Lab_VI_Image_classification.ipynb

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  "metadata": {
    "colab": {
      "name": "Lab VI: Image classification.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN4SiNkUzPGbxLRWhgeWSF9",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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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/imcinstitute/ML-labs/blob/main/Lab_VI_Image_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GDfY9JadQcuK"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "import tensorflow as tf\n",
        "keras = tf.keras\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvtJUyWJQwSx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50ab478e-be4f-4c59-ec56-aa9259dd7425"
      },
      "source": [
        "#load the dataset\n",
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqM64hVGm3jz",
        "outputId": "a3cc28ec-eb99-4520-fc07-3d8a96c42c88"
      },
      "source": [
        "test_images.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGBgUhb0Qz1A"
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWTa0-LMQ4DP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3edcf9da-9bb4-43da-8e6c-dcd4a3eb4fef"
      },
      "source": [
        "train_images[0]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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          "metadata": {
            "tags": []
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          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wm7fnPf_Q8ob"
      },
      "source": [
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXSBSxHYRIjW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7f19b09-9483-49cf-e139-2d0d630e4628"
      },
      "source": [
        "print(train_images.shape)\n",
        "print(len(train_labels))\n",
        "print(train_labels)\n",
        "print(len(test_labels))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n",
            "60000\n",
            "[9 0 0 ... 3 0 5]\n",
            "10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1oqv-qrRUL8"
      },
      "source": [
        " import math\n",
        " def plot_data(num_images):\n",
        "  grid = math.ceil(math.sqrt(num_images))\n",
        "  plt.figure(figsize=(grid*2,grid*2))\n",
        "  for i in range(num_images):\n",
        "      plt.subplot(grid,grid,i+1)\n",
        "      plt.xticks([])\n",
        "      plt.yticks([])\n",
        "      plt.grid(False)\n",
        "      plt.imshow(train_images[i])\n",
        "      plt.xlabel(class_names[train_labels[i]])\n",
        "  plt.show()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXLUmf_jRoD4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "c1428b29-c8d0-4e13-88c3-dbca0847e9e1"
      },
      "source": [
        "plot_data(25)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hj/LlyfQ8BYVS5QRkGLlxwpxL+fsUPZ54dMIpPea6G+AHUpSQuNme9uGo3M43PzzX6STRL+GUzCug1bwYfxBOsp5bGiQi2qTvT3q4Pm/11sJ+JrK0BlHxsHxA1Ox1F22TvjpRyhVtK5WuQ77Hw2Jwe+2DP90AVnoAAAAAEAgw6QEAAABAIDg0zFtORmYT5qU2m2WTQ3jYMKV3Rv1yX96d41DklpyuQFsf5qy+7Vlek23q1nqz4xy+vKRrii83xLRJRB5vQ1oH7c2I7/Dlm3ee48sTE7q6d/ac04mIyL40sFWGJ/wPm2pa/0e33TWGw767j5royzs+kVR6XzmKQ/hXdoxTbd/dw6avN7vYnFgVdkMkndj0MpAVf93QzD0ZDq08rJJNc79ce6LSG3WJzkKtYfNpkExakTFcidmtni4zKEszSLkmLBdpFvMifPBoUttYslUi228V963XObAZzQc7tRvEuHAqZHsxkZZDZPmo2qw7MySyzKeG8zFiLc57W1iH5fCWYfNE+6ahAPtPpsw0Lzln3UNmWvbEYE5Zx41APCs5MbjDnYN/HWXwXyEAAAAAQB+ASQ8AAAAAAsGhYd5ylupMhC9bmrc2X3u40ju7ks0qzyfH+3JDpF3pSc/0sXHOJlwzWptppFlseIRNG+05nYFXZqh0z3VMjLM8f+ZxzkZcc8QepVcbzc9HB3FQSnbHTl+OCnl893yll7iL16s95wvVRdgUKO+9zPhJtG/0wF7CTlhQSNhU5D4jo7of2rLcZ7KPUouGFzwPYGwXR4bskxm53OTpxfSc572YWcwtMBpr4x1h0iqfaKcoHGlLvGzEMHP7JMd1h5U5Mt6sOzk5ko+f0Yl7neMN4pfeIUK0zCLdYad6bELaFkWT6x7giWdFRiR7DYM/og4rPQAAAAAIBJj0AAAAACAQYNIDAAAAgEBwSPj0uJk4vWSyoN7I5dqe2JhjW2N9iH1HYo59UlZJP3n4el/e7fjqLOme6ss1YfZraAhpf5GJUfbPWZ6cqNoe7jzMl6+9+HFfvveO85Re7JGeEGhju2jQ4NiJQ3E25qs+cXyw1qU5FD1WwlfHDZ+USN+dXG/jnwXFQuDjrQU/JiLtS0ZEZHPiOSozRHQoYG3hSur9jRHnlX4k4G3wilfSDmV4XO3aU6vb0jzOYi3Fx1y8heVMht8RWf36pIpd3H8yg3akw32InMzQYL8Jl3AGlX48rk+kzDrfSTzIQo5epcg50OWx3owJu2iwg5UeAAAAAAQCTHoAAAAAEAj6zrwlTB8mwmYlE3bmVSHe9pIi3rXEEmy5heV++NNb1PbmLKcR3ZFhWWZMJiLKiaXAF7vrfDnhmEAaIm2+3OY5a7eCdo+zOruh1vKYXxjxpi//ofXcoscbNDgmHC/lxiv3EF2xXm2v7eIMvhVhfU+bs4VjV93QdhmKXvxJ0cu18t6756mOFL72WFsJM1XYWYbPZgvrDXFcM59qKxHa3Jf72JCT7Vc+FCUKbAaSEvcjVc99WV+nM8s3dXFbaji/g92RYxrZ/cCrFKaTWv3e9tJFbKFORub2Sfz+lKMWBUbLJ1QiZF2Gn4dKmBKlGSxDuu+ke0DS49/7C0avUnqPkjaZDgaw0gMAAACAQIBJDwAAAAACQa/NW/tEsoilfrkMafugeFz3Jcer7c3v4eW5D85f5Ms7sjVKb6koClonoq2qQnqBVmaU3JbmoqWueUtmYR4lTF1uNNHWjC58KpGmtS1ZkdX53ToCrP6eoocYNBQr/Jpr61B6bcK0VB/tVm1dOV4alxEBISdlrzR3SROWqyeXbnOG+6U5q4vHjo1xmJZc4jW54ERh9RZTJe6lc7tkzUqZ4NcJmFRmrHIjwKw0obvRcuJkoQo2jyA7M5U08VXu4HfhztUjVFvtVhGJVcnvyIgTPNs9ShR7FSas2CY95mT27ox4VVfs0H3ZNQ5jsDeYY+f6cl1omWqTpn43glYiI5vlezZstRksLAa+jN46rnKd0nuU5r3dZR90sNIDAAAAgECASQ8AAAAAAgEmPQAAAAAIBL326bFlhutGxo5R25mpHL7cdDjbfLvG6BC7ee9c7cvXjP6Fatud4zA4mUFyc0bbpOdXbvDlJ1vn+HJjpFrpSX+fk6s4jLzF0zbpcREO6fzC2st9eXSl9se5c/LDvpxxbKFrMmz/bBWlov/fnH8ovT9SAw12rFfE9u74EKQ9fsw8x/9JVut1K/lKMiIs0vW1ksjMofJ4nlNBWtm4hZ4plQy22PcNGjIc1q2KLrdL3a4DLKRtS4TkGje1ACjK1jP4HVe9QbfVbeBxFunmMRJp0T6R2Xp+pyWH8ziVFdyJiMIpPkbHeJ1lX9I8iveLTOaM9tmNm7UiUhMomo7k38VHunTK8o4c+7nVhLRfpSRhuM9LhbPLd2uT8Nk8Ja73Sb1zgS/HH3656PEOJljpAQAAAEAgwKQHAAAAAIGg1+at1IUL1PaomzhUbV7tFl+eU/Gs0ksWMVOs6h6v9Lo8Xv58M61NZK0i/FiG1e1K65D1767nLMdPHH+7L//ntncovVAFr8PvybHp67LqNtLwtV836WlfnhbTRdb+0jnWl7c54eujoxwqPSW625ffW/OG0jsUzFvlcuawNb68qmucaouL8EkZ+u+autzCePuLe7x2sdwrTWIHs4DmIUukD26SNH2VMHUVC1O3Yb2T6rdYlAJPEdNPeNZhSq17Nsef5zZok0i6nu9jajgfr2ZdQunJZOedk/lc0Vb985KpkX9jF7d9hjtYb91H2bw16SuOeQsmLUXjmZzyI+cMqqgKRRfZ7a2b+Z7fhV6JNRGZkVmmE/lN+yil1/QJTl8y9mEaFGClBwAAAACBAJMeAAAAAASC/TNvGc7EfML/aE/sc2pW+nKXFd78nl5qds09e6mL6CKgqQxf2q5M8aJlM+M7fPnSWp2F8ulbTvDlU5Of8uW3ztbRYE9089Lt7iyf68r1Zyu9JZt4qfXEKVxU88iarUpPmt9qwjp9qYw26xSZLF9M6oiyQwJbnslJZrx2qYtwJIF8VlxzVkiYNmQWZrcwqcoUKmweboHR5gz3kYwoy0VL2VoOzMQ2ZJAmJ8fCUCwjc8lCoiWivJRJK1Sib2TTCPGOadxT4sRDmCKmn83v1uaHitdZziV0R8SEdb9rEj/7NVv1OGiaLX5GRFPlVt1fLUfw8RO7ZDFTfa2xFn5Yusfx+9LMn6v07NKVBJj3Hb3Yl9tzuiC2NEfJQqI5Kl4QuxQx8Ts2UlQqaMrp37EvHP6YL99DE2kwgJUeAAAAAAQCTHoAAAAAEAgw6QEAAABAINgvn57MqCra9uGeiudfqfuxavtt04m+PDHR5MuTY41K7+iKjQWPXRPSvi+zatlm+JfOCaptYctsXx4bbfHlZ7qmK737vvJtX77mM5/z5ZMe/qTSa5vCc79sFduda4/W/gD/Of+vviyz+LbkdObm4XGu7CyrqrtI3yc3S+be0FKzQYf8H4o0ipLKcafCr0xNEBd24owTOy59d6TdudWxXctQzUpR1tnNBL3DK+wnlq4/wFTBAcDG2ffK9dWxxW6f+/kBJrc2ueJV1r3KOIHCdM7Vvm1VK/leuT5TOXkbY9KPR3d6sTQPxslgbjw+fkhcRsX4DqWXbeexGWnjg7cfpv1FqpcWPm9Quaye/WyXJ7X/jAxZz5VY65AZmXMlHfEY6S80Iqz78oyK7b7868pZvux1Ff9d7G+w0gMAAACAQIBJDwAAAAACwX6Zt0IZosqdPcucf2mbp9qmVXB2YWnOeLTjSKU3oYKLdspCn4eJ0HMiomXJel9+ZLcOVRxXwbGUOzN1vrwnU6X0ukRI+M+//z1f/u7Oc5XepcOX+PLRMTZptXh6TrhKZIZu9zgrqRuS3ZqTIevabJWxfMvDIgS6PqSX+9qO7CmemtvZ66TZgwbXVFUMGabuldhHLtWGSthJpEkr5ITAyzaZOiCrk80qihZYDRg2KvrGLTgqu62Pb1coW/yAKtIWf8opQkewO0B4hy70KU1Y0U7VRJ589WS5o7MVxW+wEXpuEnWrTGT8oCS79TV5DWzmju/gi+hq0O+EQzDJR58SGTNabR8b4/vzfJd+kQ0XZieZhVlmZybS70X5u+ZmtJeZm+vD/OB88ZX3Kr3/O/k2X+4+k3/HB7L4KF4PAAAAAAgEmPQAAAAAIBDsl+0knPaoZnOP273nhGk82chLqKMT7b48r0YXiVvTxSai5d1cfHJJZJLSqwjzenVdTEd2VYnsuiOjfK6pcV34U0ZYvZzk41/fsFDpbcpyBteHOmf6slscc5jIGr28jdu6snp5NpXj25rMavNeXZy/y4LhHMm2hsYqvd1H98xHs8/RIY9aGi0RHFVutIDMal2qEGmpAqby+ZVm0GwlTFhvh4ze2reRRdk1ZXZt2Tir8sq8la3h/kT9WKLO6RwN5d43YW2nnH6N6egtEXnllfjV8Op5bIayznMSkQVjxccbtSnGTuP3rN3NJ0vXKTWKjOXfkux27R4RBFpPmaK2w4YHWVdORzA2RPh3Upq35LuUiKhBuGPIyON9o2nFucT789Rpbym9SvHe3TOHn4dxA1h8FCs9AAAAAAgEmPQAAAAAIBBg0gMAAACAQLB/8dAd3RR6qicN5v2PnaKa/uuS+335KZEx+S87tE9LW5rtfw2VHOpWK3xziIiGi/hJtwJ7Qtghm7Mcpp4KaRuyzM67I8UG4ee8GUov47G9MiVk6VdERNSUHunL4ypafbndiXPe0D7clxtbdWBlspJv+bM5ziD9jjG6YnDFrp5rdxIYDy7s/vu/lFvF1/XvKRaaHi9xPJnF2Q1Zj4TY1pwUjg1lRtcHmlxc3CTXt0Y+r6aoWq+Qj4Rb3T2U4TO0zOB3zIiFfXDiQxwvwh3hZsyWGTWc5ObkRfmemnTxUHTZubGqtC/v49OTLlw9fcQSPehGnMhpQ9bu5IvynLHpjWJfTAqgT8/WC/UgWJzie9/h+PRIn5y0eN9NieiKCfKtK6sEjArr3+c30hwu3+5xH51Up316usR5O+akaTCAlR4AAAAABAJMegAAAAAQCHqd7nfaF15Q2z957XJu++c1vnzhmBVKb0kbh45vEmagV7t1eHg0xGuolVG9LJYQZqdYuHh2XmneqArzMWTIO5EuEFoT5pBy1yQiCYtzLWqdotpGV/JS4GG1evkwK9bo5VLgXetP1sf48fNERLTBOmlSBxNG2i+KGzDahPmvMlbeEqcbIinNYqUyhRbL/uwWHJWZSFNe8QKaClv8eQgSHROLp61WJqgi4etE5WdulkUwZQFL10wjzWqVjY7tK+B0j+BO8WL6ZotE+tQ8x3l/Jng70s7HcEPb5b2vq+4WejpDfijJx5g4h81R9uFRSm97O2f090QWZ1uv+1VlBg8g06boFC3TItwRp9esUW0yNP3V7sms5wzlE75woy/X/4p/43+zWedOGRfZ4MvrMoWLNxMRTRAzjAUz1/tyawHdgwVWegAAAAAQCDDpAQAAAEAgwKQHAAAAAIFg/316Qnk7qqftq3W/edGX9/yGP3/gsguU3gn/wdVVL57yqi/Pju1UelFRxTXhOARUCTt/UviSuDO4Z7sn+nJOtD7ZfLjSa8lwyN3OLrZPRsPFfQNkGYNuJzSztZsNpeGQtpMnF3LY+/pVHNpfN4BVZw8mUScGX/rTSJ8s11dHbkt/qpxT1yJcxEHE1SsWAo+Q9bcnkuR75zlRydKPR4UYOz44sntL3fOwCEWXx3N9hDLVfILIBvj0SJIjxc133kcVe/heNdY6Y0KUjYjs4Jufc/yC4s283S6qe1eW+Sd1rF2nnehoqfRlI8pf2C79oHROZJ+hylfKO9dQYtdjE9R20wweFLIKOpFOATI6WtyjJtZR2G+xy/HZbClSi0T6WxIRNeZEKajXp/ryTNpDAwVWegAAAAAQCDDpAQAAAEAg2H/zlrd/S8dVD76ktlc8KGTi5S6z4N1Kr3sMm5zie3SIeftkbqt9i0O6QyltOvFeXV3kqjpKXHGbL5WXO5jIieCkhpLab5R51EOAMjMyL25kM+PECU2qrUvEv8pwczf0vDqcKktPLuOmxBJsZbi4DUXuY8Ol4qdRgfShMcAAACAASURBVJ2IqOYJHlfNM49Qbal6YWbqpqLIkPNQVmT+LfMWd43R9jJp7kos2+DLMHQRZav4poa79X1LDpPjQr8/wwlRMT3D41RmeCYiSo4U8h5+N8eqHJvmSE4HMmcYh6wvmjFWqVlPvHmFOU6auoiI0jU8bispeIy7+Xm1Pf0Gzv4fombV9nJqvC8XS+tBpNNCqP2TOqWMdEdp89ikOT2qzVbTo3xNh3+Pf1sHclxipQcAAAAAgQCTHgAAAAAEgl5nZO5r7MvL1XbxnK9Etc8X/hz5cgcnE2taWI5q81ZliDM0L6hY58sxpzejwn5RFypvcbRL2FASjt3koQ6O4Bsf5aXgyqltVJSQW/EwmMaTXBvfo4m3vKraWi7hAsPdI/lvqoxOzqsyN4dyjhmkiJ6M+KrdoJ+P4X9eVfD6AJGdxgWb7UZtCMqWeNGGxJiRxUhF0noiIhr3HJue132A+9IN8Bm2kE/2WEhErjp/elfWsV20u4vNI1UbncKkD7GZNZgjUXP++67x5cfuv9tp3epLTZ50yNDOGV2jRMFt8flpFduV3qiwiJwznBl6alQX2D75M5/05ZpVL9JgACs9AAAAAAgEmPQAAAAAIBBg0gMAAACAQDBofHrAIUiZVdZfWjHdlxfFp+rGVlHhPFrCK0tMz8MdYsMtty38EEzWFPq453AiKjZdx40NrxT3LwmqD88+iH73OjtVU+1v2W4vay9Hxo5RetnJXFk7NSzOh3b6qWIz++fYDVuKnlf1TJnPZVCYdjX7vthMWjcKP7UG5/kOHc1+b3YVH8PMmqb0vBWv+/LMJ8q7phF3lmi8o7xjYDRqzHPLfPmCcfNUW/Jdx/vynjn8s19xWqPSG/0E++7IBAYnPHyD0qtqYD+x6gdrfFlWZiAiqqHB4ccjwUoPAAAAAAIBJj0AAAAACATG7sfyrzFmNxFt7L/LAQWYbK0tneS5F6AvBwz059ABfTm06PP+RF8OGEX7cr8mPQAAAAAAhyowbwEAAAAgEGDSAwAAAIBAMCgmPcaY9xhjrDFm9ttrExljNhhjRhb4vFT59ELH2S/9Ese5xhgz7u01hz7GmBHGmGX5fzuMMVvFtluQXu43xRizokjb14wx5xZp2+feG2OuNMbcZIw50xhz8oF9o+CCvgw2xphcvq9XGmNeNcZ8zhgzKH4zgg7GZu8ZLHl6PkBEz+b///IAX0tvuIaIVhDRtgG+jgHHWruHiOYRERljvkJEHdba7xzgMb9U6HNjTJgK3/sLiehHRPQuIuogoiLV2kAp0JeBp9tau7f/RxHRb6kn/ZJ6RxtjItbabIH9QT+Bsdl7BnzWboypJqJTiehaIrpSfH6mMWahMeYBY8zrxpjfGGOMs2+FMeZvxpiPFzjujcaYl40xrxljvlri/N/P/yXzhDGmIf/ZPGPMi/l9/2iMGVbsc2PM5UR0HBH9Jj/Lrih2LtCDMWauMWZR/n69ZoyZkW8KG2N+lu+Px/beS2PM3fn7vHeV71vGmCXUM0lW9z7/jMwjoiYi+iQRfSbfdlr+r5wn8+d8whgzSRz/dmPMK8aYN4wxFx/se3Kogr4MBtbaXUT0CSL6V9PDNcaYPxtjniSiJ4wxVcaYu/LPwlJjzCVEhZ+PvO5fTc/q0QpjzPsH9MsNUTA2CzPgkx4iuoSIHrHWvkFEe4wxx4q2+UR0AxHNIaJpRHSKaKsmooeI6F5r7c/kAY0x5xPRDCI6nno65lhjzOkFzl1FRK9Ya+cS0VPEf8HcQ0RfsNYeRUTLS31urX2AiF4hog9aa+dZa7sJvB2fJKIf5v+KPI6I9qbanUFEt+b7o4WILiuy/x5r7THW2l/Tvvd+PhG9aq1dT0S3E9H3823PENGPieiX+f77DfX8lbKXKdTzvFxERLcbY0rUnwYC9GVAsNauI6IwEe1Np30MEV1urT2DiG4ioiettccT0VlE9G1jTBUVfj7eQUTbrLVHW2uPIKJHDvJXCQoYmwUYDJOeDxDRfXn5vvz2XhZZa7dYaz0iWkY9N2wvfyKiX1hr7ylwzPPz/5YS0RIimk09He3iEdHv8vKviehUY0wdEdVba5/Kf/5LIjq92Odlf0sgeYGI/sMY8wXqyaewd6K43lq7N5f6YtL9Lfldkc+Jel6ofyvSdhL1LNETEf2KelYY9/J7a61nrX2TiNZRzzMD3h70ZXD5u7W2KS+fT0RfNMYsI6KFRJQgoklU+PlYTkTn5VcSTrPWtg7AtQcBjM0CDOikxxgznIjOJqI7jTEbiOhGIroiv3RGRJQS6jnSPkjPEdE7hK46NBF9Mz/znGetPcxa+/MyLglJi/oBY8ylhp3sjrPW/paI3k1E3UT0sDHm7Lxqqf6WdBb5nKjn5ftYLy7T7Xs8CwVAXwYXY8w06unLXfmPZN8ZIrpMvHMnWWtXF3o+8qv6x1DP5OcbxpiCviRg/8DYLI+BXum5nIh+Za2dbK2dYq2dSETriei0Mvb9EhE1E9GtBdoeJaKPmR5/ITLGjDc9jnguofw1EBFdRUTP5v/qaDbG7L2GDxPRU8U+z8vtRMRV14DCWvtH8TJ8Jf/yXGet/RH1rNgddQCH9+99fjUuknfyU215nif2G/sgET0j2t5njAkZY6ZTjyl1zQFc05AFfRlMTI+/4+1EdIstnNH2USL61N4/Qo0x8/P/7/N8mJ4ooK682eTb1DMBAgcIxmZ5DPSk5wNE9EfnswdJm7hK8WkiqjDG3Cw/tNY+Rj3Lay8YY5YT0QNUeFLSSUTHm54QvrOJ6Gv5zz9CPTbp16jHJ+jtPr+beuyTcGQujyuIaEV+KfwI6vGV6i13U/7eU89fNY+LtoeIaO9fP6cR0aeI6KP5/vsw9Tw/e9lERIuoZ8n2k9ba5AFcU5BAXw5dKvL3eyX19MVjRFQsKOTrRBQlotfy+l/Pf17o+TiSiBblP/syEX2jH79DkMHYLADKUIAhgzHmTiK601r74n7udzcR/SXvlA4GAehLAAYnh/rYHCx5egA4YKy1/zTQ1wD6BvQlAIOTQ31sYqUHAAAAAIFgoH16AAAAAAAOCpj0AAAAACAQYNIDAAAAgECASQ8AAAAAAsF+RW/FTNwmqKq/rqUoJhZV25m6mC8nRnCYfzoX1npJsZ/01w5r5+36yi5fbumq5GNv1ikErOeVfc19RZI6KW1ThbJOHxAD1ZdBp52aG621DX193MHYn149j6Vwd0612VTKVS+IqeDSPJkqHt+RxlLJYg8OGJtDi/4Ym+jLgaFUX+7XpCdBVXSCOWf/zu5WiehFtFhk3ES1vf2dE3x55oc4oePm9nqt9yZ/55B4N+Xq9Av4kmOW+vKfls3z5dk36GSRXnt7eRcsv/MBRse9ZJ84oP2L0au+BAfM4/aBjf1x3D7pz4IVXajXz3D3Wcf7cvWqPaot98ZbZR0jdBiX5tl18jBfHnnHC726pr4EY3No0R9jE305MJTqy/7J01Pmj35kwnhfXv1vE1Tbu09Z7MvDIvoFuTO925drIrwa880Jf1Z6U4+qLnjeDk+v4DzcNdqXs0fxX5MNz+pJzuqOMb78yoszfXnWt9crveyOnQXPC8Cgxghrt5crqhaeOd2X37hO/zH16OXf8eXp0WV04PAxUjbjy13/lVFaJ9/5eV+e9NXnyzt0SK8Ml/rOAIChAXx6AAAAABAIMOkBAAAAQCDApAcAAAAAgeCg194KHX24L7/z3md9eUSr9p9Z1zHSl7uzTvSWiNLqTHMk1wMr5yu9yiqOEMnleH6XTuuvHY2yLX/S8GZf3hQZpvSqI3y8c0571Zd3L9C+Qzt/eZIvj/j5wDtcAlCQMn1aTn41rbavHfZLXx4eiqm27eIQC7t5zDWEdbTV8tQ4X16dZPms6tVKb1yE3wvbsjW+PDqsfXoWf/wHvvzaR/h7Xb/8g0pv1CWv84b7feX9gH9PcRyHdxPm+2Zz4r6VcoAv5jT/dvsVIfXOBWo7/vDLfKrjjuBDL155wOca0vRxMEO57Pg/nhc0/LBCtYX/scSXQ5UcEep1dVFvwEoPAAAAAAIBJj0AAAAACAT9Y94qsRTW/E1eln6hhUNf17cNV3qJSNaXPauX3FLCvGUMn0uas4iIUin+ellh0opE9dJ1TSWHsEtTWiqnb09bihOlhUO81F4V1cv/h32M8/u0/UGbyHLNzQTAgCGXr0uYcA5fzM/+jSMWqbZnk/xM14f1ErNneWm6PtTty0mrTWlnVGz25XMrt/jyNifBaIvH5rPR4Q5f3plzTMriq9SEeDwvXXCf0jvr75f4cuw8J5WHvB99mGsrUJR7r3pxT7suPUFt7zmCn5XkdP3uP+NL/H4O0QZf3na2Np301kQyKCn3mS2lJ7elXpn59kw8rtVEElJ7CufAe/+djyi9a+s4NcVZ/3GJagv/Q2z0QYJgrPQAAAAAIBBg0gMAAACAQNDv0VuRaVPU9pEjtvvy5k4uG1EZ1dEYqSxf2vCEXoJsqGDTV8TwclfW6jlcWpin0h4vhdbHupXe2EQrn9dj81Z3TkeNpTw+3s5uXj6VZi8iotEJjjhZc9XRqm3UrWVmiwWgPyix7N30MY46/O6YW335ke5apRclNgPVGD1uMyKrszRL50gvj6/LirpcojBe1GiTm2xLCROZNHUREWXE329dYgz/ubNS6f1u9m99+ZKrPqfaan/7Im8E0aRVbskg53ObzRbWc9jx6ZN9eeyz/M7delad0vvQR/7uy881sQvEv024U+n9ejcfb+GKWaptyxcO8+XQU0spEBQzTZXQM5ESUwARlWdiOkpTlWQSUY9uTb3uS7gUzY9+8GNfbrPaDHZ7C1dnqPhnbeKWbwSvzJp9pcBKDwAAAAACASY9AAAAAAgEmPQAAAAAIBD0u09PdpT2Bziljn1anvRm+3JtRNvqxsVbfLnL0/bE4RHO7poRdv6Q0eFs0j/AE/4+8ZD2QwgT75exfEvc40l/HxJfa1m7rhBfGxGhumfqTNN0KwFwUJF2+1L+Fy9/4zZfXpxivWmRJqW3Kj3Gl9ut9rerMiLVhPDjSTi+OjEx5lx/n2JIvZR17f7cJsdtrQhfJyJ6PVPlyy9853bVdtELHCqbXc/h7Caq3z82o1NUAMHxR/qijeo+Sp/K78I35rMfZE29TuPxiz+e68vjF/K9vvkfRym9zDkzfblyge6jUIp9vkLz5viyt2xV6esfKpTpk1bSH0u0ub46CpHqITzrMNX02x9/z5fXZTnNRMLxBbz7v9/ly3Vvvqja+jp9BFZ6AAAAABAIMOkBAAAAQCDod/PW7vlValsua51c95Yvu6GqUbFM3pjVJrJnRRjjq5vYtBTepEPHI528LBYWq3PRTr1EJq1duTjv0zJXL/19+ozHfHlXmq9pZtUupTcp1ujLz1ROJwAGkmJL2NnHJ6nt1Wk2PW/IsAnrPVUtSm+VsO5kHDOTLivKxOyBZ1KVuCYxuZ20bIZ2l9E3ZTnz+67cNtW2/R1c+LThNjZv2aw+xpClTNNBuFa/j1sv4GKRVVvZnBhp0k/D6Ls5RUnmU3t8efsOnbV+xpe4SHNk8kRfzjrXl1i63pfNcbNV26YL2JQi3/3jl1EwKLOYsEtkCr8TsqM4lUCqQf+27jxOVC4Yxce2Yd1Hr6a5cPjT7dxHMxM7lN6IZ7fyecu60t6DlR4AAAAABAJMegAAAAAQCPrdvNVw2wtq+57Hz/LltR8d7cvxw1uV3vj/EVkeX17uHHW3Lx0mZHfZ1dTwEqet4kJzXq0uOper4KW6SDuvhY66VXv6/414efbYpbxcf2rVG0pva5aXa88dt0a1LcY8EwwSvjn9waJt9WE2TYSNfmal+chFRkkqE5QToCUzLfcG17wlzyujMd1rrQ9xtNmIkH4PNM/nhfUG2RCQ7Mxudl6bEyYRmcV3mM6gHElyW+PRnAG77TTdR2vP/Jkvn/zZT/ryjPucaB1BduPmom12gvj9aNJ91DWOz33hFfwb9OrTOkO+ef7Vosc/lDFRpy9T3Jeho9kc6X1PRxdPqOHf061dbNb9l/FPKb3HW+f68qcbuCLoJ968Sun9vfUIX64TUc1NOe32YqMHPhVRz28JizR+gQEAAAAQCDDpAQAAAEAgwKQHAAAAAIGg33163rj9eP2BML2OfUrYiZdpf5z0MLavX7lah4RLm/1byVG+vKpN2+i3trNPTyorfISc8FljOMxydA1n8rx2wkal98CuY315yT+xPXlZqw5Lt9t2+rLXpTPWBp5eVv8t6l/Q22y5MqSzzHBOFxPnSsE2Lc57iPiA7Mhq34z6GNvztS+MW/mcx0+7p8dcTYjt9p0e35+EkwVd+uCkRdh7uMys6mknVF5SFWK/vD25atUmfZW25/TY/OW57HPy3zSv6PGHKmqMERV9jm2HDkUX3UIdp/M9HXefrqR9wVV8T2uouB9PuaRGs19Iula/V0Yt5t+Ph9Mn+fKYhM4snBjPaQpoywFf0qChVAZl79XVvhy5epxq27C1W2yxfCvNJA2P53+mU3355vUPKK2GML8Xv7OL/Xnvffh0pTd1LftdlXr3qzbH17Dcdz9WegAAAAAQCDDpAQAAAEAg6Hfz1vjH9bLjNl7hosZLePns5uN0+Ozn/vohX77nP9+l2lJ1PFdrE5albJWzHCs2bYQ3bFTrmTRfY6fHS/7f/v2VSi/Wzvs1f4GXeLOZeqXntbDJ7YtnP6Ta/nQ2F83LbtdZKQNBuaYfZ+mSbOE8neUuaW75j5PV9o+u/akv3zz9SFe9LEoW4RukeKfN9+UF8WdV25uiIGBDmENZWz197xsibFra7WRLl5nUpYks7JiUZWFfGX4uTVhERDmx7Ym/0UKkjyfNYtKs5prLjoy1+XKLp99NXZ42xwSOMsdmbo8uQFvxp0W+PPVPxfcL1dT4stfBbgQlz1ui2GTHOH6+4s26LdbC5pdJf+Y+755Uo/RSMznz+FAyb5VLdqvOSi7N/jLsvdx33b+s1iHrTx19ry+vbedEEDNP2qD0pPG7VBHUUm2RMk2VWOkBAAAAQCDApAcAAAAAgQCTHgAAAAAEgn736Tn9Jl2GoiPHdvPFjVxB965tpyq9q8962pe/fIUuB6GO53G4eZPje5C0bA/OCbnL6q+dEGGxdSGWJ0R0uOvKNPsg3bTxPb78ZuNIpZd4jSvS3rLuPapt7PbnCQiK2OzL9dXZ9S/aV6flSH4GvnP2fb68I7tH6b3SNc2XGx/icMyR79IlRUoRSnA/v/l19pWZfuMLhdQHBV6U/85JOH5T0p9mYoRt+CmrfV9kCYmacHfRtpgYV/uUnRC+PyFZQsLxwZH7pUu4fsjQdhUq71RZ7/T4IEnnPfCOSv7O3y9+KuAgw4hVeHG4eFoB2VbKT6MU3Q38XEY7nIdDPtph1kvX6GsKZQ+N9BL9hptCRPjelfLjkalC5Lva3qsKuFB8nijxFOJjXz76FaV3b40ojdGuS2MoTmSf2NHf36CaXt0pUnBcUvwQWOkBAAAAQCDApAcAAAAAgaDfzVv3P3aK2j72VK46fuP0x3z584vep/TeeoTND/c06OyNVVt4riYTs3rOt8lV2IJ6LibLS3yiECw5SWQpI6xdyYm8pLf2wjuU3kfHnenL90x+WrWdu/hjvhxeuKT4RQ0lSoSdFgtXNfPnqu23ruTQ6GnHceXlhbO+q/R+3camqsda+BibO4cpvQtHrfTl3x91ly/L7KJvx7brjvHl6cdsKnu/gWTXcWz6qQ7pEG0ZOh4Vfdbq6T6SmZynRBtVW5uXoEK4VdGLhaK7VrCwzMhM0jSnB6e7vRcZQk9ENDrMy/LrUjqb9KZsqy+nLzjOl2OP6qV4oClmnnI/t8Js4WbdVZR6XwiyXNCdTn7fMtX2jyc4+/Nhd/N5Y+0663Sks3emtSFDL7PH75O9O0/9r7Rp/7Wvs/vJlCp2MXgjOVbpNV/C7+qajdqsdu2d/ye2uErCkXEdbv9vH/yAL68oct1EWOkBAAAAQEDApAcAAAAAgaDfzVsVs1rUdnOS1ySfEaaIqpf1UnP3CZzx+KIZOnpLRpnEXRuUICNsWnKfkBMhEjK8xBcP8XJn1tNzwiVNHG3W9gBnf/zGgiOU3qLNk335yB06Q+XEJWt9uXdlLg8yJQpzyuglL5mkopRYQg2P5uzVa74z3pcfPPV2pbc1xyaVhW3s6f9v285WetVhXhptiHHW13+sm6H0ukaymeOdv7rRl6eQXp6NTOY+X3/1RNX2ynU/8OXLLvqIL6fPPlbpRZ5cTIMFGYgVNdrmK7Mkt3vlLXu7Y0kWIB0R5vvvFgiVBUjlOJVRWPsgLsk1l40QRUZfz/E7ZlKkWenFDUeTdDoZmIeH+Pu3/Stn8R35aPFLOuTYJ1pnYKKXpOmrlKmrVGRXQgRk/n3VHNU2ep4oUt3C5q2Ww0YpvbFPdVDgKNN8qPT2yZCvx30x/tbO2e6nVXBB4yMTm5XeN25e7ss559gvCmuXfL9cv0b/tlasW1/WNWGlBwAAAACBAJMeAAAAAAQCTHoAAAAAEAj6PyPz+HVquyLMod7vqHvNl1/YcbzSa+tm23t3Lqbatnaxf4fM8pjK6q8TDbN/gPTPsU6GWSN8ekYm2JeoKxtVenPruSr6y13s0zM1vkvpzRnDetOrdUjviimzeOO1NhqUCFuuCbHsmnFL+vEIOi8/wZe3v0dnWv7babf48pLkBF++dZf21enOcV9MqWRj/lHVupzurgyHtu9IsXz1nEVK76XmKb581bue8uULrlqu9Hbk2Afrtk1nqrZLJ53ky+Hqrb6caKlTeoMpKDZawoVBhne3eny/26z2fZF+PDEqbtuXemHHB6c3yMrq0rZPRFRpONxchrYPD2ufvzcy/E6IGX2MFpHRvSZeXkbwQ44B8uEpRbkZmb0z5qvthqVdvjz6dh2k3PShBb6841L243F+SojWlOcHMqTozTPg9c4D9ckjq3z53BXsW3VOhT7eMV+73pcztfpd8ePr2L9zYoR9hPcs1GHvEwg+PQAAAAAAPpj0AAAAACAQ9Lt5KxLSy1hNaV7uSlpeQo+1ab1oBS9LZ62em8XEMWNhUbjQSecqz50V4blumG1WhsyKfaqjWk+Gx1fuLr4kO7tmJ+uF9TJ51yQ2uSReo8GJLPxZ5tLzpi9x4c9/vfIh1XZa5Q99WYYwEhH9YNc5vixNWCfUarOoRIZWe86zIUOesx7367LWCUpvUpUOZd7LF9deprbj528QW9qU9ta32bx153t/6ssPtcxTequuFeG0Swue9qDxkX9+2JdlsV4iok5vuC+PCLHp4OiYLioqQ8xluof+RhYwbcpp07M0Yg0XofI1TqjtuhynVR8T1ublbaIY8sIjOAvsBUabVQajiehQQxUpLfGOWf9NHmOZYfo3YvYt3H/bPqXdIxJ7uI/G3M9VALKzJym9ck30gaFIOLssMEpEZLOZgnpuSoQHN3MKkLUZbrtg3AlKr4GKF2lu+TinoJCZ16f8aqPSK9eNACs9AAAAAAgEmPQAAAAAIBD0u3nLzbAql8OlmSLeqJcZExW8WJXxdDZXabbybPGoENnmiegRd6bXLaK0MlE+V4UT+SEjxRJb2BO9MVur9FKi8qnM8ExElK4VRROLXvnBJXfWMWp70/m8zB8+jE0FFU5Ey9GjuODbgsQzvryma4zSe6qJM29PFUXniIjqI2xGOayCz5Vzeml7ut6Xa8L8rLimyqSIOpL9l3HMYI0pNnM0pXn59L+ma9Nc+C1+XidHtDnk4U7+Lr/azea90XGt9/onRaXa62hAeX8NR7k0OYFXMoOyjHr6Y8c0pTdOZDkOOyZlN1Nyf+H2e4sYc1OiTb5cGdJmMHl9cefdVCmi1x7sGMENMGf1OdKkFZ47