Bearing_fault_detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "6H9sOUkmY1lw",
      "metadata": {
        "id": "6H9sOUkmY1lw"
      },
      "source": [
        "\n",
        "\n",
        "##Testing IFD\n",
        "###Dataset : Electrical-fault-detection-and-classification\n",
        "####Data_set_link : \"https://github.com/KingArthur000/Electrical-Fault-detection-and-classification \"\n",
        "####My GitHUb_link = \"https://github.com/Tusharkn12/Intelligent-Fault-Detection-IFD \"\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ifd import IFD\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import sklearn as sk\n",
        "from ifd import DataGenerator\n",
        "from xgboost import XGBClassifier as XGB\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, Conv2DTranspose, BatchNormalization, Dropout, MaxPooling2D, Multiply, Input, LSTM, GRU, ZeroPadding2D, Reshape\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.activations import relu\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, f1_score\n",
        "from tensorflow.keras.utils import plot_model"
      ],
      "metadata": {
        "id": "BKnuTI-svZ9R"
      },
      "id": "BKnuTI-svZ9R",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extended_tanh(x):\n",
        "  \"Extending tanh to give output between [-3, 3]\"\n",
        "  simple_tanh = (tf.exp(x) - tf.exp(-x))/(tf.exp(x) + tf.exp(-x))\n",
        "  return 3*simple_tanh"
      ],
      "metadata": {
        "id": "FDwL6JsRjh6H"
      },
      "id": "FDwL6JsRjh6H",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_set = pd.read_csv(\"/content/60.416.csv\")"
      ],
      "metadata": {
        "id": "B3yL2fgEvpd7"
      },
      "id": "B3yL2fgEvpd7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hNqK2hkavv1G",
        "outputId": "9d6d92fd-b7c3-4e64-b37e-83cac0b4e3c1"
      },
      "id": "hNqK2hkavv1G",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       cl1      cl2       cl3       cl4       cl5       cl6      cl7       cl8\n",
              "0 -0.72819  1.11200  0.109290  0.379410  0.040286  0.015388  0.52117 -0.150310\n",
              "1 -0.71638  1.06700 -0.085559  0.316530  0.040382  0.019720  0.43941  0.033369\n",
              "2 -0.71757  1.32750 -0.064726  0.340750  0.041731  0.016973  0.39931 -0.074302\n",
              "3 -0.72706  0.95937 -0.060435  0.155150  0.046381  0.021985  0.39251 -0.057436\n",
              "4 -0.71088  0.75190 -0.675710 -0.017809  0.045642  0.018873  0.29961  0.021419"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-63145411-e4b3-45f9-9da4-e36154e141bd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cl1</th>\n",
              "      <th>cl2</th>\n",
              "      <th>cl3</th>\n",
              "      <th>cl4</th>\n",
              "      <th>cl5</th>\n",
              "      <th>cl6</th>\n",
              "      <th>cl7</th>\n",
              "      <th>cl8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.72819</td>\n",
              "      <td>1.11200</td>\n",
              "      <td>0.109290</td>\n",
              "      <td>0.379410</td>\n",
              "      <td>0.040286</td>\n",
              "      <td>0.015388</td>\n",
              "      <td>0.52117</td>\n",
              "      <td>-0.150310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.71638</td>\n",
              "      <td>1.06700</td>\n",
              "      <td>-0.085559</td>\n",
              "      <td>0.316530</td>\n",
              "      <td>0.040382</td>\n",
              "      <td>0.019720</td>\n",
              "      <td>0.43941</td>\n",
              "      <td>0.033369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.71757</td>\n",
              "      <td>1.32750</td>\n",
              "      <td>-0.064726</td>\n",
              "      <td>0.340750</td>\n",
              "      <td>0.041731</td>\n",
              "      <td>0.016973</td>\n",
              "      <td>0.39931</td>\n",
              "      <td>-0.074302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.72706</td>\n",
              "      <td>0.95937</td>\n",
              "      <td>-0.060435</td>\n",
              "      <td>0.155150</td>\n",
              "      <td>0.046381</td>\n",
              "      <td>0.021985</td>\n",
              "      <td>0.39251</td>\n",
              "      <td>-0.057436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.71088</td>\n",
              "      <td>0.75190</td>\n",
              "      <td>-0.675710</td>\n",
              "      <td>-0.017809</td>\n",
              "      <td>0.045642</td>\n",
              "      <td>0.018873</td>\n",
              "      <td>0.29961</td>\n",
              "      <td>0.021419</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-63145411-e4b3-45f9-9da4-e36154e141bd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-63145411-e4b3-45f9-9da4-e36154e141bd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-63145411-e4b3-45f9-9da4-e36154e141bd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d62a83a8-2845-4f26-9525-cf3477ae0ed2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d62a83a8-2845-4f26-9525-cf3477ae0ed2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d62a83a8-2845-4f26-9525-cf3477ae0ed2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_set"
            }
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_set.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lz6sPyzl7QWB",
        "outputId": "df910e57-32f1-484a-d634-fa47e4fc2fad"
      },
      "id": "Lz6sPyzl7QWB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 250000 entries, 0 to 249999\n",
            "Data columns (total 8 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   cl1     250000 non-null  float64\n",
            " 1   cl2     250000 non-null  float64\n",
            " 2   cl3     250000 non-null  float64\n",
            " 3   cl4     250000 non-null  float64\n",
            " 4   cl5     250000 non-null  float64\n",
            " 5   cl6     250000 non-null  float64\n",
            " 6   cl7     250000 non-null  float64\n",
            " 7   cl8     250000 non-null  float64\n",
            "dtypes: float64(8)\n",
            "memory usage: 15.3 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_set.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Zzr-TYZnEd_p",
        "outputId": "58c15753-b1b1-4abf-b74e-eb1fff278a95"
      },
      "id": "Zzr-TYZnEd_p",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 cl1            cl2            cl3            cl4  \\\n",
              "count  250000.000000  250000.000000  250000.000000  250000.000000   \n",
              "mean       -0.001323       0.002044      -0.000246       0.000569   \n",
              "std         1.781814       0.594890       0.498967       0.282529   \n",
              "min        -1.453300      -3.040300      -2.458100      -1.396800   \n",
              "25%        -0.747950      -0.391120      -0.345143      -0.187480   \n",
              "50%        -0.722550       0.019726       0.000520      -0.000149   \n",
              "75%        -0.616177       0.414153       0.338383       0.186920   \n",
              "max         4.906700       2.280100       2.451700       1.460000   \n",
              "\n",
              "                 cl5            cl6            cl7            cl8  \n",
              "count  250000.000000  250000.000000  250000.000000  250000.000000  \n",
              "mean        0.002452       0.002041       0.001453       0.010941  \n",
              "std         0.225419       0.044311       0.823892       0.214199  \n",
              "min        -0.931720      -0.170640      -2.865800      -0.468260  \n",
              "25%        -0.131150      -0.027160      -0.606295      -0.170070  \n",
              "50%         0.029387       0.004697       0.116385      -0.008590  \n",
              "75%         0.161422       0.033646       0.619805       0.170440  \n",
              "max         0.531490       0.137420       2.517500       0.847180  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12b50b3a-6503-4276-804c-513a2ce65798\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cl1</th>\n",
              "      <th>cl2</th>\n",
              "      <th>cl3</th>\n",
              "      <th>cl4</th>\n",
              "      <th>cl5</th>\n",
              "      <th>cl6</th>\n",
              "      <th>cl7</th>\n",
              "      <th>cl8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "      <td>250000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.001323</td>\n",
              "      <td>0.002044</td>\n",
              "      <td>-0.000246</td>\n",
              "      <td>0.000569</td>\n",
              "      <td>0.002452</td>\n",
              "      <td>0.002041</td>\n",
              "      <td>0.001453</td>\n",
              "      <td>0.010941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.781814</td>\n",
              "      <td>0.594890</td>\n",
              "      <td>0.498967</td>\n",
              "      <td>0.282529</td>\n",
              "      <td>0.225419</td>\n",
              "      <td>0.044311</td>\n",
              "      <td>0.823892</td>\n",
              "      <td>0.214199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-1.453300</td>\n",
              "      <td>-3.040300</td>\n",
              "      <td>-2.458100</td>\n",
              "      <td>-1.396800</td>\n",
              "      <td>-0.931720</td>\n",
              "      <td>-0.170640</td>\n",
              "      <td>-2.865800</td>\n",
              "      <td>-0.468260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.747950</td>\n",
              "      <td>-0.391120</td>\n",
              "      <td>-0.345143</td>\n",
              "      <td>-0.187480</td>\n",
              "      <td>-0.131150</td>\n",
              "      <td>-0.027160</td>\n",
              "      <td>-0.606295</td>\n",
              "      <td>-0.170070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-0.722550</td>\n",
              "      <td>0.019726</td>\n",
              "      <td>0.000520</td>\n",
              "      <td>-0.000149</td>\n",
              "      <td>0.029387</td>\n",
              "      <td>0.004697</td>\n",
              "      <td>0.116385</td>\n",
              "      <td>-0.008590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-0.616177</td>\n",
              "      <td>0.414153</td>\n",
              "      <td>0.338383</td>\n",
              "      <td>0.186920</td>\n",
              "      <td>0.161422</td>\n",
              "      <td>0.033646</td>\n",
              "      <td>0.619805</td>\n",
              "      <td>0.170440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.906700</td>\n",
              "      <td>2.280100</td>\n",
              "      <td>2.451700</td>\n",
              "      <td>1.460000</td>\n",
              "      <td>0.531490</td>\n",
              "      <td>0.137420</td>\n",
              "      <td>2.517500</td>\n",
              "      <td>0.847180</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12b50b3a-6503-4276-804c-513a2ce65798')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-12b50b3a-6503-4276-804c-513a2ce65798 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-12b50b3a-6503-4276-804c-513a2ce65798');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-84529bc9-326e-4903-b9a2-c61cccf63edf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-84529bc9-326e-4903-b9a2-c61cccf63edf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-84529bc9-326e-4903-b9a2-c61cccf63edf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data_set\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"cl1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88388.1887138017,\n        \"min\": -1.4533,\n        \"max\": 250000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.0013227780334800006,\n          -0.72255,\n          250000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cl2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88388.35374704664,\n        \"min\": -3.0403,\n        \"max\": 250000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.002044331664520001,\n          0.019726,\n          250000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cl3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88388.32310766281,\n        \"min\": -2.4581,\n        \"max\": 250000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -0.0002460560517599996,\n          0.00051959,\n          250000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cl4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88388.33019681204,\n        \"min\": -1.3968,\n        \"max\": 250000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0005691580337599998,\n          -0.00014886,\n          250000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cl5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88388.35334150057,\n        \"min\": -0.93172,\n        \"max\": 250000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.002452328172079998,\n          0.029387,\n          250000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cl6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88388.34642027685,\n        \"min\": -0.17064,\n        \"max\": 250000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.00204085872648,\n          0.0046966,\n          250000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cl7\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88388.31700609114,\n        \"min\": -2.8658,\n        \"max\": 250000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.0014532172255200166,\n          0.11638499999999999,\n          250000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cl8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88388.31755464649,\n        \"min\": -0.46826,\n        \"max\": 250000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.010940641933439998,\n          -0.0085897,\n          250000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "data_transformed = scaler.fit_transform(data_set)\n",
        "X_train, X_test = train_test_split(data_transformed, test_size = 0.2, random_state = 42, shuffle= False)"
      ],
      "metadata": {
        "id": "0eF_BTjJxNOc"
      },
      "id": "0eF_BTjJxNOc",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (11, 8))\n",
        "sns.heatmap(data_set.corr(), annot = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "jrsWAQXUx1Kb",
        "outputId": "7a3d76bf-8701-455f-8fc7-03b30dfea0de"
      },
      "id": "jrsWAQXUx1Kb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ig, axes = plt.subplots(nrows=2, ncols=4, figsize=(16, 8))  # Adjust figsize as needed\n",
        "\n",
        "# Flatten the axes array for easier iteration\n",
        "axes = axes.flatten()\n",
        "\n",
        "for i, ax in enumerate(axes):\n",
        "    ax.plot(data_set.iloc[:400, i])  # Plot data on the current subplot\n",
        "    ax.set_title(data_set.columns[i])  # Set title for the subplot\n",
        "\n",
        "plt.tight_layout()  # Adjust spacing between subplots\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "qh9gbeXbBvni",
        "outputId": "b15409ef-9ec3-4da7-dcc5-d486d1573165"
      },
      "id": "qh9gbeXbBvni",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 8 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Making dataset ready for ifd\n",
        "X_train = tf.convert_to_tensor(X_train)\n",
        "X_test = tf.convert_to_tensor(X_test)\n",
        "X_train = tf.random.shuffle(tf.reshape(tf.split(X_train, num_or_size_splits = 400, axis = 0),shape = (-1, 400, 8)))#Total 500\n",
        "X_test = tf.random.shuffle(tf.reshape(tf.split(X_test, num_or_size_splits = 400, axis = 0), shape = (-1, 400, 8)))#Total 125"
      ],
      "metadata": {
        "id": "8Efr07YdDoEy"
      },
      "id": "8Efr07YdDoEy",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_val=  X_test[:int(0.5*len(X_test)), :, :]\n",
        "X_test = X_test[int(0.5*len(X_test)):, :, :]"
      ],
      "metadata": {
        "id": "r-z1XJJEIaPA"
      },
      "id": "r-z1XJJEIaPA",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ifd = IFD(transformed_img_shape = (20, 20), n_f = 8, T_x = 400, num_pred = 0, batch_size = 32, replace_part = True)"
      ],
      "metadata": {
        "id": "q6nGsrAVIFlH"
      },
      "id": "q6nGsrAVIFlH",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#############################################################################\n",
        "################   AutoEncoder Architecture   ##############################\n",
        "############################################################################\n",
        "\n",
        "inp = Input(shape = (20, 20, 24), name = \"input\") #(20, 20)\n",
        "conv1 = Conv2D(60, (3,3), kernel_initializer = \"he_normal\", padding = \"same\")(inp)#(20, 20)\n",
        "conv1_norm = BatchNormalization()(conv1)\n",
        "conv1_act = relu(conv1_norm)\n",
        "conv1_act = Dropout(0.3)(conv1_act)\n",
        "\n",
        "conv2 = Conv2D(40, (3,3), kernel_initializer = \"he_normal\")(conv1_act)#(18,18)\n",
        "conv2_norm = BatchNormalization()(conv2)\n",
        "conv2_act = relu(conv2_norm)\n",
        "conv2_act = Dropout(0.3)(conv2_act)\n",
        "\n",
        "conv3 = Conv2D(20, (3,3), kernel_initializer = \"he_normal\")(conv2_act)#(16, 16)\n",
        "conv3_norm = BatchNormalization()(conv3)\n",
        "conv3_act = relu(conv3_norm)\n",
        "\n",
        "conv4 = Conv2D(15, (3,3), kernel_initializer = \"he_normal\")(conv3_act)#(14, 14)\n",
        "conv4_norm = BatchNormalization()(conv4)\n",
        "conv4_act = relu(conv4_norm)\n",
        "\n",
        "conv3_act = MaxPooling2D((2, 2), strides = 2, name = \"Middle\")(conv4_act) #(7, 7)\n",
        "\n",
        "# Decoder Model (Mirror of Encoder)\n",
        "deconv1 = Conv2DTranspose(15, (2, 2), strides =2, kernel_initializer=\"he_normal\")(conv3_act)  #(14, 14)\n",
        "deconv1_norm = BatchNormalization()(deconv1)\n",
        "deconv1_act = relu(deconv1_norm)\n",
        "\n",
        "deconv2 = Conv2DTranspose(20, (3, 3), kernel_initializer=\"he_normal\")(deconv1_act)  #(16, 16)\n",
        "deconv2_norm = BatchNormalization()(deconv2)\n",
        "deconv2_act = relu(deconv2_norm)\n",
        "deconv2_act = Dropout(0.3)(deconv2_act)\n",
        "\n",
        "deconv3 = Conv2DTranspose(40, (3, 3), kernel_initializer=\"he_normal\")(deconv2_act) #(18, 18)\n",
        "deconv3_norm = BatchNormalization()(deconv3)\n",
        "deconv3_act = relu(deconv3_norm)\n",
        "deconv3_act = Dropout(0.3)(deconv3_act)\n",
        "\n",
        "deconv4 = Conv2DTranspose(60, (3, 3), kernel_initializer=\"he_normal\")(deconv3_act) #(20, 20)\n",
        "deconv4_norm = BatchNormalization()(deconv4)\n",
        "deconv4_act = relu(deconv4_norm)\n",
        "deconv4_act = Dropout(0.3)(deconv4_act)\n",
        "\n",
        "# Final Output Layer\n",
        "output = Conv2DTranspose(24, (3, 3), activation= extended_tanh, name=\"output\", padding = \"same\")(deconv4_act)  # (20, 20, 24)\n",
