGXLlgHwr3+ZXjFyGzvCDcMw\nYs4Gp8jbtm0LeLXWrl07AN5///2U961YsSKzAysHUqLvvvsuAMcccwwAv/32G5DImAWYM2cOEB+/\nv+b1ww+JHLKffvop5fWNNtoo5X3hwm9hRR6FhDON0blSZVyX6XeKyvzd5UVW1GeffQZAp06dAL8W\nGqOsrC+++ALwa2qUD1PkhmEYMSceEq0S2GSTTQBYu3YtAIcccggAzz//fMrz8p03adIE8NEs+nwU\n0JhkXUybNq3I933/faJu2erVq4H4KHKptocffhiAVatWAdCqVSsAJk2aBMBmm20GwLbbbgt4K6p5\n8+ZANJS4xj5r1iwA/v77b8DPUVZTzZo1Afjxxx9TPl+tWrXkPM466ywA3nnnHcArcn0fDRo0SM8k\nSsHixYsBPx8dc2E05q+//hqAefPmAf58yyYam6wK/Qyv4ZAhQwCYPHkyALfeeisAr7ySSF5fvHgx\nL7zwAuD3Z8LWU2Xv25giNwzDiDnxkGgVQEpHilu8/vrrQOE75ogRI1Kel0LabrvtgOyo2gULFgAw\nfPhwAEaOHAnA9ttvD3gVNGzYMAB+/fVXAPbff38AqlatmrGxVgZSQPL9V6tWDYAtttgi5adQ5JGi\nWs4991zA+2X1ea2d1jad0SxSc1Kep556KuD9/rKq5BuWgtVPWRkff/xx8ndJ8WkPZNNNNwXg//7v\n/1J+ZtJXrrHtsEOiRp7mJTQ//dR3rmP2888/B6KlyPXz22+/BeCJJ54A4L///S/gc0/EkUcemfLY\nOZc8FnWOzpgxA/DHbrNmzZLvrQxMkRuGYcScnFXkl112GQDLly8H/J1PiiGsxvS67sbyZUrdyS+m\nO2wmUIai7uZSNUcddRTgFXhYoRZHEASRiGwoCUXZ7LHHHoC3SAYPHlzk++Ub1r7HVlttBcAff/wB\neAW7zTbbAJmJK//www8BOPbYYwHvv5d10Lp1a8DPTcdbmzaJ/iuPPfYYAFWqVEmq3r/++ivlbzRs\n2BCAe+65B8isEv/ll18Avw8Rjvn/8stEjbxwhJEs43r16gFe7eqx9n2ygY4LfY+6dojrr78e8Jax\nrgW///474K2LIAh4/PHHATj88MMBbx3qd+66665A5e295eyF/P777095/PPPPwMlhzmdfPLJAIwa\nNQrwG2eZvIBrrN999x0AW2+9NQA9e/YE4OabbwZKPnF1cul9CxYsSJq0Ubyga7xyDengl3usdu3a\nRX5OF8Ww+0w3OF08MzFnXbBkWusCfdxxxwFwzTXXAPDvf/8bgJUrVwL+5lPU8SlT/oADDgD8Jvan\nn34KZCfh6aCDDgL8msltpQ3B8Hetx1oj3QheeuklAG644Yb0DrgMaKx77rknADvvvDPgw1r79u0L\neBfg/PnzAX/R/vHHH5ObnYceemjKZ3WjruzgCXOtGIZhxJycU+Qyp7UR1LRpU8Cr2uKQklDhJdGx\nY0fAK49MqB+NRSZbly5dALjllltK9XltmMmcX7p0KQD9+vVLWhpRVOT6bvfaay8AXnzxRcC7Trbc\ncsuU94fDxYpTOZmcq1wlSjDr2jXRc/yuu+4C/HGoMVWpUqXE3zllyhQArrzySsBbKAq/zDTr1q1L\nuvtEt27dgOK/a7k033jjDaDwhmH16tUre5gVRt9v+HvWcSYrRG6Siy66CIA+ffokzzm5ZC+//HLA\nX5cqG1PkhmEYMSfnFLn8qvJJyRdZHPJh7r777kW+PmDAACCzfkhtGEndaC4lIZUjJf/QQw8B3sc3\nZMiQ5HuiWEhKCnvcuHEpj+VPVSioQt20dqVRtelG/tIzzjgDINlMXHs1tWrVKtfvXbRoUfJ3yQeb\nzcQf8Hs44I9RhcQWh/Y9zjnnnCJfL+0xHiU0d51L2nSuVq1a0krU9UPhpOkiemezYRiGUSZyTpHL\njyzlefHFFxf5Pu2eN27cOOV53WXvu+8+IDsJQAqTU0r6TjvtBHhlKgUqP5yiMuRnlCKXklOiyPz5\n82ORpq9EJvnE9VP+fSXZDBw4EMiuIteezOjRowFfLEoRM4p60vFWWh/p7NmzgYRvXevZq1cvIPul\nFnS8AWy++eZA8RaezsebbroJKJwwdPvttwPlt1iyiSzGp59+GkhYT5AIC3322WcBn0iYbkyRG4Zh\nxJzoy7MyooB7+fGUPqu4TsX3Pvjgg4BPmRaKDDjppJPSP9hikFXQvn17wCdLSN2pQJGsjn333Rfw\nSSQzZ84EChdXWrVqVamTh7KB5q14cSltxU0rRVpROYoEySaKSlAUhxS6fOZjx44FfLq3IpCkzOVL\nlb9fRdwuvPBCILHHc/755wPw559/Atn3J1erVi0ZfaPIqCuuuALw+xsqT1scSmbS56JIcaWH9bzK\nK6uYmSyl++67L+PtBk2RG4ZhxJycUuTr1q1L+ih1F9Vd8uijj055bzhGVCpPj6Pgs7vtttsAr7zl\nZ1UxKe2ES90oS1BlBZSaLhW+YsWKEuPpo4DWTupW89ZayuefTd+4VJnixXXcaKzyHStqQZnBSt0/\n+OCDAb/PIUtRKetS6G3btk0WlFL8cjqaVJQF51zy2NP4S7KOlLEajuaIYj6DCI9Na6sIHEVPaU7X\nXXcdAJ07d874vEyRG4ZhxJycUuSzZ89m4cKFgPdBykc+YcKElOcV96qdZqlf1VGIElIz8pvqZ3Fo\nx1wxu1J0mawXUxFUHEpRONr5//jjjwFfljebsfBSxX369AG8olbdDannJUuWAD4bU2NXroDKmUrV\nyRKU7/mUU05h7733TvlsFFCBKNUOUZGs448/HvBWoawqPS+rUXVj4oTOQ2Uey1KWRayiWtmwMkyR\nG4ZhxJycUuS1atVKKm+pOPny1DZMyumtt94C4NprrwUSfi2IRpZgRZEvTxE8agWXrdocpWXMmDGA\nL1ernf+hQ4cChZsTRAGps5YtWwLQvXv3lNdLapCs16W2VS1PirdZs2aRUuJCayArSYTne+mllwI+\n0kY5EVFow1dWVEcnHOmm2kjZbCAdnTPCMAzDKBc5pcjXrVvHJ598ApCMvVU2pKILFOcabosVxepr\n5UUNpDV3fSdRRfVwVEtFlsP48eMBr/IURy0/a3H1yTNBaa2C0vpLFY8u6+mYY44BUrMo40A4M1q1\nZpTrIEs4m+q1vOy3334pj2Xtp6uiYVkotyJ3zu3gnJvknJvpnPvSOXdx/vO1nHPjnXNz8n9uVXnD\nNQzDMMJURJGvBS4PgmCac25L4BPn3HjgDODNIAgGOueuBq4G+lR8qCVTp06dZEadYo51t5QSV0SE\n6kTnIsqEFI0aNcrSSIpHURpPPvlkMnJIXXLUSkttv+TzV03nunXrAnDVVVcBhdVduK1YZXdjqUz0\nPUyePBnwvnJFqsRtz0bnnzI2tTbKaYijElc9ePnGVftHx20UKLciD4JgURAE0/L//zswC6gHdAWG\n579tOHB00b/BMAzDqAwqxUfunNsJ2AuYAmwbBIFuVT8B6S3EW4B169bRu3dvwFdZUx0E+VcV8yn1\nl4sokzMvLw+Ihp9VMbfhmumHH344/fr1A3yWoHyR4ZoWipBQTLaqJLZr167IvxllJS5Ug0XWg45b\n1R6PC1or1QUK9/KU7z9OKOpLlmE4UidKWakVjlpxzm0BPA9cEgTBioKvBYnVDYr5XE/n3FTn3NSC\nheoNwzCMslEhRe6c24TERXxkEAQv5D+92DlXNwiCRc65usCSoj4bBMFgYDBAXl5ekRf7svL1118n\nIx20a65ICNW+UAf6OPrqSkL+f2VwdujQAciuclCMrVT1K6+8Avgqk7Vq1UpG2civqsp5H3zwAQA7\n7rgj4H2U+p2qz62onGzX6S4LUrCKrpISV3y1atLHBcWLKyNVvv7vvvsOiId1FCYcFaUoFWXlRomK\nRK04YAgwKwiCuwu89Apwev7/TwdeLv/wDMMwjJKoiIRpB5wKzHDOfZr/XD9gIPCsc+5s4DugezGf\nr3TWrl2brH2hTDL5Hjt16gRAjx49MjWcjCPlLVdVFKyO3XbbDfC1NmQZvf7660Ciu4p8+upCftRR\nRwHeJyk1p7h4ranWMk5KXMj/qj0bZRwfdthhWRtTeZBlofo+OgZlCUe5/n1xhPMXNCfV0Yki5T4D\ngiB4DyjOZu9Q3t9rGIZhlI34SZn1MHr06KRak69uzz33BODVV1/N2rgyhbrUyB/9v//9D4BLLrkk\na2MS8gUrg1HZm5MnT072TZVKl0qVT1zKXD9lXUWxUmVJ6LiU9fHRRx8BUL9+fQDOO+88IFoREUUh\ntao65IoTV03uOCpxKe5u3boBPrJNdZqikMFZHFZrxTAMI+a4cLWybJCXlxeo43t50ByeeeaZ5P9V\nhS4KfuJMMWjQIMArcNUhL6l/YiaRb1jrUjDGPaxCFYusNVUMepSVUUnIWlIMvKI7Ro0aBfgs3ChV\neCyKF198EYBjjz025XlZxHE675YtWwb4KBXtuciKV2XUTJOXl8fUqVNLZZpF+2gxDMMwSiQnfORS\ncrkckVIawjVWovh9lKUDfFiVxknlhZFVcc899wC+gmODBg0AXz8/ChZySQRBwH//+9+U51TBMo5r\nNGfOnJTHsgRVaz4OmCI3DMOIOTmhyI0EUknqHaiYbSP7KHNTaq9jx46Az05VRb044Jwr1BkozoQt\nWVmCqukTB0yRG4ZhxJyciFoxDMPINSxqxTAMYwPCLuSGYRgxxy7khmEYMccu5IZhGDHHLuSGYRgx\nxy7khmEYMccu5IZhGDHHLuSGYRgxx1L0DcMwysHff/8N+KYaaqaRjaYapsgNwzBijilywzCMIgiX\nL1G5bDU4GT16NAAXXnghAKtXrwZgxx13ZPr06SmfSTemyA3DMGKOKfIQutvGsUB+GJVMFVFvHyY+\n/fRTAG677TYA5s2bB/jGxf379wd8m7E4t34riSAIIt2IWar1u+++A+DPP/8EoFatWoBvCK72g199\n9VXK599//30A2rZtm/7BloDmovPm559/BmDVqlUAVKlSBYBffvkFgOHDhwPeV642hr/99hunnXYa\nAI8//ngmhm6K3DAMI+7krCLX3VV300WLFgEwYMAAAF544QXAt6i65ZZbAOjXrx8AkyZNAjJbXP73\n338HfENeISUtZaYGtyqIL1WzySabAPDjjz8CUL169ZTne/ToEWn1KvXWqlUroLBFIaR2Dj/8cCA3\nFLmaMU+ZMgXwc//nn3/4+uuvAdh5552zM7h8ZK0uXryYZ555BoB3330XgDfeeAPw6jTcODuMjmU1\nQXnqqacA2HrrrdMx9FIhH/eXX34JwGWXXQb4ph/XXXcdAM2aNQPgsMMOA+DDDz8E/LXi999/5+ab\nb87QqBOYIjcMw4g5OdVYIggCli1bBsCIESMAuOuuuwCvyIub78YbJ4wTNSzu3r27xgZAnTp1Ut6X\nDqR4NNbx48enjEHWw6xZswB4+OGHAa+G5EP+66+/AK/cd999dyCheKPoJ9c499hjDwDmzp0L+O9D\nikjWldbwoosuArwvPYpzC6Ox33777QD07ds35fmikN9ZPtpsMWjQICBhtcofXNL1Q3tNYYWutXr+\n+ecB2GeffQB/nmVjLefPnw9AmzZtAFi6dCngLb7Zs2cDsMMOOwD+PDvggAMA+OyzzwCYOXMmjRs3\nrvB4rLGEYRjGBkSsfeS6yx9zzDFAwlf166+/Ar7ZrZBPTn6sHXfcEYAGDRoAXiFJGShyQpER8uWl\nE6kX+R1vuukmwPviGjZsCMDIkSMB+OabbwBo1KgR4BXrH3/8kfJ7t912WyCaivWjjz7iiCOOAEiu\nnXz7itOVz/zVV18FoE+fPgAsXLgQgJ49ewLw6KOPZmjUHilKNSO+7777AD8HKVcdj+HICLHZZpsB\ncOSRRwJ+znfffXdyPbOtyF988UUg4QMOK3GNv169egDstddegLe29P3I2tT85RNfuXIl4I/VbKCI\nGilxjbFGjRqAX8utttoKSKwN+MxOzTG8x5UJondmG4ZhGGUilop8wYIFABx66KGAV3Jr165NKm9F\nashHruwrvR5WFGGFpEiBjh07ApmNK//hhx8A76/XT/nGpUQ333xzAO69917Ax7fKvy81JMUbJcaN\nGwfA+eefn/Q1XnHFFQDccMMNgFd54uijjwagV69eALzzzjuAt7Lkw8xEdIespuOPPx7wx5P2UKpV\nqwb4Y1PH1/bbbw/4SAcp2HCsuI6/Bx54gPfeew/wlmem0dyuueYaIBEzrrjxunXrAn6fRhauPnPm\nmWcC8NNPP6U8r/nqe7j00kvTO4lS0Lx5c8D7xGXhSokrok1rrygzWRU6H/WdZJIKK3Ln3EbOuenO\nudH5jxs456Y45+Y6555xzsU/NswwDCPCVIYivxiYBVTPf3wbcE8QBE875x4CzgYGVcLfSfoZn376\nacArU+0y9+7dm86dOwOF1VyYsAKSKtRdVgpqzz33rIyhlwkpzw8++ACAnXbaCfDqTrG6LVq0AHxU\ny7BhwwCvxIV86FFAayi/fxAEyboUmk9xyI+qKBbFy0sx6fV0orHKl619B6ky7VtI1Sk2WZRUGU+R\nOl27dgUSc6xfv35lDL3c6FyR3753795JBf6f//wHIBmlEa5HIisybAHLcjnxxBNTHmeT7bbbDoBn\nn30WgFNOOQXwUUPaoxGaq6wynafZoEKK3DlXHzgceDT/sQMOBkblv2U4cHRF/oZhGIaxfip6G/w/\n4Cpgy/zHWwO/BUEgSbgQqFfBv5FEfmr5QBXVoMfbbbdduetSKGY7HOeq6INMougUxdQqg3PgwIGA\nV7WyIr744gsAHnzwQaCwH1J1L6LAtGnTgNQxlqTExeeffw541avfISVUGbG7xaHvWtEYyvzVGsn3\nHY4MKmvW6R133AF4f//GG29M7dq1yznqykVRMzVq1GDvvfcGYJtttgEKH3PyoSu2OqzItWcVhbmF\nxyar/q233gJg3333BQpbujoW9H5Fr9SsWTN5HGivLt2UW5E7544AlgRB8Ek5P9/TOTfVOTdVxWkM\nwzCMslMRRd4OOMo51wXYnISP/F6gpnNu43xVXh/4oagPB0EwGBgMiczO0vxB3TkVx6k7ZbgWSVmQ\nv/mkk05KeV41WbJReU7zkZpTdIZUoJAvXT5izUVj1s9wTD34PYDXXnsN8Nmf6fana2dfYytNzK3G\n3759e6CwglLGoXzn6SAcwy01VlnIp6x6HprjhAkTkpEg2UYW8XHHHZfMKladH41R69qlSxegcDSY\nrENZHlGoMiqlLfWsMSnXRL5z7cnpee1jTJgwAfDRVtWrV+fJJ59M+Wy6KbciD4KgbxAE9YMg2Ano\nAUwMguBkYBLQLf9tpwMvV3iUhmEYRrGkY6u4D/C0c+4WYDowpLJ+se6U++23H1AxJS6uvPJKwEcX\naPdcsdjZQApBLidVmgv76JRZ1q1b4r6puFchNfTtt98CCdUnX69i8KVin3vuucqdRDE0bdoU8HsP\nG220UdJvHlaeituV+gsr8fDxEGekyLXGssZatmyZtTEVR5UqVZLRXMoy1j6N4sLnzJkDFPadn3PO\nOUC0sowVWSPrUOeCInN0bZBi17GrCCRlYBfsZXD11VcDMHToUCD9862UC3kQBG8Bb+X/fx6wd2X8\nXsMwDKNksh+8WQ4q4leTQlDNB3X50POnnnoqkJ3sLCEFoLv4E088Afh4cVV41Ouq8SD/tjJDhVTe\nsmXLknU7lixZAvis0UzVh5DFoxj5/fbbj9atWwN+PlI2JdWyVvZktmuQVAZXXXUV4L+fdu3aAX6O\nUcI5l7QU3n77bcDXuQlncApZfsrviFLXI8XpKx9Fe05S3qrKqQzpk08+GfBVN3U90nHrnGPixInJ\n/2eC6Ng3hmEYRrmIpSKvDOQjlkLVbrrustnMNAtXktNdXVaEYnRffjmxj3zccccBJLuSKJJAPjzF\n/G655ZbJ+GRlx0rxZTp6QFEys2fPTsZgKzpF85XSVpSSYpYVVy+VFyV/a1mRcpWvuVOnTgCMGpXI\nqYuSci2I1kRRNsp6lCIXilK6/PLLAV9/PUro2NdYtSaqB6Q+ALomyMLVWskakZKvVq0ajz32GGCK\n3DAMwyglG5wiV4VA9eVTdIB8lNnsGSiKU8dSnoruUGdyEfan3njjjYCPFNl0002TER6KaFFtmWxR\nu3btQvVIwsj3KL++9jE++SSRi6bPl1RfJ4ooMkLRRA888ACQnZrW5UGRHOqjqj6XQr7+a6+9Foim\n9STrR7V7NFZZ6Tq+1IErvCejOavvb6dOnTIeSRW9b9UwDMMoExucIpc6lR9M9TmkyOOMYnelYKUc\niqr3IWWk7idRJhwVoAxXdZNRvH22qwSWB0VAqMKearfEjTvvvBMonFWsfY0oKnFx+OGHA96Pr2uB\nahtdcMEFgO8RK8tvl112AfzelKLGvv/++zLX2KkoG8yFXAVttFGhA0tF+3MBFZXSSbR48eJsDqfS\n0WaU3ELa/NVmaZzQHLRBphO/pDK3UUPuLrklhOajMrdRRmn02qBU+Vqh4llq96hrhzY/VV5aYcEN\nGjRIio5MBU1E9zZpGIZhlIoNRpGr1KTCDU8//XTAtwnLBbSRK0UeDgWLOwp5E5qnNqOk2OOAmntL\nuWnjOqrhhsVRXOMVWRwKM40DKkKnhDq1lFSZ7LB7SMEFKoGhonUzZsxI/2BDmCI3DMOIOTmvyOUD\nlwKSz2rIkEqr5RUZwmF8KvuaK4Q30rRhvXTpUiBem529e/dOeXzIIYdkaSTlQ9+9fORhtImeztLC\nlY2OK1npJVnr+g60H6BQ3iVLljB37lwAdt1117SMNYwpcsMwjJiT84pcZWrli1Rp1Lj5IktDuASq\nGhznCmElLhSRFAdFLt+xmjiLgw46KBvDKTcKxQsXOtMaKUkmU63OsolCR8W//vWvZLvGTGGK3DAM\nI+bkrCJX01e1Z1JEw8UXX5y1MaULpXdL/UgVSSnELTa5OKZMmVLk802aNMnwSMqPkpekYNWuMJtF\n2srD1KlTgcKt3JSEpjIKuYyiVJo3bw74sh+PPvpoxhOCTJEbhmHEnHjJgDLQv39/wGfOqRRlHFLS\ny0q4BZx8yOGfcd8X0BoKzSdOxbIUoyx/fs+ePbM5nHIzZswYwKfgy8KQQs9mY5ZMoXIKKhmRzaYZ\npsgNwzBiTs4pcimC/fffH/A+SdVRyEXkG5cvXApdmZ0qLhV3pPIUKaGMuzghi1C+8d122y2bwyk3\n8g8r1lpZxYobz3Sjkmwg5R2FjGJT5IZhGDEn5xS5UNMBKZ5M7yJnEikCtd363//+B/j9gVxDFof8\nsnFC9TkOOOAAAFq0aJHN4ZQbWUeyeI3sYorcMAwj5rhwllw2yMvLCxSXWlmo7ojic6Nc2N4wDCNM\nXl4eU6dOLVUIjF3dDMMwYk7O+shz2SduGIZREFPkhmEYMccu5IZhGDHHLuSGYRgxxy7khmEYMadC\nF3LnXE3n3Cjn3FfOuVnOuX2cc7Wcc+Odc3Pyf25V8m8yDMMwyktFFfm9wNggCHYFmgOzgKuBN4Mg\naAK8mf/YANasWcOaNWsq/fe2atWKVq1a8csvv/DLL78wc+ZMZs6cSRAEhbrpGIaRe5T7Qu6cqwEc\nAAwBCIJgdRAEvwFdgeH5bxsOHF3RQRqGYRjFU5E48gbAz8BjzrnmwCfAxcC2QRAsyn/PT0BulN6r\nBMravzCsptWl+4MPPgDg1ltvBXw3JNWVUVd5PR46dCgArVu3jn1NcsMwClMR18rGQEtgUBAEewEr\nCblRgsSVqEjb3jnX0zk31Tk31QrvGIZhlJ+KKPKFwMIgCNRIcRSJC/li51zdIAgWOefqAkuK+nAQ\nBIOBwZCotVKBceQsYfX80EMPAdC3b1+gcL/EJUsSX7WqIf7xxx8AfP311wDstNNOyY4uRrTIlS5O\nRnYotyIPguAn4Hvn3C75T3UAZgKvAKfnP3c68HKFRmgYhmGsl4rWWukNjHTObQrMA84kcXN41jl3\nNvAd0L2Cf6NcvPrqqwDceeedgO9YsmzZMgDmz58PeFVbu3ZtwFdNvOaaawDYY489AN9xKJtcfPHF\nAFStWhWABx54AIAuXboAvmfge++9B3jfee/evQG44YYbuOSSSzI34FIQBEFyDdTPcscdd8zmkMqE\naqO/+eabgJ/DGWecAfiqm5qjrKRhw4YB0K9fP8DXVn/iiScAOPbYY4ENR6FHySLRNUA9OVesWFHk\n+9QX+Pp8efsMAAAgAElEQVTrrwf8HLLRHalCF/IgCD4F8op4qUNFfq9hGIZRenKqHvm6desYOXIk\nAOeddx7gewsWN0/dPbfeemvAK6iVK1cCXnF9+eWXAOywww6Ar3MeBTS37777DkjElYNXN2+88QaQ\n6BfZqFGjjI5t1apVgFek77zzDkBynY455hguvfRSwHebUXTPXnvtBcDBBx8M+LXq06dPyuNs8NFH\nHwHQrVs3ABYtSgRqaeyzZs1Kebxw4ULAW3rvv/8+4I9PrdX06dMB2HPPPdM7gUpCeRHav7nlllsA\nf/7Ikj3ooIMAX5VUlsvll18O+POsfv36mRh2kUyaNAmATp06ASV3oGrcuDEAM2bMACq/d6fVIzcM\nw9iAiI6srAQ+/fTTpLqTEixOiUsZdO3aFYBvv/0WgNmzZwPelynlLcXRs2dPgIwr29Ig1Ss1JEXx\n559/AtCyZcuMj0l+e3VZr1evXsrzTz/9dHJ/Qh3Y1cdywoQJALz99tuAX0up2rZt2wIwefLk9E6i\nCGQt7L333gCMHz8e8ApblsfMmTMBGDVqFJCIHALYeeedAa9M1ctTz0cdnR/y8atPrKwkrfPRRyfy\nAbWH8MILLwDQpEkTwFtX2223XSaGXSSffvopAGeeeSbglfXAgQMBuOCCC4DCFqDyOnS+6Rg44ogj\nkhaWjlmdm+myIk2RG4ZhxJycUuTr1q1jiy22ALzCueKKKwB/Vw0rdPnmFOVywgknpLyuO6uUQ40a\nNdIx9AqhMcrHpzlprlHwt9aqVSvlp9TO0qVLk4pbFoR8lFJtN998M+DnJeSblDrU2mcC+b4VZaKf\n2mM55phjAL9foT0bWR9VqlQBvAX4zTffAN5SiTLr1q2je/dEMNrEiRMBOPLIIwF48cUXAQopUvnS\nn3/+ecDnNmRyzcL89ttvgF8jqWVZjyWNTa9PmZJIpZFl+Pbbb7PrrrsC0K5dO8Afy+kipy7ku+66\na9J0UwibTpzikAvmxBNPLPJ1mcS6AEUJnSQy7eRW0gVRFxtdNKKENpdr1aqVPMjDDbKvu+46wJ8E\n5557LuDnqzBMhYtlA5nhZ599NlA4fE4ndEnNv+WKOeyww1Le//fff0dy/YTmrwt0eP56/NZbbwHe\nxdC0adOUz2cS3UQUWqzzaPjwRImo0t5cNDe5ywYNGgTA8uXLCwUaqFxGujDXimEYRszJCUWuO+oW\nW2xR6g09mXraXAqnu8vM0oZaFNFdX3PQpmY4pLKsxboyiXOu2CQQPa8NMyVeKMxSLiMlbGTTaipu\nDiUp8dGjRwNemf7111+AtzIWL16cVHxyu2i+NWvWrNigK0Dnzp0Bn2xW3Caewi5fe+01gGSJiM8/\n/zzdQyyErhMHHHAAQKGS0j169CjX71XikNYnCIJkMIU2xdONKXLDMIyYkxOKvCxpvdrgUGKPNsqE\nlIX8aLrbRpHHHnsM8GVrt99+ewB22SVR/kbJGVGk4KZzeP30mjYz5XvU2uy3334AHHLIIYD3lWeD\niqaWy///008/AfDjjz8CfhO0efPmyf9L3ctnni1Fvnr16mTYpPaWZNHq+3jkkUcAeOaZZwDYfffd\nAZ8IVZKlkk6OO+44AB5++GHAr11511BWlPZunHPUrVsXyJyVaIrcMAwj5uSEIl8fUghPPfUUAGed\ndRbgo1WE7sZSRiVFu2STOXPmAHDVVVcB0KZNGwBuv/12AO644w7Ah1Tm5SXK4WQzpT3M4sWLgYSa\nls/7nnvuAQon1TRo0ADwhaQaNmwIwLvvvgv4vYBsUN5ED/lnZRmqmNuIESMAX5J4//33T6rfX3/9\nFfBp7PrbmVa3m222WVJhq3CUjsEHH3wQgFNPPRXwYcCaZzaiVITO8dNOOw2AMWPGABXfQ9K1RYq8\nRo0ayZIemcIUuWEYRszJKUX+zz//JIsXKe122rRpQKKEKxSfsq/2aVFW4kJp0UIFnBSDrOgVKQ7F\nOEvZRgGldN99993JMgjar1CqfvPmzQFfElZKVGpHSUXZLPwmZS1FHvaZh1O09X4pblkmet/jjz8O\n+KiQ3377LekLb9asGeBVbrZKvjrnkvHxUuSyFv79738Dfn5KdJL1FAWUY6Iibcq70F5Taa8BsqKU\n7KU1bNq0acbXxhS5YRhGzMkpRT5v3ryk/0t317lz5673M9deey3gfXxRRqpOPvLly5cDXlGoiI98\npophzWaEQHFIZTrnkj7x8DgXLFgA+CYgKvEqH7nUrHzIUonKzMuED1k+33CWrcoyK7JIkQ2K+Vfz\nj223TfQmlwWpCBxFVmy55ZZJlRsldL7oWJRFojIKH374IeAbhFevXj3TQywWRXc9+uijAHz88ceA\nX0udT8Xte+h4VSaoji8dAwcddFDGSy1E7ww3DMMwykROKfJatWolfXPff/99ymvhu6biw1WfJAot\npkpCc5AfVb5kFSq69957Adh3330BuPHGGwEf1VEwezXbKl3f++eff864ceMAb2FobPIFS80qK1C+\nckVIaC9AtVh69eoFJPzv4P3NyuhLBzp+lNGnpgNh/6uOz3DLQfmYFXl04IEHAtlfp5IIR3xo/joG\n5W9WXkaUUAz88ccfD3gFXtK1QMpd+Qw6r7baaisgERWT6XWL9lFiGIZhlEhOKfIqVaok1ZnKin7y\nySeAV3GKE1fkgxSSfHhRauFWHIpekBLo2LEjUDgOW2pQ/tcWLVokfZpq3pAtS0SKZfjw4UkrSVEE\nei1c3VA+Sa2hIiHkl5a6VeSS1ly+80wgRaq/Levpq6++Anymo6wQNZ6QdaXysAVbDkaxdHJJaH9D\n84jyHMp7zl922WWAv3YoakzWWCYxRW4YhhFzcqr58qpVqxg7dizg6x2rhZTmqVZtqluu6IDBgwcD\n2Y/RLQ2ay+uvvw54laci/6rXIWWgFlQrV65MxvUq9lxZabJQpCijhOarhhPypasKoNYwXFtGn5Nl\nks25hRtkK5pD37+UqyJx5IddsWJFpNVsccgKknUoaykXkOWo6BcdVzrvKmu9rPmyYRjGBkT0HcKl\nQGpn4403Tnb9UOytlLWiWORfVayxYkil2qKsxBX5oMzN++67D/AxyfoepO5U6VF+8X322SepJtRM\nWupBdaMVox0ltDYdOnQAEg2bwbfkUnZhcZECUagxo+NKWYXh1mg6HsP+2mxWdiwPWisdV7J8cwlZ\nVZqr9jeyWUfGFLlhGEbMyQlFrkqGm2++ebKbipB6VYadsh+106yOQlH2Q0ppT5gwAfARD8qekx9S\n79NPxV8rgmL58uXJzEI1jFUGmvx9UUJRObKmpLi13lK1cWhYLMLx5pqjjsNwLHOUuzsVxYABAwA/\nP6nVXEBrpXwErdGFF14IZPc4NEVuGIYRc2KtyKXMFEe8+eabJ1Wbqh4qu/HNN98EvKpTFx1FDUQZ\nRZRoTpMmTQJ8hI2iN0RY9amqYNWqVZM1TpSFFmU1K2tK/nztb1x00UWAz+SMEwX3c8CvkY7TcKed\nKPj3S4NqrKiqpSxj1SHPBZSTIktXa6O+ANnEFLlhGEbMiaUi191/6NChgPehXn/99YX8yYorlxJX\nR/YhQ4akPB9lpNo0Vqm3vn37Ar6Ho3zl8qtKFclHXqNGjazurJcWreErr7wCeH9+t27dAJ8LEIcs\n3DDhOuXyjSt7NQ7HY1HIalIN+ZEjR2ZzOJWK1mr//fdPeV61f6KQe1Gho8Y5d6lz7kvn3BfOuaec\nc5s75xo456Y45+Y6555xzmV/loZhGDlMuSWNc64ecBGwexAEfznnngV6AF2Ae4IgeNo59xBwNjCo\nUkabj5SYssekMu+5555kjWH5UxXrqagM9USMk/JRpMk555wD+GqGymiU73zy5MmAr8991113AfGL\nfFBtGGU/ipNPPhmIpxIXUuSK5pA1+fLLLwO+KqeO16gfp9rHUNcqZUhns49qZaPzSdcSoVryUaCi\nR8nGQBXn3MZAVWARcDAwKv/14cDRFfwbhmEYxnoot7QJguAH59ydwALgL+AN4BPgtyAI1ua/bSFQ\nr8KjLIb27dsDvqbI7rvvnlQI2v1Xt2+pvKgrnPVRXFU1xbWms952JlAUknz/s2fPBuD0008HfKRN\nLjBjxgzAH6fKDejatSsA9eql7bSpFLRWykiV1ad9jFxCGdW6dsgiVM34KFDuq5pzbiugK9AA2B6o\nBnQuw+d7OuemOuemKm3cMAzDKDsVcTZ2BL4NguBnAOfcC0A7oKZzbuN8VV4f+KGoDwdBMBgYDInq\nh+UZgOKjlVm1Zs2aQvG3w4YNA+LnJ94QufrqqwEfJy9/cq1atbI2pnShCo7ylatLjXID1GtW+Q5R\nQxFROt90fsV5/6I4VFtFe3GyohQtVhT6XjJVu6kifoYFQFvnXFWXGG0HYCYwCZB9dTrwcsWGaBiG\nYayPCtUjd87dCJwArAWmA+eQ8Ik/DdTKf+6UIAhWre/3VFY9ciPeyBf53HPPAT4K5+GHHwbWr4CM\nzFK3bl3Ad9g65phjABg1alSxnzHKRlnqkVfIDgqCoD/QP/T0PGDvivxewzAMo/TkVIcgwzCMXME6\nBBmGYWxA2IXcMAwj5tiF3DAMI+bYhdwwDCPm2IXcMAwj5tiF3DAMI+bYhdwwDCPm2IXcMAwj5uRe\nhZtS8ssvvwC+NGqmitukEyV3qQB+LhYwWh9r1qwB4PDDDwfg119/BeD9998HfDs1lV41jFzBFLlh\nGEbMySnJ9s8//7Bw4UIA+vdPlICpUaMGAPPmzQPgtddeA7xaPeGEEwB45JFHUp6Pk5pVmc0GDRqk\nPD9oUKLD3nnnnZfxMWUSlb9Vm79ly5alvK6WXAMGDAD891EZVpgaXo8fPx6AI444AvClTvU34tzQ\nxIg+dnQZhmHEnPjIzvUgVbTNNtvw+++/A14BqeC9isKrkL8eq4D/Qw89BMCJJ54IRKuxanGosYaU\neLgAWrt27TI+poqwaNEiILGO4JuDlMSCBQsAv+8RpmrVqoBvXl0RJf7bb78BcN111wHektN3H/bD\n62+qObEsRB2f2s/YdNNNixzb33//XehYzoX9nDggq0prq7UfPnw44Ev5HnvssQBsttlmmR5iElPk\nhmEYMScnFHnB1mC6S+bl5QFw8sknA75llpoXjB49GvARDWeddRbg1b0iIKLcIq5nz55AYSUuxTZt\n2jTAzz3KcwHYbrvtAFi7NtG7W1Enan+mecmaErKixo4dm/I5qdxevXoBFdv30He8YsUKwDdQ0HES\n9onr8fTp0wG/F9OxY0fAWwlS8GpavP3226f8npdeeom99toL8Ps+Tz/9dLnnURE0p3/961+FLJCZ\nM2cCfoxaQ1lZ+u61V6VjV/sWUSCswN99910AevToAYB6C+t9Qq0I33jjDQAaNWpEzZo10z/gApgi\nNwzDiDk50VhCc1i3bl1SIUh9yr8YjhqoX78+AIsXLwa8Pznc+DeKqHFvcXf9sPKWgh08eHCyJVdx\nPtls8ueffwLw8suJNq+vvPIKAHfccQfg5ys1G+b7778HvDXWqlUrwEcqldbnXhbU+Hvw4MGA93mr\nOXGbNm0Ar9bkZ5Wq05hkjWhttS79+/dnzpw5Kc9JoZ977rmVPp+i0F5M3759Adhtt93466+/AHjz\nzTcBmDFjBgA//vgj4OcV9uuvXLkS8N/Pt99+m+7hl4iscFmwTzzxBAATJ04E/Jy0b3bkkUcCMGXK\nFMBb+fo5fPjwZC5DRc4vayxhGIaxAZETPnLd9TbaaKNkg1756MIosuGHH35Ief60005L+V1RRJbH\nDjvskPK81LV8xJdddhngVZJU0FlnncV+++0HeIskCkiBy9ettdM8FRder149wPul5Xf98ssvAa/Y\nFSlyyy23AOlR4uKuu+4CoG3btilj1WP5vuW3D/tXNVflP4S5/PLLk/Pq06cP4L+ndKOxyv8tpbrR\nRhslx6RjslGjRoC3UBTBcfDBBwN+/qeffjpQ/HwzicY+ZswYAG677TYA5s+fD0D16tUBeO+99wDY\nc889Uz4v60veBOVtdOvWLfk7ZGmlG1PkhmEYMScnFHlBpHDk627fvj3gfXWtW7cu8nMff/wx4KNX\nooiUdTiiRpEC++yzDwBDhgwB4KSTTgJ8pECNGjWSESBR4e+//0767aWQZBUpY1NKSM9r3qtWrQLg\n2muvBbxCUmx3OpV4wQgO8LHE2o/QWJXxKWWqPZmwMl8f8qsr4iVTtWI0Rlkd+rutW7emTp06APTu\n3RvwPu/w8aXzUbHXmr++H0UBSf2mk/Dxpflp7LpW6Hi74oorAB/1FUbHl/YslKOy6aabMnDgQAD+\n7//+r1LnUBymyA3DMGJOzilyRT4oSkB32XvvvRfw6jSMMvXiQLNmzQCYNWsW4HfX5Z+Ub1lZrlKw\nm2yySVIBqepjtpAaatSoUSGlJIV92GGHAYWrOeqzsqK01kOHDgUyk5UbjoYqLkZ/1113Bfxxp4gH\nrVlY2cu3rKgQ8HsgisLJFFoPHUdS03vssUcyNl9KWuPWZ7Rm33zzDQDPPfdcyuuadyZzG8L7X3qs\n3JNOnToBXpHL6igJzaFx48ZAYm4//fRTxQdcBkyRG4ZhxJycVeSKvZW6K85XJZ+mMuqijHzjiqOW\nj1ixx4prVTy1/JNSDB07diy1ykgXUt9S24sWLUqqOfm2jzvuOMCvTbjOutZYewGKoPjss8+AzEV1\nlAXNRT5zrc0DDzwAeP+3Mo0vuOCC5Gflo850RJX+3sUXXwx4K2Ls2LHJCCKdZ1988QXgrSbV/1Hk\nlCxBzVt+acVwK9osk4StqXHjxgE+9r+037fet/vuuwMJZa79qkxhitwwDCPm5Jwil7pT1Mqrr74K\neFWnu7AeS/mE/bRR5P777wd8XKvUtTLvateuDXiFLhUoNTRhwoRkHH1x2ZHpQmOUb/Xtt98GEjv/\nl1xyCeBjrsORH1Liitr56KOPUn7H8ccfD/gY5Tgg5aq5C81Vc99tt904//zzMzu4fHSuyHes6IzZ\ns2dz0UUXAb7Wisareek8bNq0acrvElKvqgaZTXQuyCofMWIE4OvBaI+iOPQdqG5Mt27dMpZ1K0yR\nG4ZhxJycUuRr165N+vF0pw9XM5Qy2HrrrQGSu8uqbKZa2OFogmwia+Hmm28G/NiWLFkCeCWuOapq\nmyrP6fmlS5dy0003Ab42SKZQJqmsA81p8803Z8cddwRg8uTJgI9FVsSH4nVVC0MRRsoOlG9d/tcH\nH3ywyDFE2erS2JURKlq1apX1blU6B1Sz//HHH2fYsGGAz5TWGA899FDAK3H5/o8++mjAWxzyu0fh\n/JLi3nvvvQF/zSjpONHxpIxqRei0a9euUIXOdFPit+icG+qcW+Kc+6LAc7Wcc+Odc3Pyf26V/7xz\nzt3nnJvrnPvcOdcynYM3DMMwSqfIhwEPACMKPHc18GYQBAOdc1fnP+4DHAY0yf/XBhiU/zOt6M64\nZMmSZCSDVJzujPopxa0YZCmKcF1l1RjOVK2E9TFy5EigcP0YqSDVsVAmpKq1FRU3e8ABB6R1rGFk\nPWjs4Wqbq1at4tJLLy3yPZqfMgrDMct6v36qWqJqZuhva+11TGRb4RZEFfNUq0RqULzzzjsZH1Nx\nNG/ePPnzzjvvLPI94QgjZaUqKkX7HFFQ4kLHg+oQlWS5hS3fU045JeX5atWqZXx+Jf61IAjeAcI9\ntLoCw/P/Pxw4usDzI4IEHwI1nXN1K2uwhmEYRmHKK022DYJgUf7/fwKUSlcP+L7A+xbmP7eIDNC2\nbdukopH6UmU9ZTLKd6fXVR9BvlvVWjnqqKMAny2ZTW688cYin9cc9t13X8D3H9XzHTp0AHzs8iGH\nHJLsdpIppIovv/xywFcklOoJgqBQ5IN+Snm3aNEC8FUNH3vsMSARPVGQF198EfDqT8eCfKBhxR8F\nX7nGqGxcVXIUVatWjdR4SyIcvaIYf+17qDpklOdS0tg0tw8//BDw1oeOU1mYmaTC+j9IHGVl7k7h\nnOvpnJvqnJuqjUbDMAyj7JRXkS92ztUNgmBRvutkSf7zPwAFi2XXz3+uEEEQDAYGQ6JDUDnHAXj/\n9lFHHZXspSgF8N///hfw/mOp1YI1zAs+ViU+VS/T3VbqVvGvmfCBSYkpKkMRAMpEk2JVRprerwiR\n//znP4BX7L169cq4f1jfk7rLfPLJJ4DPYOzevXsyTlr+U33HWld1kdEaKatQ7LzzzoCvRaK/Ga4S\nGMWepVorWYCyMsJZvHFFa6osZBG1KpxlQVam8jm0j6a4c52XmaS8V6NXAGVfnA68XOD50/KjV9oC\nywu4YAzDMIw0UKI8c849BRwI1HbOLQT6AwOBZ51zZwPfAd3z3z4G6ALMBf4EzkzDmAshBXbuuecm\n46PVt1E1H5SVFt5Vl0JV3Ll8uIr8kL9LUQVSlJmon6w7v+74GqvmIF+w5q+4cSlYKVXVfZg1a1ay\njkQ6a3UXRGNT5Mjo0aMLvackH7D8yKoPrWp8ivu98sorU/5WHFFWodZMHWYWLlwYKx95GEUSaQ9K\nx3C2q29WBK2NavtoH01dxrJBiRfyIAiKq0DUoYj3BkCvig6qrOiifP/99xc62HVhlpmt12W66rEu\nAioz+vDDDwP+IiNzX+FimbiQ62Krm4hcDUqgOfDAAwHvWgmXH/jf//4HeDdR27ZtI3kxKG5MKo6l\neegm0KRJE8Df4NTUIc7ssccegL/gKWGtevXqyeeKa7YdZXTshsNO9XwcUeiy1kObnJkSR0URXwlj\nGIZhADmSoi+zvU2bNsnymHKthEPOpKiVCKSwL5l8el4qUUpJSTmZarNVELlY1HJKCkAJDFKqskwK\nJkhB4SJUUUeWx3nnnQf4+WkttVYqURwll0pZSztoE1ClarVm+j377LNPsjhY165dK3WsmUDNT8KJ\nYXE5FgsiJa6EM1nlnTt3ztqYRHTOAMMwDKNc5IQiF82aNUv6TeVf1V1TxbFee+01wKcOayNQyltq\nUOpXTQqyocSFfG8qVHTZZZcB8OyzzwJ+80/Iv6qN2rioH5W67d49sXcebsIgJX7mmYk9dLXxixKl\nVeSymtSeTmWXpdC1ZnPnzk1uXsdx0/PRRx8F/LyUkBcnZOkqmU4t73T+RWE9TJEbhmHEnJxS5A0b\nNkwmUMj3rbvnSy+9BEC/fv2Awg1tdXeV31UNfPV7somUqAp+KclJPtOwUpOV0bp164yOs7woguj6\n668HfDPlcJMFJZFEoWxCcYRDRGXhSaHL6lAzDBXF0ncg9PmVK1cm9wqioPxKi8avptPhQmhxQs0/\nZNXrPMx0Abr1YYrcMAwj5sTv9rgeqlevnkwFV0LPwQcfDPhGCkoZ1s+TTjoJ8Mk1UuhRVD9SM2qz\nJZUj/72SmlRUKopzKMrPKx/4mDFjgMJ+ZsXwy2cepSiV4tD8NN8FCxYAPsJBfu/i5iKLsXHjxrFU\nsTq/wnWU+vTpk43hlAtZSS+88ALgLURdI7K5bxYm+meEYRiGsV7id6tfD1WqVEm2AZOvXOndiuR4\n6623ADjiiCMA76uMg8pTWrP8/EoRVhmCk08+GYh2oaWirAQVGVKjbKk2rZkyO0tqghtFFMPfsGFD\nwLfrO/XUUwEfkRRuRahCZ0899VQki32VhPachL6HLl26ZGM45UJ7UIpw07GrPYsoEf2rl2EYhrFe\nXLgGQjbIy8sLpk6dmu1hGEbaUX6D2oOp2fKAAQMAX6L3888/B+JhKRaFch0GDRoE+JwGRVxFGUUa\nyYrSmmlvKhz5li7y8vKYOnVqqTa64nmUGIZhGElyykduGFFH+xeKhBBXXXVVNoaTNtSYOLwnEofs\nVCntOXPmAD4XRVEqUdyrMUVuGIYRc0yRG4ZR6ahSoAg32I4Dyi1RRdUoY4rcMAwj5pgiNwwj7UTR\nr5xLmCI3DMOIOXYhNwzDiDl2ITcMw4g5diE3DMOIOXYhNwzDiDmRvJD/888/ydq/hmEYxvqJ5IXc\nMAzDKD2RjCNXjebKRAr/l19+AWDcuHGA7/ax2267VfrfNCqX33//HSBZn1s1rktCtTLUh7W0ne4z\nwV9//QVAnTp1AN+X9IwzzgDghhtuSGYYGkZxZP9INgzDMCpEztcjl/r68ssvAWjevDngq7BJAa1Y\nsQKIhkozUlm1ahXg10yKvLSW2zfffAPATTfdlPLz3//+d6WOszTIMpw4cSIAhxxySKk/Y8dmdlFv\n2Q4dOgC+TvkVV1wBQLdu3YBELXnVZ5HVWB6ryuqRG4ZhbEBE0kdeGUiJq2fnk08+CfhO9Lq7qhqb\neiaqf2QUkAKVH/W7774D4MUXXwTgvffeA2DRokWAv/trzttssw0ATZo0ARL9ItX3M8r8+uuvgPeJ\nv//++wAceuihANSqVatUv+enn34C/D7ImDFjgOwo8VmzZgFw7LHHAt5KUA2S7bffHvAqb8mSJcnP\nbrnlloDvTB9nVAVRVlWcqiFq7FqjpUuXAt7aL+jdaNSoEVA+JV4eSlTkzrmhzrklzrkvCjx3h3Pu\nK+fc5865F51zNQu81tc5N9c597Vz7tB0DdwwDMNIUBpFPgx4ABhR4LnxQN8gCNY6524D+gJ9nHO7\nAz2ApsD2wATn3M5BEGQ8KFz+RN0RFbEgpAQUHRAlJS5kJWj/YNiwYQCMHTsW8IpA7yuO8ePHA4mO\n7D/88ANQ+oiPTDJ9+nTA97GUb/j1118HvHotbZcZ7X+MGJE4dPfZZ59KHnHJSMVdcsklAHz77beA\nj1LReoSpWTOhjZYvX56cZxy664TRWmptZSm3bNkSgClTpmRnYOVg/vz5AJx//vkAnHDCCYA/LrUu\nkyZNSh57YeQJ0P5OZa1liYo8CIJ3gF9Cz70RBMHa/IcfAvXz/98VeDoIglVBEHwLzAX2rpSRGoZh\nGEVSGT7ys4Bn8v9fj8SFXSzMfy5r6I637777Fvn8fvvtl/ExlYRUiyIb7rzzTgA+/DDx1YazXuX3\n1wUspMoAACAASURBVOfUW1C+cqnCX3/9Nfk75W+uaMx+ZahEWRYHH3ww4Mcrq6Fjx44pf6OkSCtZ\nKFJMzzzzTIXHWF7uueceACZMmAB4y684JS7kh3XOMW3atOT/s4nUpI639b2nWbNmAHz99ddFvk9r\nHge0FlLku+yyCwA///wzADvssAPg92SGDBmS3JfacccdgcL7XbKyDzjgAMB7EMq7xhWKWnHOXQOs\nBUaW47M9nXNTnXNT9YUYhmEYZafcitw5dwZwBNAh8BLpB2CHAm+rn/9cIYIgGAwMhkQceXnHURJ/\n/PEH4KMAtFsutfrUU08BXr1lg7DClKLs27cvADNnzgS8EpdSvf322wHo0qULUDjiQepPca+rV6/m\nxhtvBLwSkHovL5WhEk888UQAfvvtt5TnpcylqDXv559/HvDWlITAjz/+CHjlNHjwYMBHfWQSHXcD\nBgwAvOVz2223rfdzykKVQguCICtRNuAtvCFDhgAwefJkAE477bSkr18RRPIbL1u2DCi8b6PjpHfv\n3oCPTIoDOq923313wOecVK1aNeWxrJG33nqrkPUY3rPbeuutAbjrrrsA6NmzJ+D3RspKuS7kzrnO\nwFVA+yAI/izw0ivAk865u0lsdjYBPirXyCqJ77//HoCRIxNGg0wbfcEycXTg6UKfScKLrouAfuog\n0MXgtNNOA/zia7NFJ97OO+8MePO94I3io48Sy6GDLtsEQZAMowyjG9Gjjz4K+JCuUaNGAT4MMxyW\nV69ewptXv359soU2u+QumjFjBgDnnntuke9XaKnWTutTrVq1rLVJUyLWnDlzADj88MOBRFjofffd\nB8Bnn32W8pnWrVsD/iKnsEvNR+GvxX0PUUIholpLBUwoVFTXEgkQJZo9++yzSfeTzlmdm3peNwWV\nBqmoi7PEC7lz7ingQKC2c24h0J9ElMpmwPj8i9CHQRCcHwTBl865Z4GZJFwuvbIRsWIYhrEhUeKF\nPAiCE4t4esh63n8rcGtFBlWZyLSVeS50B5QSnzt3LgCNGzdOeT2TadFS5nKhKNFHilpqZuDAgUDh\nkMnwmKXk9HPNmjVUr14dyI67oSicc9x///1AYZUmM7NHjx6Ad5kcf/zxALz55puAV+T6/mSuZhON\nRaGPCh1VkpPW5N133wW8cpdyu+CCC4DERne2Njl13MlK2HvvRADa7Nmzk8eRxtaiRQuApOtOx6YS\n8XT+yZpS2KFCS6OE5i03keYqV58K7919991AwpUCcNFFFwHQpk2bQuG9YcWtc7Syri+Wom8YhhFz\ncj5FX75x3WVr1KiR8j6puUceeQTwCl6bMUpzzyTaCNGYNZddd90VoESfqZSDFH3B0Cb596LEOeec\nA8DJJ5+c8rxUTdhSkapr2LAhALfccgvgv7fu3bunecSlR0rsnXfeAbwfVWFp8p3LdypF26dPHyC7\nIYfyc5966qmADyVct25dcgNd41OyVV5eHuDnI7+6ggu0FyBLJMrMmzcPgKFDhwLQvn17AM466yzA\nz+nmm28G/HFcmjWrbEvfFLlhGEbMyVlFLr+V7o7yc5100kmAVwRSrY899hjg/bB6XoWWMllsSipO\nO9uKDJDPTo/btGmT8jkpd4XjKbRS+wAbbbQRvXr1SvkbUaKkAkOKIpCK/eCDDwDo378/AFdddRWQ\n/cSZgsiKeOmll1IeKxJCCv3II48EfBRDlOYgf7dCDZcuXZoM/TzzzDMBnwyjqC/5xHVMhhPHdD5G\nEY1Ra6E9KV0jwglrV155ZcrnsoEpcsMwjJiTs4pcO8oq8C5V8fHHHwM+SkWxskLKSaiE6hFHHJG2\nsRaH0uiVzhz2kSuOVYr7hhtuAHwkgGLopYbq1KlD3bp1MzDyykXjv+yyywCv8i6++GIAOnfuDESz\n8JlS86XEpdqUvKTYZMXIR0mJCx1/yl/YZZddkrHTKsurIlhKMJNi1zErK1KvR3GeYZo2bQr4/QyV\nqxV33HEHEI3jzhS5YRhGzMk5RS6fo3zgUqv6GU6Hl09Pikmvy/912GGHpXnExdOvXz+gsP9RPnD9\nlDJVRIDmEm6iAV75xQlFFkkR1a5dG/DWUteuXbMzsPWg4+3SSy8FvALV3sQee+wB+GgWWVlqgqHY\n7SgoV/nGtSfRokWLZIz/XnvtBfgIIp0/KiQly1c0aNAAiFYD7OLQ+SPfeDjiS7H+USC636JhGIZR\nKnJCkResUSIlLuWjmE9lY8lXJ5UhH7kK4xx00EGAL1iVzegOxfFKvUmBSnnLF67aIlLoGrPmVLCE\npp6LgyISimZRnLkyN7WmKh4WJZQFqbWS4pZVMXv2bMBHRWlPRyWLpVyzVWelIDpGdOyMGzcuufek\nY0+Fo3SsPf3004C3IuVHVsGzOBx3OkdefvlloHA2pl6PQgRY9L9NwzAMY724kor0Z4K8vLxAVQjL\ngsau+hVbbLFFMtZ6wYIFgI/FVpSAYj5V0e2KK64AvE9cGWvyw0YBVUFU3LiUgGKR1XhBc1SJUcVb\nS0nttttuySYFUVARJaFxq6JeuEmBsnS15lFAMcaqZSNFKqtBdWPk+5ZCV3nXZ599FvDHZ+fOnWOh\nXsPoWFQTBmXd6jyP0vkVRuebrgXKLVEUi/YxTj/9dMA30ahs8vLymDp1aqk2SeJ3hBiGYRgpxNpH\nLlUj9bNu3bqkT7F58+aAv7vKhzd69GjAR3IoHvbss88GohETGka751J3e+65J+CtCDUeUK1xqTn5\n1qWO2rdvHzl1FwRBss7NKaecAvga1hdeeCEAX331FeAtEak5RRMoN0DKPZtojaRAlSGstQgrdTUn\nHj58OABvv/12yvNLly7NSr2fiqLMTZ1/qksSZSUuZOnpXFF1TtX/1/6GKjimS5GXhWid1YZhGEaZ\nibUiF1Lma9asSUYJ6C4pFad2aIrvlfJW1IBiQrPRIagkpLw/+eQTwHf+UVSLFKv2CqRopYKk2K++\n+upIxCWDV2rjxo3j8ssvB+C1114DvMLWfMLRAQ899BDgY/yj5O/X9yv1psqM8q+Gjy9ZW3pdVoZ8\ny99//z3t2rUDol2fJEzYso1KR6r1IStIx5uyVc877zzAr616Fii/IwoRYKbIDcMwYk5OKHKx6aab\nJqNUFNEin6Oa2gqpONW2jkK8bklozKr9IFWruSrzLqxQpeSipOhU02bhwoUcd9xxACxfvhzwak6K\nR6gHp94fZVT/RbVWVKfk8ccfBwpX09QaqhaOKjr++uuvyXrYyqKMA9qTElGymsJIUSunROfXc889\nBxTOrtVctN8hayOb1xBT5IZhGDEnpxS5cy55t1Q0iuKmw/HyyvhUNbY4ojoWUn0TJ04EvB9WXY5U\ngW6zzTYrVBc6W2y33XYAzJw5M+nzV1ZuuDOQfI/aC4gDn376KeAzOxU/Ha62qfWQ9TF27FggtZ7+\n9OnTgXgp8rBPPNzDMkqErSFVe1y4cCHgK6gK7bNpL0r7GabIDcMwjHKTc4pcmXVSc6ouJ6Wu5084\n4YSUx3FEFeikaOVblmJQLRJlhG688caRma8iBHr37p2Me1fstaJtFJUif7oiPOKAKjLee++9gB+7\nHqu6oVSdus0r5l9Ur1496V+PE8qYFoqjjyLaa9I+mvz7ym+Q9ag9Gl1Twv0AsmntmiI3DMOIOfGR\nOKVEfR0Vj6vqbPIT6+6r5+OMsuSkdl544QXA+yflK+/UqRMQLetDexMjRoxIdv5RZqe6lWvN4oj8\nrePGjQPgzTffBHxs/DXXXAP42iyK2FEkRIsWLQCYNGlS5LJxS0OTJk1SHqsuUBRirsMomkuWoOrf\nq4KjHqumj/Ic9DnV+D/wwAMBU+SGYRhGOcg5RS4Vp05B2oFWzQvVUQjvRMcZ+ZilHOSPVUy9Mjuj\nSOvWrZPRKlGJqKlMFJOsbvOa4yGHHAJ45apKj1KuUVKs5UGRHEKVK6M4L/m4lTGsmvDz5s0D/F6T\n+pDK6hfKCchmJdnofauGYRhGmYh1PfINHflXVYtD3Y+kyJXpKR96lLPrjNzivffeA/x+h+LIZTUa\nJWP1yA3DMDYgSvSRO+eGAkcAS4IgaBZ67XLgTqBOEARLXcK5eS/QBfgTOCMIgmmVP2wDfCaZdtEN\nIyrss88+gM8N6NOnTzaHk/OURpEPAzqHn3TO7QAcAiwo8PRhQJP8fz2BQRUfomEYhrE+SlTkQRC8\n45zbqYiX7gGuAl4u8FxXYESQcLx/6Jyr6ZyrGwTBosoYrGEY8UD7MUuWLMnySDYMyuUjd851BX4I\nguCz0Ev1gO8LPF6Y/5xhGIaRJsocR+6cqwr0I+FWKTfOuZ4k3C/JWFvDMAyj7JRHkTcCGgCfOefm\nA/WBac657YAfgB0KvLd+/nOFCIJgcBAEeUEQ5NWpU6ccwzAMwzCgHBfyIAhmBEGwTRAEOwVBsBMJ\n90nLIAh+Al4BTnMJ2gLLzT9uGIaRXkq8kDvnngImA7s45xY6585ez9vHAPOAucAjwH8qZZSGYRhG\nsZQmauXEEl7fqcD/A6BXxYdlGIZhlJYNJrNz3bp1yRKahmEYucQGcyE3DMPIVXKqjG0QBMnGEWpy\nqwYGKiSlImH6Gaf2YSWhuSt1P5vNYDdU1DhCTZd32203wAqWGenFFLlhGEbMiaUcDatqNSJYtWoV\n//zzDwDvv/8+AK1atQJ8W7T+/fsDvtHE/fffD/iGE3FC87/11lsB30xDracef/xxAFq2bAlEs6h/\nrqDCZcqJ+PbbbwHfTLlDhw6AKfM4Iyv/ggsuAOCpp54CvFWvJs1XXHEF5513XkbHZme2YRhGzImV\nIldpzAEDBgDQuHFjAK666ioA/vrrr+Rreq+a2aoJg3yWTz75JABdu3YFYOLEiUA8VauawDZt2hTw\nilwq8YcfEsm1Rx11VKzaqGltJkyYAPim0oo+atiwIQBjx44FfPu+TM5RY9FejI5JHUfNmzcHEk0C\nAGrVqpWxsRkVQxav9p4GDUoUc9W1QmuuZhlqAXfJJZckLTAdD+kmflctwzAMI4VYKHIpsblz5wLQ\npUsXwKts+cW33HJLRo0aBcDOO+8MwCabbALAlClTAPjoo48A79e65ZZbgHgqcSmGBx98EPDNYmV1\nbLvttgAceuihQHyaGmsNX3nllfW+7+effwbgsMMOA+Dzzz8HMhOto+9eaqxt27aAPxb1c8aMGQCM\nHz8egG7dugHeVy5Fr2O7fv36AFStWjW9E6gk9D38/fffgG+yrDaD2rcZN24c4K0oqVrt32y22WYA\nbLPNNpkYdqnQPocsXV1vZFWdf/75gLf6tf+2evVqevbsCXgVL2Werj2S+F29DMMwjBRioch115fq\nGT16dJHvW7lyJY8++igAy5cvB7wKrVKlCuAVkF7XXXe//fZLx9DTyimnnAJ4FSSkfqTQpe46dy7U\n6CmSKCpAVKtWDYCRI0cC3oqaPXs2ANdeey2Q2CMBP29ZZemwtnRcTZuW6GSo+PEwF198MQDdu3dP\n+Vz49yiq6s477wT8nKJkKcoyXr16NZdccgkAzz//POCtoKVLlwLeItG526JFi5TfpXlLoWsPQVFk\n2ayI+txzzwFw4omJ6iSai6z4E044AfAKXLz22mtAwjLs0aNHymeWLVsG+HWu7HWNzlFiGIZhlItY\nKHLd5XfYIVHqvGbNmgCsWLEi5X3Vq1dP3un1U3f2U089FYA77rgD8OrtsssuS3k9DshXJ+UgNt98\ncwAeeeQRwMe96jtYvXp1pLM9NV6pF/lNtc5SMUceeSTgras5c+YAPmZ7+PDhAAwcODDtY3733XcB\nr7w0po4dOwJetRW3PyHfcvv27QGvevv16wdES5GvWbMGgB49eiSt4nAuR926dQG/hvpetLaaj45h\nWcRaY1nO2WD+/PkAnHbaaYBX4lLR2rvRWv1/e+cfa1WV3fHPitZfdBxQKgXxByqItDpiHiNqxwzM\nSKkatP5IGGkqKpJMJmg7jCglWojBFDqOSpROsaK1WpE6DCKTCTo40WgUBZXf0HGUyjMg/hwTSOmb\nYfePc753X857Vx7Pd8+P5/ok5N577gXW2WeffdZe+7vWzqK/N2/evJqC5aKLLgKgT58+QPOuZ3l6\nieM4jtMlKuGRK2PqgQceAODee+8Fouf14osvAjB69OiackFPxDFjkh3ppB7IxjLlGVSJt956C4jZ\nqPKG1q5NtlDVjEWxZCkB9uzZU2qPXCobeXlXXHEF0N6L0Wedt67/1KlTAbjpppuabqs870suuQSA\n9957D4izBa1HSDXVCOU7bN68GYiqBnnq0iqXAXnL8+fP59lnnwXitbrzzjsBuOWWW/b7O5r56j7T\nDPjhhx8GYjtKzaNZZRFozUUzD80mdG3kmTdCazI7duxg8eLFAEyfPh04cD/4srhH7jiOU3Eq4ZFn\n44vyKm+88UYgZs+dccYZtWzAF154AYiekjI5s+jpm431lRF5L/K4pReXIkLej2J5Tz/9NABvvvkm\nAEuWLMnP2INAbS8lkbj//vu/8O998sknQPTA5fUp1tlM1E+GDx8ORL1wZ6tqSgOfVRxJoaMYc69e\nvdr1yaL76oABA2r6+ezsKIsUU/JIZbvQ39N9W2Qtmuuvvx6I95mUJwfyxDV7knb8888/r3ni0pg3\nG/fIHcdxKk4lPPIDIY3qvn37ah6NYpcLFy4EGnsMimXqKZzNuJNndOGFFwKwceNGIHryUlbkgRQN\nH3zwAZCsCUDMbNS5KEtQahXVKilSEfBFaO1DyANqVJdEShHFY7PrHLt27epuE9uR7U8H60lKcyxv\nTp6tFBHHHnsskHiwZfPIu6K80AwkWzFQ6zxSdxRxTnv27AGiOk62aTYhxY0iAVKzaGxYsGABANu3\nbweSfqtrlNcMwz1yx3GcitMjPPKOlBh6Emr1WBXMhJ78iqVfeeWVANx1111A1JtLq62n9pw5c4AY\nn8/TI5dSQNrbwYMHAzEDTTrqc889F4B77rkHiNreMhJCYNasWUD0hFpbWzv8rXThUuMoTqtrKU9R\n2YZlRDO9nTt3AlGlIZ3xfffdB0RdtWaY9ZRJW95ZnnzySSDOZFUvSesgRZ7TmjVrgPb7HGhdbenS\npUCc+em+GzduHBDXoHRus2bNqo0neVG9HuE4juPsR4/wyDtCXpp0q6+88goQ41166kr5IC2yKgXK\nK8zG7C644AKgGH2vFA6qJ6OZiLw3Vc674447gFhZrszMnDmz5o3dfvvtQPRadZ5TpkwBYjxZZOOP\nl19+OVDu3Z50rrfeeisQswF1bZX/oFlV1dF9pgxXzWAnTZoExD5bJJ999hnQfu1BsyXdX7rf1L/k\nyStHRcqc3r1712ZYeeEeueM4TsXpsR65kHJDT1PFsbTyLK9OT2HtpiOyuuAiPHHZIG9GKh1lBc6b\nNw+IMXLNQuQplJmXX36ZTz/9FKD2qkp4yrSTWkfoWujabtiwAYj6+iqg/iadsV6Vxax63iNHjqzF\nycuc49CIAQMGADHHQfXJFV8uwzmNGjUKiJniixYtAqjtu6nMaO24pQxq1cuXiqy+IubVV1+dh+k1\n3CN3HMepOD3WI5d3qv0c9XTVDkGbNm0CYlxLlRX11M3qQBXLk8eUZ00IzSLOOussIJ6bPABpjhVf\nVTXAMqP23bZtW212lFUwaBalGizy6qQgUr0OxSPL4N11Fp2z+p/qdKifqh+2tbXVciG0D6TWEMqs\nXhkxYgQQvVWtBSjjsUxolq1MTtXLkUpF3+ueV4a01myyapdshnIelLcnOI7jOJ2ix3rkQl6MPB5V\nDpSyQ97cq6++CsCZZ54JxCyta665Boi1IOT1yjPPwyuSZ3r++ecDMYasWLHqfSj7NOshlBF5M7t3\n7655OloD0K4xOp/Zs2cDcPrppwOxgqW8vip54kL9Rueuazxx4kQgziR3795d2y1IfVPX9dFHHwXa\nVxwsEnngq1evBuKM9pFHHinKpE6jfiQPXDMfvQplDuua6T7UzPmII47IPfvWPXLHcZyKc0CP3MwW\nApcCu0IIf153fArwA+APwC9CCNPS49OBG9LjN4UQVjTD8EYojqo6CVIySPu5ZcsWIGZjSS0wbNgw\nAE466SQg1sJWjQh5wdKhF4Ge/NnKerJdT395EIrxlbEGueq+vPPOO8yYMQOI10bZtUOHDgWiOiCr\n7c9bq9udZD217DXTa1tbW019pDr80tsru7hMaF9doRlxFdGsSWOA1tdOPPFEIOagZNd0iqip3pnQ\nyiPA/cCjOmBmo4DLgG+EEPaa2XHp8WHAeODPgAHAr8xsSAjhD91teEd8/PHHtUJSKnQv6ZAWAFVA\nSkjsr0JNp512GhBTqDVd0nRL0/8yLTRpU2ItnKlDaWNghSjKyJFHHsndd98NtF/E02CnJK3sdLWK\nIZVGqJ+pnIL6mRwTgGnTpgHxIVimImgqHayQpMQDChFVEV0TJQypJIScQMliJQ/Vw3fs2LHtNmxu\nNgccjUIILwKfZA5/H/inEMLe9DcqN3cZsCiEsDeE8C7wNvDNbrTXcRzHydDVx8UQ4FtmNhv4X+BH\nIYTXgeOBV+t+15oey4W2tjZeeuklIHprkrSp/Oxzzz1X+209mg5p01yl7CvUomm/NnMoupQoRAmb\nwkZaWJJtVUmQURs2KvmpAmaiyM0HuhtJKVUSVYXRNH2HuMhb5tIDui/kgSqppoqzJt1XO3bsAOKm\nIUpYU7lbzZpUtkOJRWeffXZunrjo6v92KHAMMBIYASw2s1MO5h8ws8nAZNi/0zqO4zgHR1cH8lZg\nSUhcv9fMbB/QF3gfOKHudwPTY+0IISwAFgC0tLR0i06uX79+NQ9AEjXJoJRgocVKxZMVk7z55puB\nGJ/VwpoW3LSgqONlePgoZqdymzo3tYE2mJBsrYreEUSZnTj++NwmeU1DXt+KFYkWQBv8qniW+uek\nSZMYNGgQUI5ZYBYVNJNNWmOSNLRKKOathB71O91nmmUoFq5zljxUY8TRRx+dk8WRrq7YLQVGAZjZ\nEOAw4CNgGTDezA43s0HAYOC17jDUcRzH6ZjOyA+fAL4N9DWzVuAfgYXAQjPbAPwfcG3qnW80s8XA\nJuD3wA/yUqykttbeS2WibcSmTp0KxCLx8ngee+wxIMoLVdhfaeFK/BFajS8D2hRDsiipPhSfy0rB\nqoaSrxQ/FhMmTCjCnG5FahR5b+vWrQPi9nYqiDZw4ECuuuoqoFxKKc0Oli9fDsT7SYk/+lxFtD6m\nV12bbPE2rUGpXHSRMt8DDuQhhO81+OpvGvx+NjD7yxjlOI7jdJ4em6Iv71ye9YMPPgjEp6cSgeSJ\n62mqokWKjasAfvbfFUUUMJI3pPKtmn0otqfVdCVFlSmmejA89NBDQIwnS6+rUqhVQtdMXp08cikj\nVO5Vmv8xY8YAybmXMaFL2v9sAp5KLFcRzWQ1G9LsQmtPSkCTrlxKHc0+NAbs3bu3ds08Rd9xHMfp\nFD3WI8+iLLjJkycDcTPm7ObJeqrOnz8fiJmhjTzuIuKWespru7BTTz0VgGeeeQaA6667Dii37viL\nUEEtlQuVd6MM1jLFijtLViu/atUqIPZDFQqTl6f+WsbZ1Pr165k5cyYQr42K0fUEjb9m8Vo/0/qa\nlG+Kjc+dOxdof855bsguqndHOI7jOPthZSh12tLSEqT3LhrFvLOF/538UBx55cqVADz//PMAzJkz\npzCbuhudY3brwSps6zZ06NBajoI2NVH5Wqf7aGlpYfXq1Z3qCO6RO47jVJyvTIy8syj+WsU4bE9B\n6gFVPdRrT0Jx1bxrcnQHvXr1qs0gVMrVKRYfrRzHcSpO9dwBx+kBlDkGfiDWrFlTie0Ev0q4R+44\njlNxSqFaMbMPgd0khbfKSF/ctq5QVtvKahe4bV2lJ9p2UgjhTzrzw1IM5ABmtjqE0FK0HR3htnWN\nstpWVrvAbesqX3XbPLTiOI5TcXwgdxzHqThlGsgXFG3AF+C2dY2y2lZWu8Bt6ypfadtKEyN3HMdx\nukaZPHLHcRynC5RiIDezsWa21czeNrPbCrTjBDP7tZltMrONZnZzevwYM3vOzH6TvvYp0MZDzOxN\nM1uefh5kZqvStnvSzArZhcDMepvZU2a2xcw2m9l5ZWk3M/v79HpuMLMnzOyIotrNzBaa2a50m0Qd\n67CdLGFeauM6MzunANv+Ob2m68zs52bWu+676altW82sqXUUOrKt7rupZhbMrG/6Obd2a2SXmU1J\n222jmc2tO96cNgshFPoHOAT4LXAKySbOa4FhBdnSHzgnff814L+BYcBc4Lb0+G3AnALb64fAfwLL\n08+LgfHp+58C3y/Irn8HJqXvDwN6l6HdgOOBd4Ej69prYlHtBlwInANsqDvWYTsBFwO/BAwYCawq\nwLYxwKHp+zl1tg1L79XDgUHpPXxInralx08AVgD/A/TNu90atNko4FfA4enn45rdZk3vuJ1oiPOA\nFXWfpwPTi7YrteVp4CJgK9A/PdYf2FqQPQOBlcBoYHnaUT+qu9H2a8sc7fp6Olha5njh7ZYO5NuB\nY0hKUiwH/rLIdgNOztz4HbYT8K/A9zr6XV62Zb77a+Dx9P1+92k6mJ6Xt23AU8A3gG11A3mu7dbB\n9VwMfLeD3zWtzcoQWtGNJlrTY4ViZicDw4FVQL8Qwo70q51Av4LMuheYBuxLPx8LfBZC0PbeRbXd\nIOBD4OE07PNvZtaLErRbCOF94MfAe8AO4HfAGsrRbqJRO5Xt3riexNOFEthmZpcB74cQ1ma+Ktq2\nIcC30tDdC2Y2otl2lWEgLx1m9sfAz4C/CyF8Xv9dSB6luUt9zOxSYFcIYU3e/3cnOJRkevkvIYTh\nJOUW9lvrKLDd+gCXkTxsBgC9gLF529FZimqnA2FmM4DfA48XbQuAmR0F/ANwR9G2dMChJDPAkcAt\nwGJrcpW0Mgzk75PEucTA9FghmNkfkQzij4cQlqSHPzCz/un3/YFdBZh2ATDOzLYBi0jCK/cBWtDJ\nWQAAAdhJREFUvc1MVSyLartWoDWEsCr9/BTJwF6Gdvsu8G4I4cMQQhuwhKQty9BuolE7leLeMLOJ\nwKXAhPRBA8XbdirJw3ltek8MBN4wsz8tgW2twJKQ8BrJDLpvM+0qw0D+OjA4VREcBowHlhVhSPrU\nfAjYHEL4Sd1Xy4Br0/fXksTOcyWEMD2EMDCEcDJJGz0fQpgA/Bq4qmDbdgLbzez09NB3gE2UoN1I\nQiojzeyo9PrKtsLbrY5G7bQM+NtUhTES+F1dCCYXzGwsSThvXAhhT91Xy4DxZna4mQ0CBgOv5WVX\nCGF9COG4EMLJ6T3RSiJU2Enx7baUZMETMxtCsvj/Ec1ss2YuThzEYsHFJAqR3wIzCrTjL0imteuA\nt9I/F5PEolcCvyFZjT6m4Pb6NlG1ckraGd4G/ot0pbwAm84GVqdttxToU5Z2A2YBW4ANwH+QqAYK\naTfgCZJYfRvJ4HNDo3YiWcx+IL0v1gMtBdj2NklcV/fDT+t+PyO1bSvwV3nblvl+G3GxM7d2a9Bm\nhwGPpf3tDWB0s9vMMzsdx3EqThlCK47jOM6XwAdyx3GciuMDueM4TsXxgdxxHKfi+EDuOI5TcXwg\ndxzHqTg+kDuO41QcH8gdx3Eqzv8DPY8Zg0BX8cEAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Generate images from noise, using the generator network.\n", + "n = 6\n", + "canvas = np.empty((28 * n, 28 * n))\n", + "for i in range(n):\n", + " # Noise input.\n", + " z = np.random.uniform(-1., 1., size=[n, noise_dim])\n", + " # Generate image from noise.\n", + " g = sess.run(gen_sample, feed_dict={gen_input: z})\n", + " # Reverse colours for better display\n", + " g = -1 * (g - 1)\n", + " for j in range(n):\n", + " # Draw the generated digits\n", + " canvas[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = g[j].reshape([28, 28])\n", + "\n", + "plt.figure(figsize=(n, n))\n", + "plt.imshow(canvas, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb new file mode 100644 index 00000000..62e70727 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb @@ -0,0 +1,390 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Example\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow.\n", + "\n", + "This example is using some of TensorFlow higher-level wrappers (tf.estimators, tf.layers, tf.metrics, ...), you can check 'neural_network_raw' example for a raw, and more detailed TensorFlow implementation.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)\n", + "\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.1\n", + "num_steps = 1000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of neurons\n", + "n_hidden_2 = 256 # 2nd layer number of neurons\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the input function for training\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.train.images}, y=mnist.train.labels,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the neural network\n", + "def neural_net(x_dict):\n", + " # TF Estimator input is a dict, in case of multiple inputs\n", + " x = x_dict['images']\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_1 = tf.layers.dense(x, n_hidden_1)\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_2 = tf.layers.dense(layer_1, n_hidden_2)\n", + " # Output fully connected layer with a neuron for each class\n", + " out_layer = tf.layers.dense(layer_2, num_classes)\n", + " return out_layer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the model function (following TF Estimator Template)\n", + "def model_fn(features, labels, mode):\n", + " \n", + " # Build the neural network\n", + " logits = neural_net(features)\n", + " \n", + " # Predictions\n", + " pred_classes = tf.argmax(logits, axis=1)\n", + " pred_probas = tf.nn.softmax(logits)\n", + " \n", + " # If prediction mode, early return\n", + " if mode == tf.estimator.ModeKeys.PREDICT:\n", + " return tf.estimator.EstimatorSpec(mode, predictions=pred_classes) \n", + " \n", + " # Define loss and optimizer\n", + " loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=tf.cast(labels, dtype=tf.int32)))\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " train_op = optimizer.minimize(loss_op, global_step=tf.train.get_global_step())\n", + " \n", + " # Evaluate the accuracy of the model\n", + " acc_op = tf.metrics.accuracy(labels=labels, predictions=pred_classes)\n", + " \n", + " # TF Estimators requires to return a EstimatorSpec, that specify\n", + " # the different ops for training, evaluating, ...\n", + " estim_specs = tf.estimator.EstimatorSpec(\n", + " mode=mode,\n", + " predictions=pred_classes,\n", + " loss=loss_op,\n", + " train_op=train_op,\n", + " eval_metric_ops={'accuracy': acc_op})\n", + "\n", + " return estim_specs" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpu7vjLA\n", + "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_tf_random_seed': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_save_checkpoints_steps': None, '_model_dir': '/tmp/tmpu7vjLA', '_save_summary_steps': 100}\n" + ] + } + ], + "source": [ + "# Build the Estimator\n", + "model = tf.estimator.Estimator(model_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpu7vjLA/model.ckpt.\n", + "INFO:tensorflow:loss = 2.44919, step = 1\n", + "INFO:tensorflow:global_step/sec: 602.544\n", + "INFO:tensorflow:loss = 0.344767, step = 101 (0.167 sec)\n", + "INFO:tensorflow:global_step/sec: 618.839\n", + "INFO:tensorflow:loss = 0.277633, step = 201 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 626.418\n", + "INFO:tensorflow:loss = 0.407796, step = 301 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 624.765\n", + "INFO:tensorflow:loss = 0.376889, step = 401 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 624.091\n", + "INFO:tensorflow:loss = 0.319697, step = 501 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 616.907\n", + "INFO:tensorflow:loss = 0.39049, step = 601 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 623.371\n", + "INFO:tensorflow:loss = 0.336831, step = 701 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 617.429\n", + "INFO:tensorflow:loss = 0.312776, step = 801 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 620.825\n", + "INFO:tensorflow:loss = 0.312817, step = 901 (0.161 sec)\n", + "INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpu7vjLA/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 0.24931.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Train the Model\n", + "model.train(input_fn, steps=num_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Starting evaluation at 2017-08-21-13:57:02\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpu7vjLA/model.ckpt-1000\n", + "INFO:tensorflow:Finished evaluation at 2017-08-21-13:57:02\n", + "INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.9189, global_step = 1000, loss = 0.286567\n" + ] + }, + { + "data": { + "text/plain": [ + "{'accuracy': 0.91890001, 'global_step': 1000, 'loss': 0.28656715}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Evaluate the Model\n", + "# Define the input function for evaluating\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': mnist.test.images}, y=mnist.test.labels,\n", + " batch_size=batch_size, shuffle=False)\n", + "# Use the Estimator 'evaluate' method\n", + "model.evaluate(input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from /tmp/tmpu7vjLA/model.ckpt-1000\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiL8QFKEH0iK8ANJEX4gKcIPJPVP82g/p9/JjhUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 7\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 2\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 1\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADbdJREFUeJzt3W+MFPUdx/HPF2qfYB9ouRL8U7DFYIhJpTmxDwi2thow\nGvCBijGGRtNDg2KTPqiBxGKaJo22NE0kkGskPRtrbYLGCyGVlphSE9J4mPrvrv7NQSEniDQqIaYI\n3z7YufaU298suzM7c3zfr+Ryu/Pdnf068rmZ3d/M/szdBSCeaVU3AKAahB8IivADQRF+ICjCDwRF\n+IGgCD8QFOEHgiL8QFBf6OaLmRmnEwIlc3dr5XEd7fnNbKmZvWFmb5vZA52sC0B3Wbvn9pvZdElv\nSrpW0gFJL0q6zd2HE89hzw+UrBt7/kWS3nb3d939P5L+IGl5B+sD0EWdhP9CSf+acP9AtuwzzKzP\nzIbMbKiD1wJQsNI/8HP3fkn9Eof9QJ10suc/KOniCfcvypYBmAI6Cf+Lki41s0vM7IuSVkoaLKYt\nAGVr+7Df3T81s3slPSdpuqSt7v56YZ0BKFXbQ31tvRjv+YHSdeUkHwBTF+EHgiL8QFCEHwiK8ANB\nEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivAD\nQXV1im5034wZM5L1Rx55JFlfvXp1sr53795k/eabb25a27dvX/K5KBd7fiAowg8ERfiBoAg/EBTh\nB4Ii/EBQhB8IqqNZes1sVNLHkk5K+tTde3Mezyy9XTZv3rxkfWRkpKP1T5uW3n+sXbu2aW3Tpk0d\nvTYm1+osvUWc5PMddz9SwHoAdBGH/UBQnYbfJe00s71m1ldEQwC6o9PD/sXuftDMviLpz2b2T3ff\nPfEB2R8F/jAANdPRnt/dD2a/D0t6RtKiSR7T7+69eR8GAuiutsNvZjPM7EvjtyVdJ+m1ohoDUK5O\nDvtnSXrGzMbX83t3/1MhXQEoXdvhd/d3JX2jwF7Qpp6enqa1gYGBLnaCqYShPiAowg8ERfiBoAg/\nEBThB4Ii/EBQfHX3FJC6LFaSVqxY0bS2aNFpJ1121ZIlS5rW8i4Hfvnll5P13bt3J+tIY88PBEX4\ngaAIPxAU4QeCIvxAUIQfCIrwA0F19NXdZ/xifHV3W06ePJmsnzp1qkudnC5vrL6T3vKm8L711luT\n9bzpw89WrX51N3t+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiKcf4a2LFjR7K+bNmyZL3Kcf4PPvgg\nWT927FjT2pw5c4pu5zOmT59e6vrrinF+AEmEHwiK8ANBEX4gKMIPBEX4gaAIPxBU7vf2m9lWSTdI\nOuzul2fLzpf0lKS5kkYl3eLu/y6vzant6quvTtbnz5+frOeN45c5zr9ly5ZkfefOncn6hx9+2LR2\nzTXXJJ+7fv36ZD3PPffc07S2efPmjtZ9Nmhlz/9bSUs/t+wBSbvc/VJJu7L7AKaQ3PC7+25JRz+3\neLmkgez2gKTmU8YAqKV23/PPcvex7PZ7kmYV1A+ALul4rj5399Q5+2bWJ6mv09cBUKx29/yHzGy2\nJGW/Dzd7oLv3u3uvu/e2+VoAStBu+Aclrcpur5L0bDHtAOiW3PCb2ZOS9kiab2YHzOwuST+XdK2Z\nvSXpe9l9AFMI1/MXYO7cucn6nj17kvWZM2cm6518N37ed99v27YtWX/ooYeS9ePHjyfrKXnX8+dt\nt56enmT9k08+aVp78MEHk8999NFHk/UTJ04k61Xien4ASYQfCIrwA0ERfiAowg8ERfiBoBjqK8C8\nefOS9ZGRkY7WnzfU9/zzzzetrVy5MvncI0eOtNVTN9x3333J+saNG5P11HbLuwz6sssuS9bfeeed\nZL1KDPUBSCL8QFCEHwiK8ANBEX4gKMIPBEX4gaA6/hovlG9oaChZv/POO5vW6jyOn2dwcDBZv/32\n25P1K6+8ssh2zjrs+YGgCD8QFOEHgiL8QFCEHwiK8ANBEX4gKMb5uyDvevw8V111VUGdTC1m6cvS\n87ZrJ9t9w4YNyfodd9zR9rrrgj0/EBThB4Ii/EBQhB8IivADQRF+ICjCDwSVO85vZlsl3SDpsLtf\nni3bIOkHkt7PHrbO3XeU1WTd3X333cl63nfEY3I33nhjsr5w4cJkPbXd8/6f5I3znw1a2fP/VtLS\nSZb/yt2vyH7CBh+YqnLD7+67JR3tQi8AuqiT9/z3mtkrZrbVzM4rrCMAXdFu+DdL+rqkKySNSfpl\nsweaWZ+ZDZlZ+ovoAHRVW+F390PuftLdT0n6jaRFicf2u3uvu/e22ySA4rUVfjObPeHuTZJeK6Yd\nAN3SylDfk5K+LWmmmR2Q9BNJ3zazKyS5pFFJq0vsEUAJcsPv7rdNsvixEnqZsvLGoyPr6elpWluw\nYEHyuevWrSu6nf95//33k/UTJ06U9tp1wRl+QFCEHwiK8ANBEX4gKMIPBEX4gaD46m6Uav369U1r\na9asKfW1R0dHm9ZWrVqVfO7+/fsL7qZ+2PMDQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCM86MjO3ak\nv7h5/vz5XerkdMPDw01rL7zwQhc7qSf2/EBQhB8IivADQRF+ICjCDwRF+IGgCD8QFOP8BTCzZH3a\ntM7+xi5btqzt5/b39yfrF1xwQdvrlvL/26qcnpyvVE9jzw8ERfiBoAg/EBThB4Ii/EBQhB8IivAD\nQeWO85vZxZIelzRLkkvqd/dfm9n5kp6SNFfSqKRb3P3f5bVaX5s3b07WH3744Y7Wv3379mS9k7H0\nssfhy1z/li1bSlt3BK3s+T+V9CN3XyDpW5LWmNkCSQ9I2uXul0rald0HMEXkht/dx9z9pez2x5JG\nJF0oabmkgexhA5JWlNUkgOKd0Xt+M5sraaGkv0ua5e5jWek9Nd4WAJgiWj6338zOlbRN0g/d/aOJ\n57O7u5uZN3len6S+ThsFUKyW9vxmdo4awX/C3Z/OFh8ys9lZfbakw5M919373b3X3XuLaBhAMXLD\nb41d/GOSRtx944TSoKTxqU5XSXq2+PYAlMXcJz1a//8DzBZL+pukVyWNj9usU+N9/x8lfVXSPjWG\n+o7mrCv9YlPUnDlzkvU9e/Yk6z09Pcl6nS+bzevt0KFDTWsjIyPJ5/b1pd8tjo2NJevHjx9P1s9W\n7p6+xjyT+57f3V+Q1Gxl3z2TpgDUB2f4AUERfiAowg8ERfiBoAg/EBThB4LKHecv9MXO0nH+PEuW\nLEnWV6xIXxN1//33J+t1Hudfu3Zt09qmTZuKbgdqfZyfPT8QFOEHgiL8QFCEHwiK8ANBEX4gKMIP\nBMU4/xSwdOnSZD113XveNNWDg4PJet4U33nTkw8PDzet7d+/P/lctIdxfgBJhB8IivADQRF+ICjC\nDwRF+IGgCD8QFOP8wFmGcX4ASYQfCIrwA0ERfiAowg8ERfiBoAg/EFRu+M3sYjN73syGzex1M7s/\nW77BzA6a2T+yn+vLbxdAUXJP8jGz2ZJmu/tLZvYlSXslrZB0i6Rj7v6Lll+Mk3yA0rV6ks8XWljR\nmKSx7PbHZjYi6cLO2gNQtTN6z29mcyUtlPT3bNG9ZvaKmW01s/OaPKfPzIbMbKijTgEUquVz+83s\nXEl/lfQzd3/azGZJOiLJJf1UjbcGd+asg8N+oGStHva3FH4zO0fSdknPufvGSepzJW1398tz1kP4\ngZIVdmGPNb6e9TFJIxODn30QOO4mSa+daZMAqtPKp/2LJf1N0quSxueCXifpNklXqHHYPyppdfbh\nYGpd7PmBkhV62F8Uwg+Uj+v5ASQRfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+ICjCDwRF\n+IGgCD8QFOEHgsr9As+CHZG0b8L9mdmyOqprb3XtS6K3dhXZ25xWH9jV6/lPe3GzIXfvrayBhLr2\nVte+JHprV1W9cdgPBEX4gaCqDn9/xa+fUtfe6tqXRG/tqqS3St/zA6hO1Xt+ABWpJPxmttTM3jCz\nt83sgSp6aMbMRs3s1Wzm4UqnGMumQTtsZq9NWHa+mf3ZzN7Kfk86TVpFvdVi5ubEzNKVbru6zXjd\n9cN+M5su6U1J10o6IOlFSbe5+3BXG2nCzEYl9bp75WPCZrZE0jFJj4/PhmRmD0s66u4/z/5wnufu\nP65Jbxt0hjM3l9Rbs5mlv68Kt12RM14XoYo9/yJJb7v7u+7+H0l/kLS8gj5qz913Szr6ucXLJQ1k\ntwfU+MfTdU16qwV3H3P3l7LbH0san1m60m2X6KsSVYT/Qkn/mnD/gOo15bdL2mlme82sr+pmJjFr\nwsxI70maVWUzk8idubmbPjezdG22XTszXheND/xOt9jdvylpmaQ12eFtLXnjPVudhms2S/q6GtO4\njUn6ZZXNZDNLb5P0Q3f/aGKtym03SV+VbLcqwn9Q0sUT7l+ULasFdz+Y/T4s6Rk13qbUyaHxSVKz\n34cr7ud/3P2Qu59091OSfqMKt102s/Q2SU+4+9PZ4sq33WR9VbXdqgj/i5IuNbNLzOyLklZKGqyg\nj9OY2YzsgxiZ2QxJ16l+sw8PSlqV3V4l6dkKe/mMuszc3GxmaVW87Wo347W7d/1H0vVqfOL/jqT1\nVfTQpK+vSXo5+3m96t4kPanGYeAJNT4buUvSlyXtkvSWpL9IOr9Gvf1OjdmcX1EjaLMr6m2xGof0\nr0j6R/ZzfdXbLtFXJduNM/yAoPjADwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUP8FAfaK+yOW\nZZUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model prediction: 0\n" + ] + } + ], + "source": [ + "# Predict single images\n", + "n_images = 4\n", + "# Get images from test set\n", + "test_images = mnist.test.images[:n_images]\n", + "# Prepare the input data\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={'images': test_images}, shuffle=False)\n", + "# Use the model to predict the images class\n", + "preds = list(model.predict(input_fn))\n", + "\n", + "# Display\n", + "for i in range(n_images):\n", + " plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')\n", + " plt.show()\n", + " print(\"Model prediction:\", preds[i])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb new file mode 100644 index 00000000..346f2e5d --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb @@ -0,0 +1,287 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network with Eager API\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow's Eager API.\n", + "\n", + "This example is using some of TensorFlow higher-level wrappers (tf.estimators, tf.layers, tf.metrics, ...), you can check 'neural_network_raw' example for a raw, and more detailed TensorFlow implementation.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Set Eager API\n", + "tf.enable_eager_execution()\n", + "tfe = tf.contrib.eager" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 1000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of neurons\n", + "n_hidden_2 = 256 # 2nd layer number of neurons\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Using TF Dataset to split data into batches\n", + "dataset = tf.data.Dataset.from_tensor_slices(\n", + " (mnist.train.images, mnist.train.labels))\n", + "dataset = dataset.repeat().batch(batch_size).prefetch(batch_size)\n", + "dataset_iter = tfe.Iterator(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define the neural network. To use eager API and tf.layers API together,\n", + "# we must instantiate a tfe.Network class as follow:\n", + "class NeuralNet(tfe.Network):\n", + " def __init__(self):\n", + " # Define each layer\n", + " super(NeuralNet, self).__init__()\n", + " # Hidden fully connected layer with 256 neurons\n", + " self.layer1 = self.track_layer(\n", + " tf.layers.Dense(n_hidden_1, activation=tf.nn.relu))\n", + " # Hidden fully connected layer with 256 neurons\n", + " self.layer2 = self.track_layer(\n", + " tf.layers.Dense(n_hidden_2, activation=tf.nn.relu))\n", + " # Output fully connected layer with a neuron for each class\n", + " self.out_layer = self.track_layer(tf.layers.Dense(num_classes))\n", + "\n", + " def call(self, x):\n", + " x = self.layer1(x)\n", + " x = self.layer2(x)\n", + " return self.out_layer(x)\n", + "\n", + "\n", + "neural_net = NeuralNet()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Cross-Entropy loss function\n", + "def loss_fn(inference_fn, inputs, labels):\n", + " # Using sparse_softmax cross entropy\n", + " return tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=inference_fn(inputs), labels=labels))\n", + "\n", + "\n", + "# Calculate accuracy\n", + "def accuracy_fn(inference_fn, inputs, labels):\n", + " prediction = tf.nn.softmax(inference_fn(inputs))\n", + " correct_pred = tf.equal(tf.argmax(prediction, 1), labels)\n", + " return tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "\n", + "# SGD Optimizer\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "\n", + "# Compute gradients\n", + "grad = tfe.implicit_gradients(loss_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial loss= 2.340397596\n", + "Step: 0001 loss= 2.340397596 accuracy= 0.0703\n", + "Step: 0100 loss= 0.586046159 accuracy= 0.8305\n", + "Step: 0200 loss= 0.253318846 accuracy= 0.9282\n", + "Step: 0300 loss= 0.214748293 accuracy= 0.9377\n", + "Step: 0400 loss= 0.180644721 accuracy= 0.9466\n", + "Step: 0500 loss= 0.137285724 accuracy= 0.9591\n", + "Step: 0600 loss= 0.119845696 accuracy= 0.9636\n", + "Step: 0700 loss= 0.113618039 accuracy= 0.9665\n", + "Step: 0800 loss= 0.109642141 accuracy= 0.9676\n", + "Step: 0900 loss= 0.085067607 accuracy= 0.9746\n", + "Step: 1000 loss= 0.079819344 accuracy= 0.9754\n" + ] + } + ], + "source": [ + "# Training\n", + "average_loss = 0.\n", + "average_acc = 0.\n", + "for step in range(num_steps):\n", + "\n", + " # Iterate through the dataset\n", + " d = dataset_iter.next()\n", + " \n", + " # Images\n", + " x_batch = d[0]\n", + " # Labels\n", + " y_batch = tf.cast(d[1], dtype=tf.int64)\n", + "\n", + " # Compute the batch loss\n", + " batch_loss = loss_fn(neural_net, x_batch, y_batch)\n", + " average_loss += batch_loss\n", + " # Compute the batch accuracy\n", + " batch_accuracy = accuracy_fn(neural_net, x_batch, y_batch)\n", + " average_acc += batch_accuracy\n", + "\n", + " if step == 0:\n", + " # Display the initial cost, before optimizing\n", + " print(\"Initial loss= {:.9f}\".format(average_loss))\n", + "\n", + " # Update the variables following gradients info\n", + " optimizer.apply_gradients(grad(neural_net, x_batch, y_batch))\n", + "\n", + " # Display info\n", + " if (step + 1) % display_step == 0 or step == 0:\n", + " if step > 0:\n", + " average_loss /= display_step\n", + " average_acc /= display_step\n", + " print(\"Step:\", '%04d' % (step + 1), \" loss=\",\n", + " \"{:.9f}\".format(average_loss), \" accuracy=\",\n", + " \"{:.4f}\".format(average_acc))\n", + " average_loss = 0.\n", + " average_acc = 0." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testset Accuracy: 0.9719\n" + ] + } + ], + "source": [ + "# Evaluate model on the test image set\n", + "testX = mnist.test.images\n", + "testY = mnist.test.labels\n", + "\n", + "test_acc = accuracy_fn(neural_net, testX, testY)\n", + "print(\"Testset Accuracy: {:.4f}\".format(test_acc))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_raw.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_raw.ipynb new file mode 100644 index 00000000..6d9dbd24 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_raw.ipynb @@ -0,0 +1,224 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Neural Network Example\n", + "\n", + "Build a 2-hidden layers fully connected neural network (a.k.a multilayer perceptron) with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Neural Network Overview\n", + "\n", + "\"nn\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.1\n", + "num_steps = 500\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of neurons\n", + "n_hidden_2 = 256 # 2nd layer number of neurons\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "X = tf.placeholder(\"float\", [None, num_input])\n", + "Y = tf.placeholder(\"float\", [None, num_classes])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "weights = {\n", + " 'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),\n", + " 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes]))\n", + "}\n", + "biases = {\n", + " 'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", + " 'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Create model\n", + "def neural_net(x):\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n", + " # Hidden fully connected layer with 256 neurons\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n", + " # Output fully connected layer with a neuron for each class\n", + " out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n", + " return out_layer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Construct model\n", + "logits = neural_net(X)\n", + "\n", + "# Define loss and optimizer\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 13208.1406, Training Accuracy= 0.266\n", + "Step 100, Minibatch Loss= 462.8610, Training Accuracy= 0.867\n", + "Step 200, Minibatch Loss= 232.8298, Training Accuracy= 0.844\n", + "Step 300, Minibatch Loss= 85.2141, Training Accuracy= 0.891\n", + "Step 400, Minibatch Loss= 38.0552, Training Accuracy= 0.883\n", + "Step 500, Minibatch Loss= 55.3689, Training Accuracy= 0.867\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.8729\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, num_steps+1):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop)\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for MNIST test images\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: mnist.test.images,\n", + " Y: mnist.test.labels}))" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb new file mode 100644 index 00000000..48fe57a8 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Recurrent Neural Network Example\n", + "\n", + "Build a recurrent neural network (LSTM) with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RNN Overview\n", + "\n", + "\"nn\"\n", + "\n", + "References:\n", + "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "To classify images using a recurrent neural network, we consider every image row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 timesteps for every sample.\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.contrib import rnn\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Training Parameters\n", + "learning_rate = 0.001\n", + "training_steps = 10000\n", + "batch_size = 128\n", + "display_step = 200\n", + "\n", + "# Network Parameters\n", + "num_input = 28 # MNIST data input (img shape: 28*28)\n", + "timesteps = 28 # timesteps\n", + "num_hidden = 128 # hidden layer num of features\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "X = tf.placeholder(\"float\", [None, timesteps, num_input])\n", + "Y = tf.placeholder(\"float\", [None, num_classes])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define weights\n", + "weights = {\n", + " 'out': tf.Variable(tf.random_normal([num_hidden, num_classes]))\n", + "}\n", + "biases = {\n", + " 'out': tf.Variable(tf.random_normal([num_classes]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def RNN(x, weights, biases):\n", + "\n", + " # Prepare data shape to match `rnn` function requirements\n", + " # Current data input shape: (batch_size, timesteps, n_input)\n", + " # Required shape: 'timesteps' tensors list of shape (batch_size, n_input)\n", + "\n", + " # Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)\n", + " x = tf.unstack(x, timesteps, 1)\n", + "\n", + " # Define a lstm cell with tensorflow\n", + " lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)\n", + "\n", + " # Get lstm cell output\n", + " outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)\n", + "\n", + " # Linear activation, using rnn inner loop last output\n", + " return tf.matmul(outputs[-1], weights['out']) + biases['out']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "logits = RNN(X, weights, biases)\n", + "prediction = tf.nn.softmax(logits)\n", + "\n", + "# Define loss and optimizer\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits, labels=Y))\n", + "optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 2.6268, Training Accuracy= 0.102\n", + "Step 200, Minibatch Loss= 2.0722, Training Accuracy= 0.328\n", + "Step 400, Minibatch Loss= 1.9181, Training Accuracy= 0.336\n", + "Step 600, Minibatch Loss= 1.8858, Training Accuracy= 0.336\n", + "Step 800, Minibatch Loss= 1.7022, Training Accuracy= 0.422\n", + "Step 1000, Minibatch Loss= 1.6365, Training Accuracy= 0.477\n", + "Step 1200, Minibatch Loss= 1.6691, Training Accuracy= 0.516\n", + "Step 1400, Minibatch Loss= 1.4626, Training Accuracy= 0.547\n", + "Step 1600, Minibatch Loss= 1.4707, Training Accuracy= 0.539\n", + "Step 1800, Minibatch Loss= 1.4087, Training Accuracy= 0.570\n", + "Step 2000, Minibatch Loss= 1.3033, Training Accuracy= 0.570\n", + "Step 2200, Minibatch Loss= 1.3773, Training Accuracy= 0.508\n", + "Step 2400, Minibatch Loss= 1.3092, Training Accuracy= 0.570\n", + "Step 2600, Minibatch Loss= 1.2272, Training Accuracy= 0.609\n", + "Step 2800, Minibatch Loss= 1.1827, Training Accuracy= 0.633\n", + "Step 3000, Minibatch Loss= 1.0453, Training Accuracy= 0.641\n", + "Step 3200, Minibatch Loss= 1.0400, Training Accuracy= 0.648\n", + "Step 3400, Minibatch Loss= 1.1145, Training Accuracy= 0.656\n", + "Step 3600, Minibatch Loss= 0.9884, Training Accuracy= 0.688\n", + "Step 3800, Minibatch Loss= 1.0395, Training Accuracy= 0.703\n", + "Step 4000, Minibatch Loss= 1.0096, Training Accuracy= 0.664\n", + "Step 4200, Minibatch Loss= 0.8806, Training Accuracy= 0.758\n", + "Step 4400, Minibatch Loss= 0.9090, Training Accuracy= 0.766\n", + "Step 4600, Minibatch Loss= 1.0060, Training Accuracy= 0.703\n", + "Step 4800, Minibatch Loss= 0.8954, Training Accuracy= 0.703\n", + "Step 5000, Minibatch Loss= 0.8163, Training Accuracy= 0.750\n", + "Step 5200, Minibatch Loss= 0.7620, Training Accuracy= 0.773\n", + "Step 5400, Minibatch Loss= 0.7388, Training Accuracy= 0.758\n", + "Step 5600, Minibatch Loss= 0.7604, Training Accuracy= 0.695\n", + "Step 5800, Minibatch Loss= 0.7459, Training Accuracy= 0.734\n", + "Step 6000, Minibatch Loss= 0.7448, Training Accuracy= 0.734\n", + "Step 6200, Minibatch Loss= 0.7208, Training Accuracy= 0.773\n", + "Step 6400, Minibatch Loss= 0.6557, Training Accuracy= 0.773\n", + "Step 6600, Minibatch Loss= 0.8616, Training Accuracy= 0.758\n", + "Step 6800, Minibatch Loss= 0.6089, Training Accuracy= 0.773\n", + "Step 7000, Minibatch Loss= 0.5020, Training Accuracy= 0.844\n", + "Step 7200, Minibatch Loss= 0.5980, Training Accuracy= 0.812\n", + "Step 7400, Minibatch Loss= 0.6786, Training Accuracy= 0.766\n", + "Step 7600, Minibatch Loss= 0.4891, Training Accuracy= 0.859\n", + "Step 7800, Minibatch Loss= 0.7042, Training Accuracy= 0.797\n", + "Step 8000, Minibatch Loss= 0.4200, Training Accuracy= 0.859\n", + "Step 8200, Minibatch Loss= 0.6442, Training Accuracy= 0.742\n", + "Step 8400, Minibatch Loss= 0.5569, Training Accuracy= 0.828\n", + "Step 8600, Minibatch Loss= 0.5838, Training Accuracy= 0.836\n", + "Step 8800, Minibatch Loss= 0.5579, Training Accuracy= 0.812\n", + "Step 9000, Minibatch Loss= 0.4337, Training Accuracy= 0.867\n", + "Step 9200, Minibatch Loss= 0.4366, Training Accuracy= 0.844\n", + "Step 9400, Minibatch Loss= 0.5051, Training Accuracy= 0.844\n", + "Step 9600, Minibatch Loss= 0.5244, Training Accuracy= 0.805\n", + "Step 9800, Minibatch Loss= 0.4932, Training Accuracy= 0.805\n", + "Step 10000, Minibatch Loss= 0.4833, Training Accuracy= 0.852\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.882812\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " for step in range(1, training_steps+1):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Reshape data to get 28 seq of 28 elements\n", + " batch_x = batch_x.reshape((batch_size, timesteps, num_input))\n", + " # Run optimization op (backprop)\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for 128 mnist test images\n", + " test_len = 128\n", + " test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))\n", + " test_label = mnist.test.labels[:test_len]\n", + " print(\"Testing Accuracy:\", \\\n", + " sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb b/tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb new file mode 100644 index 00000000..76ae0a91 --- /dev/null +++ b/tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Variational Auto-Encoder Example\n", + "\n", + "Build a variational auto-encoder (VAE) to generate digit images from a noise distribution with TensorFlow.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## VAE Overview\n", + "\n", + "\"vae\"\n", + "\n", + "References:\n", + "- [Auto-Encoding Variational Bayes The International Conference on Learning Representations](https://arxiv.org/abs/1312.6114) (ICLR), Banff, 2014. D.P. Kingma, M. Welling\n", + "- [Understanding the difficulty of training deep feedforward neural networks](www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.../AISTATS2010_Glorot.pdf). X Glorot, Y Bengio. Aistats 9, 249-256\n", + "\n", + "Other tutorials:\n", + "- [Variational Auto Encoder Explained](http://kvfrans.com/variational-autoencoders-explained/). Kevin Frans.\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flattened and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import division, print_function, absolute_import\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import norm\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 30000\n", + "batch_size = 64\n", + "\n", + "# Network Parameters\n", + "image_dim = 784 # MNIST images are 28x28 pixels\n", + "hidden_dim = 512\n", + "latent_dim = 2\n", + "\n", + "# A custom initialization (see Xavier Glorot init)\n", + "def glorot_init(shape):\n", + " return tf.random_normal(shape=shape, stddev=1. / tf.sqrt(shape[0] / 2.))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Variables\n", + "weights = {\n", + " 'encoder_h1': tf.Variable(glorot_init([image_dim, hidden_dim])),\n", + " 'z_mean': tf.Variable(glorot_init([hidden_dim, latent_dim])),\n", + " 'z_std': tf.Variable(glorot_init([hidden_dim, latent_dim])),\n", + " 'decoder_h1': tf.Variable(glorot_init([latent_dim, hidden_dim])),\n", + " 'decoder_out': tf.Variable(glorot_init([hidden_dim, image_dim]))\n", + "}\n", + "biases = {\n", + " 'encoder_b1': tf.Variable(glorot_init([hidden_dim])),\n", + " 'z_mean': tf.Variable(glorot_init([latent_dim])),\n", + " 'z_std': tf.Variable(glorot_init([latent_dim])),\n", + " 'decoder_b1': tf.Variable(glorot_init([hidden_dim])),\n", + " 'decoder_out': tf.Variable(glorot_init([image_dim]))\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Building the encoder\n", + "input_image = tf.placeholder(tf.float32, shape=[None, image_dim])\n", + "encoder = tf.matmul(input_image, weights['encoder_h1']) + biases['encoder_b1']\n", + "encoder = tf.nn.tanh(encoder)\n", + "z_mean = tf.matmul(encoder, weights['z_mean']) + biases['z_mean']\n", + "z_std = tf.matmul(encoder, weights['z_std']) + biases['z_std']\n", + "\n", + "# Sampler: Normal (gaussian) random distribution\n", + "eps = tf.random_normal(tf.shape(z_std), dtype=tf.float32, mean=0., stddev=1.0,\n", + " name='epsilon')\n", + "z = z_mean + tf.exp(z_std / 2) * eps\n", + "\n", + "# Building the decoder (with scope to re-use these layers later)\n", + "decoder = tf.matmul(z, weights['decoder_h1']) + biases['decoder_b1']\n", + "decoder = tf.nn.tanh(decoder)\n", + "decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out']\n", + "decoder = tf.nn.sigmoid(decoder)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Define VAE Loss\n", + "def vae_loss(x_reconstructed, x_true):\n", + " # Reconstruction loss\n", + " encode_decode_loss = x_true * tf.log(1e-10 + x_reconstructed) \\\n", + " + (1 - x_true) * tf.log(1e-10 + 1 - x_reconstructed)\n", + " encode_decode_loss = -tf.reduce_sum(encode_decode_loss, 1)\n", + " # KL Divergence loss\n", + " kl_div_loss = 1 + z_std - tf.square(z_mean) - tf.exp(z_std)\n", + " kl_div_loss = -0.5 * tf.reduce_sum(kl_div_loss, 1)\n", + " return tf.reduce_mean(encode_decode_loss + kl_div_loss)\n", + "\n", + "loss_op = vae_loss(decoder, input_image)\n", + "optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Loss: 645.076538\n", + "Step 1000, Loss: 173.018188\n", + "Step 2000, Loss: 165.299225\n", + "Step 3000, Loss: 172.933685\n", + "Step 4000, Loss: 161.475052\n", + "Step 5000, Loss: 179.529831\n", + "Step 6000, Loss: 166.430023\n", + "Step 7000, Loss: 167.152176\n", + "Step 8000, Loss: 159.920242\n", + "Step 9000, Loss: 160.172363\n", + "Step 10000, Loss: 150.077652\n", + "Step 11000, Loss: 162.774567\n", + "Step 12000, Loss: 156.187820\n", + "Step 13000, Loss: 148.331573\n", + "Step 14000, Loss: 153.757202\n", + "Step 15000, Loss: 158.050598\n", + "Step 16000, Loss: 163.068939\n", + "Step 17000, Loss: 152.765152\n", + "Step 18000, Loss: 151.136353\n", + "Step 19000, Loss: 157.889664\n", + "Step 20000, Loss: 149.112473\n", + "Step 21000, Loss: 151.694885\n", + "Step 22000, Loss: 153.153229\n", + "Step 23000, Loss: 152.662323\n", + "Step 24000, Loss: 150.556198\n", + "Step 25000, Loss: 142.779984\n", + "Step 26000, Loss: 148.985382\n", + "Step 27000, Loss: 150.923401\n", + "Step 28000, Loss: 161.761551\n", + "Step 29000, Loss: 144.045578\n", + "Step 30000, Loss: 151.272964\n" + ] + } + ], + "source": [ + "# Start Training\n", + "# Start a new TF session\n", + "sess = tf.Session()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + "\n", + "# Training\n", + "for i in range(1, num_steps+1):\n", + " # Prepare Data\n", + " # Get the next batch of MNIST data (only images are needed, not labels)\n", + " batch_x, _ = mnist.train.next_batch(batch_size)\n", + "\n", + " # Train\n", + " feed_dict = {input_image: batch_x}\n", + " _, l = sess.run([train_op, loss_op], feed_dict=feed_dict)\n", + " if i % 1000 == 0 or i == 1:\n", + " print('Step %i, Loss: %f' % (i, l))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAHhCAYAAAChqv35AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Testing\n", + "# Generator takes noise as input\n", + "noise_input = tf.placeholder(tf.float32, shape=[None, latent_dim])\n", + "# Rebuild the decoder to create image from noise\n", + "decoder = tf.matmul(noise_input, weights['decoder_h1']) + biases['decoder_b1']\n", + "decoder = tf.nn.tanh(decoder)\n", + "decoder = tf.matmul(decoder, weights['decoder_out']) + biases['decoder_out']\n", + "decoder = tf.nn.sigmoid(decoder)\n", + "\n", + "# Building a manifold of generated digits\n", + "n = 20\n", + "x_axis = np.linspace(-3, 3, n)\n", + "y_axis = np.linspace(-3, 3, n)\n", + "\n", + "canvas = np.empty((28 * n, 28 * n))\n", + "for i, yi in enumerate(x_axis):\n", + " for j, xi in enumerate(y_axis):\n", + " z_mu = np.array([[xi, yi]] * batch_size)\n", + " x_mean = sess.run(decoder, feed_dict={noise_input: z_mu})\n", + " canvas[(n - i - 1) * 28:(n - i) * 28, j * 28:(j + 1) * 28] = \\\n", + " x_mean[0].reshape(28, 28)\n", + "\n", + "plt.figure(figsize=(8, 10))\n", + "Xi, Yi = np.meshgrid(x_axis, y_axis)\n", + "plt.imshow(canvas, origin=\"upper\", cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb b/tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb new file mode 100644 index 00000000..f70b2429 --- /dev/null +++ b/tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Save & Restore a Model\n", + "\n", + "Save and Restore a model using TensorFlow.\n", + "This example is using the MNIST database of handwritten digits\n", + "(http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", + "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", + "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n", + "\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.001\n", + "batch_size = 100\n", + "display_step = 1\n", + "model_path = \"/tmp/model.ckpt\"\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of features\n", + "n_hidden_2 = 256 # 2nd layer number of features\n", + "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph input\n", + "x = tf.placeholder(\"float\", [None, n_input])\n", + "y = tf.placeholder(\"float\", [None, n_classes])\n", + "\n", + "\n", + "# Create model\n", + "def multilayer_perceptron(x, weights, biases):\n", + " # Hidden layer with RELU activation\n", + " layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n", + " layer_1 = tf.nn.relu(layer_1)\n", + " # Hidden layer with RELU activation\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n", + " layer_2 = tf.nn.relu(layer_2)\n", + " # Output layer with linear activation\n", + " out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n", + " return out_layer\n", + "\n", + "# Store layers weight & bias\n", + "weights = {\n", + " 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n", + " 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n", + "}\n", + "biases = {\n", + " 'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n", + " 'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n", + " 'out': tf.Variable(tf.random_normal([n_classes]))\n", + "}\n", + "\n", + "# Construct model\n", + "pred = multilayer_perceptron(x, weights, biases)\n", + "\n", + "# Define loss and optimizer\n", + "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n", + "\n", + "# Initializing the variables\n", + "init = tf.global_variables_initializer()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 'Saver' op to save and restore all the variables\n", + "saver = tf.train.Saver()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting 1st session...\n", + "Epoch: 0001 cost= 187.778896380\n", + "Epoch: 0002 cost= 42.367902536\n", + "Epoch: 0003 cost= 26.488964058\n", + "First Optimization Finished!\n", + "Accuracy: 0.9075\n", + "Model saved in file: /tmp/model.ckpt\n" + ] + } + ], + "source": [ + "# Running first session\n", + "print(\"Starting 1st session...\")\n", + "with tf.Session() as sess:\n", + " # Initialize variables\n", + " sess.run(init)\n", + "\n", + " # Training cycle\n", + " for epoch in range(3):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples/batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop) and cost op (to get loss value)\n", + " _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n", + " y: batch_y})\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if epoch % display_step == 0:\n", + " print \"Epoch:\", '%04d' % (epoch+1), \"cost=\", \\\n", + " \"{:.9f}\".format(avg_cost)\n", + " print(\"First Optimization Finished!\")\n", + "\n", + " # Test model\n", + " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " # Calculate accuracy\n", + " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", + " print(\"Accuracy:\", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))\n", + "\n", + " # Save model weights to disk\n", + " save_path = saver.save(sess, model_path)\n", + " print(\"Model saved in file: %s\" % save_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting 2nd session...\n", + "Model restored from file: /tmp/model.ckpt\n", + "Epoch: 0001 cost= 18.292712951\n", + "Epoch: 0002 cost= 13.404136196\n", + "Epoch: 0003 cost= 9.855191723\n", + "Epoch: 0004 cost= 7.276933088\n", + "Epoch: 0005 cost= 5.564581285\n", + "Epoch: 0006 cost= 4.165259939\n", + "Epoch: 0007 cost= 3.139393926\n", + "Second Optimization Finished!\n", + "Accuracy: 0.9385\n" + ] + } + ], + "source": [ + "# Running a new session\n", + "print(\"Starting 2nd session...\")\n", + "with tf.Session() as sess:\n", + " # Initialize variables\n", + " sess.run(init)\n", + "\n", + " # Restore model weights from previously saved model\n", + " load_path = saver.restore(sess, model_path)\n", + " print(\"Model restored from file: %s\" % save_path)\n", + "\n", + " # Resume training\n", + " for epoch in range(7):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples / batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop) and cost op (to get loss value)\n", + " _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,\n", + " y: batch_y})\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if epoch % display_step == 0:\n", + " print(\"Epoch:\", '%04d' % (epoch + 1), \"cost=\", \\\n", + " \"{:.9f}\".format(avg_cost))\n", + " print(\"Second Optimization Finished!\")\n", + "\n", + " # Test model\n", + " correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " # Calculate accuracy\n", + " accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", + " print(\"Accuracy:\", accuracy.eval(\n", + " {x: mnist.test.images, y: mnist.test.labels}))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb b/tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb new file mode 100644 index 00000000..62aa8d76 --- /dev/null +++ b/tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tensorboard Advanced\n", + "\n", + "Advanced visualization using Tensorboard (weights, gradient, ...). This example is using the MNIST database of handwritten digits\n", + "(http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 25\n", + "batch_size = 100\n", + "display_step = 1\n", + "logs_path = '/tmp/tensorflow_logs/example/'\n", + "\n", + "# Network Parameters\n", + "n_hidden_1 = 256 # 1st layer number of features\n", + "n_hidden_2 = 256 # 2nd layer number of features\n", + "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "\n", + "# tf Graph Input\n", + "# mnist data image of shape 28*28=784\n", + "x = tf.placeholder(tf.float32, [None, 784], name='InputData')\n", + "# 0-9 digits recognition => 10 classes\n", + "y = tf.placeholder(tf.float32, [None, 10], name='LabelData')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create model\n", + "def multilayer_perceptron(x, weights, biases):\n", + " # Hidden layer with RELU activation\n", + " layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1'])\n", + " layer_1 = tf.nn.relu(layer_1)\n", + " # Create a summary to visualize the first layer ReLU activation\n", + " tf.summary.histogram(\"relu1\", layer_1)\n", + " # Hidden layer with RELU activation\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2'])\n", + " layer_2 = tf.nn.relu(layer_2)\n", + " # Create another summary to visualize the second layer ReLU activation\n", + " tf.summary.histogram(\"relu2\", layer_2)\n", + " # Output layer\n", + " out_layer = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3'])\n", + " return out_layer\n", + "\n", + "# Store layers weight & bias\n", + "weights = {\n", + " 'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='W1'),\n", + " 'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),\n", + " 'w3': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='W3')\n", + "}\n", + "biases = {\n", + " 'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='b1'),\n", + " 'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='b2'),\n", + " 'b3': tf.Variable(tf.random_normal([n_classes]), name='b3')\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Encapsulating all ops into scopes, making Tensorboard's Graph\n", + "# Visualization more convenient\n", + "with tf.name_scope('Model'):\n", + " # Build model\n", + " pred = multilayer_perceptron(x, weights, biases)\n", + "\n", + "with tf.name_scope('Loss'):\n", + " # Softmax Cross entropy (cost function)\n", + " loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))\n", + "\n", + "with tf.name_scope('SGD'):\n", + " # Gradient Descent\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + " # Op to calculate every variable gradient\n", + " grads = tf.gradients(loss, tf.trainable_variables())\n", + " grads = list(zip(grads, tf.trainable_variables()))\n", + " # Op to update all variables according to their gradient\n", + " apply_grads = optimizer.apply_gradients(grads_and_vars=grads)\n", + "\n", + "with tf.name_scope('Accuracy'):\n", + " # Accuracy\n", + " acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " acc = tf.reduce_mean(tf.cast(acc, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()\n", + "\n", + "# Create a summary to monitor cost tensor\n", + "tf.summary.scalar(\"loss\", loss)\n", + "# Create a summary to monitor accuracy tensor\n", + "tf.summary.scalar(\"accuracy\", acc)\n", + "# Create summaries to visualize weights\n", + "for var in tf.trainable_variables():\n", + " tf.summary.histogram(var.name, var)\n", + "# Summarize all gradients\n", + "for grad, var in grads:\n", + " tf.summary.histogram(var.name + '/gradient', grad)\n", + "# Merge all summaries into a single op\n", + "merged_summary_op = tf.summary.merge_all()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0001 cost= 59.570364205\n", + "Epoch: 0002 cost= 13.585465186\n", + "Epoch: 0003 cost= 8.379069252\n", + "Epoch: 0004 cost= 6.005265894\n", + "Epoch: 0005 cost= 4.498054792\n", + "Epoch: 0006 cost= 3.503682522\n", + "Epoch: 0007 cost= 2.822272765\n", + "Epoch: 0008 cost= 2.306899852\n", + "Epoch: 0009 cost= 1.912765543\n", + "Epoch: 0010 cost= 1.597006118\n", + "Epoch: 0011 cost= 1.330172869\n", + "Epoch: 0012 cost= 1.142490618\n", + "Epoch: 0013 cost= 0.939443911\n", + "Epoch: 0014 cost= 0.820920588\n", + "Epoch: 0015 cost= 0.702543302\n", + "Epoch: 0016 cost= 0.604815631\n", + "Epoch: 0017 cost= 0.505682561\n", + "Epoch: 0018 cost= 0.439700446\n", + "Epoch: 0019 cost= 0.378268929\n", + "Epoch: 0020 cost= 0.299557848\n", + "Epoch: 0021 cost= 0.269859066\n", + "Epoch: 0022 cost= 0.230899029\n", + "Epoch: 0023 cost= 0.183722090\n", + "Epoch: 0024 cost= 0.164173368\n", + "Epoch: 0025 cost= 0.142141250\n", + "Optimization Finished!\n", + "Accuracy: 0.9336\n", + "Run the command line:\n", + "--> tensorboard --logdir=/tmp/tensorflow_logs \n", + "Then open http://0.0.0.0:6006/ into your web browser\n" + ] + } + ], + "source": [ + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " # op to write logs to Tensorboard\n", + " summary_writer = tf.summary.FileWriter(logs_path,\n", + " graph=tf.get_default_graph())\n", + "\n", + " # Training cycle\n", + " for epoch in range(training_epochs):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples/batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop), cost op (to get loss value)\n", + " # and summary nodes\n", + " _, c, summary = sess.run([apply_grads, loss, merged_summary_op],\n", + " feed_dict={x: batch_xs, y: batch_ys})\n", + " # Write logs at every iteration\n", + " summary_writer.add_summary(summary, epoch * total_batch + i)\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if (epoch+1) % display_step == 0:\n", + " print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Test model\n", + " # Calculate accuracy\n", + " print(\"Accuracy:\", acc.eval({x: mnist.test.images, y: mnist.test.labels}))\n", + "\n", + " print(\"Run the command line:\\n\" \\\n", + " \"--> tensorboard --logdir=/tmp/tensorflow_logs \" \\\n", + " \"\\nThen open http://0.0.0.0:6006/ into your web browser\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loss and Accuracy Visualization\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computation Graph Visualization\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Weights and Gradients Visualization\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Activations Visualization\n", + "" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb b/tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb new file mode 100644 index 00000000..71a15649 --- /dev/null +++ b/tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb @@ -0,0 +1,217 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Tensorboard Basics\n", + "\n", + "Graph and Loss visualization using Tensorboard. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "\n", + "# Import MINST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "training_epochs = 25\n", + "batch_size = 100\n", + "display_epoch = 1\n", + "logs_path = '/tmp/tensorflow_logs/example/'\n", + "\n", + "# tf Graph Input\n", + "# mnist data image of shape 28*28=784\n", + "x = tf.placeholder(tf.float32, [None, 784], name='InputData')\n", + "# 0-9 digits recognition => 10 classes\n", + "y = tf.placeholder(tf.float32, [None, 10], name='LabelData')\n", + "\n", + "# Set model weights\n", + "W = tf.Variable(tf.zeros([784, 10]), name='Weights')\n", + "b = tf.Variable(tf.zeros([10]), name='Bias')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Construct model and encapsulating all ops into scopes, making\n", + "# Tensorboard's Graph visualization more convenient\n", + "with tf.name_scope('Model'):\n", + " # Model\n", + " pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax\n", + "with tf.name_scope('Loss'):\n", + " # Minimize error using cross entropy\n", + " cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1))\n", + "with tf.name_scope('SGD'):\n", + " # Gradient Descent\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n", + "with tf.name_scope('Accuracy'):\n", + " # Accuracy\n", + " acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n", + " acc = tf.reduce_mean(tf.cast(acc, tf.float32))\n", + "\n", + "# Initializing the variables\n", + "init = tf.global_variables_initializer()\n", + "\n", + "# Create a summary to monitor cost tensor\n", + "tf.summary.scalar(\"loss\", cost)\n", + "# Create a summary to monitor accuracy tensor\n", + "tf.summary.scalar(\"accuracy\", acc)\n", + "# Merge all summaries into a single op\n", + "merged_summary_op = tf.summary.merge_all()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0001 cost= 1.182138961\n", + "Epoch: 0002 cost= 0.664609327\n", + "Epoch: 0003 cost= 0.552565036\n", + "Epoch: 0004 cost= 0.498541865\n", + "Epoch: 0005 cost= 0.465393374\n", + "Epoch: 0006 cost= 0.442491178\n", + "Epoch: 0007 cost= 0.425474149\n", + "Epoch: 0008 cost= 0.412152022\n", + "Epoch: 0009 cost= 0.401320939\n", + "Epoch: 0010 cost= 0.392305281\n", + "Epoch: 0011 cost= 0.384732356\n", + "Epoch: 0012 cost= 0.378109478\n", + "Epoch: 0013 cost= 0.372409370\n", + "Epoch: 0014 cost= 0.367236996\n", + "Epoch: 0015 cost= 0.362727492\n", + "Epoch: 0016 cost= 0.358627345\n", + "Epoch: 0017 cost= 0.354815522\n", + "Epoch: 0018 cost= 0.351413656\n", + "Epoch: 0019 cost= 0.348314827\n", + "Epoch: 0020 cost= 0.345429416\n", + "Epoch: 0021 cost= 0.342749324\n", + "Epoch: 0022 cost= 0.340224642\n", + "Epoch: 0023 cost= 0.337897302\n", + "Epoch: 0024 cost= 0.335720168\n", + "Epoch: 0025 cost= 0.333691911\n", + "Optimization Finished!\n", + "Accuracy: 0.9143\n", + "Run the command line:\n", + "--> tensorboard --logdir=/tmp/tensorflow_logs \n", + "Then open http://0.0.0.0:6006/ into your web browser\n" + ] + } + ], + "source": [ + "# Start Training\n", + "with tf.Session() as sess:\n", + " sess.run(init)\n", + "\n", + " # op to write logs to Tensorboard\n", + " summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())\n", + "\n", + " # Training cycle\n", + " for epoch in range(training_epochs):\n", + " avg_cost = 0.\n", + " total_batch = int(mnist.train.num_examples / batch_size)\n", + " # Loop over all batches\n", + " for i in range(total_batch):\n", + " batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n", + " # Run optimization op (backprop), cost op (to get loss value)\n", + " # and summary nodes\n", + " _, c, summary = sess.run([optimizer, cost, merged_summary_op],\n", + " feed_dict={x: batch_xs, y: batch_ys})\n", + " # Write logs at every iteration\n", + " summary_writer.add_summary(summary, epoch * total_batch + i)\n", + " # Compute average loss\n", + " avg_cost += c / total_batch\n", + " # Display logs per epoch step\n", + " if (epoch+1) % display_epoch == 0:\n", + " print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(avg_cost))\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Test model\n", + " # Calculate accuracy\n", + " print(\"Accuracy:\", acc.eval({x: mnist.test.images, y: mnist.test.labels}))\n", + "\n", + " print(\"Run the command line:\\n\" \\\n", + " \"--> tensorboard --logdir=/tmp/tensorflow_logs \" \\\n", + " \"\\nThen open http://0.0.0.0:6006/ into your web browser\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loss and Accuracy Visualization\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graph Visualization\n", + "\n", + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb b/tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb new file mode 100644 index 00000000..9df1396d --- /dev/null +++ b/tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb @@ -0,0 +1,291 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Build an Image Dataset in TensorFlow.\n", + "\n", + "For this example, you need to make your own set of images (JPEG).\n", + "We will show 2 different ways to build that dataset:\n", + "\n", + "- From a root folder, that will have a sub-folder containing images for each class\n", + "\n", + "```\n", + " ROOT_FOLDER\n", + " |-------- SUBFOLDER (CLASS 0)\n", + " | |\n", + " | | ----- image1.jpg\n", + " | | ----- image2.jpg\n", + " | | ----- etc...\n", + " | \n", + " |-------- SUBFOLDER (CLASS 1)\n", + " | |\n", + " | | ----- image1.jpg\n", + " | | ----- image2.jpg\n", + " | | ----- etc...\n", + "\n", + "```\n", + "\n", + "- From a plain text file, that will list all images with their class ID:\n", + "\n", + "```\n", + " /path/to/image/1.jpg CLASS_ID\n", + " /path/to/image/2.jpg CLASS_ID\n", + " /path/to/image/3.jpg CLASS_ID\n", + " /path/to/image/4.jpg CLASS_ID\n", + " etc...\n", + "```\n", + "\n", + "Below, there are some parameters that you need to change (Marked 'CHANGE HERE'), \n", + "such as the dataset path.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "import tensorflow as tf\n", + "import os\n", + "\n", + "# Dataset Parameters - CHANGE HERE\n", + "MODE = 'folder' # or 'file', if you choose a plain text file (see above).\n", + "DATASET_PATH = '/path/to/dataset/' # the dataset file or root folder path.\n", + "\n", + "# Image Parameters\n", + "N_CLASSES = 2 # CHANGE HERE, total number of classes\n", + "IMG_HEIGHT = 64 # CHANGE HERE, the image height to be resized to\n", + "IMG_WIDTH = 64 # CHANGE HERE, the image width to be resized to\n", + "CHANNELS = 3 # The 3 color channels, change to 1 if grayscale" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Reading the dataset\n", + "# 2 modes: 'file' or 'folder'\n", + "def read_images(dataset_path, mode, batch_size):\n", + " imagepaths, labels = list(), list()\n", + " if mode == 'file':\n", + " # Read dataset file\n", + " with open(dataset_path) as f:\n", + " data = f.read().splitlines()\n", + " for d in data:\n", + " imagepaths.append(d.split(' ')[0])\n", + " labels.append(int(d.split(' ')[1]))\n", + " elif mode == 'folder':\n", + " # An ID will be affected to each sub-folders by alphabetical order\n", + " label = 0\n", + " # List the directory\n", + " try: # Python 2\n", + " classes = sorted(os.walk(dataset_path).next()[1])\n", + " except Exception: # Python 3\n", + " classes = sorted(os.walk(dataset_path).__next__()[1])\n", + " # List each sub-directory (the classes)\n", + " for c in classes:\n", + " c_dir = os.path.join(dataset_path, c)\n", + " try: # Python 2\n", + " walk = os.walk(c_dir).next()\n", + " except Exception: # Python 3\n", + " walk = os.walk(c_dir).__next__()\n", + " # Add each image to the training set\n", + " for sample in walk[2]:\n", + " # Only keeps jpeg images\n", + " if sample.endswith('.jpg') or sample.endswith('.jpeg'):\n", + " imagepaths.append(os.path.join(c_dir, sample))\n", + " labels.append(label)\n", + " label += 1\n", + " else:\n", + " raise Exception(\"Unknown mode.\")\n", + "\n", + " # Convert to Tensor\n", + " imagepaths = tf.convert_to_tensor(imagepaths, dtype=tf.string)\n", + " labels = tf.convert_to_tensor(labels, dtype=tf.int32)\n", + " # Build a TF Queue, shuffle data\n", + " image, label = tf.train.slice_input_producer([imagepaths, labels],\n", + " shuffle=True)\n", + "\n", + " # Read images from disk\n", + " image = tf.read_file(image)\n", + " image = tf.image.decode_jpeg(image, channels=CHANNELS)\n", + "\n", + " # Resize images to a common size\n", + " image = tf.image.resize_images(image, [IMG_HEIGHT, IMG_WIDTH])\n", + "\n", + " # Normalize\n", + " image = image * 1.0/127.5 - 1.0\n", + "\n", + " # Create batches\n", + " X, Y = tf.train.batch([image, label], batch_size=batch_size,\n", + " capacity=batch_size * 8,\n", + " num_threads=4)\n", + "\n", + " return X, Y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# -----------------------------------------------\n", + "# THIS IS A CLASSIC CNN (see examples, section 3)\n", + "# -----------------------------------------------\n", + "# Note that a few elements have changed (usage of queues).\n", + "\n", + "# Parameters\n", + "learning_rate = 0.001\n", + "num_steps = 10000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "dropout = 0.75 # Dropout, probability to keep units\n", + "\n", + "# Build the data input\n", + "X, Y = read_images(DATASET_PATH, MODE, batch_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create model\n", + "def conv_net(x, n_classes, dropout, reuse, is_training):\n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " fc1 = tf.contrib.layers.flatten(conv2)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " fc1 = tf.layers.dense(fc1, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)\n", + "\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(fc1, n_classes)\n", + " # Because 'softmax_cross_entropy_with_logits' already apply softmax,\n", + " # we only apply softmax to testing network\n", + " out = tf.nn.softmax(out) if not is_training else out\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Because Dropout have different behavior at training and prediction time, we\n", + "# need to create 2 distinct computation graphs that share the same weights.\n", + "\n", + "# Create a graph for training\n", + "logits_train = conv_net(X, N_CLASSES, dropout, reuse=False, is_training=True)\n", + "# Create another graph for testing that reuse the same weights\n", + "logits_test = conv_net(X, N_CLASSES, dropout, reuse=True, is_training=False)\n", + "\n", + "# Define loss and optimizer (with train logits, for dropout to take effect)\n", + "loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.cast(Y, tf.int64))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()\n", + "\n", + "# Saver object\n", + "saver = tf.train.Saver()\n", + "\n", + "# Start training\n", + "with tf.Session() as sess:\n", + "\n", + " # Run the initializer\n", + " sess.run(init)\n", + "\n", + " # Start the data queue\n", + " tf.train.start_queue_runners()\n", + "\n", + " # Training cycle\n", + " for step in range(1, num_steps+1):\n", + "\n", + " if step % display_step == 0:\n", + " # Run optimization and calculate batch loss and accuracy\n", + " _, loss, acc = sess.run([train_op, loss_op, accuracy])\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + " else:\n", + " # Only run the optimization op (backprop)\n", + " sess.run(train_op)\n", + "\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Save your model\n", + " saver.save(sess, 'my_tf_model')" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb b/tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb new file mode 100644 index 00000000..d55f63c2 --- /dev/null +++ b/tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image Transformation (i.e. Image Augmentation)\n", + "\n", + "Learn how to apply various image augmentation techniques with TensorFlow. The transformations are meant to be applied for each image sample when training only, and each transformation will be performed with random parameters.\n", + "\n", + "**Transformations:**\n", + "- Random flip left-right\n", + "- Random contrast, brightness, saturation and hue\n", + "- Random distortion and crop\n", + "\n", + "For more information about loading data, see: [load_data.ipynb](load_data.ipynb)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "from IPython.display import Image as IImage, display\n", + "import numpy as np\n", + "import PIL\n", + "from PIL import Image\n", + "import random\n", + "import requests\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download an image.\n", + "d = requests.get(\"https://www.paristoolkit.com/Images/xeffel_view.jpg.pagespeed.ic.8XcZNqpzSj.jpg\")\n", + "with open(\"image.jpeg\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load image to numpy array.\n", + "img = PIL.Image.open('image.jpeg')\n", + "img.load()\n", + "img_array = np.array(img)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image.\n", + "PIL.Image.fromarray(img_array)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create TensorFlow session.\n", + "session = tf.Session()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly flip an image.\n", + "def random_flip_left_right(image):\n", + " return tf.image.random_flip_left_right(image)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display randomly flipped image.\n", + "PIL.Image.fromarray(random_flip_left_right(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image contrast.\n", + "def random_contrast(image, minval=0.6, maxval=1.4):\n", + " r = tf.random.uniform([], minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_contrast(image, contrast_factor=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different contrast.\n", + "PIL.Image.fromarray(random_contrast(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image brightness\n", + "def random_brightness(image, minval=0., maxval=.2):\n", + " r = tf.random.uniform([], minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_brightness(image, delta=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different brightness.\n", + "PIL.Image.fromarray(random_brightness(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image saturation\n", + "def random_saturation(image, minval=0.4, maxval=2.):\n", + " r = tf.random.uniform((), minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_saturation(image, saturation_factor=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different staturation.\n", + "PIL.Image.fromarray(random_saturation(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomly change an image hue.\n", + "def random_hue(image, minval=-0.04, maxval=0.08):\n", + " r = tf.random.uniform((), minval=minval, maxval=maxval)\n", + " image = tf.image.adjust_hue(image, delta=r)\n", + " return tf.cast(image, tf.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAHQCAIAAAC+yUweAAEAAElEQVR4nOz917MlSX7fCboKHUfrc7XKvKkzK0t1idYNjQbAAXZIYnbJMZK2Zvswa/PCf2Bfdx92zYZGs9kZzg5tOARAcEDIbjRaVVVXV1dlpRY382pxtA6t3H0fblWh0Mgg+oA3OzOr4mNlaWmnIiM83D1c/r7+hZnzMKvnYUh8mwpQtCwLQJ4v5iI9cGw39EMvdLACzry0VJrLNIeHaJzRJVXDyq1rt1wzzGVKAIqNVlfFKgQo8iLqcQghRzxgXsSiirTgC5OVK7Nf++brtx/eePHqi3/5p9/d3TxAHSkAPizBgeGc1r94sfI7Q9tffEms1u73DhsrC6eWlr7Qapbe+o4BvZQsSFEkMUYZ4CFgQRgFfgi4qIo6RTaYBgzIVNdHPHyi94+Dguixv3POn+hzpyUuPSdFXD48rfeNS08c06YzZMFU1yOEprr+eQdC+ETvH1deDNIn+twn/V7T8qTr1RMvx5jb0xNqruLuP+17EepzG9qO7bEIqKrksiCwOGOMOxxDkk0XIxp2hq2ND3cpnZ9fWXnv2gOBsExWKcynzp8/r8j63bubDSMAkcwYBSKnIOCQKZosS5IXMt+duNyMkFeo5LLdzPVrN8Yjc7a2UFiptR4ZGKgoHHQOaZDDK7WLUYdf+sXyu+RP993u+J1xOJoLnRALQuTL48CIKEMIiaKIsCSKAmWQoSf7VSQkJCQkJDwhyGJ9pdE7yM9oM4vVXq/HW6GWVkgou9R3PBdRQdf1lJQdtce795oiUFbmT/WHhxcur5dmMgAABWZfvfq6xPP3bt4TCEEIcScUVencpVP1xcrEHr7/rTvAjQ7aW/ce3ppMRg/uPYpskCpkmtFeoXamCM7hqN1H/UAwBAUhR/zwP/VY4ZSaVptHkj+IIqYymTe7hym9EPIQUAAiKGACAaPM9yJHl8SnnYcJCQkJCQlTQ5ZmV0Udz58vvfyly5tbjz744e3BriNLkqxKvhOCCFm2iQWMBIAYDrwop6p+y2ecpvT0o7vbP/nO96ORht2cg11RFDlk1tiXoZ8vZU+fW53Yo3AIj7p7faNz/daHC/Pz9YWKOXRGk079QumlxZWcXy8dwMLi5drsQuP+/t79CbZqgbnGtIzPqUci4ukSU/L5UGCqx90wCnhEAWeMg4iHDHoAJB1wQkJCQsLzB3nr++9k6nJxSVc15coLl0mkvGNdiybI5nZtoS4ReXdnj4EoW1YW12fmV2Z2bww8J7hz897sbO0f/cN/DMw/+sP/5XvlnAQACMMwk85m5rJI5N3m8MN37wzG3UnTEGVhZWFpZPfG4z4icHaxNFefxxI9e6k0aXeNxj1ottJ0nD2DXr+cVpTMve8PJ/tE9DKqqnrAFQGQoALlQBJ9J/JZFBAOBCgKkhRiDvynnYUJCQkJCQnTQyzLdvbt05dWpChlTCYvX32lnJ79kz/884PtEUKomC9rGWV2sYYUKqRAe3QUuELosebm+O0fvHv16tXf+ee/GujuW+/8WDFkx/KQyGfqM5xzo2uPOgYDrLF3NH+qMjezmPU0IuFOt+1YdjabdnZsAt9TdKk32fzwWmt+e+bM5bXLly/fP/iL/On1aFL0ujBVcMQXmmqWdR8yTcxGYsTE0BhzoxOFjoYB4Zywp52DCQkJCQkJfw9IvpAd9MfBhLc3jZv3rv/Kr//SL/7SLyAM//X/+9+2DwZG38nmMqV8yQGTsdM96h4U1NMSlokkHj7o/t7/7//4rX/067/8X321Ex3KVrbfHk161l5zO7Bo6EeiKMqyfO7lpRdfvprNZ4iIl5YXOeePHm7ev/ugZ453rz08d2m9tlrlEEXQvXvv5rgz8V1BuqAsv1DdowbWB9mLnfKcNruu9bd62TlNyCrDvrj5jjHcA4GrEShMFzOakJCQkJDwbEDGxlhRxY0b26397s7uvjEyv/FrXzl3cf3c2qVb1u3QC82R8+jhdtc8qK7kT58+vfV+3/atIip4o+hHf3r9YLOdX8oMRqYoWXJB05jY3h0yhlOpLI/gZGBdKJVee/PlRqPx/k8+2N3affHKS+sL53p7oxG2Bm1zQq16sVTBpdG4Y/WcQma2Zd9pHm3IV7Tlf6Ixa9y4+xCamdrMXCB3omxOns1rml4+lJ0eJ1RgTAki92nnYUJCQkJCwtTAVFVlIWMMcM45p0RGpVqhOlMeTezmUQtwSCkNowAKXEmJa+urFughiNNaZn/7qN8yCUGyoEQhLZRymUxmNBqNRiNZVF3Ltw23kCtCwf3mP/g1QUU/+skPZxdnLl++vLu5t79/UFhApdlcd9yVRKWozzy6t7vfOEpntNpsrZTPLc0vYY6svsk9dnhw8Fu/+RuHYDjsjZ0+nxNfCLvr+w/E3b2QIl0+ITkoY8/3YvaT1uNOq2d90ul53plWTxwHYwwSHtEQA6xiBTLksSgAkYweH5x4Us990jwtXeyTfu6zptt+1vTHz1p64vS+JwVxXVfEoihKGGPGooB5/V5/Yo45IpRSjAgAACEEEWeMdTqd2ZXy1tYOLAivv/KmMbEf3n/U2G8pimoMnE6jTwghRArcSJGVtJbxvQBh8P0ffI+TaGyOiESic7Rare1uHexvHeULueXFFcNw9vb2DHOCENjf6ubSYuXU0muvvjQeTH7UepcQobow88OfvNtyOhktL6B8s9c2DgTbmiNihj4fjUlCwpNCU0jIXYh8DDBBIgSYUMQYAMn4JyHhmYdgjDjiURREEcIYSqLMcURDZnmmrumcAQAAQggTKIpkMpmEGyENoA2DQWcCKLp49opC9MP9I9N0QoeqWaJntJAHgiCIouD49tLanOM6lmtQDu7fPBgP/2huZqHXG4XMGbWs+txsJKJu675teaVKEauwvdveVw6HK6PeYHj9/ZsAwjOnzz3a2KouFNNiNpeZR2J5fAAZ4AhD3/ZlIj3tPExIeGpwFgpikE9jAqFjeNSngDMII8ATeV5CwrMOgQIgECOOIcSKIsma7FPXcSxMkCRJruNBCDEmmEBN0yyXAZ9wj+03WoP2JAqj02vrnIJ8Pm8ZDtQg5zz0IkiQ67qWZ4iyoGQFLgrtvimLcmUxyyPQbvR4CI0GfajsqGlNySgIEGNkS2l5ZWVp/MB7dLvzLfYeABGOJNe3J+1xPVsbNEyv32P1SkEWVa3gmjIWiKpI4DlZUktIeBIIBPjhEIAIQhFAVZDLmCMvskPvaacsISHh74LMz896rm+OHd/xIISCIGBEECKCwCilURSJokgEzCEFABBCuvt9WZYBA4CDMAp3D3bCMJQVKQQh5xxxgjESRIHxgHJGiR9iHwhckIVqcWZlYa3T7B/sHxoTkwaAe2h38zBCoW04oiCoonZ6dV1fWbr9wb3Do5Ekgd/87d9JZ8QbP/4wJaU6P2nZVO/YMMphxwK2E9AgAgyBZ2tLJSHh58pkbPRHj0zbQESG4UwmlY4oNixDldWnnbSEhIS/A5LL5wb9QRgGjum7jk9pqKZURVAt27FDm0ZMFEWEEEfc87yIR6lUCiAecZzK6JXZYrPVADKfW13UM8r+7qFvh5DygdlFIkgXFAqDSThiIcyVChALGxvbo55hG44xdhbqmeWFFRdbDx4+4hQUy0URqcbQ1mdgvla8ff3ecNz5yje/+MKrX/j+996/d/PhrPZNjHIEV62hYpgsDKDAofC0sy8h4elCiF6r1668tAIR3trAoS9jKKZTIJrOQyQhIeEpQHqDrmO5AHI1JUGIGQO2YQMMOOJhEApECIIAEyTJQhC5HHFFl9vNnqiDq1+4/OoXXvpf/93/0u61K/O5r/za6/ubR/tbzb1Hh+NHQ98BQcEtVfODSc8dAAEKzBpEFqzk6+lyTuB9xnzXdWurtZBGjf2Wa/rbD/Y2H279wjekgjhTkWcYEjeu975wOZdXXt3p35tYlXxuHqC8OfH8kAqCooqqrmkda/C08zAh4alBKZ6ZrVx9aTmK+OHBwXDoM4oVORmaJiQ8BxCIAAccIYQIopQHQQAhJxIRJIFFAcbY930i4JSosSgEmIeer2aJ70XXb3/w0muXX37tKkesVq92jaOlc7MzMzML8/PzSzM3797wQieTS0fMTWuiyPTW5pABLmApcENKuePaDx8+rK9VXrz6IqK3mkddBFgUhbs/2SienZlPn87AtUfvme/VOqx3fja9Nhkhn6Q9oFBfRJgGfuB6jiAmC9AJn2sgUsIwDALHC6IgCEKKCMA0er41dQkJnxOgVlD5xwAA4MdIknC8B8w5RwhJkiRJkiAI3UmbcwoAQBgBxFVV/cJrr37zm9/813/+/6yUq1cuvfDO2z9KqekXrlx99HDz9u3bnWa/Vq4V8qV7tzbOrJ0bdscP72+m9DSpRopAXrh8TtEQSREK6OFRb2vzKFtMzWsv1sSvdPeK/S5Kp4uioMiCEAZPtq9FHD/295Pyf53W1/YEdaJP9LnT3mdaP+Mn7b97UkxbvtPqpOP8aDWR+KD7W7+VLlfS/+bfdEZ9NZ/Bpo0RfjpOnSdVjk9LD/q8++BOy2dVb/2sEacnjm2twjBkjFFKj1sKSimlFCGUUlOU0iAIGGA0DCcD687NexgIaS3/o+++P+5Yi3NLpmF9/ztvs5BZQzelZCSiZvRcPps3DMOyrXRO55QBw8wuzs2vno1C0Gr3cqn0q+vni3ijGzZEjDzLBqyU1jOqlIIQsoiBJNoqISGeT4bOTzshCQkJPyuxHTAhhDEGITz+k3N+PBtmDDAKGAUIYQRxRN2jg9ZkbGpzInPxrR9vyDRdLJbfufbBpOcIMhAEMbJBSs5iRBqNxng0yeWzhmGs5EXHm3THo+XKGxFzlLGyNlcUqnItn8dmvXsbYEAyWo4DmXMeRX7S/yYkxPHprvf4a3266UlISPhZIJ98q58eO3POVVU9ngFHUcQYO+6AoyiKQg4A4AAiQRAEjDXieZ4xtAIov/nmV+/cufPeD26sra0xBzEbIYEQIvEAuWaQ1rJhNiIiunDhAuV059E1MzA2du/X068Cp/D+e5uNbbO+UvzCN5aCZu7t+ybFqkgU22EYYwQFBpJtrYSEWCCECKFkBpyQ8BxBPtn6/eSn41+OO10AAELoeIf4+BeCRAghQIADRiknmAiCEEWRBFTfiNYW17fp9ub9nXQ6rS+kh8Nh4FAW2jvebq6YlWQJkXShVKjXq/n5bN/s7h807l57txC9Zo3gpkvVwukcyrZdDrkEoBpS5AWBIkGRCB5NjH8TEn4azjmAf73+nHTACQnPEYRzfvzRHv953Olyzk3TPP6efzoqAQHGGQSAMRZFARYxIUQQiGcFH37w4alTp+Zn523b9n2feRQAIIiEEGIYRsgCSRb7/UGn3a3P1L72O7+6JK9L4f3+PWtxufDqq+c7I+A72ds/7A7bcuDkQqpHjALMAWQSkZIOOCEhjqT3TUh4HondA/Z9HyEkCMInffDx5x2EPqWhKIqQQAA5xghhgDAEkEdRdH/jfqFUAJBHNPBDL51Jh14kqQLAGiFElmRVVVzLH/WMb//hH7/x0jcu137hYZvTYBaCgiyEgQEfXlMtE0Y0ZfmQAV/AAAAG+dMJ6UxIePb5ZACd9MEJCc8X/7kgLIQQIQRjfByKBQCglELIKaAccUkUKYMIQwZZyEJN0xRF6Q/7pmVEPChXyxCBg6P9XLogyRLjzHGcKIokQQYMCliEpn1wpyVUX5NpvddSIIxkEeuEh3bZGnuylosi0wcTSRF5xECMRighIQH8LVFHEoSVkPBcQMDHUtFPbwZDCDU1TSkNw9BzvWMpMCEEYxGASGCAhYBhLkgSxkiW5Wqx1ux2R3YfqiElSBU0HkIkg9nF+sGNZuai9LVfeuODWzfMvo+t1Ep2tahXD1ozrle43xFzeRXKiu8zynDfcDSZSxrgYJRRAABpEAEOwCRiZMpzJ6fW3cLHX0/4yRwqRMB094Hg8VOZaXWWwpS+sNMers1jguOmvQ9jj1/kmDbfpi33aYk74oKgKcs3Zqoal/64+mDbSrYUOoHLcEFNTYYD6PiFKJoI4tMZszLw+HKMS38cT8s39/Omi4177rTfy0mdW/Cs+SWfGDH5/NHa8vE683EU5XGRRFF0LAL+5BfGWBRFn1x5/MtxJ+37vqzKmGAIEOLEtfx+b5Av5v7rf/jbq0srZt/NpHP/9L/9J2987VUj7I5pxxftarVWrc3mMjkIEGfg+EGylHioJSRMged5EENCCKX0eKWKYJTJZJ52uhISEv5uPgrC+kTA8Em0cxAEx1ccd8Cf/C5i4ZNfKKWMffRPqBBxwEQiFXMlEKBGp7F/sHf/Yf7LX/z6t9/5ix+8/cMXwQvnr65X63PjPff3//c/+8WLv57OFl0X2Q6C6Fi8GEEIEyfxhISfHV1XXNu6cWPrsJFzbImQHGMRjjk2KyEh4ZmCgL+pIPzkWMrjdelPFig+iY6m9KNvmzEG4EcTa875xJnQCBAZjI0R9xFEIAj92w9uHsJe122gDq21sxzxzVtt4ldqufX+0MrkYEgRYwBjBCGPoggjBtDTOUowIeF5RFbkoTHsXH8/l08L5EVZJp4d2r4lK9LTTlpCQsLfAflkhfmTDvijXzgAfzOa4/j34wMpAQCMcYg+6rk551iCqaJSq1dZAPa3jkRVPH12uTSXHRnmL7745tLawsL84rUfP9y83ZuRl15a/8bOFnc9RiMSUUgEBADlgPFk+puQMA2+53LOKKWu66p5FHkRhkRVZJZ8SgkJzzzk0xNf8DdCsf560/jTPTRl7OMlaMYZJwQf98f5fL5Qz5w+txJFzA4M07A4YpIiKZAiGZu2sbe7F1j0yy/90nzmF0eHIJW2EVIoAwCgMKKURbKIIeQ8OfAqIeFnRiCARAhGPAhcAChnIUCyKguWGzztpCUkJPwd/HUH/MnaMgAAQkgpQwhh/FH/+sm69LEw6fhkyuNB9vEMOAiCiAbN4YEXOlS2uR/0xh21J3aa3pY/FiVWLlX8iQSHiDiDlLKSSikMCJzz49AtzjlCmJ9QKF1CwucEIhLuRoz6IWDHHymCIIyS3jch4Tngo5hv/imOfzneAz5WHwmCgDE+vuy4A/6kVwYfz4x9L5BUyQ6M7qQppGCxmlUUCWM82qbY0N0hNweeLCiuZz3cuW+HNkRCFFLOuShIH90NcM6SDjghYQp814EQZjOZbDarqwrnnLEIJOtICQnPAySfzg9H/ZD6HABBAXpad103cKkspzjnYRh6nnd86XH3nMvkgiAIwlCSJCxiP3BFVVhdXD4INrcfbS8tLp+dfbHRPXCpI6Zwd9jOpbPeEPIgfbb2m3XxvHMqcGCATAeKKYxhJABKXQoQF4jt+1CKMiD92ITG+drGvtiU+tHY62PkeU/6rIM43eS0v3P4+HyD/OnoDmP9cdHJ5CcCJ6Nki9UTx8gU0Qn54E6rn9aJGmGFiYKmIgLMel42LHVohwJ+fLnH65ineuzU94kr9zif42cNGvO9xL3XtPkZlw9x9/dj5ipSTBDrtOcETAuPqbdx9USIM8h9zplWz02+8s/PTvrW+2/dxJ5q9b3BrrWyfKpttJEAj3vcT9ucAQBGo9GxGQMACCGkKEqpWJyZmTmzvPL9t76/e7DjBDbmxBgERrOjqDIIogiDQAn33Hc96mr5FCTw9PxXP7wZClQEjPAIUEYRx5qgQSCAGCF/QkLC34ZS6jp+ELiESBFhURDZbui6gaAnaoKEhGcdkqkpl69eJFD41h+8hZBQnS+1RwdAApORByDABBNCjgX+x3PQbDrLOWeAEXJsEhwahtFsNmfOXXnp5RcebN892GpFYywwBYjUCK1gMp5ZLc/NZzqD6wdHGwtkOZUpqz09EM4GHha5CAFk0I8gFZEsAMKTDjgh4WcHYc+P/CBK6RIFCEGAEMZESgayCQnPPgRE+Dt/9R0MhfOvLx9tt3VRKKI5GrBe0/A8z/f9KIqOp8KCIIiiyBgLwxAggAnBCEMGx5OJ47o3tq7XT+frq8V8tnB0d9Te79fmi1e+eHp1tZrNFoQM2ri39/af384sgje+fOZS7tK/+R99xw9BFEEEKHFFhXKfuZ4vC8rTzpOEhOcGCiLKGQAQIcQBDVkAENBUHbDJ005aQkLC3wHhOMrX9N2D3cJqnuiV8b6fgplrH9yS04rv+5TS46grxtjxErTjOJRSLGBMAEccIYQBDsNQzahrp1Zf+4WrMCLvgGu2bzLVHoSN87m6G9jcUX7rl3/76uxXG5O9TI50/Qc59QoQwyiKIICAQURFjkhIoym3bhMSPteEzFZkCMVUJqWIIrM80wsUCFHcXnVCQsKzA/n2H/zw8ssXQIDHk9FXf/HrOsm9/8NbubbodWAURSxkx4HQAIAoihhjCCJJkgRJ4CByXVcQsazJGONx1Ldte2/rwDJtl5mnrywKKeBGk0azJ4Tq/Xvv7tzoC2627ewaUXUuc0oXYKR6LvQxk7EnCZFGBBkJ9tPOkISE5wlRCrKCnMrki2URQpFF3HN8Gnqx0YMJCQnPDOSLr335nXffGRtDWZNuyLdfff2VF76xdvEri9//H3YODg4ajUYYhMfnvAuCIEmS7/oAAIQQZYAxhpCgyDKEkAX87p17Gw8fiIIkCpLneUTA1ZmyerpUzFf2j1o3710fHwYkBzIzWrZS2oTXeN7NSXlq1mAng0IBc5ECOxm4JyT87EDsSDiEwKOhL6m2olJdB77NApo4eCYkPOsQMDNaebHywXeH7ohuX2+2d/+iulhoDQ69Xmk8HodBKIiCoijHVki+70uSBP6mgZLreY7jVC6nX736WuTzzY1N27Np4Bldi5vyt4dv1Wo1rYYry6XW0cPhUd//nmM9EFA2J+ZQfvksGquHtkjHmhUwm06yODFySUj4WXHdURT2DOvIcWkmTyAoYJwjmCYdcELCsw/53r+9BSDX5RQVIymDFlbnMELcEQ4mDzBSCSSUUupTSEVFEajiBWHg2q4IRUmSwpCBIMppqVIpG5gHeTFVn52LHNMmfVHLsgA2drrhyBiN3XR5br6+HF2EzcPe0O5///CPLsKVDFdbGwY7esMwKj5xMA1LEvR4jC8vPpnN4ZPS78bp2yCK8XNl0+nkaOwSYowOGE235Bina4zLn2l1jXHE+cXGvW5c/sSB+OM7nrhyifU9ZY+vb9Pmw7Q+uHF4PoCAMBhxHMpYyksaEh2fdAC7T8OxPYFpUi2rBccPsGLbtm26s7oiR1EEOFRkIaRREDqYIOGENoeFmP49TgfJ+OPLPa4enlR9i2NafW0ccfpdyuP07o+v0CTGBzdOF65Mqd/F8PHmHPHtz3TfS5wfNsQx7WFMPZn2e3/S9eRJ+zQTo+mHLFA0Sc1KlmkfHh5ceeniP//GP/0D/p13vn0NQKynFMf2ccSZD4AQuWYkSAIhBCAgqSJlkelOGNJSvPhX3/n+6tklUSMyS0tAqK6UipViYHDMhZnqvCgog96tyWQgIAn6UhEu27brQ0nSgRIIfCJBDrGCgPdE3zch4blEkSGj0A15SKkAKIeRJIVYYghgxGUB6ZRbrjdRlAwDvqpi23MQYQICgEGEIIgiBBCBic4+IeEZgmSkomGNgQ8JkwLPPdw7qtWrr7yiXL7wwoNru74dQTECfiSJqh/6siAQAkRRZJzJijS3OMMhO2odjKyu10iFksVTrFgqZNR8s9HpTvozZwq7DzeRq+5ttDEWBqNOOp/OSrUMLGfYLAihj/NEkgSRF0TZCwSfBkkDkZDwt5FlSgPBjzBnMEIBxFySbU23J5Mx446sUMe2ev3dTAG7vuNHRJAgEVUMJUYFDJhAMQZYwGLA3Kf9KgkJCR9BlByRswVBJHpad1x7Z8f8wX/6YP9up6IvprW0xS0P+YVqFgWyN3B9P9LkNEDApy6AXJCInlUoqZA+DEZE07KhR5rNgbCgMxHsNY86bjuD1GK1eLTVdsZuqVIoFkooFFfna7QrscmqY856jkCCKKMAzknHNlNSokNKSPhpEAohEiWCGeeiGGDiIDRGoBtGIx55xaKiqIrncYhNDqmsqhy0ZUUXhRRgWhQwwEUQKQigxKUhIeHZgYhFGgXMMR2358KQCH5qMjC3e91duaeIuumMSZZSAu2JqyiSD1zOOQRQkqTJZPLBex9mK1p9rjYzM9MAHccIUmqmb7VumTdKC5lcJt896r/w0ounLizPLDUaB63I4oHrBGxoKerMmcvyoxLdnTEdjuTQl3wfoiCMQNIBJyT8LRw/kIGiSQQjionBkBUEXY+28xktCMFMvaZq4mRiWjYDOCCCPBn0JdEjcgQ4d+xIYjr1RUhjt+ATEhJ+/pCF1dmdzd1Wsx8aIJ3S02pOorpnhY43QSxAIpxdrrmeM+mMdFmiLqWYIgBSWkqgZGQMRKTUSjOVahlLdNiwEAfMj7JVdaZWzqqFtfK6Z4WNRosovFwptOyBKknZlBRyI1WVWGcioLEKC5STgWeHEVblkzkZPCHhM4YXQEWAkhAhwWCoTelgYvSCqAuRF/i2rqqnTi2mdOT5Zi5D9LQuBhMkmkBAlEM/CKMQIiqFUSIOTkh4hiBWO4SeLAqYIooIyJb1iWg0e31ZkgkiuVJhbqkuySIJdzu7IxYBSRcopbZtI4J0JR269OHt7b2HR/l5AEUvCCxZ5evzpxQkj1rjakl2mffgdtOxLYEQZ2xXiiXFkyZNIwc6igRSdVEYKoNJ2gw4hkyXCGNx9kM/34xJSHiW4FyFgBBoQTR02aHvDwJ34niTfrvp+SYNorSawRIJg6hYzuuZDOuPfe5DybV9BGDAAI6AGnEKwOOjYRMSEn7+oLf++MPBgZUS8jQCpm0pBbL6wuziizkBC6qqer63t78jqeLC4rwoCcvLixGLGGCe57GIFbJFTUq5pu9ZwbhnaBnhN373G7/xO79s9u33/+rm1v2t3aNHL3/56tnTFwQvNWl4gR04hj06MlhXuPvulhk2lq/6etn3QhaGEkACifHOS0j4vAMVxjHkAaeTKBr44cBxTTfwZ2cW65VFQVAphY7pmRPLtX1rMskpooY5ZAGAHmUO5S6AQcT9p/0aCQkJfw2ZXa7LqnTYOlg4XTvYaz3ae/jVr3/5qytf/v3eX3bsrhRJ7RsjhHcvXD1bD0udRk8tY8Bh2PfSed0L3VRaF3V81Dgq56NvvPrNN196w/HDftt4sLWDXfnoUdB7cOvS0i/3W5yZ98sZ7DdChnIt09XDdGej0LxzK1ffKi+f9e6fB37RSzepW6HIBtgVMZaARoDgU99nroC0x74Aho+fGof08b9D+Hjh2PT64Jjr+eP3sOPsd3GMnhLx6QRudEofWTFG/hqrC5zSjzmOuHyO09sROJ2/aZz/cZxPcMDDx/4eJ9+NW6CJ1y+ejM61kOqKounwgyDYIcSf+FbfHC8vrr34wgpC6M6dO+99+L4gCGEYjg2zWCwywI2x+eDudq/ricJqdeaNUvWUHSBNmG4VelpdphCjR4/1A47ZlI75fGPzLS6dcc8N4vxxY3xq2ZTfV5x+F8KYDz7mfafdso/Ln1h9NozR6cbU27jy4jHlzmLOP4h73zh9+dMCxeizT4qPrAaLhUK5XPZCxw2czc3N8Xisz8Fzp6+GLr19/yZX/UxFOU2WhpO+se+mtHQ2kxWI6DhjblEOmZqWVqsXf/xXd+9e33/jy2+eP3N1b7u7s32gqfKP/3L7TuX3kKKUlDm741LO8qmZ/YPDcDAoVE/1OkZzrzM7s5TWdMqU0MMERxGngEF2bNIOAcZYwEISPJLweYYz2/fGouS5kTkeHdrOBEMa+JON+w/r9XoxX+IUGIbR6/R3tnYRQuXcrCTKMBAW6jPl8iVZWQRICwWRAetpv0pCQsJHkMP9xux8fWlleWl1YX5x7kc/fnsyNlKp1JU3F77x5V8yBw76i/Cotb93uFfJ1hZmlx7s7wROxCgP3DGAnIjYizw1LW/uHIlI7jYHfHy3OlPjvgwJHvq9Ej4TQKuQVbJhTfbKPuCTrq1TNUPqFf1SipS0slmu53c3Bv4wTCtqIISQQ8ohYJBSxmGEMMcw5ryZhOeNJ32yzGeVdrvRHzzCpMnAEQTjbF6XJMk0x82DPdOwlpdXlhZXAAAry9ZgMFBVtXHQSumZYr6aK9Yz2TnbFno9I/AkIif5n5DwrEAkWbAsa293HxF46eoFXdc7vU6tVmv3Wzdv3ajl586fuyBJ0sHO4d32lj3wc2rJtpzAD3zf1zIyhyygvibJIMV1MaW6JSEoGQ2u8tzc0pyQpeZDv6Liq1fP6NH5rXdq7UOZGf1T87Jgz2lCRUnny2t7Sj2w6La1P1PEetNCAsAhFTjFUQgwjBhgBMccZBfPtA193PXTLpkmJDwJRCkrClk36AcRTGf0TLaKkOD7LKulacSiMGICwxgrsqKpWrVaXVivgwDREEIi+5GDkKlECgWi5yf1NiHhWYGUiuXhaNBsGv1xDyBuTEzXiHrdfqpMbty8wddxLlc4dWoVY9xv3OSAt1qtMIhkWYYQiqKIMc5kMrVazYRW+6gpU/Pcgq5pmiYUqZQ6au/2xB1j23751bO1eYhVp5Y9u1R5YRzt+nDeiHRmeebDm8T8cPX8ev2V6tGHm407FQ5UAaVCJkAMKAsFCDFEMTt1Cc8Z0w50Eo4pFldSGd205VbLC6PhcMQQoiktGwSNMAw93/N8jzGWSqUABBGNJsORN/ERE9yIjoxIVk9LUAr9EIDc036VhISEjyDmxGIMVEoVP3In48n66TOj6gAg8PDmVn22oGe0kHkDsyulUWUpCwLSvjOxDFsUBEKIrqYc16IwaBy0zl18YXDwAddtv9TpDcZ2wxcjbePOZvWrQk2dD0HYdba0ubo1OYBIBFQwhLbZ1vIiYkPcaF5/OHl3aXHZ7NtC+C9EDCDUESQAMoARIUAUiDdlNMhJzWiTjiHhWcBy3CCwx+OJadmqxjgHjHPKIYKsUi0tLc8bhjEYDACkYeTt7m2trq9hwdVIeuiMTWyKBGtIEJV0NzmJMiHhmYH4vo8EFAahKEn1ev3SCxev3/jQtIwaKRXk6s69o/2j3c74qD5fXViZzyr58e4HlmNGPNAkTRAE5nBZ0QLHD53QGtozeqmer4wb3cbe/qnlta9+7ZX79juv/uLVbDlrG6NBdDQmW3LwqiqdlbnkIF4oFFO5pcEIbbXujYJOPlyYE0MJAUoxZzCIGEQRAERAGIBnS6GUdNgJP088+6jRuheEW6rklEvi6mpNlTWEhFyhkslkzp8/zzlvt9thGA6Hw8Fg0Go3qAtd6BiBRWTCoRn4RiG30HWTHjgh4VmB+F5QL9TG5tCduP3e4NHG5rvvvnvu/Nnf/to/vf7+rR/80U+scJKdUSIbjjvWg8NtP3SxCGWiCCKxLWfYHy2tLGbk7Ae334EMAEO6+53dh1sHxZq2fLrsI2umU+2b3a3uJjfoqNVT9PIrL339/ndd1c5SUaTICuQWqXlFVa2pp5aM13kkEiZEAfZDEEURgGEIefiEY8ETEp5xAr/tOw0t5acyUBX9jE7rMxnAycQet9qNQjF3+vTpTDZFKWWMUUr3O/soQP3mcL+za/DAMsftw4f67DyIkWMlJCT8/CGSTsajiSBKDnIj3by/c8ttwwDlDuqHV1+86NHRxuYDRZB7O0NXoRmpkK77477hjANrMIlCmlVS7YOji1cunH3tCxxyJIGNjQfYYqffWAkx+eBHj2CV7/35BzpIz6dPX639g3L2NfthrVQiw1EhX+wKZHR030UzteVzAHV4OSxe754GXGAMAmJD1SEAYCBhIKOYY+Rj55v88TPmaYOnptWBndTMeOol9Cl9Z+N0lnE6vHBKHeS0xL0vjfFnjSsXFqNXjvNhFac8GSru/hjH1LcYHSSN8b2GnIqiyDkPggBjLIqi53mTycQe31T1iSgHtfKsDKXDjeBgZ6tndP2hI4jk7vs7FESnTp86e2E9nUnVarXlwlJ1NbO1u/mG9AIC+O7O9r+6/30jvET53GOfG5efcTr1OOLWqViMXh/F6PLj9Ltx+u+4z5THpChOp8vj9OhxvtFT8qSDN+O+6zj9LoExPr4x/tlxO4Fxv0/bfp7UOudJ5fO0t5n2uYREsuv6vu8LGcBEn+qWWAm3rGt3/yjMFTP5cnZtbW1/f384GOOC2G50lCzL5YqDcb9SnSFQ6PeHIhKbR63UMggiT89qL7384vkLrucG3Ub7zMVTuXqtudVEFnaHkZ02SYk3m61RH+cKsDG5qSl+dVYnctbutAugUK8vHDRlGuAggF4IaMgQBlAgAAOQRGElfA745AP+9JcMIYTAK+Q1LBMvdEeTkW26HggGk0E4sfL5vBcG3V7n1u27qb9MSbKQyaavXLiQmVFzufSFFVmRpJn67Je++OX9jUQEnJDwDEEkTeCY+tgur6bSswJhaEaXW3fsAq2Z9qS/0StVC4Ik6Jru+z4DTNLJ7ErVss1W+4hHACEMIbQDML/w8u7ezq1bN4mAc4Wsqqoja1wqlRWekqNMTs9Z3Oaqg7M92EDV0vx2749Jpa/mRGSCwHTUIJ/VS0fto5BmMU9JMC2JUgA1hqKQcjPywBOegSU8m5yUnOw54pNFDggh5xxCiDFO5eR8QTE9t9FuuhOfMQwFURBVJSeUazXKqeq5kcn9gLpB2O2P79x6GGAnn8+cXliolEpLp8/XK28yR7u/8XRfLiEh4a8hBh1UFgsUo3RJGY77HjdX1k8tLEC6WbMd69Hmw157kMmlBSJSSjVdxZDXyrXKV6t/9Z3vcs7PnTt37969iNo3b18nhORyOduxwjA8c3Y9nU4TQv7tv/5jaIuLpSWXmfvbGxvWnXn15VMlNSvvN/EDJwprmfmipjARAkxpepyt97xRQB0AoqwEic8R45EfUYEke1cJnyOORxLHnTHGWBBFN3D6g26n08ZclMS0QIRsOuUMB5l8vtFoAEBkJYUQ4pwHvpHNZyMeBbb1aHOv2+ld+3D7lVfT5eJrT/u1EhIS/hqCdaAX5Mk4CAzAbDSxwGF/TIDc3rpLIyYSWZUZYmSmPut6zqMHW5Dqo64l60K+kjl1ZuWrv/Dl+ofZ4Whw7/YDVdEEQchl86VyMZvKd7rtw6ODhbNlt09b3V2oBZl5ubAYFXMmpbdSGad3//Z4NFHPRqsLZzYO9sfu+PJadWYlNzwUutvAGHh+gEMACSJYIIkb0ueTz9sM+HjWC/5mB0wIabXHXjSaWMMgoIqAoyjyLEPUoKLL9blab9hlQAEu8H0/lUqJRPQCpol5C4iS4uey+e3NcfPQBsAFIPWU3zAhIeFjSKmSlbBKbcu2KSEys6Ltrb4iKhPXFgRBwKLn+vm5YkpNG+ZEVLHAFKNvN9ujhaW581fXQ+y43NhuPKzOliZjY/feGDCgvZFShVTktxwjsIOxIKsumsgiKlYqq6vVak5pbT1yqFmeyZG0bYZNpswtLs26DqQstG3f9whlEEAOIOcMRCzCgMQFayQkfJb4qT1gxtjxErRtCb2xSwFXZF2WFMihIMCMrsgSSqUlxvx0RqbMoxFP6XLbnGh6hnqAhVDK6vX5lXyqUChenfjKU325hISEvwEpFPI0oJIiWGPXm3iZfCpT0V546fJ3/+LHUcAIFJGCRUGyLSelp//Bb/9WOIi4ELUngZ5W/cBzXDw7O7u4uGiMzdFgoooHhzutR/d3UkouiiLgk9buaP10+Qsvr7iBYzSH9ycPR/VhSlc3tna0AqiUZ48eHdwHd+eKp6OJsr/TgpOQeZhEKmYKhAAhHzDKaAiExMf088jn8ECV433fT6bCAACM8ezcJdPzXZ/KhMuCqEoCkYGukuW1uXxBK9cyy/Mr9+89GBM8W6se7u2m0gpCWICoUCjMzZ/W5s+1u3VzwMVkHJuQ8MxA5lZrd+/cM9kEZXB1MX36wsrQbcPZ8e/+7u/++3/3+yk1szi3ZNpGGPpIBK1Wyx95AXDL87nBeGDdHa+fO01D8PpLrz3afzDo31xYWJgtLd384M7Oxr6sShNjrILUoGmUMzMVfa6emlM1pGfhytn5rd3xxvat6mxW09LmxPrJxrV0sHB+7vXJuCaBLAZZn2EMAgYjjrhAUIyqKCHhs8knM+Djv6+tXGr3m2BkYOTJRFBEKQysQbt59ZUz+WLq8uVzL1y6ahgjSSQXL50ZDnt6WpVBef+oNTNbmV9YcnvlXtcNaTrpgBMSnh3IS195aXZ17nvffmtvs7l2araxNzp/8cpMvv4Xf/IWRMwDI1uSXWjIKWF2vn7/wd3FxflzS6vv/vhHEhFOLV7pbLYPdg7ISKAE795qY4IK+WKpnu+1+v1hpMkptUQ8MLi++9bKzPqF5ZfKxaqcxYOxDbrepcI69a1B0FOLhXLlPHt0wTe/5NK8CwAAxidJhABFDMT5IcXpBXGMgJHSx9/nSfs+xhGnK40jboYX53cbN/OL01PG6QhRjN/nkyZWDxrjYxpHnN5x2plxXD2J9XmN/fnvEthyAAHAHz9tODo1W/HHgwnjll5dbDZ2CaYq4ALH5WxR4LjVOFyYrzn2+LCx1Rse/ON/9t8dPWgY5lE5t+BYp2/cYRHL6RKLU7NOu3f+tExK4vTuiD8+P+PS87RiBZ7WcwU0bRDr469HMfrgOJ73fJ7Wl3paiEutbDn9C7/0jT8x/+LmB3etLt+8vV8oFJyhSzSUKWskxecLRSUrRsDKzgvFekrMgExB8Z1QUNCXX36j0+kcHR7+yZ9+L7BZsVAIPWZPXAzEUr5Qq9ZH4DBFFFmTHN/Y6d/3sTHeGzmBdWi4dn+8vDZbL61SK5fxroD0xVEvCXVOSHgMCEJVVTU1RbnDGWUsRITJsvzo0aPTp09HUSSKYiqVmp+f1zRtbm7u/v37QigVS/lSuSiJkiRy30WO7xIp2cpJSHhWIObY0uXM5YtX7n+4PWw4KEupxduTYeCHWlFcPTevKICREEu41W7Y7mi//1DOoIXV2d1HB9/+7nf6xuALb75SX33BYsHmvZ32/sByfBlrkHIQoWq5OujuSVhaXztz1Nnf721w1R12jP2HTRmtdex2rgbq6UW3W/T8cyJZdkxLffxE5cR43qNkEz6fME4VWZfVrOONBQIkCQBOGYOU0mKxOBwOwzAkhJw9e5ZS2mg0IECZTIbVgSiKg/7QdzWMFcyTBeiEhGcI8u/+1R9dPH+x+Ctz1WJ9drZvjA3P9RHEmIqGP6yWa1DzOsN9goDVsd0Bq5VR4/BAAZkzy2cWayEmyDCtVE6/+splAUre+J7P+Vxl0TTN3cOtD29e6/vtfDaffiW/vIwbo70I+rO1+d5D1zx0Za6Pbwf7nUkeraZFjTkgL5Z8YD/tPElIiOVpDeCiKMJE1NR8RNsIAVmCoR+5blStru/s7HznO99ZWlqCEFqWlUql1tfXAUKaoHIGZEVqNV0aKRBBgPjJHdH3bA1kn7X0JCT8LJCwI22Fzf9p5986gcVAZNKJmBIUVT7YbGhpkqmpEaTD3d5wFGIGL6yfjrjT3O+2Hx1kcqmZ2mJ/0tvZPlw/v1YoFfKZYrVS2xs1iIQWqrM2GBrOEFFh1DPf+u47a5fmL5y5VCnUVK98oE5mzs0gNw0mlYy7XKmcpzQ9tqlMZD96sh1w8qEmPI9EUSRBWdPLY3PbtEacWYRwQOFgMFhbW+v1ekEQTCaTVCoVhuFPfvKTSnmWYI4wXsgvE0RUSfNCFDJXQckSdELCswJJp9K2ae9uHtXPps9eOL3f2On3B6OGmVvSAm5f23g7VSQL60UQ0e5h1w8mhVwVFqQe2OrsmKo45oD4Nmjsjb/zxz+UBCVyGeUMKiHQPK56ge9IUEMYbm/viVl+4dIFp0ddc1LIF7uhI5j5krQ+m3kl9NSx6wcI254hncyZ5wkJnykgBBBiWc0BgMxJH0IzpagRhjMzMwghjLEkSQsLC/l8nhBy69YtyEQ/sLAIFFWKIsoZ5JyjKZ0VEhISniiElIIsEsI0yFSUgdHtNgcsArlsmoZ8ZnYml1NzVYHgcNLr6RlBJKx12F6cW/raL1fu3HvY6bRYhL701a9dufqi0wwarUPbsZDCW8b+EGCQ9ubq+d6mky6qmaWsoKPbN+4cbvQycmFuZoH5Ac5YHO3T/IJvpXmINTE7cA0oqI9N6EnpOD8DOtGEp8jTqj+EED9gHEgQiQhSTZFlmXR6hmEYd+7c+d73vrezs3PmzJnf+q3fevPNNyGE+zstDtRsSa/Vavehc+wyAxGPiyJ+0m5dT5pnLT0JCT8LJLtMBEFIhzOyoA6ak8gD3MMIqCGnO8NW65C9+JXlpbXybLHqG+77P7zW2wa7G+3ZtXJpJgclsrfTvnb9gx/84B3JUCmkqWzKJ6YZDvV0ca2+WCoX+gX33s4NSUYRVoYD2zadccfZPzxcSp02lEa3dDtVHxbZJXywnIKZLFbbiV9LQsLfQhAEy7UjxjERVE3LZ0gYhoPBIAiC3d1dSZKuXLmSTqffeusty7J2d3e//5135xcrL79xZXkNIYgJFiCHlEWJp0lCwrMDCScwxEEEgv1O48K5CyziR1s9I+pzFKVJmlAqEJGk5XOnX9Z4moHM+9LtdqvXHA1mUvLLX75UXswcHB7WT+c790G3OwgCIhJJRJI9Apw5pez82kvl9Qvr73zrwxt/+SCj5rmrLCzU5+dnr935YR7lsjZuP/jBvcl/vPzC67MrX7Eb0nDyHwMrI7ZfzaE3A5QeoBYPSR4sW8HTCc560nrHOL1yHNOO6ON0xgQ8Ptw8Th8cp8eNI+4+0xLnYzqt/3Hc9SdVjk+6nlihLUlRluB+CwzHzPZcDKNCsSILqVHfbh2M/u//t//+e9/73nvXfrQwu3b/zuYLF075odzcoQfZERI1g9uMiTVZMYOY8oWPL98nvfI0LXH19lnT+57UuQJx32+8Hj3GV/uEvutp/ZvjOKn2IY6n5bs8LWg0nACKZqvziqQcHTQQwDO1euBE2UxaVIkfeFY/6O5Odh8esBB85ZVfuLD6wmx1Npct2I6xffRQSIGZ5dLY7dv+pDJXlDTB8U2OmO/7hwdHN6/fttrhSv3M19/8pXyu2G0MEccsZDdv3kQ4O7aCjjnxIbN5sNfaO7K25UVv6ZVy8aymLTiuMKEcEKfIXWXodk7mdRMSnk8oZRgRggVOGQ0Z5wBBDAESJOHixQszs/XvvvUdJS2pmhIEvuVYZ8+e/eY3v+n7fq/XzucyfuAJgoDQExb5JSQkTAOZdM2sntPljCTIruVnVE2QRBYBe+JBiQY27e9bvh1NGtfZBL1++Su7W/uWaa+cm4dK1Go1WQBkoo/3AzcIZ/N1LaP0B9x3A+ACwEl7b/Bu59Zkn1XzM/Pzi4HDAAAjYzKaTArl2QAbKI31WtoBXrt/dG/3vW6wSzh3TV0gEdD72KllYdkN/ACNQfi0syoh4ekBGSKQECRgKCBAEBAwoIBCLIJX3njp7t273/rWt9fW1haX5ueXZ6u7pU6re9T8MUAgndJMJyQC9n1flQWQ2Io9VySqjc8GceVIFFlXRDUMQkmQ1bwOQmxPHDUlZzNZMR/1O33qAmaSjtf/XvsHux/2sMYixx2ZvapeycjFSd/3J5I2mA/F3rhnQgREqIwGY1lSqqWFVqszMOy/PPpeuVICEVayimt7ZmCkKirGEEIWQg9AltEzkRPxwDDGtjK+KNlLPJqBKvHg2HdUxAQkiE/6KOikoif8l/Ck64+ARUY5ZEgWFUDSgsRZZLOIqykZAI4IOn/+HGPs7Pmzq2srHEd/8G/+gEHtxRdfrlTz4y2fccIBpHTqz+jzZgeZkPDzhMzO1mVVpowSCQW+TyMe8EjRRUWRstVUAI3I80IuiqLYPGhu32guf6mcr6SscNwahBmlVChpphdBXQpD2j0YWKajpAVAAXSAhwCyVa0il1I5LoaH+4fUxoV0ORi7tuVkypkwoCzgaTGfqRbRaIuYlIbBzMVZ8egMG53qGMD3gsCfqFiTIGbJyD3hcwwhJIrcEIWSJEliBqDQNp0oigzD6Ha7x0dR+r6v63q5XO4NO4qqn7/yKoAc4TCfK7puL5+dsewuwMlpr88TyYDms0HsDFjWRdMbwyD0mN3pjEUZKqruOmZvFORWZ2srxclo3B8cZUItX8rirDwejy6/eH7iDvujHiGjbKncm/QOOx3Fn9WEtA2cyKO6rltjr2G1a6UZP7TWVheY4HeHvfHQz6mVjFzqjdrFbBGNJALVOf3lcnqucZtDy9OhCGZ8mTGjT6SwoFCKtRAjziED9MmGbyYyhoT/Ep50/eGQARAF1A9pgAETBEQEDBAP3OjhxqNep9fr9efm5u7curN+9tTRfrNcm93d28mkUxBSPS2pmhJGUUgZmVIK/Dm0g0xI+LlBrHA8cUaBaHIp1CogpaV4gBRKhECglObLenqR7z50gBul07l0Tt/aMbrNoes6BGs0wrKql/KVrjz0Rn6mpM+maiH3dF0XJWHUtSgOcrmSQGQPBeVijbatkrA0U5418pNURRsI/vBIGG+uh1pmeDS3vDC7VFhQRpPJEE4CE9Ac5jIA1At8kWGIn45+ImlQEp4F/MhTBQQhGBtD6PcLZSbIApFwIVt+eG9zOBh//au/oGna7/3+v5//0QdYwCFlj7a2Lp5fN02DMltVpfHIF3FyDNZzRtL+fDaInQGH3Oc4soJJqZY/f2Wdh2Bv6yidTQ13rc2Hu3kAVt/Ilk5JsJUORt7RsBtEtLHfrpVmFipL7799/f733lNJyjWJlIITc5QqSqomZQt6dbb86N7ufmvrFy/9lqoQWSSp1WJu5Fb11ZnU4ujoTmpuLVueLzNFjApG3zMn9chf5fZi5q5i+23KAiuc0ED2QSAiIYXz1qcMChMSPm+EYSBoCmJwYhqR3cvk06okiJJgGVY2m9O11Gxl/v3331dkVRSlZrPhu/4//sf/uNXYebhxt1yrWBZSlErkRzDZyklIeGYgXHNWVucG44HjOB9++GHkgepsARCaKYiQqO13HNCMZCU1sYbVmXJ7J6CG6nhOe29rbf3cK2988f1v3xv0hxYbIzNVqtYiO+i3e92D8fr51Suvnb1z/xbDfo4t5fLpgbLfL205VtTqLT3cVSC++qlkKOvrv0IBeNgAm4gCUAIAIASQDAQgAwAsMIl7AcgePzMOY8JN4kaOAppuSS2IEYI9eV/Vx/8epxfEeLoGN86nlvOY+8fo//iUJz7E+v5OqReMy7e494orRyEm+XHpiauHsf7NU+pEc3qeUcO2hyldxjivC7Jl9VQV12Zzy8trIQ//7Id/ASFcPDUfMtsLx2kuFev6xFjobzuLS7mF5aB3NMlmiGFN99xnzSf7aaXnWfO1nTY9cfrpuHYyzveX8pj0oMfr9eOYVsc/LU+6npzUygThIa4UZgKHHXWPJFEXCKcWkVT10msro94YXEVZvXCwd0iGOK1l5xaA21Es2+wMeu9d+3Fg8E7fUrCup3THG44DKsqCoosQiQTIwBWoITDKZVHZerR9b+vHzMUKtBQYXLl8xU1OvEpI+JmhlAoC5JwriiIIGmMhY8zzPN/3l5aWfv8P/mh7Z2d2ZoZRr1qtptPa7q329//qncAU1pfOaWk4GRmeLXMG4ozWExISfv4QQcFeaLuB45iBzQIWAEOwBZGMnX2ZaFfOviQAhAhcWz5VLpc9z7s+2qmlUh42GIrElFCqZxWS9iLbDW2EgKAgRKHlWIYzVNJEz8qTodGSjg6a+5HPX7xweVG90N0VBn0fwsef+fykiR9R/pwTkvCZ4knPkKIoEkVIKVVVVZZTjPUppZ7nybI8GRuZdPbM+rqiKPls6rUvvD6e9G/95PcsG5Sz+VxBkHU3JWuRmPOgQZ6Tip5E/yZ8HiDZutIY71IhzM6IZj/gEtBTCo0YBdHcai1XTd+/vnn37v1ittw67Ha7XXMIMpk0hFDSSCFfsAUWGcgzWT1f9yNXTctOaA4bvZa7g7kLyvbR3UN75IuaUMlVOGNE44pOeJs+72fSPmu+qkmD9ZkHYxyG4bHxkeMyzjml1LX9Gx/e9Bxv0BsCAMr5847lcgr1XKaoFPNahkJTy/JcOtPdY5ZgZ5M4rISEJ8bUWwOUUt/3FVnlEfSVUFVSiqQ4tnvm4sLFs5fdcUQprdVqiqB5ttdrjWbmFyOfRREN/DDi/tAZmQPPNB2L9yMa1jM1KUdSkYAlCDGXZZGK4mFzFxKIQbS7vc2v5MvyfDY7Ozaf9NEaj2faGXASbZjws/CkB0AIIQh5EARR5KsqgxBCCAkhjuPKkmYYRhCElUplPB7/5Cc/yeUziqa02m1bMGZEDWMsiCSIAqzAJ733dlIkA8qEzwNk50anNl+ShPRRY8e3uVyA/eHItYP2Pl4o2hLQKoUaiZRBd+hFdnk+M3L73iQKaBj5TJKkfCWTyWQMc4JyvjkOAsECkUyYFA7YwdEwcMOipFHogwhyQAVJmqCO4rfDaA6A53skflKb8InMIOFnAWPMWEgpNU1TUagoCQghWZY5BY82Hh3sH/3CL/0aIeQ73/4zxGkun+21e4btZarLtkF3t1seVVLZrEsk4D/tN0lISPgY4k2ATNTlhRXHdBtHDS2lEhETyd2/OUqxrfXTZ+2J1zhsTswxRODU6bW7DzclRRQi7Ex8w7CyxWy5VBkZ/Ykxpl4vMKlj28AVgYuCoYeA0sctoksL84syQB6d2OHAQx1JhNBLOp6TJJkxPF1OKvr9PwOllHNuGEY6jfWUeNwBz87OtptdTdXy+Xyz2fQ9X9dSgiCMu51zb15ZqJ5/cP3h/vYkBEEE7ch1Baw/6XSeCMnANOF5ZNp6S2bX04VK5vzVdTUnfv8HYyHFUnLKtPnpsy/tbO/enNztdrpz83O//pu/2um3AGQh4XQoDjqTg+5OS2/VarULl8+awegH//6GaIXcDn3Tx0wQuKgIRNdSco7vt3f6/TYKueH1F9Pqck0E46ez/pyQ8JxyvOMLAPB9PwwFQdAghAhh3/dfeOGFXKF6/fp1x3HW1tYQQlEU2cNhqZYTZWHY92vLBawqljfM6UqYzIATEp4ZiOM4lNL2UWfQGUNfVJS87md7W6HzpYkFnIOdbj6f71qDb/3wrzSUffnC6xkt9EHw5lff3Nne/eM//ZPb0WZGL66cXvy13/iV//V/+t/ajV5GLhCgAIo0RcpmsoKQq2XFo+aGoPD60ozuzchuHorDQ65KhIhQAgyzCFEKIgYA5CJ5fHQWitGJxp0RjXGMLyZ/vK1SMOXAGqHp/C/jdI0sTuc65YQW8sfrXON0e3HEZH/8yA48/r2EE5K7TKsnZuzx5RKnJkYoLiYgRh8MH/9e0+qJGZxSNyn4iIYEorXVU1b4CKuIAdhrjJEWvfnlr7/1//h/vffjDxcXFxcW63pFB8C7+rVvfPsPrlWKo0p9Lq/qhmmW8sJgLMvk8c+NYobEUkw9j9NDx90Hx+TntMCY8ooDxXwXAX98PsS1M0/t/JIpnzv1SgycTu8bR1y5x+lx49qZk2La9E8dPBVz+dT3yZLavfe3jzZ7vX7Xt0BQ4oFzGLhR/35GY3lPgKPeJOg7WlrN5xlT3d/8rW8+fPAIUHD29Pl6Yf6tt9759/+fP19bW5ufr05abjUzjyNp2JlUCpV8tjgej2unarokZhRJ0JmqZugkFRp4Ll0aOekwjEDIGOUYAIQRwjRK1pcSEh5HFEUEQSJgUVZdAw8HwyiK9LSuKMrOzk6hUPiVX/mVKIowYadPn15aqv2P/8N/0FM6INzyDSLDnJDm22YQBDKZbuCYkJDw5CB+DwiRJkOdeX1JQBktdzg8BBCwgeq4I9tyRI2IKBO4nu2ZQparor68uPLg/sbmo618pjRTn92+v9/eHvZabWvilrKqJiu5DCKEuL7teFbgea7vBDQAIWeUSgL2o/HEOQLerAAUiBQGGOM+AxEFXBIEGk43M3heiB0ZJUOOhJ8BTjkDLIzCrKrqNGVbLUopwtC2bBp1up2e71LDMGbmKulURtf0aqlcKIkhwwBANQt9FzHqy2Lmab9HQsLnkdizoBFCl69emlutzR7UjvaatuHKslybqbY3GgO7DSWQyxdlqAduGAX+/vYBHt8/bO1LKplfm221Go8at0Ha09S0JmXzpXxjt31wcJjL5lyuUScKabjXuetEEyvsqBE6N3fp3Hp9NVc0e0PjRsb1KA0445gQjjDADIVs6u4oCT5K+DxAkBhRd2wMMzlB13TbksPAsmwrDKL93YeSIG8/eiRJUr0688Pv//DipVOe503s0dh1c4Wc7Q4wyFBOZUlkdLqjPZ93koHvZ5vnvXxJo78/hwpSrvba6ks33r33Z//hO1pWKteK5VI5EMpcDIftydaHezJIQQjf/daN9+nWUevwa7/ypVdffdU27H5vQCDRVF3CpF6ZjRxuDPZNe5LNp0VF3tndZn0kaDRT0HJVJTuj1ZZTesErraclgvb3nf1d17VJLiWoisB96FoMwOf8hI4YnveKkvB0kUXF9SamY3R6UanIBEGURGnMQ0EQBoNBqzV44403RFE8Otrn0Mnllf29XSuMIgAlRThs7C/NLmiq1G07opgcRZmQ8PMmdgbsTEB/1OtPCssrS4VCQZSJqqqjyXB+YVbQtVI530i1t643JwOTAFGcyEwcqorW2R9+/09/vL9zEHQIBsJhcxBBu6UPJUFZXljqdrucgeWlFdu2BzuWJMsz1Zl0WekcDP6y8ZeKqJ0+ta7bLzBUEJRi4CHbG3MKONcwSsXEsiQkfK4RBcnzIQB8NBqqiqTKRBRFPaUBjiZjMwjC2Zm54XB4dNSo1rKypLZbh6cvvFCpL4QRbey1K1lXkWUIo8/bWdDJClnCswyZKVXDMRy2jPZR3x55xWxZzYhOYP3xv/0eFsG582fnFmdOnV41B47ZDoVIc6DJKXh0Z9vsuVEUWZaVSqf0oqTK6YP9Qx6CWr1m+/bQ5Hru0uxKdXLwiGMqy7KM1V5/2OoNRv1Bd6d9sZL1QxFGcxJRCaecWQiJkogc/7N5kEXSECT8l4Ah9gIfEhDRSBTSqoQ9eyjJRJE0VdZOf+Hyo41Hk8nk0sXLW1t3RSIPJ4OUqpUL1eHAdMc9HgGEkCiTmODfhITnkvh29dlq/+MgwQDf3T2AGFxef0mX05qscxCkMynB1822fa2xcbjQyc9ry6cWW2DU3TE6nUGpVKI+6Pf7UKL6DFk8kxdT+OuXf3Xz4ebNm7cppVkulGppkqV2q5+qSkoWI5UqaWl9bV09nd7ZeKTI0kJxodHOGBYSoVTI5hRZdT00MCh43g+JTkh4AnAOPM+DkAuSkMlkVNHrT1gURYqi/MN/9A//8D/82aMH25lMRhzhldWV0WhUq1cghP12LwpALlUoFypHR3ZIQ/I5mwEnJDzLkMChgq88+F5r+OgPzWCkZUUoscEQ5CuSb9jIUuz7UB5IwxbNKpWXvvLm9Tt3tg83LdvJzItShowMc9i0RKC0Vjd+43e/Prda/NP/+BdZXZ8p1OvpmZ803xeBmdazoT96tN0SoZ6Xi/X18pn1NSV6UFrY1617w54XTiLIli20ZpKSTB8vk0AxHXPcTDdWj4in0xnHEffcOF1v3HgM48frFKd9buwTYmY8cSPHk9oCoDEPjkt/XP7zGB1nXD6TGJ3otL7LUUw+8Jg4QR6j643Xl0+XTi8yMUTLs6cN457j9IgYWHaPAwqlMKWlMcAzS1XbtiM/Wl1emz+VxeCX33nrpjlu1RfOLS5lLffezMz5G7dpPjPdWeg+i/E/nvJM9bh6Ne0KVlz+Y/h4v9sg5ruIr28x1z9hf9lp7z+tX/K0Ptlx5yWc1EpeFFOOcemPKxcaU7Gmzc+4+8e1z3GfNY0tl5g9YMBAOpWxA4NznslkQu7YljUzV6/NzEBPslqhKqY7g9bm7mZ1tuAG9oXX18ADAxxOJAnllNykY3SHvctXzz3YuEsgah8OOKOyKLcP+z82r3kDjiAXiLiydmowGv/k3ZsDwbhy6aLjeep4BgGRikGreGOItyW7WrH/T/PoVBfYf2dmJSR83lAUDWPsBT5C0PMDMLGIIIiiLIpio9EAAGQyGUVRCqlCOp2+dOnSo3vNSqV46cKFo7alqPLC4vxffa+RTi8B4D3tV0lISPgI4vs+ARgjQrCQSss+Q87QGvQHfieEUMQCCpkHBVCZzxVLhQ+ufQDro0AaFxYRCDxCuRopms6+9tXXW+NNwENBgrW5sj9hj+7sH2y0NC0rpPnANg8OmxPboQBSxLcO9rb2tt84+6vAnAujxcW5vAP+YGAfFtIb3J4D7umnnSefKZK952Oe93ywLCsIXATDUrkAILAskzJkmmaz2XRHZDwe942+bdtXzl1xXddxHCxCUYbFstboTSzHlFQlk6ERNQl5/Ewxjuc93z6rJOVysjwtW1iCCPJ9X9JJEASto5GalXKZ/MQcdzsjd0gjG2pyyvYtiLSZytwL514+DN9P17ORSqyBN3zUGbdsIoJ3v/uj137pEkKkVIDyOb3TGIcB7jUmNMSKXiwVqpMBt0Px9S/8ckpP72/uhL7vq2lzt6hMXtZ6wizM5TPXMeY0ewsMTqYDTiroZ4PYpc6nZKsXfyTnk70/hAAC37T7igI45EFINDU9mViapt29dr9arQYgUBRlaWnpxo3rhRmh2xsoinDv/ocTA6ULtcOjw3R2NmITAAonlNKnw5P+rpN24+fDtPn8WS0XomVky44QgY7jmLZlWtbMfGWusnSw31UQzFdKlFIIuT2x3nvrg9Wl00PWzK2Twro4v1YZHW5DAGq1cm9v+O73Pszk8oxCVc/uH7QsP8jOle/febCKXzid/SJAYm8wWiCrwAO2n5mrz/UfnbPHBdMW9UgC0roU6og4EXq+PQqfQZ6XKPEnzWcgHyCIHLtvSJCIEiYpSQHIaBFCBoPB0cHDr//K1wkhDx48aHX2trYKDze3zi4vTYYdVZ+VJGlze0dRMild4sGUD33+8+0zyWe1Q3paPLUZMFQoiCKfBnpKq9WrjUajvT/EM4oiaAASRvnewR7HPF1UTMPc3t4GkjdxorEFXvpivVyp8HPm+bWzR4cNo8dQwFvNoev2+m1j/cy5K+evUlN17644+8vFwlI+8Lx9OYh82k+FqBqNXmTc4VnDQn3MoMaWZSpBdmK1Kqmgnw2elxnwSU2B4zs8BkCIuBcEUFJ0jAXGfYyxYRiqqhJCcrmcbduNRoOIqFwuu/bd/Z399fVzlfr56/c2HTtcXZEFHE7Z/z5zPK38T3i6fFbLhXASACEkkBBCdCUlIdUZjRtuL6elbMcQBXlmuTx2+47jEgWqGTGbWTzobh+9y0X/oD5XO3WuRnRgNUftzbBaUJiR83oAGNn06sXxvjY+kuZq5yktRn4eccCYiHEoSUrgayEMMA4xACCSEZUZEI2QMkSntgFKSPgcoGnaZAQxjhjllGNOAQAsn89HUYQQ+sY3vnHv3r3BYHDu3Ll33v0rhJAsqCwIirlcOqVbE0sS0+lUBtDO036PhJPheV+Z+Kx2qFP7AQsaUpGkSel2o3e006Y+qFVqUUjNYKSkNcS5khYlnFdTCuHSzbc3eyNQyJQDHu5fN8wBXbpQJRm4enVBjYSFwhmNLTArHXjSuQuX7u1uE/cwd1pXU6mh5e4fHoXICiIzdMPVhdNpvQoplRnRRZUBceQEth9S4qtQfZL5k5DwXGJZFo28VErN5jU1W+kPx+ORjbRwdXU1K8//6O1rDx49gBDOlGbOnj3bbDY9j75y9ULrqNFoerlsSZFTjuVqqhI871PghITPEIQPsORn/RBrtEClkYs8wzGL6swgHEZoKKhg4cW55bXT59fPMxvglLHx/YAJ0GM2jSJsSPe+vV1ZziyertD06Fan/Q+++n9eqyzcvvHg1uH/HIjjl78Ov/7bHOKO5C/+/r/a+PGPv5Wbwdnsai1/fuPwhzX9rEQKXc8BskdkSUdKFKkcxDiGo8frq+LGUSRuTYpNFwUapyeOe3KcD+W0OuOT0rnGETsCZVPqHWPKJfbyKfMh/j7THSgRp5uMY1p9NgKPD1+I2Mn0eAL2iZ4TpBe7rXunK+2s3uYTvz8cSoJcrxdFVVhYmRmNRvuNvddevbq6Uq3kMrsfRu/e6admxJfPLJYygqYFPoMsRk8fJ5uM1ZtOW474ZOpJ3HeBYvSsT5pnbSY3tY42pr2K01XH6d3DGD1unL/7tJyUDvukVg6mTU/sWdC5WiqywdFeb3BkkjxbXF1ATGjuN1MF7ezVVaA6C+vlN7/yWlrJCoH0L//lv/xX2v8GYXTUCju7Q0809YrKRXjj5n0s68wT3n7vB7uFo4cbmz3jYG6lpEvi6O5OZnH9aNTuhl03ExDBKOjCUff3DhpSYV3N1VOSiyZ26Do8pBgiLDxb9Tkh4ZkAIRTSQCFQkoTBaGKPRxijfD4fhuGo249CSghZW1ujDvTc4MUXX9x8cO/t//SDr3/9SweD0dbmxtXf/AZVRMAHACQrTAkJzwrkoLlTLyzMLszQ6HA8MdtHPU3TJFVk3Hc8W8twQGihmpOBSiegaw1Xr6Y6vV5FUwWNMQcBTurz9QqqXv/hXcbJj37ydkq/7UWumiZ6paaq8Pvv/+Wv1xe1SDxTezEa+l3n3ebgwaPeu3O1r7noB4ejJvcWVbKIhSxlPOCOiBLD8KfJtCP652PH6enNVE7quZZljYb9bD1VqZZsblt2pBJMw3DQHz64dxSG4YMHG7Ztf/HVr+i6HoV0YrtXrr5gghCTcGa2OBl2q2v55QX19t2TSf+0QXDPSz1JSPh5QnyXdfrtXKo6Nz8rdnrdQZ9SWqqVKQu3d7cWlMJh0/vhWz+4tH5ltXb63bd+cuv6NYiEfLqMclI/NMdjq47w0urq6MBmHDXaHcM2OWAIk8lw7Dk4cyaVOaXMkmIlpSl4+8++749Ci+SErvW+p2+fr9BKul6WShNTo5EbegrAjz8CLeHvx9QdQNJSPpMoiu65E8f0ReIbnoOQKoqiFQ4t0+l2epKgKorq+34pX/Jc/+23ftTs9GqpUxsP7ocSCiHa3Nq4/MbLCO0CkH7ar5LwFJg2enxavfuzthQ/LU8r/eT1b1zZ2txuNHZ1RS+vlvPL+tgcjf2myBCWAGdgf/uo2Wz2DobhK9SnTtgXBEHpd539gwZFAAqo1Wr5YShpkFJeq5WC/ZbreNyC7a1+vVZtKO133vve1fNOcb1WM1V1Q+21wNL8gsbttatLVy4sp/q19sbIdfoilCp6zvqcGYY/ayQz4GfzuYIgRKE7Hg3TugiRlM6mJO43x0Y6nbYmluUML5y7OD8/H5r02vvvCtr5R1v7nowlie82doWSkC2nHzx6yGIOdv57pD+ZASck/JdDoMQWz9WVGu51RhMUlMv1lJAbmhz1xUw+Nep3Q8pW507N1xcB4i++dgVO9B+/e23z4XbEwLkXTkOFGc5kbLUlEQx6BvVlTpkm6DJRQcTTSnpldsXYcLbGzjAjuN2rC5mwXLh98XXN6B3OzOWx6JrBw3Eoq7VMhlSABazudEFSCQmfBzzPi6gXRqYoFrCSI1gJjDbnHDDUarUgkWvVOuBwY2Oj0+mo8quSJDWaR5XF6sLCwlzptDFs3b+/ly9cfdrvkfB0mNa276RmzAn/eci1H95dvTBXn6shmXcb477ZmZubO7dwlveg7weHe/sE48hCteK8LMuAsNRiZvSDYSj7a6sLZ15essJBu+NGXijrSoQCZ4iqpOwagW2My5V0ZkbGRGjutrfevVbIawvVL6/k/4HBaio5UitIlBAWWGlNnlvPhKnQHfQefm8H9l5+bEKfF33bs8bUR75Nq8N+Topl6pn9CdW3EzthB0BNkyViIMQDxn0/IgCvra6ahpXLFlZOrSOE7t+/TykrFSsCESvV8oc3P5xfn3nt8qtHj6LN7cHXTl0NIvmk0v9ZrScJn0+e2hJ0gS10N42xaegFtZSVB32zvTUMLThothEjxUw5o6cf3t4evjHOFpSHmw8ajjXBfZTiTWvPvdNS0jCd1kUFHzV3BSmTKaTy4szWw91OM7j0euELX7lkiCo66Nzb/dOy/Yi7/bL4ywK+uHsHmOw7GUPMZCullZTFhtvjW1bASiur+7tPJR8SEp5pJpOJSFA6rRUKhTGlnbYj0bCWz2ua9tJLLyl6+sO712zbXqov7zpWOp1+79aNUqWQzWV2d3fNcRmTVHfg5LLK036PhISEv4YEWevypSsjp1dZyfTco1HU7m3abieElFXmU5KOLMMIfHbtnQff+dbbu81H/9Xv/g6GBInhf/Pf/NPbN26+992bX/rFS6ZhwH3JRW53sL+H9qvFen0m8+D6Hve+O7uin12qffXV/27cZe/+5Y3bd949t/r6mZUrE/u/Ptz71sHCrsIgdGAK68JQFcflii70rF4qXYKRNrAHQsamFsmyqoMCDhgAgAMOAPj474zRGD/LWL3gtDrX6bbN4vw1GYsx5o0hboYR79c73ZJR/AxvunSSKXWHjE93fdzINE4PGssJzcCmTSeI1VnG3ufTj4AQHisO3dCuVV5ttW5YXveFyzPeZKvba2zcl//Fv1islRb/8I++OzaYYYQZzT51Zjmg43wmv37l/P0b20cHo9OnXz61spKScxCPKZMIIRiLAIAwYpRCAACERIjR6cbp4OP8nuNUDAhO68/6+PvTmNNq+RM+QS9O/xpXr05K7z6tbjWu3WD88d+LgB+vp49i9N8s5v5xutg49fe0M864di9OZjz9/WN0ulN2F3E6+7j7kHaze0e89cbXXkUqbXT5Ym11f9wcNSZpmNm6t+e7IFcRa6XZB7ce9SadF79wmcHw/KUzt27deLR/X0pjooHrt64JSPA8dubyWplqvhsu1dYmPWdjY6PVPQionn6puHS2vqs1yNzwpdp8RcNheGR4/Qfb9w8G5uHa6qmV1eVzayQLonw/Q7vWduR77sS2fJ+meTYlqKou2LYHPu59PwFO25smJDyfyLKIsacpku8HzXbX831d1wVBgAC1O+1MJp3x7VKlsFyfaxxuLC68MjNz6vf/9z/FkT4/vyyKoqxLsuynUvJxw8Q5TzbtEhKeOiSdSR3udHdXdyr10kJ5LaWmFZB+f3KdWXBmdr5ULfS6fdsOPC/SlezDe7v327e+8UtfP31ubfvgUTlfOnNlYdib9A7HnIAja1PRxdXzp5Zrsx+8fdPlxtnV9W6r++Gtn8gVKOqCPDvmAZDyWkXnQ9grRBk9pY1G5ns//nDvsLO8fn5lZf3KAhT+AjU2gyELZCWTkqqA+QOvy6Y8+Sgh4bMEDcchivL5nGmOBsOx7XjplMAAazTajYPG3t4ekJDnhu12u1qqlkpVhFA6pVTL6wRkj476UGClQqY2gxBCP9UH/9ScO+Gp8KzZ86FYGVIybjtJSCU1S9mea/qzlYXTK2e2djbrK6WqK4/fjwx7XEYFhCEmwPbNlZUVVVdu7Vx/69s/DpidyqrpuUy+WmiQTv/w5rkv1nOVdG/U6ZqHK0vLv/gbX15cre839l7+6pXxpMexv3x6QVaj9378/oP+/hjN7AkTtcqKWhEEUfOoP9huG7uacQa/+ApfXDmtohTcFXsj3rE6EIVKOuQUfbJ6DP/GX5K241kkfuns55yQE+Zp2ZbRaGz7bqqeoizdH/TCMGQMMx42W4ORYRDEe8PuzvbO5TMX52cvXvvwzvxMfmV5aTTye+Mhx4hxV5FCxAyMU0/idRISngrPewUmZifASHFH4dvfffeDH384dLpLVyrrVxcPxv6je7vv/PCGmgO/+uu/fOc2aI8amp8uylVm03q5Ikho48YuoNsSUTKpLJF4oZgiSrR5f2fj0d2vvlY/dWbpqL0TyZPVuUUQ8nAQykACzLXwuOG3A00NbP5gr8EcHIxQTqjX8vMr1bO2/WEqE6bmTL1fsX0NqmEUEermEQrYR403RABAgJL154TPDykd7+23ND1kPMRIUGQNAMp56HnB1uZ2fW6+Z3b0lHxq/VTjqHnQNttHWc8VRmPUbPnl8ozrR41Go1Q6hTHmnDN2vIkIk+nvM0LcjJPFlM6010/Ls2YD+lmFDPtDl5vleuHB3UdIAVffPMNABAJeXS6raS2t5SRJknTJjmwvCoZHTR2mIYaRNVFSQiqbThcVJSVJEt5p3BxPRqVqvlaZaR+1/+AP/iCbziEB+tyBnG8/3G8+6pVKWVXIMhCEzBEgkEoSoD5XRSwxjw5G4lHHyZVRlC4aWGrWzAinlZ5peqNcmuSGUQQ4AgyBT0XxQACTGfCzyfM+Mo3jpA5zn/Y+ihQE7tgwsCCiTLaAsWyZR77jqnp2d+9gPDbOvrD22hdeCgy2ubk9f7ra6xmDgT2/eLHR3He9SbE4s380Xl7BCCHGGOf8uAP++yU+IeEZ4Xm3ZUT52ZSWUvd3Du0+0PNida5s9l02VN547U3P9o92WwKQPSvMqLl6eV5gynAwHvXHg85IRKom683ukU8mr//qCy+fez0vlbxxqEB1tjyXUlPj0ahxcOj3gtZeh7rAs2hGr7588asV/bTgVSRbBWFoGKbtu1JOUuqIZAH2pHZHB0E9pemFc6PqlUdq6SaW30H6OwAygKJj7x0GGEjGYgmfJ3zfIIRKAomiSJGUVCqFMRYVUVMzgiD4gVefqWayqY2HDyzbTum5hfm1iWnZ9rBYTi0uzeqZ0mAI7z90PtkDPgZC+FkdKiX8lwCn5Gmn93mFDOxWuVrb2TyoraXOXVp6tPVo473W+QtrP3Y/GPbN/YftnYeHRIHm2NNETWCKlqECEgcN4+Gd7dqKLlUiprgbrRunF17sdDqtZkvF6bny3ITbjuXKsrJ5c3duDtTK9aOD9lGjffb8Oa8vNTf9lIj0RSVfp1m6JBBlQkc6yuvebLuZ2ZngQNotnrOXLhci5r/XuLlx+CdL+r8DHALIAGSAfyT1gQDFB7onJHx24MzTdE3R1ElnMhxHRYIkWSgVao1GAxH8C9/4hus5t27fzOfyHCJd10U5ZVtus3n45hd/IZtf+Pa33hsO6KtfWEnayoSEZwconAIoFDJ6Vq+I+TWJaLy9N2QT4R/9s2+++51rN3+04UyCdEldWK1DAg92D4ihACXkUkAwkokkazhVI6maeNDtzc/XT59aVkVJIDKBCqOkVpv90Xfe2rM3vvjLb84W1//wf/6Lnbtbi7U57sH6hTWpIDLF6XX2F5SFWXK+s8epmC7lV7aPro3MD/OZcO1U7dWXv+q72n/64++6R/894IgzDCjhjDCGMSCUAwAfr2971pYgptaPTnmfp0aMf/DTOkmK8sebecT5m06rJz4x3+UYBAyPl4g/UQBTSimlxuSvAsPn3F1azxeK4ubDB77fWz5XfuFCfngEIMl/9+13DcN4/aUXAreDUxMtdf7BzY2XXnrJDIzR0NbJ7KBnf/nrXzb68od32NjKKXIEgB8xKQioLAcRfbZUBtPqaOPKfdr7x9WHactxej/jx1+P4/S1MbpkHHOfuHMR4njSA7Vp28OTSk+sXjnO9zrmegFPl544v2QyNzNn9B3LtB0wUWulnJ5CDI/H1sN7W93mAHKcyabLlUJ1piqKhHPe3GpzRnUhnUmnvMDygFXK5OdX66996Ru7O1uTgWljGwG0vHyqWpvzvCg9K9aMGh/LCKRnU2uP3M1H2w9r5aoE3Uf3bl58/UK1kDUH7Q3fvLvVvXzqFybUPjR+wtye54ft9qh9mJopvgwm5z7aAP50lgEGkjishM8if3tZL62n5HRpa/uO41l8SLudRj6r7m42/tn/5WsboPf2O7ckSfJ9XxKFKMC6rstatLwy3+2OHu1tq3KmvKwKddV0+wDOgI/PsfmEqU+nSUhIOAlI6FJAIYt4YAfeJAo0xjwYONE73/2JMwxFrkGAWu2mw8e6rhqGZQcWYDDDc0EYjZyBXOC52fTiyrxO8hJUc7lKqZxrdRsM0DAMbt2+vb+7Gwb8rTvXwtE923REUZSITA0yGjcKZU0h0usv/fJk0vmrt79FMpEzcZruNQjNXCmLQnHYAf29vGStqrzuHbcYHAGAP26fIAAQPOeb8J9Vnhf3oalPGnryes2/HZkMIWQRByLAAuuPW4LjYoLTqYzjU8aA7/uFYmHsuS+9fGVvf29+NruwMDOztHjrvYP7dzoiKWpy3raD+kwum+dDj3PEAOQc/PWZUp+BdemTOvN82siSuOeimPvE6Wjjrk/4+fDUzoJuHrRVomOAIwo7jcFwOAodjji2Bz6MBEVRAea2Mx72fcexXDtU5ZTv+1Zocs9lEldrhHN+6/rGzsN3ugedM6unvvSV12vlGaKSwXiwu7vb37Qa+70gBOfWrxZqtf6QIlNQvKKk55YvrEWGrFsvFEpWtbSbipRc+5JtdDLaTE7LhobgBZTDc545Z4wUKHgAAMARBOiTSvzpRuSnOKkMfdYO5U/4bPPp3veTgzIAACldY2G4sDS3231vbA7rudnAC0Qit/vD4WRyeLgjyxLgfj6fGpsmkWeXVkvv/eCuIqtz86eMiT8eTgolrOnqCDEOGUSMQw4A/ziGJpkBJ/xc+by1h3HvS3QlLSDBj3yCSOCErgEwhnm9BEBkDl3P9WVdLJaK6ZIKIew2ezLNelHHAZaeB4W6ODs361nR1sbW0sopboFxz966tze7Vg5G/t5BY9Afry+fz6n91tFEx3kFiigUS7nKC5des6Iz7JESDNPvXNfnz7Isvkx9LyMtw90vhwSOJeDYEaSymlrEWEOEHe/h0eN3+eh9OISxI9ZkBvx0idMpTsu0usaTslF7ujPgn/qHEMIwdDBEui6jPo/CUMSQBxEmwDSdvb09APnO3sNer/fK5Zcrlbxte65t5nLpcnbOdiXPDZV8FgKqKiLAACIGEf8kRyBEMOZg6ueI6fdop5uhTr1nOeUMO9F0PF2e2gyYc275pud5ak7UUhnXcyDDjDEEIWMsZCH0OcJUcUUBSygSQ4ciBaVmlGxdIgjavUCRtHp91mfOaDQyDgJ7YuUrXy4tFLf9I+CT+5t3Lpy5Qvwc7ysSyNPJQSj6xTU1vP6V8SELLDmfUm68/b6jg3xmfuAIF5f+iWkPvcDM5qKAMcAky7IYgAQo4FNbvn9n6POzNgNOSJiW49nwcU1+uPNgvlod244I5Xy6GEWskNa9yGi2+w8ePXzt5dePujsABGHg1+dmd/a2ALJbR1Y2re7styyLFlOFnW2jupDhKI8EDgkDkHH+0WeEIJ7WhCMh4b+E512/e1IQQSKiRrDKC6V8sVwIwxAyxDlvNRuiJMhEpTA0jElEw7SSA54QYK+0kMstCp7ttHcNFHnzp2FtPaunMr3doU0C1/Q5hQszS/dv7RxtX08T7fDgqHVkzuuXUSAPjiaOOzSDkR3sacqCNQiiVNoytFFvYeXllycC6E4YozqCkiAggkAY0TACBEIMCAfs48JhAAAAGQAAxLjrPGskFe7Z5EmPfKefMX90SvNPzYMNp98bIUEAgiLnMhmBAtMeB8CKGPUD78Pr1y6cPzs/N7O2eGZre3Ozcb9a+mKnPQBcCdkwiHxJzhOq7+30i7VliPjxf8cGORhiBHjSAf/9eNIz4KR9+PnwpNvn2CXo0XioZ7Qg9LuDtuUaCOKUlhYFMQj9IKCAQYA4hJAgEXMRRL7Bezmp6Ae0fTRyjkAxm2UcGmFPw3q1VlnOrW9uPPzed34QocgxvUKmLIxU6kObjY7sRwyEjLFhe/K//X9/b2ZhlCOvFqpfGruhqueK+a83H3Gfwgg3VSXFfDCe+IqgqUoaA+g4nqAxCBD8WwGcCQmfMR4bhEUk3uq01lcXQo4yqVxOUd9/fztbFlRF4xAOBr0FWvvSV7/03T/74dbBrpACqpyn0a4k4nQW9weDcqWQS9e+/+7bxdrLEP90m/J525BLSHhGIFjkCHKRylKgjDsTTVcjeTwZW5msDoCLRJDL50wLY4yLM1m/6BsPR94BOrzZLc/mKxdSR+1DsV8QhcqN97YUVb794IMvv/51JNHvff9tVdbM0ZhEMBxHeiYNqN0MNhxpFGIHzmlSbliSdGZptgcFHbiQcUgkIoV+QEUGIM7KZRELIXUwpFlJ6rkTSZY5BkEYMMZkIipEhIxbQYyuLka/FUecDgyix0TEAADi7H2Fj5/7STN6fOwfw4BzDjg6/vPYJpZzgPCUPsEnpBs+sRHf32rNj0ExvshxxD03zm84TtdIpnTNivWNjnnutPlGPr4e/s2/+OzxulWEURRRiICqSjT0AWREwM1mu4Cd/NkXxg5zbVsWcCMwoCyL8ghH1Dfcl179gq7r9+7t0JAUlOJ40pZFkNJSW492ilXxv/3d3wRh6ff+8M7AWwQKK2qZaCQ5YSjIFIVuxAXDwxhPV15xIB7jhx2jW42vb9ONs6f2G465f5wia9rvIu59CXi8Hj2uHmL4eF0vjPGvjXtuXP6clG74xNrbuKjyE/oeT0pnHOf7G09M+5BVCqIgTpwJJiiV1mVJwRCLadX0RwIRKaW+72NEPM9tNpuccySAMAxVVXUmPouACrNWLzK7R6VMKp0r5FP+zWu3sU7La9kwCkzLQ/7EiwRBJCktk9OLKSUzdnvWxKysqaLtKXKEWNp0PAAjRUQIh3ImDEMeAeTjKAAS4wRhghQgmJCx8LjQjhsLxhh68sszny74v1lIcVE9/OP//dfB2hACABgEkAP6kYMT5Agg/tybAyX8HXyqof/k4GUA4oPUEIIYIwwBQohCDiDnnGPACqUFw/JkRV2aXxAA7HXDVGZ2ebk2Moarayv1av2gfdTvDaqZai6bGtw9pFFgGJNMtiAqZGe/J3ACEKmWMwhyBhgHDHAEOYYQfKzoS2piQsLPG2L646yU80LHY46iyIKqBXaEuRB4oZJTAAC25QSh77sh57xarerZ1O7unoCFlJ7iHmKM+X4gy/Le1qFjuwISJ64ZRKYeANsz83Ni87aTSperuRnCJe7CTLqQL2c9MNls3pX7WpksFIoXFVkMIhqAIIiMbFBGHGgSDqhn2FbIKEQcEqqrWd+PwpBBjAkigMEwohjyk1JQxI+8/sbvPyUR+Vnuc/xLxCgAHAAIIOMcQoAYYAiiRMf82ean6sNf15+YCSfCECHEARMFyDkCAAgEUkDXFi++fe1BVgD5nDbqtDrtppbFqZz4yoV5FGgS0R3TO9g9KpwtlCqZQi3j+ebMzEx19vTW4fZ772/XS1ImkxFShJAAoQgjACjgEHCOEUL4+beJTHi++LztesTuASuzNJdTnECyxj4RcKagj+A4cBxN1wgWIhqGfhRGEQAAI0IIgQpAGAgSKZaKjmePxkNKIjN05QwYOX1RFIqzpXStlJ4ju3sTUReyFb3b7UJfvLB8RRQkk42ymfT82pmj3hCARWCqNCIE6gCKVuiFJBjRcSDulUpCVkhVVDs/b4kKjIZw+06JcT+KOAQC5JhzyjmnP0f5xCdN58d/eXyGUv43loA+WbjGiAOOADxedoYAcMBh/Er2ifG8VPRnLZ0ndqDHp6oo5x8tinDOccwSFiGQc4QxFETIASEEIsQJgXqqLkl7EbVCvx+FhiITQVAtKzx7cT00dpq7o1KuYlQsGrEg8s+cX5ckUq6mxxOj03UMW9FVXC5m5SwRcCSLEYKMA8gYOp4Cixj54bMVhPW814cnzbOWnmeNk8qfJ53P5B/+X38j6OAb4dbBRpP73Bzbg8EgCtn8zLxhGJwDQRAghFAGjLF2q2MxQ5JFQSAUBhH0hSyvVQvNZnvxTLFUKdojj3BF0IGkw2I1P2m6WKVzK3WnF77zk7dWT62+8fVXpTTc2LyfVivFco4pEPE+IZiFAXVDTQXlWvdB+IMog1PimiLzProlh3g1c7WpMt9HYcAYA4ABRgEDQMAnZsQRdx8W00XGP/ejfd+PL0MQQoQgRujjDWDIOeD8o5aZxURxJzPgzwafriafqjMQxWyfYEwBAALBmGAJYUFAnHNCoGEH1epMGB31e03quZlcWdH0iA8ghJZljUajTD6/trbGA8q5XymXL12++OBW9+0fvwPEzKkz54wus2xTzaqKHAmEY8wBx5wjyiFBHPwc9nISEj7F520AEX8QR1a+d3u/1WwHdhRAD4oMcZKWVcdyfd+XZZlxFgQBxpgQIonyS29cEYl8uH/IGLOCCedRvpbWKqRYltfWlpq73WHLOGwdAdMvFctLK4ssaGelrFDVdDlbKOc3m3e23t10XfvMhVL5dG61mAr5NqRRZLFKhNLVsux0Ht55T1WrpWL9/p2Ntx/+Oabya1UJiucBhKIg0RCHEYAAQUx/DqX4iRDzZwzCOjY8P+b4PH2MMYSQCOj4XzHGOAPHlqyQwye99ZbIn546j60/ccF3EHKEEEAMEwopIARSSikLjcmgWMiPzFG/M+JhIMqZCHBEzMZ+q9vueoHb3X0IOCzoOZmgYW+UTmfnZ+FsLe0ASdMkE3sAsYiakphVZAoR5AAyjgBjGD35Wphw0kx74EzC348nPgPea23t9XdY1lXTRIJ0pp41O+r40DMM4ziOl1EmSwpEADCo67ob2J7rGxPTNlwv9H0e9Y8m1YWiO3B2gsOD7Ubocko40WAURXpWX7u0sFo709zp72zvGqPmyBgM7HZWzy+cyadmbCt7a+z2vP5QFTLV/KJUHI0O9zMqm53NCrmutbsNhND2grvGj6roTRHlMRIZhMd9B4SQQQZjNtNOyrUGfsoZ89hL9RjPDR6foQL6dAeMMcYYI4REETAKGGaMIk45pZQxwPl/5jDNzxfP2kDhxE7IgvTjGgQ+qT8AAIJjYhegDxHEGHAWURpRyqPI833bYyOKURjyiImyIApEwgQoujjojhpHzXS6cLRzMByM1hfWT51atMfmh7fuAActzOePBkbjaEMWFvO5lOc3ZRkQwjEGgCPA2cdBv/RZ8zU5qWj/k+JZq5/PO0+6fJ+X8iImHWTmhZnlsqiRCHhnly+1bzvv/vFdOgwZY57jCxJZXFykLNre3m43O1vtnaWVuqZmnFGQkQuyXhs2O0bf9kxrZrEIuRjZPpN5WtYQQoIocOpMvMG97ZtH+60X3ji3PDvrRQ5z2b1rve0Hd7JzeaQ50WhSoTU5UzD73HAxba9MtjKDzAHn/Kvrv9RvDO7tfSgL/UxKUuUsRhCBj44q4DwmGP/k+KTRRB/zcRsa0wETwjk/DrL/dAeMMUeI0wgCQCnniCPOadxGcsJnj58awxEhNooeIcY5ZIwxFkWUBUHgeZ6GfHPQs70JhCIRBAAwZR6Eoe8EzUarNjOfzuidbsvx7HQ6K0S80+nv3N2sVIqMWa2WUS3khkPaGe1CcEoiH6WBf6SRS2phQsLTgYRBhDjxvKA9aJx7ZbUXHqROFcDsUB8Ve1ETIRSayPO8UJsEUVhTF3y2JWosW0Omy/kAcYYFGZ36Qk1RxUnP2ri+GxhAlHH/0D/3Wq5D+u3xdrmW0et0NVtempuToIqXxbd/+O6k6WK1WehqPrAL2dz6F64Kgrh5eyPT+41qjnJvbHUkODwdCutZUFrP+FxaooxZPuc0wEjWBAFyEXLmwOl8QEVEKAMAAAYgZQB+fAyuIlicc0ohQhggDAFmjAUhF0VLEARZlkURI4QopUEQRFGkyh91sQBABhD6pKMVEQ1YFDHOGEJIFgAhECEQ0QgCQDDACDAMGOOUcsYYQxRwxDgHHDEGGfgobuekdJnT6u1ObE/9hIZGsenkMf6+Mf7QcXpHAT5eNxyng4xLjyaolEWcU4gAwoxgQFlEWRgxiDEmBAuCQAg57oABAJLkAQ4p44DDKOIRZZxDxkEUlIgcMe45PnE9RZYJj7qe1dnrXa9UVjIpuevQ6lz2aHM7JXI5l3N9YWtvnE4drcwU6xVBzc/vjpp3b1z/5V/79VztcsR94oVawCLbZWrq5asv/8n/sbf+sujjSVk5PwqxyxwRqhEdClB77HvFETeTYChOv/v4fBNifJqnhU7pCz5t/T8xXWnMdjuMWQmLOyErTheLY/T3cfU/lilXFk8qP2OTM+X9p23Hpm1vp/UPjt0DPr96WVpOPbq7vbO9O2hNZudmPCtMaekH9oGsSJqmeZ531N9NAaU0kxn1+/lSfn+zLeo4VRW2WvsowNV8CZrKL33zq/vbR+29Nk/B5eXlnYMtAaNiNqcop5mLUlKuMldZO7UKGQKYZR8pZof71M/n58RUcX935ztv/cWVC1fXXlodNAYiTXm9XGCmRTVDSTGwa4pa8MIhggRiAgimEXdCTyJQEFHMRPQ/m3fgo74TIQDgR6f9YwGIgowxppSFQeSHIUY4pZF8ST3e/8YYAsAZA1GEKMUCTgVB4LhBFEQRiyAkooAxwRQyhiLOIw74cVlwzigFUcg+mnZwwDnkDAKO4HEwDkeAc84ghPD/z95/BUmWnWmC2P//51zl2sND64xIWVlVWRIF1QAa6J6e6emR3OHscHe4XGrjG/lO4zNfaJwX2pJrtFmzEdyhrY3YHnb3NNAQ3UAJlFapMyNDSw/X7lec8/98uB4iE+kAAh2JrATiqzAvT/fr5557zr3nO7+Gg9wdZ1LJMwYSBAQBIVaKlCMIygFiNkSoFCjFRH2SEBFrLQJZyyBorVjLImhZrLVJkgAYa8FaMgbEGAAwWsjB/d39uXNXSr7Lo3Dnzk9H5wLHVZ7ndbvdTDAxPTF+Z7X62ef3up3OTv329PDrS9frvuMOT+XJl+euXN1Z3STJsfER2AgjMpFoAuQTrs5nOMMZTgOau2rh/IWL0y9MTk7e27q5dHO1uRM6jjv34uj6nZ1eDK9/8xp45s6NJbEqmHIRsdGF3e293LA38pwXNbi6t4+h96//u3/90rVrf+sPvzM5ORnHce2PN0eGypfmL7773odeOXdh/jKLff+D96yYUrk0fWFs3092qzu1WsMJSSvHzzkdqG317vcWN21nwqPzTjsLnO21sdfuKMx4bi+JCW1GyFGaGUzMhi0C+Ce7YhRHoQAbFiQGFAUAyEGA2awTBEEcx81mhFHkeV4m483Puwf65zR3FTFrEQXKC7vYbkm3w3EkIOhqEZSQhRMhNIioiADZWrHWWsuH9r9+R1JVJGphRBEGSGn3C2ak+BXxpJ0XBrZ+wty8pwVBK8gMFlFAoXLQc1xFhNg70DyDiOUDiGgRFhFhYRbmVO0sSZIQJojGWjCGlBJOEqVUrpDzMhm32Yua3d0wJPbyxbJbUK1WPZ/Pjo6M5fIlttLrRGEnYptUKlyvNus17bjdc8+Vp2fnTVNtL0thqixJVjhmBkGjCQIFDLp7MkXSbx2e+P38lO7bZx0nnZcvWniSznj5O7fvjQ9NXrx4KZQ2uhKPc94t7a7tJz3TrLfzU86FK5c5kfff/FQFQMbxdUbaGjLuwvnpTj28ubmyvbvfw9ZwqXzxyvnRiWJ1f39mbvzyhYuO6CuXrnZ7nVajaSTZ29+Nk1iU9bPu/HMjF3Bhb6N6+9adrO9Rydtc3e512rM8127dSbAxe+Wa6gyxjaiw5Y3ttG7N7GzGtd1eHAFqBYgiQDIwkeNA1Qf0tbyuAwKSOqMiSTaLuRzmcmAMeS4lifZ9J5vTw6MRERGJUqk9mABIRBrtKJPBXM7pdshEEEUMBkWMRCiOEWYR0hoBxFibJMZYOlZ+NWViBAClURCBGQREwNovVjjmGX5JGDbMFsASAJCQcl1fO5oyvgMHdyMzWwvWCrNEkQLBtL4IkZAAMxChiBhjtE7dCIiZjTGe55EzEbaTF56/2DUtEbW+srq4cCXv22azOlQpX75ylaT+3vtvoTf0wgvXHtz6cNQrr1oyKqedbC6XQ5GN7e39XlyCSU4yRGBQCIQUaBJNTjc5WerHwc/XFwu/hkCJZxrPOoF90XDS69J//qffm59eGPvyRGVoOBNk0eNed7/W3SlNF1740iU2AJ7xsurv/IO/rcV/66dvCRvbBWXLMTgr4Y5lQ6RiTorFfGO/uba2NjE1Xi6VFhYWFOilOys//fw913Vn52cvXrowNFK6fuP6xvpmLpcbmisuTF24Aw/ufryiINvZsm1TKzxXKmzPQNgqj5SzFUky9y3EcbFTxd252f8qYqfbzcVNsUYAKeNkM67XCHsnumBBQURFohRoLUqj1oAEuTxkgsRx0Q+gkPcBXc/TGV/ns6AUKkVK0eEyKiLdMNGKHAddRycxcAJJxNZy0gZEICTL/dhfY1hE+MDGg3j4l3rliAAKgHC/QLp84RaxXwXPigR8Wl6R1loRRpQj6z4zAGUCfaj5SDXPBzKwA4JIzFYARMASoeWjIPJUbk7dpDKZDNLCnRs/GanQ+ELp3v2N+8srI8NXXFZTCyO7GxJk8tWdra2N/dGZkXOzc9zaXX9v8/xXf2e3s1IsjDWr7dW7S7nSi/64Se3QSqlUG05Eh4F2Z/g5eFpeu2cS8M/HqUUrnNJ5TwrtSfalq68Wg6HPPrixurQ5dW7MWruxvf71N75RH+2cm1n08jpfyM1NTr/02gtr66vlsULSxK0H1epWPZu4PdOOenZ8dnJxYbqXdB7c21xf/RPf9+/culcuDJ+fv/jtb/1eu9Pa3dt55513Mnm/XClJze7t7yjfWZy+MDo6WiqWHXbKhZKpxyv3N3rddz0uzUk56jZX95eavT2MobXXql75vmpMOfq1fDboxGJNjEDIA2MnBg+QIILW6Lrg+47jiVaoNRSLRmvrunHgO0GgXYe0RqXEdb1jCyjbVJvMrDWxYQEkJa5LhhA1guiMtYqUUiLWYVbWoKswQUxXOjiozfCIHHy6k3qGXz9QEEGJgDCbGBJllFKHlQkOzP+CiNbaA1es/i2Q7sYOg+tEhIisTTUunBJwNhhtjq7uVTeLk/b2vRuF4qSrs/kg+OpXLi/f7W5ubsa9cHxyRpEbd8PxsRFudwsFd3TCbddbu2vt0coL9UY4NqPcJNJe6DhoUDEjAMVGRJ24wthpOdc8aTwr/fxtw9lal0KrXu69H322sfHnW9vrCxfnX7g4oQrue29/2Lna3e1swIYdKU80a+24Z5q9Gkfqq1/9aiFXePenH3z88cfZnE8dm4RtCOLESL3abXXiZquRzWbDNrQ46nSj9k40v3jh4sKVdz74yeb+2kRpZGxuyK/Q0s3lSuHmlekXL79w4eP3PgoSLIxkVteW4kJrYfhldBVBeXZolJOksblXk/bSzffHfF3xmEkjxeSx5Tjhk5XsAAAAIQKl0XWV72OQQddDrTGfJ6XEcSgIIBug54nWDABR7An3BRprU+kXRUiMAgERCyTCDAqRBIR8D1OfrSRBk1AsBGS01lY0iDAzC4sIQF/edYXSxFhp5/or8qnO8Rl+DVCYqpqVkYQE2EASM4GNE3MgywIAHNqArQUQtJbZijFsjGUGy3JgG+4fn4a0ua67urU1MTmeRDsrd9YgzpbLo7v7dfI2Xe+q4+j6TlcpGBmZbHUliU0uyF58fYadpoJer9Gr7slzl1+48/47V0oV8jvKy7qeFxOAUaKxk7Cnv3BxwL9tOKkE/KTr1A6sNXO2oQGA0/P61h/95DoiOq62Yj+qXk9i85XfffWlK6/++K9+4uTw7Q/vNe7J+Suzf/gPf788XIpa5p//v//54pX50kg+P63q1b1zF87Pfmuu1+vd+OzG9tbO6NgI2qDXkkKuYq394INPPnnn5tjU8MtvvFAqltmPmJKN6uZ+o5oJxn74/R/RV/XI1BB8mnShcW56ug3ZkXhktjjuaGO7cUFPGZvs+jv1oftz2bkCTHhJ0GnFvajj5a3SGgyB+VUcOJFEaXQc5bro+0orzGSEFLgO+B5ox5ISrUQr6fUca621YowVkYN4YGRjGJBQCzErBjDWGGZ2nDwiKkSF1GMUZCJCskqplL2PT0YqDB8Q8MOi8G/X/fzMQ6EGFERCQYEEGMEiW4lNckjAKZtyP8IXQTAxlq0kCSdHBOwJswimmpL0HlFKsRtlClnHjD5Yu/v6C1+9e3cvSerDs7i0vJQkST6fZ8OdqJ3LZIvFIvei4ct5T5V++MOPKvmRdZDVzboJKWNdckLtxqS0RhJRIGCYlToj4DOc4SlAS5ygpxPG8tBktbm7fr/+o/q7+Zyf1NEZc2xVLn6p8rd/929dHH3xL959s9luOBW7X1vfeUBj08OL3yldvXZ+ojCHrpTHvH/33/15Yz0cGi0XZryd1nZ1rZXXw9qTrbX9P33wExvHOm8uf3Vq9sWZ0qhfqT0/57V+8B9+pH2qTBVI4/KtzbHCxden/7fxlnWKnXvRh7OVKM8vDHuXuuO2ndzz1Ua89yCXv2LJ77TCLkIxyCIOiEPCx2vVehJ5QBoQlNEu+Z7KZ1TgUSEbpwulIoWi2FDMlCBaCQUFiEn3G0RkRCzlXWutNSJC1irLrtXWGIkg6HFi2DBznNgoNizk+26vnrqZChESKTjYK3Ufdj9VhGmKJAuc6roRMU1myczWWn1Cr+/Tiic+KQbVWz0tDDQwDIg/lgH3Aw/YmToD6goPqrdqqd3vGKFSSjkOaQ2o2ECSSJSYXshRiInRhhEE5aC05iPtxInRjivghHHCYnwvEanWaw8m85Ms6s563GrJ7mcfX7zylVxuXMH925/tNltr1Wr15s2bMzMzIuJnzg8NDYWr2p9pXbhSKGF5ZIia9q3KxISvp7LwzpXhqXCy+c7akuc/l3RqlYJqdj2tHz9ug+JHYx6QiObE99vJdpqDjh4YRzsgLnPQduNJq0ZPapu0MkDJNyjx2oAvGAbUYx7wnD5p56ynVT94EE7LZnzSdnRJjUSSRBC1413we0GlmGCv3uoUhrPV5s7Xv/mlc+fOf/rezR9vfXjn1hK4JhMEQNZgkth4c7VuDbcnzehcyQnU4pXZ7eXm7v72yMXzs0Mzjuw6nOvstXO5kgdeq9sCtwdWafBL2crU6NDrX3k5Wwg+/PBDw8n83IxJbKO6F1zdGptz3r7+vdvXb2EHvjn1NV7OzoWVln7AZpUxsCabd877UOmYXpc7MKDA9SBochAABYCBjRCwAvWwUbZvpk3JD8ECCKEgHbrGIACIZUQQEmABQgBGAVAC4rA13dCGESQxsCVjkfrOLnIoAeNBZuBD2ejRCetHESMCYhoxDL/G2k9nOCHSHOBwaNc9QJKIMWyNsIG+YxajDBY3D2+UVAuS2oybzabr5Ld2mu1WozyUQxW0mjutVn18pNNq0fb29vDw8OXLl2u1GiJmMplKpXJn5bMZ+6JpY11XVzbvV0qzfqb0YP32zHDUbvccJ4tKJVFkbMKMSjtwwgLsZzjDGf760EHBpQQUUa/TdTKqWPRdn8nlC+cWNneCr7z+O2t397737/8qajMjjC8UpqfH46AeFZOp6dE2VLdWqnH9VuSPJeLNXZhqNcP6at2KmZqcING7G404YdchK4isHAoU+kVveGg6u7tenRidKlSyoxMjoK3nefXW1kZ19eP1713JX5irTOVfyH33h3+sxqIXL/3BcP3F1V1uRi3H3wdb91hcyHatSaip7eMJeOCOQzwSRrZiLaXVVxEckn5lXhRJCyUc5JI83HnhwzCSWoNZjvxogFCMVWEEna7thWyNjmIQUAqU1swsACmvAyKkjjgo6rhzFhyQsXDqLkYICIIACAKE6kw1/cUEokY83EgRM1oLIhJGli3EMcSJtQaZmS0x2EHlCInIHtvsE5ExplqtmqRXLI7GgUvYGxkpRybeq+5opYIg6HQ6586dy2azu7u7ly9fnpiY2NnZSXC3VItbtVCXTM80Nx5sexl/q9ks+v76VgukhIJxHGfcXzER1RfNieaL1p9nBc/KuD0r/RyEgXwUDm1FdchwRXq5wHV89jDbDMbh/OV5L+Ntr1R//J9+2t7isfFhk+lOnRt/6ZXna3pld29vbLTs9aRRbVcbe6OJf+3a14KrpUpp/Ac/+OHG+sbY7Ojo+MhPf/JJ0Y60wp7pthKTuLFsLe+VxrLZXEbFwQfvf/TOO++QwrGx0Wp1b31lw/Zgb30Th19sLUf7YWNudkSV6p+3/nwiu7H5iVsoT3o0B35FethJ2tZawOCkA0FAChhBHCLPIa3Rd8lx8Ljm7JALAQDBwlEiXzwgTmQBEUYAEEYEpQgsK6DYQJJwHNk4EmutsRpBAZDSiIwCAJhKwIAkRAhMh2c8zsEKEBAFDkJE0l8i2ifslPGs4yk+qIdzlxr7+1JsV5g5MWIMpkHAIgwAgD+HgB+KRDLGtNvthfnKzNzc2lrYbm2VCrS9t9/Yf6AWZmq12u7u7kcffTQ3N3flypWJiYkf//jH6+vrL74yWfPrn17/bO78eDE/tL6z73H+0vnnO/ubG1udXJmtBSLOZoPYRnEce+6ZDfjXgSet2j3Dr4aTOlWdFvRr37l656OVndu7YVMcN9OoiRewFwW3b9/+9KObjfVwfzkaLo8gKxNz1Itvr1zPzwM4CZPxAweJ67XW7u7u+/vvB1LUlP/yG19576P3mvvd8+cnBKHZbFsDLjh+JiDH1Gvt1Qc72Wy2mCsMVUqzV0ZD20EVxbVYhJXSWc/3M4DDrb2798ZLl2Y2vjpcmd9311vxJapfcDPPgww3QtO1NQDflQJD50QD5xIAgqfR98DzVCajvQAdR5DwuG/U4Ssgp9Sb/h3GjUCSnuLRnyRJYm3qroVEhIwgwMyOJoR+udcjjffPzRmLfZH8iJjTKftV5vkMTxiJYTjaBqX0yQBgGZlRBKxFZgbpZxv9OZ7uh7cEIqbZshzHcRR/9tlnW5tLjrNdGcLV9d1Oe5doWmsdhuHNmzdHRkauXbu2vr7+/vvvLyws9PZVa6Txye0Pw87lKxcuDE1kR8fG5yYv/mC5mm9TpuAhWRFGArG/irX+i0YMA71Pf839eNbwRZvHQXhW+jkIAyXgv/sH//DtwlvvO+/URuvDwz6iSNtp3ICZl7ycV3SHC0Wle3XY3tqJkuTO5w+Mlz//+mtFbxhjRzvWdYLeXmvzzj6OFH/60ccj+dn/1X/5vyfrvfX+W2OliW99+2t/+i9+goJBMSgU80JxjJ3mbvvW9fvjE0Nf/s7LF3LntvbWR4cm5sYWP/vgxo9++OO6bH38+YegrTskXamv3dqaWPhaScZR6ySa6ZpxwxJKW3kWDbiiwxNmjiLFjgbtoOthNktBFt0AHMcCwjEVohxmY1ak8OFSSEdtCQGLMAqTtWytGGvZhCJGOwikhB2IJE4SRFEKiYAIlaJDB1cAYUuPCK99aVv1TYlp+NOhSnyQs8mZBPx0YfomVDw2EQIAzASgDmacAH5BEes0Sjg9XmllbYyIw8PDk9OT+4290tCUTZrVvd1CTlcqcwBQKpUcx3n55ZfPnTu3tLTU7Xa//vWvK6WSTuznfcPJ8v3Vsp+/9o3L8wsLG3d2ak09LnntZLUbmq4kcUjIjuPCACedMzxdPOvE86zjiUvANpSXrj0/N1c2NlSgP3nv9uZqe/XBrksPLHKxVFyt7nYSG2R8h1SrW7u8cPVrL37n07du//TD97EUQqilR7JdqCyMjpR7n7+19Pbse3s7zTvvb5VLD775N37nxvm1Rq0hiam3a+RItuh0u9HGhzsZ37n50b1Prn8U95LJb1298uLr9pxz6+bdsYXCqJn40R+/k5/Ha/9wLmfy3bsbhe7VjIyTcttJhMxIpDkPSKFpn3TgPI8cLZ4Prk9OgK4PyrGoE3zY6/XQG0trOCb+HpE0iQYRYUEWscBGxIJJxJquIpsJCNBLYhIx1hoWBnAQgQiIKE0GfBCQchTxebzbQUanC7G1Nkk4zVCJCFH415/032QMjmt8shuU4451xz9MElIEAoxIiIygFQoPdL4Ga63nqJS8U/2zUmpiYuLevWXBobHxsbXVO+1OZ2SkMjYxXi7n9/b2SqXSiy++2Gq13n777fHx8VdeeeXNN980JnqFvpz3RzIFJzfiZd2iI3q/tZmYwHXLAg6BIU3CCepfxU/+aY3zIDwr8alnhPqr4UnP79OaF720cffla1cvXZxVNtnc3L7xyf1CJSjFwf5mU2cJETd2VwMc0jpo73e9IHvvxvLW3drnP1n6yf/4eeUqjAyPmrrqGBGL//gf/ef/7N5/+90/+34xO6wV1ndaNz699fpXvnTnxu0H9x+0uq3KcPHc4qJQ+PFnH1bXmxA69TsWusGbG7dv/2CPC+3x4elGY/X5q9nh2WKr2fmzf/Oj7/yd7AuvO8mthndjEdwQg54J0eOipqCd9Aw1NOdOdsEOkRbHFddDxyPtiPYMiAVJRUwAOFpEEICO4XCSRARBiRgSsmzFili0xoJFY3uAKuM7SLpHGMaJo8EYi6BTMzIAMIIFSf9Lfa2P9zD9pxdoxDQzsAARJLbfvTMC/oJCiTzkAp1a+kXAMgKQVkiIAowICDho3WBmxH4GytQHkIiGhoY+uPdhaWQ6lysFfl6jt7a21kvCq1e+s9+ovfrqq3t7e3/8x38sIr1eL5fLeZ63unM3q4vj5emROfeF1y4le7DSXWFllB7RbhBFCTN7nqdRGCFJkjMb8BnO8OsH/vf3/tlGdSUxvdnJ+a17u3/5/b+MOZxbnPjggzu7q7XqSifcE2185SA6KMpMTlYw0c1mU7ykbevZEapUKp1GNDZXHpkYunXjTlJXbpwvDGcufG1sj+5D6FZyYz/94aedHfull79mAe/dv9vsNpTw+NiEiXh1dTWReGSiPDxVypezU5XAyy4UgsutB27teqc4GQ/Pz8rWpbX1K4+9AK0QABgAgUTIct81yvNbSQyWtaNdItIk2Qzlc05ldM3znULBy+f8TEb5LpAyBJKKmIKpOzQiAipBRBoQ+9OoO9aySWwSi4nAGmQWEFxZN0r7ruNZI51O1OuGYWQJSDIhM4E4wspYlcRgErQWmBWhpIXhPFdcVzkOaQVT4woAWCiKjUmsNWJYCFWXI5NwHNs4AbYq/UvbOXT/6U9tX4N9Oju709ppnlZ8HvAAuY0GqFIHtHPSqxoYf3zCdgb1x1PCQAlCbBMEm3GhtrM8PzP81ps/Cs361ZdyboY/fHulurrPev0f/G9e+p/+nf/8n/83/+q73/0uuonnONGeHR0Z+5v/4NtvvvPTucuV2mr1latfbSZGyYWRwuvvff757OWRK5XSUK7w06X4xnriyjwkwBT5JxSDB83LwPkdVD/4pO2csD+DMGgeT2pLPi256cTxpidtf8DnJx3nge2fUrzsoP48rfXnSbej/+r7b4JnHA9MF5q7zTiJt2ubDzZvmSRglCDrY09sD5RWTqCU622ubylwKiNDQ1MjWByqTOdfee2VjJe9cePG5u762PTwdlRfe7Cmq9ii7ZlrlV6wa7shZ3v5kUKj3Vi/v7e5sus5QeTVfJ1FIBHJZDLFcoGItre3bWcIwltjFXu1/AcLV567v3N389Z2QfYHXcCh6k9EBCwCIollIcwgMaV3qlhF1nW05+lsLnBd5TpOKl8yC4LEYhUq7CeBlENPq4dDOh8eOCfNvA86jeMgUQwsmM0rpRggIRLPJgIYZFwAdPMEqIS1NRRFEIU2ijhJxFpLyIDgaHEdcl1wXSIi0gYRwYrvYUwQkfhAAuAkiSLyXFdAsVVxxN2OiSRmzsExzfmp3Bxn+PVDgBkAWKOkd6ONkyhJQoe8Ymlka21Decm1Ky90R8NPb3/P5Uw28EdK5csXLulAHOW1y3G71Xn77Q9f+9IrX/7G1c/fvf7Z+5+3TPylly80mzu7e1vncTqWuvjaUeRDToQBRaM+iwM+wxl+/dD/4v/1b779N79x6eq5W5/d+enbb9d3W9e+cunqC2+srtbjhqzf2tsMax0TAokFE8eJ9j1rrcqq3eauSdrOMORHcvlcfhHOd7Ed70VOQZ27OsMhVTe3O92NoResVyhOjk91d2Rnd7vXTQpuCRLtlU2n3UPETJB1M8TMwuB7wfX7S2Yjnsztl5+/dq7yHRKfSBeL+f3HOzunxQWPu54iAGgFxnoAjCgAFjAhbbwAcjmnXM4oIq3B0UDI1nJiDBFrR/cJ+IB3UwIetNdUmkkJajFRP7GlCFoL5eE0EiWJIkMOBzl0tHa1ky2BMFpLSYJhaDsdiUI2CVvrCmBqF9RKlGbfsUistWVARyNbYALtEAMiIiVKhAgVgssMvR64Wro9W6s/1L1De+Rp2TaeFVI/rZ3404IVsCKAgKAQjFhrknarQy9ceS5Otm7dfxA1ms/NDZ0brXTDex4E1a2tvc3tkcKQV/bY0HPz50i5tx7cmp0cGRkeGh+bvu2sjZRy5bK3dHtrfDyPialFK1NFX+tchgo9I9qxCl3zq6RV/w3EicOEntJ5T9z+E239NxdP3Amr4Jff/fFH7/zkHSdD07OjC+dnxmZH9mo7r73+ZQn1ncwqRrc2zE6320UiRDFJkiRJYqLNnV10IZttrt7aqtY/f/eDn770+tV8KdusdjjBndV9SZxatRvW2Ywm+WIp3G9tru4KO/li0URxa7+DBHPzs7PnpnaqWysrS7PnZr78tTcWLm7hTj7c9WpNm4W66wbgD3XAGez0kY6TYBrbQzZlnShmIlLERKyVyWZtsSSlIchlPQAgtIAsIiwMCMyHAUb9W/WQuQaGM3nWWlSKtBKHkQ2kEZ5+VtiytRxF1iSIiK6jHMfJ5owwJgmYhLs9yQYQRyo2gOKmNV+NZYTUDs0CbAGIFBEBooNgWTQqRHS9DDNbIyIJMyqSjKcKOa/eOJJ9f9Yb6K+PL5rzyGnFU570qp78KAgAghAiEyiQpBs2mq1obLZS3U0cV4ehXV1f0pNQHi4yJw/u3neVRs+5v7QUR4iz5ZHRqZHRuVIxx4YVZS5dfMEtCVIIYOfnJzEx4LZyY8ZzxRXdM6HFCNiCPplK7bTuh6fVzhfrbn56eFrP9W9qPPRJ+6lHhyZ3q9uO40yOjSzMT7biqrVWa+dP/uRPJoZma+tht9sVEa31yEglW/Q3NzcZ7cz8jM7olZWVjZuNd52brXZ95uL8xNiscnDrQX1zZ6fX45I3YtraLJuVB82JmWy2OORyr9Fp+joplstBjvaqu72o6zhOLpeLOsnKvfXxyvrXv/XK6MI3lj7VG0tE6A3lM0013un+nHw9SsCmAbWkbCrdEgnbSDtKaVRkPV9KZTU0pIeG0HVFRBBY+lmDMc1hNAiDJCSFpDQwiGa0LOwKW2BhYBYAtuL7ylhA0YpQKat1ZC1oQWZxXVQErkvWOlEE1gopIUPW9pNgWkERscwixAzGsjAKChGSVixiLYuIQkqziui+HvxIBX0mAT/6+aAffMGuS9I4JhFkTSqxNu71Gu1249PbXS2NIKMSRffXrjcbO+TWhrl099b9wCuUS5mbq3ea7bjebE1Medeunn/nJ//fWvj82p1ec7+jux2Xm0rN9aJuZ68+WmiG3Y5SRQCjlPJ8H4yK+Is1Dk8LZxLwGY7jiUvASZTs79RdV41OVhzt9ho92GeNamN9q7kVLX++1dpKHPARsVFvdsNOo9M6f/ncH/6DPyoXy//mX/4Pf/Gffvjh29dnF6ZX7+70Wvaf/tf/xc5i58Enf9ELY+m0hrNjXam66GbcgsteyRsNVMlxtYeZb/7+G3eX7m5ura+sr7iuWymMxaHZedAIOpeaGxe5NeqqEI3fqZrdyOuaTDBgHFiAUCGwUqw0+i46DpHCwO16nqcdJYh+QENDqljCTEY0mdRZ6VBlnaKfSeOgSqCIgMjh548ZONV3irYopMCwIHEaNoSgmJSjka1iRhC2LEmSsIgxaBlZCEABChKGYSQiURpoJMoyAChE7PUiIiJAw5IkqWHQKoLQmrRjjtYKCVP3MTaIDpxJwCc//mktuIPuK0YGUCCEiIQoHMdRo9mKSPcmh7OkfKPdXhgubywFfvv13GS7E7//wcdf+drXh0fH795/t1KcGB0vtDs7t2/fGZo71+pmgmyJ3I3t9b2cO9KLyEEvbKmtpbZSbiQROZ7WnmELzzgBn0nAv9n4oq0/pwW9tbPVaxnISbcVAmDGyyDjznZ1qDictKRVjZM2ZgouEYmBXjsKw3B5dfnt937y0gsvVet7gkCevPD65V4z+fCjD/7jf/+9+/fvF4JKMM7V9WbCcUK9fKZobNLYakRJnClmmZKQ2p/e+NjxHXJxe2dLiU56UgjKY4XprTuzsJuxrJEy7TAXxQwe5QqW2wMWUFHYT1ZFrkOeR66rHI2VgnY9R4iMiOtSoYh+IEw9QCFACwIIIP0kGCykheGAgPuL4/HXn0GgHCsgwEQQW6MEAIDFihAIgSAIKVSpmtsasSQsJEIimkXiBEwicRJ3emAtWhZrMGVfAA2ELiUKFQsYi2w4SqwIIdpGLyEEV2vPI0cTITCzHLg6nzlhPSv4hd62WqFCSMSYpNuF1vzsfD5woqhVKY4Xc6P3o/tx1C54ha4TLW/unNvbL5bKL750dWpkMkxqqxt3xajAq5TK4x6qCLq71Tvb4bJfmXr1lde8GKvLvaASiBcmoUpaUWIT382eSv9Pit/UhfWkeFoS8NPSkD0r837S+OMTq6D3d5peRg0Vh6p7+599/FmX62NjY0nP7LebWVV0fKSMJtaNRs1KzsvqqZmZjb3Vn370zvT89Pnnz1Umy7cefL7VWV39qNGpJ+/++GPDybVr15bvrDEyagx00Ov2onCPDYMre90G5c3i/Pz96/cmJsa0S9PTk1Ojc1HLNra7EOq9xqjfJtdviMube76Js4XcvuNtc3tq0DWwgO5naRZS6Gh0XRofdR3XMaCMRSGbCUCREUkIlAUhEuY02hJY6MCXSwTkKB/WzyVgEVCIVoiQNVIMDCwigqJEQBhFEAWEJU5ErIgmQg2oSWs0bNh0wziJreGAhRODJsHEgjAwMxBqzxHExApbjmOJYgBgFuhGgAKRY6M48VztakRUPxtr9CSE4Gcaz8xQELMFAiQgAGDmxCQsPQ1ubafuKLMwN6VcL26ZZjWOGnZzuzY1vbi0vIqZ6B/9o7+/cm/jX/5//nmmDNgwiL5ShfW1jfyInZ095+sLExdecBzoLdP2dvV8RXl5SyqQCF0d2bP48jOc4dcOrQy88M35zIzdbTcuXJ3Mqec/eOvTkdLE9uatkXNDy8FyYpLp+cyCFO/evC9dt1QoXLr6xsXLFzKevnrt0u5adXVp/foP1y5PPFfRI/vVWtSL9m+1sanHgomoGxlHs4cWbeDnsjpXi3a0DWdGhn/n//Q7cdyeGZ28+956vD48M/3tVavvP9gb9ifaANADDLGQbyA2wRDXJ1zKW7EARimDaLW2WosAG9sTRkR0XMllVbkkQ2UoZMENDGASEAqKo0ERAoIGLzEADxc/ELEA0O1hKkbjw4knlYpS5yxERAIkQURU0kniRzZBSikFqhNZAGAWZLAs1pJCZUhcIBICgcQYAkSNmayWQNcykTBag2LQxJQkLIaNgY51mIVZrAFrKPXwEhGOsyKWYysOExvtk+uC0pLNeMZak3BiLVsAIQFEUYin4906iMAG1ekchNOqAyoDwmZoQJGD04o3HYRB9D5wfAapxE02cB3HcdrtRrnkL681ur3swvzi1HTl1r22kSncGKnvrGpUHY4SbQuVcHnt3bGxsa++/sp+dX1rfaMYjIAI6qUct1BXb+33VlbChXl+/as5HXQ+fdv75Hrp0oXhQmbftlWnl3g+1ZpxyfNOZRwG4gsmMQ+qE4wD6kYPjP+Wx7ej8PGB1RaEEPpuK8CADCKAjKgPGnwovbyXOq0cZIM/NJklZoBkdkpx/wMxaHxOetoBdbufNOiE8e5pFdrHYJDEP2B8FA3IBf38703OXR2ZmB+t12anhmcvXbhyYfFCO24sXFlYun3PLcDCpen5mVnX8UYWCkmYzL947tPPPvnwer1crrRrvV6bSbSJeH1/CREzlez86GRlaHh3e89au7uz5+Sy69vr1trE9tpJ3c/qibmx0siQG+vmXrha39paa9JeYVjn8tnJK5dfjEOAnxHgENGaSCkRNETiOOI4pLQQYZxgasBS9JDDkWUAQABxFBkriKAQDQtgqnZGETj8A0iTBSKyEBGyEEmq2c6oNO8VplWM+kdbNAkgHpXyPXxIEHX6sDBjYoStgLCxgg4gCDMYiyCISNz3uE6F79QqDAdO2H03K7b9lhFIgBEQ2BAyKeu52vd1JqOUBkfhXvXohkDEsyT0zy6YOY5jRExTlRaLxUwmU6vuTU2M1Vq92emJYkCdNhdHL1nBVqNx7tz5r3/tmzqjb954oDnz0osvb+2vVS6PghcQ63Pnp7c3ewQRsQ9JTlM7n3OVQmZynQyGSjjO+mdlLk8ZLI9PCMOghAGQFKWRjkQIgCxwyAyHiXQAAIyRhwm4/3dmxf7NgP7P/qu/6xecVqfhZZz5mblEoq3aelByLlxarNZ2v/zN184tzN+/f3e7tnz5pYtXLl5a39mbSyare/ukRIDv3rsTN8V3gkZnb3R01Ei31trWnjBFxVIhTLxb95anp8cvXrmw+mDtzs17Q5nRqfEF2/G+9+/fBID56Ysj/lV/5LLrTDdrWStFxH5650c4WKkIERyHXRe0g4GP2iHHwXaHrWVrGVGUAqUEAKxYSGu+AVlEQEiMgAJESvhA23zstn745pZUm516Y/mOIKIgg6TF0gGJETGOjzZ9qVdXWgSJWVsRsSQMxhIKMSMIdqMEwAogsLJW2KKwSqzYRAuQsBhLzIQABgRQEpsAk4iwRRFi4ZShPc8iotYqCJTnK9fVjk7V33zMA+vXt5o+cdvVs6I6Pj2k95JSxMxJkgwPD2ezmaU7H37pa2+MjBaTpLW2tuy48chUZXx6bHvz3uLchdmZ524/WF663z43MTI5MecEUI32dNbLOJDpxiOjxaGcL3F+dxcSWy+VR7VjolgcJyAisXHG90z4bIzz07rfTsuvQlgJCjJZFAJUiIKMoI4lsHtIpDbWHkoLkCYpAAQBNUBx88Sfl1Nr/2mFIT3ZtfHENuBP37xTS7ZGzuVGZ4bv7nwGhpjsznatY/Xa+qbjoJEkU/abMbRNbS/cnpufm52e2tjYHi6Ot+php9VbX9qybF957tq1a9eWH6x89tlnGztrUS+utapLd1eyMOaanBP7KlQlvzyWnS7Z6WgVop3pjOtFNNHWI0F+mpMAWfnaC6Vz/EoOr8f12FGiNGgHfB89nxwHtCZASRIyBhFEa0FEAmEWtATAaVoDRaAVRSyawPJDAushARNpOTgvHtQcQgSTAIAAChEZZMA0T5YkkTrsm4hYK9Yis8SJsZIqKDQCIWgRBQARJyJiLbAVtmwNGQMiEicoktZTQpMgWzFGhNMUkgJAQJIWcQAAEcxmEBGVAs9VjkJrbZwYFCviHHuGEQ6yDT/pB/LM4et0gf0Ebex5LpEkSTJcKbguEapuu74wkbt3bzOMejNzs7X29TCKvv27v/v5Bxvb253aPheLs1oXksiOjlQ++OEH33jjZY2m0+1UhifzQWV53exWGaVRKM4Ffs/aSCnlKi3MCqyBX6Ukw7OLwV7xJ25pwOcDJGAmQFaEwMLEYJEIBUCgr7qUR6t6aIBDqfehlk7a0TPAF895UL/35off/MOv9Lr1D9/6dHx0QkRi6M2cm6rf7qBV95aXtre3Fy/MZTPFTjuuVZufv/MXOzu7nXZ47cVXy/mhqy9cvHrt8ieffFLrbqztZGPs6YwJe52gFMxMj3RMNVp2lu7ejzoNUjI2Wm7Umzc+Xinlpl+68L/U6PbaSbeRtMQDTrKZIJ9112sPOfQeXqfvEyKSEs8DpVFr0VqRwnxGRYm1BhPLjkZFwmKZwcRCiAmKowlILIsmNCDSD7Q8roIWAExzSh87Yz+7VhsE+wk6bJoWI+VgEx5VMRIBa4EZmTEtj8iMKCgCzELAViBiYAZrJDHWJmwZrCEREdYiVoSEMUnEWrTWMiMAAQAw9XcCRNBnY5Om6DIsELOIxCYBNmku6GOhv6k/9KncJD8PZxLw6YKIrLVKKa01kY3jWKkAQCZnFuOku719v9Vu2aS7t7u/XduOo+FLl198/837O+tLoaUgCMIw3N4Oy0Cz42NZnSTQ1k4nsuVuXba2TWwKGdUtDCnHFUWRolhRwEBx8guKJH5x8Mz0c4CEqpT0a3EQIzAiCDACqIdyzsvhyyAfhcHjcCYB/3w8LQl4QDjrH/1nfxAm4cqt9sZS93rtg/Jk5tXfv4J+8pWv/u63v/O773/0/p//2Z/cufmg1axrRzmvZv/gO7+/vr797/7t//hn/+570/OTz129cvWlFy5eXnz3g798sHrLc3wnK9X9dnEkePWN5xcuTf+Lf/YXiepanQGXs5ViI+z09vbj1sjUzMVSdjSrOclEjocIkiRhrb6JB84Lj/rxIimFSGxFNIBlQcsKwPUYEIwCZclRqFMFsOWEERG0QmtZKdAKLbIiNAeq2keF4GPJ/Y/7QYRhWsEGiRCIU3pD7G9w5aD+QRo2DKBAKWE0BtiCMWyNFUZjxRBYg9ZKHFuTgDVirQijUpxa+wDIMqc/ZGHQAEwAaQl3RBQQspBGMAMiKqWIUASMIWsPK872B+1MLn1GkRKw4zipJJTmnQs8PTQ22w3v3Ln7WS47QsB3bt3JFnq+cpPEsCQTE0NW6Xq9HWAwNpoX3Xnt5efPzY4Kxasb95ZW7leG5jOZocbWflBwDRutw2IhyARRoxmheFEozs9JdfPbhNNSQQ8iTkWcSsCInHq6EyEAOM6RLez4axjZwz31YU9EBPHxTnNnD/6vB6cmAd9dulPbq3/69l2OoJAp1R/E+3fD+bn5m6ufXnvxpWuvXt3f365X60v3lvY2d+5+tnzt2sXX33i5urf/p//he51a96dvfvDJh59Pzk5dvXyxsREGXiDaDVQPe25ju5fLlKenp6NuO+ur3dpqsVSoDA/ncldVNNuqxz6gxpzWRcZeL24SO3FXvMxDvleHr4kRY4FIHGEAFGQRsYKFjAGglI8UptzD1lpjFZKktf5EIP2RVmAGqKDTBo9/lfYhjp1+lTmCfqEkAkTMOYdlfdPnAREJERm0ZTaGbQK9yIoBY4SZI3bZQpKAsWQTYEZrEQFdF1gAES0zMzJzPx1WDP0iTwDABEJ9D0pESNOPGCRCZrIWrD2y+6b6Z0gv+4snMZx0ITtp/591lXiqgtZax3HPdRQzh2FYLhYTDDpRVGtVC/nC+cXFRXaqjQ+jZv2n778f2V4hY7f21+uNfZ2b7Pa4kzRKI2O5SqZQKPq5DWvrviflTBTWO34w1On2Srn2+HBhd583NnuIGWEPIHral/5L4UmrEAc1f1KJk9QAr3tIEJEUKCKlwHFSepXA18cXpcOyZti3efU5+GCvD4l5PMHbZzyhyiCc3ryfSjOnBn33s5VcIfPa118cLozf/Oje8v2VlY/373/yF72J+kZ19aXnX6pMDlUqFRLVbYTrD7b//Id/IiCjI5WFhYWlO2thL+npuFtbevc/fqS1PnfuXK+TZMzw3q3Ov/noj4MgyHozmESTk5Nikm4thjC+cO78G1f//vv3GIXDMLTKTSDqJVExV3CCALGZ9uwRCTiKmUgUiQAAWTAAiAoEwBICKErdxTUhoYgIi4AFQVAKjAFRqfgLnBY8kuN/AoAJ28dKxmGo+o8ISvrkpF3yCawVazl9VIhIKSKiOLYsYo3EiVgLYiE2IgJhhGyEGY0Ba5CFCAhRMSeWAQAE0DL0NwICzPYY+wqAFUYAdl3HMgujAUCLlsFaMBZcfSD/HuBMDn4WkRIwIsZxzIEnIsYYrfXS6irbenmouLm5nnVzr73yZeE7xWxwf/VBELjdXv3evQ+j2MbtcGV12agkN6K39nPklUqV8sJCQZkk2l+9MDm6bfPNRgwSZrKQywpKLJgFzDwrBPysYCBhUKKISIHroOuQ0sp1FKBkAzykXmZmxgNJtx8YeUjA6YJTb55M1XmGLyZ0q7mvff7b3/kDcnRXte9U763s7ILRrU+T+odv498crm403nrz7eGJElGRQn3/zfj//tN/df7lKc7EwZS4LXXtueeev3r5//nP/sQPvN1Oc3dvp1AoXLh28YP3Puh0G0EwlCnkri+veY5fzpSrdXn3w/vK3cxlX0pYYpcthOR4npNrm+0g0BQX054daoHT/zuEIMiMSYxpkYMUYcdHsAxW6UQ7oj30PFIq8FWS/jAt9Gv6YXPiOoCAhhEErQVjGRgSK2GsDwfluAoojtzHDlwzsMycap7TqglaayLqtPo/PFBNHxB558hjiyh1kbICttP72QcGEdAa/TMfAwBE5jjPWiBQBMpRbPo2X0x3eSp1qhFrTxaHelpxhE/L2eGkKsSTHn9aqshBiEPKFwLGiBGzvlmcaEamVmv0nE7+jVfO3bn++dTMxFrVLu/t393c+N3CxNK7y3k1PD+9mHUm9jYfJHpvcm7sj/7x3/rBn7/9sa/h6tjmfdOqd8sFvVbfUT3diy5l5aZt72y3yjGWvKC8vd2ZGJNmZ9D1nk6c9CA8LQ3NoHhfQX6sBo4GSJZpO+lalNJkenwu30vtVkqjUqAVEBECmNgQkdZEOnI0agdQiULMu/4j+uf+MtIXGMRyWr0UrLUgkCu1rUW2ZGKdJMRGCStrsTcgvvak8fqDcUrzNaDO+uD74en0f1B/BoT19g0Kv/x59be+9ntrG2tv/+j9+cWFmbFzWt41oR0dGSsVclu7m9/73vcw0Ybj3d1da23gZjt7nXMvzI7kR+/eu9mrRUVvqFOX5dt7uWFXhIkoW/QMhbMXx85f+7sPHjzYeqtDoEwT252GN5Kdm3hhtPLKcHG0GOTWtjc9kmKlFMVJlGitJpKWBW9AQfXHob8fTPNHGkACMpAQAovA4xcOg/24W8sCDMagtSyCJunf6HBE+igicTRg4glShgUARCESZZgIzGMSfaTZLo+3fyzW/gkvcGd4tqC1TpJOmHSsBWul1+t1o7Y4vdxQ6fN7t0ZGJ8Zn5taq+2vLnw+Xcp22mRyd+8H/78feVzNXnrscJT2yzj/4O/+Tzz//oNlsDg0NGWNMbDKZPGnDNnEQOp2NsfGC8swHHyyNjc9mfB4aKnRNfBZXmmKwanrAQkwEBwR8XFdsWBSmLs0KBQyAFkASQJ06aCFAWnWS0uYV4IFyLj1h+r/E9NcrBHFcQAOgSBh89NmCSRAERDBhMGyI1Nk8PlvQP/ruO/l8fn3n1sfv3Hr++ReV8TzPCTuhE0g+lzexdV2VL2eTyJQLeRHBBOOa3VmqmTZlnKFOA27tbbXH9PjUaJLYrbVtBAVoG53a5OhoG6rdXiISEerAz7naHxkuBxlne/dBt1S79eC9IJ+8Mn+x1+p1W95U6dVE51u/XOKmQw6zFlikfx/HiAgCYmlgAhOXwTACSGqFZebUg8nG6iFePJKAH99OYkHkSMerFBIBEcAgJy9JnbwOfbD7QU8nVRgNlsBOtnAMbOUJx0Ge4RcitWjk81nEbrvd7kZtL29Y1VfWd7WfH4F8eSi8cf1mznM8DLq1SIlTr9ezw+7C+YUXLrz84bufhBLGcdjtNq2F4ZGxWzeXPbeLBNtbGzOLi9OTE1HLXV7+fGaKCOrCw40kzg5w6vlNxS/0Vn3EBKbUwDCtwyOPE3ASg+3nXWJBUkCpDUuhDyKWARmIwRoEBUiC6oh4j/ctZguAhJJmFFAkac0XII8tGGIQFLapMwqzATjzpvt5OC2N2mkpbvTWeq2dSzrNpNFkldw3bZspZNmadqfrkp8pZeYvzpjY3Pl8KZ8r7Dd3Ay/TqLeUy6V82Rqqt5pOpHFoZDjnT1Sm3978act2fF/fvb5nMSiVZjf03fp+LVcYCYJMNdxzqjfyXWPscNnnmv0gSuIHjY/q+63ufiHr64AuA5R/fo8PhM4+h0WJRQBAUJIG/pCKiRSgfbxkGVNKh8gCqQsxi7BFEyHAoZDap0kASCXjn4U1eEiuxxVQaTjBoSSdfg+Q+kml/T96BQAZqFl5NgjvtG7oJ41npZ8mMVqBUioIgjDcN8aUSqWpqanVteVsdmh5rV0oRsVyPpuLVjfuu8VvdhvRxYXL+Vyu3qx/5zvf/sl33115sBrajjtkjTG7u7thnN3Y2HF0a25uevHiYml4hI1euWE0TLiuygd2r2q8XA67g3a+v6lhZj/v+TrOvn0V9ABTQmppOlyO5CCKly0ggmZkS1qjFiEiQrGYJt/oZw5QLFojIsXGHDvj0bAQkSCApKFLgIJMLIz9jJZA0I+TtAfBxI8n4C+aM+YgPCv9HIST9l//0//1f/Enf/ynhCUbCFrVa/aQeHZ+usWqVzWtZmvu/NTU1NTWg2qz1oo5TmI7NFqcnBhrdmvrW1vF0sxM5eWJwsW3v/f+0O8sDKGvoRU12iuffSJ1KU2N5QPGYjI9NQ/S29i+W6/vWOUEfuvGg9WcZ4h6W0tbzW4yM/GVSjko6vLdnV98bcelMWMFAAiQGQmRWSyhIrTm8cx2EO+rLEM/fldEGOP4qOWHRGH7M7ZYAAA44NP+I4eIbAERDR6p0B+SGh8nXsORZPyYMwz4/GR40jbRM5wuiIiZIxMpisJGw/f9ycmhUqnUrjU8r7S7u+l5xa2dB7nyUKNzH91or1qNmsnodOXL3/ryW2+9ZQxMT09v72+sbd8s5LO1/Vav6zpudm97yUr8T3/nDxiLd5aq29vtixfm9+t1P1NRFmWQvug3F79Q1fyIBDzo+MOwwEdaYFAEmIioNPWzBUUA2Dc4EUFMTATaAcdBIuo4/cQDh/7O6f+USwAgFhnEWDDGptXPTEjWYmJskkBixBgQAH7G2evXgC+cBHzu5ZmvhF9yOfPhmx/Xq/VcKZst6F7SiNl4brbW2gtNe3J2tFAodGt7oth1fLDU3u/U6vVeO37pwpW5kZdz+lJzc7+5PXV5/jXPcSMbzk4tKS+5v3Sr5F4+tzg6NTNz/977cXNNF3s22VcFp6XvLl54bsgv31v63HpmbDFfKnJrawdg9JcZiEM+YEEUYEhVPMBMCsGm+SQfByIhVCxCqKwFy5Lag1MfhePW3/QNDZIADpwdUt+ng4TSj+QYP/ZYyuHu4fiJwA4itkGn/YIR4WkR/CCc1vU+KxsR13UZdcKpR32cz+c9z7t169Zz556r1VeuXp6dmJj6q5+8f/7izMRkLew1lSeVsfJ3fu/bb3/8V3t71awqjo6NdqXhtgWQi6XK8Oil5eW9ZiNDYPdru56T7/Sa2lGun9mur43l5lH8uNd0IXii1zUIX3wJGH6JTh6/Kw58PrQgEoOQMAuRGBRCFEYRUSSCohQ6VhID6mC9OThRqlcDAAgCBAAWtAxsxRhM0mx6kWUhy2AMJCwsyIwDF5NnB8+6BHxS6H/3o3/9pee/OuxMvPOTtxOJxsbK+SGv1t2dn1zYudMUhs3qxo1717udXqVUCbv1gHPhXri8XmfHjIxMT41dCmQ+R+f/5h9ecF3XyRYTNp6vz19YZDH12vBIOVsamtQga6tvbW41Rv2Ook55yM262A7rGT1swPOyudLwMFNondYgAj6O47e7tZgWmmAGTJmVSACSAbciISIKCwmIMDITi4Ao1BYA4LAW4eGbAd53h7fJofYJ+vHEA5yqDmoPP2oGHlQV5LfrPnxm8KQXCBFRWgVB4DhOFEWFQiGbDZbfv3Pp3HOEzamJkXarrnC0kH0x60Y+BNkh/5uvf/Ptt9/ebuwkUdLiZqGUG5st62CBELP5QpAZ6fXM81dfmJoqaI0r92u19u7U1CiiMQQxZ4rZoUh3sHcy783fVPws6fYl2gFexI+9H0QEwQGBflChEUYGZAQQAUI0BgFBKWZBbYEIjNHwOCIPewAAgiIWWChJhBkYBBIUQcPIIkaIGRmARR6vr/vtI7bTwkkl4BOroDf3VzD7BpO59urVqbEZgGRidmh8uuJnR/9v/5f/ZmNvVTu0vr7abDZfungxqbeqH4flQgEYTBSPZieHs9PYGfegMjZd2qvuhCCdsOt5HltQ6MzOvWS5nSmMCvWCcn5srpydjP1hU5mlfO2llc+2XJeCeEarTHPVaXR7TvSLE9L+rAjCAiBEACyokFKltB1AwIzAfBBZwAgAxgKAKOfgATugXoFUV/x4VbADfe/HVO6F/lMHlh8i4KP5GKSCHnCZZ4/LbyeiKPIUC0qv12s2m0MTmenpqXJ5eWt3I+t3jdm/eePezPRrQ6VL9a07lUzlv/xf/ON/9d/+D0rcfCXDVm7duB7azgtfvmC3bBAEvV6vVt8wxg5VRpO4vbu9Kck4kSwuju/XQjcX1PciMUjOoMfltx2PNXsdx3Hb8OExiGiMIgQRQGAAOKzYjaAFrWEhJAAGREICQE4UHIs+OtyjW2sIU3sZAqRx/0pENGjDwgJWxIgIAIsw4CACPsMXE3r3L+HjcFlg+fbKZyPTuX/yT/7+69eurtxd+/STD668Ntvj+vJb1UsXK/Wtzf3xT//ut974t+t7o6XprFu6dfPu0sfqyszc5cU37t6sY7bpOr6JwFN5MEAAIuB5nnYl7Bgw+ZcX/+czlZdurf7LmG+U3Ins4nAerzjtazn/tZ2qqa97w+XhxMSoBzg9HW1AH9FFpxyWblB/iVgxcQD6jJn6IJMGABDzKPf/fApMDk//8C8eeVAf89ziQ63TIArmAY/SCVXWA3dkA9phfvyGAwdJ9gPw88M2HnfeAXU0B3ufnqidQThpP609Hdv8QHgqIKeZhNptXpwTPzf84w+2Li++rP117QQf3NpIcObyhYut9vpu60YwWbGdYq7ka627nValUq6NlBxw2mvm3OyVbHF0r7U2Ouz93nfmuKdu3d0qjThW8PZ1k8+2J0fJs6jHqx/0utV2dmhQfwbWx338vAx8agbF3Q64/3FQeN6AdgbeVxSnrSEolWa1QNRIoY3gYVVzP+rBiCLSCrQjSoEmQbSAbCWfsiMfAwDwAGdPgIgfMjbhQVmFhAUAiQHYKmMhigAA8Cge4qEFQvEj49z/vHek4kPdr2GatvN4Z7rBsvHj8aRNLSeWyOnxz91J4+zVoOVwkKQ7QAAbiBOGsWlF8Ob3f+pmITukd9d33nv/ncCjzZXtzQebbN2hQmZ3q3Xv+rLv5NCW/+zf/1Th1Pr66u9942Vj8hvbHaTM3fsr5fJcPa4/9gSNelsBe47jZ3KzhfPsvrGyW7v3UeulV86N5Sccfy6JMsW8sggiolzn9ALGz3CGZw8sJgxNwjab9yxRL+wqjZVKZbe2FGQwn3fCbhj21jvt3TgOO714fXOtUMr1OuGV5y43qs2vffXrSun6fmN6amJ8rFRv7A0Plx0bb6xWHRdyWb22Xh0bHSKgJGoPVbTjK7kXuVL5Tc2EJaAUIqIiRDxQahlmTo1HB69woIvSSiEJaSAixHR7jwSqT7cPZ9f5dfT/hGGHZ3i2oP/J/+GP4mZy/aMbjWa1MjUSdbrvvvO+Ri+x9bHRcygOs3v9k/tgdLeLmzudst+xMezXkueufqsw0qo3oJx37eBgmrHhcWscY9DYWDuFkaFrOzs7Dz67PV1Ws+MLrjfVinzf9RIxiYl810vCX+fln+FRDN6ZPhvhKE/a1vXEbWksAIACwiaKusq1jtKasFbdZ46SpNpp7yt2wl43jnutVnL77j0GU2vsd2/2djZ2S8WhuelznVYvm/ECz9HK5vNOdy9sd/Zdj1FFjcbazOT5QDvtbmNqtqC80NrYswGq+ETdxEFx5wN/cELv08EqnZO1I1oACTEdVASxLAislAP9TDiHZwQA0BpQHZqWAABEiKWvWflZ9j2tOPtBOLW4/xNuGL5wz9EzYsM+sQ24nVl55eXXM6Vk+Z67sHhOkb57Z622tTsxPTY8cr5Y8GanFXffvHXz9srqquc5yDaKeGuzc25xZmLSq1ZbfpDr9jowQMPR68SWTWzY2ETIKReff/Xq2P7+1t7yBxU3bz3V61lBZKaYjXYIfsvqkj4tnIUbfTGhSPJBwFHY6ew3qruFSiCY2d7e9AI/jutRXM+4gYJN5Mj3kXRmdWN1bHjswsXzH73/qed5jVpz5KXRi4uXdrfvVKvldqce9hrr26uN2l6mmKk1djMZP3Bhf3srlo3pBTMyNBy4lHSe+XRsPyesCEVYJC2pjYiOg4gkRCmTHjfcAoBSFg5WexExBxkoDwn4eMuHUb+PPfGpXNdJn8ffNmer0worOq0N00mh3337k6XPVxcnzl16bh5JZufnn3v+tb/8/rv57stB67nAukr5Rd0cyUKnt8dxL7Rur62Ghy+02y5rf2JqtLZdz3tZOyBuVSkXCUCJsmQNhF3Xd+crxRFIolYjZzKiyElY2Aqhjo0ZZFs6w68HZxLw023f2lhrT7MKe8ledTcSH/yRloSZArbasav1UKEQdTqt1i64YRLL1voWx3Lp4uVer8exDJWHHU95Gffe7c3pc+PGmGazub6+Vm82imOFtbWdwJmqN/a2tvZU0Nnd2S+PxKVyJWzZ5KQ5cp8RCdjB1MxsEUUpcB3SWmtNielXBGc+JGCA1KNSBCC19dJB0KAc5qw7zL0DfQ4+nQX9SYfbPa12Tq39Z2RjcdL51V5n9OaHO90Z8/IrV+pRvWecV19afP7Frzbf/Bu9jQJbJWyw/cqrF1+NTPWHf/ln7NH5869VKucdt9BNuNPpeJ6XOvQ9FrERQEmSRDsOAiWxBVYs3vTEK+24x+i4nicJMRNpYDxTQJ/htxpsbRiGDJwJ3MREzXovO1wOcn51b7Mb1sfHs8Mj46utjUZ9Lz/kWSEHg1a9u7tdnRifXFlZWVic36tt31u6wxJGIbtOphvZMIobvW6r2+0l4ebyvZnp/PjUeK44tLfzIJttuO4EuBGcTAP9zCDj9/0tlRKtUTvguqC0JAmKwGE1MzxAEiub1hkVZJY0hTuwAuovcI+ECPOA4iVPmgiflsT2rOOkTqlPXAL2ZXx0yI1b3ep2WBmdCTeCtUQV+CVSI92e4+oCW5qb+dZQJReb5q1R8grNV176fa3KjvJdCGv16vjIlOkmg04gCjWpKIrAWqUx5tiyFbKkRzQ0QXcNREQZRLQGwDnzov814eQ31pNVqZ2WZHBaD8zTUsVrTSLGGqtc8Rwd5FQmH4C1KF0RUU7B9YcR9x3HKxaLQeC19rva0btO9cLF8xcuXLBsbt++tbWzuTg9kRgMI3FD8YL8bvW+pQe5cgAWh0aGhkdnCTrXP3nQzDFlNXt1iDMn6udAle/gH5ys/RNKwIP6k88AELlagRKlQWkmYiIwTpoH/qD4HyIiElGIrrFoDAmwiIAgMwESQJye5ZCAD+KDT3RZJ8bpEfAXy7R0WhLws77hoDt378Y2jo1t15ISTS34Xx5pvDIZfd0diiDbbZj9Rhx5xZlqO4hk/I2v/ZMvvfGHudxUsTBWr9dZkrHx4Uaj6rqPr9kHAFEck9aOdl1H+55yXKu8WHlRsxUzUMRxu9dMTMRW4ig5M0Ce4bccnuf5vs/MvV6XFI2NjZWLxWazqRwQkV5PavV4r9aNYsPM7WaD2OEYtra29/b2rl174bPrn9y485mg7XTjdjuuNbq9MCLHr9WaS8srOsi89NIr07NzidjafitsQMYZ8v1s21Sf9nU/Kfg+FbKUyznlgpfPqyAgz2OtE98D3wPPFddhz5XDP+2Q62it+iDQhBrp0eD+X4jT6v+Tbv8MTxd6Njf78uJXRguzRjpgws9W3n3w0Z/NlS/lxv9IJJiYvBRajKK655I13bmZod39C5GByCRe4IGFuM2+m+kl3UHxWBkHbNTVGqzYXmQBXE4AwBGvZRmIswCQWAvKekqBHRj3OTAQ/pQGYhAGhUWdNP7spBhUxpf048dBBsYjnhADd5oD4oZPLKEO+maAZPyEZ/jUnDhOPA6Pn+Cs29uouuXycNh+oAMvV5jO5kZ6QxHTC63WR9eee4Pl3MpuLZtLnCB0HafZrXXj7re+8425xfnv/egvfW9oeuwagTLanRzLIfPWaufKxYvbr8GPfvju5z9+b/5/5l9emN5Z0v/2L02NX7riNRYrG3c+Op/ogUqsx18vPl5njegeH5DDYaF+luOH9Lf9JFGPIxWFivuqYDn+EyIF/WqsgsiEaQUCCLsFJFGKSUVKJ0FGcjnXD1Slsg9CkFpzBZEpLeMdJ6Em1C4gquO9UrplLVoLxoBJtEmsscCMcpSfQI7fqyZWIiKMLMKC1qKIgJDWj59fHnifDxjnQRn9TpgmwBlQJMkMWDcGxcsOwqD1UGBQkY9B+QZOxwdlkGJi0HPqDOCd08orMOhzjRnT5J32Vv2D2+9Yvzc8OpTk1I1qbev9rYXFa5WxYjY/pbQLojudaL8an4WfneEMTw6en8nlfFKwt7c3NDTUDePVtZsjo6Wd/U6hUHBd99adB4vn5oJADVf2e52OtfKVr3xjfubi7lZtZ7M5OjI+OZ67cePm9taGVsoYMz4+bU2cy/rGGkJV1OObO3ErYTfoTGcW2/u7ZsxLnAbI6aighR+1laZvrJGf/aFImqFVfpaAhQ0iEh6qovvHWCZAtiwAQIgMgEQIkMuGjkZURjvWcdjzJQis64IiHwCQUESEARAcpUSEqL8XPFb5QBDRgtK6Xx0cBC0DCSpEM2BJdzQBgBVgAyxChGKJAU6av+Fp4Ww5f7rQd+/dXV/a8jBo2dri/MxrX3mpvtX5+M0bBsLd/fbte16p9FylfLWYP9frSJI45HZPdIIT24q+YHha2p7TksxOC4MF1zOrwWmi0WhZViiq02m+8tKl3b3Og5UbiwsT1Trkc7k4idbXNxfOXzbxrqOVZfPcC9fOL1wC9gOSjBOZSA9Pji2ex1v3akOVcqujosjLZHTg05UrF2enjMejy6vIkgTZ9vxcaWdnr9lVTqmZ1E6HgBkOqgPh4WGCiKTUceHjsPiAQJohA0Qe8m9CirXWWisAMMb0iw4RsRWUfjwuIxIRiUKiQqHjOtp1UTvkeai0uC5oDQIOACAhiiAJMKRcrNyjCKJjb8CiyyxaIaIgCDOgALOoQdEZGkUEGUWDYUEGAUAQ86wQ8KCEYydsRwalgjixqfeUnNdO+PnAdp60E9Z4Zaq+1XQ8v6AqG7erP2m8n3S4utwcPUetxu33P9p31YWrz/3Rc5fzjpMHChhORsBn+M3Gk9+gPBsboNNqX8TxXAcxUkoK+dLKWhMAOr0mgInjeHd703EwCnvbm6uI3dnZNy4/ZzY3d22iRobGPMrt7dSy+XqpMvRq4arWOo7Ne++///f+6OuBLy+//GJG71YbvTAutFrVYo7yhWh739Y7zayXbZzwegepHA8LGz50gSKpaelnc0il5UEP9cyHonA+oxxHO44jIlFk49hyP4e728+FgYCigIQQiVBp0FqUVtpBVKQVgkiSsGFCFKXQ1QpJUDOhJUT1kAr3kIzFCrLFCNhhYQvgAAhbK8CDmMoKAghYFARAEQZBoYGpW5+wt+1J21FP+vn6LTNXn3T89eL5hevtW5iQC5nObm+n1swGxeHcpIGt8kjed/Xu+kqrc0vwGjpAoAakCj5xh56VafmiScBPK/zgTAL+9UDrDABEUUsprNaaYQwXL14miPf394vZblXU+MTzO9tb65vrE+O5xYWFTz/d31pfL+SHFYnSODc7nQ28brt16dJUEkU3btz+7Prtv/eHX56cHF5Zt0vLK0GxN1z6Smtve3q62DG7pYnYmP2Ahhon7OdAm5ZzVJoejpncSCVyDEfeROqo/ED6AREhYqnEngeuiyIQxxTHxAyImCQkIsawsZIWx2WxANCLIErEjZk0+S5imkZSiHSoFDoOWY99Dz1XSKFWSD9jgz8Qyg0SOAotsqPBGNYKFYIZ4JSRsE3zWyIAAggwCrIYAP+EI/p0MMgX4aRP9eAN5em0c1I8MxJwM9rvcatj2jldzOfzruMZY3u2067tX7wwVhkqiA1LlW5luNPr7jebNYDhJ9qhMzxbeNYl4JOGRZ20nZOiF8akLEGvmA+azU42KJRKfqP2qYhxXOVqGBkqPVjeEI5dR4dRl8CUisHYWDmJTc7g0HDeWtPsdBztVobLyvEvXLggaC4sTn/6+Qf7tWap4I7plpIQcajZ7gyNdzJIHUsnn8cB40b9zBSPlNZWFB1nXyJKiZYUpxUO0qMPPy8VE9dF1wWllIhi9iAVkYGs5TC2YShxbMNUOLYQxkoMRzEjqE5a85tZBIOs1RoDnxFRKXQcIARAtscSaBzfKyi0FkQAtEa26LsYozDjoERDjkqty5gYFmEFIMTE+KRrdgzCyU1Xp6U6HvCFfVZEracD3dxvl4plF4K4bcJO1EzqsQ2Dgj9cnhPX7UZdy+1G8+ZHn/773W1EzF29/L870Ql+UyXgs4ipMxzHadnskVzPVZ52TehGYaLcyv7+Xn1/e2pylKDGzI1mY3io1G54XuCtLN/fWV9Jkmh374EF2W/XN3fvO06mkC9qxaVSaW52zvc6vqtGR8q+pyvDI4qcbrikc92OHQ/butLdvvxc7uNWF6BwGsPQd8JK/adSKk2v1PftQdqpPvtqrYlIq36JofSBIiKlFCKWSy2txXHYcZy0HQBABE1GBBNDcYJRpLo9TmIbJVxreklsk8hYaxCQABmIAG1CBGgMGJMqwIGQBdhaF46o9/jjbBBJkQCR4xAhKQXGotDjvcQVOcaKMCjNSSLMYi2LYJx8sRI+DAINsuWfkhe0fcYLXT5pXxwd7nIccph02WImk/MwqXZ76IiG7PZ61XObaMDp7X3++V92Ov5L1752otbPcIYznAiEGkASm7Q7rSSh0UK2uruUywTDw7mdjc12u9Xsbk7PLmxueLlMUK/Xd3e2yqOlam2jZ7rV/cbmyvbs5GKlWMjl/GazGRt+8ODBt74+LWJzuVyvE2g3342W/KGsEeBeSUV2ajT7kW4CjJ9K/y0niEhIRIoUEPVpOJ/3Dg3AKcvqfrStOR5ulH5FROWCIkrTV7FSSKnrlDBSD1EDOCJOGFM+wSSGxFjtOr1eHHYxiQWFRDQBWsYwIWZkBgQQsSxsxSqUQ5XyI/UVCJkIQEiIHI0imG4J7IAyf4q0FjEGNINS1hhgFmaBzqkM55PHaUnA9KyIVF8s6BqveoG3sHh5b73e2KsGRc9ziYRtM86oLAkaTLb2l/2sX5zL4dDH1n23lF3stLK92CGXANmRIO7Y4axjIY4kArRMzBaFHbD6tFI7D4wzG7CjPKmEelpxvaeV6WlQvO/gYhWnMw74aP3RXwB1WpmnBnx+auUpB9SXRRp05tOJbx48Oo//JlsQiY3tNbZWPxueLO/UwtGJku06H3z0H4cnR7PjxfNzQ9Jrtar7E+PfMCDb26ug1XDl3E/e/qjXw4w7ND09dnFx9K23/4q1Zdsuus6D23Zl/bpyVgO3lYRra5uVYvGaTXZ8/ZNb11dfeuW52cng3t2Qgdi6IgoAlBLtGIWi1ENEBQd3VBIpRyERkQJXAwOnKzARa42eB67PhZzNBNrxRCnUKjoemHQoGfeOpd9BxJR9icjPK0QkQkRGlIMUzeA5QcrEImFGceD36TOxTiHPxhhr7XGXLkcd2ZsPJHKNjBlNBxeVSsD9awxjL+2MIgBirRnAAIAvyWP7z9xBxETEWokj6HUxicQmlM9zP57YirHETMzIgi4NqKM8oN7toHXD0SQiABaYAfsh0Ugi4j7cvT4cchDR1UQKEAFJnPSBoLT+FjCTZUTBxLKIhHy04egbC4gQMdCRIkQCpUQTCnAac5vx+s5xh1Ur0g5s7HqHnZHj1ZRFP+KRlx42lLVIgARIKMBWYsuJiKApAQCzxBZYxIikEdWO6LTz6cIoQmn2UH3CuPaBJpWTLm+DVAf0+HVMf+sb3+w0Q9MRcjBXzpJC07M61pLr7e7Xfd8PvMxQuWgNt/aa98K7y533X3upMFQuovYSjBmZKTJoOxZYLAMLWhKlwHOcwPHcdtQ84RWc4Qy/vYjj2AFCxCRJOp2Omyu3Wq3O/q7W2ibSkxCElldW0mxZANhqm0yz5zqNXIBJGJ2bn//S61/9/vf+fPFyWaGq1auL5xd39rbKOtjfr+f9fJfRcVQUbXpOkMmWx+aKy8sbfrbiueOJZYsJWyEi7bBWolDUQQKHlNQA+q++B1ohkjiKlJI0GleAfddTChwHPA88DzxftANaCSo5iOWF9C/9p090fBUmAiJAFKUEERCZiI5VBnxolTxOToGPzMisrH3En8s+ljjFPFpesC+CD9rwMR5vBw6WZkXKiihG1GINOo6AJQUIERD2Y4exH3KFKvXTOg0ciexEgKIUKUIkCTw4NsLA3P+LjUUSREKNhEIIjIwAYhWk7CsAAFbAMoCgEnU4yChISASEgJ5nEVErBJTUkU0EFYpSqRcaiBzubPqx2ml3AQABUiZXRKDk+OAf3hi5rEICIEACAbDssCAzs2EEMlbIgGUgi1ZALKbDiSQgnJ6acHBx3C8e9Ltvf6SVwwl42h+dGo3jOEpiIuqFtSBDlXJ+Y31TUWV8dDyXney0u8VMdnI0yGZUrR21TSLIwC4CWSUiIEAijjCJKGbhAbR/hjP8kjg929ggEfUpeY8PgFLKd7xm24RhGCRuRuswDMMwHBkdSRijHiYRV6u7oxMjSqk4imudZOf6jT/6w/nJyZHLlxfn5s7dX15J0OvVkzjiWqP22kuz3//uD/Nji3vbtVhhMF7udtbGRmu/85VXlVJbW/d//Na7f+/vfqdQkCiSKDYirLV2HNJKiIzjHkpsR0G6AOB7QgREQspqRUQCIJrAdx0i0Bpch1FZ17FILChaH4b5Hv4hAOS1Oi4JHfIjHNiPUxwewMCAcCC1ioAAAgLmcsiMIn0CPZTYXH2k+Th+L3V7AAAPi78gInaASpYTOt7IYVMKKWEmASvADrJFYkwQEViQAYEAmUQxMqDgwIxyg+7zn685S4eRFCkFWhGSZDIJER0jYOmrxI1BRIWAJIoAiUWEAKI2Ge7nveKDAGsQUHh0valNQZFCRM9TiEjIaZxVOjMIrFXft64/XNz3rcv6D5VxPJzQBM3DEnC634Jiqe+IJ321B4iQYZMkbK1YC3ECNlEmgdgAEMaJRUDuJ1kBwLTy5KnhxOvPQFXYgKgBG5OX8SzEkY1rzXrSM0o8MORlvAvnz7/+pdd+/OMfr22sNcO9ifmh8fnJ0YCcYK3W2ItsMZ8fjmM/7KgMBb2wpRQhOYoQU10Bh2HS0+4AlcvJLuvEOL2F+8m2/4Xr5xcsbu+3zdmt2+16Ob2/vw8AhULB9/2d/QYzR1GCmMn6pd3tvVqtlssme7W9ILaOUwKhOIq+9KWXHcf5T3/2g63dZjY33Apstxd1e2EYheu7Gxf4HKJ78/rdc8HL5+bHXro6k81GzWYQRkGhMOIoWypRFJleaIW164LriVYgAtp5qPre4ZtsPkqVxVoBkUGSVAWddQQASFlEIWWRmEgEWLv4WAnYc9Lo2cOJljSlg7UPCaypN7VIP/Fkn3cR8MDLOpezD5lyqX8ifZAA5PgrAIBKJW884OA+ow1K8Rj3jqjioXtSwFNohROLrEQ0xRYIgAiARSlgRgKxwMCp/Hf69/OBIl1AhIVAqD82ACz9PxJQKAhAaZpJoTRZF4NVRJYNAAIgAxOhZdEHA5EOIxEQChGCmHRGlAKFIABEIoICCSKlDnYAAMjWWiTJBA/FeadO7kRo6XD385AEHGQZURA51Rv01dqCkbFswSREhJZsjEoRYP/qRAmRgBWL0leoyBdsHRsEjaxAUJGOw6TVa2W8XKFYNAknHN69db9QzIem27H1Xrc2joXpxeemz3dN6073ARTteKXs7W8nKqLAzW71kARJFAoobcE1CCJgOT6r73uGXx2/bRKw53nZbFZEFhcXL1xZiKGU9GK3FG/VNibHp+emr966eU85qtHaDaOe6zpra+uBB1q7cWwePFipVmtjo1PKyeULEkcmTmR5dVVEdcNweGT09nv3tfY9N8pmMfBs5Kn1zb2Fc7NxbEaHodODXg9FwHXBdVFr0IQHGSTlUDZFRADJFSwiKyRSoBWSEgBAEY1huvwLilbCaDWhgKSS9CN6YER0nUedodIF1Jrj7Hv0bWIBfkYMBQDHObKhHqd5fSzO+CFtM9JBiNQhB6e/HTAxB4k4jtgOQESI2YgAgNZgDVllHU3cr2pFaEQE0KCIoJJDPfYvj8GSMRz1hwURLAiKdLvquPP5oQra0wgCrqbUTEoIAqQIEidOE0unVmq0LIxKRGE/cUraDpEQCSIzhyLoagWCqNI8KqgQje0hESl1SMCAzMz5fF8jDf0YM1EKEAW0/RkVNCKidk2qqO7/SlBESCRhBiEidjWFFlzFRsBYVBoVgGUGIWS0IghWDlTrf308cQk4TMKAM772EeMgk3Mdr9Vu93q9Ylm1eq1au0E+gidjC5XRxdL05ZHZF7G+2V7duVdr3lCy17a+VfNB4VLFKwqDtRwbZrZAjK4BBBzgjn/iqMMvmCT081VDZzjDrwZrbavV2trampp0arVaM+wppTzljY2MjlTGirmySXhkdIQFRkaGO82qo/FLX3r9uReuvfXWj3u93rVXXiN0d3dr+YIfJyabLYC4i+cuRSGPjAwXhoqjo6WNlRs7Ozsjz80rHcRRI5sdb7Sj0pD1PAgDEkHHAa3F0aKUROZQAn4ombPvIGnQBKSAiAkRkAmBUgkJJVVcAvbJwPdSjeUR9R6s7IfPUd9p6EALquDYU5bqMxHT1fW4NrvfjlZHtt7j7SuFxzTMcEi6SEck+hDxD3CiSbNDp0cd9FEAAAy7iDELM7oOA6uEwQJ6njIGABhS8gWxloUGpn6Uk8b99Nnx6H2qsbUWmIWoP1+HKuisi0SkCbVS6d6IUAlwNisAVoQOdMZoLYtImnApFaoBoG8DR0SwjiIk0Vq5WpFCpQQRIUEiTO33AEAKmRFAec5DxgVEIWJEJOcwAAwOphIAxFgU4XSLg6nhWAABxChhBO4PO6MFIhDQDgJAGniNxCjI3E8vftLxfCrQheH8UHGIIm+v1RgqjKHCnb3dcqVog/3RkZGL1y76Re1d11dePF8ZL+cqxXurH3mca4a3b91+cMd7M5edmSm9XFM3TOerCC5I1lWZ2Go2FFvFwAPrFD5hPGnV7knPO4iYnzSRP+sq6NPDsyEB+55vE6uUmpiYuLd6u9Zxrly4HDX3PM9T6Ozt7LPhbtydWxgplUrVnfWXXjz/xpdfv31npVa35fJYvRE+f3V2v7oZI7mBPz48MV6ZzDqlRrhbLBcm5ycI2vPz87ns0Mb65t7+8uVLEwsLC/eXbo2OJ1qD6yljgci6jjjaEoHuJ1KQR25Ix0FEdBQBgVJAxACkEFynz7gmFZ4oXXc50HScGg8JMuLkuKRLRAccDHAwOanIiWkfUgI7aOAgQhhQPVTUCA72C+lxh5LcsavAI7X2MW7W6vEM6brp/iAl4CMOJlSIiALGEiKwJRQERY7jKMWIyMJEDBYQEYQH3xADTHUD14ejohdIeGgD9jxzfHgPnY7JUUQCCpSTBnqBUgKAGU2GAYFEyLIAQ2JRRMKIRPrkfXzqg8BxFKEiR6PrKCQgEkR03INMKQ/Pgu9F0Pej7lNs6jWt3Yd0EoduAZ1QiQgbsJYBgK0gIgqK0czCFoVBRASFSQBZsyMIxEgEVgANEIEIPK1EKCeFbnaaI8UxBFKsK8XRMO4kSTw8Vn7x26/+2Z//yXff/O61V6+qjAuO85++//16vfbG1ydePP+in7Hg1iLdcdz4Zme326DxBvhuORdM5/0Zz6lY9tlYa+1phSGd4Qy/DYjjmKyUy+XFxZnrdz9ttULf9zcf7OfLJvB8jtzhysj9T25evHKxWC4MD4/8jd+f/9PvfjdJnHx+ZG1tr9HYHipnclk/tr1CqRB3taMCSXqEOpfPjk+P9br746Nz+UI5E5R2dm6Ojc0ws3YrWhkmRSSOCIDVSpRiRwNQXxUJD0ciEbpaERGQAkehIKU+sb7vsAAiIqeqROQ+lQ7wRj5ceg/IFhFBBI+lfjzOoMcF8ePtHEvW8RDR9iWhQzeuA2LQnLLBMWcuAAHRA+JZHUUAwAjMzIB8oLjWpCyIEkIUEeU4SKIsgKNVYoAIFAERpHI4i9AAEfik2205ls5TKdIaHa2QJJ/t4YEDWjpfaVyWKKY0tYgCpfo+cUTiakQgFhQhEIgN6zQTGKXlINPdBh+Op++7ROQoIoVa99lXE+Cxdf741KDuIiJRn4MPSVc7/as4roIGAM0eGwAQAWRBQAEBQATWYkVYrACgAAmSoCAaEhEmQUEFCIQMB2L1swBdMotLdxsLC+XiUNaliULJL+XWMuXK6k+2eRs6fv3mnU8n5qeW7q/3dnxTrbz9F/elNzw+Punm9tvVyLbt8ARkJpPNnf9HRlUm539nZvxVCScb+/mCGe11UccUjDXc4rbtAcalep1azUzGm/WdNkqQWKeXmFg1QIcKlGLPDFDF2AFlJZ+WpDsIg/pzWv180hIzDIhHfNIYNDoD45IH9FMGHC8DK4SerD8nHZ+TzpcDw4l8oLL1ve6L25ujr1ybun/r7VbYtc3Se59vZjPjhcxc0aufG3v9448+/sf/9e/9n/+P/9dqdePq1atbW0vNZsNx9OfXP3zjjTdsXJudHDPNXTdWth2JW5+antlYornFb2+s39veXnvtS/PhZ36rFc/ONGcqVUrapcqleisfJkAYxabpKm0SN4rNsQvBwxoDKFYsE4DGVAwC0g4iWEyQUAC0ephoCQSO+Fu4/0aRmxbY5WPBIwiojhVpEJEDTTI6D6/XiH2GtQcpDx8ty3PsX8fnop+JEh8yAAMgD6hz7LgPhS0ddAwshw4pAQQhZmusNQkb5oStHAt7PWynWu+bZkXQsjCjMLKARu9o03DMXywWACabqr5tWqEJWcBRrogoEkez1hB4qLUgIaGrFGgNpNlzgRQoBYToexaREZEevoEtGHiEMhERMYnbjx0HEklL+R6fXADwguTheem/GuM9duMVxQmgaEIhUYSIFgBYLIolEhErJKAg5WwAQNcSCwkqi8YAikplXNFoGFAoMWAsC7K1KABmgKqfB9QXH+Qbd9J68DhQ4Hz88doG+8NDQae1k/GG7y5/ZEIczk8tfbhVr9+InfDFr1zJT+X2dncVioeZersb5PIrtzbr263abiPqGhHJjbmVobzO98rTegM/tLo6Or+gdG7zTjQ3+lI5O7JS+6tmZ3esMlzOjY3ni50u7a59tLc5rqWIWBGtEWMT2yQESBIv97SU1mc4w9MHIRsbI8nyg/uTUxNaQ+A71Wa0MDy9vLaZzxVRdT0/2dtdGZrMQJLMzMwsLCwkSaK1npiYiON4fHx8fX39D/7GG6Njozc/W9M5Aoc7vU4mm5menazXa71eL1sO9vZqxhg/yNVqzW69Nn5uMkrCXuQCuoBAqEicxPRtk4go0n+F/vLaX8cPtbjMnBp/IZWAD9SK0K8zKMd55a+Px2mV4fg/D4yFD2lEH+Lgn+nPMTn7USAdhbce1wSk+4Z0dwLHaEZpFjk0SB61nM+nYVeUpsZMs3SJiE2Od+PolY0VZBRgQQK0IkygAAgUISsFREIoLGIZQSQgAQQkdBQhseOAIsXAWsOhxv74uBEcUeYjWoTHgi0fXCYceOQBIqb5RQ6UzIdfCcsxrcbBt/32BS2njuxgBVKXagESQZE01IwFJHXndnRGDIsAcFqOEtOgqcRgahRIrACjZbIsg7LufAGhR4ZLYnB/q1GaKnAubMXtxelXZ3DmxlJtt7ENiSct19adublzQSXffPCOZ/Tug1Y0ZAMv5/i20d2PuAtu5m/8w6/fX7sTSXV4ZizqfO4M5ScveoEOe15pY/ev1pZv5+7lRocmh2fH0c/udrpR9ztarIuudgpaO9pzSDtkvYjDx3b0WZF0nxa+aP05NZzQq3DQ56emIXjCQORupylid3e35ucvdBt3AIyrZWZiZGXljuB+GIVupru0/NnU7MUH9+5HUbS7u1upVBzHyWazSinP8yYmJuYWplzXrdX3s6rWCuth1PN9f2Z+4nt/fr3Xa+SzmZ2dnUwmPzo2aZNdx884HnXjXpiQ1kVhcFABu0lkVbqwpuzyUCiwhSNCOiJge7japlbW9HgRZOirFaTfoKQVBU8+Lz9Looh4WKXtgL36zdojEyMSHem6WcwjjRw29diTKjoqp3j8eBaDSHJwoSmNEQA5RkTwmIq1H3blKxBIrICkebIYBRMLYfehbh+9iTi9CrHI0mdrBlCSirPiagIEQIsALKw1IIHrgOOC0uQ6QkqUQq0BjinqDwdKKzhQ0h+pExDBdR4vyiU2zTB13BUOAcBxjtzlDt6ksuOjZSjTN8Y+5NnH3A9JipnYAjOxAbYkIggkImGsLTNbMAaSRMSiMSwCUcLpASLK2jSU+SFTxV8TT3pd1e0HnajHynF29ja4ENf1/p+/uTZTuZTL+p1WfOuzpUzej01c1uOSdXv7UZDDKDSxzxnP74WtqG1qO030bIFuD1UKnYbyo2xBOZt7W++/++HYyGjlwisLr5yzTvjudz/+uPlg4dLFN974nTcuf3mpe1VxIYlzvRjCuAcqIRJNJ9UUnuEMvxRO+iANPPoJmxIcDb1uS6AX+Plc1t3bbHW6LSLoNnaSqBqH2Oj1clm/09rL5196cGd1ZGSk1+tls9ler7e3t7e7u5vP559//vm92o4Rs7e312l9vr29PTKWC3uh9sgLPLFOrVZrtQVpvLpXs6ZFEKGLJkqiOGaOXQRAcBSCVaB+8QMpIsxCSJKa4w64KPWITWnJPqwZPlyO6Zju75eZo59l3/6bhxMwHa34fJQVC45JqI8Ivr+wD+pYPPFxGiaiA3+uI0IiokOV9fGTIqLTt8yKtWBsWiKCRcRRR2FOD42PwwDA0CfgVO/KIGBQqz6hpsWKOU1EosVRpB3RDvkeKI1aMxI4Sh3XDx/233XhUFo9JtSKHWDKcR5KkXt83PQjQ3pItI8MWv8CWaU/txZFxNi+X1sYi4iwBWYWRuprbqnZSlgkDQU2hoUprVplGTCVswGYSQBASACIBtgsv2DQYMFzXC/r3dm6mQV38fyFpjJJQ2019keGpotDAfnSbDce3HuQ9faKuXxsq0HWd13XhNytxS7m3MTnuhOVkqyDiv0kJD9X0EHcMfrW8k6y8VdXX7jUqsWOW9RF1J5vYru3sePnDYQWRaIE0aIoQERUqj9jP4MvmoT3tPozSGL4oo3PqeG3TAJWZC2HYdSYqczWaruBT51u1O7UNzaXigWnXKR2cx/8ook6WtH6xuadO9szMzOtVqvVavV6vXw+/53vfAcRP7/+2Wh5ttPubm5Vu93u+QuvrS/v6MBsba6KSbrdbrGMY+Mzd++tIbSQa9OXhpkhMdYaI0ocMkYLAB0Sw6PKSSJ82Es5lYCT5Gg1P+ZcJaQefa77TfGRN68cOBb9kmP1EHHKgXh9kFUj/TKRQw9bIZLDBB3Hy/AdP+PPOftx2j7U4qbK50OxO+0+9ontKMbmMK+XhhgAQDA2rBiVSkcPlHrI2e3wAl0fRUAAU2uxZWFBALTxkcxqbeqxDMzse4J0wL6OuK44GlPnpOP8eojjtszj1z4wB/sAG2eSHPb5YXk3dXbrx24d+ryJSRgAmNEypLWqrJXESC8xwsjMYtOUkpaQAKQTWmBiQbFsLVkrIMBCIhZAEBVz6q+lTlf//MQl4FA1lXJURl544cVctjReuaAy5Vsfb25H70+PjXVstdnbzxS92u5+rbpbKYyUS4XiUF5p7LaipMuKdHsv7nXDEpaq68vZiUxjo91ohJ09bsfO9v1WIct/ufSDdrtdzgyVKll/uOlW9muNpj80Iu2hBKaAc5RgnLCJMOLE9Z8Z9f0ZniE8LQn45BsmQ5B0Oo047oWt1vhwpttzzU6n08HhseJQpdBtFzq1jjUsgK2wu7+/v7CwkMvlPvnkE0T8xje+oZR6//33K6MWOlkTSmxC19eeytz69O74fIGtVSTdbjeMt7O5xe3ddilPYnl3s+Znhwhdk4hGQoDYWnQc0r1HDKgHjEoE/eq/h7Y9ETH8eG9nsIe20sOmBABSCfu4YPpLztSjkuKxGUspLWViK3C4GyARTIOJAVzn0Xn5+ed9RLY+/Cf23bjkMJAJ+jbLowQUhyI4IpIkzAJIWoux4lJK3uR66Vn6fTm4LrQsAMgi1gIIcpqrGcD0xB6oZxMLzCgC1irtiNbgOqA0OA5rDaSEjtJD4cFMcUqFiXl86i+TPF4QIv2YjQIAxAe+a+kVAxxsUI5ivfAYAQMIMqNljGJJEogTiGNkhlYokqqj+SD/thAiguo7iwGmmxVEQBayNk1FyUohMx0kSntmoDV6naTb2O9UKlPQLXS2Mteuvj5c6L73XuvKlbnPlt5ar9YnxxeCHC3fXo9Ne2fd9LphlPQkwW4zzOfzJLqYy63c3fnqd95YuDpzf+s+2fDOB5+G+zg9PdVar+cyhdJoaXJyEtyuxdDG3QByu81t1U5ULwc2o5WvtAvsAGtje097TH4pPOmd0bMisZ3hdGE5abeb3Xaz3W444Ie9nueqsdGhTtKgpjgOlfPl6kotk81n8nk/l6vVam+99daXv/zlfD7v+75S6u23397e3p6aWUwi6TaikemRTCbTa8YPlleHp650e53A1VrrByvrleHO5OTM3MyQws6De5+cu7BA4oOg4ziIiSB7nkJ1tOU4NBACwIFKl/sEfHBQGkPSFwSPpZw8oKK0haOnx8KxnJPHvvhlnq/jNID0qDn5Z5XMzEebAFZ8XB97/HXAvBy3eR+RcZpZI/3gIGQWAUAdc9o6vDAEQAJFCIJISmuwjKnEpvVRRrDjb4wFAGERFDI2VTOzAHSM1ZD6Xae7IAIgpchxUCsgJVqLo5GIkWwaGnt47SIpJYuIWKsee8lhPKB6j300tWf6Joz6B+DDcVZpNa2Dgw/YFxBACWCflRmMEWPYWjCRc9AmghCCIiIR8DybWs0FkFONswAwKwdYRANZZkQBIBabaqFPBYPuw9Nan3VjnYdnR2OKl5Y2dc/kM0E3/LGThW47mZtexEI7ur8/PJZvOuBnCA2MD00lEm3vbZuY457VWqucVxoqdOudT9773Kcsdzxf3FFnOHMus7u7Oz19ySvqpeX7GxuNkakCNO3y503YT2YvTUA8qmXCSrGXiKFIsAcACAOSsZ7hDL8FUGhr9f0w6lkb53OlnZ17QTaemZvd21na298VYxemzoeduFAsCqJByeVye3t7m5ubv//7v58kyQ9+8IOdnZ2hoaF8phzv62attfjSfLFY3Ltbb1U7DgX3799fmJtZWFjohlvj4+NzcxfjqL69U93Z3pubB6UcRFEukWjQjC4qOiKt4+zIoo59dLQYGX5Iou1TjwjK0efHVc3HKWqQOvTn4EgnDISYxjL1ldHp749rUo+36bnHfnvklzRwVT2ssgfHaPj4jw6pN/23c5Rr+kgIBgBSigXS2GDL4ChKCZhUeLwPhz1JLQCGxVrWCMJoBUTE1ZLYvn49tUSLIIBoBUCgNaSeYYhMKOmQPzxs/UwiiZHjnx5dr328JpL5SLo9lPsBIIoP75OjycVjUU+PjDaRRkBrBVLX7jQiS4CRAEhAIE1qTSSSllDqCTNR6vzMzH0Z2nMBACz3a3Kk6TsAwT4jvkQaLzWdSnb7/k5O51vJpqbV+2uJn3UZvf/wwxVdsuMzBSrH9XD1wt+uFAoF2hq/e/tuo5uQgkvXLkVJb3h8qBtJLi4sf768vr45tTgdSiSeirWzZdpXc5cg39wor3e327Xb6oWLX75y/m9tqiBpvi4iIbNwjEAeH5STHFAHF9XjH8hBtoqTSqh84g3NCWd4UD3aAf180vWJnxkMdAJ6/LjhL+E09NfDqc3Lcb3rYVH6MFne3tq5sHju0vnzn362tlvNRWu9oVInlrGvf/n1H33/329sdsbnJoxt1mvh5oNihSaWm3e6sDlzOfMX/3/2/jPYkiRLD8TOcffQV4unRWa+fCkqs7J0d3V1d3WPQPcMMMDsAMslCIJrWCy5MFsYjUbjf/6nGX/QjD+4IAkszbjELpcgiAVmBtMzPTPdPS1KdcnU6ml1tQzp7oc/4j6RWRkNPGzWlJj8rOzVfTfv8xvhEeHHzznf+c4f/Gzt/sZUfWb/oN/vdhTD7fHD4mblzW9f/oM777iGNxzUhVd65Y1/9N7bH585a11+Po7VXadw9u5DY2H5b4aJX6s0RFDQMRRzGMogjkXOtQGAkBARkFKJfGRksvhRb3VCr+2NY4I0JYlaIRGlYowWGCeX4KOQLFrR0TwwdlwM4zkaYCJkCZNOA0BEGo3D2QNEQpg0+0vkifpdBMSJD5oXCQCotHsDgNSQ6oSM4gR0OjpHQMYYAgcA0wiVJilBA2nNNCFwhogQI0FajqvSOGfaVgGVd3RBJ5FWBH7C235s72JZRx5kqjdxGLoHle4ZkA5NMjAAMEwhNTENkjCMSUmQChKpWz12YhMAR00OwkQxDgTM0KhJm4KTYhq0YOIxzzX92RuetKbHECnrWyMRAeHhrgZCqeBTuwQiyrk6vVJp7GNSe0aglTienBMvekOTiKRWSlKSUJxoTVqRthkHSLMYk8LldPai4Lg8Ne1ZmSI67DUwGX9ynRVmFOTyrL6/GQIsQBnvZ3TNOm1gVCzOLnebPcGNSIamK2L0z6zME1OVwgIIPUp6e5sNpyfOr1xhHIbd4cHaRhLL6akpmUgZqzCI93cbyGA65yon/sY3XivPVN/94APLch3T1TG+++57K9+Yztc8y4SkG4/kmEWllxe/9eF269AmISISUEr0j+G0jZSf4Rm+fDjpAsIhdRYAENCyrPMr52emZ9fWW6Y5XczPbmx8cu35y4aRMJ5z7PrC4sVcPqhUKpWy96O/uPe117/z7W9845/+V/+yUpy9fOXFfi9wHcsQZhLHtnAWZ5cf3N04v3KhlCsVC6ULKxdzXqGQz8XR7rA3KNbK/mgUjhuVs+cNMxScTItDgoyDAExSlfuJGhEdKjwTaUB2VOA7KU86XHz5I1sjYqnIVKJOhIsPM7IAZPBj9ixjkFoRREzEI5KKx8YGFehDHWcgUpOVM0vDOTWrMu1iCKD0UZmqCak/pxlNcsYaAJRgihCISZ0aYEr7C1tMpBsLAAFMp/smRJQJwLEpwiOX9+hMjyYi/Z84rvfFEz+B8UnjgSNKVwqpSGlKJGiFidRKYpKQ0kSpJ3qC66QJSFOcoNBH38aQQDLgjIUS4DgdS0cOfVp/fDJEnB4AcSCVWl82ObBjR/8JBjiKDXjShgPjCQnr6NvTU06ShDQq0iohpUhrrdOKMUhD95Pkekrj+7I7Eb8CIhqo9kHXNE2NsjJV2T3Yjo3izPzUVC334Qcfbm/vmtwslAv1C4sHrf1xW42GY9Q87xU6rW632VckYUyGza2lwrcuvv7Gm6/fe7g2HA7Ho7iQo6uXrx50QscoT3vnTFfv4IPWePdB72dBqcDFIgCkd79WCggVKQbsWQT6Gf4q4DHre+T5bd6/v7iwCMz66ONPXMd2HDQNWSxWTIMbhsgXFgdDk/H6hYvVufkZQ7y/uLpy9blv3Hy/2dhEY9mYnV0qlrSMNKiQaX7l8rX5maUHD3cXZhZL+dLuzt6FC5dG/Y4l0HLzSIIkjAbtM/OFWj0fxi3BpeAiAWYZSMyQChIlEVGnfJhU/08jIEklHvWAj0KLjBC0TK2yJko5NQSTPnFHfwJaEyJSJI68/5SozDkwhozrT8WlJ1wvIsUQSGHaNSAlIh0pYT2GWBlwGN+Sh9ZXT9b3SbmLkkCatFZAYDBTAyKiBpSKtNZKa621Y6ZpSw2gGTOQA0vrenR8chKOjnNC8f5UHloeRu5PAgBSLtfRfoVNaltRSVSaJYmOEx3HKCVFiSadtjqYjHRkVom0H5DgIAQaEk2DEgGcMcQ0eUwnaFATu6jUsUd7EhM9SI2pV5mGHxCYkk8ulwqV8embAeBofDj8OXk/kZKIaa2VBimPQ9AsgwRATyny9FnjtJFXsbO5S0SSkrmF2ouvXrEf6OZox46o22yEVn9qsTRdmr978+F7b33YajWlSsAUkR/n3aJjeY7lMMHa3aYKaXNze2ZuPhhHrYN27EdBPErixLR4ZbUyt1CreksF0y2V3ffX3+tSD7q3bTnNGGPIARgyAkIGHBEpw7U/7QmfdiI+W0rV6b/htMf/DF9MZKUA8ATfh4jYITrdxsr5Vc7g1q3rL7/0SpxE4/FYaoGobKdYKM4iL4wDa2+ve26lYojwje+8cfujrf314dULb+71dgcjdWn1MoWg9XvFQqlYmjvYbRbcYhIpaau9vb2/+Y1fe+etbdfGc8tnbJu3mv3t9b0rF8/kcyLsxIBKCETJOOcCAYnFUiMiMExNLwIgJyCI5ROsLwAgcNKpepEGYpN0o0al8NAHOupwhwCAkmuN6RIMQJyDEMgYYtozJ23NfuJZ4EKnFl1JhoSpkhQAygwDnEQCIG2hBHLiY4E+pE0pSVoypZRSk2ENxnXq7k8c94lspM/iQ/YZANOcc8aBMTRNdTQPR9YUAA41pY+3WYfO8cQzTguijsqTeOr5aSQNWmsgSLcsQYhS6SjWMoEo0TJBKVEDKX2UUAdIO/9p0lqDT5yDIUgaWhkoBHBOnFEYnWQ149FydLgFfDz7rkinB5D6wTRhXOskOWJ3H1O+4UQo+OQgAJBM2NSPuPsAILjWoEGjIiJiQJyASJ9c/Y8SFulvWQY48/nK+PzTwdMaX7z6xsthNP7kzofaiIw8/Nbv/ub16x/GSRiywdJKfdiIhKHjZBRFo6WlOUC69urX3v7FO5sPt5NASUc5ri1jbZiisTt67+fXtx40FShKcH5q9tyllc3N9W7Y2TeKlBvtj6JQDpeWFufg1Wn2vQf9gKNgjGlNMialYtKIiNw0nsqJPcMzfBnhOsJxHNLAhZUkwXDUVYlqNTurF2qDwaDfD86eexV59Mn1n3z96/MvvnDhJ3/x4du/eOv3/sY/WN8+aDQHkuLVsytI4Bbcs2fOfvjh/Q8/ur+6+jxnhlZyerau1SgKWwW35HleojXjHkGn4HlxEpGWUkpElUiRSK4BlDQ4xgSQNltFBGQEEpBT2qUmPeaTNhhx4vJOApga0zyiUsce2GE9TFplwpUipVjK5eCcKYWcs7SRw5GVOpofIbTWCMSkRCVRSiLNaVJu9AT444mrl6hJNjO1wqlh0ApIodaoFU+dPGMiuKE0KQINOOk3AAzSdr6ElPbgYwwYI62O2yaeDMAq8YjBO7LB6jj3n5rhdDaAJWxyPESkWcqRQmB+CImERFISQywhiUlrJEzTo8eXQOt0L4IJaq0Y6FTLEZUiQ5BE9P1jzgGeUJ0UXMGj5iR9LXWqZJWylPFo55Akj28s0p/+ieKVo30GwKQr0afNlecAAFeagDikvi8x4iCTk4NMXgIAZeViv2A4tQf8W3/717d3Nttyu905eOuDn/6nz//91159dXtn56NP3uNOkCTJ6pWzZxaX+v3hcDAaDAbFct6whNRJtV53LIeIcIBxHFfLs0W7tn5nT0rJTZNCbmmrXpwyjVahwpg76u83MdLLS3NVU+3dv5kkCyTI4AYiQ0aMGDBgjGVlsbM8iac1EZ+XB/wrPKSn8q2nnbdn+MvByeuChzwsIuKcPDe3tdPg3BACZTgKJZxbeW5vby/n6nb7YG5hwFio4mGp6GiotHc3X3p5tTZnN4aQG/BKOW+IqJC3phfr5y+t/P4f/Hkcx+12u1ypDtrdC5fPm7Y8f34KpDn0/eZQFquzfuzeW9+fmStFsbJiHcdJHBgMINYUagTkk76s6UtkyAgS0OzImcMTL8AUmohNanIm5gQBUMrDwpdH2bCkUwUJpImUFSNiWp/wDfGRR4EhSAVKgVaYxEwmKBVojTIjcBaFBABKExEpojQIrTQl8cTFBGKk0vA4AwDLAEWaIREoZGSYAGlclIlJJDvlcxMSodZwGIE+KrGdvDDFMYEIDl1MRDxhR1LlqSOScKojlpq6I94ZhgkkkqTCOKJEgZKMiGkAAcfl1wBHUWXOkaVdqhCBIXAGRMTZ4/fb0YtPrzNpenhSdaUnVzCN8yMwKR8lPR0OezIXfvK7tH6s+HvyYtJKARCIEbFUJ+0wEX/Swz6crMwV+umsn5+1fcmCeO/Wzy2Tf/v7r8fBWMq42Wrtru1+8vH1UTPU1DNtcce+m8/nb964dbDVKdXybt4q1QqOZ9uuGfjBaDRKkiRtv1wu16OhOthrCGlu3TjYe9CYmq1fe3WxknfPnlu8srjS3Wl3hsMPG3/SDHLlwT+0bduxPVPYXDDTEga3hBD94fhzmYivKp7Nz+eLrAebHlV9OuLF5HI2F2YU6Xp9lhvcD4defupb33rz9//wnwmGrs2C8T7iOJfHKBqtr63NlArTCwvDZP381TKJWjnveXZSKlorF5dMh7uec+nynBC5MI5MS4RqbFpOPs9bzVGY6HZXhSg1y3PL7naGwoZYQhDGKrRAQZBIyS1hIgCmNhgm0ktIqJn1SITwyAwrJE0KJkWxmNbGILA0w3f43yERCjFK5MnZSK0aA8AT5cFHU0REpLhSqBVGMSYxi2LUCqWCx6Quj5DEE9ax1qRoQvRJI9gAAJod0lzZxDZiwpCAoTAEF1oYxDkgI8Pg+iiGjBqRMK0kPjJIj17rhCae5YTsfSQKpsXh8Ry2FCYCgDBKdwBEGmki35gGXbXUJBUlCSQqrQ7icKjjdXI3k9p41zEQ0RTIhRachNBpVtU0HsvZT5LAeII1dtJ8apqQwkhjmglGQk06jWSkw5w0k1ofC488+pPB8S5k8pMIgEgTIDCZUsuBaTpOgdOnppQyylQyujt+aSBWrizt7+/ENN5ubtSL9fd/+UEylHP1xTu3N1FoA43N+1uvfv2Vl1548WHuYblQ3TnYZhyLlfxUtb67tT8aD+M41qQ0T27evFnO1ar5ut8LPFYShNTnm7c2m3rTmBteXF5Nuq31j7eMZPWsuaryecuyTMMGABnrOApCDDkzfkU/p2d4hq8MDnNvqdWhIw/YdsyHDx9qzS9dvHz3zk/3drcvX51PYoko+v2OaWEhzzk3DODdbns0Hqgg+OGf/5tf/73fbvfXo3iws7kvx7vtKes7v/Mf7+40p+fqnjPrOOVffvjh6sUzceLX6rXWftcP+7nCVELj2/e2uKbXX7mwubZRNJhSIoqUCjUSjaOYTKeEx6HXVEhCp3IHxkTx6piwighpB/jUN05dOgIEBgBSTazgo04wKFKIiAzTHj7pC0LS+vH+vqnxVglKBSphYQxJjFHE4gSRMMmKQaedCjQRgaJJES0Rok4b0k6cYDwk3woICZEZzDA5F0wYGlEhIyEIAKVWlPqsE2VH0NqBEybn6IU+lKU8pHZPHHquD5viakzb3U/Kz6JJv0Y4lOZITSMToBSTUkupleJEiMAIGaA+mkhEPLT1zLEAERgHUzBk2uDIEDVo2zrZFIFOHCqnTwEAAHk6M5PwxiFDO/XOD2/i45/iRN3zCcrVhGp3dKufvKBATJNOt1yplDWAZozTCUWUoxcyw9MVfwmxy88S4oN37yQydnOiXls0OU7NVnVVnVs+u9fYna5NHxwcMM5vP7xru+bS5YXBsP/OO++0D3oqBIcNUDNX5MueFcvIZCpOBhdfvLL2YKMf9JWPXq4yGAzQdPdhPDRvxG8WZsvzF1fLBiuXpnBwe3/UN8veajK2uGNGYjAMDwB85iRyYM3OebH9rl2iYvLC9t3awK+j03/yGZxS+TPLI8m6jKlG66eRVX+cWU98yrqxx9ua/ocjY34yj+fp3NBZdcwpK/J/PLKOMzOUlHWfnHIeThuq4uLJ44QJy5kOShkmWjJlOdwzodfYEIkqmCI3VX9492MVj86vzAsc/fgn/6aYGzYb96u18tyC9dFHt8slCILCxx9FMxeXXlucbd0fkcKSIbbGO8aZleULl3/4w19wXRz15O7uWm2mOzXLw9GumTjheHWqkG+Zveu3rueZ87ViMXTZrf1fVPlFv/2j11/92vXb9tqOA9odhn3umG2MOGfC0KYBliDTJCGQMeLgatCckQaNKBE1ATGm+6HxqDUiIgUAMSWYttED1BpBMaU0ETnOhJHEGOOcp0w0RExgIqY/IRIjACIQJDJWChOpYgmR5LFisWZaA8YWgT7uRMfSfkza0hPqLQIy5Ic9eyCKk8NhH7kuWoFWpBUlEUu7KqR3cmhJAOCc8cmOhAyOGrRhnejedpioRADTFHBodY6sLwD0x+nkTLhdR7F3FcEkKY4ER+pgAHGESiMA42zirGtCAIrViaNPb0mlAfRI+gIZA4iJCY5hSog7LpeCE+QpJKJoZD/RA0Ywj+zxSWd9MMITCeBjHpZtySc+MSdyt8eJYQAIEzw24GmOg6fX5eQox/lmxCc/d+qx5/HYy89YZzKaTGSuexnfm6nTcMp+4eKtn/6Fl3deePmqJZwo8F3HClTw4YcfTJ0teK6wEjQMMTc1PRr4D29t9bp9cIzBYDRoBuEgyTtFIYRhGLmCN2q3vWIOuFo4P723v6sUtfydQqFQrc11qbf5Tut/uP7TGbtUL5TmLs0PvMbO7mbYgmr+uVr5PEnKVTSFt965+YupWa/MZt3hWYO123ItqN3N6VfMjdfasXWqE3uGZ/giAxG1lgia8TS8hgCgZXzj1gcr518SkVufLh/s721sri0uXdg/GJQKw+cuX262Wh9++KFhGBdWzyHiYDDwPOn7wcFOY6o+fePmzZWVs5cvXf7BH/7JN797ORiME5UsLy/40ajVal27+Fy9UL314d1oLPcanYSizXY3sv2Z4uWdDb128MHKuWQwDk3bURCPfUlcOY5Q4wQ1U0SSNCNIK1oMDgESABIQ44TIGE+XV6aOOxPAyShl6mIe+8qIjKHWaY4QjpZ6xiZRbvVkjweVQq2ZpkPC8ETDBJCUPvxeSinYqI9yjZ+Gythyp9VPaVntRHdJEyKGYcIQAIlz4giAlKTReHg8xJq+CMMne34yOnLoU2fxSLFSIT6a9UzrjI+qeNIYwKQ8KXMjKGOmAblmxEEniBwYagCMoseDw+mLKNIn3zz6iY/qMRxWEB1XhT02VFYu9hl+NcTF1WVN6qMP3tUhXHnx3HRtunmwD5JKq/lyOY+FWq/d12aEiH4/tnXBKpovXn2lsdP+5Jc3W/trOadgmiYAhGFsV+2x7C+szEzt5nli+8NocWHptbm/fn9z4+baAwxiH6PtTk/nnYqLlWUz9HQht1Of5h/c/rAIelDeH5R+Ht63RovlPe3lxwW1+feyTwABAABJREFUPaBCeOlSMrtqtm987amc8NPy8LLG+eIFRLJ2dhnH/yWh759+/NPNw+nHPx04B6WUwZghWBiFHE0EPRqPwmTYbG+CgYap/aBdnypevrIi7MbW+jucV6fq9bv3u61Wy3p9tVarKaUaO+2Z2WlcwI2N9cWzs9/9jTfffeuXu5uNwD8zGGidMIOzRqPR7fRtL6fB+PCD969eeNGxizl0B/6D6xt3EzbX3M9Xposgko8+2eJmBdAlNIFhGAQQGYqB0ClhmGQClgER6NggQuKMCFNKMEOWinPAScfoONjMMe2tnuYUD2OmkEh+ZJ8OmbmECCIjckDEtQZNKW+LtCbUDGgigUREWh+pNgFA6n4/cZwMqUWm4bB1POrjCijOtOAISFoTcQQEzgARooQfnuGjhlY+bpAmIWt57NU9ihA+ZcgnkVoA0phW7cCkqwHJjG57ScARkScoOTBEztNALgz9yVGc8FwBAOPomMYFJ64Xx2MhlNSNfywlf3TWNMkZP/l4sgJG+pSs5tM+d9ke7RdrhRaebSSaDILaUul7v/mbvh88vHNfMGN/fz9f8s5eWNra3Bo1B0Gs/XDkYGE8HpfL5fmpJYrY7et3QbEkSeJ+ZHgwe7Y6e77qVHltuSD7rFKpzExNe6L+xitXS8X7P3vrRyM5fPXim5eWXup3tLDu9IMtMd0rr9SwsQ4129Kjl15aJcPBqjGW5Dfl7mY3UCM22BrOXOfwdAzwMzzDFwGMEyjF0aC0xoZp0mo8GuaKZmfQWjx7fjA4COLglZcuA+het+26brPVqlWr9Xp9NBrFccw5z+fzOw/vIuL0Qq1SL33/+9//0z/+80F7DAoZ8mLO68VRY29Hq3Bqano8jDf2H7R6jUptamNvp9MChIrmLafgXi1fm15g/d6925/cml8OTSE4M5IEkjjh2jQ4Sk3EiSQyDlFInDGfJyK1Qxw4Z0zotOGsMQnZPYlbe9wpnZCOwpjsKIusNB0xrhg8Uv579JpSno4G5MQ1GhyAFGpMU7OagBHpVExAIWE2CS4jNXPokp74IAAACItrSlW3GAFxBoDIOAUBADzhW5JHQ51HHxCfamWf/hTikJ104ksBIBUYOezBwNIEexoDeCKikCMi58AY45wYagAOqMNAwKfcViJK5NEkP1LXa/JHypZOWt+Tf378a1aKJ3P+v9x4WgZe9NrNwahfKHsrKyv9fr/V7IAwlGbjRnJr9HD+zEyYhONwLBFYTmntdzv+v/23/3amOm8zr1qtDrojrbVpGzzHVq4tPvfy2fvrdy3XaKw1S5axvbmbn7lxZam6tHxpbzfpdDoGewn8b+uuzcxkuLvbsLusfturm+fPn/vo/etMlYsXakviGxgu3Y/ecy9t5oyiEa5CY+VpKWQ984AP3/5ye8CZufyn5AGffvwn41fkjBE1Y1qj5owYQiKj8XjQbHeXly4HUbi5vePZQghrc2On3xutnJseDtb6/b7tLCwtLVmW9fDhw9FoxBi7e+8eCflf/q//y3/yT/7JwX4zHMl6vW4YhkDrYNTp9HpnLp2bmllo7o7W15qx2e0lozgStq5wbk/VBnPLRRMdE2E0cCK/nDNKlucMe5IpI2ebYUyKFKQJS6ZRpbqUwAUgkhBp4YsWmnEGyInxjPNVeFI+/bC0CcWk3/Djkcw0JP1pl4vzCfEVOTLUDNEwU6EPCQBKY6qQpTUQQ02gskidWaStk3VCJyyNJoMzognJiGnNOCelQB4qPZ3MlQIAoPXoIIfRaTZpq/eYxwxPulWISKVaypiGrCf2mJCy7ucwYpwhMhAMOQcCloYlkjijfEhnGE6BlFrP4z0BIU4UuD5tyHVGTlRn7RSytJcz8JX1gIUQ5WrVdMxOr/dgbaPbHXZ745yXX6pc/PCT9xub3Zmlaq7o6TwFfpiE4QsXXtha2w2CINE6jmMhBABIKSM1COLxwcHB3vZ+3inl3YRrsbW5FTs/tluVOn/jN771t7v9VrPb7Y37XqlswxnXvX+we+9m892Fc7XnL14rmtMUm8wvjBhHGLaChpJssfidXPFaLxbj01UnPcMzfLGhJeccgYC0YTIhIPDD0XhYLs9889t/7c6dDQZupVzd3x10WuHs9Irr9m/d3BZCTM/UoygaDodKqTt37hQ9v1arfP3rr//RH/yxiikIQq2o028LbrYP2jdufSyEefHaRa0oiimXm7618f5uozG7fLU6mnm49kvJwXPHcbitoNrvS8+dZ5B4biKMBJDnHJAnreYJE6E1MYZSQqqQpTQBEipMPuXhpTCAKVIAKYMJCCilJlnmUZD6kZVRTtraHJbv4OQzNkcATKQmAmUypTVppjTJtPpJo5JMKZJyQioOMtrqqYz6FUUnzOHxIaGUJCUwpjlDqTRnGhEQjxvO06FgxSEz+fC8HpsH45F+yUcnTvDEPrvpP6Xnkpb6UPqrztC+BkIiQI3EU3ESBFAAR2XWR6mBJ9jFR45HP348E3IWe9z0pi+yjicr1E9Zx3/aJjdfcggnlw+TcGNzLxzB1HKbSHQHfqE4LUdo6Zz0I1Pmi1Y1iEeAnTAJdnZ2kkRqjYP+GCTLeYUgCILI90qO9OnOxw/iWOdzdrVSG3ZH3Na723v+jM8riVsY2FP9KaGYbg860fuf/MA3urvNtZh1LD097vfmFmqgzbDJc3PNlvzYNbZhx2m0PwnzjhQVgOnPe64ewWftAZ+Wbfu08Fl7qM+QgkAKIZRSUmthCkSIw0glUbFW63bCODYuXXxpcS436LXioDO/cCVU70opy+VyoVDodrsAYBgGYyxS4//kb/2dd37+7o0bt1584QXPygXMbzb3tIRer9duHVy7dg1Jb2xsztavFFzrxo3ScJzMnXE726h0XPKMnJMUa+Z+ZzxO/JWls4FslAy3OOXJyDaZ7zEbDoUs0rxd6g+n7ZHSFGPaFRgAANRhWfDk59HtxIgjMAKtAQGJcWCMISPHniy4R/XE6Z8M/EfaGk46VSBYgjSBaaZmAOVhWWostdaolVaSpETUNFGVymCxxvDk5LCKJ113HnsMJrsHBaQBGWiW1tceqm1AWvyDR5qOabOjk1Y2feF6ACdUO+DwiQvDx/vswmTHkxrO9POTyLnW2YJFCBqBI6blTqn7i/S4yTx+PI+z9HgyO00ntLtPpofZ4WV6/AHPyOlm1e9SRne7p4XP2gN+aiFo07TWNjccz6nN5kdBpGRiufm9ZuvBw3u5Qo6j1dkbcjScvDEzPTszq6ve+cZuu3PQ1xbmS0UVU6vVMixxbvHCytKltb07paLZOeh324NWp2EVjPJotbs96hnbi9eskXi4t/uA+1U9qq6H7yGPB9H+VMVzhbh355Pq7FQYRXutnYX6dMnLf/eN1fu3H/z8l//8skvu4HeeTvHKMzzDFwNExBhTUjIgLjgDSGSUqOTc2ctv/eKDUnlueX5J6yjnlgO/E/iqPF26fPlykiSmab744ovnzlUHg8HVq1dffOXb9+/f+/jj647pjofByrnzfb9TrOTiOBbIa9Xqa6+9RoaxtrX+8guL3YYs8gXBXO7FZI3Prs4VvDFEIeGo53cS8OfOzHc7u2ZumGcGahQJyH4qyABKoaK0ehWJQMWU5oARFQMATFvw4PEKjY+8QMWOU5xIyIBxYIwzfmSojj5LiMhE+tdH/03KarkAng5BBEAmTOqMx1FCGrXiJEBJ1Aq0JtLoJ0/2qOS/q+nap+LDiACQOr4ARKlqPUM4bhqBJyQeSY8Ozyt9c7IyW+6nF2gCgDCcWLXHbBvipP88ESckBMxqOzEBS73v9NKgBgWAAIcSoI972JNynU875fKEh4ondK0Ze7Ihz4roZzm6X6xA8OcHcX37TtCEaSbK+ZJnhr4K+n4keO7FX/u16x/cgsBYqFzwYnft1r1mcHDu4uLH6sdnz13Mc95s9RBVrMZU7r347Rfq0+7B8EGp4rZazdZ4xynbl84s+L6/MF/Q+sGOeLj5sXzw4EGpUrQda39/f0BaB+Tkzc4g+uV7D23Xit954BUcNgdi37y190l7/tyLF79x8JCt33z/1Ss1f/g7TzyBTJZjRo4hO/TxZJzWI8xKbZw2FcL4Zy3x8hmPf8pc7FNKFWV63jrjffaUjjOrLjzr+A1kQRw5Tn48HhtxXMx577777tmVS9yZc80DjzcV4X6bBm058ruS36/Yueu3jDe++Z/trG8b0K+9Wc1N2d8Sub/4s7fefuejc+eeV1reWmsuaufq1VdgZydJQrTx2uuvbDa7hcLczMwFJ5e7eefDl3+tkrfCKSEP4p+tPnc+CJYL+bJgRvfG+hsvl1bPbu+YurWz+fy1c1vbvZjYVD0CYlpxKQUpHkakEkYaAk6HnhwHRNCYig4qVADAWOq/aiPNPKIek0RGHJEx4gIFY5wjIiAXaSCeM2CoCcngSKBL4mS5y3Hw2+bHZT+HIESMJSPEmChRGCuIE1IalCJQFjLgQpsGcKEZ04xLxojPqMlmQoOSLEkmDXkAA5w0gWBH1hQAHOMRYZDUsAKAsAaImFYw46GmChENRjYAIGjBFefaMHRKjEI40bf45Dl48tNxXSLKqkNNIkMqSpvSS3V48AAKDnPek8YG/PA3dRyNOHEirvlIEvoISiUaAIkTMA0AmqXdJjnLCOln1HuJzGUmy6U6Xd/0LM87a3R+yjrdpxURzBpHAIFXZdViLZ/Pt7vRwV7T9Upf+9rXNnYeoqXiSO4NtoqsgKaqFkpzS9MvXDzf3hm04x3bEmhqy+DT03MXr51jpkRE13XNEoZsJFXCXAKtTMeM43htba3b7dbr9Zn63O7urisK/coeDpBHFmcOJ6EosWdw6UK14cs4dl04R4OzzTtl2V50TddXpacyC8/wDF8QGIYRhkmSJEwA5zyOY8PAfM6JQ5kkNDUze9A8IHK2ttvnlhfqU+WtnZuuY87O1gySW5ufHDSKV168sPnw1vrDm6ZIOMZShYLHB7sPDR55nsd56dLli55X+clP3i6XZ+M4cD1DqThvG5zzre2Nu/fueLl8FMThOPrWG99+653ryMD341KpRNrUKnJcW44IQBEwIk7ElEIkkEoficTQYWujI9eNKBVp4oxpRKYIDA4AjHM8MsCMwaFTiDIBCZozVAw4Y4ZgilICkXpi1FRmsItBG0qRTIBS91eRlEgKGFeCp942paw3PLQ0x8SowwooRHTsSXOFx6Lfpv3Iwn20ngohjgzw5ED0pLIWQac9Ezin9CsEz9zA2Tae3PN92hg/fv8gJYqIiDSmjRNSCxgfu6InU/ZpNfAT6pVtSz/RAIcxcGJpQ0mtmeYSHueHP4KMfcIXjvT0RYN48xvf3b2/u3t/NwmTQtUrlvNxrDc21w/6u3bBIiXb/UYCoZFDzjGO44fv73bb/W6rF1OYKxgLZ2eMIq01by8uzCmlbENwlyIYxyou5fJTpeqZlaWb1281Gg0tScX64e2NB/cfnjt3Lu/lwTPCmEbjIVlusegxUu2D/qAL+42mGbpiZp68cLr42srieaWdkf95T9UzPEM2MjkBGesP5wZRnCSJEMgZRtGIMfI85+7DtUqtPj2zcP/d20kyXl29cPHC8p21d+/feLs+dfnc2Vo1h1ub49GoZRmXBML5lZnh0Fs+uxAn0d27wWDY93KwtFR95dVr9drcn/7wJ8Ph4PadG9NTS7dufDAet6bKZpKEUWwkMlpff9DrjmzDPbN8plTOc84PGq2pepELOGjsMD7luuXRMNYEmlApFiWQxFoqlBKQH3mojyzrSulJyS+w1OKqiW1WoJEYkUINpEiRRkDSiQYkwZFxtEymFJuEnBngcRFRmisFIpLqEXLQYXqS4giVYlpBnJBWqCSRIiJ0LU1IggMw4pwYV/ioZUqtLOcsZSohHplnOHKFEdHJJYfn+4ipQc2P3OWTBwaoU8loxhAZccEMgcjAyGAL8xMRr5P3Eukne2yJUGpSCQ1SAhGlv8JhudGJKSIASIc/maFPX3jucdOIkwYYAFN5SA1EijSh1hI+rRx2jM9JlDnD8j8ts/+Ze8AfvvOJIQ0ZaMpDPpc/655ttHq37908s7q4snShuz+8d/Mh59w1Xd8frt3e2VjfzOdcyxUzC+WrL6+uXF0aJ53tg41mqxOGodIguNCKIRieXSoUCt1eZ+nMomEYW+s7mw+3816hVq5vre2cW3hOkgqitmJhxNiox+Nd3VKSwvFoNMoVC7vs3k7YnsrpC2fP5cziaU8se0P22YagM8fJHP+pDP+lx2c9P09r/KyPn5Z0prUGEIhIpBF1MB4ylEShUmpqerbb62iSQZR84xtvMIx+/4/vMkHIxj/50b/yewNE38tb62t3h71GqWgKbql4WKuU6994sdfrMsZcg6rV6tRMTWOyurp848a9G63WjRsfnTt3QWkdBNG1l68oSG7duqOUjhm/d/8Wkl5ZuRyG4dbWGiN5dnV+MBaN1oiBJyUoRXEMSUKxBJmAVNo8NCRHOcv0ZyIJJAhOqVUjRoxxxnSSSETUHBggMlJMIwCgTvUetQYDIG11h1orRC6ekLMkmjQfPDITNKEXYRgBpk0JNQCA4JMehpadABJjhAwACJhOM9Z0qMmV/mSpnSQmhD6SaMaJv06MsbQdEAAcCnxMDBs7fJ3mIOiwGAkZMUDOAYXiHLkgJoAzxjNSS2KS8z72TY9urCd+Pkg5XARSaanSxLyGQ01s+hQOHfQJ/+twfHLsSSrhaLuTzqqUKdsrNe2gFAGAUkAZZUtZmsynFdw4dXlkRkg8s6NAVtnV5wSxt9nOcy8ahFG42x23phem6lO1sZ+AZI6R61MQjaXhoMsLnMxBr19wco5nxThEh0diePfh9Sj2c3mv0Ql2t/aaW8NKuRr3hZJs/XZDRrvFeuHKlavLZ5dWz174qPyJiqlcqNy6dctEoZJYU+IVXMGccS+2/KJtFGJsOLYxWzlXnjob93OOO6PB0egARJ/3XD3DM2TitB5wFKmUxkwUklZ+0CMKw6Dz/LULfqe5uXHXEDBVLw6GLaX8fN70fT0aHYy6PkRRqRaalrr+yXvxwDvY3ZybmxuNe1uDlu05jdZBEATPPXeZQN2/d+fgYMcfJ1eurDYaPcY4Y/7O1v7U3Gx9qmy6FzY2N/LTRQH2YNDzPI8x7Tg50zQLOW+qljtoDm17dtTnUoJUOkkoTiCOUem0++zjC9nR8s2YVooxpoUGRORcAwBBSoJmE0+JGIEiDcfqzJCWverUhOqUdfwpA8zQ+LR1AQCliSFHBMNAE1FqQEQGzHUnClMT0eCJgwsaGKWq1AhwSA97YjA2/ZVHT/YsSURa62Pu1UQaU3NucEaMkeDABQmOnIMQmmW179H4xFsoK2SNkw6DyBibUMsh1efKMsBHjOtHbDB9KomQfswwGBFpjVoDY8QYao2cg1QZOdqsdpBPTnl/afCZe8ClXEWPpGflnbzhFYxKpVKsVOIYmo32wcHB5uZ2q9VSOSq6Jdf1uGUO9e78ufmQeMxH3VEjHPtMiwKrnKkvDPai7l6XxqbFHZ3I9v642WzOXikDYKlY/o1f+83puen7dx744+DStVU3Z5DCB3f45noDlagVasrEbrNjeCUgm+uLVf7N2vkrrrMQSGr6Axs+Hy3oZx7wXw6e1vyc1hP9vKA0CMPgDLRmnMvQHwJFMhmVS/a4NWY8ivz2/MLsB+//xfR0xfNwMA5Aq9laycRce7AXJYOt7fUiLlSrZcPghXx+5I/W1x+WKsWLF695nvejn/zo/LnzCLrXb967f7eUrzqO1+l0TBOqM+Vmcw84Y0wrLXd3HkaBvLh6fmtryzCKQRBYRhgGAymlMM2xL0lDIimlNUlCTSnP9jjievgCAUBr0sSZJkSmNTFGUgFnZJoGMhKCcw6IIBgQKCIyDIUMDMGQgSGIISgiBke9SB6XTgQmIe2kS5MWuOk/2TZPexgwxhA1sjS2TJYxce+URqVJp4xuAK3EpE7p2FppAJQSidhhi+LUGhEi6BNM4JP3mMHDk7xlfQhDEAAwDlwQ48i44pwhMi4y2iZG/DGicvriqP/uY4gT1HpiI9NeyykLcBKIhqOK5COpSASAtJ765E8/OOKZI042ImlkG48mWBMDYIikNctuXvLZepbZHnBGCDpjGaAv2IIrPCufhBFoPR4MIyBtyL1GUxKvzVZiPY7RL03lOMFea7dUKOecHMvL+qInLRwGxJgKx1HUCO7sbFSWZRwoLVm/PVbJKPRj0mDzfBzFrVZrPB6/88u3lpeXa/Ol27f3u0FHg5f3itxCYXIhjenCXH523i+OJbNdc+rSue/mrJVB34gCM1GKZa+q2QYy48J8wS7AMzxdZHqimZ8/5fin+/ivyAFzANBaG6YwDR0EY60TIVi7vVkoMBmpsNmJwm6z2QfqDAYHS+cXunvd/Z17Dlqx2c/lLRX7a5t3Vy6d73a7+wd7zODFYvnll1/WWv3gBz/47vfeIKR8wXuhdvXO3fuvvvy1wWDw53+2NT01xzncuPlJdWoqDMdaUbPTTAJ5705o2DVhyE5zd3+7Ozs7OzV15v2PNlQ0pzQlUmsNCid6SAD85PN1ZC0AAFPlKQSaqCDp1D4ZREgMEQXjwFJHjWlStq0ZA0MgchQcOQOliQHFybEzCidsnmEeV66evBr8qGyJSURkOHHsbAGIoImUZokCTUwpzYkkCgWktdZKaa2VmmwgpBREkNrgE04wSX5Ur3yiPgfREeKIrvXI5s9ABOAsNb2EHAA1sgk//NMgso83AlofCXqM/QyWr+IagDQqSgugQWkCQnWsQY3wqCg3Puropz9H45OEMzzaYTCUmtL4OlMq3e8wANBZXdqyPPXTtpc99QN5uuoD9TnpfGR6wDubuxZZrjAtx75w+Uxlrnjj9p3d/X2ryMZ9fyyHpemyDGh/s5nomBkwf6ESwlCCPzVfNcAc7gZ+mIAwf/7Td+rT5XptWkZqb+eg2xlM16YvXrwo5oKPPvrIy3m5nCcpKVdKOwdbXs4dbSjf6THAs6sLe1uj2zc3L1WX//qb/9nuYHccxRbXndHdxkFcyp2r5hYI3Z4c/CVP2TM8w2cHwzC01lorlwshdJKEiNpxjCQZlfLOjU/WOU9a7W2G9tb2Q+4MbKeyuDx7e78LhJVqoVguakraBy3Tsy5fvnzr1q1Wu/m93/7+wsLCRx99VCgUS+ViFIfPv/B8MA5yuZzt8KtXv54kYalQSVS0vbNVKOU1qUIx/9zFi0Ci29z96KOPHHf6jW9c9Qdrd2/fLNRLjLtRQlKRTPsOIQJLRZDxKPl6aHwn5ocxkablGIOUDo2oiZiUMedcKUpdSZ22GCDNBRmccYGWKQQHDdpGoUlperJ0omUoOBaNmAAR0Uz7FBMDItI4OQYtwAAAloZgJ9JdqAk0MWKU6lURkZ40pEq7DyEAEqAmfWgOCYzkpN2aWNxDFvSRAdZaqzT+zhgCIdMMEZlO/44yq2Mg3dMQTbr86klfZQjCDI4LTjxgpZlS6VczouNvOJyix//8MSJ0GLET9vd40yO4TKdcayBgx5l+zOgCobO0gp/1d/9VEFN8OTZ8aYYql5TO1SIMoUB1q9DaaJ+5sFibLt784F4yhKmFSmWqcPWVZTlvV4rVfntcdquFXKm3AMztzMzVv5X/3rsfvLXf3clV7RevXuj3+xwMdPz+rp52VtqN9nbS7+4kfnSzXC9VvNKZ78/knTInYzzyz6z2o1flg4/eeafTNgp5VovtorH54L45Vzl74W+M9u8xuQAxqNB0sM510fdjzRLD0RGNTOk9+cQyd1JZN1DWjXJKck3G14rPvK73y42s2cmqr81CpseZ8fnThqxPW5ecBQ5+mGjbtlGPaqU4Ht9henDnzsOzS0Yz0JWK02g0dzc+IsBKZapUqDdu7y4tLc0uzd66deusPJODC71WgVX19sGB4boLZ88unjtXq8/+we//iVIqCqmUqw47ycbm5srqpVgm/f3BzHTw3V9789/8yx9InSwszRZz+ReuXOZo5dyi5+TGyYs/+/EflUtxqTh3/Uav02u8/oaOyR8r27IsihVpZll2olQcSyGEPlzpj9btNAhpCw1MM5MYQw0glZYRqIQhMxIFUYIjTDOmadJUFOtSIhEmWilGxDkiIEdCd4yIHDjARDiLiNjhc6RTxxr0JNSI2tP6UROS7gzQAEMRAAFoYofVO6gxNasWR2EKzjQCS0UTc65OFGitlQIpQSmVBmMnAdnDrkBHSd+0XyFpINJISMSIEAhDmTrigjEmEpYeJkNwvScnRSPlk0KlQEpUMdNKSAlE0B89+c59IpkLAYT9ZK5MMDKf+L4m64krHD1W7nX8efuJ42Q9Ryf3HCc/Ywvrie+DqQGAFBJR2jgrHQV52o+ZGNccNWPEGSGS0jytsGIM0qKvNMYQhbknHo+fRdrNXAeeViT1yeuYsPLCcQtmodgNDx7cf5hANOiPpJSVSrXTaxfKucp0oUej+TPTM/PT7W5rZWaF9dhCbj6Swc76XqlYvnBxtVItzzir733yVrfTz+XyZXM6Utg6aO2O2jmz6Pt+HMdEFMdhq9Xqj/qj0agwa7nTBYH27PScSvTe+gNAYgbWFkrNwVYAQWnJY8SV1yvPl7Ufw/50JJJQdULdJ1NYwmFgM5lXf8W0Q5/hqwEhBEskY8zzvOGwvb+/T0Qz88VCsfjJJ7+0LTU9PS9V0ul1oijyXLNg19vtdqvdyhdy5XIZADTJTqcz6g9rtennnnuuUCj8+Mc/vnXrlmma4/G4XKj/6Mc/+eM//lF16u1CofDyi8+PhuMP3//gxs2bz79w9dJzlxeX5j/44INuZ6+Uj2emhGG5juM4TjEIRKcbE/F8zo009tumTLiUmkiT1hxAoOafivs9th6lbhzBsZecFiMdZW3TQCciRgFHRGTEGTIkzicWnZAhIkfkDBFRAwkGisgQTINGQECalCexCRfppAN3dCRSEwIqTVIhEiRaK4Vaa6UQCNLuCQhMawQgIKZBCcY0EBFwDjQpf4LU76a00TAAUUrfSklGSGpCHp5IMRNKTojAEYFDookBpA2bwujJW81USpM0JjEoiaRAqonI1xORaTC+YCxfOFGddXJ3rB5llx2dDlcAaQYDFfLjjbNpIGOaMWKoGUt5YRog7SZ9SFCHlCHPAUBmSXN9wSC6QZspypuO7/vbm4HtGcAZQzGMu5Ywmt2xn4yXVucvXT1fKOcSHbd2u5vbG9Xpcn2mur+/v723zQWrlKu1/P1mozVTXrBV4cZbD+/dve95Xr1eb7e6YRQIwfOFXD7vjYOxH4zDINp8uKMGAhW/cOECAx7LuFwrOXmzGqxs39/fl1teWag4yQ86M7mzfi/2zJZhuBiIOCECI5FCJwkpMJ1T0taz8NTYbk9lmGeY4Kuas09PK0kSxsx2s62UOnPmzGuvvrq1vd1qx4bha7CE4QjOgKJi0d18uNnv96vVaqm8HCXRvft3pqampqamklBprYMgrlQsrfH551/0fX9/f98ynGAYRkHy8P66aZrnFpe313feeeu95kHTFFYxXw6C4MaNG9c/um0wa25m7uor18qVqufVtvfGibRdF/xgSJILnCOtGCFRAgoEQ41kHOZhH4tnTgKVE2IT6KMUKiBppPSTiPqwzQEijAcmMGKAgJpzYoiAGgENYQqOyCYUYkDSnAjJndQREaQ+xYRsQ3iiUTxNhM+ISCsJACQ1aA2kQUmUShOBUkREiQR9+E8AIDUZgggUIAmGRCCAESMAlDE7SqYeggBA60MNZ41HHhsRgUUMgTESijClHGsCgFg++X5OBUakAq2YliATptO+vxk5V54hSMkyNJaz2NRZOO3nM59T4vAk5kQqgXni18kLNgl0E2MaOSFL/4OCjQDAGAk22XUhagDgnLTWpDGWJCVXCk4ZMvucIWLyQRIMpR+E4INUSaGYr9fq3WDPyVsAUCjlzy2fKc46RJIp3VJbS8/XbMeanimvXF7ut4YP7q6//+6HcV1AzA3hdHZG3VZPjsEpu5yJWnmq2TmQKtZa2649OzurlKrVapEaN3ba7UavfdAXFgZBgMLYa21fdWf4oKijfcV4EMrI1vlKWYPahh/XnHMGO+/E00oVpQZtjgxLQvTlyDF8VQ3JZ40vC6v5tNBaT5oxSNlut0ulUq1WE0JobZ9fff7Onbc31ndyOR2GAVHc6zaWlpZu3rqBDBzPaW03Hjx4gAyklNw0x4PB9t5us9M2bKtWrw+Hw1K18uM/+1EUxq9//fUw1Ds7O3GUjIbjqWr99p0H7W6v1enMzk1PTc053tba/c3t7X3J5fd/83t+aN+9sW2YXi4nWs0dJ1c1hZFIMC2uFHBGlskZMC4gVsdG97EXcJhVpRNaGakU6BHJ5+jDo7GRJoUZckQCJM44gXZNZgpEjtwgJTQToIjgUAlZQ6rRmBYRaQBgwE98tdY6VaRiMiFFgMCkIqkINGgFRCyRoDXTWmvFkpQOTQwAGJOHfme6t6AJv5f4Ydr7kZ9RnNYFM0rpxxpT954hS4nYkgFLhyIAAJXRHUghkELSqBUqmnCwSTPkT04bZ5OMsnLGT/58VoYt6/OZildZw594fk/eIYzrJ77vcgAEjRqZZgYxoZFr5FQwDzdYQPwEIz1NUiVyIs+iNSaSKYWnNcOnXZ+f1nouFs8sIGIsQ2HxSPmJjEbj4dLKAnn5bqc3Pz9//vx5BTJQQyK6f+/B7PysYzlbW1t5uzJdmrdr+aXpczO1uZsfrA17zZ12U8U0Ozu7fH6hOl0O4nE81MVcmZm6WMx7OTcIgr2dvb3txrnV5dFgNO5Ev3z4oZHD6dk680Q8Hu4ln+jpVtTrBRjHPPZzs2Jmq9ve6+n3PdPnaqDYEmPzhvKQpCEwiZ+cA37mAX818FXduBCRbTtaaymDdrtdq9VGo9Gf/tmfVarz+XwOtIoiP+dx2zIq1eJw2EeS3W7XD30FycHBQS7vWa7VH/QLTpkzrNdr3W53aXEhjmPTEJzzW7duFUuVhYUziCbnPAzD/f39Uqm0sLgYhvGdO/eYEPWp2ZWV1V57PB6Ni8VioZgPI9IycD3Xc5Vl6rNnpjr7IUBk2znQQmllGkLKiDGWqu9/2voiHi/Rh2U8qc2dCBk+9idhgADAedq2VjNGHIEATMREkiFAaASJwiDGEFDHNgCkkWGW+tqIHFCz4zaCQMSUmhTShJFGQEWKAdOaJXLSvlBJRkSxTMPRaZ9dAuCCMQKQh3ZU60luWB63HTxJPQOpMO3WCxqBOBGlko2IHBE5w7Q/FFJKDM/sv6snRVxIRJpYSgrThGZWn90sC/k5da3JrD4g+Rh5Lf1pGvLkr0d/7piISIBIjJhBTBAKQkTjsNxI6Uc2DVEMShNoUgoTRUnCpCRNvyp6/4WCKBXKQRBorRfnlpyiGcjBQW8XuCrm80rqQX+4u7dje8aBDEiDIrXxfvNgr9Fpd3PfnRls3drYWTt3cdlyjXa7NQoGMcRGTpglGMbd4W5HqqRmLZCGfr/fudks75cMYSaxBoUb97YNdGzujfsBBXG1Xi6W84kjf7n5QzNnROao1Wtw4A1/607zvT4fF7R1sH1nML7piPmCedY1LpjJjJkU/11NTZ7hGb6gSPllQRD0+/0Xrp5pNpu7u2vnzsyNRq1SyZibmRkHHS2553mtdlupeHZ+tlwp5vP5MAo06uXlZQLd2j3o9Q6iyBoOW4jxYDBAxE6nszQ/x5nY3duxvXyu5FmCJdEIBXvh2ksEan+vYdqmZVmFfOnsmbOmYZdKlTu3bo/GwhClKExazZZhVoJgcezHSlGlzJRGFWtFQiqGjAPIx0zp8U98xE1KKUvHiyY98n/FNYO0wy4yBnpioTFUCpEUIAcUBIoAGSCw4QgJKVWgQg6p4CUAI3nSAKeGE7XWbOJ8s1iT1kAapSIijCUppbUGKVFTyqJCAB1M+vIyDQQaNaXCFkxLduT4nvSAlaY0eZymhg9fgFICcRKRRgKW6kwRqcxQ7aGUNCDRxMNlE+rYE5Bl8GRW+78MVzeT/Jjx/mnjjYY4bst4ZGgR0XMfN8Dpa8tIbyECBOSEDJAjgJbJpC0jESmNWqMmYMBSiWKtMVGkFZcSNKHWINjTIdVm4al5wL1Ov9frAVIu7zHGpmamrAqWpwv1esm+lFt/sBFKX6NutPbHw2C2tujl3TPWGYrFD3//x8jAKEFn2KzMFt787dd7vd5gMCCipcUzjd3m3VsP9rb3umGYKzvCE0E0GgWjqcp0wSsoSVEQc27qBMv5mrKCOIkszwyjYBCOV+bOIVI81J6dt6Q37o7narW4WRv0t7Q/5A5atmmZDvG8jHNPjaX2GXvAX1VP7suOzyvEzTkf+75lWVrK4XBYrVbH43Eul2u37hwcbNhm4rloCEfU8rZtG4Z1/vxyuVI0DMP1nItXLnie98ILz3PObaH7/T5jrNVq7ezsjMdjIUQYhga3gjC6ceNet98GANcywnDQ7jUce8qweZD4oYxd1yUNwrTqtRluGK32QTQm2+ZEQRy3e914f2+72ZKMGdWajcxKFKAQUms8UQj0WBqYMaZTakwqcAiT9VU96TklIoMrAOCMUiqWEIhpvz8zQUSNyBhThEpOrLqf1qrioQ+cVvgw0tHxYaTx5DQELYTShAigCZQCIlCaSKNSoHTatRAUEWhQGhBRJ6kEI+qUiwuHzijQ0a1yohD56HlHQAJiMDl61FowRAnAABmAPBT8YEbGOpCGjjkhADKdhg6AaYozDHBGDDqjKVGmYEUWntZTYZmTdoePwXHkp98EgLQ95fFhHBZWRTFojUBpviCtvGJENA6M408eHna6kzsVPq/1WQy6o2AcOa7dafZuPdxaujCz/Nzs2dXlopcru/XLK1cj8m88/KDV20finU7HyLVnqktLfMq0+NUrLxQq7lbr4bVXLw+DUX7G1K4RR/Hi+ekzy8ueURDKlkOcW542C3Bv81aYhHEcB34c+XKqPq187LS7xUreruUTNrZtc8vvr+2sV2cKChjjlm07w+HQHw5KRac69T+Lx9tVS81UpoEVB6EzlNYglsXTFno/wzN8AWAYRpL4rutyNIIgCIKg2Wz2+/2iO/THzXw91+8N8gV7qlrbbzWLpdru3jaB6vf7Qej7kT/2R3fv3XEcZ77uFIvFOI7PnDlTuHSuVqsNh0POue3mLTN39faDg2an0+nkPdsQ1Os3Bx3u5tydvc3+uNfr9TgJjiZjbO3hhonDdqMfJ/uVSt0P2hrZYMx6fc8QXqLmBRYlAUonIY0a06rPTy9bmBrQ9P3DEDQi4nGI+ERSEBBQps2COAPOiTFkTCMimSq1Z5NeeJpSExVGqRgTIiNASOPPACjDk1uBSQ0S0YSqoyEtk01FNJGIlEKiVOtYk5pEdLXGlEOrCdPqX6WAEEljVi423RcgEmgGSMAIiCHDSE3UpQhg0iqZHonAf3ogQA0EnJNOzw4IDyPYn0Z2ivO0Pu1nC9MASE8OARFShW5EcGz1aQOMiAmow1Q6kZ6E9xGYTFBq1BqUQq3ZxAkGDCMDADhPGQaac0pDMDqra/IXjFOC099wtBNcefXCcC/audGrlafNuiye5cVq7euvf21je83zPMf07t18ePeThwKt869MYWi992eftLaH9Xp95crCi29edqtibWtDYTwOR83t7mJ15cWrL2/vb/7Fu39mmPlXXnmlVq1/+N7HB5vdxlrPinNG4viq68e+nedTS2Xm6J7f/sYbX/cK3sb13d2tTl7Or86+MlWaf9i4FRfW+7B1KfyfbNyLp6avXH7xVRnl/LZhMTdMBvWqUMzsj9Uo0ACWAM5Z4lhsHJ8uOH3aHVBWve8pN5qfOU7Z/vgzR9b8PK3jpAyt2lMjo2/oaa+vIieOAs+ziKRWEpAFQWzbub4fTtW4wbbu3PjBw7s/Jhi7rhuG0vVMwzBM00TEOI5Ho9FgMPB9PwgCABBCpEGmSqWilArD0GLadV0hhJTSNM1SqeR5HiKWSk6tVqtWq6ZpMsZs2/Y8z7Isbhx39VFKpSwwrTXgaHp6GgDSfcBoNBoOh2EYal7L5c5MTz/n2tX9vaZW+Url5d19y3Cf3J7MFBkh0IyFzzQO3clHPWkFigEA00jAUiosTX6FVOKRA0cmRFqGBEryx74i/dXLZ/SvjbPYwjw1zymTS6oJtQdRE0LayklrRho0EGgmtdA0UYJMX5BmRJSVu81K3eoMRafs/uUZLOhTFqqzjO/lGdcxkchQGwy5UIahhFAGA2AkyAYARYojMKbTkAYheHn/iYbWMqMjrzdFGtvn2gUA0qAVRFJrNSkV60ZaE5DiSjIlGSlUGoEwTJwnHmdmjjxLtDoDmXYhIzWQqcyVcT+Ir3/nJadkjOPhWm8bLDx7fqkyW9huP3z5u68JMG3hdVv9PoySQHVavYJT2ryz//zFF8+dP9drfbTf2q4N85gIFtnxQIcybnX6D+9u7ulec7NrOsZMabHht95+961zS6u28JgaBePIMQqcc4GCIpKJ3t04WDg/Mz+zOBgMuAWvv/qNe8WNpGdO1esVO/dwL97fbllltte5ubR8uVLRUXTPFDNeoRbFYzMXjoyPPXspL6a18uIYiGQiQ80TAPdUE/0Mz/BZgLTUWksVA2iGKAQni5uGsk1545N393feGg1uMxoV8iLnOfm8iJNYSjkej4NDxHGslIrj2DAM27aLxSIiprwqwzCiKGSJRpn4vq/UoN0bpsYYKUybPSRJAgC2bTuOI4QolRzDMFzXzefzhUIhl8ul78/M57lwbNt23KJpmoZhAIAQYhgNTbs2GkPkj/Il3miM/Oiema+RKjyV+Tlq6vBYTFsICQCAwLhmKbVrIuI46XKICICaCIGIITJ2vOE+uWhmGRhmPDlyprQGAMZVGqZmDCmlTiuA1JHVDDkpwFS9AxWbkMEmnZGY+g/SG87aNmbt97LMyGdNPTINYMS4oYUgQyBHzoRmCIxrAmUQMKaRETtUA3WdlJL2eKjZMsXR+3Ais56k9WEaNNOAkKphAgGXglGa20AEVMAYA6URvuQkIFGvze7sbt9/uO7Yudwyv3XwfiWqmoZ16/17u60d4AqQZqZnTeZ0G4PEYuM+5+O7YRxOL1WHw+H6+vqf/aEu5IvduB3poFIrfevV7zBp3LxxZ3Nr48y5peWvzVrc6+wOHtzYZqHlMDcK4kRpacau65bL5f2DHU8UF2amGoPtulU2TbPVb5AytNs1CkXbwaiRmLbpGxv2TF0zvbk9LhUvzNe+3m0OP/rk3e9+TxWLXtItRUNHRlISGQ6ZtghHT2eCsnZAmZGkp/O1Tw1ftNRz5n7+aXnAT2eYpwZENAwkjQjEDWA84ixI4pFjhXu7H689eL9W0TMzRUNoooSBbrfbURSlLm8cxwDAGOOcG6YAIGRgGSbnXGstDO66rrLzaTmTTQZjzDTNJEn8YFjI5xhjSilggIiK2MiPkmTU7LThUX5yugjm8iKXy5mmmdp4x3FM0/Q8b3m5zLnpB1GtVpueXrTMcm9kDn3d2n/y+WZF+LKeo6MjoUfb8qRNC1jKyEkVGIABgCJkaeCZgDNAlnKflBDHucaTL2yRcWNlhJTTaiKpkEhrjWmrXQKUkpNGDaQlSQWM6UljY5k65oAKNIACzQkJf0Wo+clvZ4ZWMjwtlrkuPS3uy5OPxzIIGXFOgjEutEBI201ySxERMM0ZMYacEROACKYl4NE7LX1tmseGGR6htiWaUg0sLRQwBSlFToRcEaHmyJE0oki7M6EfZp1Wdqj/NMiez6czvvizf/VuFMaNTmd2tfT6b1+bXao39zu3Prj/+//iNghZqHl23nz+8kuXzlzZvdfZerBbNsqb9/YTNj5zcTFf9u5df3j/xmYlX8+fMVeWVy9fu7iwsAAakWOYjPP5fK/Xem513pbe1vWGP47rpWkOot/vm641U5udn1lkjA07/r1bDzZad8eyL87k723dqFarDVUZ9ZsDdsAtzOUKY3NjK/gwGRmNfX+pH0zlXySNN2/dz5doZape8c4DmMziArRWEAd06hvxGb4SeGrXPWuBO+UwpsmkNJAREgdKorDvD5thNCwUtW30Z6bs6SnTtWWv1xiNRlrrRmuYttMhIn4ChimCILAsi3OulLJt2zAMznk3CJgwmDC4JsaYMC0mDA2odMS4IYxJY900oB0n0ssV0+Bz+h0AAASIGPh6NBykJt80zSiKkiRxHMcDi5vacwzbdSMpCtXnXnzl75SqF3/FxGW8n8XOPS5POkqREgEDPOzdS5wDA4aMGAKmzY0IGNeAhKgZIgBatoYTSzxjExaYnRWSzZAuTjW7pNJAafckAo2KKI45adAEmiOXoIg0akUExsRUA0OpCIGAoSbKDHVmpa6yNigZhbdZ7+Mp79CsDWvWcyS4RAYMAdNDwDREn5alIRIojciRAEiml0ydHBAnLDVIpDq0ygCHbAEiihUnANKgNKblvJqIFKYpBk14FIogzKR2fwGROZ+tZvvMwrlatX4QrO/t7FemCxvrW7fe3ao5U/WFSj9qBf7YEpYprIJX7DRuOjKPJIRpjfsBIha9CnBT+7zf71957vvz87Pvf/TeOBo4JefcxeXxKIj9ZP3+2gurr/76d0v/6r///f3WzsqZlX4ou6PO9FzNzhnnz5/ba+6EyWBp/uzS3PL67ppXMsuLdmmWlSxrMOTt27tmHryzDLnifW5LkyszDMji5dWzV/3NZASrbm06UpbiCaCERFhgh08pNvHMA366+KvmAWudpDU4hmHE0bDX3hkONsOgMx6NOLY8Vw0HrYO97njclyk0TyPMjLFUXnESsrNNAp0v5IhoOByaliGEE8exaZqu6xqG4ft+kiSIaJom51wr0zAMREySREqJiJYtuDCFaWmtgSlQatJmAAAQuTAJJOOTAS07iePYcRztc0Vhtx/SMImVQ2YlTipKPbn4HiAtqH0CDCNrftLvx8dscFoFBICMMa0JOSAxAuSIgJoxACTG0nJP4gxM4zikeRIiw7PU7MnrA0NSmhgHIJSaSKPWKFKWMxEQJpK40EqB5kgEkpHSBERSAdOU5iwBID4lqSFDwAqIP/n4s8jOWeNkIUsYJHvDpJFAExAjkqg4YaprFplwqJIhBAgOjDFgYIojrsCkD3E6Mh1rdx9xy4mIZGITkZw4vqg1pqz1KMKJthnQRIzlV9KpvjQecOKND8LNvFOKQnn9nYcbtxvKx5X5i/2D4f52YyQ7Zy8vIOJbb721t7tfKdXiOJZax5HaDztCCFt4jMRoPBbjpH3QH4/HH374oVMyzq+uKEj2d/a9KYNHw06nZZpuba4QjKJe0BwGnfpSySxgL24yi4/jwSgYOGTuPGi4hXytWm81GruWs/raSrQ8Q1yBjVPl2VpudmzLQs69Wn7VG1rDIPzeb10OtxeK3uI4hiQCCRFhZJFjWyKKM5ouZOCrqrj0Vw1fNA9YyoQAkTFEEYXjvb0H48F9rXrh3o6MoyD0R6N+MB5ygxuGkUhtmOLQh2NH7i8iup5tWoJxkFLl8m4+7ymlhMGkBC50In2C2HEN27aklOHQNw1umsgYY5wbGhljRCQl03TcWP7kPW8JQ0rJkHPOORMSFGlIYkmmpbRod8KFxef/2m/8vZm5a5qcIBkDPDkHTJlZyF+lEEGH3eDxsOtOEnNgmgEyzjgQaURODHBS1sIIkRii4EBAiMg4hyM3i012Lb9qNcxcoAmRIYImYsAICTkCgIOkNBEBMq0UgJjUE3OuNRAp4ForDVpC2pchDk6Xjc0s5sjY0ODntNXU6lAbM+UzH3IeVSQAQDBAJGQgBAiGiCjpyaGG9FqffFonJCxpElGiIaVApwW+AKBIQconpyOp0a9CGwDxu//Tv9HYbt/8+G4wlnbiKGUXciVOBigM42jlysrzL18K4vEvP/xlY7dfMKtOzoo7ofKRiIHBISeY4NxC8M2/+MHboR6PdPviS2eCcdhpDPxu3Oo1V1bO3bx107KshQvTOTu/vbkr0Xvu5VWDm8E4SmKwC4K42z7oNHc6ppPjdqjZeLW+YmnHQdc1c0ko5W3WtPrKxPn5mfl5d7y7aeWHhbPUI9BjYmHd5payRKzCJArjyPnCuaLP8FcShoGJnBiV0ai3vfUgGN22jHEQ7YdRohQqBQpsUzimYSjlGwYdWV8hhGmaQgjOOTLFOe92u/l8/uWXXyaiDz74IIoiz3UrlbLjOL7v+76PiIwZhYKllCKiJEkYaS5QCC6lVFqZfMIaTU3dkRk2mHHUWS/9UsMwhBCKcwCPC/fs6muzc68iq0o/KDjmIGN/m7WRzdoY6ROe4lGmEACiBBkKZMQ104qYAKYRGViYAAAeGllkE8mFo3A6QCqXSJPDOSUryWDsaAxCAOBAAMCQaYGgiGyGUoFUJARTmripiUgqzTUpCdKY9BSE4Mndh7KQFYLOrLbIMD5ZWtBZ0BlfkHW9pEZMtUo0TOqe9UQpDAlirRgQY4jqsK+GfKRO9wiETyZhCcUAQKWKYBrTw1MKhRB6IvqpNQDBpNlCVhPEp4Wn5QFnhqC/8cJ33x6//Xb3w7xZtA2ntddii4yZOgrU2Ytn//rv/HqA/Xv37kVxaJji4OBg0OtXyuV6eZpiSJLEFnZCUW/ULkKtF4wUj+1qruhWCm65XkzEdN6ugoRkHAwj5YdhODezUJsuF4v5nYMtxpgMFUfDLbozU7PhMAllnGeVTmcXBTbXe/966w9kIubqi5dfurr+g/c7frN+edo5Ix42Pj7YaCas7WL42gt/P9jWnfugVCUBrhgwi0hQVtfBZ3iGv0xYloWoE6WJaDweNxp74Xin4IWGHUdhIBUXhscYSkkhaALTdSfBZyFEaobTRWo46gFAt9uZnZ35znfebDQaf/qnP9zf3y8WnMWFb166tLK9vf2zn99qNpuLCwvnz5+/fPlqt9vd29vrdrsAYNs2AIRhSHrSTi7NNE/C3lq7tomIgoFSyjYFItqm4JxrZrQaLcecma9fEXLKD3nOMvN8PMhwP07rlBA9ma6hVFoOnC7NnANoRkhIdNiwAZngKSkWAelk2QmdUGHI8hSzcqXEJhUsBCwtAdLAAMBgGoGBBqVJcEQEpUEwRKaICBVoBcg1KtCK1H9AOC0jZ5zd0P5zitiRkQaRNTFMWycQ0wAgUkqBBqY1AEMCRogYhRY8an3T1/wwYvGYATYx9XcJNNNAitILDyQFABAj0IwAVKopzjK7EH9ZIG7dubHX2EuSBEyIZGhYIvYlBBQbo+1W8JOf/OTKxef79yjcFoyz6ryZFiDGGDslRwAfjUa+71tmjgaGcqNR0rNZqbM3ittNjCy/MdgbbOatUr7mOiXGQrp165bJnfOXl3nEewcDk5u1hdIo7ERW9M3fuPzR27ccFxxrTo95417UT+7On5t+/rlrg+3mjWi/XKheWXnT7VXee+sjzkStttI8CN/3f3pp5jfyUzjepigkZrpoaIlBVt1VpsRaxuezbvSsx+KLRv76ciiiwoRw8++PLM/gqYXmTjlxWX2LozgQQpgWT5Jo+dzzf2fmf//OO+/87M//7eryQ+F0dTKykNXKZW7HxGOGDNFBxCiKCjkviiLGsN1ux3EcKc/hPGcVeqOthSXrx3/89tXVc5qGf/t3/pODg4OD3QN/6E9V6rc+ubk8v/TKi69ce/1iY7/13/2/bty8fiMME0X4yqsvX3vhhcbW9p3bd1MDPwx6y+cX55cXGu2DeJAkiZyu5rvdQbHotVsdL1eI41iGVKkF82etqTNVsrga75Ot28iE8eQ0sJIZ9aMZ9dl44rk7dFoBABgz0reUAqXgiNEh2JPLC2UGq9nOMLRZOg22yKjfzbCpUmcIdEBG0jtjnCQ5LXv5yStQlkebWRab+X6GB0wnr1eawNcIgCo1hTx9jwBkAvDExwgBACaBhsOUcKpvBgDy6DliGgGOrgZjGR5Vpg7T6Tyw7HU7K3WSJcxyyhzwIOgJi3s5d9gd5syCkJaZOP1+/7lvna/Olu7cv7m2ttbb95WP+XweQ+KJrbWOMZGRYowpqUmCklppaQmr5k0zRVv398LxOleWiqC8VIj6FAZ9K9RoaoObjmsxA01XV3jOonyn0UsgPHN2maGKrZ6WxEzDtDxDGv0DaO/1Hhrrg/5o5A9n6/OOaxVtb2phent9Z2t3u1QpNT8O8+2ua0cSIrAMJgRJUFF2TuUZnuEvEUQ0UboASCt8XnvtNcuy7r//f6AcVqZKNuWRADkapslQaC0QUUq/3e5EUeS6bpLIMIwMJz8aDCwnXl29qrW+c+/OytmLv3fl9/Y299bX169evdpqtZaXl7/3ve+9/PLLP/vZzy69uPiHf/iH1Wp1ZWXlwdr6t771Lcfz3nn7nYWpqavPX/now4/v3rtTqhZqtdpvff+3C6Xc//X/9P/4o7/4wfLy8sLS0vLZxWvXrv1//z//g23ayLwLK2evPf+Ck3d7nWG+kGMsUPJziy/FWYYqw8LEGfuxrGUVs7SUMwxnVvehZ3iGfx+ICPuDuGHkFGNifmZqe31PsZjbIDyYnq+u75njYUBMA3BORjSMiVQaLlChTgshDDCV1KZpDjp9NYiFzbyCU7AroR+hQYZ0I19G41jzxCmZrmm7rsM5t+v2g50tIwrUWJs277R6CY3nL9Wm7NU4jrQCOQZJybA/7nZ6zd7e7NSsZfIHW3fny8nifD2Ow+vXb/VGvedKlywqM3AlqEQDAgrGGUf9lEg0z/AM/z7IZsuztOwHJgE3mJmZsSyruzHfV5tGjrOYoiB2hOAGS8aq3W+nYs5xHDPGvFzesh1NoLQ0LUMYqlBwW52WUmp5eaG+UP3Bv/4jrXUYDLudhm3xv/U3//of/dG/vX//frVW9cf9B/e2f+3bv/47v/M3P/rkk48//CBfLChIdvY6V5+/kst75WoZCP/ZP/2v/1f/6D+fn1n8zpvf/bMf/fC5F54L4/F4z3/phZeuf3xzemlmdWVxYW4JhTvoDm2nFsQRAyNLbOKzjgCF8ZPfNzKCkTIjMpHVr11lsI6zgsr0V2xFyby+p6z/fmoa/l8wnNoD3muvD5J+ccaimC2uVgPoMMbYSN+7ez+Ow7xVqJ2buR9s9McB+dyUOS1ihkyT1oqIAzcEcGSYKJCMMdcthomf+GQL1IqEELvbe66RL9VLpZotHAyG4bjjr4dbrCEpZnEcR37MhZuMtQQSlthp7zCb6lPV86url84/F41lzMYbB/f6+xTL6N7a7W6798LFV2eny+vrdrvdyS27XsExbbPv8zBgQBYx0hQDZoSAntLEfdbj/JXDqcsDnsowp/+CDPwK8lGK1BUeDIacc9d1q+WZQfvAsLghWBxJpWk0SgbNoZ/EKf2KMVMIoTWTEpRCRXHesU0TXdvK5+wrVy4mSXLjxo2VC+d2d3ednB0mwcqFcxplouNYRSqJ+73u9uZaPp9fPb/ywx/+yY2b17/57TcA6eat61rq3/7t3zZt87/9f/832/ub//O///e0omK+9Pf+3t8jQevra1cuvFgv5QSaSxcul6vYOGgvnwvLJRFGfpIw0zZ0RmP5zxpJxvdmpTBkhmeclYrSGSHlrK17Zh1t1vvP1of/IHzW8/Z5XRdx65drC4vzM9P1h/fX252Wkze8gjva7WDbONjszi3MSq2ZYLmi6+/7FWdmTFFKEkmLCI+S51ESCoM7ns0V2q5ZLpcHIxbHcaWcD7pJo73fjjQzgGvLdk2LofTNXM6JKFo6N1/MFfe3D8bDIRdMAjJHSxVb3LNULo6SmI26ycHU1CXBjSRUO1ubez/ZnSkvlopWqbDYNdcj94whyiav5UURwUpkmEBs4pNZiM9u/2f4LPArHuC0qBcRpZRpqa6Xc2am5ze6t4OxH0TQaQ7zOcuxLNPKj8KWTGK76HAmDMOQiUpiaQiTMwr8sRCsVCqWyyXbMn/2Fz8eJv3nr7xYr1c8z37zzW82Gnu/+MVPX3nlFQBl28LLub/3e7/baOz93/7v/2R2duqb33y91+nMTNeC0OcGN01zOBxqrTkXmxtbxUrhvY/f/d/+7/43f/KnP1SJnp2Z291unDl7Zvn84njYv//gxvJip1bx7jwYIFZkyD4vDwYzkn5ZXYA0ZrXze/Ln6ZQssqxI21c1A5Z1fU8r6HHa8b8sOLUHHG5DzBho2xjldm+2YhXMLIg8lJRjmpbBSIz9fm2x4Ijcu+2PHW6pRGqa3OxKQ5wAETGOKDCmsBeE3IZcrmLkwWW26sullamDzU4Uxm7ZNEwBkjMDtBPS0O61eoOoc255qZyvXD+4TbE6f/48FRQiSJIHuwdRuA1ITskABru728vLy5eurjqe/eEvb+w09HRx3rOKQ7fTgw3OLWBd11rmOO0rJoSlM0JVnzW+7DfQ54an5AF/8aQ4AADSoqAjMA65fC0M9Wh/bPNC3q67rkk6FgIN04rj2LKdMAwJMIoTpcmyLcZ1opTrmfm8Qzqp1aqVanmxPD0ad1955RXLsuI4/m/+nz9aW1ubnql8441Xozj87d/6azc+vv2H/+YHnXbvW29+Z3lxcWl5IU7C1Uur51fP/ewXP+v3+9eef2G/NbWxtvn9733vo5sffPDLj4JhsjBzdm93n3PRG7Wn49Afa9IYBnu5Uo0YR7TCYCSsrAjTZ2yATy3ZeDoDnGWBTy3skBH6foYvJj43D7icK49a46bsuJBr9VpRQiMWu/ncbm8/X8iVzdz0fNksgSms+U5BDqJgUx3xyDUopaVhGLZrAZJtcbLjXNVmPN5tb+iY6QSAQxzHQRCgk4RKIwg3ZzollisW5+1Kf1xu7LX2H7T9UWB7jBelVTcFWfFQJ0lCEGpUMuJJDx3T2GvsAqf+YBirwDStcrW0NHPm4/Z7W527PbFfts5aeoiQEFWROZgp1fZkPDOcXw181kIcpx4mLYaUUiklhACAJEmCcbKzuz8axFOzsysLlxxuDoLm1s5DkrJcqWutZ2dnDw4OpJSaJIFSGm2Hl9yyl8NKuaS0OrdyZmFu2SqwJEl833/xxRd/+tOf/i//i3+4sbFhmuZrX3vFtq2vf+21997+oFwunztzbmtz3Y/Hr772iuPZL1x74ac//vkv3/1QxnI0Gl15/lK30ytPFS8+d+H2zbsqwunZBduwAxkaDg6GvWYzKBVK/eZ2ZWrVdWuBz0gbp633fVpCNzxT4jHLMztdVyvKLAc67f1wuhz5V1UI6Gl5zF/V9VnYJRYHMpQjS7jEaH5mnhms1+4qlEEyzlXsq6+vdJJdztl3Kq8FTf0n//R9KRUAAGeAQKiFxXMFDxGHSQeEvHjt7OKZhdZut7nV399s9juDKIxt286XnAjGYRhqU4uiaQsMg2G5Wko6ut3txUlcKDhv/NbLLb8l+6K1NkzCIQp0HNth+SAhBbLT6nDBCsXS/MJMOKRm+0CQ0djqDTvtYl4Ehfb5KUf2S0HE44DlzNMVwj/DM3wWIKK0nDftCai1juOYiHr9gePklxbOL0wvNfea7caQNCydObff2HIcp1KpjMfjlDsdRRHn3HVJh9FwNHQ8ZzQabW9v24YX9ga16tT1jz8Jxn6lVI7j+GuvvtZutwe9fn22uLG1+Xf/7t/9B/8Lr9sZjnw/jALgePb82WFv2GsPPKfg2m4Yh1EUXX3hKgr9/d/63v/x3f+z35WkDmzbnlmasvMiDKHV6tbOVn2/IQzpeFa/p13LlTD+XOYTMzxUzZ5MwmIZhi1LIlpmZIcxw5POLl78ahqMZ3i6EOGuDUiUc4JYSQUHvVahULA8dxR0FpbmudDv/vQdXw3cgqO1DsOQifH8Qg0FjwMVBknCY2s2hFpcNqr+g9jQbvvhiMLtymxhcbWCZrSz0YmFX1+cK9UKjU6AQPPzC6+/8Xq70/nw3Rs01O3dvgy0IwoeVpq3k5++fWN6enbUG29tbV+4sLq4sNDqNc4+v7h+c9eumfl5ATBeyNf7+0G/1+ryJB/l5WC8F4yajd1+dx160bmlYr1cTEaG0onWGjgh58hThRrF9ZOzM09vg5XxwGfWGZ8OT6sN2WnHeWoBtSxpIp6xsGbk9rLazEHGQpnZp/PJn848X8xs9/Pkt2Pt+UFoGBE3xTBMokDOzXofv/eLjeb1l1++0mq1Phl84LrWWI7cfClfqiaqEwTBw4c30y6/QRAwFhNB0EtyeVPYVjFXCoOwMpfjzKqa1U5v943vvpYkajzwOeOtZmfQG7cb/doy03q48eAAUaCpE9IMuBzDw3v7pml+7RuLb/7aqtbaMAwi6vf7fqdbKBT+4T/6W4PBoN1uT09Pzc3NWZb1yd2dV55fMCTZ1TpTrQv10qARxvki659OAiE7hHtK5awMacPM65KBTKpVVh/fU40OgBn3c+bnM9al04+foVuQpYuQESrPmocssIyMBGbMtJ0xvGFET3xfZsxPJE85b6dkZWet29mCY6e7nwXFiAKZ5iBYkiQyko5rV4vVCHKJjnJV23Mr2zujrfUtHYgzs+d/+3df/da33xiFwX//z//F9sZBuViO47HNvE6nE4R+vlKKYv/jTx6c12deeeWVUqn03BXbMIyHaw9My6hUy2N/dGZ+ubnd225ua6lN04zDZDQcIumdnb1/+S/+f0GgRZKLwxgj08a8J8qdpL95e19Ccu3a82DJYW+Ud/KtaDTs+ImvQyWNxDKCvMfnz8x+zVt+zrCne71xzvAQEVmqFA6ktVRKKcXFV5Ue8QxfRJAEBnj0GJumEfvR/v62ENjtdjvthm2ZliVc13JtM4rGqUKQlDIMw1SRI41dCyG4EIypOI65QMtyRoOg1wur1Uqr0c/lco7j9Xq9em1qdmHm3t0HSmrBjVyuSMCBJw5HIB4z0sqKx7oTxVqHSZKk8pOj0ahfll//+tcty0LEmZkZKeXa2tr8/Lzn2JaTk+E40QlAki+xYgl2er4N+c91Xp/hy42McmugU7LWv+wQtuVqreIwkRQbwrRNO0mSra2tQs3c3tkpLBnLl2defPmF8VKy+UmzYs5+7c0XrRLKAKcWK6ORnyREip1feG5Hru+MWkM21NqLwmSqMn32zLmNhxtbO00t9Z27t6vl+uWLz+WN8t7dzs2bNxeuTu1uHnRgqBWzbXs8HEQ6NE3PrlBl0SXleCPDLOtI9N0ab8fh3p39l19/wfK4Dljgh51WBzXTIYKd2EaRjYpz5RcvzX5L80rHB8NiWseABAiMMUCtiPCE8tmn8VXNMXzZcfquJqfUaD3dKKcOLJICNIlzHisCgJxnjoa7W5t35manHMd0HNOxLcaIMZ0kYeNg13XppE59mhpkjJUq5SQalirlhbmFVr+pOn3bzl+8sCLD0DV7iFiplmAZ2p2WH4zPrCyWyyXt93WIUZSEUcQtjsgB0LRQKcUYMxhzPc9xHMMw4jhu91pbm9ukoVQsdzodIUQh7+3vHUglQnOIEIf+8KCxXZ3JF4rzo4EXfcYr4mlzpV+03P9n/b1f9rLJzAjHUxrnaX3+s4ZIksS2Lc0kgKhUy7Xp6mg8XN9YQ0RbOA4W/JbKcVEv1h7Ee7c2rp99ruoFdr5amD1b3dtt0FBBbL338w+qXiUnylPlecs2u83e9p3mvcr63sFuPxzv7+4zEPtbjd7emBI82G+WC+W71x9UCjUV6H6/X8oXFxcXh8EgDgOryEdRfzz2gXC/q/pxO1dw3aKlxmzj1u75S2cq+XovHtqepRnrtQa2Z0d9OdwfzOZIUdQbdLqBzNXychhjqneGhICCMc2YIJZZUf8Z46k9MKd8/2mN8/Ru29MZyC87NUVwnbZm00nCGVkONPeanc56oaClpHzedV1TyUirmAlUSnW7I621bdu2bacFwVpry7La7Y4fds5ffpkbxvvvvf/+Lz86aPQN4VnAf/c/+l1hip/85Oe2Y07PTeVyXi2XV1p0ewGXXr0yjfYMtzgnIwmIrH7KCDtEHISjMAxtyy0Wyq1Wq9vpl4qV1C3OeQWmeVc3LMcq5WaC8bg12CiUSiyaebj3xVq4n+Hp4mkZtsyAb1af44wUVZaU5meNbIft6YwjOuODklESQmhQiYzH43EcJZZpt9utOE7u/XI7luGlq6vf+e75l159IQiCa9euGTnshd3aQjE/ZQ1GHdNxNnf2EgKOllC2GoHf0B807u497BKq5795JfG1w/NR0t/daaBiTIu8WbZz7PyZ1Yd313zfz7u5OI5lJD035/L87u1Ou9U5s3zWtapxOyRtl/OlohnceOc+xXzx7Nz8zLzJ7HvX16IktLt130/avb22vHWg3wW3LKWVyCnLqmg90fhWWiMxjoJxpjIKBp892F9MfNk9YGEg5xO/DRFBUb+7a/Kx74dJYlgmV5LG44GSviFsQ2jL8qSUaStfKaXv+3EcCyF2d7aY0LlisdnqffLJTS5sx0x+8dYHDje4MAzTuHHr+mDcm52buXjpwrnz575uPP+v/9UfPLyz77p5ZlKhUqpVpkv5aqHGGGNp0998Pm9ZlmU5rlsJhkNEvHjx4u7urpSSc24YRrFYDAd+EBjccD2nGPqhlGPPDaTZBJg55UycDs884H/Xx7/cHnDW+Wb1J6ZMctyX3AO260zyIJSSFES9oNFqyFgiYr5WoGg83Is7zZHfup2MkERsOPxgY6+y4A2oW61VLl47x02r1xhSQ3EppErW1h9ybeZ5RUrZ2w7AUDfevj8ajYKgWc5XKoW6JSzP9ba3t1967krOLoZ+lMvl3Jzrj30l9cLskjKo0+zlnPx0baaUL65vrQ1Hg6WlxVKh3Gl1N+/tjMfji1fOJ6GKfZm3SqqRO786u3IlqS/nzPlGvuw6u1KPhzouc0DSTCkgBYo0kiL46lbIP8MXEgaf9BFPtThGo87B/lohD5bJlQqThAuuEGLLEo7DAJgwLN/3kyRJ+dL9fj9lQeeKpTDsuq7XHwxv37l/Zmn13NmLtll5/71fzM7Otjvt8dC/eOHytReu9nq9d3/2y299++v7jd7uzoHnhaNkpFSiJJrcdgwTAFITa9t22vEwl8s5hSSfz//jf/yPg3H09ttvFwoFx3GUWr9yadU1puIQfcZzubqTN0xTx7iFOPu5zOcXbQF9hhSn9YCzYltf9ohXFjI94Nnz1XKxImPd3GuP+wFpZgjT4OawNajXp5fPLg1Hg0ajceeDh0EQeJ63vFrPV1yzaPZGnVKu9s1ff41Jc/Hjj41WTlKys7N7sNFhWjjokiHditXeGObyJU5RMtbaSAzPkCDtnHn7xr2XXvQ8Jye4cF23UigOh6P1tfXa4pTW2nZtpeU4GHe67XaviQLKpSmvbCutOs3u7ev3HMObyi30u9HAss6dPz+17Oian/AGcrtUmImCpOEPhRCc24gGFwASlUICndUv88sidfZXLQR9es3Jz9YDPm0Kg3PSBEprzi0A1m42dnYe5HOceAJEggPn4DiGlMiQAGBnZycIAkT0PC+1jmnnImAsCMDzvFwuJxO1ub6pIrNWm37uxatkYBjHl69cef7qtb29vXffeT+Xy5l2TmuOYCIaDA3DsZEMgSaFEhF1QiomUIoxIkfkHGNvd8svBr1u/8GDB3u7+7s7ezdv3hRCtL77zc4+/vhnH46T4de+tnrpwsrlq+rK155/sHGqaXiGp4zTrienjRx81uthNgnrdFUSp173nlZo/SlNj8iVra9/82VLuD//83c+eu+6jMBEWyvKWUUE1u23ielCOe/5BT9K/L24Wxi89dPtS986U5x2b9+/ubPTeO7C86+8fu3qzCudXuvnP/uFP/pwGCWQSN8fjVR/ofLc2B9VvFKju+vVvXwp/+DhXduzHO2trW3s7+8vn1ninDUajcFgGAfxysK1fpIgMWNYmCnPm4v5TbYxY0/vBQ+742bRrRQrUyRhbmbp1XPf+fG/eafTX++NevYwLla5kjLy+7pryrHqDw3bdG0HrbRhBCEDrjV71ij4Gf4ygaAJFBEJIaSk/qDbbh9UloG4QNuwLGFw7A/7/V7HHw+lSmIp4jg2TRMRTdMkorRxb+oHI2dxlHBuMsYajUYYyOdfuUIE09MvhePwk49u3rx+03W8uamFKIxVrAC51iRjCciUSqJEOQ5nnDNErYmETrTkwCRLCoXCxx9/3Gq1ACCO47Nnz47H41u3bt25e7diXq3nly8t537zr73eP2jdunXLWRQAZz/vqX2GryAy2yb+5R7GXxqE7tvv/fBWa7fbb4yKbNrX/jDq54u56lyZc47EXDO339nnnOtcyMw48I24a2/8aZLP6/7QWo+2Nird+aWZ5ov95XOL3/n+m7Xy/F/8/rvbtxoyBMd1YuhzR43k2MpzYjIMR9Mz9SDya7XiOBwVa67IaWQ6J5zSQqHT6YwLm8aZfq89aFJQjO3Vs+eVTAaDzrde+mawOm52W4jo2Dkjh9d33+54D4PC+lq72xqXr+H3yrlf13p2Y7tTzDmlwlS6eYkTQNSAgEaq156VY/hi5YazvvdpRdBPO87Ti9w/eSebqQSUsSHOrmM+HY8y63szqyAz/iFrnJgsABEl45rHQ7Xba34yM+VKiJOQch6RGm/v9fYbARdeqeBJ2cup6oB1xnHX9mwVY78b1KeqfjTKI9sbbi4sT+2uNc9Unosw7I5777z/bm3e+E//4T+4fvv2f/1/+eftnfFUcb4/bA2GbclGAYwDqaXPEsr5w7HnCtMVuXxFadUbHTDGLNMxkAHDjZ3dQqFQNi79+c9+/nf/89/5Z//Vf+s3rdq5pZdeWZybo4Uzf9tdkGdmqWjpndBYW4suna++fO3B9bvlhGY59bWOgNXHvrTNMcKTBXBOXwd/OiW70wYvs5/rp1Ovz06reJUlzXVKnLbeOjMUfMr67Mz5z+ySlFFfm3WFKUPbP0tR5ZQQGeOorD7EmYG20x2PqFemus1BvzcYDEamsGzbLpklSUlzv805JyLLsgaDgWVZUkrGmA5YNJY7g33b6zKTzLyIk+jDjz7oD0cz9Vl3znv99a+1N4d3P3lgegYABOOQiBQkwuGu5Tm27UcjPwn21lrCYm7BhtAYh6N+v28aVhKBiqlSrp5dWsHE2N/c7g96g7CLhurxrZkzs/bMVHO33Rs00QHHdZNSd7my6CB291v37n28NFc6f+Yc1udG/fHTujDP8Az/Y5CS/gzOCRUR3bx5s9PenJvNr69tVSv8/Ll5ZG4QRpZtKLLzecuCXNQbo5kzDKPTHYRhyJALYfjD0cLCQrFYPFhvxkkUUeR53ptvfmemVr93++Hu5l61XBu1k/6oR6iExVKDh4iccyIgEABMa90fjJRSlukxxsJYIinGmG05UeQLw9jd2YtC/fo3v712b182LIE2t+q2FV44i3k7abSpP8jniysHjXZtet4UEIcxNzgAS5QSxtMxXc/wDH91IO7cuD8ajnUErutyzk3LSCQN+gPBGOc8iiLDMLTWIBkRua7tDwIETlpzMpIodHL22cXFYVAZt/TtD9d0gqvnL1RmC27NlKH2PC/u6UTGiY4xAJVoe2RJlUSxLJqVnO3ZlsliTIYQ9bVkMQLb2zrwik4QBJwMX0bMVaKcCA+Gus1GajSIm522lOQwx7K5WzXuXV+fLpfnF2cW61M0Hqzd/8TC5Xp1odkP0jNMtyT/wY7sF80DfoYUT61O9zMuS0vVYJAhkDw42N3d3ZuqVy+sXqjXV7QejoNuu514uTkAd/+gVSnxsh23mh2nKLTGIIoFN6Wmse/PTs+y3Nh2rLE/2thcmzs79/wLLzByGjtribrfHfYXFhYEOQ/v3Rv5Qy1iwQ0tiTRpqSVJAtJaa0DTdaORTxoQlKTINIUphJLKctAu2N324GC388Krr9288YcicHd7owuXbY6t5XkvGMt7D3VvmFs+d2Zz+/3Vi9rzIExGjBtElpKJKSytnt23nzOy5/+0dTOnHf+pfOvnhtPmwrOpKaf0gMNxxJGjQSqRYRSMoxHn3HJMk0zGGElAQsd2iUgqyYgrkLblWJY1MzPTbB/4Pd8kb65S1o7xsz99+5133qnPVgSa+Yrb2u0VCoXeaCyEsLQtVSJ95cehYQpP5MMwtKU1Hid+d6i0dGxHcIOIWo2DfGmRiPrDniJtMpyaLy1dnl375eb67laoYtty8nlvMBg0ttv93ohJS4bMdko5N9/tj9qdB55tcNMBcOFR63s4NV+WW+IZ/jLwWRsMpZQtBOfIuPzkw/dLpdLFS2enZxdqdYNo9Bc/+qNq9cx/9B//F0TO7VsfCBHu3P2Z1to0LSGE5+YKtWKxnA9jP+/mWZ4BgDDYzPz08vKyKazN9b29h7tMGNu7uwZ3F5YWLIvfuPvLUdhHRCU1EiqlpFLAQDPSWo/8xLLKMowVhURJqVxYXDi7u7GpYejmbCVw7f7WN1///tmLVwvF+eT+A4QgiTpIRhBEURiSZpXy7Pb21MHBQTGfC4IkjAGZC1oTT5jgWRqPX1XD/GUvg/m88KUvo3pKEI7jIOJgNEgwtAt2GIaJktVKFUaotSaTENFzvSAIFCmVKLdsktSxDInIFHav27/zyYPBcLAwd6bT6DuRAQBTM7X55ZnQj5rtBiRGSuPkghGRAomao+YJGzO7QFyGwdhx7WIxD4RAWJ2/FFPQarWCIHBtT5gcBN27f+dc/fntxrrS7XzV8jzP72jwjWJ1xirWK6Vawavcv9feftjynMX82eVAdSzmpWf4zAP+SuLL4gFrrQkUQ61kcOvWjeefv+J59s1b6ytnL+a9omEWL15+5cL5l27d2Rr75u722kKl4nhFN+f6YRRF0qnnDdP1vGKlUpu9cCZOokiGL7z0fKzkvbv3S/npaqlWq9Q++OSjVmt9uj73jTder897jd6WVgQA+Xze4MWhP0wIgBKllZnLPXfhpX67Pw57zd6DXNG7cuXKsNVv97tOztTK+Pj9G9/5td9eOrNwsB3Ztl2pKCGEHzn7TV/JYaWgbFt5xcubmz99+eWlQgHG+5oZRcYiUrEwDP3stv1c8cwD/g/D5+YBj8dj4BQpv1grTM3Um81mY68ZKd8Axw/9tJeLIhXLWJEKoiBfsjUjfxQ0Ww0ismxTaSVV8mD9XrVaHQXDnbX9/f39YrFYLU53g6FXcoMg8KMRIqbCs2lbcpZHbiK3TA/cYjnvOM5wMBLcUFparrm8emXYGz+8tzbs+M89fynmo9vv3YrRL8x5S2cWlhbPjtrq45/f2324N8MrmC9W85fiXH+f3+l2g2QxqlSK497xdBxNCiJ+eW6JZ/jLwNPa6GQZ8lzBVnEMJDc3HhIl11544c7t23t73YLX2ksGnpubm126dXvt7r29en1hPPr/s/dfMbJkWYIgds6917S59vDQ4mmRL1VlZuku1aJazEyPwuwOljs/S4KgAEmA/OIfMQD/hl8LDBbzsSAJcLZ3MWp7Zmq7a1qUrsxKVZn5tA4d4eHh0vS99/DDPDw84oVV9+vJ6qqszoMHf+YW5teuPFoMdvbuI3Dfq4xGI03Mstwkk6ZlXTp3mZxBnlB90Am397aV5Gur524ddhzH8cvezk57Y2NjbXXx0sVLjaGvpeZczDRbpqhSh8KEslQC4e/9/t9bqF1cv/94Y+vufvdhKlMi1e8OmGmUar4MYGv76Shuz7Ss935002+VPbfmeOVRaHZ6Vhjrei0kuTPfWuvshVp1yl6lbZhJBowDIwlQVCT4VxYK98/PO2HIpwAAn/z5FIYtiKnWTKO10ASkrB0bPqvOlKhrDMMBASkNcRYlMhZCKCVViAyFySCKIs2k4bCF841fu/L69//8JwYKK3VM02SO5sDiJNKkmrP1brerB1JrzQVDRI6Cc34Y7G9v7IKhbM/0FkpENOgOfa9klUlpvbO1G/SjqjdTsSuyz3pBNIz6aZpmEkR0yHt10sbhfu+g226UwkEvlIvNpaWXKt7LG48ee6LFMuck0T1x8YmAT1Zv//Phk14PtWi9LBcGUeZY8MGH76ytLTebzR/3hkvLF1eWl374w29zpoMw3D94rJVVq7bCVn/97qFlWZVyLYqiWs1vzczu7G0xFEEQprpXq9UWlxZuf3hXStlszEZBlKlYWKzRrJESlVLlyaP1WtO+/OJFABAoXMdDMJTUUkoEtG13OIo3gl1D+OVSHTk3bdNxnFdefvW9Oz8ihIpbCfzKfnv90uWXtq+tlGfdnb3Yc5UQ0jRKceKblkjCJ65NqlLv9bZqDdMvNQe7yrVRGCzLNGJRtbFfrv38Sd9vvyj4uCTgX7b98HHB846LhVHADXbp6sUXX71eqfvcgoWluesvXqvVq7ZjGaZABkpLTUoYnHEUiZ31NSWIiAqyQdLJjPDyq2tf+MpnO4cHw/5IgLm6eG5pcXlvd5+Idg62+2FPo+YmB8w9QRRyWJxZpgwHhyPK0LfLTItRLxr1wmqjfOnyRSJq73V8u5wO9b0PHnd3gpJbrlgzFTkPO5X2B9nWBz05wpWlVeFSa3bW9WbjxDH47NL8jaW5KyWr8p85L5/Cp/CxQNCHNIot23z6+Mnq2nIYhpsb24tLK+fPLcdREIyGw+GwVqtdvnxZSjkYDIi4EHacpu12N4wSqajd6R4cHu7s7LiuS0Q7uztKqWq1ahhGlsqrN65cunZxYWnhc5//3O/8zu80680sla+8+KoQBgAopeIw7vV6QRDkEQ337t/f2Ng5d/7S2vlLSqnN7c1Hjx7V6/U0Vd1uV2ttMPbeez82DfXC5RUhktv3w3anaxqh6zlhUtHQCEa7TN5pNBrD4YHBMt91pSREtExDyk+D7D+FT+E5QBi20R8OURkLzbX2du/G9VeSNH7vw3fjJ8hQGJwzEEoqy3BAM9t0Ix2kLDUso1QqYcyifrb+094fDr7/xd9/wXKNmqgf9PfSnd4Xf/31QM0/eHcTRp5pVDzP6xwemBZ6c+ZI9sCJEokhDlpLLdd133v//dZC8/Kr527fu3XnHTVoMaKqY0Q/uffjC9dXarVauTKzd4fCwX7Z7GEFNrd6Ki6HbfPG8vnXvvx/Bu2M9j3UHinTdqqjJA2yATPO5sQ/rrq8RfCJofRFYVrq+eq8Pi8Uxt0+Z6Dxx7WKH5cglOc5fRYMC+ZmGu+/823b1jPNlQ8/evLyK6+2GtbO5jvBYJ+b9dm5ZdNiDx6/t7R60a7U4jSpVFxTYKtRhYQOd/cH3dH86pyoLly8Wg+3e7ff2gky6/H9ux7raucxdOThQfDWex+WS9Wtp23S0inrD+58cP2FOVJRf9A3xWy1Mt8NdqO07+jg/vsfnvuNud7wLlL8mStf/N4PftRe5q0X5yzeMCseeTJ9wjbf7hr/9ajywujDf7W+YJvdfZqdm2PGw8+80hyMDO6/tJsks4ODwNp159ps8GHZuqwNrzvsz/h8mDxfvOkvCj621NE8PfsPhefoY5oHfjav87x1fJ8fX/1icjJzVlDP++c83ufFM0hF9ZXPfl70ul3HcTr7B7vre67wTWb10/5MaW7f6imlpJR5Ih7IE9pqbZpWmqYkiXPueV6cRKPR6MmjJ+kf9culSrAXmaaZjqInt7db1flHYotpYpwyGXu+PUp6PNFLq0vEpU6RNDJiKlWGMC3TMg2rXCqng2xjZ10mUvF0fm7xwtrlYW+0+/Sg18+41O1RGiSUaHRAOJ6NzKHUJzAAhCbAo0VixNQnhA5+Cr/aEIdJJNKd3S0AUjor+/5olN65e9tQ22tra8Ke2d7crtRh7dxaq9V6+uSR1GpuflbrYa/XW50755VLyHilVAvDkPOZg4ODOI7LtfLMzEw2UB/evfnZqy+7nnXQ2d/Z3bvxwmcqJW9j5979249XV8tE1Gg0atVlp+tiO+10RmEQXb7y5Upt5q2fvGebYnl52XPf3dx4cOXaSrValWkEHpiWcdhrP7j/5MqV68sr/TATQdrr9vcFh/k5r9NOYhXUqg70XA4+U1aj6ezuDoPQ9P1KRqNf9Hx/Cp/CLxKeN1GJmJ9dsG17Z2P/W1t/zJBzzrMkc13XMIy8lJDWOi9QmpNhIERgiMgYswzLsV2lhkmSPPrp9vzcQgoxAKQ92r3fK11t+GUv68TINYLp17zokKVpWqlUpUrbvY5juIg8S7MkScNB4pf8cysXHj58iJJXq9U0i13bxMQY7MXDTogpb1XPjRhzLNtJB92DvmeUWq1rkFWIoSaODAG1Rp3nUvnESKK/IHj+KkMfD/yq2t6K5tMULIpGO9tb9ZofR8Mk0aaF/X6ws/u0XJlbWlrtDVMrTldWFqWUH7z/I2TKNkW3H4NWwCAIR4hiZmbJYOQ4zuMHD7a2tq7OvXjhwoXhfvDe3Z8aPqCpVs4txiFeu3w9DKOn64931rukKEulNGWaJUE4StMUkQOppeULM83lH/3g7Va9srC4Wm+Ue/3t/d0HgMb+7mbdK3s1f+tRdPODOy/feC1DBYYpk3Rz84lK1cLMPKNRr921hk4QWQxw9+motdSoNaN4IxbC6A9Sm//N8sP6RZ2jX1jVpgL4eUvAv6r4XOhE256daXnY7pIGVEgKedUES2upSREScuTj8uCKskQioiFMUJBSanCzUW/6vq+HrN3eMV0RDmI7K5uhf/fDB9WFUq+fZJQikiRTcwkK+p2BaRnRMGXIlFRxlCRS9jp90zIq9ZLhMZPsmXJzZ2e3fzDaMw+TQ6qy2YXFiwtzr4RRSXDead/fuffjcmN1fu5LnBkMUTOtmQIAIAAEKq4e+UkpuvCrCr+q80MFHK7jifZOdzQ8vH79chgODzq9udlGs15q71BvMHql2XJLRrcXhsO+1vGov+N6YjDoyiRtzcxmMj3Y2rZtf25uVSbder2+TdTpdJ48ebKyMLe4uPhb3/wtmbQTir70lc/rxDVM++mHD4c9afuObTla636/Pwq2t3a3EjUQnHmOLTMyLWdx8VzZs7NU+Z6VJLC/97Bev9Z58oSIl2ouID55sDkcxLsHO35phaO7v997/OhR2VvQytrabYeRXr50xZYzj+92hOPMzi6POlkWQaqZ/Wm1sV8o/E0jbL8ofP68xS2K8IPo7BzqmBzTdbgLCKQBBY+HKWqllCJFHHmuZ9daa9JKKcEMBjyJU01KgXLdUskrM3T3eztmxZJIllUSYB3urpdmbcMSSZIAF73gEBERcePhdrPZrDi1cBT1O4dSSsd3dQq7m/udg8PmhUp4mB0edg92O4YpEp9E7MjMWLz0xsvXfzfOZuJR2nWfJIctv9wql88xMVDEMimkEkoxQCAEZIhFVTU+BQD4VAL+uKFoPhmore2nncNdpVcHgyBNUoRyFI+klJbDHa+iAff3N//8z74NNFLpgeFIzlSaZr7vpxkE8WhmZrZWbhmGY5pmGIa9Xo82N2slr+40L1682O+SU3KE7ex2Rg8fPF3f2AMw5meXOQqORs4zAydDcEFoCKvfb8dxdPHCJSAdRSnjFpE2BF9eXuuOtrkwCLUk6nWi0TAu1/3NRweeY8dDcdhR/RDm5hZWnEWZieXzVdW379x8G5yNV16bbTbU+nZoOxVU2V/zzP9i4VMJOIdPJeC/GgjX9KN+okyKg0QphYC2bQejyOUOAAAwxhgSIiKARiQhTK11bhsGRoAUB3Fbto3UL5cqWkrbckxtaa193x8cjnjmEpFt21prwzCYFPtbB8MsLdeEjoFrQ3DbZmaWJWmWcQZpkqVxxlPuWv7s7GzDm4kStbfdDYaUpJEQaRBFc3NXSr9WB5Y45ciqbMjUioJSEpayxFaExEihQvWLYcV/VTfKxwW/bPPzc0/EQen6+qPu4f7G04ea7GqtpXS8v7shtb6wvFqp1D3f+ZxbvX/33ds3P+QwGIa9NXMxjeKA0HBcLrhhOKNAVitEREmS+L4fBMHOzk7ZqPpm+atf/apm7Ps/fve73/nB8uwLrlUJolRqnqWSMaaVJlKOY0tSWRiQoijcHw72mzMrprAtk1aWzz95/H5rdi7JFEN3NIozoUzTFNo+2D28+MKFzvaub3vuQjnNsFxpkMEsdA3TMUwWSj0Krc4eRf24XEtoL0FY+rlO5qfwscMv23n8ZYPnlYAL8UnBNAtDGnEcC0NYBpFJmUwNi4sMlFK5wJpnk8+vGWOcCaJMS5BMcoMJLpRSMpDDOPB9nyKGmmmWSsos14rD0ANuG45Awy25oBHRmG+4MtXDfqCkFtwRQpDSmsjx3Hq1drC/LrQtNK9XGnMz8w73ysKOO+ze+o+Y361U59q7LFv8jOeY3NlGL6F616d5bCvKTJ3YipgihUKJgpQ8n264T2EaPrb9UNBMv9/Z3HhSLrmGQMO0XdcKw+FgcJhmSRBG9+7fr9XWFhcWtp7yJDpkEKGBpKVtGPV6bZCkjLFqrYFkPH78+EvfeNW27YsXL95ef7C9vf3q1ddc1914vFVu1of9wWAwOBCHb3zm2jAcOH4ltxxFSZKqIXDFCLUiwYws7Tx9cnthdplz07LtxeWLacZsp+J5nu96w9GesmWlXnOt0s2P7v763/1KlD1CxLmZmYX5hmNJS4SmZclEjXr99d3Aq8yVbLO901++7JaqxmEHCooh/crCL5sE/EnBbx9fXd7na+eXTQMnslgjMAZMSklMZ1kiLL/hVvv9KB9b7gU9nccqRzZEhMQ454YpEHFAg16vZws3yxJmArMhjAPLsqp+TYNSmQKN0TC2hHH5/PXRINnq3RsMRkmc2DaaFjeYKZApqWaaLRWwwX6oFITDSDMsGQ4iOxjd/+Dh44WFhWGvnMpQa12abc9jsDa/EClpeufTfpYwDZoDgP6Yant9Cp/CfyZsbWzu7e1evrT68ssvZNLsD+Ldne1MptdfuKGUsbm93etit9O9efNmHA59VzQWWmmW1Ov1l2689PbNj5CHi0tL9Wo9TlzG2MMHDw2jsra2xrS8cvnyo/2nb37w7qXrV+rV+qVLVzefBoPhaBQH+wd7DGumaWKIirRtmpogRhRCaDV6/PjW9atvpGnEOTeEi8yWkl555TWpolu3DzKZlkol07Dv3r73a3/785kIEo0a0jTthiNZqyyuLMzrSOxGbiL3lpdfrDBre+vHi9dK5frMsPuLnu5P4VP4hUKxbfjs5wWvA2o2TIcJJQZYvl9LQp2lKcNxHBsHzgGAADKQmSbMGGOccwEGStShlokSQtjcYnaaZgEIEIZlMMOwRZZljUssypLt/S1ySrFU+53tWqMmhNWTe0M1dDx3GA8NMubm5khCcqiwJuyGNZDD84tr8S7ee7xlOQdoyEgcpn1B0njlpdWlJfno8b17Dx9sHTpRFGHadcCquKuoK1yDQGBZCrwgK3xRndGPjXM8u/2CMNFC+HnHKxfGI4rns+Fx4ADjADAEYqjHX5XIX6CP3pQ7xqmiZfk5w9iH/xkoXPUCH4KiutGWcNJEa06xSlzXHQ269WrJYPzDn/wvF9cuzMwuB7IRBP35xfmn6w9nGks3Xv5fzc/P3797Lwr7rRYtLFk7e1mGynPqB12uqLnVrXW73LV8Q8j93pPWUocBayzOkmJfvvBGkrranP3Bd350+dLC5pY3Mzu/shwBPRnEO6Nh+9K5BXBNyvxR2C/NuN1tzQm4qZwaxlJ5vvXezZ9kmTc3szzXKl8+/8rt99Z/9+/w5YXf/g9/+GTh/HZjjh08ac8uzN96+92vf2X5vfdot3vO9RfV8O2ZkhqE6ykubm0OonZHNtJwbmm/w5L9zStzfne/PYhqBieDKcEUR5REilAhmEU+GXj2ukhd4LRSEL8uC+IOP65zzYvC5guOETcK4oMLQBfsRFZwTovxw/PKHs/b/tlQPM1F+Pb56nYXvvc58TkV1PctmucigmqIovmxzr6NZ+8HkWWZbdue4yVJkiVqksumUMcNeXQSTT9GRCoh0mQKkxucCSZ1JpWUlIXpyPatL3z5s2vnLty7++h7337zzqMPqu6MxZxQx0isWq0yxsIozHXdSRaXywuLpZakOMCo2nIymY7SvoeuYZqZDG/e+8lm567myp8VzRl/4+CpI8JL8xcqpYFMvNGQKUCNyD62VA2fwl8E4/BrGiPTgpD5X23QCriBUirBuNaZKQzTEE8ePekNRpcuX3F8/86dO7OzjVKp1Ov1Fmebnuctn2s+vP/grbfe8jydRDuMMc/zBsNRFJmujwAKEV2nlMTRR5sf/NY3nY2Nx1mWuXZpNAyDKOWi3pxpCGPQml9ot7vD4fDKpQvCnOFsoVrJ0jjxvZI1iA3D8H0jHvWTOM2xUrPZ+PKXvn7QToHMmbpfqdXv31l//OjppYt/a25ubti/519e1HWj1+8+vJ987o1XPvpoKwnCasXxRDWVRpjgIB6Rjsq+9N0QWVirzj548rhas21LhJlCAgZ5SfdPFVGfwicDntum+5xQ6KTJORdCCCEMw2CM5YG/+YvPhEm38icnYKBpoi2YyRgj0MSU5YvGfDVOoq2drb29vYPugWGKxaV55lKGoQyhUZqxuB1H8Sgcokl+w0kgrNfrwMEt2WEykpBW62VhcMdxzMTp7h8eDvZj6Mesm7BD4SaVpjWUvU64vdV7t5u8F6bvAl/nRgAswU84fCyr/tcBLDe2E+ZZY/KvqIFpZBpQM9SYf0VdlEbnrwF+3uulSCFHDVKYDLR2HMMw4eaHPzUt+/U3PscY6+zvq0xubW1lWbawtOL7/re/9f29vb3XXnvNsqzt7W2lVBiGDLnWmWlAv9+Jo2RhfmFxacE0sN4olUru/Px8t9f78KOPwmCIoBBlmsZKZaPR6O7tO2kqOeeuYzab1ShJpVZZKldW1/7e3/0H3/iN3/RLlc31TS2l77pXLp+vN8qGgZpgprUUJri73bY9wzB473BkG87S6pIQ4v69hwxIoBqNtrmh/eqcxrIwZqS0R8HANFODHxisPz+3trsn+kNlm4EQWnCVM2GaEACBEcNPKfEvKXzi8c9zwvOOt4gOFraD+sx/eRWgZ/8JwzCyLEuiRCkFmnHOiQgIlTwbVwo+jgnOE3RMSLLvVNMsziDRWinKuIX1merMXDOM+ocHg7sfPHx4b7NaqVdLzWyE/XZfZJ7vOQCAAoCT49mNVl0ziUNen2lVSv7dt36QHsCFl65lgR7uDWYaTYmROyNmV2p+w1aYDUeDp5sPWssLvf3+5vadzvb+fP3F2dqrvleOYvFJZ74/KWcAj7RADHWuiGZTyqWcODOCsUIRf2Hj+vl7O0vT5JBqzrlSmWlYaZA9vH/n2qWLzWZz/dt/tLy0dOHc2tvv/Mjz/Uqt/s4772itX3rpJdtid+++rZRqtRrD/jZy4TgwP1+JIqk0VOuzs7Oz29vboCPfsyzL2tne29ruau2USrPt/Y3FpZnt7U3fKw0HvSxJTI6dg73Ll84vLizVKvXkwVbnsLtr7g56A8tymc1llgGpXm8/DodErNsfzM6vlmtLu5t7hwddYQoGZu9gODvr1Zu1ZK+zsbE1N1Pa3Tw0jNT1a91ef2a+aVtpkhmO4cZBm4uaYS7adiMYHszOuf2oBMRIcgIAYgS5SYKwKDT/Yyr39rz3nxcK+1P4/HO+t8h59hOCB35V4XlV8UVP84J1FHEcSynzTLaCjV0YWVHmSgCeW4SPIHeWVkppklwwQCNDTYjcYIwjY0z2uUNVZmK32++MRuZMKQtUEqWuU+v3+8LA5fNLxHR31IniuFarMWDDg2Gl7J07tzJwop3O+ubWlmJZZtRWLy+VW04v2k8SXFxa4CDWn27ML11hkHb6O+1Bn6eVeukq55wdZQ75y8PPe6P/qh4jBALUuew7vkBCAkBNACyXeREYgKZ8Ej4p4WGFqPXMuwpAUx60p4EDar27vRGOBp/9wt9f39zq9Xp/6/d+szU7861v/dtRONra2Z1fWJmdnUXAhw8fKqVs2yaiVquVpsqyzKXFmd29rmEYo1G8uf5kd+fRwvxna5WS7/tLS6um1fR933WMc2sL12+s3b7dXZxd+ezrb7xw9YpTahl2srq20G4fhnECTHA0Nrd2d7c2wiQyhK5VbNc2d7Y3LNP33PL2Tm919cLa+Rdv3XznC792uLq20j2ob25sA4Bhm3MzC4/uP37jjd86OAgtK9bgbG53K40VpAR4ueSV0mSEo0E/VY1quXf46PpLy+t7mcoMqZmSXCskxlDo5zdMfgp/TfA3jcAXjrfgfk7vnqf9swXXIgFAhGHIGDO4wRgDDWmaMsYMwYo6OjH65sDYOC3lMO6btjBMw2RGnpJqOBglSRIf6DBOS2W/YjeyTIb9GBGbrUbSSzIZozCGw2Ei41E0zLLMsixgqrfdzozhxSvnK59pPLq7PuCccXEY7TjWAljOqJ32eiGXvkqNWf98544cdEEPyyXhMyqD9kGZTNvPNWufwl8Zjr2uAFhOj/Ok3ApzUsWO9M5j75lfkBPWzxtQgFJKGExpaXKRyfj+vVv1qlep1t55+yfLi/OVkre98SQIgiiOkLOXXnrpww8/HPT6tarXarV2tjBN07JvEbOSOAhG+3s7u4PBYHt7L026WTq0Tba7t97r9cIokVKmabq3v0k6DKMBY9Drd2dq9ThKhtF+qeJWas7hfldpGgVxtzdIutn+7l6lxf1SpVoyfNdIgq7pmL1+N07San32/KUXbv7kPx12D6rViu+UQQ4VyVF/ZIO4f/fRV76MVy81e70winmcJkk8sHgEFJYr51QWcNtRaeA73u72OinXNJQE1IpJ4lqzXAnCGWFBWGCx987H4yz5yZGAP60f/IuEIkm36H6RUycrIsAFxjdRKpWyLAMNiAgn4o7OfrE6cmPNqS/n3DAMwzACGoDBNZNSSg3KtEySEMQxEk/SGIbkeB5wCrIhN7mwRIapcLntWu2DdhRFruumWoXdQRiM/Hmz2+5tupvmmtmcr9RaLz3eeJSy1K4KwzUY2slhuNcf+U7lpRdfffhooHrKwebiwrnFueuuvSoTl6SF4vm8EH/ZJOBP0MGbaJ5zXfSkJAYSEAIe0WDM84R+YsZVxICe/TQXKFVqWGaWxIZnDDvB3du3lpfmbt25HcVJs9l8eP/e4ycPm/VaqX6+Wq+///77aZreuHEDIfvhD/9Iaz0zM9Pvbs4vnDtob22sP9ja7lhWzTAtKbO1lQXbYX/+3e+MhiKTKomz0Ag/+Ol7nAF+cLC3w0wsHe49GfYHzcWVbv/gzj2wSrPVSs21XdOyDd/fIxoMQssUZtMmqVzHSLLwww8fVWtrwnTmFs47lvfuu+/OzDZmm3NxFDiu3Rt1swDDw97mxsMrV658cPPJYKDrNV8mByYPfHfo+5fjoGRY4C9U436PcXMUZqahgFiGCMQIWK4U+Su4vP6yabB+VeFnC1p/c6CI0H5s81Dg1c9mZ2cdx5FSJkkCALkr1iQLx7MgpVRKTSo05ATYNE23ZJuOkDobBoMoijjjnuN7jh/EA26h5Rl2yRQukimlSPrhgV9zSSiv5M7OzjYbTUMYaZhZ3P7cta+37OWgk0Eq7t9+/N//i//vD3/wY98uVVa9xSsLzcVZ1CZLSx6fbZUvrM6+8MVX//allS8ttT5z6dxXLqx9sVJaVpklk09P418X5OR2gmEnXxkBIwACIMCpf7+iwBiTWnPOlc4E41mW7u5uzrWa7U7n/PnzpiEePXrY6XTm5+cvXb704MHDnZ2dlZWV2dnZXq+Xa6HyhHGp1GEYRtGo7LtXr15dXl42TbNc9irV0g9+8IMoil588cVr164xzh4+uMeAXNca9PtpmsZhsrS09M1vfjOKw82Nx2sXzjteqdfr2bb9jW/8xje/+duLi4vhKEiSJInjmZk64/j4yUNCRGa4Xtl13Dd/8qPhcLi2uobAkiQxTdMynVTS9ubW3GzFMnWaBvW6Fw7bWdyba6HrQBwlw35vpu6HYVirLxHWEDSCLgou+hQ+hV9OKKJ3P9sZ+ayG6Mx/RSD2Hu4RkUUWEYEa2+0QjyXdSefG2uajUkj5JxFFURTHMdZ1Ekgk5plVKeWgE+kSq1arrpdIKTlaMtLRMCVitVqtVC8d7ke8Fl/55iIxPdgvP3hzs+HNMzT9Re8wwhuXXnrxhZdu37v5+m+/vjy71l0Pyh/Vdzd3jMa+1QBHL12ofy6OGIoF4Jev37iR97MfAIBCU1nF6XiKOeWzdaOFHGKRSqpgaQqj9j6uREy/KOcmyJOeHIm9NPUJZ4iRwiio2f4x1SEuPBvPrRE8ez/wgoZ0qi3EeNQvlSzg0Vtv/2m9IQwrrAnv5p2bc7OLwvPSzp0gjCy+dGH1BeQV3xclX4XDR+Foc35hJkyjKDOiwfrahQtKJhzSz7/+lY1ttrS0MDe7vbEzGg5nv/Xvb33lK+b1V65qwVob5z3LhqT0G7/1e8ur529/9O9W1la/9WcfvPt+cOPK5eHeg1v3t1cX5nXv6SjY/8Jnfz0Z7T0mox8/3hucG4SzcRT+2he/7pV4e/+d5dUrWKra8Qe7T7/lGZe39jcW0fb9ck+bHi5/73vv/tpvvhrLgyjM4kGgVfA42jJZw7z8RDH3Bz+49ebb95aXfJlsjYb27/3Dne3dr/30g4qGyPGzUcQMXRGxluL5bA+FYZZFSe35z5e3K3TGKVBFPm87ZlF97gIoPO9F+78g/rVIOfG82KRoXKrIlPCcTmdFJgziBgAgagRNRIxpRAIArRwAxZgGUIJrxogzYgi1UgYnDan5hVKKiBGBVqAUJw1aAwJLyJzu2KR7rtef3J/2o570c3ogiNiFs/GbOEXVf7ZP9pkTNL4TM8G54AYyZqKttY6DdHu4K3XGGDMZkAaTWUIIpvngcMhLUJnxq41qGI2kHigzBUp1Jt/60Q+i0mBhrWVx+/rlFxaW5/c3O8EgenJnw1sy19xVX5S9xnK9utROg0cPducXL/+M3n4Kn8JfD5iW6PZHpYonOA4H3fb+/tzcnBDi7qP7s635VqvSaUshBAAwxq5fv/Ld7777YTo83K/u7G7JNOj3M8apVHUQAFBk2ahSqe2195Rura4ueVYfCG68eOOJ3X7//fcPeu0v/tpvf+lLzs6Dm8AiITQj6HUGt8PHiZqfn1+wfDcMqdmc33zytFqD+w/uH2wNpQoPOltS71QqQ6Uz17Wc0ImiqNvtVhqDlZXG+qMDgLpl1ZqzF4J4Y27OzxBuP92dYXDnzjqQ2e93GYlet7O189R3e06pZXsryytzjJmt2aplVMPw4Dvff7ow35mfb3UOzUyBYRiCKYMMWWQE+xR+pYEVEdTnbKdQEMoFJ9IEwJgG0ETAUCOmiJohMUaCK85QCGCcDGMcZHvqM0nyEFzkDAFIEeMMlNaIavrtUxfTDAdO2B6tj+j0SQ5jYhs+1ZSYRP2eGm0RP3nmRCCiSY6BBmjIU3kIbiCwOI11BtzgKDkDYTFumzYpGvYDo46ri6sGM0bD0PdK9Zna4V43k9Ix/MbMfKs8M9gdStRxpp7c24wOCGWdJ/6Cc6NUXV40r9Tca7VKMIqHBd382KDYa+65bv+MFzzvDwqa+aTbwH7J+v+8th9C4JwJAzlSt7P/9MmjcytzWzvbQTCo1+u97ubOzpODg712m3Z2R477tldqPX3cuykkqQFAYposVWkWK6VBGYnOdKVW3d3Z9CqlanVBMCEzJYQwLH7QaYdp8PIrX11cXKx7ujKn9ju9+/futXc7phFro2I6nkRqtM594QvVe7dvMyPo9w+f3t+4eskxzajhzwPQzvYT224sLM7HSTSK40f37rcaBmNRJlWqbGEt7Ww/vHa9xIJOqVIrV62drcHq6ipjPQaW7TbdUsRx4Lh2teb75ZkwlqVypVJZvnXzvfV7WPLaC7NLYWi3B1apZBiQ6qTQqfOTDr+q4/p5w/PanrEovQ8Bw7HgyYDGEihqjglnhKgFR8Mgw0DOQAi0zfwt41aJKEfBmjRoJEIpAdX4BmeY6SzvLpyknQL5cW/puNvyhCZwerwT+fZ4+IgoYIoR+MtspulnpuORg15i+2A7pmkZuRsXY8xxXMMwAIAhR2BaqyyViAiEMko97m4/3F9/un3t8vVWXW3c39SZ9rEmI/rp27csVWo0mlbNXHAvVG4sRnONg8FToZd8tVgpXU4C1/XKljuTFqSg+8v0/1P4FIrgefdJlESVWknLBBCePn6wv7djMJklo5m5Vr+3l6ah1pFte5ZlBSE9uf3hwsI52+KSZVr2g7DLjch2Tc93Hz06LJd822Se53W68Yxn7He2luZ1Fut2uz0Kg9nZVrPV4syUmVpdW2mtYfoh+/Dhxv7Ofrky4zc4cjjsHZb8WqVqCO4EwT7wOIp7oyBqzVXiUVLyLGSJbUO9Xo0S1wxjpTTB8MUXLsaZNqyqYYth5CKaUsWRBEWmztyZxtq1a+6jR52G3wLR5PCwOdsyLOaiCQOWaZVpqzfipv2Z0WBgL+waYj7NHGSEoFKt4TnDOT6Fvxo8b5jNz935tEgjXqDRL2R76WwVOgcAIARE1Iia56kwABhTAMC5NjiYghkcLJMhB2GcIXACgKlAkSaFAJABGIhKARGJI83xKRW0ydkJPTaO25QMp9ufXOQmkmnr8vh+fnWK+uYtngmT56fvIKJpmlLKKM7Q0olM0ih1Lb9eaQA5YRjmTltxHGutLcsSQqSDdO/h4Sge7e5tNa1ZJG6Tg1LYdomiZP9gsLK4uDTzQmm25jplV5XZ+csb24/qlYpgXhRavU7guWVkDH7OdRc+lYD/muCXrP/PKwFrLQ2Tx5nM0uyD995zLLNaK1fqVUQjjgeHh9tA0nGrnlevNczW7KrjuXEw2N16ZPC4UnIcx2AGQ9LlUgOAXJubptlsNReXZrd33r5y2Ttot23bbM3WdUO8+OKrN669ePvuR083251Y+uXPM94+POxJxatzrFKr2164u7P/7rt3cl+NKImqtQrwsDXT3Ev7tm0pNQgipzeshqHMsjQYDRjXn3ntxne+/36zOVeultp79w96URxn/VGX7yTdzlpnv9uo197/8P7C/PLi8rl7t++HUSLSge2C5zqDYdI+OMykWFp4PRh9Jxh0Kn7L6ctMZpznBbp/udb344Jf1XF9XFBk2n7eWVMFSeSFIERkqBERUXMGAMAYgdKA2mTAGDAkwVFw4BwRcpXy6YXjQjMizZAzhoy0QiFQKrBw/N5TBBgQEHO6OSbC+VfTyCVjgCkbMwAAaMQ8EOSEnVfkqa/oKAMl/EWicO4YNv01/7R8axh0Y4iq5ZJl2sOBjKNRJ5Ytb0GTyuspZTJVSnHBDMNgobF9f0+j1ESbm+slt9IszVh+eXgIWtNMdfYbX/gHV6+9HKiR51aykRsn/uXLDc55EhNow3VN07bjJHzejM+fHphP4S8Dz7tPDNuKosgy+Nb6462N9QsX1l5+9YVS2R/0JYNyELQrlcrauRucVUdhUK9XqhX/3XfefOcnP2rWrdXVRcthQTSMkvSFF77w+NEDzzfK5bJwm7Wmt707LFebt3667TjOytpCGvUWZhcZ8O3Nbak2cIe9+CK3bL/erAXB8N69D89dqM2+sbK3u3Hn9oe2xZ2S190J3fK84ehREhrCDoP+xvpd5Ac7e8PhSPquI+NeuWL5JTMMD7MsvHr9i/v7Dz766b/mGFVqhsxk9zB4/PDpjZeuuLYwbLc5c/XWB06/G9Xrwhbkl+wsC56u75jC8t1at1fr9mV1Jp2pYzegVDqGaSlV4Hz3KfxC4eeND4sY2SLbcBHbqwv+wlGPCTADhsgYISMGQISADEBzBIbEEUgrBSiP6Pi0KhgAhCG1ZkyTZgTIQJBUJAzGj6tt5GRxTBwtdkwup7qDktG0ZHysXS4oRjImwNP5n59p9ARMejyh0zk9lzqO2NCvWhdfWj1/YXU4CD96/9aje09KVkViChyEELYwOeeMsTRNgbjBTGnpZrU2s1yjDDFlV2evyeWZx1u3z59/YW3pDR1XohDS0EyGIlFBrVYLBpEQVqaUYTPCiAsFBd5lf9PgE89YfEz9f17J9eMCbjCVxcIw33nrrZXluStXz1uWFYYh47Vw1I+iaGXl0ksvf9lymvfu3QuG+2GkavXWTGves1WW6ShNDMvw/fLi0srDB499v9qaWzjsq92dTcNE04JgFPV6g6Vza5VSEwDa+we9Xq9aZ436JaIScPPclYv7uxvrm3u9wxTlDDPM+dnSnVv30Qgtu8qFAdzv9DujrX4m47nFuXqrJVU66IcLrVZjqbF38CiIDjyH3br97mfe+P0LF659788zx8H585VsYJCUySi0DLrx0vmnm2Gc6LW1l5VMy57rWtpkodBRv7NfqS4aQrmlCweH65a71azPhpkXBBwFFSHcTwoU2iZ/yerRPq8K+ucOH1ORA16A5gUjAEIGjBFHyAkwIiotEPU4jkjrLCMiYEzDkS02J6WTWWFCM66BMZAMDULNeC5NqykP5ykhmONETD2pmmZ6QkNzvW9+nWXHNuNp2izyUCI4LS8/HyDizGI9PRyByCyfX7i25tqe6znIoLceE9ecc2Fyy3Jd103TtN0OGWBrsTlUXX9evPTZKzsb7Sed9tql1Tde/C//4N/8f+r1RZmYwSA1vDpDS3MHzYAxMezHMzMlpYNMSqWzUqkUh7+yoaWfwicIiMiyLGTqvffe/p1vfrVSK31w86fdweFv/9Z/8+j+geuWWq15Lhwu/CyFd9756MqVNdvxa9VGEvWyDIRlCcHjOAPiURR5njc3N/fo6cPORmdxWUmZlLzycLjVG/SaJZcz4XvVRqMhbOk5LSlFmqpKtVypnl+5UG9UnYpnV+vlL3/pc3c++nGSxJcvXwZdac7EcTukLupMMkbXr1+uz9x4+rg/26iC7HX6tlJqdW35gzs3UxnPLSzZbtWy7JlZJ2T8oL1vQLy712wtr310e99x4/PnXthY/wA0hMP+sHcYBRQFhwtzq6ah6vXVjQdblr1+vll2vWq3i0Iry/xkE+BP4a8GRTTleRmawjAwlAwQGfEj8VcwBEakGBESIVGmFDDUStEkv9SR+DslBzONmgEBZ6A1EssFOzZtrD5BgNmJO5Nuc6ZhHPYLWgNN/LMoN90e11DIhV6RYJZmKRGZpsmYUEpxzi3LigbJqTka02kUSkspVblSirMoSMJauZplWTvZOn95lZheX1//4bvfuXzl8ig77PQORlFacssvXXtp89EOSjbjzfdkd8iC3sr6zBfOn69cGA6HkUznFhYZOO/v/8CJ04tfZElngM5erbLW6SSGY0V06HE3S6LmjE8QGRzzOsUyzpBnAIDAAbjONQ/EiIgz48z1MjgQKNKaQDPUDBEZccaimE9P5eQiKwhf1Ow409b0pkFlwRSnc7yBCuIU87hSAgaaEYLWSAQEYBjxme2rorq2BcaWIn/2IpcYVRCYTAU/KDLBPzczx56vDnER4HO+9/ltvWfPv0pTr1b5k299a6beeu0zX3zzJ++QLtuWo+SDn7z13eX5tXNLL5rEb777Vvvw4Mtf/fVoeOftn/wwSvut+fqTx3dnZquu6VXc0sH+OoNwcfHcD3607pZmrsympvHQc3F55frf8y/fenDzrUeP5hf4xdk1HZUUi9rd0RxLWs7sKOyH2WZr0TjYe5TJ6/HAypRbnfvG3Zv/7oXraNrNmx/cXllosAvBsHM4HA1cs7TYXG3vPdxs78zM2q35rz96+O36gtF/508+uv3tz3/ub1+69IUffed/ev31KzMrpW6n0w033n9v+I+uX6rX6fH6d1Y/+3K9wVDoSuPCw4cDiUKY+42ZSLA/t7zPL65ce/zg4bUbpmuMPCep+pUoKchMp38xZUOf1wv3eSV4RRk8g6ARUcuCerR0du4CLIifLgrqKpaACQgZABBDAEYMEVEzeTI+eCKcmeaJN0yajWWCiIyx3L1g8jwqOxd2GWogDUyPZd+Ty3tMqPj4dZP7Y3ybnS50m3+VOnt2MgFAcK5A5dnWDEaWwU0DGcPUDJ6ZMQQA+yxnQCLSqQVIiMgYCpMQNTKFiPwo3CgnmscdLsDnlBgwFopP+B2maQoAiNMEnUEeBzwJT8xnVmt99DQ+S0g454whAIRhmMg4r2NYLpd3ehu71GYGhNTfOdj88fd/UnVmLq5dPrAPg2HY7u/ZNSOLs48evQ9cC0/MVucF2b394dbWVjaC5Tk7i+STx+u12fJK85JbtggCQyS2LS1LOq6lsrM3LnGhJRGR0hIUTg4KQoHXHJcAwARyRowzzkEwjog4piSn1QC8oBL3SZX+sdVcooKzCHARmhccgZBAaSTSiByIGDwTxz25NooqgxceybMpJz1nZcBf3RxWHw9YlpFl2dtvv/XC1WvNZnNvfzeJ42/+zm9/8N4fLy8vL86vPHz0oNdPm7MLn3vjs2iwf/MH/3Z7e3tlZXFxYabX3YvC0LVNp17P5MBxDYZwbm2lXK093XyP8dixRcw679/74NatW1C1K5XDg8NHQdIul5LdnacV/1K96UWbaRwPDXNmcWXG9cXuzuYoajoe+GWm1GBr+8F+++ncjG8Cn63WZxqtcHhw9+77SYA249FhQMDnWyscqeJa3f1t3wbPshu12UF/qDzT8aqtZtV12dbmjusoz0sOOiNhVg8OBl7JEEa5s9fV4OzsjtCqjFTHEeWZRnl/f8Nyzjcafq8zEkZBofK/SUBEhZJcAaNfSFCfl8ElBACt2YRRz8km41NfiQDGXkVpejYWsu38fs7ZT0XTaoVcQ573G/OLM/o/RXFPD3jsRawYjO2bRy5OkDs3iUk3TvhDUcYAcFztlGlSihgS8gLJ4C9kuI8VwUSQRyM9k7gDAFAX4NWCBD5mQWYZAQBCiAmtnRBggeY0FaGjiKM4joXgpmlKlZVKpTyNZZZlrulHo1gzaVdNy64MBoMgi7DCXM8Jw3BzZ6NcqpQrJRcNx7ODeBi007gthWUZqYOhaLit6moTYv7DH/6Qf945X6sYdjI7z6tVS0pSlKXp2UYASag1pSkpSUpoIs4YAGDRjjZ5whnjBppCCIFcoEAEpoWpp2f5eK7pbEla01QcGMCEwiZ4nCt7+oFMnb1gpikBgDQjrQlA6ZweQ5Gf4PNuLI1nzxsVUFQskkg+Lq/vQvhk2ICLEKJpiIcP7na73V/7ypfvP3q0t7f3wgvXX37xxve/8z88fPC4ezj6tS/95uUr5yynpEg+vvukWW/cyjLHsmSS2JYdxwkpFYUxUXD18jnOYb+9tdfe3t29/errdSLttSyjymrz1WqzWStbqezML/nXXr38+MHAMrVlazRSwCRO+ufOzb72+Sv/3X/7h/sHyShsKwokBZIgCAebu4O1hXlDpDPNapL2pW436muodb+705hbCYNelnQura3e/ejd4Tc6Zc9l4O0fbNalBdowZmq2a+zt7i+urCTp0zTNms3mBz9dr9RS1/f1fndp6Zxg2cZml+mt+XrqecZouFfzZ03hp2n6N40AnzIcTjxmCp2PCuJcn/dUFBLsvFDZ5NTnNVQACKbRFE10s2qqO9M0r2SPCSRNZVZBRAkxMM2RISMhgAEiB444iXQ9RcMmBAWOzKW5bjaTY3x7Cn8iHRFgAMgnkwgAuCAkYIwYAjICRAJNgIVFFAoED34iduj4P6VPJ+44mr/nzB1doKE8ln2zLIMjYqzUUS2bkyFNRESKAMgwDEQsl8u+72/tbAZBYFa4X3VjGoVq4Au30ihnoX6y/bDlL8okZyNgptVcPr9Qrnsa1Q+//fbTextZliHiYs1p+DNa0QbfYiSGvUGH9spOQ8leo9bk3MqybDA6VlYczwJiECmttSlIa60VatCMARAv4hBdlxgjU3DOSRia5+FZBBxPeLUdbxc8W5KWxxriE5M+OqsdAOAFHK5j5u0zrVAR6ZwAE00I9gkxmghZQcrMs5sH0GcT4KLEgEW10wvj0n7J4Bfl1Ykkb928OTffsmz77bfv1uu1hfnZjaePuoeHvX7XduoLKyuzsys3b93Jssx1jNWlhVrZszhsb27oLPZdxxRs2D0YhIclz/vo5q1eL+ZC2U7v6pXPZJm8d+eeYRsry6uW4YKUve5908Rw6CVpIOWe1mUmsuZM1RBIOs5U0B2kGh00zDBJ4lTVZ+bmlmWcbTzZ2LMsVWqUrUrLsyCMg7A/MjnWlsrdj8R+O3rl1df+p//pT+7c+bBcaxpu87D9Uau+2ulGh53B/EJlOOreePk8knX75ub87HnThCzrW4x0ejAzN+PYXkQ1ARVMI9JZv3tQmz0gbczOLoyCAo3LL5lz1se7f6Zby3dOUeuFqY8Kpq2IkBed39xEhUgMgEAjjSeeWDLp6jRw0tMq38m1Z9M0vZz8VbIEETkDxsEQnDNkIi+Ce+zeewrwKKVx3pqUUms9IfynBZhkMo+TGUMAsDgiI4aMITBGgiNjBAiGOHt+1F8iI9uR6JXTO336fj7PRXlBikx+BSa8MXbWWucFGACAMSaEQHnMuNFRlkoAcFw3n/8kSaIoEkJIKYnocNi58fkv1RdKG/uPh6OBVhSyKE2Cfm+wtbldLVcHNNjZ3rV9I1W1V19/ZWN1d29nP4lT07C67f6je0983xfc4JnZ3mw7qb9QXxkGe55tWKafJqFlGZM+TBu9pVKgmWuhJkY5G6kBQGHBgEueYIiMgxCERAQKUdGR4Dz9ivybKjgXqZCnSPV4uoSAZ3YPESXp2RvCdSVp1CSVRtKodV6aGSgSk98et4ZgFBHgggOZFkm07OwJKrahFnGUHxd8siVgmQSbTx8vrSzcvncbOV66dG5ra31396ltsq997ctcVB89ebTT7nPOz51frVZL/+b/9y0kaVosjgdpFlYqFUTV6x96vgiCoNONqpWm7UjLisol9/0P3n90a3t+dj4Nw631xyqLorTr10vivfSHbz9aXH6RiPZ2PlxcqL3w0o1+v/9k40kmS+XSYrMuH/ESaevSxZfPX/jC4ODeD77/H2MVHvRH89w0bfvWzXubj7ZfvnFZeGB43n4nvHr1FY1/+kd/8h9X1244Va+zl3qeB2TubLcP2sPdnf2vfOXq9RvzD28/1co5v1b1vISxju8e+q7XalXvrse+C6YQcWyOwtAQUa2UDEbRCP6GSsAToCmJ6lkoqjtbFP/KxNmMNSvQkAnSAMA4MdTICIFy2WPyWkRgDPMCd4wx2wzgJDIc22iT7EwCbBkaERkjzjnnSghgjHGWJ4k6DZMpyuVUIlJK5TV+JJ0WPPILk50Y7+SvjsUQGSAZHAEJUOc9FUYBviqwwedU5UT7z0q9BWbBEx0rIMBmQX9ErnOeuJbkGazY0TJOK1Lya845YwIRpcqCIEiSRCllmqZhC9PlfsNedGaztDHsjfZ5W8rMHIravO87zv5eGw5kg9Wzvfjxk/LqxdlqzVUpZCH1Ov07H92emZkRQry0+mq1VDu3duHC2vlKqaxJhlEQx4kwNZxk0+CII0PUHLkm1KT0xAnrbM0xuA7PlRUsHy4pjqRB5yaGvGGcivfSBRQ40KdtA2M25chGe2oD2dbZC2+YCjRTRFqB1qiISIEGwrP04VQcol14vyCDTFZUbq9Ig1Vg8/hlg+eVYD4ugr23vf3k6cPPf/GzaZokWWybXr+7HwajarV0+fKFdje9eefOhYvXfuMbvykYvfPWW/cf3Eqy4XB0kGQjhNQwK0kaa4oWFq5cuPhaktYYF8FokwstJf7oxz+F4ay30jQbUbDbzfShN0cLS3UL3Vq5tbR0yXXF9asLZZeXq42nuz/t90Zri2v7HQOlbpSchmfNN1uV6lq7RL3+jXb7QRylBjMXWsvtraxfalerRtDpLy3O3P2gXK80P/PyjbffuW2b/PzaQtDzh/1Dx3Fmmr5hmLv7B/fvPXn99fPNhvn4/k0ULpCuVhzLSBwzVXI0GPQoGs01Wply+4NMq2GjoUbDLuL82RP3C5KACzUZz+mcVQQT5erpnxe9t6hoQUH7Ra5rrEA0szkhADLgQnNOjBPjBKBdZ2JKI8aIMeKcGCOLn3bOyi8CCUQEPPfvzVePAIgJRETOAVELzgQnYIohwDMYciLOHRmSgYg0z/1PmT4uujDxQAIAQAefnUwiMjkAakUIqHOaQGMjYAE+LGBQGOO5W0w+IjxKmsEKvKCL8czZK4OigDHKNQBEJIQgojyTM2MMjziRU6qJyR3f90fRMAgC0zaklGW7tru1v9tbV5j5XnnUC0b90Ks6UI7mLlWZ5gOFXt00y5iq8P3bby/MNoJBAokwlFMu+/MzC+VyOQiCf/z7/43necI0kXPOTaVAS7RdByHGZwAAXIuIiDQBSKlAE0PUQIwXDBgQcKxbJoMjIQqOiPzkAaOjlYCi/AHaAYCxl/k0cH7GLgEAgAIvXw5EkhQqDUprTajleGEm+3U6nkyq4kpPZ4HBz35v7q33LBT5Ij9v4PzPWxL9uOB5CXYRvP32myXP83y32+32h8N6xS+X/F5nx3EoDMPFxVUSZc/3BqP+4/v3//AP/y3pGFm2sfUIUJoGCUNL0vMLDdN0Wq1lwKVuv2+ZabVmlaszhlF59LgtGS2tNC9eW+SmGeP67Lx3sK7+4d/9J9W5i0HQI7n77ls/3NkJh8nAda43Lfhw48Og/XTGlnqws33/zT1r43D/tsUCG9Rwp3P4cCuu7TVdBstVTA/C9u3WytzKTEkOBleX5j5487vd7bfr7iVhOA8f3VtbmjeYnWbacby799e3tweO4zx+ejNTVhAMfEebhgDm8o56cP9eMhjON88Br2RaP350xxRsafG1g8HHMs2fMMAjP2E4OhGskDAUSMbP6SVetJ8dUyIics0FCUFCEBOAjDx7rAHOkcy0ezOcJQAQo2lCAEdeq0IIznIjN3Gm88QTRJA7ZOEY6477eNQpmmBahsA4AwB38reTAuh0nqgThFyCBjI4AxyrlzUoIiokwD9LgMEx3Z0isRO8d3LUhTZdLPLdKVJB59V/ASDPyJFlmVJKCGFMec/m784XxnXdLEvjOHZcO58Xy7LSNBVk+XbZrlUSGUajdH/zMJHx3NxcZo184djCO39txfdKGxsbgIZpmoka2RUHQiM4iFQCwMG2baVUxa5Wq/Uky4ZBZPiWAj0MRqbJZqr2swQYERkkSimlSCkiIE7EGNekRQEBTjOSSAxAM9A8d8VihhhPwvS2yy/SApuBOEo5RgQ5fcz3Q647OLV7iMgs6o9GICZRM41ModSaCSAAzhg9AwCQFJQ6LlJVmUaBajo9uz/ZJ4Nu/tLB+++99/kvfr7bObzz4PbC/OyFCxc+Gna0yny/pJRaWln2a/rhk81bt27du/nB3bt3V1ciTSoJgrm5GlGmKPU88+LlC6MB39k9HAXKL5XOn1v1vY5tubPzyz8N3vveD743t1n64qtXFquJJUK3SuHDzubmgVu7mGS0t3XwnT/90dVrL1ZmnZJfe+eHf/DeW3tKqcXyIOoPf/DH/7PXWIG4d5julSwme8MPf/zWxu29lIjztDtcR+c7F9YudLbb2zdnHINbUUSYdDYyUiIYDJxzC71eZ3t/ZPleEAXrm3uGMVeqtAxrdmfn6dbW7eGgW29dLVWaV6+eG7U7tfJSY/ZGuXrl8ZM/eP+9773xxhpA4xe9RH+tgCczBk6g0Au6wJhT7Ez0fCC4AgRDABPEDTBMEgKQkSVSpZQkmZEkrbUe56dIyYMp7n8iAwjBEIFzNiFIOWnkPKe/eXjTWDAB0LkT1impEREn+Iqm9NiImKdyhGdtwFLCWfiZoSBCIEUADECSAlSaSKkiSfRnEeDxxbifJyTgaakPoDAMSWVF7Z95G3Dt6nkpZW7HPR3ahUjjQolk23alUimVSvWZ5t7eXqfTCcNQKVWtVuv1OgBUrou1i8umzyVl5Uppc2Pr7s37i3NLYA7ckmtaRhSH/UGvUW8uLy4rRfsPO/vtvd6ou7i4iIr1O8FCY8UxvNeXf/OFa6+06gsm9zKlw2RouIy4NMmCsxYSNJ8sySTGGYptJJahp9t5Vp9wauGTjMGZC88YnLmxdHZmO/o5w1yLGIIwOXuDcizYcNObZqq3o/Ds90bRcTz09KyazNYEikABKESFY/aVUv/ZzuQ9OrN900gpj4gg1DpnMhgU267oSGFFxDQh0SSUPjnz+SIVX1H8blGuWq2N3C4FR16K+X3HpoNOt1adlRkAEVJWKVt//mf/6Tv/6b9fWbmEzBOGPT8/HwYHG09vAo2C2HRd17btarVaqVS63e79+/fb7bZtbHPOpZSGYdTr9Wq16nleuVx2fTNJksXFhYsXL3KDAZJt20kab29vBGGgVGKYolRySiUHBYBWo75dK9XSML178853v/Of+qPOy69d/T/+X/63/+v/2/9j58nTOncaht2otUiUrGqzG2qLGo7jdLtdy7I455zzXq+XZZlnjJlpKTPDMsIoEEIopQwZZLbQkIqo9+pceWWpVb12dT0K3/jCFx/e3e3sJ2hZ+92nh/2d8/Mr19aupPzFIBxIKWeadcPA+/fudrvdVqv+m797/fa9aPegbLtziFkcDSqV+kEnKPuO1pq0FohKSsEYZ0wwHqmz44YL684W2UrPXl4AXeAsYxbsn6zgvUWItQDj8oK6vEqOK/kw1ICENA7Ukbmq7RkwWTT9ouP96YwjSE+huGZdTp6f/sk0npm+mORRmOb+4WSegEnkKiLazvHPT0jMPIYz8W12NhLWUxL/NO1P07O9o8/kcgAgSY7HCydQtD4W3hibIvxnNgNFglM+PdOdGWOqgrrmBPrM8Ypc5M09sGjKNXzyRO4dMJGPu91uEASMMc/z0jSVUkZRZFnW9vqu7/urlxbL5XK1UfGdksFMDkY4RJfceBhvP+2M+qOsLnjgz87MxWEajmIDzWgUVUq1y5cXZypzZb/acGuOZ2qtUkoZMyzLMm1OQqoiL8qTS/6XhAl7MbmYjLpogad31XQLkzaLeN7pyTyzJ3/55xHR4PxU344+T//qVAunNiIv8qIUk2coZ4ABNGlNWgEgA8wjFRAwL3Gp2TFnemJcBcrpyUIRnZ7qs/uT+9VpBgAMSQNQXnDs52ySxikPkelDAQyaM60wSAF5liaWAamE+w/v7O/vC9OzrYpte67DR0FnOOxqFa5dfIFzPhgM1jce8G0OAMiyWt0ru7OGYRAR59xxHCLqdDqdTuewt5+maas18+GHH0ZJqLT0fR+QWrONKIoc22zONBhC/7CbZonBueUughwkYTzTav3jf/JPmjPlfrD/+OnWP/57/8XosFcShg3CcfwoUaZflciRVXKWWgixu7ubJMnBgRPHcQlYHMfc5FmWMY6sG3GOhLxk1VLBpUqtVJRrLjGexClp3Nvbe+mll779Rz80JFtZXERIzp8/LziPsq2Vpbn19S3SVjAipWnt3OV+v7+5vlsprYaxHSUxkDI4j6PINows6yJxzhBIMESttZIyJYYfU+asIsariACTPJuQU0EYoS7g4Aol14L+kKajCENiuWIWAAAMfjbBdqyJIHFssEREy5pg78mRP7pxjOJyZJX380SKRDyq6oNTNuwT8snUvOUOQ/kzShWoiIHDxFA15epNCuA4uzJMhmCcdFb6y6CIM2HiBX0K7+XLmzeYVygYMyIF66iLBAN5PD/TCJkVrS+eIfgBwNiHGU4O8hQDlftFK6XiOFZANE6bxaSUYRgyxgzD6O6PDhuDC+cvVJyajLPDTncwGGitt+5tInJGbDRMG9V5V1UfvL+z543ACqJBsnxuqdms55lAtvc2gZExw52SKYijZlLKKE5SDcRSi9l40h97TDhpEpd2ggYXrdlk/9DUTOV4cPqHU3N6tvEfjlDz5OLor+zoYlxs8mfvnp9BgKfZgsmLmACAydrnFwAAMqVTq3vEUepTjYw/CzbW5AAQUc6AImrGiGkJgCrPuoosTyGGiJmIz6T0mSxCQAyRtAYEYgyOGD9WNA9jrMTGiAuB0TjPzNlQNNdFDFChagiJsdwrJFe+jTdebzAwDSdTKAQyjtW6f+ujd+8/uGU7drNRqVVbmdKjoHPQ3hoFw5JncpE2Go25+UoURUqpLMsGg8FoNPItQ2s9Go2iKMoDCvr9vpTStJnWOgzDzc3NvYM9rWWtViuV/Y3NJ+122zaMC+cvVUrl9s7uwcGBwblVm3ds6+DgIAqCaqV84dJyLANussOtGBFd21Uy9TwfkC0tLc3Pzz/Zf5imKWPMtm3DMeoNl7liYeFylUr9Ud+yLM6ZYYrDw0PDNlzXFUzHhKPRyIyDOQ6UprzeAGDBMIyCoWDq0sW1+ky119kLh0O7agjYM4VZq0jBh4NBYjui0agkSdLrHF64fDmKSFCWZVnJ94IoLJUdy+JaQ5ZCEqdMWKAF44KApzIqWMnnW98ir+MiAgwFknRBfoViH7KC5PtaF0jqGhE0IiIQy/k/REQU5tmaAM+lydsnfUAEIY5VpjA1LTQV3zDZz3AkWY0xyfhJAAClJrVycQrVEJ00yeXHEhFlenb4JZvyXp4+4+zoXYhI0wQS5bQsNFFqErHpRv5CkmyZcCbeC+PjdZkmGQV52gCLdCjy7PGiPntDnCLAk/vHuaDP/DMAcM6FEJxzrXUcx4N4KISwLCtTqaSMm6xU9Zuzjd764d2fPgj64etfePXSC+d9O9KK0iwdDpO9nb1GpdWqz60uXBZC7Gx09oc9p5wJtCDj0TC1bcZNpZTcb+/+pPdjpfVa61LJmZ2k7RLcUMcc02TDEeaZo57RA9BYdDsDlJoQbICpNdBHB/I0AT6KYzvVzmTnwZTsi4hIU/6M0zu6AH4GAZ7eQJP2mTo+beOujr+dYK9OdQyeWVxewKFbbFIdizQByzViBCZXGtBAVKQRmQYEhoAo+NmqHlaQACSTAgCIs3yJJrJ2MYEkAOBIDEkTag25mzkv4FhVoaLk+ZhozLkfRETMcUA+p+VypdcfNeqNJMnQIGHQd7/zbYTsa1/72tLSkud5URQNBt2Sp+dmSyXfHsYHw5FkjEVRFATBaDQaDodhGJZtQ0o5HA6VUq7r5ktmmqbv26ZptlozcRz3R31EqlarrdkZTTJNs2QYg0LfqUROMoAIUqhVG1przjkTYhCOOoPB0vLCtetX/8X/81+Ynis5bu/vOa5d8m0mVKvpf+WrnxVCtNvtfr/ved7jx4//47f+3fnz5xtO7d69e0E4qtVqy2vLw9EgTVMpsxdeeAGF0e/2MYy3kFmMi8NBalhgsizUvm/e+ug90zJ92+n3ekBaSjkc9BaWzyXRMEuHzfqMzA5aM6aAMOw9gpTVS81+b+QaKcrIMZMgCRA5ap6l0nbqkkAp1JOd8XODwv1WcC6o4H5R3G2RKaRoFxocCTCP0BAc+PhwoemcTRpcZ3x0poRgOCmgHkvG+QCOceMR6oMxI3vC3zj/Svq05H2Ebaak2yk8I6c4lBNnjc424Ykpwjy9FgrSyZ0jRJRj4LPDk4rWMfd9OYWUEDFOzyDkREQFzm5xUrBiBfkeoIAATzJaTn0CAIhc+sQp0++kr3QUK52rU/JYYTCoVPENw+j3+4qkEIJQpzIpe5XkIH1yb0NriMI4SAcbT7dqzeryylJ7ry0zmaVyZ2eHC6ZBelV7f39nfn5+b7ttmubC4vza+VV/yd3e3n68cb9SrTrcYw3Hc6qGYSACaK3kuOunNlye/+xZ2bdQAlaT9TvBARGeXtoxYR7Xjzw9rblkOb0JjiTy0xLzz94oP4MAP/sVEdlUGaz8kaP22bP802SAk9U8JuQFEjCysd0FGSHTR8W10DXG7UsNiKQnzRbEJRc5weUpgY+sv0iQU2JWqFPOU8EBUwqQdE6G856e/XwBPK8ETHC0URgg5i52pIkEMwUHmaWCgcqSe3fu7mw/+uzrLywtz6VpONjd1yoVAktlE9AI4yhJwnZ7dzQapWmaC7s5vcyCcdY5y7J8388PWq5kMgwjD0kQnAuD538ixWzhSJYpSUxzS1iWYWtUVcff29/tdrqu75RrZSK6dfv+hx/dsUzbc0ojkIpYmCYs1kE06ge9j967WSqV4jgGgLJTsblTssv1UgNR94Nef9CbmW+8cOOa67tRHA4GA98roeAHBwfrTx5/0O6oOIs1Ril8/mtfCOfiRqX++M4dmUrLc57sPG7NNff3RkR09eqVw8PDvf3d1eVF27YNw0A1GA0D16+tnb/66PEeac44zs/Wdw5FpVwvV2Y1M5eWyoBMkUoS7TjPZ2MoWt+i+NrCQvEFTjq6QKJlBTnMi30OClTWyACRISADPibAxBiMa/KcAScwT34LEXWRBH+CEE7jujMyPRGdzlR7PL1T03kkumhEnBCkU1Dky1KUYVBwPY2sTv1w+r3Py1KPu39kwnumzefzLZguL3gSzxcwcPpsBkXgkS2aToZXTx7Nb05MGp7vVqplxlgQjrhgSstevxvFIQCrlCtRZG7c2e7sddDUZMjZmXm75JiGE4ZxDwdBEJku12ZGjmjO1VzPGfRGJrfiUfrw7pPVc8uN+kzDt7yaPYh73qgrhKMUpUkax5FXKo9HflLrwgsWvugAHGkOTq9f4UZkcIrQwhS/Mrk+/qs+W1WCBf0s2kZFkutEdYyTZ4jgGZvTtNf+2Y0XEB4GioAQCRAIx/sJEcvV0z8YK5HwROKRyeuC6OxxmYYiohydaY15fnOtmSyIt5YKAUApYoI0YU6GibBINaR+tsLhLw0MaRKYIQSHMQOq41Ew22gM+qPmrB/10x/d+uDc8szLL17c29vrdPaD4V6SDNM0TZIkirM4znr9g8FgwDkvl8uMWZwxhoSIrsNyKss5NwwjTdPco0JRYppmmqZxHCutDRTD4ZALNjwMkiRlKLTSYRiOolGcRkJwGaQmCUMTA+XadrVaHQ3D+7cf1nkZbQss03ZNQGKMD4fho0frP3rrf0nT1LIsz/MQ0bKsOKY4IM6lYBZHc3/38Ic/eDOIgyzLlJIv3XjRdfy9zb29/cMkiFzTRQLONUNRK1cePLi9t7NZ92e6w06z2njx5RdufrBdbzZsg/f7fSnjfv+wWlnp99p3bt/rtLfn5mc7h+2Drnr4aLPkmS+9dNmrfF5pQymWxAqAI2cmF8ep/P/z17FAci0iwEUZqYraEQWMbGHGpQLNkAbGUBMigFYaNAFjpAl5gVNPnJwoFD91cYL7n1xwcXZGJ32W+hAAQB+HoU6/RYjjCKWTCrYTqu/JxURTSKcdjM7oJABMBMJpIZiIlDpbQCqCNJs2jU/6cwZFH7MRBa2lBQmUJgIAHdsZ84kq2j8nUhcfj1dKmTtDTrqVD37yRC740pE0HASBZVmImDtw5hiKcz4z1wqHkUyUhQ7PhDBYHAdbj/bqjWajPJcMMwsczlBYzK15paY74y/Eo9QQpgBTpzgIRoPKaG3lvON5tuHHg3CUDO0w5GgAMcb4hKKc2hD0TKD0qRGegmyKEznBNT+7BQGIiBdQ31MbC452ZE5nn22nKNdrEUz3f/otRoGtWk45TUx27fSd6UagmBPXJOFoyzKGnI/jAsvV8ZHgiIg4yelOJ+E4ZLkoI4yUWjMiScRydVn+NS3wEg8jg4gYak0IKg+bJgDSskCyKTTKnQ1FjwvOpSStiXNmmkbu8ZBlYDJm29A7iLs7Sfdwa3P9Xtmnrae3fvLevTjuI4akwsPO3jBIheEJbnuu77vccRzf9+kozl5KqVQvP0ST45ZT4iTTE+cv27Icxx6NRv1+PxlJRDQMwzRNEJTKMNGBIdykG9iWcB0ry5JOe9cwuWPa9WqNA8+Y5MANZFwwz3a5cA77gVQik4oAuICDg3ZuDH7wcHOuWSNtm6I6Gsr79za73a4Q3LIsX6z7rr/xZHs0ihkawC2tpSnYe2+/d+nCqkrCTmcPY+a6niXEj3/yY0dU4oAyzvu9g143UJIxfqCU3j4wypVzlZmm7c3+5ue+Mn/zEWfyb/3erw8Gs4ZpZgo2t/aA8SxLBKDWkjP7zHX5q8k9Z617gQnjOW3AvCAgqGgXFiF6icSIgIgxYkBAgKQBANOzCX8eHvmsBGwYEzxwQrFs0An8eXw9xhsw+cx/WBj/CvrMqePIp/szeUZietTOiYGosUvHkSQzpYDEI5h8fRab5X+apqOnIEkn1HeiMc0J7bFq/UQ7BSroIlcBpAkCPLqRk4kiE8b051SvRY4RnlV4Pssp5HQ6knI4HGqtsyybiPOWZQXxaH//wBV+s9lSKhsFgyCNpJTJQM81FlLS7f2DpBewVF+cXVlZWd56dNe1SrOzs0Evnm8tnD9/Pk7iOEhmFmYEWWGgsiyL4kCgbZiWaZpFEz1NHU9RyrMndMp2C1M7ZkI54OQy5/emxdxpYvYsmaRnRE86ubufC559LxNHrrmA+Scc0dfJAKcJYXHLZ9+feC8jIuOM8TENdsvjM8EY4wiImFuRdXZsqploUIgois/GWJyNZQNFKve9Opqws70948TI8RJp4JxAwc/dPJj3k7O8NCRjaBicGwAxV0q2d3bv3j7sdbtPHt+Lw87TJ7crvr7108On+5HMwkqFVyvCcUzXL5dLM4w7g25slXzP8zjnYRiCRiJKotQQaiJU0ZSzSaPRcF23UqkAgK89z3eFEARaZJSmKWqNDLlAYjqlWHEzCUNUBEpWyq5wHRNFqpTKpFstxXEi01jKzDCcarU205rXWvv+oNWajeO4XC7XavU8CKJarc3MLB8cjIaDFFFUSjNClJIkiaLANX1T2I7hGjU7Z8Gj4chkglnOgwcPoiiolfwsiiLiFEAn7LT87M7T+1euvcQYK5Wq1fp8fwjXrr2035u9dmXGNNLhKL36whd2Doz1Jw9S6SltMm0SkCbI7V3cQEIsTFb+nFCINwqeVwVOUkUEWD9nalgswOgKSAMhgFYqD0PKjU1xXJB4h9izSAwAkB0z6CfoEyanqCMiwoTQ5nhmgtAQ9XH7eMRO5L9SZ07ps42P+XX2zEsBEDFJjzSORHh0AQBKHYcJTQYCAFPOrTB9v2h9J2VkT2lMi7Lgq79IE3l6vMcauEk38nc8R5w3EYlcg5+T0jRN8ywcnHPkYqJ8zrODElGmNIR2vb7QaNafPn26uLBU9ss//sFbUYz/p//r/35za+N73/veQWc/y1LH9q5cudrv99OoXeV25DBW95aWLrzw4o2LF8/9+K0fJXIrTMPAyspuzTabn7vyDygrrW9suR2OiI7BOOepYopDXrCBkcEQGUfOCQkUSQBJRDJx4eTyjxeen2Agpi7O3tBCTJPIY65Q5o44gAZnhslMgxuGwTkfBv0sUWkiNSGCkSdbJU2GcUIonHRMFoQxiAJUcERoJ0t8tNhT0TvTW1DJE3Q3T2gDAAxIk9KkEUEYTJjABQKAQccsC0zRAGYyBJ1PiBBaCDINzTl3mIdj2feEsRmtcbD45NU4Vj2Fk07qqbSdUp2w/WhNREpr3axYWutUagRGhEqjUkoqbVshIhIxRFSapMxVMhDGGhE1MNIGAZOSKQ2KyAOEE0HGYzOzKAjniFMTERki5wSoiZTKpNba4dvNsteamdnZ3jo8PNzaWF9/8mhnZ2dz5+nOzg4iep7X6XQQ8bCX1uv1hflGrXZxMBgYhpGzp6ZdD4LAr5i9Xo+YyDTF2dBxHABwGI9CTOOx1J/ISAghhMU4ZxTalvHyZ1biNMpk6joewqXDgz5oFoZhHn3geV6jVbr6wuUgCEpu/fDwsHmxORgMSqWS7/vtdtu4tIqmnyu3glYzCIKZZp0zqJTLi4u1wWDgukAUMAZSBuVyuVIxSEBrcTYDORqNNGK5Uj04OJidW1TGrDJs4cUyjpXWwDj3av0gmLESDRhr0VWKDBmF3YVkQYeQ+k20rNrMuUHI/EysrMwPh8PZWZfr/v4Ofu5zn7t58+bdj95t+NnA0wKpnzAHgHFpWIQ6LLl2GCWmMAkLVCIFtrpMnyHhQbGJynxGXZR/NcXpczG+KKimwqdUqdNHkhWUn2P8hOp40luLx2c+Pw66e4a86ZSddvbMKcFU2dYJ5QQAhuZxO3DcjoBx3WI8da4FwTMeQwCg5DgM48Q0E0TqOC55mnwKHOPbk/SSkoJ8BlRkgz+ZCWtC8Ca6Wzi5arIgQJuIn8GIFGs+ZHqsG56+4PzYNyjHi0fTePa+tY0pb3OCidZaTPDpRIhRSkkp4aTWd9Ld11/8XGuputN9Wp63vvF3vtjyFzy7dG7p8tzc3OXLl3/84x/HUbqwOLe1tfn0ybomJdNufWbh6pXXapXFWm3+pZde3W9v37v1MJxPGzNVStM01XJIo4PEYM5Hb9/97K+9Apjnc2bEmNYM8h6O6+YSaWRIhAhaEKjcR2F6AcZrjwTPHAw4Gv0ZE33SfRZxvHvxSI9xamNZloUktYJM6tyOmadRlhpyZo6Oc6UCYqFKVhaw4qzAnXds53lWIYOICJwdxesAYZ5aHYGIadIEGhGIQCsA1Mj4KaF5Ql/z+AfDQCGYEMIwJveOh39qtida08k85yxdbrzIHxvvNGTT0nnuXQ8AwmCZJBNYJoGQGJICYAhKs8kaAhFnxJA4Iy05McwlY0WARIyAI01yyTKmlcpXUEOBZwQAIGrOEFmezVQDasNAQJydrTmOc9DZ+Xf/7l9tPH3MGQ4Gg8ePHxOSbdtRFB0cHOA4MVwGAFEU5ancTNPMB5UfKMbGgQb5sUqSJOd083piuVYp97oSQjDGfNsT3LDMSq0xxzgawjSEeWHN1CwbjUa5hcwwjF6vZ5qmZVmj/tD3X0qSJAiCXEFNRIPBgFI6PDy0bVsIkb80yzLbtvDSAsBC/vYoisIw9H1/ZmZmGMSWaTYbS3m3O52OIcy1tfk0jXyfj0Y6ipSU0nFEmpJt61q1Yjum0q7R4UmSGCavN6pLywtuuXnnTv+gs6kpdD0GGAtDcpHNzfs7Oxt374n2wc6jx0IppXSaZoe+1RA8E0I7nIPWqDgHA6QBBQSpCKac4unZXfosPGv7HCPT/HoSPThBG0XvHReWn3yOESsryJSEdGysxWPtKBRtUZkzEBM5NVeGQaFzFi8g/Ec+KCeChgHGxeDhGZ6jyJmrKKrrmBQdoczx83RCIzh5y+ScnnpvEeE8+TBOhHWdsx3Tn1Oay5/ZyPMBnrRFnoqCmXwWrbtSdGqk+aeYNCelnGTk0Fo/a1SHcY5oZjlWNogj7N/d+PBBdh8McenSpe9953uHvcNqpVYud54+fep5Xi4VCcscDAaH7d7Bdvbmm//+lVdeuXT1/OuvfuHffPdJKbWbC80kiu8+fSTiP7pw7oUXX75EZAOg1kg0zs2dA9cMGTGFwDTmvt4IDDhDenZCaZIw6TTHAVTkHKFPxNHS8Tk522ZsWRYS1wo0ZZkirXVeTSRPejr5h2MqjkVORkZRAHhxPM1kjDC1mfDIX31is8+jt4EUQ8YAiRigRiRElk9OHmAGAJNUaIiIHDkDzrllccPgnHPBx41Pv3d6qvO35xR3IkkbhnFKLM6JjTD4mMM7mmqllNZomoJzTDPNGKVSKwCGWoHKpIOo89RbQijGKJc5BBP59lASM6UFgkKmiJLcy04zTch57ruBWJy5j6NkyBhqAq1JCg6mMIQhKmW7Wi3Xqu5Xv/p51/7q/Fzr7t273/rWt5ZXL66srLz11ls/+clPyuVytVpdX19vNBqdw6HjWESK89ycobIs0VqORkEcB5znm0chas4ZESuXKpP9ls9bmqZElIyyJIm+/913JKVKS8/1bcupV5uGD71eL994c3NzOTFWSnkWK5W9Xv9Qa02ghMEsy0JGrdlKrepUa9U4jrXWk4W+bJ3Lj7lt27n2K8syxth+e/fFF5Zd193a2tJaVyqv5FHLUisAaLXI87x81eI4dhxnFGCeg9YtOznDUavVDMNI0+zChfmlpbkoquzv70s5ME1EjBp1RZrNtlBwa9B/kGXZ6upqtZI6dpxmCWPMFJGWgRYuQzOTRc7Lfyk4sUULznsej3u0BNM2qTE+wbGh52iHF0je3DjWlk3OIhSHJ7ET75pG3wU+DfJZwpm3UyDZmwUJQ06a5yYX094/0+JKkROrLgizEQWSq5Rn46tprdg0ZSrKpXyK+z/+ivk6jn8/+Syy6U6ATmqnqagKzTPdyC9yiXl6BXNgBRJVUsCIiImYkqPLydimc2Dm65Ff3Hz47v0dNoKuVeYffXhTJeBB5V//cf/mh3fSJP3s599wPTvNSq3WzOMnjxqNBlpqfnn+6vXrqwuv3Lvd/pM//tHjR1tf/rXP/v7n/g+GEzaX+Hvvv7mxd/vt4fe3Ok/+y//6H6u+Q0d90NMTbUlQgAgMCTnkNFgDAMhTCzOeoGcPXj7RBTYYDRpozMGxPKRnelFPzjIRMUTOuWmaSoNWahJGrDQA4ITu5G0CFVZVUkUbrggBTSEXOnGtERkiMIZKEZEGyNOha8Y4Z4LynAQEDDhnnCjhglu2laNgSGCSdpFzNE3TNLlpCs55ngoDp1whTiX3mMD0A7mL7+T+hABzYUkp8901qQaqlDINnilErpQilDxLlSaJpIiICDkHxsjgZJpgGMg5ZpEgolSilDrLSEqWotREmRIEjDE9TYN/BlgiY4xhXo5ZScHRtoXrcMZgMOhrKWeb1ZWVlXqt2mnvOBa/ePHixYsXP/zwwyiKFhcXW63WaDSam5uzTAcRlUylzOI4TpIESGVZZplgGowzEoIjmKZpGIYhZV5rYywW5zA+gIkEpP29w932zmg09Dw/DuNyqcJMGg6HuVZ5bW3t8PAQAAaDQa1kLy4ubm9vAwDnvNVq5cnpfM9ijC0vL3c6nXyZGo2GYRhBGDqOg4j1ej0IAsdx0jR1HGdxeWZra8s0TUTMLdA5FyW4cBwHGPm+r5SqVCphGNq23e+pPIZqfn7WcZwch0gpCTKi2VqtJqVcWZ3lnCdJkmXZCzeWwrBeqVSWlkthGGqtXdfd3bt7ePgwSVSpXA0jMh2wfN/gdhY+Z+JWAHYy480xoi8kwMfhLtOcN4I8dgjKLxAAQBUQniNBFCefYxVlAWPN2Ql8MjlHBdVBgfEzkBtAUdQPGGc7TU+C6o5l3+m/TvOC44P8vJ4WE5vxCV4EVIFvjdanZd/8MylyduP8iF7i9NThMxpQGGtz/+IuT6zMcHJuTwzrGWPE0W/P1gXqItX3cX9OJGgSNOU+A0eeVkIIJk7Uo51oKZ2mDgZDjoanK+mhlJhlZvvB7VtmUkGEm7c+ACTish90K81SLMPPvHJpbeWaltrzyi+//DqiU627+/uHn/n6V1vLzmHwwHG8Vmtu0Es08MPuoMzysBaYyI75ihpAgJohIieGhIA5UzrNNEwvjJziRE5s3EIV9HEjWudMMSAig+N0a5MJ0VrrTAOxXH8oM5BS5bQ2VzVMc5RjOa9gRxQtPCtQSU3fnf6lpJQBIHICIlSEWgNoAMNgQnDOuVKYpqnMNGkEYkIQMhAGF0IgA02KUp1jpVySzncCYyxXXqmpgU16npPV6bFMMNqknckA88ZtgZzxPFHcRHGtNSeSBgeTDE1MKZ2IVAdMK2CMGNOckWmCZQrLBNtinEOGSASm1GmKGddxqhkDIhrGY53zNA3WxeywEJnBODBNTBNXwuSuRbZJpmkmSZKm6Wg0arfbaRIHQWDbdhiGBwcH29vbo9FIKRWG4Wg0GgwG7YM9IsoTwwGAaXLLEpYlbEu4rptPiz6qOp6maZKE025rcMSOmIbBONq2XSqVsizNp3F1dXVjfVOQbQgUQgiyO7t9x3EMdECyzl5v2A1z/bZvV7a2thhjd4dDKWXnMGm320R0cHAwPz9fLpc3trcZY3EcLy8v93o9z/O63W6pVFpemX/48KEQwjCMVqsVBEEeK1yqOCsrK91uN47jLMtefPHFfr8/HA4rlarnefV6vdfrEVEeZ8U5F0xyzmWacM6zLDNN07ZtgzPDwGrVN03T9+eVUnmklmVZhp0Mh4lhBmjyUl36FYqzOBmmLj/bC7oIcEpJSETHfHMBw31UpSe3jEzO+5ipx5POj5PnnwU+zkw+fRbzvX22z8EkPv4UAS6KHz2Z0n6KGBR57RY4GZ0k8MdzckqSw2OLUlHKzALCfIwYTvZH0RRfciqp1hkEuNAbGQCeSV2JiHl/TmogAAAz+ZegwFNQSIAL4qEnuOvU81n2F+D50xIw4vF04FHWSc75xCafM+YTjUG7t1d2qpZ2k6ECAPSYX7Xmzl2pqoXt7e0kjfv9fhAHynBd121WaiPVXz4/79JKo9n86le+/uKLr5sWvfvBj//kvf/31ezcgye35mpzX/vabz683X/tM19uuat5kQA8kkAn/gKmqRBRjP1yCUFrrREoTY8l9WkarOWxjv5koqUCAwDhmDpjLrjS+Keo8+meMCJjgqoywUzG+CSKOu+wVidswDBWcBVmaCpWlBT084gNhGc2zUQwncidSinHHWuSIQGtIMsyJbVWZJgEqAmUJgDU+T8ClfNk+UjHjgmYZ2OWdNrJi53aVceDYizPDZsT8ol4pJSybQMRDYNNOgmAACxJpcDc8ZpJmUcJK2AaOSByxphpgOBomvkGADSV1FoDolKolWUyQCCCnAwDnaDBx5bxZ8AWinHiyBAJEIUgy5AGZ3t7Hdd1mTAVCEDBhRmnenf/8Pxlub29PRwOXddNkuTw8DCKosPDwyQOiIgzsCyR+2FxjgAQhkMASNM0/0zTlHOeJ4OczOfEz5FzbgkWRQGgXFxqWQ4O+sNM0tVrl4a9vpZZlmW+66wsLbb3dqWUo9Ho4vVXoigKh5HkEjVbWVwddIfdbrdSbZimOTe/tLt3IIThl6r1RktKadllIoKEKtVWrx8FoRyO0mrNfbq+n6ZcGM7+fheZK6UMg2Q4HFaGXr+X7u/vCyGiKIoj3N3dZYxFUa/RaJRKpXa7rbV2HKfZbA6Hw3A45Jw3m03btvOSLa1Wa3Fx8enTR5ZlCSEWFxeHw2HOvly+fNmrsr39wyjWgM5Lr+iLl6pczJtWsdH1L4IJZj/SQZ0NfFxWDxBpmgDrIzsiHmdFBipK0wCAU8440xcFpljgxtnhlEXjPSoOdFoUywp0BMWMwjEFnp6fCQGehr+KAPyM+uFI0j3b2Wpi/KapoBIA0AWq4IkP1vRvp+B03t8iVfmp3k6JE2c/OclrON1bmFq+U+Mq0rfRMy8dS8B5LO8pNnx6RqaHyhjjsUXEEkwUSL/iaq73ttth6O3HA2HwIA26UefqtSuMsfWNp699/hWjGRglURWlg+5ubxBWqrWrNy64FfaHb/2//uTt9w46+1/57G+8du6r9VJ1ZenFbi+RLEBEIEAYM6f5dDsOF4wLg5km44gatFaZUorojEQQuViRj3U8iqOBFMW/TmZ6wj6Pf3jEeUzEvzENZpj3MyctUkqGnDOuThLgozks5BwLNNO5m/cZIBie2gH5J+Njbj+/xrFtXGUyyWXZcfc1IEfOOOegtY7jeNpsPBmjUkpKQiStda40UyqjKUXIhGZMtuAp8p8/nz85ob5aa9PkuZvShKfJ95VpckWUq+S54KbFCU1hMpnyiTyNR4nApALGJQARU4xTrs3lAgmBMa01AzhBg6G4fJshFGMkhM4TbgiOjJFgcZChqTgiJglkGWptMOYI4X33u99VSq2vrwsher2eZVmmaSql6vV6nkkDEbMsmyS9UirK3aByK2yapqZpai1tuwRTvp3HA+QpE+n8YvWV114GpEcPH7/547cMU33z97/c6/UeP37c7/cvv7RYX7AGg8EPf/hDswLnrl+sL/pxHN+6dau24L3sX2u321vre5zDTMOfa1UMw9jdjetVxzTNIOgSkcnN86utcLgfhmHq4qXz83EGd+7ciaPQ85ylxYXNzc0sS3zfPbe6DABJHLqOMxwZlZLXPRSO40TDfskpCxCUgVaEJuMk9rfbFb8WDaMBizMH0xBUwjt7Q5XsBhHsbLfTNB0NKdeKDwaDXjezbHl40EtSbbszq4tfhFUbtMEkKyimVQiT3MI0QXYFuq4ccs3N9I49Ev7OttWpItUiHJd/R5xUsism/PwExj+mv0VOl2ML5TFDkBOhonrnRRnu+NhZ7BhP5i2ro+IENIX0AEAVVAYnfbaOW0wlBjjhPV2Ab4vmp4gAq5NFMibrwmFSJen4EwB0gQ3+FMmcfBYmbIHjE3qK6p/ZDtDZGzdTcKYmYMyt55qxaS4mRw3sqOjeBAmW2EyShnaJZZiOVMaIZZHSQiQUUEphFHzjN77WbDW2d7bml9+48+BWTeKw9+2vvVhaqs40ml6Wqv327tb2po6yfrtz8dKFp5tPvi9/9PKlr7eD3V4Yl/3SmFhiLpSOt7VtoRDMsgxTCGRAlGlFSlGWGfnumZCQsbCu2dF057vq6AKLWJRnUovl83CUGm1CnPL/hMhpz9h/TUop+PRjxzAWRgvQARZXRznzPheYC/Rjsf7I9pSHPUyjklxvEUXREe+GAJDrzC3TRgy11kmSwBR7AWOFMOUllhGJsTEqzKd3IrHBkVZgMmOTwU7eDkd0Jf9VToPTNMrLayGC1kprlbNZjHEkLZUGTcgUF8wCUwjBrHwteF4VI8tQ6bxwHgARMiJGgJqQoTgKxGNaa0YECPov5OQZI8ZICDQFMs4EJwStSEnJgiBLk+TBgycffnhbMNxcf3L/3qNURXnyitz6q5QyDGM0GgWhjqJoYt7OHZ1yPiP3R8sjiPLf5nQaAHJv5OmzTTRkXNoOLC01V1aXL15YHvT3bQde+/xVy7Leesv50z/9U8uXX3zhZcdxSIz2d3tf/Orf5Zzv7+9v7D3mDn3ljS+Ypvn9//T99fX1GzcufPazL2qt/+W//JcLC7UvfvGLWfb1N99884MPPrh+dfXyxcUsy773ve8xiH7rm7914eLCxsbG1tbW1Wur11849+TJkwcPHtR8d21trVH2d3Z2IDM+8+ILVc/Z3t4O/cr5cxdN0wRivV6vVCpdOH+JNO7v9VqzjcXFxcFg0OuHtlOuVqvNZnPzvZ86Tnl+rl6r1bY2D6rVqtUs12vz/f1dC2vlqtOau/DytTfOLV3ab0N/GNjV51MhnglEJAqrFY2l26PPiUB24jH8i1TQRzkbMT+ScKSxK3IiyxE6HmlQJwbIIkZ84r08RYPzn5xNCIv8/ac22LHoCScJ0jQtUQVx+YXFCdjpFI9H7R+XFJt+dZEcVASn6NwE8zCYhDkdfwKA5n+pKJLji6LUoQWJR0789kTfzk6xScfpP0/agKWUaZranh0mYW6VKZVK/X5feEygECg0asa5wQURkQYJI8FQhhkypmMCgzzLBVJYGV25eNmxfc8S77zzVjxKG9UZQ1qbH+0doGoG7y59/Vyj5vQG7b3e4Zsf/mvgOF8+X5Ur83PnXHNusB85kK7UWv1YA2omlEBliMwUYAhgjGzHBACls+hEziTDcSKtGWmWZixJUSqepFpqIGUzRoIrS2hDZJYNJZdbFmMm5siRiHLRMJ/f/X2cZkQmFtAs87XUmpBx00DLEJwBZTIdDSMEhYhSEzdNmzGtmUStnuFkc1Iki+KNjLM3tMltdQR0pKVExERNSb2MHekJUMYnqC8R5WkupBRJTJxFhkGmBfWaUSo5tm0pBb3hcNgLtOKG6TFw01RGcWq7WjDGhTYyJhgxkYfyaJKY64rzDcc5syzDNE2DSSIizPPBEuau1hxyj1kYU/SxewEAdLpgBZnjCNdF0zQQTYDcV1kicoEi93whrRhoAC2Mo3VRSikgYgyBcRaNJCJqEibiOGw4k5lSnmNLKaVMiJ2ob41o0pGn4bQHZqxTBkymZiI5IpFGLZEIuUmGad+9e/eP//iPg2CIOgHSfslJ0kwplYHa3XmaZdlkw2itOeekMsOwkyRxbFtK5JwjGoho256U0jStIIgdR0iZtWacbrfr2F6SJJxxx3EGg0GtVvPEUifcEY7JDUhGUdOvXllbARE0q9X9/f0vf/7zH73//kytNj8zQ0RvvPrqd771ZtnlXt1cXrmws/HV9tbg3MJ8qW4tLvydf/bP/hlzkhdffylN099LvvHtb3/7v7r696WU56/9/of/9zeZE6/Oz7/22msp9r773e9eOd/8/GuXut3uP/2n/3S2Lj772c8ifuEP/uAPHjy8/43f/PtKvfzo0aP/8B/+Q7UB//iLv5Mkyf/wP/6Pmd7+x//on0TRG9/97nc/+uijy9dqn3nj68Nh8K/+zb9aurR8/sIrBwdL3/vuD3xHfPXXv7i4UL5//36v1/n1r/9GtQxZlr355pu/8Y3X0yt2v3doOjVmzJTmlx+2hwQoaiNA78xzIQoSZWSgacxz6YkGChAEHdesnexGokkuYs0ZEClAzQEISD4TT8zGqSSK4onzwrCnfVYkqUkLJ1SJWgGcrgJEAJMky9NMLQAEwRkZphAR+HEVu2kCYKqzCcCURIhTn5DKMTEYz8pRa6m0zmyfwdRYpkZH/Pi8T49CnK17Qs4LUnLq4/Km059JQXW1SJ4druagAVMzNhlFkEW+7wnOg8FQSWlZlmkIRAzSkcqIM0MTBqO0WmuGo5hzUZgLuoiD+FlqlzOyW4tER2bZtMpsbWVhZrb5+MHT7a11BkL3da1WY4KFYagy8mzPMIzC2l4AtmsqkM2Z2kxj9vHjx1rrct1r7x/Mzs54Zr3Wqjqe2d4/fOe9nzSWar/3e7+3t7HHmNlqLS/PnedQyVIDyNaUGhYyIEQSQpsCbAdNgxsG189U5xiDBq010CRRFAnNUQIxhqA5Q2DAODMMblqmYQnTpgkKziWSfDoM41gNC1Ne8koRkMo0xEwTSqkMzihH5TnxO0LpY20im8pEM830FeWMLarLm2VEhEQ8X2sikBIQAXUGU9zrhDADqGcIcM7YIQMiJIJx6UatFYC0LMuKMs7jLFUyTDhjAEwIIdMEhNaaaaU0Z5gh44SIkzCtydHKSew40QECIDHGmEBAhfrEKcWTkKap1poxbZq+MAAJs0zCVMFqODKFcM4BFBEDUFqj0kCkiRAgDy9mlOu76PhFgieckWnk2u9jZ/44QRpX2sGj6ocIAAIZEJBWmZJaglaglCINNvLOwf7O9nanc8BQOpYZRWGaBEEc5Is9iRrIIU/PorWeUP3pPXaMj4SYOLiZpmmaZv6MYRi5ifSw37F8Iwz6++29vY09k9mNZotY+O7b787NzW1tbF29fPVg/0Aw4TiOZVjz8/ODweCgH1iW1ev1Ll6+eHBwIMELguTi+YtLC0uddmd2dtaxnIvnL6ZxWqvVRqPRqy+/mkTJyy++/PD+w1dffvXOrTtuVfTC/bmV2Zdevxpk3cqMEwTB3/r7v/Xf/bebjuO4rruwsPDv//2/932/2Ww2m83t3d0333zTcZxGo/G7v/u7H330UblcfuONN7a3d9/76Xvtdvsf/Rf/cDgcMhRvv/nOuXPnXn7p1adPr//zf/7P0Yj+N/+7/+rw8BBEKGHwt3//d3vdw7v3n+62AUBnSWpZHhdekSqyCPI66znrfIJmaADEaQvcROAlUCwPl0BiiBoIAHOTHDxDSApVlGdSWQACNTn74zv0rOWyEKbJ8LSQevy6Y2kPp1WahZq2IsvbVNGdidZqgl7O6PMzzY+7qs8uw6qL3LULYHKmTs0/FZkACu5rfqz4POpVvh+yOBwRkdbKskzH5kEw6na7jm8bpp2mkZLAuZBpdHCwZwizVK6d/d6CeebsbMbi2U01JsDDaOgYZrViX3ll9eLlCwmO9jrbBpqYmdVq1RRmGIZRGjmmg4iFriwA1WY5yoIoC+qt6tUbF7fX96p1X9igIE3D+MM7P614Lcfwt3c3++nhb1z+2udvfL0/CLTinNvZOMWeRk6UZqCJMcU4mSaahuna3DC5kmcTYC1BA2itiJCAkBAZcYFKSYbEmAZQ47oigpmmaVkTEwhOtIVaa8NUKImAEaBSmog05Q7DqEkJBkkmNWSkDcaZmbtYoNIakDTmpx+V1kwcZ9I54fJXlFO0YL0gTY+cI45KUY3J6ZH3Rd5/YqA1IKJGOa3JhGNLT876MiRATgCKAeR4yjRN23KzOI7SLFHEmcWYoUGSRsCMAUoxTgeNjASKaeoORFkqtSKZZTkbgxw55ygJx+Ur2CRNJuUm2aM9q7XOsiyKQAhma5uz3LWNJscepzyos4xy6qu1Vory6AWtGRIqrTnyTJHKi1AhAjHbjI+0F4gIjI0n6rCXy6k0KTA6niY0iEhJrZSWWmapUpK01qnKwjDs9g6DYJQmQaXkRMEwSiLLtuAIFQohphkLPgVCiFwmnmyz/GZeGgjGZm9zkrUDEfMU65Zl2A6dP39+cWHh6f2NUTDyy265WtnvdP7sz/5sdnb28uXLlmWNRqPvfOc7a2trv/4b39g9fLx7sCmlfPmVF2UCW9ubP3zzse241Wr1+9//fqPRyP2ZZ2dn79+/HwRBpVK5cuXKcDh85513fN9PkuTGjRv379+/cOFCr9e7evVqGIZhGCZJIqX80pe+tLOzs7i46Lru7/7u7/b7/dFo5DjO4uLi+fPn88ReCwsLly5dMk0zCILV1ZXXXn31/qO7tmX63uyLL1x/fO+RY9vVhmF7S9durKAIZxd8ZkSLK1XOZbXZKNdKvWEQp6lnG8FQqARIEbfPPjBF3lD8hM3vGO2qjAhgOvofGQJgpjNEUKDzpI9qrBTVQh1LnNMXRRRM6aNnjgIOj4TaqeJjR03lJ3i65VPk7TTVeUbjOrmJJ0njMeH/mZbvZ0GPNbdENE4blF88S31xytX0jBdNdXJ6FEUyW2E3p3LpAxzT+6JxFd1PM3UmPozikeM4gnGDUZoEo+Fhv9vZ2tpau3C+NTuLBhE3TNPJkvSgva2Uunzl1bO7WUQKeQGjcHJyJhei4lX60eEg6B0M9qxdLiFtzc/IADAVRp4T6MiP5hTLfwrCMLx+/frCwrwGyU3WDw7DNDCY4fsud/nuk92Pbr1/9epLiU427qyvnV++Pv8NE6xIJlmGBAyZBiYZQ0PESMAYGYIZgplCIQOkM1S7OUiVEyamtFQKlc5xMWmNyIjGRr5cTtapzEwaV9uY1k8qpRjTR9koxxJnPkucIQBHRhyRMeBcMwYaSXDMaRsjROSImgi1RnmUe3yaDQTAIq+8ooD6M/cVIiIjNgWTx4FOH9SpV6AQwA0UnDPGgGlESmPJGHdsL0swTqI0yYCAczIsVKSIcj9z4pxrjYwx5HTSVEZaKyk1wwwRc+rLhc59+IBpwYxppDC97XJddJqm3W5qWZbv2bZtT3yyppn9Cc76/7P2p8GSZud5GPie7dtzv/tWe1Xve6PRxCIABCmIsiiakkyNLGmCkj2SQxOyR/JE0COGFY4JzSgUtuzQODiKsEOeCZsjDrgLBEGCIAigsXQ3eq+ufbl16+735p7ffpZ3fpzMrKxCXUrw+ERFRt6szG8/592e93mMMUqDUjb0JNbBVQYUaINEazSGaE0QoBJSzpkNNI0xiDYkNWMVppm2n/HF4Y7RoJhiVFMgjFCtEBEp52HoP/PUU1c//vDG9ctaa8JItRLBTCQ0u4ZaPizbRGB7b2w3wewqNu3vmo3VyJiKxABAlmUBBSWLjY3V4XDY6/UWWmt37m4Ck0qpLC26nf7SYnbl42uEkO37u3Othbd++L1Gq1rmUmm1srrsOeG9u/ffeuutSq22sLDQqLeuX7t569atZ599ttls3rxx+6OPPqKUvvzyy0VR7O2++f777587d+6555678s7mW9+6vL+//8ILL1Sr1T/47e9sbm6urq66Id3e3n7jjTdWV1cJIc1m8/333zfGrK6vc87feOMN3/fn5+cvXLhQluXt27cdJqpR9MxTT9y9fdNxHE7huWee6XU6GkfGmC98/nNJkty6eWN+fv6JSxePjo6O2j1GSuEQxGI0OBJ0XWsQTqBo/Nh5cVKExx5JHU/BTQ8/V1NvCRGRGIJjpXQKYP+cpYiZDjh5wdX2pxOspd0nIYSyxzc8PtDxnLHNAA/Qto81Y49YYkSkOLvfB+CvkypdJ+VGH2aPesj8T8esO4IT0NaDLdhfnQAyOolb+yTsCxj22Ctwcn/tCREwor0+0/XKbkeVMkeggJxzrVWext1ud+f+FjBDCQrHr1ZqrqC+G549vabUiYQkJxn+k1LWJ/2WO8QL3FDm+t7dewiGEOI4Tu+wXXWavV5PS20bJ2zUAidDWuaa87JQP3zrnTwrKlF1aWmJc3716vWzwen1tVPNcKnuN+fmmvNzC3lZnlo/1++WSlGpOGOMCspQK10aYyKfUCCUAaVUcEYZgDHSGHnChTCaoKHaLq0KLCkkjKmMqRCEM8IFoZRKjVTrMT3hDIh8XGfVBhApACOETkgkAAAoZYxQShkdIxiprXdSajeFQBkl3Bhl0BhbK4UZd3JyY05QQzoJlc2dh1CIU3R6IIJp6RonAGylFJrHoz4YA0cwRxBHEMGJYJQxQohWkqIZF8aMAiUNIRpAcwM2qTWmAjKEUAIGkD/QpYdZj1sboEApZRypAsYYYUgIcdmDGpgxRmtjc/U2b6y1RpSEICHE+A6llLEH9bnpG2OM1qDsP2WkRGNQGwQAhUCA2tjXGKK0AaQI6LpsivqepIpx7CshEjTUVgcBpmdBKXLGCACj1GiBiAQod6FWC5cX527c+Gh76wYaJTitREFvmMyuy9NXm4K2YbGZ8JDY99P5Zmk0LLe5lVcrisJ2JSGijSC9iG1srFYq4e1bt3v9/try+cCvdIcHb7355mc+85kXX3zxa1/72pkzZ37jN37jl3/5l2/fvn3rzvWX6y873H/++Yudbufc2frt27dfeuHV+aX5f/Wv/tVf+kt/SZbqb/z1v3lwcHB4eLi8vPyJV187d+7c9773veeee25/f//FF15aW1u7t7n11IXnPvrwo1MbZxDJ3u5BVIl63cHq6rqN1/M8/+53v/vSSy/VarVut/snf/Inn/jkJy9dugQAX/va14QQTz31FGPs7bffbh8e/8SnXn/h5Wc0qrfeemt1ee3MmTNRWNm6fe/g4ODMmTNn1zfa7fbta+9VKpUXnn7t+LiPejjXrHfqOov7YbSaFAVllJ5EmHPigjj9/OEJRQEQJ4rOQAgAMUCIoHTc40goY1aTwNaQHh/hnRh7PEhsPzQvGJDZzx88bDO4L0IeGOlZQgl4rIWbsb5wcgR5gr07cZQz6OKHTvlxKGLysNjeQ8czQ0IyexYnJC5PTInjSaI4J6xvcEKpwlILTz2T6dVzRDgajeLRsNWs1+v1wA3LTCoJy0srlUoFCNdSbx9vB37VczzFsTwBHUdOOIGTDPNJgytSnjq9oZ18JLthGNYiYUosZeFk/mg0KvOSMcaFgwqVUozyk3bcihbLpPR5VBq88v51KeWrr3xidXHtzu27ncORTsnafHFq5ZLHXSgg6xdlSBC5krooJLNlVQJC8MAbx9zj/LBBadAYo06aANr2rdqObGSEAAPC0HE5p4w7lBFgbFyZVdpMG2kQcTbqsn2uAMgYmfWAgQEhhFGw3gAhSAG1UeOEE6IxSAVFpESiBpTWt57cdTLtJjyhBkxOQGm6zkOsZNPIqVpxp7G7UqosAQqNVKFyH4mA7W85M0wQxhjnhHJDGRJAY4xWqLQsclUWSilFgHLKOXcQ8/F5U86plUOmhKAxtltpGlOOT3CMHiTK5oyZ0IwxQhHZAwTEpGhqjDGUMSWVUoox9H0vDEMb/o77d2eq7/ZNKdEYY2NfY4xNeKChiGb8XaTaIAJFDQaINsIgsb/SGrUeb8cavOmYPsbSgjgMteylFMb4eddlQohKFMzPtez1VwhKj5nrZ7uwpiuvvUE2vrcUV1Nnwn7BCvFmWSaEcB0OABb2aCbknUqpufnWT7z+WqMeLa8sDzvFN/7om4vLC6fPX2o1m4yxr371q0899ZTW+qd+6qfu378fx/Hrn3rNd8Mscz6+fGV1Y1kZORj1AqcxGAzm5+d3d3ctX+bt27fTNO33+4yxxcXFTqfTbre3trb+2l/7a3fu3Ll+/XpUFeAmf/vv/p3/4pf+i9c++dpHH7719/7hf3zz5s3f/a2vfupTn2o0Gr/4i7/4L//lv7TH/4/+0T+6fOXKm2+++fTTT7/22mtnzpz54z/+Y8bY2traS8+9EEbB3s7uU88+4TrCd73dnZ0szW/f2F5aXrp9cxuMmyT59r2jJ59qpbEe9nNGpbvAWrWIsQIgUZplueT+CZHuCZHc1DQ8sgIy4BaNhQ94HJEA8T2bCQNKCSNoKXwJRaLdieenp5C9R2bW7NAPy/w9MLdTXAI8ZGsfCQCmv50+//CI4Zkm0x/Nij8mdw0AJ3Usn3T8J1Hk2rLaI7t+7GbH0/ZxBw8AJ3HgnLRNM4mkYeJkjF9PpMZ8/OdSPVg/Z4/TcSLXpbIEyiI0DqGkUl1aWNhQimjNBHelwtGooKCiqCYIqh8Ti/Bvtb+PXFVuwry5WmnMrxz0HEOUYmbhTG1uvXrve4NpNYtTLpX802173MkPj444E81m6/Qa39zcbB/0i6I4f+ZCkRtNyVxz/vy5CxuLp+vhciNaOjjs+r7PGSGEuY5nn3ifO4QUYP0XBcZeN000/ikeE9hsh02TUkoZoYSi73NCxsRQxijUBIhBw8ryQcfzdK3UWhubWxm7p2SqPkAZGGM0amoMZYzZeUJRSzs5gRCCxiBQBG0MTlMujzyIJzFbkRPaojxPU0oZo2NisskBcWEZroAAMKIdCpQz15Bh//GpNkKQAmEMrXUxxkhVGCR5bpQCWWgwYDmhCTA0iACE4VgYjgGl1LoOaIhWKKWFlphp9tvYuB80IlIOrKScc8YJcR4cxixqSZDxQ+W6zPdd3/eFEIwTnOkwni55iKi1taNaSpukQKMJABpl9Nhgo0ECZkzmmiS2u2nqR7OJ6KHBB+woD5Y/hJIQRggSSlETxPGuKQ2NloJ7fuA5DvMcryiIw7gBQSb1i+nchpk2LTORM7EAaTLDDjZGSiNOkVn2CzZZXalURqPRwsLc0tL8WAGM0m9+85vVeuPnf+Hf/0t/8c/9N//1f/PGG29UwtpLL7/08kuvvvGdN5JRdnfz9uuvfpYR51/9T//jL/2X/7DRrH/pS1/61/+v333r3e//jb/xNy49canT6fzP/+9fbTabL7300vrG+kcffvTr/9/ffOap5zbWTj3/3ItvfPt7RVH8+T/3F6oNFxF/+7d+96d/+ktJkrz22uubd7eMhj/zZ/6MJZ3++OOPT506JaUMw/Do6CiKomn1mlI6NzfnOM6dO3c+/+nPXL165drNy/Pz9ddeeUVwr3f88XDQB0Gef/mFy5cvX7728YULF37qZ34aEd98882N0y9UwuqgM0KtKC2zvBdEc4xheULo5J4gQmCmKmHwUKTIYFLTna2S2nKRzRUDMobEEEKRIFEnqHmeZDC0fqjd0f6IEIIn9PEbfHxt+EHR80fywLNL9vT9I6nX6fdPMqg/bmT2iJ86PU5C6PQIZxecB8xcOPP6pxrvx45pmxM+qrj6b8lA/MjxP/qn/aSUzPcblAah7yZJYoypVRrLK+c6neNmixOghIpKZcFxPakoGjgBe3dyRuSk8/qRmzg2wO1Ob1i0X3/+xY1y6dqtjw+7R81WIzdZr5dmWYY2t0sBzQNShcfu4P0fXq7X61nWP9g5Wl1dXZ5f9YQ/7I0Odo+q1cb83LLneWVa1Crz62un5xqLvVHou57WGo3SGhihjsMCNyzKGBHREI1G6wcqQsI5qQZgSzgEGGEMBONcUEopFxoAtDZKl1ZhghnGOS8oTg0wnXARz15N8vBANBqlkcpQAoRTIpAYMMaWB4yZruM2G4zTGvysQAdMVDv+3YfghnPqOGOpnClaOymT6XFSRoWgLnUIIcP+SY38Vj+XWSUpADQKAEiaStu+xRhzXUoAZYllWTKhLWkOJZN6LhAD41XJGpXppQMArcEY1KgNKqII59QAcrBt0g+8T2NsMIoc0fc93/cdh077qwHA3qOx00MpTtYpY2wGe/xVowkiGiATi2qFnibhA5I4ldOS6sRxoWgRerPrxeRW27CVEAZIDTGmtLvTw0GvyF3OQRZ5lmUOR4qgQbvumCLxkTjJmlXbBzwbM03Pxeodua5ro95pn6hFcgkhKpWK7/uNRsMVopT5/fv35+bmPvGJ12/fu5Om6e7u7nPPPSeEyPNcltIYs7S09K1vfWuUH37ylc+MBsknXvvE+fPnL1+5XHVbZVm22+1/8S/+xa/8P39lfn7+3XffTdN0OBz+k//bP/n6H379y//6d1Y35v/BP/gHCwsLv/7rv14UxRe/+MW/93f+/le/+tX//v/xr1566aWf+ZmfeeXlV37pl36p0+n8/C/8zKc//ekbN27803/6T1955ZVf+IVfaDQab7/9dn84/OIXv3j69OnvfOc7v/qrv/rEE088//zzp06d+vKXv3zx4oUvfOELvu87jvNHX/9G6NVee+21TloMshEPnBeefdIyVN+9e/fl11+NO87qYm1//9rcXGXt1Oq9XeQCR8nQ4f7j58UJGSOJDwhhYMbeMPL47gBVlgDAGBpCjEbBATRQCnE8FZAfr5LjdeCEeWpZeR75CfxpkdCs0Mv4m4QQgEft7qM/e3jhnqZ8pz+Z2MXH//ykLc8czINTgIlYxeznj9jjx278Ee8BEU9Ezf2p40eP9sd1IMwjEHQY07OYEozGPDeMgjHCEczz60HYHKS9wK/KUhlFo7BGiCgLSSk7iVL05PakfztaftaR4gThqXMvNMTK7Y9/cHR8dPbZtSou3vjOQXd/r1qt+oEvpZSFtMsHEIyq1aIoHMd55plnkiS5fPkyBY9oHpDmF17/s4N4sLV9LyvS4TCNk8yAyYdokrTpwOq51bXFFQbO0xfPxqO4XonyIlNADXU1Uo7aEAllxxHUcZ1SFoLRas0tiryUeVQNyuTx9953H7GYNhml8wwAAJEijlN8EhHAFC6dyd48qBsZwOlyjIhGjbuSVGEz0g4hJE4sJJkSwgqp4CG2Hit+APHg8ZEo4+VJN8YeDaWGUQCCghFCSOhVgIIQggtKCCAaA8oYE7AAEbVBYoiSRFoMGoCSts1mGkcSmyN1XG17lwoFZc6n3n0uOUzSAMKhXGBZllLqtISytAnfhwQ+Td7QiFoTY7RBBYBANQC4fEQepGSJoqTIDEDpzVMuhCMeAH2toYpqoTGGUk0I8T3hOIwQgmCmDdlWhMfaY0opUEnAADWAFJUxElATpUkBDiIaPaH7YIRRRikFFTM+tuKUGouCJoQ43C+KIk5zrYxBAsAJAQSMapoQAUBlwYoc84xrxQGokglqNuqZGzcPomgFKBgmQevBqF2tVgFAKU0IGQ6TMAzjOAkrUV6WQRDmeU65owxUao04jmWZep5njEZCSpX3hxoRlWGFZMYYaUycjk5vbCwstAzqi/4pWimcyJFSdo+P/FXxyqdO/dwvfOro6OiH7353ZWXl9U+/1Gq1hsPh1vbN3YOt51964oWn//qVjz+uNaLP/pnX61H9m1/9zoVzT/zcz/9MVKlyznfuH8zNzf2Tf/J//+/+u3++uLi4ee/Ol/7iz0lQ3//OHxs1KrLuf/5//j/+i1/5H3740eWk7Lzy+jM/decz3/3ud19sP6npk5//6de//OUv/9ZX/vDSMy8FlfnG3NrvffWNc+dfvHTpUpa6/9d//N//7ld+Z2d75HnzN27vDhJVX1idm2tduXPYHul3P978D/7yX9bSOXf2E7/1m7+ZpOzFT7z4R3/0R4SQC2cvDrrDve392zfu5EnxyosvDvOecSvnnnqOMxa09+pVWKjAQacMggAAEDHPc8ZYo9FIkqRSMYSQoih836eUWm8GAJRmUso0TTnnvu/PGFo0RhdFYRu9pJS2Ql8qsDUCg6iUIcz1fI9S6qtDq0XBGLWiEc1mM89z33PSNFVaOa5gjBVlJnVBCIlc4MyhNBglZjSQBn1KvDQrdamq1epgMAjDsZC2hekp9DgFQ2VZ5pSg67qgoSgKTqPZjDdMKh2WdgYRAR4kThCRI6GU5nlhO9mU0p7n5Xmucko5A4BClkio6wkkWJalL5zpNbHHY31HW+qaCcDHpiLL9BRrYhsw7G+l4r7v51nmuq5WJadUKxUFQT8dCSEIgL3CY6o7YwwWUkqLkLAMrJzzsiyZQ61tJsjGQg5IELEo83F6iSJhqLXWRhJCGBvbHcvqKoRI05QxpiYJj1kjh4g+q0gpKWOU0qn8mud5yGJKUXBmTO76nHPWG3aExzyuGKRu4GS5oowWhQLGlClPEJcCe9l/dOAJNfgZg42zYTuvhJWP3r3y8fvX7u/da2y4FafBMmfYjaMomnr0nudNV0agzP5Xu93WWi8uLtbr9TiO9zujN998U4NuzTejWqiNUlqmeZKLXo70KHEPR+uFzqpOJLP8eKdHm7nntQipq1IqaXwPAh98n4FRR8e7juO0FlqT7GtQFmhO8KRKOREsnDXCAEXxoKY4axHVCSgFPbZbY+jQ1GCYcpzCemT7J6ExLWHTIy4hnIgVAAoMiIFJv5BgnHOglArHJp+tniy1pM3GGJWPBYIMGm3AAEGwzTkPnakdlFIrXG/hTjCJ2xCxLB+IFNlZbWkl8mKcEEN8kDslhBRZf5KlNxMkNqGUNprRxNo9xHgQhMTigYUQsyh6+yAiAo7hb8AYe4BZfShCBQBwHUdJBKFQ2UAYFKAgmJc2GQ5gC3to0IBBQwkdQy8IRSA4ViUfd9AxyhXI8VMBQIBJiYzihGyBEKINotHg+w4hZDAaFEXR6/U8T6yuriqZB5EAgDRNhRA2H+u67uLiYqmkXbCs62BpsJRSvu8BgJX8E0L4vj+lwTKoXYeHYSBcXhSFVCVBLEvwfb9ara6urvb7fUrp0tJSmqaDXpzn+YULFzzPe/vtty0V19ra2sLi3Ob9m8KreL7X6/f2Du/HcXx0dFRv+j/3cz/3z/7ZP3vm2aeu37j2S//o/+S67n/1X/3yX//Fvy0c+s/+6//2m9/4xr3tPT8M/8p/8PMvv/Lqr/5/fu3ll18+febcT/30lwaDwdf+4Ouf/exns7zkXjQcDhu1xpf+7M+88nI7CILbN++urq2+894PwzDMi6zRanz2s5/NZYZo0jT99Gc/jYjXrlz+5p98Q3D35Rdf/ImfeN0WGubm5j788MO9vb3nnnvu4sWLQoiPPvrIvPjC2voKFaLX627e3Tk+7K2vxk9cfG7JCe2VdBynLIUxxvOBcacSsOFwWK8Gvu9tbW1RSpcWTo9GoyRLASCMuJRyOIodx7Hk1UWmrPXlIkBAygyCdlwGVDkOIILjuPamFEXOKGvNNS03EdUUCAqHF2WeZkmZ6+WVJcdxjjvHUiVB4EkJg8GgvtQaDpOyNJwJKjQjQCkIA0RqxlSl4jgO6fV6nuf5vluW5Vh/l3Df8Q1orZEB9f0gyzL6MDkrIcQY47q+tR+zthkAkFKkBCkxBCilgNQQAEaDyDP24WZCKqVR2URummU2dzU2k2Vpywd58RCXNUySvVw446zbuL5LrDGTSmdZliRJWZaeK1w/SJKkKArh+0mSU0odx0HUeV7a1cN1IuCCEgpImO2o1ISAo0ttpzsgAGgyYfhBA7a5c3wwYACYQSTMGGOmrjlM+voCTmcXjel7AwVhqpBSCEEZJagopcokViZyuu7ZyMFxnLzIEFEIIbUGoIwDp4JRrnR6wjp/UkT+46XcuUnpvRs7WZYVZRFVl5IDDWmOMbt48WKv1+v3+8YYS3hrT3uUpFJKKaVlMQyCwC43iHh4cGRQG9BcMAMmDMMkSSilzcZcs9mIi8FHN98Nea17ODzcO+Y97/TKUwvz1ciNjAAmCm0GnUF35/7W4eHh+trpeqsGAIjE9yt5WZbpSamAh9oGYPIwFelDqLwHNyk/AUwxplCYqHFN6nlTTolHdqFPSDXM2kCYBLgEgJ0kyEMBDQGCWhMhCKWUUE4Y4UwxhlaGjxBj67eEYIHaPrQKbfMsMYCIxJZAcUbI04zVXmHqc83a5qLQU/s6rUcaY6gR4/oUjuvWdh66bjE2tIxzQblDbYK3Xn0oWUcn3KXCNYwxJijlhFKCCEApMVCWcrq4SGkANOec8zE5xtROT28cpZQxVIpQaghBjQoRlDHGqJk7gtMnweXcom3QEEIpmnFKJJNy0pVApTQGNSCl1CgJyIAS22RsEDQhBgHiuJifb1y9euP4aN/zRBRFS4tzUsrBsGPZPcMwjOO4VqsVRVGv1wejofW1syxjjNnZ4bqu44x7zaeuiT0vpSWj4IXB/HyzXq1qowhlnuc9/fS5sizv3r2rlHr66aeNMe+8806e5xcvPpFlWZZlt27dOn/+vF0sKKXf/f63G3OVrEhLXRhQB0f7siGfeOKJ0Pd/4zf+l5deeWIw7Jw7vyLL0ebW1Zc/8dTu3o1Gs5Lm+RNPPz8cDvf2t5dWWkXZuXbt5tzc4vFx9/XXF9vt3q1bdz/3uZ/MsnKhEf3wh++dPn1auO65cxeOjzr379+/+MSlb37nW69/8pMN1gTafOrZZ/uj3gsvv7S2vkqBI+LVpy4mw+H9rXsXnzh/8eLF/f399y9/tLC0/LLjEsa3tnc2NjZW1zfa3d697ftE0Lm51lH7sDfqpGW6s3fv9JnTpUza7Xav18uyDADq9Xqj0QAAVQR5nmcZ7XQ6b731FqX09ddfr1arw6To9Xq+79ugbW5urtPuX758+dTG6bt374ZheObMmY2NjUoUDYZDYwaex6yMVRiGlcoyIbooYsYcpShjrNHwlVIAjFJaFHkYMs/1t7bv5nlerVWqtQhRKZM5Htk7utfrDTjzG82VqOZlaUxoWWt6qPw0HZaypMwbxUeePx9VgoODATWKICWEuK6rFGZZxh3GORcOn7KFIyLnzBijpMIyBQBCwLYJwOQhLoqSMVYqqczY1SuV1FqHHsuKhDEGhGlTYAGcc4IaOEdlDEFDENFIowghQnBUhJAxHHyyYCIiloWmdFoGGldYKKWcC0JIrVolhKBRSZLs7e0FvjfnnpIlMEYE52iI4JxSaqVfAIiSetZG2i3BBIKKs70Vk1KCDdApYQCgjARAxqgQnHNmw2ibEnCImi53iGjQjGVnBTgOjWPpupxzUpboOHZWMkInhSEKCIZQEA5nFOznlGgDyCglBKWU9N/W1/sj//H4j08aHDKOGiusXguY7MGN97aE9tOeWt+oWqpb68vb8pXjOBohz/MsyzzP01qPRqOyLKWUnuOPAzMNcZ4olI7jVKKq6cMLz//ExafOd/rt9279IM6HQcWrrkVz7EzYIp5X5nGmpDaY9oabx/17BGhY4425wHVZmpajUSmYkUoROCHk/xGy7HGEWjJ42ABPvv94Q2j7tmGCp0AERGoMUHiICm76yk4Ag9i2wmnNf/wh4okobqTj6iVFohhjQBEYUOvzaqMpI4QQoGjQomqpnjiOBhERtOXgmxjLWStLCCnKhzhaZ5zoMccsTBCPaAFYQk+v52S2IyEkCn0kyBijjNhGWyYIpdRhcupqAMCYPhpAgtJotDYM1NTAI6JRNts86boYg1amuoRm1gAbY1CDUtoGy0orK8GBiNTOlolRM5MhZ6g6yUx9DjQgWO5uOoHKEUKoklwrAFDW9SYEuUDGMfDCZr1288bVTufo4vlzSsvRqC+lTJLEzgirpLuwsGC1eG1sYQ+bc27DXCFEWaae59kQcPqh7/tocs/zwtCrV6uOK5QyqIUNuxuNxmg0unTpUhRF+/v7ly5dWl9f95wwz3ObkZqfn6/X67u7u/V6vVrzF5cWg8hLslEQeT//l3++UW2+9onXiiTlrllbW3viyYtJOrpy5Qrj+Lf/o/99fzhot4vf//1v/M2/+Xeffvrpr3/9d/qD3evXP/xP/u7fP3369Icffnh81F9e2vjZv7BGwKlWWs8///za6jpj7NqVa+koPzg4aB91Ln/08fq5U08886Tnu/uHex/fuDxKR+1+J6gFQRC4rvu5n/xMp90djPphtdJoNJpz9aBWQUTHceI4npubu3Tp0le+8pXNzc39I+f+3n0/9OuN2urGuu8PDnePb925vLi0rlQ3z4/TNImiqNWab7Xcoijax1ue5/U6PUT8iU8/nyRJb7DT7WNRqLIsl5cuAZjDw0Otuu12++jwlsPzw4N7Fy5c8L2y39vq90BrXavVDg4629vb7Xa7Xq8nySmb1UN0OsepzWB3u91arVar1bIsazab9zZvv/fee0Hov/Lqy+moGCWjWr2ytjq/eX/7qLPT6w5Pnx6dv/AUF9jpdBE9I3E06jmOUxT06tUfLCwsfO5znzt/fv7O3Q7nLqfCcYTDBaLhztiWWIDedPbZeWS7QCdIhQeAFcaJEAyIg4i270BrLRzGeKlNzoXn+Q5QhoYI1zHGAGXG8GkY7XpWfYsWij68WI3nC6VjJvPp5LKflzIBgCgMCODm5v3rV6/cuHblzJkzf/EvXzSoAABBK10KIQjFvEgrYYUQIqUxRk/Ogtp5MV2xLY0Z5YRSahQiImMCkRo9xieWZYmEIAoyhrkhpdrOfTbRIUakWmuK47mfauM4DhWCCoGESGM4IQrRd2zqjsC4WVkTgpwTQKVVWRQZGsMYB4ZKKSUlP0GY/UQD/KeqMP3o4IGoZmpYGsUMM8bs5Icu8eXQ3dzcjOPYzhnGmDW6jLFKrS6ltPa4LMs8z9M0BQBfeJlMucNWFldA4M1bN/b3DhYW519++pMXN56shEGm41A6WsKwOOgMCrcapPqwzlrEVWhMprrdeG9r9/qF8xfX1laac7WsGJUlA8MKhRS8XJ5Q9KYPMddM36N6SK9x+oaeqHw9A9+zKkdgebE4TN2aMfwBAKwY8WMGjsE+ADONgIQQlCegIsEANRSAIijQpCSEaGNQFNrqBDBGgAKZGFZlEDUxgGioUaCt12MeuJB0Qik3DigNmR7SJLEMAOA6D5itYMa3cH05G9FO/8t3CCEUCFBKCDVAStREG6PhwXUen6nVvs0VmaT0p841Ik6E0ymlFCkSQrUe+9rTVNs0NAcAaghFpAQZJdzeCovDVGgn0iS/DVqjBlOUD+HpZm6vXb+o0tpoMMaiy6jREzEo0JQQxsf9RevLyzdv3vzgg3c55+sba8fHB/v7+1JmWV7a6qN1FGx5ezAYJFlqZZHKsrSWeNbdsRxYiOh5HiHE930jWaUWeY5jUKfpSBYlEk2pWV5ePjg4+O53v/vJT34ySRJCyNra2u/+7u++/tqnbUmyWq1+5zvfefXVV9vttq23feMb3/jpL31Ra723t/cf/rW/Nhok777z7u793T/3M3+WUrp59/697c1P/cRPfOrTn/nud7+7cebM0fFeFNbv3d169eVPfOHzP729e7XbXcFy3guihaWVd955p1qtNhqNH777vh9WnnvupUajceXjK3fvbne7XSrr9igAAQAASURBVK2hWmvcvr3JI9Ef9ALtX7957c7m7Vt3bnx8/aOVlRUn4oEX/ty/9xcrQVUaPUpjgzpLc8cLRqNREHmLyzXG2PWbt+9sbm2cPjvKu8wX3WGv0qzUGiHnpFLxGrVKrelJ4yKtXGhsNBoN3/dd1+12841Ti57nuR5EUXTmzJnRaHR0dKS11gZ6vd7axvxwOIxTPjdXX1ppPPn02atXrhJeuj7OLUR5nsdx7DiO6+Pzz58PAgxDKMuy39/lfG5ubo5zvbN7e2FhwXVd4ej5+UgIurO72+vv723fiyrs2WcveR689cNvJ0ly8YkLw9FxWvaWlupFEW/eu2pM7npRmalGq3Vv8/79+/eXlpYuXLjQbHLHyYv8KM+wVEXgzxGkFCRQxjkXnKXlCCW1KRylJAAwRgkhnDPXdaZzBxEpGa9CBvTUOFmsoVIQBB5jRagpgOZcuYJJpVCXSqLC1CafLQiTMwagZVlQ6k03Pp3ChBBH2BZ2hYgEkE8CwUrF29vbax/eCwJ/NDxMk+NKxJ55+myWHo+GA8/zWq0WIV5RFJTScKliM21ANEek1DDGOGeUQr0WAUFr3KUuEA3jhAk0khoDQoBRkGWlIwinoGVGOLEt6TAJjgWnhJA4G2NCDRoNGidQHlVYprmIEK6UMoZrzQAcR7DJQj4u2wEgpSQvcyVz13WRUEKM0gaBeZ6Tl8Vj1+3ZteWhz39M0BkHakpZMGCCBKZEBZpwBSCOjo7ohC0PAJRSUsqyLA0QREsjoo0xnudZ6fIszRnnqlTdTq8+V3Mdb9ge9rr96DU2op1h91BD6XpOhde63e5glGwOriElgvuVYA1R5UlvmO0byLr9g3qz2h+24/6+y5uc1stCC07xBIFNhg+ZkAefP0waPv1PQx5PVk4eacBHHBcPH97sgyziCUQBiHSGgXHc/4BwomdkCBI9bkUiBJTWmCMhhFtFZmYNHhIKAAQI6nKMDUYDShuDBDUxMzTfUys4e8Czf9rBmZl61jBjQT3/gfGbbgcn9FWWuwDRGD0OvwGc2V0zxigipeOUOPyIlgin1iZRxtgDGDSi1sqCDMyEQnl8DMAoAKXUdZBSoZEjogHIswlKC2BigEFrKIoZTdDZ1gUqEFEbpRUqhcYQxoimxhTM0oQQYhyXui5zPcEFNbr842/84c79rSeevFQUqZQZotRK2kuhtU6SREoZxzEApGlqYfAWMDGlwVJKVSp1m4uzs8nzPFvBIUaCMVKVxkijtDGl4wrKSLVavXbt2vnz563cb6PRuH79er1eb7fb9s8PPvigXq/fuXMniqIsyw72jqqV6u7O3rPPPdPpdA72D7U0X/7Xv/WNP/zehx/c+E//s7//n//D/3Jvf/c/+j/84qc//el//s9/pd5Y8tzw2Wc/MddovffDdxdXmtc+vt3pHg36u0KILMtGSR6nxb37u2VZzs/PHx23KeNXr94wBtEQLtyo6nSOu27gehV3e2/7/Y/e7w47WZl3eu1qs7K/v8eBzS+1XnnulYWV+ahWrQShNj1g5Nr163/0jW/85E/+5EsvvXT9+nVCaaVSaWeHlaCa6nhr9+5xZ68WVZ9+4pmVtXlVmm738MaNGxcuXDCmSJJkcXFRKXXQHWVZ5rpuEPo3bl5PksT3fSllksaDYf+47XY6nSzPpAqyLAvDsNGsGFP0B23G0fVYmplK1afMHOxvVSvuM09fsJVRC5UaDoef/MRz0zSM5yJj+tyZZcdxlhcjz/PmF1qbW5u7u5ujeKgx0Vr/xKefW99YazQq9zZ3OS1drryaEwak2XI2t4ZKe5eeWD17bsHWa65fv84ECytNXRpBTZrJUmYi8MEUnITWt3fGKgXGIFJOXIfZljYAFIIzNm6IkDL3XKa1LrJC6sJOotCvBRXmuDRNcq1zLhxCSJ5LNCbwxbirwXEmW5BgDOfjTOHs6gEArkPLUo0RVfwBsw0luVG9vb07q8uLcw3ea7lGQhLvv/3W7U6n02g0nn/++SiK9vb2hBDnzp1zg0BKaUvyUkrGiBC0LMfgJgOyKDMkiVJSAzIkXPgEkQoPCGWq8IOa5/qEwP7+/UajYWkDytIkSUrGNXKPMWbxXvZo7SoUeC6nhfCJUjlQXY0YpdpzGAWjtTY2bQAAqAGREOoIDsQwioQwRJWnGWVOGIYmP6m75PHrOf1xa8ClzjWTricC4hYZNVAi0Y4HEqhtkLA1Cbt8GGPSNLWrjFUXtzQCdqUO/DDL03v37oXtwKu4p06dmpube+fKD87lZ2q1GuHk4GA/LzLGvGcvvNTr7Hb7ux9u/mCxcdoj4WBwPBoeAdEfXXlPOHxhQYKKONGucAI/cng4KrPHn8FEduoRe2geVuF4YDhPQtJPsqCzwS4AAHtow9MuwxMy0EDMAyrzh/Z+Epm7VQmkBKgBIEZrrSUBGMWEEKR83IlrjThhIMZyAqg0Kg1om7UArJAEPFzoRURCDSASMGOXYjIYU5yDEJTSB8oBhBBUY2lyMkY/jjeoHEIIsYoLFtxk95flD7XcMIacEyswCDMTe3odqMWIIAEAMelvRkRjxqxetlyK07KQ1hrG+WImxgBnoIbgw0xbFDQF5DTlD+13+kYIUUqpFUpplDEELQuupsCUNgBGOMA5dz3h+4IL+r03vnft6pVqrSIE3bxzy4Ap8jTPcyb8abrbcZzRaGRtKhNca+15HmPMVmcopYPBwB6e1QaWUiKi1ppzHvpUGyWoEMxBR2sF1aiyurpkY+izZ8/2+/2VlRXHcT788MNXX3317u2ter1erVa73e7FixcvX768sbERRZFg3pMXn9zauTMaJO/+8P1vf+vbTz/xfCWsP/nkC7dv7//6l7/23HOv7+599Xd/+5uc1+rVNc+b4wTu3tgMiHd8fLywsnxve9PxA8G8JEkQsVKpGGOIw+q1ptba931V6v39fTTQ7w3KslSljuMYBD1oH/3JG99656P38iIFh7aW5pFBVA8A4eNrl8+fu0AEubt55/XXPjU/N//9H7xrtRfjOOacR1FUluVwOMxksXu443q8N+hsbQ3WV9aWFlv1enTx4ovtfuetd98+7BwhA0rp+pkNSulXfusPtra2nnzyyc9cuMQY6/V6g8Hg7bffvnbjyvnz519cXiacd7vd7mDw0UcfRVFUjeoaCVBugN7Z3Lpz584nPvGJ9fVG4LmEkKtXr965c2d1dfXSpUsW+7a/d/zxxx+HYfjCCy+UMsmGGQDcvrPjOHx+fn53L97d26rVw/nF+sbG+sLCwvJKLR4NXE5feO5JAlQrQwijlHacslHnvq+l7JVlKSVxXbdSIQnmPNCUKFTlYNTvDLqLXlN4bLA/sE+UbbC2giVCCPsGJ/psxpgkSfI8X1msCm6SIu2197Iss4uwy40XVbmj824/SWSt2nLdgAAlhESR0+124zi21jfLMvscNpucTgbAuIfeGGO0AVSCg+M4jiOMMXGcZGky36rWKrB554MP3+s7nCbxqCiKXve+wjRNU8/z0tGdIAh2dnZc183ilxZOXbQgiTAMh8OhXSW63W466gOAgTIv00xlSpWEGspJLWoB0IpXEdyhKFx3o+4vGiwP3r1a5k1bBrVFz2az6XlezWOMWbCLLLCwVR5GmeDDJEk453mSOI7TbLWsJnc/De0ltT6BValBgoziYDDI0iKq1D2vCqC1KspSkJPsxUkg3JMoP0/4Pnc8DoXJZSYgV9IgQ2U06NyAtnk2ywlgsf5SyiiqWFiErWkVRUEIsWRG9olxjZskCXXJiy+98PnPf/53fv/L7ePe5uZWtRExRr3IQ9QGtSyz7mDYSRPNyHx1IcVhWsQySer1aH5p7tKlS0xVR20sUo6GaKlP6q+aCmA+coZIH0L34YQ31ZzETUonmDzyULw46dV7eOMntoUDxYd+YreAOCYEeMx+CRJCCCWUMEI0IhgNBlUpLaPsuMfGsmEQilXuWgOsNTEaENG+qpkjnCY/EdEyFJAJ9J0AUEIIEIMFECSUUEunMFZzIyDHDBowwV8YYxAh0yUhhDBCx9UoQKIBIM8eyIdZYV3NDaUgrHzl5DCmIbXrTjwY9gBebr84jTymNw4Rx4rFFAkhhFGg9mowz3uQ1jbGtl1RRBT8IRm16eBCaA0wxm8BEGO/QhCMQSDEpYRzzgXlgjoO/fKv/3q7fbS0tDQcDrd3t0+dOkUpdV03L1VZlrYMbK1IvV7Xtp9dKVsJppN2+aIoBoOhnSmc8zzPrZH2fb/V8jilwuVB4IIxcTKKqsHc3JyFOttY2Soi/Pk//+fjOH7hhReiKEqS5K/+1b96eHj4pS99qSgKRDy3cWln9/7zz7zse86bP3jzh2++H/fkX/nLvzDsv/Hhhx/+4de++bM/+xdOr90+ODoA4/7dv/Offv2P37p782rUCr76e//m85//fPvgME8Nd7xS6zCsOo7T6XTiOF5cXHQcP8uyubkFIYRwnTQrDo+OW805A5pyLhx+69at7/7gu71+1/c94XAA841v/vGl5880as37+zvaqP39/a///tdHw/j8uQu/9/tfNcZ0Op1f/83fGMajCxcuxGkyPz+fy/zmrRvr60uMGENULrMPPvpwb2/3uedeX15edhyn1+shYhAEeZ4fHh6urq11ul2bVSKUCscJo6jRbCKiBYoSQlZXV13X3dvbI4RUKpUwDG2bdRRFYRieOnXq9OnTDqeEkBu3bmdFubq+8dwLL9oKSBRWv/nNb0RRdPr0Rr1et1W2O3duHR62X/vki5VK5fBopywTpSii3NhY2bp/6+bNmxcvXFxbXf/ggw+uXL5y4fyll156iVATVXzPFwgKQUmp0mx03D7Y6Q444SbzUXl37uxu7d4/XawtrM1t3uwgYr1eX15ejqJIKkkpZdwxUjFGLB+SMWY4HOzu7rbbbZ+fQ8R2u/3xlSvdbtd2+LSPLpzLT4Ghuzv78ShfWz3XqC8Yja7j7ff3r127trOzAwBKqX6/TwhpNpuf+8KftTBgCwGzCHCt9fHRtgU61Go13/fTNN3f3z86OrpxfdhsNo2OtRpK4J5LfMeT+ag0g8Bzy3Jw9/Zl++jWarXR4GAeVg3mjhsFIaRZaaPPJD3+4VvfpZwY0HmZxuVQmpIJygVbWVhHjZWg6jKPgZOl/TRZybOi392Ph8f9fj/LMillvV5/6aWXauvrg84Wm3DLZ1lmm5QopY5DDw4OGGODwaBWq0Xu6dFo5DiOok9bA2yBTdN1aTQa3b+/hYaub5xZP1WPokhO+mseu26bEzA9P7YBTpM4oBGlVJrEMEMUEURwhxjtokYAIphLCAFDCSGu4LqUnnDAYJ6kWinOuSec0PO1hBLjjA35vNYmefqVJyrz9Ns//IP1i8tXrl6tLFT8wHdd1/e8vf39/p1NleVe5LiOUWkvKdiZpQtzF1+9effG7Su3dm92zzR5sz4vHYOoRlkMdFQVHgBYSQNjqLVIFFhhOfgBKDUASAmh1ACAkicYvJMsJzFkpmw7/djnxY+GcYQQaQ0SIWSGF4kQEriW+JCgsTgpAoZqxErwgNt5xrBhVsw4FoQQAM4AgJuSA4AlAzPqwf3bK+33p8lhsKaVcSBAjUEAakUpLFCLAsOHsFeTmm4YFQUpCphkidnkUXs8Fys3BWNMgOCE2eSPpSLRRky3CRqkIgAGwNSCyMKmlCoR0YAhFAkBDYQx4nCmDRit0qxkFCmBPHMREIAb1EAMogRqACDw+XT7xBiCxDYy0XFGAQEQiKEMrY8SVUvUZHx1DUFEChQAciJzk2WFRuWgpkohUGCcU1JIVTCKnlvnFEIvKov01371X4+G7bm5BiFGKTU3t1SWxg/qUkqnUlBKrZB2XPQQsTPMhRBxJ2OMJaPYhrAA4HneXLNV5t7+/r7rMGFMrnNdkLVT841WTVMz6Aw9VTnYPiLUJMnx+qnGpz/zdFid6/cGN29t3rh+I40LLTGJ01qttrCw8uxzz77/3gcHBwfLy8txHCuloiiiZvhn//xnpDmoV8/89b/yV4+2+qWUX/43v/6ln/737u5cPji61Ym3/pO//x8urywareLkWKmR8Dl6zsr58wnisMjml+fu3bunFX7xi18cDodbW3d3d3cvXjwnBM9zQyTrDwado05/0Ften8uLrLbooedsHd7b7+11Bp04iYNKmGellPLC2ScHgz6lWRRV/vW/+Y0syxzH+doHfwgf/OFHH3/oClflUilDvic3dy9ceOr05u07C/XmoR+ixFSWSSZ151i43F+o3b5/5/bdG+GCl2RJX2sE9cbb30ZDQxIsLNSzbECJvPLx5SeeeGJ/7xCxEIE47h/f37+/sLAQhmGn0yEO0Vo361WCGrWMh/1LF84d7O1kychzPalklmYrKxv7+8dFro1iBJhDvcglayvn99u7mpFMy8CPUHvPPvHanTuXA14XxE1GuZbGr/jNRqssS4ct7O2+p83OM89/0g2bh93kNDoLS+eOfvjtd25+7Pv+4tlTr7z8auAGt+7e3v7ooz/83tc5E3ONBUGdvb29/qh3Nw6qtyqYOoP+kFPxMz/9F1ZPv3jlg6tbmzu1Wu3iE+d6vd7h4VG1WvEq7sHRzvXNjz++cfmD+xvzcwudTodTfjQ4brePQ78i5gbtD67f39taXJ3zfG90eP2T858h2v/un7xnFnp3du6MRiPXdbXWEEKj0SCVdPPwq7KQWiIj/Hi/3W0PXOHKUu23t0+fPl2v14uisHmXWq22t7d30NuqVquSFoqNFldWHeHeu3uPOjQkLalK7nDCTKIz4xoT6P3R3ro+f3C0e9S+d+bMmUql0j8+TtO01ghe/9kXv/71r1sAow3wVKYggzIaOtSVdD70qzLT+3fuySs6HiVVvcwYK7KCCZapNPA91chvxVfVcaCN6g97QGxWmaKGo8Nj8HUrqtJB3j8+qHl45mxzJzWdYhCZq+nRURKnkbdSqy519vt79w85cWT80VxtpT0aNuei9fVTncMyEHMyI5lSSDDJRhqV8HhepN1+u5R5Jeyura0lSbK1tVWWpT2F+fl5jq/axCMiI0gpOKiFUZhg18qd2WSYLUkopfg01Jhgu8Eur4gEHw6qyIQnASdidtblVEoNh0MCFD3ZWmgtn5kbJJ1Bfxj3bnMi5haeOXv6PCHk6PjwqH2IYLIsW1iYLzCL81Qrfbb59IuXXqtXGofD3W568PnPfqESNpvNplEmyzKpQXDu+AJmwDUTmBEgQTCAxDDbx2nLgSfRof6p4wRoFkwuyYNN2suiJ+i7WRcJJ/zSlFIzkwWFiezX5GtgxqEpWNb+2S1PAsRpGG3fjH87w8mDM68WwT+GcM+OWe/hkRs6vafTT8hM0Xf2+4hoNAOkgGA0AoxZwIx5UGedbsq+SdPCGn5tEAEJJRSYFVCkCMYAMURJ4IYaYgBMUdrMhEHbskzGrmRZWDAXHWOeKbUukFYPgGYTfBkgImo6+QSNQYLEgCFAHd9hrMSxZSZIEA2CVlIaz6u4HisLHfhBPCrffvvtyx9diyohghnFQ8sC4VChtUrS2ADaZkSLq6KUZnHZL1IAA3LM7eA4ju/7hECaJ81qFRHjUd9o5bpurVYzxty4diNRKUM+P7eyu73n+YLSvCiCxcXFNM4/eO/Dm9dvdbt9wVzBnUFveHRwnKZlpVIZDAa9Xi9JEmvbhsPh6mIodXlqcfnevXuNRuNv/a2/9T//2q/eubf5W7/9mz/3c39hfn5+dX11fXW122u/+c47jJJWqzUYDDqdzvz8vOu61o8ZDAaeGxwcHJRlGYZhrVYbDoeMMSGEcPm1d6+1221N1OrqyoUnzh0c7v/e7/3eYdx2HKd73AvD0EiMB4nv+81Gy9GcM54M03iQWJYMQR2tNQUax7HHXRuS1hq1CxfPr6+s/tpv/1qtVsvLrN6ot+ZbRZk3m3Ujzfvvv9/rt9vtdiELS159f3NncX75f/ezvyCltHzanPN+v7+9s7OzsyNc1u4evffBO61W69SpU0IIAypORx9cfm95bfH555/zQocKOHfxTGuh0e0fFKg54aNiIEKuqNo+ut9qzFFKjwYHtYVqjP1+2lNUFqrIklKxMs6TXBcCRFSpNubmkiTeOzyst1q5TOYWm37FvXH72r3tu1E9yGX63R98+4fvv9MZtIf3R7Va7dTZU/NzC7c379zf3S7LMi3yPC5d4WVZZrRJRqnSctBPsyxjhL1/4+397v2bN27Fw2SuMd+WW8kobbfbnuc5Ps90ot1y7cLi8HBAGdnb3wcARvni8iLnPMni/e7OUeegIHGjWZ9vLgZNd6W+AQD/5q3/xYuEFzUtIqEsy96ovXt4vx55jnBLJctMxnKUmrgosjwr3IrnRG5tvr6zs7NztOv7flwmiupGo661kVpGlXB9fd32mh4cHABLFSpGSVgJXCaKMldEDfPBzc0bQoheu/fDD95mjC0vLwPA0dHR4pnm/MJclqd37tzxPM8PPEF5tVpNy54WxkKO8jIvcpmnxSiJ41H5j//xP/Yc7ytf/cr1m1dSFd/bvStNIQYNSni/39dSSynTPPV9n3HiBaxXHMjRaDA4Zl1zY/vK9qAdY9EQrTiJ0ywt94rBYEBKp1DZ1t69vCyAk6gR9NPje0c3VOFoDpryTGdhFMi8f9je9QIRVl0Jx9sH98IwVSxBxE681+v1AGB+fr4KbtjsFrlWGWEscERENBZFwjgNUCFKAqQScsY4ACBqADJVbUMhhDWoY8KNh5kjcMJba3unDAKbGGwLKqGE5UUSNJ3lheVVZ/nu3bubm/cYEcYw4QjH50KIJ08/yVxyb+uO1DmjImyERPO9zg6/8VHFrUssGbpzrSVXRIhUKWVpFBkwYui43WgSwFI2Xn8tb+I0ErTBugFQJ4CkTi6ePz405gynAe5kxQdjjDaPoq9hnIa1euCEUoJmTECJiDjbEjNOdU/7fB6T5WYzDDWzhpBKPrO3WcNMEJAABbvpcXcsmTDYTCzVZFiq3QdEFuONgDGPN8AEmQZQBAnRsxtU+iEDPNkvlKQYb4EgpUA5YYxQSgCp0cQ23DKKkqBGZIQUhQQAIFb+YZx7J4Roq5hEbIMTTqSXySzSe3pqiCitlrMCY9DoCRU+YD5Ki0wqpQhaMucxpZdBUZY2cSSrVb61tfsnf/LG/fuHUQRgn22tDCDmWRzH/X5fMC6EEEIwQmVR2nnBCCWOEUIg8jzPizKRSWYzY2nKPM8tS5FlZehH66vrnJN7o/tHw4OF5pIQIi1SDTwMwfPdpaWl/+F//M3d7f2iKJNh3usceJ5nlEnT9OVXP/Hhhx9evXrV+klJkly6dOmpp56qhPL73//+uQv//u27N3/lv/2fPvnaF15++eXjUefVV19+6ZWXfN/f3d3duX9/YWGh1Zzf2dmRcpyBPHPmTLvdtrW61dXV4SC28DFr3nx/XOr+yle+cv3GtbzIClWM4qE9qWqtendvWwihSqMdbB91O51utVo1CkSVAzCUWimlS9AAeSwRMQorslS2Q/rOnTv9Tl9K2ajWKSOBCKQuh4ORNgoJhmEYhqTT6dy8fWPvcAcoOI5DkOxs72VJ8btf/ap1EW5tbnY6nVqtdvny5f39fafK+v3+zv6253k7+9vVavXevXu9Xs/lYmVlZae9vdPebjQaWuvbO7f6/T7hpF5rXL97o9ft95POztG9c2fPu67ri8Bv8PbNw9/56m+6rru2vD7Xmg+D6Onnn46zOM5HtVb9yejp7Z3tTrf33e99n/m8n/ZKnv/R9+7ev3+/ElTLu/Ldy+/dvXeXEJLk8fXb1771xrcCN3j/gw8ODg7KQva7gzTOQq9Sq9UIIXmeKyM1KamD2pT3Dm/tdDZ7vR7nPE8HW9dvaGmKvPQ8j7sUmfYih7lEmnIQ94dJHwAqYRWpkxZJVmRhw6UuDpLeMOscHO9rrWt+6+D+UQ6J67u2DM8kxO1Be3CUpund3ZsrKyuu63mumA/qQcPRysRxfLB7WOjEDVljvtLplOcunorjmAh9eJSWsiy1BtD9eNicn3/pE68AkiRN9/f3O702EkM5Y74bVsJmq6FoEYRujUfDPBwMBseDAwCIy1G61V9YWJhbaHX7nbIsS1U4juN4otstwSW2yimVLrHUXNJAuw5Bv0CHenUWtrykGG4f3PMrztMXTp1aOrN5e/u9tz/sdrt+xalE3AjVNoOq64fLjteoleXg/RvvH4w6NHRv927lZcYY850QJNUpgBSC+pQ7/dGQ+LDd3TosjiO/VtNtShxSqjRz9vv7WwdbYeStOosFT6U4vj/a6+7se55XsrIIUkTsgTb9rPSQs4hypxmszzUaqsQ0M1HVO9w76Ha7WZZFUVSv122nog2FufXoER/0ohFCzMMGzGIFAcDxhJ2ZCpXRhhCCFKmgnDMscDAYHOwf1+cqaZIXmfQ9cfPa7agauqHwK65fcVbWF89fOqdBd64VfpONZGfr+FYvHjx/+pMXV57j6CSxNo7kNOMcHFcQBUoBShzbKoYTmJkeGwZrIQAAqYExExieDLY6CaN2Qr81MGIoGbecwkR7gBAjuHjEUOFD7bBkWgyw5sEAwiTiREAkY3YM8yM6x/Y7bAzWflRXWJRTtPaDVwDQBgkBAwYIEKtdgQiGPEKORSZtQna3P2qAtX5w2DBrgxUFAINojJ79r0LP9g3PxMFomyWM7RRiBhg3lEKWPrhKnIKZULcppWzsS9m4uYhyIASEEDNSxw/S49M66yOvhTLjFLS2Qh0akALgqEiMpJQyMERrrbRBohGMI1xlpCm177G9w/3vfPuPr9+4OTffUvrAdV03cLJMZ1la5FqD8kIqPOK6zHE4Itop5AkhhJAGEA0geo4Q2rqPwBjLy9ShTlDxh6Mek6TRapR5trGxsffBjvCEMdpxRVkWjgvzi60kid/45g+iKDp79mzVr6v8VrvdZozNzc3ZerNlHVpdXX311VeXlpZGo9Ef/P7v/r3/7BdLWbQ77SRJvva13//Zv/Tzv/zL/xeijDayyA0Yc+PWrSTJgiAYxunCwsra2hqldGNj4/LlywsLC41G4/j4uF5rPv/881LKN99800JU8jzf2dnZ3NxEMGmRSll2eu3vfu+Ne1v3jtqHutCgwJRm0BlYeEvgBGVaDtKsUqk4jgPKlKkqEikzzRhzfKKVMRwGgz5R2D46RjCXzl8AgE6vvb6+bml/FpcW6pXGM08+q3Ny49a1SqWSl/n+/n6j1gjDkFJ6a/MmY+zMmTPHx8d2OWv3j9MyuXdnj3MuhIizYZwNhRCj0SiO42oz2r2yfWXzsuM49Xp9OBz6vs85V0YvLCy0jzqHh4dlUfpecOrUqXq1sb29TQjZO9itHVaFEFt790I/opRCwgghGxsbYRgOh/2Pr1+L47gsS+2XysjWQuPw6GBra6tWrUd+lQJDioPhwHHdXBa//4e/b7QVfmCe41MSJ6NU5trzPCCY5hkrKHM5I7xU6f7BnhCiUq1E1bDT6TDGSilLKQuV0YwQDoV2XF/0RwNGOFD0XB+JOe4eooZarcY9r9asco9QSrQ2O8f3NrM7e9sHS6fm4nzIOa/VasxhpckNVXNLzZvb10uSB0EQhZXADyFUDKEaBWkZoaP7WbcyF6Y65iHFUitWVhqRUwjqkHiU7Lf3w2p0/vz5xcXF9uBYu4XxSqWVAZ1myTDvZ8fxmbNP7B5uU0rXT6+comv7BweHh4eaSKPx4HC/1Wo99/yztqptUA9HA9REK0jTXCkFDJmgYcWtsiBQzV/77V/VSjvcVbRoDw9HWoSFF/ihEzFFi2rLd6M5w1TKBoVKE9VTxmVBw68LOZAHg92tgz0iONHCArgWF+bSYbG1e+/C2qWf/7mf/fiND7YON7UskzjNRvsGwagPQr9SbwRZlnV7nVE8dApxXASEklKXRmTdbBTS0PM8NHlRFKpI+keH+6Oducriani+5vkMar3R4Lh7GOaeod1+ev/w8ND3/VVYrdVqWmvLnMr1zKATQkHzsPDybKhny9c4w5AihCAu1oMaMn37xh1vx+0PeozyWq2WiFxiIZNcQr55t+yPelEU9Ae9/qZqJNHCRq1eaWWHqhxovhA0qi0RUiE8BKK1FYIHThkBKg2hDAmhjNpgiABqRATkOOYoBmLImMfo5HFSBMxOkAucrZ7ChKJy1pI9YgDG5tbYf8YY23qLsxGnhWXZf5Yh9pFaMgBw/lAAOn2lfBztkYn/8cDwGzoFkBGKYAiccFKz25yeyPT44XGOhUIyrVvPAhAeXBsy8zrz5/SCgAZjTJra4sE4J0/IGC9tydMJJYQCpYRxK8oEDn9UXQongK/Z+zJ9rxQ1xhhLa2WsSKWhQCk6XHAkvCx0oUqFinJDGMlkz3G4NrkR7Ob1y29/8G3iJLW5ecqb46nRLwtECrQW1H3f1yLNsizLhojohLZJD43JhaZHR8dFUbRarYWleUJIlmVKqQqvJElSq0TtLiDVwqX7+8fN+UZrvlWtVUpVzM/Pd7tHQcCfeuqp/aO9NE6zJAu8YHV19eLFixp1p9MJosBWvi9evDg/P7+6ulqv1zc3N9955x3P82r1ysLCwk998YuH9wfHh8lnP/fpufn53vF+e/+40+0vLa299upre/uHl6/eDLzg2WefPXf2rBDi/v37YRj+xOuvHx4d3blzZzRM7ty5I6Xc2dk5PDy0E7zX6xVlHgRBEAZh1Erz5J1334nTESFk2M88z6tW6r7vx3FsjAFDk1Ec6xEjnFU4GOIKDwDAEMpYmRdCOIxyNLC0tBB4fhD6rfnm5u5mkiSVSqVWq21v7fhO6FA3j4vDgzbnfGNjozfodTodx3GW51fWVk9lSba/v08ZGQz7BwcHthDGOB3Fw1qtxrjLOddGyaykjDBO7avrOZzz4Wiwf7BPCGk0GpohcCJ8EedJvzeo1+ubO1sLC9luezfP8zhORnkipewM+vV6rSiK9lY3DMNhPjx79my1WjUMjzptRDw62ueCEg8kauB0lMVZXoZBBQBHceo4Shoz7A1d15+fmzOmDKOq7/mOcNFAEqdKyyQfCZfXaFMIQY1M4pgxWYsaHJxkkNXrdZkW8TBL41Rr7Ufe/FKr6jWU6jDBoyiSpYrjeDQaOdwNw7DX6yHBqhtWKhVLgp2oOMNhp0OmbeuWvjCKolarpSHPTTHsjrx46HleWZSe54Vh6FS8UZn0N29evHBhkI8+vnWVUprLfHG+qaT2I88LvDzND9q7GuTm/TvH6VG3203T1PEd13VLUmYy07keXnnPKn2VJpNSdjqdNE9t+SaO40HMVldXF1cWhsmgKAqFMvQiQkFJneUpE8QJhOPzMPTb94867S4xrNloDUeD3rDjFs7hceG67sHBgYxxffH0/Nry7fs3+nG71gqFIsfdw8PD3YX5ed8NROSxHhkMB1oRz3czOerHbYMkh1HBk+qyP7+0vNfdV1AmSTwsBlTwNC2TdHTUH9k41fFplsed3q4yklIa1UOtNTCTy/Tw8DBJkiAIHMcJMzdA0VF7IYs84uzuHV69fjnJRuvPim7c7eQdk5ohudcqWgCQJMlYed5Gt7aC9chKPTUJdhQycxzHdVybu5ZSGqO0RpWXrutSSkb9BAA4EdzlslRPvXLeio8SgKOD4+ODztzcXBzH3GOF5qcWXlu/dH5wV3KMXN+rLroq9RzHkbIEipSCybXRQNASZRDOCGeEC8PIOLlIDZMatQaliNJgDCFWdPPHa8cCfUIKWmogBJQxtvhq139EYHTCH2Nx7BNzaK+h1sRoNOOGXWIQtbJYp8eYukds+eSAKJkGvmMAMwEAix2e/HZ8i8YRNkFiEAwh1JZdbTPPo9VtewBmokuKMx8CgC2g/6gBLse5bJg6JGPDz6abHW/AbsoT480SCpQiWBgzAak0jNU6CRqiDQWKBJnDOZAJ8RZDIi0QGxg8AnYb+yhlqSYH/NATW6BGTYxBo4nRAIBgKAASxsAwq60EAMJhlFOkZmvvQ0Ihy4fVanAUbzdWuCtaaXHoUFpoVEppUThVJIQQXkqm0VGIClFJKaXJSy2EENzhTDtuRRAXeUAVLZVSmc600ZxAauJWVFk7u8yp8KpOe3AoAvrkU0+43Ds+7FUqUZoN6s3wzNlTdzevhGHo+z4SPO4czy20nn3h6YODA8fjh4eHw+FwY2Pj7Nmzg8Hge9/73tHR0eLi4vPPvbi0tHT1+tV7d+99+rM/MdfaGKSjH7z5g43F1mjUGw0HS8srrhN0eoPd3YPVlbV33333ww8/1Frfu3dvb29vf3+/0+lsb2/nWdnr9SzXAed8e3s7DMONjY0wCrZ2tqrVSr1RG20PjDGrq6vaKKD9sixdX9QaVamLXq+HRAuXBRAAQFEUtoUpCALbHEGoVlo5jNvw2vWdUTI8Oj5M0hgRb9++vbSwnGfFaBhXo/r2/b0PP/zQ9QVSXRRFrVZjjCVpWpblUe/ooHNgmOnFvfagbYyp1WqI2Kg3GWNpkgVBQClFA47jxibJ8sJ1XSC01x9IKf0gzLLs7ua9qFpVhWk2Wlmaj0YxJyIeJGUq6636YDB0mY9IynSQxQVi/+josOI3JNUf3bysiP5zf+5nVjbWf/j+e/3uwPg6qobDJGecR7VGkZcyN4NhSo1u1lpS6s5RR5aKg9s97hdF2ZxHS3AEhqRxWuoCGAruWIw9AKDGPM9HvYRoZnLSOxxqZSqiJhw/SRId68FR4tGQE8dzfc/1to+3i6xklDPKk1E2OOoY0PVGbWV9mTKURnLOwoofuhWlVJZle/cPbPlcCJHH5dLCsjEGdWxR1kVZIKA2etgubFutbb/e29trNBqO4xx2DwR3CllooiTIYTYUseN5HjAUHqeSpGmSJLFtkCnLslKt2UlqP+n1eoQQ13WlKivVqNvtlLJYWlqqN2qdTsf1HJf5hJAsTzOpi0IVRaFLrUvc29tnlKtc7mf7jDGGDkpSqzS63e5wkCSDrFTFglw4HhwMkl6c90Gr/nBUqpw7bqWCDccNg6jM5EjlUTUsyvzG7euO42qitg/v/frv/Nq6fxYCRRgZHvQ6o24YVVRpUoXd0U6tVqtWq2mWdrtdJZVtDhoOY6XUsJIEQVDmGiXNY5maQrQw95OUdAfpQbNVXVgLeklt637v6vZVIYQJTVEU+3E6MG2be+Y2AptVxp3kEjnOgHSmCUZDUKPWqK1OMGHE6qMh1XmZhSL0XL8a1guZG1BFUWx37yzOLy6vPxn5tXu3dq5/fFM6sFhdpy0Vj7KDne6FSvP8009Uw3phMjRlkijKXaWMYFQwUlAgGlFbzBOhlHJBHIcwCgQ4IZoiFYYUJVIKWICmRCmbXT2h7/YEy3wCShy0eUDhO94AWGztgxrt1PrCjAFGAwbRIEFDEEDph2TRYGKDp/Qu0zGubppZtPMD2zyhhH1wzOMI1RAgSJCMTZ39c7bwPD0BC6azxzsRP5m5LOM9AjykqGiT2wgIjExywoCIY36OGd/CvhHcZlOAMhvXjs8dgSkNnBIpQRNAQgxSRHRQoCU3IYBW44giAApuptaX0jH7FSGkLB+TmwEAZQwaMAaNAhz3ZBsAoAaMKbUiiCCE0EQOR73eoHv53tt5FguGUcUrTRwu6qJMR/lx2paETPQkPKG1GuVxHud+6dtKjVIqz3Mry+P7fpoP/KobsUApddw/tGUdAMiKgaAiU6OF1ZbreMANcKOgXFs53e8Msiwr8rIoimp10fWc/mAgtax5Ndd3Ot3jYdZdW1tZXp8DAq3K4vz8fBAEN27cuH79uiXJarVacwvNrMh29u7uHxwsPLnhec79/cH27jYWg4X5pbPnz+c5fvtbb9zd3M1yvbd/VBSm3+9Pyexu3bpFCFlcXMzSwsZGlt16d3fXdd25ublnnn+q02+Xsri/s5Xl2fzinNJyOBqsn1rf398fJaMkS+zBVGqVWq3Wz/p2FsSjUSHzUhVSSt/3K0GYZml9aalaraZpmudplqeu4CsrK8aYPCs6nU6/38/i/CbeDIPI1rmTfOT6ruM4/W4/HeV3bm0GjZAQkhSpF/nVZm04GsVFqpSquL4sZL8/UKW2y32r1SJIPSeSpRzKtN3uaa1XV1clxWRUIGSUOGCGjggCV2apopR22gPgJItlmap+b1AUBVngALQs9YgMfAgY5fvHBx9fu7J7sMs498Mg1Zkq4Hi/V61VQPM8TolhjNKiLGvVQMlMS6AolMIiz5RSlPdHgxiQjJt9AQPf91xPsmxUFlobNxQaVLfbVVJ7rj8cDTgTtWq9FpE8KeJsNIoHh3vHlKCRSKtcl+hwzxUu59xIM19fGgz7WFKTEwlKgao2oiD0q6LR7/d1nhW5SlRu+9yyYSnRI4QYA4wKSqnr+4yxYRxLRfKiiKLouN2mlBZl2el2Hcfpj44bjYZSSmujCkUpbzlQnave3tkXrlsVtSzLlNTGGMq40mZlZeXu3buHh4cLCwvNZjMIgm632+/363PV+bk5qcr+oFevVz3P8Xw3LzJg1PK2espXRpVlOSozU1KjwGgTDxKHe41GAwlhSBebq7e3r3ueF1b8ApNh3nU87kjn8OAYB0gdIL5bZCbLumVpZFqq0swttMIwiCpBWYZoQLnaSHLzznXnSVpEI+CS5oYoFWe9uJsRzUflCJCj5nEct9s9hzuVSgUMTYepUkonKdaE70cOhyzLktGo0VCaypIk24Nb2i9Wlk+1zkYxqV6/0rZNzMYYpdQoSWxjN7dT0WacLA2CXe8Y5T9qfQHAMm8opSzhgOM4tnWs3qgrpaqVqlIqS/Nur1NrVmrVWnOFhpEbd4eCurVKrVFpLdZXL5y++MNbbwHlw9Fg93hzLlxu8jmdeAwD39Ou8LUp7B4ZIZ7rUocOVDkOpzhQSrigFJAQBoYxBAIkNVZ7DggwQx4lYHowTjC0hj4+AqbIEB8Ks8YR5Al9YHoszE6tAUagth3IqgKM/9kDQUAE4Y5rydPsrt2XmAhf235cmMTHhs6cwkM46IfGNKqeBtyP3EeYZIAf+1v4key0Rm1TxOPfTv5RNuulPfi5hTsTCowB5wwojDlAmcMUKolIDSijJAUzJiMhwBCMNhqA2noqAFg2dgIEkFi6ZmODdnxwGWdPxBpd1GTmWiIFxhwGaDQYy1OfJKOtra3bmzeuH75dyHzj9HISkyQZgi6LMmu2au1el1LqUpcRoAQNNcgUEYZoVubSuq0eDyilRpq4SLvDdrVaFUJYEr4gCBAxSRJk2qsGw6xXq1VKA8NRr1qLWnONqBIOukMAyPPc8Z219bVWq7W4sPDcC8+mabqwOL9+euW4cwAMV9dXLj1xMe3Ru3fuHhwcjEaj1dVVzrntDLEqBUF08aknnz7c7n3/+99fXFs9ffo0L5MzZ08N+sm3v/Un3/vBu6tr586fP8uoWFlZjuP47t27RVFYeYO5uTml1G62b5mG7FWzjCK9Xu/K1at5nrfmmtvX7qd5gsQct4/qjVqSJYQR7nBErNQqFrR17/69hbV5y8SZF5kxRmmJYDzfVUoZhRsbp2rN2vHBfprGnHMmWL/fD8NwdXV1a/O+I1wW8du37mysn6q1Kjt7bcfnlVql2+0O+0NPBMN+XKdqcXFx2sth7bRSSiUFpVSWSitj+6cXF5bmWvO5VINOz3EcUGzQHaDcp5QGTiVPCt9R/WIYhZEKzNHBcRhEyTATLvccXzCmcyOIw6mjSi1zneui0+/Nzy3sHx/83td+P0syzgQSUvOahcz7yZBzBxH77WGrOr+4sHxwfH9ne6fMJSUsiqIorBAkWZYRbiil3OGCCFlIZZTNI7pzkMSxVmZ5cdWP3KO9DhVkfm4OuEmTLClioillzPWdVDED2uWeLBQBWovqFOhwOEzLnBDiOE46zPO0rARZaXLkMooCAjQZ5Mf7XUSs1ZpKqSRJMllyzkVSMMYII1oBEaQWNVxX6FJTFy1vxt27d+fn5y0bsVJq3MCBKATnnBdpcdw+iuP4aHBcr9Ucx7NsOg53giAAJN1Or1qpea6f5/nO9i4iam2MxiSL+ZAJlwdBkJWZNFK4/PDwsOoaC78PA4aICSR5npsSGAhllO+G1NAyVspoLUz7oEsMnSYShslgOByigizNN6pnQeiCFQQYALjc466IMeu2e3t7e2vrKxsbG/1+vz3quk4QBP5espmVaVB159aqQYMf7/SSUSocmg9UD0eouFJa5lDowihOKfVphfmMUipjLIZj0QRjqOs5fuSUJNu9f/vWzo3Tp857PNw92i8KiOPY3iDbgEQIEUKQuVMLs7lQ692TiTKuzRsgouu6Nkfh17xBf+ixIAwipSVxkLiYZokTcFf4AlyHus1WQ0JJHPPcS89oXsw3FwjQzuA4l+mta3dlj/78T/7VrWud7773B3wOP/+FL7z25OfqzhpKz+GO7yhDFXUBOCkKlcaoc1fQQImEUOkHDKgMPY8aHvezelgroEeIIMRVhhYF5IUpCjBI0JxEfHECOIs+nqADjXqsI2JlDQkCI4QRYAQIAcFIf0ZtaTaFe9J+p2CiKWfF2ADzMR/F1IiOU7vOeMuzX0bENPdhYshnK7WmfDz15knjR2sQdmiazR4PmT2yxw2Xq+njNH0dB8FjIUI9S/5cSPdH946IVX8srzT9uS2X5BNA1uxFAAAGYyZLK0Fq3UopJUDQbrerlbDUWVQn2+3rH91+f/Peda2Oe70eE1QIQSgQSlzXXV5dGvZH/V7fc/3RIE7jPPACo5FTkY3yJEmsjbE+rG22YYT3hz0rVuEGruM4aZ4IISAqqtWq9VaNMXEcM8ayLKtV/ValWY4UU1wY+sXPfeHC2XNXPv74yo17iBiGIU76DjzPW1tby4vs8Piw0+kYo6u1ihBCahlF0ec+dSaKqlqq/b0DWeqrl29GUeXll1/ez8rbt2//5m/+5trpU6ur6zt7uxcuXKjVaivVtZ2dnevXrwOSwA8559WoaowxJYxGI6VUEARWsdWaYRp4aZo6jsMFe/e9d467x9ZHT2Vq+ZkrlUoQBMPhsNPpIOLcQgDMJFl8+uyp0Wh09/bm2sq6kSAawtpLpdTi4qIlsvZ9PyAeIs7Pz9+/fz8IAs/zBoNBvV4P3Ghvb09rff78+Wq1euPGjcPDw1arxX1iobyWeKEoxplSUox5garVqm11tblovxEdHR6PhiPfDQR3sjjP0xwAonokHB4nI41K6rKQuR94BvXK+poQIonT/Z3DPCuqYc0VXp7nnNKxPKJwLYGTXUNNToK6K0nhRY4fesQwlZvDvaP1tdNa672DXcdxGs26IUYIQRgwl9gHcsr7Zp/bWrVuZwTOiIQSQjzfFULked5uty0Hp+V1cRhnlIOiWZzng7LMDDXcEb5yRzb7IlwWVsIgCmzdt7lUHQwGNmdTFEUYhtVqFREdn6dp6vv+OO5izBhTr9dHx3mv1xuNRoSQMAyDIBiDhqIxVteybSBio9EYDodY0uZcY5QMCQVHuOko1wV22t1KI2jWW0KIfm8w6A8R0Xd9x3ESPag1qpVqxDhVKKUssjJL04QULuXgBJw71PO8+eaCLmF3e799d5QViQFTqVUajQYFmgyyQXdAa9oiiimlE1nJwnVdwRwb/lBO/NDzA89xBVDYvrdj01rTK2yXjsZ8RRopBK83akHFS9J4e3d7/2CvxTbKslRKqVKXhTTa2EvXL7tBEDDKfN8/PmofHRzX63XHcWorLAiCspCeFyilkmHCmYjjWCkdeCEgkVJGUVBtRrlKDtsHD1gOZhFAAGClF8bGQAgb7AIAEqxUKlrrHGPNlYayEgWnLyyDkFv3tjm487WlQsRGoUCnc9DPeM8yCUhZFGVeqUbt7uiPvvH1wK2Bo8OwYkqTZVlISk4Y85nKjV8JuUuTMjYaPM9T6Mi8TMteEDr9wfBgb69eq57ZOOcIOhp1RcQti5UxRmtizDgX+qcisX6MQSif+WMGNEwfgIlsupYTYqV4HmvDTjJsD8K0iSEZf99M9jUNmu2zYnf7oPN18qchD94gRWMA6Uk7/V8xCFpVpof+jVPUjz8v+3BTAGIVIAhBQoBzNjlRAmDNKiKimCkZzJr1cTKDEA1jaV8z9lce5OFnAeGTu0GMhjESXIPRkEtZiepewLdu3t6/fGO7fas/2hkVXVcYQogqdZ7n9Uat1WqlWbq9tVOv1QEgSRKj0XVdRjkDgoiWgXWaGJjOHSDoOA6hUJZl1s8s/K1SjfJBSpG7riuom+fFsBdbz6DfGbnMr4bVihOGTji30Dw6Ovrh2x80F5bfeuutOI4vXLjwzDPPCCEODg6ufHxVYgEAx8dH3W6XccoEAwCl5QvPLQB1sjRrd/tRWDl1/rTLvSTLZEx/8O23s0SrXMfJqNaKOvFhzpLOQacsS+ZTl/uOELJUw2SUZ8XS3EqAqLUWQhhjpNZFUSRZFh/mCIYx5vmuISaKIsqhUqm20yMpZZqPyjiTtEpcUluIXNethz4V4MaO53lKKet8xIORI9woigglRVrGg0SXBgwxEuMytk++vZLdblcpdXR0JPN922x9cHBg1Zc9z+t0OtwnrutGUTRl2waAIAiGScoY44Izziij2uiiLMqyHOaplJIRHo+SNGmXeek5XjWqR5Ww2+0WRRFVQ6pItV6pN2q9Xq8sJKPcc/xGozGkI6UlAHDOyzynlAruGGWSJMmSjBHheV6qRiouDNEaFRgihDAGpSkt1ZQ1tNoo5rBGoyE4z9JcCMGZMBodwZzQsdTiw/7Qljwsk5pSKs3ToigarYb10T3Xt6ViexigjO8EuSrjYWxKiCpVJU3n+KC2GIZh6DiORimVHA6HjuO4rms0NurNNE339vaKvIzCCgEqZcldOpvR5JyPRqPj42NSOPbEp8GYECKKol7crtfreZajKqKwmqZpPEgDLzIMj4+P8zILQr9WrYOme+0DSqlRaL2KvMikLoQQuUp7o05rsaFKPRrG3KFe4LmuK40EIIRCXuYoeFRrCMF7w54g3tLyIpG8fWw67a5sSy217/u5ynOS1rDmMS+Xea/bS3nRaDQWWy3XdTvdtkHjOCKMQi6YLKWUkouxXLd93ixnjiWINUwiImGQZklzvukHTr1el1K2b3WNMVYk0WjDCOdUOMzLs5xRFkURGgjD8PTpkBByfHzsJT7I0hhTprEsVFmWjgBOXG0KLdEoLaUquNIKPTdcaC2OrcsslxMA2JDXHih9oN4q7X/5vp/laaZSwoykKQ2djYuLfovzhmbgrMytBqJ2dL93//ZuFufYHDnsiFJKOM4vzVXqYYcPb9y85nuuCJzl+eWN+TNVv+44VKmiUIVHIqklGHQ8QjmThdZlpsGM4oM33/zw7tadWqXymc98moJ2XJd5IjcSAJRBKUGPE5QMTtAc/F8xEB+sszjDdIEU6bhUOoZvleOs6AOvajaFe1LKetrP+tjPZ0PNyUMzMbSPGOCZMRtM/7g2+KTvU0bh5Dj+MYNQBDAIaNCgIRO9JqUfcTjGAtlWnGl6ytN9GfMQpShMa9IzKP0pGA0ApNGE2PS90caiP7TWmOcmDN3BcHj7ztWbd95OsC2hV5pEOUFZSEQsZZl7RVnIJE76/b5lPB70hqFfqYQVLQ1FSggxCo2yZX0kaCigUail4S4VDncD19NeXmZKS6UUYQRLyJIcDAk8w6nwHN913SAIkuFI5rrgGaGmVovCeq2btlVprly9YZCePXdxcWnl6Lg7HA6llHNzc1JnURQJ36s2G5xTDToIgqLI99uxBC/L8t32sCbJwvwiIezu/sGb37qqcvb80y/2s+7Vm1fDukc8EqWBSL0oitzIdQVHZUypZSnTLNvf35/ygE4XJmMMugQRFapU8UJmhhrKufB5o1IdjUYFpqXOMk2FEMQhhsvhqHQ8kef5KB46jrO+vu4KL42zMtVagAaTDHJQA2s4ZWbiYdfa0VarFQTBaDSilPb7/dCr2HXfQpaiKJqfn+90OkQYNKiV9j0/DEIlFbF8k24FAIbDIaIuVKa0okiTLGl6K14Q5DRPRp00TpnVl1XKSlNKXWrjVaKq4wpGeRhEx51O4UvP81zHiwIsyxINAEE0AIQYZYwyspCqNIQawwxxQGqppTYSZWbqzarjCxHQTqczxToVZW6kEULQnKRFEkWRJXkmhNjMIgAUueVQlJ6HjuNQwhjllKgyt/LA3HM9a5iVUoDg0pAapvJUKeX5QXOuXhYyTgc2fVKv16UueoNeIQvLvN3tdGu1mlbaEY7X8CpRxWhTFqXjc0e4jnAZY/bVaBwOh0y54xSaMRZSJ4RwXVdLkyW59WlQg1GoUfkutbBEALAZUyvswZlI49x3Sy6YRhVU/GqtooxU3VyWKk0yykhY9avVanO+0ZBF1sh63aFUnuPzeq0hhOh2e3E6CgN84oVz/e78/tbR8X6nzEsk6FecSmvh+PZA+cZxnNANsyJL43SyYBlCiMNd13G10ckoVUo5nkADeVYoqaMocl0XOdiOiSKTBnVeZN2uybJsbnHOGIMaq5WaUkpJVWRlUZSFlmColuj5vpJaSa3LnBBarVUsHGTQTinNXeGWZSlLRYCmUPpuIFERzRjlDB1dYNLLHZ8ZYPwR7vvpqm1FMS2QxBhj2fUAgDiEc44GUANowqhDlEgGxW57u5QySXppml44dREc1Y+7HN3GgiDMHLePnECsh8tlVvTjLvWAOkAMrFbWn9x4vtVYzTNVQlYWeb/sVD23ULo3aBvQvgiHebq9c7C+upCWA5knp55+4qkLT1YqlWFvpIEC52isohxIRZT6sfHPf/qYNUjT6JYQwiepYxtr60lM98ggD/cp/ejQ8vHyeQbHEOWZkJMgQAkP9TtNt6zk2BThRHNwbJ7/NwqCxxG2PcjJgdmjfvx5PTgXnI2S7RM1a2LtqyMeqILDuD0J4IGI1EMni4iyYDN7n7lugJQSg2jQ2Nh6rC8MQCkZZd3eaH+YHFMv8XzCFSWEMeDc5ZwJmevOcVdr3ag3GeGBGw1MDAZA0267iwYY4cRQ0ATo+GpoNLJQDFSBMivSAALKCHeZy5y8yBinrXCuLMsyl71OnzGmtcl1IQsVuVGaJQoKQcF1eJwMSlk2mq2RMksrKxsbG71eb/PuXQCoVCpZUSimh/mwF/eD0GeOGHRHKMCA+c6b75w6dSqKopFUWX9QAEuS5Pbt23tHvfMXzw7SQaff1aDzQlJCB9uHkeOXULjcN6qrczAKUZE8L/Lh4dRTtOGOEIJQIjynVIXDeRAF/YSiMnmZHhzlxCNaa4d5hBOKrMzGHMKLzRplDmEQJ6O6qDdadWKo6zs+9QVziqJghGtpjEallCTKxliVSsVigC3ExNqMdrtdlqXneVmWWQFy13UpI0mS9DvDPCk556gIAqSj3A0EY0yUzGYj3cB3XVd4zEhEo7M0z/NcCBGGIQD0Rh1zVLiumxd52S43Njb6vUFvs9tqtQa9UZ6WnuNZ2xmGoSxVlmUuc7XWRVGgJgJc6ghE1MoEdc9IAE6KTOYy19XI9ZzGXK2fF1JK3/MqlcoohjiL8zxngmqpZSEJEjCgjU7jVApJCKlGNXuaslBBEARBEPpR6EelKsCmWJAAEgLUHg933FE6HAwGqAnn3BDDXNpcaHQ6HXutlCmllJZKnRCiUXWOu1Yrc641Rwjp9/uyUKPRyGoh2wwzIcSKNpKysHUWu/gbYzzPq9frvhP2OwPf90XoDnuxlDIIgmSYlTpvtVpSl0kap2mqSxRCaGUYMlUqyojv+44n6q0qMHQjPtzPc5UbQ+JBylhHa40ECQVCTRQFhSo6nW6tVguCAHSa5XF7tF+UWoOyd4EIItwgqvttrz9M+yELq80KL0le5JnWnHMGXHABFEtrCaVGBFSk1WpZp3bMK0WI3eDG4npZlqBJVqbpqOjRfimL4XDEczDGSIvHVoYSRggBAw7zsiwrUmlQB0GQZVl/2IuqYecwK6V2K4wowZExxpJRSmTpVDgDQQw1pUqyLIsL7hHGbVfpw6GSfW8dGZwB8tgbaUDneU6B+RBpJSn1iw7c+OHWsOh6oatIngZyY6lY2pjv9Nr7O4ethXmZKzfjYdVPiuHhftsNxbMXn8Fcb9/eh8JZbKz4XiUdHnMqNDG7yU2s1Nuj/au3PwJiVlZWBp3Rh5c/+tsX/+PPfuqTu6dPg2RbW/fLTHPHY4yitijpsRqdNgCAhFA4oa3oxx2UPJ5oghKb7ARENDBuQ0IA2/Yz69A88sNHBpoH1/bfxVYqaXcKk2bi8X2T6kEkPWObgf5vZIAt6Yo9ZJg9nZNEMuAhkYzpqyX6IBNr+yDvMol0yaTca/+URM+e1IPtqBNq+UgtbcqY2dIgIkGkjgBgRZp2DSSKFFTnEfcpj/zK4igZGmOSNEaCDve4z6rVqpJauzodZS73iSFlpoxEQpS1EDjTIGCHFzqGKMKBUMIF8wMvqgZRNbx/b9vO8Ha7zTmvVqtWNjg2aaGzgHPmOXGW3N3eLPoShXnq6SfSNO31Ozdu3tjf3z99+rTnO8NRv6s6hJCdw91WqxXVoszkpCRFmR/3svag5/s+ABg01WpVKdUb9qqrwVB3D/oHaZ64YSBIQCXlIkqLNlDmOVorREUEcUojJSgWCMtPa9fccTqXsRLLXGb1sB5VA2/gADhSicFgMNwep4U55zrXWoGRRElMvdSW3NIsGQ6HjHKX+9VqlbHAruNhGNqQN01TxtjS0nxRFCsrK1OtnjzPbXrZHk8QBABg6fkQUUmDBpTUozK2NpIQomRe6tz3fYvfseU9e52POkPP87hgtXrVNqQimCDxeqMOULTZ1zzPkyQ5OjiWhaJMCOqggSzNtWOiSggE8yKLoEYQQBotNSKiAaOJ1pobEgSRx/xBb5RkIxu0OI6zvNzc29uzV9L3fYWKcx5GgdY+IYRR5gi3LMsszXJSuK7r0aBESaw6NTOGW1+XMDOWYSjicswWLEkRlyMxKsuyKIo8z+2ksJV7a02zLAM6RjPZr1moXZ6WkunMLzjnqjRGgSyUcnSqMlvSptTSb5YCxrIidkra6NZxnLERAmo02hAwS3MA0KSsVCqDUT+KIkTs9XqVSiXPCqIJQYIGGOVG42g0clzhOp7gJnAj13NymWVxwdhIOLzZbHJOAWi/Nzo6OgrDbq1WM6ilKomviswUZckdPjc3F1QD5kASp06Foctrc8HicpPwppRFqYuiKEzKLTIjzzMppeMKwR0ASOPUKGSEgyFFVuZ5niaZlPL2jbtaayTG9QU6JE9LJOg6fjootNZKKq01ZcQRgjFmwKS9nFDIdG57B4wxWZYJh1NgpVJ5VpRFyQgnYgwMrC/OEcOKrEyTLM9zLlhEQoc5fEJq/2ioZ7E809VwSpXFOTfacOI4jkuIX+o872d9nYVO1Dnq1RcjL/C73f7C2vzyhcbd9pVCz+eqbM43aq3KcffwoHN4qnWxtVgXCto77TQf9kfdtIj32veDoCKYv3X/9pU7/d3BVme4D0zduusbCWme3rpz66mLz6yvnClzQqlLmSCUpUXpCoJAtKbTtKsxhNATma1+3OE6anpNYMbwMErRWgJAu2tDJmHaTOA7HWM93R8Zahq9PYK1PsGuSQ2PGCT7p8ZHqStnA/R/9/GnJJlnD++Bu3bCcaKaCV0fygHYdDo8UkuWE65vQgiZAbGbyfYn+fZJX9aMaNLsMVMYh9z4sBaTUv2j7b3LV9/sJx1GJeVUS6MRImDM8CQZDvuxF7hBM9Badzs9z/GKvCSGyVyWSlFkCIYSxplglAMSRABKKGGOcD3XH6ZtqUrhRQZUVpbSFEAQqa43a5bQw9YylZEMqeCCuZyVtMhKQVmaZp1BLxKVpbX5zjA76h8CQIF5brJUJU3RECG/8cHtIPR7cT/DYslZDGqhcEU2LFrhXJIk7fa2cARQzMvMkosdJruyK9dWNo577Xv37p/eOI8SXOGVoIku3Xrgcoc6lFFuqCaCRW7FklogYpZlaZrmMqFAw2pYaBgmfU3kKBlW65VqJXIDUcbHhBBAYtuoKKWUMkpMf9Crz1XrzRrhoJQyqJmga+urd27v5nluuatwgjq0CwsAWBwNADDG0jQ1xtg+qDAMrfkBgCRJ4jgO3JBz7glfSqlLTYzmnFPKdKkUNbLUjIECAwAsEoK6yvRcv1KtN7XWeZEDQcfz6gsVt83TNG0065bmrl6r69IkSVIP65UoIoT0R/2iKBinSqlSlaUsreFRspSlRkTUiIijvgwXq37oZ0VmuM84TeLUoK6KqjEGlUHEIAgMMVYTGhFtqsAixawsoJRymCb2NF3Xc4VHkBZZqbXWqIQQ9ps2McAoF9wBbmqVih+6lsmrSAtFdZIkc0stRFRKcYeGYRiQwBrgXqcXhqEr3CRJ+t1+o9FwuJPG6fxCk3OeZZkx6LqO4ziciTwrGArra1pXpixLu/ckSRrNuta6KPMwCpIk6fW6lUqFuyzP89FoNDffstwgi60KJWzQGRhjWMkII8qUhIHne37ggSZGIyApM5nkcZIkhIGSGhjmaT4aJERynUHOSi9w6tWmLNPQC4OlWuoVZaoIITJThVLUwcX5uXqz7vjMDYUXVkfpcG9vb6m1zDkv8rLsZGgUFUyjzPNc5joMw0qlYtvA0AABqrUe9WNKGeeuQ4UqdJ7mGrVBfeb0eaVUnhZZmksptdJZlvQGuSsCL/TKskzS2HHFwvLc3HyrKIqoFlABWmmgiFQD42HVp5RqozilhBIgyDn3Pd8RDgHKbQViZu0bW1wLDbBTAnFMQE8ppSgYYwY196jj8yTuKZ6tLM2HupFvZw7ziKFHR0etflhd85761Gns4fLKou/7VEAivfnFlsvF1s69pYUqq6rD4b23r32T+/zw+PjUwtn1pbOyZMd7nZ32bqb6WTmURT7fmDu1fgYIGcapzETkzxH0u50hYYw7Y+rM8WJtqGVaIGBOsHc/9vC8ab/pg1cAoNpioYEaMAQMgAa0NBjwIwVg67s8dvuUwsP2adpbTMkkEfvAUAFoZWbqvg/KwBOVxZkuXgR42Gr+/zPohHHjkQ3qx4PHAcnsGT14pXQSk48BXJMaeenAY80/1ZOdktmzm+X9mG1/0kAAqE3Caw3GjFM427sf39269sG1t7wAkZmiVGmSEYRSHiVJIlVZZiUAdNrdsiyVlkuLy4PeME/yIi+ZEY5wFdFGQ73eAJh2ByhqxREZ9z3PMbxaq1BOtFFSyiRNhqOh7/sUiB96jVbdC1wbpQVBoBGRue2joSkMq3JZSL/mlqI83j3eOtwKgsCpOv8/1v7z2dYsvQ/DnmflN+1wwj03d5gOExEIioRAEWJSlVxiSZar7HK57P+QZfODKVsSCZgEAXAAEDOD6enpnk43n7zTG1d8/GHts/sOgv1Fu7q6bjj3nDestZ70C6f89OjhUVLpenU9O5obo7jigMQUF0YmIGmUlppzHqJXWiEHU+jNdu2Ciykgx8k5G3xKiQJ5m1ZXl3oBbpgYDUwgQWIcI8W6rju/EyQKVgghJho248p7L6Vc6mXd1JdXF5tu5b1XVvhoy7I8eXiUeSnJk+BsT67o3GKxzDNIobn3XqCcpkkwebu+EUIAI8K9p7iPLiQcx5GIzs/Pp2mqqir/NoQghV4sFlmb+gAPFkJkyHLOrnIjUWtdFEWkQAFzizXvu6bks6pRxW0g74IVimNKg+2jV0yXi+VcaUkJul2fYqqKej5bCC4pQrAJGHHgJERKKaRARG4KiBm4Txy54Cqx5JwL44SExFLkrmiULlS77SjBFrYpJQDa7XZVXRLRMAwJIhLbE6iIUQLJFVKgCNHddf6Zgog+hgz3C+QPbQnQGFzkXNRlw8tYGlPWJWNs2E7Rk3eRPMvgLymlDFwlJZTIJbgbXHABCBiw4EIKSTBBkYqiYIzlFCo3HjJcPwuIjuOIiJlr6r2fpgkZSCmFKEIIxmiCtN6srJukqXIDI4SQ4fQhBADMthnccx/3NTQLMY02+rjrdlm0riqrZtYkilVZd31r+8CSWFQzzjnFaPsw9Kt1d3P/3sO6aEY3XF3d+CkYZZQw+qgwoup3w5vzddmYk/tHgFEpxTXFZCM6XkAhDCUY+7EbWgxSKdV13TRN0zTlmxrH8Xh+Wpal0pJYiikopbLvKvHIAFTJpakQmHNufbvppl3F58EGqVXTNF3XLf388ePHb87fGC0XJ7NpmihBCokSZDT+pltxgSJwJlCQyHCwfuj3DGC6G23emc58C4TJwHR2Z/LjXTBGhOiDsPVcJz8pDd/9nXef/eSmWVb1vJQVG2H94s2zpSxO35nVJw8f33/nq6+++ubZV/Oj2fd/8L0iLe2KRn+h5nhx+eLf/+X/rEpprWca3vvg3R9++Bsff/zxB+ff+fHP//2bK//g4YP3nrxzujwxRVlWs4I1WjYhCMYdl4wJTuQBICVMB2IosBhA/m8UgLX8WzhIACAYhEQMyQP5RAjEiP4uSwe8I8/8bX/1a3qWb7+Lw799+3VQwr81AP+t5en/hp+3r+ftH0F/Jx/6EC/h7YQghF8DnR1+oZg+fM3bf56yrMrfmDQzdlcl/zpKHJEBMcgamCll+jUy9sWvfnZ583Kcds2i3m2Gq6ur5NJyfuSm1TD1mVA72OH89UVI/uzsbBrs7e0tBYqOKiNPT+8BsRDCcrnI7dO8e/N+SSktlvOu60LyPHLGmVSSehrtkDDs+ljXtU8eeLJhnLqpnzRXWqKYxslwjcRGZ6+3N+evX7WD76eOSVRK6VJVszLGeLO+Xi7n1lptVN6J6/XKOdfMasGkLGS6TZvNBnjSxVlZl42o/RSqZvb865dlWeon9cXVxf3Th++fvtcO68mO5LHrBxtGYVhVl7N59avPv0LEPH201nbTjjGmlVxvVlVVJYiJYnaIuri6ms/nVTHHlLgAWWrOy0xT8TCFEG5vbxOECEFK6by/eHPFUSSIVTPLkgPD1HPJpBYxxuVyOY7j+fl5jq+bzWbPctEmpbTdbrOiZw7Ms9ns3sm93KzOeyrdmdVDIs640UZrba0dhqHdtTHEo+Nl2+82rV0s59W80IEHClzher2bNfO+G25vb7U0dnTtrpvPFu3t2Pc9MDKFrmYVCgopHCpXZwNHPpvN6nJmrdttd5KDEsoHZ/2kq0Yb3e9GRBZDFEJMbry8vKyHSmgxDANhEmzf6geA7H+T04u6XuSg5b3PxVZmv+QSPEdBlCy6xJUodWXZthtbzYvZYqZZcXW+ckPQoiYccieDCYgxJpcyCvo77314cXGx2+2IMLg09lYIwUAM/WCMyRI44zBaZufzeWEKlnxmCebrHIYhB+NqVkzTeLY845x3XaeMnC2a29tbXagY42Kx6Ifu6uoKAJxzMSQmmNQSCYNLiGiw5EHY3pqZDC6M/ZgwVqrknA9dv13vEsRpsH5MIHlRFKYuPU3Xt1dnD+4vZgvOBOMd48gYQy/clHbTJkwJRIqQgqTtbQuYUPDXly9CSAisUEVRl9FF55nUsixmiNh3fZ6ANHWjpIohrm7XRBSisn6KFJp5XekSGXzz7GshhFamLMu6rJWuxmmQneAo2n73+OjRyYPjL5/9quu6onhXCLHtVvP5nEngnNsxTsNUCoOMLRYzBLG17TAOfgqAxAR2fSfIQ4iBKw4iTXFEBr/5W79xcXVur/d91Bx3tdZ5QQxjKyTjglOIm/Xt0LcPz07e/+jxT//oU5fGq9vNEV/Mjxa8A/eVXL8CV6/ZxG/Pr/vbgY+6GgPXPtp07/QHQ/tsiB1d7fzoF2ZRf1BoDz988PeeX3z5rBOzcnljLo4enPKyPH38joWbTVednjyKYxqnbd1Il6ZZs9y2ow+TMtXtzaqpZo0WjHxwdsSdkbMUhBsFMk3AQvKMCyEUIwCWgAghESZGAFkKBknKpCRoDVqilCQEKC4PnFV6i6LHWU8ABDIEZh06D85iCDT6yBhwJC5AcpAKM7+UYIS/EcURsdc2c2E551rrnO6EENpORk8+xBiQSBKwmCDEpDjEBCmlBClmRwqWAIB5Q29pTxzCdpAOEmMoGXHJuWCYKCaKNlFKiSBPWAnv9DR4KqdpKooiY2JTStM01XWdyB2u+e1bgL+Db11rbd2UKEnJkUMIAYA454XM4ZNLiVVlVrfXVVVZa1vbC66GYZBSITHvvVJmmibJi7/WIcgfLjBfYdaTyc3MlJIXnEVglDj6k5Nqs3n1lz/9Y2vbdfz81frrmLyzohRVzWdQwKxchBC4luDRx8iiOJodE6TtzW6sB6FZ4MFUwlR8kluttWRsE64s76kIzMfV9e1MzM6O7k+u571ODsbRKaO0UURkRDlNEzmmpZZgiNh20wkhjo5OtNaj79c3q+Rws+61rj75+gsbx3pRXn7WFfO6t9NttzLGfPqrz8Fxw+a32/O6bARj7a7drIMxRkvT3Q6ro02McQrxdt0CQPIbrYxzrpxVzk5EKiVn3SRKtKy76NqiNFLzbmz7sZdSVsW8KZtu65UoGWPRQ7sdY4yCGSLqWxuKbcTEmGKJSazWm9U0hPmcb+1NxCi0MGUtuAzeAYf7j+8ROs65g+RcWG3bEAIrOJfibHk8TZOURd/3dnR28M45wcXLZzdlWWqxAIB+lyAWdpoYM1rU2/O27/u7dUhCCBvTdbzmnGcOa+7Z7guGKN0QASC6SSg+a+YpxbZtVSEbVQohDBg2MQjASYogpCA7tX6azo6PFDd9O6TJXe3eKJydnB4DEnAYN72NtqrLmZ5ty/XmqlVk6nuz6iHUR6HynF8poQom4PZ6vb3qfUv8vilKM/lRsYJzPhczgmStTZiOj4/7scuNXMmk9x4jK02VWOpdn5KnGCFFyZnkOggOKXo7EZd5HF6WZeYX5X5koMiAD3aYNZIb5AUVnAc/MWJ2sIyxUpUJAmMMCTebTUut5LqQZd9OIES77iIGUyrbB05JomnbNsZYVVW/nbz3TFPZlIHMq1evJjsKLp1zQAiOa17eXKwJUjOrq7LMEAeJhizfXPSz2fzjd45ytB7HcbidfBebppkdLdq2zZrJs3mzPp8W6j5i7PzGWV+YQin15Zdf6lAXlUEV0CQ5x9bdjG44uj+72V2Aioqbk/vL+XL27KsXdhgFlzSkGKNgPEy0Hlrf1UVRJIrN8Uk/9dvtdhJhvkBgpEt5ev+J72h720ULZZxjZHZwY3AiaVGlYRiCj+Sp203+mtHCKGkSSgBZNI3WurcjYQJDvMbLm2+Ojo8GWNXL+2fT0WazWTSz4oOPYs9++ctfdsOkjR5HCwg9Tt77o3uL5WKpmPFTbDfdbtOFKTTVPDsZxOQTAgGgVqoq6+PlyfnNLfwtAzyoi0Zy2U9dQHt2fPzu0bumFG+eXyJiVVXEYtu2gezJ/WWM0Q9uaKe//POflWX5nfc/2K52n/3i8+P5WfLwL37n//x+/QP7wfDTn/7lm9evBJi/+otfHFX3Tu/R519+enH1SnH90bvfFwZ3w+bl9VdPHj0yM0HCgmBErt31ujBu8s2R+uKXr++dvnu6fDiNYz91BP3F5bNmeWKOKq3KFCHFmPamEQiRiCUkAEwE++iLCFJ4wUFKrjUoAULkJjYhCnjLgyFTIxhjXIgYgQjyrDzGjMzJICw8VF8xIBIysTcEfqtmQ8xYYiYYR8DEOAfM/yEgUcREiSjzfuPeeZcgBchEnoRE2XwQxOEFfdutuiu7neNEhCkwBBAMOPgYIcXeRs454zzfHYSUizlKwXufjSlzL+tuHrGP64fu+q+F4b/xORyOmSN7+Mp26pUUkokUkHv0EBMjkLScFYwxPRaCyxhj11oAy6RPpPaJxR2UOicZhdCAyIVgjCWidKdgoEBbN2gjjVHb3fUX33zy9atfENhfPPukLMuj+dJbZ509OltAwtvbG4laKlGWVcLYj13O95umObq/yD6bOQFljO1FW5nIGVgqUtM0dV03TZNSAhn3J84wOG+VUt77YRgaWY/jmFPYoigyxDSEcO/+aXTJT2tvo7VWO5EwUYLedrHzXDCOKloIwQUXIQJWHIlxzqVQKRIAJIgRAhFZa523RWnqqqmKehiGXbtd77aZg1uVNRDGkASXWmvCmMsvRMwyGojYtq3hlbU2jyT3TsB7nakdBey2vbMeABiwumqMMaouvffBRzu59bDxNlRltVwu580iDx0ZscpU+VkxxpLbA+uyED8lSCkv8G814HIDMzM1c62c0bm5UiyKwhiz2t0c8Fm0Z/87ItKqIEqMMc5FHp+13W4Yhntn9xDRjm6z2uZbU0oJIcyiSCnVdS1nWotiqAZIuN3swpistYlioODChBy1MnUl66Yq2db3CYn8FJMDRpwzMbQjFJwlxlFGS7vNThUSBXVDG0Ko67qqSynl5CdEFGKBHIQQyBASxBD6qQ0+juO4m2Ie+eX6NVFCgbrU3dg3TbNYLLTWROSTY4xJI1arPus/QwIGAiJ4F5z1UgkCii464ZATAIQQsre65D5Me7FhhMTUXn064233CCzv94Mzgt26nc/n7z5RN9e3KZIW5vz1+YMHDxIk51xMIcT9F1dVNXRdUWmKdLu+zgTusix9tFH4EMKinFXzYqLBdhF08sxG0ccEYIkI3eg3tztTqaPj5bByox+tm+a6mc/nFRXXN1d29PP6qCkWfdvfDLd1MXvy5Em7a9+8eVPV5XwxK8uyH7uu75y3ztuUEspUFIU8kcPYexeUloC03W7Xr4ZgY7TZFRUDBRttiH5pjiLGwhS84VJL731HG3CAkrnRt6x3zknDTaWFYhFCI2ez2QwA+m6oq+bm+vYv//NPfuu3fmtxb3Z8ezS87HPuCADtps/PfHOz4yiycKRUwhjj7CQIvgW/QEKKMPRD3mz5nD0QVfcnr4cJpkSxWcwePXkUor28ufzsp1+nlIZxJBZR0vvvf/fv/+5v36yuv/z6i69/+WI+W1ZNqaQ6Pbk37Karq0uK2N30jx8+bfv1//h//B+ury7/zf/zfyoW2uE0VJfVO/APfvRbjLHV9vaP/uwPn118sRrOm4Xp693F+mXBjmSlBaAQeH7+bPP6pYAq2gDABMmff/bpi6u/mh/L79Z/nzGUUjjrUySeYfyQkGUYccbLJiYIABiAMUEIVAKU5koSIiBEgJjD21+rwBCRMYmALiSikBIBJMYoJWCQII8pUwpIFCAlhhH+Bsp3H4y9g5QwJUwRKBFjueyERDm6M6KUfYUpEhCFPf4DiAgYErC9We4dY/uvDRQk1CGEuPevDzEiESCBluWhfZqdJQkROGcia1nHDFXdy05NE/K3ZuDfQqD/dtURuKMb7S8JvoWCF1WptJRCUPTEQRkjNAeu2ulWCEmMkKmUgOsAlASLwzClO9OtfJoTYUqU+3L5T3KDIp/awQcOAMnd3t6+ufrsV9/8xeXuOcGU27nGqG0YxzDoUislxcjSEAHE5EdrbTe0RWmOq+N6XgNQHonl55nuPij26FCIIOWQJYpSSqJGxthkp2madKG1ztzKYRzHaZoQsa7r+Xyeo3JKiSFTUjdNM8lJyD05dRomVYlh6BkJzU2gSER5ogwhieR1wQQq0CCVkEoCkyEFRDTGJElE1A1t9MkYs227aZqy4k9+SiEEIUSIvqoqprizPobkXUgpdW1fwmxs7W7Xee8zUxk0Y4zZ6Cj00zQFmyZhhWZCCG8D07wsqijT6mYdXNRKK6nHYco+oYxxY1QGHubl1Pe9914ro5RqmiaGFGPsu54VLH9BXmwZoMQYy8aLmY6c7yKHimma9vmBlHinpRBCSBGIEuccGQjFs9qR9z7viJxLZVpRFsHP0iuccVUYwiSE0IWiTfLBDcOQtUc4k5xjnphWVTUv2dbtJjv229HIQnD0Q+g2PfMSvRAgydOwm0IK9az0YbTWKi0bVnPOuedSSKXUbty6YDnnTCBxIEqASTJOIY52stYKK3KKoLWuZ3N348q6qGdV1sOapimFxCNPAVyYGLBS1BTIjt7bhMBTSllKJdhgKgOQKJIQYohDcGO0iYHE/XHBpZQ++Gma4A7rkx+v1rrfjN6FWb3QwvjRM8bm1ZKT3G63Usu8qHLtm8XnZydVc1SmlLbbMLW9CwQhBuZRJ+RJN2J5NvM4dW7b2g06lEtpBweBiajsFNY3myMxPz4+vncqiCjTwYHQjylZJnmxLI/IQrcZvff1w/nR0REA4AVeXJ0LxetZtTxamlKPdsjvOoYUQ1JaYYnZ3j4rqAw7i8Awog1jSgkllrWRpnY3KSGxijXzpllWLtoQvY9ud2l9CMF3MZncSmFCBPKCmzwWWa829+/fv3d69vr16/tnl0/eEfPj+nZj+ssOEmbGXWHKfjs6505OTuqi2WGrjVwsFtvtVjDGhBZSSqn5tt2OvX3xzatdu23k8u2j53CgkwdHoyhZPSsZx/X17vz5lVFls6y4YKoQLk3jOL548eL88s35xbku5fd/9L2st1IUha6+s71t+2742fM/eowPHzw5/eXl6+12Uz6El1df9z9vr//k+t7Dk3/yz/+x1oVr+5vbazu6ENK/+Z/+53/w/UGP83l5sjxZVAuznC1/8fWnv3z9l3/vo3+k02xeKK31MG2/fPmrZRQPTz84cYNWJWFiHJGzSCHGKJhkBMgIETgj5JSr4dIkKZFzFAIEB8SEmBBSno7sCQCHkg6AcRn2rr5MsESMMQZKYD8SZQNCAkopJYhAiJjoWzMGxr6Vm7DTwd+QON8nPTFG71kkSECUsqxxRAQEligbHAICSxEROMs+9eTgrf7wYZzsbWKcmVIBJsQIEYCY4tIlHkKw1mXbRAAAQmd9Pdu3c3NplYOQc26vgwYHdvKBFvy3V8D0lgJJgm+TD28BEgXuIZHwhKDsgCER51xxTUTexeASAw7AKAYOOvtOYkJEDoQpxhCSNkEIISUgEovEEyIi51RqUKa+vXn1+Rc/eXXxi60/D9gFPrz34dPry5vR9vW8SpA6u2tEc/bwdP2mRQTn3GgHZKAKpUtdluX16pLffQ6V9x0YArI1b96BgkljTLttEdEFS5CMMQw5cNDK7PpNDhuZnVIURf5Xu22bUqqaUmrBBRJQBtmePTp58+JyWE0AggiUktIwYhRtCkNiFEGC4JJzxiXjgsUhhhAYcmDJWT8NFgC0MoUu3OSCC4KJ0pSQYBzHsR+ZYIUpEXHox3EcnfVaa+8CL5kpNGCT47SUUkqBiCxxBCaFouBTSkA8hrjd7KgLZ2dnRNTuWgR2cu8eY+zy8vL2dsgoU6XQu3DIw/J0cxiGrPAjpSyKIviQF1gOwIh4kCbNBcRhGee6YRgGoXmK5ClQAiklAhNcIrD8ZTHGaZqAkTEmWxxeXl4OwxBCaJpmuVhmnFff9YuqzhDAEIKzHhIqLcvGxCn64HJaqbgCgrG1IYSxtcmnPF1WQfV85Bi67RBGmsgBMY4KOSUMucov6gIFosCcQ8SQpEAgzISxfJbm1CpCAE6LowWXPDNx8yEjtSyq4h4/zSC7RDHrMI/TYK0tTRFc8N532y6GNI0uI6oi+DzGymuVcwlglVIwg3bbB4qMM8ZYpL3CRoDwtjgU3aHbokta6qEb7GiDS8kHFnujzRC7vIzzxYQQcgCWmo+2V0odnSzK2nRdJ6VcHs/zd/NkPdnZUb0byq7risLMT6t+M7ktpA69jdGmYJOUMmFgiMRiPwxZ/iL4VM2bOOJ6s+p2Y1WXKab1ar3b7bTW2QtrvV4DQkwBORhjZrOZtXa92hhj5osZV9xHD4kJpgSlPayPYoQgGZ8fHZ09PP1ifIaEg++nm4FLxjizbhrHkXpDHJngmBhEZkdHPHgfCq5u2lshBCS8uboVQtRl8+LZS25gMV/ef3BmRzd049QPfgqVqq1NDKWRZe7c+OiQQzUrBXDgwIUQUgqO3Fm3ul73fV+eztJbn5wiMcbM3AhCm4ab69tpmta323HnzYx778dxRGGYZDc3N9t+Zf1UVObJ+08H187r2cnJou129T31/vd+0G7az378yVern77XPg0QIOHWrm673c1qrYxZNdfn/Tdswn60TAEk9uqri6cP3z1ZnN206z/683+7C6uPvvfO93/0/Uv/6vbm5Y+HPwwf4YdPygDlwyen9y6PPvn8z4/Yu0bq956UnGvESOARiIvEUkROHBE5MSSGiBw4srpiAMBYrnr3mFvGmPPxEHcPfdSUknOQEoVIiQA5Gs4SMETkG0d3jgCZihNzsZu+xVIxBgCUG9HOYUrsroG87zWkBCEiQZbUSEQEGBGRAUGCRMAIKNGdHgTmJi/9+iefXzFGzhFYJLA+TJRQsSIxBeB8HKfJMcZLUxlTElEMEGIUd2bP+WpyEMqkkUNkpf9//OZc+O5b95j4Xa8bgXubBEejJUPUWkAKHGF0iCgZMjuOkMCYMkVKhFIo4vvRe+7/7yvRor+7zqg1E0KHEIahB5W4khc3nz978bPddDmElcN+tihTSv3YSSlns1k+m5hgkWJVVZGCc26mm2pWNU3DBPro8hvPPzE/jdwdVTIaVQghMCFDnuNEjLEu6m7sok/AKIQw9iNy5Exkl1AA2Gw2ZVnmug0Rp94BQlGb/NOBkxK6UJVSqplXLAoamJ1cjKxUVa0r18WEMfkIQEhgyYUQhBS+933fp0BlUUmhk4ChG23fqsJ4FzjnMSRRyLKohn4ch6moTQ6KOdSlO6bWCJ2oxXxR52gXQghgEVFEIZhICIwiBy6YTIgJ0UbXd32K5KfIGPXdgMCmwTlvISIFOAjZ5nDeNM0wDCkm7z1FEFxm7I9ge0TSYd0e8t0D8DNLSmUFUAAMIabkGfNKRaWUEFLKPDGBGKPzdpO9iudN5nP3/UhEi7ngTAz9uNvtpsnaSVFApng2j0kpITGppFSYkUfOueD3/uhCiG7shBCeXEopOXJtYtyFECiBs4EiSS2rxpAMTKMyUhhZhkJyBRGc9Qx4cNH1gygYQUJE4DFbbO9nNABlU9TzKif6uQ0gFDdSW2t34zY/lkTJg7NpKlnBJZdSMs6ISGkxDtYFh4i5u0BEyScOnIHgKJumGXtryeWVnCD6yfloq3mVOzp5px9+ek67h2GABJxzgSI3qLNspJQypuB8ICKhuNaamBvdQCwJXRW14YqllJSRVVVN07TZbDjnDx8+PD4+VkqdnZ1Z7MDz2E0+eiAmgCdPYz9Z7JGw69vkwaiCIQ/eTb1dX236vpdaSq63q7bv+3EapJSFqvwUt27HJeOcM84oTt6G4OPUuxQo7zhAElIwzQZOzjkQUBYmUnDeTXYMLh4/mIcQNuvtzWotmGzqGUPJQgTOhVRcIiUY+rEfO8LEJZtXrKlmRFQUxXa9G4ahaZrg4uXl5ayZL5fL9qhnsPVTnHq73W4TJGPM0I8ppeCjddMtvzXGCB8tAKBHrTUSL2TFkc9KmYcBh8/htBUlU7yOrbs+X+1ue8mV5oW30W475y3w9ODJmS5Fb3ez2ez07GTeLCKFcRxxm6QUQjKu4ejB3HDz9YsvrleX//j3/6vl4uSvfvxlrY8Zqpv2wt5sX15+sziet4Mtaq13qttsv/P+Rw8fPypk9asXn/h+R9Vwtf36+dWX87J8+PBIzMZU3eoGH5qT46/vP11/9PL1s6cPnzx5/H6hK+fIeQ+YtDHkAhIwDgxRcGQCGCAykvJwr+xOIwVTwgM//a8dE8HFw/EFAMBAMGAMGU+UAIgiAWavvQSASMAzPxUR76g7hAgMs+/RW4N2IgTmfEREluWWMTFCwYExlJJixl8lSjFBopS+rXoPRcMhczo5WrTD7eXNy9vN62HstNCL+qSplqvh1TRN3kajqsX8qIlLzmQIkbGTrGmeUsoE1pwwcia/pTjf/R/ynf0dFXDaX0VADuzOPboueUyRADmnyU7e0zj0SksuZ9NkldBCKEREYHbsYwhNk18GUBZOzF4qRDFto8csLj+bzYpqMdrdxeXLIb5wzn3+2We3/UUI4+3mAot0ena02Wy0NEqpzXrbd0PTNJLLV69enc7uc8a5YMRIa80EWjflKebbUT9DgXKszStBa43AvPfTYFNKVdU458YwJkzeBS+9ZtooU2iV67mu62KMxpisFNFtRqFYIypdKJ9sjohDP0QK1awwuhh2U1y5GB1ibXQR3WCnMTgvQXLEGCDYEDjtrnfee4Y82YEzEX1kKIwsZbnvNA7DkEmcKSWllFEmhRRj1FI3VbNYLGKM0zBNfjDcCM6AKAbvos3BL9PPspqQVkBEUsmqWUzBUAQh2MMHj5z1m/XWjhYAkLEYkp1cPqaJyFk/9OPJ6bGUEiVTSrnJexcgC84X8pBUHXaZEAIjHlL/vLAZY8YYF13OhA65YI7Q3nsphZTSB3fADyuljo+Pc/qYew+ZIWOMGTsrhMiebzm0xOgShtGNHHhRFDGkabQueiBW10opxUukoIAlmQwljJS44sFlti9I5KbQvDYgItcoUFIkKRRFSJG8DW70280u87WIR8LEFCojpRKAEIIXcq8rAgBKy9wbGHZDrn1TSomQMVaUxhQ6TE4Z0dQzDsJbzzn3wU2217K0k08UeORDPyodOOcTTcrsR2n7eQ1PgOR9yEE358c5VSIi59x8MXv96s2u2xllELFoyqPFSdu27bhlggkhGGf5we5H1zxXcfKgOJYnCLb33sV+O0rsjmbODaHbDIptEwtuCEM3uCky5IgiTKlfTz1u57PFfLZAYhxFt+v7oQOAzWrDGGuaxg0ujzNijElA7p/rQi+Pl0Yb5+2wG2KMIfmyLKVQ7aYP0VdNKXgFRNW8kBVXRpRNMU7D5cXV9eVNu+7/i3/099zkU6R206UAYYyCcRm0XhhgFKJ3k4+TDylwwZqmGWhUQq9Wq6PFcfNg9sknn0S/nc/n1xc3TTHXymREW2kqrfXV5bWUkpDatiUiJXUMaeo8RiGyJGlKyQQTY1RcUSQttXWW/TpzZt9CxFExLZnWrFSgjDS532rK8l5zKg1fLpeDbVfr9Zya29tbPlX3zo53q3a3bh89fRgBLl5dK6U+OPte6FN1ov/zn/3k5PReVVXXL24asSThJzvYyc3Lo4vVs+SwWZaLp09+/uVfEdHDew+P78+OC/2j3/z49ctXQ7975+Txf/Ff/EZ9dM/HjhdNZcp3z97/4dPf/vTFX5zdP9VaMA4iMRcDQUJMACl7ygqGnANjIDjCHdwbABCBiKVEMSERpbsAfKhfc5i0PhIRg5Sy/Q4mIEYJOScChISU5RgJgXG6E8TIKT69LToW+SGu35UjAIAhJsYJgOUKk3NijAnOuElE5B0GRwEpBmIQsmnxYTPkWjPPQq6vv764ef7Nm0+uN68ihEIXjZxpUV7ZFykRBNRSl6rQsmIkiejB6T/JFq35XMvqRZhv5G4hvPX/v/OTnctSSgDEkOVDvO/7r1/8JQDUdVFV1Xp94+zY9/2DB6cffvhfrW43RVHU1czZ+ObNm+fPXyDix997kG8t0/y7rsuXVM95Simryr3zzjvvvvvu9fX1J5/89CL9fOyHaeslqqHvhnaYy2ZcOeCMM8FRdsN2u95qYVStIbK222UdxEBhcuPo+oOkba45MigmF2QHA4Dtdjt2o+Ayn+ynp6ejHRhyIYRPXjBpdCHVnuCXRXSJKJcXucCyow2R5dpXSK6UDFPquyFCKIoCReQxaeJxAh+d6xXwZOPkveeKS9LBJWsDQBh2kxKKCxF6GsaeiJp6Vug6QSh0yRjbbrejnYqigIQcBRBOo40xIjCjC62M914KJbkAD5Pbu/QgcimEZNKjF1xiwhwL7WCtp7quOUgbLHAstBYop95OyaaUlBaCSS2NUYZz7r0PzlrrVquVUkpJnYNl3w+MsWEYOOyr2xxiDyJWw647/CERZf9BAGDEFFfECLIOSkKIAOxbU5PDtIiIvPdITAntnOvbITeQhRCCyVldF0UhFOecm0JnMmgCtb0MhzUvpcTEh3Ho41icotYFL5XSUrpibJ31jpQnIGQMIRHGyLxWJomYWAg2Ousll1oURBRdmkbrnGdRCc2SJ+ctSoIIFCEkXxaaCRmSzw3zDNlDRGAglGBi3yQ7TPSnoVdSV2U5dCMIMHNVBcMHxr1ywWUPvhycpCIIMLkh53/Rg/deSVnNy0xV8t5n+m/epPk5L0/mSsvXr1+nSAgspWTjWM7Mur3dZ+Ti4DbmY4yu3YtyAwBxyElYjBEc933iUaUJr17ddl232ey2192snkVPtvOUQCgRQkhdUkxvp06iycMFAGf9hIJMLZvY2NHGGO3oXR+0LENMFOhkNh/HMfk47Zzvo/UjIRljQgqSK0zMuylRGrrRTZ5zjiCEQa4ReFJGNnXj+ri52n3x82cxxnbbRku5+g8sVFUlG0YRpm7ccw69wMC4Vr0fpJS7bdst+rOzs8KUbduWReitffnNm8xfL8vy4cOHZVNs+7XgnHHiAiU3SJhcgsSYFSJ/331ciRAoOOuDjMT3yBf4a5xUGa2bgHBZHWtuXLLEYjUr1QJ8cM+fPyceHz29PzuqispcXl+Mt+GTl5+DTvcfn7Q3IzIoiuJye/O7R98rf2guhldBuQePH7T91w++cypT8Q9/+INvrj5vynkBzesvrl6+eAX1+PB7R4/e/a6e8dlxxQqQpXzy6NGymX/0zsef/odfXF6+MUelF/xnX//Fd45/46P3fnRUHN9/v57PjgXo5CPjQgk+2mDtaJgEIAaIfO9Wi4w4shCIsXy/PBHGFHOLDu/k6fGtDwBkVFOgxFhiCDESQnbNQOAEBIgMDxIawPCtmHsQkCQC58MhAB/qVwCIiSEyYrnPHBGRM8YlSgMpYQIigiw6kigBSwiK7rS785mVt9af/9m/W+1e3fYvk/J1KRGnbrsaNgMeDZJryWSIctdhipAcQaDV1WnGcVRVdXR0lKV6tdZ2DH9rC/rv+sQYc1qDuIdiZTbnn3/6Hwqjzx7cO/LLq8vX0zTG4Kvj+LNP/+zFixfzZvnkyZPg089++leffvqplsWzc/mtP8ww5GEqY4wjSyltNpuU0jj+UKl4eXn5+ec/hafXMYKZV4bpm5trgbLRi/ZqMPf4Zr2d1bNZNbeDc5NHw85Ozp4/fz6fz5cnSyKa3LjdbYZh4JLXdZ0f5kFwMec3dnJc87IsFVeCS+99u21ztzAjP/O8PLed224bmG2aJuN4c+wZxzGlpKSe/LBrd8QDIlZlLQyzyQNAgjD6YQy9rLSW2m1j1/b1fc1HFiNDREzox9Dt+uCiYQZDtn5iShggNvaub69iPdV1vVwus5Rj9ibq+z5BxDv9qYxmQkSlFFnMBWIu/YuiqKpKGMYV37ccBQkmd/1m8L02yifnvXfWI91UVVUWFQO+Xq+1Mlm0GQgynxWBlUU1jG0IYaCx67qhG70L8/lcaz2OY9Z5SHew+VxI5d/m3AUAch2fb+TQljgcSkRU1zVjmLKI5p24UAghOsqSyLluzjE+50AppWkIWb2EMUaYGLL79++fn5/3fR9DUqIQQkBi3gXXDlikotGlKKQsxtaN0xCSlUlLJhAgQvTkIwjASJCsTavVSqCc18vkaByscx6B29FDwgTRhSgIOBNaaE7cB6dBE9Bkp2EYxCi2u633/un777w10P12IHJ0ukBgFKEb22DjrJk3y6aso18j9QQMEDFEl1JiyH1ww9Qj8bIoA6fcOD0+Pjal/vrFV9n4Ln/z/eJU6uLy4sMPPyRMXdfN6vnrV2/aYffdj7+32+245FnjLFEkolwQD3acpklyWde1t/768vrm5kZr/d7s+3G45ELywFfnGwBYFEfe++EmJEghRaUlF2wcJkpQGnr86ElOrIlosVgsj5bZU2vRsOfPXnRDx5IQTAlURJESIAkkgQQQMPgYXJJGFrKSRqRI0zQwgYUu2q4dp3VZlrtNK7UQmgnJ63JWliU/UtvYrV/2BDERVUXDJa7bdYieF1gIk9FYAGBEkYAoAHoRuU2MtDLXVzfbza5re4ZcCsWt6HeDd94Hn1JiHJtFJQ1PIRGwsi4UK8Z2DGMSTPgQsXnY5JQK3mrpMMY4yjx0yVs0r2ylVOQ+pgAAmQ0NjIQUSik7s9W8qE90wun4dC6QXV5cPX70RMXyzetL5+3irJbzNIVBcb2sTx5Mv3V//uiP/uCPv7z65bu/dU/N2WK2OK7PXn99vjyZ/fB3vvurrz97df7qFz/79MWX5//wH/zD//r3/1nq0Hbxk0//ks/cP/uX/6go5OpmHcbISb/7+LtxJ7dv0u4i/eijf3C0PBvSTdUUAGBUGQP17cBRppAsswf8ZC7ycmxY1C5zb3IOmE/8aZq4boQQ0zSllLJdedbxqVmJnLhkKCBEN00jABZFMbbyMC+Eu+ExY6wdknNDSqmuiqpUiIiMhBCXK9H3/TRYziQixkBZnyw6SwjIgHMMycXkq6ZoZpWb+ixVk4ubXKQSEYfZYfPEGOfz+Xaz+9f/+l+v/J8656wdAzpttChYpOC9r8TscBwH8rpQRVF472dq2a76hw/e+ae/98+/88730wh2CEabqzFWVTVMbhxHpQwAt9YCiFqfvE3XORwQESZImrMi+ljPxOff/Pg//qf/+8N3jj/7xU+qqsj3JQpmnQVGx/eO1m+2EPHk5GS5OFqt1ldXV84GIpJi3xuIe1MFukuCGBEBEipigp3eP+n6Nv/NxcUFJYguTcMUxsSQP3r0aBfW4zjO5/OqqrIZ7fHxsTFmvb3NrWZjTNas2W63t7e3948eOWettwAgtZRKAECIvmo45xwS2slN0+RtcNZ579fbUZf65OSECVxvVkRUNVVGHWf71YP8Qg45XLAM48oC0VVVIaL3PgbHGMtQnVzf57rk3tlpPpgyRDnrDT1//hy8mFXzqmjAsRw+QSRlFBEwyVQhiqLgTJCD6Ig8+uC0MsYUNzc3gMnBeProuKjN9lXbrQfB1DhYRDw+WYbk2mlrVGmMMaXqbb9a3xalObl3zAXPEkIHN7emaYwx1loBKlerOWXJ1y+lNEXGSEPuTs/qWbfrrLWReymllgUD1rc9Bj5r5ggspP5u9iGEUAAQXLDWLk9mWbYpFxl93wNAWZZPPr4/DIO3gQHv22G72aVEdVEziXcdeOp3PWOiqWvGmJ7x7OsACGVdjOMYoj87O3vzq3Xf9iEEijSNVgghuXbOVfdNSgmJG1E09YyDvL1en5+fq6OUceM5W82YAGMMT/vEMUPPcp0jpRRkJjeNUx/Jq0I186pZNlVVXV1fzmazuq6vr6/zVCWPV+azxWazuXfvXtYdE0Lc3t6enp5iEayblkcLrfXFm6uxs6Vsoo3IsW/7vu8hMQ48pQSEjLHl4+b+/ftd171+/Xq73R4dHX300UdFUWzW2xDCarXqdp0SioFAxLIsA7cPHjzQWudFeCgPnHNFUQBSIC8kZxJC9M459DLE4IMzpW6q2dCNl2+up97+o3/6uzc3N9vtlgHnTEyj3e120zQ1bJlx7xmdl62gp2niEqtZdXx6tNmtfXJKywTx6dOn7iKen5+vVqs8kvfe53/OSjFMndQSDjDhSCdH9+bHTUrp8vLSWpsh09nGu1/tctdqNpshYu6oCSG838/dcxQ4uDUoYYapYxqYYMMwIGGtZ5BY4uHJkydt2z578bUpzenZqXOubduHjx6M46iNEkLcrK7vnd07Ozt7/OTRz//sZ/n7H9wynHOMsb395NsDzn0jCFjuMOCditMehZTAWY8MSlUApwihaMqzs7MbdYEi3n98ogqM0RGE+++cPHzn5MPHP3j51Zt23fvkHA7vPGqacr697W+uL0wt13h+M7yBl7265c28ualvVaiAw/MXz1yc5oumaMzpw+Xt6vrf/8kf7K76y5c3Tx7fvzdf/OQ//+yD9991YxjXblYbRaUoK3ksa4ZFVXhvQcqUpHNumvoYKEUqjSJKikH0kTHU0iBiNtYmonVgjCXOOeLeGT4lEaOM1jMWpVSC877NEE3pQtBlSjFiAMYZMsa5QcQUxWgPs3MEgBgJYkRMKcGeXaAEAHnvY/IA0Hc+BeAsUnKMCSUZJQo+IDLOkYCscyhiUSopow9tWUvvh2fPn63X65RSdsIpiqK/TaXWBAkYEbP9dLVqLx1ex5ByRVsoA4KctREiF3yapgRRa6219snFGJFYXdfJpn7snJuUlsjg4voCE//+4x90Nx0Q00KiYQQMEZmuUkpjaAEB1Z54lSdMKSWtJQUK0U/WTrfTm1ev37x5E3i/WMzGcSRISqnQOhec93573T558ji6NE3T+fn5ZrPd7XZATErJGRwSmsMAPh8rXDAmGfjkJ1+0hQ+hKA0QKmGmYRr70U1BcVWYUkqpUbfbtt22DJjkUjARfdwMm7KuiCgjcbwLQogUyeii61oAQIY5J7NuEkJIJbbrXebXppQKXZ4e1d779Xr95mKVNXulNGVRWTe5yU0w3bt3r8MuIzjybrLWpki6kIwxrTVjbC9lIKUQQnDMJvM5YKc7Md6MHz4MVvM7XS6XMpYxJNtbDFwxLYwgEZlgYYKqKZpFzSR4732MCWKgyBpiLDFJSkkAYJHFAQZrp2nqp15hJMI9Egf2DQDCCABFUSxo4YPruk4Ice/evVGNbdt2bZf5PIUptNKcFCWYpik3uomIc54YxbAXls+nSowROSqjHEZkoAvpXYgUkSWXbKGKYTPs20JIMSXBJROsEHuLtpy75HlqXh4XFxfBRc545pEzxqQURVHcbm7y0F1Kbo4NQz5N0+1qVQdTVVV01I/dOIxSyaIoGQmllNMOET14IbngQkhGJF0fYoxAIcqUIjEUo++ZAi0Lo0rGmJuCx8hAIOPR0zT1hzMzZ1Q5O3TTxCU0qs78z+BSe9t3m25wQ4pECYBQcJk1io0uXr16lV/33gyKscVikVKaFbWUkjORHywAOG9TpGV5HKbkIPjoQ4opEhFxJod23OnWey+ZUlxPvb2+uFFKWW+zwzxHPg2TnWxueh2C3CH6Zkky773z9uhkWZeLyY6Mo1KKEpAXXPFcWQZy1lpKoHlxdXHtXYDIEoGUvC5rTnxHO3J7PfBDiMnpWuLovb+6upRaSqHadlcURdu2NLHct3i785Hjd1EUzbzphm4YhjyxbtuW62/duzPROeeFzoUQAhFmrMk4WuccYz4lPHRf4A6iCACjH2OKQzcKJYwxSGgnSxF1Lfu+V0rN5/Ntux2GIftpKqW22+31zdVyuXz06NGTp0+GYbi+vn785NHnn39+eXmZAfmm0Iwj51wcLC8Oj2OP+CXMW+UQffOXlbIOIUgjmnk9hWG3XYXdZObi+3//u5/+4pfXF7dloWN0s0WpjO77/vn5Vz/7+S9ffnY5DtO9hyf/zX/7z+7PHpx/+VefPftF+QCXH6nH5QkjnsUEZA2F03/wh//2O99/58l3Ht5sLut58fjx44tXl8VC1vWpUIxCvL66HqfhrHoAgAt+/6Q8WxZnBZtNgLFUhdZdOyVE52iaYkoghJBK2ujOL14byY+Pj6UU19dX2a7k7Ozs3r17mw157xmLxph8ShKRUkppmCZfVQJADIPLaWyM1qUs70GIe/o/MhqDnUI8ANborU+KnHNGmWnHUub5IWLTRCUNInobEVlezdkQJld4wFRVF8jT8xfffP3p19//wYfjOH7x1U9evHihtY60zcnjg8VvF4Ue7OjjVM2lkPirly+/efNJCl0IThvVNFUIrh96wlQ2Bd4hkxljhhed7bpt36SmLPVs2QiJLy9efvWrZ3/8R3+mmPnn/+yf/+i3/2m2tFMSpymkhMgZJGRyD4cJmfBPCTkKyaK3lAgJtWIRUEhWN6Wpi7muhtf9NE0pEiIvy4Wqdc5DOYiu67q2H8fJOSe4EkJwLuDOyuKAytknqgIZYyG5YEPXdcMwtC0/XpwopgMLFPciu8MwXF5eLs/mHHlwYRqmfGT3bd+27dnje5lmineE0b0Gb4yc8+x7P0yDD65pmmZWBz/EkBgJLY1WWisDhIWqHj54pJSiQP2ujxBjSETEBRv7cezH3GXJY0VIQJGc9ZxzrQQRxZAsOYZcGsWQ51SAM8GQM+SccSGEDzaH5HypuR01m81iK/qxj+NeoJizxDhwya9XW6NhkpZ4iuCJyIEf/aSJJ4rJphA9EPM+jlsfYwBOebqYlUZC9ImSUmqYOkScpkkZxblwydnB80rY0SGx0lSz2lMEijANNoTAySMiA4aESIiAKaTBDocTLSsutUWb2Sxlbbz3plaxDYkFyfUUBmHY3esGgJCHMlkYBAByfpDrmDxIBoBgYwiBGAhUSinOBAAyyY6OjoZhWK1WADiv50pqIqrKMtpIClOEsbOOh+OTEondXN1O0x7DFUNMKfk72DBOaq+imiClDhGB0/yoKpsy95z6viei3OpwzpGHGGOKpLWqqio30mOMt/HGFEWhCiIah2kYhqkdACDxGMeB7H7K4/su89ZyEz6Pw/PKn8/nfd97G0JIXeilEk3TKG6G3RSSH9vJDT56gsQgIYUIgBQxDrS77ohIgi5Y5SbX3Q5FQZaNxhhjDCRIIXkb8/0ySflNYWaZp7TnW1Mctr0yUinlXcAAUspptNRGbhipSDxGFyY7hSnwoK/Pb6WU0cdxHLUs5vWcRUGWheBzNpnnStmlo6qqMbU+2nbXnp2daa1TJMZYt+tTCwdL3PSWMAAyrow4uXfS+Or169fBBc21QNG3Pec8hYSE0ceUUgopYdoPkqzNjfe83wGAc3WYER7ANJzzBBRc4JIzwYZpgACGl1ywXFI/fvxYGTF8sdfpzC+rruth7PNzCyG8efMmUfz4/e+4aEc31HV9cnbMOQ9bT5DEIbL+zake/rqqQ77zYANLAoExzozUVSqERuKxMUfD2r/58jmkdLSYf/j9RsyVBZJLpcikkdk1dcp+8/mLLz7/6k/++E/f/fhpecq+8/D+4w9Pzr9ef/7zL7fb7dOPHly9utq0m9/47f9u8O2vvvpcSlXV5Uff/87x2fHVm6vfefqj2/PVy69ePf/8jbb1f/e/+5ffOf6+lHppThkpXqQoOBAAJx+AKDm7B9EkdG/OX/75z/600vSDH/ygKIqf/NVPfvWrXy2XS4/fdbA6WX4cOhtTIkaJyIYeEbWoCTCmtFqv85HHEK1z1jmPLZLUssQkc+RGROtHofQBJPz2f0opKTHPZYlTrmNCCEcLxTlSQi8hRi84MeRGsUChKsuiKm5WF988+6vnL7/54psvV6tVZy8Q8fnrX23ajfHmy2d+t9sxxn7v77OyKt68eXOzvvjOB++99533IwWheT+EGAkxOh2EEFppqWRVVLtuS0Rt29rRVVWVAo39FGyUD2RVV23b/sEf/rurV9eXr1c/+uFvHZ0sGaZCM+cTxiQ4ueBjAMalZDqllChxAMY58LsUlfnoIfqotU7otcH5spKMAJKUfByg70cgJAvJAHpxeXkxr+9ElBjTWmcuV1YGuIN7hG8HB4qIUqSYu8da6q3d9m1fsHocpml0RAQcYowRIo1J9Xt0T27+5HOcMbbbtkVR5NQ1d02n0U6jbYomJ8I5Kse0/0Fu1NNgQWNTz4jo5uo2h9L7xw99cpvNphs6bZTiGgSUZXl7e7Pv2gFyLgSXMaQUyZQmBxLv/WQnm1y2Gahrc0hociM33/thLEp3eqh5KrRab6MllrgQPIUUUuARBZcKTHDQrYfIXTEz88Ust+zafmckA3Ixxhh8HoVIxbu+i+R9wAgpxrhtPcikC91UDSIOw7DrdrkZwBhDYJQ2+7J4vpRCWWv7bpimSTOTM0tIkLtoKaXoY7frjDFFUbA5m6YpI3sBaX7c7DY7VYhhAK6Y0nxoJxumqqy99877lAJjEMHH4IkoRJUH6nniTncw71osMLmUUgxJKcUkOef6vjs+Ps6lj1JaCLHd7TabjRCiKutxcD4ETEJrbXQZfby6uKHAYowMuVBCCAFpvwZY5Bgp27wTUWKxrM1sNksWpJCePAcOAAKFQEGMSO//Ya6/8U6yRlfSGKWV9pNPkYIj8EIIYX0XIfVhwD1TkQkhgGFVVDmQbFabbtfN53OKlEKigAjobeKc5k3DaLy9XK9XGx1G51yKILkUggMySsAYo8C3qz6/Mg6aEVDgipeR+zwmyPUGleicG8dRMMxzN2P2GrcAoJRqZvXt7W3W4s5p8ThM0+CUkxPZFG0htClNWYIVyfYRAksEfoy2C5715JAS2jEQxHwG5lIk17KIeHL/pCiKeqiBQd/33oV221s3NWmWn2d6ix4ppfQ8uuCYorN7p71tz19f8MQLzbzzIJhgigmBxBkwjpKj1MpgdkolBELOBGcx95nwzhs4pyD5wB+GwSd///Te4nj+/PnzbbdTtUHAlDCnDqY0dV1nx47cNsgzkcOIpO9766YQ/b2zU2SQWwg88aI0RVHsrb4Oo7tDGMY71GtOgtid7rkUinMefWh3ra7FcnFkKmmMPn99rpUpTLi5uEo2ai1JuGpWui7ubkcMQvPCW//s2bN6UXz40fvNvXLT30b0Z8tH/oQ/N69ut7swxS+//PKDDz6oyvqLz3755OljY8zN9erJkyebdjPYXutitb6NlpbF2fff+a1/+MN/vBRnw2CDIyY0g0iMQgi6ZHHkDPdswl27WbXnz1//6svXnxnVdnTOOX/16lWfegX2y/Pxs5fhH/+2zUSRDJ/J5/5oK8AmxrharYQQT58+RY9XV1fOOa+6khf3j5825iRFiCEKyZG4kDlryU/yDjGMbNyNGUEjhDCaSymdn7quA56yjg8QEhFDXhZVVVejc68uvur77vzqzYvXz1++en5+/kZp+eO/vDbGrNdrKSWHeLnqr66ulFJ/9ov/ta5n2+2u3XURp9X11ZdffuW2A4ssxeis36zbolRlaTRXlMgnlwUQtv02hqi45kn6IZ6/vmjqxvXh5no7bqfZvL7/4KyoTaKprBpu/WTjvCzHYWq7oaoK51g+apVUB/BXjDEJF0N0wQvJnR+GoU0ptrvtxXajtRZCpBSU1PmAq8rmZmVjEdmeAMqJyE7ee9+Ou6IotNZ5JB9CyIev1HuN7pBCcMGOdhom53II3FlrlVJFpYFBVVV1XTPB6kVdFIUQYhzHDP4nou16S0RDN1hui6KQUnLkHPeE46zhpbUGVDkEehsAIIY09tM02vV6kzGrQA4ApsGyJLQouGRExIixyBVqzUyMETwQQLIELFlls07TYQPmFtzYAxAyxr0NMSSlNOd8GAYpGN4pg+ZEJE8EU0pZD9U5l0KKFJFRiD5G5kZHwgdmuWEpEkPOUfBJG1Yz5Ilb7wdTq/JI1fNKnOPQDdPWAZDSMhEDnUxpcuctQcrcj7yAFRhM2LZtsHE+n0umRj8561OgoqoYMe+8HT0AGCOk0FxKSIPY6yYyIQQysVjOZ4umanQeSSAHbZTRpm+HYeyV0FprY4wPbvJTipFxlIrb1uIdKyELZ+aOfbvrc483RXd4sHlSzjmvqirj9sUdqb1veyssISGi4BJiHgHyhKkotJJaay2ECi6M4xh93G0cETFEIgohIkJOyPpxmkbrvU+RUkrbzS7vAu9Cvp7g4zTabwd5BaUAPnk7OTv5FBJnUgrlA89nbKIkhChMWde1lHJnV8GFWT1rt+1ox+PlsZtc0zTjMKpCcpYg7UO7kLxuKr+GQ5kYQoghL2FMDsbOMsYEKMYYRu7HGAviDc+zdiSkOyqwcw7fQp3GO52yoijqWcUl2263wUVTF9basZ+U1PW87n07BcdBLOqjgoJbk+87RizYQAmyOk2KkFJUQgVyWbc1S7LQnUtjRaqsjx8/ffzs2bOLiwuO3FsvpbTWvs2xPIwzWMVsDGPoaqmrI92MZRpx3d4qZg4Ns/wP8y8EE4klIqJIBMSAcfw1HsoBbpLfl7WWS5Ranp6ezmazr7/6Zn29FqS00OM4vnz5splXSikjTCa7E6TtdpuDcU5fqqpqu91qtXr06NHx8fHr16+z3EoONN8G4MNFvN1kPxTB+Wr2qQHyGH2/G30QFRnvwu3V5tJfS6nP3jmqZkJKXhbyZrWlMZ2/urq52LVtJ4RgUgQKx0cn77/zwZvuWbseV6vV8rsPjhZHT588vb6+fvnFpWDq4vXl/+vf/L+rY/XdH37XWiu5ijHaNpwe37u5vhmG4YN3P368/OC//+f/vYpVSpB8GkdfVSpnjgmcELwoBCUmGJum6euvP//q5SctbiPvO7759PVtzkzVsWrZeru9jTH+8V/8m/zcDw3kXHip8v4h4LF6tNY+e/YsxmjD5ri5r5mYPZyVShKRMpKAB9ynaQftYkQEIHZQaSZwLjrnJjuM41jULJ8gKZCdnHfeGDOfzyPjP/6LP724umCCjNF1U8YrN0UvkghTmMLENXfJISLXvKiL9XB+cfucJX528nA2qy7O33z2i882q91sXkBibhwDBudUXl6MMVXLQpeSqU3c2sGBZCmisxFcjGMXPPHEq7Ke+vBXP/urdtf9j/+H/9PZvQdSKyFQqpiAQoKEveI1MYoxBh/CHXmp1HKITFaKYapLc3Xj+nHHJHhrYyRjSikjwCgETyl5cKTDbDaTUuYsOCMgsgj/4RQ4QHbzrBQ4CS4ggO3sdru1ox97K1AMrmOCVaZaHi2MMTGFsi6MMRzF4eUmkJC95GLI5j85tB8ovzHGPAVggnHOpZZcMM553/cMeF3XCGy93my32+ST93FzuyvKI8D8bNFPIXkeIbjJIzDOWPbNjTEWBXAmjDE+TIRvJbsEkCD6OMUkpQSOwUfvg5KaIQdC55wxpq5r733bts45zvlisfD32dANw3oa+yFF4AIRaHAuOVWaQhvjEaJPu9s2RZoGC4EBsbRXtAgEAjgJxe4/OGs33aW7xoSz2YwrzhQAp6m3nPO6rqOP0+Akk8YYIwqfpuDClKZCFzHGFBJFQsJs1X7grjjrg48AABFSAJus1NIUmgvWzOuqrrwfIYF3gQHPlumcCWttb/umacqyYBF95xJS1ZSz+awXNldCRNT3fdq7cfDtqp01jSlUjNHHoJQ0hWasiCFlgNvNza0dbF01x0dH/TB0ba8LzTiGEHxwm3YDAErJKVkppZBcSmlMEVXMsbwbHIM96yZEzzybhOvYwJBn4lae5R/EtL39Fo+dSxchhBACXAySONsz7FNKyINPk5Ccc26t5ZwxjjGFmEJyMXoCwFm9aOveWquEGYYhFdBvR8YYV9I7t93sMcP3z4qdGYZhyCbz3sVsAghRY9hXjfHOZCXj1ySPQog8d/PeZ88iIYQQmI+sQzMmb+3VetXUjTHGjs5ZP3TTOFgzL1Utxh36PkzbMCgbXArJoYjd2DnnBIqmngkh7OSij8gR476lmh/aNE3GmLOzs017NZs3s/k7TdMwYCklBmzezLfdlt7iX+SNE2M8PlskXrAi9nFVHvH3Z0+6W/vFp18zK6LbC/BB3FeSgQIHPITY3FvKbyfd+V0eDoHM2uCKl5W5Xd0Ulf7hD3/IGPvJ9qcSZQ7qbdsmCM28yZbJ4zjWTXUYL+apeV3XS7fs2t68WwguldSWOYYshjT0O3EYqr0ddBGR4b41/fZgOKW03W1UoQijz2+FOHKYrHv34ydC8uXJYqjL6EMKkZEUzvTYTrGbn9T1rJ5C3227rz97efHV5t3fOeO2CLdy9bJX2idK7WrYXjx/dP/R5fXNh99778HD092mXW/WRhZFUVTsZLW5dC5898Pv/de/8d/irkCv1ldtagCAxT38J0mp9lU7g+iBM4UQr65fffblz7GwE2sblNM0pJQk48G6XOnWdf35+acZBJsJoNmMehxHzr7OEr6IuPOvxnFcrVYZmTLZ/umDpxyjVtp7jxAIAosJAATu9ZlzWkVEbWuFAM65FMxZH0IATEopwKRNIZUe+tHHaZjcaH0/2q9efvPJZ5/GFIxRjNPN6vp2tVosF3XRpJSkUlyI3NnWxoQYK6xv1rfRJcOLRbO0adQNv18spy4wICW1YhI5y/3Soii4EDnkzJpZGBOCAEpAAYCNvXeDpYiMFBDru/FXn3/x7//of7l///67773f1POUQCjjvb+8vLq3vF+WpdIqgu13O+99ZrBc33ZVcQRJx+Rvbq8uLl6N2IcwUYKu7QGJIAF6VXBEGtz6YLeVgx8iSqFTSoXRe1g+57lnlRfqdrcWXGaZiMwUKnXJmWBFmi9mRVHMj2aM42gHxtH6wU97hvR+qYvEABgD2wcGTAlFRCzbSSbgyHOvNRfogoRUBWMsBN/UM2PMOE5+8hRISU3JB98z5CEmBE6U2l3HOWccU0oJI+dcsORtCCEUutTGzOr5BHv1g7xvM+aAMVaWOl9nBrxYa/OND348sGgO6chsNvMLKKtiJ9oY13ZyUitZckLS87k2gmsEVsYYvQ1h8H5MsgbUMVlgDClB8jCspuSpNhVyjOT9GBGRaTC1ASShZO4WVEWDYYghuSnYcZd4oIjIuR19jFEwJQudZR2/FcpI4ILPzzxEL6Usyvr4bCmN6Kc2pnhze00sehs5RQQGEa31nAkOMfporeUcE4tSCaPkbD5r5nUjl7kFxRibpomIMmJcMsWYSJFCCCjQGCOViDESg9zWms1mUz9N1mZhprIs54s6S1xxzgDAuimfeIFCGEMuuQBYpCi4WJ40iBhD6rou9pEiGzvvp81iOQv+Tmo97RubGXh8aDv/2ghvII8euSOiBAl4SkiBEjKKKUQMRVUjYte1UxjzMlBKbTab/BgzhHgcR4kqTCmkMIU+piiEMNrEJOtFJQzXRjnnptEO/Rhj5AwRSRmhtfTeUyLOefS+H1vRpTwaEFwcissYo2IqQ7hzoMrQJM55t+0O8yA7tm5ymNBbb9MQU3BTsO3Wt0QYffTVSeFH7yP5ZF2yEClE76NF3D+isizn83mM8erqinM+m80Wy0JyNdnxwYMH3a57/s3zybottpmszPmvVavWWqagbMyIuwmmoigqWZe6Gofp+vM2pv3UHChnURnIvWdw5qxxH/IIkR1gB3tCeW7mFbWWWg67/s3F6+XxQgp9dnY2DVO77Q/ZVZbKR8TdbocMlFIxha7rXLCmMIh4dnbGPfgpvH79+s3L83TH8zSy2BPm4G+gy5D2PZND/ZHX03w+Z3Ivfx9sCDyZ0syKYryJPo0Y5G63vr2+gYCC9NnD01a8mR2VTx68o5T6/PPP2l1Pnb7edl+vf/lg/nRz0W8vBlFT3dRNOZO6Xt9sHj958s/+yb/44tWnvR0fPnj44tnL0+OzWs86tn109nj1chNc+u3v/uZML3xwIU5SKx1jpOiD09oQkfcBwNkx1mVTGIbku2E1DDfROHBH4ziFEKbe5ZQCEbvt4PnAKyW46OOUUgqCrLU3tzfLZobK5QTn9uWbPVq1aJTUIU0pTpwnKdC74J11wd6hyrNtAMSYYoqU0jvvvCMEIOI09udvXjx79myzXSHiuv1msVjUVZNiipGkUEophvz169cvX74M0SslilJFCkVRnJycbLqWMQbIuVCIaIqqH6b1et1eDPnari6uMs6o79da6+1mCtEJxbRRIYR+mEIIUso8nTC8nFXzcmYYSu9Ssun29tboQstisFMIoapqDqJt23//R//r6enZf2n/y/sPHnfduFgsEOWnv/zF9M7tkydPFsePykZM3m67636ibjT/7g9//uDsvfns9Ghx8s2zr1+9fFXdS8qwJ0+eWDtKJZQSNo2AsOu2z15+8/j46dRb2INc9iA1IlIKcxw65JXWWmstKxggZVq5YKJSTbfp+74/e7g8OllKJYXkCYNQTGrpusmnPYgpUciNvoSJK8z8kEPHMqf5VVVtt9t8Eg3DwCwTkmesllJ6GMbsPT5r5ohcSs85HwYXQjClFkJNaciVXAhTCJFrwShrySIFgIgQsR3aA641/9CM4IjBBR+yYlqKyVlntNFKjwEPkNQMa8gzqoSxbirBpZtCWK+lkfWslFqiyya1do9f8zT0tt+Oj99dGiV9H1mQ01QoZnzvhsnhEdRVU5bletxu2g11cQ5zU+hptF3XSS5rM4sxhhAZ8hgoguOcM2LdtgshzOfz+WxOROub1tmQJSDyxxhTFlVMHgG2221Erwo5+UEZGWN0wSqhOQQpZIpkJyeYEExkAMt2txOaVTNTNWVRGUDKOzEv9cwYzFCaplqklLpu8MFVsyLXH+vtyshyvV5rrY+OjrHA3a4joqZpnJuEFkqpipV1UyHianPrnJNSMMZGO1KgrEaCyLnkR8sFY9xNHhEpQXSUPAzDlA09052i52GawFFQ+paXDwAhBiIysgkhBOsTeuTIJTAOiOTCnuA+W9SMsWHqsplg1u1q2za/9BhjFndcrVbtuENNyEkakckz0zRxishJ17KAohidkNxOjogYyDzVHu1IRLrQGjUiTnaX17mWWgiRAuSuvk4izzXyG8xbTwhRVdU+WBA6Zznn2ugUIIBlAiXKoXfrtpUG69Py/uNTP6bdZtd1HVF00QXwyIAoIXDvfdM077///tHR0ZdffvnNN9+cn5//49/5BxcXF1dXVx9/9N0PP/zw6uIq+mG33incw5Xyw8wVaghhtbupuXai4wySDXZ0C3Xv8bsPV19++XZD961u7n5ZHv42xzupdf6GhzeId0gL59zR0ZGL0+eff/74wdPT09NnXz/L3yFngd3QnYrTxWKRIXJFUbx81a3X6wTx9vbWWrs8WpxUR+vV5vLiahytVmocJiCsqgrvPb1Pd0Bw/pbWblXWgOS9Bw5MgLXWBZffxDANMYWiNE+fvPPsq+fjxs6a+QquFosF5/wwuq+qSkq5jVdN1Vxd3AhQtg+FqCSqQpfd2nbi+p3fPD19vIg+Xl3daC0nGn7vN/7pvbOThMEY49p0Nnt6Vj75D//Lj9/70bu//OSXP//JJx9957vH9ez/+n/5v6XgtdbrtgJMMYQEQUtujPLeT1M3Eyerzc3iZB5x/P/8p3/7Z//5P96217t+e/bgNB+1GWSPhEaWUkoOPKOZEqbJjc2sLopivV4nD8vlMvdnMu0hH9m8JIm6xNkPP/7tjz78uCjN/QenR/cWR/IeAHjvN5vNxcXFixcvrLXf+973um1cLBYA8PXXX282m/V6/ZOf/OT169e15r/zO79zfHx8fn5+dnaWNR9CCP/pxz9//vL5rttMvj95sDi6N79dXbtoUbHj4+PNZpNrzbwih2GYtj4ENz9p6rru+16CRlA35zf9VZJKhOQiBV1yFIQCtJFSi67rMlIAEe/du5d7aC+ef+O9FyiqohFc2d5PoyUi50I91/cfnz557/Fu2Nze3s6bhRK6T93TR0/Lslzdrrtd3zQNI/7s2bNgHee8ntdM4Ha7bfsdMCCi0w/nWpqhGxkJiGgHb3t38eby8f13QvBCCECa/MgYk1qklJq6Okx/i6LIMplt2+Yulu+Tt4ki7bpdP+60UacfzR4/flyW5YGlCgCZOJurzJzLT9OUbxzd3mw8B7asvhlCYF5mgmBeKpnr+dFHHwU1bjabXIgYY05OTkIIL168MNMy796MmeB36tkMWIIUY0wUkIHWuqwKY0wspxxCMm2U7qgm46bLHKSsz5WTDyJSS73ZbE5PT/Nl5yE3ANzc3Dx++IQhv7m+cWOIIdnRppCULhjxLCG5971xse97kZgy6vj42BSq61trbYh+HEdZ85N7J8ipG1o3+b4dXRckk7PqiO7gb+HOKYGIiopn2lW+kqZpcgfV9f729rYsy7qut9ttjHGxWEgpxzAg4p33WkKRRwm8WMisnEUBd+t2u+kEiLIsQcasWpXzsKIo5vO5lHK73Wa8cTZ5lFLmRnQaxDRN/+j3f/eb598IxUL0oxulFJsrF5KdH89AJGBQ6KLdtrvbrjIFIlZVNZ/PU0q5q6+UAgb5ZACADNnLKKrgQqSoNJdG7Ha7yzdX4+CUUIZXdPc51E+52Qt/2yfrorxdxuX/C66k4qBjIo+ICCL0NA62eVDmmQtlYS9EpZTW+uX1N2VZlnVR13VZlYA0DEM/doWYzxbN9erKeWt0obh+/c257bwhmVdmbiPl0pYxVurGey+VUEaklJABIQ3DkASUZckECCWUUsM0jNNQlmW21Tk5OWGMffHFF13XnZ2dVVW1nB1fvL7cXO6iQ5YEEXGJutDzeUFEu92u7/sc6XMty4QEgMVy/u6H73z/h9+7WV//px//aQghQXz//fdLU2Vm4G7bZhB7pathGJC4ZBIRpZEJQtd1xw+WIfnFyWx5Orex7+xOaCxKs/1F2Ky2XdszxrXQDKVgQik1dEPuKu3zFc6zaxZxyvnN4REBgBAie1OenC3P7t/L+lxjay8urmKM6/U6z8VTSnl9AsB3fvO91e36/PzcWW+MyYg5rfXJk+LhvSduCD//6aevX7w2lTk6netCiUPMf3v2iYjOOWR3pwlyIQQhKaX2M2NBxFLivjkuXZrW040sZbfrOOd7qCqwbMBS3uOUIAWy0YZAnvzkpnbbVXpWlGYcx2ky7733XlmWPvp6Udw7O2USnfW71rohLaqxOSl//1/8LsTav8s+++Tz2/X5rr3+jz/+ww/f++5ythh6ZgqtlPKRrLUhuLxpvbdFY4j5Fy+/ef78GxsmVUgYKYwJeGIcFRciMYoYXYwjbTabEEJRGSFEdMmraCRyJvquDTbkrWiDHccxR2K7Gu8d30cxvHz1DBk9ffp0Nq/Dm/imvbq8vDw9PR2G4csvvzw7O3v48OHr169Lffz8+fPVapUD8DiOl5eXbds2swqUc9BNaddasRlijFFKWR0pvHY02QePT+4/OPVk2UAsQiYpZZxwboDkwLlcLsexF0IAQkpp1+2CxynYyIAzQpYYJERORBQhRbjenBdFMT+phmFYr9cXty6Ht5P7R9Zabz0j0EaUZWlHOwyDWwVIbLPeJha5ZAx4t+u3qzcnj09+8cmn82bBQWw2m83NjjHetYMSLGHy3lNI3dBaa8u6NMa40ZNHbwMD8EPst4Ngcjk72m63KUVjjJA8lwI5KdztdgfUawhht9vl7ktVVuTB45glZzPSeLGcn50dxxiz5mo+6fKQKXmYpj0jkN+ZOiNj4zjmYzGP6HJf6xBXsgZLHlIAQN/3Wu27JnVdZ2gYEZ2dnV1+vsstqRzC82mOiCkkwn1hlJmpmbd9PbU5sB3C2x4BHvYertvtNvM0cizP6Xb+spxDpJSUUnVdAyMA0lq7yeeA7b0XUmcr6QOoLd55MJClzWYjeuaDizEmijFGI1WiqJWcyVmsYllUU2HjRPEtEexDpMlt/4PgVFbtz2mBIJmJpHll7tFhKRFQJhYDEueYMcMxJkiotJJSuruYRzGHhxhCUkoYXcQYg49d2zPGhmGkBEVRIrCu74ehzbIGCZzQ2I99jLEpGqI4jvb88gpJTXZECeXMlHXJGKOIOVc43FdKKZs0K6WaeXMIlvmh5XuRQqaQvPcooK5r+VhtN93QDXnQ+Hb5+3Y38W9+8jf8awGYiMyMzxa1aWQEP4yDnWziXur49mbPmh4ZgVxUpVaGAc9pojZaSimdynVUVVXSSe99iDGvAYUq3Dl4vo10ybc/TVNITAihtBRSzGaz1vbOOU6YkRkhBM72aqxElNcY53w+nzPG+r4vVHWnxsMZZHmNAAzW6ylHEyFEjnA5bdp1fcY8tu3u/Py8mpX5nBy7ERKWZUkJtDL3798Hwi+//PLV128W9ZHgsus6qcTDJ/eZYq9ev7TW5kN7GAYbB2VkVZcJAkpqlhVXOHU+hgiMhxDc6BlHxL3fRh7S52dCnHKOfoiAecAhhOCCZ7zY2dlZDsBa65cvX2bqRM66tttt27ZFUaz+7HaxWNR1nQoiIjvaSNFHf36+UayYlYvlcrlarTKIT0opDpfy9jAYETkTWcuXDrjWlFJKl7fn1aycLxrkNIRWVtik8vryBqGappHfaXdwJoKPQz9Cz5UwIYTokxJFWRU8iWmaItnJjnGVTMNjjEwi46i0+uyzXzIJZw/v1U0FFC9uX3rvC17X/dPvf/jx69/6zZcX31xcvPmTP/+Td995T2qDLbeTs1PiAsuyYiwNY9d37W67vXd24hG/evnls+dfOZiKmRZS9Ne2XCq9kIn7ECiMFGy0uwiBKyZK0RCk0U+uC14EBabg1G5by0Is75ozWnOubNe6wqPBV1fP1+3txcXFZ59+0a46gbHrut///d+PMb588c2906NHD8/s1P/kP//s5cuXq9Wq67rcYjXGnJ6eNvfi4w/vKaXacHt81gDA+fn5zfb6xfXVRC0roq7ZENpNtyaRtJYsKs6YuRNwICKtFDbNuB1dsL63PvhxHDer3TR6JYuzB/cAoJ+6yaXIPWAiokS4eNAopdSci6YSDeajeaJ+tmyKoId+tIMLzOpCSV1wDcmJolTOj1M/vfP+O48ePcKElxdXo7Pg+AfvfnR0dPzyxeury+vVauVtQhaR0PopL/cs0DpfzDZ25ccp2CgQbe9s50lyrUqgiYgppYTkFNKh0QQJvPUBQ3748c7Uz1qbD0TnJ2s94zCbzY5Oj5DYbtPmJCan2znWGl6RRzfs99gBTpzDJN6BkPNpJYQYN5tcnubyq2ma/NuQCb6cV1VlzN4T9Pj4+IK2GT66j7sp5ZPr29MZv9UAxzvD+bzVM3shpxHLqsk91WmanLdSCSFFCEQRJFfRpyz2BAmDi0ZxIfkwDAy4D957T5CKqlB+fw05pAFgnq/nn55/nHUBGQAAF7yqKlkxQGKcaaVSJCUiRFz3W/Tf1naH8RgAQMoTAIaEIYSJptwlAo6HAKy15gfZDYEQIUGGJkkpOWMsQpwGK7liSU6TTZFyop9C8skTkRTi0PPIL87fUdHgzp9NKTVrGl+EcRyvbi4IqdDFNE2CJE8CBDKOkTwwY4zBiNFFhlwKFkLIqyvGGFxIKSWWKFJGkwEARYo+UiQAMHOjuY7JHQZ43qVpmNyd+vrh8/8j+uZddviCtwNw57YygJaNKYWclz4I70IIoXvDGGMZJZARXjkfjSGmSN6FYRiGbpwtGi54DKltW+sn6yYmWHCBa9lUsyAis/u55tu1FtwR7UIMCfaOKZhQCKGFJkZ5SbvgcqyaRpsw5EzLOVdVVTbzyDz+PVbrjlMTYkiQpsHm11TXdVmW4zimkJRQ9+5VeZHvXm3avn3vg3ebplksFpRgHCY7uYwLOz4+Pj09ZRyvvll1uyHFPaS5G7pF3dx/cvri2csQQ0qJA2eMc84ZMALWnBSSq2FXXry66reWg+JM+MkRS8j37ukZ9OOcA4Lcdsr5blmWhy40ImqjnB9vb2+llCnS+ma3H/kTbbfbEEJVVXlYQEQpUPCRkss5B3JMkLqhUxCmaXp4Vr373tO23V1cnt/c3uKKBN1By97O4/bpdgayQxTA8pdJKalwjz94793vPF2v129enZNHBlJy5b3P7ZrshpGB40VRaMWqonYz3+9GyZXSsiwqY00E369XUhhE/ub8dTvuikoPtm2vxuvb6yfvPX769OlytlzfbH/+6c9Zkt89+Z36we/+3u/97m7z/Z/+5Oc//cnPPv38y+X8RMlFiI4oIMI49reryy++/uXL589JxPfee6+qzdfffH61vuCKkaiCD+11q/SRnKXEow1jcBAs2imlSELwGCBRihYCJfCsNOWInjNJlNVcnTGmKhuGLE24ulmXjTGVAYCbm+vNZTe0fjZPTdMcn8ydc0+ePpAKx6ldLOsXL1588cUXm80m13bZr7Su64eP5G/+5g8BgCh88MEHp6enP/vZz37y058yhicnx8PYQ8Tb61XXdfWsst4LAUhwQO3mAFNVVYJoKo0MkUNRFmMxpYhlWWy3O0Tw4KRm8+NaFSKyICRnhY8x2tgKIepFduxxwzAME+Occ40swOTG0AclFNPcGCMEA1CLxeLjjz/+0Y9+tJwtGXJg8urq6vGjJ48fP5Zcfvn5V//qX/2rv/zLnzhcx5QAjNSintUh+qI0xhgZtPMuebDexYkQeHBE3qpSEGXaK2OJwR0KyXCTTx84oEkZIPHJdRDRp0CQCCLnHJCcc3bl8tiMAW+7LjeuAbDvByLK81dkDCI67/u+r2blYbCUl31WP1gezzebjZ1GxhhyGm0/DAPyGVl2oOTmaiNXrplblWkV+cTJfwV37aVElHmBMYVxHNWxPKArchgOITjnEiUGTEhRsAIRGWeAwAX32VonQYp7VSnnXDTp/v37uc9sJ5cgVlVldDEMQ/LoY8i5Qkr7IpuIxmHIB5APNiNvGVdFUUR04zgG8tooBMZoDxATib1d+OLdJ/cS3i4W89/uzx3EXKDnLnqMUXIJAJj2oy4lNSCxxIa+p9BxPkUXQ0gMOUPGBGORA0MiBGCIPCWw1nPOz87uX19fD8OUs3ytMEWKkU7OlqsVWDedntyTUl68uWg3gxalbESzrGXBmWAMWIqQMVOF0Zmfkz3JBJcJE0N+IMXm/O9QLGYvPC4YYbLWUgAAKMvS9e1fewh05wnxtwbg3If4m0XwuOm8d5MbZ8dVvSirqqImxRivv7rOgSHLZeRFEkIIwWmppdSDHdu7wXYMMUbYbabRDnVTcSYKVUDBbPJ26A/Q30M6lVddXq55QuqcSy5xzolhRmZtu3Fy4/HpsTHm9vZWqSJnWkRUVVVZlqvVynuPBg8pBaSYviXXMEqAjAkueUbJ+chZWp7Ou65DBlVZVE1lreWCLRYLBqLd7aXW+r6/vLoQkk92LMtyu2q9jTmlG+1YRW1msqzKyY6THZujY8Pl4Lq+GxjHalYJLkMITGYXz2gUr8p6jO2BTJGdSfMbz5OgO9yJykdBHngbowJIgtR1HQIri8KYsuu6vLnyIs+pfF3XI41jP07TREiLxcIUmvGSiCa7ubg8R2IMWTfuXHSSyRijOCyUvBry+kgpxZBiCtM0ESPke+ZlVVUPjk4++MG733n//RffvHr+9ctx4xUWMHEvxtOTE6XUmzdviCUX7bbbFEVRF/P5bC65uorXzvptu83nhZqh0qoqa6MLUxle0tHxsqqq4rv1v/uDP3j1+sX1zdVHH35sWIGSKPpfXP7H2//4+vd++E9/+MHfPzHv3LwcX319ufp4FydjCiUVbtrVs+eff/75L15evuq6reB0fvlcSrnZrQGAImxut1070Fh27WBlwiISJkosRgjRuyEKJRhj+3Pc2pzCbzedlFJJOU0TEKvKRgq9Xq8Xi2WiPeYzFSSSockHCzbuEqbV5jYrLW/bzRdf/SrG+PLly77vtdaLxSKrhuYf8eDp06kdrbX9phu2PSxOeGK1Kh89enx9fX17u7JjHEc3DUkk6ls7u4elkSmMYz9VVWVHt9vtTk5ISlXXNbHkkwOB1ayEZDnH9aqTBmXJy5k+ejQrF3qKfYTQ3/bGmPz2vZucc1KI05Oji9e3mTDKJOeJWTv56I0yMckwBl3zsimJx92wKevi6eN3ZtXpvdMzIuLI66IZhvH8/MJ7zzUPKQCjelaXTeG954IRpNKUiusp2U23s4NHEJAopjSABSBElEr44BExG+tm+zm6A1PkOphzPsUxuBSmEANkPTwmmfVjolBV1bxZhBA67IGhYDKGuNu0mVYBCRhjkkskDDLkfDFjmvLRmX/c8fEJIe12u3Ec277NVQgTrNL7czDLOCulpmm6urpKad9dzNO1nFATEcKdlRZRoui9z9tq3pS5wXXYdLkBnuFdGULB75R6EDHgnlmYrzCXJtM0TVYiYqJo3ZQomUJrpba7jRYViX25cOhgIWJmQnvvpZTNrNZac8GMMZe79TgNjKMyUgldFQ1HbqQJ9tvCDt/Cs6QQImZXDA7sznWEoSMPd/QSzvmBwK21ZsD2ZWVIDoMQDJEprt3oU/KHNGUPLpsSEU3DtMUtY8xNLvrIgAkmvPUHzzsu+JjGaZi8L+q6lkweHx+vbjar1ardDsaYqihni1oYkSf3LIngY7Cpj30+ZPNjOTwlLt7yPYNvc462baWTRamkEfl4FEIsl8t2NRxSw7d/8XfVwYfIhL+ulKexopHaaOMIdheLRquKc8nKstxfGOd5w+b33rZBctkUTfSx73s7+qokDrJZNM5bqcVsNgshxJCGduy7EazPC/vQk6C3xNvzHYcQACljNbhU02iFDAKFUSVHLrk8ILAOgSov+LwR9t85BJaAiLjIIMF9hnHAUuTVOwwDEXnnudGc867rEkWlVHBh6Ibs0tF2u8F2Ltivv/m66weppUA9jqO3KLkMya8vr2fLJm5cTLlJjoyYCy7aICvmvHXJmUqVjYk2EktlaeKkEDFSjBRdcERESEywkpd5spOHWeLOhEZKiUhVWQBS3/eUUiF1JgFyzp8+fTqfz6+uri4uLvKT7LejUEwIUVRFVVUEKVE0hSbQ4zi2w3axWDx8ev/49MiO7vLqSrzdjkh3IiMpJcG/zXwzagkYOOdGZ6/e3GDku3Vnew+BAWApGqeGcqaNMXAVPQUthEtjGG268FKozMLMiWT+bs7G2WIOCcZu/N7977AyVVX1+NHjbtd/8PF7Qz++evHm/OWFEuZ4fsIUtzS9ufj6E7GIO3G2fO9/+Jf/+92wC5bXjenH7auLi29efv7Viy+vb84B/exIRRdv1zdt20op5/MFY2yz2aDnQvOhn1Iai4WsZgWTMkgLImbDr5iC0qKqKmunfujabofAlRaJgg+WMYaMYvIx+curVVmWTCJEnBtTFbPEiJWyWZQnJyff/d5HecMvFoujo6PcbMkzg/l8nsFB4ziO4/jVl1//yR//qbX25ubm6vL6k5//4vXr1yml2eL45csXIYTC6OhSu+po4mVZJ28ZYamL6EKpC0zQ77rkYxdaWQim90u/KAsMnBJ7/H7BJEayaJKNY5xGTxYFBRdJgnM+H+jOet2YqqiF2DrrI6WyqMpSc+aCjzFGVSgXoyyVg+n5q2eXNxdVVT198s7Tex+VZdm1vRBiGqb/x7/613/6p3/y+PET5EiOEiQumOISkAhTSJ4JpjkXTO62rQ9O/n/5+rMf27L0PhBb89rjGWOOO+WclTWpplQVi5QlSqIky5ZoQG4I0pMtoN3dAvwv+MUPRlt2Q4ANSAIMPxgwYLXplgy02iRdoqpYIlksFiszq3Ie7hQ35jjDHte8/PCds29kFq3zEIgbN+Kcvdde6xt+3+/7fQwTSmUiAsGQAZNtPwBCm6YITjjCCBBajLGQIhUpYla1BnsctHXGh+it17oLWZERxJwJWlsUCI6ob7XWevt8vQ8e6rIYYy55lqSDgxkyV0KIMiovc0xx3dZOuclkUvCCSz6kLFBtBdJ73/daE0gmoEwbYwSxYvDBkORGRCMMkMAYkuMBaYfCGCEk4TTGKKXMihQhZDdzPhCiHKjRGGFrLGecYBJ8uLq5GhUjFDGmqCzLclxY7YwxTCSAvcMRJlsR5ulspq323stETqfTPM+dt977qDF2xBpntbcsUCtJoBRJG9UQoN+2FdCdjCImGFLVGHyABAttp2FCJMG20wYxIhjH4EPXdJTTJEmk5GUxqetaKYUJxpiE+DkHBprP0BMIEPrV1RUUcRBC8BGQwVycXh/s7ydp2nXq8ePHmOLZ7phz7rGF0UB937d1m9EieBQ9dsFjjDHFkWBCKcUoIOS9t8aRW7NHEUKUUkywEDzEYIwhHFNKEcVho91BBkc+QAK3sYEvvP7C/8IYcyIRQtgRW6FV061lW06ycpLfu3dvsVhcXl4Cl3s8HpdlOR6PVzcrryIeUUlT5JDutWq1C47jlDKWJXmapn2r6nV9fXXjbCw4D1udpQHPgLDPGANhh7VWSJ5mKUIoSYv1eh1CyIsMIdQ1CmrtSvVCiDzPAdiAe7+t9hVCQNsgg1IaNEEB+RBUZ1AgwSEcKY50vV5DWae5qpRRTG4QLN9iH129rmOMxqnpfIIpYpxmM9neKNV7jBhyuF33UVqa0iSVo1i64FarFWYxz7NUpHVXexeC81LKg6NyUtp62SJPMymJQMZaGJGyrjZ4JOdMcAkhQghhMpmMx2Ow1XVdx+illFwwY4xWpm3b1arKsgyq4IDDw7gtpRRF1Bnvvc7LPM0S563plNJqOpmNRm6+O9vd25EiMb178vDpzdWC/f/bNIQQhhnnPODNXldGxRir2n64fvwwPZWJ0K3jRGivE4Flzk1QRZIlhUAI3X/xbj5OT09PV4s1DELvuk6KBBMkJBeS7z0YMSQWN8vlsu76LsvZulnaRxbRcHTvYHe6/9Hks9VlHS3emexd39zM2YEv8eXV6dXl7718/OXfePNv7KPjtlLlVJ5cXL/97k8fPnvU9osQFWKhjSoXJVTIrXYoUIwY8oLEJATvrZVSpCxPWWatJRxlk6Qx2hgdlUvSyWQ6xQwB0cBpD0BNOcqllJhE6/RoXHR+LVKesAxHXqZTZHHA7vD4YDZGQogs5X1HMHIPP/vo4WdISnl8fGytPTs7e/z4MRxaION85WtHL79yfyDaKKW6fs05bxJERn7ORpKlPlhlMuyDSFjT3gQP0kvBWTMZjwRnGOOVqpXpBeUyFYJJGgKPEmNa++skTV0k1tumr0IXCEdZntrWtqYFPXEpZTTRYNP6VoqEYRF8xAhTwtKEOeKCC/k060zkOW111Z6v0zQlmD47f3q2e51l+ZMnT7K04Ex8+uQT603TV6MJDyhAnBFR0FZBm02vWoyI5HI8GVHMgkF9ZzxyiZQheMYYJtvsAUMrTgQtIdBunUzGIHo+ylPdWdXq9U29uFp0urOIaqullH3bq04B6ApBNyFkMh8Dl2TTwBAsJZQnHAAJcIdQOoGno5WGohRgFUDIGo/HJvRAxAMgGvqjRqPRaqG7rgOOa57naZrCSDUQxN4mPQRtx4j1fQOWa/BV4K1ns8mGGRs9Qogywjg1xkRLvTWU0hiR6nSZjxKRcip4IUaTkbc++MgIT0TS1gvrTO97QSUhJPhN8QhFYoxpmqZTXQghRAeDWSIKSinTOyFSEq3WGnnSOx0siu55FD74xY0njgFoVuT2CCzvQTd4YPqU5QhIOowIynGMsVedMw5q+YwJ7DEQNr33kW3Iw0r3uSgYYUYZ3WtGGOc84uitZ4QlAjglXnWKUqp7ba0tY6kak4hMObVcL+/du1MUhRBiqdfaahtMr7q6ronkKCCCqYRZFIQwyiillDCMiMPOGEW30+HwILf0vAHGb0psRIQQII27Dc4PtYz/hA++jSUMP/TIoEAIZhRTZ1HUoY59dCi6NbA+YetWVeWck1ISTFWvVauijxTz6KNRpte905FLZp1BCJFIokeAFYPSMTyy4fKAVAXbWySsaRrAFWDo50a1w0fGqXPOKYcCERMGjNTVagXT2ACUSngWtq07BG/kPqy1MSCCqQ9BK4MRAfVHFDGMCCvLEouCCZbkEhN0cnJCvRQiMco0pElzSRnlnB8c7Ys7yds//aVbmVFeYoxV08oxPbiz33UdppgRZqx2vaWUjkbluBh7ozEiQogsKdLEU0p1bwgJEzlRSgnOm7btug4IzIwxFNEginl0dDQqSygntW1bFBlUAUajUUs60zlAF6y1JycnXdeNx+O9vb31er1cLjkTwaiu7yO+SXKZlYlMpA/O28Aka7vGnpnRaDTOp+Px+PDg6LkSFvm8FodSCjgahBBKSQghhuic26F3XWvqdRWm2DvvUKd6laZpMZdtX0/xOC9TzvlrX3rlsNrXts+YS5LEu+BdiPy5itjhnf1njy66rjOkvbq5PBzPEUIffvQBEYggqhpzdnLmNR6nM0blxbOr4I+ojHWoRtPxR1dvP7h+8f78q8d7D/78vf/4wUfvPTp53OhaCOwwqXXVt20ZtGQJp6KqGhRvGJXtutFaIxI5Y0VaTvMpwrHTHWGkGGW45+v1Wptem4SKsUwFE9RYHVOstRaYg92/vLys63pcjqdsggw22vPI+spetk8wiy+/9tLubnl9ff3ZZ59BK+Q777zz7rvvzufzelFgjG9ubpRSk8kEpqOEEI7uHzx49f50MhFC9H1/fXNzXV3d3Nwsg1q0l8EiZgRO0J37++tVXbVLIVnXdVCuoJQeHh7CgXn7w18q3VukCSu9Dd1aeR04Ta7j6f5of1xOgpdt265XVXAx9AQ7JkQaGJZccspDxBwlkmRNNFmaoohUp7U1nHLJk0gQ4RjHiEiomjWiYTwrGeVt23zw4ftHR0cXF+eEUMZYiP7+i/eTJHGoDiFoo9q2tc4Yp8uyTNKk61tn/WQ8LaZ5nhXtqm+as7bT1okYg/ceTjshhBLCGGvWDWBuGOPpdLq/vw+Teld1jSNJkiSUSHfaIy8SxjwbsETwE5AeTSYTQhGJWDIBdAkAkaCFAFwvBCIIIRA6GJVjmD60v7+/Wq2A/yWlRN7DHKqyLKFmBs1IYVEP4vXz+Xw+n2/2QNyijjEiDIJoCGNcVRVYduCgYowhXsxTDvEu+HgYPkgIaRrrvQeEYGhH4ZzXzQ3G2CjTd6pIR9a4db2y1nrfk4QCSLjx7sFCEdp6C3e6XC6bpklSGUJYXFXj6RjhED2KGDsXgsVSSGXaLzhg+Ort57OfiHGMOG4YXpAPJUkynU6TJGnbJnrMOUckQNQSUOCUJ0lSL2vvffRRGSUSIYSIKKhez0c7IQQQDR2akaD6AOsD3wN1nxCSJyNvECMMMo8sTzFHVOKRHK3WSwu93z4650iUCBPwfQP+jwYVfhtRiJFAuh8xw4gSyNiyIiM0GqOdczwXgw7D7aRl8Lv+V8hZv+qG0a1isKfW+0AcT3gmmNCu75pO6X7ddEO/eNM0IBrFGCvzcYwxBsQ5z5McUUQpRQFxIqzVy9USUTSfzPM8H49tWYx1ten3RZ8fagLWHjqanHOcMyh8LC6Wo3ERY6zruijzcTHuTe+973sD0RW0D8AjADnugc9LMA0hOGcjjhxBgXmjLYW3DfeY4uVymWbJeKcELwh9PvV576LrdRyNRnmZKqWubi6F5J7aJJciNTBVTBTIaHt9uWAJ6fueC1YURds1XdelaTKfzy+encYYke9xpNEjj41Fylg0KmaYoDRLklTe3ETvvZQ8xqj6DaeMMVbkufceBJe6rpvNJmgLxiwWS9P7shgtFos0TaH8BGqagPAzymbTeWKSul+fnZ1lTcIlS5Kk6ZqJHFln667y3guSQI2ZeWehHyN4Z8LzTgMpM2N0CIEKGq2H6pGUkiDUL3vKZHXe5vMiLeUSL6bjmSA0+rg4XTrrXn7xxbt3Dj/+rOr00laJjH69XnMmrPVYU2GTruvO318uryvVqrTIz95e6xN8cLz3pcNvfvD0vdHuOFqcy/EHbz960iyeTZfrZtWPzGQ0dcYTY0b7yfnpo4Tgp+dv/fs//nfXVzeUUmT6q/OKUupsQI632KxdJ4Qos5FSymLLJLEB5amMMdJIiKamNuraIYToTBIdkMYMMW/sxfmpzGWaJRGFZ4tzQfjueH60P//Sq2/89Gd//u7V+r2TT0ZFibHfv5tFrDt0mqQyp5mkcdm0xXT2//zv//sYY9t1xjqfpG9/8llYiqIodndnQghnnVGm7fsY0MWT5QfpZzBD4/z8/OzsLMb48z95d20sxpJhDIU0hJCQpO10Wox84z979NnXv/yVb3zjG9/+1rd2ZvPxePzxRw9/+KMf/fKXv6xPmuls7gKWheSci3AknLALhRDKiMS8CDSMstGZuqzWl3mRMclbvZIjSXO60kvKKeOUES5oEmNENFhvrNPNoyAkRxalMUUk+qVLCkFM6Lru/fff9y6kaZqm2Xg8Xi6XWmuC6GRynBfZ1eWF83oyG6vG9c2y4PtPzx7GsNo92Gn77nq9rBY9siJNgyPqzouHXpjLR0+DQvcPXrw8uaGaI+3mxXy6MyKCON+t294YncyQdZpJVObIc1Ev22g9d8zWAWdhsjd2SF/fXLOc7u3upTJr133fa4xx328UvyMJLBXBxnrdEELIiMaoMMYRBUKxQ3rVdGZpGKfT/bLtcd1UT68/3Z0eEEKMss2q51FiTxa6beou4H40lQh5Z73WteQ7jITglKCp0X2SJONy5IJbLpcuuKIoCjKlhKKOGhUZSSmlHHPGWNeGDWuJc0alswwhxChLuQk0eO+LtJgUs7PTM+fc/fv3GZbRYlU7gjgJzJnAY0I9twvX0BqiCog5dNcZpSRjkjGEEHY4wQn22LcBITSfjWIMMUSBGfaBsiBzRqlBvcUIPc/U4uarsQjIot57IHkBDyUGv7szH4/Hb731VprIzz79+OjoqFqv3vzem6enpzc3N3W7lono+/7unTur1SqXhdJ9oxuKCMNUMpGmidfuS2+8XhTFL976xWefPry+uL57975pnNHOTxFFwumOk6RvDPL9fLbXdd3yepHkyUsvPSjLAvnAGOGCPj15VJCdxcXKe6+VzVkhCW9tLaWMSGzwUo/wRrYZU0Y44oM/jtt5dgghKWW7UJA/GOIUXQ2g65D7Di+McdM06JayEvwcfBuw8zjnk8kkxgjd0r73hBDCo6cqYEwFwoFHH1GL26rriXLOFUVhlaOUCiRW62WSJFonBS9m03lVVf1aMSwlpkjT1Cf9effsos/zfD6ZtG0dE8sFxg4xJtaryhP/4N6DPM9/+fa7CKHeNL4zAXuPsUjYq6+/nKbps2fP+r63zsiMSclCgzHmKmDkaFfpUTZVSt1crMqynJYZjqbIBce4WbfOIZkKoqigMiJbVYudnZ3d3d2bm5v1es0Yc95GEjjnjJJ22VprTWXq8wYhhCK2vZuUM0EZx5JJfnV56b1/Ye9BSYo+UYEGzjlmiGoabpBPQ0rTIssTIWb5uO7Wi+sbHPwkGe3O99dXzaMPnmZpfnjnwBVmubq+UWcJzwiiAfu0SBGKhGJE4ljkL7744sXVQjWGOHp9vVxfqiRBMcbF9UpUAtgYIRCKcFM1e9N95xwNzDn37NEppMgpyxhj1WJtrM5lHrpQV52Uko2FFd5dNbCReoQ/vHoMbA82iHtB8D68hp0EBDkIdZMk0d6yScAs2LoTWXF8fAztVufPLjFFeZGhiD/9+LO+77RRlIjI2JCOOG+Hovf56QUiUXDZ1O3N4rqqZnt7e0cHdz5++lGRj4qiSMLofLx6enmeHCbF5Ki6WQcXUcDW2vV6/dmjT6+vr+tVvVysjDEoYKVUmqYxoGp9E0Is89SjEDz0E2DvQyQII1LXdVmWGOP1et11ndY6SRK/VRunNCGYOuMR0UIILth0NG2r1rtweHh0cHAAGRKKwTknJWWMMcE5Y8Gi5fXynXfe2dmf5nl+fn4eQtDGEMoIIfP5nMncWtv3rXOGS84z6jDqug7qqbAvQwgHBweHh4cuWtARharneDyGwS8hhGpZff0rX33jf/bGV9748v7e3ng81n2bSfnaKy/fv3vn8dOnP/zDH3308cf1ahHLkhVF8FE5PYwfgDQuS3yZjxCqQFrPOgP4pzXOaxssItEC1ZZxihnGGMtcGKNtp6nASSIxQXVbX99cNWtPCBEsiciprr25XjRNk6U5YjiPgrGEc9xp1TQEU8Q5t1GlaYI8Wi9X3gRrjHUmZTIgxyXr+957szPfDTaC7M718hxjjLiwUau2t9YgGnxwrx290qkWR0IwliJBBUWGWhOquuGYOG/TIj04OKjrerVamcQmIhPJhn9hrY04Wm+VUdHGzZgzgoTkMMaEMdapNkmShCQ+eGgby9IcIQRjOabTzJmgaxststYppfJSIISCC16bel1fXd7cXK6stTElTNCiKMqyVEa1bRttJISILbAZtwNeIKOChlqoJwF3CcrJujNN08xmszzPQV4ny7L1eo0yGz1qmibhqRfBWgu56SDdMDTtQKJ/u1S5Pd2AfgV0i5c7dHANghK/UqvatAjHWwN/OOdat+v1GtL3PM+B0pJl2U9+8hMom41GI2gBgORvtV63be29H41L483Nzc14PGKM/fmf//nXvvY1QAVGo1GWpkoqOKQQT0CyBUggtI644Oq6ppLAxedFvruzd3PSQGc23FSMEeDceEv4D22h9edhxue7QtCWIjd4XFjbuNVuHMDk4U9ALJNuRRzh5xD5wTGEr3ErgkSIGJw09KfBo1nerOF7AEXAoxtjWM6MMcvlEhqFB/EQeO6DSADe9tftzucxRpiSaY3TMAcXbXQkKKWc8ihikiVZVgB0UVVVCEFIXk5KQvB6va6qCksuhIA2JLgXY8xkMvE27OzseBOfqpPraqmjjo6giLnAMFQjTdPZbDZsRY2e92EDug635lzUWiMciUWd6jDbyB4sFguI8DwKtx+T7i2miAuOcBQpQxFzyhnl2pvr1U2vdFImk8k4H+e9Qagm3HNGOIk85SJBBXQKIYKQ0l23GZp5fX2tlC2KIsaY8zwRCaU0uADa1MEHa63BBm0b4eKWvOmcY5y6aBFBgQSZiJxlQjAppXZ6NJ6FEJbLpbE6z/MklW3bsqEkAK9bB3I7TZqRiP2wM1razO+O0jE3Hzc3y2tGRK2XNIuCZ1CjLse5TCXymFERTBz6wzBBwfkklVywiEJn1Wg0kqlYVQtfeRQIJZxh8dlHj6eTmcRJ2/SJTF955ZVXXnll3S7ryzYYHEJUVq/iynp7ubhYLpdSpBSzVrXehNFk4r1fr6u46b7YxJ5oOxCDcx6cFlxiRNqmgxiEYOpd4JxnOGOCYRK17WlkjLBUZISIjKXUo2q5ujg/98ZOxyMEJSIWtTK9soJxTkREwQUH1hzcJyYEYQIAWkbLqqqatvLI8CJPUkEzRjL6la9/dXd3dzwej0ajvu+hsf3p2bPddb9cLs/Pz5VSnNG93R0hRNsefO1LX/7mN77x0ksvURxR8Iwi5cy4yC4uPrlz586DB3/56Hj//Pz83fffe3pyslwufZs45yihKG5hsYi883lZwI7XVjnrnfU4Ysll06he98Ei7wKlNM0SjimnMt/J1tUqopgVaVYkMpWdaigjeSlxxBhjQpEzxvg+TeX+wQ6bstFolIpUhbZ3rTK91S6EIGmbjLiUSYxBCJEXWZOpVPK2aRkRq9XKM7N3sOdNML313kcSMCE++q5vbTBC8t39/cl03HWdaqyPjhBieutNxD4GFzDD1tuqqiLJszyLITZVSxEH/BMTIlNOGHLOBReMU9FgFJD11te+7wkU2HrVaWXwlFBKXXCMsSId5WnAGC9ulqpXaZnt7excny4effLYGlcWY0Gwcw4jQpEIFpveoUjSJEdoY7WVUp3qwFtIKcO21D0A5gPCDKKDMJaRbeeiF1khmJiOp3laOOM45WU5klJ2sfLeo4g4E5wJrwLFPJV5vWxul/oGHwAG+jZZF+x+QBtMcvirgZv9BdcSN0IcgdIIhB5KQa/fxYh3d3e11svlMs/zO3fuPHv2TCnVtq1zbtWvZrPZ4eHh22+/vb+/Px1NL88uQYYM6KY22CzLjo6OEEKPHz+BogOAQ71SbduuVqvje3fgkvC2f7qua8ZYPsphPFzbMmf9zfXCbxU2YIVBKRCqtgihoUEc3OeAJJPPtw8NjhnkRIYKGvqL4Ovbvz8kNmHb6QvXA78/kMjwtg9NO+MDCjGGGEL0HBFOCCEUb8fmoI2Wy4YGCGAlRt7zwCSnJJoAMiwOIcx5As02Qog8L7xHBNOIYsA4hI1KUlu3HeoZEdZb7wIQ5YwyIAp2enrati2lNEkl3AjotdW6J4SAVYddqpQqisIaMyrH+bSMDmVpKalEnnImrOtjjBCApmkKGIAQwhILi9B1XVVV0NXmnBuNplB7dsZHHGkkIJlg+k3NyNNACGGSEoact0bbiAJlxAXuAkckEkyjjwQj7fokT9M0jdFX7QoTVIwKZDjFDFvmAyJMZEnOOQ8oXPafLRYLbQMlXCk1Gk2/8pUdY0yMm3YGpRQwrUAKt27Xt/fJ8JRpQtIiCSEgFKjARZlxzrz30QfoxyEME4Z9dCLh5Xif3Q4Db28ma6HDlzPJfLQQpxhjuraXEzzLi3JcLquuuerWphFT9NVXX725ubm8urDWzubTbO8gzdNH+GltmqFgiRDKRymiQdnOaNt3KoTAiJiU05QnJ4/O2vWPl5fN04dnweKr02XfqoPpHVAhmY3mlNJOtUYbjLFMBKZRplxg2aimbxQEhtCNY40PLmzKVIjArGlMMRc8Hc8wolpZ7yLBDEXkXQx+e/YCChEFhzAjxLKocF3XiRQoxK7uzk7O2rblCZ+Q0njDGOWcOxtNbxvTYhcxCUByVkqt1+u8yGWSIYRiRMYqTIJMGebBEaWwIRnOi+S9j98rz8vd3V1oAk7KpG3byc74G9/87tOnT999992TkxNg+QMBkmN8+vRpu1qtVqvZZDIuR4vFwmuvVfPo4cdZlkkuX3n5weXVxccffXh++kyMBGdiVPIszd1WTFj1urVaay0TSRFXrbHmBkXMOBVcWO2ss157zLAXgQdGORUZK3DOBcuLDBEUok/TdH9/HzthtPXeS5nggDklMaCdnakvbYx23XY+mvGk9NbX67pet05oKbJpOdGu996PRqXbszQwkY24oEQgi2MqshBjpxRCaG9+APNnulpbb0nBeEym2e7Nxcemd6Blr5WxytPIg0c+2uCwjZF0KMTgvedMoIjrttrguoxJJojFxGFMkRQZIyIgF2MMKCQyJYR4FzAiRrmITERB8gRLzJjgnE8m8VJfhq1CPZw6znmw3igfQogBUcYpZtAzg1kkBBljQvTaarSdQ2y9HbKc4dCFEMDugtEZ3EOSJO2yL4qiqbrlzZoQorVu6/7g4ICkRGujO8ux5qhfLaqu7oy2cTs2+La3gPwPinCDM4CPcHaLw96ig3whF7ztgGN0hDAYYwC6C5SiJOGwV+u6Ho1GBwcHFxcX3vudnZ3pZAZp39079x49fHx4cHR8dCeR6R/8wQ9hegdjDGmU57kQ4vLyMk3TR48eRbfJGheLFcYYWrPAniCE4IOqqgJRE4SCMUZ3GiFcr1rVaWMMiQK0zO7evYcQ+uCD96HiGLa3eHv9b/vR4SusBugioS1xaWBmua2S4O21QggN7UNhO2sWdguYQehygZ/DvSBMhr8liKCAg4soBMAPoDVrNpvVdQ2UPe0cvCcANt57QRNjTMpzQkjgAehRhBDsabS4bXsgAxJEMpkLKinmCCGeCaV7QghhyFiNAlov18YYZXug+HLBtNYwX5UQkiUZCgimORtlQBZ0vVwrte47Nco1xDrjfOwNwohQNhlgA+DhA/xOCEmSBEARaBbH2+ZySimhmCacS044JjQRQvRdBxbeKpckCeeJjxassfcu+Kh7A0kwwTQ4lIySvu8Ri0Lwet00VVOWZV4UBBNJMx4T6iULCaPCW6+UOjg46Lqu1y54BEFwlo+stQRFwCoEly51wNjo+17bDUK51f8iIK0jEwbTFau2CsFH5DFhwXnGmTaaEFKWJWxjxthmHvBw6uK2lQ0hFAKGPn3OOYKkuzfW2oQl66uWUc58sjsuBU5C4wiJwYciL+BSgsGr6xo5WojxmqgkSSgjIQRE42g86vq2URX2tG1aIOBJmXodn356+ol+iCX7+JcPGZKqMUa5q6urp6ePD+/uz2YzY4z1BjiozjlMEKXUdq5eNX2nGGPrdZXIjFHRqDXx1JvgvSeREkKiQzEiihilqK5r0GoABiA0dKreaK0R2Sh/xYApVnXsjVe20XcOD1979fUsy84vLtZNXZa5xbJrW9XpGLx3zmg7m47vvXDv61/7el3XV8sF8BWRBpvLjeuJwEnCPXYOmeAxY4RT3vZt3dYPHz/88OMP5/M5sG0RQh++9/7V1dXi6lp3PQ5xvVjiEBljFamqVSVhUvSdO6vF6uzszCgTuIUbQYSFiP70z/782clp8LFaNsARkCylyFHEjTGmNwb7rm85FeW4hOhKMCmFxJT2XnsUMaGMMBopQYwgikjANBKKY4yqV9ZaLhhjzBnvehNCkElZjMpow/Jmuby8ccaa3rRtRwkr0hEjlDNOMNo/2C3KfFKOlpVfN+siK8XRflu3Vd0pazilIcTV1Tr4GA2uqqrAhXNBa2eMs9ap9do2aHnWxkzHiGQiMUFd21tnEMaE0q7viMflJI8xrtdrEqng0nTGIIUxDi6ynBNCUHAE0eCiapVHnjGGSCSECCaYYM4VDIbitT2mWAsDLUZQ9SzL0hn/5MkTVZnJZGKNq6pqbzoHg4sZyfMCPIr3nnMKaC0mG9cbcVRK2biJBgbDBBlAq2pCSJZlo9GIUgq9avEWtQewTWB89H0/HuWOBYxIcEH3tlk3bdM560tZDA51E5vTzeQof0tUcnA5QJAZXrdD+y94YviaJgmULYa8MMuy3d3d3/pbf/P8/PzZs2fe+6Ojo8vLy6dPn15dXf0v/xf/ZD6f/+7v/i4h5Bvf+EaM8d13333hhRcmk4kxCmrVIzq6e/ful7/8Rtd1GJM/+7M/a5t2MpkkSaKUkkJCIQZvxQJBEw3+uVzeJEmitWacFmkRcCCYeKfTTIKjKssSIVQURQghyzJO5QChk1tzrKFSg2/VdL+wjOiWkBljDLhIg8MevhlECslWY39YarJtOYM3hP8iVH4h84avBD+XrpzP5zAJajQaESrpLen+ISwAzlockPYYhRCE8Ka7xgQzKhhj+axIE9CfoTEGUB3GGCvTa9N3XbdYLXKRQxrmnMtYRinx3nddx7js2x4+0SiDI5ZCOuOKovAuQHCTJsUoG3mDBJeYeMh64Trrugak13gTQgBC9cHBASD8dV2fnl4ghAhlWZZlRRawD9GHEMbjcQyIkbZFHZATMRUhehgnyhNmgyaRSpZijKNHhLK265U20+mUppQgaoPvjVlWF5Nitj85ns53J/lcsqTv+6Zpzk+qPM997LtWhRC6rjMmYIyXi2u8ZZChrdQMHMlwS6J1eGrWWtC1JYQYswHqhRDa2VE5gmJBDLEYFdbap0+ePs+Ah8e8Pa7Po3LYndZZ7/1oNGrXob20ve5hQzvkYhvf+vO3Oecgwq61vjy7GqT4MCLeBRs0F2wyH+nLrhhlWAvYfCRSp+E2EMdS96atqvOTS91aifLpbHq1Nsr0NDbGmIACIgghFHzsGhVjDIoEH4UQaZo5G6y1nAlrLXZ+SOUHviLoJwAMSBBx1qlOMcYEE87522kBcsT2XimFuMukmE2mRZpmafrg/v2zy7Oqq9NJZoxp61VKEylElmUPHjz42te/sre7d3BwsG6b5XJpnfMhCiE4FyH6EL1HHtpLvPeus7o3UQrAN/oeQA5tjDk8PNRL37YtjmhvZzdJEkZova5ijIurpYfZiMvFhx9+2LZt1zR37txZVishxGgy6ZXRWt8sl1lZlOPZ6eVT53yMG7VRSinnwvtw9+j45uZGJqJIRwTRXnfRo9VyPS12oo84Uo4JJ5xiigLCkSYyhdmufVCAVgUbVTRYRdUpYwzxmDgUtNWtstZixJxztg80IXY7og5TfHZ+mlWZ9baq123fSCkJxwb141lZ13WSJNa4YBFFXGQiTbtuARrum/qWc66pW4xI1IoyjFhOKOl116qWYc2oYJzCChtj1qs1wyLhqekcTTGlNDgrmKcUGeVgP6DOGasZY5hhTCNBRKYSWjuQs1ZbTLHpLaaYMiK4ZAlRSjVV621EhpCwQRq16zwyESMUEaGIS0Y5VUoJzGIMWmulA2GEc44Ics4hRm4b6MHbQQ8xPCzA+oCwTSJZ3ayKohjlo6qqSCSZzJp1s3M0kyIkIhEkQR5FjwniYqsgfTsJA3/PN9O6yCA8Amcf8rDbmS78E0qYt10yvB7cv+uca5oG+OGYxDSTRZm9+eab77///u7uLuSIk8kEUOjjozuHh4ff/tZ39vb2rLW//OUvf/CDH/zkJz/d291tu0ZKubu345Gfz+d3794djUbr1frDDz/UnR6PJrAC4PaOjo4ACQTPB/NRIKnazCbBdDadA56fy0IkklJKKQPPenh4BNefygzSF0h3IFqCPAz/CqMKXDg8nWGMR5qmt1GKL4Qv5FYjExzwpmm6rgMcFWr5YADB90Qi/sJ1ziSt6/rq6qquazi/k8nkwYMHhGRw2VAbHtCOdVUP4DOY3yRJimL64UcrHAnFjBM5Hk2n0yko0mgLqs4gQJRbb+pmHUK4XF7Ao/ceOq8YdCE3jTHKxRgZwwSzUVGAEGPdXQouy7Ic5eNU5pIlTsdEpoSGgYoftkN6MMYqKgguN11VZNMjB+1PGOM8LybTsfFa6b6u62k2EVymMl+z2hhNCElSkWYJipSSFnMUTcAoCi4xRcbqZtXjQEWaUEq9C32nWttLkeXjnFLado3tT67INUNcKdV2jQwU3CcoVVxfL7UGGXAzBE/wsACFBswM3arRgK8xRseIhRDRxRiRNR4IqqFvm3W3iZlsrzvbdd3l5SW7XR8aDiREyiF6Y0zAIaAN5YFSurbXCHHmZNfcsImfv7SbrsjiWYNIEkLQynTtFQy+stqnMksKRCmt6iZgl5eZkBwTtLu/216Zvu+9CzBEE+gViUgoE6PJSLe2XrXpZFSWJRLh5PKxS7z3XjDJBeNE0Mh0Z73xPqI0Tb0PQgiCg+p18JpiapyjlHkfrHUYkxiRc04p7Yy9f/8+DBQ6OTlBgVDMvY3bKJLHGIMPBHOMGIrUtP3udH7v7v1UJASh/f39EMLFxdmUzUP0gkpGOaMsTZLZbDrfnUEqMB6Pt+eWgeFTTAMC7J9r0RGP/Mo0QgjKRd/3qG4opau66fRjrvhqtQoh7OzsSCm7roOutapSieBpmirtRRKNRzItOmXryjjXtV2o29YYjQnb2Z1QQlOROuesssorkAuVUkYXnzx6UlUVE2y1XBOKQRRJa12tKtUpr6JgieCEREDDQvAxBuSc8yg6FxDCwSNjLFEReeS6sOxXaq29D045HIlZO0qpjGIkSsZp5zqeUEqK5XLJCME+SsKne0f379/FBO/uTE/Or5lh1tquUanAlMTgo/e+i2tGmaPOxxBjNMEwwjIqskQiGhhnTJJinMF8CIrZ3mTfR4cJRggxypp119Zt0CglaSAoBG+JjwzbzmOMnQm5zGKMhGIppEcbE+yCM3VFCOFUWq+NMpRTxhKCqXO2bVvvfVGMXBeqRR0DKsuyMzXM0PbWGN97ZCPyITrOOWHce6+NCluyFcYYczr4uSFDijEWRQHGfUhY4ddoKmNAjPI0yWDqreAyhLBarEIIwSCDDEYkBswpJ4gliUBbRSqwHbAPwe4PJavn+S4JX8Bgt3QQ+oUMD74/2Ju3bYujy1PBOYegVjD8r/7VvwKoQGv9x3/8x4vFghBy7969f/P/+rfW2sVicffu3fV63ff9ZDS9vLxcrdcIBZCDaPrm6dOnH374gdZ6Z2f39PTUaTceTWKM9+7eRQFzzuu6RggNzg9S5+Vy2bQrKSWjPM/zoijqquGcj0cTJjksI1zh/v4+uCVnIDjx4FeggRvg0Nt3etuheu/7vl+tVsBwBj0KmNN3a7m+WFRGW3qO34q4gRPa2dlBW0Dbe+8iux0wDXlRlrIQwmKxMNvRXgDyGxUijRFHHDEjDCHkjDPGEMxQJM557zba0VKq9XrtjMcoBh+NdmWulFRt0yGEIgrKKIRQjMF5SwjR1lrjIJOD1Gu1WhGCx+Mx0BcIIU3TWGsBTYVsDzCGyWQiqKSEUkoDgTnoNTiOoXoNoQzDDGOstQazRilVSl1fX+f5aAhDGWMeOe9927ah9olMg0NN03ZdR1qc5pJxmslSKUMdUkYTjrwNIUatjGp8XmRSpjFibVxdd7p1eRI5KYzrb/pGtzZ4gFKi85aaSCl1ARvtvPeUihipcw4zQwndjBKJCEUfcIjE6V4PgezgQEMIKKB61UgpRSI4kaZ3la+d9t4HEF0oiqLr+rZtrbXBRQaB8JDpDmdPCKGNcs555BHZHGAhBCkU57lEiUjE3S9Nf/1vf/vq6frPfvBuvSQwMeb02RknqMzL9XLdu/7lV19uumZdrXzwAXltVN3W+Sgr8lIro6zOsyIRCUzgMcrIJCuzfNlcT8bThCdXl9fjvWIymdCIkSUyEZQwEinFzCmvlbHRzWZzY7quVRiRpmlw3GB6Q5EG7g4il1SK119//f79++++++7Z2RkEzhjj7XQzEkN0LljjMCLR49lstjff/e633/y1X3+zbZsPPv6QEpJlWdu2PHLvQ9VWlOCj4vD4+PiNN96YFbve+yenz4wxSmsuQI8eKddHH11w3vngECEkFakQuSHdb3z/17/+9a+//fbbf/jjH6uu2989uHf3rlnqR48eLZfLGIKztmtbUB/teuu819Y5o/OyEEJKKeqmqRtd1zXCgtIEI9z3enFdGWPysfAuWGO7vu/7PklMmiS9UoFG7z0y6OrqKkSP8REhRPfGdJ3tPfaEpCzGiCLGkQQg7ymLEMLIWeMopYxwb2J9tcqyTBKute5N55z33nMmkaZYUIIQI4IzinEfUYwxQOG8bdu6rrI8FUIgGufFtFaGEFIvGii3oK0+1MtfvleWJWMMrkRrLUWys7OzWN7YoLMyFSlTujfaUsSjR0leUk5CDEKI4MMnHzx8+OFjFCOQOJxzCU8QR8EGSinyqG1brXWSyjRNQwyAGllvmRfQFFi1UWvNImOMGWUqvUzTVJQSBWLbLk1TjEjw6P79uxDpr9c1xbQc5aNpLlLKKCvHhZTSOm2cAQg6hIAYcdvB9XiLQocQYIgn28opQ3ztnGNCcM4BLkvTtG3bpmnm87m1WsCgV8QJpnkeGBKMsQQo+oyhraYxmPuB0fMFh0r5F/UZwLCCCvcXHAxCSKsK/ms8Hs9mM6Btw1AyxtiXvvQlcBhQ6Lm5uXnvvfd2dnaMMdfX1+CQdnZ2ZrNZxIEyorWOKBS0SNPUObter997773j4+Nklsyms7Zt57OdpmpDCMpqMEQADMJCgXOF60/TNM8KZz2nIs9zLDCM3QQaCigCpWl6dXENVzjEJWQrqjVk/7e/h4WCMgG0+EOpr67rL2TM5POj3fEtyjTZDlCBEiB0tsBXKgvQywP9n6EyuMIOGr4BXASQ/PLy0igCbwtJPFQWlFKdtuDRMcbDdVZVlQjsg/c+AG6BEbm+vvYuyJQrpYwzzlnvvUxExB7GD4cQ+r4XgslMJokEeUiojDjnqqoC+Ul47tCtfnV1dXO50MqNslGwGCMSovG3BnQO7cKWbLJ2SDrRdhQEACoh+lFVZEWqbF/X9WKxyFFGaRsdsso556INyvYYx2mB2r5lkvamZZyUo8Ij17fKI6qZcd7yhEmRFHnpu8Yod/lkxTCLjjjtg0OUY4+c9TYj1HsvkpwQ0ve9EJRz0fc9IS7iGGO0zsYYrd8IzSKEh0D2dqzGedr3vVEmlSnCqFdN23Rt2093dsZ7Uyll0zSmazNRYIlXqxVjNAkhYBQIfi41F2PsVQfnhzOulLLOJUkSPBrnh/wg4PFqX+DZQXJzubh4vF4vqriUfMYmx9nF4pR3ubtKJbe9XFVmYaON1KciZUh8+PZngstP3nqayDTGCAJgta0JISxhCCFPVL1yLArbuobXJKXXlVMqlGUqKSWOF0IQSqy1UvIYvdBpfdHAM44xUotDCAFHxqi1BmMkpYgxMkaTRCKEjksyk5FGbx3utM/yzEc9KbOuEVmWXVxc3D0+rKsq6nY+H9+YzquwtztPyiQwHBia7e5ih3ezedtdU4RkXkyP5lmWpbnMEaPKI+oQQsvLm9Oz0535jmv7etmMsqyQk8urS+hSb6pKSlnOBA3cVD4L5ShOc11+efe1+e7Y+jaVYvTgeC8fGWeV9o9OTpfXyrgYCQkEsUSEGEmaaBS55LWzJEv5QfXyG/fTNPUunD2rQtUYRgMJTc27TnHO83RCcYIxliLPs6nV9fz4Aaj6YYw54gfpHmaRvxzrdYMDdcaH2J1frPNsVBQFyVMiKMbYR2eC6bomopAkiZwXV4tFCGE6nTLGdNOEgGhGx3uw7GmaC845HiNr7YRP1OlnLtP5eLz3ygt5njexpZFii/ORSIvJzsEIoSO2HRMUY4RpdMMBhikULW6cCEU+aZpm1bTj8Zhx0bZtkiRd2zvnOOc91t77g8P9nd35YrF48uTJwcEeDESr65oJvL+/m6bpJ+8+nE9mEJ9laU7piBJWluXF1ZlIRdu2hCFsMdTS2r7FDUmSZH1dc8on+SjKmMiMEDIqxsvlUhszL/ZAMve1l9+AxAv82c3Njfd+Op2CIWZUhBAIwQihXnVVs+77PiLPU+qca9sWeZyIJGWFtsY0Ta1Xm9oQokF7ljCeslY3s7xIkpQG1Hfq6uJid76bpYXkyf7eLnh3cFfOubquATEej8fj8ZhtFSKhDleWJdCVnz59GnFw2h4cHHDO2VQARgr0foB8vfdtk2h9xRgVYiTEKMtYnnuEUIigX4YpkYgxFJm1djyae7e4Xt4URREJIpwKTm1wMUar9abCZS1osGtjsOdlOqmXnZXe20gIefz4McYYQJq22eT0QxG0yDPrNpEKjGomFDNJmr6SPiERq7bnnJOIle1RiH3TIeaKScJ5KYQwvnv8bAF4+92D+wAaG2PatoVUjBCCeEiShKe4nKZUAnRMlWumo1nbtiHYpukYY8C9yvP89PIEiNND/YtSOt0tEUIYx6dnD0/OHw3QJXw0LAJ47ueopHteDnj8pAZH1Z/WOFIIQfS2ZgHBAWPM2bjdYzjG2LVrjDzCkjJMMBZCNN2qbpfWuRBCZ9HgRRgn3jsAGvs+YkIlTqMN2DLr46Krsixr1kuEUCZ5ujNDwZm+pYQEqxnjfaMr1TobaaT1qnM2RB/LSR4Cds5Z2w85XowxII8Q6mw/TCdDHgksXQ+adLS6rohDRZnPsunOq7Ozh1fD3G5YFmwx5/xicSqEaNe26zohhCBJWZY5ml7pq+VyOZ1Ox+NktWwQQkkuz8/PX7p/jyDaNX2l1l3Xe4ewxxgRxSTGGNUrtxGkXEAsJQTjnEPdAXoCAUgPaEMaGLBh8J7WGco2CneMsDItNx1iJrAsssSOMjre26VetAvj13HTpBs+3x2IEIIhpkNyTLbNPKyIB8e7IdGVWlyfrB7+7I+bC+dbLCSp2vXyk0vVa456o02SEmvc4nIlhMhkHmNUnYZgnLLnvVMQPSGEYII6YchaSwljmEPtBCMCVXFAe2CfQW0shJCKDdQ2kBrARkNQuql+CQGfhRAyBmVZluc5Alg/IkYx9NsYq4UgdVsnqbxzeFSW5YMHd68WN6vVCs4GFEVAhWdvd382me3OD/Z2DjDGq2rBOU9k2vdrnmQ7k5290eGTT5+Ny/KFe3f3dnZG851vf/vbu7u73vu33nrrvffeWy6XGOOAzNnZ2ePp46urq1VVpaUUklHG3vnsZxgTIfMOOcM7NosMC4JYJGw6nUDADiU9SFYwueuciwFJLousuL684VQYY3hKp6HctHlYC86JMeYQKYpkV4zyPG/bNsuy119/vWkaVNi6arwLzbp1Olxf3jDCOROsEBvyCMXGmV51UGdCrT/opwgh6O8ETRwppRznADex7Ux72EvT+WS4mK7roC4Cud2QfgWGCNnAoVenC7+VSkbbbhBCiNWuq3vAr6za4HtJop0JYIZAV3I6zcajSVmMxqPJdDqFxwfjwRnlfafG43GMsas7AJ8TmRJBfHBU0KZp6nYNU0vbvoWmtUwklG7nHWGKOYZSHN4W84boAczKZDKBDQMAI0Ioy7I0TVUPdpNgjBGOPjrOeUQ+SEcJCw5po/u+t8Q77ZRSO7s7txkMGONtg182ZE4bUFQkABUMdhwOnXPDcKQAZVR4KHDkV6sVZFrQWwJHDOIVtK1vwSmAY9X3PWyAGCMohYEXUdoMzgMsFEBQ8M+hZjzgUhh/rodn+Ft4HCEE6xzeSjiRbdv0FxxwCIHzzZpLKYUQTdMAtnFjluNbOxM2G8a47Vp4QLCjYLkYYw8fPtxYUhgQaS1CiGAsCz60Vsctb8taWy1q51xZlgBQQ1uOUkrmAn4zbpmtsP6DBxq+hxeUh8mWDoa202ptv4Gv8bbKC/crZeq3ryEFhwNyG8hE2z438HNgPIedAOduYBLBm8CFQTEOLhUCNW11XdcROVAfw1s9c+jxrZq1c94b0BLwVgVrvPe+7tYApYQtpQueI6aR3KKhwU0BgyxJkiSVhJG264zVabHhgcOC3N7M3vtA7UCMhyfSdR1jbDQawcmFi4f13NnZWS8rKaUQcjweBxtVb2OIzjpOP4cMDWDGsPJgUjZpHqUikUNl4fa+BVMwQCDwVoyx5XJpfJ9YTgRihBNvu9Y451hEARPMt1CVc85aB1jE7X1DtxPTHj39zLE+LSXnormsr07WOR9RwYgIwaO20kVaBu9qtSzEkTRpvWwZ01mRUcryUSEkr+u6bVtGEKwOVH/BPHHOEfacc8EkwxxFjHz0HrpcmPdO6zhsIw+sO2SsfW6VvNfea+89QYwQFoJLkmxvb8dae35+3nXd5ODO/t5ePp0SQiTfAFnee+fs4ubyr/yVv/Jf/Of/+YsvvPDw4cPz02f/6B/9o9/5nd/54Y//gDPCcCzGo5urqzxLVNt6768ubxaX9cXoKk1TTPHde3dijNqsfLC7k92//7f/s9WyfvPN79y9f2RNZ6N+/fXXJ+NxRKhpmpubm08++eRHP/rRj/7jf3A2OOtn0x3O+b37R8VIcMnP3XWWZTJJm17FCZq6OcbUOr+8vBYp4xkNITR9FUwACC50sW1bRtjd43sUs0SkmcxIQcQcQ9YyxPUY4yRJEM0gpEhTvqg7ZP2yvlqtVtdPr5xzgiWqU5KnTFApOIqoVx2lNGDPMI0oEII8ci6iNONJObLWYhqICNgF1be67/R6HWMEGX2QXYTTrlYeaIF+O98Xalo3NzcDGEu3gv7ee9UpyB4g/BoQPMEEeCPGWL/WYK+NdD56cNiw74GfiRBa3ixBNBjOOfj7uq5LMYbPzbIsy7IQwmq9vLy6iMKHEBBGgO/CIFuMMWcSY8wIo3jzT04FZ3xg8MGRGdDaq6srOJBwMiHJW6/XGFGEEKWEUmqdgYOASVyvKow26HSIARHikScUgwQxmOAsy4wxkBDs7U2gFyX4yIgAj+idW61Wt00JmD84JhChDm4PgiTwH0IIu53Hd3FxcXp6muXF0D3iNmI1FCGEUYTfBI2Xpm1DCFKIiDbFbDBAfss0TtMU0G8AruHphxDKIndbVjZcKt5OUwbDDV5/eE/wgnEL7eLn7OLng2QgXgcs1xgHFC1YgbDVr3Z+0xI2uNXNf+FmSEyHZBQhRLvNm0PYAfhB3/dQiYdoBrBuiDjbrhm8Gto2VdOt+ung84awA4BcyIPBZ2zqpgEBTD18LnwWY90XHCrci/+LFhOcBKwnVOJijIB8wJXcXpktDkoAjweCNKWUI04pbdreudC2PWx4KSVnAiOiOxMCssY544KNCJEkkYTQm8UVSLpCCqT1dtkxGXwqfAPXIKUM0SulKKciEQRTr0NjG7iLIZgjW5ETSzETDDR2lFKtao03aZqWopRSQlIHCNBmgKCP3nmZJjhja9Lg6ChlVoXbcQwQFeGsdV0DGcXARgQEHoogA2A8BCtDKeR2zIQxxhFzzhORRuK1MuvFsln0pvVMSum3mvVg6bIs45y3XTNs6LgV6PDek8iuTpaE4p29Hduh6XTy0guvXF1cX5xd+hZhz5I8a21LGMERj9l8tk861XDGe917zmWZ1KiGS4ePBuALPmU8HkMrLQp4vahCiOPxmDPedV2RpXCiKKWJlFhI6CDCOFCKKOXbtiLRNNgYk2Qp58JaM5/vvPTSi0oppVtju/l0miQJwVhwtr+/LwXWal3mCWfZ9773nb/9t37rlVdeIoQY3T169OmPf/zD73znOx99+oH3/vrmZlWtPvroo93dXcEE40gry0lirVssTiezsZTSW9/1a05wIctvf/3N89PrVEze/tm7v/jlWzy177z1Fqj+Hh4e3rlzp8zze3fuJEwKxg73j164/+JysWi69fsffvLo6cPkLlsuO9WpZbVWylAuEMLOeGxC3/aNaMCWgfmoqqogpbeecNLWXVu1lxdXMDFe3TTQZgdLFzYS+eUoT0C4QErZtu1sNvvF1cdnZ2fgJzgTbd0JLo2xFNGu60SeIoIYjJiG3CY4hJAgGJr5vPeAv0H/lW4Jxhi0kJbLZVEURVFUVWWVHXJ3sE3OdX47VSbGiLHD2/FwIQSIsggJUW4CUoQQY5glpF1ohBBNqbLWWiel9B7DZHi4TuvtarGqVtXG/WgL6hYYY5i566Rb3qygIQ9qbEqpq8XV9fX1/Giys7Obpqm11iizqcUaZ7313gcXAgqqN4FhFLCzvutbkLCHGBxtK6lAGoKMCiFUVRUcWmeBkEU55z4443SMgTLS4R5Ka4QQb71yHSSjWEQfbdM21lpte4QQE2R3NIcEzlp7dX3tTajXNcFUsOQ2OXcIzxFCiZQxRnerAwfsn9IasluQGgaw11qL0OVw9sO23YUxJqUwt8BPiKWUUjE+94K3Ice+7yFNhEwdbTtrheCD8RmeO7if0WgEAQGw3m4buNvJCvxTyg2EC/nZer2GsxYCWi6XsDkHEIgQAgIpQ9I5+FpC2BdyU7Rhg28GDFBKhbBghUEXAR7rkAwBDlHX9eBf4y2I0eQ2/grnZvMpwYBSFRApnIXRTMz74H3EGAHkEWP0PlbVYjKZwIh7qPXeTryGJz6sD8yFg0OHt2Vp7z3MvR8Q1GHPQMgVYyQURxyFEJnMKKV5mgP/IISQpmmRFYwIo5zqrXPOGeetp4TlaTadzPI8V7obIgC85ToMn0W35PYhTOFiozwTUGCMeRKt1t77LMluBxMQkadpKkajAZIZ0mu0FUIBBZi9vT2oTXjvX3vwklYGfLnRFgUkExkcAixqSAAgMiCEWKvdVn9tYOMPua+/xZi7/ayHGG44fcANTNPEOqOcNb2JMY5GI4ZwpIwgzBCO1lrnbVBeG0IpHWAfsm3MJ4Tsl3d89Mv1TRyT+XTOU/r6X3rxL8++/u//258+ffiUIoEsJoFMy6mqrCPE2S7imBSJwCJED8vEOcfoef/7sHCU0ujc/nynzEercTUuxl/7yl8aFaPLqyvVdxuMmpAkSSBiUkqpxkDqDFSdtm2Xy2XXdQ/u3QMHPxqN7t69q5Q63t9fr9f3xvn+7g7NJq+9+sq9+w/ynOtulSWCpelrr712c3P9f/2//V/6psmSNITw2WcfffrJQxJRcO702UnT1qvl4n/6d/+uNe69999llM/GO48ePv7BD37QdW1dNY8fPS1KLBNpTFet+pPHzz7+6JOHjz9uu2Uyxo8fP9Za37t3r2mat956q+u69XqdZ8XV5fWPf/zjEGLTtTyhynYB4fOzldK6Wld1XeNIORWEchIJtdg5Zzn0nHkpGWOcdNIwJ4SURKpGLxerru0Zkc70yigjAiD2EMQhhHyFl3FNKa3rmhAymUym6eH5+Xl748km3aS6iyxn2JKAkKmQ7SzGGCGHCMIkYrLBNrvoTUYgsUsSyxjrOqeUyrMRIYQGjp2ngUuSZrywzIskbrIZiwJCbjsldGdnZ4jEwQCGgEPAKFAUcMTYIxQwELBRZIEEqztLKfU0GuW8D4LigNGonAz1Y+89DMcOIQghBEbYUbwxuDF4hCzBkWllgt9kKkyy2c5EJMwxjWnkkkYU4ABGHzHHq8XKGRdCQBET1AshKGGEEOvMEMIOyRmkm7DPBz7UxuBGEmMEBxxgQi7GXLAe9dHHTVeMt4QQJhjlNOIYcUyyREaJEDLGMMHG03EupZRyYRd1XTPMnXEYkTLHXdcOlnSwDpDog+cAMzGg5W3bEkIgGoNJNUABgYGVgx2B9IVzTgj+QoaHN3X67fPdfgefDl1DgMYPbScIIaX6wVWQW/24MUYpZSI3N2u2E1jhr267xi1irAHbaJpmkwy1LSEEekIgdAOyDwQT1m5CscH/baFs+QXvu3VmG1MLuxRt0QUISmDCyv7+/nK5vLi4GEw2+Izh1iAQ+UJgNPhLeFu+fW3wAxsooYwzSHY98pTShCdszO7fuzcej29ubs7Oz+FvhzJZvIVCw+dCAzS6Nc0Xjh6s57BV/FbbK025j1Cd3KDu2OEQwnw8B4UZymme5oywptqMiIBw2XvPCPZi02+5s7MDFR+8BXLDply4UQbFW7LhBizBMckSiqm1tmu6ylWMcs55mVOojwwpPkAF83IXTplSfZJIAFqGYG6IdQCmxhgXeanVTd8p01sAWiRPoyRc0OGYDOkvQqgoCrCcEDrANQyG9Fdf7tbw8tuXQQLu+77pG+89RbQsJuOMlMWYKas2G5FhgghmWwzQb5TS4MPgOAkhuhsznY9b3iASeUrPbk7e+TD+1b/2P5rNp8/Onnb9mhGOKU5lft1eTmYTJlHX9TeVZpTlaRFjZJQ76zHB4Hdh3gvsv7ZtX3/phfl0Zo1rqxp7ZJTmE1Zk2XRUDhsL3cLW0mQzPAsAeqjBOOcSzgFgAfCVc3L37tH9+3fMxXn0HgPyxkWa8YTHhOFF13z22cfL1er1V1+az2ZWa2ttURQnn51951vfvHPn6Ozyad/Wk1HxyksvnT47++53v+tMzGQeHD7YP5zNp/PZbt/3053jpvWPnp6cn64oJ9yFlJJyPDt8bQ9qyUKIm6ur8/Pzy8vLpmkElwSzi7MrY5wLTqaSptg6Y5lLeJrFEaWJc8Fp510IODiLGRMIsV6ptukw6TcFf+GDjF3oY0B11XofrLa1qnGknrkQEIocRQ4YVN0pWtjRaEQFsda6aAnHdVdppySScFCV7andYKFVv8qLMUYY4Ygj5pQxyRBCwVrssfHaReeibZWTUkYSbDAWaYJIZ6N22iKtfEc17l0bezoE4IPFSWXq7YaGM4CWcHLyZBSDQwgFhBFC3qEYI/LRmd5qjzix2nsbY8TR44BRmmSQZoE77DsFcwwJpjEgzgS0Q2itg49GW4qpdVgHjRvkkJ3MRrOd2c7BztnN06avA/KSJ1JKZ5wPfjKarc/WbdtSwqz23nvBJeR8hKJhfw4eBWi3cF+DqvMG/hFpjJEQzBiLKLhgCCHcsUCRxd7baL0GY5TlGePUaIsxnpWzPM+rqrq4uIg4+ujrum6aFpRyd/f2uqZbLlaqM1uNqi86YLQNegafN7gxcM9Atc2y7OnTp13XcS6+UOiC28F4I4Q7NNeBPEIIn6OSDAYIUkYIRAA8hDB6tVqSW1IVw7pBHdptgUG0FWIccqnBwMFL637wPYOh994nSfbaq68WRfHo8eOTkxNCCDwRzuXtewdwCDCA24HLsDJhK2gFCwg+g3PJ2CYKmc1mr7zySl3XUsq6rrW222R9k8agrarJYLvwreQYVtJ7jxChFGQ6XAiebEU//JasA5uKEw6FT1BaxluxiMFzD58Cq7qzswPF6bitU9Jb068HGOBzBhYTIYSQHIZ8i1yqAADvkklEQVRnG2eMMc26dzYiT52LjesR6mEDCCEjiYzHyDY+FcT2Dw8PwdkjhMA1wrVF7CEkHVZ42AbOOR+9cw5FTCIPDhm3YQ9gjAH7hUdgre1Vl6YpZSRJJaW0LEtKad/3TdsKIYD2AU8cVH7ruu6aPniEPElEQiLFiFDKpNxcG9rSJmC7SsmHPQb0Asi8tTW3g6dbu+WLdmDrmoMxputUCGEyngqG6nXdti0bTKH3TspkPp8fHx/P5/Of/NFPYN+A4YBAXkrZ1G1r6myUEBmN7/u+++XPPj55cprZORqpdB4O5vPVab+6vsn2+Fe//9Ldu3c/ev+jd95+t+t7TLAxBqZXYopjjHAIYdPAY5vP51VVnZ2enz49S5Ms4VnTtFmWWdUOBwCeGVSJdNe1bTsIxQ21lsp7OCr+lhg9Y2zkvHMuobRrqtOLa8EjIw55q3HgnDurGQ7PnslEiL7tVquVbsLZxbO332M3q4tedaNy/NH7n52fXXApnQmciuXN2mjbdd2f/fTPtO0Xax8jvrlZp0L+2ne/lye0TFlVLZ6d6Pl8/vLLL19eXl5dXhCEwOS0bZtnpddWGxMxda0yvTFeJ4ko5qVMJQk9lTj4WK3XbdsSmsB5QwjJZOMsOecoIqOttRZFEn3kWEqeevt8TWD1oIzUNA2ysak65wLBTHXG2+htpJh7540zLrgQAoYx5TEQjpXuoRqEGUaIM0JccNZb1waIuwlhMUTBEpGJ6LHtHaXUa2WtxZ445TvfexsoZr+yNZ9Pmb1tiCETGnbzkH/A971pEIkRe+t1xIEyikiI2PedstaCagGU0BjlaZrWdU0wxRgDNSMG5H0w2gZNYoyMCoRQ27aYR55yicXRncOmaRgWTnuttNUuWuyEG48mwUeCWHCdsz5G5L3HEdvgbhsytIWOXnnllcVicXp6CpU8pdRoNJJSOgsgJEII+eB8tIQQhGORj7lgkcRoPTwFpXts0d7OweXlJUyihOoviOtq1yKEsiw7PJRfevVL56fnKGJGxJBZos/nr+HW4IfBtcQtOQtjPBmPhy7Vuq6tdRA0gOMcbi3G5xIiUAfZvtvnZroM1wD6POD/4EHDHFal+oFbBDHTAEjCcxysM9wC2b7irTIwIQRmMAMBB3idULQejUZ5no9Go73dXVC5gcBulI+GhHtw2FprZdVtFgLaclyR3zA9wa2CVRFC9KaFRKXv+7OzsyRJjo6OLi4uqmUD72/RJo2BLHwwYrfNNEIoSRIANoejSiihgnIuhkwaiFHwIFb18uTZs3SxGH6ygXAYGw7ObYMJ0cmQsuOtVtfwh3ErVgrLq9RGUCLLUkQQIcQ4E2MMNlLCpCDwKRjjRKZlMdriHSHGGFEAt6JtT8gx287ZBdgSNhvlfFhkvC2sYowjjkYbqy1UUoRkznkotwMkzrd/CNe5WCyg1gamA/ZY2LYzjcdjIQSw2RFCXdf1feusx5gwLAQTunOd7mJEqPe38e1hTdrWgL+Hj7bWAlXCx/CrJCy0VZoLn+/pRQgliZAisSZ0Tef1RqVca80CckywNC8ggIUBCYQ97yiHpwuryznnOVl1i8P9nd3DST5O3vzetxdXqz/6j39Cx/GbX/1yeSAfHL78099/74/+4E+P7tz59l/7svB5wLZVrem8bcPNxRIhElxAYkNJIGTD2ISV/fnP/lxrjREZFWUi09OTk/PTMyEERn7YH4NeY4yxb/vhMCCE4IwhhCZlCbzHwVUDBeCbL7505/6LJJucnp7+4t33MXZ5Qr1VKOPeakLIo4efrJfLclRyyjDGoWPG9rIQPMEhOkZFtezG48n51RWNHCNqtDXGVPX66urSY9vr6XxnmhbJarl8dv6IOptTfry388Zf+faTJ09gJ3HOgdQqhJBCjsq5MrZpOhOiDaZxDXGs65cujoK3IdoyH2VJiqKrqiXmwiNntDHOCCFwQF3XRRMf3L3jve9a5YzTve5Vzxg32qQj0bdqs7E8pRJTTgNxIibr64pSWhSFVwg7LnDGoonBY6B5IsKi8MHHwFjk1mMKlKGIJU45EsgTYp0k3LmW85RTrrXGjlMmkWWxZ4gxH0L0VHLOHMeB8BgRM7BxMXDBENp03dxKHMmWlIcQQnhD6wHFYYQj/NBaC9DikPHAgRyVk77vtbLWWkq4iyF45GxIZEYwM8ZQ4hgVjAqCAyXceYcRZYzF6Nqu1b5HBMlE3J0e7OzscCpOT866rkt4yqiQTCY7SYwRBUIwxaiVMok+xhg73dw23GRbKz04OICACdLfxWIBJV5GgWAcCSHOYxw2crJKKUIymQicRB89ojFE741vmubi4gKkDyBiBiQ5SZIYkRACIwdUkdFoNJ/uPnnyONxqUkRbiHXIvW5nYCEEqNdgjOumadpWSik4Pz46WixX0LE6UGngDPZ957ZKhyAbopTqui7L8iGVwduiXYxxd3cXhGXgNwczN+j2+S3ZakAaB4M7UMPg1A8OeDAIlFKEQpIkQ4oJYhfgzp+dnlZVVRTFiy+8UFXVuqqklJnMhjcB6wGeOMEJIMCDAxacc87XywqAZbD1AzVVZnywwg8fPtzd3T0+Pk6SJBQb7TkwQdCFHG69/OdJWGQ7hGP44ZY8/BzMv+1Q5/M51LAGLWhYqyHlvR3gQnBJtzIa6BaUOAQBA7QA+9Y577e894g3MzwQQqvVCtZnKJ9v4AFEbbDOOYjMEI6IBBTQarUC7cmh8QG+l3jDaQpb6iKojKUiYYJxzoONutfeKYopYyxJxCZCUgoPVGQhuq520fnogBYHrEwpZZZnID2mtV4sFvD7q9UqCYISJihHEW8qxzYkIun6Tc0e/hxsC0Lo5mbBGANEDXJRKJwnWTrYKDj4sHr81vCrIamglCKKYPFVp6pYp1kymUxEIlhGJs6rpbmhKXr57ssU4w9+8aHTiImYSGGMPj87heOqVX+lehuj2CX7r85e/7UHn55+GO/pl954cO4vmpur+VFxeHcvIdiyq8PXZTrRP/j3//av/d2/WiePxi+bEd+9/Li+fGKI4gWfMp05X4t9/OW//FI+Sj77+OHyvJ0WO7JETx8+JphORhky0fQ9CthT+7UvffXZs2dN04Bcu9kOQr+6eJYkCSPMGkAtRjjSEAJLKKW7cLDDVoqPUsrJKNnZ1b6bz5Ivv3FUlLkxZrG8Lscl3U4RT5Kk7/sQwmQyoQ3TzuFUOBo6VQkaxjmOxt7zLzEiPnr/k/OnV5hzVojJ3lTbXqxwX1+Osr2T1eX7H5r/7B/+z3vVv/f+u5/82//h3oNj5ZXnbufOXHdm7nY5z/JJMh5PlstlPpOgh/Xue1e66wINVbNWrpepqFZrEuju+EDmYxJp3yhjjHfe9o5xJlmCELo8W4ftFDkAb9q+p5TqxtEoMPIoIoIQTIOJyBsXm05Pd2Y9RoQxtjurHn+ywE5oxTlnQiCElqt6Czbkyq6dN7PZLMbYtb3ZEpoYCx7ZZdXleV6WJWaxVTUVOInBuT5EF1FwnhAqCOWM0RBiJmXgvO97qxRjTFDKMJ6Wk9Vq1feKc04oDdYjHwXlznoUMYrIWT9kJATTAWoDSNk5lyTJaDRaL9chBIqp8aZqKzgh3gK1Xkgu+7ZXnUrTFCFEEGESa+28JTGikk91p0/eu9zZ2UnkOoQwm83KdKITp5RCEhmq8knKEWWYt75JWV4vW0rEqJh02nGehhAgQ1XK7OxMuq774x//BDbq8fHxO++8MxvvIITyPFd2owzDGBtnGXxPKZUlopSiiAo5VrqHFhcA3w4ODiilbdumaToejw8ODqqqqoy7d+d+dOzkyemP/sOf5Mno3p0X8rx07pRSOhqV8Cfee84pNGiBCyRkU9bK89x7n6UMY/zVr35VCPHuu++en1875/f35+Vo/6233irL+XQ6vbq6grgHY4wiFZyDP+g7A/skkXme51rr4ANjFCMcfECRUEKqxco7L6UMAVlNsiwjKF8t1okUmcxDCNrpVMiN2Qoshhg8ppRhxNbrlZRyb2/v5uaGMRZ8dNb754rWDiGk+56D9q2nS1RJKa0K+/v7dd1Wy3ZxtX7ttdeMcZfnC8aYN6iLevBAMcbYdvBPmZSEyKurBVQNEEKcO++buGEFWlRvJJC2sQjSWk+n09l8/vjR448+emodo1Qsl5e3c6Cu02gjWRqdc+C9lFJ7e3s7u7sPHz7UurmdGcfnIg9fLEjDS4jk/v37QojHjx/HqAHyQQilCY23XoODR9bRW/w1yA7v3bv3+PQZDCQGDABthzxCJrd0NWNsPB4DurBcro+Pj1erlTGWEB9jnM1mlNJnz54NteTb7ifGOC7mRrub6+WAaiRJYo1aryopJeOUMJSmaV5kSS4zl1ZqAXM/81EqDa/rGsXYqsYuwfmF4bljjPtOIc+SMlEtpsJzmSQs4ZSWaYkwCtETE9t+ndOkqxWnMjVFcCFS6gkK3kLtMoTQdLUQMsbAGEMRqd5yliRp7pybjHe7rsMoSiEJJiEGRpF32PTeWotwFAlDCBmrEUGcc001T7jpjVKKIqq9Hhfj/cODanWzblcm9CzFgbm0lDs7e3mesxAdQkiwxCp9eXLDSF3XDSNCW/M8C7lFdpjujbN91pvu6dPHq2Y9qlbTfOfLX/uSvr5LKcrl6NFnT5TRb/7aNxWqguidc8cHdybp3PVoNa7JVOmVjdHeORidLdrzyyX9wL36xovf/82/zBh78ugprdkkL6MJ3/j6t978S98hAS+X64SzYDftE0O6A98URZIkieASRRRj5EwQzEIIHBRejBmSeDg2jIp1taKUvvnrX0cE9X2PcMyyrFddWZaQjcGZgXtP5Uj3HSWIc+5ipIxHTBrV5iyv1u3v/Ov/7k+7n15fX+d5/sILL3z26JM+mBjjxx9/zARDCL39zjsvvvhCluYHh8fz3Qnm8Xp5BfYUKm1W6XWsqnVNGItIOecYFXlGdexTmRGKueAuumCR9yH4WFdrxjdVNLgvyBWsUhRqR4xhwFJCIIT0qscwaZxzQpHzzlobkc9FOi1HkzTlgkxGo0NBZ15T5CpKob5CCAHKDPAXymLsnIPMUggBaWWWJZxxwZMYMIrEuxi8AxCC+M+NjsEb5otVSsFIg23hY1M7XK/XYHH6vi+KYjKZNE1zeXnJxUZ0EH6ZbUeD3YY3IfZ0zvV9D8l02LbPDSnRkItApA/2AkryfjuLFC54o2fea2OMM248HqOAmqpxiWOE+Wj7TuGou66LjnrvY3Bd30J0PBqNtNZpmkopz87OyrK0wa/XayHE8fHx0MXbNA1m0loLWoZAG4R8QhrCGKMMZy7zwWGCcMAY48vzS8hgoo9lWSYi0b2WXEYfB45SjLGqquubmxBQ27bDDoG0g20n2g4RPd0O8cUYP3nyZGdnB3KsxWJxfn7uvZdSIiwWiwXeEtpheb33lPjbfmhITMfjcV3XYNPpVuOaEBKscR5kCFFEDBjOIYTLy2vgBw3PFNJWTDEEVUmSQFoDn3J5eRlvIbdDfu+tHepNUKiDe4emR+89TLeF2YXWWh+fW7bbb3j/wZwQ4pwD8Ve4iw06eqs++tzBjEuYKQ5NwJCiJUlSVdVtHH74XtkwgN5AXQTgfSB23XbACCF0a/zt7ddAFqNb1T94uG7bbXw7A8YYY0owJYM99xh5gh1+DieQ7TAefItcBnsDDgscH8AwBrR28NlDUeMLDhgs1ZB5D6YV3hljDGMe2rYViZBShi4SQjKZzWYzRvlyuV6v17rXCG82EvpCASsS8AWUIs45phHEbaD7iCe07ZqmbaNHIuFpmrZ1Gzeoz2b1MCLxFlD0hRe0kLjt/OnB92/+djthBROEKRZCOO+yLAO9neVyCV0PWZYJRpqmsco64RgTzoXz83Pda8YE0ToKnHgT1xcN58JaiziKYdM6dvtuMcZYxpdefzB7IbFpW7tkcbMM+tOE5W3VUIqu0tXRzr0P8Sdv//IXX/u1l3cfzBbrdUZH1VJ3fZsfiZd//fDy4fr6yeKDk/rw4Ojw8IX1zfLtP/5gNp7ff+24Ccsf/c5/tMb9+vd+/Xvf/s4br77WNe1rr7xYLVc3y857v16vIQo+ODiArobxOM+STAiBEcGIUEpjiM45SxBN+FQIKK6s12uYnKWbdVVVIfqPPv7k6cnTXnWc8173O9Pd/f39siyBUD2bzUbjnHOucZOVScoQDbjvde9sHzlO03E2ratO6R5zhDlKs2Q+n7/3/rtCCEoTH22SStWrj97/aDab3Tm+8+n7n1XNZDwrjbOMcue6m5ubRKaMU5gQIpJE6U1LSd/3RGJvQgwYOcywoJwLnpR5aTtHMAku9EaBqdo0DhEUgws+Bu9JDJQCqQHjJAFkz3vvgrfeYoyFFMjqhFLbt6p1uZR1Xdddr533FocQCSdCJBRzo5wzIXJsowVTAnUKrXVVVQASQjVoAMrgkENLD7T9ASwDtgkEY4GL65yDIDRJEpjT0nXd9fU1PCmEUJ7nPmwELoA/D/gHIcT4jX8dvDJQMYFHA44EVgbOPPj+IZQcqstDYD4YBVjS4JS3/qa6ydMiEanVllOOI1adoZgKmkihTPBCCEal4KLtnmcwy+USvlmtVkf7O3Cnm3nm2/GuGCGYo5emqduOUm+axqPIGKOcEswwiZSRGBDyKPoopCiyIoSQyixkoW3boii6rluzlddRSpllfnVTAx8VVh5iKbDy8Llwm5sJChhTSkHOt65rrfU777yT5/np6Sn0Sa9WqzQba62gngohFHzlbMB+N0sK/wRTC0StgTsaQkgFDxE554xxjKeAlgN/By5yUJCA4AnmEw84x9AAepu1C095E95tRUUglhogbr+d9w4BByy1cw7T53W7wUcOYQR53g77XP4C3ypFD7sdfA/bDnKG+yqKYmhvjVuofPNP8rxDNG7LtyD2PnziELV83o1+7jV4aLj9z5fnn7+G94yIhIhD3FK+I/YB+fBFB4y3xOmw5fPDo7Fb+ZEBTAbRDL9tzRocxK86YKDN022n0+CA4xah9d4bqyOOSZK88cYbwYaqqrq2p9Q55wgigAkNDx1tScEIIYQoPNBIEHXAPNzUpzlnIMXvrCcCntGmx90HH8NmkaHU6n34gg+G64eC0XDl6Pm0QKC5bYo7EUVQwI40LhYLoGgMEUzbNjgGTnie584F1euVXqEAErOpUEoxxAWOxhrGORbYI5+lKd5S/txWZIAQcnp2OjlLTFlY3tVdG4OqrlvG2AG/8967H/zip+9/++vfW13ok8+aF17Ro1k+ycclnV3aVd00h69MXv7m12LPLh8trz4yjz54ZpvwzQe/VtXrP/n//PKnf/RzVsS/9v3f/PCD97zSru9t36u2y8Zc922SpyGEVrWEE5GIfJTnRdb1PaHcI2RciNEzyjFhIUYXUNe6m5ubi4uL8/PzTz/99NGjR1rrLMvu77z40ssvxhj/5Cd//NmjTw8ODrI8efLkyWcfPYb1Go1G8/l8f3//7t27h4dHb7x5bzrKD3Zn03KcZ4I5R5XHyi718u233765uS5HeUS+KHJokVq1FWPs4HC/bdu6rrnlJ49P5l+fT3bGMCEgT3JCyEnz7NHTR1mWpTzbQmmkrhtKqdV2tVixTGBMKCUURYyw0qpdmOVyBePDhqrV0F90PJttAuqIPEyesMo5F7k0xkQUOOeIBBgvTxneSVNEKWLUohDTrKccT2Y9WifexxittdDq57eCAG2vNpF7ynSv+76PPhplYDho9NFbH1wIITjjCCFN0wBdsKqq1Wo1mUy+8pWvvPLKK7/927+9t7cH5boYIzARkiShmM3n8xjjz3/+8//u3/ybH//4x1VV5Xnu9UYwGU4C2iqa3Y5G4edgJiATRduy1tCrCvnW7SAa/nCw+2CJwHwrpbw2IcT1ddMUXZqmtg0ORcWtjn2WFAwzzkRnau8jwc46DK4Uemy6rrt3797f//t//5e//OVbP/tT6FuQUoIOGtT+I+HAGCJbkS8w34QggjH2GEccAwoOO+ut9pKkNPCU5c65oKPASWv7aJ6rHYGbAbgCJjrcLo0Pr8ET4Ft8RkrpbDZrmub8/Bzy4N3dXYTQeDzO8kmaZsP7QEU2TVOM7PCGtxdwUHoZPh2cX57nEF9iTAnlg1sCvCHeYlCCoQfdbKjGwfMFqwq8Abyl+A73AtcHif7gVr33ALAHGJOXpsD8EkKAA/7VF6whbBhY20FE4letP8aYMYIQElLAyoNrmU6n4IMhdhxY04wxZvztYjy8IXDCbzuAwYF5b//C64Q3GZ7mEFskibzt/wanaCxAQUMqFWNE/tYwrsGJ0i3FCd8iaeOtohPAEsMRGzCVzzn7Wy+/VTsfNgm8yfZPnjPDvfd93xfp3drVVdUYsyCYRr9R3fHuOeN9eAQhBGjns9bZ4BFBJGDqMSEEUWSMQTQkSUIJi26DkG3sRogQQYFPxxiDN7+Nc6BbuG/YMtVv/xDitA14hiP22HtPJYXCM9kOgdZan5ycpEwwxrzb6uFgKoTIkoxF7AknWZpyzus6IBwRRcE7emtIw