Transfer Learning on MNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNoNInw9lZdBbbeV7pO4iu8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Anikate-De/TensorFlow-Deep-Learning/blob/main/Transfer%20Learning%20on%20MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ThqxT1G2EfsH",
        "outputId": "220bda48-2bb6-46c0-a292-d05f47073e0d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import mnist as dataset\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = dataset.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.imshow(X_train[2], cmap = plt.cm.binary), \n",
        "plt.title(y_train[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "W015qthbExWc",
        "outputId": "17631421-a6c2-441b-9037-0729b24b0bc7"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '4')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']"
      ],
      "metadata": {
        "id": "2TyNqXjFE8I3"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "plt.figure(figsize = (18, 6))\n",
        "\n",
        "for i in range(10):\n",
        "  ax = plt.subplot(2, 5, i+1)\n",
        "  rand_index = random.choice(range(len(X_train)))\n",
        "  plt.imshow(X_train[rand_index], cmap = plt.cm.binary)\n",
        "  plt.title(class_names[y_train[rand_index]])\n",
        "  plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "cQQhPp_oE9uz",
        "outputId": "5f97d1f3-81c6-4633-841d-ded3b08d1548"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 10 Axes>"
            ],
            "image/png": 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aAAAAAIBMFLQAAAAAAJkoaAEAAAAAMlHQAgAAAABkoqAFAAAAAMhEQQsAAAAAkElF7gXqUtOmTaPZQQcdtB43KV977bVXMi8UCutpE6A+W7VqVcmz3bp1i2atW7cu+VxorKqqqpL50KFD6+S606dPr5Nz66vKyspkPnjw4GhW069BKh8yZEhyFijNkiVLotkHH3ywHjcB6qOxY8dGs4cffrjkc9esWZPMzz777Gh2wgknJGdTebt27dKLUes8QQsAAAAAkImCFgAAAAAgEwUtAAAAAEAmCloAAAAAgEwUtAAAAAAAmShoAQAAAAAyqci9APXfww8/nMxff/31aHbnnXfW9jpAIzR58uSSZ3fYYYda3AQav6FDh9bZ2YMHD45mlZWVdXbdhmjIkCHRbF1+jVLn/ic58O/Nmzcvmr399tvJ2Y033jiZN2/evJSVgHpkk002qZNz16xZk8yfffbZaPbMM88kZ1P3BGeddVZyduDAgdGsbdu2yVn+PU/QAgAAAABkoqAFAAAAAMhEQQsAAAAAkImCFgAAAAAgEwUtAAAAAEAmCloAAAAAgEwUtAAAAAAAmRSKxWIx9xLkV11dHc26deuWnD3qqKOi2fDhw0tdCWhE5s6dm8w7depU8tlvvfVWNNt+++1LPhcasqqqqmjWo0ePks+trKxM5tOnTy/5bP6pUCiUPDt48OBkPmTIkJLPhnI2aNCgaHbVVVclZw866KBk/sc//rGknYCGYfHixcn8kUceiWarV69Ozk6aNCmaTZs2Lb3YOmjevHk0GzVqVHK2Z8+e0WybbbYpeaeGzhO0AAAAAACZKGgBAAAAADJR0AIAAAAAZKKgBQAAAADIREELAAAAAJCJghYAAAAAIJOK3AtQP1x77bXRrF27dsnZQYMG1fY6QCMzadKkZF4oFKJZTZ9B22+/fUk7QWPWo0ePOjl38ODBdXIuQH33+uuvlzx78MEH1+ImQEPTtm3bZN6nT5+Sz+7bt280++CDD5KzN998c0lZCCG8++670eyMM85Izp566qnRbMyYMcnZiorGW2N6ghYAAAAAIBMFLQAAAABAJgpaAAAAAIBMFLQAAAAAAJkoaAEAAAAAMlHQAgAAAABkoqAFAAAAAMikIvcCrB8rVqxI5q+88ko0u/DCC5Ozm222WUk7AeXjvvvuS+YVFfE/ji677LLaXgcoUWVlZe4VGo2qqqrcKwD/halTp5Y8u/POO9fiJgD/mTZt2iTzn/70p9GsX79+ydnLL788mt14443J2fHjx0ezU045JTl78MEHJ/OGzBO0AAAAAACZKGgBAAAAADJR0AIAAAAAZKKgBQAAAADIREELAAAAAJCJghYAAAAAIJOK3As0RtOmTYtma9asWY+b/NPYsWOT+QYbxLv67373u7W9DpDJypUrk/ny5ctLPnvJkiXRbMGCBcnZPffcM5r179+/1JWAElRWVuZeoSz06NGjTs716wcArKs2bdok8xtuuCGavfTSS8nZP/3pT9Hs1VdfTc4efPDBybwh8wQtAAAAAEAmCloAAAAAgEwUtAAAAAAAmShoAQAAAAAyUdACAAAAAGSioAUAAAAAyERBCwAAAACQSUXuBRqir3/968n8lVdeKfnsDTfcMJp99tlnydnPP/+85OumtG7dOpn37NkzmvXo0SM5u9NOO0Wz7t27J2dbtWqVzKFcTZgwIZpde+21ydm//OUv0axYLCZnC4VCerGEuvr8Av57VVVVuVdoFGq6B1oXgwcPjmaVlZV1dl1ozF5++eVkvnbt2mhWUZH+Wn3MMceUtBNAfZX63FuXruYPf/hDMu/fv3/JZ9d3nqAFAAAAAMhEQQsAAAAAkImCFgAAAAAgEwUtAAAAAEAmCloAAAAAgEwUtAAAAAAAmVTkXqAheuihh5J5dXV1NHv44YeTs126dIlmzz//fHL2qquuimZbbrllcnbTTTeNZqtWrUrOpt6Pmt6rQqEQzcaNG5ecPfXUU5M5NFb3339/Mj/ppJOiWbNmzZKz1113XTQ78cQTk7O77bZbNPv444+Ts4sXL45mjzzySHK2Z8+eyRygVFVVVcm8R48edXLdysrKZD5kyJA6uS6Us5rur4rFYjSr6V5k4403LmknoOFYtGhRNEv1LSGE0LRp01repu7NmTMnmr3yyisln1tX91YNgSdoAQAAAAAyUdACAAAAAGSioAUAAAAAyERBCwAAAACQiYIWAAAAACATBS0AAAAAQCYKWgAAAACATCpyL9AQdejQoeT8gAMOKPm6f/nLX5L5TjvtFM0ee+yx5Owmm2wSzZo0aZKcffzxx6PZ7rvvnpx96623otl3v/vd5CyUq8mTJyfzQqEQzX71q18lZ0877bRodtFFFyVnly9fHs3at2+fnF20aFE0Gz16dHK2Z8+eyRzKUWVlZTSrqqoq+dyaZlPXzWXIkCHJ/Iknnohm6/Je1ST1Xk2fPr3Orgvl7P33349mt9xyS8nn/vSnPy15FmgcDj300Gi2cuXK5Oxll10Wzfr06VPyTuviqaeeSuapvaqrq5Ozm266aTRbl86sofMELQAAAABAJgpaAAAAAIBMFLQAAAAAAJkoaAEAAAAAMlHQAgAAAABkoqAFAAAAAMikUCwWi7mX4J8WL14czX7wgx8kZ3fbbbdodsMNN5S6EpDBggULolnq93oIIbRu3Tqavfzyy8nZSZMmRbOzzjorObvFFltEs2nTpiVnx40bF81uvPHG5OxLL70UzTp37pychcaqqqoqmvXo0aPOrjt48OA6OXfo0KF1cm5dmj59ejKvrKxcP4sAX5g1a1Y023333Us+94UXXkjmXbp0KflsoGHo1q1bNPvTn/6UnN18882j2QUXXJCcPeaYY6JZdXV1cvYXv/hFNHvssceSs6tXr45mrVq1Ss6OGjUqmvXp0yc525h5ghYAAAAAIBMFLQAAAABAJgpaAAAAAIBMFLQAAAAAAJkoaAEAAAAAMlHQAgAAAABkoqAFAAAAAMikUCwWi7mX4J+uuOKKaPbCCy8kZ6dMmVLL2wD10Te+8Y1kXlVVFc122WWX5Owbb7wRzY444ojk7MiRI6NZhw4dkrPLly+PZgceeGBydtNNN41mjz76aHK2efPmyRwaoyFDhiTzoUOHrp9F6onKyspodvDBBydna3ovgfrl6aefjmb/8z//k5xN3TP8+c9/Ts7utttu6cWABu/xxx+PZr17907OfvTRR7W9TgghhJrqvkKhUPLZrVu3jmYPPvhgcrZbt24lX7cx8wQtAAAAAEAmCloAAAAAgEwUtAAAAAAAmShoAQAAAAAyUdACAAAAAGSioAUAAAAAyKRQLBaLuZcoJwsWLEjmXbp0iWYTJ05MzlZWVpayEtDALF26NJmPGTMmms2dOzc5e/TRR0eznj17JmdzOe2006LZOeeck5xNfeZCuaqqqopmPXr0WH+L/B813eMcfPDB0WzIkCG1uwzQYF1wwQXR7Prrr0/O/vrXv45mxx13XMk7AY1fTd/fHnvssWg2efLk5Oy9994bzWqq+/bbb79otvfeeydnBwwYEM1222235Cz/nidoAQAAAAAyUdACAAAAAGSioAUAAAAAyERBCwAAAACQiYIWAAAAACATBS0AAAAAQCYKWgAAAACATArFYrGYe4nGJvWW/upXv0rO3nXXXdFsxowZJe8EAAAAANQ/nqAFAAAAAMhEQQsAAAAAkImCFgAAAAAgEwUtAAAAAEAmCloAAAAAgEwUtAAAAAAAmVTkXqAxWrVqVTQbOHBgcvb666+v7XUAAAAAgHrKE7QAAAAAAJkoaAEAAAAAMlHQAgAAAABkoqAFAAAAAMhEQQsAAAAAkImCFgAAAAAgEwUtAAAAAEAmFbkXaIxmz54dzQ455JDk7Nlnn13b6wAAAAAA9ZQnaAEAAAAAMlHQAgAAAABkoqAFAAAAAMhEQQsAAAAAkImCFgAAAAAgEwUtAAAAAEAmFbkXaIweeOCBaDZq1Kj1uAkAAAAAUJ95ghYAAAAAIBMFLQAAAABAJgpaAAAAAIBMFLQAAAAAAJkoaAEAAAAAMlHQAgAAAABkoqAFAAAAAMikUCwWi7mXAAAAAAAoR56gBQAAAADIREELAAAAAJCJghYAAAAAIBMFLQAAAABAJgpaAAAAAIBMFLQAAAAAAJkoaAEAAAAAMlHQAgAAAABkoqAFAAAAAMhEQQsAAAAAkImCFgAAAAAgEwUtAAAAAEAmCloAAAAAgEwUtAAAAAAAmShoAQAAAAAyUdACAAAAAGSioAUAAAAAyERBCwAAAACQiYIWAAAAACATBS0AAAAAQCYK2kbgz3/+czjyyCNDmzZtQosWLcIee+wRRowYkXstoMxcddVVoVAohD322CP3KkAjd+qpp4ZCoRD9Z8GCBblXBBq5uXPnhuOPPz585StfCS1atAi77rprGDZsWFixYkXu1YBGzn1Q41QoFovF3EtQut/97nfhiCOOCHvttVc47rjjQsuWLcObb74ZPv/88/Czn/0s93pAmXj33XfDLrvsEgqFQujYsWN47bXXcq8ENGIzZswIb7755pdeKxaLoV+/fqFjx47h9ddfz7QZUA7eeeed0Llz59C6devQr1+/0KZNmzBjxoxw2223hSOPPDI88MADuVcEGjH3QY1TRe4FKN2yZcvCySefHL7zne+ESZMmhQ028EA0kMeFF14YDjjggLB27dqwePHi3OsAjVzXrl1D165dv/TaU089FVasWBF+8IMfZNoKKBd33nlnWLp0aXjqqafCV7/61RBCCGeeeWb4/PPPwx133BE+/PDDsNlmm2XeEmis3Ac1Thq9Buzuu+8OCxcuDFdddVXYYIMNwvLly8Pnn3+eey2gzDz55JNh0qRJ4YYbbsi9ClDG7r777lAoFMKJJ56YexWgkVu2bFkIIYQtttjiS69vtdVWYYMNNggbbrhhjrWAMuY+qOFT0DZgjz/+eGjVqlVYsGBB2GWXXULLli1Dq1atwo9+9KOwcuXK3OsBZWDt2rVhwIAB4fTTTw9f+9rXcq8DlKk1a9aEe++9Nxx44IGhY8eOudcBGrnKysoQQginnXZaeOmll8I777wTJk6cGG666aZw7rnnho033jjvgkBZcR/UOPgRBw3Y3Llzw2effRa+973vhdNOOy1cc801oaqqKowcOTIsXbo03HPPPblXBBq5m2++OcyfPz88/vjjuVcBytijjz4alixZ4j/rA9aLnj17hiuuuCJcffXV4cEHH/zi9UsvvTRceeWVGTcDypH7oMZBQduAffLJJ2HFihWhX79+YcSIESGEEL7//e+H1atXh9GjR4dhw4aFnXfeOfOWQGO1ZMmScPnll4fLLrsstGvXLvc6QBm7++67Q9OmTcOxxx6bexWgTHTs2DF07949HH300WHzzTcPv/3tb8PVV18dttxyy9C/f//c6wFlxH1Q46CgbcA22mijEEIIJ5xwwpdeP/HEE8Po0aPDjBkzFLRAnRk0aFBo06ZNGDBgQO5VgDL2ySefhAceeCB861vfCptvvnnudYAy8Otf/zqceeaZYc6cOeErX/lKCOH/e1Dm888/Dz/5yU/CCSec4PMIWC/cBzUefgZtA7b11luHEP71h9O3b98+hBDChx9+uN53AsrD3Llzw5gxY8K5554bqqurw7x588K8efPCypUrw5o1a8K8efPCBx98kHtNoAxMmTLF31oMrFc33nhj2Guvvb4oZ//hyCOPDCtWrAgvvvhips2AcuM+qPFQ0DZge++9dwghhAULFnzp9erq6hBC8J8cA3VmwYIF4fPPPw/nnntu2H777b/459lnnw1z5swJ22+/fRg2bFjuNYEycNddd4WWLVuGI488MvcqQJlYuHBhWLt27b+8vmbNmhBCCJ999tn6XgkoU+6DGg8/4qABO/bYY8O1114bxo0bF77xjW988fott9wSKioqvvjbRQFq2x577BHuv//+f3l90KBB4eOPPw6//OUvw4477phhM6CcLFq0KDz++OPhhBNOCC1atMi9DlAmOnXqFH73u9+FOXPmhE6dOn3x+j333BM22GCD0Llz54zbAeXCfVDjoqBtwPbaa6/Qt2/fcOutt4bPPvssHHzwwaGqqircd9994ZJLLvniRyAA1La2bduGo4466l9ev+GGG0II4d9mALVt4sSJ4bPPPvOf9QHr1UUXXRSmTZsWunXrFvr37x8233zzMHXq1DBt2rRw+umn+x4GrBfugxqXQrFYLOZegtKtWbMmXH311WH8+PGhuro6bLfdduGcc84J559/fu7VgDJUWVkZFi9eHF577bXcqwBloGvXruGtt94K1dXVoUmTJrnXAcrIc889F4YMGRJefPHFsGTJkrD99tuHU045Jfz4xz8OFRWegwLqnvugxkVBCwAAAACQib8kDAAAAAAgEwUtAAAAAEAmCloAAAAAgEwUtAAAAAAAmShoAQAAAAAyUdACAAAAAGSioAUAAAAAyERBCwAAAACQiYIWAAAAACATBS0AAAAAQCYKWgAAAACATBS0AAAAAACZKGgBAAAAADJR0AIAAAAAZKKgBQAAAADIREELAAAAAJCJghYAAAAAIBMFLQAAAABAJgpaAAAAAIBMFLQAAAAAAJkoaAEAAAAAMlHQAgAAAABkUpF7ARq+H/7wh9FsxowZydknnngimm2xxRYl7wQAAAAADYEnaAEAAAAAMlHQAgAAAABkoqAFAAAAAMhEQQsAAAAAkImCFgAAAAAgEwUtAAAAAEAmhWKxWMy9BPXba6+9lsy7du0azZYvX56cveWWW6JZ375904sBAAAAQAPnCVoAAAAAgEwUtAAAAAAAmShoAQAAAAAyUdACAAAAAGSioAUAAAAAyERBCwAAAACQiYIWAAAAACCTitwLUD+sWrUqmp1xxhnJ2eXLl0ez7bbbLjnboUOH9GIAAAAA0Ih5ghYAAAAAIBMFLQAAAABAJgpaAAAAAIBMFLQAAAAAAJkoaAEAAAAAMlHQAgAAAABkUpF7AeqH+++/P5o9++yzydlmzZpFs6FDhyZnDz300PRiAAAAANCIeYIWAAAAACATBS0AAAAAQCYKWgAAAACATBS0AAAAAACZKGgBAAAAADJR0AIAAAAAZKKgBQAAAADIpCL3AqwfkydPTuann356yWePGDEimp188sklnwuwPtx4443RbODAgcnZ7t27R7NHH3205J0AgPXrr3/9azIfM2ZMNDvooIOSsyeddFIyv+CCC6LZ4MGDk7N1ZeLEicn8hRdeiGY1fbfs1KlTSTsBNGaeoAUAAAAAyERBCwAAAACQiYIWAAAAACATBS0AAAAAQCYKWgAAAACATBS0AAAAAACZVORegP/cihUrkvnEiROjWd++fZOzG264YTS75ZZbkrM1nQ389957771kvnz58mi244471vY6DdojjzySzAcOHBjNVq1alZydPn16NPvzn/+cnO3SpUsyh3Lz9ttvR7M//OEPydkf/vCHtb1OCCGEbbbZJplfe+210ax9+/bJ2W9+85sl7QSUbsSIEdFs5MiRydm33normhWLxeRsixYtkvl1110XzQ455JDk7EEHHZTMU26//fZoVtPnaqFQiGZ77rlncrZTp07JHHJJfW845ZRTkrO77rprNJs9e3Zy9owzzohmvXr1Ss7uvffeybxUs2bNSuaTJ0+OZoMGDartdcqCJ2gBAAAAADJR0AIAAAAAZKKgBQAAAADIREELAAAAAJCJghYAAAAAIBMFLQAAAABAJgpaAAAAAIBMCsVisZh7Cf4zL7/8cjLfa6+9olnTpk2TsyNGjIhmZ511VnoxoCTz5s2LZt/+9reTs9XV1dFs1KhRydk+ffok84Zo7dq10ezQQw9NzlZVVUWzQqGQnG3btm00e/zxx5OznTt3TubQEH300UfRbPTo0cnZiRMnRrMXX3yx5J1y2X///ZN56rOnWbNmtbwNlIfjjz8+md97773RrKY/848++uhoNm7cuORsp06dkvnChQuj2bBhw5KzgwYNima33HJLcnbAgAHRbNWqVcnZ1PtV0+f96aefnsxhXfzmN7+JZtdcc01y9oUXXohmNX1GpKq1upzt0qVLNNttt91Kvu6UKVOSsykzZ85M5rvuumvJZzdmnqAFAAAAAMhEQQsAAAAAkImCFgAAAAAgEwUtAAAAAEAmCloAAAAAgEwUtAAAAAAAmVTkXoAve/bZZ6PZySefXPK5N910UzLv27dvyWcDpRkzZkw0mz17dsnn3n333cm8T58+JZ9dX11xxRXR7Iknnqiz615wwQXRrHPnznV2XVgXM2fOTOape5GaDBo0KJotW7as5HMboprex3HjxkWzs88+u7bXgUbjuuuui2ZTpkwp+dyjjjoqmad+z26yySYlX7cmkydPTuYHHHBANLvyyiuTs6tXr45mzZs3T86ec8450ey4445LzsK6WLRoUTK/9NJLo9kbb7yRnC0Wi9GsV69eydnzzjsvmtX03W7s2LHRbPHixcnZ559/Ppq98MILydnUv2+hUCh59q677krOpr67lTNP0AIAAAAAZKKgBQAAAADIREELAAAAAJCJghYAAAAAIBMFLQAAAABAJgpaAAAAAIBMFLQAAAAAAJlU5F6g3CxZsiSZT5w4MZotW7YsOXvkkUdGs759+6YXA9a7yZMn18m5O+ywQ52cm1NNn3933313nVy3X79+yXzAgAF1cl0IIYQ333wzmp100kkln1tdXZ3M33777ZLPXhctW7aMZu3atSv53E033TSZf+c734lmP//5z5Ozq1atKmWlEEIIw4cPj2Ynn3xycjb1XkFDd9999yXzkSNHRrM1a9YkZy+//PJodvHFFydnmzdvnsxT9t1332Q+derUaDZr1qzk7BFHHBHNVq9eXfJegwcPTs4efvjhyRzqyv3335/M33jjjWhWKBSSs7169Ypmd955Z3K2RYsW0ax79+7J2TPPPDOaLV68ODmbum+r6b1KfR9NvY81Sb2PxHmCFgAAAAAgEwUtAAAAAEAmCloAAAAAgEwUtAAAAAAAmShoAQAAAAAyUdACAAAAAGRSkXuBcnPJJZck81tuuSWanXHGGcnZ0aNHl7QTUDeefPLJZP7ee+9Fs2KxmJzt06dPNBs1alR6sQbooosuSuZz584t+ezDDz88ml1//fXJ2WbNmpV8XajJ008/Hc023XTT5Gzz5s2j2TPPPFPqSnXqxhtvjGapz7y6NHXq1GT+0ksvlXz23/72t2i2du3aks+Fhi71fSiEEKqrq6PZxhtvnJwdMmRIKSuts+7duyfzhx56KJqtXr265Oum/iwIIYTBgwdHs9T9EeT0/vvvJ/OavkelTJ48ueTZutK2bduS8y5duiRnFy1aFM1mz56dnF2X95l/zxO0AAAAAACZKGgBAAAAADJR0AIAAAAAZKKgBQAAAADIREELAAAAAJCJghYAAAAAIBMFLQAAAABAJhW5F2iMrr322mg2duzY5GzPnj2j2S9/+cuSdwLWv8mTJyfzjz/+OJoVCoXkbI8ePUraKaelS5cm8wsuuCCaTZgwITlb0/uVcv3110ezZs2alXwurKuTTjopmn33u99Nzi5ZsiSavfPOO8nZjh07RrMrrrgiObsuOnXqVGdnpyxcuDCaffrpp3V23csvvzyatWzZss6uC/VdTX+mp/Kzzz67ttepFQ8//HAyT/07rcs9zvjx45P54YcfXvLZUJcWLVoUzcaNG5ecTf2e2X333Uveqdysy2cPpfEELQAAAABAJgpaAAAAAIBMFLQAAAAAAJkoaAEAAAAAMlHQAgAAAABkoqAFAAAAAMikIvcC9dXy5cuj2Q9/+MPk7GOPPRbNevbsmZy97777olnz5s2TsytXroxmn3zyScmz77//fnJ2zz33jGYbbOD/A4C6sGTJkizXXbhwYTS76aabkrO33HJLMq+uri5ppxBCqKiI/3E2bNiw5Owuu+xS8nUhl80226zkfObMmbW9Tr22bNmyZH788cdHszfeeKPk63bs2DGZH3XUUdGsSZMmJV8Xytn8+fOzXLeme5wZM2aUfPYmm2ySzH/xi19Es29/+9slXxdymjBhQjSr6fd5sViMZhdffHHJO5Wb1Pv4n+T897RnAAAAAACZKGgBAAAAADJR0AIAAAAAZKKgBQAAAADIREELAAAAAJCJghYAAAAAIBMFLQAAAABAJhW5F6ivnn766Wg2adKk5GyxWIxm55xzTnK2ZcuW0ezjjz9Ozg4aNCiajRgxIjlbKBSSeUrPnj2j2R577JGcTb0f2223Xck7QX3QrFmzOjt7+vTpJV839fu9ps+ZsWPHRrP58+cnZ1OfjTXtVZPLLrssml188cUlnws0fM8991wyf+KJJ+rkuttss00y33PPPevkulDO7rvvvmS+xRZbRLN99tknOZu6z7n22muTs6tXr07mzZs3j2a33357cvZ73/teMoeG6I033ohmNX1naNeuXTTr3r17yTuVm3V5n9u2bVvb65QFT9ACAAAAAGSioAUAAAAAyERBCwAAAACQiYIWAAAAACATBS0AAAAAQCYKWgAAAACATCpyL1BfvfLKKyXPDhgwIJodfvjhydnXXnstmh122GHJ2YULF6YXqyOPPPJISVkIITz11FPR7IEHHkjOtmvXLr0YZHbWWWcl8zFjxkSzZcuWJWdTv7emTZuWnC0UCsm8PmrVqlUyP/HEE9fTJkB99Mc//jGanXrqqetvkf/j9NNPz3JdaOjuueeeZD5w4MBodvvttydnR44cGc1y3h9dfPHF0ex73/veetwE6odZs2ZFs2KxmJz95je/Gc06dOhQ8k7lpqb3eaONNopmLVq0qO11yoInaAEAAAAAMlHQAgAAAABkoqAFAAAAAMhEQQsAAAAAkImCFgAAAAAgEwUtAAAAAEAmCloAAAAAgEwqci9QX3Xq1Knk2WOPPTaaNWnSJDk7cuTIaLZw4cKSd+revXsyv/jii6NZ69atk7MzZ86MZtdee21ydsaMGdHsf//3f5Oz11xzTTKH3Hbcccdk/rOf/SyanXXWWbW9zheKxWI0a9myZXL2sMMOi2Zbbrllcvamm24qea9evXolZ2t6r4GG7dNPP03mhx56aDRbs2ZNyddt1apVMh8zZkw06927d8nXhXK22WabJfNbb7215LPHjx9f8mxdmj59ejTr0aNHcrZbt261vQ5kd+edd0azxYsXJ2e7dOlS2+s0Wvfff380KxQKydl27dpFs7Zt25a8UznzBC0AAAAAQCYKWgAAAACATBS0AAAAAACZKGgBAAAAADJR0AIAAAAAZKKgBQAAAADIpFAsFou5l6iPevXqFc1mzZqVnB07dmw023fffZOzXbt2jWYvvfRScvaSSy6JZldffXVytq4cdNBByfzVV18tKQshhA4dOpS0E9QXa9eujWYfffRRcnb69OnRrLq6Ojmb+hzabbfdkrOtW7eOZhdccEFy9vrrr0/mhUIhmr322mvJ2d133z2ZAw3bNddck8wvvfTSks9u2rRpNLvxxhuTs6eddlrJ1wVq3/vvv5/Mt9xyy2iWug8JIYQdd9wxmm288cbJ2Zq+P65evTqa7bLLLsnZ5557LpptsskmyVmgvKU+92r6TNx7772j2cyZM0veqZx5ghYAAAAAIBMFLQAAAABAJgpaAAAAAIBMFLQAAAAAAJkoaAEAAAAAMlHQAgAAAABkoqAFAAAAAMikIvcC9dUll1wSzQ444IDk7IsvvhjNdtttt+TsW2+9Fc0222yz5Gzfvn2TeV2ZOHFiNJs1a1Zy9rTTTotmHTp0KHknaAiaNGkSzdq0aZOcPfroo2t7nf/IihUrotlvf/vbdTp72223jWZbbrnlOp0N1H9/+9vfotmkSZNKPnfjjTdO5rfeems06927d8nXBda/O+64o+TZn/zkJ8l84MCB0WzzzTdPzg4fPjyZX3TRRdFszpw5ydlnnnkmmh122GHJWaC8FQqFkjLqhidoAQAAAAAyUdACAAAAAGSioAUAAAAAyERBCwAAAACQiYIWAAAAACATBS0AAAAAQCYVuReor3bfffeSshBCOO+886LZb3/72+RsmzZtotn8+fOTs9ddd100Gzt2bHJ27ty50WzkyJHJ2dTZl1xySXL28ssvT+ZA/fLpp59Gs9TnyH9i++23j2apz0agcfjDH/4QzV588cWSz91uu+2See/evUs+G6hf/vKXv5Q8W9N3vM0337zkswcOHJjML7roopLPBihVsVgseXbXXXetxU0IwRO0AAAAAADZKGgBAAAAADJR0AIAAAAAZKKgBQAAAADIREELAAAAAJCJghYAAAAAIBMFLQAAAABAJhW5F6ivWrZsGc3Gjx+fnL3yyiuj2RtvvJGcnT9/fnqxhHHjxkWzKVOmJGdXrlwZzZYvX56cnTp1ajQ77LDDkrMA/zBq1KjcKwB16JFHHknmZ599dp1cd+TIkXVyLlD/LF68uOTZF154IZn36dOn5LNnzpxZ8ixAqWbNmpXMC4VCSVkIIcyePbuknYjzBC0AAAAAQCYKWgAAAACATBS0AAAAAACZKGgBAAAAADJR0AIAAAAAZKKgBQAAAADIpCL3Ag3Rvvvum8wfeOCBaLZmzZrk7OTJk6PZb37zm+TspEmTotnixYuTs717945m3//+95Ozhx12WDRr2rRpchZoWJ5++uloViwW1+nsrbfeep3mgfyWL18ezcaOHZucTd0jNWvWLDk7Y8aMaLbjjjsmZ4HGo2fPnsn8oYceimb33HNPcvZHP/pRNOvUqVNydtiwYck8dQ/19a9/PTmb+i4GlLc//vGPyXxdv79RuzxBCwAAAACQiYIWAAAAACATBS0AAAAAQCYKWgAAAACATBS0AAAAAACZKGgBAAAAADJR0AIAAAAAZFKRe4Fy07Rp02R+/PHHl5QB1IaVK1cm89tuuy2aFQqF5Ozuu++ezFu0aJHMgfrvwgsvjGb3339/yefW9Pmy5557lnw20HicffbZybx///7RbNGiRcnZm2++OZr169cvOfvss88m89RnXPv27ZOzADE13XulPntquvfq1atXSTsR5wlaAAAAAIBMFLQAAAAAAJkoaAEAAAAAMlHQAgAAAABkoqAFAAAAAMhEQQsAAAAAkElF7gUAqD/eeeedZH7//feXfHaHDh2SefPmzUs+G6gfpkyZUifn7rLLLnVyLlBeLrroomj2i1/8Ijk7YcKEaHbrrbcmZz/++ONk3qxZs2j24x//ODkLELN48eJkXiwWSz67bdu2Jc/y73mCFgAAAAAgEwUtAAAAAEAmCloAAAAAgEwUtAAAAAAAmShoAQAAAAAyUdACAAAAAGSioAUAAAAAyKQi9wIAALDnnntGs0mTJq2/RYBG68orr4xms2fPTs5OnTq1ttf5wvnnnx/NDjnkkDq7LlDeCoVCSRl1wxO0AAAAAACZKGgBAAAAADJR0AIAAAAAZKKgBQAAAADIREELAAAAAJCJghYAAAAAIJOK3AsAUH9svfXWybxfv37R7Oabb07ODhs2rKSdgPLwwQcfRLMlS5YkZ3fYYYfaXgdohJo2bRrNhg8fnpx99dVXo9n8+fOTs2effXYyHzRoUDIHqAvFYjH3CvwfnqAFAAAAAMhEQQsAAAAAkImCFgAAAAAgEwUtAAAAAEAmCloAAAAAgEwUtAAAAAAAmShoAQAAAAAyKRSLxWLuJQAAaPiuvPLKaHb55ZcnZ48++uhodt9995W8EwBAOTr55JOT+YQJE6JZoVBIzlZVVUWzbt26JWf59zxBCwAAAACQiYIWAAAAACATBS0AAAAAQCYKWgAAAACATBS0AAAAAACZKGgBAAAAADIpFIvFYu4lAAAAAADKkSdoAQAAAAAyUdACAAAAAGSioAUAAAAAyERBCwAAAACQiYIWAAAAACATBS0AAAAAQCYKWgAAAACATBS0AAAAAACZKGgBAAAAADJR0AIAAAAAZKKgBQAAAADIREELAAAAAJCJghYAAAAAIBMFLQAAAABAJgpaAAAAAIBMFLQAAAAAAJkoaAEAAAAAMvl/lj2A+qL/HckAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_norm, X_test_norm = X_train / 255.0, X_test / 255.0"
      ],
      "metadata": {
        "id": "pwZXhnWHLjrc"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_hub as hub\n",
        "\n",
        "# Set random seed\n",
        "tf.random.set_seed(4)\n",
        "\n",
        "# Create the Model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.InputLayer(input_shape = (28, 28, 1)),\n",
        "    hub.KerasLayer(\"https://tfhub.dev/tensorflow/tfgan/eval/mnist/logits/1\",\n",
        "                   trainable=False),\n",
        "    tf.keras.layers.Dense(10, activation= 'softmax') \n",
        "])\n"
      ],
      "metadata": {
        "id": "dLqjE_amFAT-"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(\n",
        "    loss = tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "    optimizer = tf.keras.optimizers.Adam(),\n",
        "    metrics = ['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "APTrYRU8Jihm"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the model\n",
        "history = model.fit(X_train_norm, y_train, epochs = 5, validation_data = (X_test_norm, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bh56VABeJkqB",
        "outputId": "b46d7d21-0252-4645-ed73-5b3e4bcbd13d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 9s 4ms/step - loss: 0.8768 - accuracy: 0.7984 - val_loss: 0.1324 - val_accuracy: 0.9755\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.0913 - accuracy: 0.9825 - val_loss: 0.0652 - val_accuracy: 0.9849\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0534 - accuracy: 0.9873 - val_loss: 0.0466 - val_accuracy: 0.9877\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0399 - accuracy: 0.9899 - val_loss: 0.0383 - val_accuracy: 0.9887\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0337 - accuracy: 0.9909 - val_loss: 0.0352 - val_accuracy: 0.9888\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "pd.DataFrame({'Training Loss': history.history['loss'][3:], 'Validation Loss' : history.history['val_loss'][3:]}).plot()"
      ],
      "metadata": {
        "id": "q3Bin52oFMgg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "7c3407ad-ed04-41ce-bb2e-71ed9456dd56"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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du5Ynn3zy35aXy6hRowgPD79m+9GjR7n33nvz9VhXmz59Ot7e3gV6jMKkUFOE+bo7M+2RprzSIRh7OxO/bDxC5/FL2Xw4xdaliYgUaQMGDGDhwoUcOnTomvemTZtGkyZNaNCgQZ73W65cOVxdXfOjxJsKCAjA2dm5UI5VUijUFHF2diaeubMG3z3VnIpeLuw7cY77P1/BNyv3aTpKROQ6OnfuTLly5Zg+fXqu7WfPnmX27NkMGDCAEydO0Lt3bypVqoSrqyuhoaHMmDHjhvu9evpp165d3HHHHbi4uFCvXj0WLlx4zWdeeeUVateujaurK9WrV+eNN94gMzMTsFwpeeutt9i4cSMmkwmTyZRT89XTTwkJCdx1112UKVMGX19fnnzySc6evfxE+kceeYTu3bvz8ccfU6FCBXx9fRk4cGDOsW7HgQMH6NatG+7u7nh6etKrVy+SkpJy3t+4cSNt27bFw8MDT09PGjduzLp16wDLcg9dunTBx8cHNzc3QkJCiI2Nve1aboWeKFxMNK5alnlDWvPS9xtZtO0Yb/y8hZV7TvBBjwZ4ujjaujwRKU0MAzLP2ebYjq5gMt10mIODA/369WP69Om8/vrrmC5+Zvbs2WRnZ9O7d2/Onj1L48aNeeWVV/D09GTevHn07duXGjVq0KxZs5sew2w2c//99+Pv78/q1atJSUnJdf/NJR4eHkyfPp2KFSuSkJDAE088gYeHBy+//DLR0dFs3ryZ+fPns2jRIgC8vLyu2UdaWhrt27cnMjKStWvXcuzYMR5//HEGDRqUK7j9+eefVKhQgT///JPdu3cTHR1NeHg4TzzxxE2/j7XvdynQ/PXXX2RlZTFw4ECio6NZsmQJAA899BANGzbk888/x97envj4+JyVvwcOHEhGRgZ///03bm5ubN26FXd39zzXkRcKNcWIj5sTX/ZrwtRle/ngt+3EJiSy+XAqE/s0pEFlb1uXJyKlReY5eL+ibY792hFwcruloY899hgfffQRf/31F3feeSdgmXrq0aMHXl5eeHl58eKLL+aMHzx4MAsWLOC77767pVCzaNEitm/fzoIFC6hY0XI+3n///WvugxkxYkTOPwcFBfHiiy8yc+ZMXn75ZcqUKYO7uzsODg4EBARc91gxMTFcuHCBr7/+Gjc3y/efOHEiXbp04cMPP8Tf3x8AHx8fJk6ciL29PcHBwXTq1InFixffVqhZvHgxCQkJ7N27l8DAQAC+/vprQkJCWLt2LU2bNuXAgQO89NJLBAcHA1CrVq2czx84cIAePXoQGhoKQPXq1fNcQ15p+qmYMZlMPN66OrOfjqSSdxkOnDxHj89X8L9lezUdJSJyheDgYFq0aMH//vc/AHbv3s3SpUsZMGAAANnZ2bzzzjuEhoZStmxZ3N3dWbBgAQcOHLil/W/bto3AwMCcQAMQGRl5zbhZs2bRsmVLAgICcHd3Z8SIEbd8jCuPFRYWlhNoAFq2bInZbGbHjh0520JCQrC3t8/5c4UKFTh27FiejnXlMQMDA3MCDUC9evXw9vZm27ZtAAwbNozHH3+cqKgoPvjgA/7555+csUOGDOHdd9+lZcuWjBw58rZuzM4rXakpphpW8SF2SGte/mEjC7Yk8fbcrazac4KPHgjDy1XTUSJSgBxdLVdMbHXsPBgwYACDBw9m0qRJTJs2jRo1atCmTRsAPvroIz799FPGjRtHaGgobm5uPPfcc2RkZORbuStXruShhx7irbfeon379nh5eTFz5kzGjBmTb8e40qWpn0tMJhNms7lAjgWWzq0+ffowb948fvvtN0aOHMnMmTO57777ePzxx2nfvj3z5s3j999/Z/To0YwZM4bBgwcXWD26UlOMebk6MvnhxozqUg8nezt+35pEx/FLiTtwytaliUhJZjJZpoBs8bqF+2mu1KtXL+zs7IiJieHrr7/msccey7m/Zvny5XTr1o2HH36YsLAwqlevzs6dO29533Xr1uXgwYMcPXo0Z9uqVatyjVmxYgVVq1bl9ddfp0mTJtSqVYv9+/fnGuPk5ER29o0XM65bty4bN24kLS0tZ9vy5cuxs7OjTp06t1xzXlz6fgcPXl62Z+vWrZw+fZp69erlbKtduzbPP/88v//+O/fffz/Tpk3LeS8wMJCnn36aH3/8kRdeeIEvv/yyQGq9RKGmmDOZTDzSsho/PNOCKmVdOXz6PL0mr2TK3/9gNms6SkRKN3d3d6Kjoxk+fDhHjx7lkUceyXmvVq1aLFy4kBUrVrBt2zaeeuqpXJ09NxMVFUXt2rXp378/GzduZOnSpbz++uu5xtSqVYsDBw4wc+ZM/vnnH8aPH89PP/2Ua0xQUBB79+4lPj6e5ORk0tPTrznWQw89hIuLC/3792fz5s38+eefDB48mL59++bcT3O7srOziY+Pz/Xatm0bUVFRhIaG8tBDDxEXF8eaNWvo168fbdq0oUmTJpw/f55BgwaxZMkS9u/fz/Lly1m7di1169YF4LnnnmPBggXs3buXuLg4/vzzz5z3CopCTQkRWtmLuUNa0alBBbLMBu/Hbufxr9dxKi3/LqOKiBRHAwYM4NSpU7Rv3z7X/S8jRoygUaNGtG/fnjvvvJOAgAC6d+9+y/u1s7Pjp59+4vz58zRr1ozHH3+c9957L9eYrl278vzzzzNo0CDCw8NZsWIFb7zxRq4xPXr0oEOHDrRt25Zy5cpZbSt3dXVlwYIFnDx5kqZNm/LAAw/Qrl07Jk6cmLeTYcXZs2dp2LBhrleXLl0wmUz8/PPP+Pj4cMcddxAVFUX16tWZNWsWAPb29pw4cYJ+/fpRu3ZtevXqxb333stbb70FWMLSwIEDqVu3Lh06dKB27dp89tln/7reGzEZpeTu0tTUVLy8vEhJScHT09PW5RQYwzD4dvUB3p67lYwsMxW8XBjfuyFNg8raujQRKaYuXLjA3r17qVatGi4uLrYuR0qgG/2M5eX3t67UlDAmk4mHm1dlzrMtqe7nxtGUCzw4ZRWT/tyt6SgRESnRFGpKqHoVPfllcCu6h1ck22zw0YIdPDJ9Lclnr52rFRERKQkUakowd2cHxkaH82GPUFwc7fh753E6frqUVXtO2Lo0ERGRfKdQU8KZTCaim1bh54GtqFnenWNn0unz5So+XbSLbE1HiYhICaJQU0rUCfDgl0EteaBxZcwGjF20k37/W82xMxdsXZqIiEi+UKgpRVydHPi4ZxhjeoZRxtGe5btP0PHTZSzfnWzr0kSkGCglzbJiA/n1s6VQUwr1aFyZXwe3pI6/B8ln03l46mo++X0HWdkF9yhtESm+Lj16/9w5G63MLSXepZ+tq5d5yCs9p6YUu5CZzVu/bmHGGssjsJtVK8v4BxsS4KXnUIhIbkePHuX06dOUL18eV1fXnKUGRP4NwzA4d+4cx44dw9vbmwoVKlwzJi+/vxVqhJ/jD/PajwmkZWRT1s2JT3qFcWed8rYuS0SKEMMwSExM5PTp07YuRUogb29vAgICrIZlhRorFGpubM/xswyK2cDWo6kAPHNnDYbdXRtHe81Qishl2dnZZGZm2roMKUEcHR2xt7e/7vsKNVYo1Nzchcxs3pu3jW9WWVaQbVzVhwm9G1LRu4yNKxMRkdJKyyTIbXFxtOed7vWZ1KcRHs4OrN9/io7jl7J4262vWisiImIrCjVyjU4NKjB3SCtCK3lx+lwmA75ax7sXF8gUEREpqhRqxKqqvm58/0wkj7YMAuC/y/bS64uVHDyplk4RESmaFGrkupwd7BnZJYQv+jbG08WB+IOn6TR+KQu2JNq6NBERkWso1MhNtQ8JYN6Q1oQHepN6IYunvlnPqF+2kJ6VbevSREREctxWqJk0aRJBQUG4uLgQERHBmjVrbjh+9uzZBAcH4+LiQmhoKLGxsbneHzVqFMHBwbi5ueHj40NUVBSrV6/ONSYuLo67774bb29vfH19efLJJzl79uztlC+3IbCsK989FckTrasBMH3FPh74fCX7T6TZuDIRERGLPIeaWbNmMWzYMEaOHElcXBxhYWG0b9+eY8eOWR2/YsUKevfuzYABA9iwYQPdu3ene/fubN68OWdM7dq1mThxIgkJCSxbtoygoCDuuecejh8/DsCRI0eIioqiZs2arF69mvnz57NlyxYeeeSR2/vWclucHOx4vVM9pvZvgrerIwmHU+g8fhnzNh21dWkiIiJ5f05NREQETZs2ZeLEiQCYzWYCAwMZPHgwr7766jXjo6OjSUtLY+7cuTnbmjdvTnh4OJMnT7Z6jEs96YsWLaJdu3ZMmTKFN954g6NHj2JnZ8lhCQkJNGjQgF27dlGzZs2b1q3n1OSvI6fPM2TGBtbtPwXAw82rMKJTPVwcr/8AJRERkbwqsOfUZGRksH79eqKioi7vwM6OqKgoVq5cafUzK1euzDUeoH379tcdn5GRwZQpU/Dy8iIsLAyA9PR0nJyccgINQJkylgfCLVu2zOp+0tPTSU1NzfWS/FPRuwwzn2zOs3fWAOD/Vh3gvs9WsOe4pgRFRMQ28hRqkpOTyc7Oxt/fP9d2f39/EhOtd8QkJibe0vi5c+fi7u6Oi4sLY8eOZeHChfj5+QFw1113kZiYyEcffURGRganTp3KuSp09Kj1qY/Ro0fj5eWV8woMDMzLV5Vb4GBvx8sdgvnqsWb4ujmx7WgqXSYs4+f4w7YuTURESqEi0/3Utm1b4uPjWbFiBR06dKBXr1459+mEhITw1VdfMWbMGFxdXQkICKBatWr4+/vnunpzpeHDh5OSkpLzOnjwYGF+nVKlTe1yxA5tTUS1sqRlZDN0Zjyv/rCJ8xnqjhIRkcKTp1Dj5+eHvb09SUm5H5uflJREQECA1c8EBATc0ng3Nzdq1qxJ8+bNmTp1Kg4ODkydOjXn/T59+pCYmMjhw4c5ceIEo0aN4vjx41SvXt3qcZ2dnfH09Mz1koLj7+nCt49HMKRdLUwmmLn2IN0nLWf3sTO2Lk1EREqJPIUaJycnGjduzOLFi3O2mc1mFi9eTGRkpNXPREZG5hoPsHDhwuuOv3K/6enp12z39/fH3d2dWbNm4eLiwt13352XryAFyMHejmF31+b/BkTg5+7MjqQzdJmwnO/XH7J1aSIiUgrkefpp2LBhfPnll3z11Vds27aNZ555hrS0NB599FEA+vXrx/Dhw3PGDx06lPnz5zNmzBi2b9/OqFGjWLduHYMGDQIgLS2N1157jVWrVrF//37Wr1/PY489xuHDh+nZs2fOfiZOnEhcXBw7d+5k0qRJDBo0iNGjR+Pt7f0vT4Hkt5Y1/Ygd2oqWNX05n5nNi7M38sJ3GzmXkWXr0kREpARzyOsHoqOjOX78OG+++SaJiYmEh4czf/78nJuBDxw4kOs+lxYtWhATE8OIESN47bXXqFWrFnPmzKF+/foA2Nvbs337dr766iuSk5Px9fWladOmLF26lJCQkJz9rFmzhpEjR3L27FmCg