week3_task2_fine_tuning_clean.ipynb

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      "name": "week3_task2_fine_tuning_clean.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/GaborBakos/deep_learning/blob/main/week3_task2_fine_tuning_clean.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "wHCFWk5eUH85",
        "outputId": "fef1bd74-49fe-448f-f328-bb879051a246",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "! shred -u setup_google_colab.py\n",
        "! wget https://raw.githubusercontent.com/hse-aml/intro-to-dl/master/setup_google_colab.py -O setup_google_colab.py\n",
        "import setup_google_colab\n",
        "setup_google_colab.setup_week3()"
      ],
      "execution_count": 1,
      "outputs": [
        {
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          "text": [
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            "setup_google_colab. 100%[===================>]   3.55K  --.-KB/s    in 0s      \n",
            "\n",
            "2020-10-09 17:04:11 (51.2 MB/s) - ‘setup_google_colab.py’ saved [3636/3636]\n",
            "\n",
            "**************************************************\n",
            "102flowers.tgz\n",
            "**************************************************\n",
            "imagelabels.mat\n",
            "**************************************************\n",
            "inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "**************************************************\n",
            "cifar-10-batches-py.tar.gz\n",
            "**************************************************\n",
            "mnist.npz\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LkQHNyxnTq5S",
        "outputId": "2af5ef81-43ca-4bea-af4b-71c870072b5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# set tf 1.x for colab\n",
        "%tensorflow_version 1.x"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MmsbG-N8Tq5X"
      },
      "source": [
        "# Fine-tuning InceptionV3 for flowers classification\n",
        "\n",
        "In this task you will fine-tune InceptionV3 architecture for flowers classification task.\n",
        "\n",
        "InceptionV3 architecture (https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html):\n",
        "<img src=\"https://github.com/hse-aml/intro-to-dl/blob/master/week3/images/inceptionv3.png?raw=1\" style=\"width:70%\">\n",
        "\n",
        "Flowers classification dataset (http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) consists of 102 flower categories commonly occurring in the United Kingdom. Each class contains between 40 and 258 images:\n",
        "<img src=\"https://github.com/hse-aml/intro-to-dl/blob/master/week3/images/flowers.jpg?raw=1\" style=\"width:70%\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g10vFgW7Tq5Y"
      },
      "source": [
        "# Import stuff"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "CorhLEofTq5Y"
      },
      "source": [
        "import sys\n",
        "sys.path.append(\"..\")\n",
        "import grading\n",
        "import download_utils"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Tm6J9kVTq5c"
      },
      "source": [
        "# !!! remember to clear session/graph if you rebuild your graph to avoid out-of-memory errors !!!"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "pGx5Y6RbTq5f"
      },
      "source": [
        "download_utils.link_all_keras_resources()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:39.210959Z",
          "start_time": "2017-09-03T13:00:39.057800Z"
        },
        "scrolled": true,
        "id": "98A-8lN8Tq5i",
        "outputId": "f22c3643-6ff0-4a42-c319-54302bd1e0de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras import backend as K\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)\n",
        "print(keras.__version__)\n",
        "import cv2  # for image processing\n",
        "from sklearn.model_selection import train_test_split\n",
        "import scipy.io\n",
        "import os\n",
        "import tarfile\n",
        "import keras_utils\n",
        "from keras_utils import reset_tf_session "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "1.15.2\n",
            "2.3.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzUEFe6qTq5l"
      },
      "source": [
        "# Fill in your Coursera token and email\n",
        "To successfully submit your answers to our grader, please fill in your Coursera submission token and email"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "0gdj35H8Tq5l"
      },
      "source": [
        "grader = grading.Grader(assignment_key=\"2v-uxpD7EeeMxQ6FWsz5LA\", \n",
        "                        all_parts=[\"wuwwC\", \"a4FK1\", \"qRsZ1\"])"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "UIJ95RJ2Tq5p"
      },
      "source": [
        "# token expires every 30 min\n",
        "COURSERA_TOKEN = \"\"\n",
        "COURSERA_EMAIL = \"\""
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jn60h2LgTq5s"
      },
      "source": [
        "# Load dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67Zl_p-LTq5t"
      },
      "source": [
        "Dataset was downloaded for you, it takes 12 min and 400mb.\n",
        "Relevant links (just in case):\n",
        "- http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html\n",
        "- http://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz\n",
        "- http://www.robots.ox.ac.uk/~vgg/data/flowers/102/imagelabels.mat"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "__rYPLY9Tq5t"
      },
      "source": [
        "# we downloaded them for you, just link them here\n",
        "download_utils.link_week_3_resources()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdIJtrX0Tq5w"
      },
      "source": [
        "# Prepare images for model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:39.214524Z",
          "start_time": "2017-09-03T13:00:39.212453Z"
        },
        "scrolled": false,
        "id": "jDLk5n-VTq5x"
      },
      "source": [
        "# we will crop and resize input images to IMG_SIZE x IMG_SIZE\n",
        "IMG_SIZE = 250"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-10T12:46:09.790818Z",
          "start_time": "2017-09-10T12:46:09.777771Z"
        },
        "scrolled": false,
        "id": "Qib2BlsHTq5z"
      },
      "source": [
        "def decode_image_from_raw_bytes(raw_bytes):\n",
        "    img = cv2.imdecode(np.asarray(bytearray(raw_bytes), dtype=np.uint8), 1)\n",
        "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "    return img"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKukma4ZTq52"
      },
      "source": [
        "We will take a center crop from each image like this:\n",
        "<img src=\"https://github.com/hse-aml/intro-to-dl/blob/master/week3/images/center_crop.jpg?raw=1\" style=\"width:50%\">"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:39.393675Z",
          "start_time": "2017-09-03T13:00:39.302130Z"
        },
        "scrolled": false,
        "id": "rNouCRs3Tq53"
      },
      "source": [
        "def image_center_crop(img):\n",
        "    \"\"\"\n",
        "    Makes a square center crop of an img, which is a [h, w, 3] numpy array.\n",
        "    Returns [min(h, w), min(h, w), 3] output with same width and height.\n",
        "    For cropping use numpy slicing.\n",
        "    \"\"\"\n",
        "    \n",
        "    h, w, _ = img.shape\n",
        "    \n",
        "    h_start_idx, h_end_idx = 0, h\n",
        "    w_start_idx, w_end_idx = 0, w\n",
        "    \n",
        "    if h < w:\n",
        "        w_start_idx = (w - h) // 2\n",
        "        w_end_idx = w - (w - h) // 2\n",
        "        if (w - h) // 2 != (w - h) / 2:\n",
        "            w_end_idx -= 1\n",
        "    if w < h:\n",
        "        h_start_idx = (h - w) // 2\n",
        "        h_end_idx = h - (h - w) // 2\n",
        "        if (h - w) // 2 != (h - w) / 2:\n",
        "            h_end_idx -= 1\n",
        "    \n",
        "    cropped_img = img[h_start_idx:h_end_idx, w_start_idx:w_end_idx, :]\n",
        "    \n",
        "    # checks for errors\n",
        "    h, w, c = img.shape\n",
        "\n",
        "    \n",
        "\n",
        "    assert cropped_img.shape == (min(h, w), min(h, w), c), \"error in image_center_crop!\"\n",
        "    \n",
        "    return cropped_img"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:39.506473Z",
          "start_time": "2017-09-03T13:00:39.395470Z"
        },
        "scrolled": false,
        "id": "N4N8FjOjTq56"
      },
      "source": [
        "def prepare_raw_bytes_for_model(raw_bytes, normalize_for_model=True):\n",
        "    img = decode_image_from_raw_bytes(raw_bytes)  # decode image raw bytes to matrix\n",
        "    img = image_center_crop(img)  # take squared center crop\n",
        "    img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))  # resize for our model\n",
        "    if normalize_for_model:\n",
        "        img = img.astype(\"float32\")  # prepare for normalization\n",
        "        img = keras.applications.inception_v3.preprocess_input(img)  # normalize for model\n",
        "    return img"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "bkkLgtJOTq59"
      },
      "source": [
        "# reads bytes directly from tar by filename (slow, but ok for testing, takes ~6 sec)\n",
        "def read_raw_from_tar(tar_fn, fn):\n",
        "    with tarfile.open(tar_fn) as f:\n",
        "        m = f.getmember(fn)\n",
        "        return f.extractfile(m).read()"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:39.961301Z",
          "start_time": "2017-09-03T13:00:39.508004Z"
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        "scrolled": false,
        "id": "Mikibqh7Tq6A",
        "outputId": "7bbb9798-8d53-4c0c-a456-f68be6100859",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        }
      },
      "source": [
        "# test cropping\n",
        "raw_bytes = read_raw_from_tar(\"102flowers.tgz\", \"jpg/image_00001.jpg\")\n",
        "\n",
        "img = decode_image_from_raw_bytes(raw_bytes)\n",
        "print(img.shape)\n",
        "plt.imshow(img)\n",
        "plt.show()\n",
        "\n",
        "img = prepare_raw_bytes_for_model(raw_bytes, normalize_for_model=False)\n",
        "print(img.shape)\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(500, 591, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(250, 250, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xpF2V1gq5zhzmTJwri7Xg4xIfBHFmqUvLaCukPJHbiDLjQ2VYeBbFd0+c2U075pzZjSPr/ZbNcsNyGIhDwAUDQsVXxBVCrISoOG85ZGuVkjM1F6Z9Yr/PjNOe2tmJ+9SijZ7ZCobay50nbtp6pFNtUdnSvjtJPbR+B/wTOekiRAR3CozvuzDzRM4JwTso7aRZ+EUeSgSCwLe/9SlPnjyxeYh0HQS00ojBGfjnjMHQ1tUe4vDO2e9DULmbjgj2Hn8vYuFE7nGaVcdQWocw0WBfcExbjvPUhlTb1B7wKF4giBJFiQLBCz4I3tkxkHv5Pr4ZAxTsA8TRX+M7BgbVRACIhSVdEwBUQZtYYKU/fyzvU4XvvRE40X6uUrVQmoBzNBqlmFgj1Yn9fM3t4Uu2h5+yT19Q2pYCiA+IN3S3qZJKg+bxLNC4QSWhRKpGRAYcA7QBxDCCVoWaoBYxgEV6HtwKqRRS7fe5WH7dlKqVXCc0Ffw4sVhVwuKcsFjjgzMWoyaUDD7hQyEMyqI6SnWk6ilVSXMj55k5J3bjzPZwIPpoQqTgcAFcULxv+NgYFrBcOsLg8A4DLWullco8FuZUKa2iTRA8ThQ4WbbTvXCkp3zfMHLvr994lu7l0nf3dyjgfYHUu/nncXF6Z5sW+l6Sd1/99Rx9sQh8+ukLhuWKqoJr4FqzyNmD80IMJtQyOlZNO9B/z1GII07vYQHuJBIS14VC34Ck61F4pl83hsfn+nFUISJ4UaJTBicsorAMwtIJg/MMgyNGT/BKcGYUCIpbiN0GY0LEctC74ypCU0VaQxq4Jrjm0IqxFk3MabU7w/T183eXErzvmICAhEZzheYKWRWtSj6q30pinG+53n7B1e7HHNKX5HaFuhl1FroLStViCGppqHrQW7wsWPqI8TCBpgvLuQxtscVT1JiAAloFfO1ipHTi4nO1VKF0Gqm0Ri7GTIgfGcaGxAIhs/IDgzdWQyXjXSMKLHBUdeTiCEk67qBUGqlUailMKfdQteeNHryHEGFYwGIpLItnuQwE7xBthlvURkqNnI+GE8AjogZuSkXknkc4bhLvOnIvXc32LoB2Oj9HRPze/x//FaHnt+5kXMDybRE5Lc4QoqU4Od1n2wDzfu8AhMDF+YYXL16A88ylEp0nqNFnEjwhmDozpcQ0ztSqhJPMDn4eDJQ7avC4Br6+8U+8+h2dclKeytGgmVEVgSF4Bh9YDJ7l4FkvPaulZxk9Q/AsxBOHwBA9MRg46LvgjQgSPTIIOggSBQ2CBkWjUYVIRaugVahVcdWiD6p2rIeuFDQW52TM35n7vwWRgO3HbPlpE0S88eU59zB8x358zfXuC7b712TdUTShUlHf6fkA2uXFtSO81D1UR41mrR0erQFVQ2B6BkAthvjX0oEXTVQKqczMaSaXTK3NBBkd/WmqTCkBEyrCYgqEQfBRO94Q8UGNUdCeL1ZOtFDwHhFbTNoatdgGNmmq0UYmT4YQ7L2NYzJpWoHoA9DsN1SlzEpOypwapQDaCSsxYc39ZPbkFft//cl7IN/dTXs6cW/b3+Xxx43uBKeWDlia0UPlozFRJQRPCMEMTke774/7hsA74dsvPuHhw4c0hVQqKhnnIIZoxktMKzHPM/Oc+zzdKeS/+5322Ly//YojbXg/BUC/Hg0cN1NDyTStOAchOhbLyHq94uxszdlmzWa9YrMZ2KwG1quBEATvHEEcIQS8FxM/iXZWQNHOAKhTw0icoRJVK0UaWROtJpSASkbxVJ/JoVBiITslU4HaMRXpt6+Bgqq0rx3r++O9MAICNCkdiLNnmkKuiUPaM06v2e6/YD+/JtU9pRWqOpDB8lgK6hwSjmGU2etSMzXdUrJjHTwD3nj/otAarkZDWLOQ52bGoDZmHWmSDQ8oMzlnSq0ggveO2oRSK7VmhIwPkZwac8rEPOOz4IIaL+zkxPW+66E83kdbEK1BFWoqlAagNGeeJjpnnqCBF6V4T/RCdkpzgDpqabQq5BlKcqRZKalQK6i6bgDuHe9TtPnN1NlxjndzPUb99yKEY2TQea4jGn8E+ji9v+flgPeBGKOF6B0E/Ob8Q1itlvzG97/Dar1iTplUjJWJ3t7rVHGiOKUbgQnUdfWd3nn8+ymMszTAOXdKFRons/bOghSh12U0lMqwUGKMnJ2vubjYcHG54cHDS84fnLG6WLFZLlmuFyyGgUU0URoYRiFH49P1Hg21/F7tvDTUgEe1yDeXbMatzvg8kSURW6EuGlFmQphZ+krUTNSZ2WdaaqSspGTKzOO4qxJ+79kBqGoGoEk1wANIaWIcb9kd3rDfv2FOO0NHxXdARxATkdIaSNXu7wz9VWnUltC6RXOgiGchDq9Ky4qUSC2mutLqugSzMKYtqc0A1GZRQVPTCFBt0eVyQDURgjJkSFlZ5EbOhZyz8fve41z31icRillskdDD9IZopVUouZGL6dJb59LxRyMgPRcWvLeoxIkzfKJkWmnkWSkZ8lypuVKKmpH7hoVwDNPvc9XH/1yn+rwz1N91o+qknuA+4c5QOJV3Hncqouff/cVVCd4Tw2DsDO1kBE6z6+7XifDg8oLnz5+zXq+pIjjvSDkzB9PjBfHEZUCKkHMmzal/tTMg9ZjuCH2l0A2UAcNHUFS0i7HEjPQRvxAHwxA4c2tWElicBc4vznjy5CGPHl9yfrHi/HzD4myJX0dCjMQYraCsKzzvF8NZPGH4Fq3hmuL1LqoyHYBRtkOu1JKJdSbmkVIKJVZ0odSWOS8z07qwjjPjKpMPhekwcXs7o9tMbcUk2h0sbJ01+EXj/TACWpnrNal5fB7wElBtzPOOafeWnN4CBS8eZYVzlUKmtmSgopiqT5sJi5wzQdBRaFRqxumIlxHPDLrElYpkoRShNjtpTRpjPXDIO/Zpy5xnSrMNWmulZiVPM9vtDSUnYhD82gAjbc0ijNzQnGlRackbhiCd5SiNXIXSAqVlclMKShHMq3vLyUu2EF+ckEk0oOEpCpVGrjNx8ES/QJATMFiSkmc1tWNqlhqURq61U4WW/qgKtTa0ZdCGI+IQhhC5OLvgo6dPePLoMQ8vH7IaNhx2I59/8Tlfvv6MTMFpI4ggtF5x6bEKTKE5R3POcICGUVilESqIKku/ILhghhujxo/8vAgmphF48vghFw8fI84xxMB82NGkkASaRtbO0XCIwnQYSfOEUE1wc6QBVTnVg6r0vNl0BkgEAkfxsmpFveKisIyewJJzP+CiI64HNpcbLh5ccH5+znq9ZLEYWCwGwtKji14deKwiPRpDtdAfDLT0OFyt+GZm6TjHow5B1OHV4xYNXwdcWeDmwQqXFiZhdhqgKNP6wHq943CYOOwnrq9gpxM1TeQ6Up2i1ejRqkqVX5wPvBdGAOjhdUE0d013Jc17cjnQWgbA+w4AUozSuUcLdhmhlWCKyTabE8QLQoTsacVAOEelFos4aoXaPA2orTDniWkemeaR3bRnnEdaUTQLda7M48x0GIGGLiNDhVI8JVdyrqRUO01owE0IZoNzy6Ri1W6t9hOK5cExeBYxQhVKVZxWqnQL3tmInBsiSkrCPAurVWQxWN4pWDlpKSZ1LqmRs72nFj3likcwi7toHuc83gdefPwpv/2bP+Bv/e5v8d1vfZf1asMirDqT4njz9iv+5Z/+c/6PH/4p4+4rHFDFmebdKVLp6YK7H2/Yv8cAoYlVGLpwgieMhuP0OhFYLSMfffSM5XrJOE20SZnyiAuOWn3PrS3aSPNsFYzTZB9yBPzkXohv+/9E4x2jA2Mz1XClhWcd16zDBhcEFwW/8AyryLBZslivWK4WDItj2XVPL6Lr7OU90PEYIWHgLtAVqN1BqXbJs56in2MyoqrgPTFAiMYSlWKevTajsLU6lhFk4QirAQ2em/FAksqsmVmLGRgvNBEa8m9DJEAHxRpowauimkhlR9XRwBQ8OE65lnaytCEorgs+ABWcC11o4UACUgZE13hZE1pEjp6/KUUtS2uqBkyWmTlNjNOBcdozzjNaoSUlj4U0JkoueDG0tmSl5EZKlXkueC9Y/wNHzM6MgEBu1WjGuTLNlTybDDJ2LMMtzZ+W0ijOWdRQCrk5CxNLByab0goWOtTCEA0lFzU9PVppDUpppNzIXV+u6mhU2/iiho6LsBgW/P0//Pf5oz/6j3nxySeslguGsKA1T0qRacw4dTy8fMF/8Id/xPnmEf/8T/4ZX3710igs19kHZ5sceDfXF1A59mkQPJ6Fjzh1JsI5VsB1eYGPnvPzM54+f0pzMOYJxJzEcoh4F/Au9MpKpaVCmiZqyqeURDrTYYhgn0PPe5wYloBraKgwBOIwsFx7ZCUMy0iIwSKAZSAMgbgc8ItobIQ/mpYOzsZ4AiHhzhgcF/YRenTOWZWl96eNj9wpAO8DeYCVPDtBiYi3CCJ04VxTRcqC6istzxzyzPVuy/V+y6EkMndMhnpbf7/EBrwvRkCN26aCZmrJ1DZR2oSKlQ97Df0kd9BHoKigGrray7ABMxQBxOOISBhwLHFscHmDa0ukBKp0EQlQWum1+SNzHo0VyDMpJ6pWwKr7UjWAsLWGSPf+yTGNxdB+Z+Fo0Uoqjjg4QqiI2IKfcyalQsm2qakQRPBRLMf1w0mdV3JlTjAXJWehOEctFl76Yy7b7ryI90aTNC8U1w1bLzgp7eidDaQ8ekAR4fd/7w/4D//Bf8R3Xnyf5SJS64EmzoxyqfzoRz+h5Mzzp8/49KOP+Hu/9w9Q8fwv/+s/YZv2dC1bjwLsO+h4Q489rMpOHK45AsLCD3gcWi3tOan0xVK7x48f8eDRA1PotYqIkHImxIg2M75pngnBUeZESwVtrSP/Hdlw7m4j9KYdzelJnefXnnC+IpwtiZsVYRNxK7FeFjEY9hJM0SfePu/Y20E6uo+q9Xy4L6jq47ix749jFAA9DerYwdFAHMepAArDOEQV3/dJdWbEfFBcC2St3Ox3vLq6ZnuYyLUXIqmCVIsAPfdrnH5uvB9GgEZuM00TrU6gCdVsCawDUY8Tj6MXFPlGcx6vrou4zAg0inULEo9g+gEnC5zb4P0ZUld4HWjiaceuQTRKzYzzgXHeMc17crXN38S0B60qtVhZsHGxRjG2KqRktJH3BRWrcstFWBy1AMHjxJkENRdLQ7qKz2FSVucFXZg7VFVKduQizKkxpsY8QymQs6nFfHCGR3h7v3dKEBOcMEDJ9OYrfRNI69RiR+EEXBCePX3K7//e79OKcnu1wz84R1zrjIhnf0i8ev0FpWV2+ytElU+/9Ql/9+/8AT/78mf8i3/2JxZmcwr8bYOcKETpRTEdxFUI6hncQPQRSdVqNTo6aFy849Gjh3jv2E97xAkxROac0D0IHh8yZWgs1gMtFco4QWlfIzHpUYj0Lj0Cg0dWkeFixdmTS5YPzxjOV4T1krCKyMLYHzmyCscOUWKHzer/6c8bjuDEE8S20f0mHsfffqzZOD139Ph9k3vvTwbg69GAKVPb6fVA13U4tFj2u9tNvH17y83tSC5HYPx0Qo6Pflkg8H4YgdYq43RD05nWZkRKr7LyuJ5DOtcLihxWPiyCNKVqM95f3lV3CQEh4GRBcEu8rsAtkOYRPcqLK7kW5jwzzgd2hy3TbDjAlCdKzeScyMlAwVYNe3D0E4FQijJPxbAKbVQN5Aq5+b5JbUE1zEAI1nHmWGCyiBEfOOWYqpVaHLnAnJQ4mxHICaP+iqHqMQpxEIbBqtIc2tMBIWdLRXy0KKMWUN8R8oaxHCifvPiY5XLJm6+uWMiSoIHFqtso8fz4R5+z241IqKQ88Rc//gsWZ5GHj8/5w3/3D/nLH/6Il5+/xh/DYbnHEJxGr4lXELWIJ7jAIg64KVFVrG6/L/4YI6vVynoUzMpitTQAVBw5F8bDSFwMOD/QaiWNE+NuTyu1C53u1v9x4TvvGdZLVpcbNo82nD+64PzxJcvLDWGzxC0DfvCoN0OlTbtYuPcbwMJwRI3a1P5sc6cqv3fSgNPQDjrqz/1d4ecwhFMrNjWxcuusQbl3fKQ6nEJJjcN+5vrtLVevb0hjA3U4jmKnr83k61O7N94bI3C7f4OSEakEr0QfCX7RZZ+ud92xIp2q9ZTTa7PcUBvanJgAACAASURBVFRAfY8pbUs4Ao6IdwO+BRR3asZQW6MU67AzzxNzmpjKzG7cc5gO5JopXTZskcA9a9o6EuEtzM6lomMXerRCLo6UpZcgu05LGTcdvHWZESe4EBmiY7nwLBbWVQisB0EpgWl2hEEZh0yalTRAToYNhOAZYq9J70IYmn12LcpUGkNu1KTUYPXx0oAePcQY+P73fkBJyjzu+aJ+hVZ4+OiC4BcslgMlO1p1WPVi5Xp3w5/98P/kb/3t3+ajZx/z+3/3D/jjl39MzUdHdR8T6Mif9PC0/zU432vqTfOf6TgHDe8dlw8uWG82RnGVGV8CLRqbLyJ47xniguA9ZU7sbm857A5Gu0ngnbUuBu6tLzc8eP6Ehx89YvlwzfJiw/JiRVwvcAuPVZh3PcPR+x4N/rGHodR74pvjfEyM1fHou3SkpwtWqNYjE9N4U08gCP34mAMz1vSu4vJoQJz3p1TgGBWUUjnsJ968vuH1VzfsdskA4Aq/cK+/7+lAbZX9eIN4JQQLH5szQENPBxek8/W1lY6YKtICzvVKwFatKlM9IhHViGfAetEJ4KydWEfRUy5M88SYD4x1z5gOTHVm1kTWQimVVhytHHM8m0WTrmdwjuBsIWRt6AhUqBmmoLhoun+kEMQxBI8GxQ1iclFRvHcsYuigl7NKN5RSEkM0bx68MLpMjEpZeFo/MN4d1WiWk7Zm/QkGFTbN0bKgSWhJoYip0potqEeXT/jk+XepdcGUJvbphqtp5KPDI1589DE+Lux4lwTNGI/cCrc3Bz77yVse/+1v8Tu/9ff403/5Q15+9gVaeySCgta+GM34NGlWVEXDq7KSyIrIIJE9pdfvw/mDBZ9++hHDJpA04Q4Fr8LKLxicpYRn6zOGIeJQKJmgdj4tZLa6gugCPsBwObB5ds7lxw958NFDzh5esthcsNysiUO01d9betE3NNp6RamlbQZAZxTTjXgXjGKEU/qmTaliTVVMjmzUpYqzFAybW6Mai1NrP14O11vqiVNqMfYDFE/Bia2HijO9h7Oqz13acbO95eXLt7x8dcP+kCy1dUcmyI79qWHKL9MM854YgdYa0zzio1g+76zj71wyTc3DVaf4YK2uSqmGlrfjMRYEj2sdAcZ4YIcVC0kLtAw1NQurU2WeG9OYGKeJwzhxmGbGcWaeq3VvaViBxr2urqeabThZdcEaSJhHaFZlKKYIk2a6AZFGdB5ZRJwLKNZA1HuHDw4fe4FJ9D19sO4xPjokuo7uCiEcG0uY1zoJe3z3SEf0uEbKACk2UlBCEEquZjQxb3H54JLNakOeZkQctTZutzvmw57DbuLy8iFfvXlDysUqFrHkf06Jt2+vuN3uuHz4iN/4/m/w6mdfmQBK4a4tjs0Z7f0xDJAHHAsfWPmAtXlRsjaGpfDk+TkPHp+hvjKlgm8R7wNzmgluwAvUUtAhslpvOJcVaTjg1WI+j+AjhDPH+aNzHr14yuXHjzh79oCzx+cszzaE5QrnjaJU1PCdk1hB0S7ZDhLQar0rinqrOYFTRGdNbapFWBjtC3pSJB7todNesIRFqdoEr77jNNoNUO9IpCYYsnJI7WmW63qRilQTGx32I69eveHLL7/i9nb7870bjyjgKSL75eO9MAJKYy4zg3h87PhVsbrp4gUNDg3G5+uxDXTpFk4x9Fas/NhCqYhoRHRANFjjhWwinJIgpUaae5ecg23+w5SY5l6FV3u76WpFRa30nLCZfBe9a3Bi9aMdzfWOJvSqroa2Aq4RPIRoYaKF8UY3ncpZpVkUFC1dwCmuelQqCx1IuVBLw+XezORYAddA61Ei6y1toVlvg6ERBmefGTApLdZXwAt8/PwjvPPMOaO0nueabuLV6yveXu1sEUqgtWKiHxrihdvbLZ9//hk/+O73+Na3v8W/Wv9vHMbcc2hHx7LNE/YajSMm6VAWLnAWl2zCyM7N6AI2l571eSC1A7vRip1CWIIIw7DkfLM0g5ATS9lwdvaAS7fiyr9EWsOpEiKsHix58u2HfPydT3jw/Ambx5csL88J6wUSA3hrp679nHqO6Zqh6ccW4ypK7Vr/hlKkG/0KrRRySsxpouSZkhMpZWqtdynLEFnEgVXvahzDYFGEglexPhfHz+ycqvY00xicXluAmXuPVdXmMbPd7nn55SvevrkiTdac5gSE6rs7668y3hMjQK+Zd12zrdRsAongBZqj1IYTD42T+gt15pz1WJThgKMaLCItQvO0DDlVymS9A+e5FyaliTnNvZ12YpozKdfeb55evdWLL3pXIiO0O0etULT3IXAOFWta4VTRYt0enCoSAiF4YvQMQ2BYRIaFlQpL9wTHvNAFz/E6C9KFRMvFQKsNJ4XSmtFVzvpRWThqHrd2XKQ5JURhGDwxNrw/AlMOVRii57vf/g4xhG6wev550vja60yabOentgad4so588WXL/nBd7/H8+fPefL4MZ+9/QqadnGMbZyqBrAd2YOjY/LiOF+suFyO7MoB7xwXDwYkZm4Ob9FwzhAGcNYTUsQwoeVqbdiP2PUZWlO0VkQabmicPTrj4996wfPffsbzTz5m/eCSuF7hFiaoqafd0puXVDj2CDBMo6IUSr8+Qi6ZlAtjGdnnPaljR+O4Z7e7YZwOTIeJaczM00Qptc9XGIbI+dmKB+dLHlw+4PLiAeerC5aLFdFHmgZKs4jENXC43pewg5Kid0etR8a5ZebDxNXra9589Zb99oCWO6HRcS/9HD4JvwQseE+MgAj43pCtydHTN3KWfk0BIVYlOIUqOHUG+0k45c+guN6+SqThe3GJFiipkqdC7sU1c0pWdJIOpDRa04+cTaNdqkUZR6//bvHdHf8icqwHMVDGNYqqgV7aa929KctiNwA+3NW3u77xlUZVOTWRdEGPkSTHirjBB2q0GgSvhkY7kc4GuC5rNvTdqemIjLaUzjpYxd0xRl2tVjx59JhWiolXmiOIN4VZsx/c7v1w6bm90zuq6/r6ms8+/5yPnzzm2bNnvPzRK0s3pJ206k0qrQuxFOuf17rIZ4iRy/WGg04c/MRqEy0NKBNDXoAEXK2kkgxMEyEOA4u4wHtvdSUlM6U9EisXzzc8/+5zXvzut3j6/Y959PQxbhiQEKzOrnPtFqE1qpajqUK7RiWXiXGe2I17duPIfjywn0b2h1t24zWHcc/hsGcc90zzhGqjZqHOxlwcqc6cC6qwWgvnl57zzZrNas3l2SXPHz/l8aPHrJePGPwl0Vm06gk9ireLyFiqQcdL7GIy027m9mrHq5dvuLm6pSZjRI4YwC80AP+a8V4YAWu1BKC9f71a8Uvxhk6rINHCfdcsDPa9NVRtM6VMJwGPUPCu4aUxKGgx75+SUIsn58acZrs4SR7NCKSJUlOvz269oOhOeGLeQu769Mm9q+wY8mKly16oaqKQEAQXhCFYNx134pYb2ipNXVf2mRhodhVVu0qQD3Ymu5PGiX1GU49Xc8++u2ht5r86wkVpvXmHt14E1ifA5owADTbrDevVCu/sCkqqXQwTPUoXR1X7cHG9AKArEZs2HI5aM59//jmfPH3M0yePWS4Dc8uUYoIViwRKr5GoZG1dL2AFUk6cbQxmHAYE51ZgnkEOzKExDwkUzs4eULvxWayWLIaBGB3M1rTl6cePeLZ4yOPvPuPJ9z7iwfPnLM/OTOsBiDb8EapsULWQ04ExWev0eT6wn61V+812y/XtDTeHHYd5YkwzaZ4o82Shu4qlC0enULvk3HWJd9fpt44l5VzY3ia4vmYVv+Kz1z/j6ZPHfPzgBS8efpuH58+sN0A1/EBrZ5haoeRCSoVaGykXrq9u+OKnX/DqyzeM+9mKyo6pDEYn/hvYgPfDCICt0dZqp8esNFbE41SotZKl2IU5mnnDGKzXXC4zcz5Qa+u6gNrFM83aMhVPztrpLk/KlbnXBkzznikdyGXGarJ7Lq8n1v3dugQ92ls48t+mcOmP+6aNWLgao7NbsJ4A3ts1BBq9IYQKpVXmLKhzZIVUK0PwxBi6JzDkW7ANjVraEIJtJDtOFs5qOzYQMT67o10c6y2NSoWnjx6zWa8ZYuwdljJVqkmeuwHKxQBSq8VwHYvhVKSlqmx3W/aHkfOLcxbLQMvVcIRcOEwTu2nLXDIl9664GM4iWEqgwDAsGCQzMVsUNiVK3jK5mbJeE2Nkd9ixPuxYLtcshhWX55dslku0CJePLvje73wXv3Esn645e/aAxebMUkI1q2fqyUJKMzlN7Ha3vL56ydvta662r9ntb7jZXXPIB+uhOGdSKxQqVRWpgmal1oZzViwlEno6YtiCiDd5b7N0UsW0LJVodQI5M+XMfr7m7e2OLxdveH3xmu988hs8Of+IQVZIcWhuzLUwFmslfziMbG92XL29NiPw9jVfXV/Rmvb10Nee3IG+7+wr7tjIXzTeCyNg6CnkCqUKVU326yUajWLVPVQtSDPALNVspa5hSWwOWjGqpgIuI6GrCVskF6EVoeZGnrNZ2GKXKWsUVAz692KbS9VRMkYRVdtMeuR/VbvYqNds9xxYBaQFpDq0WgsoUMQXrLGRQ50VdOTKXQefqqSWmIoSfWVwjtUg6MJZu291vQtyBwExT+8JvVZdDE9x5oFECmi1HFMFp9Y609Pwajbr0dk5l6sV0YMsPdU5aqsmWuqX6kopkUpnrcNgdRLJIg4Rq1zMubA9ZFaXjwgDlHnLw/UjighLZ+Hx7c0VV+PEmJSqHlVPIFj+Gx15aFTvEQ2E4zX2Wia7xjgJcosZJicMccHl5jGuRiieoo3l0zPOPlqx3DjcCiQMJ9ltU0dphXE+cDhccz2+5Hr8kjdffsnLN694efuGm2mPtGpOCEdrVsTVWtc1dJl1dv16D61Y85OaoR2vaaCIN0eRtUJoVoDkBV+7ly6WWc5VGMfCjd7y6s3E569e872nH/H84jGhRsoMYxWuppn9fs/11TVv31xz9WbHbpvITWm+72iPxf8dHzumbtDRhHYUK90RBt803gsj4MQRQ6SmAhxpGE9wnijeWjSLx4vHhWilry4yhAXeBVaL3iu+V9LV2kzXX2sv7qFfVQjrHNwmUp0oLdEk47wSnbN8vIIL1hWo1UbNVkqsNGq9qwG3edKjAzsR6szj3e/z7pwthkE8sXlcDYh6Ko5R6ZLnhpOZ6CpDcJTBUYv1pjvWg9deTShic7QLVbiukHP9OOpJwGKz6lTiaQHYM0MMhBAIQdisHhJmT22JGH3XyHtSsUuu1VZpKE4aWo8gidFWKRWur3d88uQpPi45TAlfdlysz3l4fs76wrPceJZX17x8fcPNbmIuwgFLP6ILRHHWRstjJeTHJppA0cK+7REPy8XAw8tLUr1lToNJjkticzFwdnbBsIxksbSjtJGqBw75wM3+itc3L/nq9Wd8dfMlb2/ekq+2aLVmNNNhRIIQ+tWojhf0sGK+QBx6WXozQU7rNJ821y/4Ye3fbAlYu/PO+RG84LytHys86j0relVoShPT7czNmxserS84X56zGNaIBvJsjWe3+x3beWLbGmO09upRSz+fSvc2vTmKA40WBTasK3EvVmrH3PIbxvthBJzVspcKpd21gopOGLxtfo/Hu0hwC4JbEP2SGBZ4idbuuofjtSlpzhwOBw7jSMWuRlS1kVph1JEkieIm1BccxqNDr4qvgi+CD73tmBcTcWQThFhXotPM4X4Qpli+Xzu/K9YMYwiOh6sVTx8+Aidc73YcUmEqjVQN4dbSiK6xGAJp4chZWa3uGITWr4lwBPCtw5gF+qer1B4X56nP9B3a360VYIs7hMByseSTF8+pzDTMCDgxefZ+3PH2+jX7ccecRuaU+4UuquX1ainD9fWWT558woNHH/FT/zPGudLylvO2YHOx5MGLb/GdTz7hZjvy8qtrvrza8nq3Z1tmdFAYsnWadq0bsIAQ7fgZ4MG4z1xf3/J684rzzYaly2woRO94cPkxq7OB5oWcEmOamOYDYzrw+voVn7/+jJ++/jFfXn3O7XigTsomCU82F9xe7ey4OTtPoav6Wi/xlS7GUho+9I3XHE4GMwRiqZiPnQXJphlwmDFwQXHuVCh90pJIL4M+UtG1Jm7GtyyHA5v1hgeLFZsm1DIxHrZMU6ZWAQ04FcOFToF/n6tac12poVcaNtM9HDGrY3OHbxjvhREQcawXG2o7nDJY7zzRBwZn5adeIsEvGcKa6Pp9WOJk6BfBNHOsDcqislom1tOB2+2WrTtwGGdaKXaVIDdCKMYg4Ii95lrVPLnPgsum4GpZ0AI5C3nOhpmXUwEfx5N7jLuMTqscLzceY2Cx9Dx/9oC/8zvfJw6Rn33xkr/8/CUvr7bUUpkPlTwXHJXlopFWnlwaqTTrLOx7yHksaKkdH+jqPwOglJys0Oh0rbojgCViGgfVk8BotVpzcXHJR89eQChUPXScYYFzjikdOL84Y7e/5c3VV1zf3FiE1S/nrg1yKWy3O2qDx8+es9icIx7anNluDXh9qGuePbrk+w8+pn7i+erqlr949SV//vIzbt2BclaY9UAVaxKrxVqtiQ6m+kRoVdhtE29e3/L4cs+0uWQZHIOPiPdkKodxz9ubt7y9vuWwndkdbvn85Y/5yasf83Z+y3XekmkMxfPi4TPqLjGPM37pKM0ao3hvG8vac93p/VtTY1m0dwRu/nTxUOe6KpTWfYJ5XOfUettiLE2t/foLza6fYQ21rR/DaAuP3XTgbZ55i+PpYsF6teHy8hzyAbefjIVqvrfkD93YS49UsoWHxffuRfLOlbTa+34FIiee5bAmt2Y0khqINnhHFIdTT3QDi7hiiBuCrIh+RQzLXl/QL0LReXCrDyicpUs2ywdsljtudjf4rft/mXuTWEvT+7zv947fcKY71dzd1QObTTabkyRKsiXZmiIYMRAHtiUgQRIvBBjILqvEq6y9ySKLBIiBIHAWBhIEMJQAQmInjpVJVqhQsUTapEj2XFVdVXc+wze8Uxb/79zuBZtELBvoAxSq61bXqbrnnO/9/sPz/B7SuidSyEpGf1oJ7L0gmgNpJcCO4u1Po2wKdM8k/RQCUZoGQp9AYkxDw2nFOUEgxM2o2I5bLtZnvPbSi3zu4V2c1cQxEIcNY1SM3RQ1PkRicqRiZaBUFJUzE1ZsAohM4560Z+BlRZhApSmKizCM0rPHkCepMFOLomhnLYeHxzx48AJtu2DMW1QZMRZZFaKofcPJ4QmHByuWyyWPnjwipWd0ZTeFwUSUmki/Q8fq8IBqPiMQUcaTx4HtsGN4esXYj7x0x/PinRe5f3SXo6MDlE08Gp8xHI3sSiZqRciBMCRC15NDoiRHygqhSXu89rSuwdtKWi7t6cbM7nLD2cVzzi6ekyI01QFvvfEqDx+8gPojePQnz+giKCdblocvvsgf/d/fojhNLoo8TKvCxqD1BC1BTes3GSxqrckotLFoY2TIigS7FBWBhFNZMgam4WzOMKYJ6DKZm7Q28nORKiBM6+g0JTerIAixYYjM+8jt5SEP7p1w4HbszjvWu8Auih6kZE/O+6xMibErCeE65GkYm6dK8cdMBj8Th4BC4U2FdyMxJ1IR+IKzBqc0tjicqam8OMos1dQWeJzxaOuwxk5wh8kIkgtlLLRuQe3m1HVD1XhMlXHrwq7rJqTVRMSYRio3oBCfiKMmWuERgplCORKpyB8R6o+eKoCPS7RSCmniDqSUiElzse353jsfYIrip996i5lfYWLFqrnkUd3zfnhKP1xTQsKOBuszxiWJCi9gnb35GwxQUpZU5sykExC4SUqFEPfS6kRKefowTAMjpVguFqxWKw6OTmSbkHtRIap8k3OoVME7j9Ie72qsrUnJ8eGH7zOM3eR+S4xhx8XlM1arlqqt2G0jxjpQmjzC9bjm4uyay12i7xUPX3xAXTuUDgxxg60si5kl2pFkpR8PgyYNkPrE0BXGPqFReJeoVEalnl3q6LeW836NUp55u+K1By8TxoHTsyecP3nGrVsLfvNf/zdpmhm/87/+A+IQ+OLXXma32zFQyJUjjJEyTPr9xk6kJdgDSlOSO7x8XaOmpCWtIs5BJDEEwbTVtaauLM5CioGuL+Qg1Kc8Kc60sVS1ximJkRNLoNzBSTLMy1nRYQhDZLg4Y+ErFocNy6MV813i6vmOy7OOGAs6e0qWz0i60RVMxG052khT4tSnPT4bh4DSOFtRlSBJu2mQtBY1xVcZR+MrmQEgpiCDw2qHNmKgscagjcMaN0mIZd3lTEJpD1aLoccnrC/Y7RVjCDez1Fwma3DOuFCI0ZAcjDYRx72oRJSLFHlz9zyZMrnCxF4MN176qb+MsWDNihdfeJVXHr7MwfIutw88J6uXeHM98sHFlm9/9wd855/9c84uz1BKTXeJKCs3nYn0mALOeApeDoA05dFlSHHKG4iFYUoQHsaJLnRTDSissyyWS+bzhfy7p0QcNVGO0UV23ymALlS2RWnF4YHh5Zc1Qz/y0UdPUCRKGikpsdluuXt8RN3WoLfEVFDFknAE5djkgd3lmuur7/L+04+4jld86+1/xpXb8MLxCbOlJftAtD3WGdqDFlOcwF+SZtxA6SvqurAbTnny/IoUe6p6gauOeOXhW9w+eJFlc8xud87F+Uecnz5lt/6Ih68+5M99/Ru8/e77PHr2Dm++9hr/x+//PqVyFGMxSfQeztibDAamVbHcHKRlFNWincr6gLaJqtEYClhRSi4XFW1lUSoz7AQ1VwryekyflZQCKcnL7DwkJQEj0mdqOQS0JiThNe5KYtutOQ0b2qbmeD7jxLcsD2ecPenYXO5IWQs+LOuPsxM/ceizZzZ8yuMzcQgAqCLhoNb0k1otokhoVctwZUqc0YihqBRDxmKLxRYvFYLxWCthpjlDJKNygBixvsarGTMdZP9rInRX0ispg9JiNY4pk0PBhEyuCtpHhi6QCAILLdO6cL82RHrFghLjUBFZsyrSA46h0CbDK4ev8FMv