S7Vt/Br3ZUWYo3UyjVVKb92VXES4bq3zHIpu96ZypG0o5RRB3Y9rHjBkNG1Y/Ba6WZIF+xQE7WX0VdHjFRkTC5bq+3v1unf5cufpu131gshM/0TapPXHVs52n92ylXoDVnoAAAAAEAgw6QEAAABAIMCkBwAAAACBoN99elykj4usvBzZpLMa1yS0/bYY0kfIDW1PCN+fiKhpLn1ziIjCwj8gLfyHXH8ciUly6VfXv0Blifb0LfbCB8fn4e1Ij62izf/U44dyzDt1Ffsj4uzbERZZcNuyOoNtVYTvwU6R/di9H+MqOFuuW7l+c5Lt8mttgy8nHH8qGX4+PMZ+QO65hkW5TaYYaIjp442IcuZt6SP0Zkr7I8m0CssdD4Auj8M4R4p7NiWhs3APNOEZ7JMzNrLMlxentI/WuDDfO+kjk7b6GZah466vlG4LC1kfo8qkC+7jIu+/fF+4+zSJyuqzouxT1e74MuzOcnbeGdFW1dYpns2Lhe/ZHaR9mgYjbqXyclNN9Pm5hZ9JqEL7ZuTahK/b8Zy6wvvfJqXXtY4z7Y4Zz/5jYz7+uj5xCV8rmb25dSb71A17QqfCODBPl/2kVIX7UpXPe5FCpM8p4RPU+QiPjz+srVRtEy9f4ar3ENJ+uvL4JqbD48dFeJz+YTWnA5lGy6g3YKUHAAAAAIEAkx4AAAAABIJ+N2+VCimX4a7ZHTtVWyLCmTPdY0hThzRvpHL660REmwxZ93LF53pJEesYcUwn0tRjq3jp9g0nRFuGYbvkBkmcenx3iqb89E0iItr68mGq7ZVTRLznbDbbzBuvQwQnV7AJYE4lh69XhVJKT4aRy6KWREQLqnlZ84TEZl/OOKHPCWHGrBNLo5VGL4VGjbNsmmdTVlfa3JzlZdgWj+VOTxfXlBmfdzupCeqEOWhrikPqm7PaNDvxb+I6Cl5d/7Lj3NEFP086Jqd6Yc5tzfL9b8zUKL15Cc6E6hYjzYn7JU1apULZ+yLMXfbhthybK6WJjYhoWozN6JWOuWG3TKFhdKj7YKek2cM1q6gdDzxoW55bgtNJsgAAIABJREFUmrqUOYu0mXXtZ0UKkWd1luSGY9k8XHvhW727KPHqli4FtnUAizy797qUSasMzAKd3X7Ntex+MOe/t6m27GadTd6nhJkpVMXvMa+zU6m9+WPOqHzecC4tsOEdOnt7UUqYy9zw+IQwZZsNFa46o+5ncTWs9AAAAAAgEGDSAwAAAIBAcNCjt8qlThQ5dKOypElLRkqFHXOURJnInNVeuSTvWT5GR1Yv3ctipLkqNqss3KjNQ1fNfMWXW52IpxLWvoNPqOdiKl56U308+dHWQtrUWqk985+du8CXm2dzhET7ZP0lk2P5vtm4s6wpVUNiTdLTx4jsEWbHTpFVVwd+ULyFj5FoyQk9HakU7hDRd+3Fl2RtQpjPSpkJtrHZZE2LNjVU2EXF9zsIhDKFP98jim8SEUVjfI9kVOScCm3WjAnbQbtjPpLmy1LRVjJTe6dXUfBzF3kMj/SXahHRW7tzNQU/JyI6Os7fJeGYQjutNpUOGfo6o3SJKKRSZra1X+fnLbeD+yU6V5uchl2k30e9wXh8TcmRwrXhYBcYNYZMPJ4X9X2zOeEukeGxJ01HRES/uug2X36q43DRot8rt9Yt9uWFZ+jfpN8frl0wfFwzk7hGadIKz9LHu+ncP/nyA1eeJVpWK71QDY9Fr71dNJQwq41uUE0yQnTcs6XMuOWt4WClBwAAAACBAJMeAAAAAAQCTHoAAAAAEAj63adnc/cwtT0mwfZbN3xZMiLO4cDt2eJhxNnibjy6srrwUQg58WzRIlXbZfV1dz8bYr3UFu0bUTmb7bPNVvsU2MIR1Qcdm81SbmePH0q4vk61RaZNYb1QcT+W0C6ugDxiLYdEjqxyvrOT+VdiIuKGSN+DsL5RtlLE+ot9bFz3kRfjtlylyOZbq/WyY/iZStdwuLnnRCpLfxgnuTZlK/neRNuH86Vn9PNVu174ETz7AB1sRj8iAuW/xqLn/M2TEf5sMhOyO047RZvrFyQrIsvjV4a6HD0ec/JcbtX2Yn5B8jwu8nornfQJNcIHsMvxdfGU72CJF8tgpISfTXg0Z6H2Jo5Sap0TOSy58o8vlXeuMn2E1v7gRH2JOe6LiXN2+HL8/A1lHc9Ei/tcSZ8YIiIb4fuRHDGAtdStJZvq+d7lXsXsIzar7VMS/FzmaI0vx5x80s91T/HlEyvWq7Y7rr7Ul+vveaHk9RZiyq91yPs3XrzIl2e+uthV91F+PGWSmjRcbW8VqULiD7+838dzwUoPAAAAAAIBJj0AAAAACAT9Yt4KJdgU4WZTliGpa1NFwuhIF7PszBZf1pRL0pURvcSZFvYIad5ykcUt5T45pzimNJfZKLdVbdJ61aKAZcqxl3jRwRSz3kOuxQlRd7eLIMMRTVz0kRu2Ws96tkL3pRcr/AjaiL6n0sxmStg0bZj3M2KpNtain43KDcLcYmS/OmY1eX3ueeU1irZQuzbl5NbqpeaDzZbLJxf83A3nbvH4OxwvzIbPJbscPd7PDUWX2bhlKoikMw5ahPkoKpbp3ezMCWFfjJXQqwlz2gGZOTvhxOsnRF8nbXFT2iFn3iphcuo6lvu/bZIeb/E2YQar1RnH3YzK5SCzLh97nA49HyYKBW84vszMvRKr+8TN3KsQqpkJxc3r/Y1XX0ndZx5PRPve+7F3vcp6Ijz85BG6KKrk9dQ4X17ROV61NabY1LylZoRq+8xN9/nyL+4p/D5wyT7OmbI/1XCfatv4n2yC6usSqKlh+l2xLTusiKbGSFeMEo8GVnoAAAAAEAgw6QEAAABAIOgX85YVS62ueatCmJKe3jNDtOiCo3FR/NBzlrLdDM17CTl6xSK23P2zolCpzPDsXrtcok/X8T7D1+gldLnE7177oMrIfIAoz/xSTvo7SrSVSbm3zRSRXUqsfh4w/Xns3hA5p7Hg5+05nS28yWPT41Tx+Q3f+Bel9+evfNuX60LaXLk+y99eZlJt8fS5ZMSWNJG5Zitpvk6LphEhbR5pEOatmZUckfTRTacpvfdMesaXV6eLFwaWRKbogpjZDQehbOxeM1y52ZRLRG/JiBed61bTJ8/tHWza/9BoHSV0yzVX+LKhZft96H2yPZfIkC4zMh8znQvk7n8s0YGRixtqndbzW/H0576r2h7/FBcC3pge6cvnVq9SepvE9+4QFasvrtf38PxK/h1KWf2bJAvo/vvt7/PlWT/ThUST3+TtX8z4tS9/ePXVSq9qa3ET3IHSMVa7GKxNFi6Y7GK98sYKVnoAAAAAEAgw6QEAAABAIMCkBwAAAACBoN8zMnuO/4wMWX99J2cHnez49Eg917dGhqZHRGX1eFjbfDNe4fTHIacau/IbEPuUCnNP1rHeiGUtqk1mhHWzP5ftnAJAH1ER5edxfabDlyfG9ii9TJF04cPv0r4ZJy/4rC/fct49qm1ahMvez4tz1usnuvWDPyJU2J8m7fwdJsdmm8f+DFNjOgw5JXxYPrf9GF9ecccR+gTfYJ+ejHMu6WdEoor7pismKL1xNx8En579rYxeSl/4vsQXav+I00ZwWPlvb7tAtY269fmyTv3Wdznz8uqZt/ryzL9dp/RmPvdKWcfrC2RG5pnVu3x58UH+Oz+6s5PGfL/nPt501Zmq7f+NetKXj4xv9+WkMw4Xdk3x5QlizM6JNSu9xSn2r2sI6/ERIvYzXf/uO7jh3fp6F6X4ud8pfP4qv67TGeiDi+t1q7b3grQuEEBrO6UnWhMVpcxzY6UHAAAAAIEAkx4AAAAABIL+N29R8YzMmS1VrrpPS4azvq5tGqna2jt42c3LFbcX2ZyY04V4+de4ZitxCBkFGY1pc1m9yCiaqRaKa/VytyyamHHMe27RSgD6G/m0T41y1tZVmd7lUp15/SJf/hHNLmufUJUe66HhIsuqzKTqhp0Ks41Ncjj0dxu1aU7D5uvh5BRX/AaLbnFTmWpiV45Dd8dcoAtA0s0lTt0HePVV1HX2CUREFE5pU3yslc0PkV0iY3Jbh9KzXRzC73VwW1sqofQ+VMtZgVs/rjN0L/3LFF/ObuR70P5+XUj0gff+0Jc/upFNZLP/dYXS6/Mc1yVMejKDe0tWfq/kvsoHiee2TVXb3x/HffHXLrbp1DjpGE6r2ODLMqH/xqzur+Eh/m4599aI/V5Ls16Tk5WdiE3Sz3bO5N2fK5FiwO5/z4acotQy/UmmTl/8mkZ2gxklzFvuO0VmtS557rKvEgAAAADgEAaTHgAAAAAEAkx6AAAAABAI+sXDxAjHmH1CtgXRjuL+OPVR9p+pjOmU2ukEX/aEeg4XT+X010nnOJSuVKS4DE0PizIUjR3aZjg2wTb0l8aIUhuOLbE+zNuy7AYRkVNsGoB+p+4jwt9jKYvjw61KLypSOaRs374a3DFSrv29r/l9B/tOnJzQfjAr0+zvNEJUZ9+4SIesTyXHx6ePycWJ2qb0vLs6JjlvrpHsh1VVw/4XmYx+VyWbhe+Ox8cw23RY7xl7uMRIZKU+RvxilltP4LDhs2ZqX53PvcVlDWKf53voJXU5hVAl+3F4XeWVAOkt4U7uv8eemufL0+nFfj1vKRpu1v5U0fv59+nCSg4/DzlrEZuE692aDD+/LTndX5kwP881Ie27VCOe56jwrkoY/fs0OcL+RP/1lTN8uZJeUnoHGqZuSpQQycX1nKGlkZ+pUeJzEy6cYuPtwEoPAAAAAAIBJj0AAAAACAT9E0AdZRtOZ1ZXYe4SlZxLVRz/3SOn+nK2Vi+fxRt5WWt9mDNFmhKrbDLJ5T7nlSHrIvrOZLXi/W2c6XXC4uIn6/R42TntxKgXKRAPQL+R28kZad95Dpsibnjo/5TejCgvsS94+WO+PJZW9+7EYgncXYo2YR4ItlQ24SKVk23OGX9yiV0unTvH/o+X3+vLr53xU9U2Pbrbly9ac6kvT/13J+y9n5FZfMslMn6c2k5P48zLyQZ+57ZPiCs9a3i7c6IOPU4cz8/DzGo2nTzzjyOV3mF3833LrdEmLUl/m7Qk4U7OSPynyzkD8Wc/f9JBuwYXN+z7gnFsdmv7AKcBOP3ftAnuW6N5v+lR+dy3UXFib7NdmI9v5pQDlX94qYTmgbHP+BXMP26t2l69q3CV9ZLvjRLgJxgAAAAAgQCTHgAAAAAEgn4xb4Wq2as87BT3VBmZ64pncpz2xYO7pNyXeGIu6UavudkmATiY5FZzgcn6sDY3yGzN80Zv9WVdCpgoXM8RJLmWViqKMDlZJ8LDZlzlvsNE2LxuM7rwYmI5Z3PvON2NXGG59WcTfbmWttJgJ7t1m9oOiW2Z+9bNv1suKqu3cz8OvMRk35NbucaX3/XnG3x5hhuFNEiovZdNWsvu1W0XEJvBzLFzfXnnCboyZ8sRHOZVPVZHJo6v43FqhX/HWzt1tYPpVxXJvOxGWx1gYdFSps7tP5qutie/xtnX5Vltt85cXS5Y6QEAAABAIMCkBwAAAACBAJMeAAAAAASCfvHpyW7f4ctvvLVAta3dzjkVG14uMecqkbGxVHXdwcBnH/2gLw+b3KzaRi4b3NcOhjhiXH38h59WTYkmfjart7IvTIQWKz2vs3e29INGiarPid38HXfkdBh9i8cZc02flwQHA8WM/zc4/Xh6g1280pdH6WGpshXvs1+Rz6fTljJP3Me/WyWOV32/7q9i3kM2my3SUhqs9AAAAAAgEGDSAwAAAIBAYPYnq6ExZjcRbey/ywEFmGytbXh7tf0DfTlgoD+HDujLoUWf9yf6csAo2pf7NekBAAAAADhUgXkLAAAAAIEAkx4AAAAABIJBO+kxxuSMMcuMMSuMMfcbY0pmUDfGLDTGHJeXNxhjRpbSBwcXY8xNxpiVxpjX8v16Qh8e+0xjzF/66nigNBibQ5f+GKey/w9EB+w/6M996Zc8PX1Et7V2HhGRMeY3RPRJIvrewF4SkTHGUI8vFDJ5lIkx5iQiupiIjrHWpvI/erEBviwiIjLGRKy1vUv4EFwwNocgg3mcgv0H/VmYQbvS4/AMER3m/kVvjLnFGHNNqR2NMZ/N/0W6whhzQ/6z/zXG/IvQ+Yox5vN5+UZjzMv5mfFX859NMcasMcbcQ0QriGhioXOBoowlokZrbYqIyFrbaK3dlv+r/6vGmCXGmOXGmNlERMaYKmPMXcaYRcaYpcaYS/KfTzHGPJPXX2KMOdk9kTFmQX6f6caYY40xTxljFhtjHjXGjM3rLDTG/MAY8woRfdo9BtgvMDaHDsXG6Zfy932FMeaO/ORy7zj6Vn6cvmGMOS3/eYUx5j5jzGpjzB+JyK/yaoy5zRjzSn714asD8SUDBPqzAIN+0mOMiRDRhUS0vBf7HktEHyWiE4joRCL6uDFmPhH9joiuEKpXENHvjDHnE9EMIjqeiOYR0bHGmNPzOjOI6CfW2rnWWoQg7h+PEdHE/ED6iTHmDNHWaK09hohuI6LP5z+7iYietNYeT0RnEdG3jTFVRLSLiM7L67+fiH4kT5KfBN1ORJcQ0SYi+jERXW6tPZaI7iKi/xbqMWvtcdba7/b1lw0KGJtDjmLj9BZr7QJr7RHU84N3sdgnkh+nNxDRl/OfXU9EXdbaw/OfHSv0b7LWHkdERxHRGcaYo/rzCwUc9GcBBvOkp8IYs4yIXqGeH7Cf9+IYpxLRH621ndbaDiL6AxGdZq1dSkSjjDHjjDFHE1GztXYzEZ2f/7eUiJYQ0WzqeaESEW201r54YF8pmOTv/bFE9Aki2k09P2LX5Jv/kP9/MRFNycvnE9EX8/2/kIgSRDSJiKJE9DNjzHIiup+I5ojTHE5EdxDRu6y1m4hoFhEdQUR/zx/nP4logtD/Xd99w8CBsTkEKTFOzzLGvJQfd2cT0VyxW6HxezoR/Tp/zNeI6DWhf4UxZgn19ONc0mMY9CHoz8IcEj49ezHGZElP1BLUe+4nosuJaAzxD6Ahom9aa3/qnHcKEXUewLkCj7U2Rz0TmIX5wfaRfFMq/3+O+Hk0RHSZtXaNPIYx5itEtJOIjqae5yApmrdTz/Mwn4i25Y+x0lp7UpFLQn/2HozNIUqBcXod9fwVf5y1dnN+DMq+LTR+C2KMmUo9q7kLrLXNxpi76cCeE/A2oD/3ZTCv9BRiIxHNMcbEjTH1RHTO2+g/Q0TvMcZU5s0jl+Y/I+p5mV5JPS/X+/OfPUpEHzPGVBMRGWPGG2NK1XEDZWCMmWWMmSE+mkels5Q+SkSfErbm+fnP64hoe95R9cNEJCtGthDRRUT0TWPMmUS0hogaTI8zHxljosYY+RcN6FswNg9xiozTvX94NObv/eVlHOppIroqf8wjqOdHloiolnomqK3GmNHUYxoF/QT6szCDeaVnH/Iz099Tj8PieupZUiulvyQ/+1yU/+jO/PI5WWtXGmNqiGirtXZ7/rPHjDGHE9EL+d/bDiL6EBUv9ArKo5qIfpz/McwS0VrqWXK9uIj+14noB0T0mjEmRD19fTER/YSIHjTGXE1Ej5DzF761dqcx5mIi+hsRfYx6BvSPjDF11POs/4CIVhLoczA2hwTFxmkL9fTrDiJ6uYzj3EZEvzDGrCai1dRjKiFr7avGmKVE9DoRbSai5/r8GwAJ+rMAKEMBAAAAgEBwqJm3AAAAAAB6BSY9AAAAAAgEmPQAAAAAIBBg0gMAAACAQIBJDwAAAAACASY9AAAAAAgE+5WnJ2biNkFV/XIhJhxW28kJcV+ur+xSbW3NfA3RHf2XjDU7Un/X2IiULyfb+fpi2/rvGpLUSWmbMn193P7sy36nkpN+puv0cxOSWVtEre1oe0bp2VS6P67sbWmn5kZrbUNfH/eQ7s9DFIzNoUV/jE305cBQqi/3a9KToCo6wbxdotXeEa6tU9urv8SJJN87f4lqe+TBE315wv883y/XQ0TUeJmuYDD5Q2t9efWTfH2TvtJ/1/CSfaJfjtuffdnfmLlH+PLGi2pVW7yZ5XCSc1CN/sdOpZd7c10fXIj4vSsz39Xj9oF+KYh5KPfnoQrG5tCiP8Ym+nJgKNWXMG8BAAAAIBAMaBmKt37LNQs/M0//1XSZ4VqTL7ZNV22/+fj3fXnRh6f68uN7Dld6i9dP8mWvPerLkXpt2rj+qKd9uS7MprQZ8duU3hPtXLrpovcv9+W/n6cLy7ZezyWBvNdeJ1AmZa6cjPzRFl++c8JfVFuV4Xn8sHAlN3xFH2Pm01f78qWzuGjw2FiL0vvx0rN4n+vXqjavvZ0vPcJDyWazRa8dAADAwIGVHgAAAAAEAkx6AAAAABAIMOkBAAAAQCA46D49nZed4MtHTeAImp+vPVnpjaru8OWQ0f4d39l+gS8fU7vJl9/boKO8RsX5GA+v5IifC2euVHrtOQ6BXtY+wZdv33O60ps1fJcvP759li9PrNF+IN3fSfpy/HwC5SL8ccjmiqqdULfel1ekR6i2+hD7ZLWkOK3AnNgepffsKeyvNSrMIaUdXlLp3XD2Bl+e9uNrVduMaxbzpVdU8KULXx8AAACDB6z0AAAAACAQYNIDAAAAgEBw0M1bW89hU9XOLeN9ORbXGXOTWQ4xT0R029qWkayX46/gmsFiIj3v8TPYJNKU1hkydyQ5wd2OTpaPGbVZ6e1OVvtyWJxrxc6xSm9kNWdoTl20QLXF//oygcLIrNzW0+atUIJNkJfWsHlyS7ZC6SUMh4sfIUxaG7OVSm9ZcrIvX1XD6RFaPE/phYjTG8z6js68rTRzxc1xAAAABgdY6QEAAABAIMCkBwAAAACB4KCbt6rGsImgSxTtpLjWS2b50qJhbTqoirHJoSPDO+7p0mareIRNHdL0lfH0XG9sVZsvD09w9I80ZxER7eyq8WXPcvbgcEibRGTbjtP0LZ76VwISkYXZhLlfrLZoUsulnL17QuRFX17nJD+uMykqRE1IZ+GeEtvtyzJzc2VIn/jbe4705VytfkijU9lEll0vSr2EdBFU8mD6AgCAwQBWegAAAAAQCDDpAQAAAEAgwKQHAAAAAIGg/316HP8GGc69qY3DkLuETERU6YSwS+JhduRIhIWejkqmhNDrzMZ8uYJ0aHtE+OQkwuwTEjXaV6dShM43pZyTCXLS32d6R1E9QKqaupdMFlXbfSzLTwu1VcnxSu/aOs7Q7Ymg8m2OW82COIez5yyHva/L6OfuF0+c6cvRdxjVNmoJ+/hUCJ8eE9J6Vj9GAAAABgis9AAAAAAgEGDSAwAAAIBA0O/mrdCRM9V2OMTmrUiCTQmZNh0O3NzK4eexiI5Lnl7X6svJHGduro7qcGUZph4R2ZndzM1dwvQlzWWuXtbyHFGGpbd3a9Oc5PDRO9R2ZxG9wCJC1qWpy+X4kzhrsif6YVXXOKX3/sY5vvyeUUt5H2d+f3yci8d+dTfv8+WGVUrPRvmapt3XpNpyq94oeK02my34OXh7wnNnqe3OqXW+nPjLooN9OQCAIQZWegAAAAAQCDDpAQAAAEAg6HfzVvcEndU4mWbzkZWZkXXAC4U2s8lot5PxuKWTo22kdaSuslvppUVW55xnCn5OpDM+N8crxD56TtidZlNa207+XqFKbc6orGYz24aW4apt7EQ242U3b6GgY/4/e28eJmdVJf6fU3tX9b4l3dk6eyAhBBKWsEQ2kUUYEYZFARERHUbGZXR0Rr+uM+I6+sNRcdARFFxRRBRE2SI7ZCMQQvZ96fTeXd1d+/39UdXvOeem3qI76TV1Ps+TJ+ete+qtt+59762379l81KcmmXDVu7qeCrVO81EG7TsbZQHXdXHqe26e3JKoF3o37lrhyBdUkUnr5FXXCL25H3nJkQ/LqzxI05xSmNZblzvyhz7+kGi744VLHHlu24mOjC+8OqLXdOgjZzhy1WZ5X/r/umpEP1tRlJFDd3oURVEURSkK9KFHURRFUZSiQB96FEVRFEUpCkbcp6evTn5ESzOFoIbLKbXux5Y8IfS++6d3OnLmYIloM5PofQGWuTkak2HviSR9Nne5yKTls14CKWt00E/+OfGkvPbuFvLjufCk1x05lZFZp1dun+PI/lLpZxRdQiHWIfXpAZPKn3k7dtmp4vi04LOO/N22sxx5cXiP0DshuM+RX4tTtuYzQruE3jMBSqVwY3mrI/880jeYy1bc4BnYWXV579xZQq3reySfU0d+U39sPlHoLZ+/zZGr76Sx2XLK4C7HW1Uljnfcfpwjx2ukr6AJsbQWvdQWOSDntx+OUdByrBykn9ruz5P/U/0aWj9LDsgEHQfOYmt/C/Vv1SuHhF56y/ZBfe5g2f418hmr2ijbqu59YVg/Sxn/6E6PoiiKoihFgT70KIqiKIpSFIx8yHqd3DINRij8847FDzryKUG5xfnbJVRh8uALMutu/fGUkbmlm0xOCSvE3MNC3ZNJ2qL2B2SIuc9LemVBCnluqpAZeF/aV06fG6PP/dqMPwi96gBt6z5/aKZoazmRunzaw6C4bKHvv84KE2Zb7yEPmcTW900Tei2pMkeOpintQZlHmhlb4zKVgvP+h+T5uu6e7MjzPijD48W1a/g6AMhiq7zQaqq+XOj947SnHfkvzQsdeVebTPFw2/F/d+Qzwlsc+QO3f0zoNf6FMp/vvpLGbM7F24TeGcH1jvzUKwtF29x76J4b6ZD48QhPHwHgnkIi+o+nieNZ95KJObWbTPatN58u9LoX0bztidFaXf4B+Tmbd9PaX8GK+k56qUdeyMuvOaKdyXvPxTWObKaTWbS1RGbPr7oXlMFgmT75PVC+iX6PM69a9sNxuC7qTo+iKIqiKEWBPvQoiqIoilIU6EOPoiiKoihFwYj79DR+83lx7D2eQoW/9Z13OHLp7fL5a++H6hwZ58ow4micqqILXx2/LBSQYT4+vM2OzIwnqBs6+yk8viHcJfROO3GrI/dcRddw0X98QuiFGsinZ8aNMvyytG94wzEnGuiTt5xbRfL/WvagOF4Tr3Tki8vIL6PRJ311ypDGvDMjQ5I5G8v25X190p3yfsV/pnDc1OPTRZvvgt10ME7s1cMGDz03Vj8W+K5u44nPrRPHG6KUTuDmqZSO4D+7LxF6d2+h/n+s8nhH/uW/fkvoPfqhRY5871byN2j5vvSpS64i35+5218CV9gigYGAaDKs1MmEwyPD73lagcN8eE49wRG3XROhUzTKObf/QvKh8nbRuHqsWyG0j3yGkLXt7JZ+dJFu6vveKXSvbf4nOQ6+qygUHa1bdPYvyB9zyyRKWxCZI9f0Y4ph8J/xLF7gyLu/QPdKbHeZ0DvzdCrd8/LfyDduhu0KN8zr4p7PsvQI62S6k+CfX7HV86I7PYqiKIqiFAX60KMoiqIoSlEw4uYtm/Qbmx255B3sdUuv8g2qij3rNJl19/WDDY7MLVX2Thrf7fN4qJFX3wYA8AZob7Srh8xbsUoZwhnw0FWmDrBt8tsPghvuBpbixGTctzszZy1x5KtLpTnkVz20RV3jJfNhS1pueT/UN9eRry+n8MlHrND2WCZ/Xt1/3rJZHN916Ww6x2f/KNreAUtgQlPA1CHkEWDr5ykz8hfufsyRHzrpbqG3PUVZfB/ppGzNz/TPEXp/+NwFjjz5Dy+7fm5+41se2GIyIcxZts2emXlFGgEX8yMAQPd1MsS84Z8o3L/mx9TfiR0Rodc1N/8ql6yT5ockm/reMF1HOmqFynvoZ8kXo2tPe+Ta4eujtpn3yzUY03RNOIXMcY3l3ULPO4/mN2w67CuMD+yxHcD+wStgSsIghf6nTycz8Y7LZRUDmEz3ehDI3Dn5OJlS5uW/kjk5UUl9bWfSDz3sPhfdSJ23VByf/K01jnxP7Tcc+dJ1HxB6dX8e3Pl1p0dRFEVRlKJAH3oURVEURSkKRt68ZW3NoZdtqTPZ3kKuXUPbkIeukZ7jxrDICpZ12Y7eSqXo/JkMt3XJS/Sxc/Bzt8XkNu5ZdbTd21Kg7KAdocQptL1cFBQwm+y+iEyL0UxhkDdvAAAgAElEQVRMtHWmw47ckqb7IeyR982sYLMjV3npPX/vmif0lpZRAVL+WZfLIYfPfFUWu+Xs/R1FLUy9coOr3qgxMNcKRUzw+VhgLHwzZzjy1ltkRvRTziWzYcsZnUO7xhyBx1Y58gUv/pMj37X0fqEXMzTP+Dw9kKgUenvfTfNqrkyQLuBz01tXK9pMJd1XmQht+/dOlzdFqC237b9KRvqNKWgtajwqC2gdxGWLhJpZRYWTT/nX1aJt020UyVNaRqYO7xa5htX/z9CzV3snkfvCwStmi7aOxXTtlTM66Fp7ZTbl8u00RsnGCtHW20BtJSwKtzcpzeH+WSwD+EiZtwbmnD1GPCqy0Jw9gggo73FzxfG+O9h976Fo6PQ++Tvm30197Omgta+/R2ZUL7mIouOS3aS3+3L52xc4jSLsgu209nisOtPd82jMI1Nl5u0HVi9z5CvPp3XjulmrhN7jIJ8T3NCdHkVRFEVRigJ96FEURVEUpSjQhx5FURRFUYqCkffpseyRwqcl7e5T4O3qdW3jWZiDQTIOch8eAACvl/vq0Ot2yHqG+fEEQ3S+jj7pzxFN8fA+92B0w7/XsZap9wjgfhS2T5MnTH43/33tTx35Ky0yfPbWasraW8FCcLusEPhtLqHomzonieOrayiU0o9032xLRoXeG2fc58h/6JWV2f9r8UOOfMcNNzhy5c9fyHsNo4Ydim5nVHZh8/+R7fzSE6iCtb9P+lfNjVD46gv3yYrbc65fO9irdGi6gdIEfPTDHxZt8TPJvn/yFKrg3RRuE3pPn3OnI7//8fc48oGnpwq9/il0/3lKpWOB10f9lE7T34OZpLxnS7Zl14XEpjH4m9Et667ln5W8kMYyVk3zr+yDe4Xermcpw63vMpktPr2A5tI1//OoIz9wy4Xul+cnnxm3Ku0AAOlmuofq7pLh0HVMNmdQmoL2f5b93XIenb/NmvYzJu135Fo/6e3tkr4/8miEGBgnM8g0ENb89VaT/5pppN6JzpZX319N/dMtMzpAcj99dtVr7PyL5TUZ1sVXvfdpR97SWy/0XnqGUk7wUfFZvjqJRnohwbJr8zQxAAAmTtcUbZE+dN4Izb+b17zPkWfVyjXAW8kqN3SAK7rToyiKoihKUaAPPYqiKIqiFAWjnpGZgz7ak7S3Qk2Q2uJpuRWWSdKzmi9Mbf1JuS0YCtDWXTJNbbZ5K8UKk5aGKAS6PyH3TP+6m0I4G+ENcIWHJg52S/MYplCY/qavUVHDS8MUAvxGTIYtPhylUNtbK8gcUuaTKRF2pgZnykkalvWVhfQmjPw74ECKzF2VHtnWliZzV+uFZAJKlJ8h9Oq/P0qhzUPdRrcofZNME+8/9xlH/mnr2ULvL/tpa/sny+8Vbd847kpHTm/cMqjP5ekq6tfIYpaLb6RUAOU+6uN7X5Xmzwf8Jzmy7w3aHi/fKe+HyS8zE1awQNqJFK0RsRq5roQPZdeqfb0jaLoeMHEMstjr3n+X91zfDJpz5Rvpvm17XmYmn/u2HY4ce0qmJth3NoUv3/EcFYJdsFaufeIK+fXamYT5tQ+yOObO20meM0mawQJeus+nhmXqhJklLY68uZcKorb0SdNJya4jS7lwRNhm51Mp5UX3TDLzx6rlOpNg0eKpMPWVPyr71/DTW79xFW/Setd1Bs0x7JAh/KEWOuerrHis3b/h/fmzRKfC8jj4Op0/waxxPllDHFIRut60zEwA/l00TwOsYsIbS+W1L2hkrglq3lIURVEUpdjRhx5FURRFUYqCMTVvFaKviTzW40lZJM4XzG8uKQ3L7LyJVP6vx6O1AAACPjpfPEnvKRTlxQvVpTdvE3qiwF8xVhwtVMjS4geX3OPIGxK07XpJ6etCb1uyxpG/3kamjJsqXxJ6JwTITPq7KBWmrQ9LcxnP8AxAn5sBeW80+MiEtSslQxP+Y+27HHnODUOPWhpOEBE8oey+MFbI7KnpQ7TVX8iU0PgNMsN9510UofPiCwuE3ux/fdGRH14ti67uuYyiSxqZecs7X4aTNL+N9HpmsmsNyuvbtZnMn8G1ZJqosII70yywsnwX3W+Hlsq/63qm8e12+Vmle2nsM+wW7muUemWPZM+PBYrnHjVDLPhauk9eS/1aOs6w9Q03S732DZR5Oz1d3vv1a+h9uxtZNFufZZtgDDrjfIH7sOdaMl1ufttdjnz5louE3jWTX3Hkdb3TRdvBONlSdkepWHEqLdemzNadg7veIyRVH4Hma7Omx298VBbT/fCfT3HkUCv1r18uVRBqZ33FApYCUfnj0ldH5wi1ynPEWeLpktfJRFS1xYr6K6Fz7mNFZvda1iwPC2Tlc8/yDoBEZf5xjtfI41QtW1uT8sNSZWxe1tBaffoUGYnYKeL+3NGdHkVRFEVRigJ96FEURVEUpSjQhx5FURRFUYqCsfXpKeDwcnA5Cym2fHMCLBTdyyovx6wQ80iI/Dt4+Hk6I5/1eJh6dz/Fy/GqzrZeYgrZjL2bQcIryR/LVdUHmR2W03njcnF8UXidI29M0DkmW25Bc/xUKbnJT9Wgv9b8dqHHw5rfVUl6AY+8po0xCs9NlVIobMxYH8z4VYfMPtx0zXpX3dHGhEOQWZStrLz7Ellt2CDZ5o2H+tjXL23nPuYnM5f18Q0X/F3o/fLLb3Pk6SnZB6997AeOPK+Kqqfb3YpsWlSy6tbpoLym8sdo3jaTCwRE9rmvHfEKmt+Nz8j513oinW/ak9JHq3satXnYklNhR96nx1+W9ZZlVn9U5p+Dk+q6xHFvnHyconukL1hwMvlPnDeNQtulJ4WEZ2T2zJJ+Nqlq8smKziC/kkREjnlfAx0v+390D1XslGlN7mslXx1Pt+VnlKCx9caorb5Mrv2pAlmjhwN/RwKm/H4nAAB8JnWLaDMn071ZfaYMx3ejk1UJ8IekD+ulDfRDFOWONgBQ5ac+aPBT+Plkv7wfQpjMK8/3S79a7usYN6TXl5FzKuyhObU9SW17UjKb9M4k+eMkC6zBXSwm/qzSTaLtE8vpXoEN4Iru9CiKoiiKUhToQ4+iKIqiKEXBmJq3TIGCo8mZrMhhSj6bRUpoWy/kpy1C27zFQ9ETrBipbd4S5w7SdmdPv9wiDAVoe67tODKD1T9lnWQkQ1nHE0dQTPWyT8rO+l2UttR/2XyBI19Qs1Ho1fkojvPKUtpq/dFUWdzzJ12UfTVm6H64d8aTQi9u6N5oSdOYl3nsTKO0XV/rj1ptVurQAQploh0hMJ0Bb1fWHFG5SWadrdxA29kdJ1AqiFCnNP1EG2g5WPmbpY5ctUXqBWfS91t792LRtmA6HU9ay7IfByxTWpzaOubS55bvkmaa1sXU/0GWFNY2g6VZctaMn9o65llrQieNxb6zZVvtelagmN0HvY1yvQjsy6Z7xcTIZFs3FWGIn5215e16l2wLHKK+CnSxFBp9Vpb5EpaBvoTGLxqzzB5hMmGVzZXmEi9L2bHqIGVyDl8nTRMZlhXdwzJZJy2zFbKh9bBbKmBltg5uIkX+nu4ZMgNvYhF9l4y/SrTxusMeZnGJ1crPmvdDdrwThh+TcTKO1/1QrlWDC7CWRAq0rQmyAtnWb6snTDHimTiZphBlhm4sdfkEy03DxOheybCM6qNZYHslyHQZ9TWbXDQlutOjKIqiKEpRoA89iqIoiqIUBaNv3uLZelmUD/f6BwCoryUTRl9cthmWGTl/2bMspf780VuptHzW49u4Mdbm8citOp6tuXsu7bvWW59byGx3zOIyrgAAyQvIVPLvNTIr6WpmIvjKtD868muJBqH3YOvJjvzrQ/RZ00pkZbkvTaKswtvZjuxt+84Vet9pXOnIlSxUp90Ktkuy4p3b+uwNaSt16gCjuMXrkM4A9mZNFdEp8v6OVVM6Vl68MFkqZ0+8io5DLfQdeqbKZYIXBe1qkia+OmbSaltE4xSw6joGeuizIvvpPVHLlMSDUHz9+V/PHrMs6OwU4UNWsWJmigl0y+/fy0IGWT1aMLa1sidn5hxi1uTB4ulNQHjVLgAAqJo2W7T1sZqg8Woao5IWy5TUzNa7ECveXCbHa081S60blN8nECa7EF8Lz/zEOqH33D5KqV1RQm4JpT73yFUP0PnQynx/2zQygW+K0zqwtU+utBk20BGfNM1xHt16vCMvnNws2hI/KWQwOnpMKg3p1mwaZW+tlYa4mkzN4OMRv3IcMEn9aJgepgrcf9YaZEpownj7uOuIdQ6Pyz6IZfbHSmbSFA12EVTWys5tSqzfdD//Xu6RmcbLsjMH5bqE+9rpwMpIzdGdHkVRFEVRigJ96FEURVEUpSjQhx5FURRFUYqCUffpcatA7q2tFnotHZRVdnK1zAbZ0UuheXURSiN7KCkz0Xo9+W2DPq98nVdT97M2Yywbt4+OS2fKTJYCbusfg/DlYYX56qBXZsoUvksF/Bsav0xV6Dsy/aItY8jHoMVQts1LwtL2fiULOefn+N+Ok4Xene0nOjLP2Hl5tayC/t/tFFp9axVlH04aGcYczZCvwI5uaZMPuvn0jAWpFGRyvgOYmSGagl3sfkf6O8druUHw6s7hNhrPrhlymWhdxEJjrdu7v57ukYqtbC5ZSVZTIe6DQ3KwU86P6o3kVxKroeso3Scvvu14uo/49+KfAyD7oqTNDvOmvinbTukJ2hfJdSXdnnVQGinfPZNKQbo5m6G37q7BZepFnxwjDws9xgpy5MpUWD4szM8iWSX9fTyx/P4TW8xxQq+8mvwzMh76LO9umSXZ00c+ltyHI1Uq/Tu+VXM9nY/5YHkTcrwSLLsyWkMR7KIXpkfpHkpus1IdHNgOo8WAb4+DfawAAMBgfyFtf97B1j7QnR5FURRFUYoCfehRFEVRFKUoGP2Qdcz/nJWYI0OUyyJkwrC3u3hm5Iif9rKNFVtaytrCAdq67bVC4DPsfRVBCudrScmtYJ7VOcHC1zEo42cNy1B5mEloPBYgtUxwHvZ9MjHqDzPIEN2d/yWLit4z9ZuOfMv2K0Xbd5p+58h9zAayKSn7LYTUpxUsfPa80jeE3umh/MXqTvjvm8VxfBmZLz634k1H3p6U3zHMTC/722Qm2pkwfjDGOGMV6JIzpmM+NxHQ6zxEHQAgWcbMvFHWj7aFlk1hK9oYMmxFiVWTYsyK1s0EWebeUnYSK01Ei599mJebjeU4e3pYCDS3Xk+WZrA5DWQu6oyViLY+ltaim60JydfktVeNUKj60WCvK+lOZn7vLGCKZ7iXeSzcFnZ53V633XrN/kUoyat1OC750AsyDldfZZTRnR5FURRFUYoCfehRFEVRFKUoGNOCo5y2hXKzclIZbUPv65JmhcZyiubqTbJMkz65gRrykhmsMkTmMtu81Z+kbe3pZZThtzcp9fj7SlhhUm9drdBL7d1HBy7mvHGFFVHGTVocO0IEF8xx5Dc/ShEuOy79odD7yL7zHfnUqp2i7eM7ydz1H9P/5MhNvoTQ62QmC269SBTYeD/tM//kyI0/e160Rf8yK+977PPx0Ut2y/tBwE2EYxyhV/MTWdiwhkfgnUyRN31TpWGil0Ve9bCioj4ZhCOyIaNlLwiwQEtu6irfKfukpI3e6O8m2c7G6t/d4sipAyyib5AmJvue9U6f6sg1CRlJWF3CCl2yTLWZ1j1Czz1frKIo450J8IusKIqiKIpy9OhDj6IoiqIoRYE+9CiKoiiKUhSMG58eO3y2PEB+JTuTMlvz9FLyu9nSRZWvfT6rojKLrfWxONagXzoidLEMz7Mj5ENwoK9c6MVT1F0+Fj6bnC59epD79ExA2j5IIee//38Ubh6yQtvrvascOc3Sa29OSl+Jj9c/4chf2PdO0Xb71Mcd+b62Mxz5k/VPCb0w++iuDPmerLDiVk/5LPnxVP9M+rZwAt7B+YRkmAdH8KDfVY+nJhh3aQmY/4tZ9bojl6ySaoMNFR5NjrYn7bFIbd95lGdUFGUiozs9iqIoiqIUBfrQoyiKoihKUTD65i2PXSYsS98MuQ0dZaHods3OxlCnIz+/t8mReaZmm+mRdkfe0y1D4JMs++/MIJm3NgRllujeBIUs8yKliQoZyizyM7t83/GEb1aTOP73T93vyL0Zei7eni4Vem+K1LekF0LZHzUeyor73Wl/Fm2f2HuxI19VS/aW7UlpWlweonNM9ZGZ6az17xZ61T91N2lxeJoCTiwjX08bCp0PFEpsOxFSEyiKohQ5ulIriqIoilIU6EOPoiiKoihFgT70KIqiKIpSFIybkHU7t3s0QZ4x4ZCslNyVouBa7o9jh6I3hMgJ44QwpZJ/JjNb6Pn9+cOXfR55Uck081vxsdT5BaoOHFZl3V11zNh1daM4XhLc78hP91GpiWn+NqHHPXfqvL2OHELZnzFWPb3a6oGvTnnEkR/pnefIX991utD7xCwKbb+ylGodRC7aDm54wlRmIdMnayl09+Wv0Vzp7cv7OgBAuFkLECiKokxkdKdHURRFUZSiQB96FEVRFEUpCsaNecuTkM9fSRYqHbLMVq91kDnGML1YQoYbl3rJLBYzZIzp6pLVpQMhCnXfFafsyj60Mjxn8j8j+vrd88aa9OAy/44l077/mjj+7GWXO/IHJ6905Fk+GbPNe8fPIvNjRobpJ1lm7P1p2achpnp92U5HPn2BNFsd56exPfufb3PkMLwEbphEwrUtlcxfnb0nI81eYQ+NXyDqbt5CP00lk3T/XEVRFGXs0J0eRVEURVGKAn3oURRFURSlKBg35q3K2e3ieFoZZV3uS8kMv7NKW0kuo4iicp8sdLksQiaSuSzy6JEZJwi9kyopsusLdW848kcSZUKvtpQilDw8Cik+/k1Yhcj09IjjjjNJ/va8dznyllvqhd6l57/iyF+a9IwjT/ccfenKOk9UHK/45MccufzBF4/6/LidmTjfRuLxfmnCu79nhiOXrdor2rhR0yTHWZFRRVEU5TB0p0dRFEVRlKJAH3oURVEURSkK9KFHURRFUZSiYPR9elxCuKPrasTxKzWVjhxskZe5Iz7TkUOt5FtjJQKGRxsoq29sMjVWr5PPeruClKH5vmnk4GHXR/f2sVdOID+YWbsOCT3h3TEBQtYLkd68zZFn/ds20baRyVfDckfGpQuFXtc88o2KVcm+L2mnMPDyTdSnZu0GoVcOR+/Hw2n6LFVjP3vVhxw5fED6hfmayccntW+X6/k0TF1RFGX8ozs9iqIoiqIUBfrQoyiKoihKUYDGDL4EJiK2AID7Hr8yEswwxtQN90l1LMcMHc9jBx3LY4thH08dyzHDdSyH9NCjKIqiKIoyUVHzlqIoiqIoRYE+9CiKoiiKUhSM+UMPItYg4rrcv4OIuI8dBwq8rwkRX3dp+zIiXuDSdhMiNlqvXYuIn0XEcxDxjKP7RsUNIr4LEQ0iLhik/k5ErM3zejSffoHzDEm/wHkOuz+UwuTmzgZEXJ+bt6cNwzmfRsRlR6ujDA0dy4nPSIwhO/c5iPin4TrfWDDmtbeMMW0AsAQAABG/CABRY8y3jvKcn8/3OiJ6AeAmAHgdAPazposB4E4AuAwAogDw/NF8fpFzHQA8m/v/C2N8LUfCTXD4/aG4gIjLAeCdAHCyMSaee4B1/WNFGb/oWE58xvMYIqLPGDPmRQrHfKdnMCDiQkR8OffUuh4R5+aavIh4d+6p9q+IWJLTvwcRr8rJOxHx64i4BrI/xMsA4P7cuUoQESH70NUOAB8GgI/n2s7O7SY9mfvMJxBxOjv/XYi4ChE3I+I7R7tPxiOIWAoAZwHABwDgWvb6Obm/5B5AxDcR8f5cv/P3liDio4j4wTzn/RQivpIbhy8V+Pzv5O6FJxCxLvfaEkR8MffeBxGxyu313D0j7o9h6ZhjmwYAaDXGxAEAjDGtxpj9iPj53Ji9joj/OzDeufvg67n5vBkRz869XoKIv0LEjYj4IAA4fY+IP8zNtQ2Fxl85anQsJz5uY7gTEb+EiGsQ8TXM7cQjYgQR/y83hmsR8R9yrzch4jM5/TWYxwKCiKfk3jMbEZci4kpEXI2IjyFiQ07naUT8LiKuAoCPjl43FMAYM27+AcAXAeCTeV7/HgC8NycHIDuJmiCb/HhJ7vXfAMD1OfkeALgqJ+8EgH9j53oaAJax45MB4Gf5Ph8AHgaA9+XkmwHgD+z8f4HsQ+NcANgLAKGx7r+x/gcA7wWAn+Tk5wFgaU4+BwC6AGBqrs9eAICz2Pg0AcDjAHAjO1c09/+FAPC/kE2Q7QGAPwHAijyfbdg98nkA+J+cvB4A3paTvwwA332L18X9of/ecsxLAWAdAGwGgB+wPq1mOj8HgMtY/347J18CAI/n5E8AwP/l5MW5ub2MnwsAvLn3L9ax0rHUf0Maw50AcHtOvg0AfpyTvwr0u1mZe18EAMKQ+02D7G/cqpx8Tm4NPgMAVgPAdADwQ3a9r8vpXMPG/2kA+MFY9wv/NyF2eiD7I/kfiPhpyMbfD9QK2GGMWZeTV0P2xzMfvy5w7osA4FGXtuUA8Iuc/HPI7mIM8BtjTMYYswUAtgPAoHxYjnGuA4Bf5eRf5Y4HeNkYs9cYk4HspGxibQ8BwE+NMT/Lc84Lc//WAsAayPbz3Dx6GaBxvg8AzkLECgCoNMaszL1+LwCscHt90N9ScTDGRAFgKQDcCgAtAPBrRLwJAM5FxJcQ8TUAOA8AeG2S3+f+53N2BWTHDYwx6yH7UDrA1bmd2rW58xw/Il+myNGxnPgUGEOA/GN1IQB8BhHXQfYBJQT0IHN3bsx/C3KcjoPsH6KXGWN2A8B8AFgEAH/LnedzkP0Dd4BCv7+jzpj79OQDEa8A8ge5xRjzC0R8CQAuBYBHEPFDkH3QiLO3pYFto1r0Fvi4CwHgyiO4TDvBUVEnPELEasguiCcgooHsX3IGET+VU7HHit97zwHARYj4C5P784CfGgDuMMb8aIiXVNTjMZoYY9KQXTCfzi2SH4LsX/jLjDF7MOurF2JvGbgX7PvgMBBxJgB8EgBOMcZ0IOI91rmUYUTHcuKTZwzfl2vKN1YIAFcaYzbxc+TGuRkAToTsDnuMNR+A7LidBFnfRwSADcaY5ZCfQr+/o8643OkxxjxojFmS+7cKEWcBwHZjzJ2Q3RVYfBSn7wGAMgCA3F/8PpN1phZtOZ4H8k15LwA8w9r+ERE9iDgbAGYBgLhpipCrAODnxpgZxpgmY8w0ANgBAGcP4r2fB4AOAPh+nrbHAOBmzPoLASJOQcT6PHqe3DUAALwHAJ41xnQBQMeArwEA3AAAK91ez8n2PaAUABHnI/nYAWT94wbmQmtu3K46/J2H8XfIjhsg4iKgOV4O2UWzCxEnQTboQBkBdCwnPi5jWCgj9GMAcDvz0zop93oFABzI7czfANk/YgfohOwGxB2IeA5k75E6zDpRAyL6EVFWnR5HjMudnjxcDQA3IGISAA5C1g5ZfoTnugcA7kLEfgD4NmR9SQZ4GAAeyDlz3Z7799PcbkULALyf6e4GgJdz1/FhYwx/Ei5GrgOAr1uv/S73+mC2Nz8KAP+HiN8wxvzbwIvGmL8i4nEA8EJuXkYB4HoAOGS9vxcATkXEz+Xarsm9/j7IjncYsruD73+L1+8Buj+WM1Oqkp9SAPgeIlZC1ndjK2S31jshGwV3EABeGcR5fgjZubYRADZCdgsejDGvIuJaAHgTAPZAdldQGRl0LCc+bmPoFmzzFQD4LgCsR0QPZP9QfSdk/YF+h4g3QtZ/VezWGGOaMRvA8yhk/V2vAoA7BzYScufcMMzfbVgo6jIUiPhjyDp0vTjE990DAH8yxjwwIhemKIqiKMqwM1F2ekYEY8wtY30NiqIoiqKMDkW906MoiqIoSvEwLh2ZFUVRFEVRhht96FEURVEUpSjQhx5FURRFUYoCfehRFEVRFKUoGFL0VgCDJgSRkboWJQ8x6IWEieNbaw6NsRpLUxYWx4kadpCmr4lp+41MLHDXehIkZ8L0Jp9PnjDkTTpy8s2M+wmHmR7oaDXG1A33ecfj3ES/35FNyiqufAwEUBwLcxN53d9QULSlwpSPztdD88UkEnC0IPusVFhOaG/72CTwHYm5OV7mJXppLDMR1vcl8vb19dG8RDZHM365P+JJsjUzOv5SmRUayyE99IQgAqfh+cNzVcqgeMk8MSLnHauxTJ6+VBzvvIFNsvaAI/uicjJ6UnQcr2EPMEbqRfbS5Ow9gfJF1td2C715VZTbsHm5bBtJHjcPFMqOesSMx7npm9ToyOlDraLNJI/+h3NQoPVMgmzxzthP1kNjzOemhyXJtb9LoTauFmJVIBbMEm1tSyocuW7lfkdO7Tj6W9g7e54jdyypFm3lv3yJDgb7cMy/L8ARje1IzM0RnZdD+M7eiipH7j91tiO3LQoIvbp1VC3IG6PzRafKaiGR/aTneWat+zXy+TeKf+gUGsuiztOjjD77zpWT7Kx5lLQzlaFJ/K66NUJvtr/FkZcG6RzrEzIR9puJSY68sX+KI2/oaRB6/1CzzpH/F+Rir7wFbCHrvvY00dT5bvor/T9PfMiRzwjtF3q1XiqTt+BJSpd12fGvCT0/2/K7rYaqwNzVfpbQW3P7Ekf2PEtje9hCa9gPg/1ANNF2nwz7a3uQP4Dbf7FEHAeCtIOTiPtF29Q6GrOKD9IfBhmQDyllPvoBfPwNqrvsD8ndvXSKHjhXzN1Kr/f2Cb3Nbz/ZkSMVNL9Dj8gk/DU/foEOjvChb0JT4HvhskXiOF5Buzsd82n97Fko//gIdFFbqIPur3ilnCsp9rBc4TnJkT0rrQegcTin1KdHURRFUZSiQB96FEVRFEUpCvShR1EURVGUokB9epRRJRWWNt6X98xw5IYq8hv4W4e0ST/tIfv1T9jrlT7pD+BBOv/WXnLe39kl/RAqG8j3xLtwviOnN2wqdPlFg/d4cjRdeN8W0TYl2OHIC3sru4AAACAASURBVII/FW0JQ74Ua/uaHHlPUvb/u8ted+SPnfykI19X/obQ43F1/+/A2x15Rqhd6F3+o6ccuc5H99HnH7xW6M38DPMDsf0Nxsjp8ogZpFP25h+c6siTKmW/NR+sdGRPQJ5j134KrWwtpwik4+qahd7zD53oyPPuoP5dtEr6gazrmOrIrx4iJ/fO9lKh5/HRqMf6ycckfIV0ht817QxHnvGF50UbeuizzegFZ44pnhOPc+SeJhkxVra1x5H7JlOfeoNyzCMH6Z6KbG6jcx9fI/RCh8iPK15N/kK+82Wgiu+J1YO69tFEd3oURVEURSkK9KFHURRFUZSiQM1byqhSMbtDHM+ppi3rxpIuR+YmFACARn+nI78SnenIQY8Mi61g5q4kT66Gco+7zEOhsPsuoK3byRtAAYDanxx05IXhfaJtQx+lAtgXrxJtSWbe4ikItvTWC71HD5L58rhK+qzrm68Wes09ZY48qYy26A/0Vwg9D8temQEybbzt3PVCL/gKbcVvOSUu2iaESYvBE84Zy7zlWUyh4/PmU+j51v0yXxs3bxwW3Z+hfoweIBPUzkBS6PVNoznYeuvpjtwc3yj09raRKS0RJRMLeuUHG5Z7yyRJbjkoxzx4HN0P6JM/ZTwZJm87LEnmMUSihhK/hvfJVB7YT6HpkX3UpzdcvlLonbacUgl8YNX7HLlkpezfsrUHHNm/h8avf/4koecpo/mb6emB8YDu9CiKoiiKUhToQ4+iKIqiKEWBmreUUWVWVZs4nlZCZqwpQTJhzbcy+L7aR1Fe3KTlt4p0NfrpfBlDz/TVPlnPJ8Tel6gEBQD2/xtFw7yz/M+OvKpnptAr8dJWOTdn2XiYSTHik6akVIjGpjVOppP6ErkFXh6gbfoAi+BLZOTnJtK0lHXHKVvsjnYZNVZbSvdBzy2Nok1k+J0AFCrlsfci+t71hky+obB8T5xlYfZ63cOcDDNBtTRLM1OgmsYoeTGZvp7bMFfo+Uvps70lNIe5GQ0AIJOkewM9zPRlZdAOBOgcvZfJqKHwg6yUBR5Df9uzTNOexfNFU7Kc2g6dLOuole2hDOhl+6jfrq+QGdB7MtTfQda/fQ3SBHnoPIrEi06jcYnNlPM8cC6ZsWffS6V/0pu3wVhxDN0NiqIoiqIo7uhDj6IoiqIoRYE+9CiKoiiKUhQUjU9P663LHXnu+2TW3Ze3kM9C7dMUSll1z8Sy8U8EZkakT8+qtumO/FK6yZFvnCFDLheV7HHklpSstswJYP7MtAcS0g/By0KcU/P7bPWi5Kr3Pu3I3B+H+/DY9Kak74AH8/uFcP8qgMNTCAwQS8tK39yPJ5oMMj25dPUkqC3kI18EnqEbACCRJr+Hsmuk3xj8OO8lTUii82nMJrM+KA/LedWWoH60i86zKQImTY0ev5xjqQT1aaKPxg+D7noeP42/7dMDcaZXTt/DE5Th5jy0vW2h9PEKP8j0Cvg+TTS4H0/P7DLR5u+lPq3aKvuq9QQa50kvU9sz/Q1C78pSymb+lYUPOfKn19wk9NqW0diW7KW+r1sZEHp8OT54HqWtqG601uOn18BooTs9iqIoiqIUBfrQoyiKoihKUVA05q32ZbSlNyMsi+5NPoG29L574SpHnrniFqE37+ZVMFS8lXIbb+NXaXsyWNvvyE03bBZ6Jm5li53AeMKUKbQptEe0PcwKi6ZStE16L5wu9KaVUTj7edVv0vn8LUJvU5zCkLlJa2PnZKH3XHg2nWOSNLkpACGk0ONSr7wXefh/c1xusXtZeHAy4x7OnmLmrgwzU3AZQJqxeKblkFdu33uCZIvhIev9MWku4+aukxp3iLbtc8jMnd4q2yYac5qoKGg8RX3ITX8AAKESMv3ErL7KJNn4MfOWNUSAPNSdhZuDZcLkJrJ0iumlrBMG8pu+AiFpLkM2lvHa4qgqmqih0PPwfmmqTFQz05KVXXv6o/Qb522POvKPbrpC6FX+/D5H5mvAtL/JlB+eFPV3xkdjGauX5m5/D41ZJkB60SnSDFZdRZnd0x0yG/9wozs9iqIoiqIUBfrQoyiKoihKUTDhzVuDLSb37qWrHXlHb41o41mBb9y1gvQukuEc55/3AUf2Pbka3PBOIi/1s/62S7T9S/hFR57spS3Hf7n4dqFX8oeXXc8/0fBMpv7YHZeRUvF22q4NVpO5r9QvIy4mh6iveBbgem9U6H1u56mOnGZb46m0NLW0Jsks47H3gouE3b89QRxfGXjCkaewzNaXRLYKve0pMle+1CWzNXezKKpSP5nFUlb0FjdjJdKDW4Z45FXAmz9KD0Cac2bWSVM2L266t0+m4j5wIUWy1E8w8xYGpVmhrZfGqL6U5khfUpoVFtdT4ch1B6eItlScmYw8NF5YIHOzHbHF8biYrUyBv739LCvwlIou0bb1ABVPjTTJtmMJXrQzHaS+8qSsiMhe6vtUWK53/ZPpfgiUkBnTv1kWE+aFmD+/412kt+Og0OtdSlG3mKL103ikqZJP+4yPtVkWzcwcyvAMr6h5S1EURVEU5ajRhx5FURRFUYoCfehRFEVRFKUomPA+PSbj7o/ha6Aw5X+soqrRdyffJvTKfWTH3NVH1Yl/1l0r9J647yeOvPzVKx354B5ZyXnHO+925F/1VIm2v/dQyPrsEFWdDTUfOyHqNql6yqDckwrJRuYewKsmRy3fA15Z/Y8LaVwWbpU26TMmky/GM/tnObIduryjn87Rn6K2Eigepnxf9knTjyn8vz1Nlc8brArpFR7yt2oISV+KHWnyl+OV0O1QdA4PRU9lLD8FD90gMTZO7f1hodcXp/sl1k/ye2dK37g6H1VxT2XmiLZ1Z5JPWf0PXC93XOKZNV0cV5TQmpZmjhX1YVnF/vhS8ul5sV/6Z/EK51z2eKRPDx9b9PI0znZse/612s7IjAm63rMXbnfk5piVHsFHPiwNZfJ7HUtg4yR2QKK3T/qwpsMsNUGLDGdPh6gtFSHZXyt/nz7yxX9x5JI26t/Q9H6hZ1j6bl+c9IxPjqWH+ftEa2g98CbkvdA3hVbekldgRNGdHkVRFEVRigJ96FEURVEUpSiY8OYtyLiHSO64mcwbG+IUjunzyPfEM9QNC8ook+mBpNz6+98u2jb/7cJ7HXnqiaVC73sdMxy5Ky0NJvNKKPSv0UeheT1NUq/8GKp1mi6h/j3YX+aqF2Tb1ZOsbfgNXawwnqEteT6uAADtCTJ7nNlApq7N3fVCr58Vtgyz8PhiCl63i/x9e85CR47+hebOycf9XOjd30lpAS6rXCvafpo425F5Rub2uDRHhX35i0CWB+S2fEeM3hdkWZg7+uR8qS2ljLG7Oqjt6db5Qm9XJ83pqu9FRNvsx93TUIx3Wk6Xpvg6H6XK4Fmo60MyxUOYZdvOWJmR3YqCHmaqLGC6dNMTJi3b7MWWZz5Pm6xixVt99J33dsrM9zMW0timN8gC0xONTJjSESTDLGS9QroABNqYSbPUKvzJ3EC8MepgjMoUIl2X0GclnqPftchmqeepJjcFHoqescxbPHS+r4Ha6tYmhV5vw+i5GOhOj6IoiqIoRYE+9CiKoiiKUhToQ4+iKIqiKEXBxPfpKcBdH6C40zdZ9e1ZJa1CrydN9kkvqwxc65N+JbztoehxjpyxUuzvjlMIe32gW7TFMmS7LGcpvw+dKtSg/Jdw7MBM9h2WbwfHy0Jh90eljf7aGVTh/lGg8gEbexuFXnMfhce/eIhCcI9vlGnUK/0UgnmAveeYnhBDoPQiChW+Hc4UbYceWuDIyxfKEhUVrF+5f5XtB8JD0RMsAtrW45W0eWX1VFrOue4Y+SJMbiRfuf63NQu9epDHxwr9kyx/HNZvUVYaZFKVXI+29lE4NPfhAQDIsDIHInzd+mxkY2nY+GUytu8PE0U1dkvNT4rdSVqbr6pbJfSewHl0CuscLafSGly9ASY0SeY/E+hh86ZCrlb+bubvE5fh7JmS/CubiUgPmvBK8uMp303nyERkqhFfP5uL7NzeuPTPSodYaDtzC/LG5b0mMpnYg2mG19NSd3oURVEURSkK9KFHURRFUZSiYGLu5vPtL7b15Vm0QKitCK1z5Kd6yCRS65dmK27eqvVRSCc3Z2X1aCsw7KFQzzKfzFa5sY/Cqw8lykVb1EufdXyIsgmfuHSb0OuFYwg2XOmM+3M2b4sEZEhzU4CbJGksV+6dLfSun0PpPO/afo4jt/bL8OSmUgp/TbLq3RNzQowuPGuyH+U2emuctse5CfEQyFQFPsxfqduuuM7NNDzVhL0D7mOVv4MFKrDLk8tK1GDYNQ3zlvpI09skx4GnBOiO05pzUnin0LuvZ7kj25mRPb78Y4RWRmYeiu5h44D2IPG3sM8SWZwBwPTSuARYJvYQyjBnbkqzs0R3keULZL788Y+3XP5m9FbmNx91zZT3rz9KZsxgm0z9wDPfI7MzZoIyK3ugh1VM56f3yrHMeGkN4GHqaFVISJaQXoyStQOmpV4qwsxgU6TLQmqvzLp/tOhOj6IoiqIoRYE+9CiKoiiKUhRMiN18T5ncGs/0MjdwQ1vZu74ov87LcdoO3d1Pm5whj9wm9WOayXKbmMNNWhEm70rIbKgRluU07JVmmno/RU/sZO/76aw/Cr2rYTkcM7CdzERabsl6++i5uzdGWURnV8kIu31WduwB+rfKKK+px7c7Msbp3AdapR7UkegfrDlEAQCZ/bgQJeze59mZAQAirIhpNEnb8iGfnJvApqPHpWAlgIzm6kiSGXpyPuUBCmRzn2iUT5Ym+6oArZF7mGl/iq9T6HUnWOSqV5qIjEumZY/HHgcW2cXekkwP7m/qTNwyMwbofLu7ad6XTZFuBPw6eIFVAIB9k9yjRMc7xjKtepgpKFlKfZq0ktv7YoO7nzN+OocvY0VRsWAuf7+7eTJZTmPGi4/ahUQ759JnxRtobmcCVmHhXvYdp8nfU1TzlqIoiqIoytDRhx5FURRFUYoCfehRFEVRFKUoGL8+PcxOmOnpcVXrvJF8X94444ei7WfdFDo+M0w+IjxEHUCGpicNdUnSCp/l/j4tKQor7EjKcOiGQJcjV3hldVrOhv6pjnxjuZUl+trTAQAg89iLru+fiNgZd1l3QyJO4ZMRyxdqUx/3ziDbcOWb1gdcTqK3mvxG0PIH2dBJ98ZhVaOVgsxh9+rBVKVoK/HS2CRZzKsdoh705PedC1ivpzz0dxkPZ/daIcqJFLUl4uN3WRspplVKX51S5lfI6TWy+nZnzL2mNQ9N5/4zh/v0sPfwFAPWMPAMzWk2lp6A9EXhUc/dvXKt5gR8dK80lnaJttTkifv3PAblGPmi1D9pP/OfsYbYf5B+J5OTLYcfnvWcncN4ZD/5mW+Nr4/elAlaqSSY705GRr0LUmHS87f7mCwzg6dD5NeXDsvPGu7ZPHHvDEVRFEVRlCGgDz2KoiiKohQFw7dzxGMVkReqK5CVM822Ne0MqAUyom7/Gpm0/v6ebzry9zuPE3plHgpxDLIw9Y6kDGcsZeGzgw1Z72FZae1QWl5U1I9B0eZh2/zcrHIoLcOAWxdn21J/d72ciQO7BcJ+GZLcEmL7rgkWjmkVcd3fz0POybxS/2yL0PN/msaPZ4e1s82W+SnEtT0mzZNKYRaX7nXkrbFJoq0uQFvsB+I0ZrY5ipu3MoeVsCR4kdH2FG37l4Xk3n5HL5lpkr0F9tuPUaqDcv3ga1JFkO71ZVam8+4+Mh95vPkzMNvYhUS5uYuHvcfj7uOA3vxFSgEAMMCKasboHJ0Za90O0ncp88n7wb7fJhJYViqOjY/9nrKfmniVlcm6hJnF7K/PllNPkr3PyrTMp2J/DTNH9cjfRW+MuYRESC9VItdtlkQdEnV0jv4pcs0NtdE1pUpkCgM1bymKoiiKohwB+tCjKIqiKEpRMHw7R9wcxbIkmyPcZex4H5mwbvnMQ6Lt1gqK0vpq61JHjhvr67Dd1V39lOUxYm2F8izJfRnmRW5tu4vILhaZEvbILeMKH0VsHUjI6BauW8EKlR60MhUnq7J9aLzuZr6JQoZtodpRVMC+X2kl9YfX0lu9pcmR5zHzFrS2gxv8liy0da/RWzlcCvnacFNxmxW5OLfkkCNv7aW01wGPjNBxy64ctPT6WVZfHzNZlAbkHO6N09Z+f8zK8MsZ5HecCHjC7lmHa/1UOHlhxQFH/s+WZUKvv4vMW1V1Mko2lqAFlJu07MzN3LyVTPpc9TjpftJDq7Cpj0VzpVro+vYkaoTe/Eq61zxWdCAvIoxBWtNNPH9U23giOVn+ZqSDLJKZraVWnWsBpmR/ZIKsP1h4HMbc3Tm4h4G3X7olpFmf8q5Py8AzCLaziL0Qy+Js3Rs+Zi7rni5/x93j944M3elRFEVRFKUo0IceRVEURVGKAn3oURRFURSlKBjx1KW+KY3i+MBlMxy5YzHZbm9f8bjQ+0Q1+e38pEvWSv508xJH5j4y3DcHACDKMi/bNl83uL9CvECqyX1xsrtW+WXWZe7vE8/ILu5O0TXxrKmVViZaHAjfPgb8TXhF3eqg7Ku9LEtnYDL1QYVfGqwD+/KPRbpN+vRwnyyfL39GWQCAWJrO158k2T0/rTJAOUsFwTMwAwDU+WgO8urptaGo0POymFqehbnEysTdz8Ypwfw0bJ+gMAtf7prYrjqDxjOpjh11iDa+jjWF2hzZ9lNEfwG/G+ZPxd2fEOXfyjjItTXDq67zMHfrGlJJ5vsRpt+IvYlqoXdT3TOO/M09F4s2nhrDM32KI6e3bB/UtY4l3P8GACAdoDHrnMPGxFrTcBf5biVPmyPa/N00P9IlBX72uR9k2t33J1FBvnyhNurr5lOlB06ATcZQC1177yT3UPm4dN0adnSnR1EURVGUokAfehRFURRFKQqO2LzVd8Vp4njGpzY58oXVrzvymSXPCr2/9FLW5NkBCjncnZR7Wh/cc6Yj2yHFPJsub4umZfbj6UEyfZT76D3cxAQAsDNG4exhtr0etLIzN6epyCgPn+XmLACAzaw4pg9lCK5bQcbpPpmFc9JL2f9bZKLVCc8JFfvF8ca+uY5c4qf+tgsmVmwb3Pl3xGnLP8jO1xeTsZSVATLRtPe7h/4WFdxsYdKuatyEVeuXYc7dGTIQxllRSTsUPQ35Q9FtsxWfL/x8FYGY0BMhypXSRCaY4GHqnHQtrUeVfjmvvMxOMS1A5q2H25YIPV+A5kjSSpvBQ855yLrdhTz7sUgTYf1JnczkN9XbI+Lz072SZOkH/rB9sdC7/KQ1jtybkvPb76VzZMonVsZ1T0LOFeOl75YJUm/xcHAAAGRFO5MRq5Co9PxwSJfL30xfnM6fKKVzpKqk4Z+HmPtbyWWhd7p0Q/C9SeM3+UWas7sukp9bvYFk26MDTzmB2l557bDvMFR0p0dRFEVRlKJAH3oURVEURSkKhmTewkAAfFOmAwDAtf/1iGjjkVKrojPzygAymmm7l0wR3Sm5fbawdJ8j74tXuV6Tn5mP7K1x2+w0GL2DcdoyzlhFL/f1UQHFUj+ZX86okrYXbhbrSEnTSUOg05GrvRTRciAlo1uqHtkIAAC+brmNPxFBFgXQ4O8UbbFGMl+UsIgLntUaAKDqDdk/bmzsIdNiQznt6W7prRd6vMilZmQeGg93nuTITaFW0daaLHNk3q92VBZv87Hon6AVxdiPtF1eFnDPpsvNGV7fxC02ORTSLMqnNyXNBdykxdeZv688QehVLiS9eEqul3bE4wA+nzS/8Mg5nsXZb+nFubmMjX8wKCMAk0meTZnOEdtWLvSaTqHPnVQizaw8cjBaRnKBXN3jhlRY/izz9ZNHcjU+1SX0TIaZnKK2iSz//oYnac0VNuSZAk8Hvl5mgqyl37jS7e49HDhA63GwQ67H3gRdB1rrcf8kejYYjuzMutOjKIqiKEpRoA89iqIoiqIUBfrQoyiKoihKUTAkn57YJD+8+bFshuX3eJ8Tbdv7yT+nPkD21bTlF8P9Z3b3k69OQ0jG1PFsyFOCMtso97Xpy1A4X8zKoNycJBswz+xa45dx4CGWvZRnWuZh7gAAb6+iUPxzSihE9Jn+BqH3VB+F5W/prhNtj0fnO3Ka2S7vL5M2aejcCwAApkDo8LGAJ5K/yq/XCmT1HSL7tXtdYIDXD9JYXDzzDUeOJqTPQ4T5mMSTI56YfEKAXpYJN8PuO5Q29oYAjUWflSaCp43gldVtPzq3KusVXpmJm8/bEAtfT6TlmHEfEU+B6t4C63tNtHB2T8r9e3pYxmuepdxjRfMHfDSb2jtlaDcynx7Dws1TXum30dfHqpinma9WQK5dtu+O8/42K2UEG4baKXSvpV+Q99qbSbreiE/6ezX3k29ZopLulYmQcd0bl+Ma6MmwNpaVPGFl8bdzBLiQDpGev11+lpeFrKdDbCw75Lzsa6LfVu46W7ldXlPPFOp7s5cyRqci8neR+ypF9sp56I8WWvGHju70KIqiKIpSFOhDj6IoiqIoRcHQ9vUNwEA0dpO/RTQ1BymcuytNm4i1PhlqzAveTQ1QxuRCYeQdKbnt2pGk7VBuBuNF9uy2mgBdhx3KXu0hc9c7Kinj41x/m9D70Ob3OPIdD7EidhdI8xvfNE+m3LOcVpSQ+Wx+RbPQ2wTHJn4rQ3WohPbba0I0DvweAgDIHDwEg6G/le6N9Ex6pp9eJsdoXoT6e61nCijuoE+ajXnxXtukzOdW0Oe+Lc1Nyn6PuwnXDmF3Ptcyb1WFaPu9o6c4MmynItT3vWmZkbglReaHGrYGe2PSpMcLc06pk+kkmjvJRBSO0DydUSnn0rZ2yqY/pYKZPpPymtp7aVzqqsicX9Mo01Ps72bXHqY1ocXIrP0rWPzynXH5G9ETJ1MYltA6MBHMW/ZWBP+54gnQsUv+tppyyurvScnfU+OlcUfWZvzywzJ+dn9wy5cXXfVC7XRvpELy944VOwAsoQGb/JJVwLSU3ufvt8xbnawCAxw9utOjKIqiKEpRoA89iqIoiqIUBUMybwX39sLsT74IAAA31H5QtP3LKU848orSNx15S3yy0ANDH7k7TtuVdrFQHmEVt1JD8gKFPg957dtZSXnWXZ4l+eTITqGXYPuHn7zvZkee/sXnhV4AdjlyPZMjV0tP9E0tlG3Szmoaj9N36feRbEeKTYzcoUOHR5IAyP6pD9Le7e7+aqGXiVnRbS7426nfepK0nWqbQyq8tKVuF1pUJJ5SaTrwI5ka7bnZz8wsPNrKznrN52OAmbBs0zM3h/LzdVu5WftTZOopjwwyiznadoSJFSmZDtL1LyqTBUcXBOk4ZqhvUmVyPeLjUmplvN6fJpeFBDPTh30yBGxmNbkpvK+B1syvbrpI6PW2kHkrWUnn81quDXURWvt59G/koByfp/upbc22GaJt0UzK6H8wINfn8Q5apiluSmLBypDau0/opc5fOqjz+2LUj3amZhE5hjRGSbvgaD8ruF3q/hjhY4HSqblTHTmy/oDQi75jGrsGuVYkauizhyPOVnd6FEVRFEUpCvShR1EURVGUokAfehRFURRFKQqO2EQ296bV4vgvwUmO/MPPX+zIH/qHx4TeBaWUJXdxFdnl91pVxrenKPyuMy1DUA+mKGtyhPn01HllVucLw+QDwKuYr/jlp4TerE+/4MjTQfrxDIaPTf2bOH6tluyTto/CZB+FdK7ubaJrYn5QAACrQ1n7LMYmfgXwNAsZ3ZuoEm3I7PmTAzR+q1unCb1SGJxPTxm5WoksrR0J92BVXtW5qPHkv9ewoizv6/nwsfBznpE5Y2VmjzPfPh6WHvZKvxLbZ8g5t1f6d/CM2zx8/VgmUUb37fRAq6temYd8nBpPlr4Ue9toLV3YINsyCTq/L0zjksjI+cJ9svia1tMr/a68paTHK7Uf6iwVehfN2ejI26MU87ztfHl/VjO/vLJKGfbew+6H/kn0Prn6TDy8BdzVvH3UvyZghaL76Jj7DGFaBoHziu586nnicr6lwnQP8HDzYKcMRQ90s/MFSS99SKa8MV5a79HKjO6NDa+vne70KIqiKIpSFOhDj6IoiqIoRcGRR4B55BanidP2Z9NnyVz02GfLhd5jcLoj9155miO3nCSfv3zHkaljaeMe0TYvkj8775qkDFv8yAN0/pmfoWuaBS/AoLC+I2Tyb7Pdfudt4tjXR9tzXrvAX5S2E4PttBW4/km72GF2H9NMsCKI+UizkEteQBIAIOSnPqjw0RZ1a5fc8pZH7oQPUf9yk4ptXilj+8SZzMQ3IY4k6Rpp3uJh5Gnr7ybe5hcFR+U2eh8LbefZ2EMot8f5OYLCdOae7bnER2btY9nQ1V9Hff9A8zLR1hShbPKzSsiUsLdFGngCrAgoT/EAAOALURvPLG9nz68K0LzdGyNzWSZjhUMzk1aKnS/ZLdNYTAtRCHxnkszSJiA/dw9zc7h0xgbRxgtRP2Emdsb1FEtNUL6b+tBbKzNUNy8mN5DatVa25jCtuxkWpo5+63ecZV7mBQ68/bLaQWoK3Ss+FubOzWP2OdIsW7M3Yf0w8vdYU9v4WDZp13cNHt3pURRFURSlKNCHHkVRFEVRigJ96FEURVEUpSg4cp8eF/+WoRD53UtMdtdrPuw4lFfPrsE6c7C+O24M8jtO/u7Qw9yLiYzw6Qm46nF/kGSswK2JzLJr+Tzx9OjdrCyJHWa7NUYpFlLx4UhuPvFB1q+8V1Nl0ueCj5NdXsJO0TCAW7X0t2qrYnnsO7zks9Djsa6JhbD7ClRtP5bglXtKfTLUvydFa2S1l/w7bBfBeD/5erQFZGoQPi9S7i4YMD9MK/SjzQtd9Xh6ikSM+fZ55UWt7iLfTH5/eaPyb/TtcZrDdmqDLuYLVOD2GpckKqTfY880+t71a2icU/NlWo8e5tJat9q9yronRWuksdJUcJ+csv3UcbFJYUuPZF+UDhKVqMjlYQAAIABJREFUchz8zL+1dxK1Bawbkbvy9dfKcUb2mzFY385C6E6PoiiKoihFgT70KIqiKIpSFOi+vjKqbO+pcW3bx7I1m373WxO9ZEIxKbl3HWym8NkYC4+3zTAZFvxoYpqRuRB2FWWeBd2G93NPkuwvhULMuXmrNSU3sHk1dh6+fNg1pmkMNx4is8dUbJOKx0AKiAF8vfRdmsLyez51cK4jzw5TyHp9jcxaXxcm82FbvzRheGvILJZK09/HLf1yjHaXVDtymZ9SQYRCMsxZXHsp3UOlIXk/8XuIy56pMusyD0uPpqW5c0c3rTMVOyaWfSv88nZxHHmTUkaYVgrnh4Z6oTfr9/Q9U6XSjYAvf5jkldTlusgrsPOK6/EqaXILdNFnJVlmcLQsy4E+eqG/ls7hCUkXlZqfkCuKt6ZatJkY3R/SgeXI0J0eRVEURVGKAn3oURRFURSlKFDzljLi9LHMsadUyqKGm7rIFFHro+10jBd4HmfmLbDMW54Ey/DMttpt81aFl3L18kKIxYxb9u9EmZVZl20yL4nsFm1vD9PxFmaO8lv73l4WHxZmJqy4Ff0VZqavPRHK7h5CaTr5behUuqYmuobfTjpJ6KUO2rGgE5fa9WTuaY7LzPe3ND3nyD/87ysc2Up4DTtqaF6UtMrxTzNrYs9sarv4/DeEHjelpVkW5gtmbBJ6m7vJHBP2UTjYayvnCr12T50jp8rpXgu0y/vwwfITHfljs58QbTuZGR3/vM6RJ4JxM91qmWTt4xyxFQtcz1GoSGeyzO/axqO3EhX0eOCxMi1zk1Yq5L5WJ5hpXEztBbOk4jq6p9Jt7TCS6E6PoiiKoihFgT70KIqiKIpSFOhDj6IoiqIoRYH69CgjTt2r5FvzaKOsBm18ZCv+0cwKR56ysoD1Pe1urzY79zryM7vIblxfIasOr/JQ+tLAG+6h0MWEcal8XP7LF8XxPW9e4sg9c2UF9m+xbKppFpWaishzJktpfDOserZVjB0yfmrzd9O5wwetUFsWvr1hyyJH9hxcB66Y4QiAHTvw+VcdecP3ThdtTy2nzMjzf77WkTOxGBwJdUxebf2tXA7b8r5n42Gv7HekHvZqE7Qe0TV56+iqvnLXJaLN8yKtJY3JvXCsgH4KRbf9dnz9rIp9ifSN8/UxZy62tHqS8hwZVnWdnw8tvf4GWjPDzbRu2Oktgl1s/vbTfePp6BF6YiZ6rBQiw1D9QZx+WM+mKIqiKIoyTtGHHkVRFEVRigJ0C1PNq4zYAgC7Ru5ylDzMMMbUvbXa0NCxHDN0PI8ddCyPLYZ9PHUsxwzXsRzSQ4+iKIqiKMpERc1biqIoiqIUBfrQoyiKoihKUTAuHnoQ8V2IaBDRPa+21N+JiLV5Xo/m0y9wniHpFzjPTYjYOBznOpZBxBpEXJf7dxAR97HjwFufQRlNjnS8ELEJEV93afsyIl7g0nbYPELEaxHxs4h4DiKecXTfSDlScmOwARHX58b/tALr8OWI+BmX8+g4jjGIOBkRf4WI2xBxNSI+gojzhniOSkS8baSucSQZL3l6rgOAZ3P/f2GMr+VIuAkAXgeeiEI5DGNMGwAsAQBAxC8CQNQY862BdkT0GWNGrRAWInqNMcObBOIY4q3G6wjP+fl8ryOiF/LPo4sB4E4AuAwAogDw/NF8vjJ0EHE5ALwTAE42xsRzDzquD73GmD8CwB/znMcHAOeAjuOYgYgIAA8CwL3GmGtzr50IAJMAYPMQTlUJALcBwA+G/SJHmDHf6UHEUgA4CwA+AADXstfPQcSnEfEBRHwTEe/PDRh/bwkiPoqIH8xz3k8h4iu5v0y+VODzv5P7C+YJRKzLvbYEEV/MvfdBRKxyex0RrwKAZQBwf+4vIM10NwQQ8R5EvAsRXwKAbxTo+6cRcVlOrkXEnTl5ISK+nOv79Yg4N/f69ez1H+V+VAERo4j4bUR8FQCWj8mXPoZw638A8CLi3bm59deBeZEb76ty8k5E/DoiroHsHzxiHuXm+xIAaAeADwPAx3NtZ+d2k57MfeYTiDidnf8uRFyFiJsR8Z2j3SfHIA0A0GqMiQMAGGNajTEDD6a3I+IaRHwNczv1uR27/8nJfH7/BqxxHIPvUuycCwBJY8xdAy8YY14FgGcR8ZuI+HpuLK8ByP4+5+bXwBj/Q+5tXwOA2blx/Obof40jZ8wfegDgHwDgL8aYzQDQhohLWdtJAPAxADgeAGYBwJmsrRQAHgaAXxpj7uYnRMQLAWAuAJwK2UVzKSKuyPPZEQBYZYxZCAArgXaZfgYAnzbGLAaA1wq9box5AABWAcB7jTFLjDH9oAyVqQBwhjHmE+De9258GAD+P2PMEsj+aO5FxOMA4BoAODP3ehoA3pvTjwDAS8aYE40xz47Adyk2Duv/3OtzAeD7ubnVCQBXury/zRhzsjHmPjh8Hp0EAK8aY3YAwF0A8J1c2zMA8D3I/rW6GADuh+xu0ABNkJ37lwLAXYjIckMrR8BfAWBa7iHyB4j4NtbWaow5GQB+CACfdHn/wPx+Nxw+jsrosggAVud5/d2Q/a08EQAuAIBvImIDAMQA4IrcGJ8LAN/O/THyGQDYlhvHT43OpQ8P4+Gh5zoA+FVO/lXueICXjTF7jTEZAFgH2cVsgIcA4KfGmJ/lOeeFuX9rAWANACyA7CJskwGAX+fk+wDgLESsAIBKY8zK3Ov3AsAKt9cH/S2VQvzWGJM+wj5+AQD+AxE/DdncDP0AcD4ALAWAVxBxXe54oCZFGgB+N+zfoHjJ1/8AADuMMQM1IFaDnLucX7u8DgBwEQA86tK2HAB+kZN/Dtnd4gF+Y4zJGGO2AMB2yM5/5QgxxkQhO59uBYAWAPg1It6Ua/597v9CY/xbNSOPe86C7AZC2hjTDNlNgFMAAAHgq4i4HgAeB4ApkDWFTVjG1KcHEasB4DwAOAERDQB4AcAg4sCTY5ypp0Fe73MAcBEi/sIcnmwIAeAOY8yPhnhJmrRobOgdhE4K6CHd+cvdGPOL3Nb5pQDwCCJ+CLLjf68x5t/znCemC/CRg4hXAO2+3eLS/9vh8LnrZvYtNPYXgvsOUSHseazz+ijJzZmnAeBpRHwNAN6XaxoYZ3t95gxmfiujwwYAuGoI+u+FbOm1pcaYZM6tYELvnI71Ts9VAPBzY8wMY0yTMWYaAOwAgMHYej8PAB0A8P08bY8BwM2Y9RcCRJyCiPV59DxAN8B7AOBZY0wXAHQwe/MNALDS7fWc3AMAsvKiMmTeoo93QvavTQA2aRFxFgBsN8bcCdndv8UA8AQAXDUw5ohYjYhUYVQ5YowxD+a2tJcYY1a59P+R4syj3K6fL+dMLdpyPA/kA/heAOCmkn9ERA8izobsDt+mo7imogcR5zNfLYCsGeRIswzrWjm2PAkAQUS8deAFRFwMWRP0NYjoxaxv6woAeBkAKgDgUO6B51wAGFhHJ+w4jvVDz3WQ9STn/A6kiasQHwWAEkT8Bn/RGPNXyG59v5D7q+QByD9AvQBwKmbDa88DgC/nXn8fZG2a6yE7wd/q9Xsg6zugjsxHj1sffwsA/gkR1wIAD5O9GgBez5mxFgHAz4wxbwDA5wDgr7nz/A2yzpjK8HNY/x/Fue6B3DwCgMshu50+wMMAcAVzgL0dAN6fG98bILsWDLAbsgv2owDwYWPMkZUWVwYoBYB7EfGNXH8fDwBfPMJz2eOojCI5q8gVAHABZkPWNwDAHZD9vVwPAK9C9sHo34wxByHrL7cs9zt6IwC8mTtPGwA8l3N8nlCOzFqGQlGUcQci/hgAfmyMeXGI77sHAP6UCzBQFEURjJc8PYqiKA7GmFvG+hoURTn20J0eRVEURVGKgrH26VEURVEURRkV9KFHURRFUZSiQB96FEVRFEUpCvShR1EURVGUomBI0VsBDJoQREbqWlzJVMrPTAdJ9jePXLLP5GT5ub5+cvrGrr4R+1xODHohYeL41ppDYzTHEj30bG0ymVH5zLeE164dRWf+HuhoNcbUDfd5x2puDhYMyqLciSq/IxsvvW6sOz3QM/pzbrAcC3NTIUZibo6XsUzV0TVkymgN9vtkcnoP0nwLeFKOHEv7hV6yl44D+8Zfwu1CYzmkh54QROA0PP/oruYIfmz6zj9NHHfOolWy8VvPH931FGDvB84Qx7Xr6SYI/enlEftczkvmiRE577CM5SDxlFJeyExPz6h85luBfvoRNsnEqH3u4+aBI81kW5ARHU8cwu+6y5z2zpgljndfOdmRE5X0noxPvn/qU7QoB//8yuCukV+Dfe3D+IB7LMxNhRiJuTlexvLQNfRb1rci6shTazqFXokv6cjTIx2OvLFTltva/3KjIzd99oVhu87hotBYqnlLURRFUZSiYNwkJ/RNlk+SVb+neoVzI8+Itr4M/ZX+m4VLHRm91l9xGfZXnofaKivldpxhe+rH1TY78sWVjwi95Htoh6n1K7R7se4kUArAd3c8IVmrDmdOI70tO1iD+/O4p7yU1MJh0WZKqQpIYhKNUeBQVOil39j8FletDIoCOydd15/uyGXv3yff1tnlyMlWNobWxkz3h7odObWA/lpt+La1wzvYHZwxMmsqyrDgYbbgjHvd5O/ulPNjjn+1I29N0m/rk33zhV5ToMWRdybIOnRtjUyMPnc+raehG2mtvnaatI4Ixsnc050eRVEURVGKAn3oURRFURSlKNCHHkVRFEVRioLR9+lxseXt/H6tOP7EpF858i9alou2fhY+d+mi1x355UPThV59hOyO25+YSZewTIa+9sXIR2hamDzWt/fLiLcUi629seY5R/7Lx28XepO/M3IRZROdXffNEcc/OOkXjvxmvMGReegkAEBHikIuwx7yuwp6kkIvw/yzZgUOObIXZaj8N2afMJTLLm7sOcv9Coz0K2i/meZq5NoDjsyjQgAA0mn6e8sToahI0y5D2zt3Vjpy2dk0NzMvS0c6zzNr819fAb8HRZlwFLifPUuOd+QAPivavnCI5suCkv2OvKq7Sei9kJntyEEWss7XXwCAh5Lkh/fuqlWOvPkHpwq9ebexKGe+jvA5CjCq81R3ehRFURRFKQr0oUdRFEVRlKJgbEPW2RbX25s2iaaHOk525CVle0Tbuh4Kcy7xUmK5y6e9JvT2x2lrvONMCmW+cuo6odeTpjBqbgZJZeQzYYmXtuifjNJW4r/c+nuh95vvTAYlP++avV4cP9s7z5HThvq72ifTCpxb+oYjT/NSyOVDURlyuS1W78jNyQpHbkmUgSQOyiCxE/wV2IrufgeN25IKCn89u0LO7y/tvsyR/UHaRj/7NJlKYM2hKXS+OtqWf/YjMtnhrFdoDmdiMWoYw230o2Kgz0cztLdQIscC4cboo58Rk0676h1RAslxEuY8qgwyoaZn0QJxfOgrNI+2JGtE2/nlGxy5zEPzY3Jtl9Cr9JLrRyNbZ/9krbNhD/3urovNcOQPnrVS6N3ztfMceeZnWBJDex6Ooklad3oURVEURSkK9KFHURRFUZSiYEzNW5t/SCas030y4imaoqqiXekS0RbP0GW3Jig7b32gW+jNLKHt9amNFPnhsSJ5/Ejbadwkxk1nAAC1fsos3Meqnu5OyK3EtlsogqXmx+OvLslYsqJMmjle7aeIu4NxMket7pSReJc00fbsr3sWOfKhRLnQ606F8sr/M0VGM7wTloIySIZiVthKUR5Ppec68rObThRqQTYF2S46vPKs1EuzYK5nK2me+Y6Tc91TR9GfmT17HRm90rxlJop5y6XPzZlLHNmToO+SLJdRb74nVsOQKTTOBdpMKuXaNqhzHOHnHrMU+M67fkNRp989+TeibVXfzLwyAMDyyBZH7snQuhjxSDP/X3vo/NdVUFRWU6BV6NV56bdwT6rakffFq4Tel6+gKOwfLV3hyIH/v70zj5Prqu78ObV3Ve/d6k1qdWuXrMWyjSzJtgS2P2DHYBLHHowxSQb4BJIYZvgQZggZZpgk88EzMWGAmeTjcYBgmxgC2A7YBoORbSHv1r5LLbdbbfUmtXrv2l/d+aOq3znnqqtoG6m7OnW+//Sturduvffuu7de399Z3mulxuLz8hJL0rrToyiKoihKSaAPPYqiKIqilAT60KMoiqIoSkkw+zY9TK/7nSvJxXxlqF802ztBbnDDKZlJW3QHpH/aeuI1FR12cwAACKGMDvvsGLmfc3sf20aI2/60BUnjbPJJt7+Oj5Hb9Plv5T30kqTaY0XDzpAtQrWf6o6lGkW7XTFyUU5kKCI3jxoKAJDO0P01nCRbsAxIOy7vZcxVXjOuXzQ8KykKeks1zZ/eTmmX511NNgGJDgonkAlIewYnzMaNGQKlJoKiXaaWhSRgES7QK/+vM+nid4FGrwe85VlbtZ5PrBN1VTdTlOueozRHLDNF8L2b7ArL2fVIVEt36FQFXYOqU7KP2AJqW3Yu/7XifXoT1M4+JjY1IV5HnwmO5m/H+7PhQdszfnlebIkAZn4JiVrZ34oH2O/O9D8Xc0709ze75b/Y8LhbfmhAZjQPemktnExLGy9uqzoi1kV53drCQ275M50fcsutkWHR7sZqCiESZxe7O1Yr2u0dpvAyH130qlu+96u/J9ot+zzL4q4u64qiKIqiKL89+tCjKIqiKEpJMPvyFtu6emMTlXc9fpNods8qiux4PNYs6vwe+hzf0uOJSAEADkbJ7XldGbmx7hyXkSz555qCtCWfysjLsyBAW/Lrg9TfPcc+ItpV3WLtE5c4ngi5MW8Kyu3Un47SXjYPRcDHFQDgdIJcknl05bG0lDkWhUamPYYDMvoARNvIPT54FBQAGQkW2f9DBbabez8vt9gTvaRp9B6ncBL2v1fxfpbAsJw+Y3xSfrhj8+tu+anOtW6ZJywFAOi8g7bv2w+wQ+fRmQEKJkstFlK1ZdB/R/Zcx9bLG7fpy3Tvl72HrkH9Db2i3XiC5I3QAZL9x9vldfOkaMzPXS2vR2CI2vLcy+laOTcxTu2Cg3R9kytiop0ZDkzbbmSd7M8To/4CoywxrRVEPXQNmRgsqx4SdXuOkBxe0UHrSrpcnuPEOnZiRSpvDd5Fsv84C99SHYhN1xwAABwj19mhJJmI8EwD48mQaMezDiQcum5J67fwL5+4yy17FtLxbWvvFO1qg1R3NNrilitXSrlsNtGdHkVRFEVRSgJ96FEURVEUpSSY24SjjJbbpMbw8NNksf5Pax4Wdff23eyWa/2U4DBjbelxb6uDUbIid6xnvRbmPtDop3I0I6WTFQGy9L/74f/oltv+m0ZdLgQ2kzdbwsit7JMTVNdSZrlxMHh0Zc5IUnr2LS6jbdNJFs5356SUNBPVtL0uR7mEYd5M6KPrY0cxnryd5mb0ckvCiNGS4qR5EkE5N30TNAdTldS/f1hGY318xxa3HBymPlIt8phMC23Ln/wnira98mNWZGJ+LjNM7DjbeJMGKnqyxzmclsfYv5kkw9QV5Cl3bjwi2kXHaL54rqM+WnZJl6qJFrreH7pNRi1/eNd1brmqleamLZ2Ef0xScZQ5XXrCUpoL19BaPRiiz6xZ0SPandxNnrtBplqNLZdjvqKSrdWWt5J3nM4rXk/j+jubZMLjzm8vg2LH76c1M8w0vuVlZ0W7J/oomnJjeBxmQtAn12Mub3EzEnvMK5aTGUGQHd/mKilvdScoijo3I9nU1C3adc3oaC8OutOjKIqiKEpJoA89iqIoiqKUBPrQoyiKoihKSTC3Nj3cfdSyGyi/mbTBz679uKj7myfJxueRIdL8K33SPZXb9GQ8pEmWYyJvO849NTIj+G1rbnTLbWNqxzNT0gsoE/qAY+n8vqTdHAAAQt7UtO8DAPiY1mzbcXlZGNiRBLl32tG6Y/X0vC/ztCsAACad//oPbKZrZ0alLYUnSeOBzTQf/cek7VV8BdWFj5H9SToi7Wp4wO3QeaqLtknbFP8A2QukK6jO19wk2qX7yC7vggzsM80WfolJhxCGVmeX5voWmd3au4jObfIYhXHwL5E2HEtbz7nlhavJ/qJrp7RtizbRNf1Fzxr5XbW0Tk5GyfLNvCXHMsyi4nvZ0jrRUy7aVa+kY6pvIXuc+tCEaMfjo4cH6HxDN58X7U6dp/NPHZOzGJeQrdldaynswbLggGjXEZbnXIy015CdYsrQPXtdRP4+vRAk+6SJlLRU5Ospdz/nGQ0AAHzst9DDQl7b/V3dTFnSR1O0znbF60W7z9S95Ja/PfIut9wQkPfr6Suudstm3xG4lOhOj6IoiqIoJYE+9CiKoiiKUhLMvrzF3USZpIU+eShiq7lXuua1+Wirjm/B2TKVn+2NpxzmouyRkkrIM31/QZQRnp0xmYBUmSGYv6raT9vQMSeQt10NS0YaZuPXH5fb2vweSLHMhWFrzDP5v6p0wZkl40wzF3Nv5fTyJABAeoLmT+VZ2V9sEY2NU8bqrK9Nsei/QzxZpiVrcknL+KmT0WvaRLvIoyzBpCVvQZHIW+gABIey57Ci5pyoOzZIPuEhJml5PFLuW1tNiUkPDi10yyPL5Dp740173fLPD8nkpi0t5C8+cIRCS1x97XHRbk8bhQPxHCNJa8PlXaLd4d1LqI8tJM10j8sklS0baIz6HJInG6xz3NxCEssATzgLALVBco/vYGEx7N+ITKD4/+9fFJ4+yny1taZtrulyy/vGWkUdd+kPePLf50NJCn3ATQ981rXnkhbv207SXe+ldlyaa/bLc5psp/smvC/v4V0Uin/EFUVRFEVRLgL60KMoiqIoSkmgDz2KoiiKopQEs2/Tk8dWwGTy2xBkotG8dWEv6Y5VPtluOE36JNdyuQ2PTYSF+Y5m8tsrCIo0nH2xkAmSlpuyDHx4ZvW+GNnnVPpl+IGTk6TLv/QmZVC+m7mjAgC8GSWXSe7OzvVkgKzdhCLhLtzcps7Xuki2C1Odp7tM1KWbaf6UdZNNz9CV1pxjY5NopP4qj0o7uuRCZrN3lu4VY/27VrmG3JmHztJ9dH6dNN6KPMpeFFhz5hQPQDqSvT57zkjbjNArZPtQdiO5s1eG5Hx55k1yTY+Pk7uxb4E859ceuIK+drvsY3U12VKuvpbKz+1eK9o1vkRjOUHmQ3Bih0zx4F1LrumfbNrplj/2nAxJ0vwMjXOwnfrmdkUAADVXkT3gqgrpis75WScd76vxdlG36kz+1DfFwifqf+2WX4qucMvVBbYsfChtcPh6GvHRHM1YEynNXvM0FDY8XQV3e09k5PzNQGbadjbn1zH7y8fzNrso6E6PoiiKoiglgT70KIqiKIpSEhRNlnUwmfxVCRlBeZRtS5d7adsuWsAPuYK181hbf3xLzo+01d7nzFDeUgqSrKTbbKTAGPGwApEyee1HUyxrNAsrsDAwLNpx99R4mr6Xy2gAAJbapQAA4PT/A3XfuVg2G6T5U3VSth1Lk5TiBGmcKk7Ibe84k1nSEeovKQNnA8ZooLxxJleWyzlcGyapY4S5QMcb87vnmlRxzu+MDyDWkL0+yeGQqEuup2NeW04u67G0vL61FeSyXVVP0l/9Ghn9+MyzK93y+bQc/483kKzy6a99mvqOSZkififNwasbz7jl3f+yQbTDF0ma699IWdYjp+SakGYhDFZ/gG6wZeUyOvUvu0nCs+Xw99SSS/zzoeVuOXNCRol2aopP57ajiLeyEC38N67GI++NIDPbaA5J2a7eT+PeGSMTgNrApGgnshgwCTpoubnzyPcRFoab/x4DADjM1CPDTBsca78FN86ezKg7PYqiKIqilAT60KMoiqIoSkkwt/LWDCPA2jwwdJ1bXhmi6J1nkjKyJ5e0+NZfCOW2Nvfs4d5bx1MyeZryzkiH6Nl6PCO3ZNMsanIyw6Nmy+3U/knyyHH6pccQp8La5p7ClrfSoWmblTZ5JOZEvTU32bQd3GZJRBmqDL1FW/HJKtks1UDzESdobDKW/GJGWaJDNk+dGnl/DIyTbOGN0jFkwlYC0zAlyyzkFTqX+KIADXuzY3HmVim/lHXS9Tg50O6WV27tEu14ROYXvkWJHt9skN6TtZ+j9bM6IWWme75OklZkgI6j/E96RLtVVeQ5NcnkzegmeX3rfkaT7q++f5dbDl8rZavV9XRMu46R/HasY6Vo1/a+Lrd8c90hUfedbvqNCPlZZP51MhJw4mUpdxUDyZXN4nWQyc6O7bbIqPDQ2jfpyAShPNp9zJFSKIevxyLbQQFPLh75Hqx1NWqm9/Kyubyp1y2fz9vq4qA7PYqiKIqilAT60KMoiqIoSkmgDz2KoiiKopQEc5tl/R1GLl4cJNUvmiHt0s6gO8QiMrcFSDfuTC4Q7ULMTb0nRXZBdnRJXxNlOE73swigtquvKT43yLnECRRIs87gLuZ2WIFYisYiMELXuy9Z/Y6OKRMs0mi8c4jJk2W8bLVlB3GQrrlj2YH4lzM36hSNU1mPtdSwy197iO6PscmIaMZnYOQMfSjeIu+pSJBsixJ8+lnD7FxOEW3x5QNQjGDGgH8iexJf3vqEqPvuwmvc8vijZPtxpEZGbu6ooTWuuZvGteYOGbl4YYTG9pZaaRfzV3vI7qb6FM3HwR/K7xq6leykvCwbd/BgWLQboyTrIpxBa6V0V+Z2JRW15FJdt0+OeUdNm1vefYPMRj+4o8Ut3/DvKGp7Q2BctHvGsx2KjclmaY8znqEbmtuf+lHG3eBu4I6x1lz2Msjsc/zWOuv1Tr8u2v3V+WlcxtJkY3lsskW0GwhTyIHmAN1rGas/nqn9UqM7PYqiKIqilAT60KMoiqIoSklQNAlHwWOFyGVber2fv0ZURTw/dsvHYrSd1ugfE+0Shk4vbmijfMKRfnUVftp262EhYbeXHxftvvG1693yso+wbeKMylmF4NGP6zz53YQN2/Ks9EnX80SauVIyL+kKKwKoHyvccppFeC73yqjezuztps5LuJQ/YYH7AAAY4ElEQVQ7GZXb7U4D3e9rVp0RdceOUXLS0ADNv1i75drOdrfTYXpRdlY2izN3+fF2et9bbkeIpXZmBW29+zLy/7r+rSSfNb8MRYkTQhhanb1B//7Ue0Rd2qHzGVtN59zwkiV1+Ok8+66l91NHpTv0mXHKEPr6yjZRV/4W9f/WXXS9Ny19Q7Q78AxFRk6zEAFlW2S0dCdF94P3EM3TkYQMQdE5TCYGMXbvnb5FjmXlKbpvXvzHd4m6+HJ2HCw5pp14eKy1eJISTBGrl+cZyRMp3Yb/rtmJRHmmAS4tZawE0BfIYnngkaHLWNLvgUSFaNfoJfnMy5KPpkCajiyODLnlE3Bp0Z0eRVEURVFKAn3oURRFURSlJCievb0CCUd924bE68kCHluceh9Fdx1xyJOgyhsT7XgfQeHJJbMffmjNXre8R58XZwwP2Bm0xotH/XSYFFHvl14WEyM0fnX99Jkqr5TLeLRRxPweWsav3lsFCdEcqyyX88Wzg+SIrnoZBR3YdY30UDkwKvXEqYSaAADjS2nuZ0JyHQjUkHzpnKF7IBiSchkfTQ9LKpmslfcb/95ixRs3UHMyK8l4Pijl2+6zdL0rO2i+4IelLjh8gqLJV50gyeKjn35GtLv/0Da3vGyBjIx88n3Uf2sdeVjdXHdYtNsdWUXHzlTkPZu+J9q998jtdB4NNJbXNUi5rDtG6+511afc8r07PyDa8aDDle/vF3Xjeylp56NHr3DL39ryoGj3wvEtUGwkrKS7O+MN07Y7mJT3xisj5B5XE5Drou0N675vuTc6TO6yJTIO97AL+2guVvqkGcEXem5yy2vLKeqyvW5fFqa6EyC9qy82+sutKIqiKEpJoA89iqIoiqKUBPrQoyiKoihKSVBENj35tfZV9VKv5lpjmPkvj1uu6FU+0g0rWPb00bSMFFrupf64K17GeibkkScBpHYp4O736s4uGDfSVZG7TzosQ3d7QNoX+PpoXGqOs3G17LNS7N6oLcvvHp8pnju/KOn6CLmepyYnRV05M8+pDEu7gj9ds8ste64lO4L2gIyY+372uaWPfcot/+8bHxHt+lOUnn1oDdnqrAr1iXZf3Pt7bjlTTt8bapbHnnir+LJqXwACZPzZudD7ykJZx6795DY6ty8s2yGafbGf7Gd8cZpzT/WvE+22LZH2NJwThsIWnN1FoUGevln24dTTGuwkaO372tBq0a5nL3OXL6P13rbfq/XReYWQ+rajek8sI/vLQ+sfF3Wfqtnqlnc+vdEt/zH+oWhX35g/4/hckS6Tv4XVLMwHdzE/nmwU7U4NkR3XLYuPirpzSbrvfSwis23r42X9pzP5Xdv97HWCLaYLrIjXP3qD7KmWLKc1vd4nw8uEPCmYLXSnR1EURVGUkkAfehRFURRFKQnmdpN/hslHr62WW7BxFmmZb412J+pEO+6K7jDZg0dqBpCRIrlcFrcSji4PUxTmDg/7LpWwCuIE87tBTrLQyAkWsTWMUj4M91Ef/l4KYdDulzIYT1yXYm6VE46MKmz8+UMkKDKybmpSzoORy+jamYEqUVe1lKQJLg/zRL4AAINOh1v2j7Iow5ZE3eQnV2m+Be61tuW3tHW55RfOraFjOCojxOIyKccVI04AYaIle+/6x6WskI7QuCRG6Z7+x7e2iXY4TnPJ/2FatzbWWBG0x8i1ezQhr/09lz/vlv+PQ9HoX9u3QrSrfIPmWZp18b033ivamY0kRTfU0Lr9nY6tot3SGprftzRQEtSGbb2iHU8q+qWz60XdKrZWP8+WHNMjoz9DgbAWc4U3Lsc8zEwzylkE+u/1yes2MkxRuJeskHJy5yRJXzxCtZ1wlLuw83AiKSuyOV9bfSzxKTcpAQCYHKBjqlpF4x+wQpfwZKkYtNbqRAFTkneA7vQoiqIoilIS6EOPoiiKoiglwRzLW3zfMb9E1GZ5fpxIkBcAt+5PFHDJsbfDOXxrjVupO9YzYZOPttp9DRSFNN0/AEp+WADtC7wF+HaqlyWnWx2QyQr949Quffott1yB0uqfb8lyzzC+pZt9QyXJQjjMgwQDcswwRNduy5IuURc3JC9y2dgmyuRsnuPQa8kNHtaHUyBC7MYKkm329pB3UapC9heOFL+8hQ5AYCx73JOW81aCJXut3UeyQm//ItlwCZ1n30C1W36s+yrRrGXxebccTcio2d/cfYNb9jI5ONwhk3ayHM3Ap23cin7t9dGxDw6T7Lh6oYymfHqEOvzmeZLVGiulZxBspvX4qdNrZd0zJKemVtCxY62USiK9M0uwOZvYihtf47h5QM9YpWzIJhJPMAoAkGRyVNDLko9aXlm+AhkOOGluLsJ+d1v9MntCYIi+dyhNUteKoBzzs2m6HzzLZOJb5+jJGR3TTNGdHkVRFEVRSgJ96