/DxhJD9nf3PPvnsyrDJQcmoZEyOJiOZ80/ffvjo/cuDw/lnHzzzFd/fOfzsnctHnzxlo/e+/vq3mOCM8bOzS0v08dHd41cPJiPPU/zwrcsf/ehHJHAsvJEtzeP/6n/9X/yd3/xbn3zyWb1s86T0Jqpe3Tm6e9OqdVUNXAzY7mmadp2hjFBCcUBdp89Wl2dnZ5dXVz/9jx88efLk9PQ0hAA0yIOD4/39/QfHdw8Oj621X/9L3/zGt761t7eHGW6a5vEnJ8vlcrlcQt728NHJz/78nfV6Pdub7uzOX3zlxa989Y2vfuXVB/eO8pzPchb57NmzZ51uXbTK9jera/+Jz9Kc7SXQ8ggbq+u6s2cXd49Xr732yuXlpVLGucAYI5ju7M53d3ex5UVZFkUh0/TqZlGWpXO+qqrJbDaejZikjBPVm6vz65vz9fXVAlH3+PHj6+traELA2IXgMSYglAjqNnQ7cSVNU884QkhKURSFSBh0FDBOfOdDCEWZc8nyND2+c7RbJB9++OHHHzzLskwptVquMEKciRBC36kiH7VtixGOHlerJsZY5mPYi4lg0WMUkNXeGKO1SZJkVOagTnD37t3vfve7v/Ebv/HGG2/M53Mw4hDVMcZmsxkhpCiKJBXeIEzRvReOf+u3fuvHP/7x/+Nf/+sf/vCHPGDwkUP7wYBtDi4kbBvevfeQUoMdAdsHJsY91z587njAfwBBBkwD1P67rmOCcS4pFkZ5RgKnCaeJ1cGHGEhUwVDC0zSlGWeUxxiXy/Wg83BwcPAP/sE/+Kf/9J++//77/8f//f/uww8/BFyaUjrsjV51AJParUgnpBTbnNJHRyIKnnirvNY2xFhXjeASY7xeVXmeY0RUr4uiyPPCduu6rtu6J5EDWwrein1eIxPsCyQuEG2gbcYJSlvAHwZTCDHN3n4BSe3g7TYXGTcSP4OJwJtiZwefNaD6YKqUUr3SkAz5AG3BlHNuwsZ8D1nmEBPcTkYHWzS4qOHNh9/HW+KS38ojQMURWiQA2QJ3Til1wf2Fjg225WDo/bYdZQBvh9d2I2HnnNEGEBrv/SCPM1zzcD0YY7BgsP7wy1BZg+39BR+GENL6L06Brd3ATrfXCm0Z6fGWHMQmj/LQxz9grRGhGLas7OEr2ubQ5BZ7Zkg94RnB+sCqxi0Jzm3H5d4+mPAIBk+Mt91c8A6EkBA3rVNSCOipe+utdxBCfaeEEJiQ6CN6ftXPHTDZguTBbzYY5UQIEbC31sQYecKtNdqRvMhMtFob3TlrreTSex9R/IKjHe59wDbgNcQ34VbPG6UUY8QYQzi6YLz3CG/EIjHG+/v7k8kESkvQV+m9pynpe220dc5JLhORxBi1MsxaywRJxoIYVFuvnQ4uYIwH9Vp/q90eIUSVVAt6+PL4hcOXl5fN6ZMr7rM8Kderbv94loi0aZqu7at29WB+KER8608eEfTul15/IytHH39yfvWsZt8YTecTI7r5/XyxEFe2ZoE64yVL7x3d+d/+b/7rX/u1Xzs62G8qv1r0ZT4JIVjjBJMkEoZZJjOKqO60L8JkMuHEW2vXi2q5WC4Xq+urm2fPnl1eXnqT6y46TSaT2auvvnp0dAQMQ1qiLtQ2mtFekWRSu071KimS17/1UtyKwocQ1uv1T3/60x/+8If1MulWJ5+8+8Ef/A//7uh4742vvfrN73zjy1/+0mRkLy4uQvQBeyZpkkrO+SsvvdIpnWUZJ7wsRicnJ3/8R3/S1t2jzx7vHI67vq8WNWciSZJxMf7e9763d7B7b++lNMs45z7Gs4vLyWSS5wWltCim5TjnCZWplFw6HZzC0aMPPvnlD37wgx/96EfL5ZJSOi43Zcurs8cvv/DiV77yleOjI0JIrxT4HiIS55wx2jkXsccUhxBCdLGUwbrZKE2RLyg5xl5mAmH/UcCcSYOd9xEhTCnHOBDChiLlcK6cc03TTKdTgFmG3UIpLYri6Gj/b/yNv/H973//61//OiRVA0wN8CZEeGDxhWQIoUGNNS3EX/+bv/nmm28uFov/+v/w33z88ccfffgR7OYQgpSyKIp6vRrOOaQWMUZoiBr618Gr0a2ELNrKW8attCraaucCt4BSCqJsXddlSSGEjxbXy7arlGoM9hQhNd0rrQ6LxQIFZIxhRAQfjTFSps65yWRS1/U3vvGN3/qt32KMvfHGG3/v7/29f/kv/yVUvkE/sigK5xwIXNweLuu9b5omS2FcIHYuxOgRjlZ707nRvOi6TitDCAG5SoRQ33Xe6CzJwLEJ4QVNYQWG0IRsa29fyIoGgBEeJXRnZVk2mWyUxgFaBK41LGbcSMB6SilnmwRiKEBscwIyQJRgNzb+NULjLCeEee2NMZw7vNWpGK52yMMg44Tq7/DzeKtYOwReG6McnkOIQ4YKcR74AMCuB6UXcMCDOx++gS0Ut5TjwfQBZjN84rAUQyo2BFLWWAgx46DmtiUZhBCUMnCO+K0+nCH4GN58uM3/xGswzsMfku20peE1eERBMcY4kg1goFFgOOLghjCU3KoBD854iAi9f97NDIG+UgqiNAgi/8IrHDwubInhaiml4FSBuAO/EF10zvnecSZijMEFh5xzDujKt7cx+nwZmBDCOJUZy4vcR9u0mzW31kaymSPQN9qqALFyvNXxjDGGzilo8Rq20PARgJdAjwBsb/gJVBhC9MZR7z2hmAAII0ie5+PxBCHEeTNUtYL2gkvOhFHGW991nbNea81QIIQhkkTKYxaE1URVliER/W15tg3W5JxbnNdsgr722jd3duf100/dCvsRUbXvVnrvcH5zc3Xv6MFkPvr5n50nYzaaJ8cvF01XnV4+ESG/+rCNHj+YB+bVH/7hj/7OX/+fvPmXD35BPsrp5Gj37sWzy08++rRMd//wD34agv+93/3Df/f//v3f/M2/+v3vf39cllzQw72jO4d3Qwgw18X0tmeqU+Hq8vqzzz47PTk1xmZpOp3sTyf7KJbz/Z2TkxNjjMfh/PoCWoBav3t8fDSfz6UkYCOklIwyRCJGiDNGGGaMHRzuTWbjozuHf/R//2/vzMe7YiyJd7i/+cXbv/P+W/86Ke4ef+Xk9CnmMWLPBS/H5bScZmm2qqqDg/0vv/Hl+3cfGOXuHt3/wz/8cdf11zdXRVqGIlodVsuKCbx/vKt0n8r08uLy2emp9X61rrMsY5xnWRYD54K6YBCKaZoxnFDM8rRs9HJ/f/8f/sN/OJvNkiS5vr5++PDh5eXll15/uSxLmRXXq2qQG0vTlEfsvQc6yXhaHh4fjsdjQvEknyWMRqeIVdM0nZbp9eFeKuVT/kQIcX5+PvS6hBCKogjRpWl68vRkvapmsxnPhHOuLIjqNSUMIxK8jxShiKWQk8n0H//jf1wUxc7ODvDa2LY9EUwzlHJBkzZNU+9i2E59IZRQjghF41kxnhX/p//zf/Peux//9Kc/ffbsmd1OFqKUPnz8CWxuMA2AVWKM1+s1vC1Qjeq6BjIR5CV+I5cYh1IlDKIAnVhCCAglUkp174ZoXWs9Hk8gUX7plQfW2pOTE4TQ8maNBtF8E6CmC/TRn//853/6p386n89PT0+HQXjHx8fT6XRTNus0QijLsq7roL9oPB577wWTPngXHUYkxBADAklCwRM+kjBe17uoeuDFWBc2RfTJZJJwEx0ZEspfTYNup6SweuDnIH0BDtF0OjXGQB4MhWRIkf32BUbKOT94i8EzxW1vLlwAaOxAkU9QIrVBCGm9AbEAMY5bKOK23R+8wm3XjreS2l9wMHiT4fkhtfqCyYK7g0BnqKYhsjGywzt8wXnjbQNJnueUUmCchC10DDun6zqMn9fCB3cFrUGDEyXkcwIGcAubfGAL/7pb+uH/ab87vMIW3B4e8a8mcMOtMe82F4kRITgAZ9LoL3wcvgUwbIJjIbTWTdO4jYQnQtsBDMNADiDQfeGtBr8OyDzIgAxAwhDNU/a8rpQkyWgy8t6vl2vnHI44+BhYiB4TRm77zsH7Msbhz4mNIQTKNopXAQeME0QDNE3hjHZeexdcdM656CPwzxAicQtI3MZX4DWsIb1FIGeMSSmhJdN5GxCjlHLBoKhhozXGrNerAZbAGJdluepXjDFOube+azprHMaYYs4EFYYo7XvMYznPkCPr2GFFI/UD/DIcM+dcQqf5ONet/ckf/uyDnz8UMS/LvlJLZ2JdN9kofXbzqMxKsYMUrb75jdd5Xjy6+giJOBJTGcb6OqxO9Lpbj/3hjB8dHB3SvjCt38kOd8TR8eiV5erq4uz02enJs6eXp0/O/v0P/vCFF+79+q//+v/47/91aILs+/7x48fPnj3DGE8mk76Lfd9X66pXKk2yVOSMsuBjh9TenZ3xbrlYLK6vr1tVU0aLWXZ1svYd5TGfzWbBci5YlmXWG+P6gbfS93Uluzwvv/nVN//qfxUnTk+r1RhHnJW/qPTvPjz75dXFf/gP/+HV118px5lHNsuy2Xw6K+e9Uutq9c47q1E5aqr2zW9/95/8k38SQ1ytqjsvzL/06htex3rdnZ+fZ4X8yl9643p5xbQ8efbspz/9KeWcUM45b4C/SgspeUQe05iILHjitMtkrmMH1gS6nOH0EkKSnC3W68V6jbfFP+1cqxTbSAcEaCe4d+/e3t4ewlEsq+ls3hulnZkfHHgUFk27mOx8982jw8NDGBg1nU6Bu1QUBaJhtVr9i3/xL37/934fHDNkdYwxIZ+Dw5BKYoz/2T/7Z1LKyWSyt7e3t7cHQqExRugLHI/HUkpoij0+PpZSnp1edF2XZdnR4SFj7PrmBt5/7+BouVzCENzj42NwlovF4ktf+hI4NkiL0zQdyEQQfMBkBeBzAZkWwlj4CYwvVUrNZjMhxPX1td1qZYNQxnrRtW0LxreqKrsZXJFyQYQQi8UiTdPlcpkkyfHx8dHR0XJRPXz4cKDS5Hk+m81ms9nf+Zu/+V/9l/9lVVXz+bzveynlarUqiqIzG62i3/u93/vn//yfn5+fA2yVitR7r43mTCCHnLchIOfc8mZZluXN1Q1kTlZvOEf7+/vj8cR2zmpveucRooQMoDraKrwOJg9t63AQZwwppnebLlLgnA+WDjLjwa3i7Vxw7/ohR0RbKhDZjmofdHvyPI8xgjg2uJm+763bTEEYkki8RVCG9wTu5PC2w89v54W381e4eCB+Q8YMRnNYB7wFqOGtjDPD3952HmLrhodegLAVMnS3xtKgbeuqEJuAwLvnEolDaDJc8GBCh9gCbWXFIO4ZLP7t+xo89K++rN1k1bfXBN+iUKHPA8I4BhwxxpgRDD3sOIboHcJ/8fs757IsK8ty6FAA5INsi9YbLiHGbNucjT4PPt92V3ELlgxOjmynL8MNaq0pp3mer9drgkgIIU9zKRLnHArEmZZs+RnxVlkaIcQFjzF2XdubSBjJigQSVuMNAElpmsYJYlic+6vrqxtOeQgBBUQg2sMY4c9d/HALcQv13QYwbvvp2z8khBBKINWBvQeVcqjIgB6IM65Rje41RiTLMhyp954h6b3VCOOmrYVgRTH65q+/3jbdn/7oz1JSUCyU6z3WVGARMxYTR67VUr797z6o67rkRZqmN+9cSyldaNPpKCG8U+34aOI0Qu307f/v2ccnn7z02v36cfvpw7eEzjJC1Sf1t9782ujVuX8Ygwl35AHLGSP8ult89Y17P/iDp3mef/UrX7t///69e/egrVAp9eE7p1964/Vg3aefPb6+vm7bcHOxOD9792B/nmT5dDrN0wlCKM1Hy+XyZ3/+8y+/fLfMMrVesdXqMOFaJEulGOf75djjUDVrS5H3vpRSEsxjqDo3Gk1hK3CcMsYykYUQ2P6b6zRZuA6ffSQXD2OJHny9/OjUvjreYTQIQq+ulq/efXVvd0/1erW6vlveI4TUp01/qczrlu+Khw8fLxaLX/v6m+eNDFJ4U//sj370te999513P3z3z/6kkrFv1OjuxJrQrnqjA+OpUtb1zXQ6Xa1q731ZBhA/klJyKqBOeXFx0bYtsOQZYwyXEKIOFS/nnJQyLYlSCgqQ0+mJa0aEnP/2b/82f73zMR6OJgihqm9/8Pu//5Of/KSu64zRYkIxHxGrv/P6V8BGd10XnJuM5csvHX90fw8GE/V9TwihePL48ePpdKo6lyX5qGDL5fLs5Mpbw3lo1+7k0ZX37zxnCcncOXf//v0XX3rx5OnJ/sH+w0e/mE6nEGIvbj45PLoqimK9XlNKl4vl4fHTvu/Pz8/BfL/66qsXFxfGmGrdTKfTg4ODZ8+ewaafTCaj0aiuqrquocOSMQatqyGEBw8ecM4fPnwI5wr87s3Nzbe+8xsHBweXl5dXV1eQ/r700kuTySTnEqGkNh4hj1BCURIMak2k0bS6pTL96OMnHpnJKD88vLezs1sU07PzC+v8er2ezWaPnzy9uLwqimL/N78/n+2OxgVjLElFjBHh0Xg8ln2rlHLO7O9N8ozfv3PY992kzAjDWnvCcIwmYXg83iWElIWUCTk4OFhcnQsh+r4v0o0oLu5Qc1XlSb66vPbeU8pj6PJsLkTgnBJiEHJpip1DzhlwCow5hJD3LaWC0uCcSv5/lP1XsKRpeh6IvZ/9XfrM4+uUr+pq3z09wwEwcMTAbigogqEVl6EI7a4oSlfakO55zzUXCkXoQjdULLmhCC2xArk0sUuAA2AADBqcHtO+u7q77Kk6Jk/6/O3nXl18mf/Jqq5mxP5A9GTl+fM3n3nt8z5vKLWOvGAVQWQJc5RrMESGkpJGGDSbzZCzXqtJ7IrssLJAEQgB59A5r5g5ZZyLuCiXjEeMsbww1mWc8zxXSSwAQFvLBJMhq1RqbJE02HxOKKfWWm21A1djoxzqLF8GYY8LQhkqXaTZnAtitQMA6gGKG0Up2jopPTEcRlEYhsF8PuOca+cMIgCUWhPOC6W8S4TwTOKzFuuUl1JEkoGMo7Is0ek4YgYLXeRbW1vD4VCKuCiKbD4Ho5tRaMEAojVVI2lrVUhBCVhwBglywauqMtYEQWCMqVTFOW/FkZftjDgecEKIrXJBnKb26woAEc1G6plsHHzdpNmrNyGEj+f5Ggr/5coQr6owDI1x6MnJrUKDyIBSMs+XhAUUUButrfE2mTHGGU0JcUYTdKEUJgpnlGhnGQHC7GI57nSTKOYyINrkWY5xIiqVEkKY5ztSlRBCSFFVyqF2ToUh14YwDhKYsRWhgOAItZwxguCMjaNYSlnkBbPUWhvywChd5kWSJGEkgQRloYUQjPOqqghCGAQ+shJEVGsdU0kpdcqWi4oQkkMZJgFjjCIdPfL1jYVTVjLOkCOgA+esM3alOzkjxijvIVijKSNAnNKlMYayJIqlsRUCxEFEKUV02pScrwwdCgStq/LKT1BbtiCHLEuXbmGtjWxojDm7d+pzTEZr3+bXWUOIJRR4u9dssdjJajh/muaAiOcn4/OTiYSQgZQ8BADfMts4Qx1tRE2llNKaWsa5oJYRQy26KEk6SV9jhZowJ1WpTxanDHjUiq3C05PzMi8P964Ww2I0nY7Gk1//5d/O8jRpxV5VnBydnRyfTYbzV199bTgcttvtd771rXa7/eTJE2vd3u7e6Hz46d1Pw0QGYbB7sH8+PB+ejYCQ3mBLSrlcLgljd+7cieLk/v37Dx/c/71f/972YLCYTkZnQ8LZ8fnobLpQyh0ti8UyZUdPW90uIhZpShEFZ06vkkxlWdZhE6215OR0OgqF3peuB9nWYa/aakTSiCa7ceNmt9sdn49v3bz9yiuvoIPHjx8PH0+uXru2u7s7mUzSYjkcnoaRzIs0ZSxHp1VljV4S9pcffTLLlrOT46paNhutIAi3t3bvvPJSlarZbL6zGweiRSndu7xdFxH5ICrgKnN5eGO/dhqMMfO5qgNcZN2vAxHBEyk7V1XV3qW9S5cP/urdd//Rf/2Prt888L3ZPerk8ePHxRw7jd3BtmDrqr6joyPOuS9qtFo3Go3f+73f+43f+I1Wq+W9zHa7/d/9t//8v/0n/8Q3HPRxOUrp7u7uYjZFRB+Sdc55/i/OuQFRlqVzOBlPp9OZEDLLMsElIdRZdA6tcVqZsqgopcZYa1BVRlWGEIuIRjtASoD5eg/vSK0oVoTwUGcfKfGVpj6Atlgs0jTd3t722NSaLqcWvoyxbre7WCx8+NoYEzbbL/QMAiG4UgYIISTP8sVsvJxPP/3kAxkkf/Znf+YZjAeDwdOnT4MgODw8fPdHf9xoNKSU3qrwPkG/3//q4ZfGmEsHl4usFJIdHl7yJAO+RryO+oZhKKVstVp5MWeM+V7CHofsxS4REIZhHMetVgsRpQh6vV6/39/a2vLQa7auhKmTuN5o84WSALBqViFoGIZCiCSOe+02ZSyJok6zyQWL4tA6s0wXiCgkbzQSKaUbTzYjq7hOmtblxXxNOIxr8mHvOoh1Fyy/XD0Xnnd53bp9PQAQuuK+95ACu9Flr54L3Eh/8nUdql9s3p4DAIALYn0AkBvNm+FZBqj6sf2NBBfdbpcLtrO7XZmiEbTDKCjKXBvlnLPOGKuttWEc1rFfsibdcyum6BU2wkebfVyawkV8m65RbIgoQlm/lNs4yAq9TJ5TzF794JoZ0N8XET0Avk5D+AcTQnC4CIrUI0kp5TKsYyR+tN26xhcA0nTVVNF3M5NSWtCI2Gq16nXlH7vdbsM6yCHXjaV9vUY9yB7BTleFf5WXLbVw8At7Mh17D5Js9LHgnEfRKrqeJBFbs1j7IHAd8LcbzGjaKLIBzPRmdxzHYC4KzMhGzN/aiyqjenYQMYnj2n33eHifCPPdtOr5qnM0Slf1s3lI7MpTF6KqKt+Ars5tMcb4yelpZ7vZ6ob9g06lSq3Vk5Pjs/vTGGIFxhHNAx7yxKEmnAVhaInVqDnjVFLllC61A0coMTnoFJdFoRZYZVaXriwqQKXzuQBoh+3LN67sNHaOlkd6qk6G58fH50Bcpe32ztbWYLudDC7tXqeETxZZs9WhlE7ny3sPHn322We+md3WoNXuNlvdZrvTcs6NRxMgePnq4eXL1y5fvvzll19+8MEH7+cfMMamo8l3vvXtL7744uzkZNDvb+3uLNKMc77V6yJlFdJLV68Zpe999eWTJ08cgnF2ulhsBQPOeavV8oR5zWZzxY/YjJaLyU6/HV66BEx/Ph5OR+dKynfeeKcoio8/utvtdimV6NjJycndu/efPHz8+ORoMBhwzr988GWj0eACXrpz494n72GYdPu9rWb47Tu3jybjdHj88qX9pSW7u7unp6fLRYr7qEGNpsM+7VvnxuPx/v4+FaQoina7HTsOAGWu4jjWerXPfT9BRNy/yj2Mtm51slq+htt1/5bXX3/9tTde+tkHP/7Z+x8++PJuUZZea4ZBkOU553x3Z+e3fuf3pZT9ft+nf8Iw3NraiqIoiSKvWWvKTy9Jv/Od7/z8/ffTNPVN3/r9vve29/f3fVGQ50D3GK4kSe4/epplWafTsdZyIRjnxtogDB8+fOg72IRRVCk1Go/9zmy1W+fnk+PjM8ZYFEXLZT6fp0VRFEXhHInj5nKZ+wY4nAdR1Hjw4NFqZxonpfQcF8vlMooiRPLo0ZHP0TYajXa7fXx8+sabWJaqyCtKaZ6Vk8lkd3ffGDc+PX+hAm7EcdJsNnvdMAyVDUnAozDgFLa3t30dVLPZ9M1FnHNXrlw5O1Pj8bjdbl/q7WfFcjabtdvtZjtxxCJ1ImTno3mh0vHsXPIVdNmDgHxezYe+vVhERJ879yNPKY2iyID2+ti3PeZMJEmSJIlHHXo4NNkorvAEHXXJFqXUt2R3dhX1bbeSSwe7uE59xY3o4HCfEOKFqQfQAcHdvd3N4K211tcK+3puWKeZ/VKklAaS1EHdzWC4Uso7B3Wgcn1rVOvWgd1OBwC0MXZN37h51JK0zkOnaaqsCuJACEEJF5xwzhuNqNWMd7b7XnUxTuvQItko2ZrMRpwEQoitwda1a9du3rrxS7/8i51+Cyw9Pj7+x//4Hz98+NA5J0ORNGPOOSEr3JNXt97m8yl5vmaIrJUQ55wyW4fBN19Br5s8PaeAlXaEEF/BiO7iT4ytVoVXY3zVO0+3Wknt2bN1JXQYhgQbdYAaNzLTDkAYEZiVx1yD3Xz8X3DuU5lBtGqRqXTls7kedVGX+flJ9ArYI+r9chJ8lZ/yg8N81ZxzWnuCydAPUW2veEXuf+JdDsZYEAQgiV+oHuTvF4mUsiixpvA06xXinONcSBkQQoyxACu8RRiGYC4K2OhGOyzGLsoUNxYhY5T43zq34jpdZSqtrg0+v/h9Yq5StjawvHtAN/qCE0K8D+DDMJxzXmbm8cPjnm1cf+PS4e09W8CDj051CXIal0VljAFGANEiAFjglWOVY9rSFTUMoZQglqZkNpqfpItqSZnMJqUubafTIYQMT06np/ObN27sb+3PzxcGq/ZWJy3Sf/kv/1XcjIXgd15+6Xu/9Muv3nmt0+pNzqdpoT0HzXK5RAc3b9zyHALf+Rtv7uwPqCBKV6PRaGd3TxW6EbX/+R/+i1/8xV/0xsXZ2ZlW6uDg4Nd//dd//uO/evT4SavZjrZaX91/pCp989oNY0xjayedTat0Sfd33XL+dDiUMtjf2dGp5iFtdOKsWjIm9w93PXXO9TfeWUyGjVC88drtsBF88OHPJ4/uXU46t27eOT8/P3p8gkg//uizRw+ezufz6WRGGJ6en50MTxqNRiSlZzxoNZrLR4/yPO+125Nu10jBrTvsDV6+dfNbv/LbYRj+wR/8s3/xL//w7Mlx0kyULpvN8Px48dlnny1vF4yxLMsuXbrko7iHh1c4C8OgcXZ2lqYpABTFeRRF3a3GcDjc2tpazCe+4rmqqp2dnSJfetrOOI5Pzx7/6Q+Xxua/+3u/Qe2qXtbLOK9Q+/2+t/U4555KtzbhvdjyNVpxHAdB4DG9APA3f/3XX3/99XqrPHjw4PPPPwe2wuJ6qQ2TSVmWlbbdTj8M4mazOZ/Pk7gZyAiQcibns+WqJBcpAYaOaGXRkSRuTdmCs4Ax1mp2o7AhRVrkqqqUMRYRCKGIHkrKwjDq9Qa+IqvZbBNC8vwkTXNKeavVYUzM58u1QZ1wLgFonMQ+6+m3lrffASDL8xcq4CzN9oKoJwQPJCwxjqNup7ndb3e8ktDap6V9+9UgCN546/Wfv//+7u7uL37vF05OTp4+fbq1tfX666/LhlBK7e3tzRezoip82LDRaIS8udrJVeUNHS+7szz1iXM/ql4+VlVFBNRIJf+9Wh+wTj3SDYhTu91m6+bnnHNftiulzLPCX1BXVb/b5YwRQpbLJQ9ou9fUWlezIkxCGXF/vjW8Br3LdYM251yRV35t1HBiL7/iiHsYlM9f6DXts/dTawe3TqoxhsaYs7OzoiybzWbNhwXkGQIQf/hH8oh3pdRoNCKErASupd7p55zvbG1du3KFEFJVVZjIOqdINrrA7qu9JGyqUgcy3NnZ6fV6169fv3L90Ocvdv/n7bRYeF/QW8AUhXPOv5rfGoyxwWBw48aNzUhGbRBrm9WO76buJxjUyqDWxIho8SKruml8lMXKw/Oj4ZzzcYJArmwRr6i8sgnDkNJn+vbAOtYt5KptVO07ekfQgxO93+YH1huCjDHfess550e4xlv4QfBeAec8iiLnnOBy8/VhXdnFBXNrcLtfKoSQNE1b7aa/iBfpbt2co91ueyI5b9/QNW1IYmK6AdzzlmJRFISvGG/q9/WGqaQBbATza2OIUuHRJN5g9TIwCALfkNwbFt6DXwXqrfYz6OfXx/mcc5UqNw0pv39xjZPwO4Vtlo0Rw6pSL6YpIoZNsXtz9/L+1ddfLv7sn/6VhrIqSwTGgCEigrPOWe04k2DXlDGEOnRa20azlS0LwYPuTrfSBSG4e2lbuwoqXMyX50ejbJznRR7JIGxGk/GU6Wh7b6vdaSFx7/3kxx999PGlncu6MoulLooiiqL9/f1rV2/ceemVZrPZ7/f/9M//eDydMklu37n58suvpYv8B3/0p599+lfGkY8+/kxw2up0D3b3rbWddivk/JWXX62q6uXXXu92u44Kiq7f650eH//5j/96OR4zawRiP5CDmzcs48PZTCVhkiRbO10aular9dJLL02n0+Vy+dYrLx8fhe0ovHH1VmtrsLt35d7nHz/45ON8WQQsaicdVGQ4GeuukzJoNTrdnZYxZj6dlWURBEGn1ZacBUEQ3rgus/Tla1da27sqaT+dL05OTqwxX3z+RafbZpTtbu0JyTjlSG2Zla14h1ixnBZ+IQYs8doukV1jJvt7e4/vHTvn9vf3HxwddbvdLMvufXVPvBkfPT6az+eHlw/Ph+cMQ+PSn/zkJ3Ec/62/9bfefvvNyWRSfZFPZjYOm2EYKqUsWqusQ4cocs2XS+ZbYJo1iZJnInTGeOHiK4yNMfP5HBEvX75cluWrr77a24mdhfd/8unTp08558o6L6RwXYuiV03XiyBIWq0WpTKOW3EcK+Uajc7Nm3dqxuMgCOK4ZVbtwJJ2u4/I/BYSIorjFgDnAY/imHIuw7DpRUkQWMQwinwwSBuDiNoYh0gJCaNIBkHSaHiZJYOAMiakLHNlYgdItdKAtNlohzLWWodx44UK2BjHGK+UFkJ0Op297X6nKWPJAeDq1at+6Qohbt265T3+45N7k8nEN073iK3t7e1Wq/X666+nadrv98ejyXg0aSZNT5d/cjRaLBZ1bSgiSik9cs0bUt6F9YNZVZWIOCGklOUq44DEK79NYqnaJVo5zau2d5DnuY9bdDqdgyuHQRCgdYN+Twoeh2EchmmaBq3G5cuXtdaj0cj/1svZ05Op777gbQWPVbHWGu08aKAsS78AVqjUNWNDDekC34kyimqWJVyzcyD6BtAwmUzyovDkaz5mThnx3gNdd+zw53ug+8qG0DrLMp+JQAdeVTjnOr329u4WAJRlGYSy1nZkI/laKpWETbRAgPp17gW6VwaMsX6/f/XqVa9dyrJEQ70D5Jzz8H4A6HQ6165ds+saJLVusmmtDeNV7rDGVHuhbNQzIKb6cLDidXrOU59NF7Uh4q/v38XZVVir0Wh4j02smCAvvGrYwHkRCptWSO2ae9yQcy5NU6+KvOVkrY3jeLFY+OXtX8EHvfz+BVh1HfB061V50Q7HW2k+O+bQem2HiN5s9R98yzX/yj53TikNgsCgoYJSoBYsUgziABG9e1ObbnX0CBEZMlMZAKBIQxECACccDfKY1+NZmy+UUmu133Q1kN5fSvBVR42aCHlli/AV3ds6IMFWJeYF9VrZy88aZuj5d/1kbU4lP3h5RwxJZhcPHj0eu+M7L78Umu7oONU8Zy0TNylYAEOsoo44wtBltNPrknX35nSRcs67yRZQ1LZqNpt7+7tn2SlN2vvXBpPF6NHPFgEL00kxGy+ilhQRnS7LRbZIkkZWpd2wY4n95MtPTp+cXb18/fLB1TxlrVYrjBvKOBnGO7u7AHA6HH34808ePX6IYP/z/+P//tLulYrg5Hxxfjq5cv365599OjofNpOIOswWC0pIq9XY2R3sXTq89+jxfD7XWktKFqPzo8eP+zvbr758p5kky9lsMBi88vrrDuCzu188Wo6vXb16586dVTOlTmexWBRFcXn72qVBc293J4qT0+HZbmcgbrz84V//9Fd/5Tfe+/FPorAZRXGnNciybDHPtNaK2uMnTwIpOEAzSphFFgR7ly5bqOBsPP5geI8Geu+wADbo9pHxNF8WVRYn0Xd/4bvWuOl0WlVVv9+fjVy/t8WZrFA3khYlXCvbbrefHj31QMHh6bDT6cRhvJwvQxkulvl4OCrTYjlbzKez7f7WbDw9e3oqIlMVRbvZ7LRazSSZT6cHe3v9fv/p6LjZj7TmbM0GRSmN41VphIfzeUDHkydPnHOT0ci7aD5V49Eft27d8tUI8/m8txMvp9XHH3/80ccfdzudIGn6difOOQQIoyj0FnHQJoQkSdLpdf0qb3c6g63B7v6eL4r1ScFuv+fXKFqyvb3nY5U+mNbtDoQQhFnvcPS6W97U9Wq+3ez6N/Jf7u5eQkQfAB8MBnHc4pz7AJoQQsp4sViGYeQN6Ha7I4QgxLegeXEVJuNivlxmVS4DtrOz8+qdG7pcZoupEOLXfu3XfBzMb0VKaaPR+Ltv/p7vEbu1teVzBF4b9bcHRVFEQeQUzCeL/b1LTx4/FUKks/e8UqRrtvNWqzWfz72+8SwrcRx7YKpb0zTaukwTiReXnvLJCz7YiNN6M86um3V6Re6cu3znpa1ebzDo66oixoSB7DQahJDDyzeyLPMdJjxps9eXWbFiGLDWFkUxm82Gw+F8Pn//5x9aa9M0XS6X3pX3UeskFl7Z1HFmWIc6zbp9fV1zYYxxTnmJvxkvRUTrLEMKhANBBAcEAZAxqo1CcA7BoaWM5EVWqUacRJwx7RxnxAIwCoHkzjkIBFAkG0zIiOjcWlAiMdY4u3LN/SDHQaPT7EUy4WQRijjgISIyEMZYH1+FNSTYrnlgzJphFNahCKWU4OCcs2DRAfHJQsopYfEKnLUidq0Ft3Fg1wygXpP5gG0URd7vr+NYXpkRYH5GPCG5p2RCROvQpylrBex/mBcLXHcB8qrdP4PYaKwkpWScllWR5zk68BYJpdRjQTwBe7vd9veia3izv0uW5nWE2dtGflgQnA8d1zla78dnaVorYLKRBq63AwDEcdxutwkhZVlOzdRLjHLNfOAF1Plw5DY6byJiYUutDLpVlKhOiKxTzrWHsAqHrOwSQETMsiz17aSk9LAMbVbdRWvhCWuildrQqS/lRVBtOMKakAAA+Nu/eefHf5Xnj+cANEur05Pz06/uPfzj+eGdpN9phLFUqZsdZ4tJbrR2Vvf3drQthqejpBkNBoOFVvN8YUTJUCK3CkpDlYhomCSNQTgxOl/mYSOJeFJCwSUEsQxiJkTIHL/78MtH54+2tgfOohXufHZelGU7utJud63F09MhJUyKYLlcfvrZZ7321hEczxfTk6ejf//uz46PTh89eLoz2C/KsqwUZZxSOpuMz54+RbRRGJycPn1yfDqezsez6f7ObicOqNGDdidqtYyQT+cLC7TTbH/y6AidfemVV15pt3Z3dw8PDz3KNMsyva0554kUOwe7QRQ9fvDoq7t3m1EjCMPO3hXOwqfHZ4yF7XafEjY8+8w5ev3a7SejJ1/effDaK3dkGA5PRsPjp9PR6OWXX/7O29fiJ/cno/Gi1QujpNS4d+lSp9c/14VWttdtAZJ0kX380Sez8+modCLp3bpzw/Ma+lYk55MzGXGjDBXgiAliwSRJi4WyJQ9onlU8oItsZlAxSZDaIBaj6bBppNEGHRIgkgvOOAEyHo3zNN/qb4UyrBFVXj7uDrpRFLVaLc8z3Gq1fEagf+vWYrHwmKbpdNpsNvf397e2tu5/OXz33Xc/+uijdrs9HA4/+fTToiiuX79+vTswxihV1G7Nyo4WEaUULZE89Npo0N9mhMVxEMq4FsGSr7AtSmkpgyiKap5n/08mwI8MY8K7I35BJ1EjDEMfvyWEdDt9zrkvPWo2m/6feZ77F+l2+s5RX6HkwVN+w8RxDBC9UAEr44zVWZYZy2czGA6Hi/HZfHLW3FU+9etD8V4KFEURBUmn3UUEAsCIYJShA3DAGG/GLQIs4PF8ku4OoMirpN/wT0LW7L7+sb0T48tzfSskH34IgoAHK2qqFWzEQe0H1G5WHacl61LaOkTsmRqNMf/4n/53Ozs7L9288fThwzLPGEAoeBQEt2+8enx8bIwZjUZenXsBxNbFzV7lLBaL8Xi8WCzOh2NYh+m8gEPE5XJJSeQdI1xDBf0JXmDVvKobEnxF2lCnBrzO0FahQUdWHF5e1Qkqlsvlyh3UChjkVZ6VWWITCkJXlfW5RmfRWWctQURnCKXgCCLZHCshIy+gq1JlWQYEvYTNFhmjjFPBCAdH8rTwDlAQrcj6/ev4VepJKryt4xWA/68xJg4TB46i5ULWgVDiyCJb4BrOYzYosi2SWgH76/gYZhQm3tTzugfWafg4WpFf1orZD5S2ura3agWMiAbLlUlEkRIKBLTRSqlFtvAZpSRJRCiAgKpUoYp+exDHseeroes0v3PON2X3OtLjjPxuLcuqfjaftPJOeZxEPj/tD1yTmXi+dD/dvm5w5QqXK8UmpVSqyrKUrds71uOm1w0t/OjhRs2YXcPK/Foya3oWWJdxMia9Be8Huc6qeA6Deikqrb3Rs0JOuFVg33/22wo26r/5mlWNEOKZUOsguSda4I/mn2cwPbxx6ZU3bp+mjyjSQXPbvhIty8fdZtzfbVVzVy1NmWpklnPxf/mHf//dd9/9kx/86a3bB7/7u787n88/+uij6XT68NNzmQTKlLPlVIuiIQMFxbKc3r56c3q+dAYIJXmeHdzYv3H1ZjHTH/z4faAYRtJaU1WVkCEVxFG7u7fnEJdpGgTBIk2/un8/TdP5YtGJW6+89CoP2OuvvDkaje599eD+lw/2dvcrppfLXHI+6G/t9LYOtna1KghA0kkckLjdeaX5WrvRVOm8n8RvvfzykJKd/YMfvffe49PT9uEV5qwtijdbnVdffvP09PT9H3/oS998QacQ4kk1CTq9agrD4akQYjIZlUjD3vYf/uG/+KN/+8eM8iiK+t3+2dmw2WwqZa5evvFx95NLl67Nxuez0ZyzAJCOh6OPH9qXjTkY9Hq3X2+8/e2/+vlHj2bLkaHaLfM8P3k6XExTXZmnj06GZ+dRGH/vt1/Z29sLgsBLlg8//DD7ZJqVYVko5cq0nBFhlcvPpycypnuHW10VNNpBqx0RPtBa7x9u9bdbs9ls0Omjg729vXark2XFeDShhHW6neF4bBUaowWtjDEkZkEgKHIfoPO6xAcnt7a2ptNpKGVNoBFF0auvvnrp0iUAOD09/cGf/Ann/NatW0VRTKfTnZ2dq1eunJ+fz2Yzj6L0wkgI0Wq13vn2rzkAH61CQpxzXrUU1Qo9iIjEOxDWOmMk96oXrSVCRGvHgpdV7hBkEAbaEMqcdVzIIAisUl6j+Epfb5X7pJ3fct5M9qHCOI7RrQkTkHhwh685JvwCbbt5UAqhjCQIAjZdLM7Ph3q5widLKbMs8+SavqIpDEOjbBCAVY5LyigDBKOsCFilUAiutWkkzUbSOti7NBqO262OF6lekPlgr6cUrVTqA26eHsHvfCmlDEWdnwMA61Yat87YwUaGFdase8YYD0zz/NXz+XxpVFQVhsCiyrUqGeJyWQVBkATHjx8/ds5NJhMvU7xvvSgyo01tWnGxApvMZjNKabfb9fFqrz7TNA1k3z9P7e/SNWyqhsrXcTmtNeeSr3kl/YuvUPRawZouw655m30uma4hvj744S9ondFWGacd+sITY+zKvtnUQ/VhrQ1kFIdJnhVSytpPQsICGXAmAIk1vkoYORNeW3v57oW+1zS1N0nXfUFWt3MEHSHAKF3lLK21xtjlclmHUjfTxpRLt8Gt4TUopdToVarV2y7+3dM0BYw9RLG+iH8ML+59/HPzxbUra+OMr0ubiqLodrvez/ZZXr+iWq1WO+lorc/OznzvE2/DMcbmGwwEfM11StaNe9maptFb+XEcL9OFt+y9QvKjt1gsHDoAqLPObg3dD5OgdpQJIX5f+7vXt/Nxcj/+gQzr0Le1loDyr6N0xdfN9HAjHa618Vg2/6Y++uJVtQcz1mp1c2+ydaOO2rhh7KIdJGxkpn1VQh0ggXWgnm9f6hcq2xscTGfnW609pdS3vn/z/K2zP/r/Pl4W89d2XlZdNSvPU5mTyr715tvBq8UvXHr92ncOnh4d3198jgi9q52j4dN0mQfWEAr5NO8NunREn/zZvJgGDRHycDldnAgpaEWiItwL9h6XT3q73cFOj3I4PnsqW2xcDIOtnf2XbsWzFReuLtOTJ4vaxrl3do9zvhVvffThjxeLxWRy1O6xXJ2dnCwODw9v3Ly2s7ezszs4ONzbO9hrtZudVlT/djN3ErLYanzy6OhnP3r36u/87u/+7u+WZTmfzx88+rSqKm2r6XzpbXyHmJeWUZqenVBKoVwUZrk0i6qqtjqdf/Lf/kEQBCKO0anR+IRQt0zHH3w0urR78Atvv3398PLPzkdaWWfcYOsACD6+P5q329evXrkSdPGjz+giPSu+2t7eSQ6vPD568uGHn3oHsbnXvf7G7bIsGeGj4XgVlskytHDj2s3pdOoUzseLaTQ7Pxn5FNpymv7sxz8PYrdcLtNu9/j4mHP+rTffevnll7XWWudx/D0hRJKIp8cPluk4CANC9bX93Z1u20tJv3sZY4043KPBPM2EqDrdiDPoBJ3GldZjfuKqvHPY9dxGSZIIIVyJzjkmZv/bv/sb82Xe7WxTJrUmxiCPQrccskDHlJ6eDk+Hpx4e3N9Jgkg/ffr0+vXrVVUtFqNOpzOdHhNCejt9VThnraDy7Oys3W0iGBETVC6MiNaLIBSMgbVKBgTAhJLEcbsscylRShqGvqtgEYZJVVXesfNRMm/z6so0Wk0grlRZox0jWEIYOCKlpkAJZaoCKdl8mYZhCITZr3H/1nsVLRJgAIxQeXSiAGIASB/PGMVWq9VyotNoxjE41JTSL+4/9BRXWutr164ppZJGYBBkwK1BGfAr1y79X/9v/wUh5Nd/85fRwZ07t/7hP/yHR0dHXkP8zu/8zuuvv/7yS7f/m//7P7p27RoQlzQibSqHxjmX5ctzU4ZhWIoKGuBj14GQZ/ZEhWoymVCCnXbHEZeli2tXrx4fH9++fjludn728fHx/IRlvNVo9dttlRb9AvWj458cnfpov2/hxzl/cPcRbIB3CCFkRgDA41dXSs4wo1dyhxJglBCnVbFAdHEc8JByJP1+4JwqioWUUikTBO2qcs45ggEHJjSJZBQFsjDVIs0NKmsDa11RoKpIUeB8XnFuwzAkzAHBIJDWWjQOLFIkkQwDZvPFlAAlyJiDa9uXAcl2PLCEU1jOp1kUh1rR6SzzWcwW6QWhSMsFUqt0hY4kYXc8nlRanaQTH/a3FAaDQVaoqoSiWGqwPArSsppneRzG8+WCEKJd7qW5KspSaedcs9mstEnBpeCAAHXGWivQMsZycCbLvH3go6CIuEIbRSuwD1JUoJhkKDBNUzVx3paqtWkcxzKU1aIKZMAYc8ZRQjnlANBMmgR4HPGyRErCqkRAWeRWSpml3oOklK7ouhw65ywzEgBarZbWupxrzrlR2Aja3DFmKQMqmXDWKlVRSsMwLFUWhqEM+NnJTHLe7fTCVpcBM1a3O01nqjxfOCCIZDJdao3ttiAUlK7Oz8ftRruZtMMwzLPCs0Ja46u6mXMOkCRxQ1eGUpoELWutNVYyGUVRIANtZmWxbDXbe9sDay1a7Pe24jg+W4x0ZWnFHHOMMU5JoWYEq9QB03Br73A5XRTpotXrZFXhQtEMAu8SGF04JLV5kcTcrpH86IjgCIBlqeJojxK6yOeIVEopAx4mwXiet5qojauqiouAizDLTFFqKaUtFOeccomIylp06JAyxuazKo5ZHHNCBCWUAAO06AhXJ5IsYh25pBH3t7uRSCTG86fl/vZ+VqY/ee+nhLtmN7m5d4Dg9m+1wNFeu/vF9OFf/NmPiIAwDFDR44ezGpmmKr2YLZVSDmxRFHE30cqEYUgZpZSen5//xZ//pVJ6b3B4vXt9/+DgpHeWlVle5Z1eJ0yj0+HMx3A89YQPsltrmw2mtU6Sx/v7++12u9HqR0mHc27Zl81+4js4WUvBBQxj7lqsDCRjlAGhAADOgivQWvvF+X0ASPN8kaY/evfd2WLh7fosndYGS30AgDf/PX9vTVijlH3jzXdqy86LVx8mWhapAZuWGZUsSEJVFJWq0Dka6jTPj4enRVmWRTUejxFJs9n8xd/+Xy9nOQPZiJq7u7u7u7s7OzuEECAXnK7W2svX9pW6o7XWlZ1Op/1+//T0tPY5wjC0uhqPx81mc3/vwBgTBtFykRJCgpgzQR1YZapSFcpUlBNl2GCrG0YCwQCxYRR61PRsPsbhV812u9vuyQbVxXJeYdRq718f8DU8k1FPneMcoLX20uXrv/nbMaU0z4qz83MPwPFMp1qZKIrzrLh8eCUIguvXrzcajc8+++z8/PzOnTsAMB6PlVInJydVVX2r/XYctb66+8Bo99FHHxGKUUN2eo1Xb7197949X5Hs87j7+/unp6fKlF4kXbt2LUmSo6MjY0wURRTlYDAQQpyenvokmbX28PBQCDmfzy0USlWLxaLb7UnZGJ+PkgbXWjMqAEBGCSHEWA0WmXixB/xNR5qm6HSe59qUabuFiIvldLlcRuGq2VG73f7ss89v3LjhISeMWl/T5cNlo9Eoz/NerzccDj3SzTvug8Fgb29vf3//V3/1V+0acuwhwascYSfVWnc6Eeech4ox1unKeXYSJdhyHBFJaDghnUZr+/qWjcxZNunGJN6KDlv7aMBUOnfLZTEJoIvPchfX+rXWvrUJ4l2QOgTHNnorlZXvGlsxThhjCHG73Wj3WiE3jUZDa+EjzEnClVIAtNCuylWuFK0EAFRKU4EypKAsWoY2I1igzcCFgkVxGBYGwyhgnAJBV1l0KGRTBoJLnpAokFGeFXlaNlvJzs7u7du3zydLINhqNwbb/SvXLitb5kU2m83Oxk8qVVa2TJKYc865UKYqirxcZpPJ5Pj4OIqi+XzuUzBZloWxsKgtKmWzvJpbKEfzM2tt3I0pUE64o66ipXGGIZGOUwE8MFVVVcpYa5VllFLrLMOgzDMA4JwTQbRSyhWWqHS+9DkFv5Z8TNsYYyuK1vmgxaoC1SFaR9yK5qzO3Xp3zf82TdNWs8nX1bGLxaJQWW1C+Z8wzrgQvda+d0N9HqpOKqdF2mq1ojiM4rAsC7eud0rTzKz7Lqh1N9I0TQlQZ0FpYiwjhCACAcY5ZKlNYhEEcatpm62W5Nw5zPK03W3aNQyNrbucaa0XallnfGvX3DlnHGk1+61WG4FJGTrtlsucs7AR9ro7vVCG1tonjx+dDZ+GjUau8lZP6kyfnD6ORLS3t4WUVqYq07yyQAgBRMo4W6OijFZBkJB1/bQH8XmHNWIzwngzyIFSoBaBVGVR5KkuIymlqmgxK9rtsJn0CQaLxaKVtL3tTjgEjBlrKWUi4Gap6tSAdwlWEI0///+9Z0X57V974+ZrV06Hx4tJPnmy/ORHj176bvPXfvOXZCTvfvVppYvBXnf/cG9nb7sZdp58efbw7pMqs72tdrvVvHblxq/+Quvf/tN3lVLGGlSQmWK2mPnwy1fjr+I46g5688WskTQOL11WheFcqFR/8em9bO66ve1uuEt1FqgOdaEQOecYBK7dvlgrlNK8GJt1T9DxtPSxuIgG7/zSO2VaTMaTzz/7cjHLrXWCSkpYN34GhFLnUY7njy5fvnx+fl6U+qt7Dx89fuoLfy8fHnjCQr+U3RpT51mCF8vKT4xf5cPz+c1bb9VBFbKGpDnn0uwcAInkva1e0og459zXsdFCCJFEzTAKrbJVpQBAijDPDKAc9Pc550bT6SRbzB9xzi9d7uMGy24dbImisCzLJEkuXd7z+UJ/QiyS4XDo++z60JBXSHl57lttobW+10SFaLXutZJKZZxzyiiCKkpzdHT06aefvt4WMoqzbH5d5Yaxdr/FKWpAcKROt9S7AhGV0SKQUnKLZme7mzSS2Wz25OhRq9XinPf7/fv371+5cqXRaLzzzjuEkPNhyujs0sHl0WhUFqrZoKoyRV4ppfZ2VtwXT548yYu002/uqa23XhFnZ2cPHz70OWlK6dWrVx8+fEgY+lBbv983xnz22WceJKxK89Zbb4Vh+PP3f+o38GKx+NVf/dWd7YN33/1RWsysNXmef+tb7+xuXfrRj95FzLUycdI4PDh89fWYMco5FzIo18bW1z3gF34vg8BotNZmWSYYLYri7t1P7969u7+z5btCXb169S9/9KO/8/u///HHHwPA3/nf/EdxHO/s7NTwmePj448//tgaPdjuJ83YI84Ig2W26PV6vXb7/Pzcd1MOgkBQ2u129/b2vjz6YjabGWMYYa1ez+ecClXs7obLZJkXJeEMBYviOGq0qJyqqsw1NFr9vbhVZsXZyRNXKk6YByfDBkeE2aChfcYDJsTL6zrk60fAnyMk5ZxT5ghBxpmjlknS7/cSOj/ca+G6WLPZbHqg3Dx3D49OHx0PHXhKwhycjkJyefvy/u7+S7cutVqtg4MDX2PDOadR7HOQVVVlWaa17vV6jDHrXvEMf0aZqtSUUl/ZPMjszt4WgiMURMD2Lu0QSobD4fnipNQZSFQEygpJzs7z89lksX9w9crlw4cPH/a6HSFEVRU//el7N25cS5JWmi8JN1RalBVrMlYap/V5OWOMccsRsagKrXXOEhVkpSo8vGhFVMe5N56yycTPbKfR4ZynOp2Xk6IorF5hczzGgjHW7/d7vd6jyVPKKACoapVfDMswLENmmI+4eiiTz3pkWebD5lqXi+UMEZvNZpxEzsnlyZStO1n55/GOtbaq0iUAcMGTJAmCgDDQWhNK4yRstZphGPoqNh+Ync3mvn+5VyT+gUejUbPTUYpUFSi1avFLCJGcERI567ShSdJpt1vWGkZIpRt+JWwOzip1EqwADWyjTpcQYnUYhkm7PUiiBmdiPB5rbe7sHX72+aMvPvvkzu2b/9l/+p9++vlH/4//538FAq9dOzwvhozQdDQVjABJtCqjMADOjHHtdjsMwzIvlou5NZoxxhqJMVBntb1UD4IgDEOVPXKWaJ1zGQSygSC0IaqCXJtOKwSUxqTpfM7BtCPZaWxVivp2hGDQER9zMJQi50CINaZ0zlmrKSVS0jAUPAjEdLH48rMvDRYfvP9J2GSXtg/f+s2rjS310reuBTw8n5x+8PMPnSLX9m5L3fj8wy/e/dOfPvz8WGKcjssiqy7v3eChkDTQ1nAQzAnrLGoEgDAOAW0cJxSYMWY+nx89eqKVJY4Adw6plSCacafZkEErrwizJOKRc44Qh4hAiEUCjhEkDmIZch+PXWZLY5wQtlQ6SGA+y4ans/PhLE0zaxARCdCss6qrW+fYV9PZ7+8jyiBoHR7e7PW2gyDo9fY8YnDd4G9Fa+yty61t7s18H5dL0zQvFnlR/dVfvefNWF9i6/MHhBDH89ls9uj46Pz0NM9zwbjgrNFoOFzV7RGgBCiFFQXr8YnRWm9vb/v94F2iVqt197O7ddjcv8UKApNwAAiCwPvlWmufEWzG5WKZAxHNZrPR7Ho13On2mpZ5BLwQIo4SZ1dmxNZ231Naaq0Wo5m1Ns0WlIHg4WyWzj/+3BB5cO1ayEIAki+zqNEDzztoV2z1FCkAtFuBNZkUgpKGazRarQ7jwXJZNWJNkKElgYjOz8aT0UzysNlsUsoePXr8wx/++cnJyXA43Nvbz/NiNBp/8vHHVWaM1oGM8iyzzizni1Z7Vd7gMy7z+dwX8yyXSxFccOX4PexDEUWVhbGUkqf5wguj49Mnpcqrqnrw8J62JePk/Pz81q1bl/bpaDSS0uZ5XpRVr9fjnBLCjXlxo7r/8FFVFaD1G9jX2gPAZDLZ6vUmk5lzjjPx5RdfOYvHx6eI6M9ptpJQhZyzbrcrpfx3/+7fcU7e+fa3ms1mHXE5PnlaVsXW1laSJJcvXx4MBkqpTz/9tNlsvvXWW8v5QmjhqTe7ve758Hw4HCZx0pLxztYuMK4Bj4fn6ayaYqVmguJ2OkMZhiLpKzUjy5hb0aK9wqw4Lvwr1HWNm8q1VsDgW6BvdJWpDwVlGIaMABAUgjtntFGUkUTS7V4TEb3WjCUh1iUhk5Q9dQVWMxI0B4NBKFocK8lsms3zXEppytLMZiTLhLe5pyUhhBwfH/vBQcQoimazWacdgm8QZFyv14uiSCm11d8qK3p6eiqEsGBPhsdlVTDGjo6OenvtqBSUUqNtwEOduy/P7udFxTk/PT29efPmb/7Wbz569Ojo6Gg+n9+9e7e/25ovZga0ZVph0QgCCIyDknALFJGhtdbyyoFBKSEwxXImhEBSOlBIRKWdq1zoQkIF5RSkVVAUlU3VPNOLQhdmQfr9fq/XzfO8zDNrrdUqT5dCrIqGKUUvFRGNNWoxy2sHw8dRvYBqt9txHO/ubXtQ5DKdF2WWZdl0NvZ86TJIZHBBBDGfnxdV5oFOlIO2lTLKOttoB2FDInVpsTTGiEBQSrMiC4JgsVh4dEid3/VRXKV0VVmlqJTEGMuoDQThPCyKDC3KMGBcEgJxEhEGJycnZVlWSsk1jZ33bZLGisTGV71e2Ls2CEQjiTtlURV5VhaGUuosa8T9nQFrNgZlafd2D379+781WZ7KSPSDJJvkbntLpUrnJWfSGquKCoFbbQzVWlVVVVmjgyDglHIusizzRqEPA3iZ8/f+k//zg0dP//1Pfnp8MsM8CMJW3Gw3OvtMjytVcsp3BtvoCqsLy2y/1RlqFkaC8so5BYCUonVGG+VLwvyuEcLT9nFKkV95c+syDsazUbpIL13buXnnan+ne/XGZZdWD756+PDLJ7PRQmU4NrNPP/n8Zz/9+XAyOb03UVMqSMgDVmT53Y++UtVn1jBnkTLiLFJK47Dh0W6NRmc+n56eHe/sbbdaLU7Fwc4OZ2JuJ51OP11UP//k/a32wcHBS0JEjAutCkIIMEbW3oZv0g2UGweIJEqarU5PrJvNEUMlDbb6Ua+7zzgRAeeCUkqD8Jl+47Un3WnsLJfL4XBojEmSxBebNxqNMte1f4MbHa+MMVxQRBQSgrATJ2WzVRRFMZnMPf6imKXjycKtm/FZNkvT1M+us1YwrrVuxLEyJSIa5ay1jHIhAkKIs8hoT2uttfORdm/JttttrKxP/te0R/6ImkEdn/FE2V4NtzuRV8ZJkngOGn+dSo/brZan5kFERmWQxIhoLAMSxEkIAMaOlVLXru+99vo7uy2plKqUHmxvRUkClACSVqMJiECAMqAE0G8MyoCQ/e3eVq8DVH5x99EXd+9bezqfFydnw+U8nU6n8/m8KvX9e59aa3/+sw8ODg5ef/Od0Wj0b/7NvxmNRtevX3fOjUaj+/fvF+VsfDo9vHRVUEbAtVvNssr0mhjSW/dpmvrCU0T05YneD/OhMx+BKIrCI018A6g6c7Zczo0xjVYcxnIyGxGKQRCEoUQsCAHrtDYVgnWIlS6Nc6t2OV87vskDVkpTslJX/nkGW4OdnZ1mM0mSSAixvbO1tT0YbPV3dracc7u7u2EYZmkxGo16vR7nvNfrvf322199ddcXznrp5st/4zgWjHda7SgMu50OIv7J8cmf/9kPP//0s9//u7/zk5+MwpZ86fXDOI6bPXbnjcvb29uJaceNBhPi6OT0n//rf/Ph558o6wghzZ3dsiwDGQaO5/NFtiwZamqFUsuVoFsHBnHNIPhCBVxXd9Q62P+pLPN1wNq7OFVGYblcNuPKGieljKPEShuGobPIKN9qxwf9ATrW7u/sXzqIA86dEtwuNfNoLx9UGA6H4/HYGDNOEQCGw6FzzmP1GWOnp6doy6SZeHDfa6+9tru30+/3f/GXf6Eq+D/7Z/9MSnlp/2B4Pux0Ojdv3tzd3W1sB0WZCyGWizSS8XS4ePr4VJLw3r27zrmTE/zLv/izw8PDL+5++vjR/WtXD//ih9n5+Pz0+KlRlaoKih3iLDoTCN8ughAHnABlNBQ8FJwAckajMAgD6a3qqqoEZ2EYeazTfDzxLA3NKG6E0dPpKAyCOIrKokDnfDVuVZZyXbcGzhFf7boKSnhUc0EIUSpY89+Vs5m2ttnpdMKQAxitldYwmYwsqFIZJEqZrHbHGWPWmUIVRVGIShSqiKKo2+02m82wCZ7Obzade7pHwWVZlrkuPGlPb9CbTCZZljkLURQBRecQHDAmJGecaEJdt8VH5+M4DAl3ZT4HojyHz9lo6PemW9fRkTUziUeleJvbrXnTCCFvvf3O7s5ev98/Ox3mWdHr9UIZhmHYbO6FgfQw1YNLW7/zO7/zw7/645998FMUi93u7t/+j//2g88e/dH/9KcUFXLRabXKNIMqV7pEY+JAkjBwANpaPy923bS03gsgb/AwjuIqSibaMhG1BttXo7iRT744PXlSqWzQaScxXyzO82x6fK7CcBAkkhCX5XPnKiFopQub6WazWVOaeIyR3188aLk7L738wc8/PDs7u3z9UpLI8ex4UMX6qfjpX3788KujZrvR7/VuvnL1ykv7Xz2+q0hbz8j9R+eGqNdef/Xk/OlXHz8yBWxFe0CQUqZ0JULRarXyMjXGBM1we3vbnOlAhG+98fZvfv+3fum73yuyqru9/ejh0X//3//z93/yKbGCaFYURRRQBFtv8k3rm7EVbbrXu75aFxG1DQGlFJRLQrkDaoE6ymA58wVkbH0pi2gQ8Yw8AABjXKPZQkJL5ebLxWyRC5rU2HS6pqqxaz4UXJWXhDIIhGxxkXd6uz6sbdad3r0k0mTqnKuKAgDQOcl5nueBkEiAAAMARoQQMggCRj1VrBwOh8fHx2l+DgCEUMJ4pY2tXKWrrLhotLkyR5y3oYRXt77TuzGm1Vn1OKtDUogYhiEw6yviPVjROecBwPfvPw2CwNMjZ5lxDrb63auXb5UwbYWhMYYyOi/L6uTMa3owrJ4Ot0GXWuYVZ7wsl+/99Wc//ukH3fYAiRwOz99+50Zz1JxNZ2++9ebpyakQYjweHxwcNBqDX/qFX3bOzWazw8PDZtK+tH85ChJghTXWQ5evXLnS7XUWy2mzE3mXt9Fo+IBto9FI0zQIgmU296zduNF0No7jRiMBQGO0ENwYba1tNBLGqAx4EAmtNdWu0YyllMqUjBNEEkURXRPbEooAwDix/wvd4CRJAE2Nh2w0GpcvX7bWtlvh9t6g1+t95zvfIRx/63e/f/3WVf8T59wXX3zx1Vdf3b59+/T01Bhz69atSmWTyWS5XDaaSafT8Yuw1+sdbPVOTk4+/fjjxw8e3L59u5nEg17XavXSzTffe/ejqqrOT5b37r2/XC5v3rw5Gebb/T6bcsp5pVTckrdeuuIQgyA4mUyVqrIie3qWqlLlei7QSsGFu6C28ALIvwtfcyPXs++3Z13riRu9dyilDIVTQDiisxZ8xwNWlaoSMi9Z3Ohu7/Z8qygPFY45v3TplVlasiAWQihdOVsK7oZzvVgsjo/Ph8Ph2dnZ6elpURSc82W14jtEREAaR42yKIq8MlVBQRhrOp3O1ctXr9241u/379y5E/KtD37+0Vtvv/na66/+4R/+4VcPvjofjh4+eJTfXS6Wi5u3bxBC5jY9Pxmfn573OoNOp/nll18GAb9//8vh8MQ5ffv2q0+fPn5w9GVZFk67XqvXajS3Otu2slVVWTA1FLYQhTcLuq2uWeoabSuEsGs62F605e2qc3tuy5XQsMZcu3bN07gul6skqPdQF0XqFS5QBIoAaNGgdb6IyRjrnDMWmUVCCKHWoZ0vxoTabq9JmUsagRCiUlmpCQBoo5SuVh6nDYQQlAEXNCKBh/FHUbS3v7O1tTUtToKYM8ZFxlcMcSJIkuSLz7/06f8kSUaj0enpqbOQJAmlWmksFZUyIo1Al7MwVNBUb7zyUrfXH01mP//wg8fH6db2HuH83sPHTSm0Mc45pTXkuRdNxtrjk6e4Llwk69ZbQojtQb/Il4+WM0oZZzA+P/NxSkeDslQEbZ7n77g3r4q9fJHFPFjktrETfuetb/fDnZ+/+8Eyz7VzSUPicNgOZBSFi0pNFoul0kApEGptBgC17cs5bzabjUbjX/7LHzLO84pESVen5Wy+pOwsirNWuBc2JanyZQmlQcpaNCyrPIsTEILl5TJNi8VyIgQyjtqoZTr3w0gpVbpk607b/MHdR82o1ek34xbv73aybDkcjtTS7HZ3D6/v9wad8WiSlkuQNukFV8IDYmasjM6+SCHnnXBQRkpHSCNapRXjlAW0VHkoZLvfhKWbTCZJknznO+8cnTz+6c/eq6rq7bfevn75+mQ2azd3HpTDmLVfvfOGlCHncnQ+qbDANU9srXVWiqe0NUagrEqtta9Rq4ocAZlgzFOWlA4BOOdOA6UUKSUbhc/OuaQlGWNZlpWFVko1Gg1KRRw1ZlO1LkhDQlbYE+fAOqCMKqUYA0qd90uK0kUJA8q5ZIRdMMoSQhywIAzS5ZIR31Qr8pRYjPruXYwCR0RrHQHKGIviAABPT0+E4J5Qxq8/EP6BL+o4vaTjTBitJeNWaW0rJLZUOQAsU63X1I911SAiImE+Eut5+QkhzWYTAJpRWIePCCFBEFy9Ojo9TplMoihKs0IIYaylnHkeBjArWiVfzFAnCAVPVFVqVX7x5RfLedZvbTVijm3xxd2vyrIMw7Aq9WCwHQRBs9lOkqTI7ct3XvWPRCllVLz+2puUUspzrbDXGTjjtrcGXPJS5UKSOOp997vfvXPnjrczfMf4X/iFXyhV7lGL3k73/ZQ455evXAoj6Zy7fOWSW/fllgGXUu7sbKfFjAsaBIEM+WIxZ4yVpRFSUCqsNdpWkkZCsCiK0mX5v0gBIyKs58irlsuXL29tbbW6HBG3trauXr16/faVfqd7eG3fWnt0/yGldDQaHR8f37hxw5dIWmtbneYyWyQQ7+3ttdvt5XKZZZkylae/Pzo68oGl27dvX7lyZTqd3v3ZUTVlAPHkSXn81Ww4HD79cpqmaYYT70N0u11jTNJq+WdrdTpEBOO8ms+naNFVqByIkFKKta6tawfIRrOgr4ega/SDHwGvbAIZA4CgxBFjrQ05D3hYZurRIl0WnzHGfNF2FEW+RI0xli9LrRGBKGOzPDVW8ZDOZ2ucRxQZY+bzqqo0Y6u64TiOnXWSBu2kc7o8Vbluxi1bgbPEKsjTCg0psuLTTz67cRhcu3y9lXSm43kjbr1y+9X+oHf54Mrxk9M/+uN/G5LmrVs3nXPj3nh+nkVBvMhmv/qrv+qt2K2tLS+U2+22CJnnQfSRyf2D/UFz11q7nA99chTWNaZJkrTb7U7c9//0IRxEtNIqpWyFFowgwcHO4eHeFUrpKkjDuO8JfeXy4cH+3mQ69Rut02p7fYwbHTUAgG/UmIGXeM75QuqyLPv9/q1bt8Ik9LgtZZWUnheHCCE82MXXcBcq87mbJEn8XO8d7A4Gg9mj43k2E1QwSaI4NJXWWnvLyTlntL1582Ycx0ePn9y/9/D8/Pzq5R4Fy7lst1utZgzIGnG5t9P81hvXKJf3H6rHT4J5uiirPEySg4PLT+5/AetysizL6lwvD1dMMnRdMk45JZw8fvRVVVXDs/Pdnf2tra3peEqABkFwNL4PjpR5eXJyMh49vnHzSlouGkEoSOfTn3/6//5//RNJAmIxCUPLSaMhbZFFSSQ4c2m6mEzmVUVkyIOAYXVBL7qmJ2OMgTuTLGo3kBCeRKooy8Ega7XY2fkiaWCcsOVyOU9zH0wllGqbEkbDMOkPdhnHspxxQTudRFc6DgWAcM5Za3xsjxHkB72rp0+Gr3zr5s3XrjhiRyfTreb+bLgsk1Gv37keHpw+Gn304ccff/jxk7NHYUPcu3tyrffyrVu3vvzJo5/9+OeM0yRpLOeZQRPwgAmCGkUguv2uI3a+mD1+/Pi73/3O/v7+u+/qyXh6dnZGkWdpfnL80XQ6bbTCIGKLxaLZDuJGwzkXxQc+BOHxzzXwKo5jzwfmS9/8olkul2EEBIAJygh3yjjNCQk4iwybA0EkDom3zQkTAgCGo2Gr1dLWEEYdoHG2yNKsyJnrEiI4lSuLHoEACEbzLCdoy1JTuiq2C0OmK1Am9fqs9gA8bmCR530RIFJCOSWOMAGorIFSa3RAqSVgfY2/s8g5L82yKIqTszOtNeXcVRVjrCiKpEGQIAGQjHEuao9csiTP86QZGVRUAFIwRlFKwXFnLGNMcgEOCYKPqBSaOse1ts5ZAGuMyXOjtQ5Z4NOruK6b/OD9L/+i99OrnddkGE3mqQWWa0TCZ8ssCGPuVhy2dk3T46XzvDRFMd8axLEsgSwl05K7Kl+qsCml3N7ens1mvV4vz/NVdTVElFLPoeG1jufQAeqEkKenp1bbw8NDn21yqDwStdVqeViQpzjY3t4WAYvj2Jf8h2H4ve99z0uoNJt7WuaDgwPfMsg3mc+z6vvf/36YcEJxPp9HYcJJ+Dd+AZOYCR7kZeEM9Hq9sqzyTJUq/6YQ9H/gqAMnnsm82YqjKBpPn4ZhqG1zPB32+/3z6bDb7Tqwly9f9gmnIAh2d3c9o6eUsksao9FoPB4fHR09ePDAFyV3Op1MGcbY2enpxx9//Bd//udKqRs3bpyfn0/zPPB9l/d6r3z3972zBQCYS2PMYjYv8/zJkycBF7PpOMuyRZYyEhjKnM0IFZarKksrjbnLPADC+zfee6vjOl+3iX2UZXP9+8BmllWIyKlwjiAiJUyXq5rj+eMzT9PtUYQr9komwQFFJqggjJbGWmohhyaPVVVYJECJMaTSTFsBVIRCZFnGiWQMe+3Bpb3L6TznZIyWBEHQ6je5oEePnnpSwx//+Me91s/CMPzxj38yX8y+851v/87v/m6j0QjCYHK8/Isf/JXJ8Pb1O1evXUEHiWjfv/9Axpe9zNnZ2Xn48GGr1UKwRZmNp5lWJkmSMivLonIlzqYLzrmgImBhBBHnXNpKORVBFLjwYHBpNpv5vJIiyqfAyrJkVHgWi2az2Ww2vQJGxHm+EEJ0u93Lly/v7u7OZjMvUlSl+brawvuIfkYCxjzB9UXsTeuyqrTW0+k0SZLDS5fKPHfODQaDK4eHQSQ3oSSwRh0rq3zixmM8GWPXrl3b3t6eqePFfMlADPrbgsj5dAlI2q3O3vaeMWa5zL799rfTNH386OhP/+SH9+7d0zrVijhwhHQ4Q85IEtFWk5+d3M1LM0/11ct73f4eyNbf/PXvR0nzRz/4n7w4rSto/RBVrvDKzz+V5+NDxJOTJ7dv3z55+vT999/bGmwRYIyxdJkOq3NORMjCViMcDk/OTo+u3Tg8vHxJBv3ZafrpB5/1W50sXSq0d9589Zf/5i99Na+Uc/NKt6Pw9vVrNoorpKU11WJliCilhBB1v/AifaJLbqwJ44CDEyQ36ixdyEa0rbUGwgQzoVSIeVHo0XjWjAaDQefalWtbO508H5+cPCiqtNWMQxn6mL9SKk1TT5sqpSTf/S+u3Lx+o9/pl3neSOIK85PzR61+Mj7N2+12d9C/f/8BsdxZ/v6PP4qjVrGs+vE2qeTsZFEuKiEEZ2CsHs8WMpSddqvIU074jcs3337jnccPHv8n/9n/4Q/+4A9+9rOfNRqNO3fufP/733/llVeyLCtyDWvUX11rRAiZTPPacfGH3w+6Cl/sebiL3lK1/e6ca6470tTxsdVBL/K7mwdQ8cLrc1wTNQDU+HhK6bKq6kvWxqlzjiEQQsi6yr7+bbWmpNmMVyNilj5xziFqpUqtlVaF0pWzlgvqB2FFJGtXTkkIja+7JogYhPFqQJ7lbZcitusD1hhXQsiydACADpwFcJRTLoQUXAb02/U5mzDXUuW1gvEWvQ/8NrhxaK2zCNrRwrEKRUWFubK3Ajf6QeNrcpk8XdU1+ggMAHg6iFdeuf3hhx8eHx9vb297ig+PtNRab21tkTV9/2Qy8T0MZMRrZHv9MIwxVZjj4+MwDHd3d2tk1mw2i8NYrFu2ecPOCzjfxcUHur3n6qGkhbqoSasTIogo131AYc1m4M2RdnsFFvMNoHwzRELIzm5TSunRy+12WwYhEArOAoAu0y8++/yTDz9Ix+NmHDGANF3+rd//Oz/4wQ/+4kc/kkHIGDs+PaWcNxqNgsp0sZhOp3m6tFr7hW6tBbTgABijQLkMozhuNJphnFy+ctUXLEUbhxDCF/i6NUF0VVWz2Wy5XJ5Ohv6lzs/PfcsH59x4PEaQHh2zmbGz1lK26ojuNqj8KaVZuqjrVj1Klq0JNRtJQggpyhIA4ihCxLwoNt3uzX3BgPtl5k/w+TMAoIJmWebLuzudzp07d3Z2dm7duvXB3S+Wy+WtW7deeeWVoij+1b/6V2dnZ//gH/yD3d3ddrP5P/4P/8N7//7d/93f+3v/+d//+0EcIcIXDx/8N//lf/mbv/EbZZHvDgbOmr/+0V+98eYbWbnURjnqjDEWyXyWnRydUiLjKO91B51eV4gIkFjnSmW11oMeH41G0+n09u3bnU5nNpsFQZCmKdCLRnU+ZebXaiBjP1BujRpZyRAbwbOs1P6/8tlG9/X4TPK03W5TwubzuS61Ne7x48d3P/9SGObAAnetdmP/8u7WTs+inS9mg+Y2pbTZbBJChsOhbyUuhFikKgiCMAkZ9/3+XBAFcRz396+kaaq13t/fR8SHDx969zoRkVJFHMleuzEajqaz/Hy0+OGf/Wi7obWlCkXpTNQVlC1+63fe/OVfeevf/X8+UgZvv/RG0uoRzhfLmZDQ6TSLvNw09WoBm1cmiiICtKoqRphzeHx8/PDBo/HTo72D/fPz8+OTk2a7/cYbb8RJ8td//ddgmdY6K1Lr9I0bN37pl39pd29nPp9bm3nrdjgceo/2ypUrzWaTcFMnWehGcFSX4E3nxWLhYcwedPLZe8f+AyL6EfNz6tndV4RCa/LOKIquvtoejUbb29vf+973tre3z8/PAeDw8PB0OiWEUKDoAACEkEmUBEHAu732PJ1//snn4/H4zTdeG2x18lwRCkjw6PhR6corVw8pkU8fD/s7PXC82+wf3zur5q7BWn4/cwZBKBPZ8bAyygOt7cnw/NpiubVz8PndL2UQvfHm2z6kfj6afPLp50VRtOJ2/fL19gYArTwtGXq+GEaBcOac4zR6oeJ0uNJGmzoYAGryH1y1EwEAJASUKmAjkrb6QAijfFMK1JpbULdp+9c3qrSuFWEtQRBxXSa7etRadpNV06sVnWl99PtXrfFB3dJa5ZyjxGev4bmt6y84H0/qh9+4L8yX080b1fedLRY1rJ8Qgg6VReecDGPfdwiQEkIFl6EMhRBg0vo1Ny2GpHnRusutQQqEaEqQUyo5p4wpB8oZZZ1xVRJ2fBeNZrPp0VJe7dnOKm5WlmVV8VoBO2eF4FEcSSmcs1rrosj9llCqMsaUVYWInu4uCCTCauW4NRO63x5lURVlAYBZltajlGVpHMcOnLbaWlupFbUhc0wZJYQIoqDRbMznc4dOBIILzuzKgnHPktfXC6zWzV7ZlNWSEBInURRFURxywRinhJDRaFRbP3SjjY91piyK0dnZ2fFxlWeSELRWq+rj/+q/ni0W8/k8SpLKmPPzcymDtCh53MyyrMyzMs+NMYDoUStBFDjfgkYptczc+TnlknP+F3/5lz6OWttSfny8FbLWzbH3oiilIhJ13rHX6/mKgCzLkqRdr+Q65IOIWb6sozI1wSGldDw6r6MjPmQFAGEY4rozRA0y98ZlPYabV7PW+pYYXq9rrX0oXmttwSjFCYHV3DprtCryzM7G0phQlyxPE8TrWz1RZvnZyYxRtZgPmvE7L93uCja6/1WUNKy1Z+/++bea4U1ijsZnR3c/OT0+XozH96bneueACJRR4NAq5c5ORw/vPa60KbJ5r9c7PDy8dOlSnESISAAFY0dHo9ls9vnnd588GV+6dMnnRAaDgXGKEEIZCTgJo43Og3ixeTd9A7/cNoXSask5u7mj61917C4h5Hw4Ojs9n88WFFiaFoyLKIiMMVRi0mhKGWmFymoAFgRJGIbdbtdaOx7P5/Nhnp9ba6NuM8Z4Uc2NMTIUcRLNJ7Pp/Wn+k488mNGneHyReq/X+5//1b9tRMHhwe7Wdv/sdDg8HStNAHggKDqnEIosnywW128O+p2t93/6IVA6Op8E0dHOJbq3t98fbFFilMoPDw7r4DmsqSI55w+fHhNCjLahDKMoMtpY40aj0aXDS0EQOES/SseTyXQ2ixrNclEYY8IwJCTylEqDwcBai8CMhUSE167f2tnZ8YO/WCwIVfUW3pSicdK11lpHaFYam+eFWqZFmhbNrUi2aFSK2pT0WyCblzIWPGT1qkZEjQottptttPjw/sNQhrdv3vZV+z0kxhgCVAhBKSuy4ujoaDya8Juv3fzgJx88eHDcaAjrzOdfflEU2a/92q+VmJ8NT8eTESEky6rxcNHf7pydjgESznkFKk1Tk/velmS5XG73XiqqgjJ0VjGGhEbIou29S+fH42bY3ursCCHKsjw9Gs5HS0LIGZvWXtozupOEeAFCJgQ4JUgZiKDxnIL051u4YOvedFb0s3WctcKmlgMAAULgYpUTQpSqmY8oIZQxoBQBgNiNzQAADhDAIXImL7YKJYgINUEFgKfy39xIYs1FB8+qf8ocQQYBZZQDWEKREUoYsrU8es6J1zcu2uRtDkU6z71C8nKtjt4nMvICl625avM8V8ouliNKKQFGgVPKECShEVLZSM7pRleyCyXHL3p+1X9yzgGJKPONQylxllrkFgzSShlR6aJUQmrhvMoB6yBuJnSjctpLdkqp0lUUh7u729vb2z7PpDd6pFRVVbfc8XlBWPdid+uGM0opVemizJ0zDq32kXlfVyP5kydHtQfsXTpYt/MMw7DVagnBz85OPcs8Y6zSZPN9L1x5SmqfrzYfjTFSKm9H+rKQ2sXXJvUf3LrK0Cu8QpVBECRJHAkpEpItl/PJNM+zR48e9/v9II7LolwsFoXSSRARhzCbKqUIYhxFAECcs1obYzJj0DhjTKW0thCEUdJoJEkSN1qenhcIazYbQoiiKOaLqZdWvrGadyu9kkNRw5jLZrMZhWEQBGVZbnW2YB04ketu9ojY3+5uxni89CSM7e3teZrATSrQOI7jeKVQ66LPOqNZx0VxzZioteZUMsaiMBRCLNP00aNHDx8+nE6n8+mQuSiOIy14wFk6m2bz2Xw8Kk5PtNYPy2Ly4L4QIp/PYT778ic/HpYVaC2taQn+8Q//9OTD9xuNBhOi61SrKh789MdFWe3v7GzfulkcXvrr9967++VjIC5uJZQBEl4VZjKeEcIWSymlnExMVZ5ygQxQqaIoire//Te+8623pmP9gx/8QMoPfffrNE239rp14McX7PrAQK+z6rLl/yrWzekMWwuajQMAArlqrgwbHRQQEU1slD05GT5+dDweTzjnjDBEAoQ7tAwEoMhTs1xOtNOEEGFHnPPZdKG1fvLkyenpqY8oHG51sjLL87xSZdJKWLAtA5E0G1/dfeApuh7xR/6BB4NBp9NpJZ3T46f3733ZbMRgIcs1Ywkg16UJo0bAQ4tAc0ORT88WDx7cvdR753T4xdPhvP/o5NZLL+3v7Wzv9KUIRqORjz/xNVuyXwa7O3vGmDzP0WIgg8wWfvscPX66vb19+/Yrv/Ebv33txvXpYnH37t39S1cPdg7u378/Gp8/OXly/+GRdrhMc2stoQbAo8NoHN+31noQTLsR1WOLm+j9ljeOWavVjeNmHb2way/xuUgbVdJX05yfn1dVFQSB5zDO8tNvvfOtK1eupGlaFIXSVkgwFrUFdEwIEcctwaVWkyI3Z2dj/tfvvatS2x00lFKffPm5VVW/M1gMCx3lSdwYjsaEEK318Pxsd7A/X0yXedWMO6SS55Oxq7DT6UShkIF4+fYrjsN0OXl6/ERpHTV782V1dDxOwoQLqQ2WlbK+q7DTQRBUaVlbzWKjR4QfuFox+/8FAC//AQkBSgAoIQiIgJQLWKffnrEc5cU3tafonJPBRagWN7CdiO6FG0CtQ82bOg8QPTQaNoK99UNvXrn+LeMXoezajAUAawkA50SICAHtioMesSorAIZICSGMXlgYQC4usva9ERFb/VXdM90oeiaE2LKoy2S11r7PRFVV47PZ5psKtqro395iXmp4nY3r6LqHmXgMvQ9m+gX68GRqNFa5LfPKFJnTubGlherug888aK4WPasUslq513V820vh1167k2UZpTQrMrpYiRuPxVBGIUEZSqAQ0IAxVqpyd2uvRp/6Ckh/qd6gFzdij9WqUWbWWsFWa8znYHygqWb/JxQoI9YZY3UcxISQkAt4xu3QRjtEbDQa6JU/EAqUIaOGMsuikI/H4+l0CgCNRsNrryzLtMk3Le56XsJWgxNSaV0U5XK5mI3H4/E4XSy73a5otghjRZZWhIhGk4WhBoANL3MlrVaY9sgok5cVSTMoNRJqHWgEEQZQFgYdI8CkEEGw6gXDKDCKlCCiBdRu5Z3HQURW9abUi2altVJqNvy89vi96eAHpNJFvWfdGgNBKeVR7H8u1z2kV8smCDbr2uu16lvZ1FZdvd5Cueps4QH/s9nMNwoLGAPBJaWEUtR6Ph5nWXpyRFuU5nk+GY+93ea7HE7GY02IK8vdVrPb66ZFlh0/6XU6vV6v4LTd7c4rzePG4NqNzta2AfhsNE3f+9BYXVSVcw4oJSCtsoyxy1feuHr5EuN4evLImrTXiZVyJ8cnyz/7ye7uk7ufHelKgOPHyxkiCiFmyxPcSIHVomB3t11nPTd3AQ+eGZZaboQy2PyyliGcNcIwqqpqdD7J0pxzLpjQ2uoqd84xYysLZJoaqx1aQkjRtXWNTVmW2jAhm1LK+bC01nIhOs22ZLKYWxfSRtzd6pZVVaUitevGXMTy+Tjb3dmHHVguAsk557yRaCGavd7e9PgjRLGYpafHJ91egyhezN1v/9rf/ujDU+DCKBiP0/KDzz///PNut6FVAc6FYdhsNn0OqG79mynFKTfGaG0Y5Vrrp4+fPnz4sNNoPj0eRtG9Rqu9u79PKZ0vlpcuXYrEPM3zvKisQUpZVer5Mk2SpMytEEJVYK0+fvp4Op1yzhuNhi1M7V3UG4oxponxcrI2KFcGd7SKHdY4di8bE9FSSmVlnldFWZbK6lJXiFhkc86/ODmeekptKaWv+K9wxVYdhTEhNFtk08nCWcKFCFr9xo2DXlosHj75godhs9N9fP843uJC8ulocfPaS/Fu++MPvpiO51KEpTE85IgafZULAOd8/2Cvv91rtBrbarvVaZWFHXS34qAlWVRWjq6ABlTKkBBirLOFZ1byxY6EAFBPGOmAbIR8N9WbMwU8e/jlSAkHAEBABCAEYeXbUio29ahzDsARgp4X8DmtiYiNJMavHQBAkG1aTLXfRum6kfXaByWrLpLyOcXsv7f64stNDQ1UMIoWgQISIIjMl0tR1sA1sBYRnAXwJGrQ8PF04g2TurF2XGyOGPqoO4LkAaWUEcYYE0xGQdxudpxzl3bX+g/NpnO2s+vbLcS+n48PmfqjZt3yaq8sy6qy+1d2qlxliyqd51JCpaixwoHpbQdkg4K7fuvx8cJHJmvfy++HyV+c+JRhnaYFACHEYDDI87wuBvPozTRNm41OfX3/5P5GoRHGGIcaYdW800PMkqRB1ilbpFZGnIrYudD7K9PFGKnNqxQYtLoNIUSRW49urQfBC1Ofma5jErV1bM0C0RmjjTGEgFKVTyMFIds0NfS6B0t3Z8ta++Tk9PjpU4+44ZQgJePFfJ5nlDKtNVAignBZFErrngz9uDmjCeIFXRCjAJRyLqPYglJKzbJ0UeSeZs+/+2y2MrZ8fzoPZPPfeCwVpTQrUz+tAOChed7AivgFI4d/0dU/6aoa7bld6a0oP4N+ABExy7LxeOzz4lmW+TBDjTit91QtDSmlkgV1jtCPs1fkINBjc0zdKdbXeTNeAeEIBojTpmLaWos6F+gEYp7neRi0QonGWKUEpfM0jRsNKaUDGM/mVgYaQcaxX1S+ck8ZYzSUZVkUi7PJl8vlUnIcjZ5ypp3bEZQ4jT9+78NG4761Noq7AFCpjFJKmfTFDr7hCGzYXmWBWjutXY3JWEknS2EjZkbWETJrs+e+9GspLR63Wq04Sowx6Cgaqq1zllbGcsGsI2maWmcYY4QCIppy4mNI3ix2DrXOAfIqtwAgJA+CQBtVVIUQotlqcm8A0Qic8VzTutJVVX0xv08JOqNLWjWSpjWsKispCqANS+ilS1s8CPuDzmI2+uG/e59D597jp9bxbnfQancopXk6B0MIkq++ul/XHCOiB1sRQhylUkpCqLMIAGiQUd5pd5O4SfJchlGaFnfvfkWZKIri9GzcikMAAIqBDC8dHCpdPfjqAQ94IGIfNPYKvixtni+Gw0kI4QsVsBUXNcd2g2PVal1jGrzI8grYulUrKp/M8jm1oij2tndH5/PxaLEOb5TpspJSLquUEEIccc5Z44y2WmtnkDtLH9x73E6y3YP+7sFOq51sNw6efH4+P8kb7WT0ePExfj7Y3gkgiXmz2ZapKzpJRy28XkBvMpRlSYISGe11o3bn9mJegOZlSkUYalUJIbzDas1KdntFBkAR0SGxzqtOQAQhnmlJXWsUjdXmOqaU+tVI2MVpmyuVbACINtWeLnFtUNbXIYQSKuQLFTyDxua6rw+1bjZi1t1YVztkbcj6n9ShclVW9TX9bf0J2hpjjNXGoiZgKRAmCCPEIdt8jPqaurjQspuvi3BRMbx5aFJvbGRrLjohKOESABwaY4xFA8RxQRlj1gpjuDHMGMYYJ2TVnnoxXfp34ZwLQSnlFCknwhmkRCZhQ5KWTrSxJWLlwDoxxQ1Hvx6K/u0d7wE8BwpTKq/BYj6KDgAeEmWcslprWwEAUIfEWtQnp0/wa8gdRCxVjoh1tqbeVJ4Zpo55euIzv7XiOG61Wnt7u0Egnz59ev/+veFwmOeFb8vYarV88wk/DnGjUcejvObwOEFiFaW03W6rdW924+l8N55t9WBCcM5Ho5FzbjqdjtZ+MyHEGNNJEl99a9EBZYQufSVJQTkAUC8vCNXGgrEEXZEtGTDCGCEMgFIRCMYJpWgv1gMCrHYXQBjHdQQeCHHrdu5CMh8255x7S8iLG0ekQ+KQ1FkbfzXrLKVktYUoq0NzRZp6rb/yRMOwXq6tZtNHI/zIeJS+74ap121iV2A9zqMg8WzwdWLFy1OjjJfUiMgpIQ4dEHCowFlCLaFEBs65AoEwzjk3VSk5s5TlDmMumQhSwkaVFlU5ffoEGTeUziZjHsWLonj65Kl1xqEBipQBJZQTAgwqU07nD/L8JAw4pRiG/PTshHNqnPXE1HESOeeyLIvicN1CNHbPHugQAdMMnSOIghD5TLBKPwMWqT/kplob22TFb4KIiDKgCFJp4hxzjhhklFIghDIUwtdEOWuQEsGZcOgajQFjRZZlQsgkaVhrPRwvDoTXIotZbp0TMgHLllNTqjNv9dZt6v34izBAilq5rEzBUMbjotJ5WoqwOZ1OGy1mHM1zK2SL5PbRgznhoYwa2uFsVmiljo8fxAFBZxbpxHcbRMS6/JIQklXKOSRAAYABM9qmabpcZEYTh8i4qzRUykSREDIpS2P1IgxDABSSR1Fkq3I2nwohmjHTWpdl6UshpJRGUUJIgZoQQzZiCV5+LrIU1uFYWLfSYoxRBM41Xbc8QkTGNGNladPntDgAWGs/+/xeu91uNpuE6Kqa+ZRZGIZUgNZaV8Zngr1vBQ54OssJyvl4mZazxi5789uvDJKDex8c3f3g3s2bN4SJh49noIJ2MEh4W2vLmFosFpTSfr+vM+NfYzabfXL3xwi00+nvbF2yOoygTwyXkBjO/P7UWleVEoIFQezWUKNNC9r/0zrvIpCVF7f2Fa325wNSpJSgQ6/DzIaIp9Rz0lACBIBdOJArgAMFAGNXsoQiUlp7zGCVreX1Zkxs0yDYfFSmlN0g4qh/W6qq/jmu22v7ROMLFSTlwlrrqLXOAFrGkDHGKF0sqs3nqcWfCFYo3M3VAwCCtF58fXEh/a0F51Y2B3MBADhk1lHrKGEIhAKh43Nk3HJeCmEZW4UZAQAtWadstV+RKwwnbzvtCeINWu0DGw40F5E22ii1MTicAGRVyhgTIQ+oJBvhehEk9bvgRhEqW3f9XIFIg4BzrrU2Vq0iY+t0sh/nqtT1fG36DdOxDwqpoiy8rN+YYlFV1ZPjx2maeo7DPM85ET7Z7CMBZM3UU65dWLJuabfyCG1VW9beJludxp+NHK7D+84pznkcRbdv3TLehTZGO4wIVlXFGC9VZYxBQouyDIIgS4uV/UQIAWf1yohpNZrWuEoro61zCIQ4B0hJHAQ+xUsp9U68z6P7Z65NMUIIZR6Lq/3C8MkFT60qhDAaPQfThWWL6JwLxSqVY2t7xKEF5/PffvC11ss0xTVp8HQ249z3YFj9sP5T7XP4qTHGdFo9L/rLsvS2mk9npMWSyKBOyVuf8LdWUOYIyauK+LZ61nLOBeeExhW6UsiMMqstBaAOjs/Om6aklGoA7XCa3psulpTzIIranU6hM6RWWaMNCh4GoWCSttrGuYW2VFBRVGSZTR1aSumgd+ns7KxScnt7m3Ecj8dAzPZOfzKeEkI4pZSLzZBypS/SXqtdjABA6uYfF94CAACIINiUk/VejsIIkCAiAcYZgOe4JUQDKEspUMpi5iwicY5zJrRy1oA1oNAqYZ1z6KjgQhtHCBWBbIWCUgoEy6ooy5ICcwZBEIKUUxHKEBHDMHx8fLK73dvd3s3zTCmllSFOhGFroRalocfnS6dpXmAgg97WjdHUjYsn6AijQRyAFMJom1tLwbQaHUS0GqtCCxYAELTE+QSERa99meCIiI5Ya4ui4jKczlLKOGWyqmwQiigKJUeH1lprCl0p7ZyLoibn3DqM4kTIgDG2XCwpc8Y4xlkcNzYDlmRdR9dqd3x0apV3q5Qx1hgXBFJZC760DwA9CavWlApjVilhAPCbkRAeJ5FDOpkufPyGritHpBR21d2EMsopYX7fcE6j5eysTKvdpCMDcb4YxbRzcOXS/Q+emBJ2ewdhHBBK55NROTdx1MjL/NHHD3qt3uu335yezY6OjhgN+4MeQpVmubIKgTbEzs721YoEIZOlQ2eREk6AACKjIWdhZSpl8loC+hFZyVNKiRfBAB637Fcofk1h+yVq3Yb3udnOiAvcyMFsuIq4uZprF03KEADQgXVgwQGsc7Tyeb69ekytddY4ZxERCCUEKCWMEEbpKjiIiAAGkSDaJEnwRSAyYxwljAsqiSSACBacQ7CNZuu5LYeIziELwDkkziPF0dXMRGrV3/Q5BSyYT54DIYCI6NBYBAClCBAHQB0yRyxFQignhAe8QYA4Syq7ibLGgCfrBVSno5hzriyUc4gWAR0Qg8RRwQgDLANiuXAB2cC4OudYfBF2q2urKKXWrNKx9VDXs+PTz9Zph05p1IZYa8NYIqKx6tnoJWmIuF5RtUYEgK2tLW/DPjcF3uadTCaHh4dlWXb7XT93ujA1OIgQUnvnylyQIZCNEHQ6H9fX90rO78myqHzQzyOYvDkshEArlFLgbBgnUkogJAiCMGmQaoXqrCqlrXFAfIC6csSnuvM0VUVlPQif8TTP0HmTRQgpgVJrnTXGF0h4+LFds/35cJz/Rq1tI6+JPfjAg5Z9oaonbzGG1puIbiR9GV9lo7FmRgMCBMhaifpx86PkvRB/U58UqK+JawBXjQ/3Z56fnyulCCG+cqa2aaQM1s+AHrcRBCEhhAFSSuu39hPBCdFc5FnGOBcOx9OZZ1cqleaByMtiNJ42221DyGyRh0kScdzPZ1meCiUAwCKJIzCojNHG+Q7wJMsdAUaZIIRWlXp8dD8MQyDm6fEjSmkQSgBzNnzaCJu1UHLO2PWSq9yKAAcAENwKT0CpI+Vq2fsxWKtgR58Reg6dx76o1CASSmkoA8ElIlrjrHMApCoqRjnnHBhVShF0gpHFsiCEIBGEcodMaVtpDCkLm8FisfBkxda4PE8ppY1mHIKv2qeUMMooZyLPc2eLXrevlJ5Ml5SYJEq0oYt5NZ/OSmbiRjvLllEgk0Z7sciazWZZ5oRQIQNVQV6WzrksKwPh4pB5akY/RB6c76eemxB9aZ+yxhhVrdgJRRA1Go3xZBqHYRQ3zobDSqlOr7tYLJ1zSSP2zjqhK3talTYIrIdHIbBud8sbnaPhqd+ztULwYo1yqTVSSn1pgB9txtj5+ZGfuLX2WO2mZtJVWhk06F0aYIGUYRiWZWmNRkQCFNAZveKHR0TOBWOMEoYOnHMUKKecfPfvXPVd/zw28urVq4eHhycnJz/8wcdFlUddfunGznZ/5y//5N+3t6P/+P/0vxp+ufziy7uT6bjf7x8/Pj19OH3zlW/duvZSZscno+NGuPWtG7/NdHs0XRLXYHqQmlPGqRAiEIIgcYY67ZxBzbJalNee0IX1tyH6a/EKG7UftfKuEZW1IQ5rwhd40aHXjZTZmiB6BR6hQS2RvfPqI3V0DVas69Xss8jk+oH9z+1aRdVO2ArlqFZS29+6luk+ELd2Lkkt0XyWrrbR6oMwCRvmcz1EhF8sqc0TwKrnTBZ/bFrlmxYJcbaWiXX6zT+h1yJkzcXhNlAk9WPXnigw89xNV5NLXtxlyLlncv/1IxF8MSckCbIXfk/ZMwn4+oMUbPOb+nsLiqxd7foghJDixUQcLnjx8yud18ugfgVCSFnmXuV4BczWnfuoXcWcfUsMH+K21mrAOg5fax1EtOj95Gdq9hCRA9ncJv6mlFKAC6zD5s5y2tXPWR8AkOv06/sOEQPB6i/tGreMiFDyegzJxmHFM9CH+nMsw/qmZn0450IePrOMazvZPJ8T/fra3nxrtxGFqv1jQohPytbgr3r/6iq7ffv2r/zKr/zrf/2vx+OxB21ZaxUl9TVhTf2mtY5IC57d6c8tpK+tB/dcLM1fUIrohefX9dD1lf0huNzcpM+d8NykE0LArga83uD1i7zwvloZeHZT+P+G9AI0+syEcvHcN6v3Ci9CbZuzI2JNCPElZFLKqqp8L4fDw56fMk9o6DkgnXMyiv2ArwJaVbWCExfPwGzXPgDUcDZv5tYo/VBEuDbuazQo53w8G22u/3rDhjL08AifOvGkBZxzZVR9ci35EdGZ1capGQ68zHcWyZosoS5FIYR8+9vfttY+evSoblXnZSnf6ezfm9w3BTRbndl0/scf/bA36Oxsb++Ehw8X95QrF+4cChW2RLm0/+M//aN2p0UoWDCj2dAJfeWVncHV5N7ZJ5JKa7AZdTBHo6mkQamM1VkgEkodWlu5ylMHWG2MMVESb8r9+rAb+VS+Ubpjvtajxi8Obx/hhpL+Dx9+pp/bG845BxeOV72s6xOe2xvkWUdzJVL9BKxVOKz9sNUEh+Ez8qUuv1HqOdHmjxqd/9zdHTyTEr4Qggw3zyQEfEEy5S94VADwDjri8065oBvAImu9ceqZIxHRw3P8zz0GwW40riebpaKwzsGvYdv+g/2GXDX7hrnz7/WC7+H5nP3q8PVF9cjU41MLVvCBu/U/KSeEEKAA1MPO3TrX8cLLo3vxc3ISrkBxDDZtxEaU+Fyax3PVrp4qC7LGblhr2ZpW8Gw0IUgJIkFKgSGAQ0cAkK6SUj5EXN+Xbqwa2NBYUiarB17/afVf8+L3MmjgxQpG4bqEd/NSTrv6/E3JqJ8dt4s9Xq2u/5y5nOdlfdP6J4QQSeXFml/d2rn1QgKCq7zo2gS01m8o///g7RxCYNDtw4b2qg3EZjOgnC6zZavTarQaNap/lhf1a/oZ8WJUp/59wYe7/AdEDMMXK9TGutyfbFRhICJ+A8l4pdSFviQXo22dqdfShdhZ/+8qOrha1YQAYUz4E5yP4aGPSoD5hvtasP6OtUihvoM6OgCgz0QcHQAsslkt7jbnfbGR+qlfFgBEpT0NAAA4ZECQcUooyHX3FET0MtAvDCjKOuXv1gUOZVmGrFnnwtfpcA8zFLj2c4TgtfyhbJ0yB2edURo9JE3rC4fEvxxjFAC01UiQCUY5BQCLVpfaOZcksdf4jFHOV0EaAFgu09WwEHRwoT7iZBWBM8ZQRoxhfmFbZ4BAGAVcMA/s9xKY//y9zz2fH6pplmXZBMDmkpSouKp0oHAxW5Z51Wg2iOFHXz05a571dzuFyjrddhKEnJGKpg/OP3/p4LVGEO8MtjEnRZbTAJDkTBICgdHGWIXgKAPKgMWGoqdZhPp96kUfx/ELFcOmFQnrJDmsFXMt8l64yJ5ZcNaStb0MG9Y0p6uyome0smedXZvt9RqtH/tiz2wCGjfuVTvlFej6zPql/Puusllr0UDW+NUXKmBlLiyA+mEIIZQ/UxZM6kbF5hmFXX+w1nqNyJ4dM2urYMOBqK8DlCqltLUAIITgUvpRMmsDqJ5HL/msNbUU21QAhD7DQ7KxGZ4Pntd/ePHX9pvOX+do14mMlf7Qz6+31fmsAkLQEUcIWHBkxQT+TSuJfIMCplTWL0KQUEopUIIkieNVrIVw6jgYupKElAEhBAgTEuiqPsTiM5P4jO5EQv3/rQXxSg7aF1NFArvwVJ4Zf37x2831nBYprFdabZwBgMMXj7MBtfmE9dW4e/H5IrlQVJvOh29q+ZwCBgDORC0HNmNOz+27GsRUliV51rCmq3qt52NU/ghlQCmdpYtWr9Nqtfy+K8syTMvN0+pQRNW8UDCbH77uGPhD6VV3NX+iTxsB4jcQ7gEnF11TYW0rOOd0pQkhvhch2WDeXn2un+fCDfCztrHIEQBA2xcbvr6r49d3XyifqSKpB6QwK/w8xYtgJKBvXwew3nFk/WDKFsCk50gwWGmnLCgkeDIc+pUWhmGMGHo5w/kyXdaSEFd14coYjczV2pcQiuD/CXo9/tZRn5X3I5PZVYTsuf0ug1V54XMRAkoYAHoVzjZoYSpVbq43QlZYnEYj8QNC1/BDf528XFH2AgEuGROrIRqOziilRZUDAGFg0RitrbX86VeTdrfhHJxOJ2EYDrpblNLlqFqwYZCECWu6tHAcwigO2wLaPatQSikkf+PVt9Jl9tOf/rRcajQQ9WgYRI1BaJ+kaTmJGpInuN1rL04LVRkorbHAiBRCMk6AOpXD5rjUw1QvaLIuMvH/3Fzom2Naa6zn1so3CfTasyQbNLbua2xH9Qz5rBj52lEvvs37AkAcx5tgHG/KIaJggRcKbk3jQDYsBtwIfdeS5blH8g9pnNn8/uIDUkYYpd7zvLBPK2PXK+y5Bfe8yF6LtMiH3GGj7AQAZBByoYJwRTRBV6i6iq1z7bVk9Ld2FX59cgGA0RfPy6YKefbzC08HdOyF31Oycvlr7ev/6ewLHsb/YgXBB3DrwQEAzl4csuPsGzx1sTbaVlde1Z+ABUIspWbTOkTEIHb1JjdrRL21Fg0CAlggjhBHKK5Y+i5Klhn4mUSHG+91EThFBAR0Xwvd+xM44bB2ZzdHQ9DnFfbq87N68eJ7HuGzvvLqBPHMFS4UhjFkw26oBXqtVDZ1OawcsBccmxroGUWbEfJsCHq9T1e/qretl5WBCJVSyqmoGTU6DU8YopxqNpv14MDalan1ATy7fjbthucOu84abm7hTfn23BFFEdmwJ2qLocjLzZ/X0sMbSV8ff7Zh4G6O52akavPwCuY5bwcRndGbXre3IZxz7Tj6ujAEAOf8e/nZvFgqnPc2k3dSyyAJAKDMM78aLLjKKFehP0dEnDnqnAfQgDCchyzUAcWVPCTP6s6aQIkQh9S4Z4MEhBD6bATCrLFEuCHAAYAzZoyplLKl3bxFVqX1u2xuAU5EvYCJvjifcerRTIQQCuAByIyxPF8SQpBZxhhw401csMg7zQ511Gkd0LgRNI0yyzwPgsAken/7MjCEkDe6oS7ceDIZHPQqVValsiVAIfSMzJ8obOo46g/zJx3Rzdz5pDgZzxZGlxgha72+e+OVKiP5GKqUWiXRcLPyXdRzSwRqs+5rCaQXLh1/bKpA2PBoGXuxgHYbcJL6ATbvezGshBBCNjfM11fe5hSu3mWdYMZ1FtlvGEehtpLqZyCE1Em++gHIhnLdvLJ/ttpA3nwdAECEtfC5oHEGACPECx/bN4h2GwBUfzSSFlzIHQSgUsgVH4JccaZ7kEJRFM4CIN18/logbvorm+LY2RfPC+KLBRx5scMMFF/8ByTrNnmr6NxaXuNaAD0bmoYNsb+5FhFeLFiJ+wYPnvlxuMj9AwAhpMYcUIqbiqGqsnrcrHVKreD06AAR0EHNObOWgLgpR+ohIrYWQM+kJNgaSHLxhF5AkIsw2qbO21R4zwgafpHrrQcMAHzE6OsKwMj8hfNoN7CQFwOPWLc13PwSAIzT8LUdV58B4AnpgOK6nIIjIQDEEUaAOqBAKCUEBLuITLCNvuDgiC+4AgBflc4YK8tS8AahFDbsY+5bj6+xFJuLGb457VVXWm/OCyLGwYtD1rW8qn/i/xvHF8RBz0UEceOof6usem7E/IdvMhSQvBgTYHX1nHDz960DT8+tilhejPPmB4orDQTrdKEXUJzwTasd6s5Itth8qdow9dt6U+TWn+Fr+mK94y7sM1gvrUaj8dwg+w9Wo1LKN7AiaxlOCJnOx/i1cCwilrmqrV4AAIJAgFBiiGaEWqfdOobhbyE4N8ZSShGsc6sun5QBtw6ts4QyRFTaGGO0sUFIA93iJpxX58m27VzuLk+rR/cXNqPRLoKh6Sx/70fvq8ySUhIp07yMtIm7O1GXQJwP9aPZ6TkNiQhMI+xwEvJImJKgAm3W8onWmuOZxmebucl6GmrF8/XjufF94Tmbx2bI5ZkdYk09i24D0LF5381p2wx9w8YGy/Lczz2uE06rF8S6M/HqUv7iPvy1eUdYk59tLqyNAfmGUOqGJtk0L4IgfuGCA2DreI4PcK0OdJ57izMpN5+zLEtjnNaaEFPkyo+AMU5rt/kYdQjXrGPvzz0qZ9+gOMHWqhER19oQEb8BDPUNMWK6rp9+TtHWkdHnFDPi857B6jnti29g6TcIso3Gzd7s9WtDyA1puHa1EVEprJ0Sa1EpNAZXubxn48n+QyQjeFbQrJLr5Jl8RD3UmwK3PgERLyySDfEKAIAvTlWU6mvjAwgARgEiIF5k41Y/MXRzHn2UFIDU1K31IDt0hADnzwSrauHL6YtBc+RFgE1CSJ2yeU7yMmCb67DeVk4TSik6Yg1maQEAGiwgNa5mWQFGgFLqrSAOzxs0/qirqp47GlFj86Vq2W2/IRfrG63WL1XPQh1ar7/xEvI5BVzPl8/lP6elyIZF/vzzQwmefRcYwIVxzDHcHP96juxGSH9zanAtVzeXEAAkcdM556GyUCKsTTpTWR+rqx+VAQMAixd6wR8rj9+9gCmMEFLD62AjkEkpRXR0jaxcSbbVMnjGnavfizLLpAwTUS8h/wxJd1VH4DYQpojoy1br54S176FsVd9uE1rrsdl+lp1zwbq6jBem8CibLM0KU8RxDAJynQOF8XC8wCn0+WQyiUQrjuPJ+fTVm9fRgM3o6HRmKpu0IpC2KjIGzSuXrhwc7JVjjB/RqSopinQ2v3f/87bYEtiBqmE0WGSEEeAO9TPaqB7ZLMvql9ncSJuW8jNi6EVu9DetNgDw1Dz1qqoDVlY/0xwD1gwpNWnw5r1wXXqx4YCuDhkE9YKotwohJBBRbV0+t0wR0ZtddeDXI55gQ6pubDVeP8wz1wEOAOiIRXRrkD0ACPm8wkMEQohSF7nbZ8YBDSWEr+tE/TIy1qZrBqjNVUUIMc9G7/3QIKJzz8QY6jmi3+jSPg92Q0Qg4Og3xaBfHFLzLujXb03J+vNab61uR9zmyQCrHfpNuc/nFlt9GHeBwCfoKFKfJBPgcJ1SfObndlWzRCl1zulq9ZlwAgiAa9bV9VNIQeuLrB7TCx2zagJdLxXnHBCorK7P2RRAjrzYgCiyCwG6GVUSGwbc5npz/BnDq/4T2hi+Po8ACFU9gKvts2IIR1jzetd/QkRC8evH5ixcjPZqr61cDcbcptwIOas9FQR0YK13mSGojGaMERDOOsaY1jqQiXbPR8X9Z85fnLz9JrQAmguDHghQgo6seGxeeH62zMnXDBEAiGVCNkBY9SzXpz03/nRdT7wpYeCb163W1YvP1xrxBaklShm86AH8fvz6+nHEaKcdYYioXYWIFphSSooaJOgAnc+nAACRF5ayT6V4sSy+QQF7WKrPNPhCS0IpZWCcdau64mdEN8EXjw9lnFDiyVIMGqdXWH3GnylCAYKEIiAG8UrO49rYXVWXVbaeLNxQzIjoiAkj6VEFNfMo55Jny7yUJSGEMmqcMc5EQVSF89G8oo7mZ/BgcXzzWtLf66R6upyleVaZ0lFGgkSIiCAzvX5y8sU4fbtMW9lsPpvOJukybSbNbJKN/v+1vVnTJMlxIObuEZlZ13d0f33PdDdmAF4gRS5IiMdyZSvSZNTuvmnN9KzfxD+xpkfJTGb7wAdRL1pJywNrAxAAMbjm6un7O+rIIyJcD57h6ZlV1cBAUFhbdX5VmZERHh5+hR/dyzT3c6KCyxg5MAMlB1zwmQLC7iU1xXDWIOXPkbSeG+I0BkP33rEzD3XoSDnXichQKfTroWqrANSai2G887VPSzg0AZMuufQTuz6ATMfJWS6WDAmIKAkT5GxVTE+4x4DJ91kO8n7QzFs+i9jDTkBEGiyvox1YVZXan2XKcv/p6UKyH7CJo+Ucpe1MdmgrYKrEo5J+BYedgEJzWAPoGaSoVfqPmY6cxaYjTiWqO4Mq0eZXzP7nuohpDBZd1nSEYB1jYNaF3XYVYqv73DIGYGQE/SdqFnlnM5rpaEEV99w/5ai8tCePylM2DNcSGo/T8n+yXpK+StFJL9brWjfmSCDGncUowj5XjIeC+9P3NKwkgMg1MvyYWHdhPxEkWafESSJhQ+idnvpnszevrq1MWscpIiwRASdw2KvnCF0anT0jIgMD8HxW1LvakfPOJ0xEVId6PpvHGCELykDEiAyQmJMhJxbg4TCZgdQGNGe6RCjOA22oD95f+QqNpju4XzACICeI+UznHdw38ySzldQCdMQEHXgIF7T7dGYKZoBJEdo1jU7fAjZlpzMiSmYibei6ruvzupS+p/B9rfOB+CueB+60f4tglnJaQClpUkYopLWcHw6LCs0o5//AVnhUppCIANg56rph/yqhQ8S26SRRPw/WJkaEohwy7yZBckZiqqrKeSpKjwFiCuSQxXy1ulhZpqIktSh9HdZYsCPnoHj49N4Hv//w7OHsnz968dOf/vTy6u3sZHbv4YN2x9989C9uzR/+7af/4fzuyb/+qz9/8fp5cxN++r3P4Wb+4f3fvB3+ZexEHEQgBB/Qx0Qxhu3t+QdzuIAEndvW4arruqpYxDAQGlUZAcAm9BdAiIp2s7m0S6U3SO405ab2BjBUyb7LopReiLt8jEOeLGXeFnWUPIV2eJfLmYqdc7nMYtIoNxmMz25yys9U41cfNHuWEEKQDqWrlJK8IoZRpKbiVtt2upkph8R575s6kImYVBjGNJzNW0z1rqJ8lpOy65D0aQVSxXVJ42G9V8CY7u0GoFwjQeFpCUEyCYfZqOB+rBkPMPdg+xl6w8Ne2YyHCSLBEcp6RPP2VbD4oG3m+6QTCSJDJJerSkNMKYXYxRhjCr2ijxy7I4JFOhI/fUQgKF3JhzQYGFsmzIR7gWlCXuGIV7O1DNn9ktwROKdwcF3qsD3Yf+Lu8DqGdvJGuejafkPZhojFvE/c0c8yf78olrITFUQZqw/HH/M74bDfiP2ET6Tsm4KHBFbIGny/fbKEElJ9YFHGdIyMxln4arLv0IgL0rnuO0Tctjd2pRTOEQ6b1rt6Yx/X+5OxbNk3ztyQS9+OIYGpJmcYuZRf3L+/jYfx31JmS4tm8+VB/EnjMDx9UWpHwLSv1ms7JCwDm6g2/dWXh9ddMunur6O3L7PTDl3sulhRQVA0u+7FZ6+T64rPeHFxdu/JeXxWA7v7d+7PT1YP3j+dn8T30+MXr55//N2fx5B8qhbx4r37X/uN935vdzknKLsWQwBETBAjJFcw0mlVlOTqBB3gmtowg5PT4u6r9nOFl8VIKSalzk2S55OZyU+ptsxCY8smiKhZTvR+3Rhs2LBuEk1Mrz1wVkEsfVF6xDmpuuq4IptD9tp3ORegvCXE1mKJMlrRiSmfJcM4PorGKUFijFLMyfJI+V4ixCnn2cccJK7PWvYJALP5NGWmTLBtWhhveGHeeqZuNwARpdSP2eIu5qRIOjwxtnP2DrV7Rh5RQNlXMzNymPRsl2NKtZnNoe/YFPxrazi+7v/pBBMkQJY6lcycUsfMiQf87O/6NTXdQf2A9Azs1zTxEdHM+84CHPZoFozXpd9HfNgpD93gHGQvMMlLkRmIRG9GRBTDmeUN/W6NAy4hIsd8eAQMETH1/g8AwMQOUkqHywLikaOTYyuGPFXU5HPiHKp7nAE4cUyDtoBAjsgXc0scBrhlj05ERJCo9V51HnWb30s0tekLHDwWAEP6LciZB8MRga9w5QgsObyeqsriw8BgUv+nwqG/v5jvYwWM7VW2ifPgfiOXBRFAZIQEMovGlKO1oypoyB9g8dMVdq0HV9w4dnLUawkzs/bRnlsh43jFM7il2yxzQP8ur3dP9pIjD+yYfUnLhG234RefrCM2cPHi4v75YjGfu/OvXfxWuSyvti9+9Pq/vH75an21DtdQ4vLy+aZ+Hs/O71NTYKxmsyUxAwfnMULnKMyXxf07py+fX6+vGgZAj4UrE9Hl+s1ytVSN08Zj5WSbqEqkTN6XOEFNyJor76lxAxnKM+WxZGpXRR+3Jg4ehwnpnSnHNTqnOjr2ngGcgCF2vXVBn5IN0IVmf5CQJXr7UmF4NjxJRih+tgiDhXD8IIExoeOQsmoI90JDtt68uXGmxJCOuSxmGewRoKcjzKkoektD23ZWTKkWCz5E63e7nX2vgp3o8JmxSiSTfvp8GnvnW/uEvv8Spyvb/3lYsTna9kfy7qYaEksVgyxOgZjQcYyNR+nPV27piNcxwS+Olf9l2mS9YICqrIh8h+bX/tP+g+Pw9FmDj32NTkAWu3bSvOhJCS+A8yONShXElACRJijEDDFKgBUhgt01kQeTtd2PdIQBHDPtYhppfoiARBNGmH+SufS0SMlR78tpnM6IBpOG23NmFOIeQ1ZwsU/okZKcBRD0+WdSfjcDsC+nvhcTIjmdbx9G2K+fMpIUpqZ+uV98PmTJ+lcCAEBpGL9wfUH+iGDBLpuCgdEfOWs/NFTm3lCVQTFkiSBy+1wfAJwr7MgHvpkOgwITMTPBEBwlLcCQGMpCNZlwWUU21Fo3B4COQOxSwx10BI6Ais47LrbPtrCsTk8WD86f3PVfo7poN/zlz7+7aa5C13Z1c7q6ded0vjo//9rT91988breEfnYdbDbbdE5KDt0qcL56Ye45mZ701J3yl2RHEO1DYt1yQ/SOFen6Lt2JpwjuIuiABrOdC1AlTpb8QTHEo02e/NkdXU/2wWzW1SRIJmEIQp97SqmBJgSD4aLHtePhLu4bBKkHBzc95M1V9SI25RSruJnTW2ygdtdr0n3B7pDNZtuNEEmB0NNGw0tV5NvWUKIbds1KSXnXEx9QHNZlgyMxA7RwUB0bIYy+6kra4FjRz5pkkZ1gs1gdvj+DpxA3r59/89fW0uD968wCPmXUjfgGRHgoO9aAp96LWh6Jv2rD2cch2pAN6ohaK+/UlMLEExNzX3H+UuY3GMXkZmPMTYCUcUytyWSZENMUxOx/OlpyJqn3wMAQokg+UtGGzMkBHCSLo4HXg4eR3lORLlExBSPMNojXvoKdksicJIVy6bGi8AJkAlZfJLJoSeklhOApJoHAPBH0J6z+ihSTV+9KoNO5B0EBGBnFg4ACGUYxv1C8ugdEdQiD9n9wBxtxPHRkkIgKwzQn7TmpRcFY9gZ2GfjAh4dEfaMOT++3yzdsMgs+a3UIKdcIIaUj8b7fxZ7R0tv+MKB/pPvnb64j7sX34JknDotniP0Xg7M4l/SL4G3r7Rtx1s3p9jx1faKyFW+orSclyfnp/fb9u22Xgdo31y9OXdPHi2++afvzT+mv7m5Xi9OK/bthncM8YvtTz59+/mHv/M7qyWHpohfXse6hFQVNCva+ecvf1THcOfpI9rML1+1N5t2PvMXt+4211NnP2Ews1zXjHOmYsmvu2vWPE5UK+OX8AA7L/3JLsmAWFp3utdbe19oKSBjnY/s2tCg5/Y7imhI92/vceRljZT7EhHmYhowoWLMVVnpaNFIwX2RuOy9os9KFakBHdFBSgxU5/EndjTSWoZsRxZj1Dodskir09FDXz2EVm9ta2qT1jTTHNS6mhZuE0zdb8nEE4OVkMwxvH3FMX5iCSIfUp1/yXaUY7FhwMYEbbgvIrEq3IgFAEit1sRSmZN/Xdz3HU130Lvm8su1CSTxnZqTENap/suMR7zcQxh8EZSMAoDHIY7WziLhSOPXC8LhTM7uI4zGkpQG46f35aR/accERDhmUeDBGcp2qN5vUwbMiOg0+a6O3/WZTfJ48kXisRe6vJOZcphQyu7lvd9yHIVN6uwiK5xHyOCPpOxCHFkcFbZVMbJADAsRxuaHzLQQsgldZdX+z1ERM5UJ+kxbey2ZeHF7IfdPiAwASODcviUmdBoImgcCAIBytKfA1w4tPlhGC8NZWLYRgEQIm5vTcP9hBszM7oQuzu+srzcv39zMVzPwsQv16epsW726aV67Ijb++vnuJ7Nby7Plw4fuwedvF3WqU+oavr5sXrztsMG3sHCfu//0Jp6V4bRz3sHtBdxalss5Fp/90/O3uzfF8pP3735YPbzovizSbsZ86twOjJOnjEc1M8plF2OMUm1NnGtUUVOMFIZt115+TSbidljm8WawoHAmL6NwHctIFHSDVYEYRSKCyMCI1HvJY098kcAhYa4AIwRI10PH76gnEHrqaV2xJmo9EQFG56gg44OXkrjbIQEgM6TBVpBS4WYmsxsxRklqul6vyVQmUPj0qS45MVMbEoQhXMoipXollOWJhb+d2jDmPVTex0abGc0eM4d2Wuxh0ol9HY8lWXvnr+3Q9UhTWyjocSUiIhaFZA6XwnCJTfjjwX6+KqvcB7t8OcR7jEX7r8r5J90O/cT+6CTvunxPz6hGmge+YwWkYLHSZcrk1Y2KTAwjkaNcBoBR1CxSgdlMLQotIgAzBERE6qOd0mA6GqXwHIaT+LBzHBxLWhrlPT38+43AkGAU1qjATIwikesplZAan4uvWLYHyqUmY8GcEGbAKNGoETnKfBUDezoG/fVkv5T+cNwz+yFHt3kvunTAVwMA2A1GWjAAKXAoFN13lxjEeU3s1f2C5UeOCYv9wg6W6x68LHgorzAaPw0WFwu/lEDemZj1a2bmpLxY7s9Qj0bayNyVmbvU9VaE/IjcL5lEJnR+xIAnO395q7q4f96kbZnwg28+nM1mX/7s9Xr3Js2vV/PqZHFy59a9+qb99Pr7z9c/jV367ONXr9fPrx9eLS+K87tn9+89iDsooPrBD//Ow+zEX0A7c2HxwXu/cfb+zNHm6v959ZMv//lt9+YbH375rQ//spxdbDepDl1XbHUkSqMR0Zouk3FvPj1fKmpaUm4LObAJuiAamTiM5tpXQILxxpYyNYMJNxuB87JNM/EC5sgKk1dSe8PeQWnIfwlHMkYhBRUXrF8VjtVH7RPd4OGpnWM+ZNVTLp17HEyjJGxYzqpPT4e6wnZqN+sr/YaN+5818vRiAaF3pN/bjcfMGoHO2UNNjQ26Xpa26NwttC0FmaDuHlEafY/jMCQAuzd/qXasf2A8rAGTBiowAouHABGVpYsxhpg96SDFGI+dR/wKDY22B2b6aRyF9Sv3P+lh2FB9akw1SOgNx3JLHjHhwpDhSAkdAJAUuM4sifUPYADNEzc8wkkodD8qxfkYk3M0LjcgvGhqEs904HDxj6OaMfME/v14YCSvmxtI/oGJ12LWM0uZnT0PnsJHLlKS83Jy2BtVIDEDe/DipqS8TRieL8vxYPOWPyJX2LNY+0jMqfKVxPVkEAedHrJGC9CfofY1UVQqA8lfNmx/6NfuqHfEkFdAuwYAYCpGir7pTCKGM0PHfiLihS63KJ2Z0HYlZYhYFpVFywEOMCQgUgJIRF0cjUc/j2RFAMCKows3zTUX6clvPzo/P7+8vvriJ68e3lk+PHvsu6Wvl1evn3364p9ev3kFAIurxfzWal4s3rx5to3XxYzWl/WiPHGvFwBUnvtyUfhZuvs7+Pj3HLi4+b9fb5o3V2/fvFld35w2ZY1AnZunGEeRiPq52+0yIQNELIqiL/0NBzQhHKfIENYllE40Y/u9gFsSdFj7sDTNeMU5iF66cqYMoqrFiJhgZPrWJAyll6oaaJ8CAJfLtzGPL3wh1mCAkZOIFSz0ZkTs88tIOkNzdK0pAC0PBoCb67XtlrME0LZDCj0VOxARkkkskIE8QT6Lc8DeWg71HslprCfNWgdUVxbGBEVmJKCwos+8Gpnu7ah4rO+yEYMOtP/fjb4q4CcExtQDRA41MPW2jQhRGPD/J6OwaWjkS/kmC0PMY7vxrzwpBfWEpuzDHwCwrx6PRgeWYb1r/LCH6oAuW1hTYuzNB8yAjIicmKh/BSECIKQB9xSrASHF3v8KelkJDyKJQu9YedNjYUgchlNVMAxYXmTNP3aNdCPomEOn+wIRgLKyJQoJ8MjHBQASRHvSpKZBGtMNJX3VvLCvUzrARzT7mEYrzv2RMOswrHEREbuW87RlSL1W2tW7EVgGi8X4DFjp6hFBx9OojKOOn6rhiMF+pnEUoQKE8oNi5lNroWALitzCrNfz+XwCZLnfuSHzGtrw1KzwjHQVRLz96Jac9glll/DTqqpOvlF87enTf/r+9+/euUfo6m39/Plz59zTD59cvr16/uz5++897uo4q+afffpZjBGI0MPZ7ZNq5ecn1eur54+ePPz9f/F77eX6088+w7o6wTs37dvVN8Lv/8WHdx7d+uxvr/7j//q/F+2tR6e/d7H8+tP3f3fX8Gw+f12v7SoqWF1g9cYasM0wGzR+wphdkxQ0CpGu66TEnhixY4xynJzSKEB72Ei5f2EAANBXlONpDmcZ83a71e/JhPZut1tr2tU94Eruuk6CpqwKy2lknNBWlXPKwUhiAZZuq6qKmtA/f0nmjHnSqmJh3axUt4YsYaTsU6a4Yuc7+d7SEezPYHrNW1cq5uSU9trlRChIZI/YFbMJphtM2nxZWdS3AdC6cGAIqDnrGlsaDkIHAKiZ3CkXuazw+OAHABMCJgBgSFIjUP5czPr6aGCoHjNzUeeFDjF1CL0Lwvr68iCBiPFwvLLET1hLw2QhYEyA3NhvU78XEZOTwITEqR4Z0XsZs66pQNtTZcEywNN7BY61DxHGCdDkIpq8MfZC6+MqaUsmx7sl0H2HMEo8Ym4YDD8WJuq0OKEhdKTMpTuSW96SGsVeHEshFhr6Xks3EHG73R6EwwTCv7AxjsoCKnzm82KCzPLZ8W4ihefwwp39UuHDY7KmgxSfG3tzT8C7I2E/hkHaX1N7ZEceSQHrjggKtMfIJ3t/0lJoD+8jRYyxf24yqYItqFveSPks55xkXBBCXbnlwf696kxsGFsIoUzn27fhzefr7esQukDgmqadzxbXb9Yp8LxaOijW9Q4CVcXs5NZpy+1mt1mv17sOq8Xd5XIFjF9+8eK7//ifP/z6Bx/+5pP1l+FHHz1/+ckn1zdXf/VXf/XhB7/7R9/udq/dKj50afV2fbPeNLR2oRmRhoHwVaTJHywmiWQ6EfqYWXPDYvZhVuirZgl5X2lVZzJNxlBmEw2Y4Fpm3m3XExEB8m7EnM9EWIuQLa3CJDRFqV7X1VbN1c0ARsnQqTGzBB2BYWOalINNAL72to928vhyPhSHsAPQuFWdlDRNyp+ytV9uOD8/t1KOIplYLOz38tN6vbbj0TWaLxaWMes4Sz+zJFt/TZtwkEBbC8do4of2NWK2Gx9puHc0c2wbIyIo2RWyYjanLrf2WRSzRBxjTFKqLoAUyp6Et1n4Hxxh1EMpO5KxGd/yHtFEFc44aSDWC5B7IAGOgx31U8M87GgVXHbWFpL73zhThMD+ZHMs64aCvbA03YASbjfMYxAsRv3vw2Ry/wSYv7Alc9ZDJvR2MsiUY/ch75HJbTZzmf0+Hsnod6xJDk8cS+0AoOE0EwQWU6ost0QhR05MYPMi2H6UHiqdkaHaFL8WsCGMlnUygP19ZM9oR/M6Yqra70G/319K1iDAvdbHGfe5zQFA9gI0g2l97LmZabj23OOhR6X5dqMJzWdjfOoZ8Gazmc/nVVWFEKQml6S8+Pi7Pzs7O9u9DPOLkqIrXUmxgg28/OKN975r4rZobt5uuipBpNJVXQgElBLGNjW7po3hy93zLz//8uz20i345faLN693t+6cUf3oZ//46u+aH//+H9+7PftgPQfeLNAtd21EVyTk5WymC6k4CgCRIxFpNXLFeKk6AhnLVQsUK7EEs2LmWCkl0RRlP0hJeQGTJOiwr5aLy8vL/Q2fcaX3mFCOzibTDeXKg5LKQ3NZ28cREf0QOK8kxk5c34hGswSzReVdmklKqaSutN0/ujGuby4tv5cviajwlXZrnxUNXh9RgHTjnLGUT75F9MOcG0tH9eDBA/u49t/mfjRAqz90T6NUHtp82Q/PCgR24RTd++/N8tlFPEpwx2fM+kgcF8kYlklDJTAXZ81YZ6m8WqicA4gxISIjoUPHMYKWSYEx1YN3EKacR2KCoseYindehSeLV7pGRASMwJRSUvOpXaxjcJNvurGpcGBvkCwkbZ+TC8jkaYLndoL7tA9weiaar6d+v5aM4lizQUQ8KKkdb2nslmFV6smY7b6221M+y3E1oaNoaWZ9+HucxvXCHpDtsxMtQtHA+RmbQzq937kif9+/hwipL1NtYxaGKh12XsNFHDYvGKp1zNJwLECAj3qnTzHKYvt+64FgmQ4eaAMpQOQ9AZQBvCsdFcO8ABGBHFByiEO+FGAgdAzshQkJx9KD1RDCrbNbROQLx4HbunUzXxYVAGw3TQdt27YlbLgFJo4x7m6aBtoUebFcsQupxaur65BCjOG9975WzovFaYEJv/7kt08WJ9/9u39exotP/n4782fbVy3yovBl08aiKplS49acBQeLrwUulElYHixOPYpGijFd11l9X5uV/RX0zjkN7xmMhNkgA4fYmNKIlE8oIbNkNE7LeqisGrminfyZsuVZH5QBeDdKnaHNskxltGOKM6VBBxGdcJoiTv6U+OP9DqUqy4RgcRYsLA2CbB7HnGpbJ6XA172hoBbN3lIK+Sl2B86eAcBD/zoyLm8wPvW07+IUFG485sHHGu7xYIAcsrJ3roy6ITHrvgaqtkP5FCkxhA6JPTgsXHQhRgc8LdO5TzRtm2h4k5HzHsObHChMhifxsggkiduAANwQrsbMaiAp3GFv0n0y11/z9E57v17rn0qI7dvZqFyK2/oaMHhrUPcA97Vjm8DnqzLg/Q5Vd5xgCOXUeJPxS9tnPJgltoPvTccYDx4+09Vc6zLJYQO2vcmRuI/BdeScc+CQmZmmZbVijDYolyU6O6UII8OYjt/jYJGy0HCOkokkVNpytFjLkZbwSOrWIxrwMUsSJmTgJElLACD70ns/wjcYrDtpuojEzOyoFxyn7kR+tE8V/v7WrVvb7Xa9XjvnLi4uzs/ONtvter1++sH93W5Xbcom7nZhm7pwUp4h4jwtY4yEHtmVfuap5Ng1TcMlhzbCHJbLk7LyBcwAWkR69smrs3vLD37jSXqMp4vVyfLk9uenlz/dnNQXtx88Ks9qhNL78nrdVPMKEKGoJ2JXv0nyaulZrHwvpg8wNLHP3JQlSouIiCgzpexdhVnTUpOXdCLWY2sQtqvIzISF3bqqHaoCJ11J/VFZDzWj6UktAEjlugnbTilRcZgBi6CgQzpAiQ7h4v6frq+uM+wu2TvoBycCMoc9q9VK5Ql7EMA5a48NoT5KHQDW6/WEOvSzGHt1Kpxtxh+7o9TkMPk8xoC1Bx7zpGPk1mrM9uKrNrt1rerJMSWOzECM5JAQgQAYQ2h4r8FxZx9fDQTXQkn3yGS+IujYQxZ5th+b2MqACHsfOjZcSsdvoTGwrncqbcegl4yz5P4s9oH5jle8o38cS12T8VvaeuwVx8avbNX2RqYYzITxo2n787Jv0aEee+/B7xGl1DtrxC0zSzkmAMN680XiLqepYuxtAIRAIQY7GJV7tOiFpUsWsScMm2iADBvC6JxHTAARslt4f6h8JDUpHVl5PFasZe+oYnIxacwiaCpiIDMBICABjgqJSiMW2W6q4DGzwSlmTgCE6EjrcPfg7+/w4nUlZYpjjDfrNTMvl8svnn8GCRkjFW62mgHAtl3HGOe4EtMuB+5i14aaiBIFCi51yWNZ+VnpyoIqTsARX/98/ebRTbdLBPTZs09OVqd+RsXMraIvPS8XGGNgTMtFJ5VTNzsCRmRy4/2QqM/t6cZNMkJP1FYAUIYxSRahTAXzea18s1qtFIJKoYjo+vo6Y0x/piv3zKpCmaWcQwvUlTHw2CKquZ3BZPkAgLIc2LyemFo11/bDzGIAYKMBSz/qtMI2xdJx78HN9mayW2QuJwtSSApktOJmzM0a2SYF23UMWgzDjlbEkX1Si4jOe2t8Vop/sPAvMzfdbgJhzgxY7wFD0Vxx2KR8rOkGmlDtCT21Q8pEbdhkMC4mAVmzBwBySOCRmDBxSpFH7vR2DHl1fgH75+P02s6iC51uAR2Pbh+pCkzoCNk5R0AxRotmujVidzij1mHI7JnuLcs5uBBhHNaiYxhSexoxS8BuJ6tCw9F60kdSnP5qTZdVd2UyXo12wGS8VfQnterZn9hY4PbbsZEjE0JO8ciSAAuYmdxhm4qjQveapaJtN+RSpr60JiEiBAZUj3pxQGNgcOTy+0Zeb5aGK8AREZjEU10SISBKXTA6UusE0B9jnEfu35NypB07A0Z0DICuT6eDwnERJAPaBF0zxRmpCtKk2hIRpcQpIYBDdIiuD4bjYZ9Kl369Xs9ms/l8nlLabrdXV1eLxeL09HS73SECR2y7joAgYUjROd/GtixLh1R3dRcbRp4tZq6ktg6r1erB3QeBw9Xby9jw++89vXX7/O7i/M43lg9OH61vNj//4tmV2z6+/RuPPrzXXkVXvS18C11IzJ4QyRO6rtvYpeJ+3Bz5tuAHGiOPqJI9BTe2YjAep7olZBufnp7qTrA6rrSUI1N1DMJIwHgd98fModS3i+wm/Ga51DqXrBJASkkSZwr7QeuezrUdLWQJI3TBylHayqLQ7+VO+V5OxyfOUCq36kx1j8n9Fj72T50yZ5ex/SpGFsXVj1rnrrDS8aRc+3ayN+SGoiylDiMAlGUpMg0zh+5wAHs3LmKhP9mztDGjPcJKD+/fw2fGiEgZzYY7B3KWOUHmxKCZuvfIQQpA5JASAqcIKaaEA/ed3MzMtlj6dKhm+vq4EvqB5ElzB2prwsC3ehO0nFCo3djev3+9/167UpwZsB2Jfk460WEncwZsAb7POPtXjDNhabdEI29bi3U09mYw6/gV2sQSw5kTp2yxm/Bgt4c/ewt94HO/HdWAYQrS/k+20xR8lqMBBBhKUPeHlIyDxgb9T5KSgsbOSlIxAozOoJOS1nSHi1tYuOlFjJGOzPdYys9jCsbRo4ojjSNOtl2PuTxsBABgVfP6xJmyfKz/OEkyLERwnrxq9pC6yaJL82VZtm3btq2UzXHOdV232WxizUXhZ1wECFU569rQhragylXCupBTLKqCClwsZ2VZNrv1vbv333vvvZ9/9vMXX74sZ8W3//Dbf/7nf76Kiy92P765fBUbulg+rKrq/YsPFs1F8YQk8DHGSOh8MSuxKnzZxZGzTwYE/5d/8EKgxWM55WJ8Gmyjiprcv9lsdMIKBWlt24YQtN6ARDepaouGYaPJHS2vQA3kDW3KfoD2++vra5dzKQ+rm73vdORsKrbqvk25yG5RFE3dWCKlrYNOud1ktx8MQ9JoAd3PPafH/h8SOY+ISEwpwayc2dtSPrgV9LAUVqfPOYEJmPCk+XyORkfXfkIIlOOAFTKcNXsxsKN6YOX4SwWREvT58tSCZaI7wh79kuou+IsU32PN8mC7KOPX5TfmvQsAMZvsFKWxNwUH55wjBCbmwMzIRB4miSkM5zh8dnU0QeIRBiwpTicMkjIN9a4gIkKH4JgZEpAx27ApBW01S8vDRkKGWS+9sESOjUVhcmGgOiCb9jORCQCAeXDysp1MrvVT+7FjFsL6lZoV1sHguXyjEqedryItGEKMv5xh5pdpk04srk42BTNzCvbtmB3OnZ8GUExgbudI4/zSaAR0Z6ohKfVAxKYRgcz0KfBxx6Z/eGGOMWDnJsUwUL8/eH/dMWKfJ0zGI7g7if9WlMY9UtB/TwCZ/auZlohCc2Tdz+7f0t7twgAP9SnVfERE5WImnTbdLkEqqkLMkn/x3/zx4689/uSnP/nZJz99+v6T89Xp+emtP/+Tf/n29SsYb1H5nC/nloQpsE5OTgCZHPnCFTM/m1ezWeUKd70umrrdXO1u3m43l/X2pmm3IXbpZUMxcAyp3sYY/axaFb50SKH6IsZI5Cu/5ATtLqUEjvzWbZiZ0JVuURYzhyUnTCkBXoogAjn7lYTn3r97V7jagwcPlsvl9fW13LbZwnw+F9eq1Wp1fX09m83evn3LkVQ1ZxMORAUlE689QQtdFb29C43d0piPk9vQx6dGkwmSiEo67EU5Xy4sH9VtMysrEUSKolgul+IEHkJ4e3lNpoHut75COam6L4LLarWKMbZtK/KQPihxh2ncmLn0czC5RRExhBBCYDfEVSuUmLkq5wfhJs53uiss4bMYZe85uPESHz5b1UxkE9YulgNlCfoWhxhTl2JESOQBEqfUxRhD3NnJ6uw8BgDoyQ0yS6UBBwV2fOhIhasjJrgwqtOuU/Y4PZLoCWU8wmE8pZQ4odRNIyAHnoh81TtDJFPFEgDAj5iZwt/x3PY6wJ+DBaZFpEkngxvEoXhT55z1QkDN9VYMBdjt28vycO5o6Kakub8gbyGvvOTYGXzTNCpNKoqiqSOexgXtQ+j0dWP8HJ0g6ABsKlYcCxAHx5NC6w7VEQcawgt1gsxMMIRropGYMe9Hu3l57ARnJfLYjUiZtjYM66gEjfdsJ8P4x5kB9YKOMGbCw3Ws6UixEKl3vt8UtlM4B69TVqpradGkf8bDJu4Ydi5H5VAOvYkx9rEiFsUFQMGYzmQ+fUiPc+L35JJLMWkAz4cffvjg0f3rt2/c524+n3/wta/FwD/+8Y8vbp1PsFkWTHP59gQbe0ektmuYGbj32i9cIYznhhqH3lN5ctudnJ609axrYmjD7Ma/fP766nJzfusEoGjqmjCGyLFd1nXddnVKdUopxNY5V5ZlJKyqCsm1bbvb7ThnJkrhRjaS6GFlWd6+fXu1WrV1XVWVMOPNZnNzc7NcLheLBRIJHJqm2e12okM3TYOS6IDAj2xfbJEVzMa2zAaMBtl2w1G0yyWEnXOpFjEZICGkPuQFABOMmLqio3gvwzgLBCJu172pX8PPZOkXyxNZL/V3Vdyygxw6vImWIUEOD43R2ZHo3BNHAIghhNhZ1UrxzbIN6h0RvZ6yK0KrV7alBczctu3kvTrNgxvjGAP2mZ7rNwpGuXYT93hzG+fq7coeLBDkwXpziYiYEB0j5UO7AJFGCV5UYzhSjEesiSOxI49h9Hg/2ZSOpX7MiQVIvAKRkXik+PK4zHvBQ5EAZiYmh46BYWzyHeCZWbhNbWbHDIbgWio/WQXZpCoG6feczzvRnDchYkiHab3NKGehF3mEhDoq6z1r7xdhHcYyEBGJ0yVmJwxLY23TtdvXzHTT7b/Xfj9hM5jrqdjxICKPcxHr5+RoSQFiM/ShsXv5nGgFJnnWDiUwmTDjg99M2sHFQsRfMmu7Pk7js3Z9bzqSesf6pij0mLmgBWcRls1Jv4WDXYJjmX0IC6VRVhfyKVtxBQN0j9lOdVUE4cqy3O12zrkEqQudGLF/8IMfnJ6fPH78+PL67Ww2W61WCO76zZXU5bUamLB8h/2CpQRAyAgcGRFiaAAAIoYuxRaAk6dUOG5W194VzrnCFYTOlegLZMbZ+en5ub+8Xlc0W9+0b15fewfrdd3WZ8RuVs6cR/QxYUcEvqDtNSyqBRHV7bZ35ImcUpr7Uuyf8/n81q1bi8UCEdu2lbjhpmnW6/Vms+m67vT0dD6fh5iKomjbtq5ra15OcSTxKWfS6kxo7DPCs9Ohs1vV8JLxPQaAFHLAHAL5ArOho223FgUVEdu2dTlFgHVJg8SSEUyIhaYYq+vaKiLa1ep0xhy60NkNCQi7pu7H7IgM5bq+3oChOzrlkFpFA6tEFsVsYi13xlkdsmioQd62W6KpOXQChF+47feb1RJ04XQ76J8qfLihTvNoT04IvWpCs/KCmRli7BXlLkFMKaUgD6JBFkLE7kgmLIGrvm4YZxoUX4uHxyysg8ZJHhEhAQfgfMSgK6U+dwUN1clstRd2R84mqWdjE8vBpHC6Nj0jtMTHQl69H5LxaWCjsfXwL2agZwf9aegAq9EIxzZw7Tnlo5b9e2QYStys3KDMCfOm2BcBeRRfflgQpPHZKhiiv89NmdnhICeh4foJOji0O44xePdLnA5YTnEwTF2eUHgenOD+I7wnUDLzMcGxr3CzJ0hNBJTh7Uf6iWbfOTOZFHh/5GjilS2jBAA+otlnmRxilPH0Q/fJ5NMHg/FWshOEbts2xuiqPoljOROLaBtjrOv6o48+evj+w2/9/n91crZqtvVqtSqL2bJa7LNzaTEOuqBzQETgARFWpysA4AShDW0TUmRCdj6d+9vMDImREYkRgQiZ4GbzcnW6cM4X3p+euXv3F1Wx+PLZ8x983HmPRVX4gshBgjKlwMxni0VVVYyRU02ziooEmLquq3B+fn4ueZRCCHVdi+KVUqrrerPZCHmaz+cnJyeYMz2JZszMq1XvHL4LO2BClqpXA/QoV3FRakg5J7Ca+NCYoBmiCi5WpD2d37brAtm6xcV0g/UXoZMOrViHhiOiUebAKB+GowAiRqgjhIQhcUJAzjl8Qz8Rh+j6aHxAZhCvctijFOj6ZN8xQtfFFKPwJZEMdCSKkMk446jR0lJwHOsWNpxssmcObox4xLtYKjdP9h6avCKKzxOLpSqFYPR+OwB5ZF7MGGN2TWp7npEgmfq4hJQAAQgB8UjSZKIRaVOYsMnZa2WIo/VrNcUgOURMnGKKKaW5nyfjTzSQ4yCcuD/q1prnm3bLe9wCAKQQsgWUZVR6G5nGRgVUSGrcuZ2aCBYoQvDYpOSLmZ2mrp0mxplsGSBvWY4OLJiC8/aGrutEIgSTJEclALtV8+c0UYlczGYHTPdofFBgvJXS+Ox52PI8OLup5J1SyglbEHGoiQQAMM5Fr/M9VubPzmt/ISY/QRYQ96fwjmZ7sELML7zZXkwSH+njx86AwaysbU3sYDy7iVVpMjY84hyXhipkgoR9n0NGezYiDxEBJ5WddZ/EGNfrddZCkJGLojg9PT07O1tW8Pnnn9+/c/H0gyfunFLT1XUd2i60I3I/DNQx58zXXRzOP+pIzlNVVWVVnqzm1awsZ6UvXPMSyIEvnCuQHEgdt8SpwyvCtnU753CxWGIiTnz7YvYbFTFzaFNTd03TNrsutTGEUIfQRY/EXWgTtoUryqpaLMuT8q4ga9M0YiVDRO/9dreTVFaiLy4Wi7Isb25u1uu1Wo9TSkVR9GUbPACkyCmGznKg1XxpQc3jZCMKdm3b3VZ/peznhYirRZ+hpuu6rhPeDCFgMRuMtBY5JABculKeSkQpRFF80bC6lNJ84XEs38nSN1kzhhw1IT9hKhg5hRiJ9cSOyIk3cjLHtzIGKkidt8UDrt9jPPjQZZRNMcbZbMGmKRZFk7Qyjf1cDl7QkY1xrE3khslPVscaCFAuWJb6oY4oCBi6zMx6BACYgBIDAzpyJBWC93kYHit7B1N/AmlWQRxRlCMMmDJ/tROcREZZ3jmxEOgAbCYv+ymhWdqtPivxF8pR2FiM2Ag3OnzrFCPfyH5p6xoyJ/ZEqJZ/o/7ai2DCS+xnCK2ar48RdzCS9CRoAnIGhn3WJV+27ZA4wq6yc94C047BkhG7CmAsTPqWFPpiJ9pDtmaNkvboWpfFtC5T/4kjzXtyMVn0yYqMIQZ2Fl+1aW/piA/HZBiT8bzjzkmzJmiL256GVMSwJ4jswweOTDPyoF+BAbhX4isiHmflo2s7JdnymNDN6+0NM89ms5QSIy+Wi1u3bj148IC79fNXzz/66CNfuttnt1LTdW2khCfLha7ZyLmmQuiLkYCyHkRsQ0uMgOAKrJbFbFa6koiwuiWA4wgxSFCVZwC8dXYaQyqc967igD/9yafrTf34vcentxqHngPUW9hchw3XN+22bbdtXDVtH7RAHgiqgnA2n282GykwLJuciLbb7du3bxezmXNutVpJtk5hWsJ91aosk9psNnVdo5tqRXLPxJSqpC2Z3NHyiGxUyl7ZChm5+er6Tb+iMarbERFRnNIL+XSFV14lMoTg0Px0Jup7VsJ64qiSPhhymVKaLWZMXNBQmkl+KtxcpyneH7L5692GjT1wYMBIyETgiKhwhcuKkfeV3qlkImUf77iXBtzlULTJxrYk0m6MYww4piOmv1zY3PamTfePpX0H2z7l6nVoJuzVWgIWcxQDRgIHCEmraAEAAgNT8QsY8LG3KxblgR4eqoSvxBiTcMrYA7aua4t+oBlY04hepwiICBGoLCzPGEa5ZxSVX5XwTbZMP6oxbHF89G77mbBSnW+veOxFne2f6cpnG7opxLJgMQGsXOhhk1VXVEmyk5VWFH7SSe5qxNqVWCdjAbLAwSNNfEH2x2Nzd2dQyLxGGccGkuWm8Zl2wPZ7+dJlpzwLNEQUxjMB2juapZlgtvOxBB2MQ05m+4gKalNUPJRXAACa3TRVcI9X1WgYCqJ0xMSNRyprMxMiAUgFTBRKAJAZsPWXEwEqhg5M+HxRFFVVFUXBDsU8G1LrnFssFuIBG5tmt9u9ffv2+fPnHt3ZYjU/XWIEh6M67ZjdUhL1uZqrqprP55KP2nu/mC1j6rWixKlt283rbV3X7z15rAQaxCnMFUTUuC2hm81WyD608dOfv3j22c8vzu49erKczxZ9tGtI4kK12Ww+fzF79uzZmzdvCledLE+LouCIu3Xz4uUrGQYA1HUtVfNkt0u5JOfcdrtNKckROKLXSoXCtzabTdu2xUxyirIko8m4yLvdUH4LzBGjOpVMclbHMGT7YuYQQu+kzVu7q3uShG67PZx5p5xVMddskFnIK6qqKstyNptJ7Q7dqG03ZIu0bTab6T3O1JsSJwXOWqYqwY4OKFLMDCyJMMm7nlXLu7rUdl0nvmzWdKZnhMLddXZozjV14ojYHYobfkc7doMl7nZPWpHUPstZ5QVA4Z69BmwMhmBMWB4rSUEAxIkDMyUIwE5tU3qcJCNxRwSIkEaEQ4etkFFMkG/oCAOOKULP6xERs6BFXdcJ2uhS9s4N0Mq5r9IjSdIZtx3vNQAIsbV8RdnbZrPBsZInsJIwNh28ztEqEBa1ijF8BvbpSsjcC41Bgo74DSij1TstR5lMirO4jMYkYNkPmC0vTRiwxSvuBZGCjWFZtdjNZgNmEfU67iWUsG8nk59LNpQYPvbfOzFx63WKB+iAQnIfbnbWSqAQUY9IJk8da3YMaM6D8VgcPI4sQNq5ru90C6fD/Qi/Q0SiSRm3aFdQadpXtaiV+SiEE3OvJ5NYa0gVFGU5lMseqDlFsMF7f+f0zrNnz4gIEghLfvHixXa7xbjd1BtM8fnz56fLk7vntx2566urwpFyTck1LdE7i/NK8UxDcp1zpSsACkd+ViwKN0sRtpt6t6kLj0VRVGU1q+aOCkgZw8p1CFz4smvTanHx9lX9n/+v73zvo+/9T9/+d2XlfUkMjI59SYx3QwifPLv7D3/397t6M6sWD+49cK548+ry8u31kydPmqYRcr9YLFarlYTZqEO/qLwCrq7rAHoGLKUMU47uVVu/tWIhYhdYDQkWOaR8od6Wsp/RenMtjFnJn4RBr06EAKUYY4i9qZyIUrfYx0JEDKkP3KyqSpc1pfTq1auiKJxz4gUdYyzLUvy9wVAfUus3EHPqLYly3icf7CC79iAjJGLAlHrNRnV0vU4hH855L/hDRADovY8mp4oouCEEOZKU6Yw08ly0Q3m/PHswBSm8K3fu4e8Re+9QS4aUIqiJXvcO7FEWXVYw+p8KKDfXW0QkByS2UsdirWMOkxXM7WhVnMlMIVtcJmbMfr8cq/MqTk8pIRARIaEjcs7FLlpqoGI6F9zzavHDjj1hWi5OBpZsBpa4PAaiMb0b4AmGU1qep+sim0KsOJDtRirO9okBpB/zKS+O47NM7XlSoFoX0ZrN7dTU616pnNqBIO9oq0yL78ihGQ0s0+KJ9f+3nEDXdDKe0g/mimjSBAlE7bz6Z8NUAJXxtGFn/9QVmSj3g5N/mmrAk6FOFv0gEk7WVy/ezbNhD6/geEpaOsLIJYGSXRpFY50ym0xNWrzHjvAdQ/VF2fPTjCcZRgs6OT2pqqpt26apEbEsK+dcgiDYoxWCZVefzFbg4vndk013tdlsKBXtOi6rs+p8M58tvv2tb92/e6+gYjmbp8ir+ayBbVmWq+Xq9OTsbHU+q+aEBIybdqWKr9RNlLigrz/+Zq9rcgypi6mPNC2o7WJKKTXyH7PYyuZQilYqNOLVq1d//dd/ffv27f/x3/93F3fuARIzXa93p+fnKWFkfr25+fLLV8++eHH1crO94dAwQVWVs3/+9AUgQ2Ik9h7KoqhK55yL3t1cXSPE7e669PjovXvO8Xa9efWqJKLFYpFSms/nzrnLy0vv/fp6Z2nKQAExyKaCsTnu+nJjuTJruYgiWGMva24N9LZ+MGThYLU8pZzQSs+wiSimoT5oMlY+ZtbUIspdEBFp6nzUMza3EJouFEGifoVrSmwJAGiUGyLebNZkmm5I5+o+8JfZ5YwliLhrBxuXJSjz8kQJ3Mjk7vrjDGUw0u0k6bleYOrLVtpFQUSBZDLkuGfzqbb36yOxq2VgZAIHQgjoRpm5FAf2M4j1zCa0+daExEjZPAvNQQINdESVpwCGPg6E0qguFp5Srg4M8+vX1y8sZIbusyBlYycAoJqVAACSOzdk1yJGKmZoGOpAy5KPOYmHLrpdwcn99kyOjYxlhw2GYet5kIU2AJSzwWUmGY+EmT/hQ83P+jrTFkSC22KhgezII/imAt+kWScgu5TiJKWul/prSENKXW3MXBVLlxsaq8/1eisnSs5UyyAigtGRv96fTNZi9etMKS3nMzCsXS/seOTLvGSHz7BLfzLBc/mzbqdhTjowfaN9r4RETvAhpVRkDVK/7+Fcv8uCtd/sfgSzyyY+p3qhufcn+0gnOHkkmbLu9hU4IUf5T7+cL1OXbnY3KSVRMZm52TZuNgBRu0BEnHW3b99OkS+fbxHp1u1VVXVNe+MoFnNXxyZhcGXJBZydn5yfni9PTquqqspZ4QpCqor5YrGYzxa+vKPKZT+9wA67H//oR5vNhjF9+OGHq5Pl1fXl5mZNHusQVARNgs3omLnhtm3bq/W1uChHSNVyPlstvvOdjx4/flxUCwDXhgBUlOU8AT+4uHdxeufDR1+7elu/eXPz6ss3n3/+5evXn3oHKSVGduTKslou5/P5vPDVpu7Sgp1zVVk6Ak+Lut7c3HQApVoLtEYmM9++uKWKWm94j0LQO10Yi7gs2cwInKfMgz0zCyHThe8VOEZyJIqjZRuIGE0ZX90AACBhYLBH0DWSB40oDQDzalpNRTetEoWkLvHOJWN5A5Nx0J7B2BrDhHXM0VDOpAxr0+jgSmllV19PSJXclrhRhFZETyntNkPVJm0AULpKYb7Pbu038l5fDOFDsJfSebK9dQy6XyytsQKWikelckhgTkl8HlNK+4Xr5fOYCfrdTUal0AMAdKPc3YoqWuVp0oNaROyM+t8YAQBjPmBlBByUAMWrPmwMUJFHoc3Z2Wp/2NfX15N1FAbQjfMT6G6S+AWLovL2kA6kQnTO7b8UxrSOjVoDACKogUmpIdKn4Lm+V7udlBfUFtpesIMsbcuz5Adrk4p9ALBdt4qWCj1mVpOh3b/MzDBy9oGMnOr8pRN0JvpUm05B4az95AF0OBbUMIu/2oP99OXIC12bvneyrWzYnnYugizu8VRmlgxxOGZvcAiT7VtGaHz8rIrHli1dNfuu/c99INhXT669dwUzoydEFJEqxhgp8TgMQzWqF5fPfeFnbvno4v0uddebN+U5PHx6e/PmclNvfvijH87n83/1J7/z8P4D5zyHeLp8VJblcr6czxdFUZRFJWe319cRkwMA4r7OwaxYLKrVf/jf/mdfuouLi4s7t5cnc+ccY3LO+XKukErm7Gd99byqqso7Zo6p2WyvADrE8Obt5cWd+3NXNE0dI6Nz5axqQ4zrbj6fnZwvzpfhwb079dNHL18+ePXy9U8++XzXtJt13QVGkgxNnFIbWoidc+CJ596RoxMHWHpumr50qzBgZUjRpBdQlRQAQtYRZHPpXLyr0FicdHm6oGUTyZEvsrPidndjc6lwVmST8am2VruD0Ycw5otglAPAITOOQpuZE5ClrTpgObVVjWeYu3dKvyzhIHIM4PJ5qsvHwAV6lfQhq5Uxxt22UYIycBFEAm/VrJStfwDOEu6BguRdkIw7CcDIOUt3OwAUxjtUaToAOEME7FZURYSNOzEiiqnKguLQcjBAn2+O8UCO3F+h2bGN6MKYiCglDXF6UNfjyfhUT/vp4xoTAOZSOghSRdg+ngbDdQ9JRLQe7MpXhpU9pAApthMNeGjtGWmvHlE/gFzSGI0khIixmcJWEV7/1AEIrioQ1KNFR6WPqKCgZ7eW3Ns/7RJzNmXv8+Dz8/MJ0ZP3+nKmmyLlNPXMLFn/7XplmjDEVdvBCD7vMwypCm/3o7TNptaxOTfscfFlRE1MNmTUmprQFYYwRu8J7bLrAgCIB/YCYu9lM3kFvDNHNIw3hX12Mhgcd27bBE9sJ2yaXQVddEsffNd1y+VyPp9TTpJMRMvlct1e6SDAMOA7Z3eBYdusT09PObQcu7OL24uTWf167gB3Nx03tChP7pw9fP/Ro6qqCnevazvNAEzo6rpumpqwFJbECRIwEwAjAlWV/+Y3f/f+g7u3bp1yilXlkVAGZmafIDHGBMzzRVFVxbC0b0JZUYh1MT+dL6qyLHf1TSu1V2PXNJ0PWHcBABjZl9Xt2+Wti/c++Pq9b//ZN1+8eP2zn3/xxRevLi+3u7rZ1U2MkLqqaxsOPsaAULW7tF2nZteDT3JKQ64wmFLabG/A0BEi8o4AoHBzK0FjDg8oXG/yShzElVQAHrqe7giT6lMl5ZzMludlabcnZPrerIgfjjtcLpcyAEUamcJ2u6Z8MG9RSs+iKGenkqGmlA6a1lNGNd2l0mLASDG5oeyE/LrZtRazlRRKgWpx3raU2nmnYLRa2unparJDpHXtQCjtTxNbAmQNWNj9ZOdMYDhhwNZep/dIIhe1umN2p+CuEegREoP4ZQjDm3KF/bn88m1CmwCgiyP5I/WJnRmOxBk7k8mLjY20KAhFA5a0uITAhIQxMANGZhabOROhR8qGHpNIHHsNYBR+A5nqSZyFbhkdgM1AhOPTgcnqyMWuGcyGyRiWxW9hInBM6KylpErQ2UiTbKRY5UlWs9yHf8hn1bp55SfvKjAUf8AlP0rpqhxUvXMmv/pDFqzJdNA0yAIKj23CYZxqURmwlgyY+LJIuI4ViaQ13Sihil4cdJYUOsOHBDLnBkueBWmKI6PLPhpM2njuA0xobGGa9LP/aRm8fZddEfs6O2xdBWb2WEDHLUV04CLGiAEAgJzkrFGCoo81V+xPccc3u83l/ffv/Nkf/cVitvybv/mbz3/w+sl7T+azk+efXP7H/+VvL2599Jf/+i//9E/+rDxdlRVEH9vQhDYwR+9m1ekidFGUBuUBKaWu637rt77xJ3/yR6uz05ury832hpG9902ziewAgACclAlnRgAGPjmtdrtd0zTirBTbm7NVAdCVJRBFxNa72LnkXQKIzI2jRdfuErIvfeRws40AkBAhhbOz8lvf+s0/+INvXl3vPv30xcc//uTZsxdIELBh7jgxMDXdbrO7aZvgKs/MckAu+bB6/qdOlBATs/oYOuiFdGJiMAy4KFKMIfWZ+VRIr2Y94QZIIbYc+j0wX5b2iBdzLknvSl0pixCTuEm7qSz1wRx8BVjpBkOjBEvezTQOoNKdibkWhU4h8iiXr1KcLnCMnBIjYmJO3Pv69uOHvmwLAjgiR31iDR1GzPm5YhpS/UkyhJ69uaksz9nkfmx3HWxKAqSpRgWjCso25GN0sKqqiSWOYCL6YkCQYCqUKkgj4nhsVF+pWaqk18QDwRr9arKz6c1sRJBJz4kRQp4sekAUP1XC/ogoxR5FC19gDsObsAQ0h4WWyCh4Fc+1FogigKKE0jL9xjJIyWms5FI5REnLg+CCsdudNg2LmJhtY07SKQNToXbC+fSzKhyMGY+0aEy4Fkoch8QmlnkIY5s8klKCNJ+uLABmq4NlbNKqYpSpTR9JMMqbqHCezYYzfjQWe5dzj4tArEsQeUSR9OIdZ+d8iAETDV7iupQWAvbiHTvICgcWdO5Qgg7eY+2/cHsKU1Pg6Lt4T9SQufhi6Zpmt92sxRUZHTJzYKjKSj190PD8Gc+9L04Wq1BuT+7M7713a7dtb7ZX9SV/2j1bFItXn19iZEr0vX/4+P/43f/zv/3Lf/v4yfsffPDB6epkW+/Wm5sUIkHVdo1nj4jiSwkIkpJ+uVo4T6Fttrt10zUxBZGJFvOaU0yh7Zq2C11oO04REn/xbCNK/GKxeP78+fe+973dzeuTkxPGTd28Zpj7wi0Lcr4tfLlaULetO+wAsfAuQWzaJqaAriBInNoYtwxYlOWTx7fu3zttQ/fZz15fXq6vr6/fvL1ibmIMMa7JE2IRc/obYcCidBbFYIq0Cxw5h/owxTToE9vtKIBBF565ZmZ1vUEAR4CuNwaIuVvUUPHWPlmVaDSM/qmMLZYESBNvzImGAQCywWAs9qaUMG3RZB5Qw5QbV4HUB9ucgWuioBSlQyKUreUc5kPf+WxKECHbya2grW+RYZDJJiavaJtpxiISiX7sXazXSixgLL0WBm6TNmEV+5uZzBHAdrtVFqL9xxjJ5r1BYhg8YOEQweJ3xvseG+SEWHDWMMDICtK6lL3bxnG6dTuED9kb2kbGj4gIiAAOHQGT58FdDhAQicA5comG+l2WvNphTFaZTZAPZ4O2hbOd2rFc344Go59O5B1wU9NRMme6mIt/KEpDlg8s/ttm5zLafWmaD0D2V9Nt9xcdAEp/QLMEAF8OBnMdDzNz7MDIeXJOTERYN/vDQ0TNzGURBhFtlAFnyz8AEI3Ims4r8SjvkMKNjnjda5Uz/Uaubd10O7B9AUIwx9Pga2LBfmyV9+0l8qCFOe9Ry/1m0dXePEx872zrYD8+Ubc4rSRGReJwyrJcLpdpi5qlyJkoQJiF9ab2AMh89frmH7/zndNby//+3//Ff9p9/POf/PTV+nI1r89PToDcd7/30Q9++P1//Pvvf/0bH/7Xf/xHf/CHf/D4yfvL1YI8shioKCXmLke+N01T1/ViPp9VFWNEZE8EDDG2IXT19ou2bZvtpt5s6s2mrZsUOo7p5VXbtu3Tp0/v3bv34oufv3z2ycXZ8unT9xPVhe+IYDFfRQZO121TJyBfrooCu8h1rGPiwIxYABCSTxwZY0oRY/QFFSUi+vce/Pab11cvXr1+9sWLtoubTRPidUqpbjDG6L2fz+fiHikHwN1Y0le0CF2fYSeZM0hE7MIWxSHCESIkDjFwSqmNDZkjq97vg6jbUVmWlhCImxWn4fhkYgCxiKXjEacVGNuNiYihtaRBTesOOg2RtMFRlliDiRRCNyUZ0qpypm+0vt9d29k7lQha5ormhEnqxqE5i5Kp1WMLlXaohNJ+cibrlvnlP2lys3weOwNWoOFYG4umoL182cOT1TkIAKcbeLLJf7W2T1MA+hzTvKcEu/F6WZJhqbDh0NznD2FCdESE4JCQyKVc+NlCxg6J81kJG8vkZOIiryjjYWaJgxfNaR+xJYJft4yOc9fc2DUaphMPw5myq4Hq39I017TijATQ60vtgSgzyxnwBPgAwDFYCOtPGvdMpiEipOFIWEHHzMvZQo0BuolijM1uoxPZFztwLOUoHPZXQRQGMCEJ0B8BoBVQhrGlwWlLlwAA0pGjjf0dav88hLo9tWHD4YhIqn7BmOROYD55rzZ720HKYNvkzgnE9DPtHULprrcTV/bst/Xm6f2nT58+TSn97Gc/29ab2eLkvcePnv34pa4ZmWQdLW0BwHVVCLCm5vs3P7j1/vzf/Q9/9eC9u1+++gJ9w5i2oT5bFuBwNit+9MN//vhHP/zOd/7+z/7Vn/6bf/tv/vDbf3BydtI0LTpAghQTY0qckJA8ksebmxugROiapqmbLRGFFG5ubrz/InZts9k2221b16nrUHZ4cs+/+BRiOytoXroPnrz3jW984+nTp1e7V0VVJsb5fLVt2mrmQtcRupvNpqoqcC50ISaqqjkidjEQeue9IyocA6aUIkJi5jeXL8mnhw9PL+6eEpRX1zfLVbHdth9/vGVm7/1sNlOf3q7rMIPbqo/MfCwMydNg8mVmCfYNISTXOucYvWg+GHq/p2ZHmk8j5gwbRVF07ZAQAPKZPSIKYeK9loxbrx5PQg5rjiaqUka7mp/IcZ24I4qPRjaSA2TRMsYohpPVamV3o76rbhrItr7+nyCiKywr7U92OXZhyMTERlsqyyqNg9d7xkZDWTrNJwUAvnR8qB000ev2sfuKe2G0v4HHDGOy+RXIIrVYgiWwitnpBhEAk5qgAQ4QDkuPfsl2iIQBmKCDPE0UTHBU4SEGbIsc5NNxQsTEhAjIhIQJABIRITA53y+3ijU9/sPAa63EM4m7VbahKyueoSklzcml6t2EhsKYwUuHemYs32g5pmV12ASqpDOZUwNE3Gw2KSczEAlYuhXJO+Xzl5S93MHwGIv/293Gjl8/JZxMf1I4iBe0DknHvzqFYGqegnrvZ+/6ZIxbmKtCKV9UFJ2VgyZqEXhW9T4f2psApGm2do46Wk+VIpsiDAAU1XyyRpyPhCyvVZQTC5wyHWX/zINlQgFFRBCdfSkbae8d62t3uu6LCQR09WGP+06+t0/FHHtteTxnsolGwJI//184Hecetwy8vgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display image with different hue.\n", + "PIL.Image.fromarray(random_hue(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Distort an image by cropping it with a different aspect ratio.\n", + "def distorted_random_crop(image,\n", + " min_object_covered=0.1,\n", + " aspect_ratio_range=(3./4., 4./3.),\n", + " area_range=(0.06, 1.0),\n", + " max_attempts=100,\n", + " scope=None):\n", + "\n", + " cropbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])\n", + " sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(\n", + " tf.shape(image),\n", + " bounding_boxes=cropbox,\n", + " min_object_covered=min_object_covered,\n", + " aspect_ratio_range=aspect_ratio_range,\n", + " area_range=area_range,\n", + " max_attempts=max_attempts,\n", + " use_image_if_no_bounding_boxes=True)\n", + " bbox_begin, bbox_size, distort_bbox = sample_distorted_bounding_box\n", + "\n", + " # Crop the image to the specified bounding box.\n", + " cropped_image = tf.slice(image, bbox_begin, bbox_size)\n", + " return cropped_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display cropped image.\n", + "PIL.Image.fromarray(distorted_random_crop(img_array).eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Apply all transformations to an image.\n", + "# That is a common image augmentation technique for image datasets, such as ImageNet.\n", + "def transform_image(image):\n", + " image = distorted_random_crop(image)\n", + " image = random_flip_left_right(image)\n", + " image = random_contrast(image)\n", + " image = random_brightness(image)\n", + " image = random_hue(image)\n", + " image = random_saturation(image)\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display fully pre-processed image.\n", + "transformed_img = transform_image(img_array)\n", + "PIL.Image.fromarray(transformed_img.eval(session=session))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Resize transformed image to a 256x256px square image, ready for training.\n", + "def resize_image(image):\n", + " image = tf.image.resize(image, size=(256, 256), preserve_aspect_ratio=False)\n", + " image = tf.cast(image, tf.uint8)\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Display resized image.\n", + "PIL.Image.fromarray(resize_image(transformed_img).eval(session=session))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf1", + "language": "python", + "name": "tf1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15+" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb b/tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb new file mode 100644 index 00000000..a6fdeec5 --- /dev/null +++ b/tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and parse data with TensorFlow\n", + "\n", + "A TensorFlow example to build input pipelines for loading data efficiently.\n", + "\n", + "\n", + "- Numpy Arrays\n", + "- Images\n", + "- CSV file\n", + "- Custom data from a Generator\n", + "\n", + "For more information about creating and loading TensorFlow's `TFRecords` data format, see: [tfrecords.ipynb](tfrecords.ipynb)\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import numpy as np\n", + "import random\n", + "import requests\n", + "import string\n", + "import tarfile\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Numpy Arrays\n", + "\n", + "Build a data pipeline over numpy arrays." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a toy dataset (even and odd numbers, with respective labels of 0 and 1).\n", + "evens = np.arange(0, 100, step=2, dtype=np.int32)\n", + "evens_label = np.zeros(50, dtype=np.int32)\n", + "odds = np.arange(1, 100, step=2, dtype=np.int32)\n", + "odds_label = np.ones(50, dtype=np.int32)\n", + "# Concatenate arrays\n", + "features = np.concatenate([evens, odds])\n", + "labels = np.concatenate([evens_label, odds_label])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " # Create TF session.\n", + " sess = tf.Session()\n", + " \n", + " # Slice the numpy arrays (each row becoming a record).\n", + " data = tf.data.Dataset.from_tensor_slices((features, labels))\n", + " # Refill data indefinitely. \n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=100)\n", + " # Batch data (aggregate records together).\n", + " data = data.batch(batch_size=4)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + " \n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[82 58 80 23] [0 0 0 1]\n", + "[16 91 74 96] [0 1 0 0]\n", + "[ 4 17 32 34] [0 1 0 0]\n", + "[16 8 77 21] [0 0 1 1]\n", + "[20 99 48 18] [0 1 0 0]\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(5):\n", + " x, y = sess.run(d)\n", + " print(x, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load CSV files\n", + "\n", + "Build a data pipeline from features stored in a CSV file. For this example, Titanic dataset will be used as a toy dataset stored in CSV format." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Titanic Dataset\n", + "\n", + "\n", + "\n", + "survived|pclass|name|sex|age|sibsp|parch|ticket|fare\n", + "--------|------|----|---|---|-----|-----|------|----\n", + "1|1|\"Allen, Miss. Elisabeth Walton\"|female|29|0|0|24160|211.3375\n", + "1|1|\"Allison, Master. Hudson Trevor\"|male|0.9167|1|2|113781|151.5500\n", + "0|1|\"Allison, Miss. Helen Loraine\"|female|2|1|2|113781|151.5500\n", + "0|1|\"Allison, Mr. Hudson Joshua Creighton\"|male|30|1|2|113781|151.5500\n", + "...|...|...|...|...|...|...|...|..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Titanic dataset (in csv format).\n", + "d = requests.get(\"https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/titanic_dataset.csv\")\n", + "with open(\"titanic_dataset.csv\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Titanic dataset.\n", + "# Original features: survived,pclass,name,sex,age,sibsp,parch,ticket,fare\n", + "# Select specific columns: survived,pclass,name,sex,age,fare\n", + "column_to_use = [0, 1, 2, 3, 4, 8]\n", + "record_defaults = [tf.int32, tf.int32, tf.string, tf.string, tf.float32, tf.float32]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " # Create TF session.\n", + " sess = tf.Session()\n", + " \n", + " # Load the whole dataset file, and slice each line.\n", + " data = tf.data.experimental.CsvDataset(\"titanic_dataset.csv\", record_defaults, header=True, select_cols=column_to_use)\n", + " # Refill data indefinitely. \n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=1000)\n", + " # Batch data (aggregate records together).\n", + " data = data.batch(batch_size=2)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + " \n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 0]\n", + "[3 1]\n", + "['Lam, Mr. Ali' 'Widener, Mr. Harry Elkins']\n", + "['male' 'male']\n", + "[ 0. 27.]\n", + "[ 56.4958 211.5 ]\n", + "\n", + "[0 1]\n", + "[1 1]\n", + "['Baumann, Mr. John D' 'Daly, Mr. Peter Denis ']\n", + "['male' 'male']\n", + "[ 0. 51.]\n", + "[25.925 26.55 ]\n", + "\n", + "[0 1]\n", + "[3 1]\n", + "['Assam, Mr. Ali' 'Newell, Miss. Madeleine']\n", + "['male' 'female']\n", + "[23. 31.]\n", + "[ 7.05 113.275]\n", + "\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(3):\n", + " survived, pclass, name, sex, age, fare = sess.run(d)\n", + " print(survived)\n", + " print(pclass)\n", + " print(name)\n", + " print(sex)\n", + " print(age)\n", + " print(fare)\n", + " print(\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Images\n", + "\n", + "Build a data pipeline by loading images from disk. For this example, Oxford Flowers dataset will be used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Oxford 17 flowers dataset.\n", + "d = requests.get(\"http://www.robots.ox.ac.uk/~vgg/data/flowers/17/17flowers.tgz\")\n", + "with open(\"17flowers.tgz\", \"wb\") as f:\n", + " f.write(d.content)\n", + "# Extract archive.\n", + "with tarfile.open(\"17flowers.tgz\") as t:\n", + " t.extractall()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a file to list all images path and their corresponding label.\n", + "with open('jpg/dataset.csv', 'w') as f:\n", + " c = 0\n", + " for i in range(1360):\n", + " f.write(\"jpg/image_%04i.jpg,%i\\n\" % (i+1, c))\n", + " if (i+1) % 80 == 0:\n", + " c += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " \n", + " # Load Images.\n", + " with open(\"jpg/dataset.csv\") as f:\n", + " dataset_file = f.read().splitlines()\n", + " \n", + " # Create TF session.\n", + " sess = tf.Session()\n", + "\n", + " # Load the whole dataset file, and slice each line.\n", + " data = tf.data.Dataset.from_tensor_slices(dataset_file)\n", + " # Refill data indefinitely.\n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=1000)\n", + "\n", + " # Load and pre-process images.\n", + " def load_image(path):\n", + " # Read image from path.\n", + " image = tf.io.read_file(path)\n", + " # Decode the jpeg image to array [0, 255].\n", + " image = tf.image.decode_jpeg(image)\n", + " # Resize images to a common size of 256x256.\n", + " image = tf.image.resize(image, [256, 256])\n", + " # Rescale values to [-1, 1].\n", + " image = 1. - image / 127.5\n", + " return image\n", + " # Decode each line from the dataset file.\n", + " def parse_records(line):\n", + " # File is in csv format: \"image_path,label_id\".\n", + " # TensorFlow requires a default value, but it will never be used.\n", + " image_path, image_label = tf.io.decode_csv(line, [\"\", 0])\n", + " # Apply the function to load images.\n", + " image = load_image(image_path)\n", + " return image, image_label\n", + " # Use 'map' to apply the above functions in parallel.\n", + " data = data.map(parse_records, num_parallel_calls=4)\n", + "\n", + " # Batch data (aggregate images-array together).\n", + " data = data.batch(batch_size=2)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + " \n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[[ 0.1294117 0.05098033 0.46666664]\n", + " [ 0.1368872 0.05098033 0.48909312]\n", + " [ 0.0931372 0.0068627 0.46029407]\n", + " ...\n", + " [ 0.23480386 0.0522058 0.6102941 ]\n", + " [ 0.12696075 -0.05416667 0.38063723]\n", + " [-0.10024512 -0.28848052 0.10367644]]\n", + "\n", + " [[ 0.04120708 -0.06118262 0.36256123]\n", + " [ 0.08009624 -0.02229345 0.41640145]\n", + " [ 0.06797445 -0.04132879 0.41923058]\n", + " ...\n", + " [ 0.2495715 0.06697345 0.6251221 ]\n", + " [ 0.12058818 -0.06094813 0.37577546]\n", + " [-0.05184889 -0.24009418 0.16777915]]\n", + "\n", + " [[-0.09234071 -0.22738981 0.20484066]\n", + " [-0.03100491 -0.17312062 0.2811274 ]\n", + " [ 0.01051998 -0.13237214 0.3376838 ]\n", + " ...\n", + " [ 0.27787983 0.07494056 0.64203525]\n", + " [ 0.11533964 -0.09005249 0.3869906 ]\n", + " [-0.02704227 -0.23958337 0.19454747]]\n", + "\n", + " ...\n", + "\n", + " [[ 0.07913595 -0.13069856 0.29874384]\n", + " [ 0.10140878 -0.09445572 0.35912937]\n", + " [ 0.08869672 -0.08415675 0.41446364]\n", + " ...\n", + " [ 0.25821072 0.22463232 0.69197303]\n", + " [ 0.31636214 0.25750512 0.79362744]\n", + " [ 0.09552741 0.01709598 0.57395875]]\n", + "\n", + " [[ 0.09019601 -0.12156868 0.3098039 ]\n", + " [ 0.17446858 -0.02271283 0.43218917]\n", + " [ 0.06583172 -0.10818791 0.39230233]\n", + " ...\n", + " [ 0.27021956 0.23664117 0.70269513]\n", + " [ 0.19560927 0.1385014 0.6740407 ]\n", + " [ 0.04364848 -0.03478289 0.5220798 ]]\n", + "\n", + " [[ 0.02830875 -0.18345594 0.24791664]\n", + " [ 0.12937105 -0.06781042 0.38709164]\n", + " [ 0.01120263 -0.162817 0.33767325]\n", + " ...\n", + " [ 0.25989532 0.22631687 0.69237083]\n", + " [ 0.1200884 0.06298059 0.5985198 ]\n", + " [ 0.05961001 -0.01882136 0.53804135]]]\n", + "\n", + "\n", + " [[[ 0.3333333 0.25490195 0.05882347]\n", + " [ 0.3333333 0.25490195 0.05882347]\n", + " [ 0.3340686 0.24705875 0.03039211]\n", + " ...\n", + " [-0.5215688 -0.4599266 -0.14632356]\n", + " [-0.5100491 -0.47083342 -0.03725493]\n", + " [-0.43419123 -0.39497554 0.05992639]]\n", + "\n", + " [[ 0.34117645 0.26274508 0.0666666 ]\n", + " [ 0.35646445 0.2630821 0.0744791 ]\n", + " [ 0.3632046 0.2548713 0.04384762]\n", + " ...\n", + " [-0.9210479 -0.84267783 -0.4540485 ]\n", + " [-0.9017464 -0.8390626 -0.3507018 ]\n", + " [-0.83339334 -0.7632048 -0.2534927 ]]\n", + "\n", + " [[ 0.3646446 0.2706495 0.06678915]\n", + " [ 0.37248772 0.27837008 0.07445425]\n", + " [ 0.38033658 0.27053267 0.05950326]\n", + " ...\n", + " [-0.94302344 -0.84222686 -0.30278325]\n", + " [-0.91017747 -0.8090074 -0.18615782]\n", + " [-0.83437514 -0.7402575 -0.08192408]]\n", + "\n", + " ...\n", + "\n", + " [[ 0.64705884 0.654902 0.67058825]\n", + " [ 0.6318321 0.63967526 0.65536153]\n", + " [ 0.63128924 0.6391324 0.65481865]\n", + " ...\n", + " [ 0.6313726 0.57647055 0.51372546]\n", + " [ 0.6078431 0.53725487 0.4823529 ]\n", + " [ 0.6078431 0.53725487 0.4823529 ]]\n", + "\n", + " [[ 0.654902 0.654902 0.6704657 ]\n", + " [ 0.654902 0.654902 0.6704657 ]\n", + " [ 0.64778835 0.64778835 0.6492474 ]\n", + " ...\n", + " [ 0.6392157 0.5843137 0.5215686 ]\n", + " [ 0.6393325 0.56874424 0.5138422 ]\n", + " [ 0.63106614 0.5604779 0.50557595]]\n", + "\n", + " [[ 0.654902 0.64705884 0.6313726 ]\n", + " [ 0.6548728 0.64702964 0.63134336]\n", + " [ 0.64705884 0.63210785 0.6377451 ]\n", + " ...\n", + " [ 0.63244915 0.5775472 0.5148021 ]\n", + " [ 0.6698529 0.5992647 0.5443627 ]\n", + " [ 0.6545358 0.5839475 0.5290455 ]]]] [5 9]\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(1):\n", + " batch_x, batch_y = sess.run(d)\n", + " print(batch_x, batch_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data from a Generator\n", + "\n", + "Build a data pipeline from a custom generator. For this example, a toy generator yielding random string, vector and it is used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a dummy generator.\n", + "def generate_features():\n", + " # Function to generate a random string.\n", + " def random_string(length):\n", + " return ''.join(random.choice(string.ascii_letters) for m in xrange(length))\n", + " # Return a random string, a random vector, and a random int.\n", + " yield random_string(4), np.random.uniform(size=4), random.randint(0, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + "\n", + " # Create TF session.\n", + " sess = tf.Session()\n", + "\n", + " # Create TF dataset from the generator.\n", + " data = tf.data.Dataset.from_generator(generate_features, output_types=(tf.string, tf.float32, tf.int32))\n", + " # Refill data indefinitely.\n", + " data = data.repeat()\n", + " # Shuffle data.\n", + " data = data.shuffle(buffer_size=100)\n", + " # Batch data (aggregate records together).\n", + " data = data.batch(batch_size=4)\n", + " # Prefetch batch (pre-load batch for faster consumption).\n", + " data = data.prefetch(buffer_size=1)\n", + "\n", + " # Create an iterator over the dataset.\n", + " iterator = data.make_initializable_iterator()\n", + " # Initialize the iterator.\n", + " sess.run(iterator.initializer)\n", + "\n", + " # Get next data batch.\n", + " d = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['AvCS' 'kAaI' 'QwGX' 'IWOI'] [[0.6096093 0.32192084 0.26622605 0.70250475]\n", + " [0.72534287 0.7637426 0.19977213 0.74121326]\n", + " [0.6930984 0.09409562 0.4063325 0.5002103 ]\n", + " [0.05160935 0.59411395 0.276416 0.98264974]] [1 3 5 6]\n", + "['EXjS' 'brvx' 'kwNz' 'eFOb'] [[0.34355283 0.26881003 0.70575935 0.7503411 ]\n", + " [0.9584373 0.27466875 0.27802315 0.9563204 ]\n", + " [0.19129485 0.07014314 0.0932724 0.20726128]\n", + " [0.28744072 0.81736153 0.37507302 0.8984588 ]] [1 9 7 0]\n", + "['vpSa' 'UuqW' 'xaTO' 'milw'] [[0.2942028 0.8228986 0.5793326 0.16651365]\n", + " [0.28259405 0.599063 0.2922477 0.95071274]\n", + " [0.23645316 0.00258607 0.06772221 0.7291911 ]\n", + " [0.12861755 0.31435087 0.576638 0.7333119 ]] [3 5 8 4]\n", + "['UBBb' 'MUXs' 'nLJB' 'OBGl'] [[0.2677402 0.17931737 0.02607645 0.85898155]\n", + " [0.58647937 0.727203 0.13329858 0.8898983 ]\n", + " [0.13872191 0.47390288 0.7061665 0.08478573]\n", + " [0.3786016 0.22002582 0.91989636 0.45837343]] [ 5 8 0 10]\n", + "['kiiz' 'bQYG' 'WpUU' 'AuIY'] [[0.74781317 0.13744462 0.9236441 0.63558507]\n", + " [0.23649399 0.35303807 0.0951511 0.03541444]\n", + " [0.33599988 0.6906629 0.97166294 0.55850506]\n", + " [0.90997607 0.5545979 0.43635726 0.9127501 ]] [8 1 4 4]\n" + ] + } + ], + "source": [ + "# Display data.\n", + "for i in range(5):\n", + " batch_str, batch_vector, batch_int = sess.run(d)\n", + " print(batch_str, batch_vector, batch_int)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf1", + "language": "python", + "name": "tf1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15+" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb b/tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb new file mode 100644 index 00000000..22c05e63 --- /dev/null +++ b/tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb @@ -0,0 +1,222 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TensorFlow Dataset API\n", + "\n", + "In this example, we will show how to load numpy array data into the new \n", + "TensorFlow 'Dataset' API. The Dataset API implements an optimized data pipeline\n", + "with queues, that make data processing and training faster (especially on GPU).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "\n", + "# Import MNIST data (Numpy format)\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Parameters\n", + "learning_rate = 0.01\n", + "num_steps = 1000\n", + "batch_size = 128\n", + "display_step = 100\n", + "\n", + "# Network Parameters\n", + "n_input = 784 # MNIST data input (img shape: 28*28)\n", + "n_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.75 # Dropout, probability to keep units\n", + "\n", + "sess = tf.Session()\n", + "\n", + "# Create a dataset tensor from the images and the labels\n", + "dataset = tf.data.Dataset.from_tensor_slices(\n", + " (mnist.train.images, mnist.train.labels))\n", + "# Automatically refill the data queue when empty\n", + "dataset = dataset.repeat()\n", + "# Create batches of data\n", + "dataset = dataset.batch(batch_size)\n", + "# Prefetch data for faster consumption\n", + "dataset = dataset.prefetch(batch_size)\n", + "\n", + "# Create an iterator over the dataset\n", + "iterator = dataset.make_initializable_iterator()\n", + "# Initialize the iterator\n", + "sess.run(iterator.initializer)\n", + "\n", + "# Neural Net Input (images, labels)\n", + "X, Y = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# -----------------------------------------------\n", + "# THIS IS A CLASSIC CNN (see examples, section 3)\n", + "# -----------------------------------------------\n", + "# Note that a few elements have changed (usage of sess run).\n", + "\n", + "# Create model\n", + "def conv_net(x, n_classes, dropout, reuse, is_training):\n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv1 = tf.layers.max_pooling2d(conv1, 2, 2)\n", + "\n", + " # Convolution Layer with 32 filters and a kernel size of 5\n", + " conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " conv2 = tf.layers.max_pooling2d(conv2, 2, 2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " fc1 = tf.contrib.layers.flatten(conv2)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " fc1 = tf.layers.dense(fc1, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)\n", + "\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(fc1, n_classes)\n", + " # Because 'softmax_cross_entropy_with_logits' already apply softmax,\n", + " # we only apply softmax to testing network\n", + " out = tf.nn.softmax(out) if not is_training else out\n", + "\n", + " return out\n", + "\n", + "\n", + "# Because Dropout have different behavior at training and prediction time, we\n", + "# need to create 2 distinct computation graphs that share the same weights.\n", + "\n", + "# Create a graph for training\n", + "logits_train = conv_net(X, n_classes, dropout, reuse=False, is_training=True)\n", + "# Create another graph for testing that reuse the same weights, but has\n", + "# different behavior for 'dropout' (not applied).\n", + "logits_test = conv_net(X, n_classes, dropout, reuse=True, is_training=False)\n", + "\n", + "# Define loss and optimizer (with train logits, for dropout to take effect)\n", + "loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=Y))\n", + "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + "train_op = optimizer.minimize(loss_op)\n", + "\n", + "# Evaluate model (with test logits, for dropout to be disabled)\n", + "correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(Y, 1))\n", + "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1, Minibatch Loss= 7.9429, Training Accuracy= 0.070\n", + "Step 100, Minibatch Loss= 0.3491, Training Accuracy= 0.922\n", + "Step 200, Minibatch Loss= 0.2343, Training Accuracy= 0.922\n", + "Step 300, Minibatch Loss= 0.1838, Training Accuracy= 0.969\n", + "Step 400, Minibatch Loss= 0.1715, Training Accuracy= 0.953\n", + "Step 500, Minibatch Loss= 0.2730, Training Accuracy= 0.938\n", + "Step 600, Minibatch Loss= 0.3427, Training Accuracy= 0.953\n", + "Step 700, Minibatch Loss= 0.2261, Training Accuracy= 0.961\n", + "Step 800, Minibatch Loss= 0.1487, Training Accuracy= 0.953\n", + "Step 900, Minibatch Loss= 0.1438, Training Accuracy= 0.945\n", + "Step 1000, Minibatch Loss= 0.1786, Training Accuracy= 0.961\n", + "Optimization Finished!\n" + ] + } + ], + "source": [ + "# Initialize the variables (i.e. assign their default value)\n", + "init = tf.global_variables_initializer()\n", + "\n", + "# Run the initializer\n", + "sess.run(init)\n", + "\n", + "# Training cycle\n", + "for step in range(1, num_steps + 1):\n", + " \n", + " # Run optimization\n", + " sess.run(train_op)\n", + " \n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " # (note that this consume a new batch of data)\n", + " loss, acc = sess.run([loss_op, accuracy])\n", + " print(\"Step \" + str(step) + \", Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc))\n", + "\n", + "print(\"Optimization Finished!\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb b/tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb new file mode 100644 index 00000000..24aa5000 --- /dev/null +++ b/tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create and Load TFRecords\n", + "\n", + "A simple TensorFlow example to parse a dataset into TFRecord format, and then read that dataset.\n", + "\n", + "In this example, the Titanic Dataset (in CSV format) will be used as a toy dataset, for parsing all the dataset features into TFRecord format, and then building an input pipeline that can be used for training models.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Titanic Dataset\n", + "\n", + "The titanic dataset is a popular dataset for ML that provides a list of all passengers onboard the Titanic, along with various features such as their age, sex, class (1st, 2nd, 3rd)... And if the passenger survived the disaster or not.\n", + "\n", + "It can be used to see that even though some luck was involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class...\n", + "\n", + "#### Overview\n", + "survived|pclass|name|sex|age|sibsp|parch|ticket|fare\n", + "--------|------|----|---|---|-----|-----|------|----\n", + "1|1|\"Allen, Miss. Elisabeth Walton\"|female|29|0|0|24160|211.3375\n", + "1|1|\"Allison, Master. Hudson Trevor\"|male|0.9167|1|2|113781|151.5500\n", + "0|1|\"Allison, Miss. Helen Loraine\"|female|2|1|2|113781|151.5500\n", + "0|1|\"Allison, Mr. Hudson Joshua Creighton\"|male|30|1|2|113781|151.5500\n", + "...|...|...|...|...|...|...|...|...\n", + "\n", + "\n", + "#### Variable Descriptions\n", + "```\n", + "survived Survived\n", + " (0 = No; 1 = Yes)\n", + "pclass Passenger Class\n", + " (1 = 1st; 2 = 2nd; 3 = 3rd)\n", + "name Name\n", + "sex Sex\n", + "age Age\n", + "sibsp Number of Siblings/Spouses Aboard\n", + "parch Number of Parents/Children Aboard\n", + "ticket Ticket Number\n", + "fare Passenger Fare\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import csv\n", + "import requests\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download Titanic dataset (in csv format).\n", + "d = requests.get(\"https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/titanic_dataset.csv\")\n", + "with open(\"titanic_dataset.csv\", \"wb\") as f:\n", + " f.write(d.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create TFRecords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate Integer Features.\n", + "def build_int64_feature(data):\n", + " return tf.train.Feature(int64_list=tf.train.Int64List(value=[data]))\n", + "\n", + "# Generate Float Features.\n", + "def build_float_feature(data):\n", + " return tf.train.Feature(float_list=tf.train.FloatList(value=[data]))\n", + "\n", + "# Generate String Features.\n", + "def build_string_feature(data):\n", + " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[data]))\n", + "\n", + "# Generate a TF `Example`, parsing all features of the dataset.\n", + "def convert_to_tfexample(survived, pclass, name, sex, age, sibsp, parch, ticket, fare):\n", + " return tf.train.Example(\n", + " features=tf.train.Features(\n", + " feature={\n", + " 'survived': build_int64_feature(survived),\n", + " 'pclass': build_int64_feature(pclass),\n", + " 'name': build_string_feature(name),\n", + " 'sex': build_string_feature(sex),\n", + " 'age': build_float_feature(age),\n", + " 'sibsp': build_int64_feature(sibsp),\n", + " 'parch': build_int64_feature(parch),\n", + " 'ticket': build_string_feature(ticket),\n", + " 'fare': build_float_feature(fare),\n", + " })\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Open dataset file.\n", + "with open(\"titanic_dataset.csv\") as f:\n", + " # Output TFRecord file.\n", + " with tf.io.TFRecordWriter(\"titanic_dataset.tfrecord\") as w:\n", + " # Generate a TF Example for all row in our dataset.\n", + " # CSV reader will read and parse all rows.\n", + " reader = csv.reader(f, skipinitialspace=True)\n", + " for i, record in enumerate(reader):\n", + " # Skip header.\n", + " if i == 0:\n", + " continue\n", + " survived, pclass, name, sex, age, sibsp, parch, ticket, fare = record\n", + " # Parse each csv row to TF Example using the above functions.\n", + " example = convert_to_tfexample(int(survived), int(pclass), name, sex, float(age), int(sibsp), int(parch), ticket, float(fare))\n", + " # Serialize each TF Example to string, and write to TFRecord file.\n", + " w.write(example.SerializeToString())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load TFRecords" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Build features template, with types.\n", + "features = {\n", + " 'survived': tf.io.FixedLenFeature([], tf.int64),\n", + " 'pclass': tf.io.FixedLenFeature([], tf.int64),\n", + " 'name': tf.io.FixedLenFeature([], tf.string),\n", + " 'sex': tf.io.FixedLenFeature([], tf.string),\n", + " 'age': tf.io.FixedLenFeature([], tf.float32),\n", + " 'sibsp': tf.io.FixedLenFeature([], tf.int64),\n", + " 'parch': tf.io.FixedLenFeature([], tf.int64),\n", + " 'ticket': tf.io.FixedLenFeature([], tf.string),\n", + " 'fare': tf.io.FixedLenFeature([], tf.float32),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create TensorFlow session.\n", + "sess = tf.Session()\n", + "\n", + "# Load TFRecord data.\n", + "filenames = [\"titanic_dataset.tfrecord\"]\n", + "data = tf.data.TFRecordDataset(filenames)\n", + "\n", + "# Parse features, using the above template.\n", + "def parse_record(record):\n", + " return tf.io.parse_single_example(record, features=features)\n", + "# Apply the parsing to each record from the dataset.\n", + "data = data.map(parse_record)\n", + "\n", + "# Refill data indefinitely.\n", + "data = data.repeat()\n", + "# Shuffle data.\n", + "data = data.shuffle(buffer_size=1000)\n", + "# Batch data (aggregate records together).\n", + "data = data.batch(batch_size=4)\n", + "# Prefetch batch (pre-load batch for faster consumption).\n", + "data = data.prefetch(buffer_size=1)\n", + "\n", + "# Create an iterator over the dataset.\n", + "iterator = data.make_initializable_iterator()\n", + "# Initialize the iterator.\n", + "sess.run(iterator.initializer)\n", + "\n", + "# Get next data batch.\n", + "x = iterator.get_next()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'fare': array([ 35.5 , 73.5 , 133.65 , 19.2583], dtype=float32), 'name': array(['Sloper, Mr. William Thompson', 'Davies, Mr. Charles Henry',\n", + " 'Frauenthal, Dr. Henry William', 'Baclini, Miss. Marie Catherine'],\n", + " dtype=object), 'age': array([28., 18., 50., 5.], dtype=float32), 'parch': array([0, 0, 0, 1]), 'pclass': array([1, 2, 1, 3]), 'sex': array(['male', 'male', 'male', 'female'], dtype=object), 'survived': array([1, 0, 1, 1]), 'sibsp': array([0, 0, 2, 2]), 'ticket': array(['113788', 'S.O.C. 14879', 'PC 17611', '2666'], dtype=object)}\n", + "\n", + "{'fare': array([ 18.75 , 106.425, 78.85 , 90. ], dtype=float32), 'name': array(['Richards, Mrs. Sidney (Emily Hocking)', 'LeRoy, Miss. Bertha',\n", + " 'Cavendish, Mrs. Tyrell William (Julia Florence Siegel)',\n", + " 'Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)'], dtype=object), 'age': array([24., 30., 76., 35.], dtype=float32), 'parch': array([3, 0, 0, 0]), 'pclass': array([2, 1, 1, 1]), 'sex': array(['female', 'female', 'female', 'female'], dtype=object), 'survived': array([1, 1, 1, 1]), 'sibsp': array([2, 0, 1, 1]), 'ticket': array(['29106', 'PC 17761', '19877', '19943'], dtype=object)}\n", + "\n", + "{'fare': array([19.9667, 15.5 , 15.0458, 66.6 ], dtype=float32), 'name': array(['Hagland, Mr. Konrad Mathias Reiersen', 'Lennon, Miss. Mary',\n", + " 'Richard, Mr. Emile', 'Pears, Mr. Thomas Clinton'], dtype=object), 'age': array([ 0., 0., 23., 29.], dtype=float32), 'parch': array([0, 0, 0, 0]), 'pclass': array([3, 3, 2, 1]), 'sex': array(['male', 'female', 'male', 'male'], dtype=object), 'survived': array([0, 0, 0, 0]), 'sibsp': array([1, 1, 0, 1]), 'ticket': array(['65304', '370371', 'SC/PARIS 2133', '113776'], dtype=object)}\n", + "\n" + ] + } + ], + "source": [ + "# Dequeue data and display.\n", + "for i in range(3):\n", + " print(sess.run(x))\n", + " print(\"\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tf1", + "language": "python", + "name": "tf1" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.15+" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb b/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb new file mode 100644 index 00000000..1089b3e8 --- /dev/null +++ b/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Multi-GPU Basics\n", + "\n", + "Basic Multi-GPU computation example using TensorFlow library.\n", + "\n", + "This tutorial requires your machine to have 2 GPUs\n", + "\"/cpu:0\": The CPU of your machine.\n", + "\"/gpu:0\": The first GPU of your machine\n", + "\"/gpu:1\": The second GPU of your machine\n", + "For this example, we are using 2 GTX-980\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Processing Units logs\n", + "log_device_placement = True\n", + "\n", + "#num of multiplications to perform\n", + "n = 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Example: compute A^n + B^n on 2 GPUs\n", + "\n", + "# Create random large matrix\n", + "A = np.random.rand(1e4, 1e4).astype('float32')\n", + "B = np.random.rand(1e4, 1e4).astype('float32')\n", + "\n", + "# Creates a graph to store results\n", + "c1 = []\n", + "c2 = []\n", + "\n", + "# Define matrix power\n", + "def matpow(M, n):\n", + " if n < 1: #Abstract cases where n < 1\n", + " return M\n", + " else:\n", + " return tf.matmul(M, matpow(M, n-1))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Single GPU computing\n", + "\n", + "with tf.device('/gpu:0'):\n", + " a = tf.constant(A)\n", + " b = tf.constant(B)\n", + " #compute A^n and B^n and store results in c1\n", + " c1.append(matpow(a, n))\n", + " c1.append(matpow(b, n))\n", + "\n", + "with tf.device('/cpu:0'):\n", + " sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n\n", + "\n", + "t1_1 = datetime.datetime.now()\n", + "with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n", + " # Runs the op.\n", + " sess.run(sum)\n", + "t2_1 = datetime.datetime.now()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Multi GPU computing\n", + "# GPU:0 computes A^n\n", + "with tf.device('/gpu:0'):\n", + " #compute A^n and store result in c2\n", + " a = tf.constant(A)\n", + " c2.append(matpow(a, n))\n", + "\n", + "#GPU:1 computes B^n\n", + "with tf.device('/gpu:1'):\n", + " #compute B^n and store result in c2\n", + " b = tf.constant(B)\n", + " c2.append(matpow(b, n))\n", + "\n", + "with tf.device('/cpu:0'):\n", + " sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n\n", + "\n", + "t1_2 = datetime.datetime.now()\n", + "with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n", + " # Runs the op.\n", + " sess.run(sum)\n", + "t2_2 = datetime.datetime.now()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Single GPU computation time: 0:00:11.833497\n", + "Multi GPU computation time: 0:00:07.085913\n" + ] + } + ], + "source": [ + "print \"Single GPU computation time: \" + str(t2_1-t1_1)\n", + "print \"Multi GPU computation time: \" + str(t2_2-t1_2)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb b/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb new file mode 100644 index 00000000..2d4746d2 --- /dev/null +++ b/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-GPU Training Example\n", + "\n", + "Train a convolutional neural network on multiple GPU with TensorFlow.\n", + "\n", + "This example is using TensorFlow layers, see 'convolutional_network_raw' example\n", + "for a raw TensorFlow implementation with variables.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training with multiple GPU cards\n", + "\n", + "In this example, we are using data parallelism to split the training accross multiple GPUs. Each GPU has a full replica of the neural network model, and the weights (i.e. variables) are updated synchronously by waiting that each GPU process its batch of data.\n", + "\n", + "First, each GPU process a distinct batch of data and compute the corresponding gradients, then, all gradients are accumulated in the CPU and averaged. The model weights are finally updated with the gradients averaged, and the new model weights are sent back to each GPU, to repeat the training process.\n", + "\n", + "\"Parallelism\"\n", + "\n", + "## MNIST Dataset Overview\n", + "\n", + "This example is using MNIST handwritten digits. The dataset contains 60,000 examples for training and 10,000 examples for testing. The digits have been size-normalized and centered in a fixed-size image (28x28 pixels) with values from 0 to 1. For simplicity, each image has been flatten and converted to a 1-D numpy array of 784 features (28*28).\n", + "\n", + "![MNIST Dataset](http://neuralnetworksanddeeplearning.com/images/mnist_100_digits.png)\n", + "\n", + "More info: http://yann.lecun.com/exdb/mnist/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting /tmp/data/train-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/train-labels-idx1-ubyte.gz\n", + "Extracting /tmp/data/t10k-images-idx3-ubyte.gz\n", + "Extracting /tmp/data/t10k-labels-idx1-ubyte.gz\n" + ] + } + ], + "source": [ + "from __future__ import print_function\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import time\n", + "\n", + "# Import MNIST data\n", + "from tensorflow.examples.tutorials.mnist import input_data\n", + "mnist = input_data.read_data_sets(\"/tmp/data/\", one_hot=True)\n", + "\n", + "# Parameters\n", + "num_gpus = 2\n", + "num_steps = 200\n", + "learning_rate = 0.001\n", + "batch_size = 1024\n", + "display_step = 10\n", + "\n", + "# Network Parameters\n", + "num_input = 784 # MNIST data input (img shape: 28*28)\n", + "num_classes = 10 # MNIST total classes (0-9 digits)\n", + "dropout = 0.75 # Dropout, probability to keep units" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build a convolutional neural network\n", + "def conv_net(x, n_classes, dropout, reuse, is_training):\n", + " # Define a scope for reusing the variables\n", + " with tf.variable_scope('ConvNet', reuse=reuse):\n", + " # MNIST data input is a 1-D vector of 784 features (28*28 pixels)\n", + " # Reshape to match picture format [Height x Width x Channel]\n", + " # Tensor input become 4-D: [Batch Size, Height, Width, Channel]\n", + " x = tf.reshape(x, shape=[-1, 28, 28, 1])\n", + "\n", + " # Convolution Layer with 64 filters and a kernel size of 5\n", + " x = tf.layers.conv2d(x, 64, 5, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " x = tf.layers.max_pooling2d(x, 2, 2)\n", + "\n", + " # Convolution Layer with 256 filters and a kernel size of 5\n", + " x = tf.layers.conv2d(x, 256, 3, activation=tf.nn.relu)\n", + " # Convolution Layer with 512 filters and a kernel size of 5\n", + " x = tf.layers.conv2d(x, 512, 3, activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with strides of 2 and kernel size of 2\n", + " x = tf.layers.max_pooling2d(x, 2, 2)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer\n", + " x = tf.contrib.layers.flatten(x)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " x = tf.layers.dense(x, 2048)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " x = tf.layers.dropout(x, rate=dropout, training=is_training)\n", + "\n", + " # Fully connected layer (in contrib folder for now)\n", + " x = tf.layers.dense(x, 1024)\n", + " # Apply Dropout (if is_training is False, dropout is not applied)\n", + " x = tf.layers.dropout(x, rate=dropout, training=is_training)\n", + "\n", + " # Output layer, class prediction\n", + " out = tf.layers.dense(x, n_classes)\n", + " # Because 'softmax_cross_entropy_with_logits' loss already apply\n", + " # softmax, we only apply softmax to testing network\n", + " out = tf.nn.softmax(out) if not is_training else out\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Build the function to average the gradients\n", + "def average_gradients(tower_grads):\n", + " average_grads = []\n", + " for grad_and_vars in zip(*tower_grads):\n", + " # Note that each grad_and_vars looks like the following:\n", + " # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))\n", + " grads = []\n", + " for g, _ in grad_and_vars:\n", + " # Add 0 dimension to the gradients to represent the tower.\n", + " expanded_g = tf.expand_dims(g, 0)\n", + "\n", + " # Append on a 'tower' dimension which we will average over below.\n", + " grads.append(expanded_g)\n", + "\n", + " # Average over the 'tower' dimension.\n", + " grad = tf.concat(grads, 0)\n", + " grad = tf.reduce_mean(grad, 0)\n", + "\n", + " # Keep in mind that the Variables are redundant because they are shared\n", + " # across towers. So .. we will just return the first tower's pointer to\n", + " # the Variable.\n", + " v = grad_and_vars[0][1]\n", + " grad_and_var = (grad, v)\n", + " average_grads.append(grad_and_var)\n", + " return average_grads" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# By default, all variables will be placed on '/gpu:0'\n", + "# So we need a custom device function, to assign all variables to '/cpu:0'\n", + "# Note: If GPUs are peered, '/gpu:0' can be a faster option\n", + "PS_OPS = ['Variable', 'VariableV2', 'AutoReloadVariable']\n", + "\n", + "def assign_to_device(device, ps_device='/cpu:0'):\n", + " def _assign(op):\n", + " node_def = op if isinstance(op, tf.NodeDef) else op.node_def\n", + " if node_def.op in PS_OPS:\n", + " return \"/\" + ps_device\n", + " else:\n", + " return device\n", + "\n", + " return _assign" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Step 1: Minibatch Loss= 2.4077, Training Accuracy= 0.123, 682 Examples/sec\n", + "Step 10: Minibatch Loss= 1.0067, Training Accuracy= 0.765, 6528 Examples/sec\n", + "Step 20: Minibatch Loss= 0.2442, Training Accuracy= 0.945, 6803 Examples/sec\n", + "Step 30: Minibatch Loss= 0.2013, Training Accuracy= 0.951, 6741 Examples/sec\n", + "Step 40: Minibatch Loss= 0.1445, Training Accuracy= 0.962, 6700 Examples/sec\n", + "Step 50: Minibatch Loss= 0.0940, Training Accuracy= 0.971, 6746 Examples/sec\n", + "Step 60: Minibatch Loss= 0.0792, Training Accuracy= 0.977, 6627 Examples/sec\n", + "Step 70: Minibatch Loss= 0.0593, Training Accuracy= 0.979, 6749 Examples/sec\n", + "Step 80: Minibatch Loss= 0.0799, Training Accuracy= 0.984, 6368 Examples/sec\n", + "Step 90: Minibatch Loss= 0.0614, Training Accuracy= 0.988, 6762 Examples/sec\n", + "Step 100: Minibatch Loss= 0.0716, Training Accuracy= 0.983, 6338 Examples/sec\n", + "Step 110: Minibatch Loss= 0.0531, Training Accuracy= 0.986, 6504 Examples/sec\n", + "Step 120: Minibatch Loss= 0.0425, Training Accuracy= 0.990, 6721 Examples/sec\n", + "Step 130: Minibatch Loss= 0.0473, Training Accuracy= 0.986, 6735 Examples/sec\n", + "Step 140: Minibatch Loss= 0.0345, Training Accuracy= 0.991, 6636 Examples/sec\n", + "Step 150: Minibatch Loss= 0.0419, Training Accuracy= 0.993, 6777 Examples/sec\n", + "Step 160: Minibatch Loss= 0.0602, Training Accuracy= 0.984, 6392 Examples/sec\n", + "Step 170: Minibatch Loss= 0.0425, Training Accuracy= 0.990, 6855 Examples/sec\n", + "Step 180: Minibatch Loss= 0.0107, Training Accuracy= 0.998, 6804 Examples/sec\n", + "Step 190: Minibatch Loss= 0.0204, Training Accuracy= 0.995, 6645 Examples/sec\n", + "Step 200: Minibatch Loss= 0.0296, Training Accuracy= 0.993, 6747 Examples/sec\n", + "Optimization Finished!\n", + "Testing Accuracy: 0.990671\n" + ] + } + ], + "source": [ + "# Place all ops on CPU by default\n", + "with tf.device('/cpu:0'):\n", + " tower_grads = []\n", + " reuse_vars = False\n", + "\n", + " # tf Graph input\n", + " X = tf.placeholder(tf.float32, [None, num_input])\n", + " Y = tf.placeholder(tf.float32, [None, num_classes])\n", + "\n", + " # Loop over all GPUs and construct their own computation graph\n", + " for i in range(num_gpus):\n", + " with tf.device(assign_to_device('/gpu:{}'.format(i), ps_device='/cpu:0')):\n", + "\n", + " # Split data between GPUs\n", + " _x = X[i * batch_size: (i+1) * batch_size]\n", + " _y = Y[i * batch_size: (i+1) * batch_size]\n", + "\n", + " # Because Dropout have different behavior at training and prediction time, we\n", + " # need to create 2 distinct computation graphs that share the same weights.\n", + "\n", + " # Create a graph for training\n", + " logits_train = conv_net(_x, num_classes, dropout,\n", + " reuse=reuse_vars, is_training=True)\n", + " # Create another graph for testing that reuse the same weights\n", + " logits_test = conv_net(_x, num_classes, dropout,\n", + " reuse=True, is_training=False)\n", + "\n", + " # Define loss and optimizer (with train logits, for dropout to take effect)\n", + " loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", + " logits=logits_train, labels=_y))\n", + " optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n", + " grads = optimizer.compute_gradients(loss_op)\n", + "\n", + " # Only first GPU compute accuracy\n", + " if i == 0:\n", + " # Evaluate model (with test logits, for dropout to be disabled)\n", + " correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.argmax(_y, 1))\n", + " accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n", + "\n", + " reuse_vars = True\n", + " tower_grads.append(grads)\n", + "\n", + " tower_grads = average_gradients(tower_grads)\n", + " train_op = optimizer.apply_gradients(tower_grads)\n", + "\n", + " # Initializing the variables\n", + " init = tf.global_variables_initializer()\n", + "\n", + " # Launch the graph\n", + " with tf.Session() as sess:\n", + " sess.run(init)\n", + " step = 1\n", + " # Keep training until reach max iterations\n", + " for step in range(1, num_steps + 1):\n", + " # Get a batch for each GPU\n", + " batch_x, batch_y = mnist.train.next_batch(batch_size * num_gpus)\n", + " # Run optimization op (backprop)\n", + " ts = time.time()\n", + " sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})\n", + " te = time.time() - ts\n", + " if step % display_step == 0 or step == 1:\n", + " # Calculate batch loss and accuracy\n", + " loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,\n", + " Y: batch_y})\n", + " print(\"Step \" + str(step) + \": Minibatch Loss= \" + \\\n", + " \"{:.4f}\".format(loss) + \", Training Accuracy= \" + \\\n", + " \"{:.3f}\".format(acc) + \", %i Examples/sec\" % int(len(batch_x)/te))\n", + " step += 1\n", + " print(\"Optimization Finished!\")\n", + "\n", + " # Calculate accuracy for 1000 mnist test images\n", + " print(\"Testing Accuracy:\", \\\n", + " np.mean([sess.run(accuracy, feed_dict={X: mnist.test.images[i:i+batch_size],\n", + " Y: mnist.test.labels[i:i+batch_size]}) for i in range(0, len(mnist.test.images), batch_size)]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 6d96494e7f0d5b955c3a931d46ac9a121d9b813e Mon Sep 17 00:00:00 2001 From: Hossein Sheikhi Darani <64957461+HosseinSheikhi@users.noreply.github.com> Date: Tue, 19 May 2020 20:47:14 -0700 Subject: [PATCH 150/166] Modify tf2 linear regression loss function (#371) --- .../notebooks/2_BasicModels/linear_regression.ipynb | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb b/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb index 17b57b8a..83ad9a53 100644 --- a/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb +++ b/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb @@ -56,8 +56,7 @@ "X = np.array([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n", " 7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n", "Y = np.array([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n", - " 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n", - "n_samples = X.shape[0]" + " 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n" ] }, { @@ -76,7 +75,7 @@ "\n", "# Mean square error.\n", "def mean_square(y_pred, y_true):\n", - " return tf.reduce_sum(tf.pow(y_pred-y_true, 2)) / (2 * n_samples)\n", + " return tf.reduce_mean(tf.square(y_pred - y_true))\n", "\n", "# Stochastic Gradient Descent Optimizer.\n", "optimizer = tf.optimizers.SGD(learning_rate)" From a8ee3d2cf096f82a4bd88f0f923805c633ec84ed Mon Sep 17 00:00:00 2001 From: Hossein Sheikhi Darani <64957461+HosseinSheikhi@users.noreply.github.com> Date: Wed, 27 May 2020 10:37:09 -0700 Subject: [PATCH 151/166] Modify linear regression loss function (#373) * Modify tf2 linear regression loss function * neural_network.ipynp syntax error has been corrected --- tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb index 77926535..9ecf0f2c 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb @@ -116,7 +116,7 @@ " # Set forward pass.\n", " def call(self, x, is_training=False):\n", " x = self.fc1(x)\n", - " x = self.fc2(x)\n" + " x = self.fc2(x)\n", " x = self.out(x)\n", " if not is_training:\n", " # tf cross entropy expect logits without softmax, so only\n", From 4ac5751796d19b868a06136c13095228dc02acf6 Mon Sep 17 00:00:00 2001 From: Qingxu Zhu <49614979+ZQX323@users.noreply.github.com> Date: Tue, 2 Jun 2020 11:20:10 +0800 Subject: [PATCH 152/166] fix links in README of TensorFlow_v1 (#374) * Update README.md * Update README.md * Update README.md --- tensorflow_v1/README.md | 72 ++++++++++++++++++++--------------------- 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/tensorflow_v1/README.md b/tensorflow_v1/README.md index 93a8c3a9..29b188e3 100644 --- a/tensorflow_v1/README.md +++ b/tensorflow_v1/README.md @@ -5,58 +5,58 @@ All the following examples are the original TF v1 examples. *If you are using older TensorFlow version (0.11 and under), please take a [look here](https://github.com/aymericdamien/TensorFlow-Examples/tree/0.11).* #### 0 - Prerequisite -- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/ml_introduction.ipynb). -- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/mnist_dataset_intro.ipynb). +- [Introduction to Machine Learning](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/0_Prerequisite/ml_introduction.ipynb). +- [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction -- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. -- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. -- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. +- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. #### 2 - Basic Models -- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. -- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. -- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. -- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. -- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. -- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. -- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. -- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. -- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. +- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. +- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. +- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. +- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. +- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. +- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. #### 3 - Neural Networks ##### Supervised -- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. -- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. ##### Unsupervised -- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. -- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. -- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. -- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. +- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. +- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities -- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. -- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. -- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. +- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. +- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... #### 5 - Data Management -- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. -- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. -- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). -- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. -- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. +- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. +- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. #### 6 - Multi GPU -- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. -- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. +- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. +- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. ## Installation From 754c3312534755246d142d43ddd5133f60866f7a Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Tue, 2 Jun 2020 01:12:28 -0700 Subject: [PATCH 153/166] Fix links --- README.md | 68 +++++++++++++++++++++++++++---------------------------- 1 file changed, 34 insertions(+), 34 deletions(-) diff --git a/README.md b/README.md index 00610dcf..673a7d0a 100644 --- a/README.md +++ b/README.md @@ -85,52 +85,52 @@ The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tens - [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction -- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. -- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. -- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/helloworld.py)). Very simple example to learn how to print "hello world" using TensorFlow. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-examples/Examples/blob/master/tensorflow_v1/1_Introduction/basic_operations.py)). A simple example that cover TensorFlow basic operations. +- **TensorFlow Eager API basics** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/1_Introduction/basic_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/1_Introduction/basic_eager_api.py)). Get started with TensorFlow's Eager API. #### 2 - Basic Models -- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. -- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. -- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. -- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. -- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. -- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. -- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. -- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. -- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/linear_regression.py)). Implement a Linear Regression with TensorFlow. +- **Linear Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/linear_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/linear_regression_eager_api.py)). Implement a Linear Regression using TensorFlow's Eager API. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/logistic_regression.py)). Implement a Logistic Regression with TensorFlow. +- **Logistic Regression (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/logistic_regression_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/logistic_regression_eager_api.py)). Implement a Logistic Regression using TensorFlow's Eager API. +- **Nearest Neighbor** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/nearest_neighbor.py)). Implement Nearest Neighbor algorithm with TensorFlow. +- **K-Means** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/kmeans.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/kmeans.py)). Build a K-Means classifier with TensorFlow. +- **Random Forest** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/random_forest.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/random_forest.py)). Build a Random Forest classifier with TensorFlow. +- **Gradient Boosted Decision Tree (GBDT)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/gradient_boosted_decision_tree.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/gradient_boosted_decision_tree.py)). Build a Gradient Boosted Decision Tree (GBDT) with TensorFlow. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/2_BasicModels/word2vec.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/2_BasicModels/word2vec.py)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow. #### 3 - Neural Networks ##### Supervised -- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. -- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. -- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. -- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. -- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. +- **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/3_NeuralNetworks/notebooks/neural_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_raw.py)). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation. +- **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. + - **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. +- **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. ##### Unsupervised -- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. -- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. -- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. -- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. +- **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/autoencoder.py)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. +- **Variational Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/variational_autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/variational_autoencoder.py)). Build a variational auto-encoder (VAE), to encode and generate images from noise. +- **GAN (Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/gan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/gan.py)). Build a Generative Adversarial Network (GAN) to generate images from noise. +- **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dcgan.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dcgan.py)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities -- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. -- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. -- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/save_restore_model.py)). Save and Restore a model with TensorFlow. +- **Tensorboard - Graph and loss visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/tensorboard_basic.py)). Use Tensorboard to visualize the computation Graph and plot the loss. +- **Tensorboard - Advanced visualization** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/4_Utils/tensorboard_advanced.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/4_Utils/tensorboard_advanced.py)). Going deeper into Tensorboard; visualize the variables, gradients, and more... #### 5 - Data Management -- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. -- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. -- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). -- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. -- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. +- **Build an image dataset** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/build_an_image_dataset.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py)). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file. +- **TensorFlow Dataset API** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/tensorflow_dataset_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/5_DataManagement/tensorflow_dataset_api.py)). Introducing TensorFlow Dataset API for optimizing the input data pipeline. +- **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline (Numpy arrays, Images, CSV files, custom data, ...). +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques, to generate distorted images for training. #### 6 - Multi GPU -- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. -- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/tensorflow_v1/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/tensorflow_v1/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. +- **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. +- **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. From 0d0327544f2077786c946fb26b731bb1ef4d5103 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Thu, 4 Jun 2020 00:05:34 -0700 Subject: [PATCH 154/166] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 673a7d0a..aea002a2 100644 --- a/README.md +++ b/README.md @@ -107,7 +107,7 @@ The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tens - **Simple Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. - **Simple Neural Network (eager api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/neural_network_eager_api.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/neural_network_eager_api.py)). Use TensorFlow Eager API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. - **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network_raw.py)). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation. - - **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. +- **Convolutional Neural Network (tf.layers/estimator api)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/convolutional_network.py)). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset. - **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/recurrent_network.py)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset. - **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/bidirectional_rnn.py)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset. - **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/3_NeuralNetworks/dynamic_rnn.py)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length. From 86fc318c24668aff7341fdace325a34181205351 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Wed, 15 Jul 2020 00:57:36 -0700 Subject: [PATCH 155/166] add GBDT example (#379) --- README.md | 2 +- tensorflow_v2/README.md | 1 + .../gradient_boosted_trees.ipynb | 604 ++++++++++++++++++ 3 files changed, 606 insertions(+), 1 deletion(-) create mode 100644 tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb diff --git a/README.md b/README.md index aea002a2..da394304 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,7 @@ It is suitable for beginners who want to find clear and concise examples about T - **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. - **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. - **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0. +- **GBDT (Gradient Boosted Decision Trees)** ([notebooks](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb)). Implement a Gradient Boosted Decision Trees with TensorFlow 2.0+ to predict house value using Boston Housing dataset. #### 3 - Neural Networks ##### Supervised @@ -133,4 +134,3 @@ The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tens #### 6 - Multi GPU - **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. - **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. - diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index ed23a174..b6cbea39 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -14,6 +14,7 @@ - **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. - **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. - **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0. +- **GBDT (Gradient Boosted Decision Trees)** ([notebooks](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb)). Implement a Gradient Boosted Decision Trees with TensorFlow 2.0+ to predict house value using Boston Housing dataset. #### 3 - Neural Networks ##### Supervised diff --git a/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb b/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb new file mode 100644 index 00000000..cad1d9e8 --- /dev/null +++ b/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb @@ -0,0 +1,604 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gradient Boosted Decision Tree (GBDT)\n", + "Implement a Gradient Boosted Decision Tree (GBDT) with TensorFlow. This example is using the Boston Housing Value dataset as training samples. The example supports both Classification (2 classes: value > $23000 or not) and Regression (raw home value as target).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Boston Housing Dataset\n", + "\n", + "**Link:** https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html\n", + "\n", + "**Description:**\n", + "\n", + "The dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. It was obtained from the StatLib archive (http://lib.stat.cmu.edu/datasets/boston), and has been used extensively throughout the literature to benchmark algorithms. However, these comparisons were primarily done outside of Delve and are thus somewhat suspect. The dataset is small in size with only 506 cases.\n", + "\n", + "The data was originally published by Harrison, D. and Rubinfeld, D.L. `Hedonic prices and the demand for clean air', J. Environ. Economics & Management, vol.5, 81-102, 1978.`\n", + "\n", + "*For the full features list, please see the link above*" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "# Ignore all GPUs (current TF GBDT does not support GPU).\n", + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = \"1\"\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import copy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Dataset parameters.\n", + "num_classes = 2 # Total classes: greater or equal to $23,000, or not (See notes below).\n", + "num_features = 13 # data features size.\n", + "\n", + "# Training parameters.\n", + "max_steps = 2000\n", + "batch_size = 256\n", + "learning_rate = 1.0\n", + "l1_regul = 0.0\n", + "l2_regul = 0.1\n", + "\n", + "# GBDT parameters.\n", + "num_batches_per_layer = 1000\n", + "num_trees = 10\n", + "max_depth = 4" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare Boston Housing Dataset.\n", + "from tensorflow.keras.datasets import boston_housing\n", + "(x_train, y_train), (x_test, y_test) = boston_housing.load_data()\n", + "\n", + "# For classification purpose, we build 2 classes: price greater or lower than $23,000\n", + "def to_binary_class(y):\n", + " for i, label in enumerate(y):\n", + " if label >= 23.0:\n", + " y[i] = 1\n", + " else:\n", + " y[i] = 0\n", + " return y\n", + "\n", + "y_train_binary = to_binary_class(copy.deepcopy(y_train))\n", + "y_test_binary = to_binary_class(copy.deepcopy(y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GBDT Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Build the input function.\n", + "train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(\n", + " x={'x': x_train}, y=y_train_binary,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)\n", + "test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(\n", + " x={'x': x_test}, y=y_test_binary,\n", + " batch_size=batch_size, num_epochs=1, shuffle=False)\n", + "test_train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(\n", + " x={'x': x_train}, y=y_train_binary,\n", + " batch_size=batch_size, num_epochs=1, shuffle=False)\n", + "# GBDT Models from TF Estimator requires 'feature_column' data format.\n", + "feature_columns = [tf.feature_column.numeric_column(key='x', shape=(num_features,))]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmp5h6BoR\n", + "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': ClusterSpec({}), '_model_dir': '/tmp/tmp5h6BoR', '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true\n", + "graph_options {\n", + " rewrite_options {\n", + " meta_optimizer_iterations: ONE\n", + " }\n", + "}\n", + ", '_tf_random_seed': None, '_save_summary_steps': 100, '_device_fn': None, '_session_creation_timeout_secs': 7200, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_experimental_max_worker_delay_secs': None, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': None, '_master': ''}\n" + ] + } + ], + "source": [ + "gbdt_classifier = tf.estimator.BoostedTreesClassifier(\n", + " n_batches_per_layer=num_batches_per_layer,\n", + " feature_columns=feature_columns, \n", + " n_classes=num_classes,\n", + " learning_rate=learning_rate, \n", + " n_trees=num_trees,\n", + " max_depth=max_depth,\n", + " l1_regularization=l1_regul, \n", + " l2_regularization=l2_regul\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1635: calling __init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "If using Keras pass *_constraint arguments to layers.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_core/python/training/training_util.py:236: initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:62: __init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "To construct input pipelines, use the `tf.data` module.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "To construct input pipelines, use the `tf.data` module.\n", + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_core/python/training/monitored_session.py:906: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "To construct input pipelines, use the `tf.data` module.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp5h6BoR/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:loss = 0.6931475, step = 0\n", + "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n", + "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n", + "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n", + "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n", + "WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.406 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.156 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.167 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.156 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.161 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.156 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.154 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.155 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.158 sec)\n", + "INFO:tensorflow:loss = 0.6931475, step = 0 (0.150 sec)\n", + "INFO:tensorflow:global_step/sec: 47.2392\n", + "INFO:tensorflow:loss = 0.6931475, step = 100 (0.301 sec)\n", + "INFO:tensorflow:global_step/sec: 605.484\n", + "INFO:tensorflow:loss = 0.6931475, step = 200 (0.165 sec)\n", + "INFO:tensorflow:global_step/sec: 616.234\n", + "INFO:tensorflow:loss = 0.6931475, step = 300 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 607.741\n", + "INFO:tensorflow:loss = 0.6931475, step = 400 (0.165 sec)\n", + "INFO:tensorflow:global_step/sec: 591.803\n", + "INFO:tensorflow:loss = 0.6931475, step = 500 (0.170 sec)\n", + "INFO:tensorflow:global_step/sec: 627.369\n", + "INFO:tensorflow:loss = 0.6931475, step = 600 (0.159 sec)\n", + "INFO:tensorflow:global_step/sec: 617.083\n", + "INFO:tensorflow:loss = 0.6931475, step = 700 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 608.765\n", + "INFO:tensorflow:loss = 0.6931475, step = 800 (0.164 sec)\n", + "INFO:tensorflow:global_step/sec: 619.62\n", + "INFO:tensorflow:loss = 0.6931475, step = 900 (0.161 sec)\n", + "INFO:tensorflow:global_step/sec: 582.581\n", + "INFO:tensorflow:loss = 0.44474202, step = 1000 (0.172 sec)\n", + "INFO:tensorflow:global_step/sec: 587.127\n", + "INFO:tensorflow:loss = 0.46633375, step = 1100 (0.170 sec)\n", + "INFO:tensorflow:global_step/sec: 583.294\n", + "INFO:tensorflow:loss = 0.45393157, step = 1200 (0.171 sec)\n", + "INFO:tensorflow:global_step/sec: 590.375\n", + "INFO:tensorflow:loss = 0.44438446, step = 1300 (0.170 sec)\n", + "INFO:tensorflow:global_step/sec: 572.479\n", + "INFO:tensorflow:loss = 0.4523462, step = 1400 (0.175 sec)\n", + "INFO:tensorflow:global_step/sec: 580.282\n", + "INFO:tensorflow:loss = 0.4581305, step = 1500 (0.172 sec)\n", + "INFO:tensorflow:global_step/sec: 570.032\n", + "INFO:tensorflow:loss = 0.45298833, step = 1600 (0.175 sec)\n", + "INFO:tensorflow:global_step/sec: 615.6\n", + "INFO:tensorflow:loss = 0.4474975, step = 1700 (0.162 sec)\n", + "INFO:tensorflow:global_step/sec: 603.042\n", + "INFO:tensorflow:loss = 0.47046587, step = 1800 (0.166 sec)\n", + "INFO:tensorflow:global_step/sec: 598.262\n", + "INFO:tensorflow:loss = 0.46371317, step = 1900 (0.167 sec)\n", + "INFO:tensorflow:global_step/sec: 591.323\n", + "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmp5h6BoR/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Loss for final step: 0.46488184.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gbdt_classifier.train(train_input_fn, max_steps=max_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_core/python/ops/metrics_impl.py:2029: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Deprecated in favor of operator or tf.math.divide.\n", + "WARNING:tensorflow:From /home/user/anaconda3/envs/py2/lib/python2.7/site-packages/tensorflow_estimator/python/estimator/canned/head.py:619: auc (from tensorflow.python.ops.metrics_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "The value of AUC returned by this may race with the update so this is deprected. Please use tf.keras.metrics.AUC instead.\n", + "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", + "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2020-07-15T00:50:36Z\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmp5h6BoR/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Inference Time : 0.56490s\n", + "INFO:tensorflow:Finished evaluation at 2020-07-15-00:50:37\n", + "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.87376237, accuracy_baseline = 0.63118815, auc = 0.92280567, auc_precision_recall = 0.9104949, average_loss = 0.38236493, global_step = 2000, label/mean = 0.36881188, loss = 0.38619137, precision = 0.8888889, prediction/mean = 0.378958, recall = 0.7516779\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: /tmp/tmp5h6BoR/model.ckpt-2000\n" + ] + }, + { + "data": { + "text/plain": [ + "{'accuracy': 0.87376237,\n", + " 'accuracy_baseline': 0.63118815,\n", + " 'auc': 0.92280567,\n", + " 'auc_precision_recall': 0.9104949,\n", + " 'average_loss': 0.38236493,\n", + " 'global_step': 2000,\n", + " 'label/mean': 0.36881188,\n", + " 'loss': 0.38619137,\n", + " 'precision': 0.8888889,\n", + " 'prediction/mean': 0.378958,\n", + " 'recall': 0.7516779}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gbdt_classifier.evaluate(test_train_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", + "WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to \"careful_interpolation\" instead.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2020-07-15T00:50:38Z\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmp5h6BoR/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Inference Time : 0.56883s\n", + "INFO:tensorflow:Finished evaluation at 2020-07-15-00:50:38\n", + "INFO:tensorflow:Saving dict for global step 2000: accuracy = 0.78431374, accuracy_baseline = 0.5588235, auc = 0.8458089, auc_precision_recall = 0.86285317, average_loss = 0.49404, global_step = 2000, label/mean = 0.44117647, loss = 0.49404, precision = 0.87096775, prediction/mean = 0.37467176, recall = 0.6\n", + "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: /tmp/tmp5h6BoR/model.ckpt-2000\n" + ] + }, + { + "data": { + "text/plain": [ + "{'accuracy': 0.78431374,\n", + " 'accuracy_baseline': 0.5588235,\n", + " 'auc': 0.8458089,\n", + " 'auc_precision_recall': 0.86285317,\n", + " 'average_loss': 0.49404,\n", + " 'global_step': 2000,\n", + " 'label/mean': 0.44117647,\n", + " 'loss': 0.49404,\n", + " 'precision': 0.87096775,\n", + " 'prediction/mean': 0.37467176,\n", + " 'recall': 0.6}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gbdt_classifier.evaluate(test_input_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GBDT Regressor" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Build the input function.\n", + "train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(\n", + " x={'x': x_train}, y=y_train,\n", + " batch_size=batch_size, num_epochs=None, shuffle=True)\n", + "test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(\n", + " x={'x': x_test}, y=y_test,\n", + " batch_size=batch_size, num_epochs=1, shuffle=False)\n", + "# GBDT Models from TF Estimator requires 'feature_column' data format.\n", + "feature_columns = [tf.feature_column.numeric_column(key='x', shape=(num_features,))]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpts3Kmu\n", + "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': ClusterSpec({}), '_model_dir': '/tmp/tmpts3Kmu', '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true\n", + "graph_options {\n", + " rewrite_options {\n", + " meta_optimizer_iterations: ONE\n", + " }\n", + "}\n", + ", '_tf_random_seed': None, '_save_summary_steps': 100, '_device_fn': None, '_session_creation_timeout_secs': 7200, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_experimental_max_worker_delay_secs': None, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': None, '_master': ''}\n" + ] + } + ], + "source": [ + "gbdt_regressor = tf.estimator.BoostedTreesRegressor(\n", + " n_batches_per_layer=num_batches_per_layer,\n", + " feature_columns=feature_columns, \n", + " learning_rate=learning_rate, \n", + " n_trees=num_trees,\n", + " max_depth=max_depth,\n", + " l1_regularization=l1_regul, \n", + " l2_regularization=l2_regul\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpts3Kmu/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:loss = 584.82294, step = 0\n", + "INFO:tensorflow:loss = 560.2794, step = 0 (0.369 sec)\n", + "INFO:tensorflow:loss = 606.68115, step = 0 (0.156 sec)\n", + "INFO:tensorflow:loss = 583.2771, step = 0 (0.155 sec)\n", + "INFO:tensorflow:loss = 603.4647, step = 0 (0.160 sec)\n", + "INFO:tensorflow:loss = 605.8213, step = 0 (0.153 sec)\n", + "INFO:tensorflow:loss = 577.5599, step = 0 (0.157 sec)\n", + "INFO:tensorflow:loss = 585.297, step = 0 (0.157 sec)\n", + "INFO:tensorflow:loss = 545.26074, step = 0 (0.156 sec)\n", + "INFO:tensorflow:loss = 597.91046, step = 0 (0.190 sec)\n", + "INFO:tensorflow:loss = 600.55396, step = 0 (0.174 sec)\n", + "INFO:tensorflow:global_step/sec: 47.5449\n", + "INFO:tensorflow:loss = 539.62646, step = 100 (0.280 sec)\n", + "INFO:tensorflow:global_step/sec: 592.267\n", + "INFO:tensorflow:loss = 573.9592, step = 200 (0.169 sec)\n", + "INFO:tensorflow:global_step/sec: 573.943\n", + "INFO:tensorflow:loss = 617.79407, step = 300 (0.175 sec)\n", + "INFO:tensorflow:global_step/sec: 583.88\n", + "INFO:tensorflow:loss = 593.62915, step = 400 (0.171 sec)\n", + "INFO:tensorflow:global_step/sec: 595.888\n", + "INFO:tensorflow:loss = 594.5435, step = 500 (0.168 sec)\n", + "INFO:tensorflow:global_step/sec: 610.997\n", + "INFO:tensorflow:loss = 579.5427, step = 600 (0.163 sec)\n", + "INFO:tensorflow:global_step/sec: 625.07\n", + "INFO:tensorflow:loss = 555.19604, step = 700 (0.160 sec)\n", + "INFO:tensorflow:global_step/sec: 674.427\n", + "INFO:tensorflow:loss = 585.61127, step = 800 (0.149 sec)\n", + "INFO:tensorflow:global_step/sec: 652.597\n", + "INFO:tensorflow:loss = 645.147, step = 900 (0.153 sec)\n", + "INFO:tensorflow:global_step/sec: 656.608\n", + "INFO:tensorflow:loss = 65.438034, step = 1000 (0.152 sec)\n", + "INFO:tensorflow:global_step/sec: 660.171\n", + "INFO:tensorflow:loss = 57.25811, step = 1100 (0.151 sec)\n", + "INFO:tensorflow:global_step/sec: 676.676\n", + "INFO:tensorflow:loss = 70.39737, step = 1200 (0.148 sec)\n", + "INFO:tensorflow:global_step/sec: 664.916\n", + "INFO:tensorflow:loss = 63.969463, step = 1300 (0.150 sec)\n", + "INFO:tensorflow:global_step/sec: 679.204\n", + "INFO:tensorflow:loss = 55.910896, step = 1400 (0.147 sec)\n", + "INFO:tensorflow:global_step/sec: 680.936\n", + "INFO:tensorflow:loss = 58.16027, step = 1500 (0.147 sec)\n", + "INFO:tensorflow:global_step/sec: 670.412\n", + "INFO:tensorflow:loss = 66.20054, step = 1600 (0.149 sec)\n", + "INFO:tensorflow:global_step/sec: 673.441\n", + "INFO:tensorflow:loss = 52.643417, step = 1700 (0.149 sec)\n", + "INFO:tensorflow:global_step/sec: 684.782\n", + "INFO:tensorflow:loss = 59.981026, step = 1800 (0.145 sec)\n", + "INFO:tensorflow:global_step/sec: 684.191\n", + "INFO:tensorflow:loss = 65.427055, step = 1900 (0.146 sec)\n", + "INFO:tensorflow:global_step/sec: 683.812\n", + "INFO:tensorflow:Saving checkpoints for 2000 into /tmp/tmpts3Kmu/model.ckpt.\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Loss for final step: 42.740192.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gbdt_regressor.train(train_input_fn, max_steps=max_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2020-07-15T00:50:45Z\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpts3Kmu/model.ckpt-2000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Inference Time : 0.24467s\n", + "INFO:tensorflow:Finished evaluation at 2020-07-15-00:50:45\n", + "INFO:tensorflow:Saving dict for global step 2000: average_loss = 30.202602, global_step = 2000, label/mean = 23.078432, loss = 30.202602, prediction/mean = 22.536291\n", + "WARNING:tensorflow:Issue encountered when serializing resources.\n", + "Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.\n", + "'_Resource' object has no attribute 'name'\n", + "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2000: /tmp/tmpts3Kmu/model.ckpt-2000\n" + ] + }, + { + "data": { + "text/plain": [ + "{'average_loss': 30.202602,\n", + " 'global_step': 2000,\n", + " 'label/mean': 23.078432,\n", + " 'loss': 30.202602,\n", + " 'prediction/mean': 22.536291}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gbdt_regressor.evaluate(test_input_fn)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ab1b58beb18400d19e7ce06408fc180e13d78bf5 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sun, 26 Jul 2020 12:25:16 -0700 Subject: [PATCH 156/166] Add tensorboard example (#381) * add tensorboard example * fix desc * add tensorboard run cmd --- README.md | 1 + resources/img/tf2/tensorboard1.png | Bin 0 -> 77368 bytes resources/img/tf2/tensorboard2.png | Bin 0 -> 167312 bytes resources/img/tf2/tensorboard3.png | Bin 0 -> 137359 bytes resources/img/tf2/tensorboard4.png | Bin 0 -> 221040 bytes tensorflow_v2/README.md | 1 + .../notebooks/4_Utils/tensorboard.ipynb | 350 ++++++++++++++++++ 7 files changed, 352 insertions(+) create mode 100644 resources/img/tf2/tensorboard1.png create mode 100644 resources/img/tf2/tensorboard2.png create mode 100644 resources/img/tf2/tensorboard3.png create mode 100644 resources/img/tf2/tensorboard4.png create mode 100644 tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb diff --git a/README.md b/README.md index da394304..1bba79c4 100644 --- a/README.md +++ b/README.md @@ -40,6 +40,7 @@ It is suitable for beginners who want to find clear and concise examples about T #### 4 - Utilities - **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0. - **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models. +- **Tensorboard** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb)). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard. #### 5 - Data Management - **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...). diff --git a/resources/img/tf2/tensorboard1.png b/resources/img/tf2/tensorboard1.png new file mode 100644 index 0000000000000000000000000000000000000000..e0609a90fd62c0a723f2fc785439982c6d8334b0 GIT binary patch literal 77368 zcmdSAbx>Sg(>+RrBm@W+JV6F`cMC9hfZz_n9fCU~A;9477TjG24epTO?hJv!VQ{&J z=lQ+w_uYSQ-MUqGS7kD1hI7t7y}MVh)w?J7lY$ibYogakNJ!{1(qLsIq!+)Cke->n zdJ23plt1SK{D<;ITFVIu=}q_JzbEkwZ-|kQ$dF{fqN?s`drMxrsxJ2;M;FlHfK;@n z{IoAH7Mm8NOroFHD+l%fPt*3`;w`@*Xue`=sy9{Bi&0IWV1n$8XTN zAhQ8qh=@s-=)Np(sFR;E+3ZHeBu3Bl@Pm-GPTE4Y! zs{21{eEPW~&gT<7-LXlzVj-Kws4#IEWu+9=1UYc2a2O&~9U*73=*`7qkM)KsS=p@4 zxb6k1{{$_B$Y~qgAl)Z>y zsuYD-%GHp_c+?$zyOH6B4N>K{3wj(LMNE1kh2O2kBs}4C2A1bh#n{}}DGJ1;+l%&alBjG;ml6$ZEK8rcRg6gomZ~>@inCZyN;HHv1MldM zuqITa?b+>?w&tzHM(Lfdy|LvJ#9LQBNsO2*-U7z&3}zV#5~h%;)bB^`w$hiuieTdK zxe%GAsYCJ7Z2#LN(r_zT=Oq&<3IW&unrle`!d8Ri)=y za{bYC@tHJsH!5N0n%|&HJjQy0J&!WprVdr-&S7_p3#A;m8@aC}!)K*sTj+R^*-Kuz z=RW;3+)lrAXJKH^_-@Z7#p<_|?uRr71S_;$Spe~hO;`LGGpc!gFkgvuf~JN&;)luc zq~vKwJDuqlavasW`1=}8~+!2teMK`-0;ra=TxH^)N2#*ppG?Y^`C0^)qt zXu04F=h$5-HBT2HR7ax*jcmTBoFr@~k^|>DZhEzMxI`MhTgod&H9D|5wxNd~H+t?} z!+K=#IpX}>8^3BY@2RQJ$QLu=EBY|N%36J527?a;(&20nRfUsqBR2Cq&BmE|`qY^` zFz;z=Qgd(p%gcVAtIFr6%NIR0MfDeAXy&7uWcNqm2pMbCa2@Q;5|Q6yvkikKHXdX| zxg|6sL3o3>$l(|Rj{9mm<5+9`BJpDaSXJp6n>VAM`9&mP{6)0#yeBMkn&R@plY>1x zqr~kb_BRRYk-EdK9`i>M1`89KK1uw(TCp%Kfk${;)rdrq-c(S)D%pEC-BXSMnEk6@ZGnZxc1=O3C@fgI??oTGDp~qye6VzXd@#XK zokq{P(J})Baheqp`1CHD5Co-Es~2RBRmdNh=3eZ#UC{}gwlv?YzWxNlc2q|RiYcWy zx%3V8zAW)>2e)ZFwRCZ#&o7JXc^NFdk6%{rrcm*`P2>AMmDuRuO=%)(gvPjd*Y?_= z5IesC;%H3MhX6~)6q^6OX0zA`2vU04iP8SG9J3G!%YClID`82p*W6dD`v%i!;Te9M zDCps(d=#cMNj}D0l-DB(akgR{7JX@S;}LZVqM=9h~|pLG>2xto(#+r6k|0MkG`R^!^v=^9_(<qOu! zlA1DqK@nWm-d(0T=<$3$qloAhzx?KN5u-*+QjgHI;`@ta%L~%HQ!l&B--j2y_WJ5i zRdl@nf`zRToVR{xHbx%5+RVHsbv$6)%bYo9_Q6#3f9SkI!yL;WY>?Gjc~UIuM;;i< zY>G1XKn;@I?+$1K#b?HZU|>djpMtQC6~i}f9z?ES634@?y{9N@s-T6eP^wr{f{|01 zFk#)6FJH?ujeTAe2QvTEJA?$>#P4qIDjydd2CwmMxvbT7{_)cx?m`fVfpveeE+8p& z++f(gB|9e=|ND1rsiFGItq8c=!bSHt>MlOsLJJD<8>q|ncS$yWQk4@pxZBlJOlTKX zi#25XqVkR1B98Igs3Kvze5Ef^&xFoq3cK8n%gn;^+oCf7IJ)=yxHR@NQGtj$f_J_= zR_f}GOJ^c6d74dF9D~l^E08d7--*4uv#!?BrrOqBYa0I%Jef=k{e~gE>X;9iL!|;` ze_Ccq$azX`oyGOiq%;68ZxBl~%#Aa4=?k=hMXAd9{Hc;T2$ZEctUY_i^W;*iFo>q= z`V;#iCu3xNL@_zlr-d}Kce3N7{g!=aQMLRFcgpk9h{eo+i2;*Je6NOJAje}RyAD_9 z2fg8L=!IVKUrNmQkuYoEG8kriUYhSVj_s2Aew~$UGQ4%Fx%w*I8&KHvfj`}3xmA`u z%0)tK=R@rPt2{2&P}2_Z#h?u5{_@gD$&Gv&u!-;`PoIeHYUb75v)7nvO2L%L@;dqbTpGdBZA!yZRMktFH8A<{ow7l5 zefkF_T%kk+DRsu;&v!<5L4#Zx1T>1mp9`T5UoBcg23CG1CkhC_Lz4Cr!p>AENc4jIe|@Tb=`FCsaKqzgBr8`CLtrt zZ^|UsgdyFGO2Yszf`taeUnQ1WnTiO_S2tJ}P%r-6d=6{E!1J3gZ>a;nO!wULRC^F1 zK)uirH_XowA$`Yk+X&sM>o|7Y=bD&ap)+TvxbJxxf%%4sazoxzn&2hRsQFK4-v*Dv zObEnL!;;!|TZ|?|#HZqnWMeWI)klJ!rP7%it0*1X&$C6m9iEEQud9ewLUK=dv1$K9 zC3OqF&D(AvNy>bCR2OwjhxI2IHzl#*3s!scd$MJE-0DQ^=%`lJ(-X6UB8O?D=$*5- z8{=wCF2TnUCY+;#e+5zh^#APw%m%I3_4p2F`vHFDpuM&B7{j@X$XLONiiJVmokD!- zyyuzJL9{UOgX3+j3#6ix(&80HBh86~8Af9zFL@ZEg7u7_K;=y1YYWJ}4%C95;En;d2jXr(Wqto#N;KU;ctvT#=_mS9Kps(08rVH8^c zYfvJ>a`62VHd(asRq$MO6T+3g?yL4&$6w6Xyci>vM zge1d6msAlfrXEvG0>Xc{!t9SZcf*p+b)B5Tqde7`t%c7duH8!&2Cq>*9W=0=!T$`3 z;Sx7kUvx&p+Q9Bc|EMlSm*cAo{S(C1g~O{Tq=rBAJpmD-)*6-nKKmo%3@g^ayhf~h zJ6W%&96i$p;Q)sGbQ^nY6}m(#_SBt`)=Q=$n;@BaMl z{E`x3{w&0-@^=ee#Xm(X#5*}G?iCGidcZso}0z4n*pFTqqct@ofpcfocG>Tn4YPFQ~yQHpNvt6pVv{2pc?R*0v#?@xjCj zMmHYax~4xKR`2cbN4&PRh@}Nm{`GBys=R?|ph~o?wGU$CC4N$U`v3&PrINdV8sunk zDPvO3(TTA_OTj6V@MEzKj5c$}2QngsD}u+10<*ruJjvad)Dp2* z_WVq$X$@|om6}=6MQAU-W>uTuaoqP-(oeJWp6eEIrb1>({F!Wz(9HPVL(xyWce2jQ zbRFX$1nKst;^R}X;O<6GjyHB;!XN2n8<-auGZsH&D|rY!%EezoZ2dF|(%&<}r9Y>T zoFknTyaSknv z8c>6na!xHTcfBLs&P#fOXn1`o8!q!mYECjj)ev%&AaZNN(4DO6gEb-iWzdzR!zoQb zni}F^+j>6j^L*N;2fsEk1ys4sI(Nf`D&Ic<#yWc~iJQXBHyThJ)v5sD+6@_0wF_`$ z$A9N1f>^fu&>UwZMfT81lZHB=kwAdIyL+EoX3zj;$C+SK!gUY`HN9kaa57m6`na@> z(uoclcNE$n1dUT23HcDRLz+8xA7B~}4P?f?Y|#nfDI=D_g84-oMgVWMs5kspe<0p! zC{_Hi`_gATN`xM5d}pqAD>{$`TPG=_6k(X7Ui{q!{DRFZnB6QQTqpZvG6wo#mO%Z2 zCf{yN4#x088M#Pbdc77r%2L$nPrxoMWegWNFMiO}XlIb8HWD*CC7E<=y`C%9Zv#_i z%5yUQo*u#AAQ_2It=pRE@ZL1oT!kG?V-$b$Gtq4tj5h}eR=@6$>z)F59C13$(ZjJ2 z4&Nxt{35(b#lDO^mJejDRez~EOn%OZUb&<<%V5fxl_+~XY}EQ{-#_y!?>D^U$?IxV zJyn~&{CscM?2*T;2a#@b8MqG9)iIoprp>r752#7!$H4+ZOv*b_wEM`&Fh#RA(_y;i z8dJc9HOzn1^_F8}Z=lJO!5}QB8R2o`Vq2KAhL0tgdkk| zEkaP1T6_VQFUM81GnstP$zr<+AR~?96^yEAvyhSC?~^sIX}72FUE5%5;#lkQke(fz z#O0JKYv|ADT*s*L^qBI^T4nl@Q*zK8OodScS4kX$^?eie!1xF@T#3!T%(9Qs>XZZQxZWFrz8j4q_M}W^Vq#;i94*@jDqSoTo`jEkL zEGnPFPfZq+WmNR{samcqSGRkgj+msroJrK7F(D0!p@0rB2hFyZ!AijRB}GEz3(m-TJ$w%3ZNo z9adWf3&-zkr$;xs&;C@`)sCokHeEF}iLgy~ynfxgcK(%X%Va_N*jKQhZur{Ln}C5H4fVvN?J&Nn$>*chhp;V66I+*>UJaf$_UiVUJ{$6GTqjWS_jcC zSrv3Wyvp(8oOo{ZvX}^J&H69ny{8?3<8M(WDZsF>)Vyt%-}R0pjWl(ic9a{9%5I3n z1VhjU``L56+0}@MMxt5ZS>$lvj}7ptN{S6FP_ZJHtv>A(rcy>y4DIvWBHwU-%mDGs z=psp>OO|4C(vlYs68$>X^Y?g!_R;gngJ#bAhWyt!U6t3YYAb{%W!O7L)L4Y=Bo!nlY%AMtyMGLdGYw1>QtgEszy5vF4Q&a8 zn5ewR!s^l%`BnXyGS<{HKZYnOrvM-tF%riurp+kReucVT@@?n-s!jnfT80n=^zmCO zZ7k-B(^ERITJIxTAPP|HR}Nx2{A{b3#_Lpi%O+IsR<<&U#zeUE>4&%q?(zmAlV0|J zl6c=#s}Z~BqMhS(ww9v7n3bigMF#V8S-JQCRD8_Yr=q-XmO#YzSke21$~)tRJId4x zr&O@XhWfQQ(tzSe3JkHDmwsC;TLv%fhB|gW}YM_7{C!|LGM7F22gjK7KnlL=TAVSxtEP> zLB0!$<(6>Oos(nk`X3nS-9ZYxET-pvxlD|QcNqojuRV2Gc`-x;5sTNI zP)2z`#I5j78mrF=IvMPcbLAQ{y%ACEeYUK)b}nrS6&sbEa`ICrspl2dWR$JxO$3S) zUOM&t>I`nO;M^%o{bez*S!!7_Q|f?wz>@h?$V);pS5#vN8txhCyhQs0XI}RfRPIS%T~*G2xL$>Z-I0ub$zbO z@e5-Ps&Spk)gn=`b`sR4JO*7hPkVfqAUWLrsjP93jf*`GzAo^H^g%(QOqV@2( z($s|GH#up}>=ttn4Ar$ir9l}=T2if+1Fmsn`<``Ssrq!?`9&gqf0KeFc+W}6&2jO0 zm8aZZkeBT{AYs}Ddls@~zZhDi4 z1%6P<&e?NP*Bch56JWh~a5Q~h;R~jqdh5S$pFEq=ggO)+|Fm#ndeL_=or@<>hEjAA z=DukF?*4n**x#VaMw;T*>ecQh3WjMb3*dBV8lajEC)!w@89Z}>nWr2RtKOLnPKCq7 zAO*2Nj^$hVwy3hv*eAuRkZ!{xYiDqRa7s|SfSKg2VG`x2|3RC$k~ zc(3pTZ)t0(C~{9>W9968@shDDL&c&?ZnP;k*5q;^!VWLK_gf_)%}O)Arq?G-upqXU z)lf)!k@I7sW3&7ai+G@(J|r;qJZ_#Sa3yk>u;~IAJx3?&mnu zLO=ib+=ltcp}7g9#Uqn@1v~G2ITd%5Po_g)P@+^r10jXSM;|07I?ip4ZwBJ+(+aq8 z#_~d@rzGs-Ydx7C*HEmwI`l*+g-hUpBb2?sgO4b2Le=Tu7<#3@y>QM+)7Z21+?S)3Gq*W?Af=! z;IL~G8!f)kGOKdIVEtfH%O41l9&&%drI++|W*KU>2JvudxfP3{=Y^n4e}0CoY7)g# z#%U}6cBnQ?T@FaG#eZnjgOSy=t$b>~I2hgS2JZZw|K$6zn4k9}msxNvtwL=rRF-@8 zfK4x-(l<<9YO^a`pcG{qM@>coaC!D*j16&+j;2Re*7typjC9mw5-8j7-}9_x`S zAtJsd9#jFS5L1442LB;V9uc_3`}!L)VUzDypJKfCU$z02z1)bKmpymle1m|~$Ez@O zK@PqIGXfbwvD-rbk|%jLw^|Tn#4j}986dZP(a&Yt89fFN*@$+QO)n}z2$E8pN3gVd z{=uNj%!t5pjGDqOdFE_JPVNic{^$7>#F8MwDh08U5?us%3_4{00Ln9a<`MZjb>Odnc2q9I8UOGSqH z92>iG=t5)~db#C3gghe9><|5V4CxC4!dJwj=BxWd6`42@cR>y(tC=2Bk(BYOV8Ko>e=adW`3vx8fbw>f+>mZ2WQ53 zmY?ggA71ASv+jPYkzwtR7r~15uK5wCP_tea=0vlzi_ySRbG5mQdnNhDf8jOCW8T1D z)s6Y>L{+h81timtUns>*mCv#Num^S|!V`a8_i$*TZpv9Yc5`y`CeU5ZSBEe;oL#IAW&+>U6$W2Pw4?Sm2>K>i} z`%OWdP}!y=x(1J5STp7+R!35~j+OP{5SxN^VP5ZU;_fda#vT8<|7*z-3N5lflrw6B z`8prBF^w24+u1{!_f?>Ioj&01fOaD~t&L&ih&VWd4NVe5_g-H04&DZd316&Ai4n{p z+I~q`9BSa1ixTsh%*h8%`VuG{GESZ<-t}HjgrPphH#ZSZ=s8X_ z{jC}(2Jm7QGbQ;Pwj|k7y0lwuMuqL~`k$A)gCYAw9Toy11#MMr+j(-yz%K8GiJnRV zIcR53p_c-J{*EqfMJndA4;HD|%3!r-qXy$5Lp+If8j1EBneB^LeeEXBZMwf{LD@~O zcdSUZ@on|aqWn%I)@uhB9$gseU<{dU$d%wT?Tox47eG!FcG6A|0q#X1X%u`yN>eYg z3d00(rg9#JG2At{98p%}A2*Gg=Ov-^YRdN*nof;7jS~7}14~PP{?iK}A-_dI)b@Se zduRM`d%bvsw-Aoq>SnE==pxwyM_1igggd_zqs|=w80ts zDD#AT-k0x*=J-v~!mDA%IJ!r)Aa*VkHe!W(flC;(5THFY9H~M~)S`7OBcp8f1eLqo z|89nqxV5C9T$cb2$~6F6T9M)NP#zVld3e2Ff>Nr(xh>xr|Mc}M3LnPpmQS_a0p+?M z2*kl^&~ZI^EOoPw41;gv5Z(NL=f1M?yLF%BN-=Irf?q%>69q*+Lm;nnYtS2h!S^sk zC_y>8`X8T&Zek6b7;tvyw~w+awW)#9G&@A@bVeA8fL+7hkw}bmN^Bx= z_$c07KdHJzg5!=Nqv5=vQrhQYc4j};Uh9^HCs9<<%1_zjt(;OFZ@vJ50+rB{Y|A8C zk_P^IAAXT}w${I0{14@L|8DKZ3c`1xSo*%;xDt!-9 z@7q7#(gl2+0yZtdlq>YM^Om^C>(F!{0RXih!s6Lc@^!@(9wK2p_UJ=4>Bh*Zk#6H5 zfX`Uv;W^tA zilG=q7>GbKYxNB7u^iO)G8(0CJJi`#dWe>dtjhQXY{~XG6*+E1mEbB5U=_KGkX}B8 zmnK$Znv;430vlkI3WA(6SBV?8xCdQ!ga@vi-lG4YUrvzvzq(n6CwgU z&m4Dd^~YUTJ<$^A$6kxrwS{66_MW#B8EKVQe_?>*F8{r9Dw3xjeJl{)UdJ`$% zD=0a!>J$p>J4Y`1R)$V?#P48YWBCzZxV@Mw(Y%x)cY0NOM zt7H3zLC*A06WT zmi*YoJH=|sw5xkpIv3pWJVs`+1kQ3W7_EfJszylq+FPB}HAt=$9BVmtld@8(B6Qjg ztMxz(WvF3XN1;ZTzoY3_!yj(Gw-~)Vd*TwH>O0@K!B!V_^yjF-lMyN52DEE0 z)<&A%!I)&b_}4-BC?0SUaBOM1+5B}gbQdh{4m;%0fmh|6uP z@l~aa;|LeI*TB-jH`959lv+2j&95)&q@WdA?!#+8qqTdyvA2Ggfqqt{>|KeF;T=*r zo{+{>rQ3;$0R+zv(%7E89yc|Y8=UaoO&(RB{OYzpVO^}y>yR&-2Eg93fUdJ6P$a*q z9N5EQ8Fbl#E00h8F{ghK*tm#w%Ldaf(ze=|$NdjEzmTMY|mp6YxiuKrm^{ZlH zfUY+-!G5b0rB{-Haw*QWkBjj`{jXYQ(FyBN5l-g2%C!zPx|>MqW~YSH9|$G+`$Xz} z-_hC2WHWTK?2`3_!u+n+tJ@$ATvRR8tHR{@nRDUuV~H2dDH_6SjHhqW6QbQ6tfdB2v4O$T(`}d~&XJ|V zP82aLBiz2w57?Z0{ZDO4fj$d%#*``6WG2<3DykPdpGwK8(jn3U9}$w(gDRHA-&E^u z+hn~Brid-+D`4WU(AUjc+4_Dfx0dhy)s<&4kc481_BCml>B~KKfgPN)kUTPk$MHEU zJ-Wn3`LXlKgeh*&QKrNgk>&0FoxNELAajh7b``roI$vnbUFfX!7+iW|Xc+-?ThwcF zdz07^$8%#~YWXC&O1{A&oB{q6>G(}8o<8C7+Bq$?;u|?biE2S)=)guh=-|fH=3&)k$wvakEtGV$*@122NlriMK8hyh-h+Gk)Vp9wQ(0F+9 z_tQ7J;eRa#yndc}whaDn?;?`#0BZo@|M`+c3D{Nenw9?=)OB>YjM0qpg*cz}KgLG- zs+ZJ>_TS6=zuSoU|53l^|Lr#Gq^{3a6FZJ6t_*qB42x3_B?F6tmLB_UAIH}qB#Qst zw`#UfZ;R24{_mH->k(~?X|kK6qv!0%e?R*w_4a>tv|1!H4z<+OoOap$Pmif!h=~^L z{kwN0t{ne8YZdE%PfMG6h+RcT=cY3CKMkKqNRhPvzv-y`-;eT7q=r{Rc6Zd3KY^kSATsAS}K$pLcnK8U2%{g5G!kI^cP)ScK{Y|!e7=nlp)G&IC$R{CdJN2tAz zZO6Y@y1qSo8F$9Yz`#IB3HERVZ1U{on|FMUCaS8F^L5rV3TeDwR)49#c5rcd&t~*v zpF>|?UvTW^=EiMrw(9CC)AxEuMNtSBAKy)c>EV3AerRyeY&gk5B%n5cQihI>?(P}^ zpDdd2FNe1Wg@%T7TipR2$`qNAfnFoe3*?`~}^Rzmn)_jrN1p40Cq zX1~Ar-O)iVD0sWt;s1XSZZ)G$@oQ2{jN>V6q(0L?O)WklArJ{`72o9Md>?FLA9M2c z$ur2`o5S|@pcrJ)Bp&uq# z%dK9D>HKO228Ra+_lr(rLdV@WgM5iB2FqBP3P1)H- z7Y9p#w^cK`?ay;@bE{X(;3&Dep4Zzh1fmlsL`NUb8b`_}v38snmgwl{ z$dQVq&PK&&W;AFMh$LCq#d3GiW zM$DXmAP#!-em+kQKTimF!|!=!;o{=b7xDfj8o^KSl=)bOUX9sE7$FD0+d*lIWV7o& zJsq7=kt*<*-BIM?ac{;(M;|R0N2j8!tUNe0w6wG&1uVRN zhCVO{4TYPdE(~KMqZeodQR(TePTQkc6p|(;Cc5putpKu6NQQkfi)(NMK*Y+*^JeI^} z3@q48g#oFEuQy;n$lp}^HaZ3dVt!`};PCb9*W7lCyteb4t@`$yL>d+PWm8sC5$^_A zhwtxhM$BrIN2f|O9#6-{W_LeY1zcUo>s&g<1RYVztlNl*i)(rlW?S-HR1@l$H)rF! z7Jxr&c3Q62945H|JbW-M#pB3P%oJW~c4MKZAMEe{;_7PWWk49-yET;P<>}dWxgPqi z&(zEeIEkB$Eqa?tWKA)Rm(Bg?GX_Q^HGchU=IH3C|4WSf`$G@i8&kGw`yx zgR9eRAt50EyEL`5pguR}BdI*?*SjTdE;S1c_AHEy@3?J})Juc_U@KNDS@wi8nVW9| zSh~Lmb?T2I_iVl29EeTjwsmxNuI+W5#y6U)HWd~YW@)=Ll9wOJ5b^@NrQl<7Co3A( zKy{19DGWez3(ge4>)-4IKAny?ShDKOurnURrU`|!%d%#4Kh%TN22HeX*~ zAbzDHNz_I)oZ-{qhDo9LETDgXh4R(ww6Oe}1>;h)d&hd*UiOfrJbpVQX$Hr<$7Kdn zsh8BVRZoa#KlJjAh!n?(Fd;vc*J8fC!QuVtuKO=H)h5_h^w9S<=}cmW2h+O6$uK@8 zdjo~VI-?e;of(y0<_n!#Te}~kE0vN6tvs5`Vht;$(%pe zQ5(G0{Zl`39tXuxbwotO;P0A~lar0U$VdXVY+$Lun_F9mIrGfB%ZMbSM1mXlLe>-|YEO-5l!2?H~;T`Ns00f*VgL}tA>TgkNabbJhZu9)YKF}#(8B&9sPB@@b?1h zGF~(vla3<&Za07wKaC0P5$}s6ttc#XKONzTXB-vZi#}edAAOCMOq#=IS)q6)0u`bGiV?QQi!^FPwO$WdsOe zfQTuNi-S%sE&$q2PfuU{0y2GEARD8jqqEQ(vv7jl&_7T9p5OWJBD7|I^%@TY7Z-O2 z;R%=iKtogT_3PKu?J=DStF5Vqix{EvSz~~pPA6YZ?)gZAwae5!LjV0G+M>S{`-hhJ ztEILTE%p(Ozd=-&IRyHk?;7b zWdf5jGarBhhR4WGO-SfwJr?)_224dD7Ko}r-Egct6->6i~FvJ?_EaFIJ{jtzYP@7-*(V9w? z4KOJH8p${~P67UP2pG!+ebD#r+LAK`U=$>R?!V{e_}JO2{T}Y?@91P=IzC&RWH`9G zwiz?K_Qe$0vK+tXLNl9qE^kn47dKx$Dx4g0?!#_+?B5{|L^Av}0TnR|OIs!3@7LH5lsj`+<3e5_1fw`sD-xQ3kNIdf1pa#w~n zx0S^8EhIs~nw#@jeL2PBG+|#rrR*3k!knEV?Q_#VgBp`?IMi(8d>jf%y+0^zJRlmp zG5H&JJCMK@foq^hn6i0qIluDK5<+Xg{F@FrwxKN3M)!Pu_E2m)qPb3zj%U3Nn%PG3 z$64JuRBy6TUq11txqhmETMOcAYkvJNvnFRl;(236O>Wacqm4#f2D1QHRy@kF)CPBh z4%3``xknpp_r5fw+{iHn1O@>5x(#-EfWS!Oa{~Aj>*rrzfm8yJDO&&6K+>MQ#v2Cb zDi@XBXfHC=X^&Lz0UXUHhg3?cubCc<`6DeYZ9CIXpRmDVXm0Kbz{zvwv(v}Hkvam< zzNI!FFMzDO?280cpQO9Yb>l4@Zf#AOMCAkCj16BD$F_0-lv0UCMH^yTXRwi5D`CM= z4SweVeAwA32EDH-C-S zP4u?iX*o)+F@eV2p9ciGHgqfI?}ZavE*8MK%TgEGubcT!W9)&RH_CP)Q~mm(+;;o< zddq<`=Bv~E;oGLTLTfO1CS-3<83ZyuU*5A)(U%Q$6!kTWmIHs>U<+?xhsk^SWm4Zy zAX3$bNhRh8;}f3s?jfrz%VatDt<*Yg=U=f+5wCbWz?J0}vvK+~!kjdMTAC5=*SD_( z3b>%HA#oQ4O#O$~*V*!^>Mb`PEnPE;$a5KWIcx{NKW*QU&!LQ4$a+? z+e{`#s!BmY!NI{{`s~vH-hu-uF35Se`DTdN?3>{QlT3Xx7tk>+y#K6$(un~9yB#wE{sq_5> z9-G+@;K9-MtgI}Ja$QShM}SH5Mp&D!36hd#!e=U)q0qT{Ak9O?gWdqQ1X4tOhNVs& zpo?vj>w`+;bSjrsEiETV`I)TpL?o0J>uq&De_s9jC+GC!#D->;C|nZGkX@^l+`ibG z%EZEAP-~%NXh@C)D13$?I{Ngh9()5@oHc-C06KDeYs=;8WD8~s$hBJb*;q(ONC?1< z0OqFMK2aZMb4aDWu4LUXGdZbg-QWs2k@xVp0$|zZ;>hwiDl>BhVBux<9R3|&04aE| zv>&l#Hzf6IYHDg=UI^?6__UF>X3=Q0k7h#zAiTPK(|byp;b2#bk>^|t-AIUV;- zxI8w@gL(xws;DWNjT|)+_k4iH4Yu7qpX7%14Neb2Wac{q>ljiqyN^W>cehq{kEWZ& zxht;%+N#7}`|)f<#TBv((7-t}(~DfD6{QE>Xr}L?3g%3iR%|ZgG?jnR<9nSra3=sv z_s=Pnmy z+2zNvH!K-38lOb5^!B;yY_%=Nw&M6s@!kfE8n89Azr<2Qu_44IQznlGrKY#vswzid)+5j*G&~c1cuPj0*==XsbfwLwni2JTRCF{zwFXQ;*+)l502p#vPXj3Z6*4|CQATxnb>(x1m;pE? zz;_z#mH;heopA{;_u4^y4$q4N!24POZVZ%>_yq*;-@e`4+#F8hn|Kw`(EFcW03ZN> z#4O-;FloUF=qi8~iQFFfyYO2@k_bh-7nlaaISjk&=>zOV$873vlJQ z#6)vbQ>^G61qVNdyX5F-B`qyJ!_Mb!ZWjPO+Fxh@l&$#OL4I}h$%1`5ItE5XY3X8} zwFYo$Z8XgNrP83FmwgTZKMM~I1_;6E$cU+y`rP>=W3#lhq>nEKlsy#{6%f8C7#Q}- zdp||}6PWdy?3S7U#xp%VeX!iR(AP%;NOy#Qt=z!5z7altBA{Lh^70(s(a0zR%sTc- z1`z9h2hi>3qO;9z6*)P^75c5(B4=k4iljoG&Oq6Ojg3ukjFk7w*X=9w43A#@jfG3B zzbowzfVKqq4^TGn0&uCNMR4KH8z6~ck~#a@3ID_ylH1lFG|XV>37_Ak68>g{a;N8c zrCRm3>A`cj@M#9ry&^qM^ivD2-8W!KP?OrHPsML!(WDS*Tfm3#krK{EGr8uYh+HodV6gbyZ@a9|Exls99IZ zmXbZ0wmy$>f3!f4nKtjIQWkpqyd|gA7G7a?;m>g)&PgIU3($3tvaD}(bdwZX$j>o$ zRND2EHt+UEm)Ow!J41suwsm#MZ*tvdfoQ5_4ocQUucr!&WLnFdqop(=Ijn_K**S84 zX3twt#tyu-;H;mXG^wQaV_alG} zA;iwaHUMY9>c0o7OwG+VKyji0sGB-%D*$xk@87@K*&x&9D(x~~#FPf$?SPMYYv@*!Eaa{egnxx<5a)KK?_#b~Y2JlkV*70QJp5i|*OkS)hC< zC?wSPYMsC+6nP+B1o@|<0GRuG@+vlXuaoQ}A=SV8^%8>nC>8u)4jlLes@ z=c|`+u(Jcq$Ay3Ck+-)7+U_8(=~VLf=Ih`k6>Z~Ib&A4Qn*dWU6=&JElp82(=>}>i zfMu*59jk%rx~ghi^;)AE;POCS&2G764!Al1R{%Ai#sFSc?#~;Fii!Y>Q-KT&RsbSj zU+ry3Eb??v#{$3~*<_%Mel}NQUKa2c7nhHhmsXVNAJ6~wNW;5BI$M-aQ1_R=Nl$Qb z9w2@v{eDAHz^Icd$d z3BgmQualD-x?P$VFdt0IZd6RXHT`t)#+kvx>Z}EC)Cz9t3jCaWG6iOhmI5gh2Y>w3 zlj7r%EHS5NgxwtUT;g|H+-=gs*m7&1gcj_9^Z{w`Cwm%YRmV7aeAU~SHkB0jR}*P> zlE0aQ<2Mt3IoI{*D&opAF_b~>Smo4lIPj}E8HTyF6_bsiqs8Cq-lUKX{>02@EgZ%? zx-mvlBAnb@T|?aFq(iscD&S5l;%BowGop^~o^7PeV$Rg(lPc-&=E_(uJ0*Bm53fu# zZD!xg=IP_d_6Sh+$0f77*?zhkooAj;oUw`QUrwl#Z)Y-fJbBvQs5T@%7qtajhry=3 z19<^0P0h0RJ4?LFjyT*P6cQOZHk`~soOuP*Xdh)|V(stEwkC94U0rI$jK!nrrKLOQ zc80Zk$&~(O-TLnCbtfu|rh(4?#o1d&McsCPqu2q6w1R>R0@BhADxrXYbc1v^NQbuq zLrY65NSAbjN_Tfj#}LvD?*{MZIqN;=cg|Vsoj+K>%zWqiUb*)t_AIQjuS7VXo$P5= zn@&@#)tsHe{=2ZSpi0%Rp%L!qCmx!tATQ6i*+d@1f0(wj+7eF7$A>x%?1mXZ$yj#$ zTq2IUXv$15C!^n(c=GpLfZ8c9EDs_=!ofegfjnytaLy1PG{A z@*XZGVPbvVsA8?|tyn9krJ-2{jHXQ1!eZSk)3nItma1l6WaJ>kcf=F?$A4-A1a|iJ zYIp-pPsvrKQkof2G-vqV3kpo;No=gGuitqv^xc{5e2%GQ5?f&k6_Dv|78DH3sdFYS zX2|%~kG1{gAPi_24N2l)eehIwE>=c`YL2FmyolhWC5-OYgC)kq?rV-~Dv=>kxwmdt z+J<6>i_i;mmCZ%kf2xqM==mt(U&v5r$Ta%-p#y$u`f()%BUR0mK9PolabHWKscWrk zjLr5iK~X~P3nAX3=h4Sm^{8zc^-n=hj&0x1XN843g}Q&5?7!|(Oe&r3rdW-zG%c-3 zeIe9FAbZlm#4cVXSuMSV8&S!oZuTlR-1w;<@*Z<&C`-V^Y5AWet5%L2B2+%D-gvS& z=QQQp_PYR&!%x+Be02l*%uyksp|IV(n{5g{IodPO)!kn1ZykugCt+=6b)VPa51>{# z*2JOLV>m1{ij4cQ1dc5MtOkk-4-Zc*MCMY>FsxQNH8s< zb+3MkS$juoE6XAa;jx`wju_#{*dIX&A-PXR<6}eqraI|NJS_a|*?o*ol{>cySMLT$ z=W>|XxgikIU&zExa?~W=d|61&F1;%Jk)}Wfhh?j!UM)H{p*QNW3Z5-X1sb!&z783x&>e>xgbTeI13(7Ii!7q!+~atBQrz zYyZ6dkjKjCY-Z}>byau}vv!LF=UYcVitox5S0SOV{YDZ_Ro#q>`2i8`wd68RnPDke zg?)4%$ec|^vWD7ZwV9NoX?G5lyq_NmJ#O!wPi+$$i1d5XYSS~lz|hpjP88skeV;2i zaKcytRnEnxiyS0MEaFL)Ip~v>f^f?fh&!M*=(ldXgw!%Ju+zCrt zYf(}*pyTk}M|KWT>lD{_y6SHZ&BaU?vP+rBrJJAT_mpkq{lxl=N8_&kMRV{_`aM5( zYXlc1CFRTOgi+zf*$i6!w(71;OSozB^z4}FqiiZ)`!S`>dF&4EwM=dz@!s1+v@zAE z)_qKV(l?SPE&ERoi$tOb~|Jua?8g;5(LQUsn<^d)q!B;y~b>4xM~zt@SZ z^VB3*pVrrEznjfCQW0U&3;9*1vRvMMz|-ytxE;HnZY7wMw?G18Uvm^^Cq7NAyd_(2 z!O{L2%ffBmL6te`HFsPNP*AJt=l&m$n3(Z?x3ql z9UdIWh^SvH%gZVX3hyMyZv(%g^3CM$Q)t<#dLoaOZOct=UsYFECv}_+CRif+``j~> zQ54(VOJf<+)6*jrf2&D#@dH8gU+(%D%g}!m5*`x&Em&pxFX#^v3f%wJs*3rqCSyqG zKV_4JWR+E4ElTTHrb&NgTAPPVt3wS8tH3P=%ly79MVU1BQw zSG6IY=}+}WSkJdb!Jc07YVhxqXev^zSCIfg4>~ooT?_p8pCiJmx$hTeX92UxaTpo+ z>~R`wB0l=%%YgoCXUew@l^Q~Ahx^?h$Onm33lBEiK2O!^Ft_FwL}f)?x)jQsQxZO) z`|8e}o01*4*i1PA4|usXEX>zK{$AW1-HhlFHy4*gJnuO;t{6%-Q7FcOxN^NwU;Ulh z*8yRP`gz%UML_jevm=}#O7k&5cU1X(s z5?Y>>Q>p22J|I9QrKP+)X67!&3OSuI?pMAoK7Zw`<4tGYf3$&d;of?nHEE13jY3R0}oSDYK5& z*OIS?zi>o{PmFX$PNxKIR~OnBSeb}vi-{;yi%zBA$6?}Qc!5G79Gib?js7!W8f!i- z%55_L`?IVKdwX*bhH4Zeg1PMM`fBBB<}c!+Fnk`)l*MF;%wK%MLjKClKFtIACw9FH z?cE1Y9y|YBY*Kgox;)mF)$@g7lHzLg7IWxXbUqo+FAAUL>rw_Ek`2j!{W$o2(qJcWgNwg=Chg{W|~U%Y>vZ(M{#rv>Yog{;A9((IXf!2%oft^ zh~F}?rLpNKuiF<5o)_;l5@K=I zI5X|LdfHkcbL%i9K)7MvWa0d>J8}wQrY^c=3k|~W84%jtxvC4q zk=s8j8wR6ye1Z$|S>cYbXAPTCJd5 zXlAuCCb|(HgL4_bn1<<_WOUd(7loF5$Vi@{>U7(XGR>sEO-+jX^_GHZs`mCxA2Ai& zX|opPl1Kc5i37vtS0gjC6=h`wy)Me?{a8&R<0~vEC@3i@fxv7EZ3Bq8KN}lSypGm_ zZya2gz9N}e=*1fzw+G#{#l}v!x|L&Az+jrtEl(JU_f;N=wD0>A&Q#_lvtd70Y#rC_ z-jT9I&L>>we5e|6+^8*EBGK)|?aRr1y$X*o2VOp#oTBe2k{#3wmOCjSHCz2Z#p$l*@!}PR0!GEOq^dQ_y`;l z4!u!i9foVH0`>GFF_XpNE0eeS5C9M)Y;b1>rS3a4Qc$yCDk?qiUQhlOX%~-}X3;ZM z(KnXN`Dje+YTEX?9cO&=qhis#I4Xs3;oPb}OsV!eeFk>n=cb6!XW7A14XwV0LTiU^4xc0hW5Q33>1C?gFR> zKj|Mlsy;hC8Yu0GCi88wOZG;{=sxx37Lp}>M2#1Sz`T^0^kPXV;Vt|zfVEkGNnn@M zE~Zu}$Qlw7%J{V!kqCW-KUa3>B5p3G^i1p|ioh?;9}#f5?s&z;k8<``p_YYvc8Q~^ zWVOF-=%rJ7JtTn{=me)ap)Gxaxb91OQA zk~wMcckHMis1xy}9l8~43FA(wPQFL{nPYq3u8CS&O_Pepub{6;>(eF3Q__9ynnW0{ zYs32?#b2Ee_ejaI;S7QA`(hkS zKb&13Ud%!r{xW|#fR+d3v&mr2s`}rv#E=vy*bst*UuvK)Fdb$#G2CwyIF%M!tK90C zj3*h8X61>^y%p&tJl|Ttijr<5vl(b)i?_`zHAN;Q6~5TjCD?E_l9!noZn`Z+$fRZ+ z{Q@`7IJDyvh@DJSN7}-K5~wpHgiYLzc54TZAGMY=h&0TjLL%Bpmu3)i_d`YMG))rz z=OoY7R9<_4N~EL51UH2LR*wTsmbkcvNl>X|fHx@s7bW&w7bn`6aXCfB(c$6Y5{HTb zj17n~LEWle)VD(;&+)=ZF9IjdHyq1ALb?u(3HNK|lE z*|d>zbQ;_O*+V7gpo~d>NMtf&uDfP*oQ_=DNgpE77O&vog|Y-iSbJDn#BnS_kA)9% zladrlgL@Cy$OV&TAcxg2SE*r^aK{{H?amd$;3~sRCSoR_Yicr?|7Xn1DU^~``E45AnYCI?xDx8`CX|Xmoln zmPC;Bv+EDb+dQ>{h3f&Vy2yV+sId;rrkGnHnT&| zh9fv>>03V;bma!9$J!;|ZKZf(oK?c_s9>xR-~BlQ}=;)PI zl;>B%7M3hWQ*YXZ+VY=^v>Wa+?Yq}#hMtma&UP=rark~-r+;Acld2U~PYMI|p%tsbkfPQmt-Z1&Cq-Kher)x|G=;F zyiL0bnY{G6b3mQ;f5=lTM#IxsjfLIxmsP5Meu|6YHTlWzH%G%f(^`sgKXYp={6S24 z|EI{*PNe3ipPUUQ!v98@S(miYrL@c~AxixA?J>#^WzsCm;jX+iw%_;pyRIj4^y>?6|w8SdaIa}o^$jg?dI9% zLeWm5`7BKm^O+P8$}BN4$=5#a`Ww_s%f$Dh?-DLM4o9@dmmg%d;Nr?sdgDiZ%T&n} zSHkITbE{MQDH~Zh7W?@#GgoO-)|(>~jzatW;jw(*32f0@2R5NI3F-!G%c(KUdRbX4 z5yZj_SPvPiP1e!rKmI=x5NCE-9|nfzyD_B>GM|-~i0?7!RZ{RZ_UU@J+uZV3WfyQW z-^}`s=h>o3h&RM`CMLjc_)V*GE;>4TH|MYFw2&B;lkpB{%&OC34fm6sfyx#aE&Iuy zqF{0Yd-Y;h>E9Nbx9*n2Kp2gkH(v@)*YCG5g@o$L52tc#PK_ZE*0<@5=Nql~yQ_Ec zgOUsHws7WaEpzF)D3%WiKAlY+v~2Tw$@M9V^5vGGijUSCXuNTwQ!TTTC1QSQuz|{e z`ifx81JA!IWRLlxHava6JVD}RQeM~EidvSmy%o*h_z4D$eJ|`u|H!9rdcDn9R@ZK? zhH;Uh%V;M^&@t1>oj$i}U0}_-P5C#F&q>ouc}k4o1+6SEE#0T-w70VZ^#oDC{9(oQ zuKfSVy0fxmKdb6r1sNw9=D$UD&G|S^iI9J@3>}prY9a8>d&@nkXI6Mh& zArtgEKAJo$COO*~)I8^)fei-M00XqVfN2iQfat4+)NNl2z-R!efXV3Z>q9mHw*czU z;V%8RZ@=uU0*SD?vci9~!UBq-j~}mMH@CI909F8m3W(VfoR-r=siagGxd1#?Qu+*> zp|^J&K+#LCKJ_p?3s^XwI1 zUbCU^WB{-;G37-y;NSK{x`6)KsmcvD03c0(_Jjxg0tX97LER5~6S55W_r`2M!~*|1 z+FKWZ^Q8a)jm4y(jQ(ZVdzGd3p~nOO<_1Vz!R@cLr0~1f_S1{U8x;XT-P)qNkgYHAADL79)k}`T5Meyp=no7j{`R`V=dtp;ReS)X%J}EYL)Y zs+cjbTtaw6Yy-ev1L|>@VeB#O0|!kDoe2WYyDQD1RL*;AW_*p%FWlSfAOHnr&~n$; zHNtNAJvYVAYcdGJumrdhKurTgbU-u%s>bY01PQn8Dw1?qaDyK{e7JJ?GRAd5E2|BTNf#YH z4OLZI!~pLGjs%J6=>wFIJ~j-7)aiDrKzkHR>i6$|Knv-9G&&h=G2Gfp3u09`C^Lam z`>u6mEAnEFaU?-OI{5^C*DRZ;3Q$52@!>qWY{+zW(COZhAV5k?JPKLHQD;S}+_7>TLjO0jGhvdoFDX0*3Pdx{NSbGLD-qzSRZpg|g!O{XmDn5}*dv zM8mkUdjPwwJq*&vj~_MNPvnKpg<7D_MDX#cC(B^oXa1&VaiO8`Q0Rwir^Y)ihiw1x zB~=#yOoToK)+Zt)JX~H?wZf=3rLd3{o0^UeFBm}S>XY43pkvPU=q9;U5wWo=Q&UU; zmIEo-5zDm&JpqH}dU~*sO*Lsh=?W-bdR7)*FwDVGUFPBCHQ;eTdsm`F=SM)#?eOPI zvn~xIVU z$`*hdLne^MxWE7-$jJd4KLoA2E6}?&N8{_iYHQC`tz0(q{vhIoo`NRrfm*iuaG92a zg9EngbHyr55|~;}(>_!UL8ND()A@^zovONyjy0Hp=TjOK z>4Paj=cpviyNGcZ@6n3uXdIv5aDxZjaKCom=XQP6mtU&Q5O9J~0Ay!_6P_2hY?(y+ zScq_j!e!sH<-8UAbNvtSe}~N0NL*zs3xmgFDBvd zl;<8de-{Jm*7be-udk)l$u#k=AP((+M#KKMW5H0lp>5y5>&Esv>D^G}iHV|%cA@<) z$9^=Oq1za=CgAjZH(Zjo9T}u<(DzzYrP#%HV2)!4(3Mhm%ls*he{%zm2oVotL|9yM z$-YRN&%iZfw)nwGHH~G`!y9d5PA8ZbtwsCIVH}K>XFYQ@h#_+8=2__`k!nj6M-%zu zs{NkYYfEIy&n@N)g|Y{y+Zt}2TyIz@9f&c+H{idsagWov)1tzmv+_L&*^+@uabn76 zdID_1ISXQ^i%c5LgZPc#RitDR!P?}-4spbsdgsk{T(p*JfxGDUUJDD`m;NIUCd=tyvpwksoHo#%-Hj^rexm6VyQ_2udOy#icH{2~*5dLz1@{GTs z)?h?(>wT#Q!xe*d?DS*Kv82hBhjyu+HCr1iOjqRL`py&(b&kKIs=s5(@x1|t64<&3@5@3Uc?m0D9+Oe z;zU$E;I{t8z-vBy{7CQQE+O#(u0W=ma%7cw91*UAg&3Q8Rg)A+&zrxRt4 z_8pvQgdUtEhVm;FJys!1+rh$cb#!_&=u*X>I~>UP#^G>3O)d~2CKXUrRvZ}_S<>LO zUVW6qM`KKmc|Wm=ul3u-c3^pwUs7Umg0|5%KdeK!x_GcWa&lCofunY;zgXJ$&wSwE z{o>g#Of$pgzKxR{1lg`Ft05v-tJEaLe<~^yPuBT0j^{rb5itjSp7(tH%HCJwbo_K(At zUpYPANna=6cM#2;Zx`>aO368OIvODyXK!p%)ucn@Q)&JB)}GUyS>j&BK48CfNH4*6 zWh<|p7@b6Ov)^8`if#M#^2eWFY2y+V+)va+Dm5j4v7&wj))P~CQsVJe|3RHhX2&lV zyZ!oR@0*~auIqAUC0vq*p)*mDltuqgH6fnIcnZ2mg|pDXs4d1hx-7TlVL`3I!hVCk z>6z1xTxx7wYj%@mzZ`>rOi;K&3Od{I@t0EOXP=pufrqY9l z3(oXihQG0XAu%YHAe048#Z-KQaQ$2BzQ3mw-{RvQn}+3~_LB&Paono!sR1*DFLBTGa+;lno!|7Nbi3W)A7W2`y=&L>Gz4E_Zmm*RcZ)~xn6dNt z$eFv@=xwCUX$oW6;4U3ZZ+afSXIfwJceM{gKJn^My2sbn>QodHiYzT-@6Me5d4Ij* z<}OZfytE+h^zLknL+-;R6c%Q{_*0#*NaQ|4RJqk#CtRtYc6}JAxWk4(&2qQ&$xQSF zr#C__sn;1Ff`LKg7>YyL*h~(cFa~boYy<=*rIac5qD!JQGwk^e4 z+KHWOrBhCYAtg73?^u53Lk2wql43d2m{vxHzUqh{6PsMTf$Fs0B3@RF zh?ae1CwP=&?3DE@eT@}zpwt#IY%ZIuFQuuPnz>%qrluu{X=gv0n?=>sQJI!9PlW2| z!o9b-^mud*9qamB-?BR&;QKkKk92r)%1_NdmQkkmHXR!r_r6VY9UzOkO)a@BU?T5T?h0(w>9#e7yBN@aJX;O z$4psNVpkq#Qv9@DVqGNYL&UJ!qibx+A9s1B#HulhW5o=#xeoiX-$!pXn}?%%zXvpvxW13n)MSil%$1z; zROE8Dj&quDwV%PV=~<{M7+&F7Nhc)CnW%o}qUW?cue~%nI7n?TCFpHn{cfJ~V7F>h z#-rT^g+j^4hT3RNhP2Fg6gQ_YjJvj4`?d3-939Kb)zuFTP}NSmt4m=qz2Dv2ic$-F ze;{va>Zq8SioDZ^C!;k}aG6i{3QBq19g!1@7aAL!+}Y9cI`01NPyu>=%;GOq;#rg7 za9*>YBW-Pk`%O)C55!Nti&$hUm~~TiO0X%3MTQQ7}XL&iK_a>-m)<7pnuYIz^Omsbf3*lZbr*N(s#Puiis(m#~S^%^}C!&*?A!5X(W?U z0{0HJcfW1(U8}$5ER)(k;b7m_CoS>O_O_xT^+W*+Rmr~buOBxWy~nFq$mZkPxyra= zCjFH&YKj|WI~fn_zcx;|OnGkem0c@y?$Nx*#A*IA85x@St7!j|o0R#+9FN4{WOl%j zH4#4kd?7!B)=MkBxv1Da4i1uxir;PV+m7iHD{8J>65E>;*zsIG!BQWEDfQDEmKk1a z@Ax>F^dARDnE5aT2Q#5F(+kcs#;i^Dv06%OhkVQ9Z@_eq!;jH_BjIQw`6)NGtWZrz zh=syP;}bKF<3OQu^XOw+u{VP$zLwn*QHg8@6dhu1Z8g5p-Tx3V*Zp7HFN&cpo7Z#L ztS`jBL|@8Tr|rk(jZI>88a<>@+|3mF}`=9@zs2IFcwHR7Kx=rt3GxnSB zns0oP_Fy%>%Lc2)Xss8J=kq_&}G1WcI4X#|6s-^61J?OZH z^lZ)t0k_@0=PC)-PV<3GZX`9)TfIKkyz|FW>y=bZ2U6FmWIsw)b9uuty~roF;=4;k zq<;qLvwPGz8C`URYSW|t2q%2hp>{b%x|v^MIJ)p(pz|hgatl-;kA;l$Hy*= zjEvY?Zqs(njy82)2z%^j2?%FRV+kWQc(>D~zj^=7L`psHKGGnp+1Qw&a2+8(foQO* z7u6r6z;bx^?+Z%8V$1+5DX#xloM0{mN&nxcEcN~;1%WJ_yJ#GVrKP323{(2}q@sSvs-x?T1 z#>JWOO?84|L=cOwPhaYTg@;P0rSU+8}uW4;>2XC_LW-}EiL;+rtR!|_o#-5s@}u1kDtxF&1GLC(x4hlP(g~#sD$2`25gpyGkIi%s1C*=-HZTUo zro)?Ef>`%4t^-VAGDL-#nwkPfpJ}x#1Rp3rko2JdBt%Wt)PNut;4tAjQ}$8gr(mY! z;W+}xgQmrbay}V_xES4IS!fnyV6*L(6FD;9?3rKh_1)6M%UE&PMsqvF~%3k43YYR3FR}!_+>1M85=J7JuAsQ2n;2Ht4~*Qw+A@^BK?-Ob*64XTWDb(NN=dA`(P$a=g_Erpa}X=ANT4?x&SV46p^z z)2~7&?15uIM@J`SB@Mi{=Y<+T%~yTADURy#)h`%U_yF`ZNzNb&ywjtAk`+7MyJ*c= z7~m|0i9Bni}!^L)1OD1p$P!0u%6G^JW;HRSCk^4pG)EzN!`b4YjptQ{wwbS>DK!@P%gk^ zod``W7jWZ>D)G{&2Lf)DRpJ+){yU}n{n*770MMcbVCsuq)zMl7OmE}$zq{cN3y@oV zu^az>34Gxa&XE^y`yt_RK7GnT#+_eKK#o8FvHB4E0IGtVo7C6VNN{juG&C3xutr@0 z4;&vG1M@2hR9Qe7Ve$U?0hkzZiOqp0;KV`x{b`8USzk%mA7Ov;5xnHrVFC zM464`vvOy`SsMz;beK*goGyrfR*x ziahCdv(jCZ{bqPDJY!6GzZ{RLQ;|e zrgnEyBb0R@aatq;+din%{F*=*c^?O-CmQRbG>`}J-+g-Z7Z|FcQ7{#9k#K&%hUVJ@ zXnFkS&x;W5&CRiC*WE-#f%vispYa&X8DRPVdks7eVc#C;>vMte0WerKOr(O(*;BrEN`fR?ADHscs7%Wowv|QexQh}^_-ZP00ZXt>W=2-bDn92 z9N=sL0J4#`055~fO`#PysI}3ObGswRy`7U~97PLYGQfalBQbyxgC%cKxzTWTx_Thi+T-l1&p2{R3(8`2BE^t%nUT#4BN`m^5&g8 zZg9PS(<@72kXd$>Q%c6(BPOnJ*qA;K8Hm`H&yRR?bRhLU+iA|gfiMZ{4V>YQa2l`@ z1?+^t`U0tWl5Bl!Gi4!p;t-Qa&8j^@Sw%t1KVfUJ9r5ET`* zsM=A0E+wW4!G!fw2uzShm6M)sI z_~O-t?8Ug(y5h_2*W-hOyC>w;E{XG9JYjAC#OlRw|1aX!KI==`!W#3_iir&;{QG+| ziTVs`HG8&xMJ}FhC6${;X~|x#feTfnENkIy)9e}lV*jNSMRFcWl@wF`_>LMbEc?`dzZERQPTJzkA z+3!iZ;^MU3g>gHE&FY!xyq9Zs#3PMs2JJ~&wJr}v^ZtF$@07u>o+C>`(ixJ32@#hD z$lNxnZD^i%pTY7O}S(EmFnNy7`v=ucs23p%XcmH-Q+eGv-*fDp7@CA#kr&< zEzC=q*7obD4cnCv;XIu&_gAiVBRy9x;Z7=T4y8Hq zr@wjNqv8aIlIg>}+m17#H7}3eZMz|sYHOtWkVv`km5GqOAAKAVk0tI~9&Xi}+)-1| zlwTX@>?YsAj7RCStLW85fc27e<9i=PKH(Lx6ga> z(PG}a%yZK)XdG1%f6Kv0x(Ur7n1rl=k_0Jv;4;Y)eaaq)WxX=}1o7};9E$RZy)7~O z2E{nqOYDU2Us3jFZ-$=Y@TR~kyov&zvw!M#>M><+`^(+^}ac; zMJpOGOsHy|{QkxLF);8U97PN}77ywX@(2n?&j^w$E^(;QjfY*eA&N@cTl;elc;2O> z>bi%e>#Pq({4lkWINz;2k=MqirlzynzTcJfwGdTmhsKdQA`%D`KkQi}#dZMjvKVv} zTBSl*&jqwNvAyxrdv4&rDPDnn{}A#Jv*B|Ny3A$)UeMDJ7Z0=}c;(E-j9}BIIN*VG z>!GTI9`KAe&VX~gzOk`Y`auN5JT+2d55^0y38G7WrJ$}S|Pzf4B@b|k^ znRFtJNZ2Y?hBi8QU?4BI=@-DpT;UvJeos?FE1WE#I>>-N^Jxzh0zmLj43zss#8m>^nWp7ZVU zbVXVTLgoKRnDH23}Im>B2t zeSL!n31#9Ip#T(d?t75I2pk?96e$NGtRZrPaPOR#(@TY{f`OJ61%;wi-#I}X2pKoK zhQm2t@{G8(vvY5AGlXE=^B_ew_Ml~UaA3V1>hO$JxGj18BB{L|O!9BX1Qd6I9PBrM+RZA0bDa{fKi{c=u&VK!Q!hnc!@)Y~9>h9sd_2vFz3PZ&l71I52m@dq`{^-AL-214tG)wAiGs9r zcLbmBDrt=p5+o-hhnDY%Co)G(22nm8xG*MqhCbZ**0k^Xt__ zH;_=7;Le>p0YQcDLF57|0`@hzbh+}fGP||06z5hY9mu*N{UI?ITK!7-R|by_bFipsrAQN4$sa! zekPI6umrGo(DbL)i(JRTy2s0sDUo@_lJ;osn_O74k4rl5;6(EIWj$_Qnl>Y&QXg1+ zJggd0r57N1$E9oH+W0;w+jT+$POVH%i`Km$hB>zr_1;z5thry`hRltkKE|Ps-zqc{H7MG&^Iz>gt=DGAv@(oFa zo%?-IyRZ=1EBWn~<@EICetx(yn(qN~4bnu~@Rs70LMVa=@i`1H3>idZ|Ct%GZ{Bo) za78?jC!SwRQ`XFCH%;2sd_xjyLW0T+w6YA;^?6xC)hQ#>BiSh*bXX#J1oP6&9`swUX*0Wljrh)Zm1VFJ8G=_dJMamC3Hm zl7XvXD%cf%L4*-npW6SU1z7ncl@MXTJerz8oWLY7xx%L)ONilgWEj@qlBCQU5c?#|&08<8tggb`hsbj@zc(Wz16&cXoR5(eM*r9-+F9pI;kl z#Z9k9zLVFlkkQGOAv3q{Qe(oGTv(7IrAL;m+6NL_$hB_Qm1qPiFRgv>W8=dDa zNRM4%4+m6>s0BA)A5c+R$QDqaZs!56FeBP61P-&{ka2{xYx;dB(5XP?O!!U zoRy?PN(Ku$aWspQUOv1hJ=FFccCS%7C;h_*b;%|+*>dr?y^M*Aww}GqAUISawKzF4 zrluySP_cT4{nAq|Omw)jXGe0$TG>)rmOk}%{mgw+Daz{lfo=!#IkSO>|GjzcPYl}Re5Rc5 z(Kd{M&9iRK>yZhiuvd*Zi~T51RKU;0?xvFObkce%g#4kG(I#U(R-KVwycx^N%kNaR#xq zoU{}bvD@`B)Pa(Twzyl4(&c_jg|XaZMg7w&ZrxFhSj~HrO{clZS>1`=0(yfr#@i)d zI%Q^mb|<>Nb~cTl6IdXcJF;*U^2J64U{UD_+wHd)cC()8Os zGuT@z&Pd_IBw}4G&*AWEN^Xr%rqh2}6B*Iy>Ew%~)H*EgZYH+Y*fn6p|E(>rPtC;u z6YsV`S(3LJ|K*LgPVMxtkQYk>N%$;vGzG`jGD3AlCgY0&D1pwfKH-n%_G*33^+wdx zVbhnn++KA=2`w@7*R-g+)_02@dy9LE56YYV!^X!N7m*~;0^;_m(efGl-H?u)o_5u> zW!mwz5a;7LZ}pI!ve~ulqwLiQiR&1_Q|_N6JG6Oa?E4w1l4na}IYc+CxI*Zi*_-1f zdg_HcA~gG!q=q+@eR)}q%H#1bb|o)tWC@dg!f@iYoLJ!SW9>LS!@SSJUn?CfzxGIX z8%7kQmZ%uf(APLbuyplwC^jAV7RROlbeRob13kT{EA51oU7);NXU+%Tvs9$vr6fzA zERvo>V4%oVD7AYm?$$!E?))vZpl<-FD3zWa+`%w?e*`2NN zNCaXDT}WtcBebKXT%>Xx+dkK?I&up?|495`W77KZ(XZ!w=GNsxk`M5);f^L!7Z!Bw=p1cua5W`pp;e&Hsh5h(qeeI-p{e7502w){_8sFM;)+*aJKZU@sv8%z0AWk_Sxb}*HJfjb;N^dG zg+7;#leHJve=_I$Civ27i3aONtH8vk5S<-1j>8XK2e8) z5?c3l;kSmLbY5FeD|9{y;G$(}|3bnu`BJE*U@)g`!^gL)94Y=*4k@ndk~Miut(iwMP%9?k40+&+p@OR3=` zt_(tN_tYk9lpSyA4lZoYm@_31Vq2|K>QFX$kZrb#a=9xA&8Ds=7mN6A&e{5K+C}g< zy~=g74ytj;U@$XoyNVs$VgvJ}fn1wkkwbKN+YgVFaEptH#Okh9ap04cRmMv}O(*0G z?VC*{A(ir^7i)6jqF*^_a@hGy ze|M{|tjy(9YacK51j+Fa4&YrZ^J}n^M@hJ#GI+PnhM_PmW$V_)jN<5HiP5m)H-=xS z3zNmL)1DylFX8{`l90weaB7g{=&W^p-=6Ma{?8R#e@x4NB)fcUo=6_&NVeEMEwcIY zpr!L&Sn;#lMn*BkqR}>+N94(PhT>SwxVIDbS_e&%A{89jjyK81;sB&Ufh${ETYF~yq~1(#JCOs!9iQ>))AFfOlvLOs6}g8Y>u2Qh zH|ClyNx$^%q}9lki!I(O%Va1MF?V^QbX2W=XTzbc`>{rD>4SGRg5i0_O{KTuPBbdx z*A(BI1hrTs;1n#?QTvYEb6l)fprX1S+-O2-%B?9vDc*F^ChCyu;*;Oz1FDRRO4X>$jglx-sDis!3FePI zD>FXGC{mb5I`CDK>%rOW!y)kGME3F#wNzptUJbp0>QJi)mEzezSl> z7j@mhF1dBe|30@`gZd;-N8@?=)iwI&884QuEK?nq$mtY(knysvQT>7_f+ow0y6w>7eU&jxXSR6xXYkK?4j0_Xw zW&P96Y~U+P+CL8r8A1YvrB%!~ZBhMT*tplq;p4UhfqJY1uZ*+Y>wazCCofyR?Hbzb zyh#c#b%XgzlP(rTFUvb3(o-A+)%*_< zABpCaDd>#>W#eh&ZS0KxeCqpf15>BLf42sx+Pwo8`VZvfY~EPJ%q-3ej)A9lex=$E zuEx+|(-@`U7o;DE*=7kXJ!W41Ci%!ba)@5LL^M>{R3W^KAxnimfDM6=X2Kz0h<*jj zCxh16LRCLUU_dS3)Htl=ip+7sd``=)E3*TQJR{%7E}dDwIB(LA;rEvj9uqC*_D~@? zq9m9oAiH^Blbt%<-Fnkw@2t|OVU@#TcvINUE}(Y_LpYjm{+&>@eQ3>!O`qdNRyJ{zx0!P`($f#7g&w-je-I_I_ zyhG}ym9^G)I0A`-s>Wb{|FCH>NJtrmj7s!biJ<(qwvG)x_qe zbr6*$PZ7^g^Aac;m6e{p)FybP(Ue^^TOqXBcK+*oSw%(rZec>|`xmL!O=;jTR59;| zsL0EkmGBgfyuzlY%`82NkIKHGW;xZxaklS_ZB?c=sLpfp54v|eb1l>aSJ7A>E=WyD znGX?XxaBrY1xkau`g)4Tk8N3-9NxrU`Q|QGfsVnm^uv7kC^>8`qUFjerjI30k_oM_ z3qlKlqi&N{d*2o3J~^R(M4KPQ)-*D#%?)uPbiIU44y+lZ{AzX$lr-;WHXOiy@a|o) zHn#9LxDnt&YP~RJnbXc0AH!GT;_z#?p{eTyLGEEgd`;-L9QZ@9>~5>?!nZGYaeLl{ zqD8G8BxP--uDo9mhb zhMnKt4`OOsy^;|K^2S#6EhUEwx5G`&iVBOwR~K*qHgvJoF1kF{8#WY9_uO?f}+9^v; zNi3p?Hw#-1E?P8P2MV9ZGY)5Wka4Q6!QDBe^t*T3KIKrY*mAGFb9N|Ar0qI+E>AOe z+Oy%nd-}y!7!SK*JO|4CpWru8m@%sDz3LNI23dt+X<9r!)#kr*tC< z3?LxgAl)UMBT~}cDc#*MbJh(0_ulXM?e}~;&!^EDW}aB<&g;G|JUZX?G@U*?nTgph zcwkokfk_JX^hw$jDb-Ya`OlyIoA#z<lu8?l37NN`4AS`g6LJhbgY zd2Zo&`w-O-RlE@3`Jx>AR~(|m%+rnZekVCn@==ydyaC9hs|D>6=`vC|wFTxYz2A{L z^OAbQdowaJ0QKI7Mju+jL48&Zc6Oiy0$v)ipSki=zCb;IJI}#`$7RJ^s=gyy7o94j z=^qq9lK>S-80T*`2tm41LH3&H`@gKAcO{0F@HIS)w@teKMke7_30XrA2^r5&{Fv;nn9IU5;YQ{gt=yh#HCtPE79V2## zsCj-Ddc14Q)3-Pu2M_3?`@%}GKR0WL2HgD;2dl$#j>rTRo{m3yG?TsSp~L)p?`qK* z$qC-X_((ES%W${t;k2L5@AR7V0*QyW|5UDjNgzV4ON>m^UTn|d@P{=~!8ofG=Px^O zvabr_Z@+@&OtW&b^wDp9BzxCRgm#K?{OJ)h8lv{r8#r6-POHkj2F%3DPZx)57I9^U zJ@txP0Qi&+kWr&u16yZFF*vTLi*TXPzv43Ewa@ypMftS=W6YX_U;ix@7W4^>XM({= zxhxxuUPZuljBSUNT>QSF161~1+`9HQC+ZPnwcc?8V?PwK)wlT119bS}9kvfgWC1H^ zm&-}EEE4g?)>i3G$>!eD$2eicehlo-gX3E)=naX%oyKjsj*a15Q_Za?F1|{pty7>N ztjXpy2T)87+e}1k2BpY?p5@%ESGK2%dcyjnMN$zGAT3Bp?j##b%tIgp zW-zkc$C*oQ40~-2yU9*pGNuUxTx_{}c-ed3$Re@4TIY99zR-bP2WI5LamIUldq+pJ zta@8oT1H1z|HoQ)&ds999~vLr8`dorDdV2&{cFX4c~HV!cQA=R->Mu=cD5UAXlP&% z|MO0<-L<~IR*t-h!|$$BcFwDeC;{n+SCIL|g;OBh(X+gN>Y6DKadzqg(QQvO!?u+G zJ%HrohQC%f?Z<=E7$U(H!C*3l^&2dOBc{cwUc27*zbq=^x(vL(aB_0eA;pP_h`{;= z>jZ6~opYZHDS+5`)81Ewm=^N!Bfi57Tgjq@)9IV24yWc5Ye;A$zvD{pH@=$V1O>s) zKYxsz%!^goiKGF_d-hRTG;|(cYjRbT(8t>w05(9Z5q~DOYGIrg@%5UYKqU`L@9L|B zZh!?^S?LB^v$z4xp^=dpJ6O=%A);{RQa8|60r5B%CIEOmc<=z=KqYPm0;=PZod@6y zXXBd)Q6cfkN#({y$odF;*}i6f|6aVzNR~W-pP0M*8*_6liFU!