/xT37t+iXtRU3jE/stwaE/e7kRdv3eLl2w/47vff5vz6OevxObGsCcGSi0KzA72lzgf43KBVJpZACBMvoCRCGIkBxlHRDYpuVAzRMsZM3Nt1nWYxn+F9g7aOpHfk0qFUQhcRJMVcE0qPtTsiFapUGBNZLTwv3H/IbpNZb57hqsg4aMbgUHbJYrHimX42SSeUbD1IVEmTY+FqHDi7fszp+pRn6y20Bb22NHfnJDWCHtAGrAqYolENNE0hnyhyX6NjZmN2kFsO1F2++rmfpW1OWC2PGcae7fqUYdzQbUd2w5qBkY9OZ7z4wut84wtf5aVbC8a+43rcMmaNw0HUBJ8oCzCNIU1zIz1ZnSkWleRCslZjVCTqjqrRWA9NpZhrjXOFWSP+ijTCUARgi9GUOPkDinAu4yChIarylBhRpqCJoqZ0Cp0KVTIy24makhxdFxl3PZtmxFnN7eWC27OG9nHm/HHPONTELEOIUhJ7/N6+SvhxPqLPxCGgpkGgKbIWzAiHTdaCVlahKRIJlDJilMeoPPVChVI0qoilWCs/uQhBm6nPddJSeFUTS0udemZ5RlIDOUufh5p4AxNFKMdCCAmtg6jEYhJZ8YiUrUkm1/vlwF5fWCY8d5qGZ+RC4xtefvASD194lcXRDFcZWdMZS44JU61RTtEuah6+fI/Tiwve++Bd3n7n+1ytTyk5kssczy36UAj0FHbknIlBthelFBH4xEQMhX7IDIMAR9PUE2otH+TZrJZ14ORXKEmEUPswkFQkALUgva7UPB6tFauV4fj4kH44YwxiZ44p4lzFweGRqAxvZlBRKqg9ScfIeymRSpZ+6NlcBO5kT20tpnYUE8V9qQpFG0Ko0SgqV6hsR8shd2avcH/2Ap979YtQHCEkjKnYdR3X63O2uzWZTOxHaftS4XOvvsHJ3QW/90//ET2ZbMuNLd16Q11ZjJHKpnYGkkcVRxgKKSZ8pWkbQ90WlDUYm9CmgDJkPEpnQhaA7DDA9VDoIoQptEZNa+S95sMYcX9mZLiYJyiB0RYM5BCnkbNQsEjiEo0Bckw8H6/Q8xWHLx1jXcdH714SkoVi5QO5hzhMo/Yf9/hMHAKASIPLPkxSjKFOO7ypUVi5CMfdJAEtVAa8K+hiSNqLyadIJKSQhRSKUeAOWpxdtjiBSsaGMTWE0hBzQSmLnqLKhONfiCFhjKTmlgxjiIwhYByYME3go4iEmIAQMrCflHGFGxfisl1y/+59jo5OWBwscW0t0l6sEInHa7J+zjgOaFVxdHCfOweHzHXD2+//c86uP6KtXuXzr/863/7eH3K2+RaJLSUa0a4HS8mOGK1IdVPPMMA4QBjVlF8va6vFcsbhweqmZdkfXnlvREKhdBLj083HQ6PyHFUCTRM5Pl5xeV0TrzqJLQ8DShuWByu0lTQnVJlEWAWFkVdFi9goGwGKllFxddkTukJ9YLFWk8xU5SVAK4HCZEOlYWmXvHLyJe6vvsTM1vTjSM6BuqrJMTHGHZfXpwxhN2HLLPt4rpdffJU/+OOPOL3eUry4HMddRy4FZz2qaPIQcFisr0hByWxIJbxXHB5X3Lpj8U0CU6YZjGLXFYZBVpzGGmJWbPrIZZ/pRinzjZHhYE5y09JGY60E0t5scDUYLW7PkqdkqsmGbLRBaz+9E+LEHOLI4+sr4kJz98UjSh754N1r6Jub9+wTunZ+nGTwM3EICAd/slIWjVEWozXe1FhdUbImRDmRc+hRqTCUSOVH2laQ4t7FG9KQ7M6VaN0/0RtpI1x3Y8W776JD7ysJY0BNNFldpo2Bwk3Zhm50uNGRPKQgh4R80EVIpNT0sk8r+pRlUEeAk+UxRweHVL6irhf4eo7STjYTBaw/wtgDVPGclw/p8paDheX1V16grhIfflTR1F/kX/vz/w6xX/B7f/Q9hnwpTstgSCOcHD2k8nPe//DbDLGbQ9uSjwAAIABJREFUcGSidQAZdpWcmM9nrFbHaN2AcrKTn1iNetquUALgINcoXVOyJsWKnGqM3nFycsTV+pjdbkffCZl52/XMFwtc7cjjIIey0jAdAHteXsiJSKY40Fiu1wOnT6954ZbH5IpgFFkZBgWoEfIFtVmx1C/x+q0/x72DL9K6Bf2w5u1338E6x3w+I8aeq+tTzi5P6caeDFS+IWdFCoFZvaDvIBaNrRylBEoqOCzGaoauUKKnbStc6zA64KpAvUjM5xWHx4pmPpKKIPBDNIxjxXYTWHcj7cxSOSdR5sUy5kQsBW/AV440yrBWW42deAYxynCgTOMHY0R3UNIUuupl2ldiubmerRLBUETRhUS8vkCRuPPSnDFl3n1bovckZOKTB0Hm0x6fkUOgkIaEqLXFUmyVDP4MHrGsyJQ7hUIcBtI4slEDu21gudorqAyqNnsa9yTEkD0zTJp6lUF9Yg+spXTSWt/s/pWaFF5GCQ7cagF+OIN2ki+IkYSjMinobgYv0zro5hCIcDBbsVq01LVHawd4lKpQ2qGUpdIeaw/xtqFtFpw9/4CLi3MOThZo9zKL5YLd5hCnFF/+/M/zzT/5XTbrUzHr9Ik8Wn7jV/9t7tx6mf/0P/uPudpdiBAliHhII2skbaCqaur6mBIrcvRol7gh5Yg+UVRoeUaOS1CKkjO7DYTgaBYVTVNzcnLC04/OuUojIfZst1sOjw6pm4buaqBgp9czQpGQGI1MsKPKRF1ITqLi3n//I45feImZW5DihqjDNJMLqFJoTcOD5Zc4cG9QuoaL9SlDuObi4pyYIk1d47xhvb5ivdmIpZqKUjRhDHTDhpQSbTXH6YraVFOakkIbT0iF3S5AsHirUTONawquKdStoZ0rfBXIjAxDZOgVu7Vmt4Gr60QohWbhp5ZSIuSdUVCBdxrvFGMp2ISI37RhHJN4WsQgibHsp7NMW2v2823Z98lNMmlJzihFT9j2wvP1mlIi91+/y2Y78tHjXiyb+2rgJ7iKPiOHAIQgh0DRZfLKa0oWhxvZTiY4EVWkkGX/HXs2257NbsNut+PocOBgVairOaXs6S9TVVAyIY2E2BHjTuzHCoFpTHy5fXn8ScG18A2lf/vYCjypAiZnXpkmLxPjRM7cVIiloLLmeHXIajGjaSzKqGkYuU9TNqA1Rnna9g7WthizxFYfcXl1TtFnFGVpa8v66vu88sJXePH4a5xefJ8YemKEoVdYc4v7d9+CfMww+InavA8klYl3KRlnK2p3wPVFYo6isnLHUFpP0yPR/IehJnYL1DIR0gW7raPklroVr9p8vqKpV1CuyTmz3W05OjjGWkG9KTyluElCLUq1QppWwKK1iCpiasX5Zc8HPzznFXcLMy8YvwYXyNkxN/c59l9mYe5z8fwZMfT04YpYxknUoygq0ZhGIum1Y5+qZBSUMjJ0W8LQ8ebrb/IHf/q/s1ufoZ3QlFQujCmTCFgH2ke01/hWcgdMnQkqMfSyiek7y26X6LeZzXrHrktUraUoGUZ7oxlLwumErcDVGnKi6IT1AojVyEanL2riRe49K7CPEdd5SnTaT/aViNsSiaQNKlucsqgMSRXOQmBRNnzpqw9Yb97h6jxS2DM3f/z192c6BJRS7wJrZHoUSyk/o5Q6Av4b4GXgXeC3SikXP+55cs4M40ggoHTCGZGv6qnHL1km8Ib9AREI40jfjez6nmtzyW69Y+gEDrJanuBdBThJN849IXcMecMQNwzjhpRG6YfV/prP0w5Y+mNd8o2/e39xi0BQ7v5pmrzuf2+yEd20XvuWQCnNarGkaSrBoylxS+aY0DaTtcIQJjFJS1XVrA5naHeIr0+p66cY/ZiL8pyzyx9wfPRVvvGVv8iT82/y9uNzxtBTSuGP//j7fO2tv8TJ4St88PhbqIk5vs8j3qsN58s53rdcnHW4tsHUcbLpWtBTAAwwDoqL00zX92zHx5RwRFMdQDGoYrC6pq4aof0MPV3X0dQNbdui9TUpGJrqkIODhuenP2S7PZUDc6q2MkLOGUvCavjgvUuss9x/dUZ12JAyGH3AndmXuXfwM2wvOjbX7xHzVjQN2kyrYS/VXNJ421K5SEkarRzGZFIeCGlks11TtysOVofUnacvHWjhRVpvmBuP94Z6VlB1IulECAE9+TRCkH4+BRhHRJxVEsqA917UplEGqSUGaiOkKVMZQohMkCaMzVOoiMGNgtQ39mPqcIxpmodMEmYt1nNMxtgJIqMKBBG66yIJRUHD4/Nz2qPCV3/mRf7km085fy4TmSnj91Mf/zIqgV8ppZx+4td/C/hfSil/Wyn1t6Zf/0c/7glyLqz7gaQTzipcdphcTTgmCZAsuUy2dim8Yypsdz39Nog2IHSU9Ixh2NF1F7TzGdYvxH5MRyhrQtoyhA0xSoyYQWG1aO1FyzGV9sWQQ55yBaPIjSfppeCx041WPyclFA8USQlJSCc5FUct7hLvG7CerBRGJxRSIpckb3zRBkUF2cp2Yiys5itMOaGtblFXB8wW73N5CjkGvvrGV3j2/JfoujPeD99nF675wdvfodtGXrr3Gt/5jmPMneQgMPnNdUFlxWrRYCvod246va5JOaLKAUVfkVQWa7VL9EPFdjxn4Blzf0jJCa0jRtUCAK09RgeRL2eFVTUzP0NNZOfVwZvcufsGF+vfpQxn5DBSksStsXfjGcGRDaPm3bfPKKXw8uvHzBcH3D58nVfuvEkaDKpd0g87eV9UxLsZtatp6wVNNaf2LeMQ0eUZpr8gkUH3xFzYDoG+BLqwJthMO29JY0/Wosi0qWBmHjd3OK/JjIQUKEVjtCfmREgjSsnF7IwcYK032EYCZkoYGVJCErD2h25BM+K8wbSKOBasBlUKWY14nyS4dqrSJFxG4SpNiuPezA6KiUlhMNONKVdqom1ZyBZC4XxUfM9c8NrtLV/6ygHf/Wbg+VWgN4OsDD/l8a+iHfgrwC9P//13gX/MTzgE0iQ4US5BZclRUbyWcnmyn1L2UU1M7i3pi0oxguHOPbEkhtizGzYsujm+bbAelBlJqiPmjjH2xBwBJ9NveUJJ9Nm/UEVSX0qKMnyLkRyFFLt3uZWJ7yftyr5OyB8zCMX9yhCCyIe1oKW1AnQhl0RhRPo2w76E6PuR8/M19+7dxlcN1h1T1ZbFouZoqajLMdvrwBde/wrb8Bjz7Y4fvPOYp4/eo9tseP3Vt/i/Fi9ytvkhkYCQahQWj7eKo8MTvFswaI/SaUptdlyda3SlGNUzxrGntQekENj0Har22HpOiqKxt9pjbKadVTgvg7++K5RSUVXzKQbOoJQnJUueUqZjSqQcyfSyfiwaq+YYanK5JsTMs0eJyiTe/NIL3D/4IveOXiEFz9AHDpYN290lm+0VWlus8dy+dQ9dLEY5VgupttLzkSEOjHkglcIwBnIpnF8+53L3DFVlvNEoPLRORFhOYZ0mZ0HP7W+fKouM15iCMTWqOFk5T1oTpeWAHXMiZbBW44yV4JoUUErjnYci3hCNoMKU1tOwFBQfp0vtl81ay0C6oLBWWlaBlmSxbeeAsuUmz6GUTFGZblB8dJp57bbj5S9rtt/qyTtHAjoiP+rxZz0ECvAPlNA4/otSyt8B7pRSnky//xFw50f9QaXU3wT+JkCzMGy6HS6C1Xq6u0qMWJkSb8X4ktFEKILimtIwJ0KrUInUmFF9QtlEpdaYXFA2gh7JDMQSQYsr8WOXFROEY6/JFwdOjokwjIQhEodMCpkUJn32dGgIgHS/gZDnKJNqCw19iMSSJWVo2ktrlcglTNw+g1ZehkIqobXi+rrj1knGGk1SFU19Ql0bvE2cfziSsudw+QJffPUt5m3A8y2+950tp0+e8KU3vs6v/fJf53/4n/9L1v0Tec4yx6kZy9Zw+/gulV0Rtce6KHv7POP5R4p6btmVSwpQLRpyzozDSOXkwx+DomRLKT3WaOra47xEHZc8o+SWxfIErX+I0gZtK2KcfAZFy85cB/ABncGWllce/DzL+R2+84P/icW84qff+ktsNxccNy9ze/kmy+oObmHp+y3GHBPCHa6uL3n87DExRLabHYv5IU07o63nZBLn6+eEXSQHWdeOo5hr+nFLly7YlXPp0a2hsi3eGFSOghmbMgUoipTihGkD13iMmmFZYk3D9dUF280zMAFTKSKZMRbGmHEqQRB3XxwzlFGCdLToTfQ0a3Jeo7UlxkIpEWMt2lhSjISYqJ1DtZZxCBJICzgHxsGgZbAZd1CUpqSCIREGw7VyPNqe8sLnjnh5U/Pu/xsZYgX/ig6BXyylPFJK3Qb+oVLqu5/8zVJKUepHq5anA+PvACxumbLrdrTKkXM1TZVlt7qPEzNakVSEyVabYiIXEWJkpnw+DcoksBrMSNF22sNOMeVFTYfKJKTIUr6JMW5a1k6ABk1G5UwcRsYuEHrRsqcxS6WShfEnGE25/e+ZcORCMQL+DCmxG4ZJkCSzh5iCEGxTpKAxqsHoBCpTVTVxhG4XmS0sMRqUrqldwajMk3dGUmo4WD2g6+4zmwWaqkGHd7k6/4Avv/l1vvj6z/OP/s/f5ap7glaFo+UD/r3f/Pd59P4/5cHdl4jBkmJBTR+kMFRcn0dctSSEmpIrgj7EKEdOkRwMMYxCgJ42Ic5aZrMZTd1QciGGGXGsOT6+j7aeEmTynmJBZcGzxQJRT4o8rXHpkJ/78l+laVZ87wf/D3cO3+DXfuG3OT99h7t3KyrVEHsFeeT68krowKamsi2LxQFXF5disw1X5KApwdB14wT8FONMmtgOMiSOYEbSVA2Og2ghclGkOE7EpzJt1+wUNa5xHqrKococrw8xqmJ9dQ2l4JxGliuWMcRJop0lg0CJYiWjJg+HzBXyBAJTVjBwMQRCyVgjN4KkZEtmrJY5QCzkmClaYVzBVgprHLOm4lqNjF1CBSBalIZt7HhyPeLqwitfuUW6ULz7w92nXsR/pkOglPJo+vmZUurvAz8LPFVK3SulPFFK3QOe/aTnyaWQipRO2pTJwDEJXJQMN/ZlUpnAiykHShEnm9ZKkN1e4SrwVcLVBuslb14ZyEoL+ScLG95kKcFjnhp4Jo5gkUokJwkbyDEy7gbGAeJYpBqI026XPb+Pm4qCvQx8+r4imU23oxSN1RXaemKaMgO0rN9Syii9T9Wtqao56+uB+dKRstiELQZfGY5uVTx+O1M3R5ycfI7Tqw2vvpxRv3JEY8HqRFsfMZ/dhTMRI+Wx4Yuv/QXYDbx076G4Xg2AINa368L6OnB8p4LcsLnwPP/hBUdHJ5RosRwRR404beWbLKpQVw11vcBoTRpnpNji3BwQrLfSXhh5cURN84mxxImA5GntA07mb/Cnb/8Jpqy4c/hzLNuHnLxmODmuKGYLJlBXFQcHRzx//pzr9blgvKZEqhAkrTiMmcuwYb1Zk5K0a+Ke4wZSE1PAGPBaqEwxwhh3jMNIKpFR7S3FTkJdKje5sAspaeLQsQ1BXJtpy2xRYX0mMEJSOGMoUdKhrdZoC1hZIqecboRlOX+8eYopTPmZ3OgpCtMmwcsNRpuCcdP14NQ0cM74SuFq+V7DNFMwRlHUyG4wPDnrmd+94qu/+gbPLr8N1z/6+tM/+ss/+aGUmimlFvv/Bn4D+Dbw3wN/Y/rf/gbwOz/5ucBZMFbY8cYyQSaYhM+wz2kTGWoEBKmlbEbZgrYZZRPaJ0yVMD5iK/lhfMK4hHZZkoMRnJiiyCBvup9TQOUJDFEKViucFsx5HCMpCKkoBVkXkaeV4k07caMiZv+LWBKX19cMY0KbGqNrjKnQk0agKCNvfJkkthQqV3F+fs0+QSbEPHkbMsuVY9vv2A2aw9uvce/Fz7M4WPGFLz7gpYctvorM2yXLxS1KkRVrDhVnTxOL+i63b91H6YKvzZSA5UhRAQltBmBk7OHyDPqdxps7LJsvkoaVoNuU8PpyTljrmbcHeLukrk5QqmW7DaAtxnq8rdltdpQkmQRFGWIpAhwtnmV7H1dqnn70NjnCvVtfxVq4fTdQNyOzlebo1hzXVBhXU7ctmERiYNd1jGPCGs/h4QlN06KVIRdIMTFOcwBQWOtw1tN3Qgci64lYrUSBqiJWJ3SO5DAy7nqGbc+w7chxJIfC0BdCHOnHM/rxKdZ3+GqavccoITNJTUEoFqcM3liMNjepYwXRn/hK2JMoaT21BVupCUPHtDFg4hEkjFf42uKq6XBVGq2hH3YysLQJX2t8nVB2pCRFjpohWB5dbrloHvNL/8Zrn3r9/VkqgTvA35/CFC3w90op/6NS6pvAf6uU+m3gPeC3ftITKSWnnnFgTBYzixJuvyKTVaDoLDJUFaWMNVnADXuwpinyYrqpZHJJXlS379WzUHFiJCYwKWOyxSBZfhQJDklC6pA46CKHgEGL4mso5DFRQkbFPVj0493LHkF+0/8U6Tg2u44wCmgEzESmsZDSDbhSDFOyqTg4bHjy5CnjGDEGul2iaSzFgKszzstmwtiG+w9eZr5Yc3b6jH6jSWnHajXj5PgOzrTkEqn8nDJaZs2Co4Mjui5j7ORtL5btpmcMI9palBkhZxp7gC6Os2cbtueexbJldStQ6MgkYT2WQlUt0EVzfRl5/PhUAky1xpiGtj2kdoqL8wXnW6BkjLHEAIWaO8evEUJis3nEV976Ai/df4n5TDFfRnKK9GkgdZ6cLdtdh7FO1mEpMowTPBVNDInGt1hdiGXJdjxn3V2JpZYMRf6tfbflJsJclL44q1HFEFLEYSCLO1PlSEqRVLyQrnWZ7siRGAZJxioyWDbFyKpRS5S5URqv5c9Iq1pk5mPFGwOinlTl4/XgHhCyh38I4EQqBGsVWQszIiepZMXPEsTuXDLGWqq5gqBJ0WNUkepoA+9ffcDXX5p96vX3L3wIlFLeBr76I75+Bvza/5/nUhpMrbGVlcBOhBasdZyipYPcvXMGH1A+YyqEQlMiRit87XCNwTYG3yiMlwGKdmoSTSmKMpSQyCWRSBJ6qi0m2amMF5dgmAQauoDOcrKrKPAJhoKKIuUsiamFkBmGKnsZMZgsP5RRhAApezlokiYMM0o+kAFh3krSMgVVGpSBqs0471lfDRwc12zGwNgp2lmNqxK37jnOniZ0NiyaJU37kHZ+yPOnhthn2pnl/u27tO6Ybb9j1rYsDxoUimYx4+nlFu8N4MmpsL2WMlQ5Q+wtYfRs1wNHd1qiveTsImLyG9x6YFH2DFSgJAn/rM2KuX3AMMzpuh1DGklqRPMqOp9wcJKo2hazizg6NAGNx5e73D/8GrtN5Oe+8Q1+6qe/Tq3n3D48wKhbZHNK3GW6cYN1FY3zoGru3XqFi6sLNusnWONpqjlayS3U2EI/DHTjSFADQ+lQ2oFuBQRqemyVSUXL0LmIZqOgGZOW9CY1+QCMQ2nDECNDCTLEk6ETxjTSFiY1idjkzqyMaACsQizBSQaNxhi0MdKapYkerQ15UsHuadKCD58CY82kYFXSOgiyXa4Xf9NSAFrYlkOMGDKL1qG7gRy0UKtKYXMJb7fvfur19y/cDvzLfCgUzlq89VjjsMZ83Arsk3n2oR26oG3EuIDzCV8H6lmiXSgWS89yWTOfNzSzmqqu8c5jrej05Q41EYgAY50wCoxDT+o9mex/HAW9zwvIRfz98ebi56ZNgWkm8AlFRiliCKFkdtueEAqlWFLWjKEwDkWEN0wxadkjjUnEe83hwQHXVxGFwVcw9KPc/Y2k9cQYSKNClZqmWbA6XHD33hHOagyGN175HK89eEitLF4bmqrm+OQW3jecnp6x3W4Qjp9hsxmYtXMKUURDxfLo0fto22GrkTFuUSrQtAVrRYAjgkqPLgs0c5ypubq+YNdvBGJqVuQ05/GjCza7gZIqCYfNAYtmWd/hztF9Hr665K/91b+GyXeZzTz1bCBl0W7XVU3bzpi1M5qmpWSYzRZ4W9NUM44Ob7GYrwDxhWx2G67WFwzjTmZGlEmDayRNKA8oW/CVxTqNSPijUKx1QemMsUUi3ytN3ViUzvL/TDH1OWecr2hnM1xVYZwVCTlykMshoshKfBKSbCwekRT3F70oBbNSGOfQzlKMIpTEGBOxgPTFjqQ0ZWIZYBTZQHZQnEZXRmZeGpHCT6ASpUWMpbRoFsYRTq/6T73+PhOyYRTIR1fwTkVLnNeNw6pMk3+9R3gnXJVIBFQIOAftXDObW+rW4WsBiBgnfybmKCrALCVbVHnaFChQRkJCC+K5n1ZDMcebYc5e+AHTsOkG17XfYrCXE3IzJIDJOVY4P19zfT0Qo8H6CqU8ISow0q4QJTJNGcilx9iKxaLh0Ydr+l2mbhQX52va+YFYWucG72HoYOwNzWpG0RvqVmFsQRfL51/4HL/4Uz/P9uptPJLfcPfOfZSydN1I04wiJ8YT+h3trCWEC6xxGFPRdWcUc4HzgaqeUc8KzTwzxo715pxZXeP1nGEXGXtLJvH02SO0vcC5GWHbksOMzWakG3pS8oRRgSno4rm1epmj1QEPX61ZLg/prx3Llyq0v8Qlh7ItpSrsdj1hjFRVS10phkGAp42bsWhX+KplGEYKsOs29OOWmHrCKErK/VBwzJE+dmDyNFSeUo0qI3byIhduDhFiQZtMZkTpfRKtJhehWJeiaJsZppJV9tB3pJTEGpyLtEsK8Z0oc0OY2qsClVJSdZCxzqFyJgfZ1CglFvqQhWqUkUwKNWkWMIlgpL1QTqFjQYWMLTKYHEIUgE5W0tZoGSSudz9ySQd8ZioBjVMOrydMmDEoq2WFMtkrzdTra5uxPuHqRN3CYuk5OGxZrhraWUVVebyXH9Z5lHWTeEXLe5nkwClFQJlFAVZRVJ5WjAJ/3LMEc4nSDzpxwEwzEG4gDdNBIPqAjwNLhc2viMFy+mzL048uGMZC3yUuLzvW20CMipQN41gYOmHeS5WSaOeaoU+cn3ZYp4lpYLuRO0vTwmyhWa87ui2QG7xvsC7ha1k56eh4/eEXePPzb3KwXGKM5s7dY0KIbDf99O8sGF0TRglGDXGDcw6tamYLT8jP8HXBWUdVg/Uj2+2Gp0+fMw6BODScP9c4syDmnn5cS9XFnJKWdFsYQs9Vd8l1t6VPI2PIWHWbe7e+wL27x9y5N+Pxh5d4e8DxiaVuBmazGVU1w7mKumrYpybXdSMMQAyL2SGzeoEqcuNIOdKFniH2jHkUhkKSSsc4xy5u2Ia1KAFVJpbMmMPETihYLzkPetoymSnE1Trpyffy8lwS/diz2W1JqlDPG3ztUapgVcGUiR49Kf1iCqQcblKnBSUo6z9rFeoTg26jwRpwlSGpPK0qpQIuJaGQ6b+2ktmgrczB0FBVBm2K4NMrhWsM2itZVQO5mE+9/j4TlYDRmlU9p60rvPeYymK9w1l5w4sS0EUqGR0TNkOFxlUebxtm7YqqnmG9Q5upAjCFpDOFIOnAk4iHLAqrnCMhDyRTYSeElyryouqsxGjIhA79hOcbpi/s1Yvl46/tfQQgugNnW77w2tdgULz73mNCTNLrrztSbLCuwubCMAyEYfqeK4NWCl9n5oua66vC7ahYLg7od3Kn0XZkdeg4f9qxuY7kUGH9nLbtWSw9F67QjYq2PuDzn/8SH3hDSgFfGcG3jfEmyTllx9ArhmHExS3zpsWomvmipug1i0XLRXGTRTqwWQ8MfUHhOH0e+OCdLZW5TT9ckcoWimE1e5HxdEUIgaR3PL1+wrh7jNIXBFNxcvQVfubrv8z9F5bM2sKzjz5kNtO4uqeoNG1qZOBnradtNUMvGoUUM001g2ygCFZu13ds1pIVGFIgxgJJakutLMYprrpnrIeLCfAhwh0zBUdIEvE0hDMGo6T/H0ZZWxulyDFjjFj7QsgMoRdylSo4o2mrirHrBdAiDjNkLCjDUGUyZFl/A6BEETjGCEWSoosqE2cCchE5NnlacSIgEuMNQxgliHTCiFkHWiVSlMMskASfPt2IlELsy59l5LjVhtVsha2mU85alDNY67BoQU9laQNMyKiYcd7iXUVTHdPWh3jn0c6gjLQMWUVGNYoqL4gISE13bIXsWVMZibmXqa6WrxsFJmt00BQlpV0u+WbFc/Pjk+3AlApMyeyRozkrvvDG1/hP/vZ/zn/3936H07NLYgpUdcNiWfHhewFtRpTd0vWXNE4zm6/wlQcC2u04uTXj8QcjfZdpZy0XzzN9l2gXgcWiRqnA1dVAGDy2abEGZgtHOzMMtcWmAw5P7qLUjG2/ZRh6IltCkPQmpTVxiAx9nniLI1W1YhwSvjJUdWE2m7NdJ66vO8lz2GS8W2B0zUeP1vTbCjdTxHxNUYHKH3D73pfYPj2gMBLVFae7p8TxMXW1w+vXefjSL3N0/CKrY3GK9hvNrYc9rorkJEBXbQQIElPEWkf2ijBKaOxqdUBbLbm4umYcRrSxrDcbumFHnGLsYhRuoLMVdeNZjxfE0qGMILlzURIuE0Wlqa3CWjkcvJahYTSFEKIIjpKU7847irICc82J7W7LwlYs6gZcxTjESRmpGNMod32z9/WrSTCUUGi8nvQiRi50FGilCCXSVJYU5eCQhVXGOXFIliQzk6gVugJdSahJjBBCxjUWqwy7TUSlIgeb+cxXAoZ5PQObySZP+/FCUgE9ebSLihQjEU/Kgys189mKZX2LujqUYaIBTAE1EMpIybKXVgzsj1W5qcuAjSwa/kyQD50pqCwQSBUKRWcSScJLsnDh8k3Jr3DaE9N+kChQyf12QGvD++9/yJ9+9xE/+1O/wg/+9PfYbgdu31EcHLU8/vCKoQNsYLPbMOiW2UVN01Yok9DTIK4kzeV5YDZz1JXm/HRLPVM0c81sbrg869lcz2hWYtuta5mNXNmM98cYN6eZWbbdmjEc4cwwVS8C0Yxx8k2piNUKS0W/i9SN5ehohnNLchjJURDs/ZCxxrFdF84q2yo8AAAgAElEQVSeDVAOGcYdRfXkEnF2RuWOmbeH9GPk4uw5Y16jXODw5DZvvflr3GrfpGTLbOF4/MGWulpx9wVPLluIHlSkEHDeY5KwHK3RdHHHPl8iZ0XbLDGuQ0VHzoq+H+iHQWY7srMTf0Ot2FxcMqSOEIJYt40VURpBnKlKLs7K16RJwNXOlqi+p+s7aREzdN0ISpSEKAGWbMOIzS2r2QKLYjcGJLFNBEOl7DMfQE/xeHlvFnJGhElG6FnsU7Ur8UvsD4EcM846QgjMZg2lKIxx5KTYbntyL5AUZRRhiGSSBNuKn5oxfvpg8DMyE1AYVeF0hVcVOinIiagCo+1BJzHE6gA2UbUVi8URy9ltFvMTFu2KWXvArJE7ROMPaNyS2h3gdCturBLQKmB1xiiFUxZvHThFMIngEtEXoi8UH6AeKHUkV5niZDgjkWCQiuZgfo+f+dJv8PKdb2DTAapM4SdFBD7KjKw3z/iv/+5/ReUOeOHuT3N1roix0Cw6Do4i4zbjTUtSnuvhktOLc7bbBAW0qnFWHIeP3tnQdyO+SgzbOf2mpfKG5YFlGHvOz6JAVNKI94rlgaFuIjreQqd7FLMVag1OyD65IMEemnFUNLMK7TdoLHY4wKQ5vh1ZLlpsOCHtGrQSEMY4bshJ8+T9wuP3CkOXieWcXC7RyjN2movTRI4Vfd9z9vwZXvecHNzi1//Cb/Obf/nfpcExdwaD5b23r6gqw2JlUKVGq5pCh1JR7nhF3ZClnXbUvqZpG4or2NrjasdsviTHOalfEQYnrkgbqJrCbGEJccPl+jnaJJyFyiqskW2AtUpyA0rEKk0siS6MbMLAxeaKYRiwyshcxDnJR1KgY8ZG8fgXX4gmkPRIVCMBsfZS15SqZTSWXYL1kFkP0EVLnxVdiXQlsY2Ri67jsh+42PXsRtGyjDEyhMR2DASt2MbAThWuiQxWodsGP59NCHyNrzzGWVytsbXB1hrjZUaQ3ae7CD8ThwAodNboIvRgnQ0lQgki3Ng7pJQWW27bHLJa3mK1uMOiPaJtVnIA1BI20tRL6mqB97Xw4yZUmMiSDdZZnPdY74UW5EH7jPIRbKDYSDZSlWSbSTqSdURVoJxkDbz04Ov8W3/9P+S3/sp/wMMHX8MqYbsppW5CKnOJfP+H3+Gf/MHvc+fWa4RgGccBReL4tgO7ZegqnDphCFuut884O+sJQzXl143YasvF+cDzJ4W6cRibWV8HlDYslhXWFq4uei7OIn0n02dfKeqmwmhPbQ/xuqGuHdY5YiiULPFaYIgxc3Qyx/qI1oYwKJz33L13j+XyFjlZqspT1SLUGcPA9fWO995Zc3nmcK7CVjuU6QDH+lqRY8t63ZHVBsU1d49W/MWf+wV+/Zf+MqcfOqzyzFfw6IM1241mddDy6J3Cs8cFZXrQkZwtagqUyTkTQkRNyUl13YDqKXpDysJUJC0YN/cIXQMlo3PNan6P5fKIq905F7vnmFoxn1U4B0olIJJzFIyqk8/JOIzElKZP5ZT4pBRjSgwpiuLPWmpnqawRsZEWi3k/9owpYJ2RNaQ1kkbc9YwhCExlUr7ud/zWOYqStCBxmioJN6HcsC3k+xd3ozhSwThN12+5vr4gpiDqQpWpZ4527qkaQztzNI3FukLdfvql/ploB9QkytFZidd8QnhL1JOSi5CMNp7a1tTumEV9m3l1TG0XWFth9gYdErFI+EUgYrURB5eyFCtDoWIs+xRXdJY5gk7TameQFiFksk1kG1FVxswUJoDqAav56Nkpl5cdP/ezv0Im8Lv/EL7/7h8yRAnxLATQmT5c8k/+8Pf4+pd+gQcvHQAKcma2GJitAs8fa1y9IMfIZrvh/OI+s7ZlsSqkvGV1nNHvVJw+sTx4EXydGXsIY2Fx4Lh9d0nXwenTjtve09QabTOuku/JMudgcZfVvMZXhl0Q55pzZtIyGE5uW853A85VxEHhG8vR8Yy2caSkaBeWZmYIcSCEkW6tePpopLYPWa48pf4BpoyUbkYc5lTtiq6/oD645t4Dz1t3fpZf/PO/ysrf53/79jNWRwt8E3j84UCODeenhd17I1/5Ro1Sa0HKZUspcjAlqa1wzmGNp+Bw1hLSFqM87/+gsLnoifk9lL6m1ndYVt/gpHoVr9/h7OofM6o1qSRUjDhnqIyjH0dhRxTZqKQcySVL6nMR8q/VlmEcKUXi20pIGKPF7RqiHBha0YcRw5T6owrWOcm0ynkKD5bDRKpFQYVZK1jzUpLoACZLsDGWEIKkS+0PgylOTJWCdxZFYQyDzA2MrCAx8ndYr0kRdJFtAkHSrz/t8Zk4BICp75n+oUqL1DNP4Rpk6a9UTeVXzKpDWn9IbZdUfiZQEW2lXEemqslkdJT0WY3F2VpOf2Mmd6KctoVIUYGsMqUEcglkUyg2oXxGVQXT8v8x9yYxll5nmt5zzvnPP945IjIicmZmKpPJeZREUhRVGmuQUIXqtrs9dDcMdDe6N97bSy9tb7zywjAMu2CjqtHVVa1SqVWDRoqkOIlDcsp5YEZmRMZ8x384gxfnRqoMiF7zAgECCRCRee893/+d73vf5yWuJE0tMTOJSy339j7mT/70f+Rf/4v/jpe++k26vQ5//aP/k3c/eJ1xNcIxRQhD48esbVzi4uVLnDv7Ar5OkLkjUnssrgg27jSMdjVl7dAtw3AyYXMrR0YSHXnaPShaEXUpqCpPmsF0HFOWljxT9Bczon3BZLqHaUKOYBR78rZE6QneKVYWj6BjhVQhfyDLU+IkrF/jJGKw4Nm3JZEqMBbylqDTy4mTCNSU7gLkHUvjSryT1KXCmZilQwPSfJeR2yKKPDLq0MuP46wgLyQiGbLaz3nooeOceeBRbl0UjLfbHDvZwnnL7o7F25S7t0uQM/qLCyA0kgKhapwLVB5jArE40hrvRIhZqxQiKti6C3duSEbjCcgxS60nWOo/zGQvwtQGZ8tgOooanC0RTuOdobZzEnUU0dSBbOWFDfdzGZymQoTNlMOjlQ75j1IRRwprGoyfE6EAGSmcCm15XVWUE0uRt1ns9RhPJljnmFUNdWPm7EszVwj6+xsm58Lwb44SCSNsZ+8L0eJY4YF6Vgf58RyBpyLwKmwAvAdjDGUZtixxJFE6rNs/Czb6ubgOeAhyXWfDiscarLcYZwKTzgVRi1I5WnfQUZtEZWiVoMRB3oC+/8SXMportAzCgY4S4jhH6xQVReEKEEmEPvhRYbMQBfO4ikDpcJeKU0mSSbJckOQESXJmEck+1+68xf/6v/3PfHjhEk888hX+i3/83/L8s39IHh8lEr1g4BE1+9PbfHL1Le5tzGimCRiBkp68U9NdNKjIoFyPukyYNfvsDPfY2XJUM43WiuUjMe2uYDIuyXKFs5bZtAp6ggKS3BDHv4kv17Gg1RUsrkiyRLM0WGBpKWU42mM8mdDuanRqEaohTSWtniFO6jAsiwT9QxH9pQjjPUo3nDrXoehVODFDyRbjoWCw2GVx1SGTbbyYEMcJse7S762ws71PVkCSVRw+tsDxBx4ky49y6eIIrfv0+xmzMYyHMNy3RFrwyFMFebtCkiF8uFp574Mt2IckZTUXdYWotYp763ts3nXk7RoZ79BOniRuXuD2tSmNucWx03eZNS8TyV0Su4icHUH5LtUUpJdYYzBNjcAGPYoKT2nbNIEjUddMp1NsY/G1QRiPVoqyqYI8WnosDmODXLx2FiPCKruqa2bTMa6uaec57aIgUiHm3HoTtC8q+FaUCsPBJE1J0xRjDcYYDmLplVJkWTrfDsyJw06EJCOpSLKUol0QxVHA3vngPHSEMzV/jH7m63PRCXjvqWwd/AEi0G6FP4h5DqnEkczQuoVWBVqlSKnDPlfKuTwyqP2CacdR1w2mMQg8sdJAEu5XBM3AQRUODiOJEwpvBXresgV7cxToLo3Alw6TCUxhaCqPLyOK1hLPf/kbvPGrX3NvfchXvvIl/vh7/xLnW7zy1l/R1BO8rKjMDlduvc61a9/lkbOr9AYqwESiMd0lGG1XxHKJybSk7uwwLTWjvT65zpFqwspRxfqnJUIoVCTQsaSpwwS71QlbkaLJ0LHDWIMUirwrOXWuRT3W5Jll4XBC7WbIMfQHGXnLgajRSRaGplGYenf7LSKZkhQQTl/J0mrOrJ6g6gZvUlzjOP/oKnW1x+5wC+0kebKAkwukScqd9av0Bjknjg9oH16hu3SMtY2Ge1sK46GsG3avzHB1gRQand3mzCMDjDdoukhlwAdasNbBWKN1GswzEmKdsXlPsnlnSrefsNrRDCfHqSYdtoYfE2U3WFod0G4V3H1/n0I8zvHlJ5Ftwbu3/pSZm1FO94nikJIkpAqSWxfYklhPdF9gZsE6pD14EodW/gBK5RwhDm6eJRBs7cE30jQV+3u7FK02SsdzfwD3fQIwtxh7UDJCqTB3OZCfe+8D+CRO8B7qOrggQ2Mg0FEyj777DYVI65jaN+gkbCGUUDRVw6T67RoB+JwUAescs3KCjOcBDHPPwAH7H6GRKkHJBEmM8FFolebtkBTzoR8eYy11U2KaEmfNfYkvhB2s9EHf7aUPaiwhEEqFYiPmBhIXUl4b7Uljj0stNgdTCUxVY0uLmGYsZsf59re/i5nm/OCvvs94MuX555/nH33vvySJPa+9+312hteR2rK+dYMrV65z984jLB+2pElEHMe0+oaiA9P9PrHsUc7uERdTGuOZzSKEjugtNrQHjrzo4qyj1VZMp+Fak2QRcRZhrMK6Jth8sehEk6eKIyczhqNtOv0us8bQrnP6ixmtVkix1VpjjCXSniSJGQyCIUfq8PRYWGxRFGBGBj8y1LXj9OnTnH2oxfrGiLEZ08vb5PkqUXOUO9eGdAcZrZ5hcSWjWOqjZJu7a7C9VbPYc8SRZjhNUUQY13D4ZAxqnUgtgXfz8BMd+tRIBGWlCTbyqrbsblZs3NL0useIiyFrtxqq8TKbozdxyQW6A0HRj7l7C9Y/fITV1adItGZavUYR5cxEDyJwahchAjxGzmd1QgrUnGnprCXSGrAUMsFhKZsGqWUAfEjQWmFUWDXDb8RikZZgoJ41GLNP1ioo8pwoThhPZxgbrhIHdufQ8VYopUh0TFUHspWYQ1wOrPTOgdZBEeu8paxqdByR5jn7oyFqnoEZa41EBXApwab8Wa/PxXXAOcN4ssd0NqSsJtR1iTU2ABpcgpRJoMeiUYShnnUBjh1SgQMBV3iP8BbpG/AV1hnsgazXhQKgpUcLi5cNVoWpvz/wAAeSKZGUaJWgVUqcaHTqSXJFlue0iwU67T5527M5usCf/Nn/hPX3+IM/+H2mzZRXX/8Vi61D/LM//Ff80df/JUuDhzEUjOopVz/9hLU7u2xvGZwVKDLaecbi4Yy829BOu0RVH+VrnNyktNA0GbWZ0V8ytDpzt2RuUNrRmDA8lXou7kkS8J7pbI/GVIhI0l52TP1+sLEminY7YbGXkGiJFK351SjYcpMEosQTZw4VOSKpWFhoESWEaCwXIaXn4ceP0B54dGsMSTj0SwvL9PptvDKcfniJI+csraUR3V7GeBu2bnTIoza9nsVMCrIow9gNHLscO7pKrHthBSzqEHbi6qC/cBIlM6wNw67JvuLODU+aKlYOC/Y2LHdvjJk072HSS2SdlFZxCGEPcetyzGL8LOWaZ+ODV2HrHRZVw0LaIk5SrBKE8HhJIgJfwLng2DMEzYdtbLAfR4bal7jIY6XEOok3ClzIo9BRhFYaDCE6T8YYJxCxwHjLZDJFWOjnXdpRjkbPyVhBZx66UosxFdPplMbUIZcxCloCHamg4xCSfrcftiZeEMVBGj8uy3CNtjVKSLSQaCnDA1IdeFp+++tz0Ql4oLZNkDg6jXBR0Px4EWSf4sDlF4WEIefnRh439wPMA0K9w1sD1hABcRThiKiMxblA9XXzZIdICNSBgtAy/3/l3EgSWuxIO2JCzDiNR+YZh+LzFCunWLu5zvX6Aq+//2dMJvf45//kv+cbX32Bn/zda/zkx6/xzW99le/+3j9FJBF/9bf/F9v7N/jg6q94/6NvsTB4mM5il2JhhzgStLuKVn9EY1I0R3H2Fk7sUpYd4qhFOZWkfYmf05g9YY1nTKAgRdE8e1GCaATT6YhIhbjuOAWdhDkBVtJqZaQ6Rql5Uq4IengpD0QroTX2cwy5jATOGsBgDCwtLrO4GFh+VbNPkgqWlvt084JmBuceLegNIsZVRN6KSHWHzW3FnU/HKFkQKYOKZ8imoqocy8c1na5EkiDm2C3r6vlgVSFoI1UAeox2Yu7dDn/pTifhk/embG81dA8NGe5dJJXbdLorxNGAuzcTJptHKEdTxpNfgXqfsZnQP9dmafE6gjGqalP5GtQ0tIuNn/s+5rLfeRqNihQWgRNRoEAhA2zGC5yzNDasGVMRBcKzmvtAJOA9USLxzjOaDEEIkjQCEVM3gVEpDiTt8yKkxPwzVsFLo1WMF54sjaDQVK7C2HIOHzXBwOQcymsiF9MuCspyGr4jokJhwf12viB8TjoBIQU600RJhIoVUaTmbrZoDlAIFfegAHjnwDd41+BdhXdTvBvj/RjcBPwEJesQYhkHUAlizhlUEhcFf7cS4Rrgvcd6hxM25OXNh0QqksRak8UpRdyik/R4YOUc33zuP+c7L/xbHjr5PbJklQuXXufP/uP/gmv2+NJTz3Lr9l1++Le/RIiC3//aH/OHX/uvWRycZHt2izc++CkXr+yxdlPjTReJIs0bBocc3YHiyNFlut0Bs3JEbUaYxmGqjHLmQFYg6uA310ElZw33MwT9XLo8Gg8py0kAiqSCI0cX8cKiVLDRBhnr3E0lDrLvCB3VPI47pDVZkBZPibEVTQUrK4fnwIsa52e0Oym9fotOL6XVcZw5n5F3R6wcyel1Bwi3yHA7ZuPuLtev3UKnglavCk99rzn9YI8kExjrkSR4LzCmCqpM28bbAms05VRwb71kMvLoKOPypV32RxUPP92ls7yJZQOtBK2ky/69Fnevtbh+5VNurf2cqrmKbWYM12Dj3Yh0b5UFu8JSvEgRx8go7N21jpAiXEelCOGtWRpswypOaLd75FmLRCVEQhNHCXGcoKOAIzswl1lrsNaidDQPriEUMluxP97BUTMYdOgUOVqFp/XBfEEpTZblYcUoFd7N48qAotUO3Qk1KpbBZ6F8EDopSFXBmaOP0S2WkaSgIqwAoSXtdv6Z5+9z0QlIKcnbBUQWrSU6CU5CKQJ1x1sCjx+DiwxeNnPNfrCFGiPuI7w9JUrNiHyFdhXWG7QwaNzBRGau7p+rAOdzhQClDAbPAKlTYS2ExIsMIdv0OseJzSH2NmacOfYk2UuHyeMTvPP+3/Lue+9xKPk+3/3d/4avff15vv9Xf8Pf/NDyR9/9Bt/71j9mWHv+9pd/wvrwVe5uP82dtQGHj7VoLRi03qTTLbBNRFF4Buo4Fy/P8GKMUH2kz6nKmqxVolWApUrp5saXCGNd+HMP3lvqakZVzcL+WWn6gxZChuGI9HN9elOF+643YT/uwnsCc9uUCFz1cA+11E1NEhf0er0AbvVTHBO6vYJ20SHRKRJJpD1WGNI0o2laVOOM7XvbrN25Q6focfhIizQfcW9zE6kKBksxUhmkSgig1wbwSJEQSc20GuGbNptrBcPdCmdTtjcMTWN49sUl0v46H9z+ECENCaeY3D3BaD3l3tp13vvw37O4uI9QLdIohkYzvG3ZHxvOPH+Cw8dykmbKZnmLkf0UHdXYxgRorQgD51gHrJvxYGZTpFD3tfvOuwAN9T7MkaTEWEtjLMxtw45gCop0NBdBWablCKUFIo7AhKyJygXpetpqIyOJm4VBZVM3WFOjZUQlJKPJaH59nRuDoogoSnHOcbh3hNPHz/LKr36GkILRdEzja6IkoRVnwG+HjX4uioBSkqydYWlC1Zw/qYUITjE8wZ4pLIIGRBBYOBTOeoy3gQ4sLUI4pHDzdk0TBEEQ6ZBGZOaxTOFqEO5rtS2BeUUWDUJYHAH45a0KLVm8xPnjz7McPceFN6+yv/MGZ08/wx/+/j/j9LHH+NnPf8irb15g6dCrfP1rv8cLX3yav/iLv6SVaL717Zf4ztf+iM3tS+zufUja2aFuZmzdS8i7GiKI44hWG1S0T6uzyMnjJ7l1+yqzao8sLlDEzKYj0jhFzik6iIbGNCQiAkLst4pU6BK8n5uZwvqL+6VPgPBU1ZRIJwgZ42xAm0VRHN6v+ULJE4bjeEFdWdrtbqDuSsNsuot1JXk+QOsMnCKKJN7XgcuvU7xNiFXM5tY2xg157quP0O5KvLWsfbrJo08u0GorVGSCOIwGR4VUQTVn/R4yNqxddeyuL1CPWuzsTBlP93j+W30WVwUfX7/Ozv42zqTI5hTV7glEeQMR/YLeoQvsD8dE8jCdfBktW8zqMXZbcOGXO3z3n36Psw+c54O7P+ba/l/Q6DtESs5TgOYiHWuojKERECtNrBS1qWAuIUfORUDzsX8Qi/oQY28DTyLPFV5IojieR6dB2ZTEUUxapOzsDfFRELFN51cLLyW1aUJOiwhbhKaaYeaJt+pAeDS/TkQioqlm3Lx1EV14qrJCSk/sNZFXNJPPHgp8LoqAUJI0z2icCF9aJUMXME/JddbiZIP3NSGQpApEYDnP8UMQSRHixedAS68kmZchDz7xHJCEBQoRRTgnqK2htg3D2T4Tu0/tJjhRYkSFUQYTGaQsETiqesyt9Y85dPJxnnrmWX716rv84uc/5cnHnuWrL7xIt1jmh3//fV57+30eO/9Fvv7Cc2zc2uRHP/oZUd7iK195hn/0u/+Cl1/5S06dOkz/UKAkVTOP7hTIqEbqhv1hSafXY3m1i3ErbK2PGI6mtImZTR1F7om1QGtPknmqakJVRyhVIOapynlekMRpEESFh1aYWs8x7iAwtgYCzNU6Q6Rj4ji9L0DBewTBOls3IeG4322HsBE/ZTzdRmtBUbRRIp5/blDXJUI4Akc/Zjzdp6r3WT1ScObBLnmr4fJHJUnc59BKiP6S4iD1Z4rHADHOhOzDj9+/iaj7TIawtVmxu7/GqYc9aceyN0rZuLdNPVvAjI7QsEwe32N54SZHv6A49sADvPLLj9nZ3cL6hCwFVIyp+xwpnkYNn2C6OeXo4jJDd4i7kzUiKUmyGCEUdd1QlXOpbhJR1yW+adAqHBtrQ4BKoEYzv6pyX8sgpCBJDq44br5ClNR1Qzmp0UwZDAZ005xZ7UDFWCvw1qJ1FDZYjUV4gRIRiU4ZVWNEPKchE1iJSkBTN6wcXsI0lmZvSNIWdNMOrpZM9koW+0eBu7/1/H0uioAUkiRNEN7hjJkX1ZAN4OfYHu/qefKQCio/wPtoLiBKSHQekN4yrBCDV1CGMjo//M7Oo7q9CIwAFf5b6imzZhrw037GzA+ZuhGlmzCLhkzFlBgY717nzY/+kgdX/pAvf/F3ePPVd/i7v/8Bjz38BMeOPM43vvE9Xnvll7z6yjt884UB3/qd7zCaGl579Q2OHC545MFHSFXK8lLEocMK4cfoqIXy4Pw603rE9k5DVnRYWV5iZXmJclwz3hlT5EvYJqYsG2IdDlxRRCgdYW1F0wRUmpSKdr9PrAqUkoQwjfl7gcDj8EKEdGQf4zFYGnSchuk2B8GqYo5QCMnG7Xabbq+NiiKMNZTllDxPSeMCjwrgCyqMHSNVaJGtdWxv7zCZ7vLs8w+yfAykd7z15h0eOH6a1eMZSSbmg8ngiRcyYM+bKuPKh1N+9tdDjh5xWLOHZcqphwSnHq7Ju7C1OWV9bYSbniR3j5Ooks7Crzl+9B79/gl2RznSx/z855eYlCVJ3qLbOc8zj/4uWi7zk7/5Iae+tMmJpyyZUiiVYJowXFbKk8Zhr1ZXYWakY02mYrAeqSTCCowP4E+pIjKtaRUKPZkyrQMazhqLqRukknOXIAENjqCVaMrJhCjOKOIUFeWYBkbTfWazMd55kihGKEmeFXjnqCIFUUDwNVUV4LZC0mq1uXrjMsPxkDgDV4E3nlYy4MihRQadlc88f5+LIiAkzJOaaBy/IfWIMPTweIxzNK6hNCXeK4okAx8jZCAIJfGAyHfBR+F+ZmucMyH0wQRGnLXhi3kgGw6oJxBCkdEhU/3we9UM46fM3JRxvc/IDdk3Q+Jsyqi5wUc3f8S5FcXTz5xHq5q33rrAzVslj57/Et/4ynd47+3XefvCBzzz+FN87zvf4acv/yde/tk7xFGPcw+dobcwobcgqcoK6wXK51gr2d6/zagS3LqRcmhwiDzRHDrUYzaaBr6+L2iaPRwK6bMgJFExVQ228SRS4ZUma3eJvMbPoRfOhi2L8AYvLI20pGkf0QQsmZEleTEg0gl4gRUqUHK9n1+zHAuHBuSpDtewuX26XaRBy++SMOEWE4Se4qwE18EYyXRas7jc4csvLbFyGH78w9tYl9Fe0LQHAqnKYBWnQokEb1s4m7C1PuVnP76MqRYwZYvGr3PiC4azj3TJ+xO8MmxvVFSb54jrDt22od3ZYjj6FOc9K8snWD7UQ6IZDh3XrhYcXfkuJ048zai8w8sv/9/cvPM+a3uabv8J2osLRGpAY3ZRosaaCiclSmniSMwhnyG7ECkwzmFMkACjIryDyhnSJAqZgdKGTEcckZ7nUxD4f2mqcI1n4ixV0yCModdZpNtq4yrJdDrCiqD1EFJiasfOcHeeU6gxLli7lZw7cKXA+oZZ05DmLYSvSGXBcvcM/fYqk/n6/bNen48iIAQqmjPUCDFQ/iDCQwjAYp0NYRE0CBWRyghkQuQL2skSRdoHG2NqgbMRXsY4EfQCYZDokBKiSM2FF/NtgzeBPmyYS0gjtMiIVYZWHVrJgAVdMlETtv0u99w9tqY3+Oijv+bEwjd48pHnGbSP8uav3+CjD97kS1/8MiDYhVwAACAASURBVC+99AJvv36BDz/+kC8+8zDf/vaL/PDvf8qrr7xNu/0iS4tL6MiRdCXGjUhVhyQpcE4wnU1AjdndHXJoqUV/UDAZQTmbhbQa01DXNWmSE5J9w12+nlq8kginyXTBb1RnodNijkhS86RiFSX4mUDEIfotzlpBsy5c+GIdxFoDURQTJ1nQ0tNQ2RFSC9K0BeI3U/Ew0BNInSCIaOoGIRyPPnaCpeWc8dDx3tt36Q8eYHEVOt0E7xqQVQCteg8YxkPFO7/aZXsDThxdRKqSo4dTHn2yoDUY05iYe+ttrl+cQXOCNDMsHrtHWW5Q7ybgl6irVQbL8MDpDk9unmGh3SGOB+xuvcWlS79ib+cDvNnk+tUxr/ys5ou//yBL8VFGMma/uotXNU45tNAkzmHmZGBrLVEUrp0BfitAKWzV4J2kaYKk24owUzhwBB4wBnWksdYym9R4J7A+6GSG430UEdIE23IhuwgJdR2G24F1qREiDFIFAYoSBhGBuJTLjNRl9PsLaPocW3mM6ahmeek4l2699Znn73NRBEKFrXEuSIeD/De0sfgwMAI739VqrBc444mylIXWcVa6p8IdyvpAwCUDdBiL+bk7zBkOhl7GWqxx1E1NXc9ompqmsZRlQ1MpZHMU3ygavw5qHyES2rRI0mUSsUBqt9muR3x6+6f4asL5c19hqf9t3v71RS5+9DHPPPkETz/1NB9/8DEfXfyILz/3CN/+zvP8+Mev88Zr7xFHj6PjRZaPZji7h3U1aVxwZOUUm/cuUtVTdva2WFxM0LFg5XCLextDVOQRAqqqJtYOLyKckEFm6TzDe2OMa+itdpFxKJ5hDShwTbjnh4KgkS5isjelSIOOPo0ycAKvLNKGIFiEwHqL1ily7rr0YkLth+gkRsddmKPHOMhrEBopYiQRQlh6g4zFxQFxApc/HqH8IkvLOUdPxkRJg6eaDyxihPeMxzVvvrLBh+809DrHyDuwfGTGo0+16Qw8zmuG25qL78DeZhsrN+gtDMm7OdubLaQ/j21y1u5tEXV6NPV5ZKPp9fcZT99B2Mv0W5sMi5LZZMp4VnHhrcssrg44/NgSVnpm0YzpgXrUOjKtmTYl4FEyeFOE0AgcjWmCyc2CFR4dBSWnsW5eBMKsIImTgB23AQNmDCFsFwsKmrpiNN5hodOnm7YhytneXSOSQOQR80FrlrfDdkGqQDmyATEmHHSyAecOP8Z47FhaPIOOOmyPr+PqMXb2ObcSOxyNm2FdGMIcAP3uBzI4g/AW5+dDPwd4TaJbLPWO0suWgTl9VaQIkQMpHn3/N3h/QI0Nax3nK6wrMbbG2jr4tSvDZOwoh0coJwmYHrPyHrXZxjEEahLX5VC2THdVMmmPmIwvcntNsdJ/mueefZp3373IB+9f5dGHz/PMM0/x0Ufv8+ntDR5/6iRClfz8J+/w7rsfk2QPodMBeTujtA15oRn0D3PqAcvlT24xm+5RN4tkUUKWRwwWE6bTIdYa3MzRKixKxDiv8MKTxDHXbt1gPBrxePdRpI7nmwCJbzzDvX1sM6W3UID12MqwvrbOqcEC2sdELsGWDtIG5TSMQWTglUeKCDsDEXl8PMUzQmuNooNwWbC7SkuIjYtQMsE7SZJqVlZ7tFoxYDC14PSZ4/QWDStHYrzYwYsp2AF4iTGeD98Z8qP/eJ1WcpqV1YyF1RlPPt8iL0Y4l7J1N+H9t/e4/WlF1WzQWtwmzSOuXWq4/N6EarLP9r09jn1hxnDyEPvrlqaKWD46pFvtkPkxwkA9a1PNJphdS7nneffl6ywtLPDMiy9ys7rMpXsXKM0uzlbkicZ6S+UajAkqPxVBEqdIpQMtKLZkOkbHGqE0O6M9vHdEShBn4XtojLkvYY+1JlJzT4YmJBhLg9JB7CaBxf4Ck9kuk3JG04Rrq5IR3kJdW5ROwEi0jHGN59jh80yGKecfegZUxrUbl9kZr9PtSp5+9EX+d376W8/f56MIeEPdjMMXfN62HrD7guQ3pLUYB40LzDTXRKRRmyQqEF4jpEaKBEQcfogRJHPLMP9AGhzo/vgpXpa4KHQfglBwzALMZinDvZzJ3lH2dkqm5W0q9yGluYqpBB19lnTxC0yLIeXgFtPJmK3dC3RaZ/ji04/w0Qdr3Lxxl4ceOs4jjzzCzRs36C46nnrqNEpZXn/tQy5e1MTFKc6c66GjCUor4qTNAyfOsLe7z/54k8lkmSRJkNJRtCIaI6kqQV3X1FVJFmcBcz33ClTlhEuffMLRM6ssd1bDzMOHgeBkb8zmxqcsDB4Khq2q4vInFzl25hmEk9SNwNqGTDfIScOV16+wdHKV7qkuprbMNmvSlkZ2G6wZUeRHkLaFsHEQFWkDwgVVp48QKHQEnW4WhDK14vjJBYo8ADCSjPkmwCJlRD1TbNyd8Rd/eoHJsMWpxyXHz3ieef4QeXuXpnZM9jQfvTtmb9swM5dJukNaxSqX3hmyuXaT/a07tAvPzvYI55dohit4ppx7bJPO4CZ79ybYgcPWGdYuYF24Zm4PDTu39rnwi0/42kvf5vCpsyRqgU9uv8nY3mFWlnTaPfbNCKEscZoho5hJWWGMQ6GDbbm21LtDjLNIobE+XN38fMAd+ILB5acijZIymJKkQaqQczCZDsHMSFOD957puMKLUESyImV3dwctFLnOUT5FuJjl3hFSlTMojuJ1jpApr77xGjv7t0m1YXXpHE899A3gf/it5+9zUQS8dxg340DA6AlxTG4eHe6bMC/wIkJJSNMI4ROytIvWwe0lRAGiDcTBdDTnCYbXwWGYt7QcGD1UKBzY+QxCohUkBXTSiEmmyZRkf7hM1Sj2xl12qnVqG9HKChYXVpjVC2h1h3K8z3B0nV5b8OSTp7j16QajyTarRwbo+CRXPr7B8qE23/rOYxx/YIE33/yE9Y07FB1Lu1XiaLGwtISQkgfPfYH3332X/b09Ou0l4kSiFKSppjERzlSU5YRUdJBe4iMPytMbtNnb2+Te+h2WTx/+jZsFgURSTUu8CU7NZjbl1tVrSP8stobhzggVRyQdSzMu+eWPXubZb32J3slHKYcz1q9t011o01ZjosghfYSrNLtbY9qDCBWFRBYp46DtmL/7KgprM0lKfxEse+RFgZQSRwtnNbWpKacxf/p/vM/2Bpw+m/LIs57zj0uyVoOwA3bXHRfe2WY2tVRuAxGXNHXGhdfvce/2bZLoDoP2kEgpcnWKQ8U3me7VmPTnjJo7qFlNYyd4PSRppSz5DjKyWEpYGzGbKm5+/Cnv/uQTXvyDb3EsepTddI+yGeMUDHcnHD58lPFsytbuDsZXWBzWSawFYUXY6VuPlFEAikgfotuBsgzmIKXCTMoZgfcNQnqUlsQ62BLrskYrg/eOOOnTEwuUDUyrGdZWpImkXwxwpeTw4gOcPfk4oopppgJTV6yt38VaxZHFRQ4v5BxfPsrZlcc41j7/mefvc1MEalOHfLsgVAsmEutCeriRiHnUs4sE1kiaytHKemRxJzzxRQqkHPyT/MF9GDc/DL/hhou5QRmRANH9J1LQ0UhAIVVDu79DVkS0d2L2tk4Q+xNE9V2Gkw3WNy+S5F067RW67S8QqyFDcZud4RpVA2fOHkEqi7VTVlf7WGe4fWuPow+0eOKpowyWcq5cuQVUlGXJ+M4ucRZRtAqKosOpU6fZ2dxlMp4SRW2E8qRFQe08VVMy3t/HbsXopEVrNcVJR6cfYyZ73Lh8nUeefQqfzu2qM8d4d8zarTV47lEEMJ3sc/PaFWhq6knJJ++tsXL8CL3DbfY31rj44fs893sv4G3DaHOLj1//kHNPniI+NEL1EmJyrn98h1uX7vLlb5wja3m8OIC1hC++nANuhQhCIkRN3q1p5a3g5LQRuIhq2vC3P7jMtUszDh/t89VvHeXJLy4Q5zXguXWl4aN3a4RMaXfH6CJl6+OMe59CK1UcfSrn7to+vnRk0TEW2s9iSs3GzquIzq+5/MkmK4cKpDRMqjFOFOhWl0XdAeGJ5C7r67tMp47/9Oc/okgXeOTF86xHxxE9x8jepdLb1FNFrpZYLHqMZhNqxtSiQZDS6RbU5Yzd2Q5CKpK0FeC4doazhkjr+/HoSimyVsZsUoFxRIAWkrq2ICNkpGhsxXS4S6d7hDTPEWOPjDyJiEjQiDQFJ7l29QargwdoppJjq8vkccTWds3Dpx+inRWsdo+xmB5Hmvgzz9/npAh46iYEMODmFu5g6Lu/4xdEWB+FAuAdtaypyxLjY5QoEF4ixTxp+P4XMVBrDw55mGJH/OaffSCVVeE6QBDKhAdohEciU0F/VZO0NPEmxPkx0r0BO3vr7I7usr27TrdzjFZ2BJ0qJuUau5MbJJMZD549i1aK0ahk9UiXaQU72xNaHcXJkz16XcVoWGJcxO5ol417lzmVnydSbRYXj1JXDaPRPq0iRSlBgyQq2iRNyezeFjeuXCeedTn34hlYkqRtx1IWcfPdq+y8uM3g1CJCCSZ7FdtXdthb28S7GoFie7TNaLhNU06YjnbYuHqRVjvG2haXrr7LTnWPvJvj3JCN659w/Z33OPmAppESKfrUY8WVty5waPk4eVdh5RRJAoTCGhTaAi+isEGQAuskrWJprmBsEKrBW8kn7894780hK0faPPfSAs8+v4yKa4yVbK1P+eTDCmsKeodmxAlc+fWEu9f2WVns8uyzR3n3vV9jakk7e4yF7jM0xrG1/3c0/tfI8ZDtT1PKkaa/1KKuG5pqm0SUtJJjLPVPoG2PTGpur23gTc2Pf/DnlNVznHniHM8cf5G12Rpv3PgbWkUEVrMYdxnqMVO7zthuUZoabxpc08yFWQqddCnLEbYez2lFYu5fkTTOUI4nCC/I4oQsnpu0rCfJOmRZi83NTZywVPt36LTbZGlMLGPSKGI6maDjglE9I4kibt67TjdZYWcnRbmUc8fOcW71CZRPacoZY7uDjpPPPH+fkyIAzob20dnw461AuH/wZPkH9uFZXRFTcuPmDZYXzrDQzQkZehKImNu3OPgy3o8LxgHzcNODQ///eQXyC6LE2RzbdAGD1BOytkfKBKVK6irClKdJ1CLbo8vs7X2KM5Y4WiZJFtmvP+XO1g26S45nnnqYfpOyfc9Q1W28r5jNGjo6pd/v0ipajKcjoniJnb27TCdjuu2COIpYWjrE5nZI2O1EXbTUxEjI2kzTHXardTbf/ZBWL+LYV08hWm36x1d4+/u/5OrHH9M79mUiETMbj7j6yWX29/awlcEpwc5wl9JOKO2U0WSHtTuXOHz2CL46zqWPLqG1oNOJqcoxH37wNpvjm9T5WdA9UpVx6f2LvPfO6/zzf/MIXtX3C3XIVvsHzVcoB3NJrUSIKAiYhEWQsb424cqlTY490OXI8ZgXfucYOi2xfsxkIhjudEgTzfLhGu8ifvXLDa5eWefQ0Ybf+95hNtYusXZ7RDt9jsXeeSw1e+N3qPwHiGiXpslo7BFarTMsFIfZqd5gf/ozKnEHlbYpkgGDpS6IKcIJtnfGVKMJr//8lxhneGnxFMeK80yWJTvVJdZGFxnPNsiLAcq3MdMRk2YH76ZoHdya43JMudMQeUFHB/VmnKTs7O6FTEtrsDJwKJNYomNJXVZIqalKmM3GOAdOerytaUxNnGhiLSltwwSDri3duMW5Y4+zmh5lKTtGP16i31lEqoTaWCblhN3pkE63xVLR+8zz97koAgKJVkU4pj4YJxzMz62Y+wdCcGfjwqR/Jhs+XVujVbzF4w9ruq3TCN8OLri5PyDsuQ/ScwAavDeE4M3fGGZCJzBfofjwd3BiFoQ1jUQ04enmLKSFoehMGe1L0qjHiaXHmdZbjKtNGrbI8gWcPc24vMnFax+zcizjgZNnSTLFdCKoa6hrRzkzJJkiTgRtlRPpRcAyHk3otiwSQZ7mtDozxuUYM/MUskurU5DrHF0kiHbN5vZlfvzvt/jPzv4r4iMp/VOHqepdrn18gYefO0+736MZjbl15Sql3Gd/d0wqWkxmY4gNMztiON1jf7SOllOq3RG3r92h3clJc89wb5v33nuDrN8mPeyJkpSo1vzg3/1HUt1l8XgesGM2B3SQcov5RyBCuEaQ0/t5noSYOxUF5cyzv2s4++BRRuM9nnz2CEWnwTkLPieNE9JUceSEYzie8OZr29y+tcuxM4KnX1hlsOL4+7+5xKGFZxi0H2NS7rO1c4HGX0XoKd4vkqhHyZJzYAU3b2wxm1hqqZDRiL3JNWSakSZ9BgsLKJegxR67e3s0o5IP33iPXrHCl37nuzxz4kU+2hQMzT0mDNmb3qaT9GnlCbNZCP1oTDlPNK6DwtVKjCwQVciwjKMEneTUtWEy3QfpSJMEKaHVzilI2dmd0ZRlcBDicNYynYwR1pO0InJ6HBt8gaX2OTKzxNnuOU4tfYFC9olUio8VpZ0xmm2wtn6Dot2h318ii1ufef4+N0VA+gQEKBn4e4ZACXIOpI/wVuKdwPkAU6gqw0TMuHztA2Id8eiDbdpZC+/jYDByHnn/YKs5NHKeEYeZY8aCpyDMBwKt0TmoyoKyqtBJGbz6VjKdWiajGpwIQRF+k431eyz0j3H85DGivM/2eIOdnSmR7BHr84xmLV771XWiJOfE4RW6scaaGD/Plm+amjiRRJGkyNsIBKPxPrNqTJF1gIRetsDWdIty1vDhr9/i2SeepLWS08q7tAcFUcvx0atv8MGrT/DUP3mJ7soiaUty4+pHTPbukcewffsGa9evEg8UW+tb9IWgno5pmjFlNWFvtIfxM1RUceXCh6x9epczXziNzi1blze4cfcmz73wJPmiIpEtrr57nQtvvc+//rf/Bl1UeAGzXUNjJhT9GBGHlB+lDqYD//CzDn/inMWYmqMn+uzt7vNAd0BvYQ7OVAXKa2pvaPVnbG3t8atXLlGOWxw/W/KFxy0nzyxy46LHTZ5kZeksdbPL5v7rzNxF4qhB2ofI5bMIDrN17yrj9dcQ+gqZnpG3DCoVNHIN72EgHqPQiywMJJEP2QLD2S71/pg3f/Eyscr50jdeZDVb4rY6xCjeYTS6zE61hpc+bLZcTaQhTVO8b6irGpXFVG5C5UtcKfFegRlhLcSRIkbjHczqCkeDM5ZOe0AUTWgMWCTTKqRojSdDhLU8fuJBeu4wLdflwTOPkOsMkTeYaELlZwybMTvDe1y6+AF25nmk9RRdX6Dqzz7qn4siEMSPMfcfIS6s8ZRwOGswzRwwgsIjsV5ghKe2lvFkyKXL79PKF3nwTJskEkABXuPwc7lmGPYd+OVDV2DmG4GwjQjFQGC9YrSvufupJElT2oMp/aUhrV4CMmOy16KqPEhD5a5x4+6rLB19hAePnuSIbjEcDbl7Z8LORg+98Ri740945edvoL/6LEdWjxHH89+fKupmhjEeHaUoEZOnHXCWqhwTRwnKFySmIKVhwgytFL/84U954dtfoXWiQ7+zRO/kMpNX3+Gtn/2Y5//4JYpui+Xjy6xdvcHOxk26hWL95mV2N9coVJu7t9ewdc1se4tYWqxt2Nkb4gQI0fDx+5fY3dvnyOllnJjw67ffYmJKDp3q004Spus1/+FP/pw0z3jum8/hmdGMI669f4PRpOLZrz+PxQahDXKudJubmOZycABrAw1J65okj8iyeN6dpaHrc47GjZiVE9ZujjiyeoyZucmxM57BYptyP+XTS4LF4svsbl1hffdlKnsrBJn6I7jyHNsTQel/SiM/xDQ7mPE+VbxJVQmKbkYUj3BmHekPEeUZeSLo9gtwjnjPsTfZY7q7zct/9xdcv/Emj37lOR44fJLh+BabVNRyKwBFZIyQPmQEYlFSUiQJlTVYCY0tQzit+00nlOo2K71l7u1u4CRYHxKf7Xh3DlT1VHVNmiQYWyOsQwjHtY2PeenLD7J26xLvvfpOWA9nkqLb4VBvldnWDNNYxttTXnzi65w8fpzMJ/P3/re/PhdFIDyLI3wIGsD5ediibbBN0MUrHySa1geCkPGOWhqkN+yYTd774BXa7TYnV58KllrZ474yA2A+Wwj31nlH4EMh8HM+HGIuVlZjjI+YbveZjGPKqWP5cEq7kxKn4HVNOhN0ugvcuWN4/8Jt0rzg1JkFFgctBoOcnVXL3bWa27e73L27xNuvf0ryQpfFpTZCWRA1WgusCQdRyRgpNGma00xGrN25TVT1aFd9ZtYzUw0nTx7n7375Dn/9//wl3/6v/oAiXaR7/AjZaosbF99nsrZOu5Vz5vxZMgnl3jpm2mO4e4dquoeYWj56/wLDpWUqNySRUJUle3tjdJIxnoy5dOkTjDcsnzhEWe/x6/feo3doieOnjyIrwd//h1/w8k9e5Zu/9/tEGeAdl969zi9+8CYvfucbiCbw9ja3tzlyePm+jfk3Muaw9hVCoTU416BUHD4fHx4EnhInpyjV4G3Cg+fOcWvtIp1DBVU9wZVHuHbVkIgl9st17qy/Qe3ukaaHEbNj7A8jJvU1VOtTZHIJ1ezAJMfMNONJxHRcUdcRebuHSRucvYmkQqaHSeMBrW4LGTm8MvjhmHI44pP3PmJ9Y4ezXz5FulDSVhG7LiXJJaPRDihFnGiED6Qn7QMbQSKIhMVKD1GAkcZJQuJT6mlNnmbsznZpvKWZA0RileC8I45jEA3OGqSQ1E3JjrnLj177dwyK00jZYjQcY0eGcs1w0UfkoiBSbZYHD1BaKJ0hVTXR/89R/1wUAYIn434QhPDgrcFWHtt4vInwBPuvd4TDqoJBwzUN/y9z7xlsWXae5z1rrZ1ODveem0PfzmG6Z6Z78gwGM0QgAIMIFEkAJFGkSjZLtiy6bJfL+ue/qnKVQ9kllSlLomBCoAmKEEgIYRAGM5iceno6zXS8OZ978tl5Lf/YZwYoGhBlyj9wqrrO7d3ndnfVrfXttdf3vc8TSU26t8YbF5/DUh4zE+dQwgbhYLBGZwOCkawQkB+MbBpDthUzAWHo4wcDIqEpTZXx24Kw22BntYKJPCZmBHZhwNhUhCbF7xc52PPY2Tzgxz96l8FgmpOn5snlHcYaMdXxiPGJOoVrRTY3V7jyzhrn7l+kPu4iRTYCnDUxIjAuAgfb8ih4Rfxuk/2tbXY2egyjEHfcUJ6ps3hike//u++Sq7g88uknqOVrnDh+ikmTY/fabXxXc6gxx5npWZQT01pf5vDhSYpjFkESsHlnBdUbICsRSRLj+xG93hBhSdqtNgf7e5TLeSYn66xvrXF7eZmjJ44y0ahy6eW3+P5fvYrr5Dl6egahAvZX93n+2z9BpHkWDy+Qas07r16jWMojJicxPyPjBDPq0GgsKysGluVijEaIUQHQKanxMSLB80rMzHocHAw4ctKj7yssM006mKCa93j37iWWN94kjH085xC2mWAYpAh1QKG6SqrWMIkmCXIY3UGbiCiwCPt9giCiFFqUqxYpQ4TawGiH8cIk5eoMXq43AtEaWt0+aEFnt82lF96kcgzkhIVFntj2yVkKHJtAm9FOVmVSl0o5a+lGCUEcESUxOoE40ZAmTE3NstPeII5SUpl80MW23Dz+IPoA66ZQKCWJkwTHUQSDHnY+5tjSAkE3Jg4MnltGGhcTC/b2B8yNLxFHsNvew6pKbOH9wuX3NxYBIcS/AD4N7Bpj7hldqwP/N3AIWAZ+yxjTEtlP+H8FPkWGMfl9Y8xb/0GFQJpsy2NU1quPJWmQYBKN0BbGZLqmTBhhECYhjkNCbUYm15Sby9cxSB64L2Rx+jS2mgJGIReySLEZBWiy02wHgcaYKAsRoRn4mk6/j9YRlmsQuS7DvmJzo86gU6PasKmMeYyV86SHD/DjAwK67OwO+eEPWxy0Btx/4QjVeg4lHSYmLXJuQqW+wO7uPsvLu7jeNMViNgmoEGgZYrSPSVMi36CsChUPBrkd1gY7bFxao5SX1J4+xeTpCWaXG8S9PfYuv0fBj3hk9n7Qgjtv3eburRscOzSHNV9CWD6Jm9KYqXH+o/fw7DNv0Ns5QBcthCsZphH9YEi/d4CQEX6vR6/XY3bhONVijjcuv8JgMODQ4gxpMOCl515gbXOfmaVJ7n9wBv9gjZe/8w5XXrnMr3/py+RKkrs37/Lad67wxd/5LKSKWIAgE2FkEGmDkKNujTBZDFkoMsWHwYgUjIsSObAkCQHl+pAgCClRpVibp7WpuPLaJa6/9yx+YHDkvQw6BXrWMtK5iecOMfiYOCKJU3SSEqcWkdaESUgYKQaBIUwhFQ4VkUdZDXzvXlL7AoX6UWS8RZLEWMJHyoBmP2IYaQYti8HVGG9Hkx+T5MctrIkG3STIFmiaZVUGYYQf9TNqolUgMgmpShG4KMp4dhlEDtcuIdPmT1XnShOmAfl8nsgfZo/GysqQZULj65iUlOs7b7HSuoVDER3YzE0d48zsoywVz+GPBySRpuaU0H7AQbqHsv4jigDwx8D/DnzlZ679I+CHxph/LIT4R6Pf//fAJ4Fjo18PA/909P43vARS2BgpR20kMUJaxZmuSWYDPqnWCClGlhVNGGUsOsuSMDrxv7t2gzhOCO/1WZo/R86eQFABk4MRqjxrHIQYEZAanzjpoY1B6gYFa4lY9DjordGLVknThF4sGbbLsD5BaXma8UaRiVlFoexwaKkEYghE7G5EPP/cJXb2Nnj8yXPMzo4jhUWpYnH4RJXKhCGOhgyCPq5XzXjywkErF0Ef3W/SXtbsbsP47Bi2l1CZbfHuxRtceeMOcWWPEw+c5OyRI+R7An9lFxMmuMIlAXp7bS69/CaiN2Ssep5StYA/7BDkQ86cO8M7F2+hk5BqxaPPABMP0eGAoN2kXigS+H38KKYyVkejePmV1ykW8xw7epjbNza4dmUNoSSPfegMpVyeN164wve/9Rw5u8x0o0F/r893v/o9KmoR45OxIIVGimgEzs5MEtJYCJGpPX7yzAAAIABJREFUtN7fIbw/nSGERKnskSBlgFAulvEo5qexyxbDfspb77zG25fvooNjOGmFKIyI9RUc9zaWt4khJokkaWKRxFYGRUk0UZoQa0GcOoShwpg8lcIC1akjTFdP4VhT9Lq7LPuvUMn18IrgulMoywHVwnSy0XZ/GNHZHMCBj9cRzKgaRW8M13EZ0qMvW6SFECMSwiAhThLkKL2a9cBjhJ2y19sh1j2cgoM0kKKJE0Mcxwgry4RYjo1lCeIwQ4enOqMMJWGMTgy1kg2OYbe9TDk3xkRljtQD286xsLQIkSLxDZb8jygCxpjnhRCH/trlzwJPjb7+V8CPR0Xgs8BXTJYJfUUIURVCTBtjfj7S5GeKgBBW9i4B9Cj5l6ITPeLjaZI4zkChqcGYaMQGyBh4SqqMH2ACdturvP6OT5C0Ob50noK9iKSBMO7oMcBBqDyChDBs0Rnsj5J1VYJekahVxQpzBNEtur27tLsdBt0dwsEObZPQ3G6ws55jfLJAfXyK6VoBFiyU3GR3u8fVd1bY3wl46ukHOHV2DmXH5AqGqVyRJHGRRpDqhDiWWNJG6Cz3IK0I2025e+s2b7z6NkfvmWRiyubQ6RneufwWr778ErarEd2IQVtTUh4i0mDn6aURWyub1Ms14kFEa6OLDl1C6ZPzLBzb5tjxJVobu0wvzXBr4zZxHJGGAUG/j3ZcUq3RCvLlHHvNAy6/c50jR48zOV7jxz94k24PZhcrfOjD9/DqT17jmW+8zdZKkw89egqlDd/52l+y99428+dO4+VshGWQGqS2SeKYMIlxc94oXis/mOEwP53jzmQaI7bh+2gT28qNcOBw7coVLr+9jiOPoKw8QbwP1nUKpRWk6kOSJ9WCJIqJk4hYx8QmJNQBQRITJBJtyszNnOT4kQtMTiyAhlbzLr7+OgkXKeTbzDDJTPUeSvkGnlPEdgsIuU3aOSBKJSbN0+9b9JIhSdJmarLB2PwY4/VJ9uMdWqZJIkKwstivtC38sIMxBiUMfb9NIiNyhSxqrJNMuYfJDgmDOCSNAlzbIjGSSKcwgsSY1GBbGWm4P+yQd0t0/S5v3XiO/U6TijvObP0oY50GC/VjeJaHI3N/+yLwC16TP7Owt4HJ0dezwNrPfG59dO3fWwTeZ+MZNEmc4Ps+YRBkeetEEusQtCYKs3SVbYmMIIz5YKuZpoJUZDDSyAzZ66zz9vUBqYw4viAoOwqlMuhIRs8qIIyFxKffW2MwaBMOIoJ+l+F+A1uWyJfupSAm6Sc3SONVUrHLME3Q4SL+1gztpmJ2vsTU7BhLCzZjk4qNdZflWz0OmgO+991X6PVP8/BjJ5COxFFgCys76JGCKOgSRimmJ8gVysjcGPZkn8YxuH7tLfr7Oe59/H7Gp09w/OwWly++wt3rt5jK1xEdg06GFCyPQeJzc2udUGjOnDiDhyHuxySeDXlFGhu8nMXxw/Nc67apTU9gd3eQlkKMrE2W6yBthedJpmeqXLr0Np2DLvecPoJOQ269t0EYax585DSR3+KZbz7H7SsdZsfnGa/XuH71Cs8/8wNq+cN0/CaymCU1Veyhe5KDg4jUMjTmbISV3f31SPjxs10DMzrMFSY3ioQbhEwwJmXQN/i9HNNjD7DnC7ZbV1HOHUq5LVwLSCZIoxKhgVTtQ7pFbAKCRDAMKmCqzE0e4tTxDzExdoxeu8mtmy+x33kNaW9RrnYplrcIwy7tnkU116WYL1L0XGxvIvt/O5LoYA8/lpDaRH5Me6+FpyWuFhwvHWfCW4SwgrAhFiH9IMBYCWmoSQnxXI/E0nSjHgaPMIqIk3SkkXu/SxVnoTeROQrTdGQt1pkV2ZIqI1EnEcaLEHaEn+xxZ+9NGpV5gnhAq9VivnaS00v30yhP/rylB/z/cDBojDEi6+38f3oJIf4A+AOASsNFExLHCb4/ZDgMCAKTbekimSWx9MgmlBoiYbAtsG2BbWftpiQR2bjxCB9ujKE52Oetay8xDEOOL3QYLy7iiAoCFygiKZB352jUEtLkLtub67SaGwStKYimqFXmyOeXKKocgbRI0tvEaoUhB6ikQxJ1WN/ewinWuGdphqWx+zh1OmD91CZXr7zL6so2ly5dJk0NDz98OgsCCYXBIg4HJIGP3+2ycXmd7a0B53/lUcpjOWbn89xz/yTPf/dl9r7T4sLDD3PkzBIrG29w8+5FNkyVuj1FxTikg4BWp49Xq5CvlqiUqsggIA4CHKuMcXMImbnspscq7JVzFIoW9XqOkitw04A8MVVPIeIe9YLg6GKdP//mtykX8py95yiDXovmXof6RJ77zh/n+R+9xJ1rW3iyzKHpcQbdNs++/DzN5i67rSGf/vt/h8KcBJXS3wq59dYBXb/LwtkppHy/RTsy9oxSjkKo7IDw/XiXkJhUjniFfeJ4iMBjanKRzlaT29efxcltYNktKsUGjswjsdBG4kcJQQLKN8S6ihvXmJw7x8Lc/ZTKJVqdFV546Svs7L1DnGxTKGjy+ZjY94mtPNK26UaaHWsDN2doVKcoOjWM1SAVgn4U0++0sGREKiRJAj2/jd0Jeff6kLGJeU6fuJ9hoBnogIEbsNvbYCzv0gt2M6AIKSI1aD/FxIIkyszEcRqDyQjcAoEtFNqk2DJzGSRJilLZ4012+C3o9ruZaTkv8Id77LYDOjTx631EzrB5eZmp0vQvXIt/2yKw8/42XwgxDeyOrm8A8z/zubnRtf/XyxjzR8AfAUwdyZn+8IBoGBMMI/wwJQwkie+QhBCFMdoYLGVncAuRRYoxGXtQygSEwU1cUk0WRxaCREi6UY/Lt1+i2V3j6Nw9zNROUnSn8KwYowuEgSJnz7M4W8Z1iyyv3GbD3KXb3OBg/RZF5xAT9Xnmxx6hqWbYHV5lmB6g5TaaJklsc3O1jJU/ydl7T1KplTh9+giHlia4c2eVa1dvs7N3i6tXQpYWF8nniliWgyXyiCAkDQLqs7PcuPEc3/w//k+eeuJXGJucYPHoGeYubPLOK1d4+fVNHn7yOEfP2bz5k2W2dzyi8TyyOIH0bGxVpTw5SbFcwCDwHI9B2Gbg9zAqwQlchK/JW5KSIyjYPnN1xXxZMi4HzOYSlqqSVrvN0dkqJTuk39zm8Owsh+aneOONy3S7PZ76+AOEyZC3Xr1BEngszlSIwgMuvrXBRmsbLUO+8OUv8MjnzhPYHfZX9njtL29w6dX3ePKTH2J24Ri8LxofWaHiWGPQ2M7I/fdBxlMj1egOGaU4Th7hWgTRJpeufIeBf4NC3qZWauA5VXJuGcvOSMq5GLq9GpglqoUq5coccWKztb3B6xffYnXjZRJ9C9frU3RzyLSG0gaT9EkCB2k0xjTZbbaxnRQhc0zWShTLBeqxYbzvs+93CVWC4+SI9BiBaTIQXdJhH3+lR7Qf86FHf52F0/czJOaVa89yZ/8itaLF2Jik097Hc6ooS9HstujJHsMow8hhNI6SKDKhrFJ5tIGuP0CJGEsKklgThHEGl5WCyCQUii5j9Ry93gA/irm9O6AZ7mKrIgfh/M9bhv9RReAvgd8D/vHo/Zs/c/2/FEL8KdmBYOdvPg/IUoSBPyTxNeEwIgkMSWgRhzFJoAnDKGPrW2CrkT1HJCQ6QSUBjmNQWhInMUmUyTUsQwYSVSlh3GVl4x2azTXGK9eZmzjB/PhJCtY8/XaBMLCp1KrMTJzC9TxyxetsbexxsBvgtwP2uj7SnKLqnsGSc3TDFfzkLpHeJtE9BkHCletX8eMWp8+cYKIxgedWOX2qxPz8NKurtwk7B2xtJAhTQFGgZFfJGQd/XxDmYk7cd4yrz77EN//5n3D23iep3zfB4XsXMOxQLNgsnMpze30fX3QIrJQefUJnColNbbpOsVrFUxKpIfQDmt0DuqZDru4wXqsj/JBa3uXQoXFyec38dIWHzx1lzI25Z7HG0kSBTZNweG6BfnOTup1w6ty9ONLwzlsXqRUcLpw7yu1by2xstpgaP8TMVIXd7S029vsMRMTjH36I3/zPvkAqhizfvc13v/od3vjONe574EHOPXIEy4tBCnRsMWjFRH6ClDF23sZxbLQZEbxJECIiMSEIC9fNg9Yc7G2yfOd1GhMBcxMLYBS2nSfVCqkgTRU6dUHlKVeqTE/NkGrBTvMud9ZeYGv3Cv1gm3w+JuedIO9pknQTx9vHdQQ245AotOqi5ZBhMGRtY4MkKQAO4+UZHDtPqVCiVioiTUKgUgIE/eSAYTgkjjWJidBdzXM/+BYn10MeeOxxprwF1FTKOxsvsrt3hzT1UalGWZLEJCRxgMJQcCziJM2G3LRBxwnD/oAo1UTGYNA4Kut0ISBJE0gzjmEchQidhZFinfkk9vsplsph4uBvXwSEEF8jOwQcF0KsA//DaPH/mRDi7wErwG+NPv5tsvbgLbIW4d/9m/5+yPBLcWBIfEEaKnRo0DEZGthklTFJDGmSEsoQCFFWjONpXFtgKYkYcfjRCplkqcCMgGvQVkxCyH6/z15vnc3Ou+zs32K+di9V9yThYILNtqI8XqJQP83xxRrV4m3Wyms093sMDm6zddAlLxap5eYYq55C27N04zVawxUGyT69eINby3t0+xscXjrKwsIS9foYxWKVE8fO0dnfZm93L3MtDoZsbm6SbsUk3YB0rMv46TJHnjyPH73JCy/+BUfCOe7/zCnOfuEJvFKO1995k+dff5s0SZlYqNOolCnmq7gyRy7vYNkK0gQ/SQn0gJ14gL/vYweaU1MBF84sUMon1A7NUJwsYJI8F556jJyKOWWOUKs1qM/O4pUnuHb5IuePV3nkoQVaK3fprGzy6OmjFG2Ll1+6hO06PPLIMWqOw6X3rtEkZmxpgd/+gz/AcWwuvfg6f/XNv+K1Z1/j6OGTfOyLH6M2X8+Yeb2EuzfWuHNlk0a9wZkHDpHLe0BmiEqNRn5gji5jWwKhY+K4j9B9zp+bJzo+he+H+EEXLWN6/ZQoKBIH4yRJnTDNoc2QMLpFp7tGbO8xNd9n4cgRXOs8/Y7LxPgkpVLIrbs/BrlPzgFlRvYg7SPkPHEyIElCWv02bG2QxjYOeTApBc9FGxcp2yDapEFK2BcMU0MoAwZqm1a/z/rWKv3hKvOHjpHXFjkNjt0jtAaY2MVYIHSE0hFpmBmRlZAgwXZtLMdGJEmWu9AGjELKDC0vVJJZqKXC82y01gzDLLZsAVKDY2uUSJmZHP/bFwFjzJd+wR995Od81gD/4D9g3f+178smpdIkc66nGn4a7gFlK0T8PicwYw4aGeM4EuEYsEDaGmUbhNJokyC1hNRCJ9mpcopGk0FFD9qbRGGHjr/ObHWTqrpAGi/QXy5RapUYb8wxP96gmJths3SHXW+PPdbodzYYdseoJIeYnDrK4akHEO5JeuEae50b9HpNdnZ2abc7bGyuc/jwEkuHFnG9AtWpBexCjb3ddTrpLvnxlLW9Ld5dvol/8YDJvTrHHz/LiafPY9wms4dhYd7jIG6yvT8gkn1SmeC6gsZYlhazpIstXTzHxRICIRW+iLjTP+BuZwOciDOnZklrbUzDpjRlI508ImejkwLV6TFIfWYPjyOkTUE4iDhiIagzt1SgvlTm1jNvcGa8wLlzx3jv5m22N7Z46MKjfOxTH+Hr//pP2ej1MMUqn/jEx1hcmOOFZ3/Mn3zlq9y8scL0xBx/5/d/m5PnjyOVImwmPP/tl7lx9V3qYxXuu3AEd9xF2zFK6gz5IixS4yEQWCbDwUGKsmBsskoau0ThkCgKiGIPpEWvJwmHVToHHs1mQru1TphukJhNvIohX6xgwuOEgzpR4FHIB9QaAtQqM0uTWFYdxwEpwmy03DgIYWMpB5Bs7t6gH6yz0trAEQ5BEBKIAYmMRu26FO2DiS3QIJ0sg+LrHrutq+x8Z4Njh49SGsuj8j3qtktoJeBK/MQnNjFCpch8ZmA22mBLhePYpCY7QFXSwVUCKbLuAmTzA7aRGf5cQOBHHyRjCgWPIEhI0wBpCVr9vV+4/n45JgZhpAEbtfxGuuaRYg+lFI6bzQkYmSAsjXJBeinC0SjXwsqD7YlsTHWkNjdGYEY+91iTkXQtgxaaXhSQ9jeJUsFUQVARGsE07T2fsFtjrFGiOnmcwnyVWukupdIyW1vbtHZusdPfpnlnk+n+cU6cPMQ9J89g5ZYY+D22tre5c/sma2ubbO9ssbxym2PHTzE9d4Jcoc78pIfqC7YP1pg+3iCxh1x9fpe3X7vC5nCTpz52lo994TyNKYdevM5ebx2RLzI5X8BxoWw55N08SrpoSyBVBmWNE41b8Li9u8Jz775Grpbw5d/9dR594jivvvYVrt65yvShc2DlkfYUxiqDyWd3M1XOqLdCQjKgcf4sMh4S6pjJhRIVFsgXI+48f4mZsQqf+cInuLx6g2cuvU7i2pw7eZInL9zPS888w7/+k6+xenuTSnGMz3zq8zzy6GMoy9De3ebf/vE3eOGZF3j0ycd4+vMfZnyxjnFG/IdIkQxCsA2BCXA8sKwwyxyIjIwkrTzKsVE5i5zIYXQFYaoUcrCx1aQTXqcbNzHSJhm4mOQC/UGOKLAIBjGlqoudj0DYxKlDoThF2F1nmIaISJNzahS8OSwxgZQetpOgOSCSO4R2jyiNMLFPbHwSmRBqQ5IqSFxcqXA9TZKGWXBKScJEY+dSoiBlbXmdeqtMsVjAdqdwyyFqLMAzPj4+Ou0jbINRyYhxlZLEfgaT1SlJGmMk2J4LGNIkmzC0bYs4TojjmDgyYCRSpQwJUEqg05g4SWj2f/FT+S9FEcjcdylGgJEaoSAdARiMEtnhn5Vkk2ZWinRSpJtieaBsjeVJrJzB9sBGYAsLabKikaY6Kx4iQ0Nn7kGJ0RaDOCJJNmi3NROOZDLv4GET+JrdrYChL6lPTjJVq+LZk+RzK2x4N9nfbdJrX+XO7i32ehXujc7ywEMXqFYXmZiY4/ixU6ytr/Luu1dYXd5me7vNxNRdZmuHKacNhnslOlseqdtmfHqMe586S/Dy64T7GxyZPs/iEY/Ii4iCmIpbIEo0a3dXqJRTammenJNDCTdLWdqGVKfYNrTCfV679gITR0t86Xc/wq985CEK+YTXX/VZvb3K8MGTFHIZ4fi5H7+GTkvMHDrM0tGlkVDTIbLyRJUKburjxj0WHzpFsrTH2pu3qaYDHv3U05Sn8vyT//krbOshp+eP8Tuf+wRq0OG7f/51tm7coZJ4PPngeR45cQ9+Z0Cr1+Gf/C//I5dee4WHHn6Iz/2nH6Oy0CCRFk5sEIHFytVdus02E4dK1A9ZWNYAREgUQxzbuG4VJQuZLFbWMWToMj8Ysra7ztp6l2G/SNyr0NqEg32IwxKDHli2oFgqoLUmCCPiyOC6FkdPnyKREb3+PkqUKHqzJAm0ugcM/XVSsUeQbNIetNBU8ByNER20bUBYWMbDxJnCXutsgtW2Mt8FJituxbKHUzJYCYjEhUEB088RHAyIN2O8Sp1KTeLZfXBjIuUT6hBhqYyqLRSOFROlAVEckMRDDGIklR21WnXWXpRSEYUGa9RWVErhOJnrMJH+L1x/vxRFQGAwMs54FJZB2QorkZDqjDGos+2/dDTS1Qg3xS5I3LzCdRSuLbE9g3LAFQqlJSQj5XhiSFKDJkOXCZlpzFMzJCXG2DZRYtNuvsuWtlkYl0yUiyjhcLAbcbAbUGsUqIwfZ6Y+hedUKOTfY29vk3arRa+/zatvbbLXXufRR59kfHyCQsHj1MnTHDl8lN3dXW7fvsHBwTq3di5jDxscrPvsbK1gF/qUJg2NOY8nPr7EAw/8GjOzRbS7xXZ/hcQuUSxUieM+za0VphsOdtsm5+RJEhvbdtCpJhYpKid45NF7+fjvPcr4Qg7P6yFFAsmQucYUe+uXuXltnwsPzJP622zevsj3f3iTar3Mb3/5i9xdXubhx5+mvnCS2PNwlUIagxqLwUlIroU8+bH7OPbQOf7l175Ca22LarHOUx97hAuPHee5b/+Qu6u3UWgePHmap8/eS7S3y/adlH/+p/+Mt99+kU9++mn+8//mD8mPTTAERCw4WN7lmb/4IXdvbfPYhx5i6b4yyukQJ3tEiSGOLBxrLJsoHbELjTaEacDewQrra3foNBV76zX2lhu09yzarV0QGmX3iEwL25N0oyF6IFk60mBxcQHHKbJya0iansWTEtdLKJR6SHeHyuw+QdijtefRbd7L/OQsE7M5tveuc/W9Z4l1DyMzj4CyvKydJwROGmcDazqTjiitiUfvIkmITYIvNDL1SUJD/8DQvrWDk5cUahZTi1WmxyZJ3RS7UKAbDOnGAa4bIXMxw6DFMAwYhhFSgZIZszAIMp8GxqKQLxAEA2xLEIYxrmvhOC59f/AL198vRRFAgLJSjJ21jaQR74+OZGAFYixijBWjPLAL4BYFXmFUBJSV2YyVRKGwdCaAFGhSbTKoZQo6zlKCUaTBEkhXQC5FKR+32if077Da7xHpPrPVeyk6DeIhNNcTIj+hPFaiUT5JOT/OeHWb7e1VtnbWGAz7XL/5Bps7N7jnnns5ffIstUoD180xOzvL9NQU+/tbbKyt0to5QBV8Im/Axt07bLyzQ5iW+NxvfpTpQ2UCDugGBySRIk8JI4b02j3ifh8PqJYrWKmFRuJgoXVC6CScvf8kpx8+jHTaoJoYETAc+OzvbGF5ikTAxavvsXRqgXJ1yOd/9ywPPn2W8YkjFMtlpLfPCy/8GYYa99//KGJ+lkI1lwlenDJzF87hOR7771yif/EyF6wa+YVjfOrzT9Nz9nnu6ss09YD7zp3jC5/9TZKDIcNhkz/74z/l3bvX+PXf+G3+7h/8F7hWkbQriAd93vjJCzzz9X+LdODpTz/F4782h3Q6JDrAyDyWVcBx8tiqiNEKowPiZEh30GS/3UTqMjXvAtv7PZavteg1e5hU4bgJxbJAOD1OzlZZPDpBpVYiDmP6B5LOnksQ9SmOt5ia6zM5m6eQK6IjG50cods6QvtAkysLGh5gYnJaM16cJBj4hLKPFv4o/pC5MKUAIc3o8UUhpMSxBLYRGJFkolwpshOOJEAngkI5T7AX09rvsLPZZuXaHo3xGkdPHePBjz5B/nCF1dYGB8kOa/vXSaIeSihsO5OcCASO4+C6NkmiSeNszXieS3/QR0oJ2sKx8+QQ/FJbiYUA15XYQpFaEm0LQmEyqYNO0TLFsgzSVbhFgVMQOAUbN29jWxa2snAsB0sppBbYWmJimVVhI7F09lyV6mRki00AF+VYmDRGFkK0u4NXC3FEm37ostWzmCieol6aQSR5TJoS+4ZCpUa9UWd28ghL82dpttdZ3bjN9t4a7YNN3njzeW7fucbJE/ewuHicseoMnltkamKBWnWM/am7bG3cwquUqI6PEQ4V9z10iOnFBgPRYXnvMmONBrO1c1hpgdZwEydp4gQp04U6Y+4CO1tW9gM2Wc/d8gxLZxvIXI9hb4vVtcuUxgqs3r3D+t13efThkxRLNt1Bh+s33+O+h8aRpQGHTi5gSUjFAfc+coijZ2e4+c4GN978Pis36xx/+AKH5w8h7QrVpTzG9KkfDPj4kw9y/WqbQ09/mHzF4o+//n/x/DsXOXb2DH/4h/8ttcDlzede5fs/+Al3ujt87nOf4/Mf+w06lw4IRZf9sMmLL36Xt197juMnF/n1L/8nLN07Q+ysIWUeW9axqWDwssxIqkmSgCjsEYQ9pJIszJzCZoydNUG90mFyKiWX6+HmEo4cr7OwMEuxWGVvf0izOaS9byGQuIUBEwt9qpMR5bpFmtTxBx7NDYf9TYPfc0jCAlobcsWAXOmARO+ytrHMe3deJdVd4rSPkUGWhtAWiZYgzIhql924Mju2nR0yShthZzc2KQ3SMWDHYDSNQp2xqTHaO32213ZZWWmztX6dvQ3Fhz7xGPXpSdJUEecEIvbop9vEHCDdlFQbghACXyNMnMlOwwEIgee5BEFEGIZMTkzRKNaAg5+7/n4pioAUAs+x0FJgHEVqiSzrbwzCApmCtCVOXuAVJW7ewvZslGNl7ZARyvkDjthoYkhIg5I2KYIPNKYadKSIdYDSIJSDq+zsMUGlGDvAd99jGDbpdFcZj85Szx3FiWq0tzUHA5+pWYepCZexWpWx8QKHFufpDjrs7q6xvbPKzu4mb7/zGjdvv8fi/AmmJ5coWg0s4+KJaY5O12h6RWamC0zMOVTGXbrhHu9cf4PxqkNN5HG0S6zrlAo1hvttynEBoUrkzAx7uoCyFSaN0SkcOdqg1ugzDLZ45ZWf8N7N13n6o4+ydKjCffc8hmsbGrUag3aPW1fWmJyZZHZRYNI9pCojVJ5IJjglj7OPnOX0uTMMej6dTpsb16+wcOgktpVDqJjC0RkO/6pg7H6f/NQE3/o3f873/s2PWFw4wn/1D/8hh04f49nv/ICvvvw9gtaATz72K3xi6RE6P7rG5uoet9MWz2+9hiz3+eTvPMATH3mE+sQkys6jZBEpywgKgIVJA+LEz3wUGiy7QL0wjlIORmf8icqYZv6YoDhWxPPq1Ot1+r2UjdU2zVaCVILGtKFYDahUbaQqMegH9LoOu+sw6FoEA5tBzyCFg+0IlDukVNTUJ0LKE0MsL6CyGzK0IVo2mIEiSi20GYFshUBr/b7R/afZBxOjRIiQApWq7F1KUAopbYTKVGZ2UTBZqOLWLDY39uhuxty6cptBa4/zj53l0KmjzIxfYDhxlp3BFisH79IOl+npHYxK0UYjtYWSFhDjRzGWnc1O2MrQa28T+L/ktOFsJzBa/KkikRlbQEqwY0WsQdlmVAQUbs5CORma2bKzAiCFQqYCEvHTFqOQGGHQIkWLBIONSVxIPTApYTDEKJBuDpIcpEUSmUMLTWptM0xatPt3yPUO46RHsdMpcs0CB60C7akckxMu9UkLN59nfMxmrF4VxAz8AAAgAElEQVTjyOGjtLu77O9vs7G5xerqXZZvr5MTdVRiEQx65PPw0OPHmZ05iVXos9e/xZ2tO7z05pucPbrIWK5OISdRbgqmgG17OO4EuUqdqNcgSTNARRT7OB4cPzOHcgLefvk5rt94kyc+fIalY2WUbSNxSBPD0skL7Gx32N9s8tpzd/nop85QrR+QaB9bnceSFuCjhY9diBnLVwijdV579RKrq3s8/vSTiJzE5BXVE5MUp1Iuvvg2V3/wY5a8Er/9+1/i1Kklvvftb/DVr/wZjvL44pd+gyeO3I9YbvHiiy/wwsp11nMdzn/qDB/51Ic4feoIbn4RpRoIqiiRRxiBNhFpGoDJJB7KGdmmpQtajRZbhofLFSSz8yXmF8v0ej0G/QDbdlg86uHkEhxHolObbsti9U5A62DI7laE33exZRkv55IvKFzPR1pdahMBU/OGSt2QKyRYToTBpVxbolTxKNcK3Lpzg2Zzj2E4ICFLuhptRmE2/UH+AbIw0Mj0jpQik+mJBKVsLGWjFQhXoJSgNlahUCzQqrdprnfY329x8fnL7K6ucvz8Sc4+8iT3Hv4wr129RNfc5frWt+nLW8hqSjCoEPg2Rg+Q0kan2b/jKInQKQf7zV+4/n5JioDAchQiFZC+nySUKCVxUkNiQFoayzW4nsLxFJZrIS0rE5mKjD+IIZtIG0EsjBSjzHpKohOMttFpiemxeynnT7Kzt0E3vIYOmiR2grKHaCGJ8dAiRVoHpGaXlr9MOngbEU6RC45T2Z6juTPOVr3CeCPP+LTHxFSeXAE8J8/keInx2gyL8z6dVp/11U22VjfoD3cYBju0+zs45TLkNZvdZW5tvUcqXcamJnnmh68z5uWZmuiRK7WQdp2NnTs0+z6PnryPu9cliC5ojZSaiek8yuvw0kvP05jM86nPPMHcfBnLiYhxSUQNI/LMLpUZv7LKwfYl3r16l1q9yFO/OoU2XSxlozgJGLRqA22M7jA2mfLgQxavv/Imd967ytF7TmfehzwINMWq4cJ9i8wduY+z54/xg7/8Gl//F19ncf44X/77/4DDx05x67Vr/MWzf8GbqxcpnS7x+1/+NOfvOUOjOo7jToOayQo4GtI2OjYYLZDCRag8KIFQYpQ0HAFk0QiRBcekzE7/tTEUKwo3J4l8QeDnaO1F+D2X1p5kf1vQagFKks/lqJYcLCcglVsk9h62N0CLNrHTIz8+TrVexxgXYyyk8HBsxfzcApVqldmZWa69e43VtWW6vQ5R5IOJSZJsjuWvv4zJTushO0A0xqDTFK2y53tjUqRKsSwLL2fTWKzgVRSD5YRgp8P2e12Sno+/l3L+ccmF2dN0kwW621224xLGrBLbAUkSIhIBypAkKbYlUUIy7IX/3vX3S1EEIIuQKinREpR4P1duQIuRDjpBOQZlZ/wA21Yoxxq1SrLhQm0ESZJ54eVoDj012SSaNlmXIE0FrlNlsf4Is/kcrcEduvFtgmANTAtsn9geYlRKrBU6lSRphK/X6A9voXrX6Yh5OoM5tvenKa03qNyuUK4VmZzJc/joOJVKHkt5lHN1Si40KkvM1DbYWL1Gcz9EugG1xpCt/g2uLr8NrsfM9GnW1w7Y3dlnefkO5YIgFUNSucvG7nvs9LfYDToMTJFURAidooko1Bxu3nmZfM0wvzSHYydImYJwEYwhmEPICvliwKHDD7O6so5SAStru+xtH2dq3jCIr5FTZ4BplJwC0UKLLWzPZnaxQJoGvPri6+RkhcMnj4ASqLxk7twClYka49UF3nv1R7zyV9/g/IWT/O5//d/h1Sf51rf+iq/+y39FJ2zz9Bcf4Tc/81EWvCrd5YSmUIwfrZGrByBS9jb2iQPBRH2JdjNmd38fd8xleqFBoWwhZIwRAe9bq3+KMs/062iDUtAbhmytGQ52JL2WR78jSBNNsZSNhgdhREqXVNymb1YQag9L+QzDLpo+OSoIMUaS2mDsERpdI0V2wDxWr1MsFpiYmOD6u9d57+a77Dd36Pd7QPSBagyySdgP/AtkxU3IzKwFKcbEaJ21+5QlMSbb3aiCS7lh08gndFzDYDOiu+FzpfUqzY11Ttx/P9OHjvFrD3yB7fDjvHzje9w4+CFjExHCaDr9DkGksZWN53iIyCKNfCD+uWvvl6YIIGR2RxACLTP3upJZFbWFBKGQlkY62SCGUhZK2pnUAYORJosSmyxtKFMNKRitSBIIw4QoTAkjwfUbL7F5u83h6Q8z07hAI/cosR4Sy3UClhmYXfzQJ0pSoqTPMNyGYIU03qMX3mKor5CIQ6QsEuoZusMabHvcWs6zuT3HuXPHmWxUyDk2kZ+wtrLD3raPLSYZr3SZXJzAj1KWN3axC2NMToxjNBw5dJiPPv043YMtwiDAztl0ek06w3UC1ePizbcR/WOgPEyqsW1DkG4yO25z+v7zmfLLBEiRx+gCyowjzQRCFDBmSCJLWOUqVq6F0DY/efYGn/jMcbxykyRew7GnEKMDOYQHIo9JN5meTWk0snbc7MIUlucgXJvCpEuxkSLbAZN2zO/85q8y9+RHGRQU/9sf/U889/XvcO+Reb7we3+PBw7fQ/9ai1eef5XC2BJz54/Q3W5x8aUrPPuD1zFJjaVDZ9nYfJEw7nD/42d46MjD5IoCbQYj+tP7qHg7ixmPILIGUNIiTjRxlGDiGMdOWDxkkySCbrdPqjt0+z1CAozsgNpH0CJJYtLUxXEaTExMsbQwj6OqRKGDVCFYLaRJQZc+IFPblmRmZoparcrC4ixvXXyTGzdvYllDhkMfrTVJkoySgCPcOtkOIEmyx4VshC1FConW9gidl2K0QQkX27OhEFFa8DDGJm4Cgc/atRvsbezSmHuDsw89wYkLT/HZB7/MT67UeHfzOVrRajbvMQwZpCEDEVMrVUmCX3IrMYisIloZakqNtkxCjNJmQiFkilApQmXjkpZykMrJioAxpNlQQIYJ0xqTxohEoiMbP4DAT/EDnyjqEoX77AfrrDavMF46x/TYeRqVIxSLVYreecZclziJ8EUH4/Xwc1u0BnfpWRv0czsEQRcTahLTJkFiWzGWKhPGQ5ZXuoTRFvOzE0yPTeBaHt12k+WVu2ysXqFW63LPw0+ysnEZIzwq1XGUkSSRYWFqloe/eJqXn/0W25tNZt0CrVaH/rBHRMhOdw9nMAVdQdEpUa5ZPPjIPHOnLLSxkCJjMZq0gtFVFDVS3wIbtnZWuHLzCkOVIpEUXJv9vSbf+PrrfP63Po0srmPMPJgppHTBZNth8HAdjw89/UleePnf8c57L+K5Y5w59RC2U8XICF3uM/7ESSaTPGu3V/nK1/4pt+5u8rtf+g0+/dlPMzU5TvjeDnevXSY3MU5Yk3zjR3/KxdfeprXrU65M4eaqtIa7PPnxB3joww8xNlsG0SdMO5nPTCukdFDSQ4lcpgbPfvQIBDqVoKFUUFjTGr9s6LZgdytiZytE4OLmHVL2QdRJdYkgHMOIkPHxEvOHPKqTbcoVQaJ7pDEoDVKnCJGiTDDiXyqkkiAkhbzLocVFkJCkKbdv3/5geu/9KDDG/NS5YAypfl/hbjAkpNpCa5npymNBHAs8HSG1R5hzsSoSb3FIFLeRXYe8LBINYrZubRL0n2V7Y4uzj32Yp058mmPz53jz7o9Z2bmOcdfw9QFGBHSDLtL5JT8Y1NqQJhkeXEqJxAJrhAEXIERmDsoeADVydBD4/zD3Jk9yZfe93+ecc6e8OWfNhaoCCkOjB/TIoSkO4pNI6ZHy45PjxdvY3tgLr6yF/xlHeOGNQw5H2ArqKZ5kkeKjKFIkzaHZA3tuoAF0oYBCoYasHO94Bi9OFroZJve4iIysBKoqCln3/M75fX/fQaG8caUAlMMKsMIzC6wFrMLUkrp0VIUhn1cURY6xktoUzN2M0dlt9qY/ppVcohFdpd+6zGZ3nVaYEgUxy0tLXFjaJdn8Cq1OQLhc4IIMW88o5xOKLKPIDNm8JpvnzPMpJ6N9RrN7HHR79Ft90jhl52pCf3WNJA4w0YypmRCkEVIopqM5q90tVhrrBC7i6et/zPe//59otDSz0lICQRLj8ojZsGR2cMKV3ZR2L6TVb+BzFAPPGnMpwi6jiwZUiun9B4zzB7y1/3Nm5YHnmdsGzoGRNT/72W2M7fMf/9tVlNwjilOcG2CtRNBAyU2cCVBRxY3Pfw5Tz/jR937OxvJl1jeu4wRkQUDVN3QnJcMP3+RyEvKdv/qfuPblr1ERcPfhhE8+2ufD4X3evfMh79+7Q13WrKQDNq5scflanz/6ky9y4wvP0ex3MU5gROENaFXgW0MZIUWKcMnCMdouvAj8Tmu04+RRzeF+xfGB5taHB5welzibYI33poxSi1Mz30a1AoLQIoIKJwriVJK2fTSaENa3l3WENA0QNc5VC6wiRChFEBq0KUBIVlfWeenFl7HG8vHtj5nNpli7CNRlYWkvPn2cm6oJFM55nMAYt2C3CqQskFIgggYqUITtksaypJ47RG0JlAMXc3YwYj5/ncnojFe/8i3aF1Z5/tJXGLTW+HDvNQp5xFl+nzkjAvmEW45b69C1QQm7APr8L15IkEogleU8RExIj7KC92tzUnkA0GtQOX/b/YhGgfSiC2FrTFlh8pgoWieNBpS1obQzKgqG9hbSHnBSvM5+3SFyfRK7zPKjLTb6G1y+sMFzaxfZ2lgiSdf8sdQZX+mNo64qimzKeDbidHbGaDZiNhozHJ8yrI9RoaXZNqxdXGNiMiauoJ00ySYTWkmPXusCoVnl1sfHvPvBkLt3BKaa4Tqaoq4J4wBjJNVJSexaqLCkuxETtyVGOBQNTKVxZQNbNjm5d4ydZZwc3OJB9Q7TcJ8onhMWgBAIV5HlUzSaH/7oDdpLq3zzz1v0gwGBbOCMQgXOuwCrZSQVS22BtZ/w9PV1Xv/1P/PqH/Xpr26DS4lUjZDH7LyyzfbXvopoXuWtX73NO6+9w/vvfcj7tz7m8PgRaRJyaXuF6zcu8/QL13jmxtNsX7xE2upgrcKKiEA1UDJGKJ9bYBFYFDiBcn4HPad/+7m8oSwd9z4Z8+4bp4zOZsznI3Q4Q4UVraYgThxpK6DTT2h3I1ZXO/QGfdqdDYIArJhS47xDtREY452rHAakxRgDLkBJhbACjEWgCcOYMIhZXdngueeep6pq9vbuMp1OfNyaVAtjVQ94B8q3A8J6PYRzDikVWms/WbBQGY00FUEhETJABQ2ilqVsTD3e4ARQIaXEZBUPb97h56PvsvrMLmKlwcaFdZpbn+eovM/eJORg9jFV+fuJQvCEFAHnoCpqpFWoBILQ4wOc+wJLQAbej/YxM8t5+6VFtRVWIBYBIl6EZLEmByMWM2WDcIJIXOLyyn9grf8iZVkxLY7J7SmVGFKYU2o5J1eGjJx5+Yjj4Qkfn3zAr24HbH20wtO7m9x46iq7ly7S7bSIopAwDAgCSSMV9JabXHBb1BqqmWH4YMT+zX2OTu9xNj5gez3B6ZJmEpON5vQaA7b71wjqLt/9u3/mP/3tj+i0WjyzsUE6VlR6QiOJEWnCGMtkdMYgahHFEd31HjRqjM1Q9YCgXCc7zjh477cc3v6EkTpFb1aMGxOwEOWaIK6xwlHqnCguaHUl87