4P54osv6Nu377/9/lJAynu48PVjEUz6czfjFu3kh7hDxB88xWcPNaZOgIetyxMRkRIoz8+pKa70nBrbWbXnBENnbiApNR1nBzve6hpCdNNATCaTrUsTEZEirsCeUyNyO5pX9yV2SGva1C5HepaZV39M4LlZ8ZxN13SUiIjkH4UaKRS+7s5Me6Qpr3QIxt7OxM/xR+gyYRlbjqTYujQRESkhFGqk0NjZmXjmzhrMerI5Fbxc2Jucxn2freCbVfspJbOgIiJSgBRqpNA1CSpL7JDWtAsuT0aWmTfmbGZQzAZSL2TaujQRESnGFGrEJnzcnPhv/yaM6FQXBzsT8xKO0nn8MjYdOm3r0kREpJhSqBGbMZlMPN66OrOfjqSSdxkOnDxHj89XMG35Xk1HiYhIninUiM01rOJD7JDW3FPPn8xsg7d+3crT/7eelHOajhIRkVunUCNFgperI1/0bczILvVwtDexYEsSHccvZcOBU7YuTUREigmFGikyTCYTj7asxg/PtKBKWVcOnz5Pz8kr+fLvPZqOEhGRm1KokSKnQWVv5g5pRafQCmSZDd6L3cbjX63jVFqGrUsTEZEiTKFGiiRPF0cm9mnIu93r4+Rgx+Ltx+g0finr9p20dWkiIlJEKdRIkWUymXi4eVV+erYF1fzcOJJygegpq/hsyW7MZk1HiYhIbgo1UuSFVPTi18Gt6BZekWyzwX/m7+DR6Ws5cTbd1qWJiEgRolAjxYK7swPjosP5sEcozg52/LXzOB3HL2XVnhO2Lk1ERIoIhRopNkwmE9FNq/DLoFbUKOdGUmo6fb5cxfjFu8jWdJSISKmnUCPFTp0AD34d3IoejSpjNuCThTvp97/VHDtzwdaliYiIDSnUSLHk6uTAmF5hfNwzjDKO9izffYKOny5j+e5kW5cmIiI2olAjxdoDjSvzy6CW1PH3IPlsOg9PXc0nC3dqOkpEpBRSqJFir5a/B3MGtuTBpoEYBoxfvIs+X64iKVXTUSIipYlCjZQIZZzs+aBHAz59MBw3J3tW7z1Jx0+X8tfO47YuTUREColCjZQo3cIr8evgVtSt4MmJtAz6/28NH87fTla22daliYhIAVOokRKnejl3fnq2BQ83rwLA50v+4cEpqzhy+ryNKxMRkYKkUCMlkoujPe92D2Vin4Z4ODuwbv8pOo5fyh/bk2xdmoiIFBCFGinROjeoyNwhrQit5MXpc5k8Nn0d783bSqamo0REShyFGinxqvq68f0zkTzSIgiAL5fupefklRw8ec62hYmISL5SqJFSwdnBnlFdQ5j8cGM8XRyIP3iaTuOXsmBLoq1LExGRfKJQI6VKh/oBzBvSmrBAb1IvZPHUN+t569ctpGdl27o0ERH5lxRqpNQJLOvK7KcieaJ1NQCmLd/HA5+v5MAJTUeJiBRnCjVSKjk52PF6p3pM7d8Eb1dHEg6n0Gn8UmITjtq6NBERuU0KNf9W+hmYeg+s+hzO6um1xU27uv7EDmlNk6o+nEnP4tlv43hjzmYuZGo6SkSkuFGo+be2/gwHV8P8V2FMHYiJhi1zIFPrDhUXFb3LMOPJ5jxzZw0Avlm1n/s/W8He5DQbVyYiInlhMgyjVCxnnJqaipeXFykpKXh6eubfjtNOwOYfYOMMOBJ3ebuLF4TcD+F9oHJTMJny75hSYJbsOMaw7zZyMi0DNyd73r8/lG7hlWxdlohIqZWX398KNfnp2HbYNBM2zoIzRy5vL1sdwnpDg2jwqVowx5Z8k5hygSEzN7Bm70kAHmwayKiuIbg42tu4MhGR0kehxopCCTWXmLNh79+wcSZs+wUyr+iqqdoKwh6Eet3ApYDrkNuWlW1m/OJdTPhzN4YBdfw9mPRQQ2qW97B1aSIipYpCjRWFGmqulH7WEmw2zoC9S4GLp9uhDNTtbAk41duCna4CFEXLdiXz3Kx4ks+mU8bRnne716dH48q2LktEpNRQqLHCZqHmSqcPwqZZlis4J3Zd3u4eAA16Waao/OvZpja5rmNnLvDczHhW/HMCgAcaV+btbiG4OjnYuDIRkZJPocaKIhFqLjEMOBwHG2Mg4Xu4cPryexXCLOGm/gPgXs5mJUpu2WaDSX/uZtyinZgNqFnenUl9GlEnQNNRIiIFSaHGiiIVaq6UlQ67fof4GbBrAZizLNvtHKDm3ZbpqdodwNHFtnUKAKv2nGDIjA0cO5OOi6Mdb3UNoVeTQEzqbhMRKRAKNVYU2VBzpZz28Bg4suHydhcvqN/DcgVH7eE2l3w2nWHfbeTvnZaHLXYPr8i794Xi7qzpKBGR/KZQY0WxCDVXum57eI2L7eG91B5uQ2azweS//2HM7zvJNhtU93NjYp9G1KtYDH62RESKEYUaK4pdqLnkRu3hQa0t01N1u6o93EbW7jvJkBkbOJpyAScHO97sXI+HIqpoOkpEJJ8o1FhRbEPNldLPwLZfr9Me3uVie/idag8vZKfSMnhh9kb+2H4MgE4NKvDB/aF4uDjauDIRkeJPocaKEhFqrpTTHj4DTuy+vN2jwuX28PJ1bVdfKWM2G0xdtpcP528ny2xQ1deVib0bEVrZy9aliYgUa3n5/X1bC1pOmjSJoKAgXFxciIiIYM2aNTccP3v2bIKDg3FxcSE0NJTY2Nhc748aNYrg4GDc3Nzw8fEhKiqK1atX5xqzc+dOunXrhp+fH56enrRq1Yo///zzdsovGbwD4Y4XYdA6eHwxNH0cXLzhzFFY/il81hy+uANWTYa0ZFtXW+LZ2Zl44o7qfPd0JJW8y7D/xDl6fL6C6cv3Ukr+3iAiYnN5DjWzZs1i2LBhjBw5kri4OMLCwmjfvj3Hjh2zOn7FihX07t2bAQMGsGHDBrp370737t3ZvHlzzpjatWszceJEEhISWLZsGUFBQdxzzz0cP348Z0znzp3Jysrijz/+YP369YSFhdG5c2cSExNv42uXICYTVG4CncbAizuh1zdQp5OlJfzoRpj/ysXVwx+0rCielW7riku0RlV8iB3Smnvq+ZORbWbUr1t5+v/Wk3Iu09aliYiUeHmefoqIiKBp06ZMnDgRALPZTGBgIIMHD+bVV1+9Znx0dDRpaWnMnTs3Z1vz5s0JDw9n8uTJVo9x6VLTokWLaNeuHcnJyZQrV46///6b1q1bA3DmzBk8PT1ZuHAhUVFRN627xE0/3Uxa8hWrh1/ZHu4N9e+HsD6WMKQbWguEYRhMX7GP92O3kZltUNmnDBP7NCI80NvWpYmIFCsFNv2UkZHB+vXrc4UIOzs7oqKiWLlypdXPrFy58prQ0b59++uOz8jIYMqUKXh5eREWFgaAr68vderU4euvvyYtLY2srCy++OILypcvT+PGja3uJz09ndTU1FyvUsXNDyKegieXwLOroOVz4FHR8vTidf+DqVEwoTH89RGcPmDjYksek8nEoy2r8cMzLahS1pVDp87zwOcr+O/SPZqOEhEpIHkKNcnJyWRnZ+Pv759ru7+//3WngRITE29p/Ny5c3F3d8fFxYWxY8eycOFC/Pz8AMsviEWLFrFhwwY8PDxwcXHhk08+Yf78+fj4+Fg97ujRo/Hy8sp5BQYG5uWrlizl68Ldb8Hzm6HvT9AgGhxd4eQ/8Oe7MC4UpneGDf9n6bCSfNOgsjdzh7SiY2gAWWaDd+dt44mv13H6XIatSxMRKXFu60bhgtC2bVvi4+NZsWIFHTp0oFevXjn36RiGwcCBAylfvjxLly5lzZo1dO/enS5dunD06FGr+xs+fDgpKSk5r4MHDxbm1yma7Oyhxl1w/xTL/TfdPrM86wZg31L4eSB8VAt+eAJ2L7Y8I0f+NU8XRyb1acQ73evj5GDHom3H6PjpUtbvP2nr0kRESpQ8hRo/Pz/s7e1JSkrKtT0pKYmAgACrnwkICLil8W5ubtSsWZPmzZszdepUHBwcmDp1KgB//PEHc+fOZebMmbRs2ZJGjRrx2WefUaZMGb766iurx3V2dsbT0zPXS67g7AENH4JH5sJzCXDXCPCtCVnnIeE7+L/7YWwILHwTjm2zdbXFnslkom/zqvz0bAuq+blxJOUCvb5YxedL/sFs1nSUiEh+yFOocXJyonHjxixevDhnm9lsZvHixURGRlr9TGRkZK7xAAsXLrzu+Cv3m55u6dQ5d87yFF07u9zl2tnZYTab8/IVxBrvKnDHS5fbw5sMsNIe3kbt4fkgpKIXvw5uRdewimSbDT6cv53HvlrLibPqShMR+bfyPP00bNgwvvzyS7766iu2bdvGM888Q1paGo8++igA/fr1Y/jw4Tnjhw4dyvz58xkzZgzbt29n1KhRrFu3jkGDBgGQlpbGa6+9xqpVq9i/fz/r16/nscce4/Dhw/Ts2ROwBCMfHx/69+/Pxo0b2blzJy+99BJ79+6lU6dO+XEeBC63h3f+5Ir28I4X28PjL7eHz+it9vB/wd3ZgU8fDOeD+0NxdrBjyY7jdBy/lNV7Tti6NBGRYi3PywpHR0dz/Phx3nzzTRITEwkPD2f+/Pk5NwMfOHAg1xWVFi1aEBMTw4gRI3jttdeoVasWc+bMoX79+gDY29uzfft2vvrqK5KTk/H19aVp06YsXbqUkJAQwDLtNX/+fF5//XXuuusuMjMzCQkJ4eeff87pkJJ85uAM9bpaXpfaw+NjLOFmR6zl5eJ9xerhag/PC5PJxIPNqhBexZuB38bxz/E0en+5iuejavNs25rY2+lciojklZZJkLw5ts2yuOamWZbpqUt8a1rWnmoQbZnOkluWlp7FGz9v5se4wwC0qunH2Ohwynk427gyERHb09pPVijU5DNzNuz9yxJwtv5iucH4kqDWlqs39bpabkiWWzJ73UHe/HkL5zOz8XN3ZvyD4bSo6WfrskREbEqhxgqFmgKUfsYSbDbOsLSGX+Loenn18GpttHr4LdiVdIaBMXHsTDqLyQSD76rF0Ha1NB0lIqWWQo0VCjWF5PQBy9RU/AzLw/0u8ah4xerhwbarrxg4n5HNW79uYeZay7OVmlcvy6cPNsTf08XGlYmIFD6FGisUagqZYcChdZarN5t/sCzPcEmFcAjvY7nJ2E3TK9fzc/xhXvsxgbSMbHzdnBgbHc4dtcvZuiwRkUKlUGOFQo0NZaXDzvmW+292/Q7mLMt2OweodY/l6k3t9paOK8llz/GzDIzZwLajlrXLnr2zBsPuro2DfZF5GLiISIFSqLFCoaaISEuGhO8tV3COxl/efqk9PLwPVGqs9vArXMjM5p25W/l2tWXh0aZBPozv3ZAKXmVsXJmISMFTqLFCoaYIOrbNEm42fXed9vAHwbsUL0R6lbmbjvDqDwmcTc/Cx9WRT3qF0za4vK3LEhEpUAo1VijUFGHmbNizxDI9te1XtYffwP4TaQyMiWPzYct01FN3VOfF9nVw1HSUiJRQCjVWKNQUE+lnLEswbJyp9vDrSM/KZnTsdqav2AdAwyreTOjdkMo+rrYtTESkACjUWKFQUwydPgAbZ1mmqNQefo35m4/y0vebOHMhC68yjnz0QAPuCQmwdVkiIvlKocYKhZpiLKc9POZie3jK5fcqNrSEm1LaHn7w5DkGzdjAxoOnAXi0ZRDD762Lk4Omo0SkZFCosUKhpoS41B4ePwN2L7yqPby9ZXqqlLWHZ2SZ+WjBdr5cuheABpW9mNi7EVV8NR0lIsWfQo0VCjUl0Nnjlis3G2Pg6MbL28v4XF49vBS1hy/amsSL32/k9LlMPJwd+PCBBnQMrWDrskRE/hWFGisUakq4pK2waaaV9vBaV6weXvLbww+fPs+QGRtYv/8UAH2bV+X1TnVxcSy9N1aLSPGmUGOFQk0pcd32cBMEtbI83K9uV3B2t2WVBSoz28yY33cy+S/LzdUhFT2Z2KcR1fzcbFyZiEjeKdRYoVBTCl1IhW2/WO6/2b/s8nZHV0uwCXsQqt1RYtvDl+w4xrDvNnIyLQM3J3tG92hA17CKti5LRCRPFGqsUKgp5U7tt0xNXd0e7lnpcnt4uTq2q6+AJKZcYMjMDazZexKA3s2qMLJLPU1HiUixoVBjhUKNABfbw9desXr41e3hl1YP97VdjfksK9vMp4t3MfHP3RgGBAd4MLFPI2qWL7lTcCJScijUWKFQI9fIvJB79XAj27L9Unt4eG/LKuIlpD182a5knpu1geSzGbg62fNu9/rc36iyrcsSEbkhhRorFGrkhs4eh82XVg+31h7eByo1Kvbt4cdSL/DcrHhW/HMCgJ6NK/NWtxBcnRxsXJmIiHUKNVYo1MgtS9p6efXws4mXt19qDw97ELyK7xWObLPBxD928+ninZgNqFXenUkPNaK2vxYMFZGiR6HGCoUayTNzNuz582J7+Nzc7eHVLq4eXozbw1f+c4KhMzdw7Ew6Lo52vN21Pj2bVMZUzK9GiUjJolBjhUKN/CsXUi+vHl6C2sOTz6bz/Kx4lu5KBqB7eEXeuy8UN2dNR4lI0aBQY4VCjeSbU/th06XVw/dc3l5M28PNZoPP//qHTxbuJNtsUN3PjYl9GlGvov47ERHbU6ixQqFG8t2l9vD4GNjy41Xt4Y2uWD28eLSHr913ksExG0hMvYCTgx0ju9SjT7Mqmo4SEZtSqLFCoUYKVE57+AzYtfCK9nBHy6rhYQ9a2sQdnGxb502cTMvgxdkb+WP7MQA6N6jA6PtD8XBxtHFlIlJaKdRYoVAjheZSe3h8DCRuury9jA/Uf+Di6uFFtz3cbDb477I9/Gf+DrLMBlV9XZnUpxH1K3nZujQRKYUUaqxQqBGbSNpiubn46vZwv9qXVw8vou3hcQdOMThmA4dPn8fJ3o7XO9WlX2RVTUeJSKFSqLFCoUZsKjsL9i65QXt4H6jbpci1h58+l8FL329i4dYkADqEBPDhAw3wKqPpKBEpHAo1VijUSJFxs/bw8N4Q1LrItIcbhsG05fsY/ds2MrMNKvuUYWKfRoQHetu6NBEpBRRqrFCokSLp1L4rVg+/uj08+mJ7eG2blXeljQdPM2hGHAdPnsfR3sQrHYIZ0KqapqNEpEAp1FihUCNFmmHAwTWWcGOtPTz84urhrmVtVyOQeiGTV3/YRGyC5f6gqLrl+bhnGN6uRburS0SKL4UaKxRqpNjIvAA7f7u4eri19vBLq4fbJkgYhsH/rdrPO3O3kZFtpqKXCxP6NKRxVdsGLhEpmRRqrFCokWLp7HFImG25gpOrPbys5cpNeG/LlRwbTAFtPpzCoJg49p04h72diZfa1+HJ1tWxs9N0lIjkH4UaKxRqpNhL2nLF6uFJl7fbsD38bHoWr/2YwC8bjwBwZ51yjOkZhq+7c6HWISIll0KNFQo1UmJcag+PnwHb50LWhYtvmCyLaob1LtT2cMMwmLn2IKN+2UJ6lhl/T2cm9G5Es2qajhKRf0+hxgqFGimRLqTC1jkX28OXX97u6Ab1Lq4eHnQH2NkVeCnbjqYyMCaOPcfTsDPBsLtr8+ydNTUdJSL/ikKNFQo1UuKd2gcbL64efmrv5e2ela9YPbxg28PT0rN4Y85mftxwGIDWtfz4pFc45Tw0HSUit0ehxgqFGik1ctrDY2DzT5B+RXt4pcaXVw8vwPbw2esO8ubPWzifmU05D2c+jQ6nRU2/AjueiJRcCjVWKNRIqXSpPTx+BuxeVKjt4buSzjAwJo6dSWcxmWDIXbUY0q4W9pqOEpE8UKixQqFGSr2zxyDhe8sVnMSEy9vLlIXQByz33+Rze/j5jGxG/bKFWesOAhBZ3ZdPHwynvKdLvh1DREo2hRorFGpErpC4GTbNtNIeXueK9vBK+Xa4ORsO89pPCZzLyMbXzYmx0eHcUbtcvu1fREouhRorFGpErMjOgj1LLDcXX90eXr2NZXoquHO+tIf/c/wsA7+NY3viGUwmePbOGjwfVRsH+4LvzBKR4isvv79v6/8mkyZNIigoCBcXFyIiIlizZs0Nx8+ePZvg4GBcXFwIDQ0lNjY21/ujRo0iODgYNzc3fHx8iIqKYvXq1TnvL1myBJPJZPW1du3a2/kKIgJg7wC1ouCBqfDiTug6Aaq2BAxL2PnpKfi4Nvz0NOz5C8zm2z5UjXLuzBnYkociqmAYMOnPf+jz5WqOppzPt68jIqVbnq/UzJo1i379+jF58mQiIiIYN24cs2fPZseOHZQvX/6a8StWrOCOO+5g9OjRdO7cmZiYGD788EPi4uKoX78+ADExMZQvX57q1atz/vx5xo4dy+zZs9m9ezflypUjIyODkydP5trvG2+8weLFi/nnn39uaZVgXakRyYOTey+vHm6tPTy8D/jVuu3d/7rxCMN/TOBsehY+ro580iuctsHX/v9DRKRAp58iIiJo2rQpEydOBMBsNhMYGMjgwYN59dVXrxkfHR1NWloac+fOzdnWvHlzwsPDmTx58g2/wKJFi2jXrt0172dmZlKpUiUGDx7MG2+8cUt1K9SI3AbDgIOrLeEmn9vD9yWnMWhGHJsPpwLw1B3VebF9HRw1HSUiVyiw6aeMjAzWr19PVFTU5R3Y2REVFcXKlSutfmblypW5xgO0b9/+uuMzMjKYMmUKXl5ehIWFWR3zyy+/cOLECR599NG8lC8ieWUyQZXm0OVTeHEHPDANarUHkz0cXg+xL1qmp2Y+BNvnQVbGLe86yM+NH55pwSMtggD44u89RH+xksOnNR0lIrcnT6EmOTmZ7Oxs/P39c2339/cnMTHR6mcSExNvafzcuXNxd3fHxcWFsWPHsnDhQvz8rD+sa+rUqbRv357Kla+/eF96ejqpqam5XiLyLziWgfr3w0PfwQvbof37EBAK5kzLTcYz+8CYOhD7EhyOs1zluQlnB3tGdQ1h8sON8HBxIO7AaTp+upSFW5Nu+lkRkasVmeu8bdu2JT4+nhUrVtChQwd69erFsWPHrhl36NAhFixYwIABA264v9GjR+Pl5ZXzCgwMLKjSRUof9/IQORCeXgZPL4fIQeDuD+dPwpop8GVbmBQBSz+BlMM33V2H+hWIHdKasMpepJzP5Imv1/H2r1vJyLr9G5NFpPTJU6jx8/PD3t6epKTcf4tKSkoiICDA6mcCAgJuabybmxs1a9akefPmTJ06FQcHB6ZOnXrN/qZNm4avry9du3a9Ya3Dhw8nJSUl53Xw4MFb+YoiklcB9aH9e/D8Vnjoe8s9Ng4ukLwDFr8FY0Pg626WRTcz0q67m8Cyrsx+ugWPt6oGwP+W76Xn5BUcPHmusL6JiBRzeQo1Tk5ONG7cmMWLF+dsM5vNLF68mMjISKufiYyMzDUeYOHChdcdf+V+09PTc20zDINp06bRr18/HB0db/h5Z2dnPD09c71EpADZO0Ctu+GB/11uD6/Sglzt4R/Vgp+egb1/W20Pd3KwY0Tnevy3XxO8yjiy8VAKHccvZf7mo4X+dUSk+Lmtlu7+/fvzxRdf0KxZM8aNG8d3333H9u3b8ff3p1+/flSqVInRo0cDlpbuNm3a8MEHH9CpUydmzpzJ+++/n9PSnZaWxnvvvUfXrl2pUKECycnJTJo0iZiYGNavX09ISEjOsRcvXkxUVBTbtm0jODg4T19U3U8iNnJyL2y6tHr4vsvbPStDWLSlg8pKe/jh0+cZHBNH3IHTAPSPrMrwjnVxcbQvnLpFpEgo8CcKT5w4kY8++ojExETCw8MZP348ERERANx5550EBQUxffr0nPGzZ89mxIgR7Nu3j1q1avGf//yHjh07AnDhwgX69OnD6tWrSU5OxtfXl6ZNmzJixAiaNm2a67h9+vRh//79LF++PK8lK9SI2Nql9vD4GNgy56r28CaW5Rmuag/PzDbz8e87+OKvPQCEVPRkUp9GBPm5FXLxImIrWibBCoUakSIk8zzs+M1y9Wb34tyrh9fpYLl6U/PunNXD/9xxjBe+28jJtAzcnR14//5QuoZVtOEXEJHColBjhUKNSBF1Jgk2fw/xMyDpitXDXX2h/qXVwxuSmJrOkBkbWLPP8nTx3s2qMLJLPU1HiZRwCjVWKNSIFAOJCZYuqU3fQdoVj3QoFwxhD5IV8gCfrj3HxD93YxgQHODBpIcaUaPcv19wU0SKJoUaKxRqRIqR7CzY8+fF1cPnXbV6+J1sD+jE46sDOJRmh6uTPe/dV5/7Gl7/YZwiUnwp1FihUCNSTF1IsdxYvHEmHFiRs9ns6MbfDi34IqUZq8x1eaBxFd7uVp8yTpqOEilJFGqsUKgRKQFO7rli9fB9OZsPG378mN2KOK97GN6vK7X9PWxXo4jkK4UaKxRqREoQw4ADqyzhZstPkH55bbd4oybZoQ/SqOMATLexeriIFC0KNVYo1IiUUJnnYUcsGetjsN/7B/ZYnlScZXKE2u1xaPgQ1IzKaQ8XkeJFocYKhRqRks+cmsiKOZMpu/tH6tntv/zGpfbw8N5QIRxMJpvVKCJ5o1BjhUKNSOmxZu9Jxn/7I20uLKa7/XLKma54evHF9nAaRIOnHuAnUtQp1FihUCNSupxMy+CF7+L5e0cire0SGOy7lkbnVmDKvrRQrqU9nLDeULczOGnpBZGiSKHGCoUakdLHbDb4cukePlqwgyyzQf2yZr5ofIhK+3+GAysvD3Ryh3rdLFdwqrYCOzvbFS0iuSjUWKFQI1J6rd9/iiEzNnD49Hmc7O0Y0bkufWubMSVc2x6OV6BlaiqsN/jVtFnNImKhUGOFQo1I6Xb6XAYvzt7Eom1JANxbP4APejTAy8XhYnv4pdXDL7eHU7mp5epNyP25Vg8XkcKjUGOFQo2IGIbBtOX7GP3bNjKzDQLLlmFi70aEBXpbBlxsDyd+BvyzGAxLezj2TlD74urhte4Ge0ebfQeR0kahxgqFGhG5ZOPB0wyaEcfBk+dxtDfx6r11eaxlEKYrW73PJEHCbMv0VNLmy9tdfSG0p+UKjtrDRQqcQo0VCjUicqWU85m8+sMmftucCEBUXX8+7tkAb1crD+m77urhdS+2h/dSe7hIAVGosUKhRkSuZhgG36zaz7tzt5GRbaaSdxnG925I46o+1j+QnQX//HF59fCr28PD+0BwJ7WHi+QjhRorFGpE5Ho2H05hUEwc+06cw8HOxEvt6/BE6+rY2d1gaun8adg65+Lq4Ve3h3e/2B7eUu3hIv+SQo0VCjUiciNnLmTy2k+b+XXjEQDa1inHmF7hlHW7hTWjTu6BjbMsV3BOX7E8g1cVCIuGBg+qPVzkNinUWKFQIyI3YxgGM9Yc5K1ft5CeZSbA04XxvRvSrNottnMbhuWqzcYZ12kP7w0h96k9XCQPFGqsUKgRkVu17WgqA2Pi2HM8DXs7E8Purs0zbWrceDrqapnnLffdbJxpvT08vI9l9XC1h4vckEKNFQo1IpIXaelZvDFnMz9uOAxA61p+jI0Ox8/dOe87O5NoaQ+PnwHHtlze7uoHoQ9YruBUCFN7uIgVCjVWKNSISF4ZhsHs9Yd48+fNXMg0U87DmU8fDKdFDb/b3+nRTZarNwnfQdrxy9tz2sOjwbPCvy9epIRQqLFCoUZEbteupDM8+20cu46dxc4EQ9rVYvBdtbDPy3TU1XLaw2Nge+zl9nCT3eXVw4M7g5NrvnwHkeJKocYKhRoR+TfOZ2Qz8pfNfLfuEAAtavgyLjqc8p4u+bDz07DlJ8sVnIOrLm9Xe7iIQo01CjUikh9+2nCI13/azLmMbPzcnRgbHU7rWuXy7wAn/oFNl9rDD1zefqk9PKw3+NbIv+OJFHEKNVYo1IhIftl97CyDYuLYnngGkwkG3lmT56Jq4WCfj1dSzGbLVRur7eHNLFdv6t8PZa7z9GOREkKhxgqFGhHJTxcys3l77lZiVluupjQLKsv43g0J8MqH6air5bSHz7Dch3Nle3idey1Xb9QeLiWUQo0VCjUiUhB+2XiE135M4Gx6FmXdnBjTK4y2dcoX3AFv2B5+afVwtYdLyaFQY4VCjYgUlH3JaQyMiWPLEcsU0VNtqvPiPXVwzM/pqKsZxuXVw69uDy9fzxJuQnupPVyKPYUaKxRqRKQgXcjMZnTsNr5aaVn7qVEVbyb0aUQl7zIFf/DszCtWD7+6PbztxfbwTmoPl2JJocYKhRoRKQy/JRzl5R82ceZCFl5lHPm4Zxh31/MvvALOn7LcWHxNe7gHhHSzBJwqLdQeLsWGQo0VCjUiUlgOnjzHoJg4Nh5KAWBAq2q80iEYJ4dCDhLXaw/3rmJZOTzsQbWHS5GnUGOFQo2IFKaMLDMfzt/O1GV7AQgL9GZi74YElrXBFJDZnHv18Iwzl9+r3AzCL64ervZwKYIUaqxQqBERW1i4NYkXZ28k5XwmHi4OfPRAAzrUt+HNuxnnYEfsDdrD+0DNdmoPlyJDocYKhRoRsZVDp84xZMYG4g6cBqB/ZFVe61QXZwd72xZ2JhE2fWcJOMe2Xt5+qT08vDcENFB7uNiUQo0VCjUiYkuZ2WY+/n0HX/y1B4D6lTyZ2LsRQX5uNq6MK9rDZ1hCzrnky++pPVxsTKHGCoUaESkK/tx+jGHfxXPqXCbuzg580COUzg0q2rqsy7IzYfdiS8DZEQvZGZbtag8XG1GosUKhRkSKiqMp5xkyYwNr950CoE9EFd7sXA8XRxtPR10tpz18BhxcfXm72sOlECnUWKFQIyJFSVa2mXGLdjFpyW4MA4IDPJj0UCNqlHO3dWnWnfjH8uybTTPVHi6FSqHGCoUaESmKlu46zvOz4kk+m4Grkz3v3Vef+xpWtnVZ15fTHh4DW37O3R4eGGEJN2oPl3ykUGOFQo2IFFXHUi8wdGY8K/ecAKBXk8q81bU+ZZyK2HTU1a7bHu58xerhag+Xf0ehxgqFGhEpyrLNBhP+2MWni3dhGFDb351JfRpRy9/D1qXdmtSjltXDr24Pdyt3efVwtYfLbVCosUKhRkSKgxX/JDN0ZjzHz6Tj4mjHO93q07NJoK3LunWGAYmbLt5/Y609vDc06AUeAbarUYqVvPz+vq1b1idNmkRQUBAuLi5ERESwZs2aG46fPXs2wcHBuLi4EBoaSmxsbK73R40aRXBwMG5ubvj4+BAVFcXq1auv2c+8efOIiIigTJky+Pj40L1799spX0SkyGpRw4/YIa1pXcuPC5lmXvp+E8O+iyctPcvWpd0akwkqhEGH0fDCdug9C+p1tzyx+NhWWPgGfFIX/q8HJHxvmcISySd5DjWzZs1i2LBhjBw5kri4OMLCwmjfvj3Hjh2zOn7FihX07t2bAQMGsGHDBrp370737t3ZvHlzzpjatWszceJEEhISWLZsGUFBQdxzzz0cP348Z8wPP/xA3759efTRR9m4cSPLly+nT58+t/GVRUSKtnIeznz1aDNeal8HOxP8GHeYrhOXsT0x1dal5Y29I9TpAL2+ghd3QuexlpuJDTPsXgQ/DICPa8PPg2DfcstNyCL/Qp6nnyIiImjatCkTJ04EwGw2ExgYyODBg3n11VevGR8dHU1aWhpz587N2da8eXPCw8OZPHmy1WNcutS0aNEi2rVrR1ZWFkFBQbz11lsMGDAgL+Ves09NP4lIcbJm70mGzNhAYuoFnB3sGNU1hAebBmIqzvemXGoP3zgTUq5qDw/rDQ2i1R4uOQps+ikjI4P169cTFRV1eQd2dkRFRbFy5Uqrn1m5cmWu8QDt27e/7viMjAymTJmCl5cXYWFhAMTFxXH48GHs7Oxo2LAhFSpU4N577811tedq6enppKam5nqJiBQ3zaqVJXZoa+6sU470LDPDf0xgyMx4zlzItHVpt8+3Btz1OgzdCI/Mg4YPWx7od/oA/PUhTGgEU++BddPg/GlbVyvFSJ5CTXJyMtnZ2fj7++fa7u/vT2JiotXPJCYm3tL4uXPn4u7ujouLC2PHjmXhwoX4+fkBsGePZa2UUaNGMWLECObOnYuPjw933nknJ0+etHrc0aNH4+XllfMKDCxGN9qJiFyhrJsT/+vflOH3BmNvZ+LXjUfoMmEZmw+n2Lq0f8fODoJaQbdJlump+/8LNdpZlmQ4uBrmPmeZnvquP+xcYFnCQeQGisyzrdu2bUt8fDwrVqygQ4cO9OrVK+c+HfPFedbXX3+dHj160LhxY6ZNm4bJZGL27NlW9zd8+HBSUlJyXgcPHiy07yIikt/s7Ew81aYG3z0VSSXvMuw7cY77P1vBNyv3USKaWJ1coUFP6PsjPL8V7n4bytWF7HTYOgdielluMJ4/HI5usnRZiVwlT6HGz88Pe3t7kpKScm1PSkoiIMB6e15AQMAtjXdzc6NmzZo0b96cqVOn4uDgwNSpUwGoUMGyMmy9evVyxjs7O1O9enUOHDiANc7Oznh6euZ6iYgUd42r+jBvSCui6vqTkW3mjZ+3MDAmjtTiPB11Nc8K0HIoPLsSnvobIp4BVz9IOw6rPoMvWsPnLWH5eDhjfZZASqc8hRonJycaN27M4sWLc7aZzWYWL15MZGSk1c9ERkbmGg+wcOHC646/cr/p6ekANG7cGGdnZ3bs2JHzfmZmJvv27aNq1ap5+QoiIsWet6sTX/ZrzBud6+FobyI2IZFO45ey8eBpW5eWvy61h9/7wRXt4d0utodvubY9PPO8rSsWG3PI6weGDRtG//79adKkCc2aNWPcuHGkpaXx6KOPAtCvXz8qVarE6NGjARg6dCht2rRhzJgxdOrUiZkzZ7Ju3TqmTJkCQFpaGu+99x5du3alQoUKJCcnM2nSJA4fPkzPnj0B8PT05Omnn2bkyJEEBgZStWpVPvroI4CcMSIipYnJZGJAq2o0qerDwJg4Dp48zwOTVzD83ro82jKoeHdHWXOpPbxOB8vq4Zt/tHRPHVpjaQ/fvQicPS2hJ6w3VInU6uGlUJ5DTXR0NMePH+fNN98kMTGR8PBw5s+fn3Mz8IEDB7C74gepRYsWxMTEMGLECF577TVq1arFnDlzqF+/PgD29vZs376dr776iuTkZHx9fWnatClLly4lJCQkZz8fffQRDg4O9O3bl/PnzxMREcEff/yBj48WTROR0iss0Jt5Q1rzyvebmL8lkbfnbmXlnhN89EADvF2dbF1ewSjjA00HWF5Xt4dv+Mby8q5qWZpB7eGlipZJEBEpAQzD4JtV+3l37jYyss1U8i7DhD4NaVSllPzFz2yGAysgfoblxuKMs5ffC2x+xerh3raqUG6T1n6yQqFGREqDzYdTGBgTx/4T53CwM/Fyhzo83qo6dnYlbDrqRjLOwfZ5sDEG9izJvXp4cEfL9FSNdmCf58kKsQGFGisUakSktDhzIZPhPyYwd9NRAO4KLs/HPcMo61ZCp6NuJPWIZfXw+BlwfNvl7W7lILSX5QpOhQa2q09uSqHGCoUaESlNDMNgxpqDjPp1CxlZZip4uTC+d0OaBpW1dWm2YRhwdKPl3puE2blXD/evbwk3oT21engRpFBjhUKNiJRG246mMjAmjj3H07C3MzHs7to806ZG6ZqOulp2pqVbauMM2PEbZGdYtpvsoMZdlump4E7gWMa2dQqgUGOVQo2IlFZp6VmMmLOZnzYcBqB1LT/GRofj5+5s48qKgHMnYctPl9vDL3H2hJDul9vDS1qLfDGiUGOFQo2IlGaGYTB7/SHe/HkzFzLNlPdw5tMHGxJZw9fWpRUdybth06X28CuW1vGuagk3YdFQtrrt6iulFGqsUKgREYGdSWcY+G0cu46dxc4EQ9vVZtBdNbEvzdNRVzObYf9yS7ix1h4e3hvqdVd7eCFRqLFCoUZExOJcRhYjf97C7PWHAGhRw5dxD4ZT3sPFxpUVQRnnYPtcy/03VtvD+1juw1F7eIFRqLFCoUZEJLcf4w4xYs5mzmVk4+fuxLjohrSq5Wfrsoqu1COw6TtLwDm+/fJ2t/KWzqnw3hAQarv6SiiFGisUakRErrX72FkGxcSxPfEMJhMMaluToe1q4WCvdZOuK6c9fMbF9vATl9/LaQ/vBR7+tquxBFGosUKhRkTEuguZ2bz161ZmrDkAQLNqZRn/YEMCvDQddVOX2sPjY2Dn/Kvaw9tZAo7aw/8VhRorFGpERG7sl41HGP7DJtIysinr5sQnvcK4s055W5dVfOS0h8+AQ2svb1d7+L+iUGOFQo2IyM3tTU5jUEwcW46kAvB0mxq8cE9tHDUdlTfJuy3hZtMstYf/Swo1VijUiIjcmguZ2bwfu42vV+4HoHFVHyb0bkhFb02h5NmN2sOrRFqmp9QefkMKNVYo1IiI5M1vCUd5+YdNnLmQhberIx8/EEZUPd38etsy0iyrh8dfXD2ci79+7Z0t992E9VZ7uBUKNVYo1IiI5N2BE+cYPCOOjYdSAHi8VTVe7hCMk4Omo/6VG7WHN7i4erjawwGFGqsUakREbk9GlpkPftvO/5bvBSAs0JuJvRsSWNbVxpWVAIYBR+OvWD38yvbw0CtWDy+9V8gUaqxQqBER+Xd+35LIi7M3knohCw8XBz56IIwO9QNsXVbJkZ0JuxZart7kag+3t0xLhfeGOh1LXXu4Qo0VCjUiIv/eoVPnGDxjAxsOnAbgkRZBDO8YjLODvW0LK2nOnYQtP15cPdxae3gfqNK8VLSHK9RYoVAjIpI/MrPNfLxgB1/8vQeA0EpeTOzTkKq+bjaurIRK3mUJN1e3h/sEWW4ubhANZavZrLyCplBjhUKNiEj++mN7Ei98t5FT5zJxd3bggx6hdG5Q0dZllVxmM+xfdrE9/Gcr7eG9LVdxXLxsVmJBUKixQqFGRCT/HU05z5AZG1i77xQAD0VU4Y3O9XBx1HRUgcpIg21XrB5+qT3cwcVy3014H6jetkS0hyvUWKFQIyJSMLKyzYxdtJPPlvyDYUDdCp5M6tOQ6uXcbV1a6ZByGBK+g/gZkLzj8vac9vDeEFDfdvX9Swo1VijUiIgUrL93Huf5WfGcSMvA1cme9+8LpXvDSrYuq/S41B4ef3H18PMnL79XjNvDFWqsUKgRESl4x1IvMGTmBlbtsfxCjW4SyKiuIZRx0nRUocrKsKwevjEGdswHc6Zlu8keal5cPbxOJ3As+iuxK9RYoVAjIlI4ss0G4xfvYvwfuzAMqOPvwaSHGlKzvIetSyudLrWHx8+Aw+sub3f2umL18KLbHq5QY4VCjYhI4VqxO5mhs+I5fiadMo72vNO9Pg80rmzrskq35F2Wm4s3zoLUQ5e3F+H2cIUaKxRqREQK3/Ez6Tw/K55lu5MBuL9RJd7pVh835+LflVOsXWoPj59haQ/PTLv8XpUWlumpItIerlBjhUKNiIhtmM0Gny3ZzScLd2I2oEY5NyY91IjgAP2/uEjIaQ+PgT1/kas9/NLq4TZsD1eosUKhRkTEtlbvOcGQmRtISk3H2cGOt7qGEN00EFMRvZejVLpee7i7v6Vzygbt4Qo1VijUiIjY3omz6bwweyNLdhwHoGtYRd6/PxR3TUcVLYYBRzZcXj386vbw8N6WkONevsBLUaixQqFGRKRoMJsNpizdw0cLdpBtNqjm58bEPg0JqWj7+zfEiqwM2H1x9fBr2sOjLraHdyyw9nCFGisUakREipb1+08yOGYDR1Iu4ORgxxud6/FwRBVNRxVl507C5h8sV3CstYeH94HAiHxtD1eosUKhRkSk6Dl9LoMXZ29k0bZjAHQKrcDoHqF4ujjauDK5qeM7YdPM3O3h/vXhmeX5ehiFGisUakREiibDMJi6bC8fzt9OZrZBlbKuTOzTkAaVvW1dmtwKsxn2LbVcvancGJo+nq+7V6ixQqFGRKRoiz94mkExcRw6dR5HexOvdazLIy2CNB1VyuXl97ddIdUkIiJyQ+GB3swb0poOIQFkZhu89etWnvpmPSnnMm1dmhQTCjUiIlJkeJVx5POHG/FW1xCc7O34fWsSHccvZcOBU7YuTYoBhRoRESlSTCYT/VsE8cMzLajq68rh0+fpOXklX/69B7O5VNwxIbdJoUZERIqk0MpezB3cis4NKpBlNngvdhuPf72OU2kZti5NiiiFGhERKbI8XByZ0Lsh791XHycHO/7YfoyO45eybt/Jm39YSh2FGhERKdJMJhMPRVRlzrMtqe7nxtGUC0RPWcVnS3ZrOkpyUagREZFioV5FT34d3Ir7GlYi22zwn/k7eGT6WpLPptu6NCkibivUTJo0iaCgIFxcXIiIiGDNmjU3HD979myCg4NxcXEhNDSU2NjYXO+PGjWK4OBg3Nzc8PHxISoqitWrV+caExRkeVbBla8PPvjgdsoXEZFiys3ZgU96hfGfHg1wcbTj753H6fjpUlbtOWHr0qQIyHOomTVrFsOGDWPkyJHExcURFhZG+/btOXbsmNXxK1asoHfv3gwYMIANGzbQvXt3unfvzubNm3PG1K5dm4kTJ5KQkMCyZcsICgrinnvu4fjx47n29fbbb3P06NGc1+DBg/NavoiIFHMmk4leTQP5ZVArapZ359iZdPp8uYrxi3eRremoUi3PTxSOiIigadOmTJw4EQCz2UxgYCCDBw/m1VdfvWZ8dHQ0aWlpzJ07N2db8+bNCQ8PZ/LkyVaPcenpgYsWLaJdu3aA5UrNc889x3PPPZeXcq/Zp54oLCJScpzLyGLkz1uYvd6y9lDLmr6MjQ6nvEfBrBgtha/AniickZHB+vXriYqKurwDOzuioqJYuXKl1c+sXLky13iA9u3bX3d8RkYGU6ZMwcvLi7CwsFzvffDBB/j6+tKwYUM++ugjsrKyrltreno6qampuV4iIlKyuDo58FHPMD7pFUYZR3uW7z5Bx0+XsXx3sq1LExvIU6hJTk4mOzsbf3//XNv9/f1JTEy0+pnExMRbGj937lzc3d1xcXFh7NixLFy4ED8/v5z3hwwZwsyZM/nzzz956qmneP/993n55ZevW+vo0aPx8vLKeQUGBublq4qISDFyf6PK/Dq4FcEBHiSfTefhqav55Pcdmo4qZYpM91Pbtm2Jj49nxYoVdOjQgV69euW6T2fYsGHceeedNGjQgKeffpoxY8YwYcIE0tOt3/U+fPhwUlJScl4HDx4srK8iIiI2ULO8O3MGtqR3syoYBoz/Yzd9vlxFUuoFW5cmhSRPocbPzw97e3uSkpJybU9KSiIgIMDqZwICAm5pvJubGzVr1qR58+ZMnToVBwcHpk6det1aIiIiyMrKYt++fVbfd3Z2xtPTM9dLRERKNhdHe0bfH8r43g1xc7Jn9d6T3PvpUpbssN7MIiVLnkKNk5MTjRs3ZvHixTnbzGYzixcvJjIy0upnIiMjc40HWLhw4XXHX7nf612FAYiPj8fOzo7y5cvn4RuIiEhp0DWsInOHtKZeBU9OpmXwyLS1fDh/O1nZZluXJgXIIa8fGDZsGP3796dJkyY0a9aMcePGkZaWxqOPPgpAv379qFSpEqNHjwZg6NChtGnThjFjxtCpUydmzpzJunXrmDJlCgBpaWm89957dO3alQoVKpCcnMykSZM4fPgwPXv2BCw3G69evZq2bdvi4eHBypUref7553n44Yfx8fHJr3MhIiIlSDU/N358tgXvx27j65X7+XzJP6zde5LxvRtS0buMrcuTApDnUBMdHc3x48d58803SUxMJDw8nPnz5+fcDHzgwAHs7C5fAGrRogUxMTGMGDGC1157jVq1ajFnzhzq168PgL29Pdu3b+err74iOTkZX19fmjZtytKlSwkJCQEsU0kzZ85k1KhRpKenU61aNZ5//nmGDRuWH+dARERKKBdHe97uVp/m1X155ftNrNt/io7jlzKmZxjt6vrffAdSrOT5OTXFlZ5TIyJSuh04cY5BM+LYdCgFgCdaV+Ol9sE4ORSZnhmxosCeUyMiIlJcVfF1ZfbTkTzWshoAXy7dS68vVnLw5DkbVyb5RaFGRERKDWcHe97sUo8pfRvj6eJA/MHTdBq/lAVbrD9rTYoXhRoRESl17gkJIHZoaxpW8Sb1QhZPfbOeUb9sIT0r29alyb+gUCMiIqVSZR9XvnsqkqfuqA7A9BX7eODzlew/kWbjyuR2KdSIiEip5Whvx/COdfnfI03wcXUk4XAKnccvY96mo7YuTW6DQo2IiJR6dwX7Ezu0NU2DfDiTnsXAmDhGzEngQqamo4oThRoRERGgglcZZjzRnGfvrAHA/606wH2frWDP8bM2rkxulUKNiIjIRQ72drzcIZivHmuGr5sT246m0mXCMn6OP2zr0uQWKNSIiIhcpU3tcsQObU3z6mVJy8hm6Mx4Xv1hE+czNB1VlCnUiIiIWOHv6cK3jzdnSLtamEwwc+1Buk9azu5jZ2xdmlyHQo2IiMh12NuZGHZ3bb4dEIGfuzM7ks7QZcJyvl9/yNaliRUKNSIiIjfRoqYfvw1tTauafpzPzObF2Rt54buNnMvIsnVpcgWFGhERkVtQzsOZrx5rxgt318bOBD/EHaLrxOXsSNR0VFGhUCMiInKL7O1MDG5Xi5gnmuPv6czuY2fpOnEZs9YewDAMW5dX6inUiIiI5FHz6r7EDmlNm9rlSM8y88oPCTw/K56z6ZqOsiWFGhERkdvg6+7MtEea8kqHYOztTMyJP0LXCcvYciTF1qWVWgo1IiIit8nOzsQzd9bgu6eaU9HLhT3Jadz32Qq+WbVf01E2oFAjIiLyLzWuWpZ5Q1oTVbc8GVlm3pizmUEzNpB6IdPWpZUqCjUiIiL5wMfNiS/7NWFEp7o42JmYt+konccvI+GQpqMKi0KNiIhIPjGZTDzeujqzn46kkncZDpw8R4/PVzB9+V5NRxUChRoREZF81rCKD7FDWtM+xJ+MbDOjft3K0/+3npRzmo4qSAo1IiIiBcDL1ZHJDzdmVJd6ONnbsWBLEp0mLGXDgVO2Lq3EUqgREREpICaTiUdaVuOHZ1pQpawrh06dp+fklfx36R5NRxUAhRoREZECFlrZi7lDWtGpQQWyzAbvztvG41+t41Rahq1LK1EUakRERAqBp4sjE3s35N3u9XFysGPx9mN0Gr+U9ftP2rq0EkOhRkREpJCYTCYebl6VOc+2pLqfG0dSLtDri1V8vuQfzGZNR/1bCjUiIiKFrF5FT34Z3Iru4RXJNht8OH87j05fy4mz6bYurVhTqBEREbEBd2cHxkaH82GPUFwc7fhr53E6jl/K6j0nbF1asaVQIyIiYiMmk4noplX4eWArapZ3Jyk1nd5frmLC4l1kazoqzxRqREREbKxOgAe/DGrJA40rYzZgzMKd9Pvfao6f0XRUXijUiIiIFAGuTg583DOMMT3DKONoz/LdJ7j306Us351s69KKDYUaERGRIqRH48r8Orgldfw9SD6bzsNTV/PJwp2ajroFCjUiIiJFTM3yHvw8qCW9mwViGDB+8S4e+u8qklIv2Lq0Ik2hRkREpAhycbRn9P0N+PTBcNyc7Fm15yQdP13KXzuP27q0IkuhRkREpAjrFl6JXwe3ol4FT06kZdD/f2v4z/ztZGWbbV1akaNQIyIiUsRVL+fOj8+2oG/zqgB8tuQfen+5iqMp521cWdGiUCMiIlIMuDja8073+kzq0wgPZwfW7jtFx0+X8sf2JFuXVmQo1IiIiBQjnRpUYO6QVoRW8uLUuUwem76O92O3kanpKIUaERGR4qaqrxvfPxPJoy2DAJjy9x56Tl7JoVPnbFuYjSnUiIiIFEPODvaM7BLCF30b4+niQPzB03T8dCkLtiTaujSbUagREREpxtqHBDBvSGvCA71JvZDFU9+s561ft5CRVfqmoxRqREREirnAsq7MfjqSJ++oDsC05ft4YPIKDpwoXdNRCjUiIiIlgKO9Ha91rMv/HmmCt6sjmw6l0Gn8UmITjtq6tEJzW6Fm0qRJBAUF4eLiQkREBGvWrLnh+NmzZxMcHIyLiwuhoaHExsbmen/UqFEEBwfj5uaGj48PUVFRrF692uq+0tPTCQ8Px2QyER8ffzvli4iIlFh3BfsTO6Q1Tar6cCY9i2e/jeONOZu5kJlt69IKXJ5DzaxZsxg2bBgjR44kLi6OsLAw2rdvz7Fjx6yOX7FiBb1792bAgAFs2LCB7t270717dzZv3pwzpnbt2kycOJGEhASWLVtGUFAQ99xzD8ePX/so6JdffpmKFSvmtWwREZFSo6J3GWY+2Zxn76wBwDer9nP/ZyvYm5xm48oKlskwjDwt+xkREUHTpk2ZOHEiAGazmcDAQAYPHsyrr756zfjo6GjS0tKYO3duzrbmzZsTHh7O5MmTrR4jNTUVLy8vFi1aRLt27XK2//bbbwwbNowffviBkJAQNmzYQHh4+C3VfWmfKSkpeHp65uEbi4iIFF9/7TzOsFnxnEjLwM3JnvfvD6VbeCVbl3XL8vL7O09XajIyMli/fj1RUVGXd2BnR1RUFCtXrrT6mZUrV+YaD9C+ffvrjs/IyGDKlCl4eXkRFhaWsz0pKYknnniCb775BldX15vWmp6eTmpqaq6XiIhIadOmdjlih7YmolpZ0jKyGToznuE/biqR01F5CjXJyclkZ2fj7++fa7u/vz+Jidb74hMTE29p/Ny5c3F3d8fFxYWxY8eycOFC/Pz8ADAMg0ceeYSnn36aJk2a3FKto0ePxsvLK+cVGBh4q19TRESkRPH3dOHbxyMY0q4WJhPMWHOQ7pOWs/vYWVuXlq+KTPdT27ZtiY+PZ8WKFXTo0IFevXrl3KczYcIEzpw5w/Dhw295f8OHDyclJSXndfDgwYIqXUREpMhzsLdj2N21+b8BEfi5O7M98QxdJizjh/WHbF1avslTqPHz88Pe3p6kpNyLZyUlJREQEGD1MwEBAbc03s3NjZo1a9K8eXOmTp2Kg4MDU6dOBeCPP/5g5cqVODs74+DgQM2aNQFo0qQJ/fv3t3pcZ2dnPD09c71ERERKu5Y1/Ygd2oqWNX05n5nNC7M38uLsjZzLyLJ1af9ankKNk5MTjRs3ZvHixTnbzGYzixcvJjIy0upnIiMjc40HWLhw4XXHX7nf9PR0AMaPH8/GjRuJj48nPj4+pyV81qxZvPfee3n5CiIiIqVeeQ8Xvn4sghfuro2dCb5ff4huE5ezM+mMrUv7Vxzy+oFhw4bRv39/mjRpQrNmzRg3bhxpaWk8+uijAPTr149KlSoxevRoAIYOHUqbNm0YM2YMnTp1YubMmaxbt44pU6YAkJaWxnvvvUfXrl2pUKECycnJTJo0icOHD9OzZ08AqlSpkqsGd3d3AGrUqEHlypVv/9uLiIiUUvZ2Jga3q0XTamUZOnMDu46dpevEZbzVNYReTQIxmUy2LjHP8hxqoqOjOX78OG+++SaJiYmEh4czf/78nJuBDxw4gJ3d5QtALVq0ICYmhhEjRvDaa69Rq1Yt5syZQ/369QGwt7dn+/btfPXVVyQnJ+Pr60vTpk1ZunQpISEh+fQ1RURExJrm1X2JHdKaYd9t5K+dx3nlhwRW/nOCd+8Lxd05zzHBpvL8nJriSs+pERERuT6z2eCLv/fw8e87yDYbVPdzY2KfRtSraNvfmQX2nBoREREpmezsTDxzZw1mPdmcCl4u7ElOo/tny/l29X6Ky/UPhRoRERHJ0SSoLLFDWtMuuDwZWWZe/2kzg2Zs4MyFTFuXdlMKNSIiIpKLj5sT/+3fhBGd6uJgZ2LepqN0nrCMzYdTbF3aDSnUiIiIyDVMJhOPt67O7KcjqeRdhv0nznH/ZyuYvnxvkZ2OUqgRERGR62pYxYfYIa25p54/GdlmRv26lWf+L46U80VvOkqhRkRERG7Iy9WRL/o2ZlSXejjZ2zF/SyKdxi8l/uBpW5eWi0KNiIiI3JTJZOKRltX44ZkWVCnryqFT53ng8xX8d+meIjMdpVAjIiIityy0shdzh7SiU2gFsswG787bxhNfr+P0uQxbl6ZQIyIiInnj6eLIxD4Nebd7fZwc7Fi07RgdP13K+v0nbVqXQo2IiIjkmclk4uHmVfnp2RZU83PjSMoFnpsVT2a22WY1KdSIiIjIbQup6MWvg1txf8NKjO0VjqO97aJF8VqpSkRERIocd2cHPokOt3UZulIjIiIiJYNCjYiIiJQICjUiIiJSIijUiIiISImgUCMiIiIlgkKNiIiIlAgKNSIiIlIiKNSIiIhIiaBQIyIiIiWCQo2IiIiUCAo1IiIiUiIo1IiIiEiJoFAjIiIiJUKpWaXbMAwAUlNTbVyJiIiI3KpLv7cv/R6/kVITas6cOQNAYGCgjSsRERGRvDpz5gxeXl43HGMybiX6lABms5kjR47g4eGByWTK132npqYSGBjIwYMH8fT0zNd9y2U6z4VD57lw6DwXDp3nwlNQ59owDM6cOUPFihWxs7vxXTOl5kqNnZ0dlStXLtBjeHp66j+aQqDzXDh0nguHznPh0HkuPAVxrm92heYS3SgsIiIiJYJCjYiIiJQICjX5wNnZmZEjR+Ls7GzrUko0nefCofNcOHSeC4fOc+EpCue61NwoLCIiIiWbrtSIiIhIiaBQIyIiIiWCQo2IiIiUCAo1IiIiUiIo1NyiSZMmERQUhIuLCxEREaxZs+aG42fPnk1wcDAuLi6EhoYSGxtbSJUWb3k5z19++SWtW7fGx8cHHx8foqKibvrvRSzy+vN8ycyZMzGZTHTv3r1gCywh8nqeT58+zcCBA6lQoQLOzs7Url1b/++4BXk9z+PGjaNOnTqUKVOGwMBAnn/+eS5cuFBI1RZPf//9N126dKFixYqYTCbmzJlz088sWbKERo0a4ezsTM2aNZk+fXqB14khNzVz5kzDycnJ+N///mds2bLFeOKJJwxvb28jKSnJ6vjly5cb9vb2xn/+8x9j69atxogRIwxHR0cjISGhkCsvXvJ6nvv06WNMmjTJ2LBhg7Ft2zbjkUceMby8vIxDhw4VcuXFS17P8yV79+41KlWqZLRu3dro1q1b4RRbjOX1PKenpxtNmjQxOnbsaCxbtszYu3evsWTJEiM+Pr6QKy9e8nqev/32W8PZ2dn49ttvjb179xoLFiwwKlSoYDz//POFXHnxEhsba7z++uvGjz/+aADGTz/9dMPxe/bsMVxdXY1hw4YZW7duNSZMmGDY29sb8+fPL9A6FWpuQbNmzYyBAwfm/Dk7O9uoWLGiMXr0aKvje/XqZXTq1CnXtoiICOOpp54q0DqLu7ye56tlZWUZHh4exldffVVQJZYIt3Oes7KyjBYtWhj//e9/jf79+yvU3IK8nufPP//cqF69upGRkVFYJZYIeT3PAwcONO66665c24YNG2a0bNmyQOssSW4l1Lz88stGSEhIrm3R0dFG+/btC7Ayw9D0001kZGSwfv16oqKicrbZ2dkRFRXFypUrrX5m5cqVucYDtG/f/rrj5fbO89XOnTtHZmYmZcuWLagyi73bPc9vv/025cuXZ8CAAYVRZrF3O+f5l19+ITIykoEDB+Lv70/9+vV5//33yc7OLqyyi53bOc8tWrRg/fr1OVNUe/bsITY2lo4dOxZKzaWFrX4PlpoFLW9XcnIy2dnZ+Pv759ru7+/P9u3brX4mMTHR6vjExMQCq7O4u53zfLVXXnmFihUrXvMfklx2O+d52bJlTJ06lfj4+EKosGS4nfO8Z88e/vjjDx566CFiY2PZvXs3zz77LJmZmYwcObIwyi52buc89+nTh+TkZFq1aoVhGGRlZfH000/z2muvFUbJpcb1fg+mpqZy/vx5ypQpUyDH1ZUaKRE++OADZs6cyU8//YSLi4utyykxzpw5Q9++ffnyyy/x8/OzdTklmtlspnz58kyZMoXGjRsTHR3N66+/zuTJk21dWomyZMkS3n//fT777DPi4uL48ccfmTdvHu+8846tS5N8oCs1N+Hn54e9vT1JSUm5ticlJREQEGD1MwEBAXkaL7d3ni/5+OOP+eCDD1i0aBENGjQoyDKLvbye53/++Yd9+/bRpUuXnG1msxkABwcHduzYQY0aNQq26GLodn6eK1SogKOjI/b29jnb6tatS2JiIhkZGTg5ORVozcXR7ZznN954g759+/L4448DEBoaSlpaGk8++SSvv/46dnb6u35+uN7vQU9PzwK7SgO6UnNTTk5ONG7cmMWLF+dsM5vNLF68mMjISKufiYyMzDUeYOHChdcdL7d3ngH+85//8M477zB//nyaNGlSGKUWa3k9z8HBwSQkJBAfH5/z6tq1K23btiU+Pp7AwMDCLL/YuJ2f55YtW7J79+6c0Aiwc+dOKlSooEBzHbdzns+dO3dNcLkUJA0thZhvbPZ7sEBvQy4hZs6caTg7OxvTp083tm7dajz55JOGt7e3kZiYaBiGYfTt29d49dVXc8YvX77ccHBwMD7++GNj27ZtxsiRI9XSfQvyep4/+OADw8nJyfj++++No0eP5rzOnDljq69QLOT1PF9N3U+3Jq/n+cCBA4aHh4cxaNAgY8eOHcbcuXON8uXLG++++66tvkKxkNfzPHLkSMPDw8OYMWOGsWfPHuP33383atSoYfTq1ctWX6FYOHPmjLFhwwZjw4YNBmB88sknxoYNG4z9+/cbhmEYr776qtG3b9+c8Zdaul966SVj27ZtxqRJk9TSXZRMmDDBqFKliuHk5GQ0a9bMWLVqVc57bdq0Mfr3759r/HfffWfUrl3bcHJyMkJCQox58+YVcsXFU17Oc9WqVQ3gmtfIkSMLv/BiJq8/z1dSqLl1eT3PK1asMCIiIgxnZ2ejevXqxnvvvWdkZWUVctXFT17Oc2ZmpjFq1CijRo0ahouLixEYGGg8++yzxqlTpwq/8GLkzz//tPr/20vntn///kabNm2u+Ux4eLjh5ORkVK9e3Zg2bVqB12kyDF1vExERkeJP99SIiIhIiaBQIyIiIiWCQo2IiIiUCAo1IiIiUiIo1IiIiEiJoFAjIiIiJYJCjYiIiJQICjUiIiJSIijUiIiISImgUCMiIiIlgkKNiIiIlAgKNSIiIlIi/D+Q3s8vsujySwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "import tensorflow as tf\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import numpy as np\n",
        "\n",
        "def plot_confusion_matrix(y_true,\n",
        "                          y_pred,\n",
        "                          normalize = True,\n",
        "                          figsize = (10, 10),\n",
        "                          text_size = 15,\n",
        "                          classes = None,\n",
        "                          label_size = 20):\n",
        "  \"\"\"\n",
        "  Plots a Confusion Matrix based on the Ground Truth and the predicted values.\n",
        "\n",
        "  If `normalize` is set to True, the predicted values are rounded off to corresspond to singular class values\n",
        "  \"\"\"\n",
        "\n",
        "  if normalize:\n",
        "    y_pred = tf.round(y_pred)\n",
        "\n",
        "  # Create the confusion matrix\n",
        "  cm = confusion_matrix(y_true, y_pred)\n",
        "  cm_norm = cm.astype(\"float\") / cm.sum(axis = 1)[:, np.newaxis] # Normalize our Confusion Matrix\n",
        "\n",
        "  n_classes = cm.shape[0]\n",
        "\n",
        "  # Now, let's prettify the plotting\n",
        "  fig, ax = plt.subplots(figsize = figsize)\n",
        "\n",
        "  # Create a matrix plot\n",
        "  cax = ax.matshow(cm, cmap = plt.cm.Blues)\n",
        "\n",
        "  # Show the Colorbar alongside\n",
        "  fig.colorbar(cax)\n",
        "\n",
        "  if classes:\n",
        "    labels = classes\n",
        "  else:\n",
        "    labels = np.arange(cm.shape[0])\n",
        "\n",
        "  # Label the axes\n",
        "  ax.set(title = 'Confusion Matrix',\n",
        "        xlabel = 'Predicted Label',\n",
        "        ylabel = 'True Label',\n",
        "        xticks = np.arange(n_classes),\n",
        "        yticks = np.arange(n_classes),\n",
        "        xticklabels = labels,\n",
        "        yticklabels = labels\n",
        "        )\n",
        "\n",
        "  # Set Label Positions\n",
        "  ax.xaxis.set_label_position('bottom')\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  # Adjust Label Size\n",
        "  ax.yaxis.label.set_size(label_size)\n",
        "  ax.xaxis.label.set_size(label_size)\n",
        "\n",
        "  ax.title.set_size(20)\n",
        "\n",
        "  # Set threshold for different colors\n",
        "  threshold = (cm.max() + cm.min()) / 2\n",
        "\n",
        "  # Plot the text on each cell\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, f'{cm[i, j]} ({cm_norm[i, j] * 100:.1f}%)',\n",
        "            horizontalalignment = 'center',\n",
        "            color = 'white' if cm[i, j] > threshold else 'black',\n",
        "            size = text_size)"
      ],
      "metadata": {
        "id": "A5saobZOzT4U"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_true = y_test,\n",
        "                      y_pred = model.predict(X_test_norm).argmax(axis = 1),\n",
        "                      normalize = False,\n",
        "                      classes = class_names,\n",
        "                      text_size = 6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "id": "kOPt5jbNxsJp",
        "outputId": "99f8cddf-58f6-456e-9a5b-792f99dc4134"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "# Create a function to display a random set of images and the corressponding predictions by the model\n",
        "def predict_random_images(model):\n",
        "  plt.figure(figsize = (14, 6))\n",
        "\n",
        "  for i in range(12):\n",
        "    ax = plt.subplot(2, 6, i+1)\n",
        "    rand_index = random.choice(range(len(X_train)))\n",
        "    plt.imshow(X_train[rand_index], cmap = plt.cm.binary)\n",
        "\n",
        "    prediction = model.predict(tf.expand_dims(X_train_norm[rand_index], axis = 0), verbose = 0)\n",
        "    predicted_label = class_names[prediction.argmax()]\n",
        "    if predicted_label == class_names[y_train[rand_index]]:\n",
        "      color = 'green'\n",
        "    else:\n",
        "      color = 'red'\n",
        "      \n",
        "    ax.set_title(f'Prediction: {predicted_label}\\nTrue: {class_names[y_train[rand_index]]}\\nConfidence: {tf.reduce_max(prediction) * 100:2.0f}%', fontdict = {'color': color,})\n",
        "    plt.axis(False)"
      ],
      "metadata": {
        "id": "HpxEu_-vzX30"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(5):\n",
        "  predict_random_images(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "j0G5jp9Q0Adf",
        "outputId": "52eb93ff-1f81-44af-86aa-19d5183e6a78"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 12 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 12 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 12 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 12 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 12 Axes>"
            ],
            "image/png": 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AgA0MUQAAAAAAAGzI4ukFpCWfCB9bfcueW6ZGxRql7mKS4ecdP2vu3rnacGyD9p3fp4bhDbW8y3JPLwteJr3n/LWFr2nF4RU6FHNIsbdiFZ4rXB3Kd1Cfun0UkjXE08uDl0jPOV9+aLkaT2js9PuRjSP1zgPvpOGK4K3Sc84l3rfAnvSec0m6fOOyPlz5oabumqrjl48rX1A+1SlcRxMfn6gg/yBPLw9eICPk/Lb95/er/KjyuhF/Q3+89IeqF6ru6SWluUw1RPnh8R+SfD1x60QtPrDYVL8v331puSzbRm8arc0nNqtGoRo6d+2cp5cDL5Xec/7H8T/UoGgDda3SVdmyZFPUySh9vPpjLTmwRCu7rpSvDxfQIX3n/L5895nWKUk/bPtBv+3/Tc1LNvfAquCN0nPOJd63wJ70nvOLsRfVcHxDHb10VN2qdVOpPKV05uoZrTqySjdu3WCIAknpP+f/9tqi15TFN4tuxN/w9FI8xscwDMPTi/CUHvN76Ks/vpIx4M6/BdduXvOKF8C/L/6te3LcI18fX1UYVUH5gvLxLzq4q/SWcyufrf1MfRb30boX1ql24dqeXg68UEbIeemRpeUjH+3tudfTS4GXSm85530LkiO95fyVea/opx0/aUu3LSqeu7inl4N0Ir3l/LZF+xap9ZTW6le3nyJXRWbaK1H4J93/aDS+kSqMqqDNxzfrgXEPKOijIPVf2l9S4mVYA5cPND2m2PBi6jKrS5JaTGyMei/srSKfF1FAZIBKfVFKn6z+RAlGQpK+E5dPaM/ZPboZf/OuayuSswj/Cg+38OacWymWq5jjfIBd6SnnG49t1L7z+/R0xaddfiwyN2/OOe9b4C7emvOY2BiN+3OculXtpuK5iysuPk43bmXef51Hynhrzm+7GX9TvRb2Uq9avVQyT8lkPceMIlN9nMeuc9fP6ZFJj6hjhY7qXKmzCgQXcOnx125eU8PxDXXs0jF1r9ZdRXMW1dqja/X20rd14soJDX94uKP37aVva8LWCTrY66DjL4pAWvDmnN9KuKWY2BjFxcdpx+kdenfZu8qeNbtq3lPTxWeJzM6bc/5vk7ZNkiQ9XYkhClyXXnIOpIQ35nz1kdWKvRWrUnlKqd0v7TRrzywlGAmqU6SOvmrxlaoUrJK8J4tMyxtzftvw9cN1IfaC3n3gXc3YPcPFZ5axMESxcPLKSY1pOUbdq3dP1uOHrRum/ef3K6p7lErnLS1J6l69uwqFFNKQtUP0Rp03VCRnEXcuGXCZN+d80/FNqvNdHcfXZfOW1Zyn5ihPYJ5kHQ+Zlzfn/Lb4hHj9vPNn1bynpkrlKZWiYyFzSg85B1LKG3MefS5aUuJfRkvmKamJj0/UxdiLilgRoSYTmmjnKzsVlj0sWetF5uSNOb+9rg9XfqihzYcqR0COZK0tI+EaSwsBfgHqen/XZD9+6q6pahDeQLkDc+vstbOOX01LNFW8Ea+Vh1c6esc/Nl7GAIN/zUGa8+aclwstp8XPLNasDrPUr24/BWcN1pW4K8leKzIvb875bUsPLtWpq6f4KA+SLT3kHEgpb8z57fcmPj4+WvrsUnWq2En/q/E/zeo4SxdiL+irP75K9nqROXljziXpzSVvqkTuEnqx6ovJXltGwpUoFu7JcY+y+mVN9uOjz0Vr26ltCh0Savn901dPJ/vYgLt4c85zBORQ0xJNJUlt7m2jydsnq82UNtrSbYsqF6yc7OMi8/HmnN82afsk+fn4qUP5Dik+FjKn9JBzIKW8MeeB/oGSpEfLPKqQrCGOeu3CtVU8V3Gt/Xtt8haLTMsbc77+6Hr9sPUHLX12Kftc/X8MUSwEZgl0qT/eiE/ydYKRoGYlmqlfvX6W/WXylkn22gB3SU85b3tfWz0z8xlN2TGFIQpc4u05v37zumbunqmmJZqqQIhrn3sGbvP2nAPu4I05L5S9kCRZ7luRPzi/LsRecPmYyNy8Mef9FvdTg/AGKp67uA7FHJIknb12VlLi5rRHLh5R0ZxFXT5uesYQxQW5s+U23R0kLj5OJy6fSFIrmaekrsRdcfxLOpCeeGPOb9y6oQQjQRdvXEz1cyFz8Jacz/lrji7HXeajPEgV3pJzIDV5MufVwqpJko5dPmb63vHLx3Vvvnvddi5kbp7M+ZGLR3T44mEVH2G+hXfrKa2VMyCnYt6KMT8wA+N6HBeUzFMyyefIJGns5rGmCWD7cu217ug6Ldq3yHSMmNgY3Uq45fg6pbd+BdzNkzmPiY2x7Pl2y7eSlCnvQ4/U4S2v55N3TFaQf5Aev+9xF58BcHfeknMgNXky52XzlVXlApU1+6/Zjn+Zl6Tf9v+mvy/9rWYlmiXnKQEmnsz52EfHamaHmUl+9azZU5I0tNlQTWo7KblPK93iShQXvHj/i3p53st64pcn1KxEM209uVWL9i9SvqB8Sfr61uurOXvnqNVPrdSlchdVK1RNV+Ouavvp7Zq2a5oO9T7keIwrt5ZaeXil44fnzLUzunrzqiJXRkqSHgh/QA+EP+D+J41Mx5M5X35ouV5d8KralWun0nlKKy4+TquOrNKM3TNUvVB1da7UOTWfOjIRT7+eS9L56+e1IHqBnij3RJLP0gPu4umc874FacHTOf/8oc/V7Idmqv99fXWv1l0Xb1zUsHXDVCZvGf2vxv9S62kjk/FkzpuXbG6q3b4qpmGxhpnyHzkZorjgpWov6WDMQX0X9Z0W7luoBkUbaPEzi/XgxAeT9AX5B2lFlxUatGqQpu6aqonbJipHQA6VyVtGEY0ilDMgZ7LO//vB3xWxIiJJ7b1l70mSBjQcwJsRuIUnc14xf0U1Lt5Ys/+arROXT8iQoZK5S+r9hu+rb92+KdpoC/g3T7+eS9LUnVN1M+GmOlXolNKnA1jydM5534K04OmcNy7eWAs7L9R7y95T/9/7K8g/SI/d+5g+bfYpA3K4jadzjqR8DMMwPL0IAAAAAAAAb8eeKAAAAAAAADYwRAEAAAAAALCBIQoAAAAAAIANDFEAAAAAAABsYIgCAAAAAABgA0MUAAAAAAAAG9LtECX6XLSa/9BcOT/OKZ8IH83aM0vj/xwvnwgfHYo5dNfHFxteTF1mdUn1dQLuRvaR0ZBpZAbkHJkBOUdmQM6RoiHK/vP71X1ud5UYUULZIrMpx+Acqvd9PY1YP0LXb1531xotPTfrOW0/vV0fNflIPzz+g6oXqp6q50tvRv8xWk9OfVJFPy8qnwifO/6gxsTGqNvcbgodEqrgQcFqPKGxtpzYYtk75685qvp1VWWLzKainxfVgGUDdCvhVpKeXWd2qcG4Bso+OLuqj62udX+vMx1n2LphKj+qvOmx6QXZ916nrpxS19ldlX9IfgV+FKiqX1fV1J1TTX0Dlw+UT4SP6Ve2yGxJ+m7cuqGe83sqdEioCg8rrMiVkaZjHb10VCGDQrTmyJpUe16pjUx7L17P3Yecey9y7j7k3HuRc/ch594rM+Q8S3IfOG/vPD059UkFZAnQs5WeVYX8FRQXH6fVf69W38V9tfPMTo19dGyyF3Yn129e17qj6/ROg3fUo2YPR/2ZSs+oY4WOCvALSJXzpiefrPlEl+Muq+Y9NXXiygmnfQlGglpObqmtJ7eqb92+yheUT6M2jVKj8Y20udtmlc5b2tG7IHqBHpvymBoVa6SRj4zU9tPbFbkqUqevntboVqMlSfEJ8Wr7c1vlCcyjIc2GaM5fc9RmShvte3WfcgTkkCSdvnpaH6z4QL88+Yuy+CY7gh5D9r3XpRuXVH9cfZ26ckq9avVSwZCC+mXXL2o/rb0mJUxSp4qdTI8Z3XK0QrKGOL728/FL8v0ha4do4raJeqfBO7p847I+WPGBSuYuqacqPuXo6bu4r1qXba16Reul3pNLRWTau/F67h7k3LuRc/cg596NnLsHOfdumSLnRjIcOH/ACBkUYtz75b3G8UvHTd+PPhdtDF83PDmHtuVwzGFDA2UMWTMk2ccI/zzceG7mc+5blJc5dOGQkZCQYBiGYQR/FOz0uf6842dDA2VM3TnVUTt95bSR6+NcxlPTnkrSW+6rckbl0ZWNm/E3HbV3lr5j+Az0MXaf2W0YhmHsPrPb0EAZh2MOG4ZhGFfjrhqBkYHGwuiFjse8MPsF49HJj7rleaY1su/dPl39qaGBMpYeWOqoxSfEGzXG1jAKDi1o3Lh1w1EfsGyAoYEyzlw9c8dj1vqmlhGxPMLx9XMznzM6Tuvo+HrV4VVG8EfBxt8X/3bjM0k7ZNr78XqecuTc+5HzlCPn3o+cpxw5936ZIefJ+jjPp2s+1ZW4K/qu9XcKyx5m+n6pPKXUq3Yvx9e3Em7pwxUfquQXJRUQGaBiw4up/9L+unHrRpLHFRteTK0mt9LqI6tV85uayhaZTSVGlNDErRMdPQOXD1T48HBJif/66xPho2LDi0mS5WfRDMNQ5MpIFR5WWEEfBanxhMbaeXqn5fOKiY1R74W9VeTzIgqIDFCpL0rpk9WfKMFIcPQcijkknwgfDV07VGM3j3U8pxrf1NAfx/4wHXPP2T1qP7W9QoeEKvCjQJX9sqzeWfpOkp5jl47p+dnPq8DQAgqIDFD5UeX1fdT3pmMduXhEe87usVz7f4XnCpePj89d+6btmqYCwQXU9r62jlpocKjal2uv2X/Ndvw/2nVml3ad2aVu1bolmdq9UuMVGTI0bdc0SXJcPpc7W25JUpB/kAL9A3Xt5jVJ0pYTWzRp+yQNe2iYrefhbci+d2d/1ZFVCg0KVZPiTRw1Xx9ftS/fXievnNSKQytMjzEMQ5duXJJhGJbHvH7ruiPPkpQnMI8jzwlGgnot7KV+9fqpcI7Cd12fNyLT3p1piddzdyDn5Pw2ck7OJXJOzsn5beQ8eZJ1DcvcvXNVIncJ1S1S11b/i3Ne1IStE9SuXDu9UecNbTi2QYNXD9bus7s1s8PMJL37zu9Tu1/a6YX7X9BzlZ/T939+ry6zuqhaWDWVz19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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(model,\n",
        "                          show_shapes = True,\n",
        "                          show_dtype = True,\n",
        "                          show_layer_activations = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "7f72lu2F0CVm",
        "outputId": "2ab2fdcf-8a2b-4e31-a44c-1f5ca4deb3c0"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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kn99OPuYqk08QtX79erXul9GMw4cP4/r161i6dGmrCYy6Q4XP3wGl74yfX0eO0bzeDsJykZGRfWLi/J40duxYjB07VtPdYNTE19cXvr6+Gu0DGzNmGIbRAiwYMwzDaAEWjBmGYbQAC8YMwzBagAVjhmEYLaD2YHzx4kUMGjQIOjo64PF4sLCwUJgwWlOeX1Ld0tKyzSXYGYZhNEHts7aNGDECf/75J8aNG4fjx4/j5s2b3JSBmuTv7w9/f384OTmhtLSUW0uLYRhGG7ywwxTaugQ8wzBMW17YYNyXloBnGIbptWCsDUvAq+Ls2bNwdXWFoaEhhEIh3NzccPz4cQDAp59+yo09Ozo6IiMjAwAwa9YsiMViGBoa4siRIx0uo75hwwaIxWJIpVIUFxdj0aJFsLa2xs2bN7t1nhmG6aPUOAWcgvfee48A0OPHj7kyTS8BT/R0SXVDQ8NO+3/gwAFas2YNlZWV0aNHj2jEiBEK0+f5+/uTrq4u3b9/X+F9H3zwAR05coSIOl9GXX4+FixYQJs2baLJkyfTn3/+2Wnf5DQ5hWZfwaaAZTRJlSk0NbLskqenJ4RCIYCnM/WfPXsWeXl53DLo8iXgAXBLwLu7uyMpKQmrV6/ulT5OmTJFYfIbHx8fLF++HCUlJTAzM8PcuXNx6NAhJCUlYcWKFQCAyspK/Pbbb/jhhx9aLaMOPF048ttvv0VSUpLCysDr16+HUCjE/PnzVe5nQUEB9u/f382jfXHJ14Jj54jRBFXWItT4Gnh9ZQl4+ZR68jl83377bTg7O+P777/H8uXLwePxsHfvXgQGBkJXVxfXrl3rkWXUn3fx4kUEBASobX8vKnaOGG3XZ37A6+0l4H/++Wf87W9/g5mZGQQCAZYuXaqwncfjYc6cObhz5w5OnjwJAPjhhx+4tbl6Yhn1tkyZMqXVysDs9d+XfIxe0/1gr5fzpcrUw30iGPfWEvBnzpxBbGws8vLy4OfnB0tLS1y6dAkVFRWIiopqVX/mzJkQCoXYvn07bt68CZlMhgEDBgBQ/zLqDMO82DQ+TKGM3loC/o8//oBEIsHVq1fR1NSEzz//HA4ODgDaXunE2NgYAQEB2Lt3L6RSKWbPns1tU9cy6gzDvBy08s64p5aAb09TUxOKioq4lXltbW0BACdOnEB9fT2ys7O51LrnzZ07Fw0NDUhNTcXEiRO5cmWWUWcYhuFQJ1RNDbp48SK99tprpKOjQwDI0tKS1q1bRwkJCSQWiwkADRw4kHJycmjr1q0kk8kIAA0YMIBu3bpFQUFBxOfzydramvT09Egmk9GkSZMoJyeHa+PRo0c0ZswYEgqFZG9vT1988QUtWbKEAJCTkxPl5eXR5cuXacCAASQSiWjUqFH03XffkaOjIwHo8JWcnExERKGhoWRiYkJGRkY0depU2rx5MwEgR0dHLsVO7o033qCwsLBW56KhoYFCQ0PJ1taW9PT0yMzMjPz9/SkrK4uioqJIJBIRALKxsenSisMsta1zLLWN0SRVUtuUXgOvk2pqM2fOHBw4cACPHj3qlfbUwdvbG5s3b4a9vX2vtjt16lQAmlt+qS/o7c8vwzxLlTXwtHKYQtuXgH92yOPKlSsQCoW9HogZhnmx9Ikf8LRNaGgo5s6dCyLCrFmzsHPnTk13iWGYPk6r7oyXL1+OpKQkVFRUwN7eHgcPHtR0l9okFovh4uKCd999F2vWrIGrq6umu8QwTB+nVcE4MjISDQ0NICLcvXtX4XFkbRIREYHm5mbk5eUpZFAwDMN0lVYFY4ZhmJcVC8YMwzBagAVjhmEYLcCCMcMwjBZQOrWNzQerfQoKCnp88iSGYXqJso/zsZd2vtjj0B3ryuPQQUFBCud4+vTprer8+uuvtGzZMjp48CDZ29tzdWfMmNGq7t///ncyMDAgHR0dcnV1pT/++KPLx9NTvv76axo0aBBJpVLS19cnR0dHWrJkCVVVVSnU2717Nw0fPpwMDAzI1taWZs6cSQ8ePND69uSam5spJiaGPDw8FMoPHz5M69evpydPniiU//TTTwqfhWdX+1GGKo9Dq31uCqb3sLkpOtfVYGxiYkJpaWl08+ZNqq+vV9i+evVqmjhxIlVWVnJljo6O9MorrxAASk1NbbXPtLQ08vX17dpB9AIvLy9KSEigR48eUWVlJe3bt4/4fD6NGzeOq7N3714CQFFRUVReXk4ZGRnk4OBAQ4cOpaamJq1uj4jo1q1bNHLkSAJAQ4YMabU9Li6OvLy8FJaKa2lpoYKCAjpz5gxNmDCBBWOmbZoMxrW1ta3uLrRx310NxtbW1m1u++abb8jZ2Znq6uoUyh0dHWn37t2ko6ND1tbWVF5errBd24Oxt7d3q7vCadOmEQBuYqwxY8ZQv379qKWlhasjn0Dr3LlzWt1eZmYmTZ48mXbt2kVDhw5tMxgTEQUHB5OHh0ebwX7BggU9GozZD3hMl+zYsQPFxcV9bt/dcfv2baxatQpff/01t4bjszw9PRESEoL79+9j8eLFGuhh16WmpkJXV1ehzNTUFAC4lWny8/NhZWWlMLe3jY0NALSawlbb2hsyZAgOHTqE6dOnQyAQtFtvzZo1yMzMRFxcnEr7VwcWjF8yRISYmBgMGjQIAoEAxsbGmDRpErcuX3BwMPT19WFpacm9Z968eZBIJODxeCgtLUVISAgWLVqEnJwc8Hg8ODk5IT4+HkKhEObm5pgzZw6srKwgFArh6enJzQXd1X0DwLFjxyCTybBu3bpePFuK4uPjQUTw8fFpt05ERAScnZ2xfft2nDhxot16nV2HxMRESCQSiMViHD58GOPHj4dMJkP//v2xZ88ebj/Nzc1YvXo1bG1tIRKJ8Prrr6u01E9H7t+/D5FIxE2C5eDg0OofyYcPH3Lb+lp7bTE2NoaXlxfi4uJ6f6Y/Nd5mM72sK8MUq1evJn19fdq5cyeVl5fTlStXaNiwYWRqakoPHz4kIqLp06eThYWFwvuio6MJAJWUlBARkb+/Pzk6OirUCQoKIolEQtevX6f6+nrKysoid3d3kkql3J+eXd13amoqSaVSCg8PV+l41TlM4eDgQK6urm2+x9HRke7evUtEROfPnycdHR2ys7Oj6upqImo9TKHMdVixYgUBoJMnT1JFRQUVFxfT6NGjSSKRUGNjIxERLV68mAQCAR08eJAeP35My5cvJx0dHfrtt99UOubn1dTUkFQqpeDgYK4sPT2d+Hw+xcfHU2VlJV27do0GDRpE7733Xrfa6u32/vKXv7Q7TEFEFBYWRgAoIyNDoZwNUzBqU1dXh5iYGEyePBkzZsyAoaEh3NzcsGXLFpSWlmLr1q3dbkNPT4+723N1dUViYiKqqqqQlJTUrf16e3ujsrISq1at6nYfu6KmpgZ3796Fo6Njp3U9PDzw5ZdfIjc3F8uWLWu1XdXr4OnpCZlMBjMzMwQGBqKmpgZ5eXmor69HYmIi/Pz84O/vDyMjI6xcuRJ8Pr/b5zsyMhJWVlaIiIjgyry8vBAaGorg4GDIZDIMHjwYVVVV2L59e7fa0kR7HRk4cCAA4OrVqz3azvNYMH6JZGVlobq6GsOHD1cod3d3h76+frtLS3XH8OHDIRaLuT+/+6ri4mIQEcRisVL1IyIi8OqrryIhIQHnzp1T2Nad66Cvrw/g6ZzaN2/eRG1tLQYPHsxtF4lEsLS07Nb5Tk5Oxv79+3H8+HFIpVKufMWKFdi6dStOnjyJ6upq3LlzB56envDw8EB+fn6faa8z8mtcVFTUY220RSuDcUNDAxYsWABLS0uIxWK8++67MDc3B4/Hw5YtW3q8/aioKLi4uEAkEkEikcDFxQWrVq1CZWUlVyc8PByurq6QyWQQCARwcnLC0qVLUV1d3eP966ry8nIAgIGBQattRkZGqKqq6pF2BQIBSkpKemTfvaW+vh4AOvzx51lCoRBJSUng8Xj4+OOPUVdXx21T13WoqakBAKxcuRI8Ho973bt3j/sRTFV79+7F+vXrkZ6eDjs7O678wYMHiIqKwmeffYa3334bEokE9vb22LZtGwoLCxEdHd0n2lOGSCQC8N9r3lu0Mhh/++23OHbsGG7cuIG4uDjMmTMH58+f77X2z549i9mzZyMvLw9FRUVYu3YtoqKiFKb0PHXqFObPn4/c3FyUlpYiMjIScXFx3FJI2sjIyAgA2vyyl5eX98jTfE1NTT22794k/4KqsgqNh4cHFi5ciOzsbKxdu5YrV9d1MDMzAwDExsaCiBReFy5cULqfcps2bcKuXbtw6tQp9OvXT2FbdnY2mpubW5XLZDKYmJggKytL69tTVmNjI4D/XvPeopXBOCUlBcOHD4eRkRE+++yzHp3XuK6uDp6engpl+vr6mDdvHszMzGBgYICpU6di0qRJ+PXXX7mVnQ0MDBAUFAQTExNIpVJMmzYNfn5+OHbsWI/+CdUdgwcPhoGBAX7//XeF8kuXLqGxsRFvvvkmgKfjvh2tpq2K9PR0EBFGjBih9n33JvlfZhUVFSq9b+3atXBxcUFGRgZXpux16IyNjQ2EQiEyMzNV6tPziAihoaG4evUqUlJS2rxjl/8D8fzK5lVVVSgrK+NSzrSxPVXJr7GFhUWPtdEWrQzGBQUF4PP5vdJWWzmtycnJrfJIra2tAYAbhlAmT1LbCIVCLFq0CMnJydi1axcqKytx9epVzJ07F1ZWVggKCgIAODk5oaysDCkpKWhqakJJSUmrvE4TExMUFhYiNzcXVVVVXIBtaWnB48eP8eTJE1y5cgUhISGwtbXFzJkzu7XvtLQ0jaa2icViODg4oKCgQKX3yYcrnv2sKHsdlNn3rFmzsGfPHiQmJqKyshLNzc0oKCjgglhgYCAsLCxw+fLldvdz/fp1bNiwAdu2bQOfz1cY8uDxeNi4cSPs7e0xZswYbNu2DWfOnEFdXR3y8/O5vn7yySda256q5NfYzc1NbftUihpTM7rtl19+IUdHR4VnwSUSCRERZWdnEwD67rvvuPotLS307bffkouLC+nr65ORkRH5+vrSn3/+ydU5c+YMDRo0iGQyGQkEAho8eDAdO3aMiJ6mqujr63NtPZ9O9Sxvb28yMjKihoaGduv4+vqSSCTqsI46dSW1raWlhaKjo2ngwIHE5/PJ2NiY/Pz86ObNm1ydR48e0ZgxY0goFJK9vT198cUXtGTJEgJATk5OlJeXR5cvX6YBAwaQSCSiUaNG0cOHDykoKIj4fD5ZW1uTnp4eyWQymjRpEuXk5HR730ePHiWpVEoREREqHa86U9uCg4OJz+dTbW0tV5acnMx9Zk1NTWn+/Plt7nPJkiUKqW2dXYeEhAQSi8UEgAYOHEg5OTm0detWkslkBIAGDBhAt27dooaGBgoNDSVbW1vS09MjMzMz8vf3p6ysLCIi8vPzIwC0evXqdo/36tWrHc5/Eh0dTUREpaWlFBISQk5OTiQQCMjAwIBGjhxJP/30E7cvbWyPiOjChQs0cuRIsrKy4tqxtLQkT09POn36tEJdb29vsra2Vnjyj6jnU9u0KhjLWVhY0EcffaRQ1lYwViZX88CBA7RmzRoqKyujR48e0YgRIxROaFs5rXKNjY1UUFBAmzZtIoFAQDt37my3z23lSfY0bZubQj6ngzZRZzDOzs4mPT29Dj8H2qa5uZlGjx5NO3bsYO0pobS0lIRCIW3cuLHVNpZn3A5lczWnTJmCr776CsbGxjAxMYGPjw8ePXqk1K/7NjY26N+/P9asWYMNGzYgICCg3bpt5Um+jFT5gUub1dXV4fjx48jOzuZ+0HFyckJ4eDjCw8O1OmtGrrm5GSkpKaiqqkJgYCBrTwlr1qzB0KFDERwcDODp+HZhYSHOnTuH27dvq6WN9vTZYNzVXE35WLQyQSM/Px/FxcX48ccf8a9//QtvvPFGm3MmtJcnyfRdZWVlGDduHJydnfHxxx9z5WFhYZg6dSoCAwNV/jGvt6Wnp+PQoUNIS0tTOj/6ZW4vJiYGmZmZOHr0KNMATzIAACAASURBVBcnDh8+DGtra4wePRo///xzt9vokBpvs9VGmWGKX3/9lQDQli1bWr3f3Nyc/vrXvxLR08dovby8yNTUlPT19YnH4xEAbk7UjoYpnnXr1i0CQAsWLFAo37NnD7m7u9P9+/e7cqjdok3DFGFhYdz4u52dHR04cEDTXSKinvv8Hj9+nEJDQ9W+X0YzUlJSKDIystVMct31UgxTKJOrmZeXBz8/P1haWuLSpUuoqKhAVFRUl9pzcnKCrq6uQn5jR3mSL5vIyEg0NDSAiHD37t0eTUfUBmPHjsX69es13Q1GTXx9fREWFtYqQ6o39dlgrEyu5tWrV9HU1ITPP/8cDg4OEAqFCtPxteXRo0f44IMPWpXLk9BtbGyUypNkGIZRRZ8Nxsrkatra2gIATpw4gfr6emRnZ7caS34+p1VfXx+//PILTp06hcrKSjQ1NSEjIwMfffQRJBIJFi5cqFSeJMMwjCq0Khjfu3cPw4YNQ1FREXbv3o0333wThw4dQkxMDEaNGgUAWLx4Mfz9/QEAX331FSIjIxEeHg5TU1N4eXnBzs4O6enpkEgkcHNzQ2hoKBISEmBlZYUVK1bgb3/7GwBg1KhRyM/Px9y5c2Fubg5XV1dMmDABtbW1GDlyJD799FNYW1tDKpVi6tSpsLOzw8WLFzF48ODen+eUYZgXHo86iSz79+9HQEAAC0BaSD4PxoEDBzTcE+3FPr+MJqnw+TugVXfGDMMwLysWjBmGYbQAC8YMwzBagAVjhmEYLcCCMcMwjBZgwZhhGEYLsGDMMAyjBVgwZhiG0QJ6ylbU5oU2X1YXL14EwK5NR+RL6LBzxGiCKst0dfoE3oULFxATE9PtTjFMT3j06BGys7O5BU8ZRhsp8ZTsgU6DMcNoM/a4M/OCYI9DMwzDaAMWjBmGYbQAC8YMwzBagAVjhmEYLcCCMcMwjBZgwZhhGEYLsGDMMAyjBVgwZhiG0QIsGDMMw2gBFowZhmG0AAvGDMMwWoAFY4ZhGC3AgjHDMIwWYMGYYRhGC7BgzDAMowVYMGYYhtECLBgzDMNoARaMGYZhtAALxgzDMFqABWOGYRgtwIIxwzCMFmDBmGEYRguwYMwwDKMFWDBmGIbRAiwYMwzDaAEWjBmGYbQAC8YMwzBagAVjhmEYLcCCMcMwjBZgwZhhGEYLsGDMMAyjBVgwZhiG0QIsGDMMw2gBPU13gGGUdf/+fUycOBFNTU1cWW1tLQwNDeHm5qZQd+jQodi5c2dvd5FhuowFY6bPsLa2RkNDA65fv95qW0VFhcL/BwQE9Fa3GEYt2DAF06d8+OGH0NPr/B4iMDCwF3rDMOrDgjHTp7z//vtobm5udzuPx8Obb74JJyenXuwVw3QfC8ZMn2Jra4u33noLOjptf3R1dXXx4Ycf9nKvGKb7WDBm+pwPP/wQPB6vzW0tLS2YNm1aL/eIYbqPBWOmz5k6dWqb5bq6uvDy8oKlpWUv94hhuo8FY6bPMTMzw9tvvw1dXd1W2/7xj39ooEcM030sGDN90owZM0BECmU6Ojrw8/PTUI8YpntYMGb6JD8/P4UUNz09PUyYMAFGRkYa7BXDdB0LxkyfJJVKMXHiRPD5fABPf7ibMWOGhnvFMF3HgjHTZ02fPh1PnjwBAAgEAnh7e2u4RwzTdSwYM33W+PHjIZFIAABTpkyBSCTScI8YputaPVdaUFCA8+fPa6IvDKOy4cOHIz09HTY2Nti/f7+mu8MwSmkrF55Hz/0kvX//fjbJCsMwTA96PhMIwIF2Z1xpozLzgpE/PHHgwAEN96TrWlpasH79eixfvrxH9i+/OWHfB0YdOrrZZWPGTJ+mo6ODpUuXarobDNNtLBgzfZ4yU2oyjLZjwZhhGEYLsGDMMAyjBVgwZhiG0QIsGDMMw2iBbgfjb775BoaGhuDxeMjMzFRHn3rNxo0bYW5uDh6Phy1btmi6O0oLDw+Hq6srZDIZBAIBnJycsHTpUlRXV2u6awzDdFG3g3FYWBj++c9/qqMvvW7x4sV98mnDU6dOYf78+cjNzUVpaSkiIyMRFxfX7qTrDMNoPzZM0QcZGBggKCgIJiYmkEqlmDZtGvz8/HDs2DHk5+drunsMw3QBS9Dsg1JTU1uVmZqaAgBqa2t7uzsMw6hBj9wZFxUVwc7ODnp6ehg3bhwAoLm5GatXr4atrS1EIhFef/117Nu3DwCwYcMGiMViSKVSFBcXY9GiRbC2tsbNmzdx9uxZuLq6wtDQEEKhEG5ubjh+/DjX1unTp/HWW29BLBZDJpPBzc0NlZWV3ep/R21++umn4PF44PF4cHR0REZGBgBg1qxZEIvFMDQ0xJEjR7p8vF11//59iEQi2Nvbd+vYGYbRjB4JxiYmJhg+fDiSk5Nx7NgxAMCyZcuwYcMGxMbG4sGDB5g4cSI++OAD/P7771i6dCkWLlyI6upqREZGwt7eHiNGjAARoaioCAEBAcjNzUVhYSEMDAwwffp0AEBNTQ18fHwwZcoUlJWVITs7G87OzmhsbOxW/ztqc/v27fD394euri7Onj2LN954AwCQlJQEPz8/7Nq1Cz4+Pl0+3q6ora3FqVOnMHv2bOjr63fr2BmG0RB6zr59+6iN4g7t2bOHAFBGRgY1NTXR+++/T2lpadz2uro6EovFFBgYyJXV1taSQCCgzz//nIiIVqxYQQCorq6uw7YiIyMJABUXF9O1a9cIAKWmpqrU32dlZ2cTAPruu++UapOI6MSJEwSAIiIiuDoVFRU0cOBAevLkiVqPVxkrVqwgZ2dnqqysVOl9U6ZMoSlTpnS7/RdZV74PDNOeDj5P+9V6Z9zc3IwPPvgA5ubm3PAEANy8eRO1tbUYPHgwVyYSiWBpaYkbN26o1IZ8mZ3m5mY4ODjA3NwcM2bMwJo1a5Cbm6uW4+ioTQB4++234ezsjO+//567m927dy8CAwOhq6ur1uPtTHJyMvbv34/jx49DKpWqdd/KOHr0KAwNDfHvf/+719vuC06cOIGwsDAcOnQIDg4O3BBXW6tYjx07FlKpFLq6unjttddw+fJlDfS4Y8qmVf74449wd3eHVCrFgAEDMGvWLDx8+FDr25NraWlBbGwsPD09FcqPHDmCqKgoLhaolQqRu13yO+MRI0bQ0KFDSSAQUFZWFrf9//7v/whAm68RI0YQUft3iqmpqeTl5UWmpqakr69PPB6PANCDBw+IiOjatWv0P//zP6Snp0c8Ho8CAgKotrZW6b63dWfcWZtERDExMQSAfv31VyIiGjlyJOXm5nb7eFWxZ88ecnd3p/v373fp/eq4M05NTSWZTEZHjhzp1n60VXfujFevXk0TJ05U+IvF0dGRXnnllXb/oktLSyNfX98u97eneXl5UUJCAj169IgqKytp3759xOfzady4cVydvXv3EgCKioqi8vJyysjIIAcHBxo6dCg1NTVpdXtERLdu3aKRI0cSABoyZEir7XFxceTl5UWPHz9Wed8d3RmrNRjHxMRQSUkJWVhY0JtvvsmdiFu3bhEAio2NbXcfbQWne/fuEZ/Pp4CAAMrJyaG6ujrasGFDq8BIRFRcXEzLli0jABQeHq50358Pxsq2WVZWRkKhkAICAujGjRs0fvx4bltXj1cV8fHx5O3tTVVVVV16P9GLMUxRW1tLHh4ePbb/rgbjb775hpydnVtdX0dHR9q9ezfp6OiQtbU1lZeXK2zX9mDs7e1NT548USibNm0aAaC8vDwiIhozZgz169ePWlpauDqbN28mAHTu3Dmtbi8zM5MmT55Mu3btoqFDh7YZjImIgoODycPDQ+Vg32vDFGPGjIGpqSm2bt2KP/74AxEREQAAGxsbCIVClZ/Qu3r1KpqamvD555/DwcEBQqEQPB6P215YWIjr168DAMzMzPDNN99g2LBhXFlXdNamnLGxMQICApCSkoKNGzdi9uzZ3LauHq8yiAihoaG4evUqUlJSYGBgoPY2+pIdO3aguLhY091QcPv2baxatQpff/01hEJhq+2enp4ICQnB/fv3sXjxYg30sOtSU1Ohq6urUPZ8WmV+fj6srKwUvjc2NjYAgHv37ml1e0OGDMGhQ4cwffp0CASCduutWbMGmZmZiIuLU2n/HemRbAofHx/MnDkT69atwx9//AGhUIhZs2Zhz549SExMRGVlJZqbm1FQUIAHDx60ux9bW1sAT8fd6uvrkZ2djUuXLnHbCwsLMWfOHNy4cQONjY3IyMjAvXv3MGLEiC73vbM2nzV37lw0NDQgNTUVEydO5Mq7erzKuH79OjZs2IBt27aBz+dzY5Dy18aNG7u1f1WcO3cOtra24PF42Lx5MwAgMTEREokEYrEYhw8fxvjx4yGTydC/f3/s2bMHABAfHw+hUAhzc3PMmTMHVlZWEAqF8PT05M51cHAw9PX1YWlpybU3b948SCQS8Hg8lJaWIiQkBIsWLUJOTg54PB6cnJwAAMeOHYNMJsO6det67Vw8Kz4+HkQEHx+fdutERETA2dkZ27dvx4kTJ9qtR0SIiYnBoEGDIBAIYGxsjEmTJnG/PShzvoGOU0u76/m0SgcHh1b/QMrHbx0cHPpce20xNjaGl5cX4uLi1LcKjAq30W06dOgQGRsbEwCys7Oj4uJiqqysJBsbGwJABgYG9MMPP1BDQwOFhoaSra0t6enpkZmZGfn7+1NWVhZFRUWRSCQiAGRjY0M7d+7k9h8aGkomJiZkZGREU6dO5f78cHR0pLNnz5KnpycZGxuTrq4u9evXj1asWNHqz5r2fPvtt2RhYUEASCKR0OTJkzttU/6nkdwbb7xBYWFhrfbd1ePtzNWrV9sdjwZA0dHRSu9LHcMU+fn5BIA2bdrElcmHYE6ePEkVFRVUXFxMo0ePJolEQo2NjUREFBQURBKJhK5fv0719fWUlZVF7u7uJJVKuXM8ffp0srCwUGgvOjqaAFBJSQkREfn7+5Ojo6NCndTUVJJKpSoNV7WnK8MUDg4O5Orq2uY2R0dHunv3LhERnT9/nnR0dMjOzo6qq6uJqPUwxerVq0lfX5927txJ5eXldOXKFRo2bBiZmprSw4cPiUi587148WISCAR08OBBevz4MS1fvpx0dHTot99+U/WUKKipqSGpVErBwcFcWXp6OvH5fIqPj6fKykq6du0aDRo0iN57771utdXb7f3lL39pd5iCiCgsLIzLIlNWj48Zv8wmTJhAd+7c0XQ3uqSng/Gz46UJCQkEgG7fvk1ET4OxoaGhwr5+++03AkBff/01EXU9GKuTqt+H6upq4vF4NHHixDa3PxuMiYgWLVpEAGj+/PlEpBiMa2trycDAQCFFkojo//2//6fw20hn51uZVMuuai+tcuXKlQo3Cf3796f8/PxutdXb7XUWjL///nsCQD/88IPS++y1MeOXQVNTE/ffV65cgVAoZE+9KUH+MMqz5+95w4cPh1gsVnv6X28qLi4GEUEsFitVPyIiAq+++ioSEhJw7tw5hW1ZWVmorq7G8OHDFcrd3d2hr6/f7vAZoHi+eyrVsr20yhUrVmDr1q04efIkqqurcefOHXh6esLDw6Nbc6f0dnudkV/joqIitezvhQzGN27caDWW2tYrMDBQ5X2HhoYiOzsbt27dwqxZs7B27Vqt73NfIhAIUFJSouludFl9fT0AdPjjz7OEQiGSkpLA4/Hw8ccfo66ujttWXl4OAG3+SGtkZISqqiql2qipqQEArFy5UuGzdO/evS7PZbJ3716sX78e6enpsLOz48ofPHiAqKgofPbZZ3j77bchkUhgb2+Pbdu2obCwENHR0X2iPWWIRCIA/73m3fVCThTk4uLSY0uri8ViuLi4wNraGgkJCXB1dVXLfnuyz31FU1MTysvL0b9/f013pcvkX1BVHgrw8PDAwoULsXHjRqxdu5b7EdnIyAgA2gy6qpwnMzMzAEBsbCxCQkKU7ld7Nm3ahOPHj+PUqVOt/qHIzs5Gc3Mz+vXrp1Auk8lgYmKCrKwsrW9PWfJpF+TXvLteyDvjnhQREYHm5mbk5eUpZFAw3Zeeng4i4rJh9PT0OhzW0EbyxQoqKipUet/atWvh4uLCTTwFAIMHD4aBgQF+//13hbqXLl1CY2Mj3nzzTaX2ra5US1IirVL+D8TzWUNVVVUoKyvjUs60sT1Vya+xhYWFWvbHgjGjMS0tLXj8+DGePHmCK1euICQkBLa2tpg5cyYAwMnJCWVlZUhJSUFTUxNKSkpa5Y2amJigsLAQubm5qKqqQlNTE9LS0jSW2iYWi+Hg4ICCggKV3icfrng2p1YoFGLRokVITk7Grl27UFlZiatXr2Lu3LmwsrJCUFCQ0vvuLNUyMDAQFhYWHT6CrUxapb29PcaMGYNt27bhzJkzqKurQ35+PtfXTz75RGvbU5X8Gru5ualnhyr82se8YLqbTbFp0yaytLQkACQWi8nHx4cSEhJILBYTABo4cCDl5OTQ1q1bSSaTEQAaMGAA3bp1i4KCgojP55O1tTXp6emRTCajSZMmUU5ODrf/R48e0ZgxY0goFJK9vT198cUXtGTJEgJATk5OlJeXR5cvX6YBAwaQSCSiUaNG0cOHD+no0aMklUoVJnLqqq58H4KDg4nP5ys8lp+cnEyOjo4EgExNTbnsiectWbJEIbWtpaWFoqOjaeDAgcTn88nY2Jj8/Pzo5s2bRERKn++OUi2JiPz8/AgArV69ut3jUjatsrS0lEJCQsjJyYkEAgEZGBjQyJEj6aeffuL2pY3tERFduHCBRo4cSVZWVlw7lpaW5OnpSadPn1ao6+3tTdbW1gpP/nWGpbYxbdLk49BBQUFkYmKikbZV0ZXvQ3Z2Nunp6amUP65pzc3NNHr0aNqxYwdrTwmlpaUkFApp48aNKr2PpbYxWqlHZr7SAk5OTggPD0d4eHifWCS2ubkZKSkpqKqq6pVsnRehvTVr1mDo0KEIDg5Wy/6ADrIp2OKWL76LFy9269Fxpn1hYWGorq5GYGAgdu/eDUNDQ013qV3p6ek4dOgQ0tLSlM6Pfpnbi4mJQWZmJo4ePcpNr6sO7M6Y6XXLly9HUlISKioqYG9vj4MHD2q6Sz1i3bp1CA4OxjfffKPprnTonXfewe7duxXmAWHtte3w4cNoaGhAeno6jI2N1dC7/2r3zvjAgQNqbYjRPpr66ycyMhKRkZEaabu3jR07FmPHjtV0Nxg18fX1ha+vb4/sm90ZMwzDaAEWjBmGYbQAC8YMwzBagAVjhmEYLcCCMcMwjBbo8WD8/BLlNjY22LFjB7f99OnTsLa2Bo/Hg6WlJbZu3drTXVKqn5aWlpgxY4ZG+sIwzMunx6fQ9Pf3h7+/P5ycnFBaWtpqsue//vWvmDBhAnR0dLBly5Y2F//sDc/3U76GFsMwTG/Q6DBFS0sLPvnkE/D5fI0GYoZhGE3TWDBuaWnBxx9/DLFYjMTERBaIGYZ5qWkkGLe0tGDmzJkwNDTklnh/XkdLi2/YsAFisRhSqRTFxcVYtGgRrK2tcfPmTZw9exaurq4wNDSEUCiEm5sbjh8/zu339OnTeOuttyAWiyGTyeDm5obKykqVj6Gjdj799FNu7NnR0ZGbMHzWrFkQi8UwNDTEkSNHunyMDMO8eHo9GDc3N+PDDz/E3r17MW/evHbrLVu2DBs2bEBsbCwePHiAiRMn4oMPPsDvv/+OpUuXYuHChaiurkZkZCTs7e0xYsQIEBGKiooQEBCA3NxcFBYWwsDAANOnTwfwdC0wHx8fTJkyBWVlZcjOzoazszO3fIoqOmpn+/bt8Pf3h66uLs6ePYs33ngDAJCUlAQ/Pz/s2rULPj4+XT5GhmFeQCrMt9ktjo6OJJVK6f3336dhw4YRAHrttdeourq6VV1llhZva3nytkRGRhIAKi4upmvXrhEASk1N7bCfzy8hr4xn2yEiOnHiBAFQmOC8oqKCBg4cSE+ePFHrMXaVJucz7ivY/N6MOnU0n3GvLkhaW1sLLy8v/Pjjj5g8eTJ++uknfPrpp9izZ49CPXUuLS6f4q65uRkODg4wNzfHjBkzsGDBAsycOVNhpdnueLYdAHj77bfh7OyM77//HsuXLwePx8PevXsRGBgIXV1dXLt2rUeWT1fVwYMH2Xi9Etg5YnparwZjAwMDbm2qpKQk/Oc//8HevXvx1ltv4csvv+TqPbu0+MqVKxX2YWVl1WEbP//8M6Kjo5GVlYXKykqFBS1FIhFOnTqFZcuWYd26dQgPD8e0adOQlJSk8gqvHbUDPP3yzpkzBwsXLsTJkyfx7rvv4ocffsDu3bu7fYzqNGLECIVzzyi6cOEC4uLiuLF8hukO+eepTSrcRndLW3/+//HHHyQQCEhPT09hfalbt24RAIqNjW13f239CX/v3j3i8/kUEBBAOTk5VFdXRxs2bCAA9ODBA4X3FxcX07JlywgAhYeHd9hPudOnT1NMTIzS7ZSVlZFQKKSAgAC6ceMGjR8/vtvHqE5smKJzbJiCUSetXXZp2LBhiI2NxZMnTzBt2jQUFhYC6PrS4levXkVTUxM+//xzODg4QCgUKvx5WVhYiOvXrwMAzMzM8M0332DYsGFcWWf++OMPSCSSTtuRMzY2RkBAAFJSUrBx40bMnj2b26au5dMZhnkxaHxuirlz5+L9999HUVERpk6diqamJqWWFm+Lra0tAODEiROor69HdnY2Ll26xG0vLCzEnDlzcOPGDTQ2NiIjIwP37t3rdOmhpqYmFBUVIT09HRKJpNN2nj++hoYGpKamYuLEiVx5V4+RYZgXlAq30V3y7BLlAKh///60fPlyhTpVVVX06quvEgAyNzenHTt2dLi0eFRUFIlEIgJANjY2CqvwhoaGkomJCRkZGdHUqVNp8+bNBIAcHR3p7Nmz5OnpScbGxqSrq0v9+vWjFStW0JMnT1r1s71XcnJyp+3k5eUpHN8bb7xBYWFhrc5NV49RXdgwRefYMAWjTh0NU/CIFBNX9+/fj4CAAJbPqkbe3t7YvHkz7O3tNd0VBfJll9gSW+1j3wdGnTr4PB3Q+DDFi+jZzIorV65AKBRqXSBmGEa79Gpq28siNDQUc+fOBRFh1qxZ2Llzp6a7xDCMlmPBuAeIxWK4uLjA2toaCQkJcHV11XSXGIbRcmyYogdERESgubkZeXl5ChkUDMMw7WHBmGEYRguwYMwwDKMFWDBmGIbRAiwYMwzDaAEWjBmGYbRAu8FYvmwQe724r4MHD/bmZ41RsxMnTiAsLAyHDh2Cg4MDd13/8Y9/tKo7duxYSKVS6Orq4rXXXsPly5c10GPltLS0IDY2Fp6enm1uP3fuHEaOHAmxWAwrKyuEhoaioaEBAHDkyBFERUVx84r3Je3mGbP5W198sbGxmu4C00VfffUVMjIysHv3bkilUvj7+8PJyQnl5eXYtWsXAgMD4e3tzdX/5ZdfcOzYMWzZsgUpKSka7HnHsrOzMWvWLPzf//0fhgwZ0mp7VlYWxo4di8WLF+OXX37BlStX4OPjg5KSEnz//ffw8fHB3bt38c477yAlJQVGRkYaOIouUmEiC+YFo8mJgmpra8nDw0Pr962N34dvvvmGnJ2dW81z7ejoSLt37yYdHR2ytram8vJyhe1paWnk6+vbm11VSWZmJk2ePJl27dpFQ4cOpSFDhrSqExAQQPb29tTS0sKVRUdHE4/Hoz///JMrCw4OJg8PD2pqauqVvitLa+czZl5eO3bsQHFxcZ/bt6bdvn0bq1atwtdffw2hUNhqu6enJ0JCQnD//n0sXrxYAz3suiFDhuDQoUOYPn06BAJBq+1PnjzBzz//DC8vL4X5w8ePHw8iwuHDh7myNWvWIDMzs/1VNbQQC8aMSogIMTExGDRoEAQCAYyNjTFp0iRu3b7g4GDo6+vD0tKSe8+8efMgkUjA4/FQWlqKkJAQLFq0CDk5OeDxeHByckJ8fDyEQiHMzc0xZ84cWFlZQSgUwtPTk5sruqv7BoBjx45BJpNh3bp1vXi21C8+Ph5EBB8fn3brREREwNnZGdu3b8eJEyfardfZtUxMTIREIoFYLMbhw4cxfvx4yGQy9O/fX2HdyubmZqxevRq2trYQiUR4/fXXe2SY886dO6iurubmE5dzdHQE8HRSLjljY2N4eXkhLi6u78y4p8JtNPOC6cowxerVq0lfX5927txJ5eXldOXKFRo2bBiZmprSw4cPiYho+vTpZGFhofC+6OhoAkAlJSVEROTv70+Ojo4KdYKCgkgikdD169epvr6esrKyyN3dnaRSKTdHdFf3nZqaSlKpVGGJLWVo2/fBwcGBXF1d29zm6OhId+/eJSKi8+fPk46ODtnZ2XErsD8/TKHMtZQv/XXy5EmqqKig4uJiGj16NEkkEmpsbCQiosWLF5NAIKCDBw/S48ePafny5aSjo0O//fZbl4/zL3/5S6thitOnTxMAio6OblVfJBLRO++8o1AWFhZGACgjI6PL/VA3NkzBqEVdXR1iYmIwefJkzJgxA4aGhnBzc8OWLVtQWlqKrVu3drsNPT097k7N1dUViYmJqKqqQlJSUrf26+3tjcrKSqxatarbfdSUmpoa3L17l7sT7IiHhwe+/PJL5ObmYtmyZa22q3otPT09IZPJYGZmhsDAQNTU1CAvLw/19fVITEyEn58f/P39YWRkhJUrV4LP53f7mj1PnjGhq6vbahufz0ddXZ1C2cCBAwE8XY6tL2DBmFFaVlYWqqurMXz4cIVyd3d36Ovrt7v0VHcMHz4cYrGY+9P5ZVZcXAwiglgsVqp+REQEXn31VSQkJODcuXMK27pzLfX19QE8nbf75s2bqK2txeDBg7ntIpEIlpaWar9m8jHyJ0+etNrW2NjYaoV3+XkqKipSaz96Sp8Kxg0NDViwYAEsLS0hFovx7rvvwtzcHDweD1u2bOnx9qOiouDi4gKRSASJRAIXFxesKcPW+AAAIABJREFUWrUKlZWVXJ3w8HC4urpCJpNBIBDAyckJS5cuRXV1dY/3r6eVl5cDAAwMDFptMzIyQlVVVY+0KxAIUFJS0iP77kvq6+sBoM0ft9oiFAqRlJQEHo+Hjz/+WOHOUV3XsqamBgCwcuVKhRz2e/fuoba2Vql9KEv+W8Gz3zcAqK2tRX19PaysrBTK5cFZft60XZ8Kxt9++y2OHTuGGzduIC4uDnPmzMH58+d7rf2zZ89i9uzZyMvLQ1FREdauXYuoqChMmTKFq3Pq1CnMnz8fubm5KC0tRWRkJOLi4rgljvoyec5mW1/U8vJy9O/fX+1tNjU19di++xp5cFHlgQYPDw8sXLgQ2dnZWLt2LVeurmtpZmYG4GnOOhEpvC5cuKB0P5Vhb28PqVSKe/fuKZTfvn0bAPD6668rlDc2NgJAqztmbdWngnFKSgqGDx8OIyMjfPbZZwpBUN3q6upaPQGkr6+PefPmwczMDAYGBpg6dSomTZqEX3/9lVvR2cDAAEFBQTAxMYFUKsW0adPg5+eHY8eOIT8/v8f62xsGDx4MAwMD/P777wrlly5dQmNjI958800AT8d9n116qjvS09NBRNwK3urcd18j/yuwoqJCpfetXbsWLi4uyMjI4MqUvZadsbGxgVAoRGZmpkp96go9PT1MmDABZ86cQUtLC1eelpYGHo/XKsNEfp4sLCx6vG/q0KeCcUFBAfh8fq+01VauanJycqvcTmtrawDghiFSU1Nb/cBgamoKAGr/s623CYVCLFq0CMnJydi1axcqKytx9epVzJ07F1ZWVggKCgIAODk5oaysDCkpKWhqakJJSUmruxkTExMUFhYiNzcXVVVVXIBtaWnB48eP8eTJE1y5cgUhISGwtbXFzJkzu7XvtLS0Pp/aJhaL4eDggIKCApXeJx+uePZzqey1VGbfs2bNwp49e5CYmIjKyko0NzejoKCAu0EJDAyEhYWFWh7BXrVqFYqKivDVV1+hpqYGFy5cQHR0NGbOnIlXX31Voa78PLm5uXW73V6hQuqFxvzyyy/k6OhIALiXRCIhIqLs7GwCQN999x1Xv6Wlhb799ltycXEhfX19MjIyIl9fX4UndM6cOUODBg0imUxGAoGABg8eTMeOHSMiogULFpC+vj7X1vNpUs/y9vYmIyMjamhoaLeOr68viUSiDutoQldS21paWig6OpoGDhxIfD6fjI2Nyc/Pj27evMnVefToEY0ZM4aEQiHZ29vTF198QUuWLCEA5OTkRHl5eXT58mUaMGAAiUQiGjVqFD18+JCCgoKIz+eTtbU16enpkUwmo0mTJlFOTk6393306FGSSqUUERGh0vFq2/chODiY+Hw+1dbWcmXJycnc98PU1JTmz5/f5nuXLFmikNrW2bVMSEggsVhMAGjgwIGUk5NDW7duJZlMRgBowIABdOvWLWpoaKDQ0FCytbUlPT09MjMzI39/f8rKyiIiIj8/PwJAq1ev7vDYLly4QCNHjiQrKyvuu2dpaUmenp50+vRprt7p06fprbfeIoFAQFZWVrRkyRKqr69vtT9vb2+ytrZWeFpP0zpKbesTwVjOwsKCPvroI4WytoKxMvmTBw4coDVr1lBZWRk9evSIRowYQa+88gq3j7ZyVeUaGxupoKCANm3aRAKBgHbu3Nlun2tqakgqlVJwcHA3jrxnaPJx6LYEBQWRiYmJpruhQNu+D9nZ2aSnp9fhZ07bNDf///buPKrpM90D+PcHhIRAwiKrILK5FNeidTTq6Bzmeq56XCioVDuj09sWtS3uIlqVIuKCBa6O3o6tZTrqKCAcbKuotRxoPaXe9hRGDowtWhGplU1l33nuH5ZcI1tCQhLk+ZyTP/rml/f35G3y8PPN+3ufNpo5cyadOHFCb+esqKggiURChw4d0ts51TGo1hmru34yKCgIu3fvhq2tLezs7LBw4UJUVlaq9av9sGHD4ObmhoiICBw8eBDLli3r9tjo6Gi4uLggKipKZ+/xeTYQd9vSJx8fH0RGRiIyMnJArNBpa2tDWloaampqEBwcrLfzRkREYOLEiQgNDdXbObX13CXjvq6f7JiLVicZ3Lt3D2VlZfjnP/+JTz75BC+++GKXeyGkpqYiKSkJly9fhkwm68O7Yayz8PBwLFmyBMHBwRr/mKdvmZmZSElJQXp6utrro7UVGxuL3NxcXLx4UW+/MenCc5eM1V0/eeHCBcyePRsODg4Qi8XYunWr2ucQiURwcHDAnDlzcPbsWeTn5yM6OlrlmLNnz2L//v3IzMyEh4dH39/QILF9+3YkJCSgqqoKnp6evNdyL/bu3YvQ0FDs27fP0KH0yN/fH6dPn1bZT6Q/nT9/Hk1NTcjMzIStra1ezqkrz10yVmf9ZHFxMQICAuDs7Izr16+jqqoKBw4c6NP5fHx8YGpqivz8fGXbkSNHcOrUKWRkZGDo0KF9eyODTHR0NJqamkBEuHPnTr8uW3xezJkzB/v37zd0GEZl0aJFCA8P7/KWaWP33CVjddZP5uXloaWlBWvXroWXlxckEonKlnxdqaysxPLlyzu1FxYWoq2tDcOGDQMRISwsDHl5eUhLS+vy6pwxxrry3CVjddZPdmzBd/XqVTQ2NqKwsLDTXPKza1XNzc1x5coVZGRkoLq6Gi0tLcjJycHKlSthaWmJjRs3oqCgAAcPHsSHH34IkUjUqczRoUOHDDEkjLEBYEAk47t378LPzw+lpaU4ffo0Jk2ahJSUFMTGxmLGjBkAgM2bNyMwMBDAk5I00dHRiIyMhL29PWbNmgUPDw9kZmbC0tIS48aNQ1hYGI4ePQoXFxfs2LEDs2fPBgDMmDED9+7dw5o1a+Do6AhfX1/MmzcP9fX1mD59Ol5//XW4urpCJpNhyZIl8PDwwLfffouxY8cOnH1TGWNGR6BnMkhSUhKWLVvGiWUQ6NgvIzk52cCRGC/+PjBd6uHzlDwgrowZY+x5x8mYMcaMACdjxhgzApyMGWPMCHAyZowxI8DJmDHGjAAnY8YYMwKcjBljzAiYdfdEUlKSPuNgBtBRlob/X3evo6gmjxHThZ6KtHZ7Bx5jjLH+0dUdeJ2SMWMDCd+uzJ4TfDs0Y4wZA07GjDFmBDgZM8aYEeBkzBhjRoCTMWOMGQFOxowxZgQ4GTPGmBHgZMwYY0aAkzFjjBkBTsaMMWYEOBkzxpgR4GTMGGNGgJMxY4wZAU7GjDFmBDgZM8aYEeBkzBhjRoCTMWOMGQFOxowxZgQ4GTPGmBHgZMwYY0aAkzFjjBkBTsaMMWYEOBkzxpgR4GTMGGNGgJMxY4wZAU7GjDFmBDgZM8aYEeBkzBhjRoCTMWOMGQFOxowxZgQ4GTPGmBHgZMwYY0aAkzFjjBkBM0MHwJi6ysrKkJCQoNJ248YNAMCBAwdU2m1tbfHmm2/qLTbGtCUQERk6CMbU0draCicnJ1RVVcHMrPvriKamJrz55pv429/+psfoGNNKMk9TsAHDzMwMr7zyCkxMTNDU1NTtAwCWL19u4GgZ0wwnYzagvPLKK2hpaenxGAcHB8ycOVNPETGmG5yM2YCiUCgwdOjQbp83NzfHypUrYWLCH202sPAnlg0ogiDgT3/6E0QiUZfPNzc345VXXtFzVIxpj5MxG3B6mqrw9PSEn5+fniNiTHucjNmAM2HCBIwcObJTe8cUBWMDESdjNiB1NVXBUxRsIONkzAak5cuXo7W1VfnfgiBg/PjxXV4xMzYQcDJmA5KXlxdefPFF5aoJMzMznqJgAxonYzZg/fnPf1Ym49bWVixdutTAETHWd5yM2YC1dOlStLe3QxAEKBQKuLm5GTokxvqMkzEbsFxcXDBz5kwQEU9RsIGPtBAUFEQA+MEPfvBj0D8SExO1SadJWm+hOXXqVGzYsEHbbhhDXFwcAGj0eWpoaMAHH3wwaD6D2dnZiI+PR2JioqFDYU9ZtmyZ1n1onYzd3Nz4hxOmE8nJyQCg8efJ399/UM0Xx8fH83fOyOgiGfOcMRvwBlMiZs8vTsaMMWYEOBkzxpgR4GTMGGNGgJMxY4wZAU7GAF5//XXIZDIIgoDc3FxDh6O2yMhI+Pr6Qi6XQywWw8fHB1u3bkVtba2hQ2OMaYiTMYCPPvoIH374oaHD0FhGRgbefvttFBUVoaKiAtHR0YiPj8eSJUsMHRpjTEOcjAcwKysrhISEwM7ODjKZDEuXLkVAQAAuXbqEe/fuGTo8xpgGtL7p43khCIKhQ9DY559/3qnN3t4eAFBfX6/vcBhjWhiUV