    l_9T|9X(5K0zZ6*C{CD{w9iO5eQWTv7?3O~Lzxg^}iD{;diY?>ivlNonXxWQhHh!2`3zc*ZXwe_r_D)o4!=3$ z8+X0~$WM7~xq3cRREH7!ZT%&yxYxDyDk-{?Br>3#7IrdpyI5^ACTe&+ltQqNM@2&; zx6ddgg?ogUTM$6VKpO%)_{RY&yN=*r*AKnzL%R%e^YfKo83_q}88aI;DU-O(XTPDU ztD6#vhw?37<$hqnKEEV490zVWDA!I)?r8XhjlYGv-WcJU$7c3;d0FRTsJVB>WRT$< zt8t9*`Tf&w$8?w98!C87qK~F_zZNThtlpc_mR6b%ce###_RY1G_7T&oIgNL*;RdYF)q?%UB4SK7%HQVAS`< z%fgWt1tNZa58ZseI+QLI%xt;h-Gv*pc;;&CTD}_sW2;`c$Qfg*gN!?(_%gnTbFS@Y z#3HJ9CnO9zizdf-n zr_rYE{zOION2wT>lH7&t{E3`TmNF0VmZu|vnMQf6$4rk6VNVg<%h?ZhOWp}^wGEn( zl^SoiEYfpr<=dLvz908$eYaNJS=!2@0Ha$3I}QdDnXo>TJ95u{Z9hsJ_0+wQ;p0EQ zL>z9MqD$wvO0eGUv&g7k=iHZf@RI}XXdKt((WkN%w>x+d`O^* zS37~#x;+bnU ze_A8!irt$hSuz}=^2S#;Xg&!2yqx*TGgk z?HN39v7O3^;ORJ8+t#!;nFv2yGcmkK4{KQogL>?5II(!k*!8%lVK`HJTw~dja`_ri z{}^w%sN?ab0os-d65J~C%#w)#!|eGcCO(1pBE$V)uR7XuIn+|s+wBNbf3lMi5XV1c zh7cJS{xP7{EOXW9Ed2;*(+uOGBm=d|s;L98SF##fS>iAU3yt&6Li@;f8VL}l5zi2k zJV6F}nyg`2k}wKLN>7#TubK(02W%IT6pu3aIZfA7a}j*AX-5Yiv{AhB$s%V zY**7?hh<2`gvb55Z7_Q={|S_cOvQLWYO-Ux5Mg1v6tSs;YPex;~G zK$W`)XB3E(0@SNjCE4%`)YYP`Mt+{N1qqA>ipzAW?DYQ*j8*F?aC}bxMz1|-zgLIn zsb+b8QNOZN|EG4ZNUW5~JQX}^wFH^hj0s=k#|rb4ic5e%&d!wgc0or>g_%9}X3XdP zZ|QHK(22h+x&5^sW1spWJ~Z^8vog;_xw-pLX&-mbvb?lK-d{iKn!B&5k(3j#m}^x1 zd}%yfOGwBUofZ*lDXBkLI+=Gf5T=?>lIWpYZs%fmW*(qYq56t3z52%wDJV2&@0~@d zOsw4(=k*+i4jHFjHO-yjmIS8D=xjDp4~ym!Mg0k#d(xRbIJ1)mzz0F?R0T`b(pKp4 zjES86Ag|F;d#{^6yL{gaub$Fj%c3b(M@~*V);lI-sr%qJ+D%eH8eKZo`n8F##wAqE z(H9Ke1+oy9yWH91H5Ga73*kWAOZC`dQdL#6w&eZdUf?2ARBjzacX#-0n7hOljavXq zmEz%$_e48X>Q?S>L@)rgx8&7|X9T~yJX|29B??U5nOf{XR*%RjD<7GE@S~+N7L{1&ek zK$Ckoj;?lwb#JBb^ha2;w*27-e9GM-A}AP&G_|cM^sOCy=n7LLWeGwJm=GD zN}HKL@tznKJ%5|Hl7x@2W{rE&XOk?{Wukq{QvL8aco|Kq$&~hG0zDNa>{|?n>0qkq zyQO7@Zax;Kc?7{yl}g=VzoKdW!zB0BJVP?l#^*3WIYR7r-AVAuG=1c+&u#kzouC?H zJs0`+&$kJKvRb>EDY*7etM>3*uQ)lwNV4|b>vLK3U*fK`TR-C(l&K3P-0&;`OgqXT z49qGq8WF;|$z%z#4{{7~=o=+|r)K=9DdaCLP)e@TZ}aa;GLy?5KiP&H9zG~B;5#~+ zC_G$!G%>C8-(7;S)UP=>0d?YMnYKzod2H>>+jw|t2lB$o(g0oa)+@y~Ju*BzEh9rj zG$~u$R&Wyl$^fsAxjWCBxm`yv1ObMA@>QXD#wI4*t|vQ19~#5&o3O?Z5fK5XJb1}Y z?8^xUn4LX|e{q6k7Bok#W5%Aw0Ho+TI{{;C^F7V-guKM{1lk@iNb%2M=Dd1D9WqyVPfyGM{00LTDG%;NwW2w=i7Viysyue{08 z3vMsNlYmfA2F%EyqNEi5`vcru40IklZSv>)AW%94`Ph(%znGd@^VD%C@5fG1jL*)d z1=X(cSV46MW?O#G0dqL@O(_L6y2Dl|@uM&sEubXF&dyFVo3pf!UK`_oBkZ#vzfWjg6dD&VJ)Hy1cKy6*Z*I6M)u-ie3dL+RA4J5B*g^o zC(-u~a-f5*V;r!8RL2v;8rW2ZDMmYGP~%fzjgUfTES{vOxc~F)3_P!|oaYzZ!)Y+$ zKR@CLDjc4IJC0E3?+IkO`a$}g^DC;LIviB8aZ@x5CZ?}6;c)Cu%b9z>{XLQuTpq@c zk-Jil@B^CnfJ7irWcy`Oc3c0gcXLzIeG=}2>h%I3v6kF@OK^L^IsgWe88-*!j;jo% zPWinG`O@+-s&pNt0_M6+%C^QzsDU!dpdZFc*(3s+F2sE1a9d{y1B&_~LFUKN*XMv- z#C29#qXgol>w-onaapBfUR6Jp%v8jmlDmcB%UoS#;ytNreHIm%`iZ^VT>Q5@dw00x zjY~XLt2Lk$Hl-<786hq?yZkjz(h@E|m{FNYjqv_D79!vs1h4Ld6vM2;Hv(9xAw>jdvo`9Nm2`yo1dAa?HoXH$GjbEo;pl-DPZXF9YunMvd zDOuU=7TymTbj53FmZ8kmJ+u`Nds1AyncfXfLu?$JZ9uIJ)JM6z?BhBX`_~VFzKq0e zb5K?Rh5PLAb4gIt7&R;3a!Z~9Q#6n9@RF_M!rBH$M_=jx!KM5{cd?8KepJv1WQJA< zlR7MBkSQ@R@F ze1i|{h<4!!a$cDgjN{n7de)Al-8*%a5J82gxNqY>-@)Kp0&=WIkcmpW_rZ4JCDA$bQCNe2x|aDU<8`oR-*LEgd5Xa0p4L#FFp@3raOg!6(%|&TVZKeG z5#jdFA(HWm#d#bDQak$nYXHq8ynSJ!myIlw$d!~L<~wZ3j^VZ@s5{OLY|a;@l1;e$ zvocrK;qVctCVXw;%=o$CXnLCYsd3}Hbq(5dvbr~dwJuATLgLiZWB5n3gR|N)hE3>v zb+6*vYgyj-M;;gDbtiR;WNm>Z+r4hp8~IxK=M62(yu9bP-QM$m@}=WfJ4(E4dCm4y zscUo?gTD2oZH>m0_t0v`PD5dN1(sQKHddW7dh$n9EkAFHiNIcn(+X zkn%_}f3a02ypYGSL+F6nx%j(^zAZsdyq)<*(6I-+pec`(9pbRm>1&~0VK&gQ&zY+P zAsk4G!9TCiDVW%NQZd1pLiB=ZKgcdXfWg##(!;I@wm#e1fsiwLg17l2AhRo;-?Z9k z>>R0F^pz&(r1)6CI#qkoeuUWG_2+I~;I8bXfQrMMb8d2^JvCnTSXn9+zqb8m4jIpL zZHiN$v3r)dcn;0faF15j!)1qWy!`SuRSE}sqAi7tbOk-HwcnIRZRN;)?Ecg5{|3sl z7bN$1>LAAXv~-Y0%^8p7NtF6zK!aRTbj+}8KRn&h_f1sRcv<7iHzDm* zxD+xqFqry4OMd?pY@Tji}berq`Aww5?yBW`enz4yue~e}tB|pTApyM~+7-f&>le6hR>}J^-rA#%P zWbLmv_1#U2QQqth<8`)8Db6}MVHI{+ExU zRr82>IMtHaR5VIcQ^F|An)>v@0&Dy)@S!fdOkbD=4~6iwV)^Sc2Hz88Gv^nj1=cz1 z*Cto~X4K7l3F9w071f(8Zshx9rare4-{en{e$<>^3jJes z=&KW6v#?2*lcc=hGOmzs$@;J0@WDe(&Pe{?8K8()#sJRZke5?Mxd4K(m?DW9~1|nB}Jt2kyxT zB3svu_=vg{zg|Bd)S}%Gb+FKe+muLfIW{$!+K@Tx&k}cVMU-h=+Qc~IX}IpK`q1H7 zlbks2k|BoV7g4Z($Cflo4$vTs-TEDGuSoRM}FmYux`kG$fsS*ANj{W~sq2+*9uAI4ju9 z>`?`f@wsK=1MRMS3p(yJ<{a|IOyoa32vOJFTjTxHGumo5e}Ju>f3kFf!lEO2>eM5M z=%RCo07oUFOy>>n#Hxrk-*+-Z>5V;&BG!({@iHM9*h36J!@xNYwk`JRWOs^7PWcX=%h;Z*Xnj^tKBTlMZ zbhH~dX&(0?X^GF)nmGyFSHD7v;1n{y%Z7lcAMjZm);JIZ658lC&$N;)7rJjM=d1AO zLWf

    +r%U;<15uu_dkudWM^P3tjyDF>-H_5M~g@^TBNE;4a~18C@Th=BwSgHx>i( ziiP>4{;;frM^tG;7wZ8`_P4UfigQH-SQQ;KU5CZ$tVQL!Vv-)?$)^W){oFoVxTiP1 zl2@WnTqKk9#FC)^*3*7Koz~N@ArY|Ig-NdNZ?8lV)ah?|S{w6%ZBv-%e%)EF=$&W( zX_!PI9{g*V6pWc==jI~UaxE?DPq&aUr@shvF6e2*9Va2?i~@OV5?T>PdK8pwG}dU3 zDknknj=UPj++8o>_3?O&?7~st!?@GY&%XuCnKrU6+r$mY{RaV|!Ot#3jlMSco!tPE zcZNZHi}#@+C-I^Nvbfg7_uSz{vnS0>cY`N&nR8_0#;cG0=*;!i-5RhPzY=qoAl1^z zcob^%-t7gdQExg~xZO^H*&px;z=ew?L_TF`pEE0~80>qADUXZQ<%}j~yz>+5Dj@M` zDrQq3;XZsZFiCum-Xc1gn`1MNuxBdSbbaoRoF!azWo#2Wa0$9SFBaG`tZ(XM%BjIi z8l2x&Zagq??(d)EsJqpIGkmz6G&(HBH){``g{)~q=f5pj1?e;?J93RgQAoIkmNdNH zf8!~b4S0lPWpV7Dm`~Kztay9F5mo~ehzc9T@#*e6ZIFWmA}Ro*_FAsR#d0}vU{x2o z5F+Z%t=CeTtypNm*Z3dQ2?@XJ2>@WMDqx2!ja!L^mB^-uJ{mf6Zs#IrK$X=|Uq~ zR-@ewM7IANc{d&^&!~CEiH8?>los$G_etOtjy{V!?5F@&OS=#zHrfUk8-t@OF|R6a zW_V@|ne_0kg93)+@a>$zv;3yR;ZIO`8w#Kw0$g zEaV2H^gTLJjOUTzIfrX}vatb#2iSl@rA++>Yw~Y^mjVjW&Vbs&n;Q?K&KK^P6~w!$ zn;^fI10{Yz8}nTJD$DW&S(bd}fOHZ+AGzW+!R=XlSiFj${#9By8!z+-A7kYSI%Z#( zNkKg61Xqf>sO9g!Q2FWfAB&2L+9O9^{-nagh#+r{;edhFFe~Fj*wPNd3eOPPFx}lX zZ}sXKw&?Pg+g}G(OD8t}{`E&-Qh3PyYgPmcWC0{nlSKtR89RlC|1)gg|4;NqZCHqdCcoMG ze1={v>xZPTKY@96kzyD`w_*acPQ&xU{KY`ec*x`sm80e7=cn(Z*f^{ZU@6XC{GULJ z+7cWxECM`2-i;>wxMn1rK-<-rdKo3}3KZAG#8%9D1@GYC$bzZwpdc02tclI;_I3rw zTPdyctG@||?tpeFNY?<`if6y~7t8-;L&4a`YTM8XeR+zGjXmZ5S%TR`K!8$LZ*Y2Ku7fn2 z{*5k1%3P?k#W8MMsIu7_InUmhhyyi=KI#G`Qe4#L1;^~OCQuu!Q~uxar1_Yt3@QmS znR1)}xq$k3d@9)4EBV+Ytbj{DR|yYM@x?cdl-J0J@0VFQgI2ZfTDXS6G~(CMrVb`o z+zl5fl$9CV0^KUbc-)HmrHHAmUL$r!%NHC(8e#d1`6KIfh%+!wDC>N>cuU9avTm++X@+yTBa8l%R?_R z+38x$I7dcCcoJk5zm!kECp80@Co(0le)|Up(_DK&>93_l1Pu=W1va|>i9h^5DvUXc z=8ldhzX{y8Jor{~c3+4ZxSelqkzLG=53`jrk%N+3aO=RCgv;-Edpo^U*;G3cvS-R_ z)1plwS=*iS`7vDvNhtV2^??#jOxKc+&u!nvWPQ1SW(6G`GEikug`xoq)n|bR-={3N z;<~Uf&CSe|5Mi9`>?q6)21Yj-SUm?SLOy9g^Fwl-zwyQ1$^S@2f%DuY#_wn)XM34%cT~b{c8`faUk9Z6J|d2y9`Q z;2W_+wn(beM&E16sCp{^ve5B3lwL*rlw^J|e$Crf4s9RfYm}A^r>^~~`q1Fz)We7D zudbLLTF3nFilOGugJwx4!ZQ7~-|uv^lrQ%Q06k}=?HKd<(SzQ%0>K&}TQuR*TfBxB z65jc%6YZORjs0)_Y`(D8^&%n)lRsyI7V@xf?RXhD2Z&qPbu#oe_msPbV|N{+_k1i5 z@}K1%hjL^+;ag8o#%1k_c;*fk%D4zKWuiK&7vI<_p|tKbB1AD3t6!cax6+_w7R*N&C@-*C#o{^`>#2G|u_z$R;IWVGX4 zM;DO-N9L=Ts;Q{~%cG0u#-9Z4@jd~OoJGZi87KX-K>#9rh8@+Bu8+dOUAv*`h`NRG z%-|AVd7Qe|M~+;c9fDe@cH!|iS*O%_D701^uXx>e?66UN+Flt7{R*?CGQ!9nTKj$z zxD!w<1F_O?vH`Gh1hPZlH2(grL>KWJg}>84uA{DYb`mrZew~`4JO4l@IcH#1T}NIJ zoFHH%1BJu^0O z`u%2oZ4K2m85oGT4|vk#!6QYL7{36Y0$zZlGyvFl^xjZh*kd=8T!Af-%~Cdk-}GMo$J0UYlY~tMBxZ3}nvi_Id?2KSAP3 z?&?JG+HD`iS;V?;$5Wnu?4DgfyE{w6r0qPKL)n_sbYUYOKZBOXJgp&bl}5-hZx=Nz}jyY>lL(B zU8o}kJlwa1@QNo$dic(B#UFWC0r5A$()9=_21ZdZ$sFg}cSm|E&4&~D&aX!cS??m^ zGN%sLqoU4dK)-C7cN1V7wxg1CUjA(dR8`C}aE|qQQD!o(A>^Y<`?dTx?SBK95geDp zF4WQ&4MI5lYVz)4(CNzo9c@=@@uJjHEA9k6rn>6I8Wd)*;ZMW*A!2DrSut~x^)%KP zoDzLp7ZpUn?Q9HwIb(S38`|?SULS=vE7x!J#SNe8w5H9x+#Vx53nJ@XVQWT>uW3yJUh|FlD#hA@AELR1pwWa#2_wtzm%Nh?zxCY=Ja$0T- z+kcbv4TJx5O6e5+uO9vSjs_4gPl+O6?&&7&+t23S0i+ZjH8u_+E#En}W4j_9xbg_O z*AIwfNyBRVP5LC*P(x?QSKSOZ0jlHLSZBK{uZ+BM9JJAmp zBy#D6XPnG02J?gAZ91#F*9B=*J5{tcvU9U5EH0NDab^feNFpLRUmasAB8v}`?Cdf^ zL;KIkDmBs-wAh>g`Ts5`NZg|MT<`X1+Hv0jIKD*pj!pYzzKbwBOY;?{@D8S$15_qL z@@Q&FE1((d{qb7-U(cQ}oT%9!!GQidtzrRyf~N6nD=W9UpL~g#ajT)uk_SCInUSsb zQ^7^6HwG;>Jw1NG!2y1L^?U6dIAr{^zJ>J{#{g=YlgoDa5*_20-g!%~^lh@1=a6!{ zU~JVgAf*pB;8g3^9)r!!ZsxL{Z+7tTO zxpFp-Q=hs5>?9=(Nq+6Jz5L%lbLbkoe9^a`X&G$>B@-loN>7m(f(if`v;52_Pe#D)>A9H8d=m%({gUIe zb!yof+T#JZrR$tut)V+2rB9Wl_E|k*Ee?nO#PMh#xLnieueXkDykj$tyQ7v|DojjD z>Y&MQ9;m9YA_w5#+DTa~w=QNEt+x3D>Q`HMK#FYyN_H3T7@n+8zTQvraJP`F@JAWe zS2(vaf!$s2KDWs`%`I2)v*^DJmJT&%zFf%G$)l61wvDcC#0>ki%;zH1SwX+dd$FS^ zd5@i|Gl8#cv&S0u?|-lWjFuR^8d8a@tJEcAs}o$$nF@Nq%0C`@H&Je+abpY36B2`Kgk!r zC+QT~u5POos1L(EQjqP#UgpS)IUVt~8`tqux@g=%JGO^UU>OX_n6Sr^Ax}R0`!@~^ zwR(j&PHbO%kZj0k5$Wisq`!ElQsA0eeWnV6g6mh$oWe?4i4}AN8qYQAMix6=&V`VY zmuX}4R9*b;FSf0b&4l#)@qMh$gY&JJlE0(RlZXeLsX#jc?4mVW7KB9D#C%`Y5QJ6# z&#b@y7e7BY;k3Lw2%p=Z@6WRVZ?}DoP;TIXPA!<}Y3QJ1RoLIEF(q|c$V@`&opXdy z-)8;}1AVy5^A2?&xx_~rC_?h>S^deJj^*X9Y+>PPJ`!`C%k|_I@*FM;NbqC<@m?pu za=h`MduNkZo?P4q$LL+&2bgW}W zi-^BE^=&=rA(n_Eq zy9pDUsMr?-y@4>-%OS4x^A4uEgE`OT-ccjOVF%ppkSjnYd;Ng~p<{!$(00Ay(!F~g zoBO9;c78FwyJ<{?P zf#%CW4?Yf##bFK0Z^6MX`+*fMSNWkj3Z>t0*xPtaNp(~M_JT#-V&a=Gp&kdRYLHJe zc|2!_(v^tni9}FI91w70U^Fj+F#yrG~&~1>r`)mNW=9eoi8&#@}1pb@kGW|=^sbAXO^@7b8(;`b;nQ(z>yWNX&X z)-7_g^W)f#{e~TqT->*kkhQDl+sKJV8k9@xMHNPUxG^ydLCZPkz?KNz+sCk){;%0s zw1)>xGZgUU>exg@>*(W$*1y`$|1C;H%@ti8(QBN(za4GMaoC6Y{zUALH%hp+9wp4tz;B2VpMblw;0@{1jnp=afldscoh+4J5)~ zX3}G{V&>669eOGh;|@Of@Z0%tR-N@TEzF9y9Xfnw%t<1?FtMaRB$Mhx3pkp~tL2z~C;p3WJBcF<0;zERUy9G9y%e3)g&T-EB+s5TG z(NU=2FOZ^I72Iu$T>cnV)YUAROEYSg23WnUZCe@~9b zdyGce>E?c-_C*KRR>Qie%As_sko$@IfG_1%_kBcB(`Dmv+{J@f^e0vVuPX}jmb(oF z(DU;+U*vDraOr3sAdU-`DhfdT_+aj=t~@NsI>O^(E!07_&bj_2c(ieaOtAtUhyY0y zw~IP}B-eBqfmbdx?%^8V{_)854Kccy_-l;nrAGAmdRSQ!@i$Jwr<9Na|4Weyt(qB{ zMnz$$NqgW{ljMOswLq%OGxuhE3#9V?NFj1bz}QJvi|Zx%D$VwaA^otD=FvfL0c>;7 zY6Nis-zd^wns#yd*{-lH!+V*{By;CS#CtCJ)km}*afE!!WJlfi6Zxf@LG=S1f`w0h zu;GRcgng8(l4lYpz?*mfLZ@xWrSvRp5CP##E!NH*p^iV3+PL*j>b}&0cB_Kod{9rU zNm%tJCttg2|s9K+mxH~=*bEbpxNz;eeV*U5FpS*gI=zj@l9#B% z&bggApwMi0P}3DBz|5+y#C^Fksp0YRB782@7-<-{@1Q)WN}(;V7pq}eod&NyG@o#= zWSUAL$}VJPvI};f%@h#h*hZQuB7*#hDmY)8&Pj7=g%D9fbk7j;@P%zwRVBZeM{H9Q z0dw2&T#Q;xAIIb1#*!h)+O}s5$c2nT9puzh*Kt&>Y}AEbIA&yQ=$cI?;i>86KA--NI|-u&9Bx0(Zg zm`2Hx!^}tivq8JdV4PQOZ;MF~&I3!E=^{0BEan^171~jx^r(eZoz2fSprx*v89Um8{$FdR;<6X+tv-IieQWEyHuLHzxHLdbKg-eE%5Cxlq%uvPs3~L(%ny_on-l) zm+^B_;1`SQdeq+BXJZq@{z~sAPZuUL$)dC9r!Jy?hiwPIDj9u<&sQ zkP3Nmj@zlS%ZqEK{4+&Pa7k#8`OHofq^+8RdHeI^L1S}zmkXZxITzBpgyr?|XX>Qd z<5m0l38vM)-RoEW&>8c!O8K&wHL$?vLZH9ZF=9JGSsgru*Ms+%8 z%unW@ARpss#+h{@=Y8syhO4x!)+a@&DRLiDgKO#BY@DF!N#IV7uAkmH8u`?=8O)#H z07mRueWYhNWR19pl;2IpL`B67Z?#$?>1~MZFI(N(HgOjCtfW({J2Ey~p-sw(@DGxK zh>TjmC_Xz56+%p$E*2aaVp#nK#s+lzSyeQ+tIE)I7SCyjAU&?|!K&Zh>4#K(wIkw3 z%$2nE?{#DC2i#Njhsg^1sMh16MzSBaE@^XJgoj}dndD1Am2zmxV9zGX>evynnbg|2 z^pJOx?AF`c_4J%teaI$A(@BR#PYr|hdoOi_fQthefpQGe;W8Otx28?P`q?vagrkera_EZtrNXfLr{1+l zwh?lFT?t~2WKl0zC0rw7UaaOtiNo70G2i0UqeaFswGA1L%}Ha9V>d_5;2=o{Y@AOB}#)m5W|$Di-kKc|}fly)-JRn_Wzf6J=cCCdbKdIOWC^R{^O1YcJ=<#HGg zdOVQg^tXM=$N6G=%JQCxOJ)Epl$EZ;VGjrM_2tQ@qPC>yhhf$oU#Cfg)8>DLKS&zQ zvsnExwkb2z&zP=fn*}wLQP_KCRu82&Rb?D?yf)j+_}mOPyzJ|~PcE`}=#EPy+G9kK zz;D4?P8>^8N7c2Riq_CbLXSi~X25sd3)sy2NwN(|Fl|&WRcap2y!>e^RZatb z&>hgdXhsZ~I7GUiwJ!6U)3ndMiYZ_}^c~v~?2?Db+9^C1W|w}%k});vunzXz-g0<& zxCP%?`myBu8*IJ00hZV}_UJ(-jFY69>rfWzR{Gb%PBUth?s>w{xATgOM+v7h#zD~* zhDpCBpGiZdYZgBji)TtI5*Jc^BGW9}8R!oecaUBiZlDoH#@7^$rAx%ZbP26S(mpTw z>^=^Y$BM0eBTjVwi_tjq9WE`Ej!(vG4ryq-%qB0+57^!pf#LnB0S4*EHZ5s27Nzm- z*E!Ui>}mBlo1=NR-XS@PvdED&mKE-2E$Wg>Hyn-;4ZB>PI_xDfI}f99%cps8Uo@uW zY|RmfwZyz-+7dy>30{9ZuIU=N{uG`lVw*80#3!SJJd6o9(Jo`tk)NOEiQirlX>5w_ z>h{u8x7`los+!tjvp*XTkR)N@yX~<4{xMz!^N^`eRjS%2Gi*iMpT)aYEG!23KUSjC z>;VRFtwyo#UeGJ48?yEDMIZLY-Evj%qcQI**o+jMahgj6|H)VnX5qOf5iI_*5b;7- zr7PZ`>Z`Y_iPX2bqOph-MZ>nrsfTp^{SJ@l{EcL!DU_hy$G()U^;#v0&cR!Kdhb64 zf4&^9MGP+$1|O2TT@&E{!Dv*+*l8Kefmph?{9N=>c#_x<>vaB(Jr_E1a(BHTAouTk zXjyJq+>9f|T{ZoZn^s(_rYod>Qk;}JS5DblH2{Z{O^w?YaO2ZD4260?4X2}ZKRq`4 zGo7c8I2#^dK4XjN@;6`hN!Y#Ajc+DaS$tjgcvGL>zB{M?wqc%|z6YqVvDZs z@+0?IggeREVTswq&T9MD;>*r%uLs2b9Dab7<=``IoIsa$KZ}i$`nM8Cp`+W&v%Z-J za@ZEC5t;gvTqdPNS{K7P%I6C1?o0dz9124lnD>Ix(wl5IxL(m34rS8ov0|#H+3sh) z4oWM&!Tie0l>NQ!+OK++ZHtoyHpQi1j?IY`oaY)HByb{dU1Xh?f?+8i8?bu@ysJHg6p=i7YCG+>T84*J9k(0*w|8pDV%51 z#9Jft)b}ic9)^X7JT5%peep zws?|=&e-|7R+|i+zs7EUmc)4y*+8OEd%bT|Z*X)wGEZx7n6o)PJS04gz0OGknsP2( zoyzN?d{m?HETx6wFu}G%c<@SF!?OdWjcZqAdZ%754tX)$9qvo^p@hi_%+db@Q*ZCz zq0J-4^?ZPE=j2p@+BOtWsJ2%i4iDkx(++917o+uEV;bfxbuQokLuP8YyZmH{EMn!G z{Egtec6X02fX+0Om1>HmQm;tzUk)=KPtPg%Snn-d!()tjRQW3O$1oqqueky!po-~N z#AVQT8c5eV8!+f*q^lNxm9yjSGID($}T9twts*zUhuzP z1!|fUef%K2`JYrP7W!ZLnfU*||Cx5;2EFts_bxy}9n!MAFV|BG3y8V8Y&J3A@BZDM z*@veBg7?oarf|=TBad-t1&FE?y<#nX%}))P*6BtOJRAN0cY4`+*{J?`UtMDQIz@XpbC#+?f5Io1N#kyC=SJ% z4_?)?wBU_F8IQ8f0->AkX0Cn7pwr=82k9@+;wc=NaAf``h^pxg2(-R_U9siH1B?^g z2~1+wQkeO^Nrp6@FMchH3Hp%4e1}%RS;cwu*FTw3rqOa8i_;wej6Z3>m3#k2bb56_k)BW|#MC3^;;X3w{O1P#M8|!;lBMnh^}W4E`y=k#{+j}ahd9eY{&q}NbsZg{ z5vt`*$Jc6{yV9!2x=HX|mJVD9_;y#~(#g!e zYAhs2_S_<-&gfRnjNSV}w*AcnUmQy}I{VE!y+oS0qvLICf`UY`OosJsm}Vj{^1^ zTpoQBgA}?o4z(D*RA-q4emZ&OF22^_HBFUIzW^4)o`m&;NExcjMi%#^#;N&81TWcb z(+gjIATe%ufMJb8k*p)UOtOLtDJO`_p%43KC^XyD1q5ps--Y(Cf4jf?wqFRZkA(Jd z$Uf=!eycnQHn(_yD5JbbRentP%CKTUTmCuZVH^kFz^U8V_tuOv83~nCI!?H2Pti5N z1-`{+jX0Lhd!*F!^c+(sPERjAFK^CPNKHIuf8zBGSs4L?H{!y=wo=3n z_$cJYJ<=Gxq_eX5Bra6Z5Pl!kR@X`ufP4Xh5exV>OG_qtCYxy3g`YA5H zj*nxS7`I(Wkk*1&->rPU0}7Bb>~#@zIVjejdg?gWe#H~lX!2}?Y>XnlJZjb+o-7J%COWF; zrK(cQBwkk1uirmQW5fOslyGn~liA@lQD{C>-%(^}@31)&CUiBCfwA)fS3zrYIlt+z ztn{lI+B1`w%g?Ys0ml-jeKym|Y|AA!<-Uw=%41d2ujhv{o;+O(dYrRwXj11p^0BLS zH1zUU)xr^z<djf4`u!*lX}}_HQ>E$W=hskhRa(5+3gC@E_nf{^@PJl; zj(K`Q%6ar#b&_;0mxJ7l%UV2fjGZXf&~ai8mqXV17`w+~bl(cpvhQEJiz}RVu_B!2 z3xSA)nXV}w`dFbXGi{mwXr=VPVi-$`@A5bll&MFCen7vrJ}c}=&jN(gp2dTVN~ zj3Ob^w@)dGqh!l);6T{vfgzJi=)lBV+ke$k&6W!?^5M&k&%Mn2!)gH$OdS*K#(_K z*uIU6pH4s0V05AW>e3D%A+`RqV;aD)WIfQ4Iw{cL5jyC>%BZg9hJU;9!+RW|!!13K z2$XY73#wWGqHcTI4aj5F&?E|8l_Yn{gniI0y5>PI5N!ongV z*Z{5oltxEo(Pu75JoJi>fre(Z*2yMUW;qEXB%wplv+NRX+XM7fbP#({0LL2{6$RgI zAqL;JaB;dXya33wwTs?20I@DWE2&bTiK4>?%Jr=fR;Rs%B*Z}`kXJD&13#qS9!_hH zBt&2Qom!s*D}bcg3j8?9MUk(~D$zWipF-9g6 zIDGDe0?}YnZ*OmhDZ8jbK3-nHD=Pwp@&Z+OVD=t>i`={N;k_;FGB|)Z2^=@RKYvJm zeFqMZe}I?=NF;ES+HQ`cMBO2K0Xi2c;19uoDxk4Q1f~oKFS(JR^r3)OQWX%80NuV> zAminwcm+~5A5~jTE@Z(Vgj|Do4);6WMMV&9>~UQ>uDilAGTlIR4baIKRxQOoAm)Vq zA#s?r2C{cf`%6IY#$~IDnvE?Cpj=VMh1~o>Z{~BMQdNe07CjI@AEpO?0_s7MWH*1^ zS_Kd%)BC8{M7uD<7{u0~#lpg}>t*ro@yFe{W&k+>%0xhjA|8m?6kaB}Yyl-!Bpmdo zVq=LaCP?t{7fq91et7%;u=dtbQMTXTC?-BCC`u@)q)JJHlnRKnbc52}4Fgz!ARyhK zq_jvgl+sd?LnG2HIW+H{!RL3r-*wKr&N*wn_a82qnfto0`-&Z(Yw!IrFqjgjLKqZl zka@NvHrjb0xpNnI!#elgVmC%DQOAT$v~>EV!-Zr_BH{DQ`wc}JQ9rH$Qr2)4Qj%JtmC~9+OE{D(vz-JkI;6w=I!rszwpCHDFJ$KfZ&nHv z3T0wt4Gs)!|M4SM(A^0t69GfvY+KC6pSmlHTYHIu?w&`xvyheg?;~o7jedz|TU!0) zag4$%IZ0Q;9Ilq!FHJn7TZyEuS;=W^PAnZ29H8#Qb+Q;LnSJIO*mU(~RJYg9+TOV^ z;fuLnZ5GpW&-pHKT_e6pfkOQJDP+rlwpF1TC@>vf5h;m%fN+u4lC1f323e#CxaFZq z%vQy|)x41jXJp}>xsOb`ZT%T2q zy>BX=-otWo=A#Ft)5>Qu0;QBi8OKWvC$|xX+YkQXX`<8RF?m*MawL2rQ!iY571JNa z`}^fQji@N&0eU~~^5hCBvGY9|fg;<%tk8i+Db{j<{i=~3g{G71T5fChmN#E@ULdkAkmCIlYVpSA_J|gRTCXpL z`Uc6Cir(?R)^*!)ZjPWzufAs~d~ai_F5;YfR;X{_qT!t(`-dBy*Y8Op%T`AD=PeDV z+nxKxo%|gURh~_Q)}0O!gJ#tH=!I*R&j>}3_1|9aHAyN@r;(gkp56hEy>Gabh;Sn; z>{23LFF$AyQ&G2MA#jh;rXnM;ugL$_ohxUCE0$WBGorsTGDn{#VwP_eDWwtRY$2D( zQ0%Vdppic(;x4Hb#olYGQS8{6Sf@8nXrCD5)A8CT{qX+d_fKU5a$ENGCY}2wWkl<{ z%|~SBbL~|Asy7X9=C)HAeXfzt_j>=01rYH0!x1VhQbwKlAj*VAMv?F}HMJfr9z!Hl zHPSmTU0xO3G)?e2nS>y7WDWgx*n*mRV$>RGtrn&kEa;13b+JEexsu=`$G<`ME?FH- z=;PeEbc?!0aI$2DC1<5Ockr;IeO+l;eS3xyil2znU-?1a#r!xmeb+s_{t->LP=`)U z%7!IEwzNd5vr^Em6E*I!hL+))u+6O6+mb5P@;IS9!ztrD>vt;S&HNS%>gM917FXRSMy3hmQ?-+ZGtdONHq)<|+htQ%IS+Ee zlu3K=mxqj5htX)~Qhz^}1%c^v5ub5Xr~0*&cU2f1X0Q4xXFh_kDIW9=^Bm)ytIk=A;jk<;w9m-afs_lmNZ zhTdiB8Cj#wiR7>LmBIHPa6hqTTtf-z6R372)+Pn$A>A~W-`$kA!G%!E#j>Cg3eq#-kR`Lrep2;`~p z@({z^%e+eS>w+_h>~ap$n{-hplE*usj(FzQv)?{4aHp*b1{9-YZxwTqHp5{eJmu+SW$9{r< z_SAb6h2+vl_reDWqjwU-g`;EK)@QX7YciNB_U9V&iQmfvZsZI)ntTu=$YfO8a(Yz9 z(~?3(PU4x@*lT}0em!J}bvbTYcXd8$AiJ)%K;N*ngy<3q<+3@!mf&s_w7gY!|Jge- z&gA`1)4SBv-7JU^(ZhaAvM7WldNhpNX|rK92kRc-)Y4%>>w=`RmkNqZ#(g@hpIu7-MD=If0Pjhg!1M9WMSuM@%oY-c7h-N-1&a$ zv?z|TdGl4r2cBZs4{fHq=N-T?>}ZdoT-YLU|9N74?5y=HZaqi{0=}O3<*7Y`1lfEP z&_Y#=t~^&g!L3d+L%jqhCLkZK9rW0ba(tkTK!6S5j2s0X%5N+u0`;thygZcTriF8K ztIEGL#gNofTh7Jwc#=?NfJPM?DM=MbdBfB*j7 z!)yLzb!)_Vofb`y1v8hw=JH>6;N$YI@PE-f8S4Lud!>azI=TK|KcpKtS7aLW+mTUa zHsXOO#IZ@7X?!ouv1_QsY~3YA#@u7{zG%mg_oTIt#XIij^yd9lNh}Hic%|%1r!=@~ z-e*p;A{^!OL`0W%HA{#F4%tnl2(7g$+C$ja#!S&hX;Pj#1vJk&PS7$O%hz6KM00Ot z)W(OrAhwO1v1ns<+|z$%tRtJ5W*sXbY5|Q*;W9n!C3iBGR(PWDevn*W^NR32bZ*4_BN^NXccf}%xF z@6P+Ru%?Wd`fAH>2U?01W{f3#@sb|n@6>6+Wa*rODz!AbKN9v;OtVs16g}Wl-wbse zsOmYd&gnk3Fuk}gNulSz$}ePY;4jgtx!FzJgA>5ali!=E(cL7S7;Qz|wd9%|mDgH0 z#MKwdZgNn6YAwr;_hLV@E<7968y$X1ANI3C1Loo6Tw-^bZHz{^YxnXU8)8)sqN3K> z={ukD9fk{6cEyzL^f9ybP?FxH(bMMfdbH*p)3X;V>L1mqnPng-B$OE=%+VfM>ef(E zso9pM$2%MC=CjM<9jnPhOIrdsuuX9^{$lasC zXPEMZaB;RIFg8I3fng3s9y=>oMzTeoV`lWmD8}D`i=t_h`ArOXjY}EiISJMMv-XJ zcew1KgXLsxb2KXoltgnDiJ9AZ0zFLndkR-5K29;|#On*I`w$CzHs;*aufQvFp4lO6 zU|;&A&OyfDJzF6!BUvyY6+l6)$9U&U05j|OZR-A43KEh5)+@o%j5<{&Z~NHc)(ono z*p-no6W3}*QH$ZoygNZRH-7dM8c9pr4#-AE*0`A_nofxndLHjoA&TSEvM(JJieSpz z!DSLlbw<=(Cx|)I`UfA1Gge_QJ%&guP(rHk4%^Mw-EIEKjTsCv6x>MG^NM;33T^&= z*TUGCWc)Y$5j-09c67O`S44PoT{Pj~aq0_gSaNiaZ(5wxhCdg9JQKp#u>P~ZnS@k; z?a;C;ftWmD`-?(;PA<=>-WW7~po26>GoKe+&FtH2DE4`CT;f#flI3ZliAZ-jlC znVrJ?(zp=*piH@wMVDgT-SXDQ|4wWjlvU3%tnIydB~aq`*}tz|#C>e|tR!B`c&L8k zt%kqfos9Kzd$h7r*aGYq0x`yp7FCWF!6x+vS!CV(ougk@%JH1(_!8{411@qA5OC@L zR+_B{WIX!Xk*Cpp{yljQDu<68zt2L5tER<5fqXl%QnMhFEKDJ^P4l9pWsryQkLX8h zLA$Q!oYHEIB&5u?N8ic%ig09bT{bR>POF``>RXo??fIu)jLz_&myL`k@924=ww}UX z5IINl$SeQ9kb%@hDNX?=gDyLL63qhgd2HRvGnp*vWTIKZXbztQMg~QhEK-hNV>k6l zJ3mh+mOv6`K-@Cft=gdZWj-nO*IN>$;B zY{w0nSZ3$l%scH))aA$#;H#;nz5d6l#x6GIsRm zmEy5tdKTIpCK|uXmljz)AE>}7CeP_xcb8gHVVux-Wq}#^o9g9b3-uhUQ{IJGY7#^F z`St}ud8M#qs8zCt=#e=`h9ctk5YMqFXhd5od2qy5vI#Md#YCA)Nd}O}D|RzCl%Gp4 z3g5^fP*Py;rnMf8RLB6SqMooQJs+3s&&Mp-SYfDLt=$vfKN5d>JdAHW^4PGeLeJMa zFg@AyJOAG-jGsdXg{QmJKWLKoI2)vtwS{-{ShlX$v=th*x0!X_<7=QzV?0_9?Yo_x zTCF~*NN77Xqc(|~VC(Gh)qsYOfK=*JrL#<4U{0g&zGIAo=O)N-RTMe#vU4>v8?kF zkYIh+bj`m#)L~3oLE@&cW1Ii9lAB?s>C%f#*ZgUcomn z-&GG5Mtnl^ZbY*Dkc;DG6MVurT9!Ky&R#CIpMA3SalAHq_^(c=n*H#;JXm5fMs;-G z+V-XE)D)ZWwqbOw1fy!+QeSpdRu<4ftCicO}9|8UDN^fS^phfjozh*E{aw^ea z`hs&o;209DY|7UsiD!Tt`wGxhHIV26t@!~&^X#>8bS@1QXKR*PT_R?30eb7>-A*q6 z%mjh7JtQO~39vRkvcs)s*rt9}9#q1KW5;5js_x-nrzh|X2fu61Q7^3CTgV3J2g9)r z7?X^wEG4hab722{b?#D$1=PEpwKy5{mzb=?Q(pn>BeN>|G%*Qq;Kus8=+sMGN?xe2 zR*cf&hw5z*CAz;_QyBN7e1LdRSdKIscs;cqmznkgY=nq42g67xx>aui2e*YDgxK}v?!?k0zAu5@!e~$ zC+TRC>ewXfBx?1n&dJ#_&QGU^hzL3c23+Kc6?@eL08D_v{%5D+aK8R)%;G!@1CmB; z=W#fJ0`Z}yap55ENy?k|^V?9CxB>(5t=1e1{XIAK4ghTTqvPMxkUuF;;4h-XDF)%e zVsqf}9Od#l@*o)e@zAz|jn4@m-yJ1`l<-)6J{U?p(6tS-U}C9y*6c>Ecw z(TZZ{)iD6bkn`zrl9H05^^(@(MPMJ$T zd$60uh7T;?pZ?pcvrPiJ$OC-e0EXqNWYVtK4};2~bT8bBm&WHbXzmXNRJ1~j$fPQ#W^8$W=AbqpwKq3{(dSCa=8 z*Q-~rpnBiWsVyo}yaNpkE9&(@RyoFBS)F=|Lsd35_04;GURBf;xZTR-qO#e7!KEf|S92(($}&Pz?J4WCH^8Ix1gx6=~R$E(5h6 zg~=Dv$*h@}nFFCl znZG}j6+VS{j_m^rE2a;qGgk!uz^hh)5CGVllFLj9nC%O{e)YM0^S2XHYznhKq5OrJ z*#fMbp6+gsQUQQvb-fO^uK4=Dhx)P`oq%^YKrKj^4!7O)>D#w&cM5Ds!v%xjzBy+* zjzDX}&!KhC%`Wl%&H3wOWMss|+jA+w=Qm$sw`N)88zog$T;zEiOI6T7c6N4)fva0? zjum0Yp=_|AeW^TMfDAX62Sb+#hUzC+CQ$1glE%G%xuHiPq<)1D@M$P02&-_!rVj7l z6Rh}`brPq}_JBA!_$hKlOk7-(2~&QAo|g73k{a8V&3O<=-wX=WB3X4Qm+oOddl>eA zMRfe13*JaQi-fKd0f~g^0ULxWs72sJ(FHVITe^mM=-CT^2ec|85Qw@hICX>Z#4P`f zv9x^N^z7VqQEdtZRrjsoF53~nrU9k!dvfxOK)xR# zF)?gN$iY7EG6n>~P+ZbszMHOg1H+1I3N;l#_j=F5fx@`gF+xK_!+Vw}M87Gh4~24G z8esdQh;d{u2*9vJfoOWbr8R6(0yXxD-%?C`Qn1xZPNuu3+HaCS&;eC1lOPx`n zIuo(jKbx$YusW6TB&!wG)lCc3zSq^A5wM2TZb%ycL4(xF`wS&9=jL!a7uQ^ZsDvrQ z#qt07$27hR^SH9gHGEMV6Iy8^0$bEOzxqQ_(bVUv_ZP9N8R~K--_PCj2g~W3s}WJ{YY7;6=n$u-boSsTS!1s+#9G_)+t<)%jK+I2vrJ~O`qJL z1vl8FKYI<0MQ#(z)1;PY$f~{8zK;F&kqG84g~wu|oKel0_Dc8#7qF_wW*S7WH^)2? z0YR|%8+#?}k8i<8g*U#7iE0&7$ETd&JP=tF-{9=$k7E3#BOx-n)kE;NL;-QT8YjYO2#bPXrvAM76;zHN;(3}`OwtadouI^;p@ z?rzTac<$G)a>;BMpWAyz&~b8@`S1-WMY7E!=)r?cw?e)j=(PYxT_Z!8x!x8%cCirz znqslvO29VRV|DD6`zlgVKAg<=iq-g7rMYUNFyo(Py?zhItABfCBSr(Ns)U@^#^!L3 zb@jNR*C!IPE9*P{sb1`Ow}vZ;5Baf;4eEqqURi7twJ#OCh(Gnbi`RJB%!l22-i&0h z4N9gPNRBpltSKR~$K1sBLBb24Fe*HsFGH~!89jYvuWvKul%)43iT(H3mv!I$#)5+* z+uETsT9A^aTQe3&7WhQp(Xn`SNM70UG7o8>nWLkO2(E9+&QT9^YvFa$@1;|J3N3a_0Ov~@wHG6_sggiD=X`jKXCYqhMVcb?!pfAYd3;| zBNq-TF)QfRE6mV;wXKUB8L<`*tP~T4ZS1ucpnD9|BQnyGCcHuAUq18l@v*V7eacz- z+%vDB{2H^n$N9Eqjq#uWuCLD`a9oMWV}f}iRg=+50e-Vtfa;HzXA>^_+mS1>8k|jn zpfwODjFKO{1{W~Lh+dC^msp_`FBlp;q@6(ynwn5|9jdgg7E!>-J*}db~h2xIqIPz&0{be-ER^WiV7bYBm@p-#ZI|0R|`?m`sBCAWz6QU z2Fd#hjrYcnN_OCgQ48s$ubqGt4>s>4#fYhiq38GKw)PsCXH~B=Ta}8w3AlD|{)|D- zwPJ^Sw?{Nkm$t>{41c6ToWYcn`E~mD<-0Ss)ay%y zasr1)J}S>YwjMz3akS<|im$XrhWK1Inp`gS&onuf(X}=L(9?%x#Rl`ZcqnJt1c%vP ziNKm-hxgiFer>753-#XWAnnqkp3viz*z8mARKh2EswK&IQU~?naNXTD92=Gg34TxO z0>(VbCvFqp#tMl%UW^E0d6DJkm%TkDKF%^Da!zI&dw%qbT?_=!?6u_U1Z!?`p3*J< zQy4`O6L2t_>=WSE%O~f#nfq|0hTr6`45Wqjex9w;5f!x$Xl}7rdZUd!SN3Ma2XVxC zyZ4sey@!Neg7dV4;PxhDqFY1x0APT~B7*suo_8-o_y89k7I;r!|K8|8$bd=x7w zPQj!T=H-oq6bpA)2a1MVl|7`M++kvZy2Nhdl8rc^3hkgEgf9(7=fQzKa5H6Pok~Q_ zJAmoiyX=JPqC>!EH-%Yf@84g>?9kFLU!G-L`d)J`k=K?Q>TCVkz)aPJi&!mao?lfm z;ntZb2)y(tE&KL!!3wqlsp~EXWH#->J>Azwu|i+fag6G zFvIliF_be}CnO~uvdQh42gsbXv`FcVgxNjO%5EF${BK|N6RbK_Mgzk%*u9hQeERh1 z<&~?5Fr79UUH0+Hmq~jUJ>Y17Bu5+{4=*=A|H{9D$NARjhdqxSr~~okLwr!TR`6y2 zYgXEBQUA{28-;b9!9pG1a2eB+G6mRA4|^Ok@P&6LsOw*i-0H>11)PVq;3_>V?6n~X zd4QUaqL(_VZJ5exkp(MO}IR zMAeF6^s3R_7u3`%9B1mo@*j;5y>UCahY52}B+T8$RU$s3TPq9?v0k&*5oS%Rzc^1o zP`dcOv5ZijW=b?B{>%BaJJ|b9`(Rd~Tvf*4P)T+dZLnm7#-5K4s?2oRG3;zFBFWqKcUHgbn=2X;RTCszwr4Yd@zyO z+sgu^(K~*(aj#Hb3t{a?#6)m?Qe+5dA+!ZsI7bt?R{w6j%Kg$x)vBzRycM}n8Nz)d zPun$D4Kv(N6E@yAZWF(&)-cPSe)fn_77Lm``~s6`BJ#lTtCm-toLK#Y4U^{H;ID6I z#8OjJIScGx<%`VL@0_8-m6DX4{LSQ_EZi4u^Xs80V`SQ!cYZFOUz$Tuw@#!R4g_%F zco$af{m{Hba#W?7zbRbBE)Lln#?iOi&=KFRs%R=MI#j^X59uD+_wQ!rW}T$<%T7F6 z?p0#|HZ(GmG-^^Mn{E{s+s$04n4;Wc70w-x$_|X~?#+!&hy4XV3W(p+D<8EQ?}Z&C zD`@vCqxv+};N;rbwU8}0#IIS5m35U6q~cI~%<@mA`#-d$(VVnKn0&6<~ZHQ0#PR?Wf9*wt~; zsR}h9L!0_%_?5N2;0t4tUtobJ_spNBDuzlc0QqM0xE|#lsVFEY0Jlw+nfI*Yg9PxZ z@H|u)q1pjC)D8p42n5mM7T7_Y_&sN4An+D6F5bWU5~FU`57Fm{wtsjY<|`_1b?`G( z9SsdP1xGQ^(-D$H>^vYOCW0s}5G&yk6mY1QWp*{>`}g1c8Ak^>pWMKiwZTMvg3jOE z+ziOislP*A7UcxW<%A=kdpEXlxMq&p_Y(4Lzl2+j{g|q*?l?I982f#AeSIA?FU&Yb zmx3$-p#?lqrxHt{&^$Z)daSwU9}Y7FSTdl;p$=i~#-1!~P3mz0Yh<4po}>!KPrr4? z1(({Jd#Y@?e$^BG*mV8{!S2BZmyx~F{`O2)im%u9U>1#dPDF%q0P&H2TQmWD)0sH$ zwLh#U)1#8Bp{hE&(lt5MpUZ^t1^VMhSKRUR9$77RVDk$e{=tMLk+J{TJ5o$H5Zq~v z!55c#JX_|_7)_O#zQtJvb|#OfzkDFmE~Xr=%qWKsHv?`eAVmc{PzDj z5hzg%7=d>R6R#aVnO#N*n^rcw<D~yGpRTo$cL!(us%I zZ{{4RWtbi>d4>5kqs&ihV2e@VoO%S0W!AqBTHUN;O<6KO3>jT|%wH>pb+&#EVPvV{ zrW*l4K6+$4s9aohs@3a!-rxR%#fqcxgw2l={567kudvSqb|y81Hsn}n=+{?JRyfR) znlI;Uc?Mt1er^?KR$xT3MGlm1|K3X8>>FwWi)aUXcZ}Duy6bHo@$QO&xQH zZhlBUN+~FMVG*hq!YU=C`&nbb$jAs416dA3SYJUHCKMRtDO4(S?CtO0J1WBX%dmIf zVnY019`OK-aJ^ofJ4X6HY{)VJ3SrO6iHedEm7XFHPvW}Ey{f7%iwnig+cJ(_bZGxO zL(}TV!Rtw~SXzhc46<{GRk_QzbiHp7*sgpmR903}x)Jz9Ss5ONWrir*Oj;NFGQ12} zEeI_ao!};LnQw-Rv#_$BsI)wC5MC^D!n-YUq4M72N~jhJoPro%V|ZB>e2sri4!5eYE5g&4ATSTm zljM{X=uFdOW^OLt4;o)zUno_F>!|~)5$Dl2IPdt_@E9){6)-$HWqwtRHZAHPHKSLh zb=wQ~3p?gFI2xD0degc9bqnj2d$f{{ppO4RT*`@G9CZd(ZQ0dooPx$K%vWF=+a*g^ zAKttFcZs>d68rV|#(Qe&4$?JlcfH1+P#UiK!1&hR+2(?^Q2X}hycp=$%v`HH&Eq)H zzz?sq8?XDGR>YY0I5@fBwc@ZmbGfAR+JsZ$xQp6Oc`V(#7ppBFngguseJM3oRMadE zftMUV(m<~L$MLDF`XcJTe(*-p|0PBt=^j@h9;e4F&NJoV(EAvx&4KQ8dx?Yf=*Y{+a-`^Yg;14_#~KmAU^SxYt6>te*5>vs<8if@*Th%D zL5xeps_lh1meKWus=GzwLTm0aJ*Ty^3k;3f0(y^AGcK2li5l!`=4zb`z!3l}A1z(C z2ggC@XlL{U-e4x83=* zz1)s;nm$+YO#hAFQyJxe2f^jyy%+8d(8Wa-o5`OH4FwDh_tZi*|79w+dgnv0VY@tJ zUWOl}qS8Cb{^4?xeJ}jX?h63FetJX{U7*y`aH9sw3!xUY-+PrKv+)-LJrB$)`I5yx&oC>qqa zFgqoys+?jUjzaajCO_cpo2`F$9b5u%sUIBF$J-@n{0X#QW8Bs}Zmo$pcm_ujSid7z zuhh&|pv%ow=_tS-|4$>w1(G1+)tf^cymK(YZl{Vy9vIlI4Vzr)x1 z$W_M|pN(3NlN3Y(LYFTqD{umeuIA?Dna-`+D3R5524!PPQx4m`t28U?uzjpm-2Pt6 z%ij+KBB{!HLWQ;O#6G#P8PzdyPg~_F;+)L(gg za@?v6yGlyhefM~I)YYSRE1_u(J;Kp4bvE>&->LF@kDno_-(K|3{#f$Gzk*1qcPOiP zxL7;!hP9x zYHiD7DtP=L2nzfOE4XYfqtW|A5ff;Z9!sr{@hz!hXaWgL$&U@ zd4~rH9YvR2y#VR}3Lb!(*|kusXyA;=xy(m+k%*8GVww%htT;(6&COeXe2}5Pev=wj zW;dC%$Wa_+9MlxxKzkUN)`uSP`Piml-tYQWn%iA6`%ms-h9I#ml&N8M8OwIxW2ktnP9~HGy2{+54VYsO|BD7ev$1ALG5jWEnyvN)4_sY zO95`^$}j7kO_cnu6W{GwD}VXh@UmNvjqGl8ekgL7cNDHZiWH|jI&2Q1iDX7*IzNh# zwlAMv4lk8j@$3}hU(~EPyjZnYMDL}ppOIR~huu;N-Vh3Q+hFXVXSQ7!(Rmoa!t301 zZgzWAk-HK?>sLFJbQ{T8Bo_X;M;uY^b=zoHt-bsiBUK*wjNL{X+4Z$mfR5T^6klR$4J><<`kP&>a&mM`Y|(UYoHR729C z@iFTH#QYN02=ac7frrl+m3%If7V&A-3n^7>A11`#T6`R(X-^{6&4K2^d`eLWfwC>E$AG>|bYY!tWAQgJ_h3U*F zb{uRTf`+_>r)3qP@*_syU#z#t@;IBLQ=7>+FAHT_7yEt_+uB1fjM3z#W_d5u@4-lD zZ~y1K5=6q1o%xE`wd_o6$ME_3Eg)dfR34CGu=x@#I;9GRD^#blJ(cEHAaczaPAbHWX%se;|UHJ)Z&m z)KE;kv{W;<|F(#aL3QBF%*-nSakZ2>=4BawvB`^#{yzfQ>;I@$n|kf|Bo}+x2i@qi zZ)-V|39PzI#8N6gBV%JaT3SnX?{c+)L^!4duM0oj}Iwb zCNlvT+p(D#X6CixqeqWcOX?=Rs4*eX2JZp$y>sV_M((|P3YuYVuhvU5Qy+6T>2xHr zYtOQFQAL&~?gj!&PcI*?HV;t$Os-kr@c}gwxLtLQmfV`g#>Of>@ljDxNl8h&-Ehvb zb_p{Qr(Zr4pzJ(}W>&{Yi(51n_z~~JM>9>7m>qpuv!oMJHlm((Q$?CC|B&X!+WxgV zm?=z;X1e3&z!yggcBQyF@!9dsi-B!@arV}LWy-sIYbWLl4f!A z-hi>e!AZinAcMc!29Ie)m)owM5gN6}!-_kcTa1_`D7DA&k353CemMW{y^DWSKkYY@5d$(K^taQi@=qsDGH*I6t@USi7;FG^>9`DV{-eBtx8y)7z({P9y# z)&CUOABSC~D<5ge^y|<95wIM^L4?JbK3KtP;4`C`%fHP#M7JZ4oHUTz$BAwWgW^=k zdbMKJC*CglhIQY)LKT9hrWrb60A9y)(iN?*nk`c#EXr zxut>tc1MUS(Ut^%CNIw|$tx+TuB-&)aA`!nz)}H|H5m6KY`x-fI8 z$gceKSg$j7_3e;@II{VC>HKRaU%`T9n}IV&5AWajI=o!Pz`L~VE&L&EMPcd|{cZZP zkyxY}N;rK|E-aNKa&bmXqkM#LGp$+z)2G_mP_AJ-oLbK^EtVwKo4&3{L>_rm>-6UC zK^22bm5^$Xd1*H05F0rv&Jdah|cl{0kK?~H{^a_^H((a{AT@hGC5am=VW*&SWdtBj6fy)LmY)ZG`T~}Aub%I-)IqZ!~;Of@aX8W z!-IV<3CXjszePqy+U($5A5b!}uz*7H5cF^}k1U`8gx+5DeNKJYppb0Du8Tb&P{E94 zuX0e?Kj^1 zbDh24KHr8{!1KT*yO_#r@X7b|M&{7pb>gjN{GZLPa~_79w4dM5UA5=5;#&_xZM7`o zZV4g-sMg*NM0LDJtm*FCe{C@!<+C%8m6es0L??}}I+tsKRoH2xEdj}G!ooFBAEC|> zq!{hBmLIH6iq~03Fn)!QIP6OD1zCQ}?O-p5jmq7O*_j!k?YXc^9WknEYR{CpOn29X zb=>BYqjWSzd#)!6xc;_M9kf8Q^A-+Tu*dAJwA_FUyu!gfqFX~2qzYLb+{>TC+Uc{k z=?j}sK#uPc=va-t%BvT1@b<1UNfb2d)l0BqjMgi z;b9DM+M(q94F5bp^J#v`l{eSz6+WhyE7VcTBZ)L$FomA~aMoSHx(4^}fY{=F17~ZM zyZut`E4mKf%E3M=T}}Ktk#TpQNdPwub&T%gG5 z7(_24{EHRqEt0FiwEp_3Z*P(xoaQ6q|EBM{Qkn7W6SWChSAN5SI}Exjr?<5K&=N;v z@bLw*F~~Kfu~YRbwT~Q3H8$JEqGhUfE1}f|$1EKNi7;c6^~<4HVSFP8)9#-a_RTXp z(y}D@cc)aHk>6i9mS!Whx#R3E5eCMHcyd`u$bxe+NOYPqe#GPL!Mf8u;yzWDo{@&3 zFHgw&O(w}&@aV!s0}2fuzQt^J9N!xtx_RU2sKuN^zTbA`C)$KL5zkIT4liu|;L_?K zCyJt2`r47CtVJ&|CR^MK%2jdJWP$u#B{XQRYPPW_8p^BUFB_l!Pm`%M(W(Sy47?wK zb&7NTT%3p)ox;1*I_E0slXQW?U22L&(lDa>rk-h94mXH#+OD2(>1(AZH6ag0s_MHE z4QZtOV*0^d(OFZ)uS4#3>t940AhE{cQxfRr4J|FJ@)3eCnk7>yTes+ETIq=&++LZD z<|T(}Gd?1TGVMRzuJLT6gN)|2<@Qjp(qXkgdkc{&Em4%&`sIjV3Z)l%wT#xjxt2)2 z1=P<^xV!#Xk;hvE%cb@gVQK2J^-gGrS*EJov-aXp1Lq$rA4YG-DdMa0T0b@T&9=?z zr5s9{6=t5RlpmI%o_|2MiWIyXS18x-VpPr_ln9z%J44`{V^4< zayxx-uQ){~RD)DWp*q_&TZTA}o}3{-Su72gn}YqCiZk*++y6;lAH7`IgJFMRPgHzd zM$9pNlx9c#p_c?x>=JeXr)Gk?ssQ@DDMs!q^fD9|5k8~oi+_kwF%ZBn=&mC zPoZ&h>(waTh%KXS2|p-u4bIozs_$U!l;7nIOi2FC9?08$KmYPxUx{`xRrbpDK13G9 z*2Qzz#N*%DB~@oj=yo44ayis{uINZr(MG%{f95l13Yn{(^XYgOx$v?wzGJLyats^= z{z_YjlMM2b^ZCh!w6E$!EAq5Ci66hecw2XI$4OhSz+|(F)Hz31`%augp&VY!dkq? z^rmYl54j}$yLVG8bE=$K-f}}X@5WjpXE-92n)O23Z^wzOso8yt%;R=+?H!D4+po}w zP|Z2G9csXxU8ImE{)V~>)Tw`1%~x~@edj=H$4A{U{Nhp*@u~$OdCe4!h2Fs-2I&Lo zCwChh4I&yTQq~Jv%0GnC9{1;#Ov>8D7pQfP%p>D)@}JR4El})zmitkzZ;|ki z*`m0awMvw@zxk9)ps<{Pym+3ffg zuy^Qbo`CPiAr68Gzvm}G*1_ZTxD*1%rKdiL_)$KA;T+uwBk>@|ka_jx7n$rUe15Ji}bu*C|9gl$LKt<3i&n0IX+e)pEci*s&Iz>2V5 zF#B)};``{cb9EO3pR#L)O6Wee{uTY*$X`3cw_qZ6v;`g@@%=CZAC&D}Vm^ zv`~UgMYVwLqD~*9m;BEtkHm`9$4(iN_c#~s1l~yTR!yqRa8@;KeNtRX%fKv2%1>9G z+LmbEe716EGl)AMwTE){s?&d!ApsFeu$n)(!2&p+VZ%FuqzGsLmgaH6o7dloexI-b z)5m7ihBy9B!bHUo-;_fnw^TRfc+#ue!`h@8pFWOHExzZskBDQHSgYzCYB%%z}^(^%g2DbxOvD{Wk4GMd7iT-L+;r0fcn zpX+u|zK>@_UbR?rug1BQ|l)9zicif%^Vc;=qZ z^A|B~MpAd4FAm+l8_z?#U_G*7zAPVAzIi8tlE{8$sB+mUzo~!JXmNX>ic(6hc7Bkh zrFZOF@qqh9-`yXLn}p#(E|YRdghboQUOh>m;X+hpZm#9Q&RR?+#(llD2r}ZbEQGgJ z4KJedt*xvQIZYL2hW(l*#VH)a`(o#7EQW`N`T6<914$#m7qsBk1pH4{_Lx#mmZD}n zDJqKR#rHSxQa~qm>EcE3i=CFuhSC#1uKQ21Jd;ZBnP$v4K|N-3U*~KY2Cb&--vFjg zcgj;ayLJ6LAM?1S)YG!d+9F(t^jn?@+K=b8CX~+dRsjL`kAq4L=qb)1 z27P&$g?CqRQpK^fbv(^PY2_6n%{SD))qoxJeq!Y4l|c);8I!pSM3)DPE8B9;#R|}< zpyz|o$Lek)mje^N@3mqY>c#*y-b#ghuyhkFTQ8!jM07A>pljL!CtjjPho&L%q8+) zKjdZVLvd-jwUio5C)szrYFI>heONw;U%ycV^GF^U$#OWjIj^Yex>cT?SFyB@8olvO z^y?_rFT{C$9bC>jVAZKMRorTbV*09)%g`xQP*!F$S`j#Cb%HZ}gV31TiHv_;E?OZ< zd92grq>1h4^DkeGkf(5XpMxJ^Rf_g>7+5~#^}N&`4o%%kMLs>de?zO#NXBPx5#lZ> z;4-pLKQ@=Sf8+Ao$9Q;nH}e=~78f%La)yRt_V!ZuZ`eJc2sJY+VX634 zIoffP!$^9yq;s~l6H^LggJUR-))ZpBVJ9ffWK0&9mO48*1(9;yt8=8Y94g9$11T(W za_H#=C=|wf(o*dPfAC}<0FFZ%==tXxE^ID%ap2jzOBrlj4Uv2}-Z{T0z(*H;`}Ul& za#$qmu|DlJ^@88iiHU!vTCNw45@IL+WWK;lN9Ahodlecb?*3V&)Gb*`A3KLti8v3< z)-G^(*C`DC>Bgn1Oscsz9!I&gM;TOA^J!ujv1ogg&uOoNCPpCrbuM?Za0)Tvn}P&QVng;laKVVC><_j zHz*t&L3`<8-v2xgjMM8LKLQ%JQdlyhR?eVYHXy_=n2d*o;J#rf@TDy+0iBxzbQ+?> zgQ2L0Jr-b}-RSGL@LLlT6O)h_ms!zXgTn@WK=lR&o?`d4hvy#%1U|X` zALpWZYGe27=oxPjRY*%v8B4(ep*Ua7TO*m%($e(K{}l<|2mpdln)%;O-}8?v)%!=8 zO-by_ZxpW!CwYEVS3qdjrA2wTH*WIHGO1O19^$^^K;^iaA03Esq<2=W`t5t1|PU;{)8$2eW;`Zb#)LikfsU8vLVb1SU75bakp`KMAfj0>HB|%PcKUmaSUj zxvfspA+Yv7Uw4m6v$Xe~rO37$LipfYJ44cDUyrQd{!84*RVRo18XQ=|gCmtR(+L9T zQj%H3)a zO_TECM(p*f>L7qngk}H8k>?3@$coi!u-9e}kcTa(>KqR5tm%8MZ6Fl~UU$wOHyV!1 zgv*vTQM#|EksRAjg+#auR-lj}_{_}wkz+Q)fjoz(1=S;`5t57%r(tc=d$jY3?v(GF z^ci(_HfUN`4|~EoEB99_s>M0Dr*bvTR?8-NoJU++g;kDMrzEahBeyy@iLgDchAsB| zW2H{gaKfBdOXRA`=u8%v=Bxw$ZItO(=L%fJ(8( z%uVH6N;!8)GYYQ*Tj!D8p7@dYD$3Cft4)7V>hZEAB)|y1;YAo!VO3R`!jd~qj?YlV z(h@N)l1Z^%WO}?#HC{e4?{Jw)$7wT_bxFmXN+i;u)0%Q;%-QQ|K*RYlpV1e`^KxFC zRwE&pbdkFpiwmTCl$KUwwlgAyQr@DJ-2GtO9?PaD z8bh0pLQnX{rAx>3qUVHs4u1boHt(-=T(sngZoj*O>s;^mb)G}^!_<~r5**+$B<`e* ziF|uL7khlrrL3y$WRbLw-8^?+w1Yaf8Q@5*W7&3 zYa`C^ZvIXuPruMsZ($8$4)rd0p3Lt4jlFMIJiq>0$`7JEjA<%Glds>+Xbat3Yf2ii zU$v%k`4z>-mq%E#){0zl-|S+5mvflaF5O(IoKH_ma`_$A!MjmJNQ_-%XN_*1u6?Mz zw{?k_MWAWkA&64jwSQ0-q2I0%)jGei>n`^PqZk3DT;KCRIJqgtF)sHBq7O>a44WXe zt6TdsD$!X)!906*MR!!#V(!D3dQ9e5y@uA-$iuxvcWWwc3llxLSf7#6<*Efu6r9~o z#6{^=4u7h`SMSa(9eKeWv%ayRINXBHtF~(=V~q!ZmyxCB0cEh%-hOL-_1C?v?ysS} ze>)3e^~PuP*znbhbmK-h0!5dcH2+n+l1n+6gAd#s45vT)*Gqm2)o?C)TQ@OLC)G1L z{Qd2vYUiy8DeE%kYwyyr!z>b7_rhzvW!!S$&xEi_RpfUKkUU4A5Ra2^J^RpW%MNhL zxv;z9e6*-5CpiCuk#%%qPREyNK2kGZcyEsNU~gq#v9FOKNvWo48-qpKcO~2NSdR_$ z_p{7@PH7ZeZ${Kq?|Z?4r-CvTW^uzHArD6nvyVOJJ~inTsDTJA?9Ag`t7j!5CRS3G z(WpKM4tZ(-qS4*$J1{H8b1&~e?XoK1nVPWK`Gy+>fwetGg=xrAfB*Hujy=g2FTRP? zAUJd1s^qwo=eDO*jp9kTx~>2a$1}F6TRQ5b>h3NsdC$qw@%X^FreY&c(-)t41u0A+ z;yE=j9!%jpwL8l?hul~$9Um^r)Hp8^sU**4*Wx(ty1SYWWK!;TO;d9F><_m}`%%_W zn>3EU3av)YChJyCgD5fQNcp7 zDMP$l!?Y@@*CN5`d#3I_x>GuQe{D-D(dG9%I7PH{#l*}9jYZdG5wpu+yw$~JB-kuS zvF1iP%u=9x`?;HcS<#Z)w@vZc%aw2S^DA~YE_hXrb$`|WHb^4FKhaw+CoXQh+SQA{ zo9~4<%I_3FY0d==d(q6Z$_efdWm}shiLl&hKAyO8ywR8@yt@B%|IvQ;X67Z5MEhNy z8h8&?OizYa%gR@Wm;y+=+GjU}*Xn!DoZys*7!QO_^En)(sg)I_-~*8WYpj&;7HNuP z)|8ZzYPkHb?%schRp;>a|JF>$-iq`8!Jhy7LxU4h9&;xLbKG(~ z_xI5CJnXNYGQw7R-5!E=z4ALNmT`1n(70v%HQv~3i7Rwm4oy)x7pt8ft$foy2_`H7 zcceLJRIj>vcT@=9P?cJcb0LF~R$@1(O4oe~G4?vH0=ohpI>8!$Xy&n;?-{l^;>tH@ zg*7d_mB2*&Amg6UjzVtyqI}ZfZ?1Yy*eXCmKqfW?u7cc|Npdz1o!W<51w_)J=pK$N+!U zI=pq52|KE>@ZX%Bf`8+8Q-up2Vx#YHd}-k2d7rNpC1B&VYd`sjN1wXPJ+C85H~wk1 zv$fnw`HL_^lHj)=aD)$+cO5A-Y0w0th2H=A{U|<+`SATYi7VJn`SL@LZdkAv`e7X& zoTTgU79UQd@-EUxDo?~1Vj8Hu1?f_^;@pfcZ71OH);2{JvPb>d9_+ZHdHDP9FFs-t zsxsG_c45%4?RxjZk<$l?{b&Z>+W$4pmY3D+N%Law+Opd_Zn~3ewSyb1=7bU`7JYxo zCGPuQa(&W%cIiDIn|ExxlN(jWv77ibO%oFn;@5<&2KLU%Aj>fn7T4I#2W~scnccz0@W$Ch>g|gc9WF}H zM9h%VcQs2_M+bQJAiTq~f!_vpqBYfWFHL+33GSih#{c|!^*{K`zij>ja=)jmpUXO@ GgeCygAtc!V literal 0 HcmV?d00001 diff --git a/resources/img/tf2/tensorboard2.png b/resources/img/tf2/tensorboard2.png new file mode 100644 index 0000000000000000000000000000000000000000..27b746ef630e78da0d6f32d9275e367cb7a992c3 GIT binary patch literal 167312 zcmdSBby$?q_BMO*-B|RU|~IMyZU!6hUptynGC#6^|P6wJkP%Gt8|JR31n{pK_# zAtq5X-Q-iy^SlpvA3l8j`swS}>1BJ?W|4k&E(j%~MPb?zQY79X)uG>^zn7nrA{E=x zJr#85U&o8-zLLMsv9QkIlpHtzeM(Q`|L^C&Keyri`y}}b@zv1&aeN?nB=+|?maVA4 z-G86ROFg*u_oU@s;FT5rdGJyF?=|ws+WFSsYhhhz-LsQ*lrDX#tJ&-&(WC7=gZnh< z$F0!CvrPU=Gl-J>3S+JV>J!?2etf3l(s}~>?>Adua9nv?uY7UW@jYAo?z;R^uAQ$u zcTi`Ue(xD9bZXJ{rc0mDM|AcP%JjyO0C4Ty^Hd~Cvy|cox)x4#?#i*-K zo7QzvBJ|6gOjEsJT^lF&$}CLExtr^pd|$mkw3w}at6L#e;4%|I6s!%Apkv3qk${X@ zaLMje4NY4o2jQ%;uIg-?aGNETdchssP5K@2T!`X`ov1qt050 z*Q4>b$KEnGl6gaTr#~1guC?KW<7k$M1%F1$(QvzQG>2;)bAOqvZQ9*Q&A`l+CRABP z;F~DpnIY(*qP4!1@n+}(S2|*|5Mpv5Mr12=nunvJ zG(T=X%cx|$;(xx_mc@(ufv&1-=6kYk($I${vG%r6KJ8pLi)x|{giEO;6~qKJ4$bn8 z#=1iwWdZC9iry_Z$1>xTe6;(|)mi1Y4w-%z>2^KdOC{oe7d@%X6laC7zF^@V$BW?G zcC%ZMJrwOTMMa$!CHZ8kQaD=YAFY>FY6|&e7*C{x856;1r$#14Ehmxt=>ueiy%ZrT z@|)!p0uYVM7J-c%JsFQnaj5pL>x>Yaea`bp*wCPjRgzC3kE%}iyC?o4kZCkF1frtE zrYv(y59yW`SzxB}s8h)1^uooy`w3*>Bm_@VxWnBq64Kr2BJDA&oHP&=Si1N6UflNB z$*7q2rR!;GV3$(q$YUws92K-sn%RR#6au6Bdyf&AqWQAMw&St;$&o=kJ^W14Dtx6_ zGoR4=^W0S_KGk{|>DX<@mu247pTniLSHJes_age+?)}(J+j~D8pHyvMqC9!@(SF+d zJ3=AFX?Rh_IJOG<>|Dfi={j<~*20V5$z9Kpt<}&Y=G|#ADTNF3;7oPU!7A*+41&K# zaT1$lf;4pqoNOlJOMB_$pr%u&g=%Bn@_8!!A|l*7p6l>kqQz3HUbkBsA9mpOkGxLI z$D=Q+C1Wv2ex@bEsb>g#H$BGe44b9tONYkmqMK2ojzO@ggOcj3suwpeN1N1j62)m8FkRU!qN{~p*#Q>gM09-<9}WQt}}s?kCi)MyP3VTGuD`|l#j z*blQN$H;EevqKP^Dv0J1rTt-LNwRhO`lIM{hvXf~p-IB3Qc~Uo;bDBAARiD0PN5k{J@= z(FNKY)dP8Gl+rS1Hgn%DGz{;kQajdpbSA1aJv4pk(OrS&lms@-i$xlQYI&Jdw|vmYKLc_G>H)6&4162$y?zn?} z$8nSDv?!nc>FwV?ayHy?%Zd`%h;j?KZ$aB`(b_iQD{bE|N8m*aDQCXfU?qfo?O-&i z@x@JJ?RTF$nP{|-)k#VzBw|Emgtpz&ZjrKEB9M#7o(_Lm2$>%-@lRK!hE8vZw4COKH?QR=5yEQyZtbxc1S5V#YLcQEF$pv`P6PSF zFaD;yRK=N>xd*!9Ia&oO(Md}O;e_rj>fu2i@!?WSqb!UI{FjZ}(JaZ{vzO}y?7swIV8PD-!aG}szCN!?~Lgo|Q+_txI$GDNj%SQ_% z*o4rmk;ki#^cJa2kO5m00hN~`no+M%OTsgoxPIx_S#qZ}X`V}hhzq`h%~r{N%Ov6| zYg*%``9rxt>*<}|IN1kRl!B-)O>W$t15u)#{D;%WbO?d#JBeSrO1g!i?GWmd6l%js zUVU1KAA!e{60d*i9H^uw5A|$i01>DCn>hQA$5ylY;BcBJ^^$K|exIohukw8!GY?gZ zF!(*6CDEKNOySfs9T*?&*5w!YYKx?Qbg{PWb*DsP-NUa>OLp#hw?HwgbW^x&coCZ> zBYirwVHgDJm$T$M4QEL>UIa6l#+38a!)@%-fJ+m7>shjyhm)g#IbrhBo^$&h21`Q) zaD@-05hWw#JbqRzZF~Ti&wig~wOWok(0bZl=W3S<5RP*`_q}TSVl{cA$wMwQ_}Mfeaosf$O5jVOaP1qj-JQ`(xEP zIHUI7jFNPmj$84e=JqoN-(P+_P-ozML}I|NcVl#G?OO`mM#wZBtKylB?`H|chJy{IPQLD=`HezQ)t;Loq z!KHk5?gNSmShleWC%$w7vXTP_179jUJsHq((y4FzQ+Y9{JoD{l|8PrJi(n=ug6WS4 zCTWBC+GYv*r2D|lhML*{Fz%TRCp*o0ayIWnaxbO(JC+&#_9~pV7l8}CFIs7e9cof% z5VO0xoK$?twIot6*yq&W@4L4uq?(j4b!#76;v9JR z_FyOd!K?(`BM0mSQKAj+I>`q|6>({0k|L&oKiS|}?g+nlmm=FE_ts4}qAhgB;>jM@ z&>t82DYj28MeGMU-|Jtp?u6qmia)}UTPbO*-0~c)Ta%{Ks`1`y+dxrUrm$>2^rDK} z`7O9r%{uP$B9MG&+t|o*L5$7?rLHp#&F`m&EEB$J2!nSOqlcz6t>y%c6?p4NHzvX{ z;yvP|h?*~O;Pl<%bL?URve-z%1Zdj~$Ipw&Vj>aSjOb=f)+ttKzr%S&EE8HZ+x_tQ z0Zy|g%vA`V1NT%=yvl1QNli{ke!G4ss_y>K$ICB_doFZ3O0*I1u0BZ*S_%!*nLjn| z%TnV}8#VBQ=I+A3Wev)-Z{Zsu83(X?zmy3dN`c0SKZ za)*ufFhdMFzRIfclfUfkOG?OyDR*RHrGfDKaCbl1TKq@-tkzyc?y43cH?704<*BhR zz1$#OETja>MY^~0qDV>aDM4wVL|Xht4T(2137wP&%(PTrxa_KV{rxgi1ATt(9DFl*sT& zj=PK`pdF8?IN=c*vyW!>kABBC2WUNvB_OG($!_@AWm;x!0ijkx#wO}%5PS+ z2)AIveCal_RF*gDqUSvRZFk6*`A%p0^TFyHXhAKg)MJsuwZhbY97N_@O~0(>B=Z*~ z_pkX6H{CpVNC$yM3x!VF_Uw26aVEW4mU<1kw5r4OAVD^nl0t)?LZh?c#Nm8FfbX`X zkY`^0n6Zf01R2$#4|%^^Z}#SqR27N$ezEmrQnJqj?s3NKxkA>Rc66rZ(~%s_?XYXG zSfu1wB;#5R3sHw4((^}`cB!&SesM~9ecMbpylcpI?P*6aAo0uZ9~mft7-BSUF8mQ= z(D6!%HhY%iLD0F~K*Pz)SgHEUIFg2Td=bkEnB>Jr__V{wr3S96-4zxQy;l* zKZvblaz0>+<1L?cemMxIgMf0T zEDK|7ct8=K>Ga9JmR~E++)szAjmcifgEr|2_W1)SYN%RvNfR4C-%r-+)&W{96qIAe z+k-Osw`7a?Jp$ncsw=#grLXpbU8u^&=Qs zrM!`-79#;EP~}|F@{~6sJ6cHygl^_fLLptvd8I*jTBdg4!AL3% z_+1EumcBhPO%kenS_^}{xNKAE=hA@hMmngfU$_Y2M`)08^kgjOGDZ=&-e2(ivvdp> z2rpbQp>cqYL|W+7Yn#(#2x97KmCO=dnRvLOAC@S=`anVKr>Jm0lRPfm+j0tnTh491 z)ev7?d!DDvZu{B=iCW8&l9_P!{%us^)35=vB}}r_y~fC~OwfT_s|PC>6F8r!63z{C z7bO-{lRJ)LU)FHD^eORL8gw>)S37mU+`;8?+MrQT+&-u=M84~GIH+(=&rt4oEPI-O zlaLFT3Giujr+J5#=#rpiyY)({$Dy&y=P#>t4}CtnolI$8*runASNW7k@Fy5wtdh_C z@wr!~aKB7}k)D%z!#n5AxGi=3M!soUm5PAFuk!b0e9P58zPN!u8I=;2EjAdr!`4ta z;rcvNhlp4KbDXthZU}?KC4q-bUMfp8uD5gF$}1=!RpmU9>t}ZhW3i!2W-}5{nRfDQ zh;MN6z(XHYojC}bBjwFU$$HV&-`ipREL&NcYVI%DQ|=&A5fa?Ic=&76f$)3oN>+yw zZ34J-zoOU>+5N)bGQX}gT!$0Xe7)JvCc`jOK)8Hn2XriiD&=4%55$ zZr2)B@E!6mua)v7FW;7rMh@#(I{B8e;M?h5gJ!LVif$4>v!2bZ)fMy^24KT!A@;Rq z&@4?>TcPCU8aTn_^6^C7d>PLTd`D5Z`&;mLI^m>BaaAo%eGu<+Q@f|r5vtv5T+52P zc;i!7a)H#xUNcqs@q_fTh5`HhSSd~dl3xfYCEDD+&)~br#{>3*^9Rb+l1 zw%17Hkp&EkpQ*%}2BN09J3jCIK;rzy2E1#TQ|Gg)dhpZIA85G;r{Y`?NEAEQIRB5V z@2{Y})XCHy2CGBY+cPX~Jh*#X0*cr`4a@TRMA`aKsiiU!W%U^6E9E@7>AH6@4~44a z2OS6tvJvelQa?)INaXr*F2OxsJxns8TAGOPecGjOlo1i^`TBW3f{>g(O}#%9UJ!jf z?&vFzN{LpF8>#xM7pSbLiIy4kC*r6UOnm(6s;w#IxU*pY;?wwQ>8U_j6&vVjT#`7e zIvWve!FPK(V0vJVKc)7l%Gy)gazE4gr${vRv(}rufJO8(y~-DPUBLWVuFM&*eG`I? zmyZY#Wj%=}mL6ZIw6bSPk*Ak^esuREus zhm1S4Iqv_Uz^OIHs)Vo(A*W+?P4-;HcJ6!yQ5tv#Iv_bGv=}ef%KaodyaYB`)1EzS_RRvn$lAqh|$)R;MbzrIVZyBFl47Vm9_g3 zT*cDdI95Rk(IQwAgmXxmrq?q1R+TuzRU zf6q>VOUI*nmT>lA0U{6{-<0d{%*>Y_KLQZ}SJvWMbZpy@u7VqS_s#L^Fd3dRYgoFj zCK@v4;;OqeWSsW~$WcKM+r%q`Uq>GA`?^}UKx?^QnSRn`ouLbJi>2U`7WVq!c8qK< zwQUq-69^}Dh3TZRWXUKU*2}b~{)43z6i2{U7jA!$n;S+OQX4lf9t%knGi9?z$toFl z-c9Gt8f6EO(h?4@9FIjvicl%S9Wm`w^a%%a8PL6pwlQ%D))yfb!6PT`8I=3G5R0w& zXMf^}EtKqaqo>SM#!7%(Nrmay=WvvcGlJynGE9UdD6k^AUu^o<;KAW=65j_RGyM&| zS;;ZuoXQ5I;~X4Dx2dTioa}SERleAF{gD(Jla{A^8Y#7MA$M0q-_`tl9MNmhet*NC z);{JV$pdIs;s#rtIO5sc7jIW^J|N>NJ*Ou*k$AMd(xc+gV24?i*?7jO4eioI9n()G zLl9vT_D8q&k9Gzn1A8E+_@><^(u|DBq5%cd}ymc`zm$eLtoF$ zJ9d+#p3Sbyvsy4dacHus9;Q+#2z4^s9v~m_&^srUiBL2bF9N7YL6F|w;UW!VK2fF7M{HFWbGMA6!z2HXqd}P5D-*h z5tf&4A(62Nur_v-aAk8k#Mz9Pl(14w$#K)AdS z=cSodtIl}B*m(u=Ad?AAUnA_oQTj?!OA5xUg}C@^$$@Cn7C6ghH)?XP zLJ*qLAGH%YwQX}EP|2RX6DN@r7hTzb^D5I1Cmp*t!>)}?x+S>XQrsYXyP@!QPORm!>i`x)y9ZlfPWY$PBS2`s(YGH^!`s z_4mndH`g`u7C0HlnFr$lKex4H(x4MB!ShU^<}8D&OQ^^k)|WR9isDhE{kTP6(W^EZ z^a5Sd4tNdI*|hM4U7Kf#UTvb+tvkxWM|m9c+R*EYqvAS|^D<`XR7A-?h3wLc{h+*b zbqJBIZuIwr48PR?GB(Vv#-FXoU>V{N&!2pqOjIZDF zS@W%r79}@0>96L}wh>P}SwX6mi1z7}-Ky*gtveeT3r)&a!H<-DOqAFXKy!e9PrJGO zH2BH z(%j?kekyVNfGX1#2Uabs5#W>fAaO#sjJrAP2A{q&k zGL(%X8SnW(8+?}E?*_=Y&J5D57m%@RJ`s1tH z7%nbX?#mr44=r{U=~{VuZ;tsA$-uq1mDzLr+^z{SMbu*eg?%r(~HM7(7@bg!`+bBbC2<(yzpQG^( z1&Oy*HKw%A8bda2i1-}1u?qjb^p;=7&L7*~B zfW6l0rO2@`$MXA)v6T@A+Wp!`thPlP%VE!=&}7{K1AucCjKny2?HKsK1j{mfIScdD zCidqG@%7;r6q!-@5lRgSX}gf|ic;nBn8$(f=Qb~R=O?R#wFER!C%@8{HM0f6apk`> zoLEK_vLEX%;PjMHl&IlcDNfeZov2=AZ<7rvzsK!IT#AMT(G?Sz(F{FKqNm6tsRB)w z7~0@;S;vZ2lqMHfhyBHEMlC9xL8DG|zKqWOoi`&jxiBR3dt0Ya2me!=7n>3HOF46D z$)xz1@{;`TzAMI&bIc?cc5KRsGMJ3_w-_ct3>)ZBwPn>f7mYcWB%Mb%r8q=Ou>5df z(RYP();SL%9)#p1_oL(#wQYZ72U1aR1jCD9O31NpB>^j#1OIqMd;4>Rzk31H_Sdxi z4EQ->!|z6ylTiz=Cxj~&7dLH=F`wBOd=7j%K2_yiytKr?kAzWCkdVvIHxL|SMamN? znY~DOOGUVmOkky!8+X%Fkmn|r!2q1IUszN zB5k1(cG#!ae;yx>$!EpfKq)_5A?>);){Q9~i50l$r&$0C?Gl|wwwMub@6{XHT}B>f zo$+{43w%M^F-W(=+FrWiEHTUDF1LlQ&gg|5u=wWpKKnkv@b{@+S?8Bc z@@lOoeUDlPhGx90#ClGZxyKJ~>`+0nx7Cvjde&~(UzmcGLDt1Ii+4EVZ1O$lzb0CO zq~Jah#Ga*2!%=EF1X`1_bslPULRFFi<*_$?`OZv7w5PW3ewguV=8D+h4LS&)KMDDQ z97f45lSdq#YbEbyc=zAO{BovSt$`brkJQVX4)TM{!a4!6NQAwJx#=qj1s^IpapRDK z(BEeP-6(U-U|D|CZ1(lIIt_~F^hb70@3k*E?4=7Pe~so3M)x$zV4SI}=vLk<4aC@= zOLF0gOdSnGF;*9+qCQ2RH}Rc}AI7E367eEIk|@Z2J$V`nURr3RD2(yoo4acg6cOf` zjmA-9tXMLfJGeo=HsiJQ?i8$su{s7>G=5c|ytpPgdrC*a=O6ARgV|P_6r@+$?zCt% zKUGxL7zY7k8@YiMZ9n0xctJMo#z^E7>zzD4hAzvLG12PSIMLWyGx5obwx$+S@cs=& zS(P~&Z4qwnMVeCPL!rq&z2Zn&rKvxDcR)zhpj8@~Vrb#tB_NP@kd@cUo{FaLv#oIb z=uQ^ z+^W4LTPdy~$`WgjOtu^IYpXI%{N)^nb6zQuvQbFr#9BUHrFsK_|7<1YvI>Zj?7A4~VROE#>qd5-uFUHE8?i7FFLJJN&I!!W_IM|xIXip54E zrd!`g?NcimOZsfgd&c%+gX6T7u0aZ7$J-GwNi5zFE0v^v_M_QedQg3^;@~8#c6k1f z+OVP5Dn0*DHG^!cp*7mI@SxnG{QfdKvv}lWv1$eu)x-or72(l0v^4mBuAzmAwvw~u zJ4#zWZ)17i@d|NqtU~W!w8q(ZQb+fjtkp1>zaVZbz6it0tK4FP7IE`)8P<*8f^Ar# zh7#2+)1^Hb)&9_;Ul||;JSy;c7&^DM%`7xvnA7VC8K2X#);@(ovv{Hljg|z#lP6j6 zYQ@UZ1KXN5@dzeH|3;^n%$IE_~FJ41S%M~Na#gm%wJxv?qu}mWT{4ohc)s%l~L#5%qv5sM! z5e`K}(HppxH&X|~W*=l^(;yzf2r+1^N=2yv$7X}uGv|E=Xv7E9nVHWz=tXMJ$WbxuD z>Mbdr%au_ki@NkGWd_0xn5Ds%9Tk28@6@cqHLz$oao zuaDi`2BZ}Wdti!unt_viVo-~xRey=lZ9MY##ARy?;COarLuHJX-{yTJDe$7BE*Xi@Yc|wq)`7%jn-6nrL{5^B zDi(`apF4LWDp^PVYpY_7jJ?KF3J&C+5`W5+^t?5n_V<(6PiRjlCq+arM$#eYIYg;o zKYF;<{`-mLz(~Yoh6&|EjV9>?lR}GyNA^p*l63y=m#i85 z+3{aHK!0_FlG-?vVf{AYB!0kKf3^4Z>e!`*)YR9{Z3u-$Zed-vb1s}<$vubGTr z!?LEU{P#{$wf_q@vi^_r@&D&;+5ZoX%m>L(2z&BTQ)lMn_%GIc{rYupu`@|7XG57Q zfZK8``K62X-a`BM=_}};8J9W2$B)m})tc95XG$~?1>e2_=~Dq8ELBxcWu=}4?@M+SgPdmcMJSsNOdYhXQ705U?y>I%jq0qLzlE2Q{ zQoMNoQENy@$ZOx*eTm$%9{fgKUwCI-sfC2RW*gkb)$fTb@@+rap(q~TX#Me{GA%PB zLsL`Jd1p4XZFhb8AqDZj`uHIUb^no=bma8R3>llaVlw-CQ3r>L!}9!m25xRdULG8N z8|Uc@r*-8>#lz1e0h1f^Va0(^z?MI&*jq6QmT+gRsL*YX_J6K z+uh*y$hf#pyXmj5eIp|yT~o)$$OT<(SNc;B2!xQw;WK`FBTr93!w|T#%1jk(>f-zq z#-pXKuAcO4{b#z^+S(fUoUpKP6urVj9GuZuA|fK2sjA|foQ|0pAprpaDyn&z8$~TG zEs>Frg4;93HweY06%^*GZS-ViW%c!c|5+ObUCky$B9T)~UZIxy@5)={&%!^WiI@1LgK9$dIp(}zK`e(v4&CT6i_;j-(#q(}bQW7t0 zQdwC!YkULDr?>ddcWOsM>#+`>t%gc;G87zT;frFnIr6W~~)D;yKIUJUI z^WWt@AtWr;Yu05tXeZLUnwb5iZd{>1KR;Z%F;=AUPDKUvYXuAvT)PCJ)!Wr|yqsin z8<)I6|NEzFH*R}gU{>1O+cnG9*VjQia><~RZGnV@g!%dTTVw1XE^BMuz;!@$O-xLj z9_@_Bt}HEiVo-4(KYql1!VC`ynJza*0v~vmwE3&duk+Yv@WFEiAVUcd>_}sM_r8ENCVW4*%8QR$+KBIXMX~@MQ&a zy3_2_2FKT`vg+>Xfn)$I@o;yypvAs_Uq)6oDl+nqyBEF9&zv_&_wV1&kxSro+15_# ztFlM+baZs^fV&)Q=9c@CBtwYHfOUkuPOipDOG_Ik)HyVyq!CNRs?F!JJ>ApOgRmUe zbyjh5LN{NY+jRVDJeqy^{>NvrfQQAJ2pJU>7|ht%7&wW&y}g;)>PY@O3inkiNTPs~ zg^bL7rxPmedgradOi6mVcy`!iCAc22K9g7TU$nBOQ2W_oJ6UNCoOWvx0Y(!D046BN z{)GgxuTKb!d@)>Pdu2ru8d3&&GB!R=8ro4)!3khN}QzA5x&klm`=kGr@HfCaMY~FYB;C8DRu22@jwy3ZW31+0)|NQi*`DoUI z#L)LP4kHT-m_iDE`>c%TpcfFOR&!1>hE6sKj#QQwm0N%gZ^rxEu~QCxBhbnHAE6bso2W`GThrJCG*gGf|jZ zP|$8b1t}bLU;UZh(9m#v>z#Gcq$>wkC~VJiRh% z@vSu7(zzPD%&O|j$``!6)py8UU*UcJ_U+rr$q6tT`24zK9YA%T{cd{6aPpZ3HwSg~ z__2(NDBirREE+mGI!4AIxE$SZozq5ts?f&#{JbUxU>=x#5a0fAQBhGVo~c@g6;_X) zrluxoX=&)v$_hPlpGePBQAH)Y5lvP++2mCp5YXw1E1bvu5Pf)fal9BF92E4UwKcE{ ziL{*m;TN`?97%&8F=+M&jow;%hWJPw6%`eLLn;9U?Olz_me2oXZ*vrf73LE|Sz(mn!033T7+5ysLqsI|Bn4Xdn`B>wM ziRq!CgTMXbH;LnL3Y8x!2vy+hD2IXQGLE=MnS8XUm;g=(eCeDao_xiLGdIP2r3 zdb+x)gJut~vALdxhP}~7Z;Y3I{i+XeUCjRhM}pkZoR5#1yvAek&Vk978&VHmYB~W- zdf)2Le-at+`7@x4aWOIGf$8=29`udPF9jr7@|+rwf&=xnwNBX=f1|HDmr3dYx7X?6 z)>O5Pfq{X%e7`>qDey`F7(*i?j?29XBA1wf5?X}OTT&5g0Jp%50x8?)M{_nV)swF9 zl63VX6Kl2C8+`@s!C}vTmaQx=o3JQ3>Y%4~@bsK*v!TpQamt^@$`Oz_tN3y_+eua2q>`-+Jo z6>3)2ZID$!I*`a5v=acC&S9m0rpz#4jCOipV1VOiRw0O{M%}cS7=*)$JT7kduV247 z3_G+3e}=j@rY`br6*yPQC_V;0v%Ce8jcrKGL~U4qS#7Nb-c1-g^siNFic0AGRa8{? z`S~*?!^d)->6w@6L;hi-V|2;WQ4C&pkLg8m~JSaP{zzjf{?p;sVgNy1LqbRLXs7 z!K+v&p$T8ETk-e54Ot(|42bPa=v$Vp9$IsRzVJG6^zrfO6qTH+{(W`mC-tdnYd4q= z=YSv%pzy9auz+U}6BA28fzJ;hk*61@JBTq)ft0Yt8u8#KwYO~n4pBlB6b77IIXmLz zbAv-dM(QR6T;1HvMY6K9YwPPN1RTwPFMuc+3me)dg+kpMy@#?untmhTak$x^@=~o- z_q>Zb+)TwuXaNo!I)r%e*Du%I-`_!6#~TFPb1Pmb=fHs;!h&bTtL9X+z$c%Sp53Rp zWudJqxuUME%`6>BBmK#?9mDr8leNI|3j5lK4MFU}ZsS>5SPUt}1Ih!Y$IZjz$PxgU zkf0#*li-Z!VW~e%2U20Ysnie=5s^mSo%r?7a31UFW6VmLULU_sk;~4krsuM{{pwGu zu*H+3ol+-f=ajZqMIrEU-|uhL)K>VYDJUpdyrwG6hu2371ErFBc^Mhk0eI?^;u>0= z9q)l)9u6aY{5b1*P`iO$qvPbaaNy!BjEwe6cA7u)*XEjioSd9qz4}lk{pwW<;6mw* zL;%aPv$Ju9kH2;sOW<7ScrfZ|%f`Wnz zi;L&y=ZJ@snoI5g`D87n09$QW#C-a69bzA%H!)ZM&f#{rh(^9clY+ zCJ>OY$~Rlj5c`vD|E_(1vR)iDUwWzaxVgG&*Vs}4Mv|WX-J}AzBIsL8bTptWgL8Au zz(kos+!E+gTd3ivYKi?zp^zy=`5C(Xf@#c#tlwLmhISUJ!h_`3b zQ@~H+<#Iq4+d_T!y<@Bq2FRIaNp^0o<>CZtWhFN^H=KlvPEatJqk|YE5YupSA*iAt z;NB7v{<@{%!NH4s@_3&C$Y>R-cBmU01Jv}M`1Om=_V=r#zS!7U6P_fuR1xp9jf%>aH%A>RuurtfR6H1-^0h8oIH8*BsMNCoR}j6_z|!SaJf&EQjyB) z>c@a=Vopzw)<=<3Qy$|bIx#UZf&-xbaRl{4IkP1})m8ZfoP%B=^#$<*@b&YuMg)cNZNcEGy{c$&+FH(!MG9=69JUj0b~R` zw`vW?3q0%Xm21?ssh9wTh$9Fikch7U6@Z0?hQ{dVs6a|dO^quk$9R%p07rCnbpb;a z2BQV37O*sF>2AQpd_R4vtbHe-FVJSHte75>RNR^n<|X&!0=>4sXphi@baZ5)3~BgE=q*)G;)W z3Lm{SHE#A|8KebcWAHsdcKbPWq?^6ZX{37qpX%;@A7Z{WvaqmFsR9fG{2E|XJ}3c0 zLO@w~z0(fB*x})!Fn}jewaF**;p5{+L`T!y{lts}IqPU|Z*Y0qc9f38CS% zP+Il&_GV-NY-(OwGHTSl?BktNR#9P5|MrT>VSBo^sHiADo)~0p=Q+v_btn`HgB4dL zbMOG3%A*q*Xx8%KS|kmKbuj& zVej1BC2+LojEqm&^*^YJZQ*R0#Q=5%>X|Tdp*oMFZBPT?0-pqIQgf=?nLz9{p1p$u zDAXiEpN@71u+z|F6%<^N7jX5pscJUE-oCy!vJWaMDnL2!|B!65z5aLXeEE$vpeuNRN}RX;3GW<;Yfu@vR#;wL%a&B5 z(rggK?n}*jcl7$)*)s1|3FGtU@tnpzPK88Rj)dSQ?hmBvfZ9fBg4%E+KR0!<-o=I< z6&xJQYdtLm1^5L>V*kK^i_YngEZg~BxKBe9_r(qsa{mUdpe+Ck>xrbr9)dwoL9Gm= z0f>Q{0bm2ZJ!AjRTf#q3{NsLd-tU*^bC%1S4e46l#VmFAaH%UDB z5#2^Xfq@fMup~njK&wnlOq7&{Li6@~ZltcnvFbd1^k|61!$I2(0;gIIEaF8?llKCsDs$$_yC+mekt>ES1ac+_ZzzA@VI>21YD#zVFW@XXb(J+W@$jclpX$1=Wdi|6lH6?x;8580E2KKx>oo0+ z-&=}OAQy6vhWDq5h?oy$0d2?xT#C!I|7>|j2kg-K=kEec&0WwT?UR`FG&N5)D+V4Z z%EB@nS7rP-lyr&Eq}^7S{qf=K|<|i(A!m{r&y3WrmVTOgq1}f(V&_ zl$aD9{b##==OHmWB_AIj3ri?`ePaXUJwc!zka}laMe5Av>R_hN$#9x5W{E&tG1YY; z5Qsb_TGiG-(!4xd5g```P^AiaS(uN-gyQBltZklnVC1PwS;~IKeX_5N)SErF5n@V`*!ULP}+Gd zM)KrkST!r3gMB3Q7?7qwSQmG#^mxbdr0>S|y}P@+jEv0AY$IS{pvJC5 z0cqhLHg-MOjD7QUJg7l4o&)F}R2ACqqE1asy>{)|`}glbC8{I%Q|M^A!t2x-0P zCD$}eAVA!}Yyt*hSf+k8 z`@mfK#$2}r@UZla-b&PiXxsg5>+GysV>{0~=jqVN+zd#7yQ?cu$hVPf&28^3V z4jzMuU${>f)!p5lf9Ve%L5VzWfdJt6E=TVBnhkJCP{F-)-dtT@|KZE+?%@GDo9%dv z(3}|__C3vy1=2Llqizt@yYD_xYHDeLI+b|?+_187r-6os#`7FK&Y7^e&>s9n{|ah9 z+#)S^A8#sEjz-nNdB8Erlqot>4(YXgz{1`>fM zDkLOSQc|Ky?)CLXf-DwR>wO6L`44?A#u!E+AstFQPoVIPF~3j#@?{sm03gyLz(s+P z90krnY}D}b-~mw;R1v{MY^h*@pgoANX7>zp0G#+_H8Z>;oMLWdq!I)~QbS4^vc26; zj;^}8I-Ejewj7A-LO|I#SniAUvp~3a-LZFZNqb>F1ZZoU!QK^EQ0-i8M8VQ80=2N2 zr6u*<>vsrH_897LK?9&(0LDh%_8Cvq9y)O#bFjG@NJU0PirBdGY{jQ&y{gQ)Y zZy$vOlM0Lqgc>ll#5CH!E~9wkm>%D6)D2JTPvBYs*%s)v=Qag~H(x(~@?@b%Ax1_; zRn;F^s$FZ}iLkpGGS)Wl^^YwxGhRpAGoXmd&ZcEy*(&QK7rP4_QRl&4BSvvjgZIa08 zU%zTVReX5p{Q2$c*WXvte8TY7x3++s(%ITjzh0h|u2|*_=fe5&qPVRF$HFtJ?4w%hx8-YTyh>v$qG{ccAC9;3?O0tfK z7|aDBPC!$v`f?J6g%y%BB_DgoDx;ua9Wkz3!JGhyI(?car{>nR*6%U+ozU7cc~2ZWx^$=@xqL9yRt{fU(Jw-{d#iM&c65{*6Qmbl*prh@xSj~Do1^a?i#-X11ujJrdnC1jewH3uc}aG9UgZp( zopf)WNKf)+bl4IDj}gZ`%JT9=Si*ywA=SjIb9FubcT*yEwl{CKh+wyTeXGwcU}-{n zw)6h9gsPb34H7wTP)qpk((*1=)jgYcde_<}uyXMRMeGP&Pdw#sIlS?!f|Hc?BH_c( zdDX%{+nHE)yK+W4?no5x{F|L>829bS>urEs!WSjkgUM;yf-PoGYC3EsS;9v~N2UE% zylxowa4M3(xaa2P?vGc`>@+Ic5?+r-4TRI)vw0vb(0T7bIT0NlDs)3bXx*T#Bzu^0 zt%cZv&zfy9W5k)&HWB&MHVZN4ttffdi)bTuut%z7=UG@Gs9zi(P-0QtBbMiiGd`` z0#^3h<>I0zxBDpZS`i6sg>T-d1d}^Hf3}lkr+GU3C z!2Z2Ow$aVEWM{FrnoXR!??KNVTqQ>%>w~C zy1Ei*n7}KfIm%;u0+uFX`n5K!iOuyk2KY}hF;xMi|4eb4*gZ3J z5&jdo#Ukbv3oEm2d-v=SF)RCUH}|!rtE1y&?Cz5epP0l{q@<-^zI?f#`DV@4QkC=q ztyj&qlJnyuID+R^E4LBYT>mmJi&H-)4LW~*y6>-g(rUe*_gtCvZQazAJSAe9Qm>E_ zOJ&Ke(68wQKV81My%63jQJ$05ddzi%$P~*|;`>HISJ3LZ{`rzW`woW)9B`Ks+ehSm zlyJdjG(qb5&V6|)Q$d@jO=ZQ^wSLOxj=DvPXN8n6)CZeZRxEg2Nc$NQF!XjyYB|pr z*6#{Wg$lL4>%Wy!p=rCd@J(Ptc%WYJ^7}_sTg>0qikDJ%93E^6AM)i=9X-E^5J4Bn zVRUuu@Ytgq9m&743;zTv(`X>qzSu_pmJZH1qKg z4t}E)85@@ee)}~Eyu^*FbW-e*9k#E>9|9YL(<2?CvdEd3oZKCwmatnqiH`U6h?}pE zPl;(>X`gug9rX<3DghGF&fdPftZYC_k}XAJPr9}sCqrgikM-hLQltt*J8^}2o9-#8 zamZaS*D7{|*P|`Eeq->)MQ7(%SN`lL3-a=A;PpU95f#NClGr@rmg|3FPaNRK)vH%~ zpPm7^h-*pkET2Bb+}F%yHFxk=7)Z{+AmwIrk??wSb%7YY9KK zE)$LBido~lZwI`MocYN=ZY}6D`1ZSj2=jxRbNoFs3SYLQg(`=(Pl*5B)xy%OcO{4v zJU&wYnBVHQ&91|z6}OyyKC^f`c%!b-l`948yg|1*9_aB6KD<4$h4Yb!yxTW%CSlV> zAN3h3rKSR1YvZ6><_X?YIVoJl4kJa)LrJYk4Q?X~7LP~83r^pv5VjuoFgVlEL(VQN z-at4Xq?rkg4jhU!dnEJL`i6$Ra&kT}8MwN-`pEC{%%uKtS2s(K^UOE5(wLZ-+3#)9 zVPRXgZms>p!NCDa53QogJbk3Mc#^P?5Ur=qblbB#ckZBFRaH?L`WB^FmN{zj)kj_d z_yh<>PL5M`iz~DEp+ns%R#XJBUI=7;Am?y!qRsGMoj98y`l7g)0?nXSe4AZFb99t^ zgXad0;Tf+AC~)XISa?5lcFsooukZ^CYw(Z&#A%!@WS`J zKXGI+Ho7$JM#} z&iQpHHm~t90d9Unq=p8(48H!5!>9J%-sK?2g@P91Xl>+ovCfA1rI;-Axk%;IFL!t4CO;ufXm#)gka z_6SEzSsyg&8w=jT>1dSpGn!XP^;h6+5TECd&&M21U3ETtB~2roM1B*}Y;C9M;}#?u z-ZZMx7CFKmuYQzFJ$+Khwei-^{f4&$rmTy6Ve4YB%W$ifb0So;rFV`)LwCuP1DksAATHBQCWW`{ z_>JEs0H6SfchX*RLlQ0A+7^#w)i*M~37Tn^Ah2VH{5ykEAF)zUkxtgRK> zN!Qlc=-#v`teYAdQkG#wg`7Kf&vb?NER@xv!oszs?pJ(46vbg*{972u}PVr4!MwYXY}%>tTtYbg5Y zMc5uh&sGUDg>iCnQre^~>pY?d4%`C|lW@SqrS;Y7q%-)YLI>&4y0GZzSBY}0I9We@ zko?wKb6q{Vi+c0s{^6c8CMNcWtBcCZmy>MngE!GSemvOP*wV`C8&)PND@ag$0>9Ni z2LAr*Xb#!wLO_xH>@P1aEOY}1Pq`sko2p&oe8( z86GYnAz^Q4HzkFBME=_$snK@Z=g_Ciii=qlD*aZkq$==B!%uWUlAVi-Ytz3k*R6aOH>4;N@MF0(o$C(+3Pe#=DDpHkNkLQS;*_q0;Qx zL)}2%+AmG0ZsZtxV#T*|%Ki<%?#Z=NtCu(qj?*V^GheHFyEm$(+@Is_sC2r0Nq27A zt0;3*gY#h(^wXdGpQ!x3X?Tk%=22dLg{jy0`~Movf)=JA9}#ZQHt|!=}es z!gn);MX01J^A$|4ZSW#YdC3Lcii_(E+)O(&GsAMgIH-Aeco;W(1&u126?7Wu9e3{C ztGqmEeB{Uxmhek|ew?Acj_S?J5)Q^Kg7jm2yhcfUS|9`*2$mr+4L8&#)t4pun#l{h zE-*vzDopW$EqG>8vr#%Zl5{g7;x%iQ#-6L702#?3RL%=Qu=(BT5Xd8VJI%BCiQfJs z4G|EsvX`E|dbNJ*?mu`GUrD62viU8XQ*Zf^PKeJ=6p8Ql|2R)uN;xDnt_ zls;|ovLI2RvgjEZfo%vQ**Q3Tu$5F=Ti>X4(sHq=5%iGHj=9Y4i9}%|9Ye#h@863r zP3Tvee_eksRz56c>AizLh>b2pP2eqjNI3p*T`;=*lID@Rypt{j&9*NbQ+VDINg7~3 zz;e7{RC51SWS*Z2TJIRojp*pJ1(x*?P0%3#FZgn1_>N~Jq6ofp=qZG~5_Ci%9{c*~ z3oUojx+fezIfN;@8C(oui{ePQecS8Gm7#(kKJxx|YQ4#6BrW?V+JYb%P&v}mV}IWh z>WzQ@k>}MPRs+$*i-LI$2I1^)mQt=SOJuxSKXv)R5gj1Ruy>X^AJn^YS)UiywhYPZ z^6=3l2p#Wz{8-F6t#dqzc(6y~k^aSTmW;WB?9Vq74C^bNg+P3o;@aH6caAMb>VusW zyVsfR$<-b7LE5QZc^e2&p}kacQAW3XmptcIn$lzytM&-gP?E-D)X<=yASk!o|a_)|TvH;Oesb`}^xUcqN2+Cw|R+4 zN=ac8;9{^VI7K{MT`2}aJa74O2=#pY{0ExB?@j$|BJwsDth33pT(4=jdjT{wJ~=ru z?S%>tbVW>?nQ?1=t8dParDmMV!s`K#7NAqx{iDmc4iJAi`T4IVL$8r2_H3uVXFg&d zfnQSe@NiHVV1ja0ktl5Rg=OVUX`AniY-<zkG2LPf~f%180#z&PSVG zEK(4b!wnjHioRAkQhTPkuG165YD0FLTAy9nRz9y2L>eFcGx%wp>oh0Vz7T1n%Gjn0 z5?sz_-zBrhUKLn={)sp0WVbq`zOg`Sx#sA)7}f)gN8RStUhdNzIHCGEWXK^aCayX| zcT9J4%k|RC`Z*a9GyYcjGqyJv#%C3EN*D)QR=vlQ<{o94GoP~7ys-J|^i!|PV|q@5 z<1HSSb>yPYIS95?Sluu$mGMxPXC_qJim5)f5>Fc0cthICTw`ZjG6Oa2KIm>kB5qD)1>>uhk+T2%%}yLADRxt8b)^Z&v5M4(uzV{9h|-b zP?;AU-3Hi=lbMB^8+Qu6PuJd8UiKezV$?H$y3t6(BIP&Nq1q}6tJOuA1%ce1oSX!h z!WxFr2T@2U4m_0S7q^GkLuUJZto5-@)@a&WnXfnN2I)M86*D`rAR<-xnZih9tPZa} zL9#7WPqf4eXuxxficC#SL*{n|9{d4!=c@D1X)n3`@$9Uup{b)IBVZjjQ&G`L3f9d0 ztmU}dCL*ylI5r^xhDvpa-hkNhn||~-%%jSSkci-AcX4y)b=d*Q{)HCo&?C;unkaAc zBEDPA$7jXFx$=e2;tYTbr~vFa&&!t|KYBD^L=^7o?rvyk=>GT-YSJzkB4F}7q)^iN z-SZV}U^rsH|AG^3YHK_D%CE9x!9?Z6df)fn(M#BeI4o;IpWrXaiHkF}owVH!+WF)6 z;?Zy44CT2L{3=pY&w6{8Hd%p(s5=--&8tkJ(tITKHEJ^k&rLGYJZM?F;$LYo^3+y8bx3r+IlViBI8NL- za+s!5ROnPiFb9iq$cA;pyo3v>?+(NjqnqbcG}YOd|Fx3Q(brYe{6u6$>ora`hhHCp zN*TR=D7}ze_PCs6dfvljM5X1_&G`)e^tspTnry69B?})nyeLf3?XjkJG;3^%V@;Mc zXuPaU+!=43l$2<97d`*%=`-l|_miC~)0bL4@c&q#_h*z-*7d*EL3Qr&U`XZ$bWr59 z_R*ve*?XD-Ou)-qg9H5&KVM$o#(hr{4!K~Gr5GF5k@Wg&w2Y8yNMuuy#5Rne{Z5Fu z2qCHxMr&wHVxQ?t_jGbkl`!fok&rUO&fHs&W;Ysj#1ALmy){V z^`)K5wDL-q8+*WiZs-X0lOZPd_SwaiuW)}_f zaHp06&ZUx)5>(ojdq)n5iA|x01u_A9yabY+Lis>30f{qkJQaq5B6!Yj-@c8WIP=k? zJKJDfQS4Qr!w1NHRen)EV0C3>!AJg{rYiA_fx&S7-vytlR_Dm=RHt=xa8pChOS=%R zNrmjTR4aY?RlK}i((gjFYRV)<+%O5eG3dfigok2R<_7#amr~PYy0G0uGy`29KMo~- zf`y|_c4|@4lHtc`!P{WXL=x@eO48Ibp62Df8E(g?IBY{jxOS9{u}&UzrXpz}zYH|g zD2=0;8J8w0`G}{;S`$4t6O|&Tf?uE9-(7J{lKCy}CQ35p&tRQ_+SUuAaB*=GEqH8E zNb+IunRl_pTaqNc`pmWt4Owy?luXh{mUfi&o)LKG>o**Eh(v~02mLwwy9qr(W>!&i z2N6khMOPb-LYy{1~I ziat#@qjOwue>^$O*3q%P^2wlK>aT$A_I7}tmR%1*Lqn63Il}ANYh^4~E&mDHv_^_bXiwOvbs1y!SM_ySjoyqm6!bc6CX~$gn*uCR{rb_yCF2Cr`#8WWy>3 zgzcqS2ll46(L;<1ycm|Whb&k0#y8pf-AkF7jnA09kXxap6Z6#FRygT#?m1FA|>!mn+fj0c~;nv=_zH~)TLj9U12)anek z=SIRa-WzODVw@Lr1ep^mAC?EY4^6F_j2-Xi=M1g)4opa^D-VoQnQqa2>BvnO*g&mV z7aD3^LQ1r{aDhQyVCS_0;$Jrt6O-j2hFsM&Z5LM7E2V_KZNK;|ZEC~$dw$~9 zwvWTISy$h4G;%vV-r%w;;KrbBjRb_Ro5_cq?OuFdJ1KY z|Anz5$nICCqPpD>ak!qGPv~=oWknw^B$hqeViZqT6@{n72A|_#Xxq z0zkQhEE+^XxJ6unvOhXVX&7)q1wT!b9=pNoQ_YiaF;$bdsk5_am@@mLjZtj4ZD46&+VA`$3b6Ykyx3uVGcj+@|Re8@|K<*38@$aWc2e=D*enP*7PWQE74+E?0 z;lqfm&~uBtwqXsguk{Z8Xm4x#Z1Fv5^u{?Go4t`LMKiC&lFw2k-(TCOPEr%FxbGKE zy`-HXn_kRu_wbmTo0Dt^Y_f7wRSh-IFBY%fP<=hOs%j0nD6=2lj5_5V+J%TS;iepAc^;{_0n5-OK_&3AP$X*|q}}mWQ?H z@&>F9-qYO66oz$1t+0gs;g8<4tV|YRGw$d^YOUz9R#tpgUWH+cn={gu_p$`mTzp|1 z37@KdZGTF7s5fKFX@LwwTR3PUsD2RIw&%Yis2VIP;uwr%A*qZhR{RMgvj+ zr#pRmHi(89GBJ8%gi1O(rsECF4|~r%LaGdbLo`*b2Bvm)KfbQ7{YBGuI_m=-@he~% z&*jH>Tzv*V|1*MUmugly z_x>d8xk5rh%MiuTtX-!)A%N3x=|8q}1)L_uAO@7y`@)`;Yzv|X2qEXmlV?Rm%}q@^ zjh7E|YMPq%-HI!9i8=>0eSrV!6TKgJPUwsch2QYsK3HJrV1$;%9^RnpL9$5V5v&9l zaKWb$Ss7>$sTd!J0aXqT+<;>iHa4LoN*+m2kj*v@dJm^aij;jqcz83{`%!Nnq&h^rwjM@&e5odhzh^evgl~7RH1hkVmXEdmcT@CL+fU0 zzUV^>pblFe49(bA@H2pp2qcNKN8$mazt@tRRz~?5*HYCTCwjo_T^sgI_)~yZ2BZUrGE&XJyIH0!J17v)h{b5E6vQzkjd=r?Vb4X z!%$d%U9TPF0;DDWprs&Mtg;qe(%r;2wPPN#iOpOy;(MqR2M&{3cVn9arN}58+OuaA z;nb2oAE`g3LyvT_ii+GF9E9`YVq(sHiC5->1v6S*Yc4A}}I;9~+Z-eh6OK0r?FI7BJ^L0yMeg={f5qm*PNFSmb7-*r<3p<6)4B zLQKxxJ+65KpGEeb({mG=>L&%@0|agJ{;^tx*jO+SE3D3SmfTY2qkk@YAWRu{GGI^3 zf|9JPFbtQi`s|-EK*`Z!BYML2=irc|%!dViW`DezK%vHPnGlM@zl`0x{{Bjs2oOrB zs7T?t(X0PPQ3=!^Q<&e%v}(npFr6%AB9=P@E0p@!rf*|owhOP$f9tS)4v~QKNR`iG zp>gg@pGyy1c*$@)|9EpCVPs>#gW_TX0Vevzbt2e<(2V4h)TMpxlX)dIqKAMid`hto zv83d8ESb8}Q7Z{B!3#Q98%&E*N`3vDtuqqx)xH}kU5D61Sh%K57eGWoApBLWpazHE zcVPYd@v-DI{B)7T><(K9SU6NwioFRD322p&qw}y2FgOX-H?tF{G*|!XXvV+@)jd zG#3CcP)Th9jCS=vLIy2L1jD^!=T7jF;H`2%i9osxQ;tnax_Ef*6BBp)O;YX7+oOBp z6BCEiXBT~FXlR6zH#j&!n3E9*ojrPxQ$&DB7G(Y+eW%$r%T zA5+cX{zx_BWKb1kHm+hOksfr8&&l5L8VcT}lFb=X1?8f~iI$E<$r z7(v5wfu|s)wcV77n>&hB2BzoOhK&Sn2YB+^8XJjpbQ-5lQRMKj00-&iKiy|piBy1G z-=@7}eoY|;hBoXF)H*cSlkO%X*v>HOqrig(ya1!4<2Ke^yUyz96#6W>;euGrz0Sc7 z3s;wM?mRryKXifi{P2Hp0BNSl(PVNJF$&p9!n5w zNn{F^ycn|3D?sE!ToC!2^*va~THL9~HF_^Hqj~kdJg93sRU`!ccts~w@fY(}_{kih z)${W5^2I!9dvCXzFLU+kG9`rs`WL?yUkULbhy0;jP>fV^+`o~Dp~R*g6-cu?ioK>S z(II6zm{<9Dub=K6oe(;DbQPgzY_b0yN(^=C^__&NYKqo9xkpYJ$a%!=aCF1$?5qSU zQnJfSOHr2D)x`F34?rZSFld#1Y;6T|qUgEzBLIfrBz6i&Eg%oJnX!SZdzrT$*n6vz z@*sx~(xi3R{%au4Wa%-5DW`YH&3`&i*I@_UUvxS9ysXo3A7(#PW05* zQ&YcU=V2;A$ZtbjF}DTjAl1M*8pgU9Soffz0p`LMIUwPG2VA=TnKM{4$TNdciMWiH z-lhLWfny5$hJWhqmx975EKJw`R+93Z!51}I)w;7IrYZsp=<{A$68TAC;S3sNsMSd= z!_!L|dkhT>_Qt3IK}?OEcNaS?%pw&37EbmUXE9uhOEE=ksAG6&z@H9nI4zA=OsxHG zhuuKUWBqKTS8!ojxq4@pX5!8VW6)+k~80K&70 zw{L&w?gl@)m1?8mJTqb7H{~n3p#Y-7U@aIm@zf&P5srHye z9c(D5mA8+@Z}{I<%y-XN-t0r$9aw6~GSjWspA;^I2{DLdOy- zR34!6FG|^}M*8|6VZguWXOA6&&kW#@-n-uf#3T?9#9fe-f7wKSx?h%(lzjg3<w1 zo;-Ex#f9w!hNdPaK!8jf(${&2xOH&mrljCAwHO8AcaF-^QJ+VEZ8Q@H0MK7>ZMpG| zRhVg!0UerM1a8x2B&(%q-%dMg?8)5Au%#Fo8?*1&A+T`Wt8xcV#pNcc0|J7s7Ogx7g3+*gD!1q~!9SND+;ZSsu!F>35JEBcD*F7{sxy4_~ zyrzd{+Z0mo+$r%G*Rg-e9wo#8ys}F8hgVD?rRZmavk2bzqj~@uXfSyYEg%ePK;p=D z&^Nr;;GzIm*^3LMQ#dha+Wgmw;+L8KIm3$1t&r0@kj{f90b>Yzs`z#vGP7}-nMH#d{^$k}Lga?T@I;wTNl7U;$+|E* z`y|Aa`XQ`R1qFrK-eS3OF?$AxyITUQGegxg${GTuR()||eM~Koel>)L*SGhLp=u8>_vZPReAR=b~J^=j#WxH1{uR06G(@>Z31=Ak-vD~*%_}sqPolXc2d&5#O7khUby|B&b$ZapPckX=9v1rE4sVex0y># z<n93P?Wv_pc`aldCIlTocX|asPJp1!dSv`CxwvLv*iXH^Ck0|pCW`D}VVN%-a`A!86#>?5c)fQ>C$~qplE_!AT`!Nu)GggU^J??n za)BQ2+W0Nz#ok&!3g>C6vC}_Yqh#JWccnJfvGq z-#VNa*d(x&|FiX2iyPV4*kGps1==3kdJUr@vh(-s-TP{BG#M5TQ2q2Gs6nWlFx_V) znj@`+cF6?MQ#^{Z2$CV*1OT)cdFactoaq1I04VA_9PLn7!fYsvB<}B#Bg;Tu@DWY` zzaxKyCrctjx_xzd@-7@oXzBsN5Fe5Vnm0*Zykr8I4mlurL+})R%?n%hyy+4?3;9EU z>5QHp#-NdjXhczM6){Ffm_bd#N=dVXI)(# zd>GRh6abdm4>1+5y;$`WTjWX6N@fm@a8eoeA4CRM*8=qnw@a5W8R0D)#3AQQ z`A?oykD!KUYThFgkq7Lbxp@^~Mv5GaI3xx-VDiORw>_ugnr_F&&VWPPOihgrw1M*A zO5HO%Y%x6M%KGZx^*{Ok8IK?1f;)Zc;S4~o7oa3kWfp#eTv#vK_Q9ZT;jq*4cBIFw z>F8`94 zeGGZUSG0d_rhW1DXnp;kY1#@BAHTRxcv1b}K1ScKaRqBSnq0m=-qtfZ1mW+q=-xj2 zh&3V0n^A0x@!E0&j^k;1Z>Yk+25jfYJg!(-S^Yz#Cn*AkmrPC}{hKm0(-ec=aUu%H z9a?^Ve%II!?=wy!hG!$m4!1kEsrO**X3pP5InmZuR-oQ|rfO)2X|P&V7`7uO#7O?n zO6B`Q)TrU?|7g%x8v;gV_*W6Mq1+fo!oxDH+T4XQS{cK_HiMAJwP`>ISrJ1C22; zam4wHTp$lf4on)Va_TQbR?c)CTW-;R{rCD|8JPj=dw>;}Wcztq!SVTgzvs!$!I6nU zMaTz)DbL_n;Ws$;m2{Y9K72TYPmSXZzI6NpD}k?0zYU%Q0UL;3NR z_9#oPo6a6YiQLB;J9f2^iQ^NrS~jfBpXr#(4AlDig0xxc4P&Z=de3#d*<3~BwSL#V zY#kV%{m_WG(|Ygadt!T>4R2`veyiM>e%dH)E~KXBUg9&abGE63l+$Noe5H$Z@}y)- z-1wPd(|xDc1MDtpUT`u}GGTR_p$pT}WE$8|tKvN!*6Cd+oqpYh*jD59y|~7*hzpp4 z+nYD2vF}sXkCWa#)@lBXD+LCf`kn0~?0gHA4JQYc8)$_Z^*J{X#K`C(P!&f92E-8V zq%7z-m%?q=YrwstehLe>ymar~BvuRm3^jY})BoeRiY7sc++^MTpNSzL&~tVBG#Dw4 z0zt(2*nbO5Q zwMmr@YZ8;#fXIrS`fu9o_+a;fVg$ehxnr#hV1(f?hx1ts4kS33tg*aQW+uuu}D^)*Zz&I^r7Ac!DC#;uEcZF1&gqtTlS~kxh}%8e3pzv*s?j{$tU; zmaTVh3*^0=YK*bmK_>2Vk4ky+NW-k+Lfve$C6`swUXg6S?E7os?=LM4#qSHx)cMwG z=2>+hr`s-)xvVailxOb|d&@jA?m*XiTEgkvkFh%)nr-(4A5goojqChkJVCCcWjJQh zr%FmUZIc)Yvm3DOwFPfOzrYLvnhQyKQzYDblnEsw2)#evC(=6xq;9-0o3UxAWMx z7Bn3c-SAjuS{ge4{^?VnUXa9rSx~o{^OV9sUB|*y00Be3gKs_Tp}pD_Z%ehpWtBi%Y9{DB3!%z8k({G zG3JIl+jh8>=Jn{5vJ}MRs5DRi)%)1^oRO?2YEYykQzEK3A8p3gdP(^P7l&VIp>j_M z*7!*hTo)kDvVWQ4sZb%uB_u0jJNnzI+x?Yt(qqpO-m*e9OvB{+`moJ^|72 zyKf4PED8{|ZLUbc7@SNz2ucIu4Hg2_G#EFu9q=9Cq`Z~C;mt!>z$h#{yeaoFJT6SR zFr`1f^#Y`*iH!jkm{^4G`;Ql;r2L_nK{xZ|vrhG$$Ml2=KzZ0=k1^IQEG%H+r#-MD zST4$h2RKJ5)*(2Bkk5Ro)rw_`?*YLb5)vZ6Z7FBK;oTs51g0=5(KArL;cz06Wp*%x zUIot(T6d5=5^fHqcMJf7->1Dm2P*aUvBNMy!V~JcF9A@-9%;Iht3N*)jP9_!E}2As(~Sp`{a!Yi#TUj zX-cC7I8`J?QyrU9%x<)aRI~tf)bO63?df{|o^s;*4AHw?h8qa&7Hab6o}NEv8%o!A z*dYCc!foB0P+C$lXXFmja;;&PKZ^b^CL0N)IJNUev_$-0}lopgJ~Jb5J+zbG7z)k9jPBDcIOTfn4?5F zMjFqkjWz$VW_kM~l6Ozm%>#ySgpTmUk8l1KBE6>}G3*v2Z;)W|%rX|P_}#Tz=bzjz z=AGA@l@-u9D|#ib;#9cLxJRMzp;f)(?#VSb0MutxiM%fg=}XtD{3J%^cNLw`b+fqI z5gUE{}11=BK z5+FEuo|A-VgURXBf91d6DOri{BsX?<7q?vx7;q3@VGAv5DI)O&fr*)laFb-uZL{S0 z7#1y-_5zI-kykY8@Rc-V8WFjy{u|EWU! zm2ZDX+8aHW9GWzFoBWgB?crB;eIzknywje%?bKbr#fd%#*Ppp{x_9z4^s*e3ZbXvy z%3E7M@Az=5Qi0&i)lw(LI1-m~(tJdPzTBmdRxhthI#$hWd3P63ts*-YD+<6G z9JIOj$PV{3>t7NK7co_`^DWoV&bipQIOqtz1_w&{_rzf)2gY^j!@hbJLLY=N3>Uh( zJX!20FGl$Gv#*yK`3c$CIOG}dC9u-+232^OKMpyWIMTAk zRMyhXn{ILBBt7-nyl$SWYh1nIVV~K*6%V=Dm6}deU(VpaRAf5AE*szA1{W!P@sgXy zOQH9Y#r^vtR9!6!jWD3@Qm+f|7hUD|dXMGupDpT(#V9qy2lHE0MW(U!;GlUwD4cWQDXF5wYV>j`jtEdNuG|^< z`J(3cGm4A$Yz`1Dp^V5_%Ji_CFtcfG=e1?}I_LVA@lAUZ`Z~XMKA=q)VfvC%bi21!O|wA->u( ze&fn%8X5Y{&6+j_d|y70WXZ(SGIB4=N_%4JtoecrP0u!rUU;!L@w^#3>8QY68{s3# zMtcmh&lIL~daHNe$f_cC?9@;ao_&^wYt6=2;5?;tcNi__PO`#_NJzbSWZa$Ks70$@JMYpey@FU zkS32|fB~2puQ-h39dON;p%us6I?YT_NyCG0h-m;_%~BdmF5oldyxn|#D@W1*_`hEL zeN;~MkRe23@S<0ly37 zGI5pR^5b-V7Dc`cIl12&?=uB8GUS8tgmFUk z^5hQHcT@(2pA$oimV0L(z%GKdzgg-i`lX$hwpDBJfSI6EewSNI>N06mG8X&bs{Y-EghZk zfdjsnu!C;}d4~)i#5b5LK}R9YYMIVwy@wKk6~I0eh6kx-GPXTV67j8Qma~^xMjS(U%Y(D z!phod`y2>d=&!fI)Kf^f$hS3)xW%YJ5(LDeT>jkF*OmuU+d}go?OZ!p^El}uza&%A zy=SBAOw>N{?;_Wm?=JLKR7V*8u?t#EGI(ER(5fX^5uCra79nCf|8%POluk>swqfAr zeXHM(o|}o)@j5Y@Il4)^f{!JCanx{eSMDs(|(q6|DdkwD}v9y{c zP6k|kscBSVg^QpAyfRKy_e zIk0Wv^+V7ttE;P_JfJDLSwBRvE{o`5;<@?xzrc4G-Ggm}v_t}}f9ftmYFNVz@)i7lz8G7q7b358@@fsuE==lH7G{Bz2gij5#^E-_ z?#8Sm&qdyTBUM!zr~S}x&gx@&uik#d-esJS#Im_Rq`g0$iR$3DGp^N}95Cn*dmV7u z9-e^X8=#OUIr9M7Kl2=3?- zUe3(~v5iv#BNmO&J*P`Dk#3Q@euTiUl--4>7Gh1vfja@|x-4xFL+X^?d{#hIG+LPt zmSPtf&VTbDj%Da9Ys?-Z!eIh^0n}R{4~*Q=4LBJd>SQs6J*ueaYih!d6J)*#HBj!- zMTqtH7o6{`;PZffIHjTS+|+`-FF_?8=?_)nVOiOCccrq|pLSx^1K_I$U=k_x*muX2 ziCcon0Pwy2OjH2UfirTOZi zDQ)aI^(6c`_y` zsYmtxU0M0mvlvJZt796Q7G~+=*$WE`yWG8lO-fFy{#r?CXoKH>`I5&T5);Fi-1_Uq z<@?Hflml7B%f`y;_3*Ub(e@o%LGD0*huHucgjWXa%kO%3!_SFkGT*RZqgE`up%f|! z9owmGwxW0efK7Wl?D{n|X=pNYawvMzk9zv)7w68OPj^j$0}|yHW?)^`GrD)+>5Nhs z;>Upt7k0m~|sYgWvand$76PcZaf0PIJLq zEk1tye94^VSSAd%Sb#g}R1n<6%{4VMyXr@^dBFQ)e}8Uk$`Z!(QO4*QbH}xP2iP?_$-=KN_^v>h{ z!4Z0t=2&`%hy1}(Od$fYf_3cBGt6Vm&(Eif9i9JG>BcbD1q6#*hYlFg^DAqcUt$`3 zLBT~Qr+2NbXHq}MCnrNLalLi=(j^GTco0uT*dm+5>MgSr@Jf|)Vr)NzF4=%fu4<=)PZ`P0Hi$X%#0BulYk%GAYo80^PGk^|s%AoaXiqqlY zhR82fMF9N<}m z`xn`e2VY2DdGPCq!aau z^eN8rr*K>05Lh6Bb%Z#MnTrmNj*(GOBBG+uTfX`&=fcT?Y0RL);F6x0n4nbZVDdCv z)HqNeboiqnV8kF;Rd8BgFp8RiQ%3ArP!0+mD5&A-_OoF@pXI_^FBJ7S1l=O1J;}*| zyFfqnv7NIsOm0^$i@3SC*us|j^r_C-i^w?7QYi(sgP1_z4m%&zGp4`+TL6*C!lC>m z{Ie)bAuwOz=3>Mtj)$kFr!SR=-dw^sB3HD!u+Kr%Xw1e<*FWkO`Om+G_7)TCLC_lN z>EXbYf)y9;zJOzxTNQKfUao7y;417RC4CCvFDV5X_A{JGz!p#f{ig;3a&sNwn8k`p zOB>_u`~4*ohw#7#?KfnTs<@Zg8GQWMkg5HO4z zv%l}$z5DXTi;F)UEG-{qX8uJ{qxrk}eN&jn4Bqo2n$W-^4b`>17%vnFo2}l$<%4X<{7jZKZqAM`46{WEPmZN0ns-BLH00SMt+vpasi!w7Z&>?0# zcu-waGl;qB#54>F&6@9)`GAr1+js6X!boL`Acv_W`M^l{Xq0CN2RtZ8iiHk7M#_UF zrhyj#2L?(T3ZxktXNzH*0z(w4>*J1RPM-#ryPceztt|*Dl>(6`CYDQ7zN6l;)62q2 zTa`t5DbB#bxwnX*fXu=kz_7K{b#(hkv9VS;x5t`_G|+-lld*+m0}5`J&8}=BRp5Og zYjG%k--&^2EnUY+$|p`>qK|$qg?d5=6O8#F=J@^*KE7*LDpoP-VB`@^5vZtDEBLH% z%*yoY9naEJIer|&WiWR1Du4`*wFVn3f=U?mP|M(t@A_SWqusQ^s{r#eAfEH`rrEI3 z(orWiOG!u^aT&N~D&p+m5D^}JbtK+^Llj7{!rswQW|p#giM)_X5X_HUMF4ge7uQjoXc!yX(%0u>G2AUbJK?H@ z&=`($oQ)*~C%!IY!Ue!#mk;dbPIAFJc^`4JZERy%GlQ=Q!tfou-6bh$P(w*B%ap%U zTYw3Z=FR&v+JF-@ zlJl@Q4-esY-0j#;IS%1VVP@7LC2zPXM18Oc|7Ac7gdKKm+I2|#S3k&b*Dr(jwCh}l-o+4k2mzP%s zoEq2VFzn+aToJJPn*_CkH+pMX@G;K}E2kXW2S~ckgn?kL4wD|?njb%r3`>HVK-UTv z;XVyiaNH#%xl0gQ%{9UB*6~YzoCK{Tbdw_WU2z(Fk`?^@@FD|P6)^gTLY!fO%TXt4 z&M;+!POtPCNFALu^;;U>E1&9*N%GgOU3>ldb-<38#@5yhZ9%UuV+2V_$s6^Se}07e zlERcx*C<{(6I0V1c#`{|vmxCOapFFDq5{yZU~0!rOp+ru(k6ScQLgYCTS`*tb-7iZ% z6M(IjhT#}q)PV^Ed_nL4q!;)@D~2}BNCw`J>3p^qwcS)AudM6}$Va^80i&g6apYaS zk12P1KmI9uP&?v==@o>TWtUN8i)Juqe2=LYViXuCfp{y;2>xHmKoE;O2(Uxg1&|gZ9`(LMS&sMupeRBu-WKj&B2nX zR(=F;u{j~YgdXn!w`U4+9$1^MeCMznu?jy=u<3dBp?}lQHI}k|dmBwHUJH{B$@TlY zg|*n`5gB*=7liwdF@pI1;#sH6e-h`G3XRcIqjdo5m=Hj zn;h>Bf=tuq5=5Azysij-VRosfn1s1^uPVRjMKmc5(Yw04O7RS*8pxP+3JaTQUUp87 z#&Yb5fsA5o3AjbUO+lYUROXMmHz+AMVn^Ld;le^7Vj+6c;H>W-ibG@bX0#Ma=PidZ z&Q;gU4B1ppHny>$q4yKx$UdOW2a|ij#)d)&!rbVMDeO#Ph|!)3dddn?@w zkaa%vcwrZgVCYui%6uu|2wCbW)|vO3+gpgSJzdcaIHqlE{IstI>*V=iPBl z06RkZIpBf7A?=P^w^280JMyl&dI0hR@+m3e*6M(_OYf+usVUhYbfLe#eFF%3jNHJy zEO!?dI0`xH>w`G{!KGpV08nS-(KCO$acsg-euJcQ00fa2LyY9og!DF1(Km zmIzP0mKPQ{F04IxXAvG++m$nO)G6n~CuAT$x4kWOy3EI@eo7|#Q3!4fiOYFmD8E`&^c zz2R~|ROyBA>Cn1h7~cg1UVr+;#_O>(ckxderA5UmdH$T4_Z#dPyX)@rCTS$xzmM20 z8d}&pr{CgU;>}^)(V=x#I0|>~&Zw;H2X<&^P;KTP&Tj7Lh^eu_@Fu)&Z2ron6B+Mu z1f4wD+|c#90t*tOm;kp>u7J8RR{zp;*)m?@XUS(37{SFrKnviVhNgVKk?V(RZaUiI~<+*34)4N=)Q(%-`!&r2TKKlwsc&PUQc`+Iz=i+5Yjv zSEZ69(Xg{ZDl;RqP$VlUBN^Eb{^Ei*=^Lc;X1L_yZ{X=&jS^@w7v}PwWbNGQF!sG(1giqkqykE2O zwNT=Xmyt?^oO;|pF`r&UjF`U`E$#|dc7A2=FdFP9$;!9R$vF4|9yO{4ud+ayvw)+ zFM|n>T)Gn|uZRVRo>3C&cwh$t!ougDIT+`Ah>D5jKDP!0h7r?sR!*9es;b*SfG8>P zxskrfNCWjftg;hfV{zfaA({|G3uq;w=l8*iMyrEU8kX}dZf4rrA8`T$zEB722U`F} zf2o!I;C^mmi@kH_rAaM-W+I4)uub^u0C3;^CfhIg_JQywA7?^GLjxrJI!7RiS%L<6 z#%rbT2x#XKBO|X&A))B0j2Zogt7$-8|xqE;lS#r4de_tl*4207|jv$ zIqzhumrS66qi+8Gy-GV9G!A{d4+SZ+5-qf5TWw)$rDoxi`-B9@pLw(NPli> z+Qx>wkQp$i*!&@Ox}dGShmp|+SQ~crk&*b6l$#uZSd5Pg3y+Z_g~j?z>ej8>wt0cI z1@H(XB^apzL=7ASFL%9Wz*g|M?%g(q;vBX&*cZV(AztI-xwmWW%*}V>8sECb6{qa@ z;yiHoh4`N?Xil*~4KT!G42_eKL6VM<@rT<>jC65X@7xae3q-3W?H7p6Se%-&f_4AS z8z>9_Phix|?Ro-}lpMLl90Lpdinxn-LB2FPCBZ*eCmDW+5JgC_vfhn2DZP{8_}n9) z%3ImAu#6x>XJR^WlW8$FY#qg$f4KmgD10X|wPlIWhV~AJFeV82P}DRO0ngUuK1az6 zw~wMZaPC{IAK=^^yP*G@u*o(vBOJ}3Uk59D?jxxJZhmN355Eb(Dy%#iAjP6y{9viu zLRh$nUf3MG+p`CE;|*Ym0p43uLIP~uZ`7O0{nQ8o$cQZhDB2B!dB+RV($?`qhS4|57FzA( z>KgbE5f&6gEm5iF>G?7_`2uQ2m|`^F8LNNu<{tiZ$wpgSTPDGa3ee1lpBPU(L#o(X zg@FM)2f=wbgHjQXCuVD_0_n|-jbUmJh~hmcEeMu5cyz9_f;pR>YqEY?Zd^p4hb9CK zUN|DH9xTo)Ja`X5^=U0Fmyu@XKi0l@&d$)!In?$$fYTnQJYlkzB?78wY&;f7FzV~` z)y}yd2rd~&XFG4;Q~?I@tYcr_Vp~TCyn&Ci??|z{&e(>26qPixcx-Gw<8$JN+leBw z1;lsMD`&aO=MjL|O|gYdfhz{qZxFH~7nk+wEdV+4V1{|4!en>V8yf#19|f^4w&IlH=^5ZFuq za;J)l3U&ZWN=ht(BTVowxA2!U#vTR^@(h(e-^b3*1q5B7#yq%tFC002`mQoDgrh2Y z;CKMaNk#M*6N;<<*Re~F!(O`sEW_Su%C!tLW2jZ1OP91ksYHZ>qj3jFkoV3 zb&{G4CLT^P*w%}SFGHwwEB`brHRry4{ol2=A?BdAUk=W2V3UG;kCqk*re@BrXs9n* zS=A5B$jQrdtKK%Xu?Z!fMv%1IczkiFq_F-u-Qkdj;uuVb>rLjcLFWOminvrbx=YXF zd+iKSfIJ1wD*&~*A3xxw=cjJgH*^;oW;|tfc3&`E?CyRE*FT2vOzS1Mq=OVO2O26Q zr#so8H}AY+JpoQFJj-wbtpMu2Pz>Eb5)OHAYs=SG_z|xO`$<$&BGx+(({JXNxDySt z2ZsxxxaOcm0P0On4v=HxQx5(S!V-V1!{!+^TP$4BySly&45WL>(xN&3W9^Hr#lqr& z3RmUXU%rnZGl3kg_vr@|SFEh805nBJc3nC4dff2{c^s}cvjgZxKuTr1Rhh&l+X z7aHAH`HzgNksN_SZ+d>db~~!*#p!?E3nQp{7()7W(hZ>^Yo`{-6 zJ)&v&W2Sa*?%Nnyw`o>M$t2|rwcCeJwr}0&8!wM-fo~8f9uqV3NzQkz$u{f@(Aw0Z&c)zDi`MrA=L3EcFOu_eg`&Jp%XTJlr9N^sc1Ef9V z<2VreS-M$1;lM8Ap`+RZkQ3HlYAfmDsV)2m4gdhvuAY1yN>x%UC@NZWK?BxDSvv$3 z{+Rnqd&3YAvUW63sAtE%!d(ovg(6_R`u;Mfp&J1Enol)@1A@SoAd~=DGJp;cDb+fx zEAL(_ttKb;1{EO~pBSh+L`w7UKtc8z7aFt4sj)GFc@AS*aBV;^A$+_2TT`Sxz#6mx zdb~SS1U`Z|rlH%(gMtEHXn0`IXW)#*f8b+@w^PB=1W&tmB^UWU+$ntdq-1a~fZ%9t z$G!-MGL!@OKEc$p)6*3nLz=N|q20XBih5{?XAyq%RDT|ZZuI99j!8I5FnC3?P^!=8 zzx8zknl*Fu5RgXzYr&KbIe{yx8ErKL7}E0+%A;#%Oh_6ZfwZe;xdRvm$@=!KS~wm9 zV(GA32Ic|tD;(K)E?0Nxl@;^Au;Zk}LO(EFOck^Y8*nZIalomVV*DD56YqksO7cCg zRKQUppz)lX1y7lXT46CUZJwRD@MFJG13i2Ge0I^3X?4Isd`ysa&XN`AeX0Y)&YiH4 zk#sR!n(Qi4{Joeb?^tW)r@{rhwlz$9iKrJ`JAl{_9iVO6AqQ3*H8~jww{U&N+_Ygx zPe+G{@jxebSWxh_k4mhv83GK5q@-{bX?H;K0Q}a#=nZmjyjUnMCU(W>(tdz^KOkWI z@nhzL+gw4W{U5I)Sid8Wj*>+8^e!vxgQP&NX@}P#4!$R;9Gfaa1JFP(E{3Cl&dWQ* z&HW%fU4G;NVd#M&1K$0i>I(m~CiD;3oM-XTp{szUrKwOCupm@T0OOodd;=>+jf1#k z0EM*L&A3z)l$2G!2RM)(f&CpUW>i{G)AGfS4-CX4Cx=rbiS`q)S}@;Ho)bSJ71={* zxXZvbMa#HD4uI`tlp%oaOPz)WC_c*Uu;1F+tIr4*w1yjKrXY&I<;O|E9u?d&a)u`s zge*#1x4u%Kw3vT2ynTza72HqrmK5gQyN4$yAMrY|V~mm>w#pTXDg&;|`HujA+t?D% z4z^UxufYT2Qo94kEqpk5>H+n|_Uh5&=;ouXt$nu67M&N61x131OZfO<1!6ZqWTYH9 zJp%(l@?w&7eTdjnAjeD0Scxm}E(5XV`Lk!lz4ZItFAaDICaqoNKC*I_YpW}GMB^HU z+|Hn+ad9~uAV-hk2QVq95Rjc)tXpKBn3NP#Zz>g`z)Fo1l250=w|0P8#&f~ZY#+ut zmt~x8y1KtwgnRcHuTz5e`=MG0oa`g{?jipJp@ zA}Ekr4o!&WM80DdCa~N>LXQgyrm>Y6mbr@p;etSb z)R6~rBR?-MG;KusSVhexD=R4BKlI*O&wH3bl`kH%{gi&W?g@o74QLpTmKDU)JUUlD z^22e@PAnIsqk=4ntApwmBjg=qf(h1+Ang?uohj4(h5dl=HNd|B>u2vNMHT_FA|DqQ zw?7*gbV7gK5W#drQ(qrpPGoa_0|yafynu%Z9^-cKb|q)K&WbQeVwi-L2=I4KauPCS zkn9ig^S1zmR8m6T3}a{LJLoz=1ppIA<>bjl*aYB6&%WV|m321vIbh~eeKnX6M~}h< zA}($h*vdZ^*I(%+Xfv+9`80)QoEjygJYtLLjmer#Soq|{5c zejU}m+~>R-{%Mtt@@xHwl9rOHGv(qSOKM$r2cWJTf1JG0; z&u&qfN7TaU=-}XB*zpi?S=iLkfRLwQ$r0(-CO!v|90kZ(isceG3aGi2GicZjqjd3) z#0th>wq}*EroVX;ZuoVO$>5QAaxL7DH;zgu8JuBw$7GM(AHar*Yr+z-Iq^IH0g3=1 zZucm*<#xRP<^pPmkB<)=cfQnk0d_zY<6z=GC#h8Y@PEPbxn~0`@d0Ac-+cJY49g3& z_h^CRVf=&}bvpgTx9Q8$YAPy?7>mdNk;M7bPWW;nCpMUsJVb$J@7}ey2l9cI8a#K| z(n5hX^(Ng5^An&Me{AuHabAAyUTOCy_M4@APe|ZOqoM&%yuwG=@#cMkp&L!yc3+M?3cWjv*s*685Bflc3&DgKIjX3dkeF3jX+j!+x(r5hm^v_Yu3d*7 z>`xj;W}yKqb>G#uk@c%i!lI(TW@ZT9SY2J~=&GrSNla4GfjH&OS1)I4OMU+jITznL z_5T5z|JZrs^g)A{3zvL;C!LU)#gf4`iafMa)K%CTv6F^V>mlF{CnoR{ENr+)T)Xxf zr`c2E%0S>h1v&@s^mP*jGAzYVerNC0j^QLR5)vezR&h>%HUW?*xhmksH}zS&z^YEc9!?KXe{msc?&ht71Pc#80VYAjt~s#|Q^XV&o5EJOF0U zTXAfCU!;Hb=uvY`P3FgqyI>AQ#GxpZ^Vu?4VD<>ZDOAA&rP$X1%)v|nG{N_)34I$g z+BR;?hmsTJ+t(R4w03Uczj`&S^)HMi;ZiwfcH*u)DJ33Abl(^#qVlR?e(?6q`2wS3 z=zGyPfFs~Rb>ea@l3BlEkU?~~03lJJ;k{Zc#b1?AOL+k(YrigkC)s|Rknvg0SybTSY0H9<-o+tjp zVRuSdnY0`+zd(1JqzaBcF{=p#f!)h9zT#pbP;}gFm^b2-I?9S72yVGx+Un_{l(f4S zv~U41lGfUZ$Uk8*6`q(dcC;|bj|6y>2R~LMuV>iW%+yUQTyiuvdg)f^WDSSSB}J7? zWB&7;Z(Mmpw;YKg`L5=ovf01CO^MS~fX?kX;$^=tZYs;j*vQ{kl6J9>t~?o_7r+H=a$&qxD)6 z>J8LAIGSrt(OTQv6UJ}d^^oUntz{`Fpz;358VqV%{TbR zjL*5C4kuz3ADcCxtorqLmavE#8$d8I5A38J`K^WW@52YngO)B1=K>hszk4?=kD(EI zwCRnP03qR|$A`t?d2ly|q{9GIR3*;7F!+21^s-| z2-8Gq0Ay)p?9Fx$gA3?WFs(GafZ-EHJz2Hw$GKt#zJ7&-6}UbelQU22W{faf7#U4t zDvUS8<7~6s)*-Z2{cngXMJ!wmo~mkEtcP~Ll$>nRn0sEM5?74llfv>t zDFDXAXuK^lH+I#Nk0<18e`HZMukEYhBJ{?~?Dd-;$A4dU5vDuYRT|UvQ1`U=aM_(# zv)wb49S@acf5fv|@*I#_Y`VG_J9oP0lQ%eut>5T3x+P*lAd{MZW7k)=y`k0cv2B+b z{5XE4K;lmRH4{GIpa3t?UAMOvW5Hm`0|QsNQRt@i>>;@ta4KD~DKE#6$*PtM?vJ)P*N(8@8jaKF3P@te-PA_XSFkZiu>Y}q4ru*BuJ%y`l<}CZ5&eX zQ49Xg(ckV5e=9uQ)CQYTO{29E6%_?h9rD$(qpWV{IQnPY#*Pf5X&t+yDk$Z({lPRw zt9?>GwGFOrWFve5c88)um{zox3M%}_aX&K?#zKfNS$F^3wLNg7!5l0azNNcvTzFy` zwM0b`Kwx0y$KiKTbw#-)7mK8?^ihOl8|t<78#bVKgvkJAC=g6b#`2FBqP4iiZ}`Z4 zbZF=@N?!}1=>DTHKeIY4-B9{#-Eb?3v?Q7k%$BnxYT8}f=(Vd{@vP5bLCzkr!rA=J z{*2;|9U13;X!br<+~>iiCNcWEa!;ebPpu)ZorOfpqET;;NLizq{Ya{m!SL?V;06V9 zHr+G2pxztr6LFh6xZD<>rG8UV-9L}K#G~IN5Toptn0WDvI(m9HESCC?rlqG3u$~eh zD4E69C{7kKUT2xg5`Fvj7_fnv#RA}9=qkjwm`#KC3k8b9NvAlBQx3?;V5n{|_W}g`M#>5M+V#2`ioSz2gpWWjk$X2=dWg z&wc?HpbelkWw8VX1Z+G0J_(e0CUJ+OeDNqRKu_re20ikC)FOOmDr{9*^=vOnYOS>> zHf68}@4Y7LH9eaV?j&y!+A~bOmS3}QMTW{_s?5tK>+FoAtT%T}v)B~KXkP-dnwxh^ zHoZn~HYmTvOzY_pwp#_{Ut3N`=dD0I^KQZWbWh{4Fz|q-`dV4!>Z+YfEL_w3deX_$ zKwYpgJYkXW3$A%n+F&5$evA%nv3RA~70hJmcES*?50YA6nRnz~- zj9k2RrtfK--FYp6m1GpTv_~+OBZ^6^Fh*oIr{}VoXZnxZBG=S!(y{mEg>d)aFk;*5 zGNGbkueNLUiFWU`to;W$oOkOEC=RbSbf}APel%Q8JMri#tyfsm+1c=~u9wPJgD69O zJq`<{4-m<768iE&w1&tn#qK)YbI{+kU5qc| z%R^h5hN!45pAYeP=FrlzeQ=r6&`&$7L*i}ZyF^2)CH%lw*O=+#uj;|&+vlz~7Pux$ z2bWoeme&g5lVlJD!+c`IPWo!wV+Ik>VPg&4YGP$&U; zc(;4D1v4Vd2daGLo2uDbzw`-muh zZfXhwIvF5>=xIPaQ&U%uXc+qH1^NK0GpAqQb)}`tuJn3|p~D{8FC{JQ@ra4C{iW!H zRn<1^u3N5;UV8fQ7Kl|E8m-`Yz_^;6Ox}@uy?_9AP(q z4!=mo*ia@}`1*)qe$32q_BImLSxrrhCvf1xk+=NHltK7v1E$};kA4aZUH&Z_qqDX+ zeyy|x?&TA&^vEc;P3W)Q*v`NxoS2n!A+u1?X3i_)WMsWzy3qHPyMgD1!#c*?-{hAw zYnYmi4A3@EUhp&DBs^qBZ)HJCUb(AC+{ONx?3#wM)3E=pGkNDPaqUrVN%Ht#R7*G8?BaZ#Cb2AzpZwRp|?`ja+vI@s&`G(Del~G>i6~hVT$qE zJt_^d&OH)b(V;O{s;b3qUw^Y_V`AN;h2e9z)%(#m{bKaOR73Y3AoV%k*W90C_U-Gv z$_|w^Ka^iOI>^s~NEW^TzvjP*+h^tHt7m9B4mn+b(i<$L)5jkRUD!e0+ROm4hw#x!67>n4B?a0V?3pOz*?aBnHhq;w0ho$3$fj zOhW?rhl=?qt497hiVEQp2jIpYyTx5y)m}RIh+6aI zF6Mqse^vRRPPfNi&HiP!^ZL%LGgi`o$y0&V7ojj%h9cuxNh4yHrXxnt zaX`&pSWp1#8t~l+qiC?G@k3#a5&0%w4+b4)<2Om?P}%nzVMG6=QqEEcb9r z5!cR|49;#vmxy52aakW$7iZBjo(UBvGp}3)HEN}71@qbRufC@8ONQ4pd6RP@M!AN4 zq%+@VEA99A;MX$UwmqTxsR>VLPC#((s@!iTMv@PzZ5UEzE?N8r5IBBcajfCSiFA0r zO6|dv$b(0%t+mznMH^y(4I0#TAn!`+P_FqtSk8g!i&5IonJRBbOk{Ki;H?U09x#;@ z6%5n^j5ucnSnPr zzUVDc{GpSRkNOEz?N1~|>mr%dB`iAPiN^1wL`4aj)IilU2o()D63R#(jgQyH(~6B_ zbtb~+r^?$CU^swh%uW#i6VKVYKQA=z&>+c4ue^Nn1Og(kx;}SyO0|F7k|Mj4q8jZR zy!iO}^Fc`x7N)JuFD^k+*h^|g#uw&IkgCf&u7d!VE@dgvWFdHDZ2PcA{)&hCZAv!( zgx!HzWrZ`adPgVqJDyV!xi9{DcPAXlOKDDj``~@^EMF9dmh-n4`KwOf%@0I3tc8)H zo65Kg=HlwYM&`4&I!ns-FDweZsrOMEeB7R!a@ro2;jE;S3h=m8xjed0a$v@NyOrj* z4~iD;_O>LW^b_Imt+ZX<8dQ1Bp`W(@o)zkR3@<-ky{HXSWr!A#e8udoWIm?r!JLUVO35L&7lysa9>Cf0KkP-nD_gg0 zIjN#jb=4Uzc$#@W^?i3jK@HTKGp1zQ$-v+a(^@G!`@Aj!lZtXGX8kHu&g1vx z50fMg!>$$`3q~D$dQX&0Vp3E0%GCig2RRjuIosgmWDojj8%!y&>3%F@}cA zE7)ZAHa2JSc`>WQj!z}FUIPe{IG%9Ov9C!>gPV`72&0O29oe0G;5g~qZ`H>v8a86< z)#CiFR&u*NxqCyk3{NY4^`z&>`AQxS-TFjnN&W_?z}2aV zW0!hqbJAqqS!_N;c`>(K|M?bg1uYBHkCE9Qh8Yvh44m&!!tYO~G5?sF^T^_ns2y^_ zcgOD0hP=FRJ!@D?NY1*y;#KV?UkdyF+aUfsepPr$xkR|m_xH0=ll#l{qwqm(2eARG zM-8SXpq$*eLBVt#NXUwNdVbqJdS^#R=m-I5GBs8RTxZ2o3iH^3*;4qmtEI}6!Kknu zP@@wjG+-fv`3nvRp3{wNKe5A=!36@%E(U;ZpFTkNQjQV;usrlM2-oz$49;)wgfsq5 zoWQRirzDOfdh+g79Jh zG{K$v8cZZ;>+z)jlFs)iTL!DCyxhm!JX4Dom4yVlX9w|vyKMv(1JP}vPRY#N6vT>b zweoUVExQdZai~)c8N5u8OFIbv8qhTHy*)3E%m9Tnhw zI(_-kR6a;RC9rIxsf!m_jnY47yo6qM>r z$**X$jlJg^YG5`$G0UsYWuP+r*us%=ho zsm~?(01n`Qn73)obElzgzw!f{uYtqAf?zu2S6Ar_Hd~ zeVg>hs*D^jchB)M***F_4c1=j6DN&-AMnVXY5D4Y{!1Ny?6Vn`j8i&6wYFNl4`j+U zCK+nJ`NY2e#9xuNu$<`Tq~V*pMfaUnY_e|s?IL%o+JgN9?B!n$8u5L+Q%z-Fn_Qky zbAj1jQIx(?^!a`znWo5Rg@bWok3-Mz5Q?f#_2k<2)}ww)J&zHkyz83zr>1ip5!oh( zi;l6#KJ`xBM!A8S?>o|HcQG*NVTI`G0yx@o-SW@t4+6W*M_4^g4DHAmkl( ze~HzEmM?k!7DyZz=W9u8@mp1y7i})sB2|Jha5~GkGi!Os9g|98IkPtOtI%rNArogm zMAsGVM;!a|pFD|G;Sx^tG5;k@M>*GEurfJ%Bwd$Vw~RztfsqdG?>sh^jFIcssi@Lx ztEvcs26S8>`@HkrRo|YQZxZXp<}EHM8KZO`Y7Z3){wud;8uEBjHyYGki^)*#=V+KI~@f^D&0%V7dWT@SAXydyV0t>JGxJr>y&O% zxx3r^+-;R`ijgM<(mUfXy>b{}qLQ)g`sNjqM|)3u^T77w(Tp>_85$O=c01y`(V};& z*7%bu#~96;lI!XkMcmGbe}t2M^sbcU;g_Comv}BMnbe9#?%7gt)bdE`&_0lrfd)D7 z+%J#Y2Es9@u5i9N>_Fm|ZnwL)gU^S3lV6&$8q>W(5LAg*%FU7e7lR`oE{$a)yEur45Okm^c?MEy(kd1X^fa$>=^Ex9p2ALi5mtm!kndvTSOeaqFT0@+c z;!S>4)5z^Fo}9D%8ht-@j8DqIjJ^elCr}lEK100RtfUNgRK|l`_j>C^w-cb){qvd@ zcUSL$w(q}}fbG$nY@|x&L9jjlc^&=V|G}@ZTnlFV_xJGcz8wVj@&CU5AHO|Y#8UBo z8l)ZBaW;~`lc$$@S04ub_vd}%$$lztGJ57`Q2*brX>2Zv{_i8=S0j!RzVQFOrvFUK z|NGznKY!b=9q`ib-LM3oe#X4I*x~~}xQ8EAVer|6ohQ)N5_%lJ|M~hUlJKL%L;<5> z-R)$){p5t-?msWLN$i!Ex*nS~CJ45;vyvzk2Q9!A{r4p$jBW)YD#$1>*2Pu`6Np(t zIRUybFi#Mh|MH1h{TDn}uWi^2k_MqW zjGjTnB;)s*4ufDsTR1orU}FP)4Kp)$fZVU@#vs-^2J2lx}zTCECr@)YR|~B!i|J0eOU_;G;)uUS%ZTSFayp zV)f5!8L1U#cy_*g_~1d1@Mb(%xHi^%p`XQN|EV7Vc$bE(5cc`Y1aFR>-b$7PUOq|_ zyk5VgAXTs)`=4d&Rruryj3hLrk?V=MAvqh^MDT$OPsS043JwGCpFfQZI7s}w&piHH z`>pZ#njkwMHV=9N{0C;t#!xkpr{ToIA&kZrpBysjfn>N?qs+%Tf!(CeK^fdEH6^An z9Ewm4u=!(Rvh{%B0WUkRsWJbPdrNkS1~ zofdaWlCMR!;lAVlyrGf6zf!HnzfvvT@Xe&w19lvNR*Xl4w~Nd@HT(YJW0B09v+qEi(lKEEFq}U!`)TYQPoElb8(LbM z5_-mg0kNCHTNa<*kKYh`LBeoK)FOwOp3UF%jq+-{@QmyozN%Wb^P5{-Y$SYMDRulv z)XREB_d@d5#W~-Ut^fDkDyHv+Cce6NiqYcXGt=_^_s=GpKm4*OoVpa&X&F@_+elsi zt|uZaMA3Nf_s^3mc@nh0R|_=<-#6^I{a83B&(Gp9wVaY0$EuE~v6-rUeAc#`B5V~) zC!EY~flM!B{oY(>p#4>)edLmo^h^r39C_0gA$yn4ojpHD zssTeZ&03-?TVq~0l$EgQCajp4Wh@=-(wFS2euFii_gn1wmlx`+{u-+xUlz@ewH;WA z)!tq`B+TUyXBHsh)chvgS^RP4nSU%g+j;AZ+W~WNm9gj)J_`jvjFN64ZD6`(ZV+S!r^V@(cEkyu>4O$XN z(TJQH9jx!*pe>})y~zrc6J8f`sJxkWIOpBXDZFsgbiyeUUoCEMC+qLy&RTO+hYp5q zJKYkTcK_9@+r~j-^-;H=>vXEJFNMoyuL4c(dOSha^~AafR)t)+Mns?$dcr3cK>@nE02Rffx!!Js0Lgi zp)ByHhCEPcZJE3mE)g0$)N&wsY~G!rX$7Fv%xoV~dlz=uo}gd;P#IwqyJhnMaBw-4MCCoJC8M|ZgTW7J}r zD4a#kCQ*4O!~LF&$Jo6iQg`@xihiZizBc{bf3DFr>$!N^YbC!}{33;SMe{`jLqEk6ZWA9Jv;&T%|!nUO%ZV%JHM1 zJd=&X6uZepKmvL3t=_2*77@lVTcnt1B@b9PuZ*3my2cP{6#S&P7;a}V3hdb(VaU}5 z1m5DH^!KJxVn3cchhRMzMB#BX03DOa2uJO`4;;R!kwAZcPY{TKn!eXDG%$dbd>BFv z=HejinxCD8O9G?dMY2SdnjI1VMEt3dK&YI7{u4yF59Tpo20{)7GIa4W6Zpu2)*pDl zDNXqiu;}ScBp(7XM10GJ;F&+%)N35*#0!uB2QLHOID>NUF92D)x=PD8<5an&CXxCi zcV58$V4YPg2d5(W)lKdUHHOc%X0sn`?fJknmiM`ktvxcbE|_~;U}QsFU}MjNgWIp) zA5~fIa_{i(OFWa8prw`Sx@?R6yxxpVPAf-mj3e>p8o9Em(EWq5zniqjf&`!{#VhY)Aur`y`rhe~VX~&KK@}h%qITD~F(n~9Fn7c0V{*3NySNnM8cfOQTe_%=ohQpVI9fY+ z)U_@9c5XjTeU_hw*)$H#gsVEZqe;M>)wLb=5 z6ZpPF@+&~^@S;FeHs&$79$=Oe+M6!~fdwWANMr%?fS`tr?1vR=^O=cQ<$b!L^xnJ- zj`9%kU~Es#aO3b%C?+t8rKJN02sl+5GX<$7lFx~9BK=owoUNzq>Pmv!)|MlTYRei+ zQ3D9@?Vyx(lynd&abTBJ31yTU|GmGNhMBZ+Z%YGU(?VZcSPp{|LsBuC!!@st=(z@JHN~YJdc`Uob)@H0J^PIeM zk0G199G85cMin)s2>M-UfZ%K)HGYjWt%R*mRW%%9O63d!H|nVTa}>BSz?~mg54DQy z+Ojk1Y}8L4Zf?YK0T|5o6MZY@qV}JkBA(G3!w@i?uYx**i3xCNC}RpRJB6?Zyfb8p zPo_u62nh&uqHep0!xWzl`P+2N zcKrGl@>0FsHAM6C&+5I-uPPe$dB{bkm3c*+<8jfR5~SDO8X#4!s2g~&JZ@?!d*Z!8 z)gGs1{ms!ssuu4~Td2yEQx%`M5fz6Rfb?D;E@N z=1P63MD|Q5B=b$<6Za*5RvZ61wbj*gj{CW3H41v^Hbr-jSeQR?V%JtF4!p}oXHCsV z)&e&uWHxK+=~0}PBi1FQI@vM*z(52g?ui{F|mTF^Gh}6RSTMwY6dN1fN=8 zAMKARCOcd)R>sEHsCjvKEbZ-A5hG14=T!PKJG;K8=hcWVQbU#$295#TAUl>7uQ3VGdWMC8zgvqd}|R@_bhvM@Ja6edQ)>*unk(0e{|`U-l^;E7IWR5k&&V<8J)*2 z{QKBx;d_3G_wU+Ghn3#>@ZnT;&|@uA^MPT>{d^SPR~g_uN4tYnfRX-js@{yI$RhKJ27<>77! z8*#c5{8{?Vmi9Ta~;_X!gJ0ZvZS3g2~Q z%%9%hvv{w6t}xZ^keAOuP4#Ex49{BMnN#!mx(x0u4^vC0?(1B2at+~fZ~ek%rTkb~ zio1F#^f_H%>4^WHJ?aY+ZKLtMEOwr8CgYMdDS+b5IseG`06>0 zJp=1VZcZ>r(0}d{as&jIUq60$WaT*!3wntJ6(BV^CdlBJ&v%@^bja{k_oq*{8-~yy zK^F`Zj0tRi1oB{9Uf6Tw5qsgY*kB078)Srl zC{AJD++*nJ2uvQkUCHCe2s=_392p_*7h&sBTnsA)SlDX3V(qkk_<%C_d8B~=9!<y&R_?mJUKy4a__z^V&g7JHrnvhy-Y8U%k(eKakqrt`+${=&G;9krKhh_1r@k9 z0=0xtdAQ=khbLDE*9J@lA27u?m{Ea|J9Xk+$=pg98^l-aWd^;I#P8x=+b}v8xZpT> zGPk{_C^8Z=iz_iHfy5vC`}(;aY6p1gl;@)E&~fiRkCpJxZ}}SB{W!iik{0m}5yEr= zf&W0A4u(W!50xLxAST7`f)9g@fQ^kX$cmU%00R*sSdek11{Y9q!0iI@bh2+_){~A- zy|XT!{e6Zvu8g_w{Z{(Fu-WKQjP@`|l;($|P-fT&?|t_H7UG}Jzzo^42I6XgT+M7I zR)jRgKYz`Q8k#BCLZN`kX&-Y1u=AWVz5%13?gQ6wVUBJ;HWI@U6xi!U8CpGtYm67m z0XP!F4wDe{H#QPRVuliz$6`Z5PMVqyVj}(N3l59RVEDl{3<}(GpEY#dk&)B`>!7^o z!5iVvblSQi9}bocpPF!OsxLZX`j4nK4IX0ugu^W+Ssq_SP}Tbino_<qq9Z}VgLq91gfez(UfwBCHlZ2NnW>u7Q0)$sSbYmd8LBY7! z>*tQtmZ`Rn1VU3s=gQ3$fB)n7w#^fKuW6gakJK@75Lp)_bw40_*cjod1YSQ>;m~aj zF9RqI;Q{CE0)GQyB4EIQEO8=c3D1c_Y-D5saxi_6pL9%`Zd1m;T!3T8K-%NJhk6{@ z^caZqaB&3&|8!r0{|IU&%9uN#X@gyZ@#qTSUgp_>%ZJwzD*0_ziufp_Y@M|Qp9PK_ z`*I0Ml88lGKCxB(mFrYye;JplKW^+@brYE%@L3$6Tf%`az>O>$V2}#z#C9dZ5I(a2 zB5Jal7`*$qE+{GBS;N2vzQ`)ScVdK}lmtVuxA(V(06tJnmS}g2M$=WG^ANVr^s9$) z%-dYN2suVqIzO>?YV9e0a@9^oeLh02XTsyYW3so)L$7Fa1N`lZ}K*=Ga>sB;zRnWp> zA%ci#?U_pp1oG%}|Noj0|%R#pJ0e5FJlH_o?~bZY+# zWb5I>jZg6O6{3+b>EI$B>YrEfr~Ec;LmJ>c@F;WJ37X%C08%8`akYeHuEIosok75e zv;*v$9jCEbtfT}t3_Jq_{3(*ZaxLl63qk$xQ_X2L>33 zYE=xQgV1Q|NT$!)Dz=bSC=+ji(89o=tbpPL1OvR6^OY+WHOpWHc_JJSG56E3p+mj{ z5tRcqeN7A!pWr#g4T?5KHtW`{!$$>K9E2LM&!krvh_8sHO4`C<)F(;&&l1={!kJG5 zkhZ<95+Wmv#fL{n-N1~*C%{z$ag1G5v_qhxscC{RP~EhRfiQjn=`>O07Z{LqEfwZP zP~ZweN(;Cj&lyq$UX+%uATSK-3)Agohw&}~M)gCp&Ebu1wO77IbyhB>9$%U74iE2d zdC2%O-`L{&$`r@jt3I6xe(6*rmu|YZW(7sg+?=Y4rI%e>GPV>`T?nQej}l=}g~_#_ zpZq1op9ODk`XpauS(_d0p<14&sjbY)%LB&CL=^`wA#JQ8*3CWLmBg_)PS?~>WpSXz)((!*_bh;geR>n)Te)G+B^Qk zW;RBYxooAkRw|V~bVKOV%;^-L$20+deh~iQwM!^aeUH^-lwZ8~=|cYBk_8#Z_ix_5 zcSlp|uAd2813Q127WPjTp%WD6^k>vF2p|{UKRHNqp789-S@3zY(F;1s4$0P|o%`+&OP%=-8Whzr_ zT5$LgG~koVmv{22&&+QRO^UNO*9{mZy&iv*l`2}S&42cYVj0cg8*cXVy`u%)6C3x< z-eqOK7nrN~>JU#3M_WbKvk^brki*5=F%b{;h-A5m`muAXQ#kceTisvGR(wZcJ;dp> zm2ofQa5Fc%Rn$VTc!_d#q03AvHRFVp8fVjV@TJS(qw2ct zV#LGx=j9S_KjFzPz0@A{I^RzEa{tc``sG+L3qIu>D_MoaB0>AFUw5u{xBO!6S?kwQ z)|C>p|GbTz{rSvU{ccJ6bIxK@zZa*Q&!&ldwrF0QpIwIcu<)!nuh4XPm zvB^VcE?ab~ZwtSF&idQrMN2CSmYwBp=ctQ#OViGY2#7k)92l_UecuvqsdR=*CiW5K z4RueOnTM=qOKgrIfmxy}8p?T}@5k@qPm#UuGglYoc_b>6=g{I!dE2j_Lq`;6XCqSg zb42S)#Og`%h&noc{b666U9T-!dNwmkF_n0R{=A-Np4Z;@<3t{)P7gVDY+H4BaM+^r zYkrLKfZ@CVLz|1~ml~$|k7`r;3>Dn>vQGEp1c!`^W;U*vOZDkL(v7!=kG(q4l9MMn*R*s-L7{3=cE4cH z+9M!Hu;M(>x!)<$A&SA zXvQgLYn`@?i4LZ4cDhfMeo@}%Mg7~hDD~EjOrlALc~v*3cPiX(uNz`;7He3$^Z1F# zN3D7h-qN?-uV-a@g{7Q#DDuVA)(T6=2u-NWmRKn}i>V~?&46qK3@q=1>c?z&bo9vQWf8FWxrdv^Y|q1KE>?= z9UpH_vPX9)g=D$3G=_vW-0OJQwOB$@lu7oo=)Ab!Sv!2E?i>5pg_o)8-}1&8vk!;v zZ&6(m^=S>AIW|vcZQmSo%1ptNojp;ZS#ZSBZFtxzy6ren?Qq24=sh|j9O%iC8mqeY!sqUj z6URM!%FbMU$F)ByXw-*qmiah4I9Sy5(SagnnlwjdgOwv}B~J`#>$Gs>S$tf#R_EyyP`jM!?M-ZF%s5i|BYkQ?`e;hTO zIbyv@?bqP5;7zkJ@A%F(lRLNWPOguB9zH!7Bc=Va#HjTly)l)PvrNR`eaC6~sw1(k z^|E}80UY(}4VnFI+q+5!+bsTmm4%gy=V(E>`u%s(k(QQ*Mu{jI5AgD$s-6F2lMjk3 zEfK*6g(D=L$a{xX0|z^MFp4Z@afd$qMO#rxgvtw8U`UNJ5?o94$`9bfiyoh<($dau zO)1PoWR?2Cs>r9;4v#_FjeOC_GQWBY=bqL2a;iwZp^&p`wCkzPUVy$y_JA6Ony@ zR>$ClYaaYiAjI_B0729eACb(Tq8124LxU$x0G9w+=OshEk(S)y^9RV9Z|oZ zkzY;q)S6ed>y_jAw0WB(3%aq5XDzf_3{`T?-Olu<%xkCas`h4_YO{~-JbS7<<(d7e zXV2|;`QsuM(BDD{x45EkGy+*W!NHCnR&y-wWn*BW^T^k9xND5Za3t=abA^u zDzdXlHa}4^Avkz%vH3_xX7#uzpBh`lX=mBbdtO+4iMq|OZ`<(uzKq}VVu!p(Rzf`P zvro;;I9pI&i0|6Q57>lw-TjDxYGubY6qkB=w}QbFXJ%geeX1C>nXz&Iml1mKCon}p zIG!=fn2QA5MK#uq&bgNvtnuWpCoM$>jeB0)B`{yKcf{MZcl>1}eUj|?g3>QtzT185 z7FsQ%TQ~MDzwBeV@?*oWx3l}C&)(WvJ`pC#n`;hh`6pry?)&>Gd}SIU*-bI$LBcL= z6v|^QUOTZ);98B1wqW((!$pWFlSb30)9-(V4M3$&|F>)N%zyr_`y2SiK(qn3k77ML zEP3>WfIAfx{(5I1EdnNQAb^@}`n|9atZ)yJN+73Tq=?p*!GiGx;C|w8iPFo1Sp&tH zYi@Wyke3wi(38B^A_c#1s@uJ8Lob0bqSv?p<@vcs`d86y#~ZkR+q4<;}7aWjGpjXpYIARfB`AOew+w|J#}j9d%IGAJu7%%!A=d%f@%lkRlrsX zaHy0el=D%BOr+(W!h~|V3Jy?bC6d+B+aMPQB5{{$dH>(2I{0RaGu;dcsi$3f1!w6w zSMq5O*v%uKSV;kf&g?sG#}h}plyOJCp$~L21Hu4qcE|XTxgo>+Vwr)g>-WwV^FRG3 zXB~diy;&&;gE3{AM(5`puLoG;8^U{+dZ_*&U8Fu1!g;fYY8@#>fPfKphgO>xpos`LVgmw+I`9vKgfXA@T`tk)Jb980@-HLxp=_MM#C!1pggz&6^2r4E9?h&5E5Ae~%;*31}T{uDP zc9XfYIMcPGMgWaLuex~g`hB)PcLyCMJ@GL!X9Do?kUBp+%p>qUH$g}q8qpyqu+(Wk zP5=FbOEefOYw;4jvyikJK6(g-6n83LPpy)s9j4Eu6N{|Opd~g*a6jjO3!7$;m#^cl zw6vVqBS)|OeKRRFL?&~^;6oyEI!i03xtkq^UF^i#EiJvO9<-UHE`je7!)6B->lWQN zGELJ49NK^3t%z`#GUnysBf#g_+XUC>%!SU@RwK`pQrHNGHJQVT7g!8B=GeXKE2R4s z<>zhUPs#NZL>#;E=StmefsGvYEI^fbfs20c-u2*3R7?vR@o2B%K7rwIi9e7}xRwF> z2vh&m*$HN=@ZvG>>pf)RK@`hvS0H>Iqm)3vI+ttrhn#Cv)CHUq1`1zj*hoM3)zSSD z=S=u}X<6D}r{s+N@s9>Yc(@VFd2W@20x;L%>?^&{gB1#4_f2}?%rA@RJ>hx-WON_~ zojb%$F8LQiCKspI#p8ZKfef=cN!?m)ICf1=dbhkLIDFOBDmJR{S{odMhu!$#;DY>KoAqNf%pgzls4YMo*tn? zgXaN)Exa~uXMwMnzNRL7d;^h~>oTgrxouqqN!s9_6M2ik<*^U2Qxd90@piG$4Si^1 zCE(qgjADFJQl8KyVgRpz8-al-gl0q_8Rnll#L$$zB|nm-ka(zuNgp8sW-%de)fQ zSX;k6c!EXLmM*?wqGvj|4!br?8VU-KQ-rX>!*c6Lo4o&F{8Z51&C9ccq#VHRGSCjf zM-bG`hlb@T!SGnNH#P>t^q2yGWDL3El{4UrbmxdOS5Rdk;@y7!QU_+F#oh^6A&(y2tn7Cv)yL(U9J}iz2K6XR{n)MT>_lwc$w}%*{z2JE zeOoF%(40_Ekb*ldYE9Up>6SDMIjO3wz8JWr_TWsm*1_Gm7ml-pR^P_HJlvGD%_5=w zRSgD4P<3Jjzc^qnlR7m#T=RtK8{vs>y&TzO4psc$5jiF%=W;#n)kFq^SDLs%UYnjo z`u-2PO;XV8Au~xxNRoL@=FD@ZcU|>-hu`sg|9toH9QWPRUEAK*bzbNB z`K+}*YkB42L2aCk|#i>qsYe?R=95w3!%8%!-a zolb+(GGHzR_@&Z8)DMD9F@}Nkhbly$e{6)A^i14naE+rpJYF0_kcLm zzV!$T3nSeW$Q^cV^b;_A#kKbLuQ_4B!^hWaFA;UI2al}H)5D^OK0(_M#>l;gx*X&f zOc)v(-fxTCjYI%=sr=pi!bEUM@X&J$-akme+YZkFgB

    $^lHNB5s=w6ZB(A?#2u z!{+taYgJ-@o-hC=dF-IGpsCA^8|`C(2?=JfTOjmlW(f-MhXe#Htk-Q=dk4d2nB|N$ zQO>h4{eIUvQ_oOUz(To*0q11NxsCfrG8CMVn>@R1wY_gLmGf&6v%9GS!mVx|e@a24~VZ*O&XfjDzqK3^L9ONuFd2;RnHe7ooeEUrlG8?e|r zU?Ic*#cG79QNXl*->Jf>C5|k=7w32GTbVB5t7s19iS|*Ou1pTCnJ%t5Lj0uLv%jw8 zXz*Zl;A-(Q4ud2nPh*pW_dTu_wp%24U|e))=4S9I&r|HTUu;*uPx{7}8z#idf5nLL zfXLuSN$*KqJcCL*_PA$k4FWa0Xa_8oI|A9p+@CCuok+Oj-mrJXid!mYNG&pg$wp$* zpVr7>wp-JODK$;%9ot5^0pfGyszJJVFEsTy6G~`SfA|Nop74%Qh==nEPx~gY5Z&!{7VF z`o4$zWL|h9Zfhtd{jEmQ{CQ7>!;yZ0M(4*uDQfLOL%Cg_^J%5ne{T4ypLNICuZ6@=Ut@~eT`uWY(46eDP+s(Jc|*f2_v4fVo*P#`iA9*|1nTIk8P=p478yU^ zOLtDM_>Ng!(D~CX`thNwS5E9{IEbhE!}(N3g+6di5_<>3T^$WMI1vbBl{i)zk zWj?WCVyHMg@_o#3%ilWKWfBvSFs6h09Gvw5gQhyL;21W=lxX_9cP%3aW(Tl+!)o=x zgYuG&0Z2}4ZC`68BUR{Tkq|r*aSCqPwhg`cZimFQw5zzy2NWJ5$|%QeL6}Ykrxx^F zwlJ@I%yxo$pRB&Nwgk?0h_$vbaM9MHy1?d0)Eh9`kXs&919O0V4sAYH+&+(`t7ujX zT<7Q6xVM0VRWmFr($>VHv)^o~YBKoxeM;>twBFNI@Mzo_>3{2xcKNhj=-yv+nVI{DCTn~LoCl1) zdqSlOfiMo3g1zT1n&VR&85L(7PKCUqrshI&JxJZHp2p){#ZfVe`)P>~E=(^=y=fc< zcm_uv6s&oSvAU@ZmZEcQR%zMUO&>q9BkY(WFE1ZP^-P-=d2Gu+eFP{-MZihK{?OZ- zEe>o-V}1Sg`A6_YxB+i{L)cnQPfvsGcH*mzZtBsi zoWgL5hOo{=&blN{9_#aTbmy)PRT%F~dPrlZ9O6md2mNfp^#w}5GrkquC; z!O{T<5zmf?PR%FK*ns=(b7@G?+2=NX9C!a<{01C z%im=}?F{HAiw-Do@zWgAVUX%;7N_~vrNWnk6aAkr0PO|!dIq`xPP0O$-E=}G=a-*f zKdfMUo`9R?4PZTtyfWN)MAf><%0bzK&b8Vcen8Mepyr2*hDeU|39hufw)`9C<7+|E z-zF^yWs1Y!0}*&psuoHm?P_Q`3=N5iF36#MMZ9p4;fv_?^bIDaP9;D-3xf{WNWeLl zogJ>?0ZLT9S}=w{j;ab5tV8huFp&t_UBzp{J;wys3}@-h;=3C>=1cZr@VN5n63`et zSSRG=VcjDP6Cu}*fnUFHQ<-<{_>`gp11_Nhva-=uA(MdriUCpKH}-m@;Tv{2dvS(V z<~jAFY*}q{t^D{X-MZrOv7*^+LFrR0jkm5ABxt6Od#VQ}T&I0u3TwJo1IEF^-NdxecAbf3ARSOrtW9km$^P{h=Cg?WdxGEe^t!wgPrEqY!3x0F0h&&P6ed>`c16O-Jf81Q+fhE4ka!{pY_)X?HOy z{1we_mz0!9#RzF6X`rqIraLt;0q>WA z#y6eoHi}4_D;?&i?36xf7KTs zWDF2a1^qF$4_JFrZQdLP-4Ys6#NTyb!tBPj1rZ-*~L&7PRiCn`Y+}tl=+@GKx z9vp0T<;qBZKh_)tS@2%r5T41{Fn?pfEGRy9*rRS4pRK^BS_*n#J_&4aFlzFb{A;2} z^PYX_;o{~VsP`;pW}$MXacjiG(|VN8l_kb6{p%^0^8EBTfXpkYF(K>t{pA0m(ajrI9rW?{45xI2{PPOrRZbM{Lco0@A@1 z2ZJwXR0J4DfmJlH#Ggbzyz~faP`rW6jmhWm9zgdCb;euc(ucUFDB$1=1IH=vL4m4y zNNVu4(4b0%D!id+f&V0l7B!F8yyYMX%r$^ zrIXi>QVkgXGfm(M0>st+=HySQSB{=Yq9!F`Qmebcx=W`~?AS zn%;?FovOKex~T~2g?T=4@-yv()Ciz=SIJi5--T$6V1zipj2%jo2f5eAVk;sTfr9wB zv%;N={Hfdi(TgjWFXR1%Y|kuGP*mfd!O#7Q{_dt5Sdi4z)P$(0^EN8#6BnS-tvTqZMDxVqwc=a?Wqo8hA&p-Uax7=31Li*ey&kc!0HXXyai zke=m1b_Nd##rWFKy^|0TLO~9I>oiX=Bb3XvD3`*FkatQ9IMJbL5eoLA!GD-Rya}o+ zx>{_Hz)8R1vGr~{~0NB$k9LHmHh7Ar2D3gz+3-8+yMI#KvzHXHNOds0U_WC zVFX^g3Bolc?j5?TUP(_x?VX(b9zEJ4V*UtTpcc{76R7a}kuu@|lSk&SOIVNo+CLW7 z8>MN>gz`U8;s7YY6a0{ZN=1Xm%#SF^a;c6Gu<3Z72poVRSVyj;dVDQ3j20j7Zy`_5 zG+q?YetV3AzPYJMutauwx{ffi#4;*1hR;fFi4_#K$lIG~bEv_!-i|>P#E9SoM*9Vt z19d!ZDMZIEOK4aTa;N#v3JaVJAuh*$KSJGdggFg%>6W618N;s16O55C2}#Mns0_kP zpnN?|#z%wIIhC)Wu-gCD?3_Rc8qTR{5eTkeFmgz%=U#V;@^T#?L^8SK<-RqN^WtL~IDWX7HdJ@$jQx*)DU8_(|BC5l3SWM|M0Lz}byTysE>5 z?kz|b!5<43PVc>qky8x^dk(taN&$FK5y1i~eRD9QMwGe9U$d0SnVF~~mqQ*r*d!t$ zB$Ng`7ehzj%up=orWtRTvUNhrkba?Cc&V|F_ZT%ut&a%8s;nFy&1XxO?!@M(^Ajm$ zl5L_QAV}Z8cWQcT)V@;W3P=loF7JFjS}?>Rq5gBon?kS_dKDrFv+5UsZah0s7#9IH z#x{)Wly}e)eac~kff>F5yPAR-JG=7l7BXu*{5Imwx^R0ewsuO&&Ep|NwE;*M&sr5r z|EMwdZ`f3s%6=8jqs5?;Q{|HLi*2r7VbbL^)^Wwm3_KopfuRRvcW5+f!bJ}9^ZV^W z5aOT4q7}^~4fKTwR_Vkitbe7cQrDkX9WJ7nq=8MZ4jHj%t-V@GU`c}nMu43Pii+Z5 zVla>&1T~1N{7zPuv&CK5{S%fX_14!>;{i1Vl#Lk+9AZ;IEhb#@@li0MjsoE7(2u05 zc|;CqzNqtM7P_m<^!L_e*=41tBTh#HcM*xjsP`}^r+ebdJD`y`%uvO^?Pjoc?-p#> z1lrKMz4zJ;;0rM^F~{}~EGdLTl!2^^@*YeeA}%a?*ru)kPZrmi2r?N%^HL6dRDanO z|9yY|HLkIUM85fQxs|l6_;~~PRJZ0izrm41gyiGpi(iKfpZlFrO}rO z2xA*zvw$f=&15a1p6inBkT-o<-?^d8EI6w8*LGmW0Ikn*{}^<{wYuuy;M&x0_T2}g zJP12MH&Lc}2gn3Elf&CQ+g z<57~qk$pFd;jm-w>SEC<#EHC&hlPX)@MdqB1D-pW6#?WxoodsPQGp49{Ct#Go*)r% z_7d7pMb@)eEQa$-xa!EWwlmkTY;~VmF@s_N216>~c56pO_-RN0XcH zx(6OH=BR^LO+TF1hG4jf#Cv3upnZeR{UY!dTm&sZVo;sKVy|GPK@XrhcAEonpzo2@ z_f*cKFem3I56_qJ?e=mkzUKHcus=BisT%-86oTB0KArF!#$QHS)ek{Ff~LpB1Ms!u zxkTAHae{ydF(vQ|l{Hz^^fPTw5WLCBj>8Rn>^uSj2jh<4+bDBNgcil!hYuex+5^us z&_+~yC4ZEZfWiA2%awy_8SW?G+B4C9mnefd-a|fl5~rO(uA|kLCx_630KP%IP}v(H zaq-X_CvjRN)C~eK2=}ZQ$!(L~fx@Fpp=qPa3Hx=J%fR!6=Ne|lu#0udzS_O~cgWIV z?$xjEk_Qa6K>R@LhF5^+0lSNtX(A*l+Tg+cTE`*(Trh|{RayVEMh zz}Dz$<261Ab->=0 zjXqH54iv(&gGhOSrWu`}yI!CQSvdR=WmE?OeV%<2*M+FgXs}j27Q#Rg9%3(rCOi|E z2}hhw_q7}Y7bkBr(TJU!>c5$V0}#{R|OhzJpxjx=*G?;imeu}!vRX~ z($mp|zXz2B>>i!R*EaXA4{I`Y3(#Ven0u(Oemlm!W8;PmD9(hIeF_lY92FIQT7~`h z#nnF-`k9W;J$1^F_ktUBOpP6QOA!&wbYV-_+prrj4`#bCdW(|c?Lr$KPy)MvY0vEJ zEI3uu_b=chBLRH}@ko1quX>Afvb?Me{l7a5k_pb>-iy7Wmb7)6T*w%Or#M7Uw?RYU zae>`U2(L~cY-?cSnv@i)pYgZ*hhn7t*7#Y0?YMf9J?B;6b)NF!C~#Eg{vRsW7-T{pWq|m_U z?G(oCDBQ@-ng4~O+S?KY8Tl7TS>rSW*H8_6U=r`|NZ?*`6^Ny9dB3;`!_y6cKhTdMWM*Mu!G3{&o4P@&yki|Sp_T3MF+|~oJa6b9XKOrG zf+)cC4lw$}+~^C3?gF+a7-3_U!@hzV7bg9l6r96hb|3R-a0i~X%BcGqVGI#Y5p*!a zrDBy1JH!JwpCTMF7!Jq%!F?N@yR%Xe92DdLPF`+xp+-h%0nHRjQHX#&qr>(PH~j#L zp9PWwHgdT8U<>@(i4hUxKNb`efUv`36cohZ;EpQJO9<>5US1C{HItD6umthBvNkXA zNALp72@cWa>*!7f27C#cJrcB-qJ7eZ;}9(Gw@;riauGqnvcit<1l<(=dRQ1UJBE0G zyt|KQASE>REPBI4g_PJ>BV0e!t6q{v`gnrEhp0AfdVp>p+7#phu|ryeFvoy9;SYe0=s~&nR<`!(HFZMml$xV!$Q4ecl!wtKK)TsOS*Y`DjbNw{WQG#&_?5`tSI#8Yz<&rzEOI z*_=#FDx4%3`FI& zanmN}`TRx17GzkV_}RK;%K}E?aZ|&rnr)l_JsBCrYV(4I0@RtP!~w_;X#alp6Pr3V zB_}0eZp!?h1tXfwBc|uz}Zg!MvC=AScCJ3^HMFCr{*MkR0b_J1Bd?;@Az;a-rybd?L-|FK%1+1#A?$U5=O*G(spmwcAzOk|0U`(hapLM@@$I(D zugz>?g%9&loZMKw`#4RY;L3*HCp%lh!^0z7Pd)zf9UV8hHFF26yO*H-3PQ$=*TI;g5odQ z7U_gy7;qXfwLQNJm14td16K?_V;qt5bO_Kkqtb^v0Vu#k48C4iO+i=dywJNMV-~`g z{GdU3b|fxup{5r5vD4`|8XK(aZWCn;6=&fdVJ|P@LP2DycZ& z^-)s&P$u{FHFz3-FXR&yoyMOm*t>z`cn>WcdXHN(^%qoL%;e#K`C49X0n*a>VhcV8 zEY;CS(v)u|Mb(!+iZv3Nl&SNi!r=wWVQib&Pc~UlVWJU@B5^dMh+&TqHv59TjFa9Q z4L*1Upo%}NXMjAS>(0?1#hQfL2NxMxgb2*Q)-dnp4tqKr^%&g)z?-NRGYS{=#$jA) zbZv0z0%|feJgjG6P=KUy5I^tU3CEt5fVnvUI-BT8TuZ`Aun4pI$BvQCN592JgrXh9 zCknyk#m?2&uU;9;3hjPhF6M-10@qSld~#t4u$AzcOK|gxfzf~l54JKSBVPsQ_BXnf zK=v@*n%84gcLxu8T>?yLOr&eWg3GiMK7?y7B#!-X}5e&fGfe;L0 zjC6|_pck+V`d@77BkW)Qi%oUP*3pBs9wZ}2r06^^Ye%kvF9*ER+q-)yt z!+S#Z%W5_>L7twa5DNn4$vgd*z-D3bM?er1vJfmuD{$|Sl#)WK0QNXJ8-_r_OTI)1 zeY(n>?qwGj7Zeg-#veC-Z#lcd2-^fECJX5f@VAg+m07q*8@jnIE&q86GMxF%fkF`M zf_;&ny5MwSCC-KN=+meBH|nJF+b?13rg0xyG|GJw77>AL(H_lT08C}hUE56h7HXN< zf;6dHa7h6DG&-V9ik39^?WV4yqfFkr5^uIq@ZP>>s0tyKRc4~6i>zMhx<>s6xad`S+qdWWt^8^s80ctrzMjw{+@}&z> zAz^rcXqgY9I!L4hD~eo$>6sZMc-&4;7fJV-LZdPk`ezBge+c|WYcGPhXb2f5qD|06 z`7+%`OTB*mdS-s$S$K?AK#8bK6@ns!%dT3gR_9(C2c$nK=Uu=?Hm*@SJc9)3vyt!& z_zN*xXI%;*klsJ_oBs^~oRy#xQ1lT!f#xrO3Ws@lMGXyXQ%a7sV`h7uSmbiq zZoBER&Z4Y!Yg|~a-_jJOyPn<9^le*jaS0H#^=pgU``_s2pceedI}#3?8XV>#*M~IN zCnqM1+zHVd6w68p-@*7nBmp@MW))Oo&%)CS{RqaRHTTnEwc&a~8U<&et?QA%q4vjP zc2t^wNOE=Eyz6LpSU1b)X$87T(n*DrbTn0;>Ys0`ejX=U?Id%mPP4InpOtk>M8N=V>>G71?8F6Iex&z% zJOWXcTr5C&z|26{+0 zWnLxSaEE`_{2K{|?AEf;ePgd@y3y212jUiC3N{+z7%Xz&?MuMSQQ+b5$BlvX0vnA= zI{GQ%l$?kAa68y`VgoYFwg+Z33?vE%c4NFEjjuvba4=_3Ma1ep8UONR z7-jnH+`8es?(>B^p2*II_dklg$ekvUB9YZxL5KG5e^y;{p#gOtKsdb2-bbMGI(f2m zS&a(#9TJ^yqQyadKv#?Hb?4TTxC^dgq4{z+G_WM`&qoS9K*&5gqbsZCO#5^nlH*Xm zVqpQp+nt5;7-*x|g2fOF*o6;lZA+S|BeH!J}E|?F1DHF5nfT9_p<5OU6 zgz_5Dz9K#^3u=q{`Wb-Cm-=o+sw85dA4zY}?+e;ett_?E#63>)6*8Z-giJHTJ3 zX}A4pLgD@*#{Css5jNgywy{ma#Xz|g#b1j6sT>00ApMkfJj~iZz>LICO@OVKoeXHd(9uZ1BE-WDTqz%C$pnqB3?9X*xsoom_k=1s$# zYo(^bF#5@O=c*FwI+XMR7JG#31_Mk;B0*{`!D2>;4i-0m(Mi%k?odNrodaqBpjmVM zu~^VmFZ74o`S_07K?xxr%Z=*o_3J_uwK#GRY47WMMnS==_|oUEUs;7sKS!-Bz(fx@ z9fwBX7|e-#lYBuS_N=y{;p=*9@6YN_HjG;tvr8CX1Z+y!AOXcE z&;H8*!=jjzo{ zH%7GjnB=!^1uP4ZNnd3E1ziVpt-E~{`p&PwRURHf$YI(hkbi0U>f4l*s%vdcO|V3W zaJzyUAkt>HsaPb6IRbM>P$#|(8gIt(v7R1>-Y+}3lJbL47kj)0@Od3p}Ygv7)? zY)sh7KynbiMhGm3sX_599$5|i(K@pQ?Kgz^iHT(hG=p+^(5FI6aA!#5TZGPDy$Z|% ztTp~BpSx48B9;wqQOx3OwjT^h;O`he+Rxyt&;v4;xPZWi#q2Pvf!7bWFKi6LEL2{= ze(*pX=^VvX{0-a!(k@`yp!gTwjtmS~qHYup$6OU^KzO%K-@K)&>W6$>CRZ*LJ)LNk zZ)BmD1+Cn$sxE>hvvcp>_FtWaQb+B(j-k4S2^UIj0vhI;dAFvEHC)i>E4k9P1`S>s}t%$*dvEotDTs=MR((AwHfI4U+$F?S6Npwxot#0N$@G?FL`7P}wp zQ}Acy3$3JKmCiP;2*fAF$HenXcMjkH&{7J3cUg$OBqZk1-VWEsKY&SoxC+u@5vK6> za0bJ66evC}^xp}Sx{NI5-mZitZ)ZaI7+tTu0@R9gtk70HO%wl%Gkq1n+#l95(2aM5L?(dJoGg>?FWimozGN6KnDzf)Du z1j-Uj>t^bvn)w9s@7S})ZRp3Ld&Q4YwPM2N^~jzU0XexCoqcsTAGer(fy)1_N|i=( zJ!|a!AVrfq>8jZoj+Oz23KV0Ism(%D6Fm#0gi=-1uboHKCV$}O}3pZDIGy{ zW8X>7wp|w%f^z0_MMXem@A2cuowshsP7JN#vHg~6n(=rXS!LZ^O1!oMvcgtY#kA<| z?#{raG7kyZ@bEC01}WH|Nwv#j&Ik1nHgi0kK$(M}LPm%a(K)-%$2Qwg)6f7hMu2X5 zS{l)(qmqOo$Ph!b?8fQMV4V>sR@4Eg1bIJrYQQ1D7dvP{pObbozAThkm`($%e_x(0 z{Y(57Ma6P-qVU`x((HOCT(E2bs-Kz?4+{*uhBF1#y_{STPEj}^_4S~>xxM%sLNIq| z0>=gq;E=_OS3oLbI-3D0^R6>P*8!+u76Ld;C98nF9P}C@4CW9k={~*Gv`3`(@j^$7 ze5zLeuV42)JyA`-2n=*QLLOJP+=Gm(D!K(r0?Wf@vL+fWGdg&nDu{odDl5ZvPNFa)6s}*s;F;D%RH<$%3bAf*?yU|U(b4p5-gdOiZF^dEGG2&?kt2K1IMnDwSJMNgh8I^5p>O?caYaxt>+hky}Q_Aoi>!wjwPMxfuV2NaA_DHE_(ZD}U!K zyW9!$Ad%SLIA%d8n!dNZ?~0U3B4}GhA9{O6R@RQ#76FI~$fkC7HC5tJKcnZoe(f3_ zS(GtReh}X9GCJh6^_6Z6AM3+r0I?SjqYvDMj~zP(hzOfC8coa!qIZ0>ON5XUu}j|i zBbwgtufkB8BwXZ7?Jade0zU*T*YbKvRc9pJ!mu z-X3Zq={7GomHsj#!x7~mu#<*^Yo1lM*Jo7&umzze=Q^80NX2>^Lf5Z7j(p9Z@_%9H zVq~pQG2?(elE~-)^Zm$b=mnd^7Yx63mOZI8Ic4V@$L;_`FNz-s2GFG!b$N!uM@WR0 zudc~P3>9mou6%lPO5Gp@E?W}|Zo?rcnt>!V?4HyX(6#X^@6i!Bhy59Yyw&C=?d-?q z(9sxwi4+8?W_}dAFdFHS@ScOwF2?90&Wysy84O9<(jH=fo*E2C2h=WHzTwe`fjT@~ zPInHY4-miD-aw6~cKRN+Na0O@FU_biH^Zx`%=hy*>s) zAN$h5GZCNqr@*v$h;??q@CKa%P!)9Q4U~)*-6l4qySjjjLyslqv9bg#fNKPc?Zk<7 z4*I&f2pcXoPK(%=v3hIGhkKV0T7}2`_vks&vE8|s6nA@*C!cxV!#5#)K>h_rM-H$D zpQ^{#2MXMNz?vz&S^wO*Zh(rky~m-fLa8@rS*e5*gzbda03mh_j)}1Yn@dZ3>5zE~ z{_geBwxdB67?^FTT`wXb;R2cwv_iw6BOuLFgwhn!iUMq=a8wNs4P~n92HFda`i`AD z%|uyVsDv9|zTA2<3#$|$1~IF0ZMVyx zDDzmEioqcZe0&=0<-+j?9Rzcmu_+lRQC>NT)SgsLGe_EZkByVllR`|Ue%*P6L~>3- zbYARPpc_VVi+ung(cEkBAT$<2WiX6%-efiDiS=|*P~qn0>hc8tSy(_D01PxQJG*T) zSDfH(P=)rI0Y`)0>&A;RG}Ki*3E1%Y^s~oN56_uB0(L}u8p*7>oi@w9bs6U_2AV+20aa1|EH{ zJL3&+^uk@?MnaS?fLGvQIu|ZLK8r5hO>GmQVj8*sFPDRr7garWf3m443kVEPE3Z`4 z{+O1Ue8-Tt$0-l<9QzPJlBc+aWbh2J(@7do$5m242gK2^oi|w%ttMPrKN(My%%9)Z zC3NRM@47!^ix-uO$a<2?W2;Qv;8U8Y!69vSYp&aR`nKg!{0Nv{pfs^lWH10!P;YfVWm&+_50f`hOK97k{ZG`5{`5I5)t@wC35amAB8jZ^Uv0t71-puGi* z%qAt(dQBm|b_pjAB%%hw;@>U_LPUz*{C&E(Ifr`E4_wwi`C7>Zj)U|;92n8xv|+=M ziZxRG5ToFHA*7oq4(u()7nV@v ziDi|qC1uB;CMMGFZsY*S;urV{T_c(O+yy?)KE%r79nac9(1W@hFZ?1T2$u^*o<6@g z=GH;K7|=7(2f#cTR(rtRy#EdIygi+};kN3D|J;`K=0!xE3#o@lQ5`}F09oMw;$vuN zX*tc2){xxAK`tS!MX205QxbX+cx+Zxq6Z{^V$3oj?osmU&-HYmD~~$#fl*b7mR`VF zt&$FW4m>GtgKU7f;fIf!wUIeZu{wG8Lr-KfH~Tf|%i12*l>L`6pO z-vWe5Hm36@r#BfS{OFVmiKI)%$QXj+9Mv2^0M!2&ED$+t?QIF11D_F5p?09sVcl^H z6(asKAXOq{EH^h|3nMtK_h9uRA}}K_0+&dnj)4|kHT2hsJq!1kc!%@vFI!u?FV9@S zY5YTE@Kus$^}?S&(Q+Qqlnh9i8VS5ksj5bcSg2x?GMFW3 zev-epRH^4k&Q_9r4D<=uM~KWD;9tdnqM+%C$7rZfC|I{VM5of8yL71wr6laLr%-aJ zg$sMaPBAxkRxIhVj$V z01e>}8Av?m0ti_L{z=XsK5K^zuQX!-omU9Z+CyI?Y^Ct>HqRXABdH_Hq0a|g=QW7$1@&giuD>NHg+j52z5!$x z-s~(?8~f;Ce4Fmkx~9rTY?wHTJ$STeDV!{J*nNA%#(1Qn@0VQP^X##D zQSFWut*(H34}UcRTErc)P3PZDZ1mnJ?QnP6oi4VRIF$TZ=HfMO%P`y2JJRy@*<_dN z<=$eStkL$IYYNZB6Ay!{#p}bZpMd``KZNF|aPrIHhJM(|Pk{@d$M#T*PkZ3N0m8@P zcaso1R`uaShuGrKFygR*Ig>91^3ZUZ!icVGImF?;aw`Lr(d=N|L1E#*LrUl*5wlIV z6W$SjC3=qC>3B2{vT2~Dr9eh`H2N`m%`*ncaON+)*UW_`ffd0SqAkA|-wbzKiHxSt zcEaMK2%Aq4PO$1~nOmmy%%v?kyT4}jLRC{Mi{Lf*qJooiyJrD0>JE>uN)UsV8G}bTkS9G9uuiW^=;N zfgmSO=Tr(IUL{DfDamZw3%=DWhAgD{7WM-xl3s$RO=NbFRw+t}y%;KLU88Lzn5&boU> znEO}76iwL;o!oMAn`+9#j3MRgvPXrk>fH?ZDs}Dam*9!muv$bn5-;cKcacTt1^Two z*W#Qg+XHuQc$^H+LZc47<{8Ow%=ADi&nEzj18~$n{^=UKF!XRL;T;BKb-ESFX!fl7 zIdMGo_cyN0HDMo;G5`zh;OLkS$rOxa{=V4R6ZbsC{^$BIZeZrmtJahYhM1gyB;3NH zapfvZWWHB?dc0r_|Iha`=6HaisTut?$NW51x@h&8{toFA^1xv)!}6SK7vA6h zv=sK-&Us^U9lb@3!RRdxtKxpWA3Y&cL4Lej5`8=qlLuB z2TsdN&2j;ZAYQqVGb(%S?CgfOct_Q4vWFS@0G86Oj!hdTLaT)liV+RQ|9@P zo0xM{hi(~QJ~uuecGmm0lKc2sv+0MsVvX6aPTw7L<=QKq@X*^TL24xF#EbkM^|*o! zWT6QYF^fTTB#C>^m#&xS%T0rXRZ@Nlj`CqMa zrw-TBYnpSuQI>skCG$V!1>W-P3x*OPw+LIu{*7KlDzl=h3Jv~SC60%Fextv~-I=5h zS}7m<6Zhid$LQ9xyFxRB6~32khtb>c!BDqpH?nqh~I|J)uvGFD12@KgO0s_g2GVJcw(GcsATxx~6iWhH zk8p+!yT+L_)&KB&&cOGD2T>{CE?2=gU$YIc#SB;#JNx$r)GvHF2wnX(j9B34zUVrV z2_Of8Na4kukO3hbWgI9kSrkp$r~meWCD8=ry$Xh!ao-qVVLYVRk&W5qw*@Fo12|&m z|1X<#l1p}*rm+$k?>~t>6q^a)WIUPP+}zyoMLwyhxCv^g(#`)_XJ`^ua#}w|PO z$)4r%3dy;zqGg$1Z7RIWSK+h>^@9?O#boL9du`tS-&cawZR8Xc-&M9CdK~C5{%;)i zRX8y9!i=5mK?%Gta1ht@15v;jm^U>Y%l}-f`4&~GaM5qDtWx3P;)XxHWE5fqYoW}; z3y2M7E-4@w@VYJ;g;2+>p2#cSPdn}+O`xzgV!LDi^R(b7UiZ4dfhs&3^KGC;5WK~X zY1%CGO16lY!{`bfNOEEZ8Ajko4-g(T<_;yU z42n{G?Va1r4UcsqRXnJ+LofnN6L2W>@AGae?FIh~?}+=BeRw3o#8QwstQ0vU%|K(8 z3)I2{W5Hiz=<=4{R4Ns`@Gwh-7bc}aD1cYhy9j-iupBxuHSQp zx>AfPuOIek*>$+^;PSt^2tPC9?yVQw`dS|;95}yAXFn|nSjXu#5a5E(x>?X+DJraa zHq3VH#|n^Ge4=3LQgt1U1G;_k9OHo}S7&v(3hd}2MV&Oi2M)Lf{a3(ih9a)8zJ43@ zP@_M(5<(5ZaPb*J5#)lm+~oKxs0#vahM=Kug&+3M*~i|3tnw_9$>Wm$MdAhDJ5yG`9;@nD^7=a|5kvD zUz2$+XiVPo-qOR4K2Sk6OR#z9?#Z%u6!##p&)yW2y3^Yt^8^mu7~3ntyaEeVY=ci~ZP8p_2?+X>S*U+($Phgf|3#i=Yc$Hm9`TIRiFhu4%! za^O7&hL9J7UII#|PU-GB@Y%&~HvbkUK1GO7#X{T1wbUOmuoPI9$2C8xqO_4*EBi~jC{ zsS^}=!%kNTGi46CV2+xo%tPg_Wv?(DR84bkuYUAf-c(O2hJRnTiyzGsXwlH}p6heR zNY5fO)BM?Vu9yvsj&|f&z=JBw;Vv7Z!JI~qQ`OGz)YBsScvCb)P1+3wbY5oNrkKXw zO)=S`qvN@QCt<20Qgx!-t~r}SU47S108)DbSDTQkb z@f*3mbNdpgb@{1FIp-A}dfxqUN&!nhem_lNe`spEnW6{B^)Slw@7E9iQma1Q;UVj; zJP`GVPU*S2yB|e~iFSYRLvN+XAkM}O0@{1_^_(FGta*NG?1}vG`!aA{Bwnmq=*~fW z-<&9A!Vv={EKKR7t-e18vl9B`2_X7>48?%7B0q_Y1nJ%Zjt77rfj+Ra-=)Cu06jVW zqQsuBz*qThVHgLl_9M`Uz;*>8O208Z)w)Lk+C4M`0G%(SX-h-T1V$1A(2zH{gCUq3 zEm-&AM6B;ouZFx`9n~gb+27)3%0{Rd$H*K3ZGLs1zCR|-q?v6&QpPTj1!BWzcP1Ua zPSywvO6Yn}_Ssf^2bq6msr9+GUEI?2l=Yj>1DDW*0SLZp8tZm)W#NmI^lx#)V#B+g zHv=hf%hP*!bc!>s^?|XD=az5)$~} zevMM((yI-$0Treg)5t{M0?~cP&EZr)kVq)XJbD|02It}xt~9^n1VDnOaBjMK?T3;C z-&<6vDEqFD?t-7-UTch}e$Ux-B>t41*cGRDlBQ*^Qn9lAQdm6lOs*&RVV=HgF45RK zD~o)rsJG}a`yrE(wtmHvkKAm#-#?<} zBy3Ia8ovagFsSb#jEq963RwkI&p2>^aY1}Uw~Ps3(41%|rC9|yIH0Kq@DfZ>jgmY9 z#VP6;pVFV`&LakgzykpUgRHfE6b*ZjM)$L4zMB|=iy;p|TT{03vYHywyLAUK7H%hlkt6J(|WWV~_eyQwhd zl^rcfBfQcCCh~qx2xJ}Vh{`K@y-`m>Lf$poaEaQarzvr4rK0*hoxXAmZY!VPaffZ~ zoow~-EI1*b?^ymlJ)<^2#gw~K@QST!RY=rC@jKd;H1=Q)(LN_7L8s(FxG+`l@r147i2gf0Pequp`*Rhuv3BM#^{An|=tlAeD zReEAvO3v0L`9~K0_MTQ%QPPf#Kai?=hP;DC&LVsC+q<0IerBd-dt1ev&s__cn-@}N zSFU~sN3O~od=CQrq6eZDl3bjBxS2!atKgy3`0K_dD` z63i$h(B9m_!jq6mUbXb=U0N>5kUC5(hX8zR`5`MW)@QS!=f=u?P{8)Bg3uRL|# z1cWuvPB=GL(dP2wE7cq_cR52ciFrWU3T#jmtzoIihp3?N}; zWpQlmPc=9xzfw1oZ|sTWcj%sNY(VKlb(r+}_3ZdKq4<(=RZ~`8K;{qF4;ves^=sGo zmtT(F4G}VsSLhi)hVe%l%wJN9+%5LTv^XoK+_{3ELYkvMV&hD+hSl+ZIG>@eE_?sz zIIAB68M+KkHET%h?5`j=($W$?Iyln9M|&xC!v_;E5?kiocJFp`8hHd5)F9iw_)irC z71h7|Ni^}8PD*cwwgBrC1mg>gNnQg z&=~bNGI%$yAJLBlz~jHJ;P^!?LoGu?U4uGNCN!W=Z$koPBVps$`$(bHo^8xcoO%n1 zmDZ2iZ`78Rbu`+)=~0lYH70A?(OtQGCR&I=*3e44Bg`rZzOf9)x2HTEE9=+axh?uA zU%?$!m||ERTJ;&C8=41*05XN$zFmqv*3=%q zj^y$qL#F&O1%{_HyUe5BLG`H+e=bP_7^WbLuZoDdSR62$t)5l00DK?U^ow}2^`RlS zZTLpcVb;Po@eZct3>F_um;XRz8uvv{Q~cq>?Hlj;`U>uU`=H>nABAk}%6{#d6Ip*V z$%eYMwxYJSBH(l5B~kr+`*X3}^`h2aO`}=&$gW@9y_V#dDn@@|Bl^Yk<|;c!l|g!J zx=l@%moLM-1aErX_;b(1yqhe;4&n4|V8wwmbZ;4~i>#id8NWW|n7PshW4EExaOEtW)!n-1DD9Yc3z(+;EQT4 ze+tgad08`)EUF~-xjCn|+I~&f zgpnwV&zsOEp)2pHn$?CXdyXtS4)qRKl)7&qeWQ1#5!cgePI^>#edan-FM^w<$42Se z%Hk!O@M?1rhRwQKoFd-fsGtxC2~ml* zELKq^?f{AWyw(zr<8kuJiaSIm&tA#jC>V!KH6x8~(Zg{C=hL+s0O#NL@y3 z^ggZ|W^>i;6Eak~$A$&yz4KhjbBqi-LY_R)PGr#itK4a#rSmUOe|`s51;Z-h-~~e)KKOt{I+Jl&=((JiSID|V#;qd7-@ZTc-W&kCnqv%}jjtmrY)me< zE_y#3W!*+{Jc!#j@MT8wx(sgLaCgsWuhR>EAE()-Fe*~ZxAf4Al$3ncRQ_B;=I?gL zE?kFiyAJdkC|W@V!lDRf+3Yu=>6LUGMAwGnaCFpQs#+15R{+1mWr`{Pg0CQX1Te~V z*d_?B(1#DhE!=c18N`eR4}=(N*RBOwm}~l&H2&Gr&R8kyp4@QT_nElV5xY*#r`3|X zWv;dP8MJySP~|)ha!vjjQfry+vQ`Au{6b(nqh&bejDC?@wwa4<=)V+(3sJt>Z(T$ z_Ky46)0^Fr-JFQCjfJq}1*dm(BV;lX;p&7vowiJ*ez<30ILOz!E@RKzke!e!p@pklC@0&{ZRtz=P`@ljYCo`}vc8=g!jm(V+qt3Ers7=z-aO63Jd} zVEavWOl{gQhH$KtR!~u>tKQAo`%v!_?+NPh-if?PX9MtdR9k5(kH!SKvc0*uTDWO8 zL1n&fzD0K@9r(PNeQpx{0uE)5SRI7szC+{(yE%-?LUXWCDNqDILtV?(nv={Gr=C<;^eQ;WdyF^T7ZboVOrkNY!ffj$QlVz!q;d z&{Q;4{OuvW4@g~C&+44Jp8k@#^6{Ey$(EV97cRQ?*6qz}u$#Zs__cla>P+pXgt< z4H8MFA4?I;;tP(n`tTb_k7=JAA-p2L+v%dcjYym<rA7MZQshxG*yGKUq2-- zo%K+&qXA_8jZL3Aw9@v^J~5d4rEz`@95`5B<#OHp+P&cpIWBaewxk zQO%?$E}ucU4yu@{M^KCYerJH09;zB*XGqVtTIO^lorO-qwh-+Z3DVd@%287K+S`+$ z6D03cpmGviLNB6q?8HH0C+R;h6MSv97l-wuPJdkY9vNBTEuy%{Tr{S|1S8etpWE}C zi040*S+b?0V%2=k^7Ha{hy4{igPx;RXI;Y|DpRwIuC|6^NED4c5-wVFUmPk}_k|`g z*UFgWuwc;jjw!#zxN3CicFFXcV3bmh(2oUQkJ^M~oROoDBFT?J70Y8lK<3NYyMep34oQxt067y5W zSFfapzpZ^WA0l?YQZ)sVb_PxY3;~FeE{nmODz%E6E`;%fhpO!U_w>)-y;tp2Syh(e z_;d1Qw8Q6xuIbOSln(AExHp4xph0&kmo$I&ySK4tk;OM+z?UE~pUMzs zjZb)R?6_j{=$!R7;V-^LZ~6p(dv3mhG!0y`L4Ps2fSKL{J})kx&o&DwlHlJt&1AV4t2Tt(2s6B6Q&S%j12}mE+Kt-nJohlJI!-E%-BT1W>-)P=((P=hXRoKtCjX1GOj!?_hkHte7jmK;ZykIp&t7UZ9dUty;e9-Z zmW{PdYrG2AlPA$oPS;j#XeF zBR%DWEs_)9euyvBZk;7bzny3MP`395BLf~=knNJ%hJjI}9V#MA-%R-av+JO|-8HcGOf?_pGj7#D-+RQ1?>?T}lCGa)n){-s;(PqKYc| z$^%l;0409UK zUxEuPgX^lh1J{IK-JgoOL($G58YGdWIwJgje-i2512p7UvLka>epWRP*$NhEuCHzg zuBnTBgWz`1KmK<)k6+Jc=kv@Qfi(k%oKM^!_|CEwEiOtidRYu7pGBH-R;)F*<+S`a zLisD35Enw|S+X~n_?@(So9&*lL(B6q#O`-kn($sPpa!Ho167?nXdqCmdwYWa=pe^CYuhAFj-p{^lU!y23wQt zSkEQpl?lU93Da>g)e9W1qztZD%19w$6Zum{no}Q3;$#v9P3L}%Wohnv_YGG-^3>+^ zx8(z3EPc2=5$~HyMAR1+Od%%i6W-L|3~04oDohGtBP&>H`n1c4a-uzjpZ!_(wKGrn z=4KWYa~M6A8#+SYgq)5|hzY_&PqVtZi})O#$uM&}OF>CYzDsvBG+e{3YNNI=_IRz& ze!|a&h8Kvd(3#l(fdG?pzdF*%60~w6M`voGA%EkxLDf|YgydyKE10G)Ud&dOf*Ki-YdS;NJ(~(-Ltn)sj7~8{QOGfQ7X0r} z{!pnDFlzNYQ7&d)om$z}QPEM?l@OnvAN1xF)s^q5PO_*88(SKjW}{1;JHo|2^L_fX zo0U+mc<^Y^8#$i}rG9ByrC;f1;x6(U1lWSZ{Z|9InT zbWBW2uuSEp9oO}VOGE=pxvQP0+~(79y}{ozGYTx3JpN(~W&6zzn-#R0Z|EgrOO%%7 zy?f!0VRm^(3%85GacB%fzVHjqtz=M(Mf}*DSgtipEldn{x_j>~ipARx#K%6AcMsv+d-6zcfB)ROiQh%spSX@uNOm{Kc z`kj-$cTXy9x2rCXljzs;8T1Yb)~{KlKG`N)78F!&1eG1Q&p=cZ(2^p><1=6B^F28+ zQR33^ELuu==%b=-))KVS~~Cb*JZIo9)R>NUTAhi=LB|R+P5n%E|-W?d%6!CmD1Uzye;+0w9(-A z-7e12hAQ8p7ne|#rm|wI`EEA-h~4LA?GNXBys2lhM^DrDEGT~4Xb3;YtiIhd>Ez;C zN@Wm}bY(QXMf)Ra`cVm{vb__AQ#h+BCuF8I7_tBcw6S^fy{?R{II3(Wpq85p;fh$x zkHMYRLS*88CuUcuX_P6tBzmmZ#}gEe8RYZ!#rVCfJt>tCdnYg&G+COBm8cKR$FeTg zY8v%B{jIf#OrH)dDpZqe>$K$y(x0!+7|<9v;G4fr2+<7b$B{{B<)6m~K4CloMFm{! zp)~rM{c#!|Cy@5PDR~0wJR=2%LCVelYWARU>!#Iz>;K*CbNZp<57`v`n%ra4U;nAq zL^0@jWvw$ziF|p{VYL43c%+G(ohQlf7jjMq46oxb!%loAN)o2}0|^(wsmPEY9~@~{ z#dB@DVM{Phm|6+SRi)}57|fXAonk#k>3hUO;~s&o>7TK9``j4A{Pg?}0WXumAu%hD zb^GM|gVye}8-!10e^qave#RZ>wXvL3v%D**&N_oa=Nt}J{ASF}`Rg(_(7TUsp&tdL z3K|=GWDK4>`J$YpMJ{r$-t3tjurW$$lIC`wGPD%jo~%`~F!!p3o(og!mC?Hsz*^Ol zexYDQrs^8KvvR7fRD61#BXLu6_`sVIQNlpPv0GtP!o@t6o!W0oUi3G}`0U2=1Xl@J zW%hZILpBz_QgxmLQ!4(*An}{mjU(Zx?a1r@dD~L1Jf$MF2PRc z^P>x?y$GQsxedsw4z%Fsa5w~D+Sb@->FE@C%|{rO51#JsMaJ- z7-@2YfvGyl5% zTPt)IYBw6Qc9#A_Lp8h7yPQ&@O4pK-6Xp{m6F%|5`v{o^gPT~T)9=7s`cF>8ry@He zR7#k>j8lQqx+1Fbux+|&bIPo@{DWe@lg!fJ9oe&<9cbpZNgt7w_PD`3wV!r&<$c+s z*9rVBw1Lzq=ap^~mSjynq$v!m3bGeDuNnhb}U+tS|gBYc<=oYmt`2)OcmFVlzeHrK~zZ#HMsHM(3_Z zhDo5DktVP>ss=Ngm~3`09KvnjH{$au9Z3F7!|arWELh-UefW;KSWUmfuGZS7e-we> zN2{~-C5>knZ_v(`WGuMrdfom&Rq~0)`NRA4&w$s>9$&Mli#6Gl4ZeC_ptiXtESxZ1 z^IK&{_%_7}8kfX1LT(>*$W_K-15!nYh55uea_4&W|6l^T?lzEUF(3vnFenvOi z@YTMId2vJhi)c^sSeTsAt_z#420cH&Rjbudbn~B{S$R+IAI+}T+`gzOv)@h6^*Pl& z;zbFIe-Ku;J|B9kx$>!zQ)OvQP(>fP$F(y{Mc#Ij-5JCruRU%*4GMJ)%Uh(#7kdu?Dm9rPA=aG{^=IZ7d!Wc32MN1@h)ul>LPBh_ zHo|UhF*P0@8SpsxOl+{9k~L#LqgVXTB<|sYO>khK*+knc_8>+t>yaZ+kL0}TvOnoK z*#pca#ab7{SuHHA&R)UZ$lg(j+UQ`_GiDL&>wgHA!aX zKW&`d)3)>Gt%~yRW#4xy*mWLsy4RjcFMEyPLAM<9n^*4U(Ub9aG12u{S>A1W{bU7>9~qxNrl~Hqd*`lC>gq>X_xgCQeNv=8(x&&YCn&^o zp)vAQ)m*`y12=X-^OB;f_-#Rb`4dCQ(NjgwhCUzhe=J5gdeQT=<;D}sLpSB?)+r!j zVPg~W5GB7f&$z;JMQ{2#%R#NO_rIRddERYH_$bW9X0f@v#PD@|dDTZtJ!VJm@k>to z7(*+cywYzh38iDXle~!3Tya=mnBDrUgs;yfo5k{sUImR~g#L*EZmo&t=2(}G;-`U?JyW`VPaU>t?so8a4%|;U>CaQ0_jg>1;M8uOiOz|X%XzFx z<69k}C}G!J&C*Oh9{%99^m*qNUZ(@9ulMiRU&wq*Z!iVq|DHei++5p$Yj~b zUAS=RMcNxlMUBL)Ce~oWnKQ7BC&cW#LmGUREOm3>iGYQ*3Fb7Wd1+=T;N{^?{4(=3L}}ZW~KCJ%8cg z52Js^@(FG`6JHPR5Zx8t8tn|#mSKrU_+yi|6X`dOr`{CGy`S4>{F?8xZ}`#5o*bJS zJv1SqtAR=zd|wdBPY4<7N?H4pyZhD9@P&Iam-f%!P{&@NA&rZhQ&XirCb0?bapvZ^yg7UU zd7)O?^*;)#^)xig=f5D@HMjK3BZ?{Q_b4eipN%`o9jGKPZ)xWI7cDkuNDOdqHMPR? zo~h3q%5zKi&0yW$TyKYc8f3c1gWQyYjpgrbGZU*cM}hp)ySk{M7%6%3z5X=&*G0CQ z$B~owX6*z#~@KQ)W10F9lzXSJ8)^yIG&J}!u8vaYzu zda0^j%Krl+e@bO@N2#aoZ`+WGOI!o*BXcd+`p%{e)v=pxb%1nTTQxces|hp!J2pH2 zc49&AryS72U!Uf=LcI#2L!kI4twXmo%^h}%dv8p>mkwj--QQmOXYEaYr{7!IGWL@k z8uxw4ts?KAIX!+GW}FsIMf#&|Y#E*(HIxbT2fXfS+AT+18|ul~mCK$Z%p)D+vGeVE z(pAWXkIsl9%C+8?%EuM+F}lx-vZL*b{#}t4Er}>k-RyWZ1!t|>tg__H3PpBuB$DSz zon$#CjmHdc#s+K;A78|7*nT2%LbJ>Dphj06-@U{LU&+%RN{+&+V?TsCGU__Fl4{0G zil7-Y9QgWfuUA)P$67NI`!*zT(YSpm=25~KIR#HMij;Bs}&fx&*?<6V4z zzjuY19qvxW@E{@Ca5L3Bgen12kfizA*H(2&5YRsHe$h@>)m#M$EYH+-#z9apD;rMGwR?%dt{sG$4NV&kdd^$+ZX?yBJrl*xLVwpEeh9=eovp?x{kwnaQ*z)=Y3-3}@l%7Ib{F|*^h%trtfE2GB3ObgB zTt!JCOg#Yx6Yq&v;!jB#V6z3yf`d-#s8QmDau#yZa&qWWh5h2@Avc&a5c{Z6zW~Iz zZyG*02KA&l#GaQoXq?0o!k>2F@1b7G!%Cs@rZcN9$UhGuAAu8M zE;M17y$QdN7_X)8bZ z;Z|4)1jWS8TaZQpRY`;TC_@G^L*&DWDUvj(XEdN=GZ z4>(^?ci^FkQU=<3o24CUlTn}$6u6P4lKX6e*g*bg+f&XHvqlH*zDH`mF~axuX`at7 zuv{Ub*tBs?Zc43tGK7Fk=KEkBHbCd zrLn**qH+~y@fS$h$H7a60yEwt_|PnE;#PSAg~W<^PT$)tP4&_$-=`BzDk7zkY<_9? z{q{MC*ZDaek^Arc8tefxMp6tnBOo`bitr7*ZEzs-U5*R!dfEqn33EQX&b9 zcS0lm1P%SxkT{&HDs{&;xLTdlnPCA%T;Si29U>_fKo#w8sUygAb((1)8ZFiF^Z;^J zS6)u^D**cuV2%)|712htINssw>G=%_vcso^w0@l84iqQS5_)V`fjJ>Pco3@5C%aM6 z|5T+8_4Xw)2x-u1MD!v7lpQo(N@oJPTLp!Q1xyBVKlaY?KX?);xTC9~J+7v9^0@ZhrO# zBsouRL4XO$$sD9~1>lq5U4vP`s-rUm;@|=pf6%p~-4ym1=vM`z(K#AmIB4mJV)LO8 zyKYR2DB2!B$Hd4;%WJ3(Oble}7~VwIUm`53Sbg)*R1X9q+Ov}3j{#~XdZ!PQl6K`= zaFD_02*x;eE2slo&)h+69$by}rCTJ|BprLU9zt3Iqt_j88QJiQD2luNDCk_B%Y4$8 zhESV>ABR9~91ccK!!c38#Lo!O2Qm-V&~8pdRujr;1BOu203iYif}a~i1t1)r3k4dG z22EvUl{g;YQ1E1=4@FP%An`Wp>Cw}r$eyC5l_20WVd;oFf;?3nzr3t08i>-sRYZ4B z$mUv9qjr8&&o-1?&k#3$B<6p9YU`%4r4mvHC1dZ(wMg?V;&hCzTi^EZuS5vUwDbJ0=1V{ zhnt6#Ll#_xxC0ApRu>}OKqP)XXZYfUd9O25u=PyUAf-WPcBB3sO!OGPpe&(H0U(gy zK#FF7Xl81)jU=5^cdL9rd$dI=;L|;sdoC^3x*NqM>zylew=j^VuAPMPlxTb7?G5h{ zRaI4egD33%&@~Zp0~?U%j*N^zR|DBSAOhlF?;mMI7devA^2P@b9TKu#Glw{`Q5$f} zjv_ldY7JRAIkW_~wzW}gVH|-VgmMui8c2o)!v_(%4ayNd1q8^$gSnTErfZ<71g(*^tpN0n~t_Uez%J?k#*af^}KXGOu#pk{Jp%R+D4zh%j_1iF_#uFlG#bp zP!}F`!O+N%#)^0M!Dr0cqvX&5`d5HfJ_{NYfU-U1yYuR{cgkRtgI9X>DiQn9nK38( ztAZH!73+c8XR0Y7r2+oQ!q{kI%^g)@81#h&A|W?-ca`KP036>0FaoC2M<};nbN>%q zQY`RO<5-TI#qKxP&y$-kTN>v03?^bH)&6&A$P6F_Od%ulE9g>TQ_lZId;mHr1kYZn z{gJ>M7+>hA>>Z>aLQtm7e6#Y~)tb;d@KkrA31>qh-}o!KzPhsYu`Zlovy!4j*iyFU z_(OkZm(jF|4v*j*EFalfd=@izY|5wIRRXSlZTX^ z|8`kBebOm=D<-Dp4Dwf#4Ox-<{{2`?;ikD!SKLPE$_EvpO>^lN&#ID`lxOp_<};p; zx5f>5wm^K}&uJTHrMZO8GP34WK2pgCs?axmOOM#6*3wP^hse5~N&TY<8PS|fOY+E0%a5quj ztjb8M`iFN&I_6~X2k<=|ZBJ<2cTzq$?vnaMz_CXfH=4o;710A6HzLer{`8Cy=^27| zbzaH^rxt5-zGqml$?uxJeFouZ9Q^LX8lgUtdi>Zp9E@C80-){5`77U{-msj8C? z9#Cr6>Kd@WJWEC<=NcqrafFTQ!hGwf=ce_-%=fdwweJU7Dkjramk-t5J>=k0_Se8} zp=qyViupgY=WJ#_ek3gS-ak$T@tT~)x?@LD!BVw9r3!D_NE)ZfJJv5n>#-3#oQ8XD z8~Lx3c7(bq=d7haY24^ZROZXkeLqpND^8V*(Ejrrzp6LQ@?iQu15?zN>}f+&zFt{> zrYRvE{BBoGmR(Pwt@4r;MD%AtHu3TIx4@`H3kn2DSyYsOkaw`be;Ab~;KQ&2tH2Ob zSXd>hxog#O+n=wke|w}yA#DJhQxN@Fgd(TCVXQd95 zjDt2N6Qe^x@*kQtIl5vB_JkF zuWx{M?S+Bda@n5i@G;$-J2#8ci2Etri4% znuwhE%d=tf9odsTznPUsg>B=FtZ(dV&SptXKJ&NNm3h5K?kJ=0O!!k>?jwyq$%31M z@5#-r7QJ>l`twW6{>aHq7SW53_492=l)gm{c)UK=^O^M6`Ak;D{C`u6tM0eCCc7`1 zys>VZX4yurywLqlm*a+MNasLoXUdD|_F?-jGS7t~;&y84jIQmOR0ClH{*;rZFA*v< zIK`I@1T>uvbqf|Y``ag(X&inJwB3>&j>-!8(c_wo=YGo{AOhF8in`Y{_83434wnxo zBD_tF*>A(SqQHhZuNfSucQ+m=Db?j08yb3w(nDgE@Pk3`>xI<+45sNr!O%c>fqz1* zjV^Yqm4J_eLV4);K9Yu)N*Q#K|Gu$No_{f`?!Kv%Y^ArN78*mj5qSNTEsd_`Z34Za z!4;19k@ zip~Obk1eC>{XA7Ec0O8gylX+&=F*x^kucG4^yTmG$t3&SMad|__RC)pVeEfU+<75& zUzC?I+0&(PEFNE|nPp`9J1en8GgakhJAL?)$ZmVbw^i!TJgPJPjcA<<_B+~Ias|Uh zxTi3z>-$GBusN{_1sI16w3vYUkGTz>K|psS)jxKmihQga~Pdph8Uz`ZCbT?S8u z=Is`#$uZZ1-B0lu19sq^DBd(;-2TIuI5F*UQckKDtFNxUPwtTw5`}^C z9U9Ei{vk@Sy`%?DTsHo@cC#Qz04?xYdTTPj)FcnlE{V<^zS5Q$`A%@5fB3H&E$RLO zPfKqn+-)W|yVpAPAExR`40>!SAk|UJDmt-zdUu{xwCL)Ca(5<7oz}eeN~Ja$9+EdTbXiq zL7py#E>j5!n7yhe!d?YJ5%_^(Zvs}?W?gnNFM9b9!!As81v(`soF(ZzKqGn3W_|s_ z|Q>4W6FJn{$W|74YQ{l@1?o@N||&w%o(|35)QH0QO)ioew0c`H}xy zemr615VrSs>^@tq{C4`z;XIjSHCem7$*R24UQQb5G~6|?srRf$HTqahkIlle)(<7I z&fN9BHf6b5MbH(B7`{(#7!E}q=2=}?rd{KHNayi=llC^j;F85?e@uwi##ODSPd%o3 zIO-h#jWdq7OKL+X|DHcShSG}~%>vw8vi}cGM6&RoKMn8RT|rJM@L?rfJXlYkCZRQ? z5oZ29nKQIDYPY;r^HVtLWUIZHgferTCf1 zE=fo0Yup}rYO21K-I;sD`2DS@DmNj|@76x&4j*VLw?3J~*mR!Gmu-pu&Z_T3f{D(p zC*$8K3R&f`e9Pm+ILC;C>Vuqrb?w|Gud4om<_ZIQ_Xvi_{V?mJ{L7hG_YE)#GEiuo#Q1KAIomLJk$4v zL9KOT)`FjI#(W}VR>)nJV_rj^rz>P8R7lw-dLyMPX?NzSaJ9O2u4jJqgX-6m?Z1_7HzjQbS$cubea9(V{8eQ8wQPD@6{rd@V?AXcjW zkW%-VeS1~XG-Bu2y0p@pB693CI(!n8GR(h8v)tMKtxd1wK@~24*In1lg$XVysnjxWS~ut{a2Q|K^lZP`X*EUhK9k~w3?vGTUr zF-~~ZysE_0l9_4qO0(H!?#!}6^N#UyeIc^(xrG3CmG|e*GWA?(E)qanB+-R?6C35KOiZBE+l&9vi*cNyt)hEvDC*2pV= z__blRb9NwgvM+#jcKhJCTVPvg!sRFB3w<0DKZEoq>AGx3>Vs@8l*y4r_`r zKC*R-%}$10vIVg=)LVR)WO(`3F4wiiuLiya8FOnn2eNutSdn8zo?^tj_9f#CY+U=@ zE9?SzbsgxG8bhbEZ&&E|tc(o0^Z0oC&>!7nr<}nREYIhJLXBK^{lOA@fZ{dnYyQju4_s(t6hvV3-yUA z?Myr$Th9>Jvwm4)?ZglQr-IJs&gc1)27lVUmt4>CbGju!XTevSK=FpSuQrwi$&vJ? zDXJ^Pta+_VeHJ@08eA;RJ>2#qkZUt;eNX?ZQb1$^>-25S)*VNE3NvdxYiJgSWed8m z5l-uOC%@=s-V~Zki2K=6{f|xfVwQEj&d8|2!Fk^_+4#k_1fBVp2xYdt*|D}tT4+7q z^w}Z}i&|)8q-n#KSM}l+kro`ffr)EptttPi&BlNHJ23&F9y|-M(X9OZvQx@21a%xZ zn@T0MQGm)z>2B6>gBw{wM=1pb-3h75 z{{k@08?f4pTF=NC$q+_#-gkcPEZGqGb^J3zFrg4O@4FDrGS{fHqbE`*1O|#PqZA;> z-jeg-5d;Aoft|ekMcM*EWH}+G&!|~naAM^Xzu}&(wnVDo7de{w% zHF=a(;@-kX>Q{8p*Gk|hF6H>J`)BjO+iGL3wi8&A9jZ~I<;v{#{^C7%)yR`e#fNe{p)V0akT z4?mxArAf#owc#@1W%djySeB#v0lf!Dz2zN0HyY6o39AyX0^fIS&cUIhpN@LVC9|xo zSRm)SmD$Lp_5G5+-P}u}`S$cR;_=7Nv#{1k z4F-N-+)Cq}GQ@MIRF5Q1FF?{eiQPpc#WW{L%i~9@t7k6WyXRIC6P9sk^w-Cso;WCq{0JSBeg)W>4BLgEhz(Xr9F!5+`q11zmXm8|#!x9Zx! zjqUmG^Hu!BA{8}kbsqRfFhm*HvAq&~@?UL?WP+J~D<^J;-Y0(@o@eP9+rK7A3LC@| zwvO+3#C9L?hwO;VK~2pS*K&tjF?n%6>&iL@h{q-IyDcet@Ywz`7*U{^=_uG1RqE_H zinQe9$c@!15lcG8)W`l+$BQwfR3c=kl+d0q-1^Ph$POk4B+-bCCbP_NeZmWlQt%QF z0MR{XKguNb`t&mXDi39=(|VUlr&3aT{r~$~Bu4+bo&N%Aw1L0L8y`su9wQ?w5xWQ; zMq^_ms2?5aI*$MkLO!ofi4jK=$UeARM6;bVA@bIl^!f1~-#X2}|B6^79BG%$wwX;9 zzuG~x$U)x=(0xS5)e|R(Hm5h9g2jpd%r@u|dUfpuIEEl_qzn77noc*Mi49g*FNU+L z4se%ij`Arxu1vjt>7vGEws{N3ELNnVrb<$hXnXeTvm(tZ>5K@mQ5Jn#T|8HCqP431 z$e-`py}P^n%;n23kbxwm8x2Wl+LbOWC0(_^AWrK^I((s%&+27*Zdioq4c~R$5p6wF zWR@DbI^zp0<_!ZNAZ@F!DQ&L%-^~y>pR22S;xyQxl7jP(<6fRUclq)ZQs}_Ai1oSs zy|X=1UydUDR|?NNCsALT0>k#~uOhdFgsgNu1B|B)Tmz5Cz$Q?9t=A*%aGWSjkxmKI z9ReZXQ)OAb8#T5$ea)@q9Im24>1HMU9wLrzRtpKkKWY_mM)p>BA0-s{#9pQnU*Ek< ze85R(jznv^I?!^7!`kZtX}lg;)zoi7C}?%Q)qeWg>XZe`$4f3$#-inZ&|NEUbb z=<9F&vCaFQ(KSVsM?#EnC(v?)oSVh{=%tmxv520`%451RG}06?VKRlsMso(2ey6QQ zX=f$Aubr5q-i5PBlu1myuUH3j;vKse(~kZ6Pu+nH**!DygJ_v|a^n3LPAaw|@+H&M zPQ@l2lTViPKHrYtG@my2{F3vxDV*b^fQ6sHs`2Xp0+!G$(UiZcyjPO2Y@aNCQ)#F>h zxId;{tMUC-H=~|6U}_1HPmn5?pr*1IWoILVQB;?1Z!M>f5q#tc^+0&tYIFv9PQUa$ zxnGaz9v+R&h>{WFGag9b4rV9y=ocy^*5H=nI>+)oL)jHxx%QBp{%k~C#7P=rCjw4@ zl?^BQ2k;03Q;F<~R`AmDvdOa-;y0|{f458g6y?2l&DUnO?x4b~BK*1h(yFuQ^<^96ivDavqri5L;v_qj11=M}OyfMr|k2t9)WU#+L)A z*;m-E(^ZwePQRj39_P&YzH;Dz(48hvV`D!~oeDR4E)NOsWHU9+1!DE(GUb44C%)`i zqS663c)_?$#aBc`WY_i*Y)FY#Gf~_yAb?GyADluK?T^yY-R75sV>bzqy@Ri4 z#v>Zg8xWgZT-L(yB-zeadQU@PyqJcC+`FKH%BTSy2H%$R zSFfHR)|HD(FRfZi5RW6t5@hli3C>6jG&OghVv=iH^%L+{(yphoj8+C zU1q*`UfS2kW$f*L1z-}sL@Oo-0w;V9zz6~IDm*;=!-s=(fGv8}48ZybZE>C0A&}Vr zj;^VmHIm}aY!zBx`5&rrI9osD1i6P8qsUf(P$wm$lgeQfA8(%es_^*n|2x7qtzEbY z*Cd<2=w>HYrl$ALQTJD9rqwBJ!J)75yxGE~TOReQ`s86+s?NE*u$a;>AMn4`aN@t! zaPB;>oP`O`C<3j}j9DIl%;>%jG zJPF2`(H_dEAY67E$o=_SY~j+zmOphvD}!vz|aERKs9&4<5W~Dup5M)774Geo*rV&SO^ImgQ9h5 z6&3hkr9COx?o7BrK$8W^xIgNN8MeU}SCQr?%)xhC1xPNeI0PV=O!4r5hldt<7@4+p zbSQ*1)&}xS;sZCp+Jxo(X^Vajln;rv@~DTSYYWNr9vH%4P>G3&q0ovnvafF&K#8b~ zJs*UP=iGfiHR2`cQno2b6qQnCPJU@vvhWULnG0hB>+QzcY`q-_W<7j3g*ktgo=KC6!eoJVj?WN}f!p&4V(M zjoK2x(}U7*Ljgyf2-!w4AcWscA6yr>8WLn-2>uJpoGeVa(Bv zbK8px{=3V$1!H0iE@d+_^PFaQR2qyv!&alM&8*h%3Y5k0%!Lifvs@U0V<)Goqr(fA zZ$P)ONd^})M^X9y{c8mHxg^ELu2cwbJOzvZW<{0f+|y4iFZS5s9?(LP=2%L!$-?NU ztyR}AfB_3^A3(`L|3Bhw7CscHPoTZ<0$agc6Oc=je}PJORFIVQMgkJ@V^}rICos|> z65nbcuBs&x3+#gv;t5J&2__~+TTv5$n8Up2T0KS6;h(FotGfzcT-@`UNo{Sdt!TI- zmaB=npVZXcM@h&mL%9HFqbqEXCBS3@z~WhMJ?@Stm(z!@!aoWgwkTf1mv1E|DQOZm zLvSll%I87@n#t<-%M1(y00z`0fdb)u*E=8pyB=5t)JdJd2rLS0t5H3cQ;4lR?vg#aPVZ4s>2s-F0#U5 zOpE&tTspjFtkEV<93p_J^RGD|;{R%ubl1(*RipQYzZm>ugorg?qaipvV~+;o=Z_zc zP*Y>;XmCedyemW3Hzy9~Hmo!AX&3q!JKAO??7T5ebq2u+Cp;<&mO7D{nG3jK@Q*7j z5CZ8y&b^fcXWhJL&QUWPtT@_`>4Z%fo}W!Im_$}5Zmzg1LXjg zI!HFWh*L71=sXFxwtop2CwS1}kyVGmfbhmrm~v8)i&>O(98SxQk_O+LmX-^eN%D+> zH$m65btmL84@IFObkFA!FY*>Zg`=#~BHgwr5y+hYuQ;p-%NP$p`(wbCJV8q|=A8xD znH|Rp4W!c`^%-v0eATk|KrrB{SiuVyER^#{yJJ4s+ml#XtrF?v(b4BGT@qvUEdm4| zAVoNrSogW|IDsCgjes88GlDUk;o$R;k-eAX1*NR`6}d$yWnis%g8*H_!Ot?jE+`udYY0uci#Kw0D$?(WXMn*<_o8&hJmswcGfdU6?LO;(R5GNt&?b*86 z@(r?reFd%F#f$HqZhbfb^8AGUN6s=Xsi&`8;_ekc8Kc~$#K^TAoA~wYACK#bi4i%v zxYR~>B_H(<_VXp>0b~%+eLpp)H=0~_&zF^yic^%%4#&?9w2uW0ImEm6tAE(y*5|sRic2?E}5Rs&(bJA7U)!`m>K|c#{#WI1w)))5LFhz|AavlU@4bYJgMJ$=r zn^<~aX9M={CZu8Voftk76BFXI8vy3=8U9&^Y70@J@Y0_h&IEF5YIuoO{%1#9IQxU2 zVBB9xhAJ5z;2tcEK+vh>VxbdSgb}##rgh#M>@?>DTuEJbEx(htW@hqL9!$h;!jjQL z6v*jeccB&SAD?*}pYL#}8*q=0R`#z<- z1P@et@~VLvVt|aBJNfexFK-SKH<-DxmXwt-z}o|eaB(x}9AV7Dw>k`e1N6b!LhOR8 ziQ$u(X{X-<<4{;g!pAH?Wn^rLcVPd0GZr5mJK?dh)kYh=`o1sHpsy>*~*Bq@+NfLHIsk2)q{b zr}xjRCn9}u&usl9z#Fiq#j^o!HNs=R^6^9F2ndRcuL0Eo;1N8Iuna8XCbevnyVMHF zmH%2|Z0s|PQn2ws@zQC4GccG{ShAqUk&S9DD=*LdrPo+ea+ZUmxu-`M#URwho$&18 z<_f`09n5`5O^NCd@Ma<-BatdRa|GdA(AAf!-`?)S0Kw9NJ3_P0jpu3Z5|YrCE2M8q z{_qsTz;-LUW6uv!a{}f^#0rUt`dI_xlrYZ$ddj5?p~+(8H4^(w42*{6W)3bcsjv63 z8|=Ek34+D`cdsk@GOVuM1^)u%s(86Hr^?n=Qydl(?Py09n^Bw&-`Ap|BKV-g%_Sgm z6@`BAttKIQ!VIpJm@JFm(IhfVaY?c=Gid>6f4h$&K$sa!b#`_`EGrmrz#9_OH8ik7 z$75AehSO_y_GL$ZtYYxKgBRWl%rLNJ?ZuB%MC%j<1*}DVzmid5rq{QX;kI=hEN+m}Xds1C>=SUEz*A(ymu1>B(1wH#$N4K+{zrwC|B z7n*;_KO$k7k-x`cQaxk$WhzQo5AkTM+$S`dYCA>rgIiy^b$}C0-oPH0PlRAihjZ zCw>x@j2JI?QMo$geCwNr;c(#42a?0kG+u=<7%co?WicUd8L$8O1o;C<#vjJe(-CnD zRYa@>NS=!Lz@z)=X6EJ&9yq{Zc^A^`ua-aAMY@) zJbNYoaPDZV`Ym+vz`PYGuR!cVabEx~^Haj6qG5+N7Z>uDS5HY4y(%Pb6KRit41SKE zaek!l<=Bv)+u^*~G$f%I(}Pa<9QEdNqTv(L?0}g>bNL(%9A$8P=u`rR9B_5T6we=b zaYT?Q#T>M3yNw2&=3?i` zPP&$JG4~ue#b@|E`GJSZY5GtW>P;@I!Op6uf7vIu#ck#|I59fD!~B)cX|pgO{E&6i zgWdFJ*v#2;!JjYl3KxB`kSe{s%hJj!@NY-sK`&}XdU{m;N9R{Sexn1C-!Lb?hUZrQ z>@w`1RDI|<4ugB!mb-XL!~qI^QTo9|;xdri9rE(2eQou7En-fWmQQ6;_=|KLn@S#0 zq@Ztn+2}!BOL_VF@L2Df?po5FcA~H zr7FGV{#Z`gfNNa5J97fca%|?+VxKS(>2~dnbmH&5u3^Vyu9uauB+XiUA7hqnd~1^l#cY7 zTQOp-46pLqm{d`iojt(lAPD_fhjsK^+>%O0*zM&TZL63m4<3mvRy$Q`J&rClf7)-swC-sSRNiH&MeIP-LyM@ZsZhNtEO| zpIPggjEc(7{IFYLf0OeJFP=7XFxc@PG zN66gn?Y3VH>zLfy;tPI&eA>f9>0?}KN(SxK?9;@=9S0Q+=fBFolgPo#>ByP-q?UDs zhN^xQ1>nh6V8aWY4*p{2Zo4lnSIE-E#>8AWf1k@hfYp4AGOq}Md!%ww&)nZZC`B_@ z2=`dCG8Y)h-hHqUQ;M|3$uA!m3|e6*w4mPO{HDN0;dWT(9uTHr#YB7g2}zpnSJ*ST zSD-_O=;g@H!GT3LZ%Pi{Hdn4ZNNU603GIEXr%samk+{62o~1oQO6q#DEwQaDKFut7 zc41ymP4xhA-`cFT(f4gR+=T&iOk@d?Pd8co=QT=6_WoBgs zSGo1HuaCnAn_6YPilI%7^|7&>v`@dQ8%rywNF8FDBcW{0TKPT{CA6sbeYWS6+nV;Q z1CLKWv6`u?o2RxIRoAgpneXcQGrE~0&fee4ZHrZ>)p~VwJ}BGU+j{^TI5>KjOx|hE z|29a;d+G;LMVp&XdgNfTn zYDXlB;b^CRPQz{b}KQVN6pPaDqaobMJPQ^REh9`5fMcK>t^Az7!cN7o69JSZ7 zU}H1WI`m--0z)C$&F(i8aL|J)StLne^>BB5-tRAMybFfvZX$rw)@3oGfkRv+>t)=% zY%E;4xo8=ZMk_;W>%aLGq?I0B_$f80#LCR8WF#KSG|&YgCu)%N`zPj zsjYo-SzY6N+{L)%R_oLLS;tOOqXSpq{AZ=lMZAU$ol?f2pjC7cUrJHUs1D0)X>mF0 z`1BS9G*H((?gADwYytPU8|)?-Hr%qC`9phmsowvM%IWs`0XKfG=!6`9f7`H>uMc{M z8!9?zh)1~Q4&fydTzEMXab^2RF+}{09zb~JOmu2?f+PuElTJ=RyQ&}-rX(gn?Sn_$ z`)h6&uCuc7gs4@WWH+ArAh(HFqcYbc_NF*eX0{xo?7jCfDiyMmgb z<34{}*Ef;Re7|3>=j%C^=leJG-d?QxSs`hF<-7SJMhY3BY84fXarjm3NOBF@yl!G5 zDR47VV+AM9UJE|LCk#Yzno3Ga0SJRLqIfGkr-0A&ySD6#qUV2pOhSrv(9u$7*)IRl zIL4>6wEUizrF~SxTf`reeA4QTvQ*6k{36>3anaIs<9BU7w| z2jf3zMY@mPgF$GT8u(i~l#YzkFvINiSMB5I%o*es&2=&v1mrB_&$W)^JK91}S(*It z$D%i8iZSSQ8Ge^0UH7~9#WRw#UA+nK7XKzi*WuAbNaQlurb zk9bpF&h1mTg`lXDnjoL4`G|%P>VC5Vy|H-E;2OOC?Yw5~W;?nB92;%xwpA*W3TNt2 zAo|#-syIqhPb0A+9B?AtZ|#+0I?!DlANFC-0_Op$Y**tUwDM4tL^J_>4-VIW!;h@5 z+X2?!y+}kv1d-%zy7X|yWEb!-^EpDpCu^$4F8G82D+r*psj{Cq4%!0bSickN>3Ilr z^DP=4{fZcm%*9WV;Tt0#O{?}SG=GJ?gu?^K28CJkaP56>C=)tf;_d72EpmJqZkr7E zH;;2O@WrdzG5yM-N67A@MU<=0GiAMv4UEsRV_UiO+qyPe;Iy=E1B;L`wsv)m({Y(z z`rlFK`q#z!fETi|?^V)fHr$|{0S(Iku9nE*6u?1ydgG(4F?546kANkLZ2>@0GLBlS z1dIw`gYo5qUW*-&8HuQ9>F7}AOWQyKB6P;9&M&aV-@xGT@i^w5LGJPQ>#?M5KImcF z5UiZX-M2Mp8RRzI`Y|S!meb4ghyI>s-I=sfmx0X(TyU=G*O0lv(;*FNl%|7yAz`7P z*RBOD7+H-FD=BVo>`Xe8DoM*P^~!y4o3m1N1$VR0c9^$5^5|YUaC{P^UD^1KgsFDV zjHUW5b^OXd=R)xcZap`3#ooD>mz81M2v0drxgE3*Drwu8ODbtj@Sg}L;H<)gw+Jg! z0=X3+(U7>C1;&anOD?OJBaxMp6Zvo())v6I7I!?bCArbqO?Vi}-_T74J?nP$DxD54 zjxu54?myb{O70qPDu!}|foRjs8cp3enG^8Cyju!hu{q+5=J&E-g!A15&_qVY6a`Mc zUUK;UigU-d7L&n2RwUT*mD{1ml>C2Q8V%Rc2FUTlPGBtgIE=t*79O*wEO2jzQ@_F` zY7 z8W9)y?0&yDZ-S^qKKYC9P56QfZ)>Z(Z_Rp-suBoWC;mlOtzF!-fMqk&uwe&uo{Hx%l>D^s-&U{13OC!K%*n zv>to!0hw9OqFDo=j3_)IL4UaI^CkIde$d-P?gcM;6;>~Jw8bFw0Im+iLPL)UUL3bz zQ^zKslY?XqFxXQt{+E-NuK?L3L>vpj)DXUt_ud}Nlu$551aDghfN_8VKAa%ts<&A0r0LwAZK~`!Y9jE36k*_i%@(oqb(~MWaJC=Ct0AIx;s=fuan&g1KzX)D7SAybR8W^G!TaG<%KN>7| zUBNzB0QNySN@5lG^2K?qB{_VYNJsg&RV0Mhr-vZ77uMR2(;u)hTar(H5qDgWaKp7Ac#k;jSyk46V3>U7oXt* z8r)vfdMtMM`nbNuf24zx2Ra?d9_WA}h%9xOg;-xSGI+yWUT`5%LwUVZ+vV>-yJDho+`%s`g}u3na|`7SvwRhNJs|K`nw zWer-0V}+UL&iXpQ#88legt)|M@x$3{j#y-P5JN_?uNtvv+U z?=Uh2Pf|3DjQ>I|f$r>m2m}J;5UbLE2eUW`L>YbxF@BKOQ8;8dPbqQ@7Z=>hVE_d< zFUa_{u)Rwe-@X!o&4J6-%3^3H@^COhsUt9i%p>AFKmYCa_~vXD^;{h-LSM<+l*GqS z9KvT?+G}UmEdb#Fro=$m0*~iHZmCFF#;BG-D~z+iW7;HmgU~QJgJ}=ES1(??fYCl} z5)Aht(%yTgR}*T$#xPn~oeFqFVJYuJ+Hys=lJS^;+8vB?0sMtoC_oIuf1|)OMwA+I zl}~nou@(Q}LmpfwSf=_7av}Z5&Eq7O{3AGC17!=iR(r4|=+0Wim6n!@K{+rC1L6Yn zh|tiES^^2H`2g7Vv;Wis!di!EehN2vmhorIlJ+IvNoLHg%kSg9+^*#2KD$>V-gqdD z!EzqZ<${9wO`8ced(ZOk(-aj*i|R1r1yS{`*|T4dz*bHvjw8E-N)FTa zqobwr-}BiTdSggF3mmOT^7**T7C@v6f7V4(f^)hzfS!{B}>; z43Zv+iHO3m)RmG;hJnhO+5HwvyoEzBc20Nh?p+iv%+VeEeeNH}^kR8}DE?P8RdtbiKe>5b z^uD}lm7;C=$8vIOzNYsFoJZij>#mcOmX4MsBB!Jzf?&!`#}49WbeH_f4K@(iU-)ut zdS(Ip9~1msv2B}i?2gUD;-xpw#eB;J*)3PwE6*Pj5Ovqsd1~5>= zX5a#!>w0PGrqx?N%-%ibU4z8mM%?}wI5qYTV3V1k`?&ICGFtja|4U74!eEY#XAdi?Ue~BZjBdu^&;1fwjwn} ziU7U}Q6xu=z8KfZ-Q3aC95E`u9x{%=n$3?htvc!>OEY+91%T7K54@Nlgq~{vh9WRJ z&d0{aIyyLjG}u1zciEprvH6&`xhYBMv^@65M7LS48s(ns6(V%g(=s(+L{8mc2_;sM z3-wwk`6C$C-Mp507*E)BXK`X_$%5Gw-Zi7#g7KrwourA0uW=|BLE&p>2ZBnhhlS{? zKH%&b7hiI5aY6f1`GKPV1}_j+mrFv?+;I!DpcB!gqw`~r@h~TIEGMN-%dJX~<^Djx zq}8b)I|wKgx7X4*nQU;0V0d%1Pl$&mkou;Gx7AY0k{sfj zvZaZl7lXLT-skXL%Dw$NSXeYQLFeWzvbcCmXNnwO-$Q=eA#R7gAA}oEPIpIC0_z-t zpU=$CMlgx8T&LQE(1(D>_R3r~+k5HP4{Hu-uH%Q2$MItNldFe(s?ScT7~cY$^m=g7 zIrtsic)bS?e%&iWSXr%r5_))ONRd*k>G}Pv;p&Iu&rw!w6ng9`Wez%wv=3oWQXTMx zDY@6|XU30fA9U`oU*bnMLz)FgH(cZeSM+wyyt8!e81IvK~c->5w) zo{dfVophN=)NOl@6UlF@p5ANP9+%bK7pws4Bk8x&G(H8Zspq39@lE>*21Sb2fCyUx;W)B$XYDZM>|@$Zk0 zCFvzcMg)IuHUkRSY?K$0PBN4Ydr8@W%80)fN*Kuuyn1_b0ce>=jdcTe#U(} zs33q?{KzdhQ`%nn#VMneuM<@tpjh$tXODpmMFtk%?BrY*a~D;jCY+s_fe7j`suAFm z03%4)Dz)?%=EnYEu;LSHa*;o6!BU7Robb@`mmXeJU4?zuRsb_bvZK+8a|__4g)i}NezA&m{8c84>^CH?2myg9OR@w3A$x&H56}by zrWBC)4tCJ&?5)ksdEarpF5zFfcD3MiTd`C!_Rf=+sbQ(MeQ|9qVonRs-Q5R!v%qB` zpwYk_M2x}^zGdyDfGi&z^&L(w*D76V>rVBippN3TJXi@8KY9ML<7MdPL0W-9i_G-#{`OBbW$sI?DyAa z%;z^dTo2FAV1oboZM;h|4LCRgmIui`0wT7OB1QzG?e#yatS z-JeIh7EYZ!Ci$fZWs_0fL2wTTMm7wyFlN_s9wkGjS)Uni-y14IArTQdNngY{?@EfC zCZoO{3mu^>P+O#=%nooi1hBkttBCp~r<_rum9f4()llD1fE)t8eY~E)-i|wuL&q20 z`={o^-9HkQ6MXg;(FEslz9y90L7te(63;JYlZE>-4gcU2>-^sv{pj`O+dq3P&i2VP zjr4#!G!gc*=l(bM(EnIs|q-2bw|QJjE1lf zIpp6+yC?~TM)1kbjIy5&l)I};;BSme!oCNiTd-H5q{78{Fy3(+t}_%!-wXrK>Jady ziEJ|J>iS^>>)t{Ty7?1+z?DF}uHHqMraL=(NF_sUgj9VMNAy~=c5BAneWTLrjm~bo z!nvv^>>3HDXy8svkPi(Fooq%`ivr04Nd02M_WfD9up5Apc`?g(>VyQ8eP7S}7{P=Mxf^hqqpY5O#e08&@u0@oyY3$jI(4>SJm;UwyKZ z4ICP=%1$7?Kt2$>DlhfA{CM8M;vk#y;jBJ<$bMqDV}F*J4F?5G!c;X@M@L7X%VvqG z0aFPQQh;AC;dS7IG<0^}0hr1axM!g>5+@p?KqL7T_X^#N*(n#j%(@UGX33crS(g%g z@JRV(f^*e=H2L|@Q&jy;>zLpj_rU)va)kCg`5Tt$rr}7q*;7dJefQ3{iCQd@q z4gj=J4#G%SQ9*$*31&nv|kcxsVTQ^<6BZ1jbl#2cHY4!u` z)qYbr)@fzKrm8U$-BU3M;#U}pf6#W(_pt+9&!Y(F1JWYt9QpxwpIIcaEh3< zaa(H%z(9V##f9feJ&jxTqBX6@iv$ z{FCNCIIGlNh4r2MWTnGSiIHbP8j#xn8gIal%F1Kl(*b_jCq)uy z48IA+9gtZY!K6UWYt{+mBY@nX7yu9xQsdzw%D^xTc%!r9z)@oQ>)WN6v-Hc+(6 zkL6cr!>-+PHTav5zrfc31P44>o=G^5!BYs-*8*yyD;N6iS~5kL59S#IO98>_wD6QV zG*u-HBF91~g&fu@cOM}TH@YB1w*=n35PB?kL(uLye0zL+Ji-afq7?Vsdv%%m4wz&( zNk~Z0k@H&(z&VsXnq67R07#Qu0rW6{GC_c@gPi^aoRY)s-k=x2h$Pf?r5$K&d<^k< zXir^eej7q*icRpaAz(MISh?2&bn#Q|C-(k-|2_xju3hGImoPFa3K)11a4E4oUwDAG zAa5s6D7?2o#k6YEv?Uw8S>%>`udn|UJC#g&Ew>*g&mI+m3_cG!T%{u4URh|DoVg% z<}n?)kOKqI4jf8h2Lr1qLBSFb9~=lU!dfX_6Wn6X&FgBxTXg%@TUqu#O1$1rZV5ot8YR}tee6?es;QUPym363vOoh4w7DD9)FDJr zaV@z`0^_S5Hq5C=p%Ia177+ zV>8n=slbg43?IrYCc#+E`)ET35;VrfPT;k75(YPuU}`ZBa6Ht`gu=kY)YMg^Bse|Y z(%HGN$tX{Y9SD8{l_*yfN(Az{@(24^>mZn2TcR}v1}>>e$Yn~PU;vBSW4RYh1Rv>V zazPIYkuhM_PriKn!)S`X21%eaQv~>HnNs32v$8e=9_7+lTWbi5`oo7lE7a})+*;y+ zUJy8c&;OlutBhmOOcBbb`E>Y0MC`z?x-s)mNFrfWVdehVxvP~16BgE zCtQG)sVQ!&fgBr|F`#V2pGSInAlGXj=&d^RL0Ciw%&}M_Ehc}z#nQ5XGw-uEd6_>} z47L-%;GeO>UfPERWVoXDfF|JhP0bKY%ViJOLjhcv- zr@>GR64!4){v3QhWMWaHBdB*=g}a)X%t62hcd}R9m*P;W^%fojb!r})6J{zwJw~eXz(R(=C1YXV>c@P)A zN(K|ng(JTm~0FwyH+-`NIdW_}f|q<|)uo0ZG%1ibY!t6_^8E z1@^R2$S{XN01S`IFl5|Dk0CX|Sq6qaFROn0&MP7&eSiD2@N)lY8hoOx2Dw+1RHX2C zhDU3#Z%+ErKtz_ahJ&Q!>tuSM=sXKjl-t~)g^C6qffxCM0BtX5n?oozNFhym$stw_ zN*PL`NoVXZ1(0d1g2CxdC$>Kl624H<{cm$CbDsni!ZGF=A7CAXtuS@X#hU zYnd9r?e{)Dz$N79Vr#nMVtq=OaC8KY_-*imJnPhyifM$VQ4(st^s9wNv-?UEUmBf45E80X^zIt z4ppR~Z7yJG2)`0uNVWr)`Y?a+#RIO53V?bU1?V7lq5uY>fH)+B2*S>8ox-+aa)AZD z*W+?T6JGEY4ncj-+y`7{W(<&K;e-XCyZ|MJ(HSkM;+gUXOwx@Pzt6m-QB8pZj6)-;C%Hdqu_>(0mqK{%7+UJOHQ+ltU!3u#Q%v!-?EZH?EqX) zASW-=B8ecJ7j*ov(ZZow25O`iiUTSQCa2iJ%Xoi3X_^$y!o5bUN#BDtI}>5QNADW9 za078~N*R3njE!H$hyE>Eeh#R6FiL}m{2c*}A_87=W-JHs#+4Nb>|_+s`LUtTfOW9m z#2R>75?;9g!yG{QU~YT{@qtAU#If_e0+Lvv_PyA)5P$z1#|tB4&8Hy?ztbJ`|Zd476OA0cT8^ zsQuI%HP%}J2;eu#&&_sJb4LgHXPEOH5(Lyu9UXZ>#b-)24)PITK_QZCP6WX1f|HFc zUmUZ$OY<+dh9=9W$A-LbfP5Bsm1mu27R26@2LoT_)vH%fjEPa*!1+pVgvFA+3S5*u za^4YEcaMzf><(U@J3H16x9PV2%vjWbp=*8opKpIGP4VE~LG#B~({Dc$a#sT1&v2gh ztTRF5h5Bo2YquvzWItt*l#~>nyACKo#-b#cIa>|fKeXMqfKrxk{x+=c;Qk4jz95zX z1~C}MGoplkLvhj(d7} z&MmuCQjlo}Q}d##Yr0xn@4#UdjENY4T=xSaP_fV^DCPtbI9B|DE>9`+7r3Jk*CdyT zymuaF5gUC5u=6HN`Swmedy_xm=>tiY^&NHp}##_&-vjIQ&q8r^Q(%^ zA!<^ZV&~zxygUawVTU79I$3HikAv1l!thDlxqCXNZBYSF=*N(S9;a3E!`OL)QX4YI zyruZVji$?MJ_Va?>T4O^!&dwa%zuuT3uoE%e75qP^(*oZtb-rLe*U;OFd~$y6xZH< z;J#ecwu!sBJGQR`UyKG2P9GKq|@HUvlT za)=%XgKI$Zi5rR;5Jtv^Cj+!r6s&aL$LFX){1eNzbW)-2%0>bT;vJm zQ`3WV|0W5EiFGySjvmBB(7W1nEVwP^tluXI+`%Bz;gK8ja*e#F`xpVx3oMhQ#peJu z2Uz%>!08EynH}?veVi72uflqZ0nfd z({wN{5Q|wEDmNSssr3uZ8MIoa1&?nX9jnpB5=}e7FTj@(Ste1669!(DDQanU_Fu7{ z1GE%?dqDkyED(AEuc@OW58=lC5t2(BXPfwJpoU{}RUj+Y=j)HcUbXKTXFH6Y7q}7lE05pJFKu^eu~^nXb)C!#DIr=DE6}p zc5e=)ureV&#>Rr{E=mlvnZL0dUGn?SU*2=PhGTbr^&PoXsZKm_rz0ZhqX5x{plYMM zj~{RPW!nRHv9n`;pPANSuGSi{3A^J%0a(UUtI$$rG4wB55E8IPl^-03=~!5o^A!y?ib6 zu4Jsgzdx+7aD%~n{jDS+8JYQL`V14$`oZxiY;%E1|wma25BK4E&-r^X6-okbv$W$0%StlOs!|gbnjh>bes8J&M#3> zpFSSotp|_^XDoL?jS32$Hz09)_YU5jFJCC>%1%!X0~SCT0I(W&wEUeLIu&lYC0Hln zmKakomX{!O7G)p_Oi{TTF^C%}aP4gpJS`YdzWo=a?3F%Ks({rZa6B`+IywjzB}fz4#Zd5s;aH6l-O2a^vK1&6S-G3qt_%0?^VCfJe}%0pEYJONOR$ z?(qQ{_tD98W2AM`;J8tHR7ctTPJZ7!2la=~ek7F}1~FR4qa!`^-65Gz3{Sl`#Y9xN ztG}#{PFgv)*B5KpiqdID`0=`TZO(S5R2GT!tJ~1J9^w~TwYzaG=Ep0YYGcq*-Q~yn zbn}Hh#iD(lrvg#q6eo|VeiHaZNl&MSZmzD1LiJ5CzdWC;-fwOJW+_ooom&UYQ2o$s z?-2tY{-;pFT}Zwmpaas>r@KS=ynx#oqrT#y(SR8P!*J**;F`f84$HB@-ga0*4QsxN z`Chs35AY$WL;C}-DnRXja9)}t3dv0GDoZ+qM|8Rz|o z0DpXmgaYk@w%sjGO}q5jnGY39zsh*2!}TI}66#L1j@)NqVw*&D(P(SLO;!#~kYe1DKK9A)lFZD(Uxe(L5eX~wP!T6T@ySVO4(wx#;;zCXBgBsxnOn4Gj`PF@VPUwHjUE#$%<} zQc}K!TfuoJN3#J99kJ}n@%QfQ?Y{YvE$~z26Q>gHBw;Lb+{uWieftnS^0MTs4r<3)i{Of~q4IF!l(n6=ta=4crly=7Y zO@f011Lbtr-61G)VTDvqe>h|5Muq{9$*RRITRps2x?|bc1SG^T`)en~cNRM+GQHHs z+hnzKbbW}ku@yF9cpxqt3&YpUOtis^fMi1`3QMFQJOT0%fm{KLVxp8EcvWD-tM*+1 zHSep3uYnwmhKA-F?zxG+#dI>MWtGARX8qKOj-i^N{khw{Lb1E7bwo|hK6L4~WDC_u z%$G^I=ZAgQf=nZp%2QhK<@6c_U4#wa&aD4U8Gjbxup}#sjBsanR4+K%xtPv2?}?hw zs(09!EUqe{%#V3g86NI_fBK>_Ca*Ye8}U9^Zd^rdyXjSTL!a2UlMc&Rbp~X7?v8C2 z_1$#*tXS{aD&3#&8fVEG@I6IB`8kf2R(lOiLVe~Zn7a{4<_VOZrUzc;f14`@S?N^f zjo4DmlWjJFECA@CxwPl8CF*|Q;e}4IS)PC^0n-D;E=`_yP!eq)9u)RJM?XhV)HSGdF25St9EkE+h7dq>wX_>u2v`ySxgzljU5%n!XDi0XqE0%`UyQK#Hd^79?j zl19v|oX?t2=&IDcoT+Mu>lod8bDhmKdv5XT%-<}+4TYmkVvBfZq3-MV*r_xaeQ)z= zdJjYS<{vhT=HR$F2~=POGISwefaCfg+q@0zL5#`T#`5{g^ifY|5;gTW9fl+8mBLP# z4|AFi?XnnVFET(o@wFJ_cwy@kbo+II7vI-(^`Ej)VlN_&AOZj?AP+Inl^zKjJNDuC zwO7`?MN3}yKw>fz6JP1LarjW~;)lDjIi6^(Jg=vnr?vDJ-{DDbEwv-}=_)+v%yO`CH^4WRC>Dc}8&kLq6)WK(=XqF*fn z>*Hv}oA}@7Ue{l$P5DWV9gbl?qs#EvM$11!0HdecMZog1<^0(1h?GB#)q0hhiOhf5 zFj-?iKBhDY_YpUnC~b`$_BFFdhCk*UKS}7g)peJ0HwVZ`6Ho4Fm`j%~7Fg9GB3wEef<~&{9 zVlQH!9V(=RmFR~C(p88s_1hB%7bDcSZ*K+;0ocG9xGTsIR%h_(E}M2tc69>QxB$8b zZ(ySdf}1@Aw*z_<57JZ%FU2o{xnSA^Us|>a`^z)6n68n+n-PanQtJ_Ol(3B}GO3$k zOTQ>Jm;H=S_Bi3$6J_OH&astTv(e2BQM!$7b+$)$qf#2`Mo$S!t?GU*V$|0*SmtRK zpFAPR)=15Cv%eYZX6}P}=xJoXB<7=6)=$s$&a?+6v#6c|<;{K-Q)@LR(S9jbjjB)a zybgV&yr#X5T>VvHyw=)}+y5StT4_e`886V0@m;$L1M~&wcKP|v#v6fkDmq_twAjzu z1voWf!?TgAoiBj>>j1c;+}tXlvH`*On$tE^H!w}HfkdcWAfIkX~nTn$?Kl6So z&5#mPjHY)z)K6jO$gKQ^A-Eav&k=SMiL>qgKR_d@xdknpiBB#61&6kFcHsST%@nWL zYDx|jj{!S;D2F5f3ndWS?gUAD=JG9)n>VeXBmx;Lf)|A9;;|e~cajg*huW4t-okR; ze%&Kx+34?GWNl^@yXYyvapH8g-q>9}BS{^=Xa;))Kx@7>kXsu=SFo|Zu2o`3`}eKD zgN^+RKY_gYimj`d9Dp5=4TFP1yxcRAdJpYi(XqPZM5yZP-i660tX`|##Q@K2FC>TJ z(?F&P8yf)DU%$Ter=zDg<{ku_h?Kuo@tX#znL=?c!`bBAr-$8JwukEQDxYpllpY3n zrK?4$&8nY!h{;@os{)^5y@U?%#!+KMA2vON*X!glCCkt5K23Es+&A$$u36ZM9{WkE zD%ZqfKO+~xEcG{?l>d*C2VG-ht5|>mJ}<9eX%ski?9kQ-{^}U`tNow02EO>@8&4O=?GXWR9xv3ogfKcWMj zwgQu5THZ)k?*II|UsN^ywlB*sYw9Mab@zw}gY5?#jQ91kqbZ@ke>(^sN*HKLD+e?g zIXDTP{p>O6zX(<8^L*WZ7|%iDO^Pe`pCfv3`u6N-M;>}!`gixJEPFH!&kOc*aQ6&l znP?{>CtR6XQA=X6w5}U5I{UlJ|Dr{!>_9YPU@xi6JT`ds`@aV(b9N%XsAMM4vv(&1 z7AQE?djsZ#<#hVMdF&oS9tHHrxwKU3~~f*vM61d z0@43DjjW&jJn!tL@tPw@&7Qt~vmDk9_t^hOZdFkQOLket*HY#A1uc$lLS0tzsRb4f zvMA=;w{I@e@PBL}{_jDzhjCT-F1%NaqwBh!dStWd1WUmD_K@7gRQw(dRw}B;)z3RE zIp?U#KfAUHu+VYaWQZs&EcnACtbz6OfSEE+hv@Xe1K8%i!uk7dK7V9llk%!0=||7@ z&8tz3UqwrpMT&J~bd=R(J!JQ8mHzzHJdN6oS6d3QqA{1%Ub;gYVS(?RoARZhKa%1% zjb7Mq(dgXWA1an61Xq;eKa(tX9}dbn8b`@}=G%`(5s>uX4&WqD zb!y?Iu$i=N$E0jLSB596A0~^Ki-|y1v2gT<-w|aL6O$caEMifpFQbW6crKYQ2dO4wCw$yQ?l(ONH%dWYZRi6O=;`Sv4UEjpCSrzj{ZFTOWF>}4DlMwvtSTG0j zhQR8ma{V{1n6O;pQ;-Oxe*Zp}1kGHT@oqcwyCX5w`-^E?pV;H0npIbuDmo?0c*w}= zjy^V!aK=Y1sTR2oH-+R!X>+WMy1Wx|sGnjC{Qkz$XT*DEQ_=6yc>!-sl6%^#7}aQ* z;Ehmry)v`+2uBv4QxbWvr8%M)RkF)Ff5`&jUnl%7^3lECLp{I#_Gf50>$Rdo)2t(qE-7x+(miWTe zRMH>XQ(aLcJf6)-(yG7H*ip+5rB;FfRQ|U45tSFHZL@Om5)9>}oXua|(}h&u=ijW> zzpmqiXX7mXM%L8E(NnWfQW6S!{0UpXK}c8_jSNK}lJ|lQmzN?6g$g}jW@K#p%$1y~ zsmvM)(`Q4a4w$Aq!SwAEvT&+hoo1FTK`@Y^#(T{g;;(* zUfxC{@Qum?^B!9uCV*4W)hkz6zzQcM#Cs_%6_TtxfrScc2*`K8h)d#zLJ=!NG7@;q z00$HNNjOL$FpvY&6c`?Yw`C77cz{v@ zQ?&&5xoDV6Qb~A^nDL4OnE+l+)`%q6fegHi;nd?03Kel3*98K;DCrR_F3GU(5& z-dJxa#qC?tYR{~Uwo0QUH16-~6e*kLTsPyr{aNQ^&HR2=m}Lgn=Ya7S2yaP`vdPZy z%mkCLJM2Pyd_Q+p-;L_LZsNaryl3bgoEY4C>HJ$~@wZRpdAc#5hy3w8d~+Eb(WvMs zWjzTM19Eb{pH zv)|kq)uto^a=1VY(jmGf4{@#FfaASCOFB$jsD0ebmJQc@4Q%Y7{s8t2Q&fv?p+zrP znVe#ZtKsScQ@_5huJUNJ1-vdsUB?Se@T?)UZ_HyEtM}ptSvnirGC0iW<(mZWbPnd5 zj=N1?uhcsY^*NgE*Z0vaFjF=%ngKWpT!EyqHgjNT3i6DU2Y0j}X9&v1?YUUnvG)E8 zI0po%ZiJO4Kbdz2TeNc|-%RanZt$!RFIkWkVXSwyv!P?DiEoYD{y&$3U$w2OarP&1 zU3>JcgIew$Uqi8Nv)~kuZHCg?Xumo~c*bySc!yE~7iVXrMPBuQL4y?p?Vy zY^VpD^r5a2lhD{5geO%b-@7D6SciA@VOgj0XZiVshc!Z`@5z^Yo415M)Nr2x*9Zjo zD_SBDe?UeQULC3}f)I$95dI+nvrj-9A$V$I zV*`j8(~Xx2sPV2}FATvtnSt)0Fem3e3yZ(MN&A;Yn2N8iQj?Q|3&7xA2B;;01_rj% zuO6%q7>2@&H!>6jhs%ZP=W>d~+wA@#q}?ZJ#3E8)J-i{yBOxmL)LRs3eqU8?FmZbJ zHA`BK)G15AsvG8FT(q`@BDH#%tf90fo>`V~nQ?chv@j3XppNOhUPX$f?r$qz@?rlW zZ9G2Gb`~^i^R(afDb`R*74lZ2zFSnSqTMg3q@1JmuWm#oB0=9|Mf6TLr>udXBTM!7w?9%g<>wwU_5o{oAryuTvhDLX zlpjw#sJhHw)z>~lhNehL39*LaqO$EETJV*)yLIIYg1Z)Qw)Cf44 z;^6d~?y9NT^!y3nBST4ebv!tF`FT6QS0{kz`?p0N?rJT=J6SBO*0PcftOBcF3!J>_ zLznwk7_*eLnrCJdCCp$wa9WI~>CbMu&$EAyewCtY_~N@!z2EBhP2{`A(97+DxD38W zh%D=OpV+?3y2=O|$i~>%&XHQ;{lQ$Ni0w<1kl??U!erPoME}U(@bQ`)&5SI=6YA-F z>*1kZdA*!J{P$un{4*D=U|CFI_Q0+AqZWsx&f*H$?xDYGnyR$x*X^+7{DXg9-I0(u z9rEJ08PU?$ulC$`0L>Ik%Hh%?mB8)E%+}V`VX5ko3_#-){8o#9Q>ZA=PfmOL`oQFl z9a7y23wMBZ=Cjo%(4UDk-31Si?FR%T+0(KrChU((^X`foKiZ2_5I39`GxVAv7c^L^ zAk-9kj2QWqt2!TyepVvZvqL1W{QIN(RZWcS=gOH~66NOC7pr&il=LZWyc!9V;t0pk z8W{N(J%1XKne@75h((Mau0)P9iEjxKcHNS4ay*nzAj~HX-8=s1?&B2st<`(3IjXe0 zIC*+;gpen`bhB{HEf$fL^yY4;s|OVz?FngWUzq6x@MD`Q80$_t>2(C~y9b*_`t;I7 zL!o*36?cb=%f98)J>Y2s1P6mO#|VZ9{o?RHebK*xvI!0rg{$!pSlB+l1*TNg9t*IJ zgG!8sjBIct1H$&OPLa^*LcH7`AOHb@lnb1e(%(*knN{~9xYvOTL6ejV7Cv5u)*4gv&!wEz(=^@?z-(c z16%-iPMa@PdydwXlLwUnKP{Y8Pd5jL6HN4yJ6;=)ixxR~ab7g5UiNvuHC0X=t~lDp ztO9uEG59*>19AX+iP%;v4`|i?dG*yY0gR(y0R&p$I?ORZ_ylYiXzk>gwy<;-AR)yA zu?ZM`hX)5PRQqp9^%@%+KZEQYRAU?@fnb8zUy}(;3mCROl#0P1lX>~#{=m+m0I2@{-(gXBNA;LCzNY>EDf61B zH$GXDnCtsgGkNaSNo6(vV_Ts}&4a!&dAb-L<3GOB*{5}?>dDBXA(LlLo#P2hE*?09 zU2s(j3S#=RJu(E&SBhWiawcJ6=SXAM&?w!gSdGq`7!reCKLU4tHia=o{Jf{ZJN)Ik z7uGz1_effZl|*yF_%9moQ1zUk-hT<@I{iVS+azbB?QPY$J`%dAveB;eL26OunU&TF zR0*}H-pqoQjgYGVKJdX^1`B|8LHQ88hLA<_DJtOifgZ^9t$7ZbZkfv$aqMEZpHpFh zfRbA}NEdZebS@|3Wysj0NG60JBE-QPLl z!JWgOBJh0i4Si2jll)oj}U;kV%O_nX4_2+O9 z;F6c4(BU6HV(sx>RncjFLO!-zfbr7rS~@pMB>~dilfxn>G@Yr)vE{-S<1oOO};4+o!}7^MR8FS#~no;q$YX-(~5mrJ3I0PED!L z_Y6~r`C(EZ3i}#>GO@XNjZXV;vcY*sSVRV(8h=n= zdhJd6Vad_C2GXgca`1NktM)1h5g79X%fRi`&rA`e3zE6n*`W-AydGxq4N`rwQfx4R zE9dQw=7W`CbJIbf2q{vi@2Xr85)+FA^_xDs+M7pj{LdpQ9dL_lYKWyd*yrCY+!4?u z4JSBVZ<>-xWe^rR4!2>;?5WxuYyYI-xN_(8+&{a&Kg6hOuDbs<;5}YG*1grcQ*-j$ z|9OH%;Yl#TE>U=33wr-nBZitQ%X3I13sV#$KZon?kUOAH#GbpA9yqgU3R^>oCqrYihmZTQ!F^kQxyPW8+!k)wtU{46BRGq|anv zchRp}NvNIvKt%k=M(1>=kh9qEFeJC>?%z(@VebJ&gj~GW$>BYT8(sgBUx*B3JTXo4 zeet@SMybv3e`5Fps|9mpOzj1~`1@aR6^KuXyw<+!rYm9jIxV}0qR(u5Gcal@`JYi8 zU4Jt*$goG0e3>+2y@F2?ifYW<;~-ew*h z&M=z>dCYPWleSS?+E`X+x=QveUo8nKtI7nbnQN9`^+2`E^gvY^-i|{5-qO;6?$gxF zSUzhPgWrv)N5K-&jvxG~J)Sq?c2qlc{MhKT1$(I9gFJITdY3i~RRhevS$&jEkyS$d zC_DITm_HxiDs+qdW}@1s8(kP{k?*2=>(?t`x$p|e3Z(e_9UeKkGZEhbPX3^%*IYrb z<(Mv9JD<~PQhX|r#>tWpN^#-&KTiPi?mykB@2EDqzD~Bjq92bs%yd238QP5cROP9^ zRC&6)%g7uuf3ya{$lE*bY=OTVnjj2X=ld3aJ|C*ARo2gz=#ze~fxPy1;=w7!~Y}}I>87YbSX<=d66BM8AZmMuU zP6U-$d5mFh_n3(K5{URnNn_z9%u4ID6|Y)7bF)rHS22HyJZ1}*t*$D5mq}l5CcNgk zyewIa``&BoF#1#u?%TYmO}k~qC^)ioY`rY|~yU$ca37a}@@h?yy zPP{?RrPu$|t}4vm%1=1HH+Rf#J*-Axt9KSHHqn#T{gC!mR@RE5U)eE-+lxgxpLmc_ zC>?j(rgpGOKFl8khqHMo6d*vzXzIKO zjv(II%VPUDtPZlGf6#K}a(RjaTZ557O$8>fJF1vB0mPo3UI5@8rOusx+$Sak8+o0t$IsGUGZ`hxnCm<{b+mQZ}zRKNTX1X-qyzjtbam&@3T%C;n%=_Bk zsG4ASN7BWjQvleB)Ny|!>;3{^&!nR|wjxZ5QdoIru9=*l;nzV=haV03LY!k!Pp7AQ zV&(fKVY(0R4)mRy?ePvS(zvJb+AW4rEqxd_3=fs@vWgI&}-x?sGgWZoKKo3BVf!tNp z-?s=OU?W&sf;0o7*sl~ zfSgnBpdpaQM4`CwW@Gu_qvYj1Dj7Tb7tTwaaKdGFC;v3`-UFNAx+T%?V!-i!JNBBE z%Xej^+NWQssDUyh-FdLwi$ZBUx9N}MY)UhETrsI@ZP9d5eUXb?dK$Wa{NuEYY`~Aw zIs}4xB%P$Yupow=WOKrWKm4G?}x-z$oq&fo#3ZulA!7?nhfw6_A%^zHu zp6*7{g0T-!2jdh?EkURT-W$Wiu?^FN@ZEXh3r?SQlma{|^)fZUz6hR1EGtw>UHw)< z2l!a7c#r_E#234pFAUd@@tBOqdlm8(BDQYkSz#R+bOleI)0JJz?F zi@hhqS+ye=NjP!DyaJQ%t|x|QiI(`_p4ga}Kn@)%TgJd>cIzM)dg(WhiQhF{Iyi7% z>9a?NC@#AV#DnYr=+fY1Gfb0+mN>||c}-eRFP={qbb&xLg(N{H`OkMEnt*7G_3wN; z%5(zC^qn7I(AZ=I#P7#|LI6wF1;WLk;=lIx_sN}eI5sMwmUzHVl9Q7|xf*Lg@jSl; ze&wJL^01VLo5lRi_uJ{-eBO2d0a8Ux<{_BJL*lE;o&;#=%Wk7Sm>QjJ-pnDnSsc4 zpNXHzh!5Ij6aT;?4=yrh3^_pcJ zp!QGmUFYqUH4bThRp)b2wEn`wi+#Cr=Ss<)*hB44#RWZ9`|3k&1$8#XC}aj|*s81f zQ>*|VTe*97WLnDi4>3tFznpsaaz&&ShX;?RR+MM!a5^^}=N2JlWXEA|dOy#+P>0a? z<+hm?p?MSlFFpfGObD`qBpr^m(L1J_N}B4L9*1rX^>y3k zPW?;K%u$q0%|+0{bw{p{S@%bPeRCf#o7hLX#2*DE88e<~v9ZgIZ^9AV%UNR3Kro1E z&Cc5J^8mTdlIy+R@J_-ljdWupdH?dfsOpSk29a#1{TO{)8d5$V7l%~j|6=a1qoQux z_HP&iP(YE85CKI(8Ug807(yBek#3Og4vQ8L5owT?F6mUHrAxZIq+^I@AH1&ncR%l1 z@4MFf{PoP@T+5RgX69SR_c-=_+di9n@6LPw}AU-9*--G51{GKo473bq$Ji9C#HmdBYN0Yv$o()6@2fcs0$4F5dvf2OO z@t!Q595H`DTKJ;C_zQ!gx~ggq0as10p-C;O!iUsMaGNya;y_T};#u3&P0Uxct!fT$?}YkeCz!7=bmR>b$?HE5qKj3t z_pDh3yf@?A0ao@YrYGbU5Fg(o27P_BolAsCpEER^(uDZjr z#K}hdzb4z;@o6k;M6;{bT?yDGp8g0b_X&lRo;X4GFaBWCx_qTdC)1M|e2#L7Lb3>e zy1+gE#Tu@SxwNco$*=_sI)8Vo=fVbUg7aoJeJm;!sPW-$g-BA^wbTaCQD#AbLar)^ z`%rjoPabGh*Bg0k4>yP0e1o7$iVUU0dztfLQdg($M=fxk$^1>%QVHF;-@M+ef8V+1 za4-9#|0iEe$C?Nd;={%RzuuytKOd&gJET13-zC1tIdUBNbAJPC(Z11& z-SPA~f%6>CZ!JsL%o7uM6;)Iqcrb6@LO-R)1yrp+e~Ls%HBz|DEH4jJf{z;u2L}uy zU@rkJI0967dG8X=)(fhu<7A0LCU^$ek*d>~xeYsU z`f0+MP#`yC;Ch!*47*NoMmAi>-9B05a6U7b=W_Q>A~;W!-Z*qx4Or6PGQHy#*q59$_RT*R#(Iy7e9ACbz|S8c27?lFa!JYgXA6q zC9vA4G41begN4)Kz^3e=&b}$0vbL@$IeEKuG{e`&PthA9P5RAB1cwdBBZe&i*A8hA zpu}u%&+aln5uf#E*XrnjQ6jBP%lzqQ z{e^A={-!FMB1*)@D%T8VPgq}y;iI8puqCPKbCEjaVn|?aV?Gb~TSwVZC*v2H5H+|p zJN0xUU+8g-K|AjGv3+wWyUErJ=DE$xS=8Z2^P9ywrjC{fYC>nFR1&m$#d1xnj0Z;i z$A89UKFC*)O^SP#b`;RM_{7y^Cl*`i+lrcQ6 zKWhc+m|f<&KU3LJv zy((=aten?Joi0*DzkL35*uEc+=!a?g%a;R%JLT66hVrywj^D)g+ufSgq-@K{h&5Hn zcI|VMZ<8OT=Ta9o<>=1p#F=XyHfU&|Rg$wpid0&Q)&lq14P0Ck?g#M4P{QApcn=j# zfaxLTHa5H(JE2gYm+^|dl-rFjJ5m+y~k%& zPK>3NRUMJqdF&?Y*)~I({jfEBddk z^MKHNJ-m zf|S1`yFYkkPSJphssTLZ2NaSZXF1XeAHFF>Z{@|}UyJ5@6kP4A1=uC!5Dl00&U5@#o?$4g+ss`qnotMyw?DM^IT?1skIMd{31q@s&gp)CVm zBH}@~Xpt9l??>FkW{#D|cWOfAcR!%Nkr~n<6GMCt{Vd-Pa?tcKd({Zg*~*J zQC2GXM#uQr$EzpI4i8T$N`s1W%O*8Wo-qDaRBf?>sL&q*D2|(lSC%u>U}l?3brF4G z)QnHVXJU1ZGS00n_#Wu*@h$~j?$8Ge0M{DWDDw`#L8k%W5PM~3uCq)wvw!^yqbn}o z#M?qR?8|t)vW-@zDxpjp$o%jOut@pnD6@8Xh6(q4DNEX@!~OQj$;p9%65!?{t3G|g zr_$f?-G6YcYT?!}&3i=ZfY~rYuepjnn(5v>G&+*Y^?RJRIDG3?w>M~W10bX3NIH3F zb#%MTLtkfckwCAKC&pv$1L^ViU0L(eFe+-JmHs8hL~WJ`sbDw3tt)h$a?kh+c2+r4 z`dQt^`fcC5@F+4+P!Cr)cK^Fw&%^7O__BJAG1{zm&@;+ux|nUm<#G5Gw0c|9IU+sM2fYe{#OtZhuc`FD#fGE>D`z=KJc zh3^{UjeHJEbB&EmeTXocwv&*Fnx|e-jFy9${SSy@%2Zz2-0WiepqdS2ZO&+!{nN%s zK-Rq%NdkVj4DITs2#BF0d-{Z5&)M-J2|k5HBpt`eWf&~3#7@s)WeSyj8(B)}PT*6q zN@f&e!NrL%GE9rWl-n))9GA--mV{qDB6kx2Gu%3~O1C1!#y~C^GcK#t5r>T8pH;v3@J^TA5lZYzNc>X>Bz_Gh;;xRxEf+P&P3DeCp!GR9Jq7MF z=uB>7iJhN*O(`h&7@uwJ8!jFy+d5ga)u&lY)h*9by?vz(udi9vdbDyy^b^u`=P>fl z`%RAh4cpAo(i|!7tu&z?0_)9>({q?|ad=zp`K!XiO@HUTt}EdyQ$Keg^F`2P%WEAx zdv|ejEU633KUp7Nb~V)G>s&m^+-c=zR-tENj; zi>lahDH#3y9k6043?FuV{>^m`E{?%{s={IcuU-q27letI&`2JBq`dl?jM7g~C5a9UBM?8=sBa6Y42g?3 z0M(+X2tY~Dvw?GMqan;a+i=tbX;@7BLG%c>eBc@NpG%l8>e+s$%f|YHTdr`Zy}oNM zF)u4`cYiA+IB21!98abHE;mHEXGFt|!Ivn$wCRR0Lu(z8ve_~VR$W@Fiz(8|il ztmOJl-|wcq13*;u{;LIGLhIgy5Ijq+QAh^L%d)`JjL1G*eFf2#;9eRqbA-OHuvm&R z0|5a6@)b&+T#i%wgd~FP@jZQnk)F0=E2%+jXAjXu_x@o7B*i^{21l$+ zQ^>rOifT)8t5d0{1th1a!PWVq{(i_4CcJ?7qqh`yBQLB9Q}g(B8-=tno+*CnIA651|*Udk2JKb8b7gXA)xSli@Q z(%S!D>9rO5KXI{aPF8j0s5$C|XiJa(36+hFbaY#OvZFe(P?mlJ;s8)^=Tx#V{QA%D z%>DlT?lT`Vo1tDb>Up&K6449eyOq@b{ChdM%Wx^p?(mgde1#{Qf5}0D054MMD_z2| z#L)4kbdGpFn!`-bfE|qUMUp0XZ>4gDFFaH0p@W^n2h>Z^xEcR_n}w9V(xwk2ZReg^ z_oW{?Z13$jy`Ycnm(>2=p+0r@Vk=PpgI4}e zZGQ3Z{}=xcrn#9}42Ovv*zr=XTtd4iasg5}RiQArljbMYSUVrPh<$keOE^e+kVYXmjb`9sn(`KKRc; zqtV*l4oI<}&``jfAVHA?(z+@)yx;afuch(GKNc~0VMYT5ElV0W+h&r z4FhZw#6N->Z~syxv&KGsQ~xHPIEz9on@h4B!`_rGteMAYyw5 z2RaZ#++&K$RD|?C2=;iQjKyTE~iC8-(h_n@Ppt>gN=bwGlV@l!F3J57-DAi?~nor>NKhmp!R|Q6QwR> z)~OvcD*-tNO2h%gc7U-=0t*Z@7$E;C26*!_WWKAHnB5L-9xXBof;1>d008w05vz7J z#MV+B!#;up(K^s3M(TJS0f40w6bURk*U`!y|5+1Y4|hugr<9S=9J)XtmjjFqr0eZ# zVEO0+$P^+68m5`FDjfj1f$44qodTvdCXxWPfyeZvN5c0eGJwBSH*uRd2S3JQPI zPZYrw+W|8|B}&LHDpadP@!GnqCaO_D{Y4udsIEov+8bL>+?i(qks5_Mb(fj`Uiz@jkz9R*TTaY#jyNrzxbNB;77NookKdmP)r z6>AC9F<>7TsON$r=^Rj(7#a;zS5J;jznP? zJ&2Mm|hm8!uo`dPd%d#uLqQ_%^yg2w%b{SVMjtuY%~l| zV%ngI6^1mR=;&xLS=PvqpipoCL;~*I{ae?xJ+>MF%`ZXWItM2j%7zTLFVIM>3}ko3 z@roiK#SnCWW>!|T$$%_#1vr-H4lD;WuKAr_zk-INh9B^&=YD-~$p7zpH(I~KklJ%) zqC2E(=}R7!1ZZeDw@kX`A|t+J3Mdzf`e~gH=c+PBy6f?FhLH0^kDp?{9`lc!2c7_JN$OduvnZ4Z=|D>d4rHMv*D8K!-E{x`Vy`vAAxF8@5G=`n*nl<`1fEeP1Fnn;2n;VC5(?IpIB#l4 zNIeF0meBPa=ncofolGL?&lTdn9L+VZR+KNrYm(4+VFmn8@2IVL*#-m{Oe ztiONZo`S8pY0tLat&z_k~0!&LOA*<^3lMJ?Tb;%#;Q2j*v)njGGDE ziaEWLVu%O6>td0jZ#)BgRt4?Q?T1-Tfj?FSuGd4VOa&N8p zfg3H)+9m4O$MQI)!+7;jkv9C)$!7DM%i_cc*_l{+0{8by2zRxHs9~ti2>m7j7z%7w z!V9576C5!rMFoYA09I)=vebzjoc7iiDdmUtgN}14 z*Hrxv@psdbs$6*FydJ52<1sOJ0uW4uSY5>N!mitd!xlg8KV|M<_CfbSIIx^+Hg_iJ z-Cx+>Pb@u?K@3Y2m7SEAjFQI3UA44yqZ+Ax)efD$U=rS$H+~z|<{ZkZsw2qVqr5q7 z92i1Dan267W$^io;=wbuB+Q$A%p4Cz>0iT{8lHaGt0>il~CgO-<0!6uZOcgT+cHG%&e;{ z)opAFYC>UrA@G2qLXYGENJZ+~V^)Ir)!9DrsnyU!X!2sjc31z%tf*g?)?*5JWA55g zJ{`A<0)B>6*VJ?v+e50ndhSG(b3Tv*Avg#WYEQaPliL~DV>{qRotlcsRV`-z*I%q5 zyMgw)5x|lmA(znZUaz`66-eSc{QVUH%3jenzddooj`rL^#WAfc)Y(E)CQ%&o$qmO~ z8Zo1!upd@fLyQOD3p?8yotXUe{1bwzr>cFz+|=}BD&_W&{eH2@cDq!&GDTKM$)|O8 zc6J9oElEj9*hKM^*3M4ae8H=aS(kR3@?te;y8SU@K$7rNRqC-4-*$}3)an> z8|8>XgEG0Yym%Q}K&thbmB7LT2I^gRi~<8pr-3AZe@Rlq(aDX3sU?5` z*SwFmd;5Pz1a(37^K**mbMKIl8QKTot#Iy%ARrqD71=W|@`BtcmS_!uLJ6@uJ3CWj zUHaPf?@s2epsu2%raJu9%8Y~L25ops!*XWMjJ||l_XGSe1u7!<3PuqO5n8lv@4t&E&F3w=M;~bNzb+jv z+gmop(n`Bao3qR)6D%wljJ=#-;kgfA@2< zo)W9!@O1N<0s^U-H6}+}TPIsQetvu4b2T{y^^}#aI9960cYYlzTN%B1x0Uy$jn~3w zTl}!@o}`PLqULMVP*ndU<5iE<#A)o0emMk|^TAY$WbKowrLR^m*}1v*lgAAi1U%J8 z$8ug-9i2rA7N>s*ui)Y5`j-$xR9X+4fnzGJgp_yh#YuN!2_*)oql*kqug{QQxB|&z z4;Nh*9>3q=;I5ZU9lhW7TJX&fHy+SoU<@yK4fI!z9Wn1AM1avsyQ*%-zy)E8WZX}uu*>H*KbzW zj(XcFF{tj`C88w^A}8-eqj7TD$Hk&b-kDcPi+JqG@AC4QSks%FqMh5%lY(M{L|TZi zqw5ogY0^4{V`(`s=c)(GQGfhfezJp36MSPuI@rGXJ86~I@Oa_8?nu(BaH~x{l8z4M z^JBx??(u|)&lMG)A+pc*Hfd#*tS2v)5yyBJM(!)0qxC>R`V?X3BQn3fzGQ>JX)P?A z8HA0OMlNVovj4sRck5W+2$5D<*&VX}b=u-zt-QM3Qvj!$Zt&eH_h9jZP%U`S5(NcF zW-8OtWgsMQ9cQr9M2P8X3sMVy8?qk^NIUlT_HGYvk~Zi(cRoD2L6z)%zPgi6`|qT; zLR>7|aTFToRm7F}L%VRv*?;vE;y3`Tm5Cv+VZL-}H=@K*&?Bp|h;~O;b!0-+h-QAR z)2NyzEMY!Hh%`P0r~Fc4^wP zK8N!-OackoP)fmN(|tv%%GHo)R)O>0!=7-ew<)C-6Y4g3Q$LUtW)>E4oK_5YG|i#V zq>iygYL%2Y;tC%w^{g5ExTFUnOvHD`c6p9-2Wwm%N;tM)p!>H&mBlOt@#x3K(dFcz zFeXVI)-pck|1CK^gH-g(58dCwS_RezXS2g}oGY|cc3&#NI>s2$rj7pd5$SJqlR&=o zd+VSo`H3E+ntL|XGnP}XS7R;Q63~|t97Slh9~~+CX>Z`&AEv02BWO@o3Z2B1*mK4E9=(3 zY7~#Sff6>JmftLP*TS5IR@E2(4Xy7FIcd{Jg@kj|RUEccpFH$9O9^!oRE(87*qS_- zL!>~WoI{u3_JHcA0JL(Q03dSy@V;L+Jv>bGeteM;OCvlGMDk18lBf>aXs+f^+-IkCkA9EmIsfL^&u+n zSL)S^sZkAMgum0Kl%V5we;ck4Rq{eQQBb1@B0NKeD%s!v@A0uOW9MPL=z(y_67b7C z8BTSN+vJ6o9OqESru)UkT1odcJ4x`a`bA_=zH4|p6D|&VDOgLdJCZ>0#zUyi9D-?JQ63)t)qM6KwvOL{XT5v+`^ioQ zm}Z)*xJDyFjXngB!yW0o_-yp^BYJ4Y)_}4Y9>-Y!xj>U@;{D_DX1~nsK>DELxbg~5 zbc9i{I(%&{`<*0meypAC$_cZ+X0Uwj(b4F|v3? zqaSJTZJ&It6ImY2tW#qOBqJqA2?ZBOQBfhvLadiBa8@Gk3_aotck|!e*OI}hsEh`6yUfbu8_1j`%F_o3&Jt^i=()@zYwG}>=n8+L1 z7B*TqIXY9uMDggKxYR!BR#Ph859aZ;C!_vFM+ifSIGrVpQ&`;c?v`PHmtS6CuP z8+7Q2ql-l{uieC68PT~nMKJtfq0}k8X2~P0ScYm^(mcx?mId?duFA@v8`})YJM~GV z6>PrtiuMfV5lg*Kn8=bwhY)72ef^X7wwN5a4%KPf%BKfNYyQ%c?*`cJ)64TOpTqJ8 z=#&}jvBOQpVi|ve!1VC9x0jgZL!D*w7}D5zr03$C40(Goo48%Q*BLwG^$_>DiXwA( ztA~K+yDMVTNqCok_{>hjvU$xD=D#&F%!za)a~$82lSSq;D@Q6ii3uYkKf_)|^0QdC z%-cQi!|iJSox{Mge*>$iY|+T@mZF7}l#!xh^I1l5ahc~{#IHsg2BC>(51II#ttJ;b zN^akF?nvBZijqjt|-kFzX53t=V23!&O~V*Fb=##L6B z9PzP#e{rhF4jLOslTWF_DmLk`v{0}f*^W=SSfIR?D?r%_tuDiwy-r|fD9rY{!KbG6 ziD*98lm3>EOU)FXae_pav)*+n#@c1Ycl!ntL8|xRTF6L|Dyu+jn+$q~+vP+XS!P)w6UQSLrb2;t)e`#5^G9 z(^0VCa@M(>s`JR$er>4-@p&j|U$Uylg^RAaVQ9$Y@01+BNoYn|D`6mdel@$}#@Lqg zTZawng!K(nkL>$}wuc9##dQtw<4LKjU;2vM-yRK1gjAN7S09aNfZ}5{ zXQt_no-i975^13iBLB+8+#H`;kO`NO_7pro3kUuLr2F1h$q}S zYb}p0*Tl!0DZDMVe$wf<&w{Tun$qL@TYqtyU&Wyr8^?Qw6RnS)&H6*<1?uNo#(Awu zw0LmgyLfuOtJ(5UEG!PP*MEK)uaWLoF=;~{!QMwTYP`R0I;l~zk5ymvtL4}JV#9lx zJNPC2%WB*s!x#IF>8WeTt>%-sxTFAx0;Up-Z(fw3qgIH7M7?-^ualNq&&O$L(`NGRvqoX@5V%Z%gfDWV1WRU=S&eJ{C~)YVcDB5 zn+0*dzvGryHa4j7jW$PLxXk>zO;_}i2{AXnxyftVJscNDZ@;*W{#MBkm%n}fsiLA5 z-G4>{n-ExIjg2)0Q&z1;YTeko0S>XqRqHBGm<7|(Sx%O|^WW}T&{^WrTZ0-9lyXUY zT{qX~*KGY>(C|=yORGpL`1;Ymb3W04tV#}nfXXd7s|>9YXq_{9fKb)O*tTbx`-04O z*rIfViQQ-CW0Yi=T7QH0S|}1{n}XvJjg{et*q?js#k-hXrwop^*U9<5SAYGQnKX@X z!gLvjyRWqD z?JexsV>K6cd&p4JU9_>Riz%R$6{yRG}>lh5{HE7&fEa76Uk%85y(x z1g#4LrR+44b;!HNmeYiI`P&>G5j)w)9-X~B1l40;U;sntnWd%d)2Ejrn}bLxXl!P% z3qX3+-9N&YEwwbvH+{WToz54Vo0b=pso>UYR2xybx>+*!7cH-?}b+!3TZ_{GPMPn-ISGw}mb7>4fI}=Jp|_<(^o# zqP~r4&9ul>CN}ox3kzRLOhO0rN%;+$8U?g4*?rzN%&QU+mn}O$FbWleko|Bph~ zW};GD4gN-l<@sc)0BZd|1%1QAcUhcm5bv|A+{?xZ86wj2^5)itzupI6+qkJfsrPwF z?VO`+?bkz(r?xg~3QxYC(^T4EbrO|umcXL+(@{BIPxn$1f;znQ>g6~2#5yg+kd-3Y zq!#7r?xp1R@U6&|{HV9p448d4Yrw!Y?(XbVv$uGJO(!4SzbveMu@seObv4`Wf42XH zMqZHL>ZJVTL1*#SAENWgsdnir~*>&v2JFt*4-r}F9e-F2MFvQ}x+LGN zXK)Zalj}SIrII=U6sAsh>VgA;|Gc5pBO9$9!%tC<;anSSMRqEK&T|l!jQU+QV<39 zk>q>=xe3G3X&t$XRqGBM*Oreh*w!W`wfifpRZi&;R7;nrebU{4t4$jP7-?LCp=wNI)*FPz~Hbzk6hTd>DT+FUw?$9 zG&T8dv`^a*)f^dT1Z=p@1jsQfZf(I2xrVR9;h)DJyppX!it|)R+W_B}?1ngMHN7VC zlh%;{T7$;Hl>z>Lyq%vbD|plvsKI6U;Taw_jbPVpCo25F_P}<*8Wr;JA)JQa@!Tf! zbd<2OgwM~0hGihlZwQ}8_;~P3O zv@{;&`tuuFkfN~8P3BNJm}5Z77jNb8^AEieYnhTZgCmG{H|x*76DN4v!cH3&hgPNI zpUAWQ?a!Iv=f?+&A%;fh)(uS)HQ<(k@bG^t3bc9JNR=V((zDuD;=6MBeRyHLvMgc# z!*?FX58{tRcYKh`ZtoNr%#3fJhc*agJ4D-WzQ`y-dXsX9lC%3FIN80f;)#h#M11+& zI?oQh#GAlk;Q18cIl4QloWMZkwzdWMI1jtdvEfdZ&VXmv5rm1iy3+UmSuy6KQf(0s z3@0t!&O*>IM<4G0kcz6hw)V$_OYOJv)q?*ulhV;=Lw)UXv;S1yq+XEX4Ras8Z73ua z_0e={Ot^fh&VXI<-jutG+tsVb#S)|ocX_87oQ=vz4n!b1s5q#9SZpM`lPgyC-jscX zCQ>3i6PO1utI?irnYPh(n{PyJB$`{lEZc9>sIw$rQPnY114dH9d!Um+2uiafKo3F7 zL-OP7or|VbRcq=i({elOC6f{r71eB&dsCRlZU-l@VzhTy`}l}J0yj9Q=!MYrAe={0 zIO5^cW?=NgF_y0Cm44))=FpXRhudo{Psa($61RGWbn@!f_cQ-*&7UzJD+0LKYhcC{ ztQU1Kp*`WWOf?kQyI#=R`fRjp{a*4(@zH()%viwR6n%&64!<@?%T(Rmj%OO7It$4( zp%;o4wkN#OzidT+`Y)Y;Z3q?3!SOZvLsQXD5h^MFfR)M*c^Y>d`=SEqTMYXdHPO^yrAhuq%uya(-nSh;SOKI;R?-+rR=$>|YsK#SKUG@ExongxLmAEby4 z2$h2l1e*3|*RG>|B?!^ZcXq%*o06Bz$pGqmO|{Q0PZn+nANPd^MGd zv#X1Y4t5NT^sFolw)Xbq(QKdmy~a!Y{GlqPqD=u&LXS8E#{$O6{xf?(HirsnwI(Qo z{zc_~s~W59Hfx(Gk{~3CWs+G?&=bHpAON9C<}m5JiiwHs^Rr#Jw-t0YkG#)L0Lrt+ zr~Fz&)XXfa!#I{r?=7T!K;pO2G)flt`b~T5vyUX)yY*pl#=+^|3v6smzok65lw>JO@$12PsGUau)euZ!iYUa#`p?eaQx?Och*=*>NVxXxyqxt;!tvS zR#5i=bYH93SQaX<5DpHBmJlWn>ZpY1Xp~~e&(Dt%1U9pw!e9W_@!V5^PNFhH4G{V= zHr3EyLxAKIB)LxvVBGE(#tbnqT5RH}+RINzpIYiW1>pMjKlj%+ng} z@83lUv37fyjg)+>$x1n*CqZ&y_v|wx$FXUA{ZaQaIp(H`~A&_)tsT)e+?*IRa)W8zR&cOju zo9#j^d3#;l0ANIdeWn)>EAUpU6~@CSkEVYxq>WDePKjCgQaU&YKP%gIH-XSUW7nt@{dEym z-vID82#T!If8HzUW6mmn4IRpjpkw?0Dr$@ENFOi*4+wO0(-O>Hy@Es-2vnm$dTYdX zEUc`IWa5T4LvBm_9UA;r^Zc4pkR3uk={MypQVwJ2O&si>0@C!K&g&MQYXu`k)T2FR zy{+Y+Gj4FZ9?lt*TV`GoouFv@*8R0*>c<6FH}A>+h~MsDLv{{?0&YOS2xRDqAmjzM zuHr!&8z>OQ#vodvXI%-Hdzvf=r7f7Ai|nn1zHx=&gfKL3`jH zi1hz`7fnL)JMfd*SN7O6X3x?RLkSsN#~od$cE^lNphmD_jLegl+OHwZ|NZj-wKQeP z#y>)!jJAiwEf~l^yg3dGU5L~MEp*j>&$0tfgALE!csW#T;@Fr)i8@4_NJ+IpVo9Rs z{>$uvnCR%?|D~N<$jnIiB$Wn2MVtnQ@oEb?`~YxALH}G!YaD`Kw*l*j5EDab7&CWMrp{-GfW1j4~$tuG%7ZEWVEJ^H#+GZmjW^H-1JuClFOX1(sd#KH65Z_D7;U$;QHl!{J5gB z67n8_CNXMUoU2`P90pCC(9o{d)?rYMLSof;VIgkaJv5v4SqQR-$%gR% z+#~E-kO!@VoBDtkwp`MFrq8YYiBZC%cH znfatZa=sklCKFpQe0!+-Yb7Wp=M=4~idT{cb%WZ9 z*$O!s6m`e4&JOC%Ehnm@^`aos5b`JgtF=4xxwy1!irJ$_$W{mTLmdYk6DAm#IKV9_ z^#3Wo>xkp+>%jwx9qUDFa_T|@t{+J{!wGkaC40D*fmR5SnlN?px+tEaSxLnckrb_D zt&%j&d~`ugYS64mFG_%D+-Y*yI4{+VQRP4$(6VygK}FWa*RaKQSXx@{AKQTdwt2RY z*h8q+1QV^fK2Zq%*k(tMn542LZd|1efRb6;F|Y4=vqbi(mPOfLP%Kc?fvAsnJ)Hbf90yFkoQ6PD9!3JQ$Ja&`po z1*25sA$V}}1DPI3jVn7aKu_RwJ?WzN4`yED;%I*ell!5oi-beX)d1on7&7zo^JQqO zLZ+|O*Z&9f?NRL^=p&M0WnDq9DVx5(dKVYwO&>eNY2QUlc!(bkTM*R9chw$6Lo^#b3kw(6a)^9Hra>q?KR?irlc=5x6E+B+ zgIG}gLR12`IOHgStTgTUtHi6<(QN4diT67epD+JR7()$&Yw<>#k8pNYgd#9I$q9FG_uFa)7P^o22eAHiR8sJY} zJlg^+5HDtU&c*!i)Q@)qreH?szm5ccv^q1e%ccC8g zxvGgmp6b~H(1!>AMabr5wC=L<5Xe@$cvxNjS9KTiQkwyhlk6St2>*>rgM}K7MTB27 zD4lBenns~?KkG+RyWN{@aV{@>W+7?13(E;Vj+@2IUCgd$U@;}=2^gzxoz0+a%owvj zSo1yh{r#a~RzluwF6pRH)OXx*k?Gx!zira_8xs-7(@4f4?>)-cj{R+HX>hHQ;qjXd z)SZn#qAo-TxY#_QtTkXYBPe!l0I@G5fDwdg=bQBTfZ`9xt3wGaYak^6iR~9z=sgnd zPPd?;#q0i8_^$aB0>24Dna@^)S3i9s5Y{Z*);#lZ82F3Vz-@U{TV4Iuzqj}%zM|C6 zD5z86_tc&5ZC+WftDqlS=y3fyt3VVqzlDy?efD><{%bvK>ROGn>KKQ+>a%C9FD56xBUNz zT^S!T)8Cf<^{h1jwW6ytK+2VR_J4Ds+CDhI#@%<13n~nTmRfHfI9A3TBTw2}i4`^aNn^ z6{E%GZq^!&DF(uM+PPrvQ z74|TKZo*HBDvX+j&T<$KneF@D%o-sOcv~~MvUl87`={Koz0}(aWFF^7vGT&_h_vTo zxJT8sf$t>JiRCE&F0M3~a>~VU(layX3dbL;4bN`5xFyEM$3tfjl-}L_{or{#uAtkF zhQl0K>)&Z|^|k#QRGSwbat02AOZ!iz+T*L<-_qGQEb8ikF!6GpV5u}dx4ES^S7@m4 zqj}92OcJdE3siJ9;p0XB>{Q%F)8sr1QKI|(ijU1uuIzcp%^}ZbuJ!-2DKiDr2$8A zi;9NE2u)t=>T9o=F35d_zSOIh`E6pIJprdgRnV56D%<6R()oOU$5HlbvOJN(g zO04ZYw0+yI0!E#&TG++I94wYwWn*@$%mz&%%NquAoVPn-C^qiKb&+8Cj9EEDljB@I zppy|s^BOXHJG-0UN*EcbbU&~v9*%<;T&~!z+G;rVFfM><{Qi6G{I1SUA>^Y=Q-;(v zi33va)l;$IlOOlyIEi-}-@)rHZ>ZH8-~#vfqCXw^J1GY($rh zE#=k|nUDXjFg8LEdcwm%3mq*kfphS}a0p+UC-grrfa%#&eVA#X9$zs7pp%7qyr!VO z*wOG$H`EJXPs=hyVBP;YZIgqI8db!vp|+l_x)nYdQ6BUQ<(DJ5i~1W~m+-cz3yS~_ z?Z4NEEs{9-RO)M?K7>u=Kkt^Nigo|(j}SFt)Hv|r)_?vIw9-BT-9-M5sIO6f?c$61 z)|B(enwx9nWW(?N`@w6kW;nxaQX41ltct*IUx&R$eFYr%{YgB#y>yg0VvJee|ldUkRv z-{y4P3k-Fl+m}Ne#n4`3EA!kHN;#W69xOBNO7Kj7k(rfs3<=*|yG&?HvJ|VRlctk5 z0Kv|{(V@$EmtI)?rS0!ot4<1Tb=vU8N*1(wfBc(%lO8)A^1`RewBcIz9gn?FS9QD( zswUG?Q%???h2`Ysg&~yjGrdynLCt2(-k?S-m!+AtH6^#zn3nT+L{!wn_kSSC93mY~ zyv`wJou7{{`3h7WoqoQ*4ngb1w~}E~fCg=8ZXPS(x((~D8yF1`OFjt^13-ng94miB zhy?~h)9If+-iHm8P8(ySI2e(vI%kkjzqEG@YfF^hO2i)wFDzhJ1C`fb>8rZsx~Oh) zxx&)!2t9gu@WR#5s-xAKG@*RqL7)wbf0l>ZM;~m}vb^_kzmmLb&BTPCCd*J4K(RNy z4yq`<4yRF!=)G*6(;@HE$XoglY%k&u0|r#m&LFeE3-H9WvN5pG>o=LR94$p{In-ay zV;-9W9p>1)D6dXU-u_$2p>+=ft-clVtWe<%ce$;A z5>PemOXfyd2}Vez=g)x#sHoTj>Gpu>kB~Cb)6>(^(%St6bqvU=hAfAUcz$kP-iodk zSWLXoft-5PFH&ih%jvOBn-k;1!}RajqGh0b21^GHh4BNR%MD`)*Z6A;aTzQ zfh@Hl`0OU|q>XyUoR*-fhGSzVEe}F}H8!Hor#qJcp$IN0r8pz5SYjqMn{mgXJu3mb zxt3SbP&!7rNue%Lt-XnYkE2Y1!Y9+X$Sp-Q%_TLI2a-iIKy~Ny+c89TMdOg2uJtQf z6)oz!0gj3=zMBo^yaeR<78q_lg(l(n%Kd==a7pS5-9-zkp>st zDc$hR#Xjfvz2h6>9pAt2AMYCb?5zh@JZRfd2g@_d8+k#vQ^al#&Y3Oj9e&X5Dd@1t#F0Y(*6OCGbR z&9AO@Zuv}@t5TQ*;568YEia2pN!2eTxuWMD!2$#}gKrzFc@lg^Xh@)UKgzm|96cNR zRXWk-GdoC01&YjkCS-zgK8jU`B~-KlZs~;m5KI6XgK9s*uikb4Y>w3v4*?ZEfIShB zk-HYnALt>B{_7EN6@$u~<_H{pSyW-}rkLf@LM@0_qLd5&HwhLoN2q9X?_^$n0gi8z zx|W-Wa*Z+(!q8A=yrJCXnzLog2I-=vykUb|#3Ak24J(;3Lyw@f|aJ z64W93wgHv{h6vZiFDZzz(bB}=!z&(*UWbk9z!m%WYuV1-NOi)M-xwG~ScOAl$FKtz zZu(<%Gh95S?pa z3?FW=khZA?X;4r3@8fmag+nXm#>Th^81DrASa#@6(0#x1j0qiuF^uRMjYZi=-L#SU z6^&!7+g!(GH3$7W5m(cw&@UxD13ijRQJTk(rP6v~cfjKvM_gH1*}4!6(Gwy7{k(Dc z5@2EMu|P?N6B;-5_=|LA-^#9+QdrMIE{wC+udA{|12hfIe;DdC4j3_8cNaG@%IsMP zXmF@Su|&f!iJdU9xcLv9h!wuDfnH=1%I>((WDw@uR*|ngLikzU7y6$?; zfLf4Q4JThdtpP=+x;i(Amc6}4O*5OsL9H`mLMgIu-)8hJ1vU(4W6GnJ3AitZerAqHrCZisX-%cJp&&bFWpyzSHU(S%$HqymPGw07!MP(-ix|hH$mF}0e*kmq5BRF( z&`x;1ZzRb2n4xwrXT`9mYNte^;Mre$e_XJh-iu=*nw`@=Ih|uld#Ff}pWwJeh`^Ky zU#QS%(Bd#3d>gG<>^fvY)ttIw;WpWjjD9tO*4c*bN=K$UN^Zv!(KVU{8{^fQm~wK~ z2PK^>@GaD(OGcgK$6K5YO6jbjDyLnU99PHb(5GR1 zh1s9FrR^K*u2ya9=7sqhu3dz;w9mFB2HJAYcea?I@u^6>ip~TJ}$0y5^obT!n_Y>efajaGwJP!Xgt1&42#0$0Es&h&yRoFBK!=2All9J4(P`SWlcI4nN34Cjd3LXw7|G?@=xi84+es9Ov9j^&U@> z5kRCzrwZ%q8xz}!8{@?Xt*_wg9JKU!GN3Q@qxkGcp`znQWASMsiB4W{lKsw{t63rX z)36hawp%^inqvZ2_YwoK{S?3jvWkkVaEa(1(a^wlTdxn4$%n1AKX!271d?ku)i8ho z6f8&R!dMU?iuBpvUz)5`f^cCgnmH64&qn7t`bvx;Dmb`%xg)M#XI)TPtq*S%9cHYg ze>NBlYFIX)1_tKro-u_e@A8;f8bh5NDmq(#d*jtZB&6mI=i@gJep_C1?z}zqX5HI8 z1@{oo$h(b_J=VRW6&MLW?7gtdikv`>&DlH#l?{klWZV|_cR~io0!f~hl$M4cXC5=B z^{UFsvcd9S++#raK~x1^r>)!+IEJOlu&ruA%0gg^2H?7RGQ?wJ6Q4k(GO$>|{p|j2 z>DOLQAs9$Y{3f$2F1Ukny|2>pTu^8IPbBv{@KlhLN)Vxo-W51H@H|5*2_A$ZvJ+fK z5@fR^FgKHt&eO-8%5|GZHT`9ZcLM8lVQv9s z*VKh{UyqFxRCR{NU3o>EVV?~wO>qFzPx?!=gWNTS}KskVOBUNF|>NmPj=}+%9hAZri3noHA!qgz7 z0jKr3FIerZtgN8B|L2_D5X>Ewhaca?Qc_mtO`LfC1ys0zt^fh?!&^6qDCnTF2Y8FQ z80eJz_(A+l_M3A7P|5K9f5vmOf&$H8d7$b~bRkP@a`UVARRx5r>o4tzMZ2nC^89Rm zWy^}VFom#w(j=~Hx=wKAknYRX&@hAkp^m`WB5w01qx2qa+WLvT?JtOwXK7WknAYFn zUsTbZ5Y_PU@j-|;?U*z=DoSJwFfTy9(n{~lz-Vo<$D%`R8f$7Tl_N5!M3ou79Go|& ztoRr%7iG2I?Q(Ta7fF$$MUG6=K)s@xHGN**(d>q+9ei^CkoUXR3>Q=i^#q>-EgiUM1JOf`XcH11`D@^>W^* z$V=ZoApl4=B6p|+DY=S@3IWt`&(&`{7Z8XQgUu1{dDs@~IlG4ib5$g^doTJDQ@>jV-@MR%BC7Xkn5ytzM7wCjlLwM&L zq}1t(xjieEW>y^B&LKzYIdkRy%C_>KUu75yNy3Z%3v<1HBV_q&>2^(b_m|S%xgx%` z3X$4;Im=&a;g3aZFDu-ES7oEWNGg56xFLy`k;h)qrnGIv*ow=w4z6~}mq+RZ;quJl zgfcv^2EVCYpgP0__<=rG(^4J|PZjYc1{FX^5vtdM6_BAyAbI`keaMD%b^m=;?hJ7r z#-|`BTwdM2Qp8u^)vy>LBq5*58Oz`cpYoYL4LS{d2*T7);s^rgy}N_ETd(`}O;rqx znA#zYSzy(x!8AI6opOeJ1%bHhRKNRBGoQ22IWuk zcq^CTTUj++EK}mxR2iPtaaA7PIa%~~=^8W$5YPW?{{j8pG8Z=~&;UiobX@1*iyE2T z0y$l-er}W9(R71=5xxKX-Ub?z^7+mkA`{SDfi?N@EBE5gbuA-@A9GebtKIi>BmxTO zhI>g%TVf*g2AbdL!A(uL?a_|$zEK#UGh!?l#l`HshuK_%JBi4Y{h{zX4E{OQMmDXR zm~1^;W${H35)~HHY2fD@W-F4ekY1V$1T(3dRI@e|16UYdUa%S1?na15;(lM)1sy8t z;UYpm{=}D=PZvcT-&Q3`-Kj0j=d@Zima&%1T?{nN?qqffYugfhn0 z-Z!F`4l&lKQ$8*=zJFOonMpYf-nT;pT{tWS0R8@X{A+m9ijSYgD7~N4!z#(Jn)(zgfuX+ zza}87E#Eu4B}~04hBfJCopmB>TdIQjgwE6~&-Bv!P%l9B-&3V3o63hNnBU&z(h&Oi zgzDkbhk$rJ$FtYiw(5zX@NMrc&|8-Fj4Ya1m=rvBTOIC= z6V4rhH!TbbfPKM&nMBufcB)f(V-(g-q|0baUnEvi39Li(q0E-9D6S+o_47)G4xbhT znQoNMI-H77#(dF*lU9=Fst;axON=g8nUh9c7woB}BECq*41t%KGQWxs;RX#25JF3P z+^`VpKSS6T`X3oA#TyU>e&*&FY+HXR4Kd!QfLq2^rlffhx8i+Y=yl%8Wi~yAyPu2oZA3DHKLgIr#*W_IdJk!N zT1<+$hRyd{ugjD^8l1y;#-ue>P_Z{yv8SAY$o`TWft4Yiy^Q5(Z+D%K_appFl?YRJ zmTmF2m>5GgL$>5&Xbk$oSI2$I9&4tkew0~ z@lQvRr5~K00UQQdG=)9zW7&2tjzYE{K)FC$zvM&adr|fRdYn^JGiuDC&Kz}caL^a5 zTRP&6d;9k7i!)Iu1RVr17Jz-NDI(IEC6@-^U!rv8*cz10jFF5_(2q@nc=E&3Gf-cm z5`O*Kw{wn~1Dz`9UA)4xN?1_n+FY{t>aR72p`jtrmjwK)Sarw6J$ay`_ok1WB=BZ1 zh+QszpO{)agZ-k&AK2LEOY8=NkisD*&|ftyZP5d@iqxU2P4r($(6ufkAW?HqB%xZ+L+h9pu6^2no$z8 zM`}@6pQw*`LE{3=!@&^p`S)UfQV`5$gI?mXY|G6|Ni3t5trvn8+s+Z4=t>y;KKK(p{31M%z-KbL;(TAXCv0rA`IiAom^I0KxKmozUE z<+Zl9LNPrh7^3~LnB?~QsyP3^jz|Me9V z)&aPqwxB>7b8|X?)#Z88B@Z@O)Z_oQWpE#B*HxXji=IO@1(PJr&BFe|sRJgruX`a< za1lS@nYBIeQJ_viomP~TY(a^`!@~m|@n0)T40-vd#!6ZWrL4rOX1B_cYj52Ks0pUh zg9lwwb-r!K3RBDFMr>M}NTfb-wTqLtF7RlyFgJ&WIS9>|aY3;RN>hE~TIA&9Aa_Tk zWhV!w*#8U>NMS=|&ES17>4lT(JV$wc&<1LoM+xIU;Yh%|=<*GViiuSNqz3qFs&X#6 zMU_}7iAHM%AxL3OEjTq75i$S-d5L#sbAwof0yI#|2QAtEd`06#F(a4iI zRuJd`zH3J$6Y_M;GI`nt+-;qv{U1SflMMd_|N26WN(;E7J%Z;an@zxkLj%Sd6=v-n z9pC|RdM*rzD|~vmC}>1Mes??XAgYWqzj!3lk%hA60E3t*GlP3SI|U-g$^|}08qO- z(9Hw?Y#jcbU*HpeI74~v1lMn6FrNTX2QsR_i35(x_VFn|4b?!9m9gf@S3U-%4v(|L z1<<>kgvLhgSP11FJlVQh6CnCsr|0m7E9Z}ZX$wp$fUb;=GW@#luB@&yv$7_$z4`vY z$A{9RBtNGnCI=63$P&`9e^!f_EpP%SIkP#11VprUvV$3y<9H_7!S=wHZqRf-< zMo@V*vcZMBGy&Zau)%^6(-$lo$H$&*#G1u=Ex_Mphw{@X*}bBwN)>anH2HM(YrF-z zLkC^aN}IV3z_e~s(crg(OM9B2FnlYGT8Fid*KVbrrg51Ke~FIXhbD)CkrBFYi)KE9 zs;dAn)-ByvG>mo+p|AMmqSSbzAS?U8`$tz-enG+D$}Yk$FONC1uiA8|z_g&c)ubGY z3ihWqb4=Uwn2Qyc0-1(Wvr-F%h#}^7>8S8wef+_^&6OR+8 z%)XJ@(}OpTtHZ?x?fAfCE>?wx_0hRCH00Ft*FDb#pmx?|nJ6_188MRv*P`?1B?QaM zSjhd}sH5wdxKPm=g(T{VvL$xCf0l2h0;gU!uM5mXI8~KYWN;S!T^{-TR4u&@5PcXw zSTs$PCX(6*pxV}Udq72p{FH=*xLzCFvD7s*LVD7en3zD26%eO?Kl{}p*plh#v#^c{ z5Ki^u1qCeDTa>8;HL$PO;s2yOnY;m8&mETCbxp9a_Pbb(Rv!Ms)Hc@}Q@4^x`doi2xv1YnTDd*|zvAq#lGHeul>Qi%D6~ldfdZc2rmwV9 z)9YmHFHunr-+PZva1(#MBGS5h$5+R7tsLajL6GcHKeRIS{OTdu_~vW`0=efW3xa9e zG1~6C1Au+rq5_xsLl}F&qym7bwzeMH%a91duH;4@qUQ%N3hYmc0JDOFQqUV0_7@1j z_KL)JIg}8g?08XUr^h#xeQ>FXv4KeG0I>%E@a`W!Aj|+N3>*PCMELr;jP_Rr6}3!> z5P@B>99%~D)>E=!VsG_ZrGCabh2nlUbTl8cpA3{16nG#lCKk(S8l_!00odTT^#Zs9 zcYGm0xPRXPbf03@OsXU}kKt6`u30H? zg+-AX;0gM&-AjF$Js_U}!!q~p;Fxg_6Wn3)u3FB?h0|?ASOX}{_n1|2{e^=)dtK?