zk+9//GWXt+Pa3JcsDhxK74BIQlXdrdtveIl3OeOrpl3jzN3/L9/7x/+arX/8Wm7sXca6JSzXdSwHFoeWX3/17fviDX/P2zTscFRnpUptvfOPzfPVzL/HU7kX6/Q6q16Kxsk7Y6CBkk1ilYGLqDKxTxIlCKocIxWLws3Amdg5ja+pyznQ2ZDI9ZjSaczIxRN2SQTpnWQmiKCCMUuIkpJkmtHsx7W4DSRvpGpjSUeYlYS8njkGaFlpXGFEClZ9SSbFIyVbgLE7kXphWC6SRviAgaCQN1lfXuf7UdYo8RyCoqhrjWGQteEDTnbcHeKsw8JODIAgwxmCM8Z9rWRQJ61vdREDDoXNDaM6P9t6j3BnLyfEx4/mYpc1l0syxtLOCiZY4Vl0aUQ9nLPD7PQWejCJgHcXcIKwlkMK7CzsJwnqPNSOQwoOBVljPzlqMipxbGCw4TzpSKKwQyIUaUWtDXYGtQ9AKZ2KEgGa7R7+3xIZ4CSFCLDVFPaGop1RWo3VNXU0x3RxranRZoycVN9/OObz9Ef3eA3r9mH4/Zm2jx9pan16/RbPZRAUBKohopAqbhOyXQ+bHkqwouRne4SvbV0lFkzBos7vyHNXDkl/84qf8P//HP/LBu5/QsAHL15/l1S+/guto7gZjqkbEqTYUs4x4NSAMIW5blBSEdYKYBpx+eEQ5LHnzBz/l7Ow+g6930asGo3OiKUhlUbGjHEtuPPdHHJ8cU9d3GE8fcjaZ8IMf/oK0GfEX34ppNppgdxZvrcaIFrCGc0Pi5ozv/OV3+PD9e7z+2o9ZWfmvSTsdSpmgGhpbPqR+cAt9tsfahYg/+5Ov8OpXX2V36yL2pODOm/c4KeZcfPolOmKdeZFxeO8mJwdjyiym29khbvaIeiHNpZDNK33CtiXXY+bzM6bTIbNsTFVWVFVNUWjqMsbalDhqEbJKNtcMTxyBbKJU4jEDUaEiw3w2Jo4L1rdinroRknYM2uQYbanrysvajcMYBTZEuARnJMgMG+UgKw/42RAhvXRdKYlEsLq8ytbGFkVWMJ6OQZsFf2Bxr4M/iaF8YZPysdjHGENVVRhnEEg/FpX+QEsENPzH54WHRdajW+gpXKEZ7x1TTzX94yGtnTbXBrt0GiEfHbwNjH7v+nsiioA1kE2cN+EMAoyQ3gRzMR6wwluNWywotfCh82lFzgnvPozzhcN5YwasQJgUYTzWkKhVtAg4LU955/b/ytu3/wnFFbqNXQbdawy6F+n2lmmlyzSlIlIWGVTIYCEUkTG6BmMdQSAIQkMYZjTSnEhZrBbkc7lAhQukNWRnGXfe3+fDmx9w/+Bjjo7f5HhWcPWLkHQkW0tbvPPaG3z8mwN0FnH1+jPEnRXKkxGhdLiiYK25xLy1wbQZoOenpCJmbWWJpONopiVu2qI8jZCjMYcffMJ7r9/i+OQh8Y6le1Exi85oSocMI4qgRSkguxcR1c/y9S9fZHX9PabZ3/HOB3uMJzP+89/9mGtXt3jmWkoaD8A0cSoH5kBCIDeAjNWNiEayxA+//y/88mc/4Rt//i10YDGBo1QTVi9FfHv7a1z88h+zfukKURSiJyUfvr7PR2/tMZ8m/MtP3mP/7A7ZxBKaPq3GMkvLSzx1Y5crN3ZYGlxgZbtP6U45PR1SmTk4gwgEnW6Mkl0wfWajLicHCdOh4+jBGeOzMWVlsMYQJRndrqHVCegNJEtrDXqDNfr9Fo2mI4jnvshX9QIQDDF1SpUHVGWMqUKsUZ7gEwniliVKLMbV4GqwflNCScIwotvusbOzy8nJGVVZ41yGMQaxWOznlzfX9XFjXsEpHguRPK19gZUZCKVEhhFBEPhsAiEWfbF4HPoqpcRJSS0U83lFefeI8OSEjWsDvvTiyyS24q+5/3vX3xNRBIyBfAaJEsRCegpkEhIgkcItyqcn/0IFVnuBkPBpRSzYWs64cyMicPi5q4A0XGVj59vo/jPcubPH4eRtJuVDjscfcq+4RaB+gZIt4rhNM+3QjPq04j6tTo807ZEmfZrNFZIooZUIuq2ARpLQbiV0+pa1DcXScowKpZdEuwxcSZLEtHtXuPzMJgeHN9i7e4mq2iOfr6JcxP/1n1/jf/tf/gZjQjqDJda3tuguddjcGbCdpJQNGFczVJAS0cScTujYDteu7lKuPmSw3MbMY7IjzWs/+jEPbt/n1sd7DC41uPK1q/Q2Q1omJnCQWUtdtBg+yBk/iLnXrHnqmSu88tIFRJww+uv/nXufTDk8qvjXn/6ana11GvEWgusIF4AokSLAZzhsYBHEDcFgdZVH9x+RZzlBN0Ggidf6PP1vXsQ1ljnOGvz4v7zJT3/yS95/7W3s6Qg7NRCmiH6b7vYqz7/8PF//yjfY2d2lv9Ik7gtqNwdX4+QpcZISyw2kaBLIJoLIR9UJOD4aMzw95eS0QkvN+q7lmZdarK9fot1qI6QGUTKbTykKhxQJRZlzfDrGnmiqEoRN0dUOuk5xNqAovFApjCuCqAA1I00rBssBSdAkCiVC1Y+9L5wDtQAwiRIurF9gsjuhyHKMqdELAtE5h8A5h5A+F9Mu2gPwbYGUklCFn7YMBkxlaKjYuxqJyrdEXqn0+FQhRICREhdI789QK8zYsv/mPYpHpxTrT7iU2LcDjixwBMonCgVSEYbRIrbKIJzzOIGQGCqMNr4Cu4XbsDVo7R+m9h7uAp/xp0REFCZsXNil13yJ0fibGJtTlnPKeoZFU+uasjLe872aUBY549ERs+mQRiOm1Y5ptSK0DahsxKSIaUxDurOQrEzQdon+Skwca4T0xIyoGZG0WrSWlljevcS1G9d5uH+Lh8cHfPLBQx7tL/Hc5W9z5+4eDz7a587bvyZKHWtLMeVTF1l6/imaUYPD4RlJt4ucpyw3N+j32owGQ8K0TxJ1uX32DsMi55c39+hsJlz9txts3IgIhcVUTTIyrJRMDwvGH+XEps8bv36H517+Arsvr/Lssy/wl//hL/ju3/4Th5/M+cF/eZ2vfeVl2vEtkrCPUDuoIFpgMQmOFYQzhDFcvnqN05MRdx8ccbn5NAEOFfc4Ht7jX773T/z4V3c4LgWd3hoXL13E9Tagsgy2Vnn21Rd56tmr9LurTB7lHN57wPFJn51nnqa/fhmpKh82IyXWSRQBiwgKL56pzjBuyuWrXV56pU+Sel+dugg5fpTz6OFD8rlbcEW0N+agoChnBKHzi9IIjC6QMkAoRajahIkgCSvi1oiltZy0abzXf2SQqkZKgZIxDktd14s4O/s4cj2OE9bXL3B49AhjK/LCcweM1hi7KAT4dG0eF4FP8QEnz92GJc4ahJZsb1zi7t3bGLMAGR9PHOTjCZnCjzCV88IljMHYkPv3z5jVR39o+T0ZRcBaR5ZVRLIkFCUxCS5IEXH02DkHZ3DOU4QF4HSxeEMNxnlTBVMZbGVxlUPUbuFMHGCZsnf4jxzIN5DsoNghFts04hb9/iqNtImSIa1ml0Yck09qyrkGYUmajqUVR3/F0luWxB2BUH5u7RVuBmtLpLQUxRzHkDzbI4ycL2KmTZ0NcEGP2mpEIyJMuwz6CrfTwQ4DzKkmHpXkVtEIHT2lMOMJJ2cPaJo1sklJPp5T55L19U3SRhu1eo0g2oEg4PqrPU7msHmWs/2M4voftYnSDFkFBFFCYQxFVjA/sgyCTWSQcvHiZcoi53j/gOVLfZ57+nM8/PoJexcOufPRff7hez/j2cufw8oMKTXCBTjKBQaTguxgzZj1jTXycsp7v32biztXSZIUawKshgsbm/zp15eJNzdIOqvUk4CHHx0zHxaUAt741Uf8y3d/hi0SsF12Lj/HK1//EluXJLpwhA2v1XfWoqRGiAwnvFlpgCIN8HkNrolz/vOEcARxxsqGpNNro7UgDAJvROoyn2xkGjibUJWSMoeysGhTU5RzyiKnqgzGZJ4shMTYHOuG1JlDqYAgCFDKh+VoU+OExlq/SSkVYoyjEYWsryxT5COcM+i6psYh7CIjw/rNzwfoWKy1fmkL8Vj8ZoQ3xUvDJvXUUk0M0qlFHsFnC4BfFxKJch6bQHh1hUZR2YD6SScLORxaa6qipJIFJmjjEiCWoAJE4Of+Rlt/jJJeQaZrD+Bpa7zsuHJQgqgdQoNcBDlYqdHBMdPiPtPZW0xnHfJZTFmEQBdJj0i1WR+s0mu3UVqRRE26nR6DqEubJiJK6S0v0+5HqMBX6U/pq24hICmw+ohP9n9DaY+ZFlOu77zAnd9WvPPejFJEZMZQW4mpBdW45nj4iKwaYVRN0Ajp9XtcvbLG+gakm0Aqye+WJE5SV5ILT1+ivbTO+tYqcdhFAkEs+fpf7rJ1Y5fT2U+JozMCFWBif4PVRwX61HD8YML0YcX25lM00x4//vvvYVXGf/dX/yPLaxe4euUCQpVsX+ny0VvvU5gZQt8jcAFKreKsz4kssxxT51hTkqSG7c0Bp6Oa0uWoICFJYpa2t3h0NOTjowN+8+O32Ht4xtnZDFUKIkJagx5pr83g4oDlpXXWN7ZZ37rAzhVJsnyEamTIsIEQEcoFCCdwi4h5JwTCRSjptaGICuE8YcdphdERVeWoKuutuW1JXZfM5yV1qbDOYswU5xx17cFFpeRijCtYbVvC2CEU1HWO0SVCSKqqwNiCQPkiEAYBDoMxlY+xkwFKGhCCQAk219epqhyrHbNsjnWl3/2FAexCVODvHQ8X+GmYVFCVFU5qpIK+abN/8wH5uERqAYF8vPARwpOp8KxapAJpsVI/PqG4Gsoz+/sXH09IERBSEEYKYw1lWaK1xljnDUGsQxjjj+xCI4VBBA5jHHWlKWvvoWa1w1agtERpgbL+aCWsB1Kkkh7kiwqMGpNRcJoXjKeWogiwJiBWAc20QSMJaTZTOu02/Udd2nsN+r0W21ubXNq8xOryKs20SRTHRGFEEAYoKVGqIo1zOv0+H+9/wHsf3WJ4nHFj94sc/PCX/PzXHzPXIZoYCJDWUhc1+QTqskbqkuK0ADfH6SaDwQDpFCJoMXloiesmvY1Nqm7E+qCPxANKOEUQBly++jnSoymZfhvpTogTx9lozGQ8YXv7GtmNgJ8cvMete5bpeMLRvRGjYUm79z3+/f/wVS71rzE8zBBhRf/VFwiCEptNkTEIG4GG/Q9vMzs8IhYVpXlI3Juzs/Esm1faqLSBFpYKx8HhAft7e5TjnAurKzz7woskzRapDEgCQdKJcalCNRKiuEGn1aXd6pAmGXF8gJJdHCt+WYgAZyOwHij2ozuD54rEWCsocsPobE4+d9SlpK4sVWWoqqtKdD8AACAASURBVNrH1CnvVh0GmrQV0e1I4kaNCg1CVlhRAxpkjZAasZhKYf3ubZ0jCny4iR/zGYzFk6+iBOVY5Gj6Xj9UMcutHknap9Pd4PjkmNF4zGQ6ZZ5l1FWBsJnnIDgQ0tvgaWPAgrEa5TTdtIk5mTO6f4qoFRZ1HuC2WDwLcHDxOL+cc1gc2llqrcnqPywieiKKgFKCtBUhav/jGKcf+wMK65DWYDEYfDKrdBZdG8qiJi8rqlpjKrC1IDCK2AUobUE7Amd9sISVKJEQhg4VzkGWHluQFSYArUA7hxMxVrZwYg52hNDHmDLFzQfYB1N0WTA6O/HHNV0vyEkC5zQyyPjSKy+wvrbOUr3C00nCT//5N/T7Kf/Nf/8FGl3DzdsFtVtBhm0chjyfMc9q8smEejwmmJcokRO7kNhYIhFx6fILvP3hkEHYZ2lrk2FaEDXjxZjUz4pxIUoMWF35HI9OMozO0W5CPs3Z3txhdbBBO+0TNy3vvPk+3/zm8/ziH8bsf9jlwzff5srTbV75+svsLk0YZ2MuP/9VRLHMfAhJHIAyvP/aO7z1w5+zlfToN0JclJO1S6KBoD2oCNOMeCXFSRjsXOCPVzaRMqIOK2wc+zHtyLL37m32bn3EbG7o9LZY27pI40qP7vImQRoTxAlOSqTQOObYxW4vRYR0wWMlocP5NGskYSRpt5s00gpEjpISKRXWeb6JDLwTjxIVgnoBHHvRGTTQRuKk84vR1WhdIkNBFCnKAuqygQz9zmutIVABCOGJa1IsZvzWOwCHMVJI4jCmHXfpNnpcu/QUtdZMZlP27t1jODxhPDplPD6jqirKuiLL5mCMxyZESFelROOKs8NjKCXGOsLo/P/vr/OzqAcIz8VK+NMOi8KCZyL+oeuJKAJSQtzw1uKB81ZhQlpqU2MqgxIaKysf7Sx8/1YVNUVh/KO0lLnFFpAICQoSFBhBpS3Samyd4OijywRXS2yVE1Ah7QxcjXQQKkEUJKSRpR0blttdLm1dphOv0ggHtNNleq027WZKGDqEcgSBIgoTVCDQ5gjhQqQMaXfXiQfrfKFSvP72z/m3X/0i3/nLV/ibv3mbo5OQJF0mbrbQTjMeDjlTB2ihiBPH5aVVXrm+zcZGSGk1uoiwc8X60hr9C0tMokd+tixDwMNETggQLaJ4m37vJY5PT5jP5wyPZiw/tUsSpYiB4o++foMvf+0ZZKl4LbnN6ckZgVK89a83efeNj4lX4NqNa6iqgTbQayxjC8fbr7/Gj777faJRxeaqJOi0cbEizxW2sLiTM+rTgt0vXCVaVnR7bbL5GbNHJ6hWhGzHREmHwhqiaI2NCz3a/VVWL+zQWV3BxgF1qBjrGjPKUMYQ2QKpBFppGq0GSgkiKQlEGylWFgC5wQmDiqAZ+r7XM/S8Vbl1NY4a8Ao7xSIA1BlwBmu9L6CyAdDGWk1Agzj0SH0QRrhEYjSPF9o5wm+dVxcKU2OUQdfGU4S1QNcGo0qi2PNWCEOIIjpJTCeOmcw3yWtNkc+pyoLjo0ccPLhPnmdUVc1sNGHycIial4QaT1BSAuMMyqlPd38p/WPx+hwPWMgWMFgvn05af3D9PRFFQEiIGwJlJYGTiMiC1DilMU7gjAYMTlhvrKlrirwkm5fM5gV55ihzhykUlbJYZTGB9L9YK5BWE8s2y70bNJOEWBasp22cTRnPp4xmI4o8J5CSXrtDJ2nTiBusr6xwcXuLdprSTBusrg4YbDTpDCLiRKCUz9OT0jO4rBlSzB4SB3Ma0SbGDbn+7GVW+pb9vVt8/sVL7F4Z8E8/+DFZmUCQIKRCZwbKgoYQ9KIOg3SAiTvQbkNSII4lbdXgxVdvMOOUjaUOUsjFiMjviE7IBY22RSu9SlkMsdaxumyYjysSUdPuNAkann9/ODzjrXdvY4IUZ2OOjx+xGiwzCC9TjjQff/g6W8trEGv23jniB//wGu06pK0UsYV8NIVE0E46lLUhF1BMauStT7jW24CiZPrRbX7zD7/ll28POTIS1+vRXV4ljBNcpJDhTVpRk7SVEsYhCkVoQjZ666ws7ZJ0LhD1GvQuNGh2UoSqEFTg1CJqznqprzAYa8AJry8wA3BqId/10monKgSL5F9hEMqCqFGyQtjKL2YHdeXQtUKQENAgME1EUOOCGdYabyAiFzz/BbHIWktRlJRF5dvU2tvYzfMZSRoQx7G/Z6uK6XTCcDTi4fGQ+4cnPDq4z2w0opyOcbpGOkeIJBaSyElCvF+mFQt/DP7/sZ/etsxzBaTwfhqOhY5GCEqrqWr9B9ffE1EEpBQkqUIZibJi8UurEUFIEAYgoXYWbbw/YFl6E83pNGM+0+S5oMwkpjBo4UMdXOAFp1iLQtPqdFkdvEAYPIXWLWLRIxQNZCD8eDCvaEYRSRhgtaSsNFGiaLcUjWZJq6dZWklpthoEUmC1XpCULM5qjBboPKSY1fSWYKV3lXz6W4yYsXvpGVoiYTzJ+Hff+VNufXzCeApRM0WFAe0kJQklgXRsrm7x4O4+e/V7nJ6FNN0azdkqz19/mq3nLjAZ3qO7ehWhF3L3hV07LkRKgUPh6NBq3QBZ0mxIjh/cZzqsCWiStDRCzbCUfPsvX0JPG/z8h7e5/uwLXLy+w+b6BjIp0O6EfDrn9O5H3PmoYn3Qo1sqOgim45H3gCwcSue0NwZ0WgMyPWY2LagxWGWoQ0O8FNPZbTHJNOMq58GDe9Sjmnxe4GqJqiMIJCqOSJKUQbpEUzbZ2hyycVWg+jFXXtlhsHyBIHAIsYiqFz6Y5JwtZ7TlbJhRV5YoqJAy8OayziGlQFuLdTnO5VhbowJBq52Spi38qcG7AiunsbbC2JrCFZjsmLL2o+Sq8iBiXdcURc50OmNelAwnc87ORkwmU8ajMaPRhHmWkRUFWZWjF23APMv83+ca60BXjmYk6aURg0ZKJ45oxSGpUjSEQsoApKA+ZwcKfqfvB/x2j8cjpPBF4LxMnKc6amupdPUH198TUwSajQR0iLAKKRxOVWgrEC70FdxVaFNSmpJ5Nmc6mzKZzinmUGURZQamkBgMMjEQCeJAE+ATXB9m+4yGP8G62wjZpxWtsLV2jaXuBqYOqHKLqOdUylBpRVUK5AxOzjRBXBK3CsJ7JTKoCSNQCqxbTCe0xRqBrQ2Re8Dnv7jEVnuXZrxO5aa4OmBz7TlwjiQJ+av/+T/SSAfIIAJpCZSlqgvyIqeRNPjr//N1jh98QmZTLsoBT7/wHBe7zyNbEbOTRzx65w5Ls4hk0MRKTzUtck2VV+TzGUWZUdYTRtP7SJFxcDDB2YxWZ4SK/c41elhx54M5NtOI4hofvCZ4cOshy2sjRGzRpiIkQtYBSg2IkQhXUTtDYQ0ulMymY0YH+2wUG+yoDWxQEa+3cCKGKKW/c4UX/3SVa50IOj2EaFOcOu78+g43X/+Y6fGcybgi7va49vLLrO3u0llaIW226DT7xFET1QgZbA2ImhYn/InQXwoI8dNxic96Djg5mTI8PmM+cczGknxuURKszbHUxGmEUgGtVkyv36TdjohiSRRKgtAtxpBn3HvwAQdHH3J0tsdwfMYs0xR5RZ6XVFVNXmRMpxNmeUVeSaqy8tOJhVzYGIu2Fu38OK+qa4yxVDVUCw2BkKCdD9f10ZmSiJDIKULhEX/tFnohHHJBEnaL4rcAAT7TEvjpgG+GLMb5llob4523/sD1ZBQBIYiD2GvhHSAdTpYYZ3DGR1ZrW1KZkqIsfBGYZ8yLmjoPsIXAFAKTQwXk1oKxVEFOgMBpQ1Xeo64PmFeS8dwg3TLXtr7EzspLdOJNuq0OpaywdoI2MC8q5vMpRZERRpC2AmqdM59NKfIKa9WC1C0Jw4BWp0GrE7HWKZhMJM7sksoBhwenbPfW6Lb6IBxWlPSCAOvm/mFraiMRIkYGiv2jPSbFESoRRCqlYWO6F1Zpb22AUKw2HLNhRf5ggsktIlSMRhMe3N/n/t5t7nz0LmfHD5mNRuTFnCAJsMphpMEpf5S1WmAKaIiUXqMNVlHKCZNRgnWKJJEEIkQTEEqFUhUKgy4qZlVNNc+JsGSzGQ8PDqh1jWomNLZSWolEqprJoyMevrvHg/fHPKwMydoyK1vbLK2ss/v559l+5llcCVVuCeIOq1uX6W5uIlshLrbeJFMIr+pbzLwl4WPOvQfDFi2RXxK0WgHLyyESQygt0lqkrc6hU4pSU2WZz7Schhzc3aeuKqQQRHGXoNFFqDHL6zU/+ulPeefmvzApH1I5jZENb3kmQpyTWKvRusRoHy1+noRtncX6waEX/uB5MDi7iF4HhWPB+cEIi5UWqwxCnmN+AiNYtCgO6dyCIizhcQFwCCk8jV4FCKlwwk8PnFy0iHbxjFvYkf/+64koAgJvsSTEooo553tdV/udVtRUpqSqK/KiJM9qysJRFxJdCmxl0bXDGIm1hmLh4aaUQAmHqTVVWVNpR17DrKix+pS7B1M67QkvvvjnPP/cNmkrpDJdpvMxR0eH3L9/wsnpKVVhqW0DFSb0+hFyqYUgxLnA85icQ4WSKNJYLI8Oh1yrD4gbAalqIMQUOMVZ6W8Sq9HO5ylYZxEuoawmFLZm7/57aFdgjMTUDUbzmo/e/phOfIFer4s+O8UeT5k/qti7OfLyWywHh3scHX7C+OQ+89kpk2JK5QxRI0GGEQQKEYWo0OfbudoRpi0CGkTNEBk4pKpwde0nJML749cmR+AIhEDV4IqS6ckJrTimrkqSoMFspjmeO1bjgO52CsGIonrI2WiPW7ce8PrHI4alQaQpaXdAuz2g3x4w6C+zun2RS1cGRM4gqpykskjpsGVOLSxh0kYGLVjc4MJJJA4fQ3J+A1lkYIkDx0A26XRbmMt+gmTqmmxSMh3XPHw459HJPvPpPsPs0DNGM4mr26SNHmaYkRUjynfP+OT+TfKsBNEmUH7EbBd2c9aA1pKqEujKQG0QUiCDxeLEItxitCjcY+Xgwihxcc/zGV8BT349fwYef41bFLjfWS3nOoMFBnDeIbjF93aPXy28NdwCM/kD1xNRBHwZkEh1LrVceLQJ4Q0ebEltCqraeJuwwqELhS39WNDUDrs4N1nnKGtDXXu7aYGj1hV1VaG1pagcRgvajQYXd5Z44aVNXv78KtevDlBBBKxQ1CWz2SpHJx0eHd9jOBxS5o4gSAnDFmHYwDmFM5K60tS1XWjBDQkdn2Zr5kS2jcsjsvCMZhLjTITW0gOcLqfShe/lZIWlprIz7h28Q56fUGUt9HBG/fAh49f/ldGH9/n2d75Fsf8JP/+H73PyYMjRWcW0NDgVkhclRV4inCDLCwppCFJFZWoCpei2ltE2JAhCOolnM4ZBkyhukDQSnDQI6ek4thZIGWF1ibI1gVBEIkA648NAml3K+ZxW3GZlaQ3bEARd6Ky3WN3YJtCPWE62CC7VbPV2+KJW7J/MeOfmfe4fTrm79xHvTTVCh8SyQ9rokUZdlpfXuXzpMt/8s29w/eWnUD2B0AGCCBeoc+Ut59E0ny6qesEbMKhGhY0LlEtweUKWCfIyxpLSbEd0ihBta4azO+T1Q6wEEbaYVwdU+YiyKDgbTSjmc5xOsDR9NJ4rsFpTa4upLXVtqKoSXRRgDEopwihAhdLb1wuJFX5O7z6z+D+943nctH+2AHh1opfMf2pD9pn/82c4AVL4QvC4CuDl9W7xzOJj69zCgfv3X09MEfA/ikQIhxICi8Y57eevzqCNpaprqtKgK9AFVIXAFuBqrxpk4epqnP9YLoC7vKj999EgXcD6yjov3niBF555lhefeYbNzT6NxC3e3IhQJbTiJr1OiwvrGwyHQ05Px9QllFqRFx4cUoGk0Ug9QWTRm+rCcjauuHv3mBvP9UmDZQ4e7BEGLerCYI0iiAVBCKaKKeZeiFaLiFv7Iz5+f0xDbrIkNzk9rCneGzIc3mb/1hucZrdZv7LD8EqAvL5Ex5wiqimzckrDOaLaEqmIrWAJcsne3btMshHjQjMW9yFogg5IZhblYpJ4jUa0RKNoEQUhcSSIYkEchsgwJkpChJ6TiAhVSwKpcVlOI4roBBGrG+tMiznJSkp6IWJ7q0NACXNBflez99sRH975BLfUZ/Pqdf7dX7yIjFucjmccHR9xenjK2f0J85HGFgHF9JRP3s34wdmcvXdvsPHURa5/4QatvkIkDhksFHt4yqy/rw3WaGbTM4bDY4ajBxyd7ZPNwdU9inkLW7cJZJtWq0vS6HCx/Wdsrf0pk8lDDg7f43T0Pvn8BFcPqPIpZVZSFnNKXWACi7ASq2vKsvBSX6MfA4SmtoQiWOResBjb+fZASufJahKkU9jP7sbO9/hSuIVPJovTgifPnb8+D2T9na+Tny56KYW33FeLiDbpR5faaLTzegltNcapP7j6nowi4ATY0LsDnc93FynF2nmApTKOqnbUlaMqvUeAKcGU/mhr9PkRKliAIN7U0RhNWVmsE4QyZnvjIl946VVuPPcsGysrNMIuuhDUpSUMAeWQSKxLiV0DxwolU9K+JIhixlnOvQf30MWQuq4wldd967qm1BPm5YT5dEp6x3D9ypRWp8cvvn+Lv//hz4nDJhgBbsrm2hKy6pKNu5ydOe4/GpJVNWenfYphwZKocGch4+EJqjPn83/yAvOLivYXd9hd/xytZkKgLaPREbfuvsXHn7yFEBnNpGb30gXCuMXnqi61nYCyKBGTFyHDw5zTvSmH96acHj5E1zO0TYlrhZaCqXI4vUxUr5A2JImsaJqYhmuRGEurEdBIIgatLiIMuH71InNXMTdj4haI4CEmO+N0/z4fvH3I//v2Cfv5MUH6gK2L22zsrLF7fYPdq1u88NI1wigmClPisINyTcrMMhkVjI8LzkbH3Lt1k9WdFdKlBkk7RgYLeqyIFrujQMoAKWOf9qNXaNgeUZJDs8L1vZDMmoyqLMjLgLNxgCnbtBrrXL10gSt8hcnkgNn4EScnD0nSu6jTu+iz20yrI4qJxeiAurb+FGc1RldYbRc032AhfVeIQC2SiPDuQ+L84H/eFpzv8J8h+njO03mY9u8e/xfchPPdXkiBlOp32gHOv48UOPlYV4fVlvM/xv3uSeSz1xNRBJwTWK04b/R8JfNFwDhLbR117WmgZVlT5ZaqcNSlw1YSpxd+bs55IYnD+88ZqCuP3Kdph2ev3eDVl7/CtYvP0G1tEIkmehZzdtAjLAckaYx2jiq3C89ATVV6SKq/FCGEJpvPqHWBcQXT7IQsm3kDkrqmrOdkYozVinc/KLh2qeDas9f5kz//Nu/v/5abH95n/849+u2KTdGlLkLe+vV9jOvgVBdJTFQ3CU3O9MGMfDRiHE+58vQSyXZE0LGU5YT8TKEnChV1idvrbF95nkejI+aTQ6QIuHv7kHk1ZXmzQdrVIApKIyhNgI4sYq2mnTpaF5tEdULLBrjZlOnkhId5wScPBFXRQgnDhaWEtuvQY5VV1cVGTWTYIC8Vg+4Kk2xMJmpssybpKowsKaYjtKtpr6e0z2Lc/pjhLGf8wZz33r9J/K8hg7U2O7sXeOnzX+D6c8/S6wSk7ZBGOyRaabJ6RRG6EBkpbDCjDHKsaRCIBkqESGe8jgQPzrbbyzTTAZ2G4e4sYzQeE6eK5fUm7T402oIkiRa0X02e5czGFWfHJcPjirS5Qiu9yMaG41mdkZVD7h/d4r1bb3D/4R7D4Qnz+hTj5mg7J3AL+zUlkaFChQEqVMhAeRaj1Z5GXH+6q3/6+N0C8NlW4HHbcH7ggUUB+EwLcC4YetwW+Gd/GD5vCT4tAOBAPeE8AZzAaG8d7aQnCNfOYLBoqyl1TVHWlGVNWRiq0lEXoCuBLi1uQSMX4pwN5rnd1mqEFFxYWeOLr3yJP/7SN7i28zyNoE8+a1JmCqfh7NQyn0yQyvj0Ia3QBpyrEaoiiHJOsgI9m1FUGhHkoE6pOCQ3Y8q69IixC1DSEcagTJs33tyn11vi0uYS8drTXH/+BQ7u7hEzpd9YoTq5wCe3fouVTepKcXjniPn9MTIrqLIpxhX024JuZCnHJ+T5nN8cvc7qygAVFuR1jlUFMiyYjodEJqafbvDG6+8wzgpO1i3pcsnSlsTJkqrw2XWZLpjWJc5ERC6h1IJIVZTJnGlWMC4sdT4llBW1SBkVjxiOHjAUyzy1dpFWa5NxoemoLrUryNycXq9NEDpkHWJHDVK3wgvP9ug/tcqN6Zy9j085uDXi8N6Y2TDjwTjjcG/Kaz/ZZ23tJzz33BUuXb7MlWvPsr2zy8qFPi4umFdT5qYgmxYEKqERNGlEKVEcEQYJYZAiRODbQOPQVlOZmqNDy/FhRRILVjdjtncb9FcFzZ6h0XK02oL+IGJnt0GRWyajkvHZlPlsxnw8o+1SVtY+zzNXv8xwNOHg+DYHh7c5G9/jeLjHw+O7ZNUUI2pkCCpQBIHEu58Z74dpFyDhOUfZLURD5xghft8LnEA5P8E4P/mf8wH8hPF8sauFitYvfIlcuHHzeOE7PKZ2/so4h8bixBMPDDq00YvAEENNRU2FQVNZz6su65Ki8FOBMhNUc0FdCOrK4sx5P+8QwvhCYgxSCC5tX+DP/803+NMvf4Pruy/QCJfQdcR0ljGZTsjmM8oqxwiNDCVJHCBVAMKibUZdz9GmYloUzOc5RVGT5Rl5MSfLZ2hjcS4gDBK63S7LK326vVUoG5zeu8Obv9rj1a82aPYDDAGbG+us9C8QujbTsMvSRovhWcTB3SNObh8i55pABgzrgsYgYvv6GmlXM5pNODg65OCw4uVXdukt1ZTmBC3HaDdFmIClYJfb9/e5+foDtE24+d4I0TNce2XAzrUWRXGKMxZjahpBgBUSYSxGCiaVxoURnX6DbidjUmuuXxmQxnNWL65w6cIXee1fb/JQH3FlaZvIpdSxxYQlSdvR3lIEcYGoJpwePuCnP36XwxNL2U5Y2V7luY3rbGSH3BneIY8VttHBtftMtWM4esDP//kOr/2oyWr3Irvb17n63NPc+Mor7L54hWbHYQKNcRXSeQmNlGoRPGNxWM/jD6DZU1x6pkl/LebRQcnh/oTjR2MO7zuSNGF5s0e3H9Bow9JySLcfkKSKlZWE5WWFswnzSUJZOKZnJfNxSasd0ki3ubC2Rpx8jcPhJ/z89e9z9/BdZsXQM/WkV/+BWWheLALjOQr4YiA9aOUFhADCcwNCBCHeQ9nHKi3+0Z03Bh449yCyP/koobx/gP9MP4UQZjFVM57DYmtqW1Gakrx8wgVEHsFcOLXYisplaFehnaasK6pKUxc1VWEoMygyR5kLdAm1dtiFvFpJcHik1lnL9voG3/nmt/ivvvnv2d16ikbc9ZRTJEkzprvUXWjLzQKI9GozoQzGVeS5JssM85mXOlvjmOcT5tmMqqxBCxqqw+raFk9deYaNjWXanYQgTDk7MTyIByi9Sp2NacSPQEwoIyhLhwwqknTG/0fde8dYlt13fp9zbn751atcXaGrurpruqdnejI5GnKGSaICSWm90GoXclrZMgyHf/yX/Y9pCAvYwFpewDYMa72GYRhrSSusF1plzTINh5OaZM90mM5dOb4cbr7n+I/7qmdIiaaslQH6AA28cOt1vcI9v/ML3zDq7bBzB3butoh7ATJRxGlI5gqK01WiMOJgu0lpxsKWFieHh/QHBSpTHpnogxnmhq3KACXoNHuUixXCLKbZTGgeK46OmpzfrbCy5iKljzQdvKILtsZzHYp2CUd62IbL7Owivabi66+/yd/+N77AqH+Eocu8+NyXODr5HT5467s8aO6wNr/O0EwwCprakk3tbIqwQ4gtDE+R1AI+uNdk975F6XsnzBdcGlKQ+QoHh3K5wcT8Ohc/9yLFaYeTox3uf7jJ3v1DDpv3GbzRZHSYkbYMzn3yPN5MGeUotFTjEeHHN0kOmdZCUCiZeCXN5FzC0rogCkzCEQy7iv3dIf2ez+bDhChU1GolJmccKvWIShWmp6t4BUG5XMT1+njFAYNqF6cXooyYrQc94rBM0S5yfu0CdtXn4U5C4OdCI4g8G5EyT9ullBgGZDI/nH54CcA0cuctU5ye7uSZgjitAsTjf4+1A+THAEJjlW3G4ivazCOMSBRkCSoLSSKfKPrXUBYSQvyvwC8Ax1rrJ8evfRX494GT8WX/hdb6j8bv/efAr5FDu/5TrfWf/hXiAMLII1eqAzIRkaqIRKfEaUwcK+JIEQWaKIAkyiGXaZLrEyoBWo7xBTrnZ882pvjpT32Wn33t51hfegrbqEAm0YZGiBTbtrHtXB3mo1HTabvGBi2ouilJJWI07NLx2pDtMxylZGmKYxWZOzPHhXNPsnRmDc8uYkgDjIQoGWIYQ9Y3lil7qyTBPdLhXWrVQ3qqTBDN527AEjJfcPzggKAdI5RmEA1JZYoi4+7OI2q2YHGpgk2BQsnl4hPTOC4USgUKzhphFBMNA0xDUivMUz9bomx02dnb5GA/pKyrjA5H3O+fEO1arJyrcXbjDG7ZQDgRwkpwHYtqscbUxBzLC6u4ZpXF+SleeOkZsjQhDk1q1Xk++6VPsLl9jbsnNzCrkpnyGaZqZWpnJ5mYcTBDG3VkUxrM8dkXPsOl5xU3D3t889t3uXN9Fy9wcCKLkiOZqyfMVE3q05Ocf/YCRvmneCXSBP2AfuuEgw+3ad3zGQZdLEOgUo1hmzkoR+en62mT/HQEn2WK0TAlS0ws08JxTUpFqNQ0U/MhZ9YlKJsktGgdKw72u+zt7vPgUQfHKnBmYZX5Mx7TMwXsQoTp9DBiH7s4YkbYuGaJgz2fUZQy3SiRmUsEkc/B0SFxHHJat59OCHIR0VNR3B+e9oMQGik1xlhGX471Ah9fO97cj1+SHw8C4vFzPVYWEpbALjoUHYmTWtiJgTMySA8Tev7oR+69v0om8L8B/wPwv//Q6/+d1vof/sAXE+IiNzHIJAAAIABJREFU8CvAJWAeeF0IcV5r/aMLEsiRdCRkRKQ6DwCpTkiylDBOCAJN6PO4D6BSSRIp0njcbZUgTIlGY2iNY5pcufgEn//051hbWseWHkJZaMMiNzIZm5Zw+kfPm5OnS2f5H9cAEEWKbpkocLFEShrEuLLKxsZZnr68QblcQme5sGGv3+Pw6Jh2u0+5XKNek/R7h3RPbpM2R5QaQxafmsSVgsAPGLVddh+G9E8CVKLwo4BEpgQ6IBxGlMsec4vLLC1NkRojksBnYW6SL/3ST/P0C89g2CvotEwc+Ny9+Q5vfe0PWWyUsHGxpYUOivi+ot2OiGIo9D0efaeNHkk2nj/D3OoMygoRZoYUGcWiTaPewLFqrK4JXK+KQELZBhHwyc+sMDH1q7z19fcwcalPWyxeqDO3MQ1WRjoK2bp5lxtv3GP7qMvUhXk2njnPk1ee5/tv3+Wd17/P3p0jwmhA836X+7vbvP/9D3nptVf46b/1i9SXlnEn69SmZ1i+sIqOgtxWzutjOgUQNoayTm+2j913oDJF4Ed0WgHxyCYOFYZhIwyN7aXUJmyKJRvLAcdVlGqC5fVJVDZFEsPR4YCDgz7DkSbdK2HbdUqVItIo4jkDotSnXBsizJDj5oCsr6jFU8zUQvqDAXEc5hmtyvKjZJwJQPaY5ffDy5BgSY1lCEwjx/6LseEOUvzgaHDc+DOMMTZA5kKjjHECGpCmiVN0MF0gzbAo4RRteqMh2VHzR26/HxsEtNbfEkKs/LjrxusrwG9rrSPgkRDiPvAi8NaP+0FFbiGmxhOBTGuSVBFFmjgmxwbEgiwxSMOc7aXTfLQijbHyav5BrCzO89yly8zNTWM4Ci2H+RvCIQ8Ap1JL45RS5xnF+BujGZImEWEgCEcGaWLjj4qE/TqxP6BU8igXlwhCk16/w+HBAR/e+pDjoxNcz6JW8yiVDFQWEo5Cat4ER3dWSFSPT5h9Vp+wyXyb//43f5v3vruNDE3SLCUkQdgmjuFRsktUyiWUafFg94go6WIXU5bOz/DMlWeplGZRLGNYsxie5n52h+PWIZYVMjN1htXqEjPzM3RaLWK/Qq/domhV6PVGRKMOuzd2mChWKM5USDMfr+BSrRQRhuDw+AjPqSBkCbIYxBBFD2202Xiqxh/90Q7PXlnl5U+/ilNSRNInzQyU8Em8Dvf8u1zdbOPfPWT9dsSzVwLOz06x9JVPsn1/i+37Jxzt+YT9mOj4iDd/7/e5843rnL3wPM987hXWP7GGN2uQ1HKXnyTLHXcdWcSQBcD+KHiPMzgpc7dhaiadcIgSEWkyIvENBl2TvUe5UG2lZjI1Y1GsCCwny3kDLiytlVk8VyIO4OQwZNBVdHc0BgtUJwwMpwXZLtoY4Xom5iCkYNqUnCKlUplev0OW5RMBtProkPk4kOcx2Sc/2A0DDFPmyFYpMaWRm5uMS5vT9J/x/Z1v9pybIJXKjXUYS46TG6b4wxFhd8hJ/5hUKoTl0Bz0Sf4/Qgz+x0KIfwu4CvxnWusOsAC8/bFrdsev/YUlhPh14NcByhO5UII+LYS0RCNzDvVplq7yjZrFOvcRyAXU8jmpELkTlxRM1at8/tOv8dKVZ3A8zSg9JDNdbFlA6CJQRGgPreVYW16TjvEHSgkCP+Tk+JijwxbtZkjoSyYbC0xPzREMLbJY0G73+Pa3r+IHAw6PD2k2W5iWyeJig/p0Da9oACE6FdRLa8w2NpiZiLCsZ3HFh9x46yZ//me3+Oaf3SPRBobMiLMUt1pmbmmBfr/PqNunc9KlPWjjeRaz0zVsR1GtVCkXa2hlIMkwZYYAtrZvk8g2h/1DUqPPZGmJ0pkpvIlJZFYm8qvoOEWkNRx7kUzAbG0Fr1ri1sOrVMouKgs57uzyzru3+MJnvjLuXsUojlH6iDTxiXxwPM1zLz2H6zmkysfKUqyWT7wZUfBrvPTSM6j6Q25cH3Lz+i12trZ47oXLXHn2Mk+/usTFl1NazQ5RkmFIm+AoJDkysLXBve/dwanarJaXsOwaBkUUMTpNSUUE1rgBJ2xyFyiVI+IyiALNqJ8wbEHglzg6GIKAuUWHRkWgDEmcBDx6OEApME2oNxzqkxZe0cAuGphuysJZkyyRjPqCTlMzCiOyDKRu4DgWslRHqCaSI9rDDNvOdQfTLP5YhskPnP6PocOP7/88CFimkYN9pMwzgPF1amximqc5pxSJ/LtKIw99ph43G4XOZcUyQeLHDKIBxyfHtEd9QiXw44w4+5vHCfxPwG+Mt+dvAP8t8Pf/33yA1vq3gN8CmFl2NSJDyLGSUN5jQZI3TCyhScevqTTX+Ed9bM6ak6cxMsUTa+f4mc9+kY2zGxgFTaJ9kiznIeSqMiYCSZYUiQNJECbEEaSJQKWCXldx5/aA27cf0e6cYFiS+YUTDtuPGI5GHDW7+EHua9gbtMAIWFhscGHjLJcuLbM0P4crXfYPDhkNR9TLDWanahiuT79b4WirwT//7S1e/5PbxEmZTA6JVcTUzCJPPf8SYRqwf/wurV6HUqnE1MIk9YkqIhZ0ekcsry2SiRhTTCMwyXRAEvoMRx0q1TJhvMth+y690QHVwjQ2RRxh4roKqwhFx6NsFnC9AhOzBZY2nsCt2jw6eJ+7D+9iukXawxbCNMhQaGKUHpFmfYJRwAfXjkiyAqV6JT+hMgPtDwnv3ufhu31u3W4zdH2WluZY/UKNO3fa3Nja5/U33+L6zW2efuYKl567wMoLG6R2SKoUFdnADisEbYEWFjNrDdxCAaFknuqZCdrKXYnz+XeSIzT1uFg+dZ4iI4oj+sGQk32brQeKYKh4cKfN3BKc3SgzueDiukX8UcpwFDAYBHQ7I2zHplh1mZy2qFQNDFNRbUClDmEi6Q4cBu0EjAaeVcUyXdKsy2xW4LBfZg+R+14AoJB6LPCBgVYp5IPC8cgvD0C2aYxFS3PdxBTFqTxodtqf0iL30ND5CJ0sxwiYpplrHSojl+hXFoYw0ZZC2jblSpVAx4RDP59M/MVq5PH6awUBrfXR6WMhxD8G/mD8dA9Y/NilZ8av/bhPJLdpzskjcjwzlRpMYWCSS4oJlQs/PN745KORx9ciWFs5y+rKeSrlRbRUKOLxiSbHwJIcYGJYBqY0cVxzzPLKTSEnp22qkxZTSwZbu7fo9E6IokMe7rfpdzWdbhelFJVSkQsbc6ytT3H58nkW5ueoT1SxDZtBq4/nFJiZmWKiUiDodzjYfUS32+NP/vjbfOvN+/gxCJGQximVWoVPvvYqpdocb7z7dTpxl5n1GVaW1lAyZndnmywwOX9hno2n1tGmRlNHiAaguH//e/jDgKI9RdGF3uCY0TBiNNjBFALPMSm6gkrZAlnAdOsYJRdf+nRHRS5euUJxxmW3dZswiXA8B2FqcivUFLBJI5e3v3ODOx/2+NRnfi5XNNM+IvWJugMCP2MgRtw6/pDtkz6Fu3XWVs5zZmaBc5cucnLS4dp7N3nja69z+8ZNrjz3PC+9+hJL52aIkpiRHmIsZZQKJYxiByUdpOmcomUQWBjCetwEVCpDaznu6ggECteFydkytutSnY6pz4XcunbM/lbA/p7F9kPN0oUyMwuC+SWbM8sVpFREoSYODXr9iKODHqOhoFD0KBZdHNfAdTJmXItGtUY01Aw7AWlq43o283MTRErRPD5kc3+fVIHQYjwO1Kix0WjuITgu5Q2Baxs4Tq4NiSlJpSIgQeqYVGUknFoWjWt+lbMRFRot897AKVzYtm1cx8XSDmQa0xV45QKT9gSWZ9Ef+oz8j9Owf3D9tYKAEGJOa30wfvpLwI3x498H/qkQ4jfJG4PrwLt/lc+UMo/6WuWKMKaGRI7plIgxskrzUap1+pN5rZA39iSFYgnHKSCknZ8WojBOy36oDyBzcVDLUmhShMhnq1pnpGaA0xqSZiG9XsLxUZ/WcUoYKmw7YW62wUsvXebFlzaYm6tT9FyEsAjimIf37nH/5m1cYbIyN0PH73O0v8th/4Tb9x/wza+9Sbc3QMuMLPWxHXjlcy8xc7bK1WtXOeo+4sLlVdbOnuXkqMedW/cpFQq89IkrTE1LZmZnMakiqSBxEAS8f+1dDva3Cf1j5hcnKRdKpNkx/UGbNAmxfUhqBlbRo+RKRLmDUXWRJYtEDHA9g8XFVVIz5O79+2RphiFCTIZoHZOlkvff2eTW1RNe+5kvcvHSZVRmYugAGQxIWwm3b+1wcNLm7BNn6Ot9Hj5o0zm5x/zUiI2N8zxx5QnWn3qCa9dv8OHbd/juH3yH43fbXH7lNV7+4ieYPevQi+7SD48Ztru0/SOKpSqVSh3XLgNeDgvGGE/MclPSLE4IwwihJUmSMugJRgMLr+Bw5iwsLi/TOfG59t4+d+/cpfP9KlN7s+xvTdCYTmnMRaysG0zMwFTq4Y9EbmF2ktI+6VOqakpVA8exsCyBWclwbAeFTZiVSPsDFmZqXLl8nqHf4bDZIrcZ1GQ6Q2UJjKXMDENjmuSSdI7EtAXCUmQyIyQlVhqlFKnIiMS46a30OOjxkUnqmF+QswkNLMvEdV3c2MMu5mWV7UnsgkPNNXBKLt4oAtp/6d77q4wI/0/gNWBSCLEL/JfAa0KIK+MduAn8BwBa65tCiN8FbpEfIf/Rj50MjPelGDdHtDDQAjIlMWQOAIJ0fJpneRAQjOvB05IgDxRawclJiyCMKLsaIZzHKdpjuuUYuw0fCTSM5y+oTNBsdbh19wbvv3+HzYddTk4iOp0hwlCYNlRKc5xdeJrnL7/Iypk6thvi+8fcu/+I77xzm4cPdykgmffKuO0B2w/vsbW3zb3DAx4dHNBqdnKtO6mIVMz62ip/91deYfn8Ev/Or32Oh9v77O0f8tab3+Pd+zeZaUzy4vPPUywKvNKIycYsOqtgmMU8+GUjHHPIU08usb0V8/DeAalUePUQbSSYhkCLjESCn8bEQmOWPAr1MsVSBbfk5YFPRbmBSyKwpAnZCFSGVJLm0YDdh0Ne+8yXufzsOsoGrYqE/QHhdoukOUJYEd+9/pCBP83k/Dk2LvvcvfmIna0OUXOfMK3xyt96mS99+TyfeHqHW9/+Lve/u833vvFtusddXv7ZF7n42aep6gOafot2t03/uEVvUKZUmMBxKthGCcsuYJu5pbtEIg2XFIODnR6u7VGrujjuiJPjLp1Wgm0UqJRLfO7nn+Dlz8S88/YRh/tNotjGH3lkexnDXsLiWZP5ZYNavUi1pkljCMOUMBox7McERkpj0sUwU4yCojZZIKVOxoA4HrGyMEHnwgpB0KPv+6QqQekMIVMMQ2E7cuxUJDBNiWGCsDIymXddEqXGCsaKBEUqcoMVrT4GNDwt6xU5QG78VAcC0x/hhg7lzKOIiydNLFciTXCKDsL61/Ad0Fr/3b/k5X/y/3D9PwD+wY/73B9eOXNKPJZIkiIbd1HzTqsgHb8v8pJB5vBMoUFqkWsRKNjc3Obh5kMmrywiScY4LJmzsn+gN5JLPAkMVCaIQmieKG5c7/L9D7psbUX0Bwl+kKHSMunIZZQ4RJZD2Rry7ns32NxL6fYeceODu+zt9HFLVVbXV1lbmKN79z6bN++wv7PFzskB++02/YFPkmZ5fZZlTJYrrM5MIoMtPBGBdnn+8hleeuYlfuFzr7L/a/82jzZ32dzc5q23XudLv/RJim4ZIeo5lVmP6LQfIHUb2+xzbm2GVrvHN779IbGCyekClaqkVFaUyw5O0aUyMU+xvEShUKZWnaTgTpOmKYaRkiUDwmBItVwA5ZPFGQ/u7PG1P3+HhTOXuPj80yinR4rEMUoo1eDhhx+wc/MOkREyc2adh1f32e3dzDUYHYvhaETob9L5Vo80hV/59X+Tc68+zblnLrJ5+xZ3v3EHnXRIA0naLuHUV5mbmqdSbTHqt0iSEIQiTjoEYRMzMii4dQrWNEI7+IOEk4OYQbvATktjWD5n1iTn1xfw52P2D5qcHB2THhbxPI+fem0BlcH1a4ecNNsUix5Clnlwd0C3m3DhyQpuMcQuaOyCTUXXHiP8hOEjRC7M4hRdylmdKOmjs5A0i1g/e4Zut8n1O3cJ4wQhFJYpMKRAKYGQBtLIWynC0GDk8PZYjSXqlEIJUELkdprjI+p0BKjGkGMQPH6Y7zriJCPo+SQqzhkDwsTFxPLMsaPRT7jvAJC7sJDbgCmV859VJnIikNJobeQki7HGgBA5WEJmp0ak+ez/0eYmr3/9T5lqzLAwu4RtuUhh5bUVuRpQDizJS48k1vgDRa8Du9s+vWaFivUSnphl++gDmt19oqSPNEa4no1hG7QHGW+/02HknzAcHuK5Lmtnn2b9/FlWn1hhZXGa3795i8OtLY5ODtkbdBilEVEaoSUkcYKVZrmycWzz57/3J6jkImefPsedw00mJi9Qri6wsGyxtLrGq/oy5y/YVBsmaSKxrdIY7xBysPuA5tEuZC0yMuoNh6WzdaLIoFgssb1zxGgYsbMZMzdvItIK/ZMIy+zzzJUKS/M2kYiximCJlHJRUil4OLbJ/s4Bv/u7f8CVZ1/luZdewyw4aO3gKRcZgpnOUnWWeHP7Kvf3j5lcmmD1sseH90/wI4/Et3FKDpIenX6Xt1/3Od475lf+k3+XtU8+yTOfWWLj3Dn2H4Y0T454+KjIWmUFQ5WpOA7lyRpRPEJIhSFztKYf9Gl1j+nqAMssInURDAM/DuiPNLHv0jwwOFpMmD8rWVmeY+28oNUacLAXcLBvYNoZT78wSZLG7O10iIMBOi4SjCBNUxh7EOTZYz6TF48bdRYgMEyDYqVGmvmgE6I4R7xe3jjPYNDnwc4uUZqgDI3SOU7AyF30gFzkI9OaNNW5RKTO81x1ijYcg4xOM9hTL9MxHehxk5Hx49M+mT9I0fTRePlIXIA0JfoHDsAfXD8RQUCPuQOIvIGisjGz8BRqrRjjKOW4Dsojm9Lj/oDON3eWZnQ6Xf7F7/9zBt0+n//sazyxcYHJxhym6YG2kcImjDJGfU0UmgS+YthTjAaa0SimUPSYlCnNXkKlPcIoxZiuQbHsUSyUKHozFOw5gmHGcJgwN19hbX2SxkSFuelppqdmsRyL7mDA7tEB+61jOpYiEDHazOtDKYEE1ufPsTJ1gRM/4Vuv38OZmGJqZYbbW9eQzi2q9SI1t0KtsEQUnzA/fxmtLaThonSGzhIOdg65d2sTnTapNEokpsnquRpKhwiRUJ3wQFeJIs1gGPLNb77H1GSJxYU6e1uPmJp4n6efeppnXlynXi4yN1uGJOeo12oz/Mqv/n0Wzz6D5VZI4wGWakB3xHB3m8Ee4CumZzxu7sRcu/6IM+sLvPSJlzk6PKF1OKDfjHGsBrONCWQG27s3+F/+0T/k53d/mU9+4VPUF6/gTcRMd3uEUZs4tfCyScDFkAU8J/+uQkCt1KDkxgSRjx8O6PaOCP2YcKRJTJvKXIXesU1rz0Th8WgzYvlcmaVzJerTFo0ZOx/7tX2CUUIcw+LiPLZtMgoC0iwgSSQoNx9FYZKPobLHbD6t3fzAkQmWFVOpTZJlGUmSkWYJC7OzXDq/Tr/fp9Xv5gWskEhDg8jGsmR52j9mzZONg4ASjAFCef6qhcgbgXqMLtSCjNx0+zHZcPxYj4OATiEcZIyMEGMsEmO5Ftnf9HTgb3pprYh08JgIkSFItCBWmkTlbEL1AwzsUzJFbt6Y/5HUmDsgOTxu8Yd//Ic83LzNF3/6C3zutZ9hbnYFy7DHmG6NaaYYXm4znqQ+rf4Rg6yT32BJl8LsMVdmEgxzEpVUyeIpZDaL1HNoJTDkHnaxTrWuSVTK7n4TSQWv3KS5N8AwDYYqpu37JIXcKMQ0THSSG6aWLI+y6WHjUqttkLpzvPuNLT7/JY8nV0vstk/Y2t7kUaywucHJUZsr8spYwCS/caLhkNXVFZ554ae4e/MDOq0+o6SLVUixvQRpZtTq4HoOXqFIodgAKVk+s8als3P40ZDjVkK5FCPCLpOVKp5IGXQCZJZRrK6zNr1MmmVkqSTzLdJuiGxnPLh6lRvX9hmNYmQlYmV9ntGtE/bvh9gyY+3cWc6sDth+1OTkvsS0Jnju5WcJ5DH37l3n9d/5p4SbQ175O1+ivtjAmZVEOiJKjkkHKZZZx/IsLNvGEHlJBxLLLGIaFVyvimEUGDg9THuIsEKS+JhGscDUYpVgFOEPDPb2Ruzvh8wteKysF5iaM6nUqsQRDAdpzkMJwTI9ahMVHFuSxgLLBo1CiHRMysmzzY+DzQyKOJbBRM1GaoM0jpEC1laW6LZb3Lx3h0EyQosMYeQGOqQKmfJ4ynVKJPo4aF2eCgvA2EPhVCBnfJ34KBN4nOSfvoFEJXl2i4jRSLxM5zXIj1jGV7/61b+pvfzXXv/NP/qvvrr6KUEmTgVEUqJEEcaKIFDEQUbsZ0S+Jg5FLpqo9LixJ8bfXSEl+dxV5lp+/UGfQa+PaxcouSUs28k3olDYtsB1NcKKGIT7nPQeMIz3iPUJQg4peC5TE6s0Kk/gGWex9SwmFXQGWRJiSolpOBwf9jg57uK5JcqVEvEo5GDzgJPjNq1Om27vhDjsYltmHsLjDDNWlJTB0sQ009U6mWuhvQlS6XB0+ACZ7LPYsFhsTFApuOxv7VD2aiwvPDHeyJCoEX60z+SMw5NXnmDl3CUKxSluXPsAlYaIzCaNJYaw0SkULJdascjiwjyXn3qJsm1RqFaYP3eZkhNjasiMIpbpUhAT2FYJnBVSMU866JOd9JCDlO3vf8DooM/W9hYfPLzN1VsPOWoHCOlhyxK91og4zN1v5s7OMLlUolQ3CP2IQU/x1LMv89wLn6BWKrFzf5vDnRFFr0F1agKzYIKVEMcD/FGbkd8liv2cZGNaPN4mQmNgEfgm3abENhuUyxWkHZDJDnYp5czKJKvn5imWHIIg5GivR/tEMRpZGKagWBIUyimlSi53rzXEUcZwOCSK/fxessYbXp9u/I82Uo7zt5BYmIaNbdmAIksjdJbgWpIwHNH3+2iRkImQPO8HVJ6eZzonvyktfjAAjHGQeeZxKjCWv3+a1ouP/fs4VuLUI1MpSLO8o6jJs+md2+HBV7/61d/64f33E5EJoBm7tIJWgiwzUZmBUAaoBJ2GqCQjTRQ6M/P0Xwi0MJCGzl1WxkNYaYBpGBhGHiIebj3iX/z+73Hnww84v36exZVlTNNC6AKWYZGoiGa3RbvXJYw0KrMpiEVsPYsdT2KbdQSQqD7DoEPgpwhdwjIahCMHV8R87mdfZHHZY3fniOvv3+P+/Yck4ZD6/Dw7O9fJBh0ClWCaLk6mcYTFTLHKfG0KTxsUHIu4UWKHjN1OgTf+j7d4btnmZz69xuJajekrDYRRpSR3sFJBJlqEUUYoOpiGTdEusrw+yfziZ1hameKtN77Ou2++T68/ZHFxCj/uoZojKpGNVbTx0jlQAQXXo1Q8T6ebIuwSiCIpGYXqDIPRAUbcoujNoJsdju58QNqRDI72ePfq9zmKDKJKkUFhm3ZHMxr5VOsTTJ9Z5bB1QPvmDrblsvHMEsUnisyfjRjsjLj13jU+++lf5DNfeYr9n7rP5o0eW1u7lOYmabgNXFdAYR+t+2gsgnBEr39EoVhnoj6LbRfROgERUylWOIxj9nd85hbqVMsujmwSpQPCZIep6ZRz1TrzZybodybYfuSz9bDHybFgYcljes6m1jAolFLcgiBLTMLIxvdHNJtt3GGBYqGEV7Cx7I9y8I/G0zkYCOFg22XKpQZRmDdXp6en2NjY4HjQ46QT5LMoYzz2N2Te0BY5358x2E2Pg8LHp996vKHFxyPB+L3T3yNnHo9HiuPfUStNFimGgxSNxC7+6O33ExEEtIYsMcZoPhsbF8t0sE0jp8oaIxIxQKgQxtxxxKk7cYoYizlmOsMktw6XhpFLUUnoDpp8eHfI1s51qvUypUqJojNJsVDB8gRhGhOEmjQtYhszCHuCIC0SxScIeYwpXJonEceHeQPJNhKgSRi3WTlX4LkrG0RJk9dvfMg3vv06nfCQxTMNzq+fp3N4ljttnyRIcEoSW1jUPIdzs2eoOR6uNFEqo1DW3O+0uXZrm4MbGVcWXyX0z3D7/bc4s6qo1I/IgttY3lkMfwbpu7SPt2lLyUR1gZmpJ3GdWZ567mnmzyxxvPc/895b3yUdFTncOqJsRSxaUwSGwlzziJIY2ZXYdgOn3yDxLQrFOkGvQzY3T8sf0rn+Jmu6j+m5bO884t47WyzMLRM7mu/dvosz3aA+P8XxIEQoh/4g4MmNdZYvnuX9713l/vvbVM0JNj55FmO1Q3HmCOsk4rD7AXPys6w+d5mVc0ParRS7mCGli2QC1/QwvD6ZGlGtSEZBwOHRIZ3eCdPTs1RLU1iyiG1aLC9OUHIKhL4mSRtIWaPg9kiiR7RbTepVl1K1RLGSMrPo0GnbbD3qcHAwII6m6bahPmFQaxg5ucgrUC57hGHMYBDS6QyIYov6hIdpGmPdv48X2Dm/X0hNodiglvpE4YA4HjI/N8eTFy7y9vd6ZHGuMI0EbY5H1FqMsTEf1f8f1fo5ME4pzemQXZye9kLkO1+Px4Ra5U/FWFBEk0uZZII40khDPZY//8vWT0QQQAt04mAaudaca9aQyiUzTDwVMYoHyKBN2D0m0UHeLBFiLCWWky1OCdipAkOBbRsIQyBNA8PMJwromOGgSZJ2Cd0+/aGDME1SZRGENkKnmIbLyN4hSzr0+m1GQ58ggJOjIaQutjYwMYljhVIJo6jKP/udhGb7Lu9e/WNaoz1kUVKenKU6VeP5V15l0ErpdZtMFD2KKdSlzdL8HFMzk/jhiCwRWLGDv50hTip85Wf/Qz71i7/AXusOV79+G+ONh7z4YoEL5zVV/x5eYY+SMLg8n2vr+X6PgzvXKUysUp66xPSZGX7133uFcHTIyUHEw0ctLi5PE/Yt+KZuAAAgAElEQVQLbLU6PHduwDBukSUx1l6Z1B7Q7HRZnj1L/2QPDss0Vhpsbjb5s2t/RGl6jXRGcuvwgE5Ww52dxZ3cpdkaMl0uMD9ZZtT1iZKMe5s3+fzPfZ7lxS/y5uvf5NZ7HzLhTfHkykWK5yfpVY/IupvsNr9Pae5FSjM17KpiFPeItI9LASkKuHaJNBuSJENKRY+VZY/tvS129h4wqkZMlFeJgwjLsHEsaPUHZHGM47pEysCyN9B9iLsRxXoLr5jiuAaTcy6N6RppYuEPJd2W5uQAhv2EykRGpephmhLPc/E8F60VSRJzSgD6YTZgjukXgIlpFSkWG1QqPfygj9KCSxcu0h/2uH7/BoEKc7kvmYPT9LiO1xrSMSgoX/npr5XOiW3jTECe6g3CRwrFgCEEYixekpIHlVNKstZjB+/0RweBn4iewH/9m7/x1dXn6niyRsWZpWRO4eoJHFXG0WVMKpgUyRLNcOgTxxlgIrHymgrIQUQyl2DiI5NG0xTYhoFtmBhCYhgmUph5U1ELpK6i0klGvRL+sEy/Kzk+PmF354DmYUzilylYk9i2Rbu9R6u1y2DUIU6HCNtH2C0OO1c56l0jzk7wih4X1p/jwuoLkHmctE4YJX3mGrMsTi4wW6kzXamwMD/DxGydyZVp7MkKwpsjCqZ49qlP80t/++foil3+6K0/4Jtv3ODw0ORfvb7FB98PccwVmscwHCT0mvv0j48RWYRXjLDdDnG6j6BHvehw784Bb737iKPWiLn5GYQ22dtqMteYY6+1w9vffhM3KZPJgO985x06e12Om0fceud9zi9d4vbd7/ON29e5tdXBcT1a4ZAHO8fUpyapTdc4Pu7hd3wm60XsoqQ7bOP7Q1zb4cKFdVbWlmg297l3/TaV8jIbT3yKytQsQdah3dsi8Pu4xRqFchXLMQjjHnHWxzAFUloYhodpOCgF0nCwzTLtZkCzfYi2jihVJGlq0m0p/J5Lcx+S0MI2XKSwUUrQ7ynCUZ4mGyZYpo1pOBgyh/1W6xblOih8+v0Bo1FOFoKc4y+ExrJzgs9pEPjBQCA+Bm3WYzhvjmFRaUbBK1Asl2n1uvT9AcKUCDMXBVFCo5FkCjKVYwOyHG2M+th07BQxqMblwWkncbzNxwha/UO/Tt47kUbuiSFM6Oyqn+SegMTICniyRsmYxFYVVGKhEglJRqqLlByPRi2l1eoSBMljFJXK8ytgjC8YYwnSTCDNHG+QZoI0kShpoTCIs1yW2TZtMlGn16lxfAhhZGMYLlWnxnJ9geUnN5ifW0IpuHvvFu3tJpExpNSAyZmIaiOmVFVgxAx7FipdplqaY66+RNAJuL27yc27N4gCnwvVJapIatUJKqYGV5K4CllUSM8Az+KJn3qaS0+dBQ64+aff4dabH2LZBZS02O8Lyv4MX/+uyTvvvs25cxM8//QkQeeA2UaHxcWU5fMFphaArMjhcUAUw3E7YKQEgWWx5w/JVMa9fouT6JgP9/eZnjyiVnC43+6wd5hSnymyc+8hM2+8Q2gVSWuCR7e2MB9YTCxMs7v/PY72pjh3+QIblzQf3rzHod9l48Iaqak52Gpy43s38ZwiL3z6OT73d36WN/7lt3jnT95idepJnvnySxRWy+wdf5ej9jV6d3c5d/Zl5hsXKXiT+EGfod+nVDCxRBFhSEyjkMvIpR6mNjjZv8fxcYuFxQPOrTjMLJcxDBj0Uw73RlRDj7klk0IRRAiRb3OyZ5DGJmI69x8wpAaRIswE10hwCyYT8ST9fsigP6TdVhQLBQpFl1LZwrL/wk37scentGELIVw8t8ZEfRYDGA46zDSmmZ+e46BzRKTDPIiYAlNr0nSsNyAVZLlvxmkWcDr+O50kQN4zMDhlFusfCASMqwQ1fs8QGmGClCn5T/3l6yckCAik8nBkBYsChnaQ2GRakwmZyzErTakwQbU8wWAwJEk0WTZWGD395uTRMssgSyG18lFjlGmCTCPTXKdApgqSFMvUVJxpCnKN5Zkp6vUpGhM1SmYVqUwyEnq9XbqjJq3eHl5FMLHoMLUQU20kmK5PqgS9VoFRd4LYt0gsj+PjJs32DptbewwDk5J3liSr0w8zul5EQkCz47PQWKSkDaTh4VUN1p+axXRHvPedq7z3revYwQRTM5PcuXuNeqPKuac3uHrtfTY7mtXCBZT3JO+/9x1ef+MBr356jtSb591rTdrd+/ixx6NdzTDSKCTCcBmORjjFMkdZSD/NGGQ2t4+PmSx59KKYVreLrrscxyO+++BDJtZqTM5Ms/ngLkedAZPr08yeKXO4uc/0mYssX1hCFiJuX33A1v4OG08+RaJsBnt9br9zg5Ln8twXXuLzv/wl3v/Da3zj//pjzl5YZ+GZi1h2wq4V0WzvcOfB1+h1mizOXaLoTdPvhfiDHtPTFoa2QZhYJti2whJF+scN9g+gtScJugcsLB0yudSgOlVhf1NxvNtjayeiNqyxuDJBraEIo4heO2XYC6lNJFTqgkJJY9sCsBFCYTo+9YZDtVoiDFKCICZOApJUY1oWpzqWHwWA0xr+dHJgACaG4eAVyijlE8dDHGkzU53CsxzCNEDLvLa3HEmmNEmWkmYakebkuVPh3NP/4rH1/BjynouK8hHofXz6560CgTA0hqHz3W3m7knazAVO/rL1ExEEhAapbAzhYGBhaBNNni7loA0QWmJJG9cuYAiLWOUuq0KKfAwix40WlUfSNAWRQpJCJDUySVA6xTFyLqHCxLUKnJ1fY6r6Ajotk6Qh3d4R9w8+4OhgnzAdIh1BKgO00aM0n7IwY1OrgmlJRn6V1rHB7nZCEkOxIEkyn4PjNu12B0mZqtvAsybBNdkfbmFjsX6mijAcgqJNsVrDKlUoVSrEfp/N+wdcfesuo5HJhfPn2DrYxtImTz31HM2TAbfubrO4OMv5jQ2UdNlthewcplyMVnjUWuCf/c51Dg8GvPzKC/QGCVGSUnA9zsws0UyO8QolhGdjRRZSefTimEJg4I9GhIHBlFJIU9IZtnCiChPT80xMH9E5CBgFMWsbG4zaW9y794hnls6wvjaPHAk+uH6PvcMTXvj0i9x7+zrH9/e58Z0PaTRWuPyJS3z57y3x7uvXePc77/LZxS8yN/8kUuSy8a3mCXfa32L/4D5n5p5mdvIizZOQ1tEx6+fP4Li56nSh4LC4XCH0FwkGit17LXToMezFtOY2mT9T5eylGeaWbTotxcN7e9y5M6RcrDI167KwUCDOFMNBRLud4Xk2tbpNbcLEdvMNIoTAsGIKpqJQzqdWhkwf6/3lZ+6Y1Jb/RF4mALnWoQnSxjAdTNfF9hwKhsvZuWU+3GrQP26DVrlGIjpXp7bzJp4cZ7ep1qRqLKOf/xcfpfj6I1k1xJhTMEYW5iIkEgyFcATSzjnLCkGqfjRk8CciCAC5IEKcoa2PsJBCnVIocxXVfK6aj2UExth8YRwscnB17vYyLphUApnQxEojM4UyBPGp1ZmSSNEmuPmvEPoqo2FIf9AlDLuEYYRSBkVvGtOoY1gZlek2diXA9s4ShRPs7yj2NjO6HYHtmUzOKlwvIIojsiyhXptE1ktYRg0pbVrDB3SzLYITRX31Ci88+ywbF58m05Kj/R36/S7NTovRMKZUnOb5Z5cYDX3S7Yc8+/QLFFyXr73xLxECrjyzQb3hsbu5z8HJIUuri2xcukyzdcD2bg/PqVCrTbOzcxeB5ML581y4cJ6T3WNM06JYLKAiNx9VmZJipYTpOCQqxY9TtGXSGvSpBAm16QrLa2fpHG3S3GuzsrLOyhWTq9+6RedhmfNX5nni2RoDH+5+8JCicnnxM8/wRhow2FXsvzviqUUPa83klV/+BI+un7B354CN6Q0mK5cIpkIGve8xGh1w3LpPp9PmePKAinOJ/UcmO1snfPqzl3FdizjWSDNleaWC8i9yx+6ytXVEyZnA0k2CwT6Lqz2mZ6rMlV0mZyu0jw0e3j3k/p0is8MG9VnB3FyZOIbRMOFgv0uzKZmYqFKtFvBKoOUQJfq5epewyO1FY06xfOKUi4IcN6gZ4wjG96awkZQwrYBMG0jDZmZylrUzy4ziE3r9Nn6YoDJwdK5UbDmCVIKyMuIQ4kSQpin6FCc8NhVBGB+R53QuZy4eAwbGFmimgWEJDFsibYtYKbLkJ9x3QCuIooggCIjtGCEcdJqzqJRKUSpBi4Q0i4mTJBebNMzHqdi4EMhdXQUYY9ElqQQ6gSTL0CnEZm5zlhuVhCgVcNI7QGWQpBlxFJEkMbHO0FmRMAwxxT6WmeIE05TE8xSNi2w96nHz5j20GDK/7DI9U0DplMDvobKIWrVMpVxnZmYWkGzuPOIw2EMUYibmFlg8d5GNp15mdm6Bh5sP6Q27JHHEytoFzsyvsrrcot/0Odjdx3Y0ItO89cbb9Ns+T794gfW1eQxiHj56gDBTXnr5OUrlKg8fblIpNZienKFSrGNg8dyVZ/jyl3+egmWSZSmO5+IVPZLAGYtVSrxSmXKtSr87RCEpTU7QOh6SZgamdpianaY+1WLUHtHuwtz5JabuPmD31h7r59eYXrV55TOXoZdw94N7GEWD5z/7Mrf/fIdgN6R1SzF5cYJB+ZD1Z1dIj1z8kwB3aYKZyUt0Ol2CoMdINYnSQ9p9sBoWi6tP8f13jvgn/+PX+OW/9xqNGYtms8nhZoIOZ5meqFK0SwxGTVy1hp1McfTgmCyS1CccLDukMR9Rn/YYdRx2t3vcen9IuWKyuFyn3vCoNQoMhkNarQPazRITky7VuoHllMDMyV5Kx+ixkAmYaGGN0YOKPBCcZu+570WOHbCR0sY08galAC5tXGBiyuRgd5utrX322z26oxEIgWGbZIYiM3K1oFN/AaX0WO+C8f093vHqFF6Ur8cgIZ0TlqQpMB0TTIGRgvH/B+5AkgX4cY9RWMBwTZQ20dJAy4RUBGSkjILcbjvvimikYYwnA3qswZ6DqKVUSCGwlIAsV31Pc/NYtMibhVkCaZLlUT7LUJlJGnuorEQq4rFj8C62YWBZizjqGUrqK4za8H8z96a/labnmd/vWd7trDzcyeJW+9J7q1styW615EXjBQM7nmQcBDMT/wP5lA+ZfyDAfB0gQIB8iWNgEswEmNgeZ6zYlmVLstQtqfel9oVVZHE/JM/6Ls+SD89htWZg2UACGP0CRBUKLJLgOe/93s99X9fvOjl9m9ZcxfK6oNEaMuiN6J8otM6ZmcvodJrEscfKLcpqgIj3aDckc+0NXrjyKstz16lGKXc/e8CDhzcpqwHXrl3j+RdfJola1JJZxHnJ0cYaH74v+PTdm/RPKtZW13j5xeuksSIf5uzt7HHj2mUuX9zAW9jbPmF95TKXLm5Qj5s00yZXrt7gm2+9xTs//D5KaabaHaJIk9UykixFKI1OU1ozc+zvOJzXtGdnGReSqgA3FjRaNc6dn+Ph+7scH+Rce+U8X3rjRd799l32tk+Yuz7L9PWItPoy//7ffI8P3n3AxsZzvPnNL9H70JBlM7RnLiBaAtvt01ms43wPb1o0awtc3HiFk9NDKlMyMj3yokvlN1m+uEqnc4U//aM7/Kv/8ff557/361y7vkrR6XH78QPyviJSc8x1zmEGAmU7eD3P1u0uo6Vjppf71GqCKILOomN6qYat5uh2C06PT3CMWFhusHhOMrcY0z+19E96DPsJaZoyNVOj3rQIWU1mAcHgFkZsLhQC4ScWYI+QEoXAY8BZrDFoER5Sewf73HzyKaU/oZ7VuHb5GsWdB5z0hwgVioRWAullaOWFQmAxJgy8vTubAkxEQcIjJutFd+Y7mASYSj9BsDPJKowkWn3BB4MeRyVHjAyc5BIvPIls4BD0yhGFKxkOhxydHDDKezgMyLA8DdUxWIuVcM8grEqCtuBtADBaZwn58AolBJYR1o+DfdNKbKWAWngxRUwj7dCqLyLKBSK7SO9Y86P3/i1eHZA0xszNNamKjKebAs+A6VnB7EydZrOOUhXGDijLgsoUJFHEdG2eerpOI75Id1dxsn+b0XCLKBtz+foFbjz3EmlapyoNndkGtjT0Bo6q8LQb8/zCG7+AqR3TmC6JZMre7ik2l7z8/At0mnUO9weMTiuWF1a4cuEyJ91DpFNcu3yN6akpxuMRQkrSWoaQYGyBw6PSiKReozU9Tb2ZU+aGWr2DF8f0Dgcsz9WJpyXTS0227+5z8HifKfEmU1eusrN5yqPHT7jR20BPe668scS3xopv//Hb7Nw64rVfe4EsPkUvVKhYMtdcoW+fkHOMjus4k6DjBtPtVZ6//nV+8lNLMdqiqsZ0j/c56N7hyvlZ/sl/c4P/+w8r/o9/8x958+uv8dqrL/DSl9bZ2jxme/OEkz1HLW2SNRQygcb0NN39in5/zPw5T2fGkyYQxQVS7rKwHLG0nOKMJI5B+AypKubmHLPT0wxPNf3TgsFpn0hL0roENN7bMKSemHy9q4KpjXBkVZO1nDcl1lT4coh0BmMKDk+P+eDWTQ56j0mkpJFN0S8qnBTYyc3sjUd4hRYSpzxxopASTOWwkyOAm6gKz7gY4swzIESA9LrQOerJxFDqwCtUTvzc++8LUQQAjBjjvcGPHeN8TCzreK9D5PYwp38y4KTXpzQ5SI+WQRaMckgdoI1SuAmibEJj9gKMAuPxZZigSGTo4mKBRwaKsRMgHUIUaBmzuvAlrl/+JvV4hU8/esDO7h0G+fuI9ClpLaJZzrK373BVj6l2iLKaW/RkKUgxxjmHYII2tx2ESVDMosQ6g36bYT9nONyk3enz8qVVXnjxZbJaG+8lOgIvSwbjI/YPN9lY3+DG+qvsPH3Ize3vM/RPwTa5d3OXi2vXuHbpKgpPd/eIqfoM1y5dY2lunu2Hm3Ta0zz3/PMUZc7p6UloMYVknI9C4pMSRFFEUqtTazZZXD7H8f4BWVKnUa9zunUMG/OkOmZmocXCWofND3o8+WiXN37jMpe/fMitt3eojjrIlTZiZsjrv3IDc+rZvZ1T2Yz5V9tU/gCf5MgiodnqcMKQ8XCE7w0RKkInCUsz13j5hYj3Pvgee4d36J2MefToMZFqsbH6PL/1X19i6i8q3v7R+xzvSl547jqzC9MsnoP7t/ts3t/lZBhTa0Z4lRLFCwiXsfdkj3pWp93IAo1IVEFpJ0t0LHHeIGyEVBJrLEKeUJ+KSesO6wxaM8kOCEwA5xxemskTeTIQdCFcxBmLwOJsSZmPGY+O6feOOe0fc//JAw5HPYZCMLKW4+5RiLqTEusFzji8EUg7eajhiFRgC0ohgkvRfC4oOks1E89Ym2Eo6L2hqjyy8kgLyssglvNf8CIgJChtcV4wKvuMqhxXdTEVlIWhHBnysaGoDF76gBwTAiE9Qjt0JEI4pLRhdzqZkkrnArDUgtCfKzFs5cFHQQwiHVIEfKVE02zO8ZVXf5vzq2/w6Wdvs3/6fXrmDlKPyKgTyTlMmVHkx6TZiKgVI9NpKltDVDlaZQifgKjwVHg7y+h4Cm/maLUv0K4t0+tvY6xjbnGBV177Mu3OHN6FgZPShsH4kDv3PqHVmWf9uevYwvB09AH9zQNUFPPo3iGn/Ypff+srdGbqnOx12X/S5/zyc9y48QLOH1KelFxYeY7ltTW6vXsM8yFZOsVUe4bS7kIiqdXquBFEUR2yHgvL0/QPjlGmyex8h4Nbn2JONsjkAi41nL+8wMG9MTfffcg3vvUV1i+tc/CwpPvYcPHVdcb5Q2ZmFV/71pf4sLaNj+vMLa0ydnsU5oCkNo+kRrO5hjddxqMeRTenPT1FlrZYnr9KdV2Tv5dzeHKbvcMDKvsZTlhWls7xy7++wcLcEt/5k03u3zritTcusbLRoTUXEx8e8vjJDjpvkDWeRwjIai0iMmw/hpZApcdYXyJcDed0AHuIMV4MMAbwSWAXhJRxYhVUqaYIB3JrK5yzCCkmEl3QZ95/HNZXGFNSlmPKYkxveMLm7jbv3/yImw8+Ix+PQ9vuJNJP0gLPfAF2QtM2IHFI6VAibBukDOIAbwNhyzuBt6EjkGcCgcmmHB9I3FVOyEiMQE+SkH7e9YUoAlpK0jilMB4jPRZDZT1F4ajyiqoUWBtubKXO5JsOqT06hij2qMgHsKi3+IliMPJhU+CcR0Vh/+orQSksVpzFWU3ciJPtQlmOuX3vPR48vMOdBz/guH8PxxhNhhBJwJLnpziOEFFO90ShooKymqLRSMjiJLx4HqxR9LqO3lHKhbWrXD3/ErVaylPVpTXb5Otvvcbi0ipncg8poTADPvjobZKozdqFy8RJzKPNW9ze+wm5L9B5h63He5y/uMb5K4sI6eh1C5Rt8JXX32RtbZabt7ZQLuGFG69RbzW5v32ErSCNOrSbMxyaXVQWUWs0SF2NRnOW3eFTOjN1IpUQ02Fm3tLIFKOjinq8iNeGheWYhbUBwztD7vz4CS/8Fy9wdNkRH7aYbs1w4o7p97vMX17g+ahF5OaY75yj0hmD/Ih6p46iRSTqNFuayjzh9PSY/uCU1dVLRFGbleULFOXXePejHrk7pj/q8WDzDpUZsL58hZdfP0caa/7w3/2E//And/nmW7/J6vllnKzYO7nN4eERx71NVpde5NzieTpTCQd7Q5wz1OoC6wRxbIOKSOXo2OO9wFYOKSq0VkglOKPiBXFg2N0ZU+JxqEnQTWUC58BbG+YCxpAXI05Ou/T7PQ57XT6++xmf3vyI3uAE68rw+EbgcAGO48UzabCfrASddwgb7nDlFRI/AZQ6KheKiOHzaf8ZgxMfmAPGhcBeh8ULQ+oFce3/B17sH+LSKqGVdejlfTAWLz/XaBsnsBacE/hnBopgItJaBgFJ4lFahPOVC2cjpSyREs9uSBcTyK9lCCx148B085NVpBSaYmwoylM+uftHWGspqyHWlQgh8aZgmJ9gfYFUFcgRdphjrMRWhiqHsjNNszEkjSzCNRidtnl8TzI/tcLFjRdI4wanvW0qt8frX77MxcurgUIrFEpCafr84Id/QVEM+MbX3ySOMoajE97/+PscdZ8S6xkSVthY11y82qHdjhkcGg53Ldeu3uCNN19iMD6gd2JYXFnnuZefR0cRo9McWWa00ynmp5c4OrwNUqJ1gi8k7akOftcTJzHNepvpqUXsVMTC/AXGvYhmukJUj+iO97lwreKDT+/w5LM9vvo7X2f1Qp94qU57rk4sV7jzcJdmx7C40aHoSXQd0nQWoT2nJydMT9cQRMRxnVZzgZNuzvb2Cf2TTV54+TpJarlw/jLGjTk4vkutnlBVQ/Z2n4KzrCx6rr08zb+YfoE//Hef8ad/9h94/bWvc+X6Ba5fcvx0+Od89vAvuP/oPs9f+SVu3LhM2ztGZU4tyRBVxNODmxT+MbW2IatneJ/grKeWSTpTLZRWWDvJxvQWTEksHEJ4dKRRWlKWOaPxEOMNw2rIYNjDmIrhcMDBwR5HR0ccnZ5w1DtmNBqGouJdAH/8TJiG9yFiDRtWfmeiACfCAwrCAFCjUCoKujjvghhuoiYUErx3wYnrJ0EkpaNyHicqhAwrw597//0D3ON/7xVFGbNTK7iTLUQ1BmWwapI/4F0Y7BmLO0tzxSKVQ6uIOFafFwERZBxehhdMSYtWoVJaE9KLbekRKqwTmeQWykhTWUcUS8qipHJDjLPPKC/Oe6DEuB6VGyKxqACDoiwUPZ/jbZ+qhPG4RyNrEbkOj29l+PEGV197kzRpsH90h6G5xbnVBpfPv4grMxw5WT2hMmN+/JPv8fDRHf7Jb/9XpHEd5zw377zPzdvvUpmSpbkLpOI8czOzLKyMiOIQuJklS3zzW79CY04xeFyhdZsX33iOubVZSnPK8KRAuyYL0wt0OtPQlSgd02hOoUSdVrsdhrFJzLlzqywsrGA6TY6WTxjkmixZpj03TXEqWVi3zMw9YXxkcf0GC6urdPeeYHROszFNZ2aJ/eN9VpcSZFmRm2OacoFma5beYI+yGhLHwX5bq88wM1vxZLPg1s0tkIYbL66RxE2uXfwq58arRJEDMWb34CY7+w+w9kMW588zt9LhN//pKiq7y3f+n3/P9pNv8uUvv84332zz9od/xMMnH/Kjm5/w5OQF3nj5H7N67iK9rmW6EbOw8ALvfrzNe9/9IWNzioocSlUkiaTVatBo1InjGClhnI8Y9U+QriKJU+IoQktBVY0pyhFGluRyjFQSrSTGGsoirLLzMgfpwoDPaKQDYxzWnAWMTLoIOzHATJD6Xob3ssAhncQbh0YQqYhIS6S0YCyFDY5BOdkQGOsm2ccTJqFzlNIRRw6bftGLgEqYbqySFwXV+IhKjJGT+AXr3WTqabHGBoyIdCgt0BHoSBDHCqUVUkd4aUCaZzHNSk/EQy6Ei5gqzBHC2enMYeVQWuGdDf/XBqmtO1MueocXJYIKTTAmOR/CUZwL57jxuMC5irLUVPU6Zdez/yTj5RuvkMVttnduM+anTC+MWVn5R4yPG9zft6xfSRiNhrz/4Y/45JMP+Ee/9i06U0sY49l6epd3fvIdjk+6LM1tsHpuA+1m8ULSmFLBoFLv8LW3vsz55y7g5QmlKZhbOMeVl68TtSP6xwOGgxEz00tcWHseksBydE7Q6cyRVzFpPUNMVkkXLl5AJRHzGxtsz2wRj+ropMFUp03Pd3HVCS+8doPhvQb9Q0NnY47Dk4ecDA+Zq59jbm6dgztbFFWPWqtFVZxi3RRKRzSbc4xGJwg5ItIahGR2doHVFc/mo0PeeftjhILzG1epZ02maucZDfrUG57zqym1esb9Bze5e/82S4tr1KdSfuk3zpFlmh99923eeafg9de/wi+/+d/y3id/zKf3/pLNvZ+w951tXrzxFi899ypF1GJze5PnX/kGzdkNfvCjb7O1/2OM2Mf5Crdl8d5TVUH0VVYl1pgA7RA65GAo0NoRR3oeRBcAACAASURBVJ60IahNRUx1pkjiNKzppMdri05DvkWcCMpSYY3BGE+ZG8oy0J2DZN5N4swnokA+NxJ5C1iB8YJIyACnURJvKqSvsCYMuM/CSpx/FswW1IdjRy4qdPIFtxIrpZmqLzMYjshjTym75IwDgdU6rK3CUMY7hAxJrjpW6NijI4XWMVGsUZFARg6hKiwlTpgJjzBMR70TuEogVVijuCqgpW3hcEIgVAg/QZzd/GLSb7mJIGRi8ZTimXlJCol1DmMqvI/ANLC9DsOjmCydIq2XPHr6IwbFpzSnT6nnC5wcxGzePKI9Y4gajgdP3ueDD3/IL/7iV1hdvopzCae9A374zre5//BjGrUpLl94lbmZWWppjZPjLmLirdi4coHza5dRDYUtPDrLuPr8PLMrsxBZDnu7yEhw5bmrXH/+BvcPfoIVFik06xuXELNTKBUhlMD4ivlzc/RzTWu+TdTOaPg5klZCkklSHTO3sEB8qYluriO1CpuExgy7u1u02wukcYul+RWOjh7ROlfHyYJxeUxNzSJlQpzWcd5gfYkiQQjP+sYiDx8u8+57h7zzN4+p8jar5xSJTDg5llSdiKn5ZRY6ErcWc+/Bbe7cvcPc3BpJVOPlN5o0mp5PfnqbOw89c0vLvHjtW2ysvcT7n/4Zj7bf5+2Pf5+bm99mdmaJ3d0tnrvxKl959Vf4lV/+bd55z3H3yQ8YFF2chKqq8DrYcp0Pkd8BaacBTRgjhxTtaoLAq1wRFK4+bBNAoGKFQpJ4SWYinHXY0pPrinE+pqwUVVlSUWGEmcBvw3xKigkYxDrw4WGTVxbjc6QWpEohtaf0ULnAHgxdchAwnYkIvQdbOsr8Cy4blkLRSGapxydEoge2F2Cj7kyK5RDKTiKcQUeQZoo006RJQhwloRNQAh05oiTCSU3lChzmc5cVEnRASfncUMZBeCgVeBMKgFICpUG5EBhxFoXtvcJbhZMitGtMRNs4vC3BeRI1QySvMu4tU45aZK0RO8c/YFw9JK0PqPwCB1ttHn38MR5Le3bI7c0xY/OI6dkaiwvrCGr0ewXf+/5f8uEnPyCrSV54/stcufQqzVpMUR5y1O0jTEqrM8vG5XWSTOFdRSU0zekZ6s0ORA4rcnb2NmlM1bh8/hKNpZT+3jFOWLRKWT9/ntnkIlIVWOcobU5rrkFDT9GcyWjM1VhbXWfx/Awy6SOdodZqoVancKJObUojZcTS/Hne3/4Bp90T5haWmZ1a4PjwHqNBn1azST8/Jo6bRKRoWcMzBMqJBA7SuubylfPc+uyQR3e6NBvbaKGIzDT4iIPdHmumzty5RZbnU7Ruc+/hTfb2dtFKk6iUuQXFG29ljEbbPHz0EfceZ7z0wpu8+ZXfYXnzIrcf/Iinh4/Y29xERwN+9NEduifb/MJrv8Evf+O3qb2vePujP6OqxhjvQahw8yuJFJYIgRIRCoV0ZiLyL7GVw1iPsSXSBpl7VblgV4/EJE9DI7QEKyCWpJEjS1PKMqUoCqwzOGew1lBVFVUVHn6Vh1IEDJn1k+JUOmLliZVGT8JKnZ/ARJ5xhRwCETgDhI63HH3RiwCSVNRJREbkFVQSl2ts4ULOmgorGTGZkupEkNQ0caaJI02sAltATPIItQARxQgrqcw4tPkTCAkohAIROYSuQDvQEmFUMClFEQiJlo6qDHJiY8ML4V1AlRsZVIkIgfQ62JZ9xnTzBmvzb/FwNOTU7rM3uM+R3SOOc1rVFEc7MeNeQi0R1NsFperj0jG1hqeRXuRwJ8GXA97/+Pv85IPvkdQiXnvlF3j9lV+j05zHc8KHHx5SWYWn5PzCPElNY90IpSKsE9TaTaQOg6WyHHLQ3WN9+QpLl5YZ2z6jsjdJfC5oLrZYWlzk0GwivGFY5VRZwcr6HCpVdFamSesV2Qw4X8NKzbB3ytLqOU4w1JZiEJJ6bYZzy5e5//AW7emMNG7RaV3m8OiAZrtEKTg52WV2uvaMHuWFwboSJVI8lqXVFmsbizy63+fh/S7NlmamnUHlOD2yFAMoBjHnLreYn75Io7bAo0c3uX33I7rjQ5TU1LKMpdkWuelx+/vf48lff8DquTe4uPYKX3/9EptP73Bz6wfsdt9GqGPubn2PYX7MN978VbJmDaUllA4xUeUKwsBYqiDgiYRGuiAUMrbCugpjPdJqSmvgWREI1l3vHV7FITdQSqRUaKWJIkliY7yr4+yEQSAlQlqsLSjyMaP+kF5/RK8/ZjisyAuedQqV90hv0TJGonBVhfEWJyYdBBMhkQguG+89ZhwMS3/b9YUoAuFcFCGdQFiBrwQmF9gidONaT2yUBEiCjhVRItHxJKUIHwRBE3mwVAItFBKFcvKZysqdxTzZoKLykxBDqTwiCkIiLyMirXHWYyJLZSxFacjLCmtNEHj4kEKLroAS6TNqyRovX/9NZtvPsfnkLzkt71NWDxFlj2baxgzmoagjvSBO96hPF3QWBHEiEGaB470Of7P3iO7pdznNPyHKhkzX21y78ipzMytorXi6+5itrVOUyphbTJmdnQYcVTUOClYlUVFIeBZectw9oCwK5paW0Y0IcxreYM6GdWp9tk4yp1EHHikcI5PTsz10JtAioTPfodvbAn0NQULSbHP/5m1WlpZIF8ElJUKkCBlzbuUiDzZvcnCwydLiJTpTFzk+6dLtHdFuzdI92kerlOmpZZzTSBW2OAEYokkzy0uvrvHpR9vsbnept47wlxNm6ut41+DR3QG9U4lxCauX2rSyOlcvZlhnuHPvHfqDPZBTaBkx066xtt7ip59+ysGtHR5sfsSl5Ve5eP4l1jeucPPeZT69+xeMyx32jt/jP373AR5HWQ4QGKSw4cFBGE5LAsoueC0MzlRUtqIUAWcXe0E58afgHJUxwY9SGqrIkCUpVimU8Dg12dtHmkimaBlRq9WpZQlZqtDa4a1hcDpkf3+f7ae77O138bbCG3BKTB5AGoQOBd15jJtIic8ch+HGCls2LzDlz7/9vhBFwHuPKT1VAVUBpgBTOKypkJFF6nCDOh/UgCpySG3ROswHfOXwxoYpvgORh5L4OXMtKDKc8RhjKAtLUTpM6XDG453DO4cQAVAqkHgpiXREKgSZtYzzgtE4x5gKZ2KMjcBZjKhIVcrl9W9w+eLXuH3zLjsHHzOq7mFlF11lGDeLiDdoJJeJE8fM6j1WruyTpBmyukE1ukF3VOfw+BbD6mOml3voyLEwt8D83AqRjhiPC95/9w57ewOy1PHKl26QpnXK0mCtQGCJIjXZeoAXht39XbROaLXbOB+GUfm4IFjSII4DeVdIgZIKU3nyPMc6g5SOqU6bBw8/xnmDlCmNZoPRcMDtO5+xvnqNfr9LrdHAe0mSpFy6dJWPP36P1XPXqaUp7dYsvd4e7baj3oi4d/9jXnm5RaRCNLwUMWVVEiuHIGFxocNzz6+x++0hD29aao3HqKWSeuNVBjXLwUEX/4EjyeosbgiSNOPS+aukScRnNz+gP9yjjLpEqs2lC+vsHO3zZG+Hg94hp/3b3Nv6MS9c+3Xe+srvcvX8K/zV23/A/vG7DAa7eK+wLuzef5YcJGVIF5YQ9vrYifLQTjzw4SFTVlXgXRKKmzGO0hiqssIaQxIlaBURaYtTEVqH/Z5HYJwBmZLVmtTqCcp7mmlOrDOMgSJ32KqH8BXWB9OXRGGtDEj+SS7HmYT+LPn4ZwNHfv5u4AtSBJx3FOMi7NrHkqpQOBOcgFp5ZOIxE900k24AUSJUEpJqJ2d0bzzeCMxkgIfyE/uxwwsXRBSVC3qAIeRjT1l4TBGgJZEQRFoj0eBVcDECUk6QZV4wLiJKR3gaFBFZPM8L1/4xX339dznsnvLD9/6Uw8FHeHlALOtMJddZmvoyl1ZeZ3VlndzfRLYekrYMiZxlffWXKAcXuHX3PcZmm9bskLRWUEtrXFh7gXZzAesEd+9u8enHTzHWMjs7xfLyeYRQdI+OyLIG1lQ0GvEzZ2VR5Dzd2iGOGmRpHecqinzEeJQjhUZJgTpj0QuQSkEVJNvOWkTkqdVq5MWY4WhIq9Gg2WgSJzF3793GlJ6F+Yq5xSWkjAHJ6sp57t+/z+bmFhsbl6jXZhgcdhmPCxqNGnFquHXnPa5ffR1JDXyE0iVFuU+/W2NwOM360iXOzY+5fWuL7Yc9Wp1DdNag2bnOwkqdvDzk3R/vceF4hqsvNqjXm2ysX0WQ8GDzXfLREVpbFmdbvP7KVaqfvsvTg2MKN2K/3+V77zzi6e4H/M5v/Qv++e/+d3z/h/8Xt+6+Q398gBQO+zPRmWcoMYlAeoec5BBYaRDKh3+XYvLeqia7fsCH92PlAp+wLMPrGUcpkYrR2hMpSZJUxJHDjx2lKciLnHarTSPLEFYgo5RGo0O7nVOVDilHVFZgnMRaKK2hchbjQzFRCMQEKPqfF4JQ2L7AxwFnLePRmNEgZzyyAQPlY2KVEEUWJ0OrE8pCMA1ZG2S5QmUTYIrAWYE1QVzkiwl/QKggz8RhvKeqoCgE5VCQDyzlCHyl0ETIKApQE5Ug0WFV46AwBikkWgli7XGujyEnZoWvvvJ7/MrX/xmmsvzgp3/A44OfUvgjkrjGTO06r1//L1mbf42FhQ5pa5tetUnhKky5wczMm8xPX+Hx+JCRu0U69ZSsdYr30GkucfXC60jqbO3s86Mfvc9ooEmyiGs3rhDFKYNBj5PuiGRhBiHL4Kq0QeN+dHjMwX6f6elplEwR0jEuB4zG43A21SqEYQgwVRnmHcbS651QmZIUiOPwOzg8OqBZXyBN67SabXonO+zu7TAaKabnzrF8bg0pJHFU57nrL3Pr1h2Wl9ZoNmc5ONhj2Dc0FhULSx1ufvqARm2GcwvPYSuPziQqclhXsbdlsOM6G+fWONg5Yu9xj4XVimTtA6RO0H6J89em2d8dcOvjI44ONc+90mJqTrC+PouKnmPzwQOqcQ8pLBdWVjk57nN6+gnHoxKhSox/zL2tI/7X//0ev/lr/4yvfe1X6Z4+4nTzcfAF+P8UKOq9DzwLQCiLEFUIGY1cyBaMNSjC01ycidMC5AYJpqooypLKWLLIEEcZkXZEWlFZRZJ40tgFN2vfMBiNyKKgPPVIVJTR7kxTWYeMFPm4Ylx6RoXBeEtpDcaagDmZJHQxWRd+HlMWVJE/7/pCFAHrLL1el/7pKcWwxFUaSUYSebS2lGqM8Gc24TBUdhMKbCQKcAJjPKbS2FJhc0dlKyob1ngOhRdh3WOsoygd1RiKkcAWCuUjZJQEspHUaJFM1kEBJ+W8DutKFXLkdaTxPqZdW+CrX/4G/cEh3/mrP+STO99l7I6IkzatdJ3La9/g9ZffYnlukdxuclK+h1f38EVCjVeJqusc7PbZPngfag9ptkdoaWkkC6wu3aDVmKfXK/js1h0O9k8xVcra+WkuX9nAOcv29h7DgaWfOGbn60Et5gWjQcXNzx6y9eSYRm0JJVPwOf3eMcZUQIiCj3TwvwdNvAMhycdjrDMIKYhijdaSw8MDNlY9WqVMTc2y9USBh63tLVT0EUvLawgp8V4wN3uO08UBu7u7rKyso2Wbo4MdOtOedqtBq53w8cfvg5kmi5fQY01ntsPsjKY3K7n9/oipZodLF87x0/dP2XlQZ+OKxUSfMR4Ljk4aXHulzuKa5eYHOe/8dcy156c4d9EzP5/THxyxvX2MrxytpMGltQs8enzAYGsHM3HWWTHisHeb//OP/yfmF2Y5Or2Jk31wtcmKLtw9n/8ZJu+CYCtW0k6cfgqiCaPCh1mTn7TqCIFUIBS4ypEXOdZ4ksqjpSGKYiITTdp5g/OWSBuUrYKlusyJdIKQiqjWoNlxIRNR9qlcHnzxkUdagXKBSiTDmWXyM5/dXZ8nG/+86wtRBIypOOru0e/3sMYjRYyWAiEtUkThl+ol4Camn7ApKMsK7AhlwZYJJgdTeMrSMS5KispO0l3CEDAMDiVVJbGVx5YSrELKGK9i8DECjZIJkgg/cSJ6afHSYKVEqRzt6njfYDgc8Wd/9fscHu7xeOsWTjqyrEWilphtvcrV87/AuaVZlHpK3v8I4x9jbY6w87TrV2mkywzLHVR8zHQtDMi0nGK2fZ61c9ewpWJ7b4/d3X2MVSRpi1dfe4m0Juj3ejx6uEUWLVPUBUkS451hNLQ8enjInVtPODrIiW40w9HGWvr907DjdqCkCpsQwpzEWo+SirzIKYsCvAuejjTl8HAfYyqiKKXZmEarGO8J3cbBB3zta28xM90BL4iilJXVdR4+eEj/dEy7ucjTncccd/ssLTeYm59j88EDPvjgI84tRES6SaybNKfGrFwesPOkz97DGiuLl9k/l7N5b5PXR+us3RhysL1Pntc4PS45t5axMN/m8cOC/b0+QsfMLM3R7hyyvbuNLUusNczPtLl2eYW9k21OBiO8jAGNETkD84jh0/uTPXwDf0an+JnIXz/B/YYQ3KBElQoiLfFa45VE6EmGpp8ggaWYkK0lPtJY4ygLR1GW2EqghEVrS2QcxqqJTyGwA7V0WOEIsjiFEgp0TJw1yFzobktryX1JgSVi0u2G2j6hE5/Jks/IxZ8Hmv5t1xeiCFRVxdFxj7II7j4lBFJanFBYMqCGpAg6bhfaHg8UhaEwedjPFg6be2yRUBaSYqwZF2CMxbsIZDSRUjIJPQ1yTSk0TsVYmWB1itMJzkVIFSEnykCFRVGhJqotRLAwF8WA9z75c6wr8MIQxW1qaYO6mOHq6vO8eP0GramKw9NbVP4e1vZxZUqiF2hkiygtsXabWnOHTCuMb5KmklZ7jma2xvFuxd7ODqPBCMUM84tLXLwyi7UFT7cOefK4z3wnYXU5RugCU2q2NysePnjK051tuocjmq0aiIqigOPjIUxAE1KmBIVZRVW5YK4gWFaLYowxFVJ60rRO9+iYvBgQxwnNRpsoiSlyw8mx4WB/l4f3HzMzNT0RVHmSrEEtm+bp5gkLs8so0+Zgb4u52QtMN1dZXj7ls8/u4sopZpvPsxPn1K5B1hhz5cWMXtdRDiOuX7vGcW/AZ+/2+dJXL9NudTnZqxj0pujuK6bmjrn6kqZ3nHJ01Gc8ipiqn2eqtcPe8DGVL8gSyYULizzammH8cMDQFmgV4ZwBUYRBmq3jfYwTxeQRGlp66fzndl3N5Cg6ca5qAZFDKBGixCdbqGeH8UnHGkUKrMaZCmMdVZVjhKEyhsoZnI8CjmwSoSd0AJXICVEoWI1CR6FTTeIzMu8oJVgpENIgcRgcxloQdoIyD8BdB1gvJorCv/36+daif8DLWENeGryIkSJFyXADeqHwJGATsPrMzIV3Cm8llfGMRobT0xG9/ojhuGA4qijGgqqMcWWKK1NsWcMVdVzewBcNKFsI00L6FtI38C6jLCLysaYsEoxJ8C4Bn+FdjLcx2AThUqRtIn0W9seTeHSkwguNc4bxcMDcdIc3XnuemRnJ4cmn9IrbOLkH3pGo88xNfZlGc4pCPGDsPiHJumSJI8tS2lPT1NIFBv2Yvd0e+agHVpPFc9x47iJZ3VOMS+7f2WQ0VJRlRK2hwFcMTwWPH4zY2d2j2z1gOMjpTLcAS1l4ut3es4y9KEomLCZDnpfgNUIo8IJxnj97beq1Fnk+ptc/QghLHGconVKUll6vYvvJMbc+u0dlLM6H8E4hBO3WLKO+Y397TE0tc7w/Jh96srjF8so8jZZl/+Ae+XDI9sMepwcRwtaYW9ZceE7SH+8hRcKXXn2Z8alk50HE4vQa5y9nTM3C6XFJd1+S54apWc/qRo2pTkKzUWd1aYUsTcmrEaUZ0W43uHH5CvNTc6S6jhIRUmiE18GWi8FRPpOoBzbIZNPkQJ51BsJPci9Dqx8piLUkkgolwuAYL3A2zBakFGgliWJNHIdhbHAbVhRlzjgfMhqFOU2RG6rSBrWhE8FabCzGVFhbYn0F0qGTmKReI6tnISAlSUjjmDhOUEqjVMhIIPwowZLAhEb2c64vRBEI03uPVBFRnBDpFK1SJAm4GGejQP+xBOmlDZp/Z8M6xDpHYQxFYSlyR1l4XKURLkVNwkuUb6JoonwD7Ztowofy4d+ErVHmEaOBYNjzjAeSMo8wZYq3NbB1BE2kqCF8hvApQqRIkSJFgpQxzoXIp+s3NlhaSdna+zFH/Q/w6inGD5CywfzUG8w0X6D0B5yav4bkEVEiEbogyzzNdAmqRY4ORvRHhzgqIjlNs9ZibbWDQnF00GNra5tI1ohjR73ukWTcv5VzdJizd/CU/mCMQNGZmgFiut0uo2GPKIpIk5RaVgsbAS85Pe3jnAsiliRhNMwRQmOdJNIpg+GAbvcA7y1KJSiRMR5XnBwP6B4NuH3rPien3UlcfIkUgnqtRqs1zelpAXaK8ekSh7sOIQ1T7Q6Li0tUfoeHT95nMID7dzzjUR2hCzZuOJYvVRx2T1Eq4urlV7n9QU53t0atFjO/nNOaUpwcZRwfKowZESUlcWrQUcnszBxzswtY58knR5uVlWXWV9fpNGZpJjMkoo10GdLqMP21BuENeBtMPW6yWgY+d/y5oDmxgWgdzpiBBXh28wkhJ8PECSVbCLTWxElElAStgXXh5i7LgvE4ZzQsGI8LijIoBZlY251zWBuUhMZUmAA9QGlJHMdBLRtHRJFG6SBEkkIjkZNcgs+9CP/JvvA/u74QxwGPwwlPFOtAD7YqxDIJgUFh3ABvdQgaEcG9JxC4iWFC6Qhsgq1iBDG4BFyKdDFSRBO1IIAIrZJgYiMOvxjhwiZYoHCVoDRgclDKEUVReGFdKEBBz6wRPuyOlVB4ocAXOHK8Lxnl+3z42bfRqsfSeoVTA7RtUotuMDd9hcPDbY7yv0bUb9Joe5So421FvZ6RiHP4chEfK1SzQBpFLGeJ0wbttmQ8qLhz+yFlYUllRiOLUEqw99hw86Mu46qge3zEaGip16ZJkhS84sHDTSo7Ik5ikrhOrdZEysBH6Pf6E21ARL1epyodzgh0nAGKwaDP/sEulanAxXibMRpajo5OsFby/vuf8ejRY1rta8HGLYLDU4oIYyuwNVJu8OTRA1bWx9RrLVZW1tjcfMDj2+8ifId8fJXZhQ4rlwVxdsKXfrHF8dGQrQcjmu02i6vL3Pm0T61Vo9keMr+sGQ8cxVhz3B3QmVVESuPJaNQ0C/Mr7O0/pSxHpKml3W5y+eIFev2I464lEx0K18e4PtYFM491FusrrA/DurP0Dy/C9N4TXH7WONzE8qvwCBUGrVrrwKucbBmMtaB0OH4phY5ARYbSuLDBMh5rDEpWFGNDlRpc5oPYazLkDzFilrKsAhLOC6zzzzq6OI5wRuCsxZkgtbfCIrxDeheUjz8z4/jbrr+3CAghVoE/ABbCj8X/4r3/10KIaeDfAhvAI+Cfeu+PRdhR/GvgN4AR8Hve+/f+7m8CThmkDikuzki0tZ9XL5fgbBxCFifyYeEDwAspEVqjbYpUKVLVwhzBZ3hZw/sofJmJcsoRzmxCOM7CG8/w0XgR/EKTEuqtpLST1QtqojwsJ9lwnjBclzyLgvAVRTnkez/8E77y6g1+8c0bNFpQmTbarNJKn6c/3uUo/wFVdI92I7SKprIkqaSRdZDjWdJkDpUeo9MScyzRYoosayGNYOvRHndu30aJaVyZEYkYnws+++CQfJiyf7rD0dEpg55h+dIMkY4Y9CxPt58ipCHSdWpZk3ptCm8VHsXx8QAxwWcKETYtReHJdIypBFJIut0DBr0hsZ5C0qZ/WjEeFeBjHm/u8tln97l+4wbeeg6PunSac+zsHnJ64mhlLaSf5/ToCU+3u6ydX2duZpmN84scH91id+cnSBXz9KmgMRPTmk1pzg559U0YDSRVVaJjj8ljDrdqpLEiTguW1wUH+4LxICbNKqJmhnAJUvWYmZ5nfn6Jzcd3A18RxfLiHMcrjh1ZYas6hRlR+h7GD4MZqCoZj0eMRj0KP8JjENgJ/Tc8ha01FNbiq/CkjgGtwlD1rAMQQmCtnawaJciQi6kiSVKLw8LaW6oqPFOK3DCSBVlakNUSklijZSBfuYkpyVpDWZaYCTg3fO1goZfqTCgUHmYSjZoMF/HP3t3/34sAYID/3nv/nhCiCbwrhPhz4PeA73jv/5UQ4l8C/xL4H4BfBy5PPt4A/ufJn39HEfB4WWEpkJOnNROtfiD+aCAGr3GuAtyEwS4n2QMalEbIFKkylGqAq+F8hvd6siMNH2cdXugEwi/0TGBzhmkKnxnaPPi8SxDCwmQoGXavFulsOEuffY4smZ5VXH1+isVzIU8vHyzRbF4n0U22j36KTW5Sa+aktXlA4UVJrVYjljO4so1QhrQ2pmKMtSn4BOE1p/uW9376CcfHezSSOTI5BUWdnU3H3U976GiF7e3H7O6cUI4VnalZnJE8fnrCoN9HaIOUkixtoVUdUwnyUUnvNEfKCGs9RVExHpX0TyuyWopA47xnZ+cpu7sHLM/OI1yL0cBSFHk4pnn44d98zC9/81dZmG1x59PbnF9vYKuIssqRDYH3ikh12H7ylNl5T7M1y4WNl9jfO2Ew2KZn3qVwMzzdnkJGDbKpfVYvSV76RcXDu6c02jNUpeR4z1CrpUwvOupNj3URx90IZyK8y0LnhybLWiwsLLG/v8Vw1EcISS3TnF+fJ5OCfNyhMh4jhhiGVDaIdQbDE45P9uj1DxgXp2FIh0A4gslHBPpvVYH3BpRCRP7ZcUBNOF7OuaDzNwavgklOR3pSSCTGWJyFcoLQG5KTxIqspkljHXwBMpq8V4PV2BhLURacBaBY655RiM/eqcJphDeTYiAm2Qn+72oE/v4i4L3fAXYmf+8LIW4C54DfAr4x+bT/DfgrQhH4LeAPfOi13xZCTAkhliZf5+d9FxwFlRlDZSnLUEmNrUI76QXCq4CBsoCfeK9VUPMhgs1TCI0nBpFOPE0ZFQAAIABJREFUpt+1SRHg8xtdCbxzOHcGi/z8qPAsymVynVXWoBdQz+THXrggG/VRyEPw8lm3EEWK8xdnuXBlmtn5GuVwFlmco5asUdh9VHpILUmoNyN07BiPIYolWVbDjxv4MoP6GJX26Q0HVHkDSUQsY25/csKDe4/pFYf4oqLdrDPqRXzwky4nR5L2bMHW04d0j4a06m1ajQ7dQ8v9u1vkxZgodkipSZIGkarjKsH+bo8id6gowntDUfy/zL3Zk23Jdd73W5m59z5TjXfsCQ00gAYJAgJAgZQ4WRIpKSjaDlGiTIXtkG2FI/QiPzj8Zv8FfnL4xREWrcFhDSEpwjIli7QlQoRAAiAxdjeARjd6un1v36nq1lx1hr13Zi4/rNznVoMEqRAjHHd3nKjoU7eqzjk7c+Va3/rW97WcnZ2zv3fKlWtm7Ikq5+fnvPXGe2xUH6Vym6yW2ZSaNZKT8NI33uI7r9xh+tlPsDwTXn/1DlevP8vewxaRhEhmd+tpuv6CR3tnzGY73Lz2cX7oxQVHJ/+Kw/03uLf3DOhnGI1H3KjH5PERH/7UCfWk5r03zpiMNkh5zt59wyuu3qyZznqgIhUwV6olYAfD9uYO165dZ377jMXiAoews3mV8NQOF2fXaPsaQkdkSd+vaNsl8+kx42pMFYSj056kimhGkzMXn8H6O0HXJlzV4xuoKju5wVk24MQwq5yJRIIDJ2YOEupA1avpViQlddC1Pefnc+pGqKtA8A0hNGXtDexOa+daNpAt9U9YMBhk9TBxUln/95hk9+8dBC5fIvJB4DPAV4Ablzb2Q6xcAAsQ7136sbvluR8YBFQSnZ6Yxno3ou8CKQoxt6S0JOeunNAVznmyDtxtj2QxgCZ6wwJ0hGOElzHQmDbA2h2iZANOyVJGL9ejwoMzEetWl8WE4uoiJdbmgOQKIYJWBVUPliWoMJ1VPP+ha3zgAze5eeV5Ltx1Fvkq9bilkVNS45mvNnF1T9suQR2T0RaVH9H3VkdPtnqkOma1SqQ4xbuaZtTwzrvf4+HxuxxcPGR3dM61YLJmt945JbtNjheHHJ7u4RixOZmyFW7SPgo8uPs2bTpFUmWTZh5c8vRnwtHDlhgbpF4ABljNF8ccH5yxPL2JS1NS8vS9cP/eCR+50TPSXdqLQNSW5BOkMSfHF/zLX/0iNzY+wvbsOV751mtcv/phKzk0sVwtqUYjNnav8+jBPltb+1y/cYMXXvhhVumY3/7yl3h48l1yrKnCi0wnW2xcP8NN9njmhR0qv8292wdsbEwZTWraXjg5qtjYqhg1m6yycnbasrElduJKYDTZ5PrNp3l0tM/5+Sltv2BcXbC1s0vjtui7TaRekjEwuWtPWIw2aCTgNEFeseiUlCB2kUSGnEsXQNGUydGchFQDIpU5YIWhU1DEa3IkkvEu4HzAVzV1I2juUU10KLFXFosOd7xE5AKRGnEVdTW0D739vKvQ1JcsIBeVYnuQBqZgsteJ2ZbjIA8qpH+UICAiM+D/BP5bVT27PGShqiprb+R/59/3N4C/ATDa8LTxBLpMbMcFkfek1BH7JVFXNirpHOoftz8EYwFKFug9Ej3kysZVpUKogMqAlqK0MtApHbnMYQ9Ib8EIsACQ1x9YkTlzGNjiBKdmgUYpM0TN+sR5ZXd3wgc/+AzPPfs8V6bP0OgGo1ATJudo5dGjEXJR0XYdbdtR11Om0xGaPeSa8SSwdaXjLJ4S+wBpSoqZZdtz/+AtHhzc4tHpMaPr1oJarVr2D0658cx13jv4HqqJzY0dJuMREscc3ltx8Og2/fQMn2r6PhJTTxsTRw969u7NSami8Z6sia7vkTTn7HjJ/VuZ432IrQCe87Ml+/fPSYvA/FTpYmf2XFlwqnzzGy/zZ37ikJ2Nq1ycCgd7CxyBZRdREV5//S6f+bFnaVcdt2/dZTpzTGe7fPxjP8H+0YJXvvkdTtvv8XAvMJt8ktDcZFotUX/A1Wcck9kVnBM2txyiFSk5lsuOxbynqiq6vmfcN/gASCCEht3da1y5cpXF8oKUlGV7websjO1dh/Y74CYoJsy5WlWM6wbJia67YL48QGVJ12c0LZFshCETqC2YVNGpVFtERhFwZgKarXVF1mQqxSJlGtHjK/Ax2eh7zkX8Qzk/b0nxpNANAhuzGZX3eOcI3qZXu9LNyHmQ3bPgZGxHweSI8tqPw8igf8TZARGpSgD4h6r6z8rTe0OaLyJPAfvl+XvAc5d+/Nny3PsuVf0V4FcANm94bdMF2gt93xuXPVbWJ00tWU0lSAsRQ8UAwgwm1JACdB7pnQ1eqBkPuGIbZdJQsg4Ag3GDYq2gMpFgN6n0iiFZhiCg8hj8owyRONJjBpnY9FbwkRvXdvjQ8x9kd/sKtROkOmayPWW66ThfmQCq98qqXZByx3S2Q1XD4sTh84zNK4HR7JD790/olteR3vrLR6f3uXP/Vfb2H7Boa+RaTTN2LJZzuthSNY79/T2aekxdCz5UxBQ5OT+g7c7QcV/UVpUYM8ul8qhd8d67+/iqwvuK4OpCwuo57XtupQXH5z0xBlzdslyes7e3z8jtcnGxwqQwe1Q6lMSD/Tt886Vv8Sd/9OeQvMXLL73Jhz7yPPsPz3jqmau8/vlb7O5c5fpTN3hw9102t+/ywgubTJtrfPyFz3C4fx/NLSnvcffBTZx/kafrp6i2jnHNIyY7ieDH1E2Nlw2cjJikim6VSClS1Q7nB/M+c/FpmjFbWzs83HtAtzRkvk1HzDZOGPMhctwgE+lDRyjRftnOaZopVTWmihNiXJVOkq0VKPSQ4m8hWay3n20i08RpPKJC0rgmqAkZNFm97j0hBLSGYqsNmulXibN+WQ6qgCaYTcd4B957gvd4MdPSnCIpJmuXq64ty5RU1qWiScqa/SPMDhS0/+8Ar6nq/3TpW/8C+C+B/7F8/eeXnv9vROQfY4Dg6R+MB9jJ2+YLYp/ou5oUx0bOQcgSyfSomPCnTfipzVeLkLOHaAHA9Q6JhdxRUnpZ87/5vg/CaBSKtYasti21FrnwtIpIQ+Hk289E0I6sLdDhJCISEXqaOvP8B57i2aeeYTIes1rMUX/O5g7UTc3ZakGmpetWzOcXVFXFxsYYkUDudpn4m2xuN+BOWS56iDsEmTKbjrn79ivc2XuN07MFwV0Fraka4ex0yWRzAwmRtmupm5r5Yp+tWcVoUpH0jJgukNijKdnCpcIxRdOM+bxn8tSIpq6ZTDZBYXEmSGqYn3q6doxjhPPn4HvUKzs7u4gbk9SRSYis1kj4b3/pN/nEiz/FxsZVvvKNL7Gxcx3vpigjxjPhc5//PL/wCz/HybLhd770GpWMef6DP8zT15/mj3/qEzRTT1xd49ZrcPfeQ0YbE643H0Kau6h/RJKaXjcQ3yOMcGHMaNog1KZEVQKzLV5P5UfsbF9jNtvkYH5En4SL5SPGo3fYmH2YqrqGoKzcHE3exEHVk6OSo4NcE3uIvWlPDu0773SYyDauwPAo8yY4JXkMWM1Gy44p4XIsFmMOFwJ+OLnVgoQS6VbKxVmL6DGoI/WZURPImpBcvDYTaMzklA1XSKwdiFSN2ejKCiexbof/ewUB4KeAvwZ8W0ReLs/9D2Xz/1MR+a+B28Avl+/9OtYefAtrEf71P+wPWPeup8uRLhvCbiScygxEiAbIqSflYkSaPZpAcgWpQqNHkxTcNJPpcTKc1tYg0ZzWbZykydI0TSTt0JxMgFMTmuM6rUPXpR0GTEQUExMR7VDtQSNoZFw7nrp+hSu724gq7eqC8cQx2xRW7YK2P6ftllzML0gpsXvlKnUzZnUxxuXrzGZX2Nx2HK9OWS4ydTXFZyF4eOfWGxwc7hHjiCpcwTOmmdRcPDhhc3uLk/OHLJbniCgXiyPEP8XutU1Sf8JkA87jEk1jciwZlTbENhB7GI0Co3FgOq0Zj8Yc5gs4MwYbuSFFECKhScS8QnxgOt0k50T2rS01heDh/v6rvHnrO3zwuU/igufOnXvcuPrHaFfCs8/v8NVv/Gu+9d2KT336j/H1l77NfP4b/PzPCx/44NM8c/M5lt2K7Wsf4sqW47XvvMnBox2q+ga7Nz3S3IKSvSW9AOlNllvMUxLnQPNjTX88TmrG4xmbG9scPDqxeROXOF/cY2dyl2n9LKITUqwJHpwEur5lsVrSdz2rVc9y3tG1yRh4Zc8OWv9OrTwcSEOu0MrFGxBo3YRgCH9U+thbre4qBMMvDHw296Ih2+iXcH6+wOGIbWQ2m5aBLyG3QHTk6MrBqXSd0rXZQEPNZXjJI2pdrPyDdUb/nboDX+QHaxL83O/z7xX4m3/Y7/3+ywVBqgSxJ6eMdxHva7yYNoAprwrS1+RYQy7SzSkgyUGWwvJKmERkh2pnsuHq1sBfKupCKceSqpnMlWUC9v9oMi13hMHyXQe7JzFEHO1R7UAjSsRJpqkCV7Y3GTeB2K8QgdnGNlUYc3J2yvnFKfP5nK6N64XpaJC8wWS0w9XrY5rpBcuTY5bLC1jusyFbnBz3vP3OHRarBGzj3C6jZoILNTHXTMZTbr39HVJuSXlF15/gq6I1Ly2zmePsNJFTgqJll6Ij9tbuGk8rRpPAZNKwvbWLw3O4mBNX5zSjCu8DKonQRLp4jguOnd3d0iUpOo4SiPmEnJVvv/4FXnzx41y/ucPtO+8wqp9jf8/zwgsfYbIJX/zaP+PK9U2ef+7TfOmr/5jF8v/iL/7Fn+PDL3yY2jtIid0r8Md//Gnuvgvnpx1Vs0kzu0EzWYC3YC2ScJiUlyDWLroETRkuHhg1EzY3tqgbzyJGYhzTtguW/W12Ri/i0hjNQt9FVqsVy9WS1WrBfHHB+cU552cLYt8RGsVVtmUVb8EoQoxCikoOZdDIF2cgZ1hKVVWlzZeIKZGi2eWFUBGc/WMXHB41qny207tve+bzJSkqy0Vk1IyoQ0Cy0rdKu0ysumQBoLduRd9FskJVmxJ3VkGz+wMniJ4IxiAqOBFCUySTU09wQl0ZE4vsIQdS70jiiDjTWFdTYnX4MkttvVuVjixixB4tA0darM6TtbaS9mXjm1acEom5I6cIkvBq7CuTa7IUb4iERjKKQMJJxhdRjivb21y7uksImZx7prMNppOr9Elo257F4oKcMxsbW4zHU8bjDVuk9YTxaMzujYRvDli0B5xdHNCfvcXWtQ9w+90Tjo7OSbEBZlTjbcajSbnZU1JWDo/2qWqhmy8R19rJUTucT2xs1chZtr5+HpNiRMQxX3TUdc1kmqgak7Pe3Nxk1AjzR5GYI7PNGdVRIDmhHinL+RlK4tr1azivRFkh3hUcZQ6+5+33vs4qHvDRjz3Fa29+k/3D7xEq5YWPPsMnP/HH+dxv/y3+7Rd/lb/w5/8aP/IjP8uXv/oP6bpD/tO/8td57rkXUTkjpcR4tMsLH/Uc7C/p24Z+fpOmOSX4o3JfvGE1ap0i93vqXpvi8b5hY3OL6XTExXxJ343o/Jx5e59uukcdrxFb6NqOVbukbRcsFuecnBxzeHbI4vwML5GRmDQYYn3/qGZmg+upCm8fsDkCD04EF8xUFFVy7um7TBczfe5ISaGubVKwHDauCN1SYcK2KbNcdHRLqKvMuK5ogtDFSLfKdF2m7Yy30PeQeiklgYnwiHNociWV/f0DwRMSBCiOrMk80wJ4lyBEYwTmgMtGxnDqcerMXzAlMr50AiwNSymS8woBsoSCAhaOgeZCCe2JuS+1Wk/OHUk7cmnZiLOZK4dDVE3AtLAIC6UISosRIPiAd4EPfeDDPP+Bp/B1iw8No/ppxO2Q4jnLdkXMHVvbM8bNNiIVSiR4x8bmjK3JmM3dlov2lEQsVteB0Ix5+/ZLzJdzUpwQ3ITaBzYmU87PW+oRrOIFOQl1XbF/fERoMrubG9RBiVVkYzZmUjmW/Rld3CF2AR820KmydXXM9syj9RlVBaOmYja9ysFuy1mfaCZCU0+IrqF2mZiXdDlx/cazhKC0rMhak+nBrUhEDs5v8+qbX+FP/eSf5cqVwOHpm0zHz3Kwn/n4Jz7N1767xdsPvsQXvzHjx37sxzifd/zmF15iOW/4pb/8i7z4sRt4HK7JhEq5fnNMtxyzWjUElwjS4Vxrd6KM+TkyQihgZZEIR1Ec3iVm0222t3c5PD5lOW9xPSy6OYv+PsTn6Fcz4qqlXSxZLJYcnJzy6OSEw5NjuvacURPRGkbBEcJAI7M5AaIQl4ngoJLK3EBE7QArAKV6T3KJyjl6yaTeev5BAy6YCCnOkb3D1RFyplIhdkLupChgJbQWUjPoaUTzRMigqbS3MfZgzhB7E8SVUqr9oOvJCQJ9RpP1NsVlXBDwyYRAJeEL68nVzvzZsjOlX3HWHcCir8RspzkdqpVlFtmRsyOnRJ97YurWgxk5d6TckXRFznE9N+6LnZMTwanNKTgZ9OeNjWXUYvv+qKl5+qlrXL++Q1XPqMIVgnu6THAdkTWxubnN7s41gp9xfrZg2Z6xsTHh2Ru7TJqGpOfE1ZKUBclTxqObdD3c33uPLq5wjCB5xqOKG9evk1OmGTsOHhyys73N0dkZy+6UZiRsTmfE1BL9ktFoxHQ8Yt7u06dIikIOFRtXRmy2YzZnI7LfJOkZqpHxaJed3ZrVGdRNYDrZZhHHplewPGHZLrh+/SajumHeZaL2JruVTdNxqYd8/ZXf4Gd+4qd4/rnn+a0vf53nn13y+vfu8FN/7lk++uInePiVN/jmq7/Bm+98jeViTuw6/u0Xv8TRySP+6i//x/zoj36aetQBig8jRlOlGgViGoFOjUVaqLxSePxQanW8BX7KVK/UjEebXLv6FA8e7rOYn5GBtutY9kcEPbLhs8WS+cWCw+Nj9h494ujkhNOzc5QVLkCVakL2hYK7bh0h2ZF6R9/a6LCvBKcJKUNAhk8Ivgi51EHJLpO6TJtbGBnZSJwNIqEZ6jwQgMlU9NmROpgverqo+KAktWZlzqkAhIOn5WOFIdPOHIxLf//riQgCZghZyhZnNEfvMs7bRJppvbnCXqvRXJtEeC43PVjth3qcL3PUuUfjgkxlQxfYZHbUnl47wwRyIuWWXIZ/Mh1IRNSRk53+ThyuiESoOkRqvNSImLGEipDFUYeK2UbFaDTG6w6BmwgbCGc41zOZTBmPNtjZvk7XJk5P5lRVYGd3h8mkJrievj2nbed0raddNlzZuc7x8RlHR0fmR1fVeCqm0zEbG5ucX4A4Zbm84Oq1GQ8Pj0l5gTjPZDqj6zpaLhhtNIzHYziN9P2c2Pf4DWFzu6E+cIxGE6imLNpznMtUdc32Ts3xPoAyGe/QLhpi6tg7fI+D4/s89cxz7O7c5GT/LTSYMo5Gw17afMDtvW/wpa9+nh964acJX3kJ9XucLVY8fFjzwx/7cb7+8hc5X+yxWl3gqXAKF4uOr37jFU5OT/mv/gvlJ37yR5luBHzoQBIujGmqhswmTk3Y00Daxx5+rFu5A3hnlPPgxuxsX+fq7nXOThekfknbKav+lMbtsehXnC9WHJ0e8nD/IfuH+5zPz+ljiw9K1soUqqgKd3/4W5mUbD4gp4q+Mzfg4AQvFO9Byxq8WK+/rgStM12O9H2i61Yg2RSsB7ng4HBqr9/4LoFl7OkWLV0PVe1xTojR0XWJPhZXLO+o6wpzybLWqU1/uvXn8v3XEzFKrBly58DK8YK42oSWk4jQorQYgqo4cUVzoCb4ygCW4kAUagi14nwEt0KZk1nYKZtP6dNp+XpOl87o0+njh54S0xlJz+j1gp45iTmJRXksja/gkolLiOLFU7sGIdC2SrtS+q5H8wVwgrCg8n5t9Lk5ucl0tI13wmwyYXO6hfeQdcmyPefw8Jz7dxacHgWacJVbb7/L2dkJ4jwpZprRhKtXrzLbGFM31qc/uzhk/+Ad9o/eIumSuhohbsStO+9ycnZMU4/ZmG7jUdrVKX1sEdezvQO7V7fY2d5lMtmgqh2hgrqq2NmdsbHliTExaraoqjFZlL2jd3n7zreo6xE3rr+AMLbPwiWcD+AEaVpO2jt8+42vMNua8dGPfoz9g3e4fv0ab37vHh/6wGf5yAf/hIG4siSzpMtLlv2K5arjW6++xa/87b/Pv/mNf8PJ0REpLUh6CjK31q1OENnGsY2wjeqMrCOTgVtr6UkpAz2OmuCmbE6u8vT1Z9nc2DKCUB+ZL0646O5y1t5h//Qu9x7d5sHBXU7OD+niChWbUnWhQVyDUpXypybTkBjRRs+qc/Qx0HbQLjN9Cznb4SQEnAtUVUNTN4zHIyaTEZNRTVWZ/kDfmu0Z2TQZVIokvgeC4CoHDroYWSx7Li56Li4Si6XStgZOOgnUdW3SZZVJ53vvir7AH4En8P/LpULqgim2eqNlehxZtHDyDdnPKVnLUA35da7oAHq/Bv+0H9osicF1WFXIkkgSibT0ucWInJF4qeef6YBc8IeA4MlivxsxOe8sPZmAlwpwxSm2wuWagweZO7cuUM1cuXbO1sYOTho8Qh0CwY3wTBiPPJsbG4QqEnyDZsd8vuTh/VPe+O4h3/3WEZujT7OYw6vf/Z45zwCqpu03GtXEtATpWSxPeOudVzm+eItFf5+YV4yaDSazDe6/9x69P6RprrC1cZWmHrFYXrBaLqhqpRnD9vYUJ55xM2EZPbiI957JLLC54zg7TASZolpxcn7G3vEtavddquY/4dmnP0b12oyUTy2Q+UwStftGy/fe/Trf/M6X+dRnPs2v//r/TRUCFwc9p0cdH/rgi7zyemDZztG0QpOg2dOqBfdXX3+LX/nb/5Cj03P+7J/7GW4+fR2q5ftORkrCrFTYnNtQHhRgSZz13sXcf4NvuLp7jZvXrtN2Z/R5yXy1JLDPvD/mwfGc+0fvcHB6lzadk7TDefCVx1U1LlS42jwTDOiz7FLF08dMXkTqKMQIOdsITz0ygo/Sm8lIE6g8NF7pQ0UIjmXbmqZGjqXDQUloisDJ2vEql+BlhjgC1nGImaq2AaWqqgghmDiqWGkwTMv+oOuJCAKahX4JMYP4wvjLjhAF8WqTe6qQFBcVn9QwgfX0YKHx2keESrQbnyKScsl3Mqp9UZBZkbQlZesM5NIpUC1uxZLLxGDxoXMKLqMul1Qlo04NvFJTRtYYeOfNM77wuTf54EfG/NCP7PLiiz3bW1s2SJLmiN8w0QeXmU3GVHUmuJrVMvPwwRmvfushX/7CHb79zUNe+OAZBw++xt17d4rVeiDFnq7teeutd9DkCU3DyekRR0f3uIh7EM5w3tHUW4zGDcdn+8zje8T+BWaTHTan28wX+yyXC7xPiO/xQXEuEJoRYYnN1sdICJHpBoRQIUwJYcyj40MOz++RVq/R9pmbN15gOtmhnd9DRYzAI0aoydlxsrrL57/yj3j6qf+Oz3z6s6zacz7y0Q/wm//217h3+CWWqwUxrdYLWTXgGBG8INpw+/4Zf/fv/Trv3Z3zi3/5Z3nxxedo6h6R3nrx4tFy0hYhLYbSYBiNVhLQM5hwTJoJT924wen8iIPDRyyWLU11zjx23Hl0h3uP3uJk/oA2zcmSqAqzLzSBMKpxlbdDNYsR0ZwZvKqa8U1aZmJfWteASMKPAlVVW/DAho9ypeS6YdTULFcr2tiT1HQIJBWRHSeQbWrV9AIiWrpbWjCAlBVfZgtCVRlTcX3q6x+4+YfryQgCCt0K+mgsrBQVHzKh9jjvwNv8taSIyz21RirJRYuw6AEWbrQUXQBVRX0ieQse2SWyGDPQNv+SpD1R41oEAqwUyao4zeCMeWX9cFeYYNkeUnQJsrOgpZ6jRy1f+sI7fOslePXlDX76Tz3PJz/1NKNpRkNLU5tjUE6ZUCigq0XP3oMLXvnmO/zW51/lpa/vcXEy4+WjN3AO2naxVodxztP30bT9b71LM6mJuaONp7iqI9ETZITzNeIhseT+w7tcLBZsTm+wubHD/sEhKSVcyLTtklC5tSoNAqvVgq5vS7sqMxqPGC1n6MKzv7/PfHlKf36Xhw/3uH71aTZnu5wuAkkS+A4RJeWAl0B2c+7s/w7f+Na/5hf/wt/k9ddv8+xzz7FzW/itb36VtrswPCYns3nThIpH6Ekypk9TTk4b/tX/8wpnJxf8lV/603zqU59kMvWoiygmIiNqWYGd/nk90wFDFryG8vAusLm5xc7WLmenc5aLBSn3LPtj7j96ncOzeyz7k6K4XKTY6mCuV0MWMJDIc3HNJhsfJRmXJfV2j610zXgx8FJUioW5R5ygAVNzGtfElOhibzhO1+NIZHWo2N/okpJ7U4XOGH5mLW9KGRyoqoCIW6/nXIDaP+x6IoIACDkHUnLmJJQSznm8N8di8bH0gxVPtpM+9NRVjSdhtguWviNCdtjElyTzvCOWDd/ag9Ii1K7MEKxfRjlhbA5bKKQO583urIiRILEg07HQmRPgiJo4Pu45OarZf3DCu+8c86nPvssnPrXL8x8ZszG+hjan9K3SLTzLiwWnJyd8+5UjPv+bb/CdV/Y4P6kgV1a3F5cbc1RW61ioohrNButiAS7ZI9c42cDhODy8y+e++E948PB1+u6UvaMTNneuMJtt0VRTUjxD/CnLdmJS66F4K+bAfNUx3zhCw4S2m5bJOBMeuXNvj7ar6OMR333rC/zkj/88z175MA/3fpfsclFUKAFUe4REzMrvvvLrfPRjP8JnP/uzxDbxiR/5NF/46lNc7D+i11Q4Bs7otFKQ/eRMsUlq2tWIr/3uXc5PfoO/9IuJn/iZT7Gx2+CcdXi8n0Ch8Fh3QJB1Th2ABgYWYVhSVRO2ZtcYV4esuEDp6PIZxxf3ma9O6LtoWrLeEWqhaqCuoAomFaZZS7s5roOBanGuLoNoeZUR75BagEjsE1UYNmxd9Ae8aRAW3YCUMm3b0a46WtfTu0yP0PZigTJhpUZ0RV7Plq13wcpSCQwhKufvX6JuAAAgAElEQVTB4bg8fiDf7wkJAiLCqBmjau07NKPZkbKQ+uKvVtoc4pToCknHJ5xLj3m94s1dR6RQho09aCWAkYFibkna2rglrEHloZ1kl4F+IoWD7QsBREppIBGV3phy2pMw/X5RG+YgzWhbx/yNBXfvPeD112b85M98kKDP8vTTeyzmHXffe8TB/jlvvvmAr/7ue7z55iHdqrLOh1L4749zueF/BznpS+C0vfA0ApQsidOzY77+yhcRt6T2HXfu7/HhjzzHxtaMybTh/OwB79z+KlF/nK2Nm2Sp8V4J3gaSDk7eY6Pf4Oz8pp1OseX2nfd4dHBE3zsq1/Hyq1/kp/7En+dDz73It743pk0XNjyTKcBWLsuu53T1Lv/0X/zPTCeB5fmYj3/yo9y8+RHu7L1mm0iGGXhL7T1VIYCVN6mBdun53utH/IN/9K84X13ws3/uM2xv7+C9I+eF6UcUPog8TgRKr6C0DSXgXE1djWnqSdHks4GbLrYs2wuWq5YUwQdvc/21J1SOurIN650j6eV7M/yh4TmTD8+qdC0sFw6nCY2RVCV8r/jYUxc37SpUlhmIhyDUvqHxPSvp6HxkJTYjQAmo5tata17AY5u04rpFcdwqFFfLIjNZf3AP4IkIAs55JpMZ4jJdtyz1Oai6MqtdWjLOevJmpjBoAVgqbq2ggRHhHoND2MKwnZURV4KDoYvrawg060BQGFzeG4trmBRUsZ2nRFQ7krrCaR744x2qLTlVSO9oVw2vfG3FnXfe5PXvrPjkp5+hbjJ3bh/x9utz3n3ngpOTlc1E5MbaoDhLa8sLWUfzMgK5NsUY3mse3muNYnTolJeE2oCrh/cfsVwumUxqNrcCF8t99g/fYGv7A0zGW+Q8JQTHbDYmnPY8fPAuh9WYvIhUPEfSnsPjQxbLc3KG2o15uHePh/sHPPX08zTNDherY1O0cUKx9LVOV1AyZ5zMX+d/+Xv/Pbuzj9L7X2TVnhd8JCDZsjmnQwCoikaDvcuMggssV8qtW0f83b/zqxwdPeCXfuk/4tqNykRLyjyEjY4XfEgHTMAywlIQoGr2YT4IPtjGns+XLFeR2NuPB+/sETxVZSO8Q739B03NW3C27/d9IiyFXhyVOiQnUp8I2cpWUW/t5hCKVoDHO8XTWDPS95B7ulWH99HUhIOJkep6jZoheYy9HVgFCFzLnzOAgk84bXiQvFLNOOcLi8+Q/ewfi30MJ4UtkIEQMfRRxOSjB9Q22/ekRGqbK4gFBOy5bMnyvu6J4UfYj9s0mHi1lNt+E4qUyFr+LomcvQ0z5UJWygGnI0gVfdfwcBn53N4bfPG3vk3TKLGraJczyDuI2yx/ukKz8RIo04uXF9z3O+OYLiLlddQ4cVYq0QOJuGrxbsSddw442D/n6adGbG1O2DvoWbbHnJ7uc+P6h7H0uWE23WF76wonJxccnZ7h8znboyW3bt/i7bdvIRhAGbVn0Z1wsTpi/+iIri+vpfwmnJoOI/Yx+0pJckGrHfeOTvjf/8mbdN0K1RWuzHWImAek6eN5gggOK71UW7porLrc1RwdrPj7/8fnuXP7hP/sP/8LfPRjzzJqTEgEBkWpITuzUWfWfAI7dcVlxClN06AauH/3ERdnPSmZ6IoPGA5QWQt6YP/ppSB8+VpnZuWLgDlbd0bMylgKP6wjRzabcy+42lO5muADSdT0cRwkZ5RjIRJCYNTUpKiE0NN1NjtjwW6QHUvGNSCVbCCuM4I/oBp4coLAZLwBKCEEM2hIPX0fGQialukUSWV5LO0MRtFUFM2WQdggUCJn+6rZPpQ8SEEXC+ffDzm9hKsW7dGCQZRMAFVSNjVXu9mZrN561MmjKaO5L0BQC9nUhxwOjYF4MuYCj3c1IdSICCZRYZr/zhv2MLy2y6/Rnn/sk+fK3V2jGrmw2BzkVFuLTBqO9hN33j7mytbTNNUGTT0jJeVicULXn9F2m7StEqoJOxtPc74tkA/o2kCXloxnRm3VMuGmEmnjIV976V8zXyxo08I+U6TYkVEAOgCPpmDlk2vRkFnFVcnKzPTEYfJwXoJ1XBhS+kTWJREQ6dFkLWGfhZinfO5zL3P7vdv8lV/+M/zJP/lxrl7ZIIQRsAkEOzjEEHoZTCsElEjbzYmpo2lGnByd8eYbD+g626AhCKFyVJV5Ng699mHNrH0KMQxguC8DO2+4gq/M4zIFiBVkhwRHlkhXjEVilcmNEeG8r3GiEISW7rFRjiacyzTjgAvQ95521dP3Q91vXZGcI10Xy+q1YSbnwXvB+x+cvTwRQUDEMx7NACw9846uXyE4+yCSt5OlyCs5hkzAoSRSstaU0YOxcgJL++USscdaNqVUkCEYfH8bRdaDHDhQp0XKbCgp7N9oFnJuCxAkpOwLdTNAVqQMNlGGm1QCmqdmriIzLG3NiB86GsXZ6H1YwO89edaBgIFcZtLsqqYmB2IyU65Bsyk0z096vvedfT7w9HXq8ZRxvU2kJjQXHJ29Rr13DLLLdGLZzLTZYTXpOD054uHJK4TxmPGGEs7FWJMu0uV9vvT1X0XE03O6DgJaAizvc8G1ks3q/wqVgCPi1KNqsuYm3+5wKEK0GnmoZ6UHWRZxF6PRdtlRZeW7r9/lf/1b/5RXX/0RfuEXfpof/viHGY+MU+F9/Xj6s7QQLWvruJifFDpvxevfvcO9907IyQJAXcxChiDg3FAGXL4fQ2l26W1+/7pm6C57XLIsR3KFRptVWWlEdEXwI+owpvJubXwbgqK6IEZjRoZKGU0cofNUNfggxE5tHWpaZ895ACmBUFn3oaoC6hTLhn7v9UQEASdCXY9seEcMkZcyApxSJmMbzKb6PM6H9Y3JOaN0GCDjSy6Wcb7o3+ds6LenSCzZqYOLyPruPXZtdU4MQ/LDKWJR1cCu8s/FXGsGdCCVdo2x3zyqNS6bQrDgzE1Ja8OunSA+I26CuJGVNsglDEB/z+lvdZ49mXO2UwkoHWRELsuhCRAKBGKbL6aOd9865s3nH/GBD43RHBhNhO2rCdfscXB6SGaH2XhG5Wd0rbJYnvHWrZd46aWHVM2M04vbBG/9fxMX6ejzvo2phkWZ4C2ZyToASEm7W/N70BrVKaINZKvjhcbei+R1ULPOSwGyRMmyKrqPFuiSDLyQmkzFw70l//LXvsL9+wf8xb/0H/Bjn32RjY1N6jA10BG/Bu5Ue1arC87PTlBV7t/f58tffoXz82XpBvjCugslC3icCQzX+zAafnAUELRQh6WI/JW5Ewlk54jdkou0KFbxFag5Qw1l4PAIlVDVskb8bSzR4WToArjin+LKqLyNWocg1E1FXYeCCDzBQUAQ6lAhNCajJJhunwZSjPQK6syM0TsTXBT8mpUFoNrhXCFYDN0BOtRHYkjUFaQInTP3Ih0yAT9kiSY+4hxIyGXzW71qu9/SNxgkIIqSi2Z6LVw1tY6BSsLUhqx08RIImcI87BDX4H22uld8aWtfqvdLajl8NiZpbq/BObvBkIpjTi5pekHB1TPIpat6NAeyCxydLHn5lT2SXqcZRzZDYlwrm9sNq2Xm6OyAw6NHVIzpE9y6d4tvvvoSb9x5zzz00hwJgsu9BcUcSiAc/mbZFCK2YYfx2YGxqgHr6ZfZf3zBDwclHtDioye0ZFlZe7JIu+lQaYsrZY5DpSJTkzCW3u++8jr3Dvf46e98kl/4D3+WF154itqlMjwjaMrErmNxdkxsI10Hv/v1b/Pmnft0knG1EBpHGHmq0eVMwK3vjergDvQ4EGQeI/Gidr9Eh/ymBMYMlO8FHyAFXBK6bsFpOqPtYGMjMxpV5mFQvMMq74nBkQKkEIlJib0zZm0wsVNbP1au+EJhMdfZApP4AST8/a8nIgggUPnKVFV9qfmpEBb09EiOSEpEMXYbIu/PNlVRYskASk3qMl4TOSSqpKTKHIldm63ZajpNw5+3rwVtdY4CyBSgq6QAAiUoFJCo5BJZHjeHMpaZCOZHYO5EldGMnVqnwZVZ86JRp+7xixgW2GOJrPLkuv6ntLUyoglKQLDX5QsyPohNg/kkOPoYuXPnFNWOZ55NqGuYn2V2diZc29ng4uKUg70H7B/tcX//Ed9+43Vu3b5FpENd0bUXSytz8uvgaKWMjUXb61AuJQXlozMij7UBTaJNxTIzKQIuZTYcXLR+gIBKz2WBWGQwgrHPXuhwrkNCBeKJErj78Ihf+39/m9fffIO/+ss/z4//2A8zbqww0yicnJ5xeHRE2ym/85VX+O0vv8S863G1JzRim7+pCFWFW1uLFQzgUgagBV8YmIiWwch6Lblh0rRwPBQFb5+XyKB/mem1pW07Vu0580XPZOIZj8yKj4I/BG/BKARw/ZDRGA9myFSl8FtMV8Vem3OWKSKXweTfez0ZQaC0AwWLgsGbpJZ3SpQyyx+Gnq4rKV45YbKdxhCL2671SFNxaHUu471SVRCSw5cP0n3fbKWd0hZcnDMtvvIUj0VKyw1fa8rZ7hce35g8dB1EH6fIhctgv3s4zHJZ+O8f8BgUkB5zvofQbtFcxDY9mgwsK3z5glKZnJSJbpXnMmhA1dP3cPf+gi4HchWYbifGdWR0wzOOHlm2vHfnNi9/9zXuHuzT9z0hWKokvZSxaHu/5nkxwM65/D/FwLNkJevT+/tv+CWOhkvl1xSJsCGosUb1sKDmSgngiwsViA/4aoR3ilCZlLrURHW88+59/re/9w946eUf4s/86Z/kxvVrdG3H7dvv8a3vfIff+eo3+MrXX+di1SPBUdUVVS3rVuDwGILAZTzgsvW3bWgpGdGlk6lkOaCmCOykCN1kU8h2pf9MXajAnrTqSLEndjV1lddlYM52ONb1iBg7ur7n8fSkrg8KKZwHxQDBUJkCtmokxx+8/Z6IIJBV6btYam0DdURqvMsEr2Ux9CbeqEMAMPQzC2Xwwn7eiGMZckSlxUYTI7iI87HQkS+fVnKppBtScDVwcM0SLEAgNtewDgDlxBUtkFyZOdBcnneKczowjC0t84p46zjgogUYGWiv9r6cs1qPgfUIDMYoUsZnzRarA+zktHQioVrZ+6d0GHIqXYeKlITYV+wfKFE6lu0jHt4545mrE566Elh1J9y/95CH+wd00QRdNJkRTE6Y0k1mjYQ/7j/rEIMupcrls3RWcg0VPaQhesNw4qPrzT+QbShm2oNWgIhfG80MSr/BZ0JVanYE74UgGe8FvOfw5IJ/89u/w9dffpnr166Cg3fv3Ob2nbscHS9tCCcIzgeqOtAUPKCuq3UAeH8Q+P6VW5SnBiKOXAoAWn4uW9ammNdFTIImzGVLBHENkktmlSM5JVbLjn518b5ukHMlGGNUZMmxrDtdf/7G1LJyxTkxbQMxHcLY/eD990QEAdVMHxPeF9ulXNaugvECEq4oAHsoaPgA+1qkQztbhMCaFKRFa1BM5rsZOaqVEKOVAqboMkT44kuYjabrSlYwpLfDyapZ1gRFp5ZqO7H4a+mtL00E97iUEAt0Oky0uYHJ2EKRhh6OSytfrSOhDKfjJfhJEqrlJJCu8AIGlNq0ALNaCYKzzSNFDMWHCpwjJcfhwYKL8z1uh5ZJWPLpH77JtZsjVm0GgtlhJ4pgRXHiTX690S1U6vqzkyL6Yk7RpYJflzgWsNdFUwEAM4k8BIOy+YWhu6Dl51hvQhFnLS83IOiFWu7UMBZfUmCJSMBKRwfHiwUH77zDqltysZgz73vUG8POBzP1rOpAVVfUdY0fTEQvBQDg0mt5/L6Gg1iw36fpUuKWsw0alfItyVAeQIzJtDOqhnGzQ9UHunZeQMSV0ZJTWrsUD0B43/fkZKf9et6lVIIWlOx1m6KxJ/VK1yZWyyecLIRSrJytxrE3b8yoPOw4Nc0k1bJs1DadqpBzb2Ofw+YWczkeSDNSwCEvSqihVoerFFeBd0AB0XJOxB40Gi0zl5pXB2BJLSMgl/SPISspjML8GIW2lp8xFq0laQshlxFmJz25dPptA5RyYH1upBLfUwkAw/djYb/1ZFqUvnwmYiKVWoFUpWxyCBEJhpCLLwBeTqScWbXJNO8cvHPvhMOlsH9wRtdBSoGoJmdNNPJVSiZc8Zi6XIJn4eVbr/oxpdkoCwW/cFay2WwADO06XTPZLqHsUgBAlxHnSkfI43xlBhwuEFyZ7iu0W+cd3oHzCReMDTj8LFoR+0iMydR/vUMCVAhV8DQDJ6AaMgBZZ4HvW6aXgMGCgRqSr+b6o8MQY2G05uIKnEXIeMQpSSkKwMZbcLlmo9mlme2yuDhjPoeUjmzK0wmxWxVLcpuEjNnKC00WgBzOpmiLyrFVjUJOQk7Q90q7EtruMoj2/uuJCAJW9yQG2oualjiQ0SElKKe0KQUn4prx54i5I+kSvHkT+GALyuTEB+NRwFmrxfmAbxQXdH3aD77zLkBqHalTu6lqnnJItrRvuMmq5bSyiK+5bHxDYWzTFfryMNhkHO5UbNRMC0/L6xyOlCHVHmrvx/8NQSAB5sVgQSAWMFJQAgOLcp11DOm3DOHFwLicljYC6xyqgdv7p/jjOYv+nFXMRBVSNqVaTUqOZWpN18m/nXADOk5c38shM1lz+KUEMzFClCk5Z4Ymvp2wpRxal2AKjrIpg5l1uGHT1wQx0YwQnNG7A4Tgimp1xgdfMFIp+vyR7BN4NZWdylDZ2lsZUFUBV+jBw4k/4EaXQbX38TbEMkYFU/XVjKYESdeb1COoV9QN5rfW6kvl8/JJcG7CbHKdxm+isWOxXEG+wDIA64QMTkN9UgsESYq3YQkABa+yIaRI1yZStI6Y+RX+HmBmfT0xQYDYlpQGzIjBdNg1R5IWqoeaKWSMZaa6DAHl3BO1N6mwNAAiFp1zTtZAKwsvjBKVF+qR0UNBi8usWtPAPX4uZYGo+OxAqnIDgeE0oIiWpDLfPZBHKCe2AsWxmFyck7JlFEkV8cn66LlskjJPbvu9BAkdsoPinVhS50xHokXLnAWEdZoqWmbdh58s1DnVMt6KrQ7j3Ns0ZZ860qqjI5m7TWlpPT5VMonEJbJEcdUpn1d5nY9PSCyoWdaLw6+nBLXYvcMwc08BCYueQ+HAO/ElNbf6fDj5a2fW3Tajb3MW4jOucoTGIbXivJUFKUdiakliXYgqOFKlxdDWG6uvCvjq8ZiwrUnrsg3eE49tvuy9K3Y/ZVD17BPaJ3KXIJowTsbRi5gOhUs2aVk2bM6Q1FSyBjWtjdEOafI82ves0rskVhDs/vXJkaVGfI3XhEprGIvaRs+FE9K2Pe1K6XprhRf+u33AAyD7fdcTEQRA0bwqdWRpCRkCVXrx5hfQp0TfpbVjsWEBAmpBQGNHzgkfHT64ghtkE4JwivhIPc40o4rJpKKqbAnHmFgtE+0qslxmEyR1A2AFWd0wf1QCgGUhqvZac0rrBZJLLmznw2OJV+uH23bI2QDArLmwC0t7jRLYirYcWmCfopK8Bpj0cVlBMUIVsZNVC3gkWsZp7dgtpKoSXLUD6XE5gmtBWpCeXjM9arP99gYZAuK6fpfB+bac2Jcpc8OXS1/t87HAZK2RqoChlDaolLQ2W/rvE85rGaap3ofSWxAIVM4TnBC8lNFSm9P3VZHrDmJiH2VWRMVKhFBVaKd4r6gXvFQEX+FCKJ2H4U0MMyiUtN0wt2E9gJG+SCVIFy+BHBOpj5DK2Le3kiypnfzGDTVgOauVrDix9dauqEZjZtNrxLQkL8/J/SGu6qm84qoGYQOfx8S2o+3mBbdJxGh04RQTXVvMStUxAMTDCvxB1xMRBEQyPvQ450tWoGVSMNvIZIykPtK3fTHMyOTUFxMQyoYwObHcJbxmJJaF5pW6Au8Toc5UY8dss2I2GVPXFaD0XWJZR85dS4ot0YEj2RyMSvl7eegFlI1JyTQMDc9qM+ZGYBLed8SVzeicEWXWJJNctAiKg5FlD6mkjaEEg6GEyGUjDvlAGa4pBKaBqiwEk27HNA6GTWYB1VR2HC3QIy6BdOB6KNOHUQsNGQugj0uKbGPb5EL4KZLeQyC7tMjeh6KrAEZaWouADIy/ItIiCE5MVdo5o1L74AqTznz7hmBQhYrKO4Jj3RJDrIxzHtbKnmrZGZgvBN4hVQUBWqdUleBdgxeP95df/eW5jVJyplzoWMIA4VqXKJH7SGojfdeTOgsCpmviTPuCTNDS1ZKhXZoLeC0415NkRZcWhDihCjOms2tEvSCR0NyCS6YX6EY4HZN8hQi0rQONhivlrgzdDZ2lIWD94dcTEQSCh42ZkJKj7zMpZTthykBMTpnY2wfdd9FS9dSVTVO2hOtRF3HrPqydPEGsZqwaaKbCaBbY2KyYThqaqgE1A07o6DqlrTLBDdh+tr9fMIOBhmPZgRZQ8nEGMDgl2Yk/ZAG2CQaMgMESTYe6v5xWDI8BAxmm1iw7SNk2ZNZcxpkLTqDD3wAzu+zXSkcyIHQDjUl7NHdEXdricRlxRZtPiriHc/b7h67XMGwlav9e3HrEwTI2Ctp/+aSRS3W0ABVQI1ojOjJNRvXg7N7Z+KwiJJOXd4p3gg/mxmtf/VoByTbtYC0/gGH2GMBkFyhZkawPFyel9SsJvLPx3QLN2st/zNq6DACqgNOBwFUmN7NCn8l9tiygz6T/r72ziZEsOer4LzLfq6rumbFsY8usjAU28sUnWFnIB8tHwHtZuPmED0hcQIIDByNffAUJDkgICYQlgxC+AMIXJD6ExAmDQesvrLUXsASL7fXasMxH93uZGcEhIt+rmZ3eWa/QVDVTIbW6uqpVFS/rZWTEP/4RUSqtNfd6wpAm80zFmHJEhg1JKbIYCjIhMqHpklnv0RRS2nJ29v1ouoDpLnOtHhJJRpqgDCTZ4iPZ/Hs37cNJexYmjNZiCK62CEdhBHJO3Lo5Ms3u1rYGNb6bVhrzZWW+qJSLRimROYipQXS2mfWWSkrSRN9veeMsqs02c35r4NZbdpyd7zjfbtkOWzynOjBPwmY0xjACGSA2dY93GwTfPaJtc/KSewBuFIAljidAOgnOeC9t7j3h1ZobAfHeh2aTezS9EsBk6V3v3oDfOIsDbp1BuA7aIJqxJI1CnIUWad5JSWdQxxJELYqrejGVD1vpg1Ys4zzUYPOJ+GfRUtBaPfAhsiMdLyGuz0H+CBvwKVJi7g1kNu5LiAPCGSXTSDSy5zrY7yyds+e8hRwkLa8rqerErp42ltxc5+AaEbUbmFd/+myLwcFhyXG2hyVbjIkbl9YaPU+fBQ9RBISE1YqWRr0s1MvCPM1o9fvAC6lwjyxbr6z2z4jalJRiXmCaUbmLyh1mTaAT2A4x2G7fhKW3I5fZU7QxsESb4NO2xwgHhNZcX/rn7O3512ILwtEYAeHsbECSgWVaFcrcwgNo1Fmpk1Fno5ZIVUWaznJFBo+pu0U3AhxLwfZPwrDN7M53nJ3dYHd+xm7csB1GWgPTxmZjDIPPO/CpQw2iKkttzVn3g2ONhDvppRuBvVSMdRcyno/mKA6aeScih9u6IXBvQMONtBhs4V5pnHyylgr1jIF2Pr5FaIHTlyXwBTdIzceyaQX1tKL08l9hKZXtQF+Pz8kVzcVDNjJiKQqE0p4GON4gLN5JR/iX94NYA2/OmqJ7kCVDVEmtk7okUoPVrW+SyJ1DJ3a1JljL1KqUPnq7eBiRBh/Msdn1wp840Zv5pGGNvpSLp7biGetW6ZiAb6xejyIkLDkNWIvSpkadCmWaaaV2GCvU9JH1qeMoyZBByCOkbHT2aMqVxh1mG8EKpjusbkmaydkQ3syYBmq9pLXJM0ct06rvhTJBnaMe5hGb/So5CiOQUubGbodQsZY8RpeYG1gqZVZqNZ+cE62p+xhxNXd3vR+gu+ISeHTfeCKJcdyw2+7Y7s7Zbc4Y87DQMpMIWQbE8loI0N1sC1yiM7wW9F7WcDuuo6cb12fXL2VxLfde99Rl8/Hr0ezEQwIfjKIK0sbYeLbQZa2bn3grCW6/RaMTd9eVFN6Df051I2A1QFcWgG+tlfdGHiLecIOs2NDcGAjuYWkPde43As5g9Hbwa2TQrUo/beMz8XFyvt5A7UlOP757x+LeyUmjzqO30FIFrZlShVKS3wPZwggo404dTJQUoRy0opRqmCZyjqlSugJnHefpf7SqzHPx3o45+bzFOMitKbVWWinUuVJL9eG4y3sFMKxgC+M1vqee/RBPaSIN5YLSEmaF1jbQtiQdSM0LrrSeoxVnxppnx+Zpb1/sZS3ud/vtIc+9Wo7DCEhiN/rY7JoLWbw0uFRlLkZt3lizmZfKqwk92WU2uhMgvXrPL7zfaB1X8JLdzIaRnWwZ0iaALQ8lvPo/IdWgVUwrWgso5CTrSWfhgdy315NvOgimYQo70TDxychqFrSfMfABQ8Tz/cvUFZqfBsFydAPnU3dhxSPo4UbPUrRM/8ItSktFvcqyG0nHT4rjBqageU+XIOb0FEhqSJpBvBDKPRp3P0WHuF7is8JrkaDC5ogTQiw4BGZtKXET9WlOpj41yqMHb6ejQRc1MxKNFmlNLNPDXWugpVAnYZ7DE8qCDDN5BLFESWEIk2NM86xogWwr0XYtJY+ibKmgghZoc0Wn6q3PNt7uLA8JidjbP983YqnZcajAalLSOOUhbRJpKz5WzxllHhFkP6pIhtlMqUrVeyAbJJ9B2pJshDYE2Qia7SjzzDw3asWbjWqNdnyxxssx0Y+KfZD64XIURkDES4lLDCL1m6Kzsyzi4RZf275l60DNsHD5HdH1k8qL7AzT6F5bE1oELZGzB98oVeJ1qHNlroWiBRVj2HbaJwE8WVSFhUFYQgNbrsUryjSakRTfIPQ6A6XZ4P1KoxuyMwBboP3REmqPSusGYgWs9lZuwSF8HUpQVRMa7EUL9G6hIKP0JjO9tt2rGS3Qeq+izBlvRCEesaMDaI606F5zjQgrSOvtt3xDPfQBWGjQPsHJzBZ6tREk0TYAAA1iSURBVFNph9DP37+f0RqGpqW+tuLOzARlzpQpUZrTrMctDGfJZ0IkZ0ZKigySeojY203GAsR1rPeUqtGqh6FtbiTzGRM2SHQZbrRSKFNhmgrzXD1H729HysHZHxIpG3mM+YRDjjRu35Nel6Di6deqjWaeJck5Glr0sJEc96ZRmkX8r8Gqjc5Z0Xr4Ve3PZPXWrpKjMAJJYNwIee4sNI3WYDVmBZYwAn4jkPx0c3dUQPLK68aCv5/9FFNFa6JOiTpl5gsYrFHrjIhz3csM86RcXBTmqjAkxpsj483EOAxIFswKpRTmPmrscm3kIWm92U367dtQeoEQ9NPc04rFQ5AlzReYgPUW5rr8aFBBvX8g3Ic3WGLJImhkSqx5o4/G8jeseIa37vJJzjnGuEmUOOfUvNPR4HiKpIwFz8A7+boX4N2eInOyXJ9/dymFp6ROu2YpvLHw1jptWheOvUAUaCUsmsamiKlFoWkKmksYgeK9BmsZqHOw6VJ4gEMmFaMWW8MaUQhwVui81FjG+OI6btBmpUzKfOmu/iCOMUgzb4dfI0tVCrXUAG09m5EGYRwSw+gdiiVF6jI5S9IzKJEdglg7fy0n3CDnYaEAi677oSXzVHiUj/t9VONHua91/vcoR2EERLzjKw7iog1qNUqtMTjUY0OLKimiwq67iQFDrwQZCzeTiqjS5sR0kbh3BwYUnQp541NsVGG+VG7fvuRyLoy7DecbYScWrZlybPLqdd/3GndvF+7dLkz3/ESK8oNAx3EvIG7blfYL2rsfLe69p8VUemloZDzCABAGpTMDekVdrFqEBQ4ILjx+W0MVr7uI2n3LJEY/1aOhZ5IBr86LmzAbaUhIlvUziPLdpXPzyovwmoWoZU99w7lh1g6QWb/hfa6kyYxZwaQQ7P2wa97azJK5J1MzaskbbgaG5wSYwXGSsqPVgVYyS5FVFaw0rDpwp+Ibs0/+kQfc4o78m5kzPudGm5QyVepUadVnB5iKU4EFanFCzjQVag2WaDIsJ9KQkTEhg/h1JPeQTDRoWi4KdJpvEi9ky1795EaXCC2TG/GW1HGRqBlxFuRMbcUrPJeKTvau7cFddrWJOAojAIAMWBsjJZi4vID50mg1+Umf+4apkKJuXokqLQBfCDF3WVEfXtFmo1xmpruZexlSq7QdDKNvtKbu4pdSGEYjbRJjHsjbzGaTGUfvR6g6ME8Dl/cqwy6RR/Nee0Db2zOeUux1/9BS59I7RdeiqMjCVxZxEhBSseTxnaouh7wE8Us77GT9ei3cbV1+W2y+ZeiEugueFn5CNLTEW5unNJLS4DMHBkEGg1T85MqDY4CBB1iAeBYnn4anQ6qBJ7QF0Fs8D1gqLhuVJLM3JpEZL3yK0Mdzke5wNNyT0YGko2+gFKe1ivebbG4IsC2DDbQYOzekjsg3z+tb71tIuPxeLwD7HkDUo5RGnXomqlGmqHYcMpkMDVqrlDJT5vACrMf58b85Tv7BoheFl7o3Gk3X79EkeaoxDK5l8d/g4K+oA63mbFQ/7pRGoZpv/lqL9xW0dp/73/kN8AAM8BpuwlEYATMok3B5AXfvGLdfMe7dgTp7C2+PtbxMJYnnzHvhnee5WdD7vincRfbGlHVOXN4BQbFaKBeVYXQSh6QUX0xl2DXyBtI2sznfsN26ETCMVmG6rIy7GQYLw+BQ9az0GpnleghAzr0CH1rZQwTWpmgki7SftLD0fqJ2xmfHLvx9ZTld3Y1e0Yh+QlsYIVP3ELrn4Ky8kZS2JDYkRnLaBD1Xo9lJ8Cui7NeXsrvoaQ3LTD2jIXWtf+jId4iHZgSmAMRNLEwIlyS2eH9B9ZjXBMyHvHr1ZbAo+/qZA8Ko4xPZ1grNLAY5Mw6ZYagMWclZvZcD3RXxt166/ISoesluLZV66bT0MjVqdRffad6GFu9cXWbHA2rzgR5BiCSNyTtFB/ovqQNya8UHQT2W5vwMv5/T+j3390rRbl2bA56JCKNmms7UOlNqb8vf6zXcg3gjacKjMAKtGa/898R3Xr7g5ZcueOW/Chd3hFY2fvMmw6wGucSgaafEL36P34C+EAtqbl5YUWe4TFHorVCzMe4aw8YbS1pW8qay2cHmTNjd3HB2Y8d2t2HIfoLWIlxcFPL2nsftTamT0qaZNuk6Ai9skHXizF5A4PF5Cre2g1FrsOCVdh1gJDz+DobC3uUu8fQCoEIE1wHw+W6OjeA4QAocIMvGw4E0+kmZgq7rxyikldvg5dMpCp+8qlMD4PO8fnNjCntVd6xYSFitns41m9GgKIu1GFmmmLn7b5Y8FLKEJq+GpBdJmYcyot7HQeJ7T9FLz7vrGmlsbgT2jVlwDTTtZS7MQd5WWyD+Tv5pxbyCVNwA1Nr81rFGmb0kmbjG3iYtD+JYSnR4kyAwyeIJunH2Q02W7nZ98+eUwiPwGZVioFUXw2zS3AC02b2R6v0eHER+9MZ/jeTAcRiBWo3//GblW98svPztyt27Qi0bz8makdkAIBHHZploUqI5RwpjED3qwHnUKEt5avFTxNRLYvUsORAXQx7GMyOfGZvzzI0bW85v7Ti/cZPdbsuQB8xgmhp5FCzPtDJSLyrT+cjlncqwUyZdwPUAmVhPoKCcargLurQ26z6zhzeOFC8etXcL9n/gwcGSDpKmeC1CTOuUonArDUwzKW0Z5YwUKT5JO3LaMMrohJToeGS5Yam6Wx/gaorJt14k5d4G6ilNNx62zoFIHZ/wUGYBMs0r8DQZVRqeAr1EGWkyuIfenOqMDe7FdBJWGBxZ8I8Y2SUR648NGybSZkY2BdlUGCqaWhgT21vnqHNIglZ17GBSdFbabMxNvd6foIxrw4pSLaE4r6A2Q80NrGdXJEIOrxXIIjEGz0FTIWNR+ZoHt1gt2q8LjYxTodNm8DLoJEGa85vHmoXH6nhA1Ymp+SzNzlrdx4k6G3W5TwKbyeEpPEyOwgjMtfHii9/hOy9PXNwTVDfRBEQDyHLQSCP3u3SjMUOJKaxL3kfDhfWqN1NHxK06Yp+SeEsqVcYEeUxszhO7m4mbNzbcetNNbty4yfmNG+x2Z6SU/AvM3s24tplpN3O5rWzG6o1Ds3g82EE5YlO2AJ4WAtKqY3cBoVfirRI498LRv9rSd7eAgAjuR4j98drToJODHDT1MR8pWp13MtIK2UsQg7re1h2X+Mig9dKB0XBdejZAVsO0xjNEunRtQiIpPLfeN5BuPfpN2z2JHte77toxUukZDY/HpYNx8YG9e7Nfhl+fmASDUGNWhC04Shf35sNTiAIiLy93nbwLsiw6L2Y9Gn72r2VZrfAEvNubkk2CF+oflrLP28AhMMyMpBZdtltURAZQHr+799d7Wa5YwP24x6NE3ijV8P9SROTbwF3g5UPr8jrlbVwfXeF66XuddIXrpe8PmtnbH3zyKIwAgIh8zszef2g9Xo9cJ13heul7nXSF66fvwyQ9+l9OcpKT/H+WkxE4yUmecDkmI/A7h1bge5DrpCtcL32vk65w/fR9lRwNJnCSk5zkMHJMnsBJTnKSA8jBjYCI/KSIPC8iL4jIxw6tz8NERL4uIl8UkedE5HPx3FtF5C9F5Gvx+y0H0u2TIvKSiHxp77mH6iYuvxlr/QURefpI9P2EiLwY6/uciDyz99qvhL7Pi8hPPGZd3yUifyMi/ywiXxaRX4znj3Z935A8OGn1cf7gzMl/Ad4DbIDPA+87pE5X6Pl14G0PPPdrwMfi8ceAXz2Qbh8Cnga+9CjdgGeAP8d5LB8APnsk+n4C+OWH/O/74p7YAu+OeyU/Rl2fAp6Ox7eAr4ZOR7u+b+Tn0J7AjwEvmNm/mtkMfBp49sA6vV55FvhUPP4U8FOHUMLM/hb47gNPX6Xbs8Dvm8vfAW8Wkacej6YuV+h7lTwLfNrMJjP7N+AF/J55LGJm3zCzf4rHt4GvAO/kiNf3jcihjcA7gX/f+/s/4rljEwP+QkT+UUR+Lp57h5l9Ix5/E3jHYVR7qFyl2zGv9y+EC/3JvdDqaPQVkR8CfhT4LNdzfa+UQxuB6yIfNLOngQ8DPy8iH9p/0dwXPMo0yzHrtie/Dfww8CPAN4BfP6w694uI3AT+GPglM/uf/deuyfq+phzaCLwIvGvv7x+I545KzOzF+P0S8Ke4S/qt7urF75cOp+Gr5CrdjnK9zexbZtbMSyV/l9XlP7i+IjLiBuAPzexP4ulrtb6PkkMbgX8A3isi7xaRDfAR4DMH1uk+EZEbInKrPwZ+HPgSrudH498+CvzZYTR8qFyl22eAnwkU+wPAK3tu7cHkgbj5p/H1Bdf3IyKyFZF3A+8F/v4x6iXA7wFfMbPf2HvpWq3vI+XQyCSOqH4VR34/fmh9HqLfe3CE+vPAl7uOwPcBfw18Dfgr4K0H0u+PcBe64DHoz16lG45a/1as9ReB9x+Jvn8Q+nwB30hP7f3/x0Pf54EPP2ZdP4i7+l8AnoufZ455fd/Iz4kxeJKTPOFy6HDgJCc5yYHlZAROcpInXE5G4CQnecLlZAROcpInXE5G4CQnecLlZAROcpInXE5G4CQnecLlZAROcpInXP4Xgxdps8gbsoAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "Ck662hNXTq6C"
      },
      "source": [
        "## GRADED PART, DO NOT CHANGE!\n",
        "# Test image preparation for model\n",
        "prepared_img = prepare_raw_bytes_for_model(read_raw_from_tar(\"102flowers.tgz\", \"jpg/image_00001.jpg\"))\n",
        "grader.set_answer(\"qRsZ1\", list(prepared_img.shape) + [np.mean(prepared_img), np.std(prepared_img)])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "YSGoHIxQTq6F",
        "outputId": "8e820a2b-8cab-4117-9808-90cce3ce629b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# you can make submission with answers so far to check yourself at this stage\n",
        "grader.submit(COURSERA_EMAIL, COURSERA_TOKEN)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Submitted to Coursera platform. See results on assignment page!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZE66HWaTq6K"
      },
      "source": [
        "# Prepare for training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "kbBn8sLfTq6L",
        "outputId": "4244876f-5037-4ca8-8ec0-069220013a69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# read all filenames and labels for them\n",
        "\n",
        "# read filenames firectly from tar\n",
        "def get_all_filenames(tar_fn):\n",
        "    with tarfile.open(tar_fn) as f:\n",
        "        return [m.name for m in f.getmembers() if m.isfile()]\n",
        "\n",
        "all_files = sorted(get_all_filenames(\"102flowers.tgz\"))  # list all files in tar sorted by name\n",