8ZEhJiYGIwaNQpisRjW1tbYsmWLyjFtbW3YtWsX3N3dYWFhgfHjxytvQT127BgsLS0hlUpx/vx5zJ07F3K5HG5ubjhz5oyyj6ysLEyZMgVSqRRyuRzjxo1DdXV1r/1r45dffoGFhQU8PT217osxpj+DMhnv3LkTYWFhCAkJQWlpKR48eIBt27apHLNt2zYcPHgQcXFx+PXXX7FgwQIsX74c33//PdauXYsNGzagoaEBMpkMiYmJuH37Nry8vPDGG2+gpaUFdXV1WLhwIYKCgvDw4UMUFhZi5MiRaG5u7rX/vqqvr0dGRgbeeOMNmJubazVGjDH9GnTJuKGhAXFxcfjjH/+IjRs3wsbGBhYWFrCzs1Me09jYiGPHjiEgIACBgYGwsbHBu+++C5FIhISEBJX+FAoF5HI5HBwcEBwcjLq6OhQXF6OoqAjV1dUYM2YMJBIJnJyckJKSAnt7e43610R0dDRcXFwQFRXV5z4YY4Yx6JLxrVu3UF9fD39//26P+fHHH1FfX4+xY8cq2ywsLODs7IybN292+7qOq9GWlhZ4eXnB0dERr776KiIiIlBUVKR1/z1JTU1FUlISLl++DJlM1qc+ngcXL16EtbU1PvvsM0OHYpSuXr2K8PBwpKSkwMvLC4IgQBAE/OlPf+p07Jw5cyCTyWBqaooxY8bghx9+MEDE6mlvb0dcXBwUCkWXz1+7dg3Tp0+HVCqFi4sLwsLC0NTUBAD49NNPceDAAbS1tekz5E4GXTIuKSkBADg4OHR7TF1dHQDg3XffVX5YBUHA3bt31f5hzMLCAhkZGZgxYwb27t0LLy8vBAcHo6GhQSf9P+3s2bPYv38/MjMz4eHhofHrnydEZOgQjNbu3btx+PBhbN++HYGBgfj555/h7e2NIUOG4NSpU7hw4YLK8VeuXEFycjIWLFiA/Px8+Pn5GSjynhUWFuL3v/89Nm7c2OX3Jz8/H3PmzIG/vz/Ky8uRmpqKjz/+GGvWrAEALFy4EBKJBP7+/nj8+LG+w1cadMlYIpEAgPKvYlc6EnVcXByISOWRnZ2t9rnGjBmDzz77DPfv30dYWBgSExNx6NAhnfUPAEeOHMGpU6eQkZGBoUOHavTa59H8+fNRVVWFBQsWGOT8DQ0N3V6dGdL+/ftx9uxZJCUldfqX0+HDh2FiYoKQkBBUVVUZKMK++de//oVt27ZhzZo1mDhxYpfH7NmzB87OznjvvfdgaWmJadOmISwsDH//+9+V/xJdt24dJkyYgHnz5qlUHdenQZeMx44dCxMTE2RlZXV7zLBhwyCRSLS6G+/+/fsoKCgA8CS579u3D35+figoKNBJ/0SEsLAw5OXlIS0tDVZWVn3ui+nOiRMnUFZWZugwVNy6dQs7d+7Ee++9p7wYeZpCocD69evxyy+/YPPmzQaIsO8mTJiAlJQUrFixAmKxuNPzra2tuHDhAmbNmqWyfHXu3LkgIpw/f17ZFhERgdzcXMTHx+sl9mcNumTs4OCAwMBAnDt3DidOnEB1dTVu3LiB48ePK4+RSCT4y1/+gjNnzuDYsWOorq5GW1sbSkpK8Ouvv6p1nvv372P16tW4efMmmpubkZOTg7t372Lq1Kk66b+goAAHDx7Ehx9+CJFIpDLdIQgCDh061KfxGciuXbsGd3d3CIKAv/71rwDUW4Z4+PBhSCQSODo6YvXq1XBxcYFEIoFCocD169cBAKGhoTA3N4ezs7PyfG+99RYsLS0hCAIqKiqwfv16bNq0Cbdv34YgCPDx8QEAXLp0CXK5HHv37tXziED5/ogICxcu7PaYqKgojBw5Eh999BGuXr3a7XFEhNjYWLzwwgsQi8WwtbXF4sWLlVeY6i777K+lnc/6+eefUVtbC3d3d5V2b29vAMCNGzeUbba2tpg1axbi4+MNM92lTdGmoKAgCgoK0qYLg6ipqaHXX3+dhgwZQlZWVjRjxgzatWsXASA3Nzf617/+RU1NTRQWFkbu7u5kZmZGDg4OFBgYSPn5+XT06FGSSqUEgEaMGEG3b9+m48ePk1wuJwA0fPhw+uKLL0ihUJCtrS2ZmprS0KFDaceOHdTa2kpE1GP/6sjLy+uxHldMTEx/DmG/0MXn6d69ewSAjhw5omzbsWMHAaAvv/ySqqqqqKysjGbOnEmWlpbU3NxMREQhISFkaWlJBQUF1NjYSPn5+fTSSy+RTCaj4uJiIiJasWIFOTk5qZwvJiaGAFB5eTkREQUGBpK3t7fKMZ9//jnJZDKKjIzU6r0RESUmJpKmX1svLy/y9fXt8jlvb2+6c+cOERF98803ZGJiQh4eHlRbW0tEROnp6bRo0SLl8bt27SJzc3M6efIkPX78mG7cuEF+fn5kb29PDx48ICL1xnvz5s0kFovp3Llz9OjRI9q+fTuZmJjQd999p+mQKP3ud7+jCRMmqLRlZWV1+32wsLAgf39/lbbw8HACQDk5ORqdGzqogTcokzEzTv2djBsaGpRtR48eJQB069YtInqSjK2trVX6+u677wgAvffee0TU92SsS5om49raWhIEgRYsWNDl808nYyKiTZs2EQB6++23iUg1GdfX15OVlRUFBwer9PG///u/BED5x6a38W5oaCCpVKrST319PYnFYlq7dq3a7+1ZXSXjK1euEACKjY3tdLxcLieFQqHS9vHHHxMA+sc//qHRuXWRjAfdNAVjgOoyxO5MnjwZUqm0z8sNjUFZWRmICFKpVK3jo6KiMGrUKBw9ehTXrl1TeS4/Px+1tbWYPHmySvtLL70Ec3Nz5ZROV54e7/5Y2tmdjjnyrn6Ua25uhoWFhUpbxziVlpbqNA51cDI2Mjdv3uw0/9vVIzg42NChDgpisRjl5eWGDqPPGhsbAaDLH7e6IpFIkJCQAEEQ8Nprr6GhoUH5XMeyr65+LLaxsUFNTY1a59D10s6edMzxd2xD0KG+vh6NjY1wcXFRae9Izh3jpk+cjI3M6NGjOy136+px9uxZQ4f63GtpacHjx48HdMHTjuSiyQ0N06ZNw8aNG1FYWIg9e/Yo221sbACgy6SryTjpcmlnbzw9PSGTyXD37l2V9lu3bgEAxo8fr9LesV3Bs1fM+sDJmLFuZGZmgogwdepUAICZmVmP0xrGyNHREYIgaLx+eM+ePRg9ejRycnKUbWPHjoWVlVWn/VOuX7+O5uZmTJo0Sa2+dbG0U11mZmaYN28evvrqK7S3tyvb09PTIQhCpxUmHePk5OTU77E9i5MxY79pb2/Ho0eP0Nraihs3bmD9+vVwd3fHqlWrAAA+Pj54+PAh0tLS0NLSgvLy8k5XXHZ2drh//z6KiopQU1ODlpYWpKenG2xpm1QqhZeXl/LOU3V1TFeYmpqqtG3atAmpqak4deoUqqurkZeXhzVr1sDFxQUhISFq993b0s7g4GA4OTnp5BbsnTt3orS0FLt370ZdXR2ys7MRExODVatWYdSoUSrHdozTuHHjtD6vxrT5+Y9XUzBd0vbzdOTIEXJ2diYAJJVKaeHChWotQ/zpp58oJCSERCIRubq6kpmZGcnlclq8eDHdvn1b2X9lZSX94Q9/IIlEQp6envTOO+/Qli1bCAD5+PhQcXEx/fDDDzR8+HCysLCgGTNm0IMHD+jixYskk8koKipK6zHqy9K20NBQEolEVF9fr2xLTU0lb29vAkD29vbK1RPP2rJli8rStvb2doqJiaERI0aQSCQiW1tbCggIoB9//JGISO3x7m1pZ0BAAAGgXbt29fjesrOzafr06eTi4qJc1uns7EwKhYKysrKUx2VlZdGUKVNILBaTi4sLbdmyhRobGzv1N3/+fHJ1daX29nb1B5h4aRt7zhjy8xQSEkJ2dnYGObcm+pKMCwsLyczMjE6ePNlPUeleW1sbzZw5k06cOKG3c1ZUVJBEIqFDhw5p/FpdJGOepmDsN4betau/+Pj4IDIyEpGRkQOiWG1bWxvS0tJQU1Oj11VDERERmDhxIkJDQ/V2zqdpXXappKQESUlJuoiFDXIlJSUDeuWCMQsPD0dtbS2Cg4Nx+vRpWFtbGzqkbmVmZiIlJQXp6elqr4/WVmxsLHJzc3Hx4kWIRCK9nLMTba6rg4KCerwllx/80PRhiGmK8PBwMjc3JwDk4eFBycnJeo9BXX2Zpnja5cuXKSwsTIcRDXxpaWkUHR2t3KqgLwDtpymE3zrqk46S8MnJyX3t4rkhCAISExOxdOlSQ4cyYPHnqXdJSUlYtmwZ79tsZHTw/U/mOWPGGDMCnIwZY8wIcDJmjDEjwMmYMcaMACdjxhgzApyMGWPMCOg1GaekpMDLy6vT3rzm5uZwdHTE7NmzERMTg0ePHukzrAGrqakJ69atg7OzM6RSKS5dumTokBhjfaTXZBwYGIiff/4Z3t7esLa2BhGhvb0dZWVlSEpKgqenJ8LCwjBmzJhO2/Sxzt5//31cunQJN2/eRHx8/IC41ZUx1jWDT1MIggAbGxvMnj0bCQkJSEpKQmlpKebPn6/xHqyDTVpaGiZPngwbGxu8+eabCAoKQkNDAxQKhaFDY4xpyODJ+FlBQUFYtWoVysrK8MEHHxg6HKNWUlLS6T76EydOoKyszEARMcb6yuiSMQDlZt7p6ekAnuzitGvXLri7u8PCwgLjx49HYmIiAODYsWOwtLSEVCrF+fPnMXfuXMjlcri5ueHMmTPKPrOysjBlyhRIpVLI5XKMGzdOWRerp/77S0/xEBFiY2PxwgsvQCwWw9bWFosXL1YWa/ziiy/g4+ODX3/9FZ988gkEQYCVlRXWr1+PTZs24fbt2xAEAT4+PoiPj4elpSVMTEwwadIkODk5QSQSwdLSEn5+fpg5c6ay8oKNjQ22bt2qEufXX38NX19fWFtbQyKRYNy4cbh8+TIA4O9//zusrKwgCAJsbW2RlpaG77//HsOHD4epqSmWL1/er2PI2HNFm50t+rr/rLe3d6ey6E+rrq4mADRs2DAiItq8eTOJxWI6d+4cPXr0iLZv304mJib03XffEdH/lwb/8ssvqaqqisrKymjmzJlkaWlJzc3NVFtbS3K5nA4cOEANDQ304MEDevnll5Xl1XvrXx3QYKOQ3uLZtWsXmZub08mTJ+nx48d048YN8vPzI3t7e3rw4IGyHycnJ1q5cqVK312Vit+9ezcBoOvXr1NdXR1VVFTQf/7nfxIAunDhApWXl1NdXR2FhoYSAMrNzVW+Njk5mSIiIujhw4dUWVlJU6dOpSFDhiifLygoIKlUqhJHeHg4ffTRR+oOnRLvj907bTcKYv1Dk+9/N5K03kKzP8hkMgiCgJqaGjQ2NuLYsWMICAhAYGAggCdVZd9//30kJCSolA1XKBTK0tzBwcH4+uuvUVxcjMbGRlRXV2PMmDGQSCSQSCRISUkBAI3615WioqJu42loaEBsbCxefvllvPrqqwCelID54IMPMGXKFBw/fhw7d+7s03l9fX0hlUohlUrxyiuv4NKlS3B3d4e9vT0A4NVXX8Xhw4dx8+ZNTJgwAcCTaaOgoCBlHwsXLsT27dtRXl4OBwcHvPDCC4iLi0NISAj+4z/+AyYmJmhqasJ//dd/9SnGb7/9VrlhEOusoywQj9HzxyinKerq6kBEkMvl+PHHH1FfX4+xY8cqn7ewsICzs7Pyn+1dMTc3B/Ckwq+XlxccHR3x6quvIiIiAkVFRcrj+tq/NnqKJz8/H7W1tZ3+CLz00kswNzfH9evXdRJDx/i0trYq2zrmn3squtlxzNMbsXf8eLh69WokJSXh4MGDOomRscHEKK+Mf/rpJwBPytbX1dUBeHK1+u6776oc5+LiolZ/FhYWyMjIwLZt27B3715ERkZi6dKlSEhI0En/muopnsePHwMArKysOr3OxsamyzLp/enChQuIiYlBfn4+qquru03Ue/fuxblz57T+8XDq1Km8hWYPOrbQ5DEyLoIgaN2HUV4Zd9y8MHfuXDg4OAAA4uLiQEQqj+zsbLX7HDNmDD777DPcv38fYWFhSExMxKFDh3TWv6a6i8fGxgYAuky6jx8/1msljOLiYgQEBMDZ2RnXr19HVVUVDhw40Om4lpYWrFu3DrGxscjOzkZUVJTeYmTseWF0yfjBgweIi4uDm5sbXnvtNeUv/bm5uX3u8/79+ygoKAAAODg4YN++ffDz80NBQYFO+tdlPGPHjoWVlVWnm16uX7+O5uZmTJo0SW9x5uXloaWlBWvXroWXlxckEkmXVwDvvPMO3njjDWzYsAEbN27Enj17+vUPGWPPI4MlYyJCbW0t2tvbQUQoLy9HYmIipk+fDlNTU6SlpUEul0MikeAvf/kLzpw5g2PHjqG6uhptbW0oKSnBr7/+qta57t+/j9WrV+PmzZtobm5GTk4O7t69i6lTp+qkf031Fs+mTZuQmpqKU6dOobq6Gnl5eVizZg1cXFwQEhLSY992dna4f/8+ioqKUFNT0+P8b2/c3d0BAFevXkVjYyMKCws7zVkfPXoUrq6uePnllwEA0dHR8PX1xYoVK5RL9RhjatBmLYamS5E+/fRTGj9+PEmlUjI3NycTExMCQIIgkI2NDU2ZMoUiIyOpsrJS5XVNTU0UFhZG7u7uZGZmRg4ODhQYGEj5+fl09OhRkkqlBIBGjBhBt2/fpuPHj5NcLicANHz4cPriiy9IoVCQra1i1hnBAAANUUlEQVQtmZqa0tChQ2nHjh3Kmlc99a8uaLC0paioqMd42tvbKSYmhkaMGEEikYhsbW0pICCAfvzxR+XrX3zxRQJAZmZm5OfnR+fOnSMioh9++IGGDx9OFhYWNGPGDAoPD1eOj4eHB3399de0f/9+sra2JgDk5OREp0+fprNnz5KTkxMBIFtbWzpz5gwREYWFhZGdnR3Z2NjQkiVL6K9//SsBIG9vb5o4cSIJgkB2dnb0zTffEBHRhg0blP9fra2t6fvvv1d7DHlpW+94aZtx0uT73w2ugacrXANPe/x56h3XwDNOXAOPMcaeE5yMGWPMCHAyZowxI8DJmDHGjAAnY8YYMwKcjBljzAhwMmaMMSPAyZgxxoyA1ru28f6z/y8uLo5vWNDCt99+i6lTpxo6jOfa1atX8eWXX2Ly5MnYsmUL7ty5A+DJXtYnT55UOXbOnDnIzs5GfX09Ro8ejZMnT8LPz88QYfeqvb0d//3f/43k5GR88803nZ6/du0awsLCkJOTA2tra/z5z39GZGQkxGIxPv30U/z73//G5s2bYWpqaoDon+ArYx0JCgrS645qjGlq9+7dOHz4MLZv365SqX3IkCE4deoULly4oHL8lStXkJycjAULFiA/P99oE3FhYSF+//vfY+PGjaivr+/0fH5+PubMmQN/f3+Ul5cjNTUVH3/8MdasWQPgScEEiUQCf39/5Ra2BqHNzdS8lwDTJUN+nurr62natGlG33df96bYt28fjRw5khoaGlTavb296fTp02RiYkKurq70+PFjlefT09Np0aJFWsXcn3Jzc+nll1+mU6dO0cSJE2nChAmdjlm2bBl5enpSe3u7si0mJoYEQaB///vfyrbQ0FCaNm0atbS0aBwHdLA3BV8ZM4b+rapt6Irdt27dws6dO/Hee+8py5I9TaFQYP369fjll1+wefNmA0TYdxMmTEBKSgpWrFgBsVjc6fnW1lZcuHABs2bNUtn+de7cuSAinD9/XtkWERGB3NxcxMfH6yX2Z3EyZgMa9VJJOzQ0FObm5nB2dla+5q233oKlpSUEQUBFRUWXVbUPHz4MiUQCR0dHrF69Gi4uLpBIJFAoFMptRPvaN/CkgIJcLsfevXv7fYwOHz4MIsLChQu7PSYqKgojR47ERx99hKtXr3Z7XG/jrW61dn1VZP/5559RW1ur3A62g7e3NwDgxo0byjZbW1vMmjUL8fHxhtmISZvrap6mYLrUl8+TOpW0V6xYQU5OTiqvi4mJIQDKitxdVdUOCQkhS0tLKigooMbGRsrPz6eXXnqJZDIZFRcXa9X3559/TjKZjCIjIzV6v32ZpvDy8iJfX98un/P29qY7d+4QEdE333xDJiYm5OHhQbW1tUTUeZpCnfHurVo7kW4qsj/rd7/7XadpiqysLAJAMTExnY63sLAgf39/lbbw8HACQDk5ORqdGzxNwQazZytpW1tbKytpV1RU4Pjx41qfw8zMTHkV6Ovri2PHjqGmpgYJCQla9Tt//nxUV1f3udK3uurq6nDnzh3llWBPpk2bhg0bNqCoqAjbtm3r9Lym461QKCCXy+Hg4IDg4GDU1dUpq7U/XZHdxsYG7777LkQikdbj+qympiYA6HKVhEgkQkNDg0rbiBEjADypcqNvnIzZgKWvStpPmzx5MqRSab9VDte1srIyEBGkUqlax0dFRWHUqFE4evQorl27pvKcNuP9dLV2fVZk75gjf7oKeofm5mZYWFiotHWMU2lpqU7jUAcn4980NTVh3bp1cHZ2hlQqxR//+Ec4OjpCEAR88MEH/X7+AwcOYPTo0bCwsIClpSVGjx6NnTt3qpQuioyMhK+vL+RyOcRiMXx8fLB161bU1tb2e3zGyFCVtMViMcrLy/ulb11rbGwEgC5/3OqKRCJBQkICBEHAa6+9pnLlqKvxfroiuyAIysfdu3e7XJqmjY75/GdLgNXX16OxsbFTBfiO5NwxbvrEyfg377//Pi5duoSbN28iPj4eq1ev7nLxeH/5+uuv8cYbb6C4uBilpaXYs2cPDhw4gKCgIOUxGRkZePvtt1FUVISKigpER0cjPj5+0N50Y4hK2i0tLXqv0q2NjuTS1tam9mumTZuGjRs3orCwEHv27FG262q89VmR3dPTEzKZDHfv3lVpv3XrFgBg/PjxKu3Nzc0A0OmKWR84Gf8mLS0NkydPho2NDd58802VJKhrDQ0NUCgUKm3m5uZ466234ODgACsrKyxZsgSLFy/GF198oSyMamVlhZCQENjZ2UEmk2Hp0qUICAjApUuXcO/evX6L11ipW0nbzMxMq8KsT8vMzAQRKe8U1GXf/aHjX3dVVVUavW7Pnj0YPXo0cnJylG26qlyuz4rsZmZmmDdvHr766iu0t7cr29PT0yEIQqcVJh3j5OTk1O+xPYuT8W9KSkogEon0cq6u1p2mpqZ2WgPq6uoKAMppiM8//7zTDxH29vYAoPN/3g0E6lbS9vHxwcOHD5GWloaWlhaUl5d3ulLqrqp2e3s7Hj16hNbWVty4cQPr16+Hu7s7Vq1apVXf6enpelnaJpVK4eXlhZKSEo1e1zFd8fTnTdvK5U/301tF9uDgYDg5OeGHH37QKO6u7Ny5E6Wlpdi9ezfq6uqQnZ2NmJgYrFq1CqNGjVI5tmOcxo0bp/V5NabNWoznYWnblStXyNvbmwAoH5aWlkREVFhYSADof/7nf5THt7e30/vvv0+jR48mc3NzsrGxoUWLFqncyfPVV1/RCy+8QHK5nMRiMY0dO5YuXbpERETr1q0jc3Nz5bmeXfL0tPnz55ONjQ01NTV1e8yiRYvIwsKix2MGir58nnqrpE1EVFlZSX/4wx9IIpGQp6cnvfPOO7RlyxYCQD4+PlRcXNypqvaDBw8oJCSERCIRubq6kpmZGcnlclq8eDHdvn1b674vXrxIMpmMoqKiNHq/fVnaFhoaSiKRiOrr65Vtqampys+9vb09vf32212+dsuWLSpL23obb3Wqtf/000+9VmQPCAggALRr164e31t2djZNnz6dXFxclN8pZ2dnUigUlJWVpTwuKyuLpkyZQmKxmFxcXGjLli3U2NjYqb/58+eTq6uryt166oAOlrYN+mTcwcnJiVauXKnS1lUyVmedZXJyMkVERNDDhw+psrKSpk6dSkOGDFH20dW60w7Nzc1UUlJCR44cIbFYTCdPnuw25rq6OpLJZBQaGqrFOzcexvZ5CgkJITs7O0OHoaIvybiwsJDMzMx6/CwZm7a2Npo5cyadOHFCb+esqKggiURChw4d0vi1ukjGPE2hAXXXWQYFBWH37t2wtbWFnZ0dFi5ciMrKSrV+gR82bBjc3NwQERGBgwcPYtmyZd0eGx0dDRcXF0RFRensPTJVmvzwZax8fHwQGRmJyMjIAbHypq2tDWlpaaipqUFwcLDezhsREYGJEyciNDRUb+d8GidjDfR1nWXHXLQ6X+x79+6hrKwM//znP/HJJ5/gxRdf7HJfg9TUVCQlJeHy5cuQyWR9eDdsMAkPD8eSJUsQHBys8Y95+paZmYmUlBSkp6ervT5aW7GxscjNzcXFixf19tvRszgZa0DddZYXLlzA7Nmz4eDgALFYjK1bt6p9DpFIBAcHB8yZMwdnz55Ffn4+oqOjVY45e/Ys9u/fj8zMTHh4ePT9DbFubd++HQkJCaiqqoKnpyfOnTtn6JC0tnfvXoSGhmLfvn2GDqVH/v7+OH36tMqeH/3p/PnzaGpqQmZmJmxtbfVyzq5wMtaAOussi4uLERAQAGdnZ1y/fh1VVVU4cOBAn87n4+MDU1NT5OfnK9uOHDmCU6dOISMjA0OHDu3bG2G9io6ORlNTE4gId+7c6deljvo0Z84c7N+/39BhGJVFixYhPDzcoBvLA5yMNaLOOsu8vDy0tLRg7dq18PLygkQiUdm6ryuVlZVYvnx5p/bCwkK0tbVh2LBhICKEhYUhLy8PaWlpXV6dM8YGLk7GGlBnnWXHVn1Xr15FY2MjCgsLO80lP7vu1NzcHFeuXEFGRgaqq6vR0tKCnJwcrFy5EpaWlti4cSMKCgpw8OBBfPjhhxCJRCq3kQqCgEOHDhliSBhjOjLok/Hdu3fh5+eH0tJSnD59GpMmTUJKSgpiY2MxY8YMAMDmzZsRGBgI4EnpmujoaERGRsLe3h6zZs2Ch4cHMjMzYWlpiXHjxiEsLAxHjx6Fi4sLduzYgdmzZwMAZsyYgXv37mHNmjVwdHSEr68v5s2bh/r6ekyfPh2vv/46XF1dIZPJsGTJEnh4eODbb7/F2LFjDbO/KmNMbwTS4lvesScCF+FkusCfp94lJSVh2bJl/MfZyAiCgMTERCxdurSvXSQP+itjxhgzBpyMGWPMCHAyZowxI8DJmDHGjAAnY8YYMwKcjBljzAhwMmaMMSPAyZgxxowAJ2PGGDMCZtp2cO7cuV43wmFME/x56h2P0fNHq9uhs7OzB2VVYsYYe5ZCoYCbm1tfX56sVTJmjDGmE7w3BWOMGQNOxowxZgQ4GTPGmBEwA8CbxzLGmGF9+3/R7KVB8ejX/wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aImuctrc27GS",
        "outputId": "f8901c17-b928-4cb3-affc-0252ac2bafb8"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " keras_layer_2 (KerasLayer)  (None, 10)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                110       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 110\n",
            "Trainable params: 110\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P50hkUT33AaB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}