_ z&cJqu;sYKa5?G5G9~p68&UJKffaME>Ot3%!y}I;t8Y*wlmjeys)sjv)M1ijjGgHtr zfN%q20t8r}jslBp7rF(4k_^v-QM3rm08ZLoo7Rx}rhnLCnHk=ZApG2K+~MYpJF@-U zWjI!CYn1%%Js@&02Y~PcU>F7}>>M0KHdrdh+($26`S49bw$ut zB(tcR3Se#U<3cYn=)uEw9|9T tXFJsTSvP@wVf@UZK$=|`T9A>l-s7=^PKciM-{ z!Y?487C!mW_9psjz5*Z@paG2L-`~!zkP$_oySXzZSy{%v9IdZ)e?l`4AY`H}qgk|S z^jbbs6-@n|Y61=!H}?>j1aWck-VcTn0~CL$n!yzU*0qU=iH%K3+e|hH1;3Iwb0e=r~xIit+b;sIxIf zf>}lzM-au_Fl~T3@jyyv5+GJ5kP|bTaAZ2N77Uq`qcoiM2jSez&E0l-j_RC39xdm- zc<}4Ib|pa_=BnJ?PxV9i?}g`H!4N5e04GYrZaH2Te(FlxP0@2f;prlG`~wYg$L0zj zn0|Pr1N9Ze=2*$W@o@?*&HMhsz-?F0zjO66h#J-|v5fHUT2{V##zu7t~Y_O}2Ukm-iJ%!=Yzd2YH6T$aOTW`Xq_gAUp$Y1cij4Np_yGp@ zc_f{{3fEraMB8^I{x!+<%O4W?9I>dF?*;T42MVP9b$WgbvjENaOrha{;X?^|5g$t+ zBP(kqH}5B0QmGSvX#}=0(9v<%zs?)WX7w6FLjowRsWPchUp^E3x1S>j!yt>N$}G`s zXmVi5o-PC@ei~bBCY&9$^LX^;+=T*Nm;5z$>Ofvj?zUmG5~`qi4&`=>IkHDAtOQHKJeq~Pdx zc1|0u$<9FwWnjWZvHRW@j$R)`3N(YLnm=5*kuJXk6JKTO`Ts3?Nk~YpP@EWq_ox4U zsqvCJyqz=7>61LC>Xl&8%-~x+iMnj`-m=+>)1DJrT=DtP8E4*LforKudj%><6$OPp zVB_#YHr2^eYqr0R%3OR`IR@fuYgW&I6x?epDI=5o%vuku9i=lRL`7lf1m-6)`?3d? zfh__+f-#1jh`5Brl@H(4+~WE~#mMTXA0gvxHdk#OZ{9ByoEU^E9faVyL6*hS5hOi9 z)fJ+8-x5rlfPTG{R2Rm2K_40_cs1yrr&YTL>>2)s(^fLeKULQH8&NO|cnk!W4HPgX zqR?|?J^o;t`Uhl_N1~v3>c+k3Djls=~MByWKbijDbl~c(_O3qcY zYvs8MTJGCBBim_@@3@|Roj%Xb6=05RXD_7-q~Oc$VIaJL96cJjVPWLO%i=LT`!3X~ zO6EpoU&CDKehyP7AK%Bph@Di40t)UYf-Z~I=bDjWQA&181IeBxjc*D8w{4U9nd~|c z)Zrmx^IWyWb)&n7l+5I0%@88)fvgRrI2Lel1uIHTFS6UuNFu@OV)0ml`PJ3q2@ z3?k!3<8&;7l%5MZ(DL0pe1L|f+1g-{Pm*~acIrU*xK7m!e7~*jZ6Mel*$o5h40u3~ zykj7E15@9nzCHw9*TQ7st_u{ovh+tJm+2lo0u@KN!{zE)8XB%&ImgT@U~V%iGBUC6 z&+h7vbCHcZa~ZDV&NDEJG5;~OlQqdhO-iX{oRxcj z{nxPUcJ%pCWPU}7)nH__b}@bb$Yv$CHuJHIisOyi{G&vBPf3zUp5v00+c)Ry`2>$o z!X1y*#=G-dJUv%v{l92(oqvtBB`T{p7)0(J=RACi@M}MOh43pWM6Ry8WtkP?Qag5F zQwbOG{~eV;j#MrL_=n$u`&~K`R&qFlbU$e%dsN+x%X2mcX{c%C82IZcG6Id-$PT@j z0U?U-8eA}jR-KH;Ryagv$fYZ*s+?dx4yJ0)#`hbDv`kKcXLyg05Qf6GVRZvRGGMR> zycr-qJI!@O_Tqh?`U`~>dJjT37K(4vOcp~J^h7fvfkg-E{7l}el@=%=9DW+_;W}qq zs7WCYK08L9^Nl_w#xBsTw&{pq0J0ytq&!Kz1aPOFz?thqfR7~qcpR^x6CEh;id}Y> zp&WtHjJ+k8Uj!^}r@T=Ya;xiJDaXdj%7azbGuNM^WLTmBvE%0EhLiFs|BxXlpm9SK z+uM$)xQEpAxK2POIB{T6#9!ppt~F!J8eCzv(6I>*}B6?H4}#a$2iX342T>2%err#|xf~r89i$y_1;On9)fo zD&CHZkc_u)@jP8$n4_&N)GSIDoQqsLXMbpV62_M33HX~lV!rQ*CQmGH~nIkIt=cM}CYnLRZf z#ua<#E4C(~qUm{gtCBs-XXs`yP{5M9P_*7)`vpS9y{{=n0FcqJsGf^8N^;(TzdKES z=!)lto;+|Q?BJ{fCl9cPn5eQ&Ytd6HHyJ<^QGuTLeCL;t3K0K7JWpnTAd8+{#@qyM zANbgqja77Q>_f`A0JYuoSC`cD_s~xQ3k)DmHUeJon#`+l5*T=B-XJWe=U{CB2N=Q+ zUaw`&W(~5;nN8@>T7IJhgbvogtb{ymK6{#b9p8?Xbm00RZKs~v zw}eeu8B>`I@Wqkm`U=%eNKYWn4>}oLTbTdSq`CdpHfjc;wB*erFA1^{Bue<%ef#m}F4_HGndJErjgrWOYdb=|)*Hxs$foW6H|sEFzg{--_6rldEHpM}bs z=gFY~`SW!Vs^$i*=J1W-wvqoJez8~Qfoseolk!(ju{%y{qU$7)bppZlPPot-m#8)0z^rgn>ctA znGXyCaH`AfyM3?@K^=6y>oc2^5H$FqopUG|n?PY|fY}h3fbJM{dg`%(7p(Kf{ksUHdl45vYy;p48&HP>!J-gGfa~|iHGof*duPH{nyModele|M%)w(V7IA z17aB^n4mX1Sj%jZrr=y=4sviJJ!R< z)l=gWE;z;lb?8cME%>l&7lP!1*?A8b3Ux!FWd&;tl>gB+5S;f2KhPEM02DzYn&mMo z>&E&XSd*}7RzdCsI2rP^5y!(y1iGt`pw??|fK66g5Sb&o%+&|LLGWxd1Zp<)AZ|m0 zz^HL4x>S@oK*MOf7m2(jP?tT~NQ2N-9Dr?rRM@oBMgxg(oZJK0^yBDBbfa#?+yOuu z-IDuY#0C6TV7vx6$1umGoV%8Iknm+_1yyOO1CUc^Cro<)ufj;tDp_{vkDTdrEzx0; zECP{=`+dbaInS$G7a!sMaBA_a#jA71LGk{H$?ph~ZKCl()UZ+M3`9_f+pTB(8fN8@ z*Iq@x4l$W7x2aBrpv#g&T14QXk@msnf*+24KLq0YJLE1)v?)j%fZri2D=VqkKGNqV z0o`+evmk6OFD}y4(>tvew|V2jSbsAde=spmgep|Sv|dPb1T&v5sL^wCP20n0q03`* z^ajY}{}#IJFk8&-UYdT|&9|WmC$N%=R^*p2xb?CyK@UOz=Kv%P3=Qo&_nt?97vaxMpB*2C0mpJheV|FxbgpQqFTa-5rwQlLpf0 zh}j;R9}DyO{a_rVs0bAefG@A9L!nsV;^qddCWMre5f>9ons#mTjVUw7-|AV3W&AFl z$&7G6%|Iz9Cd84L+wrZT$nzMMT)eV(RuW4WvIJ zrI#7&&rRVc4}}ZnBX1SUd}aDW>y7z|w?-qFoPQY4jEFx#r=>XnOYpR9Ovi1@f(Ad9 zSS5tS$9VqzR&ZL2{Bf*80_Sw$zD2zK$j7InB!)!^dt;-$y`d2i{qBwc<>Zx= zK-0}0G6x`oLH>b{5Z)+|rH_b>ea96q!=<>tebush4$zfq+9TeY46)?aEa07@wJCZY zgrJeb5W(?N2e?wO-ucgr;6{!6H%N<%^V-acI?7Nr|Eab;&TCQ5R)kS^z2sYfdCFiu zG3n0|V1<@j^#d552(d>`buFavtyj$i^Q{vPR~we{#PwhGdReaxyWfDydtw6+B?^9Z zXj>}g0K^w5K4A3d^Y&nd3Uf{#z4HodPfx>Y&DHRXf}nBi7r$^l9uOvwt z8CmrN%N%nU2FDspOG-<3<Skx%pQ>5g?fm=s7G zi3{VZsdXP8;2s*df4DHAlgpzV8kd9`oF1z@wvCm1Vt=HWDA1^^akyuS>`8W5`jcyM z7qR0^W`=We*z%~%rRn@|{q)LV+#SV@T9{sgh!52NMO0*RvZufwEu-yHsMWe}d-QIq=@wn4rY^W&Z97<`LOvAIW z$_<{s16_U9-}7^}xG9x;QlKNiYa9b``n3O#V} z&de+jR*+u1NqK9{)r5`zg@1QNgYTy9FX)b;)j%h--)=kF`MiE>5G3A1dX&>`tK(i> zeQ=6oIiJ$^YRGM8;Na?4QKAUzOxG7#nzeBThzcZ|aG$&2!^|330{xd1_iph_8u@Ey zU&jvJ*F6KsoxedYL!LHH3gK4~6-8CJ=thKPzdmxPrrsKpg1%n)`5x%e)2{Y*h@*hJ z03wY7xV+1dtOKq0)s1=4BKC;|RO;8S2ce?Wp-K7tVBiMjt-~rq>4;XoIgJnhi9in= zwTw8?HHo*+Jr+WI4wfIIUzoEuCKUac+H^7ll8BHOplVrJ&qgAe0H+rVr@L*P4R3@O4Ghf1ialHSWEr%9UUYZz&tzex;G?xj;nO zvVuJ{aY7k8Lg2GA8Y=qF0ITObNh|*PbsoS%^?WWc8p8L#>ebk1 zVN8~&2*EEEFQr8f-~j-^0BHgc%%&RJD-UWiTC)^$rO6g7-{lq-O7}dlgt^=Fh`{9M zTgNe)HI2$d{+Q+Jv5~Q(wOTCE$yOb20H22s$AMRnmj|&C^+eCIw^kxw8B+T{01N{6 z|02~Vcq9!OPVbn$r~nh>cydl?(l)UNh>qH2P71)>(f?mVb}7&VIFRz$V4wuy08pkC zVCqXdLOekmvH|6QG{_LH7ewf~gXsVQeEc1kQ-Hrgd8Kc3)a83GIM)Nt4vPaU(;2*f zbaw+>>;M=o2jk~fF+f3i6In)e7$fh_1`#3p2PjN8kGr#MunS&jnA-2+Q8l+Hw00Ui zI?Eq3<^ZNDJQ;Y28Z6N&%F1BZSAI;#%)|r;sJnEicm}9CR#&TKD~fCsfL|YUl_2yb zg6$k6(=hG#Mj@Uy@E@X7C{-wvr4qo{+ivzp zsJFK_6i=ksJ}?j9NGVT$x860bYku(-R3R;dD#L3IX}!ac#hq$?2mDy8oD^;{2PyPa zLO@{b(WBS71Ei411QE0bo!MBbX*^Js^LVf^O-*7Nu1|IU4khnFxu0;q$y|h(z6?q& ztrq}Zc<|j1h7|>xKkGur`x~tE^uLzuE*f#qd3c-wJOV!*;Qbn;*-vP}Sh=_3kn^!1AS854WT4n^ojBI!7;@U~;)WItG*6tN8()Fu z#Q{u>o-*5PA%X!wc3uPwb3s*zZn=O{B1{IZ^+%5ty=j_@)&apT5aQavqJlhT|CbO> zqSdf2TNU*D7!X&=HE8Mz{E6G_D%u% zGW2&le*B1m_3-j#@zFx9m%tDtE;)&XM)UI3~;D#~5eA)d=}|Re4O7$?pa4 zB+82TRDAn&I8~jQHi9FjBUbrp9w&S~nGX1gBi zu>JKoVOL^GaQNEXn-#oIg?X8-F2W$bBA@Dze$&VC)~#D)njm-<(zXr2FX)X!OA>ko zOuW1A#?Z?~hR%<&`vK`%JE*tBKQI6THVz`)SM zHxUwQg51z!JAOh@Q4yL8Kz;&qGSD?@_~)NV$clmc43!HEs!*jux`BkP2BJ}bkB(!a zF|CI4vtxx!X>vgiH{ditJ+uD|1zKP-3C}it1y53dgKu?=dLtn&UZ8`7*82f8m4n~$ z7}?qFz%Z+?kF@^NbG7+K(Rl86bq1d7+9|l`tS89x*!If_{=<`juZ4eo9m2Li=s`9iHU)0>wY*F z&04<`=BSK{w``~eb0f&|PGCZiofuoV%($0&<1f&q;5sgUBGP`4isn6R&AAS=C>Lmo z3HLnT4s_J;_i*$D`$iaK0dp#leFCOe8ZrSKK*0(s*_1bo232Fv^!)jAI=WV0#wlFE zU@TA0w{HiEd0(!+L%MCA7kD&X7vyoRV3*`vxb(0Ib!O|eaA=Wq9<_0{KKK|1k3Z$h zx#RC?*}2tXpRM(f`?$Ej&Mi7Xk_I#^#lmRBwhq^+Q{Ong+&q5j1eqliRCF{nPEE=x zDt%X;|1T@z{c8#E>wNLz8$_@pRPZI)p#nx{Ik=jOR8mO6dVAMH|BDa9}}yklF>f3TW$8Jh}6gVfg7;rr@aFc{!dAnj_Cxli7y6(qJxE*GXf&R92-w-tx5ndGp57E)s=AT z!6#~ZtJbU10ce9RWbIEw@X);9)|JGsarf0rTwIfoOj@(I4 zP+B-^BndD92XT5F7#LvJ2!*BPjMu;Gki`HPh!Z>*?*)U(4(VP9!o8D|dkF?xT+f~j zPb^{l(Ruf72?FUc5>6A3nbsg2!P`Kkv7MKM7S_nfQsVR7fB9696W~Hz#=3nQh$g<~u9aXHX&@Jre6f)4K1O>{e{n`~gXINOeB~S^iudNL%*?~;PM75-P7V%|C z`z+$`?kVVg8$3epC}LAyu0zB5|9M>h=RZF}|NRO@(C3u$XSG?ul+m(oT5=A>_L_*G zX1PdYlX3hnh5tSW|=uFlmq*C)`~?< zC#Wm8jV^0wrcV!=t5f#iSLe0jr;SX#87LO_FCv028Tky9kZ9)`ZDL?}mJR@-iqCihJoo`{m`vbJUS5 zLQNHStE+z+JPcb^N><#wsdil`Nqaz1`3<2DPqZ%Qy`hdFv!}!rv`@=v>G113V zGB@s+rNX}!I*hy83rE(!kMN`oS6Unf(s87wsBq4rRdH{lLg0SAm%M~pgV^rMV=;Fs zHRWzTXX)$lW=fwke-$o-{QT09;h*75z zLz}3hEF4u;RiROj?2DcK+K(=B{-QwRfDghbkV%363&r>U`y?n6R~%~n7XhT-c4P^s zWCT7s8zV^PfwiO`5Ov($-GSMUe0s5s-ZOkyID}3^tzm zBw4iU4xsLF5d@qL%BZKnF!4nCEh;t!t z1<|QpR>>qz(|esIjg5^E5O*x~E}qQBA{MZB5M`JKq*BQ;azq50|ccEs6 z5o31w^5xXj)OiqzG3ve#mwWENuYB=x7#Ln}3Jw1AN5BN$e+FdWKM(8|E&sa!`)}ED z@q6ztDWvNCsCDwp{A(puee1uc_IvB6t)JiQS2tA`=4)pq@i`W_yh#t0_D<{LYxNnO=jv8uo3n_V@$O1kl1 z(`O7_Mq-JUqP7y1(kv_0s;jcri0aQQO-nn z)_Hcin&g<@ck4+j=hLTJ_R;(+wF8M|tA?t*GyA^)-S8wWq3$rq}h?t|cW_^0vty zkP@xV4Dc}Lr?+E1!gun`DET=*`HF5L@>P~M*X!tX*+)hO%#fH|)v)(3 z9-QAQ3e{BqDv(60F~UcfuEet(pOD_4ZvLa+haj}mg!xGvIfXKLZiMJ7;ie#3>R#p2 z!j$%TYQoGO)t*l{4bqL;8rl{n)}iu=bu2QM8{>+5aTe(zQLo@+Uc&V&I*d1hPCz+hhORR8#WY%AeOI z+7tRDeh%rw-{s!)0cjs8FDaUFK(`*~G?^`0j z=cXzNr~CERhZZx+x0g_j{>C*?Hd? zWvir{&`{eYcj7zg3ZEI40vZjx4@W%j@ZBIDNT1PJ(l_ zD|W&;Ye!Y5o$X8KVV+&7hrOPO;nY1X-P)Gvl^mn-_D;1yjZ1f#BOlF~maz9fZaw0w z#CZ(I{yyNT5^)t*5A5L?>378Xe@Z9KFH50lTxI-PDr0~aZ?j_bvwH2my&p?7F+Rz3=f;0S7>2^|qN6}; zEw2{Qu_)!~->uiBrPS7X=7!t0NtfT~)~VC&&knptKtMLqt4Eft z?dS>N<}1rT6~?X(d#nD^7EA+dN*wQ2G7jYp zeIjU~$!H1qic^79F|f(*7sr1+{K0!*dG|@h^uCRQo9sxdQzmyXn=;|cUF1YkX<9Qy zzD{Le{lgze+|vhXl(Pm#8wEwV{h`=+V&+`0XD7c?UmOzcj4N7JiL)Tx`IDVtlfXY1SZUR z4xpr1ThG!t-7R}_6a6DMWUSVS!}?eaz2kP{$Bz%!caVc*QCCCFEvGv|?%gk1liEqz zY@WYXnA~}C?BBKQnr@HtG2`IMt;)yY@!vXP(-@Je{TuzqG1JYsd7fwgP${tcL!(*ehw_0)+0z{|B7X!#n^0 literal 0 HcmV?d00001 diff --git a/resources/img/tf2/tensorboard3.png b/resources/img/tf2/tensorboard3.png new file mode 100644 index 0000000000000000000000000000000000000000..cf8771aefe20a8e8a5dc31ad60c0e2e42031f83f GIT binary patch literal 137359 zcmce;byOYMyDdt08c2c#2p%A8++9Kfgs|}d!EFNx?(UjEfQ`FraCg_>!GpWIyW3lI zpZ?u@{&;8H@!lA>239q8RjpcUee;`hev9(^BrS^eobWjU0s@-27*q}c0jV4T;jgz( z{{%lh=@Sp8gX1Q&dGlIXwjpO08AvW2vmzfZ&%XzN14!L~4 z1xgX4+fAt|CmkMn(`JWfhmwc@PgjFv-yNe~t z$r?(#Yxam6yL{u0Mm3(=@~$H@XC$`fYC13P;8wU~#G-hYKzUlRpskSie9aSTW#DN$ zWP3x`8uBu^{{>8p|Ao0~-hbU~QMw(!;d{x3%E7OEx6S8rj9URwJs-{x`U9aS z*EQ>fP;gL$V8$N8r9R8({9a)r(YdCTjb4I1I;9iz~kTaXc6nJwipbOVt0GnbM%i z=|Xl#a{;45=R?#fzKy-=-LvG|Q%=|*%xF(XJJU^YpfA<&%O{HTiLla712@o*3z@$g zTT|xsi8dhGd27AOhPTo!^`BrjY(Z*m^)V7V27Y)u7%Y85?^sf3WjHLvw6m#?_=@=& z-?daM&73zp`%S2>sjssqghx`WuNzO`=lU!0Tms21Bs)9}E{fGLOUf}sTB3QOX=7CE z!a35+MlCRi+<8c51OIiowrO8mR2Ub8%+Q;uYHbu9o1BVnw%)el_oYvuqdIevND@iv zn5uYM>MZ3QmTkzOL9QHZM{j_2I=J>nxJ9GqRV6)^AKX^cbde~2Gp;}=gXK$BjkXiA z@^0`?w3-ysON5ci5IJSCq-h2cO_4IgIUJrOqIUs%)9Sf&I5r-k!9`TUbawQsI@AkQochl_2tVz`8#5VYBD zF)8DUtI}sQMA|n$;WAzL^yShut3E}$AkzOrl7KXCY|)p00K=olIW$*}X%KS+uQx_o zi%&j%qQguvPeN*;uf(i=ok3WE?s*4!Z^p=sX?>r#&6w897nnQ5xp9}tH5(d7mue7g z7YuBXKApOoFa`->m-fJr$Z5&ywC>hM5A4pn0JvBg4bIi_l3sMQ=00VTHV9_7|7*-3Ov zWSI^$BS@ngvA@WrOCy(Q?>I}0!JzQH-VaBLp|TrdIf|@=;t6|LGgx@^K^nJ{jxpbVV^b}+3(VL2P3m7}7 zUPIz?7%6hf9gCnJh;4HFeQ#oXeDHppQ3d-%*lRnEtDRP_%R;dJ5yRXa@S7c(Zdh~6 z;^us4s;Ds$OWc!fOLE-vqo3VC*?$G}$gh=0=|0s%ZY&&XG@VwZ(JPj9gs_!-lSN(Y zd87Iq(+_;>@Kt}LV_2PgqGBOar=peWKp~XzGMi?6Fw@#TPdi^oSm}m~KrKr>w4)9Y zGc2|iHXHMKN2trU>x9ha^3c&;?9~=(A_Fo>*jHBRD4u`%_9sqB5wA+Ks++HYw zvO0n*;V&wK0$U{6x1NkM#==*J8BSyd>7%^0ojqZpdFr9vzI@a;8C7~JM?vQVFZVnJ zhR&&1IA=Uk+OH*)Qo=DhRU6VOywC#GUv@Faw2u#X;~oi?UzKdEg_FzO4}Qe(aNoqv zaKLf6kAPo-o3FB4Up-@WL7QpQdg>NzwP~JWS>A;02R|c`GP0OyZI9;k@Q0AxC&KaZ zNSQtp>Kt-V_ftZ?%&I4D(9kjqU+ulx;grC?!DUSZgSh92Tr^&2k)uDXop~PaxcpWm zedCVWX6`1TDMbfGs6(D8w=QzXZskuZlo5V*51VE=_VoN!8(%oQ{;kg*-zW$X zHbv~Pxh~W|cs?N`4KEL_%h(%_e_e9D` zLl=Z6G;_dNH)ScK@!I2b=H79l`h&sZpIOFPOqDJ+3Fe=Bxr5*tiD&>>Do>|-&5He9 z&6{2Ce~k@}>GhMMfH<)`)*pI4fmh>KM=DBFfIJmYe>X^X#(^L=-nD(K5{l`aV<~ z@J3wMlxff$yFcSE2peD%jJUQXCWITyYm3CH)fCWN1_k*K{TUb_XN}Ia>XrndVqXn0 zpSb@B#FkYoN{skCRR!Y;Hyamt!#Hf3$c!4It%0f#MQ_lVP;Hj$fuS)~QtiDYYbwLt zDMzc-7mD8W<&9ylU`618E15R`#(KwLhJwC$oQ3+Z$0sOcxJ=&Kj=UjcOJJB(cz&jrs8=b%8 z=*lv7s~O*_*O*sOI9sP37^fd3AwROY`2#0le zrKmo1j{XC*=W9ZM3}N#IDX7!B8OzA zg<=F4xQWbMM|Qs<8jDe75}<|Ndz~d2?!}6^zZdmxMx3W8Rk70e1aBr8w z{g=-Ac(~xa2ODd{TDD_tS}43G@1zxuSg^roEogkhH6_vqBYUXp`atpVHpFreW%eiX z?g~3(S!n$QHNLX6QjZN>ESKhGC-dsHOt`dbwvTaO0N#vC4q#N#u_}`Mx0$y)B#`hy z;^B{4ofyfKS=@h+bk^CMeLYiVyy7ONx78YT^f1Wcz>~pS{NW`Cd?gf)yv?TZry0ZVadua8QuJs4tTWLxslA31y=MqfGgHc!A@;D z;|{LF$hSX{GuwI#T2$pNGF@pyaylGT*)X$L-jQdFjC-70HLZxVVP(q;%=PERgZ#{{2|s-zbr3%Ub23b^8kxzKheQ8uJOpEKJdtkxAq zfo7|=i8Y~Re6MW4dUv!l<09YldckUXPu*T&8Xd^!Htvsolwc_gCWEl@2`1Tuq68XM z&b)SM8#7Ltv6-fp=MaLZQWFdDcGAWYh2^-NE}ZLJ#x03YKVyG`_e7|j2hkE!Jf6h^ z0u;QD`$@_$9SKt!K!l}ruN!(DV=R>`(RZoC#8kvnd+_`%^)k8dw@;TP6Ozd5&X$0)gyJiCpBIb9 zeC`rH_9E{VCIZ?eGK2ABKJi1ux$jy`BpTQ2ie@7f%%Fj1R5jm^o#=dgrD_oOzS=l6htmUWhHP@V#Bg-oT{1 zZ#&xn$@rj}!GO+pTjv1R{TDOEQ}(o+c#%C?L=3{C9MZ2L)|+1nySvfW`(^0yq%}od z%p>(CAmPINn@b{!!&sQ<)l6S>-<-y=S~W~t6$fRMZ$aDbcP54f(_~qd+2ehl=vbh7 zT--r*#a(8uYpY=Ij&af*bPle)>;f2Q@uZDA?*l3{h?4t^2z~^Z)jOTAcdlWJb8x-e zJqOWXcJ7Fb*U6~DG^v-+q}-z^6z+FIAPEKRS=*8J!Z-iG@x1!D*o7c_J}98>S_w-w z;Gj%SBMo*Hbw+G)*LPenXqbEXbBvsK|47Ln;{~S9p>ad+_|Iqy!bcr8`y$z&M|Xl_X7tzmPL&U*}G(T`)P+%r|0&pCpXM7 zBGSLLlc=L_&FnCExLOG1}KXufYZ}pAGAs+7AGl|%=lqs>eM8*$A`RZqWres+b4J^0D&OL{j!`Hv3 zx#OlOt+Wv_w`v4gQ85XYF9kJG2{H>?><*kK8i_vhDv4$2v}vx}LiyHB($euEnTvWW zZ|kPH=K=*XX`q1@GiNPnJv18)rfRsxw;WA=VXeF}Tnw^LIbINCdZpwvI!$iMd{5>r zTlF{bGIgBXI4$kls=ImktwuuolK?sH7`OT8v!PB)F} z7;iF7XvS&>(ZbLe6Dk++FFC@owSm`q#d;hXmFyG8WLAMXGHX4mwU=KZgmFb~aIyQs zN&5^~MOXN-r|S{k8TrQBZwzB#&@;6ztmiM(iVGkr!*#SGjU6 z(jo_)b!g`6gs3Z@OAh?|HeJdFNVYvb#_U(^4R@bqz~gUlPc9mMogPx_WWrqE8*x0n zh-OiuQQVl)d_ri7i{UO2E_|^m6Mw_6JtvfNp604wuYKWI;lH&l2(3Drm{{ZtoDZ^U zlEgn_TUliyQMbWHtMT7kk}1o9z0;e4#DK&xGdEb(k$8(|y$kYRV@qxH-GeAP6S@aK z6SSd~=S-Gqt=pg~A~Q>aZaib11jz*ux&G*_UGer`F@vx<-CX&+Ui^*vU*~;Vwby;U z2JAo__u@%&F!h`(<&mm8$Gl0hbw+Ve<}o^jwmW}ki!VCA`{V85k$#;mNW!^G2vtZ` zt|J9&lWwZUhVx2<)ScaP*IFy@9s_0(T`7q|e?x9C&h>p8YVZ+?Ud|^bnzR5~Yfe@Y z*74KO2~2{hgjj14S}{ejE@xjXBh`b?X&>GkY|9|GBcEUt45>W0B$%Y!KXZ7=%0AR^ zUz%0L+#IJUINI>>UHGKqL?Il}Ppt1F2h%fIIx~5da5>e{)Mx8wR%qxfOwVe&osbGq zm}c%c6zYF}x^d_nM-lAcf$jNak(j37aBV~fQQ0R9UW{Sl_zQGM;z@y7)~Rl-Z;VVd z`IAOs@KcDKwI-LrJ~f0+gM7WA#n&Z_?CX;FP%Z19n2L03-cYD^ScO?rEl+HY0-5%Q zWu9-p!nHMTf?-lnJ?ra_Hg{g5nUdA7*e#o}-vJIJ6GR`mSA_5K%G}dB?2v35@z*m0- z?ERpep%Ydv&ezLsFM%q=XlpjB6j7z6%UL&U#?a0B%^`K@n{(N>vB$c0m}smGXnT0z(z zmIvhHr8sYSo0`qSGl_o0wN|Uzy6+-)Q9Im%OnmzEDtwYlwyujD)|+H&=2d%Gop<%! zb-(bWW4Q+K_OIv#4h7v8y?|3gc~*$z;V{b!VsE^o{!7#VF^fB=68qJ|TA&D=J{x!| z$6Fb9LYzGsIW0g}{cxn8#0M7}BE0;K2&KT=t6CUK{Z=4KBYOgbP0?LhAJInl21a!$ zmFw@eD)ic6zsl%2#-R8|ld+~eHnLAfxA`yocGvT-clI6wZ2^I_Wt-jjf!5-b8+biu zDh!$wSV2(Ub^YUz9)(<2{sWTk?I4&Iv7)XR-k!sTgg|lWeZ^}fx-qG!-&?r?UrqE~ z^{Ushv<34*8sm3aa?Tg)<60BQWAy#v{8~3B#zLfm$D*Fgnbl^*#yRS+Kb4CF?iAvh8X zLGdCo!X3xwf;?WwFn$cPmdo&b$*PfL+!JSm@ft!W6;|A2p`6ixO3C8S`d2w`SyO0d z4;2!oIvq~Bvn=rzYT)P3$c-%@1dF2!E=pGu@J?EzPe5Pkk37{!Xm_P zZ_&so!uOUspH{tjW=k=tvAk_(7Z{MB2G>x0>-chsk#dbfJe$i#6B97`n)?WXF3oi* znSA1~mp>#UvP+ULSQ@;nmne32$&~LU{M&D&IjUn(tE`{58R@Goe9WvHO+5Cr`<||g zd*Sw)_}=p@U>Ui8S(IOh!9kL0TTWT>H!mh;y2IE&71Iji8l}3yn~IPp6cKtG2|a5g z(01FcfpZc_(v-OrDbt2c(+b1Qy`=x}Ap<0L(ctKC>`c7Fi*$yUn!Ry<-K+TC97@~G z=D{6cN7RQXy>WXm((bB%Po-2F)g6hpGMU~jt!O=Dz7qbr%7RB=_Zk1HtZ!|-F&?s< z27NEHA!)$@0u79w9TTB~y3WPSn-S^G4#_^4yN*1; ze4cLT5CJ{}0w{ax(xQjv#I;_>T{I4B&*)!dEMLz8bnce_Vxu#_?k8Hwlp{_b!3@(b zXjoTNIN=u*WptQj3@lcc6*{lnAO>~4)S|g%9&gl4$Jd-_{n?w2Eus$5BhW@h@UG|@G0f}FzYLa=kdJ1H#eZ_?cJ63+IdDQ^{_`%w zs?2_DwOE?D?Sovr4H$nR?IqyPrjO?dIEueK8%(Ml9ezxCe;u; zbP?Vgj2#BMqQ@*{;y*6FSzW%1PPy-^K|B#6sV-JnsSa0ji6M{{f*x-%-omqanhmi| zwdZkfu6QkORv6W*nm$1NxBRWI`YKnZ5)6TJ)$bfX7h|Up>F=pt9sF3!`uXs6lc}FHR2S+y%dOwor<%NbT6Stu z!()DY|EuI%7&LPNmC|SDhmTi*gKmAhr}dWXI3wH+rRhrA$*ijmamJw;*k?mB6Gb*@ z?3S7r)BHy*HW?A(+^TK3YI$*yshyr^=CnJw1<8}fOVxA)75W^}3MV(|9aPT4NLE4B zoSzznVZ=1sUMkIE1Kd6+fx0&X6Z{$M?mSwod3@iBHPA&gh svKrHNpLPGkG`F# z`ic#x(<>^_K~V|tlcIC@U#%AQ;?Qtg#Py4BVvkq z63CN0=r*lCWbNQJWT+Gm*q2P7<0H$z?G0(+pDqvJ z7baKTE+Tj}?H*az3;GIR=ggdPldak{XXDYZu<@(c5BAbTH=KYi^`19})$Qly4wvOO z>7*{c4s`bdnottS;x%+pq|boEc_c5_SVlznPA+mLOu?kHL6Cu(L}SeIX3~R=cVqg( zZS>~=p>X9=edu$!HLVMpm=il0;^9Db!|VMY(IEzPr>|8hx-%4%Wi=2dxckPpLe3g| z;j72vzAX9e*b66Z8_&`;*(@%xF~bIaV)y~`c&+?~@Ria16&nBMmvO#!HDt|ffaE}R z8wpp`W$x`Y+C5HUzU=cfn0@3)@XMqFITz^hlYMGw>Wznw(Bmmo;Q}4xS(!KD5>mAa z@&iW`=a}Z(g{EyggD=7Wrx!RT;JYafMU{sTTCSuu%`u;bmlH51TeJX0Cl5Wrwot+a5=x zYcnCY@#^IWUbU-=`XQOs`I9t1xYg0{$A_MVrELa(V+8|`ujYbTPIo%o>_6gQ;harO+pd98m`4sD{OV$5zdKhyMF6Q0LkkMFE*(t;&oJ2 zj3=AG+*$0rMjY9?8xP`3AP+xm}@*lto#MFu>z=z$`WLq4|_lmBJLK^vl#K{{zPXo0mK6YCUr_dLAQ-2$I{)92d+Eu`y)HFqsBveBKyi z=4pL3(U5Q+ZJw`rIC?*NT4y9@*Jn;X$u@@T;n!S3)rtW*=yWhDF3D z30S>(l-m=Wp~+z>lXMXe}#12I%`?g$b^1^3I)Sc@#lm}=JIx$Mf zz}vEJo%q>Fduu^D?>@}dWNad-EUlZ1EE`(8rFE)oLDR)(O0Nz-xhvPI>|mm1ksSZ> z)ntgq3+`*|eoLMW2S4PUYBYCutX~dTktf>C=U)(h3-fyXE_Z4(u1|Y9Ln~ zX+)n?l@tleX|}nyKgP#grN>@{>&SH0(!#gnV&L&Qe^*oHk!4nQ$NGDb&um;7Df^fV zOHsxkTjtC`J;WlQh;;uV_^RB2Fj@UFYpa}lMhJxAzK|LM(4Z#BGq*u*5+U?l~m3Lc- zM5YF?2FIA1^&M5)h)01-6X@h+bp#gw5PkR3xX=8Q?)q3!LQR&?lT{BF%vxXffSz5d za9i+8MlGvx+f>a3tQ;xtrCZpbZzHEy7AIzYf)N=H*kLF4V>yRgQ{!&^R5ULn+gUrCns&8`%kAqoRZ$gOd3K=X)2kK0d)>erGQb;|F; zX|kB3CCkMOKF@hR7SzRFVRn1h2lhf{cC3StUsi8Z`C{eQ{KU8WLmQO#Z%`|&*nZU^ z?%?WTg&CclR#A8{UO3jj={Sgu?u-q|>`@f)ww2KMk$B;E)%)4ToQN0MPqYK)M^*)= zoD=OH;|HhHkf=2qaxDSzDEqTIoet z@R6Z8@h1JTkC=^GI82X%n%jr@<%oVf%Kqw_-@og@%rRENKeM{AVjc-C+oG`5-%4&B zt9kDqDRAOG@(vvaF{rkF8mvZ=X;x43L1pg)-i|gBT8OG<{qK&3d)BjG2_e91>=Bns z$o+dsgTag@uoU@mSKF4yR6c#wnvGTooz5P7{dZr3AMcSt0Sm0S0*h_VNfnTX)y^p^ zdS5R`Z;{C8?ctJ}FEII|8B$T^l%|5EVgR^M>~rssRjWUb-)NFCXeRwWtczB!)jRoL zi`!1<>2>5?^n+Fj>|DmMc&9}-&sicf7pNj<9f|S-i*x5{sit~nH2uu;9$4w(>QJmO*J(& zQRVH6wd1v3VhuzDNLqY6VfxVIQ03nb`E{YvUL5HJdt=biUc-t$L5+T8Dz2C!rc%^y z+PPy89Hy*Y_8BZ5aNG&&!bVS+DV0EIwvge!(zUfc?TMlXn_o^@ne|#v)X>n-$$J0J zX5O9V?p*yv{Xx^trW(Qv%%ryt`y(;}g}J#~oBn^_^ydNI!Vpf*1ih_sK}d+C3r|+64*TTgAhn*)kM_+oE?@s}cE1g^)C5EjPQ-99c|EOd}(sdWQqe z3d14L0>4dgakHgUPLuJ(cTQz3om(T>82BuU=^^}tZ|zMjElX(pA8t1UsHNgx5)wM^ z5L)jf^1C@t6lvIO3^b16U28SVnkg$NobFXGbar%{ZI9)r?;Hz|zj^b9ntFrT<4W@3 z{?5kQdc07b+h8Cuxaj5I&u}Y!XC5x-_OoX4Yj9CJD3l~|6{hbXSh|2Q$SwWs?0mi0 z;x!foS_?M$tdGx7P}FO!*LJ7M$srImn)azFr{mS`bM@J|Ij7`{v$H*L{gsu6lavS4 zS3E4dyx021_jeZyG11XXDkY2h&y7%2qaz|B+S=N}zEqM^Q&VecEih@++1d*8cJ?>_ ziB0v<@!{@5CXt8r^UtsL1^9nAa+cLxlFe{HMzhXVL&P1i_VRlt|DA0`h)<654SfjH z1oSdS?^S7_o5j>5@rl;2l&akYo$`hFtCYLweCoHsZaPx8zj2?dZXC7O_>+zkzcH{s z4GH$x^ZS95GU#Ex@&=-Iy&1XshSURpM>Z+twqw|i;0muVp5IFwuL1w9*n+8EFFG~T zovY}1t;F^kDJ%8;)>8~A)nj@YMu)(MOAAaU1-Hw&Y~1w_w}UpGkiZE8_xYXvVmwp` z8=i$${GrrzE{g6U3EQ6EWi%7Qp@7&P&gFJ~@wB#JLW>u2^YwPV@nB1IgkYi4&cVTg z<4whR@1&lp{#0-rKPNZ$1(@~^?xIs?z?_B@2&&I=(U`-X;w z&ec1R|Fa1CE7hEjZi)6yt8$ETV35{n8h4UfOz7eEhpv!?Kua=+M|uMlt6-*m~wgDI>#^5e17yP#y@Zn zkE$VI_hgA9WD^mCQlQ?|&VCcSl{>}2u(RnZG{k3@3cpY5ZhY7@GKpJEl||TV{7-x=0Wtjnw1z2uZ@+`m?EsNT-az9Td9t_cq9P0lV0r z(^H+VG@1Az{1S|)k6(ZvCxR&Q^nr>8fV%vWAs-W5cIM?zABE2pGXrQ3~l zHu!MaC6FbRz-7DnDWbbVrz;qnT6%PB%`?R0atj5J(dO?T(8!pWYH-r=v9+jZ*W%*huV249Tl!0Nx*8f9Y`JpQ&P`rtZ=%J>42HX#M`I(d9yEr`%$p=?OBrf}*0`&bS#sF=M2p zn3#*R2`y%olAmK^ig&?P=5uN=7{k-nIfn&4_glv^AJTV@mH<&h_|8?Vt*xD$oWLl7 zVcFfB;QHM#0^^zlTbU8Qgix?-QxA zG7#)X@@P{#_BoOfPkm%;tSsE2N4n(g0d0uaLMF4=6#}OGCVx^%mZ1F@y^7;nnGMlb&IGC4B z7r8qM@hHg8S8sByoiat+3n!PWghJPWB;nG$fBP8O(^J74TKn$OjZWXo{pSgNW{;c2nRX3%2f@vgXnANZynEA{oSbiY^JJqQ2MavN3sL?4Q#3sGNlamR_W*V=! z!LDZULr~l=?JdrdDMarxrg-vRJSyEpx*`^ARR#_@J_T$RgOlaZTtZ*j-E{?g3IHP~0eTjo6I~TBcp6RB=65(x@XA%z z!}IOoOvxA~4VCD<;mm+()q;kG+ry={2x=J;R_(tqYwPPh4qK1`RG^}xD=~v6q*QP|MPCkw=>GJG`~E6&1;AdZcG&<}EurJ2UfOfBzo1MrCEC%)6uSb-itED*!p= zWN2wnib_m;_39N!8^@~=GSlX!U~U5n2Za_D6@^o{UCcRTW@Z{cv{!MA ze`a~aXbK7n8X7o!c6JubK+COR%0^9(>(j0IMwbPfK|bi}&W=f`3X@jTZ0Z0Yq@z1$ z7y`E}T~^T5<>fN%4uAP@{_2{Jhu1Cuq2FXq$drcdAdr6_8B>*gPa&kJgl!&yx0YEtb zQ<8>e6LdoQwP;RF&7_ie@&+1Gn>R}Ty$I^x(~-B_o=B>|%Bu42wFFe(i}3_oEOiI= zvl^na6^neZ69=plqp^NDSysnl8ApD17iF@w1fpzg!_oZpvw8z*!XqVyth3@EDKr?5 z<$)B=8Hc(*->A-)we|tPJRTFw@P@~9vLshNyQ=h0L_|dB%NH+F&wWOA&X|~(HU^U9 zBLo4)a1HdXudP*jJlunz1}s`ET$h7zXL}o-labLrHa3RalM@%*)7B=`U*&^Fw7#|F zbkKOg#Z*;Q6%8N#QOR{)$!oLz5sWP$^hM(jMfLlTG{DQj6rLC#M?ylH9w=+JRX40u zb8xt*sHgxyo0msdnVvYEgZtcc$(;?A-EMm{MBx5%w$_GRBye*iJ7kYRu;#iq@&4`x zJB*RQR8j{GNob+TErf*6m<9j#?hfq2V2REW;25CwRc13>mW%JXxvP;$98;~5PEJmI zQ3=w3bpmG0&gQ1dr%#_Hk>=*+#sqRO&45@Wk~HsCo~4*U+nTNfxR*cuP~ois`UmiV zD%nP$yWHK~fleq*8f&dym{qo)&~O+V9i2q#kWb$Tg+H@BX;lroXY%sp4gVVg{pX_!93%n@UOoP5VGDdA8wilc7JDpitwciNxiVDI5~a z(jO$w5Vna}Gx{H(rgLsQ={zf-MN zI&+CnO?6eN(r|~n!KVO5+yZ%H`pJW{v2LE@cQTECZw&K>ll$%E(OjKfJon2-B2|i$?W5yV- zcuHI@%o{aHfye`tc5*>RH9AN{$IC#Zc%qunu~Ln#a*y6z5y0kX{d8*)Xb_T=rM0#3 z3eUoN6WbZR;g#7k&w%^aH-87@*ZxASZ|yp2yQz`PSo{}`&UxH81(IU9uQ(hEkYz^% z-eW47%BVQE>uo1s1_O2og?gq3+&iO?iQ@4^=^1Qb5Uegu=PMV-6Yq@c@u0u-cW^B) z`nk-LQ)~NsaY$0W;VxnCUY3NjYr-vAR);{B3tM)dTXOh&&c(7eClkS-d(-VxDMkM`<>Un7wN%^2{o$Z#jhBrJdHZIO-!R;cK4{Z-X zaamc}y7cM!<5s1@0QhDCw?$DwK_a_B@ZBHV$Ane6&k_on>+4;-%%i{*G1jzsf|)HP zD=Q1~d&=IzyI;c2{AFTyuAED^TR!rWKM2wm|ku7WkGExR+v&g zvB2Y~uP|%WRk&R21L^+m{&FQbIT^JLl?cFh$rk_}UBSdNb$0Y)gIc-Ts!I7|Tu&h& z>!`UQsWGaKZ7a{_0VJ3)rt9CcwzM?-{T=Lb@ptyS>1~Swd!w}$WU{J1{;nS(Ae>K| zAco`C1iBmry=g3qX<)g?RTf}Aj4R*luwv>b)o^p{3L}(kxhUpWI~WV*>iADSbZX`d(A&h>9Up2+;rqrwKKX& z=9ZHceyq$aRpjRg8Tb7~?rdJ6Gd;XEb&j}6zPu2-Vg;0-%?5wHrG}KDt1E~5UtVtq z3{u>ipAaibaXU=ZG1p%W3V9-=_T5GzyySwS*jRu!7wrLQr!#*tk;+tASN;n1J zzugN9kJ(f-grt&m$Cxe7bOEkrU`Jy`M5L~%dB5aKy1uc2Eeiz77%j?J!qrxGN}kRz zxPhZ%O?~|hkbp!Jz)lg}OBRO$tOTQrrsQT+z(~n`fsUQf173X%^q ztmZS(!0arOg~b6Nq%$P!=|izGF&ir@Lj8ul^ozT@yPKPa%>@!GktJDKB2xPQND@NB z7=AB)tCWHelP?kcudg#P(uoo?NG}e~R(7Qwm$B`ldaWMjIfCPV`+Rt(EdV1mxht1! zmrR-D^}dAJY*zqD#a=98?()1`{6 zJTyz>tKITJ68Fm`0C?u}4OhU48W*5Kz05rJI|ZtE;+p#A68tEchTM zWRl-)t*oppEn(v0UjiN>Dk4%>TN@P}y+2vH(t=D{p%qvo4lV?w6* z{n1L78M)BS*^UDscYs2H`Exc|ssoIGAmkutXJ_Ne>9d`Q5Pa6Oh6Z33B*FWnq~ONI z-Z!;Cg8*s@SW-S{HAsa3Lo`c1f2&jK15>pLND$z;fDf3>)m3UWyGv1d^>bE%X1?k& zo2{|xi)953qo)thQ?=_41^h89E9<%E;`TOB#()W8VPOrYf@XBNgUkawLBcoIx>8d8 zAcccA+}+)&n;WT1nD5iu1G#Kx2Xgfqk971J5a3*!;kC|Z+e7K10ckaWR|D0IO2C$} zrXl2u&S$y!(;tVvGmxO|#8pj=aPY+AvW+x8-xZKIm;G7;ARRzNGRWJ?CK%?`$ zdekQ~$BZ9Nvjs9iXUx`w!GY-nNWOY3q$H^L>-Ez|-EABPW=MZ;Z#j1VRO5z^SRJN6V!v*{fPgLnFz{%jEv*+4nw$%I)j^_HwVI;h7%~w1 z05wwH*iZq5M@!q?+nZmDNEts0Zd6iIvNuy*ZH4wC!EC0=4G8M45YoZk-W!1A{{H^0 zZEeNn*9cN?-L7p_RpWR?z*-lurLuxj%WrejrNy%>9pZuR?yV4yTO9xwfJjzZEbQ;> zyah3$B$~&?;(TfdJUX)(dkupi;lMf z3H_&7_8Qc?zS?J>;){B3tP2guUR?srrlV^bdsPE7wfv0D7?DZxi+X?}qI4~C!?%E`$In2e;f^!|TU zKOR3i%fA1m3+_8tNu(5} z*jdeofqRaUb8A{MrZYA7mVS;dgug=_UDwnfQX1ce1mw*>Am1m)v9tV2eew;RXEQZb zg()^iWf=zt8@oD#jq@z)Vk6qAIm)PN+OAeF!Krr`cO)Szi;8ldg|U<(^K~YIpm3tD zO5`83c#G0*Ch`MyR3r$W9Rd?KJ+JcE z>nn80a&Aw1BU?+f(CrVXtmPu7KbyYm`G7lNrJ+bg_^;5WqoljK>+J6#T5O7Lx^p6T ziw0RDQ647)2G_BO<=P6BzKpdq^HV}PxO{oD&Fke1I!P#stmU{t!!_&C7HUh}CRM7P z@7QU>!N1|9k=7Y1Re+aIc>J`&dk%p}o>w{86CyF^oOHh2S1k{+)13V+ZOJP5qNo<_ zzlM|X>xLYTUW#va*NvX3RyA$08i}<`EAXDfYK;2##{wbu8>_Y)qE(_P zwXee(d38qeGz?zF1h~A#oEqHIIQintiAnq6I?&B=`P6UBq$=h36PH*ZopYxrE1{1b zT3w)pz4=V88Vk9alaoi1e9#2r_nde1>9EYS0tTVe&p5f-`i)pZP}8fhpdFO7j+#(7 z{In9!FwG^8OMin<+c;fNQc#Huo1a!rkQX96D9V?VYSha4|1kC!Kvi~Mz&CtEKv6)VI>zMuC$^UeFrduAB*c(|^8?Y-C9 zD}HO8>NX*TSX|Oyo0!;I#&+*1cw~f4CbkjZFKY*TP%bx`MjaCTd z*$N4-?quPtugI=e1rNSSiI4IJH9gakQK8cv@8tc7>xzRDoZpbKyKq|&yxq{hg=2*z zPA4!xjnA|3<)o$ktHl`#&P+(k4_eZKxYT-d2-!(<6>(mg5-~D&F=B1Ht&0mLq*IV7tkdk^%S1`2*B!%v| zIiCeLCp;Wncc_Slg@v-4*!WNcg@$6Qt$i(8%}Ws5!?+1k9Rm{3a3H1iR#@UQk5@u? zXs!{=wE64rv+20czePq)5lW3P67g}3rlgmuh(|ev#3k+7KJ>fUc-xpbzN=5_qaCf9 zJ(+ist5z?aq8WESv&oS+SCx#wp)48bRg+LqLsv41JQwmRV`h=@3^Mx69NI_R2D+>Ji;8t>YxdWOoj0eJIrsNk z?B5eP4Lp;?Fx!U7>((di?G-{NHKqdk|4dpA3_R^%2Qpv# zhQCQSxyA472`TB+&m?oaT*mSum;=SS4)6e3cK@*nY^0wG#P5A6W%r!SrVp0rLfJ9z zvQ7#V3JXMUT#SZw1#xZU6Zk($5M1pEb&qP*rP$z9vi6e^xt0+OVje7|#M*A*__7vI z|I8z67x!l2`bxN6$*C7dMOT~no(AdTVa&ivF4MAW zCkoUlV&E`x$fFlAW3FklEnAV28oNmtz8jHBJYDSY4$-M(bz#_eo69ZyUuUXOTn{_A zxZJf*oN82^g3ZJv?H>mxO$)~#(lZuEXGt``61Z${U9GF?v+*@9@&$<% zr*KEWqgPx%-PPw-Y+`jd$~aE42|5oC7R9zzC|wo`u{-(-iWLTdTBBvDoG>oaZOA(S zzZd?s=xz7Mr5~9K9Io{ejvvAh-CfL0s`=Sv%fw3zLxG|c#lq$8p5+Fg&oGRv;5*n2w{WI+Vspa*P1e`(kh)A z&g5PCwJuZO1dM$UJG9N;`1jJ)Qt&~`o7w&#PZ#&;lLBx!cwKkavb-u_5FqtIAUy`0 zMpfE{THEyhBk!Zt_&hFBC6n^0EEJTK8^BvI(K-)FUfbL(EGSqoYyq1%XDpM)wHR2N zXklWsUFQGFS~~`;aQ)3ellR{(l2|Dow>|1usX;U64(P8EaXaPbx)J2kt*ANK_rVnax`h+m{Xu3paKV=Z{4Xtd8Yl! zh@RSR$&f1)v|H2N&U9adTDsEGeGyEp^)@q=AV?h)3Xg>QnuzFd`^&%6z-9@&E0FMr zj*7xi`DyLc9SH1xLOzc-3tInN$MRRcG)Uho(WGKnZY?D__w0}Akh zeQ(aEc?~0hYk+V7_6|5{&A{j~~;z8Ts!;kwk%% zN0sH~{xo3;n*(0$uWc8^j`{9#mVY;rHld(kTPmyh;$SM0iGLmHOEd}ervZ3;H+xl| zA7A)AL)n6YY;3A~oBvtpgyfEHYfm75Ji*pWGPUJMpR{Ue-2Ly0)!6?t$kG3=a`Hb^ zS?T|_*89JRqY-eO_!i(H^<-cdp17d@`$RcchA}k%{v86T7WrS#;{R_n?WSPs-s-Gv zZ~XfkLS#@W*Ki04Ql=3g`frk0Zr4yCXJHjNvXrMTqW|3pRa%xZzHkl#kqah%9!Ttf z-{XisP|1-#WLM|isPylrMyatCI3bXK3?`txGgE=V)|AF>FNV+vi!Gwx)n^Z&U&&_My#LiGP`iP059+W+H_1^izo z?*Cnn=~i+ar;cp!olP37)XvxWCVTe`r*SeWZ-?jpA03s$Tu+WePU_iHF`zHCC1gU! z-Qe62vZ>+W`QJ$Y1=8Rder7_;2VGa=;WGBacaaJ1x|6AIVbwRT#6dwv7+G~*$`Lue|SE24{ z4n}(wRHm{J0lNaC|bXn+FO_E_SD} zpajnwP{Q8-g1n&d#fjP#hf6sB;Z4hz<&iS2(;btcRJp^$Y1G*LwQh`bmxaypnR>_d zzvrHK>NZ?qHlSus8z-VKvu1ygDY5zVF18JK@5|aE<&$bW5LuHjplv4(b{bOQdp$FBKPZD#Sd`sENv(#D3=(SFWk2Oh!bq^?D+bA{}^>Z7a@044qwg3IKYk<-cx3&H4wptTKa(t$ZSbs_n+IgAgapzBOs6IaK=P;c`U6##hY?5*m)SB1jVF5@tW;UTDa@mv9R65NcmBJpaC_$CZyO*Y z@SwPuW)N}jryzAm+{UDk7`gZQyT8a%o9xu4KkX|fJ~gFN@WBtoL(Y;feyRy5hzEbc z)SK1NWF#cO&%HkU>g_k7_Ki>dw~!v^(lD?8x%~)|AWoL&Mk;_-7Tm5NS-*52_qfFq z-4+hVl2kBSw%)+f(>$QWf1P;N)~ zSze%K+mfnb{8KrC5p}E$-jyWRQ7;JjwVLepdH$jauZAru6aMS@gJnB{kdN@M#wwr8 zwk@HHh+)a90}(mOsn838FP^*4Ew{=kNoy1c-zABRh7}65K^DLU`NE|@CR5Fw;%v!W@0kItF5V-5+Bd%^>+(iMyQ;~A~PQ? z?88=%^hNEd8SMK6CmdA0%I{((tDfis=~@S|zB4LhlQ?A8C^tKtc~K?a4Ps7RA;hnw z8Hs9+VaOV8uhU!?K7)n93sHc#C`!9|)EYxlWU^!{7~Gx`ku>Xfhi|30O}d*&Ts*o| zUS|6Xwsf>CtRw0FCNVs&RBCOz+f3D}!fbVHx1Okqkf7FE;)7V22;TiwIb3Y<_4Ot4 z`18K9l7;l-;=-WDN)3dB*+-T@+*nyTE;-rQXbg}zJ>A{3ufst!pNQ8@cAgt#;NV_j zz5?k%z{NDGmjH$=Dk_TK^CaKnXIBIkk?&unL>UZx{M*0d3RU!TTNenUmuS`Q2x1RB z>M2Uf{H$jqfiliveT_VMM->MIvtG;*<0lOC_N$jHRknwzC+Mc#>GbCv@&2}4SJUMb zlNZ6&);VQ5w)?h%Hy~YMwba^D&kAxvq_MyZLq{DsrA?&e=1vWj&lcbZbVY%(CxATs zL1=QNZ&(4Il*i-fory`l$Y&`jsT5}8Za}Bd9++#n0ptp7iG7ehDa`hx)n^B|RRk83 z40#-g4u^!c-kuEf_DTVJE+r)e;BM>c_W14VY19AAf7;u)AB?P1huS*keW<2`D??3F z=I^hszqni8=GSprXEVd%x}&-|05pOXAQUB|@wWlN z1GvVPNc)ZuB>U#$ZZP<3U=D-0`5D-Pm=}U)6DlAV!q4vuM9#qHxC;c3_h-{#jzX6+ zVWvbhWMZGSTZ=&Ro#Vh?gAp%#FN7;XbODod8s5M%<7 z=LY?Huk^2GGEB0rK5vps6d)O8@v%KNjTH$+jKe#V3)aC+ZzC6 z^MiaYl&|(UUIFPD0bsl}9W<6SHu8{=kT5aX19FY2^>U>zo+>Xd4+^D4bUBq=Apg-G z+MNN%b%2?i5lbr6l(NVLY+Kp%%w`%;x!KtUGBV*@o%Ue%K)hPe;ZMNn2a2~zCO}{p zoIwsi5^P$4U9P-6?l}Rtcw#6>^t?txI{}=f;8FWikP4Z$U<30>M@w5dV*@fCVhJDv z4x-BdkOmpfHh{5od_=+0VB&V!iWxHV{`3Bs?lI603xbkt`ZK!VAM9jk5T9IL>VqoF%r}$9M+9R z??XL65T3>_gFWnZ+=VrgEy#2(ki~2q(q%LQri*oi&v~sE)zR{GMAt8X*Mc!YeFAW` zmzea_OiWa{_HB1=09pkI6o64dDg|twZ9o#<-W|_LJ2aVHLJu~fmw&FI*?~lr$>CQ! z0Dodzd>1+nO+U$|D!YIB^eL}SL`Wz=y@VeSI172ewg&O}8KBe+{O`IP@lmx()k(Dh zDX{uU&<|vRfXK;a@oD9ay@J$FXY$gs-et;*Xe#+_C*i>}jpu5al81O@mUXJwd>hTHw9Dzz8EBD9J63*VPJJ}v>c7q|%k zW5))0(`L`p{9h6&)^I?(Lvo~lUbF(|f_?yqZ?fx0wCN7V4C3dm7vJ=n&Vx_{euGZ~ zeFfc^oA&@@#l*lE%HTc)!gMfIItTKlxD^YKC>h5HelNSOj740-u_K)WwzVBfy}P}h zo}P!x)lWLd!~fwXTfGFz^)FyfZemL!);96 zu1UGZ>zOt{(gD!}$cCO{(F*u%GjL0R(*@9C@?1iGueG%`m%ZQ2e9)I}sX8=yYxf>Z zD|m_~*0CnTYO12DH@kI9em7v{P;l9{3?8nhL9TcF*RSu-5j-U59*w~q1gJeuSOoxK zP<=BwcLg>q(63}%~8&P~e*pVu*)?tLqs^ zG*6Z3>DWIkJ=|Y_jd%gj6n?M2zZ;wjWhqO%|8`*+@VOt%1B#c&b*I{-FAf9-FtM<} zTb>|)3uH1hI$E9jHK;AnsA)yNoFSdwC5K>oK178d>i zX4GsXYe8!LsnGa@)JW@ZU(NaGhzL9`hn#xNe~>3OXsMH~MjmAl1X8995{SUt2YYx4 z#0S8*51~p4+z1TIt5FXsmk>Du;F$wGb2>zM6?j8>bNo0m-P%+_Av}(UQuaVkE->y9q zM6m+sd;#DNX3Z)x?yZarRFAsvPDPo{G~NKZ%fo}K$5F!o)FWWx;#yT`;^y%~6gA?# z&hd@_2oVjGZ4#qpvkn*~@b9>QOaDhE>Mz+}u;snab`sOm)4`E)I0`l>NYT;7_m#EM zzIk&5;x&rYuNm!8AQR*HS$FV=gnXA!L1EAf{HKw=_8aJxSoE6QpfMD%K>@qZLki8C z*E#mOf`|R*I|A$$0Bs0LCN$L4#KkD%p%5)?q7_(1uu)JI0QbCfvE*-1hcEmJVEnk) z*uV26KY{v*S4!YKQ&JiO2fnXs!2@_;s36$c|Nj0NpbOfi=OyK>q`5$uAmadR-o@4U zuh-pE6uv*3o6~>*2WJK6;vCHAis#YH5>V!VG3erl;vGN{h633&25U)`3m`F&9v%g| z?dZ=BDBPKlmey!7MtXcZt?$i1P5l)h89=@=*(~GgS$TPz0IC5F8t?ZNHvJLxac#MwN`uiEKtg~i0C0gPFwDSV@XwY_*DvS9jaorDr_(6A1xC@y z$q9haQ?s%d^=m-M1)yXa*AtSGpv27AuNGj6j1Hk;AZxcfItGTxZ5I$dL^qh-KL;s# zXcY*6H9+L3*n4&|$`hQQKr;cHHjd(L#tbQB9v(&D@o)kR609(n|NSjMDlYctB*lQS zJPtHhm<1Rk5N~jhX0uuT0>}1lQVe)}Kp+4;dVA>qFfcgia_|7mFsPabPwETQ*`wW~ zGrsLI0*o0@J>vo{0(f}<&Nf{7K03e-ywL18b%Km3DE<`#^A$V0@XI9`gPUQx@Gi9e2(ZUM9`Xwdd%C*x>K)#%az#nxDHk0y?lO}8+C3XaE1S-TpB)7% z2{7*8Kt(~RIv@%Y3{&tHANQmX`bQwJxLv?nss#a9a2kMt9)bn@R|KLZAoY|Nf;T|C1U-)a05b#7 zJeVPT&=yScms6-{^mtpD+jBfYEdnSGNq5CKC&LlFBl1_+;>-2o80 zKgGO{QPrc@j|!=^wH3+>&+UVc1`;3fWrLz903|cK;{iw#R3pB9`xdavUPpg~kK=%Y zS^+APIChGg&O861D69m6`_04vM( z+9?9z{u!HEklqN7zLT=jNAQipBGJbRTZRaQcbsqNMYqyoUEIc%1N>dSzQF1Wm~fnSRTt>;ZE zv&kES3Q6VjS;p?o)tfd34Y#N98+7Fr*$X#Qy9N_WbK0t#hc|1biA*hjf1OM(Ev1to z=T~Om`dsZc4SUs_LZWPd9|^^&xefs1*9Pa)MR|8GnNad+xyLd_Vn==hgS7GY;?eF-no1fXdOX=S1w_bbC50A7% zcTg&Dt+ZfLaYb`)w}0{}dhxHU^!aT90gi;+Z8d-r1e?Cb6*HOIMS2m{-FDCJhZkpP z^BU%NCDsRf7Yo;W_a+sV7XwJedNgfg?%X^$u==+%r&)hE}`%dgNkOdJWVjg<$2Dv;%EoeSg zHoJ{KH4576>H$?aPG=Iz#bn0l=r_-%Z4K_v6Vt00#I8~FWjGQeYI#{cQX$eM{kf4&s#@UcO&)B!$$N>{DGsJ{kBb1- zIfugWhlNotagDZ;pT0R&G2Ey+ncDX0%Y89Zo)(HXvJBA_W&#P%EAF5u~di@uQgwa=V`pQ+oGA=b37#s@*PZl+Fz1^eG73 zjHFid2d)upYYcpS!i>W5nx=M10 z%ct;rn~D6snum$-_{=SMHEzD)c zOPg55ZP2-cDO%Z@8j>us|26MFEFW&dT6-|;^4el-H&r;^d_;a!|Khy-L7{u6GP=q) z-fTDv2kWcCLZ-Te*m)I=j>-UgSk=STNb2$X#)Kgn^ZjY2|0Woc#+ghWl#lR|{LYhq z#`WsD{duW@!w2=x1Jm(({;j&7r?_}Jnct5gka>TAznRW-QDoG3Bi8vwz=@Q0K!#4S zi=s;c_U2hIl1!x9E~_)$&Q$(RHk-GZ@`Iwk;KS(sT}SH4>2=93Itd&c!CDOqhZrJ^ zd{b;Pu**W=uby4Vgnlx4$?|evH6yi1L!2u500S2vpV>krLw%21D>E}`NDRD(x<~YS zmXlfDx|iQGD>TpuEid(3!a^SnPEv2@<#)PMz6O+zH)8oQ17YExzF^SiQAO^f?i~`% zTkn0qHZ|0q!k8)0EjuD1Rle*kA168thuQX-Rchhz`|%glJh0 zAETx3e~8<>)^%I&;CXJD-z%YapCch5Bjd4A>l#u|&xL6Q{m50kc#d*@fwG^T!bZX< z;&}HTX9CGdb?&k-A|;BFFr}Z+*c#q_J^P!ORP95z)7S5^2BlQ48CAQutIk0?d+VD6 zT6!mQ11g=<^aHe$61f9U!+jHEg?FYF8XBF*-gZ{nT(XCw#

    MWK8QsuBU65##ix` zzOSiThY#kVufLxGSC~2Zi8L^tNyQpDCQqLHv774L=Wv|+F1Q?v9R*2wSc%_K3nOI-yz@`eI17juTc6woHkvn&I~PMgBERsw_+qkHy(p)A-$| z>s>U?4;0E`@+p})E%S`QmBWrVR|!K+uFKNnabB=jqceKt_8f~jToMGV43;tdT7-Fj zKL_sWw_Bi_DTFr9t3@k?FJoI9YLQlI{>Gb!hz_b9Zby|_zIq!;OKYi}aeYESeH+$) zm_N9CjDW{~m>o2XxwetP*Lc&S>bkPmbE9A5_Ft|SQjJ)KBPZ)5my$MVx`8{%&*nr* zDroybCOg%-o-gV0OE`jR5&gR9H-Cl#A*(}U|{nZHG`fm}9hCH5!+Raln%$8wp zpZd#hS!!^_hdH~Mq;F!Flkie_4nvn1$V(ALZEVv#^Ee~=<^3)er1ShW%Z2lZ(m7cr zlv3Pocck(E6U>}o2PGCM0jsEfMZ4#FpIz6~@3l*Fo9A57wqUI^aC>eOPew&s5 z5d?1;war^gB&egVb}GT<<=neJ>*zRm1=LcD z*Lw1f;^L0OtqMzvKFxvDU;X`EJ-ZKlxi<&GPd^<9s8VAtFF7l6nNl5cr5UIzISM#W zhg`U2u%XY45XtnJ<)W=fQg*^&=I5`a?@s0(seBJP1*O=N=YQ@iv_)sZg}N{>x~MJ= zhTm8jKU)xC^HO_%5-L3Q{#sP$B8v}PF(do@${Vkhu5~*QMszar;PP{Z(Xqfw++lc} z2u6O-3wP)hOs`UT$nswN6a#^Ve_jkp5fbR=a3Mt)k|~7RZL38{8Cd0u`Qs$&kE0UJaN)eHO zsSZ1=Oy?sXz2Sz5nS1{MlBA|Y!FFm4_PPT2YLmFQez*L+-!VU6t@5gICGZXQrs*tk z(=&W5dc0}Akt*V4ru-i0(ZAJm3ZZav(*2r2%|mj1S=d)(o{g_lr*Ng&1)qT~x6zk)NWO={Dy5&QWC>#6O zgM7WZsMhwR7LJ8^0KRBhGkDgcgdXevkl%;VN7IKFjV+D(HA+6s0nhforZoX(BL9TL zQRv=!KLiaPHN2EubXpP!NGi+EC#R<$94~b!Sy`d;S_dwdLJe$=v{H#Z7i`Y z@+EJ9LIFnI`d6>lzAWLYs;eg@Cf3-_P4Q2r4sGP-lGfeboIsrwENtwptesHcPAe2B zH!wT}brO*xpX-Zimi2@S#}NWSRmy(W4k&E*$htVSE_+b&I%>ddxLSIk z(rNy(;qFvrZR3GVZ0PPW0g%2tb%>RY`0({oxQnJQn~>$ZYz_ylrIXF~!gN@OoQRZE zsB=?u^L$Sa71wXMY!nbm{;XJE+6bE4G%Xy|?r=pnfg+8$VzOM&T%tyMzg9E9xLBrC zz0PvHn|~63Ng&bEz>pFnMIK08Us%@t=f|e@+J-+<%m2e{^L49PLjz&|yo8+pjS@lsne%@^v~<72^u1+j zaoG%pwA(^k3KXXQdkM(Urz6ZhE`D;Y@?32HVFg4B;H&MRlyDk!^6aTm2-V}vFEi3!cmx5$%tVDYq6M*MQnr{1bsfIp;VuNc#W)g_t$P#Pf9JXN7SQ+HZqhgxz3;CcPQ`W~A0>?w z>zfb^}uq&o{hqGMirVZ&CMB1EiKK1>>G_#KG%Kzt1Ct| zhM8p=B3d)+DDS}@Dw*^GS^I8_oil7i_~=zoJMUZo{B*ObtM0-^c~jGMkZ{21_Fd9< z!lzQ-sl^TJ1->?7NO!5~pRi2|@)+V6;#VVTxQL1B=egMiWa?gt;o&bp(03RY90cum zV8Vr*&+guL#8XBgAt<4bt$tEitTgprFi|9pZN3}sKRJ0T`Ri9EZ?OmD*CY>+6f;mJBWTzCN!hRiaJ{#w?0?v;P2bMIqp++&;a)qyGG5<7AzF z07OLM`zEmES_rFV2H!;LGP;`=U`7;xdYU1}e9a9W^6z+77(9uG%o<@ zOOV~NJ7CpRQ(NEg9|a36{K~FY89XU^ya4m1v&&O*(k!Yh3jO&G^byn%UVRw~^4J(L z?VGyl%0`)CnR%Z=5WNZftqS&}MZ@FjnO0~vHxn2mwzPn6-E0O`V<>!YK;bG$iALM) zsX|W@qgLx_iJX<|O}9Nwdkxo9;bHl2nVP)Ee9{zRMx?|fVIQPGtJ4|j0|v9eEW7Gw zUsd-(&y1_9&AI#g3!0)L!QVFL`Yi{Pxd{nRpTf@rII=^qkJet^UfMSBJB5R{T-Mhnlw@(XhfX%z>(uy(x6^pimNFLJ0)W>uNz|n!V-rWCcrKw_>}Bi7hRLbJ0UhH)^ev^WpRwZ{VtS3NwG$g7%?Lc}C! zp|C&3j6*|V74h zF3BX3y$xrqje96S%MQDl=DW|*#$j8R$Ah)Dca83C5)YI~>l;J98&Tl4UFpu%ddo8oDYi60InNk2yH8?N58w5+yV$&6wPDS_0=BCH!&|`*gs7dxppW z?{+d9R?(=e+IOGtZA?32`^Zm2m#?s?ohAZY~DvTwg@Ho># z-F$26K-Jp8LI(->W`oMqx_lFqzSlot*qKioF~Tw%!$7 zteb19f`k$dUos<;tJY0~S>1|5hTe~y!f1^=5p`8u8HWWX;7eER@v$rX?bJ+JooXo8 zp?Qlmq(C`N3NAJEqKwkMuIvDM=8a0}W+3+8%O(@e(=rHn$8Wy8-?z;(cXaQmuf>6k z%E+dsv6)&aFYuSBx6#{mB1OnOP}D45T`UM5wcb2yEOH$6so+@Q>LE8Ge`VlK{6--< z_9tJg5DRyo$y&i5j8+jm!IOp`CdrUL0?CE9=u%(zkKM)1+h_Ts!yZh?F*cthaQzYj z;|Nf1@9N8$uEQe_sZ19%P*$Q9tUvn_CtRhZp2A=j?c;!5xGyb0m~{@2I&I_%JYs9A zXo@6P8{UtjJd*d}tf9<{oxNq6{E&sGPv&K1?=+hdYGS+AwotbA4mMNHMpEIOI;YFv z=o2RyA*5tsk@?DeZP3S0Yv96ZF3?nTSw#*p%bRYE8nl15yKi<#V1~zEvb_z0#7NbbS2@w3CI&ENY&nXnlXtt0t`%TFi=M(Nd|E&kt(R#^@o%GgHy z3%AelK{(SL4P7YOn4Yq@jJW87$9s=jH!~q87hI#YXrvxhBQzHjSPR`@!lo52gDmIB z+fv>any)thHd~{1!OGCJ$&DY2;tiIcUfk5gR6>hcFBH?&LRKyYVesMbF zf^9&7QIMoXS2jZz1ICeBBciOdbGF}&H)YA4-0Y#d^gSTpd+VoW&-xFcGH$XcyUhGE zLSjiBFKG3DYpw}_GZZr-__X4Ku zA4&&$V6LPUeMu{(>$MCBr$^3Wyt$jj!yXbMJs!cY)*5 z9f&1A-uOUPwcTi*7A3nOeBRm#5C8J?hu-hjj+DWm+Gltf4x>&sU1Tp6$bD0#Tf_7bj4m4EG)A4Ymwt}p+VE{bq>^SB@VHmq8?PBiZBvXpec9^*!}h%dPFP#QVp z=?g*C8d=v$@=v`|`sj>T_$iHfr}LJnZE$AdV8Wk}pvGfiVsN?&CvPERTB z_S5GF&EJA|1Nm;lJ4i(@d&0a_A)Oc$gOX=Kmjg^(*~JX=WN)P zQ!;7Kno$PX<*Q_@n10VxtQyO=k(-o50hAr@>Tl_|OD3(VW&m!oFcYw|V-+SwYWpsZ zH;XJQIrQlqllvER^9~nsU$oC|&r8KbU}D8cU&Vgtw2hO(XTrxbVVK6lF~WG!)9fv> z|J#$a!aD0ir_yO4nRJ4b6`A-kexs47EG~g0TqE*afwGD;-gZr%8;x`rIZ(WvZNalq zv&U^leYXrPb$lbB>jg+z=rryiYtds~XlU!{*;bz(Jp59lWo85(a!#;yHBl;$Tfy<^ zR(x_<@4cM>CV5=u5GCpP;xf{7$D&|TA0JQ+3+*sHN!3w0px-q=zmRyojE~n&y5my5 zRva2Hd8x0h`JjqpQHa~fp}}Zo=xkRxKkm|J=Vzol{FJTZ={>_Z76IYCO&u{sVVM|W z3se;6ensSdem>^Gs|o0v^b=81W42N@?r^BCQa)Z&;q~^>T5&%l+;#tB0amO_IP>jp zzbt$ct@0`fRaMEB*JbwI1a)X@Ult{RS|8wMCDxfElKHbE%c(v`CqqdQ_lk_Ffzxv! zLk0GQrWM3Wui0a)0n|1C>|uf5?%f+~&<(IB=1Ib&NjDny?~305&KF+7B2plocxP#wCM0}MdCQFDybi1 zkps!P`*BehRZi2}K?8cuhXkM>js=^#sw$hr^0aY{Ht4Z7uWJdK1O!J}=lyjyF8~E7 zz?cLFO|!&Xc&fp?GGB}P^uO7K1PFghI|IeY83aVdICb?p8z`kk``$@Qn~n`1KS_Q@ z(4(%7M)S2J|0;|Q%?){mLrqsubF7SIzo2UzCA+vfuFGMVsE7MYzXpL)lLVw}s~v`@M{H6d`0>NBaKi;|IA z2~K^oyy3b(JK12%k~#|*m4XPJX~uXNC^L6zzq^TjwTpebn}Qw>`#vA7X9q_aCpIA; z&Ag{^u55z#0s$ETWH23NBw`tFEk6 zwp^_~xcg;0{w0c7w=RMjXpCyasPodf9;UIty06CdWccKb1)8HZuL*#eKlH$JSF?DA zoH19)(x7Kk*Fm?)hx(vx4@aPX+Z*0*>g|^@t0(Mfj~vD8##@uPhN1KkIk^W-xy!s1;K&RCEydYsR6rYVGJjxCcb^T40#V9mmc_?vNFmzea%vg;?JKCVT_=gCu>{h zQ<5S`!bsA;#ea*2!0ZGy7~8*jLe<(I;V6#Z{WiI-50Y5&Dm@O`M;!cD=cV0&!@~hc z*>0?QFEQ>4yEW`Q9TzIlNLldb>My${&`5de9G72oOw@RDFsRsSU1pdYn-cfHDIrBmCLymcFd-q`4^_*Hwo;6e^v{Pr zwZ{*Muurb=;ITxE9TzY@C%+(Fs-5Wm)cm^Bg9P(2n>b=MXqnRK@coiL*yZydMpd8l zB(IlFhWufnSNk_13dVl3yL?xTs>jwB%~MzD57V5)#3rLZtC5dect;x;UTVCiXl=C# z$UXfwjWwG7^1(R}Ex8TP-%eaNx#=UL?7hB$ zpwBDFMn$#Nbh8c$^-Zu@G8`P~+iU44pQcdM_6h0Z>pdr!8vWTRCYHY?;JSZbWW9Tk zc6~Ne6g{-4@Zhj)NlGfCeIpuoO7Do(kAx|7HZO2W^07iX+iGR>Y4=rSJ)xIKJe! zC`~*^d`Dmd7-<26J2^fV2^C=r8yKbl9aCF8YSG0$` zNTBh|t7r=0491xF(5oCFa${IvO@ypo;U=+a?TfihFNlc(_e@SMYEX|XcF4?GP7Z10 z)UHI{iUkz!U8riPsH|=T)2BU)rkIjiX(kN zJyX5JRY7kE(gQ*(-=aLCq1N|`ClLE}ITlWSs5uV+zh4tnP0K+|DOk9ue$)1xNOHq=AeIWl8Vy*yAD61N`?O&r-N@b=s+BMn?9rfF7+>dz5 z{8UBs@yWT0``?QTcLi6_TIMkp`{nK6lYI`O{m$9YDs*Xl>y3A@_|PF+)au@n#pk?; z!|u1+M$<2C`GF%KGnX%3`^>y=*o+sH-~ROC2$p>X$q`3|UD@kA?VRn>f1jKultmUz zl^Ti7-G_a0^5dPf1Kz?_o4%K&n!diD!}sfgWUPw=UxkG{^WoQJy2l#;Is#FJcVmK^ z_xtu2P2Y?h1Nh9Trqt6BW~E6}J!@SmRir-nDiBRJe2^lcc$;1muPPFWb#-a9 zZ}T`e(!f0>ZGH-|kpDevr)c!rs4n(H=rJjdsQwFb;?dW-jjFN98b`~Lhi@Y3?KJng zqp~0Fz1`aUlvb3I)(JCjM}Jo!fJh_oRr^hTom57ojYik0#9z{f5}0*Sgm6cbqk;Xi z`c~86vHVXv_lp34XCd{G&Wl2%VKdaM8u3N_6&Zop&vOM|v+;w#U^Kxb;_CAa9#>?3 zclk|OcpM21#}#ZxUS3|nOLCA91lbqjHbq%k5k1SI3L#0&Xny@1_EE|n>k3Dm9Py7c z$dF!hdSt=Aht)UY=%c5soFabpff5|RUY?%Z243Etsn%r`)Taji7;Tq7!!`7M4|d>+ zK#RDHIr;&>3R=_e+SExSB?CmSIz9z7LtB;_?DmtC3PL%4+Vq=3ci0o9H+UdmVnp9}c-@3j8bA141%5vZw&tJGi zhpurOSyLgAlY&4D2)MzfVjb>_3ixH&F}EXKUQ)|`85qkNvpBI-@kiRaRVmE&+<2&R zzZi2&ylB~K8}ZA=>WgDAo=K{RTqhO^fI&{_eElK;8=1szl`^58n>^VYng28Yyeqe* zmjar|Y}R*}q5ghSa|(u%f=+46GVWsjJ7xZ+d9m>7~nkF(~zT@~-SH2H;epk9Nh!P4=1GUT9%`G&_MW0^b2M4~&$1 z6NK(c=RzSbf&;FiyA_lRSTTOHSjY&i^Kl$o2+d(`fvQPTSu?|6B#c-Y<(chk_T9EiE5&%`%aY zwv{U#&Xv(J6rRJ1KOYLPBi+tv)stj;or)Uzoh78CVov(QIsxv-kjVFx2Q$`a^s%cr*8p$cxZdXrtBj zST7&}+ubWZ&+C{LZP92xlfTZPR78`(cqnmG!)<4KgA{`NC1`uJCPJd8n#}w2 zXOGzxPvReN39u-{uXBjG6JRzSUt4Rxp--Vie93NZx%(ZxK=N6@?ka5}O#Jhw9|%uK zZu#RI(;Cx;dq7tIS^p9gy0+|r5ptJ2NkDMK=(7)X@kfVLC|%pF!r4` z@VBk@J>IFkh;H%k_pZ{qIPPg{dI0^(JseMo9{T+2kGokpiCEJp=|!a8zX?c4<1NNw zaZnj$x8SM%%J)55Pb}A|P5HgPs>HK*)%7HB0V~y07Jemx&rzL@Af#g0Leo^=(li`? zux;TQdlf6TZ!rrAOB5L}Kl{eUY*$jR^wfCVG*9{GNEg;N7KHqT=LT}uM+@9N6Hjx! zpu_;f#0MhmE{71fs`EJiakO9|3gtIL+zf<=d}xgtV&3*sxiOSQ&uwiWH~~?4-T-YN*;_(tA9vDka0vDq!9W@C_V(Bz3O^y zuS}xzx`G1j4W=AX2@PRL5YfBim44honXN&xa-0pxP}CGcMt9mEI@E~cXx+7$-nQtb*j$8W);N) z%Vy8+(PNDMgg90Q7KSyGNfSF30)aa&(3YlKrJy_sR*0u~@58TO3l(%Qf#}&G-!zDby|U4enkhe69A7?~2y54puIFEj5Mwv41#ZT62wO3kHMlFSf7oBK}+ZDvEK4 z=prL(rHdLw-kj0L(XF1&!zt5~tJ*Y}03XS5T1`WY(~*q9>SAd$ICx^gj#-}dhg6KJg zwjIw2l~1#R9gneYz!$a=d>SaMUqV`|n|-OPu3n#z#MCE#LvVIISx5~R8X18(iSH^^T+%BDSb6&3WDEv6Ou~#P zS=ihYO4cu_-__EhsxQd$U#QE#5xNd)VX!AH@1h7i>Il_lhe`=&JR&K-^_NRgEopk$ z=9)1fcFI*Yxw}>-aKm>i(Q3`d{JZlpm#F^*j}Sqx?Y7&O7ZO`Qm@5 z!Hma30NbzQVbMS3iD$g}Ff-fneEjOOG4#Dcv-;_r`UQ}EzeiS&6C(>53rP$qpx|DO zfV}|CD@P)3Y=M^0FyF&p!1ICK&p-ogWzZ9{{`lp51#~c8_Y6uuTEsW5$^#)nNtB-& zadER$pS~U+h0<#3LHvf2`4|lIVdJE2eBBlnKdW{FwO65psZBe9)f*T8&-z*{@kO{|DwfFwA0LQ#NLTd9+W+`=l=S#M) z&ra_V`>oH;f(qBf>%{6aYsda2Nk^XJY6)k;nds>2S}%C8z?L#q!_~HNKX{rJ@%bWo zx6iim72|Lt_bDUuWjVDx^Wg|-fU`mHIehLc`JxJ{!^>3iCiwAG$IS0L@j}hxv}k}a z#7}wpS)Ce(&9B3l`WiLP`x88opSZXh0qznHqO&Uxz(kJw1t?<|z(Er~K|-QrlW6P? z_ubursbT?2;Pe)+1RTq!(7)0^ayLqPhIR+fcjwL=ECwVnWnzTZ+Ywmq@!5KY8cqv{_b(cZok8P6~3MOE694rGJ$ujJsGW*s94iRV-3k0O zHeV%uAaXP_iw@5zrWIKv!2q|SxL8`d5&1*BMyi`rlg@94+7goNd?BSsOsl`%jHM~c z2Rw3cLQhBgJGeDdM7PuBruGH~LbNU?jO<`QUvuIH0Eyn+-2s+KRHq*r(d3F}(x?H( z1pu{3SIG33y`NlNU7eUPTD)v>-lrwv_izUIGHNv+s9<7$(n8QC{~S+L^Qr&)Gw#>x z-MN~LjT0XVh-N^wmel(EBF|)cFshsMkI64tEmLLX5z=H8)HK?GJQEd>fr0qh$@fod z=@9@S=iz5E$tO2}fbujJBH(<|e)|sCB0phZHZ#ObH@sx(w_S=5>e45$5Q6TuVTw$S+HAu9NC&#hh z#t${SGRIR0SIVhVeZP{bjWog<0H^PBZpOs1^w^?1NlX^3?|FV5A?S3RXCHxbg_$;M-=Rwv^B~@_ z;ZN~IL-J=8SmtW*0?I_)x84Q+&16+~H3hnBCGNV?$eUe3OXBrx(;mi zy}G)eUzjMt+?tsb#3BQY2u(3cUO#96R7hW+rPDxuAZ84qNKbr~veVgtYOjTOYvGz& zgi<0{RKCLOfwdA(b$bYv+y~ysm8mYDf;x3Nt3d4$g68XeVT_6rmak_L6igl1%etC_ zr~eWJij8@X?g#zBhFv({rvv|N7fyy@B9BefiaD>t=6ulD z+=9#Dv4>)U6R3Y>4{2t}$J05c+Id;NVRMbSr*>8F8FO{CW3Z8OOPwguJP_bHpK=MRcf~c*1Z|F zhAonGAtZBJ2a@zN1pA+bUZ>N)!3NPt22b9Rh@`BLOelgOA`l5dV)*q)Ln7wgqZFJ# z{V4J@|A~$-OrV7_+#x>MPfre-Zs-bCQtRjCx5EYCsNp{59rtLUK=5}_Y^Q22ir8Ll zaBqDmC*5YNc_xiyEMzDo6&6+yyNT`)n}OORFHUSss8xSX&hp)yWscDbjc)jjDx`?o z^JlS7iIc&Ee~swOue!y0@e(2{3TN_F6KS@Rc~zekDe$3io%!{M*(%gCoYX2+5`a2D zV4~C793jb4Ynl==NhRE3%*^Rr4sLQIHW4;OUb*J#c|IWr<6rg-O&Q?Au_Rc= z)KZW$Q|Zt<7_0#s?9D(#KscGGyR5P@Yuk3Tpbo;tC&ZGEhr^Sd)#dN6wca!MuIctT zxYJjf`g*>`;(JYbshrsF1O>>RNgf7cXPHt!f~uHS8nf@s1XI(H3TB)cJ+I>vT{&%9 zO?9nu{bx!~)3n(s$gVpB8q1iHGkOb`{=&Bs{>r6k{L_Lw2ooR1xkvv;q;X zO8JE4ns9J&qM08xL{{Xa*U?3dQ0R~Vw6kkr zt1u>s2u|G8amRctM^Vv{pOn_?rss;;c?@ZM$rVHI2`E1yic&;Oo~$`*VX{s!bP)YZ z)>wrH47|^U;CZ>p0&8Mn#nu8I(+GlXK%JdTneHb-*dQ$+q$FDfF&Pl>fK2pW`$|_fi^%U$~=UV#q1ix7T+K7YajRSSkKQ_mu~-Qg?|4LV*1`CHE-KoM}n@Od#a~ z;A4p3j6ngIsASF<1JDd`3gIF`B0HI9Kk(@LoER$bBOnD#fpS$z`4YDls;+d2j?HQ3ufo%W$uoB1O^8;?U9gq z%wu!69ZccKFZjGJ3>vc`%54WBvgti0GwC7tY-MIHupSFNglkjPQ5N?+uJW;QfcHpV zcd$UH{UO-BcPlisQx-B_uDZcg-$cKolKvqw7#$?+uk^tsk&brGEED+`Se2igFe zkAC#(w*A-R{VuHC>)-O)5mN35EJt`gW+c~N}HGOkAV~444MHJzIKeq)w z1paN=A}`b76)X(j!ReRXmzK)EcQ4@DN^2SH+hs^#DJin8ehAcOP5mm2-1ki_IbMQE zP$k)3H$jkllz+ak*H4TwpGT{orI<$!)|yXM;j z_VfOr_1TO?Py4+Cf*l?#>bet(bh{()_L^k9&N4iOh4I*}Y$eMo3Ujf}rx93vFXLoC zB){8^myPVLzEmbF=*Yf!-3HsE*A^TUl%-~mYeJ^T7#MW0L+aXAoPoCqCRND`e4YzERSpIz(ElkiCX-a1H9hqgn%QZ`z zS$@y-Nvzi)PmFmtf;-?Ksa1#Of*bL=xAaI&O!vHOeLi0yUqXn%Lw~`r=@ia)NmyJaE-bc*^Bfa$O=oh;yE|3r2pER3OJMj*QRBF z>*OzX0!}*}9C2rf!>Clj@y6YF9N$)If=)~Grn?ee+kHy_b8016 zJ?{f}Y3}MRCWlOqwK^^zXWmKaY;YP%3o|T&R}-*e?Rt6}Ui%fxB0A=C+u-0*D;wJ! z@7uo~s)ysO9<9zS#RYW5gr1yPi>$R_b|l6S*0TuA~b- ziRku0HY{Q_E#=6<}vogsasl)pg=kOSgU?CM|Ws~ofaX~YZ=`2sT~ z1wusX6;O`9ZR}b}l!jrJeUL-&P_-!Pmp5PoS&JZO1T|BIH6s^Hmtjy84$F|YnOjb& z>Db3WXr%@D4|8lH{Q9DqU_0w_VNnz{zh7%Qb?Jv9>+)oO`F5E!BpHc-sti^t1%VcE{j5(YCWx|B495Xoh0~mu} zQ(w})Bl|`w_2FRZuNZCo=(;h4$luZ0?kwmO!E>nE?_YDZh%G!w>E2?hK7g;B#^zafmSlBoffT>+M zYF{&b>+?F>ely&#Dp2d?emF~fKcS|f%Q8H|zB=38e;(nhlSTj6v?k%}{IxtRAm28z zU#q-oV*g)xS8Du;CZCXqNcWeC^s)qWC%2n!LvcHMK>I0*o>PCXhK}y2@aV;@z*}Q+ z2WA>*bNyj>7u_iu6O#UW&w$Sj`~)qb2*P(pI_rdq6Z1g(i6r$g9A(Nm$&?SNDJCEh z4n@Xnnwyhb(z_*b1PjGbX?OBHA7Ag&SC9gF80I&S&&w3=tWYVUy2sX!eu0Pa!jdgy9i0VXaiK2#i83S1=g9E$kQ8PATAUSwe z_|C%P3$|6~5v-5?GH*o>jL-ln(Bq!?vAWjTv9{$9i6m@4C(T7G1;gtfY|5YqOSXo^ zED-tILjHY4atDT-4L?J{(cfAv{^(@Yf-PLEu`4hhJJzr_DfxP8#+{ zTeX96G#7IA1>E2yX`^s?$FE12#rdQ)3)|WY2f7HEwODP1z3*o zWHnv5dN1#QY8FP5N+lvjs>{(p3rNxhqSrUK(+dZ#K~iB=b1okcooU(fH~J{XeGLpk zcM)e)EYq}7=x~YDWLBXod9LGVnZKmP6zx|r2!$LAO>R^vmTYXlgNE<}lS~HpZNS?d z82=zNJSQ8v_~r3lMjED6!Z0heK$^Tl0`Vz4$`bWy|7$oWzW>HR3dgohMZtA^- zvVisFcw79li5%}qj?%r23c4XkE==b*5Di+(peIBQiJaxsoIE@nI^>^6oi+X`O;{jqVkzx25n!U|yc@atzH4kAK^u1&R!kjP61TzF%Sls}RGJj#ZABgq1ixzthD6~%On*AC>`Pe0 zWyUH=$L^0~u}~%_Rk6l?lh@k(&O#|_PwYz{FqoCC`2^n^_e~{yfJ-n6eU3Cn#a_Nt zibc&`G@euQUKK>@@_9PLt@x78Foc-_^jqfXVYb88+&rndTLc#muL+P|JY=1*rbJg@ z)W}FbZU!_l9re?bTPtyh`0cBC+l6pw^Hn1)T+)mY3R zbT*N^uxO~NB4FR4*(7Pq@-?YE4aI6fM`omHlk57v?91y)Ph_>n4b@Il8vEi(4y<@#e1U<9b*~eaxDB>yom*#)Q2`j)meP zKY5_d%C*k^h3V6o{7{LVd?}FU6sdi06%wTz;O4X}o30Yd!z^WLv)&F7)B!^H|8dQq z2xQiyM~z=!e|LKRX<~M!Aw@L69g}Bq~;bqAiLtZ=zO-UVuJ`v~24LmdBT!{bTb%Nd@3wdV`>UcQoqT&I|vW zzLKvTy-<|`ABWBYE2AG#wA2pn=cJeXhj6mUeOEwd*{r_$ zhA?;H4)=6x##>n4g?~GRG~vMQp61;6Nq?5A;jlFeto*<5=(I`i0tFgLD%lG6Q3>xE z^88Sh&=a6AH7Z%l!3~2L2-yx0^17N)9!&Oy=TY?-AR5x5t3MR_4}%)77pIvo4%&<1 zfKqGiRFsKp@T0?%)UfBKI21@-hd)dGt`}r}o}pE{R9JxE15wUO1Jq8D@x%giA|n>+ zG6>#xF5XFU?>{24l(OU|inaP#Up<8B{de+aHpllNAd)4;Tu6$4x`r@7)sS22qsVN3 zl_e4KE57KdF;HnBB0@z&6^J^*N1G4x5|d}rGKu|xq6reQwY)_vh-1XMKvd~#6O*HV zQ5CFnmE+6`mtB^({K3KwPs2W0lqGFpk9^V#B|mS-W9DnDPs@Hnk%YTCl|o7=UY)IP zy{=OII;BU>S_YzYnQHc$|E%ckR$<0cmO|vHTK(O132iSfr&kmvoQX#M%!p1^P!3*7 zc~_4;(kW}hJ5SZ!^$9WxJ}HfYt6bu&kiST!Iqqy}o~U4uK0)1jG_E40M?1U%>5qO`!;O(M>Snh?0%kZD_`Rqm2mYcV zbtmxmJvTfA6K9=SH4wwHQ?VgQTIZHnPlA=TbULOg#320C|KjKP#+PL1i#cp5hMPi z5nX2Bmyq(SUH^O6aXM%3+6P^gK6me{Ox1G!9;7Z@rBdP9zYRi!cqjhI*0IdJ&|B-~Solk5h1HRdrPN z{rzvE0D_@Xu#@|2ex&e4jxjMoeYiCY^d6HxSpODl&K&d&->vBq`e1BQuSW|>&q*@} zFM)N)l170eSD>gVi0U0Okc)KtnCgTE?XNa(Gvl;*LuG4vv6qda`+YHWj|Y9Sq8h9B z2L>&z$SCJeX^>j%;C|2iR~zLhwPFz&T4;eyGC-n%gNfu+qb4adezR#XrZ!LKO5@mm zd99#8`zA0~C1L|kN~Yqs$+P7{N{2 z)tOAYKkIU0RhIYiQez`Zu0B#Zb%}!64@P911e0NP;pVmy2a5ye8EP21D-P)(UgnaL z6=vNXM14&@j8F9(;2O}ZV0yF7c$DX;*P zg`EUZ$O6-hk3Qu2{XBEj^m(UV)_d+Y_KK`<)kPG<8(e@rNBeeq(#j_WKhnj0F@F*G zYa^Hp8Uu2`d8ti|(|~&n@q@~?)ZvvXua4tMi>=YR!2E+AH$NiO_I7~RiX4{cG2Td+ zY~IGzq$_hVQc-M*K>EPe`v$ZI#A<|hwX z7!rTxjS!2#_oZrw;S zKuR74W;7_aFT75(8k+(NeEJcwHADn^vpWV-9r9!O*vCEmR=y|5Z()@BL1ijR%4MCv zZoC^j9?1&xVN$4CiRHoVfv1ag#MK~i_m-BnZG2p{slsd;uc^pg{Tx@kNR^J?UHNwt z{dAN<4?i7Vnq6>|Sd^o+8aF%F0?oENRvY`qK=!oCM_NotRXJ@QWX-uyja$AyRWgIv zim^ zAs3UeD(~JcC!r$ILj)a+pRo-JAJb;AQE}Z_CwP;-rMJm=|1QlISCkxy7HEt%RsB4_ zY7@iPB@vzNkHPcj4gUdQBJqRB|CH>EnIo{vSc;8wI`oP>t{Iw38sxB`SsVl7r-LoT z%`(MCgH@@gaR8ykXiG<2YH@BsZ?3}A{{8n}ptfuTwFFsiftp1G0~aI-=o@5S6=*To{c#7H56Y65)cnhG1m3sKQ9O4)Jb-U zF)0;k^n}LGdf_8}zU7D2d*LI2MKz4)&sA+usaxV`Z&O!&kNdaG)%6qHzC@W0LUEQD z;aKPdEYnRmtxItN)~f;o0rX(3Xs$(eL^f+>Fl#D?R@rP1*ys*MXx z(un8yRA9pVqPZ1g`%*b4EHb%4HLk3MkGY{WbR-pz{HN}llzJ1ncA9BQM!$7wqY!Rs zbzCalsC#6tA}sS+7FbViuIM@t)Eu8bPlG1zZYWZ=J7>N{>0EB5M8=_g_GB1T2&WI^{& zY7Kb4dhPa(bGDX73q7L`>X3#YN;g*h8<;B$8 zc?_9i@1+DvVeT$OyG@O?X0Pu)FE?#HYEel5^Fz|$7g><8`V&H{>O6j!PMMO6} zB?T4DcONFy@M{u&1EfhFgqctW536RiAsgemZ(4qEYWUc(S}R_c!*6H?d#YHL0**4& zr6TGzgVWuU6!I-SWFzg-JXL!$;-KctcMMS`U(5$r$XsiMNi~kRRtQXtWp1$gy zz2E-G$>aTai_~DdXPX{UbGdS?cyie2Yvtxq=B8LVUYBjm(Gfh{HNL!f+q|rOs=eCd z>Bd)gyVrOBwk=(5a~z}OS>-yi}jhRQ~_$IF3UPRZD)30hae`i_Wziz zZyWN~Bz?jjW7oi2)_R zKG={e)!|MW5}7pwT8kU!%Bw$B#JIqSo82^x+o8;L@L}AFo*%X#4b-O`EmKT!gSqRP zKO4j2(2a&ml$q(d!oo#O*@*QeRU85ssd0>!ssIJ@J zl#W%4))j9_?HM~qj|Rc&+}UlfwE^mATOae9>dmlpJ&n}GMcy^M^7Pb`1LBqE-|e~G zOBRRihZ!wAlemkIQ?ZYex+7=fhO?{-Gj=PE+MX=oPM$X?4R%NNus2?>`^!+o{q_B@ zw(X}cQRQA`yFo-P9uGQsi!-Zr+x5|Gr}H;+8CStG>(Bh&8Iucj`x(Q!@*t8!cf-eu z41boBT372xI6qJDY&6ad5z(fCY=5wPDrY_Ke;=QQoO@a|`&^6!_3AXMJo4S= zwL0U(llX{6aIhH8gMZY=OmXNLWoZTy@yO$%IG2F9+1DGNt9di*xGy2()G~92d$XIL z>1b(sa@Vo2?sBJvqtcEYbhiG?+i-e-ca}CYGUBXnH@O2mH^lQU=$QBRHp(G4-}|{> zxWPpUhN1~@^a60OGp7F=94y^;9`_3qixwrCyLQ1(pBx_&i#{MwZiWM=wUQ99L+Z`S zF77V?$zj+O=%g1c9=Ku(KF8WvOpmW^U(uH#C!i_XI zVI>-dsjC6$IFj0#Ot96-e<{a?$ z*dI&A+5a8c0rfUxYJI2)Ip@DUhUGr)7@W|ZD zvEnhSJm{c`>~$C&fDx!RGSA8guBu)dfZsr z4dM_u+nyo%e$0I0eplh@cYC~0@04|!0oPD}zCxsEw>ti?HlyH&LP)e&b=}o6VeHw! zfBie-q@rbE`I*<1&}R8z?fF<@6-Wc^R+n$a3hMoJ7wd29ZGF~;()X*MdF%Nf%Pu>; z4R+ z-hocN4LubfD&X3ewh>RgZ}yLwY6S@eB?EOd+n);c0Y?~4kk_f_cWU%79mWy)C2PhX zX)zJj~-m6VKy$jX1* z8ChQRjLyl0>g;TP-dp;_uDdCDdveQj+SJ5v1JdPmJ9aPGr8$L;oVn(7Yv2;HGBX=G z$u-tvD-UYvHn!E)UbN9B(L#aSTbyN`@mYKxXLqq%Xnf^;^yr&$gOIS&aMNzS`KqT@!grF4rU3O!BuqowXCMxR2TpSwNXDR~37%-rin& zq#w8Ocb5~?yVl_fS=~O|=mMr|P_SE&4z~o=^3Kcq)p6#O!4(A@;6i=fRcG| zqb9k^kG<@(wVVrBBvdRp9kd0BtdC_=Xz03#J5%8N{zauYWNBx|WCJJ;Ir9Kk^p<}U znS}sE3hKieVPqF`7Qr~fCcxvCqne!IIM&BBvT0=V!NkQAGKJ0!_!CW@D7+_nb|;t- zvi2uEaH|U4q0@vqYai5Th+8m|q>#D#(HISHrHDE7#EMhU)9&?e60F=?Aozmrs$H^sZ;YDQR{wHCX5Z8 z{h*T|(#uuv|HT5Nlq!gfHs06Q*YE7?%=5Dr4%OSFD8GT;fRe%80x*PK_kL^}89pxL zN`=U(rIEe%Mxs6jLlMIv`Z()XPI~LdBe$<49X3g3zC=9d=PvN0#Nt40f!3VuRt}CA z+Abek`hYNt)L^5v+M_$`l9Cd>S(rKzto`+2MYRqYShaQwPK|bR$Jaa@Hr7s=N8Eg- zgv7ST)|sx}BBL(upUw82<;&%{j+*UtN2L_M%#ZCgS{)WFx9Ec(!dPmZ%Q|c(`H1TqNu3p#?>W2IqHqA0sAaWzg2-o!|#`6T=V@UwrA;d=LP!^ zT~d!wV<=<$35&c*FfFo-0v8`;G{s&=RyJWy4mEb)#5swpXe&rmdJ8bZs+~HxcYKR-Tt%}jx|K$yD^Hx zRPlLW8XD*n!+=DD0@onGm~+y@Z#c%~$iu0tFE61Sq1vJG~~b{3JD}@C`x?Bn(ap#AKl1?*I!+?@ikPOO+d1- zsI*%>$+#L$Eo2A}f`BZvZ5g0f-*PYBcG;Y@~Dkx8zIXk-4; z7YTL(H-Fe}eXfjWeSzQE{S5`_+3dqALK+xi8KyW0$zLpthY@s}8)ri$&2vi(aT~uk zV41wk0o$yEI;$UeU4KAJ%KtU3gr$Y#ld* zhelc7j^PgL45{d@ijiXNyXx$}Lxn^U_WOp=?Pu8iD|_DnA1I^|F%tOQD;RNW_wC}&|h!se*9ivR{6JU zg6NR<_UP^fqg$JJ;Ztri2SpT6Bl%i)iZ^>dC$#V&HUf20txcM}v$mVtP(6_%AGiH$ zK!c0*ipSze`eNqNv14Izo9EPeGL|A@`%{ILVY@=V)T*+Pigm3kM})#}ZG z4*T`mTDwNGp$94hs>`raVt9~E`mq92^8yk`LDDRhWocpM({VBCDC$a+@uQn-i@g@F z?TUwn2ENcx@W<4UhEuN#CceVD8vYE8rC)|aJJspD8BYj_1p6};!>KJr>?~xvD18Q6 zgVNz*p}_@4?+jIU`sfF*%D!lzjfib-ykq%N+#C}UOWnu87;QGCuG_>Uv)W2{!T5`U zW-Erse{GEnlEmMvIhQ%Hvv!RQxG%uLOro5gBp@BW_){xjL`hXX%sut5>_OS-^Gl`o zaVlURV}5@zQ^|jkKVg{-xEVwPZUs-JzK;!VZ^YEG*K0}6<*RB|m-PQhIGi+#-F7V& zJC1qV4oMmdFP(SmqmR8023Af&PP8*K>=!4y8~DMq#p;ZFi$p6gJvD85y+|JkPJGwu zlYN=D`9}rnOZQ(wIL6JsI7%QQBUgN>*^$%l#dBKggrNQE=H})dOh80LL_$(&?GFk1 zMuFIwJ3}de8?gOXRzQ;mGnz!O{Y{lM({jO32U8~!anQ^3Z12Z5Ni(6s`>2TBl&j%R zZM(KQz&~JOp&sEFFj)RlZ6r>v+=g-0$~!Z=I@bKlmPiWr|Hs#SekRes+a$2P`{SM2 zahv#hG1$<4y$m2zM7P~;COcm5hdUlmPnvdbZ`v;w65Fq?b(x;094ucHR4%;Bq_I}!}7ognYWL+Q#o6^-_h6b_Z`l+mp3?F?o?o}Glu1!sQX0@@0t#cJdGP6mI%Q8$^&5{+ur2no`_G@fMLUw0E`8sA=Bw5A z?Y_bckDXyo&x$sVm+1f}hfxx*gOiriF}CAek`}$$y7lZ>n^N_Rd8_|Od|NsWj9yi( zom@l&&7S&mH$BLSydTpWmTUHPn9=}5=@(Tx^96WsXzbsp{%|xv^U0|~78TvmmG^(J z9%H#oOnPkx0A)vJAj@#CZ9@EhvD>NpppKr9G>|)7j@z?u%I;674N5Q)TjhBnV z?=Qs#;{^XPbl=|ZjYm$#inAS%07dK;$ucG~EpNY}5iSjL{9PYz(2^NsR4Y>4qoLIeJRl2Gd*PXHDq6L1=oWo*jJ_}4mt>R=55j0c~OW)w9} zmS)`mSJRG%Jq;zOA7&ko(j*@{A8fC>;)1z-}V{2w`+XF8?|m?Ky9)?GXnIa*^mF zHbrT8G`mMV?f|H)Lr~JLg%5KCJli%jBGs_<07W1MFL_i7NCpD9aExr2v?w1HA8$SA#6#^+PWE z>yO3Am3KM103ogD&%468l3Kv!;@8?zNF2N_!3wYK=M2iakpV96YWE!||BABj|L)zT zI4wDJ)(7a2xHUt)Oc!$m>>F~w^nDWz2aCcN8Er_@t_Q9EkZiw-uYB1C_$iz9^;m!tj`>v7 z)}rrqn;riPpjM;r>-`Gg8#`|5>?C-7FH`j&kF{;nu5nd_IM=qb2$+w9{=of*zT!ss zhrZgSDCnilX8+2Vy#|W2J7RM@m47+fh#yH?ff|V~ljMFO+o~477>?zj(YbxUp ztb2|j?eVx6aWmC1>fcO#UQKu29CdqUTL3>tEnhMR8&g z3)E+@fbq@ZZCHWH!*n$S3{8xaIxVH@d%i&Uu5NP|ae3%JUk!N2*QUZ7T`?`Lbk~OvRT<8>0@`8C(BiS@=VwFQg?j!| zvx2K|qF@LPzY1%$la zEqG%Gk_wst%c%#%H$B@$7lH~Unm|L%hV{HS!0^Hsk)9ZZ89wVdyPP7JbTT(f;{yhj+EIze6C5 zVy4531CTyZy8eL(*-0xcU5GvE!bQlD`mymtnU_FjNlAg4S)JDA=B9k9f|?p@DaH?g zJJ^$F$+0*<;#5LcqTxlXtak4HjF2=(WaJjOCA?LlPBie9~l^!4Pia7_6^Pr-OH>o-0PD`v(HNxr~tBvc>LRB_3)ylX= zF|=xK%_-dNcUu9`nhrP-4XQF}FBXjD9ONRv3srSL1c3a^b2Ojsqf5ePKi=TJLiSTK zj=NOP_ZVSCMD1@W1+VQ!@Y|l>i@m)kW-kfag2;U+5Aa$8NI;TSNcERiZwe_n@QI`J z$d%?PMFQhq8?Iq{Ms*R>gPI~~Be;UN7}P0rdf$r#&J)59FurRq^%}W__KGwDrWJlA zZ+h^k%aQZ2;$mvg-*F!!#bs5`?|VMeksGmQ5Y&Yj>l&-7VVCywgoW;f)e#&N<7+%` zPdZ%z*VW1WGWBw;qyrT;dA7`)lJK3+s+r~`IVIfO^A(MAzw<1Os`tXhF(YiN13eiI zfXq-;Eh;SRoz=%eZjUQ-Il0K`p7Wn61z*j~s6Tv2QqK(qtoe4fw$ilL{$PrWi{Ip0Iea0Z)+I8n~ojAd-Te=&#TpBTYfEL8I4l+0JfU< z)|p%mzb)%#ij=z}!`ao8y>lMYQa(`iXvoT`is7hX$z$kZ$X||H(URxvVMox&z~ive z($GjrONT{8Ax*=KiUWqp0ChoL0TwVPds~Z>fYVpQ;<0Ose8rSy-2w4*-uDyfl9H4! zL?BQoJaFqkgIJognqsP%VE&x@p2KTFkE4%Rx((o^jgMF%0dZC6G71XF*8M#_bH_Jp z=onk^VDz8prIgeW1>wLW{Xu(H{PVMLc0pK0M2-|Gr79*`1Pyt3uReGL9UUFezXsgU ze&05as23*(?5nEDOG&}6l8$|vKfVF%5n}15U6yy1whkBSLf?}Dc5 zEbpw#_ItO;1hr_E|0pWH znbi4XaNreJZO`aBE_9KSqb2mE6!%>9Cs@EC&tW|F%Px0E4KS0rP~L_>HPUF@#+9E+ zZ)5X07QLk8j&I{KtWs4++^8X9xGRT}iyGU?y#^^E9T7h)n=yroJjW_00zb8g`h%>s z4o$4^c))}-_cKq96-UUN@wbd(6dr*wsUNY*JKU-V9WV2nQkj;(w`iGr{ac6YOP{kx zP0~e`2#AH!pmgMeihl2m`tGKS%X$f1_YrvVKg?w+q~5&rggr?s3Z$lc9|h#w;ZeFp z5nf)7CWq&QN~x%93|@WzYsbpy*q0XwNR|VORGW^t0nER_h#et*L>+<$iPI0W()^dS_TW@uPOT{kYY12_^s2Sm4*87ocnofb`U@*igO| zoT^)_o1gE83=$U~aa;9lvzVm%Va}BEorTs=E)C3Y#Gt%E8?Vy9wUHEriWAUj{F~ZX zW;scLrHno%EdjAaKwl^4Lw;IX+WA)OEP3IK%ei2jjdfAZgP61Lttx|2-FXq<4;N|* z+^v!lwSB7)Bm$)d+mz5qLifiUaoKON!s5!N9bCtE=dIU=?e8#o(kps;df;oZ#^tI* zDb$ZxHZEl>!hjK?$NEm!%&13)R9g=5zDLtWcl)ZsY*E?@>c?sD!fejQd2 zUO6S0F>8v^D`A2H0_C6%kouL-(H|Q0M;}KW2S**8j`Enh=?>O(v5TrlG5EF-Q5E-=Ni86UVHDe-hK93 zXZ_9}XIx8hn0cQ2x$i4JSEJrPdKNH19q{_}Q*rrP<512pXV`jK!O*MDwLkpg_dk$6 z{7Q_6Cwd;Y^`RhAYWEv_Yil1#`91%G(Cp5ybaUBV9yF^DXrkgZb$6}lejdgu@jRWz zHK1YYqVU+<*4eOkq5Vrc&J`3Ub|8Tc^!rD4i3^TJynlUzsMHz$H>uEzfm>ul^SF0LQLZzL=2o-ZBM+$P9`sCwNv zAwYNCWjse4zt!Sa8m&K)`c7H+A{r^00wtR*glYTzS02vZg|>9X*Fi(4NHGcW=xxoe ziB8jD!*5Vf%vqNNSQ`Z2XA{mkvSB4}{*p=>uwvftP!-nBgO+LSbBXbun_e%&UGG#G}ZLMOHKS4)@Q z=;?)i{aRmNuQW~Xm!#^dSvQw!C@i+)=c8{oua{})$~71MdnOBXl31F61B{q#jpF4^ z&Uz>vEBM1^&SbU%Gg(@oh1i|{g2K*KaEAODWIx*CwK)BFFo47}P=$n#`%{O{1td<2 zAIb=0EUx)WD{^d0n7#S#IJ(MBI+BYSI|6dL zrKRF+r?@cE?$L5GjZNJ(7(*r%lbZuWgihg7j5A(cXZsDw=vbuV7c=*6i zz2`P*3P_~r3l~l?(e8Yh;~mA*1#Ql_4LfcM-(LE)82P0XZ?qVyiy0vx~#Cwmue7!yTqQX*N zCZ|o`_)7(8O6#-g7j)~c>SCVs-WMKklI$oa4-)A5$Qd-Ov1l2s>cmrTb(j!u(S}NX z7L@fdic4_#+V0vo-2fX43kw^Yu;s+B!7EojfRF4^j7)O3k$xf!Kt__) z_4UHeX%Qp>lv5GAyWij49YNFtpni^8t zUSkBBxkcL&=7PfH@bFg*cz6%!K(H~J?)#{{4=QFXesdVmR4*PBt5G$WznMQJpat#J z?P#Qpsfn-p0TA``$?{z!jgytW>t~x+W702s1I7_k1e;`)Eba=tFHx_1t06O&&2(+F z3j1rZcZs6>xZOJI>!(k|1JExO#%nUo>E-6;!n9%emqyU{&S=ZU&nL;K{yul6xX24r zbTW`r(?!{Zs9yg}o>7lf{ZNv$&$RTcYVE+4Yh4?-KJDklBL zeH#tp618kvkqqJ^{F86w_D2FJqIgUmnhyMGe5n-|$t@@RV4PuUzRSa@e zlqbs_tTQH+udS8;3eYB5`z(xXl1r72J(>sYoxNtirDjpdX)~*+%l(-Gg9*yW5D7M? zlw)JS`1^+J-iH*Ky76oVm+9@Xg;@u8=((8g_Q+x~Qztik(tPXUzkKy+6KQ~$sf{+Ot@IY~= z65vML*`}L#(eX>pugkM(xlOmD`aZNotb@oqEn+p*9@+naJ0B;940Y;1H=4<6}Br zS>d!bD^9QL2_xsZjeG723y(!F?yinTfh3BIG?uXg{z~myczgz3P(8gCRz2OGP(AH_ zC%CxCXJJp)s|mqy8CkK0kLO`iEr>Iw;q{n{{>DikTfkZ*@bD)u;*Vru&DUVgaZ19^ zygcdHu78d!{5{A2^SA!{DJPYhOos>dZ?E>`$|eWGNM>47vbH#^Om-}O_F5n6Fn=&D z&Ch38<8ZpxpwzA+b&XI&_-Gw*D|Kj2LCblgcURf2y`BAm6lQbI_rCD4g#?@C><+#l ztt>jdlq$Pap+PuEh+j-}%x%sqInG^Jv0!bqYz>2^UGCdSs`e7kc$N1=^x;E$B5KQL z`&o3RZPEQ%C8f#}XZ0~Z@8lQ6JqpH#A!N%d)v%RrA7EX9@{VprQF(bm-f5QYBGUc^ zvcsVyOUwCC*t6=j|Teki6cjuWJaUMY*J-j%yGu zdz4*N%8&jd7oaDv!Q;*yVTG43>AzPR;RE{iVeKNs^O%3BIPJPu(I3SlR4~8wKiLJd z?OKY8(?>`CdVG8h;-gr(D4sBG6QxeOcsd%aV)NHlmamq_QF46!>@2oP3AeKkH$xsE zS?Nh>qxcQiTemb(3sKosZrh#r7Se?;?d|C5>f*cj`$Y!{K3wfXT)y(<&70*b+AE;; z)$^1-ZX@aR4Cu<>ob8o0J6F?qZj}o&XEiP?59SSTaVDRm_&OL2otrvKwz%QSR-;4N zbS}1ks<2zJt>m6qIy43y! zJ;Ua7Fkjk>mHheK62EHZ*Qo|;uiZn~`wq{dF@o-$I;Hm${H2= zs?tKMFRxqEQF}KpHMRG*l^e-DX6w??RYah3v2y#{AK10_YW9W|w&Nu&ra&4Ol-Rjy zn9+7i7!7SF*YHx3&BdoQMjSN9s=Xf_ zP2QUTl5!~VOUww24z6>#XV4kop{CWEKJFjLP1BTJe&2&^#UQQ8jk|m zjOhtEmJ>oJz{SDzsN9N$C|2dOqr6Y8IS*P@Zy)h;{k~@A-XAZdc|?&nWR;*bJ4AQjc?R?W8*db@3VQ$H49A- zexqnf?_OMKoi;7-@#W+`l+`!N#wxOTrCLfrz4w~_4 z1neJ31=6)gZFQLx824Sc+<9qb2YyhG;h89c)>2k@e!OUXY z=IS0OuhWKOOHm?#OAllvB_-EiCY+eCA1Qe+u(+1&q0o_BMz`rdQgV+JSyWY7W>-C+ zUM<$0ht20aNL>()qfBBJo!-wK5puy8m@Yv-vr-PzG`po)0`l_B&1fe84Vka6L58>c zbg-mCoO`)RQQO;^@aqkq87)=^Ho6zt`@N?QPYoBY+-2RlKAhG*XE^{=leI(#wUv{T zSHBCx_a^-Pk>gI>TsFt-UIH`Ctl88=l5W1W3lx$pR`66Y~43E zE|CmY%%P_0g&~nu9*G>^@2>&VALi`(OMrBWEHdPGt<6+O7EE4p7F%?7Ufj}c7(>m3 zDxaZXVq{F}EqU{&=VEtqyRa2KV!9u@oVN{KQb{7Vy3)7i*t@t^CfQZ;1ti%h=NC0*>PJ0Fv;S4%|)}=@6X;c8lY)3G80jbgqcYlptE%(Jsd^ z=v^FYpOuu7a{ZUpy;L?TXwX(3k%##jsal~?c7L^a>Z`!B0E`=UIJRQeXKSYlBHudbJEP&*pWj7LbOWK$>MOo?Fc0-MZK~T-YFuHC-%tHk z%BnTg@Wc~_#Xf2D6y4hAJlpA}5W3NFr=^`ybN8iI2mQ8a$}|I0tgwkW0AE0A(3oIT zh6XLTH}d?g#;pTl%J<_OX|PhY%2A#T-zvMs0fU)8+0$>H-38FN$>Un!>;};1yNLKX zeqDF>C$BB)ESP3`e(Eh1dJ~L?t&ewH{vke!EVIvii@!SW^1EZI#3?9%j-tG1@;w2{DR^yhg<@9_%JOyiihD>&g8*3=@jgvm}a6J!TBW z?XzTvMA1J+?7Or7`I-OC+WmL;{pUEa$^71CjbA%DjUfc&f%q$+d_7; zP!7#t{XeJF0lO)8j^TX1Y9@p%FJ8P@>v{4Eb=Utean-j&$~7$@T-q?@YwLgV%g6H^ z`ozDouSa}?_SQ+h5)ifIk|uD^I2n3^r%nxr5#1(n4vEZ^{~?|*{Liu1c!TRA`l{e- zIb^Q{FPQo8dIu)8ht|b+UjJeq^%2}x1OM4i55va~FH9&0go+1PKIk(0hpe=LVkW*k zW@*%^J-LGVKdu<~`G0qv{GU3eLHbcVJc=NQ<3Qg-yILQTVDo<*07yx9cTD|9s;$jU z2zh2z9xlEoq>5Bv2YP=OlVsGLByBR3Z=|lS4%VqbYvi>B@{@;>^Uct#CBO<^lSbC|iIs6HpHm}~FFUp!uGp)SirhqlzL#q{gV?zZdLDYN? zyH-v%`R1mr26l7}hqMXAOMauTpQTkbHn2KWuyj_r6)yRG+Lr&r z6`IITpX6X}8!YdJH#mzRfOCxAv*`F%?L)!_<-z zt?KiJSHL$jLrq6busT*zM$d3w5+1b6pS|*taZk$f^0FkzpXf&$(1nC7xC(o`_O|J~ zod&{j{q1`zt!T_q(|P*-MX}8Ljf*<|A^hGvZg05))%ADnJGk+e2hW$a#An&=2?l+R157 zj*d76?g>vA84{9bzoPmh0kRYT3i7Yyz%%s2w566h$v=ZgVGuytdvgeE3G~#SY6wEO z?&rws6b-YNOn+`HJQ4LCe{SBpw-D>H$`{7a+=OAfNlpe|Vp1u#{O-dAK^eI&sCtY; zVfd_B|KUtvzNZZfwqFktvzCGluizd4BEY2X|8N{$r_cW>#`!O9%>VV09tvb)92g$a zQWyjRubTlN8IGiGQs=7_BTnA2TJph!+76*cG!WS zgGt=TodZ|epoXO#@lyGd&)j4&4K@c-u66t}00|zY<^`%1nieOT^6tq8h{<6?WE%b@ zl0@-g^Qx|1=V=`Jm&7@dQEWMaRtZ%9l5DU+k8k*#J=w3~d5+M3aGWQ40ODXXhhgd?J8n9nj7eeX5`)*Ka zDj61py|G}AC+~s75%h0iqdoq-=`RX4ufD;o9pd3H%eZV9nj;BY+JjS?DKAFYI8D?d zBYu<@8y|nS{%|j1`1VbyPJkGXZ^H=plPv)nz&Wi`%Jt1g5hf-|scD2pN=u=!nX!k? zYvWbThYM{oYx>k~&fVC;QL9#xu(@2qGEP~tqST1uZ)bd)9{;Vn9IjC?C3sngF{RcsJnnVQW6P}$o zkMQ_p#UJAr50%Qi2CJ$?=A#Auno$sfgbtJ7_l+ssga1?U+ot%~&Ua|n2i!1-b%(I6 z(Bb@#zlNN05t$=#0`8-$Xts2;dYC?e1C86x2N7BI(MQ~U!uEFc8JVy4G?j{^GZXeu zO_krZ7w5c&X{gdba|8 zS7bW)(EVtiL$^Ldz2xD(2TWTUb()KrQUCi@&HUha4MjJre>$|y0BtQcO^2X%wLLaR z_7}~PDf#D0uk^gpQ;PKwv{^}{f0$#Gxt&4!TX@u}XJ$}qnn$L*P-Ziem2IK-CQ_71 zvwMH6dC{*?TRAn?K_X8TKeBHzT*<7vv&MQc%@KWnY_Hj&+TlISlkd-X1s%4})?}vb zLFt*C_c|AcM~=%{#hz+`F*oG+kr?%+^@4=PZpC4@7rGk;%0SS8$9>ecTY5(Qtvtd< zhFnZuwC{2mGhcnu(r3dWKZ)4kJM^^DHV64v;|@Pu6L^*{HQa?>jBT#8aqgMuzBZ#w zRmv70J+#zm8)dz?^l&Ndo8R8?rj^03y0m|-;sG>6@-Z98`3_^z0H(m^3E3})`;1ur zCWIVw>j@8-B2YF;yKmhYFIF^S?f?7lgUXRS1KIv84e-$p=NsiYZR!88?D+Nsln*J1 zMn6cv1JKowx@EgEWC!Z3{uBu2qEUM_DJiKKeme|wX*+Zkw$WrERywBa?+}X}EVp=W z-zjt-HKZZ;d4)M9S8tq#l|6MfN2rvod!<`2%|4?x&vk8sE|9N%<7f8DqwT{$5w$#j z{(fn;Qp#2+Tg7`s`no?%%l%cR zY5}-Z0$|EoOE?Emg+B$m`%-2Rpc;8a#hz@fs(^q15(e=X;8+5}q2aeJKA4RfHQzg^ zw7;33ri2q%$jJYjA=Y;62oCZuU$zGg;%)@(3t{%B#@*3o&d$ygcpClaoux4NUG;go z>x}UBidoTUp+P(C)bpwSO;G-R5->n=wA2o9FdYKNa|L{`?#GAVXM|CAn~s95k=&%g zkV30r*Nb#=asnt*b-0-Bva_g{q4^VvJ%bv$0gVt>5PFngMg&~@-FH5C=~lK3aQ{?D z?|HW+U}NvhCjcTqz<$}WU1%?WRtSLK*u8uAyuG~vx1r|RV#*vhCIQq`?ewX^C!$Gc z{3NiL<7{Y&2f-zNOM`G;i!m_ru24UE9Uu%8S_a!OS?5pZG#w0Iqk8_McytxAS#d~j zp-T^@DFEpTxE~e!xE?Mfi@d+w)YK#?BV%8pZc%wKyZ-lgx^lL=`x=p)6*32>>-_Py zE}y=T`{fInF2k)`)eA?x3^FiLucfhZpw!Of+nc{N-hiHogqhgd5URJhxeEYJ1A-zP z8~*mh>s6jUt4hv9$9EBks7V+ZrTPfM?n4mu=x`|;y|;#fLKPrs#O3VEkDZvi_TY;Z z0I%SLO#vIi2?Gv5SO$w^;dUq>h2%F@Wl2r!ywb`^XB@|VvsG3E@)9Ir`# zLqa+Mx&KW5dtV74KYoYR5l~XqUgwFfKbUxXo;F9tF&?K`J3X;`XJ3Eb8*{N(9Vvls zx3bdG+)@Dkp`3>9=j$+B_K{Wn9kAPqzTEV>lh zF?M%%!()$^6`{y_?fNx}juSwP-;lk3UL~`SAaF}ZR~MM)xam$@Pa1LwmOT$BzkjJB z=lQ8?)}1>z<-iCC;lXck{5|_fxdWw>8di1J-qx^J?#IKbSXJ{c2>(o|MeI45o0}^s zechO_{FR1GKX-+CAWK64fOz=j@A>(*HSaEh?dbrz`Wc;@?cS(EdyD{Y3;Tr&QGLqK zO3xakJPv8gU4uO=`SxuBT5LT0%Dg&Wg$1Fg4&0cl`;5d0nstLf1%n5a7bHtWLTFmU zx{0y6fXia+2sV@GwsX~um3vPJ^4x*kpya(CumZY;v)vVr9?n&l?WT`vywAi>#w za~Y60zhTF>T-}C3i*d}nWMmRN&3v1{n&8R=NQeV3B4J2TRll#39D+0CU6IV2jL#;Ncr! z&h5E4+|)D3J`TLWsSkz8iprGfbkasxyR{B2~kP;4~?C2c=ZSPTrUf*>1C z@YiUv%iW@oKM`3Ma6DF~*Zh@6&~Y6!0+Bo315QqxI=^c+vn_lp6F3k6`+o?<%MhHe zuw8fw(7W8BD?t*Nh)&Sa5+aCqUW7j|8_Eaf;xg`;hlyFVyyog)Wdbq5hY)Uuww#*R ziLdm&)Z2e2!AsB8mcF*P(0zxKv-`TlK$3J6n0x1F8D(C)I6B&Dhna#1yZMgqV1n+A zqp%azUS93eQc{QOzlk96sNzl2XV93t3*E^_AN+Az2>u5G$P%OrNR$X+w?mi%XAfEo zOeoZP^?9%h30s%Ww>69P;7$2gf$;LVz&JW}3qgn~e-$Y?{M=6Nb;i zrxoo5rNJ6ZD>Uv+6GAWNDI~oBea{tpf$3!Kb{ozO7JcnszOZ7STpkXOKYDPmAXF$U zbwH#8K$-hgbl!uHt$6K2M~?Py7;4$uScqH615$9D$VGz81@Loqv)MRux0Y142ZSaN zzr(0q*zn4gWo!NzxQYUDrzH>cVIK&>39nP{Boi0WGyB`!vN*w~nfz`c5qH7RQ@9IW z>dx-KF$W5Z9tU-zr>~EJ55nPJTm}>!bnf!?*9EHWg6VLRwMw)(;E~2*{0Xkmw6(Aw z&%@Z-1-Dw9VxHnMSUmi%!v#MX4dpMu$w4^C0Vq~k6lR!?#*6s_qvG1p=H$qUshkDMhu?ub<;ZJ)r)6>*~jydqG z^c$?mAH%Oo8May=b}WP>~9%ug{qikqHpRiO8&cuYI&j(@$y zhh$zT9rgAx1=tN@j^4Uo!vX3_522y~IK6Bx*2Qy46*gm}#0FGJ)&8VE5Y5>?IP4)S ze1O}&t+{sWLUNp^)(|Mah9J5s?ceX=p@DPUsy)wYJ0oy=(T&>+e*nEW)ei%g{Cs^E z`m^J5c^Wronn9jeHZg|TF7~A2WMD7=N(rb&5H11~4V&g46jZg46a`5>ROKd^qk}s# z(HG8~z)d>L`iEa*=V={pa&TBdIxY?#K{3R^M_+$`0oQ#?d;49086Y&4a>@xFZ*!Ea z^#l9BX+pz=DT6pS9vJc)dSBVE5mFHVNkKJZE`Z^C3Q4d~!Tkv@wL()UySSik33C#G zD>EH6@cqx!7>5&*xQIY9G-so0S!JX z-aB`akLm%b{s2q}F3FuD>3f0~zyzff>zb1B4I~ z8L0{$OGsP;57^wv3&8XB5F$;>5Nm5|K;;mHiUf>~v++WeHA`}oaXDOyv^Y=Ky<4}m zAt4S};?A9C{m^In;lqdI?3$+2=3eIrBRO=%Wn|i5OzQDWm=S+eEFL2_XF-T6)iIZb_CTE~gEz@ZDJnnmUsK@C5QR8z*z z+`J@~v$i+isSC6M2l$d~W;V(~Gw1aaScq5x>_b1eEkk657d9N-LFj{4Kj*OZ#OoZO zR!HopdG+d*Mb+_cNqIgPF>skFP=w&t1!)L4XAUcTBESiVYVa;3g2w-NgEJ{Ve>)p> zY!WBz##Xlc6V4Sdc)N=HcIPv?*2YFdK(+7`V9)DCZ-SLpv0g&}T*)%`6J+ohs6x0t zJ|iUbJ_opvr!T#(4IX|RXld1V0lcK3pa7Lc=3mCa_BP%a9n{{0~GJm(hSAi?_R;;{agZAO<|o zSEzVcx=x(1^d9i-Xk=p?(>WY-;f0=;B|QmC!iE2jxnED>7Ac~E)O=Tu665a%8()-5 zO-M`>E4@e&=uW7f!#oP8=vPS&c+xq#%<$wSOVqPKQ$H)cmc-1=e906%eOM3|Kno9( z7d<^aA;%h-uGAX62o95wfqRjuna?hD@1`U50_p z?KFaXeL0d#;p;|`Q1tai#_kqHwY%Aa1GL_Q15Tp{CF7E}S6OuqkGo%QwqFoIu7SH^ zLLD4H|B)05`Ui5g?G}4Dk{1VXFO}}J`)q8x&J{_>M|3GiI8bez_+$S>_rud@&)FG zcFLj~9h9LF%>K7XEMCePMWiT&Khk|m@&yvf)%t}8$A86*IPbO`dGDQgpWegaXNQ91 zanA(Jb7S;wS3N#x1oS>o^N$+BZM=t3q>t_oCJLVe=3IH;6l@r#df`|Pek8rMH?6}k27UF59n zF^iH_Er!{KY-8>{J*JAaPeJ5@n5d}#J;Al2ldwY@MEvklwq0pAoMdOLFmL3prPt9# zTnqE*W#ajuVutbQh=cOe{$uU9EdMk=RE%^hB0G+az7!Qk4^^f+(-)q2SZTK>Z>HcH4!YlrW*{|x9lKTrBlU-nNp8@S4Rk|vR zUy%r&Ta-P=s)T(YM3Q?( z&&H2FxA12-e;qx~2W)a;mjB{44T@(^y=gVG_PyHS26)7mj;v3AyA*}M#%Kzi*BsBp zZX4x+glVk+u(1`xfXOxqbBEM4^&)%S+mQiUTL`jc(jatCT-VimY+vF9tzwDiE?QlK zvRtTty(0xhDE0nA^$2_R@YY)#8=pV1@I<9(rYEb6)B(ZB*U~arNy3{}zE?@t=td}x z8tl!x$(a2HsC9yF4RH=a0o7`S!H8W9`U;hEKVSisS&oB)vqCPHRZ6!>Lx#`2i*dHb zfX4KAFyTP;-)ijQvK&$Eu@%i5?}-TgE*_q zejRYk@?OA@9<9-1P?YyRPtwDN!L=W;i8;M=tWo_k7w1R@)VPtQ{((Lq{SA4yf~MBMaGT55q4`;ciLq@`k( zv@9TXr)_s7`?4@DRq0va8;2!lvjj@tc|8_0GGdjfMhQGIQCp7p`AenqrFZM!`^;mW zPSMww#QP_q?;r@(mE>!6NO~I#^?;VH0E$UV%gFGe5h&y>q~b}dLM%N#Z~-s(;ojPP zdd=*;5LqQILpgTV%)%zKO2_$9$LGiQw~W&aOAUolqIXMC^a$zDUnx(d3rb?3mZEM_ z>zRr#NjkSRJ04P8sju33r^}QF2wD5bIrJXgWtnponrj&m?0^DiAfYHgj>^jF;uZAi z;pET4^3h_2pXu+la0U`<>|sTGNDdYvQLVNbdvUaa9;zf*nuou_v5GR7ZXZTVkE^aB zJwj=Y*EzvHd>RBXGbBV{X*jOv3NY2nzf`wobLr0-+=l_z85(7?Ew4~{vB57O|Bxj| z148Dis*zDqR9O`3CI<%x;4Vr+D!+XBIW7)$t9K!C(h6$4H=JtckX%S*vRoCh+(o7I zyX5j@BrEwLib#RKy|RvqB*u>e&e_!(4K1wM zghP!RJtJdXbZOi4Klz(s9D&^e=%}snh0Y%)Q%r>I_TCqEO++9g0t))ws!oS8-M)>B zM(=+At{^7|B{?RlRiq?H z7O1m@<*u+;$MDn*4DQ;ORc`S{bpce_T^9+es#p)@x9jq3XJ%u&Vv2UH?CR?JC03J} zfP2;EFOHal2T3@4kxUvXXfCDzvKs12`oV)s?Cz3-dvB6+aqk-oU=Enpi=7wK>@$ zC`oVC=Uf4`?i}4@l^~yLbS$HY?B|xv&3lAkFIG9|C9N!tk8xH~Cb@Wd9*x>v6_WUw zUP*ei7mfb&tfEx)X{LEWq!K|9>!+_Dp5%7vT;{p_`okM4Nr9UY8hQ2^Li=VP-?4r_ zuR<>F)!%$3_ig4)Q{sRyn!(%wUHfO&XWr991&hDF_>MRwQ7K(*(EzDjxw<5{8|1Qu z41EQE?8?&7Xy3rT%yQnf5@ZZ-@4?DAA3{6tjNEV4&P+>9@?FWL?F?(EFqq((;NeI4 zuO8npuy`L~`Yx?7JGNKZZ5NZCjoAP&B)HSl-Tb*)$ffkoDFBr#$B35RO}{ymq_pQ&iZaKlh)Z zW-G7^9xNgJhkrJ-uphM+5WjDfWpmbt1OBhUlt0wIt_hyhP^rYmCm`JI*T#e*&{@2r zOZhXs{Oltp*(1pX=BfZTACuSHnK3$~W&lx7;OWZ<(_6iQ(O+`#BFN zrK*YKV)b>2nY%_tmXXR8s}QS9CvuygeMWnr`Z)peXwG_MyYF-smCt;HXkj<&%D43( z#6~ijZQSKIVSilbQevm=Z{f4s8IuM>{@XjdlX!Sh>;wH}j&Tg%KPob}pW%ENpFJ;Y zDXBnDPaltbaN~A-<|3g*@g_c9@dkwjjBi2qYwWEqWuw?Vs@(VQgX0&94>v;1t3?$NOOnoWwPW)Cgbn_=Wc;orD2z7Eo1BisZp3ZR%)x!Tvg*j69bDgS=V^<1 zm&7;k#2vQWq1zwWUD+}?Tuh1Ud-Y0FV4-`3H_QGz-H@K!$x zwU`2@x3MoDP^vcRxB5~Vvdu^fzQY@;!k^E2+Z#duVO5*&ZLih zmE`Wd{_AK-eP&9Ul!^IQpg+6wdbKRO%X;uLto|TF5o)@~zchX;R0+Qj+4uPOQxo2d zA0=n*l@-*-G_qSvc18>g2k!p#t$4-25Wt%r2DIeE6WDZZzeeYH0K;+ zrh5uMM9;IdV$4v@B0sEk{p`dc-p>iy&?LDSarqb&?s*KS_LX_*h zG2VTlMNATNpTD)Xvla7(+Arr+O6($Hcx9?{Wvw%IdiYg8d-{%z#)UX70aU$Wx+F&T*bi-n()acJ4=H+!55_&mq+@>F*?xW9OFs?B?gLD_ZLywn3&hOoK zuom;C<4x|jvn&ZAd-M(?<+Ov@h=`%RI78vDj6~@f-o$}h~qamC81inB%dSY zLT&HT4tRWZ>W40elx8<16+F14`r(eIRn)a2r1#5?OD}(Im?{ya5WdFa-l!&Um)^ai zI^gz!b7Rc2W*ot}<6IFt@2OcTuv;$oE+OVuUuAMyj1c#AuSlao5y7sWD_?!!a` zxsI2gpFe-@tK6kZ!8rnO;;YPgCTp!AiR-KZ<_4Zp*BJtgV;sd4(|BSF0(BD!Zh1%( zEnjk%*1uG_X2~?BCby3^NYc!Xld-ky)-1(0+xahxGy%_U%!+;)@#bzYM6c_f;8=~mlzFL5VR z4!IS9ixXG~s9&ehw#3I{WJ3tPP*WlYhSO^w)jcCKPkqBu74_ zPh|Mmx%P%aexQH4Dj6kTe|LghVez~kB7)t8GIY3C(m_}$>VD?#2KVX$8HbC+RCcU zlQDY-e&_OhnBes-V62jG&iOJ z`Dp#uVy8%r@5G}EREFH80^~ubJvuwii5K|eBzLjvy5!~1c)_#n zUQ0g;&io+xKzzlO$ov6~_)qFzk>*;e_sUvR^9)WApC-0^yQmm@?utKFsX-;}wUMV1 zjXY_f*K0on3MU}=4M4Zk59M&Z1q9}Fm}kdItA>$Sd-tk0@GrlUI^~bT@UX^2NsNsI zgGvZ{_3R@{^+$okZDP;4%lEam1g}s^{iak2nX{^SYddk_0>#UxS1zi2e9Pg~e*T9x z_Y0mG$Ob+9{Akw=%)C8I4alv!m|dWJujX2@U6{!LlZ?8Ko{ZOMyW)`suWFaPe} z4f}g2LKJ(mIkNh6-K`p?cqVnCCkHRI2!VD;I&Xu(jAvc8i9=6P&>jAKc{sX;NgX!F zN++WRv){T@9riw3L;sc?wOLz19Csu5a+eyJBFyct=U*u}%R>7*H|#3{ihW5NLKTwb z1bSkQFFn?KK6>gj@ztlZRIg-`k+dzZ2s5{S3dG#>4X$_UP|AuIBy4yRiT8NKlLt>U zT{G}0m+UUG>(qMrks!?=kxjUsn0??ot@oVYJer?gYVaakKdP*?5B03ITX%I7{-cm; z)v}#G zb_-4J@L2Y}e`I>gm_Ee=Rf(f5+cxco5~6Co5cAzw|E+aUBN7)A5rJ75KWzL>gk6#5 zJsw$fp>v*Z9(SH>YGxbeY+A}W$L!r{AYc93>=ZlQuZ^&dwC}x-dpxE7N?bUPm+*@~ zR?;l_H~Aku!?T&+$TDa@MedMy?7VlLVSeYa#t=@EDB4EZEohf!{nwimP22z3u%XR@ z;0&JA0X%~@bq5ye6#58bDCT~MoYsM&VR5ehqtK)4T^ z?Fk+dp%j=ynI5DmU5N5glhFN=Zn>|_bV7pXH?m7a^O48De`rnC6 zuA;$%yv*#jHpANQ9)*EXYk$bg8Tju*KNMj!P|k54!cJpuWzBKPi^v_*bf*fP#9JXe zoc;yVzlIvO>#ukw<482ZvE^8Md5*&bD{C3?S%^>6XHSz6J;Y_ink{tad;2|f&5U(1z#?`I(|B{JhV z(nfFS-&I?f^o;4VIPba8@mVBMahEXA4r7B9xs^) ze>b&6&^#d%dtzCC`sZCgw<;HgGnk3tgT`QGVdpuwy%Co(bq;UW9<-`SaQ>%S`YX3I zsLphdJC~!KzdZ{4{eWfP;JTPRnV9$J>4@8s@pl(vmMRAG`v$*xfAraHFPwPt`$53V zyn5_srj?d4G(4{2<QZJyE3i!kF|Y_(1lJRH;3bls?Hvxjy>y~dD}p*rIjAu7{D zOkSV;(c1m{RqQqgcH0W|_u6;TJ0IUrKTYmqe`Zd6(7iEoba(ERbOv#Mzo68;UQ-?d zG@eDK8BY|HBkG$JsAer?UH0XX+|{|iJooDPc1 zKJSnT*SV24#8LHIHH+i<%p%NTuQGQ34`+b2*7MND+gFWlf8>a@h>^@9!u5g?|IrD} z@$12le~OHp&H%y^^30)^WX{P#d_qV_W55S5Uhl;M*MH;!+~s#aa;!!SJ6BYO8_F*` zhZ*dS-2p_Xb7Gtsx2HSMx!%G$Vvh*U0|?=S-Ed!SaL>q#Nm>{g8G%TSvddH5i>-`j zLJPuf31F@UR=u10Ag3jo7pO$z?vZMwo_~J{{Z}-Ysv$4$tn`|6H+0ZEI3PS9{DY(Z zy6z?1u{*oFyPs})ogPzJmsxIQ!-N(g`V&)62Ep+$p9+(Lr_}cCsdkmI8Xu@K#(L^( zMh}|V$02VlWxTGwrL9d>;4pgk;&7h99s~e?++_M*sDiNHV3In5~xhK%6gHbf(_F$2PRyEAehR~Ut zHw+oN{QG69;`%oLVm7|+J9^$%ob}h-K*BJe!x?yKV>?+6BTfws*<~1^UgMqb?5+oH zyz1vCHX5pgI5{g&*ctpinQ*0!dl?$6gi)ubG~i+9^xcI}%Ueu~F>;grdim9phg>SPjIoRN)9p0dVHDRei= z7T*3I1=)orlAoa{<{Dll=(h3Aue|2t!q{9F#KB)8>}HONrQ*|X4}~^ffRhG*56eGp z8s751e~4Ts>b1&el)Du{>6AglumTTr_&0lbxX1yEX+FPNae!ZCJ@M;FVoy=k&UB2! z$leB`d@5$`sA)&*31xO?=zbaYok)8;QbhOSOC4t_`?Ue*x35iF?xn{PWo}(&bIIzX zHxG@H7A5#}#Z$^Xp*xRoyKnr|N=+$C!gJD;Vyg5^H^L{30kx@K#HXJYs&T5R5SR=* zMT-rbKJ%tB4|3}W@vr0K%^17ORQ2>v@)@6g+;zr7TskS$Lp;4t=4!BkPYfZ!1p<@T zECEkSzF;$czDf^Gtz2sFYWW*rMfedeDi+Y_eC{HZcm&IZljaP&LK5E^@}X zPUy;$AvS8OaGog0IzZX4AMcIt>^hX&3H|<;?X^YpZ;k${{X|6E^g6RxBul$3f2_;Z z^Hm?F*tKB;mTwF(IjYMOh|h^xrmOKiEBMfpMAl#!YZzCEu+2OD@Od%+tEQZe1dYzG z-sT5kgWZhp@cu8t-a0DE@a^{oB}KZMp}RvsxH*p<8Qxvy=SlU?lXV5W`WE+&;8tS<>$-Ds27F(JqnRT5^>tw ziP%XVY>Ftyl)d$58W$}h7!O8xB8aiV*T(XF3f0~h{fU7m9Hc9w}|v3qM>Gc{Wz zS!(M(OH%|Vl+)w$knAujFLUd52a;gReuo6wWH#}OjV(1m$pVpiPp}Y^WW8FMXs9?kY_n7&?!H0 zm6uo1YIl7QO!0bIgMnpUN^xLfP($lIlB&gJj0<^5YpcR^*6g4oaMe zu^3-I_T9jY-9FV7+$J;e-QQfL5KT!1S3#@0AN=znVO&_UH65Q~(!Q zWwDiRT0T4B|J>>M`QHSYc2!`GMeFQ zh+(53+-rTf(7T4|cGM7;x!ibGsUqCz&U3QV<~FGZI&C2yJ7P(c-*?&ER!jaGAs433 z@X*(kIRQ>{XZ68|7Sr}(7v6=qQo>a7AC?~KKYb<=vW#P}<8vd;K_gH{v(|U^kuAPO z^T(|Yl0teQ`@)KA|00*358nm?B0BB%WR!9K3ao_Y{*<4kqI!t#935Lw_oPRZ=mQU$ zsbwC<`#V`ChG(k%RpS%Baj$-;uhf2+e)kdMLmAJzq@d}&fw4_|{O)KPBv-3fgHQM~ zf6rZO%6&Cq($ll5QRp3y?w}d*uf3D}b3|GEYe|a|f(HgIj`LDh;shHL|^G>YlN|BfPxP#J5yO_AeLI5ZOv@MJYZTlhDs(Ayyzsy6vV=sQSuPa$R#ePeTv&&m~PB-q`> z+g!d1U9#Vbu+h77DZiC zgV9;d38(hr^}pCWXZXY$8V-yka!uHRP*6*9Vd#utlPX-2fV2~0iG>&{d(k35SYBa{ z@BtVNw%=?knr8T%KerF^bQX_coo-61vdC5?#mk9SZ}BlDuC-RA z;VTZ>i%>td6MNvB;Lv_pTi}qQ>z(b4{yBHa34sPA{k+e`{`oqFz&@m&YMj8n*z50$ z8LS>>D?DUEbj!f0>nsP2V^g@;&fiuJr9%0B!|D@-1Mm<@x|~?GNgnc%Ut}e5ix#?) zh9%CQ)*CUOv~DBXwXY-z=v(Vb(!fO|XLHJ>J=@&cpdVYx(XgB_$+mfW8MUR^RUNI0k`3j|no|6V9EjP|xm)5ego;83bt;LPj`lMo=@n@| zDPgcCT}4TjD^743tcRM!hS`6b2z9Qs=wQ1wj40G8LwgO(XJD{*?8GY;O%bk7y}|kr zLkwExs9MO87I!ZJ9uO#M)@Qdce{*X~_p)(@Ns!j!HA?1-)-&BvFN85{ z7u}$pYja2$vFM!N)|x+m?vC~QV0(UK6cB1LV}5N#9;7HmrxHWuhOOhg?MNePdB82( zz^9ox6l9ELRMWV#tX3wKtEjF_ll#}Zt=_GZ;c@%h!*;E8m#!g>GpG$CfBz?xXs2}= z6}nnAP^DE$QHL=+TXN?KMPWVBL7MN|yy)zWA6O$%A2)9+clA%u%dmI*+k_!e?On>tWySyu3S zgsnD)lAE-8%O-nEYpKLH(yr!vQGKo^2xo5J!NS94AMOZ zV(%#6^3BDJQ}El(Qn>w%gxI6b<>?|iL!>PuJ?HeP1&GIH4OaeMZ2<*~I))ykvY{5DFc;j)0xf+~Wk_yk7+SKrH2p)^bfV z$GzV9#v5!2JiR~BkQcxIdlUY&3IQqJU)5bN^F_5dmu1aj2ASY*TZtF59mO%zh|?k_ zuZ*L8R|W;ApWlR7c?O-`b&xy@`o`cFGhN_*>;;L!))`$oMiM}Pj2p#mGC?q_+g!&4 z*vHh@@PK(s|9QRRa>eafy5J+2ZwSRLQtYXo6Wh(u@@Wv$f6vf1<&+aZaVyfocv^)Q zg?Z4#BJC^6`)0sDgRy+zjIrAF_r=AEmTFIQIjRT*5%Kz@rvy(M1&;2lK)S!VudjUl zOowW__643|S4I4;Th*0ulYRNVdF-3hmnCcpP}QVlB16&nm$-X-H3o60M3H(W!f!uz zS8OH|!OpBFuwd%?@smlp?AnlBTtZHD1VaV7E(U!Mt+xW0PjpbwBxZ)6R_(LM7UfkM^$X;*ySG6waLG~K{^>EkgNRfN#WubTGtLQMr-Wle-;Z{PK%aH)V&OWT1)s6EbwQhotHZ|M1JsS1}%@YnUGbC`E`xs22A} zl59e#|D0A2t0(>(k9YUhsN1o-g`oV=(6jwW4L9~2Y9uE+Bam_Sz)C40ZOK`t7`RoN zPX2msYncNDkKWNStKAtKo&T;d>vjf>%2Eqobq0DgwDSRoHt$BDE$cTi78 z=}!>D7a{H>%t7&UnM(5uET@+Ho8yO7j;C=6nfctA!}B4= z*@f)GdPlBTS8vU_KQL29?qoP6(O>}4o zaZl}|VKj|^#)H{2t2yR;Y{;oWg*^@ST`N^hW3V;eD2=jU}HsTV!u$SGvo)E@4ob)hIpUlp~A z+BcT}{xjw|?{3L~Ntw&}tPOGSRhN`|Rv)xO0DEq9oz@qkO}N z9cM;=C2rU4S4^Bnbm1Qd!&p_V_Gox-v@PRGmYSexJ1XK=I*_)ff_`yP&vTkg+D)w% zZv>vmBIKgemCW*h37e|+&*9jawO#EtC8MIBC>Yka;q`xW_r@Z>F)>3bS~Mgnu*H}-F>7xuZqDTgfkz(;xPBeI+8 zKxPIkZ*t(BzPH4J6fBk7gSzzCn7hy)&YZ>2KLky7ZceiX(XiPO3rn!nmXCNJhU2qHEZRs*Ny>V-4-uEWo*C92Vne_cazbV{XOK$IH@jG7e z#T??3B9nv30Yvau3@xlI-%_jYl^fHa^ap`nsKT^By+d z))tp~9C!@B;S3MIl)V|h3BAbroJAu|*vxD?Qhh0#jHMn?)0*S7>NbG)b8sXTia45x zlUgT63Y{O>H7t5+&iC1AkkAZ1TMxZDNU@wyvy4w(UCW{-ii?Y9jr%`#C%uEb1l>tv zI-`Rq%)^!Y7xmh$=uLCGS}pTh%^VpX{P=ib>-}dn`0*ky*~jL*1%vXT(A0SlEYC*nG-5G(yOm!EJKGWd=qabR<*|A;u5NLVf8sW# zZ<@+GcE}i!_PjIo=W9!%s5JV4jU{}2t|Q~zZ9 zla)b>c+~#%2TRjp#4tQ#YVABuGT-1E!a290#@H5HTw3J!3YTmhJkwZiLwTcP)YC($ z%lj4}EDK)KFvrtcu2ztiA4lKo9?Bph9eST7FS`L?z{6bG{nCwu z3Z0HK%5Hx2Pj^Gi_$R;AFsg+LQOjajvho!R>fb)V&oSAYz35M|DWC~B@3|v*rHqLC z)VNw?VK*q~7j=81Q2yO70_&W;O(Zjf)XpdRj&8d~`n=X5RY}=t6l&Fb+84^YbOHQS zT$OD__OqIvBFtra{XP-0a?RwyB|Sd^AUR>tJnn}dwn_s7yFri1VU_Pie(7HGJ&CRN zamNT~C2(E-n>X{#JIe~eFPY1lpde*TdaGd{A^Cy}}Jev6K$d6BEg!j$9T( zmi5B6{28i6V`hUxLzxXvA^JF)U9vi#1=AJ37|k_#a9_4b+)Q{+EBit2SUChS?*Nvs zu!p@}@}B@6ZP*=I*&DR~gz&Wmp{w!7J#}67CkwGZlEjia)d?a^vWfJBJ26hLM(od2 zkn>F-8OkK;TBZvGAKoN}f6C*inOoYm0xH@e6a|?>zEC!(rnQSr>21`sH{G8~`8T1f zmu0a(atq8CnD(XCIw(|4AO+kGpVWRr57(dEcjO)vDptFfn=!4i+LBsxz(Q6c7jg@T z88J5dW*;*YUwd4X>Il!?bE)ALqNlu0V}Up2%|Dk?NVxVNb>Uy(aB(T%O} z8%8s)($%nP@&9P3|7lx<=AxeidlbP{CfYng9hWEhw`O8b2%7e`5CPK!ZZ~r&Sv6jg zN-^w^a|cOmGluJ5K=bYe0lM&224o_ySZsf*#3) zvOOax(c8s>jgb})pZUVwQ2ZKQyzwdsWrS|6NKuAG?j%Eab0;pOa{@kCc0!r%MeqBr zn!a(6pU;_$Laq5wH9c*Z8g&24GBxd0G`t(dq%@Cl1eajFP8n!h4s{V(6^FNYU87rh zupmlsKpD+Z_z|ZtUcMr>M^$p&1L7o)pvSlT%j=<%=q=nYN<38*!+>#-U&D5+crF3y zP$$nDX(7+gB-Jt5tUVtLLk`WeS?`8jTdU|^7IR8Nsy?{s2vAe);e7tJ1*9r(|UzX?8e zcC>ij-h`F3y8d|EvLlmv0MrC`JJDFY?)TI_N`J)RkfnHT-@*9Y3zVgGPU1eZ>*%)g zjw{}~onGUXjmar5k#IJ_!S5uX$c1%}vvt8mDGm<}_1TQNbmTpE1+^!jSj1*MT!n$Q z=|-9pi7DxKnfDx}6bxGcD*2%6iZiRAZPC_^LW44XZb1D!cWiwB@d~fi+SM@Cn3= z96g$u=N_Q$)JWb?$NSpSrDYXe?70WmGAw<>LHBQVB_{45njU$~@E$g=ug_>drdib7 zjN-T6wGx%W9|L|tT4Lf7sA~J{Nz-+9W)8jeC5AYKCHRD7L`=}=J##{x3Es+94&3V? zQCxma^`w;8^$=C6*x7yQ*nC27==})({}j$|luBW4e#*;|{C8?Y8MmR7Asjtsob~U0 zmM0F)P@YyH?kDyi)tWlasgIOZ!)!IOu|JvcZ%A$8-sFn3$^5s|@BhCb@3W`vLwgzM z8J4FzKd|L@-y*s6zQZ_j+v4?g;5SStmZ!@?)bT{O2xmpi*Xer|!K9aoODMgl^YY6% zRA$qHDWdzMFIIZo@xUnkk!7=@i%<^>x{lHy$bUUxr)mLIM=h_Q$FlB2cLH;R(H1<$ z!u*c-o3!%KP4o88vGH&wd(!>>Gdr-5Bq&AtpCj&r?O)LZ{lYS_o3FLq?gZb{iJ}i4 zwGZ-T;VJtU1Of}-%ZMKC8YVwKLkWUjl;}ZFvrw&%Mv|U;wHcgH$lb}EUs?(l*HM^X z$(HSi62G7{N-kSkeEIs{ieHM7s=VVGCcWMG{8uaaZ#}*jd@h)3)v&rv6Y(+Ks7wAF z2Xg{br|XGgmhw)q`u=w6(f1k?YM$G8Q@f7RX7p;da>AZY zLIQNCK1ZDY)PXF;fnSfNIy!{i44bYuiEIb~za$2c7Rg&e&)Vx~l*BKxiQp6Nz+=f8YR9k=72R9o zK9-iJi$NAf5IjM1yM_SQ5cu0`_LKN*zF7TAhN#9d$P{v=Zg^KyI zdLAhZ9E1wU*_aYh*c!U)l;(BQHP^Q1Vj$b2$Mi8R0r_`Wt)#_b6_XaLrQWY!deNxt zyx+o^wQpJDMJx#rpz|*cct3LBsdkZL;8ke}S*mBR|BiMzj`6*&vbwY~k<2_H^mvoU zh|ZxJvTFY)NBv9M-V6HfF!HSr*!=l*c;%g)N^~)BL%nZL=u4ave7SPx$H0GYjw!YZ@EN=2&M zGUo}SX#*dh)U6L7iB)F67MmMPVvo~fv=OE%Ax}4&hP1eSA2OfLly$#4`$M)C84~|M zJfRg$bdeJ&i~ZWl!1(R&0zqL(obBq{jds6E3Y4?GooAB*OQG*qFC+x%``VU`_XHhg zd#fhK>6(u@i1E`fb~>F`joLiz7VhvNGhJ8KcIzICm)a6~H5kCE6JH9La+IlfK>AaQVyI5Fa`Expbrktmuq}9fb1WXw({4NOX z@F0ZwXA%&>XJXA9da8!CUx`GQ14frxJ{--3a|tqP*_JAmPk!3Hwty?w!3`Y^jyr(! zR1LFAu_?Rzje;;oQJdiX)MLCy_JKR)C@!cvC-Yp&P!j2hJY7aq3u*}*B(_(_jvvIq zFusOopq7zfX|~(H9x>~Uc`;SPqfpBeXh1~XJ1pXEBU|$E&)Z1OyoP%9xz?6)EI5MP zPH~DKM7Dh)=u+$IYNlbt(ZJ3c#(@>cakZnZTuX`+JhhQqy4+gsM`)!hsS=+)}wMIUpjh`w3_xfvlBSWL3O^>^-%(Xq-S=9}N{|6jFWn0) zesc<*Gb-LlmhU(Yk=s!{F00J@P1ro3gwkB+V-ap#|9~L$GbSrxp;!``MUCU``G3^{ z)Z_<}vmg!RjU@Cl+y-CS@4J!Sk$rn!rTcZ_(2r3MA!kq*dlnJ6q*w~CVLqv~(D##E zx;0hZH^}X0_Se1%wljF6>Y~M;yA2CcMXFjI=$KP~Qhy+$iU_J1MeQL2<*1Zc5yI}M z!Y{#8xh6(NoiV&7k0>?{hvLxm&)1dZyO_sKvoU-{!N*(JG2e-NH^Pw|m%8&bRpX() z$S7r)jU$Fb*bt=^V?@H(LWs+h1C}}~_M%9}Oj<0OO_*gSW5FNt4Q3@QQc6uiXh+<3 zBGIVTKMmjxN3NPFUoXMKXFiHyd>t@@YCK1HtclG}Gt%U4MAS8G`+6>0diD^`VY(#h zU&$R&7c4qxSc#HJFHYD+RJlJn$YOw z$yl!r;gR6^Ykognf5A=kr0@3>!M6c@K`I zEh<<*h)A_mBp;c`t|eMv?|Z&#Efd$UA|w3%68{yQx&O@*ysw$9emCS6XrOjnm`EKx zb?QZ0(AahFOo<(hqfhQN1Fafajw6%LN}KWY zN%V1`9s>E?$?^+D?{no*;{3Nd(C6B2-O1ZZXhPQ^-W747@hDFsrl=^Iv_7|v+NqP7 zdgig$;e1djcF%pI>OzKf;Fu(fX|BJ2Y&2T?ZH#|W5b>3j64KWu&G&zXOF1it$7Y*T zaDL(Rc}caB5X+Z(;yTHq=|?0`BGZi1xw(y*3C@^Z&(+Boz=8l-0{_=8LT9 z{$I9l_+FjyvbIq?zClBU6CLuhZz=d(-f*X}K`*5w)O5kB#ETG5n7byP&Buxu9`$2ppiyNO%&m?zVb0#_zcJvg8dqsCstQYqw>*@Y&^!7*x#o|K8m3MH zteF6*T-gArRS)sT4_TH&Vn$HOxrjY9XaFY!{6+y1pxSBnLfKiqc9_R867)=2LK%xB zETF2=F-DVW13^sGH-9eM1o`=W@DrRh)YaXFEP<7%o~$2tt4VP16Fzw)ZY5zwH{>O< zWl9cJ_UH!@8g!8V$@(6lrAQ-U8Et{ZDF$VZz!pb?im4iH)obpUxbv7}Smc(Ef5ueh z@v9aQLPd+N8LZWyIYei;;!LEwYK7N>@{>^K#a-}G`)wE}6Olv4N^P9rgx(byYqH0S z@FLKBI9(X%mAzeNav1%8<-lMRC*b3x`*;9`G@yKC3usVfsYv`LUTD>00w5_^2U745 zby~G*u?l@W^J1OteZG8X0n_uvCby5G=m8+(FGe^|sTz)^lnW8>5+zTcEP+l(Db}aO zKmUX;`%>1E%U}|Fl^mi~N?tIhSW^~ytXVw`02&N$G66n`nK2NnFcEwz_=)8!9u$E3 zVKT{(c{A<}jaRR@*(y+`@8rqC>kL9pmL#Wqy~fHwUrn!4lXN7t?5saG@d*7qHd?q1 z9jWQrRI!CX=)_w8e5$?pR4bca9<^wE^pq@>QPHYwE#cMXQ=1xW?lh#o%z89jH7E8b zm#H<=1WLbKq7$v1Vlq;(`f+0j z!h6f@aP4WcsIz$RzINKL+`LaNm7JZg)R5ZYmeofIjC(^=k+I5=vlV%=wm>j)wx2_7 zl7>RDIHO3WeRQzWNQ-WZPdc5<(o8m>Lx-FZ)7%v*6-7CUqTJL`!8s~rhMxN5mudHA z=IchBJNCU-~Sv%!{=&YtUE60gN4UBJ{D9IY3BBJ?X4!0dk zQ58+)g?>?KVX2hFPRpTb&ndA*AO=IE3&26gb>B_^w*7A-W9A?g?*&(> za)X;5Y_e(XTx)NSlb~C1(Lqwy`UC>Pirh*00Z_yU|7rJCmADKQ%gfK42Y9IN5`h*~ zbh5Ad=1Jn{y`2?P7p9|+CDiF67E{bA`X?+rlG749_V)=!`ltVL@c4ER#g{#`z=ajw zr~T&7t9tub>Ae5rE&B$y?1+BRe2G_vEO8=2NNO!jOJL-zgXgr6D<0(m<+o|u$-qz~ zXR;m9l>1`1QgSrR+=)nBBo$s%%Yh+kWePzn4gYshnC|%NjPUUqB)P=i&+8=PW2-R{ z+}|6`ewDZu7Y4_P7ACA#pbzUwE^0Mlwj3MO#)OppY%xxr3Oqcg#!3jJQz>7ns!F8a z<;4q+EcDd3>@v?ZBb)6zglZ>hr~VqhOf9I;Zr`pI(U0Bu93l((^Wv*8%DA@b?|L{Z zQM*#dg>zRHI0z#NxBOzm1jDy84<&Qe#@|EBHU4Y|F+FLgJ&eAC7bd~x=!S$6UzK&v z=~qj<_NuNYs??QouM1Vj%mV|`L>;Is6)6-_chG6|qc3F$3miYng>IC8ZHGt6ZlM*K z+ZFJROQUh0o~5fMPH}#|o+tJ2%P&_CSivD&A+W@oXoG-1YJ~WtXhQ}f)>e4p_1HK5 zhe7tLGWW>AJZYA{Eb%p?X~TTwnP*ZpN&^f~&|jmE8|N>Z89z~klH7iF_Pug8?ql5( zWHW^9QFVXSQB+{J3HefGJ2i%`R!a5AI&P&&$BUO}T8mXPmm=v=oDYY`n=)@NxDv(X z9i;7`YIf~@e%(5?If*u6JKuz_xzYOvD~D7Q#O2a^F21XG?tboA7oz^4C7sW3Hb3`8 zv!J@n`F17WdHj8o=JdM&S%Y6`QbGNL%p@87z=C#;&vmY+DVLgg(%USc9Dz&%}6j5i=Ww~zn zF;f@$Fw}XGt2Ak7WY;^jElJ-A(?3hl@wxW5=jF~dPZLW@W9Fa$o6IB;bwQ~&F|ahp ze!whEnIueFHGSM9XGvG`;{A!)T&0%Z&tDQ5SeR_dMsPY-)x}h>ixK)s$=km5K1?NF z#4)SI9(eS{v1ZFQPVFKv?V~Q9ri68hK+yy>FhLHmH&8c7QBpHS>Ejpc3@0`|uYfXo za4?dS&vBQ2l@rzay(Mtjo$k?lZhIS=-tzeE;Vm=)U#Vo__V2{==NE?2TF$HmozuU^{;KKFB$4^!wzx9K46wPOsVhaOf~9$DH> zaBRJgMjjWK9R|&^ z(MuGzrq#Y`xLixaDwDZAU2lR2(t;uvzUVa&#b^l85O~+j!VIt zUo)!9M7@2%zV`{sx9msQB6mOMODm>R_}ncVS;p+;)5P7wR>{U1|5rWy;~C51e&gZ% z^2zk|bpuWMosDUk;*Df&m+`Ije2L_0F(nIg#l`kOwSoVHZv%XPr=@d33c#aM2%6uQ}dN8-z z=uVR^eYx**(%qE`+Ft)t+kV-!?474rxWC|hlGq^spPLLJkqj{_4$Jz5ac9Mgd{_SB zYvtwMunk7kehL);qsTh zr|+8ejMrJ4z|BvbWmt>F{fO`CWBt`y>qGhS?b0RBJWv>{(S&|Fi%T3RzCqZP?Ao&F5__s^bgR6Wht7s zr|+A%U2j(?Qlum%(wWr9(#Yw!%o=w`#Jg{Lj`&=4J>s=%EuHpU`mO|-RB74Hn=!V{ z!rg%mVH;2{UAJ_zr>1+q&M?9SGar(lArsTAfmkXi`}b%Z4H`#iqh?1~cIC!7Vxqmf zXICnl!N(!Vw*YbB{Hy=8DE~`2#0b`}U&c@(u)cj;wKdg*(P~HQd3TNf9MItNFMq}V zU;Dg4$G1yuHZ6U^03a)+{hk8RdnPQ-*8$oJ??Aivq!|b3=Xh7Ksv7TXPHV*tb*`Fa zwBm-Gdw2sDU?3H)0`9*aZ`>XsWnJf!g@sRSUFYBTU#xQqRvryJ&geppy+;Tf_kW+z zhDn2FIM6QBeEmJy14F>;Y6Dhf-}37Aex~iA|FbEqoJJ}jGSml0dwLE;oI$^w49}y` z|1ktrpaorOi1IOk!+-5R)#0SDxTKVcNu$`?|NmHUyr=feefI5xk&DaboMqX=D#mlZ z+kK4711%lYF|7}O8cX9U6%$wfETi_n5H$`!mqcrO_L}E5hVsLNlOk1EHf_9AAbNhy zbN`HAN6bW}LEH%|vHOW*YexF6hvQ;HNL zwmmHy{a`1mHMZ!w?6L(qu?E_%#;Fqay;eSZR{2o)o)(UHoqql%rT`R!A5SNhN#6&MAmi_SVR7Ro{LJ{N`!-6v%VHi>UBIZ zK!|9Tagv` zK_g*y!M0Z?*x0w9*?WJymLzv0jgbsMkM1c)^zV48usTlgHM~A_T>0op#=ooF;)40y zrpmRG8GEyeN!=#p?V!Fz1vl=QOlL8>9ovZ(u&Om8f}2Zw~9{`=&QI8Snm3J14o zJ0=;&ae0IpOhhG>iFY#Iz(e%m70_};c-vtoA66;ha+k1mw& zE=rqvhwmAWX^l;%kmg@wHicD($x8gME7S3npguNMs_~~+r5F%%JS^%_!f7D}(Bb$Q zl%~EWu75s&5=z}?e`d#8Lh&3cZsacl?2E9hng87d!$|NJyb9<7)8mK;nm&lc)qiTa z+89NuB$Cx<*W$wc+s|p`+VsZ%-@R~Q!OX*|wDk)a@?n1wC}=B<4Gvn2V@-Bte2I$< zjor$AgPYHz{MRVj5av5UMZW8C*s#Sj51cX^4nlTz_U>oefXNye#P%rY=}Ky9c>QR9 z=a}wjvfL|hks7cc!}MNCM8sG|L2{t3(pzaE_^I!_nOX_3<(H){?-CMycoEyz^q6%dCVvhYSahMFzsoGoll? zGD=P=95PNSSYj}8l&EOMpUsVQZud?(4lJMxtQLojj?O6~4d9I^D0B_jV#Ia`A;`_fQ+TJYy;ToVZ6{3O{&$(+Zw77{l0;UKO|F3szZXu zl|*&`HVA->)z#Gl0-XRH#tQ?7g}xnTB9BQ<28yo+Rf#uc+k=2P=p}>GJ(y+uPuX>i z48U92>6HssJiKoHnio%LmVSEYD5V8e#YmWNR+Fa{Ujb_3mgMA(o4@Rhc&7@s&Bm(z zH45V8`y9P11c%y<*XAA$sKy9uH$+R zZ8)|mTB1K!?YN1b$A$xmW_~%&1BsZ-l7B3IVu1d!6BiW?*s~WB@=H%ozm37pHBTzt zqmG~8)KXPVQo<sss4Kgtb3JgxZ*cj~V8;cyvGoFaQuw>G7*pJzYTCh4fOOSrW2V8R- zpEqXUI$H`7zw8VO)`f(mr|X)S$YM)3pK#!IfaoUybK$n zHVB~e=&&-D!6}~SL`VM_NNWwdW*Ru*wLylf;Vo`aQL6{=@mVR?7cd%IE-ApVt)SB4 zot)td`eI>C(G@ABxM!J*bz!MEBL+#C{YfmY(qKfu%G_*R4OLQJm+dd&J`oAmy=b!#3v?!B8mC|&fv^+|21EkUZZ!^b|W|V9yL~( z))fEl1RZiQ(q1lrCrC-pOR4!zLL}mCAT_yPZ8EpjH2KN7=UZ zoy%HIFw`_yYO9ol&1?T~@fKGME4sXVG=2TMd*N#RrydC_{@dV$()8rCZM@>Ah~Y!j z!+d=hxTa~`XOx0!#P0$t!c}nTBz17bIAlaw2(EMY5PZYrbG8TNp=4Usn?`ico7DgM zqfyX5;@Ph$Qj)&A`8&Z5#Fx}^r8e^OWvr%4`Wof|p7FBH`*%Ik5fN)f*)w`||wq}Q@EL}5GWm6UP8%uqlkO*54khlH%aTKTUznC{Cq#ZTdOE zC=GR9WyrHvK9`+=(hIR2XQe~^mBB$l9?VRVT8$=-`KrqVZpBk@(8$>1C(T+HfWYZjXvz-X7MOdp$$T1=`mZ1<;k z03Lp^)!VdKb*8}<2#-wXRB1)fY=YXvJs6tF3)E1vZ=aIhZV9SBQHF)@6TDK)_ExaDDmB zaCoU{694VFwU(=>garTJAp60C&ofxQoI$; zjK`_#K$5kQo`}k1gE1V2ma8WU#bTik)Jpk)J(BoJrW1hR=@te z?Cc~aCaP93%O=FeIs(gJWkt`izpyANCBsbs)+TD9aRz4@#DH)Pp*jA*o!e`rt_3zqvHbXHhc z7?9c2)8frp96~D`XEYhVo94-JeRnz>U!Aig0z~u+fTgWJdV!6-=CQxEg^!7;H+SR% z^g;kp&*Y)#B>{n%<~x~*K}H(#&!0bcbadoVL3@guvhdvwl{e6;jJI^n8>ICeo{*ED zG+h7$_ERkL`Pz-eFG#-+W=mBxG)$c@x`Mv}Ejyowd|TZ=W;**bh!|e{{gMin>PC7E zVfg#5rZitlugAjJq)s}|vR}-Yfo8j~#t<8$=%hSE#2chJD+HySktAklcqtv;J4@W0 ziXH>37Q6l`0(fjD1a#VFH^4KMUX3sx3xB5h3>g{uS#xDE&Zn&A=4PQ{lT#!MpMP3W zK-otA``u+7(QNDT8Yy#a4e)z!p{ng!;{J|XOVjZ8WHNZT#dLwDOayl0kwYBL+ zuP3nRE}dTibT=Fv+@Z8o+>`|fVC2OQ48vYr(3GMg;1J}+k$)6R2trK+U+JC*~U62n$$kspdnv7I?6ZKbZ`jkiY6i;fa!vh625VY zvK(;UwXV_pMw1)iYgTtY#}4E_G1-&?;xGHU=b` z(zq;LK-P%4^>XPoV2xYq==eMd` z)wqZuTOXcj!@oeYJtpr@JOKP%DQtivuPhP^&jTVW#9WD!J3IeX3*hw8r?pBhJsBX_ zsK|9$?rUup=g^6!d0nAdv{fejXS>C=`5)6rag>ZN21djrl6eA<+BXuptk)*&myq$H}$a-{k&7cK@-4J!rhmF=zv6p=rob4}vyw;BnymWZy zN$M0`tuPZd_{C+ugq4RQEsb+Tlh+X+Ure-u_A8^p6TY)kaLo(A$f4~DdPc%+v@LW= z#cm?o^lR?<+v+{L_q?%$NbT?G)e3}$A=d&vVFol0FZ(2zwNpa+?eMY|a%Ehjg_0dSE!`4-~h;y~M`FP3JAE+I8lyTU-s(IWmS%Z25a zzVw8SKSwzoe|%_`xa&f8w&cF_2~s@!@4lUrwktogTInZ1fzC7Ej8nz!1_TUM*%@a+ zK|%I`>)YF8RawypHB(KI)vYZW^1K!y0K+hO7v8_gVk8|g z)>oXM-vhS0bi>5hO@RnNJt++pRbWt1BMY4ZFaV_FCl^{11FCMiQkWc;3WBBh+Dd+cri!IRIrHcoL-zin( zzyIN9{tvqfd;kCa@cuVt{8$W}EP6#)UA?uI^DW*@qzCio+hg}GhO0oG2X7u~n=ssB z+RQDXqJm}5?T{~U?X@M_;;}UOxL5W#LXn#)>wIW=RBbWK$;7m%e)`)t?bFG<9O=6& z_islr2?;Yr#%k*-b+$8x<3CMBjmAuM0d`W2QZw%&E3AvI47x*!3c9 z)>qli#YEc6pIPvHneexNg-U!~;&r$~C-Hb*Z}<}k$&7rl{@voKyDacUH|aX*WU@+E zB;l1$$evpllf8XgTGQ^rnbwEu)fwFxV128D`#Ws9%zXby8ImtYjbF58(1{`=${~|{ zwmZLQcv~ESkM;fQ-bs*0Mf(a!z8_C%8OysdNGDohr z(4!VXP{r;mn{xGWp~#rZ`>wrRXl8D3u!JU!KyZ6_URxTB;!(#g%ZN|5SD=G^@pmek zbOJUntMI*~TC>@~FTK6-1B3m$)6lb$*@;E`v^om8WUi|B!L>&%k8_PRL^Lv9n@x|m z3npjnZ7?aMcaA-lKvbsT^u6{^U_w94Rkp~cCT(eZ9$pWvD=Nx<^z`T2)0j)p~re*zpn+Y?FAESZTMKkS&41G8P65nzukx81gYb z-WUTN-C4Ji-sZp$&fCC%9$4qVUUyv>X$$*Rq+$uQ3`mKIm5Ws)wLF|3ji-_rSnk_@ zL}Ns}bYD_|J$c*o4zL=TGuKUxjlt^tWY+uV4^G_$VfA@g8%tC5MB$S2CCL}WMVktB zo`un2i<%U^=-t}scOH`zXM)$w0bf}VUhQ4$uEeY1M}zP_kW?BU87b4O`O!1O!RPR& z;C8NV$?I`UBF`+zg^J29u=348roLDk{FcifpsaN?TlJV0{oaaeK5~&qOhN+mpH5Jq zhkX5dbDaIwV!FiVkp7z&3OZ0o^4gf%`tsg?r0L?{WYeFk{s0EHLY=XqLK_?=o)>#yGNKc5 zJ1jOvlE*|vsaJ`2u$*yPsPd-RiHc%>BZeweB~_H0MD7M-ylOZdhn;~(r|*NYb=*#- z)V7*2Nk~+%d-0>sx!44ckL&f0w!biZ~*-(DHY30$odFuZ(fTBQM`gRHAG z#EiCvuU1x8eiW&EvNQ!OMDa)qio=E`FofiZi5d*5cME&pKLahmEs0mmPP)z;Q(}L5 zY*MqbvpHlyX-6+Gzcg!Fhii88U-{0KuYSWf<=Kh89%y6@(I zU;r6x`=geb@VMBY-m2A7eYl#sI?2ul{>Rk`ZgAJ?s})--Ow5(^*wl3=o>W=R47*HU zKVKJFSrkq>h5>=q(EM}{3z1M+$TJj_!^y&~02GY-^3#F5p`?!~$JLfoe|K1%cb&Y; zv}{i$GqWXP(~@pF*(Yck3@U*ZBlYXo_Wi!E7&6z1G;7VK!K2n?(K_Btt^zWR$tr<{ zeEjPC3=G9!SH}42xz=HyU4*w81JX*~dj}Z0$-L_#(U>2Vvc+qFB3twAQcBtPVL|sd z=Ku{RXe;5u?Qnm8ukDF&b-QJhpnSyTR*UxOr~8O4)l5)io3&=+-+75g_UW3RRWz|^ zDp)vjX5??Iw&Cl}TZ_#nEP)`P0VCTMY=pd!z_IhA*Pw!r-P>V{nZ$}q3;bK!Qah&W zYmI@5S~-8vgQ$^sk+DJs>aMCZswykd@JwdEe37ER1da-ZlA^3~fqZGeSK9)m{An<; z3zjoV+u9!CF(UY!wk4(dQLr>#)VU0m?J=m7Zb#F@=EB>HlU`CHm|T-#Utes#PWr7) z;OWMv_tku!UJ0H@L_BWCe(mk;Kw@oa>9Ka1%j(qad~|eFkgMDKV)i!OyY{E^Sunn? zTXz=i>guYRkJqP6pv~~g-RYx~ld`DjRe?g6@2&NOJFkPhd{{%xhG?ymZv|(?@v% zEheO=p5l*<31EeEb#ywCcFPOu6T;ci&^Aa>%P1)!fn;ALlv{3KYAPRskUU}yvVTGl zp>KK}M9}ZVpy=@9y`~5O??EsOzTIdaOM)cAQ`EP)RnpsmD^t@f}i=%OII zlJ^^z*C`=psJ-jw2c3O0nTuVLi~VID#+uLF&%-Fm*M|#?cNv#OXAT%tO4gb?I#|fE zB{OPjlF_r5%d1UB&UN*X)c(@hc0t3#3~na{4T`(90a9#+y?=v-rmDhb>mGsp%!VHv z|J5WZx4XX`zHK`f$CeEOL!2aKYq!Pn6d@Q>kn zwZ~Hxb`Wtg1KrD4(!HC3f$gXW#MwH>cV}h9^z5VogszVFNKbSJM%Vbtquz#RDdc}R zUaflc=n?;s#m6S>q{ztKjxAh@pXa{>X2}yAR)#I*~F!5D4tB$;;)do7UF*%*yAE zR5~u>R&qN0@?Uq0J8Ksk8R4>X+B-Q+wqJ6tO2~gC{Q5QPKhcsMccQ?_o#^Q3siH+T z${|{8yN>}(+_kVT$;@B9e!U>Zf~@97Hf+tOPDDFx=vaNBg-{ly;lw=eNk!pyig4Lm z87lm_0zte$j<5CdE~P^+h{*7x0A2e%JUnjWK^CwqcF~`(Kc0MlB*sok0Reqe0h_&zD~3_}Iq zaR6hKn%yo~y<(G*J>WY7*^A7F>RfDkZHL%a9j#Y@W?I^utvPB{xp`AqXs!%-R^c)h zOkA-C6SJvVx4s0S5w<*jsDASCdMD{-!Nd_35$8|GLMo7TjW?EqjSSLt!z_L3R>`Ti zwkJ4@96)i&Y}o|`DjVx%tIyAHtUd#24EvIxjyVcVl`*CYP~7l}IJw!u^kK)>AD3=tm8BFZVV@w*w_ESIxn`Vy zg0iRY->Tx5Tz01j3lBFQTDXk70PL!X|5JedACS*T@&6tg{C^4HHg4K3=#~laak3Y2 zW}`*e?!Z}OXeq4P?GG-#0|G+0v0uDM(tC5tiaKaG?%CH-VF?=bfBS}MfRh>nF(FaP zCr`k9kB#O2a~aA{%#dC{z1;u1cOCPkvoeQ!`)8grF`Dk}f4-&TzZs6kmk^`<@+qal zAR{6Eu=Wy7f$oSOF8!7p1u8FI8$$k|NNY!o79kB}ASh4m+0kA6;kn<1j5_M){~SgA z4~FW$4wo)2^xuDui$A0NkHY8w%Pu)mIRP22=)C3fyb3a>66T9R`c(+io8S|A1x|y8 z)a~cbceMT?8uIx6J$(#~Z(l&fUN~aH9{-hk(hTR~gB!e!7?(K%=lY5++> zP%=ym4gIrf2Phd{*eF&o0%Qcv&VWTGd8XrAGKNaB0e5r@xFeRpOCE9QO1Xnv)(gIG zKsYU3Jhu1yyGQ06utVYHKmcL4g*VP^5Dz0H1chj}iiv8=+1LNRKT$k$4z%Z)+T(u1 z2j*A){aaKHty8Q1)5RsGvxJ}Bn8tX7_m~7FM~C(cAN=9PU(l-m4^I9xGkg4Mc7Anv zr3@KA$7Rxb$E7FhhL1w~nqG>MUq28ou;AE1K6pjPht=H<&;&@-!=Ai22X z_CokQ2*_a)KKll)1C)g=xf9RJ+d!Neu&2G3tq5?7x8SJO+_zso`O>Qt2VO8yXwy74 zYBdXS#zY)uIn<)yUT}qAJC+?V06V8r<9Z6maOOkF2I19dN6C!sGGvnR@DEJ`1A}lX z36#RQ+E9=jXpkF&psY!f+z_DevM5{$-ItRP_UEr z_RC8UQh`gn!MJ!>1#+Qxz|VZA_V)#~m)fo_Mpo7xiZQ6P-aNQf_hL+U{U#dz*0^;S zH07hCzOLtML%HoT$T4Ot!|BU>MIf#Sf}@onn-{`=^cN+697ynx0uKMoBTfFWlnHWp zPpGK!RLi6VAzY0N3;PKl02x7$NJJ@=!qKMMpfFy!TYfQ=)Lf9dfH$myuwm%kq-Z)k zctff$!6S5`ZxB&NzY7IR$-ABlfz;nGK5!QLG7>oilA|B5pu29hJVAxkTYvxlMTvM` zzpn0fx{K20Jg@|Ba(Z$C^*sR3;r!3S!a~1m|2%zKUxw5<2%JJ|agd*1SvUJy!TVEq zIH0HIJfiumKI`0Z_esmh$Vg9TH|lRjd;=vybS#oPq3~QN6K;ecLTRnrZ@#Jm$xl$_ z%#}`SO|+jp1a(1Oef?D9Ky#ak?tp!~Fxt|63VUA(k zrtFaI{yaV*si^@d#lqyH^f=*GKx-%!bmU=aQ1-vS7^O;GxJ4MXEX9JE8nZ>R+eE5= z-p{v2G9nmYV@_)VH;+pvKaMa~fQJo&gNfJ8x>G*-0#D}*iqd(`8z6sKdGG~P{|KQ( z6_z+GYVkyF1PDc<^wyyyQ0$2E;+x*b!F@wvV#c>`AVf5%*bB}gj?EZvBnRX*Atl7M z-XX&9b7=-FmBS>&1t)_l<`Y(JPbK2nQKFF$sbzCaI=m8u^20%ByGJReqr{70mEbd~ zJFitkV#UwT4>*cj7#Jono=^#zArTLUAr?4pFMwzs#X0kZRd}uh>M`tAbImg*j-e~- z&$M1>f<$l_lVR^Ss1)5>8la<~m;r4~H&CWutJ+t`fdT8N0A1dt?EKfl!ZoluK|S^! zPQZN5y&h1ymzI$M9o-60SOm4=rluxXLmc$$%d zmu?u>w-^{49PICZHxw3*Q?a0=q~!beDa`Sp2;Ts5ldT{@Z*ueElW$1^39r6S0tG~G zAD^yRmM4si>rg8UI=6PPw<{;8FW%*RAL7St*<5AYmN3d>*CRt69UV})R4FphRCW8u zUB`rsfHX4`Jn+RQs44%qgCTut|CuA6XV0VsLE>h0{zB6a3Il-bou+?HPfdYI#Cj(b z*_lV+ey2CsklF5}@8ICXb>`!)u~j$OdTUoNnT``7zIKnrDMQBhJl zRy@7vG(G#=n#n*m&yJ=Br?RQSU1)hyQb8|JxgVP}TnrGz5<6 z;`Kj+T2Lqp(iS=iVe6CO6Y-RE9b*zWH-jQx=gt=&Q}T|ENnFaaL*d0~Ojrj18kNq9 zO@irgd)$3>(pnZc;lCwgmk!WS*lgoEDuo~#ejrda@yR-(mg4^buoMZz*TaVl9?ywH%y|$iKUJ_*8;Hx@`(b zn#=01F0zf&=mKa`{(*rfu1W^l)pdhvS`P zg8oL9lyDk#ovwR>Qk|2O{&|T+?osIP@WJ(be8Qb(+W|L8PQ*;Nh_-1{Omml+*-xs? zoq%{OwQqxzYy&DT=UXSnJx_o!fUmD!nZ2U$9b*?J>g^zE!QfuUB5@cqEg=jcW;5D> zci`#gd)>wddD0aMVqQ@G2IbO?=Wyot!JoHTzLmT7GxfX2a%xjyVK`*(<0q^4x^89& z&K)i<`@Wef$DO$=iGASOWa%{dN8G1ic2|z#I@|aADtyXFJ}(CgluYT;*}N<}Li+B&CleDhJC{SvPck z38FP$n@k_;t@*hnyFY!0sR09>?a+C1;Jx+fcP;uR2u)E`dwt& zk-p#3(cbM#xmz^dG$>T{61Re(oTBp@fxr)~uZCn(rg)ZE+*zC=C3H8@vV|FFnN$hJCf3W0}s`SK;u0_WhW zRQGtU-+%Y|7z_Y4DzO;h3#Jf|hxx(ApRp2i@@G14;1NNpRY(WOaJc9Jlwp9Q!oPpN zNVg4dzX}{NkliDoWe*x&x)bHL6kE-YLZFoN1m$B-w!843DzoZ$k_;rk9{84_Oc{L;=w0Lb=-*7W7}M9l?i(w*aL6%Gd7_^?&t5yy~klXosVHY&ECe4vr#Vwds3I3!&{ zsiwwN)*_5ApVQ}5BkQyPaSWfAFjHHmKrrNY=)vhf6Xh_qt(?_3FNoZLv8yvHfqNJ* zD`{$MREOiZfg6u!HS7fuX885I`aVVO5#Wm2x;hS%VJ7E;4dATP)6+qbdI!)ipf;$$ z)H5*no5bhjjYWC{b>pjmA8nTh0WfCQ{VA>Fz@+|44+_D+VHluz6D6?tw zO=1T!rLmF79$yL6)z#J2yOQ{X=1@*c*5a(7Hj42Zm^gYZw@ z-s8uQ=jP{8n&yJ2lst%~1KI@^%ge_nANE>|aw)_jaFI1=BiOBvM_0`|w_z;PuGzMq z8P?dV$2Om$=AP@_OSoshxjVUZygG^?=CYcLVb%t!nv9SzHZ*h-M4zh;Sw)*e(9j;B zi3q-sU5F0=5C%&O8iu!e#TW^DzWDAymIofXi~l$$`>z)qDRECKN2BHp))PLCPaO^k zNhPT51(WhXCTL-z~;?{qp#3=o ze+K<@TUg>y>=b<-cPVCaAAoQ*U|c|qW}`)h0JFdc6_2fgOIq$p_3<@<#Rh;m&owbV zUIj{__i6DB<#cquZ9ZUP>eebC=<}IqXl%6m+x-Q=Z^@La6GX<1LB#q6Ot`2bQC*C4 z&uecfE?>Sp?$#tP$Hu{-AebY+OnJIBUgLJ|>FEiX@G}sd2lS*^?Of)|nb^r#eNgK{ zYl)MUwN$=VJqNZYnZtbY)3w`xPu-4JIfk}S3h^++Yu#V!c-AZXV6Rg+QcPd_7|r<= zDII7iFX6_z7lw|hCK@ijb_}~fu~4rgOD-Esj0XG!D~5rA!3?5>i3)s-nsN{fFUIu` z2}uUa;S?!m8d(AM99EqhoC=3aD@(v(rV7{>$`csZ-@3nvy(nJ}1Rm`z1xiOABK)B~ zOk#Fc<3VvjNZ{8vZtD$gZJ$x(-k)S)=HRf0jRE1?J_L)ffT0$jOuO|D${2zTzm7>% zx*i!WW6`tI-{0@)c~yJnM;qMe5v+-?Lbp-4&Htsd^9ksB!E_uCYloZy81L!n3BY?= zzBPoU8ro=D3E)We;z6Mw>W!2XK=nM!?3u|bry3Adhi`^ue=ihf1OUAPghx=}wF{cv zP?eM>8aYmfmm!BV{o?C{ILf5u(fBI)**^J~^7t9Twf zc;DcU3r7lqe?9?^g)*()n8g0gz#8~q63gr4LsT@|##l2BE)~3l0vHld?%qd$VLm@v z&Yzu`0e{PE)K87m($Rs5jqL`VJm41hz?MKnPf0~Z)P!D{ImKx>q4x4o)dUs}3VIGs zq)nC1tZQ0L?eFiekC$!3!>hGS!zh>Atr`WYpm?6- zxo=+Ja;7RBts!tXGg|{y@o0J_EAT{=Vo?W&hXZgr^MU#Ox3Yx)tce-aeiqzb=%_i_ zYNL@!7f;|UfM5!uM^l(=!Ec~v4m|tPc9(zvm`cq$?}6{{X2&y~QC{r(yVTdgBxV1^ zULoWH)nJ~X#tud~iCO!#p56i&TU6*y;WXj2UIhvsr*Ii!4zm-Gr&r3A-$zLC?e;_2 zqdBA?h}ew8lXwps@HNCmMM2u*)PBl^!*Zq`YD(mC6fJ8#Z(c#iqVFlWc3}Uf=b6sW z5@O54_I1=o8HRcs=FdOijbOelBq*^kG1bbf<{^e12d4)F1>EHX1jB2`ux{2~2ND$M zw1k1YI$UoDR5%+034ucavG*)6kI@>gg!k@Qd##f>EB||dChO4{{ytliP$elVD}yRJ z3J7^1+Y2$h@$BHQh9a<;u<=63_(o?jue|*#sy|gzD~}3PbcqW6F2yWCnFqM8Sy;{c zDq;)J94k!H!beiXwIMi^0TK92Ao@J6V~UE28AAXJ z1Nri0>Fz^|UAjb<8k^!<@LncNxV-Ayn;le;7=c&XCHQ{?!xO0_+4~KiFRxt3a6l&7 zi&I}s;S^s!CaX6@(Q~?_5T#0eSg+dOTbnSp>JbU2s@mxlqxcD5w?FB$K0^!z1}N_( zH?W;o1y+XLWO(|J>as$`&&hqpIlwLOM|)0^hN*ejMMsNJ7?+8%wQp_FP&I9aW1_pq4N^sgpVet6O3kJcp=c^Dbmxd{?i?B!c2HaXm zCS>@)lEB{D1IX^*3@e|Q)!+^;?f|Unz(5Qf2u@*X*h6#;@aDt*nyJYk>T za3|n|D86Pbm0Hfi;?UO8nuH}6=5XcXvn-6=Q}7wzzf()}3C_VcLun7x%gPOGY;3^p z%FH?f8wo`)>;~P_$LlrtpvkXm`*aIZ+4v*rozGt#o@FAkru~cUj6>}Cy*Kf)@NsR%pRx>0JmMOt#K^+ z!M>nK767MEOaNkp4AlXAH?dO{SOH++`1cK<`Ko%gcnk<*;30d#)AFp9z8cyE;^Po9 z?|}gU$H3CS6f9Mr0`mvny&EjxD<3SsW*I3yFcPWI!vw`{6<>dWj5H8d@En9vAW>8S zXaKk;R|p<}v9=LV2HzVv7C8xA6R=J{eCgFI9l(qOyzmPMh<~Qr3gCk8c~Lz0>Pnpba{vslo6g~$Ve0Yj-P?x$n~(gQ@BZ>?8`(w9tW^&rm6{wwzqqI)z~0xc zUzbee-i5mYapNO^3_z)(f-kUk7^K`g6qq+|-GU%U(aLHIxGZ+FaRd4jaAoV2+mb`< zFqpZrnZ0a7sw`G>Z?v61x1Ahr1B7rDdI!$dn3bQJnwpRA6e6x6RqGB2a3F*1DrA1g zB9ivhMSz{%4gv|_F@X(*uBb6UE)dXxp1P3E?@!mj&;v%U1YQyzY>|I%<0Ym8(84g7 zgWy$w-zDXB=&quKv_f2ZLsOFvob1}rfJwr+(K{sxLzAL|im-8j^iwG@LzxL!YWxtO zs0|5S*oZ7Sx&<6m=5H{^khfSl7btNac!tdoChLWE)B*rj4`QUv&CSMIp#Z$H#ye#J zetse%B9K#bc#smqcy)s&t!5pJmJ2*2 ztfR{LC>5}#2(1Qxz%@4OlWKy&WPIEP-6;_9!3%y_nvBQLk2Z7~kt=T;dRPm*sw(*Y z_E;A9IInK&{rC>~c6)d>FszU{xw{E=o9}QgTr7%y1jZJ?XrNalodQ417IVm303^`V zO%Ng{Cm}8l2?njrkRNb25ad6mqJk;6g$0n6wKi7LWrP^altGRM1$k5?Wo3PU1q6Q1 z5fTFsMtT)&!0dsUABBR-)<`kqQ*1rGgg0 zX%UFOp-{WMt4m5mq{mjRuZYiS4>p`4ls!TeGzEhpBP|Wf@Bk1EK}C*IAr%YD_V}ya zl@V*e)3B9nU0u)M*R*n3{a=Lw06ljk3%G&lgU&mYJ184~&qK~dP-nc4A-I5T1c6GKW)o zAkBT{$`z=yEe@BR2+txK>;L4S->J; z(5O~@7m=q{5e%RRb_k%9?_eWB)$#x}pvLa|yH|pEjOr&4V*#g(P54X)+!g>fSZsu> z2A_}jP+5$iAV{9{0jRVGrlKs$$I}yKV!*E(jQrGv9R;mUt9Hi_4XVL9hO4rIp8}Tz zka}Z91wF!0H+vmoZ=yb*r+8vuc!L5k{F@_aWb(=u;;1CzSWSkz^$Nj%3?SMXh^Ha1 z@B(1i7_3M0^2KyvGRJS=QzL2Rw)Xcq%+xVps3K_povlPAoAJ-D9e~21UcvnnA3?}F zGzOD84MF9}(hFHRXfdF82GxLbb92BXWpFyd&xQ~!3z@N8{zal7JXKS9fmzZBwXa`_ zL4tX8+Z)lBlS5@1poYS#4wFN52?tiPEijGX=}GvV9nMY;8^0&d7>pG^EqE~fRN=)` z&C0(nQqpqeeG=?Zh-Im0X|te>;Wi%qclD~HG_o&Xgi&Zpr6jLIuL9^1bPR&GcUP}Y zE5QZ!KcIGod)P1F;qd1+ft*LZQg+_|i=h8sm^u&b7{f$!mwrq`({h>#30|X{Z?jPA zc1)twdB=GAXXGM^0QRptf<^eD0uxP?recB&7k7SDK8BWsrEeGTdmU`EY{)xurGfGu z#40#sWM-0Oz->Sb*2k|Q(FaAbGS$wG)=-xXtO?Lo5^e6NQf%meDU97YH--F6c^>-J zt1&Wbu$tr2K@J8QJ7!UB8B}f;JsH=-j}skc$0^d21u&`31DPUrFYd|%Qgs%u#l-`c zH^Jw5eEI}lPfKzUB2FmG?Q4bX7;F(JK$jcn@0UfDg~PkRsG2C#hlPf|11x^gw=}=7 z08PFzc;avX#&S6qLi7{s)m)z z!ee7lp|`pZZpa7NYWL)D2WX%rvn%{WwWT9;oWydNvWa0N zHZ1X>(qdR7RcyxXGo6H}cGecr@Eq2H41C^Z(Lx=4>bRxVXn2~um+X+xM-vT&;HQ)f zDxgMI_k4YCSc^g(wvL|OQn{HVmrq?N^g67sn@e)Z$%Ti7!8zOQG*2jC_1%UlNH~fv zf(Oi5x*0ey z4pfs9;CfV(6F~0f<~Q*j-^i`us1TsBp}lM&qOG;{*6rH?fq|?Us;;ipTwa}Rf0}JW zeq1>@weCz}JUP(D=HK|`HW|DB&d#ngfF+50|CNB{4D_pmj{>gB$jHdAxXibxh*eIG z%{t>#dRm&7r{~;U40j1PG|#iK4HUUPp@0$#A+aCc>Hg_7ufJzxel-kEFgf1**7tGn z(|q}|)C*C?A8!Q}=qdcEDf}S^`hnJ8{WF@fpv^n_Ys9LGA3l62m*F_fc7p!?zD^d{ z5h?)lr!n?5J_1j5a&jVmoYqY6RB_zu&{3;mXmw+>Q%=HW*;^5 zhv4Ahp(N@^NK7Ql0HVX}_f1Oq)M<_y3|e2ic5M{8-ZGSwpv;x!riq!^q}$o{+-*kj zs_Iu`&?>;Y@hcUW7@B7$OgzW!Ymyl(92`StCGp)>(NGi`-vP2p`g-qR1wrSR6^Y?} zHq)FuUxT;>3r>c`D{`_K#~XY?J1liaoeIx;2XWS=kgg zD@fLS5TV>^WIOJRtIN(jJRJG+o9!!uMcU;mrr18)9-;d*4iAvxD^CgL^}l(RQ%i3= z7rZZYmR!Yy{gah$o#&hnskRo!u|FD}m*;JEXCl4fMkD8EN~PWhD+avU?`tp9QN>sW z&I$D@-gAHI_d1MFQ@iCB*WAsPtJ*IfjR_W!M)B%tJ&!{FQQ&^v{#Bn^mF$pu*<|H$ zMUwSG$nk=-QmkC8oKi_K@*e4k`J_G9y4ugLTMAuVA%g}%d(TtlA|9v9@;PkHJ#~y5 zGbkCG?+-jkCHDSfdZKXYq34~Kf!>cjUf!@&VD|nl)=yU*X|Yi(rXAKC@F8o8syiZ! zL#EBqi=J{VyfG$WX*fx3&y0nc`D|F*{HR_3Y#v?U;Ed6AQy|@N?{BL4)#?o|#;V=i zDYt`-->w2jaQ^*1{9096mEU%0KKZoleAjO3=kR;PsM$e>(eC)tj*9%4%`Z8+ow5N> zv!`vut_2O(e4gl)k?;RSlvI7xqROW&_7OXH!TeEyCZgu3dFirSDdw1p`nFwXKK{!a z4?Txf_80RBQMXU4^_!p3nRFV{{Mt=)bMo+Tj5l|CKX%GQHrl9syQvu~J+J3c$x5|^ zgWW8>g`rO~bVIoR&z$7;TsZkrn#S3|`uVm1r`25d6M;jzI;3e@ja()^sj|^?p+*&|6SBQm+rrLM(ugW-cOUBVBhhN6xTULV~_FX0{L~g z@t4e8J$TFPYZkqE){i|j5f4P#Ys>uBT^g1X=DrA9M$tc%pjM!kGqb?67=J?6ZESf) z$xvPU%6#G16@mb^h^(PiS&MZp3ybp%{@t;7vXhzUvkr?{()EMFb%&;8(nE=f3U<5E z3D;dd@oM$b5~B0NchP3IyHxlla1SS#I<>Q{cvnAafF88{;>1aROuS1u>Xz^9bBSE{ zx-(<;1Mjx$x)hh2|2$U?rPN{Ld0p!I`>9YKK9Y8s#0&!Kcyp^5`xp}AAL+i=`eG!* z8}FwX6-2}!Q~8QYb|>@}jpzyblx8CJ4H-N;#6|^+7Gk;Dub8LsOD278^wd2;V;nY2 zK$5(Re!MV4SGj|cNHxK(^wK!VhpdS(=Zo~zn1axFp?5(dDMPY;06%f3>8T+rS7VNk zi?M!zd{YT~vi+{gvISPvmpE)M#87@FF$=$?z`@W%1LH{r=+)?s#h9d~4z?0S3kUet|R z(||?qKZyvA5ogE3KMCXXzX?VIilY6F>AQN&L(^hNx-z6Q_+Mzp`588*XNH)QrYOF; zi?i=|So6#+DpJToNjrVcvAybbeoL>A9`^QhJoS)>)(Kiy9d>~42GM#bvmoEm)1+hC z+xUhZseI{ru>!()eXASI*i2j=2%{oQ{RYx+F|o^oedQgdto~QKL&~#;YU{HJlU4Xk zY_Zly@46a~)zSPS&OY((nPfV!uEX?^E*;R$%ULgUbtiw8l{9@Zf-?&(8WXT^v-90f zV>6P^>bm%MD}ow_VW@@q$-!2s<-CzUFIIu z9Fd>RVT+}0u=9J_c4n3$cX@V39r%-ckoaXW)`EP!G3#efI&12CDX|0uGcQ{lJQl-` zC?#3455iPdPQ^XUtZNcBGtoY zVb_h5;9+0rAW1Zz`3_7(y^Cf2@97klV3!?5Ps6L5*tmZ`jW;7v%8(_tE#S* zfCqzEv;oH=ec>}<5}e&P6hz^&Mu8H{h>*os;^dEW?jyvA+agZhw~g)pj2XOq8>v*@ ze8P2It@n}#nTRZjCTZt=!^B<#Aw0;Nz6xw06qOqd5OQA3m4C^W?pMx=Z}60vzu}5` zC51?jIt~(n7t14&-!g)4AkJKtylI$@9j0IJq1Cy?lz*w{x==~W?Wy-?B)OEI+*7gd zEaRSU&w7%X~!JlYGB&U?kP7S2k}?NN=?YK$%(6q^^zME zvO})4$}Z)xOQu-`si0;DJgRH{otNXeorx72#U)j`r7kN6p@qm!4JY$5)gv*t#hNQW z>5og!_om4CJ{@+qeic4-;-TFc&IINaOwL%a9a6ay&nS+Q;TKS zPG7!^>sI`JUh!-I*qEDNO z9il(FH2q~);*?5~uM_*QfEFIkbNtzl=hqKfJ4G_%)uSPb@IY9b_jjdt-bpqc9c^F{W}{Ryjs&fBeX}VX_CcBC)Ok&tRY;4QW!-rB}v@ zxbdn;Dr(jQ2kZL7G*MZx3|#q}{}IXMIyAL&MCzMbAG^Rej%Pghn?84OQc^BP$ou z)Ez0-_o{3}beToR9@w~!7|z=({PY{)wWViVX2tqIca{>=OYN`Z^XrFm-q<_N97MJG zr>xs;gZqhC-R0jUrHQ<(26f8tg;(*H%!cLAysT2*-7+p}kxg3Wm2>>cJLQQhlNGZR z5sh&o-_|yx`6WD)6)llCQBRV%X#IIfJSUQODkAGn-|BG6Ttw&b>6w52PeCN1GPVjC zb&WrpLp7!-RgR)SQQc*}_gQ}^T?E#Q(G7~^>Dp4de~p@Gp}+T$p^vntjt|$z>TQ2V ztBvX4-Ti@5Wz@p*_B+uCv+Yg?2V7cQ+HiHEMfx74obXGXf6%e!E9=;a%P*GS}dD8s~J4i3b>7(_WQbA){OA8C*5)HQKi+9&} zRjJ=sQ1Sj_*aAO8sNh>Mu!$a=FViSV)FT>km7^}55YjD(g;xOqMq zgQG@Kkw=8ny*IjE<>Jv9rhW3KQ@BB#$G=u9ik3Ad+ZG+kCdqa3x!@)TKdH%gb7{+D zR(Z=QOk!Pwpf(>{U)qp_-f9V2Nv_H#xN8Yf{DyWVXq$TWDel4@ez{#hbfrSxJnE+jb zAoG&b)4|1NQ2tBd#fU^_SYy_dttIw+#C2psN90z|^fk##-+wH%%F(J+-8ann@H85O z$PC>cb43q<6HG+j*5pBxj30HV*k4M!6mj;1JXCTZVxws;;qK0XeFAd!KJ*sfXp@CjJR| zr(()KroOP1EqLWVr?9PfP%=SrMyb{Sb2xrsV7M=Y+Enm2x)?BXH3yU zZK0+=e{e}-#4)9>|M-c9rOMpW^CcaNsd!+^4sjjPc=Qz0T#wqX*?N|jjV-r!Yjn%G zLh+fYNmxOPM3cs`O`plqQp})}a+F-IOfE}V$O13j?W~Hg=%jifOzfG6J>IS^8HOr< zgidT6SMx#OWg|Fwg66>*%U_s3`qXbeeX&F8pG}iHH+JZCyT4R??e%oc?p?p*U9wc? z7|Dc@ICg?l9#Ev5i&%Xu(#vXEGUmkV-6BU6iZnu~g%C@cC`8dOVqS0E?l(}lxw3x@ zL{r<(W%BYNk6^1W*NshUrq*q^9}(k#6vK`ORGym|?VY7MRlS%ezmA$5r=))-2QmOh z;R1(~HG3bEJAa<|J()aQv#+^*FeG*U2RJn{*Ml$F`?P6izXeW)1rArIPMzHL6`Goe z&&Q??iLv=?X72a0ZEYPD zEmQk{sG4jPHj6@VOUXtp@ybz^yl8RcR}F2l} zEfLZ#x^;DqVXJazImm=N3t-zwT6YNqHMF#}LX5BH52|Z92LHH^eWaQB)wFT= zK8ugpYD#yjrNE|uL~s@d99f)Ty$0oLsf24$qqE7&tW-fQcY9+}1b^6K*h8-3b!x#` zAKzV6?F{73X(LU!4W@_~p}A&ReZPEn6sZEo8$(`#qk1z4wMf4Hg@Vo!i(J3lqOsM; ze%_7gH|SN-u6rmqSwNaec>kp@j#aS3uUMqxCNaI5a=>MXuA;!9 zfqJ7|fw#w#;x+q94ndz%>6I?Z7Hz`iez|ne-{GQu5%i?ZIz}^ zLYdQdTfgvOC5~%JXO*U2eRv$A-?^QkXUmxt8*jgz<+lInF~wRq6{lEc>y0N}7slmA zLCIs(O}!J*j$+svS5Q*=HdxAy*WcnkZ>{LjL696N{ky`KW~k>b_5``OB!mm98BpOPzzQI$=zmG??yLAumT9UI;WpFp1p$FPB zNSNpaBS>ro2wO zP>$p9zFjlHJ)xfFuarpDk}*=<^8Q7e1nWn{1w+faN_H~IQ4(}f?GPl5}C3UxEf+ z>wPhLR^*cl0_+T>gKSaS?e$ZI@2>Ugjq;ZBI06Vx|KupIJ>r&c8>-u z2XcuR^p}pS&4Jxzt?TIM2>Ll`9j<3R-=C@Ay1(z*wqHel7HaRjB$Mp8H?m$m|I~ca zp`D}VaJ4NO>9#@Ywih-2v9R7H>{8k@Bf(_qe^%V$?!gm4lu_eMS@U>xJ~k!pSF3k`L;e z&IW3ZhDzqJzVeHBN?%P=v2w8#D7FmW0Un56Z$@&h< z;OJ@z>!iVF#?}gSw(f%zjxW=^kt|LzR;%oj3x6H>2Q7|8+w1wLg2S0qyZXd52{69~ zp`U0ZpN(UmZ}5tnzjh(g%DssPNzr(+2qU%p#K8va&)&Bl5AxeWA48 zHy#a~!LyuXo0mfk;~Fh_f6gfVB48o0F=vQQYaJQ*-l_GJeQM@Ar={4op^_$MV~$JJ zmTGEF!(1ui3jH7xFI88wkFhYPey$Z3I%h%y_wYS9c}R&^y^!tKJfv1LYow2z!P);=56Ubv(EC~nu0$BWY4E`0B95Y1A!%Gv9u z^=5Lk^R8}uqszNT#6$OZ;jn__lNJRy`0#v(NP*L7Z)Majqi@lM8&0Jk>lq$Au+(^R z1wSHTXks@vKQ1|WefPtu>$!Kn#TCe!xLY}XJC(-6$8Ve8@(uqD1u;*DG&d^RxFw1@ zs7_~0VlW@|%$L%D{h@cCWB}#jHD=kqvr8uD|Dc*T;Mo5OZ=kA?XQ0jpV zXVp*(qikXQ-wQj=@mr~SpN(6hkt)yk+8EffW@#)w2gc}Iv_hHUVcqI_-AZnieUF1V z9RhViZaLNi=dhUyy;znB5p9&yJBuvq=f92z_gv@cG%B1O*BGmp-L78u_=t40j>d-# zd$vrRzv0bMscFj7_7k$lP>13mtS!uUM4C|5l03tNK^)Y0aB$F}3KRk~B}kX)swowR zr7r?*84CYR|4^(&X3NAjDd+dF)~(e>zoJGwrvUB>)9XKkxF$z;RN|@n=sFfp?qVod z{a6m*df!YMGjn8SYoPxd=$uatZ+R~pV5jwD8zv5>lx3~-Qp@a#4_K%4{ATKZMfcXA z>L~X*!WBnN$8A{yF*)!v(vpPS_3y5*{Xx&fquJTn63d`>sJ-~FM8-YkfCQ(|wOiPg zmZ!fRe$_gPcdmpwapBR+#ne0>ANycVWO=5GRTC^;`#byUe7uX&YaXGUy|@?xx0M18 zmj#ydEe~?%bNreE@;+9logR+M8V9w}lShLvg6Na`9|ux4$em|OB>8P-PUud+c~^KRx|^tr?$(-jW`eu}zj7+?r#q`(c9(`U=Q6sD zxh5Eh0>d$Js4L{El<{ku6UIcEoByB-1vZ;ogxK*)RRzSSa{((%?T=9|A7n`iwrJW+8^p6#pwH1~sh)vuJC&=>z z{Pm34?ry%o_D-MM!AOQ{Ne{vCVe)ZvpVWB|Xp%(dSM3AAd!DPG1byAJPE3`Bl^YWB zcroK8XSX-z0h)JOZG7P)n325axii|no?~10dQ}%!m?3BKlhdeZW^G!ZYrjHkj|x}{ zW(k*s2bHFd3HTO=7%Y80cTYabtbvZ7={X3Z!R*C%{H&tiQ?U|48=HtBEfXHx=ZF+)Zf@)~(& zZN+p%{lWc_$o#P?=9=ALHu|3|sZR-3?El*TMf@!xw0Yw_qT$c1_{!`D;o$vuDJ=os zWG~$D?`JO>Vs~j|@3!5X7$(ppl$yPk>O^rdi!*svU8ifbo~7mqJobM~&7ImTj)ylN z>31bUkAV;QWU4fO`1Z+=!BZxuZ`65&HcFN5w(TV4Uqv>}N_h&q8@CBe#+bA?!Z~ftR zzv-y_*q!qv<)CYO^Ofj;*4XxDe&l{^f4Owz^Jl+##w;kCG@+q#2;X9S$G4DMM7G$& zO$i6}w8NDDPQlOw}AQt?v|`jXNglr}sW+ z^F=@ErEBFpt#-Ir`Sip(By86OEYq`NIs=X4P4rL~Eq_$F=H9 zLl8nU-n7A!8#Rg2)2cXQG+x%DWe+5naLi)Hxd_OvbmE5G52+XsF3#|YmP7k6oHC+P zwfd*zR+^gGI9s;h1$S*0_z!_|!FuNY^|m}huso3@nL3A6yZb^kpYC#AS5p>CE4>%j9Ut?BS>(>=_^$|G{2vZyu8DVs>C8Tnfb7*1A(g$D`UB{|9JzWs>I4?< z6n^%V=E^gw41UHW%%>Ih5qEFV$~^$lyOd ztl3I>;%@FdI|gZM=o>1UpX9q^%XO+@$XKw>8Qs;b@|!3fo7qn8^*zr79>m$|9R03H z%6IgBvO$ZzhNs?4T>2Vw!^ zFSY(%uDOHfythOBOH~p6e!&&7XvK0tmrt8CN2JOMLD?xKKPNHER8yIS+zf@S3&MS0 zg#4&Klo{gsIYbc@!(D#-`Nj~6Pcv2XNZqQ-Is5M2y?XW9y(VR#M)4WQ^&Pa6&TZIl=2P=qV6oW55G&j3=O!^{ zwHL|0G^rAY<_uB6!>OJ;tywCUgd~krhy7|)D-6;saISOXI%En_@)VhFzIAabJ*bph z$r{}qumn+4URt;J+RLi-$iDHEm0fH9pUPMKqpfxflBa(|8>~zwid^ z-XNb(O5QF3Yq|G363JehZwY=c^J-JO9| z$bgnSQg*KQo~Lp4RBlU~$?zx{Z6rt&RI~-4p*9=6Y)j#~-g@NK6Ubd$A&Xuq?dlYZ zjm*+&A3#$c3Tx-|jrZC}_EBiYxC?hOQzx6RNPy#{~Q(0!U7{{54JHpT`n%K zwFWfNi!$C&us*=S7m9ZP#BlqH+S)V8qV(0G#OJ{4 zWH}S8DzfM#QFH1^o@%dA+^gbkT|X`b5e+|)Gl#!Yy_3DNsUJ$B_|2?_xMR&)4#h6d$Jy<)o-u#r`A)L(ObopqNET*$= zkA_eir`aS9{-I~*~AC5dSF4+K2tx@R^W3ZRiPi+qu z%8J9OjXh<7Tm|R~rY!an4-&<#O883Hq`y>U{afScm7BVy*&A)HoAk78YfjGb&ydUV z?8;WOv|G4a!#&WhS)BE%i`$&UHOe>VjN~{CrW*t95-cu~>*P5C7a}4DUEkA)gIzqJ zKO%KK8aWAXA1BiA`vjwq{@^qS=I0@9mc}OJ;Hi6VV!ht z$rG3&7NmTls-hcA@T(}@NcW$$UrV(6_Y0ZFoMefYcnHSPk{JP=j)5xSop-EmBrmy) zB$PX-Q9=eK9X#o+c9A%q9X|Z+iuc90wrd(%c=L%obU-q{#1J0liO@a({u}2EJIl4I zcXx6-AWpF;3@i}Mx0dq7^1se!<&$0vOJ z!qi>$CH8A()AajDS~6_|yRWn~u@t6}_V!2rFs+a@$3}U(V*|sNY14`BL8GOPcdl0u zRtBH{9Em6zHeW9_gK@cJLbf3BpP)*+pm zp7@ns@V~VHL~xFQ>_O}_E;Q`YiyCF}YqWEgR{Pt2c=~n=>dF<70`(i-rd6v^@8sG* z_K%Op90OICnJZdk;PKKFX&aq;tuiC84h|ekN#mE4$17GF95ii~s%b1{mMYSe<<#wN zM7iGcLI@O%pIfc*kiN7Vw!kWh$CZeF40R$%_xk^BPlRF9Ry&l8w zK-)a19J~|4T#t*q9bU|p@82K9*JRQwIICe*hu)%FpfYmK{&8-~r+Q+Km9T>_Wawx; zSv)zj*llDaZ6Uq3F__CaG3InrOiZ*F_5qN;eX#5ubDD)s>aql`NCZhxHz%0aHgM3Z zW3_njx=#bS1Kmb+I$*=U2g%eiRsr3YnYz`e%G7=%*0SHJ>t;f$mvgemnFm9sXveQy z5t~eM}iz95HBgj}pwh@HFu(f9dd~U$R z&iCmi4d@q;lsGB}@iKdH90MnqANFT!%H}RA3L7L`oWAU6bKz|Lo{0uJF#qS|FVHhE zRNEj!1%8rBPEO_^oaMT!Czcq+ISH=bun3toCN%iK-vm2FXOyMc zy7dfVbh^vm;+fA(F?B^Z*4*9jS7Tj6RXv%Z<1vVSk>gBFqw=@0I#+@B<`JS_r`6^I z(N@3>fBC|S6=KUGK-}X@o-hDS9WzSP0=7G7xx@oiESs0)7LNj8jiD4SQq{X;lUDim8pugS@Q7T`pbP{zGSe$45HK;dZAqH`@XY<{0RIy7-Ckd zL#dynIQUtVvzHF&v#0Q9ZPlY1B=beqgau8=Bk@@AydZF@PWYCg*C$!bwPM03XX93swih1D9qURuL zBzEgF4aMDP(#bfOu4b`zo+2gPtc@W(&Z7-T0&AB*Wt0ss@6;9Vl+5C}t|pIUja*Av z0nrPRW*(otd9bQ-IfP1M-@Y;aKFEqUS8p;eeT8J@Js}SJtZy`3xEmF=ame*c?+=xtc1!Kn|oGE>t<) znioP04sFzOOy~i@1jTUyyXKQZY`BlI>vB06kfC-wzIrrJ5~nTsUG86aQwEThpWzEg9tAq0)y~@AdcR_hUnP&h;-v8K4=Wm&w^Ej#@0}nOyW&`|}xwP(ld& z3e^uPW(zlq*9QtANUKbqFU3_6Dr0vnRFkLS4lccE*aQCUkDTJ^aE65I@{-7n82-!D zjl5+^!fP7BtV&oE{z=!X`5UKbT6ZRF7BogTLnvkFx;dJ^vS_n-^KWK<;h=`?nShLX zG5@5>RRQPbRV`*^1H|V5v?3N?b>5RwnGV>PaNO!~EkO147mR}3YHjW@s$UH}0vjS? z5h8@ORc4`ldkigzD!RwOr=|5<@*n8IlaPM@1!t^qY1z|Ni?Jg*gQI`7X)c~yt?*-u z0(-BQy9qh&Ppg}cUT>!@ABx#~5#px;zdOcf{E@AEB8w>Id(e4i8W-t#+J!Nt^xB|? z0p>uY7Y;XjO`4Ad8$kN#6?9w|3k*Md{7yBmb(u(MYD|V1Bk(onBeIm8N&T}0`-kWX z`>;tp+sJV<+Ig}nYC9CDN8Bp3UZiT*BGg_1~~&V;`7{*6-=iGrfyOoIcZ zl^si*`$A@ZLI=inP|AUmu}0vC*zT-0t;rp!b*2KBT4}xyqApS*<(2lxl~hMxJ#A-1 zFO69Oh{2_x1_Om8(F!njW;2Y0ej=C3q1;(8+O2|n$|Y}E$G=q3pKzRdv?qsR`6!1{ za%GBmaX~k`4~96HCC7apjO2Jeh7Q7%-uvTn()3OP?oTl{y*=9Pg{a4+iKtbQd-z## zsC~`y+;j80Xz|{B+9viG2Gs9qW;rkCfV1+xj-L5G%1(pwP{b(z*^U>NeT=bSjmX^k zRmR5^0+c_^33~KF03H!!1perE?atHV&R`lYb>zFX7HUr#opS zww(>=Hc>95kK!aT*9Vo3-7E1AjFTrs>Vk?^~W4{E;XXtWOXxyVq`kQ|xk{mZsh`O*v z;1RQ+T)x^Jb>jeKlSy%G>o8thSl7{KpEpGIYSh)NV37$KiVY%00|)WBF@VfMF26KY zj+gG2cFwd7Ou5#lksF6m70pO@q6v?3Bdy(XtB|_fg95f$Xje@mcRwz-^S_d|GXK?h=gJ@8>U@GAe=~0}pSz_EjUF$KB5{o zCR6Y9Nw_Rb=_cCj#atOP+$jla09VYkkGwWgz(j7lnw;IAWFJ6}6XZ7t_sFpT>K}d` zh(!E^O*(@ImTU?2m0HdGaU=Qx2egpwm6jr=C@8JUYd4=WijwXwvlf@nj8lyV+YhA% zMus{VIl2Y#(G3-U8wcC+{r=i&-rsAKEBQr|hTMIpal9l$)T=t^;^vW&oWpFWwUhn( zN82ORlhIb;Bh+h4y+`_`qANx~x=)V$_jk*PqUYI-`lu$43J76;o&Kh4xGqXQ`}R#Ajg`XI1s9e! zD?(d7!3EmSlyL9H@ptYPedp^`(v!nIQGt`Cn8{S+)+w02PwI@zV$qqzR8 z%6~P^YPAaZAIw|otjn7`k2?)}o@qHJENGS_c9Su#8Z>+D*flm$0GAlw;JngUtc+y! zE`(UPv}2rHj9M#?T;acjETr#C!dnpDPp0v?r+47rthwdrj7spBH!OSa;BRt`qP`<| zX3vi;W_$gGPMaj$g3;-obTTJ@F<+Zr`qop9H%I#AQDy~kf}jr>fWleDNVLj$#;`9) z3dB>xSC`-vrZfBi&V1Tf7z+0qK$X%~&cqE=+&x<8;iW`f(07!FAoa~nx~=>i8wq)n z`B_H6bBR0UNQ9Hcq=`kmOuPnGUcjAP?GhZ1b*_YIIeQ|N2rs9YJN8JPX4Wo&LM@SP zLsux=N8*q!z8Hj}Uw%#su_Mbm26C_+n6NrI#W>)g$dK}Vwq&aF4g6x&c^rs0$xUYm z*c0)|~Y@dx}}Ms^$r;=LG_1v(nt?5h7pmCm(|90vrVrZB0%|ljdj)SaXKFeq(tI- zy{Cc(CSP?VJpF(N4y@q^IR3vj&^TWlTlfAi9kh|SCMY{^+P8$?)QtGl-9PbUjudje0;zO z6YPYE#5`MX_h5=%y7j8g#U{3ZM#7)oT-O`C;+7xmfFFvqNufY0#gvWBlL`7qwzN(B zkJbgu<;Q^*+qPNGztMr=%wB2<~2{L-VPH}E9&z$Q9bVd*>755 zuJrU@RS!p1%M?UWgxPumfTmJ>A9i})rrx$j;ze?vcWWwL$d?HZU@B4@C8t7F8ZDxl zQVEJ8%1#DHv%Ma+Q4K>2y}!N3rIvvIUY6MJXzapc3LUWS0P#I8c|BJ8=HqL`0&ET9 zd;bf-d|>m5Rp1Jq^ULk7?~gEmgf>CS+FGN2qz{Oz9e*uY6nL?Wrgpl%Dc-z6-q5!6 z&?#amVrg>7IB+OZ%T8VL{(b=5PHOcAiffsawAAjG1e?1EVkZ9R+q+hXDNRjynrIcQ znF}Tk?jL9Z8fNzE{((;dH)WgM%N5EMb3=lt5D=i|)?d@(M1e~hL{`Zdtan*OiXzZd z!JL&0*MD~}l;3MWlt1U>evz2}=Z*#DSSaBa-`&%;tHt%F%l^ev>`vMSi7;qmYK4>| zaJB=77!gX_d(Ud+vH2{8u-yl-lidZfEQ$qNy$JV~<*US5&1A;B#4E4^54hn`FyC4m zew;fg!+MA+SZT3R=k-aZjKX-=9|-5{M${@{PrJ5U>qJGRbE!)bE>Ub0=je!GUZQ-J zgE&$C+dMu{Mh;j4xyrQrzdiBJ@_cv!vD4*y`xAI*)sG8kEfG6hbiw^+iquh1Gn03= z2f(OztJQVemd%SC0YBea=A@#c+F4L#BR}^C%%p*4o^y*$m6Nio!yDVvqe6>xlQg{f zNA00hs)%PRI&Th#pLc!w+XPvde}r^%R_1SR?EO0ttQ^t;z3D6dIq!fo z(0viG*WFS*X|AV}`C)wTc}%CEk^ftLFKu=u$*Nd!Gf*6T^05js(N(jB0x5B5NM_xn zrmL`0Wh^BNdr7k&$|R|TM@AW~jLxBdh=r>1^O(J=MqtzA)l9aMCSnH~_+&XRZFA^4 zWg=K6SR(yAqfYx=#|-kSEj>LwA(y>Ao6$PQ4J-y*ahdW6;tP^PPU$x`{Ufp)sHR^x zE&{WupU9%6lAcz7FF^TyTVNC|vsL6}iP1;sB1-y;o6^0@fo%7KD(LOk^ zAKk|Uph<6}_x9NGee)sfn<`C`Z;ya?=X>K!4MSJQ>%x_zn(D$s7birmhM73T0JZd? z`^$YU=UqBc~I4-V2_7x$}nX66~o8-n-SvYMy=Rc`JhI zf65X%fBYShiU|6Vio^<4Scb-StTMYcngq*iQf9Q^zt%*3RZfl*@Bi98i3~etU<#HJ z|JgHj7~m9Pg^LnL4eh2KlG3Z9I?nV!lMWV_BAax9LYiBEDU{ezsycA`>`VGL;(x09 zE>=R^CobtkNJW=mjTCg~$u>T}RZi3^{`gBnA&|=yMNqb6t65%O?l=!x<^5D0l2xZp zk|wwME>E*AGA3tBNmTpB<&bmBqRHy>eH2F_b_4FrBp>vJNYE%^X!o*e%^NZyVaS-8 zf_!X5sXmeS^OQC(6@S}c=$ZlIf?)H8uba`T;h7C9r$(M2bt8i&rWItnd#$-ar2<@) zb25zyGpMKgpA`eNFaOETUEUM-IfdT^wjZe+tG;2r!?S;ZnJ%sL^Vc5N{ef^=)x(b4 zjkEE|>N_xCaEW7^c6M4Dponwxq{}ihOdi6Rrx~qx4j1CY$;s~=YbM2~Dth(1J?H%z zQ$I<3e^6FYanY8L_Dj~bKZz~{q7N!jYnsHd66{ZTZGI_@fb?ct=7I{Y)ZEW2Ru3U=M7L7WUZb{(>kG z0fMESj|$S%bTZ3wP9fa>%U?S+l%%;N_#>^+RFML}5!N`gwM?LtF{R@m1HuMSW5`hg ztnzx`kAx_Sew+pulcU|`#2EF~Z7;nBvjUW(Z z#9kP287rrw#=J66mEf%{hF17V(zR_9JE25+!ABu?W+ejt%*3rhFfCner8TG{Kb5wz zf1{To(hobf_w!|=_Nj*lPX!l<`||?zivLPiId&GPcJw`GS`;gN36YqG`O!HH&4PJm zK4PFU(dWRRxaCh+_(YWFg$XghY}A@c?iZhJ`}m!+H}L@pVny z=3sStMQ}-Ha@E*$C_j!L=T)!6H}VkQi8<1?b#?+ZhNJXn;0n=kPJ@%6-QtM`l6suQB-AFdd+Al12d8*K9>s%(t&sQT)%Mr*(* z`W)0DuWdwlKuO~We(Dn!UQ)(i8p5n!0-A3!ZV+me2r?Z1;wK|`nsRLT7~Rz(%hcJB z5L2#ThY1xMai%<3_nN6A(;%Xs_5F3`+|AuF^flxeqLv`Q(q{!en$B^Ovj*ky!yqk+ z+d*q%_c8^bLWp?zCShU@iApmDf8Rwx6BdM%1SNfr==)v2rE6z0@~7|8v-b*8OeqZ; z#uM8A`q*+<6{{=S$rzkko&y2IDQkvul3_vm(eKx9FZWY7M~hI!T<(?CY*2~=Nze5< zH(2!Hal$c{(Rv)`HMJzN;3019iqmZ`Yv)QQ+)0&}D+8R0>ITbxxp$t$I7j#w38`^2 z&u-ivfLw1Srh1kf*4VASa=%{s^qw0&%t-C{Br&GpvPfBV}tORSc9ww1yYyl0*gtaf`i2!V`w#w{tq@i*6-eG9U;G;Burge;feTsfxuzlzo{aE)`W$HV+B-a@6 zB*h6Nx%;gT9O~64ygjjLj=4Fgo54XZHyk&}2tse&PYl22**bFJv3l#(7;#!Yk8>RD#R+2;U^G^OcPBP# zF&fOU!Yo(4q&>E97%;`{@u&iqfHYnbXdzMUWd0(}SnGctTmA&~^o%E>Z%oI<#)c6t zTFxQ6i(>XKF;=CD)4|QHC9xi8`}0i5RWR3Bos*>ynV8r?Z+H{=tTNAqu29S+O$_`| zA1R8LAXL5lfrdfCBViR?P#L-x*l)&%YhnhbA#GXVk!tpoW}-Sz z!{MCtuA>!7VChEGOHJcYgehW}aVS=j4bH(nJ=JYHbES?W9D`tc4Tp(=5LERAx*+tmr&_n4$5xx(Z zR0dDWxdac=B)7AY=83KrTAt^Dig#eJvst&Nf^chif^VK4st*Bj4e|$c05ET2xTns$ zqXiZ7KMCjWrH_X{@%Kb}r>%!;FYI-gooLqfSx(8fLFUn*jUuk&`yz zk2+RPSMb66PVj&($PtAlHLmaUuFb>z3uUZyxEbR;6pJu=1Z8=U3$!}$1NkJ8h3v0w zLTyjkVu!!@W6y^hrgUkWx+mn{Csd#EqdJ{#2U&3%5W6u?M~|aigWycJfyS?-O+nvD z&g+Mee1>Z8&Dy?clbvo(bSFICm)&P~vA(to)dBe~jD=X`^~x`A_s8-#!l@sGCic!= z$#f4PA0dpG!e+p~wvn#o2&)Js91X}N4|NYNtjf+t!lR{2igGN181^;0MmDb8eUo53 z<*wFTA==l3_s9|ycO(RamLTAk7;>||70&WC)i3>U+uSllIh(^XQ5yeh`qY+d!o=Xu zPw|Jr)yT0wi|}7rK#K2Y8H08TB;1!V6v3%>pXF9L`VdG}1*8{`dUX+Q9+o9Kkpjq% z`X-H5GhP#GE*gK%B@hkhGeU%x2j+3*rb2+orBuT&3Vnl44Ol6L#Rw{fp6LN^2b}-` zIX7GtpPTqgJ0tn|eJEzp9xysbnTEYSwyIBuFJY^_tz)j~RmVUAagUBKVb1fFCO~00 z>Tk(V~ow)aq?n($CVYOp>rF!PTT_ z(BH>NS_5ARaSnwWvEnsBMktu45kb;J1YsuNpK7m_NA5PW3JV+!ba>(}*=etU@(_2vsGnb#&6(0KE@s!k>P1ipM7je=rV+P$P1cS6gx5!^;G5sNoqSBpo+eqFfBs5 zZ9V$oxb+eqacv=8QEnuGKqvTmjzp6B)`2M?eG4WHOfJ%D&o9@9O9cxCg~NangEW$7 zqk_Z0+(3$;QNh%IMd*oocyXfSGR#FGBZ^h?$P}X9+YOb?!dP@sQK@n)NH%F|X_>-* zSjhUizP>(h9rM1qSq+N?V$g&~HuCB){o{yvqa{wroV#I%`vf`D_vH@K&ZIcPH*BU6 zso>JFAN$E;FrxC=;f9BPA{C<5{yX$W?`4AHHpj!sJubBpuG~OwxaTc|iaUe^GLmul zIZDd3i#Z+!CO!P~3=Utre;fQX7jj@1&Z+;2p+59a*DTXHTKFF(S22&ExFW+)Y>Z1f z8)_r@R`6_uQ4iZYL{~!RWJy2eiIov`2~~~Y4GX^IlVPgNO{p9-ld^voMK(5Q9r%61 zQGN8~v&6>3A(S$r(@F^PYCBFEsk4p0O@R?OFv83OLA0K-oqr8qO#wr0c69Ncj9nk$ zfsN#D+KW>)vfyjKJY#!vz8fuzRPwh1Gg@cDI!}mzX*W3#Ofdxvla@WNWA*4qJJ&^j zt3W4v?ELGbCnfxcSzCLG2v!S8&=giVvk0(SL(;;a#H|gL9V{#K7x(xi(?NPUT)JR^ zP-t3;5Gu$>-S4G>pz_j0X=zhsk|J;^jc?htO4;23_K0VP+yUaz_!D;v{ z<0%+mM+hR50MLRA1>z@!#07_O0szK^8W~ocmb0+8lcb+p;Z;OPqS}jf$i-ByB-E}W z5XBT7;nUld;F^I?MBptmn3g(bQ9_HZHpR;9Yii)*4p#!whi1$!FTo`GgHT1O0(yz; zvH7x?z&iE=*Me>G;>1WJwV~QUd12Ri4jjLjW#I-S1tv+D!UTxGq>>(j z0;S04TXPB=r3@Lnx;;pHGYDGOD#`>IK`83d(R`i*6zx|P)O0JplmD#+KoGIVsFyF$ z4P9F=vk;a;zxCsUYM<`H)zMNIA<5|bi3H;>kFbpsprBmNTEnqD1LYJRgcnT%ca^|T z3Ia+>+WHpif)QK>LJaZ zz2Eb1wH{Jf>KAa&whYB6!Y+_H3`WoJlD<@9aSr(4Coh(LHHk-)*n;@Me$U}M{Blc1 zosZn$h9nIIOInDA1FT8T&DS|UO3Pi>!Vq2V;B??f7jc*%(x)|Ln zae@^8blGknDcWj}Q)CwLw}vW)m6H%*wLFl`9aQ8`#T~Hyq5$Swm)8h48H3aqOYVLB zuNT%LHs22#ZCw(XEMp+^p}%DJhNKyO8exv@QR235Sdo7O=g*D3HAa0c+%y|ix*nHl z$jnlwlE)5Lk~AG_An?gQ;L`Hel09KhNJX>7xLG;AxgR!^*Ys?-FjgA|S70FSG_c@A!FqCO*D9G7|Zuc|yrEfWkRF9XD#M7>*Otfc8QPOp`+HEtYnUQ#Sc z7Bxhpn-X2yfvzb|X$=gWAwk%fwR%T#ooa3>K~aK?`PGS`fSfPC)s9VZv~bRyg}qF5 zNQgOUX-)8rgW|K-6J|#-wGsRTH3U>gQ8{>@rTI<-_cI%2!u;fHy;N14e%#^uwx2Dw zwk2pa9vR9X|2SpkUBcbF=LxAI#muXj_Q>ODXVJbeHustULu?$kHS+>0ft;BmX}QQ` znesJ5SE2~`F)$e+xWPUAP2VqA_{<$+*TC3J^;&%)mz8t_Q=9=RcK zP&lXJe%O!Aq}(91vEJguc+)IGI8e^u;wYXVo?C=$Pt!zYG)&b$cx24uA3as$ZJRTn zK=U`%+A-v$1rm2(Jvoy^*%YzpA`H`Zab|LRMC!y9&j}YWO!k*`U3WlhIe9=L8=Ih6_rH z!jxC~dz4i!v|36>q*H3(Sa}`C&Q1gX^{67SRW>uNv<1bYFb1ox&cd5%u#x%2sz+zW z4^i2K#$ZBPwfZprBsm=u?ev#rnb2I)n}@ z%yD|kjF0v*)Cy-Wdq~vp@9&q(eP?o{*zCeGV(Pai++~%&k-CMf&Wc=VQ}=|aBzSbq zwaaMo05=j=!d$|F^C%46Qb!@^Wg2JtnQJlaYz~UUs;qgBv^yB@QI^E&OfbYe+WQ2y z!hg4EIX3{(Cg|JVCOvT0QB!I1o*)VmHqVUgB`mj+%cUiIUX~Q)$HqIJQR34dzW_1E zfq_7c023rd264DBECe_ckjp2M<9{@wp2`z^$Rly(Eg;9vEyK-5-2wL4N+INaD zsDErTH(%}#J!@sE+>INTtc-nw2Jd+fFVf#I+d!Nokt2t7n|p6j2Y z(72xx3QKoz?d2_z-1bth*yWI0?bxRcgFG1yM_ss3?AMbyDbGy~?ROXGfiOWm$px%GSOjlUjX!D#vy*w>17Ft>Cr= zaTRka6e6etJLYs5VQZtL8N&QZx0I2>Sk>U?KV7zXY8~wfxi}y~Q3a`ZJZ%RtO5rdN zBj8jZm1sGxK7tO+@~Q%hBn)ZN>=GPn)b&;nv3bb-TCgnxy1Dhn)u9mv%y~TSD0?F$ zW#ZHiw_2O5RlC;hW?-f_2j^6m!I=uJDgl>cPF~(_=;ZyO8N)B)NaE_N&M7fxB&wqE%JFl$KVmm?xnp~+!g~J5_L!k&OUGI7>z%O zbAK#+mn9eQh`C4d7*sUC^Ma{>D^bb5YaC&gIsfNrr{T!M`kJM9mD3GUEE+5n!Gz0A z&Q?~{s(AivT=<=Q^Yh+6uCRT+vGe6|JnXmIc<9on;yUi96j4Bp5`+UbyOGgXx-UR^ z8MM<;eI3H@OlObX2Z@d1eIIKS_{E-}6^j5r2646i(W^;bQUybR_Im$q4-AEBHGU5*0je8iQHkN^g@v~CeeB$(DZABn7cCE#L$1liMV|v; zs{Zrxl&`{eGdO80gCq44B#vn?vMywLj6TXvO`$Vkj|_`jBzp)0S16UM3KqfIO<<3L zD|*`5s)!4_Y-=sOeTjMkRp<#8Wax?D5(-0*V17v=4bs%^iJaR`(q|eP91zIS$(!&E zfnJ={_^%>+==aSC$YbiKidE$Wi!vX>8&AOxhe?D-=v+5_0I!&>{_3%B+0)o$y>@t`k$hR2&S(ra=Z1OGOD>%UQ(;$WD+NrlIQ{UXU()AcR zq}0TKw}1?Ty8V2(ybQBtmW3dR2qzu2!u%{xpsxT2Bj_klMj)WUPiMrucO8>kst<>8 zPhFUgA@b`FUh#M82o%q5kbR7+(ccspZyMgMo7i4$(n^K~UehM^7ZC#nR&W4zPUsHsztT$*gNH*as1))%1NC;;3GxH#K4 z2Yh)nEx6+)3l+=s?ZL>=SUPf-JqGLCN!Z#zp*uP{`nT=b@UjMsD60wz3goF$O;uzF z0h^F&z${XMBogCikRcfe#H~HmkPZb`kp2(WUQ);ev`kj&)Gd@usop}2n04BYjDvB) zHz^isoR+i`mTjt-{#N)!)mCtf9ZAW3u{bmYw6znJGht~8P|4nP;qITUCq>B{(vrQO zo=@xNP_-oYq0^}Nr95a zgPhlESv9pgi;`FL@7fF{<(6)07iI!(3J)__4BG0N3>a{|`n6S6Ho^z}juyMDvcl;J zX}hA*xZ$FI{+4f`zy%8fSI(0XdYyUJfB$B2Wl{IYyy}j|x4K=eVF`62+;>U2<&pN9 zDYOqYv)U$5!Er&`F z_RMhIr;=8h&D@9Wb}1?C)CT6cz>s>;z9atz72v2R(IQ_ZuO3XTuFSMORTbm9zde0} z+Txn5@x57XZeD0}%AoPnlofi>h7cYpqQ?DX?8qi(WnN1A;xrf$==(3>?S6oA((Cyz zrOm2F_cI7cEMN0$7&#K(+q2UoBUG?n$BR~E3LWoB<*XaGrblpYu|-6FkZ@jjmi)9R z$9HSAbTFT9^S0y=VE+0{TzSHSWvp_uY!ft`$9%Iil!3Yhm^>S;G35U|I6M2*JKYV3 zT?t@Xcnt{7N9ZDUxFi0IqE=d~MlQNT6o;*{8k3YD-PACz+ z)d}(2|hqDIo1J6ts$?jQBe z>-UeX7&3+urV$_lPzZwQgjEQc0ciWd7mB>MA~+uYHR(0lKnu#rS-%D}(`S6?yvg9O z(`dE#eaI$np+F0KyYut=ax;g{E~nG>?=l$vpy&E`MaQSWPy$Cui-)2V-SUbGEuiNZ zG^GfayadCEj}VFy6MZEVEZkdPSspFA&lO{UIUoOdlN z$BPzs>5@(WH|MC?^m|)bSvh7~TwmA83fVXU0}_umbTHlcrECu3dldykiYfT<$l-$_ zGA>_vj#^KuUq;hsH?fgIiM_YBS*J9?w`%-+NU}~Rpn-FyY?pCd0nq(XgRwd zZUH|UY{m_j(ZPRctV5~!R0$Eu7;J3hDB}FlBkl?Os%(GvDU2>`s~_nn1)UPSoxXpPP{}ZK}<#+3GkfYPR z-@LkbZhA5!Q3E)wCe3p-6m#GRsYAjb6-gUSp$eDxGHvt3p{Wv8M}CJDNB=k;KnpO! zH~Di=oJFpMxp}YwSx(xK62NY1QXRh;AhfYrdli2R`)@z|SM(HvD#;6Ao5HkR%jUq5 z8sk9(gw2s)_~U}M#7zlartwq0?;W5M5cOWsrV&M7>4yNVfw->Zd=d;1t_XRAc7JcE z81~r%(f<}|V44fyQCaxRONE4QAw5CLk!Yb+LbD_|U?;xYPOE$4%im%1Q6BJ& zLQDbvgseU^z@LRwFybf?eNiMhphhER@!$IENcUuO2>#m(9U|->Uz{57qDEE+-nBZt z7(UdT#!o7It66< z=I7@Jxsf5j=B*n$I=-O5lv>^#e-1_gCd_Z!w_4JJ)zVe8w5ZLNKPox%WDz+AfmE0l zyu7^F*j(pe0}oQ4@}7Bwi{;h{DtQs7o}s$3aE?Xfc4TuRt7x z_NH3a`p`HD@yr5Jj+4PCDo;47d;6Wu1n3}BFLXpv!avuzRg&JKbVka zz~1inzv*dtmNbN5VI_b)0YtMWSfUA11x^Vni{sHV0;Q|XXIx*|6M(3ymDLO)NzIxj z%YGH$%u_`LXQXoMks@8hb)Z647^F#3Zf)pJ5Rh^>mC*9vo&Txc3r9cTy8$$zv;2RNtg4Fd@+WwSr~G8RUPw9& zM;Ds3b3r$#zd~QHaGt(~>in?~!SIYrS9NtY&jc(f2pb!latSwx;wbR@mSg^>w`Q1+WseI!F|C-fYgA74LkyDh>Gf? z03w}@{y0;n#h+qQf|-Yd=pd^a8zNCp5eM4x;aO(51BIVPFmn0iLVA&7!qLj^@A?XxzZ1Vh)Ft|e) z=5URMEzXPt82LCDa4?|MB#=QMZY1165T7t0W$A~45ps`(KSBfx!$-x@lu9ZYPQDp> z;2SE>_&adv5$X?OA~Kq=c*!X*Wq2YASUnvXM&E@qos3l{i%8z?@5(sHhy*;3!4Rrs z0v3c6f{#)l&IjkC9V2E6^J6D00pJ5|@Z%4~O;NOTMH)jOE73soOjdib%b%H?0~K(~gs9YI zp&*T!ap{t9AH~83Ye3|+Q95zRN4Et)Frb*IMlYAb_9*}G*waZOF$t&#UBhGcJ`evc z4f{xf7oXl2*0k>F-0Lt8n%Gsxca(Zq&*kPoMP>bEAy((|h;w6U1P2@?TAWDlZ48&o zVhAOow=VD495z1>*G6WhE#A%t9DIGLE}VjL`M@~g@Caz$#sqgy{tXJ0h0zE`_Zn93 zxV=@Wh-tlh+I*ZP&S`rr$M<{5$gy0xL}ps_eW<|Jy`D+<#_!u7(!$VwdG+=p5E~@i zi_5#}hEg+a7u$l)h;{8GsDK3t=VxWn)g%9WT;xktAthQj#!4$5v4K<%kai%xNn{{$ z#9yeCa}yu=l~UhN6u3PYM(XoSl-8qDT%e2Itp`wTKcvK*{$IiujaT ztgRP3@*u7@>2zqCardKz_afqNv>$BPS)@O1?5Ms0?(80a2i1_ioa)yHLsEmb_QH^{%g=8cPftNf6i0ut zyFu!n&1P7ucN7~6p)@odpF+eO6!@Q{PWORV=h08z+!r*gG&T3ZTDJmXdGELS_kL}a zYO$tSe?m*EVgDw+?gJrMvYoXZ@ zOD%@~1Twb}e&N`0@$a^gR}noC_fyuRtPHRk(xCAl;#T4dUCu2q(y}_ymb+pMy?w9& z?6YV;2S(NPBMIF99nRr>I-S*X-`=z5np;@laiZ*Kx#EG0^)W+xmtn1B`*sq1g_5z@ z8ZoY&+e`y5BAN#2M8)h{lmF?^Bn;IyXYZ1ik0l|gTnf+no|{LA-t$sg6%S8ym9|G} z%NoDm#zM>_pRHU8>&e3U%^kLGbw$O?+QFO2&2d4w_hi8=;$g`6-nu#8%F5q-K|4yG z&4D#Y82OTsG^4$Ex!%$K$^JTnR3=%7eto#dW`j~kY0lnoLBEaq-iys+D(JwCy!Wh>=Ay8zwkVe(E-$Q% z=jSd$?RtKsJr12lg9%kEsgk8_u1E%`N+$9GnZ)41hBV<4XQlS_?<OFD{Zd%^=eKm6kNgJ1IRBB!AFtLSn!x> zEp*zFy+LvExm5CO8on!059n;M4oQ$;ptEcxZx4tKHmk~FNqW;XoDx8+io<;uTo`k- z?H}Xk_}qGcxNUYZ;2@YbXxRBsbSbHW=FuZD-g)W29fOpM#^fczEpDcAR$k)oUg6Pi{(*s4# zSBt5Mj2504pSf~!5IXgxZuh_WDZ1V+DaN&cQ&3|k;NvPtBHvvM2OKwEq8gwFV_E<> z7v9|5LcrTJs4R7k+UYF16s?sMe<1@7+pX9~VohMwdlFl91(};0 z*k?3ft#9ks>an;=*4Zb(X=BfsvRiAl7>oe#N@6$&$-8iT9*o3J0h&bCbXZ!gwbHXm z2uMg23BLe4)bCEav+A`lH3E?_h^G1QMaE2e|Esw(4~P1D`}o+h4vL~|Nnc9}rIK|* ziN!coUwhAGXeIHAiY=Z_T`5-a; zXC>Hx-b?3D16}70p{Dbh>hG96={%s3c-yf6M+?X|=i5vM*#+MmueMZvwx;_e_IcXK zy)mp=_%8)J-sBU7>cl#^JfWi7hf89mISMTAoQl%S%V!G>EjYy;eE90ga)Z0C-h9vN z8;nY#zASP}e(rj)^5IA3HPiC5%~9`v$Dc?R9UC?7{vgHAabHoW_hM1D%@xlFU#7Ca z!U>q#+Vc%z6zSHmXQC_&(qtQ31e=P~G&4&p`$YmNF)RJ|=*UP0kQA9j%E>I4$$#}A zM9La+G@J+C(&ux3pEk&TaR)o1LbcC$HOs><0<7P{##!ecB60 z=i!&hmYmnRO3Ss*=+c?Zov_wPW)bh*uChVM?xK}dcXzgp|J8#{_mid}CarT~Yxb|TpreZ(tlYry{R%xeOv5 zbEEuN(8(^j-m1E!XC-Bx1S7l!s*?<(?D%Y$4i?q5r$zt4x{V+)wP{?6c6%;W;~>%R z`1jfMf&xp@yxvmP_>UAds|$tRJ6}d6j~->4Ja;>nRURsD{`T>&&hFc22;6huKF}*- zGvM~*w=l@*5c;eqs`dwS;1tn=ky@z9`5*w~jYe&fUI<|<%4Xhxyq!mzWQBd)*QUY_ zo^`@`PmGYln9uBvZ%IvlPL9tg9(~W)cmRv!krU)B{`z)%GPQZ=#vARbLwx^% zl58(eYS_pP=rJ4*IUasoqntDRxd~aI|zBi;7${1`r zXnR6@6LL`~l~bn`CYW}=rR|-)=@ojTh{J)dyX_NNmIyKV<|DF#jGw88zdP6@J3hux zR&JDYX3zc6zUEN(Z!po9z0g=>OE*O)aw*})wTM?vu5w{o-K!j(5bgJZ`#8zYN#TyM zs}c7&xqKxRN8(2l&#R=APLYeRhut$5r@1aE!hd@%0HqV_H+p2O5W@s#uQHE;K51j2 zhEi<_h3h%+&)G~G5RSNsV%TeO+sFYi!PXy|y>(FXa9_jGnu`}Sd!aD*7lWrvUuZ$6 zpx;k_A?R>yi6*seS6L^Y z5N;FR+aG?F+XHcTIMjqe<1X5+?YB`Youeho@xH5b^31hh={cg2b*a+pkO*q?`wC(9;;zIl`hRrjmBvrv|iQ|-{OLN*e{*h+kIR_nWR|CNaITOk^B&r9v#{jQxk z%Uwsfx#>s37j>%7K2T#gmW-X%q)kqcyflY-U3dDMW}Q0%7iB9}dl}5k)HUT7W*+{s znOkmZ;_Sz&pN`Sz2xndPM!14Isz~_!Y>v>hT@|nWc{3~HR(AIBb1(WzxwpH$L?# z6Y958DZnOiCe}c{s`cfhXc=y~?o86gEA^~T8>NApn5%CtGv^6oXx%Vhh310&5xQe^ z_=K&lNh{+l{Op`~H3Po+t!J0hxD*pRBUwX5v`NTOX^2!F?}acfeKp!P=S(t;^-^YTG=uyC5cV z09`K}%wxWP$|=y-gfuC>r8j3TR-XBAycmjKbKog<;tta9c=FNNDZ=LJC2Rg(9mjn* z*x8+t*=yq1%_#$;kNd0m(e&gbT-byXoDFXAlSzR;qyQ|fd9*ue&1#SplPu?cBzek< z(bGi%2=|VKPbU3^y%#=@l4lhqUTE8f;}7Us%Gv$T>MWvVnFSh928b8tPJI1^hKHsS zOgsfKT7p2+WBsP*Ex>VGw+B81#_>?hJNVs!a2T7t;CeC=-c_Ra)-|=+gD0HTWt*yS z6I?6p(TUm(_0B>8`h2l;{`#3*k9%iaPd%U8edO@SKzh!=C|OjPWyQyo>u*NQB5HKe zS=j_^pqav!`~8RAfeoTnvwaW@KLBR5Kc%wo#=e4C#HYf-!t%G>k8<7!D7b#o#er=# z6Q{4}dZZ#O6w-B#3BPVhfqC9QDnnfNBL$bCui^^$)vtfOz9kxZzsLp(G+pAa=T}xr zbP+Y?#UK%?l8a8n@%my_qksk#nj*izf7C{N`P|2SGR|O{DyC9K{##n_O`E)kwtje% z1GWtF?JN!D9&k!MpX+2gkox-GLak-@3`grnQv{^m!MGw+1MdMb0xIAF;ILi4+aVkM zz~NAdvtELgtX{(S>zo^fKL2YYrlzLaofZ}rAo}ayU9NrlNW0y$$(AsHPSd(U+EoxX z`J~icQ%vlTE-@@pgwPhLf-KYpy6$Sz?Y`tK{A^FA2wPYIZUiI=2MJn%GhBc(H#Ww9 z(q(?MTDxtyx3zA#r#hd%?h%nvU6r-4qo3pE_T$n0)* zi0NK=HvRl&vC7(49_;Gj=UC{_)$?UMb8d`O2%stdrkqr9JKrND2qUg0BW3^mdx2!ldib=|tXj2$3ezF)6 zqM3x69I}n|p|(q8`T#A2k94f+OnF8|qxL2B+`0KY%ClXYm2(Zu0ox+zCVsy4N-QCB zch@1-(qsLBL%DVk(v20)+;0Po;SUtc3oCd5`$v&AkZ58+$5e zkhp_!MVHmR$qLv{<6r4m5>XG0_Fh~4(DOfHy!w?!X54%iirORA(17H#@Scx4$}IEX z!hp}alG^of`_4qk4tKJyqXI@R2Q08zlL*);bb5TE>5}pIX0yC*9RQOQv;)O=z(}DO zHI;-z`r|I%HUl*tHTK_ri&A=D{s^$iGe7r%m?ba!3)uY+y)-{h{K=H5_Ge-`0_+1E zjFB)%!rH|sFDEMxjv6*T>u2}*?`1{>PDgirmcH>>)sQRQicFqP^mA1RfNkSfH+mYX z9s5Xkt77WWM&@pA<3j?jM6B1!72xVqx2#s`2$gPQh!usx5$&U{ju6TsI)K>1E=wlG z6Fn@@OFv{)cPCJm?tM(Sp;HHTNyktgm;$4Ob~~|nt-Vx_;8~|>ap>aKK-lE; z5#hxkRaInOx-XW!2Yqp#)_Zrw95N8t<5BNEDf#9UoBQGJv+LhwkhBhaEX^Q zy4K?l*+mI!Ll`PF{&YqE?i-N8rxGB`z1;lqJ8ifQ_winp+ev3@m-nR~Kc1R|&&|Cz z_ViqK=R8{Dbgx0FePK!cbBLibbnRT1G2E%T2s-lU>EN7wrDpI&)w!69iY-d`z_?7 zM|_8&IAf@Gn^-#biT?(ntY?3gNAHR)0a|5WrufoR)XV*evrGD7LlUI?K)E%X%$yzx zL>&4+3YQyI&#DT%ADfra72viOP0jZ9_j`}5VZ1xRc6(?cQ_t~_-@EN%i>&wE95be+ zIlb|9iIAUP-V@g_C~7R#+tA4%$93qyUS`n@bipl_r|a#QqP->}g@_8E{OYmX%yD*I zUS2-O%XXt_)sux05Y(00;>Go=Pje2-J=-H)Our}ox?w#^pcqZ*a3VnXIV0|uSb9t+ zWcn?ksffKO2HG(Hk(=haKeRA#Gu?OS)=?)%;&$<3N!@f?TCvPGI~aAZ1R!MOd}op# zZcQ~pxbz`#6i{oO%#FlRk2FSX1*S+0CG0_ z!AOHuZUWLrP8s3A16gh5ssXH@0tO$ENULHc)>o8|EzyQQ=Oz<}SY}#r;jqE4+tvy< z8qhl9iYgnMWi(-yz16O}la9TcxNSx7nXz7o|0YQfIYAb&Y$OHp=w2B#Ge}Rnd@y*0 zXwHq+mSohk-#U0f#L^iZTnx~Fs529GNE@;sPzvNBgt%J{A=y9~lbZYJf+EUZT!2m2 zcp0%X3R@x&oS%Hk%>C_m?3^ilbj$cG692GO4j|1~w_iVg4G%Luz`-&T!=Zsi;)>JUc%;mK5?jv#t2=pZIr_8k1@7=T%L z^35#v+9{NeCs~sVNzINBmeHZW7tf9B^S}7^&;X{E^6qyGZ01L&3>dZ17+XF|?`Ggq zD=&lza->=++Bn(6l?+BZ8d<&E`ia9A(^~<{v8q~D2D?EmMn^-(Qai@(v_1Z2kS>rq z8^pZg<4=|~$nUCEUS1zGoXyn%FZwtn^Fbg_6@WPZGxw4Zt-Z6dj?y~*H2|>zZ3#m% z42|qeETVMiP&($}z`3{F4$~`Xn7oXi#`QA>QW}G*#>bY<-In_B#`0G`g(%xVrJ%@I ziyco754|tXU4}i)pMKj?7$xNlU_PBG;$VCkAwuZ`m}#3hPIgj?v04Wf*esE74uQYK z(GXm8TMD(;IsWIFPuou*3dD$@maHi2Z>#1L%}QXaP2@%dUe!QREk6>F%I&Q!zfM5? zifzl;eJG^LjEpgj<}+4`NC%_l+t#tPeuGg|+53};BE64TCR3`0hfHrvOMA=z(vi+M zGtvuD+3L5dr=D0?c-*71$dFUmsHEURo6#&OA4DG)o?1{+^sLspa6RMj!IpEbqYV|M|)UouYby-4WJZ1__kq+F{w3nMi%5Lc+hsi{{269;EqR(Nm~Zcot;a~+h?!C@#2LlA8fRO1!CA`Tu@N3V0TT#?+Ll-Dt(j1 zIHd}NA@y0l6d{Q3W7R5sx9%Q&l74=>kh)KZ4cO$K^>>uq!@yL_+~*&<8JG>%OPB1E z_9ZB*Rp@!tx{qI{a4oy_!5%{5stJVv`eJ#0ZX_S)(I=KRwweN37^mwu37&oW}G1zdS&`g zS&X^QJ3c3$;yOx4CV45T1WkElA3Hvtw7X!8=A8I0#zp;J;}EdUlc_wp%hk9J#H^!@ zm835s#GesmzeaijwmWZCH6s1+h#oRITma;$oCIbr(O5zVoXBfEMarPr%BS766(C&` z5*nuS2l-|K)>dTEJF3nyjk_;I8YWr`V=Nyu57Ld05@gZq>x;B$t)c^pBywNSdnQFh z(j^LG3wrysLNVFYN5tqJGP8a)I6QoxtDf5G;J->rOst`<%ZjvuIpt7=F-@w95k?Hf zqBeU0J2fhZB`7pfM#1HJuQcpXNwcxEt1m$khI4d|`tbN-m3Q8O!rdx#Bb< zM+8t{UfIyQD{e<&5fUis?ATbNJj`8v{-NLr?Zir5@}ttU`;_ewb}p4w`8!l-H=^&l zi1XveD#-2{iA?uL)T>ib(lE0|kA;JQ^M_i6SgB@ZxC^-+cCB4JeDupH&zmsSWrJ%P zhd)H}20?e;Vl$^M;mlP98VRnjD*9^qS;1B=EFAa>I8 zy+fc7lBM;3FB~dSg~Y*CtWcJgx)Ep<`myvfE(pZzk;C8hFLBV9Y%J>b5@tr}53ino zi-bUaiYtfE8rA>6K@SdB`&=I+RyaT|KgKGynfS4Re@#e%pV5lqFR*|EC@r{|5x?u66(b literal 0 HcmV?d00001 diff --git a/resources/img/tf2/tensorboard4.png b/resources/img/tf2/tensorboard4.png new file mode 100644 index 0000000000000000000000000000000000000000..418f697ed9b8479919cdccbf6b47e00b82ee4451 GIT binary patch literal 221040 zcmd43WmFtp*DV?+0)fUQqyxd-9Xf%=Ex0uhTpDd?Tw*}u?lc5<2o8-UxD(tVL4&*7 zsXVXT@!cP1++X*c8dw!owX2rQHRswJ0^cb~<2)sQdgsm^99bEt>YY1}Fn8`e5Pp0Q zIAZ9r9t?bY;vl2rbmz|Vw!fcuW0{{*+`04Wjx6-8x?9TDoI6Ydc>~!yo!V)%K{y(z z#L(i&s5m^7dO}3h_po1XBq~QCS}w_umz8@VKfkCb@tag9vAjZm-{|PQ8tCYEB0{3a zX0qydMCx(%DJ!R)buhNVyu7i7RNR-M0yAreVria(RhO$39K61-foJ*mMs2+7@}HmY z+}YFn=uGwBUF}!;kN>+vEJO15P5#+FJRJ7>&(C+>|NnEzkA?034td9?^K1{=UyECu zoC8iH;f}KN%ue4gI`uA+e!K}1haQ~UoO@Nr)T{Jd-e}72p!WWI<&CtXm`ZiB_00~S zCJA9~;B|!KzMU%LCa47$rNtDi$T%opM`4e-(vu~zp6YJL+}*K7Bk0ObMkH(ANd|1P zi^Q`!SIn8bD}}VV9hO`|Kl=h^5{YN5jqrNa^UeJfT}blp;eLgqhCHABKGaFe z^PhK-v|0_3l+!5l_+;LL7`Zr`QLRGHyF}ZjbZu%UIm&vSc~Pqt$n`eWq(shs=GYQdXbamppau4B*m!*EV-(CfIlA0hv78BA+0etPfC zc`m~t<7Hz--~Fed^R#D#!EnQz-*C5ZE~{(jedFsV7dlA+ zt6W6AiWGe1(fkK<3rn7sB(tP(|;k<$7X6v>jk$H3jJZf===@mju`gxwzp@fnm z<*>d>1Ae`@g2vqug`44+0}(J`XTSh8bt}nhp)7+lNt0Bek|Fj0T8epw1iX088G zV@V*oV_NcwNlqYh)Gp$^?_*dR)>#uSC)$Gn8LN6jyuZg4CE1()wHwyFZj-yyQ^m!( z>$(2NS~bi6nWx{gC9${zzNC@)@|{ry?G_iX<(a8vFS5!-Hk=9y&0^qPIY_)-ya|uR zFi}(gNCh7et_eq#?NV#S%1kk5eHwjkf0w!1B7wt=ZKU0hQ(ZRuGP^H)l;iB9^4{mP z>J-zWC}u=^h~z=G8jhCr3*`Px4BV`vG*|nS7b6FX?J?f(;#wbd*`yljpdW)U;(;~^ zvdJz#*1D#kRM_OWS;DJ#hG*i_U-1FE;st7~`TV(L6);PTsj(uo}C^)>_)TAl|J zV>K`4*!4~`t*G)c>Z@@Y&+c@j(!`61)@a zrNzQg7+ytGfBgbo_b659)R%sXXOX56y-s})c2%}jl^L)wvr%ty)oa#!`91peb%6=!D3DW02Ss7ie1q7CO7XTk>j9zq1T|{(L94L={^kn9ovYpG3JW z>PaniVTG(=j`)1|dy}Y$4*6A(WSN_N55q|4ysa-T?q-*$ing6YL4`&so}Bvk$4tM( zt}q#NUPwI~+GX%BM6BObzczwhTGL%g=d%$wH5qLQRoq?TE%6S8=g#Fg6M!csYK|kAkET?aRygZ~^pMZQheJ)W+ zFRR8noE%B4z_9j*A@O0wam>%^K0KMm(n``+Vwtp_-*=^^)~7d`3_h6pthz%-9mP(B z2Z9`t^`C{u%0D#LDpauIRTg*=c!8Cl5NGZ+euP57P%(#whK+ z--gwC=9b%ehc2}1f!{E?epb~tXKXw{TuMX^?!*sLT1}R)*rDrDkf642T`s>$8SI_-vJ{ktSe`66$%o*BusAncAaYmPpV{9` zWn7FWD`#-LGZ;d@j$basp(P{KxLrWEO%rk!kWe3qT})dyBMF?3QhEK{E(=!S|7K?FFr)f9?p3C9;~eKb5<9>tIlZ~iz;qO#vsF$l z5f8@U39y*)vKkxOmD{h67{3gzT|j!~32c+q;LKSg!lS;159x6?h-oMg5Bjb2`)Uc)B0dsiEx|)K&G94M2gNT*(ArgELv~r^|KyHoSN9i_XYXWX^;37t-b4Lm#%XTI;4;g^3L>qD|vqgPP#NI zQRkvL_v6$X)rX{+%ujClN)G@jj$xQ8M7y%0`XIeRA)j4!&UPIFznr2*3ulmBvrSkO zxc#Z13n&5$A7tO7T68zo9`UDVxmLguVZ)aFwS#7%yd76nx;{i?E;~uH*dzZY?Jeu0 z?4*QLr-y?)PF9CIat0|i{2DAIy%}GAF(};znu#C=8T|RX>L@lVa8^{6nB;e|;D+ci zE7cX)JZ%tCA+8|M*~o^UQp3D@x)roTD79<5?l2*sW!Wx0+h;k;_ZSq&N(YWDz|Ut0 zMG2%aNbQdCD3RDxQL9`lIXj*Gbd%c6R*%)b)VJ!OjZ|VM+2!*gQHD-q#Z}Co0*A=7 zIzf>Mky!&KT@TymEU_d`ax!h$PtT`wReA4cXwy?l2z4-ip(ux4P6}5a-sAYpZ{4Y| z1Ute80gPPIZuCo`s7Fd&cjC0;K8)WLqgjL!8hbJ~5ek0I!= z2b3eMA>ay?(@C!mzGoYed4VXSZD*QEmyeYzeJ)P))CFwSUec5<6b-4FLNNVY;UD9cT#qE-a^mIYn$pyia zu*E@H(~{p;9hJEyE(X-`Yj~igE!h-*R98vUdOva%oJw%oeGjxF1Oj|{yb92Hg@o(@vhZzQ%uq`xv^43VsUFSdOXk+vI>eo ze75QpS!wB}s7*_zeR=+gFmhA0q*cOt+LAR&fo7cZ(=`rsR%pPL5F~rt*a;AAMqdJx ziP+;mhTv!#EnLA^k$wB+IHNOhU&wL(;nc{NIt!hFJZ}A-Ddw=BeO=L^(~NCV3AOo z==A`Lz>&Oc9obx0e9*+{TV(XM7;4=s+%(yHwKC+r^0ZQ%b&ZIL%uD%3agk`0OsynX<{Z|qG${~$;EpIl2rhGBT1DWIvLsDh)Xs~su zcO~PLo7$2#3P*k{u)TS2c$MmYc&Mr)%*+}e92|IRE{YCBiDYWeyU=if)=3++uES>( z=cmBrU~>E0oZ-cA^PYobHh>e-P;;871lNfpn_80D8YJb+91qdPnuxjP2F1-K;A8V< zh1I-@V@f}mq#C?QAPHSfqv)_K=;fqn4%R9NiY+kawaRgtPiwurhs==N;v*C+x)BFk3?fb^Gp!M2c0{*C@eXno) z5|6C(Wah-vFn%99OxmGBd{<^l(xb={IzE3Y)gS}DhTKB$*10{C0`a!92E8;}b+YBz z_|V=K>@=Ecj-T3V?f&c}pN^o9)_feb$#fRH6OX!DmM7T>AIB-qFUtd}qxzd!TQSX55f73NsbHBvK#xSHmZ!&4 zgP%VsdiaTQWUBOcZnm^UorhG|p9XLe_&KyIlW1K=~mP1PA-UQi7=Nls9t zHnnXUoC$}DNez|o(@p6WS8mdH6TKFJN?i(P|FT*U&ahLu)0F7(Qdns*XR&u_lZ2l4 z{d4vjU#B|7uNns*-FnOAFaly-$*AQbH?;v3>Wtupgoyx$C+rnUj$RQJF;%l?yP;6% zqpFDnm2TPCPN?H@zspz@GBnHIqiXAmKpbjARdJJ~dkpHVE+$aUvl*d9 ziHrfY2U`e;NkSa&Vy|wOGDr}diK+SJ`R=tE#60(P+*Av##!rny5vs9*K&`r9ElD0- z5A(6L$6Zayc>5xZQPaUeqmf;_Br(rAS2hlLu1aS`O_s||#y^b}5-N3KDN@&$sLm5_ zQc+!?iA^HeQ#kCBn6WRJ7fy|uc3TlT)q>l_bYO;>$n+o~ZIw6f6H0`lCDf=;!K(H`nFbz~3&X2sUCqdsl zo4yW}#QAGcG`nkad)$RATlFuq^PKvcDtXQ{tTp;0<&#DP2_jV|IO3nCnu5uZ_SS8d%OqCIv20GU98%{^Wnv}SrR*aHwH$AZU=xyylGH}h~hO;=41Zl_}v#K9;X8YWoP&gkpA7ov5nlDB& zZag7gRk~12I8R>3qSvR(252bE)Z#nCe1`s{BEpKeh1mzt>gifFYHGB>W!EEWt{E1P zkt(4;O#gaIDit;;QW(OST(L8{jzT$Nn|4RBRg&?VtB=Y)bfc7jlJLu|5$NE$ z-lU6E3uyEx#s;ajWAi@y)#v^I9~7J2gM<5Cch< z5E}d#_8^&wd}EB0ZO595FMU4nBf>TKZxec<#a$ZV4PNvJw|D_lOkT^U%#YFZ_bWGO zC7oBQbBIEo#1#6_N-kb^Buy)j7_8y@k0{iLl*l0y8IK?KzOwA5xw%SrK64s}_~WJy z0C}gqQ>}1gR`Qy9SO^4K`jdk;(t*O5WXk$dER$bHgske@GsG){Kqd=*+hT8o8e5~H zYGGaHlF7a|@x1b8$vP21KKQu&MPW=YuS%=zxtBcH!NYr6gezXv>oe;`o)9)mE)s`k zx*fHV3ANanL{+P=BQ=v)d#mEsWkX33>?`t@w-V5h`G9AhnxyCc?lXq=$84xBm;|}B zG3h2e{bWs5A@oN2djrsPZiRnAo%d8pWs>W9u?f8@h+|k0q)SGs^O!7HR4TEi(32}L z`f_f2L<2$*Pt1_?@Ohb6tAbyD{<=#201g49(A@#~NJ@=&Uw3g^rf6igx$9!rcrM^( zqt`UKEhcy*G?}HQ^yEbhd&+yE%kH)31j6yV!WtrbhVqo3&~CPT3AqgpSs3 zf-|>GFRRPE5wSfZqjtmjqyCOleBL#8fgHJ^M_u+~aaFxGIeVXcyT3!;iNR9|*?XsO z%_h1DenWB?TfjtZk8q|d0EcghO7FSGSIk?c9K$67VM)?+Ew%5g`S25EilUTl+g0f& z%#t}9j$u@Jq>VK@gY~2&_MFV;nmN*yn8tN+say*b%2}c2dk(?VJ0 z$=i=bz9O4o_K3HF6|f?IRgKh^!yv$*FJKamf1@uvqvS3WeODLf0%CO}+dqcw4quRvO!z>wa4X zZIiz8qrNc~+z?IqjkFCJZe3sxKJnYvK%MA?@daRY-Xmhi#BkAxn#Ao8<1+Vn?rBoBiIt=!Usk6tI=fD&VKy+{FVI}g>s)>@4`a0e7u~L;0pd;t$|Al#sB#U5^ zoGW1C#>cbh|u;0dFSJ7tUIA?E&kjT05T~IfP zuhDLxx&ChKASK`KWqeG9KbR~0S;|j5Dfcj|2?!ZIZvQp7o8*vHK9b|b!rEQx;r@BJ zXf$rBxgrTyLzp$YT&}Cb4_t1gm+GFfLRIK(&Phapx7Gkeig7TqH%yMcIaWT+F* zH9h=0k$J}lONln{N3T1Q_Z~I1@uwD2rf6KYSQb&@hB}nnIDJ^Ro3dbZ4t8qqqjnDW z1?@P#=SdwWESk^f-}uSB&A}({dL#*oYU;ow@?&so($+) z%FZT?DC}}p`PyF{?&hwXc*&N|GnG6q02=+@#D=_B6!|uR-a}P5ivFthC3U(I93C&usmkA0 zhbN)7H+O%KTMJLRFbE&CV4bF;e^5zu=5>rCjiRL?RHl!4~~~ z@FXN+5X@(PS$Of9@;XQWAW2i!iW7}cm8=#tK!6v2vy(RR11F;DV9X}&Q09dJmxno!Mn~u((3fSS6xxcDn0+93~J?7zBlhI!^)>iL5fZ# zjHHQH^_1n*H=DfxnRIU#UYE^481&e#0#UU;J6UMqlt5cqZccJ0eUWMXS8mWc(+KTP zg!$&6q;O#qB``^|Jz7dmcfc{hl#^5q#>#)soM`e~WDwj*`b#V2QDbi#RAOidto&mu<;4^-Cly8DO5Y;#-f>f2-K3tFHVoilKg zKQ0ahxY^Xs{jC?f8bCm)59w8knAWIgVH)fvvja)m$z!;;M_EN~i2)dx6y{Pzr@pz43U4cl@-*E@3 z0dLFT+K9_byu$T4GxU2Rf1WDDq{}w{bGSnzzx?9%!&su?CC$MO?Cgb)!?p4+oI&DK zh-9Dcyvp^{vF5Lb_e<20fa%>!exm-`tZHRAt{9I&e1uv34#U!7apG0>wxIRRz{vxB zabmCs`qfO%NrK9nwauqzW@UKs|5*!=1cm0%G4@_W>up<)U}i)}fw^o2qawVHhwgz` z#cCqfi6tH@#^=aV^Lq=RPqX?uYNo3ZZJ6*pty6%w4tpcy>Q7Ie5a-#ZP#w>Q${ zTK~D$(#2n?7ANmlPioNms6Dbwj!o3zn%SOsLfC&>Fy{ENSv_u4kgI$@F-2W_gbjsa zUBblCw`eRdc_|0>3@hrG^c7KVzQj;=k*Uw0iX_>zZVTM9xqFiatyyaMa!5}S@o4$e z-uC*l4}nFQ^@wwxnN8)8%w+e@g|3{}xwIln8vDO;Tha`^*;JpyW?6^3k9+(GTJrc4 zOFjacy2zDgY0*~6v!WP|9h*a03N$3A`bA%%r-7I3^1va%^~s1?!;AFt2ynjAE7_G0 zqQ}j*wVICsgk6qi5ef+F2_FLB)UbQV220se_^Yd(vo zZ1+>);)}BfXYPW((z_UEM=@~Kd%=3!Ho(mA`H77G05&_9gixUlQ6*CDP%3JtYOi^jMJdjj1n{r^=LSqM zO5AJ9(LPZeW^D4%3BI5@tuDQ5F5(uPX64zYWXw3&7M&8!B>kXVX@n;;ocMUk7k>|iQl#Pg z_Zc;2lGc~?R2?qf+@hwK?bOt|i0V3Uln*4<92-pnS5|r@7w1oH0#Levm+{|#*+0~| zYPT4*6r0@?SeqxDZdc|2pA`Lkp%2%(0ZAps$ILtPx5lty* z4F)BG1Esa*!Xk=7&8Lr?qa|!|Q@4IvpiHxK#6`~0mA+*wQCmbob#c4gvABu(>V|l@ zY>h*Hb_z2FphuAQi*S>k2$+ z@EhScQkATjyb9Q>lJU=Vnrc?FGMW(%)vuqmb7_uU2fi zEW8x9H{RQi9dWwRCe?2{Dn#Ax}mPTK-5jQ){6Oz1THhq|<7-I&BG(e`Qic zG1YfW?1asN%+d1OQ2{qeFuYa4eW%4~N}OLctZmfxo3og)l*N>!|GPQzrIvO4<2A7S z$l0GjnL5p)h*ab-L1Yw#y*9ciB95#mrw?*ZJMk8cG7V=W++r@<$a3LSnpz@ak~OGSxk=tr|7_5yC?ssl|(*s^q>t-DNe=m)4;B8 zV^>x&>u(CYiseAgrfku{fA3z=#`0Gaa<7>8A|FwIR*kea0JCf2M~3a&*jjBcANVt< zT}YiLeN34pYtmLoGiSPZ%^rsRZzDKZkP-)=$bwNQFkY#ASGliRnQyV`DKsgGnUI?p z`Mx9q1@uP|`*wA1HCES4Rrd8Loo-fiH0-iE7}(!PtVv}Cioa{7NAkZGgdm$S3MgbW zuClt5pK(V3^=g^R4z;{-24Re~x_L zeeme@{eS)PyM6r5x`eCngIZziTT15|i8HX@kFNh-`2OSn*!jP8#pVCUb)CL7z*~RV z9O<9$J-HZ}3tPl(KUz98mKvT5``3ceU;J2&&;GK5f41+RrW)K2=feJb;EoT08x~eT zJ>|cR2b^_9_diyiHe_Zl{l6{{7Owx_T%e$+*#AE-n5?J#_xOKrulYZ2|9^O4>;Klw z-*M!XmD9K_l)_(%zIgG% zdddVg7MPf8kJF}{-c)SGS)lZSTy=3lLGa;@3+WxomHgb)pwLkLI)s^q z1`$X@L!%g0$Hm9zGLRzZDqOh`(epG~S{CMITfcDDIy zg&jC^publ#egu#xc{U^7Xe%mR`2Cs~Zy~xeuj=GCe%A zRlCG(y4WZjhox&Y`&=7%A8S;F8_leX7i&`B;r;Zw%?i5={>0MD;^|6f-rw6>?{$s{ zp1a;LR(BQi4=c_C9HEw@u%0*U!!GmYwGjAk+rfDuqhvdvaD`GN#gZ| z^!*pTYEkpPp6N5?uEbva94ljMs`rS!IGBG%<{>&~O6j!1hnTtC$?|b7|p*%({Ux>qjyh}m(oc#_+W~gg-tV&Ewgxs9H z2AP@u-TKlX&GPYVuC6Gv@fq}tNXZY@L_A^kietmU8L4jV4ouHuL(9jSF z1hS@DSy>4tVb}D%ZN9y1mW`&vc_HxAl4obO?yxP80JvTs<4neB+}3zHUzq9y9A?$4 zPJ#H&PfZC533;^o<1BTAQV2M_0ud1sZq3$JWMs7b_(4TOL&L;W;Lf);S!OanKQDw< znzMPSp{S^6ZEfx2BLwF1UJ#G&ssj*)$P*=}lkON@U*w}b>ZZ7c8uOFY2`R!i{ zyKK!jdU_sj@Ooc3&lKv~3~p>}go|A;FfdpiEO$fIRaMJvrpgl%5>O~q6?|fRY^<)n zK25+e1B00!9JFuw@6(Ptw72kPqT%q!}o!(WCm{rao0v`J1z#=*%+%`nq;raCq*?&&9o-Nw_fm!i71d!Mmz?WW3C z_B+AKva8R`Ow4HwJH%W37~L{276e0+R1tn3^1JGvrY2jGHz z_I~{+)~g8vLwo?A#;l#Eu_@%T9V=*vS7jQjHt-~INfOp!nyax5Is6LM)BP^?cgn>6@vc#Ff3pMRn2 zOIzSY3Q|};@TqGSA*O?7APoEHh*E&j@#4-8-8_z0J_gR;N^^sy2=JPJeV$mt^1#%( zJx>wnsmbA3`?T`ndQYdK^`5BrXL`j@XWshfX_R2%KkLE{i&=M*TCOt8KR&(xGLe$< z&n6kEsOWg~i$?=;C}04bFf{q9u8y|$oJUHwu$~>@Yja~GDmq$`F51J#%E}7J1sE))xw*M2f=wR-MEDU9uRs_8)xlbK zZ^ph^6sUFyoF*^5k5mi|7l7D`i;V?E4q3v=j3sStx4%3g*s0%b(yw<(?2jA#c5rYo zR;X29SEraJ=G_%RwZ6U%xjs@mOpuq8^YZetIh~ZE4O%={=^acFtg`eC&4HomRTz(!&3A& z5KGlM>V?EJBSS-+LPA2q!k4pkPC*%2Sy{F9^?KZOxyosAG*%rUWNNuetgNj10BncO zZ^g0dA)K5#!zhK38|oEPDJdy1eSJk}z;nuJ407}9+s`@5X~5X0$jx!I5VAn_I{+qW zzBhuc9JnHT-+@8H!H_18V{1v3(afNXBEx2%pI_fPIy#oKl2NciF7EdEA7oXiBoJ}tnQwXicmRxqW zwB*n~>`($n)Ge>qCEkDJFBujT66#`w_>}+c&nG&L$qJttet~xtjHRPcb&t18@VQbe zo-i$mIK|N5`Iwt?W_wCD%sUfmu4Mhoq@uOaH4@%WuLLl)qxP04UEPYhbcN9Sf`2HB zh@L^s504j*su-nhL$6d1r>@p=Y|6LzlDpGe@tNGllaMEdbje zl_B-lY4-8f(1?wW9svMxw%OMgso4pDdQEk8b!~0!cASkd;1nQET^$@=QA>@aA$rjDdP8x_I#U!2_hkwijRrUsK0e~+>KtH~J=5RjFh~l1yB8n;d1a%HwrduE zEXo%pn{le}XuKKCS4&S%zrFs58rlbr17m^tY)C=p=jVz2tG)5; z{DJNhCHi4uVcZr2%K~927!0Oaq#c=-1|}u7w8gTwx2MK`@O!hgxygAws<065DQ7n* zKbw)SmJ1{Z{t!UoZm)NJ!GZ`H5s`*a2>^?m-1Zl_2SqYnjt=+tW##0iYwQgb6ci3g z3en+jfQ&iWo|$O&y}jIRPTN=z&(6*sf90M!v3X3w2D2Qb{1+j-t$qG7q{z{+!bg~v zI@-YUH_hOm=4;pYza#sO`i=$-WDF=TJ3dYQ2#r@DmiZb-cMKnd`o;>+`*@?x$Kl{c z(4UNa1^3^9yY_#c+sVKkReRHuWd8_nes1Fk@UKY9k-O{D+Yt_K26%dZGJj11+iZnJ zxQEh=pSrqw!}ZC`c>mkaj}%h`%FuTSiHJZd@85r&wjThMTx=I4(e>1d}$ zcTdmVh2~qJNX5p+M$wRA`(rRd)Iz>D4av#LylRX@pxxpV?Z%%&;rN$`updaygdONpPQQld;CdXFUH<(3oqm(CY74ivp4LHl8Y~j)P-FoBXlG|M{c5EQG!we)x|Vv7AAt)qfLOyF_L;shPAI zd$$_r`Zpdgx9o-wKCRX#d9$>+uIoh89K%9+`qb$_p=2nPoZ3)X%|nF|=ZJ>7JnW4P z%dL#q!)9WNKjKhzwUI?Z%FJT{QGLZu`ChB9+4Rx0kF>!2LNN7p)#kjQZqwoS*#u{z ziGoh61sa8b4C4S;A{|*PzynydzrFd3C_v!b#1~tpj#_>eGClr`EB0;9DwzwBuG=L<^8 z)|M83918Q9)N&RIY=634G5||9&3}~WaaHZoHnIK1MU5E&bv3n-Wn!Q{O$#d2R>doL zKbiJP5(ulHOnd&1SAHf5r?HK`TTdP2)5kOLqM6JVf>ewybN$qiFvWk7SHJ)%9c`Z& zaT*@|H98nAc9_CH6c98{o2~f}BG?m+@((}IzeWyQq#hdUGvk)tT+}9tB**Sh3^?Vx>FfsAwFTP}<6>yBPP#l;@J{S|2qt1PV?m8^_+XVxe(G7=D`+EvyXZfUc>y>bH1Bh03!Gp;E2oTT9^(yW~G*fCx<&hpP1T^k8mpF2}?Is{y<9_V()4|Lpu{ zv*9BlBm_urc0qylc#%#uecq;fq=69CYW|~bhN8{)>x^yDw&qLSW_5iV*p+^#5h|4 z`3fH|a$gLjZ)j*B^(W)81mNLwL1AGyrEsEj;__}Ip=g9QB*Ly-NqTVuXO%ruBVpZ#N<62V|Yw}W*p2=f8D z>X#()lSGYdX(NwZ7fj%`P75NuNc`gG@H>kIX@evZScTp1;8Hr<0x~gVO!(*ZY9yx( z^FUV;l3gL~WU_Emp{XfOK)=Pn0~mWO=QPdpV7*4mkd%Y{Zk_m9fUda+yclH=L* z%jt9gYpQ5!YU=3Z+fA2BGkY!omQ)PL?bTiy*bt)Rq=Mk#YRv=U*2>-(1t} zuZEBJi+9KqYIA&!6+$hX7lnru*L>Ynx02RQn8k;G^N)OS$dPRSkI2YFeplJb092^>|C@ZU?<=BYy zXlw#X*m8GtQ&SUwoNwR0UGP5DS67dzulLl^DOYi;{?A%~sR6Vw0Ch$)*%5lnTU%a0 znFD;_;o;%3KAi1&x&tVH=nRGo20&S8MbRJ;pMU=RSy@?G+H_$J-~qJK^VAOhL`+O< z!Rt^F$kjJ*z5^Np5W&+T&8?0pX}i0-A9Cgbv87w=0`$-%{ILPa z+SS$dRnx-8@~4z1@N}oc+43W~%0OEoxViZj&w=%ZzX5;-Gu5`I zvrfvv>n-DU)qqlZ(P=wfX|>!H36Qd>sVSD;g5c<*ttoC+Rz7wbSx^M6jMtEN`red)r^Ui#s~hw9zRe*#TdP!Nc8fMq$ppzl3s^1e)R zo`*Gh9HT(5p(=p7$!%=B{&4sHv=|RiOzvYnFVZeE2F4Z{1VHsr z1)mMzPqeV>$;k=Otvmv-%vNiz&TT(q$g&ds8ULSP{L1UJ~RPgq&6tkP03Y0hPMje;d$%lK@kKB5P_+0qHQ4 z;PMR}{^5ccU_fc3Tv*RvXuo^+o;3>y`hfu@#omAQNh(9F22Z;e!ZuoB1|fR!iM~Iw z)yBq-<(bm6ymqmjoO zV}R_I274c2T1}EU5*1!_>X2ONSKB0N7B=o+I9=xfGR$t+Bmx9WJ!O}D%a6}kKtxBYscl)Khe1-ymz^wFB8^O7PwoMQfNx9~7*jz1qhpk7mF7-vKotERhu zkK*FZsLsyWKDx6^jaOIhjw6!m`upTqIF%E!67m?nen4d+ zqDK0@zZmbkx*??&F@5>y(aQ0a3B)< z=s#1NmRUzGcmEe_Zyi--7xnGpV8wKf*PLb~JQrU=zfFRu<-6;)% z(ka~?(%rGo-1I) z|Naf$!zcoiM!&j0G&GaFVvDSSunzPRb-T>e+FEo@v{8%l$WdBZQ~UdKQ&R2ByKmr} zpcD-lOHxh5tvBOB=dDak9i#tV|9jg9Y=MOQ{O(aIr|)a5=*QZ?YwY?A0w-UEae}$- zqD7$G-)~MC;Qr50pK1L2#F&Iz))asL`~Q1R|H}{3sZW#1>uVhk#PT+T5~NV`urV8( z$rnpU6e=+dXGF7EzSetrEp(|ZY_Xv|B3ib}W=lzUhb)PvhJ(s%F?c?b(_hil?l|IS z!R*YBkR%TGVR7M^(S^iqu{4C<-M56paBw@}M`W*)gwbE7&)Gkv zNEvqKth3(ILEORCSLw;V=Udm2#ek2v$7?aM>y-1EG4!(fpt(vBr_)4PbJqCi=Sa%S zrhY}+lC0KhiG6{0iN6#X8%ozFtK{G&QS%F^|4a=q+RBzSI!<}Kqt{nn+!d}DkN4DF zB=wW0L7~dFwLu9kV#r@LYjQ%R&nfev7y^l@!!lW5*7EJ6f&Z2sf^pdWl~lQj8q6RH9;)zsh{*y{2=# z))OL5fs{3Qr+a($o$a=`)PwC(OD23h{NK0J9uf_#wrBWCbYDyK6(hR!&CT#Vg+aE~ zosD+d{=gUh7Dqoh9(nwHV(qjy@(C{(x8Ba`toEOU4hhLm&8HQ~83PRZ&V-yMi2936 z&!daPGpQ&TX?nOeFx?(KdPeuDw%-dZ@B)FULqq@5-|DP>$u9WrA7|e!U2LahzFVjN58}e?Fc5a`BHYK!-nmgH_Q#Y98=YwqzL>60S%> zAjHL^MqXt9Qb`{n4;CQg<`T43bdj#-k#LElwiX#H8Q)B$rA$Jk*xu_1yP!-M%N5}J zq5I?QHqL|Bbgi9)vrR&R56ndk&Yk`21a;S%5XGG~HpRkHPjzgi694YPGR+dZp@(7w ziNCaV)t;t^&?$-|F!lA^vTdXs-rgbo$}u;mn$*;IYLlc8Rb!^(G8>+sTwZkB%NtjI zE7g&};x+;^@fS_9v`mlod}nG*c2D!I3?hp9+cTl2h1bP&_u#`+W`=(2M-Q0eh7I*0 zr?Tv(c*R5I*|<1NKNB$$B+4IS>D)r#;|l4@xy=Uks!zCj8hYa*@cFh;Rs4kooT!~} zAwd;)t2H*=`iP(w_QuB+LgAiHi{H$f=pRxrFV#hqY_ILnL`Z&!UJSq{y(tPhaFr(Z zpG9Ou#Y9R-A4;Uds>$IaUHC%W|D@*lP-VO|<{vt>=vbNt$y0mHd^SeoF#ZQs=|#=H zJ84bE4}T2IXI-z;smQ|D{t~OG@v_{)T5DDyRQ0mhu~7;x0#CpJY0;w8QAQV(!@(|7 zC?PJj<1ZRgBkrFsv3}{7MZ222^LIn^RJqFYzpLO z@sF*c)9y(gq*|{Oo;|T|vCn=+fVjsxlaVG;c9O3U(>zvoq$1q+T~K-CCeh}b)SsWr z{wVlw98ywd5w}Rf@0zy`ZAwXJhy_cg@J^bw2EP{NsAiKTBqr3TvNw%m<61Xi@MB5z zC%r{u<6zK#+Gw4@!ypPp@V@g0Jk(reJPb{CMPJKV`&IdI{NB>ZP>XmQalXBEk=)r$ z&qz*0q-9U?YDng%q?6_?9Gul8MXZ!>ZQqgU7`o29>{!2S{yhqfeAGO6bGf+eD_%@i z*meZpAV|Cs5D?Ad{;W84c-qPR_=<#^o63$Kvx{RQ?s376!80zQ+&&s8)rQp+-S{>LA@e;Mzn~>`T1(gq>ZCytng1je&zSf#kf-S~hP!XNm`;xL5HO z9UmV*BGssse<;Lb(D<(90==uSVoO1uvZv<5Yx7ha%y*JZCbt{332r@{N#$7cTD9Y= z{%_d{uoVrjt`S){V-qs(I$Vph9=(2vyX9;EAqJ1Ci0(I81O2b`U(qZlxqoj5pDV5JZ%E&CTg~Bji+6 zjJTq!HQW3*4-Phlixwe5-}oDqK;xf&p*qL(KixJl(rLX*ArD&&id1t3AcNB$oQL%8 zXnP){cH_kDKUXVb>hJ42J)8{$bOdr)Nc?<1eApDf2N6lPOS{1iWL5JvM-a;Yw^hyC zAa;B}Nl7CU7xVdZzUd&dSg^-IFa(b4!$nO+`d}C%DjMmSyN_PIk);08cGU5Q3i|$B zgL#nDaqJfq&?6-WN=%jEYj?NRo`iJVOIa%4M;h&#ti!W|I{?f7w^@!t>6UU_< z{a;DX*BjyXFlyJFZg`?99ZmmyUc!yw7Cp$^f?D(tA3lD>3KdW3H_5Z9;b*Y(+&6pu zLId;r4j>h5whQebv<_;yu7xUV@j9$V;Ld;~N5*dOsZ`?c2T8FL`GdGd#$^`4&sUX{ zn*E3s{gQLyV13AZY05bxBLkBEev^_SgARH6(DlFj4nsrJ-hR(<(%o@T)6v_<2Q|Rk zQB)6QZ;18VihL8d7b-E{NPu2AdiIn#{N(VkZ*b7Hlv^w>99%tcz3z<*`g&y~TZATzf2jQrZ}(;;G$@GGX>%$|&s!?W(Az@F_Bzx^Eefw2 z{1>%p8E_CuNJv294>{eM1*rGW4%Ww6cypBVv>?MQpJAp|=C#v=O0#G37X;i{{L^- z{NO*Lu=~TmX8m7(@P(?p{LC5=hSL)$8<$G_xei9@W2#$!?k4<`o8Z4)YV?1FsBa`% z^wTIY5a5ji@;=7*(DEXLH)8tV-SlK7D6+%Aau6fI z+!~(87~VE*#Y1xQ&qT*94jldl&CH=Y9 z6Vv<5m#p-w{`1)|+wb@sQjhcu|H6nP_+TC7fnwgm(tlHm93S`*%Lfd9RzO1abi;qt z;{TCV{9n`iYl#0dRCnfK=D<&>t$j6+3^-Lr5kXbFmY#{n>hH`!Ln(1FXM^W)xb;fA z{n1@gQeu(27O5RYoR}1?Jp__bC7I2$XIg$qid|A_pOqFuUH>&M_w%Ke&W~J-F|`9B zkIW~C-A_A?jI#utoD-e5X0EwVAaR}JnU3nabvub!_6JwIX(dix*$1G$Ds5frED0h{hseJ1Xx-W66 zo97lkShBuk;Z%gPq)EE3j~74wf_pVw(&cb|jdMSK!gY80@=|TCT}^TIYSs~5kMm4c~AF*@{zIFopa{V@5$z_r9YlmO0CM9aJMjlf_}1Uo+Xc( z`|xkc=f)BXLhN+&ItSONDUfTwPr8y%3+IM6`%go}EZ1`hl8hR%LoOTlcksC%PKRXd zOiD3qpC*}NyiuMb+jO`bZtO*Mp){WBFrlO_7z5*bPM58fJ2?$;Ps+OaKO<~g7 zW?u31$JG^n>@%b@%dz2z0%C}b+boA{zcnWLZVB#jWbbXZ$x)9!E~k7YCIu;FuF77^ zVr&Y93Y}g9>YtbtyBR;YQFc~C?Mv6>5FfViu9!PukC)g* zHzI2n6X-HjSJT(o-G9B-4AEjh-hJ$0r=T|S%Z61YPbz{pr)#yJyvj5Gclx<+N=q!u z`P-1t$g{7vuROO`^6RwKlHJJII%vir`s-kf=;Hja`_+Eky}_khs!c$lB`tK^GQG6X z=}B$aoq(MFDvBd>QvqA2nSpIE#@)SVKQ(T(pr4DvVoKA;WJ}_*2^`cnuAk2PsbPM> zO*#%q9E&NM%Lt*8DqWU)N%^d(?2)YDXthmU^i`SAXmiBST4KSv@4L5RwyEavXLT}( zf#*5aqT$-ccyW>wrvvI;kGW}eoDlW-(=Dt_+i@ZoZ^D@bom{r+ZfE_7yiK|tD94Jb z&ijoz*RXLjsUJD*5klE5cYkq=>V?=IpkFroC8VhUIvSgzF@8nCov6Vy{9Gn)>% z!_L3{0?k32B=@X3&f>EsKHqV}LaB@H>ho!B`(s$j3OMnlyI+6-@_1Y%Q-slH!G%q+ zYA`yF?O$?c$gFu3Nci$3Yln*^@~nMjNuNnUu=`ttBz2@@SY)Jg!&j)5L#bcG#AJEi zNAPs>2b3TcFmI8QqdNIFG(75oY}lFcV{!SWHbrgD?>_AuixQC5EV;!H}fEmD$g`O$lGT5#D`q(i+N}$@)we5$*PQ81{kq|LorxV|Y5>Y`BK}19#Kb2M z^z`>>OiM|E(izETzpUuuv+aq(Q z$x&R6Y!UdGQH;;9?Ze{2UtY9%pzK%mRn+fz*LQiWYGq++9Z*kn76XcV4_> zqqhC*!&i=h%3;!h$9u@`ZrPpv8^mm9x{B}D;>~&V4>m0E5DICY`Ns;YF@!I#;@>l< z-K$d;^NY@Vp%l5po!3c-zGmP1cCY#6PQGz_f-7T6s}5*%6{yx(9HG-k3=Av5UmF@4 zVD;h%9!@E`yz;;uxnC|xjrUr4IUzt$JY2#^5sTtk==0l%p&03+IkV3soX=Ds+bf>!7oonOKbPGuSODXXq~FB4L&eACeHhGL!e@29`ZEL z&-WtuSU8twW@T!4(rTT5RI*f6=@mJvEdM4u=PK$bJ~`_b-II|dC!;O)N|Rc#JX}31 zUTE{@r>Q+`RYAp1#tZ2-+HRy|HPt-Y=n;s1J30(}%+>36r{J-y-!I|-@`Fk7&&7+A z9w%#n&}eL(f`)`pJ9SAS|yp0=W+Aq%!8TP5A^Axtp6`fbygB z<2|W>OP_l^h`7{JQOUha6?RM8pq+^YClip|BwVz-{4R8zK?{M=@Qq!S$JLQ*RweyZu}{*SMQ$|M4=`Rz_JPEA>3S^AB3FD(v- zC(EpLjf~POSwUE){u2AxSsruR4g|^ZL2XYaBrPLj6_mkC)Yc$x+-e}yE|al1?zoQx zxr`hTU=AWFAo4*Bqmhc5vj~4^3_=^ITE9n1R)A8GIZ%{&y!8|2pvN}MJ?Tb?JUrd$ z0-fsg3{8H5&X^WB(_CXz&psHlJ&yRC# z>)W|+8)!L5Eg~Z=wI{M=JZ5JZ#*>^+kPXB%W!T~_uDbHQbaK7n+?6X+&MQ$hmv6?M zFHbEg(~oJoD{T8ocvdf6I*1O}Ix9AQ_EyX?+q9~yJ=OJ8I_?l&j&3N2#)lYXA#S~Ayc(S1Yc1X?|SGH>nZSQ{M3t z*%=HFjoE@wi0&J~f&0}&>d0ZQsIDFjI%6|sV76xEK2W5Pw%-Hf`J@~CffZ!fX#RC= z1Pw5f33;4CIYJP1x+8S9Bm-^&$`(KXLQO-p#H1gj=(AeD+HnMSKi9nitet7!ww@^v z9iWXh>KPObk8)xd^|rRQdYGzp<3~!SLe5)U%yEEKcNv?21gLg^)0U9oS}t_JPCB=} zy?7P&fP#w3?{gwJHoJv!s<#@65g%Z-o12>;(l5WgN)`bqxmKPwNOWRiz8waB0RaRE zkk`>5jG|)dG8a7Q?C1JvjE69t6iXVjkN(ctc+P#P7S077=ZyCFMw0h~unOpGrUT4mOOJA}&)gFT=x zM=3{bFTD1&;mqbJo;gf3|de1Nsw85JeK!Xbk znx61#cwKOKbOhBdepXg%IAj3#$jQ#WgK&e^Av!ub&;Vhu9MK)1qY6Rchve>IWo1cX z?DQ~H4%WPU3<>{v6TKcDZVxq^Xvx|l%oQ|F@z~jBhd_I>F#`|q2%w#wKPNP&?t?l4 z;QRp79RquK(?=Lnr$o&g7r=j~<%~4Y3AruC%3#GJ@@n4?QebSRM?3F-2@ZBx8{yP% z4FL?49E=C_m_d;tg& ze09@SWg58)Ou)sQH3^+>fm*CpW;NYJ?rCNMTtC>x89WJEszw9ZBgGk)S43P>%R9@3 z26YE8s!b<;uf%Ef1yhxLZi_!-)ook_p%;XE0LWoM2SVqAj7%4}U2&6`9(!mVFhD)v z<_55G(tIKjkytkUNd~0xp@LaxHGo#CNWfYFb%4Y22F{IArZ{)d3k9Lxwpj>XgPD!3 z5Q;w?#Kc5Ild~Sv_1-|)9#qUykCs}-10fjeI6)WQ7Cn*vszeQbnFZC?Up!DgW2rs0 zf#xOXH8G8)NElz@|2c}Uk$NL+*Ol3Gf1`XcZe(Pn_H4~^-9Na=pZMUQ7Bz0f0q}qV zCKU1}p+zfKvr+^!dr?u)kD)uF+}Q_8V0>;G?4*|tJQM|*EyR+ojZc~3NdOgfMP&sa0`uO zDd#~boSK$qIb5g@=sE&3tABZRwg`wR)M*zAFwl<9&U3g)=!PtpA5SXTtOGvC+*XS% zUBLzYqu=-NA**#j=}MVJxX_77i3<=m&^==G5gR5W`1&X2LE%ix7n&@EOc=((+}tUk zUBF6==j#aXw2`~6vS#(8sVevu=-ej2PAIb^0LlTSQ{X1yt5O!)1S^22foYHTRut(K z0SQVI{iIHg&Jc{qog67JEZKeA?eXF z`h0A}V`Z2e&_bBW_{*CR8{po$^{e={YF59+c*P#vQ@_AKoa@(PU^!9-KJGak0T&2X z?Tq3yg(-n6CxSQk4^mG;brb#-1&arK1%MS3a3fL2c_=`F-CzTs1Fh@hh-`G3vFt)S^QBFGsGY(;FrJ*$bv9#+Bmp9tpPx@$_6U$x7M8Br zJq#mK?$cFQS;&f@*P>b-UE7*^4zm5@jjw@%eAYA1Bz85Fl=i`wrofI0e#mLb9VrRC zG?YKLCFtqt!5RhyDr!k&7#$@UhA8?PfULpaY7SB{aihzk2{~IsmDp0$y$6$?+A1ne zez_L=|5K*V0~AXW+p6sZO5A|z!wp>{pC9Qf^2GP`^#xd`I#)u?$Ot`E;<)?ga~1Ew_EBP7A;+9VLLI2bSj{ zatOK#0G)wy-TXXQL-xs&ClKj9efk#nHIPR;3mvxcC?V925CsJV)X!rDTpdhIGAmhW zXbhoe3XUi@2sv2DMUh;E~JEOnP8Dz7{g!@Jc08Z)`5Mw26P4kG(A?R?}z&u?ht^21PIA!t{iX;z~qAx z0VI#z4jCGw0=O5Nh{5r%L*^v41}$Hw(4xD)F^NK*Pont2tUHN-@Dpk|>A)ue&I?=; zxX=B~F0?f^xdI!!OPgHl&wMkOQH@TjjtGzE~I zGE3v&-~q@;&{Yc}(FB}Ei|=n1o93LZLPW>;@ZlURM_9V(Pk?zLaiNPB=AE0K4o$t< zWmbuSfe^$jdK&2K_YVwk?d3b~EC5^tJ&TXvxn;n?Nv*+Kv$gUzfLxQ5Y^UN(?>9;7 zjf{?l#?#Mnapyz2f#Y*sX#B;<%d`bB4^;|zM~f~bGQVioSa3bVt!0Rrvk)la8e*Xz+Olt^Tcb@>s(^`rucHA@ zzV6*+@suX5i#K4L#kas2sdYn_&1ow`9`sP10~pRywfU}+72Wn4uA>^Xd_=MeVK z`&_>wY-IGi-;Wns#^5k8e{>hxZ~NiIJ#{%yzHIe82ek@e_%XDH@z~5Y11U=Gwpo`V z5e@(<6)i0q`Gp7?mKU^z!|TS48|#Eb6INod#syq$8D9j z5FJwnZ3yjpY%oY==t>1EnVHnSW`&SnwaTfWZ5|5;r*zm%I&G)X=SDHwO`yFR+;t@K zQl#WQxXB!?YCbwTG0a;;L@tmUR72k}S2S$k4XD$YpGU~V@K}Qi2k8cE(4)C8GP(Lj zu=wFR9Cxo@xNrgdM6p^9x9nq>N_SYnyDM)NJ0J{%JE)YkC*yhC3mtuMcn?CC+uYn7 z9*s06g_he^1GM6Syf^N=7z++?T>_nAp@S4s@Bn)g=#vwsXfgp%Y!48WDe^}xH1U@% zUshC7su%@$>%7fR6?)i#7@mYr0B9Bx9ipL4M~1?hjh5 z%!-EB0T(+MXn`4iUwdZ26)n*73mmdQPvGD<*ToBuQ?j$O!9HZG-Ai^{XZB~{MWW%5 zNEZ8Ik(~uWb}b}15VqwkW?t-;A6Y2cu;RIc1x_7UOcn+P^%}RMjsrBjHrAu^>3Tv5 zIuV6UTJ&QYotxi;qXS~u2`gpCaeG2l3acz5udZ`wyoWUR7tp7$Pq}ln(ZJgXFP!H_ z=6KYS5irYFuU@IB3_*%BlB3RTseCDcv$)Q%7EWN;nlLy8`cTvsIVvbBYI*G=ycuId zmvuYEBKj>x+{{)(Gx|Fi2_4(#s|YED*Wxy><2UbX>oBsk4u3mZ(Y=7v6>sm@O|{55 zce>&^K%d5REF8a(v*>VY-@?^TxwFU>YiXHI&kKi4qt{}}z`gG&$uXRk7PMBLN+!j1 zLDpik!AT{ER@H-r_f-pq@2NVX@xx1P#2nhQRekG}f47E|n6xOdI-m(IT+T>_a5GK~ z7xCSn;i*V{Ms?LF9#3(Nd2R`%Q0n&BpoEk8d@9TFV5!$*RbnGKe#-jMQJU!z5v-Qk z6YRn@1`U&pT88(yg*Ziq56z{PJ~*?_lprwWq9%B-{BaQ~HUmtEzC=KcRxM8aqy&*6BPyi${(E)~(ROS8CtG zP^#`Kw}T&EOC(`jZOrbsv}81?8XG5~K%VU`G>{t*dE-WHzMUnPi41$KQFV?C|3^5n zuR$iBUpg-&nMinmn|Q#yyCaq?@2sSHI3sm(_1TBk)ok%tX+6WipES!zW}U|ROB6_j z$O0SZr&avIcVGg?ZI}N-JylUi?QMj_Q-=aJK28zJ>jG{-+43s=T0lTwnGDCg{=MS5)>TH~*I9;9f9^QOT10p$pWKz_NQO2X zLz1jVVqwd{Uc|j@3<^&{69hKl+DWoOd~}J%82z=%$`%D1tI>jwZ#LuJ=5T#vkjJ73 z5!cjnb4!MYAvnvOl+bmw5?E*8=`szgrgTK*4LnuR(6BO=){>f4Mh};oTCis{(F!j` zkgr|AB>fiRLu<@yIzH-bjy#pyz#`7b=Liz_mHa&fOYfP&? zdZ!;$VkuxmNyS6=cYpZ{rS@bq(s2p0@i0@A@nn6U84v!;l#Jk6(um3b8f*)XC1$6d z-g2g+*OilQ3^0#;uB@Jrh4mr6fVO7KkJWS@9)pKTfy`VyM0q$?;nrJNK8kCTIT>u! z)rtwF?$^uFh@e8^A~O8$@$0{iQNmnGa-gu=Vqfq->G1v*_AYxS|FD3M;+Y0wb36MV zy6vceTc)}*l)db>^muq)QqH_rG{=TglY4@T);nSvyR_6JNJvOC+L>uXA296D?`;L? z=v*;3(8{=D_9&vB$V;l_GyHz8_}kRT4`LrAyV}_sej0nGssCLMPRP<(2!Q8L>+*zn z4@=~Ue_0Mi%o!TkA33#zKd+748%DE`HHSG!AP}pqc1Fn!G?)y{q{KB(cb#W<>qDvy zhNY#fA{K)8Ck(S@t3dXFS$5X`3#6Zz7(S@m4H0vGXuAcHN9 zdVR3t-(h#Zt0E!0eADeARi@EwxKEb19zAc&e!1n(cegH5hCHv0C7cj1E})!YQ%W)P zkFbulR+f@h(r-^rjrB{J$zdq6F?l_kmqI9I=hn`>G<=yzI73jdq@%&CwfXl{)M|T7 zRDOgn5ydkV$>d^A_NJs?l?PA0&FSN>GZ(8>{%SB_H7_VaZhObh%KjkV3_&%(?xuZMrN zsquGIBN-{Nc}ol17#9j2q=|kR^;SKul%+p9p6zd(V-4(*y19*iqw!;XyQh1sbBUDr zABarxB#h;}f=N_t{n~r}B9UiJ+S}_03cG7X3shJXp{XqD5$6)RRx{bYVKGt7$qW1; zw3ND(raeX1eRcEnsfy&u$+KEoZxR#Q{#u@209Xux$336o`-_{kkeGlVXHbt;H;I@= zke*U^ul2D+vKAdOy}gs|Qd1h24DVh+P|y4JnSzW~2_M`q$IB1bLWy*%t7ra7rBg~& z?9m-$OGZ$NAzNx%`EydqLq!85QcLB(8&J(F{C}7-BmV!Li!mCE8MtB@j*+lds2Iav zY2dqb*d-m^KUxCTN(eY?f55m9p!P`kgDWFwEup14risYsOKt^WQ7Kgpk@HF#QtK=YDVpc8UuY znG}>}L4N&2VoOCM{g=_>{!qek;N0Gr8s_lO^eeieKOvx`UADql@m2lrA_f9p6BeZ4 zXCk)aTk*`d5SXIX8FsTmD}FZBnw01vBb#RG(PUgE14{*(j2rA!auH!;uf+YHlDTiT zckKqZ0g4uxE@Yj2+UCi1hpe|@9FJ?7t~So`MG2t^&ia;vKYWbQEiylBXxsR!n`~s< zRF8y~Cg>i4XFpU!^`JQOIQUEFdl$G(3FZZ%@k}QRFHu@&?d3w;oNCv-3BR3HI!tm2 zxd$=_c4QxVcqzEn`8|g+R)V*aSr%T764bJ1GA1xEa=z>2e#vBck*a1%_)mg)dHxQu zlnQdITZ;lIqrz>v6qzc#pSc}Xt$y};UBSnWHI^rnn62IvBxQ^QeRO+3vG&cEv0v-- zvKaRqC@@BFSB`q;jpL8(19OF}A8y8-c&(EX3mzRM=-?bVY%uBhd)j4p@h7i2i#m_+ zP1`t^s=;KPVw%vLm<4Zu%a^YcW=ulpUWQ@{LI?M(Yk}GP#e-=Qj-lUl8&W-6L&T*@ zjZo<|PY(q2SnqJh@D9tm)yH@aE!jn22Ofzwxm2PlB&XX&*cTGcY*dy>_o!EnZlmXx zlOMy7d$>Ls#eD0AchkFd_XIB6O5A!Xu^VaICJvtJ`+2qMSb<0Hmw%9zBQc=k+RA$#I!c`yXW4PI6f``hdn&Ni@ISB3bliz^uMN7xpHl6twlLooDmW2N?`)AgLiec9%NEY}E zt6N|fZx+4}ez#mbnZbVr%SSj@7AssLQEBwLH|hIN3d`@jal>9Za|*esme|LBHlt{f z8EWmkv`ebjDzpkupv4Y!u5?3N;QAj63~U!6Fnxx97ptPlO_r>Y>IUw@aMqKi@2=~4 z9g;Xi=dZ>ma_VN&uK8$&^Kdzh-9ZgGd4KaNs?+d1ebtfMQQ=DdXi>inx#_p8UhNoN z+6J}$H%)(ijOnVr;W+tN@AV7C>YXmShX^gP&zUy53t0=?N!=g2CyWMB~LSR_G@RQ+amw*(JWL}On--{5tP((HuTEFc_IZikSai&fJx3}V zFg9~XvSaJ#Hv^Z|PhPro=U-jfx42Pd8DrfQCXJUWVTgU^vU@F(LzMeT-8<(cl4W;3OX%zB#aXi19N{2kNj&;QNBF9>E~(-$2*gJ|QOK_r|NhQ$>qg;ba+ zCGKx6^^Chcmf*i%=l1X=v7?OpzSd}l#!7|;L|77z_Jd^S7uOy7h*x$3oAt2NjS~X} z)l%i7rRN#`u}l5JI}*WT8Hhflx&4N{F}kIBbM~aH6BU#x!JPtNI`NwM@j4#K!f3hr z4_`{b*h;We$OS%SFR`UO`vIjZsA<(T&98m8s*|x(7P4bGqfx_FMuDF37jC}HsgOu+ zU&>bwqjx`ZqjXU+bX#0YgbRJYh+e9(HY!9hVaJVK3w#;5jg@;Cn>W*n*1NURN+#6Q zdaQr{t{|`N9Y3(rYzzzdLB>Wt)~|E;!+z3ZIg@_S&hIACLCE^srn4(!On1b=c5W4W zvd726*S$Yt#CqnP@6iNug~SwZJy3u}rcVCHdWC};M#rm<`o;D{SO_kDS6}wq@gAps zdEBaKB4Hi(4J8X(_=2!VRFQ+ji@PstH(U+9hF$2DV_NYMwEqYz=!_Si`OyC(oRgA9 zm8FP3AjOO~z~(ydfRk;Ba1SamX_p$3*HQq~d8+s$J`3`ZU0yxw(QxFr+g^!Qpz(1Ur{i zeeVd8RZz~6hdG4qv=~u1%FvaCsKiS9K{rg&2$7!qFKbI2baw@}Hzdd1(yDD;oTygf zrZ*1F8X;MIhji}l-Vc2{$3NXOzHku0X=s66w)pngebVtRSFXnf0mX1iQCf0QS~jBD z%`G(1MbW5iHbGEGBrewS&?4o=s# z3?yk}6#w)%FK!!oC1l&sIdrh&H;^TK&b;a^MjRF~?vV7J4SiZq{!RgNreTLIF zaW?m-V2)N^AyAT@$i499>a%ChoGLGki9gi!|2olE)!-6`BEeYmtFGvI&6BdbWz9>1 zpw2eLvh&4Jved%o*~i(796ddu;m|WY#F1iiuT&c@udHxosZIQv(#qz#WW40&tKd8# znwuXQRt8{ip3=Rs6lT-3$=Dxr^d<76iRTJhPgZd)0|6tz;C>xXQ)Xa)L363y){q?9*gn&{)6kD`#@t`qfx%Ou9~LI&t9cGd=rh(qqJWOkw9> zMZ+Pw`r)KjFbh%^iDcjxJsh*G#oc~CtJ-SuYdauzj>}ZoI=Aqpp|V$tGI_tOOX~h2 z^z@;(4Iz5l2-t8$utaM9el#f7AG+LaWf^)YA+%qA1CPtE?X?5X!|)&UUs~`-FksPA z^L?9M9QW9wB)+*O_ugTTbU%Mq^6lEGv8b`{*O?gj)7+x4UnWdSMz?`$lvh2KuZmAk zKDl0P>TxPK6?YZcrk_V&8Wy*84LBhR2wJ8@^4ZxiibbU*&Zw*t!3q&8Rf;%o&}~(` z#FAJgfrvFqWqRc2VTSAHtX?tw^ETxgx%;<*rKpGd1lH5FQH~j#>6yX_llj)?2dJmL zLQikqW{%~{#l-qXjOE>%(Rbz0ZlnLRrwabvne43n8ykt;hmn5URRYhz_+$6M3Pyn> zVJ>O?50JR@p`TljxHpUBDtMk;vLa@WZ&f}vrFxm8krB+R*%}ZF5eVxVs?M{ScBSB= zVD(isoQ=K8gQqKj>UUx*d6`2-0#-}R)D0(VrG~Pj_-8JXd5kDIY2tq)iq+nCbJ&m2O(+n*T z#;?G2Eq_YSOLwqU63b8Zl3uY+lO*g)R^oT6VRzRt9rin!6?Wm0C6H@?kVlJ7BV6^e zXz>w;Nc*DDI4-iYjM>-&ObWC4#cEB$Hv_WTZuR=5yvrsrVOP?IX>IxLklKmobgH^O zr4FxT+{=`XjqTUZ?9T1+70%>e4|ZIh$)^7zeQGv(A|*URlgoaz9Kj#kXyWa8RdJAP zpUvxs0*&o8R0RLp{FSuvg+f)BlY5;tjcpj50TxeTRNZ95tzB3Y9GVZZQ?B#|wcot> zzI6_(4dUCu%Lxs<%l=_AkAXo`Zq{Q?Yg@7t=h88^(%{Kk@(kZwj6L_AWfZd5k{~fW z*;21Pi4SR+U#<~8rEc#V+xeX77^5-RBB%Cx^u%9nQh0iWC6*}|J6xW43h!4N1dYZS z2iI%MiE7S>k=SHGa1$^9^o6VUf{5{fbUPes5g1 z$n2Md&Q@iGsPQCWn#H5nqAgbF5*{{3(BlKOP z^b*wq1~4`aa zF3ow&n9~o6pWt@<#G!wS_*V$ZfS1cP}p5Oz+Cy>J;YJzv)9?QA>S97e9>` z;y6Zyv$WhSkZ^r%A(Ll?W1zWTa5-0C&ZfLqVTEHbgU_43gjD0kp1f3wMq=UJk5vL_ zf%e~l#oRD}I6qB-TRk!{qu_i_&fe!qM%^lUV8u}v`9n$n$(g@WE>365r;ngLyEs>K zjwn2Sc2K%Ly6kvx@teCk=MU-^uC;PF#2Z5={#g@q^p9tTP}L#(zB?+Z8ig4yyL5pn4f%0zY=IiCX|4=l6v>^@675cuuVG8O6KFm%#1tUqWW)RLs`9GFS?f3 z8Hej(rSLr*GcI|tXW;7WTVLx%pX~R;q@-Wr2)w)1akAxs)&!XVdU*I~cf}tJXC_vG zVjLc`;m-d1m(JiY`Ny2|O0h0>CqYgxm(CdwB`)RUST0F>2W#4nR?ntfoXQLd9HlYdmn9&{T^-iu+Pz>n@2otp+H}-w6W%N5A+m5g zbIK}hoH~>^Mw~ocPuU(T^vJ~g{R35>Cx+F-I++D`KmIAxSWrNR9C{mQF5waAFmP&k z{H}LIeT$(pJA=cX!*4nyXZMrU5NWEKVfC+JS(g5O%q&HohiT?$yhBLXexu4tS*&^HM&OP_nAB%U+R1u;|mo;5C z;v(bRe;M5~F3!V*r@KD6t3!ZIOfy8-^s|nJr%8{uU_;8y^0bn7u!YxpUCP4d_2i`{ zu5TB=y=}hlh4J>%?HB(rQ5rw&_+&6?=;AUtnX`P*)QlJixNCf=&xcodWexa^+|sNR z=hRHHPE1T4E}((c>k(hyUj3}asw{yMWt)+tkzTtwoWsY#Da2~KV*3j zDB9OEW|pTX;datK6zfkpE?-zD?6W@@wY2bQ-F6D13%P|ss05EQ(j6W4`vWTpQjkF zDC|DhyWf~elo%f}zhz%QH=MSZN#oMSX->^kn zy(pBVO*h6BcVcBZ5r#I-$RZ@^IMd{}%lw{VTpj_>-akT7A4)j8eQFZ9Mc(uRr}1j? zs8E;4i7eyoKyA8={H80a6fzoQVsggm3O7OUr1`+|%>OaNT&y04K+lGHnfwi98As2* z=Ee+XZEz*bO#BFp+sH^H0~e}=eEin(P|e~${JPg*j=fDSse0qOpKfR+N!ApVbPrmO zM?J)>=L|I$qOTM{%H||p3269uIZmn&K^D+q{$-IZ0b^0#Ij6TmS=F9~e>19hU#sST zs?dybi3b_r<+Pz#wPo8gDWd4#t}e!fB2z8AdA2sjmhzELOm6rEIf(>TsGQ=WNZjbR z*ogbc=5ySB4VQ6A-E`3#q2S8wEBJ+RFFl1nWmTr(%g@YX)B}FX{$qp+7bQF0x3JyL zFFzT@5!?>U!FV6%I#Va)xxGj3dBRY8GUZi!5^5fe%?dF$d5&8B9U%of`Q2!!M+x?*ps1-Oo?mw54g{=NkMU@L-qa72u{8VEbS3++Oi5LFb>;q4L*2TY%&48C z@86AUb#L$yyNw?01(|N!j0Z|h#4HQzhmDh{Gn8A|x5bQ&YPL(?&Cco^D@~ zdAU5uR!U#Bxahrns8Hv_cg@zQ?0qH?@shW$yv!4<5L^S{ur=RXJORl|NPd@&Pi`l2 zVvbFYwU;{6DwY4-MO>srxl&{h-ddNyk$CdHtz+k<5f(M^jPetibE7j!jFN*qrv?5s z0cw+ut*sctg^o5uC3sTX2zQ@Ai-ZWt<)^qv93*?w-L=}GA`@qOqmEbwdzq?ZKR0b3 z!}Z{mVXmjRsdsf-yege3+CDm)M1O1&m8fUrt)ud__HV}e^neD*?$gQ^gdf+{yt6Pf zMZ6o(rMt*twMdwM;%sE`$*xvAZS<-(=3Ym0+J~f|Z8gJWoK!K6aFX1TrmL&Xk2qrG z#pF54t>ainWbPg?RqL;Yqn0hS33qIXDHz0RclzUZosfycEXUA{ys8cyk#bXDa7@}3`vGcf~zXld&U%|}mf;SiQ z!$OiO=PxrjDH9%2+lQwNmTlii38oQG4_~x7kR;xwA|tnVjaBHAmXv*x7fsc|7NoE0 z_%ul&_=E0S+fTc#-0oQX=U&;9^#`?*N|XL!u|`IXpN^(TS}_l9?m(T5geia!jCKuim5NPxD1#jttA9b9L27%RC+?{^aN2Xn? zmlbTK91s4TETMxr8)bBk(0N;$4f~>&qP*TlQq%d;)q_&9iCWJCKh)vwOZM9R1JsUm zkR}{QIv=gt3TJ|BZyqn?Z8BAIo~;u{y7OZUwWKR?vkwlpV(~8DwynB6{gvL`uJ(%r zUSsU*0G=>YgQ8!97EMh?s`zhLe>UldUk#8-e@1n$h;XNRw0Xw(AyE{fSTP(i+^0a9?ekoCbOPH z>dEC-S{9V`iQ_^Zq>diWXUA6way(z{*aBQQ7MIA+!Yv_(_Fs&;cK45GWbTV8*6#3d ze>7k-p?a-k*H>zm!`VZ4bW|Io$y1H;-|j-`)YSI!TQ1pgs=#8Wbe@vp$A3BIfuPeH zj=IZLA+yZM?nHmHk(L4j)6mKcajo>Dyr6Ka$&|{oV2YOJ6js-3Y7s&-ZFy(Ib{&nKx{(TM~&jNf~wevdnxLO)0{ zwFH`#SKqcc!_4eZsI_x_OXmMP$z_%8@aub*Lm!!B63*wphEZ5avUx-k&;yP!BEjRj zf9P6K0aRj_i@}r(MO~FKaHG@RG#i1GwEoEGGoXvkf)yNFFgDx^{eG9-6X`nkNs}c+ zSGGFyux}tbK=pes(Glv^7qPn!2)wsX^!t5uh7Fl>lj6^!OfC3f?#8d?z)oIyvD}%p z(1ehb^hS?4V$;U=7IO!GQY>`{ltqornYB7Kc}Df+FUCC1Y?C$vpB1F z^_+gDk5TcHhDGNXKQ*Uo`v`TQE4YUO60Fxiz%i!K_1srx|1SE_^h_n0NL3!pI4(AD za;_%#M^+tLZs1!psu_CZ7+zt@Fv8E3{Q4Bb^Xl3|B#&+UU*N!debH+xWH+)@F&Y$g z=~AcwHi8Utdc0%tuSifvNec-Q!Lj$46Nbc*a@Ek|o8vG0IeJV1+N5l&@Ce zm@E1eqy4|_=ruEheA#L&8nxAhS39ZyXTN9t05ZQryC5=no(6zE7ho+vv^2mzjuI>rGkNGNF~9 zFmgLenpaQs_0*E&DU!eGnxIqyQSF3PGoMl;hoRQZDLJ zdi~nP*SY%NXNXmBhXyuxur0}x;1DLN3Tu@8jtcfD#pmcSKO zzX-M*xfz4&MvEWB@A~NE`t_V~uDnr_>eP}PCVudVLCA(5734c@q2`~%FdW4z=BJ6T z>Qd3>l`eLFtZWxWNLh=)cF#9MNsCs6+q^XCAkB!0#NWv3lY*yCV5NEtWa19hB}f_x zz!*MNWjOL+pPX^*Y}0r2JUJ-MmUkX?QK@}9fAzy%LH;KY0zPR{GtFJMmH$}+T^3x# zkdk)nKl9s7d{1-2J7Ddk1gH19M%flwgphB3pPjr)%h6?9f&HzfmXd8V(rm-5-f;Za z42TB;v2zbzjt70NQ;OC+xn9a934`?N&bXzYsnzGL%ogK~a^w%4aSGy+OC_*EIKm*3 z`1(G<^V&eC1J60-2+`~ddu}@zDd2b`|EXG(CSV96R3Kilun6N?qjy(wl6_1Q-oX^J z@6!enx>nG)8r@J#hLArJ&U}}Sdy5V>0{na09;#iZZ1DN|-8*+GtLj8YO*0V(6Sy7M zsf@<6(EmPeFkz@>6chZPZvaaisq4~T_x3li-}JHB;~wjzJ5jtYNkMz*UO`5t1&aes zs(PL7@QhGrqbTJ6!==5Oi#P~LqW3k%AomxDk0HAw{LbEepJ^MP_y+pIv*iV%cD=xvZ-e*VKQt8G zSrPx|@b!ns1#~vFD0Cm<2cpF$-=8ScB$tjA&%I8K`>A3TuO7-4gdZW)oMsRhNou#z zHO3TE;)A|%zMt)DpN<{Ry8%m$#fjMYe&4M#j6lnMX5b{(^;)B^Px%b(W-8(4rTfwO zSAfEnrq%eD;caJFMSAs*bG6i3`N2xOY8vD+anwW}*wIKv_zpHOvXFt^7$%(%8+V1q zungv8>;bj<5@EF`C|Uf$-%H^^$m!AIMaPgGjr$JM+sntNlttD}=q(1IfN5Z+%|hh<$73V*di~xG+B9PISqv zyGuo(V6)O~6~s3eA~rG4mb%XgL3!A+yvWol*vCy@j1{GgLrxS$o-4*5Isyy){7m8K zrq?ufQrl;CzmHOFXv83&&KawuN&jvb+B@St4CEX!Ev^;OQhw57Y4&rEK;3!#{JF3S~Qitmq-};~sXF5!(GRiRT zz%}oJHe!pE)oozJpmAb(z3$SU_C^UmJCT7m+|b4I(0h*Za_rudPC%y|RQ%YKG6Oyv zCtdyd5E#rySJddhoQy5NPx1U>)e>sHrIvzVo}_}PfJa#;)*dVikOV)@v^<(2P#X;s**ijt&aAaVtp z zVkW-)B=unu(n*?l`BKh3%G9`35XCSCb)O?Vkr-VIv?2s^bzvPk3#%#i65fav+M%>J z7KDhBM$imF(mVh=#M`NTBI6I_V|2VwA2WcS|DXg~h6cO2@T78CurTuPLHvpfJK9RH zQHXMoiDx_>_vjlb%Ci8@Iq2rqOd&_j%4~d}Tii{rt~h?jp-9Puvrqz7(5X2N8)4JxV`l)$fBWY`;34g|+snQwE#5z3mSP zzwYjk0(XZij1#CiKH*4UrF#^g+YYnjT}n!n&t59Iuu*eYZq9A%Oc18}Ah#=$js-uQ z&v73KF6|ChJ=E@wJ6`v~P@@fZ|b1q5y+enadc==FApZ6Nv{3 z+0C5tguj&!kM1!DJflqOM2;6NqFJ-owd5keF{~kumYbSI9RG;+AQor(CF;VEZ-0e; zFWr(i&mM~`ccdJ&(i#f zRt!e^Aledjh=@yr5kX(Asy^==ll4yd)Q^&LjyUePq>6Ry*)+TxEAMTK9$i zH7>Pl(aTI0_Jo=t>>J1EM$@?M51qXocQf!t)DpL|4O`gKovCNJRb4@x8q?bz(h}u! zLwVcA$j7oyfw?+FvAmQdLLcIQOzsi`KW>jn-01Cd25M~WULsnKa9ydg(s*6{9K1v^ z>^h0xL=ggc94@$~AHv)XFTcBx{HYU|y5h%K4-Ep>p4A@+$0%WY7k`eT0EPDPM)E;u zK9fDg1Ysu4Z!PL*HsVL+Yi?Y!+kwB#w*GQ#p3K2B%hHS|FZN8HoW`urA?asgPwA@2R2_->F49aM- zrjmCL;b$qU`g9B|Bph8=gpQ|YS4+7h#wSSs_)&*#OdxkgV-aVPdb6mdpe%fUYc3OF zg}kWJtPUAbvA2UZ#}yJyp-n8nU+yE-N^h`pd=&8Egp{w3m~hDnPMy9@ ziQ#m1)jOzKKDK9%j6bCBpLhff?;3~OYB{w4iCbMQ%aJB(?nd57?Y+f5V>4&=5>s z;Y0A_HOZ|BVR=5DUG)$$HFY7`i&W+6*a-BQN|t5zVlzMe@VorgwmMN%H+Yw9`zY%& zc%Ad1BFc0Q@5HRXLwkP1R-Z((bxmDZCzsv5&Hg_bAZ^IMVF>;#HVZ)Jud2WQDA!Zf zi~ReTGa%kP;qOD9+L)BzW#^wV6F@-q%zNsdzdeyNCwMVTS?74nZYu0UhAeu9JAhk_y7-$b!!^$t=YGW51{9=O(1cp}~RO$hDAElxbd=1-+l;dzi9(*;!Z#a42#m$)ajO9&$bS z0Dvou+6I>p8Ba3kmPhwRqqVvZpR|DkfyKHH_fyz&UTbjzqDfIM`cU$1t-UkG=s)olNb0JB0dKCU;I3GKMC$zSDcDp5lt0%QQZH5$9G6O+8wgS9J zI1{E!C>6R)Xpu+)S$2*Ce=C~DRf)qy^{rmlhGQ?=#4l+a6N7&Ou3*C4|6Sk)ChL*o ztYyjHJlZ`K4le5#!mlA*>;?0mV7UeTy|tZMwaE8(oSAaJ^%ISnNBk;RcXeu6CLyh( z?A0(`jEUB3dXrNsCs<+RDFrIsPc)59@fK(G7u!Y!|Lp~6KFF|n`M~qo=J@>_TJIh> z%0`d zRnJdVpvcc(u&va6x=tPPJBzn|D4O1NoY-mGMBkO=*I?pn5N6PsIDFw#qIech!-I8C ziu|rJCi|OhGYZQIBB($_LurhhJ#=rL_Y%cyOqvGL%%}*-x;gKdEfD)6HITiCM!_%r z15+nv5Hb8huOr(`w3gg-9p>n{zpY78DqL@zxGmC*O0&>k5F+ImdgTu|;+ra)t(<#KBT z!Gt>?5a9iKTAr?0QO3VtxFF%xgWM*{c3k$EbVHL+Vx~U`qjD_ z{{UP62`CtT)x^L>WvkT<-JMZj*)~o5+n0t-%q9I4{rgPU>xYI=Z@pniQTvr`SacT( zns(cx`!y{JtcLwq*L9@(((<28p|#92IuhT}PABO6VA?{pl(}+zL2OC(>d=b((gfQc z+(511Z)BjTHqXz4aYVfxnNwgyKZG_^lO-A#V!=*4eE+k6?vtBT+x%*5^?D<$_?QAG znt`VtJn!PtoDj7@=`dcCC?%GP6~a1Lg<6})F^eY3UN?UFg|P;TUJZ(JRvp~;Nm}Sk zQiao{30p>oee*(A(5{wSg|^mLyaNUl7sB37GKp%-If9Ip@zI7z?V~FZjnX}ew#+u6 zx$RWloNIxL8Kb4^^98QgQv1yT+BZ*f_&*-!1%VCJ`Qd*y}tbos}tkOE@de{{P~p~ z6KbIDS^uNW_izPfDhMA+gfT-9%Q_V6n07(KMETN^f1kk$xQ-a#v>;Dj6O-xMe?d{RqtbJ;0 zs}-g$Q()N(MDMKM_Tgo`a9JvyeI%E(BN+R%p}EqOX6Z0$DizPi-E4cdn8n`mTEt#7 zskN1o%;r)1s`M27{z)2cC*i7R+`TO`rO&{Zzh!f6Y@+hF;;VucST+BdbiCu@d`&*L z9+_ z0DQ@uQ4IA>35q1F*i*m++1Z(r)j0;I2{V#rWck!elGHXbJ~)B~zRU^&(g$1pKLBAr z1Vcdm6gWAYP?UXqUWbPMp_(awdi8|>26KVg&X>htWKg`x`-+ioXtqL)P2_I98_hQb zWZY+d8n@1aBI7D8Bv~HeFYu9igS2nkq|okyX~H)t?