        "all_labels = scipy.io.loadmat('imagelabels.mat')['labels'][0] - 1  # read class labels (0, 1, 2, ...)\n",
        "# all_files and all_labels are aligned now\n",
        "N_CLASSES = len(np.unique(all_labels))\n",
        "print(N_CLASSES)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "102\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:40.185940Z",
          "start_time": "2017-09-03T13:00:40.175758Z"
        },
        "scrolled": false,
        "id": "uh-ABW_zTq6N"
      },
      "source": [
        "# split into train/test\n",
        "tr_files, te_files, tr_labels, te_labels = \\\n",
        "    train_test_split(all_files, all_labels, test_size=0.2, random_state=42, stratify=all_labels)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "V5HDfPfATq6Q"
      },
      "source": [
        "# will yield raw image bytes from tar with corresponding label\n",
        "def raw_generator_with_label_from_tar(tar_fn, files, labels):\n",
        "    label_by_fn = dict(zip(files, labels))\n",
        "    with tarfile.open(tar_fn) as f:\n",
        "        while True:\n",
        "            m = f.next()\n",
        "            if m is None:\n",
        "                break\n",
        "            if m.name in label_by_fn:\n",
        "                yield f.extractfile(m).read(), label_by_fn[m.name]"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:40.529088Z",
          "start_time": "2017-09-03T13:00:40.423114Z"
        },
        "scrolled": false,
        "id": "9s1BbCjTTq6S"
      },
      "source": [
        "# batch generator\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "def batch_generator(items, batch_size):\n",
        "    \"\"\"\n",
        "    Implement batch generator that yields items in batches of size batch_size.\n",
        "    There's no need to shuffle input items, just chop them into batches.\n",
        "    Remember about the last batch that can be smaller than batch_size!\n",
        "    Input: any iterable (list, generator, ...). You should do `for item in items: ...`\n",
        "        In case of generator you can pass through your items only once!\n",
        "    Output: In output yield each batch as a list of items.\n",
        "    \"\"\"\n",
        "    output_items = []\n",
        "    for item in items:\n",
        "        output_items.append(item)\n",
        "        if len(output_items) == batch_size:\n",
        "            yield output_items\n",
        "            output_items = []\n",
        "    if output_items:\n",
        "        yield output_items"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "O7hyCWh4Tq6U"
      },
      "source": [
        "## GRADED PART, DO NOT CHANGE!\n",
        "# Test batch generator\n",
        "def _test_items_generator():\n",
        "    for i in range(10):\n",
        "        yield i\n",
        "\n",
        "grader.set_answer(\"a4FK1\", list(map(lambda x: len(x), batch_generator(_test_items_generator(), 3))))"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "lx36hiAxTq6X",
        "outputId": "44277a50-debc-4165-f188-e639b1b4f377",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# you can make submission with answers so far to check yourself at this stage\n",
        "grader.submit(COURSERA_EMAIL, COURSERA_TOKEN)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Submitted to Coursera platform. See results on assignment page!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:40.637615Z",
          "start_time": "2017-09-03T13:00:40.530642Z"
        },
        "scrolled": false,
        "id": "Qf1DW05BTq6Z"
      },
      "source": [
        "def train_generator(files, labels):\n",
        "    while True:  # so that Keras can loop through this as long as it wants\n",
        "        for batch in batch_generator(raw_generator_with_label_from_tar(\n",
        "                \"102flowers.tgz\", files, labels), BATCH_SIZE):\n",
        "            # prepare batch images\n",
        "            batch_imgs = []\n",
        "            batch_targets = []\n",
        "            for raw, label in batch:\n",
        "                img = prepare_raw_bytes_for_model(raw)\n",
        "                batch_imgs.append(img)\n",
        "                batch_targets.append(label)\n",
        "            # stack images into 4D tensor [batch_size, img_size, img_size, 3]\n",
        "            batch_imgs = np.stack(batch_imgs, axis=0)\n",
        "            # convert targets into 2D tensor [batch_size, num_classes]\n",
        "            batch_targets = keras.utils.np_utils.to_categorical(batch_targets, N_CLASSES)\n",
        "            yield batch_imgs, batch_targets"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:41.092659Z",
          "start_time": "2017-09-03T13:00:40.639132Z"
        },
        "scrolled": false,
        "id": "2THxO3ttTq6c",
        "outputId": "cb53af8c-b07d-4e42-a43e-97a25ba406c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "# test training generator\n",
        "for _ in train_generator(tr_files, tr_labels):\n",
        "    print(_[0].shape, _[1].shape)\n",
        "    plt.imshow(np.clip(_[0][0] / 2. + 0.5, 0, 1))\n",
        "    break"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(32, 250, 250, 3) (32, 102)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6DxHiZqnTq6e"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-10T13:16:35.109044Z",
          "start_time": "2017-09-10T13:16:35.105127Z"
        },
        "id": "WAsV43iiTq6e"
      },
      "source": [
        "You cannot train such a huge architecture from scratch with such a small dataset.\n",
        "\n",
        "But using fine-tuning of last layers of pre-trained network you can get a pretty good classifier very quickly."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:41.097588Z",
          "start_time": "2017-09-03T13:00:41.094216Z"
        },
        "scrolled": false,
        "id": "tCurJ8tyTq6f",
        "outputId": "7d467ebd-0cf9-4934-8e1a-d5c9b087088e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "# remember to clear session if you start building graph from scratch!\n",
        "s = reset_tf_session()\n",
        "# don't call K.set_learning_phase() !!! (otherwise will enable dropout in train/test simultaneously)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /content/keras_utils.py:68: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/keras_utils.py:75: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/keras_utils.py:77: The name tf.InteractiveSession is deprecated. Please use tf.compat.v1.InteractiveSession instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:41.222209Z",
          "start_time": "2017-09-03T13:00:41.098974Z"
        },
        "scrolled": false,
        "id": "mE54tylKTq6h"
      },
      "source": [
        "def inception(use_imagenet=True):\n",
        "    # load pre-trained model graph, don't add final layer\n",
        "    model = keras.applications.InceptionV3(include_top=False, input_shape=(IMG_SIZE, IMG_SIZE, 3),\n",
        "                                          weights='imagenet' if use_imagenet else None)\n",
        "    # add global pooling just like in InceptionV3\n",
        "    new_output = keras.layers.GlobalAveragePooling2D()(model.output)\n",
        "    # add new dense layer for our labels\n",
        "    new_output = keras.layers.Dense(N_CLASSES, activation='softmax')(new_output)\n",
        "    model = keras.engine.training.Model(model.inputs, new_output)\n",
        "    return model"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:45.150429Z",
          "start_time": "2017-09-03T13:00:41.223777Z"
        },
        "scrolled": false,
        "id": "U86oZ1wDTq6i",
        "outputId": "3a98c75d-fc72-4186-8a4c-243a71d73bbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "model = inception()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__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 /tensorflow-1.15.2/python3.6/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/keras/backend/tensorflow_backend.py:4074: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:45.252883Z",
          "start_time": "2017-09-03T13:00:45.152062Z"
        },
        "scrolled": false,
        "id": "Tegt8Z2RTq6l",
        "outputId": "c7afbc9a-4b77-499d-eb64-948cc12d5fdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 250, 250, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 124, 124, 32) 864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 124, 124, 32) 96          conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 124, 124, 32) 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 122, 122, 32) 9216        activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 122, 122, 32) 96          conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 122, 122, 32) 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 122, 122, 64) 18432       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 122, 122, 64) 192         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 122, 122, 64) 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 60, 60, 64)   0           activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 60, 60, 80)   5120        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 60, 60, 80)   240         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 60, 60, 80)   0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 58, 58, 192)  138240      activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 58, 58, 192)  576         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 58, 58, 192)  0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 28, 28, 192)  0           activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 28, 28, 64)   12288       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 28, 28, 64)   192         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 28, 28, 64)   0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 28, 28, 48)   9216        max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 28, 28, 96)   55296       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 28, 28, 48)   144         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 28, 28, 96)   288         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 28, 28, 48)   0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 28, 28, 96)   0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 28, 28, 192)  0           max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 28, 28, 64)   12288       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 28, 28, 64)   76800       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 28, 28, 96)   82944       activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 28, 28, 32)   6144        average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 28, 28, 64)   192         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 28, 28, 64)   192         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 28, 28, 96)   288         conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 28, 28, 32)   96          conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 28, 28, 64)   0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 28, 28, 64)   0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 28, 28, 96)   0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 28, 28, 32)   0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed0 (Concatenate)            (None, 28, 28, 256)  0           activation_6[0][0]               \n",
            "                                                                 activation_8[0][0]               \n",
            "                                                                 activation_11[0][0]              \n",
            "                                                                 activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 28, 28, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 28, 28, 64)   192         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 28, 28, 64)   0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 28, 28, 48)   12288       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 28, 28, 96)   55296       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 28, 28, 48)   144         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 28, 28, 96)   288         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 28, 28, 48)   0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 28, 28, 96)   0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_2 (AveragePoo (None, 28, 28, 256)  0           mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 28, 28, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 28, 28, 64)   76800       activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 28, 28, 96)   82944       activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 28, 28, 64)   16384       average_pooling2d_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 28, 28, 64)   192         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 28, 28, 64)   192         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 28, 28, 96)   288         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 28, 28, 64)   192         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 28, 28, 64)   0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 28, 28, 64)   0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 28, 28, 96)   0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 28, 28, 64)   0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed1 (Concatenate)            (None, 28, 28, 288)  0           activation_13[0][0]              \n",
            "                                                                 activation_15[0][0]              \n",
            "                                                                 activation_18[0][0]              \n",
            "                                                                 activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 28, 28, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 28, 28, 64)   192         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 28, 28, 64)   0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 28, 28, 48)   13824       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 28, 28, 96)   55296       activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 28, 28, 48)   144         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 28, 28, 96)   288         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 28, 28, 48)   0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 28, 28, 96)   0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_3 (AveragePoo (None, 28, 28, 288)  0           mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 28, 28, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 28, 28, 64)   76800       activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 28, 28, 96)   82944       activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 28, 28, 64)   18432       average_pooling2d_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 28, 28, 64)   192         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 28, 28, 64)   192         conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 28, 28, 96)   288         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 28, 28, 64)   192         conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 28, 28, 64)   0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 28, 28, 64)   0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 28, 28, 96)   0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 28, 28, 64)   0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed2 (Concatenate)            (None, 28, 28, 288)  0           activation_20[0][0]              \n",
            "                                                                 activation_22[0][0]              \n",
            "                                                                 activation_25[0][0]              \n",
            "                                                                 activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 28, 28, 64)   18432       mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 28, 28, 64)   192         conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 28, 28, 64)   0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 28, 28, 96)   55296       activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 28, 28, 96)   288         conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 28, 28, 96)   0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 13, 13, 384)  995328      mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 13, 13, 96)   82944       activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 13, 13, 384)  1152        conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 13, 13, 96)   288         conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 13, 13, 384)  0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 13, 13, 96)   0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 13, 13, 288)  0           mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed3 (Concatenate)            (None, 13, 13, 768)  0           activation_27[0][0]              \n",
            "                                                                 activation_30[0][0]              \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 13, 13, 128)  98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 13, 13, 128)  384         conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 13, 13, 128)  0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 13, 13, 128)  114688      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 13, 13, 128)  384         conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 13, 13, 128)  0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 13, 13, 128)  98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 13, 13, 128)  114688      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 13, 13, 128)  384         conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 13, 13, 128)  384         conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 13, 13, 128)  0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 13, 13, 128)  0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 13, 13, 128)  114688      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 13, 13, 128)  114688      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 13, 13, 128)  384         conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 13, 13, 128)  384         conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 13, 13, 128)  0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 13, 13, 128)  0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_4 (AveragePoo (None, 13, 13, 768)  0           mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 13, 13, 192)  147456      mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 13, 13, 192)  172032      activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 13, 13, 192)  172032      activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 13, 13, 192)  147456      average_pooling2d_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 13, 13, 192)  576         conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 13, 13, 192)  576         conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 13, 13, 192)  576         conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 13, 13, 192)  576         conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 13, 13, 192)  0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 13, 13, 192)  0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 13, 13, 192)  0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 13, 13, 192)  0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed4 (Concatenate)            (None, 13, 13, 768)  0           activation_31[0][0]              \n",
            "                                                                 activation_34[0][0]              \n",
            "                                                                 activation_39[0][0]              \n",
            "                                                                 activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 13, 13, 160)  122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, 13, 13, 160)  480         conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 13, 13, 160)  0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 13, 13, 160)  179200      activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, 13, 13, 160)  480         conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 13, 13, 160)  0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 13, 13, 160)  122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 13, 13, 160)  179200      activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 13, 13, 160)  480         conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, 13, 13, 160)  480         conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 13, 13, 160)  0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 13, 13, 160)  0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 13, 13, 160)  179200      activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 13, 13, 160)  179200      activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 13, 13, 160)  480         conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, 13, 13, 160)  480         conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 13, 13, 160)  0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 13, 13, 160)  0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_5 (AveragePoo (None, 13, 13, 768)  0           mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 13, 13, 192)  147456      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 13, 13, 192)  215040      activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 13, 13, 192)  215040      activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 13, 13, 192)  147456      average_pooling2d_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 13, 13, 192)  576         conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 13, 13, 192)  576         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, 13, 13, 192)  576         conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, 13, 13, 192)  576         conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 13, 13, 192)  0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 13, 13, 192)  0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 13, 13, 192)  0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_50 (Activation)      (None, 13, 13, 192)  0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed5 (Concatenate)            (None, 13, 13, 768)  0           activation_41[0][0]              \n",
            "                                                                 activation_44[0][0]              \n",
            "                                                                 activation_49[0][0]              \n",
            "                                                                 activation_50[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 13, 13, 160)  122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, 13, 13, 160)  480         conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_55 (Activation)      (None, 13, 13, 160)  0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 13, 13, 160)  179200      activation_55[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, 13, 13, 160)  480         conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_56 (Activation)      (None, 13, 13, 160)  0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 13, 13, 160)  122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, 13, 13, 160)  179200      activation_56[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, 13, 13, 160)  480         conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, 13, 13, 160)  480         conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_52 (Activation)      (None, 13, 13, 160)  0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_57 (Activation)      (None, 13, 13, 160)  0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 13, 13, 160)  179200      activation_52[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, 13, 13, 160)  179200      activation_57[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, 13, 13, 160)  480         conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, 13, 13, 160)  480         conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_53 (Activation)      (None, 13, 13, 160)  0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_58 (Activation)      (None, 13, 13, 160)  0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_6 (AveragePoo (None, 13, 13, 768)  0           mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 13, 13, 192)  147456      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, 13, 13, 192)  215040      activation_53[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, 13, 13, 192)  215040      activation_58[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, 13, 13, 192)  147456      average_pooling2d_6[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, 13, 13, 192)  576         conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, 13, 13, 192)  576         conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, 13, 13, 192)  576         conv2d_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, 13, 13, 192)  576         conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_51 (Activation)      (None, 13, 13, 192)  0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_54 (Activation)      (None, 13, 13, 192)  0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_59 (Activation)      (None, 13, 13, 192)  0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_60 (Activation)      (None, 13, 13, 192)  0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed6 (Concatenate)            (None, 13, 13, 768)  0           activation_51[0][0]              \n",
            "                                                                 activation_54[0][0]              \n",
            "                                                                 activation_59[0][0]              \n",
            "                                                                 activation_60[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, 13, 13, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_65 (BatchNo (None, 13, 13, 192)  576         conv2d_65[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_65 (Activation)      (None, 13, 13, 192)  0           batch_normalization_65[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_66 (Conv2D)              (None, 13, 13, 192)  258048      activation_65[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_66 (BatchNo (None, 13, 13, 192)  576         conv2d_66[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_66 (Activation)      (None, 13, 13, 192)  0           batch_normalization_66[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_62 (Conv2D)              (None, 13, 13, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_67 (Conv2D)              (None, 13, 13, 192)  258048      activation_66[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_62 (BatchNo (None, 13, 13, 192)  576         conv2d_62[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_67 (BatchNo (None, 13, 13, 192)  576         conv2d_67[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_62 (Activation)      (None, 13, 13, 192)  0           batch_normalization_62[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_67 (Activation)      (None, 13, 13, 192)  0           batch_normalization_67[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_63 (Conv2D)              (None, 13, 13, 192)  258048      activation_62[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_68 (Conv2D)              (None, 13, 13, 192)  258048      activation_67[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_63 (BatchNo (None, 13, 13, 192)  576         conv2d_63[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_68 (BatchNo (None, 13, 13, 192)  576         conv2d_68[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_63 (Activation)      (None, 13, 13, 192)  0           batch_normalization_63[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_68 (Activation)      (None, 13, 13, 192)  0           batch_normalization_68[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_7 (AveragePoo (None, 13, 13, 768)  0           mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_61 (Conv2D)              (None, 13, 13, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_64 (Conv2D)              (None, 13, 13, 192)  258048      activation_63[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_69 (Conv2D)              (None, 13, 13, 192)  258048      activation_68[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_70 (Conv2D)              (None, 13, 13, 192)  147456      average_pooling2d_7[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_61 (BatchNo (None, 13, 13, 192)  576         conv2d_61[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_64 (BatchNo (None, 13, 13, 192)  576         conv2d_64[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_69 (BatchNo (None, 13, 13, 192)  576         conv2d_69[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_70 (BatchNo (None, 13, 13, 192)  576         conv2d_70[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_61 (Activation)      (None, 13, 13, 192)  0           batch_normalization_61[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_64 (Activation)      (None, 13, 13, 192)  0           batch_normalization_64[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_69 (Activation)      (None, 13, 13, 192)  0           batch_normalization_69[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_70 (Activation)      (None, 13, 13, 192)  0           batch_normalization_70[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed7 (Concatenate)            (None, 13, 13, 768)  0           activation_61[0][0]              \n",
            "                                                                 activation_64[0][0]              \n",
            "                                                                 activation_69[0][0]              \n",
            "                                                                 activation_70[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_73 (Conv2D)              (None, 13, 13, 192)  147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_73 (BatchNo (None, 13, 13, 192)  576         conv2d_73[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_73 (Activation)      (None, 13, 13, 192)  0           batch_normalization_73[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_74 (Conv2D)              (None, 13, 13, 192)  258048      activation_73[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_74 (BatchNo (None, 13, 13, 192)  576         conv2d_74[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_74 (Activation)      (None, 13, 13, 192)  0           batch_normalization_74[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_71 (Conv2D)              (None, 13, 13, 192)  147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_75 (Conv2D)              (None, 13, 13, 192)  258048      activation_74[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_71 (BatchNo (None, 13, 13, 192)  576         conv2d_71[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_75 (BatchNo (None, 13, 13, 192)  576         conv2d_75[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_71 (Activation)      (None, 13, 13, 192)  0           batch_normalization_71[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_75 (Activation)      (None, 13, 13, 192)  0           batch_normalization_75[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_72 (Conv2D)              (None, 6, 6, 320)    552960      activation_71[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_76 (Conv2D)              (None, 6, 6, 192)    331776      activation_75[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_72 (BatchNo (None, 6, 6, 320)    960         conv2d_72[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_76 (BatchNo (None, 6, 6, 192)    576         conv2d_76[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_72 (Activation)      (None, 6, 6, 320)    0           batch_normalization_72[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_76 (Activation)      (None, 6, 6, 192)    0           batch_normalization_76[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2D)  (None, 6, 6, 768)    0           mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed8 (Concatenate)            (None, 6, 6, 1280)   0           activation_72[0][0]              \n",
            "                                                                 activation_76[0][0]              \n",
            "                                                                 max_pooling2d_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_81 (Conv2D)              (None, 6, 6, 448)    573440      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_81 (BatchNo (None, 6, 6, 448)    1344        conv2d_81[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_81 (Activation)      (None, 6, 6, 448)    0           batch_normalization_81[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_78 (Conv2D)              (None, 6, 6, 384)    491520      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_82 (Conv2D)              (None, 6, 6, 384)    1548288     activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_78 (BatchNo (None, 6, 6, 384)    1152        conv2d_78[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_82 (BatchNo (None, 6, 6, 384)    1152        conv2d_82[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_78 (Activation)      (None, 6, 6, 384)    0           batch_normalization_78[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_82 (Activation)      (None, 6, 6, 384)    0           batch_normalization_82[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_79 (Conv2D)              (None, 6, 6, 384)    442368      activation_78[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_80 (Conv2D)              (None, 6, 6, 384)    442368      activation_78[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_83 (Conv2D)              (None, 6, 6, 384)    442368      activation_82[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_84 (Conv2D)              (None, 6, 6, 384)    442368      activation_82[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_8 (AveragePoo (None, 6, 6, 1280)   0           mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_77 (Conv2D)              (None, 6, 6, 320)    409600      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_79 (BatchNo (None, 6, 6, 384)    1152        conv2d_79[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_80 (BatchNo (None, 6, 6, 384)    1152        conv2d_80[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_83 (BatchNo (None, 6, 6, 384)    1152        conv2d_83[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_84 (BatchNo (None, 6, 6, 384)    1152        conv2d_84[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_85 (Conv2D)              (None, 6, 6, 192)    245760      average_pooling2d_8[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_77 (BatchNo (None, 6, 6, 320)    960         conv2d_77[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_79 (Activation)      (None, 6, 6, 384)    0           batch_normalization_79[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_80 (Activation)      (None, 6, 6, 384)    0           batch_normalization_80[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_83 (Activation)      (None, 6, 6, 384)    0           batch_normalization_83[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_84 (Activation)      (None, 6, 6, 384)    0           batch_normalization_84[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_85 (BatchNo (None, 6, 6, 192)    576         conv2d_85[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_77 (Activation)      (None, 6, 6, 320)    0           batch_normalization_77[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_0 (Concatenate)          (None, 6, 6, 768)    0           activation_79[0][0]              \n",
            "                                                                 activation_80[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 6, 6, 768)    0           activation_83[0][0]              \n",
            "                                                                 activation_84[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_85 (Activation)      (None, 6, 6, 192)    0           batch_normalization_85[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9 (Concatenate)            (None, 6, 6, 2048)   0           activation_77[0][0]              \n",
            "                                                                 mixed9_0[0][0]                   \n",
            "                                                                 concatenate_1[0][0]              \n",
            "                                                                 activation_85[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_90 (Conv2D)              (None, 6, 6, 448)    917504      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_90 (BatchNo (None, 6, 6, 448)    1344        conv2d_90[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_90 (Activation)      (None, 6, 6, 448)    0           batch_normalization_90[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_87 (Conv2D)              (None, 6, 6, 384)    786432      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_91 (Conv2D)              (None, 6, 6, 384)    1548288     activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_87 (BatchNo (None, 6, 6, 384)    1152        conv2d_87[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_91 (BatchNo (None, 6, 6, 384)    1152        conv2d_91[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_87 (Activation)      (None, 6, 6, 384)    0           batch_normalization_87[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_91 (Activation)      (None, 6, 6, 384)    0           batch_normalization_91[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_88 (Conv2D)              (None, 6, 6, 384)    442368      activation_87[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_89 (Conv2D)              (None, 6, 6, 384)    442368      activation_87[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_92 (Conv2D)              (None, 6, 6, 384)    442368      activation_91[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_93 (Conv2D)              (None, 6, 6, 384)    442368      activation_91[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_9 (AveragePoo (None, 6, 6, 2048)   0           mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_86 (Conv2D)              (None, 6, 6, 320)    655360      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_88 (BatchNo (None, 6, 6, 384)    1152        conv2d_88[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_89 (BatchNo (None, 6, 6, 384)    1152        conv2d_89[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_92 (BatchNo (None, 6, 6, 384)    1152        conv2d_92[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_93 (BatchNo (None, 6, 6, 384)    1152        conv2d_93[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_94 (Conv2D)              (None, 6, 6, 192)    393216      average_pooling2d_9[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_86 (BatchNo (None, 6, 6, 320)    960         conv2d_86[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_88 (Activation)      (None, 6, 6, 384)    0           batch_normalization_88[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_89 (Activation)      (None, 6, 6, 384)    0           batch_normalization_89[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_92 (Activation)      (None, 6, 6, 384)    0           batch_normalization_92[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_93 (Activation)      (None, 6, 6, 384)    0           batch_normalization_93[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_94 (BatchNo (None, 6, 6, 192)    576         conv2d_94[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_86 (Activation)      (None, 6, 6, 320)    0           batch_normalization_86[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_1 (Concatenate)          (None, 6, 6, 768)    0           activation_88[0][0]              \n",
            "                                                                 activation_89[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 6, 6, 768)    0           activation_92[0][0]              \n",
            "                                                                 activation_93[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_94 (Activation)      (None, 6, 6, 192)    0           batch_normalization_94[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed10 (Concatenate)           (None, 6, 6, 2048)   0           activation_86[0][0]              \n",
            "                                                                 mixed9_1[0][0]                   \n",
            "                                                                 concatenate_2[0][0]              \n",
            "                                                                 activation_94[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 2048)         0           mixed10[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 102)          208998      global_average_pooling2d_1[0][0] \n",
            "==================================================================================================\n",
            "Total params: 22,011,782\n",
            "Trainable params: 21,977,350\n",
            "Non-trainable params: 34,432\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:45.273005Z",
          "start_time": "2017-09-03T13:00:45.254250Z"
        },
        "scrolled": false,
        "id": "urs5AWnsTq6n",
        "outputId": "69aa5f05-8d0f-4ad6-f17a-686bc3cc7abb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# how many layers our model has\n",
        "print(len(model.layers))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:45.370171Z",
          "start_time": "2017-09-03T13:00:45.274354Z"
        },
        "scrolled": false,
        "id": "KQxkS1sATq6p"
      },
      "source": [
        "# set all layers trainable by default\n",
        "for layer in model.layers:\n",
        "    layer.trainable = True\n",
        "    if isinstance(layer, keras.layers.BatchNormalization):\n",
        "        # we do aggressive exponential smoothing of batch norm\n",
        "        # parameters to faster adjust to our new dataset\n",
        "        layer.momentum = 0.9\n",
        "    \n",
        "# fix deep layers (fine-tuning only last 50)\n",
        "for layer in model.layers[:-50]:\n",
        "    # fix all but batch norm layers, because we neeed to update moving averages for a new dataset!\n",
        "    if not isinstance(layer, keras.layers.BatchNormalization):\n",
        "        layer.trainable = False"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T13:00:45.494833Z",
          "start_time": "2017-09-03T13:00:45.371512Z"
        },
        "scrolled": false,
        "id": "S-eNR_tpTq6r"
      },
      "source": [
        "# compile new model\n",
        "model.compile(\n",
        "    loss='categorical_crossentropy',  # we train 102-way classification\n",
        "    optimizer=keras.optimizers.adamax(lr=1e-2),  # we can take big lr here because we fixed first layers\n",
        "    metrics=['accuracy']  # report accuracy during training\n",
        ")"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1WYNgDHpTq6u"
      },
      "source": [
        "# we will save model checkpoints to continue training in case of kernel death\n",
        "model_filename = 'flowers.{0:03d}.hdf5'\n",
        "last_finished_epoch = None\n",
        "\n",
        "#### uncomment below to continue training from model checkpoint\n",
        "#### fill `last_finished_epoch` with your latest finished epoch\n",
        "# from keras.models import load_model\n",
        "# s = reset_tf_session()\n",
        "# last_finished_epoch = 10\n",
        "# model = load_model(model_filename.format(last_finished_epoch))"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wWcHxF89Tq6w"
      },
      "source": [
        "Training takes **2 hours**. You're aiming for ~0.93 validation accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2017-09-03T14:23:36.792701Z",
          "start_time": "2017-09-03T13:00:45.496194Z"
        },
        "scrolled": false,
        "id": "lgJk8V-nTq6w",
        "outputId": "8c3b7a96-605a-4483-feff-4d988ede1c64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# fine tune for 2 epochs (full passes through all training data)\n",
        "# we make 2*8 epochs, where epoch is 1/8 of our training data to see progress more often\n",
        "model.fit_generator(\n",
        "    train_generator(tr_files, tr_labels), \n",
        "    steps_per_epoch=len(tr_files) // BATCH_SIZE // 8,\n",
        "    epochs=2 * 8,\n",
        "    validation_data=train_generator(te_files, te_labels), \n",
        "    validation_steps=len(te_files) // BATCH_SIZE // 4,\n",
        "    callbacks=[keras_utils.TqdmProgressCallback(), \n",
        "               keras_utils.ModelSaveCallback(model_filename)],\n",
        "    verbose=0,\n",
        "    initial_epoch=last_finished_epoch or 0\n",
        ")"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "\n",
            "Epoch 1/16\n",
            "**************************\n",
            "loss: 4.4615; accuracy: 0.1756; val_loss: 35.2807; val_accuracy: 0.0677\n",
            "Model saved in flowers.000.hdf5\n",
            "\n",
            "Epoch 2/16\n",
            "**************************\n",
            "loss: 2.5280; accuracy: 0.4338; val_loss: 5.9445; val_accuracy: 0.3724\n",
            "Model saved in flowers.001.hdf5\n",
            "\n",
            "Epoch 3/16\n",
            "**************************\n",
            "loss: 1.5018; accuracy: 0.6319; val_loss: 1.9370; val_accuracy: 0.6562\n",
            "Model saved in flowers.002.hdf5\n",
            "\n",
            "Epoch 4/16\n",
            "**************************\n",
            "loss: 1.0577; accuracy: 0.7238; val_loss: 0.7431; val_accuracy: 0.7344\n",
            "Model saved in flowers.003.hdf5\n",
            "\n",
            "Epoch 5/16\n",
            "**************************\n",
            "loss: 0.7444; accuracy: 0.7884; val_loss: 1.0616; val_accuracy: 0.8101\n",
            "Model saved in flowers.004.hdf5\n",
            "\n",
            "Epoch 6/16\n",
            "**************************\n",
            "loss: 0.6552; accuracy: 0.8325; val_loss: 0.9203; val_accuracy: 0.8776\n",
            "Model saved in flowers.005.hdf5\n",
            "\n",
            "Epoch 7/16\n",
            "**************************\n",
            "loss: 0.5156; accuracy: 0.8852; val_loss: 0.1267; val_accuracy: 0.9245\n",
            "Model saved in flowers.006.hdf5\n",
            "\n",
            "Epoch 8/16\n",
            "**************************\n",
            "loss: 0.4212; accuracy: 0.8910; val_loss: 0.2470; val_accuracy: 0.8984\n",
            "Model saved in flowers.007.hdf5\n",
            "\n",
            "Epoch 9/16\n",
            "**************************\n",
            "loss: 0.2973; accuracy: 0.9280; val_loss: 0.3315; val_accuracy: 0.9274\n",
            "Model saved in flowers.008.hdf5\n",
            "\n",
            "Epoch 10/16\n",
            "**************************\n",
            "loss: 0.2077; accuracy: 0.9603; val_loss: 0.1587; val_accuracy: 0.9297\n",
            "Model saved in flowers.009.hdf5\n",
            "\n",
            "Epoch 11/16\n",
            "**************************\n",
            "loss: 0.1324; accuracy: 0.9753; val_loss: 0.1122; val_accuracy: 0.9453\n",
            "Model saved in flowers.010.hdf5\n",
            "\n",
            "Epoch 12/16\n",
            "**************************\n",
            "loss: 0.1105; accuracy: 0.9821; val_loss: 0.2590; val_accuracy: 0.9193\n",
            "Model saved in flowers.011.hdf5\n",
            "\n",
            "Epoch 13/16\n",
            "**************************\n",
            "loss: 0.0692; accuracy: 0.9895; val_loss: 0.3394; val_accuracy: 0.9190\n",
            "Model saved in flowers.012.hdf5\n",
            "\n",
            "Epoch 14/16\n",
            "**************************\n",
            "loss: 0.0647; accuracy: 0.9913; val_loss: 0.2380; val_accuracy: 0.9531\n",
            "Model saved in flowers.013.hdf5\n",
            "\n",
            "Epoch 15/16\n",
            "**************************\n",
            "loss: 0.0599; accuracy: 0.9962; val_loss: 0.4298; val_accuracy: 0.9245\n",
            "Model saved in flowers.014.hdf5\n",
            "\n",
            "Epoch 16/16\n",
            "**************************\n",
            "loss: 0.0408; accuracy: 0.9999; val_loss: 0.2206; val_accuracy: 0.9583\n",
            "Model saved in flowers.015.hdf5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.callbacks.History at 0x7fc842b4ecf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "P4wWZ0-5Tq6y",
        "outputId": "eda57dcf-e16e-48e0-c384-b332a3b4c4f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "## GRADED PART, DO NOT CHANGE!\n",
        "# Accuracy on validation set\n",
        "test_accuracy = model.evaluate_generator(\n",
        "    train_generator(te_files, te_labels), \n",
        "    len(te_files) // BATCH_SIZE // 2\n",
        ")[1]\n",
        "grader.set_answer(\"wuwwC\", test_accuracy)\n",
        "print(test_accuracy)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9487500190734863\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "onj1axDNTq60",
        "outputId": "cea7af03-9fe1-42a9-bf27-b700701a77b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# you can make submission with answers so far to check yourself at this stage\n",
        "grader.submit(COURSERA_EMAIL, COURSERA_TOKEN)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Submitted to Coursera platform. See results on assignment page!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cfu2GBBeTq62"
      },
      "source": [
        "That's it! Congratulations!\n",
        "\n",
        "What you've done:\n",
        "- prepared images for the model\n",
        "- implemented your own batch generator\n",
        "- fine-tuned the pre-trained model"
      ]
    }
  ]
}