        "\n",
        "auto_encoder = Model(inputs = [inp], outputs = [output])\n"
      ],
      "metadata": {
        "id": "u6d_DR_UPIvt"
      },
      "id": "u6d_DR_UPIvt",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ifd.set_conv_layer(conv_model = auto_encoder, auto_encoder = True)\n",
        "ifd.set_Data_generator()"
      ],
      "metadata": {
        "id": "bxQNeGLKPh2Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26e2093f-471f-461a-d91d-cd684e151ba4"
      },
      "id": "bxQNeGLKPh2Z",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conv_layer is created\n",
            "DataGenerator is created\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ifd.conv_layer.conv_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HXCPP2kMn5-8",
        "outputId": "2d75d184-787e-4c4d-f557-9e60689938cd"
      },
      "id": "HXCPP2kMn5-8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional_10\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_10\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input (\u001b[38;5;33mInputLayer\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m24\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_24 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m60\u001b[0m)          │          \u001b[38;5;34m13,020\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_48               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m60\u001b[0m)          │             \u001b[38;5;34m240\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_48 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m60\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_30 (\u001b[38;5;33mDropout\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m60\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_25 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m40\u001b[0m)          │          \u001b[38;5;34m21,640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_49               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m40\u001b[0m)          │             \u001b[38;5;34m160\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_49 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m40\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_31 (\u001b[38;5;33mDropout\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m40\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_26 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m20\u001b[0m)          │           \u001b[38;5;34m7,220\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_50               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m20\u001b[0m)          │              \u001b[38;5;34m80\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_50 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m20\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_27 (\u001b[38;5;33mConv2D\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m15\u001b[0m)          │           \u001b[38;5;34m2,715\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_51               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m15\u001b[0m)          │              \u001b[38;5;34m60\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_51 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m15\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ Middle (\u001b[38;5;33mMaxPooling2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m15\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_24                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m15\u001b[0m)          │             \u001b[38;5;34m915\u001b[0m │\n",
              "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_52               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m15\u001b[0m)          │              \u001b[38;5;34m60\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_52 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m15\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_25                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m20\u001b[0m)          │           \u001b[38;5;34m2,720\u001b[0m │\n",
              "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_53               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m20\u001b[0m)          │              \u001b[38;5;34m80\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_53 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m20\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_32 (\u001b[38;5;33mDropout\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m20\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_26                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m40\u001b[0m)          │           \u001b[38;5;34m7,240\u001b[0m │\n",
              "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_54               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m40\u001b[0m)          │             \u001b[38;5;34m160\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_54 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m40\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_33 (\u001b[38;5;33mDropout\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m18\u001b[0m, \u001b[38;5;34m40\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_27                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m60\u001b[0m)          │          \u001b[38;5;34m21,660\u001b[0m │\n",
              "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_55               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m60\u001b[0m)          │             \u001b[38;5;34m240\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_55 (\u001b[38;5;33mReLU\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m60\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_34 (\u001b[38;5;33mDropout\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m60\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ output (\u001b[38;5;33mConv2DTranspose\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m24\u001b[0m)          │          \u001b[38;5;34m12,984\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">13,020</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_48               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_48 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">21,640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_49               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_49 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">7,220</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_50               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_50 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,715</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_51               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_51 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ Middle (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_24                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">915</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_52               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_52 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_25                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,720</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_53               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)          │              <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_53 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_26                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">7,240</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_54               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">160</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_54 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_transpose_27                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">21,660</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_55               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">240</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ re_lu_55 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_34 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ output (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">12,984</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,194\u001b[0m (356.23 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,194</span> (356.23 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m90,654\u001b[0m (354.12 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">90,654</span> (354.12 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m540\u001b[0m (2.11 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">540</span> (2.11 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = ifd.create_model()"
      ],
      "metadata": {
        "id": "o7JRDQtfQzLP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a839e2f1-f539-4ef9-8e3e-f3a53168ccd2"
      },
      "id": "o7JRDQtfQzLP",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataGenerator and Conv_layer is merged\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model, show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "s9190qhupDrV",
        "outputId": "31cac2d1-8c42-40cd-ad37-948b9273e73a"
      },
      "id": "s9190qhupDrV",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABN8AAAToCAYAAAArEGaoAAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nOzdeZyO9eL/8fc9MwbDjBlDY5nJTkVRyc7RieyHE0kpihaO0yZl6Tg6pCyFOkIha2QrZa8kSWROZYkaRA3D2IbZzGqu3x9+M9+557ruue9ZrhnD6/l4zOPh/lyf63N9rvu6Z5i3z+IwDMMQAAAAAAAAgELnVdwdAAAAAAAAAK5XhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATXyKuwMAUFLdc889On36dHF3AwAAoEiEh4eratWqxd0NAChxCN8AIJ9Onz6tqKio4u4GAABAkUhPTy/uLgBAicS0UwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAbkCBgYEyDMPyq3PnzsXdPeTA8wIAACi5CN8AAACK2MSJE50CtBEjRhR3lwAAAGATwjcAAIAi5OXlpccee6y4uwEAAIAiQvgGAABQhDp06KCwsLDi7gYAAACKCOEbAABAEXriiSeKuwsAAAAoQoRvAAAARSQwMFC9evUq7m4AAACgCBG+AQAAt1zttrl+/XqnesHBwRo9erR27typmJgYpaamKjo6Wrt379aYMWN000035ftahw4dcqpXv359jR8/Xrt379aJEyeUnJysqKgo7dixQ88995yCgoLcXqtDhw6W1/ruu+/cntumTRvLc/fu3etUb9SoUVnHLl68qDJlypjamjp1qlMbR48edXv9ouLl5aV7771X06dP1/bt2xUVFaX4+HilpaXp3Llz2r9/v+bOnatevXrJ29s717ZGjhxp+Z4dO3bM4/60bNnSso2UlBRVrFgx13NLly6thx9+WB988IH27dunM2fOKDU1VefPn9cvv/yilStXql+/fipXrpxHfXH1WV23bl1WnerVq2vu3Lk6efKkUlNTdfr0afXt29fj+wUAACWfT3F3AAAAXPsSEhIsy7MHXH369NG8efNUoUIFpzohISEKCQlR8+bNNWLECD3zzDNatWqVy2vFx8dbloeEhEi6GqC89957Gjx4sKlOtWrVVK1aNbVp00ZjxozRwIEDtWXLFrf3B2tdu3bVjBkzVK9ePcvjlSpVUqVKlXT77bfrySef1B9//KFBgwZp27ZtlvUXLVqk119/XT4+zv8ErVWrlho3bqx9+/a57VOXLl0syzds2KCYmBjLYw6HQ88//7xGjx5tGQAHBwcrODhYDRs21IMPPqjo6Gg9//zzWrlyZa59SUxMtCzP/L64+eabtWvXLlWrVi3rWJUqVXTzzTfn2i4AALi+MPINAAC4lZ6errS0NFN5Zsjw0EMPaeXKlabgzar+8uXL1aNHD5d1rly5otTUVFO5v7+/vLy8tHbtWsvgLaeQkBBt3LhRXbt2dVsXZmPHjtWGDRtcBm9Watasqa+++koDBgywPB4dHa2NGzdaHuvZs6dH13D1PBcvXmxZXr58eW3YsEHTp0/3aOSldDUgW7FihaZMmZJrvbS0NKWnp5vKM78P5s6d6xS8AQCAGxPhGwAA8EhycrKpzN/fX7Vr19b8+fPlcDg8asfb21vvv/++/P39XdZJSUkxlZUqVUovv/yyOnfu7HGfvby89PHHHys0NNTjcyD1799f48ePz9e5Xl5emjdvnu68807L4x9++KFluSdr4d1000266667TOXnz5/Xhg0bLPuybNkyl6Pl3Hn55Zc1YsSIXOtYfVb9/f3VsmVL3X///fm6LgAAuL4QvgEAAI9YjfApW7asJk+e7PEaWZmqVq2q/v37uzx+5coVy/J///vfkqS9e/eqR48eCggIUGBgoO6//37t3r3b8hx/f3+NGzcuT/0rTJMmTZLD4ZDD4dDEiRMt67z88stZdRwOh+rWrVvEvfw/ZcqU0bRp0yyP/fjjj+rQoYOCgoIUHBysTp066ciRI6Z6pUqV0htvvGHZxoYNGxQdHW0qv/POO91Ox+zSpYtlyPvxxx9bjswcPny45SjLhIQEvfTSS6pVq5Z8fX1VtWpVPfXUUzpz5oyp7oQJE1SrVi2XfbK6rp+fn4YOHZrrvQAAgBsH4RsAAPBIRkaGqaxixYrq3bu3JOnnn39Wt27dVKFCBVWoUEHdunXTr7/+6rK9zPOsuArf/Pz8tHPnTrVq1Urr169XfHy8YmNj9eWXX6pdu3b69ttvLc8bOHCgRxsw4OoINKvpmSkpKerevbu2bt2qS5cuKSYmRl988YV69eolwzBM9Tt16qTKlSubytPT011OEXU39dTVCDar9vz9/TVq1ChTeVpamjp27Khp06bpjz/+UFpamqKjozVv3jy1bNnStG5cmTJlNGHCBJd9svqsBgQEZH2+v/jiC7Vt21bly5dXQECAbrnlFq1ZsybX+wQAANcXwjcAAJBvmSO1tm3bppYtW2rjxo2Ki4tTXFycNm7cqLZt2+rkyZOW51pNH3QnPT1dgwcPVlJSkulYWlqahgwZYhkElSpVSt26dcvz9W5EgYGB2rFjh37++WcdPXpU0dHRSkhI0Lfffms5Yu3QoUPas2ePqdzhcKhdu3aW18jP1FNvb2/LaZy//vqrwsPDTeVPPvmkgoODTeVLlixxOUry+PHjevPNN03lf//73/M0urN06dLy8/PTqlWr1LlzZ3333XdKTExUfHy8IiIidPz4cY/bAgAAJR/hGwAAKJDLly9rwIABlmtfXbhwQZMmTbI8r2LFinkejbZlyxZFRES4PP7rr79q165dlsfuu+++PF3rRjVnzhy1a9dOd911l+rVq6eqVavK398/1/XL9u/fb1nuarOBiIgI7dy501Terl07l5+JFi1aWB5zNYrugQcesCz/5JNPLMszWe1w6ufnl+eNO+Lj4zV06FDLMBgAANxYCN8AAECBrFy50uXoNklav369y2PudkfNad26dW7rbNu2zbK8YcOGeboWPBcbG2tZnlu4On/+fFOZj4+PyxGKVuFXRkaGli5datlO06ZNLdvJLbyVpMjISMv7ueeee3I9L6dPPvlEFy5cyNM5AADg+kT4BgAACmTz5s25Hj9x4oTlenHS1el5ebF37163dQ4fPmxZXr9+/TxdC675+PiobNmyCggIUMWKFV0+Ry8v1//UXLlypeLj403lrqaeWq339vXXX1sGvzVq1FCZMmUs2zly5IgMw8j1yyoUvv32213ei5Wvv/46T/UBAMD1y6e4OwAAAEq23DZVkK6OTjp//rzlIv5WO1fmJrcRdpmsdqyUri6C7+Xl5TIIhFndunXVu3dvtW7dWrfddpsqVaqkgICAPD83K4mJiVqxYoWefPJJp/JOnTqpTJkySk5OziqrUqWKmjRpYmrD1ZTTKlWqFLh/OeW246kVdyPsAADAjYORbwAAoEBcTTnMzmqEU3540k5iYqJlucPhyNOi+TeyypUr66OPPtLhw4c1adIk9ejRQ3Xq1FGFChUKJXjLZLXxQvny5U3r83Xp0sV03YSEBJfrt5UtW7bQ+pjJ398/T/UvXrxY6H0AAAAlE+EbAAAokCtXrhTZtTxZvN7b29vlscIc9ZbbdUqy0NBQ7dq1S4888kihBm1Wdu3apUOHDpnKc049tZpyumbNGpdBa2pqauF0MJuAgIA81b98+XKh9wEAAJRMhG8AAKDE8GT0kZ+fn2V5RkaGx4GIJyPkAgMDPWqrpFm8eLHq1KnjUd0rV64oJSVFaWlp+b6e1ei3Hj16ZK0X5+Pjo44dO1r205XcRp2FhobK4XDk+SuvI98AAAAyEb4BAIASw5O1vEJDQy3LL1686NHIOenqtEt3rsfdU1u0aKF7773X8tixY8f03HPPqVGjRqpYsaK8vLzk4+OjMmXKaNq0afm+5uLFi03hXUhIiFq0aCFJatmypSnoPHHihL755huXbcbExLg8FhISku++AgAA5AfhGwAAKDEaN27sts4tt9xiWW61MURKSopl3ZtuusnlbpmZOnfu7LYvJU2PHj0syy9duqTWrVvrv//9rw4ePGgKMgsyKuzcuXNav369qTxz6mnXrl1Nx5YsWZLrFOKoqChduHDB8pgdmzEAAADkhvANAACUGN26dXNb569//atl+YEDB0xlrjaLKFWqlDp16uTyGk2bNlWbNm3c9sVTmVMsi1tYWJhl+ebNmxUdHe3yvMxRavk1f/58U1nPnj0lWa/3ltuU00y7du2yLG/VqlUeewcAAFAw18a/9AAAADzQo0cP1ahRw+Xxpk2bqkmTJpbHtmzZYio7duyYy6mo48ePt9w1MzAwUAsXLizUzQhuuummQmurIFytdZfbBgbt27fXXXfdZXnM3ejBTJs3b1ZUVJRTWf369XXfffeZRjvu2bNHERERbtvcsGGDZfmAAQPk6+vr8rzOnTsrLi5OR44c0XfffafVq1frvffeU4cOHTy4EwAAADPCNwAAUGL4+vpq/vz5luFJmTJlNGvWLMvzEhISLMO3hIQEHTlyxPKcO+64Q1u3blXbtm3l5+enoKAg9enTR//73//UsGHDfO2cmpycbFn+l7/8Jc9t2eHcuXOW5c2bN7fc3bV27dq5jkLzdIrnlStXtGjRIlP5f//7X1OZVT0rS5Yssdx4ISwsTJMnT7Y8p2zZsho/frz8/f1Vt25dtW7dWr1799bQoUN19uxZj64LAACQE+EbAAAoMdLS0nTfffdp+/bt6tixo8qXL6+AgAB17txZO3fu1D333GN53jvvvOMy+Prkk09cXq9ly5b69ttvlZiYqJiYGK1atSprJ9A5c+bkuf9nzpyxLG/atKnefPNNVatWTWXKlNFtt92m0qVLe9Tmpk2bZBhGvr8mTZqU1daePXssr9GgQQMtXLhQdevWVenSpVWnTh2NHDlSP/74o8LCwhQTE2MZYnbs2NHjXWE//PBD0yjEW2+91el1amqqPv74Y4/aS0xM1JQpUyyPvfDCC1q1apWaN2+ucuXKKTg4WJ07d9a2bdssP0MLFy7U/v37PbouAABAToRvAACgxJgxY4akq2uMffHFF4qPj1dsbKw2bdrkcurjyZMnXYYwkjR79mwlJSXlqR9nz57VuHHjdOXKFdOx3Kaj7t692+WxUaNGKSoqSklJSTp48KBHO64WtrVr1youLs7y2KOPPqojR44oOTlZR48e1aRJk7KCtaefflpbt241nVOtWjXt379fa9eudbsj6u+//65vv/021zobNmzIdSfTnKZMmaKvv/7a8lifPn20e/duJSQk6Pz589q0aZOaN29uqnf06FG98MILHl8TAAAgJ8I3AABQYrz11lv6/vvvPa4fHx+v7t27uwyUJCkyMlKvvPKKx22mpqbqkUce0fnz53X58mXT8dzWOTtw4ECe+l/UYmJiNHr06Dyd8/rrr2vNmjVauXKl5fGwsDD17NnTZTiandXGC9l5stFCdhkZGerdu7e++uqrPJ2X6bffftO9996b6+cHAADAHcI3AABQonTq1EkrVqxwW2/fvn1q1aqV9u3b57buzJkz9fzzz7ucmpopOjpaXbp0yRrlFR8fb6rjatOCTAMHDjRtLnAtmTVrlkaOHKn09PRc6yUlJemJJ57Q2LFjJUnbtm3TkiVLCnTt1atXu9yB9vz58y43UcjNpUuX1KVLF7366quWa8BZSU5O1vTp03X33Xfr5MmTeb4mAABAdj7F3QEAAABP+fj4KCEhQf369dO7776rQYMGqXnz5qpevbrKlCmjU6dO6ZdfftFHH32ktWvXKi0tzeO23333Xa1fv15PPfWUOnfurLCwMFWoUEGxsbE6cOCA1q5dq/nz5yshISHrnAsXLqhatWpO7QQEBOR6naNHj+rOO+/UiBEj1KNHD9WqVUsOh0OxsbGKiYnR/v379f333+v8+fN5e3MK0ZQpU/TJJ5/oH//4h9q3b6/atWurfPnyio+PV0REhLZs2aL3339fp06dcjpv4MCB+uqrr/T3v/9dNWrUkJeXly5cuKDffvvNo+AsKSlJK1as0NNPP2069vHHH+fpeWaXnp6uN954QzNnztQDDzyg++67T3fffbcqV66swMDArDX9Dhw4oG3btumjjz5yufkEAABAXjmMnCvbAgA8Ehoaek2PXgFKsvPnzys4ONhUHhYWxkik69zatWvVs2dPU3mzZs0UHh5eDD0CkCkyMlJhYWHF3Q0AKHGYdgoAAIBrQmhoqLp162Yq//HHHwneAABAiUX4BgAAgGvCmDFj5ONjXhXF3U6pAAAA1zLCNwAAABS7jh076plnnjGVHz582OVOqgAAACUBGy4AAACgSFWtWlXJyclKSkpSWFiYevXqpddee01eXub/Fx49erTbnVcBAACuZYRvAAAAKFILFixQp06d3NZbu3atPvnkkyLoEQAAgH2YdgoAAIBrTkREhAYPHlzc3QAAACgwwjcAAABcU7Zv36727dsrJiamuLsCAABQYEw7BQAAQJGKiIjQrbfeqooVK6ps2bJKS0vT2bNntXv3bn300Udat26dDMMo7m4CAAAUCofBv2wAIF9CQ0MVFRVV3N0AAAAoEpGRkQoLCyvubgBAicO0UwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA28SnuDgDA9er+++9Xy5Yti7sbAAAATubNm6eoqKji7gYA3DAI3wDAJp06ddLw4cOLuxsAAABONm/eTPgGAEWIaacAAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAAAAAANiE8A0AAAAAAACwCeEbAAAAAAAAYBPCNwAAAAAAAMAmhG8AAAAAAACATQjfAAAAAAAAAJsQvgEAAAAAAAA2IXwDAAAAAAAAbEL4BgAAnKxfv14OhyPr648//ijuLiEPOnbs6PT8HA6HnnjiieLuFnBD6N+/v+n7r2vXrsXdLQBAMSN8A4AbUExMjFatWqUhQ4aoWbNmql27tgICAlSmTBlVr15dTZo0UZ8+fTR79mwdPXq0uLsLwEPz5s3TV1995VRWpUoVTZs2Let4zmAg8+uzzz7z+DpvvfWW6fxRo0YV6r2g4GbNmmX5rKtUqeLR+adPn9Ybb7yhDh06KCwsTGXLlpW/v7/q1Kmjfv36acmSJUpPT/e4P4Xdnh3S0tK0Zs0aDR48WI0bN1blypXl6+ur8uXLq3r16mrXrp1eeukl7d692/L8d955R5UrV3Yq27RpkxYtWlQU3QcAXKsMAEC+VK9e3ZDk8uvtt98u7i6anDx50hg2bJhRunTpXPue86tTp07Grl27irv7RlpamlG2bFlDkjF79uzi7k6BXav3s27dOqfnf/z48eLuEjxw4cIFIygoyPT9u2zZsqw6c+fOdfl9Xq9ePSM1NdWja02dOtV0/siRI+26NeTD8ePHjfLly1s+65CQELfnT5o0yfDz83P790ODBg2MPXv2FHl7dvjss8/c/t2e/atly5bGoUOHTO0sXLjQVDc4ONi4ePFiMdyVtebNm+fp3wGZX5GRkcXddQAokRj5BgA3iMWLF6tu3bp67733lJKSkqdzt2zZopYtW2rIkCFKS0uzqYfuHTx4UElJScV2/cJ2vd0Pitdrr72mixcvOpU1a9ZM/fr18+j8I0eOaObMmXZ0DUXMMAwNGjRICQkJ+Tr/5Zdf1qhRo3T58mW3dSMiItShQwf98MMPRdaeHWbOnKmePXsqKirK43N27dql5s2ba8+ePU7ljz32mJo0aeJUduHCBU2YMKFQ+goAKHkI3wDgBjBq1CgNHDhQycnJWWXBwcEaOnSoPv/8cx09elSxsbFKTk5WZGSkduzYobFjx6pBgwZO7bz//vvq0KGD4uLiivoWJEn/+9//iuW6drne7gfFJzIyUnPmzDGVT548WQ6Hw+N2JkyYoJiYmMLsGorB7NmztW3btnydu2LFCr311lt5OicuLk59+vSx/LuhsNuzw88//6znn38+X+fGx8froYcecvpPLS8vL73xxhumujNnztSpU6fy3U8AQMlF+AYA17m5c+dq8uTJWa8dDodGjBih33//XbNmzVKPHj1Up04dBQQEqHTp0goLC1ObNm00fvx4HTx4UPPmzVNAQEDW+d9++60GDRpUHLdy3YVV19v9oPhMmzbNNCq1WbNmat++fZ7auXjxol577bXC6xiK3PHjxzVy5Mis13kJX9PT053OzXTHHXdo27ZtSkhI0Pnz5zVnzhyVLVvWqc7Jkyc1ffp0W9uzy8SJE5WRkWEqf+yxx3TgwAGlpKQoNjZW69ev16233mqq98cff2j58uVOZV26dFHjxo2dylJTUzVjxozC7TwAoEQgfAOA69ihQ4f07LPPZr328fHR4sWLNXXqVFWoUMHt+d7e3ho8eLC+/fZbpwW616xZo/fee8+WPufmxx9/LPJr2ul6ux8Uj4SEBM2fP99UPnz48Hy1N3v2bEVERBS0WygGhmFo8ODBTtNNe/bs6fH569at059//ulUFhAQoC+++ELt27dXuXLlFBwcrGeeeUZvv/226fw5c+Y4bZhQ2O3ZISMjQ5s2bTKVN2/eXIsXL1ajRo3k6+urgIAAdevWTZ9++qm8vb1N9Tdv3mwqs/oe/OCDD1huAABuQIRvAHAdmzBhgtNUmH//+9969NFH89xO48aN9fHHH8vL6//+2pgwYYLTNNbsJk2alLWrno+Pj0fXmDFjhuU5c+bMySoPDw/PKh86dKjT7n2Zo8imTp2aVVa7du2s+ufPn9e///1vNWvWTNWqVVPp0qVVrVo1tWnTRtOnT1dsbKzLvhXn/eRHamqqVq5cqf79++v2229XxYoVVapUKZUtW1ZVq1ZVmzZtNHLkSP38888etZc5ciY9PV3z589Xp06dVLt2bZUpU0ZBQUFq1KiRnn/+ef3+++8etXflyhVt2LBBgwcPVpMmTRQcHCxfX1+VK1dOoaGh6ty5s6ZMmaKzZ8/m2o4dzzqnU6dOaeLEierYsaNCQ0NVtmxZBQQEqG7duurWrZvef/990zprVrJ/HhwOh+Uv6vm1Zs0a09pegYGB6tWrl0fnt2rVyul1enq6RowYUWj9y27nzp0aM2aMWrZsqRo1asjPz0/ly5dXzZo11bJlS40ZM0bfffed23bmz59v2sGzU6dOWccNw9CKFSvUrVs3hYSEqFSpUqpcubJatGihSZMmKT4+3uM+x8XFafbs2XrwwQezRgmXKVNGNWvW1L333qt3333X7We1qMyaNctpumnFihXz9CxXr15tKuvfv79CQkJM5U888YTKlSvnVBYdHa0dO3bY1p4dzp49a7kW3UMPPWRZv0GDBrr77rtN5TlDRknq06ePypcv71QWGxurzz//PJ+9BQCUWMW84QMAlFjX+m6nx44dM7y9vbP6c9tttxnp6ekFanPo0KFO9+hqh84333wzq463t7dHbU+fPt3ynNmzZ3u0A1t4eLhhGIYxa9Ysp93lDMMwdu3aZdx00025nh8WFmbs3LnzmrufvNq9e7dRt25dj3eu69Onj3Hp0iWnNnLudnrixAnj9OnTRtOmTXNty9fX12lnTSsHDhwwmjRp4lHfypUrZ8ydO9dlW3Y860xpaWnGK6+8Yvj6+rrtZ3BwsLFgwYJc28v+