@iB~)vp{6hthS`f~QLl_}h#6 z_ww#n4{5XOEXit0GGb1FoH{7UkV!Bjaf(3#c*l~rOn7`dDpPu;=^;U^g(V0nkd$Fs zqf=K5P(}|UN}OiJ`$F>{mZsgVH;+L+k*`{+Ogk#XMx5$+x)Ah#Lm_HoQ%7+HZ3Par8v%pk? zwS|RlE;lc}s)YNkscR$kj%U1oKj(7I+`l>PU4Jf4ej?5#_PH~N_%*cVPsF5pY&Keq zJ-7XnBzNadY(ApENwVD$qyLLsIcDts%5EZ{A~3d{l9B?@Sul(oGO+_QIAir~RfER0 zV)+hxufLAJ=PUIL_Dc)R4;N?GBj33QnK?QIlJg*~(A9_q>~bnFn8l#*9`cTwpX!F{ zz(O3|Jua*lCNJO!#O$DSeWRrXj@@2^hwtAPH11cvg%o-TRh%=O`1CiPXjJUO+Q(P-$*V!hlhhA-^4+RJ;X83O$7AV~S&QE-l4` zNV}T;AlK@deWGjyb?SnRaM8HFu3!h)Z{fVi;KAr(jg{KnPTku4gWYP@@@oPX-85ll zFX|g)eWkGr(}FVx*i{!LMOpr5hTK&cfRv+zP#c6l1xn0DPzA|$57$ry$i^ijv50qV zF#SROo+|vM9FzS&rgHf&CcslFLdLf1x;9j%K!Yl#J{d#fFlaPfny9dd**oyrpzAf9 zZ|2xkRZT;YmMMjbVOU~5GCc{fjIra%6z@I8B|RN2Zpl@^4?(0MUu^cUw07WRWn&%| zwq;^p}6E=(})!OwpqB*;Bet2kmY^z=tJ9Ei<`55&l#!#S4_`ZSWN=L7qj)djN6jOJXq^Grtm6u)yjAczwS7-e z?Z86j?Py)^I;CFiOGW0l+Z+IHtTn|7$>P4pQDNfUhE7;uUEsN_^^Lk!U6t<;HQqDI zWV`+>rL^WRMlcpyjWxIql|0pe5vKop>m%S3=H*N60;vF~5GY0=K4QTt8edSQtb}xJ zPp!hMo8*1$!Z^^6Lj6M;SRkgXv>M(A6X|IT?mMN0xpS!Ia1J0QZB}(G>j=gFaP$!i zwezt1`1;V`ovP*$>9E7ltbX>Hx zXK21T$1cEcMqB7cfw(u6U-P~vZzU;T`|9x0dOlj58~`S%32h56xbhCQVv#N5TRzqU zs(coyO$TQ5Y9rg|pcU~^YdYRxb2j#HB|h1T4NW%0@Yp}N5}wSk3KY2m^7mT@3S*j4 zi)9#9U?${Nt_5N7BmdQ(F1!R*T+O4O$K+dgzgpG?PMjUBFXJX$J9in_ zxNXA8x?TmW3GWl({%>+I^ly#j%U81J~oDJHM+-nnCno6@omgMsCDB0@@;U zu3lwexfRgXok}w<1mlhe`QSBv$|Q!22EiVGEjtFCvbDM6UK+ICfFbT^NvAbWFZT~P zH-)C?mQE+j4S?*ppqV);{y)g5&J8J7sz)XWT!z5oiLB{iP22S)>4Orr~S>{oD35hLvQXboR@ zx$Bu1x$EV(c!`Kkcsv7m_oEyq(rg{UIfAL>8AYWq8%w+cu=QU09B>N5yIcUU68;PC zG%UQpa-Q>K8q{dlt3Q~CImb=4Di1|X#Rn-9Dh$IJ zt3>UDfEGAoMdXWJpJKFwrG_h8!L}T-#OPqmW3^A7DFXU%{mZpFpCO4yOoeSRG%~#g zSwdQw5BiZQ^1}g1iy+-jzQyN$ptH>-YE0X-nb??xr_842yMlvaT&Oh%*!QIC)%j7} z-U-F$GUJAD(#zhD&C4T}m=HQojmyG0wKcWUBnKjqN+8$EZ<(w(SXLtCa7WEri#$@w zG?@;FNek%W{21ql)z@244et47Y)mxYa9U#2E*KS8k*J13 zT!G)Q#DC{jEtmvCyY)+r4syZeiTbX4Gqz*vXQb(bSWD zGKzvHa)rCaNH$-kwL$F459(T5w~Uh<8bVJ`4^VQ3FdIu_PhTPZR_c$@ZDV`N zipt_^;Cnzdmi}Y9^|pB>bagAY;1Eq{spaQ@I4tBuQ4V_yyw* zR4J6EfIhmt^+=OylUr!s8;q2-kVP~X))h#lNL`FpDkpl=?NS|oW0V}UEBqbK(zJj@ zJR9f$r_YEsgbBeeh-Gy5sZPz4psw67>xeV%FfSR)4_`kOJ{PHRQNB-%o`FG$_CBd> z(t0%voz4~Jr$TeMbSQ3GL91@YQ2yGp2l}nQ>ph~ekX4(duV8_yv~gPpa61R^ z&Dq{JIu_8{me(Sy(GAvo8F3mh9G0CVJ{l+$$)FC>I8#h`$|FoncNqqSs zV$`b=x4<_|0MuBIG^yzB;LDKnAG|Ru_d7hPL&#xqcK7~9F;Z@){N~|R%*VmdZGQoW zfAK~mUdHI(9a7Twu03U*?M{y=esXQMpxR6_cP^tz+5$>0nR}I+iyM9$J8a9(A$uX98s{wGM48<~TJg97jzx&oKnZb9`neL6$I4O?G5^En{Qwby{?v3lNh&t+b_k7fmE%Ixtg?)&|Gx!Vtq4IU~BbFtDV zQgc_`ideOhI+Nwjy>@_BJHy7e9s6c4(#O3ZW13V4==3Uwe9H^~x>m3L+L?#iYIiw? znmgeTOH52p@_jTWN+}htTzfd{y*r=V<$r=aajHQF-+5E;I-O%NKc3}*YQgo5sCVzg*Qymj{B{s9q#HyO! zV=5r&UiDQc87LZu5}~vCohF8aCBQ3VD^MXcC4et zS`2$d#)jtHYB0B9NqlliBi{=FEiD-1@ob-7a>!9f$wy!&ZtEBXC~Gi<4ykVZ6tKTDL(oe$2j%pe5-^ z`8}p*SK5+U%1gx518Qo83`PpFjF$u9><(yip@2^FtDl`8f#5G}b-4Jby=EATDswCU z*lgfb`It1lu(?zJROcFk3HK|X1Y+)VHY3I3ubqY&YeEIV#LaxMiT8e=)z1Txu5*UE z-7HV0|2m>BdO25&s>(U{kI~plyHJL=t}#oJozBSPt`KOa#Py>o^EsHqEVe!qz#wz^9j@Xizetwt5XDtMaZq^Q&3TUfyW_YYlzw$3IVp zY8-cn%i9GBNyrjR0mF&CgU;(BYyLuu?pV;BjHs2}*wcP^JycwIrojoc`jQ-_n6umn zKg@6CuP&Vm&*D^0XW)QePg_%SG6X}O#8o`Abumf+n}apV6wEXR%D7Ps-_%X_py-uG zoakw>-BsAVjD!0JqQvbpGO9i?e-ylFnRerS^Lrcfy6<&(4lkE`L&J)uW8mEf&aXY- zyE#UlgfT8J2s*k$H@Mi&hQ67B3RIdpp=M3ka0}s!qu8 z!EI({<|bRO&NV=;ceL)afm`L5;VQQq9{N9yf2kU011iP-(gP=L4YFOv3cFS<#B5Dy zI{=nXJk(@I@t5Hd5&HjKJZV1#S=TULZ^cp@Ox15fZY{Qc&2NISm!(wWF6yfDp7oj>NcQlqd^= z;1{+xxJ&szogY#UQ_oLz=3&r0kq^_A{jtCm6!i$iI}O|uwftDn%XZ}er!*GCSDGa{ z0O!HN%-lB5*A@F9yEDU3Q7wmlxd6c4j00IR{W=k#b7ssAI-2WR@J^v;J`BW!4UAYs!ik9lHRE z0On81psH`jrL3QQ$O}rdWNVl;VVjwcK5F{d?sc8+Q_vtmgiwk1Fp`83fgK&U3=g41jvdc@lJ}I!O{A01HvTqokYfkia+y zlaZRaZMp*cJVIjY@?jvF5b^||05&uvBSLX^aj{u$0C?O1@=D3VX+Rk39|0?X<8V=#J*pVW_5Tk}06Pcv2RCYsGTGtwXTjm#t75>othH1SM%0Z@}zTOM=0g z4UnhjhHKgl-O;VqaWNFRVphm|_8hpaL8Y0fT9WpRBS&Oqc!v^_lthHJMw&Doz=*^K z@){Y1_)y<)*6zz3IR?W|mfYC|8g*{8f#^bexkgTOWSzmaOW&i-lZr_TMsBtN#AgFW z2&Y?Q+dGPNq4y7Vxu=%`{esQ5a1JyAe7TefU%ntc5*1^Skp-6$Os`6~W2WAZ0K*bK z+YbG56V)r&CH#-Uu|EHHYuOR0;YPOo`IZE#<8R;#C)UMGn zd{xiUuOreuAQk}atlr#Kr<^`znn&uq89QG|zt{M{_e|ZtA3cl=zMZ`BYZfwtn@;&b zDaoCUay<5}KOeJ{ObV_WZNafOlJ)R^SyozhyS^Fip8P~&7JfNH@ieln>m1d8I>=pB zO+ECsJ-!R_QP!!>YCh|F{yWzSK{kE2RS%9u{KhTcYP`-WPQ&)KUaiWd|U0dS^|E5 zC6Z1d9Khc>3rY!Tc3NwDKGRWmb$jObd*l%o(uW!7djI;oFZ6<&S*ZBtJbE&i+rs#g z3AO(<{Le;LCHT%I8hf`6pYa^*^tBLG1I*gp&c)}|v^t-Y^?G68>pKx*@Fsr8DJ*OE z_JfsK#o^9rKSF5z*%ur&-e}?#gG{}d-FcQD_IJJ&0t@!ObNvorSblK>f7onmO<0L1 zRaW2_!C;5AlMWp>p^eH(RPbJjkcJRRC_AHR`*CDf(3)4OA`^l|r~X1dOq$RE*&?XG zkhA{g-LxH!h3KWR4k;Fmd0yAoXS(aOYI7g?aqsfg%_fPOLNNCOxr9busf30WO+dZx zQ?e|>XPFN_kZWCtveKFDrw!A)yoFdzi|ed#@SP~c`|rd%(h4`N_^?uBMplx_^fJ^yw3kpvQ#?CdUI_>?>+;@wNiTz?Bv)$nLf|y_5E|Rx~BD z&PH3OMLV}KZ?TQl??JxRtmOPn`p2_=A{6=3X>EJ#VYB!0^5ZYx^{*$yiWTl}ECs#c z(W1&q!ni_%N(}i$80Dc~h?+vga2~PXr9Fj0!8Hy-G2Ic8gj09iI5r~BBus0;T{tb! z@H{=caLI9s|MVY5ior(Z|H4@jb;44wkCTCbsROe9FUY+do2-&9SZ0vXNK;STmKG&s zcN_GU6CB*!J*w=lO>l^;?-F*}k&)Ua?~Gl~zO419^sbl8IrHwzy+V5qikI1^A*c_BK81@mY#{R64+cFEoSH(PVNV^BRb4HdKT|vD`CPGYu}jZ>H?JuG{V-T@pFh~$M)UGkEM@R~$T>L5E&Oupxn}cxmy4QL{@-2z^CqG8 z?mO$_Ki#WD0s>gKYxhGY$-g*FSKwa;+ezg{Z_kN!m_AaHrhGLs)=R8iwoB+xRAhn2mA)V&81AKt+S^MU# zV2e+u>(b+5m=!EhJuI@+I%U0EoRSvI%p@5NbRH3$|Gw|ifU8UEakmJ|Iqt&;un1hZm60eq- zfBxT;YsdOL`%DPTaizg7(9RFLCMYI~#ZVVA@teio)ai~4muPVhGb2Gv3(cJL*Nw{2 zZA!)COWNV2Depx6HUj$7X{09SB zyP3w(nrM8jn4gyw^3@g?ZM@OY7{gtAYg}&llHM$M`I2}x4QaW1+DAvmXB7K!S$vf# z(X{mwUSXI24${mBZ0mQ}kI9_&{zFH6tDbKz`Hbr&|AKnQDMwUz$Qo*D9WPg-2~^2R zNopDzO-)U0x>cNju!Cm460fTM!mZe5fmV&M)4v^Z-n)-R)yCVcavd@gxv_2vG|`-s z3VVFyD*@XMgEU!+ECaMDz?0~YU5mx+)(htD{uHKGVM@(-$%DvO`PRu_uVrt+5z~?F zb4Xv-G2r}ct~`WiZ&#dOqpfA$)Un(3{A2Gf*UDYUru!1f@(dBVA+gSC%PH|vG|I(f zf}>9=PLXmlzjXsuGyyWE)-FlC@H@))*gQvi*qA~``X}uv7fyxKkZP=Syn*)Ld`0iM zojgb}+p_0E@B2F%&roWR>LNyzkQf^@DlnhLu&9+WsFhc3J0 zBzx1SuN>&xVT9~4HKgCqZpKROM^jehTg&WZ3#e2ms1qn!ps7Soydc#}ea3+-sgwfV zm=d_(w_QbS?6RP-MB2$=)}Y7vdmldpZk}HXbN0V)(2klZ@I_5AT)}KS0lFAm<4Ql$ zGDE9=9|h9|sRU~~9AlJ~2Wjj?lurGf2(|ZzpGgr4iS@G;dP#jmUB}7H6|pecOuW|> zemkD)es{lJ{%S0rsM|g4wgD$aQ#nYvd{XrN3==}47Ewc>Rx_esDqH`zfjF_9aU*8G z6q5!8eH>LdM`Ds>`Vdm8S@L{GB2`yi&G0KMbCvSZj+fmt&Hr4nsK;8zRC7DM!sE@BWHrs3%(DVq#p4!*j1q&K^SqxCTmf!d0PembqFQBfygkeI&oq<-Fo8C|6o5&!}z-5>+zn)pgQyD&;9HL71mWPu({r{&8o^oXNVrU+kRuv1?Gpfvley%jLHI^chnoAj?1&OC*qe zmOwMA#h+K0fZsy@Hu)ojbx2WTE)Wma2o^YGwC zOQDj2zR&~I%O?j12Y=Xw>kzHiaZt^k*AKE*7P6{$;XOB4AcR7y;Vnehz zLUm;cNvEEwOV*!hk8T!GUg9tc^o7PTlt<6@^Ks{qaupr^bun8bf)H>1**!Ap%&=qim4^g>%43LUihk{#4 zZgGt`^PN6u|7`kWqe8tpCnfhO;QPyaBk|klN?11r&lwqE(~j@`Ix*{81bXH)4J^_= z=uc~G-x0s%D9NDpyG$u^&n7v_{ZSw-svD|AjSY(!`qYJ+mI^r<7hcC zVicnkH3?83AQk0G9h|5z6vEJX@zR8gZ%Jt93$~Sgwif-KA1`O#kK4}RP5lMV#45yz zcrPcW*CH&W;9T_V^-D4z+a@C}9mp|#p8d`Ty#1mP^kxeY-E>piER@!$g)nz2r<9wv;oARE7=l;;=ygf~gGTxYo5{4NPQTTpw zU3eXYXv(o0Q0*|5F{1xt0xS?w0I#Sbr-6bk}UX2Ag8|IqRl&bha|- z#P53eTSuKp(QV7{nMNpwDv+QCy zH}+sCAKfs31wQ-?YZDBPe6ldOoT0aV7WHYUemLx2JCtOuS#sp|T0n)9s8umg^4+rh zZP3e=tbOv`V=fJUwko$U;CrzVW>drUlH=_aeNN=6Yu>o!p-23kUC3@tnZS7lw;$;AJ`U(-e`Pk*sS(&GqQNU%gP8gFy@<+8c>@2c!R_3S zcD}VP^M$T9X=ictU0q*Rd{mtp5rhp2_-wy)w(!=wQi`Uigd6N!eazE-^V|Aw>id`H z6QQzgNw?#Kjp|~Up`Q`?2~$$R(rhcyZSr)cag%?hHm`cl;LP)2oD+vbib=&-@|3^0`AdQWYC?HFyq1IxMGYn{r+4L( z3$`0-8f0a7N#(&l*K??SP>Kx2jZj=8>S?7Lr6ul+9QrLn~^bFQuZ z;;`MgRBNfGzv(=V8&n#Dx~KR?<9Ed)CduX4Qr1fyk`Z#mpU)w3!eUkHw4~yzw5az3 zN-(u;=KIUS}&cod%_GX96Wj7RLgI89>arR=cLHOUfSS5e{)qP9HVIj5)w|SpN%e1p;b1klszsQ;RhiZJw$3;J6yAiaR!$glr}tpYvz*z)trnTM zZRc5YVD^oluLFNZuC^6*Jp4RLCAIkJ%Xs3UefKoa_!II%Oo8;U|8q4g{Jd*HwZ_`a zL_NKaQ|ZGl+(?!ALDNKm8D2U(gaa%Z3W|Q4l*$$ST8^TUGMO4#pa>RHBcD+%<~Mnx zNz}xs4jU7oE$L}fM^|zSMd7D2AX3mk{G(r&oJpHk1-^Rpl9&1+D`I~4t14)u>jcyNv&WCg*b!*`pRTI0mi?DaN0vn&l$%bmh z@Jx3~K{0Gb*RK~j&Rh9B!w#q}*j)ssiQ!**HbdJn%J_Eb$*?pCG0^9@P3$m&$yy*O zQ>Qy{w^vJTC5;gi_G7BiL_i_lh{cP%nQ&Z{5NrZa=G20aQMw_t@Skq+%si5n<2PW;9-`1c;uc#VtPH)~^l0VsBo= z9eISggQ|Gp2Wxt}L7(K?86lb2IpTotHOZ$DNREtJBuQ?O$OXrAUY22cMijJrXeT#g~ex zVXlhS8{~mEOrYGf2pWlV+kRa6AQ+1ZQm$A!G1dcrm<|2JkfGaHR+9xeaI-tN?fJ>W z10W_hi%Ze2-xj;e$*#q!;hF#aMC0*vN7$lO9Uy3Z=h zGon=KsQNrCeklIqA-g~QO4lEjTiinb+Sd`inA{#&R$+UBko2Gp$D&Nkh=lenBpdN; z5_H@y#3mlDppCZOjzgIPE5u6613F$lOiT(Fjz3JTt@)m<@J+OWPuFTq8ne#Man`?q4HTn1f@vwF zxW7fAmNr*esP;;Er{FsE?KQZN&=lmk`TUthu#^j%n<~$FiC*jo+EGzL`V(5&ag2GH z-0X0*e(^_9hAArDv4s)MLWDaaUAe`_;d?$VIsMR2QG`Ik_>BG&_$I+0IjW0qf=`qA zjFp>DB3>}bo{KknFFy!zd%Lljf~Z;U$; z=Il;EY}sv$y!##R-kUOR^Ml$;J%i(C!4-6ohm%^9O+DdfMdG)Zn4Z=|>31dfmR?mn zUQZ{9FjRkc9rNqM!of8p627^kf9~T}=dfY_X4B5=d*#vGa$SwmX?hv+a(%h|I_~G> zE>+iWIgPmuJzF{6WjGixP_-@nz9BG!-_v(ksFyy_n|*)kpfi==e}`o?>`Lwz+S7mde*Gj zH7P3xD7>rjd3y1zVUSApdG@no%!0S_a=KW*hmYdeHo$w*c)UnfL{26&Re>IEkI*Em z0#k1X197s-X#xy|T; z3U!A*Q3wiid&f&vfQzPlE)0%;BrNzzbMBw<6!LeG#YeFC$3^g@@^_Red_Z6~O^`+D zil3o;JRsC{MD*|!o=r_@g{WgL%?wG8fSfb#UUylRrgBl) zD4lbeR&yyi2JE}1l^!G3wT-Hl2eF?4y;r?zjP=pfP~s^duRlEOv~_y&?*NHiFC*9uzO(+1%5D2wCA6-dI1O4Q$&6iZ5G=}^xJtgY8Y-{ z49n(7goA}IoHDB6gX{$P5Qy%2iJTi*Eo>xmg>9jQj6XsWZwh!`NdEDYDn(ZeKZN4* zr!QTEG=ACy;Echkt7&qP7Yt3XLJ;=h6=N1cNWx=_Nm7FqdY~%5Bj&%*bw4rv=@NG| z3L8)ejsL6DgUc=S8IPavEd7y4UF0?W;6P+L=rzIR456kH=Ttw9<_`x76A4?fM*HR6 zu`ucvT5Yptjp|T-hNzy1jxJUE1+!55;O}Td{m}5Yc|pC5z?9U>Ykh5>^wPtZcxJ<= zdsc_7!GG8Ojdj3A_%#v40Mh>ts?kiN~|Qx3QZByUo?Gh43@!A4G9TD*Xh z8Os#5q)Oq#j6tN=#)s3Emj|BzNez$7{nL$!r=_GyOv?dPV{R8f?qC?gQGO zW!YR~ZZc5D8BqHxoxM+(!CfwEo@BZ6*BXP;uVx^xK-?z zrCJ_U%AeNdM}aqZEG)W6fU8z=&tArByR9=uCJE@&ce< zkUs)$E!0u2EXJkGX&4%W(m^zBqa02a>#!4kYb{o!(~RiPhoOSo<5J&O2XcnFd6B^d zG*W^TfI!H5<=N@AJN_HC-~<8Iih6>Fpr^UmH^a&kFAlQ_mWe_y%0R{)ovkDrmaGzd zQfVvh4fe3c>hDt4_|1#eT{1$93M!k82i!S~n}Q1#1&+K6yz~&QAfh$|LUE+r(>*A9 z@&d0NJQ5a#$I%!KrOPV!sk#1)PZ7z?S90^M>+kGGJ;`fBawZ3Li=Fv_|0*2Plu4_e3pPOb{!jYo;b4-p? z3lqbCM=__5_ukHbTkhV&<%!I|)S;u)RvEI0YZO%(YWN6i2n%3G%>z@R9lhW^n1-rN z#3vP01eq>;vVJ>7e4m&mnXXV{bZ!Pi`Lgw(B#K37f!NTg?SdnCs7_NYK#CaN4-5hV z7X;cNWf_~Bo5{)OV8R7XfGuQL5^(A9`PR~Ci5TS{J6$O?!_~$F$UqCh`U{aRa~mUD z+}c6)kB2UiSWER8gqekuJXfQ=AqbU8{F0x;qsTakjgn9iC}6b+;|a=Uc)rs<`AX3S z{9zq9n*j-v2?~)PDM9xw^^e%>-tlb=rd}zMSi!!UJf9p9()v}=SHosWbcrjShAc?G z5wZy&4IxD0#vsqCMPc;z;gb*8{j6u2HgzPhC0Qgy#}EJ3uM=wqzJgT=hSEllo)u;m zIuw0oQrLmcu~)ekR^+(3+Bf}*@{)L5@QUaVq(*oeY#-dJ-+3FZ+8GR$V^bKcglv~8 zF}#2Vc6#4?S?tMEvjLoCv%kn5{(|TL)ev}yEEKqA7@0U0i0+Z-Mf`Vz>p|ni-CFrY zPUgQ$@ZG;)BmAc6<8kecDH7G;E_(LTTt)?lKj=>@Y{;YV)Zo6~aO(lHAwkg1HDfxz z4<1*XWjm8car%aZZSe88{ONRC6oi;`{qM!uGUVdlxyk8r(~ZgbV0mdPu!;w(dXXR} zK1C>hK7r0uAC4*t#@C!Q_ss+n2Q|#mv;xDogN^B?Swj2zoCU|UmJ7?hw}s2FN@u;I zlzuAckT3o?Ll6uWYNHJRKy4H(0Nq_R93Z2p?$(kiqk8y6mq2U_5+60-vkMHneOB+$ ztK3ptfFQgS8&}@D@0rgq5?n4d;UB_8U+sbnfs+FjL&*ixf|J$CJKBs=1IlL(xeU1* z_gpvXOTR3q)f(~b+-U5@AM}HB@K}ws7Fo3eteHh6`!MADEdo(rzQB>wQiaGeYSVh| zI7p(sG`KW-0hbL0JXm2F(YO81K zz!eZnD5aDF4V5ycGu~~P;n0y!GT3#&3*!gS?RR6V(`6^;uXAJJl#0*W`_Rqn{)Q>w9KdD|}z|MefXti3?Dd$`mJ7X1a zND3Bxr!<0O`Cc>X%|$$P0#gyMi6xdEB*|yz=QUjM@ghv|q^q6|fh1R5UClsG&urLs z-H*ruCQF?PXr_sYiDUKU*QmGDbp3InqtNrL26*CjNN@@)Z{RY;-kRtBC=Xi2<=-V? zxvUwRd0ZpX*ciYnjTGY8yjiXDM!t#kFKXDVL8?iDV#QJ3^(`<*2kR|GhW%96Laxay z5YHW#b4M%Kc!K}dw7J}Nk94@8&bh#mGon~vd_qc)w~?}#FT^5perDwZTY-_2Zurt* zyx`csPH90nZQW)zG^*s@Ra)SmDEuICvHEoz%~NE0F@%lFUNP}n5dsXE8zESlU{$vI zw6Y>amv~T1%rITIDd$Nb`1Aj~dC09ZO=vr#k;IRVy)}|>fET|V+`IXwGy#&3-lH?d z6d%`*lx*+=qKWNr!2$wKN6(vMEFO1qI`_xBkV$wW^XwRL0I7V>4AoXvq}GIh8!MG7 z0#nJo+`uT%}7l>9e9~uzO$Zf{4tCW;F1`WcU#SS%^g< zl4~5d)&I2z6ZJ#ouk}?!U7!l#>hU`%Ms?_s`e?Z>_hi%Oe5GRDFZ$cjw26}wkBU6@ z&0RMp3sptyS*XX?N+<0FtR~1~)`i&7MG{Qc{t*?aVHnQNQ@_SWbKRVGuhFVgbuZAU zn14-}8H`8`BJd3JFnKgyzZ<40(87t+xihEBqrC0_W7f&Q)wxVjT9k)nzth|2pt{>x!BU~|W<&iFh4Fcv-2P5gtgyq@P%v6ie0h#}q2)RU*V{YOenSRP zZ(rSgKDF(h&iIwX|1SzkS45SVclGwQs5&bLgb7>-91|>>;)b|0MWbA$4<5e-G#68~ z9H_2elf||*glz(4fG}(_wEeRUJtclEy@gp+coDYhTAVjj2v{ekK1l?)4s;fYxFvF( zUEh#O*ioQa?%#OtZLYUnYv6%n=!B!$WBmC@QfN$$!N1X?;EUWR^6jQBmSpqlB$h83b;aIxBkxL?th&5wD49%l#x@MH(}fl#hiv&41^m zqkr8-IM5zVNi)=ytfW>=9E07MK8j$0NQocLNP24ZrlaSca@0gQ18j{j zCO>Z{+#Gxg52pTM;-_1@jjmV~l;Rf#G({GYj zf8!E22X$F!(w1}U`Jg*7nNc5Mqb8wOAjTl3+O)}rAQWigib&p$MHKQP(|ew_ygh`D z48gXY%{T8LCRmpH(+w!m#Ng4p(Z{lfz6&jRFilKNG26n2i&v9kB#ulMi1Yxi(=sJD zZw@z~T6$7Ew(52#$$#!GCK~P28hZMXO!1*(tp}WCtX*{v90|~bk>ya{z|FC7yFyBA z2$HZ_jeIuGxnDm63S`e)qum+|lhrYPmU z9dRuotBBbS+|*VydvSXVH;wwv?eF5u;5A?TA@p4?+&m)v5yCka$o(H?5M-smP+Czt zB9^g4$>3~OBl;MepFX)2b|ZfkmU>a(y%RVWQk)8kQbmM_Z*!IRKjeCgeH{9O%U5LI zwRPMR(jDvU8}=#nhX*Od!=PRtF^TtUS(O3r*qz(xRladY$mn9R6QlE=n(lJDb{?FD z|6iLg@r0R^jm<~#8Hb@+q_p@_oljYP;_k$B4+!CA79V#6Ff4{?O0h3R`K~ziloCrx zCWd5Y_@C9q$)Y%C$bcb5hK{&L2!ia`&{-dlC3bTF2Yo%kED< z7=QG@t;MLH3v!3rV+;wZOo7m;`e1bJ2T!+AP+fd+VZfPupG`bHv;k9#l?&6hiqKE! zZfu?zNS5D%3|7&)=y z25S6`jR3UI`A#KGAW9lY5^a=fzdX`*QK0R42PJnSeux_8sI}nu=6-hLH3*l#> zHEIGk)mu1&UErtWdiI$5OHtS6MyK;=kqYl4U)X>WY8Fd>{#)FRAwh;q@S z=_*H~zNGwu1?PpM%9WVWVkn!SQr_G+PWla#n8+O zZw0Pz&%O4+YIEp%wmvA`+`YWnZRySbsOx$En`ND!rDE>hZrD*vlPcG`CdAH;fe@b2 z@_o;SlnT%uq);-QS$EZiK#mKeX`fn9!Or_DpNUVa`H)KNb)ZO4M1ULRED3^J=i+b2 z;lt>D&KcP4&v?6!4wY1gl9#LzMbtyu*fuI4|4QyaEM5;EU<8*)z963SXYXE3@J*)8 z8N%QXaI*{>9Ibax#)4=_k*}?;13We0dXWrM#tcVWfZi@yL6pVJ(>Xi@$}U#`vPlAs z()*j*1PCA8`ueG;I!Yy!P1iojAu6ud7<`Kw$w)$rSh}-e?{lsXDXcNg(fb!d;)yy7z#Wfzlog7#`ug}H`c|fW7 z9a`hmVk0xhY=_PBgyXjte6(!4n!2pi7i}0-HE1o%9aECL2ujQ$gcWg0{IC#?DLy<{ zUf#WMBJxzPj+#nplAf6#&_^O6_C>@jyZ0~7T4ra$iYTAjD!Ot;61QP4oiV3?0YUC< zUDu1nFAC@WDDwlOWuRY%DP+G?V=0A0S8fFM+L4yUs*>;yABO5VjNrln&T~IiZdTI4vKvD^^_gPVUdzZSy_iKohcIHzoJ|vof zNc@0X&siMSNCSY`hiB){?r!6h(zB(yX;@?~PR<`KEz%{kvA8U*QyY$tfKw=xnC|NB zg>lr_Krg0R>EY7^9%;u^q*MKqJ>75;rz3jHIaO=N(>1d9tNXa|{`~Pff*)#09b%D{ z#cm-45y}31eY8q+O0;2M zSJV~fnbAVPFg-IzoIK=|dVg-zT8x$wnnK9=1tfd?^9y9%-a6?_W9(!c1g~EJXhYtpS%U=GT{=j~9=Vk-@?JzNcrv za~=8N)uA1cG^i?-D}u;%s%((a(Zh6~ngKpQRbEhQ@BrIhnH(+?pJJA7u)Jtlb$)`- zz=zs$&}w=8UcQ1Fg~vq;qe8~HO^$J*LPqX`3X0%l_<}cx=%EIVdv~{DxefB62CvO^ zi9Z0Tmo09I?S3X%m zf9oti$iQ-9z5TDBqMAYu<}!&#CUl=w8lf-cIe*+_$4k=>AC~)&%bzcMGoRouB-Ro}?|!~aaDcB6V=7FN07Z(!v?JZ(=gX1j?4R(;o4R}AdiX}qD?kM+ zqo!Wgkr|9;{2V-#yn-%0tiRGiGM178TNeeze&e4=C~Covh!7{>nv9C0SPt7~Ja@G5 zVTGwizNFuuXVt{w$RzIALNtW&0=on|H@4WNJ?sOohU`f2hgqTg#zQ6u67{7eeh+T!L7u80C5T93{(*<}T(GI`RV7x)5;Bu?9^TeaB7 zgz_XYXw*j)E{i~kf6!(X92$y{i$EIy6|4bH)t7qk3zykX?2+N~?X^11|10>TPK2~_ zF9M1!p(miY2Ihxs3T)6Mik@1W*a6mF4X)7Q@^taxa%LIpuh~dmcgsj~<37^g2%rAE zEjMgcBB2Lo{LnBxUZ0^g?@FTDQA0AM9uSIB4&?x|S(W}72ppF+FTH$!J4B&bA=AtT zJUQ+AdbN;@V88rEd9t1H1HZvXi#hd`tSGFmT#HjhWDbn+hiQ$BRq8~y5&f_%Jp?2B zYW&{Fa}lu-ZfE@zyyUYbwTs%fM`0UGb0lkva>HtvzHXxM?euwq*J}7_LRU{MSXI3# z_$n~pY12WIYugiL8xkwWfC~H>yv@4&c>pt75~q1#ak0kyW6OwS;?I)8sYUubE>-nZ z^|5d!)-b*Cw`V)Ruhl=h+zbdhSybY-UwciX)}x0TlnWu<%waOBt@vwyc48mZutPS2 z+gS-#S^bG?{8*jgb37$<(D7x?NLZ2e(P!B-xkS@cC%zygVYShdezV;n=e`Us7iSV} z>^+sVMOneJAY9kNYex~2_MosDF08uNE;*w(7Ydp05z`V1A#}dcAo3=*4oaM%Cb1p% zCM_TkqXwr>`h@AUHo#*aIzMjD+!k=hPs1>IN8bviO=}XBzT`snb|&?GS2}l#`@fdU zh&yg}X6AaR)3{OP+25aDzBVMo>Ggli;XxL*aBdp=!#=|6@hi~+L8%tJKb^JFDk4>r zvE52DkA{e1d@7~d-nLqb33Pl||`EJm`!%{8H}%%YEKE zqjk)0n@wf_CW?lhBj%5DlhtX41g;5He@0dt2yP9%+axPp#DW{EgT330R`qS~MlWA3 zEwaKciK+B6gHs>`3aY*1a$XpMB3cw#pINy59?GLXsn^dWTsKLk!c16@>UYDIj7VR< zX|9?BSA{=H3VGj|kJV_grV2z*w^ytSn3^TYlNG))gJ1jp1n8Cj8;yJ$Z`{u_SkmyO zrF%-Le|&G!an~qjYxk8O{EK14u~elL9)UY@T(V)d3=NwzHs+hGq~i!)$4)KfT0Z%} z2HvH#C76RBIwEi|G*1Fgq8PQtZ#ID7f@N==?fea#+E+~r)+)^{6&fK2F&dv&$oW|f zn0}a~LdM_dIDd3oQjCJb;|h?IVBz+V^XQ49Xgk+tzIZ68^b)|FCBSzA>Q8sfyv)vZ zp%3oVI|*!HToWk}b{kQmLSVvtgB3DK3c)e;0;T{0%~`T13x+Y)gRFo}2~poCD?)>U zQ|BA=h1R}>slBa`T0^DqF#As)^1o`8cG#_qPG?U~&j0~K05ff*CVv(2u;`bi(QP2& zkiw27gDW=1%2Zwi*npHQwv?CK`6;b*Q!)U*1P&0f(BatEx8RkAF@a>5hFu2rcG4mvPMw)Xe-J`Vt|1<(l ziv!2b3u%7f^b>Bl5T^*I5a$qJvd1a)dpbMfDW#ye4ZX2MoZoqDzv)g&~lI zse(85V-xDw8>YyC6cG5Z8V#2e*wLZF0JJVjnPp%S_4ua@a?KAPiS)hz$K(NhG*;cu}xZB6C z=*&SdU~4Gb_i8^##X+$R!h_a8uvwKfd~Ut0(WYXsjyoY(;+pWYqAM#N6oV@(OhePh z5d;sWEiO%Aj50*y!;YdAPtwMJAwMEBx8JnvvVS4h?r6r5&&&MZ>YVpKHbA|T+4vW6 z(F^J5f~WPGR-FyWYwx#|g@#v`J(au*8=xYStrUd@*OcUk8A2F>8Pb1uPO!ilqmA(a zb8b`93++(ieP+fRn6ge**g2)sTr5f&SU>}8SonZ^Ro%*g+pcVa#EW1S<=%Giy zk4Gs2S!W%rjFC&ea1)syBTMb*g+$QYb;#MQ|d$e`Lee?GxamP9-T94}aldSIdO^yTsW&oy)h8ez z=;-LMwX?%*_Lnh&`6#mg2;L#8qTNN0+Z^o0mZU~YOr!ARxsonK4={>SEOf;D^8put z%f=D%78J(OGc;efcYamBhaYFmGEN@JJ)ml8BO9UC13mwS2G`X*gE$ilx26BlH;vR! z7{c?#5TSuCid|5P)`E>Eep|ZqFiDAG25z8tNTvQG@%o@=Z~SrgogJob_qM`&M+M;i z;kZrbQvqoqQLk*G^I7rscU1`F$_=<6Vt=t^L+3WrM}L}9<=^l3Pr_^eBXAD=ALgS> z`8OVS+aL2G_c&?l(NW~Vayj;4g+-XW^wJJ=ZCFO`V%?}x(X?J9GXPXX7r7ZlW{39V zTc--d|pBWFEWDyg1FWb9sdoQte?Y_SzW-h`7ua?D%y{o#rD6{=y=pOm+>r zx6iM0|BJx|@(*L@F~dcd{7Yk+r*USSAZf5}2EQpxrkFxQYaF^nWVQ^O3_^R#6uc{4 ziX7QjX=-?|u)=TAKhCLf2%+R*>-HvIuC@27C}btCR|Nj~iF9|uY4(P_HYgZbfRzWH zzK}iYn3A3s3SODTswwkS=E7=_2H;8>`qhFPs_nXJU&y#(PpwMLSvhIBmjy>D(Q?EG zP$m+{zlzPH#Qo_#9E@;iyNAzzhG)(fU6NT21WT67<%@ZfK_lr)p9V2hIvm1&gjGwC z6dW|*BW?7~AcG}gn|U}_=jkl=?Hn{hG%$LIQ)XcXyC@I3`m)ntg$%;e$>lK;zPJCf zt$mwYBL6?nDj!9?Y~I)F@3Ra)q;J-n2ZRDQE+p=zQZR#0>6N7As1rt^@<@i4l`Jr2 zX%1uGlA*WA(llYV>&zX9D{HMCNpyX!WjqZ;968Pmt zI4triPP8RW0t=b=#4WZOA>qO7J1${^6PYe}q#s>n-ZywDi44sOnf z6Z;eB6BW|}MXRTc&9PbpzpxpAZ54)_FGJ}X&N)n}s#41iE-u%T5O=qiQy34WROJC< zST==u7xr11zbxjC1UL9_7@kC5hGOzr)JDzL0%^5#g(8RnR~=X_4_d2aV1S66p-}Xc zh`2Dc#K1k+H-e-DGb^xe{x1>?W>(udnap6B#9;z()?5hqgVZ4UYo1T1I8gZUfS3Yb z8`V&FLTSJxa=xeU5`Zm35A(|lD6z;#L5Y8vybrl1;()62q(||!*ULzN5B8c|3Pkh>hVpJ+DJ~~B(Q)Iv4G@1V*WkzQXCUAbtJ7U`OB6O97tADmS1|e z39ln!hh(td^7A%E2wVlOHmoH6+(ja7sN24Jl*~*}^rj%H96EhyQcWhl6dNp9t^7i3 z|4ztIFo%p9`d|Vi5VwtTPiIBAg*Z+ECkS4enNrWSP}WmLcB52m&iEcV_3gx|q1W0w z)mytaH2D%ujCTfAm0&zhwg$<9M z643%0hv8iLhsURTYH7bQ*q)?U(C0@)!5`78Mf;Fs+uOM(^e*1*l$f#c21H3Uh;J<8 ze=QGbJoIpDOo{j%Xl=eXFX<%E_tz_ANUR^y|QtJ7?0inb}tFkxhi4 z+heWAB!15lo8wSV>UAIG8R-Z4ELUwctGn6pXgQmJ^j{WJtM5bw3bb8Xl}-|SH~^Ml zkNC@NzZ`6U%gPZ`qXNQ-SOk~r+h-EIA?gs8wBbl;a@G_j`vRMcuKBya9)usf0oe&_ zu0!J4s7=lndKxb))5nX&aBe(U-p5BDn%MAzvwP4W zlmw zxeL$4xVSL!qJQ)Vt1@GN9-#v`OXBdmDoUKGm^P%;X}p>_h&vM2LTJj?(g_K6aybAw zJ#y$*&JeKWfaM1SEv#hGg&2tWlL!16QfJ2Z`7!~7Eu_5<^KhKSZ)oanfHBbXR!L)o!`XtZzUE3F z?_S4L{MzkG71G##6j?}8BQ*x-AIcLhn9Ny>ym$~A9Z_VQtw5^VSqazI54R=AO`Rt8 z$#~^IWKtDpZ)WC-+RbFS|4|R1A2~9<$YUsKM-Y3kCQ@10t`wR&$_&HKb?=3YhY%4- z+#?8Gj2&WF6!@Yo=Th`2Oo`ax=WbesjX26I#i^M&gX>sCB$~eq zi(!flwH5QH>ALL%e=zEh8<6Rd8Hmz;I~Dsf3Pql3NwY1O)_)1m=bf+|1F|EgZvD6+ zbvW9v{R05U%moY9Jh^;_K+xJp8lwHBc3{Tz_!B=8Bnk`TR^6v8dv3qO3H$+8gz%6J z$GW;YCp$aD$bpTGjX1W%k@?L{Wx9Cj5?mJJ{oKxtOW;NxTuA+ogSBp~R9Ny$+ zj4HfgKem;`C;JuM%BU8h`qf!4@OI8@uRfj7%{nnS{rthj>FfK%n};jFz49AMG8?A| zJQr*%@hsc&hO;)gFmw(f3aW3<(=dz0-TWve7=lFTuBbbbHjg{@GZDQVD^Iu?$tC?n zT!{Q_ON7LgWo2${|&!eMUKc@?Wx<$gyoY>Jr@?Vrx^6X5uE}5gA z`@E>X-B>AlLw$Vlw^vk9^1&c6RbFM-Z@~y`sL^vpQg}nzw!3iq^0NkSGiX;G6v(O6 zcc-XV2>rHf>kPEK`0ix1SkB{Pb1cR?NU;!FRr35!9gk8T4SKe&5NDSA%1J1Z@~zF1 zY>1f*j+dn+2t6jD$is>U37wve*QLAKuXg@j4Pw{(;;5*JcFq<1{qFofFU^^mX@bts z+?a{lJQq78wH4U}sA*t_t}T;us0-SEq3J!U6;RG9@%E#!%@Db=|5|~~irO(Ydd~>S$$(gspW+g4BQd@sggvXR4=edWNl2evh zlvI{fmRXW%Br9#)p`8}tNw5kxl0^b4Apb4P87!g*k^m`LWw$u&&l|oL9u}HCeYPp7 z!5Y{2|Fi&a%weDQ$tk|t>cuoj6IGXEYn#`7M@Vk7wQ*Z(=}^ki{5}gWo%_i?T-?=D zDkFA|K3ZNbEf%YhU7*D5fu2a`J?R$OaHH-Ob#4BycGeTwc zPS`=TKP@UspLcS(zRL~F`II(AGWDJU`wmtu>4~6JQk6P3{uILLk(^b?t6&>vpwtzv zn_>f5Mm}(4vm)I->MgQ{%I=!19z3X=B!*N%rA1SbtA4KJgMLZs502O zXzSL-u5O_8b7{7arYoz=boDf}su#hA{z>DJvZLOT2(*$4pGpta(*j}AG zdSP}(J|Lya6lU633zaRUuqeDk`!rilDJh?fOAMreh?E;dmBNb?%o@z9u1>GcY^HM! zi%;5%`#l~04%)>?-bWsK0-{W?jNB^UHTju%0hwSGUdom|GONcn(x79dBeBR0DOc}D zvWNSXtH?v<{Q616?s%dW7rwKc@HPS8m)IJbjJPKKmBzELsfJ5G^BtGQ78Vv#GJpZ~ z9z-8|l`pRwih;o1*K*yyHrm4o2B++pFC@{jIk2wj(R3F%K}7tA@?^s*4+~p?sowMGqCBQCDvvKbQ@5YD+t$Fp%Y1Kd zES!XdWNBM@EIgq}o^dVW=o`(PI1C2^kqjS7EyXuADfwu1F{5@e80FM#%7PBtfDIX5 zXvhL$FD>RhiE_jv7I>}ly(w;} zcjKW-np2{*;Wx-OC4EDNQHKBG@y2Hv{Id^gUhvy{x7+n&S#&k9Ax&RF5TH`&@4>)d z4pwiV=E_h4JJ5do@h2PA>U>jT4aYZ;gUx%H|M$0_n#y1~{tg2N3hCQUkzVp};@@{Z z@F14KYQkzNPRbbi-bmkkZ?M$op}A<%*1_6i`d$bZMk#OGvB0^rY+-F-6(gSQpZzIK zRda|?i&w04D!s#*iH(CAcAAxI(9Bed=XhCp#B&Za&X_!xY*w)t{vag_#G_;tVFp8{ zF0({}6v|*drU;^}S+nVky@wk3sWbM~sPpsb?k*sm(Gf!H2790BuRf(Dqg4N$t4@?7 znQ}88_mHY!`n0$aSrg&8gE(>YN=KEXzJjgAt+i6BuKy~^T#EHh_FibPRZF0+$=e`^ zuh+(XfN>B~nHHWkG7+s#vQdH5a}BMz_*tJL&9M;wE&*2F_LzYZwbt7;c4+rDpJ$T5 z6Lx-n9$-8_&aFil%KYypd0HLTv~_eIe;YUv^SMgS|5!2!3b@+*86O`n^6@;4O3aTH zJEWX1_!)0_Do@~R>-RmFEpA80%NPG{Y^oH(7-mg*xdZwUoL@r?;p)6CstK<4pbRC? zaQ(E3j->Tdez>u&Ud}q^cIH|pqRztrLoTs=H-ReZZHBes2AuxS9uiO#b4Onb@rBt0 z=!aiUM5Hueg<$ddM`T%)a*p{%qjee>)?fUHUg(5SSp|OmdEtG75kOI=SCvg(SEN%J zqSRN}YRKX3=TVuKu&}1XwmB}m;+-^LeMQPE`x$8%GeN1L({_c}^}#ZjV{^K_Z{U7W z?nYz&2D~?cSQgPWZtJx`9XcI?S|dIGuV{-9DC!7Rpwi0CAWnFQ0ns};B^izxyE`Lz zJs~kO;fFR~HcfzDFBDCcC9a~QOP`tHEG;kfl+dOr|7@JNI7Z`t>F53LvL|vJb4pxY z@rqx1&mlVNnR@?L+kX380UVO+G4VIo*H4+Q5}V0{vEHw%0p|!Obhn@BsZ(|EekuZy z7h&f4`JnHfa#2zNN?eu#4C0{hbC{WIo^7FgV{AE!H1kbg&rCsNWPk@{{ie*dvbU z9v&Wvg153rjKHW%tUo`mVl(QwYk|{dSFhE z?;m&c?yDwijzeoDr^4nRJRV?CZl_Hn{iPE%biAQp2i0Y|MAtIm9cg5j<0HZAJ$mvn zDaZ8ASlBn*;gOwBHh>|Tl7nKQ87+?0^^fP()SzVuynYM2YrZ5#g*Zs*1*I`HE#`-P zUxUxp$azq1Q;*d?`AeenD>lq@()z?7NVxc91*H607-hSM8p&PGAG7OH11PVc5cFaX zsFGH{3df|gEK%1@n?Ng&AixARb4s=TywV~#Fy#38p5$)}@s?4 z1LNG@eSqwdGRZ4s#Pr?eIN}s#5pRQU@x$OLcx|q==Z`;X8Z;MP=rQK$4dc4l<#nF` z_~88-WN}QSF`$U*cBsSoBUQHeg%!!2crwy)TbImMns$iDD!~k=TAWPqXb^)Ot^Ybz zg^f)!)<@CP?HDz)D+EjTH#z*AnQc^eqU{jc09!x2I64NCW0#uLY4q>*>>_swd||5U z266uE2&=YiP*^WL;u20msQJ=&`pSGjNzS0ko8ao;4|3mFL@LAobVT2p4PFsK{#-X`6WN`1qMBL}>V9`(AL!6TbOlNJ)t*JRVFK1RBjk}5_ zWr{^;;>*|v%l#hCL~2cUEm9r|I1w1)+x)dyg@!~rg_TCB+}IjecUTO{2MJNT7inH=zy{{}+TSJShLKQ}qyAm4| z+^;OHl5{y9YAnj(WY~0WO?%2prR5yW?*(`G{lQ_B=kD-_Uubbp)+ZYZ8ZAlR(ME#$ zHf2r(hRXKNUx=w;@z2bQ3!FoKGueMU#ytSnc5Fx`j_fFOV!0)EpTy}gXwcMxSplRD zdjkzthA%H$swz&-S*j^c53;3^oqW!!Tuou%D$;Ud>_|~Rj?f?K*q7E@I~~k<0mAH` zMHp{Dj=Pzpt$VOq6lJyf*mQL=Vq!37e--o{NG&|IHoCsg3a&yqbDo49rIUg`OPO@MxRWTVhhvX_e zVCz`E(0|RC1fJFo;_K-LcoYx16Zx#-cM=YjzzqNFu|NbjaYYT9yw1^S*Js|Lb%f1= zA;t%XN^Q?|1>2A@Qy3LG!H<9J8%ZCng z#;xgIpA?1b^B9*q^HV}WMP>PXa(b%&YE7T+d-d~?#CsdOV7hgk+?=@8qQu2*#D}Xo z^}%ehFCh4UlrfW%#q~r@o{9NaTlV7EZmc0rlRk*4Q3J^&NoR^2K-*qU zQ;>B?PL7H-=MeaHqf=>T`UpBr*|*9fHs2RKvc~O4s^)YkcEf$fIto(yo_z6$9%Fh) zXJxlCGsIXXIB-N~b+SCxDFXbzf3;fzV$}0jT-5iPyJQPB3pL4_+Zv0&!pkwE(Bgnk zwY6_pQIq7^=Szz_+)-r?HrXLc9*HX2wDEQGc9^g|$x|D99VP=wiE4x;bhs*;yi9>o zp8K?J=MZ{oE8`XJKGwct*ZI)335?)R@EjZ*BMHO;?#HT}1kfRUpM*`^vbmKw@Q`3a z`=0TN%^L6ks+gTyEoa6F2<%jO_)f;}2t&EQ zCIM7j9xRSrO$%v)$Jdilm9S9WqNj3m+x7R(Ba zAZExf^vU>OZ;0^Ysy0Mj3gvkO-ugd_Ti8pJ87bm<6>*{UdTF}03@_W~L&RSuGkk98 z!?l6>xW%!`|5@c+!UW0x%HW?^dT#DnZ)1x95)RMQ!UFOLUEO4iHq8?L_XgV-Gdh+D z!J#-ti!d$qmT-0Pc}*^)8B*={Bs-{H>;ww?f6jq$2l(pqL-k*^GruJaR2+D##+()B zJtkQtnW=_f29JlnfkPz3J1`-0Ly5DIdxY)%aIl<5nZr~>p7X;xAouVG?}rCUc+vvW z0hRCY93#(O^-L|KgY0~7FruvTtn#$djj^AwmJVP2CKAjJdG90rpXk3Px;^tI^Pi^J zw@0exF5q#L9C!1rp+PFi@YqtpamB0W1E1h?&I_;E=A7Vp!}p|dQuQVAfa(~Ys&4~y z{aUr+ZVn1siU8KuT9BPi%17@A-ocD83E!np)=cKdhBG;WPdO+CMwDk^hsRZ7xeyuR z8tHT-I`<tmXq3%etQrb2`1QGSpF! zk%@Vo$#7y#J*Hlam2GTRkm1=YYM{qV67*_>Mjftva&~ zb~xH{x;|sQIpP7rftj9ojFd3#w~UKZep*-nLx-6!;xHmsgq+c?^*1*-aMfMGnwg#b zWG7nURN_D?kJ#5G4WOYtJYJO=@q|&H{Q&hI{)8%4M8$Lhc2;p#L+rVCQj=5T$DN{~ zm2cATi`>6UIlT2}!!t7WLZhQT5NhNP_r%uY;N_8@ZH|t|0eCgV8M zep<({-k)jh_P$Wos;LhkEcyC;8Pvy!Rq%2XLmVLTSK)2YPT@!Y&}c(lRu?!?c3fypxGJNtvEJ%4*!eQxruM3QNLK1_rLExa^)1>U$X z4KlC@-dS%2bhe&vz0WKaeB@Z8(rl-TY`#w3ramfly{496zW+1{e1EQpFn&7pGC5(Q zK^1=3eJl`3VSKx0FfiU8H^Ou(AX0?&6nuXD=ht2I`0egHuv@RgZX*2IH%=nA=dnQ# z524X^q|~U10@Hb}((k384rvdou;_5<4v$5Kyh*EW(nG(LO0$Zy%KymMg-quE1$|5B z+GJ6|<_0HLoaN1NV^u0C|D9divX;{{>z=Yq5a37GhYlXu5bamR$|RYk0zE-X%hyeJ z9C5~g==sxC2~wW9m4zu_O)HsVW6rca3&e{d;@U918vXbBw`5;_FLExJDa|(n9M;!n z`p9TXhc%l^%1udH<$X-woG$m1Ip9|1p+p!UeDL-4Wj5+?zuA>4?Ck73HD?tPdh3HF zr7Z)3x-S%#OUYo8li=_czC6-SgBlOCIr=*w?vLw%AW(dD{9*Nt zUgsnhihG!ANR7P$!qET8jr(Glezqla1>G?ABv_zBXJ$E`6ChLZi@Ni&OiRUBhbQ1t zAi#mQj`mp}g^A(8Vd3uX1W&Nw?i$?Pf+xWj?(QsHg0t|T z!QGwU?k;DZCwuSTe(U|$`E=@3ovDHvW)1h6x#ynltGlo6c9L`(pL!3j0^Al3L>R*X zMpJbYOHw`zIkewdW8QvWjU@?h{g0!#QPgRjy&O$b3jp*vFIVj~!cSsbNH2~NXC<*l zXMT5;*6(l=BcJCf40x|MWnOL%SB3^hz4uavvNFH!^>+g+=e)zcsqN)H$-v`LrZ3a; z31qO!cIGcUce70NayvRo5}D<_E)(Tve`(H*4VO`T5Jnmay+8@U86+6LDuAP}Zjb z65(6f?fInV?Y$#R;oJSBOqu85P!Ou0fD@CT(E1V`^b}xrsSZoE z5h9f`BKvXWi%W$9_G$nO8^|zMFomCKYV_gU(J7Q1a&8zM^+m93!?Vg4fS(SH zqwkiA&ohLF%>{6rI1sJ#5r#c7Lb1#;4=MG$*j}gm!F^e5?M?%|h8-4w2$=bf41Joy zDLF~iudzw2u8&SFLI@d$<#W${D{8LsQ=pz{;N1YVGDWOn6>P#%S?>F{$4U%ir6B735ctT?`EVAHH9p7oJ3wb;6#;@ws(zVRtzg{Tw9vR5+x z#sIH&LyU#d8YRE*aPjQi3-3sqyc5F!6&fTy?}~Y(wT&5WhsSi$A6W5iaZ|WvnPNzy z=9ar1B@SE2{KQ4`3`ZWxkBr7oo8|~K7RTiC!&C5>PszLk&g#T%r0cjjAD*)jY2d}+ z1C~ohV_AyOc43w2--?v%|0vVWuo#kafJ{9Wt6iW6rs{_=>+Y>MVQwM)|2njxJ^jCr zbnYhnHeT-C)X>VRV$~b*wO=edR$k`o+WUkq#Q`y@{j$e(m;f~3I)O`}X@5EV^_DX8 z>vb`R_^E#pLX#yabhlyfxcl8+;9i>Vxv>5A9);9*v&TJ>vN^V6WmS0Mu5&wfs|mzy!rN`?0G z)~xO<->1D+1WAFbL(Ea1tIwZ#y>FXP@`C$lVi%2Ft5Xqw{-6UFtfSIGwwF0@k|=d1 z+@)-*x1Ox!iKg94WqYvHq4bvPb92~AbVl2B`Qo0+`81LdhTBESq#-ltk&|=sJIo?% zZ%tINCUto~(pOl9~d33Pr5Qx5scf~(eb%MWb2b($TiX7 zhY+VdpR8YTl-gfUQ9oQTFtQvi#S8m*ur@FQ-q+R%>1bD4sdsNz<*P zuA@RV9w3aorbr_g7eE4L?|~J&gIu$}0;BuP{-y*I<90!Zn(Ayjy$4=J-@ZxJXegS9 z8MTS0vOX_^1T*V|3>1EBO6tZ=InMjm{w>9!N3v1siaYPE_{s}{yRgVfW%0>Jo@63O z8?hzKCSFCwVUv z_rkfBam_od!x{8g8g0>-k#x(bjmOpW@14u`oqb#fAMdm`a%xRH`hQyAmX@_r{crtk z1U*H4!PGffw`KvL=#0rPf^o!GyfbjRlf5P@k0wk|2P1Nh%RH88Kl`?=99h`clj%qYGH@d)v-cNFfYDj;Q?A0Qhvw^x`K5rQRqP(a zrt+ql#-fafk_cOXgk9>ybi>Va+*HH(c-PER_zl|%OXQPDU>B!5GDKL6G+a1fz0f#V z+73O`3|qcP(WcJ7Wg*63JlRMpYrhi%H9+Ucue$!D!}H_a_wTQ=tE$dAzQ4K9aRnL> z8&%z&cA>rj3SH93r~kC&#|T~WdwFWAV6lfnnjOOCeyY=ymbV2^2~#pY%NdEM*%O8- z$AClC&?6y3jDd{vmTVJ)Lk`2kOCZu2u|AX^N@W;@tGyLJuT34q-hFK=XniXZ+kL7e#TFnj5W<{oQ-7c4RA^Cm^ z7T#y%foSP_oO&_`>6o~lOJ)9#UnE%PF)OtFh1=jo*kV;(1$jNFw)2WATDJS-c|o}S z0pykB&3fy9(n9EG@t`x>>Tv$s;PCzF3{U&H?zYa$Wv>eVYu&HwF`uJWh8pB_amiD{ zSvV=<&N@U#+b_ZjTUVn51uCNY`}?iko6rN$2A{}hf3xW>7-@*nu}HsDk{wk3G+$rM zAaav)K#!}I(}*YmFP3OtK?gBmlkpT8lx>8NCrtcE_|6i<0t>%P<3c0{QdSW`loORC z<9gRrAT^7_(dA^8oHL1Jl`&OhZF;dh)exITmX!M<3dZBi(8MGX3@{2XvVc{`TMicf zHbrORV4Hi@h?3I(ZgNwu+0w%JloDzQ!}0kAVV{5Q#w-1aYR6an0APd%eGCD@PQ*1I zp&=<0ga(pH%w}xPb2w?9V=hh{CWZYY(F(7IBH8(+Nz1hB8-rhdR}FNYBC#dmez<8l zYKkadH1#Kf$=}yw z<>pQt+}ggtS@k+;6%9rK^5WxY6!crZI_`{Q4C>4{`r8xm@Z3E4zFf&r%9P1(JL3OGRATKgaZ9jHfx4@kYCF6r%Sid01eut0g4G*`Ulhs0_|LYaBz6X%d# zyYZ^e_ufr*@Xh3ApEobD0cYNp<)Xxn9jp*A)lD}T_S>5bHVdL|gj*A}DKaNj7aYhL zxvjxfD+fa0hQNNIdJ>~c^5JD!lnH2XjlI*Xzw9NOOH+Xtx1G^R9@a$o&7|LtXH?7i z!@~!?K6rgo*NwS+bE#28xzEhid@=aILx=AQHlr=hJCL3XG$VD@KqNEa`r)i}nXQGA zs{7wu0L6;j?T}nDt^ieK6{juAo2?}2y0n8PhHDm_unSPy|FY~GG?_V(yl>j4u(~Q= zvfwS2okX?Qz30{zUIoo!2H-ZH6jP)KKi8ZHoi!K;C(DvNt+?wSdtS3dQo^<03_0%= z>U;j#i!<=tOlkK$2aVcQV$0D(XgpV+ACkOe&JrGB)$^ww1{5NngNfkYfP7Dvq9#zE z?tY1%p@&i^2ws}6-d&@jJWs25y}`8iISGvOdbvCZ7S2(71m2fmF5;qN!U!IyKBL4S zd+C5kkkhNuaJ`&lDt=MuH&deox-?BHda42ObGo6WPVHZ~XF&0ZkeRqlQo&zimV{vz z6z^?qp0RRpQ|O!~W#5y(buH&~SCV9N37n$+CM;GK3g{%F>}WHq}h_0SZNdvNSA#)A1@ZJ#`Nu zM8Gso1RSFe3;SE%SbZmnbf#lOAr5cnDGP{FJQ*16auynxPuiseU3V8zoQ|1}o3h6B zk%g6`v!Gr4b?G^AE{A0NdJO1u|CmZY?_g4-&}Fj z(n$#mD*_ATwVUC4PJ;2EFjEb<&P+@ln3|HqvEuyn5)SYmXW^%7gp1Y`WfgQc-+ zv+`!Q&!F7fkcmzaa3b#TS4waE4tu!-0jBFq?a87)Wdn)F{T)p#^7C5CPEh9Uvkcsu z@BX8HPjFGf7qg#z&knO*rWp*LuJd%-@Kzrl6iB>_4n#a(S18QN+_SWt-flN|4W9Wc zJRRL+$ui5U$P3-^u$Z=;4Obp|0)OHWInS^J?XIY?Wl*2~2iDGPBQB zgLTN=R)W=B@ByKK51V==K#!WMe~yY-_7(i613gZ6!-U_m&RfWVSRY$b>WL`vQ`$sM z_*G@|DObX$=<<{CO{~yJl@(f2R4Uhp`Zy6f5nTL-D3hY9PA*zrRQOJ)l}8V8-SA39 z*hKAOU<_{&0zzFd;4#Cqv7lN`9QnPH5!1clQ|MBPvm&2S+eq)Qpm@5V#m?d{I$Row zV8A^%^&v?CrW-$Ws#r1oi`ZS)TZ#b?IhwjM4elo^X&&8ThT)xi4KySi`WU?oM0y6Z z=`xANdLRhGOuHO>tdhDdEo#}s{y3d#A=5amPIK`~I<-LU>W2+CKR9c^xJKU=BO4DK|d8P%cvtkcT$rVr?-SObj0fvUg* z_*tb$ssOR+{S^c8VZQfKKX&8xfpt z81|Gi9f!D})gJhtH*DuMEideCq+Tc-^9p9*)~v0~_9_pOnyyWA=nlNOzK&1uF(HKT z4tUq=kJ5ep@7?MT3s2tgok-lA{Tkin4bU?)`nKYJ_ReD8>;;{DG8{EJ#mFfd>Esoz zuZW`v+ann^7(Im658hAm?#JzGY3a@M>Ckv-$$*bm(!(k2OxadfNUlUV40Pk1RCQJ+ z8}CiaG~Q?=xjPV=w~hZVZ~CJ0d^h8%`|5_@bNj33V;!DEf5Py&2jWS;r|Q<1n$mZu z164wRO?2JXH&ZPROZxLqG05+1Dl9B5^M2>K&??L;%g6Xq3ntV1EP`goOD*lgdBfAr zb}EL#)5Y$R_Tks-WuzsTlX2VzFJ;}LZ?xLo}PFz^}R3wN9}eawtNhO zXKdLL7n*v&a?eak=|jrYJ* z!=^TcaASbuvVPUi)e-;|V*5vxCD|5aNM#!X6@N$)<=_S)W5+oV<$)S+nFUdujy0wl zCadwfqRg@%#yQK_du*60p}N_+*{YO+kq2nj5qKz=qd(T%iw<(oG(e*KJQi!1 zq$&F|mhflW?B>|mSZw0)@o|q|fpYQ9ir2|^IO5YgZ-D9(WJnp;50LyK7k{!rp?_M8 zFxZ@FTta7Rw0EX8y~&2n*=DJ|)U07ke5sw02DO|*E2w#gt(QdLQxq&A;pw{|QFC10 z(K|dBlI0s!H*jMsl?x!imi=lYqtp=+zV6B{fo1eNlsR?M)DHMMzKo1A= zf~E+rL=~lD@IgJuc_64$;v-|z_vkuuEoE}fw~fpt_sWgT6{2=#%)c!nzl2{Ob>ri; z4?Q>sk>%j_4|BS)b9F#&;Cn3hO&;ECs%#eEwJL`3l^NY4P{Gq!ocw~%CPc&=Le`L= z8}Z<*vn$*|142Gz;)`w)UMWOcEi${lF8@NAUG}cDx{i;eK zMB_l}Li~7R`5pc{{4drRPYfn84BSu1!l?zEZ-zhK0HyVxLwAFgZ9rApjbn1S21{r% zZ+}n?4Gj_UyB&_@h)xss0*vX+i#rVipJ~T*ksJrW$|2%+8|p*D4(kD`E+fMx)h^{S zwNEddF&QwW+8mjxD`!FPF|@sNn5+1d)DcRzTO42cftTnLUyh>i3XrXRiLaB&fCNs!GN)Izb<4`>s9B{#BIKC3ZM#V~XL>GeG7u zp5W*TMfxL8YSV60TK_BNwqRi~70?5Txm$R@@7?ntOsYPI9hhgbF|jG(5F*TAi zU|pq&3|aVjtj@=*!c4pr)3I*y37j~?6Y#%L-8j!tZ(t|>FUh-N`#Zl=-L+;vqZ7ZO z=S$EI68?VsbCvHSSAqq-@Kw2W5RT{9uih;{B?!&cSN6a6#S$+%8R0n?q}{s7~NBXZBlJg_a~dDqN~kJv+;I?cyLv6aK9HFAR+p^i%Uu9 z@}s9tz)+V`l~rOBr>iV^9-n4qD@Ix_%n!1oC9|%Z&X2UiLJV|OMV&1gGuN6K&-)B? zIUz&|E@eU$y8YrxmLczVIsW-(*`4Ieq8oKwXy2bVZ{7fD#Z{U%QiGnh`?f$y;pYhL zjIu(((HbQ@;nTt-Wg;uY*3qeHY5adZ#CctO%p@fS;;oU42p_z zKJ*9=Yu8sb{F0|U#Oj!G?DgRSf04xG5Tmtydxgc}oU0SzxCynia}%a(2UqB`XmIPF zXv99f9B4gcprBfMpd?xe%8Hilg8w)|Nq z)NLHOZdZels-l_?N?h(xCv(78E-$qHALbB>UguczL*>u$@qW?W)u{J5;U@ox`B!bzm7^q8Hgn^Gk~H_ zg}Nw$Xv7p?1UUE!Ypcj3|Gr@*z|Bhs0^%0=fc#7`WRLs6o%*RI zRFdedAk}VeN_JW%|^HF9EoZA;&(OX32-x2#F>AHMOYMk*)6Q@W~PQ_1cQ;g_VRoDG+U@WQGJ> zgjEryObcojfz&#cS81vYA9aIB1?Aa9eG`>4zk~yqTBrD%okMj{N7gRqin${F6O@b0 ze#Hm(BWb1l4>!iL^NAL5vf@=SW4M7HS4T25a$y-ZQ8rPsI?1z7rK4#5OcmB();3RX%fUwTw|5Wo%A_((XqaI6zkBt6cg|l}_Gkd=I5!DnRgReEzDV$v!kVxX*qhBNoZNXW3Gm%ka$j z#d~9YBe-F!sRN(RQbs7%KwqG9V~YFZ9JXOn)xc}DIz4p}(aF-bbZtB7R4v|l8i|pZ zl+K~ok`Y02oUY`Q99@8(M1)rt4;Z4Xp>R1Yk&0ILSYNKvI@c_jVvkdF&>PZ~`-#+3 zbdp_#G)v9nKgIUA>+3*g2H!mvv9RX$uJFf?ud!3O#Yz)@U~)bvm%ihm2WhM$8QrpR z;QEHcNPG-5qM!IEP>jz<=qDC$&6*WVv)tFn_AA7jHHM?UJhpAYdJT9%p#!DMC8ghvn z$ocI!&zhwvYn>zhoYTF=5~MN0#?gHZhu8j7(f9I?IPI^3)^v}I0%j3}`$)`+JV(q> z9BQ~^{A^vy1xe#Vt9>U-rU8)46FN=2qVAt`W(BG?GX)%m_EJ?2-6I*0-{8hb*a`9f zc64Jv$GW=yG$63xc`Q5FAX-X$O#B%_QDI}dNY)~3zQ&jmIprkE$=QPm1i&)%{KvC) zk&+KTFxe<9l5dm?5A3{sE7B_h_mO=-FNX}1$htF3A2f$RH5qo+9BLA_@-vK!=$)WxImNw)IFp!6f4Ot z_IyCNw7P+Ltlx|Ogh;*(T zVCM3y(^E6Y5!1|w?GQVuAZNY#G+Kyv0Ackibl>Lb0A=qO%D2nY%I8L;kWFApW)#SA zx+x8?@%~gq;=-c9rNAmI4!d{zp_CsvwLQRsi~EdCm?(tu9|kzZ+JE@}#>U16{Ml&a zgg_r_ClE3mz08{*@g-)n^u>9WA{^288&)?LjTOxT*_Ixc;De^>bWn;^Oa>PABvTrr zC_FD{wQ;Cl6&^2K(ob^MbzPI3i+q^yZXW6sz?T~NqkuU`Aua>pSzvL5->fVtHGxY+ ztAs^Z!RYx$>@Hw20$X>sl|D_D+3*RBA7Z6eyC2CAYm#}T@7_5o-i%Kj)nCndcF?8C z3)OYPH<1O1h3u1ID9<44B>E*xjk#l_Hz z=bY&_6CH>S!RNte?-I}zn@GAUUGlyp&ayTAtgCaaivp@1N0A6W&yV`5O9Rvsd%MN@ z;hl>NUgz!c&m`|wJa#f>W@Z4pDhp`Q5{yCw?lGcK$mB#e{_AbEwzj7MysdA(e>21_ z?KxxGYtfFM2glu~`j?@7+vcPF+&C@ft3wMntEMgv{6+^9|MZf4I@pD97D#}xiaBQv z=m5q43aL0yo)oi^l}C-q$dB(M0V+uanA|cZ0Pa(l!|kPmSz3&H$DAG3Cz4#mWc+~ zr(^N))9wHn%G?k7bY@lYfoH01j<)|l!jj~q;WH5ac|U-Us!Afd!8xi$1~U7HlQ4Ff zdPqe%AP%h?!zBX^AkhP9MQ35i#5BS(Euow0x^R9)+YB3gTv0J9m5Q^ynSHpo*@?Bc zX(|IM11yhtwz)eTvhPpF+b3EAeX@)m3w; zK2fV$Gihe7-1k!rDyzM}wW=q(QyUM2oH)`Sdooszul_vh*zo=q^BaC_t%?(jiplum zq^R!=H#{c7Mu9~$E@BJ{rEDXOdHWop-|y+s+KGJ^F#121sXq+}Ff?owouF5hGnFz_ zi?RNGQuT~lP5d^X?7kcJ_Acp+2cT#pat-<}iBW-Lun$<<^1(Q#70c%60 z2Ocen(Pygwgsl=G5v zl7W1@8@-V>@qA){05;}=9kLNc>-X)8?p@aaNhhc*=p7VZ>ng;-c736fUWJ*S?Cc%u zYiW)#WXK-kr|D{m$@pk#3pH$~JFU11rbG66Dm@+5KMLj5tlo$@Fk}hwlI4U}ntfe( zp^Vrcw*6x{chQ$HOVPA zsXI{}spG{Dbm3!xv>>#HP?auO{GJ29QO`pPbh18)jBpBcN-&loi$9Qr5$A#*uMz`D zv#~($#kkI)e`w9hbkh>y+++3rniRiip?K#i>0SV&F25nCdedj!CerP-aT?AWDx$g{Cf{cqek}kNb*>(}gHN1fEWf zD@4Z%QRzKX*-AX%U$yd(gI$x5e_F`ti~KXh;`JYE;cuQ5LAQf`1SYuy@RJI zIw?wtxb@!k{pJ)?>^pIg-qy;FnIsu8Ruv5H@90nPNw3883;813ld9UsoM55$tH}E8 zl43@x{{btRR+p_A)F49B^6f{Dy3^nK6bN-*s)dwI4qlT5#m&B3U$!mJZe)-CFn}4n?S=G>$@g4 zM*bIeC`;4bZL$-}s#K*vxTW1%U#}t;fzKKRg^JBMynbT(;_=wimkgGIepBnD|0ksY zYeKTTpib=oyNO|0AOrCY$;@Nm8z6@+?;)9};ES=w&%rrtUAS(0%EjgG<4Yon@qy#u z@k{ZMK~s8DT>=A`<*3o9?Y>j8xA)aMI;A1TDnp1IG|a8RDgIV<-KuD(;rhS%wf|&aCF-=mHGo%3 zpnigL*Z`+T{1CARaJWM)C*n2>`-i9Q-x_n;Q_qC3h*>2g*{_Z>wK1v6_A;}&eGiha zUy*_Q?k3e3`ORPs^JpjtK!7{|0-!wRq)vz0P3b1Pyjg|Kc>O=qcl$EmeA8HT(DL$9 z$qVxanU$6vo}PZUu^J2x3WWUVD=V_8shx4NZdX5^xmsK(w|zKTw_0>#sVptOx}Q;R z&RBBNS%$E4^r#q-=K>pPE*Z)Wy4e@A+o7SGsFaYmXJf4X=Z&k^^$qLJsk1wRZT%ki zzJ4pMS)&^wK;}z>0p{o&tfyeZ;?bU6CLFe2sdw8{sCwypS5By&{3tx}s08ql7Ka#Q z9IX9E-C7KD%46=gX?bGydG`-nH^6%8cjwNOixqM;sg(o$6328>7o?VaQ zb7V5*bT(V+Z{JVM&D$pH?|L!cZLC@jnfG|B*Z#_V|7`WNg@%BDJ|0>lXCpUZ1KQDG zGXlND=Esh~kMAuT!rpunLx^&-`k@D&+;VBORo95qN*y96$1fka*t6i=$S}j*R<>5N zD;(IvGEW;g`sdetfm5^il3kL_lAUAoa;DbGCo;Q;!p=(S&wh1F-z{nlhS_JpD(F2G z%7+tX8ob)7k?NK;P=66B$|0hBXKPmsLh=PP>(XJMPIYZv9X1x$($_x`>+37goaE%W zEiFqIO*uJa?Eh@aO7YG+BjV!XYO1P~l$0(j8>Pxmhu|bW=$O^dopP=5;o!Mplg&tq zNI@Mw;>W8mCZj!i@{Y}_%!Q458>*^y4UJe069pJaAFvY!i=F71dvdM?yr%ZhAj40? z5P3@PZ}jx~<&)MwIPqO-!+J}giZ>02z*Cw!eLnXph>AZ>8Ju9}%VP{WUqfv6XA*(I1O#b-x` zrPsE5B;~=6K3wvjmnBVjgyi-ZLE|Cv_g`1xU|?Ea_dr|GN ziV|Wf(V4}|B61NhGW44~VRGlnCl)9p)p^Y^jeZzNSc&&Bgg!Hg@HwH2s^Gvkr%c100WbqbgHGRspEAd^bu~i92OaOE!Z7FRd@FS zA`c8J>0GDNT|Uz|t(~Mzk@`hL`u6%;D=`8ADqyDc3mrw@C^(T#) z8=u%HmzOo@g+!KX12fn?1G8}8$gMe@zClIT&XLY4A!L!sF3Fs=OnS)Yi2NR<1{{cf{xgh z*18zvocU|Sm$Uf)<^tr7H>p`ES*qff%Rn&d)R})yNqr`Ifzdr~k8yvT{`BvnB5@>h zz;3Sp(+O}VsHmy$AFJsT2J9MuJ}0QStl4R%?<+nq+gMpS@nt9}DG9QzEdXV!kb{Ul zM|^;nO@Xs9WX^`WW}c9M;K7!8!$aA8r{`^ITACWYnwXeaA4F+ELmC==k(de{lf772 z&~DWy!N8cn%re_~vCC{&`jq8r>eHT<5JNyfASx=#@0KoOa9p2vT~q{t^w>2JAO|54 z&2Nz;A8YWRdBBww7bBpe+GkDPA89cr&djK@4ZMBLs|8TOf<;0sIzv?^l_agKL2TNYV{JS+Wb_f35gJ}st zdi8hrD>~@EK0Y-vHCdx~v#ktA{`aHDsPX?fz4klv+Jc+a#le9z;f`%MuKUi@q*0Hj z)yef^mpQ&pIvM(zlnQ$R^=B{P_mE=m*=s#h^C2b-LUbZw?mq=mXX}}IGb&yUJ0t=s zvm0XpJ>4{NZI=5XlI4;|744N=a^7p#Y-B_$g>JK}!gJ=wKgm8={(9pal<^X?C^Oq$ zQ{}PI>aGBOwwg>Cwy)5z^ZYUr64~blt;$foKQrLYyl$C!8!X;OFlnF4$Ea)N-cmCo zaP>?EF?Dq`{Hqbp0q>@RJ5X9iS1%o0=}dLg^p##co}MYN2JHZLYm zmCo%Jt6MI7NJvP-W;KgP^78V;yiWNqgUw~cM=$3BK&is(lhsOEuzqFxannKVLgkBy z3400(u>dVO85tRHBM9%`zn2V5notL*0+yDR(7l4RM+0IQn2ta+(7lS6wT=v;ba!$_ z3Pve*%&Y!Mvj)|64TqoH9{VuXdu_^iX&EU3AH^uyxv8A%3mUbQ#5OP6F#1mCYXoU5 z;V4%*bx>wQYZeuhoMRLkiw_+xPWKvs{uZ~V#l=+_LB#s~L{@b%XU2Uf%yYWSv~-5c zgk_<)0>9hLWePjS^z~*j*jye%cG1v73Ave!r7ym3Q=|I{Ku~AXfvN89>x%+r&xHMG zwY~k%O%+h=lZ%^M;&Ve(X#$XW6GQR2Am1g+`N4nMN)YG?Sf*9Oi9bA3u5+-zFYtXk z(2Wl4bWq;l$zJwxudNv9O9q?(wEf;5PW!X5Q2{Q%i2*R6@6)}j&D_e^RAbSxusXV> zt*xNPxk;Z({|ny@QiQ~xdDq8V#|Q}y^AW<}uCu+uENV*1uhp)8O=ZN(`p~SSmDYzK z0=tm#aDlI_G(e|ju}b$&*Xcuv2%Tfzhz6N0nbe-l zSKp)!ZU&(8pjgqZv>X)`OQEBO5()dsBWjUVg!h}^^SKm-bocgV^1I6`#b4{4ot86;%Yr+w_#p)aU`Ypu zpAvbFd>PLyC)J*{FN2sdz)5RsUq*lS8FmG^KE?|gCjxW;aY{hS8=v{k_~`HwMpAr^ zbm6_n1~lkTTs>TPaMvC(!h^&J2%`YIu!|Y2r&POuRvr>!*~f2<${iNcoxkdJLs_^? zN2VPSB00I@*OF#HLyp0HJpxx?;DAw8V>L+d(%kdVwU&bFd$Asrn~aha9MNj}Ed-ea z)PC><>e`)bXU^+f%=esyca2`PF z9ZKP_Yv8A&vz+^B=*!gL52(58VrRaL3c$mVrU%Y#Ts~%LyUm3_#4BeD3kraGrNW>k zAC(5+9Z1Tb+|$<4sRS1!%KJ%rimLokc6q5vOiBV&b0kIBo7{iI$24(47N09UfWjHe znse?2*PXm-vj&?j@Bk!HBcl`#h@VhQ8rXahsHP6^stE(nC+Tq!0S4yd=kCDo+3#m8 z4zKOQw`v+_a}iL`Z9@Pl>n1M7P&Ik%=aQ-kD34=9U|vEb@Q>ut%f;lI9>%3|<176r zDi?VskTX42?rDhV%u(V~OJ!cp?wQfi3%lkuXE@EgH{V6J(aLqt9X=HHfvl zBcZj1Q43!czBHO!Nc5G*boH8}5M&i|X3)*o9sQ}Bt~nXmoDT#lrQ5mR_=m!x5(_x2 zbu>0M0&@m918|Xp0Lj^&FsszZH_Fg_0rX_z@_hs(^VbY{_siY!r-vJb48G~gEjK4y zPS+&3oB9HYq^3K~a`gis`>6uF)14vUQQgwwy-;iUoq2OZFVDSt@rd93SXNqkf3@8& z+j>s~K;eptW7(hlhs-W|qF`ZQPFbia;KXFT!)xyI_YRjRi2=)&|ELtNi~Kq+xYWqL>4xEkiM2kS@cLIk?&)T~gxp?5mmE3y!cVll%C5GtUgF)|-Tl)+VR#r& zTlf*Z9PfvZ+!l|nkNbYS$7LmDW|kKd5fwEyGJ1T(2G$cG?yi8-g{6R*{t=eZ zGKZhk)YMRdjSptZ(|GLdtgRPYJZgjs?}>$dmsIk5jP{mT_=vT%wLR}I_tNZ|=stXy zv)`&~UIMfhB8zs=vK^pTA9ly{f>B9`L-t#m=*|aO+RG{{S6e)s0pTes+6AcC*Qyl2 zDg=cfy&+%v&_hd=#zVrTbp)=f@}BMe(R)kx2R20V>-kjGFGv~>PFfi_WWt1=-x3E{ zi(>pn1KqnE9tSs>R?1>n*0z?G8RnzdRf#xRY?SY#kY5IVZsuH1bQRdYc$3&3c88ut zJY1(Mi{%ERL{uy$x!2B=gcxyQ?-1F9OA&jnz)RSAOB9TIG?nzwk>^IhS0!!RuX+{q zA47+`rbl*@VMg(~)TjgneE_PymX;Pd1_3eCg{+a^zv0)6 z7?Y;+-Y6Ra6z*JFAasBk0|tXH_)ddG$<#Z9pq$a9OLHnQa3m zC$PGBJkB>e5)lD#j~)joA_8GSua%0HM}^hJl?`MzWc|Yfh=w}?Q5nOk7n++ zq}jvhwUFeTKik8yp;+jlnJzMJzbjf=>bz#^Bpt+Dyw4AHsJ&idpCkIP2C~B;NN`#0 zNc`=A)x5Oib?)5t{(SCAJB$Fu}RuKuiWm&Y+I#x2nUz z!m{8@1^S#&Q&Anw*A&*)P93=sMD%`?i^7ii`Sa)I=B8X4cc`S1Q7%m)AZaF#8dg`f zDgY@qoW@f+ZIvOP1lUT^sr)>y#>RiXzkve;bPzJ0vWm(D06t*Yz~wjx*qXkL5$YfGz22uk>OjI}p{tW7_ima|rVbA-*&hn<6E!3q_{ z@nv_DCFEft$uj5XgF`~lT}aVfJ@smAeCy>e2$D*Eeq-lGgF9@6IB?J5U$@AYcabPNdJK3cbiQ1 z;_om^mM_-l{LGafr^;v6tnDZQIX@FHgk91#HG%oU!jb_=POL<-?}IA<7!Hc5-%A{RLrM_*V&3L5DOrlwgQWSE{Rc zf|)o$TrxKgKSMu{O5T4h9`vlfz{FT`6_Evz40T{&(>wbzMk&2EPB|9oln|(VO7rEJ zO$O9rn1I(lMH$5GVH7h`Ydft&aIPHTj438oCApDnIuBdSpnKJu>KYowB0B*NnZm}_ zj>Vz06lYHq#7+achr5kqB4LQf4=kx2$o7g4CQ;H-@!FU7hw8O;)8;1A5ADe$vW%r& zXtY+5u>cIOTnH^qA-P-sIUHtP0&q-HH`RFfoVI+YgYq+YIHu2at` zCx#Uk`_tsEQ?pf_q06;YeUpY+ClQ@OTB_}ax>rZ0(1Q&Qu`r%2DD2&>ZPLtR>owzE6srZyqpGPCCNMLZ zb^jk30MDx8;G|&|vH)L`8zGgEhOVQfX@i44-dm7Qy;}#8U2hUn$}$OOOHiBZ)>Y2U zFQA!8h@bCP)-sf0rFH@B(wPSKfY}%rq$;uXZ0?s-^`s0#<%XzwT&wn+Rg8d3x3SXB zz41y=uLbwm)YCT2T~ZAu4a-q7{FR>HQ13YHN*EZc@oHFb3@ydQ9@Ow>ud1{&^vyZu zvCzwvl{9l@^cH&Ve{t9Jm=%9LfaYG;VAyi=zP$1WhlDV5d4)2$FE(j74>xHVd(JFz z`Wsbgy1)Vzg)4b>>u@drg4CG^(7}ib|9pV{fRU;`ABu;Nn9yTtW~-%F##XgxPQ1W~ z)APlqJ)LZZ)D$O7laaf;ZE_N$P6{b;Xu00DONU_yBQ8FXi>J#00sBqs=RPv=q&B3E z>Bzs2PTU(0q>hs)xu47Yqjj)Aa|kCxzYtrA9&r%4c(Pf#$Hg;)nc8RX97?pRM0dRJ z$z3C!RQ*M&N!_O4k;}eZ+x)9hj(%l>79+R;fzd21H>x$>o^b&w1J_t~Y71*Cg(-9r z)1`{8iU3?6O4s|L;UR4Q(z6^gLcReNujyyxYt_H>%uu$h3ibYDlQv%_H=rHQrxD+j zo32*sd>O?eW%>^1uQa{|pHNdUm4h9Odbxb% zG?VH;`RBplo`0;bZiAJOBgk|bF$)rrkVUTSUW9E^bMUGcz(rdSkTLv zcYp7MdB8jm$<|aHo6S;-sFTe{IhBoNXByiTiNxTm6vbMtXR+?hJ`#1FMC1u6!L^24K~)B{LO%v>4OytMqtzz$<>n3SwRE$c9;6WwG?vw)Hq<*(!-hM0J<@{rxwaD;`X zx$ZFP=!N`uGTRk{S{H@3IO9Fkat76;;wEDsQy`X_!?}~UCN&bbm zAG1Vs;j^v(3lv{|0(P3#v?jXaaABMNikLNF;MmA)CU!N}Uo`FW{y!-A|CLAc&rEjI zNmS0Z-|v^oBr+nnko|ivx^jRI1Z3bnJ~oDikIx4femSDSu=&Umpx39ENrP997o$;x zyzHEuoV>gqJz(tb75^goZ~7*HH5(cl3iNuES5N>P0nNFzAVElNtqWjWwlVnwF_p*7 z+4kovhRT+6fa3JmP)2TA*&4#U>!4n{|g4@BTJ5Qu|yQ{|3%ze096@vf1?MHP((lkqyFzE;L6Gj2?(ULqq~QRE?vy^%-ACX5dt>h0nLG2%d^qDEa-L_$ir-r6 zx7RLG$P@lqUCsXbwRp*tlbw5aVQFFEK%oAkuNX)d3#qp!Ie}!Zx%KsMaHpBadc5&t zay~lByBpZu{H?(I8mN|*U9k9GX0 z#r8g+*Q~YPpR03i@|-C*R4@p;u1jl zE2$$ppwwoy(0JKJY(?_UdNClBlacns{G_p`zn_+tHqz>bsnI0jZ%*}Ue=>G2IPmp-(3L&g88gxDCBw&o9 z{`XQ2HV%$N>Fk7rgkk=>xe7y&DQf?M$$fwbkiq^is0eNuO&L-!++U==J0ics!NolW zr4{9mA6=^Nqo80CvA!<-_;*1^h(R%v=dcwy*4BW-XR&7ZVE|Z0SmV`x10~qiA7}I+ zN@jTT=7FZj-<4Z*2O8x*pjT#pnF-x{EX?`ur_6^JZ~uM=f&8xuQvZLbr!`-+!YDYeC}8dyo^Zl=vRgs@Xk9Gu8y~zo3U1TK(&PQ^!J>oi4wB z58dU|L>SS zmVE>a)4IyU^hcOZWQ0&z9J1E8poQmMtaWQ*Pi7cLvLI7ja6SlRlNcCbNULGrWs5=_ zl=ja(oU*vAw<@-`R%tsBION~2Ad0rN(qiZL>wRTiKS{J!Sl_p%9OYeJ3#a;haiYUM>6N>d zg6o#^WIkIRxLdb$%e;r}U6bOFIw?O)O?h^jnL+gKEU7Frqg_u2dNlvc!=hlN-v~N9 z_T+qu)k{G>mW=rHsAvVoqi}i{qko)aE|>a0T!1H62k-P{!-8GC7N#v1XjR+!uaJ=5 zBPakoRu(_0jGqUTn>0W~*SOTz0N@c-E(h)sShFou6QJ zJtpILzD(X-V2rg_vvl^rxbRG8m+m;pxv^}GAQCqc^ArCttrW9AUksPQ*w&X*JcpDf zLO)EeRBlH%!ZdSNNw z(v=KsIUW?5e0%i!_Lav)_82DRpSf`yN?T|TQe6%)fv0VcfkTpMwTo;L5y5GbydQJm z32wM?_RCw{U0s~K@>yQD192kjogSpAPSJgKHiu(v&!!35)4eOt^WqDR_U(xRkS8y` z@c7;R`y2_<$`%43*Yzr(<#G^xZ-Gyg2zPvDEmo^Any+^NrtHsdQEu)#&)e~}j|Ix}VBNqaXDQ@zN$;3|*Grb=u$-&)^}RRT zwY$5!)*l-j1l_w`A-@AB3ZIdY@mb7P0#=F3DWT}vXX0)#yCPrYANj8x;U z&f{d4o00gYiNPtX(Czgm*Y%X83^?lb`8VaTqlkPAqYfGH zK;-0i035gu)o-M@ZSU-Sq?xF90Qp$*yNkfzpC4gi$pLK9IbKsnzLKd2Ia40D;S}D)#6;+kiD7|qk$Sm-#8ns=jt3az z@t;4UAi+S9S`u*$G1aHr^wjehHx zWj*J(FCw!Y*p*n@nfBAkyMyD=f%M;USyYb4+Pn@buwQuRHz2{x&dN%{>#8I?S)v2G zy#wSh;0(rc!3_UBvxxB?2<4!nDTN{J_^pTo}&U~_Xk03Cp<@aWabR{_M~1$|f5)n^Ae zVCEGwu8n2ZY>3pUw^`i(KxlObhttEQcE%g$;LayFIEcczs}VZi&B+*GA%Og|1%KlfhCBEjy8Pz4B!!Vpf&L8*6J#r;{397wzOcE*U{|s-?O>R$FfNte{>jjc2oF!+ck}u{xexggKyxqC(DAZ}a%9L&PZtX!hqHU{SYGrzzP^%UXa5GWP*c(d61})(fPxuy)Nw2A5 zEdY0GfzmvA^2Bv<2x7khJ-I0?EZl?EFshaROiNn?=dty>Ldw*Oz}f6Wp3PFgN2#n- zDw%S7Tn*g=Mbrc58&H!tTIsIg9}_6JfirUMeFVUxO5H>I6J!b4qDIcT}N?Te=6hi_(rlB7%Z>u-O;ysk4>q*ssMCCk=8 z=WY5yIqaA)igTd84wM40nZYWlsVz`wam-o2|E&^A#=9NA zJ13j@Thpd1XAE?JpNB66l0Sa-b`c!gi0AWg)zE-OYdxG|{x3+l7zRX3a@WoDbC}m% z1MJ4~stX7L9TStyc&)b`?&t6NZW@dB{`Fj&16AO85F zVBB7B@B{Xrj?=sYC9}~$9Duf6-QCoHRY677+12%!h&6Ym;UzUS`8(@0-t~sh3pv}R ze|D);-T#1y$^o2}9p*isL(rFYr`t@aH-#8?KR1=W1lI8sSjR=`SCMbHxv#)kqGVPR z(9et^)@&mGBREnGb3sEmoZAilJ2h2Rpboch`S|!)SSnT)09^OF+0g;(3QEgfhizyg z9hNnL{{DqJEw|nLw@2WdcJQ}vjt2|8t|y=G=E>kuyJ9ctrB1#GOi@r$y88VzlsJ3g zQ@rBK&~$)?h6V#1VvQP0i!cljlJI!I?T0JDb-n~>C@5eQ9-u-B!j5K8d7CU$8#_8_ z*st#gs|idN5KAv$uecZ-Mzd~jt~gj(K^GKYA*c7wfa^ny3sfrMi+)(XzP`YcFYUy% zPJ|({jd0WyE%a8o$zm-HHML7H;F}7`Ii+ z%~|M%sIc($j?SGraEfT;?r#AS09eFd8M5Rs|^~5zYO+p4hCyLURy}WAUDRz|tZYI2eEL0b?1NK6V8G z8^YHa=lw0N#ez&ztH{Y`khEwX9Uo^k8T|?HB^F={U2JBCr2w!8R`mhytNVWg^F^9= z0}v5d(*t7E^*o@(fpOP^GAK)W+}~_V?Y$6&`_?$%F9g_5JJ2;WfHcVisuYMB@sgh5 z;wmaAEFZfE-t(OW)nu(@~z%P44Ks2%FhJd}m{&;w*fjmR7&NU?v##gV_ zuV8o0gGOdB!!p%~h=?Rvpmq=< z<~|)c2hA5wONhZvw0L;|Di`3FX?tF-ooU^Hy|ZdMRsJqIc3B9Z6Y%PC;2z;dGRVf( zM~@a_07VcwWta>yIpBHCe&Bgw-$^+CfPyeWbAX`}L1a7t8Sq!OFcFfSmph$#)nl<9Y`udW7(3ktRYHU@7F3|t!n(kEa}fapS54pJ1|-a z0;0#mGQg5V6v@GQXf?aL$jA)-lULKoUV}1KZD8JUToBp=h6@^hfZPlpKR?WF@*5s4 z&_ph6z@4UE4;t_? z(reXMrl+T`pl}l$?^X;GeHX2mxke;X&_W@_0+1_DW#vh5@6EjBIq={vz^?<15AFd! z0Wd;j70A*p(Dv=^ZF~mJi&kVTd-oDhw=dGH16N(1R}Aw(ft*~OSwO2jE|vmOP*A+C zps-L9&e5Yu8d_RlZ+nxuol@S}WaQ>C_R_|bT-s%743&@vzR8e6B9ru86@QywdfURsa z0|E!XZmV~DAeI*pG{mbx2qyfJk&zKtbdcu)6GV6maq%ADmRElL`UQYvmCj)%_)VY; z{D4yhGJOmHEO=ajpC$bG@fMi&%-_!1m>~WD_pbv{0zw2l0`O9?2MK^F0NKO?q4`M~ z;6DHW0kdw$1cUSf=^&7_2m;J*z^l|e(F7?FmvmrTWzV4i%UgLKC$gDMR`M6^0tCBb ztGl?^=4WPVTF_k-g)dgBa2F=|sW?i>*GKoSdMCnGi5RV{-gO z6Q+!H9}(uE02!n@$SSR1&BiG%>!7Cu$d9!~%JFT@JR>I7`1sMmkbuW_wFjY%z~+Nx zajlMui7{nOzRLCZQ%lEwUJe8_+0EB zUhW~Z=frd%4QbI0n3s`uEPnGEs26-Jbtr+&3~V`w=KvEe#h7<2^=8)?z+@>|*_5}R ze1CsFN%{i})XnhS^)Ltu8tQg~8JktLwGrsF#wxG4_!fYIYAe{nQ7IBKY6uyxdcj2V z@enuQ%7LaL$L6U3EN$o^;JfqDGJ&=;1p~uAkVlM%5C2#wLOdiPSIup@&wx^ZC|%?w z6_p`~SyCKig$)35>W`r(20*M$_X4=V9A&Q?t1J+p=`_1vf{|RE?!+b}%nn{GpJ})O zw*-R6GKt7nfaVH@^Mg*{(F(PB`!nmPrnx-9@xwJSgW zj@HTtsGS1@O#tUDH*7;rq84V(lVx)@J_R8%7ofGF4v2cKykx)=7xTM-bG|);@08qu ze!C7_$r?Zk0I0$QaJVxzf!PqSgRQGJ13d0PD^I5d7h^@mF+h);-C+ES;7KCGkuCt5 zod=&P2i^!+A%y+u`9T5l``-K~znb|0FbC2fLc*y~Fmbyn#t%IBr!-nb*7sf*NUAXW z+5pc0|AGjVbRvMl->?4-IoE$L{=YDdq!5Wxh|h-OwC1;r2V-dU*bv_0%bw;wjytI$ zjET)(AeY&*A>4AZU)T|WdL z;K1Us6W(z>rvmd$Q$Y}-1a{Z!tL-p-ith=8nb9*c9W2bbG&I$hZ;g=MO@S5u3rHZ( zi`K-n0EtKiDXlyH{J?%ZzJBiG)t0GoaOdzTcxM^`BC8sd^|MFY`728y`)`6q(Q3T|TM=I#3L?%b|8xnuq@hs0r`C`S@m3*DP+Re4sLyy*qyO}3FpNOGa-fMw#KiIu~QMsl^X z*!!Zj9&Nj}$(YFQUtcN~5tQLnQ2>HGl1B|3#}jwij5W`c9mGGU^&^eEpzFqB!`mC} zq&7X3H(t2*<5JCe{5#JfeS7gkl`H|Ddx$83tEEoL+94TOO^7#ND3Fwqsj3t%2Fn`6 z<5bW4o$4X5 z7-?1@s_||fQc!qFDXEw_>dr7y1=+-@?u$It;0I&*HoBf$wEZV;ec3n2W=buTw6yf^ zG+=+Hkwl*^7fp9gTf3lhd=&EXgG`+@l1f;3Fbcx_bqjh5?pH8f~LFDt-QAty8) z0g#a#mr2f}#_EsB56zh%uD(?#Kh}B{z=vE_zhO{z=RnQ!6CUGT65B0ol&G^*Lgx`wh)3-tf=fW{#RyPbJvfyuSyM z?=aKZy8y0t!dcU#RmLJOZLd%1*Tjm@oPvYY3G%-NO^S+|QDXj9H0B_^$XGM@I%=*= zWcKR|)s*=)HKNuj0P6r|VzZ%HdjnK@W)lWvgE_rVA&2y0^D_yU1uqn>KJ&#fQf@C| z-#EQ;Ef>PEnNf$D7O2aSRPVyn&X2g|BXbJgw~KVx=03O8tamw&!N=zT3b`2xFiF~h zLIM$ViW3JXt1dhz8fof@#+pP;rp%hX^A~(knfTvIwKqa&sr}HAgRVr8pG9SpGp6!^ z{wdIwF|R6MEz+j`&by#$c?TGM;iRGJFA1&D$gSN8XI?m$^KS;x9KVWxXz`)=jch@h z!f#C`=8#ozsd>6m!e2g)D<2LcHFiOu@C*+^fVKZcZd-+Q=D zoo+HxY;E)l{x?Y~N|^=@;!#Tl6cldLe5GY&x{bIna2Ra_4N4n?TmJ1@PxrhN za>%*Ut3P|#v+j`^EWSBnPzEJsGZubn@Igp;`;;={;bUbJzwtH-8jBy2Ma8#=q{fPw zoL28oDlaiLZ0S1PeudzwOJZ$_g<%~g1DR`WL9A6+xk|F_c>EB|Is4cAWcI^{55*-Q z?zvUVaqM(+V}qI51D~&Vk#Qb;`}$pSxN?8k?v2GhdqYFf;KVw1=aM5A+T7)b!9XkM zaMCzc=wnh#b6&M4d`5a>w~VL*Hz|_DB%2LjU%c<$0*(Vbw|ZSv%;VIBIC{OST9ix& zUGsF#A~a?!i=O&%aFC~Y!k$u|F-4{)izS=YZERD1M$vl6sOm;#k{wiIUN$F))$&4Y z;Mz@Ob#4gr#!B-!vl+aXx>;=N+eUL zJL<9|IMH0D%im)>xP@QR@Q%5DuIZl~U3(*I7HR0Eee#_nILM_NzDaCl$*Yz@{LG5i zrfotVtdq42FwEY3`Nv$;lo_lXG?PYm1jwy5Bo42{_6SvisZ9Gq)_+-0K6%2=BTjBY zOS}Ka`QwMupm)u5b~WsQVKNoTT*VI_J|=soYEoGB^uUH2&kG+UDZE>kXTWd?>UN9h zsYC4-_5tPItnk!}7vf&9W+2i3HFLISbL66<{?{JmsMop$TY<{AATDY07#>+%qU^_* zDgpkczkW^BzqP3&VlOf&C;^&5nCxJzk-Vk3@Wls%+;pyq)8OJ6G_QOFr%M2a3#M}I zk8mE8SMiF;-u5kKw($pYm(bu;psZArw zu01m!<|k*D%U$lM8B~-ee`R}c&zcqx*-bsZSa^|*rCmlcL-3Ph6SYH)HuUe=$`ev) zQB?2d+8Nbj!RYcRMFF=(c%A&z&Q=cKC|KXg4-Q@ z#?_GJAnc1k=_iS}FFgkY-@4Ds%k#_gwb@VkjMk?{KFfS(YS%U5;+?%SAPU14LZ|Ro zCXp2tMNcouFmURhXP@~hV)sdOXuu<4PY|7#M%lhiJ#Kvf=jBWi(ctxLV%gXMl0esz zRi|uYJ7#jeWmRKL{hX4Gd~9Mq*|sf?#P`N?Cs}u!qbdGA*FCKD1vssKuGB+gZWG16 zA>4M(tujFslQl^l62yzKU7x(w48j)D;T#Y*L)LPFns#S8y5qD5px=Zt8+Sz}tK953 zy;5g%Hf2}G7@OP`PCM#6IekUV#przPgIqGBeRwKt?{Z3VwayjMu>-oGg)xvg^gWLbZrIeH28!*=zr zY-8Xi%kup!w&7t`^Rqbl*kWO4AS535{rK7gGS?Tk3v!j!7_M>!oU>+wA>3u?9QF|L zB-WNc^A4cC2K&n5@6N6aPvE->OuN~ zca+A@;(SG#DP)1`_EePfW?^1FyF!>+(y7H^g~y(5Pgd`tonePf4^8B1JDt;~B<7EmaxbOg;7ttn%u+-Ua1f zanA-y*K>G25_v1l;e`Sj{-Wjkd^`j z#2KC9WO~VhyBw4#pF<>s1(gg8z`#t)6m9H0;d~Cu^4!g@11eIaBe8|T{F(R-yYaQM zqI=iJe2T90x?v9ylKq>pjoG~+)#+8>z!Dk7kEQw3BwVe|>O}R8d^mH+QwQNq#1hUOPC9rHP2jcdo;*Vx~y`N&fU7a@>omI_7 zw%dLRD6o{;_VcQ3zx9u^AXv@E+?R2yqD{q?Ke6PGYP23uQO+bb`Axt|{pA^+p;a9p zr|%MwJR$USMvkPA5FiJ5c`1{(cY9dbo@WGVf!ZxjGTCOb%x4Db0oblD6|@GsR!iMJ zTIBC#)Ks^TkSqQ9cCx>|SkYo^&pH#?ZYtxapb!t=B#=7&+D~hM*JH@mH*2xK7sxZ* zNNKQ^$V<<|bufRK#5UV)M9rgdW)drRQN1t6mWtnmMD>FBai*O zo1auRpEdK}6<57OgGgGYTkXXNyN&T~6OYA>K+<7UTRyUOuF``;ic&O@GSv6n9#hLU zu0b8%&ASf8r`lTa3XJBRbm~JE7d*uoUK`hUDKJqJ1}e?PljxP9oPXh6zexK%)fH0d z1uHnhwW7@zV^FvFe%fCrLPtj7`}viqmSNRaIG;-Ic677eKe4G@+(c;VAF0#Ivumgo zedj}@=L_zWipo^R>+|{q9_|+sm5(S+UT^B(z(1GNxF$1AGQR#6rI>cEY4_2}nv!K` z*Z;0{$K7#m^YasC+nbV`bhf$Vi&HE`@@rO)bq=F`6ClC7Og#}ydjk#QJ5Cl08`6(= zrV{TBd-QKM=NIR*pmNWD+)+%9r7F=;n)g2x-U;~c=$?J5MJ(ECWpj0k*Od%6SzVR& zQoGJ7$!!AOF8Q7+*V8_8@Q`+I_>u4 zIaNGvcB~;S>PaFEp0l78+T8lojbGU&t0MTIMq0lTR+Go%vY!iddT+qWm(hdKhT^3M96j9NfTaYUdKX%AWT^5!Q=v+brtpVQd=$%_nHSyaQ)p&W*M!_XtLr!D zz7x>x{MnvN4Bsg^pR#G5tf;re25Ya1b+os3{u(BsFpT2Arcga^VKRl^?R$I|X$=u> zyQF1ViRCe=*BSIkgc~0Rd8vC&)PJO163g+A0T0{cK@rp-6f+yj;9Ns4nd~{$uwxAj zIR(pb#oh<_uA8iZI!&I!PBoEnXzBQ$JAVko(JRpmTH)yVb;0)4<(b#%?v-o(&6BrK zwKzwsIoz8?<6A$gR}AjE&>QzZvz#h2e#t@KninHmwErW4OMfNMRAC(d7A9(QCcV)z!z4^3=i_P*e^ zrySc}zp-@`g!{GVtNZ3!E+35ve8C>VDaQ!7Rw(=V@ZA(6$=XrSd7TwToAh2siKZEQ z;(M(H3$&_Wau*_^F)fvj57}(Il*Vfq89%m7G`51p{<`fjlu!m?U{5Hxx%k1xBypd4 zm@&9t1SfIaI$u-YnW)ueQdy<`3c+@reaTfajllx{oSG2+!gvFe0i(UT)5mAdZ-yHS zYqYE<*gxlljp)WW4Ihg_UGxi6;qBY&9%;2fE8Z5i-Kfyz(7`c@DoYLcwuEtQ@L))N zHLqDHmT79^p2uleWj3zuk!h*S2wPU6@=oLS{H1w$=FE$pfswq7;uBthe`9_21p`ND zQ|j~n=5SS8FeX|)`?7;m!-BL%(4=-LK?8Jp;itn=EKSCLO zxB)6bwDYL`BZ~CVk^w^L(UvH1lFGK?@VgCIDR%(!e$Q7%$+kzo$=Nq@tmvI$l~LLE zTpnQxqw^BY3ItBh>&b?qX9!cp)8z9`QWJbE-D1kBZmYEwVkmy_zoJ-Xr68K$8T|~^J&MM6a9J>dGUEK^QwH4> zRrtb#Y1JsVI|#C@Lz!4)waA<#s6J@Up(3pKrWDHE0VzOmdDhUB+ZmndubasniWEB| zJI$xuT2#?B*-R3e+y@2uvCOuXMTtb)o`o_Z=)5oWO~(_+>{P}TB&xH!g+NwH<+2ZUJ$x3FW=^DsnmfbPWy~+zB-Hke*K`UPJzM)y^s@=6+c=J z;=iD1LEN>JeN2kqx{CcV?U0_kzk)HB$*BdcR%jO6Rtl)-BxM z0CRTSbisSGl1fWVOu?*=rJa*b=Kze|6dwB|?P?6RSDZLA0TpGRaD+gV4|YRIv`pBK z5Y4<#EfaPVM7z&3=*Z#O3$8x*EBviPCmjW~E7<18gzgavW5@p#k9VoIFmAperOL6+ zIzamemkbbasUGVQ!R_OCmyHs!JJ{W6Xriq)-11}>P1by=m|KzJIKWFUM{#yjw{edW zg06j+aCe9ycM9(wSN9(wFrv=*9Vr&FL{?1wRi8G#Zv8{-;+c55c|plXh7!X_KRrjJ zpL#LhWg|M?>Nc}`r1D+#|3{d%!4&vJtHcb-~OrS2<3!eZ&%}bskYcl z_t5(YymCff-}F{(oJ}Bdpi}JWnQ+x;;!df+#GFIQDy6HuI+TPx`L|6yi?aa$lTxLJ zVM^bu^3U5ScD0pICk)TMP~M~|iek2154(P5PhV+~8leb*$TC~zJP9kPLr)L7JHOb#51++IDO%oNWhde_>yRn zRZBT<+tsoWuo{5X>ga4Mzhl9&k*y4md5)Mwx)|!zU%QXI|4xzr4uh7<>neM3s3gAK zV|{#+)afZtb8D!dZh|i~a??CCg>(Ds0G`L4&iS2=7b>J{w`0YELum8ZZdAg|QRvW_ zl-@-8u|&jArE#LBB2%?0&&yi3jZZ7P4f`HC9HBQ&J7%!*l9a_@yXDzEU;cK!OWM*$ z>e^y{YR`M0(<&(lC|CEht{WZ`v#jJ5<7cI@9!DQw-E_7Ld#!{8 z-!6K&H~uvWbJfd(#t|yRhnqMw^&+^NS)vFpN5*9V@$4v{e&mG zrzO7wjr5=ISQ)l!EuMWPK99J3Al})=+{RpMNY{h}QBcR|{5Y8v_8c$M0a~|Qp1?<` z7EoE(l|q856X#zs7|uu3SB-D8fE4LC`j`1@$tVg zSdVb_&$pCBQH^`L?&RSws6<18iYybL*?E?3*F+8W!v1^{0{b zq+Q*2&z0ET)r(eO6ovDt%NsLFC$7AgocN;DPdJ=Hu%g0uWXYK(6N)W3;Cu_+pKy@7 z&i;>JJ^~4n^s83ur7^4hn-=n0tUDvM^oEQsElon+n}u>)N?M;iG!%JBR#M|F?rYjs ztqOT`L;4g?atO1|rDx&ZXuj9E&Rc$FA=`^3V}DCNMj=ASWz+FRWJvKM3z5oqSzXX#f?5+}>Tr5{0IXI&zdt);x~ zjjWjsgUi}@dEWxuBITo51ikV1vRgM2MfyA?m6GJUMFI#m*F`;HSj5{=$BAh-=4=&9?}{W)_IRBsvRxvaA9Q9?w1Gk3FmKA6d`jO8`V z2d~jvLsj_jcuMxDgYr%BlY9=rV+nC;Z#&&b@2*Z{M0i(YxCW_Rm?fEQCEAf1wq7v2 z`JrfDl@gBSIacfWpi%J2fb4mdWI)09a54&()cE>nY3FLPARSJs`z%@x8gmmn^Y2~~ z{zVr~g#VpxJSXqMF~5ftIk75yQ(J4Jz(n^;%D(0AZZxZ+t^C|mtA)hP?Ve7e%T6HV$LZ_=G+X|n$%`KTUhZ%iTPRhX z=R7@=YHW(J*Qa!0o^mZ#+*d$L+bvCtAs@0{TBe=l8R&3?T(?A+vy!1%lkB9Eb!n*d z)VW*a!@Rx63z^RJV@e2w;&~r1PLdo8Jv1SwzSIz6qU2tR1t(0%<`BW&4gp2Zv;>tZ zjcl;8oqTy0+(bT_jiWchCLugC?5u9mQqF;`;FLR}Xe0U?SCx@p!_U5t%2mH=9{r7$ zJ>;JO(>ss$1tV2GuXzXg?uwj9F;mIr(v-v1l=x&xyXY$6X6Cxb*3w3IjEtKr)tqlo z=KUc&D@lm0M0JSXW>mt>bs&L^EvH)`N^`ABvXjVCL;ddbL?CeS2nDM#e!IzNC?v4u z409XHko1Df!A*JF6$jp@-gr^Q#{D4ApEO=phkIkbUT1MGo|HPNSEQiumYuT(ee_Kb z{fI6q)0l7s3UJs;PhG57=r!K=@1&iw(hd3YOHY~m>EhQ&m(nk*>c;D8q)|OD@MiNs z-+Ef8-dz!#ydL@ETljp*Y%G7qj-$pN!Dr7pi=UmN>iVVaWfpnP4u4MEQv9qsRcOo0I2Kx*E6mZZpT09dA zDiwB2f7h4TK^3ObB|;6IEp> zlkZAnI?{5YC^-5=X+4Lx3%#Q)HDUayCAZ0pBLoR12-C1z8TwVo#@%bwJwLItSH`z} zmWPrhYE`gl+izHb7y=o$%vvJ( z%#ERb{n*&{%DEj0h)UO0D}ld*TXLH+AFn8}4jcbP-o^F#=?gC^_GZ)J5SFgbj_C`Y z^6C|A9;Z=CO}~>!Q01Q-_t(A@8z%1uRx!rJh_yLw6_0-j`X7ry_FomkWI366;l+Im zyA5m2<}Du0zlc`s5{H#@DJVA=43M+FS5yGTN%YZ9lqP}IJBLT zMb=y}?WuHosI;3Yk+a@!qODWQ8RPFK*t~g4R+U4yq9*P$@WY(#;1(u*3G^t4bNY?41uH6-fDIBFEgr!y&aUy?0?$N!=x;l6qvo@#ueZgA z3j|REngh}QM5h7efBc7H=eGfw+Y|n5w1(@QYU+{{nN!UMhXMuFeVY6a!74LHA|AO? z2Q8lZ$XSHLUfdox5u9+7FSvE3n)KRPPm9XeUlkr9K`3LVu;nMw>PP*OuWOL8^0p&v zrG8%Pah8mK6$#11(YJl(eR0lnPy~B*|Cf?O@B-wOkl<#xwZf5-m$7TD@zJsk4ec$Q z{%np_wVtF;nk_Z@J)xxMSI!X%<2BqaXD$kYn6k?^8Pr*g%vW#}lRZa1XRX&IFEX`+ zTu2`|MHoaC&OaESASGxm`!vDTGl15Af5@ADjnl^4uGIduDkgnu>++-3n8WSC#;s=^ zFGfmXg!}#<9*>O;*wps7uLSe3g!$VIEU>?#VU3I4uPrQ|C*r?LcN)e}w@UmG&$x!q zm~tz#UUMswv7%mHGx1TCY~i#}{lhvIj8NuSL zrTwwnsO!Gu17jtHR(6!wL{-%2kKn>4Xo_ETMd{lIgf1UykYAzd=H3i(h$!gJTLy`w zT`~7Ogw>LxmK^sAPT$E~t@4k4K~@&SeN*I^{l#R4h?Z-ZKGC1~^h|2|%vitWmKY`l z({SG?L1y&i;GH(ysn=9(cHZXTgx!#nw2yqb(z75)R{Eu-Mh>(OS(tRWgLPSA0;KuvY6;9Wc1(|yO6>G-#Ho4Mh^wlPC0}bi@;}`n;Kg)HzLfBJ!c;T8 zUtc(BS*AYxddHE=Vj~N)!KE9e-iUjvS$gt-!q^KlN@!m8gVJUrdOa!?8(@rRC47@y}}f@z~@q)~PWg zIz4)6Ka<%!f4Rsc@d^3?r!=<2W4{=zno?0 zTiU&tV3B+$MPEGPsy=Txp{p_J+>&FI$;&1ro%}8|F8l>S3>0}!DNXNFfZdvZ`Q8tY z^&s~};@$BQe-EpO$UE^fFTW&Lj6eNpt}OPN{_&sC4Spq9yyZqUf{*p~Bq15=5|*02 zpc@jhVtH+;T0@MVon8}P0_zuZ6NpfIn;3Tbrw=vfhNlQ-I2_8-O(9Y+I7GXM#j^g+ z4p>otUt70NM2$#qTFj5ybuO?xdH){QcUX8AG#SfTLRF3XDN zZ>pmn8Bv0NZ z+f7A~McDDZvhPY`4=5!8*japREX8#1agt5fgDhd?+~o}Imc^q7+Wg^Kba+mB7~^Yr z*PkroWqI^EDd(6uttAD}H@?jTOz!$~vI)&;XO;ev6&A$)x||VlKVyJ25c*0rMn9g8 zFee|w8$e=#mjU&2zKjCAo^9mW`H8hWLT~+Q8wK7F2xAME((68UBGG+ZV_~E*SB*{~ z92NBXiQuaL6G88+zOYpro^RcQoE(o<1Rb3k=FjObP1XNt@Jk7G<%`8k4E}?69frrD zv9A_k&D?7tYWStZI!3ze71AlPYQIsqnau`e)WQh`mGkJ{#9w|5Cik;|6P>+G9L5&3 zR~e66@wZqKbF+6Fp$d%g{cs!1IlNoIbu%VL>cR<*-+1b&jE~ zlL*AGZ2jt*|D6?sspE09UGq~&)q3{s^t~xIeb<3!q~hmQgkvj{ zb1z%!em=*$UlaS9;cj+N?|IVpXXY<%(HRfr21O}@Tvb?tmaVL?e27c@O(g7WRT=28 z_FX7!K;GZB=VQh1CZpgPnC#21xJc-bbbux|v2Z6k=_3>C*|<$Y?KQ^qHK+K-JY$Vb z?9&%9ehZ)r&x8(TK^f)4bOsnAY#-(^_Sk=~Z+c!phf`JcQZ6UXkomoFOt1Z5=)gT| zZ(>HNyXP6Z`7_T^nM1yQ(zlfC>#>QR$`vW-DCBvAozeUQ8y(iT;nbL#y7Oj^XIwv7 z>eic$03Sal8k|+)Tb*eku{^j+?SBzQHJ^DniSqE^Y}4xCss{hw#u?~`%b&b$BBRZ1 zJS2NTd={sF({-V14Moy-ACc&qqbl%WakHJ)XyH9t2t&5I{_1%Yr=TpOWywDKq9otg zqew`Qa6s-sI-Pju{jk)Zdf0uG>i2j&H=7@!;FTJ2+q1QeL(nM;Eg@Er`$)N^Pg9U- zybW7dA&<1sVzDOozifSaW*+!~LCMM&RMBNp)J;3p zE~dnqhHOG_-}enP5gxTbi<(b+`I>Vsy#t)w`%*FfZ$>{X`XK=WamHzfGNGHeKelCp zEamb`T-|I?ujAy{dWqjVie(F6fPqQxTZo&DHu-in{>E;n3oRE`$k%k~Hy}?gG`@zz zqusWLH^)aQ5gKEwhvQM&{*2iAWAX8%Wo72E>p7d5m-Oh5be%69_?!4Up1#N)| z-T8oVp#1Y9>5g^3LCk~Db_+FJrL?#=W{zz$wrbtx&o+B?a0O5HTqq{S@}pFUUm$mZ!%w`gT(1?3 zijDV_oeZ1e!rxQH14R0ivR~NdLCc_5Io(1%UDm)J`4Ud&>HakB*3z6x93yp|YU34X?x{}XKvAr#bQo2vLV^hPkNA5STK3(+$=#@^^{`E#z1=Q54= zK4FTHzA5fhQ>t`*miZL{T7rE_pP^$kr!BxNheKS5 z&J=vIOHz!~??L7;ze`F)^YWX7ooZxyIGGciqIO&>FqK5rH_6iM|6Hbv*3G-G;hITh zITJ@DhWzoYR<8v$!q;6N{hzTPu*`9D%W=bmu3bV%e3eEsBM z+L4f4d)jEK_TB3IQ(a?BgFDH)w~t#dNB@}KN5}KV)_sJIB$QjI5F1!OH@>G|zy2~q zmZ#4=n44WA;n%=3$OrkCzT)?N9%d|_Dd>owppl!^9wM<&__r^)sl+8DfWuc$?x&ZP zF-1!z^LfC*@gLvkNuWh@b2B)4dJIk(f!@p~S2v(tQ^ZTqtCZ*4dP$?9$DLiBetu*_ z9rUmjsvqP8CAADT@UdWl-iI#7bkh4l@mXYsNOeJmf)KgunFf3_h#~CxH{q#)aP6WS zUp^4Iu3xhAN1AgDeXsQDo;n$#m8DtAo9`KWV{EJXriAUPxQ=6&DS`iJmuok~9=996 z{Go1|mS#<)4kyE|b4t<`+srMG3Fiao?oWki;JX@vp%x60XkG3(X*)S@In4Ji$t=#v1Rsx`{b$(U z6tOpy*iP&W$c`-M10u;ntQY@Tf6GbJ$<6fdRJhKC_U=i;b^?9ZMVez%Z^Ir0`Im#t zz#Ao%DaEkVx~32x{hyrw2U%|!R9Dn3i*B4ia0qT2f?IG4ZoxuucS~>&?(Xgo+}+*X zg1fuB+gth0z304BcU8d;evlMA9%V=~BoZeW zlV~fjdI_;w_Hu`S@^-7bx2ce>yZ {&yPXzbFSq%1FQ1FgvobFjf{MOWYOKq@PUm zMUgkZzLwo>rC}x4VeebYSV${1V2*!cvCR!5pe9R2?Mfl!5OevEM-n80YDKpRM-ph) ztE+ked-{-#5y^Q8AIi977h68;Ao)|NdAF903lgO7=nZVKZ$>Cjf=NBr0|!m@iZTpk@^j`@`Qml;h%42WvN;shH2aAm&Y$&} zroVj-3kIq-=(B-7mDD(G^pzBJLnIYVjdZ0n0OOdOjL^TqeshQkU12Re$`1*pLQv8K zIF2kjt_Fz6xa`}B|NgR!@uEvh2N{;)!ZuV4ql8Fs-(K#Eyh)=5wRYcjsQe`j`x@hKNI&!xSvgNwwIr`Fw;0(d zYlhKq!7dt{a|n=X8(K{0-*w1(7~DrWdBk>9IS9^{FXd5OfAYLQ_GwTfeoFXV+ky2g zx~YF3xZSQk%~%9pdJGO4RKcKF9gU#vCHAq-m|Wahy0*RA z_Pil_8{cAlpK5tO?w8|8RQgiDbT@=XT`;KCkj#>Q>!gY}ZEKv-;+W$)MgdH;#TwSG zMFW(u%*s#HWjH6#|MUzlcpEz~fK1>>Pd=TuxDK9cUl+HxzVWA*0SnofO!{$3dJx7L z6L4IO+o$9HG&a@z&Y}PZs-79o>m*ilIkz6zjDU(5BTVJGn{qXNaU62{fgY+nq;w3O zm`GE+g9J}R%ruaaT5U3*Mh97Z+^?EDYnF%%8G%&am(+u_ig!F)A)Y3J2`W7LWQVZZ zs{eiUlU*$%paD%7|5LsQ-p4d0eibURMD5{PVQnn3o7T-Am+?)7Wr7n427v2SxN0ty z4@tG*3f}R%Jt^QDMN+I$u6gcp8aYKM<1)c!*Em3M_G7sC)m0lEQV$%oQq^Q(1IR%3 zBKo$_DBG!?pw5a_yt$rw|8Vi;I%q=(37{5Hp?#xhXhmp`s&+xTgf8uKHt-n6OK!V8 zQFK5g0*YG7`LnG@QBu6PNHPzy9&!zRWf{H+hdr291XU2Th#U$ln97R;0)+ON#PH`X z=LV3GF`jIJ@r~3+0NUoK8V5Ltgf`Rc;?TE%`|>yMh@>XN{MbD`Jw0H_%u(YABn6}b z*A=x?RV50hi;LIjz7g&5eYmr3eLhKlHs20kGSt;!t4 zyO&(TWFwd1$!R*=*qSRXd`FnOHvI`%RkF6CkxYqXuEq5i!K%OK6F{$@wz)rAAi4yuHCTY6b|5Zuw7_b0dk*jXrb0!<>S zQor)hs1QL;l;L?F3!(Q}{MhKk@C?M=&U$RUN~EV$(l{g@oqz~C>&veTkKi;9M8|e$ z6~2529@TlHY8R+;i%-W+%NG0oO1E(#V$!K_wpMp4wdb)+McF zP<@^ghlT=~)7M0*Z%v{{M=o3qsD`u2>DI;rz&5zCh%tTieUuhZ?*R&uHIKm)Q?Mmt zwBi;Lsv2pv5V`0uXsNywxZqo2ZlO>=zJnIbKqWQ~%4fPupx|INR5xCuJ&3!C|FcERM72h$$ z`=fyys1Q^?MgZsymf4-mEtluIYaL(F9bDtP5!UOW%8PG-O+-}keR6>TuI7AFmYo(^ zu?D;$>bLgW#jg*s%}pcK1f@DBv`*KFFKw$tm@T-NIK;$>9eHgR~KR#JlDB$ulm`>Y%HtP3gE7YU^XdDTs`g|UzHSCQ|T>NQ^) zEya&s;Dp5LT~FCdI%F8A9QF!;kM28VAvwl|d7(zpx}MIX>e@8|&uc{EE=iheBGJpI zG*4`HVxPHbxeko}X3`e;aspp#V9+EC;Vg%*=aS_B6j1q{_r6#VT>M_Mm^Mq5j z@(X~ne&tPbM`S1R$~yk*fQEP17Y#e<1T0^Hue2Rz{~) zZYn>U8h2kfD~r9csox60lth9yJZUG+{zr4T#vTGV9H@RT7E2AOo;l8aKReJy8gQGe!WlLe(iWzZ%Q+-y*GEi0@wb*lt)bfG16 z#Y_?oQ-8o{u;2y>c6^Bve(or5exDliSuYs?VvKC3H^fq?XdImT0LaoJH#&^f!!>i7 zR~#xaJT+x5f;u>*28~9JOSFN1>p#!o)=6lj*1nvNh|NGA`%SWFo3luYx0+Y25255+{KFiD zUl~mKAT%=l6EOZ-gBaRSZe6`ta1Ol|cqN!I=LKZHa3XVlTKL3-Z;nrZFO4KQFEsx- zi6}sTV#eIJN*m|Yr>;e>6-5z+iYN)s?}+}dV?xDpPlb-|u7kNp2$Z)ELP3N7qmc12 z*Y4l*pOk9}#q6q+FeZ-js~$|iY&BE2+%!_A({b|k!sX^cL-RXzUrFAUBpCk5x`J@K0thZX{tQ`x3W1n4E&Eo zq*M{OSgf}>7a2qT^}%qbrD;CbU|P4i-y!^PNuJf3nz}1|ffxjb4C>tMcO8G6;#D5^ zf})FOy7SsbEh3Ts0q}Q{@L2-Xq|j8K{m}ahGp5Fg0bG{!^8>0#7d~pM$JUi(79A-n zI#lusvl@7-7c9%$osD?6wL&{yWoo)||&m=Hto=`KP#{U5A<7szC0O ztnpMk^q(ZcgS1#zBk*~94_<71o#<6YS<**{Q`QlvgMWzlFJTJ@ChDN4V`hjUBQxbD z0hRctd^PZJ%n!=;LG&M>D9Pfuqi{!i(p3kg(Qy^!d5vp@Cjq!=d086d%=D91`^X(1 z=B84yvgP5g9%q-!E-A|mN-c;&)N+&~XZozM*kSROz^tKO%@9r@Ser^5IYeXtE&c!iBm%ej|$? ziv{$jTIjP{6nU}P-A$0kY3bzrUN2amQ)86gQEBEEcEpd_)8$tm$2TfqDlK)Zd2PK< zp1)xcUvbq2)I*Bw#FZKoG-eGohiF~96d|D^#u6mHIMiE+g1nHpO{mE}F?a_S0RgXn z9r5(-Hm-zhkPNZARPV!Kkj@KjStMxewE)G>m5cRr)-9&_*NaatA3QYt&B!Vdz zS43|2gFNQS3+1e9^)&La=et<{{~|%Xi)4w2I2eCl|+Biq1@ut}< zkEG|5GWMEbz1Zbya$>fO%C0Nug@ILJp0C{#7Qs==uke_7rw5$xaG zFd%)wCi$A^JcYyY5@2}p-xVSezIaGB;wK>~;J`6@!xN?(EAYk9uy-WfSI76k#ixxc@^y@e0} z1}%B;4B(mqxLDYZLwR=shHXT=WK1qsjpb^HEOb~Ll!4)!L~RXY1jhzLNlD2Cm`a2G zSSx?Wsyu*83@uqdZXW9F&RZG?A*DFfCmwYkp-x|kU3H#J08OrK$A$ZKTgQ*Pj;``_ ztod`FtYaZZQab6$Qb^S#zch5<+bHi}HV|?a0nRb(08n)o{(7bO`GFu1Gorw)4~Y+z zH;Fg!9a1jo#Ml2Dys_{tJI<-!B+($9R}9-VJ@uGO!EFd=UYTf3`Nh{msKHQ{ z09^Ozk~JtCi5NuyI87Fv(NTqAv$$-f8Ei}_y)U$xgD5=L_fDAqMVyw={by6NO--!z z0HcfrJwiUZIZ6>`w+Au+);dK@0b-#nqecL{t+dq+zlV| z3%X{SjC@GYX+WR-5TttaI05*;G9^9V7;WtGWQ_R{RJeuyLOO(vxkA)+YNEc9L7=DZ zMlCWJbE!cK1ww=V(jfA0xh>gfu{fPGenSNh34($2qYG~*1^FGPXe!Hi9gh_x+{Kr0 z6r0s2t*WGNGQ2JJtOb&wt9`B(GK?Rj8AL!6!5h!Q<#qIEe@Z7^qp>H5@gw^U z^%lSZB5tJFh2t+_9&N!Iu%Mu(K7elS7d&tOB@+(0ujqggsdMpE#6beh+_@bqw5S=A zT>eGNAmHpEC04T1{ugxtKsVWWrJ4ycd~MI0Oi|pZw5Rndwk&QgTBh}@_Luu~>ksW2 zrQjWeo`q>i+B~$t{rvqxA%4xh~6Llf@{Rc;0vl~{P8jK7R?JrB?HG^SJ=d@2nPMWUT z^$f%cjs%Vj1o0fix)vsR^nt1)-)yEm8lS$XbtG!O;?xrc&<^N6>zVQ+#v?KDAQ18A z>dUDk_M9?tAcLZmK7MA43iGk>G4M6$l3ybqfE5nh!qY{*Vf8i;UgU;17JOE_fF>=# zaY@zN_TYR*KJh>rC-S~^&3b08Snb!~+gwKBImP}>5__Ctpq9p^X`^wvKa2gYc9l*% zZ06)e^)oSYge}#SipSwe%VC;t3+dqN*xXE^rgDz56^UggAJ1Vg*b(ev@G<`=pQz&i zQO+vr336t;fQaA5xo`xkT2d4j+B*nLi3uGIpn<8=?=N?eBXg8mcPoj`ZaIim{|ghi z1 z8Ni$;00SJGk~pyll0P{UWg?eY4Ca7vEXu@mAD5RI;@tMA4U}N-O0F5B$U8`{)?)3l zWqqVtjr2r*1^y^O-((>w4R~Yp#OFO2C^}JDE+_K@Nlv@i@fY&YHBue=%LjjJw1({WD+<+CNCv*850Im|imPTPtY&Zqj^`4yo|SzAevNU8Zx6iOo` zG2)}64c(sCo{ez_Q-0X9j-M}t(#ZP53 zaZHmhvn=Z__m!B!dzrf5^DOarvWqsKbRsBsyQ9UGdC++fJdErhp}zHYXHFaJ1yDw^HB&Pk&6oz+QWa6*Sj$&$9%@}2iH2cdil}Jl|8G)D=OB?Ku=(Sv= z3fNd=<>bP*g3r`Hh&m2AoQ!6tPQdqk56E=$V0PQNMl2c;i4E!%WzG6!q%>?tT_6d= z!BQ~{<34@YSxz>76U1hZTrTV#WqyPjOhG?MjxM7)T=Eh!Tmf9~G*d#g0jDAKANmL2L9gO!mCC3U=--H0e!*r1$<9~g`o zKV(e5D$CcwTww%JvC?XH@mKe0yq3K^+BNZ zo_*61qR~jwN$6#f)(<7u3$=(Gzptn9iw>+ztBXs8DdfZ_v4)n&K3U z31v?2E<}Hw+DLIZ66C6vA!a^nzcTK{D_AmY^S@bIT;$d;;d0(36ek=D?{79gb!z9h z{&C#S=5q7a!uAU56{X8-xW!4WKP1#J1S74IjCtiFW@d(ggrYn2N-$pBC)`o~^F*4! zeP_72xcCh{@tgSyV_Q|TqNxrcl}-N4LMoO=^u{0a4fK)!(RT;YIxlw^rd%b?HcQh{ zYqz-+(_5`5FfB01dZfoMdET!>gE%^lgw)+*Gs#fxtUH&*Z88JjDbYlfiUw@6i4wyo z@aA;^Lyd~sPQo(s8zEv$Ieo0TS|-WCX#Ph@vE+~Vt0QG1;&0(XXlM~|cZ7C{`DMrZ zA|$zWx5IZtn~2R)EL28Hd^uOi{df209mbvelsaBxcq$>FKZoE|is@7EY(KdOYp|qt ze5fY9ZC)Ypf5mK)pQ%4dSVmqjm@nPD24iAXVT#6ralH9WBR+E5VSsMRo?m&8=PNSG z*Kst@bHEDp>dzGGXZzHXA|k_8y36Jo)^zpK^>k?82kir=+)q%bMr_Mmg!LB|oJl1l zrdS=#XRXCa(UHb}OiK*%*b3)tkd>)rL|Oi|un?LjWxH~RQ>FwQ00A#Q*WGv(5a6%I z0c^hjszC?vsywdfV90v`tPX)#-dNcJ{)Y`;z_b|{?nV+BkDJ%1e%*0&>QLc6QsbXv za2;@-s9IYrL{!T-n%7HLLee^FS+>&j4O@tuAgrP`lluKphly`hPpK-;s5j|v)Q*=S z1hgFVtH$BlHi;%elpcobt)+VdPy?QZDCx0TL17e4akCioY00ta?y5;qgsFP7MatkF z6jC|boFb?u(cC57)JpnX9wv7r!dH-bOGIuG*H>7D%|EHaA{8u~gtl@TcSzvGa;Z&h*u&ih-gJmac*+a$J~k6$yfjJx(1~JjhS=CvihSF~E{G zsw>x3Gn<*GXjU}(mku+SS$t()r2&6Pwj?d$Z|s=(^yu~mL`YY(KhdB89&{>DP*6aI zj(XYIVhtQ%W@=ieetLRpVGi|2jw+4Esr+(n8@G3iu26q2CAF3^a4a=Zk#Bp4h3S}i z_Lu$b;i`LaoSkw1>7z|s-gL)3%yr3nh(x#31=jKLdaz;5T2&umo1wY8tQLRCx#zd^ zcSRx(-~0fPVwe$@gSCJvsfAleSpJ@CtL>xtgXO?kt_NSx26s}6w=VQ559&94M;zK| zv(>3E4SsG`sAg~fFQ9`pZ$(PWG|?}p$IFj1){>pSU<$ za_jC604c4(eBe0Gh@E<|Qg4ilO2=BAsM%q*t_ZXcGXX|44$PbSYDfmA)tM`hB@R5i zWWQ~*0s{xg9%#cE$iel}@(h}R@rd)H+wlU8+m@3LiME7U_^9YY0umBKLP8P}NXAO4 z`Nb+t>hgVQG`mf4N{>pY-nfms;wwS&dVS=UP1Y-tA>{f4dLxR)NC`!w>OG~~?93^D zQ+aOeOL(}2gNRKzVY5 zh%kca?OU7r{-^8yKh6fIx>s;U_20dg4Hz6hD}v4n43LDkX? z^0sDJN~2AAt?&VLsxgA*$-~7pM&0c5a2_$`p>on9TP`Y@z+0u!j4CDAw9{Ow*@(=x z)e+}oA;>|}yJYdV5u^6J|7hoGcVv2cT7KA&*%|Qs2dpt4Uf+OqFsP=c#?H>Jt*tF5 zCkL>eG)ueOjs!xBP8%|7OC~5h?+&_QBMls`P!%v&H0ZuJnNg57WH6a8=MpH<+tuX` zi#BL($jf3?7pFOeHc0b!|G*6n)m8moZ%euzrel#yYG%=!p>3F4}|7^cbVwC&3 zYOdqE*w)z~%zG%mZTnN*<bjq`O33kT`6MgN)s z{6M;pZ2Xm_&DdkxrlZgs$zcGzzbG$#HgyO&6~X78b@(@!50(pmj{Bb`7v;osdjz&2 zE(7k@nAU@5ht{uG?txGz;Q8%%u_J_n5-EP6HRO+$hn^ND&c89|6eGVoeg6?SHMXF-a*e zjmFr97Avv)OIe+`)iK(Tv6GEDy-W+Dg0+Rj*n)?I<9*2{;@QmKbXj<6b=TT@qRFM6 zCL`?>*dXqPC}Yl=oU@Yyb;v895-)mF7MiAC)}DEvn9pKn=bJ{30HVQKSEg(Sekyphos5*kZd}q z-dx+hs(ugh-;T4ydgB>6cZK-_hXiq!5OrqpKK*;Nx8GH?YS|GE@G?zg(Bih)@5}fc{>af z3)39wlO=bf@bfgxi`h>1_^VLkN3AKi4xb7D5Rkh!uu@ghp0h6Aw1J$C)OyW-g7TL5 z_UgIG=#ka-Vvyz*sqGfn{(>d@g0s)p-8FChC}qCpJ4%qk9j(!uuhr;*007tuPwyQ5 z?%q7MtV$z{=K8F-z7y1=`Qx9E7` z6`jG&XJ31u?|aoXOxiP9#2e~*YozeHO6Dt^_B;0ewkJl#Yt3!fJEcnlZO^$Te-80G z+;KmQ)6b>fhg!!^Y9^MN6dJYdUopR@lKI{(utl4)Ki&UIbvj7bpi22;ou*F>|M*~V zqhjc=HTK^?m-znx-Sv1))8)H>1b%0>#;)DFHPx)!OiQMIqXD1oN&}AWpaqA;`h}`N z_C@B!`bK5tzO-SJm6Z^hT_d|dY1Uvy|t!y5#BtNp*G&?;mxQ04=N!m7b3;;8>+=g4l3Bd z6$zjWc3CVfo6s7hHj^q^bk2!%Yxc%h?2W}1s|+=)HZDr$=FeGZ-$TzivH66X4tVYo zu4k=o_SY3vWPd(uXO|QlG^rfCP%gNjhGLkm`{aESi3Q0q<`iKl6Gj9Z((YMh+tGfq z%&zpgA7{=l_B@?D-uVy1-+y(yzlWrY z<8-&X$-ABzHOSXiWX|3binAI;YCGnOQ{hZL-AI_!3tyrC76B+rf|Pz zSX?0mU-m->@>4-1JNhES z7(WuT5;nAoOK|)-oS7kYt+yd5eIXTHJ!ks{#q^mHH; zrSIR!=l5_k{T+pXGfIM-@8zQBpLzbLP@wF;U?L!W{K)IUpJ~KC`bsGz>0I&YnpK(d zxGw9Km^x3QX}G@dveeqVo?i76t&@zGvSb;?*0<@r%~&D1GV-__z_9=yJ1sd?lX@6f zGqe10vI4+ZdKY8eSj$ZY#PiI#k!uCtMVj4J!_pdd6%#`E;!3+9bM?k7cl`~59^+n)@>R+T%X@mD;Rf2&PB+gG^pk^l$)BvqOtw4+J;-2!J z;-2EHI+R1punciM+_AQWR$}1-Ia<99rg2X{GOO*Az=c-qWM{waLZvMHN|}eA!LrVa z9*4#_VR3#G5AXBWnYZnZ&_qxD_f4Iml1T2S(%6o8cUR6yefQ_ApuC-Yt?IG?yf*zv zExmzBC&F1rf|##=5a6T_L`2?#}k@KLI+c&yq;#NP-5tX@H1MZ{Jv=5r|9 zgh4aMx32h^@c-|dZ~>f`i1Wkda|_Ii?3)WG+(=faXz)GQsO=y~QnZjYlkWq@HT}xz z^wVRbfS*p2!!AC%nc;(xU4i*oRIu?CTvOxlzcH;iNy-02m@W(=NHJBqq1^e?z6xhc zA#*5A2VF{w;lIYaryr4MGIK>|`O^^~DzfXuYi~mm80chb)I>Q2H*x%S!GY7nS^y-=i_Mlphypj4k$n;ce$u2UFb_4GcceOjY9TKXWI#or>>sF4AVE2 zjHGGkkNC|PN;R0CMwhKx@@c!`@T$0hIvBSpcBdNd?;jv%c4L>W`8!W!qJLrS@@9W4#vH7g zoC=RutG(5A!C6M$d>Qz?n|V1qaXH1mHJrHMf|Ij9*+3kd_{ADJk>A@k>-ldT)R6Kh zGAWCl3ZuUtnipeGH**+>{X9@@U;%~-n@6X+n^P0N$TBRgYqP=nDskZvlA_#TgfC39 zb8i)=!^ay6=LLOrIZ8)w7d)36Ffk3B6Pa(sm;Ih~9d0N>gJkO!o_BU3x1@W4VED+rrGIdWwYR6$S@aFPtL7y{-@>;|9{=r zoW12uMQc8jDm^M_Lv^QF7bdS0V8Rk$WDv%5Nd=*)lcD_-h4>CO43&q9d+O!rizK6^ zoEeOpqV(o}<_=d|6lr9WCe1VJ;eYCRLlsp3CxB(Pf2k=+Y6AVy-=M~#+Mq;VmLf%j zw@h?PNW#g(2fIM`K>+fkJUOlM7c@(^%^swAxqhFNxsI7pRnfZ@gF7RF_6KQA&j#WuMTHD9GBs|HXwJ9WDONz zK_9C;qG{PN+*{pMn<)X$QY>){ok8Gu6htY_R)_TtUVP9&9o`iRu%p!+jj@z7Lt%3$ zmLI3aR^;o6px394i6IQc^VW($+N}og8bE5g30}c$APy?he%1ge*(`XpE{DacZ%rq_ zd%eSLo{AZU;2GsW%$lADD`T>%`)GKdVWbwhcHrR%$G48*_vH<#M(>OtO@6o_PL_xt z`A4PC_8+wU5=~pJ`O+9QfQ|`Ki`P+pRAMiwJhE*1J1F?6D+pk$Sq0^%KX%yJ{Y`J` zijR?~H(k{*QfP5~rNd_KuWFbb6xGg{(|I6WCje&eC_GIKXraMTy%Fsd&OrA8o3}{L zI|kV|bn*56y4V9e0UIp$=PwT*kY7P2>jT&4Tg&%TEkU9%GP_zm8^{8O$p4GLJx`S>QS8~;bR?IAk!{!Skl>omuWZ{K z`qQVC5Hw4&<*mhF(`gH4wsL7o0aTBxabNrhAWT+*gy1;@OoT2ZJChP*Kr^kPjAWc% z#yzUO9R;4GYk4wK;b)ww^peDdVLX&H1<(>#tP}NdBcI1q({@4uB zr;9CF_F6eIEZVeSP1p2}1wVi)MV2B&CsT!Kg59L75j}1_n(Ne4s{)NR@_*pwrrI%*g6KgL6o@)952F_oXM^$+Hr_JQ@B5q-k!o?%KUnkEu1C}DTm1V zshW(ns$p{tzUv6HS|7Od?40lDx+LBngHE0&Eu zVu7Nj4%Q2hP84E$#t)2=W?P=JaKWoI@)np<@Dl7L#o9(Sur>X+G^xN^soJ&VV?5+A3?~I1?!fVi zqG}VSOjog;rP^*nw8oz8o1O|iL4hjXB)Mp8Q2s)xEnf~o$sm2n33{Ho=s9w4>Pm3Q z_`~fo7^Y9Yx+ye;hbqM$+axXTC&R8T^x!_(swb`PG|HHlLDQm9yv~os-8yKC9>!E| z5xQ_wO+N!Lfv_&UYcwliJIUh<6C@@)DJsQk<8gou(DfUY1C*>Zzg@u%UBoha2_$t_ z@e5-Ae$gV*cLM#4}Yv1x+2^ctgPjQKgOn*+i2zY1W)>JA-I4 z(`OdWQ*Zo;FZ-(LyhhsrENdTrmBcqGQ`Gd=_W?vqY4NBJkIkx9it=N;Shy@gA63Fk zOGAiAY)Vs+u~?%fstjSZ0n0ZTBoAsQBD5?jod?AeUYcJy;!ypW_+FjO;=KuQ1ocEDGQ^2Z3{lUNm}3ncEKz(ONuKvw2z?@kO7{)iLVNhzrWkF z@QwxJ{kB^N{<^@ky*Lny_?4HJs7t46HrS%uW}$v#!P?x>V&RkD9}QT%C~nqpRzi85 zX=uVgLbI{cUUt1@5XY#rDzz7WJr1a*uIrsep9fA?^JH@%{vvcw*4_f^G*xp?2S`3~K8iLpcB5%9mt4S3%#Kq%&W82b8~y^;OO`-vJc>ZL&b6u8Fl6y0O{oEeLb)2_YAg- z{v(m;T37!Vei>-DzX?w0TJa*ZQAr%Z-|HEM#fP^E$snO|0}?RNF7u$HMi_@_)a+_fx(H%)h~8zQbj_ zla{9oneY#D9e6>a@iD zHc?6J^HpwF_x%-i9e3CI{r^MwZm6Z3dm)x+(yl-GbF@#|$Dqsmwtg1D;{#R7{G-%p zvCE-$g_?fZEWI4)tm0z}_-y?|`yMJ%120B}zCSBE&Tw@E#wN~*NSzUxU|+03GRCUm zaQ1i^7cXL;tp8SPC8UQXrBIjvFfbI~Orv7*FvB^3J-!^ZbzYDyM0_{l2PlrJk; zS7i~cPK&fCx6+AFAD-_>zPyn{Q`4F3tgWRQTKMZQpHO^e#n0~5ep@2j%uBrm%?cd# zZ!@E?%bR#roF?1`3x*0W8Gx=7hredTifEdc>qml|pDoliZAkohL;BP=eziZ?< z?D9@`F%5Q8jLM@Do6T4{@xGhbt}b{YgU$`J}254wo%($ddl>jBWJS?-`iB57?gN1`I+o0EQj z{yz?gn*QC)&5j#S^9%RfvhT=n?UA%P`U77yxq{mmw?_kktzEnte6Th8wu<$JlG`Kg z3FmNm&wTqXnN@M{ahgs(8NYRbW#Q0qCH=2;k*~|&I8H80T3LH;X+UUJ+4iX`ts2Mc zp#rK8FDED=a&h3`b7QJ^yUV@y!2di0HC~Cu8vrHHfddPC@y&g#mWe~ev=X8o zc>K7pA~(3<7<1Ll3G<p3QHDHC?x1!20d1J{Epy^6o9e6Xx_5Xm?wi(wJ{7#>4^% zYK{ckAPk7Ik~Tk~%0wGi#z6~EGqKP@E;vx5>JBIwYt7PXRuoDOr8f^5%!35r0TvLJ z)faCvM@YV>?6Wnk1QV*yL63|n!a_jjv zFE@uGM_-bfYVVf_Suwp~@r8CQd46Mt_5m}m=lh!`!~Xpyk$lNrDqY?{j5ZN3gL3$XG14yhY9uXr{{>;RnYo0?%v=NX2fx~+ zWyMAWR*3v_h#H%RQu=!sq~2nSmgUm8-S$!BI}Q8kpZc+fp7-o>h@(~S*p99&4(naB zo729n9rff%f18uvM8J3i3`WcUMxwNL)K|DKF38I|oplwJ$DEdKzGV{zFOW3OwYOOg zEX6Q%;aPDIprS5pA@UD8@a;@5q^8_gh)IO~SD4L?*+wk+9f9poh4Dax zm2v%I3+t-Wwc)#ZbN7F>0F<0BpV}Ykkvsfrh&MKlId6&X+QWv9|JvJI^vx>#-V_IZ z(hPg>*l;oSY@S8vto#9vJ#+-WG}o*2I5Ri&*D}!(1K$PdWvh50+lFB#^ZTY9aNQ~%;PXy z)8D0pm}>C2p+Hr&2g_12gqTv-N4)f78znkD>`i$Z1TMbu!Wi?G%c-T$KHNTN&^b(I zNv$3QJsB`NhJBqnmy+B-V(xg}~c zI012=K#X%aHul@y0@HmaJh<5@EMsPNr#)ozwLZFZ^)?#x*Ve?h?#+vfOfD{K73bKvr*AFDz}K9Rufu0B)`G?@v}I(nX)p%&mCOY=sWtFH%--PCz!H zp-IPbL!_1VPAYbhMNRuPlb=0BzzVI-_V7HssA#dwdh1BT`)A2|($##ou6`2Bn_~6* zkmcB?uAC-4RbuOB-Z^Mb$**|-U5^iDo&Q0=89y=#<)1U_>-Z;8~ava{&S*Nt33D^pAJ z2Mgf(6T!Nm%^F2b7*J272#e1TzZ`fmv@jv~nc+gr&1*8ufo|welyfiNuW3z_s!Ay{ z#Hx`lDVTEP1?v1BzpS*v&u6Ko2-&RJ;CM5AD)?=KS8@%K8~hX1HNz%ZKRnKN;v8)v zwz64MZ6;v3h$OSW?7NRtPHQ(wNFSrCrdrCro{Zx;?9yFOVZac&Hw~3kLI29=CRp@Eu?Rg`y zSR~VE`FVm+!SFZOv-d%1m~lHkb8D+WqE;YwkUrar5fJi>yu;|c!8vyL4V4@m3>*ltIe~_lcB#Sai0x>}$FQk)#Riv-d7XC3o$Js^UZbg@0p85Q|P0x@Ta$ z)jwhmXcwB7wLk6bC8;OwnVqmXAar}iH(vq-LeSoF9~Yf-mwt?rtoc2DIPpLOn*H_p z#>&9KqW2t8+AI7-M|jrs!_9i%WW(>9n48B--}gWh%fcf3qh%CSnc<96pk%bQY%OMqCOV;-t`@90APp-Vm|Af(_YxkExsUb~0Cr^;Y3 zu!POkbR=kGPYZA{VxvP;6tZ`tAwai3(t2bQLbxrcoj%MPLgMTLz4lgfaf<)AU~vc>7qW&@k^JB(z5W{a_`_zMlazK?l8D9Y>{Y^rdLrkv&3T}8K!~xb zNdnYxpe!nR3yC_?*$7*rMHp#Ke?~7k=t~NFb1^1ZQaS2Bm6FpvRHfimS@#y34cA{k z5kjX`Qy{%gz}2CRE+O2{b#Lw@o8{v@%thM%7LcYhZkQ?k(v>nR;~Uzp+m%t_wIUI{ zsE4AQ-qx66Q8^z}Sj@AE0;ofN8W^F;Baq^AZIggWC4!c*Gv~;&0wR&1K5AnaJeKqL z3Cu|RHiyR$npZnAd>}IXbD1Rj|Izi2h&g@+&e<=U}x1wUx4g zetEP;fC>Cg&3;hj19@GwX$&=GE^U=Ysc6G_GS7SPrE)k=b))&Q|{MZ2dN$ZG~qucc47^fRReggYp|~ zF2q}k9Sx)JkcZ_JLuy}-HW3ML@XOl!SV4NRB=uX5eZs>~YEsF*)j0sU%e%BW#p=UM zf2YJ>(iV;kaFvRG8}czLB%wYeE^}=D3%bQUL8pZr6b<+#R}fn4z$B<{_Uzr`hzF;i zb9VjvmsQ%Tt$T+Rq^leLX}?}{sI;F`tiklL(Acx+N++8Ip7}8lxJ0QxWiWH7l0BI8 zNflLnd1Fks8eMS@>~?igitlM^B}w(th6`N+_{Kk{Fj%~r@E^?&Fakaavne*-F-9{0 z^xE?**RsE_*I%Sc#d^Hoys*!xwwtZ~?Cf$(rNYYr=Q5sQaGTGh=!UV>(Rap5X9Ha= zD_&ebDbc=Q`d`!GIGxOoB1BC9#fZXlH~G?0VKW0Db)U%*X?pP}XqmFl4l>5$;+|lzeb|-+JN9jLt z!JLX3HPb1vrz83xl~At1@5j)Zux(WHiY{}*n{_bNl*`MWCMST%{d!e_V0L=2SU3tw z72o6AX8iMA3WxlxiU$>Ztfem;?t>7#`MM{m!moSFay zS?F!nYF-}$E&>$-cUz&(tvebpfnQ_}( zahx4T{IU$7Y9V;Sv^Y8YjKR=nULae*FcG2m@<}*v^UEha%RGSK=Mrl7;#FuuVb}Lp zeYDXwC8>(K+|k{=Q;#ozfiq_H+rpa7kE>F}{`Nq=3HSe>!;4jOTaSo%1@-%yCEYA_0h#HFB*LtHmy#0ez!|ejqb{1i& zwMuX;*vq>9aLc7FLqmtjr%R?B(est;WaT|yqqF1RaGwE6c zJd^eN7s+a#geT*n03`>nwJ|Iz%tp5Tr$TH|_&ck)6j#}<_zV%-onuf~t1=u~MW{~w znX6{h+LNLKqDPrpOBFwJAnvdu(iEn$KK|;(DRY!{Y}w?LVgvvCr&lPyuZwSuW{eJMC`(PP4sgk{vx%}r%-}4vXMqxUI=Z%DpgiEOCPUrOZPKQl!~U+cNn1T;Z^-H$1L=0%WoV^tM5(NYKJJ8$ds z))F0)O%kSX&u8~-y6Xm97*^oT{|) zrt%03>fpVF@gWjojd##%bDPL>*Gh|PIflN#66E=99qCKjzhN94l)#Fw&polC+yi+) zuI4$?g}wsypU0+^H0UyeriS22V47givQ6c{4ezv7~u&3FOFl6!ir0uUWmsA zA*Ep%RR9wdr-A*C)K_l_--721n2dP@BCD(bvqdsVIh|M*d}X@SNba68eIkQ)o$zEimu=Jl zH*#F-ENaTu&}EZ)d+I0epAO_DANORg%q~=*;A2Hn(#QWspIgK9TM@BOtfAmcs}+s& znTl!^qLaRCa_=Dxv#+_mdlh%BoPmg;3)9JRyi1D3*=+ zKWVVL2XV1wu}j*b+xW1ywSmyK(bnFBt*NA3=eQct17f&(quVS8sE(ni7#U!m4`z#r zp2G`s#QMV@Wi#7EJtM>;kzh<-+qJiXgZQ>xpX}b>^At!UskM7+NlmoYyS$tU3}o^= z2~{rs57h7f()e~oMrjY^6;nArpO&ZG(_7zBl*K2ySLND=+yRq3&cs}C66p~?(3{`_@g zP;YR)ui#TQqjv%Vl4BT9Xe-g3?tW zhfMZqtBk178jUn^+Z_^w`R+Gb;q%wuqyPo`3*5N!WP%Yn0mB@-Gx52{?`0OAo4U^- zla;(dSxqN8rd9_e=$eG8o=3VJ&qe%!KU$2Clb*1@$^~s^d(GVH?7b5c8T|~0XrNtV zo?b<)Lo9RGehsDjO_LtK-b*vZHJE6Cd%B0B&FNZ?SGd$=@cVzy1I_RLC`n1liS-IB z(z==&*O0cng8|n~L&Lf^b3}0Fu}qJ$oDTXGDt>)J7z?tXTc#aG=*QgGQ0jz*=;JF} z_~u#arn2Pe($I?2`;(Es)ORS^uB~U$~)$w#3Kz{Rq=K7Mxf2?F!{3d z3InKqg;9x7#R^j{!-5QhtT^xzIT@rF7V*8iI1tuk-bU*mm~OMEmBqI|3CK;6Loayd zjg5yr<2((UcAj*$gnvPMYJSBBrUQf2c)(5Ys=;CpRZo1ZF^XK7YK>}*CXH$tmwR*M z-d=^x6E>q98=nWZu10*@Jy3+AQLfbvO-7&YE!25n#(nv>a`X7@^s5vCd0^`cTfvqt z0YB9}qX8kjR#irlQ~rBMXbQjSVOz zU$Rcf*_2{^_d}pKO}FpSBETR6JaPxx(n8$W`3@KZ;TgF=P#Zm6H)2o7R_2|ovzXb;*-jHp2>x^sZ&hia+O$Za zQ!~o`G0Dh;`S!aykL?z!3&-ecS-Qg+=ag`3x9u6{I+@?sjSY0Q6}nu8T-yVdw+~Y9 zuTQANWt*^4ZArKFbtFa;yl%``9{O@Fua0p|?;PCUfB(~(^Yw%G9#y$lWFJbDQ7+Qq zWLwmI@^e08P?4S#OrbN5=2M&81SoCo-A-`b9b`tzX2az<;`c}lB9h3?vR;gl}Su~4zHwwjrfaN|FKoYlS?(kDVgID#nnkO(&`;6ai~y8P%8FF~TIh*0RCz_&Xc zIpsU@ym9ww*yRtl&+*pF9gP*Gz(3~;ledP#Wr4^&+NM`gE|V_nS;QJ77QNXc`{a_W zkYNzv|GZim1c6z>9ff%FnoY8~U@k;&#A*AUD`3dWh-#jTrW3GDVWZR;32bqZ(G$sl zzMb-`zE+BXJmtHWv*sMr;?{ko!xng*6pT(T9&ZN)wXKkK86^C=go{}RPAql`qVk8A zcI=U_Pm%}X;;BwvUz#mD82wmWTr3dtTTbo;yhiVDFW3Ax6B9|(N4A08o-dcHLDE%v z9f9WuWvW^5gtie$si!X0v%~x@%?CB3uceejQgk5iR`YIC$RZGAqTde9>`$hgCtrh4 zo(_xHvj|_l{*h^vy;WxM?vihcX$Xg<{?Cu@Kii=mKZy)JI)8|8$oV>0?6rKI|FL`h z2d&7pU|N=lcgi(py)J!faT=!-gH+6Wg~s1aO1P7+hr8`M0@1*%j^5<792*V>$oA)4P>1R z*tQ23<3ZbwKkP~%PuQaJyqK8XH;OhfmipkOExN9%Q*ouSrCI`3sHNo}mBKyL;s)fq zMu$07XVgqXV7X*_6s0c7$5saRT?FDnD-+064B{sFPMP+ezGuh8PC{`k%Dz)xVO}TN zh78?3M@G>}gcy8(n2`cc=slRZ>Ncct@g{iQQ_+Js*L_u8g(NHvaBiWNd9#a}?JA~V zZP6IvYfU=Ao={UCv`^q*`G-_uQll6Ya_~$2Hm%`e8)2WksGee>%TZV%43gk+rxEMPg zwi4m91V5Yw`g!V1+jVLUbgO3fXDTQY6?pcDEuTm9DwH$?+j8eM;9gf(Yxt@$Eqi(d z(QIVe`)W$q<@7mo!1v4c1b;!z@sUu_tC`BFnwgrBmqBeb1dObhh;)xgOS$yFqnf8f zj~}5t?-SNHi86C?dR$L0x;=MQUGsz<-`3p~7Bhm_)8_m)yZ@d)T5mD=<{`;n-KA1? z?{3t$T@>X9W(8UB`G+3{ci`%b5~#1wgV^J6j^E!%B-vXP_K73bw#Rp=I=+tzcf}YL zf^qRrY%c^6=9`^KB9AZXg1--1cwYM@bPeAz4KhQ_H%1RQ3VguWM@$%R+{Nz8y{6Z?EZ4!ZSC~Ew( zjj-#dZA4?+&P?Ll-7mB|UTeFIhl*GG+`oQP$DHr6T%5Nmdn_1hU{;ku#i#^=Yp>kZ zusE8_4LNg+bu7_oHmDoFbY|ABkKHEcJ*TdIVN&uAJSiY|3H%dokr)>2_9Ce}65K+9 zp=q|P5eOyU>6%8kif7!=FA!GD#B1?yC3#zraXR6;<2_~YQk>8sEr*PL7K0SGP8ni#mbgnD~Ox8no;=c0A7Q^9im3RA5- zV581qy*N8mnoW}}#tk3BdqHqtGP<(4gzF@cCl z+!gacDr=+k@sNo$bg%_isCB0h2&|h2!v7F$fPn&$los9azsSE z@B_(ak;l#M@|}duTb^sKV8zdiM2*-9ygnIeZ{Pj3F@JcO6*>~vnoUzt?zP7Wd>w_G z-9AG4I6fq#4C zrrNRWOMetn*0qN`2fv!t0;Q|5tPquc{rdI2?<^))6`Wc#1;5L5`EpM8KM9eJly2 zApVGA8|^YK zdjvkyl)5`)J#G1hFe_v*<8k9`!rz0qHs)DH`we~!ck_-PrqFt6#29PV@;q5t00qxBgz} z9^@ZvqV2=K;)0ZVZ~*k7s}ZF^LuWem$$1EO8~5Rzrj?+NOBUir8PG`)>GC97)@rZ! ze@>nNQFHHoHX{(b0L^!>%ghlo5hIR>2imaY9`pYStqVgUOOb|TiG z9pH8nipI82zH?O#h_8jmMyc(O@g^n(1~|IQ4XizLx1C&HuyqOP5S`FDKF?LCj>0#* z6h^mur=^g}N0nAAybg-DkI*ABsno$$LiBo4EoDLy-8!c+b6LiS=e!v7)GH}^dDgSB z<)cg_7Tu>|s!PB~XQPVnL<6=e%U~VmMEDrWV?BIPHmW0frJG2ZB@!h)YW2ToHMuN+ z!1~5Q-ls7Na1zfGdJ8wM1=JFajj0+Hn}6EujLLM_Qk2v*HF2@AU1=9?Z!U>Vb{<|d z_#uayo)_JU{4Kp(c8xPIt%LqTk3XJGVR z?t*^M4yp|42Jh1Sy}Uks>~Cnlk{o%>sqtsHKIaDV4w4nxe=ap0N+zEi{Jr{dxb-ow zVa_mzo=kqm$i#k&|FZk%)uRlj` zm+zjjhJIV&-X||x`D9k%6&uG5B<8u$iG@(>4Gt8m@h@Y3MKb~eE{}cZz!li12XC`G zfs2RIC(vp* z9#~sT|BdT*NV1Cl(jo6!!BJ|O8FW+%eqj6JQR_cB_Gh&QFs2tq)QbjSy+K`ehr&P< z@$u!wm71LSNP!cyqvx5*)iPb9r<^ZvL9c=iQvGvnN&jFgOty#w1wSNd zR|?67xy3f9eWPm*o>+|Ee|w6ex81&k52H3BC za?{CRXNbEIj)JG2x^K6Iy22>8feMba9?(H$4BakRbofu@E&BOyWruZX3e5+AQs2K(fw12X?Qq@lz{T+QW5*u-@Rx6YZ* z^l+Vm+2lYC2r{6RTZ4;OW6zI7Ygu5F%z9mDehVpvZW98BzR$h;P2yD=KiXFKRo4MK zrtk#2;{D1F6P=jypq6hfKujIjCD7$air(&%Svuuv&5ppI8NaOWX*Vi!A!B2+l~9da zde5`4EkHWrpzQtbO8l~$xhS}C=>yNuD*`j7P9C639OBk8*F218B9r_8-4KqniL~aQ zYZ-^g((Mg>>^h`Dlcyu1ib1q~=F*X%5>>T|L!Se75c%#)gT9|>e@``GYL#wM5Z|mt z@cB88lf~o55h~+B@F9edDfdP=*fXOASzDE2f}x;f%cCA{#fs#b>Ec6j**Gy^Do){n z>CA*6(ke?N5M~o~6#8Ok0xcrNjSS;X4N3$FB%(PgT^N!K2`VTCO^cz#2jvKCngvza zF4u|8AMafuY)6(u>GkyWOSxPrmk1y&DOl|AmQU%^_~3;~dp|_aOu-BKt#7gEAXpFe zb9!l|eb6*Me7hq$-5^aND$pq1Ame>zV3a~e;P#L{wQAZP-IPotp?q1}=qY~`GXrMy zx9?|seo0ZvqBj8G=h9tqi6_Che3P(QuQQ};xOF(uc0-RoK3S(b zrD|;n|4=X2( z59rpHOVcP%QUbeQr8I}>5tqtMnKlrEJ(sU5K^TJ)R(YqgLObP#Qr%)F)p-6th~)n+ zv%y#dHW69{|B2=9_5Z~3vd-^q0}OX=(GYi=#+A--H;0i$_%FVBgY(gusKnK9q`?94 zR873EE`PN(&~A4svMD+dx5C{?m+eCf?g57&k|77S?LFGEmD2;?vd!%uP1#`3;TSr_t%M z4a5syMw#yQ^cb9+?yY@n;lwF&xDot|`y^12Tw-MFz>M;6eMNV?|Tp~Odfd$?-m zZ`A#828Pqhzy-kVfvK#P8bL2C71=w%q8^8AoH zf4`B`VP0pp#IKu$kAEx`Bk{()Cb*vf{uU+*(b3TtSFau&{>sKn6m82bj=v%v7Q@o? zznQVm`snc2KGs*ZDl1(f5h;=q4B7z<^a9@aG=41R1>^6WH1Q|mmxiW0rgmDq4y2J^ zpL+Sty!f+H*T9fa`$G^;VVQo}=kN2smah{w-||TT1}R6+Yegl{jRL>-c+!}KG?ZL` z^8sTY$&H_AnH7@}k=C4=o;^&cp)(m-Oc9N<5~B9WbI0iw$cHU0XU}4-WJo6&v=NJDjSVhS|U) z?WSS6^|8(=!xX*Av)ILyI>@P)#pF83NVgH^4=wuIhUNKx*|YKllF6*X=~3|K4piw5 zdWq+vCwE2s9%;t-RN%T5sO7Z7uX*mJKkZT5j{~Z>(|T zzd0S@cFBbY+a^&q)z5n#KYIAyYPy#x=x5iyq)Y9~%MpqcS+X^k``wt}=)A9qdmc>v?o?b7#zk5#s>jfj6{O#Vk{A-cc$15AEE>_eM?85cqB9T+}*PBco9UjDMKx{_^&zP>X%QC@1lBnt;yZ*bJS>KxPcPt%={d}^FBN(Ms2OPfm~E$0wWWE zE&CXvn0q*+apZ^2hQ>ouSAe1k)uR*Q7%lKje7y*l8Vf(PUIE6$7{aF<#T%iP9 z4UiN~7kbx6X>Th~mQeR-?02G6^3l%@yBV(Cj2QXPDu>rKTpx-rs`fIp$pElU@ zP*61qQ{hW=#4!qrd@o#FJ>KNA(-PTJYjSn~u4WHlN3(XC?lL3+B2aZ=RX;;3!2@_n zXGbYJGxzc0n0?aTdC=!Jc1A6`KrwY@ewySvMZNW|{nuQ5H-JIML*GAK?*h(JReA># zp$~s=PrB=22SpEO6#$#)=k5LeYCY!vqeOguW!C+&v9VcFEbJt|P3H1?jPlTDHF;i^ z9`9Z1X0_DU5Y^n^<^&%AgfBxJW-DU~!LT5EYDGzr5qHtrbO=`s-0_EewLC5w8wSL7?DGt@+_~yww=Q9UwxTadCQY$dWR+? z#gbYYl-+5;8<^r;xJaRBVSlHKV*xc7a7`t*e5lKE^#>4}OUL`GM+?$lbP_!!E(u*@ z5%s|Mkcw!CEA_vlCJpW1ejH25GR%h>>;MakcZ|iTtZTM+6M7#Lx)e%A&roYc?-j`; z$2W+Q-rD5IZIs>?JZLWnY#C-EUB4SVYho6{se*n&kA$^%b71Iug__}kQ#v3p)z+>E z_fH2p`d#jr?w=;bDqK0#6raG@qvByI#qE#IO8QH@k7tVd1$Pnu!M-CNmr8*AD!CoC zoDwRwtW>lBo|%f+dwN!3LujAZV&}}9>5%<(>_FV5T66l|R}l$VeMcQLV$%SnqJ&Zp z%fwu8Dj_26)S`H-3~uCn$aPbMZsJC$~!FK0l0~gWowlQ{)1u)H8l$j|s>4 zt2aU{^^L;vof;TAAMf{J=XS1N-#$PK@I6pz~sz zGg_7fc_tY;HDL{&yVm($v$#QPsny}f-v1QW-hYehp{`2V^iE8kKI=3*(mT?ov-VgJ zNz70-_k*Tb6pVq)$ghNG4c5U3ty+jj1^@^?3hB#?2}R<;m1~(`^1}gT%_`N$Xp1QH z^||vH-<49e)S{t%f`fgsD%Iu@U`8cjjZ2;nbn($zTEr6{H->rnpQ-v#jO zIwKZoO-2vJoR&ZZ1C&|DJIJy)N0T1bZ#+6YmO>fanp-v0codXke2mE&ozXzFzPup^ zD1(g4P<;%{upj#sId59;=u2BS`m?alKiV)_F2*m`Q1cHV3eyLS zDWXKaZH5dKB=FXVdWZA#Ph0DFqU*421)(~j>X2iqhpBUPiL=hslMe%Mv}u<-5VaLN z%KaxfS7bU)-OJuaE#x_Y{O=la6g0)DGN`h?Jd#>n@ii5rt^v{~{L8oXkLj5W##F-nM-#&S zh4xX?d=;zPKUnkcMOvlSdqhr~W__^`k4ov8`0V`^?e~}ttGnM~s5%CgsZ&?ZsI2ca z^B>XXq^z&_YoEV)Ok?Cu+q2zxag*e6z(tefwcWi(vok7|Zjh5>X_cx*TScP)v1u7{ z7_w{s!zLt&rX}#khoOXRftQXE^K$czb2JZ%&g)1#9nkcBsgCJH1_QA?7P0!}^m8GO z!^@tg+{_0u7-8t~?bW~MQ0UI1_@C4!KX*QcODr#=Kb6QD{o(wT-guKfF{P%zKuo7F z$1L85WZ-`8u0NS1_7g4Tpm=asjbJsui%JPtlEc6Okcl`2fN@(LwMFLzAxFv|+z3Yk zhbODZy!y`^>|= ztPhp^;W|R4_Lh-?MV(y1TtdGbw*-yGGMm0A!a?q{5|qR{ySuFk*tocFLku}dxGlck z4Rh3!MgQqSa{M>10ZXHvs7lvq+jsOgd7_%cPT@0Bt zY_I(cLq;PwR=J;=$pYkSb?+$ZHr3E8$X9UZ@cl@BFfgfN8I9lPFL*{;S|-GGdmQRx zz4)gOj4>&CcS=q3UVTm58Ets2JN!1NF;iZDU+UY6l-Gg8H>vf3&gI+ihMkJs=D58FhRBv>gO#)3EM1sp=YpYLz{rvxU^zB>E!XQ9G%OQp^AME{Yq zVgE9yv$oo2=F-?FxzDrIJ2D-}h;GUNA4(M818-2LGBGwsm!7I@6jcNuCAHcuG&BZG z6ndibxM7>FKR*H+3xD(NFQXw?fVu-PU@dm#2woH0n|+DBvZoy>y7KasmF)H(?W494 z%eR5Q%zbDruTZbi-0z-_Fp-sY##mxEMeXmf);X|=3U2EUSF4$^t|-p&C|X$iK*|t% z&;Q^M6hv;Od!>7guKrOe6`+;~BawdnrQ=XKJL@rL89?vCG)m5It?;<-R64Fgf|kL_mpz@ER>p-U^ILER{=s2K3%iCsW+eR}h&zB41KSgX?b z9NLqpz(|A@1ujmcf(CZc;KZ~h9eS!>Py#@25(457syGxk;x0wQ?~=`BC1}^FTr1}( zU&GZPuGk`70v;R7B4Xk@qhXp-rc>)0xaXKv^~QPk5L`pD5HqXtJeS=YvtpxbgU+6()IiYexr`>H9V6Uz=P5#NC{v0`yduY~%IkP9p8Guv>|eH@ z^G2|KRxfvdw^MX=Y$N)-{rA(&(DMAd*>2qIyNe>6gd+rCB}P!Iz)PV8@|{f)~D2A@Qj!bH%}rN zyk#AuC%ucEAkY%$LK?*>Q)L_wSo9c;g-h4drQc0gSWwJNkf1fGlzzpq!Ugf;8B+Q^ z_tU83<6{R2yWE}DwUFB%CU4~#%ozt(S63&b9`6R?2;-rnA6UeFl)F?8uNOT=f$>3(lBHz3nzkbtz7TT_j!I+iv$*Up-eJA=Tm*| z@^`0voL!hYMJ{RPBpId#e`9{VU&E&A$KVaf0f@9ymovJUS3D1x0G;a>#8}ddPAc*3a$vD=Gh$`Az6Zv8rKG`z;zUP#ghF264rj)s5hrhqQj~si5rk z&V*0}*vJbCjkTR|QVf|6%?m1z@rP0Em(?q!E9plh1|~mXvow9O>l#Ax#IS<}4I+6E zti)MUk9D!*Kn^K!-3cJYDy?V(O$$_LtA(D24}bskGkz1{?M%SX!msX_}NLWC+Y`X&cY{yGWKUItV*j zeIv90Jj@``x4=HpzQBMQu>XWnGOAgn^brgWzFa@V0kN+G)a{UaM~w0%!Gt9$0Kmxk z)(Ealh`+z`0Js)Lc}IW$tN8{STw2Ne{Crfvg3~lf=2cdw?@?|#Uy9O~vuRZr%$@d{ z&2N)7=YV32eT-t8svT{@p)n;bZ;vU})-j=stud{c(mQD~G~GBZ=FOfISiFC$m8wI! zb9@+kSL){v#DLfy)y{e=RY+7BI%;2OV0PEJ?g{&oE=4B+N1<3h-q+(*|AMp@Hum8L z88-E41-5p%vZOpqYAZ|&%8$IRdF`F)UQC_)QsFeE$;?^jlJBjZo+nKO#rj7pO^?|5 zMzKmo_3gcl_#dI?1?IonkX?~Gz4gAaj%W0d-ZLzb2=_IVAq}YFY}lq#Ahm!32=);m@7UWg6%9=0 zSqvzfxJFrgDcJxY*0~i3$o(#je-@>;8k{1j!KV_Gt+PIPbxTpVvV@;kb zkdU0GqTHlVtpaE~RjSu%TtuV4W8=ks=IbBkS4j4jqP{nD!+JqPqAegA{ds9tbyjti z)}3WVv7<;2w>=B-7nx6MGs1>8*^%1iu(c{60P>M0+>AFIE zDcyL52yn{M>CnIoXezH$7gc@NUv)%;dW3vBkkd}5E?P7<`eGyWY$DX!M0m9V70yP4 zH0clI5O0UK`OaStjL1y+S>c(piG%N_|@5L19><32gq~ro7vFr35|JT5S!yR%59^4$L z_A^|qNd*ev3k}%os2M0NDBI#laUE!-`C>s((IHFffNP_;x>ZUGbC`7ODBiv~Ro(_e z6dLEpI(pF$*);b z%e;DB1xL)SoqqhcfImIJ9Qo=Md`>&s%6Uk0@RnO?U|enL_=!m$=@!43J6*$elZX|V(BqT~;;Hj(+3deMZPng`UohnMzQp&0)^TG|kp$GTT{s@oeK z*AtQRn(jaYsh|@9gOQ_5W==x+wv!Kdl;n1G7pRv%+^q%Wt7SSh0x}MXkbP-XvikiP z(1Sc%mR~ZjS|Rf@40(kV^US%>M~nvkg&0oFfQVkQUY-wlA9%jq1oOyssgw*X^0|zl z_AzxTU*L^s8Gh0(*ZE{%E9Ty41x<6zRe`7!DiLrgb;>~VrU?e^;y*T<&?%o!3&_=^ z-Ep=D9B8tJR*3?dAmL(Rg=9x`BoHGR3`mBf=()&-ONQ@(K%|i;9ZAGHv_p*<#WDuFSl?`~G4{ zx>&2tb2Hk`Xv*_WU z7_3=cd$<8Uu*g0Jdy{(jd^nNrj|Krbff?NpS8{Ne7EpNBtWN)Osu%B51xNkS+^1q% z1-9&?HLP2V3O7b?)k0)>6fVC%409}xrp#q`$k->_XBfv7r!7Aa=Ewx%dY!mX<=NEp ziphi&8s>gwtQ7US1iivnoe=>{rhn~feGl|RI;L8tTc&HJLqZ5vB5ia8v;@|NCw-nt zFh$@O25%Gs8{%)PMgRNE6IPW>LtV&SihF7lts7W&WsP z$F0{I$5;Ia+rd}YHIZFx&TanHOu1T%lokcz%s1_z*Av90YrIhW_fxfW923T%(uEd> z&QQ>YzLy;edt~dYZz4CDI+e~QYY3Ee#sic!DF^saOA7GIZi1(an> z(pp?v;|&1ll~kxrm3Mtx!}WFrETPnxrblZQfe{rLwZTyBrAh45;n+bciJ^eWY71gX zlYrZZLAQf040aHW{g3hTKN?7yq_zL-Axx|!1b^RYe}wLJ&|sj#QN0U|{Jzva=&4jG zp*y`m^unVyLnN~)xn&s2B2W?dM;+Djr$h>nqc*H^pGepXMmjxNq`v%2q#b)y4G6g9 zajFt8VbU*hTqcvY@d{%J>Xm(LbsRD@sZ7*+hpK2%_PJ+~z(LY->uXyX?NLa9cUfp#c zxtwCf6?n!Ni?1ikivpw!#tJPWn7p$*39Sx?^%5$hxvv$E3UnsTUrD+8+alIw_XQJu zm{x2VCaXv1dDI4fH62g5QeFlkJ<|_#YnD^~WjIbKr+eZ|IDt@kx<+!FY@e)jm+lB? zxvCRap!&VeHt^4j+3GilJ(2IDZkaSFdoobAKF{&co-<*>P?jGuk08bh!!}Jx)y6zI z)}dE=1*hy8#5*UJd8L8MzFr$X?DAM2iLxB)T3MRaV1tRA@=sCHJ@Osn^>Bk@M7&cy z!sXCm5B(E=KQ=aH-pVVL>70D|3t1ZHB*JR%nqEA*7#J7;^xUj0EMf23UC9*0Y0#r% zV(QJi7T4Cg?Y2(%Z9l8>Y@dV~$0Q{sU>4L#+7aVObw+24*-VHWovn zm1oq&Cp;s-pu=23T2&pK-PyAGxCeIZIX}0a{`16u0J2LdxHVOh?I zO-n)XXHG5BA%(s+y0Kb#CqOg!B|<56n(yd&3(6oGlucC85t$RDRH6ordEo8i{b68e z0&TuyqMNVbeZcImQ1(9k&E6vL))aR#IIK{Xh%|?K%{b{x&NZe1&1;YRRavzpU2bjD z(zfphoDqn9snIjx%WitAid~+2grh!N{ps&vgC%8m$TF4mmk3+ju6jnd1G`Z{d&-eS z4S1JQncGyQ7IzaLuyyd{AEj;X`jD;ZpJpvBR0yLiFL^8;hB9L$J`0{4u1e`-1Vj!L zFQ@e8l9LD%3?kTH2_oJHi$Y9l_Fx)6G{dLkOS_8>Y)j;{_4S3!wf-9#52F-v&leBS znd@??nsP*qFt_C+!xp4SJYt+!iM}8DCmmJ7ZfeSXf;bgOAP(ZBg~^D%#;C^gG;}I} zAastiPYjfIO1JenjbMfe!6Z}?gtr01T3wRgUCU#46yS(%96L^lYv-;&-W5ypF+ytsjQ zUfHn#aQDVCI_L84I`Kb5)NAkj>s#@Tcv#9oSx{^VcN1JqQq>(&rnu^S9+ss`75iN) z-BRy)$uvfsmfQh-6?tVYDv>^e7SSaw8h4e}A3atb?K0=XhU2T8TZbj7&ouu#8U4G( z1njU_$2V_MceWBNjlSYKj>7wY*zZMYJ>sZi#T;&6|t+wky+!yVr~ zF44tXyUd4N3zV{KpD<=TYh-^;{M4iJ!AYip%(Bz{b!QZ&E{PrX-Hpc_9dr8K6_4?h z(_cpm_K4aAFYC;)?lOlz&x^rdimH5-?d?~$SACiHnvdf*=FYqz>jd*F_9=;_ch;^d z41G@LdKV$=89nyed=#5hgFNKz0Yx5Vad~u=m+Y6=We2v8R&iDrm0HYn*q_C+n*Ny7 z*<=4sJyWs57SSe6D1OVQL)%{)($`5#*ROE=p!)Rkz10UfDgu4WwM~bLF9{RF7RM_b z`I7pUdHV*2hUNb7GIM`@ayEk8VXACrK08aam0r`B7*Ow>7!m4Ky{;}Q*l_3b<(S!g!96(5aLcc?cNnMyin#mb9pzL+8HY&H1NrOYz1WLKu1SK3^g zC#;d?kh^2^IXH^4TYmDolFILZG))hk9-}oUg$NmiuAw9ToOf})!lq-UwDI%^fn)ZX z#Yp{cPiC`6v)gPH3x##NfBs`zx`I6)_;VH4WJTQkB&WIaXB1tjqn|@hkw3p7t27^iJXo{G6V??-*lFxuzH5rL(N|{joA~P>NMQ32LsrOXX?9@iWn2pJMbpBBX&-tMatr9_ebtB{u3y1_}9S|lmc#xL;O(u+iVr9}0>)J|z!-aD6h zAzPiRTD;J3lhMwQuXd-42hN#;a}c`?inrdmcE+$NWJs@NKU)6%`}51kGgcKX5#;`Q z6@*_~ad*r14k(s(bdy%PcmpZh#HhkQ(~TtAW!xbVX3vXIV^hj0mA+AaPi9;l#YCm` z<@M{O9GNE^buRK;h5XcEle&!pXC|x_QCfH$k@NxSJF+$Ya!rq2jL|&9rAWxd$7z0E5 zfL!Ccc2ss2*Dz)ylfP(sW5{eNVQ|0w7?a*1NnhAf=se4~(i-kF!ja>_NGZBldt^N4 zh<|V=ZLBi4PHL#Q13{Mb2;qn-Rcv^Cu+9UonN}vO#$YT*gPgh4yMbeU_16iWgm%gX z5@JOMk~gwe75Rn`k1=751nN8xrwSK)uoT98gFIZ3d42vCLn3{hd6d_D=)5EGm|1r3 zI`*=WoJFBDkH~l8f%X2$a@@1*!;7?BQfZ-zbt*2C+Y2<_$q$}MH_Z;b$;&qP<)FE> z=Y9f$NXw)K(WRYsqh@J;Tj9NSIi_sA3=R^WJGN6d#59X~yQq7T%7oMIYCic2a>5{A zB0AeGPpz`Hn>~>x@Y>0fHgUG|AG5rnF-FsWULXHCG!QBN)d`;k4t~70#BfW#L?B(p zLD@p)qd=?AKX_O?5osUfR0*SV%^gd)+cfWPjgIwCQuZ;QykNN%GOv)h`=X4z&#oiL zKR;_dguL;_r+Hj%(3lHj_*SkL8u6_pJ4C4hB*mjsgT;uJJ_U9c4?l8*()T|)4;#=G(>7AU8ZMPk|7MH`YKwn$t4nvJLA)ai;K zZG5(X0LZLy*)OT=(eyUkezlBd$H|?W%wZ4qX>SkxIUGH5M9o^pu&U?TXTQ!1zH7g( zG+L*$QJt5aM;o6!qLO7(Y~q+>f;@S2FM=y0-V{G9O@XGRYc%UqFAY_!^5d+3czQJ( z7g^{NbwMy})f0)*mkv&tQQIJ!iw7rO^JUyyTTOuPWO z5sDGQfmKC`ythvtJkuNr`^5kfC?e5)$!kZ^Ic8fz2`RybtR}4WOi_HUdHVGMtxQaDT9hRdl8oyR_!i-(YmYpZI zC0xoz5+_T_Ap4yfl)Y&-oL_#XcJy|Igiw0O_BA~JR>Z1?*wvS>UNtob5Fy1sH3yOL zn~$>Ym&Gz$AyWp2h6>cnyR740(>qK$EviNgxO9Bs^;QT=>2k0yqZvt36g1y8$x;HH zVaqK29^0Czo?89()mzN@Oat!+`!obSnKbPrNy{WAnf*(VnI<`-5A2SfjPB!>?wg3& zz4f_oe8QFOsxEb`#H?D`1jfonQ7!-WY>8!#i9_plmYuI}i!)hO@{%Bi_cO(%2GTs~6CdSS0iM819Wq>ASR#S+9na{U z+@xKZ8CsT+cB9tBeO(VmN|?pqpFIzYt69>GB{+74R>X_-!7RpyC+jvIUgg9Ix}wsw zI_xU3Z1+s^mDE2*R^AlG^KoL)kxHarsmD&lYBa5w$$M@js)-#-tS+rw#xNEfx=V>4 zL{Lz6bzogl#822?Wl8VJsFFsKRepmL<|54Y><|abj7;O7VHiMoJ>Zj}P11_D`j#s{3_ZQtntYZi^4LdWEv}-XJGb5y^=*QgA7rDdVVjkL-xocvC3ENj7qsLqf5q~sQO$Y`9h!g zfeUkymS9Vn9{W|i_CME!;{=8aPu39^$N63{OSOC>Zv3)ji_~uW!{zm)!-gJlmM4vK ztT=Ab)bgPa7X!nKztn(Alb0{P>gdj&gYp(}c@(48@N5?`J5d}vBy1D94URX}v)hX+1; zidl1>fZf|C(p#K7j-T#cADekj6{S1o5X4b_jK57@`MiIH=Ojs9nit3A6TT80H66qd zwpA54e6ocZ6jSOxBk-$x~3T*|Y4c%`?eSp-dC~ zVY)DO)@z^s1kuXydW9||*S9(Qt70i2FbN0cSGuh0l4}#G+ONN7f{?um_s=Qrlpwhu zC~r}SUbmDz;dwmja@nNk^K)4>TLRWv>ST+{3?T(YS>jZsmOIGy6ldGThe#U!g640p zwkSPy!O(=IyvyN^sTq6%x;h6&b6bR_C*7;YWE^2-V^<>Eq_g`>%XMNBI1?TNZD4Eq$Pu0o5U5FU-Ss7UMRS~D)1@_oZXej z%#sDQz2+}waoh2qCr28|nvE3c1EC(`>*r_K8KY0o3~1v45fOGHAmC!dZg#2S!a{4* z(QdRP<%0m&G=6AtXLpjhzk4=yK{?wXq&+oUtgVYST$VOz5RHOsG<`0W7Opcx1bYoJ zZZ8;{6CPSnM6-QOQ7Ng6o6-BAOI-*xe)Y$JZJ4;pnX*0$ZG38P+bH6p@$f)fYL;5( zfJt@)add>B+(t`{P>HC=SII=l#FGn$7tt;cXZJ3T#W{@Se5i4Kn#qe+AV#e={Aa{rqWNWH3R>R!uH8^E`k@p{-&Mm12m={7zeg zUrUyx(}o!2p+NNX+b@ym$Eys=$-Oitd408=MdpVWF&C%_?&DV;^x@md&gQiqCaTj%_5~bDejJRv5&1eysbb<5_54^{F8(q&m++jqE8qhlRj@R zy?LmwR>o64u@a}t#ayK}-fHTm-Tq~Eczr{9$Rykgt8d8SRbc9Xn5ybXlEc8zP~BED z(M|lPtWrCWU`crHWb^RWK@0!sR_%U`OmBNQT}OL6rDW9RBLCSTL|H#NI$T!r%1_+S z{w(#mSaO*WG|KK5B1$fL2qfl72kLnhA>|wfzvP+iX*Y(Njar#mD%-G|4ioD4NmJ@` z2CDO|>akcP-qmO6@}Q{DQQN+TjgU?6CfkgS=r+rfLusbeXqsb(sB3d7xhG~jWadft z2;K`)$<`?4ncC^&e})%k+;3;uHe{xj7OKRpdR&%iT_+#U?AFd`EiC=xen5wwyfo!X zYUsMm(1mds7gfH-BCI~D4ra-Yy@>Z#v6L?04^HsEFGgvumFe!8*(Z}5rHpGlC*^Nm zX!Uo)z+gJrDi^_MROHvn^zvruA4!Ku5HM~^WbgKsYIc$cncA->*WC4pjmY8@xwC3_ z5)wX6skX#t#ksCc%rRW&v`PN(s7(VYcg=5sndV-=o|<3v&Yp|RZHLACwD$uBmTu~) zg{0`c$-;TU)32O!g!5$9+j#O9=dzKq$c~4herCy=MSA%G2Y|5ZA8F4$hknevz&mT2 zhT)?c25V1Q)PCbGW|y97`!YqPoXC47Zy(lhwT}Ej8a#0bKOh5wso75P-sV1KK! zpiE{O%R@me0gulom*l0RY=4MQQ&YeGrtLA7`;Zt1c~2->vUzPPMmj?%N!HZV^x>`6 zj*d`PIDfL#bSRLNx2h%LE~`G%YKuERv3;2fgIfp**i?zUn7u3jnA4U7zW0s$6+La| zakHxce6}+vAHIq9GI2=Q43G+ZsSqBMt@vD;<*8hs``T3gGG@4BZ`h8$;drm0ZU<)K zoL_+Ai)c)dEGz$H&&er<@h5M+r8j^(Nl|cJy@c_8=A+%C_d3~mYuck7O#a%Y0Yx+}Bm5;2aQm-=2|SDVx~3D3D+8;Dbe_c)9*@ zh{3F8iZfEpB;21aL*PnUj8z%2Iy0nNKjXU$2Xg|n$emMA2-Tz@8(OshRBU@u4T{^@CEN3U_`IU#sL zk8g0qPq7qa6~fsClbT{VuETbQ!Qnn&G9=)VJ>$POX z?zw-}0;t7*N|vTp3G6d$QKE{qcJ;_N?Pf=DTdhSyC@Mwdpf=*awAaFv`|>oCJnN~y zu#0r!YPMw&eMcvLNm&^?J9}3g7t5{dVPRqP8dd#8xeW~s@GNmmOpI*n$|^CYCpT<) z%){ei=G1LD^mCbSFDSw|##;^fttCR}e%OcwN_)`M{+C|7zJ;}hk1D;Od%1ycw^neRp=EOU(|_VWuPoB3KKa7l z^)cigsX33>7-lP`UQqgzSBV~6X$qYzPdVaq?^&`EO+MSNHdf44&Q|dFHdZ$b>v5OJ zSY(?-OTY`fk5{~0b(=p&R_Ww#GbP(n$v!%M|9}#qN?{6vYqolao$clR*xev;p^L5C zIS$^V%Tq#`EgrgbE(bq`&V*pN$>iqI+yZYQGYkQed$)d|6D_L$1sK43eW#ETUeZEXBldLHw9nPnH|e3$j- zXaO;YFVLOSg;FX)JtDug&Mm!9N#M=9kgtJsHse|=>8m;$?e8x!8OV0pS-5i110JM2 zIy#~&sP1L$45w3BA1ThD%}Y}oi+CdV$#WKaEE7Yr+IbHH9zQF^Bqx5?^_MR zmSrhv#)5J5$Z#nT6m2EruZnP38O!EyX+srl^GqC!hPc_3$jo->Xa)cdsF?&TV+t`} z(wV>Cu}wp$k~|U1zw#&ey=~-3@i>d{+nDDngJRrVg)Y6De%G6CYN!SDgx=$rO;y(5KY;a5va`Q66K$hCfqqNn80z*_h?%gF77+oB3nvbJKo?))y&-7-28lt_AnX^wwEL%B*CH-#5nuI%=O*cU$J_J`5mGGL&47Py*5SSRJcE0 zr7YvRWd|aA zBz7B?FrNQWW2NtXU~%7)&);+oZ{jQO=lD8ZSa5;q-G5;ZmnYZjSv!rY2Y;j!Yt?UE zA=y7px@R@Mhv%Jnp~qu1F~9wxSbKW-GVFybT+}F9@*wL|XeV{7byaBt6HY%pw~bzu zX6((z<7bi`h2|M41BJ!D0ZL}MI%-~w4-xg>E_xC7F^?xurt5QoO=tM&<`v&b;8v#BmY&yTP;ApE7Dc_W9z$(X{z?hB6kyv99*AJb|B* zB{925rP5su)$n{liH-uRgvZa2ddbP24oosO#A9v4Wy~~Gp*GnLw6XTid)hZ>?a;(>ES+Z*zemCPKHOGg7LBN&k?fk(pj1qC5CeAG7O)7Hf!j0doVt z&Q8apE4rm?@xRe{@5H{0kB`Um)Zh>V1OzypzoI46)6$;Y_gkB+arqJTtfriJ08Y^* z-dCn7GgpronC+`CGw)+{8Sk?u<|>;RzBZ*=FyNy*Oh^^`Y!$u8G(v%^{-K@D*fQf| z>tvUHe8Z%SI{i9>OUT!;eMOSWlwF0e^H{M`=c_Udb_N9mI8MhAIdG-uD5Gc zntNVPK9HvpStuP~Z;^AiV8+L|jTO5uWWHD`vE1R)63dDE7!!5}W1kI{LVEWz$;Kk$ z;-ItLHQI-3*#fH>c-8{y@o~S-GZtia!dI;bga!x?FMnBh{MU8Bb=)+v?zz?SOP6xl>Es|HB9iL!|5t!gTkAei zWgj>T)F?3e-9@>R>Rx+3an8g~X2t&rUshz!-@97cpP41ThP`Y?vOFx!>XmgOKOafs z_{qjEOyMCy^!qnKC!w)rJ%ML?7clCXUR2$x*TGV%`X-VSD(jLLD%Ms)+_4eGSFrz? zaXP~HPbLvFme5E^5lvqg!HEA1dv~Wnm{hbI{vZ80i~aRz_98;6P*xV2-E97lxztc$ z_4pxfazg&$K8(gtVpM)7L>gotwZoqtr zcb(@uF~E!;a!e-I8ZbYRXvqqzf#B}Ia%!SJ_SEiF*d>ml zk=19)_{`|(+H~wab-G{gV*$sD-KCWLeZ&^6-rJozyN;|Li_nUgS|9ZLnxFsl4$ZQm zTt*7&>M&H5_w+oonQKjPRy=dqn4CbVk|ANsMI%;cIP98RHPQaeCv|Uc@8#oXj}iZT z=aC38)B3sTy}IAOoND?(Cz5mxM_QG)`BcuDIK$M}T0}&IJ3g@K?RtU8a7M#7SLyOP zfr@fd6BA5KOw_5Pe(01?7j?8GJ1eXI1GZ%&vSD>MH#cfL?x!oyNeeCv4-Q6%r}_H& z2BKt-_cs`yK3(T6s3zl`RNWu4ln~pTh;f+{oMu+Q1AyR_kD$V0%SbX z`S3+RTA1$nybQE#`6x?`=kv1a{wMCISdE;uFG%+dNrGBzZEW5RwzNuNkxX5^SXx>- zcm2q&I^WxldyP|?lbCN$sM0AprCpO`nG?=$Z}Ix{znhQk9iv*FrRpNS zhqk45FY-Jc5^!;Gd3cNSsrTD+-&YVM=790{B`pQ{!@s}26UD#y_qWY2PcQ%dZTi`- ztpnK>4DHuBSy|Dr-LS04->a`)8u0vk3C8n(-@#Rg1OtN@!Q;3-UZ^X=9!tXO{3qHl zVZ=>ES9kVkcZDTdlJx;>l9}JV=!UfprfhArlw;hgar)(N&Mx?z=)nV!5kkYbX9ho_ zP94qvPnzMqSEM8*q&p@?MvP+?oEGCFBO`}o-l3jHUa-$BFY4BS%$(^^zEqmLm(Gtk zuHuS{cefs|o`rMuD^-Dq3=9mEcv`7<>+$o$KekF0AU&GzW<1evS><2Y<%xl|M@vm( zV`E8LXc!q2S&e%IuI)_bw|NY9JHZ1bQ_d?4V&Anlt9QC?dN+Q&+sjn_1?J%DU6=}K zcE|;;4CZaPOda26P*YSQc zLmOUovfY7C|N5Iwqn8=`Sa&|4rS|sr4#qmyp5#|BAUO4r==UbO*)WsypWk%(6~Fvh zx?b10yPXB^@4UIHm}ohl=lQ^SlE zWhmkA&Fq|GE6K+JeJELs^W-9p7n+!diax0azF+WoT$P^r=*qx_@K@WS1ImxzH5OK0G`; zymIBrM`FDVQJm6_qFjN853@isf$Hkdl)LmG)*PnOaX<|=)qMH5=Ugg1Y!w;5x-?2% zvjj=LmF`L^_NAtvcXtI2)@UYluR~Zh@Era@(3F*w^n*o-jKnn%MFmmTO>L}?f@o-J zPCiUc4BU$$zKn_+V#4AWm84XVld~Q*&cId^ZCn!sbtTXP?Ta(3!oR^C*oWxUeoi_C z1l&?aBHyMpNltotdfgdnV`rh$R>Aw@!Z&c+4e|V-YyDXY-rNy<`MotR`*7}ENJz*d z|78k;u{|Qo>Sv~dxk=b5-906sN?_vv#$$_#Y6LEa+A?*=N>XY$&4;&zQsLjbhb=9N zBA~>-gMm>mkTP)Nedr4zp+=B+iq5EKD{Pd|M(n(>YbgUVDo{r zNX?Ki=?pm5bY1$G?e2Y?Q!Rt6H_sV>_E?x7bVve?+qw{uJc3<_g>a-bqy+xT;jKP#k+DRt^rT zJ3df?sUfefuI}P;49Wki`C?5g1$C3d!vxnaKSiz$LCL1enIva)iO z{=?4B&cfI9jEs!`d$u;cy1Lp_dW7ukpvChhKF*sj3h9#3%=*4AE}(NRd#f~bbSuAq z8yOoDK70F;GIVKm^+#vtS`A&iG!y_Hq33dF^TB1Xy>WTG`yJ4pk4-_egAPUyTDQP+S8_@q9Rfi;~~3)g@pxd zBK9wI&jr17>;l}xYNnj`dl~5ItGCNsb zOgZ=U44=ba@JIA>vx{@qA6687f-a~Xn2`{N)#2baJ+!f$oB?k7YoYxTN*Q8Uhvoh( zn6Vm1&}N2P*H7jd5!Pf#tC@xu)Cud-kjwDEPX1kQ_6fE105j2!NbSxU5bcGgUPxOtT-+nrDIDisX> zeUkn^vs+1l7wW$$D=4s7|N18>@UIufx~dir7ocDN#UFzd{e7=qsOr|=-`;!MJ^K3_ z1}>y4{=P^d3HA5&U)=M5=ivW+!&#zb42(EIX_wTR;{qPbAkwy{7*yQNd~h49i%I`( zZCMZlPNG~+$`9Zb55S0HMaOs;^*21={lBmO(l*qik}VTpV41S9vE8MTCSImKzocIK z>%Z^!KlJ`;w{cKO;-2Vn;ncNyA5S#3f)OY1-wk04+a3c$noDB`+i{X~t>$)poA2MN zZ)N^p;+X%gm48pv|MQvu|IH20SIUd0#hSQfsfxGc`?)Z@zwip6#G}f#7ac`_RBUW) zTJN2!fK@Ncf1poJ8$V2jj_pI&bD)tF_*Vz8IJ0kF_>)AZLV)8P`BdX*`XjEzNKsm8 zseR_`Umd#S2gaqAEAa01%L$0n!&r+d<4oPZ0QTc2_$UE*Vo>uA=V^~?6dJajY_Lbp zMu~Bs7dPv9*OZ zdS60+qkbCw0ivb;oztbq?CsXNw>GBbwmUFObQMfcW9x@2H--HP9xVY7 zq5NyHIPEzDZI6~RWZ+zBCjXuk5hAZ1Kv!o%WtLO5Z<_#T_|C>YqkzW0>M=)KB96zv znFI)@uo(P!_dCfO(r1pms6T!1=Vv$$9pGwlH=ikiu#!?Zj->GEUU?mApupW8U{;Tj z9pIha-CeT7RuSmLZ5(gIA5kwJ+(r+;Y8#$61`F?{BMLxs=NQ#vRFZg%tQ6ee+v+4Z z3LNsl`09;ryzZchF~R+42|z;a(Q=L-KBEKi;!UlC2EdvY6*FE4)DWTuh=ZX5tur4P zSHX~}jSc5%j@qpNa967n$bERUBhn7CSc8Lu7NaFuU%yU+t2*BF=6CIRg%3VXCo=&b z-6M=k8F_hY;VPQZ{D|6E?o|bq{LN&)ry+BL1mZFLh||Jr0``yIdu(qu;v!~lYH>pO zKP1KgKRM3^aGY*O)`k7~!P1R)|GxX0sn$(T!No+xX~+|aBYtTqseq}Cpr*I5@}s5& zYrlU#R5=$wP8u-ADDZ1sa4+G22lkoAcHQ>?eZmVV);O!AD+>Df9$-8x!O6@x)Su1Dy9p43QyT0Bo1eWI;6Gkpn<&m2)8ywWqrU zPy%%-gE;ClDANA^VQaQ!zd{}8I8{~EL=D-a z5qm|@Py%YLlVA8`?gul#ac{#~oUCY_9nFQxoX%142mkTFb)8n9IvQ-m*gP`^by zJw4Bk3eJu?E!`DDjB8hBf2YS<0H}@vJP4WBM5l#lP-K}HRvHeD{o+K_>PK9@3K;Ao zCt-B!7;%66-#zE+e}+MzX$DKp6WRu{b{`K<)WpPuyEfzZ>ME9};JV%%Rl>Vpumga( zr6qrD#`!lfHZDM5e08kW?Ra~8oAlkZhaxTH{PH&tXUCvVq|0B=FME#;B&UghDcEq| zN1h!fo&o-^`e9#xMVV%0vJZK&CDRQ#EHG)V;9B zg7xZ%U^xCQ!q44*`@G-pUkHLS^AULI5Ww=kJ3F2_i=8@wfpS$=Rtk~eo!^MSN)i9! zZy^M(_p9Hz7jGTTj~&jWnPXX+gaa`)VCPiX+PXtkF)ExFfbg5g5x0q9HDB3xL!Fqr7#)|1!!k7ks0qfo> zn<0Od@!1+;!g>BvvdTVNH=Q49p`8#FN2k4I$gkacK9n}!vjGgNlljF({=>P@jTsO0 z?s^+lEBPsI{b-#;9AzLvSiwgij>{1%Zr%L}+SU?bO07%J zTnoF(*`GYOGsmt^4PVX@wMLI%NQp7emUC}c)=eneHdQn%7Q|LECY;n2C{^H&5B6^% z7A2{>gT`x;-`cnzNG$rh6&6`9tvfqzyh_o~l5 zmsIMG2KLcJ!QlaCl6oJ!*F3F8zUXbVT&ppC^=i<1aG-4AirZ8S$5vCju-FvW8LQZk^n=3Oq9w0I zcI6fYm(e@xm%17K+>FHAU{|e>(_~PTYXqe!% z01ygU20=RiQyLoB^bJq~zCm{asxR?WvG-S#-$uo3AjgzzACo`ObX7GIdU>E zbBuI!$L%V$A#Hk9fjbAYLFhAsJOt`d_TVme7Q1ob_VM7HHmg5yYHI1>BwN3yj=I7?ZA|uZL5D@zhU7_-$qr&6rbyVKaRPS@>@QbBY z<2L=(hEPWy>nccWU#vxHP9(GrXXR=PMIZHb9hEPS25o-hAFJ5l=U7))>2vs=`I>+6 zLDZt1E7s`n*`s}u=l`k&Xe2vjf&`=E>_TTOcH?lZ8Vd=*A#+odcXvNU>jU7aQ*ee!;^7a&s%`&z9R^~RgFcCjyc}bV?Gt_**TK}+2WKd^jJ4n1 z_>qX&Y9AS;epJe%Q`BLo*2s|ShOh7T2T`Tx_7vVI5uuZg0y8bn{ON>|yGL9@0=zl8 z>`}UBy%LP(W@dIl8w*b+5F8fc1VJr89p8eVPv5))nzj0HA=YuqZNG(YuTUzM-Jai) zUkTYu;`B0;FTa3iCAxqI5_!b4)dAjm@S0ZL!r!*$>MNG=k10?4C) zbc_ljhHl>dafdN;wC86r@JE`BK%BK+F^f~aX9)fes>AHYqR>0MWGR{eAssT9tGW{$ zrRu?!HZoPv#=JrL3{L5P#tzL{x1 z=Ef9a+XI@{r31hg^fXe#LDk*e-PCjiGV7Z^!TfFgbC*sz_W+>t;lzgfP$!jm6~qSU zfguhf5lh@lNy$gcDs@!=Q_tzc$H7n!381k36~2bun0Yx=!Xeo@(AVz{X=$me6Zthk zfSVG;=M3Q#aN$^k5`o{F(1oWNSJ8;}J&^uZ&>K4xH{#UQ@qsp!?Snl*mL0o#AJSJr zunIMF=y-H{Ij7(hjWY~>5I7#|2Jt$;4ejd;IadeX09=!Xa03Q5u^&jnvv-aE;Cq4l zZVqr^`o*eZZF)rzo0w`Hk3ydiz)iYMUm~jjkx%LQ?SkmSxazj52)ypNGeE}k3J65n?1YTr{RKR%t4PEaBBIVAdw3$e%A6K5PpAkOv?h`C@9QkT_2C^$j; zGL5mi*&yF5iSG+Dnqs=4;@`5t7*ZWzSphId5;KX%-n%1bR1)>zpdPFLqV!*==<{LX> zrPg0*AndJPOmKmSgYU3S2CYVkCv#L2gno#_4lRhene^Mk)`ON;|J;9XX=DWD9d?T( zj;gh;howWBJhlsxeXPicx5L#=J41D+yJDe~ZtyjC)b7yDn>WG6X^|bwKTB||2r0nwIUxIG65gn(smaUt zqWnefC?(z&cV4etJYA_fT{+q5KI?L<1&Tff%xky>6;-hg`_EJzLdXhmZUv*Ixb|qE z06iRBzJqBXOKV|q>NXp0Y8~}PQn>BGsf-Z#xu49E)xlbUbf^IRy3qo11pWXqR-`Y% zMDV7{ZVA73(-R_ifMSLySEeXb&{_v-7VAf%k$1z)gQfk}SXH+ekTPvT^1af=4 z(sn}s8j@H{k@xX9mXqB{hCl`wghmGco$ zV#T-fgT-;&x@C2wSSab|04xh!U0zy>1}#%(s417{%L<7{{pdgsRt*3s8mUI;+fN+_ zSi0&$LLP|s3D^<^mN)@|%mT>qO;$T~L(ky--I@(I%emHIFc?D?)mzOM+*GX^I_UbP z_Oa}5HI7aPq0pI2um%=wGpdI1Y!>4c`O&SAkO4vj?I}0``a=3o9o7Os@yh8ixlcAy z#`V`-w6=nor~rB&=>F4F*qa&}hbX7LqXQzwsUR@?1X{dUT7PxwD+}}c#&@{!!9g#X zqp_}j47ua;Qb%Wo>~!;5`LnIt*_oN|KrJDn?ze#Hi?g0NbkXyteAC}ncqKRLY$uD z6x1FGksZkbbr#2E8}U>lCRE5$WO|O{dCTdhKbk!tpao27MR19gkP!X?E&T7Mb;mk7 zV+g#VG`jn^&KA;9ppV5PW{ED~lp2OKUDgh~ug9%Q|0zUJ+uPg2>`77F5jp*Wk4|ml zYjS26qr1?EeqyR+>PspX-+pEHgk7H$cTw2)J{f+uPPQS~?s{?Z8kwsVWU5>EUry^e zCI_K}Xr4X=ZUjMs=^1$P=sd-5_3JU@usCCZIRN+-L9e2D35ETU+e(YLBi1zY7F)#9H-D3 zF}`KAF9$7mAb6qe5pn8EX%I0dgq;FIES+EZ7Hr!~G00 z{U~UQ=XkZ)abuEU361#xjm5;qZh*??pJ0V)zuFkfhQ2^hFMIr-N?t#iH43QQ`@IjEMy+0{+( zOamkbp!$R041s#wg+6OT85Zn)a|-nBE-4jT(|K)W*Ev4r`ePpo zS&T;>vVy8HJ#)|d`ueyWRr0G4e=^yYeFTlaxK9P~o!TXG+b?H5h&1E1?Q(+Afeq)g znyv>Qz6hyvG?khK@yT*l#$bWgh655UsT)wgT3oyXyA;Ao@MOq9!3)@waWs+CMUI>} z1(Ka!vwB#MIZv~uDlN_PGrPt30ieb5l9E4Q@Q~oXSa`uu-;eBIv-t$Rr+;7|PXjq< z#*Q{}8*q$-iODD!-`ODkTBsTUiEklwF^j{Xzwg&FyLIS_UIbv&m;=zd1*Br!+Rm_U zPpm;WGlh;is%@L-8`mo=J)U4!@x>H4*&CSFxUw2%z#orb88_^mt!;7R%mL(JNS%Ktu^{5m^MZkorkt!uRr3Z@M1mlmdJE7V zZOPcA$dd&g%BrezwcU2*(~^>sT43oY0_{VgP@!ZqBZi52xw&Ys4_+JUaaaOCaJ#$M z9{`nZ3<=zKH@kQCrx0fl1dzJ@>5<$-hupxuTe>buXk}+*sn)sIwCTmn1z@~UPCrE= z0DD8FktwWL4wSTjq5_25UmdZ9_iy*Wex&f(4vceqdvA4w<)px~@M5d7t$`}LVWqX0 zSR2|`L9ibY9?lD%)w#w!MCN>O({CZsgVB`&agHn0{kTBjiKdbPwea-4R#slvdPm76 zsHCKXKp+;1lc_Drk*TSA+vFvsr6@p)4#)|#qaVuG$e|W@apCDGLMM+$xLDrp zKhdV&1v1#cKR@Xk9vP{GQ>r0{YBo`|3-iP4day}hd3m~sd3Oq>Zc(WZej9*HC5RaF zQiBW}s>rffSY3Sr7Qs(ArN@XhT-*&Z1RXdo!sTCP_KzVaQfRx_1?h>1hzKuQzbH*W3;T5242Ab(Nj5kdP2?_6DkdV4)G4AIUxm zCdpF&2|7CnvMA?q89cZ!ce!tDjP80vp>B%>=w|uYnic1pr}(G6=qY)hh0d#U?$_>> z7Pz4cyt>a$x_KaWg@9k{oA!4AQo!4Ah=;sv^*{^n>=rS_ZOh1N@3z3yGuaPV7`QP$1$G5&^=x>2}QL zVx-udDxc*?atTAy81~gaLEKBEQBtz6N}5s{2lOGsyW3A5^xP)>Beics5&dlxB*y+6 zx}ps%mF*y$#eTZ0b;g#Fa0BBFL1Z)XZ0T=4_WRdp1Aq4S|Ea%(3k4OElas(_=r3H8YKG{@@6Dt7CYu}8$BE}6j`6cS`g`vyjRm}gTfdhq8jCN$*w*Nfo8f98Av52DS+zg}Jt9RBNf459xl zyacIx^q=Wp(8^oq2<-2L+SmWHxC5Rh`+w3*y=OXwEEhf+?g!L~8NgQkLd*@ce;+hQ*^ny{}<7p0}%@yIK{w)7z#eHG(S_zRsA zdQWo&EhiNMtYR=Nk%#nNm)=smv!1J9KACtylEVz{cKe$J)fp;*p55xu0HzY&Nvsy> z?yadIns>*HR)1&m%HKXoh&6}Xy*=Tp&hRGtO*r}3^OAh4BWdHs_qXNEciR@oQ%LVr zTl8#H)^DL(s>g$J4M;{Zu-(I_iV36eJozxhTm8!aAnl)jB%&@%6Z@_n3(Ux;kjlT} zo9J`)nEgAo*U?kUscth&g=H5sMKv*zOS5e(<=cS!C!ysV#-F_|COei zu>SZc@vX@ImuwtMJYL?n^(hpyy75o0*1U1tY2DK^tx2a|Arse z%n$&_kn`d#$%yn;*l$y<_L##Baq9S;gQ=goLta}}>}9x-QSRNQ_ul&fAs@dqxLcPv zU~rP%p~4BAdNJSkMr+aY#^05oN3Vo-(LY~RFb|dZcsEZN)y}3`biEye={s)Mh4|8K zeU9^=|Dz(U%sg|1RD8;7BS3TKOY-*}QVxt;Vlx8L_SSinq!ndt- zS8DD$NH~^%_V|AD7P@Q;LsAuO%j)&o+A4^Huq?M~YM#2A$xzBsJ0IH~*}9p((L#p! zQ^f`auo+}$?7Du!#NbM`67cmDDZ|gL&^=K3zIgxS50}g&fq9>?B;}FWPl2ldfBFN!Tyke_XCOq|Co16GL%- z@||OuoY)iL+9mW2tyItLF(utZJkQ-Pr97qmvM==VGuBFc+p^p3*;@8CZVA=Karrdf z;48=Did+dkbjLHnAb6F0TEcq))3SO@v;5~y1LGYLhb4bu348NB*^}Q1H%ZR}gTI#T z&0xj){U_m3k}^`keG{Er0nXlZE>(AeQbz*~am9k)6a2B^%_GOB!KL!Nv}koK$o1fV zQT3HkZ8p)?bwm0>aV^2!p?HDd?heJ>p*XaqxD_uFDDLhA_uvpb#ogT@+=t$E*ZRKv z;vdhnH_juj*&VGKJy(df_k87*jg4uf^e%XXOhwL%o9jN$)^>%|}V~}YA zKYU*ZNvhnZ0G6g>>J|aV9avP$mJynpl(w$I!9oO-_?uoj>tj1SPjrX7eknu(rbJ%4 z=%_#4n#2lY;NK4|C%4ZNl4rr$6vmH9@Xf!o?c5iS;m)L@JR?+oU=GIOD<_|ihxWT? zN1H~6^eqjl$PY73P(-UzPA-g|7Ln$AYagAP*Rrj7B5c?f-tiGXrNxN zIXB{i+9*HH&zcmJs?~RWstO_JTZA!5MH2t@$Pjs~?cZo$1KZ)-fU3#ml*{c~5ax4# z*p1JJ3(^|R!iBUvDg{PL^%W)ms*m;eCG8O-#{&D2MH)Mh>tu@1?@yh(&obnB^@dG4 zT_jX;YfolZZ~4|OH066nV88;0yLSo?obmh(wi4q3k=oNWUF(CST{+Sa1pm(bkMJK$ zUfX)`;USUt`t)pBdr6sW{WtD;I9H|8p#$2;PysA|LG3emOiu?6p$0y2=fX-lulYqoP!n=*PaG-4dYAda6qRe)zWJj4&Hq7xxMa zTJ=J>@Ip*)h}sd_eOX1`8SaQOeY4UY%Us1gcQQdukEiz7?fV#_|9wVl_ zW)l_@ysdW)&K{v3Rh8R1WQ$kB#F&PgL0IFMs^!X5_?rWpGWWEUKYbJv&8ExD+qAKh zjtpEYPY=FA4p-s7Ucw{~yesbRf$~YIQ1204U(ILg9K?SBoXI!ug8rp96^uv3lL%2g zjc(gWbRxJv6fA?Cn_km3SS3suo|INg|+OTwzDV^!oNg8KJh;Bv^=@(;HM!E|mN&-&3-zCm$ z6O1IR8`zl9WUaw9a}?=1?>r-Nu)$sDwK{nttnuoKbh+YpH-_6HAnD6mlm+C`l*UyE zj!dMeRQEcNOtag*5e<~|KXaIsc1a(Ns;kkb@14c9vnhZuZ&m-W2ceQ<-p7s@| zJ{BnHX%ZQm7IM^hoQnFj$Fk>uAH9k((wH9M%29c$XZXi!LiEmJ{}oh5Zhh*b`Uh6$ zby+nm4#ARlO-uXSi5efGsX~k#SnS(5S3W0VM&Y4^I?Kxv&C7t>8JT646_U0G?6hda zMBjyyKmQK{NIH>fz2=ASrl>EIHIEZ_@7zgAMt|&ehBd$jnV^5Q^LbdqjG&K zKdAmdDS?%Ms39ymL{bVYyUvL^ApP_`hSbxjit;$ab8t5J2d|uxiX$gBx2A1U4{Gy# ze1S9Q%#AAYfM=C(;Kl50>h&L~A4C^EjlxfuSe<6<2bou>njH<*E6?!`@$WDH!7m5P zN2O=UiOlo%wZT>QmCv&j1LbkpUv)W|2z7q&;UFPTh=N=XgK0c(v+$W08{?0<#kiy| zk`%6J5m{8A*?kl=j{t4n=w`bwtIj;usnnv80Xc1-FX$J8R{EZR!SB-h&BjM08%y+i z%Z=V7xd9{oHh}&w`ol&qxT*iPGI*>!nV$U(r@0bPreLY8T39B~ViS`Bqg{6w&nC;^ zhWh&??W5RER&^mGWwVll&BDl2^5k$|-&T@mr$l9IM=4yC%Exs0(t29Cb-VP2uoFLB zilK0S-BT7~lf>#a@l`HC*|v90(k_DhalY%<{>`_RqH;)ITFHm6@RVn7hyJe@z=Y=t z8JOCmGytmN3hK|1f;%x|Md9tq%SBfu0iFN$$?7F;YZb5jF$oDI2-BE~Q(5`-B*E=& zX(L#SN&J-fiN<+9u%7!n5p_o|JLMIG_E1XP3w$py5=Zt=V*8ykI$N5~iYjOqnn&%$ z+6fGw%h#s;!(6&-j~Jky;pjl#_m4T7K-TN8*$U}jelvy*@}BOUmiBY(>^GQ32l4ij zXTznCD!V_{EhO!0wauAT`SYGBt{B2$R@4do_#NeFFQHi%clWT0YPQ2hM6S;^DbXqJK#T9fEN;?`1?e9Xh?PT(+^Z+0MGDfN4VTfh=7!@UEF(9VnZ%ee;vTN=;MsZcvuT= zU)KK&d8*L3JJFNX?Ix65=bl4=_HyCM*`*hutASELhe5k-vbX96KY#tsPBXT3MZV|D zU|P(l##@y*seib5gyUcNM5(yvqgv#-S}5ww+i(;REy6(ac-eUL$5I;&qvC5~0!j17 z5Uh&=5l{CpqPGOL@;qxpwfV5$3kN1H#W_ZSEFfSlE1D0TvnDn0QKlWW&DF574ZEW` z(^PtcFHkL#Lw^h$C4p}KdizmhY!_ej_df8{p3WcL5{~bqB~ygY@lF4e`Q)*(^z$kG zaXH@68ppePz1b1iP4k^r9{+^x7K+#D;30=dcJ)84$YOHwyRa`Mt_pYPS zej{h4ka74-`^z|OT^C3P7=A{#5^=_KI z>>-8vos^_P&?Fj+KCw9ca`EaM9cyoCUwk<1ik@G6&ql_$HrauE{9v@q5r`G9RY4l0 zb#JO|*N;ETStTNf(%x^m1TU6Eq}(`niRBT+FPBToyhGvF2RNl!6<5PS>zbb$ z5AU5x{_*Mmr4PC*s!Ebvq?BSxn5;Z|Q8s?;^X~aejfi*lNZnzgw&kDS?H1<6z)m2l z?hdd?AG1-663B>L-+3Lic_`B(c>SRxQmMg(Fw47)8GZJP05|^Zi<+FHBQj2W;=9#WQ=JaPV!1(c z44zrBV^2m8dDm9om+|qe!?C}LRoBfraigG7;Ni)?DA}*ls~&5Jbf1% z>7|@+u{YGE?VGDKa0(Dgl%54jfKF-vvvW z*AnQy%-VPtLgTt6S_<`;5Cvy0(<#ezPr=R?h)Ib?Q4v$|<7I*$<>4yV3&ZSbJyJ%UqO#hOAt7$dR(b$|IEpicZsU}!sBnvJ6KrAVw2dp+*R&p0{O66sa9a@C2nVdcYVYtht<S>gLWs~|3v6{l)%h!OFO#g#&V=;&@+3&e({Fwkl!2lI(yM-AXYa<&}$csTLd zOAFf<`uYJS@||5vZ-s+at`c@YOZtP%-DvM7tIOlSeiQry7;%mVK=H&9m%+tZxbW~N zXRbajLydNwx>Hh6m^kGAbQ&DYntVo8)sx4zUsyJxst$o`UAUysnVSjHhvY9L8Ps0v zUNq8ov&O^&_|reINd#0mnE&CHueTw3t(|xUZ(9TaK?;kfEuGFcXQeO*{iVYaw)5wB2(`Fkv; zz=8;x_ZPs%0}?h!c?Dy)gQsW3X%;RQ%CGKj#cxDYGL}>VB*k$dmf?t_eKc>0X5-`j z=pgX~v0;a z{%+S>iD2guRzSbw*jwx-hN8$x;rdMpaj;=Y>IfOfW4B&ypaEn;>)mRct-? zS_W0j)QOGhdcI(TT$)WD>_qXO=PmO^H}_guqmZLJ?)3Z$Ft66gYxBU&$}qRGf18@* zAL2)+AVe?c3Ni&#?PiX424EZ zxSX@X4Nqg*v#pzAQjKBN+c|SFbZArb)o%R7A$NX}OlS7y`MflLX5C!8iwGVR{1$`u zxSX|B5u!xRL6UmZa&?fPdfyVgEn=ssW#XfsQIXG4yIOMGyX>jGA6B~Qg}6?ex=Le= z+&!cS#Lp=zbUG~2cuoQzSG&n%3%w1I!R*{};icR&N0Fuq$6xv(#kF^7d*ySyiKIFj zXJ%&%%w$thD{1hor3y3hGdH^2sF)hFuB1V~FrB{L)eC5B%KU*K`R}h@2sxBfjVyC5 zTCZot@|t^rG3fr79Qu0tzW8KUXmpkccjMr3nwCU5fNkXPR$1j3#iL>7pk)8yOwj^< z69UzLQkYpw4D-f%PSQ+ouysfQf^@5Uk7QLq2peiwJE6FLk)MID;+prK@m+Tk7wSL! zZ&RuwFnZXg@V;KCVQDEps1$d0Zgu17B<72D^b zCUplU&ClDkRpo=kTsEQIJnp%+AjfcIl$dmhu@+!DixR9;viZwQv}g+5bY%SWnL(xi z@0eB@yLqzkXH)E3yrqu+e4an|^R8XW^%Akaiw{A+y0uw@Y%o*`qWlQUZ)Hzk;Qq!#e^H_oB;-AXZpx92ARt~lX z26uh8vp%;0u}x1{!sDDCfbgj2Uxdgx{nxp6bT+S zA<+?&^o-7BTyp7I%mt&k36-VKAVV4KTEUYal+0Qf7xhU!&!>xFx+hDq8~D!bi61<* zMeXmkWO^i{SJQ2v?w4qffwqZ)L;k!iM|{ghHA|T1Q$91DP^*UWB!CugeUAr6>s&z9 z*E#*I4`bKOTP)i*K2!9Go3F4@Xq$($9Tc6hPZp4}a%(Xoew0V!ei20NEnAH<8|Q^> z-9o-~7W?oE`h;BVsHiHCg^wt@@_Zl8XVn)dIf$oz`13-mmX_@uB3@6b4k{`e z8n{4~4*G1oxdb^^sC5=h4wGma&#`Z7iYh68uY+YH(dE3VN1#;mP9|*rl26|+!a--~ zCE1btvpbr;ueSqz1E|n19Bq5UWBvrbUf=KscdaF2W|1=qdxzo$Dq%O2K}w;Kvwo8PgX-k_^uwR*6>kmav1DMHIgn%GocIHP5m&l(0i>*oxBDRXN`lZ8r)bz_ zQp<+7D!zvAcP88Twq}w4*e-ta_p?`llOKvSVpw^Wr0S*y3=M9s1WVU{@9)HK%+Uj} z0~7lAvRN15Sh`<-9-7m|QW|f~wDM15{^pDhkkI;xH}}NJZ7|_O4>??`7`(sL3-`OE z==P|7N1n^c5)AB{=?H7s(FOb}-bU+xDK zx`w0{IV>jRDK;GjRg~#re*!6IwvQIIGWo9Xkp;=fcR6{|6})i1sqV_SXxHy-%F(u@ zRMDgV%GJYtH}&a|KxUGGs_b{oObnw0wX>TvLva-^HJn|PXix6IYvgQHC8^A5rc6qd zl6ViL^lj4x0)}ctL${h7WoG_xT+$1S)p(Ktv@*AQ&wO9#GTD5Wi#xeA+F=f1Paor0 zQ2~QtZ;XdxW0fLgSaA@ViN9WXt+jm|4{*?z`rbb3Rn{+9*KRdEq7^suApEtIDaF8~ zq`8CN`uogcJ2!EE>7@o(KV)59mgHp-x7svs;(W0s>h#ytLo0dTN;v~Xqrx;>EvML) zKdOKd=^KTIw{;7v3*{w{A-&^++@tE7JO=vj{>Kw_CR%BVk6xY)SQ{i#00?|@3`f0>#C*xh4k7{H5dQKE=G@-_@ z2v{dykOF7F6GNlme7VrATI8#Hzw1lO_d)`dd1PghC?4XqMH#~4SXZ5-xpnqm@F1=* zIiu6%nJ}Sg-pE}vag}b_K*&|1vJ-!?8@B9>ZGGRAMkRgZbxVfr>k?<2V@SjYyZGOR zg=s@|)8?(x$fqr35LL<+d!5TmlDdgujmDJ91`&`^-q1&7Ny!fwIY!|Om4NZ`qQEd^ zHc~zw;02$e#!GrT5&F1Z2uU1x??sPWOBUGQMnC$#e?1`mR%dgCu-b&OASEa%f&@Cjn~zv zq6#hSEArNk1*X5$myyRg$|9PO0d$YrNXTlu)@X-B#067Mj z@zupEA73R{Vry2_Ot7RJHTj{S1o#+o_{skM$M8YF%apR?FZzYKtOe&45EF2(M zrWhH3W^4dzu#R2di8psKcoLiKR4~(W8QXh77yT!4l2Y(mGhM?3>eSGXn}DU};zse+ zR+*jshbym-R6s`gprWoF4PD|YnU8P*Rj3tRX*Q-6U2Zm}6R3YnGjNifL8drutfH$j z+j#w(1Uh=ZVXOBSubm@#=Y5gsq09e9ob}|q-NM$KWm+=s{+KRs^7^-*-afRRjXfCR z8>9S_!>|4|lQ0n{gPbFRx(>`hn0y4`>>E+(Ou)LPJ=~B?d(XHI+=qO44Yq$c$_jbT z$KV>KORw?XURA~MA;k}$UPBW6F7cg>XT?p5kRVithjQNim%aVR%)X8D;-d1c_EzI? zs+Q(7dqEMfh{lISxKXpwtm0w0M#NQ!O;%1h_`EuTH=nf9{U!h6ma;SYPy^Sq#{r|z?0tE4Ke8gp8A;*9@~UtlAdx5uLY z0@q9DddgT5x8wRF`h}RAeaPWM+b;uI5#ITEWru0Ud(2b^)9=<;?Q;1G8P3zUExZx=4_R`BJPf@xg6 zql{sU8H~*9E~m)tUSNsJx*s;jM*O@74BV1HMzT7EH0LYLQ50MPJI>$`^bT*dXIk#cZS;PH{A;MEtt|x}H zS?}v*Rya%+we!E#WT>Ley~)II=t#*Xe7r(0(yN8Y@4hayPGN~>vpK4FQA+|`nb*tf z_clf2cU25;5Eja=-b(@uYYr*0#ZLlNYvn>s9zJ6}2164(6+;FwrHeA)Vfr$eCg`w7 zrvT5k$aLuBt04ONb5jtT#t4oAlc6BC|Hytlk_iGcGN?|)P@XGi8x1FqV%4F+!PDLz z%nKgd9(qN|frq%zHbK0C!TI*neXR%+h_D({;*YZJUVJ&>P?Ajh^qaSl@1*_JW=-8Y zNQ5+faOaNrHqqRA1-)g^uXU&A zbF1b?22>tb_S@;>`M~c?_q6o#DEVmEmkHoVA1`njn*4ZVgClzZmBIuX25QaZhqbgB z@O+PPD~RpQbk-a`OLY%%R$arLWiv2GjFmtRt+2ho2MhOe*8TvPCv?4DRCos+!Ee8Xi32qJq!@ zv#;K_gygTWZuy9&7Ygg<;tq0`_xGxk!gI6skWt<-SW;mD46oGJ;F(YrsjfS}`=}F3 zcv6!Q$Vip4#`*(BK_T6nA6z~931(FuY;_*^$%D{z|Au_$S-e2}-8}Eo9*rg+r-If=pkdtbhXn&~jf?Y5)Of+#-kKehyI}0%vG-gviSt&CD~&UJe&@}fHVD>;qTh3G zEMAw}fpghp;MGbWh&mBTE^Lj*Wjw?TRk%H0n=)XS7_+5%-P|OA)?p;!Us0VrrmDAD z@HdkQhXY4p^)~Pz|7P275?@a5;o|*Ng9b{P9pZ;|I6Sv1lAjautH1C=klTi?q+B;U zDQb>96{|k68s(CiOi^~^tJP}!n|`fIsLCoF#_hYQd9Ufzn=<}6(H4n0rN$!9s!mD5 zH8of<)FA!V17+HK>~e*zchZ$ARBY+qAh!LXNB5` z?^twjW)-yy(z!YnH311h!%Y<3-XKfDxwS{kydc(OoNJgJ203?UG# zk}4bRw6vnENrz}F=2auGO*5J@h79wQRvXVxt?1e>tKZBI^@sT1h4=?rvfDW0@FbM1 zAa+;1YI*H7v$8RDSIGwc7P(@xa}s;yDx5zP+L4N(*xq9gbiV`?!Y@0IXWY5@cXxMh zZ-d)?ClTh*4Kt8*4T+(q_{jx?cW&D3%mu+IX72o7G}XK466R~YW^A*$w} z|15Y2dFSU_dll_>BzSoWdtG+2eUyu91;|Y1Ro{W*dd0@$FY;Ng$dxV$La99G1@H9T zIh_FdQaMiV7~qQgX`mMk3HA^F*)$uF2_3#PZodNCzYi#%1XYRlP@zeD{Npm>J61P78Sc{ zJ~<^$@>{T^TGy0ut=ND{?{5m^=@qL2x;VpDkZO%5bR| zF;QVMH&2+c&$Wv<`HH+59_4qFBn~xmw*r3wPv!kO{?BGQTM*aCZu+6BvT#=}vx_KW zt*K=vxef6z^{}SfM6P%R=lPN!Pw5sWevIwU;tC2~wgTYIxX2PpV`=EvqSVXE)y^Zfy z$kK-`Y6qcnyv}JEf7Vh3se!(}$qym_*9!nNS{nRINpyIq@-w|uZ>8-~G;|BFLd53PXBExYmwu)ujfzBjVTV2=+R%^lW1p_C zvY@qVWk^gFcJmIcpINoL6l8ZB>CTY^MON{WiUx`+^+?tRptq<|W^9{=fQ?a;&V{Rz zAq%a@pg7=uV~5@d48R?*@Oyks$dDxiogB#>iwfj(xv$~j|t3{CUtZnqru0%5i z(0--;$&GEpp>WYwS5Wmo@lWMLm3v5d120*@Y1^8noDr>kvluPmU&E#NZv~Xa$xK>I zYd3rw^p<@*Yv;8R&655fqX?Y&GoyvVkuZTFX%5w+w%BcdzP0amJyzW6(@DA0tOQa% zwlLc=)32t)jhXG)&e-LpB2#LAsx;NaChN2Nb2ykd&(gOkjM;Vv)q8AA*__`qs`pqZ z{}2Skj~(m8n>ikkk5)}fb^k>GbwHX~Mzb)w``Wi4zO#&q++kkxXygs_=Yo1h8APZe zU8rJFleLq>FNElffZqJ%exEK;mU!0mHy31x*$^ev#f-1Qb9^_1lZEQTc0?yGmU)`a z##6J)k&lzH=WfhEl!>4M5rolulh6B}c;tdvH4D3(3 z;L)J`YD*vlaHC`fKs>QQDU;(Q>f?D7KaS_}>H#~H@RN^OT|If^ItGPz=?{WCLJrh- z8uW{f{I1^i@>#Y>PSk>pv2(}&L$y9lKiFqvRO&OVIKJffMpZlI83F1 zOziC}XvC?^U=_|0WOd#ARJ~NaUiA#(3I-Q1sOsVJHCLl7mXCN0MKnQW&^#o8HmXyX zolOQ>emLH-o#>hcdyxrRf9vLFU<+ zYcytiOsjI2yXZcjVZrUo6wPE6<}wQrZeh)$YT2&5koj#Q`d*B8>+&Ze{U43l4t*o5 z2A}GcyDTdLZ?dX_b%6KOSmloEm-c~KSBwF!{e*X$PT{*MbAX?f8J`2t@|}P!;AEln z68LbP@|)#}XuZH6n1~a^hZOx$igwS*cRo@%E4#SpHX{tz=7Z{@-LiW687+U}T(M*H z%ep%O@r(*I{yAva$b68id8i#LVTXaM#|9n@aJW@CLHg9s>Ku5g!ox zRk}4I=;%9m;%{#Q+V$x>kM_?Avf$eh#YJtJ>XtNg=CU}D$>9y`9b%6p368sX{4^RO z0FRQv1E^vxSL8pmLm#`RRY-yMFBUO5t5-qdp>BII~b+>3d=; zjy`T;g1cEExsA;2!21B}ba`%)DK<`rppb)w7{ArzqsTgHKa?>@%DX9ktHNfZvzgw7 zCt=Y!M+iTdW%Y1t53jZUia#1KlHm;&tT+e5^uNA;&l{*gWp~mt-8FC&m;W$6Toi`9 zkjmT+-JRKJ-f6#tZH$i?MN#`}MX+WeB-~9N1a> z3o(<*arKOssA)m>g{R6q=wMXh0zn$n>C1!pXY7-Glf;&NqC68l*EAEB#2QXcDb+2+ zz+|6ke@*|VvVD=qFlW-p%@54x*%&RiL7%3It7ZE)_xr;|r5bbS1e?t9Q9i{t^WZL= zcvf+8DbU+(716vERn4I({Q|yl+pRZZ(K!y>}xeVNN?60(`LnJclEGDR#8O6=Gl{ylffS?mT|wlXy&O5?P^f!6Ar4%)Sth@ZLBB@SYRFhqfB z!Ef@DPpK_z=q}`bo4BlM_*rfKUye(w+Va$~-S}S$Z{4#?KZr0Cqm8(HpyilNoS%oHW+o}Va#nQ1WvfcdnOcM`%5YzKPPl)95C1Od0XXmq zA%~be$y`B|CK0zU)8y1r)5X+Q=}>k-V23hrT39iAU3+uYV*4&#-6PEq&&M~qz$v$) zX--kKls(B6htRlUdr*BUI??BfM<{v^K1@Ww!^rx&lf|5e`zSSz_y1xadHC{)tP)@f z(;=v?HaY%QJQ@z=-p!jvg$f1FC2;9Xl1*!Pjm!wJ8{yjUqIeAIJ zacSzEKX!GkNyy2%-JwoQomV%rcb1LYuCJ0*cHJI@+`0BkS4y>w_{+ZM^UAB#*?o%4OniU8KcG%|(IVviLR2asK|Y2dlAy$Ly;aigomrv2_ql9gk=)50mQ$&?@ALX}hSsBC) zqO`?Ulgfh|=TK9n=5l=c^jnfbGl4Qwm2Prw&hvJ^u8QGyIfxYSI?v3^yuZCH)2!&& zb^+dx1uhtm0LCGAPlf2IP7gNmfX9_R=zgcf(i4FO3Ms5Av!ybr!YE`4+pG&S3kq{- zk_#;ewq3%-yEd?QAuliblfv6V4wzb?QYRPE2k1V+Avui+vQZ#U_W-`9|&S$2R-8I6yNz zYa8sypesxbt$^4Q=X0;y)(OPE777vl!T4+{5OM+D(p}7G7w`7ZZG;P)*R34OneXQ8 zZRn@oKq98LT}@aly|v$=9uZS>s2R$diCX3q*hN<~CWKRsr*IUB{(7@iM?Z=JHubzjQFVWw@B>gyXkuve_7?-EPfdn^A0BDurn@W zH!fdx8=XN<$6Z!0u>h44(0VDhZOJxdkSv8;xS%h(F6%RC)6l91)EQM>18vI7Is^`b z;l4bj2VJ6JTC?J@3w@r376yV)wyF$kZa0>+Ma^NelXqPCUQZXEF{lI&_wXzdaQ)&{ z+nQ0VE!Qm}75yJ;vG9Qn-a_VCO|tp0%5`-^Ifc*F#6_?Xf^{=OL+|B4VF{b+H?6ChFKwk^k5G{9XKJ_xItoj3~{pc}4a0#$!8W>@j%f zri+DFLNLX^CcnU# z_&R*7YetthEBZ`a85<}oQsd)}JvPQJ#XXhbk(BBqTbgo`u?zVjmJyp$YaHL>6Y!c%-kQcwnA#XHAlA95};Dr;2%g8wf>WK}rh_+te_ia{nb&6vh; zwQhy1IU9;kNr-J(vK20lz}5L$`+hbz!6uV@9f?W<$L=7vYpIvl3g#mw;5NUVhaOB11+w-UhuMeR4t>UlDx97vzODH6{OPYJRgy`W zCQ`MOLsp2&`6|yL_eL*u1iLkIFiLA)DA$YM?s8_|&JAi>WGYk1For1>{p_vyn(oU# ziaeVs@CNFA0igr23`Mmk!qL?-Qyw_#CC-<#Zs|T9EdjSvmp)}PP4kA8e@E4uTzId# zWMHJoZYR>iTA$&XI*n$fgN0tJGs*Ez+VX}0L z8&a&gCPJ&exw`=II<HujA>rWE*WY zY7p9|BTQ25c&$ahq-%!y7=_i9>RYFN>w^oPM3HtgJlW$7oM_rX!3nZcS1zaL|GU$* zd0Jw1?0z7i0hk~l8l3YO1}2~H+??pk!DC?ArMm-2lH}a&#z;Q~??QEIA<kd_01i;n7=Pmse_5uc|y4Fc|aFH3RvX2-^p(jDN6d|n;t z*P&y61@8OLYpNs!kRsPBmKnJGx9M>p&Mw@wYmSjTF~QZrh+|!XNJfSawIS(@AMKp} zd!i&zoU0VcU8v{vvA&tbRt{}edB8hG>F8bM=RN_@-1~r^#;||65126dd|ks z7&9`ed*L*R)Ta~r@PfmB`^2&)OTuo>XgCs|eKlv7^vEdfWd{SE5`p&m{%wwAMAE=q zfX&W|C0~kBVGs?08fT$Z7h#;SE5m;ofnfa>fA(?MP0{#Q5TXgdUuz_G-FeyB) z6Brjqqg*a_1+mkECqtkYdbh4HLtw&b;H^4`~7IK?+2 z2h#L{P!1n7f16qxO56Sg`+PTz3JX#pfqw8k_RGJISo9m69*|&;yNF&F>D9l3888zT zy4M}V(=fiD$0gnLyQ?yLSYJ;Z?uffg^z|POFOIdgO~}y{@H@PHpGMjXk>SA0fu;uw zJgi^a-|;znc0q0Z3Lf4)*+F3$+BG>;>0e7To?zQ|E-N7(#NUqPwyg>*YN1(25jrRG zkQcgb6yVD};F0GjwIF`Fyiob{yH1NC(8nA!Q|4=kM@#Sh7X|6x0QYLhFGQGc+ zidBNmJ>sd;sL9eV6VL~5O+_O8UVZG|c}|QNQ0d%Q`$bd&A8BbBo|UT9omOpf_q$== z^mgp8J5@xMAs5x=*ENZmb0Oc01{jNL@tw)PD68S<=EnTqJJ0$Z!2JaA!VPb^UI=u) ztWIq$R$;32#nV$eVI3d|vbl6m%e=J(Tl6|#nfq%(_#Mg$1@mPWoYI`uDM!pxZ9;KR zI~ZmQD4l)#lSwLi>=Qqn$`}0Muv1Rqx~|>l+@#(Q*k0-8hRDWco`PWU?w#vxqQY;aJ<_B;CH)i-__2-gq-)hUB1Hg7>c3G% z9W+>AXnlwNn_`kHT2a;V;1Az>cKfzrDu_!*GRd&dgHRDP(t_E$#O2<{Fz)&5bf%F= z+=W{*8kHS{Hy`>NIEQ&}ctgm_kj%;n^p{Cau9Dpx)|>~}c0aoX*ffK?+I*Dr&Y%xnviMk?Cy#)&noffR8^`}t>+cItLly)THDZUr1V!%BKkSZ{Y zotUs23D?Z3o4g=i*GUXVBsTM*V>7kn)P7|X8EOdG>D%W6(^}8-GgrQy`>WP?gFfcn zwK~aVUsKuyBcea~o4E98Hr=~u+`x9XW7PE_!B$L+ljJ|`{A$-(`W?arsO|@UC)?9P z@diuw7u3%NG6Fc1!;El3#Z}RAZUf8?+Gs%kb&jK2C0B4nTChtov-lmg*d^3Vr5Yvy zPKmCFr?PT#*zW@B7CZ+WQ;jOz0m~f=4s9ANIxqS$agr!=p2B=px_e_M^?QzY=%qw= z!hzjIng!2G<5KIjaB9VfQgpb8WIolFGiv6tpi!s0I!W<0n=lWhR2gR$^K}=i^zj4` z)r$)F;)K$iGk)(>P!X%ypejD*>#iyjeY5etz*cW(4EN`%ri%Y{9a4Qu?1{8>;#5t_ z=ii+^>tT8AzvpTsEEmmmc^O~ajresZk0$S2A4$lsw%4q_4a7S6N) zt3M@Uc%BOj(~@L+5wx@<@&qlOUqq--FPX@iS}eqcD$b=)dh6AW1yKtp{BZlqHOTZ<2eIf zv{g#BqH3X~N&6chMSBMK$}~U431wQ$QfpkEgWj>Gmny6+yG*x){re5{^v0tA37U)M z`YuS~Ss)%9J(c193^6#LSc%l}R}_|>6E7=CUODqK@e8OKe-;i)G2_6sCx3>_iD!2kvGeytYQWmqHR@E7>00yGfI^ie-x} zM?KFKxHw0ed{>S`pu%6x9>hje42FSFH?C_^@N4kijS*m70+RCD`D-92U$v)~@H5|2 zFTVJqfTohQ$8(~4e0HfNmnAv;CvH=vvqdioH%~kPMf#@B1{>EelgLwl?^E0@RLFo2 z|B1FvVl&k$-nS|i+E3JlRN*I1S&gowXC0N8(pj$K2diMCY#&@K?3?5~)^y=d=uccr zG>L@s6R@1qhICHpmFpUox{b!PjJfp4aKZ$(qy;Z#jN>C@shk04GC<1#p7H}cbKL+Z znYeKn33Xw33HMmG`nScBEXgWV~7|bVBmHgrC*=?^T-=W96LL;j;N!E@RJ9 z&A38q);UqU6~W+X8Op!}x}co5ut*5aZpVb-7R{!ZS#+XIIhr9s2Z!Lq*7h3!e1o<&t&a1&(et^_iLvvH#t;&;?SynCdE5_?}u`*Wwtjt^4>SeAbV z7g`?KA*iRh-nK~%&dE6U{C#nLqg6fSx5k4L$+Im+U9*;rBhAhHO=@SOq@_1)vpbb@ z%wE?crJL}1>)Gzq()kIWkfIw=47V+(ecCE}R?&v2>iZ_&O!xm`>nnre>b7m0gdo8h zcMa|uAZT!R2_D?t-QC?GxVr}n7ThJcH4@z2-u})#_uN`bSsou3dA_HOH7^ z%~8q;qV<;yVqum9sZ5hd21y2~T0Bek+Xs=OZ^4DWCqTx|LRi7@_-cg4n+XmEc91X0 zm_6iWZNh6levf?%J3lx$tZww`ADK`ki|ahFDpFmE4C-L0xNT))flcG~IBlkr>VC9a zx$DsfNHM!1pQ{Y1c+Lx1Jfyqrqy$TWVAc`eS*Fw-SvjDf>omI7W61Fq#`$vdp->@e z;IJu%P}4GXW=gEn=kNkmw19{yUckR&GkTI~ZrRoTUu%i0)gdo3aukLlUB<^Omm~-n z1=@UObZ~uFnLfSArpNep->vGhB>X4&)LZQh^Q^k>!PVDH7FkNGIrWlT?(bmMVu>c7 z6h{4C6+)%Tkk`II<=WAjchWcG{a0u$yr_Kb{xyft(IFmU$o`os4*5;hOc#~nPbT_! zYP<*yH??mu3r#=n*y-P=kRpgPiv=p?NDq6;b8$}A@Qu)Bm3@8a$37hn_M7)zdRp3G z7PHYTXi2W-TeuDbs^p~0=ju1sc+9iBlI*;S%S64+V#UXU7N{_pUfwsc&C$29`-Sd` z7+$Bf?tQnzWPzH8n~fF+$ROK};eqq=zUU2e;BidyTk06Tl{;+gp8uLDf!* z1Pl!f`&>2`S6SeV0~8csguc?`VF*oqPjWgT&BL*ZG)3nH3J>_4v?i+4@7u`iHFK+5 z+;&P}F2_Uwk~T2!(#xn;6GhL@J~nUe`E?>ztU$VTYa|Jk%Wo! zme+EZ^jNeiAvXx5G--eNO81Qkd`5J_M@<=5DYg?It~G|XE<9;B0v$I0)DxPk&NvW6 z(lbEi)w!Q_Hem!m;+%4?>!*an0>6zcR5BRc%$!j947iXfn8Em1-oGiu0Aeeebj8*@%F?0(kcPO zjsaOB2TgVjKFn=8%nP-dBiN{+Fj#1X8LQM7kwokuk~yali?_jv3R@%0T!9ouNEM{h zcb4kMamv+f3so-fUt(>XXBHRlYa^c!`U)yvRK1=bK`4R)!cB#$!tlb_0|ZRFqHX=s zACe;Xc51mjR?;PK(t{Eu1>t(hi>gLl|I%PZXejsnj%Jgy08&Hbyfg#h@;XGRa($TG z&7y~(bC+K3ecZ~=PM!8V>?;3?&xXt4sRJ`I4^6gyjti|C2kTVVymj=tWBU3txZ_I$ z$Dy=O6-=W)t40Ib2jzwB@nY)6SN?A8yIHKK=Qd(fo^0uQwx-?-SL!$Xd;vwzb0a7N zA=}A3o_NK}ZM)G%?|OpN_3Z3;pJIPRHtnT(r+ctyIyOvd>CigmD_U*~fSRitf?bbw zDf-2-;LA;FU*H8ny!p`49z!jgnKkTxiSlaHwvgw&Uw_*xP$zB+KdSQC z)=}-yJBH!6Eo=<$Cz-y}m+ueSe|v*wmNmNE^>+!rk;q^!X%ysF_h%5Oa9YpXW>{K~8e$s(O-g zt_E0-^elJPPPisQI;!WKy zdiN(%n+dM-8%>iW2NRN5kz40aW1gGG-DML;iy^$$Vx|eOM*PapCceQU!9__%u7eL1 zZw}mTAVnPmDfQUprc@>w+|&)Sg#1%pvZw^_EhNRUx?+Zrg_r!erkZXlwUOP7RG5N( z2zrE=DoQ-2#7Coa;0j*+%a>De>Csn zQZ4hBfJ8S|FFEeGe=TJb*rJyKHf$CrLg1O3sI3$_9YY&0T$l237~SYKKQc)Z@_5Vp z4r`>bo@z7dF$@7O+C<+NF*`tv@Tg~NXNVpZeyeJB$6xV9%UT(Bw=_>lm0nmaWbu;z z21A|LYmXNt(KN1PjUG8yE0Gm!Fh@)l@sSc;5-kap(qB>~g|vdMsdsnfQLztR0BQCA zs*K+}xBkV>VYCO98dIO}+yHlL5HM+CnE1YZLgcI-*W^=LA(215fKz&)L@4d5YabZ@VgA`s$@7@b~qELt=VD^$9E*5H&t&)QCzu6@;OgxjzPpq(g z+HI(LAD6k}J*XRJkx^Opwc*a{!RAEq5JfsV(~O!j*icC)lEb0F>(Nt0F_|zleXq_| zZ@bKWVWx<{ThR#Zc6T;Ejx=7={LX$41vQpfS77Al6oL@bkGN0Zd=)?Id|LaARn~BD z>0$$1OuH$p5UFI)aDb#p!Y^`#a5t}nK`H|zk*glaCtU%iR1uHqbXvbRt za*z(a`z2{-Q<8f8AZd6G!&Ray5V;`PL%mX;{rgCxFvZO)ifx&}Z>YLF(#kc$qDNa@@c@SEIUSIPPG{0}SA^KVup*1+}G zK1@0lD0VU(H(B#%(n)CY0;BzYeHt$5ZvNaf1aVEFAl1jEU94sZGk%B67nhYpR|wb;slPc+9zsgs3GaPYY11ex zvrgu{AK!^ze0Ps6;<0{}?5nTKC91+S%`0*N9+zfEk)O!)-q%kOp@}tU-ezv#MU!U$ zatx<+b8$$o-n}*C$v29FETjHSvdPi)b;Y;TX1Vy7Ym5q3q0WfIX?fXL*E@7FR@#%u z6ag}Eyx7^2eCa@{>UkMS7g{f7LpgZ1nFrT!T!>W^qIQ;JNVjHKxQ9Ibq>N{65rYB;@tv8aWjiosvW+DVI}dxIx^n)?6pfl9$$upO=hv@rg5_wMqT=W=-!k?p%E^XktIQLYn`d6+ zx!FG5UWc)HjN*2F@LOQ7%Mdd^LqCVU-;Qcvx1NmVhO|hM^lE;YGwnJyLIVKg#cQk? z^N8k`dDE_)otcIH?+4k$;8?wKTh)~o&sd+|l4DDbi3`t#9DD&QskEUK^-?ci4-wGD z=~`T9qOFyz*`oVP|HylP#j1GfKf4sb=-&Jvl6v#MN$NTtxc6y9NuRA7o|t$zCoP%0 zvy(2p{?dY2>_qvlWiEJp3;Eufj!|ACeI6L3;TnCRVTa|(f1abm>l7woF?H+$nDsTW zTf37^Q+!66ZWqdZQ2*N(zBlyS#mI@aKcL!M9iwezBh31ak;B4~oIw^&kawp}`iK6^ zatD1Kx(HlWC(%>>UzVBix)=wdIA1uOwz$YOYqFmM*f@;Vy8a?+tKJg)k$*cGi684? z%PH{@T@1OIxLZ(VPkO}4ke~fNcgP2Mc@|*MHV&JRq6t+eZZ#ooi z2?q*?pBbO#&v<#Vre629q|nVEp5wwD|qazX-WLx4s`ljX@d( zVdrb3C68?@>0&tYrUrn)LUyoOfsQ?dcA=@#9V(?IO!W%kppLXGzmT7`8=CeOT%c-9iU<93Ym(L3NbT3>jd z1j9sW!v0bYjJwO;zP<{2F6fGX~CU{NMUw?eLOaI=(yS& z`(Gl@8zq=dt>knOQ0(IrtqGxp45-UH;tiS)Iz0La3 z!t{L-p5H6`F+gH$^Bf`U52V`hn(wnUY)c(`(`%5@)#6-WJU*z_9agORJy>0CxLq!# z`^GlalZNR908Cfowuubwjj3HWYi;GtPL;18EtA3x!_!f7vh+L&G*{@8FWK9vn_SMd ze|I^yUEb)fHztc5_CL4IEEU9FqfW4}bsTnjxv0oZOr_Y6adBULA~$ifXu2By`EB-7 zkKRdR*WKV<@pNl2y$oYFM!cpF?lmt6y7vc$bfVc>xcB7do~OjbdP!Z1BdDN!aHUP% z`@xm}`Y+Hg+>I~++==2P0Am+~q#0nSzO0N)rOWZlTt^${Kdhbl1^l<{)u!4~FFanU z;C9vBb5NL!P?7`nk(|kJ4xuBK=}Ek9dmLskJ<}eL-}0LrbRu0R6{#Bfv5Utl&Q!)+ z!T?yuQq&L}Vn9#uiHLZTGJA;yQsOHbz;g39GAT|fT1a!F+(`hTVC+mA>p&DNl*wZz ziyplIXsvRw9@szPvP{Pc!GP~52y3cVf0`K1Gv>KTPy-AUyX*HsQc}M$G`+NKghVe3 zIAvi4_T^Sln+-|iIrz?@Sp+!;{}SwhV`>nZ<@e~!v3y`s@|qh;R>Ojg^%>;H z7+$yGO%7X4*C-z6=uOpf^(P^wZvZQDoMwN;tIDUqk?$c<1XsW`WYEMuL&niumqAI5 zckw7m@^Lpu=^4C|*u;4!tZ}CvNIx2TxgHAn-$6-TM7%5x_th$ra(`z~SHvXV`FOj= zH}TXbz$BrtIbA;1>?wC<<2mNBpJ`w3CB%K#TF-KSxyDy0%sKW31rqfP$_n?f+#FeU zq0x;EfAwU#JdlY1*L+5N&&n!pQl!jXzt@Vwonbp2ksx2r!Q)xx* zb%O3W`3){skNf=D!tmfm!Ih|aL)Ruga|N(<%2H(_#gHjqCJVJ7;bOg7Y{d6K zj`3z{to24IKK+pKVd4#R?Y;PM_TQe9U3IcvNGc!u$Xfzn*Gk`vW zC>C%ep%=$YmZGTn^8{!5eAV+W5<{l#-2X_C>7($z7<@52P(%z%i_%Jhg%+_mozse$ zNAuI16A*5XxtNUHN&Bvr%GKcw>D4ClZmm! zv6zT9dwz%aMVK7K6v5DjaX6*WzkiYU;-vL|pmq&1{>7?~SBt2R%RE|&lc(aXh%xjr z?VU~El>T1>7Ys~rCavK^YXY@cdg5Ofw9=pO;aC=?*u)Z<=?f3W`}g2Juqc> z-8%`o7pPcjt*fzHb)D)ZSpA(jhyTVmCjm%0AjaLw2IYn7 znzq01iV19Qgf5&sgg;vk>pTJ7@P8IQu{xuZz5UV;qXyy}8N4!#kv^b2QM41b92&8orP7g<*P3*RKUblkKr6mLRDD7i217{ z*Ifo%zX3dEd-P&tyvogc5Ie#tHl?~NnAGFT1uUzw9 zJCACW%qpbbXT#$BgW|Ryg^~$jhw*)B>ZLCYb6NDB5a1?kv#||Z8ignQbJ(jp-uqE! zG`|@=+`M&7_=6OR*{y%`q(VtF_Y^d_SU~x$F34I*)64c!qjr|w5C8#Fu`34x5w}Zj zG~QRdVS=s$j1F=(-jqGp#*W&D>ghMWK)mcG(LNdVV;7SmsbbiGD1rAbT#l#=+p0; zRPSt!Y#uhwM(oyE_cjCM$mbw^kl$ti0x1`2enNl@fwW5Rbt#N&AeKli7E<V0OZvAV1SeSV;dJz!(%gkO1=2Z)K5Tm;LlDRqhUvQWcu9nU|~QoN%`hxupk z1Ue2q)E6I)Uq>gJ?#!$kJhrc>k0zR2Nr#mzv8@jTJru`c&euJCzLFD&w}u2+NN~xi z#7a!WkL1GAN-o5_A1rQ$sv%ZFj36UVrv7Lo%Y_w>v4}-4yogmNvq*_Z7y+Y-9uZoK zhk+y%rsPj$GGJEo_sLJhLQ)oC?TtJyss!7OlBOP>pDjXaUPUACLd^XNx&Ew>jn7IR zWrcHnF#1kXCv#?6_>Nyc`ff#-^Eh)~1E^=Jl)pSzCQ2_yvfa)g61Pej1uw8G*63Y+ zkkFxnA2g$udWWjN(hI>(=v8HEp|{CwcDzH+vjWe;FidMBCk4bPONmmzdG8vMNQx-^ zA_o|3BMDZ;#;4=jKUS6fKs93Py#2ouKhb5d1#TDfJ5bq&_>9z_cbDpKbZgFtWugQR zh}ncKNZAJ;+>Zbh9{6ikFU14d_b~Bwj+sDNFR@QLQ1NfD@q^5lOWcICo@X~_R|A-g zSTp{9{H<6LK4cBy$_xHByA@Iy6a$7lyhbNO6S#15qrK@>C{bYt(d@1r0*#D>+!qeK zs!ug9oFF6nj2nqc&s;V;Oh4+~Y}Z${rN(HYx60R^SUxI#Td(s1*la?}6BE}G+5O)V z%GjWxj}_KrNeOw-^wM+e=sgwHpDL<_z44VfO7bz)S}%0(!zUBhP`V&OX|_Hk&pj`% z)3REBd;2@D$1*4rbb>aiv9}Ncd6+K+MH!<$Ed>2WmSBBc53c;-Eg(af5aa;$S=|x7 zs_4pJ{EZp#|3VM%2mAnOigasQ3JL<1mY0^?59eq=fH_oz2pI^OfXnXn*_~8`8sqTb z0nVA5lk1;eB3ynNBqYWmJXIRV>}XfF&;P^##lAZ!odXSsT}il)!@`j#at>|&)1{=T z{X*A;E(KEhZ@j`3My#r$JRC*#cDJV5ZONSn(h|54X^rqh9zqZ9Q8vk8>GS47G7C}4T91%Dfq2w=r z9oa*N(?Q1TgH~nQL!I$C{=3Xy(RkTQD<$&%`y%By%;m-E(=Q)I9g9OM%SfMk@z3wS zX|>$Wr2@n`(OFA%mmw6plJs`;a`XFZw?F_!j>}#;NRsc1vbfez7na8h27MqS2djOy zO~vP&l_0!<4davmL@3n-cyC@82wnft*CGE~Unj{Y^w0(t*{z_@?&!;G|Kavc7=M^i zzfoNwxY_9(C;)ByFj2mm80gc5jxG;s9UW=FAMf` zAoV-=>U1p17=G%F!;K&h8gHNSz0eKabJ2dh_;MA-sjm;sF)ktVi90l>#O|a@RcGkL zaRxzeu$AAJ4B*ccb+qUAHx34FXsww?vAZu8DR0PN$-loa+x- zWrj6SwJD6666myBJE_dvf@DpFXY}DMl{sR4lnXP%Ox^B!6}om49To$bgg$2zXe;C% zP#*rK=t`u9K6e{6cv2RJ;6L&kM|vw!{?Z@?#=L5z-f9yHQ|ww}rqZAtCl2DJxh*6= z*1zt(+^X!B`Xdpazr+}>9Q6ySeY13}ovHc9i?ZU@<-fXuR^fw?NrAWLz~65VyZ>|r zliHJEiGz13njKEi8xTkRtb5qMrv#T>3B|BV!XewYy!!#}W!3+ipM+gRFd}M`UTdwd zCB&pg5Eovlpcm(SEs@awtiOkHF$chi^SOLJpUi!wA5jDN-ykN&e-!a-H^bd?GB9WR z7EU5Zm-^WWO1AJ8Ig!~F zajMV-rPiX%QYt8lv@nzbf^ei^;9h!kp&;wN&-z-m42ytTO6578rmtR|HE5HhnI^?k zlafGM=Grhnw;#{Ul5@#DN&fmO+=qoN3Ks+hU3`$F{{4>Sm*gj26IfI2D(MpXe3oXBhi+jSp%cQ|@ zj&xKa6p&vBDARsxVLWVMM5F8hKrn2CRL57e>bxy3B#})7G)bubwJD)@@9gBraiidS z;f(VQJjpiHn;j@DwdhA5*(>G#hp3*m`?rK+nMuWxG)lFF687&x;NMlCT>85ndR3F) zJvp;r^EBRVvi7t)L3DI!zG2ApqsTJ*D-j8{4K?oc6RO+=MO zOOiA?uVQFpmTvf%`e3=_)U)1eZ+KNNl*ePSoV+^3ZpCXmVc$%r*;c%Yb7 z^535YWM66cjx*9~jK(Htps+#l0_VyqE#%(-CtiXj;xK{RYLQzJf{5;$HgIVE#E2J@ zC{Z;;87FxeuN$9La>92xjqU#SVcOT<>3S;1uuxmb0r``6t5XCBPE4``mCzF$;V(H< zBuN^)Lw`x500S0+8(PZ+6_N%$a$ z5iO6R?~$hM30of8>@kxsInR8;XCdmG!f`zYN+Wldw zLFFp0ACXsgEViH+S6m>J^X*V~DmO>`viicFCh*&P6EV={)K!{J2@#nWsTmQ*EckP8 z(-6IZ4RoDY;UBojmPyEvDGO&vikzQ;g<#YgS$#SGgD9k_8@gtlaQ|6;ZcvZWG?BMO zZVt!t$m9w#n5xPu{fNF`3F^L(ovBUdyRu9p`u$X+qOQg) z_477QAH7v}kBV6!q=)Wq_|gCG<-yiEEtq;&KSjNt@I{0voH2nl+eURir zgj#PPk9aO4be(MXMV(^`yP^U(3pC^-Tu&W~D$hd}w6+ z!Sgq>-3QqdzhC#V;CD843sn=Fm}s^suMfnB#%t3z;+&!{B3bbQMLjevMO`TOo;9UU za&bJVahku_^q`>7*cP8K9@oY9b+x?_Ko}?is8KpF`ahtTD6R@&u*v!#VjP+mce!B z>@9mg_Z9F~s3d}#!U*@e+sLmY496umnfghHtqa%>#>VSPpgoh4n}Ga7kne?z^rbwu z5hhOlAxaOI`zCp7iO4|vmo&$|zbiMiJDb}uWEnQcZLiwGo3iImJKf%3N0}t8(J5{7 zs+%+!Sa5>LN!Ozoo-QUhzW|q0e0==< zy>j!la1jFshs!0?+@d7F>aU%RjSXmgU^8lSxg39E#yLJYA;iO52E3M+Q$h(0`Yt6k zvU!74X%oN!NkHcsO&Gfweu5O*_4v@B(n(|8j=+u)I&@onHP#K`IjKeAG0d_`uDh<)mSWKdF4*X028T+A#9FBCD0COCQ@N; z-qZ%~JoJZIa+YP9G0qn$cWOK!KO<5KE0GL?k<$=Y0t|cVx6%M4KF2HOIc&z1?`&oz zK4~w56kdc|#}#edvxZONPmM5OfA7u}14wDHCVkM=U!wwLT=xeHMOYA?~LW1*^kf@JSkxK_809rBD z;Zh6TpkWh}b<^zC)J?N$5`bb&9M zzoh{wtRM~weTJ13wPq8mE=US26uhcROyG6HWqM)jxO~g2BV0k=-s6s16H5Duo*hd+4LzWsO06i~k3gg}ivzbN&e4cO}UrAkXDcs_6rvK}jos=$u zfx;?%9?HT7J&vFeoD;eisu6G~%z{7)w(K8TGa459nCYo7Mg}H@yok&*`|vb6zlDyS z_Yes9@Fot^OT)xV>;Tr_Q7j;Umuo)JW-OtWV#a}o{RrLMO({lC%fK>l&q=pL20(1m}Of5u7HWs~vHb#?`bd=Ro?3ySUb9ED4h+ zh?BundxTRAJcy`F)8X+3Gb^g`6N5p0I7U`!2`18ppkR1{a762{uN`s$mKRwAsXbUAd=SW{@N5o!7WbT)< zsjD0!foRogzzK_65aqQ}?*T=7ZmTf)fLf=T;b2Ls`164%VYO9~X~;dwtB6tJVT^F= zy;zgDG!?TCU&&ljK2lf?@ORv=Ii;41$@^fn8;osNs2??)G&R0kA{ z5`VkCh4Ei}n4UQa+LoD&EW%YAp=+7=dId1~snCA??1#$$qRYKE-cfG7kh`J2DgaSL zpKNdAUrKjdzl{^~jX3Z9*=R!UHv4Qv8Wh&WcH2+Ve5|<=eZm_xz3y4F;@Lj~4Ydis z+90}c1-x)9;R;AtkwaqzCXO7OxPBU4p7De@U&_pP3R83 zP%`-oH1u}KuHWe^aUi9tX!C~EA|_pub&^MD;QVBEQd|hOR)baKD=dofcs~aDkeDt& zj#VegO|Nh)GZXr zD|3J%UbQ|_revX*L^-pxTpbC$aGJ4AQgEJO2QlPj{;1eJd{Ep=e|ajzjIY<8j3zlh zT6IlDP!LS{kIX1Qsh3=VlDzGbm!1kwfE;qKT>r)XKk5pxzJIGLh$b~{W9L<>VHZGhGDZ6-M3(k^~H}%r%m51n!{aM@ukN zI@W$e|47r17bh)vLBbO0hEC}yFJ-#YV#LgF~}OWwW)AX{{*) ziHYrMaNvWz6(6($HEl?BVli@EwM*N_&akVR;qFVOV6ed*#Oe-mbgL9ny*WlV%4EK8 z({V)zQULn9=b;rI5&8bCK^ApfF~?+NvT%P?b2&i|?C)5TuE5$Bi$+<#!U?;fyuTwO zD=fc$=HwK3wwgJ(4o1L^A>h7@kMi?9b^9l@VQFqEq~-v%(EbtK4bjFWS{Gef73XF~ zk7OH3J&h70coZW&=vxLBTR~bY!zFOi6Izg+DgS1sx`17c365eQYKXkwEO&@}b9G!5 z<5rcl_`{??`@5N3Ty=o_Owdo#CSXLBVZuQ7sVsT$qpBok%6W7md$J zX4^kr{d{-l{q{th*2EzUZx9IGd*2J)%Wg3jp@Kl|SDzC>2SOwaU6J8zuG%L&!jqGi zms?4~USU|(#rUA370Y=%W3EgWucd#%5*Z6Gf|ItnXsbAup!X@4tVz(KC#zIJs2_&w ziB_&@eTO=Lud*FTw#+2N@(%S`|5)x=uEXa5!L~5v%z6H^9$Zg|(X)#EqN&Pfl@=KB zx5{spfIRu1wY_OL9E^p91)KDuVxf*W4!XGHwBs^g_?AhPw7K363dWfEzEhg!NuAJV zlw$wW+K}q5=I={37v-7zl#R@3=u2n->tESJgrRF?jb`;+`Z7NWH-{%JV}_kdS(jERFbX(UWz!||BMMr zyg-P#<-1E&u5Ov$VlW~co(BEW!&eKk2JTq<#1ERAKd`Aq!Ua3%B1VlqgpQ=E-@FV3 zlbm%~dhX&oT$6i#Kq}fx_ZD>IoAh{QkBo)NvT@2*3-pIC^q&n5>VXu8%Fslw$x+wQ zU@v@xCYy^`Bqnc7d)U|eYP8w(@+CWH^yQLfI}E(Pjm@_P$Wa~+B_ zLz>|pqH5BeXhD8(BEbTMG1(Z?{CUgN#m1x**u=w{ZLUPU#KpccK?YE*R?*`bV zl~+B9t0KXyS>TdtFfkUJtNS_WG?eZm%-Uy!*w#Drs}6{8R5m%c5UFU%*QQ^~i1TQd z`wZ1B&M=DBol?G~E*Qz*qx$yYMTV;5wDk9gVr{|rB&&nz>UQqKHR-WRAxa?!JY3pj zpQ#^F?DuE~qT=;Fb@ne42!(YiI@@XIc*Sc@57CbXhw}c68LjAl6!jA1wpaY3o#Y_K zLMN@EF*4#MrRP030PHmCVC7(J}@ zIKZhH{d=QLS`wv?nLqKQK7Q0Jl*#TsV8uq+_k6HkvFAXs+NbY+lLt|D%AA#Zatr%g zrtuC}tboBa`JI5E#ZD?z2WbZ87ujp%kl&m7h=GH|2{;toV>9osm?NYN;QGF$B2-9( z4~DLZwuo{|Z{QF=Q~2XxuQOMvcogp83Je~<&sp6W;k=*DRbrUol(P6*xTjiW`Aqeu zOo$2o++Luuz0UI(o*EQ%;MvOC`iCvvb0>w{JvmuYYQ@lQVK0cO|5t0)&re?%Q1Rp1 z=H+vbOmyB3Hk=@azlqex*Grf7`IiuRzIeqP`M%%0Bt=?w&VTuVE;U09ahkD)L@LlJ zOvDlF4?8}86;!V@nx@S^A!#~iaAW|N zrZXO9L(ai%mu!@xbEOlvV)GXsU-9)7H{Dux8C7>dMR39%@v4lSen?mRE9%VeH4= z4AjjzM6}1q<_(dZ!2>SsFIQUG|DS~H{qm!{n$JoJf?&<*#kH%PS z;VRf%4LFqCAEJ?+ue5X6Qf6X4uV}ra4z=C4lX4zRQv=ml?)EN;O$1hAJb!ttH!PVT zV&vgZ+f|IahmAmFESFOR6{kkJ$#!L$=dTNC)+SJ#R-NyDuIOvQTTb78S?UAiEm(87 zi#2>rwCUHPWc{d?09`tT9D-W$Sc`vHnOfmq>!mE7>N`a&qKLlzTMF!ejwkdxx($qW zRR-uCBi-upOxuvyh}DF2sFRBV*J)>7C=9z+vp>|PW-V~P%P^^p;m9vSN>f4QAD$KQ zh-ZCO3R9o4yU0UgPWP{Kigjzc-(H`7R<~$Y0f)dXjg0zjT7d6&8@}Ct^qrip?=K*! zj5_v(iLl-JX+e6($eN$eEB|Ce4$%%at7qoUz)0SI!P`GM2tu!7s9xi5c@j)AKt?dcJEhCL^kGPL;&qRV=;llM9 zy@PEs=iZgM&me&0IM+$mNqXSkL)-!-BuPar{L5BaU&vVcr>~$5fOkw{v}}d<5zbaH z9A!AXVS8-U>5CXQ6PwHR8j(;91{pO1AyyBXEQr5G zYiR$40!RnB&zX>fVqNidI~Rl~Gv%A)`Wltm)$`nGJste3 z=JkEoop`bDQ05!UF8!Y_1iRD7`q}s<)>zw-s~Y^5(5Tgjc-=<~^lu6=Ix`#hC^=n^ z<#U7r&*z*XrMoFkI}Py!iMwUqxN@I^QoFxWf29ZnW27h&E@tt1p}6|Z>KQy@ z(IxKLnRbuu$WCabv2&BZUHBsO0$L?j-R&L5 zOupKWw4#KPgh6I-!1pYLCsJ4+P6v|CK3!_leNp(3?QPdT9PgjED{12fl~(X;c35H%~IyfUL#=hB7LWL zwRNd9p*S3`o3BX%CS{Q_2vQ52;|=D(<3RKtO=Emkgc%G+s{b;2NI$?{9(_-8LAo7S zuSDKJ-Y}{n4fmBqibRN9l8Z(de#rtS`r}W=cIzdZY3o_*QMt$)I|2O$$Sr&gv0hp~ zIOkUiZM2Ju>PJjb{Z1EB63$a_-j7lxMMYH_40G4hN9*U7@6HB5%>J|N!L|7a4D|HK z)K36{FkF7mQBhMQC((QgI7>8m%DeeQMu8E$ z2x?PRx*}ttgrSNWHhm6?ku zR)f3mWv3+h}2H>Hb?O1Wk%#?@jRepUSGN}0u@j-r=?C-VZ)%2>t>jv`GJJWN`5tlU~& zQ5tC)%Ml|YfjqAF5GgLw7gR+FMW%RpmODQWW5-p6Rg7sb(o4&og*0D@mhmC`iEc5P zzA?TL4J+i3*m!~-LX!6W-uDr>piIKhOoewPgNW~)+`%7-e3`IfZ9;8etArauIK%p) z%d1UsIn7k;TJ3+ztbp4Q+5J9HN)p|LGxlH5dtIObP~gt z=*6~d-kIN{`r!gY&qEHHOV!O)%v8)($}n2*h$4jExWp$Qw{<|6W|_yK$$<$spN|%; z9IRt3(hWk2hSxdJUTe2w!4ccBL*H`GgU?~k?~5>Ijs~$=dnL!n5_4}=JMsu^SGkoU z7+R)vZKx&(4*NC#lTV4I85?VBxqciEo(ei;iafR6V|D;XQiEYZ*xf=^LsN<=RKqnh z*7ld8H>d5}=k5^qA@@m9tmn$WQpda9+_%~BwWmQNb6L>A2TI0Glv#^SBqfHlVv1=f zHLCFl6GkQv*M^@O=16*J9=P)%RAF$WJwrYS7N8u*3t=hee8XpW$y#>Rup z7Wl;?O$rq*znDdcS~hyk`Kw;M?&gm4T*Y>iEqv;q)>o{vw`8}snz^37LxB4TZxGwS zk-O6+;=qBK4=7DBn-{GjXNw$LHGR!n+B)Yhwy`-pUmSw*%SWv{v4_^nAVinAHkM0(DAJFQp@)2Vpt;4vwcn40L34CDaoDth4ULsl;%T|MSVCtN)2)rjxpAqr!e)XIA!VmCvrJThgSIXu*<6U019;_|E2 zE~k9Ml-18+hN=>@j4$fax&lY(@?ZKN)48HosHC~2G&Cj@zkTZTtF~?^ZC(oIr1m16 z$4(L!7jX~~d$e=6=Tfw${#>>kWRdt8-({cw440-TpIU1agTIgQp~0Za8{uCoGBEaL zFeo^90IEoV+RGxBOGsv;_dtJ#ZZ3kIRwnCHi;PLUqn?2~+V=~V)Qz26p5?Jlm$$8# z++U88N*-*}Ha*K;0)g_jN^wGC!&3>0=L$uD}4h zAjRMm>P#dFT+l#Y?o6dc_shl{e${w5Ev9t_`=gj^?vrcXhl^(E&xTuv3 zFz8Rvxs8u5)_R`7yta!(I`%KI=>yRgKLAvWzsRcK1EI_{z%hdR1xW~gHn~Xi#~bwl zw->4K$j@yv>`2c81fcWx=avYVD~FGR^G!?Zs54nqlQQ`0e zGX)LGcUnnJ9=Mx~urH*V8lQW3WNTB^iWQ2tinRZ# z*d~5wxgwkR4W1pXOrg`ua0l4WAdR*h&nPoH>FUGx^rX%qpf=! zYWB^jvdqKp(6D#p5%5ex zB@t2Y_(7vGebB$;5RnS~ZV`QGh&yTXxJFF1w*A2NI6|dq%tivn@#VP7`KDPpIY}Ou zFU~IdoVMnF=rATo6U2%zCEmQe`3QqGxm=Me6e|-VOI8{IHQN4os-K@9zq%)o75^@O zJCHxDb-ds(TQHkHaw@`BOEow~UNVx#%j?@Ci`RW6%*M@5Izo!oF}i0OXXMOHw_?}e zjm`54U!_YHC-w@C?+fC2qnj2~HEFuxD(Cxcp!u%O4EmEmE9g2&NGT}x!DB_@L@kHo zgfo-1QlKb8bGViP!{LTgF54KvEmeo&3>FjyqzVCc#EmG8D2XtQlXY?x39{qUTYcve z#}4Xs<{bFScFH#enO{-ly|)Yqo4d9t{t(wb0^CK6XPS1Jm8R7x)3T#FQi(V`?LG34mvd^ND zS1{b%CfHMah%&zdv8g16D}On`iVaU%5>z)ypGM=6*SSspN_&pR@k}s|NRHxiZzzF! zp5lvVgUWGGPx~F=Zk=22oZ(g*x)wRxdyPpksOB*imwG+kS-Ihf(~IhdaeTcyq2;~A z5B&XoMyt=QVcy?OP{WArmbR%P3#VJREwycS!udM(X$h|ksX^qn=_lUvcAt$ldvAEBbWRc0dro4 z!^!oCt9ajZ-i&jqwe_@g*_Nl8_v%v!CEgdGS<93<%3kHFnElR$jO|pp&X;x9Ed%k;G~%5;)v-szWRX) zw5+yrxR%6gjxw4vmMb7RL5rCylX2#EUPS_|yN$POGlnS&)}9%^@y5W3W5huw+uvU} zo2ZqST@)q zWB%Y9mFKdk#%sCm*5P|!_p6X?g3l&Iz%b1lWPd8%45o%63TiDihE&UdO4$jwRC!v)gQ2=4W& zrC%hTk6WjwWg8b8lP0g?hB_8r{FB&w+_90FMu9svUxN^$yo5pu+Vz0=4V%KL$Ve9v zI>bcF{*aWOos_LIxDS-s@w$%y{nEiwPd*P(j=v<1UBY2uY+61pmlyS&IgapiBgiRa za4`nm%Ri18jR2rSPc}HMt88LB6wxU#*?0dWbu=FZmMtq-7DR-Yi>d$#=ZnpYEuhMo z%>(Mr=P5_?PjsKyr(Hxzl$B5AR<%#%enpNqtua2WDqok4rv%+d!iwP~ZQiO#gBf%yNUf;C?)bLWZM_dvjfg74*to7hJDHWgq}WwB}U)9#^~>SfBvT1 zub$0`)3*#o2x`7c1o*!M!aJnqsHk@3WUpxr-8L zjDC97sA(%GUVXZf#O3kl4rdNlpgG`D0X1gE#N9^ZSd>j%?m`Q*$m#ocC`#kOTa2xC zPhCCY@qp@38c7OqL`4@_I2?$y*vsWoROr+I8h<`wGHlj>E-9D#O0p=y#u@=cB)02oA{E-gB~r8Db~5^v^M_3X*?~?Ir94>ODbmR#|goSyB9zGC8-K z<7@O_)T1~9>i#Z&v|xQ_IF++ci2AkJ%VU#|X17-8;)-s}PA)VbuXfq>_W+CZ&j(rX zkESU=EC~-E++;3O`yYd?3I;}9Z9+!2OYsa}Q#Ld093-`m{>5t(IX0@}N*0f{d-_Vx z(KLT?Z6AUC$}5Wb#p3&`L+ckQ4;PIb+o)IgmdG6aLqy{2x_*@|oMkGEUkaLgmso7G zEBHtew;WuHT>VuCPD=Ef4&NuvJ#z{xW-M>6G=MfNcm$N@OKAG<`wMdR50Bl%7WUz3 zr0rePqlCP@l0w24+xI&*(`UN&S&rPfscd10MEy9+$7iJLPqUHye^m&1)@HW)fPFc` z={mKR>80g$C%ZVb64CuSUIdf7|MU1bEHMe>zm@<9^;}E4T*1wjQ{)lmMHroSiv&X{CgTx z{Yo8PN)1;jlhFQf#CVxrBZ^M&Bg;FB70EhSJ=-1d?f-iGd07@LF=%UtQnHG7i0S$e z|BkH#b?4xaAb$;DlOisXMY=H#Lhf@<;+4u|fE#(O1L3V;<7(4%1>|1ax4N~8jEW=C z8f1+%u7NF)3rp-_t}ss2*_7Qp28ApRK;A}sZGrh6sB%fj`1+O>qo-(6S;Le2$Fp<` zgG`0|f_QH}h0@;0EErcWg`lu|jLji^M=<=OmJEIg4NHAzkQn|q>k=vt)vmWt4Au-c za4jEB++1tvOj@*X0d>4VK{=ZSHXvg%Bq!AIB4vE+_AVrri=S&2TcRYDc!a;BXyxpF z!AsDS>wgKe9i9q|iD25VSjXaWog)CjB}B#7m0uYvU(MD(LP;@5Yy30)^sP?W*vf6P z)8__>Fzz9?1jP{9{eKce- zxxIg>lI;yGqUH!Vg$h5x9TT%((ePC?wSUPev4^J{-xKFE{0@>DXZ47`ea-Ns5~l5) z{^kOq=|My(IWcXybDdMvb_rb)+B+U`VR`IV`@nMz(YUcrNY=hN#>B(y^?OqEw7>kt z-AX$0>W~@?;Cdjcv(EyjjaEIEW!ZZyXtGtwu@Ybxku&Z`m`k=1%Sa z{xSO*JaP&I$` zQuPYJ`V$p7vAL`P9jPpa3!JyVFW?KeYsMW zjy~jzfPdnw2{Xh2UlsI)dmiLKPzW}(8pdevfourtZ$6izXd5C<51c6d^ z<0*%{OjD9K4BcpL%G$nVhER}4(k^VFcFrtUFK+GV@pb&uCrV8Ao0bO!%r>%~uh7&| zk=tQg$f)6G?0+uVuUx4iK4L5X!|(VNEuLhqj{>rMV$y%4A7K2f{MimY!;)Q?vfbGF zN_cbYqe_a=srIrgu}9apPKjCxw?JuZ;d+aW=b*E{e+!Y{ozWeeeCQkxG`wiI#^l|3 zG}|5>tvnVz2uOM`NINCUrAP;czN(rWk{CVj?=u~A$QXw{Tg5$HTeIGQ&#zA00|a&K zJT>9(kW+|qJ|`4==POA(f$leF=X)W$5I1mZ;8BR0Y{PH7nYcitwT#qXT=siKMRj(X zaktS^4Dvp~OQh}v^-(w7FUqGMTVHPJSer){R@4ZLhgn~rXU-NuK;^7`T#`6@#F!8n z@!!k!UU7f$2h3|VxQfbXZVtbI1!-r^0vYtq12_LI(Ey9^)LGRvlulCb521X|^=EywR;%7;I@ulx>ldsPoJ85HS8BBK4vIdA5H% zZp)#Q|5{|lsMR+IepAb3#-%d2R3tNKEOIo6CCe}+RoVg>ja{U(N@}z~4F4A!9$F*X zf}(_?#3@JRdF5?FVh<4k@HX&Y$oMMwSom1dcyz8HIDJt{=p!efs?f?&>&Gdu4)0)< zR$~iG)p5v%nikRbb5O@z6wYy!tye$Ec`!UVGm>y$>Td5ht;Gn&2*(IFICK%M!>>Ex zumBWE_u)}=C=hA9<0wuDGl=Ia%~!o~kGjPO&YrJ5Z(`3=j@PA_vvZ-T^$O|4uV?}y zB*%jk5a1jT@o0;xeFiupOD%Efd_)!9WwT*W0b z`*R#;H&uMq3mpOf`t*zRKA-gcV;|Xe;1jBkI=;U?6jg0BoRfH-=4be_yJtOHWWR-! zW(9#(P?1b@ThVemweGch@G;!zrv+#|CA?O*jY4f5fWU%Ik^1D<&C^R=WXS6Vmzby& zxJyGURJ$_k`KGt;6vhMvn0o~F>{1S4k#QcpWbD$WYwI+|*78#Z&o*vv!9Z}aLAbob z^6#J5utVT7G!OKlvJ_*ibRI}PECwImFTdA;gimtdRSTrX?T#HIwIvi4PK&sH(K&I? zHu5+oxSvSxooZ>nQh{c?f|K3B658I*qxMcZYd=QK>Gz!%;eCck^BoW3gu_pJ8m2&5 ziezk3=7Aj)Wdhl%ViznPGk|_0P7cWtI)h`&Jd9*F-?BK;Dj z5*AwF^kvgUDb+qqC_w#=k>;N67V}Eyypg@ubwtJ`_Mws|XB-pNVS*pq2Q8aWrJhwa zKz;Fx&Ioj*=Cx^~hmy3vhCGYXhbUB*P_;yBL}Nr_GWL1X#yW1LVZ1DKEugnUuq@0i zL{jKe@GXWko1L#!kli0?AM=cDBI9lh(wWIAMF_>)isUFwdL@|{4dW|t5E0kJGODU1cCl=ywJmYwBWB^Nx~!!o)kzMdZy6s&4lEBrHRI<8ExXKI*9 zAL!CAPAzsK;GbBp9+bEgH{|hI`B-)A%_RBUO8LGNOJ{_;&PAh(jm(`QK|{=3+7ae`DhDoM7Y zgksVYZdUBhV<}j&_(aFREs^5z(nUw_Tc*b|OvfXZxvO0mSll8m&)CjMfwf-+8IoMP zgqzPh+=4M!3TJd46`*%+?rYr7>afJ-W_t2~#rMnKuVNGF-a~uOhUsnETr>rk#^VW! zw+9R9{|O(fL`B=jQ$|>$v2+bb0K492F=5{ABvYOQe>585{G*@K4 ztRUOGytMpyy?bIe9P8i?(LU&EAj`YjZE?MD6}=_Nxj=0;8o}E?*F6M0J8}N#F0W0H zMRaRiYh3Brzy-ZVb%(z8XK5YdSxeB@cAJ8*L#kDdR+KfsYb_|xL{8s1i)KG59Xq|? z$ZEAB4tHzlGuurbPEtGMCLC%w27&G?2Z+Ef#$b*~F|eGNLwZAdMazLCQq zIwGtr_6*mbKNvMiujQ#J)CxXy^JdcX|7e9LQE>kvnLSUD^#eN?3uw}y{M6BOEmPhN z46$(qhhLraXc`Ucj?0sSLI;@*ASFJww7ewn1FJ2DX0*>TXGHvh1S@yX3I4o~c4-0t1Ja=aQAtn2xvW3Ywc0Y0EZ z%GMX;)g6N~%xHO1dkJ?=YEU@Lm^uD)8uDVxXtd*w~>k!0O4}7UR{<ap z2WUH?T#yb^qK2mC>+z4y5}J|Li;mWs8g%5KOaa&H`ASa}mHEaEPyWQwe*Nl`#Y&xa zH%?U8)mCR~bMxcA5DfW70rzthDj~o5kd-sf7An`J3+OvY1 z*CcB0Vyr7@fJq62eMn|Y>kTMD4_2gC82qx>?UEE*LrPP^4^yiP#K^FEE=( z=;5CtQ}bQ>w81G7u53JOIXKE@mmEaaDr}xnkCy~q#eXGvDUQn@LM>J+ z%`3?}x=q2(H?v29+e>1!2}?|}Cy*ep#}LPz)CSm>L<@n2?KK<6WkljeepoTMf$)$U zHq`zd(fV(|d#fi!`*~(+x&@c7g^ClYoqk1Y0b5N2Gn9@{?iBgxdc=mL1nI`}Fvl@r zJ<@xP42yuPwQlLCT|q_Jy;30ObUbfu$*S>zjd@vUVY#vDUmu>wpLW^*Xo*HTPB3?K z4UM_fY8uWv<@*Q0zoc^14ra<%0mIB_!R*dK2FzEx%f^kj0)-u2$C%VQ zTjwicFs7I+h$Ci9ZQb(kJ*W4ZkPmy@YX*fvZ%w78n2Syum_%8sn{zm3@wPmyh=z-Z z4mG?i98{H%93w8i@iS06n2VclNwJ zjOho5N;^a!f`y>X*pIChsP^C(Z-46f%`p;RJGP$FkwL+Xx$5aFxGK2nsR>+sNVAuH z9;Ud^>DU+trku3-GzG=`+%QYHBN$x-l<^!CCi&HZzCPKrWlHq1DW5sR_=WZT5p?ob z3jR^{wX?Oet2&xI(0mmElG-ysHQjCjF<)jfm4qF&A1C|cAIiK{%qc{Ez5j@ecOZoR zkpl4FhXJ)%=$Mc~_}#~gq9xB7&nnN_U#&+`7hf01N64n%O>LwAU+sF7!0aI>pyhP= zbp5U4KXeW9f6+Cxi4R3hjRD>qTokvywx<)8#Z60}sI>ngHt~NzucKak;B{@kSvlpO zAl)oMf@Ow%8qxOq`@QzQ@nin{g)&gh4a#O0eU{{6by-Nn9GjGNV954V?oS44a7|6> zHSA+<90cgyWrfxw>0--by*A^Z!-KS#7qdKahhcOB9I^H!AN_%zK9oZ?U8*Vty- zPMN06?43slhIL58My*oS2W!WfSy(q_w@Ug1>1+j;tpJ=10oNKd;RCzo>nD%eC5L8B z0>*LUo1;H#LYqD&LKa@Y-1XFPi9$vU?reT{)ZSkCzazK&j9}hCz?eAcr7th zM2bl9lDdep(sE;SV-2)kpdp#kQ>+l zb06sp>5O?=zz8bwE4n*3?er*cltEBT@hSiScA&|l3nwS-lU~7(#uZ~s!*VIT2q%M7 z$X4^gv;MP_Wrk=lkhV zy>sgQ#p`!;3RA7n(z=tnlkf~seO$7^Zl2om>D>ky`(!VQnyc?x@06kk__-Ge3!~=6 zdmoRR+Q))d@smM&X_ml>qq+q>g0u6+KAbl|s-H@>IuA|6;pV2~s*&-HEmM{t6$Ux^ z^{|N!+I&i4O)n16`@2xim0|4a>MA*Uc-YVr)~|mBm?4NGuEL^@YslNhGeCh*g@HG z*tyg-1+Y`2?wErTz*w?T${HJ>E9*q6BKa!vteNwkp zOp7#dQPRl$zOrMF@(Tmp;BiVU@e6^}grG0F zSw2~H;7Z}N8#N^o@EnkfBg~&A=ajUKaEf!9om*8QSV!$ZeZzS4b@6j(H73-tiaAQX zCD(pa42;5%*}RWSKA1q6;uC}Bc`#~WoUvzE+t}t}vU!31d<8Wo#g~X84pdLtpz7pq zdxAY2$A}@Y4AMY}a?fY~41NZbuIeErk(yVz>!kcSPl zCSSv2)pU)NMj0B~r`w^u02kRL{p^zB1WzJ0ufv}NYGb|k>F;7eNv*wo2s~dfl{B9? zfu=JmGB+HR{DvSSu_7;xg?nvfz$?$c*W(1~@TwRa)O6Qg=rpY79IZpf$H4xreri+O zevg*X{-zb^{=*Ph-42dTKA5Sjv59hvd1R4e&c|96@wFXME3mo@*f2K!HUzja`9H>; z9H4X^BUM*;o<`UO<8o*!_Nb4wT~nq-{PXIr!_kHqqj3$6z@-S8=b=blF%p1oICnkHNH$slC{P+e+<=XPHTQG%{R1`wiHYa-Xc3` zpMoPAvm%{l)`b0D>O!pgd3yQ_MS=Z`2K!;jc}as-h20V#!-?S6uX!$Ut~xQvwgNBY zzClBVp|Mh-OkP5$lx!KgeHh7C{^>&R83)`vJWZb8N=yItbFtt5K#)%7X^5`4Y|9@W zvO>*6fcS!ruH-gzgE<*sfv_0kB>HG1pB9+u0(11lDZlx(EAHJ^6DNayaY7qsiac1H z6f?VI`|peTkaBq}o2P*h9B5p)3@P0&xDeICk66MM?T)UDoGq>h`v=ve#L847$*@(O zV|JRPE!r%Kf+m#r1o$a$sQRa<%4~Jv9E4&){~g$V)X1LjEL90!ltN+#@1OLQjD4%| zXcBcuF;H%jStJ1pTXR!I-4)#x*(s9k$DOA1UXYgpV(7unS0F%GtP zk}|zjdbT+V2El~q(d5aNsa9H0Wk-NDut{^E8TcmJ!ErN_75A3J1 zDTnMJe?1Vle-3oqTzLF9+Wc(Wadf!dS{4Equ0txe8jBK{N0rYp5!$wFx>?B@DPd0Wt?$OSIeVkABq#qqk!6uL ze1D3GoF-vxekQP&r4d6m*X_QL0*#I^F3x)$verNeq)4R2K!aYZ zEcLFpQ5O%$Jwgsh%eBAuxYL-xhJb&XW&x_^5y`1PPzO>3BJgi+(6*B2w+V_zTlOQ1~`zk@hRyr+d!| z=R%jlmij9dxOS6#fuca9K+GZORiYh7$ABp2sCM zKfUolRhxOa0xEnj3tA|-A$hED4MMe3pPQfNr~=PVW!tb{&c$&TPk*V&$#d33mFZG{ z<^=|K4DKHsJe&htOApp$njvfJP@h{rh%Vmo&OOi=4h~js@K5=URe(2ZMW^ho`nR>d zE&LH}IHC3HaA$RP*&2A)jLg8e^P}s-hJd)@n9h3yORkF@{}8;uHF66Ki#>rTwe20r z)k5Y@>4!h(l05Z0EXHE$B#-BgeAOfB5%r|t!`1_<6D%EE8o!%o0@$Z{CycgsJp?B`cRpk*A8lm#H#t(ye%?TA~u^u#GZQTlSe_@km`9F%k^b?e9rOHX1r5qV!~ewg^J_NuE$p+uxq zSrloQ{_J6P++Mc9x52L>u))8ypI>g7Uhq>!0ij{yMC*uodKzs$-tpyF!Vbn9dTUA6 z2bK!CnFNg4!Xnb@#G&so~sUsnH^P!zt3dZ+%lJv!#G-alu{n(^a|&r1AOrD2dp@ z4qAQ7(?#T+-aS3iu{9+yip@3GhxnK{0(BqMt@Tjppcz z67&*)SDAf^)%)JFZ#g~qQ(m%VgOp? zi zL|rE`v@@ktTIbCMl)3l$4`c1Oz3NI6cm2k7GN%s1kueZbOt|iBi*a0QI78QxBI+pgE#n z@$(nGwg;h>(5=Rk0N1@6uc%}gu*+xH)lSqt_^9;3TMj1dNw=2*KfB zNU$X{N_SryJ!AaeFbXmh(~;@`n;B7HAXK~B{jh-WhmW;~MYUllpI`57yo$=>rENNM zkDR)v9;=k{SOxHk9l>2aLKAz2NfU$h*w)>Vu1EfapEj%#)2;VD#U#e`Gf|}RRluy~ za=4{f2z24sgxMn;;gylejtwFPeUROr_MhTbyi*9^thrCKdYclgLYJYNW6h1yjCIph zmNn3dq1wwhKDc9~nVTP?`R@CPoR)9%xSdw4^ICP@vXZDjqB4pf{-~_ML`- zRET6wORc@n2TzcJ37+4gD76%R=aFs!=LUC4sAI`<9>Dm3g(gE2o7)@R z6+5??&$Z!q%`nG9sUSCiJb&HkL{O>-XIXw>bpr>xPX{@{CkT|WX@8IzOIBavw!@%B zZVoFgMZXfd1x~MrX?)Z*u-DhbrXxB=3EUX8*;umP%ieIYhe|<2sMPXn*c=xb>Lmp? ze5xF{iemYIm4nRZxX<1P9>kv}coyvUTh4&}43GZ;uk1;TL}w7(Qq~ z1^1`fg5p^_u3)4B6hf5E-UWEp0~jE>0Swx44 zgsPCRxt#BR0PS^*6>pzJ(7TH3?<^dc^VsqZ_8yItXDpOVsiss!TVh7f`4lMUIRAQ~Fl zndfUJ9QkrSHg-z6_+wo(IrPUV7Y(qy@{QqUXi7TL!Y_UC zT~$h1zw?R?(O!Mj)O@)cL5pnjbMJ)o?tqMDs+k4b@u#DNvvIr(yr$&-|xM^9%k>>u%vGZw){fo{R)j0xA;vy>w-l9M*e`gOQ~DfHDw2_ zD?)J;+WOeo3^6NWj7HzIB^$&mC#CVACC5kL~Swkg@Q5*0a}%g5yu|xzeItmfzei5Ra;rhb5*g zxv&A*ha$oXC?eBYMX2bg^xz_cfj|6ay>mc{q{^`1;sFJs#{iPbC?VT>CuefEE@VrYt-Y5RGhYE{g zwP!X>Awt3_VDYdEd+tD5{|MVeSR6R`z*%l#ZB~YB;fbn18;j5bh>;Hp&8JUbK!p06 zzaJ!RSuqhZX|Kpgo&x&Z*kJa|6xkkC`GNcf0~QjLm}!27-}7pE>ByTa*yc@L3O@ma z+7=pyoN}DuibC_KF`>R}XVwqg2)dD@`5@)l9fApp37KTo?$gzq@X_R^8og`XL9p;k z>EN5>l25{2j;LRA^vKvl76rS0LEX_I8XPU4r>Cc&|1f3;L=Buj7oIQ!rMGm*s-#O;MRIPiC` zkUY>0HiT*Q2`Msd@a5yn`8#rcAxN?5oxatwYk^{vpn)>XW;Eb>)|?l{a-KY7$&64i zs+qNkD`U0moqb3K6uj>!1xDEtL*y0t{m-nF0~Z{QabeX3$1@7~e9%j$SIKa==&Yhv zz1VWD_2s(G_D!cat)C6y%X(M&r0xKV9ig*VYfWlNFEDmde9@_0!ABf>+g_%K&RAmL#oAtlznVN)EO9J z^UG{eu@d-UwRlxEax3@>s65o1b;unHp4>Kx6_Hq?4PzQ=gf||zLpkwCR5&lCkB%4O z3|M8e2a|uxpGJ}Y_$rHml?2{}M_7LlgF#1TS>VPs#$@_o`{n>hW;!)a zdLmmSV*MW_2tRz_Zj$^q#HTY?PKB+#G1*^zi5TT>UED2m8zHr|v& z5i|!;1>_gZ_@_U_0w~cozj!!-oj@irnF#y%rTFAulUV)ZOi8@){7|b#iVR~i2j{e8 z*2F18lu3{~i3kTP(G3>$7PmMQD!02o72z+#00CqRmu*%`UhmlIa^%r0gy{F-O3+XK zQ*{=SW>xWCP`R%J(%$O#QHm~|Z59ajPz7ZtL>C||^P|FlD@(~@`qGJv>`&*nOckoL z4h0gP&Qma~68AIy^}QJ5-zaAJFBCIpPawyt#^jt8b=koX+a40=j?ke<>$=8fQ1qAw@WwT57*dZ!+Z!Rhw(k*oq7U);W@$A0JMZ-xpD@ z;pK_&@bI#CuEbF-Ak3yaud^Z=n43#4-eqKDczF8JT#`f|l%v!8D`1Krlj$X0iVA;Q zY0uZk86BN3*C|C#)sB#ZMJscp(dwt8b&~z-s5nB(p9`S(wi^rDPZY90ph-bWKGbjB z6}hpJ_1?)ZGzY5iA*RqAkj9A9PGWJUCm^24ZSqjFF(Jg7559ZdN1lC>?ft?j*;99n zEhtQ^_c4n_IBDJ$&fNl5M{22)@0k8^QukdIxq97nSx2vGYXG5!BFa5glx5yqzs0rr zSN=2OGiS^j-7we~ZVv73sc0RN6QfjSUFc6utdj4JNvvYAxICd!gw4FW2P`jOvk$sS z3P`5V4v^}wDae3VLynM#C=Yu*)0-Id@=sA$h3d6tj*6I#TzUn991>@|O!%39^>tnQKjB$tQn(h5EvM0qBEtu^NIe?yL`& z#b;WgxNqiX6YLN1-39~JAEF8=`+bs4jmjfx^ES7?83aM*U{}cTYzp)H7R>v`)2vnZ z4js1`xoyD{*%1|@;ggm>QCggZ@D~D~k{E&UESCiI8xxV+k0`cVLZ3`90SkjQrshu! z(w`J@!*Q>3C(BITzxCk zUSQQuG;{!-u_Y0RekMUA>{Pd%1ZdzXq{D2|YG7+)X#$I*iP2KG4aC})LtL@)(A3Os zdjK`g-jX_^LQclDA+V+?mtWAnlE-?Z1*(8JVc3x=5ZC-qx*8NJv$cKuq+>7uE<0B- zKw77xw8PszrHww~o@%k)3>!w>Yvaa?^`%9bg2Fk{3!I~LhiRbPMt=D{A!HkZ23 zGC+0TK*Z*AAo|NWM_0v8pPY~qIR_S~6J!N#+xsL5hI%2~M)U@Q=0%_ix8&-=g^!KT z8DBfJ&^LBiqMH$@040Gd0p$jJlR5k3YA{v^~9bAMXU-xR%AaMyV2ue>aj zHsf@N#P41#Fm9!<6zh00&06J6u!WId2CG;)l=wd{L6*WUS^>V;%Itoy%|{g07t*3N z!izp1b{7}4JNRQ2MB@H6W-_B2&CXW_XdP=}rm}?g09*Ri>D#mKnEmuv{nb$m+;Fl{ zx91oJojM!0zm9BA-4U{-qDS#$b?uBoB;*y@!@>w;C9JAJnU*WSuqQsRcMlHyyT;c| z7@^BfV1^Ok^YS_=Zv^9s{7!=zJG}e&__(>L3oJ3H-4m}c#rZq-S4UPcUW$@DePBk$ zkQvthYjcT0$VcVDq#*WtozY_83G5_x1tjcIQ19zh(!tlGZu|U`F2^liJ6;ia+#HKe z7MbkS&=R_m5|_xTP;zeSYlNf@l2ydeKrxtSWb$dBpZp`d%63C?Jr$pPfcdSKhf6^& z)Z1qw48N|C%g9?Ks}69D&v)*vfh5BQGX>nTcqpU*pgW4Xfy4~N8__Q*i+wnUc&LX9 z48>H2G{-H07Z1n*&2Kg-U<~tvx5od60(D-!3Y0*n?o&S_}~UkxU(mv-?CKtLf>g;1vFtv*#o2q0wWDPXt$~@ zOnaYun(+udNv5M%Y#D>G?umspaLzrjPj#_2xu$>-0UmKK@L;7-;vzij9P$c8o%sB2 z$Rp4nX8bR?NX(b#)|t6O!{$o*&&bdHP;QH0U_{4X#YB{&pyt)xP&OA#Mk7LrkB^Vt zd~)SvWaiB4nL80{n1h0Xq6m3wJR8|fCVAVy-{y5uEiH+4~?&E72t9lH%~CImKwQp(+X|i!Q>8Wh~a4vlnFh0rvrexTus(QC(E95jVln3Tr#i(dBc97KyDl zbIr52$m+xIJljGhj!)wn5e$kZMLwoeXbT}%Y6A!TR(LP5{Vg07chccrt1*dJx5uT)5E;_m>Ss zmO1vwh_Cw+RFKlyeP?r})wW9_P|Qlz;nEppNh!I*lTR5G5)yJQaG619ljrzBv-NL( zWy4PtHbFu=bDt~~87>wW;&Sfr_Tnx&xKnnDowP)sq*q`J*@w{etDPK9?yq8G7@Vt7 zGkz@YSY}{M@ybAF32{Pw+=C%+(b<rva4|xbL7sju}0(a zG+kx$zzwzBz-7U-k7(UjOu+5hpH&x8Bq_)yCxTf_9!a?>9h$vR`0Z6{3ox6eWsL1AP z$!TTF_l&Jg$1uxG;EeHvgb_vBCK^GJMN}t$QUz{E3~8naeB->u;bJjsCsn^Ya^PkV zaO!l}t0^`!%y`ESA4%7Ez@&t7ZVrYp|2e94du|HV;ec_))H?jQ(~2X?`(x1PUX1sn zw@*P0R!TsbeYQ2&M58<~w^W|#S_l|u4@sGz#PB-Ni8)cy#^4w@?UaZxZ~NqqE z(2)ozF+-@nf+uy=k2^j@K)IEyz|gk3Qc?q(8}rdF3##-f%#vv9Ocxj8#GR-`#F{31 z^SKJ3M`^u7OKvNnho?l~OPej&yOI89Bl+I|+#%%pg)-QWc&LMilV|nGu}S#{wAVT9 z;iIJcIYb-SP8sP>TW60BzPjEXI@{G(gJ$J*`N|XU_7}RGj_7%4_dC{^nUgv-bw137 zwj7G29g{ozAM16!R-tDJzV~LJI9|@4EP6pbWw(k}A~uqq>xotp$vk6eCrNOIOhas| zRDD>5_@ICIsmaE^W}3;{7qs%DR{ZSPkINj(+ww%Y#)8+j`)$7X-O%LZu8;AxHaE}J z;S2}}pl9Sw*MRu!egojIHsxZpKyy+?hA1(6YTwy*IOOv7{s$p8NS`xGQpzdbawX7asYTx|B^E|l+;TA8*rNP95R?VXm`vjRGwR2>b=Z!sjs zHk)%_{~Y0Qz$+JUbf#2>ObePMrHc-n-O@N4q)xLvHSUddZgE|u>P2^)4SUzUDS?Oe zZf!`?#zymWxpjiKYa6l;jnRDm6Jv|^heMdalmKs)Htd}RIj@#DUC zTeh8#buD4xBD^D5HqI-;@WOA}eYx?9_wc9WbEzrWNfpV}vuy-&Hnc9oZ^cvJ@q(yG zinOZi%ZX*LYEgp1pj1&;vsqGtEOXQ30&amKge`y zhvt=R*bg+|SVY&5_94UBX78@q`^Z$9F?@XHx~9t@6P|q1Ie;fy`M%5OdFstuJjj>= z9CqyGsIdDk3q!ELmWA>;q>5&i4P=Q~)M-Csx`gu>YVE3eWypKC)a_^s+W6Lia>-l) zXu3i_uq2h*#Rfd|2@;W?EF2KLoM~|QY6nr&CkxS3X^+)-b6RO>Y1H8-0X=;u7niM# zjr7b+u|5{$n6P?JC2x;W)sr56CmW}qjukC^DvkTzWv?>KwiW70^TPKdb14Y(+)Wi# zyfZQ%a7p2x$rl!HaW#+@b4A%{CtV+vTGb!n(ZoXK#LPp?lLQx37?i5<%7X1l6Z~IU z7TUK|&)ZVGrp$|WyIO`f5Ngv_)bi|W{WinH#MHz~=V#k*$!wTehQbigy>w;n7>ldX z*OTajspTsHSv9RcN#LLKsLn{!@EO^*7rG#NqV_0Ti=%)Y-Fn4$U~s z^IUGcJ@O&Jp!}uNY1@EKH_Kp+d7wpZ)C9h9GL;AGfk+M2d4$zy9y#)Xw} zlYJ9xi%WZ{W^GnxF`vv|8RLU8LF>XeE_Pv-!vV|?sBw+;Y;!;{s&CPaMgZ!MN%tad z>$JMN;(pu#IjhLwGJ|3F+uPgS+`nEMATo#?CH|vRUtc}t>g+hB=)}YJiLJOCu2!cA zH4RX4!F>+d%9CwGHrmL=&jyJrYLM#`m;|tIy98Nm8&8&ztn-awujVb>7IpZNcAw=E zlLdG6zWFu*&y{c1uQL@vbw6PMj`eThHWgU-rJbKcg zU39&=aWZ*YoEIsgXI)`Qv?~x$X}%Ni>02uF1J#onW+B;0y1LC73;? zSA(@mZY6G%@VhOuFOLa91U-s$yxaTxdoCNZ5B-tlX>#WdAF)fNjKQ56A}+q>&#oD8 zu1Vj5yvK0v$6i>@*%(R3rl{g`l4}A^s?6gljiGp3AS$pX3vM3`XDr>?e#r3*c)da4 z`cDh4IDpe|W@aXEQrpaIc7A?7`+oSlXA$sGdq8Fp-sXUCoKV~-QIElv>}+ z7$aYAYgm||XfC%uoltv!)I904`Z84Z{nnZ< zqWngzyBR}FZdYUCO{Arts@6HkVh=C_UfnyzoqZB6*e85w%=?^1|AaR&o(EEz+VSd} znKnPC_-dcUlR0VgQQmy6m408PMutruoN5Ss5S??5FjydJf)an5NiM&RolSwXb9 z0;#Qx&UmknSrYBGv;PMwbLv)ohba!?yVrRxk=n;W5vjm7C$5pLrNZ$R&@zD3(Y0E~ zqlAr@7(Sp!M=0e!b@I-5Rd!wrp+nkvCb5~We0l0LC-IcAj%03N_J6`nHW$|KnrP`E;l~S$2e; zUbd~yV3xfW<%$p68~63FJ(i7>@efv(`hw%|7C7#ohZ8U0R~4y#`ZeLgIY63IHc@Mr z)J|O{cG;W(}T?N8AG*P?Ut=` zcEVHwTbc4$R{y4| zJPkZgywR8kOv9vFdLcjr2Sr|3F~6A+JbiPQCxlRu#6F|2VWkL7{OiEXm1uAs`B9gFu*C(3K2XhwgJ3E+IWIr!NI6V32 z9Cx(!-QMbi1!K&Qv-kBA2<-;Q@T)Pu3LVbYs(ed zBGmK~)vy1xsC#~B_db@~DbXzeY4Z7X~u+8V~%g~1sNe>vu@+YX}E9DLj z?o8jN;EAfV=CG7V*Wh@YQqc6x%4!Nh6e@3HgISVXsPB6akvD8NEIeKkQqznny*5wJ z%nVy1B9+zGN9L`N3Y@+u7ZVa1o|v$s{p;D2?h;rZmZq_(3A<<+t8TVUQ}y8kLTx|A z4S&`+mF~oW7lLnq+Fx{=Kx`lo9OP3rWA}FAsv6sNOAk$&jLMMtD>FoT zVAvzz0qLy}LG|y8ke1`QM!usjz#nN4O6)mDwCQD64Be*IR~B_4GTROfz48|*%m_Cg zL^!dwzl#s}0yowk_vooad|MSi5VHh7v9Ia=d9Thg3p5xBfvA^X4cl_z{r%l80pa@n zjdl=ljQnndAMV~I2O9u?`4E8H7fmv5BJ|fM@R#ym51B09#33Ji2grc`6~TnqjVK;p z14wZt?*r|4Km-V0MGq4B0h`m|$%T=4@0qn^ef3!*U&^~$?~GcP+2;G{#`Zh9qp8)CA1+PSIAmZ{xivGSCWeQr0vK~`?03+MZb*mmGVZAP`ppW< z4$t*;`yH}vWZB7@*}1Q`_u=wj?$k^&bsIAu{sO{x$)}^E19h~N>cqJ3u--}!0^1^N zIWJN03^++xZ#xB05KZ6x6zwO4so6^yIHCgWWGTZ5TtN`9r?M_E(F$L>GWS zh(<8(B0Zj8E+J*e@gU+L#>im@ZQ9_u@uk8N+`S;F%L{cfqZzpSHho1WIy`7=tkf7# zzdf`uB0e7rc0G~$-uUi`bRei||Ir!$Kl675@_~KMMI9YxewZxT*475S`mX^Z8 z!f^Hu4yXyQbhtWBp`Q+g?!`4UG^T_{0~kDnjm z>euCoNk~cq$5WcKIW^L@{ptdYrd8Ba(Xex@9?z95qtdXp zIj0T!nz3V6GQ!Z+eF>Z?L@hfR>NWK>T$~m6dYz*6i7Fc#iDBPE7h&4kh6kksev91+ zVrXy62AJxH$<4;s7pK6JqKYVT`C7=6KYKE#9}ZXZIv~5gOAUN~m+Jy}-3*E+5CI@y z+o-g_ZZh&Z=V`>in7uFBkasLcRqZI+?6eiJAu>FR(2Ayk+YKYG>htv&kya!ZBzIXs z&25a*YCUv0-X5(MmkX;)!p=@Tz2b~)chI=vz{JL6!UE6F0(vSzoJy^bDUd*%ul1Yz zYvTjyl4AGnA%(DZ0e-(m&ewkemJfkIYE2R5gBj9wW1MCMHFgQWGBx{y9P=VNEpqqKBhD{dVc*)lfe2E|{ z0CZK8zS7095%%j)G~59|V{=S%S*S_;G5CoQkdAK4iO3Yu1CJuz0Bo}j)uDWk&r6*Wtj=G^4PS1@k3WQP7uiO`GNMkCd-rH#$ZDfe(Ia* zLx7W%o(@#~YfMswqxL##rsLQ}0)*z;9BT0K^d`TPJ32WzSt)nt2$PBT!RcgJ6M_X6 zbs~-N^cerfo1bg3tZxopv(0TGWU%A+u=>-|1@TaM*5rq=ad9V9ke)?1F3KT~rt)$Z z3kwTyn!({=WMqWH0}m!_fI6Xilj6u9s)h_@&G16|*c`3xWA+y=%{}!F7#v~g>*pBX>zHJ-&(H>(o3o*X!}3Hti_?jA`6X>nBxVQU8*nJ`WAC>jqJfAw%V-3*nH`}fBz2HwF5?*zQhB% ztHo|A?k9JXrTh*`;BeGYp9?9o%=Gy9qvBD1lRB;9QFl|=$wsYZ#h2;n>AqAkz|a{1 zD#CBGzFX>7Q{IxAP0$d`@E zsVnna|55=WIDWohKkmu|);;aPANO7B5I2aa+YffNDS>S5Mi`?pV5yEWu@jjahKWW} zm!YFSm#X^hMI?;PA!#Vl#oYti@~w)IKYriaoj-Tr+`|JSQt5L{)y z8!o_{fAtX&!SxY14u7}*-pOaE2Y$q|pVjPa{3F=Oa=B%y9b3+p#7`s?d?2&_r-}h| zi0a`K>uY>2CU5N(6kvkl7a?g85zW_=O#jO$Sa=e;i#PGeH|+l-DJ;LHo0I-=UHd{d=(5MS4&V z<_OzIshOFx4PINtn^*o`wT;XFn*7}V?O^}Ui~cYD>;Kix{{Qdf971vbHAP8g5()=2 NRYeVjGWo|Z{s+V*BrpH~ literal 0 HcmV?d00001 diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index b6cbea39..83d9ad0e 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -34,6 +34,7 @@ #### 4 - Utilities - **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0. - **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models. +- **Tensorboard** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb)). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard. #### 5 - Data Management - **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...). diff --git a/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb b/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb new file mode 100644 index 00000000..b552d0e2 --- /dev/null +++ b/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb @@ -0,0 +1,350 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tensorboard\n", + "Graph, Loss, Accuracy & Weights visualization using Tensorboard and TensorFlow v2. This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/).\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Path to save logs into.\n", + "logs_path = '/tmp/tensorflow_logs/example/'\n", + "\n", + "# MNIST dataset parameters.\n", + "num_classes = 10 # total classes (0-9 digits).\n", + "num_features = 784 # data features (img shape: 28*28).\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.001\n", + "training_steps = 3000\n", + "batch_size = 256\n", + "display_step = 100\n", + "\n", + "# Network parameters.\n", + "n_hidden_1 = 128 # 1st layer number of neurons.\n", + "n_hidden_2 = 256 # 2nd layer number of neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import mnist\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Flatten images to 1-D vector of 784 features (28*28).\n", + "x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Store layers weight & bias\n", + "\n", + "# A random value generator to initialize weights.\n", + "random_normal = tf.initializers.RandomNormal()\n", + "\n", + "weights = {\n", + " 'h1_weights': tf.Variable(random_normal([num_features, n_hidden_1]), name='h1_weights'),\n", + " 'h2_weights': tf.Variable(random_normal([n_hidden_1, n_hidden_2]), name='h2_weights'),\n", + " 'logits_weights': tf.Variable(random_normal([n_hidden_2, num_classes]), name='logits_weights')\n", + "}\n", + "biases = {\n", + " 'h1_bias': tf.Variable(tf.zeros([n_hidden_1]), name='h1_bias'),\n", + " 'h2_bias': tf.Variable(tf.zeros([n_hidden_2]), name='h2_bias'),\n", + " 'logits_bias': tf.Variable(tf.zeros([num_classes]), name='logits_bias')\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Construct model and encapsulating all ops into scopes, making\n", + "# Tensorboard's Graph visualization more convenient.\n", + "\n", + "# The computation graph to be traced.\n", + "@tf.function\n", + "def neural_net(x):\n", + " with tf.name_scope('Model'):\n", + " with tf.name_scope('HiddenLayer1'):\n", + " # Hidden fully connected layer with 128 neurons.\n", + " layer_1 = tf.add(tf.matmul(x, weights['h1_weights']), biases['h1_bias'])\n", + " # Apply sigmoid to layer_1 output for non-linearity.\n", + " layer_1 = tf.nn.sigmoid(layer_1)\n", + " with tf.name_scope('HiddenLayer2'):\n", + " # Hidden fully connected layer with 256 neurons.\n", + " layer_2 = tf.add(tf.matmul(layer_1, weights['h2_weights']), biases['h2_bias'])\n", + " # Apply sigmoid to layer_2 output for non-linearity.\n", + " layer_2 = tf.nn.sigmoid(layer_2)\n", + " with tf.name_scope('LogitsLayer'):\n", + " # Output fully connected layer with a neuron for each class.\n", + " out_layer = tf.matmul(layer_2, weights['logits_weights']) + biases['logits_bias']\n", + " # Apply softmax to normalize the logits to a probability distribution.\n", + " out_layer = tf.nn.softmax(out_layer)\n", + " return out_layer" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy loss function.\n", + "def cross_entropy(y_pred, y_true):\n", + " with tf.name_scope('CrossEntropyLoss'):\n", + " # Encode label to a one hot vector.\n", + " y_true = tf.one_hot(y_true, depth=num_classes)\n", + " # Clip prediction values to avoid log(0) error.\n", + " y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)\n", + " # Compute cross-entropy.\n", + " return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred)))\n", + "\n", + "# Accuracy metric.\n", + "def accuracy(y_pred, y_true):\n", + " with tf.name_scope('Accuracy'):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + "\n", + "# Stochastic gradient descent optimizer.\n", + "with tf.name_scope('Optimizer'):\n", + " optimizer = tf.optimizers.SGD(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process. \n", + "def run_optimization(x, y):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " pred = neural_net(x)\n", + " loss = cross_entropy(pred, y)\n", + " \n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = weights.values() + biases.values()\n", + "\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " \n", + " # Update weights/biases following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize weights & biases as histogram in Tensorboard.\n", + "def summarize_weights(step):\n", + " for w in weights:\n", + " tf.summary.histogram(w.replace('_', '/'), weights[w], step=step)\n", + " for b in biases:\n", + " tf.summary.histogram(b.replace('_', '/'), biases[b], step=step)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a Summary Writer to log the metrics to Tensorboad.\n", + "summary_writer = tf.summary.create_file_writer(logs_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 100, loss: 568.735596, accuracy: 0.140625\n", + "step: 200, loss: 413.169342, accuracy: 0.535156\n", + "step: 300, loss: 250.977036, accuracy: 0.714844\n", + "step: 400, loss: 173.749298, accuracy: 0.800781\n", + "step: 500, loss: 156.936569, accuracy: 0.839844\n", + "step: 600, loss: 137.818451, accuracy: 0.847656\n", + "step: 700, loss: 93.407814, accuracy: 0.929688\n", + "step: 800, loss: 90.832336, accuracy: 0.906250\n", + "step: 900, loss: 86.932831, accuracy: 0.914062\n", + "step: 1000, loss: 78.824707, accuracy: 0.906250\n", + "step: 1100, loss: 94.388290, accuracy: 0.902344\n", + "step: 1200, loss: 96.240608, accuracy: 0.894531\n", + "step: 1300, loss: 96.657593, accuracy: 0.898438\n", + "step: 1400, loss: 71.909309, accuracy: 0.914062\n", + "step: 1500, loss: 67.343407, accuracy: 0.941406\n", + "step: 1600, loss: 63.693596, accuracy: 0.941406\n", + "step: 1700, loss: 60.081478, accuracy: 0.914062\n", + "step: 1800, loss: 63.764942, accuracy: 0.921875\n", + "step: 1900, loss: 58.722507, accuracy: 0.921875\n", + "step: 2000, loss: 66.727455, accuracy: 0.917969\n", + "step: 2100, loss: 70.566788, accuracy: 0.949219\n", + "step: 2200, loss: 64.642334, accuracy: 0.925781\n", + "step: 2300, loss: 54.872856, accuracy: 0.941406\n", + "step: 2400, loss: 64.342377, accuracy: 0.925781\n", + "step: 2500, loss: 74.306488, accuracy: 0.921875\n", + "step: 2600, loss: 40.165890, accuracy: 0.949219\n", + "step: 2700, loss: 64.992249, accuracy: 0.925781\n", + "step: 2800, loss: 43.422794, accuracy: 0.957031\n", + "step: 2900, loss: 46.625320, accuracy: 0.937500\n", + "step: 3000, loss: 62.517433, accuracy: 0.914062\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " \n", + " # Start to trace the computation graph. The computation graph remains \n", + " # the same at each step, so we just need to export it once.\n", + " if step == 1:\n", + " tf.summary.trace_on(graph=True, profiler=True)\n", + " \n", + " # Run the optimization (computation graph).\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " # Export the computation graph to tensorboard after the first\n", + " # computation step was performed.\n", + " if step == 1:\n", + " with summary_writer.as_default():\n", + " tf.summary.trace_export(\n", + " name=\"trace\",\n", + " step=0,\n", + " profiler_outdir=logs_path)\n", + "\n", + " if step % display_step == 0:\n", + " pred = neural_net(batch_x)\n", + " loss = cross_entropy(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f\" % (step, loss, acc))\n", + " \n", + " # Write loss/acc metrics & weights to Tensorboard every few steps, \n", + " # to avoid storing too much data.