Synthetic_Medical_Tabular_Data.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**GENERATING SYNTHETIC MEDICAL TABULAR DATA**\n",
        "\n",
        "**Author:**\n",
        "\n",
        " Maria Rodriguez"
      ],
      "metadata": {
        "id": "n_xjm1AHPl_K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SCENARIO**\n",
        "\n",
        "Dr. AA is conducting a study to better understand a rare disease.  However, due to the rarity of the disease, he could not gather enough numbers that would power statistical analyses.\n",
        "\n",
        "In an international conference, he met Dr. BB who was presenting case reports on the disease and who also acknowledged the difficulty of recruiting patients for enrollment in a study.\n",
        "\n",
        "They agreed to collaborate.  However, their individual hospitals would not give them permission to share patient data due to privacy constraints.\n",
        "\n",
        "What can they do so that they can do robust research on this challenging patient population while maintaining rigid privacy standards?"
      ],
      "metadata": {
        "id": "w4O65JQN-ywH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Primary Objective:**\n",
        "\n",
        "To generate a synthetic medical dataset for a patient population\n",
        "\n",
        "**Secondary Objectives:**\n",
        "\n",
        "1.  Demonstrate the transformation of human-interpretable categorical and continuous data to a tensor input, and the re-transformation of the tensor output to human-interpretable data\n",
        "2.  Explore the use of tabular data (continuous and categorical variables) in deep learning generative models\n",
        "3. Evaluate the performance of generative models regarding its ability to create synthetic data that closely approximates the real data:  \n",
        "  *   Variational AutoEncoder\n",
        "  *   Generative Adversarial Network\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "wEUB6M6yM-8s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**OUTLINE**\n",
        "\n",
        "A.  Data Loading and Preparation\n",
        "\n",
        "B.  Create, Train and Evaluate VAE model\n",
        "\n",
        "C.  Create, Train and Evaluate GAN model\n"
      ],
      "metadata": {
        "id": "J6qE89T8noAY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# uncomment this at the start of the run, needed particularly for the mse loss\n",
        "\n",
        "!pip install tensorflow==2.13.1"
      ],
      "metadata": {
        "id": "K1-uJo4DfyXk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "outputId": "172783f3-7f4b-4526-fd9e-d3fcdb1dcfbf"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: typing-extensions, tensorflow-estimator, numpy, keras, gast, google-auth-oauthlib, tensorboard, tensorflow\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.12.2\n",
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            "    Found existing installation: keras 3.4.1\n",
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            "    Found existing installation: gast 0.6.0\n",
            "    Uninstalling gast-0.6.0:\n",
            "      Successfully uninstalled gast-0.6.0\n",
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            "    Found existing installation: google-auth-oauthlib 1.2.1\n",
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            "    Found existing installation: tensorboard 2.17.0\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torch 2.3.1+cu121 requires nvidia-cublas-cu12==12.1.3.1; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-cupti-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-nvrtc-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cuda-runtime-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cudnn-cu12==8.9.2.26; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cufft-cu12==11.0.2.54; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-curand-cu12==10.3.2.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cusolver-cu12==11.4.5.107; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-cusparse-cu12==12.1.0.106; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-nccl-cu12==2.20.5; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "torch 2.3.1+cu121 requires nvidia-nvtx-cu12==12.1.105; platform_system == \"Linux\" and platform_machine == \"x86_64\", which is not installed.\n",
            "xgboost 2.1.1 requires nvidia-nccl-cu12; platform_system == \"Linux\" and platform_machine != \"aarch64\", which is not installed.\n",
            "sqlalchemy 2.0.32 requires typing-extensions>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.\n",
            "albucore 0.0.13 requires numpy<2,>=1.24.4, but you have numpy 1.24.3 which is incompatible.\n",
            "albucore 0.0.13 requires typing-extensions>=4.9.0, but you have typing-extensions 4.5.0 which is incompatible.\n",
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            "albumentations 1.4.13 requires typing-extensions>=4.9.0, but you have typing-extensions 4.5.0 which is incompatible.\n",
            "pandas-stubs 2.1.4.231227 requires numpy>=1.26.0; python_version < \"3.13\", but you have numpy 1.24.3 which is incompatible.\n",
            "pydantic 2.8.2 requires typing-extensions>=4.6.1; python_version < \"3.13\", but you have typing-extensions 4.5.0 which is incompatible.\n",
            "pydantic-core 2.20.1 requires typing-extensions!=4.7.0,>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.\n",
            "tf-keras 2.17.0 requires tensorflow<2.18,>=2.17, but you have tensorflow 2.13.1 which is incompatible.\n",
            "torch 2.3.1+cu121 requires typing-extensions>=4.8.0, but you have typing-extensions 4.5.0 which is incompatible.\n",
            "typeguard 4.3.0 requires typing-extensions>=4.10.0, but you have typing-extensions 4.5.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed gast-0.4.0 google-auth-oauthlib-1.0.0 keras-2.13.1 numpy-1.24.3 tensorboard-2.13.0 tensorflow-2.13.1 tensorflow-estimator-2.13.0 typing-extensions-4.5.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "gast",
                  "keras",
                  "numpy",
                  "tensorflow"
                ]
              },
              "id": "463111062c6444f6a7c09447a569367b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**A.  DATA LOADING and PREPARATION**\n",
        "\n",
        "-  Using medical patient data from https://synthetichealth.github.io/synthea/\n",
        "1.  Get continuous  and categorical variables\n",
        "2.   Merge continuous and categorical variables\n",
        "3.    Split data to Train / Test sets\n",
        "4.  Data Transformation\n"
      ],
      "metadata": {
        "id": "iU84iebJoUH7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "drive_path = '/content/drive/My Drive/UofT_DL_Project'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oqaB9fdbM_wx",
        "outputId": "33254965-f743-463e-fafe-2835c9f88f79"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def plotting(history, plot_title, plot_comment):\n",
        "  plt.figure()\n",
        "  plt.plot(history.history['loss'], label = 'train_LOSS')\n",
        "  plt.plot(history.history['val_loss'], label = 'val_LOSS')\n",
        "\n",
        "  plt.title('{} Model: {} '.format(plot_title, plot_comment))\n",
        "  plt.ylabel('Loss')\n",
        "  plt.xlabel('N_Epoch')\n",
        "  plt.legend();"
      ],
      "metadata": {
        "id": "1UATjrLqNJXX"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A.1.  Get categorical and continuous variables"
      ],
      "metadata": {
        "id": "WikH8XMCrIrl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# get categorical var: GENDER\n",
        "cats = pd.read_csv(drive_path + '/patients.csv')\n",
        "# cats.Id.nunique()  # 1171\n",
        "\n",
        "cats = cats[['Id', 'GENDER']].set_index('Id')"
      ],
      "metadata": {
        "id": "9rpE7i8LNNJp"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get continuous vars: SBP, DBP\n",
        "obs = pd.read_csv(drive_path + '/observations.csv')\n",
        "sbp = obs [ obs.DESCRIPTION == 'Systolic Blood Pressure']\n",
        "\n",
        "# get the first SBP and DBP values per patient as representative\n",
        "sbp_first = sbp.groupby(['PATIENT'])[['VALUE']].first()\n",
        "\n",
        "dbp = obs [ obs.DESCRIPTION == 'Diastolic Blood Pressure']\n",
        "dbp_first = dbp.groupby(['PATIENT'])[['VALUE']].first()\n",
        "\n",
        "conts = pd.merge(sbp_first, dbp_first, left_index=True, right_index=True)\n",
        "conts.columns = ['SBP', 'DBP']"
      ],
      "metadata": {
        "id": "10c5izMFNc0C"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A.2.  Merge categorical and continuous variables to a single tabular dataset"
      ],
      "metadata": {
        "id": "XG7eJ6Cqrhvo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([cats, conts], axis=1)\n",
        "df.SBP = df.SBP.astype(float)\n",
        "df.DBP = df.DBP.astype(float)\n",
        "\n",
        "df.head(3)  # len(df) # 1171"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "rFpa5jNOPNqp",
        "outputId": "628660f7-d98b-4da7-8392-2d189ff14c5a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     GENDER    SBP   DBP\n",
              "1d604da9-9a81-4ba9-80c2-de3375d59b40      M  107.0  83.0\n",
              "034e9e3b-2def-4559-bb2a-7850888ae060      M  119.0  82.0\n",
              "10339b10-3cd1-4ac3-ac13-ec26728cb592      M  121.0  70.0"
            ],
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              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1171,\n  \"fields\": [\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.00270648225935,\n        \"min\": 99.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          108.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.844319231666648,\n        \"min\": 69.0,\n        \"max\": 119.0,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          112.0,\n          101.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# df.isnull().sum()  # 0 nulls"
      ],
      "metadata": {
        "id": "2Z2BQK17QDcL"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A.3.  Split to Train / Test Sets.  \n",
        "\n",
        "Note:  Use validation size during VAE training.  GAN does not need validation set"
      ],
      "metadata": {
        "id": "M3eglQJ_rsdv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# stratify by gender\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_, test = train_test_split(df, test_size = 0.2, random_state = 42, stratify = df.GENDER)\n",
        "\n",
        "## if decide to use a separate validation set, uncomment below:\n",
        "# train, val = train_test_split(train_, test_size = 0.2, random_state = 42)\n",
        "\n",
        "print(train_.shape)\n",
        "print(test.shape)"
      ],
      "metadata": {
        "id": "lGf-8T8fQFG9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "192617ae-eb3a-4cf3-aea9-76a1403d07e5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(936, 3)\n",
            "(235, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# vizualize distributions of train set\n",
        "plt.figure\n",
        "train_[['SBP','DBP']].plot(kind='hist', subplots=True)\n",
        "plt.suptitle('Train set Distributions')\n",
        "plt.figure()\n",
        "train_.GENDER.value_counts(normalize=True).plot(kind='bar', rot=0, figsize = (2,2));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "id": "qhwJVubQ1phi",
        "outputId": "419ee1eb-8f49-4be1-e3e9-747524900bad"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ],
            "image/png": 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I//+l165dg9vtxt9//41ffvkFu3fvxqZNm/DSSy9h5syZmrl//PGH33Gjo6P7fEPDarWisLAQLpcLubm5IfXlxrdV8L7T6r/p4sWLqri4WI0ZM0YNGTJERUVFqczMTPX222+rzs5OpZRS9957rwLgd3vhhReUUkr9/PPPCoA6fvy4Zt9Lly5VAJTL5fKNzZgxw7e9yWRS99xzj3riiSfUrl27/NbW2zEBqLKyMqWUUgcOHFAA1NWrVzXbNTc3q8jISFVbW3vrHqgQx0uniQT4GodIgOEQCTAcIgGGQyTAcIgEGA6RAMMhEmA4RAIMh0iA4RAJMBwiAYZDJPB/E/XT73kRsmYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A.4  Data Transformation"
      ],
      "metadata": {
        "id": "DMc_SA8JsA-e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import backend as K\n",
        "\n",
        "from tensorflow.keras.layers import Input, Dense, Lambda, Dropout\n",
        "from tensorflow.keras.models import Model\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "irV8oWN8dDYl"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categoricals = df.select_dtypes(include=['object', 'category']).columns.tolist()\n",
        "continuous = df.select_dtypes(include=['int', 'float']).columns.tolist()"
      ],
      "metadata": {
        "id": "X6LK7ZOmhJPr"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# transform continuous variables\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "def preprocess_continuous(train, test, var):\n",
        "    scaler = MinMaxScaler()\n",
        "    train_scaled = scaler.fit_transform(train_[var])\n",
        "    test_scaled = scaler.transform(test[ var ] )\n",
        "    return train_scaled, test_scaled, scaler"
      ],
      "metadata": {
        "id": "v-S3-n_hbC1R"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# transform categorical variable\n",
        "def preprocess_categorical(train, test, var, vocab):\n",
        "\n",
        "    # create layers for one hot encoding\n",
        "    cat_lookup = tf.keras.layers.StringLookup(vocabulary= vocab, num_oov_indices=0, output_mode='int')\n",
        "    cat_one_hot = tf.keras.layers.CategoryEncoding(num_tokens= cat_lookup.vocabulary_size(), output_mode='one_hot')\n",
        "\n",
        "    # run one-hot encoding layers\n",
        "    def preprocess_cat(cat):\n",
        "        cat_int = cat_lookup(cat)\n",
        "        cat_one_hot_encoded = cat_one_hot(cat_int)\n",
        "        return cat_one_hot_encoded\n",
        "\n",
        "    # using tensorflow's Dataset function for data flow, convert values to tensors then perform one-hot encoding\n",
        "    def encode_cat(df):\n",
        "        cat_dataset = tf.data.Dataset.from_tensor_slices(df[var].values)\n",
        "        encoded_cat_dataset = cat_dataset.map(preprocess_cat)\n",
        "        encoded_cat_list = [item.numpy().tolist() for item in encoded_cat_dataset]\n",
        "        return encoded_cat_list\n",
        "\n",
        "    # run functions defined above to encode categorical data\n",
        "    train_cat_proc = encode_cat(train)\n",
        "    test_cat_proc = encode_cat(test)\n",
        "\n",
        "    # convert to numpy arrays to facilitate data concatenation later\n",
        "    return np.array(train_cat_proc), np.array(test_cat_proc)"
      ],
      "metadata": {
        "id": "zQUAPFWskLl_"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# run the transformation functions previous defined\n",
        "train_cont, test_cont, scaler = preprocess_continuous(train_, test, continuous)\n",
        "\n",
        "train_cat_gender, test_cat_gender = preprocess_categorical(train_, test, 'GENDER', cats.GENDER.unique()  )\n",
        "# reshape categorical array to facilitate stacking with continuous variables\n",
        "train_cat = train_cat_gender.reshape(936, 2)\n",
        "test_cat = test_cat_gender.reshape(235, 2)\n",
        "\n",
        "# stack the continuous and categorical transformed data to a single processed train and test sets\n",
        "train_proc = np.hstack((train_cont, train_cat))\n",
        "test_proc = np.hstack((test_cont, test_cat))"
      ],
      "metadata": {
        "id": "IsQTLxumiqTT"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display data transformed into tensors\n",
        "print('Tensor samples')\n",
        "print(train_proc[0:5])\n",
        "print('Tensor shape')\n",
        "print(train_proc.shape)"
      ],
      "metadata": {
        "id": "oNNfqrXrw4Vy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2701b553-07f0-45f5-f8c5-c132d6d0d0f9"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tensor samples\n",
            "[[0.12871287 0.20408163 0.         1.        ]\n",
            " [0.13861386 0.14285714 1.         0.        ]\n",
            " [0.24752475 0.26530612 0.         1.        ]\n",
            " [0.1980198  0.26530612 1.         0.        ]\n",
            " [0.28712871 0.2244898  0.         1.        ]]\n",
            "Tensor shape\n",
            "(936, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Pj31upRy1ppZ"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**B.  CREATE AND TRAIN VARIATIONAL AUTOENCODER MODEL**\n",
        "\n",
        "1.  Define sampling batch\n",
        "2.  Create Encoder\n",
        "3.  Create Decoder\n",
        "4.  Define losses\n",
        "5.  Compile and train\n",
        "6.  Visualize results\n",
        "7.  Evaluate model on Test set"
      ],
      "metadata": {
        "id": "4ndA82gRuTfn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "B.1.  Define Sampling Batch"
      ],
      "metadata": {
        "id": "DjnlPFHX0-KL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Sampling(keras.layers.Layer):\n",
        "    def call(self, inputs):\n",
        "        mean, log_var = inputs\n",
        "        batch = K.shape(mean)[0]\n",
        "        dim = K.int_shape(mean)[1]\n",
        "        epsilon = K.random_normal(shape=(batch, dim))\n",
        "        return mean + K.exp(0.5 * log_var) * epsilon"
      ],
      "metadata": {
        "id": "jjdgAqyhuTE7"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "B.2 to 6.  Create Encoder and Decoder, Define Losses, Train, Evaluate on Test set"
      ],
      "metadata": {
        "id": "e15UODMa1EXE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# VAE training code\n",
        "# epoch_list = [20, 50, 100], 200, 500]\n",
        "epoch_list = [40]\n",
        "codings_size = 10    # dimensionality of latent vectors\n",
        "batch_size = 128\n",
        "epsilon = np.random.normal(size=(batch_size, codings_size))\n",
        "\n",
        "for epoch in epoch_list:\n",
        "    # ENCODER\n",
        "    inputs = keras.layers.Input(shape= (train_proc.shape[1], ))\n",
        "    z = keras.layers.Dense(100, activation=\"selu\")(inputs)\n",
        "    z = keras.layers.Dense(100, activation=\"selu\")(z)\n",
        "    z = keras.layers.Dropout(0.5)(z)\n",
        "\n",
        "    # Define the latent variables mean and var, using the batch sampling\n",
        "    codings_mean = keras.layers.Dense(codings_size)(z)\n",
        "    codings_log_var = keras.layers.Dense(codings_size)(z)\n",
        "    codings = Sampling()([codings_mean, codings_log_var])\n",
        "\n",
        "    # Defne the encoder part of the model\n",
        "    # Outputs include the computed mean and variation for the population\n",
        "    variational_encoder = keras.models.Model(inputs=[inputs], outputs=[codings_mean, codings_log_var, codings])\n",
        "\n",
        "    # DECODER\n",
        "    decoder_inputs = keras.layers.Input(shape=[codings_size])\n",
        "    x = keras.layers.Dense(100, activation=\"selu\")(decoder_inputs)\n",
        "    x = keras.layers.Dense(100, activation=\"selu\")(x)\n",
        "    x = keras.layers.Dropout(0.5)(x)\n",
        "    # separate the outputs for the categorical and continuous variables\n",
        "    #    with the appropriate activation functions\n",
        "    cont_output = keras.layers.Dense(train_cont.shape[1], activation=\"linear\")(x)\n",
        "    cat_output = keras.layers.Dense(train_cat.shape[1], activation=\"softmax\")(x)\n",
        "    # once processed separately, concatenate the results\n",
        "    outputs = keras.layers.concatenate([cont_output, cat_output])\n",
        "\n",
        "    # Define the decoder part of the model\n",
        "    variational_decoder = keras.models.Model(inputs=[decoder_inputs], outputs=[outputs])\n",
        "\n",
        "    # Decoder to use the codings output by the Encoder\n",
        "    codings_mean, codings_log_var, codings = variational_encoder(inputs)  # Get intermediate outputs\n",
        "    reconstructions = variational_decoder(codings)\n",
        "\n",
        "    # Define the combined Encoder-Decoder model\n",
        "    variational_ae = keras.models.Model(inputs=[inputs], outputs=[reconstructions, codings_mean, codings_log_var])  # Include intermediate outputs in the final model\n",
        "\n",
        "    # Define the loss functions\n",
        "    reconstruction_loss = keras.losses.mean_squared_error(inputs, reconstructions) # need specific tf version\n",
        "    reconstruction_loss *= train_proc.shape[1]\n",
        "    kl_loss = 1 + codings_log_var - K.square(codings_mean) - K.exp(codings_log_var)\n",
        "    kl_loss = K.sum(kl_loss, axis=-1)\n",
        "    kl_loss *= -0.5\n",
        "    vae_loss = K.mean(reconstruction_loss + kl_loss)\n",
        "    variational_ae.add_loss(vae_loss)\n",
        "\n",
        "    # Compile the VAE model\n",
        "    variational_ae.compile(optimizer=\"adam\")\n",
        "\n",
        "    # Define callbacks for optional use\n",
        "    early_stopping = keras.callbacks.EarlyStopping(\n",
        "        monitor='val_loss',\n",
        "        patience=10,  # Number of epochs with no improvement to stop training\n",
        "        restore_best_weights=True\n",
        "    )\n",
        "\n",
        "    model_checkpoint = keras.callbacks.ModelCheckpoint(\n",
        "        drive_path + '/models/vae_model_epoch_{}.h5'.format([epoch]),\n",
        "        save_best_only=True,\n",
        "        monitor='val_loss',\n",
        "        mode='min'\n",
        "    )\n",
        "\n",
        "    history = variational_ae.fit(\n",
        "        train_proc,\n",
        "        epochs=epoch,\n",
        "        batch_size=batch_size,\n",
        "        validation_split=0.2,  # important, coz no separate validation set\n",
        "        # callbacks=[early_stopping, model_checkpoint]   ## uncomment as needed\n",
        "    )\n",
        "\n",
        "    # Plot training\n",
        "    plotting(history, 'Losses for VAE', 'TF VAE, Epoch {}'.format(epoch))\n",
        "\n",
        "    # Generate synthetic data using noise\n",
        "    syn_data = variational_decoder.predict(epsilon)\n",
        "\n",
        "    # Re-transform the synthetic data back to a human-interpretable format\n",
        "    syn_continuous = scaler.inverse_transform(syn_data[:, :train_cont.shape[1]])\n",
        "    syn_categorical = np.argmax(syn_data[:, train_cat.shape[1]:], axis=-1)\n",
        "\n",
        "    # Visualize the synthetic data to gauge training results\n",
        "    pd.DataFrame(syn_continuous).plot(kind='hist', subplots= True, )\n",
        "    plt.suptitle('VAE Synthetic Data Distribution at Epoch {}'.format(epoch) ) ;\n",
        "    plt.xlim(60,170)\n",
        "\n",
        "    syn_categorical_indices = np.argmax(syn_data[:, train_cont.shape[1]:], axis=-1)\n",
        "    categories = ['M', 'F']\n",
        "    decoded_categories = [categories[idx] for idx in syn_categorical_indices]\n",
        "    plt.figure()\n",
        "    pd.DataFrame(decoded_categories).value_counts(normalize=True).plot(kind='bar', rot=0, figsize = (2,2) )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "ATBlKfArNmSO",
        "outputId": "96789f18-becb-429a-f2da-0cec5836f72a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "6/6 [==============================] - 4s 65ms/step - loss: 3.4387 - val_loss: 0.8802\n",
            "Epoch 2/40\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 2.2113 - val_loss: 0.7829\n",
            "Epoch 3/40\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 1.8646 - val_loss: 0.6956\n",
            "Epoch 4/40\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 1.5960 - val_loss: 0.6504\n",
            "Epoch 5/40\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 1.3467 - val_loss: 0.6130\n",
            "Epoch 6/40\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 1.2387 - val_loss: 0.5870\n",
            "Epoch 7/40\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 1.1358 - val_loss: 0.5873\n",
            "Epoch 8/40\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 1.0703 - val_loss: 0.5766\n",
            "Epoch 9/40\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 1.0180 - val_loss: 0.5753\n",
            "Epoch 10/40\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.8992 - val_loss: 0.5726\n",
            "Epoch 11/40\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.8720 - val_loss: 0.5875\n",
            "Epoch 12/40\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.8505 - val_loss: 0.5591\n",
            "Epoch 13/40\n",
            "6/6 [==============================] - 0s 19ms/step - loss: 0.8028 - val_loss: 0.5850\n",
            "Epoch 14/40\n",
            "6/6 [==============================] - 0s 15ms/step - loss: 0.7560 - val_loss: 0.5505\n",
            "Epoch 15/40\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.7572 - val_loss: 0.5629\n",
            "Epoch 16/40\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.7129 - val_loss: 0.5917\n",
            "Epoch 17/40\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.6890 - val_loss: 0.5793\n",
            "Epoch 18/40\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.6848 - val_loss: 0.5100\n",
            "Epoch 19/40\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.6676 - val_loss: 0.5834\n",
            "Epoch 20/40\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.6313 - val_loss: 0.5708\n",
            "Epoch 21/40\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.6574 - val_loss: 0.5461\n",
            "Epoch 22/40\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.6222 - val_loss: 0.5473\n",
            "Epoch 23/40\n",
            "6/6 [==============================] - 0s 20ms/step - loss: 0.6280 - val_loss: 0.5539\n",
            "Epoch 24/40\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.6106 - val_loss: 0.5500\n",
            "Epoch 25/40\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.6038 - val_loss: 0.5339\n",
            "Epoch 26/40\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.6060 - val_loss: 0.5672\n",
            "Epoch 27/40\n",
            "6/6 [==============================] - 0s 13ms/step - loss: 0.6020 - val_loss: 0.5217\n",
            "Epoch 28/40\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.6074 - val_loss: 0.5474\n",
            "Epoch 29/40\n",
            "6/6 [==============================] - 0s 12ms/step - loss: 0.5911 - val_loss: 0.5434\n",
            "Epoch 30/40\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.5846 - val_loss: 0.5692\n",
            "Epoch 31/40\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.5802 - val_loss: 0.5282\n",
            "Epoch 32/40\n",
            "6/6 [==============================] - 0s 14ms/step - loss: 0.5763 - val_loss: 0.5607\n",
            "Epoch 33/40\n",
            "6/6 [==============================] - 0s 18ms/step - loss: 0.5794 - val_loss: 0.5220\n",
            "Epoch 34/40\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.5887 - val_loss: 0.5361\n",
            "Epoch 35/40\n",
            "6/6 [==============================] - 0s 17ms/step - loss: 0.5742 - val_loss: 0.5250\n",
            "Epoch 36/40\n",
            "6/6 [==============================] - 0s 20ms/step - loss: 0.5750 - val_loss: 0.5443\n",
            "Epoch 37/40\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.5659 - val_loss: 0.5407\n",
            "Epoch 38/40\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.5637 - val_loss: 0.5273\n",
            "Epoch 39/40\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.5753 - val_loss: 0.5374\n",
            "Epoch 40/40\n",
            "6/6 [==============================] - 0s 16ms/step - loss: 0.5788 - val_loss: 0.5444\n",
            "4/4 [==============================] - 0s 3ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "B.7.  Evaluate VAE model on Test set"
      ],
      "metadata": {
        "id": "uu7jdxsh42gr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# load saved VAE model\n",
        "# vae_model_path = drive_path + '/models/vae_model_epoch_[50].h5'\n",
        "# variational_ae_loaded = keras.models.load_model(vae_model_path, custom_objects={'Sampling': Sampling})"
      ],
      "metadata": {
        "id": "wYyr12aQ-yH3"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "codings_size = 10\n",
        "batch_size = 128\n",
        "# noise from the test set\n",
        "epsilon = np.random.normal(size=(test_proc.shape[0], codings_size))\n",
        "\n",
        "# Use the decoder part of the VAE model to generate continuous synthetic data from the test set\n",
        "# syn_data_test = variational_ae.layers[-1](epsilon).numpy()\n",
        "syn_data_test = variational_decoder.predict(epsilon)\n",
        "\n",
        "# Inverse transform to a humna-interpretable format\n",
        "syn_continuous_test = scaler.inverse_transform(syn_data_test[:, :test_cont.shape[1]])\n",
        "syn_categorical_test = np.argmax(syn_data_test[:, test_cat.shape[1]:], axis=-1)\n",
        "\n",
        "# Visualize the synthetic data to gauge training results\n",
        "pd.DataFrame(syn_continuous_test).plot(kind='hist', subplots= True, )\n",
        "plt.suptitle('VAE Synthetic TEST Data Distribution'.format(epoch) ) ;\n",
        "plt.xlim(60,170)\n",
        "\n",
        "syn_categorical_indices_test = np.argmax(syn_data_test[:, test_cont.shape[1]:], axis=-1)\n",
        "categories = ['M', 'F']\n",
        "decoded_categories = [categories[idx] for idx in syn_categorical_indices_test]\n",
        "plt.figure()\n",
        "pd.DataFrame(decoded_categories).value_counts(normalize=True).plot(kind='bar', rot=0, figsize = (2,2) )\n",
        "\n",
        "# Generate synthetic dataset table\n",
        "synthetic_data_df_vae = pd.concat( [ pd.DataFrame(syn_continuous_test), pd.DataFrame(decoded_categories )] , axis=1)\n",
        "synthetic_data_df_vae.columns = ['SBP', 'DBP', 'GENDER']\n",
        "display(synthetic_data_df_vae.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 902
        },
        "id": "t3h9jTDUyvAl",
        "outputId": "264007a2-1cbb-43ae-cbe2-2ba13fc3b501"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          SBP        DBP GENDER\n",
              "0  121.869209  81.370529      F\n",
              "1  121.222267  80.260506      F\n",
              "2  122.159088  79.773735      M\n",
              "3  121.774261  79.691544      M\n",
              "4  123.694298  78.880646      F"
            ],
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              "    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-6042ddf3-39fe-41c3-9f1c-04237065ddcd 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\": \"display(synthetic_data_df_vae\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"SBP\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          121.2222671508789,\n          123.69429779052734,\n          122.15908813476562\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DBP\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          80.26050567626953,\n          78.88064575195312,\n          79.77373504638672\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sbp = pd.concat([synthetic_data_df_vae.SBP, test.SBP], axis=1)\n",
        "sbp.columns = ['SBP_Synthetic', 'SBP_Real']\n",
        "sbp.plot(kind='kde')\n",
        "plt.xlabel('Systolic BP (mmHg)')\n",
        "plt.title('Systolic BP Distribution of Real vs Synthetic Test Data');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "sOq7J-2_dSFi",
        "outputId": "66994c7b-f7c2-407c-a537-2db089894cb1"
      },
      "execution_count": 56,
      "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": [
        "dbp = pd.concat([synthetic_data_df_vae.DBP, test.DBP], axis=1)\n",
        "dbp.columns = ['DBP_Synthetic', 'DBP_Real']\n",
        "dbp.plot(kind='kde');\n",
        "plt.xlabel('Diastolic BP (mmHg)')\n",
        "plt.title('Diastolic BP Distribution of Real vs Synthetic Test Data');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "YVVYLY4LgJDa",
        "outputId": "8994d9e5-d6b0-41df-c21d-0c55296e8184"
      },
      "execution_count": 57,
      "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": [
        "gend = pd.concat([synthetic_data_df_vae.GENDER, test.GENDER.reset_index()], axis=1).drop('index', axis=1)\n",
        "gend.columns = ['GENDER_Synthetic', 'GENDER_Real']\n",
        "len( gend [ gend.GENDER_Real == gend.GENDER_Synthetic]) / len(gend)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ygflY01gWzb",
        "outputId": "747adb74-467f-4046-d75d-b40deceef7fd"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4595744680851064"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gen_real_ct = gend.GENDER_Real.value_counts()\n",
        "gen_syn_ct = gend.GENDER_Synthetic.value_counts()\n",
        "gen_ct = pd.concat([gen_real_ct, gen_syn_ct], axis=1)\n",
        "gen_ct.columns = ['Real', 'Synthetic']\n",
        "gen_ct.plot(kind='bar', rot= 0)\n",
        "plt.title('Gender Distribution Real vs Synthetic Test Data');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "BNLv67R8iiyY",
        "outputId": "d291f275-e559-44f9-a78e-183615b72d8f"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**RESULTS**:\n",
        "\n",
        "The VAE model with 2 hidden layers (of 100 nodes each), with Dropout layers, using a latent variable size of 10 on a tabular Train dataset with 936 rows, 2 continuous variables and 1 categorical variable reached convergence before 40 epochs.\n",
        "\n",
        "The synthetic data generated on the Test set showed an equivalent distribution to the actual Test set, showing that the **model fit was good and is able to generalize to new data**.\n",
        "\n",
        "This shows that **VAE can be applied to generate synthetic tabular data containing both continuous and categorical variables**."