eZBkbNq0Kdf6edGpU3jCZxwAACAASURBVCdTn5566inLula7nb7zzjvGzTffbCr/6quvXF4zr7ud/vDDD0bbtm09/ly2bt061x2Wly9fbjqnefPmhmFc3fW1ffv2ubZfvXp1Y9++fbm+rxkZGcZbb71l+Pv7u+1vQEBArp/VonDs2DGjXLlyTv1asGCB8dNPP5n662q30ypVqpjqrlmzxuU177//flP9f/3rX7a1Z4eoqCjLZ7p06VKX53Tv3t1Uv1mzZpZ1H3vsMVPdnj172nU7HmO3UwAoWox8A4Dr1CeffKIrV65kvX7uuecsp8rkxQsvvOC0ftCKFSsK1J4dso8yS0hI0MmTJ9W1a1e3I1NOnDih7t276/Dhw3Z30TaHDx9Whw4ddPToUY/PWb16tXr16iXDMFzWcTgc6ty5s9vReKmpqRowYIAOHTpkefzIkSNq166d9u7d61HfEhMT9dRTT2nhwoWWx+161unp6erevbumTJmi1NRUt/28cOGCnnjiCU2aNMlt3cKWnJys7du3m8q7du3qcRvx8fGaOHGiqXz48OGW62Dl1ZIlS9S2bds8jWDauXOn2rVrp8WLF1seL126tKksLi4u69l98803ubYfFRWljh076sKFC5bHMzIy1LdvX40YMcKjUXJxcXF66qmn9J///MdtXTsY/39308TExKyybt266fHHH3f6eyA3p0+fVnR0tKn8tttuc3mO1fpnmT8nCrs9u1SpUsVyGYbcNkY4c+aMqeyWW26xrGv1vbh169Zi3TkcAFD0CN8A4DqV/ZdPh8PhcgpNXtSvX19NmzbNer17926naa12GDJkiAzDMK2RM3v2bBmGkfWV2a/sAWNKSopeeeUVXbx4Ua1atdLatWsVHR2t1NRURUdHa/ny5apbt25W/YsXL+Z7xzu77icvXn311azph76+vho9erTCw8N18eJFpaenKz4+XkePHtWyZcucphp+8803WrVqlct2p06dqn379qlBgwZatGiRTp06pdTUVJ07d06ffPKJGjZsmFU3PT3d5c6Gw4YNc5qi2a1bN61bt05RUVFKSUlRYmKifvrpJz3//PNOU5yHDx9uOVXUrmc9evRobdmyJet1vXr19MEHH+jQoUNKTExUQkKC9u/frzfffFPBwcFO523dutXl+2iHnTt3mqZ/e3t769577/W4jYsXL6p///6mz9z+/fst15LLi40bN2rgwIEehZg5paWl6fHHH9eXX35pOubr62sqi4uL09SpU7Vr1y6P2j979qzGjx9veezll1+2nDLpzmuvvaZPP/00z+cV1Hvvvef0M79ixYqaO3duntpwNW08NDTU5TlWx44cOWJLe3bx8vJSnz59TOVLly61DJ9///13y0Cwb9++lu136NDBtOlFQkKCdu/enc8eAwBKpOIacgcAJd21Pu00ODg4qy+33XZbobX74osvOt2n1fS9wpymmSkpKcnpuq6mvC5YsMD0LHr16mWkpaVZ1r906ZJRv359p/r79++/Zu7HUxkZGYafn19We2+99Zbbcx599FEjJCTEaNq0qTFt2rSs8pzTTkuXLm106NDBSExMtGznwoULRqVKlZym9OX0+++/m55JbiZNmuRU32o6qx3P+tixY4aPj0/W8S5duhiXL1922c+TJ08aNWvWzKrfqFGjXO+rsGX/bGZ+NWzY0GV9q2mnw4YNMwzDMLZv3245PTEuLs7UjifTTmNiYpw+F9m/+vfvb+zatcuIj483EhISjO+//97o06ePZd2qVauaPnsbN2401fPz8zMqVKhgeHl5GS+++KJx9OhRIzk52di7d6/Ro0cPy7aDg4NNn5dffvnF8PLyMtW98847jY0bNxqnT582Ll26ZOzcudPo0qWLqV7t2rWNlJSU/D7SPLOabpr9+yU8PNzyuea0cuVKUz1fX99cr71w4ULTOWXLlrWlPTudOHHCCAwMNF3773//u7F3714jOTnZiIuLMzZv3mzceuutpnp//etfjYyMDJft16lTx3TO9OnTbb+v3DDtFACKFiPfAOA6lJ6e7jSdymoqT341atTI6fXp06cLrW07lC9fXvPmzXO5UUKFChU0ZcoUp7L169cXRdcK1aVLl5wWDW/cuLHbc5YsWaLo6GiFh4frxRdfdFnPz89Py5cvl5+fn+XxihUrql+/flmvo6KiTBsAREVFqW3btqpfv74CAgL0z3/+M9e+PfvssypVqlTWa092hi2MZz19+vSs3RUrV66sZcuWqWzZsi6vWb16dc2ZMyfr9S+//GL7NLns9u3bZyrz5Nlnl3m/7dq1M+2MeebMGb355pv56tucOXN0/vx5U/l//vMfLV26VC1atFD58uVVrlw5tWzZUqtWrbL8XJw+fVrLli1zKss5kkiSLl++rNjYWL3zzjuaNm2a6tSpo9KlS6tx48b69NNPTRtLSFenDP/2229OZRMnTjSNeKpZs6a++eYbdenSJWuaYqtWrbRx40Z169bNqe6xY8eKbPSbYTHdtHfv3nr44Yfz3FZMTIypLOdmAZ4cT0pKUnJycqG3Z6fQ0FCtX7/eaSSrJH366adq0qSJypQpo4CAAHXu3Fm//vqrU51WrVpp9erVlp/JTHfccYepzOp7FwBw/SJ8A4DrUM51jCpWrFhobedsy9WaSdeKBx980PQLVU7dunVz+qVv586ddner0AUEBDhNw9ywYUOhtT1o0CBVqlQp1zq333670+ucv3i3bdtW3377rSIiIhQbG6v77rsv1/b8/PwUFhaW9doqxMmpMJ71pk2bsv7cv39/BQYGur1up06dnPq6bt06t+cUFqv1/Ro0aJDv9qZMmeIUekpXA0mrnRzdsZr2eMstt+hf//qXy3MmT55s+fNqyZIlHl2zadOmlgGet7e3y10/s09rvHLlitNnINMLL7yggIAAl33OKT9TVvMj53TTypUra/bs2flqyyrgyvlZ8PS4q8CsIO3ZrXXr1tq/f7+effZZValSJde6DodDrVq10vvvv6/t27crKCgo1/pW35Oe7g4NALg+EL4BwHUo56gjVyOW8iPnyISc17rWeLL2lY+Pj+68886s13avMWQHb29vtW/fPuv1jBkz9OyzzyoqKqrAbXfo0MFtnZzhXGH8spx9xFnm6KzcFPRZnz592inMyl7PnRYtWmT9ef/+/R6fV1BWi8JXrVo13+3Vr19fQ4YMcSpLTk7WqFGj8tROZGSkjh8/bip/5JFHnNbzy8nPz0/du3c3lYeHh3v0GXj88cddHrMa+SZdHTWa6eeff3Z6nalZs2Yu273ttttM4cu2bdvc9LTgjh07Znouc+bMUeXKlfPVntXGDO426XEVlqWlpRV6e0Xh5MmTio2NzXUDGunqiMNTp07pwIED+uOPP9y2W716dctrAQBuHIRvAHAdyjlax2qx+vzK2Za7//EvbjlHZLlSo0aNrD+fOHHCru7YaurUqU6B1cyZM3XzzTerdevWGjt2rLZu3Zqv6Vs333yz2zo5F8DP7ZfXM2fO6MMPP9SgQYPUpk0b1atXTyEhIQoKClL58uVVpkwZ+fj46ODBg3nqZ0GfdWRkpFO9gQMHyuFwePSVfcOKotwx99y5c6Yyd6N23Bk3bpxp98ePP/44TwvEu5om7MlGIlahZ1JSkke7+GYPQXOqVKmSZfCXfdMYq8BQuhrcuXr2Xl5eThuJSFdHBFvtiFlYrKab9u/fXw888EC+27Saru1up1RXoVipUqUKvT07ZWRk6KWXXlKLFi20ePFij57dH3/8oZkzZ6phw4aaNWtWrnWtAnE7Px8AgGsP4RsAXIeCgoKc1p/xZMqep3JOJ3Q3za+4eTrlNnvYkJSUZLnL3bXuzjvv1JdffqlatWpllWVkZOj777/X66+/rg4dOigoKEidO3fWvHnzPA5lC2vkZEpKil588UXVqFFDgwcP1oIFC7Rz504dPXpUZ8+e1aVLl5SYmKiUlBS3v6RbKeiztlqjKj+sRk7ZIS0tzTKsKOjzCg4O1quvvmoqz74uYG7rW0nWoaAkVatWze31XYWHnjyf3IJHb29vU6iYn2t4ypOwML9mzpyp7du3Z72uWrWq/vvf/xaoTavPjbsRZ66O+/n5FXp7dho7dqymTZvm9J8GPj4+Gjt2rCIiIpSSkqLY2Fht375df/vb35zOTU1N1bBhw3KdamzV/6KYSgsAuHYQvgHAdcjLy8tpDaqff/650NrOuUh09lFE16Jy5cp5VC/nyK3U1FQ7umO71q1b68iRI1q6dKmaN29uCkmSk5O1ZcsWPfXUU6pZs6befPPNIgkaU1JS9Ne//lUzZsxwGmlUmAr6rLOPIiqIopqK7ep9LFOmTIHbfu6551SzZk2nst27d2v58uWSrEdJZRcfH29ZntvmFe7quGozu9KlS+d6PLcpr1LhPru4uLhCayu7Y8eOafTo0U5lc+fOLfAoZKv/SHH3flg9k3Llyql06dKF3p5djhw5YtqIRZKmTZum8ePHq379+vL19VVAQIDatWunzz77TH379jXVf+mll1xOjbb6TBuGYdvPQgDAtYfwDQCuU61bt876c1RUlEfr0ngi+9SzihUrejzVr7h4+stN9umYDofD1l/27Obt7a3+/ftr9+7dOn36tBYsWKB+/fqZ1oK6dOmSxowZowceeCBfI83yYuzYsfr++++zXpcqVUoDBw7Uxx9/rP/97386duyYYmJiFB8fr6SkJKWnp6thw4Z5ukZBn7W/v79TvS1btsgwjDx/FeY07/xwt16VJ0qXLm25y+moUaOUnJzsNuBztTmBJwGnqzruRq0VhpyfgYLwJCzMjy+++ML0HnXv3t3ltNh77rnH1MaZM2ec6rz++uuW65KlpqbmGphZjXDMnKZe2O3ZZenSpabQLDAw0LT2YXZWm3pERkY6/YzLrjC+JwEAJRvhGwBcp9q1a+f0esGCBQVuMyIiwmktp7/85S9uR5J4yq5RIp4GIdmnCvr7+7udVueOXfeTVyEhIXr88ce1fPlynTlzRj/++KNGjRrltC7gZ599lu8dEj2RnJzstPNlUFCQfvjhBy1cuFAPPfSQ7r77btWqVctpzTdvb+88B4IFfdY510q81nfydTVCLD/r+lnp16+fmjdv7lQWGRmpadOmud0F1tWi/54sMu9qk5D8biSQF65Gj/300095DmGtRkddyxo0aGD5cy/nWojujt1yyy22tGeXvXv3msrq16+f6zpz9evXtyw/cOCAZbnV92RJ/08eAEDeEL4BwHXqwQcfdFpnZs6cOQUOhHKuKTRw4EDLetl/4bpy5YpHIUphjczL6bfffvOoXvbr55xKey3dT0E4HA7dddddevPNN3Xw4EHVq1cv65jVtKvCcuDAAafAa8yYMW53Ek1NTc3zxhcFfdY5w4JffvklT9cvat7e3pYBweXLlwvtGm+//bapbNKkSW6/B+666y7L8j179ri9plWdoKAg1a5d2+25BXXrrbdalpfUTVjyokKFCpahkqtASbLe2TczsC3s9uxiNULR3dp0rtZrc/W9Z1Vu9zp2AIBrC+EbAFyngoOD9fjjj2e9Pnv2rF544YV8t7d7926n0VENGzY0LTydKeeIHHcjiDIyMvT111/nu2+52bFjh9s6qampTqMfGjRo4HT8WrqfwlKtWjWnRfVPnDhh2zS506dPO73ObUfKTJ9//nme12Ar6LMODAx0CiTXr1+fp+sXh5tuuslUdvbs2UJrv3Xr1urdu7dTWXx8vN57771cz7v55ptNa8ZJ0rJly1yuiyVd3fBg48aNpvJ27doVeDSqJxo2bGg5qs+Tz9b1oEePHqayL7/80rLupUuXtGvXLlN5r169bGvPDpUqVTKVHTt2LNeA+dixY5blrkZn5vwZKBV8V2IAQMlC+AYA17HRo0c77QC5YMECjR8/Ps/tHDp0SL17985amN/hcGjy5MkufxnOueuk1bSe7NasWaM///wzT33ydErismXL3C7y/emnnzqNZGjfvr3T8Wvpflx577331KdPH9WsWVPLli3z6JyqVas6vS6sKcQ55WzXXch36dIljRo1yqnMk6mUhfGsswfK+/fv16ZNm9xeNyUlRU2aNNGDDz6ohQsXFtlup5L17qGnTp0q1GtMnjzZtEmFq7Wtsnv66adNZceOHdOECRMs62dkZOgf//iH5SihZ555xsPeFozD4bAMe+bMmeNy99KNGzeqfPnyql27tlq0aKG//e1vTjvDStLmzZst12P77rvv8tzHIUOG5Gn6a3h4uKmNkJAQpzr/+te/JEmPPPKIqe6KFSsUHR1tKn/33XdNI8SaNm3qFGgXdntS4b6XkvUozdjYWC1evNjlOe+//75ludX6epL196TVmngAgOsX4RsAXMdCQ0P14YcfOpWNGzdOjzzyiMt1lbIzDEOLFi1Su3btnH55ePnll9WtWzeX5912221Or+fMmeOy7qFDhzRs2DC3C7h7e3s7vfZ0GtjZs2f17LPPulzw+vz58xo5cqTTdbp37+5U51q6H1d2796dFfq9+uqrLkdmZLdq1aqsP4eGhnq8W2he1apVy+n16tWrXdY9deqUOnfurJiYGDVr1iyr3JNpvIXxrJ955hmnsHDQoEGKiIhwec3U1FQNHjxY+/bt0+rVq/X0008X6YYL2UfqZcqtv/lRp04dDRs2LM/nDR061HJU0fjx4/Xkk09q3759SklJ0aVLl/Tll1+qY8eOWrFihal+06ZN1blz53z1PT+GDx9u+o+FhIQEtWnTRh9++KHOnDmjtLQ0nThxQjNnzlS/fv2UmJio48eP64cfftC6detK7Fped955p9q2betUlpCQoC5duui7775TUlKSzpw5oylTplj+R87w4cNtbc8OvXv3Nv08lq7+LBg3bpwiIiKUmpqqpKQk/fjjj3rkkUe0cuVKU/2GDRu63CTG6nuybt26Be88AKDkMAAA+VK9enVDksuvt99+u7i7mGXGjBmGl5eXU//KlStnDBgwwFi9erVx5MgRIzY21khOTjZOnDhhfP/998Z//vMf4/bbbzfdV//+/Y309PRcr5eWlmZUqVLF6bwBAwYYP/74o5GYmGikpKQYv/32mzFhwgTD39/f8Pb2Nl5//fWsut7e3pbtli9fPqtOlSpVjO+//95ITk42zp49a/z555+GYRjGggULnK7bt29fQ5LRrl0747PPPjPOnDljpKamGqdPnzaWLFli1KhRw6n+o48+ek3dj6fCw8MNh8OR1V7FihWN119/3QgPDzcuXbpkpKenGwkJCcaJEyeMDRs2GD179nS6nzFjxmS1tW7dOqdjx48fd3v9nOf8+uuvWccyMjKM0NBQp+PDhg0zDh48aCQlJRkxMTHGrl27jFdeeSXrPZk9e7YxdOjQrPoOh8NYtmyZkZSUZMTFxdn2rA3DMEaOHGn6Xhk3bpyxf/9+IyEhwYiLizN+++03Y/bs2UajRo2c6g4dOtSyzenTpzvV27RpUx6ermuTJ082fY82bNjQZf25c+ea6j/zzDNurxMTE2MEBQXl+jNv5MiRpvM2bdrk9LnM65e/v79x+PBhy3at6p87dy7X+wgODjadM3v2bFO94cOH57vPtWvXzvqMuuvvjh073L73BRUeHm66bkhIiMv6e/fuNby9vfN83+3bty+S9ux4L//xj3/k+3lnfq1fv95l+3Xq1DHVnzFjRr77WxiaN2+er/uMjIws1n4DQElF+AYA+VSSwjfDMIxPP/3UqFChQr5/sfD29jYmTpzo8fXeeustj9seM2aM8dVXX2W9djgclm126NDBZRsvvfSSYRjmQObw4cMe33doaKgRHR19Td1PXowePTpfz/aOO+4wEhMTs9op7PDNMAxj9uzZHvenb9++xpUrV4xFixZZHu/Zs6dhGPY965SUFKNLly55fh/vvvtuIyEhwbJNu8K3rVu3Wn6vXrp0ybJ+fsM3wzCMadOm5Xr/VuGbYRjGokWLDF9f3zy/n5UrVza+++47yzbtDt9SU1ON7t2757nPISEhxoEDBzzu77UYvhlG3r5fJRn16tUzoqKiiqQ9O97LlJQU4/7778/z8878mjRpksu2z58/bxlAu/psFxXCNwAoWkw7BYAbRK9evXTs2DG99NJLbqdEZufl5aWHH35Yhw4d0pgxYzw+78UXX9Rjjz3mtt6IESM0ceJEp53fDMNQamqqqe6YMWPyvC5Z1apVtWnTJreLW99yyy3avHmzQkJCLI9fK/eTm4kTJ2rq1KmmDSJy069fP23fvt32nfeGDBni0dTFJ554QsuWLZOXl5d69+6dp3WRCutZ+/r66vPPP9fLL7/s0fRBh8OhQYMGadu2bbZN3XWldevWpud95coVbdu2rdCvNWzYMNWpUyfP5w0YMEA7duxQq1atPKrvcDjUt29fhYeHq3Xr1nm+XmEoVaqUPvvsM7322mseP9OuXbsqPDxcjRo18vg6dq2zWFBDhgzR4sWLFRwc7LZup06d9M0331iuP2hXe1YK8l76+vpq48aNeuONN+Tv7+/xebVq1dKGDRucprPn9OWXX5qmwvv7+9u+iysA4NriU9wdAAAUnYoVK+qtt97Sq6++qs8++0zbtm3TgQMHFBkZqbi4OHl5ealSpUqqXLmybr31Vt1///26//778/xLkHT1F6HFixerX79+WrBggfbs2aOzZ88qIyNDVapUUfv27TV8+HA1btxYklS+fHmn8xMTE02LvN97773atGmTJkyYoJ9++knp6ekKCgrSrbfealpXKNOVK1fUsmVLRUREaOnSpVqxYoV+//13nTt3TsHBwapXr5769eungQMH5hpAXSv3kxuHw6ERI0bo8ccf19KlS7V161b9+uuvOnPmjC5fvqzSpUsrKChIt9xyi1q1aqWHH37YtJ6dnWbOnKmePXvqgw8+0O7du3X27Fl5eXmpevXqat26tZ588kmn+y5Xrpy+/PJLvfDCC9q5c6fS09NVtWpVl7+0FtazliQfHx9NmTJFzz77rJYuXaqvvvpKhw8f1oULF5SRkaHAwEA1aNBA7dq104ABAyzXXisKpUuX1l/+8hdt3rzZqXzjxo2Fvkukr6+vJk+erD59+uT53GbNmmnnzp3avn271q9fr+3btysqKkoXLlxQqVKlVKlSJdWuXVv33XefevXqVaSfS1e8vLw0btw4DRs2TEuWLNHXX3+tX375RefPn1dqaqoqVKigmjVrqk2bNnr00UctF+53J+fPiWvJY489pm7dumnRokXauHGjfvvtN507d05ly5ZV1apV1bZtW/Xp00cdO3YslvZyKuh76e3trdGjR+uf//ynVqxYoe3bt+unn37S2bNnFRsbK29vbwUGBiosLEz33HOPunTpoq5du7oN/ax2773vvvvk48OvYQBwI3EYOf8rBgDgkdDQ0Fw3LXj77beLZLFoOFu4cKGeeOKJrNcXL15UYGBgMfYIduFZX7V48WINHDjQqSwwMFDR0dElduH/61mNGjUUGRkpSfrzzz918803F3OPSq5r/b28fPmyQkJCTLswr1ixQn379i2mXl3VokUL/fDDD3k+LzIyUmFhYTb0CACub9fmWHcAAAB4pHfv3qZRP5cuXdLatWuLqUdwJTExUSdPnpQk+fn55WtUMa4qCe/l6tWrTcFbhQoV9Le//a2YegQAKC6EbwAAACVYuXLl9OSTT5rKp02bVgy9QW7WrVunjIwMSdLdd9/N1MMCKAnvpdX34NNPP52ndVcBANcHwjcAAIASbvjw4SpVqpRT2Z49e/TNN98UT4dgadasWVl/Luw1+W401/p7uWnTJu3bt8+pzNfXVy+88EIx9QgAUJwI3wAAAEq4sLAwDRkyxFQ+cuRI006LKB7r1q3Tjh07JF2dJunJ7smwdq2/lxkZGZa7g//zn/+8JqfHAgDsR/gGAABwHXjttdcUFBTkVLZnzx4tX768mHqETGfPntXTTz+d9fpf//qXKleuXIw9KrlKwnu5ePFi7d2716ksODhYY8eOLaYeAQCK27W3OAIAAADyrGLFipoyZYqeeuopp/KXXnpJXbp0MQVzKDo33XSTTp8+XdzduC5c6+/l+fPn9corr5jK33777RtyN2YAwFWMfAMAALhOPPnkk+rQoYNTWXR0tF588cVi6hFwY3n++ed17tw5p7LOnTtr4MCBxdQjAMC1wGGwEAgA5EtoaKiioqJcHn/77bc1fPjwIuwRAACAey1atNAPP/yQ5/MiIyMVFhZmQ48A4PrGyDcAAAAAAADAJoRvAAAAAAAAgE0I3wAAAAAAAACbEL4BAAAAAAAANiF8AwAAAAAAAGxC+AYAAAAAAADYhPANAAAAAAAAsAnhGwAAAAAAAGATwjcAAAAAAADAJoRvAAAAAAAAgE0I3wAAAAAAAACbEL4BAAAAAAAANiF8AwAAAAAAAGxC+AYAAAAAAADYhPANAAAAAAAAsAnhGwAAAAAAAGATwjcAAAAAAADAJoRvAAAAAAAAgE0I3wAAAAAAAACbEL4BAAAAAAAANiF8AwAAAAAAAGxC+AYAAAAAAADYhPANAAAAAAAAsAnhGwAAAAAAAGATwjcAAAAAAADAJoRvAAAAAAAAgE0I3wAAAAAAAACbEL4BAAAAAAAANiF8AwAAAAAAAGxC+AYAAAAAAADYhPANAAAAAAAAsAnhGwAAAAAAAGATwjcAAAAAAADAJj7F3QEAuF5t2bJFcXFxxd0NAAAAJydPnizuLgDADcVhGIZR3J0AgJIoNDRUUVFRxd0NAACAIhEZGamwsLDi7gYAlDhMOwUAAAAAAABsQvgGAAAAAAAA2ITwDQAAAAAAALAJ4RsAAAAAAABgE8I3AAAAAAAAwCY+xd0BACipxo0bp/j4+OLuBoAbQHR0tKZOnWoq79u3r5o3b14MPQJwIwoKCiruLgBAieQwDMMo7k4AAADAtYMHD6pRaMlFdwAAIABJREFUo0am8nnz5mnw4MHF0CMAAAB4immnAAAAAAAAgE0I3wAAAAAAAACbEL4BAAAAAAAANiF8AwAAAAAAAGxC+AYAAAAAAADYhPANAAAAAAAAsAnhGwAAAAAAAGATwjcAAAAAAADAJoRvAAAAAAAAgE0I3wAAAAAAAACbEL4BAAAAAAAANiF8AwAAAAAAAGxC+AYAAAAAAADYhPANAAAAAAAAsAnhGwAAAAAAAGATwjcAAAAAAADAJoRvAAAAAAAAgE0I3wAAAAAAAACbEL4BAAAAAAAANiF8AwAAAAAAAGxC+AYAAAAAAADYhPANAAAAAAAAsAnhGwAAAAAAAGATwjcAAAAAAADAJoRvAAAAAAAAgE0I3wAAAAAAAACbEL4BAAAAAAAANiF8AwAAAAAAAGxC+AYAAAAAAADYhPANAAAAAAAAsAnhGwAAAAAAAGATwjcAAAAAAADAJoRvAAAAAAAAgE0I3wAAAAAAAACbEL4BAPD/2Lvv8Kiq/PHjn0knjZAQQm+hSFNBpBsBIx1FQYqsCKgryKqguCLq6oooxQVcUWBREVAUsCBSZJEFpEoEEQQpMSgQSkhPIMkk5Pz+8Jd8M5k7LZk7hbxfz3OeJ3PvueeeM3fKJ5+591wAAAAA0AnJNwAAAAAAAEAnJN8AAAAAAAAAnZB8AwAAAAAAAHRC8g0AAAAAAADQCck3AAAAAAAAQCck3wAAAAAAAACdkHwDAAAAAAAAdELyDQAAAAAAANAJyTcAAAAAAABAJyTfAAAAAAAAAJ2QfAMAAAAAAAB0QvINAAAAAAAA0AnJNwAAAAAAAEAnJN8AAAAAAAAAnZB8AwAAAAAAAHRC8g0AAAAAAADQCck3AAAAAAAAQCck3wAAAAAAAACdkHwDAAAAAAAAdELyDQAAAAAAANAJyTcAAAAAAABAJyTfAAAAAAAAAJ2QfAMAAAAAAAB0QvINAAAAAAAA0AnJNwAAAAAAAEAnJN8AAAAAAAAAnZB8AwAAAAAAAHRC8g0AAAAAAADQCck3AAAAAAAAQCck3wAAAAAAAACdkHwDAAAAAAAAdELyDQAAAAAAANAJyTcAAAAAAABAJyTfAAAAAAAAAJ0YlFLK3Z0AAACoinr16iVnz561Wc9oNMr58+fNlkdHR0tYWJjN7Q0Gg+zbt0+io6Mr1E8AAABUnJ+7OwAAAFBVderUSXbs2FHh7a9cuSJXrlyxWS8uLo7EGwAAgJtw2SkAAICbjBo1yiX7GTlypEv2AwAAAHNcdgoAAOBGbdu2lWPHjunWvp+fnyQnJ0utWrV02wcAAAAs48w3AAAANxoxYoSu7fft25fEGwAAgBuRfAMAAHAjvS895ZJTAAAA9+KyUwAAADfr3LmzHDhwwOntBgcHy+XLlyU0NNTpbQMAAMA+nPkGAADgZnqd/XbPPfeQeAMAAHAzkm8AAABuNmLECPH19XV6u666myoAAAAsI/kGAADgZnXq1JGePXs6tc0aNWpI3759ndomAAAAHEfyDQAAwAM4+yy1oUOHSmBgoFPbBAAAgONIvgEAAHgAZyfLuOQUAADAM5B8AwAA8AARERHSr18/p7RVp04dufPOO53SFgAAACqH5BsAAICHcNbZaiNHjtTlBg4AAABwnEEppdzdCQAAAIjk5+dLTEyMZGdnV6qdAwcOyO233+6kXgEAAKAyOPMNAADAQwQFBck999xTqTZiY2OlY8eOTuoRAAAAKovkGwAAgAep7KWno0ePFoPB4KTeAAAAoLK47BQAAMCDFBUVSb169SQlJaVC2x87dkxat27t5F4BAACgojjzDQAAwIP4+fnJ0KFDK7Rt+/btSbwBAAB4GJJvAAAAHqail546626pAAAAcB4uOwUAAPAwSilp2rSp/P7773ZvYzAY5MyZM9KoUSP9OgYAAACHceYbAACAhzEYDDJ8+HCHtrnjjjtIvAEAAHggkm8AAAAeaOTIkbrWBwAAgGtw2SkAAICHatu2rRw7dsxmPT8/P0lOTpZatWq5oFcAAABwBGe+AQAAeKgRI0bYVa9v374k3gAAADwUyTcAAAAP9eCDD4rBYLBZj7ucAgAAeC4uOwUAAPBgnTp1koSEBIvrg4OD5fLlyxIaGurCXgEAAMBenPkGAADgwWyd1XbPPfeQeAMAAPBgJN8AAAA82MiRI8XX19fiei45BQAA8Gx+7u6APQ4fPizLly93dzcAAADcom7dunLu3Dmz5YGBgfLdd9/J9u3b3dArAAAA9+rZs6fce++97u6GTV6RfDt16pQsWLDA3d0AAADwKAUFBfLOO++4uxsAAABu4w3JNy47BQAAAAAAAHRC8g0AAAAAAADQCck3AAAAAAAAQCck3wAAAAAAAACdkHwDAAAAAAAAdELyDQAAAAAAANAJyTcAAAAAAABAJyTfAAAAAAAAAJ2QfAMAAAAAAAB0QvINAAAAAAAA0AnJNwAAAAAAAEAnJN8AAAAAAAAAnZB8AwAAAAAAAHRC8g0AAAAAAADQCck3AAAAAAAAQCck3wAAAAAAAACdkHwDAAAAAAAAdELyDQAAAAAAANAJyTcAAAAAAABAJyTfAAAAAAAAAJ2QfAMAAAAAAAB0QvINAAAAAAAA0AnJNwAAAAAAAEAnJN8AAAAAAAAAnZB8AwAAAAAAAHRC8g0AAAAAAADQCck3AAAAAAAAQCck3wAAAAAAAACdkHwDAAAAAAAAdELyDQAAAAAAANAJyTcAAAAAAABAJyTfAAAAAAAAAJ2QfAMAAAAAAAB0QvINAAAAAAAA0AnJNwAAAAAAAEAnJN8AAAAAAAAAnZB8AwAAAAAAAHRC8g0AAAAAAADQCck3AAAAAAAAQCck3wAAAAAAAACd+Lm7AwAA3EgiIiIkIyPD7vrXrl2TzMxMycjIkAsXLkhCQoL88MMPsmfPHklLS9Oxp1WTv7+/xMXFSc+ePaV9+/bSpEkTqVOnjgQHB4ufn5/k5ORIdna2nD17Vn7++Wc5cOCArF+/XjIzM93ddQAAAHgr5QVWr16tRIRCoVAoFI8vERERTvnuMxqNavXq1ap3795uH5OlMnPmTJM+T5061e19slQiIyPVK6+8olJTUyt0LL744gvVrl07t4+D4nnFm94HFAqFQqHcaGXy5MmVirldhctOAQDwQP7+/jJ8+HDZtm2bbNu2TRo1auTuLpnw8fGRhx56yN3dsMuUKVPkjz/+kFdffVWioqIc3t7f31/uv/9+OXz4sLzzzjsSEBCgQy/hjbzpfQAAANyH5BsAAB6ud+/ecvToURkxYoS7u1IqPj5eGjRo4O5uWBUSEiKfffaZzJs3T0JDQyvdno+Pj/ztb3+TXbt2SfXq1Z3QQ3g7b3gfAAAA9yP5BgCAFwgLC5NPPvlEhgwZ4u6uiIjIuHHj3N0Fq3x9fWXdunW6JCw7deok69evl8DAQKe3De/i6e8DAADgGUi+AQDgIv379xeDwWBSwsPDpXHjxjJ48GCZPXu2JCcnW9ze19dXPvvsM+natasLe20uIiLCY5KAlsycOVPi4+Mtri8oKJAVK1bIqFGjpEWLFhIZGSkBAQFSq1Ytueuuu+Rf//qX1RtexMXFyYsvvqhH1+ElvOF9AAAAPIS7J52zBzdcoFAoFIq3FGs3XOjXr5/N7f38/NSjjz6qcnJyLLZz6NAh5ePjY7MtHx8f1atXLzV//ny1c+dOlZycrHJyclRhYaG6cuWKOnLkiFq6dKkaMmSI8vX1tdrWtGnTHP7+TkxMdFn/ypZOnTpZ7de6detUw4YN7TqWH374ocV2CgoKVJMmTRx6fQQGBqpRo0ap//znP+rnn39Wly9fVkajUaWmpqpffvlFrVmzRo0cOVKFhIRU6vW2YcMGk3pRUVHqhRdeUHv27FHp6enKaDSqS5cuqf3796vp06erWrVqVej17qrxfPPNN6V16tWrp5YuXarOnz+vjEajunjxoho+fLjXvg9KSvPmzdXTTz+tNm7cqI4fP66uXLmijEajunLlivr111/Vli1b1HPPPafatm1b6c+jyjyfFAqFQqF4UvGWGy6QfKNQKBQKxYmlssm3ktKyZUt1+fJli22NGTPG6vYDBgxQp06dsvu79syZM6pXr14W23N20sHZ/StbvvzyS4vtLF682K7EZdny+uuvW2xv/vz5drVhMBjU5MmTrR7TsuxNgPj5+Wluv2fPntI6w4YNU5mZmVb3l56erh544AG7nxO9xuPv76+5/e7du5WIqIYNG6rk5GSz9ZbuMOrp7wMRUa1atVJffPGFQ23+97//VR07dnT580mhUCgUiqcVkm9ORPKNQqFQKN5SnJV8ExHVpUsXVVhYqNnWqVOnLG738ssvV+j79vr16xaTes5MOujRv5LSvHlzdf36dc3tDx48qPz8/Bw+pgaDQf3www8mbV28eFEtWbLEroRgaGio2rRpU4XGPGfOHJvtG41Gs+2OHz+uRESNGDFCFRcX27WvoqIiNXjwYLePR+s1f/ToUSUiasuWLZrtaiWLPP19ICJqzJgxqqCgoML9fPrpp132fFIoFAqF4omF5JsTkXyjUCgUircUZybfRER98MEHFtvTOvNl9OjRlfrONRqNqn379mbtOivpoFf/SsqUKVMsbtuzZ88KH9devXqpkydPqjlz5qiuXbsqg8Fg13Y+Pj5q/fr1lRqzrURIdna22Tbnzp1TTZs2Vbm5uQ7t68KFCyosLMyt49Hq8++//666du1qd5ue/j4QEfXkk09Wqo8lpk+frvvzSaFQKBSKpxaSb05E8o1CoVAo3lKcnXxr0aKFxfaee+45k7pBQUEWLwP88ccf1V133aUiIiJUZGSk6tOnj8XL8TZv3my1T5Yuw7T1D7sr+mfpktMTJ0645fUwdepUzf7k5OSoZ555RjVu3Fj5+/ur2rVrq0cffVRdunTJrG5eXp7VueXS09PNtklNTVVr167V3LctEyZMcOt4MjIyzLZJSUlRK1assNjnsq89T38fiIi6/fbbNc9YVEqpP/74Qz366KOqQYMGKiAgQNWpU0eNGTNGJSUladYvKipSXbt21e35pFAoFArFkwvJNyci+UahUCgUbynOTr6JiMV/ust/P44cOVKzXn5+vqpdu7ZZu61bt9a8JLG4uFhFR0db7E9Fkw6u6J9Wskcppd555x2XvxbCwsJUamqqWV+MRqPq0qWL5jZNmjRRaWlpZtt8/PHHFvejtY/i4uLS5+7QoUNqwIABKjw8XIWHh6sBAwao48ePaz5PSim1detWjxtPfn6+unr1qlJKqS1btqgePXqokJAQFRYWplq2bGmSzPP094GIqEOHDmlue+jQIVW9enXNbapXr64OHz6sud3Bgwd1ez4pFAqFQvHk4i3JNx8BAAAebc+ePZrLmzRpYvI4IiJCdu3aJT/99JMkJibKpUuXJDc3V77//nu5dOmS2fbHjx+XAwcOmC03GAwSFxfnnM67sH/+/v4SExOjue8ff/yx8gNw0KOPPipRUVFmy1euXCn79+/X3ObMmTPy5ptvmi2/7777JCQkxO59GwwGMRgMsn37dunatats2rRJsrOzJTs7WzZt2iR33HGHnD9/XnPbDh06eNx4AgMDJTg4WNauXSv9+vWT3bt3y9WrVyUnJ0dOnjwpZ86cKa3r6e+DXr16Sfv27c2WFxUVyYMPPihZWVma22VlZcm4ceNEKWW2rkOHDtK5c2e7++DI8wkAACqP5BsAAB4uJSVFc3nNmjVNHi9evFji4uKkQ4cO0rx5c6lTp46EhYVJnz59LLZ95MgRzeV169ateIct0Lt/kZGRFtu5cOGC4x2upPvvv19z+Zdffml1uzVr1pgtCw4OlgEDBji0/2vXrsmYMWOkoKDAbF1aWprMmjVLc7vIyEipUaOG2XJ3jycnJ0cmTpyomXwqy9PfB2PHjtVcvnnzZjlx4oTVbX/66SeLyfhRo0Y51A97n08AAFB5JN8AAPBwaWlpmsuDg4Mr3bals2y0ki/u4Ej/rPU5MzPTrv2tW7dO1J/TcthdXn31VbN2/Pz8pGPHjpr7OHnypNU+nD17VnPct99+u11jKLFmzRqLZ7eJiGzYsMHiuurVq5s89oTxfPnllxbfC5XlyveBpbPpNm3aZNf2W7Zs0VzuyJlvIvo+nwAAwJSfuzsAAACsCwoK0lxeVFTkcFt+fn7i7+8v/v7+4ufnJ4GBgZr1fHzc8/tcZfp3/fp1i+36+/s7rY/2aNSokcXjdvr06Qq12a5dO4fqf/vtt1bXnzt3ToqLizWfy/LPuyeM53//+1+F9qPFXe+D6Ohoady4sea6o0eP2tXG8ePHNZe3b99eDAaD3WeyOfP5BAAA1pF8AwDAw1m6nDI3N9fiNs2aNZOhQ4dK9+7dpXXr1lKzZk0JDw8Xg8GgVzcdokf/0tPTLa6LiIiocLsVUbt2bae3WX6OP1t+/fVXq+uLi4slNTVVatWqZbau/HHwhPHYOsNOi6e9D6w9j8nJyXa1YekS6sDAQAkLC5Ps7Gy72qnI8wkAACqG5BsAAB4uNjZWc7nWJYXR0dGyYMECGTVqlMck2srSs38ZGRmilNJs19JzqJdq1ao5vc2wsDCH6lu6lLKsnJwczeRbeZ4wnoyMDLvreur7wNq8hFevXrWrDWv1atSoYXfyzZHnEwAAVA7JNwAAPJivr690795dc92pU6dMHtevX1927Njh8kSTvfTuX3FxsSQlJWm27+j8YpVlNBqd3mZ4eLhD9a1dhusoTxjPtWvX7Krnye8Da5eE2psktHYpbHFxsd19sff5BAAAlccNFwAA8GB33XWXxTOE9u3bZ/J4xYoVdiccrl+/LgUFBVJYWFjpPtrLFf2zdCfI/v37S0BAgM3thwwZIgaDQbMMHjzY7n5YO6uofv36FvdhrTh6ppgzedN4PPl9YO3S6NDQULvasFaPGygAAOCZSL4BAODBpk6dqrn8+vXrJnc97NKli/Tq1UuzblJSkjz11FPStm1biYyMFB8fH/Hz85OgoCCZN2+eLv0uz1X9s5R8q1mzpjzwwAMO97uirCVZYmJiXNYPZ/GW8Xj6++DSpUsW1zVs2NCuNizVy8nJ4Ww2AAA8FJedAgDgoYYNGyZ333235rrNmzdLSkpK6WNLZ2VlZmZK9+7dLf7T76qzqVzVv6+++krefvttzTtzzpkzRzZu3CiZmZkOtVkRycnJkpaWJlFRUWbr9Lh5gd68ZTye/j5ITU2VpKQkadq0qdm6W265RXbs2GGzjZtvvllz+f79+yvbPQAAoBPOfAMAwAO1b99eli5dqrlOKSWvvPKKybIGDRpo1v3222+tnm3TpUuXineyHGtzUbmqf1euXJFVq1Zprqtbt66sXr1agoODHWqzhKNzlJW/LLhEt27dKrR/d/OG8Xj6+0BE5Pvvv9dcPmjQILvaHzhwoObynTt32rU9AABwPZJvAAB4EIPBIKNHj5bvv/9eIiIiNOt8+eWXcujQIZNlISEhmnWtTZTfs2dP6dChg+Y6rTPHbLF210xX9u+tt96yOIdXnz59ZOfOndK+fXuL25cXGxsrb731lnz44Yd2byMisnHjRs3lY8aMsTr/XL9+/SQ7O1tOnz4tu3fvls8//1zeffddiY+Pd2j/zuYN4/H094GIyPLlyzWX9+7d2+JZbSXi4+OlXbt2ZsuLiopk5cqV9ncSAAC4FMk3AADcyGAwSGRkpNxyyy0yZcoUOXjwoHz88ccWJ1W/cOGCPPXUU2bLr1y5olm/c+fO4uvra7a8adOmsmLFCov9snYpYX5+vubyO++80+I2ruzfr7/+Ki+88ILF9R07dpQff/xR1q1bJ2PHjpWbbrpJIiMjxd/fX6Kjo6Vly5YyaNAgmTFjhuzatUsSExPl2WeflcDAQM32LN1VdOXKlZo3KmjQoIHMnj1bc5tq1arJa6+9JmFhYdKsWTPp3r27DB06VCZOnGhymbE7eMN4PP19ICKyY8cO+fHHH82W+/j4yPLlyy2eYVm3bl35z3/+o7lu7dq1cvbsWav7BQAAbqS8wOrVq5WIUCgUCoXi8SUiIkK378O8vDzVsWNHzf2OHz/e4nYrV65UzZo1U4GBgSo2NlY9//zzKiMjQymlVFpamjp16pTZNsnJySoiIkJzX4899pjFfb355puqbt26KigoSLVu3VoFBga6vH8iogwGg/rmm28s7tNZvv32WxUWFmaxH9OmTbO47dq1a1Xnzp1VSEiIioqKUv369VP79+/XrPvhhx9a3EdqaqrmNvXr17f5ek1MTNTc9qabbvLK8Xj6+6Ck3HbbbaqgoEBz28TERPXggw+q6Ojo0r4+/fTTKiUlRbN+SkqKqlu3ri7PJ4VCoVAonl4mT55s8fvYk5B8o1AoFArFiUWv5FtKSorq1auXxf1GRkaqrKwsh9sdOnSoWrRokea6s2fPqnXr1ql58+aZ7Ktdu3Z2t1/yD74r+1dSgoKC1Pr16x3epz0KCgrU9OnTlY+Pj9XXg4+Pj9q2bVul9nX69GkVHh5ucR+uTL55+ng8/X1QtkyaNMnhfpZXUFCg+vTpY/U5IflGoVAolBu5kHxzIpJvFAqFQvGWokfybe/evapBgwY29/3EE0841O6MGTOUiKhevXpZrbdjxw6zfe3Zs8eufZT9B9+V/SspBoNBPfvssyo3N9ehfVtSVFSkPvvsM9W0aVOHXhNbt26t0P5+/fVXm0kSVybfPH083vA+KFseeughi2fA2XLlyhXVo0cPm88HyTcKhUKh3MiF5JsTkXyjUCgUircUZybf9u/frwYPHuzQ/v/+97+rwsJCq+1eu3ZNjR071mS7FStWWKyvlXRo1qyZOn/+vM0xlP8H31X9K1/q1q2r/vWvf5VeZuio06dPq5kzZ6rGjRtX6HXh5+enpk+frtLT0+3aX15enpo3b54KDg622bark2+ePB5veR+ULW3atFHr1q2z2UaJgoICtXDhQlW7dm27nguSbxQKhUK5kYu3JN8MSiklHm7NmjUyYsQId3cDAACbIiIiNCelt8ZoNEpqaqpcuXJFEhMTZfv27bJt2zY5ceJEhfrQrFkzeeKJJ6Rnz57StGlTCQ0NlZycHDl58qRs2bJFlixZIhcuXDDZxmAwyEMPPST33XefNGrUSHx8fCQtLU1OnDghGzdulE2bNpntJzo6WqZOnSqDBw+WJk2aiMFgkKysLElPT5cjR47I3r17ZcmSJWYT07uqf1oCAgKkd+/ecuedd0r79u2lSZMmEhMTI8HBweLj4yNXr16VrKwsOXPmjJw6dUoSEhLk+++/r/CxKC88PFzuv/9+ueuuu+S2226T6OhoiYiIkKtXr0p6erocPXpUtm/fLp988onFmweUl5qaKlFRUWbLGzRoIOfPn7e6bWJiosTGxpotb9WqlV1j9rTxlOXp74PyWrVqJYMGDZL4+Hhp3LixREdHS2hoqKSnp0tqaqocP35ctmzZIhs3bpRLly7Z/Tw46/kEAMATTZ48WebPn+/ubthE8g0AAAAAAABex1uSbz7u7gAAAAAAAABwoyL5BgAAAAAAAOiE5BsAAAAAAACgE5JvAAAAAAAAgE5IvgEAAAAAAAA6IfkGAAAAAAAA6ITkGwAAAAAAAKATkm8AAAAAAACATki+AQAAAAAAADoh+QYAAAAAAADohOQbAAAAAAAAoBOSbwAAAAAAAIBOSL4BAAAAAAAAOiH5BgAAAAAAAOiE5BsAAAAAAACgE5JvAAAAAAAAgE5IvgEAAAAAAAA6IfkGAAAAAAAA6ITkGwAAAAAAAKATkm8AAAAAAACATki+AQAAAAAAADoh+QYAAAAAAADohOQbAAAAAAAAoBOSbwAAAAAAAIBOSL4BAAAAAAAAOiH5BgAAAAAAAOiE5BsAAAAAAACgE5JvAAAAAAAAgE5IvgEAAAAAAAA6IfkGAAAAAAAA6ITkGwAAAAAAAKATkm8AAAAAAACATki+AQAAAAAAADoh+QYAAAAAAADohOQbAAAAAAAAoBOSbwAAAAAAAIBOSL4BAAAAAAAAOiH5BgAAAAAAAOiE5BsAAAAAAACgE5JvAAAAAAAAgE5IvgEAAAAAAAA6IfkGAAAAAAAA6ITkGwAAAAAAAKATkm8AAAAAAACATki+AQAAAAAAADoh+QYAAAAAAADohOQbAAAAAAAAoBOSbwAAAAAAAIBOSL4BAAAAAAAAOiH5BgAAAAAAAOiE5BsAAAAAAACgE5JvAAAAAAAAgE5IvgEAAAAAAAA68XN3B5ylVatW7u4CAADwUL///rvk5eVZXB8ZGSkxMTEu7BEAAABssRXDeYsbJvl2/Phxd3cBAAB4qC5dusgPP/xgcf2YMWNk/vz5LuwRAAAAbLEVw3kLLjsFAAAAAAAAdELyDQAAAAAAANAJyTcAAAAAAABAJyTfAAAAAAAAAJ2QfAMAAAAAAAB0QvINAAAAAAAA0AnJNwAAAAAAAEAnJN8AAAAAAAAAnZB8AwAAAAAAAHRC8g0AAAAAAADQCck3AAAAAAAAQCck3wAAAAAAAACdkHwDAAAAAAAAdELyDQAAAAAAANAJyTcAAAAAAABAJyTfAAAAAAAAAJ2QfAMAAAAAAAB0QvINAAAAAAAA0AnJNwAAAAAAAEAnJN8AAAAAAAAAnZB8AwAAAAAAAHRC8g0AAAAAAADQCck3AAAAAAAAQCck3wAAAAAAAACdkHwDAAAAAAAAdELyDQAAAAAAANAJyTcAAAAAAABAJyTfAAAAAAAAAJ2QfANEZMOGDWIwGErL77//7u4uwQF33323yfEzGAwybtw4d3cLqBJGjx5t9v4bMGCAu7sFVBnEMN6NGAaAqxG7uQfJNydZvHixyYt39+7d7u4SUCW8//778t1335ksq127tsybN690ffkvl5Ly9ddf272ft956y2z7adOmOXUsqLz33ntP81hKnAcwAAAgAElEQVTXrl3bru0vXrwob7zxhsTHx0uDBg2kWrVqEhYWJrGxsTJy5EhZuXKlFBUV2d0fZ7enh8LCQvniiy/kkUcekVtuuUWio6MlICBAQkNDpV69ehIXFyfPPvus7N+/X3P7t99+W6Kjo02Wbd68WZYvX+6K7lcJ6enpsnbtWpkwYYJ06tRJmjZtKuHh4RIUFCT16tWTW2+9VYYNGyaLFi2SxMREd3cXgJ2IYaquHTt2yFNPPSUdOnSQmJgYCQgIkLCwMGnYsKEMGDBA3njjDTl37pxDbXpDzFFVxy2iz9i12BMLE7u5ifICq1evViJitbjbokWLTPqza9cud3fJ4xQWFqpq1aopEVGLFi1yd3dMfPPNNybH78yZM+7uEuyQlpamatSoYfZ5sGrVqtI6S5cutfi50bx5c2U0Gu3a19y5c822f/755/UaGirgzJkzKjQ0VPNYx8TE2Nx+1qxZKjg42Ob3TcuWLdWBAwdc3p4evv76a1WvXj2bfSwpXbt2VcePHzdr56OPPjKrGxUVpTIyMtwwKm2dO3e2OrbJkye7u4tmzp8/ryZNmqQCAwPtPkYiovr27av27dvn7u579Pd+RXjqeIhhvBMxTNV0+vRp1a1bN7s+y318fNSECRNUTk6OzXY9PeaoquNWSr+xa3EkFvaG2K2EN8ZwWjjzDS5z7NgxycvLc3c3cAN59dVXJSMjw2RZp06dZOTIkXZtf/r0aVm4cKEeXYOLKaVk/PjxkpubW6Htn3vuOZk2bZpcu3bNZt2TJ09KfHy8/PDDDy5rTw8LFy6Ue++9V5KTk+3eZt++fdK5c2c5cOCAyfKHHnpIbr31VpNlaWlpMmPGDKf0tSpasWKFNGvWTN59910pKChwaNstW7ZI165dZcKECVJYWKhTD2270b73b7TxwL2IYaqeQ4cOSceOHWXv3r121S8uLpbFixfLXXfdZTW+8fSYo6qOW0S/sWtxNBYmdnM9km9wmR9//NHdXcAN5OzZs7J48WKz5bNnzxaDwWB3OzNmzJD09HRndg1usGjRItm+fXuFtl29erW89dZbDm2TnZ0tw4YNk+zsbN3b08NPP/0kTz/9dIW2zcnJkREjRpgkhHx8fOSNN94wq7tw4UK5cOFChftZVU2bNk0efvhhyc/PL10WFRUlEydOlPXr10tiYqJkZWVJfn6+nD17Vnbt2iUvv/yytGzZ0qSdJUuWSHx8vMteV+XdaN/7N9p44D7EMFVPdna2DB48WLKyshze9sCBAzJ58mTNdZ4ec1TVcZfsT4+xW+JoLEzs5nok3+AyBK1wpnnz5pmd0dGpUyfp2bOnQ+1kZGTIq6++6ryOweXOnDkjzz//fOljR/5xKSoqMtm2xM033yzbt2+X3NxcSU1NlcWLF0u1atVM6pw/f17mz5+va3t6mTlzphQXF5stf+ihh+To0aNSUFAgWVlZsmHDBmnVqpVZvd9//10+/fRTk2X9+/eXW265xWSZ0WiUBQsWOLfzN7ilS5fK7NmzSx8bDAaZOnWq/Pbbb/Lee+/J4MGDJTY2VsLDwyUwMFAaNGggPXr0kNdee02OHTsm77//voSHh5du//3338v48ePdMZQb7nv/RhsP3IcYpuqZO3euZkLjzjvvlL1790p2dracO3dOPvjgA6lZs6ZZvWXLlsmZM2dMlnlDzFFVxy2iz9gtqWgsTOzmYu6+7tUezPl2Y7j99ttLnx9PmitFKeZL8TY5OTma8xl89tlnZnWtzZdSUvz8/NSJEyes7pP5UjxTcXGx6tWrl8lxGTJkiM15Lkp8+eWXZnXDw8PVpUuXzOq+9957ZnVr166tCgsLdWtPD9evX9ecH6Vz586a9U+cOKF8fX3N6o8YMcKs7vLly83qVa9eXV27dk3XMdnDG+YLOXbsmMn8bn5+fmrlypUOt3P48GFVu3Ztk/EtXLhQhx5b58nf+xXhqeMhhvEuxDBVz/Xr11WdOnXMjkHbtm01v/O3bNmieawXLFhgUs/TY46qOm6l9Bu7lsrGwp4cu5XwhhjOHpz55mLLli0rveNIixYtSpcrpWTdunXSt29fqVWrlvj7+0tERIS0a9dOnnrqKTl9+rRme3Pnzi1tr2nTpqXLU1NT5R//+Id06tRJ6tatK4GBgVK3bl3p0aOHzJ8/3+rpr7NmzSpt08/Pz65xLViwQHObsneBTUhIKF0+ceJEk7uvVObXZKPRKGvWrJHRo0dLu3btJDIyUvz9/aVatWpSp04d6dGjhzz//PPy008/2d1mya8FRUVF8sEHH0jfvn2ladOmEhQUJDVq1JC2bdvK008/Lb/99ptd7V2/fl02btwojzzyiNx6660SFRUlAQEBEhISIvXr15d+/frJnDlzJCUlxWIbehzr8i5cuCAzZ86Uu+++W+rXry/VqlWT8PBwadasmQwcOFCWLFliNj+JlrKvB4PBIN9++63dfbDHF198YTafQUREhAwZMsSu7bt162byuKioSKZOneq0/pW1Z88emT59unTt2lUaNWokwcHBEhoaKo0bN5auXbvK9OnT7bo78gcffGB216K+ffuWrldKyerVq2XgwIESExMj/v7+Eh0dLV26dJFZs2ZJTk6O3X3Ozs6WRYsWyQMPPFB6hk1QUJA0btxYevXqJf/+97+tvlZd6b333jM5xT4yMtKhY/n555+bLRs9erTExMSYLR83bpyEhISYLLt06ZLs2rVLt/b0kJKSojk/yogRIzTrt2zZUm677Taz5X/88YfZsmHDhkloaKjJsqysLFm/fn0Fe1u1zJgxw+Ry3n/84x/yl7/8xeF2brnlFvnss8/Ex+f/wrwZM2aYXMZalju/94ljnB/HeGIMI0IcU4IYpurFMIcPH5aLFy+aLX/xxRc1P6P69OkjDRo0MFv+yy+/mDz29Jijqo5bRL+xa6lsLEzs5kJuTv7Z5UY68+2TTz4xyborpVRGRobNO6AEBASoTz75xKy9stn8qKgopZRS+/btU7Vq1bLaXoMGDdSePXs0+/jmm2+W1vP19bVr/PPnz9fcpvzzYqkkJCTYtZ/y9u/fr5o1a2bXPkREDRs2TGVmZpq1U/5X43PnzqmLFy+qjh072jwuZe9KpeXo0aPq1ltvtat/ISEhaunSpZrt6HGsSxQWFqq///3vKiAgwGYfo6Ki1LJly6y2V/b1ICJq8+bNVus7qm/fvmb9euyxxzTrav1q/Pbbb6uGDRuaLf/uu+8s7tPRX41/+OEHdccdd9j92uzevbvVuxN++umnZtuUnKmUlpamevbsabX9evXqqZ9//tnq81pcXKzeeustFRYWZrO/4eHhFl+rrpKUlKRCQkJM+rVs2TJ16NAhs/5a+rWv/NlBIqK++OILi/vs06ePWf2XXnpJt/b0kJycrHlMP/74Y4vbDBo0yKx+p06dNOs+9NBDZnXvvfdevYZjN0//1TQpKcnkDMPWrVuroqKiSrU5ceJEkzFaOlvLnd/7xDGVj2O8IYZRijimBDHMn6UqxTDbt29XvXr1Uh06dFDNmjVT0dHRKjAwUPNMrRJaz//9999vUsfTY46qOm6l9Bt7ec6IhZXy3NithKfHcPbizDcXCwgIKP372rVrYjQaJT4+3uYdUIxGo4wfP15+/fVXk+VlM+e5ubly/vx5GTBggM1fdM6dOyeDBg2SU6dOVWAUnuHUqVMSHx8viYmJdm/z+eefy5AhQ0QpZbWewWCQfv362fwl22g0ypgxY+T48eOa60+fPi1xcXFy+PBhu/p39epVeeyxx+Sjjz4yW6fXsS4qKpJBgwbJnDlzxGg02uxjWlqajBs3TmbNmmWzrh7y8/Nl586dZssHDBhgdxs5OTkyc+ZMs+XPPPOM5jxYjlq5cqXccccdDv2qtmfPHomLi5MVK1Zorg8MDDRblp2dXXr8duzYYbX95ORkufvuuyUtLU1zfXFxsQwfPlymTp1q1y/M2dnZ8thjj8k///lPm3X1oP7/HZ2uXr1aumzgwIEyduxYuX79ul1tXLx4US5dumS2vHXr1ha30Zr/rORzwtnt6aV27dpSvXp1s+XWJte9fPmy2bKbbrpJs67We3Hbtm1uveumN/jyyy9NXrtPPfWU+Pr6VqrNyZMnm8z7snr16kq1pwfiGOfHMZ4Yw4gQx4gQw5RVlWKYnj17yv/+9z85ePCgnD59WlJSUiQ/P1/zTK0SV65cMVsWGRlZ+rc3xBxVddwi+oy9PGfEwiWI3VyD5JuL+fv7l/6dn58vs2fPloMHD0qrVq3kk08+kYsXL0phYaGkpqbKhg0b5Oabby6tX1BQIG+//bZJe2UD84KCAvn73/8uGRkZ0q1bN1m3bp1cunRJjEajXLp0ST799FNp1qxZaf2MjIwK3+3OXhMmTBCllOTl5ZksX7RokSilSkvHjh0dbvvFF18sPW0/ICBAXnjhBUlISJCMjAwpKiqSnJwcSUxMlFWrVpmcor9jxw5Zu3at1bbnzp0rP//8s7Rs2VKWL18uFy5cEKPRKFeuXJEvv/xS2rRpU1q3qKjI4t12Jk2aZHJ5w8CBA+Wbb76R5ORkKSgokKtXr8qhQ4fk6aefNrk86JlnnjG7zEKvY/3CCy/Ili1bSh83b95c/vOf/8jx48fl6tWrkpubK0eOHJE333xToqKiTLbbtm2b1edRD3v27DG7dMrX11d69epldxsZGRkyevRos9fdkSNH5IMPPqhU/zZt2iQPP/ywXf8AlFdYWChjx46VrVu3mq0rm7gvkZ2dLXPnzpV9+/bZ1X5KSoq89tprmuuee+45zdP4bXn11Vflq6++cni7ynr33XdNgvXIyEhZunSpQ21YuuSqfv36FrfRWlcyLYCz29OLj4+PDBs2zGz5xx9/rPmP22+//aYZpA4fPlyz/fj4eLOJfnNzc2X//v0V7HHVUPb1bDAYLF4G7IgWLVqYfM7t37/f5LJWPTj6vU8c4/w4xhNjGBHiGBFimLKqcgxjy08//SQnTpwwW968efPSv70l5nBEVR23iH1jL88ZsXAJYjcXcf3Jdo67kS47LXtpgMFgUEFBQapPnz4WJzRMTU1VkZGRpds0atTIZP2yZcvMnoshQ4ZYnEQyMzNTtWjRwqT+kSNHTOo483KNEnl5eSb7rOxExcXFxSYThr/11ls2t/nLX/6iYmJiVMeOHdW8efNM1pW/ZCMwMFDFx8erq1evaraVlpamatasaXI6fHm//fab2XGxZtasWSb1y18KosexTkpKUn5+fqXr+/fvb3VyzfPnz6vGjRuX1m/btq3VMemh7OuzpLRp08Zifa1LNiZNmqSUUmrnzp2ap2RnZ2ebtWPPJRvp6ekmr4uyZfTo0Wrfvn0qJydH5ebmqr1796phw4Zp1q1Tp47Za2/Tpk1m9YKDg1X16tWVj4+PmjJlikpMTFT5+fnq8OHDavDgwZptR0VFmb1mfvnlF+Xj42NWt3379mrTpk3q4sWLKjMzU+3Zs0f179/frF7Tpk1VQUFBRQ+pw7ROsS/7fklISLDrVPs1a9aY1QsICLC6748++shsm2rVqunSnp7OnTunIiIizPZ93333qcOHD6v8/HyVnZ2tvv32W9WqVSuzer1791bFxcUW24+NjTXbZv78+bqPyxpPv2QhKiqqtC+tW7d2WrtTpkwxGafW5Xvu/N4njql8HOMNMYxSxDFKEcOULVU1hrHFaDSqTp06aY4/MTGxtJ43xRz2qKrjVsr+sZflrFi4LE+M3Up4egxnL858cyOllAQFBcknn3xidhvkElFRUSZnF/zxxx9mk7SWFRoaKu+//77FCYarV68uc+bMMVm2YcOGCvTevTIzM00mDC9/i2QtK1eulEuXLklCQoJMmTLFat3g4GD59NNPJTg4WHN9ZGSkjBw5svRxcnKy2XFJTk6WO+64Q1q0aCHh4eHyt7/9zeo+n3zySZMzIw8ePGi1vjOO9fz586WoqEhERKKjo2XVqlUWX4siIvXq1ZPFixeXPv7ll190P227vJ9//tlsmT3Hv6ySMcfFxcm9995rsu7y5cvy5ptvVqhvixcvltTUVLPl//znP+Xjjz+WLl26SGhoqISEhEjXrl1l7dq1mq+LixcvyqpVq0yWad0y/Nq1a5KVlSVvv/22zJs3T2JjYyUwMFBuueUW+eqrr8wmZRb583Kb8r+szZw50+yMp8aNG8uOHTukf//+pZcpduvWTTZt2iQDBw40qZuUlOSyX46Vxin2Q4cOlVGjRjncVnp6utmy8hPO2rM+Ly9P8vPznd6enurXry8bNmwwOQtEROSrr76SW2+9VYKCgiQ8PFz69etnNt1Bt27d5PPPP7d6G/uyZ22X0Hrv4k9FRUUml1NpXSJTUW3btjV5rDUBtCchjrHM3jjGG2IYkaoZxxDD/J+qGMPYUlxcLOPGjZMDBw6YrbvvvvskNja29LE3xRy2VNVxizg29hLOjIXLInbTH8k3Nxs7dqzUrFnTap1bb73V5LG1uzQ98MADZv9MlTdw4ECTD6E9e/bY0VPPEh4ebnL5wsaNG53a/vjx420el3bt2pk8Lv9lcMcdd8j3338vJ0+elKysLLnrrrusthccHGxylxutAKgsZxzrzZs3l/49evRoiYiIsNqeiEjfvn1N+vnNN9/Y3MaZtObGadmyZYXbmzNnjsk/DCJ/BvNad3K0RetU75tuukleeukli9vMnj1bcz6HlStX2rXPjh07aga/vr6+Fu90VPZU++vXr5u8DkpMnjxZwsPDLfa5vIpc7lER5U+xj46OlkWLFlWoLa2gq/xrwd71loK4yrSnt+7du8uRI0fkySeflNq1a1utazAYpFu3brJkyRLZuXOn1KhRw2p9rfekvXdWrIrKz2NkbY4XR5Vvy9KcSZ6COKbyvCGGEamacQwxjKmqFsNYU1hYKGPGjJFPPvnEbF1oaKjZ5eHeFnNYUlXHLeL42Es4MxYui9hNfyTf3MxWMCMiZgFU2V9Ky7Nnzgg/Pz9p37596WNPu+bdHr6+vtKzZ8/SxwsWLJAnn3xSkpOTndJ+fHy8zTrlj4szPsDL/lpb8sumJZU91hcvXjQJAsvWs6VLly6lfx85csTu7ZxBa1L4OnXqVLi9Fi1ayIQJE0yW5efny7Rp0xxq5+zZs3LmzBmz5Q8++KDJXDjlBQcHy6BBg8yWJyQk2HwNiPyZwLdE61djkT/PuCjx008/mTwu0alTJ4vttm7d2iz5UvYW53pJSkoyOy6LFy+W6OjoCrWnNRmtrQnuLQVwhYWFTm/PFc6fPy9ZWVk2b0KjlJILFy7I0aNH5ffff7fZbr169TT3BW3lzzqydMZSRZT/xd/a2fOegDim8nGMN8QwIlUzjiGGMVWVYhhrMjIyZMCAAZpJGIPBIMuWLZOmTZuaLPfGmKO8qjpukYqNXcT5sXBZxG76I/nmZo0bN7ZZp/wdgqz9k1T+l0xLGjVqVPr3uXPn7NrG08ydO9ck0Fu4cKE0bNhQunfvLi+//LJs27atwqcTN2zY0Gad8pPHWjsuly9flg8//FDGjx8vPXr0kObNm0tMTIzUqFFDQkNDJSgoSPz8/OTYsWN297Gyx/rs2bMm9R5++GExGAx2lbITPbv6TnNadwKyddaOLa+88orZ3R8/++wzhyYZtXSJjT2TcGv9w5CXl2fXHfDK/gNRXs2aNTWD5rITrmsF2yJ/Br2Wjr+Pj4/ZGbhpaWmad8R0Fq1T7EePHi33339/hdvUutTJ1t2hLAVq/v7+Tm9PT8XFxfLss89Kly5dZMWKFXYdu99//10WLlwobdq0kffee89qXa1/JvV8fXi78mfraE1WX1Hl27J11qK7EcdUPo7xhhhGpGrGMcQwpqpKDGNNYmKidOnSRb777jvN9W+//bbmTZK8KebQUlXHLVLxsesRC5dF7KY/km9uZusadUfZe6lK2S/pvLw8p9ya3NXat28vW7dulSZNmpQuKy4ulr1798rrr78u8fHxUqNGDenXr5+8//77Dv0z46yzDgoKCmTKlCnSqFEjeeSRR2TZsmWyZ88eSUxMlJSUFMnMzJSrV69KQUGBw7eEruyx1pozoSK0fnHUS2FhoeYXaGWPV1RUlLz44otmy8vOqWNtfisR7YBaRKRu3bo2928p8LbnGFkL2n19fc0C8orsw172BNoVtXDhQtm5c2fp4zp16sg777xTqTa1Xje2fgW1tD44ONjp7enp5Zdflnnz5pn8w+3n5ycvv/yynDx5UgoKCiQrK0t27twp99xzj8m2RqNRJk2aZPUyHa3+u/vyDk9Wo0YNk88Yey7Zs1f597ity/zcjTim8nGMN8QwIlUvjiGGsX9bkRsrhrFkz5490rVrV80EsJ+fnyxZskSefPJJzW29KeYor6qOW6RyY9cjFi6L2E1/JN9uMCEhIXbVK/+LZ0VuKe4JunfvLqdPn5aPP/5YOnfubBZc5Ofny5YtW+Sxxx6Txo0by5tvvumyAL2goEB69+4tCxYsMPmVzlkqe6zL/mpSGa68hMnS8xgUFFTptp966imzM1H3798vn376qYho/9JWVk5OjuZyaxM/26pjqc2yyp8ZW561y0VEnHv8srOzndZWWUlJSfLCCy+YLFu6dGmlz+DRSkLYej60jklISIgEBgY6vT29nD592mwScxGRefPmyWuvvSYtWrSQgIAACQ8Pl7i4OPn6669NbvxT4tlnn7V4WZHWa1oppctn4Y3Ax8fHZA6qn376yWltl58suexZRJ6IOMYz4hi9YxiRqhfHEMOYqwoxjCVr1qyRu+66S/PHlho1asiGDRvkr3/9q8XtvSXmKK+qjlukcmPXKxYui9hNfyTfbjD2vjnKXsZgMBhc/uHjTL6+vjJ69GjZv3+/XLx4UZYtWyYjR440u/Y9MzNTpk+fLvfff3+FfqF11Msvvyx79+4tfezv7y8PP/ywfPbZZ/Ljjz9KUlKSpKenS05OjuTl5UlRUZG0adPG7vYre6zDwsJM6m3ZskWUUg4XZ14eVVG25quyR2BgoOYdwqZNmyb5+fk2g2NLE/va88+BpTq2fvF1hvKvg8qwJ9CuiP/+979mz9GgQYMsXlJy++23m7Vx+fJlkzqvv/665twWRqPRahCndXZAySVezm5PLx9//LFZ0iwiIsJs3qCytCbEPnv2rMlnXFnOeE9WNd27dy/9Ozk52a659exR9tKzyMhIuy/1cxfiGM+IY/SOYUSIY0oQw1ScN8QwWlasWCGjRo3SfA+0bdtWEhISpG/fvlbb8JaYo6yqOm6Ryo9dr1i4LGI3/ZF8u8HYG0CUPcU+LCzM5unotrj61yJLYmJiZOzYsfLpp5/K5cuX5eDBgzJt2jST+XS+/vprp9wRxpr8/HyTu0bVqFFDfvjhB/noo49kxIgRctttt0mTJk1M5kvx9fV1KJiu7LEuP8eQp98BT8Tyr6vOulX4yJEjpXPnzibLzp49K/PmzbN5BzVLE53aM1GppQm2nTF5qi2WfjE7dOiQw//AaJ0d5clatmyp+dlXfh4hW+tuuukmXdrTy+HDh82WtWjRwurcJy1atNBcfvToUc3lWu9Jb0+Q6C0uLs7k8bJlyyrd5smTJ03mcrrzzjttnkliL72+94lj3B/HuCKGEal6cQwxjPN5YwyzZs0aGTdunOYZrEOGDJF9+/ZJbGyszXa8JeYoUVXHLeK8seuN2E1/JN9uMCdOnLCrXtlf1MtfglL2A+369et2BVPO+oXemQwGg3To0EHefPNNOXbsmDRv3rx0ndYlV8509OhRk2Bx+vTpNu/CZTQaHZo0urLHuvyX1y+//GL3vt3F19dXM0Fg7Q7AjvrXv/5ltmzWrFk23wcdOnTQXH7gwAGb+9SqU6NGDc27HDlbq1atNJd76wTmjqhevbpmUslSQklE+654Jf/sOLs9vWj9um9rvhRLc35Yeu9pLXfH3Cre5IEHHjB5jhYvXlzphFD5uWAefvhhzXqe9L1PHPN/3BXHuCKGEal6cQwxjPN5Wwyze/duGTNmjGYS5oknnpAvvvjC7vnAvSXmEKm64xZx7tj1RuymP5JvN5hdu3bZrGM0Gk3OfGjZsqXJ+vK/zNn6JbG4uFj+97//OdBL16tbt67JZLTnzp3T9fTyixcvmjy2djenEuvXr3do/pLKHuuIiAiTQH7Dhg1279udatWqZbYsJSXFae13795dhg4darIsJydH3n33XavbNWzYUPPuxatWrbI4L5bIn5MFb9q0yWx5XFxcpc/ksEebNm00fxG35/V1Ixg8eLDZsq1bt2rWzczMlH379pktHzJkiG7t6aFmzZpmy5KSkqz+c5aUlKS53NKZDeU/A0Uqf0e/G11UVJSMHTu29HFKSopMnjy5wu3t37/f5OyoNm3amN08o4Qnfe8Tx2hzZRzjihhGpGrGMcQwzuVNMUxaWpqMGDFC87LDGTNmyLvvvuvwmcneEHNU1XGL6DN2PRG76c9zjjacYtWqVTYnnfzqq69MzmLo2bOnyfryd5/SukSprC+++EL++OMPh/pZ2blK3n33XRk2bJg0btxYVq1aZdc25W+frOeHXfm2bQXImZmZMm3aNJNlti5DcMaxLvuP2JEjR2Tz5s1W2xP5c46WW2+9VR544AH56KOPXHq3UxHtO29duHDBqfuYPXu22QTPlua2KktrktSkpCSZMWOGZv3i4mJ54oknNH9pevzxx+3sbeUYDAbNAGTx4sUW7/y1adMmCQ0NlaZNm0qXLl3knnvuMbmrmojIt99+qzkHxe7dux3u44QJExy6dCQhIcGsjZiYGJM6L730koiIPPjgg2Z1V69eLZcuXTJb/u9//9vsDLGOHTua/DPo7PZEnPtcimif4ZCVlSUrVqywuM2SJUs0l2vNKSKi/Z7UmqcFpl544QWT7+Bly5bJa6+95nA7x48fl6FDh5b+0m4wGGT27NkW/2K9fz0AACAASURBVBn2pO994hjLXBXHuCKGEamacQwxjHN5QwxTYtKkSZrH+vHHHy+NSRzl7Jijqo5bxPPHrmcsXILYTX8k324wKSkp8uSTT1qcMDE1NVWef/750se+vr4yaNAgkzqtW7c2ebx48WKL+zt+/LhMmjTJ5kSuvr6+Jo8rezr4/v37S4PlF1980eJZGWWtXbu29O/69evbfZetimjSpInJ488//9xi3QsXLki/fv0kPT1dOnXqVLrc1iUwzjjWjz/+uEmQPX78eDl58qTFfRqNRnnkkUfk559/ls8//1z++te/unyi4rK/cpew1ueKiI2NlUmTJjm83cSJEzXPKnrttdfk0UcflZ9//lkKCgokMzNTtm7dKnfffbesXr3arH7Hjh2lX79+Fep7RTzzzDNm/5Tn5uZKjx495MMPP5TLly9LYWGhnDt3ThYuXCgjR46Uq1evypkzZ+SHH36Qb775xmvng2jfvr3ccccdJstyc3Olf//+snv3bsnLy5PLly/LnDlzNJMgzzzzjK7t6WHo0KFmn8kif34evPLKK3Ly5EkxGo2Sl5cnBw8elAcffFDWrFljVr9NmzYWJ1jXek82a9as8p2/wdWvX18+/PBDk2WvvPKKPPjggxbnVSpLKSXLly+XuLg4kyD6ueeek4EDB1rczpO+94ljLHNVHOOKGEakasYxxDDO5w0xzIEDBzSfq9q1a2teKmwvT485quq4RfQbu56I3VxAeYHVq1crEbFa3G3RokUm/dm1a5dmvW+++cak3pkzZ2y2XX6bX3/9tXTdsmXLTNYNHz5ciYiKi4tTX3/9tbp8+bIyGo3q4sWLauXKlapRo0Ym9f/yl7+Y7a+wsFDVrl3bpN6YMWPUwYMH1dWrV1VBQYE6ceKEmjFjhgoLC1O+vr7q9ddfL63r6+urOY7Q0NDSOrVr11Z79+5V+fn5KiUlRf3xxx/2PdH/X0JCgjIYDKXtRUZGqtdff10lJCSozMxMVVRUpHJzc9W5c+fUxo0b1b333msynunTp+t6XIqLi1X9+vVN1k+aNEkdO3ZM5eXlqfT0dLVv3z7197//vfR5WbRokZo4cWJpfYPBoFatWqXy8vJUdna2LsdaKaWef/55k3ohISHqlVdeUUeOHFG5ubkqOztbnThxQi1atEi1bdvWpO7EiRM125w/f75Jvc2bN9t/cG2YPXu22fu/TZs2FusvXbrUrP7jjz9ucz/p6emqRo0aVj93nn/+ebPtNm/ebPLadLSEhYWpU6dOabarVf/KlStWxxEVFWW2zaJFi8zqPfPMMxXuc9OmTVV2drZd/bX02ehMCQkJZvuNiYmxWP/w4cPK19fX4XH37NnTJe3p8Vw+8cQTFT7eJWXDhg0W24+NjTWrv2DBggr31xk6d+5sdTyTJ092a//KWrBggfLx8TH7bB4zZoz6/PPP1enTp1VWVpbKz89X586dU3v37lX//Oc/Vbt27czGNXr0aFVUVGR1f+783ieO+bO9ysQx3hDD6HWslfKuOIYYxrRUlRhm/PjxFe5f+VL+/e3MmKOqjtvbxm4PR2NhpTwzdivhTTGcNe7PWtmB5Jv9ybdTp06p6tWr2/VGrl+/vrp06ZLmPt966y27PxCmT5+uvvvuu9LHBoNBs834+HiLbTz77LO2n+RyXnjhhQp9gN18883q6tWrVp/jyh4XpcxfE9bK8OHD1fXr19Xy5cs119977726HeuCggLVv39/h5/H2267TeXm5mq2qWfybdu2bWZ98fX1VZmZmZr1Kxq4KqXUvHnzrD4HWoGrUkotX75cBQQEOPycRkdHq927d2u2qXfgajQa1aBBgxzuc0xMjDp69Kjd/fXE5JtSjr1fRUQ1b95cJScnu6Q9PZ7LgoIC1adPH4ePd0mZNWuWxbZTU1M1/3mz9Np2FW8L3L766iu7P+O1iq+vr5o5c6bd+3PX9z5xjOPHtnwc4w0xjFL6HWtvimOIYUxLVYlhRo8eXaH3ulbRen87K+aoquP2xrHb4mgs7KmxWwlvi+Es4bLTG0ydOnVk8+bNNidHvOmmm+Tbb7+VmJgYzfVTpkyRhx56yOb+pk6dKjNnzjS5E4pSSoxGo1nd6dOnO3V+kpkzZ8rcuXMt3rpdy8iRI2Xnzp0uuXPLhAkT7Drtf9y4cbJq1Srx8fGRoUOH2n1tvbOOdUBAgKxfv16ee+45u067NxgMMn78eNm+fbuul+5a0r17d7Njfv36ddm+fbvT9zVp0qQK3fp7zJgxsmvXLunWrZtd9Q0GgwwfPlwSEhKke/fuDu/PGfz9/eXrr7+WV1991e7jOmDAAElISJC2bdvavR9Pmli2rAkTJsiKFSskKirKZt2+ffvKjh07NOfu0as9LZV5LgMCAmTTpk3yxhtvSFhYmN3bNWnSRDZu3GhyKVh5W7duNbuMLCwszGV3FrtRDBkyRJKSkuTZZ5+1eUlkWT4+PjJq1Cg5fvy4TJ8+3e7tPOV7nzjGOlfFMXrHMCJVM44hhtEHMYy+MUdVHbeI547d2YjdXKNqvJqqkOvXr0vXrl3l5MmT8u6770pcXJzUq1dPAgICpE6dOhIXFyfvvfeeHDx40OJcPSJ/ftCsWLFCNm7cKMOGDZOGDRtKUFCQBAQESMOGDWXMmDFy+PBhmTt3roiI2S2Ste541atXL9m8ebP06NFDgoODJSAgQGJiYqRnz55m1+3bw2AwyNSpU+Xs2bMyf/58GTRokMTGxkpoaKj4+PhItWrVpG7dutK7d2956aWX5NixY/Lpp59q3hVJLwsXLpT//ve/MmzYMKlfv74EBARIUFCQxMbGypgxY+T777+XDz/8sHQumZCQENm6dav06dNHQkJCJDAwUBo3bqz5weesYy0i4ufnJ3PmzJHTp0/LG2+8Ib1795b69etLtWrVJDAwUGJiYiQuLk5eeuklOXnypHzwwQcO/cPuTIGBgXLnnXeaLde621ZlBQQEyOzZsyu0badOnWTPnj2yY8cOmTp1qtx+++1St25dCQwMlNDQUGncuLH07t1bZs6cKb/88ousXr1aGv0/9u48vKZrf/z4J4PMgkQkiHkqlRY1BXW5YuZyUTX0ojp8tb0t+m1r6KD3dlLV0O9V9KpqzUO5Wlq0tLQNSg2l1FRaRAyZiMjorN8ffsnNyd5nSs7OOcl5v55nPY+svfbaa519kvPxOXvtXa+ek2fgGG9vb5kxY4b8/vvvEh8fLwMGDJD69etLSEiI+Pn5SUREhLRr104mT54sBw4ckC+++ELq1Knj0DHc5XHqev72t7/JqVOnJD4+XuLi4iQ6Olr8/f2latWq0rx5c3n88cflq6++kq1bt9oVvDm7v+JK+1r6+PjItGnTJDExURYtWiQPPfSQtGjRQqpXry6VKlWSgIAAiYqKknbt2smTTz4pmzZtkjNnzki/fv2s9qv3u9ijRw/x9fUt1Xg9UVhYmMyePVsuXbokS5YskTFjxkjr1q0lPDxcKlWqJP7+/lK7dm1p1aqVjBw5UpYsWSIXLlyQlStXStOmTR06lrt87hPHuE8cY2QMI+KZcQwxjHGIYYyLOTx13iLuPXdnInYrIy697s5O5WHZqasUv4Q/LS3N1UOCQTjX/6W3tKVq1aoqOzvb1UODjrp16xaeJ0fviwRz7v5aZmZmmt0Xq6CsWbPG1UOrMEsWKho+2zwH5/oOYpjyxd0/d43iqfNWyvPm7s6xW4GKEsNx5RuAcmfo0KGab6LS09Nl48aNLhoRLMnMzJSLFy+KiEhQUFCJvnXEHeXhtfz000/l5s2bZnVVqlSRv/zlLy4aEQC4F2KY8qM8fO4awVPnLeKZcyd2Kzsk3wCUO8HBwfLoo49q6uPj410wGlizadMmMZlMIiJy3333cfl6KZSH11Lvd/Dxxx936J5lAFCREcOUH+Xhc9cInjpvEc+cO7Fb2SH5BqBcevbZZ6VSpUpmdfv27ZOdO3e6ZkDQNX/+/MJ/Dx482IUjKf/c/bXcsmWL/Pzzz2Z1fn5+MmnSJBeNCADcEzFM+eDun7tG8dR5i3je3IndyhbJNwDlUp06dWTChAma+ilTpmie1gPX2LRpk3z//fcicufSfXuePAh97v5amkwm3Sdr/v3vf/eIJRsA4AhiGPfn7p+7RvHUeYt43tyJ3coeyTcA5darr74q1apVM6vbt2+frFq1ykUjQoGrV6/K448/XvjzSy+9JBERES4cUflVHl7LpUuXyuHDh83qwsPD5eWXX3bRiADAvRHDuK/y8LlrBE+dt4hnzp3YrexV/EXMACqssLAwmTVrljz22GNm9f/7v/8rffv21QS1KDs1atSQpKQkVw+jQnD31zI5OVleeOEFTf27774rVatWdcGIAMD9EcO4L3f/3DWKp85bxPPmTuzmGlz5BqBce/TRRyUuLs6s7vLlyzJ58mQXjQjwLBMnTpRr166Z1fXp00fGjh3rohEBQPlADAPAFYjdXMNLlYMbC6xdu1YefPBBq23KwTQAAICLdOzYUX788UeL2ydNmiRz5swpwxEBAADAlooSw3HlGwAAAAAAAGAQkm8AAAAAAACAQUi+AQAAAAAAAAYh+QYAAAAAAAAYhOQbAAAAAAAAYBCSbwAAAAAAAIBBSL4BAAAAAAAABiH5BgAAAAAAABiE5BsAAAAAAABgEJJvAAAAAAAAgEFIvgEAAAAAAAAGIfkGAAAAAAAAGITkGwAAAAAAAGAQkm8AAAAAAACAQUi+AQAAAAAAAAYh+QYAAAAAAAAYhOQbAAAAAAAAYBCSbwAAAAAAAIBBSL4BAAAAAAAABiH5BgAAAAAAABiE5BsAAAAAAABgEJJvAAAAAAAAgEFIvgEAAAAAAAAGIfkGAAAAAAAAGITkGwAAAAAAAGAQkm8AAAAAAACAQUi+AQAAAAAAAAYh+QYAAAAAAAAYhOQbAAAAAAAAYBCSbwAAAAAAAIBBSL4BAAAAAAAABiH5BgAAAAAAABiE5BsAAAAAAABgEF9XD8BZjh075uohAAAAN5WVlWV1e2pqKrEEAACAm7EVw5UXFSb51rJlS1cPAQAAlFNLly6VpUuXunoYAAAAqIBYdgoAAAAAAAAYhOQbAAAAAAAAYBCSbwAAAAAAAIBBSL4BAAAAAAAABiH5BgAAAAAAABikXDztNDw8XDp06ODqYQAAALhEVlaWHDlyRFPfsGFDiYiIcMGIAAAAXK9+/fquHoJdvJRSytWDAAAAgGXHjh2Tli1bauo//PBDeeSRR1wwIgAAANiLZacAAAAAAACAQUi+AQAAAAAAAAYh+QYAAAAAAAAYhOQbAAAAAAAAYBCSbwAAAAAAAIBBSL4BAAAAAAAABiH5BgAAAAAAABiE5BsAAAAAAABgEJJvAAAAAAAAgEFIvgEAAAAAAAAGIfkGAAAAAAAAGITkGwAAAAAAAGAQkm8AAAAAAACAQUi+AQAAAAAAAAYh+QYAAAAAAAAYhOQbAAAAAAAAYBCSbwAAAAAAAIBBSL4BAAAAAAAABiH5BgAAAAAAABiE5BsAAAAAAABgEJJvAAAAAAAAgEFIvgEAAAAAAAAGIfkGAAAAAAAAGITkGwAAAAAAAGAQkm8AAAAAAACAQUi+AQAAAAAAAAYh+QYAAAAAAAAYhOQbAAAAAAAAYBCSbwAAAAAAAIBBSL4BAAAAAAAABiH5BgAAAAAAABiE5BsAAAAAAABgEJJvAAAAAAAAgEFIvgEAAAAAAAAGIfkGAAAAAAAAGITkGwAAAAAAAGAQkm8AAAAAAACAQUi+AQAAAAAAAAYh+QYAAAAAAAAYhOQbAAAAAAAAYBCSbwAAAAAAAIBBSL4BAAAAAAAABiH5BgAAAAAAABiE5BsAAAAAAABgEJJvAAAAAAAAgEFIvgEAAAAAAAAGIfkGAAAAAAAAGITkGwAAAAAAAGAQkm8AAAAAAACAQUi+AQAAAAAAAAYh+QYAAAAAAAAYhOQbAAAAAAAAYBCSbwAAAAAAAIBBSL4BAAAAAAAABiH5BgAAAAAAABiE5BsAAAAAAABgEJJvAAAAAAAAgEFIvgEAAAAAAAAGIfkGAAAAAAAAGITkGwAAAAAAAGAQkm8AAAAAAACAQUi+AQAAAAAAAAYh+QYAAAAAAAAYhOQbAAAAAAAAYBCSbwAAAAAAAIBBSL4BAAAAAAAABiH5BgAAAAAAABiE5BsAAAAAAABgEJJvAAAAAAAAgEF8XT0AAAAAT/Xaa69JZmamzXbJycm69evXr5fTp0/bdaznnntOqlev7tD4AAAAUHpeSinl6kEAAAB4opEjR8rq1asNP069evXk3Llz4uXlZfixAAAAYI5lpwAAAC4ycuTIMjnOqFGjSLwBAAC4CFe+AQAAuEhubq7UrFlTUlNTDT3OkSNHJCYmxtBjAAAAQB9XvgEAALiIn5+fDB061NBjxMTEkHgDAABwIZJvAAAALmT00tOyWtoKAAAAfSw7BQAAcCGTySR169aVxMREp/ft5eUlv/32mzRo0MDpfQMAAMA+XPkGAADgQt7e3vLggw8a0ndsbCyJNwAAABcj+QYAAOBiRi0NZckpAACA67HsFAAAwA00a9ZMTp065bT+fHx8JDExUSIjI53WJwAAABzHlW8AAABuYMSIEU7tLy4ujsQbAACAGyD5BgAA4AacvUSUJacAAADugWWnAAAAbuK+++6TgwcPlrqfgIAAuXz5slSpUsUJowIAAEBpcOUbAACAm3DW1WoDBgwg8QYAAOAmSL4BAAC4iVGjRom3d+nDM5acAgAAuA+SbwAAAG6iVq1a0qVLl1L1ERoaKn379nXSiAAAAFBaJN8AAADcSGmvWhsyZIgEBgY6aTQAAAAoLZJvAAAAbmT48OHi5+dX4v1ZcgoAAOBeSL4BAAC4kbCwMOnZs2eJ9q1Ro4b8+c9/dvKIAAAAUBok3wAAANxMSa9ee/DBB8XX19fJowEAAEBpeCmllKsHAQAAgP/KzMyUyMhIyczMdGi/3bt3S2xsrEGjAgAAQElw5RsAAICbCQ4OloEDBzq0T7169aRjx44GjQgAAAAlRfINAADADTm69HTUqFHi5eVl0GgAAABQUiw7BQAAcEO5ublSq1YtSUlJsav90aNHpWXLlgaPCgAAAI7iyjcAAAA35OfnJ0OGDLGrbUxMDIk3AAAAN0XyDQAAwE3Zu/S0pE9HBQAAgPFYdgoAAOCmTCaT1K1bVxITEy228fLykt9++00aNGhQhiMDAACAvbjyDQAAwE15e3vL8OHDrbaJjY0l8QYAAODGSL4BAAC4MVtLSllyCgAA4N587WmUnp4uv/76q9FjAQAAgI66devK+fPnNfXe3t7SoEED2bNnjwtGBQAA4Nnq1Kkj0dHRNtvZdc+3rVu3St++fZ0yMAAAAAAAAKC8mzFjhrz66qs227HsFAAAAAAAADAIyTcAAAAAAADAICTfAAAAAAAAAIOQfAMAAAAAAAAMQvINAAAAAAAAMAjJNwAAAAAAAMAgJN8AAAAAAAAAg5B8AwAAAAAAAAxC8g0AAAAAAAAwCMk3AAAAAAAAwCAk3wAAAAAAAACDkHwDAAAAAAAADELyDQAAAAAAADAIyTcAAAAAAADAICTfAAAAAAAAAIOQfAMAAAAAAAAMQvINAAAAAAAAMAjJNwAAAAAAAMAgJN8AAAAAAAAAg5B8AwAAAAAAAAxC8g0AAAAAAAAwCMk3AAAAAAAAwCAk3wAAAAAAAACDkHwDAAAAAAAADELyDQAAAAAAADAIyTcAAAAAAADAICTfAAAAAAAAAIOQfAMAAAAAAAAMQvINAAAAAAAAMAjJNwAAAAAAAMAgJN8AAAAAAAAAg5B8AwAAAAAAAAxC8g0AAAAAAAAwCMk3AAAAAAAAwCAk3wAAAAAAAACDkHwDAAAAAAAADELyDQAAAAAAADAIyTcAAAAAAADAICTfAAAAPFhycrIopTQlOjra1UMDAACoEEi+AQAAAAAAAAbxdfUAAACA64SFhUm/fv2kU6dOEhMTI3Xr1pWwsDAJDAyUnJwcuXHjhqSmpsqxY8fk4MGDsmXLFvn5559dPWwAAACg3CD5BgCAB2ratKm8+OKLMmrUKPH11Q8HgoKCJCgoSKKioqRFixbywAMPyFtvvSVHjx6V2bNny7Jly0QpVcYjBwAAAMoXlp0CAOBB/P39ZcGCBXL8+HEZM2aMxcSbNTExMfLJJ5/I3r17pUGDBgaMEgAAAKg4SL4BAOAhoqKi5Ntvv5UJEyaIj49Pqftr37697Nu3Tzp06OCE0QEAAAAVE8k3AAA8QFBQkGzfvl1iY2Od2m/16tVl06ZNUr9+faf2CwAAAFQUJN8AAPAA8+fPl7vvvtvi9qtXr0p8fLzExcVJvXr1JCgoSIKDg6Vhw4YycuRI+fzzz8VkMunuGxERIcuXLzdq6AAAAEC5RvINAIAKrnfv3jJ27FiL2+fPny9NmjSR//3f/5UdO3bI+fPnJSsrS27duiXnzp2T1atXy6BBg6Rdu3Zy/Phx3T46d+4so0aNsms8TZo0kYkTJ8oXX3whx48fl2vXrklubq5cu3ZNfv31V9m2bZs8//zz0rJlS4fmWbVqVVFKacrmzZvN2oWHh8u0adMkISFBUlNTJTc3Vy5fvix79+6V6dOnS40aNSweY/Xq1brH+Oyzz+weZ6tWrXT7uH37tkRHRzs0Z1fz9vaW7t27y5w5c2TXrl2SmJgoGRkZkpeXJ9euXZMjR47IokWLZPDgwTaXOk+ZMkX3dTl79qzd44mNjdXtIycnR8LCwqzu6+/vLyNHjpR///vf8vPPP8uVK1ckNzdXkpOT5ZdffpG1a9fKiBEjJDg42K6xWHo/btq0qbBN7dq1ZdGiRXLx4kXJzc2VpKQkGT58uN3zBQAA5YSyw5YtW5SIUCgUCoVCKYdl+/btFj/jZ8yY4VBfVapUUYcPH9bt66effrK6b/PmzdX69evtCT0KffXVV6pt27Z2jc3X11e3j4SEhMI2w4YNU+np6VaPmZqaqh544AHdYwwaNEh3n5s3byp/f3+7xvnKK6/o9vHNN9+45P2RnJysO57o6Gir+/Xr10+dOnXK6mtZ1Llz51T37t0t9hcVFaXy8vJ097333nvtmss///lP3f03bNhgcR8vLy81adIkdeXKFbvmkZSUpIYPH25zLJUqVdLd/4cfflAiourWrasSExM125977jmXvA8oFAqFQqE4XmbMmGFX/EDyjUKhUCiUClxiYmIsfr5/8803ysvLy+E+mzRporKzs836Sk5OVqtXr1YhISG6+4wZM0bl5OTYFZwUd/v2bTVx4kS7xpabm6vZ//jx40pE1IMPPqhMJpNdx8zPz1cDBw7U9O/n56fS0tJ09+nTp49dYzxw4IDu/uPHj3fJe6QkybeXX37ZrtexuNu3b6sxY8ZY7Pezzz7T3e+VV16xay4//fST7v6DBw/WbR8SEqK+/PLLEs1l1qxZNsejl0w8evSoEhG1bds23X5JvlEoFAqFUn4KyTcKhUKhUChqypQpFj/f27RpU+J+33//fbVr1y714osvqnbt2ilvb2+LbZ9++mm7ghJbpk+fbnNcN27c0Ox34cIF1bBhQ3Xz5k2Hjnfp0iVVuXJlzTE++ugj3fb/+te/bI4vOjpad9+srCxVpUoVl7xHHE2+jR492qHXsbjc3FzVunVr3b4tXVl48OBBm/OoUaOGbnL12rVrqlKlSpr23t7e6vPPPy/VXGwlyvTec7///ruKjY0tcZ8UCoVCoVDcp5B8o1AoFAqFojZt2qT72X7o0KEyOX67du10r0ZTSqk//vhDPfroo6pOnTrKz89P1axZU40ZM0adPXtWt31+fr6KjY21erzU1FTNfsnJyWrdunX2hDwaEyZM0ByjZ8+eum3Pnj1r8/V48skndfddt26dy94jjiTfAgICLC7P/Omnn1SPHj1U1apVVVhYmOrVq5fFZamWYktfX1+VlJSku0/dunWtzmPs2LG6+1lKij733HO67TMyMtSzzz6r6tevrypVqqSioqLUo48+qi5fvqxpm5WVpRo0aGBxTHpXSV69elUtXbpU99hKkXyjUCgUCqU8FZJvFAqFQqFQ1LVr13Q/2999990yOf7Bgwd1j3/w4EGLV3pZu6/cgQMHrB5PL5FkMpkKr4g6ePCg6tevnwoNDVWhoaGqX79+6vjx4xZjoK+//lpzDB8fH4sJqBYtWlgdn6WlhpaWRZZFcST5NmLECN222dnZKioqStO+RYsWulejmUwmFRERoTuet99+W/cYTz/9tNV5rF69Wne/du3aadpWrlxZd965ubmqY8eOuv03aNBApaSkaPZZvny5Q69tdna2yszMVEoptW3bNtWlSxcVHBysKleurJo1a2Y1mUehUCgUCsW9Csk3CoVCoVA8vPj5+Vn8bH/ooYcMP3737t11j52Xl6fuuusuq/u2bt3a4v3ZOnToYHE/S4kkpe7c407voQjh4eHqwoULuvukpKToHmfevHm67V944QWLY6tcubLufe9SU1OVn5+fy94njiTfJkyYoL777jt18OBBdfr0aZWUlKQyMjLUV199ZbH/vXv36vY/dOhQ3fbNmjXTbb9jxw6Lx/Dx8dG96rHgfn/Fy+TJk3WPsXjxYquvld7VcpmZmSo4ONjh9+PatWtLdM9FCoVCoVAo7lPsTb55CwAAqJDCwsIsbrt8+bLhxx83bpxu/ZYtW+TEiRNW9z106JAkJCTobhs5cqTDY7l165aMGTNGcnJyNNtSUlJk5syZuvuFhYVJtWrVNPWrVq3Sbd+/f3+LY+jbt6/4+flp6tetWye5ubkW93MnCxculK5du0qbNm2kSZMmUrNmTalcubL06tXL4j5HjhzRra9Vq5Zu/cmTJ3XPfdeuXXXPhYhIx44ddbctXbpUt/2QIUN06zds2KBbX2Dt2rWauqCgIOnXr5/Vop8nWAAAIABJREFU/YrLyMiQJ554QpRSDu0HAADKJ5JvAABUUJYSFSIi169fN/z4Xbt21a3/8ssv7dp/27ZtuvUdOnRweCxr166VixcvWty+efNmi9uqVKmiqdu9e7ecP39eU9+pUyeLr/ugQYN065cvX27x2BWBpfeatffn4sWLNXW+vr4Wk5t6yS+TyaT72vr6+krbtm11+zl58qTFMYmInD9/Xnc+7dq1s7pfcRs2bJCUlBSH9gEAAOUXyTcAACqo/Px8i9t8fHwMPXZERITUr19fd9vRo0ft6uP48eO69a1btxYvLy+HxrN161ar2y9cuCAmk0l3m7+/v6ZOKSVr1qzR1Pv6+krv3r116/v27aup/+OPP+SHH36wOrbyxtfXVwIDAyU0NFTCwsJ0Xz8REW9vy2Ho2rVrJSMjQ1M/ePBg3fZ6r+0333yjm3CtV6+eBAQE6PZz+vRpUXduy2Kx6CVjY2JiLM5FzzfffONQewAAUL6RfAMAoIJKTU21uE0vgeBMUVFRFrclJiba1celS5d06/39/aVy5coOjefXX3+1ut1kMklycrLuNkuJPktLTwcMGKCps7RkcuXKleV26WHjxo1lypQp8vnnn8uZM2ckPT1dTCaT5OXlya1bt+T69euSkpIiTz/9tMN9Z2Zm6iY3e/furUmcRUVFSatWrTRtLS05tfbeLKkGDRo41N7WFXYAAKBiIfkGAEAFlZaWZvFqLktXpTmLtfvNZWZm2tWHtXbWlizqsWeZrd6VVtYcOnRIN4nSp08fzZWFlpacrlixwqFjuoOIiAhZsWKFnDp1SmbOnCkDBw6URo0aSZUqVRy+ItGajz76SFMXEhIiPXr0MKvr27ev5rg3b960eP+2wMBAp42xgKPJ4LS0NKePAQAAuC+SbwAAVFAmk0l+++033W2tW7c29NjWruayN0FjbVmipaSiJbdv33aovb1Wr16tqQsPD5eOHTua1Q0cOFDT7vDhw3Ls2DFDxmWU6Oho2bNnj4waNcqpiTY9e/bs0V16XHzpqd6S0/Xr11tM3hrxcIvQ0FCH2t+6dcvpYwAAAO6L5BsAABWYpSeG6l0t5Ig2bdrIkSNHZOLEiRIeHq7Zbm3Ja0hIiF3HsNbOXW5Wb89TT++55x7dZYnl8aq3pUuXSqNGjexqe/v2bcnJyZG8vLwSH0/v6reBAwcWJmZ9fX2lZ8+euuO0xNpVZ9HR0eLl5eVwcfTKNwAA4FlIvgEAUIF9//33uvV169bVTVrY68UXX5SYmBiZO3euXLp0SdasWSO9evUqTIpcvnzZ4r5169a16xiW2mVkZLjNlUMnT56Uw4cPa+qL3vdNb8mpyWSymLhzVx07dpTu3bvrbjt79qw888wz0rJlSwkLCxNvb2/x9fWVgIAAiY+PL/Exly5dqkneRUZGFl5ZGBsbK1WrVjXbfuHCBdm5c6fFPq0lhiMjI0s8VgAAAEtIvgEAUIFt3LjRYqJq9uzZUqlSJYf77NKli/z1r38t/NnPz0+GDx8u27Ztk7Fjx4qISHJyspw9e1Z3/3vvvdeu49xzzz269Xv37nVwxMbSS6LFxMQUJg//8pe/aLbv3LnT7gdPuAu9pbMiIunp6dK5c2f517/+JceOHZO0tDSzZceluSrs2rVrsnnzZk19wdLTfv36abYtW7bM6rLkxMREi1dOGvEwBgAAAJJvAABUYKmpqbJkyRLdbTExMfL+++87tPy0Xr16smrVKt19rly5YpaI+u6773T70HsaqJ6iSzeL2rVrl137l5XVq1fr3uOuf//+Urt2bbnvvvs025YvX14WQ3OqOnXq6NZv3brV6pWOxe9/56jFixdr6gquJtS735u1JacF9uzZo1vfqVMnB0cHAABgG8k3AAAquPj4eMnKytLd9thjj8n69eulVq1aNvu5//775YcffpDo6Gjd7dOmTZPs7OzCnz/55BPddn/+858tXtVWIC4uTmJiYjT1+fn5smzZMptjLUvnz5/XTeb0799f/vKXv2gSldnZ2bJ+/fqyGp7TBAcH69Zbe4BBt27dpE2bNrrbAgIC7Dru1q1bNVcJNm3aVHr06KG5inLfvn26T6At7osvvtCtHzNmjPj5+Vncr0+fPnLjxg05ffq0/PDDD/Lpp5/K+++/L3FxcXbMBAAAeCxlhy1btigRoVAoFAqFUk7L448/bvWz/ubNm2rRokVq4MCBqmHDhiokJEQFBASoOnXqqOHDh6uNGzda3X/Hjh3Ky8tLc9z9+/frtj906JAKDQ3VHWutWrXU2bNndfdbuXKl1XkmJyfr7hcdHW3zNTpz5ozuvnfddZfNff/+979r9rt165bauXOnpn7t2rUufz+U5DVbuHChbrsTJ04oHx8fTb8NGzZU58+f191HKaU+/vhju8f4xhtvaPY/fvy4pu7JJ5+0q7/g4GCVmpqqO645c+bo7hMYGKj27dunaW8ymdQ999zj9PcjhUKhUCgU9y8zZsywGOsURfKNQqFQKBQPKcuXL7crOHDU+fPnVUREhO4x77vvPpWTk6O735kzZ9SoUaNURESE8vf3V40aNVITJ05UV69e1W1/9epVVatWLatzdFXyrUaNGio/P9+u12vQoEEufy+U5DUbP368xTktW7ZMNW7cuPA8TpkyRaWlpSmllEpJSVGnTp3S7JOYmKiqVq1q1xgbNWqkTCaT1dc1JydHhYWF2T3vqVOnWuxr3bp1qkOHDio4OFiFh4erPn36qL179+q2/eijjwx5P1IoFAqFQnH/QvKNQqFQKBSKWfHx8VHvvfeeXQGCvU6fPq0aNmxo9bhPPfVUqY+Tk5OjevXqZXOOrkq+iYj6+uuvbc4jJSVF+fn5ufy9UJLXLCwsTF2/ft3+k/b/DR06VC1YsEB32/nz59XGjRtVfHy8zXHqXUVY1IYNGxyat7e3t9qxY4fD8ynq9OnTFq/gLO37kUKhUCgUivsXe5Nv3PMNAAAPcfv2bZk4caI8+uijkpycXOr+Pv30U4mNjbX4VNMC77//vowZM8bqvcGsSU5Olh49eshXX31Vov3Lit5TT4tbt25diV8HV0tNTZVp06Y5tM/rr78u69evl7Vr1+pur1OnjgwaNMjifeGK0nvwQlH2PGihKJPJJEOHDpXt27c7tF+BEydOSPfu3eXGjRsl2h8AAHgQezJ0XPlGoVAoFErFKiEhIeqll16yeGWONd9++63q06ePw8e8++67bd47rqicnBw1b948FRUVZfcxXHnlW9WqVS0usS3QpUsXl5/70r5mL7zwgsrLy7M6z1u3bqlx48aZ7bd06VKL7Xfu3GlznIGBgSo9PV13/2vXrqlKlSqVaP6+vr5q+vTpFu8BV1xWVpaKj49XQUFBhr4fKRQKhUKhuH+x98o3XwEAAB7n5s2b8vrrr8vbb78t3bt3L3wqZYMGDSQyMlKCg4PFZDJJWlqapKamyvHjxyUhIUG2bt0qv/76a4mOeezYMRk8eLA0b95cBgwYIHFxcVK/fn2JiIiQkJAQSU1NleTkZDl+/Lhs27ZNvvjiC7l8+bKTZ26c9PR02bJliwwaNEh3+x9//CEJCQllPCrnmzVrlmzYsEGefPJJ6datmzRs2FBCQkIkIyNDTp48Kdu2bZMPPvhALl26ZLbf2LFjZfv27fLXv/5V6tWrJ97e3pKSkiInTpyw+PTRorKysmTNmjXy+OOPa7atXr1a8vLySjSf/Px8efPNN2XevHkyZMgQ6dGjh9x3330SEREhVatWlczMTElNTZWjR4/Kt99+KytWrJBr166V6FgAAMAzeSmllK1GW7dulb59+5bFeAAAAABdGzdu1E1utm/fXvbv3++CEQEAAE82Y8YMefXVV222455vAAAAcHvR0dHSv39/Tf2BAwdIvAEAALdG8g0AAABub/r06eLrq71jSnx8vAtGAwAAYD+SbwAAAHBrPXv2lP/5n//R1J86dcrik1QBAADcBck3AAAANzFp0iRRShlazpw54+ppWlWzZk2pVq2aBAQESJMmTeT555+XjRs3ire3NmydNm2a5Ofnu2CUAAAA9uNppwAAAHAbS5Yskd69e9tst3HjRtmwYUMZjAgAAKB0uPINAAAA5crJkyflkUcecfUwAAAA7ELyDQAAwE3MnTtXvLy8DC2NGzd29TRLZdeuXdKtWzdJTU119VAAAADswrJTAAAAuI2TJ09K8+bNJSwsTAIDAyUvL0+uXr0qe/fulRUrVsimTZtEKeXqYQIAANiN5BsAAADcxsSJE2XixImuHgYAAIDTsOwUAAAAAAAAMAjJNwAAAAAAAMAgJN8AAAAAAAAAg5B8AwAAAAAAAAxC8g0AAAAAAAAwCMk3AAAAAAAAwCAk3wAAAAAAAACDkHwDAAAAAAAADELyDQAAAAAAADAIyTcAAAAAAADAICTfAAAAAAAAAIOQfAMAAAAAAAAMQvINAAAAAAAAMAjJNwAAAAAAAMAgJN8AAAAAAAAAg5B8AwAAAAAAAAxC8g0AAAAAAAAwCMk3AAAAAAAAwCAk3wAAAAAAAACDkHwDAAAAAAAADELyDQAAAAAAADAIyTcAAAAAAADAICTfAAAAAAAAAIOQfAMAAAAAAAAMQvINAAAAAAAAMAjJNwAAAAAAAMAgJN8AAAAAAAAAg5B8AwAAAAAAAAxC8g0AAAAAAAAwCMk3AAAAAAAAwCAk3wAAAAAAAACDkHwDAAAAAAAADELyDQAAAAAAADAIyTcAAAAAAADAICTfAAAAAAAAAIOQfAMAAAAAAAAMQvINAAAAAAAAMAjJNwAAAAAAAMAgJN8AAAAAAAAAg5B8AwAAAAAAAAxC8g0AAAAAAAAwCMk3AAAAAAAAwCAk3wAAAAAAAACDkHwDAAAAAAAADELyDQAAAAAAADCIrzM7e+GFF2TMmDHO7BIAAKBCWLp0qcyaNcvi9sjISNmxY0cZjggAAAC22Irh7OHU5FtkZKTcfffdzuwSAACgQoiMjLS63dfXlzgKAADAzdiK4ezBslMAAAAAAADAICTfAAAAAAAAAIOQfAMAAAAAAAAMQvINAAAAAAAAMAjJNwAAAAAAAMAgJN8AAAAAAAAAg5B8AwAAAAAAAAxC8g0AAAAAAAAwCMk3AAAAAAAAwCAk3wAAAAAAAACDkHwDAAAAAAAADELyDQAAAAAAADAIyTcAAAAAAADAICTfAAAAAAAAAIOQfAMAAAAAAAAMQvINAAAAAAAAMAjJNwAAAAAAAMAgJN8AAAAAAAAAg5B8AwAAAAAAAAxC8g0AAAAAAAAwCMk3AAAAAAAAwCAk3wAAAAAAAACDkHwDAAAAAAAADELyDQAAAAAAADAIyTcAAAAAAADAICTfAAAAAAAAAIOQfAMAAAAAAAAMQvINAAAAAAAAMAjJN7iNzZs3i5eXV2H5/fffXT0kOKBnz55m58/Ly0sefvhhVw8LQAU3evRozd+efv36uXpYgMchjiu/iOEAuIKnxXAVLvm2cOFCs5P3ww8/uHpIQIX34Ycfyvbt283qoqKiJD4+3qxN8T+uBeWzzz6z+1izZ8/W7D916lSnzQWO2blzpzzzzDPSpk0biYyMFD8/P6lcubLUrVtX+vXrJ2+++aZcuHDBoT6TkpLkzTfflLi4OKlTp44EBgZK5cqVpVGjRjJixAhZtmyZ5OfnGzQj+zl77p46b0vmz5+v+/ciKirKrN17770nERERZnVbtmyRTz75pNRjwH+lpqbKunXrZMKECdK+fXtp2LChhIaGSkBAgNSuXVtatWolw4YNkwULFsiZM2dcPVwAdiKG81zEcMRwxHBlTNlhy5YtSkRslnfffdee7gy1YMECszF9//33rh6S28nLy1OBgYFKRNSCBQtcPZxCmzZtMjt3586dc/WQYIeUlBRVrVo1zd+DlStXmrVbtGiRxb8dTZo0Ubm5uXYd75133tHsP2XKFCOmBitOnz6tOnXqZNdng7e3t5owYYLKyMiw2e/MmTNVUFCQzT6bNWum9u3bVwYz1TJi7p46b0vOnTunQkJCdPuOjIzUtP/444817cLDw1VaWlppp+1U7777rtXXrXbt2q4eosbFixfVU089pfz9/e069wWld+/eas+ePa4evtvGPCXlzvMhjit/iOE8EzEcMRwxnOOsxXAzZsywq48Kd+UbbDt27JhkZWW5ehioIF599VVJS0szq2vfvr2MGDHC7j5Onz4t8+bNc/bQYJCDBw9K27ZtZffu3Xa1N5lMsnDhQunRo4fcvHnTYrvnn39epk6dKrdu3bLZ58mTJyUuLk5+/PFHu8ftDEbM3VPnbYlSSsaPH+/Qfn/729+kVatWZnUpKSny2muvOXRsmFu6dKk0btxY3n//fcnJyXFo323btklsbKxMmDBB8vLyDBqhbRUt5qlo84FrEcN5HmI4YjhbiOGMQ/LNA/3000+uHgIqiPPnz8vChQs19W+//bZ4eXk51Ndrr70mqampzhoaDHLjxg0ZOHCgXL9+3eF99+3bJ5MmTdLdtmbNGpk9e7bDYxk2bJjcuHHD4bGUhBFz99R5W7NgwQL59ttvHdrH29tb3nzzTU39vHnz5NKlSw71hTumTp0qY8eOlezs7MK68PBweeKJJ+Tzzz+XM2fOyPXr1yU7O1vOnz8v33//vbz88svSrFkzs34++OADiYuLK7P3a3EVLeapaPOB6xDDeR5iOGI4RxDDOR/JNw9E4AZniY+P11zR0L59e+nWrZvDfaWlpcmrr77qnIHBMO+8847uB+Gf/vQn2b17t9y4cUMuXLggixcvlurVq2vaLVmyRM6dO2dWl5+fL1OmTNG0veeee+Tbb7+VmzdvSnJysixcuFACAwPN2ly8eFHmzJlTylnZx9lz99R5W3Pu3Dmz18SR/wD27dtX7r33XrO63NxcmTt3rt194I5FixbJ22+/Xfizl5eXPPfcc/Lbb7/J/PnzZeDAgdKoUSMJDQ0Vf39/qVOnjnTp0kX++c9/yrFjx+TDDz+U0NDQwv2/++47GT9+vCumUuFinoo2H7gOMZznIYYjhitADOci9qxN5Z5vFUu7du0KXx93ul8I9wopXzIyMnTX869evVq3vbX7hRQUX19fdeLECavH5X4hrnP79m1Vs2ZNzevfsmVLlZeXp2m/bds23fM8d+5cs3YbNmzQtAkNDVWXL1/W9Dl//nxN26ioKN3jO5MRc/fUeVtiMplU9+7dzfYbPHiwXfcLKfDJJ59o2lepUkXdunWrVK+Ds5SHe74dO3bM7P5uvr6+atmyZQ73c/jwYRUVFWU2v3nz5hkwYuvcNeYpKXeeD3Fc+UEM53mI4YjhiOFKh3u+ldCSJUsKn7jRtGnTwnqllGzcuFF69+4tNWrUkEqVKknVqlUlJiZGnnnmGTl9+rTFPt95553CPhs2bFhYn5ycLK+88oq0b99eatWqJf7+/lKrVi3p0qWLzJkzx+oloDNnzizs09fX1665zZ07V3efok+B3b9/f2H9E088YfYEkpJ+o5qbmytr166V0aNHS0xMjISFhUmlSpUkMDBQatasKV26dJEpU6bIoUOH7O6zIFuen58vixcvlt69e0vDhg0lICBAqlWrJi1btpSJEyfKb7/9Zld/t2/fli+++EIeeeQRadWqlYSHh4ufn58EBwdLdHS09OnTR2bNmiVXr1612o8R57q4S5cuyRtvvCE9e/aU6OhoCQwMlNDQUGncuLH0799fPvjgA809Ooor+l7w8vKSrVu32n18e6xfv16znr9q1aoyePBgu/vo1KmT2c/5+fny3HPPOWV8xSUkJMj06dMlNjZW6tWrJ0FBQRISEiL169eX2NhYmT59ul1PR168eLHmyT29e/cu3K6UkjVr1kj//v0lMjJSKlWqJBEREdKxY0eZOXOmZGRk2DXeGzduyIIFC+SBBx4ovMIkICBA6tevL927d5f/+7//s/ledbbDhw9LUlKSpv7FF1/U/RvVq1cvqVOnjqb+l19+Mfv5008/1bQZPXq0REZGauoffvhhCQ4ONqu7fPmyfP/99zbHXxpGzN1T523J/PnzzZYqhIWFOfz3YNiwYRISEmJWd/36dfn8888d6seTvfbaa2b3d3vllVfkoYcecrife++9V1avXi3e3v8NNV977TWzZaxFuTrmcfZnu6vn4yhPiePKSwwnYmwcRwxHDFeAGI4YrgAxXBmwJ0NX0a58W7FihVnWWSml0tLSbD4BxM/PT61YsUL3uEUz2uHh4Uoppfbs2aNq1Khhtc86deqohIQE3T7feuutwnY+Pj52zX/OnDm6+xR/XSyV/fv323Wcovbu3asaN25sV/8iooYNG6bS09M1/RT/xvTChQsqKSlJtW3b1uZ5Kf5UpuKOHj2qWrVqZdf4goOD1aJFiyz2ZcS5LpCXl6deeOEF5efnZ3Oc4eHhasmSJRb7KvpeEBG1ZcsWq8d2VO/evTVjeuyxxyy21/vW9L333lN169bV1G/fvt1iP45+a/rjjz+q+++/3+73Z+fOna0+oW/VqlWafTp06KCUuvPUsG7dulntv3bt2urnn3+22L/JZFKzZ89WlStXtjnW0NBQq+9VZ/v2229V9+7dVZs2bVTjxo1VRESE8vf31/2Wr4Deaz9kyBCzNsWvjhERtX79eot99urVS9P+pZdecto89Rgxd0+dt56zZ8+q4OBgs32WLFmiDh48qOnL2remSin1t7/9TbPPoEGDSjR/Z3P3K9/Onj2rfHx8CsfTokULlZ+fX6o+n3jiCbM5Wrpay9Uxj7M/2109H0d4UhxXXmI4pYyN44jh/luI4Yjh9BDDWT7nxRHDceWbVX5+foX/vnXrluTm5kpcXJzNJ4Dk5ubK+PHj5ddff9VsK5o9vnnzply8eFH69etn81uNCxcuyIABA+TUqVMOzsI9nDp1SuLi4uTMmTN27/Ppp5/K4MGDRSlltZ2Xl5f06dPH5je5ubm5MmbMGDl+/Lju9tOnT0vXrl3l8OHDdo0vMzNTHnvsMfn44491txt1rvPz82XAgAEya9Ysyc3NtTnOlJQUefjhh2XmzJk22zpbdna27Nq1S1Pfr18/h/rJyMiQN954Q1P/7LPPislkKvH4Cixbtkzuv/9+h75ZSkhIkK5du8rSpUt1t/v7+2vqbty4UXj+du7cabX/xMRE6dmzp6SkpGi2mUwmGT58uDz33HN2fbt648YNeeyxx+Qf//iHzbbO0K1bN/nmm2/kwIEDcvr0abl69apkZ2frfstX4Nq1a5q6sLCwwn8nJSXJ5cuXNW1atGhhsc/mzZtr6oy+D5Kz5+6p89aj/v+TsTIzMwvr+vfvL+PGjZPbt287PGa9v0M7duxw6RM3y4sNGzaYvebPPPOM+Pj4lKrPSZMmmd33Zc2aNaXqzyjEcZ4RxxHDEcMVRwxHDKeHGO6/iOGcxyOTb5UqVSr8d3Z2trz99tty4MABad68uaxYsUKSkpIkLy9PkpOTZfPmzXLPPfcUts/JyZH33ntP02fR4DQnJ0deeOEFSUtLk06dOsnGjRvl8uXLkpubK5cvX5ZVq1ZJ48aNC9unpaXJxIkTDZrtHRMmTBCllObx9AsWLBClVGFp27atQ/2++OKLhZet+/n5ybRp02T//v2SlpYm+fn5kpGRIWfOnJGVK1eaXZ6+c+dOWbdundW+33nnHfn555+lWbNm8sknn8ilS5ckNzdXrl27Jhs2bJC77767sG1+fr7Fp8089dRTZpf39+/fXzZt2iSJiYmSk5MjmZmZcvDgQZk4caLZ8phnn31Wd5mBUed62rRpsm3btsKfmzRpIv/+97/l+PHjkpmZKTdv3pQjR47IW2+9JeHh4Wb77dixw9pL6XQJCQmapUM+Pj7SvXt3h/pJS0uT0aNHa953R44ckcWLF5dqjF9++aWMHTvWriC4uLy8PBk3bpx8/fXXmm1Fk/cFbty4Ie+8847s2bPHrv6vXr0q//znPzX1zz//vO5l7La8+uqr8p///Mfh/Yx26NAhOXHihKa+SZMmhf+2tNwoOjraYr9626zdFsAVbM3dU+et5/333zf7D09YWJgsWrSoxGOIi4vT3OT35s2bsnfv3hL36SmKngcvLy958MEHS91n06ZNzf7G792712xZqxFKEvO4cxxnVAwn4nlxHDEcMVxxxHD6iOGI4QoQwzmRPZfHVbRlp0Uvi/fy8lIBAQGqV69eFm/ml5ycrMLCwgr3qVevnqbNkiVLNK/H4MGDLd5IMT09XTVt2tSs/ZEjR8zaOHPJQoGsrCyzY5bmZr0mk0kFBQUV9jV79myb+zz00EMqMjJStW3bVsXHx5ttK75cwd/fX8XFxanMzEzdvlJSUlT16tXNLgUv7rffftOcE2tmzpxp1l5vGYQR5/rs2bPK19e3cHvfvn2t3lzy4sWLqn79+oXtW7ZsaXVezlb0vVlQ7r77bqv76C1ZeOqpp5RSSu3atUv3suQbN25o+rFnyUJqaqrZe6NoGT16tNqzZ4/KyMhQN2/eVLt371bDhg3TbVuzZk3N++/LL7/UtAsKClJVqlRR3t7eavLkyerMmTMqOztbHT58WA0cOFC37/DwcLP3zC+//KK8vb017Vq3bq2+/PJLlZSUpNLT01VCQoLq27evpl3Dhg1VTk5OSU+p0+Xm5qr27dvrzv3MmTOF7dauXavZ7ufnZ7Xvjz/+WLNPYGCg0VOymz1z99R5F6e3VKHo3939+/fr/m2wpVGjRpr95syZ47S5lpS7LzsNDw8vHEuLFi2c1u/kyZPN5mn0Ms0CjsQ8zv5sd/V87OGJcRwxHDFc8XbEcFrEcMRwls55UcRw/y0sO7WTUkoCAgJkxYoVmscAFwgPD5fhw4cX/vzHH39oblJaXEhIiHz44YcWb7JbpUoVmTVrllnd5s2bHRy9a6Wnp8utW7cKfy7+eGA9y5Ytk8uXL8v+/ftl8uTJVtumXrbsAAAgAElEQVQGBQXJqlWrJCgoSHd7WFiYjBgxovDnxMREzXlJTEyU+++/X5o2bSqhoaHy97//3eoxn376abMrIw8cOGBrSk4513PmzJH8/HwREYmIiJCVK1dafD+KiNSuXVsWLlxY+PMvv/xi+KXLRf3888+aOnvOf3EFc+7atasMGjTIbNuVK1fkrbfeKtH4Fi5cKMnJyZr6f/zjH7J8+XLp2LGjhISESHBwsMTGxsq6det03xtJSUmycuVKszq9x2bfunVLrl+/Lu+9957Ex8dLo0aNxN/fX+699175z3/+o7kpscidJSdFv1164403NMs06tevLzt37pS+fftKVFSUVKlSRTp16iRffvml9O/f36zt2bNn3eabU5PJJA8//LDs27dPs+2vf/2rNGrUqPDn1NRUTZviN1u1Z3tWVpbFG7mXJXvn7qnzLkrpLFUYOnSojBw5stTjKXrFegG9v1v4r/z8fLOlVHpLZEqqZcuWZj/r3QDa3RDH6atocRwx3B3EcMRwBYjhiOGKIoZzLo9PvomIjBs3TqpXr261TatWrcx+tvWUogceeMDssnI9/fv3N/tFTEhIsDFS9xIaGmp2+f4XX3zh1P7Hjx9v87zExMSY/Vz8j+H9998v3333nZw8eVKuX78uPXr0sNpfUFCQ2VNe9D78i3PGud6yZUvhv0ePHi1Vq1a1edzevXubjXXTpk0293EWvXvDNGvWrFR9zpo1yyxgFrkT0P7xxx8O96V3ufNdd90lL730ksV93n77bd17GixbtsyuY7Zt21Y3+PPx8bH4tJ+Cy81v375t9h4oMGnSJAkNDbU43uJKstzB2fLy8mTMmDGyYsUKzbaQkBDNsiK9oKP4+8De7cWXZJU1R+buqfMuqvhShYiICFmwYIFTxqT398jeJyp6quL3MLJ2jxdHFe9L735J7oY4rvTKQxxHDHcHMRwxnAgxHDGcOWI45yP5JmLzg1xENMFD0W8K9dhz3wRfX19p3bp14c/utu7bFh8fH+nWrVvhz3PnzpWnn35aEhMTndJ/XFyczTbFz4sz/oAV/bay4Fs9a0p7rpOSkswCoaLtbOnYsWPhv48cOWL3fqV16dIlTV3NmjVL1WfTpk1lwoQJZnXZ2dkydepUh/o5f/68nDt3TlM/atQos3vBFBcUFCQDBgzQ1O/fv9+u98G4ceMsbtP71lTkzlUHInfusVDw76Lat29vsc8WLVpItWrVzOqKPuLbFdLS0qRfv366H+BeXl6yZMkSadiwoVm93s1Ybd3g3VIA48qbsTo6d0+dd4GzZ89qfrcXLlwoERERThlX7dq1NXUXL150St8VVfErjixdrVQSxb/xt7V6wB0Qx5VeeYjjiOHuIIYbZ3EbMRwxHDGcOWK4kiP5JncuC7al+NNxlI0nPBX/Js+SevXqFf77woULdu3jTt555x2zIGfevHlSt25d6dy5s7z88suyY8eOEl9OW7duXZttit841dp5uXLlinz00Ucyfvx46dKlizRp0kQiIyOlWrVqEhISIgEBAeLr6yvHjh1zaJylPdfnz583azd27Fjx8vKyqxS92XFZPmlN70k4UVFRpe53xowZUqVKFbO61atXO3STTUtLTOy5EbVe0JyVlWXXU+CKBtHFVa9eXTdoLLjpuF6gKXIn4LN07r29vTVX4KakpMiVK1dsjtUIZ86ckY4dO8r27dt1t7/33nsybNgwTb3eUh9bT0eyFKjY+tbRKCWZu6fOW0R/qcLo0aNlyJAhThub3n8kXfW7UV4Uv1pH74FDJVW8r+L/6XRHxHGeEccRw91BDEcMRwxHDFcUMZwxSL6J7TXaJWHvco2iH1JZWVlOeTR3WWrdurV8/fXX0qBBg8I6k8kku3fvltdff13i4uKkWrVq0qdPH/nwww8dCuad9a17Tk6OTJ48WerVqyePPPKILFmyRBISEuTMmTNy9epVSU9Pl8zMTMnJySnRI5FLe6717htQEnrfuhkhLy9P9wPEGecrPDxcXnzxRU190fvK6N2voyi9oFJEpFatWjaPbyn4tOccWQtcfXx8NAGpo/3by54g09kSEhIkNjZW9z8Pvr6+8sEHH8jTTz+tu6/e+8bWt4CWtjvzSh17lXTunjpvkTv/ud+1a1fhzzVr1pR//etfTh2f3mvi6qUd7q5atWpmf1/tue2CvYr/jbO1zM8dEMd5RhxHDHcHMRwxHDGcOWI4YjgjkHwzSHBwsF3tin/jV5JHarta586d5fTp07J8+XLp0KGD5oM1Oztbtm3bJo899pjUr19f3nrrrTILTnNycuTPf/6zzJ07t/AbKmcr7bku+s1BaZTVMh5Lr2NAQIBT+n/mmWc0V6Pu3btXVq1aJSL63zYVlZGRoVtv7ebHttpY6rOo4lfHFmdtuYQzz92NGzec1pc91q5dKz169ND9j3q1atVk8+bN8vjjj1vcX+8/4bZeD73zERwcbPMcOFtp5u6p8z579qxMmzbNrG7RokVOvxJK73dZKWXY50BF4O3tbXYPqkOHDjmt7+I3Si56FZG7Io77r4ocxxHD3UEMRwxXHDEcMVxxxHClR/LNIPa+OYpeyu/l5VXmv4DO4uPjI6NHj5a9e/dKUlKSLFmyREaMGKFZ+52eni7Tp0+XIUOGlOgqM0e9/PLLsnv37sKfK1WqJGPHjpXVq1fLTz/9JGfPnpXU1FTJyMiQrKwsyc/Pl7vvvtuhY5T2XFeuXNms3bZt20Qp5XBx5hKhkrC1FNte/v7+uk/Imjp1qmRnZ9sMEC3d3NaeANlSG2vfeDpD8fdAadgTZDrL0qVLZeTIkbq/Ay1btpT9+/dL7969rfahd1+H3Nxcq0GM3jfj9ixvcqbSzt1T5/3VV19pfs8GDBhgcWlOu3btNH1cuXLFrM3rr7+uaeOsv0eepnPnzoX/TkxMlN9//90p/RZddhYWFmb3Uj9XIo7zjDiOGO4OYriSI4Yz5wmxjKfOmxiu9Ei+GcTeD9Gil5lXrlzZ5uXYtpT1NyZ6IiMjZdy4cbJq1Sq5cuWKHDhwQKZOnWp2P5nPPvvMaU9EsSQ7O9vsiUnVqlWTH3/8UT7++GN58MEH5b777pMGDRqY3SvEx8fH4WCytOe6+H123P0pcJa+WXTmo7JHjBghHTp0MKs7f/68xMfH23yKmKWbfdpzo05LN5l21g1ELbH0jdHBgwcdDuCHDx9u6FgLrF27Vh5++GHdqx8GDx4se/bs0X00eXHNmjXT/btX/D46trbdddddNo/lLM6Yu6fOu6zo/T0qz4mRstK1a1ezn5csWVLqPk+ePGl2H6c//elPVq8icYSRMY8r4jh3iOFEPCuOI4a7gxiu5IjhPC+W8dR5l5WKHMORfDPIiRMn7GpX9Fvl4sswiv5S3759265gwlnfUjuLl5eXtGnTRt566y05duyYNGnSpHDbrFmzDD320aNHzYKl6dOn23wKVW5ursM3TC7tuS7+B/yXX35x6PhlzcfHR/fmoLaeAOyod999V1M3c+ZMm78Hbdq00a3ft2+fzWPqtalWrZruk36cqXnz5rr17nrz7h9++EHGjBmj+wH+5JNPyvr16+2+l2aVKlWkadOmmvqjR49a3EfvqXDFA32jOGvunjrvsqL398gV91Upbx544AGz12nhwoWlTggVvxfM2LFjddu5W8xT2s92d5tPSVX0OI4Y7g5iuJIjhvO8WMZT511WKnIMR/LNIN9//73NNrm5uXL48OHCn5s1a2a2vfi3U7a+TTOZTPLNN984MMqyVatWLbMbsV64cMHQy6uTkpLMfrb2JKMCn3/+ucP37yjtua5atapZMLt582aHju8KNWrU0NRdvXrVqcfo3LmzDB061Kwu4/+xd9/hUVX548c/k4QkEBJCAiRA6CAKoii9yIKGXlWkiLDCisKyKiJNVoWVRWkCu1+aolKUJqAiSpFlQbpkKYIgTVhK6CmkQBKSnN8f/pLNcO8kM8mczCTzfj3PeR5y5pxzP+fOJPfwmVsSE2XevHm59qtatarpE4xXrFiR6+PmY2NjZePGjYb6Nm3aFPiM1LzUr1/f9Ntgez5bhS0mJkb69u1resr65MmTZd68eQ6f1dK9e3dD3datW03bxsfHy759+wz1vXr1cmib+eHsuXvqvAvD/X//RZzzNL/iLjQ0VF588cXsn2/cuCEjR47M93j79++3Ojuqfv360qNHD9O27rbmKeix3d3m4wzFcR3HGu53rOHyjzWcZ65lPHXehaE4r+Hca08XIytWrMjzxotff/211ZM72rZta/X6/U9gynngN7Nu3Tq5cOGCQ3EW5H4d8+bNk969e0v16tVlxYoVdvW5/9HBOn/Z7x87rwVifHy8jB8/3qrOntPwnfFe5/zPyNGjR2XTpk15bjc1NVUaNmwozz33nCxZsqTQnpQlYv7UqStXrjh9O9OmTTPc5DjnvV9sMbtR6Llz52Ty5Mmm7TMzM+XPf/6z6Tctr7zyip3R5p/FYjE9AC9cuNDmk682btwopUuXlpo1a0rz5s2lR48eVk8UExHZvHmz6T0Ydu/ene9YR4wYYfpev/LKK/L222/na8znn3/eULd69Wq5du2aof6f//yn4YlRjRs3Nnx5URTm7onzHjZsmEOX4ERFRRnGCAsLs2pjFoNZvGb3aIHRW2+9ZbX+WLx4sbz33nsOj3PixAl59tlns79pt1gsMm3aNJv/EXa3NU9Bj+3uNh8zrONYw2VhDZd/rOE8by0j4pnzZg1XcCTfNLlx44a8+uqrNm8YeOvWLRk3blz2z97e3tKtWzerNvXq1bP6eeHChTa3d+LECRkxYkSeNzL19va2+rkgp0Tv378/e7H417/+Vc6dO5dnnzVr1mT/OyIiwu6nTOVHjRo1rH5eu3atzbZXrlyRTp06SWxsrDRt2jS73p5LQJzxXr/yyitWi8whQ4bIqVOnbG4zLS1N/vSnP8nPP/8sa9eulZdffrlQb9ab81veLLnFm1+1atWSESNGONxv+PDhUq5cOUP9e++9Jy+99JL8/PPPkpqaKvHx8bJ161Zp3769rF692tC+cePG0qlTp3zF7qhRo0YZ/lOalJQkrVu3ls8++0yuX78u9+7dk0uXLsncuXOlX79+kpycLOfPn5effvpJNmzYoP1eCAcOHDDdT+Hh4aaXmNjrsccekyeeeMKqLikpSTp37iy7d++Wu3fvyvXr12X69OmmSYBRo0ble9v20jF3T513YTD7e1S7dm0XRFL0REREyGeffWZVN3HiRHn++edt3lMpJ6WULF26VNq0aWO1gB4zZox07drVZj93W/MU9NjubvMxwzqONVwW1nAFwxrO89YynjrvwlCs13DKDps2bVIikmf58MMP7RlOqwULFljFtGvXLkObDRs2WLU5f/58nuPe3+fXX3+1en3x4sVWr/fp00eJiGrTpo1av369un79ukpLS1NXr15Vn3/+uapWrZpV+xdeeMGwzXv37qnw8HCrdoMGDVIHDx5UycnJKjU1VZ08eVJNnjxZBQYGKm9vb/X3v/89u623t7fpXEqXLp3dJjw8XO3du1elpKSoGzduqAsXLti3o5VSUVFRymKxZI8VEhKi/v73v6uoqCgVHx+v0tPTVVJSkrp06ZL6/vvvVc+ePa3mMmHCBK3vS2ZmpoqIiLB6fcSIEer48ePq7t27KjY2Vu3bt0+NHTs2e58sWLBADR8+PLu9xWJRK1asUHfv3lUJCQna3mullBo3bpxVu4CAADVx4kR19OhRlZSUpBISEtTJkyfVggUL1MMPP2zVdvjw4YbxZs+ebdVm06ZNdr+3eZk2bZrh979+/fq59lm0aJGhzyuvvJLntmJjY1XZsmVz/dszbtw4Q79NmzZZfT4dLYGBger06dOm45q1v3nzZq7zCA0NNfRZsGCBVZtRo0blO96aNWtmf0bzitXs76I9hgwZku/47i/3/34fOXJEeXt7OzxO27ZtTWMtKnP31HnbKyoqyjBOWFhYnv1q1apl6Ddnzpx87AHn+vDDD3PdR5UrV3Z1iNnmzJmjvLy8rOILCAhQgwYNUmvXrlVnzpxRt2/fVikpKerSpUtq79696m9/+5tq0KCBYV4DBgxQ6enpuW7P1WseZx/bXT0fe3jiOq6orOGU0reOYw1nbM8armDHc09dy3jqvO3lSWu4iRMn2jUGyTc7P0yOJt9Onz6typQpY9d+i4iIUNeuXTPd7syZM+3+pZgwYYL617/+lf2zxWIxHTMyMtLmGG+++WbeOzmHt956K1+/wI888ohKTk7OdR874325//OQW+nTp4/KyMhQS5cuNX29Z8+eSil973Vqaqrq3Lmzw/uyUaNGKikpyTCezuTbtm3bDHF4e3ur+Ph4m33yu3BTSqlZs2blug/MFm5KKbV06VLl6+vr8D4tX7682r17t+mYOhduaWlpqlu3bg7HGxYWpo4dO2Z3rPk9iA8YMCBfv+9mxez325HfVxFRderUUdHR0aaxFqW5e+q87ZGfhdutW7dM/9Nm63e6MBWl5JtSSn399dd2H9/Mire3t5oyZYrd23PlmkfHsd3d13BKed46rqis4ZTSt45jDWfswxrOscIajnnbw5PWcPYm37jsVJOKFSvKpk2b8rw54IMPPiibN2+WsLAw09ffeOMNGThwYJ7bGz16tEyZMsXqSSBKKUlLSzO0nTBhgtPu0TFlyhSZMWOGzUeXm+nXr5/8+OOPhfLUkmHDhtl1yvvgwYNlxYoV4uXlJc8++6xD15U767329fWVb7/9VsaMGWPXqecWi0WGDBki27dv13rZh5lWrVoZ3vOMjAzZvn27lu2NGDEiX4+/HjRokOzatUtatmxpV3uLxSJ9+vSRqKgoadWqlcPbK6gSJUrI+vXrZdKkSXa/p126dJGoqCh5+OGH7d6Ou91YNcuwYcNk2bJlEhoammfbjh07yo4dO0zvXZMbd5y7p85bl61btxouIQsMDCy0p4oVJ7169ZJz587Jm2++meclkTl5eXlJ//795cSJEzJhwgS7+7nTmscZx3Z3mo8tnr6OYw33O9ZwBccazjPXMp46b12K+xrOc97JQpaRkSEtWrSQU6dOybx586RNmzZSuXJl8fX1lYoVK0qbNm1k/vz5cvDgQalfv77Ncby8vGTZsmXy/fffS+/evaVq1ari7+8vvr6+UrVqVRk0aJAcOXJEZsyYISJieEyw2ROf2rVrJ5s2bZLWrVtLqVKlxNfXV8LCwqRt27aGa9fzYrFYZPTo0XLx4kWZPXu2dOvWTWrVqiWlS5cWLy8vKVmypFSqVEmefPJJefvtt+X48eOycuVK06cC6TJ37lz54YcfpHfv3hIRESG+vr7i7+8vtWrVkkGDBsnOnTvls88+y76XSkBAgGzdulU6dOggAQEB4ufnJ9WrV7f5S++s91pExMfHR6ZPny5nzpyR999/X5588kmJiIiQkiVLip+fn4SFhUmbNm3k7bffllOnTsmnn34qgYGBTt9nefHz85M//OEPhnqzJ005g6+vr0ybNi1ffZs2bSp79uyRHTt2yOjRo6VJkyZSqVIl8fPzk9KlS0v16tXlySeflClTpsgvv/wiq1evlmrVqjl5Bvbz8vKSiRMnyn//+1+ZNWuWdOvWTapXry6lS5cWX19fKV++vDRp0kTeeOMNOXjwoHz//fdSpUoVh7bhTo8Tv9/AgQPl9OnTMmvWLImMjJSIiAjx8/OT4OBgeeihh+Tll1+WH374QTZv3uzw4kXEfefuqfPWwezv0FNPPSU+Pj4uiKboCwkJkZkzZ8qVK1dk8eLFMmjQIHnsscckNDRUSpQoIX5+flK5cmVp2LCh9O/fXxYvXiyXLl2SFStWyAMPPODQttxpzeOMY7s7zccWT1/HsYb7H9ZwBccazjPXMp46bx2K/RrOntPjitJlp65y/2nscXFxrg4JmvBe/87sso7g4GCVkpLi6tBgomrVqtnvk6P3BSrqPHXunjjv5ORkq3tiZZXVq1e7OjSlVNG77NSTcGz3DLzPv2MNV7R44vE8i6fO3RPnXZTXcFx2CkCrZ5991vBNTHx8vHzzzTcuigi2JCcny+XLl0VEpFSpUvn61q2o8tS5e+q8165dK0lJSVZ1ZcqUkR49ergoIgBwP6zhig5PPZ6LeO7cPXXenrCGI/kGIF8CAgLkpZdeMtTPmjXLBdEgNxs2bJDMzEwREWnUqFHxOXXbDp46d0+dt9nfn5dfftmh+5UBQHHHGq7o8NTjuYjnzt1T5+0JaziSbwDybdSoUVKiRAmrugMHDsiOHTtcExBMzZ8/P/vfvXr1cmEkhc9T5+6J8960aZP8/PPPVnW+vr4ycuRIF0UEAO6LNVzR4InH8yyeOndPnLenrOFIvgHItypVqsiwYcMM9ePGjTM8qQausWHDBtm1a5eI/H7quj1P3isuPHXunjjvzMxM06dq/uUvf/GYyzUAwBGs4dyfJx7Ps3jq3D1x3p60hiP5BqBAJk2aJGXLlrWqO3DggKxcudJFESHLjRs35OWXX87++e2335by5cu7MKLC46lz99R5L1u2TI4cOWJVFxoaKu+8846LIgIA98cazn156vFcxHPn7qnz9qQ1nGdcQAxAm5CQEJk+fboMHTrUqv7NN9+Uzp07GxZ1KDwVKlSQq1evujoMl/DUuXvivG/duiVjx4411H/44YcSHBzsgogAoGhgDee+PPF4nsVT5+6J8/a0NRxnvgEosJdeekkiIyOt6q5duyZvvPGGiyIC4Clef/11uXnzplVdp06d5I9//KOLIgKAooM1HABX8bQ1HMk3J3nxxRdFKZVdimOmFr/jvTa3detWq/2ilJIlS5a4OiwAxdzy5csNf3s2bdrk6rBQxHBs9wy8z+ZYwwFwBU9bw5F8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADTxceZg06dPl08++cSZQwIAABQLsbGxub5+/fp1qVevXiFFAwAAAHvktYazh1OTb9evX5fr1687c0gAAACPkJ6eLr/++qurwwAAAICTcdkpAAAAAAAAoAnJNwAAAAAAAEATkm8AAAAAAACAJiTfAAAAAAAAAE1IvgEAAAAAAACaWJRSKq9GKSkpcvPmzcKIBwAAAPc5ffq0REZGGuqnT58u/fr1c0FEAAAAKFOmjAQFBeXZzseewfz9/aVKlSoFDgoAAACOS0hIMK0PCQlhjQYAAODmuOwUAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCY+rg4AAADAUx06dEiUUnm2O3funGn9hQsX5ODBg3Zt66GHHpJSpUo5FB8AAAAKzqLsWfEBAADA6Vq3bi179uzRvp2AgAC5fv26BAQEaN8WAAAArHHZKQAAgIv069evULbTq1cvEm8AAAAuQvINAADARfr06SM+PvrvAtK/f3/t2wAAAIA5km8AAAAuUqFCBXnqqae0bqNcuXLSoUMHrdsAAACAbSTfAAAAXEj3WWnPPfeclChRQus2AAAAYBsPXAAAAHChhIQECQ8Pl7t372oZ/8cff5Q2bdpoGRsAAAB548w3AAAAFwoKCpKuXbtqGbtKlSrSunVrLWMDAADAPiTfAAAAXEzXpaf9+/cXLy+WewAAAK7EZacAAAAulpqaKuHh4RIfH+/UcQ8fPk8+j/MAACAASURBVCwNGzZ06pgAAABwDF+FAgAAuJifn588/fTTTh3zwQcfJPEGAADgBki+AQAAuAFnX3r6/PPPO3U8AAAA5A+XnQIAALiBjIwMiYiIkGvXrjllvNOnT0udOnWcMhYAAADyjzPfAAAA3IC3t7c899xzThmradOmJN4AAADcBMk3AAAAN+GsS091PT0VAAAAjuOyUwAAADdSp04dOXv2bL77e3l5ycWLF6Vy5cpOjAoAAAD5xZlvAAAAbqRPnz4F6t+uXTsSbwAAAG6E5BsAAIAbeeGFFwrUn0tOAQAA3AuXnQIAALiZRx55RI4dO+ZwP19fX7l69aqEhIRoiAoAAAD5wZlvAAAAbia/Z6917tyZxBsAAICbIfkGAADgZgYMGCAWi8XhflxyCgAA4H647BQAAMANtWrVSvbu3Wt3+4CAALl+/boEBARojAoAAACO4sw3AAAAN+ToWWy9evUi8QYAAOCGSL4BAAC4ob59+4qPj4/d7Z9//nmN0QAAACC/SL4BAAC4ofLly8tTTz1lV9ty5cpJ+/btNUcEAACA/CD5BgAA4KbsvfT0ueeekxIlSmiOBgAAAPnBAxcAAADcVEJCgoSHh8vdu3dzbbdz50554oknCikqAAAAOIIz3wAAANxUUFCQdO3aNdc2VapUkVatWhVSRAAAAHAUyTcAAAA3ltelp/379xcvL5Z0AAAA7orLTgEAANxYSkqKVKxYUeLj401fP3z4sDRs2LCQowIAAIC9ikTy7bvvvpNhw4a5OgwAAACXiIuLkzt37hjqfXx8JCwszAURAQAAuN7QoUNl4sSJrg4jTz6uDsAed+7ckejoaFeHAQAA4FbS09NZIwEAAI9l68oAd8MNQgAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCYk3wAAAAAAAABNSL4BAAAAAAAAmpB8AwAAAAAAADQh+QYAAAAAAABoQvINAAAAAAAA0ITkGwAAAAAAAKAJyTcAAAAAAABAE5JvAAAAAAAAgCY+rg4AAABPFxwcLHFxcXa3v3PnjsTHx0tcXJxcuXJFoqKi5KeffpI9e/ZITEyMxkiLH0f3/f1SU1Pl9u3bEhcXJ8ePH5cDBw7Ihg0b5MSJE06MEgAAAEUZZ74BAFDElCpVSipVqiT169eX9u3by4QJE2T9+vVy9epVWb16tTz55JOuDtGmKVOmiFIqu4wePdrVIRWIn5+fVKhQQerWrSvPPPOMTJ06VY4fPy67d++W1q1buzo8AAAAuAGSbwAAFBMlSpSQPn36yLZt22Tbtm1SrVo1V4dkxcvLSwYOHOjqMApFq1atZOfOnTJ16lRXhwIAAAAXI/kGAEAx9OSTT8qxY8ekb9++rg4lW2RkpFSpUsXVYRQai8Ui48aNk/fff9/VoQAAAMCFSL4BAFBMBQYGyvLly6VXr16uDkVERAYPHuzqEFxi/Pjx0rhxY1eHAQAAABch+QYAgBvr3LmzWCwWqxIUFCTVq1eX7t27y7Rp0yQ6Otpmf29vb1m1apW0aNGiEKM2Cg4OdpskoL3M9n3O4u3tLSEhIdKyZUuZPn26JCcnm45jsVhk4sSJhRw9AAAA3AXJNwAAipjExES5cOGCfPfddzJ+/HipXr26DB06VJKSkkzb+/n5ybx588TLK+/DvpeXl7Rr105mz54tP/74o0RHR0tiYqLcu3dPbt68KUePHpVFixZJr169xNvbO9exxo8fn/1ghbi4OPH39ze0mTFjhtUDGM6ePVsosTlDZmamxMXFyb59+2TcuHHy+OOPy/Xr103btm/fXgICAuwa18/PT/r37y8ff/yx/Pzzz3L9+nVJS0uTW7duyS+//CJffvml9OvXL8/xVq1aZbVvs8r69evtnmPDhg1Nx8jIyJCIiIhCmUeW4OBg01g2bNiQ3aZy5cqyaNEiuXz5sqSlpcnVq1elT58+puP5+vpKz549ZcGCBbJ37165cuWKJCUlSXp6usTHx8upU6fkm2++kddff71Al0u7+34AAACFQBUBq1evViJCoVAoFEqxLMHBwTaPgZ06dbJ7nLp166rr16/bHGvQoEG59u/SpYs6ffq03cfn8+fPq3bt2tkcb/z48Q4d75VS6uzZs4USm7P3fVZ56aWXbI7XsmXLXPtaLBY1cuTIXN/DnK5evar69Oljc7yePXua9ktKSlJ+fn52zefdd981HePf//53oc0jq5QoUcK0/+7du5WIqKpVq6ro6GjD66NHj7Yax8vLSw0fPlzdvHnTrviUUiotLU19/PHHqkyZMnZ/Ftx9P1AoFAqFUhzKyJEj7T6euxLJNwqFQqFQXFycmQBq3ry5unfvnulYp0+fttnvnXfeydcxOiMjw2ZSz1nJNx2x6dj3IqIqV65sc7ynn37aZr/SpUurjRs35mue06dPNx3T19dXxcXFFWhuBw8eNO0/ZMiQQptHzmL22T527JgSEbVlyxbTcXMmnUqUKKFWrVqVr/iUUuq3335T1apVyzNOd98PFAqFQqEUl1JUkm9cdgoAQDGyf/9+WbZsmelrderUMb3x/4ABA+S9997L1/a8vLzkk08+kcceeyxf/fPizrGZiYuLs/mar6+vab2Xl5esWLFCOnfunK9tjhkzRkaPHm2oT0tLk6+//tq0T9euXfMcNyIiQh5//HFDfUpKiqxbt85Qr2seOaWmphrqAgMDpUWLFtKhQ4c8t/HOO+8U6AnANWvWlI0bN4qfn5/NNkVhPwAAgMJF8g0AgGJm2rRpNl9r166d1c/+/v4ya9Ys07YHDx6UyMhIKVu2rISGhkrHjh3lzJkzhnYlSpSQ999/31A/derU7IcTTJkyxXQbY8aMsXqIQe3atQslNl1yuw/azZs3TetHjRol3bt3N9QnJSXJm2++KTVq1BBfX1+pWLGiDB061PS+cpMnT5YaNWoY6leuXGm6TXuSbz169DCt/+677+T27duGep3zyHLv3j1DXalSpWT48OG5TUVEfr9X2tixYw310dHR8vLLL0udOnWkZMmS4uvrK+Hh4fLMM8/IgQMHDO3r1asnr732ms3tuPt+AAAALuDqU+/swWWnFAqFQinOxdmXPoqIOnfunF3H1H79+pm2S0lJUeHh4YZx69WrpzIzMw3tMzMzVfny5W3G8/e//910O7ldClcYsTl730+YMMF0rIyMDBUcHGxoHxgYqG7dumVon5aWppo3b266jRo1aqiYmBhDny+++MLQ1tvb2+Y9x+rVq5frXGxdvtirV69Cn0dWMdtGSkqKSk5OVkoptWXLFtW6dWsVEBCgAgMDVd26dVWNGjWUiKjnn3/edD7NmjWzub2AgAB16NAhQ5+TJ0+ati8K+4FCoVAolOJUuOwUAAC4zJ49e0zr7z+bJjg4WHbt2iWHDx+Ws2fPyrVr1yQpKUl27twp165dM/Q/ceKE6dlAFotF2rRp45zgi0BsZho1aiRvvfWW6Ws7d+6U+Ph4Q/1LL70koaGhhvrPP/9c9u/fbzrW+fPn5YMPPjDUP/3004YnZmZkZMiaNWtMx+nWrZtpvcjvlzC2bdvWUB8XFycbN2401OueR278/PykVKlSsmbNGunUqZPs3r1bkpOTJTExUU6dOiXnz58XEeNnP8uJEydsjp2cnCwzZ86UmJgYOXr0qGzcuFE+/vhjWb58uellxEVhPwAAgMJH8g0AgGLoxo0bpvXlypWz+nnhwoXSpk0befzxx6VOnTpSsWJFCQwMzPW+UUePHjWtr1SpUv4DNuHOsYn8ntQLCgqSZs2aycyZM2X37t1SunRp07YzZswwrX/mmWdM67/66qtct/3ll18a6kqVKiVdunQx1Ofn0tPOnTubJpfWrFkjaWlphvrCmEduEhMTZfjw4aKUcqifiMgLL7yQ6+srVqyQcuXKyaOPPipdu3aVV155RSZPnlzs9gMAANCH5BsAAMVQTEyMaX2pUqUKPLbZ/b5ERMqWLVvgsQvKmbFt2rRJ1O9PhjctmZmZcvv2bdm/f7+8+eab4u/vbzrOl19+aXq2mI+Pj+kDMERETp06lWtsFy9eNJ1rkyZNDHV79+6VixcvGupbtmxpc7/07NnTtP6LL74w1BXWPHLz1Vdf2fzMZ7lw4YJp/bx58+Trr7+W3r17G5LTjigq+wEAABQ+km8AABRDthJB6enpDo/l4+MjJUuWlKCgIAkJCbH5pEcvr8JfVrhzbCIiO3bskMGDB5u+Vq1aNZvv05kzZ3JN/CmlpEyZMoZ+DRo0MNQppWT16tWGeh8fH+nYsaNpvdmTOi9cuCC7d+922Txy8+9//zvPNhs3bjR9SqjFYpFevXrJmjVr5MaNG3Ly5En57LPPZPDgwbk+9OB+RWU/AACAwufj6gAAAIDzhYSEmNYnJSXZ7FO7dm159tlnpVWrVlKvXj0pV66cBAUFicVi0RWm3dw5NjOpqakye/Zseffdd02fTCkiEh4e7vTt2koWrVy5UsaMGWOo79atm6xatcqqrk2bNqZnxK1YscL0csbCnIcteZ1ZJiISGxsrU6ZMkffee89mG4vFInXr1pW6detmJ00vXrwoGzdulMWLF5veUzBLUdkPAACg8JF8AwCgGKpVq5Zp/eXLlw115cuXlzlz5kj//v3dLpnlzrGZiY2NlTlz5siSJUvk0qVLubYtWbKk07cfGBhoWn/48GE5deqU1K1b16q+U6dO4u3tLRkZGdl1ti45Xb58uWl9Yc7Dlri4OLvaTZkyRSpUqCB/+ctf7B67atWqMmzYMBk2bJhs2LBBXnnlFbl69aqhXVHaDwAAoHBx2SkAAMWMt7e3tGrVyvS106dPW/0cEREh+/btk+eff97tklvuHJstQUFBsnHjxjwTbyJiesN+Z2zflvvPcBMRCQ0NlebNm1vVde/e3dDuyJEjcvz4cdNxC3seZu7cuWNXu8zMTHn11Velc+fOEhUV5XBc3bt3l6ioKNPkdlHaDwAAoHCRfAMAoJh56qmnbJ4xs2/fPqufly1bZvMsuftlZGRIamqqzcsonc3VsXXu3FksFotpKVu2rOnZTz4+PrJ06VLTJ4XeL7ezlCIiImxuO7eS25lS9jz19JFHHjG91NHWWW+umIczbN68WZo2bSoNGjSQsWPHyg8//GB34qpy5cqyevVqQ0K4KO4HAABQOEi+AQBQzIwePdq0PiMjQ7Zs2ZL9c/PmzaVdu3ambc+dOyevvfaaPPzwwxISEiJeXl7i4+Mj/v7+MmvWLC1x5+TOsYmIxMfHy6uvvmr6Wv369WXSpEl5jhEbG2vztbCwsPyGZtOpU6fkyJEjhvpu3bpl/9vsktPMzEybiTuRwp+HM/3yyy8yY8YM6dixo5QpU0aaNGkir776qixfvlyuXLlis1+jRo3kySeftKoryvsBAADoRfINAIBipHfv3tK+fXvT1zZt2iQ3btzI/tns8kKR3xNLrVq1kv/7v/+T48ePS1xcnNWN9gvjbBx3ji3LunXrZP369aavjR07Vpo0aZJr/+joaImJiTF9TcfN+0XMz35r0KCBVK1aVUREevToYXh9x44dEh0dbXNMV8xDh/T0dPnPf/4jc+fOlRdeeEEiIiKkQ4cONh9iEBkZafVzcdkPAADA+Ui+AQBQTDz22GOyaNEi09eUUjJx4kSruipVqpi23bx5s1y7ds3mdu6/R1hBeHmZL0XcITZ7jBgxQhISEgz13t7esmTJEvHz88u1//2XAWdp2bKlU+K736pVq0yfWNq1a1epXLmyNGrUyPDaF198kee4hT2PwqCUkq1bt0r79u0lMzPT8HrlypUNdcVxPwAAgIIj+QYAQBFnsVhkwIABsnPnTgkODjZt89VXX8mhQ4es6gICAkzb5nbj+LZt28rjjz9u+pq/v7+dEf9PhQoVTOvdITZ7REdHy/jx401fq1evXp6Xn37//fem9YMGDcr1vnGdOnWShIQEOXPmjOzevVvWrl0r8+bNM5yNdb+LFy+aJoi6du0qPXr0MNzHLCUlRdatW5frmK6Yh6MqVqwo/fr1k3fffVeWL18uUVFRcv36dZu/LzldunRJbt26Zag3u0ecu+8HAADgIqoIWL16tRIRCoVCoVCKZQkODrZ5DOzUqZOhvcViUSEhIerRRx9Vb7zxhjp06FCux9Ho6GhVqVIlwzgLFy40bX/y5Enl7e1taF+zZk118eJFm9tZsmSJzTm+/fbbpn2ioqJM2xdWbI7ue7NisVjU7t27TcdIT09XTZs2tdk3ICBAxcbGmvadPXu2aZ+SJUuqAwcOGNpnZmaqRx55JM94//KXvxj63rlzR+3YscNQ/+WXX9q1DwprHrdu3TLdRkRERK7xNWnSxKHYcpaGDRuqzMxMQ9/XX3+9yO0HCoVCoVCKWxk5cqTpMdHdkHyjUCgUCsXFJbcEUEHdvXtXNW7c2HS7Q4YMsdnv888/V7Vr11Z+fn6qVq1aaty4cSouLk4ppVRMTIw6ffq0oU90dLQKDg423dbQoUNtbuuDDz5QlSpVUv7+/qpevXrKz8+v0GJzRvJNRNRDDz2kUlNTTcc5ceKE8vPzs9l3/PjxNmNYs2aNatasmQoICFChoaGqU6dOav/+/aZtP/vsM7tirVChgkpPT7e5zZx69uxp9z4ojHkUJOlkK0m9atUq1aNHD1WxYkVVqlQp5ePjo8qWLasee+wxNXbsWHXjxg1Dn7S0NFWxYsUiuR8oFAqFQilOheSbE5F8o1AoFEpxLrqSbzdu3FDt2rWzud2QkBB1+/Zth8d99tln1YIFC0xfu3jxovrmm2/UrFmzrLbVoEEDu8ePiIgotNiclXwTETVp0iSbY02dOtVmPy8vL7Vt2zaH55rTmTNnVFBQkN2xbt26Nc8xY2JilK+vr91jFsY8CpJ0atWqld1Jx7y88847RXY/UCgUCoVSnArJNyci+UahUCiU4lx0JN/27t2rqlSpkue2//znPzs07uTJk5WIqHbt2uXabseOHYZt7dmzx65tZCUQCiM2ZybffH191fHjx03HSk9PV82aNcv1M2BPQszMr7/+6nDSJbczC7MsXLgwX59lnfMoaNJpwIABKi0tLV/xZZk3b57ppc9FaT9QKBQKhVJcCsk3JyL5RqFQKJTiXJyZfNu/f7/q3r27Q9sfO3asunfvXq7j3rlzR7344otW/ZYtW2azvVnyrXbt2ury5ct5ziFnAkF3bM5Mvon8fnaV2f3BlPr98lN/f3+bfX18fNSECRNs3jPsfnfv3lWzZs1SpUqVytdnztZlsllat26dr8+zznk4I+nUqFEjtW/fPrtiy+nkyZPq6aefLjb7gUKhUCiU4lCKSvLNopTJ8+bdzJdffil9+/Z1dRgAAGgRHBwscXFxDvVJS0uTW7duyc2bN+Xs2bOyfft22bZtm5w8eTJfMdSuXVv+/Oc/S9u2baVmzZpSunRpSUxMlFOnTsmWLVvko48+kitXrlj1sVgsMnDgQHn66aelWrVq4uXlJTExMXLy5En5/vvvZePGjYbtlC9fXkaPHi3du3eXGjVqiMVikdu3b0tsbKwcPXpU9u7dKx999JGkpKQUSmy57fvOnTvL5s2bHd6X8+fPl+HDh5u+NmPGDBk7dmyu/YOCguSZZ56Rp556Sho1aiTly5eX4OBgSU5OltjYWDl27Jhs375dli9fLjdv3nQ4vizffPON9OzZ0/S1CxcuSI0aNaQgy0Qd87h165aEhoYa6qtUqSKXL192KL5GjRpJ165dpXnz5lKjRg0JCwuTgIAA8fb2lsTERImPj5eTJ0/K4cOH5dtvv5X9+/c7NH4Wd98PAAAUZSNHjpTZs2e7Oow8kXwDAAAAAABAkVNUkm9erg4AAAAAAAAAKK5IvgEAAAAAAACakHwDAAAAAAAANCH5BgAAAAAAAGhC8g0AAAAAAADQhOQbAAAAAAAAoAnJNwAAAAAAAEATkm8AAAAAAACAJiTfAAAAAAAAAE1IvgEAAAAAAACakHwDAAAAAAAANCH5BgAAAAAAAGhC8g0AAAAAAADQhOQbAAAAAAAAoAnJNwAAAAAAAEATkm8AAAAAAACAJiTfAAAAAAAAAE1IvgEAAAAAAACakHwDAAAAAAAANCH5BgAAAAAAAGhC8g0AAAAAAADQhOQbAAAAAAAAoAnJNwAAAAAAAEATkm8AAAAAAACAJiTfAAAAAAAAAE1IvgEAAAAAAACakHwDAAAAAAAANCH5BgAAAAAAAGhC8g0AAAAAAADQhOQbAAAAAAAAoAnJNwAAAAAAAEATkm8AAAAAAACAJiTfAAAAAAAAAE1IvgEAAAAAAACakHwDAAAAAAAANCH5BgAAAAAAAGhC8g0AAAAAAADQhOQbAAAAAAAAoAnJNwAAAAAAAEATkm8AAAAAAACAJiTfAAAAAAAAAE1IvgEAAAAAAACakHwDAAAAAAAANCH5BgAAAAAAAGhC8g0AAAAAAADQhOQbAAAAAAAAoAnJNwAAAAAAAEATkm8AAAAAAACAJiTfAAAAAAAAAE1IvgEAAAAAAACakHwDAAAAAAAANCH5BgAAAAAAAGhC8g0AAAAAAADQxMfVATjLxIkTXR0CAACAW5o1a5YkJibafL1Dhw7SokWLQowIAAAgb5988olER0e7OowCKzbJt0mTJrk6BAAAALf0ySef5Jp869ixo4waNaoQIwIAAMjb5s2bi0XyjctOAQAAAAAAAE1IvgEAAAAAAACakHwDAAAAAAAANCH5BgAAAAAAAGhC8g0AAAAAAADQhOQbAAAAAAAAoAnJNwAAAAAAAEATkm8AAAAAAACAJiTfAAAAAAAAAE1IvgEAAAAAAACakHwDAAAAAAAANCH5BgAAAAAAAGhC8g0AAAAAAADQhOQbAAAAAAAAoAnJNwAAAAAAAEATkm8AAAAAAACAJiTfAAAAAAAAAE1IvgEAAAAAAACakHwDAAAAAAAANCH5BgAAAAAAAGhC8g0AAAAAAADQhOQbAAAAAAAAoAnJNwAAAAAAAEATkm8AAAAAAACAJiTfAAAAAAAAAE1IvgEAAAAAAACakHwDAAAAAAAANCH5BgAAAAAAAGhC8g0Qke+++04sFkt2+e9//+vqkOCA9u3bW71/FotFBg8e7OqwABRjAwYMMPzd6dKli6vDAjwOa7iijTUc4DqsZQoXyTcnWbhwodWHdvfu3a4OCfAIn3zyifzrX/+yqgsPD5dZs2Zlv37/QSWrrF+/3u7tzJw509B//PjxTp0LHLNjxw557bXX5PHHH5ewsDDx9fWVwMBAqVq1qnTp0kXef/99uXTpkkNjXr16Vd5//32JjIyUKlWqSMmSJSUwMFBq1aol/fr1k88//1zS09M1zcg+njpvET1zNzN//nzTvxnh4eHZbf7xj39I+fLlrfpt2rRJli5dWuDtw1psbKysWbNGhg0bJk2bNpWaNWtKUFCQ+Pv7S+XKlaVhw4bSu3dvWbBggZw9e9bV4QKwE2s45GTPsTc3zl7LFIW10b1792TdunXypz/9SR599FEpX768+Pr6SunSpaVy5crSpk0befPNN2X//v2m/VnLFDJVBKxevVqJSK7F1RYsWGAVz65du1wdktu5d++eKlmypBIRtWDBAleHY2XDhg1W79/58+ddHRLsEBMTo8qWLWv4e7BixYrsNosWLbL5d6NOnToqLS3Nrm3NmDHD0H/cuHG6poZcnDlzRrVs2TLP44KIKC8vLzVs2DCVmJiY57hTp05VpUqVynPMunXrqgMHDhTCTK156ryV0jd3M+fPn1elS5c2HTssLMyq7ZIlSwxtQkNDVVxcnDOm7VSVK1fOdb99+OGHrg7R4PLly2rEiBHKz8/Prvc+q3Ts2FHt27fP1eG79bonP9x1PqzhiibWcMjJkWOvGWevZYrC2mj9+vV5HttzlhYtWqgTJ04YxikKa5lmzZrlOreRI0e6OkS7cOYbCs3x48fl7t27rg4DxcikSZMkLi7Oqq5p06bSr18/u/qfOXNG5s6dqyM0aHLo0CFp3Lix7N271672mZmZsnDhQnnqqackKSnJZrsxY8bI+PHj5c6dO3mOeerUKYmMjJSffvrJ7rgLylPnLaJv7maUUjJkyBC7+w0cOFAaNmxoVRcTEyOTJ092aLswWrZsmdSuXVvmzZsnqampDvXdsmWLtGjRQoYNGyb37t3TFGHeitu6p7jNB67FGg5ZHD323s/Za5misDaaO3eu9OzZU6Kjo+3us2/fPmnWrJkcOHDAqp61TOEh+YZC85///MfVIaAYuXjxoixcuNBQP23aNLFYLHaPM3nyZImNjXVmaNAkISFBunfvLrdv33a474EDB2TkyJGmr61evVpmzpzpcCy9e/eWhIQEh2NxlKfOO2t7OuZuy4IFC2T79u12t/fy8pL333/fUD937ly5cuWKQ9vG/4wfP17++Mc/SkpKSnZdaGioDB8+XL799ls5e/as3L59W1JSUuTixYuya9cueeedd6Ru3bpW43z00UcSGRlZaJ/X+xW3dU9xmw9chzUccnL02JuTs9cyRWFtdPjwYXn99dfz1TcxMVH69u1rkf+gnQAAIABJREFU9aUWa5nCQ/INhYZFG5xp1qxZhjMamjZtKm3btnVonLi4OJk0aZLzAoM2M2bMMF0E/OEPf5C9e/dKQkKCXLp0ST799FMpV66cod3ixYvl/PnzVnXp6ekybtw4Q9tHHnlEtm/fLklJSXLr1i1ZuHChlCxZ0qrN5cuXZfbs2QWcVd48dd4ieuZuy/nz5632ib3/AezcubM8+uijVnVpaWkyZ84cu/rD2qJFi2TatGnZP1ssFhk9erT89ttvMn/+fOnevbvUqlVLgoKCxM/PT6pUqSKtW7eW9957T44fPy6ffPKJBAUFZfffuXOnDBkyxBVTKXbrnuI2H7gOazhkye+xV8T5a5misjaaMmWKZGZmGuoHDhwox44dk9TUVLl9+7Z899138tBDDxna/fe//5WVK1da1bGWKSSuvu7VHtzzrXho0qRJ9v5xp3uFKMX9QoqaxMRE0/tCrFq1ytA2t/uFZBUfHx918uTJXLfJ/UJcKyMjQ1WsWNHwHjz88MPq3r17hvZbtmwxfa/nzJlj1e6rr74ytAkKClLXrl0zjDl//nxD2/DwcNPtM++C0zV3M5mZmapdu3ZW/Xr16mX3fWeWLl1qaFumTBl1586dAu8HZykK93w7fvy41f3dfHx81Oeff+7wOEeOHFHh4eFW85s7d66GiHPnzuue/HDX+bCGK1pYwyFLQY+9zl7LFJW1kdm96Jo1a2ba/uTJk8rb29vQvm/fvoa27ryW4Z5vyJfFixdnP7nlgQceyK5XSsk333wjHTt2lAoVKkiJEiUkODhYGjRoIK+99pqcOXPGdLwZM2Zkj1ezZs3s+lu3bsm7774rTZs2lUqVKomfn59UqlRJWrduLbNnz871Ep6pU6dmj+nj42PXvObMmWPaJ+dTYKOiorLrhw8fbvUUm4J8m5qWliZffvmlDBgwQBo0aCAhISFSokQJKVmypFSsWFFat24t48aNk8OHD9s9Zta3Lunp6fLpp59Kx44dpWbNmuLv7y9ly5aVhx9+WF5//XX57bff7BovIyNDvv/+e/nTn/4kDRs2lNDQUPH19ZWAgACJiIiQTp06yfTp0+XGjRs2x9DxXt/vypUrMmXKFGnfvr1ERERIyZIlJSgoSGrXri1du3aVjz76yHB/DjM5Pw8Wi0U2b95sdwz2WLduneG+EMHBwdKrVy+7+rds2dLq5/T0dBk9erTT4stpz549MmHCBGnRooVUq1ZNSpUqJaVLl5bq1atLixYtZMKECXY9HfnTTz81PP2pY8eO2a8rpWT16tXStWtXCQsLkxIlSkj58uWlefPmMnXqVElMTLQ75oSEBFmwYIE899xz2WeY+Pv7S/Xq1aVdu3byz3/+M9fPqg5HjhyRq1evGur/+te/mv6d6tChg1SpUsVQ/8svv1j9vHbtWkObAQMGSFhYmKF+8ODBEhAQYFV37do12bVrV57x55enzltE39zNzJ8/3+qSl5CQEIf+JvTu3VtKly5tVXf79m359ttv7R4Dv19ClvNSmHfffVdeeOEFh8d59NFHZdWqVeLl9b9l7uTJk60uY83Jlese1nHOX8e54xpOhHVcFtZwnreGs6Wgx15nr2WKwtroxo0bpvei69u3r2n7unXrSqNGjQz1Fy5cMNSxlikELk7+2aU4nfm2fPlyq+y4UkrFxcXl+RQ3X19ftXz5csN4ObPuoaGhSiml9u3bpypUqJDreFWqVFF79uwxjfGDDz7Ibuft7W3X/GfPnm3a5/79YqtERUXZtZ377d+/X9WuXduubYiI6t27t4qPjzeMc/+3ppcuXVJXr15VjRs3zvN9yflUJjPHjh1TDRs2tCu+gIAAtWjRItNxdLzXWe7du6fGjh2rfH1984wxNDRULV68ONfxcn4eRERt2rQp1/aO6tixoyGuoUOHmrY1+9b0H//4h6pataqh/l//+pfNbTr6relPP/2knnjiCbs/m61atcr16XwrV6409Mn6lismJka1bds21/ErV66sfv7551z3a2Zmppo5c6YKDAzMM96goCCbn1Udtm/frtq1a6cef/xxVbt2bVW+fHnl5+dn+m1kFrP9/8wzz1i1uf/sGBFR69atszlmhw4dDO3ffvttp83zfp46b6X0zf1+586dUwEBAVZ9Fi9erA4dOmQYK7cnrg0cONDQvmfPnvmev7O5+5lv586ds/p2vl69eio9Pb1AYw4fPtxqjrbO1nLluod1XMHXcUVhDacU67gsrOF+L560hjPjjGOvs9cyRWFtFB0dbfqefvHFFzb7dOvWzdC+adOmpm3ddS3DmW/IF19f3+x/37lzR9LS0iQyMjLPp7ilpaXJkCFD5Ndff7Wqz/ntZFJSkly+fFm6dOmS5zcaly5dkm7dusnp06fzMQv3cPr0aYmMjJSzZ8/a3Wft2rXSq1cvUUrl2s5isUinTp3y/CY3LS1NBg0aJCdOnDB9/cyZM9KmTRs5cuSIXfElJyfL0KFDZcmSJYbXdL3X6enp0q1bN5k+fbqkpaXlGWNMTIwMHjxYpk6dmmdbHVJSUuTHH3801Hfp0sXuMRITE2XKlCmG+lGjRpneQ8FRn3/+uTzxxBMOffu1Z88eadOmjSxbtsz0dT8/P0NdQkJC9vu3Y8eOXMePjo6W9u3bS0xMjOnrmZmZ0qdPHxk9erRd37AmJCTI0KFD5W9/+1uebZ2hbdu28u9//1sOHjwoZ86ckRs3bkhKSorpt5FZbt68aagLCQnJ/vfVq1fl2rVrhjb16tWzOabZvTN03gfJU+ctomfu91P//wlrycnJ2XVdu3aVF198UTIyMhyK1+xv0LZt21z6tM2i5KuvvrLa56+99pp4e3sXaMyRI0da3T9o9erVBRpPB9Zxzl/HueMaToR1nAhruJw8aQ13P2cce529likqa6Pw8HApU6aMoT63ByNcv37dUPfggw+atmUtoxfJt0JWokSJ7H+npKTItGnT5ODBg/LQQw/J8uXL5erVq3Lv3j25deuWfPfdd/LII49kt09NTZV//OMfVuPlXJimpqbK2LFjJS4uTlq2bCnffPONXLt2TdLS0uTatWuycuVKqV27dnb7uLi4fD8pxV7Dhg0TpZTh0fQLFiwQpVR2ady4scNj//Wvf80+bd3X11feeustiYqKkri4OElPT5fExEQ5e/asrFixwuoU9R07dsiaNWtyHXvGjBny888/S926dWXp0qVy5coVSUtLk5s3b8pXX30l9evXz26bnp5u86k4I0aMsDq9v2vXrrJhwwaJjo6W1NRUSU5OlkOHDsnrr79udXnMqFGjDJcZ6Hqv33rrLdmyZUv2z3Xq1JGPP/5YTpw4IcnJyZKUlCRHjx6VDz74QEJDQ636bdu2Ldf9qMOePXsMlw55e3tLu3bt7B4jLi5OBgwYYPjcHT16VD799NMCxbdx40b54x//aNcC+H737t2TF198UbZu3Wp4LWfiPktCQoLMmDFD9u3bZ9f4N27ckPfee8/0tTFjxpiebp+XSZMmyddff+1wP90OHz4sJ0+eNNTXqVMn+9+2LjmKiIiwOa7Za7ZuC+AKnjpvEfvmfr958+ZZ/acnJCREFi1alK/tR0ZGGm4UnZSUJPv378/XeJ4m5/tgsVhsXkLjiAceeMDq7/z+/futLmvVwdF1D+s456/j3HENJ8I6ToQ1XE6evIZzxrHX2WuZorI28vLykt69exvqv/jiC9Pk82+//WaaEOzTp4/p+KxlNCv8k+0cV5wuO815arzFYlH+/v6qQ4cONm9keOvWLRUSEpLdp1q1alavL1682LAvevXqZfNmj/Hx8eqBBx6wan/06FGrNs68XCHL3bt3rbZZ0Bv1ZmZmWt1scubMmXn2eeGFF1RYWJhq3LixmjVrltVr91+y4OfnpyIjI1VycrLpWDExMapcuXJWp4Pf77fffjO8L7mZOnWqVfv7L4XQ8V6fO3dO+fj4ZL/euXPnXG+qefnyZVW9evXs9g8//HCuc9Ih5+czq9SvX99me7NLFkaMGKGUUurHH380PbU9ISHBMI49lyzExsZafS5ylgEDBqh9+/apxMRElZSUpPbu3at69+5t2rZixYqGz97GjRsN7UqVKvX/2rvzoCiuxA/g32EQEFBBNHgQRVlvs6vxQlHiqhvPqIm3JnjEeMZdUROPaHnHqBHNrmeZFa9gNKZc44FHstF4y7LxXlELD0SEGIPXcqi83x/+YOnpnpmeoXsO5/upoorp6el5b5rp/vK633uiXLlywsvLS8TGxopr166J3NxccebMGfHWW28pbjskJET2N3PhwgXh5eUlW7dx48Zi7969IiMjQ2RnZ4tjx46Jzp07y9arWbOmyMvLs3eXai4/P180b95csf7Xrl0rWm/btm2y5318fCxue/369bLXlC5dWu8qqeKp9RZCfd2LU+ryUvy4m5SUpHh8sCQiIkL2mqVLl2paV3u5erfTkJCQorLUr19fs+3GxsZK6qnUfc+ZuYc5ruQ5zh0ynBDMcUIwwxX/8dQMp9W5V+ss407ZKC0tTQQFBcne++233xZnzpwRubm54uHDh2Lfvn2iXr16svXatWsnCgoKzG7fFbMMu51SiQkh4Ofnh6+++ko2XXGhkJAQScv0zZs3ZYOUFhcYGIgvv/zS7AC75cqVw6JFiyTLdu/ebUfpnSs7O1sy2KTp1MhKNm3ahLt37yIpKQmxsbEW1/X398eWLVvg7++v+Hz58uXRv3//osfp6emy/ZKeno42bdqgdu3aKFu2LD788EOL7zlu3DjJnZHJyckW19diXy9duhTPnj0DAFSsWBEJCQlm/xYBoGrVqli9enXR4wsXLuh+e7Wps2fPypap2f/FFdY5OjoaPXr0kDyXmZmJBQsW2FW21atX4969e7Lls2fPxubNmxEZGYnAwEAEBASgZcuW+OabbxT/LjIyMpCQkCBZpjT1+n//+188ePAAX3zxBeLi4hAREQFfX1/84Q9/wI4dO2SDEgMvupuY3h2kNGV5eHg4Dh06hM6dOxfd4t6qVSvs3bsXXbt2laybmprqMne/FRQUYOjQoTh9+rTsubfffhsRERFFj+/fvy9bx3SgWTXP5+TkmB3I3VE8td6AbXUvJBS6vPTq1QsDBgwoUVmK361eSOmYRVLPnj2TdKdS6spjr4YNG0oeK03i4UqY48xTm+PcIcMBnpnjmOH+xxMznJbnXq2zjDtlo7CwMOzevVtyJysA7NixA40aNYKfnx/Kli2LTp06yYasatWqFbZv3674N1mIWUY/bHxzsiFDhqBChQoW12nUqJHksaVZivr06SP7Iprq2rWr5GBx7NgxFSV1LWXLlpXcvr9nzx5Ntz9s2DCr++W1116TPDY9aLdp0wY//fQTUlJS8ODBA7Rv397i9vz9/SUz9SkFgOK02NeJiYlFvw8aNAhBQUEWtwcAHTt2lJRz165dVl+jJaWxYerUqWP39hYtWiQJzMCLMKs0C5A1SrfM161bF9OnTzf7moULFyqOSbVp0yZV79m0aVPF8Gc0Gs3OGFX8lvjnz59L/g4KjR8/HmXLljVbZlP2dHfQ2tOnTxETE4OvvvpK9lxgYKCsa5FSODL9W1D7vGmXLEfy1HoDtte9kGmXl4oVK2LVqlUlLo/SsUjtjIqezHQcI0vj9NnKdFvmxkxyFcxxJecOGQ7wzBzHDCflaRlOy3Ov1lnG3bJRVFQUzp07h3HjxqFSpUoW1zUYDGjVqhXWrFmDw4cPIzg42OL6zDL6YeObk1k7mQOQBQil6YULqRkzwdvbG40bNy567Grj9qhhNBrRtm3bosfLli3DuHHjkJ6ersn2O3ToYHUd0/2ixYG2+NXKwit75pR0X2dkZEhCUPH1rImMjCz6/dy5c6pfpwWlAUUrV65s9/Zq166NUaNGSZbl5uZiypQpNm3n1q1buH79umz5wIEDJWPBmPL390e3bt1ky5OSkqz+DQAvGvDNUbpqCry446DQzz//LHlcqHnz5ma3W79+fdmJu/hU8c7w22+/oUuXLoqNMAaDAfHx8ahZs6ZkudKgvtYGeDcXtJw1EK2n1huwr+7Ai6v8pt/v1atXo2LFiiUuU9WqVWXLbt++XeLtvuxM7zoyd8eSPUzvTLDUe8AVMMeVPMe5Q4YDPDPHMcNJeVKG0/rcq3WWccdsdPv2bTx48MDqRIJCCNy5cwfnz5/HjRs3rG6XWUY/bHxzsvDwcKvrmM6QY+kLZnolz5zq1asX/Z6WlqbqNa5m8eLFkqCzfPlyVKtWDVFRUZgxYwZ++OEHu2/7rVatmtV1TAdPtbRfMjMzsW7dOgwbNgytW7dGrVq1EBoaiuDgYAQGBsLPzw/e3t64ePGi6jKWdF/funVLst7gwYNhMBhU/RQf6NjRM60pzWZo7YqPNTNnzpTNHPT111/bNLiouS4magahVgrMOTk5qmaAKx6gTVWoUEExNBYfcFwpbAIvQp+5/e/l5SW7A/fXX39VnE3JEa5du4bIyEh8//33is9/8cUXioPTKnX1sTbLlrlAZe3qqB48td6A/XVX6vIyaNAgvPPOO5qUS+mfSGd9L9yJ6d06SoPV28t0W9au+Dsbc1zJc5w7ZDjAM3McM5yUp2Q4Pc69WmcZd8pGBQUFmDhxIiIjI7Fx40ZV++7GjRtYvnw5GjRogJUrV1pcl1lGP2x8czJrfcltpbarRvGTVE5OjiZTczta48aNcfDgQdSoUaNoWUFBAY4fP4558+ahQ4cOCA4ORqdOnfDll1/aFOa1uuqel5eH2NhYVK9eHe+//z7i4+Nx7NgxXLt2DVlZWcjOzsaTJ0+Ql5enemrtQiXd10pjG9hD6YqbXp4+fap4oivp/goJCcEnn3wiW158TBlLYyMAyoESAKpUqWL1/c0FTzX7yFJoNRqNitOR2/oeaqkJmlo7duwYWrZsqfjPg7e3N9asWYNx48Ypvlbp78ba1Upzz2t5p44anlpvoGR1X758OQ4fPlz0uHLlyvjb3/6mWdmUPg9nd811B8HBwZJjrJoue2qZHuOsdfNzNua4kuc4d8hwgOflOGY49a8FXq4Mp8e5V+ss407ZaMaMGYiLi5NcNPD29saMGTOQkpKCvLw8PHjwAIcPH0b37t0lr83Pz8fYsWMtdjVmltEPG99eMgEBAarWM73iZ8+U2q4gKioKV69exebNm9GiRQvZyTU3Nxf79+/HBx98gPDwcCxYsMBhATUvLw/t2rXDsmXLJFeptFLSfV386lNJOLILj7nP0c/Pr8Tb/vOf/yy7E/XkyZPYsmULAOUrbMU9evRIcbmlgY+trWNum8WZ3hlrylJ3CUDb/ffw4UPNtqXGtm3b0L59e8V/1IODg7F7926MGDHC7OuV/gm39nko7ZOAgACr+0FLnlpvoGR1T01NxdSpUyXL1q5dq+mdUErfZSGELueAl4mXl5dkDKqff/5Zs22bDhJd/C4iV8Qc5xo5Tu8MB3hejmOGk/OEDKfXuVfrLOMu2ejq1auyiVgAIC4uDnPmzEHt2rXh4+ODsmXLIjo6Gjt37pRM3lho4sSJZrtGM8voh41vLxm1X4rit/EbDAaH/wOlJaPRiEGDBuHkyZPIyMhAfHw8+vfvLxtDIDs7G9OmTcM777xj1xVKW82YMQPHjx8velyqVCkMHjwYX3/9Nf71r38hNTUV9+/fx6NHj5CTk4Nnz56hQYMGqrdf0n1dpkwZyXr79++HEMLmHy27B9nL2lgHavj6+irOkDVlyhTk5uZaDYfmBrZVE47NrWPtiqcWTP8OSkJN0NTKxo0bMWDAAMXvQcOGDZGUlISOHTta3IbSmBb5+fkWw5bS1XE1XZy04qn1Bkpe9wMHDsi+a926dTPbNadZs2aybWRmZkrWmTdvnuR5LY5FnioqKqro9/T0dFXj0qhRvOtZ+fLlVXf1cxbmONfIcXpnOIA5rhAznP3cIcPpde7VOsu4SzbavHmzrNEsKChINvZhcUqTety6dUtyjCuOWUY/bHx7yag9gRa/xbxMmTJWb8e2xtF3vJgTGhqKIUOGYMuWLcjMzERycjKmTJkiGU9m586dmsxqZ0lubq5k1qTg4GCcOnUK69evR79+/dCkSRPUqFFDMl6I0Wi0KUyWdF+bjrHj6jPAAeavLmo1pXf//v3RokULybJbt24hLi7O6gxi5gaMVTNAqbkBprUYAN4ac1ce//3vf9sc4JWurOlh27ZtGDp0qOLdDz179sSJEycQERFhdTt16tRRPPaZjqNj7bm6detafS8teGq9Ae3qrjelY5G7N4w4SnR0tORxfHx8ibeZkpIiGcvpjTfesHoniVp65R7mOOfnOEdkOMDzchwznPbcMcNpRess4y7Z6MyZM7JltWvXtjjOXO3atRWXnz9/XnE5s4x+2Pj2krl8+bKq9YpfUTbtglH8wPP8+XNVYUKrK9RaMhgMeP3117FgwQJcvHgRtWrVKnpO6XZdLZ0/f14SlqZNm2Z1Fqr8/HybBk0u6b42PclcuHBB9Xs7i9FoVDy5WJoB2FZLliyRLfvss8+sfg9ef/11xeWnT5+2+p5K6wQHByvO1Ki1evXqKS531QG8jx49ipiYGMVGmDFjxuDbb79VPZZmuXLlFAOJuTACKM8KZxr29eCp9Qa0rbvelI5FzhgXzx316dNH8lmtXr26xA1CpmMKDR48WHE9V8o9zHH/46wc54gMB3hejmOG0567ZTgtaZ1l3CUbKd2haG1sOnPjtZn77jHL6IeNby+ZI0eOWF0nPz9f0mpep04dyfOmV6asXUkrKCjAP//5TxtK6XhVqlSRDMaalpamaxe5jIwMyWNLsxkV+u6772wav6Ok+zooKEgSZHfv3q36vZ3plVdekS3LysrSbPtRUVHo1auXZNmjR4+wYsUKi6+rVq2a4uzFCQkJFqebv3//Pvbu3StbHh0dXeI7GdRo0KCB4hVhNX9fjvbrr7+iX79+il115s6dixUrVth8V8tbb70lW3bw4EHFdbOzs3HixAnZ8p49e9r0nrby1HoD+tRdT6bHfqDkM/l5ipCQEAwZMqTocVZWFsaPH2/39k6ePCm5O6pBgwaygacLuVLuYY5T5sgc54gMB3hmjmOG05Y7ZTg9aJ1l3CEbVahQQbYsNTXVYgNzamqq4nJzd2cyy+jHdRIraSIhIcHq4JA7duyQtIC3bdtW8rzp7EtKt7cW9+233+LmzZs2lbOkY3WsWLECvXv3Rnh4OBISElS9xnTaZD3/YTPdtrWAmJ2djSlTpkiWWbsNX4t9XfwfkXPnziExMdHi9oAXY5Q0atQIffr0wfr16x062ymgPPPUnTt3NH2PhQsXygY4NjcuQnFKA72npqZi7ty5iusXFBRgzJgxileYRo4cqbK0JWMwGBSDwurVq83OfLV3714EBgaiZs2aiIyMRPfu3SWzigHAvn37FMfyOHr0qN1lHTt2rOK+HjlyJKZPn27XNgcOHChbtnXrVty9e1e2/K9//avs6mLTpk0l/wx5ar0B16/7qFGjbOqCk5SUJNtGaGioZB3TMiiVVWkcGVI2depUSQaJj4/HnDlzbN7OpUuX0KtXr6K7JQ0GAxYuXGj2n2FXyj3MceY5Ksc5IsMBnpnjmOG05Q4ZTs9zr9ZZxh2ykdJdmg8ePMDGjRvNvmbNmjWKy5XG1wOYZfTExreXTFZWFsaNG2d2oMR79+5h8uTJRY+NRiO6desmWad+/fqSx6tXrzb7fpcuXcLYsWOtDmRqNBolj0t6O/TJkyeLwuInn3xitkW/uG+++abo97CwMNWzTNmjRo0akseWpnO+c+cOOnXqhPv376N58+ZFy611AdFiX48cOVISMocNG4aUlBSz75mfn4/3338fZ8+exfbt2zFixAiHD9Rb/CpvIUtltkdERATGjh1r8+tGjx6teEVqzpw5GD58OM6ePYu8vDxkZ2fj4MGD+NOf/oStW7fK1m/atCk6depkV9ntMWHCBNk/pY8fP0br1q2xbt06ZGZm4unTp0hLS8Py5cvRv39/PHnyBNevX8epU6ewa9cu3ceBOH36tOJnValSJcVuJmo1btwYbdq0kSx7/PgxOnfujKNHjyInJweZmZlYtGiRYiPAhAkT7H5vNTy13oB+ddeT0rHod7/7nRNK4p7CwsKwbt06ybKZM2di4MCBZsdVKk4IgQ0bNiA6Olryz8NHH32Erl27mn2dK+Ue5jjzHJXjHJHhAM/Mccxw2nOHDKcXrbOMO2SjXr16yY7HwItjwcyZM5GSkoL8/Hzk5OQgOTkZAwcOxLZt22TrN2jQwOwkMcwyOhJuYOvWrQKAxR9nW7VqlaQ8R44cUVxv165dkvWuX79uddumr/nPf/5T9Fx8fLzkub59+woAIjo6WuzcuVNkZmaK/Px8kZGRITZt2iSqV68uWf/dd9+Vvd/Tp09FpUqVJOvFxMSI5ORk8eTJE5GXlycuX74s5s6dK8qUKSOMRqOYN29e0bpGo1GxHoGBgUXrVKpUSRw/flzk5uaKrKwscfPmTXUf9P9LSkoSBoOhaHvly5cX8+bNE0lJSSI7O1s8e/ZMPH78WKSlpYk9e/aIHj16SOozbdo0XfdLQUGBCAsLkzw/duxYcfHiRZGTkyPu378vTpw4IT7++OOiz2XVqlVi9OjRResbDAaRkJAgcnJyxMOHD3XZ10IIMXnyZMl6AQEBYubMmeLcuXPi8ePH4uHDh+Ly5cti1apVomHDhpJ1R48erbjNpUuXStZLTExUv3OtWLhwoez736BBA7Prr127Vrb+yJEjrb7P/fv3RXBwsMXjzuTJk2WvS0xMlPxt2vpTpkwZceXKFcXtKq3/yy+/WKxHSEiI7DWrVq2SrTdhwgS7y1yzZk3x8OFDVeU1d2y0ZtiwYXaXz/TH9Pt95swZYTQabd5O27ZtZeX01Hq7W93VSEpKkm0nNDTU4msiIiK2H6KpAAALo0lEQVRkr1m2bJld9dda1apVLX5GS5YscXYRiyxbtkx4eXlJyhcQECBiYmLE9u3bxdWrV8WDBw9Ebm6uSEtLE8ePHxezZ88Wr732mqxegwYNEs+ePbP4fs7MPcxxL7ZXkhznDhlOr30thHvlOGY46Y+nZDhb2Hru1TrLuHo2EkKIMWPG2L2/C392795tdvuumGVatGhhsT7jx493avnUcn6rlQpsfFPf+HblyhVRrlw5VV+6sLAwcffuXcX3/Pzzz1V/eadNmya+//77oscGg0Fxmx06dDC7jYkTJ1r/kE1MnTrVroPN73//e/HkyROLn3FJ94sQ8r8JSz99+/YVz58/Fxs2bFB8vkePHrrt67y8PNG5c2ebP8cmTZqIx48fK25Tz8a3H374QVYWo9EosrOzFde3N7gJIURcXJzFz0ApuAkhxIYNG4SPj4/Nn2nFihXF0aNHFbepd3DLz88X3bp1s7nMoaGh4vz586rLa2/YGDRokF3fd6Ufpe+3Ld9XAKJWrVoiPT2d9Xbjultj6z8A9+7dU/ynzdx32tHcqfFNCCF27Nih+hyn9GM0GsX8+fNVv5+zcg9znO371jTHuUOGE0K/fe1OOY4ZTvrjKRnOFvZc+NIyy2i9PT0+y7y8PPHmm2/avL8Lfz777DOz23bVLPOyNL6x2+lLpnLlykhMTLQ6KGLdunWxb98+hIaGKj4fGxuL9957z+r7TZo0CfPnz5fMgCKEQH5+vmzdadOmaTo+x/z587F48WKzU5cr6d+/Pw4fPuyQGVtGjRql6rb3oUOHIiEhAV5eXujVq5fqPvVa7WsfHx989913+Oijj1Tddm4wGDBs2DD8+OOPunbdNScqKkq2z58/f44ff/xR8/caO3YsIiIibH5dTEwMjhw5glatWqla32AwoG/fvkhKSkJUVJTN76eFUqVKYefOnZg1a5bq/dqlSxckJSWhYcOGqt/HlQbHL27UqFHYuHEjQkJCrK7bsWNHHDp0SHHsGnM8td6A69ZdawcPHpR1HytTpozDZoV92fTs2ROpqamYOHGi1S6RxXl5eWHAgAG4dOkSpk2bpvp1rpJ7mOMsc1SO0zvDAZ6Z45jh9MEMp22WcfVs5OPjg7179+LTTz9FmTJlVL+uRo0a2LNnj6Q7uylmGX255jeI7Pb8+XO0bNkSKSkpWLFiBaKjo1G1alX4+PigcuXKiI6OxsqVK5GcnGy2nzfw4oCwceNG7NmzB71790a1atXg5+cHHx8fVKtWDTExMThz5gwWL14MAAgMDJS8XmnGpz/+8Y9ITExE69at4e/vDx8fH4SGhqJt27ay/vVqGAwGTJo0Cbdu3cLSpUvRrVs3REREIDAwEF5eXihdujSqVKmCdu3aYfr06bh48SK2bNmiOCuQXpYvX44DBw6gd+/eCAsLg4+PD/z8/BAREYGYmBj89NNPWLduXVHf/YCAABw8eBBvvvkmAgIC4Ovri/DwcMUDnlb7GgC8vb2xaNEiXL16FZ9++inatWuHsLAwlC5dGr6+vggNDUV0dDSmT5+OlJQU/P3vf7fpYK8lX19fvPHGG7LlSrNNlZSPjw8WLlxo12ubN2+OY8eO4dChQ5g0aRKaNWuGKlWqwNfXF4GBgQgPD0e7du0wf/58XLhwAVu3bkX16tU1roFtvLy8MHPmTNy4cQNxcXHo1q0bwsPDERgYCB8fH1SsWBHNmjVDbGwskpOTsWfPHrz66qs2vYfpscKVvPfee7hy5Qri4uLQoUMHhIWFwdfXF0FBQahXrx5GjBiBAwcOYN++fTaHLE+tN+DaddeS0jGoffv28Pb2dkJpXg7ly5fH559/jjt37iA+Ph4xMTFo3LgxQkJCUKpUKfj6+qJq1apo1KgRBgwYgPj4eKSlpSEhIQG1a9e26b1cJfcwx7lOjtMzwwGemeOY4fTDDKdtlnH1bGQ0GjF16lSkp6dj7dq1ePfdd1G/fn1UqFABpUqVgp+fHypVqoRmzZphzJgx2LVrF65du4YuXbpY3C6zjM6cet+dSu7Q7dRZTG9h/+2335xdJNIJ9/X/KHXtCAoKErm5uc4uGimoVq1a0X6ydVwgd+ap9RbC8+r+5MkTyXhYhT9bt251dtGKuFu3U0/Bc7vn4L5+gRnOvXja+VxPrv5ZunKWYbdTIiIn6dWrl+yKUXZ2Nv7xj384qURkzpMnT3D79m0AgL+/v11XB92Rp9Yb8My6b9++HY8fP5YsK1euHLp37+6kEhERuSZmOPfhiedzvbjDZ8ksoz82vhGR2wkICMDw4cNly+Pi4pxQGrJk165dKCgoAAA0adLEY25b99R6A55Zd6Vjz4gRI2waq4yIyBMww7kPTzyf68UdPktmGf2x8Y2I3NKECRNQqlQpybLTp0/j0KFDzikQKVq5cmXR7z179nRiSRzLU+sNeF7dExMTcfbsWckyHx8fjB8/3kklIiJybcxw7sHTzud6cvXPklnGMdj4RkRu6dVXX8WoUaNkyydPniybpYecY9euXThy5AiAF7fYq5l572XgqfUGPK/uBQUFijNqfvjhhy7ZpYSIyBUww7k+Tzuf68nVP0tmGcdh4xsRua1Zs2YhODhYsuz06dPYsmWLk0pEhbKysjBixIiix9OnT0fFihWdWCLH8NR6A55Z940bN+LMmTOSZSEhIZgxY4aTSkRE5B6Y4VyXJ57P9eIOnyWzjOO4XmdjIiKVypcvj0WLFuGDDz6QLJ84cSI6d+4sC3XkOK+88goyMjKcXQyH89R6A55X93v37uHjjz+WLV+yZAmCgoKcUCIiIvfBDOe6PO18ridX/yyZZRyLd74RkVsbPnw4OnToIFl29+5dxMbGOqlEROQJ/vKXv+CXX36RLOvUqRMGDx7spBIREbkXZjgi52KWcSyDcIOO9du2bUO/fv0sruMG1SAiIiJyirCwMKSnp5t9fsmSJZgwYYIDS0RERERkXWRkJE6dOmX2+fHjx2Pp0qUOLJF9eOcbERERERERERGRTtj4RkREREREREREpBM2vhEREREREREREemEjW9EREREREREREQ6YeMbERERERERERGRTtj4RkREREREREREpBM2vhEREREREREREemEjW9EREREREREREQ6YeMbERERERERERGRTtj4RkREREREREREpBM2vhEREREREREREemEjW9EREREREREREQ6YeMbERERERERERGRTtj4RkREREREREREpBM2vhEREREREREREemEjW9EREREREREREQ6YeMbERERERERERGRTtj4RkREREREREREpBM2vhEREREREREREemEjW9EREREREREREQ6YeMbERERERERERGRTtj4RkREREREREREpBM2vhEREREREREREemEjW9EREREREREREQ6YeMbERERERERERGRTtj4RkREREREREREpBM2vhEREREREREREemEjW9EREREREREREQ6YeMbERERERERERGRTtj4RkREREREREREpBM2vhEREREREREREemEjW9EREREREREREQ6YeMbERERERERERGRTrydXQCtzJo1y9lFICIiInJJDx8+tPj8/v37ra5DRERE5Gi3b992dhE08dI0vs2ePdvZRSAiIiJySwcOHMCBAwecXQwiIiKilxK7nRIREREREREREemEjW9EREREREREREQ6YeMbERERERERERGRTtj4RkREREREREREpBM2vhEREREREREREenELWY7bdSoEZYsWeLsYhARERERERERkYto0qSJs4ugikEIIZxdCCIiIiIiIiIiopcRu50SERERERERERHphI1vREREREREREREOmHjGxERERERERERkU7Y+EZERERERERERKQTNr4RERERERERERHphI1vREREREREREREOmHjGxERERERERERkU7Y+EZERERERERERKQTNr4RERERERERERHpxBvAQmcXgoiIiIiIiIiI6GX0f+motZXXZq1lAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = \"adam\", loss = \"mse\")"
      ],
      "metadata": {
        "id": "ENdvpAINl1tZ"
      },
      "id": "ENdvpAINl1tZ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "his = model.fit(X_train, tf.identity(X_train), epochs = 100, batch_size = 32, validation_data = (X_val, tf.identity(X_val)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6MAqlMOpPMy",
        "outputId": "4acc4afb-ebf2-485a-fc71-bd9daf093e4e"
      },
      "id": "E6MAqlMOpPMy",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 357ms/step - loss: 2.6013 - val_loss: 1.3816\n",
            "Epoch 2/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 262ms/step - loss: 1.3787 - val_loss: 1.2625\n",
            "Epoch 3/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 284ms/step - loss: 1.2765 - val_loss: 1.2055\n",
            "Epoch 4/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 440ms/step - loss: 1.2427 - val_loss: 1.1354\n",
            "Epoch 5/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 236ms/step - loss: 1.1674 - val_loss: 1.0888\n",
            "Epoch 6/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 271ms/step - loss: 1.0858 - val_loss: 1.0199\n",
            "Epoch 7/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 312ms/step - loss: 1.0556 - val_loss: 0.9741\n",
            "Epoch 8/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 232ms/step - loss: 0.9805 - val_loss: 0.9452\n",
            "Epoch 9/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 264ms/step - loss: 0.9732 - val_loss: 0.8967\n",
            "Epoch 10/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 269ms/step - loss: 0.9328 - val_loss: 0.9098\n",
            "Epoch 11/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 234ms/step - loss: 0.9048 - val_loss: 0.8756\n",
            "Epoch 12/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 333ms/step - loss: 0.8997 - val_loss: 0.8673\n",
            "Epoch 13/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 233ms/step - loss: 0.8730 - val_loss: 0.8471\n",
            "Epoch 14/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 322ms/step - loss: 0.8456 - val_loss: 0.8532\n",
            "Epoch 15/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 255ms/step - loss: 0.8378 - val_loss: 0.8081\n",
            "Epoch 16/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 234ms/step - loss: 0.8135 - val_loss: 0.7910\n",
            "Epoch 17/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 312ms/step - loss: 0.7972 - val_loss: 0.7821\n",
            "Epoch 18/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 233ms/step - loss: 0.7913 - val_loss: 0.7765\n",
            "Epoch 19/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 232ms/step - loss: 0.7750 - val_loss: 0.7639\n",
            "Epoch 20/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 336ms/step - loss: 0.7718 - val_loss: 0.7554\n",
            "Epoch 21/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 253ms/step - loss: 0.7671 - val_loss: 0.7442\n",
            "Epoch 22/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 356ms/step - loss: 0.7579 - val_loss: 0.7401\n",
            "Epoch 23/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 234ms/step - loss: 0.7407 - val_loss: 0.7218\n",
            "Epoch 24/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 330ms/step - loss: 0.7388 - val_loss: 0.7238\n",
            "Epoch 25/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 232ms/step - loss: 0.7241 - val_loss: 0.7237\n",
            "Epoch 26/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 309ms/step - loss: 0.7196 - val_loss: 0.7093\n",
            "Epoch 27/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 229ms/step - loss: 0.7378 - val_loss: 0.7077\n",
            "Epoch 28/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 314ms/step - loss: 0.7181 - val_loss: 0.7131\n",
            "Epoch 29/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 236ms/step - loss: 0.7204 - val_loss: 0.7194\n",
            "Epoch 30/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 232ms/step - loss: 0.7241 - val_loss: 0.7030\n",
            "Epoch 31/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 264ms/step - loss: 0.7074 - val_loss: 0.6852\n",
            "Epoch 32/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 259ms/step - loss: 0.7035 - val_loss: 0.6849\n",
            "Epoch 33/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 231ms/step - loss: 0.6894 - val_loss: 0.6882\n",
            "Epoch 34/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 306ms/step - loss: 0.6811 - val_loss: 0.6655\n",
            "Epoch 35/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 262ms/step - loss: 0.7258 - val_loss: 0.7126\n",
            "Epoch 36/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 231ms/step - loss: 0.7003 - val_loss: 0.6738\n",
            "Epoch 37/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 270ms/step - loss: 0.7021 - val_loss: 0.6688\n",
            "Epoch 38/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 307ms/step - loss: 0.6965 - val_loss: 0.7045\n",
            "Epoch 39/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 231ms/step - loss: 0.6789 - val_loss: 0.6591\n",
            "Epoch 40/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 230ms/step - loss: 0.7054 - val_loss: 0.6561\n",
            "Epoch 41/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 355ms/step - loss: 0.6773 - val_loss: 0.7057\n",
            "Epoch 42/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 232ms/step - loss: 0.6818 - val_loss: 0.6744\n",
            "Epoch 43/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 331ms/step - loss: 0.6584 - val_loss: 0.6513\n",
            "Epoch 44/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 231ms/step - loss: 0.6604 - val_loss: 0.6439\n",