\n", + " with summary_writer.as_default():\n", + " tf.summary.scalar('loss', loss, step=step)\n", + " tf.summary.scalar('accuracy', acc, step=step)\n", + " summarize_weights(step)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run Tensorboard\n", + "\n", + "To run tensorboard, run the following command in your terminal:\n", + "```\n", + "tensorboard --logdir=/tmp/tensorflow_logs\n", + "```\n", + "\n", + "And then connect your web browser to: [http://localhost:6006](http://localhost:6006)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![tensorboard1](../../../resources/img/tf2/tensorboard1.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![tensorboard2](../../../resources/img/tf2/tensorboard2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![tensorboard3](../../../resources/img/tf2/tensorboard3.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![tensorboard4](../../../resources/img/tf2/tensorboard4.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From e3414d654d40b46549eda95245b264532ef2093c Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sun, 26 Jul 2020 12:29:43 -0700 Subject: [PATCH 157/166] fix ml intro --- .../0_Prerequisite/mnist_dataset_intro.ipynb | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb index f1813c85..74f8a91f 100644 --- a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb +++ b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb @@ -1,10 +1,8 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "\n", "# MNIST Dataset Introduction\n", @@ -27,12 +25,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [ "# Import MNIST\n", "from tensorflow.examples.tutorials.mnist import input_data\n", @@ -53,12 +49,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], "source": [ "# Get the next 64 images array and labels\n", "batch_X, batch_Y = mnist.train.next_batch(64)" @@ -88,9 +82,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.18" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From a1516d2303f31942b4cff615e7c39f1b548157b4 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sun, 26 Jul 2020 12:30:48 -0700 Subject: [PATCH 158/166] fix ml intro --- .../0_Prerequisite/mnist_dataset_intro.ipynb | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb index 74f8a91f..93c9e79e 100644 --- a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb +++ b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb @@ -25,10 +25,10 @@ ] }, { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Import MNIST\n", "from tensorflow.examples.tutorials.mnist import input_data\n", @@ -49,10 +49,10 @@ ] }, { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Get the next 64 images array and labels\n", "batch_X, batch_Y = mnist.train.next_batch(64)" From 6b11799028f90f43c6591f9996ca853b7027a80c Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sun, 26 Jul 2020 12:31:39 -0700 Subject: [PATCH 159/166] Fix ML intro notebook (#382) * fix ml intro * fix ml intro --- .../0_Prerequisite/mnist_dataset_intro.ipynb | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb index f1813c85..93c9e79e 100644 --- a/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb +++ b/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb @@ -1,10 +1,8 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "\n", "# MNIST Dataset Introduction\n", @@ -29,9 +27,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Import MNIST\n", @@ -55,9 +51,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Get the next 64 images array and labels\n", @@ -88,9 +82,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.13" + "version": "2.7.18" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } From fedf9e88b0fbabf6244f8811b88bfae9c9a754fb Mon Sep 17 00:00:00 2001 From: LCB0B Date: Sun, 26 Jul 2020 21:33:49 +0200 Subject: [PATCH 160/166] Update bidirectional_rnn.ipynb (#380) Replace broken link for Sepp Hochreiter & Jurgen Schmidhuber's LSTM document. --- notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb index 2435b229..9595cc50 100644 --- a/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb +++ b/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb @@ -23,7 +23,7 @@ "\"nn\"\n", "\n", "References:\n", - "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", + "- [Long Short Term Memory](https://www.researchgate.net/profile/Sepp_Hochreiter/publication/13853244_Long_Short-term_Memory/links/5700e75608aea6b7746a0624/Long-Short-term-Memory.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n", "\n", "## MNIST Dataset Overview\n", "\n", From 26c4c7047095139f25a6ca380e9cd4d028b16460 Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sat, 19 Sep 2020 00:53:22 -0700 Subject: [PATCH 161/166] MultiGPU Training Example (#387) * fix ml intro * fix ml intro * add multi gpu example * add multi gpu example --- README.md | 26 +- tensorflow_v2/README.md | 3 + .../6_Hardware/multigpu_training.ipynb | 371 ++++++++++++++++++ 3 files changed, 388 insertions(+), 12 deletions(-) create mode 100644 tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb diff --git a/README.md b/README.md index 1bba79c4..e7de7049 100644 --- a/README.md +++ b/README.md @@ -13,13 +13,13 @@ It is suitable for beginners who want to find clear and concise examples about T - [Introduction to MNIST Dataset](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). #### 1 - Introduction -- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow 2.0. -- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow 2.0 basic operations. +- **Hello World** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/helloworld.ipynb)). Very simple example to learn how to print "hello world" using TensorFlow 2.0+. +- **Basic Operations** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/1_Introduction/basic_operations.ipynb)). A simple example that cover TensorFlow 2.0+ basic operations. #### 2 - Basic Models -- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0. -- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0. -- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0. +- **Linear Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb)). Implement a Linear Regression with TensorFlow 2.0+. +- **Logistic Regression** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/logistic_regression.ipynb)). Implement a Logistic Regression with TensorFlow 2.0+. +- **Word2Vec (Word Embedding)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/word2vec.ipynb)). Build a Word Embedding Model (Word2Vec) from Wikipedia data, with TensorFlow 2.0+. - **GBDT (Gradient Boosted Decision Trees)** ([notebooks](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/2_BasicModels/gradient_boosted_trees.ipynb)). Implement a Gradient Boosted Decision Trees with TensorFlow 2.0+ to predict house value using Boston Housing dataset. #### 3 - Neural Networks @@ -27,26 +27,28 @@ It is suitable for beginners who want to find clear and concise examples about T - **Simple Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a simple neural network to classify MNIST digits dataset. - **Simple Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb)). Raw implementation of a simple neural network to classify MNIST digits dataset. -- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. +- **Convolutional Neural Network** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network.ipynb)). Use TensorFlow 2.0+ 'layers' and 'model' API to build a convolutional neural network to classify MNIST digits dataset. - **Convolutional Neural Network (low-level)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb)). Raw implementation of a convolutional neural network to classify MNIST digits dataset. - **Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/recurrent_network.ipynb)). Build a recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. -- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0 'layers' and 'model' API. -- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0 'layers' and 'model' API. +- **Bi-directional Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset, using TensorFlow 2.0+ 'layers' and 'model' API. +- **Dynamic Recurrent Neural Network (LSTM)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dynamic_rnn.ipynb)). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of variable length, using TensorFlow 2.0+ 'layers' and 'model' API. ##### Unsupervised - **Auto-Encoder** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/autoencoder.ipynb)). Build an auto-encoder to encode an image to a lower dimension and re-construct it. - **DCGAN (Deep Convolutional Generative Adversarial Networks)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/dcgan.ipynb)). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise. #### 4 - Utilities -- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0. -- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0 Models. +- **Save and Restore a model** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/save_restore_model.ipynb)). Save and Restore a model with TensorFlow 2.0+. +- **Build Custom Layers & Modules** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/build_custom_layers.ipynb)). Learn how to build your own layers / modules and integrate them into TensorFlow 2.0+ Models. - **Tensorboard** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/4_Utils/tensorboard.ipynb)). Track and visualize neural network computation graph, metrics, weights and more using TensorFlow 2.0+ tensorboard. #### 5 - Data Management - **Load and Parse data** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/load_data.ipynb)). Build efficient data pipeline with TensorFlow 2.0 (Numpy arrays, Images, CSV files, custom data, ...). -- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0. -- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training. +- **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0+. +- **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0+, to generate distorted images for training. +#### 6 - Hardware + **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. ## TensorFlow v1 diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index 83d9ad0e..ffccd7e5 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -41,6 +41,9 @@ - **Build and Load TFRecords** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/tfrecords.ipynb)). Convert data into TFRecords format, and load them with TensorFlow 2.0. - **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training. +#### 6 - Hardware + **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. + ## Installation To install TensorFlow 2.0, simply run: diff --git a/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb b/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb new file mode 100644 index 00000000..46b07000 --- /dev/null +++ b/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-GPU Training Example\n", + "\n", + "Train a convolutional neural network on multiple GPU with TensorFlow 2.0+.\n", + "\n", + "- Author: Aymeric Damien\n", + "- Project: https://github.com/aymericdamien/TensorFlow-Examples/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training with multiple GPU cards\n", + "\n", + "In this example, we are using data parallelism to split the training accross multiple GPUs. Each GPU has a full replica of the neural network model, and the weights (i.e. variables) are updated synchronously by waiting that each GPU process its batch of data.\n", + "\n", + "First, each GPU process a distinct batch of data and compute the corresponding gradients, then, all gradients are accumulated in the CPU and averaged. The model weights are finally updated with the gradients averaged, and the new model weights are sent back to each GPU, to repeat the training process.\n", + "\n", + "\"Parallelism\"\n", + "\n", + "## CIFAR10 Dataset Overview\n", + "\n", + "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.\n", + "\n", + "![CIFAR10 Dataset](https://storage.googleapis.com/kaggle-competitions/kaggle/3649/media/cifar-10.png)\n", + "\n", + "More info: https://www.cs.toronto.edu/~kriz/cifar.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras import Model, layers\n", + "import time\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# MNIST dataset parameters.\n", + "num_classes = 10 # total classes (0-9 digits).\n", + "num_gpus = 4\n", + "\n", + "# Training parameters.\n", + "learning_rate = 0.001\n", + "training_steps = 1000\n", + "# Split batch size equally between GPUs.\n", + "# Note: Reduce batch size if you encounter OOM Errors.\n", + "batch_size = 1024 * num_gpus\n", + "display_step = 20\n", + "\n", + "# Network parameters.\n", + "conv1_filters = 64 # number of filters for 1st conv layer.\n", + "conv2_filters = 128 # number of filters for 2nd conv layer.\n", + "conv3_filters = 256 # number of filters for 2nd conv layer.\n", + "fc1_units = 2048 # number of neurons for 1st fully-connected layer." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare MNIST data.\n", + "from tensorflow.keras.datasets import cifar10\n", + "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n", + "# Convert to float32.\n", + "x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)\n", + "# Normalize images value from [0, 255] to [0, 1].\n", + "x_train, x_test = x_train / 255., x_test / 255.\n", + "y_train, y_test = np.reshape(y_train, (-1)), np.reshape(y_test, (-1))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Use tf.data API to shuffle and batch data.\n", + "train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))\n", + "train_data = train_data.repeat().shuffle(batch_size * 10).batch(batch_size).prefetch(num_gpus)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class ConvNet(Model):\n", + " # Set layers.\n", + " def __init__(self):\n", + " super(ConvNet, self).__init__()\n", + " \n", + " # Convolution Layer with 64 filters and a kernel size of 3.\n", + " self.conv1_1 = layers.Conv2D(conv1_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " self.conv1_2 = layers.Conv2D(conv1_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with kernel size of 2 and strides of 2. \n", + " self.maxpool1 = layers.MaxPool2D(2, strides=2)\n", + "\n", + " # Convolution Layer with 128 filters and a kernel size of 3.\n", + " self.conv2_1 = layers.Conv2D(conv2_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " self.conv2_2 = layers.Conv2D(conv2_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " self.conv2_3 = layers.Conv2D(conv2_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " # Max Pooling (down-sampling) with kernel size of 2 and strides of 2. \n", + " self.maxpool2 = layers.MaxPool2D(2, strides=2)\n", + "\n", + " # Convolution Layer with 256 filters and a kernel size of 3.\n", + " self.conv3_1 = layers.Conv2D(conv3_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " self.conv3_2 = layers.Conv2D(conv3_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + " self.conv3_3 = layers.Conv2D(conv3_filters, kernel_size=3, padding='SAME', activation=tf.nn.relu)\n", + "\n", + " # Flatten the data to a 1-D vector for the fully connected layer.\n", + " self.flatten = layers.Flatten()\n", + "\n", + " # Fully connected layer.\n", + " self.fc1 = layers.Dense(1024, activation=tf.nn.relu)\n", + " # Apply Dropout (if is_training is False, dropout is not applied).\n", + " self.dropout = layers.Dropout(rate=0.5)\n", + "\n", + " # Output layer, class prediction.\n", + " self.out = layers.Dense(num_classes)\n", + "\n", + " # Set forward pass.\n", + " @tf.function\n", + " def call(self, x, is_training=False):\n", + " x = self.conv1_1(x)\n", + " x = self.conv1_2(x)\n", + " x = self.maxpool1(x)\n", + " x = self.conv2_1(x)\n", + " x = self.conv2_2(x)\n", + " x = self.conv2_3(x)\n", + " x = self.maxpool2(x)\n", + " x = self.conv3_1(x)\n", + " x = self.conv3_2(x)\n", + " x = self.conv3_3(x)\n", + " x = self.flatten(x)\n", + " x = self.fc1(x)\n", + " x = self.dropout(x, training=is_training)\n", + " x = self.out(x)\n", + " if not is_training:\n", + " # tf cross entropy expect logits without softmax, so only\n", + " # apply softmax when not training.\n", + " x = tf.nn.softmax(x)\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-Entropy Loss.\n", + "# Note that this will apply 'softmax' to the logits.\n", + "@tf.function\n", + "def cross_entropy_loss(x, y):\n", + " # Convert labels to int 64 for tf cross-entropy function.\n", + " y = tf.cast(y, tf.int64)\n", + " # Apply softmax to logits and compute cross-entropy.\n", + " loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=x)\n", + " # Average loss across the batch.\n", + " return tf.reduce_mean(loss)\n", + "\n", + "# Accuracy metric.\n", + "@tf.function\n", + "def accuracy(y_pred, y_true):\n", + " # Predicted class is the index of highest score in prediction vector (i.e. argmax).\n", + " correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))\n", + " return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)\n", + " \n", + "\n", + "@tf.function\n", + "def backprop(batch_x, batch_y, trainable_variables):\n", + " # Wrap computation inside a GradientTape for automatic differentiation.\n", + " with tf.GradientTape() as g:\n", + " # Forward pass.\n", + " pred = conv_net(batch_x, is_training=True)\n", + " # Compute loss.\n", + " loss = cross_entropy_loss(pred, batch_y)\n", + " # Compute gradients.\n", + " gradients = g.gradient(loss, trainable_variables)\n", + " return gradients\n", + "\n", + "# Build the function to average the gradients.\n", + "@tf.function\n", + "def average_gradients(tower_grads):\n", + " avg_grads = []\n", + " for tgrads in zip(*tower_grads):\n", + " grads = []\n", + " for g in tgrads:\n", + " expanded_g = tf.expand_dims(g, 0)\n", + " grads.append(expanded_g)\n", + " \n", + " grad = tf.concat(axis=0, values=grads)\n", + " grad = tf.reduce_mean(grad, 0)\n", + " \n", + " avg_grads.append(grad)\n", + " \n", + " return avg_grads" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.device('/cpu:0'):\n", + " # Build convnet.\n", + " conv_net = ConvNet()\n", + " # Stochastic gradient descent optimizer.\n", + " optimizer = tf.optimizers.Adam(learning_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimization process.\n", + "def run_optimization(x, y):\n", + " # Save gradients for all GPUs.\n", + " tower_grads = []\n", + " # Variables to update, i.e. trainable variables.\n", + " trainable_variables = conv_net.trainable_variables\n", + "\n", + " with tf.device('/cpu:0'):\n", + " for i in range(num_gpus):\n", + " # Split data between GPUs.\n", + " gpu_batch_size = int(batch_size/num_gpus)\n", + " batch_x = x[i * gpu_batch_size: (i+1) * gpu_batch_size]\n", + " batch_y = y[i * gpu_batch_size: (i+1) * gpu_batch_size]\n", + " \n", + " # Build the neural net on each GPU.\n", + " with tf.device('/gpu:%i' % i):\n", + " grad = backprop(batch_x, batch_y, trainable_variables)\n", + " tower_grads.append(grad)\n", + " \n", + " # Last GPU Average gradients from all GPUs.\n", + " if i == num_gpus - 1:\n", + " gradients = average_gradients(tower_grads)\n", + "\n", + " # Update vars following gradients.\n", + " optimizer.apply_gradients(zip(gradients, trainable_variables))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step: 1, loss: 2.302630, accuracy: 0.101318, speed: 16342.138481 examples/sec\n", + "step: 20, loss: 2.296755, accuracy: 0.108398, speed: 5355.197204 examples/sec\n", + "step: 40, loss: 2.216037, accuracy: 0.299072, speed: 12388.080848 examples/sec\n", + "step: 60, loss: 2.189814, accuracy: 0.362305, speed: 12033.404638 examples/sec\n", + "step: 80, loss: 2.137831, accuracy: 0.410156, speed: 12189.852065 examples/sec\n", + "step: 100, loss: 2.102876, accuracy: 0.437744, speed: 12212.349483 examples/sec\n", + "step: 120, loss: 2.077521, accuracy: 0.460693, speed: 12160.290400 examples/sec\n", + "step: 140, loss: 2.006775, accuracy: 0.545166, speed: 12202.175380 examples/sec\n", + "step: 160, loss: 1.994143, accuracy: 0.554443, speed: 12168.070368 examples/sec\n", + "step: 180, loss: 1.964281, accuracy: 0.597412, speed: 12244.148312 examples/sec\n", + "step: 200, loss: 1.893395, accuracy: 0.658203, speed: 12197.382402 examples/sec\n", + "step: 220, loss: 1.880256, accuracy: 0.672363, speed: 12178.323620 examples/sec\n", + "step: 240, loss: 1.868853, accuracy: 0.676025, speed: 12224.851444 examples/sec\n", + "step: 260, loss: 1.837151, accuracy: 0.705322, speed: 12101.154436 examples/sec\n", + "step: 280, loss: 1.799418, accuracy: 0.736816, speed: 12185.701420 examples/sec\n", + "step: 300, loss: 1.790719, accuracy: 0.755615, speed: 12126.826668 examples/sec\n", + "step: 320, loss: 1.732242, accuracy: 0.807861, speed: 12229.926783 examples/sec\n", + "step: 340, loss: 1.732089, accuracy: 0.806885, speed: 12167.651100 examples/sec\n", + "step: 360, loss: 1.693968, accuracy: 0.835693, speed: 12060.687471 examples/sec\n", + "step: 380, loss: 1.665804, accuracy: 0.862305, speed: 12130.389108 examples/sec\n", + "step: 400, loss: 1.627162, accuracy: 0.890381, speed: 12152.946766 examples/sec\n", + "step: 420, loss: 1.594189, accuracy: 0.920654, speed: 12057.401941 examples/sec\n", + "step: 440, loss: 1.575212, accuracy: 0.929688, speed: 12196.589206 examples/sec\n", + "step: 460, loss: 1.569351, accuracy: 0.942383, speed: 12147.345871 examples/sec\n", + "step: 480, loss: 1.520648, accuracy: 0.974609, speed: 11998.473978 examples/sec\n", + "step: 500, loss: 1.507439, accuracy: 0.982666, speed: 12152.490287 examples/sec\n", + "step: 520, loss: 1.495090, accuracy: 0.989746, speed: 12071.718912 examples/sec\n", + "step: 540, loss: 1.490940, accuracy: 0.989502, speed: 12049.224039 examples/sec\n", + "step: 560, loss: 1.476727, accuracy: 0.996338, speed: 12134.827424 examples/sec\n", + "step: 580, loss: 1.475038, accuracy: 0.995850, speed: 12128.228532 examples/sec\n", + "step: 600, loss: 1.469776, accuracy: 0.997559, speed: 12113.386949 examples/sec\n", + "step: 620, loss: 1.466832, accuracy: 0.999756, speed: 11939.016031 examples/sec\n", + "step: 640, loss: 1.466991, accuracy: 0.999023, speed: 12095.815773 examples/sec\n", + "step: 660, loss: 1.466177, accuracy: 0.999023, speed: 12035.037908 examples/sec\n", + "step: 680, loss: 1.465074, accuracy: 0.999512, speed: 11789.118097 examples/sec\n", + "step: 700, loss: 1.464655, accuracy: 0.999512, speed: 11965.087437 examples/sec\n", + "step: 720, loss: 1.465109, accuracy: 0.999512, speed: 11855.853520 examples/sec\n", + "step: 740, loss: 1.465021, accuracy: 0.999023, speed: 11774.901096 examples/sec\n", + "step: 760, loss: 1.463057, accuracy: 1.000000, speed: 11930.138289 examples/sec\n", + "step: 780, loss: 1.462609, accuracy: 1.000000, speed: 11766.752011 examples/sec\n", + "step: 800, loss: 1.462320, accuracy: 0.999756, speed: 11744.213314 examples/sec\n", + "step: 820, loss: 1.462975, accuracy: 1.000000, speed: 11700.815885 examples/sec\n", + "step: 840, loss: 1.462328, accuracy: 1.000000, speed: 11759.141371 examples/sec\n", + "step: 860, loss: 1.462561, accuracy: 1.000000, speed: 11650.397252 examples/sec\n", + "step: 880, loss: 1.462608, accuracy: 0.999512, speed: 11581.170575 examples/sec\n", + "step: 900, loss: 1.462178, accuracy: 0.999756, speed: 11562.545711 examples/sec\n", + "step: 920, loss: 1.461582, accuracy: 1.000000, speed: 11616.172231 examples/sec\n", + "step: 940, loss: 1.462402, accuracy: 1.000000, speed: 11709.561795 examples/sec\n", + "step: 960, loss: 1.462436, accuracy: 1.000000, speed: 11629.547741 examples/sec\n", + "step: 980, loss: 1.462415, accuracy: 1.000000, speed: 11623.658645 examples/sec\n", + "step: 1000, loss: 1.461925, accuracy: 1.000000, speed: 11579.716701 examples/sec\n" + ] + } + ], + "source": [ + "# Run training for the given number of steps.\n", + "ts = time.time()\n", + "for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):\n", + " # Run the optimization to update W and b values.\n", + " run_optimization(batch_x, batch_y)\n", + " \n", + " if step % display_step == 0 or step == 1:\n", + " dt = time.time() - ts\n", + " speed = batch_size * display_step / dt\n", + " pred = conv_net(batch_x)\n", + " loss = cross_entropy_loss(pred, batch_y)\n", + " acc = accuracy(pred, batch_y)\n", + " print(\"step: %i, loss: %f, accuracy: %f, speed: %f examples/sec\" % (step, loss, acc, speed))\n", + " ts = time.time()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 35de963a4d46cff5f11f269f6d14f9ac71a5e2e2 Mon Sep 17 00:00:00 2001 From: aymericdamien Date: Sat, 19 Sep 2020 00:55:12 -0700 Subject: [PATCH 162/166] fix multigpu typo --- README.md | 2 +- tensorflow_v2/README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index e7de7049..09431ae2 100644 --- a/README.md +++ b/README.md @@ -48,7 +48,7 @@ It is suitable for beginners who want to find clear and concise examples about T - **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0+, to generate distorted images for training. #### 6 - Hardware - **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. +- **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. ## TensorFlow v1 diff --git a/tensorflow_v2/README.md b/tensorflow_v2/README.md index ffccd7e5..ef6785f1 100644 --- a/tensorflow_v2/README.md +++ b/tensorflow_v2/README.md @@ -42,7 +42,7 @@ - **Image Transformation (i.e. Image Augmentation)** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/5_DataManagement/image_transformation.ipynb)). Apply various image augmentation techniques with TensorFlow 2.0, to generate distorted images for training. #### 6 - Hardware - **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. +- **Multi-GPU Training** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/6_Hardware/multigpu_training.ipynb)). Train a convolutional neural network with multiple GPUs on CIFAR-10 dataset. ## Installation From fe8b8122f7362b5d75bfb725ccbed4a5f0072cca Mon Sep 17 00:00:00 2001 From: ShanksAndSS <45415847+ShanksAndSS@users.noreply.github.com> Date: Mon, 30 Nov 2020 12:16:51 +0800 Subject: [PATCH 163/166] gengxi (#392) * Update README.md * Update input_data.py Co-authored-by: Aymeric Damien --- input_data.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/input_data.py b/input_data.py index d1d0d28e..0ebcbdad 100644 --- a/input_data.py +++ b/input_data.py @@ -90,7 +90,7 @@ def num_examples(self): def epochs_completed(self): return self._epochs_completed def next_batch(self, batch_size, fake_data=False): - """Return the next `batch_size` examples from this data set.""" + """Return the next `batch_size`examples from this data set.""" if fake_data: fake_image = [1.0 for _ in xrange(784)] fake_label = 0 @@ -141,4 +141,4 @@ class DataSets(object): data_sets.train = DataSet(train_images, train_labels) data_sets.validation = DataSet(validation_images, validation_labels) data_sets.test = DataSet(test_images, test_labels) - return data_sets \ No newline at end of file + return data_sets From d85fb5c279ca1312e7adeaa1ba8722874bdc45bb Mon Sep 17 00:00:00 2001 From: Aymeric Damien Date: Sat, 5 Dec 2020 02:49:21 -0800 Subject: [PATCH 164/166] Update README.md --- README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/README.md b/README.md index 09431ae2..9c205400 100644 --- a/README.md +++ b/README.md @@ -137,3 +137,13 @@ The tutorial index for TF v1 is available here: [TensorFlow v1.15 Examples](tens #### 6 - Multi GPU - **Basic Operations on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_basics.py)). A simple example to introduce multi-GPU in TensorFlow. - **Train a Neural Network on multi-GPU** ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/notebooks/6_MultiGPU/multigpu_cnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/6_MultiGPU/multigpu_cnn.py)). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs. + +## More Examples +The following examples are coming from [TFLearn](https://github.com/tflearn/tflearn), a library that provides a simplified interface for TensorFlow. You can have a look, there are many [examples](https://github.com/tflearn/tflearn/tree/master/examples) and [pre-built operations and layers](http://tflearn.org/doc_index/#api). + +### Tutorials +- [TFLearn Quickstart](https://github.com/tflearn/tflearn/blob/master/tutorials/intro/quickstart.md). Learn the basics of TFLearn through a concrete machine learning task. Build and train a deep neural network classifier. + +### Examples +- [TFLearn Examples](https://github.com/tflearn/tflearn/blob/master/examples). A large collection of examples using TFLearn. + From 29df154ef110a7999d9f639228c370612dda72c8 Mon Sep 17 00:00:00 2001 From: SAJITH NANDASENA <10287973+snandasena@users.noreply.github.com> Date: Tue, 29 Dec 2020 02:42:10 +0530 Subject: [PATCH 165/166] Updated run_optimization function and tested with Python3.8 (#393) Co-authored-by: sajith --- .../3_NeuralNetworks/neural_network_raw.ipynb | 111 ++++++++++-------- 1 file changed, 64 insertions(+), 47 deletions(-) diff --git a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb index bbec2f13..2e1032ec 100644 --- a/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb +++ b/tensorflow_v2/notebooks/3_NeuralNetworks/neural_network_raw.ipynb @@ -180,7 +180,7 @@ " loss = cross_entropy(pred, y)\n", " \n", " # Variables to update, i.e. trainable variables.\n", - " trainable_variables = weights.values() + biases.values()\n", + " trainable_variables = list(weights.values()) + list(biases.values())\n", "\n", " # Compute gradients.\n", " gradients = g.gradient(loss, trainable_variables)\n", @@ -198,36 +198,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "step: 100, loss: 567.292969, accuracy: 0.136719\n", - "step: 200, loss: 398.614929, accuracy: 0.562500\n", - "step: 300, loss: 226.743774, accuracy: 0.753906\n", - "step: 400, loss: 193.384521, accuracy: 0.777344\n", - "step: 500, loss: 138.649963, accuracy: 0.886719\n", - "step: 600, loss: 109.713669, accuracy: 0.898438\n", - "step: 700, loss: 90.397217, accuracy: 0.906250\n", - "step: 800, loss: 104.545380, accuracy: 0.894531\n", - "step: 900, loss: 94.204697, accuracy: 0.890625\n", - "step: 1000, loss: 81.660645, accuracy: 0.906250\n", - "step: 1100, loss: 81.237137, accuracy: 0.902344\n", - "step: 1200, loss: 65.776703, accuracy: 0.925781\n", - "step: 1300, loss: 94.195862, accuracy: 0.910156\n", - "step: 1400, loss: 79.425507, accuracy: 0.917969\n", - "step: 1500, loss: 93.508163, accuracy: 0.914062\n", - "step: 1600, loss: 88.912506, accuracy: 0.917969\n", - "step: 1700, loss: 79.033607, accuracy: 0.929688\n", - "step: 1800, loss: 65.788315, accuracy: 0.898438\n", - "step: 1900, loss: 73.462387, accuracy: 0.937500\n", - "step: 2000, loss: 59.309540, accuracy: 0.917969\n", - "step: 2100, loss: 67.014008, accuracy: 0.917969\n", - "step: 2200, loss: 48.297115, accuracy: 0.949219\n", - "step: 2300, loss: 64.523148, accuracy: 0.910156\n", - "step: 2400, loss: 72.989517, accuracy: 0.925781\n", - "step: 2500, loss: 57.588585, accuracy: 0.929688\n", - "step: 2600, loss: 44.957100, accuracy: 0.960938\n", - "step: 2700, loss: 59.788242, accuracy: 0.937500\n", - "step: 2800, loss: 63.581337, accuracy: 0.937500\n", - "step: 2900, loss: 53.471252, accuracy: 0.941406\n", - "step: 3000, loss: 43.869728, accuracy: 0.949219\n" + "step: 100, loss: 571.445923, accuracy: 0.222656\n", + "step: 200, loss: 405.567535, accuracy: 0.488281\n", + "step: 300, loss: 252.089172, accuracy: 0.660156\n", + "step: 400, loss: 192.252136, accuracy: 0.792969\n", + "step: 500, loss: 129.173553, accuracy: 0.855469\n", + "step: 600, loss: 125.191071, accuracy: 0.859375\n", + "step: 700, loss: 103.346634, accuracy: 0.890625\n", + "step: 800, loss: 120.199402, accuracy: 0.871094\n", + "step: 900, loss: 95.674088, accuracy: 0.890625\n", + "step: 1000, loss: 113.775406, accuracy: 0.878906\n", + "step: 1100, loss: 68.457413, accuracy: 0.941406\n", + "step: 1200, loss: 80.773163, accuracy: 0.914062\n", + "step: 1300, loss: 85.862785, accuracy: 0.902344\n", + "step: 1400, loss: 63.480415, accuracy: 0.949219\n", + "step: 1500, loss: 77.139435, accuracy: 0.910156\n", + "step: 1600, loss: 88.129692, accuracy: 0.933594\n", + "step: 1700, loss: 92.199730, accuracy: 0.906250\n", + "step: 1800, loss: 90.150421, accuracy: 0.886719\n", + "step: 1900, loss: 48.567772, accuracy: 0.949219\n", + "step: 2000, loss: 54.002838, accuracy: 0.941406\n", + "step: 2100, loss: 58.536209, accuracy: 0.933594\n", + "step: 2200, loss: 47.156784, accuracy: 0.949219\n", + "step: 2300, loss: 55.344498, accuracy: 0.949219\n", + "step: 2400, loss: 70.956612, accuracy: 0.925781\n", + "step: 2500, loss: 76.179062, accuracy: 0.917969\n", + "step: 2600, loss: 44.956696, accuracy: 0.929688\n", + "step: 2700, loss: 56.581280, accuracy: 0.941406\n", + "step: 2800, loss: 57.775612, accuracy: 0.937500\n", + "step: 2900, loss: 46.005424, accuracy: 0.960938\n", + "step: 3000, loss: 51.832504, accuracy: 0.953125\n" ] } ], @@ -253,7 +253,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test Accuracy: 0.936800\n" + "Test Accuracy: 0.937600\n" ] } ], @@ -280,12 +280,14 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "

    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -297,12 +299,14 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -314,12 +318,14 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -331,12 +337,14 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -348,12 +356,14 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
    " ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { @@ -376,25 +386,32 @@ " plt.show()\n", " print(\"Model prediction: %i\" % np.argmax(predictions.numpy()[i]))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.15" + "pygments_lexer": "ipython3", + "version": "3.8.0" } }, "nbformat": 4, From 6dcbe14649163814e72a22a999f20c5e247ce988 Mon Sep 17 00:00:00 2001 From: AE1020 <68134252+AE1020@users.noreply.github.com> Date: Sat, 23 Oct 2021 04:03:00 -0400 Subject: [PATCH 166/166] Update dead monkeylearn blog link (#403) --- tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb b/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb index 226dd66f..0ddf5419 100644 --- a/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb +++ b/tensorflow_v2/notebooks/0_Prerequisite/ml_introduction.ipynb @@ -13,7 +13,7 @@ "## Machine Learning\n", "\n", "- [An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples](https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer)\n", - "- [A Gentle Guide to Machine Learning](https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/)\n", + "- [A Gentle Guide to Machine Learning](https://monkeylearn.com/blog/gentle-guide-to-machine-learning/)\n", "- [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\n", "- [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf)\n", "\n",

    H-BF|TY3PH zT&^~8^C0njJta|}8hgW5bbK{I-bo~lR_PO5+|rUct(7;|`ErHI9Zohupp!8fAJ2tv zA273hQ%mzK@^UW}6Aq^!*=ilnZz7YafY}3wAF!~a?~lT8tsvw})v()$?yom!L81b3 zJNtKTJo}VcoZhySI<@Ot9|UxuyXE?n{5wG9QQW?Pe3)P@Mw{D(@rwJ|UbtLdPX8B! zd8#+#<@yvzJ2`s^cEEI`1w5zx{+_PW(Ntu^W;ANKpFC|vy0>#vRwPH_69Ml;N(cBWMgDRfqCzHlPsqEsl{bLjP5V0f)`(iD z%8%vNg^BCjPHt(Hu-9!b7&>*&EPM)Wg;$;)yi%q;fwDhykh9oqSLtDq=`^%#WGC|8 zMOiH(QOjt%SXY92zi<4yi|n=*ZqA0i47xGQ=CxxWfZ08W_&4YFe^OHubJGAn5mS*p z9{IQUEAk-)Q!_R_$Hwc(fDCy_&Rb6bt8xiS%_`C@e}e+Xc=ESr*9+eR~6>FCr)A3ln}OHr&g&%oSjy3Q)j-dnQvN{B1srymMAzU%vdc zoT|4^x~43M0agZV*gb}d7=V2+Y6Gma>tD*Wh^rSTV_dT=fUn7Hs{K3!Z{&8UPg(Xw zrQ`X?OBq|0X^X8yIGpbbUd!(m&!na5SW9YixS&0-z`tP0^RO!*#8EU*Y9VXFLL6GH zhWIbO28ofMp6VcMG*i7X<{7ZLP=BPRwv$k?-=oMuQJp`-4j+o}(g$K<`{)c?v%S1( zG%?0v8q#WG4cij9iCaJ*0X1&Qm?Nrfng6{cHAL#3cn-F4>05Si{wsYU1U$SjbQ_VP zamM;*H}NlCBPniUJmm0mJjbU;&ERS4 zjvY0uamNCOA55ouxS&nly_YAcrt-D)Z0mvIQP61vkRMh|1H7F=IZNFY3Q zaC`&$ue2qfcItI|6+O^yFM((1@BG0Oi8Or*7^Mz0~Fj&yVp=Ke`)! zw6bMh6KiTzy+#o4L`ZJL*uZZL6^+?DyR%r2S_KE97hjh3C&$7EU82nOk!OS2#To^6 z(({X;Ay4!>_8v|!YtQ!NRNU9Zx7aQJtg-%r5l-H6gm0rhQ7+Vf>f_%wk=CUE3Ad;a z9M4Y2cF68GOvPTWL7!Rbmm6bp6%g8B_K5STptI#B>@vLdBkQ2Hr%3bpg-*(yNqQfK z?`O;uR)AftAy?q3h)#Mg{+JGV@K`=M6gZ{flbm?y_L<@F6S=$YElIj_g{mTtcQzCz zt+UF|YRgj55+PS_-a^!9Ad4@3YeR14NJ;fe=)Jt&6<+DU8;rOT4Aj&$?ZiZO-cmYx z)uIDApg9KSE@Hmou{!m(2P?KJF)2o8cY=@RDpp>Ob^41h8_LNdPC3n7*=;(yyxS+Q5a=2s1^k!JctLKf{nVtyP3Y^SVLa#ksjWWY;WBcg6UmvGYW5~-Tt!RmT zsrf7kZGL!BxV^Nh$^Me+P)?98E@?EFma_0_#L6#Uu}KW@fUV0Pt{t=t8!}l;ebE*LWh%s*5oJF_St+eS-CSlg4J9bEmPmRcpJn2VN!!$ zK67H%THG~Sl;Vj68+pzW@EdhTD7A0q%*)~CB_CO1q0@NtDm${ZQqV=4GtuEn)f@N{ z|EZ_}RsDqZv+Di+5ez)#_8BC-2?&wFNVbVX5H-({S^JSbm}OZ%m=ND%5nphWFf{UV z7nbc_Sd>WbOJYzpnS7q0Pc2;%KL~r{+2=AtxAzRnIBCrgqpJ=Myw_Z8a%^KsI8=F` zrZ)v*1rUMe-Jcs6XY&`UVqLy5c!%4kQgHhU3P;b89f;Xx^}#O3LvK;U&^mqj@a`ah zdYPVF2CPd8Ny$31;D7~JC)9wKr=DDL)$c$%3SuQD^qOJdUbE;Y(RQh4!^1sf(b1uE z2GMTK&LSd@@*aE6us$|F$AiX%hh^I@tHC!%ZTbpdg7uBG;-^#~`_Z_?Zt01s`=MSw zr6>uy*r3X&tY{0#V7OOvTIi!A+3_FGE~SC$C0vI^C{G!vV(s|qV$5D&&=3ppT)9Ti zsWWe!cBZLQPi92Lc}d9Ot7+o`^BWy&p&6}*5dHgq__LpDF8NeeR!+4r5LbZ>rihWM ztrVe^jg1OXl3BOFk$2%>_hbWjY|o0Sd_{POZ=qmEsxE|Il3R#>)_HArOt0Eu)7^Pc zr?-E&{CHoI7$oCa_DzOa)O@K;%GXgORL_^m>AB@svl}4=%Z-K)O{5DfjAVm36qeUc zSW4(^4V_gbW%e(F!8D%+^Q($D1FW)!bMiijlxqou;?+0s#@xdQU#q1xnN{&Ex;OO0J*z=y#6f`!%MK?ab$Qy?7y6=25^0yX8*<9> z0;9J#)SfWunCPV1smzHhP!gdzvG!FL`3r`M`CW&@W|%%G_7Mg|7~=#bKej8%jI)pE z=5yA|2P@iyJk?D5UOgrdb+%dSt#jXJ=}E4BZYcQkm9{J5q> zwYC(vd(qf+GdLx!fAC>F>yb*UE;;e%0S#;8b$P0qel&*<1U?qIK_rs~rGIk$U% z?~eBQI(6xC#|vZzf0dXu-WU?Uj&&(+qRu^>q6^>#Wph0f0rhcdfyptLd0H73e>GX) z?znDxZTi{Cc)Dg44k7WCtWE5NIj+*;3AKYwo;ZI+pjNHA)Jtm`a7zjM;TPgiK)TD&=y#{OoN(oB{*|UUabp(OiR0_DsQJZ(<$-R5Sh)B{sr~&Uf)PaYF9yf2 zp2en|0m_r7?$J?X4Y+b^YDS-ClekkIC8xXGItYZllP>Es>kK^RV-yOHJYr(u6D3~B z%M`_K83|W8sSAx`(iqkC&ax~eF+S^n;%*?W!34`(o+BEUm2873DkaLzhMylG?facS zuDH512L%X!v+r&AE`KEac^YK+~{Wu3#D-z9R`n6y97Gz*fE8@B(amE94nnDGQ4J zVpeN9MtAPz*p!bijCz>MC3*w*K;@i#+TC2=0AV?Vn|_sGw64hM)~xk_r^s6KFzbY@ z5z?F2{aaffmqjznRXTNMy{j2zj&$cN%k;cEZ1_Ny#8#xvv3kSMS^<+tD6u;(UB59^ z`uBL*pL48HrY|K~0m`7Yvfqr{M*y@5yKEO47`v&cxllmt*uMNeFqjalcf0xAIVH2r zVr+psb-6ubl8&EK{kX1Gcf)oT*#z@99sA&9FRu3pIX#GmS~CaFf!W}*{A9hTqph>( zmyjTytAjuJ;=frolVwz+RoQ;*zp~}8kI*2we%qgP7b#!z1^^hplVku!x?Be$mml1qq%HdE~T9V=S&e&e<^#BFVEUAt)O#y zS%#PAa;LB@g28L*M}hkIDFNt;w82xLze$(?$Y@Hgaj8t?F(L_f3$JIa1G}#ibYqfP zeAOtbIt_Ub^$7SAL;Xp35~Gno9#ud}0u7Ac8Q!cql$QW%J5Rm>Q4T*wCvl{`prm=eRjk(f@G! zSR=@Ma#6ffVc&SOF#Qs=igJQHHQVGEkvBWo;0vc<;-@t?d%e4XnV7ryoAk!?h>XlK-K`Wc4m;X?09JN0n!h=4?m& z1o0&-iuzTec~+o9@yw^VOP)AU9?{y0JyCY zg<|=AJF=TS@rK1cjV=we>S#A410#$_mw!^R?+DhHgq4emUwk8gCXh# zKpqsIrfQJs>uk4OyOi$W@v)|ROVn)}Na3R@w+=rY)lUZTRV}&pGM1;@oL&3~8?Cjb zar!F5%@Im=Xqh-qi}F%m2`q2L=3Vj&0PbG&K_?qkHmJTo_0B>q* zmX%y!3;`Ru7V5%&2s}eP&MCXNp#5mp_AR`pM3k8aFss;IJH9>5;nlqki~qDYNrXWKl8Nr43dCexvk*HF zepH+FjA9s%qEDqQ%&sC}_?biYAfidW!jL^`MSZ)|HCmX8QHKxxJrFx{Q|~C-uiDub zGWmktwKsf4aQH!|Vr6Rq+Q=x_zOTijV)0&94VG6)x^*!%YxG}H)}&Nhxv{BQ zsD#GOgw8W_z;d0F7DLv#huKEatOu;oQ9L7OQ!m*n;3*uZBy4!_AAj`lJYKO@X)2T_ z?@)=@LFvQU-{`233J3op5euoKenMlQ3Pw-eN!>8MW5Xmov-KY7cR<%+y7a7H=$pyk zY?R;Pu>bq^K|GKX?JLvs1(s(Zm|hpUYg&E~GFgShejQ#p;JRk6w(?H+R>{!g%MzM>ewgN2wOWu_v(LuWR^1ha-D+l(He6_s$=pyJf_`VP zP(H%`nlJXJl>J>_lAc%CQWW(594r4^bC-IO!=BC0j1mFiSKdoxi$fwCHlNfgE2-|T2Y$bN=;wm&6{JG&MT!KlDqha2Qu~F zkRyeMZCSAfMxegA7kR6PJv%DicYKPz@`O&u(w-f^#zUc+%eNcT<8VMWW~{2$bNw;+ z!U4hY)%^R(1=8VFp-&cqZ{l3fjY4jXwGBOaSr@L#YC$GMqR2PNUb69qbh^GD{P8t) zKP>8)1YnOs>X~P74TI+}Ig4M|pzDwaev;IUisSjOF)P)ekw+d7o1EyrlmgA~>)vgF1r zlI0*S6E4NNuu0!c=2Ec0V=!vz#qnWsox91Gp3x-eVho6Ko*wsvMe;L&Mwjjle^ZX* ztT{?lmwCSV-Db=xOqLsDmsVT4bW^lm{s|)?EZay#hN3ivhqa{_wC;9pwqYnpb>H@Z zk<34=hChS@m^ocTy4h7arEf);xfSYa)spn_j5YmW%4FJnIsRpr5)t`YXFNHGOe{r zkaxFKoM8z(Xd!kPm>B`9_jwFEZP7S#Xtz6*$um+H4xgtkr_0Lp9-BCHgv|k#%#TfS z?xa-O$4mTDI`DQ=>rBBX*=+gjPDkGdR*6anU-X8>4X4h39prJR+ldX?GHxJc&FQYD zW8pOd|i58<%nZzzT5DVnyV!Gq0A|_Dz<|9zOEr6U=ez5WdWB(^I zN->#U<1Izd48+R8ch!%g3G$Yi1o3bED(-EN#QH@eJ;)DdGW%tWu7)VCSbBC*eeC}f zG(9pO^EnUl*zE4VhxI3y8C<@jvJ>FGEUV8}g(a3qtp*<&fV(w7ZtV>E7T(s!$kAkmswJ*I1vq1+7`F@AThms=npZnw63;EE!m{K%d7_ zTXq+L?E1_z8|lD@CpRN)E16i3SY;jrSdTuCCxKqotVlECP~oR;SIeT58{)xs6T~t=PASJ5b&|_k1@0;FL3V>$GZ~_K%Z^}^@;Th6ECa5T=eC07b{D!=Hs`z{r`g#r zM#l2Rvp?WiDXg%tdx{+?NIiJdE_G@z=$o)c!Y>Vl>IumWs+UxWQi=6>gbm~`sFK1A z&>cJC`PAdQ{OhV-@wrQ_nB|sVa&hl#W+f;cOwNvEzt5wj-@k_vTCO)6$c8J{$^O?i z0BM3Cai5MCE?DR3xt~(pD|9J@X72YP%9DE}b&&S%`tq2w8P|Ax9cOMr;%^2z^h7bI z>G(Yf5b`jQq7V!J+b=>i# z8OfZT4;+?Xh$CBO-$WTjDoqNlKy;e#hAjA95@S`$cKhCiF!F#+zSiC9lj`@Dvfu=} zW1%@!LyEIyTF{bJDnLi^K$W8&f99szf7mTP*RJ0|j5#)*J2a+>gX2RpuNCVU4ANw- z+}7I~h{{KO*W7f)FZA}!Rn=4IiJ|Ka1F;B!*RAdLvuFD&6()1+XI+MFf)U0)K31Yr z^Qd3T&O45^?VKsSUA&j_<-*Vh#!~1(Khgw^Dg@q7If%Zu?Y>+R zls<#G+u%PuVwPXNalaqkmfw=YkE%oyP%Bri_35?heXbscL+|<%9Pjb6*cV?L5!#;n zoXC|&Lw`h|xaHPtW_14lvG(3UO?F%Rupo*BMMOcm2!eD$>D7WrmkvUx2Bd@@dO$!# z1?inoq)M*|y@`YpdheYC2mxuKguoY`=af0mIq&a}bG{jdxhI+2ZLhueTGv|Zx(NR- z&zONa;^Dx*tt4U3(h00*_bjBBSExGf&zLQ;laXrNO_x}DH+F^oVF|4o1B5P@x;S!4 z%btB&7?COnT~xa8-so$@uQeXE_iu{f;`@R;s#Cjn4PiTH8UaE#H9* z-6!%i)va%Rw_IQ$;#Ze-45!02Sq>CT4Ab6yS{Y)yZRYZQZR>hy-G%jRJoN#4pl2Q4 z?ZN;Qqk>|P_*izpyzNr)o}^;r$R@j3atd0MAG-czQup0j$qr!7r^rs$Dr||afUEIf zBvs9Fp(9&nFS28#!6^4TeC6!}%4eP2efu99-%E5d^wBEz{sT?Tua@qm2iTnom~=} zI^f}4G$E#-`_54prCr6i11GV{0-y(}PY|@LE_boOY-%Qe%mrYAc0=a-N9}EDGo5NQ z(L*vmxS&Ak80X&>EL;_XdMgo55_F_=+AXiYw0Q%H4lzGhHj8&*6SFVZWcZs^D` zwH50G=F3Y1=P~*zd@mYB&g!U(Bfh&7iXuXn52q-wiOv-JcBz>mULwO%7(o{IwEh1Z zCurWj;yN4Ct~LMHzKY!7=9xP8Dx%XzPTkC;NfXh#2s@ON33Y=0sdgD*N$opSO{D&tPJ5MSI^6?m|>jjNsJ4 z7$j6=Z-fVFCxZI?)5fm@Qz4;Nsqma7=&cF&7~Hq`R<<<$k)?rWH|_J2H%zaD+e!<^ z1O-KqQOZYUy>B%3wThjksL9?d@CS?gytp3z0QrT!1-5CXo8sEq5-wgnrxcn=zm#1x zJR;SvGM^BX zsa5hOAoG_eEG_eL3f(CO7m5n3i{*0~ojiGGklA*L1*_?NQ*@F#IUw%+@})AaCVRVL z*7j^4RKw;rvnB`logZ9M)Sp(>^u!2V;Q{s3(p&Ff1Q_(5h#gBK&fS=(3v;4JDbb)_ z_q67MlEzwm$lf*LwihN(tXkYo7Ihe%dlwG-h^73gmiwXah@c+45P0_eEWHTcS*pHM z2O%_6pV}9l&9BIMAxEb4^8xn*P{+=z1tJNDklHwJ2wmZnv+oq=J)Nd8TuT9;sSaF| z6kH~}Y2l!KUr^*#0B7s$>$>B1+Lk&>;EUKIVNyt0jX8NF0UYPnhl_d<0N#C&UNu=# z!m{PS5WBcTNIr!@3_j80uZWlnb-ut-^>?S5Y3z=l4V90*HSSR42vBY7T3fmGIo)|C z?aTb^HmL~YDPjq(ODV~pwtG^~ERA+wjNRU5O2?gqr85Ip2nSyf{2MjmqC&Nk2YsJb zY%-0^sOO&jcqx#Ff=DO5!VaWa+=~OQ#ALojS~L*G8LML2=ktkbf1O{*^VSy)uHs+7 z2aNGQ;f6>cXtjg@@Ro9LD%np7e_j42Z&d(fRs}SYNsoyXxOR)ab0`23Tc0KLl$=Yi zl{WxJOW5A{L7&r;K>6LKV&sE?6wh~qzN-PXZ**dmweK!l_csQpyTw_nwe?*Jt}U}4 z*(lm#*(%$z7_UyvM=X^Set0&e`r6GgA7r|r>M=XRkpzRS-Lg$^d1bWosgoLu5LbLT zS3H*B(;>!`A}6E*R~Rd;4xf%MYs`Z$buS41iEdpd#V>f5|EPOMntYqskguf~_k+y6KH~l5 z&!bWS^0sse{yffK>0QA$HMTiNp?9ok8H;Tq_>ZJ8WH5WSh2def+cI*jy?{cKT%q}4 z9W|QbUI4)R>gQPAn{0IT-b=X0(>_{0<=Bjqt2T+!w+qkVb?hhso(@Q<#J|32MW!h` zDjgY4&qWnGnUuB=fk;9M+mh7H5LUMoC5l-^>&r7LPmT2WCQDwu2fwdo>4k*=_7ghzk43kmlx)9eN z8|7h(7s{vMPxZQ;TpRM(_7(U6CnbeW3nw(3T8TKMLFO*pNxpQ~QK=0u zdmJLOTQMm+2a}2?U+t303v?nR#FyKupO?{2UiHCESY3=CT6^#tm3#~#Tv7@aq;~rI$S4B z{nY-xh0wh!2Mn?S^l|R)(H5$@R5bm`ZtbT;yKnJzZttFlIdNyQUDWHu&yT!K{WjGO zUVhN8C(>T3uEg15{`;9oAi#oAMA6O|%x@QK`ch_$22+6PhQmvQv1PPtUxuV$2NoPf z=8>8ZJ2GaJs6-FnS+YDw6?Mnr@ZxnetDLlW56*rmv#WCf2 zCvnht6C9Qpg$0*4`doXYtaW6N7Hup7&Q`e_YBxZuLvx36U{K;0rI zn)|`_@CdQlBQ!YnRN`TF#CEvFt@kH%b%)a^2(q3eUfZmxnuTXh_Iv(%xhf6)A8u@^ z4ym`dcPlxIPpM9duAA?9HMQ?1%-x1)2U4Ure@QcohW|^!k-wIrX@LW;;c*}`@7IES z(|ginqL6I~iGu$XQk{rL{zcG9FN3iWhHL#gzVhP!S)#5&H}q7uIcs;~N9pU6{A;gW z(1{>6tLlLYt&+La7$Yoh`!_4qeC*`9^8Vpt1AOv`lv>HRXm{F{da7ZDUPocacqg(A zTo{PEZ-?EOv2a}Pay>kn?GBl;_l?CA%&!?S0I34+sOtR^sMAd&7yGQs*f?)3_1SMC zMMWviNM;)VCfTG;xEqEejQR15%8YrToyV8>4qc8hzVD|>KxACOD!5i{B9UU1tERDg z*>KFmKCdo?!1IPkDOwUnuAhk+5^d?BUMQd0<6|?dVCiXx6J&}%9^ZhHX!lN+iDMXZ z;T7Kz=fFVx!c-F*XOTd0e5Yb48&Gnk5i~8Tlgl^cVK-VaS97(Vf>g?#@C4EiSk^on*d!$i8$)^KxEKAv_yv#@#3K3Yb_g za6sRVUGfq*r;2HAAw#%@J4q|FH`y9!`06)Me=`^8vx1kf^^Z}m_w{)Tq`3@;2#es| z`K*e9v7BR4592`{?39z`P;A3=by0k_1NTF{ zrd)hv?Rk@hCT20cGU#CH*9fhsPB(x_*odD2tj`I~_iX@ebOs!Lx0m~7r>@=e1Bp`BZ_pcZIB_Y-9$@+VEjQyBi_N_PFZODiM%n>=DxtRT}|NK(ik}^ZT1}c`j;CSywn` zO#U=nq^$^360^{;sxs<*ZLR_GFS+)rH=PL1?#<9whZwmuM^)U(y<#N|t;=VNQ#1NJ z03@1L(cp>fSgcPk5Qdnc{6&Y;mA)+T6ugqo{>80(?ASRa>1 zl{>|CPN4udC;Fk-T+%8pFbC4B!&aatFC8V0w8)VtpE*Nkh%8fRf;4)30jzZ(|jl!zv>i5I+k)q6F2xzq)8UN5-Ce znpAaEp330%Z{_A3j`#elh%MKTsSc9=Lbp?|@gMpCRpwo9T_NX45&1noG_t>VNX>~h z)=Rf1mj4I35~+$XlReHO1N3FT9e*z5T_>jV+*85O499#gy1Y%Ndz(y~J|m|TSTo?#G`ENQt9d^EZmM5(@57sC z#r8>RiESMdz?hP<2ZK*b3)26np^kdFhkNCOr0p@n{ICpGyxpp)12NJ z)9c*97EHaS8vxo~<0Pw3)cWv!Pjvt2v6kBm^Ynjs9qU&d%|HHO`%rGuaem+`6te4$ zM5YQ^A&|%UApQ+EtreTI5Zb*w*_F3yO~tgarLAh?H*(=FtG5xj=Nk~4kP9Q0JilZ< zV@pwH)ZJcVpCIBCUCpQ{Cc%=*hQYqWg7O;kklxcPma3j9hoe;$dgJhDFYz|Lx*o6D zC(d^Ho-B{rbxbl|KXx6r=mdtxPD8B%qrJUJ`;nZU;jy^7uv6S`wQhaTW-P`jO{ zGcs>P)kbyZ?NeXkyKF&rlB)4EYC{bN8sZJ59P^a$w0t#wp7Q_<^KZjJ>D2osYz*PbQw zi@)o!C>eH{n@|8OaZVL<59)X3T-38I@e)=~79AZC4;18J++|O`32_p5Z*{}Q{jbmc zPdH!6^t98vHPlqiPR|-|1An|>P|j(FWEFN*V&$T7e^pDZ2+dV|#^`r0e5Ji|L-(YR$f)I(GHRcF(iD&w%vZM4>+Bc4(|AD~ z6aZyU7HNqq_(se93h}K!y8v(8h32w*W{T)nK zN6Fq@Kv6?)?0)I?;3xvOQVZ{&faPuo!naueb7{aIhQg^VH)`^486jeQ0HZLJScyf| zthl-`V^(Y{MD-&-+GWamjEqb1?_2=2ROyE5!)-O|`CWQ*iHu4XV5_9Ct8FA)jQz_W zV|q=*9u0iD_!jJTE%)KEw*kVQ&FPpVe(-zREDssBi%xy4Fr_%TrM7E#55Smgz9i=D zl>H*onKAT=$7V|WVCNMDxh{$@xs}L^`eFgVsh@&&G+42KvM=)93&EN3`iSiLcL6cp zqcACSmT{_oJ45+}akIQR3klsz$v?ken#qCUWYSzo16gjqT^uBzyDWOV-sa`Z4BdSW z-*Esko(_*5W5#ph#h=D+K6AQQWa>PV$>4FYJ+D2}h zaT0h!HLdgV=WfLuVcg^kP%(Lt_&pc(+Jegwy&Txj9c#YMlx$mw>@b-Nu{DtxthX{w zW`=d0wv0rN!DRe_tt4c6cl(}Q2oLD?H(JrIuw?NA-iy99TTWXwX-2h81AUNQ36SU9 zKI2ooghT-a+}?F`&*(%;vE0^Eh~FoK3Ebk9x?y(L!qf za_^*tm=*55V4J2#l;lWQDFp)My*xT90@WFUD2F*`bMV(`S7uxoe+_vaQ0z@9xtNTbnW$U z=I3uqhgw(n!k!bkI(naR=f2QN%kA#^LPbwYQWrub8|zNq|GXL-x;Fu z8paByY$%?7fuxho4ulW*W)1!u=Kd!@toD#(;|JP^9 zvNFA(ze2v5OBOX0Gv?4=0R>e+4q6?aqMyWarZ|0?B$Xz@LR$$2t*WW;oz!^xI#yzZuGDo?j2md;7k1ka-%^dEEnLc)Lo`&x-J4 zSfYo;!os+7k0B_($<|h-R(cSTFwHh+{eRo$o2%n*mlgh%&a0QopU5}n%2Nly!p6Q@ zkoZL)CYp&hDnXLEt8ic*ui)lGTtql&7)2IqdFP^e3n<$wek^^^or(|e9Qm|FP7L5_egf-Q;@(Dotyk(=w>S) zu4bXBw@vRViu%TIt*Ts1E*Z4kBnIj@rF$)G z8lCDAXFc}P$wVU2HB8gnsdxQ&67mSN>!vQ5GIO}%sioe96TjYhnpA<4fi?822m5LV z7uJl*m+nfTbY%}>V6PgwqU4hX3`6#ZBSzK25pCn#LzoWF3T23Lt;9s5^hd9h7`#_< z6SpqQ+0xmG{)o;bt%N2V&Vy;cBYXlKyxTD2HM+))1psyfj-8D1&V9!GX3k>Z`0U3?{6QK5cSa4(O{$)Q9x7f?5PN*5`V z+9b2R0ZbQ)*1Kv?s5WAa9#)3hk;Ys^|ZNWx7iZR*eUiufP?;)7pinpEUmc=T1q zKqA9Y`v=sy%Ypv-q433DrzMdTqBZ!T{-);ve!ZY1PBvgi!JFuQ_y=Ciiq=BMj50Fa zWUg6ln_z>+=Lbf1In}`eSD#W?Z^slf*we1tLIV0~7vrw2xxKv@JFv4Ooq^aj2 zL_XA6L#|M`{`g}iC1K&wciE2l`Zg9zlyiRuCll9CGiB$AlILq$%)rWFCqaunWm=b! zMn~osyg)kT=Wf%wUT%w4E)S^29KFnS9*1ScnHt!_Qc_rxPcl>t_u`G=jCyo$U4Lc` zHQai%%lFNSv1gTaAbUfSb1nheIZkP5O5`2AU$@d6x8rSh9)t+rQgprVq?L`*SG>*3%g_E<-q6(9hSQ=e?!JvDEN zO>v7OsngIi#Ek7eGc=(zn0#l*xg8qRkcHCN!d6rkF!yP~(Fu*l$Z=~;t4U8=!>hCP zKkO&WZQfXs6^_y(dg$^W2KDfSz8{W>8Td2_A@*M=-j6l6&|wZ7R!iqB%`Ca8txM_= zvI&_89H4eHvTHqNISco{w*GC~%#a&owxum?*U{xUv228Ug{TN1Tse_fcw{!1p$-`s zUJ(>9x8XSx@SJh2%!S;|i&mM=d`@N)bAvteK3zvN)Un?bx>r&H60yY4X6c z4C6B#z2jGz!yJfs)-ZW|n^EQHoh2m`8-Q!n+JK*BhD9>d_TL?Fin4!__EQ_Aum0(nqS^U z>*euajk`}SGS4Dr!i|FQ?+!kZE1;gynL#8XddlBQM)8~mmL1XuB4VfXSF|YyhDxa})Os{8fjcFL~7 zsG%|5aoUV=Ea{G)CX2wwDlGa-ol`l#f9(isQOar;S3O`VihA-F!_ioGlZH!g#Ve2c z6Jc`&TQxNQy+unxI6 zD&mH19-buG8`Y^6jI&shz1Gwx8fB-PeQ=Q`x2g(#} zPSxoT|9p$ae@+>rND$~mc~se;fzz7o^R=Z*{zk$eyQNl{JGptc3zg+8Yr2#vfmJ`< z?)s+y{JMaqRwlXF3wZ0|a4F{G&Y*ljY!f-^T5o1kN`}&hk^pu}#z2Z^kZ9@yq-4X2 zkH+#UB)ku{^bhe}p>$DCF=8vyz6^~Y}3Q?ey80=06cks>0^&n8yWZ;NCvTNUf z`{3EiBd%P1U*6bAi}1+co;%_&Yd)!#-|R8zJf2m+FPZVd^Z`Je&JApEM`Psy|)-_28l zj0V3{1cV^=&+cZmxMm_@8@Vw%>L01TQV3}|#b1j_&)Z^z%3-pBt>=%e6RtU}&Jo2xV|H+wof^JvVC(Y|<|-IWhzc#ugL@T>#ouYtF~u@=~lgX7F@{n0>1g0Phe zk304Qf{xce*O$KkMVHgEvNJVA$E~yTqX^*8AJG)4W3?eL_K<3Sv?kxD@<-vmAPp0l z_O9*`MInC=z;LKV^I_k3hw#_!MF1kbN^AoLDgT&+xcS7u;S7;$+H+1A*6W3;)Z$>u zda^ER4SNC0BR_$y7x* zjhEt0>d|qR#AfiN*%ZLFIzQgv0TK)Iy+M=rDz^Hi(26q#vX90Tn`*Xf1cn3Fr(Jow z%oN(-1}jQon-jO*+IVXu*qX8^m9eaVDD&dCb!cjI`Ms<)Gf$fmL@^I}^?!ln|EySV?z|^em|C-)sN!z&W%g9eRKMg{BKr;#)>e^OtfCEcFG>myZ@za1`wox%-)TDX26G>mH;lfJ^8KM4rtnlWakBWhKH4zGWqLiqwTs(jJ#DnMcd=V% zdABj95eOIeb(fZBf(%w~o3iUB9 zAxLXYK~}=tFLZTu@VN2|G#6{}JZzzjyr^4eZ!kY*N8bAPdjbrkSgcJm{nR(bM;7go zTFCaIX6?0%KkL@_(G-Sv4O}H~_)A!<)iVN2I&Ph%Pa)l(rJyCx1Ewmf@GU#g*PbZJ z{)_X)h*#h$n>;MkIukQqwgjM?PYb?%<@fL4_ut=cDjeN3t(O5iQL={XFISE_Tbp!L z39?RLM#t+-ZoiF59Qz~G7vcO(BpF{gCE#15IWr%H_2}sDkX`XXOU{Y=8%Sppvq{K5 zT4$#mnxyM{{q!Xgl!1 zacp~N(BY-R1h2?McKR%RVV%Ik%5Xg5Sp(fYMx0?MkL(`1scUNNuSW9X%-qcBoJ?EU zpI1^1#1RKBpglspNIwj1eDqLZ+M`2XZcsP&x0pps+?HokZl(_ zl~NlYGIeWAJ;^mWe)G+4HG2>dXSz9n~{!#g*~XPoV}H|yJA$CT|BMF z^mfukTF^!NE!Q;yrO1=SuhLIZHV^WYJ&$CyAC%3Nw{uT_c_5X4xflQo;utD_vgtIF zDG~|sl--@k7p>e-UMw@Y!QR(%;M3o#MK$xi>hoPlOtjtSf3!T?eQSPlR4?W6+th<7 zGBoP?cHoGWm|Hul8Jo=`j@J-#TM?~RxH8HxT0%-yGOQjG4e=I+s@*(%jvw}m`m@HF*DeA(eXCZolLeAHL?9* zZG-|>>dFHaHd~e@DqhfvES;ll3#)vZ#9A)jzI?LU@@H^N&>cENu!VMF0e=n<9Popd z$!1~Cf&=C9O5?c8?duZ?RrX~?*4}UlfwZ1B-}mo|+F%sAf|T9ETCUC4Rbv^j-Kxqn zZ^V*UetqVaPunT~`u_Pvn~XBMItrzU4W7YMPj*r#fu|3)0@tO8G2jg&*HN!M1%rU) zl^n#aQ|$bi{3mq40vGjz{n>m~F6h(!>6hZZ$L&s6>~|#{PK;1{f`rP6wCremyX=ZF(^KAP8T4Q*FB&u7fvh0h_c&pj~f!b@=~)x zeF4C-yEQ3yCvt0Gu;|xTH!yrX#H5i1t7)`sooh4}>Dzhp?kOxTk6#H4`JT}$w1hX4Xk+|t29rG)^fJ%TM8eFG4}cBRA^Jh5 zmax@8MFBk;CRK{j%Wukm=xqT&}KVpAy!f5U8(UD8*w#Ap%4z<0*?>wH~GUXHXUW9Zo0(kdDi4 z&NkEmvPb_;j>d6|jPWyu{NI2+2MET{Z~~jHe5|twIek=f8Xjtx*r7!9&U;x%8ui3_ z;tF!IWwouAo{6iy6aIYgyhi{Y?Ku+PQm?FLMTT4VzM)W2-LDbo6&Z5Dc=BF2ucvYR zS89Fi_2AOsfp=Zi;{KuE)sG%!50UjiJlWyeDN)HbhV;u{{{X_9Bde8N^Z*WXI2cyLx zO|lSx;LH_ID>~Sh^mY2@r@DLb&QB&ju=U4_bKJ&}Y+Ptax!-o&$d%Z7JW%mPr5F8& zia~08^LX)uUsVx?p-Og(UTXG_vV!wFTO}jLHG3>My%)cQW(rtMWZ19gZ z$3G9|pWd!^!%mLI0cLMC3WIh3aN4KX`Wcb9q+Yc1qk@)tWG?_ZoE)al?M_nV6yQ*R z+#BrbC`eU%>y%M4`CK;4L`Q_F=QtezYh&CK+=>cCXi!=SG9n(0!Gh|K|5GCS{4Sx? z<8ctFfW2OEWrsO35su~%+B6VOa|~U+P|W+9q`^#~h=`EGbcv1pE>=gRh{{~<@cH>- z$s;WVRPdUT;p1bEns=j}|HozjcxK~!B;MwY zk1KWmlYjz;BgYjfZ*D6=K<3_bn3=MFHJz9tQ^QZsYiK@kbwKm?g49}uu(usKfjV7p z`f%NJ$_BJt$W@AlJ-tN7&2>yl=<#Id9q|&zUo>i#V58L(X9)~m$L;@2ZW6Pc{;$1&&(i>tbi%YSe_}~}^5mVSc zNt0P$n`Hs+%AH~XaOloQJ0=Es;_+O&-tTO?TmZ01XBSb1HWG_CJ-2phK$2jyrusUvj6j-^g7vL!%+~O zqnlZvaUgMDN08y}uGY2qHwov-zqf!J;>BGEe#Ay(+eKlR@MDwl!7c z7oV=by^dLUAS?+SU2C;8(CW~wd#uXEG7|;#4G31u6K%JW-l$&%7)bV4Yi!QNXODuNriQ(%rprYW<%w`sX`TCUJ$U%M?W*|7FfxwQG}_{#Yjfa z*`nv~;OWOk62esX6b|$)GmMkXxPJS5&)jR5W{xQr?6@;}72>_oBjnhw13j;oUz zQ)4*@ZejKN?nnv9hA@W?zgbyq)He7Oiyz(>=qzYLNVPN4%eh-?`QO>e)*mgKmMu6& z5x4j5kbt0YSCExT_rRWQvBZoro_9H}S1#34hQ2KT@yaz&r3eJ{ti-3`^Fac`Iw`9dRF;0!B-NKjH4)bs5+D~5c)n}sP_Y!Zty*=Nf8uW9EucIr zEJYdUu;E(P8%u!esQ)(2!d2N!p;Aq;ycPl3dv|n0YH0EYz9-c}Z*sm#t>C8Zqn(%d zFp-1Wsn#vsH?TV(xmyYQzE5Nb9{_Brjv?+%m6I&~iI4n#KwNKdcIOUj$Qw`!`U4S} zN6&M3hY;UGpFB~CuP>-C@L$5q`Mq0*o$9_qCn(V-y3xIh%gYdsHejOyG#wq8|vt3*)<`pvl}+4}ua!&aV>`f}9~!=e4H zv(v3sbef+`{WgN3waBmvp5vbURpw|pdVwe<)QTx-lBqvlFPI`;98eezE|(dt3HgHW zCZJ?on%o7qJr`VB(LsGZ{2Yz$ho=kP!CPqnmGG7B5R1oQ4d)Eqi4WuC#>%1R5<@43 ztxbW2f+-(0QDWey!I>H}PV4z2ar@ZdPCp_8{MYM^rF&AS1Y>nA-;QaE8qn}jcZA#i zlh(;|MWSj^pM&Pzbh5d?aUuBD+)Ek6uQd*g70MfW_G#Jsv052d0;uTwy+v)!;5T90vAC|`!6@eS3}mY7!=xjT0Vf1>&3kzTsrQGri{d`VaolCQ z??2s{*X|oaiV3Cr`vmdJGZ4xBypI$IpXUgNk~B>(4y>t}8cxpoW>fqWHAg%zhS}5G zZRC7^_KZ>Emc0BeJtGariv!|TsI5ekS=slN(8SE@(Cc5T1*&=YKi38J_kXCAb8qr_ z-UQTk)75ze*3q*hrbD%26C)!NozkkE*cNB;-^8ZS)(R?hs>gx2+6Go+XM-ei{Cir*F`^qK3~owe?1?tY7sors&2uFf1CV1tWJ7Wa^1 zmh;EuIj*m?b3u%$a@(L6&1 zXo{aD-T0LvP*bPnqLxxg0a3UrqRIr_?QXC)=@x(AyESNEF-C~K>Q)pIfdxx?cR5TB z705ccE2Br}QbS@nQVQT-dg9(EPPSL1#$^-S5+`Yw%4cvTJ);D-IW1o_lB)<% z<$e5ZTH5j7zvb@2vUU4^JIzz>`?snF?g~u(k7J&E{mQSI>Grz+@ihPcnGQbHXQy*0 zxo;8hPjjvl_?;_D$UBR)Ev~UV@aq=%ugv^1DW%kJxwG}JJjM4^uoCXS0PPEx{x=)F z;NNtWD9LyGf17t?SGuWgeU>C|WB=zP|99g->JWtlzI}Mq z=Qm53CzoAK^+YAYi~!&fof*Tq<5A+)46z-OGBBE7%T3#q*-h-*%h5CT1iBMN>IF_0 z5a^R7ma}mP5sh9;^;rBO{6$%yp;90`n3Baf8P)0KKa{KPJ`v|f6k!6>0$erRk$hE! zX$xB==K@{slro2=2OAZAI4#`P++233rME0m2NLC*ll>(`B2Ka1Z@<6 zw!BRai2g{^to@FY3LSno?Q9xC{A%mmoP-OM`D?Pg$LeNB03HiLwnJ}ygb+@v(as(R zGw$x;vZo#2BCdR~GodxTL zF}T=8mwQZss=yQW!E4ELCzeemVL(k(-DWMOcB~NxYn@2)JH?os)yiI`e7W9mK0{tm zqa_#yT@A%g_i|;izB|!ru7WE07TZ+~Sdi*g$=ObPlWQ7&8jbpCIo0FK-u_hQpl36F zr{x=N%caRRgW4k2?K9&+%!r*Xv3Ry|=X~nP8g~hL_EJ~nU$ks;=_;Wu>pVS5_1;`# z_B~t>{<=|8;n!CKfBB)==E}RdL1(t=jex9d z5Eo4z4}86ZD54pf=sxO|Xq@Y_JpCAQ&3)RbecGjE89X4;lgAj2lHx!k*Bm^FrZIB{ zg3ZDu$kU!D8n_yA7swwM=9D){xNYb+rJN9TyiDdeJht0tXn>`kdgDg7UsYFZH>6^Z_UkEJLVAOP6@}*cuNt?8KH7 zJ}AeD<)|?|*_x!}?Tlt@&1yP4mxP@h&)Vy2IOj2)Jw%A64){vv_!J#)Z@C?Z*uvCm zPK%_x*yTPnYm@a7Wqa{eXQHv)9przXdd=(Cv?J6m>nTZcM)+gS6%%W1ZWTJH{eCGC zSki!-d_v!B!yL|V-Dlyt$ttt=MwxG^{AE3>{-{cr9Q}=<*MN&d7;5VuXKez$1B4-2 zf|mR5`Ie-OZE=4xRMthol-9dhE-ja6p&F>cegZK-D@V#LF5ptvu8!l0;n;-3R?+1R zzIFW|e*3iJ;oM`>5QxN*>=#z4(++>t zrvLWgVDc1aR%-B?0LI^F%1K0DjqAH#{oSI=I;^Ln2&G@fzZ?Nk#4XXi7E^KR7D-D_ zC;JUg=(5D!<1Bg7{Fs(i%z3#(BFXia@9|LD=1#&<`OTg29k+&?t2IP>JyIF*tiUhy z5e467&V?nxEJ&v}q?;}S@uPto*Eu+9KEHsE=yS-O5VwaPRec_=#V(f>WzBfnhjWQB z=h@_k6Q4)>)?sbbDyc0;442e<&)U{hRA=HKs$Hh*wG&Mnbte1ydi(89EpS^yRBxp? zh4uYLOO1(ywF1WOjZp+Q(mrQiZLnOgmGNE%E#GM-b&3mBa?H%Rpsf|-3xp(=;i}hY znD_wc1HX0Kad@ZvW#{e`kt8KPZ;%_~w7H~SnB*WlzA`sxDW7a! z^1n)SVP}qFlRiPGkNCcsUUlnYdOG*}ljz>Xp`rT;pAM$mTGLyca91}KmkhEg>hF9u zq-T)LVYE!Qy?kbQ!E5v9E)k=sVQG#=jAyy}Qjq(CPC%gg=nFsWT6JV~iz{Y&<7&42 z&hWPWk7`;S;RiHNEZUM}``SE#=!vZ?6%>K&^$qgew%VYl2&YlD6v*0-uO^zLjxGluXf8TE zX%-BeigG3iu4*D+34&^2kbJ7A_?X%yi_0w|Oh#Ip&Uz?{>AaR`uK2tshEt7JOiNK% z7~{|aZB@dG=jDfJK_EYVySm+PRm|aoSyKkbi2#Sm$s( z91_BXpCIG2Jd4DN(KdC6D-#~Rm!a)Xm=OTM{2Y3ORtoqlqOkgtc~llyQv3z_!(#>`}jj_*hKEKSRJsWCoHgn(!MB#hjypKNg;dsswlcr+=|Kg zbw&1WIYpB1yg#{D6g2Q$_;K@MtI(m;lqL*}Vsy=)8ccA*30}596$#a-gy$(xUy0jo zvO#z5^w`!hdG}VVw`X2O^<64(-p)3gECghrrZyOTMv&bJATH(!%D+t@UO)bv#NDFG zOp2jW)zX}B>s^z5f(|@;Ml98EpEz6n42c^JPTp!L{>jR*dpA6$HYfJjeFHb3N$~EI zJ}4Y%nxGvya_uD%I^_^`_~H9Q5WJq?Y1>q#5?=HE75`~u%u;Q2){HnPTbQPdKR_Dp zQ?&EPh7*hd6F0QSX{p2r#&m?t2HIwKfAdra`FZ8x-ys9y7lQszeROiSB#CmEz8_RC zUz&590?iQLV*RP|;5IuSe*|LK2xdSp5OI0zei%vHv6ob1IWOgDq z5eyC+m%py1B*KJLDXEf`M1J?^zq zjgs8UfaRg@@znG7(!`x7PFt?x-0sqZHe)QS6oyyDvLLWs8P_ej4QyoS%B2d1bB z=eCmYAvLSk+-p-J$v<1Mrd0Xz9I9Fp+Zb^`8=P}Y_Xn~ zKMsHRT(q-kWM%Qah>`k&@zkN=>`olN|FsEpmR(LLhtA_UM3c~rd%1eANTO-~#tX|c z8$Y9|jR*f9dtV(_W%m592&jahsC2jrs5D5YvIdPHok~eJNM5f2DiYESDk0r*X+=RA z=?1AQ-F2zodEDiK`-NTC{qOhs{G-o{`#dM+%$b=p^PV~DD`Q5c;aQXBsVGNB4|Fe7>n@+iWz-3!rHnJgRcq+=|*~%z4)iU zvD^=Yw;Ebg#Rb;aG%@@752b|LSNvuwi#R4D=S6AN=L6J9LcPP;u{07LR{cJz2V&bJ z&h*6QA;r(eEi5a;7bL$v$gY;ePAUPOTdh@f-9PllNap8U|M{%@QLU$!mGt~joWzfk zbppQH`6D}pDzewo=C_Qvy^)G8YGlp*ZGg+UTzKVEVU=bQ3C5}k;D73rmeS7lmg|um zCFfim43+BE7H27Zy$G zF_u(Y{t;B({I^%YQzB2a+I{R{!ic!*o->>B#A1W?bDX$i5_~E1e4ew0rzMXJ-k?+= zX~!MLu{j>|%n)1k&XFM$q9~N!$4W7QOl!i{Hn}bOYiJ)z2is&A$2Vtl^m59AoJkFL zL^HW*1G!YxN`!o9?mL}%ib@;j3kvSy+i&JzIyK6CIGGypwR*E(SVyKcj^o^NHyCfm zlQp9Cw52E*u0PzGL0Wz}zT838QPWnFZ^_O6-t((d5^lDdQwCh@)kWoN+@a=1&GHSg z0RdZEG~R#xSrrr)^Up(jX7}84V0&uA`(l<^+uA!E+5W9 zTIOgym#J$`Y*(y&pORx8i=WuF)VIC067@yXhuzPAQ(FS5XDb~#6`LEvlby}vX=p(G z{U~ssdRy<7`w7bNBZN&sej_!#(%j-Y^$u|s0-+v8`t%lskvpEH3JKmQ^@|sXzeqaScBNanFL#;g*GRS2WHnFNmVgt>#^sqbyM+g4 z0eP{>?U%_t7CqaGJhFLH95>avD>_QTNu<6TIG^4E5na3diZ8cXxiv4#TRs8i=e^iZ zn?DaO=+X$PYoYC|a679r-omz^&l|`T%W`Fk5%EeSXVZW*D>)=`t4Um^x;?SIRA=48 z{RZHDhA7C9%gT8eo-eQVc3u?ix*C|lXw46}QTA2)VNry11LmN<>|L@5_s8vr>xplP zI%zBk*bAS0!ryQBHerlM>aFo~a!?XU+iu8xmFvVS`)bT46jgzE{?c0C?COKY@-rn@K%j>a{(DGU*B|~xN zzElYDxVwC{-2dwMT8WJ$ZR2Ii;gpyRuMuDNwapn=DJ-N=59i|)D(M8Wx_m*7Z_M|U zvTCI;?{wAThF+APi=3Q>0(Zm{(w=5j@nS_KQ>sxx_#0c!HNG1~KE!B&4l- z>;ojfngp+F6)2G9uM-gk3h!(Vcib_snJ)8Lnde)N@OE8%>k<*%-dVDsk-(xVO?r&_ zEvAp2TIob;Vg0~6m%7P0smV-F+>cWQ&~dw1RI(3T@@xkJ7AvG)obM6RV&QA~&vdZM zD|kni+e^1>VON8k)3b2QwMpOVkia2=PAQZ2-F!5Hgb&dp1i3d$<>?ktsQ zHGb!EpUTfle{sMM47b2RC!D?e9+{3GSI&Io(hCQ# zS5_q&l~{lJa*fc{%Kg zMbErD+h%zz2IJpP!m;0(Tr^E_ynlEpF3haDoboR1Swd!Ely0rW(pPh zD{Q0>{0wt?^GvvF*Y&+t`kc)BLxK)pf!q&;pc0J5>g|T37?5KKb!bL1QQKbt2A%hh z_R=X20=L!!*sI4qmpIb=npE;%Qig1Ph|<(^WfDPGgsf;& zSl?n?Uo2nW`g7PecOi_lB)4;2eEl-wF`>rLQW(#4oEFJ5YsVj;`^vQ@0tkVvoNqZ6uL zMx)2LKGhTZdRaI}h9#-z_Qk73q=z`o)9=)Aw^p)o@y2z2E23?-o>Z>%m0$8@!H_4u zj{uBIMfxqof5^WF06MN*Pn)7R+7PKD35;qw+b^~1zth43tl)L(7nv*bA)M5DRTdSe zIaR!!=JE@rqBk{V^Q+DPXFRdao5?h?`r9SqmT>&p@&4d7?oXzMbRKFY6p(?z1bgt@ zAlSaXd2sIA8{|M(N_9d&$keh*U0Z{Ew^l43W7lYuOLuZ*I$p~7mQfzCAsp1Yl1okV zrx%IjPqHb2c7BDQ*yoga7u6C#Nw9d_ny6Z>8dzBUUM zd-`ujq?&FA6^e;<6Mc?&!XQF_#OT4k2If|p*+rKM-xLE3(-Dz5WR1=onmYF!-#QLUd>XpDL?jgnlzDDE&b zm+xARS>LSQ>Igw_^&oOY)cfRD?Jf$(}~j`d)ed5Oc+o2zOrc1AP& zCn8I4xFhimvt$}g>o1LC=mTGyIqE%fL0GUi!In{tw-(`q^;N$M*xYB4sZWP2{U2IK zmRfoW9;JR-^ns&&%_h?ar4rRDtf*gOy5)u3x_v@F%CxPYi{D&gX-40Lm#Mup%v2%; z`j?uit<-R);S;y@)ghb@b*{X~Lrn1|q6iuj^~=Y&skr+d40YwFI|zjZq&Y044Q5|Q zd8nnrlq@4u^??t)7I-8g&di1W5tIER=yL>)8LMj{)@0kR(M~DkoDb7HA~&89^{w9x z1KeH>oVLYW9o$Op)QABKDdUm2%ZGFfN{T(rt3*EHnaR+o zVy7yxsy=GDoS%sQd<}7@VzlsOL_vrSM(Ir9Ovx65wUXyuZ9_W!ZE&G8_nbP@a+21j z(e`hL&(1hBIBhLg^lu}LipFNes{@|sgm?|c@W6n^8>e1B?z!dES)p=Doo=VW`ieEODe!SE&e(hn%M7JX2*1>YSotm3_6p26FsBmlUdci| z|9a9j11-P80ZohdSexr(jpl44%BiX^v3;Dg^?)hFI!%Ll2-F7Ftb{>Dk!Do)QuDgzek*cvtiUb zY`x|jrP!d3GMWexN?MvI9N~ghT2ItmD+U59Wi+8OaJ)YSWu=-5Bkm1UO@ifh38^N- zn)`!>w!=l+Kh0#Bx`3#}49iPpTM1)tUg6r{wEP%Ywj65G`tUag!=!o68IB|#(^p%A zK;$W?#|MT0hReb@s!#u5PyDnj8*>zEtVIFk+kAykd;#m;!crQn7{=j+c-ykM(%(;@ z-!QNogGW{$J$@yhF8@v_i_f8VWY6Y-63#4LR~s!Fiwi4gfNd>omQ7$r=!#h2D@Sch z#hKCi@ZSeMRpChki4d3V_5RYqys3%lXRG{?b~rB!KS;YJWsQbS!dHMvm{D@2Nu}+8(0rCzc-nw*DM{OJcSeDYSxc z?UoFYAT-0`a68{^f=?(VV*N8@yc>U9b({CwTea~?F`b)r#zEv$YkP@ zhk{al(#?s&_~l>}BXQxaYRjd^1VSBJt8WA`%b5V>RXokrW53pvZd4}&}XH!YYy_*?tG))}wFtizMn+@3TL zp78dTt4F2;ech0EDS0M^-GMJ?z-0CE(n*?hK8yt~(g|leM2Cr!R)o4)af~5S`jC;F zmr@^qT{UbrwiIWURA#RcHwgssX;lfxGu-6WB8dx6i%VHch>sj^;Ba$@G>}J9m(M7V zAbH)y#S7=$fdfBB=%u>sD5&N&?VZLh!+b~4FjB(Uw&4$l@?~LWtNJXJV)odfFB!;z zoIbV793G1*rlagRo2|iI+v>rIxv>s<`mM`Kg_Z)`4SL|gV_8j~-!NfIsT-nOE+N3d_ZRE^MCb zJW9~4IKr^dCgik=a4Zq8EWPPXHN`c-BCUCLs)l^|z0B*{7Stc^Vq(H)h2zGJ^SidtagPNjzdllZpPr3Bz9Io0YTwl&O?T?5<251)!OYdAf2Z7Ii4-j~YDrx=yWM73hqy(`kcX#zOHftG&GYw1Avz=tfsaG-tX_VZ z1?B!a%#}mgWiZ5mNKS4^ONlj!Z+%Vd-eZ|It*LRA_e5f$GE&;2+`lZKBQ814B=_ty z@-X#RJnvtZ*EcGUdO7fxJg=Ly)Wr^tSt?XlNXqzrA)xm3D6Eizvupd@<6JuMFli z@5!7ItEQQ2lMBcj<|odtY8^9>UuIeJ(5Tn*9UN6hQp@R3Si|EIi8%7#JhE6YO&~bM z6X_wO>m!ys)wbMzN$vQlI-N%%?zXy&^L#V-@hq9J?aD4Aq{{S%KzKT0!m*dB$VzM7 zyQqGPHf}A+v>2?9EEuJp7lO&w>5SqJ*~FVcTZQMYl~2yEuvW* z`xyPmZwe6qG$>o9Jn%GKNaL?YDyKfROQjIKP)BtW4_A`%X10~)S-%2SV=u$zY0XQO z$9M@ouPH1Q=43Jjyf3PR9J~=LYLGOwjtum=SsD2_?~cBlqOTmmh(l{(h$7M+xFFL@ z$(l!_>}IUgmh3)82t|ff=VmLw)N+HwQ(8;amPWEwx0y4Ua+JZpF4>CPW-U}$rfsN(4 zU9l{5`|&&Z8`c5!cg1})<@hFzOTMl%320@%)Z6xB58jmGyXSW4afhE=X)Xy(KoQG$ z@Kxu|(%Na5+u_>d;}Y(Q_!p{H@_gk=gyx>hBx7A27XPR=<-x#{_Oj`{LWDYJN^Ir& zbYU7nQB7})S zeKRAJ;>TyRlgmDa$;B^tfloRa5lKy!bJ$ZBH+*dX@PI7Xk0O~PQtQ|hfVbKU)0c2oK1 zu;?lYZEJ5MiJ2mUXjiqlT6yn#5%{Wh2I^C~rB#xEfcGkS5=vl1_Z3$injsTej9v*P z;JTEy#G2dE0X^-D~NlAGMN-Y9XG?c z+Ix6;r!o+YqpY78-usC;M1DU8nQxCsEV8U^R&bYmDQv;VaPv??z8vvgnJ;=e)X%p0 z3E0BrR)f6Y*d9mI*q6c{m)pRZVCfKiia6+ zH)>-H#D1-eW8*SZDTy&q45c08inHJe9Zw`76&nE-7torQR%m1;WC0D zg%3tv#ET^mtKvCQ$XDF+zdF5E3uU-&F}EYgjHY#iA9}>#Z6Nd4a#0|*5uX>jNnF|U zm9={TxWoF?(=`P$S%BFo)Etyi}ZODpPd0~n*I(h23hw7 z4TVjHS>JYtqJ1u3YS^T_@GB?#hq~p&U3EeVll7>5*+VyF9tlk&kdZkoC?0_oEN zJ`cZS*PjXOkS`_SV9T&|%YkEH@YLH_6!&_6_)&f8 z@RGIcyf{HFP2eS(>2k1LLV7~cheDG|P&d8WK?a4idfoFf0Rcchs85nkuwIvvF3Cy| z!8Z4%Y}Nw)gcgp4rlOLfKra|FB=`mhVe6d+HpxrlPwXerg1qA}+$KNvHT`33j(6K{ zsWb3yx!$%5;2LFYX(wF>rIg7!iZ8X<5;#3`rN zFw5ja(QS;CeDwbtpP6f?s{pN^LluOmFJ z;??K~`AIjOJ+hn%kSMOR_xWb5f<8b;KjCsZIKLR;Sq*ShCT%%v>A@n`4CMK=`hhdQ zK{|4A0`o>}bCDOK`6{1Ug(filSKzndpEs0*?SZRf-DaSnK1%1cRn(Q9Mb{D&#gw#^I*s`#d2w_?h|V&aHG09)osx!l|)H72v_zX&VLHtT%TN# z+_Ys@_8AH`iPAzf1=W8xn~sr1Iv&Sv*rFX-+*+TR@s4oQJ^9?Kc;{fC-f~1nMJDwY61Iih8njMX zp}!-jrT<=>Ur}O8-H_q)nvqwH72AvLjfQRYyzQE&wTbw#AQH`YR-!Z5UfpNvA`(k1 zrCm@5KW>sUSz?eqSIXL`@@*CsTPGtvk~*?M5>Z?0_;M^ObZ{%NT0}5VroQ}-u-avZ z{uBVp=nt$|%%fE{kqm3T++NE6C5WSEG)`{Yt2c1GBb7Nfw5&!lY3oG`f>U3+b)r9` zk{-m{RMHTHHWmE3#qol6xpl%WvMj(Cfa-1jj@QVUQH!e#Um5-o!wZuZ?wzE3oRv}p z!bUU14kl}=z@|5W%@x`5eL~r53Q3&@_@>jC%QEMP=6A!)56hh4_C-UK#`v(1x7fbJ z%-+t-pyJ?JM@^*7KsYMq2};-*b(!=(EdO!Z}FM#PK8PA5+lRO^(Se|%F7mx8jZ*8v$}e3d^$lMB&gJ?#^A zs5#~?0=xp#8SU4fsjKBn2%MAb(>l-?SGBYVQy-*RSA?}oJ(izQVf})C_vJzdCU8(J!T=Ttf%j`} zRf*2Lwyhl7H1<%z6J`k!40`9LF0eR$@o@C|y2WC85s_pSTiXplMP8rGT&8~uB2|yL zVpDZ!Bb)#)FYjz_tkIFsUd=@3muw^)<}TZr!lTad<87*KUwOYwI&sQSO%x|kdMk#I z^5HEMcP@P8eJ~m(7jh}cGdGsaMG<&!_0&H13xu(mZmoF%=pFtF3%M!19;e;9@`$OS zCL(z0QqbLEKST0ZNArM*{%Y%a<)xaU4QTFLms(48@?sUo`!l{{@|Kg1=ci_J2pkP= zFBSOi$+ZYmcSDkz2Xi$WIZ-jo*L#|kZZ30XWsDeurg(IVy~bFmy6S|Y zEZOVaqVJ3i(kuw_7|c6va?!_;gYt6qnA`8T#kaei_+S&v8n`Zvl9+Z^2RbrzHX|(M zsW_fxVk=I5kV$D#8dYWkJeI2SDND`PTVVbc%y#nnGUZ%-37t1-t=bXC2D9T`>x)C) z!hQW3JnNHqhd#G{1YOhmpsp;5G`)X(be$ccRFnC#RBiq1_3_Vo>RWm$afvHd6n=+h z>#$``AZ;hJT=Bg!60FNHIkhZ5oe2v~+dh+aj8OS30Ll!tVWxQ&s~REsEC+wL-&mca zI)5b5;3x(`fKh4J2M3iA&b9vPPq{5irWZVxW#-Au6egnhsWfJ8r>Qc4>Vs!_2x)Zv<(epXoOKzaxbcK*eO(PO~P`}p2o z*z_;81kbPj$*~{UJe(IKP9UR$f8T%32Y!bR#r^ppLX*n=?@9V&^V(h@Yl8P8-L8#; zHg^8i1L3nIyk7$S??HJ4p9h&2CtisCIKqEVB+S9fNB(OY{~xl;z#)v?lmX1Zn*#vA z-TRz~zQ7>TA#NLr7ZghJ+m7F{M=g%IUR{~$ZlCHeEd{xR3k64xp0ELFnXcOqK-slx zez?N-fW?M?!p!z&8P#Rp)773OrV$zF0SPUFE2FLu*yoQ`D&vD=_6aSu$Zu+4SmcQx z|6KPlWM#IA{l`%+f1jN3%}iKjj-{i1_GRRW%g^i#+KDljpyjm{rE7M^Kf~tSo4T$)Q^AP zB?k!u;Ns-qwcSL&@TVf!B(%4}O-x-(D%i%H&_%Atp7na-WivY7f^8!!EQn#> z={FwCd_lr9c|R-&dVfV*0)692e5jLthfaHk0!F|9Wk!8civEOtLD>}!>5Ed1DZod| zHg|~*Z2)|-4M-%=Du6I4#OfCBBZ29N{@{le;fA>PsiT%r0sIFrOXG{OEk*XrQM;RY z`P#s9`p9okD8t5y@(F*fQy^`Pzn^iz}ztgKw;tQp)tMk}G zpk8gJIQpj_AGGPgKAL&*X66Tq?JP3H4uN7KG}G)U_%+vdro?(2?oQ?S&S}1iUWtxH zrHwJ`prO~_07LKmw#K-(!M!w0PX>D?$hJ-yJvn&5>CXXMP-;qYPo_agFRA^AVi?xG zD!r@;P+coj;qKMFP3~nXIOSPzwl0q@@!DbRXbredrB_NzgoQ!-o#~fh0=Iso$Icu@ zv|oY^$fi2F2NH|^?fZkG6rf9D3R?|2pD^7YLq=fkyY&>Sp38j6mxw&k%IeU5`TTQV9vbX*(c~WpBGngD+I2h$PS99<8 z0{Eok5|9`Cv|~=_{zFec*qEyY?IryVH{_&wq7SLU`95`iv{QFL4+T(yB<4fE|Ab`jB zceL9vy{LtS#zb22V#KLv5BQMvbfb{a9%J;cqYo}9gC`isZteCu=~mW>ozQ33u7}Ii zhMaMJ(7a|h?!<+9g%`(XHGC@%R%*OzqN~fdFBMczH461Lbp<;uVzqSD@J2$st1kD`UH!)Rl zq)jiS@yqczAK!Plbc~I1VU`jO5f-lkI1d=IMH`@eLuey0y1#5Xi@}f2r$Y=P4e7e8 zjOQ8mAFxO_;FxaBZ7Z3-68%q^rh0~_(Wqi1Y-dFumf2xNI*unyx!ROBxNOzCb32=7 zadV-1OzkytJ1cLL1_@9LJg`_qIv>zr+<9OM@J+}`A4sH6SUo50Eg7tLp+VoU>~x~P zWH#ppBlWcCK@HFS2!&LQ{**g-cELHdCZHeqOZI)K22zim?LH&u7nCo<{pe-v8Uz+< z<(Qfg%E80D4ng~mEm{gFU(-~3#J&<0>aReWCRJ{?YwagxKy7MEU^azy06&iX>q$ z<(oiwxg}TtJNVqyosa+WChIxC;6$n+nP^ifeH~+cCC}+tuT`IYsqPxq zz8^B?{keA#60x!9P5l>x40$8E_u?b#p$R4QH0kIp`W%o_j5j?D;zx>DQgx;GZC+A7 zdW35*a6zc9iBV~HLg;3o2kr1OxMfz@?p^^x_2>zTZ;`I3v8)^UQ>is=E|rjy>Yd1P z$sq5KDw5d0rN%g5HgUBVsvodd4`XN`z4FBmqbUbJi2+W0Th9>joW?Z{QEj3AwO1(rbWjvM23mN6$)9VgQuOX|8U3){`B+a#B)%Upukv0+y=Tc{AP_KgL@2>j#Yb z1^@<99@n?q=^LJlRS!?V;v~xZ@+lcyl^5D(Z2#jWF&tvo!KJ75f)E{f2He^SpRMU!yaP5Th9PvwP2#& z7fs&1ss(zpo7m!J&s6u?g`I(bq98{Js3z-$w>R{yqKfDm4luN{nLt=^YKdiKe|8m# zRsdTzCDmaK%^W-o@N##8s8*!XSUJ;(Z9toc-u`Q=c`Um{zei4UZgA zPf7*7HQ}+RJ`aSstS>XQvQJffxZsUVHszK)v#;_iGXjgORkx&{uzSe=kuvZp+)3yh zCS$%mB(!_@)Hc9`QvR2;Z0b1MCu@l#ADcTsj(|##98R&()VKK&O2X5ys-ktuJLC!> z1ot6NpfoU*n^I2U9>9T^$0i>FSFAuItmcf8dd74&FnK85iFaE%OAHdRfg!tXLy+K? zH+=gb$B8H~Nks7*4xD2Gt|YgS_PDY1uJydEP6JPrFHtj@=Nb_i5@ZoMyN93l%!UDS z+FVx(|Ab!NzcPI;Esz;%CPKTD95BcDUV;L+psFk{$W(>x{`6xfqsM_;A^slOp8j?x z4@3~_b--Kl>PhqH1}vnpyr={nrC~Gb8y%+?Y?ymC_SZJ5r-JXP&7WRqkqhxZ$i`16 zhlG-7t3BGtyh01GwZt|-8g`w!^WjAoWDeLAgdLJ#Y4!Q~4pKIlalZkoTsD>yLQB%+ zI|IOH)%)uF&1D->yTM97_u=aYEvN@{F8{qs;0J2#%8G|&mw|6iVKzjJbOD6j_=)N}%Ok0=RPnzN6H zZ6L&IkRvIQNj;FahI#+v#3A?;^W}kDBL)c1 zpDc81jaH%^e7;U=|9Ht;dGL=MBYw>Eoe zgVdX#j8Lcfbyxr0@$PN33p!D^p#s(R6NO;HtdOFR*T|YjtMPOjjN=alZNAAu;j*on z#j$22D4hn}*_6CiG20TXspxJ07Y4151vZj(!c^XYbc}$E-R3tXyQjo5It*K-F3bv{ zxsODf{TvMfSylA8+ih#esD3DMV4LV+q07xEG(Y|z<=|Pwfa{jsN_O#Cf1yDKIhdT& z(6kbI;mC+TKYUvt0r^`CpprM+{N2$#nEjfdYR+aq2sV-{1-k4{DP=CeB{jJ>7Ka-SfJ35Y{Qyuu{mCOD_{Sc`7*UVb6vjIB9KWL!KSrwU`dl1r>%g zurGs)*ByG{M~)U?_K&jkzcM_CS-+?MC#456p8rel=*6HRR3hD_Q~K{1$78^1HlAX7 zb^Rc(Tp<7{Ij?wbVjR?Fx*&YBdp%g@0MT;LXXr8QJOt+g5*emxAr6$#2l392U3A2O zc1%C4k^K1Q-D&T1`1gM)%LX21X{XI<%Kz977ihQ!oB|^H%qDkw?KA#TF-xM$3z~q!}ZnqAv9Y^XMq-}q4n9V@g^G_`?*#F0Nx*;5)6c6=(6dC^$J%79V z{wI3=j;;SEdj3`z{ZI7#MOFQ1PW=BXdQO-uSnshiw3V=cU%WV}f2Ia)asMr4{=!wj zW8HoFJ*6f0$Q<1c3CqlIFAU^9GFw;$l`>O{`=nunqLY1}mvgAWsc)eGL~(6Ow>M`Bp^EAda%Y?gjxatI`E%ydkLHH~18WiPbd?`{ zAyg)CQ;9D3wcIDcKfGG)zk@vRokr`Qicr2B%^P0>r=MoQ`OoK#)7)nwJoM(tci%z3 z{7(-eY8d4Ydjv`snrbKrUKn?;Gg0x9f@(~K^Rs7(xF!Ck!4h&tmFz* z7^K(9sig4$XVc#gBiO%8HG0bWa6W=)8n`)7G(D2kCG>>$a!DHcJEN^(l7;7~RO6JS zRMy2Y*{3r8ioKm~gfa2RbeqbV-@FU1P!qgc)l1n0j#EM@6v=06WM*MD!nJS^D&%hs zBH+Y$r`?V6Dx%tgfLndz)L4V5L$i-|^n!LC=4Btd)nMeAhfZU$LDOwA>zaS7Qg>$m z7y6Z5!+>zG)WK?p5fkOY+CpPHwE(P$Q8*mC#v~6v9CP5rwsfR`gPqp5=D#$vGpxU# z6D8rTm*uRZS81kCM5Azah6}gE;H8nO?zNck${#9u-}6gNM-bnt75&6N+)XgKA+vg8 zr|rWS3s~bXSRL{r2?(T-uVetK&^U&$S0mVlaEDutV?h5!=_$;_1 ze}4G#Z(CQUgTJtTmwyx>%ChYkasO;U`whk!XSL3x$6vnc6eD+0dq3YUO}Y|Bxw5+e z@v}lD;}5%qwHGmLAHI0}FWNy1sPuM>EXxzX)8y#6y5sHukq2H@sZc=?Q-9Ii9pU)% zD|QG3bV(|BhY=z@w91#x?zm(C1dVmoE*Kdq6g~;ABef9OP;0P8ZeBdAg_h4<7B_$5LXfwNMOeXlb6FHCLE&V>s? zhaC4+*B{Dg{CjGFqIj5t%rgZUy8r&$J;OPS;dBvr5D4Zrul&lI?Y2}l502UzeYQGP z|F9;zpX|R3%d+5%Of|S}_#f#!at6@3XrjUw%}W19aO_TE62fK(eq8w_sl11#H4xg> zq1}`AKb~$oL?KV-vzhpVb}k%T3{rg4P@&D^2uz=3niz5M7{+m&m<#tmAAV1c^V^$a zzHyXH-ro`hm?R2=+vH#=v9S?eAwdFnDfkTyWAC4%Y7r5|Jae7x_C{l(Q1|HDtA=M|JnDcScQ7H%vH|&Y*)7f4j7B6oafBEJ$DPKl1C6hIVtD z)ZOLD&bv$Ft*^7Quc(;S1Tu*N=c?#TU-88HynUe%Jil;+4OG|Nk<9&PpHbThZOoPr zpHs`ee^NEaD92nlJ^o)W1)S{6gUzLOKboEhe)~}k``!ZEyKTuTci%t4`udWi{+K5j zXEnL`MSOTZ{)m(c+WB9mNtBO_bo$T~D^WgL7gi2}34|y3C)06e&KyyKYP7o#4i2W< zPPEN>nmeBdF5K52)@p)S2%wV7*A_wsCzpZ3JLFKQ z{A7N*XX9-tYR|6bv+t7gLS}-kFZnaN;{{3Imi{0rvJ@= znmTfVM>`LmpH_l0$_LfnGi~uG`L2Wm0Gmpo3GJ3+1C-7jv)*IffKNmxd6AoN1=NW< z4)D^BePaM}ukU$uutJOE#l-e|0#_gVwCp($w0*K_4~l3!f$AfSq;f_~>@GHX)&Jq) zDHE&H@+96sbJUg%&uFkt<}x@35JAPq2}>(W^y@+|@Z_0x=XO32O*qZ%|5O#dnjlI< z%c@@+JlVvdUwe$-Zt`=N9s>ky)oceLl!Y#Y=jz>ank-Cg>0J6vbxdsK)8O)&5>W7E zIy^MFAEe_sT+IieBs{1N;c5fOl_Z=nM1aeh%G1CN0NG>zekPdnn1lAC!l-X=V_5V* zP+qw&+xH-dS>waWn-s4WKOvG7Qy`}F+_?DAEz@XXH^d_Q7z2dy>r$7&dCM)wxLtp$ zN8HfRa1~U93+@ik^_CLm8OGmud-Ab~HLTa5?BQVtc}V3_uVU~CV*Kt|=_N8+*2ozM;6_}7cC>()im;d{~iy;+y~te9115-}_%dKofkc60>1>+H9d1bH41Tk`8pRmq}bRLP9# zp-omtunT}Yu^et7YMfB|I@*}|p;Qy#aDK3!=il=W@6*QG}DDJ9i{Mt~3XVz9|vI)42a${?y zw1JuQu@$)aE;6TC9LUOYD2kj-N+%soa=CgnOiJz-mPZfso>dZezca^}r;4>&o|%UJ zjq>|AA5BSSsxG57({g5+&pFXde#tN{rpbbn+i8Mwl4|OZw zE1L!)EQDL=eq%HwWxmVxk6O!}-FkJQoSjUd2vkEr7(RCdVbP^;M4&Q1GyZMDd?<^?{hn ztX(sO_{Pv)G~km1(S_j~f^gcd%6vvi2k*lxm((KT&we`KfL zXnjOj3zYSv>rzQ!Eve`o0B&yNGZtR%&=C{F|KREACnLmrnz(%`mmeFC8dw>fd{HjT zoynTF^|aPTUcZQx5#DoJ_^0jj2Rlq6L?rK@@zaWp1;tLjwkm1i&#kW_V4$|XdLSc1 zM7OM$E&0htS^s3e9l;l^jfq;G3KN`|?937cx;~g)PrRu;%b`J!>nyT1%BtD80i@{+5_CRw)lav509aIY*%PI5!f<;it543BM@Aa?I>|^ zdMeSCYpV7DuuIn5*5#Cq}K#Wg=Vsmq?Xm)BPVgfhJ716$@cdn0#< z`%t9#)J10I6CgheUq)z6SGmB(F#p>dj}M`o#=e|h6ug-|IdSkpYUkOj!K`<$oq9=5 z9|>5X5OX@v39kC>rHT^}8NmWI;AW}QeU1|;jQ~z!HM0TARWb=|Z!Aw~5<9D{MR_>EHCwhleO{3$n#j|gNuhm8l>u_Md|(0YO`T3=DmGxQShj(c(n8aj@4 zLjB_d4#?0mJ6CMlfX2?tUiz+hBL$sC>pZXKOm*iqOh}M3spaC)M>$({NqVtaKo`8! zP9)XbxPALJ4P5%^)D>9>@(o-!JSKSf=Zu$ z=VEv-PPSPu9|cGnb+88Vhmwrb$iF?Pn+GK1T8Ev(UZ`NqUP=hJX%f2&@Kl|?5L(#+ zaJL(==7Vz|YfH7ZQSIdbwi@oK1&~~K8>6qPsG)tGJ1qZ?v^sVg0ta4m8j3`py9bD{ z?qPSFJbz=jjvOdz%|er*cW=F_C&;+X)Pba}nn;oaEJ+yWMyqW z0GZj4uxg8}b~d;ME>*rPgN`H@nU`Ok6TSVf^(Fif(QFFpk<$oO@Xm&aX2$>t(Y0~- z;J63Q1Luu{lz2h$gL04%6@pI*mPe)da)1{lzkYMr6+_=v$i_|vI z%v6vubpl+%TcD;5?{g@3!-pglfm*JK7<)gLaU0`D$$X%*nPg9T|9fBJQ|5H%wfROc z5?ML9Q-E=oLCkWZ*l{UwUXG>U+Z$E@G`!H5J6Q!z+h(KvnL}Ow zUf@PIV!gnnEc>w=Hfsyp>NnRtd>KEs^0xE-QmeWjnHIM(1kf5w)8Y|v! zd3QUuy$~>5BXH_pj*uVo2tyP8pSgQSU*L!Uk#M+3*Bkw9<1Cgc@ZGrKkv7dze2URfTDF}f>)aWa~@5zG8&Yv`Z0$PUGzy3K35Xd@r$R7 zpBw_mIQuNDQMt*}(9+WK84PD<20t%5Xhfn@v6^_?uS~y=NhKcMLbKjedLOeJ&Cxyi zViDlf01n#tM=5P&t$H$aZgwE74f?dciaFfdS%-hoZ!{g2l@8gx;@fX;h&{^4>v!en zwVUqYMQHHJ(3nY$7oOc|tIQ0msjQnsJIw4>vk6e-WD)`u86DI|tcQA(n{=9H4@8Uq zour8-!!R@vB`fry(xDI~^CzFk5Z*feF$FtQ9!>jYNgA`?b+k>%o`k8f0Ba7zLI;M* zGqAV$rOhx9g<R1a-q%Ii|6#~aTE@|QKOi?5SH-^0R!H}{HjE%0X2CVH8R>8TQ zU;o7-rpw^QWZ=&QV-Lo$oj~Y-GxOLso&d$4Ek$n~`zUq4)-25FWs*l&>y`6n0&!On zdK_OXa)06Q0BSW5!AaVE0(%~Ilk^jIkFik_Jqt79MQ`esP?704&SFC{AMzuX){iZ) z2I8)Hq510PA4H^%gGm{dnRXuP$<_kQ@x1WF4sFWlaln+wl*0<|db$|WmNG?Tw3X;0`AhO6W;$A^Anf5d^3L(z zPb%nN|HT8m`uIl@w<{USWDP!4Sc8i7jf^`R2u^nTS=g5=L#736b%JqAi-BW3*;8m! z?C&^wN5byB^Db4SGymy>h-?Q^4dnu-?(nwHwU}ERnMn zPRsuPf^GI&90h=CP`l3B{I5N2Ke7vsD6shM78%IwW`h3s0(yb}{^5V*`CB&nA9?;q zo?qYa|KpzjanJv_XUD$zQ_#iEg$ca%t)?m zE1Ql}1DzrL4?3<8=kv3|q~}yS#@T)fc(?U~HRDy9)a_#j|NJveU>@@awMX+PPHX(! z+XwFyal(UtdsCLxAVeO>q;?ka%ii!LI|IVQhunU~bQ8G>QU{s7wa@go=fqNeJE{J#B1-|jGPl%+Cf#~EV`{_qmxF24eVerj3@5S?* z&2$f<$H5S}v9?hD;VEwU>fDeh2u?OllL=O^%*lkX(}F1YxzohN?oCZi7ENOGv|8WN zzVRB_9A-wRWXfJTyxx5K%yd@GksfeTg`%OSlDj~B&7$M3djNOJ)n%wg_g8Q;5r}C6 zumB?=x6lHF1!4sOEmqL}EVw9u`Sh7HoSzM{@Bb7ND4Yc5*cm2kG!2fm_>_Tb=SZb_ zNBoD4py&>0UrQ9=MC=hL2$Dt&ux5mhO>|T;b;Z7QW+9mu27?CE5Tl_`vEmClP+33( zJRgLI@gUTflBR>6a+bc-HxPR!VY~PH{r1#e=Jp;xFOtu5BP=jp0Ob-aR38QR-Kkjh zLy!+=RC83Hp8C-p))kq;?TE6&Q|OFh)-2))cb>1R@+g?f?Osk0=>Z@!gYCl=)F;Gi z`nQ(zX2CUOmkcW!C_zz^w7G_pW5quQm1v=d52CV3jCTVWvKKMfj1@~=H+?HT-)Df3 z@#U2-K`dI4SYIoSLorjt!o7hBly^7D&6cZrM;Tp z`GhRh1jGT3v)6+|bORp%T}(=|9pll*2hhUXZSm+MEQm5J$18M40a!n;g9_Vy+uC3@ z+m*o{-jC7Wfn<8$j3<@hfq>b)Q`(<$dfy8U7!;a9XNxlo=g|sq_@^vjwk0f!z)F#ruh;Lde>3d zG+xb__ZmmhLD!5v3$aKm7*B*^7mXmCQlKekwcXQjbD}*hjo3Kz*iY~-+(iQ5e@m8x z1b4j>M@~NL40Ss}8Jdz?B$+pdI|7Aux#$(|$xZ<1(>&_l5D0zOp`-!y14LJRbF3K* zqFn6q0f5W8&?uX#eFI$ob*^xf8r-*ff#0m_3ka*ucIbFN4bI^Xgf5}{TD;OH;ZBL( zEaSI;e4bx#Ue)1+pkjS3AZSPN!eb?6yRHPpHE^~W1OQKbz~}Dq(LL<7UDx8?1pGlJ zq*7sAbw7!PF%pm7Fh{nxv}(BTN;NnRmKWdnJCjKeA3HvWI|HFfQGFol%4$7Yd*QLk z#v9l~TS_?)P9J<}B|dz)NgYw=5x{Fbsyn6Q#Y>k|DIUgU`XNTl@58&>Z$ChoQ7EK! zFD?zjjnySRzk!$N`Tj%3;$$aB_=CacVuCAC!EBSD%&K$#Qkz;i6s1P;4m>9Pf+ho9hsG|l2`8(+dn@(V4# zei#NGIb{0w);fKAM}e&|gc5`tc9t-Tb!wNzFS8Zo=1%~?@+k;OKV{Y^&|UFZ7>X3q zWZ2AzndREhQZ3GB(mM35po`OK}g($;8gU1gvJ2UO3x`wT#ou^mJ1?Ltd1h`!*o*R3N z0AvpVeYnN7K4ffupz<3PRWOgB>>(GYm1&LX%_hXpd5A6pRCivc+~}?+|J_~qbZ?=- z)!YW5bs)R%iUQfFVHXrU!iAk!Q3~)W`iE25c(}O5H2^bYo5Ky|(M5&>- z1PFvK(ot$4p%*Ek6G|xW$v!K%`#!rrzq`*r`^hTA`<(lnnQN}OW`=U-H<}7D_MDLoZnjw%VEQ9GlL9NEk)&igh zqq6^O%#pJZGy^g~xKd;G+$!_-Ft|SVmr3Z7DAxS<%CBUj?Yk@zy+F@18Dg#fEVf{& zo@V1NF5k}hBIraduQv)N%drA^Y*yW(AyA9jf~h*DN1xd@1;jue7|jBWfIX%HXf-Q9 z=Z~0$b?~)!WV&xhUKFuWwz`H{#+~7??+BEf1zNDZv%;YlJ4RpYvHuD$gczqithsb; zbo)(kWtve5>g0YoZyx{dzJ11#l1>h#wenSv)Qy4Pm-Y~m@hFfLob_E83;S+-BQ1Op zm97Z8(F;U=+}hY$T_tzNt>pX%kA0mn9&=<$1K1n|U;-M(Ex1i>i-ESI7We>rja03- zz==4n^+)CoO~nsJmwbHE2}U@Zg>m7-Ma0p14sDSBJ+jMJV47+HI5T5Y+`_y(ZXYv2 zw9$703Ub?z6xZ+va=wp*9(mrKVOa9$neAW^82Z9>9O#5j&>R>I1$lAP1~7wEIGFW0 z8_zY&ks&r&x~jaPu7|8&l7oYn*}om%5BU}ID$NASBK9~dZ>RV&d8lT6CuWsqEL=db z+c(QjAioSe%I8=qT(C6VD;xQ!mbb<^atXi_Q~O>pdnT1bR7(tozXi4EW#0>%dT$U4+9(Q?uh!7c32gU=ll0fe?^y*qiSWK+6w2Z38D^f_pR zVrm?cjJK^|xZfCgiK3>jF?+gvsqZPB&=+MekOvwxQ3Ax#3Zb3h_ay{@;?!OXTnL~5 zksv(2XYgSX7$sKFH}})&d@<8EM1V(n)_ji5M&b{OwkBv>8*k#wV%U4Y;MAdl!GQgg zhGDpcaE1KI65Ap6be z$*=Zi)zVV|pm-p4qHrRe4$ST~rdXQa#9T8&K`Jk5r*OtQVD(m#6b&jcs?9|%^O=@y z$jQ}N`>XVocQQCdk5Q&*Xn%mM;b(vV)NH=jZMyiJ#kaUrY3r`snUy9_nSGXiM@>PW zy2#z78sbMJFwL@jjIwLC;j*3{KpI4~39c`Le&J(du|%LopJj-+`jSS{y}j6{BEYnTK}FFXfC7 z(v<3+#HWqhsjJLAR1`V))wQc00<%ghB0=f`1=>+B7&)UC7J+%KQxyXWU7(L|e-COT z)F`j@rTyXU8`H)N_E>8QY=a7o@rQ8*o~Ycq%4Orrb}F1ok@nzXoLR(sKA17D4eGvRIGnlMLASjVrUmaouI(GE=p6@;5=$m;V--1v-IFSH znz|TA>*Z;1B2CVjzV&mP&VC49r?zJOgKhwyQ=@co!282;@UHYP1v4&Kd^>&o(U%h^ z{HvSx*c8p)a2|Sl^D6y851`7c2q0 zULH7gl|I#Pm?HS0q!qzzD9dj$z@1e9%MsQ(2(paTF90&WSowhfN?Q2$b@?r7DFfv+ zasG6U08AD%B!WROwb?Zr6D1qam7qti-VAK0n}~Ew-Q+RRQc67F4kqz6v}`?Yy1g5Zu{?=x7b zR)6@9SZxQQ5L}Lir2s?*rJUy*4&yZaW+F&32f#ONdk{ct7oXv`6Eq}ub(8~9*IOqX zMTHAM5D$FT+wEJ^p@N_XBz$fWxa1ZN0RbXR=pr(4s+=M<_E0x>vVZ-P%l_;2B?(h; zdO}OEsb4N{pX$Tmr4-#erI5hgWj(LMpbq<{2gC&W*ddBG*qc(-apXA}*zXsBO)%BD zdED-rMg}7VGe6&V=*;tQNSYNT;>8JB|9FK6*ONw`?>A4f2pb$vP>G3tOuKdi zOkv_30JRS9Z_`=_qT^b+ayaGaf>(VET$5Sm+5P1z|=Upd5shgy38%7Z1+y$F5%R!O)cBQt8c$iuM-+bPw%7?jzksk8cY`*)#k6r)+ zNJvUKIJ11mdImQD&YPMQ)BArAg8u;qa@rsV`0#q}R@}O`6(Q$>ORA-7^iv{JKs~x# z4BCDgQ6ysYF_|x}-VxY#ZtVcjN?M(slV-ujr3V80DJ^5ShJmm<0 zqS=^duaAlAru z%QQeGZz8-T_ETiT{cgQv1MyM7Gc=Ch4X) zUXotO)hjcLb|=Jxl8h4)#*i+~fT+t3w3H%4m?lLDYk(1AqmT!VWbl}$DN^5lVb2vM z=h-UO3W%nD2&*$w$(o@-d+#LGcF~Iiuo%a#*6(NYymlrud>;kHDZrgJhiC>q8+BAL z%1YsZO}`I@1@(vO!uCDe!`MidKp^DRvehwE7XJntzOjjtK7YVdDv=^V+~ATpY>M`i zeLufG?J3Jv0!Gyb&I9s7_66Wr&Qd1VMR(wKJ%e3i^O&gXN!C zaX;jF?d(RwJJCeGuwEh_Z#2YaQzVk*3}#>lk|#;mexjncZOS&c^gE-?$E&JVGz6 zo_bASSu6u#v&OG*Hg$DV2+60kLxs3eyn9*@g zfEVu92J`j)ar; zZagY#dOLq(bs{IVAm*_LZbD<>(#ehd#u(64_}rtRFD9FuO=Lk)}W~1w!cfXDOhV`H@UV)Y--pL zw7F!;8T>452YaP$#Rl}o4~%SZff!+%u3QUTe;m7KFpt40)5!I5R_q%I9)ekQY3r!~ zsq;sHVK8`k)Ob+3<Vj_sX4^0yMj$-7UP%o!yo^6T}W;tPaTo&=t&hy z^%D)lv0#%7>8bp|a!}GM??B2FNrBNHUa#KtW(0VXan{@RLFNPqEu#e1fEA+!Wy?v+ zCmcl&?y?#jO`=d+fL}cee1Ebc^tIapBt?T8>hsQ?@$&Z%d{HG4@!Vx4cGe<0E~bmp ze47Gc4@UA~K=lB8V7{`6UD9caUL+;w*EWZb3RLANB~(uj46(@Wi%E(7X|jRbN!7bF z>4e!z;i522zz*vfpk=nbZwN~gaUE0uSzM%WnWLa0krE{GQ70*bEXY0|zjt<+$76v~ z#>WmH+vRRkIF>M3Kwg@TX-m<0btQdlF~t=Y$tn8Pak@wPk{Hdt4ij8U9>h#=Hd-WH z>z4v$)fNh@(P>@WzDbk2s6_gVKm-AC4IDSwoAQm}_Et6QSuI3YPYlZZ>q$K~VjhI# z0cI^`)1xVTz%nShiZn6zV|4}YtO9iMJ`nA>u`*OB?M9*pW1h@Iru%nqulsMV@NP>@ zAU`3xVt(o_Omm7W6IE8kNB09&0|k$KXUs_Y>|C8NfX3LFkS-AiDVNnJAi|cMY8e3A=Ke{vqRU_a{e)Ao`*wt!m)){V`Pe}{`K+Ef% z@+EHT%i9UZzmxo^22h;gV3_f?jO-5^(VV#(gjp;oE8IcTrf7C>f|)@m$^q&HIlIs{ zoFI(mxB)B?Fdi+L9JQ9)kmJYw*?x;#e{whz$fc<~Ryh7bq-1IE0q{qZ%-gLE%Z>5$ zUz3-CTGqoIBMtapO`iT?nLll=oIJ>pb*uISJN()X{`9MVe3Pw6Ifb;IhK8Sa00ds3 zp8f>|L%fr=Y)zt6Mkz*HY&Tj*yaz}-&E=lvmJR>Ax7W-BLOb#2XcNEf)zGR^3}8P% zVz;8*K{pIgtTa=yGvG(0wPrOnXs6h>G9hA-T{QB{FKWOQ^k7Bkd^=Tj;rUSX%#*w&? z5Pr=w^OUGeZxGC8+zY19_XhPlgDQ5i>Cy(MkAE*Ci zx9C3}UV@|g%{O)r?*rM3Nn4OmcmZ+~JjN8Rb@^+6M{p|- zXc|E2L^2skkxD-%c8OUyO?Hax^S7puj~;;1DIZIWiEd~}yAAw^9)Ka*%~PNK*eXD( zk}Xt7|JV)x<3}lGCK}{?@%IABQcHMy3iI2an$uXz=IiHN+-*}LoeK;-8@W~!dl2L> zdbvA5xvJSRAS9#xsucC+!9dCb{uc*c7Hts3_$E*o=#i~Rx)bstn=C+XamUma4#&iA z^*aCK>i=|D(02GwC0U9cB?w4Jg ziSwvp)_hK(gOaQ1(RKoYP6P+dThDB#LF1DmF8mQaNstq(K|x z+mBp+Y<_S@q{CE#8Z(-p_nGO(eU&?kKu+dNs>ps$@#(HMOa2M9|INz(i(Mhdl~QyL zfZup8)eXAb2bFdmQO5w5mMO=#>Y!)~g}h2eTj9=wYPc+Sz<=~_u9s|vwF0TaQNY&; zfTkx5xr8DY`(-daJba;AAeU0swn;A8WCKO<2Psw33)P;mg5l6i(0c?>gav>W3Rbe@YRy7U(oVA?F@VfsXirudB@p zS4JY{M?yQ2kh(4%&0FoOGgIUsOWLHw%?qi>?({j=t?Tl)ACj5?CYhM;&e|FqgMjPw z0ftF2Is;^V6f!A?JjfAr7g!#VC^<^0OO9g~9h?H3N;2co^QZMdzDr>}tYqzBJ}?&L zXzXHI9D9a6=okbV{CL9VeL(>+{fr+XLxgMTjr@Lvkt$D6U(J*b(j0N?U?e;bq8Qh= z2}uXCiy?e|v=%r&Z>g!O9glNLI$FJ4dNus=l>c4|)34SG>=0@sZg6vB3`Fh+FBJ89 zkhv+W{XPl=30#qIycXm`<-9q4uqIL~!$;S3+b+meQG$ie7>%`F?*qSti@f+il2f&} zXmksWCEY`WrvVy&-8%aPB{T;qR<9#Pg%9gOhlFWbI6!4*%`%YiM6gD>f+)iKf+!JC z53CeeX3_(?CNb}qnkk(ucrR{!>_H~1-jeN^o$+TJrJG36I;p!KC>9tg`2=xvks=yK za2)e5%dj{qW~r{z;Gd?GS35oo@t2zW!qgF>3rbv%W9Or$PwVq*r0xY+xQ@LsW%`wF zkJp8*H$AtTWD@hxQ>PdX@1bk{$?pG+OZ?D7yC#|kDb|nn+P+Gi64Vb~bOZd)!zckV z^39+nfugK={WmcncQ^;SHcdwQ3KgDx%^`gmI;!!2%cfpk5)3}WPpPChnx*?w;nELhe+G;y9sWykO9;`9(uic)pFY^ZLR9Rd}uWT zgwWy&QELmomdfx`e7I_qT>IhEH0hsH^C^OcLXhJEJ!DX8YooG^l=!(FWJ4yHL~)c* zq`%*rb3dT)YCex=&w3!>5YhxQTntN}=LL?xn7s4Hhm%8qQJsQs zScxKAxN$tAv;#}Zn<<`#3kQcG#M3X+OroG2Leo28#f$9%dnNX}1AP)BD8j9aprjsO zOr&FkF4+Lmnxd214^lY_U9&OwRHVgTgC19uRHV!N2m}jvz$4uc@%+FJw5lI2Q=9=4 za!}*Rri+WoFCBLVFa!?v%e~lWJo&el`IfWFG z$OWA$Y{8(S=P~P(X0b_0XOH^AEk1XHRw^>WdnJ^C_MriD!}`K+nS5)?;KtMvpid(L)M7%nqB2APH4@X5Et}SZ-7t0$`=zJdJ)R?H2RDe!`{|r&QUfgq%jefegQR*W z`O#>g_R!~W0A#~`O1uJaBd(L;lL0CT&o!Qb9mi1etR!nRfdwS)-!{NE*Ozi!aaNew z%M{NqZlD_ix^Z1z19Vgqh5!U0Ie8RL`|(7(pp6vzu09$M-ByfD@~INwq3aflq(uyN zr}Qr#T}{f-U2M=|{o&C{WrS@3>t@yjMlgM$Bqf=isda#GWCI*p3MI$`PmFw|2=cu* z&o8y>+Ax6bB`V{x8?ymHw+ktW<{;xqDbUw95L&qk^ijtd6<(EL0%SLwDfzmC3zRJT z3?P$ujDPPkwOaDdZHXyofs!J--(T)^!c{;5liaQ!@RBcPYy2yx~8KB zAbh8^!P1YOjI?i5`0OuD>O7rE0+OKm5j>ENP&ArQN}rcV;4Ku$sBRb?P`c4xzkdBU z_Ebbk8#nifbN#IFUgw%XZd@?P>zrAnNCx2++%!!I+~{ah9srNYp9L_(A)tT63rcDq zLk}iOJ?sZME_#Xs`8=@Nq9(2cdr0^;zb*UX<59)IHP-rB$t?kQ_$NrSM}N`oUO0^S z2yzHhR%d|prE+K zSqG<4h^ZCuU-yA2u4#29TnTWH;Aend=L|)6SOkoaLPGD6W;W5C zn&`rxMx1>0R3S|2TWe8$1JDcgN|*F6^(#dvQlifb zA+NSh;qJZannpnIi%SLp+aEetjb8;}txzlB#~S_yH_JVds{jR0-RF8EAX`c~CUaFP z3f)A}!%|WSppS+~+?8S}-yh7;o*1_RUasGN8e_Fj4 znE8pG=_%mZLf3!#fvxY~j45_w^q<4dOK*-v6t)HGurht&I>i(oQN}{U#%6VVZy%GS z-Ep@-(P$G>(ODCFOD9W}JH+HJq?L8lIuvc0LMmxhXn2RKue3iesvOz5Z~rWVl@(0< z2m(Cp3B(S>4h6yF>9BQ~fM$l%u$`xx$qM@%WI)v*+I4UU;pQ})Xt~*ho+^+f&$Z-VPdDZ^f-%Ho};9k2Y0 zC2k*|aA&IBc04`Oqdo_=|L|XYg2{o^GH_+l{cjdqd5G(!Q~oJ&aJpRSqnuT`4@Mz@AK{=2~>A+G3yDu8+k*-?LXTz#j}&=^@LPJ=D#sauRRfD znU`$7Rp#aFn=$gHFMm5ArOyxAmghH$(*B$CSGvrSKm9Df#^jNE?@m+i-%s8lwI^o5 z`&_yU%>VJr&ad7*MdnIxxmf-5$8mlwu`~Ma=Wh10z(+vxMLa{M@cSZyMH#R0+7`Tts89sJ-X(hM zpS*iKWap;7v?R~X?SJvNPkkILw{pe!U1#j$8)HI*_2P!Sa%h!ZRO6L2Yjjm@84Zu# z`}42-=^Ot0|K5DUB^0yk&ld=Gl`E!!&vEU8Yry2aGhJgG@0v`5iDOfo+&m6N|9j9*$V_!h;g7a#6r=gTUS@U1XJG0{pET1#B+ zli+(-a;^Uoql&G95a<8>s3F`au%y_jDf~~cq%@4{%|)mn+RrU`Y%F`cJ2D$y97qkd zXUQZ&yUd9BZXtfS!hR(gn^=N0OS84w$~`EvTG|j}($b7Ul?n%B>`=u* zu8z}zcU3Lno&2Xg##QfM=U^QVBosr)SeY_!50Vdj_Qs zuVy@JaUW;C?#SH~!(lJ3jIsW@c9}ZyHu|Yrr-iyu>^BdWezl!HFlgzBt(rjs%WRepkJ4Ur^s;RIB7<5TA`l@D*@jy3cah^l$*!!sOo2+*nmfrw&?h*h z==5@8W9Z=Tm&@qszBu9Ndut{^18MCqp$z$~GHXN#6~c#?PdmR=$7-}@a2{-8e2|Z> zEqeM^@%gXHE9F&u`q=iT`8lcUj>SGB0^hiySO*k?fHbf{rfCJkDrpYYGc3D@7+4C^ z3U`rb*#6PE^roCkJI?T`-G^?WU|(getcv1OqA~qqX({YS&re$Qq&37$YPe+t8L=1a zR)XeEzJnlBUcJWKwqfFqp-VE-+;pQNhakS9jXn<0TwBX!r1dF{(_MMQiX}+zLG~=y zzB?wSQSEStc;wYx&=($zPWy!MA2?If*}I(mG%ErypJC4;=Bj)bB|P&KPciS22v|Q! zKz3|;d|J&&RTJL$E{Viye7HccBHKJ#YGM^^?OkI`u>tvXSA4jrisi(_=7$$OB2JkM zGS`u&(6JW&jKXSMi|%3$`WXIM#XL5hGc`(VL=LCX?KEgk_K%cr3lg3fj)xfgf4bjp zjJvMV0j=93*(P2)YJhz?B7;?fhPL}3l;I%fTKyimAztzJJvVVnD-gF^ef}-1aiW}D zq9zpeNJPpYgBCi1bS^u{tgg&vS(3LtaEE+Y)=rDVQ|@hiiu!8hu|;;t4@%R;u}_hE zAglS(n|GoTl7%-yTuIYvDivn6qpLBf9Qa**{G22Nw#-O6JCd{LvU2tQ3Q_60)~{TJ zKi?6fH4t^O%Pu{Acl$IVwOiXwNi(uwO?Zm&IDTfTDQ{8Bs*eaM2_mF*p=KS#v@#dT z#?-optXJ1PJ#~2wX|l`Ymmf8&%kWH>S2Ad>Tv&u6K$yV)3PYg<#n+)Q zY|lPxavF!zE_uT&C5^A7Gnnc-U1IhH25=9Yk@>}-g4r4~m?{cBdPA%ceuqveJ^XWD zx0Pp3PBCZ0hm|0STno?Yz8=luFE`q9?StO?3$`<2F^V0p0t_84^8db5Mgx0IjEl!D z@SlbX#OeASlLm_odD#MLps}3;E&<3d9`5}gH+I0pQ}V@dB<&p%@K}P54fMirx2$^2 zDHTPQIESl6@ho5b1Ps#W**33_dM-8jw5F*$Pkr(c$m-3b!#m>DtF>7#+H7L_%E>mC z;}Rv);}HOCCFR`&3u7vIp8y>y$M)UE<~oTX_gl7kf=Cgjz9A0^03Qb6wc%>?gm7 z+^jiBapw2h7ir4YYl-N`kF;jQ&YQi{XlsDPaV0kfRp+_gEWRuh(1LaPgi`jrlafyV zm2~0zC`&oJkJYL=#wsMV9xhjA6{8bzEZ5#-tE=~E*r7f5 zvPL^@U&swh*op9W>hkdJOfq*~yTnzsHd@IKwa(Yx@1VUs`cu?CAz8&op)18b730hwey}QY_2pj z^o_Vie#X;O1#o7jTJ=0dtBfIJH+AU-8r4vVN;RlMwOV^6?8ftH1+Cb^*Pd`J`<6f^ zJFi4-HprfB$0vPDZ0A=M*S?3{KvMATQgm9a?gUKeD0*f(Lu}D&nipqxgFq6vN2;`R zaW|G`Ah0gz?f#>8|Dz-Q3pBd)ddH^ew6uLn_;)zBQCZo;x|{nh={RR@XgzaF?ssv} zA$|YekTbQvxS=Z-1AoIUWgnjx-p+sux3tW&+IIvCBI zbC|@XwwE!btN2P3At8D4f*HfZkg1E9URQmL@{$ul+&xpj&LiLTTDZfKTlWhu+mzh? z3B$i0+8`RR$phIKM`fVJjet(VTlWtZdl2-e^6 z%sB-0+`HUD0s08ym`2wzOJSHxH^a~>t*?5)6!Xf{EAeUE=%vz8#b|3tSzPjalcrkk zcC8710aV|VOO>E%gcVdH)uQAe31d5=Cy~k)y%}CE&B(tj=V!SwwRv~6-LG(=hmBS? zSF4I;K=7SmV);?~*1W-8RVwWE6(x-;JQ;9p8R!79XFbcyuH%bZ>7nKQc4PklYrSSf z@PE`AG*+vpLcHR8^SDcAPpB2FuPv!0LUvOt3n3Wquv*uD0*_c^3DMt}OL@#`f1$d#te48<_%6swNwi&NdP=>eXjD1xK4r{;za@B6tqMo8ft6sbk_p>}KfK9Jyf z&u|bHuN-3`;?LJl^_mz7sF3FItKXenLe*KFMnvy$z)aO7;8 z*BN$BdNB>lmB8Av^>o*}QYyYFPeXHvviAf;n3O^;`yYpR1|ZoXg<48y3>_Bwd~Asi zF0I^h3YXK!f{yy_3BX{4FSj;d?9-2D#gd=W#C`N*7%##EVf@U5HHVF$R=2C0==J8U zrK%wERk>qCefgN06qF(Ky4u&}&h8W>r!Cv?Q*0)m;$%+U7ICG=@_lXc)UcXDm2Lo^ zPRX6DksD5RLv6k42ro-}X?-=J#CI%Mcpup_@oDXz;EQ(GzOqk`4RCrS`%7#Z)C5(h zvM=${^=s$% z^BRv_9f{NEFd%c_={m;h?amqqUiX=56@yr|*|+2o39B3P`kiTayji|`QNLw-z<1!U|ijrcH%e;S8w>GPi8W+-Q(g-bLN+PxjRAp1Ps$KHc$t zEpM9d;=}im^M<)J28qcMs+ScScT}-7I=pw4Z!s&hA7cC`QQ7hwb^1{Zc`mWy=mELD z8@uOl8c51;6APCt*b8bF(K^@HYsgX109A>bx8FCv4n$ZUCT|fZ4tVTDaB@)VJ_y9 z89z=Q;y#sfx0QH%B6iTvNB5KUsQ(fn?QJ15ZIg>uwBC)eDdmtft&D3E2OK<>b)(2j zo5*Omq`JA`7qt~)hx%%(D>cuZyIg8QCG+>hamVZvc~uub%&ioyv3z8>jZ`ZOwp>xF z#iQ^(4xS0&QRHu&n|I)3CtmxpqBCip*IPW7OSJiIf}~a=X^D0ACms0HE=gq2>Go&< zFcUF)C1Uh$ys!g3hSuO8fXXMOVZ7&pqsix+Y73a;`cw|ya&R<1_ekq_`{L$F@(t_c z;>WS}<{V)a1@io`HAwXewk%3i{y=}3gW-s;Nu9>}WC@IYKDf+D*IrhWkEHz)l^6=})-qJ;}zrwPqaXHo^r z6S!id=9|-vD9gDQnjuncsnNnRkZ(PbW!=rsNNe9`Yjst*4DV-2k-WsCjbf>WLuz%O zrMEnp*lk{nc=D*`IlvaQ2^zXjNRdUcndcq#stPp^qm77OP8%4Xk&fi$tVCw_4>#@^ ztRX-Emun24lKQ4UnwGYdBviDSkf8MmRz4EgGUO20@~&`Jr`z=441N*XTe-acW_WO!vWi zDjmZg#+k_lHZ8=ig@~m#`?j;$=j;mF*TLkhOUd5C7Bzat$WM1-;k^c!@(u2_nFo;l zQ|if8?cci`D{;qKdY-W(^)Kf-*rM&0P9_VMEGMJ%pa$4u@QH{3J!n98Zkn2*+x@JK z`^L{C4M_Fk#hzB_?F|JUPf)HazOL4epEi~oHS!ksW0G;-OC?y# z4lmOQQd+f&(|r)jN37O`ae#aO8gzmV1Jx>^YL zb32@rr0xvc6n)#`J|Upo+^%^ztG)YCyreK+3;eDOUG+$d1$P=wgC#tq`qNVjvD$0G zHEcqTXt&9W@x+K*MKaWhO=^#qs>hw@OL&1n^kS7tI7o+cobGx+zM(!_KR^E6ODPOomN4(T{Sw};G}XpOyT5{C;V?3=&x2P zqqRTenFfbX^a&Yb_l8}w>Xxfts3n{qx0+DBU^l$FvBZ9NV#tB*<}+GWT`ma`0s2d8 zOmsJ*u}tiiGwj$qa35`sE9fi*aYLOHv#|DCxVt096FvzloFdPlC9jbEa^zwCr#jhI zCP7oF@k&FI*$u=Uu#5~%tFg=avR7S+qCLd4x9{o}23`#NxGVIVClVYN>(5wQj7>Yk zT%CegTCrBv6~Jf)v5r@j=p=P|X^3%Hw#FuCkc;*cmL|PStyim>a@RsizGf#Yn@9eT z1e`(26)h$mv~H+fc$_>)Fys$Y)QKRP+%cH{_|O>Me%bAIP&?MDtV_8{;Q7RbUKVxf z&R#ianNH%)4vP{8EK~KqUQ^dkb2RdbG0=OflM2wT;txYvG~DkQd5fG2y8S1zh|1r^ z*`{+;rw>1LD}-DuR<)q}9x6TWz^xLa`nbbeJnXp?g6evx%%`a)*}y}qQAH%~sLk~W z780@fI&>;(tu1UwP(ZE2ECPakP#iKpXC-=9woXst{~V~5hEdX4brPemDXb&)FodPZ z8d#u}*Fh%5J%Q?v&SE6FLcpbhWfXz%$@u!DY-a!t|VNa1TcWitTgj)xwSt^U!>k4SZ7--%GjZ1czX3wQyaYgV!!3c{mOB!PAm;9f?fo!k&^QH(zJj zR&+f6UV??M1a+p%!YtdNO{}jwM|Etk(%KVLH&pg zz>{Ocf+iDaCYX?xdBpr&h(>1&sa{Xb^h(bM+JxJhO&jb!>|H4Xh0J^|CIzmHlT$Ih zg~x39>VNbC(0uGMSdzbU00wOq%p0UNYsfh_#S)?`D)f)C%I=o{tz2^4tnq27HS^42 zRtv7FaRsfER6mZQ_M-<`O;*0v`La*E;FYeWue#;nkdv+0x71(q+o)Kb)}pDpF}2Sh-YDjXga2JQ*F&rn~Jes^lK z&mX;9uA9cdHZndM@gBc3P;h!|P@bRQ*dHOSn;F+NRpykNnrjrwYZ#SQPRG5DFZL#PJs!A}Mr;$6wC^O0*gsI$Xm!PY`K7169RGE%f~hFMwu4_$Jpm z?Y7vu#9EZ4ll|QLQ9lmovdb3gFik^tI2J!js4;eH^B_Me)!gqH(G#)B%w5~YuUR*C z!;uwAB4pbPvD0f@W~g6RBB_lUxlIj2(8HSfj!_!QoG-)79E|4M5$Pib2Aw!M<0vIe ztMAr}HWVgaT37TgC$w8M6wUM@-G=T-Tr@QJJs(<1Jn-FGCeFF;0C)F$ko_^YM#(?E^9lNr~)0Ql-LY4@lSIVpd$^WPWw>52;#(1 zt15;j{nVNAc=75%A6*R0=$kqnK^Y~CQQ+5F{7|cz>Qp_BC#44_>K@J!iL$j5?{RW_ zbL5Ag_yM_p$(g3}%AJ}LbLo6p>dgGo9Aw_9hC}QQOrfAKP`H#ow|!J+|~g z+taQxaeXh#3utxZwB!CSmZN(Xk!}`~rh>gkHY@iwx_@~Z={IicZPC^aXLp_bT4p}_ z5_fAOcHS`Sp;WOSNAh$DhnZTu@8URr9do@&#`}*=F|Fx_n`#W{_c0`nSC$n|55TiE z*+r)`v`!Jnc~TZWD?>$5@x+z}OT)EnBn=jM;zDa_>a_@EUCbk*9MhVQuSJ)eySZO? zk<XzvTt2! zf$g%c)v=L8mrNVD0*d1-h}7n4!q=mxX!HkfF6rJ{dgAqHLjC)Ne%C1Bv>N^vhqCe< z?~u$LHfvhX#0E(QRjipJw4(xQnWoX*(NM2ESf<29V}I8rt32E3yTdKKiym1Jk`!gq<6;qLqE9p|1Kl=i(A`AqNT4)g-s{D2G+<)@54xP$g?tC6~j*+ zvm@!T?cHTLx_g&;J(Er8lu5!6odOeQx%NaJO-5>)95d9|BI7Z^K*_Fe$)D1Ub79(V z?5i^Ex@EvPd(KD31)7YlEl5&|D81(ND5lC&iZE_>%JdDSB06^FQNy>(A>IO9caLMo zd5-XD>JwCt%IN1RMdQtW=-CPo6)uH*wi<9-(8Ch;+5GUO+3?Q zEn*D}^R?b4N+dmA^qpf~^i=RYG)Z)wW>}nDmD|TA+2&Du%!w|9hziy7#b2J^C+vK? z*bftu*$x;Tje-WW0Ub@4pqobQxX!TE6swGL3)N?kvFr%W%@p}ZZWXVM(5>+z;Esx1 z2Bb~6T#`nwYoSibTCVK5chPjC1wj>Sk_Y)--M=VeSGgjRpt6|{9-rq^LoRn&$KkpDsF@#HQn#nGZv>O z)W!BDG}t7i*`;M{W~9jwFY`OJ*4qHxVbUQd zPBr5uMM3NUlZs7{xaU9))7bZpQ_yaP4uQ7w(90ZHaGENr^Rqfabk?o>4A2v{q2D)_ z+SZmCg(P}k7AUKi*L-;3Yt2=ESslXW+BS#yy51foiE;=f3x;s+Wiiatb<;ak_KOlVv^CS zz_2LGe#iHAX>TQBUi0_lswnrN-K*>i7iOo`RkhO;+DF_r7&noR&AeTY$=FBO7kk}j zzKsm5cA9U}U#cq}x%_|fCja^2{1$-jrgP5?p0eFTUCRl-%O+HOKen=tn1f3?<_Np% zsrP8;6@OUK2L`$uFQoR6lJv>Q&?RiT{x0i;SB}J%+c^YXjC%MaMt;- z=@=}}1**4ftRf^nZII$EX`bbS?03?x?pl!tX{Q_m1av+XU_3l7-DYnYVPsP$uRU;z zF>2r|&QHB(Gd;!Vp?e1(9wQba)S4nJ$R>iCAGtBoH;JBHS+rcPzH{nnZ$&_w{yI`4 z-Xk`xT4;}_TQ_7gdHv!Z!@S*jlksB5)`L9T8dHDIG$Y7z6dlx2Xs*LCGhuk4!bafv z5JAJi7q<6Un=pv>%9=CKcoaeW;SI*+I`&A#KPg zDc95sdXN+_el=k_^*iGWe<|0#C@4@3( zed$G5!Uw%k>tj_&dItTvnJD+zmzJE`$(o)Ey96UH&*^z=ey^sN^;n)z{ouJ7AuVKh zq_xvb5t-ITL^-JB+13>GCmx?)#Bza>G zN^*V1?hI@ptxaZr9{4crLxAh7;Tad!SyK-bSf;+TRuv;%B5!!Q->J)3j`T~bWcfkP zjZzC7X0wq-VvYB-xtV8tV{J+v7-N`oF0Os-gnZC8uNV!xTNJN=y;VRf@KX2O!$N(O z^t_2EEFVQ4^iwpvnDX(arujFt;k}_62in`HcHCpdSMr8Q^HGLp3m2Cjy|?l9wq0kw z2U%M_Mr~H_aCE-igRQ$nUVnN6ldaD_cT<3=i1aW-D$f99fG{ebM>?$i&Ks#VMoIQ+j=M*E(`OACBRWKvMnkT>Py5Sjir}D7h60fmCf=7f(=DCly<_mCbw}5?d#^aMHH)zXL3HrLD zO-4w1@w1N6=X$Fj$CXG+d&$qP^Oa=tuI!JFjyccr_a0Ybss7=x+ZHJKRiqGO4H5K1`^2={wLTBxA%O0Rzs8gWK(H4e< z16lN`hg_wzRiq_*>?6#5!=yfyQ!5QEItvE7-KhDrCpeddz|V>n>G-_2(crY__4p#{ zD(`nis6Ca^1tot2Yx~9*~+@ltv9pn?<2LBHV<`yqoJ+__9iEfyQpmAVzH3 zwaw{q$?AUhgyv`V>gE$sBtol~qs!I%Aotqg#ps!$0L8`+tT@0VVJ0=TW%jc_sqhdx z5qf<0o08XJvUhQ;{YiiOXNAlUpqkEYJ5agLX&!WqxH)N>_gnz%P4C{_0`9sV^9Wo@ z`lh(wV9p!4g7r+iHx)~x5a%ba6{Qj#x|HWiwZu^+Sy7&A|M@C}-RU<_o3`oEdnn;>`oTNov|`6Zw%3A@B_`*d zv#oy@jBm=*cL>4W^18|>sMbuC+vu{^o<1Qclt=2y%_GnZ^9!hbK;FoD5#=K>b45U34eKN(viVcu9GbT(xJxkXdn&^DN^;>rU281xNh{6&cqEfoCaz-%4BKu zWPXV&TYCi@?B+GO{eKc1=aZ?jM&uuPfK*rT#g>mgW`=K?u4W->AX7sFD8Y`77S8gh z;^uajC_cTa0Cro?RnoMo?PqyDkIAFxeP}as!rK#iBMucj}e|J&6D2D>25mI_Y#r^ zCVKBRH7>QGZ7^#rpBGsUBTE|x?XXsr<>-RjwOy_ggf}n+0_}C#ZULKoxwqUZ{O#Z3 zqSskk^Uh^vBwZ`p!x7x_Z9B6HYJ8r^&DASV^WE@}pC}dg_*GpxK77aKiz&ZLS*(BD z*wTy4x3s^AJ+#hOJ_YM&WIX&{yyck9XdS0xd9o<}gLOh;H=Nz$`+ffMV*GPxEFo_F z;+4+QR?F)eFkAV2xS^FpH#Dklk@@D~NNXZGhvf?^U+z-;oc1o5Oq5!CW1?O=!$87& z#X%U{LS~evqY%%z$>&x|q44Wj32^bSH)0#&gxZ;Iw7sCD82e;xIB0m^i{J_D5UTg`=p8hB*rETA zva0}!Biq`6K(GM82^!oXKyY_=g1d%bgS!WUy9Rd%u7f+lAp{s?aCdk8XWx6f$v)ZI zzp9J6O?S`0J@=mPJK7yib2e~^@ewKY&huSO=6P1)dPoN^h>K!a#XHY6ZO*K#8UBzhG*-@{_#Igc*g9JUwVtt}1UBZ3?0&$X2|+4q8o(sFfvK(}C@Utq&v)w@BQ z+$A4IyLT-=DncTUR_!cUvgzfHwJK0NRKJpx(WDNOTwTF`ON#%Wr~h*wdO%ktc86y2 z7MR@8ToBF{R{y3xFqJyo5%&nP|&v`wpUN^1o zS?qy!)_L(-=mEIge|MJF%{?H(*t$MHj=WH+w4~d+Fk$bY-We>OuZ2QmyD~GL=t)~V zV@|9ga)=g~RF*ic<%IN=s1^h!a0#DbsahgwqbK8<^_zYD_mA?r1&I^m!( z`}F(6$%0BsXeiwAQh<6DJsz@Fq-?#onc4weJ#K8IbdH)EW2a9VSrEN@_}SCJ!N&o zTZDRUXw#PJF24ZO;EZbJ+C<_1h&TclxIPpPY7Lc7IP(~t++bE0%;~hK-#88~#P5aC zI^`%4W!4>QQO~R~DRyXC^66#z*P((v+F9hOuP2#d8L0P%1q}S=XR`S~Iv|?Ny7?Eh z1#^#jmg{Azqr#XwXXf)&0ig2KnjltlyE%Ig{Hq4!Mt-E7!An$5D5Kw*9mI!&9y zM$j0fq5(8%`=V zn&hClp8SP5Zcc}S z^4(UuCUH#+$6p#9_3}&F4CNXpHfllQ98cebON0` z=t}Fp>1o#1FZ-oj@>}+SJ)?+g0oGw`H={`vp4Irt!G!lZZL!M0*5RYlGgnON)Y9-k zx3l6rUCY20?@arUWX}~Nr=v%W6-^{7lPCu~*fQOupKfIXXe1NXL)JnL-1rK>IUP9B(7xY}=$WT`>BwJSv5dl_sS zS3pwwH$k4A(TL`wDowc~c#svC%&iNX@ZLs|ZM;^0T!Ev}a5@%}ISTQ)^6tbp3Am~Z zn!Rt@q`=}o4>m!rPjM)myc*ZGk#xDHZ^3p(X3{VGVf!e!0@1~LXLR7jud+iV6j#r5)y4l?Qiwl&s3kQR@RfqNNPXOAvDuP2*t zP%De$opE{Hro4JVjmSy2$3mix=0sUVk?QHqIue#9#vz3%gD8PJpdMo4EIhMcbqqr< zRG@z6Re&f>XZ3^*o?~sBX3(p|Zn9|AD8RTJC$xaFN!L7S_NSUf$lxJ^#DBHe$K(Al%)S-JJ z1qZ}N((*Rf2-9p3(7|U^8vO?7`X>LD!=R5Vq8--xVZo!GR#DI5pi$3_z6Iai;9H03 zFXb7KVh~RK`&cV}@~zn)mScSo)8l`n2LIcIX>44&;Jx-sLo2a=<4%yge-n4^gk8?u z^L&Th$9rQezS@4_uc%u1HrZ^;YTDbES+JUXHmv0n&l0Pmt>*A-(tRyITy`Tex$EdP zr)?J=Q5zrB#Jp#serM^&HmKIHGShaDO8*vYBeWRZ6~$lH)u%RKdr*6$QBjS3Jd!4m z@92=rVy=x@d z&!FZ9kY=ph1t=@E@_VU7=WJLR2}fBDMf&dDD#pzaW#d|s_lB6$)oZph71PaeiVi6Pa!~GZq z3Pv0!IGxi-u0bEtzq=Wd0by}EXFG&Lt+*22h|SWF_gk?16S91X5%fLfQ)`iqQ37ie}FPZTEOO2%uTL) zGNaenV@fCYxgqC>-m5(VVVd*e!LJGt~o-ma$IH3&qZo8dfG% z_mp~350nPAjML3DGhy|NvBTxGa;;ao6^e>%0)<(Lly|R%DDYgGk?R(_Nab$#GBr3s zg_CGV(;%Om9vos#i!yas`429mWHv?9#w2sYJ8RO1cd9fvZ+5oA3`2F#KW+#)!9*-q87R57Yj^(Q$>R ztZFu<0dr&nDIRubgCzetr1$qdcsw@Qf%d1HT!{79$8>V-maJr|7X+o>1FgHu zqj*-F%({i!P??3Lov^KiXt5992#_}Q0MDBSE>)zHr3{_QG$9FrxQR>9O}}Vl43RhH zV|heT5_&#}mclk_2cSd+WS@G}8;lDeY{HlJd?y>IvsyFj=r`iqdoMqxSsz3aJy4W@hO52@WprBOc)qZ}6!6XKYI;vLr zF{pY0XUaHxzT-J7rrWJs;3jXWqh;I=>po2IO-3oBI+o9g%c=H+)ol+6K5I5K(k{1L z4N>N_mW_7&#~B;s25@}qjLk@GN(=_UhVJDLu6zZego->_e*8XjD6RZBY4&{pzDs(R zO((=nm)t68htFZ$sR?NZv46yrYQoTn6$)nKiD=B$WKWV(+4$s%(TCb#xX`gq%b^-` zu=l>4iM9eZB)!9W^vf6`Z6RILma!#beSvN>duCuYnk8G=xBcYqi+A`4gaho+aeVg3(MhKep>z;Xp*uLDl=?wcXo$vJE4z za^3x&nACLsyPy82`CRBrc&o7}y^Wl7n>QRua!a}DUA2Fcyfy!p=XSXC-0a^-2>{ve zdcb(MCUH*Vj0Ct)=elByEq+1w&bVILTq}*B%pc^qcXC(8Tt9}qa=GuR->Xns_o`eX zBo`U`yBuBhtr(V;1_#)!6M5>==Ao4hY=(6+mr2)PP|QMws{Nr(oyaoE>5I8>s!y)! z+SXv-T{&|zJ~+M#V7=e!qtbMfh5rSauUgm-j#zp3I}y*35-bjX`Re;6MUX$b7x<~D zHqh!}BLNb?ELnxnZ;OLgNZ6pEPh5@9cDh#I0%_6Ax-GG?(rh{9ur80@m!LFbBZ!eS zig!nzvB~x`dZBjw&c5R4T!7}!1$5}+*V1yx3Sa~o<5;2&kBYGV>X!L8ZuY0s4hBdo zgH{T56yZPfv}+a^dk|^rA#cTbK8jfTwdlssbOXtR;0-2+>TYenJCZ+brJ5#{YAUCV zpDq#*TQ-xzM>7yVNABGeBqM?%@FQ{k6?6VrebLn#FhPMrwRY8#*3&a0&@h!g3rwP5 z7{faoz6xe<%3cJ!MabiFHXf<;3b@NOX2_f-y#H$Oh;GW1@xJ~bIW+U30M?(_L8R;C zRw3adR~|IdOc0S)>2VGfCb+$O&R%=t0*4xsfC+KNs^)A|>biVu^|o#3>8i(JD8-L% zZy45rru8tYbH{g~Glp8y*c*R*dv?%5{q5)fi|Ye@leP|IpI@{Y$Ph|rDm47NjgT6g ze(M;s2NWm&Luo=*EFQw22|e>Wp?xLbrc<>RvCGq;vuoga9xF283E-E z;hq6wfhI&9vzDzj}wru$ze2+i|!)BNRtw)W90*_h64T z&;5|UM_+X)084Oo0iDb5aYp{|=P%nJ9f#WkkDhHwJoEDqdNTpeuZ2~e(8a;Bp2`1V zI@Z5;LG#WyQh93*^6Heh_8jF!fX0o9aPb7y77t@k}0w(ter6a@9Bxg)CEBvS$b z3PrYel%~y0#ReeggtqHxY;S9&&904C<=N!UEhYPbmrQ|FPL7$e9{jtgYe9$yErkid z#)PLEpX!@Ly2oC%`~y5?swnDr4sD_8Hqlg=YF&6fe0oukv_s{i!>D*}hc0WlORNMf zq+^o}Rw+w?Fxs~kp)DY9wZ}*(KFeU3QQe@}Y+(KcEEUADjZtF4?M?^v)02Ny%2O&!v475re z<-HcqoQ3=4!J63<_m;-_W9iPR054b)So%*o)!PV8KJ~rU<8oTZoa3N-Q$5P9pZoP1 z9rkpz@kjCFoATD-K89#Z{SRxNZpeRI^{w@0?1jX?XnqIbD}Zpr zXbr+YJ_*uY1yVszH@;W^N$$9fWT!seRyoytTW6r007)D)u~4xLN#8<=-D%1i?seD& zx2yDm(D$maXCT%nB9r$I>#>nE3#Hq2C7>?I+;B1U=*fJ;gFC@mfTn_N+?`i1KXU*5 zTvrpY+LeA^>-c*vt=1DUW~s**#~)GWT*i*W?64 zd{bpo2feV=^lJ46(rVDfuY8yL`@1KH!$#CribZHhr`pPAC$Gd|(2VhUIO~nm;^P{W zQ>sgbrV>h}Dh?gXh8@)?Scj(44~usOc6TOshiRwdJ?1pmzKS*KSnwDVtl@%Z;K{;W z|Iv&Q1|9ypc2ktYQHl@o0-Y`b8h#(U!4uEkZbl#f*8W2}ow>eU+k?uy+`J;sdrcTo z!QYSN%QpsgFD)o5nByj98es{pdJXLMxBS4WKX3Wt-oG5!BzJ5H_aGk|uEoZpuWxKf z^|!7>1*5!zLi^`&%?XbSH61QCoZJ&G_peX)=JET`$0a(JE_(Fn664cH%PCtH5zpWY z_DC?)gMvW}y{5f1{Xvak1c$mWeE0(ormIm>dGKOYN&2hhy{cvsLZwIqkKx9;@nGE3 zi>%=2yfh@ln#6;S;EEG6xTlRq4MFRF8fV>>U(a97^$21D0$ETs{ zI$Z9SJ_Q(dd+Xqk4tZ~q@GL8d(^vfi0faAeikvZCL>>l!yCw>j} zg%AGyn8$?95U_cNZsW@o_9SEHIox?9XA!|j3r+gw|8W3$pthWkIwEQXI5==N^GW~X zQNSL0upVa^wb(2t>>Txkt&GnRjeZYuqO8dIu$}&!{xSnRMwPZ4YpNB!Cc(y9q#Kt- zncj}>H~HWkx%SVm;tbacqu#g|Wt>_=t7P1wO?(UhObh_cxZH}7$GvR5Kj3cSe)hMj zG@>CW*qe)gO`mGieKP?_`G~Jki=N#@17F%qi~tHdSdyx$e+n2ItF2zq1xGjUFweNb zO0rbpV19C#rMi-)3c_3L_@1%!b_yi)Va0x(#SLfv*~9c`z`upkZiZ^PxYb8GdE<3{dCIW2T7b>`~r9xSd_AecySYg8Z-b_S>rS z0|yBX=B>Qolt-4}eB2Otu4y1_6T4=9RlgH}_y3)9bia-$@AKS3+x!>B48MviQvo_4 zAi%p%?qg*rd~nBB^0RzDk~q+uJ?_F$OK3|k-K%V!FRZrA8XqJc(yEuG#yGz%!K?}6*uPl!U4Gfi8OcQj4ToN&c@ zl--B!y*Tay!3}bDY%Puqq%JnX!2>Nq5HXCCTXAbWlFM?Hm@zeiVovm(LtL61tU^932ziNhD~On=3uY` zwL)_-U*;61GK-$DtEzQAP{hmMr9^rVaSd5M+%itsKr$=^$Gd&DhiVv2)tHFRHdM%p zP}*e>I0j5A_Ywr32^Gw3E}pTiFFYZCN)g3>`PtAZT_R`2sb}#Tu7LrLrU&KDypp;d zZ1-FKjg?2eo@VZtWwt-5ZGL>17FPm@K3wCRMhslBv&W_rg_=fua{>O=mKkwag7aUd z?=RHQamcIa;JXlmDJil4Kmwb1-#+qL(JNE(avf2Z=dg2ZF3i0{;th^O=;_wadS~9| zYrn-}_g#Z2g*ScV%gR|!v5dS0OW%CXP$lJt=3+n35t02C@tol7i-`^^ z%DP#^9shHnhVCEG_;&`^=Lj$m?L*A>u%oZ0j`=&P{zJ>1z`THidqAe3pjdPHAP4zg z2N8p!3)~k`#65y%-jsEEf_4Y3!Lpgig|2w9vY~ZpZ+m~R2q3e&-%{T?TZ=4y<>rK? z2+rhs!IKuyGNpe$VS*tGBFq`)+3>k$b!|)8 z{u#kY?MjS1V!r9gI*(ERna<$L0!1_U;WE+6O(GcV_uXcs6)}DzY|v*%$EKerr(}+A z%SqiXalu5V`OAXis+?zs#~79>d`S|cYy)GeR}>nC86_q!uG}S&&pSxMj$_~B&b1Oc z>p?-^h{4-yr-cBC#B>WkAWn>u2B%xDvf@b*RJO6{n%hhi{?UrQud!u&A)^mSb=K5RFNaKfv8RD;)Nd3 znP`v%UmiWODaRXEsQV)&lGd8Ab-O&5oNN!hE?1A{_4!!aln{q%KFj@y_!`@v z?nt-;zJao_J{N1CwW6Cxf+ShEyygRJ*OfQ&LkQa~&y%&^85t>DGhu%>Uxh3vtGn~rq%z)B1PTKP1QHO?>V zxZwgJnc0z+eK=??-VQF=H}&0F)7#e^o3=8&6BtADx4KG5|EYKWilr_ml4Oa_Yj3I* zk^Ec$$2XLaUyfZps2_$9ol#+H=rtGpcT*SDgL zYZ4eO>#gMmb_b(23=iU!rbRT(_!|jg17k}Y5kv`~ zC9_49F!uQy-Zs_?K&nxja?Sp~yF^+rq#={mMpyP19r;frS?)#$G*`T_+dxNeRUe-| zCkj2B-=_KzWHu_EX2dX}Av%cDDG7U0ABW35?>ld5Y518`)~9tjEI%f2=zemVd_wm# zYD-uSgU=^b1sp;hNsQbnyXm*qFd%W zF(_YYwm=vA@gvrI!7G+JB~RkF176q_JjeH&CSTF&e2dP@ZnCa3*nl$Oi1fAHcd^x$ zY}^6XE-p`IT=P$ea!IkA0;3}@YODzrsY0_}oou3mMB{Ucow}xg^$YR+Wn2AEaPh+@ z>(F@f-=1o4g9)PPM@i@Mdo1AV&#v9YloLv(si28lH`N3rQ(M9Y04kJ02i&Mh7L_rf zv<03N%#Pc9>t#(yB+}I<4h*>b*NpaRHomO6FN0A?*xoINeXV*p(g6lgzGtsqA%PJl z$(z$&t|$1U;zZSj>WS2e<%FpnX2`5G9b9)9iL=b!5*aTAc@v(ILsG}=^?Ocz&re+k zMlgpaIgI+^?rg#p&1V$82W(QconM;K?z@r6izf+}%Z*lM1X_Yh2(H2& z32$ob+up_N-m~CgT~UF6ts*_7A-nG`Sw!9Dkz~?#>VFmo`7(uWWQ+;{-D##br`|cI zVK|K$uf?~3jMTTbv_G}ZU$ClQE^$Z-k{%*Mt2q6KB>keId}D;yWSyKm7>$obF%Z>l zvtHqg1Lo*^n9{ETCjhg;0%+{chvYA4_OK^yazsBGa;Asl7pQ*xqyjvnU*d`Od76$f zIW0NDb(QKtY2PaBk_UYZD{kV_zen_MbUSvHUG3ljB$frJG=_#W8)n^+#@?miHa{GB zR)G{ShM2?+Zrowo&*1=SR=&FLu+`DC|IDRo|uz|=aNw~+}`Hz(Tcj+G0%SO@X7$uCf2C3H~w8=R`dlyeCblS z-==s0x_%D;O!LI1iC+t0K7a7iWo)Ww6TI@*4ECqkLkQ4m(G+gC@VxSh#*iuk$++Z_ z|1_C@k;8wH8$bkxe0A{^<9qD?*jm>O@X3xK>NwMg`&pn5&gqc`EEN@MOf{_JrOpY3 zQlTqtIuvS1SiH#%;yRWxC4+GF6>V&Q7d(^J3!VyJ=tH-c2VNL~`fmPG4c%?$yU6FH zP8X!;g;Da0CWwe!`kuHbV{jQvCy%M{^cpWd$CjdbEkIx!=#8(8BOjHwG^+WmF!JK) zh7V>_y~;~S!%JOU57WRJu?1Aig-O6WBirNjb-H`@7)wL;TrPvR=iP!2%nXr(PPCH< zK5<}g?SgB!v;HbFCMHSYq0p079NSFm_t2h@0LcrdZulM=={B*bd)9*Gwuto&B=1AKaVFnbHTQFgFr`pa^5__;Hdf@@b1)Mb&w+O z$Bso@oe21+bOd2+;z5nlw z`FWv28i+FzKK_AA)x7kXzxM+8gOGWK!@P(*kcdaOS!IdSpb5gJ*WU|k5E=v?PRU>b zp2LsPU*&(FxnadM`)GOigRL@UliLx1a|)Udc)KyvGrJZ{G>c@e#?=+3-etUM8Ol;F z6&0aeo?fegZ4dH7=%isaP?48ZY(hSEjSI3Q*NOArQ?jYEM|xG%gwc8DL+m$o)sexC zR4>!%clQe4zwXn4rW+1D<(&JLLZ?wza;pJXsU%90N@d}2Uc((540c73)P$3Ztw^;6 z&Q&9PysSjOWNZGJ+k-lqBxUjk9;6tyEBQH-8fI^+%uJMh7im-Yh8viQ-xg3_B$H_9g?x{liem#OS**!5)2w`>rq!0cKM?JFSQ6du zffxlA5uyZ|)#w9$M0v@$1k_)k<7$7S2q%#>ZCM0{Un(6nM2J(a*V_Rguo)EQ#9S3Q%VZ$3nHQtAP{C~A zyWL=J75XP!F4u?{7#MUM$=cbSm=~(-hCoD|2O}#y-7zlyvi6;!uT=SXeOz+&$`Cxn zSJU$e)YHoznj`dSi!q0jJ9vAb8~4L}NK1m#`3w4 qX1E{kE`_irF3YmlFdzECF) zu{dDVQ^1LRYWhH?Xgp}?E!Nc>vvH9SEBTf7oi_;R1^>fJppYpdVc5)SPi$3@(Zeb7c3y*xZZ{8WB5Kx}a-7^}`C2hA2 zzpKR!G-uhe2kgqetS9j2;q-mht3$ROL@iMAhaUSUOHU3?CD=aeD}U@^z_c&xDy}>Y zGjK>tXE8Po%QXP8Vk{N?`oLd}JIo_|-M%UQLtx%G(0qJ+k1IDfY4^M(oSH+KV`8D7 zm+4QgOU<4aUJpm?kY?7X-B82ie7K)Fb9EyY^OXwiCNJG#O)?rH=Ku5qe9t<0x;u7! zpJCIU#1{4RO8YBm-1+sUb*!Vu+05SHPl!FX!@$k+#WyD{2ZJLZ#5X(d5bE|Bdl#l9 z22Khm#O^TSs1R!trxoVA6CKGwJT)Qb!mHiQ_bx>>6^{9=8J5B~02J9On+o6Ru0j zP7?(y^~T~B)n`BGQkPBY4sy+?;`&8X#3l?e{c;cU$m`hhRx3V5Ehx)PUJVtqH%?ze z(5-MmEj-l1!(y>CO#=nGWC+bFW+dlD`VX24h#`Gw zV?2(GkByWDYm=gzH;G;<^U7CS4;0tDN@<6w0-2G|Y12@vzbUkb4f=A`8QjbjrmozU z9tIQlDej>aUU?K5Tj2tBoko8qT&AMkw?%A-dEOh3JwKx5CjRrEpwh+-degQ@Z*W5+ z@kk_q?0{_txVe-va?L*AL;tq8;TA4H`%&+!-%knrCG#PU=rB19S)Y&krYr$55W}ee zsU-nxJPx^*))fK)g-_iI$LH-20^fNZb0wBuT&R?}ZZ7dhWZr)+JV|P|YD32J++(Y{ z34HR@MVPJy5#k9-?2(*{+P2m&0m80dsuhq&*Tv3W)3o2AMx?;?{ism5Ic-Y&#Vy=H zCHiLEArSiTeGm6ow#St-Bq|imYKzt->&u2vO$mk5`6{goJue7u*;!X`e;(n}5caiE z8BUZEOIe<1htl2#Ns zt|$Z$rj1TZrd?nM){eS09Wpa&T3TTk|ELR3@`ux#e{Q!E08lGrHa5?dK&iN8-%i z!9nTmp2*haA?V*lWQeJJSSyv#e&HF{`{=kfd_mb0Be?uve`*YMbMG7NwwHKcG=pGz z73OL&9q4G*QEvRVk3%Te=c|I8=nIyJL`(*xVayX9Zr%i4wgy75dPO!fKvkW`bP~$f z-=ehm5zm($b|%d!6%F7onZX@y;(;U#F_kd%kqnjI$q~51b=F;}yg=z@#7FaLtx5a7 zE!`)1Yb~+Iwsrp&zh#tFc9aj*lKD=t3KMj04^vRp+IOWCTxrOf0?L-$YkPW;c7fW- zzEexbLE0(0^!*lY1yey}H0U$)JxHK4iLROvPoN1-h!U=_-1%mpF1s-Sp2d&Dc^BRa zaWwmfTR%nfO72~>N-GK^ZWr+Qa0JjtfZaBNnYn`aR2wj3G1M7JhT|6}8{??;9GJNE zZ+#46!=5+ZJkqs)bOI&R{fR4-pdF*z$#=vWt(J9H)qIXevsD>zWaU~}F%aODKVR)# zJ3fW>mOLy2!PQCpVqpDYY5lhWKQi=dTQRA|%oox2#Hu5ckOv&eA> z8NOrt{`p=`U*uGoJ3VorLMhJClKfMtQJ{lo$`tbe5Jzi;2)ri)bRK`FAq#s(F_YlZ zImjTGm4?pv`Bo1OcJ9hMVlqSD;U-tAAy*E7aU9tOXFEM~EPr^lq31y}VK-|Vw$tu> z;s`^%+k{+k_Jamy5W7pWk^rF`j2SD(4KP`FZZ&;<^rj%Qsv9|;CWuC7MpExU6smV~ zyUI&4wnna)v$_FK>XbdC(T^0d!7VUYpECdk!$n0Tp#MU}?m4C%k0;l(A#FvRzHb-S z-uGr(svCXr{U2^mY;(j6zY>9Ydzhj{|0V_s9eIKs0E!yr(ii4hfuMEHA8YHZi4scF zEe%Y)%wHj~&3|L*|Krp%1EJsehIh&9LKns+tz%2A$9mq4TCN1f$x^zepX-s8mJHwi zxa{JMCM#^x6%&MS_W}1#=$Ag=@CRY^QLTXRag1m~Gt{ zg1#(@+#BI5BZR(=XX|pH-8Lpi%gw@P)ED*R_yi5Ap?Q0??=_^r6v13_~n6gD6!-oR(@L_mM3 z^kjLtXpaEtUY{b4*4S0YMn>qMJ*NoahneY5qeMyeZ}o3Xi}X~w$w8&YsF2>B)HsFPG0RGi3IrWC=|OUQDv3337@y1YQdx*=Lh__A3lyK&Hb@Fr`L1Xzz3 zh|cyNE7;SQWpQfo(-NySZg8Xyy^j{`S9P^)@_4Vu^Xjpbhn3yhK&<}8$?)_$?N=13 z7A~a{3j~W3VdE!hqa3+)3jlki;m?zWKEWQ#`V6U=v&-wbIbY4@x4J9(K#sNbB!e!6 zSp*vsl-}I^1qlPXR*ojz0%5%v-cD=J@9!h$r~75kG}9}6)=As%6ucj`qlih75Ha~b zvZU(wdi0F#!KHa+)x3_7DH9^$=?toB4 z!$5yYon0g0Juyn~w^!8kK^!xvJQe*((ijJYXgqfCLWsH9utU*CTGoT)UP_d~2R>(C zP=c__m#-RAAZ9n3&l57I4Cp$fhEj*U@knmQR7PBMW~at*1E*(a~1@GdDNc-Vsd~ffsJ!9*r2I zwX^VJ+TkDen91kl$u#Ppaovw{&$o;zbg&JH_e8kp8ho@70250TnQ-UGcBWT9$~4qI zz^*fCMP}#Fcq`lKnO->EjvFoK(j+Fg#*SBU}$O zE{oFtKRn4~FyY~Q3Rd)Qe)|RhAvE#&sP0xJ*u!DE?S*4ub7fBXH~r9zIlrxXcHN1p zZ%G*7k2>L@5pJXf?F0wc#gx$cqER7 zwnMHa1Kug!ZO6HOaN61492Tx^g&KQWV6TlBoJip_xepEa@;S37v2G(J;?aZMe0@{J zl_NFvFyg?<7oPP`$R=(^@!`O!mfDYgaDkDUZ(@o zyF1I2G03|_h`Xi1i%BQ>(H`)Y(O*;`9~=rDB_JCOLPB3~)Q5g`X{HSkh{f_0^iRW2#EWZM0}rF;MEi|KDh zv;)mD%LwRcEwy(x0DTfoML#~_o4hhKN_ie;T{9Ea5z5_w zT+!VG_=3=g1z!#hzaC9mJ~Y2mw-t5WcyU>SIv{h9TPSzw*hxWV{)i4^me`HIE3u-U zA^K$L6D4?}`iV}RGzkINYI{%Szi!hrzew2rN#+J*idSVE8;nEW`zEE2ntMy}TJBs= ztjLkkp6;+?>UR278W(bSp?IDYc2dq-JJC-{I$7#+QI7` z(Av;S(`$j6pd{&eEkZ@hpLqMtB3*^k7UJ{L7uGF*I%i9OR+(Q!K{MPh3^o{*xL zA2!u;vF=sy;Ue`mtBLp4gIs6^+2?gH`Y`@s6#6dn{+ z24W_nsEV6$5Vy0yXLBmahBb{d7elcA6AW03a-K}DeC23hl{-_tgokY9i*pKG$2aLd zo-sYL6*JZz0FU>@u#fl|jGD3e_t;;-t4y#XZ-Fv-m|M27-erl$^HCsVbTy9j0l1N)!TmJC#5`~~1TUZ2{che(vNlsV+@k6RAK0U5bzsuPC zwYCu=;N#KWF=NK_8QW%0=H7V%PD}QxkyJI;la0z^0Bhz79j!A#kl5imqi)y=*^x?HWwg&0-?n+I&Atv& zoZ}h*QQ8%?fQ6D10g|lQg&BfeLJE>OCA#EbGdNyHd#UpEa$@S6dp5;SyX%#owx2JS zVCWh*t5ihNDsePdqQh5C8o4R08P+Z6c{57ilRkUB(Bg(2DN@_O`V!<{f$*=!fUs9@ zS<3%mgUE`%kwH$gx-tjg$I0POtm+MX;U1QBVODO0Rd~9=9xPgLJt-6riDQx!KmLB@ zMu)+p~H3!bHO5BsY-F)HZ3c1 z1)}*fc}8Rz_unYvV;jNKq1I28SP=(g)?DX|E9^_dea;7rg`@->a!URVZZh=3WDjI_lR_>Z_9O zWd#Pt&TF$iLwOfuKJ@M-^AeXL@i^B_30_1+MqS$tnIw z(XH0h2i-rLy;?9Y6y6yY)xEYWs6ZhAM-}4}3%$YgBMf=-u7qHGqK|rr1sP}sH35Kw zq=zDQkKDxo2PJFRa}KDKDo^Mt=V9*G)un;GmWPlW!f#395t6UGo<=O!6$`D<2r`x; zk;&{%!kKF3WVB7ZXl!hly8Q2^Fi^asqT4q4jn=T`@iR2J5HeMZ;MRooCX{2go1i z&XS-Dzs$7Vt+ZY3#W-UpzUjZ{A)=hHpEU~wxc0tydF1VM?x@EbIy2r1uRh($NG*Q4 zOM%IM4pV-=n$Y^}107Ufz)OZ7*2o@Mx($Y^N6| zCAv90=NxnOLb2A2%zeoRsWINeINtrUtBy@??6{@npLoY6`djGoOvhBml@vP*`T?iM zRB;=q4!fFk^_LY_)dGnZoT?YAY4Ma>Km|!R2amjxU%i!oVc+V&TN>b9Lgj_|2%&Nv2*NJ8A=8CfA`(JnS^8ES#Y5}($< zCbOmn2F_>-4>NY-qSqG(WavdTCJeN4nJj_UZ>z24KhQr+OBJ@8k?U;k7;TkAq>w3I zL>#R#>vyrG5~wcRkrU~sSOkVGc=W-Hj0ZF*9uP|)OrL`zuluZNhkPS)F7AUv5owFl ztzRp#`b$ipmvbeF2ssa7L`72GYKahTXT7x=6g-QprMCvr9#5iX5X1mRJ{G~?w_iNx zt?enQAj^NO8}=Hrl4{2REyMX-lCv|Gh>D59Qp`Kzd&wK4lA09ok;}|Mz#=Q+IBx5Meq!3?y$!jXf+F?`umedlZ7bI$qFNnsbG5D$6gy2Nx zK+@6aTB@8qfATu827avhISHjJ1!FC#y9pB_@pU58x|Uy7=KU8=Cm0%O*x6qAvX}&? z`$r7HoJOclNLb!X`u`~V>!7&arE3^YfS|z%Zo%DMLvR>8*x)XMySoH;w*)7+ySoP+ z+$BJ8_jk^7*Y_QM&t31S^Zs`gwX3G6eeLemt5>fscdf^irrJ;M)8vmIg;^4Rr(!0U zz%^h;EgC=C5oA z@v?>T(1NsDVdN+UTk8+_n-=C?UEM*ED+*{A^6L{%=(XUPM5++|NiC>|gVy&A%ig|l zlsi07fOY5Oia<{{ZYZnJ-kVf)7wJqYqy|dqxOtgoEG9&VTv|AH@aA*(&hlC2hmH+_ zCf_1v-hHOZ{*BFBw0&c{$Hb^ZBp%d@j$7$gpoJ?J$|nZb?+rzJYY z$KX$xH$LB03x7w;ASZDaCEvLryq)LJfQ9%Xh6 zU{(|6QQKc?Cozzn{LB#}6r<}OdP39+&NItUi;$GOZ(3;e-4Wt5Q5~9%&bAyVxO$%@ zH=ajkwMRHb9+me9C*K%(!mOv}yqdo@R?U607Cb(&UN3pE0WB1%q|pM;_^8XdWWtBqI1h0a>Ykc;WhJ5bQ%Sp0LkTkV8^g9BXt_S->IBE(90kt7_O3*n}JHUVAl>` zuN%}!`0mH`5Z9MPUqILFt}~RXYmc?pOiBt-;p`HYLjrY%1W!$l&x6gLm@z(oRf0vE zv|FNu%1(;VimCL_{O9By z$#2uOc)edIK>jdAw?N!6QTopMRIybMebct;42a%Nc8hPnLc1P|hv(wQeZ4cRVkiHq zXb~a^vZ(7uVzMzZyiNb`MELpnGH?3WTojI9qF|#8jDKKS^HP02T2^vGNMZL^|2q?)3Pv8A3-2%Y$tA#a^>TQ^N4iwj zad8#kY=0t9preC8OOF+U4Kpsyt1XST*P5FB>kYi@Yd#4$=C3i3>I--*{i*LhiQX+H zy-S-#XyFslJ)m#-cbxQvGxlb58o341_M{!r%TL5_GvjXixg|vXCy*s+Rs2P`uHRJe zb0YeY(sZ?xr;-PAlt6F4ibh!f?&MxbmvG{0cJz;}<(J_H=I-I)yeB+kwj-~PS>^$f z2Bd@E8xo-naVAJ7U{=rme(1nL<9GZRH)0w7!x0U{8?;G|O!KlN910$n22MMG>f*L6 zsqX1lkGrmvVQ3eZ5>Zxeuz5hIx854Jc7h3Zrv+o9`HK$GgGRkRQ03COA3n@Y^#o~U z3@!kWFOL*|*VF8!2Labn3NtM+oa6`?5{N+*dbPbKh?-#c@FuI*IyRIm+EbV8K+dE< z^MruW4&Yw@k!R^?h^qy2%mUAxCRG>!a((Yxz^1GoBTHw?E$P?rb;_; z*zNik7NN*@2`-`$YGgi!oqCf`Qo1KW%P!Ne9R#UrGR-cy;(9O8SDoiE@QQztnRSC# zc0p5*8NOoQZ$CpA>?sIGvh4hxsbHD3GON~(Ho@UmIZ`MPqKTnO%iXib{`K^0?$7EY zw!;WEY4;cH!6v0~23f9rPUZ24Lwq6t4*llh0Y|h~(iQhzf`4R6C zTQa-)FL~^d#I=|Is{DWWa1y^r4AcWyLLs@VYd^i~I&pZC6blFWl%wELHM%7UY`nOJ z?R|1eh^|I$ZRIJLK0RrdVpE(!S}E_*S5H9gMYa?jLpiCd<$l4q=Dad841(Uh^?}oZ z&F&t3Wb=;5jf(%}e!L+vk%(yJvBNs6YV4#}_$Ru7J!JDpcT)Uyf&CcV`ZUrf8O-OVQP9hs)aU-r*DH%MTX|{w{pTSL1Y!fN;ZW*TARzT zLV@G>vNn`-zJrPTahN906#G-e{W=siE>T)ek8!Jce(tgKXs~m^PGR~r8_Q#Bpmidv zhnZJ&=+|A_s!o3Iz)(-SGW*6?w(E%f%Kqd^!JzOSP|Q5`KTwk_sY^*#?jB`RySn}L zsx|y%Y9K^4l7>P0Wo=J3v@KLrt2%wc=+{CiD;j&Z07pKNzLf^DClUyi?kw2dcx?1hwpm8l z*un!=(Yl$%K#d-{mD2(`viKc*qGQT2vL^|R#Nfl5=K&)vDjkTYcRp_WW3GO2TUijv zy?GN8|dEcBkXQGBx+BEsF7Nqlt zeNA8VeG9(G+&R6zvoy><)?RRGJON*X#P!1`lhK9#dE<%SXS{C6R%6TiC3M=Q8CHxV zm_8wxjrDPw5+agsvC*a^jNP%%QQW(N5fYYG+ED(y5kkF1Q2+aE2f`eJZ!S!Vcd$M7 zESL}Lq89IZ$(Q#U&1$?w!{?I(e%chB+HJI$bPO-j!?C_Ri3?H{k`Al~31xrFjn^!o zXi3k`05=2W!L;(`7v7YzYNUAtjae4r;IOpRx-wHU2B_xFM0$^ zAregSdd*jP!(gz5k*Q;Y7eC#usf+1a7NN0+@rUW*#;Nzlw8&}cBrmJk(TuMbl6NTz z-k6}22}#|FJI_@|Q)ZuY-xsXwETfzdR9}a=x1(-%`S7XV$~7bjLVz(pUxyw$BDHEZ zMPP@P;P+Bk?CAmMw{6U@J50J^SeT~U`wHYT(i}DB!LM+?7D{@DN`Qg-!+@p~el)>0 zf)B2?BEN1C1rkoghe_w_9@CkEPBoxI9cXC4>9fq_CqPz|)HE9qM6Z!u8#{{bwy)5` z{|)t&ZFi~6P-Uy0CKou9Y6ybSAwH@n{>jifDLeuEnwApQ=#!2+18Q%tg!c47*5;90 z^xq1kh4$`R)9d9QP6Rg0&}F};@5EgbFI-J;csyv5*5l$TX}zfdE{q;YM9aMbh}J$- zMg8GHjtJ|Lh(>16kuAB!Xp4q8I0aS}jThp2O)0P-De$aOd>o89zTDNO$GtMy>j};7 zVtKT}aAZWp-xQaP+u9K+=^%y)m)XEdCiQ_3#YARt_$QJT%<^y>`1h$>5_X49uRw9o zX@6HWbX#Z}^KlWY+r?Q^1Lz;K4KvjGiz04#CG%2^5+l2A3xFV_v@mlX2}z!@^G%k) zU2MJgZl)M%S2+&C&)7%dZ}aVcI@>*BuqfN?IX~WfWrU1>H7P-Dk2eqqK5+e7U#bEt zpmWMqw3i-oS>E3jwv@0kI%N8!TJ}$nPsNSY&i~vaFAQP>xm*RrnZ#`Cd2i1c?P9T) z+;tD$8)7O0`UTg%A9pSZ(WN)E-5@T_3+~F!##Nj4$O)ecEuBpT8(hT%a9x9eD{7+x(j*@Z^Y!8b!H9g-M?4bi(lM_k^HTOZp!XoKuw- zNwf{wkzZ$M!l9^XNXX84_Z$3f?vF25rDjH4rGIkL zs%-zDJNn-37*IV<=W>nXeK7aE^3NX63dNM}PSf)D#?7mb!dMpVmqlI7^odS`16T0D zev3#3s^x)ZH=Nw>%+hHevPonIvxc2kI!aAhkW00LAsGXn*Sq3)JSN zIXe*e_R$_y%C%~(=b6FVK{hqXmOZIav zfg-4>T52?qaKEP`T*0dD&V*iM_qNHOwQEpN;e_8ZI~)Syw8#wg+u0}ojIUjQx-D^? z=rrD>TnN9Cnt-`Mnsi0ocfK`+e$QpPc0cG$e#FKD4gjoVIP^Lxy-Buj)M)=V;`g8Y z=nr~IcbZI0e@tucKl~0Ki7_;dC)wx?i_`MEs;KugB}FR`sAt|b*UEceZn3f|uyl4X z!?sxBCGVvtDB%JBZC!^QxG#Z|>HZ0ek-)2&-NTqaouvHhrySbXzL8xCfy)S@L^>TT zb>V30&ctNtWB8HM!?SOyyxHy#i%#nmhFwhq&FJ60?c}}uaz7Ia&pPGEyNQzy+PrBu zmoMt?NsA~sFxwZd?l|j1A^AqK>-uEPF$kjG1y`B<6-rK#8?{u1QJpI=U+8b;oefEL zdL$pUWFglnwQ}Yp3~yqBooXry#@R(_BveHjq((5yy2@h?8Br2pZOSlYSuy=9z!y3A ztD;?gxr22IVr29YFf77y$=a|q;-!dcdy__jeJUK7*CV*SB{$;Og~|E9q%lFu`nOWdY~^c>|D!!i&btUxU0 zH{1fRcwu5$&Ri`|`#}bx2~Ngf`LH-!eLF}5QoUHRB1!E!x>iXx;RhJv?lJifidS|L z@OX|X6cf3UQfiFpp@7aU{SI0?2(@MGUDw#$u9+Xh>K88Q+U`}W=Y<`ZNIbG>^H~BHZjE`SKiasB`;1k_#L$Qr_0$XTxK1#ahU)cuW8D{ zP2kIyzW-cr+mOY1L4c=Oy5n*<@k#UumXyAQy5JHBs>pfe#lVV$Rxwp{sSt~N4$tQI zM>E;T3!myIAnxjaeOdp#z9JRJ6Ix@3 z8(cb2^bozB|BvrTA=1x`Dz@61g4gF`YBeQwJFAh&UrfXjW>Pk|JW=Q67XNvG0wWPt#0)66?)22kwb?_K=olJ%{rUP zvDjuB)>p1UGw~at2d?lF^%I++yl1Wzj(_G-B>I=ehGEki4J17!w2av-o!vcBBHCMN zjUNi2oK?Xp7&EFh>opkK&*NCka%4wR2LlnEZh^m!`lh2&UJoq07I=PY>BuV>=!Ci--7H&J#4? zRdY?%_WA;M8XYN!xN5^rbvzpl&CE=xYiL zFXL!R#rg-ImaSeC)-QWB>VH|)35M#ecpMA1toW515q-V>RdDctUZyLE2p#%*AYLaY z#eA~xOm?GyO-Oh=K>A_1dsF1E&oMAWU%%J<9OI?e5F!b;CVs^)FZu_|s*w1Fj=1-C z8YZsLYNWr>BYIIR|851`Gfc%hOpS4R`|7M=Z4n5z+N9p3@@(B?L*VB99r|up+qR0t zMV<*F$ClGJ7yr9surnw4E@r>sD=%b*pH$ugTxOQC(PIJ`TnZ&3!sQ*TQ`SiQs2|@M zUf7l0h|A*}U#TKRw2<%>WSCn5Ca!QEkqXN#NlR!}I`XT`ILNP7_8w z6AYiV5t}6d(R!9fHTJ9twncZR5QB(6)+^!%4{TdlWucRVlO1)*Z->~0D50cZkmuMI z8#2%O0YD=vpK6*tGOTmzr0#dr=|3|gNGWy5SCu@rTG&go+Fk3zME_qf|0^$&EH9*E{TL(Md(%Gtr)ed4e$V4+)feL7b5RDU&q~5p z%&L)F_?FxpACWcrOquQ5j!p!yumr71y9krW_EN@4lB80way#A)KAh;!sNa+Nv@{*_ zpPjzUT$)=Qi{0*W{e9YHzq_Y58k2!+{8T*9Dyv@x%$n;tZsm(Pu~IhCrh4^(q0ou5nTp zA5?UEUmVJwDPV6pf>#P$r?2Z+#yD114w+d@I3tZ+Q<%5+Dl|};xxO3Q;_S|e^eJ#g zfgr{Zp=UKf9+!7vg{yW=Ruf50jIVM`gaCdp0z6QFsAgqP8WBKqZ zVeo2zPC~CYRInV)phPqML)MGhk|lJ>K9uSsmOWwUVc+b2Z!=sl7-@1{L#2}T_gEt7EAHNBTvuQis;oK-}Epw#0zhFX^ORI?g$#S zHeDUzh%%C7->c`TV8=Wx{!jhNw-V{DV+m3%t8o{7n@}I)B_IAeRclCbi%ZTD2F2ZQ z$zF|RJ8n+L$A@+hjIXtCe2j zynPBj-Y$!}TkF-qvXO*&Ut0NrSvbWs%{xug`EDX`J83y?wV+rRL*(cj{eHQmSmGYW zq{GnpQ>G4)#RNc7mrY2O`;o2l(SbMnX)W;XpxACXaFzKLs+Br>fxegIY1q^!zDA}P zQNi1r^W~7_wut*?BP-<<<FwH zI@24-_?F)H@_-4EC-PtRob%OLZAON;x*@9hyypsD_+8d4b>sS2(kSnP-m{DJ#IbVM zoB%MiHjQUA(J9=(Kj)HS<>K%Z!T{Ju5rM?I3kOpJMt8vaFgA6V=Xr_RY2Pw*$T(fe z(Q+4L&$$%&axj<^I#b_)*gt`@*+vy!E|{_c!DB|WRFT->^=ji(5C=X05)$;YIt_!J zzGb{qDD5~t0yuFIFqYM6CDz8tRH@6T0$1X9G>LXJy~H9vZ?eD7(=LlLF4?^XIWy=fK}F>oekh`36I8#@%?N zFrR7oEdFFMIDgC(tPIDTQN-@(3)(zz)K41w!QzF^6@$vG7y9R=8od#R=;5;K+;E|a zW!z3@iCO-c%C*j9-A?58D~jw)8gm1)Gv|1^zS2O9$Anvb)|mQ^>JICU*g~oG{-C^jh;Sm`Djdi$9c6N09HiXD1$-lpox zG;XQtVGraiI3O~|-}2a0W?&lo6@S<>0d}bk-G0ncB?raLUeH(HSJ1(v@+H$Q-tNDe zosI__jZ7bnIZqJTvj)OQ$G(GDGVJpv@Wv#GCMm0?|Dx1X)^J#uI*iS;=3SXm@`)v1 znX(Snd-(Z0djco9Y$II}YdJ9zmNrZ{v=f^t8T(FZ>@t!+NB6Y@2FaLJIu<6D)X^HD zt#{Gv)Vss6_Aa4qUH7ED|M9Hy1Hp^i(8zLql#DHL++cP-Zz~jf zxEy3~q}F`T_+*j`^?J}r-1XafZ6MMXj59}1Irey;o{mM6j=8}Z*$Po*kpJVz=n z*AX%rs45jie{+k!ge7m2CKy67Hh#|aIx1fr=Fl2xJ0}aT!E#P>BKUX>1!i_&82LB~ z?gFi-;eubbY@FUzZN`a@D^*FlQ zpXEUE92wYfZs|z0dKP3R_x+QSSx8C@hZUtX){|3H)#w8EBu-B=aMPP7g$|tZ8%rS} zZ)?Yodx(v~9O;%xMnn_jr$#fmLt4@$QXrmazwmQvfLW~A2x zQlj7^5kv;!?PJzwDJlO z4>vT-lu1&NYW27NAo@P=O%&Smg;aCE+9-{-;<<*E+5Typd(=iIN-&1KW-Tl?R}sQR znYX6po^lD^v)sj#c9iLV+oAFgrDmbr6+(!}gj_eI?oeh+56DTcVmRL@^c0 zMRhmwbs2ZZS0y-x{Z#4GsWnF!$;)-;ARhIW+J@`voN%Y3W&JpB5ASMzFz>eG)B5&D zi{gy`qsHxG!1!}=;*wXc>s{1$rgFYu4Lgg z)PS%j0VSXp`BREw*>#)|ly!W(c|(SD9e2899O?PtExJL@N>~hfZYI8=VL3u6asTYu z(lHiND*iHn_IkS$j)tOX;PR=cW1ItsSeujxfJ-yyr`+6ggpY26KJz*av#sSHhY9bJ zic;BW2cnseVs{(KFwxXb8`}kl{+@%XQ%cOjHNddJYa0gy*CYEe)X&2J3)ejASF9lB zYRQ%anSwBxci;DwBda)8ttagm!ZE27?2u{poE0TdmMTeoFZ2yoxqYr(g%sRfQQIDn zhcaKV+{3G1lnYTVC#~J*m)GbzCI`b%UpJF4zeXhIekXvs#|pMa@m-`#2`5;ugF7#O zOryx$5WFwJEM%nRc0a~oTRo=nesHGX`s*~@ZB&Fx&dnM=6c4;RhoMTJAm!(co_Kxn z%`@JXPju`p+;U!4Y%^ug1#qijzM(o(J)xSb4Y;LaWqORB(~eqT*DcpZ`p?oQ?Q2tX zVVnuH1=HId@QFdZ^z)a1DP&b95f?8k);SwT-#iWoQTG-a|G%BPz%u$jWt)+_z4ea9 zXLiU~CSlSukT!*7Q<&OK_V_bW8lBt|^@6G&!gRhG=mxa6o{IL=v*uA2Yr5DzvuUe^;lna(wz{5v?Sy+t zI*?C_IKAYvVsl+vl@dG)Cw}NVcp~xevfUH3hx8*9| zC0l_GYAJUC-a37QGB7F8%{dtu6$<69lk{h9W3~Cq{!F)K2rR0}NFsKJMn3|2dOElf zjjmawQ)ruhSRsy7tTpT-UK~eVE&pgoU_k)yES5;%ar4NefsNs)wms;#*5TxWXQAutlUE(t=jt#VWIaoUFDEJO=XwT2h zjiLkB-o>`-H0lAv2n*KHcGnc=l*NWTJp$~aGiv}?DB$wo$?f;2viDlAxT3y~BXXg=D24@Y&i@;wXhM60IZ^yg~p9aN(&Q1e$d)B6{2- zw*nDAnT5o1E8b(q!Dnq%weFw1tY|l9zrBoO2=8B3-Gq4erfsYpaboJr)f4^9ESgs8 zU~e+F%7+_?A7K*!WA(S?%%F3{)a#=3|L!XL!3*rcV0*|^#M_fxqW)kyttLH7jcBs} z>v7Ha*Mq@kE{mB=w-wda9@>)=y-Aw11>5(=_T_jy#nqEdRcfVtvX^j_>y_~JzLpDR zD&+O9(|N0Z-2?Lx{|Ig3W@}bbcrXSP#|6HM*Yf5h({t+4fJtKi|8Il@`TthF>Teyo z8%u`7{^a4eJKG;pQY-g)zD@1;WRIZDN@Xp_#cEG4=3i^i&L9X5TD8){8Ho0Yyh!Pg zMepj}DG>!|@50JNq>zsXRSc`-{M_<<#p*^&FqZ?2LuL)`7a1Kx>1fmEZDDFn;+owE z(XX}dg<=FVuU4P$9nh=XCGHz`&e0VSMx zoA>Jm%8dOh@3WUzfl{4ODtGm!2(7D(eh5wE&nM;lFBo=XpndeKhELP>jN-1VHZWO$ zx#LqmvmQIn;1%#IOp{$AZQCiCRAP@7P(g(3aPPf)8p~hy18!k?JaEMzkSlOwi`6fz zk#4GJSX=(IKxC zOzyr{CEFG?$Cc~0Uz^Hv`2mAJTVl8ahvnjY61~ulpzsGF9g}34DWx(|Ie@Pt1owmP z<41@|nSa=**>Bg4L5+)f67u07BO_q-{(X;NCeV-gEfHv%^ySOqe8gFf`+!ELgkgmX zibgHjm>hn2VN`XroV^O(^55W$9hhp|04#BB8{O?-L3M;${fs$I3zq+`5A_~hV+f{D zAU!zs3q79eXJ3apF7ypBGYmwwy^$fU|I00Pv=p^+A)Hg3+jPifvupiD`0n=gv9rJP z@#ysk8v)0p^{kb-EPJ%T-0ICQ`fF0e;+;sQBa~u}OL17`rfLpyXp@Lv z6U62grzsR=>4Tt(5b}qUBXA!tFtN(lLliA|XKX#=ax%rr?CCGCY-?AK;lZ=)Qw9W(35=v5sR-w1J7s9<0JM*@b#Eai5A5&!VCwAj!GgE+b4A8ah$lk-yqul^8vY&%oTkS$ikXj=cv>TLHR}R zjda8MHdsW$WGBLW<+lBL`Qi$%2hNcN%s;OigG0&_6HzgSJAdP}_P|JQU&bP3tkJXS zE6?8-*5qsU#z2Fx3^N@mTJE9d^d(Et%sLAW)&C(xcUys%>eG>O<=}UB^qn&riiuHJv+Z6{JII9s?LK`Y`Bi*AUtKH;R%MOkN4$U(I_ zI*SfYUT<`9qorWmTt-@mY}(wkJ|pE=I^~M;#)kyUcE~{Vk1NZtdV#LjNnTd2y9;QT zP#;+BPALn2kVmZPK~ty?5X~)3?1^n~w>6zn3uh+mN>(8`(4m8K8X;M-pw~>?yjKgaL=#=HD#6;gwa0ry;ke5H z6+V9dGawQo?O3Gmj5;TWi={+r#p!R&F=Jy@ac~#L%(x|W-X&y%RQW{sP6(}Ga}f7z z;!LuAsa*`Q)MC^9B&!vFj?--xlcQl!3hhxeDP)po^vUC`-hjmu=Auj?&@Y}b!CuD* z*_w@s4A8f`-VeMem+g%j*sQLcT$-HgX}JH$za&Ag%kfZpiIn$8{mr#|0}21{?)Q82 zI|;O=gzXwMrYDzE)6ZqnAjl*7u+gLpgixT)=gBiO&%vf2EOaTCNE?=9EO0P;uiQH_ z0*|Z0{k67~Q_J>5-o2WBWUyOF#USk{jE4`FZ_Df$6#qDOJ<|TG*_8$O2h^v90uZCe zIpZC`h;x%mw-ru0JR_Mwt1dmPCegxi+i2*@#qcTuRq>_AmL+U-aAz@(2H5(kT9qKy zgbkFwW$JZ=dDin}L^n8ctu4BDs99JSy%w~sWav!BG&z~cA24)+!?1#M-#~vx&`3J= zZSBVqU)Om+p-S*&_~?eU2Zj&Au1 z#^N5k4*(4tk^KDAfBNYJxqmxXXVPdZ;x^8bZyUpT+ZhyRqcQpDhFu*pPP6z3b-MZ} zUn8HG$cN6StrWa6gvREr)3zT4pcV?d(3v!>*=3VZAQ8zt7^1rO06_c+lgPDYUjFee zY5`s01~1<9ph40Mhm1nEhFv+H5I^Zg-S1zSxKrPA+rB??4U+q9QVwdU7;Y-z09~>; zl&L9GtcZ9$Icm{Z=pCnI^$(MRk zwOnzQ^J4hjrvL|cP1;s zZGrcjOmsSZG*18L_q^m_1_rD|F@EH3&0h^vTJWZ;L9!$Y43!r1XU^YIJM~l_eIB@i zS5UtF^~F2iEv4C%S1el{n4J|ehAadq(G6LcP69N@8C*=$MtTqLtWa|P3!gqMu&nck z#d|Z8k2tb}Gg$1e$P-`pge6!2KRI~){f6rkLe~vinEL$*7+ZFwxov*0!o~!Y>!}_q zgW4TNit9m5i?Rq%RA5-ibx{`0yLBwz+tD*X4JW!&ej_QN-I#u+MXjC>%xPMqUlIZB zCR^HkJ`Cn3w+hP5bTNS=>wHuZSNac2bcpDHrUFubsj+bc#FlP-)PPZHzx27_`h{xN z3g;;{s#oO|lLzbHni?RMFVe0SjN+>%PnX82Y$v3Z{%Yd(ybfSy^1ejiZKksQ8b*+9 zoxr^Z7Gqm3h8tiEIhBFXVkLaa^%W&9Jqez;U>r*C?P4$z69; zXsHVKvwP{2_a{!Th4XergZ$TE&$q$Jl{&UyC(ZETKx~FLlAYE$_nr)+6r7h@(m-Vp z(tIF8gD-0O16*K1dC1L!z&*G2ciN6!@K~yB9U-T+KZ?i)m9kY&tgK>6x`{TtrC*_4 z!k$!|mqSF!70rUG46^HCpa$?Rip>B#G3i?0v)`O8T?<&ghWKRb0Z)yW{G_Y8(So(O zDqV;uwGqqaoS7(b0FMD-?Ij2@O>i5>-XF{tP@K4H*%hkJX~Go$LS!PI3##)Zt2PrkIe0lV7qi zhPvQ{b7!Yw#?DA37WIF!{+Hl~*c+syW*3T5Qp%Q7)_@#P- z%+LFA1^Ve0ps$9HDu5m$Nt-HgfpbA+c8kg8)c7L}Zcwb*lBL_K3%708tZEEG>(2+q zMB7%{pDv_LcKLR?iD8ulE-F1bO0jmBD@k;cB}u~F_Jyl93vgDsKbF?jVle@31>XrW zpT3(O+%Y;%-ib4?ZdX(o5fp5)=bY0Pz-Rax2GzjKjOpHcHx_P8+Yb&=USBM+uE5!z zJGcSxZw}{MKL_lw!+R{JQmjP$9zhn+?cK-nU_9rVxK{juqVzYaV%dFk399fYJrV>} zC23DS=(IeY;!ChNaWqudsQCL>vdkXIiz?5=lMmg{!)EG%DA8V6wB}x*AnGGo1Zb08 zb-Zr{lJm&`1J`MNY7MvUVmOQ#w z^RV0vBKhC1t){IDp{RCSlBLOi1RaUr84Wj=oyP6Pq!`iJgb*AmuBpBSs{b3_{QEEd zA0+gDx|7X-*>j2u_p&B=Ia45Fv`S3K%9JC(RW!T+7tCMo<$LqX=ZuG0uS;j|%Wg%- zf9JQZuT(59n=r}%;5eJ0^F}X;ipF@}VQ}MyBzdCspi;{=EyZ$r908FFn`x6*HKuNY zIwYO1C}~_+H|1h<_t7Xb;>7BIEgf#clc`S_6#jZZ-x>OJ2R|60(nBQr@T8QjP`x36 z#CpJ4)8_B+zTiEkB&JE%0-9ns#ftdo`*7&@Dh`1&IhO*6AFN&qz0YLmX{i4qmvl1FGwbVkJNqP+E5Bv~_^(_EWWfPAgB8H5J?L6Yyj zd3M2jD5{D~BcKD$^b=vpTj(Jm@LsmQ{%YB;rrf#=W2HC=-Eemx9o-WquKXYqx z74=H8RgbCau=3@-fst!xGF-ebd-%IE7K{@1V2Txi;ECVAoB{LKnF5FEjH1OZj1U%E zQ3Khdvb9zq!4Y(*uhCkUzs*irh#FBb8B_EPZL$%p{}Dn^B}senR}T%T7i$!+ z%5=xf4i=Rw3i6S12+9{iIL?*lJ$cm$mIj!!=c*eJBBfgZv&eTV+Y)sCS&)+WEim{G z=NL49+Ck&P^COK1uSx66e_c*5L71&lJ{My%8p*s|3YU`oh#M#SL`*F z>BO?CWM8~qc>I!V(FB0LyzP*|bNMa>}U$sQEgl)4j@{S zZS7Va=$YU^@>4PmKr@e6S^#(-#&Cj2F~5pJKVJnam3MOM(--IR)g=E^k zpW@mo(v%Kf!orN#+#5>_(&Nk(etK%bPkr!)N3KR!S+A&@CfD%HnkRiLVKb6w5SL8p zmgLAxB4WE+)^E+Y=oS2OVSM~-kt}+lhT=ofDBaYa38L{t;MiPT?K+S41cB+q<_Jpy z(;%&^w{hYGK*}!wFPyFa%!RTZwCpu7H{*kTq%=RFF9(b1@VL}8EK4b?x17Q`$)bHD zTeSHp=}d4w>n5g7&Fmtzl;l((Ywf@FTKDHA*Z+xGySisM=2>npw4XWIeB!*y#{?Ba z;Q#Yy&uyx#%-w3Zglv`c!n~9`aY0@9F zmInn1<#LI1aI9ws%;-$CqW$cZQ)woAjGeQ#2aVE*E%!Sxxw4kD#!P#+x0h17yfMQO z)M%P5@rG3Wi&rr5UOH{tczu?Kw3}Q87dzDODqB$um2m8>u}8-eK0gQ+vq7CLA76HX zUNb^LH1~U{hliEs#*==ux>%C*sY9~v9Dka^A#5&HzkJJO+jZ+Z!~z=Aixh-%svc8z zCRXs{JPGS{#OXD={rt-sQ;sOa(^*^avXuAqD)RDYG{ym@ZO`&OM%zF+pm!C%Oam^p zi)CnbwE^jiBqj;%Rr)*a)}OJ_t*4wn;w+j03GJZTZ#6S;X|tnp2sK#LWx|*?N|Qm$ zf|XEu#5CIE382+$R^Ek5AfoT(OHUP+Uf}$tcJHS`m`eZ7CfNJzqu_5V&dYJ_cLm$# z2R-t8Sq-UJGd9pv$9ptGOV3D2>YuA0-PiII;!JbxA#36iB6E8Cs!>!WT5|1ullB~R;AmwLf>c9AWESJ_hcPYcGzf%_Uzg7O7 zHqiVZslWIqbC%22>>3GW1)C{b2N5(@6^btG0Et=fQk*O22K*74$~tts$G{a7^71_q zA#vM<-2aGu{~=_NojId5t)8bwaRQ=v35>>_BJVS2_LL#8DOC^Z_Kxc8iAkas62}Ax z{}r~JD)v(8cub9+4vge{U+kQm8~)ur@p6RUp{++D_CY=HCpJ#4<2&1oL02@@x$3c1 ztcpCBeITbhP;`P}Pr2zEkEPk14 zKu)`%A9CWz3$SSF){o&wy=REW5-w_lH&O45EcunF(mpK{;&u-|5VFZi$yH~G`FjS@ zsi8?s+f{dw%#EJAOKTu#Nteo3JoRI8cK#0!*tLjGXbPHf7d`rXAI)@z{egb+nWDC9 z-u-R~-Ch-2*7vU5yGxv1mJ?dz=Z=YZ9=!9;RL{CX(Hjf=@bcb48`B1eb?VIxrZO!B ziFU4CTAgrf9o%UXvLbgjQIUZOB-nlMvQwOR;JzI>g?9uupCQYy250rCKDF+!6<&HI z>b9Gdt`v7uRs>3ScJ~!0B#S1=qATX!G^0!^Pt#Cp;oDn5o9rG$85PQV4^-1KsQemt z){{KHTo`P6h6}dB%O*^3De`tNfAr47lf2ZvSXe!99E% z&V8K!LvY~nH1DF>mR+#=b_6A(k=Dy8lXZkQtEWuijp{nx^C#T<< z(;__URXaHi5J$5NSOPgEZm~a2)Z?7n2ro%lxwMT-;a-?94i;e7iFpX?+LrfQulYZevVR(SYQ%beQFwY< z`Nl+h;XtZKnSy*7 zD5vA|DVz7)`VEH{)tO;H?yrI^6c?3-P4_&)(BVcfGo-k~?Zy!{>aihswDxbjMV6@sp=Aog)U8Vm z5|J3DGtD>>xO=T1bpd6n+6pVJhR*{cLA0jH7a)+yRz>5{haaY&0%mf~h!v+fs~akb zbgvQb2;986s4y+$20o|+JonQ#=@z2L+^CeDp;@kk{G}}1x^PcKXcw9ZDr#hy9KLow zBmNj~Pq@K1vi3y2R*D+8D~Oz;)+Dx#r4lc<^1WPpe;OQ*RWWl6pfn9|Q0k=;qy4bF zPqsQ2GEJnwZsEF8u+8KX$Hra|vzbS7&h9|8ty8~RrAj8QuRHdGJzUe~=OdH0>c^GZam_Z~?JH=RsqQZ$<`F}_k{#~~HZ%WAj zgOvWrr|Q8_3zcnwFg-6goYgh1oc zIKiEujax&o;I8@gGc)fzdDpDqE0z?~k|B9Wp&Xx;eh8yZGoi?Aqa#bmldnEpxbh8U=}^NO zu<4W#AaYGR27?(?DvfI?08p0+D*W4pXZd9NoVw9onvpG_X%#O!n80bevALaSTdCevJO>;U zFlZt7$xpQ$)JzK#4(~~a5Pz*zo~u~;%`RZ^`+H7{)Y4}arV!?z=Alo0Y}h^C{UOI# zE5n=vFr!!Vk4J;M&aGHB(?5bUYVCO`T}P~ziT6#wBDMh)363^XFZOdxvgE~K4-c-&ddJZ%l>xk|5MuXm#a^~F*r@;obVK*wN8bIo=6=@F|D7W@O`m>&CC^l&5NS zmiSZZ6z=5|+KLJ`O@0wak3^fdX{QQmw5epW#k<4-6q1l-G)|qMopdUo-BumGnfa}?6au=zmd+fTH=LQxXJSlDzb%p_raP*;8QFN9R`3k`H z69mgR&>UQA_u$+uR=jA7lxnZY{=H~9|9~)HSoRsEFX>y>ZM~k)E25R%WhM7kPQA=H z{Vcu8pjST^l;Y}N>f7~tq@;tlg~MH=x-1Vo{C4YBvNipkJ(=ZIgP`jBN1GSkmgZ9H z7S=yRJvi?GvlOFaa0Y&bX3&OiZP?x@S31qLcm zRoS^=rG}*KPXh$SMxJ#M3tptnSPx-Mw1cDp!56g5%Q~Qnnentl;h^b{Z-1C6#meP9 z*1<)3FOk$Rdt?Ts%7=ha)7i#RwvR_M?|bdMD! zmev0UzRz)Bhld`gGK>*r6%EDdf#Roi%x^AJ(UB(CTgs73H`nxcNB^9DOEmr`4gj&5 z5AV6uKnO*pkpU)7hTOb6RdN``vEnUiPHXhUS)v4s3Wvw8XIQc(y{^f;AdGm*iEd*f(@+SNoFFbFbK(Q%-U@v_AVe8%40ZADc{^D-_#wW6TGAz zH(OxO=-5Z6E`t*hVKYC|@;I1#Mrw%qo~JZgMx=7A)l zeAKV#5Y&pxLG!drZKyucNwd6#ZWZKcbepP3$y@!saE>KSa6CLB)sG^d`1|YC+ztd| zW@&??YA$m_Z*+ncQS0CrUi+O^+F0?Vt?i4N5lkI4==nXv7@neuJmZX^Gj^)e^n}PJ z&FGi*>5JZo+?E;nQ3LMZjdfx4hY>QH{W4$d+Av}z%lprl*m@U!)ONi{W`A>>F z%xmTN41-iA8r&Z6JiZctyy<;>nPAx;tJe2X(#mU@#0{85b+@_A?1K z-;c;6C0C`J@>GD+UV>!t1X*0IvV8KyO`ixSNShr$~G z$MKPv_;07UndJRYUcr5`RKb!#`Wd6!FT)pM{68@5MaDs{2McboMnL9f`u;@T*nBjz zPh9J>lRn?;j8wZYLOj>GkzrE$37=7mm&r*h>jFT$uuu8yjJk@Oqulz}=5Q_K;23)w z)NLRw*$XLaI5p7gvZ3Wk2NZ{3s75=YcK(q%0y>--B<%C+os`v{-YsO1f&i-_*(+mr zckP~zm=DxfRDPg#7zWXb4rjIavS;O#(=-Pf8rlrg%jR*T*kC2c+Bg}N-bFMD`}ch5 z{EYI#Hi*aslB^h01F-NOl3Fy1A8-1C6+kH`e}02dZ&J&m7&300 z>bCynBFEDEHzg}tfW0WT-}ZUTofVJy#x>FOjt9=N{q1vHDbkJ;&pgKZ_Py60ssb|C zOHF3yJaovZSJaW{YJ}E6?wGAlhp!U_*thrx-+u@aA)euDo-U^yFpZZqyIpu*SiwhE z{UCpZ{)mv?G0Z|=NORn{gzZ8P7|?vH8vxwtUPVwv$iGHBVZE{H#4VcZU$y!qOE|qHdIoPqTM6J z>2-HyX{gEOpjQb1(+xxW~#WG@fJ`edNziMIe zJpl{LyP4Wm7v7j!-y_c7cnG0$L{nzU?v6Lj{a4zb%DM^vEAR+pFfw5W3yoW8EUxwH z%vy1YHQr81>CKMWGQ21wZOT(L%J@Dfza-ahic$ei&Q_!oFk0T52M8}grT@%I_)}^1 zALk@k{-GC=#bX_-`|dx^4*ABtuGtY&vaLeBUwX}f5z*DU|6**fnHr1|J7gDyUo&>| zX1n^)Z)f8#&uNX|uN!2~`z+^UU;48VRXxFPi@o1On@kj9V%fO#KE4_#VNR-H zX*!~P-y`+@k|i%L<0GMMLnSu10A1Pc}K5h*S} z-^$8HFYzZs^jSx*1Z&~SQ-=Tm&sGo@`V%le|CA29K2E^{hK@2SY@Amq%CZ44!C{Ea zrA8W=q8`B>sA({|xnho0YXi*H-|U%YNrO$=wMhvPd86@?g76uNSyuxni6d3v=w0>CB#w(SS6 zayVT^PHi-qcSs0rc-cvd$HOR(OzVt^R^fAr{PfP?)K8x1P5#u^y+vU>_54Qpr<=Ro zVHJ9T_7{#3(5KHf-n44r7!S4i1Xwq%{IVBMu#~ez>VWq+ICHm?wF!t{`vqpjy$S{lT?!wrBbk(e;I$Zh+hr(s6VVDHeRP!1}i@uskQrBNSu}@CZ#o1_FwPg zl7Qpf(n@^CmB36V0he0Q70ppT!!xg$dk;f2YBLmt9`D4X>{$+!DGZUYuWt^{Ux;Q! z3mv-R?2FTEk6`${`Fo&FQy}f~l>d4`4(;&f9MF(SMwv?Hcc}PVBg-;tfFj4krMP5a zK4H^3a^kO#r~$$2^X;2MXC9N2~0F0JO^qC zUiMu)WfI>YYgGY|wzz9NFVs~Ot(8$*(5<0dc@ty0+vajW8siuPK29aE=BXf!J|SnZ zPEE})ndEA^{V)^8#fZozn`82h**=Aa*EePJCwAQQ%c;d7$V<~2*@^!4?>=9rs( z9}VL+x7lXq_g*gh2X;s_mpI)bR5*XkP(QT$Pl+8h=5-$9kFgnPbH%*=Gky)>ieVH( zRUsEp|A@t?J}cG5#6I}ld|{bD8xDAz?XEL41i%Qw!Ba`ne=wb~ zFOV5G#LNZWr_|D7MyUs7etHv0JoUke*x{+z4LTy;*V;6T#<UOf3a}jw4Xv^%!{GcwgNm3wyj7o}YX_$DK(=KheE%$i; zz>B~rpFNc_1PlzKqPR~A5|;atGT8RE->g&0Kmm8<8~d>p%L+#xk|M#}sOeyB;s+(N zCsPBNJ;Fe4s0oW|!vJQ3f6=c?SK>(r7AeGskO9GzY1%XJ8EpdF%Y6_Yzh&3$ZynyW z)bLgVzRHZjX1Oy;tnRmGFJijSQfYss50YL^!N$Pk0h)tXaOvX|0WNfRO83hvrH>VQ zQNNl@F=nP)&ews%y#YI(2$6#kXMR1feG8m}`Q1RRT2VEE2tQ^MO{q7{rYI6rOL4AH>@izKB&KyK^gNjR zc7DG2uiW+HFhb}m+FaA2FK5)1@b9+1yKnF_zH!ab$}8zLwU>Ww;wKfBqx4+Soex9b zrj)Z9OB(v_w72!HavHH=Qh$$ejPZ=j{M4rAh7bhI7{$ZQ-6r)79Mn)K7SIt&uq(?p z8)8M2B!xdEfM^HH&6of|QH7ZD#x-9E%`ruW;s=wlIWAtiU!W*lsfTTy5C^ZY5q*wC zbl>>I7Yr<}Ql@sNIk8cpmrUao2eKrylmW_%W2jQo8;(E0?G|J^W@RfLUfriB*-~@L zxKB&{_=1dvs*l0!rgJuXh@y~2JABb;@hv6%DWEic+A9A0oITzA?50+N>(MludqcK~ z4Lak@KC!A&R}tBMynX0h>T6-;?@94IQ{AmpoV;c(oT@F!yNwsbGHD8nPSJ#1+eO6I zlL(NRTBr&}u_J)-$-fos{j8r2w^=Zh>leKngzQ3F#kk~WAsLn(fCmrf{q;0TYp3N> z+)LUW(;BW`Q&L?Gkn%*pWDVx?sa{DNV1<6|7&yitEUp^I-xK3*IsPWNpw!Pk%VhYauhB2Wu!y1-v1I5_)(oc zywaGy!nmbls5nxulh$7zawaKa&NScjUmMP248l~f_?Az92Wi)xgWVzD>~hRFOvF_; zFa{BCk^IYQ4rfg}iy*A#nLt};+P=?NR#`jm&9kNPCEgb*-v7iR=COW+4i>RRGKBuS zXgu#8b`ZP{AWxZg-X#gMK!3;k&hCpD=o?=wJs1JD%@+>%ukQ&T*ZRfZ5lkr!U;1Lp z19r?kgrDhGlS{M0pgog78tI`+a~zs$0tFU*oxNnnlD}dyMJgR3J$-EhJO|S~K;g!X zJ751y###^#Mk-~-&$}kuh{7oys1HL|+3fd{_iOPMX|3d{dl#tz=`}z~JZ3r-KF`B6KPBTiR`zXCAU(oyuD)H14fe_5J#wBLsGt`Sfl9F?IVsET=T(7v}>gP7kJ8q$Qcia;* z)-Ui(m&2Fhu?{{YH*d3avj0eITBDHd+qEhScSZ`J7` zZiI_oCYn-XP&W!zc%`*pqa2e`1^8$K4zPWFXeE-9F^D#gBhtPtwQNVJtzY#A7w~UV z;6G0~CF!_ySt_o0$Mi1RNT=@7t_)0<53apNof`ReQZYncp|v}PH(~!zEr3@9n)HVS zj!)~y>|Xlc#LLi_XWt3AR@|=ab~}hWE$@1@G5y1l=lYHIa$`)~Y4rM0xDh)6#!+w-NHf8`m2x@nHI%Msa7Wd{?w@R_h5Gc7I?~{gdg!v%kGQCRaM&U> zXwh%uVsW&fXhe8-h34kEBup(VN`OZ#nSPIXJd&dl5zVON$>m2;RLl}N=NM|gv%t`{ zVmI?MvhMM|5qG|SCvY{*NsSJ>CaR>TjHX+ii9&FNl(n=-K`?8CNiPa$02DX-Y$%_& zis9$b`zI*Ei+~|!l5AThv0W9414_In;LP2IWIWHuAJeZpJjyL|2ku_Ce#juG6FBh# z%%$h5Y8-5$yV@puC98Hq3MCZX$-0UC*e{X?D=M_rpwPy=KLnF#d^=SGTSo~N7pvrLb@aS zHo?Xn-O#`bv7D9DhvhKUM0P!IzvFnF;+adqyaiT{yst_usW!jX#-H)#HP;0=%4Iro zBqXJj2N}MXq7yjWT=WCh1U}pYm1+B?w|*fq91;T z8M;zV0QDR^J=ggM_+I;h;76~%G=0$7VUR$;obB zou@eu2NR6F>7T3uzH@B_D5-z~iry=R!_el|ru#cMsx;Vx8_yVXznD0vlqpv8 z{>x?LVl)-Lq1j|8iGd{Y?Ct4ef1^nFgyEwmV58z=KKqo-jnL4gGoAbH%Z)Mt`W|;j zAULDdE8crAGYBjuSJxa;+Sok+I;?jmq;TO9y(0SPW@|m@*prNCa{onOes*LYwYa8; z+hI#Yyo5OQM)_+YP68t3(EN>~HTO|T5%dq?Yo|b%gst^6y4hqQTM^S!NEuNA5wR5! zTMiaId*oNi2Mb9E@TgU*31CA~ImcY)(s2R>JlLQ^+~d}9cB(NF1`Vc0LVruNqyRclh$DZz;A^pY#W z7w!j+=N$MAa^({?=t4w+4GD|?Wdc|Yi6k6Q4Wl%(vK`I2XH2M9;Bwz3zFmC~CgzRiZ?|Rb z`TzuP6*CPcBie}=7QMdVer0f7p(vunm-Y5^PbjhDRF_$&gUAwaSj>?vk&fAr)bU%K zL6&=#*VWk(C9ODNfQQA^<1Ak~?8y6t@es26P_bBovi>c&l`M~Mkw6s>x57~Ju}clB z9D+3ov8CDys4=SBw{Df#uohU|PKmI$L=7`Gie*a#%SZmys=x`l|+%Nx&UwE-zxvEiIfqh+gG?0H_|zI|LYz*8dOtCt z>6*NlvN~wZEvx@g!d2G7r6cl$iWazv7X6|`eQbd{7!HTw1vJop zW^`M{wk*Mi7bpg%A1&GGhc%5)_t2SkLKIjD45a2$nY#hIF?e*t+}|&f4m{^YX@nlR zBlgIjm6nGTZ7CZt*{uZ9=JZPqO9R?(FmydKmId%$_x1c_OGsv*gkh^C{~`MDw?p!0 z6R5(hrXE&_Vj$);Iq1dJ(DJ=;DC5&sta-J`2%nBg|8^Xw$3tg@VXq?Wq& zK~L{sSZCUbBi6*urHa(pHsF5=p#F@r{>S;5(%M_8iKjyS72x9SQJ0Pu?yOxsR|>d6 z|4-8xPsrPTX@oK}F0AFpQQRyOiU7t)DCKRH)5!UK+Ifb2&Z0Qwne*7e*MQ-G{0;QP}O;dsFGLbwZ)>@ZHv<=5^LRS3Ine zc6lNqUHmZxI7-#O*{e==zr)15dO^8?o1t14G0B?2xNk0;!TouSMz!IJkqPuOO-I;c zCxv?J+HEa;cEvzsqiU7(01YE+AzLeL`7phfe)O-EtwgdziVHqcybC{sei3qvHDUk1>pHp zo$1?3W_VX+%8%I{^ zU(QtiTJK-Bzh+ZJ)*9$|M5s`oJR&@|`|)X|KT8*2hUYo#qj7>y8%8ij1Yt5xlxh$> zJ$D$)eFA%F(jp6hYuNiFI2`C$10C&aI1_9pMo*ZfSFLAD0tjC{n5qUyYd_!q#a!64 zurFVXp*~hWy_%avSSQ~Ekfq=q`c55;lfk>{gqz*gaK4mhaJ@y$$!xj#X24QsSS>1M z+K%l!gX~>Yjmzoz_95p>QG*P|Rh{P3p51qerfHvmH~c#4p(jK+&3reM;vWYaCcY|K z4|79=mRB}rRa>_(i(hLRuRZumcu9+PnLU@Au9W?D*L=se$OF2MvPfMbDRKjMVWJqg ziA7dNGNn~yybYR!T6Bf~QGi0E53PLcN_+KZ6HBDUMeWQNuR~p%l+2IO{kUKrOWU7a zw-Z+VUyyD4K!=-p<9n)8cG|q?|1~!fsH&m z{HR@G(lW&p#2OCME;E|@?sk2}y-WIF1^=AxFhvBcfejnhiZ`R3mcD!vOWC;@{rQJT zLdB%M&5rj~sGKussdWOVs(1d2f_WaAu-bCPJvP0h`Z{E9^_FSCrIBIq<>ET$3PPG} zN3XHycRj?X{EBa#d)bScA?FbV;p;H#$POkNF3{W@%l?9e#g`ab)Y!7{z&K6s*Tz!= zbYkO4eTNyB=!Z8!mr7JVDWJo-Y0#2sSZT?LJ|{gea(YvBHD13zbS+7PuGN+)z;>v5 z)Z*D|^?)L`{Ue`nmWpU%%-fsvL4)L-nPo|W%isVcaB2OEV*jk7AYu1?y_J|0zi3Zw zzZ#Y8=S&Qppmq{<$AdOy2meX-ZN8>i%$m0`gs2J5oRlcQF23~{a-y!Pc%I{Igkkp$ zsCi}fl*lSH?T7H(dJrfIm$>ICR)Yb2O!Cz{QY<$eX=0M7VpF|yD%v`lrC4PJ`t4l* ziU4;P+A`Z#_-4@EiIoQLd=$(Yret}U%s=_GGDOeXhS;@@9I$L4b~rhb`4N~m7irHC zTNBJ(?h^W1iu78)V8z&JW_CkxHJd;Pa#`am(1=<^dUmw+;e;9>Zs6 zd}FUIz-rmLRSClco1-a; z=44pO_BH_AirR8!SbT)wA=>`!HFI{a@!CYa*-gMk2vO01qh49>f1lh#M*yYqKliy^ z;2W#^4ZIyK90x+jW(6C^`844xh@N2xeHRchp$ojO>Rbp?8By?AOU5?u=l6IY;X(JAILLTh@^fp`m@-{hCqAy2jxh5P5xoN|qY{o1e^ysd zFy|NJNvqvQ7_;atnXptf!nHHw^v17lu9xG>>D`<_9l*WJ-N64HtoV&@a1!KFODXP@ z#ItY1{&?>CteDbhqhy@B^_jF<0wzMfpRd6ae93d#zvAi$(d>A!G88QlumOI(UO2P1 zAgur$Z-H~{f~`~YEwOv&ha1Pyp4ZZak*jkXz&e+zD9Bl) z5yZk8b;$s|WsFsisW&gJrN~BU=)jqC(~f8c%r1;Gk8{(xK1W|}#>JQ;^|tZK*<4<#)I`)n%DsqjE_3`Y0KT4%tvNeD-rd2az^R4 z>*T*C$nVgAl1}s-(mHczBA#g?)^ILd(&TfnB@|UZ(W2kY7izA!QS^CT`;@CF(refq zi(E02IkU~9m=+G28_*NgP2KLRym_<@(uBZe1D)*epG}3KVs#?IOlj@O64qNf*R7SM z4P|l-28DVp;gcLkOfVszTMQyc%{SVxklJZIA}d~?9<{I&8J$6Gn`ZOAHjd9d(N#)@ zaq(H7P>#NA`YyDBd!BuHaq#7mx}Gc^T`_cvdGnSyhPOMAtix+)u!$f6@`oF_#!Vkr zuQP@vIlHOcQ}2=POTslcYr2d!yUJ{?B-l4j`Wcn<{luCk~tOrvfnuaup3MG4%9G39!^iI37}@1yCtVDvlPG2#hl7G>kp(K!bv zhZakv?H>Q^_mV1(*e$kW>&-pRF*j2mg{}uVzZ7n!o85lO9xRnFUcGF)dRORaJ5V6N z!Q1>9{aY%6a2@H90E;huFc}f3Ekv?Uw2ugLC;j4}a!36tvML^-Ll#{?| zv;@b+I9qVvjxk`X{aQ2#SXKBwquGP{Ij8zB{f;Tdb@PhMxACRLW~;Yi^gvNuXt7Z| z3Jd!9^t!7cXUDlXq?iA|8S|Uil-8HQP76euuh4V>7kM#l)R_6&DJB{fu{ulgt#G!f z$3A&VD!8js;Xmp$i7H^(@*0VKb>uhXb8j~ZGbI*e+ znhKR1%y6_1qObZB6yc$j_f;#Yh|IoqpWPSRZ@GU47M*pXo1NC^z5}c|22xfAT(Fwy zj`J6;$67d-6m2*rP{;aaBTao)YL?( zEAs1_SLnBa_o1`b?Zs%}5%3lm?T$c5V-x>ENcV_Ggny6l!3(52KQ4y<#3mCu5kl1I za6Gjcd#P8vhDYsz&tT}Y5!6>&c6t+hd)mgCZ?dWwrW%3691)X!jW+Camm@UK-&-Bx zZjf4IcJk9ZtkT-A-|1eEsQQ}mo-IM8_Cb{F@wlIP{y~JE*!_&yMOOSbqe70?^YDYa zG$Y{%Jqzr=sw=KD0(ra-uD+N$Rw%lSn#GCvAj*#r$utS(uoRu>kJJ4MjT5?f(Uoz@ zrIl0)0m4r;`5kc+{VD~xaj*P{LfiO+*DDux2%Yr5L5i9$^-5^(Ox}K~4>OD?{oY#; zOuWE?zSDA>P%}bzbZ6MV>7Yk%xflXLe~WDejbqtj$IF!K5q9Ru{WLD}^dxe*Ltb;!9MR zWogs!9(H^sD$#sXKI#ug=YkZbKpNME{tU5(Y((T<0)eLB_N30Zv5W`_o)nGCeOjh(rP$RQ zv4_5uzaCuF;@#!-Ru=tdQic{80#R{9-Q|8@V?)ta2!ktab2;9d;!r&z!AVuX}a_}c|xh`2XJ zc%KsUH^b2U4`A$4&3lxN!K4?(G3i0VrxPmA4C-*gafQ=rlDaXrBW56P2Z<_a^+0`p zl?0}_$CUP?{PuWlD4aG`_`}c9zG9yop~bSZB<7FI_CQ4tM>ok%7;3LBSd>h17cq0! z>xm5)D-anzL6U4lmMkx>7juF;TK!9_63y7FBSB@Fjmf7>Tl$I}R1_QoRhlZxJkOKW zw3g3ap}$us0~!Z2w(>YM($0duntfwMI1}L)zwM6NOn)_>igc`c=NW9h`l;1xLd=^a z9br{pIJP|H;1O`p>p^NTK1aQXieuCnVr;aI)O|_w*d2meMTTE>*=%9qF`Ly=q|sQY z5vhs6N1q0qL^k@tm9TnuwPS+Zr>xT!LdsuGq?}i*U37g za1L9!w?7CuQEHKMY4|;@===^L>t7*+6}|aD=IV1%%=MFuR;3ycaeVmPBvD*n4P3(mseZ zX`b$H6W-R9&;HO8qQurG*-jD?o*s^yR3{fhiKw#22GA%wzj*eo<|~$ij{D~zX$>TY z;pY@>USlEQzw35lNnU@!u4MY3q5d`E|JT816>EfqHmh_6xV7s(_TH}ebiN^BHp5nU zvAr0~z}}K!Q)F{H``j&#*#6no+vap14*Vz-C)}tN%ez`3pW-*XNhPFC#U8FY*_UwL ze~#^cr-}U3;b;{MXJ~1}idvTvw*Q=S(SC-7bhyut^IF$9zYS@qoD%H7%?>y4uo;g; ziac7=3m)EeBb*jI+U3+5?r!0hD59o4;xk&Xeiz7&+QsgyUpGD$~Xb4qu|NQHBSwl%q$-2R1)|fDktc+ac zPJgni8MjsNXZ`AtVkS17db&D`3&GwYeUj_Wh0;ZAAgka9WyqaDTNCG)sK@E>_xjl} zYD^pS&JXsG%#U}-f+>|sxf;lAFiTgA+^gdZ%By-bAF9Hbz7FD&wIspQAg~%3v+i@D?fZT(%m__9bzt%SB+EBPa@v@7aa|-gV zOBV9aUjIhtc_^6u2gJ{qaRx?4f;5M9DBXy3)_?+9`^Z{SYtJEn@WaJ^sdo@)YcUc3 zAcY!~O(Xte)6|SvMS#cG$V=eLiD9gJj?3yUa8r zEj;4I4bRE=4Td7mphtha(F@wf^xMILQwrgidu|}*K;V@4_Jn(adhrIjtIa4CeynR) zP?UJ4Cq?#0&xc!e*EH2wQ^{`FS>_u=?X)d-M!t~^{e zZrR*W%$i|CF>_J8m}6r&RsSV@HVNUOe`xjDK3^q_F6Ou#GQ& z|M+{0;?r}=k}?7Pw3wVykpt6j2U*nOA6l}H{`y7ySnc-_PkQdB-w@!3p>)PEK>Mv6 zJ&zVU8=?L&dOIKKMI->U7Jl?;?|o5!cwh1DFJ6TVvNyiLe6M$W4}&~enSI*5ivx>h?q2_Ns7cy!!WYZZl^N+}=Rp#cKz1kD?k+m@AZD z`1vm|iG_08tSHbt&N^oJTO*6XBcTl(+LrS%qsUzlu0t(bLqF7s%Mx~!AY7zxv|5xc z)}#Du>X%6}p=@F|$P&mGWmA1jBMsh9Xi0WlzT%hYWU6eT%mxqta_$S(tyo6_BV{;# zuQ#UrF6ObEZ!@08j{1WNMG*b#b0Z2=(jMqmpC*!di!$8DGfr%^k?d;WQ}4T|ZGsr% zC~QsS{tb~bpG_W;bseug(WO2zhM4afQ=Luacrm7q-#_Faa~S+C?Df_s$q~+k&S6#c z=8gPBUA4j%BsYSi`!#pgV@6?XRXchkS;C$C`cSNrkT^_o)iEMsvz*b7ll^RPx!#eq z1bDgApgQ$C5+&;{Kv8X>2DfVA#n@>c<8KPi~Z`gyUS+a{c*J55LwQy)vc)#-# z2vFBi2P4HgBXYDU9qB3vBnvKSkKIpY6 z$cj9j?Jx8qO~>9U+da=coH&zcS{e>qx^qmAglg6_$7KJ#$#p)5%3?+7w&92ULZ%01ZH*TK_5D=Qz37<>W5;mtJQ@<}2 z0hI?Ek`R;;;`i!|$Y76ra-cA&<8i1rDZr_AkhZbxXh^=6b1<~lUigvERG>Mw_{?Eu z?c{+UDO33KDZ{?_xsTR;;lQ;bs&~-mb9tl^x1of88jAmWN&dr@{PUK^Z=G3wc~exv z`e}WF>h2!YyfZCRr*%1L4ZIh;Qzw=;%T`P_AYRzL6Iv)m%%+?|i`X z9W(dhok=UzgIN{7nMzX8DMcW-I`6kKGJj7-k%Y*@4JX;2daQPH|H ze268%$D2Ezco1Bt##hdTsQ6BtV*8Uhd`KetYSc>xo8hKaY+AoO@(z~3O+KHnU|XCv zc`h-U=Uu4_S2wI$a9$*|cn?^_Kk%M89TqM>sa>s^yn$n~4P6hP8}@{MHhCp-spl8L zD$)94Ns~bj3}-E2*Qr~o5&ZRp8Ofes3_DI?$l7a!=gjYQB`P+O#gi+|CBH2k>on^P zHesYFpr-(%-^`~o**shz&*4^m{9n?x#?0>czWiwEnC0Tt!i&H6-%N74wq}aiz{V#!Sy~>BvHY zDo3viNvLfSs^<@K3C*jX4gTRv)SbPB+$2i7Xit9ZDmA1o>W7`I*gF$DoUhGQXNo&Y z>QG7nk>6CTD2;dtIsMfT3|i36+_TK2%8v8OCekbIgOG#HHNz@!YsceoM`N-@hxzkN zOKhtBCK?A5xCadKJyD)=NmVhZ_#4tuRVIGt3)z3_YkGk%Y8{T#(0ujz|7uPq_ z(H;ofG1297Mb;;?C$EXNvv z^y0W{O%OA8`UP}0e+6q&(cE?!E}s0{d|CXa?@a&S5FcUvh|o7cgAp)f8)REWShJs9 z<%UrPR0`8$X6YtO{y<^N*l+MyF&CIRE#`PgJO*O2@9p)j@9DWLC5(QJB-0?$ik+ge z&^7bzDsXjj^@CCG?>Q`1jm5bebq%zrgT*@vN~sjB;e>HrV|fa^BIjh}xTr`5jolVo zM!*HW*L4Uk+_q@C$~0vLvPzwa=-+(5gjFV>yKLU66+O!4@nn2;~a+M2+%e+B(gs zZkXq8tA7|TXy;rWZc=#5+wx@*hV_XK)YmhcUEi8XiVsx44rE+qNb zV67PQG|9<9`~dss^xXRusT{{%{TK?iT!{W*Ax)nL?(uUn2IxV>3LX$UY`Be1LU7>* zH`4H?12v2i(;I1_p00DHLCL%X%7I8Hcsf85My5LZ#N!91s(W_cRy zUu$|uj!k_N9<)Y&W=*r{l%J6K@&RL(4|uP=QcRl!RDLyLR0&R1f7y4Of2BFD`^281 z))A6)xo*^Ild=V~?S6js^}}TQG!;iPS%~x{HGqsizJ;!0Lt6(=*s=X|8(oNyZZL^N zR_>kv??!UBKZXpjn_eC?vJ{H{rYIl-cvFh z+2%n4C4}2LZ_cxEPlu2KVfB#uuAv!?7V8htP*j|5I#i^l-u=oo6xcJpysg3vuq8T| zwvqPQl89eTXS^N*Oqb@WJ zlCUgHg58`C7?nk zE#(q=29EQi)yE1kZbFOGz^o_34h9}#gQu-u(3dX$|JjWkTl8-);^rl4K&+zX-)>*! zQ~+&&A1Zi?b{C)&TX_jD9y2U)cqtEi{{rAo{{rAo5${pa33(OgQ@y(9&qIn3`!?t_desf{pbw^F~|N+-7h83%OrCm-BTgS!`JiY}~FAE3ThbRfS%6gAe~;f4D}4QBv(D9D@Q5AhGe` zhpzUaSkF9(bC!GOSBU~b)`w82Lid3i+1S?4AWs5p4A9Q8fQu;CI!}Q=pni{dTA9so z4lO+9VK@79TsRTZ$q0@1{b|Q*R2f>UC(Y>O)KtJs4;7y{zm5PVG$4i)$;(!gtoe7b zx~*3!gV6KeX$*eVWkRxbgab&^6gh7?=a5^_@nX3T8KZFJUiPufKdE4%J*=4_LDl`J zfego@UvH1l5dAn`uM3%WPko5iaNFeNKhL1?T_W{lfc}_8eZU&{f0Uh7R9k`8t&`wR zOOfIgqy(q9r9x?owWVlqcM{wQHK15qihC)gNP;JU;_gsff?KiRZZ|#WKmR>LooNtNZe_-o zTNf)bPq?UPEA@hogAJON`H<#KQUwjxzLzVtjHwfgZ{+=acl&gb{HK(JII|3um_EC-t3kdp5bZ%h(Z;6 z<+Z?+axYNXj$NnAj$%sxeOv5}i+erxO>9N6R=jPfx^cF3I2TbO8y$4^VCJU4eLT${Q7yWgJu5tYINR+d(}e0dd?Zf zP+74T^DpI8&u1J$xZu)ago8YU&dnSMd@J| z5Hj5Cs{u)ncUGw7+HK_<4((WVi}_}R*9D@j-GY9`ax&PI7HIHR59%gX_fnrVHZCQD zG9a-}v%+^uqInp9i9iWgxu46FJCt7NAH{uAp1h(|cM$=+kaC`(xC(uX-$oEDL`O{# zbllZO9kq*2c6}`2@Ok0YrYV#(bimj2_)b@eP@%pAUX!H#Ca@=6!O1#vZdxyx-3&)Q zTfrDQK=``pRZuzIo5dbP1UtQt4lR88tEH@*=F|B05VH_nv3?_mxr+`CTUfR zfy~4ub^Ra7T>u4fUAnA?yIwh?^SJdBQ?EJwSETxm#`QJoi*#liv!18fQ+Rg-nnH#R zST{j`f+)Mzi`1oa{Tpjv3Ud9(PqpACWH zV6snjn{!UIMLTgsmq17pvZ#5)w73ouDp<}$fE=FM%>Wp-`uLk}Jy=Ipu#nS!?|H3oQD z*+!KlVoI_MmXI0m`<^V#LKieE0Rvy-NJJ%C$;o9o6&(y9G1R@Cm2r-R9{5EcEowRd z#%D1QvCh|c+=*MMWOPKX?gcwth+HCRh}I}aPzH6wYu+2^SAC4Llg!AIGE+=rr>7Kp znf++t7Msty_31`d!`Lov<;|np^&ijYb$P> z7Q0i?<-Wg^$8n(J*eQ0nXvU^O?YKJN@|V;H$E^xXFbO2uX7KxGf++jrHR0L%^6f8| z^uC8EUtq7R6WpG*cQ7DeVDf=R$CS&)1S4Amg?{EU>dZSZ8G&sOYr?eKG-F`wBGDgM z3}t`#ZuJ=5=;P7%8ChgPMij3^D#DTqPSM@7YrgjqMkBYU7hqceSjBE=gG}Ebum_&Bp$_v4eCG;BlEUIm5 zl@Fl17U>KYnYfQkLUU(=cYf+pRlVE7EuP5+w1GbI&h!bkHlR|?PnK!JUH`D%tFJE4 zX>oiGayj!ysw?PPKtZ*`jf$99hlC=Vr>V%+JlgQR`*`}T_wR#QqFVXQu+~`unLxHQ zd8%&u?&7C3B5A+oV{c8|lG5wGzFR&^Rq|AQ@{JRpn%Jm%MCxl1k8dif4q?|AB~;w% zmnt7pUEQU{)R%!`?-ghc?+j_2Ao>RiZZKe0lRq%@ckF^#bvGDH4CVz}G^%g+-aEf# zL|+QAtK4yDxUt)$M78-RuuxyEtxSi#h3yhfo$R38tm|-RBW6X1&L#e=>Pg+?p^uUD zv=;y9{%9mG>UbBV{ocSjKs%P|}XY6kttWN8h26nHT$khI|DbQ{T&@d_4GNEISXL!(4VP1}sfT-}j=r>VW5a8T?|L7}4k0wu}$orG9b+pi?dni8-7&?+a=&^a`TI?zq7k zIPSVG4w*X!Z5R|7Ow+!bY=qYb*N3F$XbwGz<}AN%xpvW`1=>iowg{l2cVXfi5ntH! zzp!nPEm&O@fdcuoWjEr7Fa3)QgH%E#0}km5Jq{lOVm;-=gblLy<;9jJM+|l8d15b? zekS{$ldzbXkwfM-o*21>NLr@aZZ?MZe-PF$cL`P5OFHg5R*yXdlHIPvMAL{TrBfW# z7&CbXd1P<(=kgA@jywN$n~?VwOwE==-$fwn-+UNpCZP^}#UL~3-!k8o#-wDpD+Rz~jRDsgVwk^7 z2Nd@i(g0|AIr+p0Zck=w(lCDu7}{wqgZqV4vt!PkehOn4U9hJo za;S0(@pHBSubA;|)hz>Ed=ECBQ5T76H8w4sHU;$Hg9q0zo8>>+GMZj`h0xSqRnZXb z$75%by#s;Ao+V4fAuzlFN{d6YwLz8!n@v~jwLL38QvP;lHnX7b*_r93x0H{M2yNv| zS;M0(=mMILs1T|@Vxv~O69Z3MCoqm&ScZ~$c0%uuM;RXSUW_l&^*lRABn@gkl@Rzw zQ4`tak#2(h+M4b<@7@|1pSP60RNFJ#%x-2Bf*uHJ>`U7Z^gZ`mwbeRx=iQV*W;<8) zX^z$Hq<0c7EHcdGS`RDFd`+m$&`+ zGlLw^sa~Va89K|v0{1;HSko)zCg(Du;_7{0;IESE#bhI`{%1Bb4 z0#Z5!oxgrs`(LCEckk2Xh9<^+bTIeVQe^;4Z?Xbn*8*n7hqtSD&}r(rja1Ej1dmR# z(HaV985KX|tiygf6DsRx@XnddJlIi+dbbOsTa&5$G+<&)t!iY%DQQp_`N$~|!Noh{ z*myzjtKOd*y$eRP^Hdto1$q8@#?bDlSBRFIZ3J;U&$q@ASOjz5*qCp~yWB!jzJB!8 zc19@0W)lKqiFhCATKo9&dCFCeGA|e3OYR5uu_WwYN!{q#%-@`|e6qSqPiXWHsp!!P z1j0mJRVKK6_Eho-Ye5FHba3)aN7GZvjhbYc!w( zH3A*KD@fbLi2yGY%17N07?5CRF0Fyh0dGV+s?n3W9jIUK_Q$xD3|wr%p;;bjP>(cf z7tSDcsI~d+kZOMhf6f6SuFsMr#ns$HHVS!d(ng$BG1%#vJ>bBX=^=!Yx{IEjfoKXt z{bSb|GL+1I^XfX2^+mW_*MXQ#`OIU z9n-|#xS4s#FII0OKr}DvZRV^BM+3zeDz)@u>uv8+uLi6$MI^Xwl7rBw12XX=wnPC= zQS5qsj;pM_St7u4__;CljK-Q=6oH9RI}^3SCr*HO!g z?H@us{bFy1me%nlPY9f?ZZ`)0y~gbARxPX91bJq*&G4g<<9)rtt)*aq$<2}{nLCE; zP7)eOnWalyt8hBnQRE`6LgCD_GgKW_dAiZ5h-P9o3aO-uN00aKTzC-@2=PRJoSS_s ziv!|@UtumpuE^Gvx6udZ=l)VKm@B}|adOmvb{h*{pmm((@v-^Z_Zji|GsTOYHa^ci zlW_kBMRQa*u+F_v@xEP|m1Us2gy3WDPj|sSk3}iApEA+wiK^d#T(38(Tq4C1{~_jn z!uvs2ydSY`mR(eW283jAwk%9sY=|tsv0EK4pSbRp#LH!`Ed_ za!6io2jMzP1*NBPhe+`D>^&GJ*z0G_2h!Sg&gR#QlccJ8EvC0zI6}J`Yc0%b!mLk% zYKvl9m4j#*TiNWZQy+zaT~oW-R(|d!-A~u6?bQ?hmDRK3_i;ssH%ynRaMmjYgk`jl zs9fNM3GEpB^nv6N1s^ZEZq?@YwsV)%&Ie^e&4&Hch;y0Z%S-+|O_6l+yX#?klM@6X z!7in~bs3jayt%`n$quGi3@Y$>aF(j7Z}dd7QSgREv-VCupyeaq4F+Aj;%k$o1XzYt$|EJuO-_~Y7i#@j7=tMNY1@tEgs zbvg#1bqBQ?de?iL^MPR@GTtr9pwEm~{JQzk9bctQMBqIdYdE7Az5)8HyYRR*jrty2 zg&D1f?_(^VtV*Nw)=r^02K{?d3+?_nZ)>^m%ihs#xkqcAzRLemvcaNGWcSykP_ z>nmfMd_ViNyw04gHFaI3sS<6DSd&=!dl{=^>ckoVZ8&@g`JWK7 z2f)CPX_sXD*rbqJoA~e!COP=mW=@-qYwp^ws_3m>|H=-~<%9C>3>l@F!kUfColwbj zc~krH#?TA5yMY3k*7ly9b#}D1S8{}E^CvG zD7#Zl7rVF`(7|E)-J@<`Iq!4d{C=fv~f1J0DA z+^c&G`<4a!plSyd$9`+Scb>)gQElxbew7x(b2q0y_PbMsrUP@O7m4Fwz_<(G~*0)Pn1<)tQnr2hjOwPlg>_^dQQHP-#-`)ayoghRtR6$#$h%`AZHp^<5y0B zTXi;5B9}D>wFh4qu^CiY(NWL_6O&K{snR3gH5)|TQl!o|uF9(3mKEa+IFECIsl5q5 z`;`bXWw~cx2`N3LyW$}|k%XcP!Ma~?NElz4EE8Ja3?q`|dD}K}s+^&nw7q(s2d;jf zzqrQ7^ugwjMy-_3lu;LFGbu9D)H6B^gUxahHKRMLzIvm>L8r-MZ5M|oGoz>K-$g(- zz-9XCt7|;q?i4z~WU68qDkumLNnvpvphHF5aSg4|c@OSQuFjPB{G8e1>Dv2qdTj=9 zDT_wfJ;t_gF`0|A;@w$(%k8ChT~${Hd~-o+##`Fbza5;nq%ZEL-(P-)8TMWEDd-w- zOVdlvaN%3lI8t)v+gx}suAAZR7tOGzbZ=#CXN_;^`H9SbrmkiOe#C-$JO_3=I`d;P3Ip=%-V$2GXlJQF%@I?%^t{f2L)(eo`?aglb+MMYj-y?Kx}gKK zG5ALX(JBM2CR|#v;#`s@lGnzdJz8y^giL{Se7W=g3_@k_k}wH@K>Hpu_vja@B?D;x z9B#mkl;}uDCw^0zCvgHPWIX;eUdpoftJJ?#yKA^-eOY>Y(Aa#pK{Q1E+#QO z8EhtDe)$yuI=o(Jlsg-cWksZ(8<}-G1qO=(u^M5TV9@sUdYd?b?3DzdyXa zxB|Ls@MnzN&bRsF3V^Vyz>4Nqtiz9bO4FrP1XYbm}QN;~_~nHetB zqkL*4^S$|GNlMXN7SheKElDa9-F6oBN5sK6wVe31BXfuzoI3($OJnAu%q{f77M7!N z#xR7n#u!YV5Rv!*}a5G%2)Vvfn_@rTUU6A23meBi%Y>KnY4sLjBJ z$RHL(7AJr?>}Vnfut}+&C_d-HCS*vmNZFr%cdQg(*X`S!25CT)OAn0EUl2iZ@z|aK zrX^UHf7fVonrzsDsHk`^b_Zm($XP$Dkvb_#TdR8Ws5OluQvPYm8+g$I|A_mw%mjC0 z{%B8Hw_)R}pW`7j(|<;KVa5sKPa2xt8jZ<`Bb!PxZ62AfG5}q>>}4A5W$?9;mfPkL zAudS!5NkaN&b1aCSyCLEAm-8>oS|TsBnd?x&h<_7J1QSzMGiYhQN+2p#>3o4LEUHF zrL>MM&rT+0E=8n{N4iGBT2CsVh4?HE-DY#|8{0>xT{C1=7s*S8%g}Y^I>*4ele{>; zs*O{y@}kL7GF*!pdnNEFP-Y!XYFm}u(gn|BD>;0`e3Q+ zUDrlCqF#%caJHixLBb!Sh!VRPfIB7mq@SP>W$iUO^eA=BZY6{UJW-;9FEBg6L`{%cMGfLG<3U!Qr-b%I}o-rFMOH{&U0!n*qP*iBTuX*VN+^ zN?9EXU()7~)(NWklrK?Iv*tGAP56{+)q=W9|Ka*%c#sT;96%LgD&<`Ay<8NWk)NE1AQSz*?#3K+S~J1IS~uG{@OOg6aBjSnJ@s z%LXSxJ6(LzEZb$U4}1dFXmeXjQj zMZgP@>;V$8oci2rW2t~FkR^QX`d!uv*YhzBW*Kg!5Pug>!;%IkqS<@B1)yhHG~LPY z#2Q=@fy{x1DZexB`#aRBtOJ}AP(;sFECCY$2_v}{f9{!RXG960zDA~dUL3rFdv?pH zfeumXHc(;6t$&Rs#39qaa+VSiwk9+Iknukd1c3Vs*gFR9dK*o|$ay7paOlxu9n|`p z<9Y#)AA_Y0jGnx`6pr9L`Pfo-;on-<_a+;~jWeGm2V!yZxlBKv7X&*U;&}!>2htE(JE$J8|OU0*67c)~8ZQU84NLr8Zn9Dj=QY zp;7+qvl3WHV&cjhz;f$XaK|mZdepprrC^tAf>(u^R`)}`qz?*~a<*@)1wh{6aQ8h1 z7Cp&WkJhb_8<#CbSqKHS zJL68jOKzRNu|{Y(G6_=5{@`s)0A9CT9x6zO3^rW2PHfK+o5fAq?Uk993HIvl{P}rM z7roA3?EE2oPb1@mmG+x+yy*m!k$ab7g41kNhL~7gYjWM5tu7v9)8ZL1e%kQTkzB^D zw9nCeFP3RoY&11spS7-XSp5!CT$YDc;>*fe4{qOaq5rbh-T(&c7i*CmMhdee;*-MV;%B{-pgk$SqE)h+A7K zN7GNMT8e`D=Be61#2mBZVTZrDQ@75oBQN}5*P?X+tgZo|{&>d(C4oN^Sy=!FqOatq zh2H}?nhq+SWKJ96!eLtm_wk7@Lwxt)!cxDW!3@y5`f_ z=azY8u=O?2`OBg<5=^uhEnNMmuP)Sb|B)`0k#LxFSLKMHkwrx9VT{w9-jy?34Qzrm zrrMQAX8DjSfLU*qbPcsshNY}0Ry9M+80}RrzGoU|I+W$Bx!nN%H^^SknD_wLBDjb+8iP?ty@kXW%00bQXu-iMU^;JJ$bW^lK|P2uUhz3*srjc2AZKPn5Tu$ZXQ?) zaW_2_f0?cl@PX9cGE&9dq0s2=CF;ID2i*Kl1n6c-d^#b`!HJ&s4(f5&h*=5?cOagp z>>ZxxX+HO5I(_LBzmS9MeFPO^TI~22HTi!VnEwhNha}xdB>%0cTj$q1Z*?%Z8*bg1 zk(huEE@)F9jh{&U#~aUH>q+>GpPBxhQMqzlyyHQFuLRk1i3n-2lh^++%>Nl3mSL1D}rs zkJ@dUM2Z9eX*W|H%L=3yNGGj7u;FREp^$lUtc0N`ZS0|u`s^g_>{y87GcvcYE!WU; zDk{S*EcVtTZ+yLnt!Xq z-p@j1kE5gR(%Lx0Dp_*VLCIi(?Z@m8_vC0dlbGOpg!OuWHW~W1qAPA0+=okGQFeqQR zExqit2Pci(b2jUXkn&osHojV#F;Jwh!lmEIjAq3-9?0IXLQ$c7lt*!_^#>n>V#t05 z{@FhB+N|JuUrPyw*BFmCTui4UZV#nh;iR1!RxUsFt&^}RRP~VpgZ)h|UdqITIr6t* zj4U1P^j^3C)QOV;1S7%; zn^frwq?cFW&`CC>K!eB8_Dbl(d_jVYlHSDVz1&=USEeKU@2szo79S3#vF|PmwNXflnY3R z!-%DT9oq2c>RN%51)Kdv_=$arHiv}4MaSBYO*?s=6XdcUMt1P>wR7JvT%uxqi4gby ziFk{po7XvTKON%F6zQCNhVjRF1ZoT~Tb;x%+gUzyyRiz&gzll)Id&}w21Y528 z9PgFr>=WroN-xi^&m0Cw(EjBi0MyB0u&gLZV=`SWe@c(aoQ~&VVq_ImqN1Plzu7hs zXA=R?0IDwuvSs+h1YL*88kU5V9umK%FV8$|{mIvViw}Eib>umOy1o>cDD=u?(Cl}< zGsUL@ALBO4b`p~)tnSY0#Xe1>Li)S}8Hj~_O>j*VT6yiyF_GEQAfV6}u(Sm9sd$J~ zk*~AgNvSWxZnzEd^rAgAD9j?eisn#GdVPj>!+eQ~^SP~Mv+e=4n{DZc6kV6AE17T- zztK}nT;mabk=L+|2&{w*^<;dSuJ2L8+*sne5Z^-gscPZCeJ=W=hk!DPE%a|kqD?uC0AHLpNKQ|K#>rfxj~@FsC&It00JCYUiwsc z_NLFBb2~e1-Hm7#gJ;%=@|J0b;0}IS`enPIJ?wq64SRds0{9MPzoh zTT^;=?q3&B#8187^;sXEeBx(C+-$)3%C1ljIfDe5-gWggr~~WS0S3jz)2hBE0V7TR zYj$kYRM?RVR05tcleOVsVlD74miKF3@A0M2mA7N;Wp!H5-mphte7|W?<#Q?~n5*AE z-1Ju=l^?aXo*11%D1$Q6hiU>y$@_>6+28BSDKJDTQ{C+&T3k(fu%ko>JA4|hO|>GK z-W_lc`NFhF;j6&R(nl1&E~JTRMoJ?l8)}Oos+*k^x@y(0Fg6nK^V_ux?#;KsVa^Cw zgP%5E-+MnXQ0Fsv7s?_fgPRU0aftV(U${F{@sVq&4xlf@{tYHvkBJnzO?n;C)%5Io zs6)d|i+K9J*4N#PGOE29`aHasRAwj~ydsmPBz&j)Q=ZVfmXIfeB};W$j21FN++R(f zoIe&!E9<_a8H4m@0ckaDe6shFVk@CvN+KX+f6Sw5&`WwN}1NU2&RZ8MtplwG!2 zV>93nOME7F4cam>@k2ygp)gTkF=}gFafK^X-(JDRqWhN1D+LvUQe_j~z-8_nv9HOs zgE9`ZA67Ipzd=aC0@X7{e|=R@s!@CBC-I-bk6B9fZ;*qTSsRIj`$$t`?@|TNobxrm z;VCU$FZs>a7}?2-j??tb5|ciHV#7y)Xy>Qd(rO4JQME6*X+S?%`(G~+ko9o%E}y(j zX?1y2N2g6n0$NO`Zg}Y*!xn_6fCUSDy6o1%9IfyxpJZ31#A@B;=QBlTUhK-?#}&g_nj3dCu3`k&Z6#3wyFGY^afeE5=!Ot#(iOQM*93&mCg z|J*mIxy}5V_ov={gW!h)IB!L>VE|6@Ro#bYODBG~42LyXyVa{o-X4jYH6Ar=34}wk zD@O*a7Fq~jLd|@;Fieip6X+s(PMI!Lj=W&0&FK-$sdeDEUA|1dWRP3OR#A*xo{PvGbcJMzUCKRj$>#r_H!$OsKq_k>9{^ciZra? ztL3-g3w)8`GgN++u$^ARu=l=bvA}f=_4i2mn_LX%Bmtlsj|rC0#Pw=+AnZxkh<>D+ zW5DSPJJmK-Y*lO5C}>`|gat8P1u%ruL`x}Bn05IBL2t6<>0bF)YikUU-H>GWSkQ(b z-!LpuM`Sx9tWppMi>##k_C$&{U``NiCdb<2sJ%kBOY&lo`g|kz1dkU9j^iZkMkMV| zUMZ=XLRVA74yRhiBp*$=A(L`p;v=wVF<%bl+G2K za>Z>k_*T~p#>Nb{QQb1~#?g6ZU0K8EN72!kXFZO+w>)YR7z%uO8#h9!HhCNt5BqKW zss0|Il%&<9In6L5!;n2C?n$5buAKeFR-Ji`>jkCvsx_dppvhKvR3XEQS$`cbLKF%J zFNc{DjjUY?P}Q6720f##gB)81{2*B*mag6@TXqQj+5yu5DigmUXX;q|5*Q&7-{K(5 zN^*PTLY1;^j8W$LAxYFbm*)={uPr+81uEBSEl@OPyM@SLJ`>ONE_p|` zkzwF3&*=$k2)@-Dop1E_4YLM{_*Gpol1IqUXM^!nC9=RrKBWD+C6%=X;WgxAO=S(v zsV@&CAQPI$CKS#C_~SB_ftp(IUh`9#j^+J;hckX3$Sr@z+lK5X)BT7dUNaJut4Bw*m%*k^`kkff);<&BzHt&0QJoEBm0KTXRl~b(1>V=uryTcEboQQlbhOc5Irifk>I z&H{O}H6I-B(~5*~;r}_<@$Um@|3}nZf0t7I@Tg|3u1j6lWGZq^voMy^H0`fD)obPa z)2Z~lW7j1&Eh+k0m+ge6boy%aF#P#2tA^eT0TpQ#fH2a(b_et0GcaHn*S$|czo#N@ z^$d+#EtT~Q9@|2_OZ`?)2;sz$9ppcKt-p^@FFZ}8+AtYNKYCOdU6WBaI_O?7{bNCM zOGLva0qjhHMGpnul5vZs2CdtJl!~s?TjgQ+WYpvQP=+NKd3D67)#2#@Gz3_X<{IJy z^t;9rvY7Zi-O}xsQPvwO;n*5gP0m)?@=?;o+kQ&{a?}Nr#Zzl=P>!n_^|Ts%>v_JEe?7}jQFhM$%GD@-*-vSbcMOH*A zCs9NJBO!M^qlxB?N%zDC5wD2DXJ*^aDVky#0nrFgTwb8qX0-P6#Sw0(&?24BJ zFJwZLm%E2x-T`VHW8DrY9uruT^Uh^LPjt2U%Sqi#Q=^Wdus^iiWGQtZnWUXmlE}?0 zA(U$XwU2a{@6UkgR}e!ZPc6{z`CYam0H<*|r6?vAUQ}X-<$7_G|6yhcVq%C<&jsz) zk|UU`#m1IVeB9X4OrwA22E^@a5L!9c8^{}t#+Ul^X zC`a&Twom%&roA_r6XdGUHzxC}W`-?2&)*o3yzN9L*uJNa46RRN@wjxGw~oGrVz6Sr zHro7g$FuMNqCe91%3+v}im(SBbwPJly!eV4_QUE}%<7eQ%z&HJ%UE*rI2dnYMpgppkRmUU z{^BCoPeffI;mm(1qxbhJ>+tZ7;Gh58I-9N4$vky^zdAFYPbRNjx%R`8m$T@6d~pgX zHQ>d2n0LN9V6~8nx||X`3p@;z?rWNKBUye<@XuDB8@P@#V(A#45`E13Afzcp7$F}0(t6iZuCH9XLz+aK~cesu7pTlkZB#Zho39Gun=AtPjR*Bpu zfu=YYE{E*XvAn~Awi~B$mORlv+ymWG91JNY z4#=lC4>f3;t4<=9yG#_16Nv(V72P{s_3L7dzuL#!dyku*#*3F0sw?CdrKwg&3v{s;RVb@p@uqO8zbO}{1P)BoO z7Aj(}8K2u|t4QbZieB*IT-tfI$JB6V*I<2SkE&qu{N1oi01N+-Z*2Bne~35Yq1+=q zOPs5v$Q4_GL9HCNCGMoqhBXDr8=4~e>AB0bZHZ_YkCKF&e;1|UvILU}7P$maW#t;@ zP}0k0mUF%#>b>3XFRgi}NwKg`7iFJrT}ihGsY;4xQYzAjH!r-Ix<%YWKGHKZ@}b&J zK{@-3sa_>Rxog*V{&KjF>mnfUa@;Gs?55X1hBYX#AXw>#v(A;1+MbQwE{d_wI#|{_ z>6hbZ2b&uSUixo2Ltgs&)TNch*gB95Y&{)*dQ^bX=92a8Xgf;h)ES%7*MbZ`EKQ#&k-w(OKcgc~ZV5 zKUF`dz;4UVMK?7VzJ6L#Hw|7*)-c+*y9)EV(u&OZAtw-p;f>dd^t_)Q?#mFfb958~ z@;)>44e+}%f^vobPqFjAZ2|Z<5f&hRBN6Xf9-H>yV8)wQ-b-qUA!bmltV6BMz2IPQ zAs>SoY_)15^tn@)Y3v1Fp49`7(;oh~TA>W+WPEBP640r#J?KrMWKa3s)CnVwpOk&F zspXBcvM%dM$={S$3_>TXkMudk9QggK-LB^|2(fI7?it@Ec_(CCbsu4l84Tyvm9-%K zB(Ba*7Q_);^lW=INYHzbLNm5(Q)Fb(o%bu59iwgUwn?Df7g=YAdB4PJ&eIpN{WeK( z3bz~+Yhv5Um$Q>ELon6l*0+NS0~;zWn{+u@$fL7X6TemY@n(q>PoO=`Uj@?ki-Gkf zcRCjH6aLNxSF^?TmdWh5D?Z;w@{V;O(Zt7z=3o2>)4n_qg!$~`b*PK8} zCtuu}lp<#k@Vdmv<&g=Ig#z=(C70p-ZoRkec3IBIs5C`jfd4S*%<(-Jr0XWMwOhD2031J|fUQ8*YgFqyK`TD;cCZDEirOL^2*L1mqbe0 zkeHDf8kx>)B;xhdBW&3OOeDIv3uHwPTxo^01I-xU6;p;us-7uA3S=f*)6Tw(kR+i3 zZ~SD;)EWICMNKw!ObG$5+zFkwn_7TWr;L-CeVTNko5)vpvH0WIgYBoh&1t0$a?^8} zPt`T3-r~W*j{qkw=Baz+rCE18A~U;v)c|xbZN*bB#C=d?f%t%I3t(w6Q zt>`Z^6^kVvj$1^$r~cA8yi3s3 z@<6`M>22K!u*!~}E0-lR@OU$!@KVB*F4Oh>I$M?jK~HyAUG)WXDm}V`m7;<%(|>8% zK}P$lZnyP^V4N4*$)wJI%QxJ@1f6Mi5xL#px^zQyklhQS>jty*g=D!ps)Pky-q5o1QQ7VO*~J?ZJHD=%qwPqRL0p0ExadUH5C+|oRn8&QrYct__?UtOb1d%$rzLC&%$Gx zzjyD2;UAgypBlR`V!nnRu#1YWKD=A!j$#nMQ8aWtVN}rmj_}jkqJxS)jDm2v?L#O2 zdNe5nQ(XOa41~~KL2Vs+gs|^ERN%AqYx#Y8nZWz;-gm8ZAKCkIC~8DR!b@?H%g74n~e+~cqOum#uJ!PKqS z+qQli$apO;+-u*#3Pl|QKLH2#A9D6n9`b`?gq?$%$0L7z#xziJ$?QLcE-%zxe5A*o z;4QZPt-9K5WqI-jX?f8aZIZ)Y`RE_{TPOcrdf(+CrHrKwc&$gpe2sc#K#(_& zqIt`S@GWnSU<;8znLDX)$r59F*AEmcmAtd9AC>{s@nok&?a)S9HQ~yeyamifbA{lx zR}Tq#C9T2JV{!jC+oKM#o3*QXBJUO?g#s2ARiy%Ek;J^Sfv}FiLpJk)0L}!YShUar zw;Xn5rQx?8jbzO>M?v6*EI32nyk$_e(dw8z`kOW3WOekGUfj$x60;UuK&m$!#W8sV zEf_zD%XB_j4C+FS+ZeEGTXLHMQ{rL(8g1HMgsjuzQ-MFPCcy+$;}(ey{P65*lrR`8 zeljDR{+tf@oJ4ABTZZAhLsi?RW&sqjx1m$7%snxS&p87njA75dsKf*tJg^1zq|2+D zLUO@CbCT{uFW{U>>2dS)cUp*RIa9kX_uEu`GW#+z^1a&t@F&Oe(`#ian_(jT%m54* znRrNwEF{FY3wB}c^xY?u%~Firg>9Kc&t#p%q?JQ@;)Ek}Jg~mdl@E{Y8VPm0$-7I& zXN3U*Fng#TzKZov8k#W*Zo_r$?bQA)mDBe`nU&@D_rx!J{kD3W z{i}pXh@sJZ-+rZhsU6JMHVnEG{LnYK2pO$DQxNqj_k1T|cR$diX1KO?Qim^)mYng& zvDQM;8l7O(RcR{pbl~W_iC>YkKL3ZYJG{J@^e(|#_i&%(`)>uHKk|kNZJPNU{d|mQ z>*LVf1N?S3jEH5qKjO=f!{a*#;-*n{v_G3NYi;(c(x)dMEvU^%r z(RzVS2Q$FIbAnQx$vr4f)0f)0u=4yNUt8rQe8|T(gFJMXp1=+(5lmKC9awZ&;qzG_ zPD$zSGzkgGK8cU?pw(uNl-5F`{{IL)1j74w;`_6$T2d#=G#gj5jl+OT9W&r;( zivc(RIpz9P0v;4VC`a8aP-w{O#f20XT0wzD9HvIkowBiuor#-oXpM>AOq6e6@_X&t zEr6RDoI3lg@9meZgwt)BYBxRti1-Aqyf{wbU{&lqY(?4{diUk=iT$vcxRTba-e_ee z*B4J7z!|mKatnYefoMnGdo^w9rfqiNQma|JojLxImG-`B+4VaBQd@Dw#R(T^Ho|q( z0EFY?AL91<9uO<;tfXiq_aJVk##ju;(EypETeNg_0tw`RVaK{GK6T*ZneRFdm^50j z+~Tu`Ad7jF6@1!t8dv}r7Dv(m9002t&b-maowOx~-8LZLvq|4e*ClSWf@}q~0>wfJ z&=%mqx+j2o>j5osgzfL*)eW4$@w@&oaoUyp?KQ@#`kIbkL*16eK$)B`AZ#pmUCqJA zr7P|OfR)=RPPTFPIe=mSP@Q|59DvtY{NYQu6FrmSfGF3(ZFDTP!|vFhHJ2b14#0-1uEwJ=!~nOUZMn3q+Gh6AZxh&f;Y!92fT6wyfl;MVjN2PfhppKT2z)qk z&}En*ojqcD)JK7l*#3Cpx?0Jg=!tYZ^~ zy8z|qPX;Gk1Bxx}eZ_LX*hZZ)KuHl8c48`?J#Jax?OZ$e0p~mh+?!tmjQdai3%maP z|HDc^wk7WOf!BU)jZc3uxIz~?9sk)p(_I40apaA3vSV8GT#D} zjiX$p4|ir9-E@!~q9d+BmPHL}0dsTI&xX$yj<`U)pXSus%Khxg6>tSyf!k2P5DmE%xo|rJVU@0Il{m{EwGp1oUF zz!h)>Tme_W6>tSy0aw5ka0OfeSHKl;1zZ7Fz!h)>Tme_W6>tSyfm>Cengy8(u1(vl zF4u9@@6$SId(~wV>rPgu`uORNRlSMrrN>THHeLOxj;U^QuJ>me-(2_a?Yjc^n*#qo XMx9I!DM}Ey00000NkvXXu0mjf+m^9d literal 0 HcmV?d00001 diff --git a/resources/img/tensorboard_basic_1.png b/resources/img/tensorboard_basic_1.png new file mode 100644 index 0000000000000000000000000000000000000000..c3d578171e8a87e4f8c08fdef3dc0a781511c485 GIT binary patch literal 284683 zcmeFZQ*>s{);1iwV|Hvi>DWofwr$%^$F`l0ZR?Khq+{E-<9xmU!@c*xKi=nbk5TKS z4r;BM^P1O0%~}x(a^m0MzQcilfP9mb5LE&JfoA~$f$D&P`a4raU*!b?f`DNuBBCHE zB0{9#WN&6^V+sNy5s{(}t)V=InX9b?151w#N?pJqw!~4nv3dB^~ z&Cc#e1(;wgkv=y5!Gw&Alv$z|C?XXIh`7PTQMi+DVd20oBh#NR-K|ZC@Zw%G`iRHc z{V!FOc3u={5Hh$um74G*@LOgO2U)oqCPENl=tbPA>6w%rq41?&@kAh^IzbDj8p=V+ zrXE3y6hHnT8DWB$&}7leKnqF^WNG$EhB+pa;n_m!^nmPHg?Dq}*f0Qq#JTZFtnfyr zEP$i(?IFo8?a9UZbQ`xP2!}_FU0ju*)-W0^l@>RFb$*whmL9{H^M(})pDtb%oZtP2(iJKtJoei41d}L zMN|G!>x+vE(16`zP*jsi-vAmKhUx>#VifS`cWn@E*VzmWnkJzZCQ)LJeyusI&bJ%T z8&thVuNq`9rjDYKhsHVW+`WUF;4v}CBU2uS_MhFwq`qMun~9HI)>n63UPh?w>7r%?GM9%fgFvPgeY+#LyjBMb`n7#BU@#NBCfNq_)A<=Q z?k0Iw)+?gUB!ONgY(9^Aa+dM2wcv-HE@e1}uC~E(ACQ?Ra6g&74_Em`?*TWzI~o!` z6_K}q@kBNoo7Y8Yur7n7uUb~2ocGB|-Dr^WCa4XZ*vu)}X)+=DFAld%w`@P%BRMx7 z-b*h_C(mRhF}`5Gn;lML+%2%)W$-(295IVP;AO$ecx&@ufslE3+MIg~#$T<$?9X%P z>cNB9kt<|;%8vX1+W{zpoLu*uM(Yha>BIMpF~n+U5G_cy7z+k`;f&u@&VqUM!K4rR zTm-V-fY2d>2n&&gLrNMz>D(g3gLBP5-S?v^AV~FV)uE^a*&0x;!XpNd=7X{Yv+nS= zA$0V8b3j@aBlG};>gQpKCW3)2Aa)UjpA(lsrUei8O|wGjTDAt^iq;0aI#&;vvOm_{78%#PurB$f$P z61l|^s9DMKG!(WJmy|gay5x@u{P7rLuO{A3L|hqke|I=B_BL8Nsy;R|ItWJtM*(*Q zmjI{ps~yf7-Hn=!T1W9j8T)sc(j--GvW66eDQroQQ@m5GQ{LTAMa4OVIa$X@5`D*d zqhEW5L-3K^k%Z+|D$*5%uHRK#6wG{8VC9g;&_GpjTcmcT>A$#xKI1`#8aSReEK3gnO2Zi+B!!qcp2PQXI;rV^2z41`pdE6qrD@`b>Md{KE(%QK4yhL6d` z#@*%AYjPgv{Alv8r2xpIQx?C+#6Ld9Vy*BL9gz*Lbak=gS7!;*JBs8 zk-5p=!P9x%N$B`BG5Cjc!Z>f_UeYYdBgswH>%#J6>}g|nZGY%N>h{mo(!TA^)Kkr^ zAArTkQL|HX#)r;_#rNs0>!JA(+E+V7c#m|)czcbY03QxthTws1ft^mE zfyIe^fX>EPMxKt0huOvOtQKwuLs|U8glUo7MaV)Mo1KoLiseGF&9sfF%aVng%jNEM z3`ik;<+tBlvb4Ew z&K+wHTnmiTv$+X;`v)?I85bGi$}rTh)Ne}sjihVr7X^#)i)V{Mv4rMm$B=15n|+tZ zo_FIOGxNL)bxhlwf0r!SL_U4 zJgcBtLm;k7PJ6R`*mG*R8PFWm%(3EGyRQ9POHJd+POcmr}O@h)5x7|7F8A?%Zp}!_B#izywv+2*n7@F-0ktOaW+RAPFUAy*< zl;+2sADgx%>-@II_PyPt7UI4!ma;E$d(%NV^6o>|R$oJpX@55VbV|GXuaT}UG;(;^ zYgbF#Uh?PoTA$5wJL-OYTE2B6J`-B-TKGDQ5Dyp35AM`G-w^ixe)qbUSWQ?isu4#b zVC7Qt&GzdmCMeI-7Fdm+n_Wm>T=gY+5VcMcU%1`2@mpW8nbmPs+CRutPF9|fi^*o- zN7g`y>6dgqmL6DBz7*_(Vd=e%T3yR zt?jLO+haZVXLkLYyWUaPXjf^x3Z4p|3de?10(75_Wh3p!CbjdSv3wGklT*(p200};zZ3rY} zkab*bRfg#IfS%JstSRVXzhTvwSn`npW0cr04(x%jganQbkoZiHXA(RdoGliXJ5ww& zZ|Hzj-nATg5>bzyZ97sSU0=rjxI6hFMq4&@FsF&Xc>=71gr+kH2qyWz9#BaovKtT( zK@dq%Ar%kMiyY_;^r4ThUhOM_4s=kVOczTqHNlH+QRA37m83lKOryvMYO7BQ3ady- zwjr|bMG>2;okVa{;UrSm^FpBIGtTavZ(oyJww+HK_Zxg8_dDi#yc-vj7Z(@a?&jv^ z{P*~_$MR5|!s1|Lg8yi!lI5%4YVe}N#)JH$(FQ?fauoB=zJ*M|6%3t49iIzH;vema zOpuuM8Wff2pNzl?wEXz?vr$jMf3^-NT!tsoKa{OX4#=-bRq<)?#DAtEh0#gx00@bH zGV;&<>bZv7i6H);trH+@wgvuAp-U-9?7Wn4AMg*?L1(!q_(xa)MJ?n0tLMttY-qB7 zf;mvs{P+yuKhtr%;1aCDsE+FaFVR1;=lH*n>w^AASP6irkP8q#svtcEmHa1|3-}kc z@coZk{sW``qn7_lLjR+d|3*Upqn7_lX8)s>{~F&U!Td{FL|KAU$M$IZeT}63o%Y|! zq$bv^R8p~(X3Z0E2jp^MZ9v?MuT ztp&=z67XV@#mPh6RZaL^`Trh52|mJJunU`6mPo!YZnMMo0~)tO<6llG#mTKowB?# znev>m5^mvLL&wYVf0tmqpx|GZ8UX3*%?e7&aAc@1b@5z#F=amHO#0u9{y$vCf7?JH zrbwR+GkJAwzH2Pmc3ZUtjqf(G5dt4;;;4Ank+zpXvr_)Uj+bE?(WmHESC?CMnXshMND9?r7lLL!Z_KdegLzcB7*MQq}?%P0o>eQ@LeEU34k)p~8seNB0;>Izsl!k{yQk zxs+o|Rs3RM+K?U-tv!nX^1#h%TND7#H^Cz3wd7varj-uO`jM_awU=ReedcBC)RF)q znwfIPlXIIsTDesg70E|HsV4=~==&U*Im}mjKc$9PE zV(ql2(>zriCiu3^@xrn_=Z096AMNpcBuUDBHLgaPy{)h1u6k@x2Ftkw<1cncQ2L!x zGXInVeE3B4UV2}UiDKd2whC1tneXsD>p3Ry0v_1)VP2<`>^FaPeAo(f+QcoFz@LS& zF3cFoz7ct?j2O}TC{`iq_0_vW?mn6zS=Pc{}`GrN#VJ6!kXe5}5~-p8MnCxKP6HAycKvh`tS7 zKQvB?c&7d+2k1+TCor;v;io`Xjs< zT3E2ejGwYWKmGfcQKgsvC+(ii5E*xenTX)|AKAUqk6zxxKnc4QGapJDWbZ+p#Wedx z9wA1zvsTfWXt6cgQc0(~-n)aSBSF%uq}7BxG~d2@&wE;OrrX}@-L=)&j{Mn|3Z2Db zM%U?cv6kwT#Q{l+MxShsx6l^iU>OI5MS&pqw6{+!UYu>i%HWgu8O~=DX7TL(P|i*c z>AeENUq(fG)v~k^dH$V*={}bnb~(jJ-^uH5w{He<&m?V-!cCn4igHY)&KWe@H+qa^U_Msj#)G^+j?$*7oCnU_%1K@HJ=8=WC-p zuYmDvs*LG}-p}C1waoy(@wF;0nxj0(_mk`XYFnK+PNNfD4kN1l6p!384Ul;InD;EV zXM2F>qTNnj^5!T-+HI~mGv5)FuL<5kWrUgUeee4lZ&IQBV|oPO?Hy*Ce4 z!g%^Vh=_GRTl*80EVqZje{<%QbfCP%CgeO$Y5aohcKT(;eY1F}tryPkyaH7NRz_p> zpym294zqOq36f$<<&}4#>iZlf-C|nu05HJn&8I=FirnI@N-u+7H1O+@pqtEXUjAbh zfF=;`Bf_T1BZOZ+`N`HhTgB>wPT#LQ_QSa-Z$!;8BG0+eqTY>x#TEY2PEk+d7O=x8fHR`uQ?{hU*-J zglkd)#NKQ}Xvg{wA1J5_3yAGfFrw7Sqith1GJ3!x4HCGImnMIimnVOD1&5q4i{{d^ zntnC5RrjwnDPS|%nU)syiRu&JJzC&hvtncuwaKok>hbk97RUv6@+2XpuMD$=DUAGuK2cQqr? z>%P`#%CA|W2EiT?;=8we=oRr4S{x>K9w*&|=jnNaXl$GPM_bj0DqqbQ3=CZyRz@8@ zaA7+VKEBJCD9^~$X~;NCH`BFgk|LyZI6aCegc}Jhz)y8vwD28=xv3oXWEClB z3~v|QC@5^%xe0J{T;(l~ICTe!L5#om>7 zcm4*%u-y*ytK+(0HR=W@cK&=J_v5BkB1n;F7Hhg16b;)3_-1BCz_Jm_z&`Y>rH zURd-0v7dfn#+XA^&T%!3^2pxwt^DpOn>*CyDy@d+=7N^k=x#`x=q5r-eQD3uCs)fi z1$^~q4QUUCZfTDzoQ{4i?~vaTTwQJ@@YM|U%oi%3{*{#uDay^QWss}<$pz2<$s0-1 z>6O&;yxT!rWvRLT77Hs+rfizSCpXuD+r_6_U4Ky-IoKnKu8Z?=szsg=C{O4DMzJG1 z{Ps}`DMq?w_g+mBDAn6243ye>GH@=dWbtT5yd-yf{q{_7kpqth?A}h*ypJW*>pNHh zBN2BuXMcH3uRZoxue5LS^@{pY(FzF5B_1pHpA^`w6?V0_8q42gxriQhwn5-+^XSww zO)TZ5$rBwDK00CEbUMudij~Dz2?#T3Wpe2FCo8zoCuBU7&8zKy(%e;7M&>tP=h`tM zh<=T)G8Wo?#l52;)B5C|t&X#|(QDK(mZQDqdKXTACw&bp?L=cts@ID;Ld2o$7cs(d(_((!Zo9Yrg&Vi%&HF9i@>^<;KiWqv`NLZ*x=A zTkDNKy}A{uvGLfzjgLF~Tr)$>ItuCB!^}5xi^@}1}!97NaZ<6CE?