      ],
      "metadata": {
        "id": "E_TO5PYFhr0c"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mnvbaO7chYZc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**C.  CREATE AND TRAIN A GENERATIVE ADVERSARIAL NETWORK MODEL**\n",
        "\n",
        "1.  Create Generator\n",
        "2.  Create Discriminator\n",
        "3.  Define Losses\n",
        "4.  Compile and train\n",
        "5.  Visualize results\n",
        "5.  Evaluate model on Test set"
      ],
      "metadata": {
        "id": "ST8olgBkmhey"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "C.1 to 5  Create Generator, Disciminator, Compile and Train GAN"
      ],
      "metadata": {
        "id": "xrSPdlhXgRtt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GAN training code\n",
        "# epoch_list = [20, 50, 100, 200,]# 500]\n",
        "epoch_list = [200]\n",
        "batch_size = 128\n",
        "codings_size = 10  # Dimensionality of latent vectors\n",
        "\n",
        "for epochs in epoch_list:\n",
        "    # GENERATOR\n",
        "    # to be fed random noise (code below) , and will output data with the same shape as the given data\n",
        "    generator = keras.models.Sequential([\n",
        "        # use selu to stabilize output by normalization\n",
        "        keras.layers.Dense(100, activation=\"selu\", input_shape=[codings_size]),\n",
        "        # use sigmoid to for normalized output for continuous variables\n",
        "        #      and probabilities for the binary categorical variable\n",
        "        keras.layers.Dense(train_cont.shape[1] + train_cat.shape[1], activation=\"sigmoid\"),\n",
        "    ])\n",
        "\n",
        "    # DISCRIMINATOR\n",
        "    # to determine real vs fake data (code below)\n",
        "    discriminator = keras.models.Sequential([\n",
        "        keras.layers.Dense(150, activation=\"selu\", input_shape=[train_cont.shape[1] + train_cat.shape[1]]),\n",
        "        # use sigmoid for real vs fake\n",
        "        keras.layers.Dense(1, activation=\"sigmoid\"),\n",
        "    ])\n",
        "\n",
        "    # GAN MODEL and define LOSS FUNCTIONS\n",
        "    gan = keras.models.Sequential([generator, discriminator])\n",
        "\n",
        "    # use binary crossentropy loss function, in accordance with the sigmoid activation functions above\n",
        "    # Generator and Discriminator trained separately\n",
        "    generator.compile(loss=\"binary_crossentropy\", optimizer=\"adam\")\n",
        "    # Discriminator weights are updated to minimize binary crossentry loss\n",
        "    discriminator.compile(loss='binary_crossentropy', optimizer=\"adam\", metrics=['accuracy'])\n",
        "    # Do not update Discriminator weights while Generator is being trained\n",
        "    discriminator.trainable = False\n",
        "    gan.compile(loss='binary_crossentropy', optimizer=\"adam\")\n",
        "\n",
        "    d_losses = []\n",
        "    g_losses = []\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        # Prepare REAL data\n",
        "        idx = np.random.randint(0, train_proc.shape[0], batch_size)\n",
        "        real_data = train_proc[idx]\n",
        "\n",
        "        # Generate FAKE data using noise\n",
        "        noise = np.random.normal(0, 1, (batch_size, codings_size))\n",
        "        generated_data = generator.predict(noise)\n",
        "\n",
        "        # Train Discriminator\n",
        "        #    on REAL data  (1's)\n",
        "        discriminator.trainable = True\n",
        "        d_loss_real = discriminator.train_on_batch(real_data, np.ones((batch_size, 1)))\n",
        "        #    on FAKE data  (0's)\n",
        "        d_loss_fake = discriminator.train_on_batch(generated_data, np.zeros((batch_size, 1)))\n",
        "        # Combined discriminator loss\n",
        "        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "\n",
        "        # Train Generator (with Discriminator frozen)\n",
        "        discriminator.trainable = False\n",
        "        noise = np.random.normal(0, 1, (batch_size, codings_size))\n",
        "        # use GAN model to train generator, use 1's to mask fake data\n",
        "        g_loss = gan.train_on_batch(noise, np.ones((batch_size, 1)))\n",
        "\n",
        "        d_losses.append(d_loss[0])\n",
        "        g_losses.append(g_loss)\n",
        "\n",
        "    # Visualize generator and discriminator losses during training\n",
        "    plt.figure()\n",
        "    plt.plot(d_losses, label='Discriminator loss')\n",
        "    plt.plot(g_losses, label='Generator loss')\n",
        "    plt.title('GAN Losses Over {} Epochs'.format(epochs))\n",
        "    plt.ylabel('Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.legend()\n",
        "\n",
        "    # Generate synthetic data using noise from the train set\n",
        "    num_samples = train_.shape[0]\n",
        "    noise = np.random.normal(0, 1, (num_samples, codings_size))\n",
        "    synthetic_data = generator.predict(noise)\n",
        "\n",
        "    # Inverse transform continuous data to human-translatable format\n",
        "    synthetic_data_continuous = synthetic_data[:, :train_cont.shape[1]]\n",
        "    synthetic_data_categorical = synthetic_data[:, train_cont.shape[1]:]\n",
        "\n",
        "    synthetic_data_continuous_inv_tr = scaler.inverse_transform(synthetic_data_continuous)\n",
        "\n",
        "    # Visualize synthetic data distribution\n",
        "    plt.figure()\n",
        "    pd.DataFrame(synthetic_data_continuous_inv_tr).plot(kind='hist', subplots=True)\n",
        "    plt.suptitle('GAN Synthetic Data Distribution at Epoch {}'.format(epoch) ) ;\n",
        "    plt.xlim(60, 170)\n",
        "\n",
        "    decoded_categorical_indices = np.argmax(synthetic_data_categorical, axis=-1)\n",
        "    categories = ['M', 'F']\n",
        "    decoded_categories = [categories[idx] for idx in decoded_categorical_indices]\n",
        "\n",
        "    plt.figure()\n",
        "    pd.DataFrame(decoded_categories).value_counts(normalize=True).plot(kind='bar', rot=0, figsize=(2, 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "POEXSfi9NmH8",
        "outputId": "a95d7188-1aa3-4da3-eeb2-6fc2d7fe4f4e",
        "collapsed": true
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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maZqWl5enFRQU2JwvKyvTgoODtauvvlrTNDWA3MvLSxs8eLBWXFxc4X6pqanVvldVVq9eren1em3IkCEVBqEfOXJECwkJqbQOUEpKiubg4KA9++yzWlhYmDZhwoQKrx8dHa21atWqQs2e8m3XtOoPUi/vrrvu0gDt008/PeN1r7zySoV/03379mkODg7ao48+ajmWn5+v+fn5aZdddpnN82+44QbNzc3NZiByZapbZ+dsg6Dvv/9+y7EuXbpowcHB2qlTpyzHdu3adUGDoE+/ZtWqVRqgrVq1StM0TcvIyLAZJK1pmrZnzx4N0D744IMzvjchpBK0EKdp1aoV3377LZMmTaJNmzaWStCaphEXF8e3336LXq+nWbNmAHz77bdomsbll19e6f0uvfRSHB0dmTdv3hmn1l9//fW88MILbN++/Zza+8UXX7B06dIKx++77z7+97//sXz5cgYMGMBdd92Fo6Mjn376KUVFRbz22muWa9u3b8+QIUPo3r07fn5+bN68mZ9++sky5fjAgQMMGzaMCRMm0L59exwdHfnll19ISUnh2muvBVT25OOPP+bGG2+kW7duXHvttQQGBpKQkMCiRYvo378/H3zwQbXuVZVBgwbxxhtvMGPGDDp16sSUKVMIDQ1l3759zJo1C6PRyOLFiy1jRcyCgoIYOnQob731Fjk5ORWWKdHr9Xz++eeMHj2aDh06MHXqVMLDwzl+/DirVq3Cy8uL33///Zz+XczeeecdPvroI/r27Yubm1uFMWRXXHEF7u7uANx1113MmjWLMWPG8NBDD+Hk5MRbb71FcHAwDz74oOU5rq6uvPDCC0yfPp1rrrmGkSNH8u+///LNN9/w4osv2gxEPpMlS5awb9++Csf79etnM3A+JiaGAQMGMG3aNIqKinjnnXfw9/e3GcT9+uuvM3r0aPr27cstt9xCQUEB77//Pt7e3jaZxJdeeolly5YxePBgbr/9dtq1a0dSUhI//vgja9assQxsro6vv/6ajz76iCuuuILo6GhycnKYNWsWXl5eXHrppdW+j2ii7B2BCVFfHTp0SJs2bZoWExOjubi4aK6urlrbtm21O++8U9u+fbvluo4dO2rNmzc/472GDBmiBQUFaSUlJVVmgDTNdnpydTNAVX0lJiZqmqZpW7du1UaOHKl5eHhobm5u2tChQ7V169bZ3Ot///uf1qtXL83Hx8fyPl988UVLJictLU2bPn261rZtW83d3V3z9vbWevfurf3www8V2rVq1Spt5MiRmre3t+bi4qJFR0drU6ZM0TZv3nzO96rKP//8o40bN04LCAjQnJyctObNm2u33XabdvTo0SqfM2vWLA3QPD09K2SgzLZt26ZdeeWVmr+/v2YwGLQWLVpoEyZM0FauXGm55lwzQDfddNMZ/53KZ+c0TdMSExO1q6++WvPy8tI8PDy0yy67zFJa4XSfffaZ1qZNG83Z2VmLjo7W3n777QoZkcqc7Wfnyy+/1DTNNlv55ptvahEREZrBYNAGDhyo7dixo8J9V6xYofXv319zdXXVvLy8tLFjx2p79+6tcF18fLw2efJkLTAwUDMYDFrLli216dOna0VFRTbtO1sGaOvWrdqkSZO05s2bawaDQQsKCtIuu+wyy8+aEGei07RaLEErhBCiwTp69ChRUVG8/vrrPPTQQ/ZujhA1SgZBCyGEEKLJkQBICCGEEE2OBEBCCCGEaHJkDJAQQgghmhzJAAkhhBCiyZEASAghhBBNjhRCrITRaOTEiRN4enpWWJ1YCCGEEPWTpmnk5OQQFhaGXn/mHI8EQJU4ceKEzRpJQgghhGg4EhMTLdX6qyIBUCXMCw4mJibi5eVl59YIIYQQojqys7OJiIiw/B0/EwmAKmHu9vLy8pIASAghhGhgqjN8RQZBCyGEEKLJkQBICCGEEE2OBEBCCCGEaHJkDJAQQjRxRqOR4uJiezdDiLNycnLCwcGhRu4lAZAQQjRhxcXFxMXFYTQa7d0UIarFx8eHkJCQC67TJwGQEEI0UZqmkZSUhIODAxEREWctHCeEPWmaRn5+PqmpqQCEhoZe0P0kABJCiCaqtLSU/Px8wsLCcHNzs3dzhDgrV1dXAFJTUwkKCrqg7jAJ94UQookqKysDwNnZ2c4tEaL6zMF6SUnJBd1HAiAhhGjiZM1D0ZDU1M+rBEBCCCGEaHIkABJCCNFo6XQ6Fi5cWGv3nzJlCuPHj7+ge/z999/odDoyMzNrpE11JTIyknfeecfezThvEgAJIYRoUKZMmYJOp0On0+Hk5ERwcDAjRozgiy++qDCdPykpidGjR9daW959912++uqrC7pHv379SEpKwtvbu2YaZVLbwV9DJwGQqBmlxVB2YQPShBCiukaNGkVSUhJHjx5lyZIlDB06lPvuu4/LLruM0tJSy3UhISEYDIYaf/2ysjKMRiPe3t74+Phc0L2cnZ1rpK5NbbnQwcb1lQRA4sKVFMD73WHWxaBp9m6NEKIJMBgMhISEEB4eTrdu3XjiiSf49ddfWbJkiU1GpnwWpLi4mLvvvpvQ0FBcXFxo0aIFL7/8suXazMxM7rjjDoKDg3FxceGiiy7ijz/+AOCrr77Cx8eH3377jfbt22MwGEhISKjQBTZkyBDuuece7r//fnx9fQkODmbWrFnk5eUxdepUPD09iYmJYcmSJZbnnN4FZn6tP//8k3bt2uHh4WEJ+Mz+++8/RowYQUBAAN7e3gwePJitW7dazkdGRgJwxRVXoNPpLI8BPv74Y6Kjo3F2dqZNmzbMnTvX5nur0+n4+OOPufzyy3F3d+fFF1+s1r9JQkIC48aNw8PDAy8vLyZMmEBKSorl/I4dOxg6dCienp54eXnRvXt3Nm/eDEB8fDxjx47F19cXd3d3OnTowOLFi6v1uudLAiBx4U5sh6wESN4JRdn2bo0Q4jxpmkZ+caldvrQa+PB08cUX07lzZxYsWFDp+ffee4/ffvuNH374gf379zNv3jxLYGA0Ghk9ejRr167lm2++Ye/evbzyyis2dWby8/N59dVX+fzzz9mzZw9BQUGVvs7XX39NQEAAmzZt4p577mHatGlcc8019OvXj61bt3LJJZdw4403kp+fX+V7yc/P54033mDu3Ln8888/JCQk8NBDD1nO5+TkcNNNN7FmzRo2bNhAq1atuPTSS8nJyQFUgATw5ZdfkpSUZHn8yy+/cN999/Hggw+ye/du7rjjDqZOncqqVatsXv+5557jiiuuYNeuXdx8881n+c6r79+4cePIyMhg9erVLF++nCNHjjBx4kTLNddffz3NmjXjv//+Y8uWLTz22GM4OTkBMH36dIqKivjnn3/YtWsXr776Kh4eHmd93QshhRDFhTth/dRBTgq41Gw/thCibhSUlNH+mT/t8tp7Z47EzfnC/yS1bduWnTt3VnouISGBVq1aMWDAAHQ6HS1atLCcW7FiBZs2bSI2NpbWrVsD0LJlS5vnl5SU8NFHH9G5c+cztqFz58489dRTADz++OO88sorBAQEcNtttwHwzDPP8PHHH7Nz50769OlT6T1KSkr45JNPiI6OBuDuu+9m5syZlvMXX3yxzfWfffYZPj4+rF69mssuu4zAwEDAumyE2RtvvMGUKVO46667AJgxYwYbNmzgjTfeYOjQoZbrrrvuOqZOnXrG91neypUr2bVrF3FxcURERAAwZ84cOnTowH///UfPnj1JSEjg4Ycfpm3btgC0atXK8vyEhASuuuoqOnbsCFT83tcGyQCJC3d8i3U/J6nq64QQopZpmlblWJopU6awfft22rRpw7333suyZcss57Zv306zZs0swU9lnJ2d6dSp01nbUP4aBwcH/P39LX/YAYKDgwEsSzpUxs3NzRL8gFr2ofz1KSkp3HbbbbRq1Qpvb2+8vLzIzc0lISHhjG2LjY2lf//+Nsf69+9PbGyszbEePXqc8T6V3TciIsIS/AC0b98eHx8fy71nzJjBrbfeyvDhw3nllVc4fPiw5dp7772X//3vf/Tv359nn322yiC2JkkGSFy44+UyQLkpVV8nhKjXXJ0c2DtzpN1euybExsYSFRVV6blu3boRFxfHkiVLWLFiBRMmTGD48OH89NNPliUWzthGV9dqDVQ2d+uYmWerlX8MnHEB2sruUb6b8KabbiI9PZ13332XFi1aYDAY6Nu3L8XFxWdtX3W4u7vXyH3Ke+6557juuutYtGgRS5Ys4dlnn+X777/niiuu4NZbb2XkyJEsWrSIZcuW8fLLL/Pmm29yzz331Hg7zCQDJC5MfgacirM+zkm2X1uEEBdEp9Ph5uxol6+amAH1119/sWvXLq666qoqr/Hy8mLixInMmjWL+fPn8/PPP5ORkUGnTp04duwYBw4cuOB21IW1a9dy7733cumll9KhQwcMBgNpaWk21zg5OVmWOzFr164da9eurXCv9u3bX1B72rVrR2JiIomJiZZje/fuJTMz0+berVu35oEHHmDZsmVceeWVfPnll5ZzERER3HnnnSxYsIAHH3yQWbNmXVCbzkYyQOLClB//A9YAyGiE7GPg07zu2ySEaPSKiopITk6mrKyMlJQUli5dyssvv8xll13G5MmTK33OW2+9RWhoKF27dkWv1/Pjjz8SEhKCj48PgwcPZtCgQVx11VW89dZbxMTEsG/fPnQ6HaNGjarjd3d2rVq1Yu7cufTo0YPs7GwefvjhClmsyMhIVq5cSf/+/TEYDPj6+vLwww8zYcIEunbtyvDhw/n9999ZsGABK1asuKD2DB8+nI4dO3L99dfzzjvvUFpayl133cXgwYPp0aMHBQUFPPzww1x99dVERUVx7Ngx/vvvP0uwev/99zN69Ghat27NqVOnWLVqFe3atbugNp2NZIDEhbF0f5k+veWaAqAtX8A7HWFT7UbwQoimaenSpYSGhhIZGcmoUaNYtWoV7733Hr/++muVK4R7enry2muv0aNHD3r27MnRo0dZvHgxer36U/jzzz/Ts2dPJk2aRPv27XnkkUcqZFDqi9mzZ3Pq1Cm6devGjTfeyL333lthVtqbb77J8uXLiYiIoGvXrgCMHz+ed999lzfeeIMOHTrw6aef8uWXXzJkyJALao9Op+PXX3/F19eXQYMGMXz4cFq2bMn8+fMBNRYqPT2dyZMn07p1ayZMmMDo0aN5/vnnAVVXafr06bRr145Ro0bRunVrPvroowtq01nbrNXE3MNGJjs7G29vb7KysvDy8rJ3c+yjtAh2/Qgxw8EzpOrrvr0WDiyBZj3h2H/QYgBMXQQ/TIa9v4JfNNyzBeppgS8hmrLCwkLi4uKIiorCxcXF3s0RolrO9HN7Ln+/JQMkKrfrJ/h1Onw5GgpOVX6NpllngLW5VG3Ns8Ayjpi2h1VgJIQQQtQjEgCJyqWZBgJmHIGfboay0orXZB+HvFTQO0KrS9Sx3BQVGKUfsV6347vab68QQghxDiQAEpXLPmHdP/wX/DWz4jXm7E9Qe/A1FRQrzlVBU0me9brdP0NJIRRkQmaCGiB9Ntu+gQ96WTNJQgghRA2SAEhUzhwAtR+ntus+gMxE22vMA6DDu4HBE5xNZcvj16mtVzPwCofCLPjuWnijtRoY/VIYfDHqzFPm17wNaftVV5wQQghRwyQAEpXLPqa2vadB5EDQymDTZ7bXmDNAYd3U1jxYOmG92gbEQKcJav/IKigrAp0DlBaoa9a+V/lrZyZA+iG1n7SjZt6PEEIIUY4EQKIiTbNmgLzCoO90tb/layjKVftGo1oEFSC8u9p6mAKgeFORLb9o6HkrBLaFlkNg8m/wVCpcbSp8tfVr1S12usPlFuVLrv1y6EIIIZoeCYBERfnpUFYM6MAzFFqNVMFMURZs/1Zdk34QinPA0VUFOACean0bTh1VW7+W4N0Mpm+Eyb9Cy8Hg4AgdroDAdmq80NavK77+4b+s+5kJVc9CE0IIIc6TBECioixT95dHEDg6g14PfaapYxs/Vtkf8/ifsC4qqAEVLJXnV8Vqvjod9LvbdL9PoazEes5YBnGr1b7edN/kXRf0doQQQojTSQAkKirf/WXWeRK4eKtZWQeWWpfAMI//AfAItr2PfzRV6niNuj77OGy2rgVD0naV8TF4QcwI0zEZBySEEKJmSQAkKso+rrZe4dZjBg/oPkXtb/jIOgA6vFwAZJMB0oFPi6pfw9EAfe5S+0sehlUvqcySefxP1CDrvZNkHJAQQpyPIUOGcP/999u7GfWSBECiosoCIIBet6tZXEf/hRPb1DGbAKhcBsi7GTidpbR+v3ugr6krbPWr8F5n2PCxehw9FEI7q30ZCC2EOE1ycjL33XcfMTExuLi4EBwcTP/+/fn444/Jz8+3d/OqLTIyknfeecfezWiSZDV4UZG5C8z7tADIuxl0GK8KG2pGcPUF3yjreY9ya4ZVNf6nPL0DjHwRgjvA7/erAc8AOj1EDwNHUwCVdgCK88HZrep7nToKnmFqzJIQolE7cuQI/fv3x8fHh5deeomOHTtiMBjYtWsXn332GeHh4Vx++eV2a5+maZSVleHoWHd/YouLi3F2lt9/50IyQKKirCoyQAB9plv3w7rZLnLqeY4BkFmX6+DBfXDTHzD2Xbj+J/CLUvdzD1TBVupe6/UZcZCwwfo4fj282xn+uL/6rymEaLDuuusuHB0d2bx5MxMmTKBdu3a0bNmScePGsWjRIsaOHWu5NjMzk1tvvZXAwEC8vLy4+OKL2bHDOq7wueeeo0uXLsydO5fIyEi8vb259tprycnJsVxjNBp5+eWXiYqKwtXVlc6dO/PTT9YirX///Tc6nY4lS5bQvXt3DAYDa9as4fDhw4wbN47g4GA8PDzo2bMnK1assDxvyJAhxMfH88ADD6DT6dCV+336888/06FDBwwGA5GRkbz55ps234PIyEheeOEFJk+ejJeXF7fffnu1vnenTp1i8uTJ+Pr64ubmxujRozl48KDlfHx8PGPHjsXX1xd3d3c6dOjA4sWLLc+9/vrrCQwMxNXVlVatWvHll19W9VL1ngRAoiJLF1hYxXPNukNEb9N+D9tzBk9wMmVpzjQAujJufhA1UI0zihmmjul0ENJJ7ZsHQpcUqCrSX462LpMRv0Ztd/4A+Rnn9rpCCCtNg+I8+3xpWrWamJ6ezrJly5g+fTru7u6VXlM+kLjmmmtITU1lyZIlbNmyhW7dujFs2DAyMqy/Kw4fPszChQv5448/+OOPP1i9ejWvvPKK5fzLL7/MnDlz+OSTT9izZw8PPPAAN9xwA6tXr7Z53ccee4xXXnmF2NhYOnXqRG5uLpdeeikrV65k27ZtjBo1irFjx5KQoLLdCxYsoFmzZsycOZOkpCSSktRi0lu2bGHChAlce+217Nq1i+eee46nn36ar776yub13njjDTp37sy2bdt4+umnq/X9mzJlCps3b+a3335j/fr1aJrGpZdeSkmJmo07ffp0ioqK+Oeff9i1axevvvoqHh6qyv/TTz/N3r17WbJkCbGxsXz88ccEBARU63XrI+kCE7ZsiiBWkgECGPcRbJ4Nve+0Pa7TqaxNxpFzywCdSWgnOLxSLa/R8xa1sGquaQmNpB3qdcwLrxpL1NIZvav3SUgIcZqSfLVUjT08cQKcKw9oyjt06BCaptGmTRub4wEBARQWFgLqj/irr77KmjVr2LRpE6mpqRgMBkAFDQsXLuSnn36yZE2MRiNfffUVnp6eANx4442sXLmSF198kaKiIl566SVWrFhB3759AWjZsiVr1qzh008/ZfDgwZY2zJw5kxEjRlge+/n50blzZ8vjF154gV9++YXffvuNu+++Gz8/PxwcHPD09CQkxJpBf+uttxg2bJglqGndujV79+7l9ddfZ8qUKZbrLr74Yh588MGzf29NDh48yG+//cbatWvp168fAPPmzSMiIoKFCxdyzTXXkJCQwFVXXUXHjh0t79UsISGBrl270qOH+vAbGRlZ7deujyQDJGzlp6slK8xFECsTEAOjXlZZm9P1vx/aXKoqP9eENpeq7e6fIWEjrHvfei7NtFxGxmHrse3zauZ1hRANyqZNm9i+fTsdOnSgqKgIgB07dpCbm4u/vz8eHh6Wr7i4OA4ftv7eiIyMtAQ/AKGhoaSmpgIq4MrPz2fEiBE295gzZ47NPQBLYGCWm5vLQw89RLt27fDx8cHDw4PY2FhLBqgqsbGx9O/f3+ZY//79OXjwIGVlZVW+3tnExsbi6OhI7969Lcf8/f1p06YNsbGxANx7773873//o3///jz77LPs3GmdhDJt2jS+//57unTpwiOPPMK6devO6fXrG8kACVvm7i9zEcRz1f0m9VVTInpB5+tgx7fw7QQozLSeSzf1W6eX+yWUtB1SYyGoXc21QYimwslNZWLs9drVEBMTg06nY//+/TbHzZkKV1dXy7Hc3FxCQ0P5+++/K9zHx8fH+tJOTjbndDodRqPRcg+ARYsWER5umxU3Z5XMTu+Se+ihh1i+fDlvvPEGMTExuLq6cvXVV1NcXFyNd3p2VXUBXohbb72VkSNHsmjRIpYtW8bLL7/Mm2++yT333MPo0aOJj49n8eLFLF++nGHDhjF9+nTeeOONGm9HXZAMkLBlGQBtpzR4ZS55AVx8rMFPUHu1TTuoVprPT1OPo0ypaPNyHUKIc6PTqW4oe3yVn1BxBv7+/owYMYIPPviAvLy8M17brVs3kpOTcXR0JCYmxuarumNX2rdvj8FgICEhocI9IiIizvjctWvXMmXKFK644go6duxISEgIR48etbnG2dnZJqsD0K5dO9auXVvhXq1bt8bBwaFa7a5Mu3btKC0tZePGjZZj6enp7N+/n/bt21uORUREcOedd7JgwQIefPBBZs2aZTkXGBjITTfdxDfffMM777zDZ5+dtkh2AyIBkLBVVQ0ge3IPgBEz1b6DQXW/gVox3pz9cQ9SdYoAdv1Y7QGVQoiG56OPPqK0tJQePXowf/58YmNj2b9/P9988w379u2zBAnDhw+nb9++jB8/nmXLlnH06FHWrVvHk08+yebNm6v1Wp6enjz00EM88MADfP311xw+fJitW7fy/vvv8/XXlaxlWE6rVq1YsGAB27dvZ8eOHVx33XWWzJJZZGQk//zzD8ePHyctTX2Ye/DBB1m5ciUvvPACBw4c4Ouvv+aDDz7goYceOo/vlm17xo0bx2233caaNWvYsWMHN9xwA+Hh4YwbNw6A+++/nz///JO4uDi2bt3KqlWraNdOZdSfeeYZfv31Vw4dOsSePXv4448/LOcaIukCE7bqYwAE0PVGNUDTNxIi+gA6KMqGRNMnGf9o6+yxnCQ1G8zd316tFULUoujoaLZt28ZLL73E448/zrFjxzAYDLRv356HHnqIu+5SVeZ1Oh2LFy/mySefZOrUqZw8eZKQkBAGDRpEcHDwWV7F6oUXXiAwMJCXX36ZI0eO4OPjQ7du3XjiiSfO+Ly33nqLm2++mX79+hEQEMCjjz5Kdna2zTUzZ87kjjvuIDo6mqKiIjRNo1u3bvzwww8888wzvPDCC4SGhjJz5kybAdDn68svv+S+++7jsssuo7i4mEGDBrF48WJLN2BZWRnTp0/n2LFjeHl5MWrUKN5++21AZasef/xxjh49iqurKwMHDuT777+/4DbZi07T5KPy6bKzs/H29iYrKwsvLy97N6dufXcd7F8Ew5+HAffbuzVVe6cTZMZDy6FwZBV0uQHGfwhvtoOcE3DryorT9IUQNgoLC4mLiyMqKgoXl7NUbheinjjTz+25/P2WLjBhFfuHCn4AWvQ/87X2FtBKbY+aagD5m6Zq+pkqU2fE1X2bhBBCNBgSAAklMxF+NVV57ncPRPS0b3vOxt8UABlV8S78TIUXzUtznDpa500SQgjRcEgAJJTf7lazrMK6wcXP2Ls1ZxcQY/vYXHjRL1JtT0kGSAghRNUkABKQlw5H/lb7V33eMBYUNWeAzMwBkK90gQkhhDg7CYAEHNuktgGtz30NL3vxL5cB8ggBg1qrxtoFJgGQENUlc2FEQ1JTP68SAAnrVPKI3me+rj7xCgMnUxXU8kGbeRB0TpJaOFUIUSVzvZyaqkwsRF3Iz88HKlbwPldSB0ioNbagYQVAOp0KfJJ32i686uoLBm8oylIDoWVJDCGq5OjoiJubGydPnsTJyQm9Xj4Ti/pL0zTy8/NJTU3Fx8fngqpigwRAorQYTmxV+8372Lct5yq4gwqAAttaj+l0aiB00g41DkgCICGqpNPpCA0NJS4ujvj4eHs3R4hq8fHxISQk5ILvIwFQU5e8E0oLVebEP+bs19cnQx5T9YC63Wh73DdKBUAyDkiIs3J2dqZVq1bSDSYaBCcnpwvO/JhJANTUlR//U83FCOsN30gY+GDF41IMUYhzotfrpRK0aHKkw7epa4gDoM9GZoIJIYQ4CwmAmjJNa5gDoM9GMkBCCCHOQgKgpiwzAXKTQe8I4d3s3Zqa4xuptpkJYCyza1OEEELUTxIANWVpB9Q2oDU4udq3LTXJKxz0TmqdsPRD9m6NEEKIekgCoKYsK1FtfZrbtx01Te9gndH22VBY/gyUFtm3TUIIIeoVCYCaskxTAOQdYd921Iax70BoZyjJg7Xvwpq37d0iIYQQ9YgEQE2ZJQPUCAOg5n3g9tUw/Hn1eN8i+7ZHCCFEvWL3AOjDDz8kMjISFxcXevfuzaZNm854/TvvvEObNm1wdXUlIiKCBx54gMLCwgu6Z5OVdUxtvZvZtx21RaeDLter/eSdkJNi3/YIIYSoN+waAM2fP58ZM2bw7LPPsnXrVjp37szIkSNJTU2t9Ppvv/2Wxx57jGeffZbY2Fhmz57N/PnzeeKJJ877nk2apQuskY0BKs8jEEK7qP3DK9XWaLRbc4QQQtQPdg2A3nrrLW677TamTp1K+/bt+eSTT3Bzc+OLL76o9Pp169bRv39/rrvuOiIjI7nkkkuYNGmSTYbnXO/ZZJWVQM4Jtd8Yu8DKazVCbQ8uh6Jc+HSQ+iortW+7hBBC2I3dAqDi4mK2bNnC8OHDrY3R6xk+fDjr16+v9Dn9+vVjy5YtloDnyJEjLF68mEsvvfS87wlQVFREdna2zVejl5MEmhEcnME9yN6tqV0xpp+Hw3/BqhchZZdaKyxpu12bJYQQwn7sFgClpaVRVlZGcHCwzfHg4GCSk5Mrfc51113HzJkzGTBgAE5OTkRHRzNkyBBLF9j53BPg5Zdfxtvb2/IVEdHIMyJg7f7yCge93YeC1a7wHuDiDYWZsOEj6/G41XZrkhBCCPtqUH/5/v77b1566SU++ugjtm7dyoIFC1i0aBEvvPDCBd338ccfJysry/KVmJhYQy2uxxrzDLDTOThCy6HWxy7eahv3j33aI4QQwu7sFgAFBATg4OBASortzJyUlBRCQkIqfc7TTz/NjTfeyK233krHjh254ooreOmll3j55ZcxGo3ndU8Ag8GAl5eXzVejtPNHmDMeclOtAVBjrAFUGXM3mLMnXPOV2k/YIAUShRCiibJbAOTs7Ez37t1ZuXKl5ZjRaGTlypX07du30ufk5+ejP627xsHBAQBN087rnk3KunfhyCrYOqdxF0GsTKcJ0PdumDhHZYPcg6C0EI5ttnfLhBBC2IGjPV98xowZ3HTTTfTo0YNevXrxzjvvkJeXx9SpUwGYPHky4eHhvPzyywCMHTuWt956i65du9K7d28OHTrE008/zdixYy2B0Nnu2WRpGqQfUfsHl4Gzu9pvCl1gAI4GGPmi9XHUQNj9s+oGi+xvv3YJIYSwC7sGQBMnTuTkyZM888wzJCcn06VLF5YuXWoZxJyQkGCT8XnqqafQ6XQ89dRTHD9+nMDAQMaOHcuLL75Y7Xs2WbkpalkIgGP/gXug2m+sRRDPJmqQNQAa+ri9WyOEEKKO6TRN0+zdiPomOzsbb29vsrKyGs94oKNr4atLKx6/Zyv4R9d9e+wt4wi811WtGv9YvDUjJoQQosE6l7/fDWoWmLgAGYcrP95UM0C+UWr8k7EE9i22d2uEEELUMQmAmor0Q2ob3NF6zCNYjY1pinQ66HqD2l/2FBRm2bc9Qggh6pQEQE1FuikD1Plaax2cpjIDrCr97wf/GMhNhhXP27s1Qggh6pAEQE1FhmkGWGAbiB6m9pvKDLCqOLnAZe+o/c2zIWGjXZsjhBCi7kgA1BQYjdYAyD8a+twFPi2g4zX2bVd9EDUQulyv9ssvkyGEEKJRkwCoKcg5oYr+6R3BuzlE9IT7d0LbMfZuWf3Q42a1PfwXlBbbty1CCCHqhARATYF5/I9vpFoXS9gK66YqQxdlQ8I6e7dGCCFEHZAAqLEylsGqlyDuX+sMML8mWO+nOvR6aH2J2t+/1L5tEUIIUSckAGqs4v6B1a/CtxPV+l/QNAseVlfr0Wp7YIlaNkQIIUSjJgFQY5WTrLYleRD7u9r3a2m/9tR3LYeAgwFOHYWT++3dGiGEELVMAqDGKj+94jHJAFXN4KFmhIHKAgkhhGjUJABqrAoy1NY9yHpMxgCdWetRarvmHfjzSevgcSGEEI2OTAlqrMwZoB43m/Y18Glu1ybVexddBZtmQdp+WP8BbJsL0/8Dz2B7t0wIIUQNkwxQY2UOgNwDYMwbMOZNtf6VqJqbH0xbB5O+V9mywizY8Z29WyWEEKIWSADUWOWfUls3f/u2o6FxcIQ2o6H/ferxtrkyK0wIIRohCYAaK3MGyM3Pvu1oqDpcAU5uqoZSoqwRJoQQjY0EQI2VJQCSDNB5cfFSQRDA1rn2bYsQQogaJwFQY6Rp1llgEgCdv643qu2eX6Aox75tEUIIUaMkAGqMirLBWKr2XaUL7Lw17wP+MaqY5M+3QXGevVskhBCihkgA1BiZu7+c3MHJxb5tach0Ohj1iqoQfWAJfHkpJO2UQdFCCNEISADUGOVL91eNaTUCbvpdfS+TtsOnA+GdTrDlK3u3TAghxAWQAKgxsgRAvvZtR2PRvDfcugLajQVHV8hKUJWijUZ7t0wIIcR5kgCoMZIZYDXPryVM/AYeOQyOLlCcC6fi7N0qIYQQ50kCoMZIAqDa4+wOQe3UfvIu+7ZFCCHEeZMAqDEyB0AyA6x2hHRUWwmAhBCiwZIAqDGSGkC1K6ST2qbstm87hBBCnDcJgBojWQajdgVfpLaSARJCiAZLAqDGyDILTAKgWhHcQW2zj1u/10IIIRoUCYAaI6kDVLtcvMA3Su1LFkgIIRokCYAaI5kFVvtOHwgtNYGEEKJBkQCosdE0mQVWF8oHQH+/Ai+GwP+C4Y02sO0b+7ZNCCHEWTnauwGihhVmgVam9mUMUO0xB0B7F0JpofV4bjKseRu63mCXZgkhhKgeyQA1NuYp8E7u4ORq37Y0ZuYAyBz8DHsWpv8HekdIPwQZUiVaCCHqMwmAGhuZAVY3vMLBPUjt97sHBs6AwNYQ0VsdO7TCfm0TQghxVhIANTZSA6hu6HQwYQ6M+xCGz7QejxmmtodW2qddTVVJAXx1Gax+zd4tEUI0EBIANTYyBb7utOirxvroy/03ihmhtnH/QGmRfdrVFB37D47+C+s+UBMBhBDiLCQAamxkBph9hXQEj2AoyYOE9XBsM+z4Xv4o17bsE2pblGX9PyCEEGcgs8Aam/w0tZUuMPvQ6SBmOGyfB4sfhrSDgKbGDEUNtHfrGi9zAASQfhjcA+zXFiFEgyAZoMYmM1FtvZvZtx1NWcxwtU07AJgyP4eW2605TUL5ACjjsP3aIYRoMCQAamxOmaZfm5dqEHUv+mJw8QFXX+hiqgd0+C+1LTgFP9wEu3468z2Sd8k6Y+ciJ8m6ny4BkBDi7KQLrLHJOKK2fi3t246mzNUH7t0GjgYozoPt36iAJjcVNn+hiiceXA5Rg8Ej0Pa5RiOsfB7WvgPNesGtkjmqluzj1v30Q/ZrhxCiwZAMUGOSn6EyDAB+kgGyKzc/cHYHjyBr0cRDK2HL12q/JA/+fdP2OSWF8PPNKvgBOLYJ0uSPebVkl8sASReYEKIaJABqTMzdXx4h6o+vqB+iL1bbVS9CzglwdFGPN8+GzATrdes/gD2/gN4JfFqoY3sW1G1bG6KyEshNsT5OPyKz7oQQZyUBUGNiXn5Bur/qF3MAlGUaoN7rdogaBGXFaiFVs9jf1Hb0qzD4UbW/55e6a2dNO3UU5l4J+5fU7uvkpgCaWoZEp1fZtfIBkRBCVEICoMZEAqD6KaIPOJZbl637FBj2nNrf8R1kHVddOEk7AB20uxzaXqoyQal7IXWfHRpdA5Y+AYdXworna/d1zDPAPMPAp7nal4HQQoizkACoITOWwTdXw/wbVMrfMgBaxv/UK04u0KKf2m85BPyjoVl3aNEfNCNs/xYOLlPnw7urgdGuvtZlNcp3gxXlqmrHWcepNZoGp+IvrBvp2BbYv0jtn4yFk/srv64wG3YvsO0KPFfmAMgrDPyi1b4MhBZCnIUEQA1ZZryqLxP7u8oUyAyw+mvA/RDaBYY9Yz3W9Ua13TYXDixV+61HWs93uFJtdy+wBiNLHoVlT8KSR2qvrdvmwrudYMNH5/a8fYtg1csqIPlrpu25vb+qbewfsHC6eh+/3AlvtoGfpsKHfWDLV+cXdFkCoFAVXIIMhBZCnJVMg2/IymcBDi6XDFB9FjUI7lhte6z9OBXIZMZbMyDlA6A2o1XXWfpB+O9zCO+mptSDyhgVZoOLV8231VyzaOOn0OcuVd36bMpK4Ofb1PibNW+p8U16Jxj4IKx+BfYshIuuUsFOWbHtc118oDATfr8P4tfDlZ+eW3tzzAFQuHSBCSGqTTJADVn52id7F0JeqtqXIogNg7MbdLza9EADz1AI6WQ97+IFw55W+0sfh1+mWc+VFdfe4OLUWLXNjIfEjdV7zsl9Kvgxtw3UWKfed6jByal7VAHIsmII7wEDZkC/e2HqUnjkCFzyohrAvPN7NXj6dPkZVS8uaxkDFGrtAjN/GBBCiCpIANSQZR2z7p/YprZu/qoQn2gYzN1gAK0uqZht6XMXtL0MjCWQth+c3KzPqY0ZYqVFtuNndnxfveeZf/4iB8INC2DokzD8WVUPKWqwOpeyCxwMcOVn6twlL0CLvqB3gH53Q0Rvdd2Rv23vnX5YdZUtuL3y1zbXAPIKA39T92/GEVVUUgghqiABUENWfv0jMxn/07CEdVVjg0DN/jqdTgfjPrTWBRo4A/pOV/uHV0JBZs22J/0QGEutj/f8UnnmpaQQNn8JeaaV149vVdvwbmrw9uBHwOCpjnUYb33ewAet43RO13KI2h5eZXs8fp3KHMX+Xvn7NWdCvcLAu7nqeisthKP/nuGNCiGaOgmAGjLzL36Dt/WYBEANi04Hk76D63+GVsMrv8bVB6YuhvGfqK6joHYQ2LZ2usHM3V/hPdS08sJM6wy18la/Cn/cD0tN9YrMGaCwrhWvbXsZuAep7r0B91f92i2Hqm3catvsjTkjpZXBkdOCI02zrgPmFQYOjtD5WvX4lzutAZoQQpxGAqCGzDwIutME6zEJgBoer7Cqgx8z72bQZZLqLgLocIXabp1TMUOTdVyVRji4onqvX5itvsAaAAV3sI5PWvuu7cKspcVqphjAvsUqK5OyRz0O61bx/m5+8MBuuHWlWh+tKuHdwNlTLeeSvMN6vHyX3MHT1kbLT7eOOfIIUdtRr4B/KzU4+te7pCq0EKJSEgA1ZNmmMUCdr1VjK0ACoKai4zVqcHHCOvhytG0dnaWPqu6i3+9VwUp5eWmw/kO1SCtAcT581Bc+Hai6tcwBUFB76HaTWrbj2H/wyUBI2KDO7fsD8k6q/ZI8WPe+GqPk6medhXU6RwM4Op/5PTk4QdRAtV9+HFD5GV0Hl6nsUHE+HN9izYK6B1nvb/CAa75U/ycOLLUGa0IIUY4EQA1Vcb514VP/GOh6g/oDFDnAvu0SdcM/Gq79Tk0hP74FPh2sVpw/ulYFP6CCg90/2z5v6WPw5xPwz+vqccI6FUifOgoH/1RFC0F1swXEwC3L1Myq7GPw9Vg1RX7Ll+oag2kKvrleUFjX6k2ZPxPzOCBzAGQss87o0jmowCtxA3w5CmZdDD9OUee8Qm3vE9IRLn5K7f/1P1VAUgghypEAqKEyf/J19gAXbxjzJjwap7pTRNPQ+hK44x81tqYgA+aMg0UPqnMewWq79l1rF1BxvuqyAuvYofIDjjd/aV1OJaid2oZ2VvWL2oxRXU3fXQdx/6gp65e9ra4pyVfb8Eq6v86VeRxQ/HooKVAzHcuKwMHZWiNp/g2mZUOwBkde4RXv1ftOVRIiN0VlqU6nabBpFnw2VAWPQogmRQKghqr8zBed7sI/eYuGybcF3PS7mkmWn64yOM6eMGWx2p6MtQ5iPrjMWqvn5D6V9TEXPQTTAGNNlVJwD7QeN3iqLqXoYVBaoI7FjFCVqj3LZV4qGwB9rgJaqcHXZUWqy808/sevpSoMCep96vQw9l1o1lMdC+5Q8V6OzjD8ObW/7j3rdHlQwdXCabD4ITixVY2lEkI0KRIANVTmAdCVffIVTYurD9z4CwR3VI8HP6y6r3pMVY//fVONmym/phioP/qpewGdtYAgqPE/pwfUjgaYOBeam9Y063sX6PXQbqz1msoGQJ8rnQ6iTVmgI6us43/8Y1TQZXbxU6rQ4s1/wm2rYPCjld+v/Tho1ktlqVa/oo5pGnx/vVqI1sw8vkkI0WRIANRQmTNA3hIACdRMq1uXw61/qQrLAH2mqUHMiRvh75fggCkTdNFVarv+Q7UN7Qw9b7Hey9z9dTpnd5Vtun+3dayOeTaad/OK43DOV/lxQOYMkH+0uv+lb6gii/0fUMf1DqrrzcGp8nvpdDDCtC7ZtnmqdtbRf1UNJQcDXPGZOpeyG4pyaqb9QogGoV4EQB9++CGRkZG4uLjQu3dvNm3aVOW1Q4YMQafTVfgaM2aM5ZopU6ZUOD9q1Ki6eCt1x1wF2quZfdsh6g8nV7XKvDl74xUGo19T+/+8rrqvfKNgkGkh1dJCtY2+GC66WnUrgaoxVBUHR/CJsD5u0Q8mzIFrv6m592EOgJJ2Wpfi8I9R2163qSKL+nP41dWiL7Tor2aqrfvAOgC822ToPFEFb5oRjm0+832K8+CHydWvji2EqNfsHgDNnz+fGTNm8Oyzz7J161Y6d+7MyJEjSU1NrfT6BQsWkJSUZPnavXs3Dg4OXHPNNTbXjRo1yua67777rtL7NViSARLV0W0ydCxXJ+qiKyGwjbWyNKgAyDMYOl2rMkbmLqjqaj9OZZFqikcQBHUANEjaro6ZA6DzNWCG2m76TA3i1jtC//vUseamJTjO1g22f4la1X7p41BWeuZrhRD1nt0DoLfeeovbbruNqVOn0r59ez755BPc3Nz44osvKr3ez8+PkJAQy9fy5ctxc3OrEAAZDAab63x9fevi7dQdGQMkqkOnU7O1AtuqmVSdrlXHWpsyok5uENFL7Y/7AB6Nrx+1pMxZILMLDYBihqnZcsYS9bjzJGsmy7wGWeJZAqCT+9W2IAMS1l9Ye4QQdmfXAKi4uJgtW7YwfLi1Cq5er2f48OGsX1+9XzCzZ8/m2muvxd3d3eb433//TVBQEG3atGHatGmkp1ddEr+oqIjs7Gybr3rPvA6Yt3SBibMweMBtf8G92yCwtTrWaaKqq9N+vLU6s94BnFzs1kwb5bNQBi/bWWnnQ6dT66iB6uoz7wM076O2xzafObOTtt+6v++PC2uPEMLuHO354mlpaZSVlREcHGxzPDg4mH379p31+Zs2bWL37t3Mnj3b5vioUaO48soriYqK4vDhwzzxxBOMHj2a9evX4+DgUOE+L7/8Ms8///yFvZm6VJQDRVlqX+r+iOpwdldfZs26w4xYNYOsPmrRTy1qaixRGamaKPPQbhwMeRy8I2yzXEHtVZBVlA2pe9TgaCcX8I20ff7JA9b9fYvUkhtSfkKIBsuuAdCFmj17Nh07dqRXr142x6+99lrLfseOHenUqRPR0dH8/fffDBs2rMJ9Hn/8cWbMsH4izM7OJiIiosJ19Ya5+8vgbV1xW4hz5Rl89mvsxdlddU3Fr7nw7i8zvR6GPFbJcQdVT+jwSvjpZjXzzNEVJnxtLb5YVmqdkaZzgKxEVYwxrEvNtE0IUefs2gUWEBCAg4MDKSkpNsdTUlIICQk543Pz8vL4/vvvueWWW854HUDLli0JCAjg0KFDlZ43GAx4eXnZfNVr5jXAZAC0aMy6TFLbmLMsFFsTzN1g5iCntAC+mwQ75qvHmfEqG+Xoai3IKN1gQjRodg2AnJ2d6d69OytXrrQcMxqNrFy5kr59+57xuT/++CNFRUXccMMNZ32dY8eOkZ6eTmhoDdUpsTfzytv1YbCqELWl6w3wSJxa7Le2dbxGdXm1GqmWF+k0EbQy+OUONR3fPAA6IAbaXa72Y/+QleaFaMDsPgtsxowZzJo1i6+//prY2FimTZtGXl4eU6eqKraTJ0/m8ccfr/C82bNnM378ePz9/W2O5+bm8vDDD7NhwwaOHj3KypUrGTduHDExMYwcObJO3lOtO7pGbVv0s287hKhtbn51M87GLwru2wHX/6Cm9I//xFR5WoO9C60DoAPaqDXYHJzVMiPmYpLVUVIAq1+zrl8mhLAru48BmjhxIidPnuSZZ54hOTmZLl26sHTpUsvA6ISEBPSnFT3bv38/a9asYdmyZRXu5+DgwM6dO/n666/JzMwkLCyMSy65hBdeeAGDwVAn76lWlZWqhSJBVn4Xorbo9SordGg5HPhTTaEHVUPJ1VdVl176GCx/Wh1rNeLM9wP473NY9aJaeHXi3NptvxB2pGkaugYwQUCnaZLDPV12djbe3t5kZWXVv/FAx7fArIvVCvCPxKkBnEKImpefAa9HqyrRXs3U2LtrvoYO41XX12/3wLa5agbZtLXg0/zM95t/I8T+popQ3r+zTt6CEHXto78P8cnfh5k9pSc9I/3q/PXP5e+33bvAxDkyd3817yfBjxC1yc3PWiTRPPEgsI3a6nQw5i21AGxRNmyvRqX541vVNjMeChtArTEhTNJyi0hIzz/rdYt3JfHa0v1kF5by74GTddCyCyMBUENzdK3aSveXELWvdblxgzoH8Iu2PnZ0ti4iG/v7me+Tk2wNosA6kUGIBmDCJ+u55J3VZOWXVHlNbFI2D/6ww/L4ZG5xXTTtgkgA1JCUlVpL8EsAJETtaz3auu8XpYKe08/rHCBlF2TEVX0fc/bHLGV3zbVRiFpUVFrGkbQ8CkuMHE3Pq/K6x37eSUFJGW7OqmciLbeorpp43iQAakiSd6p0u8EbQjrauzVCNH7lF44NaFPxvLu/dTbmmeoCnZAASDRMGXnWTE5KdmGl15QZNfacUN269w1rBUC6BECiRlmmv/eV8T9C1AWdTq12DxDerfJrLHWBztANdnyL2kYOVNtkCYBEw5BerisrNafyoCY1p5BSo4aDXkeXCB8A0hpAF5jdp8GLc2AeN9Csp33bIURTMvRJCO9uOx6ovLZjYMnDkLhJjfXxNFWx1zQwlqkPK+YusG6T4ei/kLrXek6Ieiw97+wB0PFTBQCEeLkQ7KUWVK6sC+ydFQeYvSYOTQMdcOvAltw3vFXNN7qaJAPUkOSlqq0sgCpE3XFyUVPfnVwrP+8dDuE9AM22G2zO5fBuJ3WsMFMtstpurFpOoyQfTh2t/bYLcYHKd2WdzKm8C+x4pgqAmvm64u+hxsnlF5eRX1xqc903GxLIKSwlt6iUnKJSikrLaqnV1SMZoIYk1zSt0D3Ivu0QQthqeykc3wxH/oaet0L2CYj7R52bf6PahnZSQVRQOzUmKHknbJ8Hqftg3Adq2r0Q9YztGKDKM0DHTBmgcF9XPAyOGBz1FJUaSc8txs1PhRlZ+SWWrNCS+wZicNTj7epUy60/MwmAGhJzBsgj0L7tEELYam4aCJ24SXV9Hfuv3ElTrdkw0xii4A4qAFr2DGQlqGO/lMKk71UFaiHqkTSbMUCVZ4DMAVAzH1d0Oh0BHgaOZxZwMreICD83AA6n5QKqm6xdaP0oMCz/2xoKoxHy0tS+ZICEqF/CuoLeCXJTVKFDcwDU4UoI7aL2oy9WW/MMTnPwo3eEg3/C2nfqssVCVEv5LrDUKjJA1i4wFewEmLrByg+gPpyqAqDoIPdaaef5kACooSjIUKtTA7gH2LctQghbTi4Q1kXtJ26CRFMA1GoE3LIM7lxrHUQdfJH1eX3vhjFvqv2/XoD4dXXWZCGqo/wg6LTcIsqMFVfPOnZKVYkO91Xj5AI8DJbrzQ6dVAFQTKBHrbX1XEkA1FDkmrq/XP3Awb79pkKISpiXzTj6LyRtV/vNeoGjAUIusq5q36wnxAyH7lPUoqrdboJO16o1x365E4py7NF6ISpVPgAyapCeZ5sF0jSNE6YMULjPaQFQuVljh1NVEcXoIAmAxLmyjP+R7i8h6qWIXmq762coLVSrxvtHV7zO0Rlu+BnGvqumwet0cOnr4N1cdZ8te7pu2y3EGZxe0PD0brD0vGIKS4zodBDqo6bAB3g6W86ZHTFlgKIlAyTOmWUGmAyAFqJeamYKgEpMywU062nN+pyNixeM/1Dtb/kSDq6o+fYJcR7M43i8XNScqdMHQpsHQAd5GjA4qrpW/u4qA3TSFDwVlxqJz1DdZBIAiXMnGSAh6jevUPBpbn18rgVLowZB7zvV/tLHVKFEIewov7iUghL1c9jWNHPr9AyQuQiiufsLIMDTtgssPj2PMqOGh8GRYC9Drbe7uiQAaijMY4BkBpgQ9Zd5HBCcX8X2oU+CizekH4TY32quXUKcB3P2x+Cop2WAmr2VmlNEaZmRhduOcyqvmOOZKrNjngEG5WaBmbrADlu6v9zRVTcrWgckAGoo8sxdYDIDTIh6yxIA6apeO+xMXLysWaB/31Q1hYSwE3MA4+/uTJBpiYuU7EK+25TA/fO3c89322yKIJoFnjYL7FBq/Rv/AxIANRy50gUmRL0XfbFa8iJygMrknI/ed4KTOyTvgoPLa7Z9QpwD8wBofw8DQaZurdScIpbHqr9Haw6lsdK0X74LzN8UAGXml1BSZuTwyfo3AwwkAGo48qQLTIh6zz8a7v4PJn5z/vdw84OeN6v9f9+wPVeUo5bbWPc+pMae/2sIUQ3mLjB/D2dLAJSYkc/GI+mWa8qvA2bm4+qEg151dWXkFZfrApMASJwP8ywwWQZDiPrNtwW4+lzYPfrerSpLJ26E5N3q2L5F8Fo0zBkHy56CH26SLjJRq6xdYAZLF9i+5ByKSo14uTjaTHIsHwDp9Tr83dU4oJM5RZYq0DH1qAo0SADUMGhauTFAkgESotHzDFELrAJsm6tmhP35BJQVgVczcHCGtP3WgounMxph9WtwaGWdNVk0PtYuMOcKs7dGXRTCpReFWh6HlesCU89R16/al0pecRlODjqa+0kAJM5VwSkwlqh9qQMkRNPQ1bSK/M75sOcXOHVUFVe8+z9oO8Z07sfKn3v0X1j1Ivx8K5SV1klzReNTfhB0gIfBJuMzqHUgdw2NxlGvo2WgO27Otmurm2eCffbPEQDGdAzF2bF+hRz1qzWicuZFUA3eas0hIUTjF30xeIapD0B/PKCO9bgFnN2g4wT1ePdPldcLStmjtgUZcGxT3bRXNAonMgt4+Mcd7EvOtszi8vcw4OSgx89NBTV6HQyICaBDmDe/3zOAOTf3qnAf80ywnCIVgN86sGUdvYPqkwCoIbAUQZTsjxBNht4Bulyn9ouyVbdXr9vV45jhKhuUmwJxqys+92S5AdL7FtV+W0WDsPNYJtd+tp7tiZlVXvPi4lh+3HKMJ3/ZTUaedRA0QKBpIHSnZj74mIKhdqFeNjWAzMzFEAH6tvTnovDznBVZiyQAagikCKIQTZM5AAKV9fEMVvuOztDhCrX/71uwcDr8OAUKs9Wx1H3W5+1fLIOlBQDfbkxgw5EMnvxlF1olPxOJGfks2ZUEwJb4UxxIUQvzmgc0h3irHohBrc/+Ydz8HIDbBkVdcNtrgwRADUGezAAToknyj4b248DZE/rfa3uu00S1PfovbP9GjRPa/ZMKdk6WC4AyjkDawbprs6g3MvKKKSi2dpEeTVf1ePacyOavfakVrp+9Jg5jubiopEw9MA9ovnNwNJd3DmNKv8izvrY5K9Qy0J0hrevnh3fHs18i7E4yQEI0XVd/CWXF4GQ7y4aI3nDR1WrZDL0THN8MCRug1UjVZaZzgBb9VIC0fzEEtrZP+0WdKi0zMmd9PL/vPMG2hEyiA91Z/sBg9HodR9PyLde9t/IgF7cNsixNkZlfzA+bEwF4akw7/rfI2o1qzub0aelPn5b+1WrHJR2CeWx0W4a3C0avrz/LX5QnAVBDYCmCKBkgIZocvQPoXSse1+ng6tlq//BfMPcKSFhvHf/jHw0dxqsAaMf3UFqoAqmBD6mB1KJRWhGbwsw/9loeHz6Zx/HMAgI8DCRnq5XcnR317DiWxeoDJxnSRn2wnrcxgfziMtqGeHLLgChWHzjJvwfT8DA44uLkcM7tcHLQc+fg6Jp5U7VEusAaAimCKIQ4k2Y9QaeHzAQ4vEodC2wLrUer/ZOx8PfLan2xzV/Yr52i1u1LVuN2BrUOpGWgqruzPzmHhAyV/fFyceTGPi0AmPXvEcvzFu1UY39uHhCFTqfjNtOsrciAxhssn1cAlJiYyLFjxyyPN23axP33389nn31WYw0T5cgyGEKIMzF4QkhHtb/9W7UNagfe4TDoEWjeD5r3VcdlVlijsudEliV4ATiapsb59GnpR0fTzKsDqTmW8T+RAe5M6tUcgM1HT1FSZqSguIz9pgHPA1upBbcHtQ5k7i29eH/SeSzq20CcVwB03XXXsWqV+pSRnJzMiBEj2LRpE08++SQzZ86s0QY2Oamx8G4X2DrHeizruNp6BNulSUKIBsAc4BRkqG1gW7W9+Em4eQlcafqAmrgB8tIrPl80SPd/v53p325lz4ksAOLSVaYnyt+d1sGeABxIzrEERpH+7rQMcMfb1YmiUiOxSdnsPpFFmVEjyNNAiJe11tzAVoFEBdSv6s016bwCoN27d9Orlyp89MMPP3DRRRexbt065s2bx1dffVWT7Wt6Di6HU3GwyfTLKjMRcpPVgMbg9vZtmxCi/mrex/axOQAy82muskSaEQ4srbt2iVqVnKXG9ew6loWmacSZFh6NDLAGQPtTcjlqCowi/d3Q63V0ifABYGv8KXaY6gJ1jvCxDIpuCs4rACopKcFgUNPiVqxYweWXXw5A27ZtSUpKOtNTxdnkpqht8m5VATZhg3oc2gmcG28kLoS4QBHlAiC9I/jHVLymjWkJjf2L66ZNolaVGTVLpeV9yTmcyi8hu1A9jvR3p40pADqcmmtZkT3SlNHp1twXgG2JmZbCiOagqKk4rwCoQ4cOfPLJJ/z7778sX76cUaNGAXDixAn8/as3RU5UISfZtKNBwkaVrgbbX25CCHE6r1DwjVT7ftGqWOLpzAusHv4LSgrqrGmiduQWWtd5i03KJs7UzRXq7YKrswPNfF1xdXKguMzItoRTALTwNwVALXwA2Jpwih3HMgEJgKrl1Vdf5dNPP2XIkCFMmjSJzp07A/Dbb79ZusbEeTJngADi16ogCCqmt4UQ4nTmcUBBbSs/H9JJrSZfkg9HKllCozpyU2HDJ1CQeX7PFzUmu7DEsr/vtHE+AHq9jlbBHoC1qGGkv5rVpbq7IDGjgMQMFQx3bFb/lquoTedVB2jIkCGkpaWRnZ2Nr6+v5fjtt9+Om1vjnTJXJywZIODAn6rIGUgAJIQ4u563QuIm6Dq58vM6HbQZDf/Ngr2/QhuVvWfbN5B2AIY/D2cbA/LvW7DxYyjKgcEP12z7xTnJKiix2V9/RA1ujyw3cLl1sCc7j6kB0p4ujviZihp6uTjRKsiDAymqayw60B0vF6e6anq9cF4ZoIKCAoqKiizBT3x8PO+88w779+8nKEimal+Q8hmgtP1qwKJPC/AMsV+bhBANQ7MecO9WaDW86ms6Xq22e35R4wwzE+C3e2Htu3Dsv7O/RsputS2/4Kqwi/IZIIDle9Xfj6hytXtamzJAoDJD5Qc5m8cBgcoINTXnFQCNGzeOOXPUNO3MzEx69+7Nm2++yfjx4/n4449rtIFNSnG+KmEP4BZgPS7ZHyFETYnoDcEXQWkBbP9OdWdppvWiUk0VhHNPwrxr1CKrsX/Yjhc6uV9tM44g7Cu7oNTmsTkjFBVgDXrMM8HANjMEtgFQVwmAqmfr1q0MHDgQgJ9++ong4GDi4+OZM2cO7733Xo02sEnJNXV/ObpCTLlPcBG97dMeIUTjo9NBz1vU/qZPYevX1nPmVeR3fg8Hl6lFVudfD58PV4usFpyyFmaVAMjuTs8AmZXPALUJKRcA+dsOUena3MeyLxmgasrPz8fTU31Tly1bxpVXXoler6dPnz7Ex8fXaAOblBxT95dnMET2tx43D2wUQoia0HGCWmH+1FEozgVM3SLmDNCJ7Wob3l1NqU/ZDVmJcPKA9R6FWZCfUYeNFqfLNmV8vF2tY3f0OojwswY6IV4ueLqo4b7mGWBm0YEe9Iz0pWO4N+1CveqgxfXLeQVAMTExLFy4kMTERP78808uueQSAFJTU/HyanrfxBpjzgB5hEDUYLXCs2dYxYJmQghxIQwe0Pla6+Oet6rtSVMGKGm72g55QnWXARzfqgZKl5cRV6vNFGdmDoB6RvpZjoX5uGJwtC5eqtPpGBATgKNeR/cWvjbP1+t1/HhnP36/ZwBODk1vadDzesfPPPMMDz30EJGRkfTq1Yu+fVWGYtmyZXTt2rVGG9iklM8A+baAm/+Em34HfdP7wRRC1LJet4GDs5pkMfQJdSw3RWWF0g+px2FdINy0FtTxLWpiRnnSDWZX5qKHrYM9LFmgypaueH9SVzY+MaxRL2txPs5rGvzVV1/NgAEDSEpKstQAAhg2bBhXXHFFjTWuyTHPAPMwzfhq1t1+bRFCNG6BbeDOteDiDW5+4N0cshJg54/qvFczcA+AsG7AF3BiGziZulYcnKGsWAIgOyvfBdY2xJONcRmVBjmODnr8PQx13bx677wCIICQkBBCQkIsq8I3a9ZMiiBeqNxyGSAhhKhtga2t+0HtVAC04zv1ONT04Tbc9EHsxDYVKAFEDlDVpCUAsivzIGgvVycGxASwMS6DHuW6w8SZnVffitFoZObMmXh7e9OiRQtatGiBj48PL7zwAkajsabb2HTklBsDJIQQdclcPTrjsNqGdVHbwDbg5K4GS2cmqGOtR5uulQDInszT4L1cnJg+NIY1jw7l8s5hdm5Vw3FeGaAnn3yS2bNn88orr9C/v5qttGbNGp577jkKCwt58cUXa7SRTYZkgIQQ9hLU3vZxaBe11TuoYCh+rXrs4g0Rpmz/mQIgTTt7VWlxQawZIEf0eh3NfGUlhnNxXgHQ119/zeeff25ZBR6gU6dOhIeHc9ddd0kAdL4kAySEsJfTZ5uGWsd3EtbVGgAFtAG/KLWfn6amw7uctobU3t/gp6kw7iPoPLH22tzEVTYNXlTfeXWBZWRk0LZtxanZbdu2JSND6kKcl7IS9csEZNkLIUTdC2yDpR6QZ6htJto8EwzUuCEXb2u1+rSD8P31MP8GKCtVmZ9VL4GxFDZ9VmfNb4rMs8Ca2hpeNeW8AqDOnTvzwQcfVDj+wQcf0KlTpwtuVJOUa6quqncEVxnEJoSoY06u1syOufvLLLzcjNQA08Bpv5Zqu+RR2PcHxP6uBlDHr7WuE3Z8i7W8h6hRpWVGcotMAZBkgM7LeXWBvfbaa4wZM4YVK1ZYagCtX7+exMREFi9eXKMNbDIsRRCDpe6PEMI+gtqrcT1hp9Vz82kBbv6Qn666wEAFQMc2wfHN1uv+fhlCOpZ7ogYH/4RulaxOn30Clj4Gfe+2jikS1ZZTaF0HzFzpWZyb8/pLO3jwYA4cOMAVV1xBZmYmmZmZXHnllezZs4e5c+fWdBubBvOnJA8ZAC2EsJOhT6qq0L1vtz2u08HIl6DrDRB9sTpmzgABRA1SdYOyj8OBpepY+/Fqu3+p6hZb9jT8fj+YZwpv+Bj2/goLp4GxrDbfVaNkHgDt5uzQJKs414TzDhvDwsIqDHbesWMHs2fP5rPPpN/3nJkzQDL+RwhhL8HtYcyblZ/rfK3t8hn+0Wrr6AJj34X49fDrXepYiwEw6CHYu1DVC1r/IawzLZTd5XqI6AlH16jH6Ydgzy/Q8epaeUuNVfkp8OL8SNhYX0gGSAjRkLS5FDpNhCs/U9mgztdCUAd1rs+dag0xr2ZQWgDLnrQ+79AKKMy2rjcG8M/r1syQqJbyU+DF+ZEAqL6QDJAQoiFxdlPBT/tx6rHeAW78BW5cCO3Gqm6zNqOt17v4qO2hFZCwATSjCpBcvNUirLG/1fU7aHCKS418sSaOxIx8yxR4yQCdPwmA6ouccoOghRCiIfIMhuih1sftxqqtqy/csEDtH98Csb+q/ZiLofc0tb/2nTprZkM1698jzPxjLy8tjrVZBkOcn3PKnV155ZVnPJ+ZmXkhbWnastSaanhH2LcdQghRU1oOhqu/VLPLgtqqbepe2G5abyxyIEQNhtWvwIntqmvMxcuuTa6vNE1j4bbjAOw8lkW35r4AeMkMsPN2Tt85b2/vs56fPLmS6Y7i7LIS1da7mX3bIYQQNemich+cY4apAEgzzfqKHKCyRuaV6JO2qxllooJ9yTkcTM0F4HhmAQkZ+YBUgb4Q5xQAffnll7XVjqatMFuVkwcJgIQQjVfMcFj3vtr3iwYv08Kd4V1VAHR8iwRAVfhtxwmbxxvj0gHpArsQ9WIM0IcffkhkZCQuLi707t2bTZs2VXntkCFD0Ol0Fb7GjBljuUbTNJ555hlCQ0NxdXVl+PDhHDx4sC7eyvkxd3+5+oLBw75tEUKI2tK8LziZFuyMHGA9bq40fXyr2p7cDyueg4JTddq8+krTNH7brgIgT4PKWxxIUdkgGQR9/uweAM2fP58ZM2bw7LPPsnXrVjp37szIkSNJTU2t9PoFCxaQlJRk+dq9ezcODg5cc801lmtee+013nvvPT755BM2btyIu7s7I0eOpLCwsK7e1rmxjP+R7I+oOUajxh1zN/PCH3vt3RQhFEeDdWZYm0utx8NMa42d2Ka2ix6ENW/Dn08iYGvCKY5nFuDu7MANfVvYnJNp8OfP7gHQW2+9xW233cbUqVNp3749n3zyCW5ubnzxxReVXu/n50dISIjla/ny5bi5uVkCIE3TeOedd3jqqacYN24cnTp1Ys6cOZw4cYKFCxfW4Ts7B5bxPzIAWtScI2m5/Lknhdlr4sjIK7Z3c4RQLnsHblkObUZZj4V1AXTqd+HxrXD0X3V8+7eQskftl5U02VpBv+9IAmBkhxDL4GczyQCdP7sGQMXFxWzZsoXhw4dbjun1eoYPH8769eurdY/Zs2dz7bXX4u7uDkBcXBzJyck29/T29qZ3797VvmedkwyQqAWpOUWW/U2m8QJ5RaX8tuMExaVN8w+JqAdcvCqu/WXwNK1Gz2lZHw2WPwuxf8CbbeGLkWpZjSZmx7FMAIa2DaJ9mO0sORkDdP7sGgClpaVRVlZGcLBt7Zvg4GCSk5PP+vxNmzaxe/dubr31Vssx8/PO5Z5FRUVkZ2fbfNUpmQEmasHJcgHQhiMZAPxvUSz3freNL9bGVbi+sKSM1JzqdRNrmsbBlByMxqb3x0jUEnM3WMI6te13L+gd4dBymH895KepxVdTm1aXrqZpHDKN92kT4kmYt4vNzC/JAJ0/u3eBXYjZs2fTsWNHevW6sJWEX375Zby9vS1fERF13BUlGSBRC2wDoHSKS40s2qkGUq47nF7h+hk/bKffy39xyDTV9kwWbj/OiLf/4YEftqM1wU/kohaEd7PuOzjDwBnQ42brMfcgtd2/RG3LSuHY5kbfLZaSXUROUSkOeh2R/u7odDrahXpazssYoPNn1wAoICAABwcHUlJSbI6npKQQEnLmJSHy8vL4/vvvueWWW2yOm593Lvd8/PHHycrKsnwlJiae61u5MJYAqHndvq5o1MoHQPtTcvhj5wmyC9UCitsSTtlkb07lFbN0dzKlRo3NRzPOeu8/d6v/X79uP8FPW45VeN3P/jnM4wt2cvNX/7Fqf+UTGoSwUT4AanWJmhU77FkY8gRMWQRDHlPnzKvNL3sKPh8Gm2fXfVvr0MHUHAAi/d1wdlR/stuHWmvySQbo/Nk1AHJ2dqZ79+6sXLnScsxoNLJy5Ur69u17xuf++OOPFBUVccMNN9gcj4qKIiQkxOae2dnZbNy4scp7GgwGvLy8bL7qTFkpZJvqO0gGSFyA7YmZPPLTDkvgU34MkKbBq0v3WR7nFJZy+KQ10/PXvlTM8ZC5wFp5iRn5lsyQ0aixqVyQ9OxvezhS7l7P/Lqblxbv47tNify1L5W75221eS0hKhV8kcr8AHQ0zeo1eMCQR9WU+damQdPHNkPyLmvgs++Pum9rHTpo6v6KCbKWSCk/DshTKkGfN7t3gc2YMYNZs2bx9ddfExsby7Rp08jLy2Pq1KkATJ48mccff7zC82bPns348ePx9/e3Oa7T6bj//vv53//+x2+//cauXbuYPHkyYWFhjB8/vi7e0rnJTVZVUfVOsg6YsMgtKqW07NxS+x//fYgfNh/jl20qI2MOhMzjBVKy1WNfN/V4a4K1xsryvdaM6ekBUEp2IZe+9y9j319Dak4hB1NzycgrxsVJT5+WfuQXl/HITzst7V65T2V8bh/Uku4tfMkrLmP6vK0UFJed0/sRTYyjAYY/B11vtJ0ib+YdDqGdAQ3m3whlppmNCRugpJ6WOKkB5urPrYKs3V4XhasAyNvVCUcHu/8Zb7DsHjpOnDiRkydP8swzz5CcnEyXLl1YunSpZRBzQkICer3tP/D+/ftZs2YNy5Ytq/SejzzyCHl5edx+++1kZmYyYMAAli5diouLS62/n7PSNLVKspm5+8srDPTygyzg8MlcLn9/DUPaBvHhdd3O/gST+HQVuJgDGHMANLJDMD9sVj9nIV4ujOsaxqerj7A1PpOJPZtTWFLGPwdPWu6TeFoA9OKiWHJMXWe/bT9hScN3b+HLG9d0ZvBrf7M5/hS7jmVxND2P4lIjUQHuPD66LSdzirj0vTXsS87h0Z938vo1nTA4Opznd0Y0en2nn/l869GQtANOmQbx6x2htBCO/QdRA2u/fXZw2BwABVszQG2CPXlkVBua+brZq1mNQr34i3v33XcTHx9PUVERGzdupHfv3pZzf//9N1999ZXN9W3atEHTNEaMGFHp/XQ6HTNnziQ5OZnCwkJWrFhB69ata/MtVE/WcfhiFCTvth7LlBpAwtZ3GxPIKy5j2Z5kCkuqlzXRNM0SuCRmFABwMlcFQGM7h1muu7RjKD1a+AGwLVFlgNYeSiO/uAxn0yfJ8hmgdYfTbErw/7LtOBtNM8r6RPkT6u3K6I5qbN23m+JZslvVKxl9UQg6nY4gLxfeu7YLep0q5X/1x+uJT8+z3K+wpIzP/z3C/uSc6n57RFNWvnZQcEfocIXaj1ttn/bUMk3TOGAaA1S+C0yn03HXkBguL/d/W5y7ehEANRnLn4HEDfDNVZCZoI7JFHhRTmmZkYWmkvclZRrbEjKr9bxT+SXkmbqYEk/lU1xqtBQ/7BDmTcdwb/Q6uKJrOF2b+wAqtZ5dWGLp/jIHSqfyS8guLKGkzMizv+6xnHPU69hzIpuV+9T1vVuq7ufreqnB+wu3nWDVPpVJurRjqKVt/WICmDW5Bz5uTuw6nsVl76+xBDyvLNnH/xbFcs93W2U2mTi70C7gZfpdOXCGWkke4EjjDIDS84rJzC9Bp4PoQFkmqaZJAFSXxrwJQe3VuJ+5V0J+hkyBFzb+OXiStNzyBQzPPiMLbLM2x08VWO7hqNfh4+rE7Jt68NvdA+jYzJsADwPN/dzQNNWltcwUAI3vGkaAhxqEmpCez5qDaRxMzcXXzYkXxnVgSBs1DbmwxIjBUU/nCDUTpVeUHzFBHhSUlFFQUkaEnysdTivWNqxdMIvvHUiXCB9yCku585stLN+bwlfrjgJqXaMdx7LO4zvGWWsRncgs4MbZG2U2WmOg08G18+CKz1T2p6UpADq+RS0q3ciYB0A393PDxUm6jmuaBEB1ydUHrv8JvMIh/SB8dZnquwbwkS4wAT9vOQ5gCUT+q8aUdLAdt1NUamTviWzTfQzo9aor6qJw69TZbqYs0FMLd5ORV0y4jyu9o/yJ8HOz3M88SHpYu2B83Jy5slu45fldm/tYxvLodDpLFgjg0otC0ZUf52YS5uPKF1N6Eu7jSlxaHrfN2QyAi5P6NTT/v3MvP7F0dxIXPfcnP5zhud9siOffg2m88ef+c76/qIfCukDniSoY8mkOvlFqIkn8Onu3rMYdMnV/tQqS7E9tkACornmHww0LwC0AUvdA8k7TcckANXVZ+dbuqCfHtANgS/wpSqoxG+z0mVvm4CXQ01Dp9d1aWNcT6hnpyw939sXZUU9zUwCUUC4AMneZXdw2yDLltk9L29mXV3VrZglkynd/nc7P3ZlPbuhuGUgd7uPK+5PUQO/fd5ywmSm2PzmH6fO2sjLWOkMtLbfIMrg7PbeIxxfsIr+4jF+2Ha/yNTccUUUf95zIJjmr8c4WarKiBqnt4b/s245aYJ4BFlNuBpioOXafBdYkBbWFaevg1+mqzDuoTzGiSVu0K4niMiNtQzwZ1zmc537bS1ZBCXtOZJORV8Rv20/wxJh2BHlWnM147FTlAVBQFQHQZZ3CWBGbSu8oP+4Y1NIyldYcAB1Nz2NHouqS6hqhgiUXJwfuvbgVczYc5Yqu4Tb383Zz4oubepKcXUjnCJ8zvs+Ozbx5a0JnPvjrEP8bfxHdmvvS3M+NhIx8Fu9K4qru6sPAx38fYtGuJBbtSmJs5zCMRo2le5JxctDxv/EdWXsojVP5JQDsPJZJmVHDQW+becorKmVnua61v/alcl1vKTjaqMQMh61fw6bPVEaoy3VqEdWcJBj6JDg33JlSldUAEjVHAqA6NGf9UT7/N47LOoXyyKi2cP2PsHM+FGSCf7S9myfsbL0pU3Fpx1D0eh09I31ZEZvKvA3x/L7zBIUlRk7ll/DV1J7odDqOnMzF180ZX3dnSwbI4KinqNRoCV6qygD5uTsz5+aKS8iYu8BW7TtJblEpbs4OtAmxfvq8bVBLbhvUstJ79osJqPZ7vaxTGJd1ss5gmdCjGW8sO8APmxO5qnszjEaNfw6mWc7/Xm4mWplR46EfdwCqF8RJryevuIxDqbk2bQXYHH+K0nJjhP7alyIBUGPT9jLoPhW2fAnLnoQVz4FRBcY4ucLFT4GxDHZ8p+oIhXS0a3Orq7CkjL1JqitbAqDaIV1gdai41EhCRj6Jp9Q0ZXQ66Hwt9LnTvg0T9cI2U9amu6l7qleUmq7+45ZjFJaobrDVB04yb2MCH/x1kGFvrWbSrA2mKfDqZ6pHpHpugWn6fFUBUFXMGaDkbNVV1LmZT4WsSm24qnsz9DrYGJfB0bQ8dh7PIiOvGE+DIz9P68vAVgFM6tWcRfcOYMaI1pZSWjf1jaRbCx8Atpum9e9LzmbdYRU8mbu/zIXj1hxKq3ZpAdFA6PVw2dsw6hXQ6VXw4xupzq37ALKT4O9XVMb924mq+n4DMHtNHFkFJYT7uNI+tA5XJ2hCJANUh8x/jFKzZRyCsJWWW8SxUwXodNCpmRqs3DPSz3I+2MvANd0j+GDVIZ5aaK0jtS85h8MnczmRqQKgftEBrD1kXei0qi6wqpgDIDPz+J/aFurtyqDWgfy9/yQ/bknE2UENsO4fE0D3Fn7MvcVaG6xDmDd9WvqzNeEUU/pF8s6Kg2w4ksG2hEwu7xzOxE83kFVQwje39LYEQFP6RfHmsv0kZRWy/nA6HcK9KCoxWjJeooHT6aDPNGjRD0qLoVkPVXMtcQP8cKNaPgMg+zjsXwztL7dve88iLbeIj/8+DMDDI9tYxsyJmiXf1TpkHrtRfpFKIQC2m+r9tArywNO0uOFF4d4EeDjjoNfx3rVdmTGiNb1NWSFHvY5Qb/Xz9N2mREqNGs4Oekv2yOxcM0AhXi6WgogA3Zr7nuHqmjWhh5oJ+dOWY/xlqjU0pE1gpdf2ivLjzsHRuDg50MU05mh7YiaLdyWRVaC6Px7/Zadl/E+fln5c3FZN43/2tz30f+Uvhr+1ukLV63O1JT6Dl5fEUlzauFckbzBCO0NETxUQXfKCOnbsP0Czrib/3yy7Na+63lt5kNyiUjqGe0uxw1okAVAdCvZSf4xSJAMkTmOuytyl3ABiJwc9P0/rx+J7B9K7pT96vY73r+vK7YNaMv+OvtzcXw2c/3GzmgLezNe1QgbnXAMgvV5HMz9Xy+MudZQBAhjWLghfNydSsossNYEGVxEAlWfOUh1IyeHr9UctxxMzCigzakT4udLM141h7dQfwISMfErKNIpKjWysZp2lqjz72x4+XX3EUgFb1CMRvaCdKdMT2hluXqq6yOL+gZP1tyTCHztPMG+jKpT7xKXt0NdBF3RTJQFQHQryUp/Y84rLyCtqGP3Qom5sT8wEoEuEbcalhb+7zcDeIE8Xnri0Hd1b+FqyI9mmdbqa+bkR7OWCk4P1F2agx7mvf2cOopr7uRHgcW4B1IUwODpwRVdrOYi2IZ6Eerue4RlKsJcLYd4uGDXYeSwLnQ6eHdvecr5PlJqyPyAmkMs6hTL6ohCGm4Kh3cfPr/giqEGqsUmqTsvhk3lnuVrYxdh3YfjzcN2PaqJJ69Hq+H+f27ddldA0jfdXHuTub7dRZtS4oms4faP9z/5Ecd4kAKpDHgZH3JzV2IZU6QYTJmVGzTrl/BwyLjFBHoT7WAOE5n6uOOh1NsfONQOk7qMCoG51mP0xm9DTGgBVJ/tjVj5TNahVIFP6RTL6IrVG2cgOauvsqOeD67rx8Q3dGdNJ1Sq6kABoz4lsykwzzI6mSQBUL7n5wYD7wVMtrk2v29R2+7eQtNNuzarMwu3HeXP5AQBuGRDFG9d0tnOLGj8JgOpYsCkLJN1gwuzwyVzLlPPWwdUveKbT6WyChAjTytDmgb2eBkdcnc+9fP6NfVowuHUgdwyu+9IMbUO86B3lh16nKkpXV/muw4k9I9DpdLw/qSsrZgxiePvgCtdfFKYGmu9NsgYx52rnsUzLfvkFXkU91nIIRPSG4lxViT9xk71bZPGXaR29qf0jefqy9nUy+7KpkwCojllmgkkGSJiYB0B3auZ9zr/0hrQuFwCZAp9mpkDofLI/AK2CPfn65l60s9PU288m9+DP+wedtaBieeYZc37uzpaxPo4O+ior6LYM9MDN2YH84jLi0lSxuUOpOeQXV79runyBxbi0PFnMtSHQ6VT9tYg+UJQFc8bB7gX2bhUAW0zL3oyoJGAXtUMCoDoWJFPhxWmsA6DPfcZV/5gAy5if5pYASHWBBZxnAGRv3q5OtDqHTBhA1+a+vDOxC19N7WlZo+xMHPQ6S22VXcezWBmbwvC3/rEpMXA2O8plgLILS8k0VaUW9ZyLN9y4AKIvhpJ8+Gkq/DEDSky/kzUNfpkGX14KJQV10qQTmQWcyCrEQa+jczOfOnlNIQFQnTN3gUkGSJiZMwldIrzPcmVF7gZHXhzfkWlDoi0rsPePCcDZQc+Ac6jM3BiM7xpOp3P442FeHHb38Wxm/XsEgNX7T1Yrk5NdWMIR08BnT4MqpxYn3WANh7O7Ghg9YIZ6vHk2LLxTBT97foEd30L8WjVjrA5siVcfgtqFeuJukPJ8dUW+03VMMkCivNIyo2XBw/ah5x4AAUzoGWHzuEuED7uev6RamZCmzBwALd2dzHFTIcn0vGKOnSo4a4HEXaagtZmvK+E+rmyMyyA+Pa9O6yaJC+TgCMOfheZ94ftJKvBp0R/WvG29Ju4faD2y1ptiDoB6tPA7y5WiJkkGqI4FeckYIGF1ND2P4lIjbs4Olq6rmiDBz9l1NAVA5uDHbJupJMGZmLu/OjfzISrAHYC4tAsrqijspPUlMPQJtb/4IVUtWmf6/xO3usZfLimroMJyLOYAqFsLCaDrkgRAdSzYU2aBNTYFxWX8c+AkxkpmEx3PLGDTGYrt7U9W2Z/WwZ5S8KyORQe64+Jk/RXYxjTuaEc1AqCdprIFnZp5E2kKgGQqfAPW/35oMcD6+NLX1TZ5F+RfWLHM8g6l5tLvlb8Y+sbfrDEt9ptfXGpZ9PT0Su