chest_xray_pneumonia.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "chest_xray_pneumonia.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN4a/C7hdKu9jTdJVubrcQ3"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSN3VZtTP4fw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get the dataset\n",
        "!wget -cq https://data.mendeley.com/datasets/rscbjbr9sj/2/files/f12eaf6d-6023-432f-acc9-80c9d7393433/ChestXRay2017.zip\n",
        "\n",
        "# Unzip\n",
        "!unzip -qq ChestXRay2017.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ktxOZN3T2KY",
        "colab_type": "code",
        "outputId": "6c6e0b94-6200-4cf8-8622-3f04f898617a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "chest_xray  ChestXRay2017.zip  __MACOSX  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYlB0RQ-z8vg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import imshow\n",
        "%matplotlib inline\n",
        "\n",
        "from keras.preprocessing.image import ImageDataGenerator, load_img\n",
        "from keras.applications.vgg19 import VGG19\n",
        "from keras.models import Model\n",
        "from keras import layers\n",
        "from keras.layers import Dense\n",
        "from keras.optimizers import Adam\n",
        "from keras import backend as K\n",
        "from keras.callbacks import ModelCheckpoint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tz8Zan7IFEQg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_dir = 'chest_xray'\n",
        "\n",
        "train_dir = data_dir + '/train' # Path to train directory\n",
        "test_dir = data_dir + '/test' # Path to test directory"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3N9xhXtU3k74",
        "colab_type": "code",
        "outputId": "76ef8dc3-39e4-42d1-9c66-e5fab3b98c4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "# Display Normal chest x-ray image\n",
        "img_normal = load_img('chest_xray/train/NORMAL/IM-0115-0001.jpeg')\n",
        "print('NORMAL')\n",
        "plt.imshow(img_normal)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "NORMAL\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MJgHEz549C1BAL3BRVlJoTKenp6H9YOSSAtQ2Nze1s7MTHt2zM9vb29ra2tJkMgk9bjKZ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\nTkh8cXER38XSHPrYJ369Xo9jq2azWdRXwb4dTMi0Ac5kFGE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SKZVKQcwzzwgfiAX0i7Xn\n2m5UoAQgvHGNQYagnCzLYuw9Agt3x3c3Nze1sbERkU8CH7jd5+fnMXd0F+A90BPu57e//W3t7+9H\nrR6pH3fv3pU07tXF/bhxZt2gTJlHlKGT/ige5syJbCeti3LpiIr/nfNzNMX1Puv44+oS8C9Jeppl\n2fNP+cyfkfS/Zll2nmXZRxrt/fYrtzm5k9OuoJxYZiCc+OYzriCKCMqjJX49BEEah0XhAbAouCJ8\n1slddwu5Hu8ByekOsLKykmvEhSBRVzU/P6833ngjhwhIK4BHkhRQ/fT0VN1uVx999FHwLITNQXaE\nxrMsixycw8PDED4S+9rtto6Pj6OI2NEoZRwgAFAgSgKXg7HGLSZ5EHIcJQOywu0hGohwgdSIFCLs\nP80tkcadHIvuC9tULS8v68GDB1peXtbDhw+jKRtKpNlsxveoTXNlWqvVQqn6tulZlunFixf6/ve/\nr52dndyWTLjCrK3r62u1Wi0tLS3l0laY1/fee0+Tk5PqdDrRlYIkVQ/kwE3BYULiMybIECQ0a915\nTpRV0di7S4xhKHoL/Oa77tbx/z9WpFQ4fk3Sb9v//3FK6ccppb+WUmrcvLYqac0+s37z2seOlNJv\npJS+l1L6niMa6eMPmlLK+dNOUDvRjcX1sCSLl2v4e+7WAE1BStLIClEcORgMcglmWCsQAxtOSqMF\nce/ePaU0CquurKyoXC7rxYsX2tzcjL45Z2dnsSFkr9eLPki0FPEID8oVC8xGlAsLC5qfn5ek4CkI\n29My1SE8WxgdHh5qa2srCNR2u63FxcXghojuoNTSTVRtYWEhXB8UCsQ5EbnhcBjENmPqcwSpjVIi\ng5wETxY8RC/fI7hAhBFlyNxSX1fc1GEwGESLYlIeiGYuLS2pWq1qYWFBb775ZpD7zDdzDvFfrVZz\npL6kUEYvXryILP6ZmZmoV8QgsB5XVlaCbC+Xy4FEf/jDH6rRaOR4oPv378c1UFAodc+yBj0TOfU2\nMa5AHBF5IiXj7ArZlRyKhu+6DKL0ioru046fWymllCYl/WuS/rebl/6qpDckfU3SpqT/9mc9Z5Zl\nv5Vl2TeyLPuGP7BHBm6uLUm5Be0DxYB4VrWUTxTz+h0m0J4tfljMzichAM4nuZJzGOvnJ7kOkpnQ\nP1YfToZIG7uheEcCIhzwThsbG1G8STgfV5KMcMaAXUFAZ7SOnZ6e1sLCgh49eqTV1VXmIqJNWTbK\ng9nb28vVne3v74fSR+gYBxAWBDL7pdHDiHHEtfQQNs8JYpQUboW3yHVOBITHOIB6fU5BPNPT05G8\niYKkVId5Bh2Wy6MC3oWFBS0uLgafJinQK+tyMBgEb3R5eRmBjMPDQ3344YfBE2HQsiyL+cf1rNfr\nofjJ6t/d3Q1FdH5+Ht0HoCSyLAvU6usPCsC5O8aAtYyb7dyZ0xKOhhhvj8p55jfz4lE5acz1ftbx\nxxF9+1ck/X9Zlm3fPNy2LYD/SdLfv/l3Q9I9+97dm9c+9XCBdi0MyehEG4Lgn0dD4yezWD3EycGg\ne4o8n4NLIlu2qAT92u4ewsVg/R88eCBpVOnPXm+Qt7VaLTgRWuOSbQ3Jy/PSF4daOhITCbEj6LVa\nTe12OzoH8MykLxAJIiucTHFvnObKNLshtAlNez/tSqWSc5fgU5gD+kZhTLJs1KcaQeI7oC3uia6K\nIAfq/3gfwSKKxVyBaiXlujOAOEFGCBBCDYLkvgeDQaRqkFkPaqKbw/Pnz9VutyMBE7QBF+TCuLa2\npomJidiVxueEccyyTKurq7q+vo7WxbxPmgH9210moBUgrFmjICn6MJGVjvJBkXMgL45mHRlLY2SE\ncXdvw7kmPuvn/7Tjj8N9+3My1y2ltGzv/euS3r75++9K+rWU0lRK6XVJX5D0ndtcwLkihJxFxAA5\nX4Tw8eMLzKMBrricXypWReNzw4WgDFlI3CMWw0O1HolD2HDNhsNhlGd0Op0oS0kpRSsK/qb8QRop\nJFywcnlUyMpilBT3iSDCYzUaDS0vL+eUBdEv3C8yqUEclJCAChF4UhFIHXDBYoHCU6FwFxcXYzNL\nipFBmtLIJXvx4oUGg4F2d3e1ubmp9fV17ezs6Pr6WoeHhxHdgoD3feuJ9FFTmFKKZ/FUBnKvULhz\nc3OhjO7cuaNOp6Nms5kj7slqp2XLwcFBIEASaeHVSNjEpaa2jTVbLpd1fHysly9fxv2DNIgGXl1d\nheGh8BfCnULmq6sr1ev1XAoK98F4oAxY/97Vk7Xt8lCMTKPYUfysdfcMWPsoLVdMRaV1m+PnQkop\npVlJ/7Kk/8Be/q9TSl+TlEl6xntZlr2TUvpbkv5Q0pWk/yjLsmvd8gAZeSpAEUV56JwBksYoCqsI\nh+FNqqRxnovnO0mKvsqgCyezWWwMPOfmul4Yi8WanZ0NhEFe0vX1aLNIJ26ZbNyIk5OTCNXTVB8F\n6hFEEvqw+LOzs6GIQBLSCB2BTkBs19fXuSghi59kwHv37oWCQAmhmHGXBoNBcCkYAeq8PMrlGzmC\nEFutVuTZUKcHgvMM5VqtFkS0b9ENGmKMyar3tBEQcblcju2weVbq7Hq9XuxSgrIiYub8C/3XsyyL\ngmWMEYgV/oj54btswZRlWWx/7kESSOosy/SlL31Jx8fHarfbsc7Pz8+1sLCg7e3tWPPuTrE2mFMQ\nGzIAmvboqMuXpJARFB9y5BFezyEEdXpKgAehbkN0/1xKKcuygaRW4bV/61M+/5cl/eU/yrU+ifDm\nQR3C8xngJoJDQy2spjRGXSg2rgOn4VBfyuc/UfDKgUJD2ZHNfHV1lYvaoEBIlmPTw6urKy0sLMRC\nguhEWDc2NkLJcR1QDrlC8FQXFxeRaT0xMWryxhhcXo52nZ2fn496MleA7EBLaN6jO2QteyU6r19f\nX0eBcNGVJrGUsTk7O9Pc3JwkRfRxc3Mzdk1pt9uhtLz2iuemhxGcHM/A5gfcM61ZWA8e2UR4Tk5O\ngvvhtdnZ2egf1e12VavVguNiZ+LJycmcSwlK8lYjbCRAFwLQG9tMed7QxsaGFhcXY0wdRVJGQrBA\nUqAmDE+5XI7SIyepCQg4r4orx5rxSBzvcw0OIp3IDYrdEROgwdET53T+77OOVyKj2zVsUdsWBxxh\ncK2PWzMcDkPZsFCLfjRIiEHHFZCUu45Dcd7zlhKErLkG15YUC67b7UZ9FNzA3Nycut1uNDOjOVu7\n3Q7lAmmMYFOzVamM2p7QgQA3E+7J+zCdnZ1FFTuL/uLiIvJkqNqHl6rValFFD7d2cHCgwWAQ9X3N\nZjOu40JBxb0HGLx5G2T+cDiMKnkytyHcEXznlbweUhqHusnxQYFXKpXc5gx8bzgcBg+XUopyEJAG\nJUIXFxd69uxZLreo1WpFi5nr61GRss8x7iDJl6RVEGhwro2NN3EFV1ZWAuEsLy/n6hj7/b4WFxcj\n6gcy92iglzxxoJRR7hhOkJp3A/AxdVqE14voCGTH50DMKFTPE7zN8blXSmhb549w2zyJjs+4MnK2\nv+gzez2ch/xRLhwUwHKARNzXRlF5ZriHP7lH+AxcMqwYigzEND09rY2NjVwHApqIcR0SA3FlPJ+H\nXCNcHZ4VrmNqairaoayvr8czkv/jqQ+4cUTXyE7ms9J4bz0+78rQkweJckEANxoN3blzR91uN1xI\n7ptkRPoJOd/HtYfDUQIpKQq8zz1zLSJ7RWFFMEmroMUKSJn5hpCXFEJMLeDOzo4uLkYdQOHoMHok\npKIE2+229vf3c4IP3wZKOzs705MnT9TpdPTw4cNYY61WK1Dz/v5+cHxOMPN8oG0MuLtnV1dX4bax\nTp3TksZpN+6ZeJ5S0UVkHXoajSOlYhT6s47PvVJCqbhSksY5SLznCoeJ8kga3BHf4T2UG5OIIEtj\nTiilcfOtYpjdI2xYBrKTvd9No9FQs9mMyBaKjZyYLMvU7/cjgbHVamljYyN4AnbIJUJEzgsbVMJd\nEP51Hz7LRi1iJyYmtL29HeUmoA+QCUiPREUWba1W09XVlXq9XkTxZmZmdHx8rHq9Hi4cHEyWZYEg\nGG/SAaanp1Wv16M5P0XNQH8nRbHs3BeuCgQ3CpbxIErqc8U9oLh43VEBSpnEQxQZ80UIn/wk1gs8\nXL1e14sXLySN+Ee6bHoUjHEm4ZV8MeZHGlMAlNmsr6+r0+lELhPK3HuEe/Ku9wJzF4yAB+gT5UPH\nTXglxgMFxf2AwHxNOX2AcnXlx2sOKH6h3LcifJTGIXgUg79e5J5QZsX8Hl+YxQzWlFIkwkHkomiw\nfh4yR/nB70DolsvlyGtBcHz3VcK7uEOE2iWp0+loe3s7pzBQlEtLS5Hrw4LE6p6fn6vX68UiTGnU\nCQD37+TkJPgJkCEROyczffzppuBtNOBkUMrlcjkQBYS+pFBkKBWE+erqSrVaLRSvuyQcWHWP5CCI\njkhAV7R1Yc6np6fDdZLGO/L6PEMBMG8g57m5Oe3u7gYXBjmM2wdxTBE059ne3ta9e/diQ4J6vR7d\nIzziy7MzHlk2SgPwAAeuKryeK1zKi6anp2O3XhQ9zycp+ElXgKxb0JUjax/vYpoL/+OFuMeCx8Ca\ncTkryuKnHa+EUvJWIq6Zi4PF+1J+9xMv9yjmIXmZiSMqFqUvInKG4ENY0E6Qeji0XC5Hrx4UGwgH\nATo7O9PGxkYOufmkv/baa7HBISUNbBQAqoGj8oZwjM2TJ09yoWbPY3Hk8ezZs4haQVZLCmGkhQZ9\nxEmY7Pf7kR7AeeFWPCIFd0Pz/JOTE9Xr9RAyKb/5A//DlaF8h8NhKDsSOjEErtA8T4deR4wDCAqC\nXhq73Cgnom1OZBc5GcL5cHw0/Z+bm9OTJ090cXER0UqQC/lkcFMTE6Nt0lEuXIv/2elkdXU1SHQU\nuyTt7+/HNt+OLOHzcO28fQ0cD2vAU2iQDV7zaJrPj7+P/PAZT24ugolfGKTkmpgBdY3rsNIzrD2s\nLykE85MG11EXFoQIDFZGUi4B0ycBiw46worfv38/F5manJyMFh/OLxDRQpjdIi0tLalUKkXeiqTg\nFyC1Wfhzc3N6+fJllJTMzc0FSri+vo6dZp2/wh1jUTtSurq60t7enpaWlkJoIOEvLy8jiRDh9eck\nE5v58GAEIXAEEGXmXBmHCxqELmPNe6CS2dlZ9fv9UNbu7iJ4IDuSL3H7QIqeSDk7O6uDg4OY+0ql\nEsXIPCv3s7W1pdnZWXW73VgnGxsb0R8JRDcxMaFGoxHjzRienZ3lOC/crOFwqBcvXqher0frE/g8\nFOT9+/f17NmzyEPyUDyROi9Dkcao0RGyUyEoEDdQReXkoIBAhDRWSG60HQF/2vG537a7eDgclMbR\nOAbQlYvzRRwMKJnDLAAmA06m0Wjk3EIUBsLhqI33vYg3paQvf/nLurgYN1SjdmkwGGhjY0N7e3vR\nTtbzmXDRWq2WFhYWgkSEEOW6tVot3ERybJ4/fx7dCRFwlAQIiDGAO+CeUbTeJZFz4+pR38VOJ7T4\nYA5o3saCBNWQc8N88LwoNAhxNxJFrtAtPf3GPdyMW4lQMn+sGc91u76+Dv4IYWVLJlzDYhmRFwSj\n4KgXBP0ghKwVkhufPXsW5Dm9q7w8CgNBWoB3iCC7fnd3N/o3HR8f6+DgIKJv5+fnWlpaimf2SCAH\n6JFnYbyLyokxcwPpHgPnQA6cIEfuQLju5t3WfXsllBILERdKym8b7BOBBXfUU3QLSqVSDl24lkcB\neNoAnBGRDb8+5yPVQBoJN6FtXL1msxmk9E9+8hO9fPkyrNzp6al2d3cjl2VhYSEE++joKELbEN70\n1mFR7+zs6IMPPgji0Z9lOBxtcw2XhAAhoJyXZyHEv7i4qGq1qna7reXl5chdIkqG4k5ptNkBBwsP\nt41zE/1yy42yLHIgHkDAUEgKhcF5icKhBHCJfX64HorV695KpZLa7baazaaazWbOMAyHw0hlQLhI\nkXBDc+fOnWjihotLoik1jszzBx98oLW1tdyGBCBk0Bl9l0B+TpJfXV1pY2NDvV4vemfVarXgwiTp\n3r17sRY9SMS4MRekgBC1Q2kXa+GKtAiJw+6WuYLylAFPFHW+6bOOV8Z9cy3NgDiB7YcvRiwxn3Of\n2t0wKV/nBoIokuGE8Zlcj+g5mfj6669rMBhELsjDhw/V7/f13e9+NyaOPjn9fj/cGXa4ZSFKivuk\nmFWS6vW61tfXtb29HblA+/v7qlQqkUnMM6GcJMWCJ3KDYqIUwtGh70jifJrDeULoIE84CVAECYQs\nTM/bom6vWMPo8wmvIinQH8QvOUU8H4aDvx0JOwLjNQpePZxNpNFJZbaywj0mFaBcLgeJzzoi+lgs\nZcmycWb9s2fPdO/evVBCbHfOLsOStLCwoJRSZM5j+E5OTvTy5ctQfCBgEjk91QVFiYInGIKSxXjB\ncbLemAd3ZVnXHiDivCitYrCo6NXchk+SXiGkVCS5fXCKvqpH34qkuENWJy89F6nZbMZgs0AhDoG6\nDnlBVljsVquVq5NbWFjQ3NycvvWtb+WuS3Y1bVRxEbHkLAasM+UOu7u7+vDDDyMKBXrhHkBQhIIZ\nB8+xurq6imZt8EyeQQyycIWC8K2urgZ6w1JDkDNHc3NzIeCMFchHUq76HQVaVJ7cj7sDg8EgV1BL\naD3LskhLYH2goFFUXNMznuGzcNlQ1B5VXF5e1tTUlBYXF4Po9zwm1tfU1JQajYYmJ0f71h0dHenw\n8DBQImT8xMSEXr58qbW1tciNIkrI/bNG2BkYZXR4eBh84uPHj5VSCveOXksPbjpTuoLHOKGE+R73\n5i4vMuFEuaMur57AMHMdN2pFWQVNfaa831Iv/FM93I/1BDsUxicV+znXg0aHD5Ly6AtlApxGWDyK\nxGdx95zg9uLScrmsBw8e5DY2fPTokf7gD/4g7gUUBmnp7Tx4Liw693d2dqajoyM9e/Ysx31lWRbX\nJnxM+N0btsENzc/Pq9lsBspgLLHIzWYzlAu5RuzqmlKK8pWlpaVwTeGNipGfcrkcpDCbCdD21d08\n3E4QEM+BYErjrb4hlrkez5dSCgVH3ySUlyMplAnChhL2a2dZlrsHSVGyQzM38r7m5+dDsbCVN0R9\nuVwOpOj8HZGvLMu0s7MTRcNEwo6OjqKej3ns9/thaFA2V1dXeuedd3R1daVmsxnrlUx/RzZOgaD0\nPE+K16Vxa1s37h544X1Hnzwz53JCnHm6LVJ6Jdw317KeMMlvjxwwcH4wgd7gy5EWnymVSpqfn8/V\n9LgSchLbLREThOBR75TSqO82WwUBkamsJzt6YmJCe3t7oThYAERjcHW4X3go0AQtQTx6dHV1FaSz\n82bkxRClQvmw+BGGmZmZUFDNZjNajEDcViqVQEkgKhQDiXWgG/Z8QzBJX8iyLMbB3Uw+Qy4QJRgo\ndK6BkDv/h7KnANjdB9zaovUmix0kiIsjKYzL3NxctLIlYubz9+DBg+C6GEPPb6IfFq4i918qlfTs\n2TPduXMn9olz140eWZ1OJ/LY6DRRqVS0s7Oj9957T7/yK78S/Nr5+bneeOMNffDBB4GccdNZt5Jy\n1EWtVos0D/dKnAZxDlJSVCUADjzszxp27hfZ+azjlVBKrkiksdbGwrlbh4ZHyUjjwcc9cpTEBPFZ\nd4OYABYq9Wl8DwgMp4KwUwcljSItuBlEWOCRgOpeNEnZBHlIWFWugdAiSCgeiFlpxDexVxtdFeFR\n3EUj/wvXkX3k2N2k0+lIGnNl5FuBPmiIdn19HajJy3QoR4GEBs3VarVQYL7brhO/g8EgiGzGoUg4\n4z6iKFDU8E3cC+sDRUGKBkqc8aNfEYmdIEzWBO/PzMyo1+tF9A+lw/mHw6GWl5ejTpH3PSJF8iNu\nEoaNrgUgNeYJAp2ulR6c2Nra0uTkpBYXF/Xs2bOoVZTGHoG7Wo4AkS3WLWi+6Il4BM4VDbJCHh7n\ndBoBuXTy/dOOV0IpMbBOVDtnxOGRJCyvKyteBwIzqbxHb2YmAsvjVpi2qq7UnPNot9tRPtJqtSJk\nPDExoU6nE60+KMIlUQ+ug9omntch+3A4jHMhmNIoYtZoNCJFAFJ2YmIi+gLBHdD6hNot8pUmJiaC\nA0opRV1crVaL7Zi63W7wX7i5/X4/FrlzULgToCKEnqp+UAvRH/ZOo3p9fn4+kJMrBRAgTdeojMdI\nnZ+f53qA09mROWacvVYO5OWC68gRknphYSHQAVtX8TySYlNO9l3zSCHvUTKCAiPpsVwuR1dPWg+z\ndulnxWYSjUYjun+iNH/v935P3/zmN/XRRx+FscIAE232vDv3KjAc7mKiRJxSKLp5fBYvRcrXSrp8\nSbdDSdIropSKDL5D/SJ3VAwFgyycFHeFxuDT48fJYoTeyWwEiR9JuXQABMLD+Fk2yqF59uxZLEii\nHk4+lsvlXAvU6+vr4AdAMSxWBG1ycjJ2tsBaUdkOOc6C6/f7arfbKpXGnSfp6829EiHCVYMnY0yK\n5TQI6cTERCRAlstlPX/+XJ1OJye0oA9SARBc30FXyhd0oiTIGmeOfU5Iv8DFxS1CCaKYeP/09DS2\nEveSChQ152YMQTcoTua61Wppa2tLkiKZk26hjuQuLy+1uLgYinhqakrdbldZlsW8cB8nJyd6+vSp\n5ufnw8jAozE+NHpbW1uL++ZeaamDgnceSBpHkH2tOz/kMoaCAaXyXB6FJgGVted5T0UXsCjLP+14\nZZSSwz9/SC8klMYknZRvUUs0hvNhxVNKAfWZBLcMZDqTC4JQEgnzfKFSqRTwn2iONE5aI2EOgfZ8\nqsFgECUiCAGkJ+d35Le4uKharRaoCCUJasP1AVWdn4/2pO92u9rZ2Qn3EGQCWX3v3r1cITHIiCxu\nEAWuCQ3Mjo6Oou8QSZYgEaKJCCVKstlsBvdDjhEtgRljxg9h4xlxw5gbhAQXC8NAtIrxc4WBcvWu\nD8XuD+VyOZ4R7k9SBApoRsfzocS5RzgndpVhXrydibu8KaVobnd2dqY333wzWpuAeOB37t+/Hw0C\n2aLp0aNHwSWtrKzoyZMnOQ/B01hYeyBp8qpQUu6hoLiRDc5ZBAHOK7lMAiRuc7wSSgnL6OS0KxsG\nxR+aQfRaHnfN3HIUw6FY8yIklRRcAFac11EcoA2U3eHhYShOmvVzTko2Li4udHBwEPdAO487d+5E\nz2yecWVlJRAOvBSCRoErPEmv1wtXZ2dnR91uNxQkaOjs7ExLS0uxy6u7KDyv528RBaO6H4TiRDfu\nj/eJ8rSC4+PjUD7MGfPkPBBlNSQ1ct8ksWK9iVIx/rhh5FCBxjzR0p+Lvkjcoz+D5+FcX1+HO06q\nAZ9/7bXXNDExET26UXwYM5A3fBZEtqcmXF5eRk8qxur999+PHU6gFCjgJep2eHgY7iZr4+LiItYC\n69sVCBFMaYT0CXYwnowlaxslBcLyeWV9IycYfBSUp+Dc5ngllBLKx0OQrpEdcbgL59DT80k87wnF\n41snMWkMPp0nK5VKEJZYGtCIpNy2zfV6PdpC4No0Gg3t7u5G3Rs9n10AUIqgBK7DLrVEtRAu+Cnu\nH94JLoNiXu4T/orv0L2gVqtpdXU1Fj47cIBevDgZAthD4FyPhXl1dRXdCCiBYR4QWqA/84grC6L1\ngAVCBAeGsLkVd0SKMiKFhNQE5hsjgjJFEXJ/5A1Jik0KJiYm1Gq1gh9D8C4vL7W+vh5Z6jTT297e\njmguRsOJZNIqTk9PQxFhhLiv6enp2JQAFxY05WgeJX9+fq5OpxOkP8+FMvcIqCsnDALK0XkhD+1L\n4w6srsAcHDCfHJ56cZvjlVBKHn1DobiLxQJlUDzZiwF0tCWNk95wv7AULGZXVh5Chdx0KEsuEwuK\nNABSAIOBkwkAACAASURBVKiZIodma2srMrHhIrhnDxXPzMxEPZMrZO/ueHU1ah6PezAxMdrVFgE9\nPz+PHBtXagivNFJ87XY7EKikyL3BVcOl45mou0IxUmM3HA7D2pfL5XBDuL7zcm40iGgxZ7i5zDnu\nNQaAZ2cuPJxNeoaXSTB+uHKsBc+HIhrKPRAlBIF4kIF5Yp2wXdTW1lagpbt378Y44Lqx+26n04m8\nq5mZGe3u7kYKCM9XKpUChZ6fn2tnZ0cPHz4M7o75ZQt3Nz5wTEQmi0qauXZyH+KcdeEROpcdj3gz\nxj6mHnxyPstl+dOOV0IpFSEoiMJJM1dEDDiDysJkMoCS/A3Scqvg2/NwfoTZ/WcWHGR5qVSKFreH\nh4eanZ2Nxdzv9yOqhQKRxv1uUADlclkrKyuxkFFStD2hVQWLAKGn/QjfoaEYmb5YfCrcs2zUv4eI\nETlAKAqejYjWcDiqvVtfX88hFJAdJQ/kMIGYGD+ifJ4NLY07JMBpeL4NAQMUMq5LtVqNTpy4eIwT\nnAsCg5vl7pi7GsWyEgwRiAJjxHhTw0jum3OAl5eXOjg4UK1W09tvv63JyUl98YtfDOVSr9d1dHSk\njY0NnZ+Pdjohl2tiYtTGxNEcAR1+3n33Xb311luSFPlq9XpdBwcHWlhYkKQwnETrWGNOZpPf5vws\nyN9lgrHxXDdp3JoHBcbcOv/r81Hkgj/tuFVGdxrtdLuTUnrbXmumlH43pfTBze/GzesppfRXUkpP\n0miX3H/WvvPrN5//IKX067e5tqScsmEAWFBFCMqBkBdzK1g8kkJAUHTAceet0Pz8z8AitFhzoD2+\nNkLmpOrTp0+1t7cXUR8npkEVjUZDb7zxRvzvWdqtViuqxFkMzWZTL1680MuXL3MlNCQnIkhEFxkn\n3D/urVarhTJjUU1Ojna3pYiVBUdCJW7n0tJSfA5XCOKcFAruhUZndDwA0XDv8FBwPHAw0hj1gtiI\nSnG/jDsbYGKlcVvh+UDErnhAqWdnZzo+Po5UANxhD4SwCSRo21MrEETc34mJCa2tranb7UadHBE1\nxqtcHvWxomsl4+xKg2e7vLzUT37yk1ASjUYjOiyw/t1og+hQ3C4XnmriiAoD5CiT8/J5j366vBUJ\nbpe923JKty0z+euSvll47T+T9A+zLPuCpH9487802pzyCzc/v6HRjrlKKTUl/aakf07Sr0j6zTTe\n0vtTj6LP6pqdwfD8Jd5HYWHRHYLym0VBFILve94KURppHN2Dk+H11dXV8PXhufheSknPnz+PcDK1\nZvBHpAG8/vrrmpqait1V4RR840OqzycnJ7W+vq6NjQ21Wi1NTk6q3W7HVtNsOIhbycI6OTlRv9//\nRJKYsYRE9rYjKDDcHAQbdxDejYxxabxBAu4rc+fJg5KCr5IUJDAkMefGnfUcG86FkSDiyXrw4IY0\nNloppWiwhnJ18pyM6evrUVdQFCPbPKGweF5cQrpMrqysaHV1NergUhp1cqBEqFweZYiT0wapn2Wj\nbbwfPnwYkV13kZgzSXrnnXc0MzOjxcXFXAcGXFq/d5S2R36d7Ce44iiKcWLcWKueeoMS83nl+FmQ\nUfG4lVLKsuwfSdovvPxnJP2Nm7//hqQ/a6//zWx0fFtSPY02qPxTkn43y7L9LMt6kn5XH1d0n3yT\nRkxLH989QRq7bM45SOPcGHffPEPVc5FYBBxEqFjgfIb8IhYjSgZXCk4Bd+zp06e56u3hcKhWqxUV\n4eyMAQ9AWoCkXAQL3ubJkyfqdruqVqtx7eFwqF6vl8v2xk1jIRKy9uRCIjgotfv37+vevXtBeBKy\nJrTPNt/s7EG/7fn5+XAXJycno54PQcINYVzIUgdloHBBroynoxBHjYwl6RyEyhk/xgV3DGTgPBDP\nSNIreVk8KwqWeWdMIfcdFeBK9ft9dbtd7e7uBlJgzGdnZ7W2thaclKd4nJ6exqaiIOZGoxEKkPvA\nOJBmwHNDZqOESf9wNO+BAeSIA7To7rHLHHybB4o8tYXDAQPP5q/d5vh5OKVOlmWbN39vSerc/L0q\nac0+t37z2k97/WNHSuk3NEJZOQXkQo3FYWKBkmjroiXlb6yppxUQceF6LExcGCJq0rg3DXxCqVTS\n6upqRE9SSkESZ1mm999/X5Ii8kMnyNPTUz18+DD+lhRheIjzUmnUcA00A0cBh4E1hTtAcbF7Ky4B\nhDrCirtAVjQEaLVazUFzki8h87vdbvAzV1ej1ra+WSML2qNw7jq6UUGIJcU5UCCMMyQu+Vq4JUTy\nqAND2ZEdTV0fisU5I0hgXC8io4w33JM0Ivu5R1qVUPbS6/W0v78f7Ubg07jW6empnj9/rsXFxRx5\n3W63VS6X9f7774cy99rFk5OT2PyAQAfuJCUr09PTarfburi40JMnT/T1r39d19fXwUcSZGHrc9Y7\nY09agTRWHrjLTuATAEppvLkrbhgkPGiJg7n3CDjz6Urw044/FqI7y7IspXS7EuDbne+3JP2WJE1O\nTmZFGOgumqMYDldCDDAWEaWGoEsKy+XuIRbJyWSSA6V8Gw4mBZTCxHzve9+LxuzD4ag2jaLKTqcT\n98nEukCibKenp/Xuu++GC4RCgP8g1wf3BaTAtuCgLBRiSinaqXrdGgsTN2BiYiK6RXrCIXwOhDsL\n/Pj4OMYNZeQcF8/nSg6SFqHGZQLBgiA8+gfiYZ7gXpgPUCypD75eCHAgaIwziBCESR8mnjulFGMH\nKpUUhqhUKgU6BrHgYhNY8KAIirPX60WvLXpcORlPLSVRO9Y65UqQ7M+ePdMXv/jFUKqkL6B4XeHi\nqhaDQc6bshbIFCcK67lKvnbdLXYeF0P/s7pyP49S2k4pLWdZtnnjnu3cvL4h6Z597u7NaxuS/kTh\n9f/7NhdyBOTQXhpnZ/OeHyw6T94igsUgQxAikAgErwN9vT0H91CpVPTWW29FNTuLt16v63d+53dy\nxaSkC9BniEUGV4LlYjFMT0/r6dOnOWEZDkebNVLw6wQtXFG73Y62quREoYCoV2PxgN6c/E03YWYi\nbow7LguoiK6TCD5oksgPypFzNBqN2OkEtxhFNRyOK+u9NguFAbplHjk3uTqcgyhWlmURNsfau5UG\n4eJeErljjPkuRqlY9F2tViMXbW1tTYPBIDghOj1g6C4uRn3UT09PwyBNTU1F+P7y8lJPnjzRl770\npSjcptuoo7rl5WUdHBxENBVkxT0+fvxYjx49Crcc9Ly6uhrbP6EcUBDO03qUz9MwuIan1mBQOAdj\n50bdSW834Lc5fp5+Sn9XEhG0X5f0f9jrf/4mCverkg5v3Lz/U9KfTCk1bgjuP3nz2mceoAYp76u6\nRYWo9e/AA6AYnH/y6ItHeTxiw49PoDQWQCApiEwabTj4+PFjDYdD7ezsaGdnR4eHh7pz5060lZUU\ncP/y8lIzMzNBZjcaDW1tben73/++jo+Po280aIcF688HH3T37t1AM9vb25IU3AotcbGUu7u7uagI\nuUb9fj/OSdQJlxTkNDc3F64jcwMHAWHquTmlUkm9Xi/mgetC2oOCJIWCAKlyDkmBColaXV+PNkKQ\nFO4yigtD4rlMnqDqlp7scwRLUgQ+KCHhOvQGn5qaCheYnlrLy8uBgOiJhKBjDPhbUrQlvri40Acf\nfKCtrS0tLy+r3W7HPaDYG42G3nrrrZyLDfcFknz77bdDkWMwUkphQECvBFxQHKx3R2ie8Y2yQbF4\nJUORo5XGUTo/3JX7rONWSCml9NsaoZx2SmldoyjafyXpb6WU/j1JzyX9Gzcf/weS/rSkJ5JOJP07\nNwO8n1L6LyV99+ZzfynLsiJ5/okHkNA1trP+KBtHUHwGd8ebe/GdossH1wH0JCqEoNBx0BczE0rI\nfnJyUk+ePIkExqmpKbXb7di8Ee4D4Xrttde0t7enUmmU4fvtb3870BMLC3fQuQlJsUBBWeymcXp6\nGoW80mgbHtrgggDpi+ToA8SBe1gul7W3t6erq1Fbk7OzM925cyd4DvgJiOyURlsuoVDo6khLFWm8\nYQMuFgpNGmfTozyp/veqegzC2dlZtJEF7eCW0J/Jm7yBmOC+cG3crWd9OceC4mDOcP2IHNJFYW5u\nTr1eT3Nzc1pYWNDh4aF2dnY0Pz8vSTEGXAeez/tPVatV/ehHP9Kbb74ZSalQB81mM9fq5fr6Opdl\nD9lP8bFHJBcWFqI7hOehIS+sp2LpDuPu7hqHu8bwr0X3ljnlHl3ePu1It/3gP61jcnIy63Q6uYgL\nlqIIK6VxDo40zmspl8vR+wfXgcUN2YmLw4TTWpWkSLpLQjQSql9cXAxu4K233tLk5KT+3t/7exGF\nW1lZiQXlKICoBZzXj3/8Y9Xr9SjSnJyczAklETkEeGlpKbKI+/1+JM5B2oJkcCvK5VHWNmFt+DHv\nc1Qs1JSkbrerXq8X6Gxubk7Ly8vB79TrdZ+r+B6KAgFhnCXFxgcLCwuhVJlPKd8V0nkyyi+w5tQW\ncs9wKRgY352W987Pz6OuEKGD70Nx0JEBjg9hpBWvc4hwRu+9956mpqZ0cHAQqIzn9fQJoqns51aM\n4E1NjXaJIS0AVxrkQXsdUkn4DIbr7OxMv/qrvxooE2V6eHio7e3tXAoKisxrHSHUPdoJ4gMRXV1d\nRdPCq6uryJFj7hxxMvfM78HBgS4vLz8VMr0S7XARyCKy8cPdNyeos2xcZOsoic+zKJ1gdejpYWnC\n0Uycbyo5PT2tubk5vXjxQufn5xoMBlpdXc1ZfHag4DozMzNqtVpaW1uLtri1Wk31ej2iVlhBLPDy\n8rLm5+cj8jE5OeoJDtwmazilFCFhEhgRtlKppJ2dHe3t7UXaAAoE4eeZCd2j4El2JLrDInQC1Ptk\noyR5DhIZIY5RDMwHYX1v4cJruGzMkSs+BAcB5vfJyUmUiXhoHUNWzOYm2tbtdrW5uRnbGlGuw716\nBjmKhHPwjOQ+wftVq9WI9OJm+9pzDvLDDz/U9vZ2GERym/b398NQtlqt6IZ5dXUVSbXf+c53cmkg\n19fXYTw8KFIEJO6WIS/Mr5PZUj7lBreP94vnZcz+SaQE/BM7XNP6A4MAimFJvsPA4u+CSrwoFHfM\nXUE/H0Ql55QU7o5zQliqra0tDYdDvfbaa0ophcB3u10tLi5GLsnMzIzefffdIOBRQmQ8kw+F4iJ0\nDTryhcWzgCzY1aRWqwUvdnZ2pv39/VjEfJ8dbvf39wMZ0MCMaBnKe35+PpRApVIJS319PS4gdrhO\nNAzXCpcEZEKpDIS0IxcnUXErEBLmnd1SaDdMagOKFtcMVIDV5vlQqtQf0loFJeIoAp4OIYRTIjp1\n//59PX/+PBAurjAKM6UULW85L0qUA+MDsjo7O9OLFy/0y7/8y7EBAbupMPf0VSJZEqR4fHysdrsd\n/Jt3vsTFQ4bgJ30NQQm4K+YIsUijOM/Lmvb14N7MZx2vDFLycCSvufuGEnK+yIXDIyrFiJ1HsoC9\nTo4yUW4V3IIQnWFhrKyshEIaDoe5qm8W4Xe/+91AXFg1v39KOejbPDU1pZWVFVWrVdXr9YgAgdbc\n7aJ3NpzQBx98oPX1dUkKtII7AUoA/aBssmy0BZQ0zm2hYZmHynGLPHKD0MGpkR+EMHnbECy3Gxjm\nDzeCpFKSLBFyBAx3Whora9ZFlmWhbBE63HPGjPdxVzy6Ko17ds3OzqpWq0X0jM/StA6OizknrYJ5\noSVylo3zxeiuAHoEHTui/v73vx/rgHkdDAbq9Xq6uLjQ/Px8uLHIxgcffBDIjMgsOWuUV8EbIgde\ndiKNd2FmjhwR8UzugfA669hBBMryNscroZQkBaLxh0Ux+OEP7pEz32vdU+U5XLm5ZnfN7wPutVTc\nx+XlZSAkeB0pj2pevHihH/3oRyGQ0njyq9WqVldXtbCwENna9XpdCwsLWlxczGXzcj1fVNz/4eGh\ntra2gt8A6VFT5gr6/Pw8wtS4PUSTms1mPHOtVosWrLhZRCtREiiB+fn5sNJ8xpEbDdGKBa1swOho\nxEPMHpL3jHGasOGqMHe4mh7uBy0wZiSSEoFD+FBUROukcXdT/gf17e7u6vj4WPV6XXfv3lWv1ws3\n+Pp6vBsM0VMihQsLC7p//37sisv4M14o3mazqXfeeUf1ej0+w5rZ39/XwcFBdJNAIdNvq16vxzpZ\nWFiI8fZwPc+M/LDevaYUN9TpELg6DIxHqV1OfQxvc7wS7ptHxeAvPi13iZCvu3SuZPiOE+FYTSZF\nyvdxwqJxMPEsbM4/Pz+v58+fx7VI5qvX6/rWt76lwWCger0e7gILAnQDmQzfAl+R0mh7o16vpxcv\nXgRPBGRnCx7+9k0GcF8g750/cz4MReFRLCww5CWEPRwHbhXXwxDgmnE9/vdunU5Yo/zdooIgKJL1\nnlHwVQgG1yI5E+VcqVRCOUxNTUWSI2PA/DJWuNFkrPPM3vUS110abyXOeB0dHWl1dTWnlCYnJyO9\nI6UUShAkPjk5CQEcSHZ2djYifczL48ePI9GS9cmz4uIeHR3Fdz/66CN9+ctfzuXcMc6gYq6XUgqE\nDEJFCTlvKOV74SOLTmhzb7znQYzbHK+EUiqGJF1ZSAqXoPi+lG8Ch6uEG8H7PpBYb/e5sSLupqCM\nPD8JDsTdRwTl93//9wNlHB0d6eTkRLVaLfJepHGlteeTpJQiZQCyFaSQZVlE64bDUcX4zs5OnA+L\ne319rZWVlci6dh6IwkyIaCwxpQrwGKQTUCeH68GCrFRGfbQREq9SB4lJ+c0biwLiQQcWM4LtlfBF\ntwxkAykPH4LC9y6aJJGSkuDzxeskLiKo7AyDEFO+4UQ3LhXJm3t7e+r3+7p7965OT08jAss1Tk5O\nwviklLS0tBTIh3tzI1ipVKLTwIMHD3L9o1j35JHR6nhra0tf+cpXwkNotVoR8oejBPF6jaMjyiLC\ncRcO+XMu1hEt61kao9LbHK+M++YPR7Kia19n+JkkBpSsbAg+T3Z0ElwaZ4iDDoC6TICHzWm4D5pB\n+RFebrVaSinpO9/5TpyT84CWEC4WDvdQKo2KX8lt4joUwlYqFe3u7qrf74eVlPKV+URcarVabJpI\nzhRuJwqNNAXC5vv7++r3+5qZmdHDhw8jwXN+fj44MBR20Qg4mmKeHEXxOeYKpYj7wThhOJxrq1Qq\nsZ04806UjYNnoeAWVMZce1ADJYHSRyGBGD2Hi24FknJKmeeD15qZmYm6wsePH8fnnWOZn58PtEXu\nFp1AQToYFDpTokh+8IMfxL3gVoH6mWdpnEsE2mJto1AvLy8jOkl0k8M5WTwVgghwaax3rg/ycwXl\nke7bckqvBFKS8gvT4T2vI9CeEMbfRQXkrpsT5qAHJ/CkcfdJrCg+9s7Ojh49eqTFxUVlWRZ+PnVn\nJycn+t73vpdDTgsLCxGBu7y8jN7cXvowGAxyfAg7U+zu7uZ4IUo+IJVpKge8J8FRUkQFvRAWRQey\n6nQ6EfYHYdB8DCVAGBtXhDouzw/iddq7eMsPEv5AQ0QGUXK0VXFlRfSIUhSiZZTJeIoA5L007qfu\ngQrWEJ8BLfjc4gpLY2XivcH9mJ+fjw0juTY5UBMTE3rx4oU2Nja0u7urN998MwwBc0ZRLgXH8/Pz\nyrJMh4eH2t3djXVLfeH5+Wi34T/8wz/UV7/61ZgnDyAsLCxoe3s7iPX5+fkoE4JfI9Lpe+65oXBq\nBOVUlDlSXfieey9FBcd3b3O8Ekjpkx5QUk6TS+MaObeG/n0iICxkFJM0ditALCxGR1UQgOVyOdye\n2dnZIFHhiNrttrIs0w9/+MOcsltZWck10IIngmwm4ZJkt0qlEj2eNzc3o6cPQupbLjUajYhUIci4\nJjTgJ5FydnZWi4uLgSDpEsmPE6G4GCxESaGQJiYmIirEYoe3wi1FkZAhj3vl/aL9OdhIETREaoSH\nwVNKuV7lIDeyr3Ev/cdTD0BDcH58hjkANbFmuF8fGyJhg8Eg0iHm5uYC5aCcl5eXQxE/ffpU29vb\nUQ8IEqImEP6M/DFq2A4PD8N4eRT68ePHgaI9cnl+fh5rkAAArrpHsqV8RJIfxvqTPBEn+4utXdzI\nuwwCEG7rvr0ySMm1OELiLgMDweHpAHxGGmU/U2dG9MZLDTyaBQmLwDkETSlF5wDIwZRSWJ6XL1/G\nZOA20e/I3Rp2JOn3+9HoHRhOX2Y4GQSWEgUWBDzA4uJiCIqHtVFYLty9Xi9QBG4MuTlEgDygQHkD\nvAO5R+x1BqHtPA5RJsbN+xqRmIlrwHjwGnMA4kLYvGYQI8H8OyIlCuVuvpOuHgQBKaKMKP1gS3GM\nkucP4T4xZrQB7na7UcJzeHios7Oz2CIJLunx48e6e/euBoNBoFWUAKh4Z2cnxnkwGOT2JYR8z7JM\nL1++1IMHD2IDCsYpy7Lo5819wuGhvL17AWuF+SGqhux4agaeAujIFZLPBXLi1MttjldCKfkDugvn\nEB/XB6XBBLkyA3pi9VEArtFdECkJABlgKYHJNNtnkWPVP/roI0mjkHGr1YqWJaAYIDjN0FgI3W43\nXKgf/OAHYYGxoCw2onEoBkLFcEJwWoeHh7lm/I4Y2UmD83hxZ6k0KmbFLXFezQtncfVcWDAG8EGU\nxXAuLDoZ7M6zwW/RZIzaNULQcC9YaVxJdh7hGb0sA7TBGPguIZ4/BTmOYfAqePLESP50QwZKw0ig\neBmj4XAYLYsxWJVKRZubm2q1WpEOgBIgMODC3+l0InGT8WHM9/b2IkeKdcgcNZtNSWNuCQPmEUeQ\noHsjzCGyAzpCjgiwSGMEJY27AXgk3NMFfuGQUjEpq+i7OqH60yIEDNTZ2VkIMud2tOV5G3A9TAaL\nQZKWl5eDeL66uopSg7W1NV1eXuob3/iG1tbWQijIIOaaWD+Q0cXFhX70ox9JUmQMl0qjXj2QqCxK\noiazs7O6f/9+wHSIcxQM2+3Qg5ui2lKppMXFxYji0Ebj8PAwCNyVlRXNzMwEKsC98ejl4eGhGo2G\nLi8v4xrSGL0S2QH6szU4JCnoqnheSmIwAKCU4+Pj6OKIEMABoZhR9iA23ES2WUJZwDURTQOBX19f\nB/oDPfAe/BlolWgta41kRZQWrtvrr78ezfd4b29vT4eHh/rSl76kiYkJdbvdyEUjrwnEhIu9s7MT\nVAPysLe3FzlgrA8icSheR8UeVPDxZ325UkYhMaeMD2iS19zTABzgWjrxfZvjlVBKziPxgPzNwsQ/\nx/JhCbDeKA7/v2g1OC+uIYLtLUyIlFUqlaj8p9ZsamoqihNXVlYi7wOy2pu0lcvlyCe6vLzUd7/7\nXV1ejro/ooAODg5CITDZtVpN0ggB0GQeAfTxODg4CDep2WxG2QHQXVK4cLgPKI+trS2lNEpSpKzl\n4OAgx7/gvsJVFd23UqkUXTZ5fuf8arWajo6OovcQWw6B1OCN4KU8Z8tTQSjjAcGgkBBaj+CxZiDG\nGefBYBAIA6MDQoJYR+HhQkr5ekvPJE9pnFzLxpu4ovCQHg1cW1uLOkkQF+NIkTbKqtPp5OYWLvPJ\nkyf6whe+IGmEWGgiWC6PkkPJg8LwgigZE0czrAMMPmMHQsMo8j131zi3UytOlN/meCWUkvM/HkFh\nIBw5EWFw39Yt8nA4jKiH77LhhLf/z2BifVigjx490sTERCSrLSwsqNfrqdfraXFxUZubm5qentbq\n6qqe3TSMJ1sZBFEqlfTOO+/EM8L9OCTG7QEVefdLhBElintwdnamxcXFXMdAUhjYDRbBIRIHmUou\nFd0lnYz2LGMPtcNHFRciEB+3Bzfhk5raca8gIHga3FNf+OTxECYvuhe4dU70TkxM6Pj4OHe/cEWc\nC5fd+7I7OewIIKUUShflgsG6c+dOGIq5ublIrSDaSuY6iKvb7UaT/7t370Y+GdFDgipXV1dBWHtp\nEWO+tramt956K5fnRLb3xcVFUAWScoXOHnHzcWNcUxq30ElplJSKbBSDUP6aUyM/i1J6JaJvRDs8\nAcutVJEvIdelSIrzGecLioPFxEjKQVcWqWfdet+eSqUSyOPo6Cgs+sXFhRYWFsKtIn9mf39f7777\nblhFQt5nZ2fq9/sB2xFwuj3SApfwdKPRiLAyglMul3V4eBhbNPF7ZmYmV2jKs6CMGFe6TnpjOsaU\n0DOorF6vR24T7hcIg3o65or34LDIaoZru7y8jGZwhNbr9XrcGwp4cnLyYy1oiIZ5AIR5glthC3N3\nH339kPeDYbi+vg7lQkdMFBmuIPlFc3NzkeCJgiTDm8/4tkqXl5fa2dnRYDCIQut+v6/3338/FBHl\nKUQzWU/siMuYg1p7vZ729vb0+uuvB2LG3Zqbm9Pu7m6MNxFKnt8jZvzgHaCU8S48p89RkSvJYgTu\nZzk+90jJI2j870jJ0ZA0zqtwP9YVlodOPVojjRcnEJXwPVDes70pHWCCEFzcPdykubm54F2Iwvz4\nxz+OWjcWDdE+7hWLTdIe1p+IHRwCFrff78eml0R5cEFBC+TawDucnp5qb29PkqI3FD2xPVSPsEM6\n0+7Wc8cQclxN3KZyuRyZ9CT1sZCLkVHQHjk8w+EweDV3xZ0wBdGBkAkK4GZiOLJslBoBQQ6H4o3S\nSqXxNk4gIMaW+0OhZFkWfa8cYZDZjqLIsix2HWFM6AIqjTdxxJgcHR3pvffe01e/+tVAVIwDTfOI\n0C0tLWljYyPWPbwfrizjD03AM/mcudvFbw8cuZsJp4esuevGb+SL83h6yW2V0+ceKbnfyg/oxN2s\nYqStSIBL+XR4J7aLr7nQYJ2kscJbWloKizg1NaVOp6Nyuaz9/f0gXuFYpBH/Q+7N22+/HULrCpdz\nl8tlLS0txXY/WEn2EJMU+T6Hh4ex0wXohDas0qix/cuXL9Xv9wMVsUUSCYugmJWVFU1PT0cWOvlH\nvN9sNqMvNNwNQuKoAwGWxkmnHsbGvUQpYoExBN5DCQOAgLnxgUty5MaYI5ygDyx8MVwtKVIAjo+P\nKzwaLAAAIABJREFUI8kRZcK5QTd7e3uB0HgflOdokigc84eA4z5R/wcaJRfp5OQkgh/vvPNObp3g\n3hJUwBiurKwED0dEdHd3N5AcdMNwOCozQQ6KKTO40542gQtHqQ8Z4Cgbl08UEufw9BmPbt/m+Nwr\nJSkfdgTae9GsL1ZpvHgRVI/AIfhYVj8373u9D9dCOFJKarVaOjg4CGvXaDRi91O2/fGi4EajoZcv\nX2p7ezvQglsiBIaOjoR+QUg8B4v/6OhI5+fn6vV6sRU4XAVRNJ4DvoQkTKJrWHgal8FjYJlRduQb\nIWwIindWPD8/j5ycwWCgvb29CNE7KqFCHWFHaUmKJEQEHSSCAuO6CAqtXynpYO5AnLg/RKF8bl0J\nUnwKMuR+cYV6vV6E4wnXQ6RTkMx44p7iVqJwuEc+S0sRlJjn+WAEhsOh1tfXIyJHwIDndy9gcXFR\n19fXWl5e1nA4jL5ZEOyMbzFx18P1nvPFnGIgoAVYB/5dJ7I9wvZJisiNwacdn3v3Tfr4lkpMnCMl\n54FQBhyusBACFj7f9dAlvjFW3CE2ionyCe986FXp9Mqm7QR7lXlyISUakNIk5VEDlWVZcAqEhUnA\nRHGy/xiRLXKYcD2Ojo6iJOPo6CjHt9E4jFQAmqXR07vRGG1gTJU+iAgXxiNw1Wo1nod7g7xttVqh\nBFDGkPcEG1CSWFyPcqEkvRUNiAg3DzLY3WiEjPFmTEBWjvDK5XKuj7v3yCbMj1u5tLQUCgJFKynX\n9dKjcKBHon2gPMqAiBjyHdAUHFuv19OjR4+iWwHKWRq7RK+//rpOT08D4R4eHkbt4+npaQQd2u22\nNjc3w3AxDi5rLjOu/CC4iXZLeeXjbra75O623caF+9wrJeeQOHxCim5aMeIm5bdrxlJ4e1WKLvHV\ned3f9yhClo2Tx1qtli4vLwO9cD8HBwdaWFgIMpvJAUaTaQsXBJIhakKUhX7O19ejcgRPWkMwWCCT\nk5O5gt1iy1nO3+v1Qvl6tI3sacZBUixEOA8UEWgT3stJYDgeFKyPLZyXpwdwXpS2t3S5vr6ORnaM\nvysrkBXIjLEhlE9aBwrPkfL09HQUxXoHCBQa5wLx4YL2er1QJigehByDhTJjLjEOIMQ7d+5oZ2cn\nxohNIDifu0UpJT1+/Fhf/OIX1ev1cmT55eWl2u22qtVqoN3r6+vYtKDRaORQa6PR0ObmZk5Rcx2M\nnUfonH8qVjYgZxhxV0yele9R0dscn+m+pZT+WkppJ6X0tr3236SU3ksp/Til9HdSSvWb1x+klE5T\nSj+8+fkf7TtfTyn9JKX0JKX0V9LPQMm7NnZ05AQjyshdOSwr6MAHiBwWFrrzSA6P/WAygOrAZkmR\n1EYG9cLCgh4/fqyDg4MoVej3++p0Omo0GlpYWAjFQ1QGISG57+DgQEdHR7nSlMFgoG63q8FgkNvo\nkO6DkmLxHx4eRhsSokApjfKacPfm5ubUbDZD0YCWrq6uIgmQZnPsaYYgg25ALYwP8+LuBp/zLpzS\nOOvZM6tLpVJkVqeUovQEpAq/QrQMxIHbdHR0FEgUHsddFZQLyornwUUBBaIgpXGNGLza2dmZut2u\ndnZ2Yjsr7h0uibVItAzhrdVqqlarUZzNeKPMGStvuXJ1daX3338/dhMmb+vevXuhzJeXlyMt4eLi\nIop8WfPwf8gOBy6voyMUMxwga6sY5nfjz3kdNGDcPLDxWcdtOKW/Lumbhdd+V9JXsiz7ZyQ9lvQX\n7b2nWZZ97ebnL9jrf1XSvy/pCzc/xXN+4uED4JDRQ5ZOgvtnEGbp41sreR0PEJ+JwTV0bokJ45xu\n8VJKgdSwYs+fP4+oC/d+9+7d4DOurq5Ur9dVr9c1HI532+j3+7GtEYJ0enqq9fX1KMpl4uEmyuVy\nWElJkTZQLpcjmgSaobgUN1BSfDelpHa7rfv378d+Z+yCC2IE+SDQJJY6PwLKrFQq0VoFJVUqlaIt\nChwXJKqkOCeL2Ps9Yd2J/hHyJ4jgvAe5OpzLlSY8D0Q5Y+YN8CCjnawHRVPKw+dPT0+1ubkZhbSD\nwSCMF2uJ6BcKuVqtqt1uh1JknU5OTkZ9Y5HgPzs703vvvRduI2kI8FXn5+fBWRJtJJWB/lhFuXGS\nmrEvRqUJHBTJa9YyBp858uioE+C35ZQ+UyllWfaPJO0XXvu/sixDYr+t0W63P/VIox10a1mWfTsb\n3dnflPRnb3WH+njU7OYePpaf5KipGJp0tOUT4dAVxQPf5G4jigQi+t69e0EGcuDO7Ozs6OnTpxFN\nqVarUQoiKaz6cDiqAWs0GmGREAiiKVtbW8EB4JIhUHAfFH3CC1GKkdK4ayHJewjr0tKS7t27p2q1\nqmazGdnhIA/ulcgd983Y0j3RK/x5DzfNkxvhQ8j+lpTrgy6NoT45WITwea1Y/sOcDAaDEDgvD0EZ\neJW/u1fekoZxhPzH5eHHgwfcK8gRDkuStre3oysD23BRIgLPKCmiqjw/202xX2C1Ws0pZ4IMWZbp\no48+ik0cMIg0APSI5Pb2to6Pj3M7IA+HQ3U6nUBfbni5jufnMcYYwqLs8D3klIg1svfT5PfTjj+O\n6Nu/K+l37P/XU0o/SCn9Pymlf/7mtVVJ6/aZ9ZvXbn1gqd2X9fCuu3BFxp8BcoVUzF71yA1w2MsJ\nOD8Cij8PlC6XR71wTk9P9ezZswi7s+h2d3d1eXkZrgflI5DDoJ47d+6oVqvp4OBAT58+DWTgOUNY\ncYQXIpPnYKwIIVPDhmXH2nu/ISmfozI9Pa3Dw8NAK4TWeXYijWRBO6wH0jt6gvsh6/zi4iI6JOIu\nUPRarVY1MzOjarUaqQe4byg33FZPZEV5wKk5WkaJcd8IPGPFD1yNpEBRJBviVsInIZy4S77N0/b2\ndnBjExMTarVasZ06+95NTk5GDSZRVlIyWDte3c82W1QPkN2PgYDXJMn24OAguk8gC+xA4+jGZQaF\n5ImZxR5SnkqDHIKSpHHEulgU74rs046fi+hOKf3nkq4k/S83L21Kei3Lsm5K6euS/veU0pf/COf9\nDUm/IY3zJooP59qYxeFKBuGQ8rlOLnj+OuctoicnbRG0drsdygY3sNPpqNvt6sMPP1SWZbEzBXkd\nnvhGIh1Q+ezsTHNzc6rVanr+/HlE8khYZCsfBIq96Z3DKZfHfb5ReCmlsMYgBh+XO3fuRGdL0OHJ\nyUkUFpdKo117Xahp94qFxVpnWRbtcKXxpphO+LJfHVEoBA5hpp0JvIjfK+4yc+IIBq6HaBzK/mYt\nxRpgbNxFYUwmJydztYooUXctPeqFAOJmc25eA0VTIzkcjtoVs4Mw16/Vatre3tb5+Wj3W/LHUCwQ\n356zRBRwc3NTq6urkaKA0smyTI1GI1IeXMmAoEGljP0neSKVSiWqDJxD4v1PCiw5Z+vpDpL+8SOl\nlNK/LelflfRv3rhkyrLsPMuy7s3f35f0VNKbkjaUd/Hu3rz2iUeWZb+VZdk3siz7Bg9JVnExiuJ+\nsROZrlxcO7u/C0IAonpfHwbQXURPJkSZ8H6j0dDa2lo0iG80GkG2QjZ7hAKUNTU1Ffkla2tr4b87\nCe4uTqUyag5/eHgY5TLwE7gERHQ8IxyLh4CVSqUoL6EuCsHGsqJ0WdQgJjY9xC3yNAyuOzc3p1ar\nFTVzKCbKPnAnmNepqalIpYBP4X7Jkmdu2d4IwXHug/nDBXbDg+sG+qJNMMoYZOJunt+ru1HOVRJR\nxOX0a5K7BbEOOY8gw3s54Y17S1EtSgrlBprr9/va39+Pe0QRM1cppdhZhfF0ZMtadHJaGqMlZIy+\nTz6e7roVjf+NfsjxordBSBx/JKSUUvqmpP9U0r+YZdmJvb4gaT/LsuuU0kONCO0PsyzbTykdpZR+\nVdL/K+nPS/rvb3s9BogFzITy3sce6ob0A1H5gQBJioWNdWQxENlwFIaVJHTLfXh7i62trVx/ZJAV\n1t/zT3zBHB0dRcfJ2dnZaEJHtT7Z2x7OXlhY0MzMjA4ODkIBYwFpCEdHS1CSoxp+sNozMzNxP9vb\n29rZ2YnEw5WVlSCtQacIPdG6druda+kBueqGwvO84JqGw1H3R0/R8HwhfiOIFxcXQfBCoEMoo6Rx\nNzxbnLlAecDH0WCPtitE7lA8IC+SNeGe4JMwOtw78+vcE1wU55uamoo99Vi/JN1SKgS/1Gq1IkCR\n0rgjBLlnGEFcpb29vRjb1dXVQJK+yYOH7m/k9mNKivkq8k6uXByFSuN+So6qnGIp5iz9tOMzlVJK\n6bcl/QlJ7ZTSuqTf1CjaNiXpd28u8u1sFGn7FyT9pZTSpaShpL+QZRkk+X+oUSTvjkYclPNQn3oU\nw4kIlGt4FzQUj7t1LGoGHcXiqQGch2s6h4VV73Q64aenm8iY71QxMTEReUC4URC11J5Vq9XYwvvw\n8FDValVZloW7Bwn78OFD7e3tRT0aO5lcXo57F+ESNZvNUNz07QZluJCjUEAUW1tbua216QDQaDSC\n2wBNUavnxDZKQVL0CB8OhxFmh4AmT4nfjC3Ic35+PmckEDyvpsfKLy4u6uDgQNJ4hxJJUZ8IwkPI\nnMsAoYKqiLoxx2SB851erxdIiXXF509OTiJFgE0kuF6Rc/R8LVw4NiOVxlX7WTaKDEMRULIESsUd\nv7oa9e86OTlRt9uNYAmEu/NkjvpB4p5A6pyqG3wU/CflACIXzGMRlTKHKLifJSXgM5VSlmV/7hNe\n/p9/ymf/tqS//VPe+56kr9zqruzAAqHhfXFBRBa1v0fepI/38mbhY505h6ScInJ+ypPt9vf3o9Nf\nu93WycmJNjc39ZWvfEXvvvtuIAIWLgoQn/7ly5exIMgHIiyMooWQvLy8VKvVUrPZVLlczgk+C46t\nc3iNZE5cIuqleF7qu0BRXNMzjnH9qGKHfC+VRn2SBoOBms1mXINtiCCFCYujYEFjjAljBOntrjio\nFFRA0MFdd5QjBbu8R/9r1oFvhcRBcTON9Rw5VSqVyCsj9E+uEVEooqqsGRQlaQ6sI1AGSqpWq0Vo\nnTpEaYwIMXaM08TERHBDFxcXqtfrscEo6PLo6CgUNBuYQrxLinw45gWjiBy4rGDQPejDPEkKFFvk\naDkHz+Jyx7wWPZZPOz73Gd1oY7gQlA8LmoWM8nL4+UluHN9xkpMFjhLiuq6wvNYO602kaG1tTfV6\nPULBjUYj8oC8kn84HEYdFeFozn1xcRFcDkp0ampKCwsLkhQELc/DQsN1mJ2dDRfPkRIuHhwU1h7X\nCuHxMalWqzHGtA7xEg5JcS98H9elGP0jSojAz87OBr9GmH1ycjIieTMzM7ke0r43G64pY8Q9MLfM\nPUEAlLoLEsiY8fOOi7hZBCTowe5BFb8+64U9/K6urnK9kDCCRBZxrRjHlJIODg6Cs7tz504k0rqy\nxugOBgPVajUdHx+r2+2qXC5Hp4KXL19GyxLvfY5r614FFIBTH+5FIE9ZluUUtK9N/66jK+ah6LK7\nG/dZx+deKUn5zSh9klBWrkx4z7/nltQ/h0tQ5GQcIblFwCUh/OvJduzNtby8rCdPngRcTylFo6+X\nL1+Gq4Kfj1ChMNgv7vr6Olpc8JzD4VD379+PWrRSqRRIgYLYTqcT4XGgOoLjPaBAJOQTOSEtKWro\ncFHhl4DzRMLYZ+zg4CB20MCNRSF1Op3otri3t5cTWN87jaZzKA8QAwrMDRFKw11L0AfX7Xa74aKB\n2OCNvKUubip/e78nXwvePA6lCFcIKvItxaVx7g/ncsEn8fHy8jLqDT3SBQfoUWM4p8nJySg56Xa7\nSinp5cuXevToURDs3EO1Wg2S3b0F7o01xrVdCftGDkUXzUluFBD/F6OnyOttjleiS4CjHBaJ55g4\nDwQkdRfMQ5GufPDnpXw+k3+G65bLo73YLy8vg/wEUbAIiRLRNoLIytbWlnZ3d8OFgcPp9Xo5Ihzu\ngJYk5XI5SGp4KZQNDctSSgHvCfGenp5GFwOa0FcqlUBpjni2trYi8jM7OxvbBKHsWZTeEoUkRq/q\nlxRohxIYPlMqlWIn309SMvA/KH6E0duisKAZL1ALCi27iYayXnhmkDbIgdevrq50dHSkbrcbQQbv\ntAlBzPx7cAMXB2QJ30JLGZ5bUriY7HnnUdWFhQU1Go1AmaASkBb8IN9DwaJAK5WKDg4OckqKdYc7\nznjT3A5Ujmfgz8HYsUZAYi4/RXnDm+C5OQgUMDeedvJZxyuhlPBZi2nu0lgDuwvHZxg01+h81wfU\n+SomyhPBeB+FUalUYhcJJwu55uLioubm5tTpdLSzsxPCRfSHa+GuAa8RUnJcEAxcBngNBBAUJCkW\naK/X0+HhYdw/SsIJby85Ad6XSqVcdImND2njgYB6U3wURzHpkM/1er2w/rh27tahnJjfy8vLqBWk\npGVqaiq6aILSQGqMkbf1BU1ICuNBaxEEkTG8uLiI8pSrqyttb29HkqukyECXxjyUJ4NKI9esSM5j\nmNh2CfIZFOilKxgelFWv19POzk64cHzXiWla1UDIozQg5iuVSvBXuJ+1Wi3QDYrH17fLVVHx+r0y\nxo7eiuS4n8dfI03hs45XQilJY8EtRgpcEXnylpQvOmQgHb56dKCo+D5JkeHukFAHikFgnIStVqt6\n9913w6oTdXJCdm5uLnYIwZqjAOCL+I6XbpBdTdFtt9vV+vp6QHCsFgsZN8cjJTwnWeZYWzgqUNfE\nxGi3D3bnRTHislxfj1uScF54Ew/Tozx4TlCJCwDBBMpAUDDkWbn7wKKng4AnkuKmwZ3gotBUDtKc\nZ8c15n24Jp6f9UA0lbkArRVdH0dLRPPI36LJHmuS/krl8qiur9Fo6OTkRM+ePVNKKZQ7qIe+5TzP\n4uJiuNZZNkrnoMZuOBxGsTWfkRSI0hUH8uJROkc2rsjcVXPF5FyUKx8Mzm2jb6+EUnL/lcXpiscH\nwSMBbgWYAP88FtLzM5yQczcRIXNSvFQqaW9vL/gfOCTQAe6MpwaUy2UtLi5KUnSqRIj39/dz5QxT\nU1PBi9DveX9/P1wzKv15Nvr2zM/PhzCxk6wTzt5rCKQAavEdhEETfA5inK286cJIdMo7USKcdE3w\nkgNIZpQQ8B6jwNh5ZMjzgkBIKDt4KYIAICkUBQKRUorui742QLxwScw5+WF7e3s5jgbOBmXFmgBp\n0seIFIj5+XnVarVI7eDAGHnBs0ctP/zww3D1MXygTo/usvmlpIi8ViqV6K3kHSJKpVIk6RZ5Vkk5\nWXCjDsJ1pI8xB/XyfX/dgcIvlFJyMppF6g/vLL8PpOdTFDkl3vfOepJy4XxXQh4tAg7DhfA6SKTb\n7erk5EQrKytxP61WS9VqVXfu3MlZSoQU3/3k5CTcDhQffFW32w1B6fV6kW4AGpMUmb58j/Aw9wqB\nizsJJwIvhEXFDV1YWMiR0M7J4L7heuAW+eex2IS/mTeSH3EHWbwoFkmhUEEmRIHcKKGQQDbHx8fq\n9/sRiXSBI+UBgWPM4dhQEB4t9HUAemXOGEeuw/8kZYKgQI+Q/cwJBHu/39f8/Lyq1WogaNYl3SHg\ngSTFuRgbAiw8a7fbjRQEENj5+XmgKrg95gMjzdrmdUdDzDcyx+t8x8/DGGNsXB5vc7wS0bdP0rQ8\nPAKCheTBQUVYdyx+8TPFVHgWrJdBcH0iPpVKJarqURRMGtwOi2hxcTHHhZCTdHl5GTk1ZEp7sSrt\nSyYmJqKXDSF+Euva7XZOUbbb7VyWM+4F3BPPh9LDkuK64focHBxEfV2/3w9XhUgROT1EtLzODRTh\nW2Ezns6rMf68jpCgoBE4jAFEdNFVp+SEeeH+4FNwz7hnd11QNnBkHmkjYldU4KDH4+NjLS0tRRIk\nKIQ5mpmZUb/fD6HkeWgKeHx8HPNGwi3Kg3XA+tza2tLBwYHeeOONeJZ6vR6dQ8k1W11djWDGYDDQ\n17/+9UBXpVIpSqGkMReLC45Rck716OgoZ/h5n+fxXDNk0t03d+n8u591fO6VkvM7KA+HnlgcFpnn\n3Lg1wMqwSHgPUpPrePIkC8lruwaDQXTza7fbOj4+zkU53nvvveBesmzUd3tzc1OHh4cRJkcofVHA\nAWF12u22JidHO6aw9TcWklQAEBTuIUmHEPGPHj2KhS6NXVusJZAetIWCrtVqufIEauLm5uYkja0g\n44zA4gpBasMP8R24J8aWqBT8C9yPo0OKUUFWKFGioChx5hBlRbtZFDNzVC6Py198bRSjdCA2Pofh\nIzHU93ZDGRKNROl7axHWEK5rtVrV6elpGDV3MVkfGDPW7OPHj/X666/Hpgg8D+NCFBeXFcV4fn4e\npDhuMa4f68bRNO67Bw9cuRQVjxsk/kZRFcnu2xyfe6XkzL67cD55vhgd/fB9adzzxRcNC6bIFeHi\n+HcYfHgUFgIh2dnZWb377ru5Nh6EZuv1ehDOwHpcoXK5HL1z5ubmghuBHOXzjUYjFifuAQWsLHZ+\nDg4OYvcRUI407mCYZZk6nU7UT1EJjoLnHAgpxCklDuzY6iUbIC0yjRl7XBcgP24yFhwlQ5U8qOro\n6CjC2EUhoObLEaqkcIs8GAJvxfZWcG3Mk68rR5UoKYwGNY8oeEeEu7u7QcaTv8VaYo2R28a1GPNG\no6EXL16EMcKIOJ9GPpskPX36VL/0S78ULXnpk0UJy9LSknZ3d4PgRlmwbjY2NmKNM0coP/7nfkGG\noKWfhoDc0Ho00NMH8Dpuc3zulRKHE3DOCXkkwOGhE95SvgEV3BOw1TU9guUhTkcyWDqiUMB38pU4\n6vV6oI/r62utrKxofX093LVGo6HT09PY7BHBZkFDINOdEESCsiuXy4FcuE+gPEmMKDc4BFfq3oZD\nUiCwVqsViJFoH8oJZU+mNONMEiGLGSVEPySQxOLionZ2dmKcEaYsyyLHB9cL0px7Q8AIxbM/HM/A\nZ2nQNjExoZmZmYhuYgAwOETWJIVAowTIPHe0hDvo5D9uHQrZ+zRVKpXINUOxc13WM8gFpUxJDCiS\n+wJlsubffvttvfXWW7HGnWM7OjrSo0ePVKvV1O/3c61mCEj0er3wODgvcsQ1PF+J+8VVYw25u4tc\neDAKxISCcr73045XQik5EeeCxIN6OF/Kd8JzsrgYroRr4vPuJvKan5+yEnJ4nBN4+vRpcEo04YJM\nhZ+ZnJzU3t6earWaZmZm1Ol0IvwtjYWDpEy/Nh0FmXBJOaFAMJyYB7WQ7ewkZEoplCvk+/T0tHZ3\nd3Mk9mAwyHVMwJqzbdPKykpurKkJgxeisBcliTL0Mee+UGiUlvjzF0lU7sOblvl3EESCElxnYmK8\nuSTjyv3RooPOAShfLD4BCkkhzF7fdnFxobW1tSgNQiHD1RVdRsZ3Z2cnFNbFxYXm5+dDCYBEIdhR\nAE+fPtXXvva1KBgm4jg7O6svfOELsfvu8vJytOmlzcnOzk4YW+eQUDIEb9x9RS6cQ/K1hOxg/JBb\np1F+oZASioUBcJfMk8AYLIQWzVyMzDnaQgCLmt+tT5GHgmuBf9rY2ND29nZETvb390MRggCybNR4\nq91uazgcRgP4LMtiB10EmnwoFiJCxyIlqtVoNHIT78lpwG8UGUJKEiFRG3gXX/i4BJICjbHwvdgW\nIhyCF9Kb++czjCXWlEgaLhQtd3md7GYiYl77hlD4XKN8+I4rkpRSIB4UM6gIhe4dI0mZoOTG14ej\nGq7N82GcyuWyut1ukOzNZjMQGWsURESGPu48WfrcD64n98rYcd2nT59qdXU1EGapVNKDBw/CnWTj\nCGlEQbi7TG4aXFaR8kABF102BwW4dR76d2SFcWTtwS9+1vFKKCVp/JA+MB5ydKXi2hkLUERZzk15\nuNPJu2LekjRugFWpjPoLVavVKKpkC+hyuRycDAuwXq9HpT+fIRJE/yQUAiQ2ixxEhkCRKcwYuPvJ\ns6EEII6lsRtErZVzIJDlnpcEB8W53dKB8FAiPl6eM3R2dhZV6c5LoCB4JkmBEola8fwoXOYRNJNl\nWaA4dw0QEsYRJTc7O5sramY+cdnhbnDTPRqLQJNc6kqMZ/H3+CEa5vfFvFFOhJsHqoYH9LYhnqLA\n+fb29rS4uBiu8vLyci4RtdFo5Nw3kDjrHS/BjTLXY20yTr6u/H/WpaffOJrnfI7MPut4JZSSu1QO\ngZ1ow7I7vGTBcQDx/XCij8XgBDqfcdfOk8U2NzdjIe/u7gYh6ikFr732msrlchC5MzMzsXWSZy+T\n9Ys7gKUmEQ5+AbRDnx7cJaw1bomPBflN8FXec4jnq9VqEWL3fCDcLpIWUSIoBf8sbhp1fEQQ4chQ\nCigv56jI6yFahhAhNOQ+8cwQ0PB08HjD4TA4Mkc2RJwQbhAe3BlKkR1JUNiMkW+EMDk5Gb2RmBfG\nB1SJQTs+Po52J8wd9Xa4SawpOnlOTIw6dG5vb2swGASC8m6nlUpFH330kb761a+q2WzmuD43fKVS\nKdAbR9Fb8LpK6iedf3Rj7cbbZcJdbOQTPpG5vk0U7pVQSq59i2Q2Gb64Z26litEBJ0bdz2XwsPb+\nv2t/Wmy4UkAI6KmM346Fvnv3brghKCqUCfdB7RML2WG1b5goKTgGFgZCMj09HYLsnJFHGL1+y+vV\nPKmNVAYUekopSNeUUmyOABFNBwHGnLouR7FkNfscoEg9nYAxBqFh0cvlcm6HEubZhYdeQ+52oSR4\nLowFSgoDAZfjqIjnA4nBPxKOJyv/6uoqlC73e3V1pf39/RBy1hkF2LOzs7EtOqjQ0QTzfHBwoE6n\no+3t7dw8oRRBsAQR+ByBCZQ7RDnEN4rCOSW//idF0v7/9s4lRtLruu//W9WP6Uc9uqq7Z6gZakwS\nMgElC5khFC1swYtEkrVRnEUsZSElMaIYsYAESRYyvIg2XiSIHSCIoUCOBVtBIjmIHUQIHDhyENib\nyCadSNTDYTQiKYrDeferuqdfVXWzqPqd7/9905xuUiSnm1MXaExPddVX93HOuf/zP+eey/+DOhJq\nAAAgAElEQVSrxkkqVwh1chv59gjwSdqZMEpufX3Q7nb5e9mFndyVyrcw+MQ5Qczz4I08Q5ejFURQ\nKOLfaDQiRaBeHxVia7VaUd/GDYK7Bp1OJwSd90FOO7GNosHvkBwHN4DrhcFD0f2mElwAqbj51PmV\n4XBYOkMGjwXycUIU4wCnRWSJYnKU5CCkz/XfXIaAMWTjQJFwH91lkYqEyuqBZpBFSilO2O/v72th\nYSHOCOJ+cEyENSWHytceQwbyciMIz0SfSHilEJ5n5OMykWdF2sW5c+fi2BBHepBhcraYA2Rsa2sr\nEPa1a9cCOWKYLl++rH6/r6tXr5ZOGSAP0sjNGw6HwW/yQwSx6nLiwrvh8SADMuDNgxfIlvfBn3Fc\nOxNGySfICU6Hnwg17plPgO/Ejp4kRdkPN1AeheB9oBB287m5Ob300kuh1PAouBTvfve7tbi4WKrK\nCBnsi+rXB7GDYfwYE5E0dn7QFIbM+TFSFUBioDSU2fOC/DwTCowwkaWNYWKc3CFGn3HvarVanLny\nYm/kdBHZwtXDEIFe4DH4TnedB4OBut1uoFTnaDAynLnLOevVV1+9h7gFzXjlTgwjEUFKC8ORwatw\nbu3u3bvhlqK83EKLYa7X61GzyhNzCf/nnHX16lUdHo7uZeMcG/KBiycV0Tv4oE6nE8+o1Wp617ve\nFXlNvV5PTz311D10hZd3ccOOPuEyI/Ns5qy7c0qMhTVxea26dcy7A4F3jFFy4WSQ/jcgskNOJ+6Y\nXCfZnNxzyIzwuKXn+xYXF3Xt2jV1u91QjI2NjeAucDmWlpZ08eLoSjtCwSg8+U+QtCgsiIaoHMqE\nkIMGGJ9UoD5KeMCLIHRO5HLkw+cTUtcRE4bRo3TuOiPgftq+3+9HtjBj3tnZUavVUq1W3B9HX/h+\nXFkIbNYWt5i14+8UQYOsZzM5d+5c1DtnnXH1Njc3A0VyThCDxLqDbjAsfjMICNldfeadjOwLFy7E\nMzh/xxyChOCCcEkHg0FEPd3YgZKZD+evGCt5WBcuXChVMpiZmdH169f1+OOPR8E/SXFgFyTnPCu/\n+w8biAeW0D8QmLt6zim5TkKZgPLdLTyunXqjhEA4p+Rwk9/Zuavvx11gkjyfAmPiguCRN4ekoAsU\nkJo9+/v7arfbunPnji5evKjz58/HIhDlgRdyJAca4XcuYOSqH4wNpDJn5kgCxM1gtwdBYewkhcEg\nRwpSH4TCAVFcPqlwZUFUzCHKDbJh/tyggyJxKzG6ICx4JOYb9wbjxNpANqMUrB08WRX9sTt7uoGk\nQD5TU1NRSdPddxCv83ueu+ZogWRER38gPJA47vja2locsKVSBIeFidqS54YrBooBucFR+bGn/f19\nLS8v65FHHtHLL7+ser0enxkOh7p165bOnz8flURJ3WDeWq1WuMauM1IRGUXWqtFcfq8S2s67SmWK\nxfUPlH6SduqNEs3dqervTri59XY/2HdjPuOCDn/gxs6Nk0c1FhYW9MILLwR3w6lurhnCmMBLQFS7\n6+VIjAjc3t5e1MT2nRy0w8FTcmCI0jB23DY+jyCBghBgN/B+QBZBgrcgpQGugs8hyG7YJIV7hKvH\n+0BYuLbwMVJBpoIoWQME2cuJ9Hq9mGsMiEcGCURA7JPGANfnKAy3j7UAlfF/6d6SrqR8IFOe1Y8L\nNDs7q4sXLwZiIlnT8+RWV1fDGO3u7kZO0Pr6ehzmBfmC1NrtdolCoAQx68Dcrq2t6eLFi3r11VdD\n7pzPpBYTOgJawkhy/KfK46I7DgiqRqua6HoUGX6Sduw7U0pfTCndTCl92177XErpakrpG+Ofj9rf\nfimldCWl9HxK6cP2+kfGr11JKX32pB10OMhAPVqAcWIymMAq8SaVEyw9iuORnioX5S7E3bt3I9wt\nFffL4bKRNMj9Xa1WK8hevh+F5fPtdjvcDbgYoDBoCUOA2wCa8p0H4yapVGTNQ79Omjsv5YoAp8GJ\nfDgv3DEn332uyQmCPMYg+FnA4XAYVQdqtVogP78Ikj6wVqwLIXjnFzmtT7VNDBR9wxj5Qej5+fm4\n7np1dVULCwtqNpulsDe/k181GAwihM8mwDhB5zMzMxHZmpoa3YrL+vM8v+l2dXVV3W5X7XZbly9f\njugkY5EUZybPnz+vdrsd682ckGzL63fv3tXLL78cF2L6fC0vL8f7MOL0v4qWnCZhjV3HHL252+xc\nFWtW1amTtJMgpd+S9K8lfany+r/MOf8LfyGl9F5JH5f0FyS9S9IfppR+fPznX5f0VyW9IumZlNJX\nc87fPe7LmQSHm1XC2nkPdlVPVPODlaAp/k4ExyfTnyspcmx4PrsyN1fMzc0FvE8paXNzMwTKQ/ge\nih8MBrHrE6HxUDqC4rsj3+9kMOSo5/941BHY7OQ0Aot7QGQIA4x76nML+oCsx0jSD9xZN+IYCcaL\nQjtPMjU1peXl5UByhOQx0gcHB1EyhXSBWq0WJX69QJqf54Oz4k491tyTLuGOmE8QIj/wU7hU9Xo9\njCrRUdxF5sPvkCOCyMWgEPEcvsYVJOq5uLgYF5Nev35dFy5ciNws5ktSzAd9AzVPTY2KxN24cUOX\nLl2K83ug3a2trXiv80WOho6KvI11O9YOOWb9mVvmHhTmRsx5zuPasUgp5/zHktaOe9+4fUzSV/Lo\n+u4XJV2R9P7xz5Wc8ws55wNJXxm/99jmjL5zQkyGQ0V2R+cOci5fTjkeUyCIqhDSPFztt7GixPSF\n82ueOevuGd8FJKa/ZHGDBkBL8C4OmflODCICgHJ4YhrCCi+Fa+guLdEdR00oJUK0sLAQBpXmYXp3\ne5gX8q0QQvqNADM3bATMsx9G9fQAjJdnKTMWXGrKiHjEDx6OPu/v70cBNYwdMsKYmAMQHeODRGZu\naSAnryrKfDLHGOiFhYUIZnCLCohuYWEhbrDpdDq6ePGinnrqKTUajahndPfu3SDjB4NBpDscHBxo\nZWUl1qHX60WtpoWFheDUPI0D+UF3PEXDiXqnOZgX5ui13Djne91AoVdvB9H9mZTSJyU9K+kf55zX\nJV2U9HV7zyvj1yTph5XX//JrPTil9GlJn5aKaBoT4i6cR5jcZx8/I/4ldCnde0cVz/bQMK+jxERm\nJMUttlNTo9tyu91uoLNLly7pxRdfDJQEj8H3sVtw/u3w8DAywDGgIBa4FBSRCAooiHF46NuVyZP+\nmCueCYLBoKAwRPoIU7ur6Tk0zE81sVBS6ftBkeyuGF/6Q6ImaBGFJiGTOaFfKBY7PoYP9IICu0sp\nKW7fxTV12UA+OLeHAkOOw3NhaECxRCHdDa7KJkrL2uPKe6kR0FWtNqrQQKb/k08+qbW1tShDgry4\nu9bv93X79m11u1298MILgdK4fIGAA2tKCRf6xGYNqss5R0kY54uO4nH9+IjzlB4c8NMTPOck7Y2W\nw/28pCckvU/SNUm/+gafc2TLOX8h5/x0zvlp92Ud8TAZ0r1niqpRuCph6RyHQ1X3rau+MAoxNTUV\nZOLKykq4aCgEfQHag1ZANIS0USh/9nA4DKHwiyOHw2GEl9nVMcK4KYwBxWbenFtBqHD3EJ7Z2dk4\nIMyVP076+onxavqEP8dRRrfbjfpEjIddHneHWtySogwwSuqGFmON+8f3QWzD4ZCW4YXlarVRaV4q\nL3iyolcswNAQDCBT21MjiHQyZxhLNiWIZNxbkBZrA3/VbDbVarWU0iivbGlpSUtLS5qfn1en01Gz\n2dTs7KyWl5d14cKFqEIKr7m1tRXfWavVdOvWraivhSxx/IX7AdmYyYB3mYfgTimVSHAMFevl6SjI\ngxPbvN+DHJ6tzpoe194QUso53+D3lNJvSPqv4/9elfSovfXS+DXd5/XjvqtkfYGbIAV3SeAb7oea\nfGLc93d3jtCyE8d+SJSQLu/x3RrC0l030FK73Y4dxElqL9FK/SO+Gx8fI8BxiZyLO9lwI8hF4dko\nVBViMyYvWetJpyQPgqJAW857wS/BVx0eHqrVaklS6YwVBLhUBBrYwTEguB2eme78D67v5uZm5HqR\nqMpccDhaUiAQXE1+x4DDA4Fy3Ig4IgABYqCRNU8XYUyclfMseg+Hk2WObHDlObyaFwBstVpxdx4K\nfePGDbXb7ZhPPyaC4iNHRNvcsHtf4bb8d3QEHXJU4zrI85yTco7Jm5PkVZm/X3tDRiml9EjO+dr4\nvz8ricjcVyX9h5TSr2lEdL9H0p9KSpLek1J6TCNj9HFJf/Ok34cA+fkfh94skk+auxRV0s7Gcc+u\nUP2b94HJRwkxHB7RazabunPnThhHNwLsSk52O5cDwczfQEU55yDb4U1AD3wHSuzKIxUCw1yhZPTF\n38u4mVN3Peir35QLWpAUbhqF90EBVFckwRHDxlhAYV79EASAu4ICeGVKJ1ghgdkoIMcZO8/HoCE7\nGF0SK/kejJfv/PSLtUkpBeHsxgpEw1yDiDG6Xu2AVAs2QYy1k/UgKklRjcIjxiDcw8NDraysaHNz\nM2poMY+e4gEiQl6RDUml23kZL7Ll/696KciIb/7MFZ6I68lx7VijlFL6sqSflrScUnpF0j+V9NMp\npfdJypJekvT3xoP8TkrpP0r6rqS+pF/MOQ/Gz/mMpD+QVJf0xZzzd07UQxUuGdaYiXF0UyXRfAJQ\nMrfazhdJCqHxHcFdFFwedqi9vT11u13lnCPyRGu32xHRw3D4GTOUst/vh5LSf1xBDBQGSyoQjR8V\nkBQ7LM0JaXZRXJtqljCEedVdcs4ONwRXCb6C93JuiwxoDCcheNwrDBVXDXnpWvgWELC7BEQnWafZ\n2VktLS3FhaBsKhznYUy+GWG8IdpRRq9Y4HOHkSS6h/vGGJ1bYY5AK3wvhob5oBInqSW4kCAx8tG8\nsqlnx8/NzemVV14pRVK9/AhInsPZ6AyolFIrfI41YgyesOo64PPI+qB3/n9H5JJKqJTPnqQda5Ry\nzp844uXfvM/7f0XSrxzx+u9L+v0T9erezwasdxiKQKOQLJRHrlx5fYJRcLfk8CfVCB7/wiHgg3Oe\nip3Lz4fhSnmo2ZUCofRbMzzbNuccJLgXQJMUO7y7by5MLkSOrJxTwaXxmkn+XcyxXyaJIlJGw91p\n/27cQsYLwqIsSkopLmD0GkS43fQFd25nZyd2flyeubk5tdvtOAzLxgSZ7ZEhXCpQFgYO1DgcFgdp\nUVbG5n1y1OX8HjLCQVrnnuDBfDPELWU+MXQgNvpKlJA5w93s9XpqtVoxf4uLi0HUt1otnT9/Psqv\n8L3wXY5wHPE471flVF1HXLfc3XV05IjJSwod5a0c1U59Rjc7gKMjJkUq39wpldPenXfivbzHn09z\nIXK+AoUkmsPRCBQWPoFdnGJd8CcoMFEWeATcSwqQ5ZzD/SHLFvfELy4E8pOqAIJzMpvxOF/mR0yc\n/PfMYUmlg5sImdfx4dnME0rImlSrHBA19AgfCu6ohvUClTJ/3HFGGVd3Iaanp9VsNuN7kBEUmEa+\nD7u354/1+/1AcrhuBCfggXgd9515wqgjD9RDwnV0vtAvFyDxljOIXAAB8nGE5Wty/vz50vo5YiLq\n64ESNgXWCn1hA5IKdEyxOal8AaxH1aqGCt10gFDd+Ks6d1w7E5dRMiD3bx2yHsUNOSfkKMXzLJxA\ndl7IdzF/JkbEyXQibnwPqAO0QD/52dnZCWSH8anX63F5IgKJ28f7PPHQd23nTnzMcCseLfOcJHZ8\neDo3LBgahJY8H0q7Injs7BsbG6HQfA/PICpGP/gOEJmnKYAicLc5hU/G98rKShgFDIET1ktLS5GL\nxNyxdkT2fJfHXWejQ6FrtdElEFxjxBxgDHC/MBpVhI0bt76+XuI7Dw4OIr8IRLW+vh7uFQYA+QFB\ns0EOBgO1Wq1IEcAIYJi2t7e1sLCgbrcb4+I5VGlwfgx0R8P1ZZ0wOlLBGXnABL1xGsQNFLLs6Okk\n7dQjJakc1q9yMhgaQqSOqJx/AvFUyUqpXEqXZ0rlolXk3bCTcVqbIxn0yb+P/BgfA+4Duy28hbtW\n7NSuRLgZKKwjFowWO7ond/KvpPgbvyNgnlOCW+e7nrsenGQH7UgjA0h1ROYJI8LfibKxfrhOnjIB\neVvlY0AYXD/th5AlBbLa3t6O4AM1r3d2diJpkbX13b1er8ctJVJRykZSyAwGl5A6hgJuDUSLkSYR\ncWZmpnTDjSeWsmlgLEC8zs34OTXmttfraWlpSTMzM5FzBAojwjs1NRWnDfjea9euBRrEUCKX9Xq9\ndFswxkQqUC/fg7xgwDFU7uK5oQddVgMq92unHikxMA9Bul/scNff49m1jpyAsI6AXHH5vBut/f3R\nnWQYFRZla2tLm5ubIVRSEWr3XQ8XjN3DC3yBGnwhMTCMsZpF7m4sBonnS+Ui/04QQ8JC1DvP5Ibd\n+wkCQOA4SuMGa2pqKkrpooydTkebm5tRBoNbRzyviaMXHAKlMbYq8Y2RAhkNh0UpEkdMCwsLESXE\nwDmicfcINOCIEjfdkTmVIkE8KCtkPcitelh4b2+vlM0vKfhIbrjl/B+Jjn6hA+gWBLm4uBhZ4Jyl\nYz1WVlZibanlRekYH4tvwC4bvtE5T+Ro36OyVd7JuVjkQiqSNN8xnJJUpKpXIypMFv+63+r+uHNM\nzl1UCXD/rE88ZCTfBSGKcPd6vaiDVPXzQTbs4vTJI4JO7ELqero/URlJQWhieJzfwrBg4Lijnmex\n87MLMkaa8zzVuaeh7LgGUqG8zA9ubc45znU5aex5SxjtlIrC+258UhpleC8uLobxh1/xvCcMvxO2\ncHRO6jN2kgsxaG7siLLlnIMs9vGh0L6pgWrcBfQABsXlQFegbqcg9vb2gtdxpMI8MR8o+8LCgmZn\nZ7W+vi5JpXwq56RwN+G/qkEc5tVpEkdArJXzlf4+/xedAUhU6ZaTtDNhlBiwC0I1IuCRACbJDZbn\nXGB0HHE5LySpxA9ggBD4ubk5/fCHPwx04lUlvZQHAu6n/92geEQCjuYoA4mrBepBKPluqTjCAn+C\ny4GCYhD4l9pQQHlQByiA3dlhPoYN5QXleQlfBNLD2s6HVedZKnZqah7xWdxc4D91qyjj4oeV4Xio\nfgkS471kjhMNrd7qwQbBvIEkOdMI0c86Il/MHfPCevB+3CJcX/hD0iXYkCjnknOOeavmboHM2Bi2\nt7cDEZMLhUFlM5mamoo7/fykg3M/g8GoCoLrUZXYds+hytU66e6GzI2epwoc1069+yaVC4hVjY67\nG7zXSWpPH5COLkrl8NLzethtOQ7hBzedcwGVgGq8bjaNMLobIlw+lJXQOYYIwpvFrt44i2FwdOEo\nBOMFzOc8GYX8URjmFVcL3gVXjb55sqivA2kNfshWUvBOHHUAcXl+Tb/f18LCQqkgmt9uwsbC8QoM\nCnwRxfdxyxcXF6P+tiNreBleo+IjCkjaAG4YCo3yYuhYN3g0Dw7wA+rAYDrq47v6/VHl0rt37+rW\nrVsl5IIBYM5BWmR4Y3jgmbgRGURKHzwq6y4bsu3Bh16vJ6k4muObhuuO6yRzCyd6lCvnwOCk7UwY\nJZqHsY+y5kyCk3BSObfCfWQXFN5bzSr23QC/nsX0OtdSUUwNQfJqgn7C3I9n+IKRi+SkPZCbvBNC\nzhhLz2hHkfh+7ye8BYmBGBIfB/NGn51UBlF4dI81INoE58CP78AoBeS2c4UoG+MHpYCMpSJdYzAY\nlZL9wQ9+EOPFaMKvef95j6M21pHEVNYGgw+KdAOBPFC2FjQqKaKcR5Wc9WiVl51h7W/evFmKvpEA\nCYcGIuQWY7hIKAKM9BNPPFFy90mV2N7ejiM2fiLC++YBBtYTfUK/fDN1Q+u64mP39Bs3Tidpp94o\nuUA4yqlyQm6lJZV4Ez7nRLFUQFLnqNwl5HkIYr9fVAqkTyw0VRcRCtwhvgNl9/AwY3M0B+qBqPcD\nuJLucaG4y01SGCd2R1IO/AyY8wocsWAOUAaMJt/vKQTeZwwWCoULhZGvutf1ej2qLIIkQQ4YFAwF\nv0MW03c3FI5emA8ig8ylH4th/pgrlAQkBulcr4/KgLihBUEQXPBbReC9SBdgbjD+rAHcF7loIDWv\n9ogrh5yQv0QVBfo5OzurRqMR5ZdBgnCTjIP0Aww/Y8YLkFSaL9Ca6xl64IiJZ6EDzjcxx46s/BjY\nce3UGyWpECDPJ2JSj3LjHCq6lXYS0pWmuqu54BOe96MBFEUDusJBEGXCz4foJiwtSY1Go3T2zUlB\nR0wsLGNDsD0SQoVCP13PriiVExyZR0938CRG0IJHDf2As5OvzD0Kz+7uJ/UxnlVDyY7N86jZjdLy\nfaQuMAcoC/wdCoeyga78xL5vZI4i4NkkBdckKQwaJUmYR48G0hxRgl4d+Tk36e4dUcZGo1E6FO1p\nBHwmpRR1rer1ehxL6fV6cb4PbmpnZydSMOjH3t5e5EWRPOquN3Lu5LfzP+6SoX/+OTYV1x02sKN4\npJNmdZ96o+RoyCGjVBgrt+4YLiYaC++IywXIJ9shJxPrBg/CmtCsowSuuea9FAHDMHkOCr9Lip0P\ndFIN20oKFwQ3DAKUoxeucHt7e9ra2iolrYEy6P9wOIyMcHgIOBXIagwrY8QVlQpE6bWXMBYYAEnB\ne+BS0XfG6PfMYbyYc9IZeD/5QKBNd7NxLRlHvV6PqKRzi3zWAxh+vIdNAg4R44Ss+dgguT24wobE\n+vlcEzTw1A8/HsXcEjiAE2PjoIwvsjc9PV26Cp7PUNZ4Y2MjIo+48xz2drScc3F2k7X3NXaXzQ08\n68ZYHEVBL1RpEn/f/dqpN0o0H5hHCPx3lI/3OsdU/Uw1SuAIwXcE3Bw4BDJ94StYJBSCnRq3i9wW\nJxUllcLeRGUQWlwp0AuCjDHiOAOCXOXHUAA4GHYoXAqvJgB6YY58fpxbgFyn77yPqBEpE41GIyoY\nUjGRXB2fZxIaeQ6K5aVaIOUxWvBnnt/FHFbrLYFk4IFwUUBYuIisi9cZYkPq9/u6detWuK39/qjG\nkrt+vHdubi6SZeEFQYHMHeiUjcKDG8jv7u5uGAm+E7mu1+txTTnGF7TFGJ988smYA+SSufBUCe/D\n1tbWkRFp5NWRkRsWDJdv6k61MAb/vncEUnLozqB9ghAqJz0RVN8leVaVR3Jk4hE8f4+kqHdz+/bt\ncDsgZuEzmHR2S3KTPOuc/vrlAxg+/15QiGcJ811UEURQJZWKwoGSIESdI0I4mCuUBoPhZXc9P8kN\n3+HhYVRI9FP65M34WFBEDMj29rY2Nze1vb2tmzdvhoEAeXm0C4Lfd2OKqGEop6enI0+HufDPoJSe\nUwWC2NnZiSigo2LfkEgJwPUBBbAeIAPWlppOKKInn3rahht8d3f8KBAGzJFjrVbTo48+qpxzuJ6+\nmbBZSoooHWvNsRlHK2xypF3QLw8UOXquumRu3HhGleutclPHtVOfp+QoiAGBHDAKTIxbeZRAKpfj\noDlJx2dwvZxj8mdDBPf7owOc7XY7BGJvb0+tVisUB0EgLwklolA8/AE7KGiFz6CU8BpSwd2QqwJ3\n4aiAhpC6u4LweElfDhC7knhelJPouALMCQqQc1HRAMPnyov7hlLwzOnpaW1tbQVCAh3CKeWc74kA\nQn77GpIb5uN1/pGxuJFijBwFqfJHbH6Q9/BWoJ5qSJ0cItbb0yAcUTB3bKL8jiHme3wNJcUara6u\nlo75gPY8wHPx4kVtbW3FsRh4I5C2953+VY0NfUfXfFNy7onfq5u9Gx83Uidppx4pSeVIgRsM54Pc\n9XLEwEKz6AhrFQ35xDmiqBo0/5wrspcSmZ6eDoTD92M0MDQcRYEfYHF912GHZeennzyfZxDJ8jny\nOjvV/sPT8BrlPlBsz9MZDAah6OQPccQBpYDXkIqDq1UOBaFlTrmc0TOQZ2ZmSkmNuGr0C3SJcsCV\nYMRYB8+RkYoUDF5DRnB5QECgIcZRDayg5J4XheKSpwYq9ssBqCnFOnExKf3B0IPIiMzxnW7YWQe/\nSxA3cGZmRi+++KJqteJCT781WSqy9jEkoGmPzHkUzXklmrv27u7zL1wZ8orxPWk7E0bJF9AH736q\n7278jUmXijR5V04nwBEuqYysqgYKJUOovZIgpCk/hOvZWVEGfubn58NAuYsmFZEgILzD4+GwqKfk\nRo9x4ZJJKuXtgAwRfpAD7pojDlwnnoN74obYYTnGw1EqNZOoSgkaoi9edheFJ0yOIYQ7wUDiUvlZ\nv4ODAzWbzRgbzyTayJy5gUspBYpgfT2a5smo7kqB0jCCuLsoJ3PP5kblAhIM/YgI0UtkHGOXUgo0\njcHgTJ/nxrlLmFLSnTt34lKFTqcTCNqDDHzGkazrQdW9Q4+qHJN7LrzuOld9luvbsfp+EqPwIFvV\nGEkF/+OD5LWq0vgzeE41jMkkIoAIlEdQmGSQGcJFFAfDRmVBjBZQHJjO7ul9JQLHsz0Sww6LYrhx\nIacHo4KS+rk4SmSwW9PYrekbOTAgFak4DY7h9FPuGBtHZ3BtICh2b+7F63a7kXGN++RVBXCnQETO\nt6DYRKTm5+fDDWUNOZoCcuC5RBY9pQH04WvriA5ZYB0cReAm4j6zWbGGyNj8/LwuXboUBekYH4aA\neaY/vsGRXsF8Li4uxtrCizmd4GfXMIS4y/CPGH8PJPgYqrpCg0pgPUhARX7cADFn/iz65Wj+fu1M\ncEpVgyPpnp0CofDdWrqXyHZyXCp4CY/uScXRDyAzn0HoiWLNz89H3gi7HYvH8x06k7/kiAtXCMEh\n5Z+jKXAtkMhSUWCMZ6IQhOelouxErVaLg6Du3koKAtXzbjiT5RncoCJHBPBioAjmm4Q/5tCP1/jt\nvjyLkhq+nufOnQvFxIDVarU4sMoYut1u1KgizYEzc+SP0eDFvIaQoyCQDH3xs4YYelq1DhTfj7x2\nOh21Wq1IAqWQHijIo2qS4l/6xzrWarW4xgtkxfcxX+fOndONG6O7PJzkpr+zs7O6dctXukAAABbW\nSURBVOtWSc5BNr1e7x5DgZ54JM0j0h5EcM/FqRWpHKTCkJ6knXqkJJVzdqowE1+YgfsO5K6dVBio\nagizSsR5dArh8XC0J/q5EntJUwhjjoZ4igAuCIvrOTEIAIQnRzKck4JHkQq/n5pEkoKfAa0x5ipa\ndNLe5xZEwb/wW55X5HMFZ8EZO4whuTFsEimlqBI5HA7DYEC+4m45ac84B4NB3MHmB4YZI+/DiN29\ne7fkVtHchXJXBAVkDXBvMD5sNI1GI0qwIAcgZzaXy5cvq9vtamVlRY1GQ41GQ8vLy5qfn9fi4mKU\nuXXKAQ7UN9hGoxEVKZhjj3hKo03l9u3bJdnGDfc0DPiyamY1lxG4EfIN2n94vifgOhhwmajOuQeV\njmtnwij5rupIx1GUw3C3zCi5+8C+Exy1QyIovhA8h2MEvBd3B8RQr9eDazo8PAzYTcievxN1o8+4\nBBCZTqR7sifvr5ap2N3djR3YdzQUGIFn7EBwkCBowPkY5wak8nk3kJVnj/ttrKQ0+LVJcDgQ9CT6\nYdwxBjyXfuC+0k/GC1rCOLkRhfCmRhHzw/pjWEEann7gpDp8oCem8l6fF+ofdbvdmEfKoywsLGhx\ncVFLS0vq90cHcW/evBlul68r88pzQMmeIwYqhmNyA0Ck1zdT55Ac6fixIF+DKnJCFl2/PNqGQfRN\nz1Gl/+0k7VijlFL6YkrpZkrp2/ba76SUvjH+eSml9I3x6z+WUtq1v/0b+8xfSil9K6V0JaX0r9JJ\ne6hCKWlulT0EXbX0LKTDyuqOyCQTrWIH8t3DjwrkXNQyhofBJeNcF8JGg0PCKDl8p1+4OY6QnIBl\nPOzantCHAcB48p2gJYQSZWs2m6W8Fr6DMaMEjNkRkhOmkPl8lxeXw51hnTCMuJzcCALqw8DDkzka\ndCKWdWNO2al5NoiCdBEMmvNvKGjOOcoJc5TDx+gVF0l/oM85F8GCsXyH0XTuEOVFRl599VVdvXq1\nlGLBfDtl4LLrqQS4lN43+sC8kT7AxtPr9WKO6bu75dXgz1FGidQBj3bzXueOHDS4B4McvFlE929J\n+oi/kHP+uZzz+3LO75P0u5J+z/78ff6Wc/4Fe/3zkv6uRnfBvaf6zNdqDNZ5HalYLJTfk8aqRDbW\n3NGSE+QIgoc6QRZSsbPi49MnEig5HAl/AwTHIGDU6DcGgGMO7FocjfDcHKA3/XClRAl9wau+P7st\nxnt6ejqysN3nl4rrz2u14gYQjOHGxkYoKyjBjRkXUS4uLgZScuSJwkoF34HLg1uD4vjFBaAdH5Ok\nMDYoCoaQMYIYBoNBIFfW2113v33EI4hSUROp3+/HIWwKsfE5/k7fOLANB7i4uBiu+pUrV8IFdRcH\ng0mZWzZCXFkCCPV6PVJIdnd3tba2VpL9er2upaWlQIWsJf1BRuirH8R1Y+GbgPcVBARC9/msNtcz\nN0xHvbfajjVKOec/lrR21N/GaOdvSPry/Z6RUnpEUjPn/PU86tWXJP21Y3snlQySGxLgpkfJUMiq\ny4bQ+ERWF4LdyF0zRzK4PBgcr4TI8QOiKLg+GAwXQN6DchFq96iKHwWRFO6ch+wRUsaK68hui0Iz\nPyAHDhXz/Xt7e/FcXFB25IODgxiXJzR6jo40ylxfW1sLN5u+eQSL73KUxDPcXXSj7Sfsq3lXjIXn\nu/EHUeIq48YT+eNIigcgkAFQBrLD/CEPXjYWhO1Hm/zoTLPZjMTQZ555plSvijHynFarFdFPnsX8\n0Q/yjvb397W5uVnKXYIv4iZekLbzOC73yGGVP+L/7opVA0ReWof14z1VZDTW/5K8HNd+VE7ppyTd\nyDl/z157LKX0f1JKf5RS+qnxaxclvWLveWX82pEtpfTplNKzKaVnnYSUynCWwXpUjuYQ03mRoyw7\nwsUC8D1ScW22K0JKKZLl3OUBkaAozst4vhCLifuH8ADlPWkRo4eQIoAYXXY7lNFRCc/xWtIYUsaN\n20haAoqHccAIYyCl8oWZEMteU4iKCSA/hJb18iqQjMfX2I/PMFfV2ke4orjUXFuOcYUId6WqNkez\nh4eHUSbF0wI8lYB1hKdivvwyBr7//PnzMcfPPfdcZPIzD6SLwPuxcYD4PNEXZec4DUgSw8v7OBJD\nNnmtVtOtW7dKOuHu28bGxj264f/6e93YVINDThu4wfY8wZMgJNqPapQ+oTJKuibp3Tnnn5D0jzS6\nwrt55Cfv03LOX8g5P51zfpoJZ2IcAvJ/V3SPZlRzL8bPvicsyu7l7wdCs8sCXRF4SmZMTU3FLRt+\nKwaktfNd/I6b4YXC4EhQXD/Y6xEoDACGx8lndzk9HEzkxfOHXGAkRf4P6AJF5bnD4TAQhmdRQzJj\nFDCcZCTjZnp0iPEyn4yLdSbtARTA2nNFNbfpbmxsqN8f1TC6c+dOfJeH2zEeyAY/GCzcVGnkRjWb\nzRL5y1pKKiE3T3LEMHGwdnV1NYzG1tZWlFgh9YG15TIKR4sppRgncoLL7De+SOX8oMFgUCqLg3wQ\n8segI+PD4TDymNzosFZsJPSNeXM31/WQ/qBjPMPl+qSG6Q0bpZTSlKS/Lul3eC3nvJ9zvjP+/c8k\nfV/Sj0u6KumSffzS+LVjmw/eJ8b9XCdBUQSHoEw8z0MQ/DV+PCXeQ8Y55zgvVnUJERLPtqVyIQgI\ngSOPBgTjSZiuwBgGR2zOHdBv+grqgD/xIyqOrDY2NkK54EWOco3gQTwLmt0bEturOYIgnbAllwlX\n1BNB3aWEq2IequsBnzc9PR3u0Pb2dhDsrDu1g5h3jBKbAg1EIxXcIecW4adQRtZ0OBxG6gRGGJSG\nsWes8DiDwUDf/OY3Y75ZC+aNIyCsL5TA7u5ubCSe1T0cDnX9+vX4HdnAkDEHoCdK2HgaABvScFjU\nBUeG+Hv1NadOqjQJuuSuHHNddf1eC7FW24+ClP6KpP+bcw63LKW0klKqj39/XCNC+4Wc8zVJWyml\nD4x5qE9K+i8n/SIPm6MUDM5Dk/y93y/utcJoudsnlSEqCEZSuEA0fHkEFs5AUpwy53M559KVP5Ii\nqoMi4J5JKsF550gQtiqJTdkMXJmpqam4z8wF1XkEjIkXR8NIcz6ryoWR51MNFWNkIPxRNBJG3YVl\n3B5a97lk7TDGvIbh8yAFbX19PSoMIORO9vv9e6wxRo91xqXl+zB2KPLBwUFcL07+mSsffcZYk05A\nXfB2ux0uOQQzybWO+geDQRwzajQacdU7G4kHGnDNKeqHkhOB29/fD6PkQYjbt28H0kJeQEvO/7jr\nxevoDY1NuOq2VT0Y56voq3skJ9L3496QUvqypP8l6cmU0isppZ8f/+njupfg/qCk59IoReA/SfqF\nnDMk+d+X9G8lXdEIQf23E/VQBTxE2T3k6EaGiUMofRIRJISPz3i0bTzee3YV37FZfHZPFhxj6ciI\n57J780x2bz7P3+FbUOSqm+QRJP+9VqvFM1E+h+vs9k7k+0l3EJsnArrQ5VwUMMPoYAggZjFMGFev\nJS2VjyrgqrFWPG9+fj44HHZkjED1VLynTPBMuCdHmIyJPsEFsjF4pQOyyNvtdlxk4Juh523VarU4\ns8gB2U6nE1nlcEh8hjpcUALdbjcSK3lOzjk+A0/FOoKc2JyYE9IdfONDNpFj1s43r2pqAN/lPx7N\ndmOF7DqKdq/C9dYNlgc97teOPWaSc/7Ea7z+t4547Xc1ShE46v3PSvqLJ+pVpbGzIKQOJR0FeURH\nuheKOppw7mncv9JO4c/FKJD+7zsdQgrCAiGg/Nwt5r47nAYn7t3ILi4uBqzHULZarVK4GpIUTgO0\nhBHA6GAkMAIoFsLpBswVen19PWoiuUuDQZUKhOoJixhKMttBX3A39IG5dINCBjY7u1eFdHSJIjLf\nHMUBXTAH9MXLwLB79/v9iGiyRsgNY+r3+3HjCwjGq2+6cZQUKA0DOBgMtLm5GdnYnU5HOzs7caMM\nnJunhNA3r7DAhnBwcFAyXm684di8pAsHc7nkwNEK7jDv9Qij65afrfONvMoP8ZoHipw6cd73JO3U\nn32TyjwDxsJDkk5ce7QJuI4COE8hFeiKCYSXcr4B9ELpWYhvyFiEB4VgB/PDqg7ZKZfBQnu2LmQx\n0S0ECF4BY7S5uRnlQ2q1WulOMj/5jsK4MLiAebIjkJv0AMqw0DcOu7orDf8hSZ1OJwQdV7HZbIYb\nuLW1pZSK8icYKi6aZK7hsUCY9Bm+hHli7kEBcHjIB+4mY/SDxh7x9Kxp/zyGC8VvNBpxyBWDx79+\nmBZjwfEN3EoSY0nJ4L1E1Ah8gAY9kspcS+WACJ9j04EQn5+fD24JOfINmw2O5obGqRDm0dEh76/q\npMu5GyDG6RG649qZMErARY88uXvhHIEPvkq8SeWDuVLZj65GEVBYfy7v55Dj9PS0lpaWSpwBeTge\ntmUn9zwYlI/3cnaKnX1ubi4UXVLcJ0fDHXAoTp+r0S7nKjDEbtwRfOYMZUTgyMdhjHwPCZC1WnGe\nDnRF8TTQTc5Z6+vroXiMjeMYkMTOE9FXv1Sz1+tFCVg3/Hx31f3zv3v2N8rsUVD+5Rm8j3QHV0K+\nzwv0YUhAN358aTgcxi0pGEE/Vwif5gEVvw3YXaqtrS0tLy+r0WjE2mCYKTYHqe7y7UGZo1o1YuYc\nXdXjqPJInu/ltAnz6hzV/dqZMEpSwe+81oDdFan+XSpQkfMU7MAoZNWSexSP5yHsVSIQ1wiXjvez\nsI7KcAFRQJCdw39SDRBIVyJ3Z8gPqgYBeIbvVHyfjxcB9PlgnjxsjzBj5BBEP96CEcb15DzY3Nxc\nEMhkztMPzm/1+/3ShQcoLWPGdXHXF4PAM3idyCDGdWtrq4QOQTNscF6GYzgchvvM81nzRqOhw8PD\nOEDNWmNQIZyvXr0a88hY/c6+zc1NtVqt6B8bFUQ7HBHyxIHuo9AKc8gagwrX1taC22Ts0AsetfPN\nxA9vV/OzHC3RBzf66Atr5FQFz3C65H7tTBglPwtUDUm6UUGYXeGd9UeR3MI70nLoyudQVhSUXd/T\n+IHMXseIHdC/W1JcKujRKUdVcDkO3TFoKMjMzExc8wRymZ+f19bWVqk6I7sXn93Z2Ym8Jchezu4R\ntSEBj7IfRKukAmXu7u5GFJJdfmlpKUqNEKWDL3P0hSDv7++He4eysCm4W+RcUrPZjOuqGJcHItw4\nD4dFITyiVBgrV6pz585FQbTNzc0wbi4voBCQraRwwZCB4XCoTqcT5WWQEdxbjCRrjTHDqMA3uXtF\ncAMqAHTIpvroo4+WAjVTU1O6c+eOVldXdf369ci54uYd5op+Mxe+mbERe1S0qhdVYOA65nMLWOCZ\nknT79u1j9f1MGCVP8qtG1hwNSeV7ttywuNtW9Zc9uueTikGi0qEf+0B4vHqg57c4VKbfGDV2fXYw\nz+WRipK3tVotku58fAgoyokANZvN0rj4XowjyZ0gDyJ9GEgXtMFgEEcW+B4QCakEEMLcTQba8/wj\nqXzpAEoAV+ZolNdZNxSZpEbcNg83e5E4ng86qNVGZWExykTe3JBRqYDNAcId9ILRxVDRH54BOms2\nm+GeNxoN9Xo9NZvNcMGp3Q0P52Q8mxNySZFACvMxFhA1HFyr1YrNydM4ZmZm1Ol0NDc3p5s3b8Zz\nkPPp6Wmtra1FvSdfH+SL76zKEejP+SqezWbAM9z74PeTROBOvVEiGlJl8T2Uy/uc+HbXiZ0eBWAn\nYheH42AnImzLv+xgS0tLSilpaWlJ169fLyk0yMhfYwGcu6nVakHqAtfZCem3P8/hMIgRwWP8ICiU\nBH/fjbOjRN+hicQ4L+ShcHZ9XEgMqrsAly5d0sbGRowDBMLf3TiRsIkxZS4ghN0AeP4QRpL1JiDA\nHLuMENXCEIByut1u8DWsFe9HoahfRPb67u6uGo1GyKFURH09mkapFJAB0TZO6HsKAzzQzMxMuI58\nbjAYZWb7Ws7MzMT1XfBoKysrpdIz5K5duHBBs7Oz6nQ6Wl5eVq/XU7vdDg6JO+1u375d2tj5PHqC\nXLFuTmE4bQICdjLeZdZ1kg3ruHbqjVK9XhSt8rwhqdiBq2hIKvI7XKGc4HVeCcPAaXWUnp0EQ8NJ\neGrkeM4OaIpoCAsJ5KevbixrtVrUW8LdyTmHUKK8ICPcHPrdbrdLWdPMkUdBCMW7kKBU7Hq4BBhB\neCJPQMWA4CKjMPSPeULQPRKJsoHmnGNzw12vj065U/cIRaGvrD38Gjs7iuuR2MFgoE6nE2veaDS0\ns7OjTqcjqUDUhOAxOp7B7a4gVzR5SsPq6mrwPVJxqwnpBxha+sp8eKCD1ARk1pMooSAwoshavV6P\n8rr1+qgsMSjy8ccfV6/XU6vVUrPZ1PLycpTDBU3Nz89rbW2ttHEQtHDagDWn+d9c/6pktiMt52k9\nbeR+LZ00TPegWkqpJ+n5B92Pt6EtSzre4X5ntIdlrA/LOKWTj/Vyznnlfm849UhJ0vM556cfdCfe\n6pZSevZhGKf08Iz1YRmn9OaO9UyUw520SZu0h6dNjNKkTdqknap2FozSFx50B96m9rCMU3p4xvqw\njFN6E8d66onuSZu0SXu42llASpM2aZP2ELWJUZq0SZu0U9VOrVFKKX0kpfR8Gt0T99kH3Z83o6XR\nHXnfSqM78Z4dv9ZJKX0tpfS98b9L49dTGt2PdyWl9FxK6akH2/vXbunouwFf97hSSp8av/97KaVP\nPYixHNdeY6yfSyldTcV9hx+1v/3SeKzPp5Q+bK+favlOKT2aUvqfKaXvppS+k1L6B+PX3/p15YDe\nafqRVNeoOuXjkmYkfVPSex90v96Ecb0kabny2j+X9Nnx75+V9M/Gv39Uo+qcSdIHJP3Jg+7/fcb1\nQUlPSfr2Gx2XpI6kF8b/Lo1/X3rQYzvhWD8n6Z8c8d73jmV3VtJjY5munwX5lvSIpKfGvzck/b/x\neN7ydT2tSOn9kq7knF/IOR9I+oqkjz3gPr1V7WOSfnv8+2+ruA/vY5K+lEft65LaaXR/3qlr+ei7\nAV/vuD4s6Ws557Wc87qkr+mEF5a+ne01xvpa7WOSvpJHF2q8qFEp6PfrDMh3zvlazvl/j3/vSfpz\nja5Fe8vX9bQapYuSfmj/v+89cWeoZUn/PaX0ZymlT49fO59HFytI0nVJ58e/n/U5eL3jOuvj/czY\nbfkiLo3eIWNNKf2YpJ+Q9Cd6G9b1tBqld2r7yZzzU5J+RtIvppQ+6H/MI7z7jsvReKeOy9rnJT0h\n6X0a3X34qw+2O29eSyktalR3/x/mnLf8b2/Vup5Wo3RV0qP2/xPfE3eaW8756vjfm5L+s0Yw/gZu\n2fjfm+O3n/U5eL3jOrPjzTnfyDkPcs5DSb+h0bpKZ3ysKaVpjQzSv885/9745bd8XU+rUXpG0ntS\nSo+llGY0us7pqw+4Tz9SSyktpJQa/C7pQ5K+rdG4iEh8SsV9eF+V9MlxVOMDkjYNNp+F9nrH9QeS\nPpRSWhq7Px8av3bqW4Xr+1mN1lUajfXjKaXZlNJjGt2D+Kc6A/KdUkqSflPSn+ecf83+9Nav64Nm\n+e/D/n9UI8b/+5J++UH3500Yz+MaRVm+Kek7jElSV9L/kPQ9SX8oqTN+PUn69fH4vyXp6Qc9hvuM\n7csauS2HGnEGP/9GxiXp72hEBl+R9Lcf9Lhex1j/3Xgsz42V8xF7/y+Px/q8pJ+x10+1fEv6SY1c\ns+ckfWP889G3Y10nx0wmbdIm7VS10+q+TdqkTdpD2iZGadImbdJOVZsYpUmbtEk7VW1ilCZt0ibt\nVLWJUZq0SZu0U9UmRmnSJm3STlWbGKVJm7RJO1Xt/wMoV532STW3lAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnH8dcMN4_y-",
        "colab_type": "code",
        "outputId": "cf210412-f3f1-4f9f-843b-8428be2de73e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "# Display Pneumonia chest x-ray image\n",
        "img_pneumonia = load_img('chest_xray/train/PNEUMONIA/person2_bacteria_4.jpeg')\n",
        "print('PNEUMONIA')\n",
        "plt.imshow(img_pneumonia)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PNEUMONIA\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GYHCe1zNYGFm5XM5UCHB0F0fpLZEUkyQp0gomGUeE2mI8pBSck36VbrcbDYWLxUK9Xi/z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sI3LwwMTUwGP9kPBh+Tmgx1ibeTa8kYWoI/QNOevK8DPvVHhlo0kqp+nVSWH+Ri4gz9ALiJk8tn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\nWiXbyDiN2qiNWiXbyDiN2qiNWiXb/wFTJGqK9KNVCgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ISDFMP05WbM",
        "colab_type": "code",
        "outputId": "6c42a6bd-697f-4beb-9708-8b8e299567cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# Import VGG19 pre-trained model\n",
        "vgg_model = VGG19(include_top=True, weights='imagenet')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg19_weights_tf_dim_ordering_tf_kernels.h5\n",
            "574717952/574710816 [==============================] - 17s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xf5cPKJ1_XB9",
        "colab_type": "code",
        "outputId": "10411df5-0c47-4fce-f378-261b618a4407",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "vgg_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"vgg19\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv4 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv4 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv4 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "predictions (Dense)          (None, 1000)              4097000   \n",
            "=================================================================\n",
            "Total params: 143,667,240\n",
            "Trainable params: 143,667,240\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wm8aPYFh_Z3J",
        "colab_type": "code",
        "outputId": "24cffe58-c400-40f7-d6b9-7b24d8555def",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# Pop off the last layer\n",
        "vgg_model.layers.pop()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.core.Dense at 0x7fce948ead68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuCdGG9t_906",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Compile the model\n",
        "predictions = Dense(1, activation='sigmoid')(vgg_model.layers[-1].output)\n",
        "model = Model(inputs=vgg_model.input, outputs=predictions)\n",
        "\n",
        "model.compile(optimizer = Adam(lr=0.0001), loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejVNv9MhANEA",
        "colab_type": "code",
        "outputId": "bcc9ab30-d899-4a47-81c6-a566d3c4ee7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 224, 224, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv4 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv4 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv4 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "fc1 (Dense)                  (None, 4096)              102764544 \n",
            "_________________________________________________________________\n",
            "fc2 (Dense)                  (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 4097      \n",
            "=================================================================\n",
            "Total params: 139,574,337\n",
            "Trainable params: 139,574,337\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56n2NH8AAP37",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Prepare data augmentation configuration\n",
        "train_datagen = ImageDataGenerator(rotation_range=40,\n",
        "                                   rescale=1./255,\n",
        "                                   shear_range=0.2,\n",
        "                                   zoom_range=0.2,\n",
        "                                   horizontal_flip=True,\n",
        "                                   validation_split=0.1)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whYIwNWVCL-q",
        "colab_type": "code",
        "outputId": "021e55f8-c348-482c-8612-c15c2b296187",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(train_dir,\n",
        "                                                    target_size=(224,224),\n",
        "                                                    batch_size=16,\n",
        "                                                    class_mode='binary',\n",
        "                                                    subset='training')\n",
        "\n",
        "validation_generator = train_datagen.flow_from_directory(train_dir,\n",
        "                                                    target_size=(224,224),\n",
        "                                                    batch_size=16,\n",
        "                                                    class_mode='binary',\n",
        "                                                    subset='validation')\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(test_dir,\n",
        "                                                  target_size=(224,224),\n",
        "                                                  batch_size=16,\n",
        "                                                  class_mode='binary')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 4710 images belonging to 2 classes.\n",
            "Found 522 images belonging to 2 classes.\n",
            "Found 624 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kK9971lJFsTk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Number of train and validation steps\n",
        "train_steps=train_generator.n//train_generator.batch_size\n",