            "Epoch 45/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 279ms/step - loss: 0.6622 - val_loss: 0.6569\n",
            "Epoch 46/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 284ms/step - loss: 0.6629 - val_loss: 0.6516\n",
            "Epoch 47/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 293ms/step - loss: 0.6508 - val_loss: 0.6393\n",
            "Epoch 48/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 231ms/step - loss: 0.6538 - val_loss: 0.6383\n",
            "Epoch 49/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 233ms/step - loss: 0.6482 - val_loss: 0.6316\n",
            "Epoch 50/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 350ms/step - loss: 0.6444 - val_loss: 0.6363\n",
            "Epoch 51/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 233ms/step - loss: 0.6510 - val_loss: 0.6229\n",
            "Epoch 52/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 311ms/step - loss: 0.6564 - val_loss: 0.6340\n",
            "Epoch 53/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 236ms/step - loss: 0.6335 - val_loss: 0.6257\n",
            "Epoch 54/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 238ms/step - loss: 0.6396 - val_loss: 0.6136\n",
            "Epoch 55/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 315ms/step - loss: 0.6350 - val_loss: 0.6237\n",
            "Epoch 56/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 268ms/step - loss: 0.6620 - val_loss: 0.6264\n",
            "Epoch 57/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 309ms/step - loss: 0.6303 - val_loss: 0.6137\n",
            "Epoch 58/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 232ms/step - loss: 0.6420 - val_loss: 0.6190\n",
            "Epoch 59/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 235ms/step - loss: 0.6296 - val_loss: 0.6173\n",
            "Epoch 60/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 354ms/step - loss: 0.6278 - val_loss: 0.6119\n",
            "Epoch 61/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 238ms/step - loss: 0.6288 - val_loss: 0.6112\n",
            "Epoch 62/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 362ms/step - loss: 0.6339 - val_loss: 0.6198\n",
            "Epoch 63/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 232ms/step - loss: 0.6183 - val_loss: 0.6042\n",
            "Epoch 64/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 337ms/step - loss: 0.6137 - val_loss: 0.6026\n",
            "Epoch 65/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 234ms/step - loss: 0.6209 - val_loss: 0.6085\n",
            "Epoch 66/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 231ms/step - loss: 0.6133 - val_loss: 0.6015\n",
            "Epoch 67/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 335ms/step - loss: 0.6235 - val_loss: 0.5971\n",
            "Epoch 68/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 230ms/step - loss: 0.6024 - val_loss: 0.5996\n",
            "Epoch 69/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 351ms/step - loss: 0.6229 - val_loss: 0.6008\n",
            "Epoch 70/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 230ms/step - loss: 0.6029 - val_loss: 0.5975\n",
            "Epoch 71/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 353ms/step - loss: 0.6117 - val_loss: 0.5970\n",
            "Epoch 72/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 232ms/step - loss: 0.6015 - val_loss: 0.5994\n",
            "Epoch 73/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 316ms/step - loss: 0.6007 - val_loss: 0.5939\n",
            "Epoch 74/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 251ms/step - loss: 0.6029 - val_loss: 0.6066\n",
            "Epoch 75/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 270ms/step - loss: 0.5934 - val_loss: 0.5884\n",
            "Epoch 76/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 235ms/step - loss: 0.6005 - val_loss: 0.5960\n",
            "Epoch 77/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 336ms/step - loss: 0.5921 - val_loss: 0.6062\n",
            "Epoch 78/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 235ms/step - loss: 0.6008 - val_loss: 0.5844\n",
            "Epoch 79/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 353ms/step - loss: 0.5937 - val_loss: 0.5818\n",
            "Epoch 80/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 233ms/step - loss: 0.5794 - val_loss: 0.5902\n",
            "Epoch 81/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 353ms/step - loss: 0.5875 - val_loss: 0.5823\n",
            "Epoch 82/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 232ms/step - loss: 0.5875 - val_loss: 0.5879\n",
            "Epoch 83/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 330ms/step - loss: 0.5862 - val_loss: 0.5697\n",
            "Epoch 84/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 232ms/step - loss: 0.5809 - val_loss: 0.5731\n",
            "Epoch 85/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 233ms/step - loss: 0.5865 - val_loss: 0.5694\n",
            "Epoch 86/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 346ms/step - loss: 0.5766 - val_loss: 0.5686\n",
            "Epoch 87/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 234ms/step - loss: 0.5845 - val_loss: 0.5631\n",
            "Epoch 88/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 234ms/step - loss: 0.5737 - val_loss: 0.5648\n",
            "Epoch 89/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 342ms/step - loss: 0.5685 - val_loss: 0.5563\n",
            "Epoch 90/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 232ms/step - loss: 0.5709 - val_loss: 0.5786\n",
            "Epoch 91/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 235ms/step - loss: 0.5803 - val_loss: 0.5690\n",
            "Epoch 92/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 283ms/step - loss: 0.5679 - val_loss: 0.5656\n",
            "Epoch 93/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 232ms/step - loss: 0.5748 - val_loss: 0.5662\n",
            "Epoch 94/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 232ms/step - loss: 0.5576 - val_loss: 0.5530\n",
            "Epoch 95/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 346ms/step - loss: 0.5600 - val_loss: 0.5525\n",
            "Epoch 96/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 232ms/step - loss: 0.5596 - val_loss: 0.5564\n",
            "Epoch 97/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 233ms/step - loss: 0.5584 - val_loss: 0.5590\n",
            "Epoch 98/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 347ms/step - loss: 0.5620 - val_loss: 0.5606\n",
            "Epoch 99/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 231ms/step - loss: 0.5646 - val_loss: 0.5568\n",
            "Epoch 100/100\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 305ms/step - loss: 0.5540 - val_loss: 0.5486\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Taking abnormal events in dataset too\n",
        "data_set_abnorm = pd.read_csv(\"/content/60.416 (2).csv\")\n",
        "X_test_abnorm = scaler.fit_transform(data_set_abnorm)\n",
        "X_test_abnorm = tf.convert_to_tensor(X_test_abnorm)\n",
        "\n",
        "X_test_abnorm = tf.concat([X_test_abnorm, tf.expand_dims(X_test_abnorm[1, :], axis = 0)], axis = 0)\n",
        "X_test_abnorm = tf.split(X_test_abnorm, num_or_size_splits = 400, axis = 0)\n",
        "X_test_abnorm = tf.random.shuffle(tf.reshape(X_test_abnorm, shape = (-1, 400, 8)))\n",
        "tf.shape(X_test_abnorm)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XIkBxQdOskSI",
        "outputId": "6bfff743-5ffb-471b-eec3-85197714baf3"
      },
      "id": "XIkBxQdOskSI",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3,), dtype=int32, numpy=array([625, 400,   8], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test_abnorm, tf.identity(X_test_abnorm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QenXNoiLqLOM",
        "outputId": "4be9dccb-eefd-4354-e737-45116fb92af9"
      },
      "id": "QenXNoiLqLOM",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 90ms/step - loss: 0.6896\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6880791783332825"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, tf.identity(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCvc0p_C0_gj",
        "outputId": "3b5984c9-1283-4f1d-f11d-0afa7051a97c"
      },
      "id": "gCvc0p_C0_gj",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.5428\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5491299033164978"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_auto_pred = model.predict(X_test)\n",
        "generator = DataGenerator(img_shape = (20, 20), n_f = 8)\n",
        "\n",
        "img_generator_output_pred = generator(X_test_auto_pred)\n",
        "img_generator_output = generator(X_test)\n",
        "\n",
        "plt.figure(figsize = (10, 8))\n",
        "for i in range(0, 3):\n",
        "  plt.subplot(2, 3, i + 1)\n",
        "  plt.imshow(img_generator_output[5, :, :, i])\n",
        "  plt.title(\"Original\")\n",
        "  plt.subplot(2, 3, i + 4)\n",
        "  plt.imshow(img_generator_output_pred[5, :, :, i ])\n",
        "  plt.title(\"Predicted\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "s1GM-QvosWkW",
        "outputId": "6ba81b63-bda3-48ce-8c87-ef0b4918cd00"
      },
      "id": "s1GM-QvosWkW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 397ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_auto_pred = model.predict(X_test_abnorm)\n",
        "generator = DataGenerator(img_shape = (20,20), n_f = 8)\n",
        "\n",
        "img_generator_output_pred = generator(X_test_auto_pred)\n",
        "img_generator_output = generator(X_test_abnorm)\n",
        "\n",
        "plt.figure(figsize = (10, 8))\n",
        "for i in range(0, 3):\n",
        "  plt.subplot(2, 3, i + 1)\n",
        "  plt.imshow(img_generator_output[5, :, :, i])\n",
        "  plt.title(\"Original\")\n",
        "  plt.subplot(2, 3, i + 4)\n",
        "  plt.imshow(img_generator_output_pred[5, :, :, i ])\n",
        "  plt.title(\"Predicted\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "Tf23iWCRsZUZ",
        "outputId": "ca5b85e9-b534-4bf7-c568-b501762aa940"
      },
      "id": "Tf23iWCRsZUZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m20/20\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 89ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_comb = tf.concat([X_train, X_test_abnorm], axis = 0)\n",
        "y_train = tf.concat([tf.zeros(len(X_train)), tf.ones(len(X_test_abnorm))], axis = 0)"
      ],
      "metadata": {
        "id": "wm7DPcvy1d_W"
      },
      "id": "wm7DPcvy1d_W",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "threshold_list = np.arange(0.6, 1.0, 0.02)\n",
        "f1_score_list = []\n",
        "y_pred = tf.reduce_sum(tf.square(tf.subtract(X_test_comb, model.predict(X_test_comb))), axis = [1, 2])/3200\n",
        "for threshold in threshold_list:\n",
        "  y_pred = np.where(y_pred >= threshold , 1, 0)\n",
        "  f1_score_list.append(f1_score(y_train, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-Wy9H4nq3kr",
        "outputId": "566b104e-2850-4743-ccf6-48fe5b0afdf6"
      },
      "id": "x-Wy9H4nq3kr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 183ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(threshold_list, f1_score_list)\n",
        "plt.ylabel(\"F1_score\")\n",
        "plt.xlabel(\"Thresholds\")\n",
        "plt.title(\"Thresold vs F1_score\")\n",
        "plt.title(f\"threshold = {threshold_list[np.argmax(f1_score_list)]}\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "CejtZnMrq5xG",
        "outputId": "37be4442-05b4-419a-a425-ed75ab0e7e8f"
      },
      "id": "CejtZnMrq5xG",
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "threshold_auto = threshold_list[np.argmax(f1_score_list)]\n",
        "y_pred = tf.reduce_sum(tf.square(tf.subtract(X_test_comb, model.predict(X_test_comb))), axis = [1, 2])/3200\n",
        "y_pred = np.where(y_pred >= threshold_auto , 1, 0)\n",
        "print(f1_score(y_train, y_pred))\n",
        "cm = confusion_matrix(y_train, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_3nNOHYq7x1",
        "outputId": "5421a065-70b2-4d9b-f343-906821f397cd"
      },
      "id": "8_3nNOHYq7x1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 88ms/step\n",
            "0.8342585249801745\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot = True, fmt = \"d\", cmap = \"Blues\", cbar = False,\n",
        "            annot_kws = {\"size\":14}, linewidths = 1.5, linecolor = \"black\")\n",
        "plt.title('Confusion Matrix', fontsize=16)\n",
        "plt.xlabel('Predicted Labels', fontsize=14)\n",
        "plt.ylabel('True Labels', fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "IGpkflN8q9d1",
        "outputId": "c25aee76-3491-4c74-cd90-88f72cb32a03"
      },
      "id": "IGpkflN8q9d1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#making convxgb\n",
        "xgb = XGB(n_estimators = 300, min_depth = 5, verbosity = 3, booster = \"gbtree\", early_stopping_round = 20, eval_metrics = [\"f1_score\"], shuffle = True)\n",
        "ConvXGb = ifd.create_and_train_ConvXGB(model, xgb, X_test_comb, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MmjfaT1U_6ho",
        "outputId": "32baeb1a-8ce6-457b-9b6d-f44d8455c816"
      },
      "id": "MmjfaT1U_6ho",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional_13\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_13\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ Inputs (\u001b[38;5;33mInputLayer\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m400\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ data_generator_13 (\u001b[38;5;33mDataGenerator\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m24\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ functional_11 (\u001b[38;5;33mFunctional\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m15\u001b[0m)            │          \u001b[38;5;34m45,135\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m735\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ Inputs (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">400</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ data_generator_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DataGenerator</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ functional_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Functional</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>)            │          <span style=\"color: #00af00; text-decoration-color: #00af00\">45,135</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">735</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m45,135\u001b[0m (176.31 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">45,135</span> (176.31 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m44,865\u001b[0m (175.25 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">44,865</span> (175.25 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m270\u001b[0m (1.05 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">270</span> (1.05 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[18:13:14] DEBUG: /workspace/src/gbm/gbtree.cc:130: Using tree method: 0\n",
            "[18:13:14] DEBUG: /workspace/src/data/simple_dmatrix.cc:179: Generating new Gradient Index.\n",
            "[18:13:15] ======== Monitor (0): HostSketchContainer ========\n",
            "[18:13:15] AllReduce: 0.059743s, 1 calls @ 59743us\n",
            "\n",
            "[18:13:15] MakeCuts: 0.073879s, 1 calls @ 73879us\n",
            "\n",
            "[18:13:15] PushRowPage: 0.05912s, 1 calls @ 59120us\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [18:13:14] WARNING: /workspace/src/learner.cc:740: \n",
            "Parameters: { \"early_stopping_round\", \"eval_metrics\", \"min_depth\", \"shuffle\" } are not used.\n",
            "\n",
            "  warnings.warn(smsg, UserWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[18:13:32] ======== Monitor (0): Learner ========\n",
            "[18:13:32] Configure: 0.013577s, 1 calls @ 13577us\n",
            "\n",
            "[18:13:32] EvalOneIter: 0.003917s, 300 calls @ 3917us\n",
            "\n",
            "[18:13:32] GetGradient: 0.100832s, 300 calls @ 100832us\n",
            "\n",
            "[18:13:32] PredictRaw: 0.001268s, 300 calls @ 1268us\n",
            "\n",
            "[18:13:32] UpdateOneIter: 17.6209s, 300 calls @ 17620850us\n",
            "\n",
            "[18:13:32] ======== Monitor (0): GBTree ========\n",
            "[18:13:32] BoostNewTrees: 17.4974s, 300 calls @ 17497402us\n",
            "\n",
            "[18:13:32] CommitModel: 0.000539s, 300 calls @ 539us\n",
            "\n",
            "[18:13:32] ======== Monitor (0): HistUpdater ========\n",
            "[18:13:32] BuildHistogram: 5.01055s, 1053 calls @ 5010546us\n",
            "\n",
            "[18:13:32] EvaluateSplits: 8.7694s, 1353 calls @ 8769398us\n",
            "\n",
            "[18:13:32] InitData: 0.456801s, 300 calls @ 456801us\n",
            "\n",
            "[18:13:32] InitRoot: 3.62441s, 300 calls @ 3624409us\n",
            "\n",
            "[18:13:32] LeafPartition: 0.000136s, 300 calls @ 136us\n",
            "\n",
            "[18:13:32] UpdatePosition: 1.18894s, 1126 calls @ 1188944us\n",
            "\n",
            "[18:13:32] UpdatePredictionCache: 0.0909s, 300 calls @ 90900us\n",
            "\n",
            "[18:13:32] UpdateTree: 17.3917s, 300 calls @ 17391747us\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pre_convxgb_auto_pred = ConvXGb.pre_xgb_model.predict(X_test_comb)\n",
        "convxgb_auto_pred = ConvXGb.XGB_model.predict(pre_convxgb_auto_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "piNJmzxfHjUA",
        "outputId": "98451397-d8e9-4bbb-8444-9e97f99d3d15"
      },
      "id": "piNJmzxfHjUA",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m36/36\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 66ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f1_score(y_train, convxgb_auto_pred))\n",
        "cm = confusion_matrix(y_train, convxgb_auto_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LrSYxFq4I6CN",
        "outputId": "4cddae8d-f9b6-48eb-81e7-e784b8058b0f"
      },
      "id": "LrSYxFq4I6CN",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot = True, fmt = \"d\", cmap = \"Blues\", cbar = False,\n",
        "            annot_kws = {\"size\":14}, linewidths = 1.5, linecolor = \"black\")\n",
        "plt.title('Confusion Matrix', fontsize=16)\n",
        "plt.xlabel('Predicted Labels', fontsize=14)\n",
        "plt.ylabel('True Labels', fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "v7HZHXlnJCv8",
        "outputId": "2be4ed39-f6d7-4c18-87b5-a879e49621e4"
      },
      "id": "v7HZHXlnJCv8",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cy6ldW1zKB41"
      },
      "id": "cy6ldW1zKB41",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}