FEURVEUpSTQhx5FURRFUUqCeRGX9pcj0h1xaRnZ+ESZwQi34QCQLuvcNoC7/dmf4zYmtnt1NcsMm2lkLrhq01MQPiy2DU7El4TpWOyTkXOrO6e3xehI1YvXPLvw+ThpyH5Lq0Zv8bmqFhXNdL0rXpURzHnw1IqVclwizL2Wh3xIGWkHIuA2PZYdEHdt5XYKkxnp1srndKKGxrbhcjk3F5SRS32yWrrbOyOjUAygAZgyfXOkmQ2870py4T6xlCLyVnvkdWsvJ9uKl57e4JbLT8v7/qoryT7u6Y7LRF34KPmfs0D1sOXufaLdeIradY5SKI+FVkT0N59rd8vNu+kmin1OhkT48NI9bvnBH5Dbe/pgRLRb/Dmy47qmrlPU7bptuVu+fQFlan/yjIwmnayWoRSKAcscBwLMDjLIJt/QgLTpQR/77bL2M+IOXWOeFT3hWI8AbJr6WeTmtMnvss7LTb4ReewjNLl/cpp+x+/YsEe060/TXEwtkON8sXdmdKdHURRFUZSSQB96FEVRFEUpCeZU3kIPbX0Zy7sVffmTmDlsP5xHq7RdzLlsFbJdlvPAo77aW/JcIou2kotd6MCMulYAIGS5rAc9tJcbDtC1j2ak7OU/0u2WuVB1MLZYtGsJkkTRhTJCt1IY74IFbjkSZq69KOWtiXbaRt9QLiP8viOYvGWHNBDHx3x5bXlrWYBkLKeM+hjZ2STa+a+nqL6RatkHFIm85UllINKbvf7Da2QE4efeWOmWA0GaO7Y7967nSEpY/DxJf2evklGXn9h/uVsue1NqaQ77aidI1/7l3nbRbmUdmRvwJL+VASl9JuqZy/p6kluix5tFu+8Ok9SRaKTPjCyVP1dvddPYBjxSvl4UIZmFr+Ojk/J6tg5c3Gi/FwM79yb/vav1TrjlQJ+UBZ1lFPF4xJFzNs0iKnOzDftXMcNksAofjV/AI01HJlnke953yNbmmJo60k2hE6o2yvFKsiwJiRp5XlYM7d8a3elRFEVRFKUk0IceRVEURVFKgjmVt0wmvweNp5x73sitWy+P4su2vAtFgBWfAfm9XBbj2+u2vMWjOqfD+rw4U7xMqQpZAVDH0iQxNERo6zZq5MZrJo/0cHKyQbxeFKJtbb+XtlDDXstLLFN8kVjnGrOQ5K2xfvKew+VyK9owL5EXh5eLuk80/dotdyRIfrDnpsOnICunrGTASTYHHSZXL/afF+22h2h8G1dQEtqRwUbRLuyndmNXtsq6rm4oBtJlHhi6LCtD3XLrK6Lu9UGKVjs0SRLGmfPVot0nPvArt/zt9mvcsu+o/K62xXSt4i3y2k8+R3PLu2HMLS+ulh46Z6MyqesUyytkJP2aJRSuOTpEXpdLVveJdvy8gEX0HVsv5/ANqyhS75lJef4+FnGdexFubzsl2r103RXsxQWnMCd4LIUoxeQo7kFsK8up9STVnU1Kzy4eqZ57Yl34AEDztFCGAx/7LeTJnPsd+b1p6YTrUsCXE6L1slblLUVRFEVRlHeAPvQoiqIoilIS6EOPoiiKoiglQdFGZDZJllkW8kda5nY3HjtDM3vN3f6Clk8gd+Hj9j4Z2/aHa6Gx/PZDisQbo+voRzmWjUGy10qzyJ6daek+a9KW0J3j8Dnp7rqkjWw9GkPUtx2R2aT0ed8m3ki2FNXNZMMx2i0jF5d30rJx83sOibrzDon4PPxDoQzpvMrODs0jy64Nkrv5I0NbRLuHBugYh8fpPPxWIG8egdYUqVkXOgCB8eycefyFq0WdCdK6s3Y1RVOeSEr3+28fJjuedJLZSITlmtYUoXGOp6Wr8OGNZKvjjJKr+3A4v5XFCHMJf/LElfLYmS0YNE0/nwEAVtWfdcuvLma2ZV655r7eTzZZi6qkzd9oio6Du9hPdEubk9bj+Y9jrnCC+etCLKK91/K2z7DfuwmrE2HTw1zMPR55P/jZ6ySz6XGsyeJjIQJ43/0paVslPsaGr8IjHz2GWQb2VPmlnZi68iuKoiiKUhLoQ4+iKIqiKCVB0cpbmSi55kWtrHv1PpItCm2b5+17hp8pFB02MJI/wnOhSNOlzvfHrKSGzJW8gclR/zK02frk9BdyuE9uV4eXUH9cFrXvIUzo875NtIHkjbExkgd8C2Ki3USQts4H0lL6avVToksewdxSqME/wx1sHnmZS89P/1jKW22PUkTmz/7rs275vp5b8/ad8RWnvmW8AInK7LGFBuUxZtiFu2orudj/4PhVol1wL8kFVYN03Qa3Sjmnd4LGrzYkE4TWVFFyVudJim7eu1Em+a1fRC7sGSadlPVJ12MeWH9iEYuunZRzs8xHY+4ZpZ+oTERK1NEOklK6xmpEXfsNXW4Zd1Jd2ErgOroEio60DKYMkyyMQ6uPZLxok7w3kjE6OdvUg8PrfFYkaw6XtOzfTP7byPsbz8iI38lqaocO9TeUkffhaJrWG0d2cdHRlV9RFEVRlJJAH3oURVEURSkJ9KFHURRFUZSSoGhtejgjlsi5sqzfLSfNzE6B2wPYtjrc7Z27BHqsZ8IUC57t7ydt1VZFC6XXKEXG26m8qaxT1O2PU5b0NSFySb732M2iXRMcm7bvsjPSzZaHI6j1k01Ce2hQtKtqLY6M2sVEaITuZG8PCes8wzYAAFSRHt8WkNeVa//c3dyepy/EyN3YaSbf25NxGYJgMet/T5xSMHzuDx8T7a765Gm3/Le9dO9UviHn8KrrmDt01UJRN30yhdnHF8tA/aGsHVX/Frn2YZrsIh75+Xa3bGXMgYlVZNuWrKY5Ejgrx2H0CF3v5E1nRd358+QuHmyg7/WOyy8bOUr2PoFhZgcipyYwsw0I91G72IBMJXOygdmcsMXVMyo7jLewtXpQ1h3roLH1NbO1v21StKt8zDKgKQYse5yOJKVzuS1CKTts09RUlGx6emPS1o67qSed/L+Z3D4nI2x6LNuyPPEedo+2i9emgtaK8Ek6viHHyhDP1grL/PKiozs9iqIoiqKUBPrQoyiKoihKSTAv5K3tlSfy1o04tD1ZyE2PR2S2o/NyeYtneXYsP9txh/ZncVxuk3LUZV1STcmQ4UfDm0Td3iGSOW5pPuyW44dlZM981B+Sro/HbqXt+tMTtW45aKUuzjxXC4okFab/gdLlzCW1Rma3rqkg1+ZfDK0XdbfUHnTLSwMklwSskAO1XpImqqqovyvCXaLdeiZvjbOI3eMZuQf+rUGSenonaWt/dKWc67/aQVm1ayeLVIaeiAG+uB8AAJpfnMXv/YZ8WTV9K2WWOZ2gEAHecjLtqOqUc6rhespqvyRyXtSNBOi3i6+FZd78oVfySVg2MaZHRXwyTPTyNgolMfLCIrcct+RuHr09E7i081J3ehRFURRFKQn0oUdRFEVRlJJgbuWtGWo/B6Ot4vX2CpK7+llE2EWBYdGu3U/bfXVekqNGvBOiXdxYbgZTfVvJ08KexLTtbNR7S1L98Mtu+fDDsi4A5HXzK+Y/0w4vw0wo+9fXxOvTT7KEkuket7zHer5vgpdm1H8p4UvQfNy44U23fHpURrsd6qU5d+aDUnp+AJa65cy2293yRKtMgBiro/GoP0RZQb902cflQfFkpBM0r+r2S++7zP6jbjnkO+OWlzwt15jNdV1ueefea0BRig1PSspKv1u53y0HkWSqyo5x0e6tZ8lj7ZmtMqzxZIxFNmeJW+2Eo8iTdDueacs2hnVRWymjeo++Rp55rcfpN3iBV0Z5vyJMvwMP1eWPEn0x0J0eRVEURVFKAn3oURRFURSlJNCHHkVRFEVRSoI5tumZme3Ly4MyFS6PrtuXJLubE2ONot1P0xvccl2I9MR4Wtrw8AjNaea+vrRcRput95MtkDMoXQKV/HhCpC9n4vECLQv0EaGs0SZJ7o0mJd2pTVq6pk+BPnmrY4DcLDPRqN28JOH2UUN/1u6Wk2l57VZ+6vUZ9efZtc8tV1p19uspGp6fUddQyBqQ3wNdfXWirm+Evrn1h6/M7MsUZRZpe3xIvP4P777TLfvYb5X3zDnRbtG9R/L2ueAiHdvbpQpOTfv+bfv+WLz2MluiJY+qTY+iKIqiKMpvjT70KIqiKIpSEqCZocQEAICI5wCYj7EyG7QZYy767qSO5Zyh4/lvBx3Lf1tc9PHUsZwz8o7l23roURRFURRFma+ovKUoiqIoSkmgDz2KoiiKopQE8+6hBxEdRNyPiEcQ8QAi/jkizrvzKEUQsS43dvsRsR8Re9jrwG/uQZlPIGITIv4AEd9AxD2I+DNEXPk2+6hGxD+7VMeozBy29h5AxL2IqHk85iGlPi/nnU0PIk4YY8pz5QYAeAQAXjTGfNlq5zPGTB+0RZlzEPG/A8CEMear7L1ZHTNE9BpjLm1QiBIFEREAXgKAB40x9+feuxwAKo0xu95GP+0A8KQxZt2lOE5l5lhr700A8JfGmHfP8WEpbwOdl/Nwp4djjDkLAJ8EgE9jln+PiD9FxGcBYAciRhDxO4j4GiLuQ8TfBQBAxLW59/Yj4kFEXJFr+1Tuv5jDiHhnwS9XLgqI+F1EvB8RXwWAv0XEjYj4Sm5cHkfEmly75xHxXblyPSJ25coXjGXu/Y+y9/8fInpz708g4t8h4gEA2DonJ10aXA8AqamFFQDAGHMAAF5AxPtyc+zQ1DxDxHJE3JHbQTg0NVcB4H8CwLLcON43+6eh5KESAIYBCo4dIOJ/RcQTiPgCIn4fET8/Z0esAOi8nOOIzBcBY0xn7gdtKp3rlQCwwRgzhIhfAYBnjTEfR8RqAHgNEX8FAH8CAN8wxvxzTlbxAsAtANBrjHk/AAAiVl34bcolYhEAXGOMcRDxIAB8xhizExH/GgC+DACfLfDZC8YSEdcAwJ0AcK0xJoWI/wAAdwPAQwAQAYBXjTF/fknPSFkHAHumef/3AWAjAFwOAPUA8Doi/hoAzgHAbcaYMUSsB4BXEPGnAPAXALDOGLNxlo5byU8ZIu4HgBAANAPADbn34zD92L0LAG6H7Fj7AWAvTH9PKLNHyc/Lef/QMw3PGGOm4ni/DwA+yP67CAHAYgB4GQD+CyIuAoDHjDEdiHgIAP4OEf8XZLftZrzVp/zW/Cj3wFMFANXGmJ259x8EgB/9hs9ON5Y3AsBVkJ24AABlAHA2194BgEcv+hkoM+U6APh+TlYcQMSdALAJAH4OAF9BxO2QzTKxEAAa83ejzAGxqR85RNwKAA8h4joAQJh+7K4FgJ8YY+IAEEfEJ+bouJXfTMnMy3n/0IOISyH7Qzb1ozbJqwHgdmPMCetjx3JyyvsB4GeI+CljzLOIeCVkd3z+ByLuMMb89aU+fgUA5JjlIw0kx7rJvIwxj9hjCdlxf9AY88Vp+omrHc+scAQA7ngb7e+GbIqgq3K7c13AxlkpLowxL+f+818A2TVTx25+UPLzcl7b9CDiAgC4HwD+r5neIvsXAPAZzP27j4hX5P4uBYBOY8w3AeAnALABEVsAIGqM+R4A3AdZmUyZRYwxowAwjIjbcm/9AQBM7fp0QXb3BoBN2unGEgB2AMAdmDV0B0SsRcS2S38GCuNZAAgi4ien3kDEDQAwAgB3IqI3N3+3A8BrAFAFAGdzC+v1ADA1XuMAUDG7h678JhBxNWTNAs5D/rF7EQBuRcQQIpYDwAfm5mgVRsnPy/m40zOlK/sh+9//wwDwtTxt/wYAvg4ABzHr1v4mZCfehwDgDxAxBQD9APAVyG7l3YeIGQBIAcCfXtKzUPLxRwBwPyKGAaATAD6We/+rAPDD3GR9irW/YCxz9lxfAoBf5sY9BQD3gIaDnzWMMQYRbwOAryPiFyBr99EFWfuscgA4AAAGAP6zMaYfEf8ZAJ7Iycy7AeB4rp/ziPgiIh4GgJ8bY/7THJyOkmVq7QXI7qb+UU6Wzjd2r+fsPw4CwAAAHAKA0Tk4biWHzst56LKuKIqizA8QsdwYM5H7J+bXAPBJY8zeuT4upXSZjzs9iqIoyvzgAUS8DLJ2IA/qA48y1+hOj6IoiqIoJcG8NmRWFEVRFEWZKfrQoyiKoihKSaAPPYqiKIqilAT60KMoiqIoSkmgDz2KoiiKopQE+tCjKIqiKEpJ8P8Bj1MGjpc0r3MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v0dGy1wXR5R1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "829e0b37-c607-4139-8b8e-ce11ab07d19f"
      },
      "source": [
        " train_images.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTVzHZ53SBlO"
      },
      "source": [
        "train_images = np.expand_dims(train_images, 3)\n",
        "test_images = np.expand_dims(test_images, 3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VAHaPomSDxt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c9d31c4-e1e6-465d-f988-e2443cbb877b"
      },
      "source": [
        " train_images.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLaD26bWSNk9"
      },
      "source": [
        "model = models.Sequential()\n",
        "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Dropout(.5))\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Dropout(.5))\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "model.add(layers.Dense(10, activation='softmax'))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUYEBoTESRvG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d30a02c2-3107-44b9-bcc6-83abd93800ca"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 3, 3, 64)          36928     \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                36928     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 93,322\n",
            "Trainable params: 93,322\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2myyFz-7Sf-y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5773edfa-5bf9-4763-b582-8e3fc6e5dfbf"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_images, train_labels, epochs=10,\n",
        "                    validation_data=(test_images, test_labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.0878 - accuracy: 0.6781 - val_loss: 0.5535 - val_accuracy: 0.8038\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5303 - accuracy: 0.8046 - val_loss: 0.4323 - val_accuracy: 0.8425\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4728 - accuracy: 0.8263 - val_loss: 0.4122 - val_accuracy: 0.8473\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4389 - accuracy: 0.8371 - val_loss: 0.3827 - val_accuracy: 0.8606\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4221 - accuracy: 0.8450 - val_loss: 0.3944 - val_accuracy: 0.8560\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4053 - accuracy: 0.8491 - val_loss: 0.3700 - val_accuracy: 0.8654\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3999 - accuracy: 0.8515 - val_loss: 0.3620 - val_accuracy: 0.8664\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3877 - accuracy: 0.8548 - val_loss: 0.3630 - val_accuracy: 0.8659\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3820 - accuracy: 0.8555 - val_loss: 0.3666 - val_accuracy: 0.8635\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3759 - accuracy: 0.8588 - val_loss: 0.3440 - val_accuracy: 0.8739\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQ1swZ4Mibw0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "fd8fdcdf-39e4-4c4b-9bb1-288e1672fbee"
      },
      "source": [
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0.5, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 1s - loss: 0.3440 - accuracy: 0.8739\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"
          }
        }
      ]
    }
  ]
}