        "validation_steps=validation_generator.n//validation_generator.batch_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9rXP4UFuPdp9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "filepath = \"weights.hdf5\"\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='val_acc', save_best_only=True, verbose=1, mode='max')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHBSALRUD7Hx",
        "colab_type": "code",
        "outputId": "650cd443-d9e6-47fd-ee5e-2a14e858a7d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Train the model\n",
        "history = model.fit_generator(train_generator,\n",
        "                    steps_per_epoch=train_steps,\n",
        "                    validation_data=validation_generator,\n",
        "                    validation_steps=validation_steps,\n",
        "                    epochs=20,\n",
        "                    callbacks=[checkpoint])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "294/294 [==============================] - 129s 440ms/step - loss: 0.5597 - acc: 0.7588 - val_loss: 0.2534 - val_acc: 0.8594\n",
            "\n",
            "Epoch 00001: val_acc improved from -inf to 0.85938, saving model to weights.hdf5\n",
            "Epoch 2/20\n",
            "294/294 [==============================] - 123s 418ms/step - loss: 0.2944 - acc: 0.8655 - val_loss: 0.2735 - val_acc: 0.8636\n",
            "\n",
            "Epoch 00002: val_acc improved from 0.85938 to 0.86364, saving model to weights.hdf5\n",
            "Epoch 3/20\n",
            "294/294 [==============================] - 125s 424ms/step - loss: 0.2696 - acc: 0.8844 - val_loss: 0.2501 - val_acc: 0.8933\n",
            "\n",
            "Epoch 00003: val_acc improved from 0.86364 to 0.89328, saving model to weights.hdf5\n",
            "Epoch 4/20\n",
            "294/294 [==============================] - 125s 425ms/step - loss: 0.2518 - acc: 0.8905 - val_loss: 0.2466 - val_acc: 0.8874\n",
            "\n",
            "Epoch 00004: val_acc did not improve from 0.89328\n",
            "Epoch 5/20\n",
            "294/294 [==============================] - 125s 426ms/step - loss: 0.2380 - acc: 0.8980 - val_loss: 0.2149 - val_acc: 0.8834\n",
            "\n",
            "Epoch 00005: val_acc did not improve from 0.89328\n",
            "Epoch 6/20\n",
            "294/294 [==============================] - 125s 426ms/step - loss: 0.2145 - acc: 0.9107 - val_loss: 0.1980 - val_acc: 0.9269\n",
            "\n",
            "Epoch 00006: val_acc improved from 0.89328 to 0.92688, saving model to weights.hdf5\n",
            "Epoch 7/20\n",
            "294/294 [==============================] - 125s 426ms/step - loss: 0.2023 - acc: 0.9211 - val_loss: 0.1810 - val_acc: 0.9229\n",
            "\n",
            "Epoch 00007: val_acc did not improve from 0.92688\n",
            "Epoch 8/20\n",
            "294/294 [==============================] - 124s 421ms/step - loss: 0.1880 - acc: 0.9226 - val_loss: 0.2686 - val_acc: 0.8913\n",
            "\n",
            "Epoch 00008: val_acc did not improve from 0.92688\n",
            "Epoch 9/20\n",
            "294/294 [==============================] - 123s 417ms/step - loss: 0.1731 - acc: 0.9356 - val_loss: 0.2006 - val_acc: 0.9111\n",
            "\n",
            "Epoch 00009: val_acc did not improve from 0.92688\n",
            "Epoch 10/20\n",
            "294/294 [==============================] - 122s 415ms/step - loss: 0.1573 - acc: 0.9386 - val_loss: 0.2433 - val_acc: 0.8834\n",
            "\n",
            "Epoch 00010: val_acc did not improve from 0.92688\n",
            "Epoch 11/20\n",
            "294/294 [==============================] - 123s 417ms/step - loss: 0.1378 - acc: 0.9469 - val_loss: 0.1224 - val_acc: 0.9565\n",
            "\n",
            "Epoch 00011: val_acc improved from 0.92688 to 0.95652, saving model to weights.hdf5\n",
            "Epoch 12/20\n",
            "294/294 [==============================] - 123s 419ms/step - loss: 0.1280 - acc: 0.9505 - val_loss: 0.1559 - val_acc: 0.9387\n",
            "\n",
            "Epoch 00012: val_acc did not improve from 0.95652\n",
            "Epoch 13/20\n",
            "294/294 [==============================] - 122s 415ms/step - loss: 0.1339 - acc: 0.9515 - val_loss: 0.1124 - val_acc: 0.9585\n",
            "\n",
            "Epoch 00013: val_acc improved from 0.95652 to 0.95850, saving model to weights.hdf5\n",
            "Epoch 14/20\n",
            "294/294 [==============================] - 122s 414ms/step - loss: 0.1120 - acc: 0.9607 - val_loss: 0.1568 - val_acc: 0.9368\n",
            "\n",
            "Epoch 00014: val_acc did not improve from 0.95850\n",
            "Epoch 15/20\n",
            "294/294 [==============================] - 121s 411ms/step - loss: 0.1052 - acc: 0.9618 - val_loss: 0.2006 - val_acc: 0.9229\n",
            "\n",
            "Epoch 00015: val_acc did not improve from 0.95850\n",
            "Epoch 16/20\n",
            "294/294 [==============================] - 122s 414ms/step - loss: 0.1056 - acc: 0.9617 - val_loss: 0.1040 - val_acc: 0.9625\n",
            "\n",
            "Epoch 00016: val_acc improved from 0.95850 to 0.96245, saving model to weights.hdf5\n",
            "Epoch 17/20\n",
            "294/294 [==============================] - 121s 410ms/step - loss: 0.0953 - acc: 0.9634 - val_loss: 0.1369 - val_acc: 0.9447\n",
            "\n",
            "Epoch 00017: val_acc did not improve from 0.96245\n",
            "Epoch 18/20\n",
            "294/294 [==============================] - 121s 411ms/step - loss: 0.0974 - acc: 0.9621 - val_loss: 0.1378 - val_acc: 0.9526\n",
            "\n",
            "Epoch 00018: val_acc did not improve from 0.96245\n",
            "Epoch 19/20\n",
            "294/294 [==============================] - 121s 410ms/step - loss: 0.0962 - acc: 0.9647 - val_loss: 0.1243 - val_acc: 0.9427\n",
            "\n",
            "Epoch 00019: val_acc did not improve from 0.96245\n",
            "Epoch 20/20\n",
            "294/294 [==============================] - 121s 410ms/step - loss: 0.0879 - acc: 0.9698 - val_loss: 0.1218 - val_acc: 0.9447\n",
            "\n",
            "Epoch 00020: val_acc did not improve from 0.96245\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNxkKcgUNgvG",
        "colab_type": "code",
        "outputId": "3e828892-466b-4de4-ce3b-9410f4dfae54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "# Plot accuracy and loss graphs\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dxrN0+tDt/7PzH4z7u2/jVCfQBKFqZv1MmxzG/wsydkByPPzwbyj5p10f09N2\neOo4zD637OK1nr8//nKI+2evJz07n4cv6sltI7vUbie3mkhaCc07QvP2vo7Ed4LC7ERHbfvB4ifg\n4E476F/UKRD/CkR18u5ItKrKNEGo6ispgfXv2mIB11YnBccgda1NFkmr7FDU65ypIpu1Pn6H0XGY\n7VFcw05jRcUlvLhkJ/9ZmkDHlmF8cudZ9ItrUaNj1ipj7B1E53N8HYnvicCIByD2NJh7ix02fOSD\n9m/lwr+Cnw+LAdWvaIJQ1ffLMjiS9Oser0Fhtty5dKaukhLI2H48YSTHw7b/2XUBIXZ2svOfsD2M\nTyInv4jUw7mkHD5G6pFcUpzX2/dls+vAUS4fGMcTE/oQHlzP/qwP/wI5aU2z/qEi3c+H276B2dfA\nVw9DULjtaKfqlXr2n6QalLXv2HkJel5y8u38/GzHqdjex4duzt5vv1Unr4LNn8Cnt5N9y3JSswpJ\nOWQ/+FMO556QCA4fKzzhsEEBfsRFhRLXMowHzj+V8ad7ceKamvDGAH2NQXQ3uHWxTRCxfSGkua8j\nUuVoglDVc/QAbP/CTlATWI1K4Ig20OcyNrc4l693xvLbg4/x16cfYVbx6LJNQgL9aN8ilLioMPrF\nNScuKswmhKhQ2keFEt0suP7UMZxM0kr74RfT09eR1D8hkbayWtVLmiBU9Wz4wM5iNvDGau2elVfI\n9K938O7K3bQM682ksL78uegzzh57J21joomLCqVVs6DGMVVsUrytc/HTVuWqYdEEoarOGFj3rm2V\n1Lpq34qNMcz/aS9PfbGNAzn5XD/sFH53QQ+aZzSHGRdwUc6nMPD3XgrcB44ehAM74PSrfR2JUlWm\nCUJVXdJK20RxRNWKBhLSc3j0s838kHiQfnHNefPGwcdbG3UcausyVvwLBt1kp9NsDJJX2Wetf1AN\nkCYIVXVr34HgSOgz0aPNcwuK+c/Snbz23S5CAv35y2V2sDz/8vUHox+Dn4faAd4ues4LgftA0krw\nD9KJb1SDpAlCVU3uYdg6D/pfA0HNKt18ybY0Hpu/hZTDuUwa2J6HL+pFTEQF03XGnAoDrofVb9rx\neFp2ruXgfSAp3vYur05FvlI+prVmqmo2zbUDqlVSOZ1y+Bi3vbuGW95ZQ2igP7OnDWP6Vf0rTg6l\nRj0MfgF2HoGGrjAX9q7X/g+qwdI7COU5Y2zxUpt+0K6/200Kikp4c8UvvLhkJwAPXdSTm4d3JijA\nw+8ikW3hzDth+T/grHsqPE+DkLrOtvTSBKEaKL2DUJ7bu86OSjrI/d3DysSDjHtxOc99uZ2zT41m\n8e/O4fZzunqeHEoNvw9CW30B+H4AACAASURBVMLiBj4ncdJK+9xhqG/jUKqaNEEoz617FwLDoO+V\nJyzOyM7ngQ83cPXr8eQXFTNj6mBevX4w7VuEVu88Ic3h7N/DrmWQ+E3N4z4ZY+yAg2lbav/YSfG2\nc5xOfqMaKE0QyjP5Obb+oc/EE4ZE+HLzPsZM/5bPN+7lnvO68fX953Bez9ian++MW+wQ0Yseq9qc\nxlW18iX47E47JlBhbu0dt6TYzpOhxUuqAdMEoTyz5RMoyCmrnD5WUMTDn2zk9vfXcUqrMBbedza/\nu6AHoUG1NBpnQDCc93+wfyNs/rh2jlne1s/g6z9D3BlweDd893ztHTt9G+Rnav8H1aBpglCeWfsO\nRPeADkPYujeL8f9ewezVydx+Tlfm3n4W3VqH1/45T7sC2vSFb56EovzaPXbKGvhkmk0ON/4P+k2B\n7/8FGT/XzvGTSwfo0zsI1XBpglCVS9sCqWswA29gxve7ueyl78nOK+L9W4by0EU9q14J7Sk/Pxjz\nhB1SfM2M2jvuoV/gg8l2wMCrZ0FgKFzwlB2m/PPf/no6zOpIioeIttDilJofSykf0QShKrfuXYx/\nEPdu7cGTn2/l7FOj+fL+sxnerQ6Gw+g2GrqMgm//BnmZNT/esUN2XuSSIrh27vEhPcJjbDLaswJ+\nmlXz8yTF27uHxjDYoGqyNEGokyvMo3D9LL4uOYOvdhfx5IQ+vH7DYFo2q9kscFUy5nHIPQTfv1iz\n4xTlw4fXw5E9MOUDiO5+4vqBN0LcEFsvcexQ9c9zJBkyk7X+QTV4miBUhQqKSpg362UCCzJZEjaW\n+XcP54YzO9X9ENztBtj6iJUvQda+6h3DGJh/j71DmPBf6DT819v4+cEl/4TcIzXrg1E2QJ/WP6iG\nzasJQkTGisjPIpIgIg+5WX+KiCwRkY0iskxE4lzW3SgiO51H9SYdUNW2KyOHSS9/T+zOORwKaseT\n991BzzaRvgvovD/bYqFvn63e/suegY0f2uP0u7Li7dqcZntyr3v3+ExwVZW0EoIioHWf6u2vVD3h\ntQQhIv7AS8BFQG/gahHpXW6z54F3jTH9gCeBZ5x9WwKPAUOBIcBjIhLlrVjVccYY5qxJ5pJ/r8Dv\n0C7O9N9Ky5G3EhIU6NvAWna2fSPWvQcZO6q27/qZ8O1zds7jkQ9Wvv2oh6F5B/j8ASgurHz78pLi\n7fza/jqSjWrYvHkHMQRIMMbsMsYUALOBCeW26Q2UdpVd6rL+QmCRMeaQMeYwsAgY68VYFZCZW8g9\ns9bzh7kb6RfXnA8G/QziD/2v9XVo1tm/tz25lzzh+T67lsH/7rUV3Ze84FmlcVAzuOhvkL7VFmtV\nRe4R2+pL6x9UI+DNBNEeSHZ5n+Isc/UTMMl5PRGIEJFWHu6ratHaPYcY96/lLNy8n99f2IOZNw0k\nfNscOHWsbQ5aHzSLtuM0bf8cklZVvn36NvjwBmjVHa56F/yrcBfUcxz0uBiWPQuH93i+X8pqwGj9\ng2oUfF1J/SBwjoisB84BUoFiT3cWkWkiskZE1mRkZHgrxkYtO6+Q6Yt2cNWr8fj5wdzbz+Suc7vh\nv/MrOJpR4cB8PnPmnRAeayuRT9ZfITvNNmcNDIFr55wwPIjHLnoOxA8W/sHzvhFJ8fauK25w1c+n\nVD3jzQSRCnRweR/nLCtjjNlrjJlkjBkAPOIsO+LJvs62rxljBhtjBsfExNR2/I1aelYezy7czlnP\nfMOLS3Yyvl9bFtw7kgEdnaqede9AZHvoNsa3gZYX1AxGPWQrgn9e6H6bgqMwazIcOwjXfGjHdKqO\nFh3g3Idhx5f2rsUTSfHQ9nSPJlNSqr7zZoJYDXQXkc4iEgRMAea7biAi0SJSGsPDQGl32a+AC0Qk\nyqmcvsBZpmooIT2HP87dyIjnlvLad4mc3SOG+XcP54UpA4gIcYpgjiRBwhJbqetXS2Mr1aYB10Or\nbrYuorjoxHUlxfDxrbDvJ7hiRs2n+hx6O8SeBgv/CPnZJ9+2qABS12j9g2o0vJYgjDFFwN3YD/Zt\nwBxjzBYReVJELnU2GwX8LCI7gFjgaWffQ8BfsElmNfCks0xV09o9h7jt3TWMmf4t8zakMvmMDix9\ncBQvXTOQfnEtTtx4/fv2ecB1dR+oJ/wD7fzVGdvhpw9OXPfVn+DnBTD2OehxUe2c65J/QtZeWx9x\nMvt+srPtaf2DaiS82g7PGLMAWFBu2aMur+cCcyvYdwbH7yhUNZSUGJZsT+fVbxNZs+cwLcICuXd0\nd2488xRahVcw9WdJsU0QXc+rftFMXeg13g60t/QZ24kuKAziX4ZVr8CwO2HotNo7V4chMGiqPX6/\nydC2n/vtSicI0gShGgltqO1reZnw3iTbUui0y22roaCwGh0yv6iYeetTee27XSRmHKV9i1AeH9+b\nq87oQFhQJb/yhCWQlQpjn6lRDF4nYsdOenucTQoxPeDLh6HnJXbgvdo25jHY9j/4/H64ZZH7orek\neGjZFcJb1/75lfIBTRC+9t3fIXUtNIuxFaFB4dBjHPS9ArqcCwGej3mUlVfIzPgk3vr+F9Kz8+nd\nNpJ/TenPxX3bEuDvYWniundsLKfWQvGMt3UabhPq8um2l3W7ATDpde/Um4RGwYV/hU+nwdq3bac9\nV8bYO4ge42r/3Er5iCYIXzqYCPGvwIBrYfyLsHsFbJ4LW+fDpjn2Q6nXpTZZnDK8wg++/Zl5vPX9\nL8xclUROfhEjukXzj6tOZ0S36KqNm5SdZlsGnXlXlRKTT41+DF4ZDs3jbIulGt59nVS/q2DD+7D4\nCVvE5XqncGCnHVCwo84/rRoPTRC+9NUjEBAC5z1qP/y7nGMf4/5h52LePNdO87nuHQhvY6f77HsF\ntB/EwaMFfLllP19s3Ef8roMAXNyvHb85uwunta9Gm3+ADTPBFJfNGtcgxPaGG+ZDyy7eL9oRgYun\nw8tn2crwy984vq6s/kFbMKnGQxOEryQshh0LbTl6RLk5nAOCoMdY+yg4Ztvhb/4Ys+ZNZNXLpPm3\n5eOCoXxWNIzCVr2469xuXDmoAx1b1eDbc0mJHaDulBEQ3a1mP1td6zyy7s4V3R1GPGDHdup/LXQ9\n1y5PioewVrb5rVKNhCYIXyguhC//BFGdYdgdJ930SFEAX+eewefH4vgpdyLny49c5b+K2/3/x53+\n8zChvZCQyyF/DBT3rf4AcbuXw+Ff4Nw/VW//pmTEb2HTR/DF7+COH2xv7eR4e/egEwSpRkQThC+s\nmQEHfoYpsyDg181NM48V8vXW/XyxaR8rdh6gqMTQsWUY15x9Ghf3PZ8+7SKRowdg6zxk88fwzVP2\nEdgM4gZBh2G2LDzuDM+HmFj3rt221/ha/mEbocAQuPgf8N5E+P4FGHQTHNoFg2/2dWRK1SpNEHXt\n6EFY+rQdXdSlI1dWXiGLtqTxxaZ9LN+ZQWGxIS4qlFtGduaSvu04rX3kiRXO4TEw5Db7yNoHST/Y\nAeyS42H582BKAIHYPtBhqG2b32Go7dtQ/lvusUOwbb79oAsMrYur0PB1Pc/2v1j+j+ODAGr9g2pk\nPEoQIvIJ8Caw0BhT4t2QGrllf4X8HLjwGRDhaH4RD32yia8276eguIR2zUOYelYnLunXjn5xzT1r\nhRTZ1vahOO1y+z4/xw75kBRvHxvnwJo37bqItk6ycO4yYvvCT7OhuKD+DcxX3134V9i5CJb8BQJC\noU0FHeiUaqA8vYP4L3AT8KKIfAS8ZYz52XthNVJpW2zx0hm3QmxvCotLuGPmOlbszODGszox/vR2\nDOjQouZTegaH2zuULqPs+5Jie+7kVba1TdIq2PKpXRfYzLagaj/Y3m0oz0XEwphHbV1E3OCG0zRY\nKQ95lCCMMYuBxSLSHLjaeZ0MvA68b4ypxrRbTYwx8OVDtpx/1MOUlBj+OHcj3+3I4JlJfbl6iBeH\ntfDzt8NDtO1ni6QAMlPs3UXyKti73k7Go6pu0E2Q/CN0O9/XkShV6zyug3Am8rkOuB5YD8wERgA3\nYgfdUyez/Qv45TsY9zyEteS5Bdv4ZH0qD4w51bvJoSLN42yfir5X1P25GxM/f5j0mq+jUMorPK2D\n+BToAbwHjDfG7HNWfSgia7wVXKNRmAdfPwIxvWDQTbyxfBevfreLa4d25N7R2m5eKVU/eXoH8aIx\nZqm7FcYYnTqrMvH/hcO74fp5fLYpjae+2MbYPm14csJpNa9vUEopL/F0PojeIlI2aYAzkc+dXoqp\nccneD989Dz0uZnnJaTz40U8M7dySF6b0x99Pk4NSqv7yNEHc5kwFCoAx5jBwm3dCamSWPAklhfzc\n74/c/t5ausaE89oNgwkJrIcztSmllAtPE4S/uJSFiIg/oG36KpO6FjbMJPP0W7nmk3RahAXxzs1D\naB4a6OvIlFKqUp7WQXyJrZB+1Xn/G2eZqogxsPCPFIfFMHnbCEqM4d1bhhAbGeLryJRSyiOeJog/\nYpNC6chyi4A3Kt5csekjSFnNi83uY0+2Px/cdgZdY8J9HZVSSnnM045yJcDLzkNVpuAoZtGj/BLY\nnZcOD+H1GwcyoGOUr6NSSqkq8bQfRHfgGaA3UFZGYozp4qW4GjSz/J9I9j5+n/8Yz17Rn3N76BzF\nSqmGx9NK6rewdw9FwLnAu8D73gqqITOHd1O04l98VnwWYy6cwBWD4nwdklJKVYunCSLUGLMEEGPM\nHmPM48DF3gur4Ur84EGKSmBX/99z+zl6g6WUarg8raTOFxE/YKeI3A2kAlrjWs6yrz5lVMYivoi+\nifsmnqu9pJVSDZqndxD3AWHAvcAg7KB9OnmAi6Vb9xHz/WMc8I9hzK1/wU97SSulGrhK7yCcTnGT\njTEPAjnYeSGUi/VJh1k6ezpP+u0h95LXCQ6N8HVISilVY5UmCGNMsYiMqItgGqK8wmLuf/tb5vl9\nSGH7oYT2v9LXISmlVK3wtA5ivYjMBz4CjpYuNMZ84pWoGpCEtGyuL5hNi4As5OK//Xq+Z6WUaqA8\nTRAhwEHgPJdlBmh6CcIYOPwL7F4Bu1fQZee3nBawnyM9p9CiXX9fR6eUUrXG057UTbfewRg4tKss\nIbDne8hKteuaxZAc1o+ZWRfxyISnfBunUkrVMk97Ur+FvWM4gTHm5lqPyNfKEsJyJyl8D9l77bpm\nraHTcOg0AjqNhOhTeWHmOrblZfFkqLb6VUo1Lp4WMX3u8joEmAjsrf1wfMAYOJhoE8Ke721SyHZm\nVG3W2kkGpQmh+6/qGBLSc+jWWpODUqrx8bSI6WPX9yIyC1jhlYjq2pEk+M8g+zo89sSE0KrbSSud\ni4pL2H3wKOf10rGWlFKNj6d3EOV1BxrHp2KLjnDZyxA3BFp1rVIrpD2HjlFYbOimw3grpRohT+sg\nsjmxDmI/do6Ihk8E+l9TrV0T0nMAtIhJKdUoeVrEpF2D3ShNEF01QSilGiGPxmISkYki0tzlfQsR\nucx7YTUMiek5xEYGExmic0wrpRofTwfre8wYk1n6xhhzBHjMOyE1HAkZ2oJJKdV4eZog3G3nyUB/\nY0XkZxFJEJGH3KzvKCJLRWS9iGwUkXHO8k4ikisiG5zHKx7GWWeMMSSm52gFtVKq0fK0FdMaEZkO\nvOS8vwtYe7IdnFFgXwLOB1KA1SIy3xiz1WWzPwNzjDEvi0hvYAHQyVmXaIypt2NX7MvM42hBsd5B\nKKUaLU/vIO4BCoAPgdlAHjZJnMwQIMEYs8sYU+DsN6HcNgaIdF43pwF1vtMKaqVUY+dpK6ajwK+K\niCrRHkh2eZ8CDC23zePA1yJyD9AMGOOyrrOIrAeygD8bY5aXP4GITAOmAXTs2LGK4dWMNnFVSjV2\nnrZiWiQiLVzeR4nIV7Vw/quBt40xccA44D1natN9QEdjzADgt8AHIhJZfmdjzGvGmMHGmMExMTG1\nEI7nEjNyiAwJICY8uE7Pq5RSdcXTIqZop+USAMaYw1TekzoV6ODyPs5Z5uoWYI5zzJXYcZ6ijTH5\nxpiDzvK1QCJwqoex1onSMZh03mmlVGPlaYIoEZGyMhwR6YSb0V3LWQ10F5HOIhIETAHml9smCRjt\nHLMXNkFkiEiMU8mNiHTBDu2xy8NY60SiNnFVSjVynrZiegRYISLfAgKMxCn7r4gxpkhE7ga+AvyB\nGcaYLSLyJLDGGDMf+B3wuog8gE04U40xRkTOBp4UkUKgBLjdGHOoOj+gNxw5VsCBnAJNEEqpRs3T\nSuovRWQwNimsB+YBuR7stwDbdNV12aMur7cCw93s9zHwcfnl9YVWUCulmgJPB+u7FbgPW4+wARgG\nrOTEKUibjLIEEaNDVCmlGi9P6yDuA84A9hhjzgUGAEdOvkvjlZCeQ3CAH+2jQn0dilJKeY2nCSLP\nGJMHICLBxpjtQA/vhVW/JWTk0CUmHH8/bcGklGq8PK2kTnH6QcwDFonIYWCP98Kq3xLScxjQMcrX\nYSillFd5Wkk90Xn5uIgsxQ6L8aXXoqrHcguKST2Sy5WDOlS+sVJKNWBVnnLUGPOtNwJpKBIzcjBG\nWzAppRo/T+sglCMxQ5u4KqWaBk0QVZSQnoOfQKfoMF+HopRSXqUJoooS0nPo2DKM4AB/X4eilFJe\npQmiikoH6VNKqcZOE0QVFBWXsPvgUZ0kSCnVJGiCqII9h45RWGx0HmqlVJOgCaIKdJA+pVRTogmi\nCnQeaqVUU6IJogoS03OIjQwmMiTQ16EopZTXaYKoggSdRU4p1YRogvCQMYbE9BytoFZKNRmaIDy0\nPyuPowXFegehlGoyNEF4SCuolVJNjSYID2kTV6VUU6MJwkMJ6TlEhgQQEx7s61CUUqpOaILwUOkY\nTCI6zahSqmnQBOGhRG3iqpRqYjRBeODIsQIO5BRoglBKNSmaIDygFdRKqaZIE4QHyhJETISPI1FK\nqbqjCcIDCek5BAf40T4q1NehKKVUndEE4YGEjBy6xITj76ctmJRSTYcmCA/oNKNKqaZIE0QlcguK\nST2Sq4P0KaWaHE0QlUjMyMEYbcGklGp6NEFUIjFDm7gqpZomTRCVSEjPwU+gU3SYr0NRSqk6pQmi\nEgnpOXRsGUZwgL+vQ1FKqTqlCaIS2oJJKdVUaYI4iaLiEnYfPKqTBCmlmiRNECex59AxCouNNnFV\nSjVJmiBOIlEH6VNKNWFeTRAiMlZEfhaRBBF5yM36jiKyVETWi8hGERnnsu5hZ7+fReRCb8ZZkYQM\nnYdaKdV0BXjrwCLiD7wEnA+kAKtFZL4xZqvLZn8G5hhjXhaR3sACoJPzegrQB2gHLBaRU40xxd6K\n152E9BxiI4OJDAmsy9MqpVS94M07iCFAgjFmlzGmAJgNTCi3jQEindfNgb3O6wnAbGNMvjHmFyDB\nOV6dStQWTEqpJsybCaI9kOzyPsVZ5upx4DoRScHePdxThX0RkWkiskZE1mRkZNRW3AAYY0jMOKoV\n1EqpJsvXldRXA28bY+KAccB7IuJxTMaY14wxg40xg2NiYmo1sP1ZeeTkF+kdhFKqyfJaHQSQCnRw\neR/nLHN1CzAWwBizUkRCgGgP9/Wq0lnktIJaKdVUefMOYjXQXUQ6i0gQttJ5frltkoDRACLSCwgB\nMpztpohIsIh0BroDP3ox1l/ReaiVUk2d1+4gjDFFInI38BXgD8wwxmwRkSeBNcaY+cDvgNdF5AFs\nhfVUY4wBtojIHGArUATc5YsWTJEhAcSEB9flaZVSqt7wZhETxpgF2Mpn12WPurzeCgyvYN+ngae9\nGd/JlI7BJKLTjCqlmiZfV1LXW4kZ2sRVKdW0aYJw48ixAg7kFGiCUEo1aZog3NAKaqWU0gThVlmC\niInwcSRKKeU7miDcSEjPITjAj/ZRob4ORSmlfEYThBsJGTl0iQnH309bMCmlmi5NEG7oNKNKKaUJ\n4ldyC4pJPZKrg/QppZo8TRDlJGbkYIy2YFJKKU0Q5SRmaBNXpZQCTRC/kpieg59Ap+gwX4eilFI+\npQminISMHE5p1YzgAH9fh6KUUj6lCaKchPQcusY083UYSinlc5ogXBQVl/DLgaM6SZBSSqEJ4gRJ\nh45RWGy0iatSSqEJ4gQ6SJ9SSh2nCcJFQobOQ62UUqU0QbhISM8hNjKYyJBAX4eilFI+pwnCRaKO\nwaSUUmU0QTiMMSRmHNUKaqWUcmiCcOzPyiMnv0jvIJRSyqEJwlHagkkrqJVSytIE4dAmrkopdSJN\nEI6E9BwiQwKICQ/2dShKKVUvaIJwlM4iJ6LTjCqlFGiCKJOYoU1clVLKlSYI4MixAg7kFGiCUEop\nF5og0ApqpZRyRxMELgkiJsLHkSilVP2hCQKbIIID/GgfFerrUJRSqt7QBIEdxbVLTDj+ftqCSSml\nSmmCQFswKaWUO00+QeQVFpNyOFcH6VNKqXKafILIyS9ifL92DDolytehKKVUvRLg6wB8LTo8mBev\nHuDrMJRSqt5p8ncQSiml3NMEoZRSyi1NEEoppdzyaoIQkbEi8rOIJIjIQ27W/1NENjiPHSJyxGVd\nscu6+d6MUyml1K95rZJaRPyBl4DzgRRgtYjMN8ZsLd3GGPOAy/b3AK61xbnGmP7eik8ppdTJefMO\nYgiQYIzZZYwpAGYDE06y/dXALC/Go5RSqgq8mSDaA8ku71OcZb8iIqcAnYFvXBaHiMgaEYkXkcsq\n2G+as82ajIyM2opbKaUU9aeSegow1xhT7LLsFGPMYOAa4AUR6Vp+J2PMa8aYwcaYwTExMXUVq1JK\nNQne7CiXCnRweR/nLHNnCnCX6wJjTKrzvEtElmHrJxIrOtnatWsPiMieGsQbDRyowf7epvHVjMZX\nMxpfzdTn+E6paIU3E8RqoLuIdMYmhinYu4ETiEhPIApY6bIsCjhmjMkXkWhgOPC3k53MGFOjWwgR\nWePcsdRLGl/NaHw1o/HVTH2PryJeSxDGmCIRuRv4CvAHZhhjtojIk8AaY0xp09UpwGxjjHHZvRfw\nqoiUYIvBnnVt/aSUUsr7vDoWkzFmAbCg3LJHy71/3M1+PwB9vRmbUkqpk6svldT1wWu+DqASGl/N\naHw1o/HVTH2Pzy05sWRHKaWUsvQOQimllFuaIJRSSrnVpBKEB4MHBovIh876VSLSqQ5j6yAiS0Vk\nq4hsEZH73GwzSkQyXQYxfNTdsbwc524R2eScf42b9SIiLzrXcKOIDKzD2Hq4XJsNIpIlIveX26ZO\nr6GIzBCRdBHZ7LKspYgsEpGdzrPb6QxF5EZnm50icmMdxvd3Ednu/P4+FZEWFex70r8FL8b3uIik\nuvwOx1Ww70n/370Y34cuse0WkQ0V7Ov161djxpgm8cA2tU0EugBBwE9A73Lb3Am84ryeAnxYh/G1\nBQY6ryOAHW7iGwV87uPruBuIPsn6ccBCQIBhwCof/r73Y3vk++waAmcDA4HNLsv+BjzkvH4IeM7N\nfi2BXc5zlPM6qo7iuwAIcF4/5y4+T/4WvBjf48CDHvz+T/r/7q34yq3/B/Cor65fTR9N6Q7Ck8ED\nJwDvOK/nAqNFROoiOGPMPmPMOud1NrCNCsauqucmAO8aKx5oISJtfRDHaCDRGFOT3vU1Zoz5DjhU\nbrHr39k7gLuxxi4EFhljDhljDgOLgLF1EZ8x5mtjTJHzNh47CoJPVHD9PFHVwUKr5WTxOZ8dV9GA\nByFtSgnCk8EDy7Zx/kEygVZ1Ep0Lp2hrALDKzeozReQnEVkoIn3qNDDLAF+LyFoRmeZmvceDNHrZ\nFCr+x/T1NYw1xuxzXu8HYt1sU1+u483YO0J3Kvtb8Ka7nSKwGRUU0dWH6zcSSDPG7KxgvS+vn0ea\nUoJoEEQkHPgYuN8Yk1Vu9TpskcnpwL+BeXUdHzDCGDMQuAi4S0TO9kEMJyUiQcClwEduVteHa1jG\n2LKGetnWXEQeAYqAmRVs4qu/hZeBrkB/YB+2GKc+qmwKg3r/v9SUEoQngweWbSMiAUBz4GCdRGfP\nGYhNDjONMZ+UX2+MyTLG5DivFwCBYseqqjPm+CCK6cCn2Ft5V1UZpNFbLgLWGWPSyq+oD9cQSCst\ndnOe091s49PrKCJTgUuAa50k9ise/C14hTEmzRhTbIwpAV6v4Ly+vn4BwCTgw4q28dX1q4qmlCDK\nBg90vmFOAcpPZTofKG0tcgXwTUX/HLXNKa98E9hmjJlewTZtSutERGQI9vdXlwmsmYhElL7GVmZu\nLrfZfOAGpzXTMCDTpTilrlT4zc3X19Dh+nd2I/CZm22+Ai4QkSinCOUCZ5nXichY4A/ApcaYYxVs\n48nfgrfic63TmljBeT35f/emMcB2Y0yKu5W+vH5V4uta8rp8YFvY7MC2bnjEWfYk9h8BIARbLJEA\n/Ah0qcPYRmCLGjYCG5zHOOB24HZnm7uBLdgWGfHAWXV8/bo45/7JiaP0GrrGKNipZhOBTcDgOo6x\nGfYDv7nLMp9dQ2yi2gcUYsvBb8HWay0BdgKLgZbOtoOBN1z2vdn5W0wAbqrD+BKw5felf4elLfva\nAQtO9rdQR/G95/xtbcR+6LctH5/z/lf/73URn7P87dK/OZdt6/z61fShQ20opZRyqykVMSmllKoC\nTRBKKaXc0gShlFLKLU0QSiml3NIEoZRSyi1NEErVA84os5/7Og6lXGmCUEop5ZYmCKWqQESuE5Ef\nnTH8XxURfxHJEZF/ip3HY4mIxDjb9heReJd5FaKc5d1EZLEzYOA6EenqHD5cROY6czHMrKuRhJWq\niCYIpTwkIr2AycBwY0x/oBi4Ftt7e40xpg/wLfCYs8u7wB+NMf2wPX9Ll88EXjJ2wMCzsD1xwY7g\nez/QG9vTdrjXfyilTiLA1wEo1YCMBgYBq50v96HYgfZKOD4o2/vAJyLSHGhhjPnWWf4O8JEz/k57\nY8ynAMaYPADneD8aZ+weZxayTsAK7/9YSrmnCUIpzwnwjjHm4RMWivxfue2qO35NvsvrYvT/U/mY\nFjEp5bklwBUi0hrKutsL/AAAAKlJREFU5pY+Bft/dIWzzTXACmNMJnBYREY6y68HvjV2tsAUEbnM\nOUawiITV6U+hlIf0G4pSHjLGbBWRP2NnAfPDjuB5F3AUGOKsS8fWU4AdyvsVJwHsAm5yll8PvCoi\nTzrHuLIOfwylPKajuSpVQyKSY4wJ93UcStU2LWJSSinllt5BKKWUckvvIJRSSrmlCUIppZRbmiCU\nUkq5pQlCKaWUW5oglFJKufX/WIxD9VUxkBAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tdC5nTemzbJsHsydA88th2H/KnsguIABaD7WN0vm5lYtVqWpCE4EvCqtpe7r0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\nlPJz/x/ZmyuceFM9NAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9y6U28CGhjn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Test the data\n",
        "score = model.evaluate_generator(test_generator)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnLgjepvTVAO",
        "colab_type": "code",
        "outputId": "5e5f07f1-d9ae-48b9-f100-426d8e0f833c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4658936753104895, 0.8461538461538461]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    }
  ]
}