#|&|^01 zxaS6ehV>`9VXxFpKTc*vaY(v8R?A8^oMVnsx8}913xW7oh^dv+sw+x>BXTOrDqUYC zBoPB;(@4%6Ma;33`#Uw(FWUo4-y1oy^4*wdg zzvo7RXlM$lTt<|Y8IOU^ErVx-Rc}_vzSOj(HP~3ewe?AMPLPoiUt$ZDd{cuniO)FL zi+9-#+`q0x;e(IPvL;vU(M9=@2@C@CBMNhLwTYq;tJJ(Y#r$q<&0>2vQHw=6Bh)y; z^Y^Q*YLpJkK17qn^k&*~_97*Raoz|?Udg`_{_w~}((P7UcGfn%ZggAv>Fsq~w*6EF zNG=6kN<2uKK+FvaoFm3lUVS*lVh^mR^iE}iKt;s_hV^CMKI}mb?X>cr6z^HW0ltF| z@9n~U%O%cXKERgjeiRMl3o@w2twQiS`T-*@T*#(zqxfNIRH`8U;Qlte!N>g&|4h9i zy|-%}xgA&cKwc-OQ5%fGICBav9?6A>e`eTvPbveEei^ByV;y6q0Oy(}RqB2LSsCLI zdy+{W=yWwk!9}bA?V95yH(*4jA^Rilli@WfZ|K8CUedSeW&BtxcjzD~d>vF;V_nVI zXMZd$ zY}UJ8B2+s|6B~@OHBA$TWnUi{Xrx>f^2SW%Mu*~UDdV_pQ_t167CXupUPYM1esL*zdT$Ow81ipPq-(SWJ#k515QR~7v6s+dYU#B25k z&))hH^hwhr+~Ba%&+Y}6PLzr!zw~}?KZVu>LBJ<}8Q=b>6DIH-EiYsQ&~QOksbl?_ zfp@2T&G8W_o=$}rhFs|hX71-!@2VM&OId)%BXvw>zkQG$5-YH?`fTMuwP0{fX_5Di z@W=e%+GWWl#x*w3W2GOU49~rYU-uqb^M+xWIZj;11&7PU2g8Y0QO+-lZ|;_ zov_Cy{;XP5Jsiahh-IO zSLMclUslD`M&I$=>y9gPXzm$qDbuizUKM20?M4wJ$$?65<=MzhHXks;K-g8;m%|f! z-(D3)a9UzT0Zi)~)OSQ4Xz6*qZ_HM;8=K4?hP4@iNY~QDcT)lZI6Q~u*bnbPy|s<5 zzb-zfU_Bysx6SR)ouAw7L^kB(Z#AU#1bft%Xa`h{R#qKXB0P~}3(RB=9a|d89`54m zL;B}=>)VBfcltSx?Tu#74qS}+d0=S@7rRDg6YiZ|4$!Y2t6ui@PgDqZuWjjlHP5cv z@yxaaXzTaC^bfkuTL>7J8&drK411t!!@_2$5@@}}8fdhk=$AWxOH;p&Dmjep6pHo} z-_Jbof4Eq(H(Wkx)_9`%8c9HzwFs^McC(Em1C0knf95&SL;IT`o*$oI)}{fPWH1!>u2{WN%-^X=U$ z;|Ff^LK2Nxzgsy0Brh#Y(4_*Mym-xP)={ic_J@nb0NWN;g90x0{RoM#pKjlq@o|C8 zf`DkDqJsl%MM6PdJg16A9=CVb)1HPFUE=pHnZ%hd;$)IDSNarwS82AoRm=x2tDSY{ z>5++9;FC2r2F#<7q36LWMEI6LhBDVjAFHVT57i4K>GQn%yCZv7m}F;pwXTp8=bFyz zGa~J--iMlQqVO*P^|D%$hl0|Gde>%+>mIyf+PPBT1-X|5;KJqud1gcwS=i>$S;5kX z()`Sie3;N515m1dfI#=cGppDIhg9K*jm?m&Z>$6D#3FTPN&F9&JP|d^D_tnm2m^++{1d|_IY`hyQta$`N}bz zCq)@7chyl{==lrRy>szvyjwm|nGi+MUvrxCx%dGNnFbFmWR!=5*0yJA^rZsAV#GqX zBR|I}!}@gb+nH|}_Tb3U`sYMiFWdyM%Ib36sOmQHArefYsO>CxIJ_Hq;13+PH+2tj zTIg0*MpyHPm+-^0_m@P3xqsyKpzNnx5w8d3NNf)jCHQWV6EYm7{tM*rYtSvP>y}j0 zWd!tNKV?J@o^2n=c8;CinXmU^EBWm#5SRQZHf99az;P>@az2F19NNvBnC7@+Xhd+| z{jlr6H_2?C&)~hWdE}o*05(-#6N$CTdR4xXxE-i!Xgo*x^F|034pD=)jLYuW*jbdrxwC6Y8#%f2BTKqz&3XJcu0OeVKEw~3KFz=Ltq_(=uw8xrc+n~>v(jmFbo zEH>|>x{_px?8Q%Hp>nbeh`kk751S^vd1o)?_RT)oJ2&CDBf7=FO+V2J+=4I6AO&*K z*2;Abte{8{w-9%ExpKB8**C)+a4w|0jqN?YaT4BU6#KZp-j{_)Ri z7Nz?=YG`y*j{1?Yq-Fd|j%koLOfN)Sq|b6z71zIa0v=R@1&^XCzK8wci7jwlE;(u_ zoelVK^+RdomhJL**xD~%!mu|Md~BPS>oOyB;T z%>5v>d}Mbx_2;dTG+gzn9V}TYNh0@SfP#QCh)u@AwW6-cgtr$lzyGz`*AK_pE$+Rc zUmMB11JFTf`~!U$iGQzb`MBh5uw>H<*3Ojv?`LwExdF@HmMaz$eQ?q|o;u3HHGfgk zYec?cK&WAdOmU*IJ`OUt?IF$Ku5;rYG3v09uBg=hfrz7Ou&x@M-(%7!$^~xiCzdU&00qC(% zn&;1CLJG}ze?2Z_7zayk`VsL>wiS7!*9c;~osw29+z4m)ql9jif}mUOq9o}fSqe}= zamTipfhJ;9YkN+1yBbGAVwRHE0F~i^AWW~};DS$=pA}Zbm{qfetgy$bsHT#A z{MubXo~XbMRWi?!J;%85T8)JQZ0k~cCt`5kFww!LsN6L(+bGU;*MT)qRCJPNehpNP zF)XCEuFMVr1iIr$7G(Ro6>3RlKw2iI%)r7c9>Z|mAg;<2rgNSF=k&{ov5enyGYo~0 zhuk&$^1a%Dx~8gh&mD)x-rC-=`Gs~@^)aQ*@7z`5V+b_f`YzW!Ne$<35$US+OP-Xu zZJ^Pt+c_{%<%f;Z*zk+ok0{1AS0TMh4$qg?WG!3%oVFhX!U=VaM>7tTMjnPQ6i%Lw z@0;GRY-t@*njGqCseEafuaq*sy@>y;1aNb6o!ZqO^PQ3hAHFy0-qsR=#4Ufn-k2uv(|#D^{H@=7JbDRl zZ$eQ>qF1YLU?k^b+~`>7GmgHQe(WYZCx3-rB}i@QKPnA%O36M+PId7}RH$)K0keol ztJ`XWY=xGf6wq2wk@REi^Cz7Vb>-hZu%r4+CY^M-E<>cAO6#YofKpEW<{#YkSiI-K z3TRS~A8*g=N7&f9wkuQHa&lj+>L|CC7ks&Q)bvV? zwBM_GB`?ZmFr*KMEj7-njv8=A-}+GJ-2hda+IPQ|Q$zO6-(eZMJhq;F0*CvOh?=^I z^^@0Ut6v6k(wBTqKc9IEq#(4NZIwI5K#XMe?5Wt%Y=biLUje%qa0;krN_ zHE6}vQ_)6~Ldp*G|2!>=Xc~_UKWB2tZ$3L2;UZT2O_=_#0QRvq!N8!`Dj0DMa@Va0 zma}sE38;$c0mp4h+;Sb_;z9#D!Qu=@F%`=cinkBFMyuzZXUo`!9ofH1GL7dfgdF8P zhp_}rjFQ*A1fltBobJp5{tS9?hHDAai@$tp#_djr+O99!a$Q31%x24T+6m}s> zbDd$Z{|bwmPdu=G>xBt!qhZmW%MCKMEr67U%xE2hzw;?w^Qky6jlP)hKp7HKjB$2v zCm{d+zD3~=Bm+eV!X=$~lr~`WY%goRSbRTzZyG15&?+fpma8_|Ylb!{k*`)O&0fvRTu-mxXpe-nsd$N1lbOm(xTAAU z3&x0^8mQZo%)6bz*B3kc$>c3pR3ARPJnP$g^|COSDJZ6B;H$qm)dex$P99)hoLsD` zIJjFc(e>J%wWQE;uGQ`c;mlRR{59fnY;@F{t9OY-rTbqxR?9^Sl;&IA$dG5?BVvhX zg9&LI?fAYZ#;AYGr`-mQ%hS`=fB|_y`6%)LM(mD6n0&?`eplUfep&29pcC@tKm%8U!M8f2 zb$m?sxCxY$;n>fDVl%#Q&;^o09k!YjQ=`M|IIO7V3yk`eZs^LH?s^-v)S7WrdD@9l zukBo;S{+<;!jI@%c!EAG=d%4(xdI#hE=cPEK12k&wAwByquya#^@; ztzPUwuA3?8m9~cc+@aw)q8C-$aZ3XWar$vENW_=H4QzT%n|oY38tI4iskBCiO9#(M z4WUCcHZpS<4|`Yff979Ss19nvi-i!i8LH-!%*Plyzj<#L=}FEQCr-hCiuZUcH-Z;9 zsVTWHXx*@+zI>x#phbyoU`xN8OrXq9YgP5iYrZzn+(II6KJg46o^~5lfp)n2C1853 zc6(`+GPw3lQEs(4C>|$Ow3vZ<`4tE_0iAXjE`uj~oW572mCX34T1zqkUPQOCqYdY! ziV-!NBmxWq6}XIV#!gFq-@7>D1S5(Bxwsc~+n~}JxH4Rd&bgwYw9#AmVOF?TH#EL<6uaKc|QZp9fp+W`DnQce6og z5|E{eZ(5#4`N*F%`7ZN3?+ujK=9yBy0}QSl!kQ{b6US$|_5$Qy+O>7qZ$-}MA&or= z(^TFuJ@XbJFG=aiLg-IJq-D`FVGKmVXO(hjMG=n&A3vQ&evR2O;5(qdl}1&J1&>-R;%_hsLiIi8b@q%f>QIS+xponI((Y*>ZK>+ zlu2r3q{y7+bM#ZDYg%ShYMGT^pRNe?eD)?p*ZZ{6LWEB21H}5VLQXO0fZxKnR!8YV z;*|42QS&|z!+EcD!`Ap@4Ew8>_1bTDDL@H6H`wfe$8!PpJnmhGKc+y|(VsoU_{}$X zu{j-2!|086wVb8L=}*uuY8eLZ;u*r99`H>A;~UtT!&8S$r4Q3qG>81i$A1L`j}6Sa zz7Tb<8h2B@QWxphoz%??;Q5YtNV6#fT!Bo=RF_YaTG@^$0nnqNK+ErRd z7{mB9BKXfz@qwo44nXXee*#k=6Zh@Gp_2QxY8f`c0E;Lqv*4S0`NzasVrCNia}ZAK zZDUYI@LaT0>jk^LaYl`G$x{ufDR3&-1b7YAIe~HXTPBPO)YWFsbFSBzVSw46sQUAo z-_rHe8ae2iaE*@(hYqug5LOvi*0ttu4^0#SJov3EFel>tm9bvkpKe+$JR;3M-23R~ zWW&2J8$CDq186~sZecx__Hx(ArnT~0sba&MdT{X*(%=T0S1ki_W*(|+9B4!bG-Gmy zzb`m&&{t#r0R@s4S$}HJ>XH` z@@RwihgL%r;h6W4m`|x+4+*Y=FoySfqXpX5dG$C7y+$W1z=K!3=y+)ASocdKFD?nZ zXdM;-hkX}XthuETWY#KE^ZkujKZy|3rkfXG6I}R9tA>67NS|6CpqAY6}czP!xHXg0_!+Z)uo+z4bN}H6C2N8;1wxxPKCEh3xSm1tmaCYR^Jba_Q ztY67z+?0A<8PAK)@UB|NCp@!TT?Nk>RMv|HUet6%<7+67(r3-<9|qOO*mGe7 zd-iF2_3DK^Q=H$x;-57IhKdvOV;}aOrNO2OYNX`<21mq~Q*#|0+p12&mQC>vnk6w! z(2-Rnhll3|^DWyy2587)T*;RTG!Dnn3@Z1FG3AA_U^;ANNS?A$$9bmI(`|$L~T`>{x2h!J16LaXsUDP-~>Fhrn9Hv%JS;&wAbi?6I zkO<91o~sy%K9}-h)hv|yZy10od9m1iyZmj=mpj)w4`(YfxpTZrs=2r5I6&n$>#IgC zL%Zjo_L@WUYH2Ub8n^}TUv%~c>meyS7oe2n5OPzK#$&|fi5>@kbCLzz)I!RN3!P)F zG6HUvT8~xfBEQS##^R^EJQ0e5S>1LUk$UWd@5i6N2H{?Ea&Qg1&$%^*bF(=lld>yA zk%}4Geio2x+GtcZq#j>$%J%q$rSJFRQ|E2n8`uco!%nm&WWK0}r`yMfa`7xM8IQcP zq1OCBo$*Hk{M|*^KYrfVY-_=w$l<5* z4jvdaYbEhpM6becTS_c{lhUqsI(Y;3pNVbXO_8YJb<>CAkq0~!5i$tlCT~wvb3(H7W}Oq8!q`cywWjCR8^%+ zw5gq5dVO+)X77|Lq-OI~YA^4p&r$-}Ii|5I=v?I<7d#27KlpPwK03PzcJxJd2JJ=t0C2a0ewk6&t^*f|PDqws^$507BTldZV7bnDHNvp z+DoS=*LtX(EUIlOW%yxJ_oGG4Y%S9BcRKMOVd_+bO>yKklWdYObIxpe;5+J0pO`?a z2mOR#XXPB-E)iR@Mvq%05eLX>qMiAD;H9#8-N9eSH_7K&PphIO#APWQ)n-tu-8+0u z5%~}+arnzVtBxJY#bMf*L<>N}6Nj@Tk1_=`Z-1L*vlM zJsz{05%xNMau=--7n-Uy_J?Pj=)Oj^2n=`gp5G$RlT&WP8QSkd2FK884Y@6_TnlrE zV521dxe+w~92Bh@Hm`=r?hfmAOIfuDI~S36E;8D$i3g0+M)|{su}DsNK~;VtV$wHu zL^ebdeqCG>e`-WSm+$J2i?Rx3MBx-M} z`ga$qu}c32=<$m^1depRR4gSn^yZP$Qqik!Zw{$1nSJc)g?8`tzMdiB4rp21G2N0o z&C9y@i^UZbTpzRg#fPR0H*Z;p2znoInPVW($#t)+Prhj{dmc?O!_ZaIW1H;1UR>fr z$lLxYk9Zj;3Vj{63!yE|Q8@iqq7%~=;C;(*$R9UG?Jn{D&T|7BSPDrS_<}eiZ?&BC zNx|n$2D8(SDON>uy%_Buf`oFr zj(dZGbbnGfI4(1MDn*9t_Fd0GQ||hQV3q9)&)M0PxN$xO?<$%vS7rgC1jo7xed=JB zz&T8T>_u+2Q{$qKp%E3b?ICz91A~d&Rbq#~c| z!X4&D(&Ql<^7s{DwUCDD0Q2jjmdhnpvfGF1mqL}^cx>>XenActsI{uK)BYzP9YzwOOMb93vY-$&^Gqej65YCfS>{_$pBvXu4-4(|*7+j+#jcFw-=jnbTqtocS$ zQudQCbx$|P`QfHDg|=l}a_$DidD8P!4l|X}wdGR6UZgH*AhA@+raGJJ=|0gynAo{w zq9D~SMiwR_N6YXOcr`eL=k>Fr5&8Jw;#C8cQ79Y7jq|$WtyZ{nuXYIUQ&ZsQIX5~j zuh3$!fTEqW00NA>i^fjscZ9M~h;}KTBd%uA>T{pn zNUWq|jtSJ1=4W<+gl>~eiJzlYRa(^bO%029Zd#p9@$>M`R;)86SONOg+Q3=0RQHu1 ziqfdZuE^A$~2aJ^O?ptgUkW|qREYT z5Z$#5z7cwGpF*wmM5zw_ssdgEl{1g^376Ul8Nv&v)DT1JTaf6=Ufy>Nm6|6s$)3mB zCV}&>rj&c%g@SRgoyc~-NOaMu`sjE~^`xRLAG+2Hzq2NBal#J#maS+O3tOJZE#qSB zqUpzV#>CZ#l4VNMOMIIOSk6zfY@#+H{h#Vijt(eRCw+i>ezWoh>{=2)U zfn)MV1`3bM3R{IOnT<&ps(PI;0=ohdG(jmUGv_Y($w0KBtDY^!ZYZH@#Dq%J4zKAK z3+516q4i04#z*yL|LAxs?gqkPUNm{#s3p2OR6%&RgzIV4?Sckciy12ou>$_I>`%h- zQyzlKcp}@%CyONgpcH-hIBM~`;KvVub8pHjthy-O8udi;kzrco&deGUmMc01JYyc= zS_Qvf3uv& zZv)#OwbB%kTHbW6i0s1K8Bd96A0(XTij;QZn;U)t2zOZ;t~fyl!0nrTcrPQ02W-aQ zza}M;?MWP-;G{AwF(;>E3_cXI<>4c%TR#QYoAcu}bIg@D^bP$iCUA`zVZ%ha;Mv|x8W(+``39koKAtf zxkLL*(f#d+QSVpyNfARhh>Tn}tC0mc>R3#7kRV@mQuiL-1V60tNd}jvhIBBLsT>*D zh3kY{DjOnz!V8m%sGgFTy^l#d8HI@uEvv z3&4cwfS@T*6N|_L=4_=a4dE)ER-MbO%OE#afUuLC5npRu;&jHq2TQAas z3jauzhlBjx~X-$JShoSomY}i*YWynW^df<4 zLLtpxh@FexaZtiX#HG>=Q}UTVtL7mNnV|BbRrBE(H7fw45zq`1_T|#9N&+{&l8;}d z017|&Buip5G0Lvv=&i5_$2ot%K%dU0e@}nxPb3-Xx1s!0!3{IUEV@^N@X0Pv)fr)y zD{0@^h||U(3JmYP#XZ z=bKMys~N4BbYgd>gpu7e?m8lu`E;I{IM%r@EDEl!e|z-cM%Y`K*J?(L>82G-3rI#A zn(y#-K!jCU#`Uk%&%O)YjCoe;WCWeec<8rj%3)SH+>mHb36+o&Cx)eJ zcg(|j7f{sw&eXzY6*r_YEii=#**rben>mL`fv7>qqUxotG>+%j!wF48?0{0LXHqc! z_j&?hL@D}OL2lVi6tldN^)1QDS>2X%6=7xbCC#n#RqE<#`eW}Ndwps*$zOs3Y-#u$ z`D=te%UN6|VGx@LyLtCI%rorofUNLbGPDh%HLyZDB z_uk*%@BDk`ednpw&wAEcPwu$u2yF*m?x)VIWPY%=^-WK$_c^q5xB1}Kr3=s9)bROJ zW}EqJqJ;9pc<{`Kr;xeTYc+&QYG%da%E0ouFC>UXh+#4r zFOvaOmht!{DXj$)k?su}dCZyj0%KhJ=MM_^-|DYaIXJ~l)ZfB(bCqS&Lndk?SL%6= z9m;q>WOY3cOjpU|Vy@&jXL@g1lw1}u))J86TpF0%DC7aa?dgPimB^yja6jOKXxf9& zehf-K4plm(OJU6mJX5R%od5x*B3^i*86uvov;gWcqC5*^lGz=~! zL%4AY^kd#fs_xR#hcKOAua9F;%4+hfC){z4X}w-NEAXCun~Yr;7Ku(V^jt!bRr~0$ zEFJT3!X?admfb6^?S9#TB4%bK@wCs)XLmEP316x0*cVs@PfEEC&szha7X~Zd65$)Vc=%qcAhE=cs^cy-c zIsO^NqD}$8-8tpEtwn7re1$vY^CRWoG7Dy1Xgx|Qh8j{Eij}%lrlxkImBKqnpvmNeZ2ydi3QTmq-Z};S{pnW!^>u&%v3D zKDAyf2~J*5)%4&`l^v>HrKt;)cf&L0@0}XTPhh=56#*NzNcAJ1=*a!vIrG(h0oHmr ztkwAW4z##b-}Scc@!gfqe7a09%YI{d7F>!8Cwi})Cnb^n1Z44)6p>LfAH2r9SH*%Z zma&*kbDa1-&qd?~U30CKjso$c-)D|y(cswflm*eW0-L;VqN_hR49Vvl3ofv@9}Pf) z>kS2%J3F?mgMbD))$v+tbsdq{R5&Au;Io#S0v@UKnAoL2RZoSwnss+$ks*JW_5mr;0Cw1oy)PrYSaDMWYNIZz5% zE=X|bdm~$shTovXhDo$eEvKP>rVT0eL0gOj1Y8QgCd?3M%`q@~cSBP8@tvDrjs>-4 zJUziPXPFIBLdB-o2U2oqbe(vqFYRa(rsr<4d>(x9$?RKf?uE8yILmzYw|B>a85c7E z&l*Jb~#)@9#XcUZ9MCk}Vvrd`V_|oac)l;{BB)!~Pn;{_@ zn1ML@aSlXgWv|Mqw+x$o=6oe-+h83&6GT#xP3L%mPw;a=R@diS+&oMp_x`Q-zWprW z^pgH_D3UmJ{i_xmH_AROL;!p+2NjNgOR`~166m~RTN!$fxz({^hrt$6Yv;p98E_!~ z!?L>sSZgKRBX-!cST`uEbv|FQnm|o4wD^!ohG$^2J+~}xsTAU)lj>oR?dFScM@Z(O z7&2>QMWml|7Po@J$Xu;(W`r{}EW?~7-u=u!7_Irl*`ydZu&~%NB#L>q!6wd}NTEGb zRT63z^+S&R3_tNmnJ!dx{7q3Ftyl^0s=11I=~#@n&wp<$1>>ayeQ~3#!+k{X((Ee{ zxiy?%u`qsP{~;IRL*`jTR)yvk5xlqjV|?eUvyJIqES>HTXUe_|mSfMLikjvT5%(oc z^JLG>g!|teP1H`WJLa1VII6y!cm*lDkyN%uH&WU9?V9akjXJ0ErWzFfNU`6=x+Q85gXw&EPv9B;=+pY4Zy@%U69YFW z&be{l*)!lKPLBsaQeIvT+^4!pYhvhi`)k&5W^qiQZt~d_&@CMLajqYgjMn{(aHOC; zC`A#Nqr^m>lZz=-f5(<zN<5>*L ztXH-e`9H%D1_%X@dj&LkvDkz7^X zY=W^;UzREJU-9pwgk+MCREg77xb^VToXprv@$;NC_eHiNKdR^D$~h-k$fp&+P~F)( zVup9B`{cgH`2D7UKI(x$si%4tr#gCqrFte)c?EzVfqCjtr;N%rdh&6(I|WzIlWk0ZQ}NX-J6GM;_vno5b zJR^R#`Yn3w9y=cUXA$*2`nf1xPuJsna_4VPDx`I&Y);Fy@Hl4jH)vG4&RRSwqf^+{ zX{7FrR`uskQ_ml)1NlpK&uSi#eG;t99`+1&CBR@)lw#2eH^lU!t$CGj<7`59gyNQ#Z#| z;R0Bn%lB~_B)61r5xT&&^jjVtG;e`@H%ko>MRZUK;txJwFTjfs zk%D0>&9{u9=Ji>2wjA~(_L_o+HrnxmQkOf%~6g}GiqH_pQ9H)3yWhL=r zL#rRG{F0N_#Q#vD{A2%Az({IhSi56JCa~5zxhFo}n0xXuw8?EjGRE_Q65zRz%Y1dK za4{Nn9|%mIe^gy5vS+KVpU6w$GZ)I+lX790y>}2WKX1|-N;r30nTy%-$V1 z5XPckK5)EHh>`w)T<7H>DYih#67_{>)vN#-(vHG3REB*3iMvn?Nq3L+VAKKuaGk()g``aC{6 zNGF^BT&K8Z*29p>B}9C;Z!X&%;`0sNX9%w)1ZX&>lWZnHoA~Ui&k;I06JUXtT6?Ku zmuVE-zA_uD;ycnXqRV-)i6t`n-_A#;xl|}x|w1z3yxjly$E>hn>?oU_% z)VMFdU_C@NO6oSY_`)C)*Q#@P>r39{s8Jm+EJr$~LIjDQvozk#dnDX^FY^^A<;Deh z)rf21Sf@1TxaPLOKtm0RN{r@{kE{{R@MC0BFD|807^SrL;5f#4EMY}~xV(qv+sc?V z8#Nm+omgc_Vodi=eC*o=m>O19Af)baj@wLS!fcb~4$Axhg-pK`5Pj!-?;d^B!4Fff z(O6;gvWq$!4B|(xOYySrBL0_ZHKQS7hm>-OJiM&V_gjTJAFIqPe)eh!3`VIF-U*qq zr3SpA2qxuZW&Uv4)dk!gn0~R)E&}5{5!{-2ygu3Y4$U?c^QS-Olk3rHqQHhBs9Gsm zh3XFP{SncE&>q!%OAX}%#XDc^JIk4aq5f`VWl`j@V(2vCR~7$Di zw50iu1NKZmh@)k!1kKN*3P0dE{s$y(BqZ=W>xLrr#%DQ(c zbAKA_U8h&MRshtX=lVDK}W`}yy9Y-CAA5_=dU6fJ2xhh^aZ2F ziQPZpg!(9YsM`6wk51|+c%@&dmrn6xoUDf!AQel_YNdIoEb48%qbUDm*qKr8P~eqd z(;DJ9-u$9qaT>i&A^lE3Zx(j*5S5TQrk!w}_H(B?gd;~^ONvVoX}6OI_3?58A<~9Z zr!%geY>VO%LD-A$n6!DlGd*nF)%hW5wZ^Jkpb2OKdF02geAkxp=c_N<^PS#R25eS=-$|SA9FLk6;D_m-RwPNYj^R+b$R^l{2Mi71Mi8^C;*))XCrCo&5?Pyfavk~ znfK9KImXdC`EAg88l5C9n!JbQz9pb-&R9%&7a;C_NrE(5P$8qWRfa{IpVB>%z;OD$ z_=a#QHh(&?OBXKnBSnJ=sXBMgi+JhPD%DB+3Vl5mcVNcM&u+sV^Y-Rhfy)?Uha6RS8xj)zVmB7jfdkd*#xiesu*2KmI+5#Q`Stye%#X6 zU+o;dGPdH)OV6T}Rw)sUaFWtYj1UY$GdtG0Md4DZsx|+nakkzylz89wLc*kmn)Y3P zfIi-D8yY9HH9EjS5n;TouL^paRYfxw}M-@1z9iTEx^ z`4S~~u;vWIM!lA-c;4XK@kC|k7CLQrjsrVjD=Ht{Q3~7Q%LY7ZKb)w0Tz`B`XM6a( zlmR?8B(BwAxdD<>bfDYV5Gd_5Hs+UM0f974)pis*Nu-L$>YoO{gcIFi&dS4ByG|)D zt8+Pu-)J_dCNQH$;UnVo03VDe6(PJY+c9N7_Wca`<|=A{@1Y_ZJ}1yFkT7S&l|imC zz%)Tq(7TM1|Na>3?);boDZY)4T%&SU;L>hf^3aP9tc@me*496BTDR2U2XkmJsITwD zg0MT>(fGvMozoTU3a@8bJtv6-$# zMKAI(3tkXbZcW+9cY@ip%hvEV^b{|S>oc6!zHpZCDnP-Dx{7&~A=ygzgGz1ew})^P_rJm`=5_*YKdULlHQ^@>jn*X?-PS zE3$ZCBbcgw3)q>w#Uk=J@%YAW)4dnQQw;j~g@0MUzB*;B+(QpvnDe1TKeUkN&|u!( zUV%f2C@FUw!?(JtyuH8&0w80%UV^*=TtbwgRNY^~X5N6ezFbeNPYGP$*LOwTD2yGl ze;afuvEJrQjoO~xFjsI=wHPeLJM_D(wKq#W@RJH_M?0Pp8Y6b|bax7tORN9l{wlux zbKpP|(~5IwC_8rN6GT!q#ruzg5$=Z*YA5>K)MN|r4ZCWdXJ&#$r;+fqBA##d;Gws} zPYRvzjp4P4?QE}crFLhKC2cNV5j1)*srl1eAyFMhu`AznBQx502b`78M}edcJ-=p- zz=;V{N{@yRsbyg$v@cS(;cRFh#(&iCcRJH}Vw%wvb*ugt^V6_WoGBrXpzB4P+t92x zbsuZ12CB~_g}W`}v(g(ct`OV3QWJCc+qis*%g05;_Q@Oo#M1-~39H_G>?yQhJy%9yll&y9YGpjbIdu>+iN4DYu4 zyI~o}`OG`;WemTD0z2^xH;0B*IOaj1t)bUYvw~?ghOu7V@gY8mJK*pT-no?9yX>L9MOg4yRpErmwUl1^QOnR zGKoMzc_r$ou0n?fzDW84EV`&@xk;|J9&-uUil*bgQ1F(;eAX{|7Mtc$Fqd84GwvhV zuSPy){?bvV-Ki2tDeRFUWr;I17S~C5<&6ls;=B4p`IGIE z{r96iP$NT^n`j)u{_&2{^l)5;M;(Gjbb2gWmEUdE4$6U&V}daq1A#SY*5e)}??N5p z>TO@2j?4Y%U2s0?vHZm0a2a`^%PajD$Q~~J%%=OhC7!{Uu_pW{H8b1KLL>jcsKb?Bm zgEKQIe!tT$;(2%oo9`hx3nhLO>4I$)*@{P~Hr~ezF5Ntz9r~7PWPThi zF&8KMBuNDzg6uj^*U9lnz3Z2HTz-C=x*-XsQ6I%2_1n?9g;lxZmJuLgCYxu}!KgUsh&KB|cq-c?L&60iB9Eewm`C_}9Ws9Nebny1EJAddZ2 zyFR2KLWm?q^>aL)($uIt8ik8-xA4VO-l=VY-gs8}&*DImgS^S1>(6s;F4C+HPug6% zzv41`mgLt+jIFg%)e&4+RPj)Vk{XV=f3m+>*V!y;cWvPFGc71rHKO$T;r3zr-t4K_ zIG*XPyATF9`!n{5D3{s*8WBr?Npy3@HhT*?y2@3CWA7DA4+_!rntA-;k)_$CKrr~E zV@lvCh~PngsQ=ATT+-koNid(on3`rk6x^>tTGljA#MYqkD!JBPz`r+2x=UtNY1m*n zDK88AeOOLCxI2G0%w?Nj8?7{^boHS+CbY696x=+s9K7DvyZpn3^To!|0?T`irYLf6 zoffXD1TsNPug)Y<-LnE6~D@idcNDT2texnpqVeS*}oO8 z>+90>soDkb%KgymZMT2Z&({73%c93~TR(`kUn0!-pQBl{NP8wNk(iYkUyX{4M`PJm z)oRd2nABuj6=RaJ@I*lyjwh3IoYI+*F2nb2)8-SE7Gck`2el2qm*m`B4rx`>@b5&L zbqu9!Fg0e8H5RT5YLwFfz?nUo-1v~8c<`;Fu)3Dn>yo2|;E*J!)LHRVf;LnEM(qy~ zeMgB~8rd@rqwZP23EUPwXpoq!a9l1L@*~Ykkmll2 z*k`9DBbNqnqGMQ@7I^E%Vr?OdDBULQu~U>KpWU&z=9Q(C@N_*4oh!9P#%o!=gFN;E zkP0w~L(>!14zkAcc+?@G9JRL^*j)PPI`I)?Ma2gsdOc@^*N==eUQ>*99YyYnOLJnOg&=nyL8(MHzWpqt^oLY>ceMGOb5}IMiN2rew2YH79a=05 zB1fFx?mnLBMSLMzMnolqD-4GXyHOJH1@wHJ5RKRO%v!l+0{z^y(P6`b@RoZ;4L}Rq z9b=Z~4`~MBEKnh)S;Fvhnr!)v2Co^1BmRqu^lQbos8oeG9T~w+7KMP{91pHUi9iY; z6wxS)sr4Qk_tspFD(w%`5bTC{?2C`~OPMszL*vt%8gAdye$sg6!nji+wOLXS1#@XW zA;L-?^BXET9I100-aLGoLulQ@sBC-JCE6W#?H7~6UF=WIEfNx2rOyNNeTQPfuVA^) zMrE*xNt(|3rjgTdManjsiu(|f6z?jF^VLV%q3gc0#KA}A0c35V$bRXFCy1nY%8yT+gdtt1Qv!xx-|PnZc>?*La55|(gKX4?j7B@ZpKT`Tq}W+3 z8lP1;Ok~4~h@M@~-s->bR?_e7jmm3SlzxN#KWi}|%7=Xa?z9S1H~a~mHx&O~mKI1O zIm(gvLzKP4`;g*_3MJH`R|Q;?U1k3~H3_b(iZ6_*QK90!$jW{H{=Lvy=?mhyC!>BM zARFy7MDnVH_m~7u%_j)BP)&4dsWE^LEhm_{uxH_-IgjLr&6fQ$X1|&`z8xM<_4OlK zWh!K&g-v2x@8MMi1vn9aT;mx`*?ohwO8~L;x~`R=^E%DXd{eb})p>LGoYOH1M~SfY zg~(-(iLdF1R$K?03_}#hRj@w$6KG)jk5PA*TJwLce;E=w^T z_YyCI>S6@#-n^V}c}&>M;mVuc0l3ve6h>mQG;aEl=l!nCI(091Oc8{XUDvRV24#z3 z8A?lu1VCal#J0%eY#iwg|=(S~S*5d2iBUs51xv)^ko? zx!s>_t??smh;WeaIH3zk_iwPlH63;DjNrz;wr_!z)6QY?@!V6$Xat0)UfS2S?u$4f zr+n1e@w}@>R34GqDtuEkQ(<|dwV`g4=mBBuv^!nQlo(b{yieI&=053BqycpCN*+46 z3Sg=cDPM&60LcnF6hXovcIV6|Dhge94+9RA%mYG#vl}!l5I1IUq6G-FS{TsWlds3L z;)$2WcgomV1P~(f(d0(73rg?vWv5d-8bFRlc&NL?NRNVJ*}nB)I)BW?9&X_ zTax|9f^4!`)W%zDN6yMLw5v4q@WOEzWk)WDXmF7U*6FL$vm&+sE9sm77|J5pjUp1Y zyD}M3)#t(;=V3&-dk6G+c};o+k)Xa) zei!Kv{cN5%o9kU8Gv?oH>$90(&3`(5CQykIGI{Un`Pmyay;TvESE};07Bj(xcPyZ{ za1)TQyT_Krc@3yeDF4|l6gBrG!8|aZh1AtxtX~7ku~6`63x%doG>}HK1DxTPNbCFq zYB1(TWFmgQj3IO1XW$R&5)EEEta{hfzH7w{Yv#e5`$4K?YI&<~-uAN4fc>JdV~75} zDTJK(ldN}>hk?9Z#lcFg&X=wFs-6onl&VK#j1l}MR3-`V?qik6qg;WMVaFk)pq5CB zz!H!l-5N8NK=21ny;I*6Jujd$*y~VxIJC#!@An(Rl6Mcfl6oGk$Id*^h$e9(^w-w_ zXlSmYYi2r@vjbs@!MKyvk6*rjndo_XDAy_ zZK9%iv~3R>x(B(&tiAJ5$-U$3X+Bdya7MC!7Pi2f&r5!|97#1#wV?xu%h*V_TU5kHO!ilQ1xO6asA7lGabGjdTn_sZ+zkUvAg1t z|L_=Au(-kgvetEh21X(8-g9PGc1KOxLJ{Vh(8d>^ZkgYH+0KE;F+En^FO5k#`>c=K zu5S{)5Q`ia&3A&kB;3v6W=X%cjB|8_CGKc!tR13#Gz&3|!3AgHF;`ySn!1%WR~l^* zg_nI^l0$@Y$k?w&dEw^CSa(j@GbogQ$p%{*b8GQC>@mcc^@*FPN`YdnVqCAJH#~_C z02+FIHRtA~md{_JAuEqH%o3Nn5tpr7RB`J$J#Asc|ISHO1>XPORoQ+iTumrvU+KV~ zZ)OgCZ_x*&S(9#mWJ{T97tWC$bm=+Qq;K@K2s(f11df?NqytV8`bJj{#2j4SCC6yE zJrBR|WYsCG0~3=Gj3;d9B&9&PGAa{?OZt7~hR+tmK<^Bv393}QQI^WCz#}Bv4s&CD zg6Gv~kg-0BS=~@)uBC@E(V8O+2H)Dx<-;*DgY7=gDBof!Xt!KA%g`tlFeD|Nva4(T zO!AcRPW`Bp-!tNb?S-f&X~bc0!(7Mt+IM%PURmvwMM!b3i#KeJ+S(rN25%gWa(wzN ziP_tPeR868tbTbF^0vN3#e$XAdShXy$QUbkudF#B*QJX-$5B-QH`j1oe~Z{O%G-6omX3@~ zfunMLy2n9hs#89T2fm5({G?FFG^YbgSkO)|V2H)dfl{EpJc~X@?MPf|$h(Z{u6DE< z!VJmu5Jq_o+~{oqw%U;UW0iL$=3ZdyLQ%11u`UZ9f4B|eyb}t?HF`-47+7}}))?aE zzFjx#5e-P@q71F+D|MnoA08z3KO(FD9vv0H@#A}46?Y0#g0&kZ(oV5!HcNnH>;nB*gR__3;Z-@kOFJxxBRU-Lp0imu4 zxLg?uhSImr>^)sXt_((uJ9c`lX2llh$r~rcDP@0_%UlpaSqeSVU`ejVI^=~$5g%4U zQmbh^ZEZ+C+!^K>M_E`unRgLIc)L7gih1}8d;vwQdHBUa9$@vG2|%;#6^0@&f4Ppq*c$Cy6 zBfXJKzJ{GP>JgdWub>Sdx_9jN<4_mf+n{-6zj9gA&qpb?%@+R!`OoNid2o zPgSs}t2BK$B)J!l!+z?XJPwXD?ltX8!MxY8uI=ac2+ApOA7%<;lYDmXRb{kurfp3& znvn>1r^U&PS|(DxY-9ka5#ASm;nn-Tuh|c4a#?BZP15eO(hGNSJ4x=a_zqW`2c*pg zAGH{#R!j0Vp*^Tzf~Ve`Zt-ediC*16MS_9e°v<;eG7d=-gIAd?pCXE zu6W;jdp6r-rKK0%KSmieRUK~Pkj(ER5AzNq)Ei&~FN>(^v%!}?T-&Tv_u9<4!532q zC~qm9gEKlo{W$Hmf$~{FNGFTy;rAK4oVh_io$18HBzGxR9$5me2DvWdQbX_Upe+j0 z5la_ea}Fi~ZMvM6l#3M0rI@9R zcHC3FEw`){&kZAuQbp#sfSF~<7n5AOKek9CRe^!I#v@FjGOC$hJ~#F9=7cF-&h=JU zk}S8c=~~*{S|~r>^XhHCy-{eLX^)gdYj5#&DD*4Cc_H@o!9yfuG{l3xEc#o?IXX(n z@?u!1p7M1K^+nZ<@%os6eI#qgJqZwHX-hfcc6R$Go^9s&oqOQo z$6lLP#7|h@YixkScM?Fi&siXRBW*S0PHv+L)`Fp+!!saQPE0e0gE5!QT5zS8B?-!pntlJ=AQQ!Zg<=n*&=Q3 zZ6qZ%Bexo!mQAEco?BgBs0_2r8Ww>}>E5MmB(e%JmS02IT$a^qK`rn}>FK4^P>VrH z<0ETrJJIsivsB4}7TMYTt*psdS{Dy9YEr=rHt_2GZO5~DcL$cPHA}#UxYhaJK>v3f z{J{f^K8wl$+fGpT;GJcKplG@G&x4tpjM~tW9vc<__rzH0`Be2611)GOQ<&|6_cNe# zW%a}vZTk6c#FmPs4Lqt2&a0j`*XYd}k>M$Ka%dl>XE0Y!(7AXide?>~oS8az4=|W7 zPc5tTzTz#mI(cJW+I`Ou0XSX`)eF1qQ&0`u{u)UTHr?U@2X28%e9|2fC!OmERK*im z7#RbE)H*8eyN}Ibck9m{9n~16&zg)o9UqI*mk8@+HVB-inxB&vxQJLvtNcjLoEy3R z;zlk7;pVOfJA5egk^)`_*JwEw@_JO`K@FCn3JTi5d++>7n`?bLOggKh{3c3)4DIN- zHMouWs#hc817Nt%D&a2y@5MyTK^ZR(G2|$y7CV+zyTOV9X|z(7W%>BSU1(N*G3=V| zLx1hDpOLfV=&^F;8c+R~`;^xcE7c`e7tNC28@=3=U%GNi`nINjpOXZ(wh2Tqc2$ux zB|d|(C@5d>V#b)e2wrAbf7$(%TbbThn2^4v;wqfHl=uv&Yb~#cI+F&|ZeRj0uU;~a z0bkeOpPbB0?&fCk*SNp@(*%G_f{XLn`t6{|e{t*|P|H94hf6i9XucHp&r{leO9cJL z$jPX4%Zq;n%!ros$jHg7DE~D4pCR$5OiI{KfXp?~}b=d&~R zKYsnGLD@Hud`BKBfc^g>;iA26Mg5BmLTvl+&F(`w7xBNL`_EDpf~_9?#o7Ibt^SF) z|HD?lY`y=m)jzS`f7t48qVhl5>YsGRMCk7|FtG^I@*U)Hx9MvWaebU8b+Fr+nU2pp zFUXQ#{@+cjz>Ke3!2N;#^)W%OizF9pIFfa3*37&43+C`|GthTH zGL|%Xi72KuC()Y~xf3V8m z^*ynURBqH4&fqsPYp{E;1E=^3|Es8WLo9qZ$r+0cH2+Q7pzp6*261ACbJ{jK8BC~2 zXNO{#-`#%U^G|j}?1oe>S*l_*HcezQhNt@%XTOK-GdGzlo%qINa|gaLOZPkQ{FB!H zx8zultR)>m62C>4!tTIA1^K`8-@jed#225rYNLLhRz=ufY$=wd?Sq-0)VkU$^-mh) z-wyKoC2*;WMK?ERxKscB!#@d~fAcBpA;MThi9Qnli^T1Ku$)s5i+TUzG)Zs}JIn`z ztbdXSei8kD7Wc2{`48s)m377bLze$4EJ$$7tn?{c{~-s6W01(n$tCPpP~`uWtbYH7 z7#*SVsW%x@ssAQJ^^-8rn6b|=zPSG(6MwIIhVU9BB+lmjniaJ~mgU#x!zefE1IuY_#%m^u$WO-EgAINPe6Dxn<&_?uf z_iFO*aQt5$0uvCvwuu&9DcN7$E1~_68Sx#lM*ZV;e4qFYWi9h#1HZsat?&Xh$!Lqe zCrwY9ED-%q7(#)+Is0&cUjr5YHUZ)gB>sssiIL;uW9o06X}@~@#}fZ#H4<6~C*C_f z5kI=WQAhYEp9+KBZ%hWe|4XaCee%g>Kv>sYm8r_VCM#7YhQ*F5rW>gq@xOrn*C%~5 zbc6@2{Lr}WSBJO^N=!v*>%#`SxadYxe_{qNnGrMaXUoX``wUvdusA?UXYZiw-hToM zN_Yq?$fyr%{2f*nPtab^e?Fo8V-nI=`ptoNFl=P|_laI0+q?1u5jTn6{{7Ye^5FLo z0eo9sqFkz9thpy8=Gsh3mijMv`qy40-XaaHYo1E>G=y6@XnK~5=#QcE3s1jM z*MEllWuyH=zr6SlF#jEw{QpHN4Qb!=6LnJn7o~dKUJoWg#)&#}=K!ZabyT5Gk5-2j z@CNIGqe82m(W>>hLBflq@4uzFRGyfB5~Ss@;*{>e+GRr{cW<_3@O56Sf`trF;5jnV z;nGwNDk$F#TPQ7o*=CtNt6H#N@FVv7APRPd2rJuQS;{ufReR7%UEW@fP{EOrg@EJw zp!pZ-`%t|`p-~_-(y?DSY-%|7ol(D{+$}jDi~OpC=c+QZ*JprML&d_1R=C?Zx)W4( zoLS~9(R$BqDnqC583^$Cy^N%&U?l z5IY@b?NUKz?DNlJ=U?R>;!V-ZiFe33eFlEe@%`kLE-Dpg2n+p(=s&4rpJ; z5=C-+!QG^njlWge*WEkS|kzB}A;iFemYK>cDU zIdSFNn80;(iP-h2VeMQctBau1WtZb*b!-Xtex2vAI+oltu&v1x5`S;&;C`uB z;Iw2N@P|k+`=qb3-HH_<+#mitwl9J-QP*`d;QV&|z5|>^y+`(PcL7q}Pr;;jc-&^Y z*=6q`D=Ql%{+ng8f6`CM)*9j9Xr(9$b4=l_ESgilE7NIhI5at=7TfSarJ%38$5#~M zKr#_z|M;oh-D#&;tD&qYf$!;(ZQ3+@6LSH(IP&F!QIxz1iNP+J(AsCCM>dX_93Y9c z^8(%-$w~{ORZFV-R=OW)Vo&_Ee8~;VdRczXIE;tF^#+{q__=l;d;h!v(HttprXd=vcJ#<&ybZ@Y8I5J&3Pn{(acHScx=5In6&K(0-R6O z4fmU>m#7qSiKN@^qr1`X4`D#cmekxly1QB=&BuVks%~fZ&%Dh4Ey@40@)3hP4%CU} zr`Nr|nNOsWO<*o2#nEkXG^c4J%ieX*1}!8=6jXYw9u5IWVMa4W%I3Qx$>s+$#rp%) zQ7b>H_(1jZy4vu!H0D-6(BG>5Y(fPC@8REguK_71p#)5fjDMw&UIAi#4<6 zPA9!7&7$o>qh^a&iV($)!@IgV#sN}PXCVjK6z7S4Ca<0@e)W~Kgg7r|hTA0mgPmx* z>f<~7g1kix0d+)3Yw6iEK=K`LW6=YzYnEOUl@5U8eeF~Z>-Juq z1ircuZ`sor=d88{ojaL81oJ9RHN5_n|?lUA~Url&EV$y%Gm@ViRG;zruZc4 zjhlruhpF&Kw1p?zVI_Ur{e{I4!IwJsGxG854j#)Zb)R)WRY#R@Shx(qj&eEY6lPR~ zf+>Y4i#+{plF}u|VSev^npb0~+=bD4Yk3Ws!CKh;rEL5~IaC;Nz14}IW0yym-4|Y- zh)i|{dBN+E8F)hXk3wKy`|1x&YMLJc2stLl%lymlUtBq?4c+h)CP^_+T0;? z;5c(Xqti8)>^e(!{=XI1cg>RNQr~QXg9{-_{n#GIM{d(XNISJtIq~EfI zS)(e;)4OrinMp5Kz+OcUyi^ip^J;)>b02OEbBcr?G`6?57g*JQ)Egs*LGv$mR=oIa zY@#A=>wtY^usL@ulINDxMi!ql-9ZP#)fYEq0N}UVI`@02#9PGWaS^#}4hQ3Q*gc%2 z+mPe-m2`8(h6|?*Vg5v~TfMAG1BCw^4wTZqd1?d|;V#$JQPE=ssEN3kjDEEoRtPG< z7rh&@FFLj~Eo{ap0Z{k1T<>qO32P+xiHM zTSbk$H(deN)Buhn?vrREtcuG(0ST)G`NP25?6#Byk=mA;6-tcU#_LZlcTD{S5f{;d znfc?+#Vn)OOF%UNuN~2qv@s!z5612821}sv_su0v{Wr$unnU++%2X4w`{bdD_2!>r zIrFSmt2tbtFzk4nhVYaW1@H9?9p@^UUwhkMTa^HR6h>O(E3dDt8Ww4f+s?+~ zt$y3Kz4yG?Ve?L=090PVK> z^3m~W^9Ez-3jMG}nhwF3<X)Cj(y73}+o7n1O>g`poCtHrU$Bl)eh!^|4M0(66G?8q=-LETD zomy|ft(BS`Svg5_Bz3~DepB{6;pXw zjuC2h#~x078D>dv7J4fnv<(Qqy UO1mR$(sY;OlfxJq*&z?h$sEZY;pSO2?RYN; zLIvzcOJFfTh7S~|&+lt9VP&37U>F~3xZzzZxW!W7xNf?{1#;m8-iNte?<6K}>$N&t z?c8yU34)t)KZ)+=#SY!J)>La~)SAfelR)K~B1yPQ+7h>haqQTv+Arp2TY1~a2-YZF zH+-gEiQsoR_LIBFeJoUvMkK+h_DE6Yr_I$h^dl!J<{uP#bH`Wi}*JqWk&=7eLaDn-ybop z{xB8I=*^81Y4If7f&{u&%#~}ai{vhffZ43fFnWN`b94|GO{a~SKf6>Q4I_BDTS3+B z;)S>~t8*KraJfNWx24Ied)*+nk)JdDX6>dPKz+4beK~_E+-NP-nd4zyTo!(RglXRA zFE26T7BpBmDnsrKJJnL9bo1cUTfYIC~vSy`T4TX~k@}!mSC@H$+aL##x-I;qb@VY(guLY_k1LCyfb0m>*Ru z=QmqM^P^@^FKDdxpY99~8&T~Mz1c%!*;OOYyPM90$5DMEdedd5OmmJFiKs6GATIqsW{`|Hg6{TV%fvu(NxE96BZvJ1u` zk<$TcW7}$XAv@u1P$Lj#Y(*a>K&f$LAmcw3<=$*myi3%_;c=9Ubg(vc;<>PfD59Gp3ugk@u?33Ow+hBcX&*qnU z&?Zz8D=#+_5BtjACh9L_Pz7r{(Y}j_Ojm`iFw9wkTO(i-_Gw#5)TmdF<;Y9O^o1WhbNG#54c6OFT@d37) z*Go6W;nv7}shX4=(ggOEamj=o)y>>4xaX5%{1J8Q_h8K20#Aa3LE<*eURwATeIguabZEx4Vh|ksdo7Nlq?%dcdy`a-A93~VCoMlL=n}J16;m& zxLo0(6Ns&3kx1S04jQvm6D#8@>H68C4l>mc>K`8KUeNgEF19b1YaS5m+md>YK00rQ(cx{?kVk{PnwC z-b7WG?Ip=QVWcU8=P&n*#7So>aswtg`pG^9>QdJc&oh^>f(SQv(FXg-sIPR$UB_=# zfE^GoNa>0xu2NLAC9(A54Kw9#LI>vxNYNue?$I|ZrMjNK#iIxaez z!hGWM5e-F6r154(N6M2oKfAHnK;)hKO@-b3mm%M8_3kh95@$nOFUcRaqn-8Fo-fUJ zG<EmLkI#5{yzCok8%@1LobchiWMowW7CNH>3E-u-@DpxriQoIF3Fyg#Ab0U=1m#u=ma3LWYy zwGHt_s|?Z>Dm}^fL8}>0h>9WzBA^sO z*}@j2NbjHsDAhs{2%sRn2_clwqzEd_MlTVN4hax?RcT5Ky#^2h1PDcHfKa}f{hY_W z&v~DHbbtT8>wEurh9 zRU|{3;WsVueJ{pxsKmbHng=!^^vSGh$E~>qhE}CI=tR<7;-xPTlkn8}wM0RAom!4Z zb2!?ebcs=!;GgE;vzLToFVPwY_m}li6;3ntN#@o!N_|N@5<$Ni`rUScnNhZE3{F_> zgoI1!<&awUW(KHeeAm-B7SEn)8>y^SIVTpGF8$#Aqv81tZ>+AqvFau1WS2eWO%t2Ic!n_DszMfAPluT}vQU^;X; zpp_@t!)#}aSgU`GgXY&i&{z2V#?=)Z^YBTk6;?$(jP;*Ld-8V zB{s8bbxp!&NU1xosCZS&r47Rbn_e4X>zqwhN@mKF753(|TRo>q63hAov8!C19H06D z@_VNX&y5UK^rIZ%I>&mwlbn^Ms}9tfT_7JWzP0SJu@M4xgd`GjpKCYC2+LCf!Nt9> zW8SA{iXb42Ao%hKGWKZYwAER4m^%M_O{EcMef)yQL+SafG?$oZ=A)CX6nDjBZ>+6A z#Lhfk`kFY|dZg=;qS2n`SyS)li1L2t1Nn{K`xc6Fp9vMulqo-^DZiHwnKze9&Zm0Z zjDF46+)N5Du9Zjk;j`RPjZ%{p5iKJt=UEVM`w)T7{O-Vi6y^8?5x9EU5Sn{ksqv&o zA?$i{(n!svkF_YwM9JxRBDlHRQrZp)$S zl^$-_jB7ER-tFan8&|p9J1OC-p~m2z@Q!<-ST>0?9V#)DNnj zY>-zk8XhamO8HrXh<^%K!9`^$*QSy0x43?f<{ME_?!u9AoS(OC$vT?R98d%b1|%k zaqyHiDteXaekz`UToZ6m>Z%q5J}2K*E~!+G8Ys{FES)2ydU-mlfQ6sejl=ufrKbi+Y#V=)%EZF?*0OTv+uKFpo53#hx< zmHn{UR@l-qNra-AjE1Gc%T03tR&;beP~8$Uq@}_;Y?$ES${te5xm3mdwje=p+IFL~ zMf>IN!PiVE3vgI~tRd}~v{*Yw`Yfd5T`_S(ot+5GJ0!n6M;eQ_wvj1fXnw7)YG4v7 zt&n@yxK_F$F)~u{Wkad;U6}kbqD%rOCXWCstH6{a)=19F%CQhud5RVHyOeLMN~wgX zues}j;#Ffky!UT z2-b+08SMT8sb`)FmG6b4pz_lBMfN}EgJ%;y7SISER}WH8Q%;}8rWV;3XQ{*=ak?8N zmb0y5RPgXRNDrrQmpWtl@Ot94)pU%nxpoIQ_$l1GE_oR7HL#3adK%Byp`zTaVyH75 zeh9jI^9xe1HR>T(-bjKTAC_fr-9k&Mfo-pPBi|`JCY~R8yBMM=wfqjl&ScRZ=NN3 z)*i1srbA$V7nu0yY+p8^glbOpmf(%{5<@=-e!o@$SK+(T$lN+NB`DGxZ&b_7;)9$t zXv65%koYJ{DyHoc21q+B2zuxJ7Ysc83%m+Fg@uHQH8dmfMhzxt7t*&--H}i+K8Q`Q zDycX(~};XLsni-J{X4tDAou;2MTi= zu)2?9^UL^eLw#4hrLv>%-tU1?`@OQfAapT&^aJk`BU>ZL2S>Z&Yci3jY9l=gli|At zM8Tu(PCJ*M~JX-X+5^ES%U7#8#(A*{5@J=@S%j?b%zwWNJ%+OhoSEZ zZ)-u+R2G8U#BZdBw8*PEK`rA8iGz-$ve!S2n1Ho@>!FMbq1~5D6J9=YTJ{yl{ws>k zEW3?3=%3(qrx9w}0W^fp(1pG@=Hw&SPP4Z&(AETN1r_R zb$KGaGq=n%F#%F%8Rd;_Nq3$3rKM^I2Zt-XH&t_)KQ1lC<}L?<96CHv!W6TW`l;L* zH-DjMv2-L_caf`MbQXHA3~gfn+)7e*)x)dnnbNUB#h|#fzMRi&%hcCGK7X;{Pvc3y z&0vcuTl62AYhbnWOG&Z#oHAGKQ>JZ&HT8C6j(UpO>d&`Xs_RAWR;@RoyICXT>tw(7 z%RpWwoBG0ES}zJLC+bl?sY{%J6=%ZuFlx*YcUx(E)^K1Pbkle43rZ01!#nvEHcOeN zNUZ6JqbP0qleOlb)NNeCzd1H{8+CgsI4-EWmWYY_l$E(hrOgAB@%tv>BS~bZt8Bb`_;Ng0V~=8W2#& zmpN@otdb5k%0;tP>XM`je%>=EdQ~Xzz-fz07|)c_BT+p;6UEzV9N1Pd=}d{eWM69A zms9Mv7(BjWJgU9q#0gSlR(^`4NV&}i71&V@`Um#K%F1JW$nw-vQcc$?id=OZ!avTC zx%i1D6_Wkqy4toY9IXE9$w6vs<&*I$p##;4Wqt5=JfV_5pq2h{1XEu*^l3i9*T)z$ zF))iK6JTYd&u$m9u`Rmc&CG+6)24APQQZ5l7vWyZoA|xBW~QF@sO+kQok|$tUXV`} z_17637oGQ9dQ3)RD07TcqYq27a@3qhQ!0Y(RY$WZLF{vj!cJFY%OM!8fNHq}MZ;YS zDWo#&*_8K~ZDjkX)po3kDRjKX$C6b_+c%o^yjK`kK-K^=JLi|miHKxweJ7Ot z`GgFg&9_%Tq3HaKyz|ULOZ5H7!OUL@T2&tlXoZ)Hd$cS%MJZAu74|){86VYBG1UT0 zz4(FoQ5~S3((}wJAASlrJJD!Oe`vu$cUX&OwoxsJE=~E+63B(VS$drV*`_7&1^cC2 z?qr%yH^+U<6Ysh;5t2#eqS`53Ms*Y9nl+v!HYlIC`?M4@IbCw>I*Br$=jg2!X5R;j zdwB#^+Le&A&Q&+85g=~0rMJEX@-2(mrAWt~pJ=7h9I%rTVjy+$D2uN^L7wYY+C8wZYfA=g6DtDAvuy+s$uw#n6;t zUDPX=Ye_8vV=EEvAAt82($!y$5R~=?MCItz*=>{OG|mVy^mMHksZCrsdN)y2c#{q8 zI0YAXSxR1biy6JeStfL`=7Cv!g$0cvP8O%l6?7ma~ce+81s+w??yLzo$6xv&tF|dm8lW; zW_+oOuq`;bVge z$cZHPF@p;%*S~JL+(_dO=wIpK=u0K-fI}7HEr&CS5X{l>yO_4K6>CbnG~lJ>a+xrX zi!Wirh4zm^v3bxrtA|I$naw)V=s`<4lY+-*3ul_vdTb=zl9va$ zI&L%9xjRa)P;Uf$#P!S(cLmZJHq{W7mP#928$^s2VrMpkR0hf^NYC#9ntUfC6@D{5 z*wUP2~eA^XF)H8^9 zcGsv~z0jx7Vvx08B3EaoUF2&a=f5eqoD&Tw1BDd(;UQ*06UZjx#d^rK5y(Pc}#X=gSq(v984qGoJxtpd=U3K#2yAHscS5|7I+-o@|D{>^7 z;LGk3GgzmE9srfAYS&7=<;=b7_7h&hP24rQaItG-V1%)H0l(R8La zzxZYL=B|&mPl(f)`NJ>Hyw#Gv(x^Sa^k_y>g*Ai1oF_BX1TNdY(~>R5vsHm=Q&XQw zG|*We-8vfYT;fuk&(J-muaGd)QOntGoxP{MF-wTdoymxE%IUkV#$TnWjy5q&blhse z+eTRE5W-wPEmX{Hds;cN1PMf*Lul2&oh!Gitomhro2&$Hot6i0WX`s^&o`2`>I!2; zk2V!9mOB^1W5Qe8i$ZO?m18}Js#&}75re+w2fFIlHY>FAwD4?V74xIRp`;oo6$_=< zBzOaX?^k@s3{|g|s%Yl&^{!R|;7489?iPxFPNqNA?mvw@686MaXWM6g8-G8@7=Vtd zrIE0W`IeN5d)BbJYaI}&b-;D!>AXXZ@kDcRZiSlerV>VA17*Xuxh_VqL#1$h)-85j z=d=y(>Ib9x1Jku_bKy?j=EH#IXb~{4z3T&Bv-7f?94oio1`4&4q)V#z>1FrlwEc!R zI$L%Y%f?MazU_e0Sb4QXYCQ3-D!c9&pNOSBRe6IaxtB_fBV1t0>*2wngW8_QRhNb|i=~TBD{1k%$fLpV)nRTTtXbqGO zA4q&RS^GHIUJJq3jvF6Ue`^iJ+GcUgDmI??Xca`W-|PJ4NA z-qkTs*77V-#q`h2^bS{ElhY$$SMner>xW@#4)#egBbVrm`9wMV*F6N_!#luRxzv`& zKy_z4m@Xxbm*j&Mnbq^>5f}S(%W)zKA#phMt0jgtRv+NwlBzoPsJ=<57KCcJd|JuQ z%GkFvvjPolQC&FISkEWM=)>nV>2D-&Z-?G%9`3usyCG;)k>_C?t%q8?h4=BbG-!AK zT!@cCoLH%NawCb;B@8xSn3>5sJdkOjEqGf!*<(912X!KW`Z%+B+{*SOr13%YG!C~h z*Jrag%-TKItTp=K4Ad&^(%IcYsCe6D4i3EO=xfKV;W?XCH-4MXcN;g&E=)r<7i8Z@ zk6P-L8oRE}DAi}baKjOkUUwv*FXapD3!nxWp@&_pW(PGQv za^51mm+<0FW7Wb>na$hyDcn@qoNrdJ^vQxk@@KW_Q4f)T*X~$n)ymz~*4BrP`G?Gx za(7B9Th?hLu&;cHRpU`9-!hZoynCxLbyrEDcT9@2Zb0NR=6&I4Y>ZUnh6i3|+#p#z zUWa}Wj zo&oyh6HJp3OozF4>W#WuE0rv{XDfkF)U)}olP$Gy;Rqan$@^H=t|FtWs)ytPnuo+r zO^RGH#IDiLgyZ=GI!2$3wH`6q=c=W8&1c;Vbp>5`G1uokv~a$pePZ4Cl`s>&$Ht?k z8>=%jXLWs{LUJ=_J`GW&Hbczh{w`4}8@;7NiZ>BLQMsB)>4jQ?C ziH%($!Or=h0k49EZm)^>*<9KQV?r>3t^+JYzx4EB|Mq9GYiEPv&<#}H4(th&D_Jm~*nU=iV|N>ZAC1d_CeX-@prh63t(3N7;Yr;W^#V zwCPTpnv2Sphaj)l#ZaD_=iTzkoJ_|fKU(Tg7?;@U8Z3(P?x=iRr#9WXkxl?~I$sUJ zc$v3M)uN8vvS2|cvl;r{%rS(+*W;xRtIgeMi5qqF$>9&}cZ2TJx~P1Fg2otiddqO3 zaX_o`U2Im_8OO?lyg(-++*#Gy3or+B;CxxqtDQGG0a3kkjUwsVj3NK$stKl)4xm&` z#i064=(tH*B!iY@inqefbD`POep~fLRaEb4LA%3Z@5XD@3kDHacAj&t14wx9M6ug& zVUrqVO1;#QzmqqPbsCbPnV_e)8a9gC-wLhU3Kh0ygT7R>3=z#PtEK9C4ufm`CP=3ReQ>_Kt(aJS%Y=1m1-`Uw)KTmGtAVb=gmQnisd|zbh z+bl{pqEm6!?wGaRV=WP@j;r$tU@`FIBLP_J8tw)+5IgXA5jrj z2YOQlk9=`nR-K;DYG8L;(NG<6Zf0*4jOfjwK#*SSy%2#RzY5G1_6+O3OQ zWiXr`+&bF8FCl65)1jYw?T71eR>|9q6B79yA}>2lf=tqse|HA0$b@R8K10fzX(#Q9 z%W60BujtR`4W~oGT9W7~0ENUK*koLW-=LCJ7W&?Y^4;LRj-#qoE~%x0s2s&meQorr zn18awm2~kmmXBp6Vv9^@p6P{B@FB}edeWURA##dFJ2=0V_ zvkhV*bLmB>EKU|277{9T^YXAYJ=HQY9_EBUYaUkE%$6G9k|^>y#B+fM;q7w%RK%f2 zLHABeetW7_A3@C@(z0PoW_Q`%{h`dhl_?rLS66p|au7$YVYxqT6Q#fBU z1Mxx7hqOs|%1^8|k61qqm6vvz3DCFX$;;;~s?18xoIQ-Aik_MOTF8$)7#ZK+RM8r+ zK?t=mnx(DUub)?N_EV-3xlv7@+7acu_GuzI*RR=U0OmC&XyOG;N^tgTiebLMN3lb1 z7VagJoS)TXytkmbF%RnYnUgGQ5!=lo=Sd-2?uFC4QDEqF4hEGf4W3RU$2Z9=-o zp5dgYt$tfd09{n*E@Z1$QI%9<4c+y*tPwkg4NAL1W$}3GKN48A% z@duQ?<4!}BXAd}BeSlu^Czclg;{^tn2uZg3f?ay3(A}ZF9k0*u;Ey4 zhGI&{8T=C76B1fhJDAnho@uJg96Oi==bvmT;5B4HZ1frUH11* zrh=+c71v#bLz74@ekvdNZ%oo-!|^$wjDm;kv0PlILa10PjH~yJconhXO+XKl0Zb~n zhpI(9O-wqIYE_=JfVOy2DnW!{sp%afzd6v3H!-s!Yj@^dSjb%l_uF#+s7Z6-!@T&D z30wBzPu+qFSshk9&maov4@H_w5WS84_BJeZ*S5@VuQa}272TF@#H)LYZ~S&@d{%N% z;yXnR%{BeV;`2T_M2YTb+|WxV)JkIWIQ=${U*VNn`}g#-`HCZf%$d%9ZibolR=$J? zQ9@5r-ENP~ao|M$GSKp$P?jQs@|b+daOMOpCXh>X=vE^x)`t?pb#5S!>2Q?_U#>VI z(U>$hmzdgZOY5cDDdg)C+G$I9IZbTxtga3Kw*%f=OkUxNF78p`xySoq9epD^MQnXm zq@L8w0jZhp7*P4jx_d&(m&Jzul#||NjHEl|b1#NVOOAQr9ajRX#*&iK+*3O!j&W*k8Z?h_Wz1GB5si);MRX)gb13#+!*69PnJBejzKwe4dy}{wB zvYJJQ=^#PiIJFos1W&Kba6Kxzb3*>tWHitP&#&3qtfkfykrbD)q<26ad_-J` z=f_Ca|IzRGU)xpx0L)*VGsR9wmZ)<06z81;{6llbmHQd4@n$+!i6T~721(Zf!a#Mw zDsvHhV-`Ohqb9j~vFOZ(s{HEf7UrX?W1xBaN-k~l4jI*5&^@==W(m62b>ACYoRK|X z)9WCQM*h)&17rctN%}{h=;_AGD?GtNxqsRh%X#^_rTeE>XP)1AtP`<^I@m!8XR+1Y zkmprX03@Hy61L(BMNAj1+|^JK$CF^Ry-1?iNN+nzgfnLQwIy{cVHTK=O`lAMw^ z#vDmKvj#pH`(EedP4NRzZJ|p@4lOrXw8nY%xmUTJ4Cn~FXG+n$JqTJN_L?hJOLvp? zf4WL?b7{T7dyBptUG{p!%1b()xNwQ+Pc2Avs9IYHo6D_uc^|leaVf!#KuOih z^Z*!G<$O+V_he(J-BQ6?hfJn>m+9bMQEPU@KKw=PgFTZUCuC=+9SQ zIm+`(bBwc8U=T3L&fE{`VDu!f*)3DV;HZqNrdp*gb{KEXxz zXhv>xFl~`RP3`@2|pB-u+!L=3qLjDL;fk;U{bOd8N2dtxiOXZV0x6QdNxDj zr!Bsxy!`Lh`v!CW(ooZ%f9>OY1~6Ue8q@IfXFle?XL6_fz`*c?UwHdJla~Ac>emS^ zP#PK<29G9cJpb?e@ShyBP&cJv$Vd=i;}#~wFR%Se<%LV@8@Ule6`QD8j;D}nOEmQw z3*`EhO)vcbbajmGjaU4be~rJy*@iu8>>Vq&pSovo z-MO=1m_2Y(_}q_iaK$u=zE9KDSHEDhlJ|aLdjeH~kN*y#=K3L~GwZJid+yo$`$68~ z;Uwc9oA&pAEAYm@_9)Qow_|@Q`n+cadlaFw*E2Sj+>bIvtp3rWJ#FkhTC!@gv0s?? zvvwJBc6kcC>h+Vz**_P@*;%8g^C*HTW$HnMT3aC-Dor&~psdkujOikt?O_LMT5JFB zqS=pa1|J3|$sz!|XU>sNqm98@?!UDF{he4kftMlzny=svos92uV`m?y?=kJ zzsbqolcK6(Gg~j-ug!L)@wG~Av1R6%EKO&#tNiJd{?^1gZ<1vS%;sPI`#0YEk*5Il zA4j04i8+C7^oAXurgw+yxO!xMn%e_;0c{{t;KghCV+h^<`o%l!mJx@iIib}mxwJz1 zf-$Unf1ZfHUtmEa_)BG5mxT`fxYJ~+sgrHa8YU3~yKj%Q7b1V@w3vX~`{d;i7WU)* zK$Gtk)%59V$XI#v6$0I6H1I~aJ(6VNlTi>oJpS1LMEBNc~$8&Oi!BXBd{;`%* z^8Rer7rePdF8zDA{`%|x<4^k2oqP84l;eNc%n^QY|DBT^s(=5;{)eCZ<6D8B{pS*^ zCcq2&OSu0-kt8k?;I;`*V(lyr)R{GT&(eqmcFJ9LPO^XVaqfA-J2>O>L`_POtqBN&3}F^=OnmRb(jCpKfPJTuak1!&moMLdlF)T6&{=W&)~zqlV9;hk^2Tr8zP)_y+TES?Ij)tJm5z(*pMMh*)9ptZ^L2G~ zSytOFIf!T5w8xA2z|7fyB{b1rri*&><_*JL5zC@KW4FbpOOxgc zUiNW`h`QU;Q&V5~P}P^}gK5?Vl;yXl7%CQuTD=`jOKhV~91=9G#o{*s^YjHUIy{Ai z6?I3X?E0@(d<&rVUdrUF!PxCNot6Sj*<*0M%S@bA3mEC(xpwW^1yU8M7inyyhce*@ zT$qT@K56_wZM@c}eHD;y{48eu<~C3w|7`|#Q$R4apA#+LYv%B~m2X(mW=G^Wa-8;^ z#JG1)9vrj)TFiE)EVWQgo2xe|*pMe93xri%0IwUz+p=`T8}NmPmD+~&F}WA*s%)$)?xm~Er0ZSo z&*>U#A8qzZs2W~f!ze5V#g6W&4d2XFLpV2`x1j59C0)F7tHz&)W>j|G2xoW-;0Dq654M=i8CnnwpF>Yqrm9tKfxh zogblwhVeZTA|m&9h|9~>j-BuQPlys~hn>KrYe(s6h21-r9f@<*W3@h2s?V(CB9gU< zqgC!G*_8$ct#QyDsky(qrj0MDd5^nl-*e}?kyFj(P-emC^CFhF!1GG~pjmvcM9-)+ z;aGLwr%}DU2NOZaDz`=6a27EaC(HA{5W)PPGXm6E7?b?sFSk_nF<#6r@rw0(BB%?# z0n@h$uoAlg@xE6H;tsYZ9Fk8y1|=@C`|ix~#7Q_MduIC-^FqOo+U)YZv6y5qasY08&8maVY7b=a>4qh?9d=$RU~F7V z(cs8umd71mzP!r5pE+zi1!Q8rpKn|2N(<>x%@t#YB?vtV=*=?_8F-{#|G{@}Cl%jU zYBwNo|Kjk4SiQ3Sskho~>iL}cjB&rV;O}LMSLyzGLv+a$EIR!mrR(di2Br3KyW4mK z_Y*co#><6;g{z?NXeJ(_r4_L-Rx^?}pw_bgxrC8V@13ddb|ag1WP0SgcW2i+lQ6Pt z8ijoFUWgACtb=TG^}+IycUiclUxBrMEw<@tixr(E_faLB>|zle zupJl{04fLI)*}pNPX7`Dq`UZ~&G;`)K=B;)Zw{jt;e{wviEXKZpda*++^=|B3j+X14QeafXnvT+dkt!9*c2G|llo-lRitkW zi_*!y*I1n>YV)S1@>Vjss3;NlAtOV&HKphd*e4?Bsef@SsyULw>2n*|6hR?C8;mA$ zDWzSsqLJzOC4l{n10(_m4+?_*^fZbOPFUaSM@C@ZUq7OHc4!o@5_OC$S{;>rk7FPB8hGZ**ybl{8 zPQ*$LxNVEEYEMfjCRr}svOV5SKlzt3myc(*cqR@V|Ldj(g)4%i))pty999ODFf7T- zJZ-ys_wL3a@dXkVijg90 zoEs)Pz}vTPJNJ)J&&|!fNldh_Ya4*t#0>`ao&+=QJ|&*nhra{ykGO}>#o?0a-uv1m zRV!hCKJ)+Olj_k+IhWQp7e_2W#cal^K&3b0TgpuZvB

Tq=P4(Q6l7{K4h6}OtaoE;-0Uvv|p#h~RPBybe zoqh&xeU2=j5rvYylqA@bV%ieHd{FLvhEK$f)!?q|lgc%47=~8a@2e)yl?4L{eiH<4 zA|AdYTE4bMgFIvw9jA>QutY2&DHcR?$Z`V?k4^$ z3Wh0a!iqrKhSHvYab5K%&9EFR22yR5dzP#ag!F-975^U78g^35z2q~e)~PWw(^Ouu z!3rG58`T>ll?m(sRQ0g2bd-90V+o-gRc5|`2DYT=OzeCCf*LrIzp1|hq~ej$Ke)+7 zVDZ3SyOtK$^qZF9hNC3-0%p3567MtX3(>AsceJ)&M2YOh(J&UJXTG3)tb~eBU6$MC zuDXlcX#zoF3T_r#-E9YYF-JthD1M$EI^v5nxr56W(KUDqmQH9xtio+st(x}`& z5@_QWUCf9K?Q`$C`6M+)Rj$?+u}a2_hNi#XF|)d~;~YAJ;t3&CGs|TPnT)Gd^K8W8 z$YN3-=5&kn2k%qj8t!IRbCd7g>XkrqZ3^NP-=%qUZJmxAt8*^bMJzhHd^+CyEfJxN zUyT%SpB6>4(nsjEh3w_v6nZbB5yXhssnnpczx`hBM;FV8+EN*SJEZX8-?IRwk}4Ha z^fO8snt_Ct<=;*I#dwat#kNkPqAjdhhAeAp4iNToc|BtgbkB6#%|A3>U zn+nNNvQ&53LK`Ey+)_!hFJp^JVrK07nnVe0D6&^b3n)uJH>ZK7rnx{t z&Q3QiLC&-e`a__)2!L`bgjI0o{!WPg?SB1ZVyJ>IN)mKi`P2L0`oy_xLXEP=SYt3< zN%}#&DHJF&D76^m)M?MIe|)>G+Dv^@tvx27xBPjFaV0x?6qxIq(zvR&rNnPrO zu^&|i&dSt91bC!`Ymk|!qhK7U=Bmyi=~Zcp1OiLV^1<0;hdQh6C4_%fZevDx(3s=* z5t794NutDo50O-Bu~FVAPZs+?%dj)rPy1FDJsYh94P$K}XMYaQ)wJH*8n1&85z}P` zmCz%kuxRInrJqJ2zRw*(!eayhMBg2Xj?w=$CSg0G$o0G;dpD+e3-ErnUWYyM!+seBQSH^kiGCE?EG3nSpBKhx}!Nr zZ894TYC>XN^OGtNesKN9J6FyHzdo|(NFVc10yEN8`>(anY@c@14JYDa zcFqI5!#QEk1Ak)Z!U9bf&Mx-bY=j&N!!3dHMj-fe>n)@MQ(BU|fdXz6XY{FMSbnT? zEc7G=C6VTFGMG6}Uxa%#>ozn?(4xfWL2eoWOz}POtfRRohhkC)5X-nih zK;zVJE!+@9Y>bydfY(M+KMG(EYfeEOSx*d@7alB<=h(0>GP9NO7jg5)=DqNtaONvd z%4&Z$?i6)x`b)jOLlj>7e@uGR9l6@o zr%hmtON?ZkxWOtJ1rkcKW%aVNoU7-2#hKc{XW^_b3C`>#UJykicvNy8EI-kvS zcH0Wk4Dm4ziqAUB6^G6|8?yFx0rW4S?r!`0S9(VMv6c7GmQf)vL!TeDC zLF?v?`8L%1B88UK`p-aW}@3{E{#}4B5TSyfD<^Wr)ZXQs58( zMqu}?tWY`nht!SU4YGB^Y{JK9HAfnXaU_wY3=nz0H2X@RNyLnu*cs&U$gx>4q7%lJ z;Khvs^N>e8$^vK~ZiRxS&M@4wS(Sv&CMz2V#+}(`1c}fe7}R)sL#Oj#*r;PrgpSUd zF{lLUVpkJ z@!jil>>tapa+CkSNBm<2cLcuDOJxi%5pPId$UIEg_jN{Nq;u@m>fp(uNOV7YoB^V3fc1xztg8zak=SZ zB_^m&ymbTHQ=!R;l%pI7%W(q{#cal!Ig8CO=|uWo-Yk~)c#$!Acy&D+vl8?P^wVQ# z7*p_GUzwlnjvC^P9P&N(qL${g>Ynq^%$nxBE`}B9=q{XH4`Mf&Hh&dh+n6ai2z}CB z6)M6@>H1J)Q8Z@%(heQs}#WZR%}Mn2?s}@^n(%k_D>k zjQwXBA2kVDO9&Q$5M-(CR{pp&`@GB{c&vU}HEi_}l@S)WDu*)vf9>>8I8Tvz|1IqV zum+{()7bP~v}RGG7qsk#ICIRgnFFMc19{{ez5U|Ms(|T+v|ZPzoHPKBE2BA`oDS09 zeJ-{YG zS3^^`D*FSi4`T0wHsm!7Hu)BTMSA1zZ!!oq{kP>~t3F*nNiEd!JNC52Chcev|uS$ysb&9T&-PAH`D>D`NR1Bx zM=7*AF19VoovuKYgaTv@c8SzwFQd)|AUl zfYnRfH`&B6%2#szt4gUsu=+wrEm8nlDGO%YN>D4_NY}+^6@a~;lVC^1WUBMQ4bxuA z8bCkmPFR@VYUpHICjaA%@GT41n$|K@B<&TREIxDjkC9vbYCxML9um~7V9ZZ^B?$vE z@wb9OQw{~sxQH4+C|;#$uholb-2%}GymiAJ82vmu%^IElrE{ijV?uF?4B}}gv=RVI z1)Ll3mG88>$d^SOJVwQ+_wW5XI+T&C>HyFNP=jy_Xlf!bDnIu-4qh987m-O3Ya&%J zi4Xqv7k^#&C(ZTvUiAtg?3~)7XXM^}_3PA1yp|R+p9H`&ld^dfjLeb?-508lQ`_8S zNxqrWZ^7f8TS@#@YhMG>RZ_c0IPP?E>~tMX6Bm^OV9;OH4=9z-^OW}JYug@Idt#e- z@lnsT;4ox09taml2=!~DwUd8x3m1R2_b8} zq?*+wI)h!n_ig#YoS^|m)j;aKNqfaYlt#fAA;ouQ%y_TEtOIAVBR3bv*JX?b@wzlcQtg z{NTT~x}Tm)$&P&il}ksSJtW$jEAm<0zdzpO7w7lPQqqT3A#-zzF-EA=yA^=Xm#`z$0_piOioD!QTX}wmeqIQ{>ZvzuadbWx&6G6BX^aI{I}O{%D>fg4n90zhpDM` zgEq_@{y(V!P{_f8doo)>3A3@Y!$Z-SkR^eWC_dd)7nou1;j3{U8XNRUDb>{lPi^+& z@rjVqGK*Vv&H}j#T!fj6ly#s1zVY6}a0av;Xg%-J{Vua4BNTG+V{hv|H;xIriM%hcl zvzu-$?#v}Hawp#m7=9wX!-vsQBq|OGu2TzaQVl?veCLwctEA3XU;a6^mT&&JeaR28 zi!6PTR^X>3{--U>Gt|yaSNhLQz^%A)D4nMxmNT}OnJ$Z*ugsHy*N(KHU^%=tfB!5gSlu9&ad(Q3n^?3j5sKo;42?&mKL@I9DXp6HR|Gm10 z*q;&+Tg0h23Ku;bLM8ke*yMGnA*MUC z;**k7&z4C6Z;0sYbc}r%-nNCd1LSL38K451Mr$S=`6YrU)wq4WUCR)a^pTC%9Dx_U z#@2EL3)*BsV>L!HM8%KUTLQYtJYd%xd|L>*@G6RrI)yAj-OB%RU@Wv+T;77$Q8#C6 z;cGztv9eyxT~**+iUZz2MD8Xc`Ak~OvJ!AT_KqsC`_Roqgl-a!arKp=4aen^ z4Q=o}FGb-r4}Ej^Tu!R2Lm8qIUbgmd zDBsA=j*wRFw7Zw~H|+sP$QZ7$&9C7J_|!p2vTf+zj;sK~kAI1XB=Xyw-h6?7DG8Tw zo?Ro!bo_lygJ1u+)bZ5$Vl_wwy+!#JXV_tT>Rmpgxo(@8b-Dg&Ar5-auNM;7xlo<@ zg0vJ-KlE9VBMXRwq_6SRb8vUShibpH(rlV@itN^efAmc6^~a~yg|_CI(-iA#xqZ}n z4EIe9vqZ+orTijDhOCX*WtzWhOxt(#SYRwt$0*z5swrvV4 zEIbEC9K3|F_465Njocy9W9=c+E$)WU2H%Don$C!_? z_WEd4wrXEhPMi_+%l|Fb0Bir;iTNZK*u>+Vu*LlNc|WDVT9dlqn-N7X#PEJO%w^p{ z57c-^#8oY&V&u(2hy4}bX}M6;Z`G^wPxZ>G9^R}J`$f1!LDj1r5K8nGISq8yAxMnVf7LyZrUYnIP!&As9vF5vZo3`%X;jSmt{ zOXpyeS;CzRF&ieKydLUdXs8fz`-oPRRdhGM<(e8#NbJU+Q;#!ygb*i3U|Wb6AOkIy zfJDEDypB}bbWUJ=^wdh&SX3ER_qz}X_WtN>OQ?hG^R>H2d7z2S7wkRWre1n*7fGI1 zyPaodZzk&ixvDsZy#2*F7(u*}#U;LpCI997J<5s>`jqsE+L9;($38Mt0DJ1VNl`Hd z?v8l``&VMjPNWoaDH%yt1%bg8Z9g78w73O`m!fhjW$SCooJ`{eI=2?uKbmafvvSQH zb%R(Hscw$tQdDhJPaewJ9}Nibu1ow6o_i&yS%{&H}Gp@Z+N>U52IhU$$ z=}SW0Y>0z5wEk2o=7;u~9Rb-Ev3G*vNs$Gh$!3 zpAqrC9oP}3%FKE$Yw7K4z)B49p^3g<+>6xFdYpO3xvN^k)4wT6Zawaoc>e3C;RPQc z!aXXx5u46wfD6f`y7hA2>P#eLf~-46&c8LE4wf*ep7Nz-aaEo8a@nFXZSutx64%qG z=TSu)bX@zh%Bj6WiO>3!oMH5X+|d178KR+s`z8XKOjJj;0q(5oJBxMv3X$!-vzu^S zcu8Gl)^FGcPBZ{O7&b9P^U@bKIQyyB#UVbw)u!)$YSZ;vZ<|eQ_6VcrL2;~jQ`)xV z`R(yXq=CKzJ)t;@Z$Ke|=Ezl5{<)Zyev5hn1`??=aqCjES?LSUd-}011bsdKOQCr8 zOi6ZS=Ty5JtYOb4d;iPw|2%Sd!=1sn+SUuSv5GX!cx^i=l)o@%zU*eu_#hZi$1HfMODKKQfPzQPYt z8s{bwBovvRIVz|51}=OPv2oniJ!zLXO!&*rfl?M{c&Y?!<{ll`-dzi{z~b#L2nMZV zH;9Kf^W|o~-NP3g4_37Kx*utxkZR&LiqaTa3pBQj?v6!H`DU)HBgVhl$)<=vxyG19NE4RAqVwvA%_72=81CHiN6=#}qE`0aHe23gxbcJl7HnkKr$5ki&3zoTp8#!JQz~_);fjc6G0}Nm6kGs z%fR_83-UjVw^#&pEHTc%^2AwveY&;0ZQ~x52Vx*CtTIt#({@Hxa+8rh^N)*GGDSs0 zR5?{T6AwL;M6Bjvca)yqMHWp-_w~+OCqC4Q27;iGkt00N(Kk^w_La|r#kd0aJ_le* zddo{tQ(?c;xAD;rFSjHkF{pX*+N_66QA?N8ota_kcr+;#@6Zv(q&8#pZlVFNw&w$% zMOwS)_dR%iNAnlHTJ?2Ao=t1rBo{x)PXLTI>1rHg^Ure+x%K%ITtoX(7U75ucVEG# zJtj)(4@K6i%Zg;+qOu`WaH;x~vnHM!2uUj}`Je2^0>MQ1%v0%6*7|R?)vjPc1PC`1 zC+qg4?MOle#D>CafsdEEZ&{!fVd;sP0HpZr~9y~Vp` zK91D)-n;o#uamctg=|tVLRC1N`zERQPa&59e0-fCHtCE`2T4-Fd>~T-AC3q@?xAI{ ztQ-3H?e8b;F_t~P)+)-{-_*NNQsFvQct8?g;k7hI=GodK1BCsQFd*!=oCTr0zgRiO z@o27cjlI0(3AF{6QYcYzoq6GQQD{^F9K}aVn;L}2{dKAEZL`r@*RIe-o{Kt9+wSe{ zaXnVH?%d7&XTbK))q3)>2@(-2{J$>wpGQD+U3-{7CIw^K-m1x&AE@JpqOU&)Y6tug zdV;1I?dogUG{yH4h)M1|()V>#XU7>@N$H~Um*0?L*Q1w!`aQ+E>#ezopQc4)76U+; zt0RXEy)o$04eMSGU~z2(K{xCwoLl1p0=TY~_Zgrfy*$o4`f7U>&-e&E75#IpG;JAY zYp$%p^JD7uZ&am=*N*+=ssZ)YmWRO|Nv-D=oQxbWd`dt>tfRFr?eyabj!4!jHJ$QpJS{M{7I0wGLjO(plYu|oky+!9dp5fx#yhn1z{O%#>ux~~ zrRNUvjofd^uomKhSjH1Bt18R!Nz6d;#9X&`C%~HSWXT52p-_CZgClp_GYjjCjNW>Y zc5A1>W8%c)o4EBq*3h7Oc!)9FgU^(|L6g=&%rHopCt1ABI^dqzfybu|7@se>OS~ed=RGh{Oh+4)i-?oj2udje|Y=0 z`7iQeJ;evEM#O=0?GnXne^zUFxEWY&NK|-6=j0$VPQ3?bWK@JNIqV*t-M0rS{GSZ= zD=-^6D0mUJp<~P>-hL2N)_Vw1*baGp5&{dJzm&tzd}4ikgz;BOMIs!RNR@0K4dGh| zl>v#?U9oy^?&xo|?b&bju`D3{>-EQpLYtl^`n=4`_h9p!M~U}O5UvT@q;ld#CG7f6 zS@cE;;O(=UbK%%!I4Z<=7+ebCv-jRVXkuLJ=;x<&TA}ca-X+p@wS$VJ26*k%Zz51} z;h4V|#ENYn%HiU4ex;HZLkF}G3S*hHW zQpLkNYf;JV_{44W7vUMi-+40rK!CeY`X36&=FcH6`yTGRej$A2hazImds?ZA-qap? zxWphIspu$NMSUR`;^6((1$xN-#t%ZtbI~k6(0;NNuOmI1hVQ5nmj{C<9g~eV1qlCg zkS{0*)^)uVD_BfMX0#?t`lxbBA3mF}uR97oVR;}HnPFfP4&Y)x6Yub***00wfr3CcF>Q(UH&|m z`JVlJg;hDCZcJ%8IjC;C*xzd%ni8~y+1rXD$JD>BUtOc|G(&-PYXJE@-nd z@>0OZyFpDDr^H097c^eh$0GtGQ&^mvjRWMOaq35-)?g6Hftp@$t8DKX9sa*B`-?~a z-=i3kQtrqz5B5&l`>wa9ezN)u!rHT|SMA^*pORfpx%HSfr3|NFoLk=f8xTV-_WIj^oa?*tmmxtTDpoD zW30k$_Cl+MqDoTpyU!+DUp~6wR*XNRy_g|#m{`o9)q-^u1xDAHe{TPKfBj3`MPKQT z(CQ4io$hSLHw&c`-L7wDckQv-p!6rOFbmiK4YptSo1n%$QdLWWFjSGr+Ni!`5;EZu zwS`Th&HviUzmFO&@>uG18&r|Ms3k`}X>)~Y1=|p`@TU$3e(caj7AGSxD44D+kgj8l zMkf6<-10numB}|fsSiCDK${fHe<*6e$$gaCU?trYG1tgDc~8@t;!AwQA1sc?g8q?% z2Ds>4^94SfeaQ$t=fTrzshq4=vr+bBA-tGC$a$c;GHJbmQ!1U%k|y)m@IS8O(JPdg zTIwK+;KgKCQ9v#Xc4O-&dh!kz53H1Q(gFd_5lInISUTzO_B8A^v~DuXC!i!gP?M%$ zZr9_ddyPkdkm1Z!MrCN?2ug|}REL0X9qdED(>{-~{%2!=khbp4k~rVMvqJvC`de56 z(RwO+oC1Y9H(zDtU=;2Tshu?6J z+tH!JV3C|FZ5fPZDAMC~GQz-6p;rj6Nm^S(S{2_4B`e-}`F2dazS`liW7Mpp9|jB` z|IApb(Xn@9c=#Uux1?>7f@`}Rmh>I!yEdOPcaSgGDynROp64LacC%@f0-t$7SPn8`c5*@Lg7K?S~zkX6A`I}uW-ukW{!1O1$^n% z(QQGG6ZQ_T78K= zddQ2ud6YRgl~DW`x_cm05JV_{>(J{%dYxu>E2Q{(Beh4n39P{K(15!Q45C(EVgKa- zZT{*Kz5}Q+;P7`fHDK&AkNoL zxwmIC@w~rq*Z0d=vzhq@&2kze(7huW9}%ZsgEP-sI>Qvwd& zE1up(@sUHB#3~(+kYW!zgT%DoWnW^Wd0`?gCdZGj87im}Bn=Sr%ij-!NmqgVIplnqv44sao<$(jZ=DeXEp?wS0eidy|BfT7v z@s7+STQ*ipwfO2b>PRd6)A0Q*9Z|S_OdZM6tc|volM~cra>s&=*N+(-YL#>FyeU%k zsL`<_-*d$Q8Pt?yc*5x|a($q%9yZ45{_0>hPl-iHK&;knT=1W2?H@l! zVAeQQso(Vxm4kam)?APM#^{X7@v-B%SX zS6K8gs_y}==T5aVilSRQ#07S~vVQ^D1$aI@S?NW>(7tOI@t@FHSBAj>JLk&bK^?tE z-c;!v_=L1`Gi)vW#^sV8ndnW}ESQ_PzQIbq>rRHeOXlx$R6CAa z`XbwFf8yH9??8Aa*J&mQx=q%S_jGdbn<%6!%0DkY$NWkq|Ko}NYfnGF0_iM3TaOzz zCT)_qa(&Gww zND^GONB+p#_SBN79j&L*K-`G(z{@`nYj(&Z7{h>l_>iE)g^KaFNLN<(% ztN>Esc(YZ*>GwUQxWssM6*vl}J)Yrgs{OY3jVr=|{)(36tE$quwes0QmPoQ)4i6HO zo@R(jgzUl_ImkS>)ljvl>!avr!D2sF>9;nuacEP)H2#!@p#c5*!|^s$p|3YXkTY_{m~d(GqvsUbH&I5 z+IQy7h=tWg_PP7f+x(pwn;7ob4gTk-;qwlApvW&$Vg({J@m~pzxg5~6fIHxU;)%q-yB3L~zq!RKz=k-` zb^guP%b_S*bltS5f`9~aPjdX9)n9BUb?14EPQ9ejo?hky z0#pEKR(Kbw&z~}K_fM>VlKsM7uDmzQ>zg?HUn@B28j~vn=e=dZAZCABDtdYee*PKY zV~H9nxE*fKjfNJL3O`f$tB^ZYL2pua#ph1GX9ze>S(R6c+Fq^QnNsX>vj(WCG&#GH zrT#H~e@94vYrYr&gXshEnd9DkhO%+)&px{#u=9x@vc;}gK>5}tCWTwfwtCDzPN~wd z0$$LS`fx8-y;Kpp&sHU_E9+CU+M;eV&aSAZTWZsRwE5$XeHNaGz)53Oz`St+j8)pFOq%#lD( zE+_vG+Pvhy_w&EMUHDl9dA?S+IW$H)$^tRmlGfNjTo6*1d+~K$wn8X-m$RG&aUp9I!7z1Bcc!e z#?5?mMJ8nQywLL{)O?i#(FoB4c^v|gzHZ=0mFl!ASL@94ys8x(i~wpC+-Q9uKd~); zIqZ5{-9Uohm_B~K3p&oA{}u(nA2SQv-ZJ8Zd_XrGN{d*AOCw3+x9;3I+gEYbuQ}Uk z-VW?x|@v?DJ#zIzoqKDm((>qUtjT#}c>1YT}+p4e5B zf{BidN0R2~#ui5C1;u8LiucAU{`*oWwrhIE0WZvXH>ij)qOT-suSKkA+o&w0d?B+O zr!@#O67|>>9gX)I{w`;`!dS9r!l%0qQ?(`Q)5+&*uQC;=lJx-wTJRupUsjzICORr2 zVs})|e(=sHfhdg1V)Y{mSfd#`B!*6t?q+9)Hs{!3V^_$M5v@)w%xR`?b`Ic~#8e3F zN~(4pempB@_FgR*Z_nc^taS47R1uLPiMosQX?zmYQ7TtnT*Rs0C7;xKPkeCZRYaq& zV_6C0k{#gLe*5;lN`vRCJy2Mw34{loliJef5EL)fGXUDt4&bR(s+5^Vdx8D}k_?>k z3YQf2Uajzs(!HR15}-C8WbjZ5?mpLfCl#I<*RO=I89EI^>~FHFxZIKQ8(@W=%Obbf zr58yXIqY@|5}SAW6K7g%x=(SnEhrm- zi=2M8{N8axH9Kmha`}sH*!PbgRr+<=bb+RlA8H^X<@@B>8I-J4H7;sgN8+zw7CPi~ z0e5M>%AoAVk0wJpTgc{qVK{yReoS?w4?AF$Y&zOfQoY_>m4m~Vzg{@3?ESP3iE>#ZWg4Q6M zCY=Rg{yw|zFwP|=213?Zq)C@&IuGyZ)LD~V4s)I;m~Gc}kzVc1sr2w1U}IT-Xa*h# z$&cmy1?9JyUFJi4nN{3xyWV5kPnCi-SJJ~NM$_(;)4QsdR9@!P=&pC@R`t~9)lqcT zi|Gi|MrY%*eC*2G$gBJB^}L9}$4_r(dNE18A5=J*rv2Z<5*f+iLpC!chS+L)%i3!; zxmDMF{vB*NH}ZJY>5VI*?#@5xirVxe={LlH4B15&N0C*LqNF#k6*i=rI?gD`D71DW zFlfH1=;F1FqN%dD3w)!YhMaz(pukSGY<$$b$MvTwY75H9`V~2|7oZ80ipD}Rl7`E! zxpJ5k9zp7!QnVDFz{vkbLDX4u^Y!eEyZOwX^zmW5w=izF=;F<9LU@YWS*x$+ekAKs z5eB*Fe&*HhiQe$qp*5|LvV@zTHLbfHP`Ed(GZ|-oQv>`Nf_Ob>Olmdp5lfewTXrs| ztcdAG#0Wxtk`}%v1rD|9+?L*0HDD)N!xxL;jPK~_hZjOd7CKL)(*kJ%hbjc3*554m z=ms0YzFcxs=<;|~{mhy5I${CDu?cfQ?GkgEFI78OQ9`z!my+U@;x1^BTb+cSL)P1EWxPI0N`;=Y_s%0AimR#s+7Z7R>7W(( zPFI)wPj>sioVu7&Zt@;HwZ+(8An)8v6Sano^A<$p%fQELg+<;ihQI*bQT=5^z2h0L zNbT-4F_JGMHzGqA$Ok$Q&IAnH+}iU0B%#CGW>PRwCdlV-jKT7g91WMHJdz?V`$MxV z*3Df)1U*kDL4)ciTO!49V1bP)cK4*)@=FdiM02T0-cz9vfeJ3VTgE`xV)51VAVDNI6fpOqOtLR$%$}^1a3KY#; zOD}PdT5EluoqcX2TB+Xlm2;-7 z&9C7m=@;&f+3n4pbgK+n`Y z9UdMZ**ZUXWEstGWMGb7YBYafA$yBJ?x#21C5-e|C=S|@;wE2CZxtXUvCEuVud=DS z<)%@qlZ}{-v8XECAbR?!bd)I*`GK^-jB1>2#0^GpW_w~s*Ko^)tZCkvKJkR<{x4A+ z-sM&IHBA-Z)d-2{;nQjD{@In-mGAGSExn>@LMUMy=#+k7{6j^*ZT&a$sExTa2@KdEPj>+dc8qL@)H z+MRa@jM~=Opc_QZ(#JEJsVPK_H0RLoqSDH6@75>G!6$JW0#khkDl2Pn=~`ob;*n5t z$iiDCYJANeMHY|K%J?Z7Q$4G$XD`MF&CIMkN)LIbH! ze41tb=1WIjzZDM*6opsTg)4sn1F8BGG+DUj{^t}lq0N9if1p^6#s?aM_J!}~$*} z`0j42)2gjL(b5qL;J(KJ7|C_8z`${<95Db#h`L-+`q~Blu_mPQ`WSu^@lE0K%jb(M zCzEuR&uUxy(be@}i>*_eg6huEwtcI|(XQ=Z9}RvDG@{b4LiY|Y2lm@cS8|wz8^sF& zh`~74ozBG+hHR=tqVlawHK(EGu|w$9eIl8YEedKd5UU%@daJ!Qom)%$nuLI8-@}h8 z4`FW;Z(YGrJf(xvHO2y4FL<&>M^qHTcxU?G1YsR#>%jweo{_G3x_{ZYQ;7e<908qK z_kg|UpG$qTuuf9@OwXmNgFffK0XE;a0Z34mL@1Qrt;;*hlq3PZ=ki9Q;2eJCmTl!-d+oedDHf+I?`ro zEIv8t69c0-_cI~0pZiNG>ub@}-C_V>Rq;fFw08cDdhPI_zi*e1_Svc-Ajz`o`X>PP zmInX*+=jNmoo7+^< z2Sb32ki0M#^T*3g{01+X_>GZ-s5nez={Qz60A1Z#JB}4;=s;r`3>mnyQL}u{Q$CA3 zP~tP7^3-dmjYsePjcX!v(iKRL9o^t|m?sE~mACbF93C9@)WNLRmC7CHpHCS-7iQ*j zpL9AoiiINi789vA`H0><9qcpKX=ytp9a&(b#UJR_=?XIaG^`e{vzQn=vF2uEIz41U zaxo$o@U@XDf`NZ~I#N`0Z^>HTK#8lOS&-OhKbT^QO|0I)IOXih%X1M^iv^}r(+E*e z{Ebnp^i{%0(ESa=yfbMWW2`j1gHq3CwmsnGrFa@ePTEXgRM{3jdoOEcwg55q6G|q? zvrlQq<>T*wf|0%hkOj)Kx0+@O)pAIv!UZ6jC3PJEa>mI>r{JSIA)`AXim9&ZKRd## z5>x$?$dPpJp~m0$I(*)7)>N{HueU~``BgyK&|8((&#Gmm*C4O9Pg}%X>d0d9 zTPh$WEOX7-u%#DZGPI>|>21kFwA1HqX*WF^KRV`o+ELbx5Vzk*mp4EN^digUra`J@ znA@$Kh(!ZkQdsq>&!i5kG$rDz&bTyo$wO1bO(0X?0KulBo7PX31TAatk`pECYQD@O z*PLM1tKqjZ8Zi#$?UTaI}XQn{_@5$p`+43;;xck|lY}#tZO&!i}lX*}0fwb3Z`cNBm=)0Iy z`Ma=}3XZP0%;bK0_&R<%7Q2!%-JoO>*rBLhtP|9=%pT&>ET3tuPjssBeS)Fax#0|# zKX@|vN(R$8;a!e_Yke4Y!{i+GyNCSkXF(;sw;`-=U*c0DRkL$y-cT&n<&w{7E?Uzs zY7$>AmjHIA%d9GF$dl>K*4OM8=*zAUPRSuHI4NmHhIbtZ;1C47XtuAO4?H}iDOT>K zdVk~khcU`@J3uv&%z}h+D zKuBhe7Kfq1`1~z0XbOdpI?egxMHqN~!U!0x`!#S*c4%GV29Oqe^e*~*CD2QmjAUP} zwh!NLBN5UZ#j(hlrGx>kYirD~lJ&BywIQGe8voPv{tu#JH;btw%YPZ#)p_c{*XQ&1ntbE@gtMMM55HPhC<9e#B7E10^Z#wO**8Z2LB|scAILj|J7tj0 zW`PFE*z1QgCz}h~&+s*oC3MMnhYD(%*i4>E6ggSjFT|$4?oE3jD}{ZR6xsMpV~|-k zQ%RZF==Ny$QOSrLe%sYtP~qLAvtmK7h&U4q!&SDMdTHDf8&&C=Je!y~&9k%Db2*Ow zs+`67#I6a42XSs{`UdAQU3=VUUsyAUD8_~f;lA^C@>(Qo0yiy9Uewlhj#z)OR{a}uF|Ewow_{dE3)Wh1>96V-{^RXHOc*b< z&i|tnO_NOAvsWMx7TNcK6~z$_yxt)n#=)+o*Fx#Q=)x&tY1E8AXynch?}Ln%!zKq%meMFE%vz*O?&vhAFrRgc=Y9q zs;<5$-sg)H-}?UBatWD=37%8Y66oeYFb3 zuG?F^d*nBsNBVHBPbE*}^B%Gx(Eep8OFY||LY1264XIb@ELMC$<093SGF`XgQGj_H zKJ)Cn)cQZ1G{8++0Y;>N>LJ)gm5nFt2BkBZS5@%t(|wwI2Y{~MtSHudHVn(L3k&TL za+`Y{{)W8(Ii>TT$!iQB!G#z(!0;v>3Vszg4r2a>h6z4U&R!3QFoU+8F`TFE= z>Tb3&!%DKX9-iwNL=2d4p%)I&1O^=|ovVfUFvpWxr<*EuJ(%RP{cL+3bR+tSLV`rt zu+GwZCAHa^_?xEEx(7ZrZfsP8C=dolZ$pH1Ezr>2r~4}3cdy9noyj`Wls%GQr_jdP z2=<=9wI8mWRGD5U-0&3~!nuLK0o&)UB2YH8uGdGDY!#dlt%H=+v|~N^W^X9a<|jQ0%zh0i zN=6-Xl=UpJE$D(3(9+qqzbV3LDh>G!>xAn#NJ9NOp0FH(uQmDu-B`)U2jkUYN2~fC zx>+`KQ#(Gj+^-$&b43vTBHYE5Q5E!F%015vD6uT2sYCFG!iBnK93T7!sVFF(+K|>X z`W_m~#Q4S-&78rq-VZ48kv1j-2J2VpmD(1W*pje~;_b`B&Y2aaoW6I{-#rCyMa;u; z%H5w=_pig7-7RYhw0&Zr$jGLUJiM z-JoG6xspN-OR4pRrXzc6UalVT4<6=sXvt1D?XPf0x2B6t$1mgWU(K%hG_^N$#MpSS z&kWyXh-O+%H)$?jpz1C`-!i5y)J_+URtApDi+OQKpo!HY$*{#MoRwHze;dwfT=q0) zm^paY_Znd=J>P@j{|O^V>i43cO(_gO{bwVkO0>1yWyQ2#(A>zgmQ;J`9!5~1C*yU! z@z=C$TWi^|17Q1Q8=ZpbWWvrEq-*BD2p-d~e@b-rTwS3X)TEIRw{Qu(0Ly+SMc%)Q zp7k?ua`?ne7D5o_)xP~Kw!Hp}|K3MCwt)fv{ij8u{POP!()M~kVjRmxU{kjWbW%J*1CJLh&$Q0A#%+qr9Va!S{YhqJpNUO z1>>yZ`ZQgA=jYGp++q?iV*JBXk0)1S11F+^xhE&D=BPtS znmz2m>B45iu%g}*2%X`O$N90#OXwL4Ab`sE>No(ddTieZY>w1<=zyL6FV!YNBPqLuQ z4?}k_(3h%LnnIM0xc;=}m=zBM4^I0!YbHxfPCMQ#2NmixxvQL)j6!WFd(J<((6t1l zV}6SnxxuZ&DQ)nYgCZepD9S#XTm z&RRNz;_w6kr&QtgPWV}Hh&BP1P|LQFmq*U}*on<*L82BVu*L{N`+0&kFYd&l4`IER zYIMnwl}b_TrmhKHPFi=OmM2Kd8{5SuftPs85;vNiI%M5vCg$~n>?&`vMyTHn7w?W% zNjf@IGqGdq@YiBmV`2SfgeXL4R(6-8N8L(-(wMGmPpkCISSDl^62HGQJih`eiQ5O; z<5J)4F)ghUGga52=n`g~H~7}4MT%W~YdVw$LV4r6a2aa@vslJ0&E?$sGR-JY0c{6F z>a4qYCf0!D3rgvQNmUN#MECs6FGCrJGo7-|MSiswX(EMYnefg7MM=0Mm$S*){3!KR zw~`4uf811>=P9$V-$P|=(A|SoH%T(k+i(C_o}8%f5P<6)dGlS9vQ|MFPE0&6Yhghg z!MX`MH|%H1zbIC&L)G$}{8yQ9(^v5a(c|!Ee_1`ooXi)?{{9NlyF{q81>Vqj9>q7f zd|≪vD3aN)Dg{nveG!V5i}uWS!u*$!Iqe+@gLXVE(!7D|5!Ijx^aW0u^-NHw7j= zmwVicMu!e>q+*xQ({H3XlB_J#iP50QPmxgdsOrhr@1`wsYwI5*e)oM}y59Gt3uLA8os3D!Y7Scep6)C2q<+e28b=3x6w4)bkLTF)-G(-+_ z^x9h9D_9YP-!Z4_b-Mj}v);9ZYirncvp8p=SlT7(PS?`Xe#GsLDE}!n%Gj}0l+)r} zLfIXL&0=>9U1XfsTHeaObh|*nS&qk4G$jc^XQ>73OuwfaOa&~V=UMlKdbMk%>-d8x zFE}pBw6W)pE^#`u_Uk&*ljbhuU87n9+P#lMdCi4_wAJl>!-F1_!A>PVx;FDr>xeed zCM{a)z=)yG>-Xl+Fk0l*nIZ^QvBX(%ric`4{j^;A5nFyQ5i5jbW_`P6P}~*RI=TPq zB47xqxw0t#=b|gU&zx^gyU$tg9XT(@&nj$rBXaGm_i8)a{j;z0&t(=9*r#0pe!CM3 zG2W}0jy*FH6C&mH&B{9t2^!pG7<2UH-n%ZhDBWvOu0(0?*$|s6lxY$oIamWRM+5?zO&!O%ITl3a8IbSzUNFn^|;>C{cwgC{;LHmMF|a8dJ8t+-rpA(rU1F$|VdU=5VTLY}2j>t|Im*8m*ev`hERq6<#<{w@iG>1Q_deiq7$?Wo0tFEg}1 z{L;_Hv`u=QuUh%QW}ef@-{F~byROg)%HB#8PKDF?j`9GT$ZXxYiuGgrfp3h6ONE#d z9+e7)np9$M%+iePt;DajU8uJn3t%~lW3w!iu?~53q=vPTn_1m6Z<&wZ3qdvaE%kps zbtB)s_=Kx_M~H9CCz-Lu_c|s zjKaNl%Hu|~8^ihy_6>pKvn3-gIy;c@Zu(d2`I6e(CeO)1ywiGwXj|*)k6cm{4+Ag}t8)bl)?ar_k1IBM6j- z{w3*~`%0XF5JcmQD`mH6eG3VL^dJ9Y)TpaYbu)hNoAsv$H`^_hf^WM7-^ zzxdTw(B_p%_d$2>Q(%PPB20lzqRZwVgKoM7Tu@h7LGIlQSEu`8cMD_@3?$LV>@y9z zsZRCh>`KHC%iBPYnxKPx>&(Y`VY@%3ckWi7@FwgOS>^8!rxg|5%!7?zUfp&ZJCYa0 z(8HnD_p+ovC$rI{Z_hz@x4OKpd(S@Q+!-1xlUwo`ZrJZdEvR3mORk=hsUYis3 zUL>)yMAyyK>4h&a7aSg?;K<-N5dbr@Tvb)d@2qo;ZjkaAtXJy+aY|K7do?NyeXkE3 z+8C?$H=hlwo$g3Te0aigQ5TTRwXrG9WY`R(S%CNl+Bks+#_UdP5?xxY*D^Z?zq+JV zZXbgM&~z%?v)R(O>(L+ENbHs#UTg4O7~L=F_8~N$f`OE82_5%C8dh?J;3jJIVT1SC zD3!9Wof~qscr7UYEFiftEiQtqMQ()QBtqei&vV5p;%pG3|2V~|O!M+o^aw(z4#-yHfT>#Th5H4< z#nY`3%7_eESD63Mqncq+=K>Bye)Y0LJG|DkWxQ)5wS7j&ri`dBcS>Zk*L$IIJH1k^o6>zw6X6WQKdy&7Bkc)z zB#L*9IiH%kgZ*~?LuZCUMQ`~gBxNk3f+AHyic22z!e=-BkTqs_C)2hX?yPc}PD1K& z=Cd5uYXQe~9Rn!!^dv-I-Jo2l%gpiY#i(ruxoz)FITb5}OQXYXZPIRlr#JRZKr5lX zN-Rn4ilt6##FE{ZnRO{;Snj*ENACi;c4l!fk>%OI&7B>jBd0fNp{4e#Qi+`qx8iVM zgfHwzd$Se zM&rA6nz&JFpc8Xet-~$deDl%s(RC~=Ygld}TU76S)jZM9YUarHn=G1&XBR{$n;X+W zK{I;c4rN2pmz2j=lzH)kSLWM;jMRr)io1g@{yXut6B{LEuQ{5VkJYm8->LE)SgrCL zRws&oq4 zSscAsBhq#69Y5QW_1sJyiNGfPl~QZX32$tS5xOH(x*4VN%4?_N#A3^-kqOVhF0z4C z@i}AcC8e#=b!U6M(-}kVE^;0d^ByJR4AR1kqm6A5?OxXoIvbr5nM2t3;P6?W>(Y3j zvUjdtxumU8Lh#3f{heiCU|YS9kEL`SLIw3vEWOJ`!|I56m@3wAEQD1hf2pTcMyE5ix!$M)2oX9#WJ4-f8&X2zcC*869j{ zl$SfZ+4*u_Nto{lvpt15zwlQ^R%{7Seht;^WoE z;#Y_-dk>)ldTYC=boXfL(b2FY6!WNpW9Yz+j*c&<8XvZnPeGn{T~1>RT;+u6 zdxh|oOIEF&5T)C#32CElEE28Iam$?p4j~lz_|NZpOa6G8FS*+L&gyDN)3*1h2&#{n zj2okilH6m$tLQ zaMNxqwQSdUm7T$IDss%_wIv`_5iagO64@Ux*ErY!JkaBGFL~Q1 z#Qiogit+{+Kds-W30!8DT6tVw51aVsj>lk55S>uNh3i!lvo5fW!2P9!hV>JI`GtD2#&XX(s8}hG$iN&adL#_5io>5UnY)P z;o6J*TYvChe{IsE_mDDiDz}CDcNZ2Ra6necMADdUpm)OTG_ukqKTs1;b?XXqjFQVU zx#-a!QSH$YC{z>G*jcYroVKgQ`dPPtIiaY*DK$QEXJF!p9iV%v!Nb`>_)&bAQkBjh zN9-uRExcov{*@*(*22Bxq8WJ;=m4#0=+?SzHRUS)0vNokc|8G0P;G4<*JO^9yqOHh z$H8ar0~4DcsuSji?fTCA&;gpy7*byc6=Qwrk`8tI|Fz8j_o+#k))q)t4J)=T^1{=5 z%v}3tK1}XaPJiwh1_q;Nx}*VhjuoGkhZi-@AFF;rQy&mpoLGRiyKERy!kq3tumNA4 z7!^1oHlw1+{3#o0892)MU&jU}!oROah z2?!YturVL!obl~`c~->XwV}}qbZc`@MU(KrqpVlA5c!K-4eOd?=0>RCy1t6fY~qrK zBJ{uI)CL`bW7(ed{!#UFULUpam>rn@X74JOaY@VH`sS)hgu4&F`|z{7cFv3a$_cJh zCSD$O8ecL`JY@Fl)9p5ie#kIZJU`W;k@9CW`w_{$lQ#Q&E2Dk1G3T?;FPC@9u`XtTyX3D0Kl< ze_MgZppET~?%qOAGaKv;?!!*;x8-`l$JeWJdF<+w+R4X}rhb<@BWD1`Fgg0!2MAx_ zDX5P1XT@Eq1xa>a_G-EUPjrOrF1OZ=^diDsJ&U7zXFhw@-;P72ki(kaeb^ z4oQUGLY>0T?a!IR%$BjmJctE!f#^YlRxYf_=FRT9?1S5e{yLg)aW(C3ymrq3Z1g6k zkD^oaxD}-n_h*6wtp8@8s(>e>!Fr%gkV${Wo9^cuMGonTM+`3k@kPy$Wwvdsb(WiI zvmk-KZ3o*HdVGH+Mp$`XUC{S7@e2S93?mP$<=5Q-ImGGikZbchbvV>u2cbxYJMFm+ zGKx6!p^_ibMMu&wKT!S0yXTe((C)4&(6xI=C#jThTpm3?@<43vR>3nTgB(~1Y3WoX zljkw?E=$Jxbt8s3FU|YKEr=?v?#OsbCGI1eh`>tHD{{3l{Yc^0np24Z6{NGxy0+Npt>A zl<#*b5r;E=*UqN@aZTu}EYkZd%4z_}FB>(xS-s7g+~ZSOV(VvJJF<=Nn6vbusj`9KYTyShBa&w_o{EiO<4>nQQi zB46L}fpZb2O3QLE?o|?#KKLSO7IfzuZZN?4OgMq#gGj0Ipmj-Nk zfkvwb?+jesn>ay8V2Z|DH4I{7jqafTD7W@4?SOUop9=g)W!Qza!}7= z6uxgok*T8+Xg3DB*b`drbiXW~TAFYtaeev!<5agYGK_|9sKDmM1s@bO>tV}I1--A3 zOqo9wR(HLad!GLrV6=Cb6{@twA3uo8uc7iz8UPgdgU)@|tzT=D0>rF1MAqha*Mv67 zG(d+L*FSfdm4?tLN-wJO%gx=I4-*u7N!bW;E^vTay@QSAFa(~17@dFlQfU()>f+`z z$2;6xON%mUG1Fa>DJT~RJUXvtF(LcjMaM!*`}HZ#JzAf`Dyx6PKgK~(0DBAQN)Gqy zk9X7hmBobLQIMB&_eVyZWqKh>5i&Jh8Bl#K(|g{$9o@U)^@UaXia9AG3y{>xvy9+G zFf**}@bT}jOaQ%8ZGjm;)%Q&pw2z#o7CfT2hd%KG=mY;JjFb}@4-@r2sL24X>bG2g z%hs(26*)i%0Bdl7A9t@TkyDk=epB>vso&50%hm3aV*4+xRT(Rhq99lB8qRGW_BxLVeufm+x0dtT4#tEOxEfNHwt+lQWj$W0EanGbQ7s;aT!?*7rb)DenZbkGD$B4Unbtak+|*d`Bo9 zh0s)s%IAqEINFTD`#8LHbA07SNY|=I#a7)-2an9TX8xQfy?`2JJ-pKEO=4TVt%RHF zHm9E00-AyMD-Cj$NW(mycuqi*Rb24LrJ@;+JNKo%&ImukHb?>_s`NF`I$}c&yqycs zLAJ!1ynI>N+19?o&((BPGAk=6++N|_Jg4jK&ncwxe7<;VeD_QvYqzdr2oGZUQc=x# z{vT67qJP9?-#h){fV|J>gO=4RP-~BYzG=1HKRfbfBKuPJ{_r_cAN=RQeCWGhdM9x= zf(I+jA|hF0Bjfk$0W}=(4>C6IEdBOW-H*m_N1+p^qU!P+QQduR{5m=Jo@YTg;zv`osC&cZubG!z&XQt%unU(+WOfG zV(LKy*Q-Wc5t@Db_3s-|t(8U(eBOde03Us8dK}rO(8R`l6#p{}?n}JVSR^1l_mBXX zB@ZIQHnz;TI2?HEvzugKadJDLghPG}>xd9Tvwx`JBAVJ;rL%3e`qjVbG5KahO>}bZ z)3*L-wCiRi=fXBI#QIC4e)!+t&`)uF5YNJ%6=$2Pm7dm=*s|t&2 z-prn#&*evar4HgjaL50%!mk|vxRnz#YfLZE`?93N>k)%+f~V&@(@Vf{t}7XT?>`1q zr`@M6sHhMJ#Ro#LFXzuFN|0B-)n_DJld6D?$gXIoa7)a}q-!#;vx_2TS67Z;pN!|i z0I@W~+||P!<8X1B>YCvPU!OT34dFL>`&IdD7{6WJnS7M_s!!e0{oAS7IlzlXO&AAI zx%rLen+z)Uxfy}mH40^fA_=1(K*{xeRoPBs4Lz+Vjh16vf*A{O9f2F3nK(+TLr(M+ zxl*ywiX|`Qit%me>ihUn-T^V=9iT5H?NbqjvQQ6=16%ZGdzyCmc}1f+L<<@$YSJQJrq-p3Uvj=9pFjn1}kLmNE^~vmo04@o@`ECyd{RmiihW{!c_|n#L_Lx5r+w zk-)3s9}NSqh6i(cf;zmw|5Cq2;9RKcE9Qqlp?Pav8kY^Zzd}xGaO&aC+eA8=?gw%L z(N1(zm0Ba1-;Vuq$qt!H+vd^#di1})P@N{NHo8_XrY)qO>6FkJYgIVCIVrYXhc_TC z^T5>$uY03b;GQ0+O;R0Fpk+rI9kC9@J^f4!_9S8DjG8X*9xUgsgXW?@+j^>_7v>la zFXR6%8}Xtkm5o%KtNb9YNlvS&Yg%qAdrRv-d*i1>v#S|LhpLn|-A<3gSP~z!+9UH7 zWVA1Ty~i{oIF#Fti^|HXj=V}n&hw2fo;lODf`xm;!JmIUe-%rOL~u4svTX)|FMX{6 zUy1}@oVau=?tgCjzgJEa4OS}#jW8V(l8RavKLfnZ1pdS*JDN;P2z!Ynb(ERD$sP4h|y5od~A}8DzR_2-fMkvz!I!By3uz~yJvs=(albWF0LxCl`Ez@aq^*JRNBL6 zM~9Kvpy!jkQ0U}#5A+thp@bfe4yjWI3z_uuT`9z4{h6NK%qAqK(#D2fK|l!4zyirf z9Jc4yWkdGw$ZA5jvfuks0BaR-^K`Q+T@caPos*GV(1n^A*UdVLM= zh&zyGk==ck@*Bit+_f-q$==0*UC?R^r*zqmjyM2#EyR89CJi&_@r%Za0o3iO(JHHAMED&(**icAH}jHEtJTT^pGphUprZaR?#^*UhJ$9 zz$v(Vh-AHWNl6T2Td2W1iC#ntyQ|ZAMNgsxD$(E z98TyF2ZAI*{1+*OJqbP=eD!Jjpb&?mGi<>DeP%+N!^>L)uQ!aSceB?2ggn4bAcB=q zOL6^*x?I|%aom6uJ(Pc4fsa6o>ELM2Fcs zkx18RtI*E1A%*8}BdVwGt;sxfjth|bYc2oT=3j8HN`Jib6jEiU~K&1=}PD?U*$h; z_iuzkgfw}B$`(f_SWe7526an4(dCO3u-1#w=0Gqnk@>q9y*QBJPtt9$F}BZlH8C#6 z-!5kmcvaz+w&TZ&YcP467KvLAx$Q@^kc4w5MknNvKEK-vz3~u6Qo!0*%|#o^eBj8z zRO4Qvz(=dTM3$<~VfKI~+O5yLO;dk&uQQTmaUN!<|R zRB?2dW#GepDI|UbuW&2mSjk_H_`jG9c4en!Y>P!Py%u+V64|=$b(Z9X_6V1mY8gp5 z=YuMpM<;Lj?0^Q1p1Tp%(ClT9s49H(3uz4}0?4>aK?;*xqSvX7lgpwKd~fx;ZPDkk zSmoB+O$L0W<=(I0JxW6ziqo`*3Tl(|qYV=BDXq>=FJY6d9>{efJ0ovZMOdTE(Q^1j zY;25chrm~1_ZBb{Z_|kU-Zl5{I$;#BgeO#n?}dX*;P4La#B&&e1DSWw=(AB#v}E!9 zn|NI&g!c$14tz>gTwx9x;;Sg)dycG+9Q1msoG620(V9GNXb44WF*z8+Bg5HaQ8dF@wI%9n8%7ekr7Q|CVUb0pi}u61T5fiOx}G310R( z>Bks_b76Z?@T>i*Q}L2V51u>q3K$Bv3RFbi#2UWft|EXrre4Ly*7RAYxRl6|Y~K)? zWl`XjT!9#2#|?W1l3TepH(8Z$vf&y@fyXMdt_$bi~weeXN<=``A zU*z(?pc5kCsGk3+j4!8isKpIIoU`%+nZk2P`acX%jIW(jXlv?mK-=AZYgud*BXDP5 z$!d(KMU2Eb%5uAMl^(t15GsCpt6m z<2m{90t4u>q0P6B5XQ&A;#R?eyxqFCOeZyDW@6zc;*O^iyZCS&7w7{1Y`mVEjJ>Al zoJ{9a66<&OR}eXDQUGBC7@()^WHP>)Kg-!yRuIb>?97^{=B-mG&t4k+Ic)KCwv;=`H`d!~Q4d#97hMBV7`! zP=~)z6Yxr&E$kanCEH;R6qubiH?j#kL08JnOYYXP=om@Jjd*jE{IG7;P{1IQ7wUG!pcMiCq)j z-e1HltzTC+4{e9S{mcOr-`3*Qv7N^FMuUEM`636@tCHPAvHXq;+n_E+Y z`yYDnlg|G&-Tw(+L{aK6T-6go@?jqvj{+&9byPvDThhon-Eu<#mI*XFFTy;-;#-x2 zX^qmhs+=m3Hwqhn_j&t>A#IJhc~Qg`wkVroG8=baZJjbflBXe}AJ(o3A@gjOs`@@z z)IOoxoLl(-=5Z#UyJ-`3#NzAXXcZ6CZ6`!HhPujDZs)E-AUNEF*@U(lj^!mDRMq9R zRi`{<5Oz@_`sAuKpFX^<{~Pn*G?aPCS2@ytr4cSS#^2eWzmNX~m!=$pb3P1gvSDh?W> z?ewfn-jiDXa8KaQo!E20xaTBq8~>LW{rb57XPvGgvG4&vh33 z0!wU%+nGU15;2gKAN!g3?h;FVCxxi03M6{fnKa62JBA}8nD~Xb^f)-FC4Ot_ke{aB zPN@CG)X9v4g6smX(RVZG?AS4yAd+4b4oatOYfw!x%1_VY72VQ=*H&oDycg$juiX#P z=D84;Rnf4y0&eJ_jeU}kl?j=TmLYL*RVvbqO?;ZnKTv{$%Z`HYA0pK~|Iq@VVZu4k zAgk3`U@NY0mw6f0;ShX7SNii)h~{RIyR!GLyRxe|hs#JuzK@%fFNx-dWDpUNjHtVJ zH@3NpYxb}0&$HzCb(?9@A$LDVz!0IowfIE9;t5lWBZn=+6-CF>AP_E0Gps?p_WUIKU3?dwuq_9qOaI)AfGn zN$QM;U&5d?%?L{g_^e__HId%)q`$FWIp6sCKMhiTe8&|<>FYfAT?c}B=RocM32Xl` zGFQi+_t%97F5tuWmz>5cxN97}ni(Thvta$XcPKw(@l)of^PA%`pR_=Wd=0 zdUAKTLE?}q=Z~In1b@xq#%4izU?f!0NPu%ai_&ykxSK81D2sUaaE$mg1ov&tH2pW{A|LdYw8zYfHdLLp4Fpoz6gW~i^y0&YC_-f zHvqRO=e>6lXK)It#JIO!>stgV`Lt8;@}wj8m?s#<`-*YX?2<3@VFVYIN*=EHCk%nrW9mhOacFn97(IE9qx z5(4<=5GSO1o$lm=ncVbkWzqmO9B&lPx5oo`c)S-Vjn~J1|yFt2Qu;^`WGkBwa#EptGiYyuKZsFkSFb}&istq z`!Zb!)0i_RiL7 z3Fj)rSFq&XSg6x^-7Z?+pHm&ffO{ z%7D?3p&s{!MSjaN4aMnngJ`TcJay27Do?e>DJmdU0T$>eu5Eu$J~D|WtwN;8b!Yt^Z!A#VT*QxNIsd=eQh zLshuRm#Y9R2dNTGR>t%*3}R!nu4Wi+H;#cwQTo|P`23`+oM5m62Va{Vm;d>QnY zZhB2B;<4^VZKLj;HERMMv?=QNhd$}@Z_<@lf|*Ey%2AA)ZlXrA%`N=cNIEu2gl3^U zjHTKbo`TyZw&1&3Bk*1Bz`(`ymb~Q3KAjtA%c~NI*;!jho?zaZb#M}%l{x*6!bJZB z^gD~GzW{m_9ZyYjm=pvT*>->%Ygxe-@xM|S29Zbzoy$z(e3r53YsFp8co3V=h_J^l zonttiBf&JE!maL+9JmwLgZ24QipN#zsWm2}-F7GA;Bt=U$Pb)p6I9Rc3LOn~*(Wcm z-h5(`%_Go>b>`e8u*7ljzbbkke?rq(DZ?tXqV~8b60ggV@*oj68igrAeHJ2fH80BG zw>KR6G0BMP3A3E$D7`^8|E=)%X7&KO(%+$kAVIMG!HUCSNpOk0WX z+(kQ~PMMs+r1vNZ-Wuf{wEJw$fs_WVb@MF?m=S%1xXl>g7s12Za&Y-#I_OX{97X;+)m_XWlB!QWnFph+f<(Kd-y?r zN4uueK%=@&9~AC{iIldT1d+Hts}k`owfJT^dJDv5ugYR~(qx6?%Btjk%!C=QPH3f7 zXszM6*;o=^{2h7&EK81)h#qu8hu*>q?Fm|jMAcQkR61gsVP) zHupZUe9*YU&I!4_0C_(G{7D=18QubiT{ONMOG;P+v2!z@SlTJnY#)3QHM72e{-) zXF%!%m`RDy$&n}m_9AFF*rI_Se#Sa_lzqsfod+83vFtL)8 z9EmipVzg)n7bf;G{XJg=wtThw5n7uh2Q0oKT959I`HfoA@LR@22p-4Zi31H`(axCQ z%@eKX6V8LynS`RXt2_%>nr_T9e&ZX|9a~;-09KuS z0u4rC5>dMt`^hIjB0XbR#9&(R8~pkC_UH_~PZ&TErKRc0ks2Xh?oa*zz98DFss&WJqUZNY;v( zigqWF7#HSUE=-wyhvfUwiP=fqH$lOuXVL?rEnKZ5yaQ4)z$YhcVP~jxDg#p7;~*X#AZb;iBm@Vrm>yqe{E*bD znMKIAEX52f4j1S(Tf}@V7PIWdDGgVvks*<&E{${jmbZyQUL>pCCGh3?TW9^i5nb6= z=IYU|#(1}<-q@lY;J0|oRl4CLUrCu8eRgvL@N<*dSpREV=sOWJS-xp?ypA8|JD0Bh zzDfil`uU6`g22jsi40<9oRp$&9|g?R=Lx4`oDiHv zTcP}F!Y(UDEKAvrLNID0UmZ>&@-`Z;`5Hi~z~>_qXQrO(bQ?yCDU%7@9Et2rf}8KC zZ{hH!M;AYFDm3^OHr|D6d={i}v@>%pB7z@Mwi@yQ-@%`xJMk&N{~1lZ91=;I!Q^S0 z5o(^YGmnusnC!V9KXgq&hO=ZX)X@1e^N-2pd6~MwG|V{ z7T)O&V$}Ae!Fz(DDm=xtU?-5ny>%4~nYi$*l5vg%Hpn0SERLcI!<{lZ3fx_<)3yXs z8ilwT@&-L&0#b?*wsN635uPE^U4jw3oJ0b3^snQ;)UuSqhtWxFF$hs}uM?WiKp9Qv zT|4i}8vT=KXXVd2H&SflEtq+iMAaeEKuMt^(;jPlxf;@G;wzp6>X$7*R$PXksRG=cX^D*d8Y#T#v+~KqzSA3NeH=172ENyz zNA!;$-07QiA+V~#;oiUF@G`*3OGgc|A7+%}XP95*Q;gQr!Tf6F^K{5fzP4tNbfoCM zlQ>3=|IP_~4H6%zquh$CHr!7~S{0+9dKW+=8l?m&StCd(hYHjGy?cYx&)jI5Z%zzBqfQ13ozX+KNg`@F9fcZ3xjFS$mg$h;#7qu%N7KN=EBYTt9!J{`Xs%Mc z;{b&E%Q>1Dz{`n&+lfl9**##_2-@7a=u-&M4GE}5rKYRz&aH>f?2)zNhR*qW5`EkD zGsEQXS1{@}{kWA+nYi&5bsne=ZU(5S^95tp9iSQ)BuG3#jMATORB%ZL6CZlIm7Ipk zt3H(zfW-9urq)6g+;w1U$r5}Kc0AuWm+3ap+Q5?YGzj}9>F&DZLoFm!brxC4*|5zA zXz!$IX2JMiq%kC3y$*mIEVT-vpA*?vWSe9mUFKH2j!9z1&VWE{6WiQK84UIHMM^))e^({~lSk829Kc(0;?CN^D<+q9ZP`B?8%bW=?$_ z`0n!En(gU)((JJ^KvW5|HjV4roA{C3U(`SKSm%!b5xpT#w0~{kRe%_bs-bHel*E5a z`lFgGb1IWV=GzYM4Q>_SId4LhMAUG^SYb612%`>^7T=TynjYwPf8~3bn)J-=d1&UO z>;#&XktFyDJ(U!G9fHwtj=$Qo^OQ4zo)T`d)wKtfG~5rAh{AOR?CLM>4N$%`>~S9u zHoh$#Si!pZhKafh1248 z--<1kIxKFDUg2ydu;e?n=JY7OTM{^^FN`i&eEBF5=v6tR?HyrIr8DvANnWcd>M1{> zN{KD!#zMX26RRjT(5AKevg6@$+##s^_eUVj`W@{%iLMX+%b|j3$(Mjuge&mYyOvg3uZ`bI>!sqt6IEd=nRZbX1S&E#p7Eh zpRflnZY`{QbJFokQBA;jZK4s0XJ3;UB_;env%3=|r5=qUDigi8Cb7^3KhK9Py?IoE z?(Xt}X^L?02I9Bd1r)N8_|ad`36(;{N&$@}_&PH^O;cy|b?vQDNAIpL#WAwv5Ihf7 zLn5)dG7lkm&NzcBvGCA#XSgG1n)HmoE$|L4f;^4eBcYsEubiRg*8G@U`4}v=q#?Cq3_qhQZFL@!iy#emQ-)+@JuA%p|(dbeLM|<+;zA62dg!7gqxj7^7&CC_Wd~tNHtsE#3ZCOO| z15P~l4sHENNc5l5o40~x8={OMlAcFqv6N|~^?}k@$DbQ#esMMeZ7gjCD@pKOZVD23 z)z{LLjOkwMm>`tSoN8DFPNiK@`;sY&&(CTY%}>4rjuBQRW`2+wKl`j~-j`u`iaS7| zL-)s5tsh3QFL!{n5>$JRe`_(;+XMr(C{*&PWJFaD6l}3PH)O2mZL0y{2@b12ue^*z&~!q zH%Nd61k?PV%WM-L%S`iOBkQo9C&Ep8%UjP&^ARkCR{v#7!V}#YNCS?f4vRpXw`Ejy zyXDc@UM8IEPXZrRBzBnSjEC@CN>f5`no?R#R`eeku*cp|Q$Gl>GNow7#^BQMuMtYD z`^e+yaV(rFD(w_9JO|Wd_TEUX5^jH*18Ka^Z3w}rn{2lBVdp2&5pv40QCiyCPr4JX zk}Ee4Rw)@w@jrVuIwoBlYhQA@R~>6yQejFf<_a)|?VW7salMj6bm#AGHk9rv0}3v< zxoOWr*WNcSqe^V+igeMq^G6}q_&$#-DEMM$B|fJ>Ys%zVd8U?tx(H|*$YnI7e?f&- zo+m7EW;0H${xmwi>Bt^uws+*^#U7FARBw-mOxl`gog4Cq8X&1v=oh~|ANLju{k*H$ zT0h3nt@!3JI{ktIK*3;KNC=33g#u6cQ62V#x*1w6!5ZyykmaDMS{Z*Ig^x?ygME72bA7Wt$H75HhyFU^h>Rc@eN0Q@zSzE`AI;iz<++Nk!m?Eo!^Ic zbLS?<)8^D%1!|qs#pTofp}3wD>ePuh0 zv1hKeQunKkjME1iQ{Ib64RLaSH&W5-f-6fvu))k8T`sk<_?j1JX(7~QDMsOv@h{}y zo+uh3D>G25iEi4Q`}%?j zlB+=@8=KH;FY_sxIL=9)pzoE0h;xZo+d$G)a@7L?fCgL#_@0$o(X5!E*o{AboYa&#{w**(2gXZI3gv~n?#j@ZWEk~-2Mv?XD(hVCW;_whG|jgb zDV$JNc%205q&7!V-|}^6-}A(H@VAL0A;lFv$SxnIqz$DV3r8o1u^6HtFFo4`?|Wjq zCY=XpUh#>mG`WVO)h-d7w>0wOT2hzS=)o)?2ZKA6_#I1h16I0^n^pX{Y9Dw3w6u`D z>&H=CQ+@WtCLkNy5-_MLyqB1ub*n{kl6Po5{wcCeAWhNW`{k>`zYCc- zeuhk~{qcwEZ_BqCc?{{OA)i#Cj1T6)$ADbfPVO48wb7QS7mA_PBNts#tq{9$CI^)1 zRogJes_EF;=RS*~WU9Ac624_IP4O)Qb&u9zP`??I@rKOk!Pf*|YlXcUtbB(KO!hnZ z+AN=IlaYE14ly7869d&k)ue<)KvbUPno&r1R-&kRMyOPdT zCyAY!Y>n{(o#*dWf4+Om>qL~d^nk3rO^OXR7wCfjoAv%xqF;VMgRcYi^EK%bYYa^; z(^%l&F7m7_uHri-n7c+Z8Muj0GVG(jOR2&;MLeoQKU?stTHAgcd1uI;4lY(qkH+_y zSGSCt_UqpWI4A8aEQtuk9d(pH`>US=Np<$-dD7u;wM9jjB4l{q!9ByE7R^j$x{WMS zxaJ%QMBoDP-q`M^*lP8jb)SRfwUy_0pRXQl6*_YT#LH((uEsW1tW7X6nh%@yFS=gP zB7Ft6T87jRiB(ZcF4`wg6->NrugB61SX97NM>sZ-`y&*Y&PMJ)vr~1#y_~V~VRwMM z4*+fV&298sWfl*rV6-zP|t#sarWF()j5;urLdB)0Rxyd8=qBZ>#8i@ zC`8r%C~i}y95g81iQCm2Vq%NBnLrb}D7-R<8!#)}lYcaVuPvan%QhY;PY=TtN@WiU zJc8q^raahJ5H>)j-q=Y#>;j=QPc>c*kE!jcDO3^^Y}}@9=60n1O|-k*0wg#ZBt=jB z+N&WUpd1S_r8p$#*#-)*6vh5w0*osz0QEBd^XOHR<#bCR=Ac==-rwDDnLiUr!q#lRB$7_3Qc054VNv^U5?1kT z7<(bes6givEjEyI6+j)oc-8s!?|4bvIWwG;ghFk+vYmX&9p3%~Q$kkwC~kJLa$2zf zKQIsU5M%0pGXjRQ_(^mVN(v5tsh>nQR!gni@FbGIMg0$*%ZxK78~T+8ZurxYkVh7x zJBeac4~dgA@f4_xD9TC=i<@aV9#mG~%Uu~^a}eII{taV*;;GKld2guJC_{aPwgE}g zvwxKGPLwHU7U-40PX=2P4q=zJQ|at8V@c< z)iab7?cdR7+&cq0u)gPOI={(Ke9Ch&A81s<*MPW}_7~WEPDk8ilU!P0d0IerVEVZ6 z!=mhP{rqqHJ8%-5Hl#<^*M}BfYd%x34tSoq||RPxgAYMf7h z!twR__lMyaH%WUir<6)`F-~nv6*_r8uSVZAFcb&siDgl<3naSEUpol}njEOU?6fm= zTf5^UODim=%~ZP|Nc2&<+84c^3bXM-I%#BR)*SN>N(6I8LO#62M&+(rv5^|)j3plr zO(iy#EH+CPfbE>EJovw;Kd@04icf6%tR1CA-u(-Q(gR7ZyF*viN|llp{rT?d8MGotTD)N^aYLtJU= zYcUQbb-`?ZXa9fgKJiL$$Yj6&+Ny}4j#C}I@pSfl*0{WG3?5#>7k@%T6?!q{+zu39 z`;_np#+kbUVUE@b*0!CwGk|_=biYifQ?bY@?2@g~I(g z!as>@X4}-xA2#8?p8W5iThRb8bfquSqlw~w-O4<{+`6|0E4-3I`C`#pg86qdO}^=e z9keOia`QR^(f6Nl%XGz;!1xWA!%D#6zS2n@%^ZTJNDjQe`bM`Q9yg+re_+2)9&H6Z zn-1t}Ve-?6hW(4mV;wo#k*ZVoY1_&%8EjjYO4&#p^KqC;^WAv0sDLJ1v_ebAP86Sv>v-n z+dAKD%2tN>`42<+8A-&!pJF^+5{So4Di+ty~LW zqFU9Ad3=gdV3GlX$q#1mogK>we{bB<$t@hCkvTg3j-%Ekt zH~UW)R(l66Bl>eT&I{7u^)tss*!N7)N{oeh(S{nPNs} zU1F3eqMsT*{{Gw4n3?X984Mwd;RuuSMH!ep+XzI;l_7*MLWGs|oiw?o=c1pSgS;3+ zKwHJ@VAija^1}kEP@`sI0(Lnl~ub!Zs^J zDq&=ci1ka+&WsC{k!h$~(kVk9Qq_wSYQ1Y238MoOz8IyJs6@-s|HLyu{v%`7&O56B zaM~Tc-R6VwC&|E{6oax#g!>$F=Bu@ny5&k6LIt1`TWSZN5h420CCiRywkAtdhThc(^~ zh-1qb+u;)Qz5y5;30VjL_wi9pk(-jD;b|eopL6J&fZd>aQqs1vcfIMs{vjT-LpbOh zDe-gC!oY&Bp$xzD+$e}pD?MnyJb~_Z##3Hfw95OY#35a8$}DqJy8HGxf0rVhlO7V6E*>Au0U2khN=rW@VZx4tJB@n#A}Q+R~Zsp-27?}n!#?v ztSRW64!g*B z)o_=CxruhzI&n|KuWww4r1RwDS<~?Wy zX6E8of5H&>dR_P;&UzE3hJNwB?)YS}+`;w(=IP+|cFGfUjpoz`V6o7*1b_1GmrlEP zT8eL-Di6d8Z_V+8E3pM;nmlt?u#XSX93TtyWBWKZlts$$tu1Z>;>*m zaPd}V5gzJYm(=&$V9?HMNyYCgyB-roJa8hv9gKguoCGi_P5)pU6-vr413kBiHRsoP z%X8~-`&R~$aGtw7fN5rh#f<$uc0PR@(%x$mllgSyqJ(Sx8|Fe5A9UOMksS3bmuYKx zlzRd%gQ3~sXtu=?AmP2UfjqdqIXRLD97`YLctDYw?u8tSgK+|USv@#sr2Bi;<-d6K zkq$BvdpE};Ar~I_oj}IBd%}b356KNMsB(W@ezWWYN`RyDbo}eB6CyzvX~!wu|ATck z<-!C%S=Q{78>?K(n$5?;@kxjk2$tMCBq!#mLNYI#H*fT(6@8IcmBr=$7F zFpH0=L1{t8E50Sjh-k*Zq1Ey_X9NxiAW4TROLBBrt-6{ACu2yhf@_l{?eWO6|K4?C zSEb)ohY|202P>%D6O8dX41hXl6^isFXh38&@m*$$&iF>t71-T#;p?1fRy8>nVrGkV z!6o|-g9i2$i1rBKA-daa+-86j(D6MlT+7Qkw ziFyw^pJ9clYZGN>G)hO@rX*<-SmQJhDElZIAqcRMqH(+WQ0+NEhSjL}{cOz_=xESo zW(8A$7}{*m0dD1_LrSWsnYVoaMf(E}&6yd!Vf-9#%FW-2HfeBCQ_ld*J8EasS2H5) zD8hN3Q#9nR>b$Z{^FdzCXyn@Lw>v_37JqtJX>A@pJL3}P7a7edP=DH;T5SE2-B#rw z`Dt!q*?*_GG#WAkw?BS&jH-~6Lh}Vs0a0&ST`%G#sE4tv^7*dmVr#lU6>g{ z!;7O77I3{9NXyD-VC|~xb8Bovs93WeZy5Nk@y(m-TExnp+wtg-l)61W zPptd+w~%e=W;N$=kwypRy1-lt&w_ufq<3^347$|MRm%9y02vn{j#7`hUmwq%a8f(* zxe49jhk7hC3X>3X%x?Yge>JLtAq@2?6hh!Z{@aS-ACZTmaMa$50R*@*Sg(U0X*!)+{njIJzXN|#{L8A4e?fj)a&3xAU;i1bj_&Iibl|uh}G(S~+K1#*Vfh6a1+S3#GjOoTWq$ zkE(;i6D3S+@*j8iLNbtFE*S7Xx;DpZo zEQ$_?9L1_E?u9pN7+_uKN5UtDI^RC=GDo(mHm~wW6tOeg-w4n-pQ){{IzyT8vtVT2Y|tWLZpDqpWE?*^>xM>Tohp$ z50DwFnEhm6x7j6qV_|*W$DgAw%Q!m#Q#*Q-m|qE>4z`d#_{p!XN?> z-ARz5GdrH=PQ{Ggya*2w!D`1d2Dg?PB!&L?CSQw1gVFbxo8-x!Z~ULPDB7j0e~YGV zJJx$QE(AILu0r{(ta-+Kz;ZOILb&`fbLDmCSssRl*;X{yT3dn!R4mYQb6&K>ri>|- z2M`z9u5`8|Pcis~z;mW|ZxVakuvp}9W^!KI=WJyh@f93>P9k34$Tw$JxATSM^YNO8ua>FukhI$9n!%{{o$#Zi|t{K z``KcB5i#5p0PdU@tkw{gMNu&bi7i2BY)+t>olk-E7L;s8sJ1pN`M!57kVNypRH#$A0kfSJNB}qKhhClB#E7 zuo*ld>3kbU*T0|YdaZ^^z`Xt5+xlalt1k@Uv?4K?*ED|FrPM zwKp0xJ1sO|Ri}j?JTFwM@%dp%l8i7#1y-B#IOf`Ig898fAF$}@?@3ql%GCxc{#ADOI1 zRI&uucR-E?hQ9_6%0UOS-DEVKre4s{g3msVxw?eD=x{%Nl+2Kd!-M>D$0$!YNLEg2 zV*59c$Fv%Dj4IONuF^fAu;6~F#qB><`Go$G;Qbbm!h#ufdw4LV9(C{crS^$A!T|qa zj^{p22tWplb_KxFT{^HQv#xgfy1cTi+5j8I8*I{0I88`dd7)P!TrbY_e6pnk74WJiaH>iP`uVpTXLlmvCks_202U;N*BrPedAyJ{JG`nYkbyO zBS;hNo!tFhfe8l4h`+k3O98B#&~(0|Swh7GeRBGtETL%mQTG9g)Dg!8-zIe>@mpZz zHWbMfkx@c$S4WaItp(N(IXEAVH`x;ST3fvOK_>)ek(~TOEc_QA1MQBm0*& zU7{udTUP4GGl+t9i$ki*$P(Zs$-gbQ)D-#NZGY7!hQJyg^PeQO-x?fKW`Cy61BxA; zzlXpU71L*0>2!Tw13#TVH9C(BE11v3OcZG#&tC+_q`1m4WY2B*EhTbIyhwqw7A&g` z=8coW@*x?Qv0Dz=Drk^oLRPmyM`7T_EbJM{>C{QBl(VOjwOYf|=7e!4bS`yyh~y6&Z-P3QGTbo~)32UDINzoeLFx zQ+bWpkkoum)U+wRn)7!&^8e?|Q`eXn{wkv5Ig`lPgZyXH=P4$hVX}6AVx>2ai=eTc z9R$_FdqU1bPq6KZxAVq##LB61+Ds>$<1004W37aGjQ7e3hVKlViRTNj7t>qQ0d~6;2}wkksGXx`muxE@w=w*j zMafxi>d&PDIsMf!V&8I~mk!oFCX6a!2~^6IKnULPclyC!PDRkq8;ot^q}%E7d&!*C zQp}p#}*OzrHLV;U9FY#TEL zhQdpqczyishYwLg*xnm=zZVEHPu>iwLBwy~goH@SLVuW32>sK7aDz*x013QrMH288;1qZcR_DIf+b`Z2Pc6gnJT-$wCv-8TiHwyIVd)EX9Qf z5Pfdvv^m=*haLP83LXrYlyN5T3V+vmg=X;tux8v>$(yu^isRxm^KL4jhTqV+!W&7x z3Yqdh_(+`cJ2KVR4^1bK>|E#mVv_UES47MzRqx+k0RLR{NF;)Rd>|1`kl*)73*Gn< zD;G#MK2F#>#kfeD4W}oW$9*W0He&(JoqO2^I5B|PO( z4-%>@vyEgUHX#<9hqL+{I5D$_Fck?+U4%ZCD|L0KvxHsltuYfJMg#7a$`-gzcWMfl z*gfbJ>5M)4tf+=S9KI~aCg8U5dzZRTQ@{%qZEdo!r_<9vlWZ8HXyh;ME$HwcjJvC># zOT^8?c#h81>B|{v)xh&DzMmD8@eu53XRWnYgBJ?x^_)q7n~s6k@i%)z%fhVX0-Fc= zB`;+vEl3g|8{KLY<93CmZhDyzfA*R3#T)B09W{fF7uowl4ejfQUzDd}1E~9d2u-;+ zAJFXrFHMEaLC9rlvsB1!ANq{JTo%n6+=V~sOWn6@MiTOEs#M@!oVpHMEOkgH_er5$ z?LF1{y;32zK1vvNTjW)uFNQ=@r$6qY9{8R5xG3cUb8mifc0VvjzM|Jd4nwZ~w&_Li zLw^46Y*+92XBUeUUyelo9&&v<8Bqe8v8S(7$jsiSujFhPVF%eonbWeZf0#)Qy0hhVcEd#V&6iRF zTAE%`-BUHL(srnayUmXo5EuW6Awq2&Dx4tG{55^H=yUTVwuyNvsbyHvNukDCKE=Tx zDNch1>bkd+(=ziKqhHmnyl)}INyl$hFqh0+WVHdm)X9vyuh=QVg~)3C8O%ZXh@7i{ zG$r5b*n--@a}0yy3}w2!$DP=Jitb30_|BqX{v@no%Jhw>Mo=dh%CTt$_ORDxCm^0G zj0$};K8?m^@v^h)8W~Q8c`K39uXz(4Sbj37T;)<7LPta%-g}O(Pt<&UJ-RM>Mt7g< zS8iF8%-P1!7n@5>SOK^V@YvJ^ij4D=1pm!;ak$08HSl5T8c;Tl)kn7PM3`8s`~6Is zi~Wle*fGzHrGAL?>Q0~AeI&R|lpe>qz!>YZPE=LrnK@(DHwAu&y@`S328tC2iv+V_peKdhR5kS82p~9{`S-;QVebpWz}x> z#qVI%v?_z%wzc}6bncDTyL8ti9dlMEjC$9mE4!IZqu|(n8_G5#>YQ!H+W#ZSaFw8a~DJq3dQ#Hqz@`e4f z!5z-Iuet86=!<8&ie>8n=F&B2aRj%+7#NSh2$jJ-Q-9Tx+5e<|jOa>#>OW=>o0ZK@ z{KO#7fL!M*O$l>lDtxm)zm2HvR%zEbuDNyDfSd$>Xn)2?xJR=DgRS+s}7E7qB z0?kgy&Gy1=dZX;T&a<%~z@*Te2tY;#1oP{DU0cd$M6^qJsdu$#?j0}5=Yqa5jylgB z%B$n(=Lpx8kFbqtX4q!YJ^HRjETgn~DAZQo%YR!M{PYHB7{y&;`0pXWO=~jjJi6I& z8i+CZc1iZQBF!}TfMA6P`MP8E#n7mF%O1H-7IC>R@8K64zwa z)Ps_1l_LD69k4ane5B8w*Bj!<&OxbZM0@o`biC>5#tvE1;H2Ml3D*v_`Ux*I?np3Z ziVE>@q||>>oHcN5WQiAxnbv;PC_X)kDY{_`zmW}pM%zc5286aV7gGh~W#W$t1g{!$AK$u3c5Q*m# zRHwt+bbRx^q8H_hnAiS#|HPE8Cvq60qV$MPtLzNJa)i&6RZArg2jZ9R(kB3Bt@3{OK_O?Kkde>SKoO4W0FfsR{9EB-=s0!Pc)OV z{K004v$43|hK#p!B$PypE z2JH;fLR-|BbhQv7Ln1nv{l@UX6{UTDr-W{rw5976CUUq>tf3>t{bpVl3x8$FLgHF+ z?qZjPdQGsdrD3MsrbZ^1FlrrA@<&cyqD5 zMSZTEX0zI{W$L~1)jcm%S`>r5N(l2(dFXx{M~y_(VTMI zn*Qz~CNb>al3=O-0eQ8YyY!RVY~XBGbYL-nEII@o$x#oNDa}6_l&!g92*&V=jfGq0KtYF@bIIjQH9$WH0$!5RjX^p3o|aWH4_bgv&bgFLKkU_fS-56AG6kM_P3)L7)y5-8M1!~SGdc$yU%Y=4lBKt? z$8gAEHU9jCu%WzE=(L3bgYJ7GwLUE6zW)16hoW*+s}|nvz)lq609hGNE(i@QVei>S zCG$m#NI2^Y5b@Xwc6+4OWB9mqLY*?v&Q^J5AvmqBuTGyWC&^&zf|!_2YUrJ+ijzAc zTVCi;=0DUgsyAj&C#YPL`>K3)N3@^b zkhxL3AZ#T7jCeRpe<#?w=|RGNmX)bcMF&B-RrbA`o_NtCcq8?VdI>E^jIo@rGag$G zhm-2IV7|(Z#OeOod3krSaJBYD64*a19FFqQMqg|W?cuKST1AGgqbm}cWWfhJ5ZH=+ z7R64R<5!ORg-d;up|6zTFvMG>=12AEz_Vep-{cP*%IpiOZkK``T}btVH$NJ=bSJ2e z-g8;PO}XcGQ~wL*<7m?IwX3^+?60TOo*dH-v0l5(7On&H1Q5-KBLLqO1qy>G#Ig8cxBUpkU(TyN8!6

$ofdj`MFFHjPH{Ubh zfJyruR~iG5q6xpOiwY))00D0qs#t@9gH?E1QaIZiLNJD(E)K{dctQZ%7l* zc^nt}#-wE~i&c?;=MyP6gzOVqbx6qs_b!pV;NZhk6&$(@bRv4G49|+Xb*)Fn2*AE) zsEILqTghpUy{xV4RJ&m{#hBePpvaL?m&* zK2oe@1oF&00AmHa#z>h0)+Qw#40RKXYqLTVDC9E`Yt&%fP{yD)omnt*_Egm6p~2pp z-T06Iqoig2sh9@Yd{&(mxN`uO@L&erNw@ISxUoBI$5X5tyV{pYTMSM&#goIznZ9f^ zX~6t9!3o-EHW^9!xdxP#Wji%0ga&9fUfG~%{gJ6ZGs1{baTM^%&~wyxIE1Q>7j`q~ zg{OgOQRlrH9nUl#tZbYXaY~QMube{QZp-l83&6lT(M)D#vH!9 zmcAxEV?_JMy`O0)4*JkpOz-ZOJctSNCx%EK*>}#?7pt@fpQzf1TC$Ty{J2?kh2TNk z0h%(s1%n1!r)HX`!#J5P>{Jf>5#srZu+C0X_G+{vs3NpntPn|7Bmv*066pK5XV}VG zL{GPitwKk>V=dIORu^XI>?-M80>zeLw>@5Ysz=f z)jkp>)%fln!<%(hunlooMt<+myLLU86c97yX&Y!;#=ewA-c#VxR_ysyrxY2}Fq|mv}*3G+k6|w3)`8?P~ zD!Ic`?UXm*vX&^<16wH5hl((-+X$o5M7SeGB9_80&yO<14ZN-p7;bX{(qrR#T|Cy7 zzt7=Y$|ID-o`g)DJVV1_^eVfM`!jdseBGe0#G-6wxh}tnr<3OUzp^!LPL;k)TT@LlqQczlUSk1e~ zu@>Rzmd~t1sjF#bdcWZDo92KE>xGcG;V1IaA{M?S-rEOn9Z%EW()92*gJn_LZQMsO z%c{nmi-CO~5!=6Pada>6e}^(n_LxDjwNhLCd&*-z>THH4=R#WtQ{P?~uGRQ%0ws^- zQP!wdyq|c6F!S{hWmh<#-laBhvd3$V_70GGU(OiO9>otlCP^Dl3VC4YmpRYi^>W@{ zdc?H9lzYY3SuEyoWL5d@##5fzC(V>*^>WyWeYzKk(Kf?*fWhG|cR>!<$bp%bb;mG> zj-F(UoT1z!oa!D$&5%_0$?o|3ko7?_UPPpjxW;o;JdU>2ZLdyMdH zczkDd=`0;7w&>PK%3vc0f6Q{`6vJxwJK|)l>E3nqO2DA{{G#3s)xN#@e#>N8vIKLZ z_F;|V*gHewz|(;JNsE?D9aExs=hBCI0zYae{K0pP$9=UzS)h(bTH%-DlbZPLal7rQQmUEuxXpD zAiL#8xqEH+@(WNPeqa{KI54!m<)qvbZGJ)`ZAVz>z&=Rqx8-SuVgwJ%vF#!^id;<< z6>FAlrt0McqRys8C+9~sHn^7JenhfIPPT^UszZXhBev4nClV;m1@1n6#sqm1j9UX* z^3UTkeVHnErwuAH(vPG?1xlG%4?I(L9vdl&Ls5NidUSHH)Zg87RF)-wzDfpVx|rWt zlqS3Y&Bxk=mz>C!S7~{6jOoB?AoEYK&GjXQBSvn}wv~t!3+iLMd>>WF{vNS>wds2# zly$hHY^}u)r5EDXXnZ|%!QIM-YM#E%ejE!+)+lWGZM!&5n6XGf{JeG0&Xh6g+9M1- zD|qA*#)%=khwk^>2)gIo7qSJMxBbA($2Sx*L#c09pTyG4Rlb~J%E!fBnuy_T+YC-` z>?pBCi$==4*HfSw8I4PT&>TL743t`7nqFv&j zgCd`pS*PFyTf_k50sg3IuMhlD9r^|PO~X4CYQZhObH(e5BYp38T@R1t_~qqOh#fOw zZF`2o9(x|#yNmKWSau4=WCVyk`E#`(X8TJ6*fG0d*Io^j$=vF8Dljz`l5)xys%Goc z-j!BzaUCk%V(>4WZ;De0BHg z?&d{kX5Vedt9tH|En8eckd_}%+JaCH5X>H!PIt=qa2BotA6nVhMm=8TMe7@&+UIo0 z$S1^0G9T6*+H{`8>l|Kak*@V+So0%U;dHgiy9x44m6qB;=G4|a@-h6Y_vHM|`y+!dK$-Rx4y7x>I5qY&lgj(qHGH_lr}itW=_a*FNyr&J}l z&o$6grBLxe50L$@)k(lW`lq*(Hhsi*r3x`v_3Toifp&8Y=8N)IEnl)?yl5HidvuJ_ zVC@@Yq*- zA|C57SE{!^v3~2w3pmxT7xaSn>9Eb`8YsAOj)xnesZtoZzt|^Z&mKgBeKWvzkuj2e z?22)f$CUoJKp6^n#=_FKL@CCKi?rhrqGP>FJYlvio?s6@tb{N{DC>LPeeszML>A{T z!J9`xX!%Nz6l_a}T&OvdMB2wDQf8Zpw2OMn|9}}U7?b(PR zgl)TNV)e4i=QXt%T-6qq*_0_l*Q(=qg8CC4;wq1F$!*})WyP)Tct3!d?f|sv7t!bx z(BiS}C_&5|jwz7NDF@QDcsIT)9a7LkNPoYXSf4t(!HMto8Uc~W2}+$N);47*9R`VZQ1h_yN%!W>Vm^i z!@E=B7io92Po|d}Y!Ss~{(Kx!f((`2x&Stp=D{deyx#;~TuzI--Zb3yf&c%TM-s>zfCLD2nbI~DYF95gM^ zNI`5v*g3E=uZD|E>`INZB6Wp^wCxgUhmHdyy0FeMvD_41QZ0LRudXHp&VI{MS8T^8 z`Nj%H8UtSy?2PHI>EO1z7b27ZS<**sUC|nM!4i|c_2FOK34GC|L<*Kay`uzdi`3`V!OoqA{k>biMID!LuxAlO>njmGp?7KNi%u<*DX))}!2DIn5_)JalSh0p)90P+#3?1tf;e6Evo z2%c^fvTq$Y4PDaNHfl48+tA(J)X6`B#e^Uo`gyn5EQs~-$r}?3hU<|x)MMLibU`#lfLZ?Jql^mZi zJbPr(nf%? z4*X4(R~@VEvu`Y-v76Db(bD1S;m5ehTMc*2BtBkNMdI{58)^wZ2$;#)q zB>;%GAH9 zG&4x-pLJ?*a@{<;7=(3=+H`|T@z!E1FMb*;k*c4=u6UM4 z#NxBh3`&y% zNAh;8HwuQK#D1y$YpChnLn|2@m&qQ1{_B9UR&=qNeE)UTc1$i^Zn=^pdc((ScHO*NcO`{=dE@Cx+90+M*5Vm0G!LU=+uG&8XibGkZCtq} z+Yu-9K&iz_j8C4kX#Aw~6hSU0(li%)QwVVronz-H=4CzI8hG9_u>;8=0j!4=F$Sjf zstI1M= z6SF8(%ags-lNL)Jdo||2z@E*uXW)^32w{C=n3D>67tOVzyN#53vI&WJeJ{T9w8sP~ zi|oN!7N4842de;b~_#F4a3gSlgoIay^D$>BaaU(2jY2 zHb}1Y$U2RLk58-r@JO2oWEx}N*sNJR?!3jAf?E3Nqa_)C`*Rm8Tp= za|+9h2QwDDHOlufBlrtbq9Vpu$GCNNiw+`)mRSiT{$Yr-_^=+N<6qMmFPCkRpC*v- zh3H1CxZ?RY(9d3AWGFSn8`lmHkiMp>{!%LoPOAsFOCvAZQ!T`7F_$(Fbh)<#J~sulKd1zAUDPJyt%O zn^BTuk1zNSx$Yfy$w@k!_oHVFl{vsemzrYrZs*)DMH8jE@`+&=U3OB$>We~)^Z{50 z{1LiToa#n_nj*B?u(s0M<1=yZtf^E>p3n8l4yG}yd+b|}kvs7nFTrIpxOq#XC?j+3 zetKr~r(V-ZA-_3u(qrf1@G^MOMQg(#4GKbq_vCW8q&**&IVwU04`9Z7>W;HeYGL|L zy@OCyR-`7PiQGI_sI;dL7=JPZIs@R8eecfNSN0Ya0|)gOs@>6RvV#Sxlb+;q3xZH_5 zq8sxx=16+(o9l0=)w1x@Nw6B{aXN3Wca3}q7yHl8S9F70hNBvr6r;NjfvoOgASmd3IH#>P(Y>c)B8AY^QQxxU;SK(&x6^CO-6Ca ziO2Xu)FH+PxkpbwDO6Z=5GJ2xRlabS(#p}=eO@!YTv>(Rx4;)axi>U>uz#91&FRR$ z^IF_7IZ{KNk1rixryG!<1tmC0D<($Lo!e6j<H>Sx+tH6yt?+#w-S<;@}Sq)wm zV8>g=tCVa-@JjQjpR&JSDiBPQ&v{1&xx98W7DL5N|q{T)A54j z=tq3LA$yf}`F@)t-_!o0ug+{me9rgNYuyWor1ikQP@G_CU4g@M?!?ix$dhH_$#-59 z157%p&0TIc%2A_T{pt^GJhexF@ylxvZE+LJ!<(Sr;NTHZFvvwd{;xmq-)7`&jE<6- zzOnyiKk_Y*YA@>L7f2c{ubILbrIcPM_R(lxe=Rn4*9+c*`f5HBDt61SK8w*L%w~^@ zXr9r~RHvZIK@yKjzpI~@F@^`QLyEg^&-rHzW~Vgx+)g|rX_Ri+3&#+ ztnx?p>offkFnc?J7$y&ia*P^1azl%{(TC+J8ST$LKOlsLl_>w(;>kr;-(X{O3r;TA z-(&sCZvXyFXpo45O;O1IN}azy|AUl1k&jWmitlvKQCE@x9`S1f^P2*|DK2ZFE zME+wCIr--U_W#oW5rgOOzV+E6lL7xH&cYZp`hObmUk3dY=l?PY;lCR6yCwLq24Vcy z#QiYU|MfxtANN785dI@X@L`w9xD)4=yy5hAqPCvXLv1$kq4WmYr?{Sm)d_^d{VPgn zSH-q_ajXi=28;jiF!P^IorMYJqBlCNu>LRD#;1@P22`e(3-13R zCUo#U_21Bk6b*~@eD%1~)UV+`pE`MRHD%?~+NU-KKi6zvg^;J1F2wbS;MZ`54=eU- z93)^$D8FeL(u*HzVobM6WY!xqY49JJ@khYeD?v`wjnhtoE$8dy_OE-;z)NoabK#{VSh*z9HHN*m)BEn6JGX2PQb(p4{)GenD{Twjbxv(a||H z;q?wH)BfB(DwWdp)uq2yLeIZW^B-2g`cEj9DzDcgs$N$Jyv(B26M0F4@!t+ zifC$Tu9dR=hlT%BYkmj~wg@@ZEIZt?FV#;nsGwX!Zl8;gV*lH7|D#OtJcWNHynoA4 zkR0`cWuZ##D>IY`lPSN#z;+&#nD8#*ykD8=U0O{vS#9_jmvMLR1t)FkA*&-V*)P zp*ElP5}j@*+xAu(_pNXCM?d0Jmi}Q;@=8Q3 zEG$%2RVDaRN4!$M64kK&m5Eo;fSIGmZ93YNKmC||MoWvJp6gnG>t1erR%t0?V`C%r zJXUGo54)U#>+=TR1$;Pns@-;}eR}ND#{c%!gX4X5GfMPdW%=+kMP#I<12=Z0KIifN z;1|BFPt~XpusNT2@+Qq2dgHpCX!aFuk(FfjC+;$M{P?}XfZ)U3d3ISD)ADJ!%bO08 zd8|M3`tQW}n=<$mQo?|!({gU=?@popIc`FU=%Tuw8dRWjTd8A)00W$`?OqIHqX8hGc(rIENh;7IqJgDx_s*j&Nx` zPn}bNvH=Gd<6~l;%)X=gvB-oX_`V3Th-~SIULxCO%%h_t``xijxH>-L-cXIN?`4J( z+0EK_=urRNgZ)9ZF?c*-fINS6dFaQV2D9+djGrNc`cSqs;BkW&WDw!=_DCwx^z`(7 zTH()am^F%+nVH?)&a{Q^q9raJQgoJ5sVG)#%*lcZauG?tt^mSJX6@s`3~N6!(Zky zS1(Xf#zhz3T-V;k*GV_quV3s&d;mGhZp^kdu(M_EmHS%#y%&HE-tVkT(S(nt_zYow zIAcDYQc=WdNeE$Tr1d(e^C~VFdHPc#S=eBTadoqk&b-uNvU@USuq$o)sj`4gv8NPXD3K5Mt0*i!N-rjcIOu>rnrAI`4~;NSjG zTpw24Zz-R&U0WXRPa<4e4s-+527bPU2Gld8+~|B z+_XD-`%dvQ(I{;Qm3p_F0q^#}Qup4n#bO?PLSC znbVEFs;wc5hQAIZ6e3_b^gVlS`k$H)`yy~@>FGS6d<*_rYcs zAf(YE4Ue{#w?E@9mx_szkZI(TEk4M;5Nso%nnsfvVz*sxzMd}k!s!bq83bF~*gO+m z#AM(9pZ59>v^?}XS`M8M`N6Z*A#9ih#|!52Tz1>RS!(*J4`x~ILHXX#brtVscy0!u zUJJqmihV)b)|lukB)L1f#3p3ToU(xO-5HyT;E9QeZ+D(e-riTUBqSvE-&Z>d%F0Mf zLfn6H=09N9WhtMDI(sYezbUAS&gx7X)X63=HZ~S!z?&)mK?Vt*vUI2>F{EGr34Z6% zf&F>_&Bm;!H=%CZo%@Qk_;TBU?g^bcbUlO__wTF!57KRLhmi7_|FhFxaZ~nP<%98; z{GMqVGsf_2IjEz3F8`0^vh*M>yKaPC58HZ0FPOkgSy{>7*dgI#EPPjIzkbsv-F&$b z;VryAEFO9L%^UyS$`E(k?@knEO>6Jz%bVFsya@l=Re)i(+AkrF7!VN^|LoaWb$#PY zXFT=#pMKTsqX-ZP9A{$BB>!PkFnClD*=W|nsr?rYobC$cF?WW7rY3~$mM@`ItJD70r_jDy*!Y^Drx>0uOIX^c#D?edfR7zr%&cO z2;_n8x?~-;3}(F_=~M|+ht@tbpcb#2$i^b%M1vK)SfaDHdNJ4l39tea0UX3%XYq0Jf#y z|IG*{(vDqrU-eoo=r$b(ovC?y4xGZW1MjA&&#%6c+h7I}{gqH|ynqo1ej9s7pO9wq zWN&6;llT7Mmh&xZjtEy1XB4)5AaUuc;ZAEkq)dKXFKH?luV+WNj&1@mLQ(hd^YF^8 zDTk?FYi#9hZH+RsnX@GqBMJr&C4xcY=Od$*PjK-|AA6w{_R0KWTfYwX|B*n+NSRt#zJLqe~Cu$fv_mK zlACi~8;dri=n5n;jnY}R=N8|Wq|Hp`3i_)0UTAYp$yQVI4O#s(Mol5@prCv&9dxg} zf#&fav^OOYo<`qd!1`{H`R;`}sl{41fLUtc%8Ps9==}ma&7tXzEy}W`bqFM-hz9tyrhYfwa4oEr-D?cVBubwmEQMD!^rdNuvT{xR0}SrxpG*= zt#bRJ+e`A1uxkRrl4wQ7E2DMY$9VHmHiZR6`nc-MWv`Ae+}CfrRmRU8Oz(!Wj|KH3 z58dq6jw$%Gl8ttj^UMd`sQ_}@DQ)I53! zPyVSV(dIE)-;f%0K;BU@C2(17@2;TbdvC#^=j z&dW@mLI2A^Bv9x>COx5dBL$c6+wJ%{2I*;(guj>mmDByP{$O60 zJ`6^wAo)NTbr64wB8!KQG4eWDTZ_=mj3^9KoAu^DTu-vrd0a0APM)k8HQKx6}Bo9E-Pc6+UbkL zD{p*j>pY8SVss)0KB+gkEGPQgg?wVbnsYkgHXekwiq89jY37X%8f~Tel7z#yB!lK_ zXRqrHvfMsjVIM(^mSz?<@|a74=Jj?vYf{Z&^C3E3zH+{^1S~cgdzCa~JrwqMCN=(U z+u`7CliPh-n$}k%@4jzMJ5_#P%I2+Up#%#oKksu7l!ZPk)tkj|ly=!QJ)yO|0Z32! zouZ=)POy(GYTUGid173RL8aFtNst?N3W~y$5$#vbfb8uH}u6N6wLx&Oe z9Q#a(v;Qd4e}XIDYtb&G7MH<6Z$u1S;R|1I=Bb+;p%)S_zCPAGn}YBOyR{lCSv{lw_3H`d7T)?uqM&BD#cks3w5tZ z8&9MwPo~rAkD!c|H73doqf~lT;+x*d%Ggiwrm6BDNFyAV&wZ>s`;H;OV)ZzjPfvpm z;v|nkznU+l`WukVPFttFKDx_?-MZL$x22G8eP>v1cJ(|_#ZB0LI%`PNeZ2lCu=1ud zZPGis;wX<%{`5x8>89r5m6|GZu-c3c=6P|x^nnV$(c~lkXywLoP@jv`4$h4mfF2k3`E(0uDLUR`V8<`8Xw~N9pATY?AO@xU{8Z5!jgsVd(u zkBQDtg?_*ymM^QCoQnoji7xI<8jpkqzx>YoZ8e~ZD*y1^$i9twBrh>HFjjK9WT4=+ zLi%8sow*s7(QtZ-w&z}${h-=8kY8dtV9@x;+Ht+f#X7jw%39s(VXjPL0tjziw)I=U z48H^1GHq#4vW+fDi`ibD*-T#;fPSOx;j(d;my&7z8I?@B6$W?WMt;-TqmhD$nsn`@ zqsS@Pb3OaoJAJ$>4eZXX^GjA&*^nsm<1CN#w70ju#T)18=DR8@q}rsG1Jk+88nM;= zSEZUBPK#XpINmKbW-iGyM8?vC(@UZ&GtNEYqZd*pl>6_>!Vd7RS_eNcG+N55#x7$Y zkP0RoEsWPwYDJOTSliTkWE$zQDf$>6t#8=_dWh_2gq_l*aBXXQ9s5Jd2U|)!^r?R0 zY*P5Wp^n2AZ)GzYkC&v23z+G8EK$E%OItP7@%SebSZW=w(9HSVOMPs&cuwm%YAbzs z4Vo>@;K-`xBAAr?la~Gg^0w!|P99R9m+uW}y$5WJ?DAqet%kK}#Uj{4K8YjSXsH65 zcWvI-Xr)=b8Syx3h{AReR>tT&Dh&_5t%r6sZ8Qnn_P=vFTEUUmeX7hICG1HvzmY4B z$tg**2kIV(l{1n)h=N0wj6~m z9@F7*J;882^3rh{Uw=&_x_Ddjg{-3%26~Fudo`_P3hCdnFHg30 zXtGl}e$ihk-cSN6PbZhke7`bh3QzHjnfk3D7#h^^E-@P4aIf!@H}+)l;lX+ zUx5>R4fJYe4No06;8HYjK#9@44V1ySI=A(#ES#W)teUOd@<{zPELud^oazmmm*~QN z-4msFFUpnjmbh@}Dow~Z!_^#ma~;cX~J%v-Fob0c1WddK{Kg~J2 z-ALy0kX95zB>Wa{z47|$TWTbLN@0e?IP7Oe80!=i$u zqJos?!a3X{wpN9W$D>g>;*cICV6! zlK%6u8FZ-)uzY`cdT-Q$P1qJ5c*wJvd`r8cwc{Q8#4WgX9Tuofwy`(mg`1C(kqe~Q z%yD%+W<&>aJpB=~wC`c_fUq-`9?;RdI~2W`iWDnjx)g1~k8RubU|F?!bawxRQwJ3* zZ#!*)v(06m4?MhK!jmqyCH0$UvGzwNC7&C*1R}56H_*Fp<&!t|@R~+1NAI+EItSHZ zUu&&l+g3=ABXCMkm*zNfWg)%mDSKZcK5qY#U5{_u(@s+dct4=wdTa-M$~jcXL1~wS z@K*iK;>_&Aj(Ej!rQqF{#&t(!u#aMRQMtljsUu2?5G=%+G_&3AKnSNsrMsy*<1~CrCq) z!OO(l%=mO3^O)$%5D_o>9mcR+9K~+RQPa_$;h8H&5@GzSmCNaCX!vpThY&KdaAa;T zdI(^T%!{}6dOV;f0_`Nq9`vwvbn4gQxx-H!aktCgUmgbQB~#J0y43|F`&@q4chZe^ zpF|w4E3BD*U-P|}xhLM%a(6}y(WjsKOXL1SXq4lvh9y7yk;#J3cGdVhWoHpah- zXWzVp(c0dfsL<>|NS06QcKF>{ujBo`>Kjs@fe+NX^CV){%ahM54Zi%=Pe0o2D2||j zylAg(_TfEzS^q<>X->A0l-d;I$ek9JIt_`Ii)}cN%slki(V=O5&rB=&|FQR1VR0@? z`#2neLvRgl0fG~Pd$0rz?m7wX?gU8C01568++lEccOTr{eQ^Ghz4ppV-rwG9WuJUU z-`R6r&+yP)Rb6%0UDe%?z$rS)QI*H3xhgc;WSJ_stxY^KGZU@t;o6y)6VG0Mc6#QN z7@=$V;$m**s*MMpYb^IgQXJ)Wu@y$K2hqo}9qNy&LHp zpK5xz#f!eM_D-YIFx3JLg7t^X*Nq0vqb-;BDYtLt7CDyM(t0Ht@1oS|z;?$~RmA}( zX*6*O%bz1Y8iOx(r;5`6hu3O1q+Y|tjaD7WA-W;#g;gE3v&DNi-TdBfXx>)7vxsbT zQn%-~kxMk5d2xB&Cz)p6f+By>GDNjJ?}V~2i1l}r3k-F6v(RxpV#88TZM8Hyrw$L7 zdW<0Zg+@*+l2#%=-nfiNsrl-ZbDf%xp9=$E@965qXeLf zmy>bDbt$_|pTF4&fGRT{50!$U4uy$@=jugNgb*; zSc0D&1P{5VD_zy+Z2HzaP~d9vU;8xH><)Mw508P`caY(y>_>CnTPZ3rfeth6-EPBbJ?rPl7AlzEQ{9JrL4_#w} zc6h1c?$Gk-cs*>8k)Yb7ZSm}BHlNDoc$-`f>3}mB=`of< z>e}2jt?rdqM-LitP{fcR5zS0;;HAt78aRBdEk%G+a>&CgvT?JDvNubn`&<+IZyA?1 zyw~?lADA8+1vDr41MuP7QM@4xN->^b(bZ9Dw}jR#V2nTEdSK+D-2=ECcW`8q(jhLf4tIL(cJ#lwT5 z9P57t!{l_k2>3DqSBw4xm!Oh`KD16%ukb^xv6xQKqz9u4Q!?Q;6|-h?@>AsETK36# z7tIy|iVOD}Uf_ds%U&0_>pJAqZaiVtd<~OjgQ*IHcbxqitC^)S$ zeskyhb!pZaW)HUqm!pSFAv^tC+xGo8OAg0gUYmWa#d_DIb9Hyv>9{Xds8|qTBnRVh zG95^R$8oB}b|@dn73dp@ax=ZKGf#^#Ta#c8hcN%nt38oOeZWX*+s;Js?xC=LNkJ;i zk1q(huw6|n0!C|-A#gqT6l3OFrDCki^p)UYU=h{uD30CAPCl*}ohe`?be*fB97ck!f(5^1s5@OS?CZ@rfs*9wa>#c~ z^V0J7Yo;pPbjxXOb{lYY9fhq}cZzE0=)e||X+?4Ql$C{bY{`YXm<|(T>8leNRsbn) zDW0Ngch923Tx1-Z^IkvW`l?OQrgzbWJ(qIY!{~T}Zw6Ne601Q|`0#EbtrD8)xfPT< zOwz3_xwX~TD|u6w@8qbi&$Fn|?N!IM>$x@(+4w{2ZO9x2r|0bAt!(qGb`?iPYuvvm z+Q~I2K!{ri!fXDB!4-b%mg=tWe(k${T-a47_w@&Cxc21jU#8)dI9x1^J74^gNS45@ zU0DZe<~4OKIf9uF&}_Qyh>d}B}E>(GK=B zxTet!qTj?-I>R;MXpgbdm1mJX^i3}AiZ{3u@wbawS9NE#)JY5@-z4c*!>^hzWwC6^ zHkxdJ0FTfpAj<>4SWVW?l<9xuGV~~rxGS2)w3@AwuY|i&K!vDP zX2O#z8ic*JL%*{coe(X0FFhYHNj09JJ~o(!Zb=vIzB8GIW$C|~67S?taP?Yy1P)c8 zjK0!h5qFk^5n-8{F^{QHK*i`)H9zRq?4GQsG4+UT;i-(=LI1_PI(qBCORMUG=rr_$ z=O7jAdZs7(;K74G%4}Q>kEN`tEA^nzI-%W=8}(^!GAK#v!QSo(HJoccCD7}pb%c)X zSZJiQTc_JvDemtZU!De;0Zi^Wbd0%U>v*dlwzas!%S=^uMmVVxy|e_}KImBttpXU` zkYw8!!!*5`tUGV<<3{Ju3K_UE!9sgp4msU6Ho8CYtp6DiFWDnwQ-Ni{3RxGo5;3=% zxeYB<)pezFG^|mHFL+Dy8KMrq_M(lmy%sB|HE}4vtCAVfuy!>+GvmBWbjY6+ZIc4G z7x^ZMg`AX$j~8E?kN3|K5PH?J&A7_>FdA!nw1ih1o}bY6_u}%Al&jfIha6woId!6W zUH2I?^4(Wfb*U?l99b2G<}+`5^;(Kyt~KmeUIAV1vW)qrIM=Yg78Ql%7i;1;lp#5V z2W|IEO(#W1oe#cP0>w)eh0U6QJ3PR5FXMF?D|_r;59EaUz0U_+`S^UXb<9wD>26># zelSk}=6_kDT3gFhbbZ;C?s0Iv?s< zW6N_zugi*l!5O$P;>{bnza@&kmz&0z-^SYV-sJ;**p$eK^97wG3P{!uc2$Z)c>Q)D zN&BSxvX8NuNl5J1&_OC%P4#(BBA)TJo;Q_MbGWMIm7=rd_8DQ0h9X%WBCJt(OkqRR zO*}d#Au*m2i7;-CqBkQ!v5Jby_AmC|vZ0(Y@LRnpP_Lt^<>6gflCr=s%1+-s4}Cq9 zh^1VHG*nNV>Lg}SP`iF=<6z`gURn8US5`LdPR2-Sa$M9kRq(FoO|98s4yj(McfLZ| z?rNvzZp1m&YSSpcr>fzt`h+|;AQkasRf+L+eYh^MKW1^8_VfjDpsMnTrFcVSQ0?U& zvC|rj{2#S@EZJ0^qPx3K$4gaU`(pFM>#6hWAmc6W7O$bO!?0VI6696CJ~+XzmDXmT zfD=*#8#n?4C43g*El&CG8j)$-9NLp<(9RY_>&;ZK%X>j+>EkxE5Ob@%3cUf`v0R(H z`a3%3JF=xtXdZr3(^E;f`-~+=h%V36O*SNn-8h|`G{emFCIy>QDC}%6K6X(trCq0M zPf|G}J*$%Dp4W{^cxBCgCa5!HN$z( z-y|u_&ZpM99%ypKIF@591f+M}Fb)5GRSJ^T#a2TKz*yjUwWoigsb(eY*EBTqI`Sk= z`&+2}2M_+F-;-0;Q8@v0{>?S&I;OA$fl6!VIza;1>@8|-9-i<;L)2m``F#6C)r2!% zYc5TH^B*jgK|K}Za+UWQ-8tsjU9YYO5GoY=D$fV zu^^SRs!fYX_6I;@W4Q%IL&BVfB#%x{6wVJnM%~mrx@-A)3fO2w8Pt}U+5^vI9QZn2B*jlzQ?qs{peda_f^rz z`mY~xsavAd>weTv^iGf>Q<^ALR0_l3XKKME_d}MT7Gn}l^{BD zF~Pf3&m`sl7;}HJY40A&w>&Pz0&hR{@xlBu~|o))`;Ye7l-UHGEec|jy{f%dRafW<9dZ1P!yw80!R_kh;A+1@h=L^=Ng zM%dhG)Fl{9WyXIbbbB-8{wy9T8sww~{>N#hA!bm|_;lLwf2aJh^1W~~{T(H?gPZV& zawYgcfleN@vi%xFG}+3b)y1ekzJ~P{f&%BPC;BfF{88~3iuf#VlRm+M+Za{42E1(S z_%1UKH+lA^V-sK8q}KpfD5U@!KW_qe&LRl8F8=CnrcN&^O&{<@dkUt^DJy-#!HJ3Ty-umMe5|S&33#vXch4O zP>`8NXP$J{aj$0}!ouOcHCEG^%F0Sr_wR82zo0A(4p+Kwb(~W;Vx{5ZnmFB`x|9IV ztnKlk8MQqu+C{F8%o88B)m#rA5l6JhI=nT%zBOBQ^TM&YN%0UQt>zDhvGzLig@Q|b znsSrS^!d{^6iJfX`?)(j_*J$C8;_Q@HsFI$%YI1^+v$DC$>zrL7+&u`zIV3TUe3uBz*{N>zbrdrj3*;m0jr!RL=%3Ua4 zK&FHGyD?TP*JT9Q^*k4wHc}7nxWkQDO_jGP*hb1JF~zam-9%Tol0WR_zdY#8!Ae4k z!m^<^1=Li3xHfSr>x*flu$(*RmzL({pZVP`*0Qd7vvN-f_MXB62Uhc{c;-;j%N^6nBmE8s|l&0aQ4Iu|qaU z(WY>-4W0WD^Da(PH<*5&iPuZG-tb zWQ;HCr&dWlKG@zdR`6|$Y$x&ktI^HD5SLBVrn|R{V{8#Xq_7L(JfP8k5y1~t>tP~9 z%KETtQ2bww|Mjc97We~rN_CJ438VN0!MYf%{J%L0Ylx#j zy!T-U`(?fSs=@!~m7W3QP!ZXFuz~VFte{`vES46;QTQ~G_5C9t|M|1AvELoVX@eTh z|KuoO@F0$2iMjjaALjC_&Zx75ObwX&@(|Fwk?bovCI@C$L;7W0Jqv10<)xDa%lD6u zHY|=MRojopxoy_-*5%hL{%&{2FADd5PQ~K#!b1^ZUga9eUTiFVHvN2{@x13QFkpMT z*Lkzf6yRwwL51`UwIpUl=`QP;$1IdM?r27tO<8zar%@*nr_(^oq-&jrZ!Pii zVCt!b1ly{zc$+_=U-^OYJRUE}JTW8W&a?~suXIup%KKNqtx+k!J;Q?w@#R3yAI(WW z>*JR>{i%}Xaf~n62|S0kn1>l`1XcT=H>k9@*OWCdPMK@y+~~|*yZscm^u_32aF0QH zH=^RTgx6XtB&Lr*$sfb21Z&ywEQxn4Ebr7Y;|eVTCOCD7**)&fCP%CJ9S??jpNZ^+ z2F1^&=})4kH%ww{R)Pl;f_kM0()62`tToNmtH|+A7)gMTjsc61ESjokyBe6B^X2Z{ zELOk4N&TsRmT8!4Q4sa)$>PF!)`bV9WM~7&7F8y*1>xDW9@-0!UZ;ob7Lu->6DLvC zyObx_?s)<^-5E0QJQ!8hZ8SXTSHOYe0h;812{Ws}YbcTMPsEWlnL_d(z~j8eR?|4U z2j)TRDe8n$hMNx&(UR*F{XJw!z;!RnK(vAe9zt^x>+v)fctq5uM&^SWH$|d7LIP$P z@jXS*LMhw9y{o4C1u|!6?%)csHUHo5jQL4DdacN7ie^}zxvNdhSat%~W1c5*0RWmV z^AkuPw)nIZ7?n@Q z?7fxpV)1_6$^tQI0K4kv3&k-K@Vp{~8C7Nu+AXf5+ZftGy{oL+3ua8H618(1dQ&fv z0GLN3T&hhD%^(Pu#DB~(076#_U z$LI9i=Y?GN6@_ZvuU)8l*f=C_ypuAoc2d39F7GF}xc+Pb?xkQ+ww>a(Vql2F_y&=qB~@+^;1`&_XbM(JDNf-oaY45qG5myTScD|Jv4y)cP3$C|ndD3<+H?F)U{}qT z2h%v;+its$r7bWc3yMNIVweKs*V^Y-^4a3S9AXk#+UoDenR5n6n`dT^&ZCXpvu+S3 zdy#k29mN*~1wH{X(k)wV_c&%Q4udB_b!qfZftEHN8N9Owo6Jt4r!5P^TW7I~i|BoL zpo_tmLB+%)x1s>P30pEAo;o=|k`6b`4jyx0ZT#8uu3f%y%sMAdvE9D?K;5Xe^1$=xwB`%u7wYh}(J*j5;{p9-HqLt;iH&POCb@|=if>~#pT3KErxM&gN7p2-! zX=QQ1JyAm$A3ijYCI!czU#08ykJ;CsDq-P>B{SJiLx@oS4D6VwJN*Fn~=Y0 zcS>Iu&C*oeTbLk<(Pe8I!cPL)ja^UG7W0;9o|R;B6OhglRKyg8CKS;jC^$lTPy`ne zKgZT6j3~Rgy*xwQJ-C@h1DvuYP2=Je;@HgY)S7dHXLsuWt6^0abJSkt;jFj=TLFcF zQ`iI3=UFS6B14c!5yB5Opfv>9RVLY5bGn)(SkG!IahYAZJ|Y7=-n?<0OeSSZdlpaN zBwlL>VNuKY;6lACJE9$S4OGp*jYw`1ojW6p@^;5NYIZW-%EIZB5{JPKf>v}n+f0>I z%d7rFL7q2zpd&_AD(}tHQJ|HsRgTLSC^jQ6qebD9^NA$jaiEyZ*fZGdZPZal)jL;3 zzKNukL6w1~y`X#bW3AN;mUIDJRja_HVycv!cMFtBapx$va7cB3?nnFQ@Ue#3-9Dz( zhQ5WeDvp^*TqcF!gdM32+#B7|YP!fy30grgROPEbNPzaYwrdCv*a0N06rrGPw&mkE z$fqDLIKG!-VKh$0o?Xe^Jkg&RGy;nWGqKH<-^E3{$4~{RgV=P$4)msqsHSEFKPK#+ zx!Re(o_rae@6c5{#iED6DJUtiI|!+s5~j23PAHm&ge+bjG?UNyR2Irv2Dv%j)~06F zOQ-I44LdS z7SMR>P|khPpkSSXosh!)B|nF!$?^zGaVRfv9aI=NYuUs_~JEnvxcuvQ^f6r)hb;_CV&@}RDAI; z5`s+|J2`RDR-V>3ke2oj?6vXXiq6PBT|LiRg9U5>rP;g`Y~WHwIOS77Q-y-&OM!$i zgX}Cc_e5)>{t)F94Fwiux4A0M%Ws>De2d|N^G-8!vAUOoNaD)Bn_haeNkMSt$3r*i z*6LT)luFE&I;`nrdai4)TYAbZfLGsqthab$+AYkA!)mAJB}Tt@m}U!ruZ(H87D@qT zMPB%jvZZ|M?5oPu%rVO$4CiAxj0>Zpr!WNBo`r8GNx3Um-HX~YB5$F`U;cTU4PDbo*r~19Jh?0suy!seB3UC80n&xcJBDpxJf)c29=iju%GSElW1}l&?CJD>3s3>7b^@S z4UBEzQvq#et}azpTRAP-g#^2-{yGWL@ z_a*OlcrlyJ_SM!{?pIpQjs-tB&dxc`o6g=_>eS8{CRj36hEZpVXSPtoQTZTAzT)}B z(ux3$@L-n`XSdPM!uG-E9esXs znz?lk->|s7aY%A$_^etXkIGy+`9rz048jxuLGHN_6w=!dkFir9!P5TLW2zHUb*V#x z-Cd0N#3oMXdxR>p1!gy2>JLwYhS9sqD+y%OCXK~SGs0%Y;=f`hhdefNP*Xi($4xID z{-%Ih&9S<*EGbQb89cdz8OhU@bA5@(!oHR!wD#6a%DRMu*hB$gN<$^D=nc&$joFVD z>zVlII16Hnwnl~VLFM8ZA)v@B2e&=ijAw*zb=CsfIL2!XfY;s5vDlPuI6)Vr;-50b zQ6qxli)+aUC0{CASBxb`v}X23N3J|;r{}5gjciF;n0;$cR&|&E%7FD@!CvOQzHW@D zehYZ)=p>78^8=mvd}8qXVCu(`UuHm=o--C5ZeQ<6hw^&jk!7V0<=x}6y#0S)SpOF6 zFdj@TEzT4kEziS~B&5|BA7@?kNL8moquPqjK6Q{SsKZp0mcKIjB)~H$C!-FH=|_-3 zRzw*&Q-By1uMqx4S%+dsQC$I5Ib5=@W3K`?wgN zWwwVs#7jAL-6%Q>+rW5onbPI)XD`4)vo<+?F6}KDZm&RXox?;A2j(cmmKh(zm^61>_pDb zaLP1lQ^7T};SM`*d{(krkqE3foj1 zOh~kL9}Uk(SZ9J~h(OcYHho-*_4&FfFcFao17$(f$Vfq_Z|O*-vVCRnpEm+1BmnQG zEU_n~y0WF@6--1wVM?>df5|{JmZYqG&(_}gW_ega0)d-BgZ;>{3{KN`rXWm8p=;yr z&{Ae={6o3=c5HY&jPJE#OQ=P0fg@iwPmj?s_Mc|~_}Zt2iJ0T-;S4c%$cDs!&T~|Y zA%g!z!895W`tdWbQZL6w~0@_%&@uuUs)9ikD&I&0s2aWjwSs46#&eamfeQ9hP+(L}kC!43O{8rMs$8zJ* zdh?5R+NW@xk~mtKCl!M-%*wLLRh6F9|{GHv`&hu2ncLJ4tlVOHnET~sgI zBh&Lmwso#trQ;}{4jZ7(O4|TVNw>09$43RPfEPdvRuQe$KD0HLussfr9H{r}y~&kw znlI5L_cXKVcVF*dic zW?AKjHFOwl@&4zt!E?niMROP6`32}&5=!QM6HMF%?+RaysD9J;J+bT)BE?VwNCe zG_HKgYW2BgRh*~)vKX;@K1XSwg2HUz@U21ehm01uDcKl`Dj~weaX_cf)3nL_F5FL% zT>POiHFP`T&%*plsk-zi6~aSkpIa-7P0)PybF1oJg0Jc)fzro#i#)=X% z%%GNpn!l!7d&qvIA`{EydCk|JZx$ftgUz54bfpnoxw_u#P9=sWTGoJ+xt{z@r}<#z zgMbZP|1vCXj)SzCG8t?pJ!&hFRb(;qbGpOh32Pjrw%Ds!*QNLG%;@0hJq~Wv=3HlC z>g(TJU5B?;yKyW#-2lEuzaZ-T0;g7}q7+FQqs6W^N;P6rG+cg@$d~>|&*Poh3o4-_ev^A(_S&{i{3)z)O5yaxo?`~_>Lhv6Wi6Zn{}Sn3 zOepPu6R@KG(e!AU;byhJH43E>U_{gcU&(*FYQPa0k5dz}nZCN+3_-OZw`Bu~7#c2P zksEF1X+mbI-GVnC_9epdwI}Kj%>}sMqRUaNZ%qtxXSBSVW-;XWp!0Ryg`z@H9a#=X zukhe{pKuQHxhb=a)xqa8GXvT{gF)f{*%R>#3t=tqBK9p4T;~gY(JUPiA%6?Mx&$K~ zD?o}S*!bi$+gPNO1dyq?8f(vY-|QX>R|hIl7AHuPiFG`AXLi+8{8!h7{-b zjQo9oTNBqUvUb)f{s==vmPBo9 zmum+IRGJCaHq5F;BxhTlrS(kvzy|BCIm(L zc%VW#=XMZIT76!3=Q{sF_qt(o|aZfU;kTeo_d)fITx22|0dL*GnuEc-kV}2;<10-&@#Ze z_{6v6JlNh89_=?Gw6`UWakliks)afUITCw1!WPooweSMqwx8gNR{yw^?O~&%qP8>k zN@mPd8pX}bmd_k+B6SmNHr(t&l#TmCn3d-O|=JEl2_t7 z{=6F@V%BaE9Ljoq@zQE=R0b{Db{NFt3$T0Hzv&3ibfMsEJnF%P3JaXaZT13!=%lF=Xhk$pvz~FN z7OZGM=G!!T)l)QZxyu_zn{R6C2nX2f=e2&|e;Tm6XP-QFn)sV3>##4(W4B?AYV4n7 z@K%UkkG?$%j4UrN@7)Rpr4MKA?=9H`C_q>FB2J+{|FE0LlQA}7ZFY1K;k2UawUCCm z9Mlv4wtCk}&G8V^{oVo|G@$?UJpV?)kesKP%#ztTqFVH?F?ioUe|v0_r4=`Me1_BA zpSv}J{(e>k&?n!8Nm-=xVN+K-xWY`gWi+9a2c+$*T)e+EGSu85|CfLG+nk+;GIh6K zFEsNP6*5JCKq0+j8`?K?Idq4a?XO#|s@$)ko+2Q~+4*Yn zvVr;;ERckfA~oT*&Uk=Ye|ri$=s;jSzoMIdyq4sA7fiiGpWAv^QxmXjU!i^Iv>!B? zl|?;1Iaw>i?tG+oUfI4H#Hc;Z+xbWBS29>mmqpqy-ohB!+1k!+NXp5n4$;%mS+8|toNLX} zJg%QHqmfNSc$TwUEGM1SVA&pkfzJFMa%|Ahnvj*27Lj-eLFUVwh1tTANvKZR^h4IY zZ@*~gzqw6sCOk+2*!PC}mk`3Fm1^B(*hTeC@vF_)<0+2!2L9N3^QGzdcBJ^bHonyE zXR{BhIb8YMCV5kzn(nm6Qn?GJDvu|h@zMG+24~`5c8n~wGG{0BQdbc1mF|Q zZ%|0N0Mi*w`7qtyBB-k8aKPl3KX=0@IGM{^uPMFS#R8Oro-b>!YhX0v;Mj!` z^TNrigUmVTwRJ8W>)o6PNaom~>SWDd5*S;m6I$e))q9+`E~ z6HNi@@m!hP#vCTI=JQ3l_Ryx7Z`Zzzv~^Aln7&%4vyHB^IISVIBu$Nac^w3DYB6jZxUBb7Uy{Vbv!fnX5L_1(N8jFs3U z@bEX0*RNlrx7}I!3Bka8q!@poy-@hA^(lmh4C8q0o*4S`e6g#AyyKxszr9VSeEgIR zYdyU^?kPfa7}D_wl*DV0O#-$1iDlL*I^g(rhg5&>y+Y448Fc4DIZ;zXSD7o*Tc8`V zELU;$>L(}r=UoL&9lyGfr=POZ@T1=k3AVFEHgTgy$04p%H% z4B*z^)Merm@#DwndU7+xT(FU7`eJUzd|c6IoC(a%>M|(7r))^`>u=5*xRR^_STVN5 zcj3OVCR%?IN;1&ynMr$4Lx9ceTSTpBpU`DtZgOU9)*1*1b**kO=`l_SJ|{Y~ zaiNh#2Jw4?z&sRfHk@|^VBNxuAYRrtf}}I!;~&Se< zl?JC})lOwqG{r5^+V5k}Jml*paT3|_U)7^nj!lj)nIl;cwpp-U@)7nfctH&{9==A9 z=+_F0!K*6lcS}~(J$g?u=L$I&l8hC&1y1X;q6}_mbt&=Gq=K$pa8$Co zLHkXE-gn7LOGjO5E`y<|Q1sUChHUXO<>{m6{KVdXD0kCUt>+ria}7K#e@hV8)sN^` zE{-{4!zBK)oZ$o*hNG@4UXHj}|)mpga4?_-6Ljs&r503Dw?xP&eP4!Zi#v_T_MbsmWw1}%*qH*0idzHlfz7dPh_ zgzhyUjFP)1ugf^fkMzug0iWawzvfSqxN=Gqy(84e25O`>rarog3Z1DGKh@?sOYu;k zy|fH)FI-r{ADf8{3q#H*Za2&wi48gz)77&Y9oULh9BJxWE1h!7Pvs z+h1b2uNA29f>o`SlAIP=c)dXN8K<0{0-$4B6w>9fHVSb3t{PJ6 zz}C&WYAECBGS zHMA0W?~p}@>IwUxMf8STk=Qz^DNMVcFnCx(hf=~JjtrUsvi0XhSdC2v+gG;JS-4zT zaQQ1SNh6`NGPWAUy0U*OKrcyr5Y`gp+WVObZ|=aJYO*Lhbu@WRinYvz8t&LD#q+tE zj@V>S{?L*>GStWB&WpFL=8_0<{v8LMX~G%?j@v`fd$YtSlO#}p_R1Nob^pRgPz*o) zCmUi18w*`8pA_Z?dLmUbrmZFYF%YSK51VFUde;g_unC8pgV4N!gn9OAq2=aPUS8hO zIY>9Xx*Ffb#pUu0vd?Tpg0v^fO6=&O`zvKmK|v&Rb?=kqqU--3~RRS2@ zh@mFB316Vov{o%trgFWTL-(#nGaW8>LPm;_Zk-zEwFbu$U(Tuujj3vu?0G(WvTN}; zm(iWUv(lNtvqBce1sP~M-5>zxSX6VFE-e#Kg+9boGj;>>?WtPb{Q*;Mq^6S6#~`Gk z(#8d={PNDOuJrP9?23wtST38i(K$-6BzjeP@F{NDgwCw1CC!P{{%aW<H8ReN*bJx<4z%nFg-JqGEr47#7 z%_OJ`mDyvwB3<5>(JWa{Y1Fl{>J`;TLC4(jL<{TD&TgX|Zuwed=1!EEkC&KM<>lOD~ytLgsIY^|!2;;a@1KU1TURL}!? zV=(@6jH70x@M~GNzOKT7E_q6AH@@BH{SEqX)J>N_o=z@XiWO)dNUTB&j$*e_#wYn3 ztoQ>mduWg~8}_?F8F(v$uhNe!F9!aczsUY*o=`}RZYD|wkQ;6OY(a!ZS(yPU~- z@?-4ksmC&v1WKIjl9@Il=q~CX3G&WsLa7N&P2_hr%1&4LAsRKyJGrt~I~CJjUm9W@*3t@ZnKFiZ;IsugCSv*0#3Ut1Hv4 zI&f%9cwzQe9ye42K9a5R=U5AuOWd(O49*Ud6V7?fenQ%C$u1^zvX*Xe#fH=9=r7^x zLM?opkr}7+SxoOGU81?m6us59&#S6R$|_(n`W8fTm7wsV!&oWTZ)`uP&S8yeD*|B& zO9v^8n{C}43}DsJL8~EKD`SAJ&}9fJ-6`N)bhpcXwY~F+@4rB`A7cC->zG~(ISH$t z8vNzjArUQA0uhFgf(P%z6ArSK7mH!&{KuXQ2*ttyDVUWZ3StXy0%Mpy(yOGz#s-wW zkdV>WmBYfqqV%^GdkDawWHBBl(-mQMar5xtXTU`c3JO|dmrLXVikS5d%}g6=>E$Rv zsVVhNh^>A_YZB^54g>V`#X0W)rCrQ-ZgJG^X4h>IR9}k9X@}M`vO2F!c9pQv>zZOP zFBQ=&HWx>~A=d(m$+d;fd97UO&OL{9AqHcoN7mrCMnzrz4s|k&2BU^iMOxqE{zpQ- zb@#X+VD@HFL;yl=4L3ZdD5uM_uPQIUtRKmE6S1~ax&TMSZ3_cQW+^V|=r)67lrjf; zdgN=DisfM3JEW#7trm-jdiQ4pShHg_%~y10N(x7_$F{BaD?k(s4AM#2QmA{5YJ?w# z-}0ZiF0##7I7(!{e0{hJ;>xVq8yICtdZ=+<{KUUW*&I~8ncivk-%`P!fX4ab#{1_L zyNB&h|4;qi-dE_&8^qqezPmy94jk3XC(A*|V#CUa3qf`mACx(4=0*e{HQo!Jp~j;l z>vddW)SQ!2){A7tvXYP00)16%Nd2!#};q+De zFW^at88;_4SJ=je)zr*vBD?yem;M+Moboq&TT1lXB1MYbFkEq%t!5#)$6VfoY1id0 z)@r%3Wd$g3H8K)=i27VhlUvnd?FZ%f%g2AQ;qM|lpxo|MxUbNEH#raY8iF|L2H>ohsyn zoXGEn3kkWFf*jVNM z;^%RlJ@*a@LfV_HLW3O7d}0R+1EV*dBhQjBfPjc-<2JcHmfg3RMqv5&?H5f_xqr5z zKiWOCpQY~R1#+SNm9s%4J8TD|p{Y4CH}@HG`ZtH0#cYk;=GqQ0&9*=`J0gr9oOY<- zr{3WTF&&$ewKq7#dGLSZ)rDdZAq>F#$i#r3 zr5Yoinwbd;3CTP8MMbENbq!K@lzBrz!@JGi&=@2nOikZ?eJ1#bBd){Xt4Q_C<{L># zALoiHNVqEJ{CM~G#`0q{-pH7G=wP2qXV#wuz!V|`08m1})S9Y{j7$~tE2SwMDBWWP zy0M_%8VG54BBhkGzf+iz0Ry?0X{Ve5h9PfBTwFZQ;);g5{62nP#GPDKSnAx;0PVdYtCWzuwfLW0fC`17mGkG22<1BjPR9h4q3 zEO4$|Suv<-Egp4Qk5cU=*IG|>`(44yeFye5^~hS#KW4uL|9H_|b@?JP1}#u=h%jL< zr_?h$S>mZb8I>AyirWPW5qjtmR8eZSqmGvcc?)^K3!SWm^xo^j*_xRV5i~k_dU72U z6ckTY+Q*d`7#OzrDlirm#SoI=T6(2%F4;p7gt(@y*Lv# z003a!!I^k38(2{AL?VntSz_zlHCCEB;DeQu#mZCu6e-Q+s$WP&^2CmfV!5*v0S}G6 zHAV)8ClD8HG?-YkBvkeS5Bz%uFzf+g?HyAXjk10?6n@3j8W=g0B)Hu}!R8$|1>SOT zDHBB5nT$s{OxTE&GAxcKR$q;Vl3FBmZjvmY`MyKFW1|`UEazRjgVG#x_4ce_gVAf$lWuL#}h6 z(9zLy+x+9KMrr}Kivlg!s=Pc&Vv5$Po}M0CXElpGKj`CrNDghumYtoQzlno`!)3b` z;99hg1*NlJfkh+x5p(<;L_JcHA3b zEJXWRp7nLW`RVzvdDn4UM+t}2nB?ND2tX#_;>DcC`>5-rD#uWE0%GASN4$2Z-xB|I z^@o$8T#sAUE9aknuiZ;KiS56JRZpl{3e!hs5M)rd2+P%8AjDN zEM291MO1{_nW@{5zI3sOdF-fa142htP+#3=B#jcC$%_^VO*dagsA>s9cJJ(QhDq zsZKMlTE}mP=t(avZTc&^BVX0!qBR8=Sv`gCyZ6%XVb+OCG1Fc)dDnjJ*&cr4i$E~` zxNZ<5=bZm!^dd5fsH=NwgayTdX3IIb4?9XmtU^`3url3yPUSh#^Tsa7^%r9sMQ`4` zc@2r&ag63Gh~mW~&5W0>YjtbYrhw-Gxd)oMk5#tOI(>?BeYeQ*0W+mpV%v z6-x&gJJ8)G;X5ks(TeDOde$BI{I$MKzB3AvH=HdB(IKkQcRHvWegmG+nTK3n99&*H z^*EQh&L_FK=%1|3LXB1*?zt5kJ)3VikD|`(+W9wjHJ*mm$_afe?*RFi&byxuBwaVk z7p+1vlz#;5)htjd?F}q~I)B9t*NP8BePy%cJL5aP8ElXfC<@^nd&%d7(JWdIHj^d{ zisL>gd2mxR_qF>KV7{9JoLt?0l0ZxCy=E**;_umvO&ZL0cQ;c;_`4 z7{V1;ysDMe#qDb(ee z%woa!Smc0Xp2fn(MjcdY5yTo z0Qw{J{}g!t^>`;wRw5|yG9~UWH9X2$yg(~(cVvb87q_e@(3?ew{=`ikU^D@h*n`9AwQe1v58(fzD6n#GTL=d$0Z; z83CcW+Gh1jeEdwhNOiAkhiCT}@>mWFf};8pNeed7=IWZ7r_Y`}>mC~N$806$vW6V@ zbiIR&v}|gX0b?t8ccxgQP*f@B#^6)rpina($zTJP#Xi#g@JA*rgyL<9W`Mp!U}|8f{Kvt7KAD-iDKxWQEb zwf!CrhIDl(o)*wBWD|nkIw5sLohwwZ>4Lw|wIo4Pr1?=$2ay)+NCocyY0YQE-jjB` zz#X(^?LL)ewIlfY_^7bVQ;Bc-a!*NEq`XLjWIMD4FA8i;y1E{2{Xc&pARRGc$;&r; zJ8{OC_Blqo+~`D^T$QjDC%9%OFMp}HbXQE%n#KwnAD_1PZm!1eJFQg=IYU6y(vr!X zBY`IYVK+#!X&(68doEO|dq4EXsUs1BJW|o*KJQAfz9zSFIv$OV^qwfAJc`iH*2A%S{=nIb zjG6Eo7~8v%DfRZ$T-Qq$`(c&*vzNVue=-}HH4%yK-DA(Q#W4)5nQu~De*xb!Iw&(U z`p1tIk7Kkofp0%r0*R^`fcR{lUjwsKW&`UM)whtg*)@+25*~y?UedX(+I2G+i%#86 zTrNAr4;6w>&N#KJ9!1IcH=Lc-XWiAfaPcg0DXh}iU#*BvA-Tm7pUfizJ z07(^Z2k;_=oIqGgF~fHI{ZKhE2VU>!CJ#wcvu)aObGJ+1Ijf!^Tx!PYS6w$^Cu5&K zV>@E_-N#amaNl_ikMBt9iVJWQtb~wkx+X_QpQ@PgGqkLJ4EZi$Z_nDv)7fvrrazqi znsBLvaD$-Wtf+(XsL|!NT%tVV2}LQWOxXv_+KY9yg6mad+dIxf_(!-`ocsmydeGuc5Hh>3+h zZRA0YlrEp|sT3}x9P|jbeo@TJ!3uqcjTUK1Ng=0*)sc~rpF%?X@_pPFTM^hef*0N+ zz{4e$AAWaX1<3L0a;G!{V>wu=uJ1+RXz&K>lmuVY%Mk}_F-2JYo{ z1D{v%+K>hQ?ZW(31~+*T1)>N%HxNJqZ^%}?^G_khZ@ZTP0&s9q!OU4{iH9^_QO*@E zN)D=vTE*Qxi0+wk)}OF>*~kTu z8$DKUBF+S-Ka57L%=5ad?bs+i5~t32`s`VDzb*m-fw_O8Os5^@WSst0Bs34aiIXZ`&liW|u^3?ljqz+X5kQ>*1V=jA6S zoOctdUDeYTlJJz6s6Q1_aVYfbRYk=sJOY9S1_l4HKr9MIKn?>Vqqs`!O6^={dA_{) ztAzA;S|;bN7l?K3ips=$J2UxJ>Z4Rvt+4k^{sp=vLWS%7gg$oyIg3biN&9u{6AMe*xWjQt_89A)Frx6!RzkxV=i`TRp+^K!mgkS; zbOf<@HOMGhuC72TF3c)KQ$A3v`453dL%rsSu)daqCmc)N65wj>70AT-T9mDe{SXX} zCnr9$o{6;h9e5-oz^0se4t}w`LY*e^Ey(-b!~&@=$T&5b>i+2?D>po69B?xC)Zy2LAGw$I2Wt7QEX1_ySO*FenYYJ!S-YgB7#9w`OV34vy2_4a z^j{;}#6A#Jna80LUtDbSlfo5Uo7&yAc=r7H*H}5R-l{w+f)M1V{&c4h%f(jTL;E0( zaxaAk=TqedKavQ1Z{s$GoAtl{C zIvv?+X}7P01>{wz>UH<3^tzaEzmQ6RCR=(78@gQ@It9nmmTDF9c$PK=Zb1Fkbg{I}5ES-VPR{d7PI%H$5}BGnEG#0E z%l2oAC5{CVWSXjA{7nngHK`5(HvIRu+sEU4e3;WFY%UmGRPr%RN3Bd zombV$PDV(|RZ^;W^u!$l@ zqDzH~nxPU>GqlnQI&+~<=D0W8S0eFtZjM~&`la6CU)(RCwcAC-4Yjm`zdn?nF=|iK zm#2|UvVloBOj44gBRQYOIM=m%eOrn&PC(}|4}Vltbaq(`SscwjY5`n1{y3Tl^n@GK`HO*4=He8JvGpZW z>^3FOFXvnHM;r$W9oyDGg~JJjm;leP3^106WZ8O4igw*+$*2kyD`~Fd~2=~6pF90H@_Cfge2}N1=S@;eb9ZXh2eEhEq67aOw zF-TmSjofud`pbfbA0Qs&^G&ey!%8E*WJ(L^(CwkZTatncNO+=;Oir3!$9AmRrQHi1 z&$bJ}=c8onvz3Sue1|@4%}|)b?l%ZcDdli-aHG*eK!Ee?x?}QwK*gWnf!|V#zp2?D z2!qf9?)Oh0PeJ$j7N}_log%s#GHW+@vtT=jO7}*h7CsCk%Ng{q)UFizODa%0glIc5jS+PQla_b!9JZkXO z4WFLUa`2COSK+PO^dubSL4lT2=lf6B@JvJE^qrdMkQ8X<&Oj-yW%Y;b+f$hzv@Ebf z#4gD~-jOfg7Hb?Yy|9SiFYehDV~vDGK+9adDcC$AQCN4aW8z27ap%ypm#yciwS!kw z5-QMC+730boiSTl;&9xUH&Tr!abXFjDg<&)LaizXqdJg#=kddi-3dD2CxSo{Jn<_y ztVDHk2rn}E-UoCU%%_BWw$rh9HX&lw0=}xUFh0J4n50T0nV=Wx^7F!PKhD#A%;T^rhP6h!q>0md13zfm zW-s|TL_nv8uB1*FbJlGuDoIol;Qv-yN=lv)M6J(jyX;Tgji0f4S_4n6R&!>{w4frG zb1oGrZ{S1qW%@v3Dv;!4QpdU$0i5snOolVfo{e@dYt64Zll862gWOF!YMy?kzZ6~5 zshlnvTsjgm?yPc;>t|{lxNxT1imy3tZq^QaF?%x3GkcOv=Col0dc1qV+In%MUp+)p z>GykH<8J^i7{T{zfL(#xIM_H0a(FtW9uQzqQk5pYIigM zcih$#oiOA5gm_Fpf*Nm4OC<@-+0gKN!oGufCjct=^Q$2Sf4V>i=eo;@pz8Z4u~W^b zGMn*?zHe|dhu>tN)pwTQkqtst(VR_~@OxB{XTBRv=qRfM)7H$_7Cxo%CuPSA$?h#km3M#XLnD`M*5 zFx_hy9E5T_kYlX&InTEpilDZ(mVs{vB|I))g$@jPp~<&u3o)s@0bd(;gOs?V5#7Yo zf&>gdpWU>kqdt5X;zP-YMjcppn-ud{fI#76|9A){28~Jt?$aoic4iI!Gw>O7QNthE zlP)6cmCg%oP92c!3^j4JCj(AK-E6Xte}5pAhtN<_C~aZWk5hLuGZEp3!YaNSy@?0F z_dwa4f7iQsE*$d%RS8fpCqw|lQ5A>W!XmT{+b6;`7>d}9xw+z)y}=-tAm)-%8RZ7} z0o<|R74y)Vx%aOMCTzPv791o$T-)sIXBRnSGh{vRh{Ei<>Nh6Dvc4DLvGP{#p8Dj_ zT+m>p98z(tj5|G~)6v$}_Hh1)XN_3*D*WTx+K!kS_$0qM_dV}#h|%BXEYGnw<6)~1 zkJV@4n#GR`@mL*~mD70q$#%R$(r!%yNx;FigjC(dIXiMF^lBlT?37wyGJR(6ucUlO z`B}o&=!I)S&;05bGthYoXQAZnv9x7_&88f0MTAGRG=1k_R{~P9yv_2vhK=PXz>zP4 z-HT0z^j08CM4kDV9WKF?km9Q`n;jPk!rlIq*+*ofB2u?> zAlaheR`W$(*`u0pMlgoII`qIz zG7$BBaD2W1opAlh$%&=v3*m<4c#2Nx#sGi+USF;9GmoJ364Zp?w6W!h@$qbOI!pb1 zAf)(IpwCuWIffLTIbR&Kir02ZfNLrhr!G|ujY8EPe0-lqbEQzwfFOKtN3B^kgar;+ za9PG`LLHa6o{L?biJa+Ju4$c-r~VOq|AWE1V+Muf9&N_Ip6$M-W)V33iV4EXl!D@d zot+;NezDKmr>%Ve7>b?TMqTFcs=D7pOy0@$!j|{=_JU$O)RQ-bQiEmq%g4a@Z%zRU z9Wr0My&s)gUgK8Kk?GAw$(^+e7}aR7%M%LTI~sUCrLlOj5^)L8kxGwKDZ;Gy++-uB7md4 z&9ZP^HshE}bEGFV6&BCA&ePs|IBTEH0-ZE>Xu%qC3vG=B=sdtFvt$aT>;6*i3 zwXqex?}t)Hbt}H_2WA~_9IG`%7b{ep&pe}@gqjunONmRsN1Z8MWie)#Z(mr#43K%0}zg2}7@tVP_Pd&5ifYne11=(gu zzFZ9Lk{kMd_h73GP06{Pf-hA}kxs9l4t2V}ttUe=rIzk1iyviA!>U5cNitGW?fwsN z-*F_Z*_ernB7OMq0fM>rkpQO#f5NI)*FMpN#(@4BC`b&1M#w!7StJ@1fonWMa!Xo^}a;p$JQkM=2Z@(F8C-3K4 z1CLx>%8_@&X=}r0i1o&&^@^=XoG~W0^gch`nI!1xtyy3b|JGtatD}>|0I;r(Crp3q z8fU{wFz`lk@Om#P3Caan2t{c`?UHJn*{98>tt%;pdXjmv0J`f@T=_?-Xhz&)H7u2T zz}KjuT`@r=LPZ-4qoYxucZ!0|YgNCRTa{W_R(|waG!&mQFE$y5pPM)w@RUueXhy+O ztxPtPXp}x9@OQ)%4pXuP$NN%YC&yR{5 z*FB{?*{}F^Vve(49^ZO`q}#ZOe$E^@XtWximW;8UWTOADH?>~&jXlA|kDZ?p2@W{N z@ZKy^x3tFB-r%mFxIJ7kO~Wbk)Gm3@piy>lGC}6T!7J5o!JOxYq#5|OyBIe@Ur9E{ z2=ks1la#e)kSgnP;pg2dTr$ zSdrfRXvAv82-uSnj2vq1A zi7oKt9rNQSI=3}(aRl(nW%k)Z@J?h@)YCxg8*S%;%a0f&AA5TxKm98jdTn5krA1WR z&_Mk3GYK~KqNC|DpoQdlhNmjaybq{Qxc~A8p)68{4bl$;O*EPnenIG3VfZ z3>;`anZhnPZ2nZ(0uS0mJv^F5{oxny7MeY|JTI#txuoy}QlE-%f8Y2Jt$ah>&sHru zBnZ_St~Xc&5ZY!AkA##twJFs(OUHVEnbMMi$j%_l@o6sZI12$G&U7iVQelOfct%Xg z7zSCTUy5JJ2i7f>M0lxcba^f0(nD#66+hIT#TXno0MtZ@-p#e(Pp_2}mZ7q>$CwFQ zDC40>;cp!;-P0>#YD(p{Ft{g_xBqPI{ zFez;p(x!eo&W+~U2cMW3{|#RX86&@a5v@U1n~?zr=P|C%_9YY*qplU#d0h{)Jz5XbPhXm32R7gnvP9&=A56+}SR0U-8JOyYaq#hHUGd&ggYuuW5T2d$C;)zDMh3 zxV05_srP_6y%*;x2Fp_#mfI%3)XD3C-VzIvXVt4sfwSW|UltXJYMZKr*G9zTT026F z>k9j5`3F=@J#7NCVoyUrrKLiq4DT?{ybPr4*VR8dbPoIYXQ1GX^DH-h+yE+kg^nv53 zn5gBv;9@7OADh9=sT2cyx66bex@?U7mKe z!I4W2zMIMhUQbyY@1*7F2^xZzT6X=rNbd~qY}g8(78yxB{PHtlA6z%*|KeAtGykPN zq9w={0l{p@;H=ah9qr?b=LTNsD->5<>1llDnt1Nd9>_tx?}NSfaU3zpkA)CY9YV^=pP2c@}O+%+V{xo^D%=CVUlJPfNIYvBTF^cmr@X?NL>R@8V0Ha56VHpS%2bd1H)w5U4KD=||z__oYCJ zWw>JnSq~@UoY5<;XosAZ){r%(X8j6!{B7hTpWcC3QV~PeoK5jCG%-9rtpx#GIz=gH zVQ?#V5FcUPhC8NDibcVLi6!tTtk~PmwOkY*Ov;}Ii0Tw5!IwI zGduN_UO-u{M&&s4PTHPjzYu$4UMOPfg;O|=&6pn2LV){}w@&M;aSb<#COYZiX@y16 z?aJ$H88#{@Z_K>D5Cw;+%m*(es61so?o)3t|0d5#@O?w~KF7sSwy{#&tJGF?)O0E;+p9-I-(uEWDA(eTBbBec0Jrh09tRHG4DT}Y4B2*Q8M znpkdkrEi@uBD=HP?{8o2SR5y>l6Ocq7{Z%|5AgsVqrcwGGZ|BQ+cTM~t4s`;{ zBLBVWeZ3gJUu>k+(mg3$?(XWMgj?1UArdk&3~v|wiuJk`BZF|W<>N|SoHTv&I=c4kNpG-#LpI8r}g$wv8mc2AF^8*wq`O);y2#> zDg1WD#RgHBC{1;)VOfYmkVrb7@dNi{PZBl(^3kzoxy2ivDk@CqH1MM{Wq^Zq!lRvX zQvOjw*?`1>Vx~4~U7A=cj4x+dfh<(dAuGf^rpn7meAijL*g&l!u*g--4n_%+zUSu} zi3HjEe#+Y5@~znt6^$;PQXS%8dB-Lve+N>d?i;~+cr9j;D@Pn-0`>jSxzh0>%HEUe zJ>smMRWU4s5qj2#0$z{Ay7HDL@5kxQKQ3-EM@ytZ^oLB(ZUD^x+$I zefsbL5e}f7lh59B6O~j`Zl6${wzO71^`m&F60%}|t}z7d6m%jx^A0+`(uVI@ck*u8 z$O$dKN-OLD&F_In+}WhfV@vj1_j4RwxJe~{0?C5=bV!KV5hj+P&ktORpUE3{mG?yp zeI|D*Kr#{iCg1s3O7B+rKwqE0wZ#MI5!!?@C4vaGJJ%tg4 zL>~*zxk`{J6xqe{#HqJ@2^*LE_N#{kYW?&_T4{U>J9&}7{3*4^K6VG<`EM={?t|yo8Uial=d?=~zf%xMwR|z^ zX^a@?iaNRYVm~x6Re!6C%2L{W!0-H;N#(oe+1f_=Y5>MI&#HUVvpFg$%MYMqgQnvU zT>}->=Gn*CUxIaS4cX%s648|RIv*a9adDmI_1@SZKZ-fw1s}L=MjWIWBszhsK$n|v zybT*aIu;5~Fn?@o)qSVL_Y;0@YuBUi3W10EbIlR6#_2d6Z@)g~@OT_0)A6_+I$#Z) z{{-JGxj|QRB~sTel_yc;F!Yktmk@sm1wcXyzlAhHCm7&2F zZK*Tq0#=s9iPXWj)s$T);P)~I_MUxVw|Y*0$mIQ|gPvmL(A)}MF>rYi@vf_)@z*M8 zF>A@T*{Ps9^CE-+WMJ7*x;f>kAGeE2Atvb|=>0>L_WI@Z|H-%fb@u#!GF%QBa92wE z)7OH#e!_RybR%J1(aseNCil4`C!bL2wG?hdx+$}^zG`NIQMJ7B;NXR$`6)xcN?ded&9Q@2TI@fuCbY1*&>zFhI?JugilHnDirW~cW}Cmm#|I?7yCuHBy)fdKel)eX|5D-c7@5)Nys!W7==8mb+@)QUK(kf?j8I<{0+7zUS(Rhsw#AmRG%K@#OesT5P?uk{$F_}fA4sBuE zwM87NkYE=>!WQ}k0sfKx`oa0-W#5*AMrkk!tDp72w~8(JX$?a|@hvSakQz(Pa!sn{ zp@|;vw`Hf^`_HN%Q0Mqu_i*UrtT!z!A`sFSM7EQ9 zEL8YgCxtbj8e!8h{m@Dy(~BdZp%EmEY}MAMVuFV9Z^`q6c#cmOZ;in`388P`)CfX7 zzvwqz?H_yj!=!P0?ngU=$Gs= zf`GmO#?Ku%`I6Rx3|!?xK|5h@EBv&K8NczH8a9OO>Z6RsX}5{IFdf-`qbde@!Ah$d z9K?pG&Cf*j3x}0sD6ktfihpG%o27jvZ@4`a!%c5;EU~uAV^U|x)m|i^zIfc%+ zlV>P$uZyF`=7aUnfr#3aB8#_!{IJt{+^4+o0y%& zwhUCP&?{DeruGY&q#WizWi_7Gg%%;Y+<}q4&iHCd_agldS{To}f~4l?0-PC=>TLkQ zlOFL|Wv`9C7t&E#*=@fLgDyqlb9zTi7Re~}Ta9cleBCsB4~(siG_4hz<}>X z#pv9l0l?n-_Kd+Tzh!PO^rjw1eg};{F7h=v8Tf=o{eDPKTz_z*aX)d6VU6-}*{vP1 z;55(uPTI*G?&kt58g!x4O`iJfd<{W{0yVKOe`CsDtau?2obOgIN%ScGbl;fq^VI+v zNYPHXSR9lAS6ZVWESb^lQ7{NM&*YM#WUL?x4|HRi$Wq^O8Bsr{v3jcxdx=rYZf$?k zjMM72!=F`cB$v^C)uZ95)99S4sKru0>DhN*&it6HcKd+o^s!Qa>A9n&Lm`)#dqt7a zPDgGFvAU^T%6P$bMMI%ffZ)2?L;`nDe`4k2=GMYBXm?)CV0R8`Xmpu!rX!{9X%(DG z?i6CxxMK@t`QTXGe-6ZK@v5tx%R8~&Q+)3ygAF1n-Q8VN7?>A($%+@O$wd;|Q#oG#|zA{Pfhk`!@2EJSic|$zU0-(D+#=LAxhFnHH;`r_^Zk74V+yqH_)F72DL5f2!Y5Q#RNg)o6u*+>hh7-eN_Rio+n%Z2 zPr%}nqY9@GEPeMAL>N!Ez$vM3iB)~ z`(Q)0pHQNH)`%F)X*J-0u}2KN2fLMwW^{gOSP)4BM#bdtv&n0U%gzqGbfo@N(WZKz4}jY;{ntuHBp_;*XkqZr3hW!i5; z?AxS=Souc2qd4n4>uc6l*@ccy&kJwxSMV=-B`yyOzKkl!95L|oYx6qomSJ()txCeH zY^ToP6ZtFK@IN;euThmxQ6+dT89mu9&#eOnA@w6 z#WlDXH9`>gv(3IhfZEv)#i)DAbCv$iT@mdM zo&7f>B*cJ(+F6YJNc&IZBQ6nom$wv5hM)P}x7_VJpks)V)4GG7idWhNIPMZ3E~;bInt*e0^4(*9Y)DVOA*RNxc&K*V`RE<&Y$QV9QLPQw;RiH=Y9 zZetiAj>lT5j^$34^M$+1_6dIF|45Gi=q^klJppBzU>hh_x^4Z2uKEzDZ48^mv>q89 zir*?df)y4A4tWuaKmA9??9Y#86Yw=6`qXGTvZK++x!|UOd=Zf6Y1Soiq@pG^In$o3 zRbN4CyV(XBS&S!^fe;<)aZS(aJ)7@`f2*q^JHfPROqT007*KG zB><7-U3j~ylzLTFD+e?u3%&kT`XjPJ4$g&aaqi zRME@eTHD;;XmCFW|If+%!}N#8krGwJXJyiE`Y}tO4s#}sCkM3dsa=i7wSq7O>uV^k zWdXLo=48S4=vtPws*S1A-efF9C()*Mdw%@?z&vE%b=*(x??Wc{nVk4pTS^)-(kicy zcvDMvamiKY5$rBqXTfT_h32KGw9v@XYICQFsU-1#@?rl&f%$eb4O<}Eo_T@9_C%}O zMgRrEk4siJs3=xkQIG=7du3|3BWbWU0aQ$-C%AhKV%`_=r-L_EArhbjz4%d( zHl(6ft%HXhcfL?8U+gDjKdgaOq(reNdtVWG`Bb~8`}-Vk*={^m^N-#nVE^yeG9?;Yh>#Y{=LnB{XW3`1G1HEX0e0XLaco6^8%JrCYo8k%lRTu?j!zXjVj*&N<^P~mSFv3#H#*m3(xTnjq zWbvFR@Fz5{55q9Mo%RKiv!eV~WXM@wUvH2m6L?;m=(MTWFLV=y@eUn{Kl_v(oROU7 zQWU#M+^>9JgH%h6T5I;i4CgLC%8APibCzMgQOvYQ zy0Nz9Ey!%oYDgO4&RCA97PB&1E%>#w(+A|uc$yvHM{Btikr;a4@_)};N>P}8jO z^=pfdDnG^kM~DMdR3!)b*X~%#d4@C9wW74MAk`UkCPB-xBJa)tx9fu>^OJNK$SGv5 z6dZdqVet;Cr1U$jP0kSowp|B?!s6kT;d$fZ%I5u;fg&LHGpq9%RkY5k@i^CZjQP8{p6#VxBqLG=*e&ya-z!eWV&zG z1l@&-r64W-zIK6JGX{8IgOn`^m?O~ zCl{XvKp(Uh!z9}NIW+hMQARvn{dPn>O;+T1*k2FrmFz+_09@(yt-eTI{!`=(Wl=B(Mp8oK!RrwDmf8;9cM=NIN!(Tmz{|QNAqfH@VrYkcL&kd&saC1 zBLWjIB3tRS+95H|6Clk7G2e`Wv#e{At2c_R+iyz7x0`C%D*Z)hoJX(Qj7usJ?@#z4 zCc}sW9N4w51@SrVXgnbh>iNs%zSooATwPBpeu@4Wzi_w+sVQl~l$-tzX9slB)zWSc z9`M(0F62BCyIdc0J`tTQG-A5XwC)7!6Wx-|g%3DsVJys^x0ltVybOc&~*+aLAgbON+Bvw5aD zD&mM(Pvp--nHIyJDTg%q=?8{u~be)nR-+AQk zi{#H&`Ni-QXmxBR#hrP}2&b#{!?*ythEkp%!zTv-j^G1N-JP!hFh|gsE$YwoJDBG) zbfFbmefM#T!szGu8?C;y51KRx3HMky#sDp!ObaOd8nrjW$_4HW;8*hf2Zh6AeS~Jk z&w8hr@3c0i`P`^PK`%QxNLsLBxMk)Y6tD4EsT%XD0OfekB`*5a!=It)I^E)&N5tUQ zJ2uip7BR4jc~8a?a-qw8jb%Oq@izGy%+O~oith}U!(JO${R}*LX!7SrmrZrbWQ6O_g)O7oa8#CntX0FR{Bdy+_}LfiGXNJ zvTC4rxAl0rPwDvRmscx`l;-*p8XaSEFK*-<(`mNWOvATr^i3q zAsNlqJX4B@vAbCTpXZBUr5j!;;4KHB%UKS*$;?zBlV3tiP8--ksXeR7no5R7@H3sG zCi}t#(AY-|a7V0&OMN0r()dKkTGUG8;%ivKBf%~a?Spq%^Sg2V)0N2+M5JYo&-dFn z?ucH#w{P6GeKFmRCAn?g$$AXVx@*b_y|Ng&ho^W0! z-LkIx|0O&A^2W&%xK`j#fA##Q=zf>SLSuw*LEREGkiBD2{$ug|_Xq#~ll?hD{eO+K zX}R8_e-x(;K)R%+fQJMLrqoi0)yOcp?}oFh>Yd2xj*0tSVg9&Ubq$vrh){r89? z-4q?~=5sbRtnBS8ec5|%^I3U4XT=w-)U3zx)Vjk~7h9sQ%l6WE#g>}cvA9MYzShb!u}iSI+_oP`Jdf0L(?E`{MpGz^s?drrV|?w6Ml6Hn0hx`H zZymR(#$#B`Bk;be!q(H~3{Qs{?H1MDAz`3SoW>oUbw}O&Q6&vl{s0+?lGlE3k(k>W z^q~Wil0r$;mIt{AuL*+!1=u^w)-B$S7p91N@9<>cQiryyBQGaT8xs;w8_PYOHnwK{ zpu_(1f4BP&0{-TVEu!PntG3RIM_{t zCiXOmMKx8@+)C-B#`t(qIpqz=JyZ`44%}Q%-=ENE^BG_8J-=NXjMm)e)&KpGe{)rc z57%27#Kd<7&4uoZ2=Ljg5qr~DXyww4!tl+8J(VN*_eJJ#FCJyfD@FyKvVi!! zo7YGd;kv4KzSfV)TtA=9MphK$0(X!;z6HG@(( zqQeZ`L2yJ`>6AR+taT^v<*oIB+sdhc;aEb>2d4(TT?V@Vu+?xy3|fNQ(QQ@$ueJz^ zHc82KXy)Nc7;lRdyCJ@p3KDL7b*kxCKwCxz8mLZ~L(3r1av#J1Xr2{1*;1^(c?-zA z8=d@ja}0MyHfV*O?A4>aBEPe6uzZz}n}h3KsK#qJ5=m)Q&cIJ`hc8{bxwG(zU#NU0 zS<{MvltMUyJDIKN@^iQ2u)=*gtpp2A*n6KB3xeYlIq#BAiOQ>*EfMjsu}%C}G z_Xgq{GplsOCn2q=;LW&EpSkQ7m~?7J%@s#64QL;T1JEffl4@%^6(ubCoOWH3dNCf` zU)#?RJyAP=JcEW#^(VLKzkPj&j{V78>F#1%oJDSWRYTLyspX92u$DGg^YV@Qo+&Gb zNd{erbPR?e97PN17`oOYaejfbDTCiVYez9r zOgigEh2~QzUOor&i`7gPbDPXnH43O|LmGBS2Hi)nfNZu-ndTzlIEi-V0ii7z#=u>q zJ4F2p?Vq0T;GC`U$WH6d{cZIp(t%+Pm7WswFN(bZv}!pjuzJnf6V%}v?>LlbGoO#A zrz#`fvdWp6{cyW}_zZQ^Q8$5Ph9fxfSR)>fQtR`1vvB3GCAB1nNdV>H?rr+FiL zbg31YAyOTIC0#tjStvn4#2~lkaU+d4&74wxZr6=Q+eta5{m*!zHU$5Q&7ZJN^wP!^ z9rFjOJP9PlEHIQe7CYW8_*6n^$96B_L+E>k%X2z>E{JLG7u#T))GlM}I|)=;^e+e1 zb0{<%o7&Hfq^095#w=SJm8Ihv5zQNC9|7gFq`lT6RiW)YUU|>ExsK3wCjla*TX~Bd z2z@gkDRhCkbu%ttgf`pyrMxBUsqGWn1Un{@+!P>ZqY)ZN>>eXWusC)(UHrZ}gM6)D z@F<({GAXpu2xENp;%K6Fo2}ikk+Je;G@HD%uMxJDhEC?Y4Kse&)eO6H&`qsNzcce* zQ&ZOQF^Tu+>P%J`_FS%;A#Dj*et=K?9a-4~?N*?35Bt&)$HYc&{lD!^`VtzF*=Aj{ zl!s-=@yR2G3+Aa_QF9$%V%Dck5KF?FvOdgqvV&fa8@F_#3Qaf1uRJ|MS_MO4<)eo! zk8!<<9d$2gR?^M)GFCp)wt{43_Ay%$HFnAvM6Bh!6hEfgUTp7iA328I7b9crRQ}8L ze&QT9+jw~gfkuBux?Yyw`FmcVTi>v7uYtX!acQ* z*Xl0bGae?i$zHy#FH^0F4r^{>H?^iZvcOQ+mLs*@_=+*%L)RyfYGff->}pW`APR#l zeMTxzMjfP6(%Hz5N21-48>%w!tYtkma<5Nrr-4;)oes$6Zmm97A9q;&9zrEWo8tWK zm=kZNSXHlmqk(zlr}QJ?_;kxh_+@D5|2CYS9~jL>4ATi&n5 zBX^N25&(#_RlS?3zvH+3(Vt+<_8AJ$y1p(>0s6m@i`U$nU~)3`aQGyl?VovxFe36w**{Iz%)FcO2q;AY0H(?zBEl!mS+zd9F9UNjqL_ev~0 zxY-kcYwZoN>!iI+Y1AHwy-&yN=Kr)>ibgdlLLa5>@KkkVq9nMk8+HC>{EH^HoAbKg zD`|G^y@w6#Cj}J@3%%q`&GesL&KA-}QoKtm^2GUwN_8bBchyfMe-4!(@mv6{6$0-WW zT57tdWsOWHdVry}sT=f39V)Mco$ptv$ z8P1EOq>_m@Z!YcZD_$LGDRKF^749##5$o-?sSl99QsOY_i6}7%YyE@}j8@$Cj8h4| zg=X?&Y3<87t~}TJA@wn0MPEg*#qeu;A=`8>p7E&&g|`b|vLgg<6nUjK6nCWARxcHZ zO{>6V==KCZ7Eii5T9w&EVOxs(zjs!FN_R>cWt*E*_1v9s;b~7UY2Qrv>+J{=ru97W zH;E)Q2RtsgjXsr>ddwibOc!HMkpdna9>ydln$2;OVxH{HHLfGoh0K*K(hmLWa6&GP3Imx7f$>!xFz|E~hoh z;S>Wm^^p)tz3daM+~_lc#k01NsnjNUrHF)(z8 z)KJ3^GlX<^*Y9?pUETM6pWWs6-^~ZO<6P%D@x9LZ9@OWAJTvaRAKtE(&=%$VNJm!T zxB09#+-7S|&UFI~;oD^lmW2YlNlH^L#gF+7NPnNt4YaYxv$$MCjd!HbV2Y z=6*I3q8e3P@IKS9mvUl9ml-?XbJq@pOvriNwZB3)&N*NaadCE3$0W0QyRU^W8D1+l?dHe-msk=RQwZy$`b?*&79F=%& zcWC}67r@UPe0}Z>11)`+x7bMQjGv9?T_*6iLu>f>?Ud?0T{hRWV>*M1EmA0q-=qw4JS%Sw z6ci4w?^0&&bMWZ8caGJ{LB5+tQg3xvYcgBeTa{M?@+un`XrOYkzr)#$V_ufZEnPQ6 z5pZdhZLfi_ItAkKNx+q~+-A|X9JUd}8B1I&;$Ro?q=JV=Huy1Y1G*n*?OsFi!>^Db<| z!`hmTN997um~WRs!x>@KWIzPdGmp#3#RbqFIZ7Rol9FmXd)7u7l6J16%|GHpn;N|K zzVPI=DCC|SG-G?LMn2xwGr^8E;bNXWeL^ba&{;j4OVqj3MW4FQylt^!OHSzPlh)*^ z+$QuBKbsYE|EA|^$F~IqG`&5zWbjr^?hWxO!8*U1s2kpkh4;|JSiSkvfq;NU+%9<-dQRlJt&?E?7lD^Bmi`rZf8 zmw`JiN{PX3CW4DIj({?*_};m+y}s_W3$?R^7754#GI)U3=`&JSCu)hUfNO9RbBG~A zHkFIb{*q_?b?5xw%@v;{x<%LoqK9%lRTX)X)3sKcM`oZ-@3}x6X8vY8l&HcT#$%*L ziZ6dA@a^=s4Z_&8jn#)`PSDDUo{7;lhw(^nUL14#>2?M%&y$-msoPbX`kj^GXPV03aNEMKB6a- zj!ISTOdpPN504^YikwBm8qOg#6@jU*A;sck_bv}N4(6X`J+v8yWdg=BM16uuGw)8e`UZ|Ai6+w~rLQ)uR1G&vmb9A(0V2TjDJr(13p9{qO zPz>?7>oBt~dbIn(WXBiLip6(YEAq=Q9;PvX>PCZS!dBku&;v6}9xpEGc{2vFCZ-(bcySx!d4+*~uULPiV3{UzIY zXsBk;($48H(scx5-(D@#Zg0tP0y42m%rbt3|HbT(bM$YS$jo)85EBqyiMi?;)q`B=QDkR)(_3l4A<9qjM70SyU#l`)#b zdjXCW{EX)T)l)wab1#YNAx-D5ggnBrGejW>Yt+%@(Ta$t=`u>=5H&^$1o3YgkeJVs1R3>6*y{P}Y~`wRS~tn6&fQRI~#&<~Ph zbHEyml$gD))+u?TymI@mVDR6%)i>?3ifgphisf+&BO8`Q zHN*Q+*ONVEzDa3t?nSLnD9yY{8cVn~11lP*O;fH|E8lY%U$c)&E*1Ya?v+s>ET@FI zuE|h1TsSS^7vMiPZwPQIM)uR`c-FUJ5;BT*lE}ODQFV!LFRv`B=3R5JC{z0JHF`L! zFZ8y$AKL$pbR;H;0`9z1Q$ZnAA(7A4DPrJNWSlbEBd)$?)@`-pN+$^sQIUa39Pz)S zRu_C{87HvQSM?HMW)#j!N*~A=UR15_yPLCxE)RSdl^#8jC93TeyT^y>B%*%2O;tHo z=_n}a7^Ku>40rKICx#AE_Gw=V2<`J!?Ch;bq=)lwNJ_dM6HYX>Qm|l0-d1}Y_%-Hs zf4amC3~l9}!#cK)RSvhM>)t}>s#K{JFlENjt74PxJ--(KFVv0TNwm^~)o7P=uhP>E zA6(g-8)*bWY*DTok1p8|HdvZmO`5vvBbr1&1Ca5XnVIR-dR6}zHwQF9+Y>bi#pIVB z92|^PIv12$48N-GETYUmN24Qfe|xeVNm##>!leC2H&XC@HfLi3-AWd1yc>5Fs2}KP zbs4VjbY{69XNZ_N?(&h43UFl%G~4gYf!@!k!A6>oHB?(ABU9?yMgRc`pKMLNUA!%b zMro8tmzyxa4w~#DK+cCt0+^B5I|WHzM;JB95_S>)du^S|M`@hn<+Q*L1S+dmzjQg=B?8Q>eRA{2T^qL{N~m#ntYO-;(oR>P2A#Je;rWyrrH_tV4y}s zSuJN7L%(vwFekaWwNtjrS_-#9Nm=T^;YV)Wx~d5&1u1@s6r)p}u2}F;6bWK|Don)u z?DRyyZo19Pc{op<6>d4I;pOEu*a7r7u~#l()@F@>p&J^H3gXnuDl}idOp9_wcLwXC zjPYkGF4=RGPhWRj9#WnarfO7IQ~Q!3-BFVQ+QZa%y1Os6hSA3y*cm2UlDC$c^-E=y zxuOP4N)yCTs}SRkXime`4w|LpR{hp<-NZ_*1qB_l;!N_4tpd>yFZ2ke-x=|hdfj;TDtWwxF^rqv!z zWiIZy-J<1^g#<&*r^jO8&|5)IxxFyKEN}HrSaH!6PA@z<1#{vy2zM>pi2|@8qrO?uvd+hFN>hBC1a|?C{jX6WWqDT+m#@-A z9uN@{Dx+cgj_lgb9ZuTb_qiqa0ZXqTEijYyk!B;%bPWGHCO%o{VmiT!D1V%`| zLvyvLQ-m>Wg&be){&#Ry|8$A$^z#}yB_y2|1HlgC8=|JQ4~i z8TIPNQ=Y>NKv?{H{AW{x+b0~G!@8!ZwhT68o*Y~+G7jqJE?hz#Ho522JTv^#=2OJD z7uz;hGZ1s*Uer+vxtsY!l8%y!F!3UnTs{ojdJn^&2#h>Q@OPiQ#ivNLvv6K=8ODWb1Lw)rWj`&qtlCKE=^G z+tg0(dMy^M1KeAA03{tKS7!3UPUBUPH^^imb<*E^#EWMkj!Q)20~-A4RxDl5D`z{N zM7W;p)tgj64mVUZp>3qduG(C?s+^n^%fW|2QcQ|E_i*Liru_3X$x3 z@)#?aRg2Nb+OPk69VIu@MUE+CYel+h!hWtjZw)LIEv&N;d9NiH9aZg4wex;KIFIJY0@&lbY%F_$8~v zBKn=XOqik)4vRs#1_+lqkVW^%obiGi`4t-I)}{AXAsyU$R=0V71a!sM-hA*L!(Jf;y6O^Eysqd%8GgZRZmGD=BBmEjIH2bzGvN}}Su8JpRD!Ssi z57>osX}_5T&JXA+>!&Xn>jKzeKfsM6?FiIB{>yJZRtvVB1J2`b9+w>PB z{aF&k8Y`XGqT>ilzXSF9yd9~pJoPaNzKEmUl?cj23w#1JBf}oRg+}3XdGr|LYU)mj5V?eeSg;J?ShzHa2}SK{)~ z{_NY+-SbB!-j;&c+gq%HT(I)&FZT&sAdA=oDFxJ`F}a^j7p(h1bxe&$zra;xJsIr@ ztA>2(8KQe^BI82V&@F-n(CAZc*Jdi(c^l({4UXp!yQ;Gt`|S~|u^3|ZO85M?5gKpN z6UL^vvO&jJu6|jR1$nUHk6S{1HjqW-@@_I=h2ex_5ci?N=(3TlA6<3XCY4F*8q>H2 zaOZ69&vP?5+D&en_+S201msKnaAF*!0<`LGFMJOf*?OAaAX~d7hn*%$Xhu!2xKaISoye@__^3rjJEyQO>IvGd#lhVTYxqA6~Rm(-Oyy*9!9nuS`lB zt$4vnKe4x@;&2h7D1tf#XLui0qUzXzY`-=82^HPVj6#uGgQ-SqmGA8zbs|6-jc+@mosOSqsM1mC>ev?0#g|Bh zNr$5RGH-wml5Fl{ZfFn2UH@0ypeGn8ukO{JS>{z(vg0gSJ<$Z69&Y+VlSx0lW?ZV~ zs8R1eqzueshpQ?@&e8~_s&Cle1e#pz6%CjcW}ms{`RW(r>ya9l*yq&qUmV zP3t>tsLzmH0^5^D{%T~Lw2!SqcdZYe+tX3a@aOBf9;ga>=R&$UR3R!?k9Moty?@-`T% z&vn7w|7^YR?I+xqegiNIsHv<6_7!20?BN>`pjB$bUH$0EqY{a)@5&I{IQfRwISR}BgoT*y#5y=7M~$1 z|MHW3e`9NIPD0C)`j)yyhL)`KGVfw%ZEmzvh_T-wW3CEeyj{H3}HKuxcJ^l~mvd7m}IozmWIpb`b(6-mhGK z$fjEs!IhIG#K*ljgKhUNzZEu2mTs$9m1i4Pozp3Fd8W@iH`-UhA2$iVqm_V9*iFmc zyzqGwLNm&`3h~Elr^0ZL$@wk9OE{IOL!MzKX+u)mmW|lSSX=NBDU?a8 z9TDs!!_3;X!?b*vB*@`J=5XQ}c1NlCVT`}d*5uJ3{yo7YVJ=7*a-rr<9;BG3#x<`a z96ac5;CZ;M9K){v1VSZQ4&)?Mk6!o;78r7HA%?0#X$7+YTE4hpPh_hjBc&a()oT$< za+azvIM5pMytG(^p#OEoVaNDMCfYHdl-AC)D5_6oP{;HAIL*qTK*87Cw)eLddQQk$ zAeAdr5X4L0!xf4+saI)gPUnUhZ6b^L{2B>jL57akE>Cd# z5n{FyyJ|%Q6Qz3cHw2bCTAxT`6EjDIZiC-BEa_Qtk^B)8_N?RH#i#N`$PNU;1-U(+ zeJv`rQVIO@$susVbpCZxT%SE_$t(NlS5^drp&=g!F9sM=DxWemYag~6j%$W0SMFLD zM4XOMTf-iwJA)C2JHdm>)|wYgNU&5A{c|0)J+p(lj*@$jB@YovNs#HNYaR=y#-2wIjhc}U8h~109PXP{f;j?4=%HOk1uc~c|$>kAyR<$VYA$uujz@E}r zn2?ha5PbFXWi?seq3Ks@e@{x=2XOPEs@j+UB(fdOre~*;{*h0>Z zNFfgwMd;54HEzO-XY9RHt20m(YZ=R4?(G$=jnb;Bd!YHv7YBlRmTM2?2J@+3MeZ)J zyTWh7=B>!zF4%3KmQd>0<~8w{p*U3Pvkw%C43QT{lFNGO`g@2?Z07gIy}j#8&F~{; z=jJ+)NaUC}5KOGty=DUrHDxVFOQQtq_&7KehlhtLruk;MoGxEX5JLb=2oy&2JB4qi zg$PG%4@9GHawLYjj_^K3Uh=YmB*|>4*nZDfFh@_Wq z!&+SC0`L12CkWXk>1S)VUn_2nzwK9$xcu+=(%G+P^LgRjcyM z=qv6k+1(M}Tse`G#G`zB=`OjuzAo)it5l|Orujq5+5H`SJ~zkIC*dr zmW#&ZWE3wB)u3Wh>8YfbD^pFI#RjMEFZ(Es@LW#m~Dz_0mej zo$N{cpJqNh$`5dg|6exn|NiKk#UVPDo^=+?f9DVH$PK=jo!&~S_AOWfdi<_q5oLJ+9NK5^lkPcc1Ma4 zoA6$9eu&Rn&~fpL9F9P)+WS*Z|1N*cM+c zxTQ26W9G-IE6v+O^U?>Y>cl-5m6l(qp)p$WZlhxJQZpn#1i?nloyTRbH5GwccM=9Mv)Gd4YSRpSY zQ!#4ITVo!{8odasG4^g56Jh9CwEr#=$HFa(@*l6>>6No91w({ImW+|ZO2k5LPO4}P z6Q5UDLy)ln2?0SkB`ysWl{p8^?b|s&JaPpRg!8aVbgZrIa5$m*jSzX~1wH$-WV9rO z{+CkAQ8$ew`(Y%qV1&QZN4K93;8i86b9B}#wTL6fP|?z^z|6zfRz)%KJY{!QSwfP% zMT><+Qe`}aYVV$q^Y`qOt6JcW#TypXr@;y~kfybnB}Y>?S5H6ooQzd0gR8SZU8VW( zHS^uU*PE49fzL;>sh|Hly453rivgzFNrW~&2C5qveX~=%DzZ#QuDwyiK|a}fo-x9a zAUW}rh|gUDjEE`wL{cU(7Ah0CI!Ts;&a7p0Jz9wRd_zN{V;)8)=EB4o!hpSbV%R`* z(zhS6x2l{V>}cF^h2EiDUlVn3>~W%|^&;TZR{drU%LO#H^$L}c!+f}`tZaTBz}+*y zzcHop2u2w>tOYwc+S3(7NJ>iXMt3n_Qd1d@YJ`bVcoTzj@f7B($({vV(FUpGtu?7) z-}=cH#1bo-$q82ad)uPHR$p6fnn{QUrlW`*R;<(EGhLN8EzZ=ma^8*`>$K+Z2id3f zMOSCCuG!%2iJUHTK)SzJb~T;D7?}|d0V7jN!oDs1sg1rS4=1i@K2M3(DFhipnVKSv z>$qh+8n};ZcHL&+Z#Szu-)wzoBsn^GThZa3cB_g@IZgLEXbb%|a;q7qOLviIIl)U# zEhI;*JIHaO^$Iy1N2Ir$^KI)8arS2!O9$Aza+tX0-HyjH;OjNH8+ zFrGs$#{|**uIo18T&wm%Ebsc+Hg9I4fbmYyieWVEXg*)5L;5P^UJWYx;6S!=e0}aS zw{du@H(x%T$G^oG0B>6u(4WvXr){r-D}k>pObE!mv1X>dZio0O%cI( zDQ z4Z4f8yL2oK1~n4ER#2$^Nd*rF8lI9cYF^xBX)sEoySpaKbW&MTTcLB1ZS0q;l&VY< z_1k^E2H#5_A}RLWbH~1yaQrssHilQWUA@Or_QsFii#v=iRdq{X1&%bZHvJ(lN8vR} ztN(nu`Qx{qiVO`44sNP+JJr>N_x|If4}*Oun+E$s0S4U_Y$dujpS7S$1t70G1rvEb zubWXZwuHX2EG%kuez?CS;aM)6E0Vt%n8C7-_7drQ7gXffooauKKJzepUz?cxDQk;U zYQJE#q6~ezxQ`$Q6ejVyYNG`)NalgeKf)}V4YGM^ZGMuKs}jrLVl=$ZWrGkT<$45S zV}q)SC)7imo161;a|uVifl1y}!X5}<7K4+9pIz4uEx(-vFxgTAV6q(o#fvqY^ni_w zjE0W%mCdL^X!upKZ_I=7`4TvmVu9?bTWC)YIm49Vkq%G^>XZ!(3fULbi`69XTJP?G zy^u8hG^0;C%R6nMjTNS=-||J&HS{1Uu1@^=V{!@D{c?hu8(GUv-(1|?Nflo>?={Q5 zk4wz!Cwp{h=PC`~1pW6Ba6uI1Hu6t~#=i{|O*MSZ5_US>xPI~|^$6kxzo}_bmD=_n z*HPJ4iYq`w(C3Q!ief(5QoM0_s`M3#8&SOY-TCllw1-S0b#xC^}de#Oe^V6P2bvdEku0&>@ew1kylcp2Ji*Q%crHJ z9qmtr8P{xXZ3$U+?V8+rwUrni-XkfvPTD^-^nh!97ck(-#oEHIn??}(*o(foxw*13 zGG=gjP3&zhljoWwsMjrzizng>fH7;#?Cdb{rA4K$Rlw<(H@Q<=Ov~SX`=7rM{d$4Y z_AThae@gdz#PQlbjEd7=>3BMEg=heccJ~7~EinNB0jk|Ja5!3?w>(z-MCXD}ZSL$(mrxio7u^BRK^Hh25&_H*Qp#Yqk7O z9<-r!^gv(_V=uXGIdK4#E&R`0{+A!;4S`;i-GaF;t6!<$VYsM`6095wWC?~T9-AMj zsHuTzRMW$`T_&nVMqe;iWs6Hoqj2})YJiZe0i4ve2{6b)!OxzF)b(bTXX zAJz?TVik%8`wc$P!PB`ndoQg@lW!cj|H3)z&uPaMh2pSw+d6ZdA-;%u>J!WcB&rRQxLornL=7}j^de3rCs9c*X zh6RF6R%Y3?9@Lpy3R#Oyz(UNlp8ToIJ*s%Zhx7M<^?vsdd8Lk4*u&)+EycfViT`+s zo*kU7#)p+&_)^RFaWZvMDp3^gSZDOyD;Z-8OHUi2eqBrO@>GoB6o&+(d3%E{x4lDE zK|3t2Y&>moya08|l3O&PJwr)3eCzr1ZRfOKA8vDq1L%2AE4?=jrxK9E1OE8wBI4hp ziXTzLD%m^}KrX;?+)7IOm?T63dXFP1O&Rud=5&=~WYa41bMStTnV}x6IVSrVN*ZoC zchQh>=w-Z@KFJsl75}!p1mqmz6y;uwDZO1&s8X4%lhMv<-S7A9Tg~Xl_%mQ=$w8xB z^2As=n_|v`B~s%6+`ZD`A;zeTg{IGWcDj!_CR8D##4b#AKupLcN>$q-x>Cqij zH%>dXIlJ!ZjHQDx&RcAIiw4Fcw2;%nIh1j}AY71nrHe&nX>>)E!<8){EGK)#5`!cHM!!g5DKewDNA;NG$3FDavZCg zv&YNfh2_80WIj<;M&=_G71dJ#02EUD_)#o{l(@X(X9@8yh5Ij84scN;&8tKAT8o>_ zwIp6ZTx+$+uD@H-wui=z4DwmnjumepKYT!F6i?<2#};;p$4>?kH)B@XAb=+M{^*=ts*`r=-&*_q#13x>+V>Y%F8M5U|TH}Kp;w-<_yXG(Sk z5hY7dm2rLMg9f|`%Tv>+Cyxw1F7Q{(z;q+YUSnZBtH+8^VkG}bVBG0O;?cO;$4cgD z;lJ!vS_&IJ8asicJG4{ZzjME8`VcO_em=YDj)&8mixpH1qw-XF$NL)GiR##%phuf^ zanKDUrBI_R!NDK4O)5f4*m^2V$CPAp#qS?jzv(KQeb-sOIT}Xo`dU1!A*5ErDubMn zRsL}Bt=qlvAAXTP_%WT{^?1oW))ZYxMw)nu=L~x<>}`z&;!|$CaJe=_Prbm-OiZ0V57>|B|vH9F~n*;$F796_MfULSw(_Gv}4 zY)MlDli7wMuX*H&O4m^{9Z~>uRJx$Fk`#Xv%m&eLPj(%wQ2Jg*hhi1cpFs9e3z~dh zJ?^3w@NRnA<^CclTfgy}rsEg4IF;+i*pY{bz`fiCbc66FP9~4F{@ku<2dbP>whVq{ z^%+gV9iS*PZnA2%NQFNtV?WiFlgZe8%N;K{zRwS*Ji9xx`PxA6-JjoI$BYX&WLX`L zbs1Sj#TcxW=l;UYw>H%OydGz1% zZ1qMBsAcFF+Ua;3E4`fkvUMVc5JSZmPLG~;WCc(-w58M!8e+{S-@YY1t1juh;yuj5 zCnxvhV(Bbgi2uIFg%zJ6QO`&emrZF{ji@-ZLs3?3(W(f>_p9sDLA35l1GJ#v>cXeU z$S=USjq|GLL?^*^hbU%AIL6V$!LzujJ0&+?gL9xI%hXKl3$GjMO1hAe#^~Zmvi2d^ zlGs_qI3O`f;S4OM0}E(pL z9AkbJHjBFW0t2vt@r^9gCeNP-ONCI8tF)faZY1*idQpDR?GJV7WB0^GSq9&thWrM< z6-xz?*<#;vw#x57RdpKaZ_bdrZ5~*%JcOu0;S>qIoo)y2iFp$mmPsf^v5&UcsNBJ< z$ViuQ39Bgn{0T#_RrfEKY)P01OyD*)HohYdz;@gwowp?={SRdQ!lU*6J$(aiBkD}} zLzJrx%b~W#R;R^0Ue+pYJqOg9X5T`#xcRx%zkS@$sW`qmqCD_%Lc=+Z22_(a^*QPl zn2mwY=&)N~>tbWll03(sF;eO{5img|4}s7EvIY8hSvk3WAUr&T5>x-%Iq(maO=Thg z;+Y%CPZPi&P>JN$In!`y$F+tYA3ccfDn|`On?F~!oK;g=@cR15#OGSqw;-Lp#0(3Q z3IozVU*LjoD3Ql{{pCE!mUoD6_7RXjhgj1*TbNfWInNth@MZr&nG+;d2}VSIBQ80% zw$5o#PA@a`G+6Q~)FWnWdG}ni+A6mN8w+uoYY7R|v}hy1IzP5^$n&<*y(w1ib>&yL zA2kj+Ir-sqgyK^j32AB8d-v{r2_^pdqW`o<`2>G7iPP>WAgK~!)AMt=wwtXn5b5zJ zu+REPu&oipSNN=d=7isV@|C@y8cO^=!0fO6X&MQrbCn zbUgM-b#F2R6-W0_EZ|obL6U>3qcBWMY73iAqL<`~^ifnLI~wR5xb@J{TtPP0cID;b zh^jGCNheWF?KL-yRn;tt`{j$RP%)9aQIagST^dfs2Q&vq$6{5xFDXy!uoIs!t3K*g zLT@@Jq#}RiO;2#>J-0t8BtM}{C!?n?7#J8hw>4WP3MVUz^R|$Rz9oU*SK)O8*t4&k zCB406DFgX(wUj!;B$~|qCgv&<@4Tla(a@gcK$BrBEPQ0QRb+oO)}PP4qi!-B|CG4$ z`ehxlLK3?GIcG3Egg8KlHqmV(4m;?-x6IxfWF2+1BxPc{-{I@0dS5!fennOeZnuoD z^OsCsT70&POtd5G2PtBh+=j3l*$cgm2Rd6& z{!`(4e;H!U#g)G6LKPOVwF2FSo41xP zomnSE>kH*-E3Rcy`c!&gw^)v**Wy22FO^w}ddu`+RL5gRcm+Z<&OKL=S#etQ+&?o& z4z+yW8N56HXxL^3C)1kn|6fvQ$Lf0;#M zi0K64i zqXK|JSo8=lxb~alM4fLqDGkGQE&{ERt;xbPh1xNM5~k<5f3c{N!bG>pl0(IK;@)F4#qTltyf z%AsCq))jM0lv8GwX^B&@cD1f<$`;-vl{04n!jA!C@>K69fddH%2@5l`*~Q1Nnh5-C zbaRx>vjC#V>mQc2J6?iJr5V)GUGSobijtj=U3O0OPZJN2W}4;JsdrSj4{VK07jZNK z{_t47!i+ZS6Jo%E`!55Jlo1cOLYO;szJ2PJ47e+ywYbD6^5(FFf`j22(cwItlZB5f zc1bO=iI>aFSEBEZ6No#WK)ECiXZkN>vi=cKZ_D_GiJWQyU2b>WgFb%j%~8%cH`tK; zgA)}X`Ra>LR|5iYd^yJT5`qnV_3=QP~@j9^W=*e;* zkba>2QfIm4N<&C!sFkwfIltcmfEL|%w_ZB_3dE~5Ql;Dt$001^hY9iVYI=J401O>Y zoB8F-8(?SOaXe=@zIF`=Kb@a2Y5axtr~}uW^htqqvZrJ#bv3awAB_M2ueY%SCMC6` zq~yF|M(Q5HkQ-7(oB3z zwwy@V_^xwpMEX%KU_@&6Ni)S1xIlGWhuR{-L{`}Q9rQ^N`~dBs4iy4 zJIlnIj^1cpMz?5=^$Egx-)MOIya@ZXTz`MH{ql$AO}_6K{Phyu`E_GVnhV|rjxLNL z)Xs0SNSOz_{p^~r1n+Vfi7xsd=5N>{uCnAEJO1lHlHXD4RA(Y!-wLKsL!^G9*7GlM zKx@J<>9JFZ*F^zt8cv%=NmBB)Dvc|ji`N}jgw@hoJ`iLM(~hi03v<1Cadq@!$9YZ? zek4S<%MpCPJ_ew(E5S{4-y16=;ATZ-1>nbwxlN_m@r*bQ!->_S&nm`-b^^!7 zXOixNPgUgU9dx+*{(TzP57X`D^TYN|-1&<8XT){EckJQBpgn|MS^fCt$wZM_ZlPN3 zSud66(8Q?ffRes|X+X_kIbV{XQSA5cNOisE)?*yt^TaI|F3K_VvDo~QAfS8Q1Vau7 z4cbNg&I|Qb)oxoGdY-qrbnI(vGOVhcqNQ4+mO|Jnxm}(|chgE-vod|R@b;hbogTIE=^?-5lP4uB>HKx9cV;&N&2MWf9PKDi%Yuy`?^MmHPl~fwZsx_%m z;N8ZjzmkN%F2c`+|BuH@^#oGBQshUCzi4L3wH*FcrQ8gR>UiuEd1C9>+{te#%5EyD z3+s0_%$44K$5D^`hg<%7LTVU6tZT*p(1FyqIPSc#$nd+3e{uoz-4hhq*exi(gc}sg=0tQ{UB85t`~PxIl-R`LidAGA!sxsu0BmuLjWIj5EjY-M$xvBX zD;+TH1akQ~hS8J$1_)bSw)sa!nKZCS?cwUe4MO|q@fcP?*r3c7RRbyHz0MsQ3 zhm4GD#X^P)5Z}Zy5n{^D0qJe>WBz1W_yfRaLup&{Kz51ZSC>NvKhql)_?kM$b^i46 zzux!1TwxyqK@smIE{{FJYc8XnrNO~dnVay9#s zB*f-?e3pns=bGFoNd@_XO*`XeT3q(VmqUH2SHGky3(W-7?f{RjpAwQ{UaXoU|UCOTQfq zfCqMu_HI2S&=PZ<9e_qbjq%3I76FJ8hq~PIw+7( z`KY6-zeaN3vd+>D3LJA9e z+k`1*TqUKXcI!~ZzX6N>!RH{E#}LS1h`baNE|$-_3-1GE5@tVYZ5{MdWy8)~;A4~m z2CCO--K?-Iw5JVdZpIlsq*9@HL}r8CF#haqF$6O$eKOrY?5|%-*29GRL2vm+SmB!0 zT|oOV#x@sVl=&32v}_4ox2}IhJA;NOaXby{uGE0W`)A!piQ3^~d$X$vXA1L2cb9?D zE#1+g@{{&%twIJJG1$B%ig8GJO5wr^ZRJVFM=I{8ZvUfUg}!*gs>^s0QN62NWP_`~`~q(}KF+pluWIL1#8{f@$0pXl-*eM4cv>k}6*yh# z_Zi@x+Wfi*-B>ZxIjkOy;`iZ*{rc=LIR1ax_kYN!haTV`T?tdHj#(~uot4`fA!-1< z-A+$Wcl)NG#Rv#qs?H-UVQpvPg`R!lY%R3_7mp{f(7vjKO-bG#*=e|>2n*#|*x5A( z_~)qf+e@-LKXigZ0cg++h^dn~&;Cdj{|QP3?sm!w7@;%I-D>kW4W8#qki{+w1Pzc` z@8NasRqWK1d0pK;(JvMUbc7Now_*W|hV*XyK=PQIJ*8qogVEVc=H&GB5c~I;yTQgq zzYbpi>9Kr}9|*3x7o&B754n%2anpTjEBBc{y(y8A3yc5;DVXt(OU6ZuV9(qO8y1CEeqpj z?zfwO$4@;Yh+R8qYkP)V^8v;}YuD1}7rD;kG@_FO;f}~wY1vup@HkiffdZ7pW z>%0igp!?XN(2L|BWh}m{*B)`%#6(7x+;(gFK=z~J41RviY22*?j06Kv^OkDWv`SXGBJL2a>GcMvj1X5f!!fqi0pNUGk zvkzJfI9QgtH6N=PV;kJV>N=nvBfq`9{n~q=#wvtPNZ`CUJs_64TOMKu76fYMqy4%L zC4;e*^)Q*OTMt+_Eb=pg<{YSxM&2xU&H^xZLSHwYN(eb!&&wJKdOYYRT)nriV*)N6-p(> zUA(%uw+9=nABuhSwi&#A(9aDXGj6BpJE%iVNfsELJTnHsp$gjNfnOg{pI47IkV*=)q0q13W1z%Bb?WLnJYZT)TwSvRhKlAsJwdhYEQoN z-wGA~Wr*RGqX4BuIKCz+Q2Vz!G zhGz&J-7%rbwHR)b(CCv1r;xIA=M(3r#FE~Ixn~|0W$CS$FK|{07Cz+aN1?E#1d-tf z{^b+`vK^2tWy2o%vS#q~HG7G&G0aAKAnR|Vb@go2s^`hc0r8gH5mnQo<^#rj3gyvS z( zt|N!5cMOjtLMrBqb$|x;qVA2%XfXO2w*Z?j%X@$WCeyQ-KBo^gLCeBokek8j_$@e2 zmj1RtMMt3`-ddCk3%H--F^=uGV~U4H_(F)C9xU#=9{NCxUly|v+nkkpwpXSaj>Jd zPSB`tpn&zd)yc=``da$x;>HA5-p=Z}3v6=g>gsulxAQ&`4p!q!YBi^Rt_}Q?BjoSn zx8Fm`o`5>$yImvm!$@mXw8daPINI&zD@NEr7QO72RTW3D?C&*Rr(LW%#YQ@@M=SYx z>;v?tuRqpQS}6_zgRtw`zVq0|84Q)E^pAYVfU=Ectr@sL?Wh|w^Ff2=!8e393@k@W z^dd%XXRj}i$9rt$MT51=bXZqEN1vHwj+t%Z`IlNq%2NcwNHP^SICDQkuh^Xcm;!_x^J~K~spM2ZMCfL9!j>%^Z61Duije53`hrSkuGU3tNdKMUyEqNja-Mu^+I*9hR3(E=%)O0<)1>GN> z5_oxRy<=VaW~80}bh&)&;>aE{)@F%MQnPFn`uY0Wdu6`_M(qXnx2(*+id+6ss4LeP zt`yKddzmow{TStVuty2hiFJJS$5fguBxN9%Le65zcD)j!Q0^rT`!i5nB)(h4mL^!E z#T@G1pT?nJ5$9U$G#(+FT=ZK{T9k_CDT{uzb`H9O4)vRNDMEBgD2xr2Y1l=g_Kq2f zVAWHyiw#h7)Jc71TFiinl`u9jelVt=`}zBI^~2AXqgOGhO!#A2k~I&ZNy%Sp9j*{1 zgUS+JU>gQR)T&mvV~nAZaZ3U$?BN?D!WLh63Ce0+3KfAk#u>+0*ZIww(Uc))jW;rK z1)DBnIW*~vI4IRlt{d6p6V0ddqxgniob$*Z?=I);rrPZjO&keh|ZtG<-T&zEb3k zZo7I0GpjE|tcvSAUg-|kjy{=n^_&|qla&cppXn8H^OyliwP1-96QrDzYc{ql-Cj!; z*G+zB(#4%*KV6gFS>fm!c%%y@T^Y(xVLOc8q-vC659mZTn-Tm!#@;+0>bGqJZb3?@ zgb*q}WDnW*3T4mESSq_QV>fnDQP#@76Ov_Svdmb<5<+B`8H`=lF?Pm2@7H}l_wzjO z`@Hw>?jJrMd_LxTeXr}h&ht2r^Ej$^3GX1GrSNyzsc?M5`@AVBul^E3A}Ogw{K?YK z-eRxK7X{gE3CCq6K`Vjd?v}enRauG#lh%@|iM%nxV=5bkP~B6OM2-}Vp?n@EAU!?I z-gVIS@SxdrZLNtRLTMAllB?o9$A9>BB7mI*(z?!yvy(M7M{d~EDqSN1@?`*Z>5>@? zpe_vzsxK1XoBtUN@J#3~4MjT<)yOOaqJE@nfq+?IO!5T#Uke6pTws&n3wXmbS=Tvv zasgcBxZ%pQhtxItGFD@(De3>prW+mKQ1uj9nljtb>}1v6hR!G~nx-5-#$>APju+W8 z^gabuYL~DD6%nG-5|Of%F3z{~r9>eBX-@mYi=S!ekg#&Kll8VwdYsy19r{LwFTjvLv)Q|M|7TGkqWPBAU1B!)E2 z&uEUQ#4~srB93608l23me>qpMsZ6h4VqD?ZQ-zC$efaC1qJ)9Wtp39=|A*~9Ki%CL zH8ceb@s4BfqK%e^FLr9tvddM*$)F9m4|j_yo2xcWxn8a9UDRNAtamcses94z+PI*= z?wuAT9=ms4J0$^biy?`vD8)p7Q$39Qz241`?zo+r0^gJTd{HjeY(x1m&fI~js>b6F{is%fQdu8rNvVG%YI$OFV4#(R=~0(jLw%sK9VQE#KoUEzFQIkjG%p=WiU^rTMzXpgPdKi> zCNw_3-sP7?#xivW_{J5=lF8OZ?32{Ctufj+3+!l^HJ#oSmvSIf<6pIa;U8Y099!c6 z2gEghet>UD&?AazmtWg6b{UMEyoq83571}RUuxbl(L_J3g$){v-k;wZ9sITR3KV8M zp&6+|3}_svGENsx6)#Bi#(Sywl>4l0t36H(c9h4sv)qO#4WA?!D-rKCKYkSp^Kl}8 zSv+f_g;1>>LO*>+6vPO@=hS6#tFYI#S|S_s za~bRjNj146f!%MX!kDxB=3#X#L9gUiM|WKWg7QB~Z6%QWKd|WD^r+k2jOr8(`|c1& zL-OPtsPtscULCZ~_U#ZyLbff&gIZo2#LecNDyp$Y#WEndo)zPWIhQLK5gLu8N#B8!2P0BloS8lqBH+$0*Ma6Jl z_hh0u2gzdTS7Y@hZW}*9-KG+#Q*9@fh;W zF2mF*6GAL=RVN#`|9V-qJ!COgZ)c}^UsiS0wyI`sliN{P#P-hXnTgc#tk{(ir48y);(`Y0uV%=bp+CdV7buQr^RJ z^YAz?>Um}=kbm^{D$S}?Cq<^J4hI$#ppMv@om>8-jTd?%{H(Qp2NnSx4{>SxOGq+| z-jl8VY?Ru{3ry_jz=Jsn$vu2eonwCEP4qpF;w05JNAEnT;opl_xxmYm&+nLwq4>Kd zbBW~PV)ixW<=6d!eEsQ-tKSvs zT#b_?%5?^IY@e8}&@kcBFL~Pl;R%p;<3k!2I5V6WpHL>-_T5POp9uT%L3QZ+Y zuJ!GjBR=zc{n3uFWnze#O0^_cJkcat3*Dhs58{@~9t?-5X6+a;)geBP*Z`c3_r;gC z4S|5Sn;3OJ#G|&qry#654(u5Us|z!+9%3}}#IujF`jY%Uuat*ecNx7--K^b({nWaZ z2$jZM{@E_{vvb_PZ2ekyuQOL?<4@!7%BK;2fpP#*ZCQaGFEDW~U~k-y?^J|k4@B#7 zfB$}>;>zg4rXPLkKVj`d{_t<2ka(2locJg zGAx=Y{e1WGO<(|oRl(mY=Tk&-mqJE&%d{I{UM9(T#`6!d({0~SpYYeadqLa9lpM15 zj~a??R7yXHM(MKeIkSDvAVfPR#Y|lOf>*kCUROWN#Tm7*ggR(TK52a4g;t>*6%1Zq zWg-XRDYK3A+tE{Nnz;mul@_&d3T|&N+r!0?T1fCM0%ZC3ep(G#0pgvaGK=y^+t9Ut zx!7dMztQ@DPgEVoIs#VMsA+@Xr!JZwc{_D4cV~%^eCi!4z2is}Ka%R^Xe%XfiPFzQ zp`u}Ka4x?wm+|M@rdxGVU(t?-=^nZn#ESzv7rwPc4{7OML5%$T99@)+O9;C|JosKG zS-va@;P`E>Wdl_{EVrvBxOU3%(YGwb-Ei_NY4>1d{GGw8L<6JzAS2JjLAAc*s%%pB z>Z|mfEo&$pVlxTQU3oXM<=uX?l0?!mGbpxP+c#!QEe5ToAPucKF;0FqZ-N^p*tRY7 z_3qedOV7Q%e+oSGAin91Zxdzw=N#rIFJe#!OLvY}Xi__ms?rlGpBG$J1|gNkJ*z&2 zlv`A^v2q?cxjC=>3>l0g??v_pSDpkM_a?Bg2VcnR&2|~PSH8?Tt9X*Jp%T}tt5>=8 z+NW_{?s&7i?+kvPtJ*?1Xm2{AyCm_$hukk;cIQM~`}YM6Qx2#83;+0A#PUpNmO!7# zdH30#y{)eTPd}WPRWWmZ5T>VlzccBK>N8C);X+7sE-SBtw?5-fz+v;pWBo5;WoVcM z1cdqx$STw1yK8Th=kZK2$Bgq?(*BrlKUS2Vm75Rv9})k2 z_{t3~JHqRDuxNtX&tVZX%>6VYYiC=7LiSyjm5O_$eR{WJSyE}+yA9_{RQbh1e)DS@ zzVIU1PND9&6Lty6^lSIG%K18PxXi0mI?WR&aduG8AjuJV8ZXu@-&eiewW2mL*?=E7 z^}tTgHM=T-p`-ni?-o&-+!q8A#mmks)vNVEs`Cb%@`G2tPxE(4y$)>uszobtJ`&!_ zudy4-mqki)K&fj)>&;y(WORXOx1NShX`!>TEjM-=&SpHP(x_eJv5c=%?Y+@BMW?tc zN6b8a-(}U7kN;(T_jfOE@%>9i2VAaBec8rE ziT&iD{L#xyE*zhi8^>uWlNH}Hn%xyjNg8U9sVjS*h5mr1)R48?luVAT-4$SlI!1J@d{DcEkix(R?M}8w&MED<7irMrg`TEz%@}QG zv0&}&rebgjMN9nt^}obb-h6ltd$pmTuieoi=GF8@rq z0r^5-V(z1;zV;7Hhxmld5=$m4j!1VT{NAR7<6s}F6Z4Swv~eAM={tR5YR(ZCSD&On zkX}mBENUGiwUm0uDYcroq^pMBieR$iCq@QT`g|8KKb~4c;kNi*D_>cyOb^KqXt+0_ zWPFs9o7JEprsO*<_eq`55V6|dc^F(&uhJNq*RD0V#~@dUzaj4b>sIUi1VUn=wP^WT zo3Cuv7x~=yo_OrtEKe`5*WuBClN9{^@7u6?X3?^VljqQ<|3e!2yAB=oYsN*FBdg&i ze-FEl(a5hPcKoEmCp!b(%&S}92xJFunR-tM+&QMpgMT4%2Xr+}w$ z9&ISs%j$#oXeNp<9iEu`4JRY86~E{4Muxv$2JGyTC)gz4q9yOD^-VUJJZAYoL^^xb z=V^%F3eWclju$t+ca7>`Rws^K!lN#{O&_);R&++WZ@CIu{*JY$i zu8|7-oGO7?f0;YV3oipoBrOXYnO;m|8!vXdz9g}Qk}>mi#b`4GmOVT1JNFP}zWLW6 zWCyxPd-$UxO-5qflhdDc*@ES2+q6aRw=S(yhS+_E8^!n#Qa;qxm@SY+$tO1}(BrvV zVqwO8pQPYzjG2dVre6I9OwBSVb0$8Vewvj+-Br$IF;*6=)adoNSBA zWWZT|A=dC7u|@W1xYP&bUtJ#!|J@%Vj}X!hMXJSu^WY&5Dz31xdL7QzvDbQTM!)B2 zVpb=Y@!4ke#)_AZ`^TCyHf+>3E*%+3y`gR1x$o%k%i5rYX0&qa3r~0X{5t)b2!+G# zT@!ujpwQ4I4e@Kci8s#H6w4Khab1%VcR`zR3G*rGg3;Ig=CvC3{KtJ%7s{)tnAnlF zTwE`zxh#AvLoBL3T&+U&!C-o}F6(jDo7Jhc-M%t$a)z5$A2jHazI^$0$YE^rMw0AT z|8cEGHCMDr?d9sH8jbs6$KmwPQG9cCSmSHoZs67fqx+AF>gp`CyA{uPuhuNEh8U~z z`wv&zt5m~j9|yr`u0!_T3D<0?9@{N2S;qHwa*9EOn#~ZOK60rk2F>cu?|ta(TKv{H ziDyj?_>(?>mtIc&?OgVo=t7A&qt3mnA4RT^ez$R(AEQ`mNt@VLk4Z<6H^x$FA7UqL zHs+YkTbUNxYAqn$)e3jv1)bs&cXiKB{wg8zL9V)OhVvNT5-|{H%SR==VL^HkVk};PNRUyrp1V9yh z4G;~h)h2w;gCj3xAE0>SU86+5ES@{dbp!k3k;Uda8N&jfdZOMuckc8T>|d&fnx&s+ z7(nlGcB&k8n~H+gjGZ5(Wbce);59B9ni7*VI{V^7*aXK(cPEV~S8<)f5}zC;2xhu=!fmS|hIvB%T-Vr#a=H6CMhmRLcFxnwNPsI-pWRL*iiXd~mlF zUy;VzBl$Sd85X56lw`r@&)FM(sQ?+_%A)7Frw=6vG*U~zK$VAVy`LvsfsRG&(bCcp z0wwR?H4HhnmEBlQ$keX2!n`*dAy*lqeH88N0}m`oHFV{+p}y z-^c4;CvcSMt;?a$WR2V`K|q4jAHY+B%^pv!AL|O!b>_2_lxm9X(z{UI34vvn!9rrL_L!m7OByoZYYkWKqf?ip zen0;8=@7bwtIiZi2%2?f^q=(z+ChzOZC}!EP{2*bJ}{vf_8KX@NzRGIWe@nMZ*kfd zE$u<;&76k-(uAZ|PIX8En#P9LddgMrV7Y>S$J&o(^_2)ffJcgnS=wAkpYzqe>7d$D zy?wBSj}b)Ws&|wiJD2U{Lv3_pJRsrB-9~NXch!o-TwLqXobqZF;6WBJIZXlx2 zfNGWZNM3w5iR`b~cOVPY%vO{iuW2s?q@#w&(dR%ypfAe`+@0K1Uak zr+NiyVUYnSS6m&4jhYbBO9+VIrIxzm@rh%Pa~6MV6T6JPDC)s{q;Dc}l(%t}`;VI6 zJ*CQx7rz_LT!tTRnLO|^3Gz;&qOUcaJc=_iKQ0+EcgH1P&(6pdQFzZhuo@>qzU6|& zO{rVVKO?jGMJk&nyw`Clcr0 znA|0m@$N)q3mXM4+rm4QmN>yvAJtJS9s>9JL7Coj+9!whw@Jb_Yv)CVnl-yjKI#L< zBQhp8mqvRP=JnV8q741{AoZVl=U9R)MaBv{_<&Kzz(ixAM%`qd%h&=bsm=vTNhV|!+?SAsJxN_P=3qv`nZgrKlugVc*ng`ndjgpRMxnJVeW z&?#&XEQqqEx08Pseah@zo-Xt`ROj&D;4W`=9W3nKP9BfNZM&`7C*A;i85ubqR)k=~ zt4i-#@Hre%^xoLW6SHj{t)+(=uU6oVmI(%5g>*47g4GjT#$}=t`fZGZ$JP?7!jlCN z*mGrM2PU}05qF1T_I#JSTP$EUyCBVi%6`3H1d~N6w4cb zg6qPE800kPj`EcWQWO@w>-?#7r^npr958VKNX5GSNCULN;0$e4fl15+OgVQX4gFTvH8tcDymj%^*hoC$M56 z)7P1Co$+$|${z#L1DVPrF z5-rVJePhA3hqjX(e*2|lX9XjWIAk$KZ7QA1QxY&-0UPIQt37`f5dchI4R{Ns)0^() z>>fFXrXi&zx1#(HbwLlshSCTVA16I7m$cP2&W`7$x%=tSS%V^1z3V>Bwm5{_PIVuv ze*3lDTO&v5l2)@PIfOW_xFo+Z2u)nMA7%jyPZf6$W3pbL^`P~v>MmB6kFl&^7SS7V z-87tV^ePgNI;Tc1g?9(gr+!Me&!=*Ru$X&6VzKf>+PmJ#%#JObH0MY`1-Z=D$kd_+ zsdgC|^LyUek}KQxNTEq`TGlk^P7jhUw=IiEHVbT8e%fB#E< zetzpjkKCI42oc+O*L-Z1f$>zNM^kfa%?^;vI`ODtj?Nk8q{CQ%y&9l3YW{zTSeqO> zW3u)%K07QQIfRthMTO)a@vKeOFf?wy#Gd?4@2pge8gL0!Wefh&8%(S+-`P|N8X_-o zb`)D5_O{gCe#j4T)*D+v)sEghq@R5#Y%(tUn7}DhoM(RvD&V%}Sy_Ff2Ikb7q@tw7UXDy)kRwdUy9TBk@5Z8~2*?I6d=g$bF}d zR|miaOdyLP{BjU$I#|?Cqo>=ENg}DgUD+(&*7%oUk0ou9f0LtFipZtMZ|SJ zefmklgm?e2jWT>2_f*<4)V8~R7TVn?{a!`V_Irx2oi$n+HYsY-q|_=2np)`Q7}~rb z%#SUu=$QX0O<=)g6}6JaKJzU#B3})k@4{X?kAtz*fgH z$4*LX!vRmbbkpm31*OlG^ecBSNL)0yUmA$Y4GJqI8q-uf|FkgDT`xU8DdO9=mz!$7 zK2(qX-M<1*x0NyzS_Mqz9h@~DsDbwIZXbV!2XMFJVexjay&^y>mV$fK2bY#+`{qewj- zj%>h|N}MotC>)e^a@616UZdr@>$|{}82MHE*ALH!7|%bcp_E}XkEAnu(1T?@q)R>X zd#mO~#)iwj-Z5p7(yiQ&%QoI=XgOx-qUY{_qp}P=dam`xJjS1^H`@3jg@-^2Vw2dP z#;|boJ&R5g)8czq$bp=@4EbefvfS4{+d$>cnlR8HaUsqW~cgw{*B;FR=#|Lc#(iKKS}X%2}w!+Fc*N8W(Wcah@Zcy{`Dn@G1epX$&`!Q z%c!7dpFUpjwvm-i9;8O^Qgsd6L}xcPZ9G4(@yf(8$bfaC(e*wV5{p)gN8Yx^XhR_^}I6CSm7LizLLr4MRV#&hg_CWZ7n z6CQP6JljR|^NFmGvGkIk3PZvQp5&WCCzcJXe%misv*ip`bp;0<8aLho5F6B0LVpZ| z!`(3R_6#!f?mi3mFUbr!$!S(H?*8Si)Q$UoDCtBXiO|<|Os*-!|n>tCp|IWSuIO)|3p3$IN^{(+qOBQapqmk$+3Qay7Vs22+J_ ziGPtE^7a0`*d@=-(g?+Fp3 z@yAM?999K{xmgM)ue4POSp>Jh2i8`Np4RL`Xc}_Hg@-#X^qzcYc+JLgs12`AzcnU& z%Z~crr{e!AfkWvoZLcYh(Y@&ZC-?0%XP?@p1X|0#eJ~0mt8mYtpr9z(tt^%w&h{w)ukwVs);la@pbgRJD}WRb@eS<|B9y! zZ!D7^`eWm!Y$m3BACaGI-JL9OcNX%KiHPJ2Ay#$ncZw*mW9v_{JAXI2jK-;J{=ugf zW|$%)R>d@^{)fWmDE!R+(ed`6t{v5+v584QkG&g2olfA{j$shMzEl`-V7PYezT1>C zARH%&$H#j1$fyIeryCSYKJBC3T6M23(C^X*0V9aQ89&ooPl}Oac|~f)C+1{9`&?tx z6U%s79E!}B**F!E)o;=msE4y1erP}QM}%5&K)-f1^`m{(Ak$Z|iOgN)%K%_=V0j$?m|g5g3|BhE-=vEmA8cQmSZUxJbRAN_#jSkPYbgCjd{^jQ z_eCaoe2L{SwC;SZL_6CNKD5Iz62NHw$xS8O802U+Wv~oB`+Dy1xygV}W?<*q5V{^=uGeSEh!dji=ydt3RfF zFb~gpWdevQp)}PGU0W-=qDPLa$Aluw5oDzk`)Fb`t>mgCbiDe5`vOn?dX&BvL!|yC z+Mj}-1QVmX_CB1%aBhgVl=g9^CGJE$4zV}pf4*F&PNPbD6L5QwviaU>0Fn;Lwo#xp$oW5rJ`h39a6O7VvA*B+Jid=!|-1 zE4>AY%M&yeb)8Q7cES&lgD)+&yh*PI&A%=(-wrG|AV9=X%U*9h1BcS-`4@Tvygnx( z$foaRcmqK{8$6wUc(44?JX7E~3isY2Y;r!|V7g+RJ7RE25LXi_f7nS_Ts#PbxjTHA zmbto^a&ET*+I>8-mZIBXaJw(~R4btpu=C+P!?wyN_)He)hwrRSTAvE%_Ts#NI_WO?S8NQ+JK{cgH%E6vrTOS~p zevDVSW_x&gzWV%v(E|sV7SPO^Pln$QfJy}h0CAl)d9=~w-Me=>LzW;=!2Vn^c>8ok z^r0{zkNhNd#V>Zxc{$)wNQ@>Wu8R~Acg)_YnLq5S8gTNHYop?1ixNg@nd#e4>#wjz z!!IQK^hv(%Mj2D#aY*qhBfNy;hkny}mE^KN;fgAkT=G4E`Ihv&O6SrOIZLx;`gIQA zMMb`Q=bq$m`55RPIKs~H>SkR&Dy>p`j~ovbeNJJn_wC#lKhRdU`9FgX5|!NLSLklN7C3DP|H;`6THNX8g~F2W?~8A3B{eTW|6GR{eCF@=R!yTNGUAr2`&Hjl-gcO|*Hrxg`z=`Et}O zaiPi)*S-SF=~IF9JbiIwp5f6n%&H!JZ_BN|Hp-QS!LX7EY@RxO0nqs0??eFN+6iy0 zg#_!Kw@?9ErGKs8VCsvBiZTcc44g{Ae8|q`{1ObZvRdV!D-GCLX8aNi9`XePS|m}& zczYs{`{^|XA3GB|-T(8Y3#0~)yuh)nLYSm%V{hb*loZgoEBlo`M_(LQ=ikuYDz1xpEKRei$x& zdu(@`!=+I!@MxA32iP)XUaDCYu$K1SU{`P(`*7o@4#9SMlofRZK-SClpf%27(z$ zbzquVGwI$iG}9>HI3M)Z7P__^Kvg=|jhnbGqrlZ})6er%FU(5nuN`$x+@G!hsGBklS?J`fv%4xIt3LLm>Nn^f81;NR`a z|K6!NK4-(KQFyn{G1S%1Eof%Uq}ynJk8Hc(X=g@dq~vWD>hNgYhi5|3yjKpkR!+1) zfc8Ebbu2fv%MJKZS*hFq@}uzIU18b&dMauT_;F@t_vbJW9UTCxG?j|e=nqKOcpdGz zFZi07rS7hd69kza>1b(rh1KLTsl57C7N4FjfKd+unVY8pHsRjOzlsj525gV1=xz?@ z)JTrRuu%W^XZ;t<|9>AW)aP^$u-BVr`V~(%SNn9)&YSOmKr1ErP5|p&ky6mmRSAbS zUBuE()gJB|qP2l1!ZN&#jWrGotc#3{%y=^Fe|)f0{`CvxA|8YXjFN!Ku$w79Kwtq> zCYDw>75okT{QP8n*KF=lXet}OKPn8aktMZ&&h$2;nR+~5Rrz!^2S9f~ zuGBvc`bOiFAxP$?pSxSESwFSvkw^>?9tdt9n_WGySZ$F2p4aOfP~zbZ*BUu}*HD*` zZ{EDoxlsjNGTN{_`}Ug|x# zqReLE7@K(;M$bD%J6Vg&-Pz)xhh+wI1;hk&VNvX)-RGEN>emffxrfAsCn~M zMf-~uYdsfq0OcY|4jl&HD#G2P(2cooq8g#q+xM zi7*FRHO1Dw{=fnv}Z*TXtS~snDd0y^Hp{}e{4mzJHq=4)RZtTOgV%DG-!f5H( zz0VkQ@C4EA5Eaw9ffl-QP>UvxpjTE(FFAq0Om`}av!IxnNP+9-XMZWQ*VKZ{Yan%)e9=oNxg1n$pirX{OBY4L4U$rx|@iEjPqungF5YRziAqw)Lzr zkh^8mZe79&*R4O;A>ySLe|5z3W?q5xHA$jvRK`|TTuz53?^c3r(%PjKYk&XT8CcQ? zS>HYMUnC^oEeni0f$fqifdZ-4hum=tBALEYH1e1*XD(TP`2ER@cKaWW76!+Q4AY5vQ;L;P_ktz}cUA{Jyug zp3Z&i%uUG3Wwkt^ISWc0uB(#=L^g-O6alLnv+Wm}OO2nBGGP;^%3TQ`Kk6ezM?DHN zjA$f+cSVM_l9U7vvjj>C&4cwCF~J|1>MZgx)?;v2>+8sj!g;5om^i-8ur)%q$U#eu zksoLwjW(_eYX>XUZzg5+Bi5ZX8af$2t9qF4m~|X}pG|W%kuv zrK&9pRmLy@DLlejktgootlvKqi@s2b($(vzq0SB5gHC9L28KO1 zk!i)9!$!l|V}14!&vK`T6?gqn%EU;<0{x7Jh(cNC(pLP1g$r-oh|floU5653Cl8TF z%iIvm);2;M+K{<2OpuP!Ek)M#Gjm7ZGPa+e~3o%%=nv@a%$N0ReM5B@0J~&Apu$CcjmgL;zBihf5@QEuJzY}epO;LH z@Bo-39reZ|j$Mw)v{bCL{=$9r3Q|~gwe-$ne~Z~lgK+^4;$wZt4=Wi?&&Uugsl{J| zH$1lu&>gFG&jYFo1klfs%bib3v9?(SjA-db#N21Tn=hpMj=;?&XGv*Qo7_R*W#93@ zCZpOdHr#S&oON;K^DJK%lMqwv1YW(cs4+y&1^gqVey@ZfZsl5$3A(N*V}LD@i3iWN z%W?C6IamDKlrQJ(Iof^e5!xF`2w*KzDaO`I;p45%kDyc7ShKy3h8KA3Dw-1YUmXlX za)ojNTLubT%KwS78t7TtSvv%8{(b_ed{FVTjiPrdz=H3=X9dBlH_$%4u+O+x4yTf3 zqaR-$gC+}gIUU>=>=<2%lfD7g>xg>%stl&>q`rzo4H_f%p)xBjRZiH4jsbCqwaHoz z?YCu5%OaMKUH<~hNSaV|pLxx29et^VJ+GAzK9`=jTQ%u4QFWrprf6Ca0NoNfiHJJ* zO+qc_V!Yg6?8N)9C6(@d3n@rgF2FPvOcFl^I@BgyFI-wxZ$y4@puwu~2pktnFL4gx zj3?g+{ik&6bgQSS-CA#~PPdmBN9~>D?2_z*YMb^ z-3GMY`T>L^lSH=`v=Y=2iy3k!*5}KN;$0gp311aMT8$5tBQa=_Bn=-vVJ$7 z-?nfa0@3u2zF0WDQUn(cUNY5rm++fE?qH(PA8`k{&@r`nvpu&k2|JBUeSVascU%F- z4LT0_FJogC3&%f7o|>92zV?cbk52>~O%#Uy!tXZxY78Oge1o$!z=;p<>`u%FDS=TKc@$u=h0v(iN^Y~Y9w z@9+~{^kq2YNu9Q=IZm-b6E|w#LDj9((0pl(s(5yE+8oq>79wq#`=dYk?!L>ACz$;; z!+*Gm|MC8Q3F+-9ta_8C1%Zdsdw9!&8V5SY79S)gB^BrAe=l-Q|M+p>%N6J%h2Sr+ zjXCNeCSv^Os?#(#$VHgx#QR~hPrckiU)k_K%Jjv(bElY{_ZYAZaK5GilfyNhg$B2A z6H002mVq0stWwa7aDbew@`_3(*N6s*WokwRfObfLQZRA2ERU~nK+tAHTsuR$EcQ~v zg`K{2Ls~XPZ8kv*0;K<4nso46&%cn>hLO=Gn+`VKwKLob+s)|ZtlfSU&=KS7*<;2n z0)AreSman`Y%tA(_`O)mHAp0%^G~(}?G@nKJtJ6q=cRJuOEyREhX^L$9pfhNht&YI z=ha+wp6c>@P$rs>7NW(5e^BEi;(7y@N&kH8^K}P7;QZwT(F{jOsl{;+DPFWtKBobc zTAc=Yc&YIX*_A@wicg)Sz7xFWsJah3tc?s;Q$c#tFY z${9t3ItDwH6!Ok<-&3W8vw5xJ%}G0;Ly#)kR4K62^VnrSYSPGUrGiWbpBU3?m~^pa#Go0KAB(`wc+# zfVlYZCg8>GrkFn+6nS(MSSYp%V0S|R;JQ|1KZ&MK0g4+2hV)~rm>Lxwomil#`gCe} z#m!x?n|n=UC>sXvxB1C2PE+Z&DIIv#Kd#vZKicD?rpu(;PHQ;GF-uJ;s0iveMlNZS z)5N(w&KLGQd9$?lH#{7C)*i4_K^v?QZ0Zq4g+RSu2Rp|hFVCyCD2a! zSM_=$m^#_zd`cM}$l^q+=|T^vy?`#0;<$Nb?*-1l9H5y^wd&W(Xdo~BJIZ-=IxT4L#rjlJPlRl9rt3Q!CSL`g71JqWptpS4Wnq54lc{RRyXQd#aPfK7 zascQKs0!*W;yLki%+qHbC<88LRM2PnDg^?p2Bb9Sw=&WQpTXx}WQRtJ@=4~5d9s`4 zwpp-)ppfFA)W9T;0ZXeDO-mtLrtIV6)7QH`WMT~;9v;>aY|i@+lT&@N=N$H}C*k5n zpX*DIPr?an$TYQ6sUjuvyW(4jox2+s1bwzrlK@u& z<-T^>{UW36`VVoYI;9)1qZbXEQ5xGj(38oldB?e<2k6D&0}^~K z<$v^w|K^zH@KMSJT9lL35rbj-%$?cDFC#kk;Ei4ridc2+%)(X!|NW9&N;)IRUJEUs z4Z{Pva>Au;PPx{`)TI?QOGQ<_%sUkppN2@iLu1S|nG0F-$P{@yt#(Ap3Ot-ZJAIlW z-ySn4)af`UKjG}RI{6{VDrDIpdjQydnSN%ASWB0Q3E-j&wwZTof*X|>FO~lk_6#$R zVTl6&BQZC(`ZmG2qEKprv;wX;f$NcL z2w-zHqjs~*KqZ6>qo#uYG41+i3HSdCA1m{I0kLLFK8zZ0_AB~Xkx;))uKZM9EH>;K zT>Gd(4LgwBg1VI7WB%z#0BZ2GM)%2+%tC$_EeqVscSr^!E%bUZtZW?)qQ&t~qrL9& zt>5vL{B%5N8FcE?+sbV1H9t$`4uN=9&C~w&7l!=$9CHi^gUi^M4n6v3ZE*Mxc+G%a zN=nM)0zF6R#u=Y3c z#{9xpG3{&>!N)teQ>UJ`%S}Mi2K+Pl-DjNqEMm5A^$iSw8s5l;$5;pkU=gR7-_&^y z30e1lTQ>Z^ej)ot*|yNPlPBQEHM{yU;|nhGAy>zLcNiq-rxh?Z9=sBhWaMkIzU(tl zs+tz$q`%G{x8Ou%D?$0RFcC!rpZ0Iu;X^d?Xs3MA)lUC(!1SRTUuRrmg2WzZk8H+G zxo&8%$XN}3)-gC(KQQ{#jsI#cQdesXqQ6;`77-G?t*KPdt4-?V680V485ijI`D;}B zjnPgbgPM~@8fm}Y{$lO=rWcvLU#AKvQ^GdW3A=j0#dqyGZ-}m16M9;E=}tm#3ij*z+0s(^6c1j zu%o=kazA&r;~e;kk{rE*+DvPm|ofq><*=Cvv6TEwsB#tE-PTZ$GCGh-FVpFDk0DU7`vx3+Ee3KDx z{p_!ll7)kh-2%TBW@>sq2wN5|TVKdFu0=i)P?RJk*_vXtJL0_4trlVOjz)ME`nG`B}gB19}}4T14q z9n}^<(a)PWI%GDchj5oPxeMF;-N7p1Y$#CoVyv9&QY9)|MEqpx6@8`qY}*+UqlrZ> zbi9^wcSJdd>muOlt%ZTDCDoJPNCt2&0a@XxJAum@UoOd(6L@n!dU||{Ju%lztb5sDyU`0Hj{jF9NfF*KUQ=>;7{4T4``cS2 zEwXaekE^1g5C^XBE4#YZR$!pNZjxp3S+iYO&R_#4p0L1?3bVF$pvEE64&ObmU84;t z80(v7DQvT-58^7x&eKdzQF~)kk(E($(Ym_6=L%xnzx8N|)kmE;{bFs~+ynoh8tQqz z20+itTWRMG0vjZmn^7|D?d@{bLaRNv%tlgbsr6z5J2(;zkk~%o2>+*`*yu}Tz;G{P zxjN|Jb)fkVC8+?;k;ah5(_}B67fhZqWb?iJ`Cno6ZSe#Kx$Kpk2%r4W zR%xGC*v{}FjuZwR?mCSZN6aZkc$>S=QrP{b@^&5W*{H=}NNHtmHCUJ7x$G8U_(t#Z zG~OwT;1wOXsfi&;`J_TrJXzZ2_X2Sc0edC1GJ`=LWQh^dbS)Q}C&mj2S$>)XNtXcx z1YDT|_R29iIBG{Z;e{ciUv&uCH3->i0*RdMdSsnro&45I==diq*>mCNeEqzL=BuM9 zjylD_m(uqh5TMm6#_4a^nuSC}T+z3{2J#ROk8*Kb#T)nZ8LM0yQ`L#Fv9=@SH?cqd zVT_>+1^}`hz~(;YhUgua9S5LW3!t4f-8;AAuz|s=F@JOqN>EbIVDA@^K4^vPR1_eI z10M>L7e9-153GQo1C^D&s_FEH6;|x*!3NIHx>S?T!gnsm z@8jiArJIIRM*~tMr;ZFhjb(;4LmKV+Ks0vcNL}xW&qm%F#evjLxXpZa%Pg!*|6u(u z^39J4tJy6Ea@n+(@*A@Ja(Y08y&)O_oS6&J(@{|-11aK6TO~sLKTVdUdhKvBIuCUZ zKI$jP%BwzkH6Y=yW-+yy03haKcL6&TgT#-vA|6HpBS}S!i9>dgA>=g^@4;;^vge`g zmQHqX{OlT40%JL=;h3ARsFTDeNm$JPcQwqKo3H# z?!7pNd0sjX&l{Ge!UR6+p_R3+!8#f7%0;7r0Jg;K_Czv*52e39uoozoM8%HJ^Si#Ly+ zb)4@k?jcjfO0|aDF!|8Md*Ku7i(Q`@9#VW7NLt))9V{;Cp+ane1OqL$?tD6@e&Q7p zE?@Ke+eVw~>n;&nse8e;&or+-tw>ZXmCa|9wmI3lK;bjl;}oD`A@y~c$_xT|Zc)V# z3}SiTm07<9vCRXzyecXd=}Cw3LZYn?BZ>uVs=gmrU-(BI|C8F(dklcGoq=2`GJ3mi zI=iOcsC~T7uY&HTW560gi0ixMA6&jy7Hu@k!@Uv>}_VmZAo?2$c=jKi^&jLc)4bL^RQj?Hm6*6%uZcb~h@ z@B8_D?*6(xa^rp8*LA(F*LaS8k0nu}W1K-?fMfNeAyDX*WS4vMnQ7Asiq0q0COBkY zw%5>DyZO1gv-wXYBSrj?^XJb?f+~HNx1=8$qJ6szhXhEdl+6C$1EbKNRxkF^rSZ&) z@_rHih$3=)(fECIx`$3}J{FU&tz(~}0E}@SRP_?S8=puHL_($Gqv~KwXU(c ztw!5k^XcGhIQscnc9&uU~^5|NT{GFMwp^sSazH!&0k7Tjjk=AY}j+O@39QROHi5S`0ekRx_Xhs zTGnag^7Su}YAUB^Yedeu-jB;q8kRCNjhDV_&r58}7O!@u%rCXa=gYMt@(r&lkdsjV z0V;3eqG0Zcq=bZgpy5gFR>A=H0U{aF&fi}J&~pxCD11F3>g4`^Kgxf|VUtz;UrBd5 zuMSZ;i}YIdeP=&2^s2-9DuG#R*9IjPdGCVWRwcc+A z&l*48W%lb7rLDG@i9%N97j4ibWr)$Z<5rUP73UuD!*y)+QU+^X%c(bBqk}wF4a`Fc z)!7^s1ZqRO?cQun0%vN&_Dh{KLPs?ZuNkw~k8V_gQZi_HkS(Jk=L@_?5fCgpgCBtD z*bS821t{tqr%=lOop=28UDVzoSvTFm(eHzcZ&XU0zqMcMF)Z29ZcmiId)Mh&pqM;S zw%v=xrjnnE2k_sW%Ycyuq|!p ziRd)8vYluP9~b{s=b{of%_t1q>vk~rF}Q{-k*Pin+;1B%HtR%60_k!|06|(SE`2(zBD38JL(+=;$5+27^rc1cwXX=CZ|e&W9-Ig zWeEaa+$5DuVmdQS^3)$*BT4SopV4>ycX1&D0tixv3i{0}xPiWY%FmNYO|2ZRaJ3zl zD{`&#)gc0ROipjda!~ZCY+iea;X8RAIyH}|i@KfN&$y?o39AjU@~~SEGZZ#=@q4H3 z<#Ksw;5IQKLYDlbfq}}Bsnd&K-uB$FYK>mH>z<6ASn|-lu+~q1zJD%%K+FMb>z0vV zfTuM-XyhQ{KK}t=b@ITEv_7j8{hexx(F#U!e8Hi`4%Wy6EsAie;J13jtY0fGW^|Z% zsdJnberLO_wg`tzzNt1MtskzSpcy`CX*1O!a&WZyL!DS;rjFHAEGGXL`-gjG64!H% z2GW<2RJpALrU|DWnUS6XY{r_mQ{GGx9>>cR*w-tnGw!7X~=R2#C&P z?<|YFx`)1B7DgLy=r*Vp$+nZ<=bEX<5JXmZC!xyQ`g-p9|AF;E`W{!!v*wKz^qhUBS?fbb3|19@FlF+gEVkeum zW}nFz#afcx3x7f3uBh!KR1i(Hb;Bw%X@nb#k&aM9Gehr*D{OD@Q~A3(4jo)Fi0hX-5PKlIE)w3AW@`;=BKg4ltwKu<~7 z4WvDL_6(&t{|~K5aySRAKn|9y7z(TUa64hmwS?##YvUa2TBo_k`R#;5Vy(duTf}YA zWu`J3g$ItujqY#Z?EKeQW~-mfJ~j{d?ylkv#}HWkcu>M;EfRU@Wkl4Uytnt8$fzrC z32$}n8k2cG^Vo9NzYE8wg8}JoCt73mn3K1`#q?BYV+G zLCV+Q;b>#|l#&2*#2mr!iM0RsNPj;x=U#o!EJ2+H!AqBV!H|k_%`NHkX}iku zIBkWr9}T)O<~z#D-C**Gu7-LnJ+d4G)3t33P(ai&83}-~8^3E1Sh zBv~PC(;b7PIwZ@}YM&=SfP5`nY->$~nL|N6ytYKrab&o;Bb(k#x+$Qu+hsp;F;S=C z^|t6%m0o+bM@-Z(G-+=e6N3vEHx>}+jHn|Jt7ggvBTld(Z{F=$LXlHcnxXQxoef{r zNQ^mgB>USDU$I>bv%kF4C{le_&g_tCNlHi&oi>YzgK-`+%=CwxXu^ zCO1O!*qvO5@0rhX4q3{3R#Ovps#vz=SVx_z_Yrd+zwIfm_Q$EsC2iRz4`$PqL7J5c zunBut;HDABCFQ3Yd6bEVncA!RzKxy7yy;-1eB39;hsio4 zjyH1c-=Z-rjR5`pLtj?UNv#VW!lQct-c=9>YEs=D>{6f)_O(9n3N$%&O$zy5o$>rz2f<78BOm~K`Nq4ss{W&K^!wkUoi z(%ZKo-9SU%xZSosSceW_aZAgCB95zBYlKrX;yc%JLFUewwjArO+x^aHv{sA6!p}mg+lSbC zt3&Sut4{^sKG`*{ZbuYWO^elJt3+-oewbO-&8}V`@Kh|l$Ju-(ml3`mwR$d$oyXPW zChNL-)r@9F;q;evZS}IvInow275MN=H=l0b>&ji)G4T~jWI}c<5_>AYj5+{1ptZY! zqo<_fBLN5GxHg!oP&*A@wzs#3gGuX1OYyOq-2nt7;Q$;AROAB*>-IFJ{YW_ukM6WF zo3;>+ce#Jwe$uIR074l7=GI!D<$ysP>YTx?App3~0nKjdrK+`fhU!CZ4{82GR`Y+e zxq%monFX)nl7;+P&raJGKH1-T;+7AzzFp;fp2Q_qSehi)dYrTCIFUc7n`aEuXOuM3 zuh4!eb|Ojse))paf;Zj?rYItf!d70Hlu^;afrCS(RQHC-{cJ=Ka=60HrRMrz zjcR3niNvUnCKXV~kAoRsTYnj2&Px}=;>gYY$D=Ntaz#?Nm6T*uw6r2bglVS+Cg_+0Y2_1IckE6< z_dpSw*g9qOTrq)SxYDCw%?A>{^HJ% zkO`SiBTZitW&oqcRmdNpDeZ z3GCz^xVIu4Vy$ha6{2q3T6*PN=?H@5tg7|b8y{}jB;D>9H5nbhNZ|Oo&&2BRd9ZP$ z*v|vfZDOy7#^@Mh=t>M`G1uXx0KBNo(#E0S=Mz3sneu#h!2uTT+feq>(aulYG_I_& z&vS82)OCJ<#J6>0C7kzu>Ir*0#Z!NlrINW%sTP~wuzdt*l>I>Ds?B<5XD7Dnqf+Gi z_o<0;P>f{fZyjGoD=#uU+o6N=xD}Sd{sRcw&SZde@@k%TGGYwXksu@Hv$?F})gdwB zd1I-XXaY(@Iyxw2`F*kS%}vh`(3|;qJH#gE4BMaaBAJP{Z=39yjLy{+28m1S*4yCd z`Vj~~qc8d_*MsX7%JF+Ge*?9rr|t3mE|!M|LY}^SrkW(rnRNlL^A%xce`{!U)tPU{ z=3t7TVX5RG%Xyn`8++SJ_yX82KIw(6Ng)>*Jtu ze_5Mm6+HE=uJT2JC*lGg$c^SeMvq>rX#u>!khN3)Jdhxli&4ixGI_OQDE%VUM6C1W zMw_gsFEy%z4VkeNIbcfF@jzcc8@9ha+JO>eZX|D8XJ=eh za7cS|qqv7Ro+0TFsR}>PD2!dAm*S=`BpT*SirozfU`^}B&=4{wol2GK@KB*o)3-Wt4ne*#YMo(5IC#)!`5=U8f+NS|8S?t| zJ3*EgzF>{xpQ^ibc3;2aE?k)jh0#(E#@pJ9D>)p%ZeMDblW6?W7fCLp0AzxkNia~A zm{$iCWOv5>IKcZ@`7|T-doFD4e2TY*ri;zL^PaquSaC;fcdBv^m%;Pk%ga@<@xEhd zM}O4i#krnLb8W}rh7G&IQu9_8W)>D3@HbR1l#YAk?gkYVUClSGH36JUT`*u``hws` z0+dn1b)-(Agwf%$4GpOutUtv;rcVrY=lS@+w9ceuL$?V*RCFlDA@%k3m4V$&^0nun zHP08U#?LF>I2dC0dqvb^aB}V+YjJEpH^vbGw|*XLX?r`r_9>O4;6n(6h%c}k1!O3? zv4Jf6heU89ya9hp)cN}zP`zRH1Kie2KD`6uP^jk(BuIL=`!=Yw>k;J@!GyADI37ur z`D1OZTqwwhg`CO7{$eF6Dk`yQ%cXKEnNds*>t22i2`T5gB{5@-TjmlYIsf0w`9JLs zc3}XbT?u$Y#sGY}oC|0K%vQt7$bgB&GI`MV0>f@_VF~c?+ydkYZChL0=ktKT)EQmcrXQk=){Johu?JkXbqHT9E3U)6T3#Lp)?~*l$wI01-08Z zkeLB#c#fp44;9=d`82S~d-X8q=m4cd-i5!|zW!aJ{hzPylZigBPqR_`cbt7VZcxS# zmn#OeI`y}Md0}K!NgE;=PUY+|-pc9OA2idU7tNeJ5n)j=rPza=xcjQ0>lm1nI|qKc7Nrf3&wRLGEJeJ zx$*I*z^;=Hb8%erX2#1J6S&X0iSEBuZ8(Nix9hGnRJGac`>?Us+(sUV{Zr)d7v}>X zr@i|8y~F;c!Vj-u93L5ZOxw?*DL%0T5U8C5FBk&yBfu?4rI4Zjd>;(;^)DI@1yzU# z7&`>Nw6QBJm~?+$T3WiC?_qb)$Z!Z+am~m>UR+@?<3~jUK=kvwWU&PP3l~s;wwP5k zal-mxs*fetr?lBm8=GZ(xL5mfm`ova9{vkLx}7F+Utxz`BWkH+|Lht6pV$9a*XOTV z5pr~Sf7DPj33?6{N_7dvF7yT;U}CpGZ7s+Mxps!O-QWQB8M0IlZD@GuDWC+8znKB7 zvOnxUVCW_*9vO19a`wnsYy)Ifb(suk{r-H_oEl_kc9G3W&BttmwJ>5td0BMh`aAJ` zmk|EcPR-HxyWUW=i$S?XQ%qA>7mtfQ*75<=^(KAdk>7daEM%VSmeI{GG|9FM}>jjJ616N*s)hrk~ouL4%Hv0n@V<=?_3+U`Q-nc=2I*xkqA}5h4 zhGi6RLZKO^6QrS&m?5|3-u@qs%gK|6Pg>5suC+(M>=D)-NY~AU+{)(iwJxSkGtKTg zVvN`y-BU(hIp^jza2lL*wPCX+>+9dQy*;p3LMor;AUS7*Db#+0kA&(59Wq0M`0X>O zgH)GhartlX)TVX|a+Z(uB|PzP5KtDSMPK2mA;WL;IgkiM$O622_f4NnApM<=b3b+} z<5|X!A0|Rb^}-|4E>(d-TP+m#@8zLD^w9Rod4SU}CT)CuExVvFtUco)c@oE?C7Sxr^tZ&=y5FqBB|Z@YlplHkYODH!aSQ(MtSRb4b~flatK)pG+Vo ztHOhi#m-1+)XX08^_jkx?OIT}xRf4O>zTbrd~NPcObEkm#93arl1lO}{m0Ni-phcT zxyK4!5RV7IZR(?*&kITys4eX5A|ZCK-@I|Xj$9m|Cv z9DVnp8w5=J%d!i5eFql?i?#Ka3VIriX|x}ycNMZ0!?ufXRpwa#mQ<<{^vH(IT9 z<|W^HN~87Xu$sL0yW4JKbq8naub|~xN+x#&rH_}Zel%0cR!fpcXZHOqrSm_oMgOOY zHy;MmkvIR#LznJwRP?&zQGPERA>k8}KaMzo^@2;qZT`mnL|LG=6QU|-3KAv}MF~1i z%>6)y?*cM1h8&pM{5&}&*_r;|K(vB~VM;zL1Y-by^8R7QPj*bgl;J=2!Fh^$1>X?r znia$0wZT7pot#rgNIl96rzEvUW8URd_YZ2nP0AdmihMB6zB?sssZKIYqXITTWIi_c z1#IEjY$e%!sjjJ4YQ?%TUIXxhCGcDfC`dM}4G+2yvbrt|V-#qHOg0@IFs0R6F2pR0B4Hf z*#>G)eEyw{DVYm4LGg*MgMVMA{`40ZWz>@rDM8lKRd;{pw}T< zpy?fOJ`;lEFTvk2*DhGS%GutDuGNuC=QC}7FuvlGdENfjty>`)lK-%+e_Yp=$CJ<6 zTY9B?ss8da)GYXEI+)DiQ6E8oA=H4G@ z`kR1yMkn1#|H~%$uX~46|8(j(g;SjYzlOmOrVD*#YVVlfU3^V+U44aS33YbJ<7;bc z2tZ5Vlrzwlk}%>9{he@t>N<3I!o5T{V&)g>dh!t}Z-!G@t8TC!LnuA}_OK;l&M|^a z$_6+wG%^@W*lK($L2auAs;Nm?nt$8@U>nR@f%HLUK%iyqmxzvfK|!goB4{jfMOA>? zBxh89Ey{U=`GUOsNOtHTK%sr|0CJKR+H(0IL8AWohUHL7PCT~zIAh%t?u;c-x>N! zzW~RGe&uTEexRgN(RGIcnLLKllQxzePE(xxT`Lngmz9ic)83P|L}jH^S>LFlVE1No z7q1u(?%!BD^mEA-X&CR^xuXuM*`nfNZ7nUW$=77k(U&jR={Usi0VN@^4wv64^!zt2 zfy{@l%DdkouUCdfIsNFxxpO_D=1+V$K4`bzJF{K?IKQngmsNKOJJ&P5Y$)fwu3lOzy$BjTkfGDU!oupDWl-YcZ@*Pul+<%1 zk5`M{-cySXQ=J*S*mhGGq|3wNLUv5#30)rRHZVw0PWAlq-LGWFLq{0UC*jG zN>Ac8970m1?ai)~11skrTi1%h9~`2&owZKvczqxOR6(HIqoShnJZI9qus|mHw+^i; zRjkzG0cve$U5EwFGSaDHH80$7r5;b14YQc-Z?DBhT;o(Y4b^$eJNxNk?wx(#{o9-} zCOT#%pD8QwW=z``?OdO|r)lC(v6RSE8}GV0H81E+QMT_r^LArV;kfHU=y22ebLa>b z=IapOn9)d+VD|TDAldB@X=5Q5bR5Y-B+`C6o~ZMx+qDybd`$^vwB3N|!VK6gu8Exg zyaxdNGFWlu=mb}1FX1En=+SZa*LScmxuQEg-J0J8gf%t*zPVju>F9d)CDZKA&`RT_ z1(;s;6DJM=u*4V-0LZ-qL{cXozUbTCM@>r!j68;oVfgfZ(?4>s*za>InJ&*L%30UI+6|D9iZTA{WsFUZI;r+D!E?3h+Vu;qQo32NsO_De(8{vp6Rqcz9h&HduG+E!ZxY6FPubJH1#jvxv zrn@=3_hY}?Dy%l2Q_V`_nf@&IGot%%oc=M_P6xP#ZPQwN&=f6h5n3| zzg~m?|G!C6yh~_!v;b6xWNo71nuygz{iD&J>0BRF)gT?;*~3=cXHauNtb`5tiS$gg zv$jT}PJu~2V&9}-e&_5=tdP^i*sE@_8OE{j&5ZZ*Gusgt@F582$8=K9wlg)0SbA1B6RWHOVuHZ8NaPoQ|W^d z-20nNi{s*De(POI1i)#U4Nuxhvqz2xh)XE&{%B_ahMMka5T;jFRtBn}W%F@VRqZ*G z)6>(q=6C1V#ce;Ne*9RSIk@Z|gHhO3s`fsu+ymcDZ6x5`qsqlg_VoiMMgJ9D|La01 z<{lB%mMuYZC7VbHbz*2$$_`a*#6ydDsb-`oXR0_>%=T{dRNTBU>6-51*-%^Ls%cPB z77VMrlM8dp%;+>d?p$R4$YZnX@Q^CCyHShrd;N9AZ69jy@YEt@ zJ~;-hHRj3JRtkLr#HzHk83&ub3=&^oKMkG+bsn&_F+D z>69tb@zI#kwQK$(&?Pz0AEXXNG=zXwp=BbA&|j@MkfYug99!EG^R!)(5VEE`p z+LsOx5a?|zGeK*@yfwr3Mtx(&zuTS}lWv^&4GOg4b1EA@KHKMkRy3^Nt3H}XW>PR2 zyY5ERxhHVcXbF0+!Vo)ZVUVrWsS22l)u-Y+YJOAC3~nG7`!WSY#AcZ4U0ai`4&R_N zlKAX<))%+Lz+7oJ)yWD(9ug*bv_bP&{(hl6$ajR{Biy3Bge0{M7^hQovYEn;wM8GJ zy*l51D3x1&L0P|^ct%;j+`>qE*QGm#p#5M{?QOlcvjx!qm8QzdK|{+6xdk=2L+){A zTyI|g+)yPR{$%w6{~;hN!yZN+seoe31_VB7w;Ynk7CR@0Z?Ya6%xFq$qJ6leW#8_2 z!;I+T-WcJIS(DCFwl}tE)N<~X4`Oj)#C^r$njOJi;te$`Ge0;zsrKTtf6Jr){{Zq= zx1xw5A@!{N-8)=8x=WcG7kx>WUSjIpOjGlw%B(sx_lk5a4Q_rv1`No9UEN|8P8t}b zwY_1k@acF=EZOs%d=jEMoax1c(+HJtjO;x{yt~wWD`v07Oha>KZbW}_NJqy5F0`1u`9)v~{+2H0g^g@g`Ud2d+Rhox%KTM!F=~VB$MSD&KTtiZBNHJNHbe7aLLZP7ULX7cNaYp z7WQ{K_gzT_b;K?uZZyc`)~AGF`V4aGZ^76v69BAmn+i#-IaPis^y1bvBvfE$p`vTrry(OkvC_Xz*PNkEjb4iz{pI$-(Td zXDYab=Ia%HiNOMzw5Tu1b>;&xj7KGE7NiIy61En;zMhkmSS%L@^#${Vy**+rzTYy* z?aO6*$)-jewjgOR~HCAvX?Y0WE23ppx3LWO{#ZwtT-g2WL8m z!1W_YEq$$VJL2~x9u_8&Mo!7S8+1K)^~66%tGaU9UYYmai)B`Zy6$t*+86IywrGo4 z*lgvB&B(wQ-k=2S{4q79cd{2zdt(!6`m_#@C%jSIRHVLe%rYZe2)7c{$LlzZb zFCC+6N>wE8+me!P4m8xRe2%)l1eX`TB6+Xe9I8>xKTb_IFpV{BMA;31qpB9fP`}gIfc!RBj7ZG71g~v`u?+YA|Pdx22Kc zBNO(cHN!EJ^Zmu6wKvK(@trbIUkf8wCaGTyfHF z-h%XkZKp-TCD*m*+&hYFY-}B3I!=Ahnpq}xn-uoOUxX>Fz6+C_+5Ab+mX1)L6tGc1Xxw9JuzXg3q+S>+mh=kE4J%VNcQdY&`kBf7_tMDU zg6XhnM{&PBmPF|>cjDt7I_8T@qB=$eM*jk0_f}ya&Gd!v2FD?FLrp=S*}0^Cs%f#8 z2_GaYha0hlZ?}0`-7gMwT=?cbeTu=muB7Y8we5l`eeE;ivk&Dwt``9N-}Mo~sg1m0 zc~V!HP1}bnIayh#5Q&l5LjVqPHs$#cbg7yIFbnI!raNDx%(WftMp-yo2SJsW@b()x zN;oZ~G14)G==!`YUm5<=G5r1eCurMze)!9$Go4Aow8sXoNQl}t*{SMW#12H)tn*lo z!mWdJu*ESUFCFuP7Rk5Li)X->Ar}v?R0VB}3h>`2%q~CBzd*yniE5tm#zy74-H{V; zP6a=sI0**~C_3sB>^BR8jPkt;b4unnAvLKh9f3G!La!krI!>_FxPYgutvhXfjreytygYC-4-Y&&3sK_Q4}7121l>N`wq-k*ENBLNv;4j1i%#{R|t)t`lV*7e#@ zMwg(*v_c}?F|_Zr^L@6Heffp&LNWRmBDrV1yoRTVfMCs{0xHGi8^qfKdLhc2QrWLAQ~1C2-R|5CV6)&2e_MC{$(B4;tZjR~bDPbu zDHyuQ3glj1J~byJ7Xqp2*iT+w%isNjAJhg>!?k(yo2zv!d0uJ)@s*+2h~NyXmU6TL zyP=#%%25id2s_hc?qMLAf2!k-p4?>Y1I6;=3D#)|5DahgB^ru{G+CC#);`=BQ0x}ocD+Ut#=crtXC=zb7b{oa(_D% zS6rFsj|@SoX)y7y$Z3sLYWM`!AEXR??Yu+bw)-8IN9rV)4#JkCSs@QCL23|gz5y;J z4WJkaq#M_PCCTiJ!hSj6ljhcfE~zx;>CoQpHspKrJ33j7v2oxjprNfDsVcwqoju0G zE`VxM^~2>9OrDFsuG|J@V9^ewIvuGWl`5YpdlNVM>nM^s7505H7vdahR^~qb9De2N zo)#C^*{IJHGOjpO^$iq;Ej9T0VUE)j;$z458?Oo`Rwh?mu?wQE@Dmny9HK%Ks@F;! zD`9UP{6}_%U7X88YsTf}_5oRFAG<9o-37DDm2mY*7PI;dG^5obg`E&%SLJSlEr(nlY4isPqx8ri0 z&^g*2IxMc+y_#xhPkjqhf&PAk0`CA@9rs_!5h~}<{5JRn)$x3XZr(iQ_2tDjub|8# zNFB&NqN~oM+)vA!dP#}3Mh@`oMNZg?Rexv0m!1yHW_|6axUystGy>|^zwt8GMRHnV zHxPz;^Lj4G?S9b0dpMigp3dB&f8Z#G^oWDB_HRUVma~pk<&O-T?YN{Yw5OD(PC35( zoY5OoztQ}{+sIB%Yl2;SpK$%8ahNuQ-a5zEX3=L}aKWrT0@e(Azb!I)880@^T{}5k zar2@~=IjUYPvs*Gm<^#)4^8G)j734^Vui}uOeYppQc3`4bWg;h4b`KjFs#*-RTxH< zu6I{iIj0L7&yIl`<4;+xOf(ef+VMz8l$8n%{M9Z?W~K!~bW)>;QioAExmasV7zlH} zBx4VFGe9*+{iA~VjcGnwt=%`lt|1EOUE_;)!-Z6^UvzZ^mUg{bs{CG%UT`HE&+qwb zS7pRwn>@ZPpG?oZlUofF=|!#72t4?mt#pRsK5Moo#kEv#x?Y}z>jZc6mo7p1QtStEM*NZbGDm$VoC1>~#*Tg^gz9DOv)RHu3K7-83&Xt)MWy#+t~MO;*GE{VRl>3qX?V+}|d*pJ8jg>_Q8@+BM81E}NY1eX~q2igv+Nu%0 zylAZ{qx`Imxo<9FEKYXVN4d%@qVinv0KZMz`-vQ3JgGQk5^oSJ{~J4OT0k8;swy$6 zDY5?Kkw%d2D@#mU%+lQAN>5l~{YIVl?wl|T+meGz9-Px$^vEAS`ZYuuowhcyzOjv# zDq-;{*H&PY8Y=c{52hqd6FTMEDFX=dq!ICozh0$v_Cn;lm?eErbcJllodSA#MB|Z} z^K%I=n^;~!T&jcf7mRI}9!3Knj|5;Z(eW{i8@A(=;D*sZSoVjQO@u(`xuGMB`UsoI z4#~+kPbb;Ctv>TD0!98*(xLQ$kMa5Q0D&h*K#tzHk=yfynYHq&3QhJp&|d*}e-zbs zj0JmIz8>{o%_{?>B0zD)&m)15Zx2~nc<8=~N#eZH9hMRfZd1;ic!ez6wa0>vu*h$T zA7%Vxn_#arO*b1eNh=pMPuh#A4HXFJNsL+5HU@+e<5Zddy(RXa^0H*}!>3-)L)d2I zN;L!Mdesu+=VJ`l_wq^D+r-f-Xe`8Ne($>=FlKfOm6Jk?@K#iBvmv?ziPh=DiHQeT zD{E20x|~ODCy08hKfUH37i*JLSIG^-qdwPsW=Ikoo-Cdn+v6luuMGPR;4}-R#Ico| z1q-!h#6Gj?(uaM3^2^t{pv3K~g}=Jd{KO-w^!xYk?2|e4G#=R5tdA)WEd1z^Z2AkP zH9lS;@BF!X;u8}W-)w_ct+D=y4`pP&Sy$?? zPRA#~z9$jbDQ&F(0J0T5YZyZZuc4j5fpeJ`f0mg~kMuD-C#}zY1hPRF)u#*Q zg~!T)Mx|QAa+KlT9&`jfP$1FSvs!PO^4@4$D%}&Y!}XQX(eFkRjnRwSzp;NUgi+!Y zPR88z%fYbJ@xbSQENE`6#4uP*rAW&sO+;3h4VcPNUN>9p{!pV4h}Vd99ZX293HqXd zQ!AG6#MQ7i0Qa*p@7U`DblARPBYii#Hh9b4oYNnu6Xc7E1w7ZAdpRqQ5>{qv9z7 zbWhv{fWGkf6EhcvKSZ;n4@0*%7Hq6;_XE{}es7(<-6dG-Gn3&43Kzi}1NiIDZ_f>0&+$_tFr+^w=q(?1P`H&md zc%V2_x^H0n)W{m8v09w?eU>puSd8EDXByuQ>&YbF4Sk>j1crcHJ<8Pt65EsLj!hJL2c5|?;xXzJ}I0s`dcp*pbOlh!O z)$P)0K>{@O#;rv0O|k5h$*}mh0>80jEay=c6i$(nw~PH`y4Je1Smd&cHQ|_NA!5b0 zizbzdW$seC4!T_562&G7acHqN5o1oZZU03EH=1c>H4}vg3_GpDf0XS!9jO5lB7Rq! zM4bAn!>ryooYhq`O%Bc&;6sgkwhDetkN$e4hV7AQOk8?P3xCoS3`$(~Hg81SLyBW6 z`wU7mJPPIPmKrZG@!mt3?ya%{39OMLPvii7(EM|GEeT!VIvFC-f>VjQqyaRu$%*o} z#}LaxrSo76FnDC7`ulqoGS(HKGiyegN(lLFaT03op{6?%tYl*rHz1n*t%yL)&vGg* zA|j27mSuJ#SQxVpI=yP-+?<@+fjf70zPJ+uw3QY_(9b;beSjpN(R|vAne|rAfpT1 ze4S($=E?G=;cD{Ppa&(@55?;R2>rD*R?jJ-j!Os%wtsueaX|(ymtN;5`$S>0kehx= zC?>P5UX?XocX$0jKZsGSq=_D}Z0Sd}S51wxjC3_U2R!fJEm5^@$g5l*!1nVO?;Blc z#waXS-Pr3qYBlR=`DpLI&kX(*Ag!Dz{Lc{$hU|Km>gr)DRf(~&V{AtHd!-qnvv3i3 zwUXN2+}+)kJ*X(*jVFOqOj}qd3fkCUydSj4wUpFQnrpvr6J`0vcxNT>9D4kXA>oaA zZ!AISe#lZvF1PUwSdUuBywWk3un832JR}*sIvRsjd2$hM^F+ zN2~L8-|ji1gXI^M=U5iJzdOEKD}q3Z<@do38OE%31VHTepazDQTgf6)^VTev*;!dL zK_Wf$?E&N<@(kRyO4r0IeZ(6t2wVti8-d?6q~ER>RJ&t+lr%Qf&#@m|k>lMe!f0^p z<_=X(Pfg9$Gbm^QDociHLJo+_qGO`8I|(9|p+d+p3VoI#*e>4Gpvto_+#f>$P4dQF z*|UvR|Jy7s?u}k^`nLq z8){7R^w$t0(6v_F;@6Ap2`8-55HIobt-PFzMp-W*M~$$Bv7}ZALs_JC2lpbXgxe%r zprH1~+D`Gf^hb8-z2)wz-x%kWT=0JjoMc9O(*$vwP0oFf1vhumu_V+p@B201VbfNa z0^W0cBf(L|@&keGda2G|X}R+3`8 zckM=Pk(#wwO$nm}3@SYovIEk#65nlizCXmStT^VBNb6j>bV&=0%580cPta~N9($40UVUg@iwKBEzvzlQ$%l3Rdb}duHx!2rU`+Zptdnk50 zt2?a&v198_@Z@10+L8*rRW8U4oXB3-q_LW;-YhA_-~~2yFFHLPezTg@YUi!zXkrn% z0y^+qHEJ}QL7^a*)I{3QgdixQruw(%*f%D{w)gJ+f|U5z8UFQ{`)4p)J@-eV>u7WY zQAlNpkQ?hgf)*HQ}(1;7EQy%v%wwu>Lp%as4_UuBLYPx*FpFc7m!aoE7C3+u1nd z73}wu3dosdaUa#>dqOdu21#-EhR;BlPIg|N^VRX1GOOrLl%Ma!`u7`|n6MG@flr8S`@i?^JW$Rb=i#8R99vnTrzKIbus@Y=;B)z zI+wQD_i^`d%Hj$YB(aCU&^sCUY;v(@-Y|4wj*<>36;R4}kiyBZnWo{&PZYs_P7MvJ z731i>y1|MXXxs%XJrYl{>kpqq-yN=S*V3v>)+go}jI;(LWCWCMsd|*8PRis!9-li@ zDUOy$3{4k`I}Dp7S90&&-U@8-iJE