6idkkAVMckA9S4aJrGtHlbmPzFJr7dlFDh/G1fb2bCp+uJNf2CO93+ZHW8zTkO+hUXztFBb5np1jLA3bLK/fbqBECmDFCnZj5E+qvuMpkK34DpHeDKTyGgDXScAD1uhsB26tzRf2vsZfacyELTICmrkBtmb+SZX3ez9lA6ZUaNUG8XmxpeovZJAFTHAk0ZoJzCUgqKZVXqhm7p7mT+3q/qdyzcdtzm3InMAssnu6qyCvuSVSXh8tWeRd0Z2kZNmb99UEtLEcrdx7MoKat6ba/dx7M4kVWIk4OOjuUyQDIVvoHzbgZ3b4KrZqnp8lGD1PEaHAidnKUy/37uzgDMWR/PHXPVQq3S/VX3JACqY14ujhhMK/um5kg3WEOWV1TKzD/2Wh5vjj9l+QUHsP6wdVV280Dn0+1PUQFQWwmA7GLakGhWPTSEa3s1J8rfHS8XR4pKjew3BaaV+c6U6RvZIQQPgyMt/FQAJFPhG5laCICSTL8fJvSI4JtbehPp74a557y7DKCvcxIA1TGdTidT4RuJ9/86RFJWIc18XS0DapeWWxRz7eE0y35lAVB+cSkJptXIJQNkH04OessgZr1eZym+WNVA6LyiUn7dfgKA63o1B8DV2YEQ0/9pmQrfiET2B3SQdgCyz2+x2yMnc1m47bglM2j+gBTq7cKAVgEsvX8QM0a0ZnDrQC7vIqu+1zUJgOzAOhVeAqCG7AfTKuxPjWnPONMvr8W7kwE1Nqh8BuhQSsWMwoGUXDQNAjwM+NfhoqOiauYlNcxdlpqmsXR3EtO+2cLaQ2n8vuMEuUWlRPq72SxUGRlQcRyQpmkUFJeRU1hCUemZu7tLyoxsT8yULrT6xNVXrSIP550Fevinndw/fzsbjqiB1MmmyS8h3ipgdnFy4N5hrfj65l51uvCwUKQOkB2YB0LLTLCGq6C4jIy8YgD6RvuTW+TN/xbF8t/RDFKzC8ktKiUpqxBHvY5So8aJLHXMo1yRM8sA6BAPu7wHUZE5APrnwEk++vsQm+IyLGO8lu5JxsfVCYBJvZqj01ln7UUFuLPhSIZlKrymadw4exNrDqksoLOjnu9u62OZ5bNoZxJBXgbLEh4PzN/OHzuTmDmuA5P7RtbFWxXV0XIIJG2HI6ug88RzeqqmaZau1AMpOfSN9rdkgMwZQ2FfkgGygyBP6QJr6JKyVO0Yd2cHvFwcCfdxpUuED5oGS3Yns86U/enewteyJtfh07rBLAOgg+2z5paoqGtzX5wcdKTmFPHa0v38vf8kzg56BrYKQNPgVH4JTg46ru7ezOZ5bUPUv+GKvSlomsamuAxL8ANQXGpk7vqjAGxNOMX0b7dy3awN/Hc0gz/3JPPHTtXF8unqI5SeYQC2qGPRF6vt4b9UlehzkJFXTG6RWlvuaHoepWVGy7jPUG8JgOoDyQDZgXUqvGSAGirLJzlvF0smYEzHULYnZvLq0n2Woob9YwLQ63SczCniYGquzQKf5k+HMgC6/vBzd2buLb1ZdzidYxn5ODrouHNwNC0DPViyK4nX/9zP2M5hFbosL+8cxkuLY9mblM3WhFN8s1ENlJ7QoxnX9Ijgmk/W8+eeFPKKSvnOdK6kTOOOuVtwLFf/6XhmAX/uSWFMp1DKjBo6kPpQ9tS8Dzi6Qm4KpO6F4A7Vfmp8hrUwZnx6PidzizBq4KjXSZd3PSEBkB1YMkAyBqjBOmEKgMLK1e24vk9z/tqXyvoj6RxIUdmeftH+pOUWsf5IOgdTczAaNX7ckkhcWr5lOQUZAF2/9GnpT5+W/hWOj+4YyuiOoZU+x9fdmXFdwvhh8zHeWXGQDUdUBnBy30g6hHnRwt+N+PR8Fmw7bsn2hHm7WH6OogLcuaR9MJ/+c4TP1xwhzMeFu+ZtJb+4jCFtAukQ5sWJTNWNet+wVmddqkPUEEcDRA6AQ8tVFuhcAqBy48GOpudZPjQFe7ngIEFtvSBdYHYQLGOAGrxkUxdY+b58N2dH5t7Si1sGRAHg6+ZE5wgfWgWpMT6HUnL5acsxHv15F5+sPkxOUSkuTnpaBcsYoMbAPHbn34NplJRpdI7w4aJwb3Q6HeO7hAPw8uJYCkrKiA5056dp/Qj0NKDXwctXduTWgS1xdtCzLSGTCZ+uJymrkKyCEn7dfoKXFu/jq3VH+WnLMV5Zus+O77IJKt8Ndg7i060ZoGMZBZYlV8y//4X9SQbIDpr7mWeM5FNUWibrNjVAJ7Iq78t3dNDz9GXtGdkhBG9XJ5wc9MQEqQzPgdQcjpo+FQ5vF0SXCB/6tPTHzVn+GzYGF4V706OFL5tN6zpd37u55dz4ruG8u/Ig+abip9f2bE6YjyuL7h3AqbwSSxZwfFeVRSop0xjRPpib+0ex+sBJjp3Kx9fNmbkb4vlzdzKp2YX4ujtz17yt6HXw8fXdpaustpgDoPh1UFIATtWr1pxQLgAqLjOyLSETgFBvqfZcX8hvXjto7ueGv7sz6XnF7D6eLeu/1GNHTuayav9Jru/dHBcna6BqqedRRen6XlHWVZ1jTBmgxAz1CdDT4MjbE7vg6eJUW80WdjK5XySb40/h5eLI2E7Wui5RAe50bubNjmNZODnouLKbyggFebpYusQB7hvemmOnCugX7c9dQ2LQ63U20+33JmWzJf4U3/+XiKuTA8v3pgCw83iWZQabqGGBbcAzDHJOwLKnIXEDBLSGK2epJTSqcPS0mlAb41S3aIgMgK43JACyA51OR9fmvqyITWFbwikJgOqxV5bsY9neFOLT85g57iLL8aQs23oeZxLg4YyPm5OlSvB1fZpL8NNIXdYxlNTsQtqHeeHqbPvHcULPCHYcy2JMx9AqB8GG+7jy7W19qrz/jX1asCX+FHM3xJNvmmEE8Ne+VAmAaotOBzEXw7Zv4L9Z6ljyLgjpBAPur/Jp5iKnQZ4GUnOK2HNClb2QKfD1h4wBshNz0LPFlC4v79ipfIpLZSpsfWDux5+7IZ5tCdZ/K/M0+OpMZ9XpdJZxQE4OOm7uH1ULLRX1gV6v49aBLekXHVDh3HW9mvPNLb158YqO533/0R1D8HN35mROEXnFZbiZgqy/9qWc9z1FNXScAOjAqxl0vk4d++t/kLSj0stzi0pJy1V1wga2CgSss+glA1R/SABkJ91MCy9uTThlU/11xd4UBry6ijeX7bdTy0R55sqtmgaPL9hFSZmRguIySzanuv355lXHx3UJtyyFIpoWnU7HgFYBuBvOP/FucHRgQo8IAPQ6+OSG7uh0sPt4tkyqqE0tB8PDh+C+7TD+I2h7GRhL4Jur4MPe8G4XOLLacrl5/I+vmxMdw23rfEkNoPpDAiA76dTMB0e9jpTsIsuAWoB5G+MBWH3gpL2aJkwKisvIKlCBjqeLI/uSc5izPt4SFLmZiiBWx90Xx/DY6LY8M7Z9rbVXNA03D4ikRwtfHhvdlkGtA+nczAeAVftS7duwxs49ABycVJfY2PfAIxjyTsLJfXAqDn6cAlnHAOsU+Ob+7rQwrTVnJh+A6g8JgOzE1dmB9mHqk4G5G+xUXjH/HlTVYw+l5ko3mJ2VD3QeuqQNAL/vOEFSprX7q/xyCGcS5OnCnYOj8ZKxP+ICBXm68NO0ftw+KBqAYW2DAFgpAVDdcfeHW5bD1V/A5F/VmmEFGfDjVCgrsRRBjPR3I9JfAqD6SgIgO+rWXI0D2moKgJbuSabUqLrDSo0ahypZQVzUnfLr9gxvHwzAzmOZHDAtbCrTWUV9cHE7FQCtOZjGy0tiuWveFkuRTVGLfFvARVep9cKu+RoM3nBsE/z9imXsYAs/N8J9XDFXKAjwMODsKH926wv5l7CjrqZxQObBtb/vOGFzfp9psUxhHynlVm4O93El0t8Nowa/mf6dZDCjqA/ah3oR4uVCQUkZn64+wuJdydwxd7Ol+1bUAb8oGPOm2t/xPfFp6sNrc393nB31hJuWxpHxP/WLBEB2ZJ4JtudENkt2JVnK5w83faKLTZIAyJ6STlu5ua9pZs9WU0GzMPllJuoBnU7H45e2pXeUH9f1bk5zPzdOZBXy1MLdNhMs/j14krvmbeH1P/exdHcyZcZzW9xTnEXbMaB3guxjlKYdAaCFvyp6a+4Gk+6v+kUCIDsK93GlXagXpUaNafO2YtRUVmh4O9XdYl4tXNiHOQMUbAp0+kXbrg8VIl1gop4Y1yWc+Xf05aUrOvLutV1w0Ov4fccJFm4/DkBeUSkPzN/B4l3JfLjqMHd+s4VHftpp51Y3Ms5u0KwnADF5WwDVBQbWQEgyQPWLBEB2pNPp+PrmntzQp7llRegru4ZbpkxLBqh2Ld2dxN3fbiW1iunDyRUyQLYBUKiP/DIT9U/X5r7ce3ErAJ76ZTeHT+Yye00cablFhPu4MrFHBDod/Lz1GDsSM+3b2AYmK7+E4W+tpteLK5jxw3Z+33GCwpIy6wVRgwDoo9+Lp4sjgZ6q4OWkXs0Z0iaQiT0j7NFsUQWpBG1nQZ4u/G98R24fGE1scjYj2gVTVGpEr4O03GJScwptSuWLmvPhqsPsOp5FUlYh393Wp8LgxKRs22rPAR4G2oZ4WjJz8mlO1FfTh0az7nAaG+MyuHPuFk6YZi4+NrotYzuHUVJmZMG247y0OJbvb+9jM5vxeGYBs/+N44+dJ7htYEtuG9TSXm+j3vnkn8OWySkLth5nwdbjeLs6cVW3Ztw/ohVeUYNg9Sv01e/lolAvy/e1Q5g3X03tZc+mi0pIBqieaO7vxsgOIej1OlydHYg01Y7YlyTdYLXFPM19S/wpXlocW+F8ymkZILDNAoV6SReYqJ8cHfS8P6krAR4GDqbmkldcRqdm3ozpGArAjEta4+yoZ2NcBqv2W6fPL9x2nEGvreKLtXGk5hTx5vL9nMwpsrn3nhNZ3DVvS5ObpJGaXciXa+MAeGRUG+4Y3JJwH1eyCkr4Ym0cX6yJg2Y9KNE5E6jLYoh/hp1bLM5GAqB6ql2IdIPVptIyI2m51l/sX607yoKtx2zOnzSdLz/by7zEgauTA16ukkAV9VeQlwvvT+pqmYL92Oi2lhXjm/m6MbV/JKDWuyszapSUGS37vaP8aBviSWGJkVn/HrG5r3mm2e1ztpBT2HRmmr3/1yEKS4x0a+7DtMHRPD66Hf88MpQHhrcGYN3hdHA0sNepAwB99Xvs2VxRDRIA1VPtQj0BGQhdW9Jyi9E0cNDruGuIKij36M87WXc4zXK+zKjhoNcRUG7hyoGtAhjRPpg7BresdhFEIeylb7Q/X0zpyXuTulZYn+yuITF4uThyICWXRbuSWLo7meTsQgI8DMy5pRePjm4LwNz18aSX+7Cw41gmoBb7PH2mWX5xKbuPN/waRHtPZPP7jhPkFJagaRp/7Uvhu00JADw8sq3l/76DXsflXcIA2J6QSV5RKSsLVdHUljlb7NN4UW3yEbaeaisZoFplnuEV5GngoUvaEJ+ez6JdSdwxdwsLpvUjr7jMct5Bbw10XJwcmDW5h13aLMT5GNImqNLj3q5O3DawJW8uP8A7Kw7g7aqqlF/fuzkGRweGtA6kUzNvdh7L4vM1cTw6qi0ZecWWIn8Oeh2/bj/BgJgArukRQVFpGVd+tI59yTk8Nrotdw6OrrP3WJOKS43cMHsjGXnFuDo50MzXlYOmcT+DWgdWmAwR6e9GsJeBlOwiFmw9xj8l7ZlhAPekDWA0gl7yDPWV/MvUUxeFewNwICWHrPymk2auK5YAyMsFvV7HmxM606OFLzmFpdz3/XaSTau9S90O0ZhN6R+Jj5sTR07msS0hEycHHdf3bg6oWarm2WRz18dTWFJmmTXWMsCdB4arc08u3M32xEzeX3nIkrF+Zck+Fu9KqpU25xWVkmT6/1kb1h5OIyNPreReUFLGwdRcXJz03Dogivcnda1wvU6no09LFRR9sfYou7QoCnSu6AozIXXv2V8wfh0sfgSKpPJ/XZMAqJ4K8XahVZAHRg3+PSQLo54vo1Fj3aE0Plx1iBk/bGeNaa21FNPAzmDTNFUXJwc+vbE7zo569iZls3yvGhgaIgGQaMQ8XZy4vdwsrzEdQwkq9zM/rF0Qod4u5BaVsu5wGttNAVCXCB+mDYlheLsgikuN3PLVf3y8+jAAvaL8AHhg/nZLlXuAo2l5NZLRnjZvK0Ne/5vDJ2snYFi6KxmAG/o0Z8Fd/Xj1qo78+8jFPHVZe0uW7HTmACguLY8yHDju0VGdiF939hdc9RJs+hQO/lkj7RfVJwFQPTbUtMjhqn3WAKh8f7s4u1n/HuG6zzfy+p/7WbD1uGW2l7n2T/kMj7+HgZEdQgAsBeRkuQvR2N3UN5IADwM6Hdw8IMrmnE6nY4RpHbxle1KsAVBzHxz0Ot65tittgj1Jz1Nj5sZ0CuW72/owrG0QRaVGbpuzmcSMfFYfOMklb//DZe+vuaB1ykrLjGw4nE5RqZFFO88vw/TFmjje+HN/pb9LS8uMLNurAqDRF4XSrbkvE3s2t9TzqUpvU9BnVhDWR+3Erz17gzITTE86debrRI2TAKgeG9ImEIDVB1IxGjXmrj9Ku2eWnvd//KZol2lAZtsQNag8Li0Po1GzVnn2sv3Fdk33ZgCWZQIkABKNnbvBkZ+n9eWnO/vSqZlPhfOXtFcfClbEplgGQHc2XedhcOTzm3oQ5u1ChJ8rMy/vgINex3uTutIhzIu03GKu/3wjt8/ZTHGZkTKjxlMLd533MhzxGfkUlxkBWLkv9SxXV5RXVMoLi/bywapDbDhScZr6pqMZnMovwdfNqUJQcyZRAe4ElQuSPNoMNjV4HZzpQ6umQY7p93mhjPesaxIA1WM9Wvjh7uxAWm4xqw+e5NWl+yksMfLozztJMA1EFGdmLgB319AYHPU6CkrKSM4uJCVbdYEFndbF1T8mwKbAoXSBiaaghb873VtU/ge/d0s/PF0cScstJjO/BGdHvaVaPUCEnxt/PzyU5Q8Mxt80Y9Ld4Mjsm3oS4uVCQkY+RaVGBrUOxNPgyI5jWZYZVVX5fccJRr79D3/vtw1yDqZYu712JGaSmlN5FfeqHEzNtcQjP25OrHB+6W6V/RnRPhhHh+r/eSw/DsjZQU+zDv3BwQB5qZB+qOon5qdDmRpvRJEEQHVNAqB6zNlRz4BWaurqA/O3k1tUCkBuUSn3fL+NEtMnoabAaNR45tfdfLMh/pyedyJT/YJs4edGc9O6PHFpeeUyQLYBjoNex1XdmlkeyyBo0dQ5Oei5uK11JlmHMK8KVdOdHfW4ODnYHAvxdmH2lB6E+7gyplMosyZ358FLVM2c15bus6nDdbrP/z3C/pQcbpuz2RKUABxKtS0Lsuocs0AHUqzPX7w7iexydYxKy4z8ucfa/XWuzAFQu1BPnAyulnXBztgNln3cul8kJU/qmgRA9dxQ0xTWTNNMsDev6YyXiyM7EjP54K8zfLJoZA6k5jBnfTwzf99ru/bOGZSUGUkxfUIM93UlylRd+0haHqnmQdBeFfv2r+5uDYDCZL0vISzdYGDt/qqODmHerHl0KB9e1w2DowM39GlB+1AvsgtL+XZj5VmgzPxidpq6rkvKNKZ/u5VlpsDEPB3d100NRl4Ze24BkHkZC4DCEiO/7zgBqLo/V368jpTsIjxdHOkX41/VLap0Vfdw7hjckmfGtlcHWvRT2zMNhM4uN5xBusDqnARA9dxg0zgggKFtArmqezNeGH8RoKoXVzcYaOjM01KLy4yWcT1nk5xViKapT6f+7s6WAOhAco7lfsGVrLMWGeDOE5e2ZdqQaEvWSIimbHCbQJxNXUJdm/uc03PLFwx1dNBz60A10PrnrccqHYi87nA6mgbRge5c2S2cMqPG+6YPe+YusJv6RQLw78E0y+9ATdN4b+VBPjXNRquMOQMUE+QBwDcbEnh8wS4u/2ANO49l4eniyOtXd8bg6FDlPapicHTg8dHtrF2J1QqAymeAJACqaxIA1XOh3q70i/bH3dmBx0a3A+CyTmGEeruQVVDCitgUO7ewbpSvhfTf0eqtsXPcNP4n3McVnU5HVKAKgDbFqec7O+jxcat8Wuvtg6J5dFRbqfYsBGqw851Douna3KfKworVNeqiENydHYhPz+e/oxVnPv1rKlUxqHUgj41S1ah3n8jiZE6RZer7+C7hBHsZKCgpY8ORdAD2JmXz1vIDvLxkn2WW5+nMAdRDl7TGUa8jNimb7zYlUGrUGNUhhJUzBjPqopBKn3vOInqB3hGyEuHI35Vfk33Cui9dYHVOAqAG4IspPVnz6MW0Mc1kctDrLN00P2w+dqanNhqZBdYAaLPpl+bvO04w5PVVVZbeNw+ANndjtQxQn/r2mz4FBnkZJMARoppmjGjNL3f1r7IWTnW5OTsyppMaY/PTlkSyC0t45tfdli6xNaa6ZwNbBRDk5ULbEE80Db7flEBRqRGDo54IPzeGtVPT83/fobqRyhde3FHJVPvcolLLh6I+Lf25sls4AP2i/fnxzr58cmP3CpMiLoizO0SZZoPNGQffX2/b5QXWGWAgXWB2IAFQA+Di5ICvu7PNsWu6RwDw78GTlv/UjVlmfvkAKIMyo8Zrf+7jaHo+c9dXPjDaEgB5q1XbW5oyQGZBZ6ntIYSoHVebfn8t2pnEtZ9uYM76eJ74ZRdzN8STmFGAo15H7yg1DmdQazUMYI5pAkR0oIdpsoIKYBbtOkFWQQmLd1kHS29PrJhZMo//CfQ04OPmzItXdGTD48P49rY+9Iys/pT3c3LlZ9DtJtDpYd8f8NWltlkfmy6whr+GWkMjAVAD1dzfjb4t/dE0+HlL488CZRYUW/azC0uZs/4oiRkqwFl94GSlYwmOm2aAhfmoACjI04Cbs7VvX2Z4CWEfPSN9aeHvRl5xGXuTsi0r1j+9cDcA3Zr74m5QS1UONM2EPWmauNAq2MNyTasgDwpL1Cr2cWl5lvvvSKwYTJjH/7Qyjf9xctDXfp0v9wC4/D24cy34tICMI/DVGGsQVD4jJF1gdU4CoAZsQk/VDTb/v0SKSs8+GLqwpIzEjIZZP+j09dDeXHbAsp+cXWhZg6i8E+XGAIEajGkeCA0SAAlhLzqdjgk9VBYo3MeVRfcOtMnQmst/APSM9MNQbtp962BPyz0m9VLrlpnrCpn/f+84lonxtGKL5gyQ+fl1Krg9TPkDfJqrIGjhXep4+WxQYfaZiyaKGmf3AOjDDz8kMjISFxcXevfuzaZNm854fWZmJtOnTyc0NBSDwUDr1q1ZvHix5fxzzz2HTqez+Wrbtm1tvw27GNUhlAAPA8czC/iwGlPiH/t5JwNfW8VOUzXXhsTcBRZgKrRmrolknqW1+kDF9dKsY4BcLcfKB0BBlUyBF0LUjdsGtuStCZ1ZOL0/7UK9eGdiFxxNqaCB5QIgFycHere0Tks3z+ACuLJbuE1NoruHxuDipCensJQjpnpfLy+JJT49r8IMsDrn0xwmzVf7R9dA7kkoLvfBTSuDkob5AbWhsmsANH/+fGbMmMGzzz7L1q1b6dy5MyNHjiQ1tfLaDsXFxYwYMYKjR4/y008/sX//fmbNmkV4eLjNdR06dCApKcnytWbNmrp4O3XO1dmBmeM6APDR34fPuNBgdmEJi0yDBCubefH/9u48vqky3x/4J0nbdKFJ9zbdaMtaKC3IUiu4oFVgHBdEBewVXEavWBhG1MtwHUAdL3iHEe+M44WBK8Lv6qgDVxEVQagCKgWUHSyFQqFAm0JbutMteX5/nJyTpDulJE3zeb9eeeXknJP0Oa/T5dvv832ep6eTu8DSE60jUAZH+ONpy9pFzWeMFUK0KIIGpFWsZa0NgScix/DyUOOhm6KVdbaSowOwesYovPFgEkbEBtqde5tNQDTAJoAJ8PXCJMuoLS8PNSYkRWBYlB6ANFP0wo3H8PedZzBt1R5lsIRTMkCysESgTwRgbgROfCHt0+oBWPoA2Q3mUE4NgJYvX45nnnkGTz75JIYMGYKVK1fC19cXa9asafX8NWvWoKysDBs3bsTYsWMRFxeH22+/HSkpKXbneXh4ICIiQnmEhIS0+nm9waSkCEwYGo4ms8C/bTiCJsvs0E0mM/5r+0lkn5aGiH6bcwmNJim96ordYHIG6I5BodBY/kucOjpGWS/t57NXlKwQAFRebUJNg9QtaJcBCmUXGFFPNX5wGP7l5r4t9t9uKYT289K0mJvr6XHx8PJQY8pN0eij9VAmavzkp/P45hdpmpCiijqUVEv/RA1wVgYIAFQqINayUOqxT6VnXSSgtSwtwpFgDuW0AKihoQH79+9Henq6tTFqNdLT05Gdnd3qezZt2oS0tDRkZmYiPDwcSUlJWLJkCUwm+/qXU6dOITIyEgkJCcjIyEBBQfvrzrgylUqFPz6QBJ23B45erMA2yw/854cK8V/bT+G5D/ajsq4RXx+zFttduOJ6AVCFZRh8VIAv/iU1FsNjAvDQTdHoG+yHuGBfNJkFfswrUc6XR8YF+3nZTdEfH2L95dfaLNBE1PMMCPfHX6YNx8rHR7ZYoys5OgAHF96NNywTxA63TNS4zzJf2G0DQ5X1/UL6aFuMqHU4eYLEs5aeCV0k4G0JgHr7ZIiNV+3rnpzMaQFQSUkJTCYTwsPD7faHh4fDaDS2+p4zZ85gw4YNMJlM2Lx5MxYuXIi33noLb7zxhnJOamoq1q5diy1btmDFihXIz8/HrbfeiqqqtlOL9fX1qKystHu4kjCdN6anSsWAXxwptHuuuNqId7JO2dXIyKOnXImcAQrw9cRrDyRhY6Z1PhJ5YrYsm0khlQLoQB+7z4kP8YNKJf0jFs6V3olcxgPDo3DrgNBWj/lpPZTMsO1SHSoVsOjXifjfp8dgiEGHGWkts0sOJ2eAYCl41hmsGaDeHgD932+Av6QAF352dksA9IAi6GthNpsRFhaGVatWYeTIkZg6dSpeeeUVrFy5Ujln0qRJeOSRR5CcnIwJEyZg8+bNKC8vxz//+c82P3fp0qXQ6/XKIyYmxhGX063uS44EIK2Nc+FKLX44Zc2GrP4+H3WNZmUI+Pkrta0OG++p6hpNuGqZ7l7fyszN8sytGw8WKt17F5vNASTT+3hi6eRheOPBJOi8r29CNyLqeaIDfRBsyfLclxyJ/mH+6B/mj81zb8Vv7xrg5NYBCBsKeNnUIemirBkguQvM3EuXODq/DzA1ADv/09ktAeDEACgkJAQajQbFxfZLORQXFyMiovWpyA0GAwYOHAiNxtqlkZiYCKPRiIaGhlbfExAQgIEDByIvr+1RUgsWLEBFRYXyOH/+fBeuyLmGRuqQEOqH+iYzXl5/BE1mgcER/hgaqVPOkYed1jaYlLWwXEGlpftLo1bB3zI3iK3U+CCM6x+CBpMZy7bmAmh9BJhs2phYZKT2gP8EiajbqVQqZNzcF7FBvph390BnN6cljQcQM9r62t8AaC0BUX0lcPUKsHwI8OmzzmnfjWJqBGosPRGnvgGKDju3PXBiAOTl5YWRI0ciKytL2Wc2m5GVlYW0tLRW3zN27Fjk5eXBbDYr+06ePAmDwQAvr9b7daurq3H69GkYDIY226LVaqHT6ewerkalUilZoGzL2jj3pUTixXusvwAeGB6p1L2cv9L5brAN+y/YZZQcTV4GQ+/j2erSFSqVCr+fNBgqFbDpcCGOXCi3ZoC4mjuR25l390Ds+rfxiAvx6/hkZ4i9xbqti7LpAqsCCg8C1UbgxFe9a16gKiOUbj8A2PVnpzVF5tQusHnz5mH16tVYt24dcnJyMGvWLNTU1ODJJ58EAMyYMQMLFixQzp81axbKysowd+5cnDx5El999RWWLFmCzMxM5ZyXXnoJO3fuxNmzZ7F7925MnjwZGo0G06dPd/j1Odp9KfZB3q+TDRg/KAy/GRePGWl9MTwmADGB0giKzhZC5xRV4qX1hzHnowNO6zZT6n/aWYMoKUqPycOl6RAeW70X3xyXMotRrWSAiIicSqkDglQDZNsFJhcJN1RL2aDeQl73zNMSlOZ8AVzOdV57ALTsT3CgqVOn4vLly1i0aBGMRiOGDx+OLVu2KIXRBQUFUKutMVpMTAy2bt2KF154AcnJyYiKisLcuXMxf/585ZwLFy5g+vTpKC0tRWhoKMaNG4c9e/YgNLT14rnepH+YPxINOuQUVWJYlB59g6VvtD/8eohyTkyQL34+d6XVQuhGkzSl/OajRVjzxGgkGnTKyulXahtRcbURAb6OH0FRXit117VW/2PrxQmDsPW4URkO7+OpQUpMwI1uHhHRtYkaKQUCpgZAH2PfBWa7PEbFecD3Bq1T5mhyYBc+FOgTJq2N9v1y4KG/O61JTg2AAGD27NmYPXt2q8d27NjRYl9aWhr27NnT5ud9/PHH3dU0l/TU2Di8vOEInrglrtXjMZZRUeebZYDKaxuQ+Y8D+DFP6j7bsP8CFv56CPafs/4HcuHKVecEQFc7zgABUrYn68U7YKysg6+XBhF6bxY6E1HP4+ULzPhcmvnZJ8B+FFijzT+n5ecBQ0qrH+Fyqiyju3UGYOzvAI0XcMscpzbJ6QEQda9HRsXgV8MMykKCzUVbJhFrPhniU2t/woGCcuX196ekYrXmAVCSZZZVR6pQhsB3HHxF6L1v/AKHRETXy7YQ2tvye7WuEmiqt+6vcL0BOW2qsmSA/COBqJuAR953bnvgYsPgqXPaCn4A2NQAWf/LuHClFgcKyqFRq/DRMzdDpQJOFlfj0HlrMbF8nqNU1zdhl2WVd3kZDH0HGSAiIpdk2wVWZTNRYHkvCoDkrj1d2wOSHI0BkJuJCZK6wC5euaqslrz3jFTnkxSlR1q/YCRbsjx/2X7S7r0XrmHkmKzJZMb+c2W4cg3D7oUQeGrtT5ixZh++PmbEFZtJEImIeh3bUWC2MyVX9KJVDOQiaH8GQOQkBr0PPNQqNJjMKK6qAwDszZfqfm5OkIrt5NlWv8uVusHkCRRts0EdqW8y4b0f8nHHn3dgyopszP7oQKff++WRIqX4el9+mbULjBkgIuqN5FFgNZeB2lLr/t6UAWIARM6mUauUyQHlkWB7LcHGzfHBAIBbB9gvHjthqDQx5bVkgP77u9P445e/KO/ZfboUlywB16pdp3H38p3Yfbrl3EJ1jSa8+fUJ5fXxwgqlC8wZBdhERDec3AXWPODpLTVAQth0gUU6ty02GAC5oWh5JFhZLYwVdThXWgu1ChgVFwgAGBEbCD8v62zb9w+XvmEvNqsBEkIgK6cYRy9UtJgj6ECBVDz91Nh4DI3UQQhpRfqrDSb8NSsPpy5VY8Z7+/Dh3nN271u16wwull9VZnz+pbASZTWWiRDZBUZEvZHcBSZPFOgr/TOK2lKgoUZaOPWTx4Ea501Ie13qK4HGGmmbGSByJrkQ+vyVWqX7a2ikHv6WIeNeHmqk9ZN+ACN03kiNl7rGKuualFXZAWDrcSOeXvcz7vvbD7h92Q787x4pmBFC4NjFCgDAgyMilQzS9pxiZZ4ejVqFJrPAK58dw/qfpf9yzGaB1d+fAQD88cEkaD3UqGkwIe+StJAtu8CIqFfybja6NjTRul5YxQVg2yIgZxNw+CPHt607yNkfb700BUAPwQDIDQ0I7wMA+HBvAb44LBXcyUGO7O4h0mSUY/uHwNfLA0GWxQUv2nSDZeVcUrYLymqx+PNjqKhtRFFFHa7UNkKjVmFguD/SE6XP+v5UCT6wBEmzx/fHTMvKzFuOGZXPqKprgpeHGr9ONmBwhPQLoNEk/VfELjAi6pW0/vav9VFAgGVRbuNRaXkMACg7Yz0nb3uPWE+rU5Qh8D0n+wMwAHJL08bEYlC4Py5X1WO7JYhJTQi2O+fRUTH4nxmjsMgyi7TcbSYXQgshsPu0lD1akXET4oJ9YRbAT2fLcLxQWtF4QFgfeHtqkGjwR1SAD+qbzPjZMq/QwyOjca9l7bKcIun8E0Yp0zMwvA88NGoMbTbnEDNARNQraTwBD5tle/wN0gzRAHDoH4CwrH9Zli89l58HPngY+Mc0x7azq+RJEBkAkbP10Xrgf2aOQqClpkalAsbE2WeAVCoV0oeEK3U38ppa8lxA58uu4mL5VXioVbh9UCjS+kmF03vOlOJ4odT9NcSyEr1KpVIySgCQlhCMmCBfDDZI//UUVtShvLYBuZYAaFC49D7blewBQMcAiIh6K2+b33c6mwzQ6W+t+69YAiDjUQBCyqw0Xvv0JA4nD+3vQQXQAAMgtxUT5IsV/zIS3p5qjOsf0mGBsZwBkkd1/WgZwTUiNgC+Xh7KEPq9+dYM0NBIawZH7gYDgEdGRQMAdN6eSmB1wliF3GLpfXLXl+37dd4e0KhbrgRPRNQr2HaD6SKtGSDbFdTLzwOmRqDEZhFRObvSk/XAIfAAl8JwazcnBGPvgnT4ajUdnisHKhdthrUDwC2WzE+qZQj98cIKFFq6yWwzOGPigxAf4odGkxkTkyKU/YkGHS6WX0VOUaXSBTbIEgANjvCHRq2CySxY/0NEvZvWNgNkkBZKlak0gNoDMNVLQ+Mv20xSW2UEguId186u6IGzQAPMALk9va8nPDUdfxtEy0tolNdCCIFsSwboFnm0mN5bqQMqtcz6PMQmAPLyUOPrubfimxdug6+XNe4eYukGO1hQjrMl0jBJOQPk7alB/1CpYJuzQBNRr9aiCyzW+jp6tDXIKTvTLANks3p8T9VDM0AMgKhTom2W0DhZXI2S6gZ4e6oxPDZAOedmm0LqvsG+LVZi9/bU2AU/ADDYIP3Qb88phlkAQX5eCPXXKsflLBLXASOiXk3uAlNpAL9Qmy4wAP3GA0EJ0nZZvn0GqLrYcW3sKgZA5MrkLrArtY348zfSfx+j44Kg9bB2n6UmWAupmxcwtyXREgDVNpgAAIPC/aFSWWt9RlgCrEi9T4v3EhH1GlpLzaO/AVBbgiBPy5w5CeOBQEsGqCAbaKiyvq+nZ4BMTdYgrYcVQbMGiDrF39sTeh9PVFxtxLZfpG/m2weG2p0j1wEB9gXM7YkN8oWPpwZXGy0BUIT9fBiPjIqBl4ca4weFXU/ziYh6NrkLTA4S1Grg128DV84BMWMA4xFpf952+/f19CLoqiJpGL/aQwrqehAGQNRpL90zEJuPGpEQ6oekKD0mj4iyOx4Z4IOEED+cKalBSnRApz5To1ZhUIQ/Dp0vB2Ct/5F5e2owdXRsK+8kIupF5C4w20LhFJt5fuQMUJ00zQhUaimw6OkBUOkp6TkoQcps9SAMgKjTHk+Lw+Npce2e85dpI3D4QjnG9g9u9zxbiQadNQAydK7rjIioV0kYD/y8Bhh8X+vHm4/0ihwBXNzfcwKgosNStmrI/fb7SywBUMhAx7epAwyAqFsNi9ZjWHTnur9kiZaRYCqVNAs0EZHb6ZsGvHRK+kXYGn2MNesDAPG395wAqL4KWHc/UFcOPL8XCBtsPVZiKdgOGeCUprWHRdDkdDfFSqvQDwr3bzFKjIjIbbQV/ACAh5f9yLD426Tn+gqgofbGtqsjB/6fFPwAwOUT9seUAIgZIKIWkqL0eP+J0YgN7jmrBBMR9ThB8UC5tKA0okdJo8Qaa4Fqo3WYvKOZGoHsd62vr5y1P96Du8CYAaIeYfzgMPQLZfcXEVGb5EJoXZRUNN3HssRQZ7vBqoqBXcuA6kvd16Zj/wdUXrS+lgM0AKirtA7TD+7ffV+zmzAAIiIicgVylkfOpsgTC3Y2AMp+B/j2DWDfqu5pjxDAj3+1tGmQ9HzFJgCSR4D1CQd8Arrna3YjBkBERESuIGkKMHAiMPa30mt/y7qKnQ2ALluW0JBXZ79eR9cDl44DXn2AO/8g7bPNAPXg7i+ANUBERESuQR8FPPaJ9bUSAHVyNuiyM9JzTcn1t6W+CvhmobQ97gXAkCJtlxcAZrM0kaMSAPW8EWAAM0BERESuSQ6AOrMemKnJWqBcc/n6v/auZVLxdWA8cMscqS5JpZFWsZcDsh48AgxgAEREROSalBqgTmSAKs4D5iZpu/Y6M0Clp4Hs/5a2J74JeGgBjQegj5b2yd1gcgYomBkgIiIi6i7XMgqs7LR1u6b0+r7uiS8BcyMQdyswcIJ1f2Bf6fnKOSnjJH9NdoERERFRt1EyQK10gQkB7PhPa6am9Iz1WGMN0Hi161+3vEB6jhljP3ljYJzl+DnpYWoAPLztJ3DsQVgETURE5IrkGqD6CqChBvDysx4rPgbsWCJtD3vYWgAtqykBAroYmMgBUECzhaoDbDJA8ozQwQOkgugeqGe2ioiIiNqn9ZdmgwZadoOd+sa6ffYH+y4w4PrqgOQAqHlmxzYDlPOFtB09qutf5wZjAEREROSKVCprHVDzkWAnmwdAzTNAXawDEgIoPy9tyxkfmfz68gngl8+l7eGPde3rOAADICIiIlfVJ0x6tp3bp7YMuLDP+vrMDusMzfKIrK5mgGrLpBoiwDrqSyYXQdeWSmuUBQ8Aokd37es4AAMgIiIiV+UXKj3bzu1z+ltAmAF9LACV1P1lbpQKkiOGWc5vJwBqr0BaHuLeJwLw9G7ZFk+bRa2HP9b+CvdOxgCIiIjIVfmFSM+2AY1c/5M0GQhPsu4PjLdmjNrKAJ38BlgSCez6c+vH2yqABqRgR+4GU6mBlGmduwYnYQBERETkqppngMwmIG+7tD3gHiBunPXc4H6AbysBk62C3VL26LslQNGRlscr5PqfNkaQyd1gCeMBXWTnr8MJGAARERG5KiUAuiQ9Fx6UanC0OiAm1T4ACooH/IKl7do2iqDrKqRnYQI2zZEmNLTVXgYIAIY8CPgEAbfOu+ZLcTQGQERERK6qeReY8aj0HJMKaDyBvrcAsNThBHUiA3S13LpddAj4/s/SyC9ZRwHQ8OnA/Hz7wKuHYgBERETkqvzkUWCWLrDKi9Kz3EXlGwTE3ixtR46wBkxt1QDJGaC4W6XnHUuBDx4CyvKl1x0FQC6EARAREZGral4DVFkoPeuirOc8shZ46hsgcrhNBqitLrBy6fnm54H0VwGNVhpVtnq8lDVqaw4gF8QAiIiIyFXJAdDVK4CpEai4IL22DYD8I4DYVMv5lgCovgJoamj5eXIXmE8AMO4F4PlsILi/9PnZfwMaqqTjzecAckEMgIiIiFyVT6A05ByQCpvlDJA+qvXzvQMAlcZ6/u53gO/fsh6Xu8C8A6Tn4H7ALb+VtveslJ79wgBPn+66AqdhAEREROSq1Gprt1b1JWsNkK6NAEitluqCAGnE2Dd/ALJel7rEhLB2gXnrre9JmgJ4+QNNlgkSe0H9D8AAiIiIyLXJ3WCledISFED7c/DIAdPhf1j31VyS3mu2DHv3CbAe0/YBkh+1vmYARERERE4n1/UUHZKefYPb76KSz8/dYt1XU2Kt/1F72C9pAQAjn7BuMwAiIiIip5MzQIWHpOeOZmD2tUyGaG607qstsen+Cmi5hpchGYgaJW2HDLiOxvYcHs5uABEREV0HeX2vosPSs66DEVpyBshWTQnQJ1zatu3+svXwe8AvnwPDHulSM3saBkBERESuTA5o5AxOhxmgVgKg2lJrF5htAbStwDhg7NwuNLBnYhcYERGRK5O7wGRtDYFXzrcJgMKGSs81JS2HwPdyDICIiIhcWfMAqK0h8DK5BkjjBSQ9JG3b1QC1kQHqZdgFRkRE5MquNQCKGQN4+gHDHrYuaWE7CqytGqBehgEQERGRK2te1NxRF5g+GlhgWdMrf6f0XFtq0wXGDBARERH1dM0zQP4dFEEDgNqyHIayOGqzYfBugDVARERErszLzzpxoW8I4Ond+ffK2aPaUmnBU8BtMkAMgIiIiFydnAXqaAh8c3JBtDABV85J225SA8QAiIiIyNXJAZC+g0kQm/PQAlqdtF12RnpmBoiIiIhcQlczQIA1C2Sql55ZA0REREQuITBOeg7uwjpdzUeRuUkXGEeBERERubrbXgYihgFDH7z29zZfGoMZIMd49913ERcXB29vb6SmpmLfvn3tnl9eXo7MzEwYDAZotVoMHDgQmzdvvq7PJCIicml+wcCIDGlEWFfea0uuCerlnBoAffLJJ5g3bx4WL16MAwcOICUlBRMmTMClS5daPb+hoQF33303zp49iw0bNiA3NxerV69GVFRUlz+TiIjIrdlmgLz8AY17dA6phBDCWV88NTUVo0ePxt/+9jcAgNlsRkxMDObMmYPf//73Lc5fuXIlli1bhhMnTsDT07NbPrM1lZWV0Ov1qKiogE7nHpEwERG5qd3vAN/8QdrWxwAvHHNue67Dtfz9dloGqKGhAfv370d6erq1MWo10tPTkZ2d3ep7Nm3ahLS0NGRmZiI8PBxJSUlYsmQJTCZTlz8TAOrr61FZWWn3ICIicgu2GSA3GQIPODEAKikpgclkQnh4uN3+8PBwGI3GVt9z5swZbNiwASaTCZs3b8bChQvx1ltv4Y033ujyZwLA0qVLodfrlUdMTMx1Xh0REZGLsB0F5iYF0EAPKIK+FmazGWFhYVi1ahVGjhyJqVOn4pVXXsHKlSuv63MXLFiAiooK5XH+/PluajEREVEP52tTBO1GGSCnVTqFhIRAo9GguLjYbn9xcTEiIiJafY/BYICnpyc0Go2yLzExEUajEQ0NDV36TADQarXQarXXcTVEREQuyjYD5CZzAAFOzAB5eXlh5MiRyMrKUvaZzWZkZWUhLS2t1feMHTsWeXl5MJvNyr6TJ0/CYDDAy8urS59JRETk1lgD5Hjz5s3D6tWrsW7dOuTk5GDWrFmoqanBk08+CQCYMWMGFixYoJw/a9YslJWVYe7cuTh58iS++uorLFmyBJmZmZ3+TCIiIrLh5WtdTd6NaoCcOth/6tSpuHz5MhYtWgSj0Yjhw4djy5YtShFzQUEB1GprjBYTE4OtW7fihRdeQHJyMqKiojB37lzMnz+/059JREREzfiGABUFbtUF5tR5gHoqzgNERERuZdUdQOFBYPLfgZRpzm5Nl13L32/3mO6RiIiI2pY8FaivAuLGObslDsMAiIiIyN3dPEt6uBGXmgeIiIiIqDswACIiIiK3wwCIiIiI3A4DICIiInI7DICIiIjI7TAAIiIiIrfDAIiIiIjcDgMgIiIicjsMgIiIiMjtMAAiIiIit8MAiIiIiNwOAyAiIiJyOwyAiIiIyO0wACIiIiK34+HsBvREQggAQGVlpZNbQkRERJ0l/92W/463hwFQK6qqqgAAMTExTm4JERERXauqqiro9fp2z1GJzoRJbsZsNqOwsBD+/v5QqVTd+tmVlZWIiYnB+fPnodPpuvWze4Lefn0Ar7E36O3XB/T+a+zt1wfwGrtCCIGqqipERkZCrW6/yocZoFao1WpER0ff0K+h0+l67Tc00PuvD+A19ga9/fqA3n+Nvf36AF7jteoo8yNjETQRERG5HQZARERE5HYYADmYVqvF4sWLodVqnd2UG6K3Xx/Aa+wNevv1Ab3/Gnv79QG8xhuNRdBERETkdpgBIiIiIrfDAIiIiIjcDgMgIiIicjsMgIiIiMjtMAByoHfffRdxcXHw9vZGamoq9u3b5+wmdcnSpUsxevRo+Pv7IywsDA8++CByc3PtzrnjjjugUqnsHs8995yTWnztXn311RbtHzx4sHK8rq4OmZmZCA4ORp8+fTBlyhQUFxc7scXXLi4ursU1qlQqZGZmAnDNe7hr1y7cd999iIyMhEqlwsaNG+2OCyGwaNEiGAwG+Pj4ID09HadOnbI7p6ysDBkZGdDpdAgICMDTTz+N6upqB15F29q7vsbGRsyfPx/Dhg2Dn58fIiMjMWPGDBQWFtp9Rmv3/c0333TwlbSto3v4xBNPtGj/xIkT7c5x1XsIoNWfSZVKhWXLlinn9PR72Jm/EZ35HVpQUIB7770Xvr6+CAsLw8svv4ympqZuaycDIAf55JNPMG/ePCxevBgHDhxASkoKJkyYgEuXLjm7adds586dyMzMxJ49e7Bt2zY0NjbinnvuQU1Njd15zzzzDIqKipTHn/70Jye1uGuGDh1q1/4ffvhBOfbCCy/giy++wPr167Fz504UFhbioYcecmJrr91PP/1kd33btm0DADzyyCPKOa52D2tqapCSkoJ333231eN/+tOf8Ne//hUrV67E3r174efnhwkTJqCurk45JyMjA8ePH8e2bdvw5ZdfYteuXXj22WcddQntau/6amtrceDAASxcuBAHDhzAp59+itzcXNx///0tzn399dft7uucOXMc0fxO6egeAsDEiRPt2v/RRx/ZHXfVewjA7rqKioqwZs0aqFQqTJkyxe68nnwPO/M3oqPfoSaTCffeey8aGhqwe/durFu3DmvXrsWiRYu6r6GCHGLMmDEiMzNTeW0ymURkZKRYunSpE1vVPS5duiQAiJ07dyr7br/9djF37lznNeo6LV68WKSkpLR6rLy8XHh6eor169cr+3JycgQAkZ2d7aAWdr+5c+eKfv36CbPZLIRw/XsIQHz22WfKa7PZLCIiIsSyZcuUfeXl5UKr1YqPPvpICCHEL7/8IgCIn376STnn66+/FiqVSly8eNFhbe+M5tfXmn379gkA4ty5c8q+vn37irfffvvGNq6btHaNM2fOFA888ECb7+lt9/CBBx4Qd955p90+V7qHQrT8G9GZ36GbN28WarVaGI1G5ZwVK1YInU4n6uvru6VdzAA5QENDA/bv34/09HRln1qtRnp6OrKzs53Ysu5RUVEBAAgKCrLb/+GHHyIkJARJSUlYsGABamtrndG8Ljt16hQiIyORkJCAjIwMFBQUAAD279+PxsZGu/s5ePBgxMbGuuz9bGhowAcffICnnnrKbgFgV7+HtvLz82E0Gu3um16vR2pqqnLfsrOzERAQgFGjRinnpKenQ61WY+/evQ5v8/WqqKiASqVCQECA3f4333wTwcHBGDFiBJYtW9at3QqOsGPHDoSFhWHQoEGYNWsWSktLlWO96R4WFxfjq6++wtNPP93imCvdw+Z/IzrzOzQ7OxvDhg1DeHi4cs6ECRNQWVmJ48ePd0u7uBiqA5SUlMBkMtndSAAIDw/HiRMnnNSq7mE2m/G73/0OY8eORVJSkrL/scceQ9++fREZGYkjR45g/vz5yM3NxaeffurE1nZeamoq1q5di0GDBqGoqAivvfYabr31Vhw7dgxGoxFeXl4t/qiEh4fDaDQ6p8HXaePGjSgvL8cTTzyh7HP1e9icfG9a+zmUjxmNRoSFhdkd9/DwQFBQkMvd27q6OsyfPx/Tp0+3W2Tyt7/9LW666SYEBQVh9+7dWLBgAYqKirB8+XIntrbzJk6ciIceegjx8fE4ffo0/v3f/x2TJk1CdnY2NBpNr7qH69atg7+/f4vudVe6h639jejM71Cj0djqz6p8rDswAKLrkpmZiWPHjtnVxwCw628fNmwYDAYD7rrrLpw+fRr9+vVzdDOv2aRJk5Tt5ORkpKamom/fvvjnP/8JHx8fJ7bsxnjvvfcwadIkREZGKvtc/R66s8bGRjz66KMQQmDFihV2x+bNm6dsJycnw8vLC//6r/+KpUuXusSSC9OmTVO2hw0bhuTkZPTr1w87duzAXXfd5cSWdb81a9YgIyMD3t7edvtd6R629TeiJ2AXmAOEhIRAo9G0qHAvLi5GRESEk1p1/WbPno0vv/wS3333HaKjo9s9NzU1FQCQl5fniKZ1u4CAAAwcOBB5eXmIiIhAQ0MDysvL7c5x1ft57tw5bN++Hb/5zW/aPc/V76F8b9r7OYyIiGgxMKGpqQllZWUuc2/l4OfcuXPYtm2bXfanNampqWhqasLZs2cd08BulpCQgJCQEOX7sjfcQwD4/vvvkZub2+HPJdBz72FbfyM68zs0IiKi1Z9V+Vh3YADkAF5eXhg5ciSysrKUfWazGVlZWUhLS3Niy7pGCIHZs2fjs88+w7fffov4+PgO33Po0CEAgMFguMGtuzGqq6tx+vRpGAwGjBw5Ep6ennb3Mzc3FwUFBS55P99//32EhYXh3nvvbfc8V7+H8fHxiIiIsLtvlZWV2Lt3r3Lf0tLSUF5ejv379yvnfPvttzCbzUoA2JPJwc+pU6ewfft2BAcHd/ieQ4cOQa1Wt+g2chUXLlxAaWmp8n3p6vdQ9t5772HkyJFISUnp8Nyedg87+hvRmd+haWlpOHr0qF0wKwf0Q4YM6baGkgN8/PHHQqvVirVr14pffvlFPPvssyIgIMCuwt1VzJo1S+j1erFjxw5RVFSkPGpra4UQQuTl5YnXX39d/PzzzyI/P198/vnnIiEhQdx2221Obnnnvfjii2LHjh0iPz9f/PjjjyI9PV2EhISIS5cuCSGEeO6550RsbKz49ttvxc8//yzS0tJEWlqak1t97Uwmk4iNjRXz58+32++q97CqqkocPHhQHDx4UAAQy5cvFwcPHlRGQb355psiICBAfP755+LIkSPigQceEPHx8eLq1avKZ0ycOFGMGDFC7N27V/zwww9iwIABYvr06c66JDvtXV9DQ4O4//77RXR0tDh06JDdz6Y8amb37t3i7bffFocOHRKnT58WH3zwgQgNDRUzZsxw8pVZtXeNVVVV4qWXXhLZ2dkiPz9fbN++Xdx0001iwIABoq6uTvkMV72HsoqKCuHr6ytWrFjR4v2ucA87+hshRMe/Q5uamkRSUpK45557xKFDh8SWLVtEaGioWLBgQbe1kwGQA73zzjsiNjZWeHl5iTFjxog9e/Y4u0ldAqDVx/vvvy+EEKKgoEDcdtttIigoSGi1WtG/f3/x8ssvi4qKCuc2/BpMnTpVGAwG4eXlJaKiosTUqVNFXl6ecvzq1avi+eefF4GBgcLX11dMnjxZFBUVObHFXbN161YBQOTm5trtd9V7+N1337X6vTlz5kwhhDQUfuHChSI8PFxotVpx1113tbj20tJSMX36dNGnTx+h0+nEk08+KaqqqpxwNS21d335+flt/mx+9913Qggh9u/fL1JTU4Verxfe3t4iMTFRLFmyxC54cLb2rrG2tlbcc889IjQ0VHh6eoq+ffuKZ555psU/kq56D2V///vfhY+PjygvL2/xfle4hx39jRCic79Dz549KyZNmiR8fHxESEiIePHFF0VjY2O3tVNlaSwRERGR22ANEBEREbkdBkBERETkdhgAERERkdthAERERERuhwEQERERuR0GQEREROR2GAARERGR22EARETUCSqVChs3bnR2M4iomzAAIqIe74knnoBKpWrxmDhxorObRkQuysPZDSAi6oyJEyfi/ffft9un1Wqd1BoicnXMABGRS9BqtYiIiLB7BAYGApC6p1asWIFJkybBx8cHCQkJ2LBhg937jx49ijvvvBM+Pj4IDg7Gs88+i+rqartz1qxZg6FDh0Kr1cJgMGD27Nl2x0tKSjB58mT4+vpiwIAB2LRp0429aCK6YRgAEVGvsHDhQkyZMgWHDx9GRkYGpk2bhpycHABATU0NJkyYgMDAQPz0009Yv349tm/fbhfgrFixApmZmXj22Wdx9OhRbNq0Cf3797f7Gq+99hoeffRRHDlyBL/61a+QkZGBsrIyh14nEXWTbltWlYjoBpk5c6bQaDTCz8/P7vEf//EfQghp9ennnnvO7j2pqali1qxZQgghVq1aJQIDA0V1dbVy/KuvvhJqtVpZSTwyMlK88sorbbYBgPjDH/6gvK6urhYAxNdff91t10lEjsMaICJyCePHj8eKFSvs9gUFBSnbaWlpdsfS0tJw6NAhAEBOTg5SUlLg5+enHB87dizMZjNyc3OhUqlQWFiIu+66q902JCcnK9t+fn7Q6XS4dOlSVy+JiJyIARARuQQ/P78WXVLdxcfHp1PneXp62r1WqVQwm803oklEdIOxBoiIeoU9e/a0eJ2YmAgASExMxOHDh1FTU6Mc//HHH6FWqzFo0CD4+/sjLi4OWVlZDm0zETkPM0BE5BLq6+thNBrt9nl4eCAkJAQAsH79eowaNQrjxo3Dhx9+iH379uG9994DAGRkZGDx4sWYOXMmXn31VVy+fBlz5szB448/jvDwcADAq6++iueeew5hYWGYNGkSqqqq8OOPP2LOnDmOvVAicggGQETkErZs2QKDwWC3b9CgQThx4gQAaYTWxx9/jOeffx4GgwEfffQRhgwZAgDw9fXF1q1bMXfuXIwePRq+vr6YMmUKli9frnzWzJkzUVdXh7fffhsvvfQSQkJC8PDDDzvuAonIoVRCCOHsRhARXQ+VSoXPPvsMDz74oLObQkQugjVARERE5HYYABEREZHbYQ0QEbk89uQT0bViBoiIiIjcDgMgIiIicjsMgIiIiMjtMAAiIiIit8MAiIiIiNwOAyAiIiJyOwyAiIiIyO0wACIiIiK3wwCIiIiI3M7/By88WVFj1LluAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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g4ICRI0dK95VKJd56661CvX68zuTJk5GQkIAhQ4bgwoULuHnzJmbMmIFz584BAP7++28AQPPmzdG6dWssX74cW7duxf3793Hw4EFMnDgR1tbWUjsqGIYdGck5cyolJcVg3aZNmxAaGoqvv/7aYF1CQgJ+/vlndOjQAbdv35Zubdu2xblz53Dz5s089zlixAjUqVMHixcvLvBZGTlatWqFvXv34vnz5zh79izmz5+P5ORkDBo0CBEREVK70aNHIzIyEr/++iuAf4bzY2NjMWrUKINtVq9eXe9+Tmh5/vx5get63TZu374NIQQWLFigFyArV66MhQsXAgDi4uIKvL8cd+7cQb169Qp1plFB5TwnXj677vfff4e3tzfs7e3h5OSEypUr4/333weAAoUdnU6HNWvWwMPDAzY2NqhUqRIqV66My5cvF+jxxtT8qhUrVuDq1atwc3PDW2+9hUWLFhX6jSnn7MKCcHV1NQjYdevWBYBivbzAgwcPAAD16tUzWNegQQM8efLE4HRkY/8WIiMjpVDl4OCAypUro0OHDgAK9rzIj729Pby9vQ1uuZ167uHhoXe/du3asLCwkPr5wYMHsLCwkD6g5dBoNHBycpL67M6dOwBQoGvoVKtWzSAAlS9fvlCvH6/To0cPrF+/HqdOnULz5s1Rr149/PTTT/jkk08A6H9Y3bNnD7y8vDBu3DjUrFkTvXv3xuDBg9GsWbN8v/IkQ5yzIyNqtRpVqlTJ9dTJnDk8ub0gh4SEID09HatWrcKqVasM1u/cuRMff/xxrvvMGd0ZM2YMfvjhB6PqViqVaNWqFVq1aoW6deti7NixCAkJkYKDj48PXFxc8PXXX6N9+/b4+uuvodFo4O3tnWs9uSlMEHvdNnQ6HQBg9uzZ8PHxybXtqy/A5pbznMip686dO+jSpQvq16+P1atXw83NDUqlEj///DPWrFkjHWN+Pv30UyxYsADjxo3DkiVLUKFCBVhYWGDGjBkFenxha87N4MGD8c4772Dfvn04cuQIVq5cieXLl2Pv3r0FnmNR0FGdgsprFC47O9uk+3kdY/4WsrOz0bVrVzx79gzz5s1D/fr1YW9vj8ePH2PMmDEm+Xc1Vl79WtRRz5eZ4vWjIAICAjB27FhcvnwZSqUSTZs2xebNmwH8X3gGgKpVq+K3337DrVu3EBMTAw8PD2g0Gri6uuq1o9dj2JEZX19f/M///A/Onj2Lt956q0CP2blzJxo1aiSFi5dt2rQJu3btyjPsAMDIkSOxdOlSfPzxx+jTp4/RtQNAy5YtAfwzbJ/D0tISw4cPx7Zt27B8+XJ8//33mDBhgtHXHynqi2OtWrUAANbW1rkGLmP3Vbt2bZw5cwaZmZlGXQ8nL9nZ2di1a5d08TIA+PHHH5Geno79+/frjQDkdmGzvI7hu+++Q6dOnaQX6RwJCQn5XmOpIFJSUrBv3z64ubnlOaE7R5UqVTBlyhRMmTIFcXFxaN68OT755BMp7JjyzTAqKgqpqal6ozs5I585E4RzRlASEhL0Hpsz0vCygtaWc02sGzduGKz766+/UKlSJYMRJ2NcuXIFN2/exPbt2zF69GhpeW4XpjRlv+bm1q1beqNut2/fhk6nk/rZ3d0dOp0Ot27d0nuOxMbGIiEhQeqznIn9V69eLVUfQuzt7aHVaqX7R48eha2tLdq2bWvQ1sPDQxrpioiIQHR0NMaMGVNSpcoCv8aSmblz58LOzg7jxo1DbGyswfpXP6E8fPgQp06dwuDBgzFo0CCD29ixY3H79u18r2ibM7pz8eJFvfk2+Tlx4kSun5Zyvod/dbh+1KhReP78OSZOnIiUlBS979ULK+dN4dU3o4JydnZGx44dsWnTJr1QliM+Pt6ofQ0cOBBPnjzB559/brDO2E+W2dnZmDZtGq5fv45p06ZBpVIB+L9PsC9vNzExEVu3bjXYhr29fa71W1paGtQVEhJi1Hyll/39998YNWoUnj17hg8++CDfkZJXv1ZxdnaGq6urdCZLTv2m+FoNALKysrBp0ybpfkZGBjZt2oTKlSujRYsWAP7vzfXUqVN6tX755ZcG2ytobVWqVEHTpk2xfft2vX+Lq1ev4siRI+jZs6exh6Qnt+eFEAJr1641aFvUv6PXeXVeYc6V1XNCbM4xv3oF6pwLUeacdditWzc4OjoiKCgIaWlpem1NPWJjrNOnT2Pv3r0YP3481Gp1nu10Op30Gv/qnCzKH0d2ZMbDwwO7du3CsGHDUK9ePekKykII3Lt3D7t27YKFhQWqVasG4J/TrIUQeY7I9OzZE1ZWVti5c2e+p7OPGDECS5YswcWLFwtU59SpU/HixQv0798f9evXR0ZGBk6fPo3du3ejRo0aGDt2rF77Zs2aoVGjRggJCUGDBg3QvHnzgnVILnLelKZNmwYfHx9YWlpi6NChhdrGhg0b0K5dOzRu3BgTJkxArVq1EBsbi7CwMDx69AiXLl0CADRt2hSWlpZYvnw5EhMTYWNjg86dO8PZ2dlgm6NHj8aOHTsQGBiIs2fP4p133kFqaiqOHj2KKVOmoG/fvvnWlJiYKM3JevHihXQF5Tt37mDo0KFYsmSJ1LZbt25QKpXo3bu3FCC/+uorODs7GwS4Fi1aYOPGjVi6dCnq1KkDZ2dndO7cGb169cLixYsxduxYvP3227hy5Qp27twpjXwVxOPHj6WaU1JSEBERIV1BedasWXqTgV+VnJyMatWqYdCgQfDy8oKDgwOOHj2KP//8U+/r2BYtWmD37t0IDAxEq1at4ODgYPTVel1dXbF8+XLcv38fdevWxe7du3Hx4kV8+eWX0mhcw4YN0aZNG8yfPx/Pnj1DhQoV8O233yIrK8tge4WpbeXKlejRowe0Wi3Gjx8vnXquVqtN9vMn9evXR+3atTF79mw8fvwYKpUKe/bsyXXOijF/Ry8/R1/16geYe/fuoU+fPujevTvCwsLw9ddfY/jw4fDy8gIAeHl5wc/PD19++SUSEhLQoUMHnD17Ftu3b0e/fv3QqVMnAIBKpcKaNWvw73//G61atcLw4cNRvnx5XLp0CS9evMD27dsL3U+5uXz5svRh7/bt20hMTMTSpUulWnP+XR88eIDBgwejT58+0Gg0uHbtGoKDg9GkSRN8+umnetucPn060tLS0LRpU2RmZmLXrl3SMb46J4teo6RP/6KScfv2bTF58mRRp04dUa5cOWFrayvq168vJk2apHfRt8aNG4vq1avnu62OHTsKZ2dnkZmZme9Vhl++iN7rTj0/ePCgGDdunKhfv75wcHAQSqVS1KlTR0ydOjXPKyivWLFCABCffvqpwbr86sIrpytnZWWJqVOnisqVKwuFQpHrRQVftw0hhLhz544YPXq00Gg0wtraWlStWlX06tVLfPfdd3rtvvrqK1GrVi1haWn52osKvnjxQnzwwQeiZs2awtraWmg0GjFo0CBx586dXPskx6un9To4OAgPDw8xcuRIceTIkVwfs3//ftGkSRPp4njLly+XTn9/+bTpmJgY4evrKxwdHfUuKpiWliZmzZolqlSpImxtbUXbtm1FWFhYrseVm5cvWKhQKIRKpRINGzYUEyZMEGfOnMn1MS//O6Snp4s5c+YILy8v4ejoKOzt7YWXl5fBhRFTUlLE8OHDhZOTU64XFQwJCTHYT0EvKuju7i4+//xzg8ffuXNHeHt7CxsbG+Hi4iLef/99ERoaarDNvGrL66KCR48eFW3bthW2trZCpVKJ3r1753lRwVf/BvM6Jf5VERERwtvbWzg4OIhKlSqJCRMmiEuXLhnUk9ffUV7yO/X85cfm1B8RESEGDRokHB0dRfny5UVAQECuFxX8+OOPpb8XNze3PC8quH//fvH2229LfffWW2+Jb775Rq++3C7f4OfnV6CrXOd1EVEAws/PT2r37Nkz0bdvX6HRaIRSqRQ1a9YU8+bNMzgVPWebXl5e0kU7u3TpIp1ST4XD38aiMmPt2rWYOXMm7t+/z081RDJV0N/XIyoMztmhMkEIgc2bN6NDhw4MOkREVCics0OlWmpqKvbv348TJ07gypUrRp/eTkREby6GHSrV4uPjMXz4cDg5OeH9998v8qntRET05uGcHSIiIpI1ztkhIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWbMydwGlgU6nQ1RUFBwdHaFQKMxdDhERERWAEALJyclwdXWFhUXe4zcMOwCioqLg5uZm7jKIiIjICA8fPkS1atXyXM+wA8DR0RHAP52lUqnMXA0REREVRFJSEtzc3KT38bww7ADSV1cqlYphh4iIqIx53RQUTlAmIiIiWWPYISIiIllj2CEiIiJZ45wdIiKiMkoIgaysLGRnZ5u7lGJhaWkJKyurIl8WhmGHiIioDMrIyEB0dDRevHhh7lKKlZ2dHapUqQKlUmn0Nhh2iMysxns/mbuEEnN/ma+5SyCSBZ1Oh3v37sHS0hKurq5QKpWyuyiuEAIZGRmIj4/HvXv34OHhke+FA/PDsENERFTGZGRkQKfTwc3NDXZ2duYup9jY2trC2toaDx48QEZGBsqVK2fUdjhBmYiIqIwydqSjLDHFMcq/l4iIiOiNxrBDREREssY5O0RERDJSkic9GHvSwYYNG7By5UrExMTAy8sL69evx1tvvWXi6v4PR3aIiIioxOzevRuBgYFYuHAhzp8/Dy8vL/j4+CAuLq7Y9smwQ0RERCVm9erVmDBhAsaOHQtPT08EBwfDzs4OW7ZsKbZ9MuwQERFRicjIyEB4eDi8vb2lZRYWFvD29kZYWFix7Zdhh4iIiErEkydPkJ2dDRcXF73lLi4uiImJKbb9MuwQERGRrDHsEBERUYmoVKkSLC0tERsbq7c8NjYWGo2m2PZr1rCzceNGNGnSBCqVCiqVClqtFgcPHpTWp6Wlwd/fHxUrVoSDgwMGDhxo0EGRkZHw9fWFnZ0dnJ2dMWfOHGRlZZX0oRAREdFrKJVKtGjRAseOHZOW6XQ6HDt2DFqtttj2a9awU61aNSxbtgzh4eE4d+4cOnfujL59++LatWsAgJkzZ+LHH39ESEgITp48iaioKAwYMEB6fHZ2Nnx9fZGRkYHTp09j+/bt2LZtGz766CNzHRIRERHlIzAwEF999RW2b9+O69evY/LkyUhNTcXYsWOLbZ9mvahg79699e5/8skn2LhxI/744w9Uq1YNmzdvxq5du9C5c2cAwNatW9GgQQP88ccfaNOmDY4cOYKIiAgcPXoULi4uaNq0KZYsWYJ58+Zh0aJFRfo5eCIiIjK9IUOGID4+Hh999BFiYmLQtGlTHDp0yGDSsimVmisoZ2dnIyQkBKmpqdBqtQgPD0dmZqbe6Wn169dH9erVERYWhjZt2iAsLAyNGzfW6yAfHx9MnjwZ165dQ7NmzXLdV3p6OtLT06X7SUlJxXdgREREJcjYqxqXpICAAAQEBJTY/sw+QfnKlStwcHCAjY0NJk2ahH379sHT0xMxMTFQKpVwcnLSa//y6WkxMTG5nr6Wsy4vQUFBUKvV0s3Nzc20B0VERESlhtnDTr169XDx4kWcOXMGkydPhp+fHyIiIop1n/Pnz0diYqJ0e/jwYbHuj4iIiMzH7F9jKZVK1KlTBwDQokUL/Pnnn1i7di2GDBmCjIwMJCQk6I3uvHx6mkajwdmzZ/W2l3O2Vn6nsNnY2MDGxsbER0JERESlkdlHdl6l0+mQnp6OFi1awNraWu/0tBs3biAyMlI6PU2r1eLKlSt6Px4WGhoKlUoFT0/PEq+diIiISh+zjuzMnz8fPXr0QPXq1ZGcnIxdu3bhl19+weHDh6FWqzF+/HgEBgaiQoUKUKlUmDp1KrRaLdq0aQMA6NatGzw9PTFq1CisWLECMTEx+PDDD+Hv78+RGyIiIgJg5rATFxeH0aNHIzo6Gmq1Gk2aNMHhw4fRtWtXAMCaNWtgYWGBgQMHIj09HT4+Pvjiiy+kx1taWuLAgQOYPHkytFot7O3t4efnh8WLF5vrkIiIiEqMEMLcJRQ7UxyjQrwJPfUaSUlJUKvVSExMhEqlMnc59Iap8d5P5i6hxJSFU2KJyoLs7GzcvHkTzs7OqFixornLKVZPnz5FXFwc6tatC0tLS711BX3/NvsEZSIiIiocS0tLODk5SXNW7ezsoFAozFyVaQkh8OLFC8TFxcHJyckg6BQGww4REVEZlHPW8csn6ciRk5NTkX8klGGHiIioDFIoFKhSpQqcnZ2RmZlp7nKKhbW1dZFGdHIw7BAREZVhlpaWJgkEclbqrrNDREREZEoMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGtGhZ27d++aug4iIiKiYmFU2KlTpw46deqEr7/+GmlpaaauiYiIiMhkjAo758+fR5MmTRAYGAiNRoOJEyfi7Nmzpq6NiIiIqMiMCjtNmzbF2rVrERUVhS1btiA6Ohrt2rVDo0aNsHr1asTHx5u6TiIiIiKjFGmCspWVFQYMGICQkBAsX74ct2/fxuzZs+Hm5obRo0cjOjraVHUSERERGaVIYefcuXOYMmUKqlSpgtWrV2P27Nm4c+cOQkNDERUVhb59+5qqTiIiIiKjWBnzoNWrV2Pr1q24ceMGevbsiR07dqBnz56wsPgnO9WsWRPbtm1DjRo1TFkrERERUaEZFXY2btyIcePGYcyYMahSpUqubZydnbF58+YiFUdERERUVEaFnVu3br22jVKphJ+fnzGbJyIiIjIZo+bsbN26FSEhIQbLQ0JCsH379iIXRURERGQqRoWdoKAgVKpUyWC5s7MzPv300yIXRURERGQqRoWdyMhI1KxZ02C5u7s7IiMji1wUERERkakYFXacnZ1x+fJlg+WXLl1CxYoVi1wUERERkakYFXaGDRuGadOm4cSJE8jOzkZ2djaOHz+O6dOnY+jQoaaukYiIiMhoRp2NtWTJEty/fx9dunSBldU/m9DpdBg9ejTn7BAREVGpYlTYUSqV2L17N5YsWYJLly7B1tYWjRs3hru7u6nrIyIiIioSo8JOjrp166Ju3bqmqoWIiIjI5Iyas5OdnY3Nmzdj+PDh8Pb2RufOnfVuBRUUFIRWrVrB0dERzs7O6NevH27cuKHXJi0tDf7+/qhYsSIcHBwwcOBAxMbG6rWJjIyEr68v7Ozs4OzsjDlz5iArK8uYQyMiIiKZMWpkZ/r06di2bRt8fX3RqFEjKBQKo3Z+8uRJ+Pv7o1WrVsjKysL777+Pbt26ISIiAvb29gCAmTNn4qeffkJISAjUajUCAgIwYMAA/P777wD+CV6+vr7QaDQ4ffo0oqOjMXr0aFhbW3P+EBEREUEhhBCFfVClSpWkH/80pfj4eDg7O+PkyZNo3749EhMTUblyZezatQuDBg0CAPz1119o0KABwsLC0KZNGxw8eBC9evVCVFQUXFxcAADBwcGYN28e4uPjoVQqX7vfpKQkqNVqJCYmQqVSmfSYiF6nxns/mbuEEnN/ma+5SyAiGSno+7dRX2MplUrUqVPH6OLykpiYCACoUKECACA8PByZmZnw9vaW2tSvXx/Vq1dHWFgYACAsLAyNGzeWgg4A+Pj4ICkpCdeuXct1P+np6UhKStK7ERERkTwZFXZmzZqFtWvXwohBoTzpdDrMmDEDbdu2RaNGjQAAMTExUCqVcHJy0mvr4uKCmJgYqc3LQSdnfc663AQFBUGtVks3Nzc3kx0HERERlS5Gzdn57bffcOLECRw8eBANGzaEtbW13vq9e/cWepv+/v64evUqfvvtN2NKKpT58+cjMDBQup+UlMTAQ0REJFNGhR0nJyf079/fZEUEBATgwIEDOHXqFKpVqyYt12g0yMjIQEJCgt7oTmxsLDQajdTm7NmzetvLOVsrp82rbGxsYGNjY7L6iYiIqPQyKuxs3brVJDsXQmDq1KnYt28ffvnlF4MfF23RogWsra1x7NgxDBw4EABw48YNREZGQqvVAgC0Wi0++eQTxMXFwdnZGQAQGhoKlUoFT09Pk9RJREREZZfRFxXMysrCL7/8gjt37mD48OFwdHREVFQUVCoVHBwcCrQNf39/7Nq1Cz/88AMcHR2lOTZqtRq2trZQq9UYP348AgMDUaFCBahUKkydOhVarRZt2rQBAHTr1g2enp4YNWoUVqxYgZiYGHz44Yfw9/fn6A0REREZF3YePHiA7t27IzIyEunp6ejatSscHR2xfPlypKenIzg4uEDb2bhxIwCgY8eOesu3bt2KMWPGAADWrFkDCwsLDBw4EOnp6fDx8cEXX3whtbW0tMSBAwcwefJkaLVa2Nvbw8/PD4sXLzbm0IiIiEhmjL6oYMuWLXHp0iVUrFhRWt6/f39MmDChwNspyNlc5cqVw4YNG7Bhw4Y827i7u+Pnn38u8H6JiIjozWFU2Pn1119x+vRpgwv21ahRA48fPzZJYURERESmYNR1dnQ6HbKzsw2WP3r0CI6OjkUuioiIiMhUjAo73bp1w2effSbdVygUSElJwcKFC03+ExJERERERWHU11irVq2Cj48PPD09kZaWhuHDh+PWrVuoVKkSvvnmG1PXSERERGQ0o8JOtWrVcOnSJXz77be4fPkyUlJSMH78eIwYMQK2tramrpGIiIjIaEZfZ8fKygojR440ZS1EREREJmdU2NmxY0e+60ePHm1UMURERESmZvR1dl6WmZmJFy9eQKlUws7OjmGHiIiISg2jzsZ6/vy53i0lJQU3btxAu3btOEGZiIiIShWjwk5uPDw8sGzZMoNRHyIiIiJzMlnYAf6ZtBwVFWXKTRIREREViVFzdvbv3693XwiB6OhofP7552jbtq1JCiMiIiIyBaPCTr9+/fTuKxQKVK5cGZ07d8aqVatMURcRERGRSRgVdnQ6nanrICIiIioWJp2zQ0RERFTaGDWyExgYWOC2q1evNmYXRERERCZhVNi5cOECLly4gMzMTNSrVw8AcPPmTVhaWqJ58+ZSO4VCYZoqiYiIiIxkVNjp3bs3HB0dsX37dpQvXx7APxcaHDt2LN555x3MmjXLpEUSERERGcuoOTurVq1CUFCQFHQAoHz58li6dCnPxiIiIqJSxaiwk5SUhPj4eIPl8fHxSE5OLnJRRERERKZiVNjp378/xo4di7179+LRo0d49OgR9uzZg/Hjx2PAgAGmrpGIiIjIaEbN2QkODsbs2bMxfPhwZGZm/rMhKyuMHz8eK1euNGmBREREREVhVNixs7PDF198gZUrV+LOnTsAgNq1a8Pe3t6kxREREREVVZEuKhgdHY3o6Gh4eHjA3t4eQghT1UVERERkEkaFnadPn6JLly6oW7cuevbsiejoaADA+PHjedo5ERERlSpGfY01c+ZMWFtbIzIyEg0aNJCWDxkyBIGBgTz9nIhyVeO9n8xdQom5v8zX3CUQ0f9nVNg5cuQIDh8+jGrVqukt9/DwwIMHD0xSGBEREZEpGPU1VmpqKuzs7AyWP3v2DDY2NkUuioiIiMhUjAo777zzDnbs2CHdVygU0Ol0WLFiBTp16mSy4oiIiIiKyqivsVasWIEuXbrg3LlzyMjIwNy5c3Ht2jU8e/YMv//+u6lrJCIiIjKaUSM7jRo1ws2bN9GuXTv07dsXqampGDBgAC5cuIDatWubukYiIiIioxV6ZCczMxPdu3dHcHAwPvjgg+KoiYiIiMhkCj2yY21tjcuXLxdHLUREREQmZ9TXWCNHjsTmzZtNXQsRERGRyRk1QTkrKwtbtmzB0aNH0aJFC4PfxFq9erVJiiMiIiIqqkKFnbt376JGjRq4evUqmjdvDgC4efOmXhuFQmG66oiIiIiKqFBhx8PDA9HR0Thx4gSAf34eYt26dXBxcSmW4oiIiIiKqlBzdl79VfODBw8iNTXV6J2fOnUKvXv3hqurKxQKBb7//nuD/X300UeoUqUKbG1t4e3tjVu3bum1efbsGUaMGAGVSgUnJyeMHz8eKSkpRtdERERE8mLUBOUcr4afwkpNTYWXlxc2bNiQ6/oVK1Zg3bp1CA4OxpkzZ2Bvbw8fHx+kpaVJbUaMGIFr164hNDQUBw4cwKlTp/Duu+8WqS4iIiKSj0J9jaVQKAzm5BRljk6PHj3Qo0ePXNcJIfDZZ5/hww8/RN++fQEAO3bsgIuLC77//nsMHToU169fx6FDh/Dnn3+iZcuWAID169ejZ8+e+M9//gNXV1ejayMiIiJ5KFTYEUJgzJgx0o99pqWlYdKkSQZnY+3du7fIhd27dw8xMTHw9vaWlqnVarRu3RphYWEYOnQowsLC4OTkJAUdAPD29oaFhQXOnDmD/v3757rt9PR0pKenS/eTkpKKXC8RERGVToUKO35+fnr3R44cadJiXhYTEwMABpOfXVxcpHUxMTFwdnbWW29lZYUKFSpIbXITFBSEjz/+2MQVExERUWlUqLCzdevW4qqjRM2fPx+BgYHS/aSkJLi5uZmxIiIiIiouRZqgXJw0Gg0AIDY2Vm95bGystE6j0SAuLk5vfVZWFp49eya1yY2NjQ1UKpXejYiIiOSp1IadmjVrQqPR4NixY9KypKQknDlzBlqtFgCg1WqRkJCA8PBwqc3x48eh0+nQunXrEq+ZiIiISh+jfi7CVFJSUnD79m3p/r1793Dx4kVUqFAB1atXx4wZM7B06VJ4eHigZs2aWLBgAVxdXdGvXz8AQIMGDdC9e3dMmDABwcHByMzMREBAAIYOHcozsYiIiAiAmcPOuXPn0KlTJ+l+zjwaPz8/bNu2DXPnzkVqaireffddJCQkoF27djh06BDKlSsnPWbnzp0ICAhAly5dYGFhgYEDB2LdunUlfixERERUOilEUa8MKANJSUlQq9VITEzk/B0qcTXe+8ncJVAxuL/M19wlEMleQd+/S+2cHSIiIiJTYNghIiIiWWPYISIiIllj2CEiIiJZM+vZWEREcsWJ5/LEiedlE0d2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1njqORERERkoC5dP0KW/KFA7juwQERGRrDHsEBERkawx7BAREZGsMewQERGRrHGCMhERUQGVhUm7ZIgjO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrsgk7GzZsQI0aNVCuXDm0bt0aZ8+eNXdJREREVArIIuzs3r0bgYGBWLhwIc6fPw8vLy/4+PggLi7O3KURERGRmcki7KxevRoTJkzA2LFj4enpieDgYNjZ2WHLli3mLo2IiIjMrMyHnYyMDISHh8Pb21taZmFhAW9vb4SFhZmxMiIiIioNrMxdQFE9efIE2dnZcHFx0Vvu4uKCv/76K9fHpKenIz09XbqfmJgIAEhKSiq+QonyoEt/Ye4SiIjKpJzXTyFEvu3KfNgxRlBQED7++GOD5W5ubmaohoiIiIoiOTkZarU6z/VlPuxUqlQJlpaWiI2N1VseGxsLjUaT62Pmz5+PwMBA6X5CQgLc3d0RGRmZb2dR0SQlJcHNzQ0PHz6ESqUydzmyxX4uGeznksF+Ljllsa+FEEhOToarq2u+7cp82FEqlWjRogWOHTuGfv36AQB0Oh2OHTuGgICAXB9jY2MDGxsbg+VqtbrM/AOXZSqViv1cAtjPJYP9XDLYzyWnrPV1QQYpynzYAYDAwED4+fmhZcuWeOutt/DZZ58hNTUVY8eONXdpREREZGayCDtDhgxBfHw8PvroI8TExKBp06Y4dOiQwaRlIiIievPIIuwAQEBAQJ5fW72OjY0NFi5cmOtXW2Q67OeSwX4uGeznksF+Ljly7muFeN35WkRERERlWJm/qCARERFRfhh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1qzMXUBpoNPpEBUVBUdHRygUCnOXQ0RERAUghEBycjJcXV1hYZH3+A3DDoCoqCi4ubmZuwwiIiIywsOHD1GtWrU81zPsAHB0dATwT2epVCozV0NEREQFkZSUBDc3N+l9PC8MO4D01ZVKpWLYISIiKmNeNwWFE5SJiIhI1hh2iIiISNYYdoiIiEjWOGeHiIioDMvOzkZmZqa5yygW1tbWsLS0LPJ2GHaIiIjKICEEYmJikJCQYO5SipWTkxM0Gk2RroPHsFOWLFKbu4KSsyjR3BUQEZVqOUHH2dkZdnZ2srsorhACL168QFxcHACgSpUqRm+LYYeIiKiMyc7OloJOxYoVzV1OsbG1tQUAxMXFwdnZ2eivtDhBmYiIqIzJmaNjZ2dn5kqKX84xFmVeEsMOERFRGSW3r65yY4pjZNghIiIiWWPYISIiIlnjBGUiIiK5KOmzdo04c/bUqVNYuXIlwsPDER0djX379qFfv36mr+0lHNkhIiKiEpOamgovLy9s2LChxPbJkR0iIiIqMT169ECPHj1KdJ8c2SEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWSs1YWfZsmVQKBSYMWOGtCwtLQ3+/v6oWLEiHBwcMHDgQMTGxuo9LjIyEr6+vrCzs4OzszPmzJmDrKysEq6eiIiICiIlJQUXL17ExYsXAQD37t3DxYsXERkZWWz7LBVh588//8SmTZvQpEkTveUzZ87Ejz/+iJCQEJw8eRJRUVEYMGCAtD47Oxu+vr7IyMjA6dOnsX37dmzbtg0fffRRSR8CERERFcC5c+fQrFkzNGvWDAAQGBiIZs2aFet7t9mvs5OSkoIRI0bgq6++wtKlS6XliYmJ2Lx5M3bt2oXOnTsDALZu3YoGDRrgjz/+QJs2bXDkyBFERETg6NGjcHFxQdOmTbFkyRLMmzcPixYtglKpNNdhERERlTwjrmhc0jp27AghRInu0+wjO/7+/vD19YW3t7fe8vDwcGRmZuotr1+/PqpXr46wsDAAQFhYGBo3bgwXFxepjY+PD5KSknDt2rU895meno6kpCS9GxEREcmTWUd2vv32W5w/fx5//vmnwbqYmBgolUo4OTnpLXdxcUFMTIzU5uWgk7M+Z11egoKC8PHHHxexeiIiIioLzDay8/DhQ0yfPh07d+5EuXLlSnTf8+fPR2JionR7+PBhie6fiIiISo7Zwk54eDji4uLQvHlzWFlZwcrKCidPnsS6detgZWUFFxcXZGRkICEhQe9xsbGx0Gg0AACNRmNwdlbO/Zw2ubGxsYFKpdK7ERERkTyZLex06dIFV65ckU4/u3jxIlq2bIkRI0ZI/29tbY1jx45Jj7lx4wYiIyOh1WoBAFqtFleuXEFcXJzUJjQ0FCqVCp6eniV+TERERFT6mG3OjqOjIxo1aqS3zN7eHhUrVpSWjx8/HoGBgahQoQJUKhWmTp0KrVaLNm3aAAC6desGT09PjBo1CitWrEBMTAw+/PBD+Pv7w8bGpsSPiYiIqCTpdDpzl1DsTHGMZj/1PD9r1qyBhYUFBg4ciPT0dPj4+OCLL76Q1ltaWuLAgQOYPHkytFot7O3t4efnh8WLF5uxaiIiouKlVCphYWGBqKgoVK5cGUqlEgqFwtxlmZQQAhkZGYiPj4eFhUWRLiejECV9snsplJSUBLVajcTExNI9f2eR2twVlJwycK0IIiJzysjIQHR0NF68eGHuUoqVnZ0dqlSpkmvYKej7d6ke2SEiIqLcKZVKVK9eHVlZWcjOzjZ3OcXC0tISVlZWRR61YtghIiIqoxQKBaytrWFtbW3uUko1s19BmYiIiKg4MewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsMewQERGRrDHsEBERkawx7BAREZGsGRV27t69a+o6iIiIiIqFUWGnTp066NSpE77++mukpaWZuiYiIiIikzEq7Jw/fx5NmjRBYGAgNBoNJk6ciLNnz5q6NiIiIqIiMyrsNG3aFGvXrkVUVBS2bNmC6OhotGvXDo0aNcLq1asRHx9v6jqJiIiIjFKkCcpWVlYYMGAAQkJCsHz5cty+fRuzZ8+Gm5sbRo8ejejoaFPVSURERGSUIoWdc+fOYcqUKahSpQpWr16N2bNn486dOwgNDUVUVBT69u1rqjqJiIiIjGJU2Fm9ejUaN26Mt99+G1FRUdixYwcePHiApUuXombNmnjnnXewbds2nD9/Pt/tbNy4EU2aNIFKpYJKpYJWq8XBgwel9WlpafD390fFihXh4OCAgQMHIjY2Vm8bkZGR8PX1hZ2dHZydnTFnzhxkZWUZc1hEREQkQ1bGPGjjxo0YN24cxowZgypVquTaxtnZGZs3b853O9WqVcOyZcvg4eEBIQS2b9+Ovn374sKFC2jYsCFmzpyJn376CSEhIVCr1QgICMCAAQPw+++/AwCys7Ph6+sLjUaD06dPIzo6GqNHj4a1tTU+/fRTYw6NiIiIZEYhhBDmLuJlFSpUwMqVKzFo0CBUrlwZu3btwqBBgwAAf/31Fxo0aICwsDC0adMGBw8eRK9evRAVFQUXFxcAQHBwMObNm4f4+HgolcoC7TMpKQlqtRqJiYlQqVTFdmxFtkht7gpKzqJEc1dARESlXEHfv436Gmvr1q0ICQkxWB4SEoLt27cbs0lkZ2fj22+/RWpqKrRaLcLDw5GZmQlvb2+pTf369VG9enWEhYUBAMLCwtC4cWMp6ACAj48PkpKScO3aNaPqICIiInkxKuwEBQWhUqVKBsudnZ0L/fXRlStX4ODgABsbG0yaNAn79u2Dp6cnYmJioFQq4eTkpNfexcUFMTExAICYmBi9oJOzPmddXtLT05GUlKR3IyIiInkyKuxERkaiZs2aBsvd3d0RGRlZqG3Vq1cPFy9exJkzZzB58mT4+fkhIiLCmLIKLCgoCGq1Wrq5ubkV6/6IiIjIfIwKO87Ozrh8+bLB8kuXLqFixYqF2pZSqUSdOnXQokULBAUFwcvLC2vXroVGo0FGRgYSEhL02sfGxkKj0QAANBqNwdlZOfdz2uRm/vz5SExMlG4PHz4sVM1ERERUdhgVdoYNG4Zp06bhxIkTyM7ORnZ2No4fP47p06dj6NChRSpIp9MhPT0dLVq0gLW1NY4dOyatu3HjBiIjI6HVagEAWq0WV65cQVxcnNQmNDQUKpUKnp6eee7DxsZGOt0950ZERETyZNSp50uWLMH9+/fRpUsXWFn9swmdTofRo0cXas7O/Pnz0aNHD1SvXh3JycnYtWsXfvnlFxw+fBhqtRrjx49HYGAgKlSoAJVKhalTp0Kr1aJNmzYAgG7dusHT0xOjRo3CihUrEBMTgw8//BD+/v6wsbEx5tCIiIhIZowKO0qlErt378aSJUtw6dIl2NraonHjxnB3dy/UduLi4qSflVCr1WjSpAkOHz6Mrl27AgDWrFkDCwsLDBw4EOnp6fDx8cEXX3whPd7S0hIHDhzA5MmTodVqYW9vDz8/PyxevNiYwyIiIiIZKnXX2TEHXmenFOJ1doiI6DUK+v5t1MhOdnY2tm3bhmPHjiEuLg46nU5v/fHjx43ZLBEREZHJGRV2pk+fjm3btsHX1xeNGjWCQqEwdV1EREREJmFU2Pn222/x3//+Fz179jR1PUREREQmZdSp5znXxiEiIiIq7YwKO7NmzcLatWvBuc1ERERU2hn1NdZvv/2GEydO4ODBg2jYsCGsra311u/du9ckxREREREVlVFhx8nJCf379zd1LUREREQmZ1TY2bp1q6nrICIiIioWRs3ZAYCsrCwcPXoUmzZtQnJyMgAgKioKKSkpJiuOiIiIqKiMGtl58OABunfvjsjISKSnp6Nr165wdHTE8uXLkZ6ejuDgYFPXSURERGQUo0Z2pk+fjpYtW+L58+ewtbWVlvfv31/vV8qJiIiIzM2okZ1ff/0Vp0+fhlKp1Fteo0YNPH782CSFEREREZmCUSM7Op0O2dnZBssfPXoER0fHIhdFREREZCpGhZ1u3brhs88+k+4rFAqkpKRg4cKF/AkJIiIiKlWM+hpr1apV8PHxgaenJ9LS0jB8+HDcunULlSpVwjfffGPqGomIiIiMZlTYqVatGi5duoRvv/0Wly9fRkpKCsaPH48RI0boTVgmIiIiMjejwg4AWFlZYeTIkaashYiIiMjkjAo7O3bsyHf96NGjjSqGSLJIbe4KSs6iRHNXQEQka0aFnenTp+vdz8zMxIsXL6BUKmFnZ8ewQ0RERKWGUWdjPX/+XO+WkpKCGzduoF27dpygTERERKWK0b+N9SoPDw8sW7bMYNSHiIiIyJxMFnaAfyYtR0VFmXKTREREREVi1Jyd/fv3690XQiA6Ohqff/452rZta5LCiIiIiEzBqLDTr18/vfsKhQKVK1dG586dsWrVKlPURURERGQSRoUdnU5n6jqIiIiIioVJ5+wUVlBQEFq1agVHR0c4OzujX79+uHHjhl6btLQ0+Pv7o2LFinBwcMDAgQMRGxur1yYyMhK+vr6ws7ODs7Mz5syZg6ysrJI8FCIiIiqljBrZCQwMLHDb1atX57nu5MmT8Pf3R6tWrZCVlYX3338f3bp1Q0REBOzt7QEAM2fOxE8//YSQkBCo1WoEBARgwIAB+P333wEA2dnZ8PX1hUajwenTpxEdHY3Ro0fD2toan376qTGHR0RERDKiEEKIwj6oU6dOuHDhAjIzM1GvXj0AwM2bN2FpaYnmzZv/38YVChw/frzA242Pj4ezszNOnjyJ9u3bIzExEZUrV8auXbswaNAgAMBff/2FBg0aICwsDG3atMHBgwfRq1cvREVFwcXFBQAQHByMefPmIT4+Hkql8rX7TUpKglqtRmJiIlQqVWG6omS9SVcVfpPwCspEREYp6Pu3UV9j9e7dG+3bt8ejR49w/vx5nD9/Hg8fPkSnTp3Qq1cvnDhxAidOnChU0AGAxMR/XvQrVKgAAAgPD0dmZia8vb2lNvXr10f16tURFhYGAAgLC0Pjxo2loAMAPj4+SEpKwrVr13LdT3p6OpKSkvRuREREJE9GhZ1Vq1YhKCgI5cuXl5aVL18eS5cuNfpsLJ1OhxkzZqBt27Zo1KgRACAmJgZKpRJOTk56bV1cXBATEyO1eTno5KzPWZeboKAgqNVq6ebm5mZUzURERFT6GRV2kpKSEB8fb7A8Pj4eycnJRhXi7++Pq1ev4ttvvzXq8YUxf/58JCYmSreHDx8W+z6JiIjIPIwKO/3798fYsWOxd+9ePHr0CI8ePcKePXswfvx4DBgwoNDbCwgIwIEDB3DixAlUq1ZNWq7RaJCRkYGEhAS99rGxsdBoNFKbV8/Oyrmf0+ZVNjY2UKlUejciIiKSJ6PCTnBwMHr06IHhw4fD3d0d7u7uGD58OLp3744vvviiwNsRQiAgIAD79u3D8ePHUbNmTb31LVq0gLW1NY4dOyYtu3HjBiIjI6HVagEAWq0WV65cQVxcnNQmNDQUKpUKnp6exhweERERyYhRZ2PlSE1NxZ07dwAAtWvXlk4XL6gpU6Zg165d+OGHH6SzugBArVbD1tYWADB58mT8/PPP2LZtG1QqFaZOnQoAOH36NIB/Tj1v2rQpXF1dsWLFCsTExGDUqFH497//XeBTz3k2FpkVz8YiIjJKsZ6NlSM6OhrR0dHw8PCAvb09CpubNm7ciMTERHTs2BFVqlSRbrt375barFmzBr169cLAgQPRvn17aDQa7N27V1pvaWmJAwcOwNLSElqtFiNHjsTo0aOxePHiohwaERERyYRRIztPnz7F4MGDceLECSgUCty6dQu1atXCuHHjUL58+TL3+1gc2SGz4sgOEZFRinVkZ+bMmbC2tkZkZCTs7Oyk5UOGDMGhQ4eM2SQRERFRsTDq5yKOHDmCw4cP6505BQAeHh548OCBSQojIiIiMgWjRnZSU1P1RnRyPHv2DDY2NkUuioiIiMhUjAo777zzDnbs2CHdVygU0Ol0WLFiBTp16mSy4oiIiIiKyqivsVasWIEuXbrg3LlzyMjIwNy5c3Ht2jU8e/ZM+jVyIiIiotLAqJGdRo0a4ebNm2jXrh369u2L1NRUDBgwABcuXEDt2rVNXSMRERGR0Qo9spOZmYnu3bsjODgYH3zwQXHURERERGQyhR7Zsba2xuXLl4ujFiIiIiKTM+prrJEjR2Lz5s2mroWIiIjI5IyaoJyVlYUtW7bg6NGjaNGihcFvYq1evdokxREREREVVaHCzt27d1GjRg1cvXoVzZs3BwDcvHlTr41CoTBddURvgjfpZ0D40xhEZAaFCjseHh6Ijo7GiRMnAPzz8xDr1q2Di4tLsRRHREREVFSFmrPz6m+GHjx4EKmpqSYtiIiIiMiUjJqgnMOIH0wnIiIiKlGFCjsKhcJgTg7n6BAREVFpVqg5O0IIjBkzRvqxz7S0NEyaNMngbKy9e/earkIiIiKiIihU2PHz89O7P3LkSJMWQ0RERGRqhQo7W7duLa46iIiIiIpFkSYoExEREZV2DDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrZg07p06dQu/eveHq6gqFQoHvv/9eb70QAh999BGqVKkCW1tbeHt749atW3ptnj17hhEjRkClUsHJyQnjx49HSkpKCR4FERERlWZmDTupqanw8vLChg0bcl2/YsUKrFu3DsHBwThz5gzs7e3h4+ODtLQ0qc2IESNw7do1hIaG4sCBAzh16hTefffdkjoEIiIiKuUUopT8dLlCocC+ffvQr18/AP+M6ri6umLWrFmYPXs2ACAxMREuLi7Ytm0bhg4diuvXr8PT0xN//vknWrZsCQA4dOgQevbsiUePHsHV1bVA+05KSoJarUZiYiJUKlWxHJ9JLFKbuwKiolmUaO4KiEhGCvr+XWrn7Ny7dw8xMTHw9vaWlqnVarRu3RphYWEAgLCwMDg5OUlBBwC8vb1hYWGBM2fO5Lnt9PR0JCUl6d2IiIhInkpt2ImJiQEAuLi46C13cXGR1sXExMDZ2VlvvZWVFSpUqCC1yU1QUBDUarV0c3NzM3H1REREVFoU6odA5WL+/PkIDAyU7iclJTHwEJWEN+mrWH5lR1RqlNqRHY1GAwCIjY3VWx4bGyut02g0iIuL01uflZWFZ8+eSW1yY2NjA5VKpXcjIiIieSq1YadmzZrQaDQ4duyYtCwpKQlnzpyBVqsFAGi1WiQkJCA8PFxqc/z4ceh0OrRu3brEayYiIqLSx6xfY6WkpOD27dvS/Xv37uHixYuoUKECqlevjhkzZmDp0qXw8PBAzZo1sWDBAri6ukpnbDVo0ADdu3fHhAkTEBwcjMzMTAQEBGDo0KEFPhOLiIiI5M2sYefcuXPo1KmTdD9nHo2fnx+2bduGuXPnIjU1Fe+++y4SEhLQrl07HDp0COXKlZMes3PnTgQEBKBLly6wsLDAwIEDsW7duhI/FiIiIiqdSs11dsyJ19khIpPjBGWiYlfmr7NDREREZAoMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQka7IJOxs2bECNGjVQrlw5tG7dGmfPnjV3SURERFQKyCLs7N69G4GBgVi4cCHOnz8PLy8v+Pj4IC4uztylERERkZnJIuysXr0aEyZMwNixY+Hp6Yng4GDY2dlhy5Yt5i6NiIiIzMzK3AUUVUZGBsLDwzF//nxpmYWFBby9vREWFpbrY9LT05Geni7dT0xMBAAkJSUVb7FFlS7MXQERFVRpfz0hkoGc920h8n9/LPNh58mTJ8jOzoaLi4vechcXF/z111+5PiYoKAgff/yxwXI3N7diqZGI3kDL1OaugOiNkZycDLU677+5Mh92jDF//nwEBgZK9xMSEuDu7o7IyMh8O4uKJikpCW5ubnj48CFUKpW5y5Et9nPJYD+XDPZzySmLfS2EQHJyMlxdXfNtV+bDTqVKlWBpaYnY2Fi95bGxsdBoNLk+xsbGBjY2NgbL1Wp1mfkHLstUKhX7uQSwn0sG+7lksJ9LTlnr64IMUpT5CcpKpRItWrTAsWPHpGU6nQ7Hjh2DVqs1Y2VERERUGpT5kR0ACAwMhJ+fH1q2bIm33noLn332GVJTUzF27Fhzl0ZERERmJouwM2TIEMTHx+Ojjz5CTEwMmjZtikOHDhlMWs6LjY0NFi5cmOtXW2Q67OeSwX4uGeznksF+Ljly7muFeN35WkRERERlWJmfs0NERESUH4YdIiIikjWGHSIiIpI1hh0iIiKStTcq7Dx+/BgjR45ExYoVYWtri8aNG+PcuXPSeiEEPvroI1SpUgW2trbw9vbGrVu3zFhx2ZOdnY0FCxagZs2asLW1Re3atbFkyRK93y1hPxfeqVOn0Lt3b7i6ukKhUOD777/XW1+QPn327BlGjBgBlUoFJycnjB8/HikpKSV4FGVDfn2dmZmJefPmoXHjxrC3t4erqytGjx6NqKgovW2wr1/vdc/pl02aNAkKhQKfffaZ3nL28+sVpJ+vX7+OPn36QK1Ww97eHq1atUJkZKS0Pi0tDf7+/qhYsSIcHBwwcOBAgwv5lnZvTNh5/vw52rZtC2traxw8eBARERFYtWoVypcvL7VZsWIF1q1bh+DgYJw5cwb29vbw8fFBWlqaGSsvW5YvX46NGzfi888/x/Xr17F8+XKsWLEC69evl9qwnwsvNTUVXl5e2LBhQ67rC9KnI0aMwLVr1xAaGooDBw7g1KlTePfdd0vqEMqM/Pr6xYsXOH/+PBYsWIDz589j7969uHHjBvr06aPXjn39eq97TufYt28f/vjjj1x/DoD9/Hqv6+c7d+6gXbt2qF+/Pn755RdcvnwZCxYsQLly5aQ2M2fOxI8//oiQkBCcPHkSUVFRGDBgQEkdgmmIN8S8efNEu3bt8lyv0+mERqMRK1eulJYlJCQIGxsb8c0335REibLg6+srxo0bp7dswIABYsSIEUII9rMpABD79u2T7hekTyMiIgQA8eeff0ptDh48KBQKhXj8+HGJ1V7WvNrXuTl79qwAIB48eCCEYF8bI69+fvTokahataq4evWqcHd3F2vWrJHWsZ8LL7d+HjJkiBg5cmSej0lISBDW1tYiJCREWnb9+nUBQISFhRVXqSb3xozs7N+/Hy1btsS//vUvODs7o1mzZvjqq6+k9ffu3UNMTAy8vb2lZWq1Gq1bt0ZYWJg5Si6T3n77bRw7dgw3b94EAFy6dAm//fYbevToAYD9XBwK0qdhYWFwcnJCy5YtpTbe3t6wsLDAmTNnSrxmOUlMTIRCoYCTkxMA9rWp6HQ6jBo1CnPmzEHDhg0N1rOfi06n0+Gnn35C3bp14ePjA2dnZ7Ru3Vrvq67w8HBkZmbqvb7Ur18f1atXL1Ov2W9M2Ll79y42btwIDw8PHD58GJMnT8a0adOwfft2AEBMTAwAGFx12cXFRVpHr/fee+9h6NChqF+/PqytrdGsWTPMmDEDI0aMAMB+Lg4F6dOYmBg4OzvrrbeyskKFChXY70WQlpaGefPmYdiwYdIPJ7KvTWP58uWwsrLCtGnTcl3Pfi66uLg4pKSkYNmyZejevTuOHDmC/v37Y8CAATh58iSAf/pZqVRKYT5HWXvNlsXPRRSETqdDy5Yt8emnnwIAmjVrhqtXryI4OBh+fn5mrk4+/vvf/2Lnzp3YtWsXGjZsiIsXL2LGjBlwdXVlP5OsZGZmYvDgwRBCYOPGjeYuR1bCw8Oxdu1anD9/HgqFwtzlyJZOpwMA9O3bFzNnzgQANG3aFKdPn0ZwcDA6dOhgzvJM6o0Z2alSpQo8PT31ljVo0ECaca7RaADAYIZ5bGystI5eb86cOdLoTuPGjTFq1CjMnDkTQUFBANjPxaEgfarRaBAXF6e3PisrC8+ePWO/GyEn6Dx48AChoaHSqA7AvjaFX3/9FXFxcahevTqsrKxgZWWFBw8eYNasWahRowYA9rMpVKpUCVZWVq99b8zIyEBCQoJem7L2mv3GhJ22bdvixo0bestu3rwJd3d3AEDNmjWh0Whw7NgxaX1SUhLOnDkDrVZborWWZS9evICFhf7TytLSUvoEwX42vYL0qVarRUJCAsLDw6U2x48fh06nQ+vWrUu85rIsJ+jcunULR48eRcWKFfXWs6+LbtSoUbh8+TIuXrwo3VxdXTFnzhwcPnwYAPvZFJRKJVq1apXve2OLFi1gbW2t9/py48YNREZGlq3XbHPPkC4pZ8+eFVZWVuKTTz4Rt27dEjt37hR2dnbi66+/ltosW7ZMODk5iR9++EFcvnxZ9O3bV9SsWVP8/fffZqy8bPHz8xNVq1YVBw4cEPfu3RN79+4VlSpVEnPnzpXasJ8LLzk5WVy4cEFcuHBBABCrV68WFy5ckM4AKkifdu/eXTRr1kycOXNG/Pbbb8LDw0MMGzbMXIdUauXX1xkZGaJPnz6iWrVq4uLFiyI6Olq6paenS9tgX7/e657Tr3r1bCwh2M8F8bp+3rt3r7C2thZffvmluHXrlli/fr2wtLQUv/76q7SNSZMmierVq4vjx4+Lc+fOCa1WK7RarbkOyShvTNgRQogff/xRNGrUSNjY2Ij69euLL7/8Um+9TqcTCxYsEC4uLsLGxkZ06dJF3Lhxw0zVlk1JSUli+vTponr16qJcuXKiVq1a4oMPPtB7I2A/F96JEycEAIObn5+fEKJgffr06VMxbNgw4eDgIFQqlRg7dqxITk42w9GUbvn19b1793JdB0CcOHFC2gb7+vVe95x+VW5hh/38egXp582bN4s6deqIcuXKCS8vL/H999/rbePvv/8WU6ZMEeXLlxd2dnaif//+Ijo6uoSPpGgUQrx0aVsiIiIimXlj5uwQERHRm4lhh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhk7f8BciOLLyysqCYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "C.7  Evaluate GAN model on Test set"
      ],
      "metadata": {
        "id": "vlMmTCTagjjd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate synthetic data using noise from the TEST set\n",
        "num_samples_test = test.shape[0]\n",
        "noise_test = np.random.normal(0, 1, (num_samples_test, codings_size))\n",
        "synthetic_data_test = generator.predict(noise_test)\n",
        "\n",
        "# Inverse transform continuous data to human-translatable format\n",
        "synthetic_data_continuous_test = synthetic_data_test[:, :train_cont.shape[1]]\n",
        "synthetic_data_categorical_test = synthetic_data_test[:, train_cont.shape[1]:]\n",
        "\n",
        "synthetic_data_continuous_inv_tr_test = scaler.inverse_transform(synthetic_data_continuous_test)\n",
        "\n",
        "# Visualize synthetic data distribution\n",
        "plt.figure()\n",
        "pd.DataFrame(synthetic_data_continuous_inv_tr_test).plot(kind='hist', subplots=True)\n",
        "plt.suptitle('GAN Synthetic TEST Data Distribution'.format(epoch) ) ;\n",
        "plt.xlim(60, 170)\n",
        "\n",
        "decoded_categorical_indices = np.argmax(synthetic_data_categorical_test, axis=-1)\n",
        "categories = ['M', 'F']\n",
        "decoded_categories = [categories[idx] for idx in decoded_categorical_indices]\n",
        "\n",
        "plt.figure()\n",
        "pd.DataFrame(decoded_categories).value_counts(normalize=True).plot(kind='bar', rot=0, figsize=(2, 2))\n",
        "\n",
        "# Create the synthetic dataset that is human-interpretable\n",
        "synthetic_data_df_gan = pd.concat( [ pd.DataFrame(synthetic_data_continuous_inv_tr_test), pd.DataFrame(decoded_categories )] , axis=1)\n",
        "synthetic_data_df_gan.columns = ['SBP', 'DBP', 'GENDER']\n",
        "display(synthetic_data_df_gan.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 919
        },
        "id": "QLDR6NlEfccA",
        "outputId": "41f5170e-c6e5-4ae5-ca60-92d6a64e2025"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "          SBP         DBP GENDER\n",
              "0  145.889282  104.193130      M\n",
              "1   99.448532   70.754463      F\n",
              "2  105.207321   75.398659      F\n",
              "3  101.573242   76.653130      F\n",
              "4  198.807724  116.497116      M"
            ],
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              "      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-12a0bf07-8a5c-442b-b3a2-eadeefc19e25 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\": \"display(synthetic_data_df_gan\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"SBP\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          99.44853210449219,\n          198.80772399902344,\n          105.20732116699219\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DBP\",\n      \"properties\": {\n        \"dtype\": \"float32\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          70.75446319580078,\n          116.49711608886719,\n          75.3986587524414\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GENDER\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sbp = pd.concat([synthetic_data_df_gan.SBP, test.SBP], axis=1)\n",
        "sbp.columns = ['SBP_Synthetic', 'SBP_Real']\n",
        "sbp.plot(kind='kde')\n",
        "plt.xlabel('Systolic BP (mmHg)')\n",
        "plt.title('Systolic BP Distribution of Real vs Synthetic Test Data');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "nbFysOFwkPlj",
        "outputId": "7813bbf3-709d-4719-fa76-c8c7a870df37"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkgAAAHHCAYAAABEEKc/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACj9klEQVR4nOzdd3hT5dvA8W+STrp3aSm0UKDsMguozAoIiigyVYaIGxGcqIDoTxEVREXhdQEqiKKIgIogoqBU9t6rlNFJ9x457x+nCYS20Ja2Sdr7c125TnpyznPupGl655kaRVEUhBBCCCGEkdbcAQghhBBCWBpJkIQQQgghriEJkhBCCCHENSRBEkIIIYS4hiRIQgghhBDXkARJCCGEEOIakiAJIYQQQlxDEiQhhBBCiGtIgiSEEEIIcQ1JkES1GTduHMHBwSb7NBoNr732mlniqUlLlixBo9EQHR1d7de69nWOjo5Go9Hw3nvvVfu1AV577TU0Gk2NXKuyCgsLeeGFFwgKCkKr1TJkyBBzh1Qqa3gtzcXw2iQlJVX7tQx/Q0uWLKn2awnLJQmSlTh48CD33XcfjRo1wsHBgcDAQG6//XY++uijarnekSNHeO2112rkH3xlaTQak5uTkxMtW7bkf//7H9nZ2SbHjhs3zuRYV1dX2rVrx9y5c8nLy7vudf766y+Tc+3t7fHz86NXr1689dZbJCYmVsnzyc7O5rXXXuOvv/6qkvKqkiXHVh5ffvkl7777Lvfddx9Lly5lypQpZR7bq1cvk9+3o6Mjbdu2Zf78+ej1+hqM2nz0ej1fffUVEREReHp64uLiQrNmzRgzZgz//fdftV77rbfeYvXq1dV6DYPly5czf/78aiv/2vdSWbeq+tL4ySefVCipuzoGGxsbPD096dixI5MnT+bIkSOVjsPaPy8MbMwdgLixbdu20bt3bxo2bMjEiRPx9/fn/Pnz/Pfff3zwwQdMmjSpyq955MgRZs2aRa9evUrUAt2MnJwcbGyq7m13++23M2bMGAAyMzPZunUr06dPZ//+/axcudLkWHt7ez7//HMAUlNT+fHHH3nuuefYuXMnK1asuOG1nn76aTp37kxRURGJiYls27aNmTNnMm/ePL7//nv69OljPPbBBx9k5MiR2Nvbl/u5ZGdnM2vWLED9YC2vzz77rNr/cV8vtldffZWXXnqpWq9/s/78808CAwN5//33y3V8gwYNmD17NgBJSUksX76cKVOmkJiYyJtvvlmdoVqEp59+mo8//pi7776b+++/HxsbG44fP85vv/1G48aN6dq1a7Vd+6233uK+++6rkVq+5cuXc+jQIZ555hmT/Y0aNSInJwdbW9ubKv+VV17h4YcfNv68c+dOPvzwQ15++WVatGhh3N+2bdubuo7BJ598gre3N+PGjSv3OYbPUEVRSEtLY//+/SxdupRPPvmEOXPmMHXq1ArHUdnPMksjCZIVePPNN3Fzc2Pnzp24u7ubPJaQkGCeoCrJwcGhSstr1qwZDzzwgPHnxx57jPz8fFatWkVubq7J9WxsbEyOfeKJJ4iIiOC7775j3rx5BAQEXPdat912G/fdd5/Jvv3799OvXz+GDh3KkSNHqF+/PgA6nQ6dTlcVT7FMWVlZODk53fSH+M2ysbGp0qS3OiQkJJT427keNze3Eu+rsLAwPvroI15//fVq/92aU3x8PJ988gkTJ07k008/NXls/vz5VVZjask0Gk2VfFbdfvvtJj87ODjw4Ycfcvvtt1tM4nDtZyjA22+/zV133cWzzz5LWFgYAwcONFN05iVNbFbg9OnTtGrVqtQPeF9fX+P9nj170q5du1LLaN68Of379zf+vGLFCjp27IiLiwuurq60adOGDz74AFD7zwwbNgyA3r17G6tgr64u/eSTT2jVqhX29vYEBATw5JNPkpqaesPnUlp18sWLF5kwYQIBAQHY29sTEhLC448/Tn5+/g3LK42/v7+xyvh6tFqt8UOqsk2J7dq1Y/78+aSmprJgwQLj/tL6IO3atYv+/fvj7e2No6MjISEhPPTQQ8br+/j4ADBr1qwSVe/jxo3D2dmZ06dPM3DgQFxcXLj//vuNj5VVy/f+++/TqFEjHB0d6dmzJ4cOHTJ5vFevXqV+UF9d5o1iK63fTGFhIW+88QZNmjTB3t6e4OBgXn755RLNmcHBwdx55538888/dOnSBQcHBxo3bsxXX31V+gt+jaysLJ599lmCgoKwt7enefPmvPfeeyiKYoxdo9GwefNmDh8+XOp7uTwcHBzo3LkzGRkZJb6UfPPNN3Ts2BFHR0c8PT0ZOXIk58+fNzlm69atDBs2jIYNG2Jvb09QUBBTpkwhJyenQnEAPPXUUzg7O5doRgYYNWoU/v7+FBUVAdd/z5Xl7NmzKIrCLbfcUuIxjUZj/Mw5c+YMGo2m1Fq5bdu2odFo+Pbbb4Er75FTp04xbtw43N3dcXNzY/z48SbPQ6PRkJWVxdKlS42/q2trQ1JTU69bhsGNfi+9evXil19+4dy5c8ZrXf2eL60P0rFjxxg+fDg+Pj44OjrSvHlzXnnlleu+nuXx22+/cdttt+Hk5ISLiwuDBg3i8OHDJsfExcUxfvx4GjRogL29PfXr1+fuu+82fsYEBwdz+PBh/v77b+PzqWwS5uXlxYoVK7CxsTGpMc3Pz2fGjBl07NgRNzc3nJycuO2229i8ebPxmBt9Xhw4cIBx48bRuHFjHBwc8Pf356GHHuLy5cuVirU6WfbXPgGo1b1RUVEcOnSI1q1bl3ncgw8+yMSJE0sct3PnTk6cOMGrr74KwMaNGxk1ahR9+/Zlzpw5ABw9epR///2XyZMn06NHD55++ukSVcGG7WuvvcasWbOIjIzk8ccf5/jx4yxcuJCdO3fy77//VqhG49KlS3Tp0oXU1FQeeeQRwsLCuHjxIj/88APZ2dnY2dld9/zc3Fxjp82srCz+/fdfli5dyujRo8tVq3H69GlA/UCorPvuu48JEyawYcOGMptfEhIS6NevHz4+Prz00ku4u7sTHR3NqlWrAPDx8WHhwoU8/vjj3HPPPdx7772AadV7YWEh/fv359Zbb+W9996jXr16143rq6++IiMjgyeffJLc3Fw++OAD+vTpw8GDB/Hz8yv38ytPbNd6+OGHWbp0Kffddx/PPvss27dvZ/bs2Rw9epSffvrJ5NhTp04ZX8OxY8fy5ZdfMm7cODp27EirVq3KvIaiKAwePJjNmzczYcIEwsPD+f3333n++ee5ePEi77//Pj4+Pnz99de8+eabZGZmGpvNrm7eKC/DP82rv6i8+eabTJ8+neHDh/Pwww+TmJjIRx99RI8ePdi7d6/x2JUrV5Kdnc3jjz+Ol5cXO3bs4KOPPuLChQslmoJvZMSIEXz88cf88ssvxi8yoDZrrF27lnHjxqHT6W74nitLo0aNjDEPGzaszPdZ48aNueWWW1i2bFmJPl3Lli3DxcWFu+++22T/8OHDCQkJYfbs2ezZs4fPP/8cX19f4+fQ119/zcMPP0yXLl145JFHAGjSpEmFyoDy/V5eeeUV0tLSuHDhgjHJc3Z2LvN1OXDgALfddhu2trY88sgjBAcHc/r0adauXXtTza5ff/01Y8eOpX///syZM4fs7GwWLlzIrbfeyt69e41J29ChQzl8+DCTJk0iODiYhIQENm7cSExMDMHBwcyfP59Jkybh7OxsTNoq8nd+rYYNG9KzZ082b95Meno6rq6upKen8/nnnzNq1CgmTpxIRkYGX3zxBf3792fHjh2Eh4ff8PNi48aNnDlzhvHjx+Pv78/hw4f59NNPOXz4MP/9959lDVJQhMXbsGGDotPpFJ1Op3Tr1k154YUXlN9//13Jz883OS41NVVxcHBQXnzxRZP9Tz/9tOLk5KRkZmYqiqIokydPVlxdXZXCwsIyr7ly5UoFUDZv3myyPyEhQbGzs1P69eunFBUVGfcvWLBAAZQvv/zSuG/s2LFKo0aNTM4HlJkzZxp/HjNmjKLVapWdO3eWiEGv15cZn6Gs0m5DhgxRcnNzTY4dO3as4uTkpCQmJiqJiYnKqVOnlLfeekvRaDRK27Ztr3udzZs3K4CycuXKMo9p166d4uHhYfx58eLFCqCcPXtWURRF+emnnxSg1OdpkJiYWOL1uTp+QHnppZdKfezq1/ns2bMKoDg6OioXLlww7t++fbsCKFOmTDHu69mzp9KzZ88blnm92GbOnKlc/VGyb98+BVAefvhhk+Oee+45BVD+/PNP475GjRopgLJlyxbjvoSEBMXe3l559tlnS1zraqtXr1YA5X//+5/J/vvuu0/RaDTKqVOnTJ5nq1atrlve1ceGhYUZ3yvHjh1Tnn/+eQVQBg0aZDwuOjpa0el0yptvvmly/sGDBxUbGxuT/dnZ2SWuM3v2bEWj0Sjnzp0z7rv2tSyNXq9XAgMDlaFDh5rs//77701ey/K858oyZswYBVA8PDyUe+65R3nvvfeUo0ePljju//7v/xTA5LH8/HzF29tbGTt2bInn9dBDD5mcf8899yheXl4m+5ycnEzOrWgZFfm9DBo0qMRnlKJc+RtavHixcV+PHj0UFxcXk9+Xotz4c+pq136uZmRkKO7u7srEiRNNjouLi1Pc3NyM+1NSUhRAeffdd69bfqtWrUr9ey4LoDz55JNlPj558mQFUPbv368oiqIUFhYqeXl5JsekpKQofn5+Jr+X631elPa38O2335b4HLAE0sRmBW6//XaioqIYPHgw+/fv55133qF///4EBgayZs0a43Fubm7cfffdfPvtt8YmhqKiIr777juGDBmCk5MTAO7u7mRlZbFx48YKx/LHH3+Qn5/PM888g1Z75e0zceJEXF1d+eWXX8pdll6vZ/Xq1dx111106tSpxOPl+SZx9913s3HjRjZu3MjPP//MtGnTWL9+PaNHjza+BgZZWVn4+Pjg4+NDaGgoL7/8Mt26dStRo1EZzs7OZGRklPm4oSZh3bp1FBQUVPo6jz/+eLmPHTJkCIGBgcafu3TpQkREBL/++mulr18ehvKv7dz57LPPApR4j7Rs2ZLbbrvN+LOPjw/NmzfnzJkzN7yOTqfj6aefLnEdRVH47bffKv0cjh07ZnyvhIWF8e677zJ48GCTJpdVq1ah1+sZPnw4SUlJxpu/vz9NmzY1aXZwdHQ03s/KyiIpKYnu3bujKAp79+6tUGwajYZhw4bx66+/kpmZadz/3XffERgYyK233grc3Htu8eLFLFiwgJCQEH766Seee+45WrRoQd++fbl48aLxuOHDh+Pg4MCyZcuM+37//XeSkpJK9GsBtS/X1W677TYuX75Menp6uWO7URkV+b2UV2JiIlu2bOGhhx6iYcOGJo/dTI3Hxo0bSU1NZdSoUSax6nQ6IiIijLE6OjpiZ2fHX3/9RUpKSqWvV1GGGjXDZ5tOpzPW6uv1epKTkyksLKRTp07s2bOnXGVe/bdgaAEwdPovbxk1RRIkK9G5c2dWrVpFSkoKO3bsYNq0aWRkZHDfffeZDMccM2YMMTExbN26FVATmvj4eB588EHjMU888QTNmjXjjjvuoEGDBjz00EOsX7++XHGcO3cOUPs0Xc3Ozo7GjRsbHy+PxMRE0tPTr9tseCMNGjQgMjKSyMhIBg8ezFtvvcX//vc/Vq1axbp160yOdXBwMCZTW7Zs4fz58/z77780bty40tc3yMzMxMXFpczHe/bsydChQ5k1axbe3t7cfffdLF68+IZTDFzNxsaGBg0alPv4pk2bltjXrFmzap+64dy5c2i1WkJDQ032+/v74+7uXuI9cu0/HAAPD48b/iM4d+4cAQEBJV53Q/NZRd6L1woODmbjxo38/vvvfPLJJwQGBpKYmGjScffkyZMoikLTpk2NyZThdvToUZO+SjExMYwbNw5PT0+cnZ3x8fGhZ8+eAKSlpVU4vhEjRpCTk2P8gpSZmcmvv/7KsGHDjP+wb+Y9p9VqefLJJ9m9ezdJSUn8/PPP3HHHHfz555+MHDnSeJy7uzt33XUXy5cvN+5btmwZgYGBJqM6Da79XXt4eABU6J/+jcqoyO+lvAzJ+s18VpXm5MmTAPTp06dErBs2bDDGam9vz5w5c/jtt9/w8/OjR48evPPOO8TFxVVpPNcyJOBX/40tXbqUtm3b4uDggJeXFz4+Pvzyyy/lfh8nJyczefJk/Pz8cHR0xMfHh5CQEKByfwvVSfogWRk7Ozs6d+5M586dadasGePHj2flypXMnDkTgP79++Pn58c333xDjx49+Oabb/D39ycyMtJYhq+vL/v27eP333/nt99+47fffmPx4sWMGTOGpUuXmuupVZm+ffsCsGXLFu666y7jfp1OZ/I6VJWCggJOnDhx3Q9PjUbDDz/8wH///cfatWv5/fffeeihh5g7dy7//fffdfs+GNjb25vU2lUFjUZToqYNMHbyvdmyy6OsEWGlxVVTnJycTN4rt9xyCx06dODll1/mww8/BNRv0BqNht9++63U52D4nRYVFXH77beTnJzMiy++SFhYGE5OTly8eJFx48ZVaoqGrl27EhwczPfff8/o0aNZu3YtOTk5jBgxwnhMVbznQO2fN3jwYAYPHkyvXr34+++/OXfunLGv0pgxY1i5ciXbtm2jTZs2rFmzhieeeKLU92pV/K5vVEZ5fy+WwPC7//rrr/H39y/x+NX9KJ955hnuuusuVq9eze+//8706dOZPXs2f/75J+3bt6+W+A4dOoROpzMmMN988w3jxo1jyJAhPP/88/j6+qLT6Zg9e7axP+eNDB8+nG3btvH8888THh6Os7Mzer2eAQMGWNw8Y5IgWTFDs1RsbKxxn06nY/To0SxZsoQ5c+awevVqJk6cWOKDws7Ojrvuuou77roLvV7PE088wf/93/8xffp0QkNDy/znZvhQPH78uEnNS35+PmfPnq1QAuLj44Orq2uJkVU3q7CwEMCk+aE6/fDDD+Tk5JiMEixL165d6dq1K2+++SbLly/n/vvvZ8WKFTz88MNV3jnR8O30aidOnDAZ8ebh4VFqU9a1tS8Via1Ro0bo9XpOnjxp0hk6Pj6e1NRU43voZjVq1Ig//viDjIwMk2+4x44dMz5eVdq2bcsDDzzA//3f//Hcc8/RsGFDmjRpgqIohISE0KxZszLPPXjwICdOnGDp0qXGObuASjVxX2348OF88MEHpKen89133xEcHFzq/ETXe89VVKdOnfj777+JjY01vr4DBgzAx8eHZcuWERERQXZ2tkmNdUXd7N9BeX8vFbmW4bOuqj+rDB3QfX19y/XZ2aRJE5599lmeffZZTp48SXh4OHPnzuWbb74Bbv61u1pMTAx///033bp1M/59/fDDDzRu3JhVq1aZXMvwBd2grDhSUlLYtGkTs2bNYsaMGcb9pX1WWQJpYrMCmzdvLvUblqGvx7XNXQ8++CApKSk8+uijZGZmlugLcO1wSq1WaxxhYKh+N/RXunbofmRkJHZ2dnz44YcmMX3xxRekpaUxaNCgcj8vw5IPa9euZdeuXSUer2wNwtq1awHKnPKgKu3fv59nnnkGDw8PnnzyyTKPS0lJKfF8wsPDgSuvuWG0UHmmSyiP1atXm/QX2bFjB9u3b+eOO+4w7mvSpAnHjh0zmdtm//79/PvvvyZlVSQ2w5wp185QPG/ePIAKvUdudJ2ioiKT6RVAndpAo9GYPM+q8MILL1BQUGB8Hvfeey86nY5Zs2aV+N0qimL8OzN8Obn6GEVRjNNqVNaIESPIy8tj6dKlrF+/nuHDh5s8Xp73XGni4uJKnUU5Pz+fTZs2lWg+tbGxYdSoUXz//fcsWbKENm3a3NTEh05OTjf1N1De34vhWuVp1vHx8aFHjx58+eWXxMTElCizsvr374+rqytvvfVWqf3EDH+X2dnZ5ObmmjzWpEkTXFxcTH6XN/vaGSQnJzNq1CiKiopMpjEo7b28fft2oqKiTM4v6/OitPOh5GeFpZAaJCswadIksrOzueeeewgLCyM/P59t27YZvzWOHz/e5Pj27dvTunVrVq5cSYsWLejQoYPJ4w8//DDJycn06dOHBg0acO7cOT766CPCw8ON3/jDw8PR6XTMmTOHtLQ07O3t6dOnD76+vkybNo1Zs2YxYMAABg8ezPHjx/nkk0/o3LlzqR0zr+ett95iw4YN9OzZk0ceeYQWLVoQGxvLypUr+eeff244ud+JEyeM356ys7P577//WLp0KaGhoTf1LbY0W7duJTc3l6KiIi5fvsy///7LmjVrcHNz46effiq1itzAMDPtPffcQ5MmTcjIyOCzzz7D1dXVmFA4OjrSsmVLvvvuO5o1a4anpyetW7eudL+H0NBQbr31Vh5//HHy8vKYP38+Xl5evPDCC8ZjHnroIebNm0f//v2ZMGECCQkJLFq0iFatWpl0nK1IbO3atWPs2LF8+umnpKam0rNnT3bs2MHSpUsZMmQIvXv3rtTzudZdd91F7969eeWVV4iOjqZdu3Zs2LCBn3/+mWeeeabE8PCb1bJlSwYOHMjnn3/O9OnTadKkCf/73/+YNm0a0dHRDBkyBBcXF86ePctPP/3EI488wnPPPUdYWBhNmjThueee4+LFi7i6uvLjjz/edGfbDh06EBoayiuvvEJeXp5J8xqU7z1XmgsXLtClSxf69OlD37598ff3JyEhgW+//db4hcDb29vknDFjxvDhhx+yefNmk+H2ldGxY0f++OMP4+StISEhRERElPv88v5eDNf67rvvmDp1Kp07d8bZ2dmkWf5qH374IbfeeisdOnTgkUceISQkhOjoaH755Rf27dtXqefq6urKwoULefDBB+nQoQMjR47Ex8eHmJgYfvnlF2655RYWLFjAiRMn6Nu3L8OHD6dly5bY2Njw008/ER8fb9InrGPHjixcuJD//e9/hIaG4uvrW2pfsKsZPkMVRSE9Pd24CkFmZibz5s1jwIABxmPvvPNOVq1axT333MOgQYM4e/YsixYtomXLliY19tf7vDD0nyooKCAwMJANGzZw9uzZSr1+1a6GRsuJm/Dbb78pDz30kBIWFqY4OzsrdnZ2SmhoqDJp0iQlPj6+1HPeeecdBVDeeuutEo/98MMPSr9+/RRfX1/Fzs5OadiwofLoo48qsbGxJsd99tlnSuPGjRWdTldiyP+CBQuUsLAwxdbWVvHz81Mef/xxJSUlxeT88gzzVxRFOXfunDJmzBjFx8dHsbe3Vxo3bqw8+eSTJYaTXotrhvfrdDqlQYMGyiOPPFLidTEM868MwzB/w83W1lbx8fFRevToobz55ptKQkJCiXOuHea/Z88eZdSoUUrDhg0Ve3t7xdfXV7nzzjuVXbt2mZy3bds2pWPHjoqdnZ3Ja3W9+Msa5v/uu+8qc+fOVYKCghR7e3vltttuMw7Xvdo333yjNG7cWLGzs1PCw8OV33//vdTfXVmxlTY0vaCgQJk1a5YSEhKi2NraKkFBQcq0adNKTL/QqFEjk6HzBmVNP3CtjIwMZcqUKUpAQIBia2urNG3aVHn33XdLDL2u6DD/so7966+/SryHf/zxR+XWW29VnJycFCcnJyUsLEx58sknlePHjxuPOXLkiBIZGak4Ozsr3t7eysSJE5X9+/eXGEpenmH+V3vllVcUQAkNDS3xWHnfc9dKT09XPvjgA6V///5KgwYNFFtbW8XFxUXp1q2b8tlnn5U5rL1Vq1aKVqs1mVri2ueVmJhosv/avxNFUZRjx44pPXr0UBwdHRXAOOS/ImUoSvl+L5mZmcro0aMVd3d3BTC+50sb5q8oinLo0CHlnnvuUdzd3RUHBwelefPmyvTp06/zapoqa/qUzZs3K/3791fc3NwUBwcHpUmTJsq4ceOMv6ukpCTlySefVMLCwhQnJyfFzc1NiYiIUL7//nuTcuLi4pRBgwYpLi4uCnDDv6GrP9e0Wq3i7u6utG/fXpk8ebJy+PDhEsfr9XrlrbfeUho1aqTY29sr7du3V9atW1ehz4sLFy4YX0M3Nzdl2LBhyqVLl8qcFsCcNIpixp6Qotp88MEHTJkyhejo6FJHCQkhRFVq3749np6ebNq0ydyhCFElpA9SLaQoCl988QU9e/aU5EgIUe127drFvn37TDqhC2HtpA9SLZKVlcWaNWvYvHkzBw8e5OeffzZ3SEKIWuzQoUPs3r2buXPnUr9+/RL9oISwZpIg1SKJiYmMHj0ad3d3Xn75ZQYPHmzukIQQtdgPP/zA66+/TvPmzfn2229NJtIUwtpJHyQhhBBCiGtIHyQhhBBCiGtIgiSEEEIIcQ3pg1RJer2eS5cu4eLiUuVLRAghhBCieiiKQkZGBgEBAddd31ISpEq6dOkSQUFB5g5DCCGEEJVw/vx5GjRoUObjkiBVkmHxvvPnz+Pq6mrmaIQQQghRHunp6QQFBZkscl0aSZAqydCs5urqKgmSEEIIYWVu1D1GOmkLIYQQQlzDIhKkjz/+mODgYBwcHIiIiGDHjh3XPX7lypWEhYXh4OBAmzZt+PXXX00ef+211wgLC8PJyQkPDw8iIyPZvn27yTHBwcFoNBqT29tvv13lz00IIYQQ1sfsCdJ3333H1KlTmTlzJnv27KFdu3b079+fhISEUo/ftm0bo0aNYsKECezdu5chQ4YwZMgQDh06ZDymWbNmLFiwgIMHD/LPP/8QHBxMv379SExMNCnr9ddfJzY21nibNGlStT5XIYQQQlgHs8+kHRERQefOnVmwYAGgDp8PCgpi0qRJvPTSSyWOHzFiBFlZWaxbt864r2vXroSHh7No0aJSr5Geno6bmxt//PEHffv2BdQapGeeeYZnnnmmUnEbykxLS5M+SEIIUQX0ej35+fnmDkNYOVtbW3Q6XZmPl/f/t1k7aefn57N7926mTZtm3KfVaomMjCQqKqrUc6Kiopg6darJvv79+7N69eoyr/Hpp5/i5uZGu3btTB57++23eeONN2jYsCGjR49mypQp2NiU/pLk5eWRl5dn/Dk9Pb08T1EIIUQ55Ofnc/bsWfR6vblDEbWAu7s7/v7+NzVPoVkTpKSkJIqKivDz8zPZ7+fnx7Fjx0o9Jy4urtTj4+LiTPatW7eOkSNHkp2dTf369dm4cSPe3t7Gx59++mk6dOiAp6cn27ZtY9q0acTGxjJv3rxSrzt79mxmzZpVmacphBDiOhRFITY2Fp1OR1BQ0HUn7xPiehRFITs729hNp379+pUuq9YO8+/duzf79u0jKSmJzz77jOHDh7N9+3Z8fX0BTGqh2rZti52dHY8++iizZ8/G3t6+RHnTpk0zOccwj4IQQoibU1hYSHZ2NgEBAdSrV8/c4Qgr5+joCEBCQgK+vr7XbW67HrOm6d7e3uh0OuLj4032x8fH4+/vX+o5/v7+5TreycmJ0NBQunbtyhdffIGNjQ1ffPFFmbFERERQWFhIdHR0qY/b29sb5zySuY+EEKLqFBUVAWBnZ2fmSERtYUi0CwoKKl2GWRMkOzs7OnbsyKZNm4z79Ho9mzZtolu3bqWe061bN5PjATZu3Fjm8VeXe3Ufomvt27cPrVZrrGESQghRs2RdS1FVquK9ZPYmtqlTpzJ27Fg6depEly5dmD9/PllZWYwfPx6AMWPGEBgYyOzZswGYPHkyPXv2ZO7cuQwaNIgVK1awa9cuPv30UwCysrJ48803GTx4MPXr1ycpKYmPP/6YixcvMmzYMEDt6L19+3Z69+6Ni4sLUVFRTJkyhQceeAAPDw/zvBBCCCGEsBhmT5BGjBhBYmIiM2bMIC4ujvDwcNavX2/siB0TE2PSYa979+4sX76cV199lZdffpmmTZuyevVqWrduDYBOp+PYsWMsXbqUpKQkvLy86Ny5M1u3bqVVq1aA2ly2YsUKXnvtNfLy8ggJCWHKlCklRscJIYQQomy9evUiPDyc+fPnV3nZGo2Gn376iSFDhlR52eWiiEpJS0tTACUtLc3coQghhFXLyclRjhw5ouTk5Jg7lApLSEhQHnvsMSUoKEixs7NT/Pz8lH79+in//POPoiiK0qhRIwVQAEWr1Sr169dXHnroISU5OdlYxubNm43HAIqvr69y7733KqdPny5XDPv27VPuuusuxcfHR7G3t1caNWqkDB8+XImPj6+y52mIMSUlxWR/z549lcmTJ99U2TNnzlTatWtXYn9sbKySm5tbqTKv954q7/9vGUsphLiiMA8yE0DmohGiXIYOHcrevXtZunQpJ06cYM2aNfTq1YvLly8bjzGs2hATE8OyZcvYsmULTz/9dImyjh8/zqVLl1i5ciWHDx/mrrvuMnZgL0tiYiJ9+/bF09OT33//naNHj7J48WICAgLIysqq8udbk/z9/UsdVV5jKpWaCalBErVL1mVF+ekJRXndW1FmuirKu00VZdvHilJUZO7IRB1grTVIKSkpCqD89ddfZR7TqFEj5f333zfZ98YbbygtW7Y0/lxa7cyyZcsUQDl27Nh1Y/jpp58UGxsbpaCgoNTH9Xq90qRJE+Xdd9812b93714FUE6ePKkoiqIAymeffaYMGTJEcXR0VEJDQ5Wff/5ZURRFOXv2rEkNF6CMHTtWURS1BmnSpEnK888/r3h4eCh+fn7KzJkzTa6VkpKiTJgwQfH29lZcXFyU3r17K/v27VMURVEWL15couzFixcbY/rpp5+M5Zw/f14ZOXKk4uHhodSrV0/p2LGj8t9//5X6vKUGSQhx8zIT4Yt+sO8bKCpe5iEzHn6fBitGqbVKQtQgRVHIzi80y02pwOpbzs7OODs7s3r16uuOkr7axYsXWbt2LREREdc9zjCXz42WXvH396ewsJCffvqp1Ng1Gg0PPfQQixcvNtm/ePFievToQWhoqHHfrFmzGD58OAcOHGDgwIHcf//9JCcnExQUxI8//giotVyxsbF88MEHxvOWLl2Kk5MT27dv55133uH1119n48aNxseHDRtGQkICv/32G7t376ZDhw707duX5ORkRowYwbPPPkurVq2M66KOGDGixPPIzMykZ8+eXLx4kTVr1rB//35eeOGFap153eydtIUQZqTXw6qH4fJJcG0A930BAe1h79fw+6twYj38OAGGfQUyu7GoITkFRbSc8btZrn3k9f7Usyvfv0YbGxuWLFnCxIkTWbRoER06dKBnz56MHDmStm3bGo978cUXefXVVykqKiI3N5eIiIgyV20AiI2N5b333iMwMJDmzZtfN4auXbvy8ssvM3r0aB577DG6dOlCnz59GDNmjHGw07hx45gxYwY7duygS5cuFBQUsHz5ct577z2TssaNG8eoUaMAeOutt/jwww/ZsWMHAwYMwNPTEwBfX1/c3d1Nzmvbti0zZ84EoGnTpixYsIBNmzZx++23888//7Bjxw4SEhKMzWXvvfceq1ev5ocffuCRRx7B2dkZGxubMuc/BFi+fDmJiYns3LnTGMvVyV11kE88Ieqy3YvhzF9g4wgProKGXcHGHjo/DKNXgM4ejq6FqAXmjlQIizR06FAuXbrEmjVrGDBgAH/99RcdOnRgyZIlxmOef/559u3bx4EDB4zz+A0aNKhE/6IGDRrg5ORk7D/0448/lmvyzDfffJO4uDgWLVpEq1atWLRoEWFhYRw8eBCAgIAABg0axJdffgnA2rVrycvLM059Y3B1Uufk5ISrq6txyY7rufo8UJf3MJy3f/9+MjMz8fLyMta4OTs7c/bsWU6fPn3Dsg327dtH+/btjclRTZAaJCHqqvws+EudX4zImeBzzTfVxr3gjrdh3RTY9Do061/yGCGqgaOtjiOv9zfbtSvKwcGB22+/ndtvv53p06fz8MMPM3PmTMaNGweoq0YYajuaNm3K/Pnz6datG5s3byYyMtJYztatW3F1dcXX1xcXF5cKxeDl5cWwYcMYNmwYb731Fu3bt+e9995j6dKlADz88MM8+OCDvP/++yxevJgRI0aUWNbF1tbW5GeNRlOuJqzrnZeZmUn9+vX566+/Spx3bU3U9RiaHGuSJEhC1FU7v4CsRHBvpNYYlabjeDi+Hk7+Dr8+D2N+BpntWFQzjUZT7mYuS9SyZUtWr15d5uOGtcFycnJM9oeEhFQoaSiLnZ0dTZo0MRnFNnDgQJycnFi4cCHr169ny5YtFS4TuOGoumt16NCBuLg4bGxsCA4OLrPsG5Xbtm1bPv/8c5KTk2usFkma2ISoi/RFsP3/1Ps9ngedbenHaTRwxxy1qe3s33B0Tc3FKISFu3z5Mn369OGbb77hwIEDnD17lpUrV/LOO+9w9913G4/LyMggLi6O2NhYduzYwfPPP4+Pjw/du3e/6RjWrVvHAw88wLp16zhx4gTHjx/nvffe49dffzWJQafTMW7cOKZNm0bTpk1vuDzXtRo1aoRGo2HdunUkJiaSmZlZrvMiIyPp1q0bQ4YMYcOGDURHR7Nt2zZeeeUVdu3aBUBwcDBnz541LjBfWof3UaNG4e/vz5AhQ/j33385c+YMP/74I1FRURV6HhUhCZIQddGpPyD9Ajh6Qpth1z/WMwRumaze3zxb5kgSopizszMRERG8//779OjRg9atWzN9+nQmTpzIggVX+u3NmDGD+vXrExAQwJ133omTkxMbNmzAy8vrpmNo2bIl9erV49lnnyU8PJyuXbvy/fff8/nnn/Pggw+aHDthwgTy8/ONS3lVRGBgILNmzeKll17Cz8+Pp556qlznaTQafv31V3r06MH48eNp1qwZI0eO5Ny5c8ZO5EOHDmXAgAH07t0bHx8fvv322xLl2NnZsWHDBnx9fRk4cCBt2rTh7bffNtbGVQeNUpExjcIoPT0dNzc30tLScHV1NXc4QlTMt6Ph+C/Q7Sno/+aNj89JhfltIS8Nhi2FVkOqO0JRh+Tm5nL27FlCQkJwcHAwdzi11tatW+nbty/nz583Jie11fXeU+X9/y01SELUNXkZcKp4jpLw0eU7x9Eduj6u3t/yLsj3KiGsRl5eHhcuXOC1115j2LBhtT45qiqSIAlR15zcoE4I6RUKvi3Lf17Xx8DWCeIPQfQ/1RefEMJo2bJlJsPjr74ZFmC/kW+//ZZGjRqRmprKO++8U80R1x7WO0xACFE5R9eq2xZ3VWxEmqMHtBsBu76E7Ysg5LbqiU8IYTR48OAyZ92+dnh9WcaNG2ecckCUnyRIQtQlBblwYoN6v8VdFT+/yyNqgnT8V0iNAfeGVRufEMKEi4tLhedEElVDmtiEqEvO/QMFWeASAAEdKn6+bwsIvg0UPexdVvXxCSGEhZAESYi65Mxf6ja0T+UnfOwwRt3u/1aG/Ashai1JkISoS878rW5DelW+jLA7wc4FUs/B+f+qIiohhLA4kiAJUVdkJ0OcunglIT0qX45dPWhVPEPvvuU3H5cQQlggSZCEqCvObgEU8GkBLjc5D0q7Uer26FooKrjp0IQQwtJIgiREXXG2eHHKxj1vvqyG3cDJB3JTr5QrhKjVgoODmT9/vrnDqDGSIAlRV8QU9xdqdMvNl6XVqX2RQBawFXVaYmIijz/+OA0bNsTe3h5/f3/69+/Pv//+C6hJhUajQaPRoNPpCAgIYMKECaSkpBjL+Ouvv4zHaDQa/Pz8GDp0KGfOnClXDFdfo169erRp04bPP/+8Wp5vXSIJkhB1QW46JBxR7weVPulchbUcrG6P/QL6oqopUwgrM3ToUPbu3cvSpUs5ceIEa9asoVevXly+fNl4zOuvv05sbCwxMTEsW7aMLVu28PTTT5co6/jx41y6dImVK1dy+PBh7rrrLoqKyve3ZbjGoUOHeOCBB5g4cSK//fZblT3PukgSJCHqgou7AUWd2PFm+x8ZBN8GDu6QlQjnd1RNmUJYkdTUVLZu3cqcOXPo3bs3jRo1okuXLkybNo3Bgwcbj3NxccHf35/AwEB69+7N2LFj2bNnT4nyfH19qV+/Pj169GDGjBkcOXKEU6dOlSsWwzUaN27Miy++iKenJxs3bjSJ9eGHH8bHxwdXV1f69OnD/v37jY+fPn2au+++Gz8/P5ydnencuTN//PHHTbw61k8SJCHqggs71W2DLlVXps4WQiPV+6fq9gepqGKKAvlZ5rlVYCFmw5poq1evJi8vr1znXLx4kbVr15a5fIiBo6MjAPn5+eWOB0Cv1/Pjjz+SkpKCnZ2dcf+wYcNISEjgt99+Y/fu3XTo0IG+ffuSnJwMQGZmJgMHDmTTpk3s3buXAQMGcNdddxETE1Oh69cmGkWRZbkrIz09HTc3N9LS0nB1dTV3OEJc3zf3wamNcMc7EPFo1ZW7fwX89CjUbwePSmdtUTm5ubmcPXuWkJAQHBwc1ETlrQDzBPPyJbBzKvfhP/74IxMnTiQnJ4cOHTrQs2dPRo4cSdu2bQG1f1BsbCy2trYUFRWRm5tLREQE69evx93dHVD7IPXu3ZuUlBTc3d2JjY3lvvvu49y5c5w5c8Yk0SnN1dfIy8ujsLAQT09Ptm/fTmhoKP/88w+DBg0iISEBe3t743mhoaG88MILPPLII6WW27p1ax577DGeeuop43WeeeYZnnnmmXK/PuZS4j11lfL+/5YaJCFqO73+qhqkzlVbdpM+6jZ2P2TEV23ZQliBoUOHcunSJdasWcOAAQP466+/6NChA0uWLDEe8/zzz7Nv3z4OHDjApk2bABg0aFCJ/kUNGjTAycmJgIAAsrKy+PHHH2+YHF17jT///JOIiAjef/99QkNDAdi/fz+ZmZl4eXkZa72cnZ05e/Ysp0+fBtQapOeee44WLVrg7u6Os7MzR48erdM1SLJYrRC1XcpZdTi+zh7821Rt2c6+UD8cYvfB6T8hfFTVli/qJtt6ak2Oua5dQQ4ODtx+++3cfvvtTJ8+nYcffpiZM2cybtw4ALy9vY3JStOmTZk/fz7dunVj8+bNREZGGsvZunUrrq6u+Pr6VniBWsM1QkNDWblyJW3atKFTp060bNmSzMxM6tevz19//VXiPEMt1nPPPcfGjRt57733CA0NxdHRkfvuu6/CTXy1iSRIQtR2scUdMf1bq/2GqlpoZHGCtEkSJFE1NJoKNXNZmpYtW7J69eoyH9fpdADk5OSY7A8JCTEmLDcjKCiIESNGMG3aNH7++Wc6dOhAXFwcNjY2BAcHl3rOv//+y7hx47jnnnsAtUYpOjr6pmOxZtLEJkRtZ0iQ6rernvINE09G/1uhDq5CWLvLly/Tp08fvvnmGw4cOMDZs2dZuXIl77zzDnfffbfxuIyMDOLi4oiNjWXHjh08//zz+Pj40L1792qLbfLkyaxdu5Zdu3YRGRlJt27dGDJkCBs2bCA6Oppt27bxyiuvsGvXLkCt2Vq1ahX79u1j//79jB49Gn0dX4xaEiQhajtjDVLb6im/QWfQ2UHGJUgu38R2QtQGzs7Oxv4+PXr0oHXr1kyfPp2JEyeyYMEC43EzZsygfv36BAQEcOedd+Lk5MSGDRvw8vKqtthatmxJv379mDFjBhqNhl9//ZUePXowfvx4mjVrxsiRIzl37hx+fuq0H/PmzcPDw4Pu3btz11130b9/fzp06FBt8VkDGcVWSTKKTVgFRYF3GkNOMkzcDIHV9IH35QCIiYLBH0GHMdVzDVFrXW/EkRCVIaPYhBDXl35RTY60NuDbsvquY1i+JPrf6ruGEELUIEmQhKjNDM1rPmFgW43fzIMNCdI/0g9JiCq0bNkyk6H5V99atWpl7vBqNRnFJkRtVt0dtA2CIkCjg/QLkHYB3IOq93pC1BGDBw8uc9ZtW9tqGJUqjCRBEqI2q6kEyc4J/FpB3AG4uEsSJCGqiIuLS4XnRBJVQ5rYhKjN4g6q2+oawXa1Bp3U7YVd1X8tIYSoZpIgCVFb5aSqnbQB/Kqxg7ZBYHGCdHF39V9L1EoyqFpUlaqYw0ma2ISorRKPqVvXBuDgVv3XM9QgXdoHRQXVM2u3qJVsbW3RaDQkJibi4+ODRqMxd0jCSimKQn5+PomJiWi12nKvZVcai0iQPv74Y959913i4uJo164dH330EV26dCnz+JUrVzJ9+nSio6Np2rQpc+bMYeDAgcbHX3vtNVasWMH58+exs7OjY8eOvPnmmyYd3ZKTk5k0aRJr165Fq9UydOhQPvjgA5ydnav1uQpRYxKOqFvfFjVzPa+mYO8GeWnqtau735OoNXQ6HQ0aNODChQt1fnkLUTXq1atHw4YN0Wor31Bm9gTpu+++Y+rUqSxatIiIiAjmz59P//79OX78OL6+viWO37ZtG6NGjWL27NnceeedLF++nCFDhrBnzx5at24NQLNmzViwYAGNGzcmJyeH999/n379+nHq1Cl8fHwAuP/++4mNjWXjxo0UFBQwfvx4HnnkEZYvX16jz1+IapNwVN3WVIKk1UJgezjzl9oPSRIkUQHOzs40bdqUgoICc4cirJxOp8PGxuamayLNPpN2REQEnTt3Nk7LrtfrCQoKYtKkSbz00ksljh8xYgRZWVmsW7fOuK9r166Eh4ezaNGiUq9hmDXzjz/+oG/fvhw9epSWLVuyc+dOOnVSmwXWr1/PwIEDuXDhAgEBATeMW2bSFhZvyZ0QvRWGLKq5RWQ3vQ5b50KHsTD4w5q5phBCVIBVzKSdn5/P7t27iYyMNO7TarVERkYSFRVV6jlRUVEmxwP079+/zOPz8/P59NNPcXNzo127dsYy3N3djckRQGRkJFqtlu3bt5daTl5eHunp6SY3ISyWokD8YfV+TdUgAfi3UbeG0XNCCGGlzJogJSUlUVRUZFwsz8DPz4+4uLhSz4mLiyvX8evWrcPZ2RkHBwfef/99Nm7ciLe3t7GMa5vvbGxs8PT0LPO6s2fPxs3NzXgLCpJ5XoQFy0pUlxhBAz7Na+66hukEEo5AUWHNXVcIIapYrR3m37t3b/bt28e2bdsYMGAAw4cPJyEhodLlTZs2jbS0NOPt/PnzVRitEFXM0EHbszHYOtbcdT1CwNYJCnPh8qmau64QQlQxsyZI3t7e6HQ64uPjTfbHx8fj7+9f6jn+/v7lOt7JyYnQ0FC6du3KF198gY2NDV988YWxjGuTpcLCQpKTk8u8rr29Pa6uriY3ISxWTXfQNtBqwV8dLCHNbEIIa2bWBMkwBH/Tpk3GfXq9nk2bNtGtW7dSz+nWrZvJ8QAbN24s8/iry83LyzOWkZqayu7dVya0+/PPP9Hr9WWueSOEVTEO8a+BCSKvZeyHdKDmry2EEFXE7MP8p06dytixY+nUqRNdunRh/vz5ZGVlMX78eADGjBlDYGAgs2fPBmDy5Mn07NmTuXPnMmjQIFasWMGuXbv49NNPAcjKyuLNN99k8ODB1K9fn6SkJD7++GMuXrzIsGHDAGjRogUDBgxg4sSJLFq0iIKCAp566ilGjhxZrhFsQlg8c9UggXTUFkLUCmZPkEaMGEFiYiIzZswgLi6O8PBw1q9fb+yIHRMTYzLRU/fu3Vm+fDmvvvoqL7/8Mk2bNmX16tXGOZB0Oh3Hjh1j6dKlJCUl4eXlRefOndm6dSutWrUylrNs2TKeeuop+vbta5wo8sMPZViyqAUU5aoEyZw1SJIgCSGsl9nnQbJWMg+SsFhpF+D9VqC1gZdjwabyU+1XSn42vBUAKPD8aXDyrtnrCyHEdVjFPEhCiGqQdFLdeoTUfHIEYFcP3Buq9xOP1/z1hRCiCkiCJERtY0iQvJuZLwafMHVrWDBXCCGsjCRIQtQ2SSfUrXdT88VgmJxSapCEEFZKEiQhahuLSJCkBkkIYd0kQRKitrGoJjapQRJCWCdJkISoTfIyIOOSet8r1Hxx+BQnZ5lxkJNivjiEEKKSJEESojYxrH/m5AP1PM0Xh70LuDZQ70stkhDCCkmCJERtYgnNawaGWiRJkIQQVkgSJCFqE0vooG1gaOJLPmPeOIQQohIkQRKiNjEkSF4WkCB5NlG3yafNG4cQQlSCJEhC1CZJxX2QLKGJzbOxur0sNUhCCOsjCZIQtYW+6EonbYtoYjPUIJ1RF9AVQggrIgmSELVFagwU5YHO/spaaObk3hA0OijMgYxYc0cjhBAVIgmSELWFYQSbVyhodeaNBUBneyVRuyz9kIQQ1kUSJCFqC0sawWZwdTObEEJYEUmQhKgtLhvmQLKgBMnQUVtGsgkhrIwkSELUFpY0SaSBYai/NLEJIayMJEhC1BYW3cR21rxxCCFEBUmCJERtkJ0MWYnqfUuYJNLA2MR2BvR688YihBAVIAmSELWBYf4j10CwdzZvLFeTof5CCCslCZIQtUGSBXbQBnWov1sD9X5qjHljEUKICpAESYjawJLWYLuWRyN1KwmSEMKKSIIkRG1gqTVIcGWySEmQhBBWRBIkIWoDS5wDycDdUIN0zrxxCCFEBUiCJIS1Kyq8MozeK9S8sZRGapCEEFZIEiQhrF3qOdAXgI0juDYwdzQlSYIkhLBCkiAJYe0MQ/y9moDWAv+kDQlS2gXQF5k3FiGEKCcL/DQVQlSIoYO2JTavAbjUB62NWsuVEWfuaIQQolwkQRLC2llyB20ArU7mQhJCWB1JkISwdkmGJjYLrUEC6YckhLA6kiAJYe2MfZAstAYJJEESQlgdSZCEsGa56ZBZ3K/H25JrkGQuJCGEdZEESQhrZqg9cvIFBzfzxnI9UoMkhLAykiAJYc0MCZKldtA2kARJCGFlJEESwppZ+hB/A7cgdZt2AfR688YihBDlIAmSENbsshWMYAN1LiSNVp0LKSvR3NEIIcQNSYIkhDWz9DmQDHQ24Oyv3k+/YN5YhBCiHCRBEsJa6fVw+bR635KH+Bu4Bqjb9EvmjUMIIcrBIhKkjz/+mODgYBwcHIiIiGDHjh3XPX7lypWEhYXh4OBAmzZt+PXXX42PFRQU8OKLL9KmTRucnJwICAhgzJgxXLpk+qEcHByMRqMxub399tvV8vyEqBYZl6AgW13Gw6ORuaO5MbdAdZt20bxxCCFEOZg9Qfruu++YOnUqM2fOZM+ePbRr147+/fuTkJBQ6vHbtm1j1KhRTJgwgb179zJkyBCGDBnCoUOHAMjOzmbPnj1Mnz6dPXv2sGrVKo4fP87gwYNLlPX6668TGxtrvE2aNKlan6sQVcrQQdsjBHS25o2lPFyLlxuRJjYhhBUwe4I0b948Jk6cyPjx42nZsiWLFi2iXr16fPnll6Ue/8EHHzBgwACef/55WrRowRtvvEGHDh1YsGABAG5ubmzcuJHhw4fTvHlzunbtyoIFC9i9ezcxMaZDjF1cXPD39zfenJycqv35ClFlrGWIv4E0sQkhrIhZE6T8/Hx2795NZGSkcZ9WqyUyMpKoqKhSz4mKijI5HqB///5lHg+QlpaGRqPB3d3dZP/bb7+Nl5cX7du3591336WwsLDyT0aImmYcwdbEvHGUlzSxCSGsiI05L56UlERRURF+fn4m+/38/Dh27Fip58TFxZV6fFxcXKnH5+bm8uKLLzJq1ChcXV2N+59++mk6dOiAp6cn27ZtY9q0acTGxjJv3rxSy8nLyyMvL8/4c3p6ermeoxDVxjgHkrXUIBma2CRBEkJYPrMmSNWtoKCA4cOHoygKCxcuNHls6tSpxvtt27bFzs6ORx99lNmzZ2Nvb1+irNmzZzNr1qxqj1mIcrOWIf4Ghia2jFjQF4FWZ954hBDiOszaxObt7Y1OpyM+Pt5kf3x8PP7+/qWe4+/vX67jDcnRuXPn2Lhxo0ntUWkiIiIoLCwkOjq61MenTZtGWlqa8Xb+/PkbPDshqlFBDqQWvwetpQbJxR80OtAXQmbpgzCEEMJSmDVBsrOzo2PHjmzatMm4T6/Xs2nTJrp161bqOd26dTM5HmDjxo0mxxuSo5MnT/LHH3/g5eV1w1j27duHVqvF19e31Mft7e1xdXU1uQlhNslnAEVdoNbJ29zRlI9Wp86oDdLMJoSweGZvYps6dSpjx46lU6dOdOnShfnz55OVlcX48eMBGDNmDIGBgcyePRuAyZMn07NnT+bOncugQYNYsWIFu3bt4tNPPwXU5Oi+++5jz549rFu3jqKiImP/JE9PT+zs7IiKimL79u307t0bFxcXoqKimDJlCg888AAeHh7meSGEqIir12DTaMwbS0W4BqjD/NMvAp3MHY0QQpTJ7AnSiBEjSExMZMaMGcTFxREeHs769euNHbFjYmLQaq9UdHXv3p3ly5fz6quv8vLLL9O0aVNWr15N69atAbh48SJr1qwBIDw83ORamzdvplevXtjb27NixQpee+018vLyCAkJYcqUKSb9koSwaMYRbFbSvGbgFggXkJFsQgiLp1EURTF3ENYoPT0dNzc30tLSpLlN1LyfHoP930KfV6HH8+aOpvx+fwWiFkC3p6D/m+aORghRB5X3/7fZJ4oUQlSCtQ3xN3AtngtJ+iAJISycJEhCWBtFsb4h/gYym7YQwkpIgiSEtclKgtw0QAOejc0dTcVcPReSEEJYMEmQhLA2hg7abkFg62jeWCrKpXi+sow4tSZMCCEslCRIQlgbY/NaqHnjqAzn4mWCivIhJ8W8sQghxHVIgiSEtbHWDtoANvZQr3jiVmlmE0JYMEmQhLA2hiY2a+ugbWCYTVsSJCGEBZMESQhrc/Us2tbo6n5IQghhoSRBEsKaFBVAyln1vtXWIBkSJKlBEkJYLkmQhLAmqTGgLwQbR3AJMHc0lWNsYpMaJCGE5ZIESQhrcnXzmtZK/3yliU0IYQWs9BNWiDrKmof4G0gnbSGEFZAESQhrYs1D/A2kBkkIYQUkQRLCmlj7EH8w7YOk15s3FiGEKIMkSEJYE2sf4g/g5AtoQCmC7CRzRyOEEKWSBEkIa5GbBlkJ6n1rTpB0NuDsq96XfkhCCAslCZIQ1sLQvObsBw6u5o3lZkk/JCGEhZMESQhrkVScIFlzB20DQz+k9EvmjUMIIcogCZIQ1qI2DPE3kBokIYSFkwRJCGtRG4b4G8hcSEIICycJkhDW4vJpdWvNHbQNpAZJCGHhJEESwhro9bVjDiQDqUESQlg4SZCEsAbpF6EwB7S24N7I3NHcPKlBEkJYOEmQhLAGhg7aniHqPELWztlP3WYngb7IvLEIIUQpJEESwhrUpiH+AE4+oNGCoocsmU1bCGF5JEESwhrUpiH+AFod1PNW72fGmzcWIYQohSRIQlgDQwft2jCCzcDQzJaZYN44hBCiFJIgCWENalsTG1xZj01qkIQQFkgSJCEsXUEOpJ1X79eGIf4GxhokSZCEEJZHEiQhLN3l04ACDu5Qz8vc0VQdYw2SNLEJISyPJEhCWDpjB+2moNGYN5aqJDVIQggLJgmSEJbuci3sfwRSgySEsGiSIAlh6YwdtJuYN46qZphNO1Nm0xZCWB5JkISwdLVpDbaryTB/IYQFkwRJCEumKFf6INWmOZDgShNbXjrkZ5s3FiGEuIYkSEJYsqwkyE0DNODZ2NzRVC17V7BxUO9nSS2SEMKySIIkhCUzNK+5BYGto3ljqWoajXTUFkJYLEmQhLBktW0NtmvJUH8hhIWSBEkIS1Yb12C7miRIQggLZREJ0scff0xwcDAODg5ERESwY8eO6x6/cuVKwsLCcHBwoE2bNvz666/GxwoKCnjxxRdp06YNTk5OBAQEMGbMGC5dumRSRnJyMvfffz+urq64u7szYcIEMjMzq+X5CVFptXENtqvJSDYhhIUye4L03XffMXXqVGbOnMmePXto164d/fv3JyGh9A/Mbdu2MWrUKCZMmMDevXsZMmQIQ4YM4dChQwBkZ2ezZ88epk+fzp49e1i1ahXHjx9n8ODBJuXcf//9HD58mI0bN7Ju3Tq2bNnCI488Uu3PV4gKuVxL50AyMCRIGTIXkhDCsmgURVHMGUBERASdO3dmwYIFAOj1eoKCgpg0aRIvvfRSieNHjBhBVlYW69atM+7r2rUr4eHhLFq0qNRr7Ny5ky5dunDu3DkaNmzI0aNHadmyJTt37qRTp04ArF+/noEDB3LhwgUCAgJuGHd6ejpubm6kpaXh6upamacuxPUVFcKb/qAvgGcOgntDc0dU9XYthnXPQLM7YPQKc0cjhKgDyvv/26w1SPn5+ezevZvIyEjjPq1WS2RkJFFRUaWeExUVZXI8QP/+/cs8HiAtLQ2NRoO7u7uxDHd3d2NyBBAZGYlWq2X79u2llpGXl0d6errJTYhqlRajJkc2DuDawNzRVA/pgySEsFBmTZCSkpIoKirCz8/PZL+fnx9xcaVXucfFxVXo+NzcXF588UVGjRplzBTj4uLw9fU1Oc7GxgZPT88yy5k9ezZubm7GW1BQULmeoxCVZuh/5NkEtGZvDa8e0gdJCGGhaumnrqqgoIDhw4ejKAoLFy68qbKmTZtGWlqa8Xb+/PkqilKIMtT2/kdw1TxI8eqs4UIIYSFszHlxb29vdDod8fGm1evx8fH4+/uXeo6/v3+5jjckR+fOnePPP/80aWf09/cv0Qm8sLCQ5OTkMq9rb2+Pvb19uZ+bEDfNOAdSLR3BBlcSJH0B5KRAPU/zxiOEEMXMWoNkZ2dHx44d2bRpk3GfXq9n06ZNdOvWrdRzunXrZnI8wMaNG02ONyRHJ0+e5I8//sDLy6tEGampqezevdu4788//0Sv1xMREVEVT02Im1fb50ACsLEHB3f1vjSzCSEsiFlrkACmTp3K2LFj6dSpE126dGH+/PlkZWUxfvx4AMaMGUNgYCCzZ88GYPLkyfTs2ZO5c+cyaNAgVqxYwa5du/j0008BNTm677772LNnD+vWraOoqMjYr8jT0xM7OztatGjBgAEDmDhxIosWLaKgoICnnnqKkSNHlmsEmxA1orbPgWTg4g+5qZAZB75h5o5GCCEAC0iQRowYQWJiIjNmzCAuLo7w8HDWr19v7IgdExOD9qoOqt27d2f58uW8+uqrvPzyyzRt2pTVq1fTunVrAC5evMiaNWsACA8PN7nW5s2b6dWrFwDLli3jqaeeom/fvmi1WoYOHcqHH35Y/U9YiPLIy4SM4slNa3MfJFCb2RKPSQ2SEMKiVGoepDNnztC4cS1bWbyCZB4kUa1i98P/9YB6XvDCGXNHU71+fBgOroR+/4Puk8wdjRCilqvWeZBCQ0Pp3bs333zzDbm5uZUOUghRhrrQ/8hA5kISQligSiVIe/bsoW3btkydOhV/f38effTRG66fJoSogLrS/wiuGuovTWxCCMtRqQQpPDycDz74gEuXLvHll18SGxvLrbfeSuvWrZk3bx6JiYlVHacQdUtdmAPJQGqQhBAW6KaG+dvY2HDvvfeycuVK5syZw6lTp3juuecICgpizJgxxMbGVlWcQtQtdWEOJAOpQRJCWKCbSpB27drFE088Qf369Zk3bx7PPfccp0+fZuPGjVy6dIm77767quIUou5QFLh8Wr1fJ/ogFU/OKjVIQggLUqlh/vPmzWPx4sUcP36cgQMH8tVXXzFw4EDjcPyQkBCWLFlCcHBwVcYqRN2QmQB56YAGPOvAaFFDE1v2ZSjMBxs788YjhBBUMkFauHAhDz30EOPGjaN+/fqlHuPr68sXX3xxU8EJUScZ+h+5N1Rnmq7tHD1AawP6QshKALcG5o5ICCEqlyBt3LiRhg0bmkzgCKAoCufPn6dhw4bY2dkxduzYKglSiDqlLvU/AtBqwclXnRgzUxIkIYRlqFQfpCZNmpCUlFRif3JyMiEhITcdlBB1mqH/kWcdGMFmYOionSUjYIUQlqFSCVJZk29nZmbi4OBwUwEJUeclF8+cXReG+BvIUH8hhIWpUBPb1KlTAdBoNMyYMYN69eoZHysqKmL79u0l1j8TQlRQ8ll1Wxc6aBsYh/pLgiSEsAwVSpD27t0LqDVIBw8exM7uymgTOzs72rVrx3PPPVe1EQpRlyjKlRqkOpUgGWqQZC4kIYRlqFCCtHnzZgDGjx/PBx98IIu0ClHVMmKhMAc0OnUUW10hNUhCCAtTqVFsixcvruo4hBBwpfbIvSHobM0bS02S2bSFEBam3AnSvffey5IlS3B1deXee++97rGrVq266cCEqJPqYvMaSCdtIYTFKXeC5ObmhkajMd4XQlSDOp8gSQ2SEMIylDtBurpZTZrYhKgmdTZBKm5iy8+EvEywdzZvPEKIOq9S8yDl5OSQnZ1t/PncuXPMnz+fDRs2VFlgQtRJdTVBsnMG2+JpQ7KkFkkIYX6VSpDuvvtuvvrqKwBSU1Pp0qULc+fO5e6772bhwoVVGqAQdYai1M05kAA0GumoLYSwKJVKkPbs2cNtt90GwA8//IC/vz/nzp3jq6++4sMPP6zSAIWoM7IS1SYmNODRyNzR1DzpqC2EsCCVSpCys7NxcXEBYMOGDdx7771otVq6du3KuXPnqjRAIeoMwxpsbkFgY2/eWMxBapCEEBakUglSaGgoq1ev5vz58/z+++/069cPgISEBJk8UojKMq7BVsea1wykBkkIYUEqlSDNmDGD5557juDgYCIiIujWrRug1ia1b9++SgMUos6oqx20DSRBEkJYkErNpH3fffdx6623EhsbS7t27Yz7+/btyz333FNlwQlR2+UWFHE8LoO8Qj2t409SD+pwgmRoYks0bxxCCEElEyQAf39//P39TfZ16dLlpgMSoi44GpvOh5tOsulYAvmFegDW2B2grRbWX6pHj/xC6tlV+s/TOkkNkhDCglTqEzgrK4u3336bTZs2kZCQgF6vN3n8zJkzVRKcELVNkV5hwZ+nmL/pBIqi7vN2tsPZTkdwlpoYzN1dyFtntjJ3eDs6B3uaMdoaJp20hRAWpFIJ0sMPP8zff//Ngw8+SP369Y1LkAghylZQpOeZFfv45WAsAHe09mdSn6a0qO+CJjsZ3lUnXy1wCSImOZvRn/3HnKFtubdDA3OGXXOcDAlSvDonlHyuCCHMqFIJ0m+//cYvv/zCLbfcUtXxCFEr6fUKU7/fzy8HY7HTaXnr3jbc1/GqxMfQQds1kLVP3M5Lqw7yy4FYpn6/n/xCPSO7NDRP4DXJUIOkL4CcFKhXh2rPhBAWp1Kj2Dw8PPD0lA8vIcrr482nWLv/ErY6DQsf6GCaHAEkF8+B5NkYFwdbPhrZnoduCQHg5Z8O8vvhuBqO2Axs7MHBXb0vzWxCCDOrVIL0xhtvMGPGDJP12IQQpfv3VBLz/jgBwJtD2tC3hV/Jg4xD/NWkSKvVMP3OFozqEoRegWe/38/pxMyaCtl8pKO2EMJCVKqJbe7cuZw+fRo/Pz+Cg4OxtbU1eXzPnj1VEpwQ1i4jt4AXfjiAosCoLkEM7xxU+oGlzIGk0Wh44+7WnEnMYvvZZB7/Zjc/PXELTva1eHSbsy8kHZcaJCGE2VXqk3bIkCFVHIYQtdM7649zMTWHIE9HXh3UsuwDDYvUeoSY7LbRaflodHvu/PAfTsRn8vZvx3hjSOtqjNjMpAZJCGEhKpUgzZw5s6rjEKLWORGfwbLt6tqEc+5te/2an9TiNQw9gks85OviwPwR4Yz+fDtf/3eOO9r4072JdzVEbAEkQRJCWIhK9UECSE1N5fPPP2fatGkkJycDatPaxYsXqyw4IazZm78cRa/AgFb+dA+9TkKTlwlZxbNHl5IgAXQP9WZ0hDqS7cUfD5CdX1jF0VoImQtJCGEhKpUgHThwgGbNmjFnzhzee+89UlNTAVi1ahXTpk2ryviEsEpbTiTy94lEbHUaXroj7PoHp8aoWwd3cHQv87Bpd4QR6O7I+eQcFv1dSydjlRokIYSFqFSCNHXqVMaNG8fJkydxcHAw7h84cCBbtmypsuCEsEaKovB+8ai1Md2CCfZ2uv4JKdHq1qPRdQ9zcbDl1UEtAPi/v09zIaUWjiKVGiQhhIWoVIK0c+dOHn300RL7AwMDiYurA/O1CHEdUacvszcmFXsbLY/1bHLjE4wJUvANDx3Q2p+IEE/yCvW8/duxm4rTIkkNkhDCQlQqQbK3tyc9Pb3E/hMnTuDj43PTQQlhzRZsPgXAyM5B+LjY3/iE63TQvpZGo2HGXS3RamDdgVh2nE2+iUgtkCFByr4MRQXmjUUIUadVKkEaPHgwr7/+OgUF6geYRqMhJiaGF198kaFDh1aorI8//pjg4GAcHByIiIhgx44d1z1+5cqVhIWF4eDgQJs2bfj1119NHl+1ahX9+vXDy8sLjUbDvn37SpTRq1cvNBqNye2xxx6rUNxClGbf+VS2nb6MjVbDI+WpPYIrNUju129iM2gV4MaIzmqH7Xd/P4ZiWPW2NqjnCRodoEBWkrmjEULUYZVKkObOnUtmZiY+Pj7k5OTQs2dPQkNDcXFx4c033yx3Od999x1Tp05l5syZ7Nmzh3bt2tG/f38SEkrvf7Bt2zZGjRrFhAkT2Lt3L0OGDGHIkCEcOnTIeExWVha33norc+bMue61J06cSGxsrPH2zjvvlDtuIcqy+F91PqO7wwMJdHcs30kp5a9BMngmsin2Nlp2Rqew5WQtSiS0OnAqroXOkn5IQgjzqdQ8SG5ubmzcuJF///2X/fv3k5mZSYcOHYiMjKxQOfPmzWPixImMHz8egEWLFvHLL7/w5Zdf8tJLL5U4/oMPPmDAgAE8//zzgLrkycaNG1mwYAGLFi0C4MEHHwQgOjr6uteuV68e/v7+FYpXiOtJyMjl14OxAIy/Jbh8JylKhfogGfi5OvBA10Z88c9Z5m44To+m3mg0mgrFa7GcfSEzTjpqCyHMqsI1SHq9ni+//JI777yTRx99lIULF/LPP/9w6dKlClX15+fns3v3bpOkSqvVEhkZSVRUVKnnREVFlUjC+vfvX+bx17Ns2TK8vb1p3bo106ZNu+G6cnl5eaSnp5vchLjaih3nKShS6NDQndaBbuU7KTMBCnMADbiVsQxJGR7v1QRHWx0HLqTxx9FalEwYR7JJR20hhPlUKEFSFIXBgwfz8MMPc/HiRdq0aUOrVq04d+4c48aN45577il3WUlJSRQVFeHnZ7pwp5+fX5kj4eLi4ip0fFlGjx7NN998w+bNm5k2bRpff/01DzzwwHXPmT17Nm5ubsZbUFDF/pmJ2q2gSG+cNXts9+Dyn2jooO3WAGzsKnRNb2d7Y03V/D9O1J6+SDKSTQhhASrUxLZkyRK2bNnCpk2b6N27t8ljf/75J0OGDOGrr75izJgxVRpkVXvkkUeM99u0aUP9+vXp27cvp0+fpkmT0jvWTps2jalTpxp/Tk9PlyRJGP19PJH49Dy8nOwY0LoCTbeVaF672sTbGrNkWzSHL6Wz5WQSPZvVglGkMheSEMICVKgG6dtvv+Xll18ukRwB9OnTh5deeolly5aVqyxvb290Oh3x8abfEuPj48vsG+Tv71+h48srIiICgFOnTpV5jL29Pa6uriY3IQx+2H0BgHvaB2Jvoyv/iYYO2uUcwXYtDyc7RnVRR7R9srns969VkRokIYQFqFCCdODAAQYMGFDm43fccQf79+8vV1l2dnZ07NiRTZs2Gffp9Xo2bdpEt27dSj2nW7duJscDbNy4sczjy8swFUD9+vVvqhxRNyVn5bPpmPrPfGjHBhU7+SZrkAAevi0EW52G7WeT2X0updLlWAypQRJCWIAKJUjJyckl+gBdzc/Pj5SU8n9AT506lc8++4ylS5dy9OhRHn/8cbKysoyj2saMGWOyttvkyZNZv349c+fO5dixY7z22mvs2rWLp556yiTGffv2ceTIEQCOHz/Ovn37jP2UTp8+zRtvvMHu3buJjo5mzZo1jBkzhh49etC2bduKvBxCAPDzvosUFCm0DnSlRf0K1iyWc5mR66nv5si97dXEbOFftaAWSWqQhBAWoEIJUlFRETY2ZXdb0ul0FBaWf5XxESNG8N577zFjxgzCw8PZt28f69evNyZhMTExxMbGGo/v3r07y5cv59NPP6Vdu3b88MMPrF69mtatWxuPWbNmDe3bt2fQoEEAjBw5kvbt2xunAbCzs+OPP/6gX79+hIWF8eyzzzJ06FDWrl1bkZdCCCND89qwjpXok1aBWbSv55GejdFo4I+jCRyPy7ipsszOmCBJDZIQwnw0SgWGvmi1Wu644w7s7UtfPiEvL4/169dTVFRUZQFaqvT0dNzc3EhLS5P+SHXY0dh07vhgK7Y6DTtejsTDqQIj0Qrz4X++gALPnbzStFRJTyzbza8H4xjWsQHvDmt3U2WZVW4avK32q+LlWLCrZ954hBC1Snn/f1eoBmns2LH4+vqaDHe/+ubr62vxI9iEqEqr910EoE+Yb8WSI4C084ACtvWuzB59Ex6+rTEAP++7RGJG3k2XZzb2rmDjoN6X2bSFEGZSoWH+ixcvrq44hLA6iqLwywG1CXhwu8CKF3D1GmxVMAt2h4YetG/ozt6YVL757xxTbm9202WahUaj1qalxqjNbDfZ/CiEEJVRqbXYhBCw/0IaF1JyqGeno09YJZrHqmAE27UeuiUEgGXbz5FbYMVN3dJRWwhhZpIgCVFJvxy4BEDfFn442lVg7iMDYwftyo9gu9Ydrf0JcHMgKTOfNfsvVVm5NU4SJCGEmUmCJEQl6PVXmtcGtank/FnVUINko9Mypnipky//OWu9y4/IXEhCCDOTBEmISth7PoVLabk429vQq3klO1hXQ4IEMKpzQxxtdRyLyyDq9OUqLbvGyFB/IYSZSYIkRCWsK649ur2lHw62lWheg5teZqQsbvVsGdZJnTjyi3/OVmnZNcYwqk8SJCGEmUiCJEQFKYrChsNq35g7KrIw7dVyUiE3Vb1fhX2QDMYVN7NtOpbA2aSsKi+/2kkfJCGEmUmCJEQFHYlN52JqDg62Wm5rWsnmNUMHbSdfsHOquuCKNfZxNo6sW7otusrLr3bSxCaEMDNJkISoIEPtUY+mPpUbvQZVsgbbjRhqkX7YfYGM3IJqu061MHbSjgdr7WguhLBqkiAJUUEbj6gJ0u0ty164+YaqqYP21W5r6k2orzOZeYXG9eKshiFBKspTlx4RQogaJgmSEBVwPjmbI7HpaDXq/EeVllI1i9Rej0ajYWxxLdLSbdHo9VZUE2PrCPZu6n1pZhNCmIEkSEJUwB9H1dqjzsGeeFZ07bWrXb3MSDUa2iEQFwcboi9n89cJK0s0rm5mE0KIGiYJkhAVYOh/dFPNa3DVLNrBN1fODdSzs2Fk5yAAFv8bXa3XqnIykk0IYUaSIAlRTqnZ+eyITgagX8tKDu8H0BepC7FCjSzEOqZbMFoNbD2ZxMn4jGq/XpWRGiQhhBlJgiREOW0+nkCRXiHM34WGXvUqX1BGLBTlg9YWXAOqLsAyBHnWM9Z4LbGmIf8uxUmoJEhCCDOQBEmIcvr7eCIAvYvnF6o04wzaQaCt5DQBFTSuewgAq/ZcJC3bSob8GxKk9FjzxiGEqJMkQRKiHPR6ha0nkwDo2aySk0Ma1FAH7at1bexJmL8LOQVFfLcrpsaue1NcihcBzpAESQhR8yRBEqIcjsSmczkrHyc7HR0aetxcYcYO2jWXIGk0GsbfEgzA0m3nKCzS19i1K82YIMWZNw4hRJ0kCZIQ5fD3CbV5rXuoN3Y2N/lnUwNzIJXm7vBAPOrZcjE1hz+OWsGQf0mQhBBmJAmSEOVgSJB63GzzGpiliQ3AwVbH6IiGACz+92yNXrtSXIqH+ednQJ4Vjb4TQtQKkiAJcQMZuQXsOZcCQM/KLk57NTM0sRk80LUROq2G7WeTOXIpvcavXyH2LmDnot6XWiQhRA2TBEmIG9h2+jKFeoUQb6ebG94PUJB7pdOxe/BNx1ZR9d0cuaO1OjpsyTZrqEUqHskmHbWFEDVMEiQhbsDYvNbU++YLSzuvbu2coZ7nzZdXCYbO2qv3XeJyZp5ZYig3V+mHJIQwD0mQhLgORVHYUpwg9WxeFf2PDHMgNQKN5ubLq4QODT1o28CN/EI9K3aeN0sM5SZD/YUQZiIJkhDXcSYpiwspOdjptHRt7HXzBaZGq1sz9D8y0Gg0jOseDMDXUecosOQh/8YmNqlBEkLULEmQhLgOQ+1R5xAP6tnZ3HyBV9cgmdGgtvXxdrYnLj2X9YcsOPmQGiQhhJlIgiTEdVzpf1QFzWtw1Qi24Kopr5LsbXQ80NUKhvzLciNCCDORBEmIMuQWFPHfmctAFfU/gitzIJmxic1gdERDbHUa9sSksv98qrnDKZ1L8WK+UoMkhKhhkiAJUYZd0SnkFujxc7WnuZ9L1RRqIU1sAL4uDtzVVk1AlmyLNm8wZbm6D5KimDcWIUSdIgmSEGX4+4S6HEePpj5oqmLEWW4a5Kaq990b3nx5VWBc8ZD/dQcukZCea95gSmNIkIryICfFvLEIIeoUSZCEKMOWE0lAFS0vAldqj+p5g71z1ZR5k9o2cKdjIw8KihSWbY8xdzgl2diDY/F8UTKSTQhRgyRBEqIUsWk5HI/PQKuBW0OrYIJIMOsSI9djmDhy2fZz5BUWmTeY0hhHsl0ybxxCiDpFEiQhSrG1uPaobQN3PJzsqqZQC+p/dLX+rfzxd3UgKTOfXw5YYGdomU1bCGEGkiAJUQrj8P6qal4Dixnify1bnZYHu6lJ2+J/o1EsrTO0rMcmhDADSZCEuEZhkZ5/Tqk1SD2rMkGyoCH+1xrVpSH2NloOXkxjT4yFdYZ2kRokIUTNkwRJiGvsv5BGWk4Brg42tGvgVnUFW2gTG4Cnkx1DwgMB+HyrhU0cKcuNCCHMQBIkIa5hWF7ktqY+2Oiq6E9EUSC1eJSYBdYgAUy4LQSA9YfjOJWQYeZormKoQUqXTtpCiJpj9gTp448/Jjg4GAcHByIiItixY8d1j1+5ciVhYWE4ODjQpk0bfv31V5PHV61aRb9+/fDy8kKj0bBv374SZeTm5vLkk0/i5eWFs7MzQ4cOJT4+viqflrBiW04a+h9V0eg1gMwEKMwBjRbcgqqu3CrUzM+Ffi39UBT45K/T5g7nCmliE0KYgVkTpO+++46pU6cyc+ZM9uzZQ7t27ejfvz8JCQmlHr9t2zZGjRrFhAkT2Lt3L0OGDGHIkCEcOnTIeExWVha33norc+bMKfO6U6ZMYe3ataxcuZK///6bS5cuce+991b58xPWJzU737jsRrV00HYNBJ1t1ZVbxZ7sHQrAz/sucT4528zRFDMkSJnxoLfAaQiEELWSWROkefPmMXHiRMaPH0/Lli1ZtGgR9erV48svvyz1+A8++IABAwbw/PPP06JFC9544w06dOjAggULjMc8+OCDzJgxg8jIyFLLSEtL44svvmDevHn06dOHjh07snjxYrZt28Z///1XLc9TWI9/TiWhV6CZnzP13RyrrmAL7n90tXZB7tzW1JsivcKivy2kFsnZF7Q2oBRJLZIQosaYLUHKz89n9+7dJomMVqslMjKSqKioUs+Jiooqkfj079+/zONLs3v3bgoKCkzKCQsLo2HDhtctJy8vj/T0dJObqH3+Pq42r1Xp6DWA1Gh1a6H9j65mqEVauesC8Zaw/IhWd1U/pIvmjUUIUWeYLUFKSkqiqKgIPz8/k/1+fn7ExZX+LTEuLq5Cx5dVhp2dHe7u7hUqZ/bs2bi5uRlvQUGW2Y9EVJ6iKFf1P6riBMk4xD+4asutBhEhnnRq5EF+kZ7PtpwxdzgqV3WEHWkXzBuHEKLOMHsnbWsxbdo00tLSjLfz58+bOyRRxY7HZxCfnoeDrZbOwZ5VW7iVNLEBaDQanuyj1iIt2x5Dcla+mSMC3IoTJKlBEkLUELMlSN7e3uh0uhKjx+Lj4/H39y/1HH9//wodX1YZ+fn5pKamVqgce3t7XF1dTW6idjEM7+/a2AsHW13VFm6h67CVpVczH1oHupJTUMT/bbGAvkjGGiRJkIQQNcNsCZKdnR0dO3Zk06ZNxn16vZ5NmzbRrVu3Us/p1q2byfEAGzduLPP40nTs2BFbW1uTco4fP05MTEyFyhG1j3F5kaZV3LxWVHjlH7sV1CCBWos09fZmACzdFm3+vkhuDdRtujSxCSFqho05Lz516lTGjh1Lp06d6NKlC/PnzycrK4vx48cDMGbMGAIDA5k9ezYAkydPpmfPnsydO5dBgwaxYsUKdu3axaeffmosMzk5mZiYGC5dUieVO378OKDWHPn7++Pm5saECROYOnUqnp6euLq6MmnSJLp160bXrl1r+BUQliI7v5CdZ9UlNno2r+IEKf2COgJLZw/Ofjc+3kL0bu5Lx0Ye7D6XwoI/T/HGkNbmC0ZqkIQQNcysfZBGjBjBe++9x4wZMwgPD2ffvn2sX7/e2BE7JiaG2NgrC1R2796d5cuX8+mnn9KuXTt++OEHVq9eTevWVz6416xZQ/v27Rk0aBAAI0eOpH379ixatMh4zPvvv8+dd97J0KFD6dGjB/7+/qxataqGnrWwRP+duUx+kZ5Ad0caeztVbeHG/kcNQWs93f40Gg0v9G8OwLc7Yoi5bMZ5kaQPkhCihmkUi1u62zqkp6fj5uZGWlqa9EeqBV5bc5gl26IZHdGQt+5pU7WF7/kK1kyC0Eh44MeqLbsGjPlyB1tOJHJvh0DmDQ83TxCZifBeKKCBVxPAxs48cQghrF55/39bz9dZIarRlurqfwRWNYKtNM/3U2uRftp7kRPxZlqjzclbbaJEgQxZk00IUf0kQRJ13vnkbM4kZWGj1dA91KvqL2CcA8k6E6Q2Ddy4o7U/igKzfz1qniA0GnANUO/LorVCiBogCZKo8wyj1zo09MDVoRrWSUs5q249Qqq+7BrywoAwbHUaNh9P5K/jpa+VWO0MI9mko7YQogZIgiTqPEOCVOWj1wySixMkT+tNkEK8nRjXPRiAN9YdoaBIX/NBGEayyVB/IUQNkARJ1Gn5hXqiTl8Gqqn/UU4q5CSr9624BglgUt+meDnZcToxi2X/nav5AAxNbFKDJISoAZIgiTptT0wKmXmFeDnZ0SqgGkYjGprXnHzB3rnqy69Brg62TO2nTh75/h8na34JEhnqL4SoQZIgiTrNMHrttqbeaLWaqr9ALWheu9rIzg1pUd+VtJwC/vfLkZq9uKuhD5I0sQkhqp8kSKJOMy4v0qya+h/Vgg7aV9NpNbx1T2s0Gli15yL/nEyquYtLDZIQogZJgiTqrISMXA5fSgeqMUGqZTVIAO0bejCmqzplwSurD5JbUFQzFzZ00s6+DAU5NXNNIUSdJQmSqLP+Pq7WHrVt4Ia3s331XMSYIDWunvLN5Ln+zfF3deDc5Ww+2HSyZi7q6AG2xcvASEdtIUQ1kwRJ1FmG5rVe1VV7BLWuic3AxcGWWXe3AuD//j7N7nMp1X9RjUZdzw4g1Qyj6IQQdYokSKJOKizSs7W4/0y1zX9UkHtl1uda1MRm0L+VP0PCA9ArMOW7fWTmFVb/RSVBEkLUEEmQRJ20/0IqaTkFuDna0q6Be/VcJPUcoICdC9SrhiVMLMDrQ1oT6O5ITHI2r689XP0XNCzXkhpT/dcSQtRpkiCJOsnQ/+jWpt7Y6KrpzyD5jLr1DFGbh2ohVwdb5g1vh0YD3++6wK8HY6v3goYFf1OkBkkIUb0kQRJ10l810f+oFo5gK01EYy8e79kEgOdX7udUQmb1XczYxCY1SEKI6iUJkqhzkjLzOHAhDYCe0kG7Sky9vRldG3uSlV/EI1/vIiO3oHouZGxikxokIUT1kgRJ1DlbT6q1Ry3ru+Lr6lB9F6ojNUgANjotC0Z3wN/VgTOJWUz5bh9FeqXqL2SoQcpKhPysqi9fCCGKSYIk6py/ivsf9aqu0WsGhj5IdaAGCcDb2Z5FD3bEzkbLH0cTeG3NYRSlipMkRw+wd1Pvp56v2rKFEOIqkiCJOqVIrxjXX+vV3Lf6LqQvutJPppZNEnk94UHuzB8RjkYDX/93joV/n676i3jIUH8hRPWTBEnUKQcvppGSXYCLvQ3tG7pX34XSLoC+AHR24BpQfdexQAPb1Gf6oJYAvLP+OF/+c7ZqL+AuQ/2FENVPEiRRp/x1PAFQh/fbVtfwfoDLp9StRzBoddV3HQv10K0hPNFLHdn2+rojfFGVSZJxqH901ZUphBDXkARJ1Cmbj6kJUrWOXgO4XNy05NW0eq9jwZ7v35wne6tJ0hvrjjBvw/Gq6ZMkQ/2FEDVAEiRRZySk57K/eHh/n7Bq7H8EcLl4AVfv0Oq9jgXTaDQ81685k/uqSeKHf55iynf7yCssurmCZai/EKIGSIIk6oxNxbVH7YLcq3d4P0BScYLkVXcTJFCTpCm3N+Pte9ug02pYve8SwxZFEZ10E0P0pQ+SEKIGSIIk6oxNR+MBiKzu2iOQJrZrjOzSkCXjO+Nez5YDF9IY9OFWVu46X7kmN0MTW04K5KZXbaBCCFFMEiRRJ+TkF7H1ZBIAkS39qvdiBTmQVjxHj7ckSAa3NfXht8m30SVYnXH7+R8OMOqz/zgZn1Gxguydryz+K7VIQohqIgmSqBP+PZVEXqGeQHdHwvxdqvdiyWcABRzcrvwjFwDUd3Nk+cQIXhwQhoOtlv/OJDPgg608+/3+ijW7GUeyVfEUAkIIUUwSJFEn/GFoXmvhi0ajqd6LGfsfNYXqvpYVstFpebxXEzZO6cntLf0o0iv8uOcCfeb+xfjFO1h/KO7GHbm91NFxxqZMIYSoYjbmDkCI6qbXK8YO2n1bVHPzGlyZA6mOd9C+kSDPenw2phP7z6fy4aaTbDqWwObjiWw+noiTnY4ezXzoHupNuwZuhPm7Ymdz1fc5z+IEKVkSJCFE9ZAESdR6By6mkZiRh7O9DRGNPav/goYEqQ4P8a+IdkHufDGuM2eTslixM4af9lwkISOP3w7F8duhOADsdFoCPRxp4OGIv6sD3bLsuRe4ePowqzefQlEU9Iq6lExBkb74ppBfpKeg8JqfDbdC9WeNBpztbXB1sMXZ3gYfF3sC3B2p7+5AkIcjjbycqndSUSGERZIESdR6htFrPZp5Y29TA7NayxD/SgnxdmLaHS14sX8Yhy6l8eexBHafS+HAhTTScgo4m5TF2eJ+Sic0Ou61B13qGd79/Xi1xmWn09LYx4kwfxdaB7rROdiTVgGu2EjSJEStJgmSqPU2HjH0P6qB5jVFuTJJpAzxrxStVkPbBu60beAOgKIoXEjJ4UJKDhdTc4hPz6Uwywd2gb8mhfs7eFGkq4dGo0GrAVudFnsbLba64puNBjvdVT/rNNjZaI37ihSFzNxCMvMKSc8pID4jl9jUXC6m5nA+OZus/CKOxWVwLC6D1fsuAVDPTkeHhh50bexJr+a+tApwrf6+bUKIGiUJkqjVopOyOBaXgU6roXfzGpj/KPsy5KqzdRs7EoubotFoCPKsR5BnPdMHDntCTjJv9nAC/zbVcm1DcnY8LoNjcensjUll17kU0nIK+OdUEv+cSuK9DSfwc7Wnd3Nfeof50qOpD452dW/9PSFqG0mQRK1m6MPSrbEXHk521X9BQ/8jtyCwdaz+69VlXk3gQrI6kq2aEqSrkzPD/Fl6vcLJhEx2RCez5UQi/55KIj49jxU7z7Ni53kcbXX0aeHLnW3q06u5ryRLQlgpSZBErbb+UCwAd7Txr5kLJhb3h5H+R9XPswlc2HklKa0hWq2G5v4uNPd34cGujcgrLGL7mWT+PJbAH0fjuZCSwy8HYvnlQCz17HT0CfPlzrZqsuRgK8mSENZCEiRRa11IyWb/hTQ0GujXsqYSpGPq1rdFzVyvLjM0YSafMWsY9jbqlAQ9mvkw866WHLyYxi8HYll3IJaLqTmsK75vSJYG1fKapdyCIrLyCilSFBRF7a/lbG8jfbSE1ZEESdRa64ub1zoHe+LjYl8zF004qm59wmrmenWZZ2N1a0GTRWo0VzqYv3RHGPsvpPHrQbU26dpkqXdxstTbypIlRVE4dzmbY3HpnE7MMo4ujE/PJTkrn+z8kpN86rQaPOrZEuLtRBMfZ8L8XejaxItmvi5otZI4CcskCZKotQwJ0h2ta6j2CKQGqSZ5WfZkkRqNhvAgd8KD3JlWSrJkaIZztFVrlu5o40+PZj64OtiaO3QjRVGITcvlwIVUDlxIK76lkp5bWK7ztRqM81MlZeaTlJnPzugU4+OeTnb0au7D3eGB3NLES6ZOEBbFIhKkjz/+mHfffZe4uDjatWvHRx99RJcuXco8fuXKlUyfPp3o6GiaNm3KnDlzGDhwoPFxRVGYOXMmn332Gampqdxyyy0sXLiQpk2vDLsODg7m3LlzJuXOnj2bl156qeqfoKhx8em57I5RP4gH1FSClJMKGWqfJ3ya18w16zLDbNpZierIQQc388ZzHdcmSwcMydLBWLXPUvF9nVZD+yB3bmvqQ49m3rRt4I6uBmtYUrLy2X8hlf3n1URo/4U0kjLzShxnZ6MlzN+FJj7ONPZ2ItjbiQB3R7yc7PBytsPJzsZYM5RbUERqdgGJGXmcScrkdEIme8+nsis6heSsfFbtuciqPRfxdbFnbPdgRndpWDMDKoS4AY2iKIo5A/juu+8YM2YMixYtIiIigvnz57Ny5UqOHz+Or2/JYdnbtm2jR48ezJ49mzvvvJPly5czZ84c9uzZQ+vWrQGYM2cOs2fPZunSpYSEhDB9+nQOHjzIkSNHcHBwANQEacKECUycONFYtouLC05OTuWKOz09HTc3N9LS0nB1da2CV0JUpa+iopnx82HaN3TnpyduqZmLxmyHL/uBSwA8e7RmrlnXzQ1Tk9IJf0BQZ3NHU2GKonDoYjq/HIxlw5E4ziSaLtjrbG9D2wZutG/oTniQB60DXfF3dbjp/jx6vTp9wcmEDE7EZ3L4klo7FJOcXeJYnVZDcz8X2gW50SbQnbYN3Gju73LTs4vnF+rZG5PCLwfVZsfkrHwAHGy1jO7SiEl9QiVREtWivP+/zZ4gRURE0LlzZxYsWACAXq8nKCiISZMmlVqbM2LECLKysli3bp1xX9euXQkPD2fRokUoikJAQADPPvsszz33HABpaWn4+fmxZMkSRo4cCagJ0jPPPMMzzzxTqbglQbJswxZtY2d0Cq8MbMHEHo1r5qK7l8DaydCkDzz4U81cs677agic2QyDP4IOY8wdzU27kJLNlhNJ6vQBp5PIKKUpq56djhBvJ0K8nfBzdcDTyQ4vJzvcHG2x0WnRaUGn1ZJXUERmnjoBZlp2AbHpucSm5hCblsu5y9nkFJS+IHBjbyfaBamJUNsG7rQKcK320Xf5hXp+PRjLZ1vPcPhSOgAuDjY82TuU8bcE18wM+KLOKO//b7M2seXn57N7926mTZtm3KfVaomMjCQqKqrUc6Kiopg6darJvv79+7N69WoAzp49S1xcHJGRkcbH3dzciIiIICoqypggAbz99tu88cYbNGzYkNGjRzNlyhRsbCyi1VHchAsp2eyMTkGjgbvaBdTchROK+x/5SP+jGuMTpiZIidW73EhNaeBRj9ERDRkd0ZAivcKJ+Az2nU9lX0wqe8+ncDoxi+z8Ig5fSjcmEpVlWEKlmZ86ZUG7Bu60aeCGm2PN94Gys9EypH0gd4cHsOVkEm//doyjsem8/dsxfth9gXfua0uHhh41Hpeo28yaDSQlJVFUVISfn+kSEH5+fhw7dqzUc+Li4ko9Pi4uzvi4YV9ZxwA8/fTTdOjQAU9PT7Zt28a0adOIjY1l3rx5pV43Ly+PvLwrbfHp6Tf34SSqz5r96nIQXUO88HdzqLkLGztoywi2GmPo65VQ+5o0dVoNLeq70qK+K6O6NASgoEhPTHI2ZxKziE7KIikzj8tZ+SRn5ZOeU0CRolCkVygsUrC31eJsb4OzvQ0uDjb4uzpQ392R+m4OBHnWo5FnPYvrFK3RaOjZzIdbQ735ae9F3v7tGKcSMhm6cBsP3RLC8/2by1xSosbU2eqSq2uh2rZti52dHY8++iizZ8/G3r7kkPDZs2cza9asmgxRVNLPe9UE6e7wGqw9gisJktQg1RzDaMFaUoN0I7Y6LU18nGni42zuUKqVTqvhvo4NiGzhyxvrjvLjngt88c9Zok5f5uP7OxDiXb6+okLcDLN+ffD29kan0xEfH2+yPz4+Hn//0kce+fv7X/d4w7YiZYLaF6qwsJDo6OhSH582bRppaWnG2/nz56/73IR5HItL53h8BnY6LXe0rl9zF85JkRFs5mB4rdMvQK7U6tY27vXsmDu8HYvHdcbLyY4jsenc+eFW1hbXEgtRncyaINnZ2dGxY0c2bdpk3KfX69m0aRPdunUr9Zxu3bqZHA+wceNG4/EhISH4+/ubHJOens727dvLLBNg3759aLXaUkfOAdjb2+Pq6mpyE5ZndXHtUa/mPrjVq8G+FPGH1a1bEDjIe6PGOHqAc/EXn6QT5o1FVJveYb78Ovk2IkI8ycovYtK3e5m34Th6vVnHGIlazuxNbFOnTmXs2LF06tSJLl26MH/+fLKyshg/fjwAY8aMITAwkNmzZwMwefJkevbsydy5cxk0aBArVqxg165dfPrpp4Dahv3MM8/wv//9j6ZNmxqH+QcEBDBkyBBA7ei9fft2evfujYuLC1FRUUyZMoUHHngADw/pCGit9HrF+M1ySPvAmr143EF169+2Zq8r1FqkzDi1ibNBJ3NHI6qJn6sDyx6O4N3fj/N/W87w4Z+nOJ2YxXvD2lnVTOTCepg9QRoxYgSJiYnMmDGDuLg4wsPDWb9+vbGTdUxMDFrtlYqu7t27s3z5cl599VVefvllmjZtyurVq41zIAG88MILZGVl8cgjj5Camsqtt97K+vXrjXMg2dvbs2LFCl577TXy8vIICQlhypQpJUbHCeuyMzqZi6k5uNjb0Ces9JrAahN7QN3WlwSpxvm2gLN/18qO2sKUjU7LtIEtCPV15uWfDhZPtJnNl+M64+VcQ8sJiTrD7PMgWSuZB8nyTP1+H6v2XGREpyDm3FfDicrCWyH+IIxcDmGDavbadd2uL2HdFAi9HR74wdzRiBqy/cxlHvtmNynZBTTxceLrCREEuDuaOyxhBcr7/9uyxngKUUnpuQX8elDtJD28c1DNXrww/8oINv82NXttcWXUYMIR88YhalREYy9WPtadADcHTidmMWxRFGcSM80dlqhFJEEStcLa/ZfILdAT6utMh4buNXvxxGOgLwAHd7WTtqhZfq3UbfpFyEoybyyiRoX6OrPy8e409nbiYmoOw/8visOX0swdlqglJEEStcL3O9VpF0Z0CrrpdaoqLK64/5F/G6jpawt11KBh4drY/eaNRdS4QHdHvn+sGy3ru5KUmc/9n2+XJElUCUmQhNU7FpfO/gtp2Gg13NOhhkevgYxgswT126lbQ7Iq6hRvZ3tWPNqV8CB3UrMLeODz7RyNlXmxxM2RBElYvW+3xwAQ2cIPb3OMZDEmSNL/yGwMowelBqnOcnWw5asJXWjXwI2U7ALu/3w7x+IkSRKVJwmSsGoZuQX8sPsCAPd3bVjzAeiLrgzxlwTJfAw1SLFSg1SXqUlSBG0buJGclc/9n23nRHyGucMSVkoSJGHVftx9gaz8Ipr4OHFrqHfNB5B0AvIzwNbpyrpgoub5FydIyadlyZE6zs3Rlq8fiqB1oCuXs/IZ/dl/nEqQJElUnCRIwmrp9QpfRZ0DYGz34JrvnA1wYZe6DWgPWpnN12ycvMC1gXrf0OQp6iy3erZ8MyGCVgFqx+3Rn20nOinL3GEJKyMJkrBa/5xK4kxSFs72NtzboYF5grhYnCA16Gie64srpKO2uIp7PTu+nhBBcz8XEjLyGP3Zf5xPzjZ3WMKKSIIkrNaSbdEA3NexAc72Zlo158JudRsoa4CZnaGj9qW95o1DWAxPJzu+eTiCxj5OXErLZfTn/xGblmPusISVkARJWKVjcen8eSwBjUZtXjOL/CxIOKzel0VSzc+QpF7Yad44hEXxcbFn+cNdaeRVj/PJOYz+bDsJ6bnmDktYAUmQhFVa9NdpAAa2rk+It5N5gri0DxQ9uASAa4B5YhBXGJLU5DOQmWjeWIRF8XdzYPnErgS6O3I2KYv7P9/O5cw8c4clLJwkSMLqnE/OZu0Bdd21x3s1MV8gMVHqNqiz+WIQVzi6X1mX7cIOs4YiLE+guyPLJ0bg7+rAyYRMHvhiB6nZ+eYOS1gwSZCE1fl0yxmK9Aq3NfWmdaCb+QI596+6bXSr+WIQpoK6qNvz280bh7BIjbycWDYxAm9ne47GpjPmyx2k5xaYOyxhoSRBElYlLi2X73ep666ZtfaoqABiiv8JN+puvjiEqaAIdXteapBE6Zr4OLN8YgSeTnYcuJDGuC93kJlXaO6whAWSBElYlQ//PEleoZ5OjTzo1tjLfIHEHoCCLHD0AN+W5otDmDIkSBf3QKH0MRGla+bnwtcTuuDqYMOemFQmLNlJTn6RucMSFkYSJGE1opOy+H6nWnv0woAw80wMaXDuH3XbsDto5c/IYng1AScfKMq7MomnEKVoFeDG1xMicLa3YfvZZB75ehe5BZIkiSvkk11YjXkbT1CoV+jV3IcuIZ7mDSa6OEGS5jXLotFASE/1/pm/zBqKsHztgtxZMr4z9ex0bD2ZxBPL9pBfqDd3WMJCSIIkrMLhS2ms2X8JgOf6NTdvMAW5VxKkkB7mjUWU1LiXupUESZRDp2BPvhjbGXsbLX8eS+Dpb/dSWCRJkpAESVgBRVGY+bM6IePgdgHmHbkGELMNCrLB2R/825g3FlGSIUG6uFsWrhXl0q2JF5+N6YSdTsv6w3FM/X4/RXrF3GEJM5MESVi8n/ZeZNe5FOrZ6Zg2MMzc4cCpTeo2NFJt0hGWxT0IPJuAUnRlKgYhbqBHMx8WPtABG62GNfsv8eKPB9BLklSnSYIkLFp6bgFv/XoMgEl9mlLfzdHMEQEnN6rbppHmjUOUzVCLdHKDWcMQ1qVvCz8+GtUenVbDD7sv8OrPh1AUSZLqKkmQhEV7d/1xkjLzaOztxIRbQ8wdDqScg6TjoNFe+ScsLE/zger22K+gl/4kovzuaFOfecPbodHA8u0xvPzTIWluq6MkQRIW65+TSXz93zkA3hjSGjsbC3i7Hl2jbht2V+dAEpYp5Dawc4HMOLUvkhAVcHd4IO8MbYtGA9/uiOHJZXtkCoA6yAL+4whRUnpuAS/8sB+AB7s24pZQbzNHVOzwT+q21RCzhiFuwMYemt6u3j+2zryxCKs0rFMQn4zuYOy4PVaWJalzJEESFkdRFF77+TCX0nJp5FXPMjpmg9q8dnG32rzWYrC5oxE3EjZI3R5dC9KPRFTCHW3qs/ShLrgUTyY54v/+41JqjrnDEjVEEiRhcZZtj2HV3otoNfDesHbUs7Mxd0iqIz+r20a3gIufeWMRN9a0H9g4QvJpmVVbVFq3Jl6seLSrcYHbwQv+Zfe5FHOHJWqAJEjCouyNSWHWWnXOoxcGhNE52MwzZhsoCuz9Rr3f6h7zxiLKx8EVWhbX9O37xryxCKvWKsCN1U92J8zfhaTMPEZ9+h8/7L5g7rBENZMESViMS6k5PP7NHgqKFAa08ufRHo3NHdIVMVHq6DXbetDmPnNHI8or/H51e2gV5GebNxZh1Rp41OPHx7vTv5Uf+UV6nlu5n1lrD8vSJLWYJEjCIqRk5TPmyx3EpecS6uvMu8Pamncx2mvtWqxuWw8FBzPP5C3KL/g2cG8IeelweJW5o6laigJZSZBwDC7tg4t74PJpyE6WPlfVxMnehoX3d+TpPqEALP43mqELtxGdlGXmyER1sJDOHaIuy8orZPySnZxKyMTf1UHtFOlga+6wrsiIv9L/qNN488YiKkarhU4T4I+Z8O+H0G60us/aKAokn4GY/9TazIu7ISVaXfKmNPZu4NUE/FpBw64Q1FX92ZK+dFgprVbD1H7NadPAned/2M/Bi2nc+dE/vDGkFUPCAy3ri524KRpFpgmtlPT0dNzc3EhLS8PV1dXc4Vit1Ox8xi7eyf7zqbg52vLDY91o6udi7rBM/f4KRC2ABl1gwgb5J2NtctPh/daQlwYjlkGLO80d0Y0V5kHsATi/Hc7/pyZGWYmlHKhR5+OyLZ5hPi9DrS0rjZMvhPZVl8hp0gfqWUj/Pit2KTWHySv2sjNa7bQd2cKX/w1pg7+bg5kjE9dT3v/fkiBVkiRINy8+PZcxX+zgeHwG7vVsWTq+C+2C3M0dlqnMRPigrfpN/f4frsytI6zLH7Pgn3ng3xYe+Qu0upqPoagACnKgMFe9FeReuZ+VCMln1Vqi2P0QdwCK8k3P19lBQIfiGqEI8GkObg3UOZ+uVpALKWch6YTa7HZ+u7otyrtyjEYLDTpDswHqlBXeodX//GupwiI9n/x1mo/+PElBkYKLvQ3TBrZgROcgdFr5MmWJJEGqZpIg3Zyd0ck8sWwPiRl5+Lna8/WECJpZWs0RwK/Pw45PIaA9TNwstUfWKisJPmyv1q7cOb/6mkpTotXangs71WQnJRpyUiE/s2TCcyP1vNUkpmFX9VY/HGwrWTNRmKfGdWojnPwDEo+aPu7bElrcpd78Wsv7vBKOx2Xwwo8H2H8+FYCW9V2ZfmdLujXxMm9gogRJkKqZJEiVo9crfPnvWd7+7RiFeoXmfi58PrYTQZ71zB1aSZf2wmd9QNHDmDXQuKe5IxI347+FsP4ltUnqqV3gVEWzsyefgYM/qCPlrk08ymLjoNb82DiqW0cP8AwBjxDwbaEmRh7B1ZeopJ5XF/I99guc/Rv0hVce8whRp7IIHw3eTavn+rVUkV5hybZo5v9xgoxc9TW9vaUfz0Q2pVWADO6wFJIgVTNJkCruTGImL/xwgF3Fk6zd2bY+c4a2xcneAscKFOTCF7erTR1thsHQz80dkbhZRYXwfz0g4TA06as2mVa2w7aiqInFfwvhxO9A8ceo1katbQyKAJ8wNclx8gY7Z7BzUqeJsLG3rBqanBT1ORxdC6f+UJv8DBp0hnajoPW9svZgBSRn5TP/jxMs2x5jXOi2T5gvT/YOpWMjeR3NTRKkaiYJUvmlZOWzYPMpvoqKpqBIwclOx7SBLbg/oqHljvhYOxl2L1H/KTyxXWbOri3ij6i1goU50OVRuGNOxZKVglw4uFJNjBIOX9nfpA+0vg/CBlp3IpGXqdYsHfgOTm4EpXiBVp292rm9/QMQ0ss6RwKawamEDD768xRr91+iOE+iXZA790c05K62ATjamaEvnJAEqbpJgnRjl1JzWLotmuU7YozVzT2b+fDmPa1p4GGBTWoG/8xXh4WjgQd+UEf9iNrj4A/w48OAAuEPwKC5N+7bk5kAO7+AnZ9DdpK6z9YJ2t8PEY+pQ+hrm8wEOPA97P8W4g9d2e/WUH3e4feDe5D54rMiZ5Oy+GTzKVbvu0hBkfov19XBhoFt6jOobX26NfbCRidJZ02RBKmaSYJUutyCIjYfS2DN/ktsOBJvrF4O83fh5YEt6NHMx8wRXoe+CP56G7a8o/4c+RrcOsWsIYlqsmsx/DJV7V/m2QR6vgDNB6rLkxikX4KzW+HQj3B605V+Oq4NIOIR6DDGumuLKuLSPtj7NRxYqU6XAIAGmvSG9g+qCwNfO5pOlJCUmcfKXRdYvuMc55OvLHrrUc+W3s19ubWpN7eGeuPrKtMEVCerSpA+/vhj3n33XeLi4mjXrh0fffQRXbp0KfP4lStXMn36dKKjo2natClz5sxh4MCBxscVRWHmzJl89tlnpKamcsstt7Bw4UKaNr3S4TA5OZlJkyaxdu1atFotQ4cO5YMPPsDZ2blcMUuCpFIUhdOJmWw7fZltpy6z9WQiWflFxse7N/HioVtC6BPmi9aSh7wmnYRfnlX7lQD0fkX9pylqr1N/wOonITNO/VmjVWfdtq2nDru/dt6hBp2h6xPqsHidBfabqwkFOXB0Hez9Cs5uubLf0QPajlD76wW0N880ClZEr1f478xlfjkYy/pDcVzOMh3h2MzPmfZBHrQNcqNdA3ea+blgZyM1TFXFahKk7777jjFjxrBo0SIiIiKYP38+K1eu5Pjx4/j6+pY4ftu2bfTo0YPZs2dz5513snz5cubMmcOePXto3bo1AHPmzGH27NksXbqUkJAQpk+fzsGDBzly5AgODmpmfscddxAbG8v//d//UVBQwPjx4+ncuTPLly8vV9x1LUEqKNITl5bLhZQczidnczQunaOx6RyNzSAtp8Dk2EB3R+5sV58h4YG0qG/Br01BLpz7F/Yth8M/qf0tbOupTS7ho80dnagJuWnqNA57l6lzB11No1Vnom42QP3H79PcPDFaquSzsG+Z+tplXLqyv56X2ierUXd13ia/VqCzoJnxLUxhkZ4d0clsPZnEPyeTOHQprcRKMTqthkae9Wjs40QTH2caetXD39UBP1cH6rs54OlkZ7n9OS2Q1SRIERERdO7cmQULFgCg1+sJCgpi0qRJvPTSSyWOHzFiBFlZWaxbt864r2vXroSHh7No0SIURSEgIIBnn32W5557DoC0tDT8/PxYsmQJI0eO5OjRo7Rs2ZKdO3fSqVMnANavX8/AgQO5cOECAQEBN4y7uhKkxIw8cguKUBRQUFAU0CsKCurAGeWq+3pFMTnOcF9vcpzhGNDkpqHJS6egsIi8Ij35hQr5hYXkF+rJLywiv1BPTn4h6bmFZOYUkJ6XT1p2Iek5+VzOyjP+0Wq48pbRoGCn09I60JX2QR60D3Kjub8LJpVFxreYUvrP1z3mOueZvHOvU3ZBDuSmqvPR5CSr61UlnVQn5Cu8Us1N84Fw+xsyaV5dlX5JHf5ekKX+k/dsDPYWODeXpdEXwek/1WTp1KaSM3nr7NTRfB7B4N4InH3BwR0c3dWtrYN6jNZWTaR0tur9sv7hG/drStl3vf0WzskHbB1JzspnZ3QyBy6kcuBCGvvPp5KeW3jdU+10Wjyd7HBztMXV0UbdOtji6mhLPTsdDrY67G20Jbb2tlp0Wi06jQatluKtBq1Gg06rubK/+L5GozF5SU1e9aseuPZVNz1HU+r+axke83a2x8G2amsky/v/26z1xPn5+ezevZtp06YZ92m1WiIjI4mKiir1nKioKKZOnWqyr3///qxevRqAs2fPEhcXR2TklY61bm5uREREEBUVxciRI4mKisLd3d2YHAFERkai1WrZvn0799xzT4nr5uXlkZd3ZSba9PQypvO/SVO/38fWk0nVUvajurVMs/22cifb3eDx+OLbrsoVbzbO/tB8AHQcpzYNiLrLNUC9iYrR6tQZ5pvers4Wfn6HmjBd3K3OJZabqs7qnXTC3JFatuKZ+j2d7Ojfyp/+rfwB9UtufHoepxMzOZOYyenELC6kZBOXnktcWh6Xs/LIL9KrP6fn3uAi1uerh7qYre+qWROkpKQkioqK8PMzHULt5+fHsWPHSj0nLi6u1OPj4uKMjxv2Xe+Ya5vvbGxs8PT0NB5zrdmzZzNr1qxyPrPKs7fR4WirQ6NRs3Bt8R0N6iKJGtRMXa2hUbN5rUbNytX7atptuH91OW4FjuTm2WHM74vPUwx7NBpjmYatRqNRyzZc2xCMoQDjeWXtK+3b3HX23VRZZZyns1e/rTp6qN9YPRqBdzN1xmCf5tb1LVMIS6azheBb1BuoNbqpMWrzZUo0pJxTRwHmpF6p1S3KL74Vqlt9gZpoGVy3Nvmqfdfbbw3K+BzSaDT4uzng7+bALaElJzfNL9STkJFLSlYBaTkFpOcWb3PUbXZ+EXmFevIKireFReQWXNkW6RX0ikKRXqGouMWhSK+Y7L+yvXLdqxufSqvML+3Hss65th1LuepRrRk/n+toT8OKmzZtmknNVXp6OkFBVT/E9fOxnW58UKX1At6vxvKFEOIqGo36hcSjkbkjqbXsbLQ08KhHgzoyoLImmbVbvLe3Nzqdjvj4eJP98fHx+Pv7l3qOv7//dY83bG90TEJCgsnjhYWFJCcnl3lde3t7XF1dTW5CCPH/7d15UBRn+gfw70C4kVsYUDk8gqKAV5xFN8IKy7FEJVqe7IIaNR4obhKXwvKKboJKPMrsrhoLwSoNHikB10QUDzxZ4gEoWYsVdhRdZlBRblFgnt8fLv2zh4FBVGZwn0/VVDFvv93zPDwz00/1zHQzxt5NOm2QjI2NMWLECJw+fVoYU6lUOH36NPz8/DSu4+fnJ5oPAFlZWcJ8Dw8PSKVS0Zzq6mrk5uYKc/z8/FBZWYlr164Jc86cOQOVSgWZTPbG8mOMMcZY96Tzj9g+++wzREdHY+TIkRg1ahS2bduGuro6zJ794mrbUVFR6NWrFxISEgAAsbGx8Pf3x+bNmxEeHo4DBw7g6tWr+O677wC8+Lx22bJl+POf/4wBAwYIP/N3cXFBREQEAGDQoEEIDQ3FvHnzsHPnTjQ2NiImJgbTp0/v0C/YGGOMMfZu03mDNG3aNDx8+BCrV6+GUqnE0KFDkZmZKXzJurS0FAYvXfdn9OjR+P7777Fy5UqsWLECAwYMQHp6unAOJAD405/+hLq6OsyfPx+VlZX49a9/jczMTOEcSACwf/9+xMTEIDAwUDhR5Pbt27succYYY4zpLZ2fB6m7+l87USRjjDH2Lujo/pvPXc4YY4wxpoYbJMYYY4wxNdwgMcYYY4yp4QaJMcYYY0wNN0iMMcYYY2q4QWKMMcYYU8MNEmOMMcaYGm6QGGOMMcbUcIPEGGOMMaZG55ca6a5aTkBeXV2t40gYY4wx1lEt+21tFxLhBqmTampqAAB9+vTRcSSMMcYYe1U1NTWwtrZuczlfi62TVCoVysrK0KNHD0gkkrfyGNXV1ejTpw/u3bv3Tl7vjfPr3ji/7o3z6944v84jItTU1MDFxQUGBm1/04iPIHWSgYEBevfu3SWPZWVl9U6+AFpwft0b59e9cX7dG+fXOe0dOWrBX9JmjDHGGFPDDRJjjDHGmBpukPSYiYkJ1qxZAxMTE12H8lZwft0b59e9cX7dG+f39vGXtBljjDHG1PARJMYYY4wxNdwgMcYYY4yp4QaJMcYYY0wNN0iMMcYYY2q4QdIDa9euhUQiEd0GDhwoLG9oaMDixYthb28PS0tLTJ48GeXl5TqM+NW4u7u3yk8ikWDx4sUAgICAgFbLFixYoOOo23b+/HmMHz8eLi4ukEgkSE9PFy0nIqxevRrOzs4wMzNDUFAQbt++LZrz+PFjREZGwsrKCjY2Nvjkk09QW1vbhVlo1l5ujY2NiIuLg7e3NywsLODi4oKoqCiUlZWJtqGp3hs2bOjiTDTTVrtZs2a1ij00NFQ0R19rB2jPT9PrUCKRIDExUZijz/VLSEjABx98gB49esDR0REREREoKioSzenI+2VpaSnCw8Nhbm4OR0dHLF++HE1NTV2Zikba8nv8+DGWLFkCT09PmJmZwdXVFUuXLkVVVZVoO5pqfODAga5OR6QjtevIvqAra8cNkp4YPHgwFAqFcLt48aKw7I9//CP+/ve/4/Dhwzh37hzKysowadIkHUb7aq5cuSLKLSsrCwAwZcoUYc68efNEczZt2qSrcLWqq6uDr68v/vrXv2pcvmnTJmzfvh07d+5Ebm4uLCwsEBISgoaGBmFOZGQkfvnlF2RlZeHYsWM4f/485s+f31UptKm93Orr63H9+nWsWrUK169fx5EjR1BUVIQJEya0mrtu3TpRPZcsWdIV4WulrXYAEBoaKoo9NTVVtFxfawdoz+/lvBQKBfbs2QOJRILJkyeL5ulr/c6dO4fFixfjH//4B7KystDY2Ijg4GDU1dUJc7S9XzY3NyM8PBzPnz/H5cuXsXfvXqSkpGD16tW6SElEW35lZWUoKyvDN998g8LCQqSkpCAzMxOffPJJq20lJyeLahgREdHF2Yh1pHZA+/uCLq8dMZ1bs2YN+fr6alxWWVlJRkZGdPjwYWHs1q1bBIBycnK6KMI3KzY2lvr160cqlYqIiPz9/Sk2Nla3QXUSAEpLSxPuq1QqkkqllJiYKIxVVlaSiYkJpaamEhHRP//5TwJAV65cEeYcP36cJBIJ/ec//+my2LVRz02Tn3/+mQDQ3bt3hTE3NzfaunXr2w3uDdCUX3R0NE2cOLHNdbpL7Yg6Vr+JEyfSuHHjRGPdpX5ERA8ePCAAdO7cOSLq2PvlTz/9RAYGBqRUKoU5O3bsICsrK3r27FnXJqCFen6aHDp0iIyNjamxsVEY60jtdU1Tbtr2BV1dOz6CpCdu374NFxcX9O3bF5GRkSgtLQUAXLt2DY2NjQgKChLmDhw4EK6ursjJydFVuJ32/Plz7Nu3D3PmzBFd5Hf//v1wcHDAkCFDEB8fj/r6eh1G2XlyuRxKpVJUL2tra8hkMqFeOTk5sLGxwciRI4U5QUFBMDAwQG5ubpfH/DqqqqogkUhgY2MjGt+wYQPs7e0xbNgwJCYm6sXHFx2VnZ0NR0dHeHp6YuHChaioqBCWvUu1Ky8vx48//qjx6EN3qV/LR0t2dnYAOvZ+mZOTA29vbzg5OQlzQkJCUF1djV9++aULo9dOPb+25lhZWeG998SXVl28eDEcHBwwatQo7NmzB6RnpzxsK7f29gVdXTu+WK0ekMlkSElJgaenJxQKBb788kt8+OGHKCwshFKphLGxcasdkJOTE5RKpW4Cfg3p6emorKzErFmzhLGZM2fCzc0NLi4uuHHjBuLi4lBUVIQjR47oLtBOaqnJyy/glvsty5RKJRwdHUXL33vvPdjZ2XWrmjY0NCAuLg4zZswQXUxy6dKlGD58OOzs7HD58mXEx8dDoVBgy5YtOoy2Y0JDQzFp0iR4eHigpKQEK1asQFhYGHJycmBoaPjO1A4A9u7dix49erT6uL671E+lUmHZsmUYM2YMhgwZAgAder9UKpUaX58ty/SFpvzUPXr0COvXr2/1Ee+6deswbtw4mJub4+TJk1i0aBFqa2uxdOnSrghdq7Zy07Yv6OracYOkB8LCwoS/fXx8IJPJ4ObmhkOHDsHMzEyHkb15SUlJCAsLg4uLizD28ovb29sbzs7OCAwMRElJCfr166eLMJkWjY2NmDp1KogIO3bsEC377LPPhL99fHxgbGyMTz/9FAkJCXp/WYTp06cLf3t7e8PHxwf9+vVDdnY2AgMDdRjZm7dnzx5ERkbC1NRUNN5d6rd48WIUFhaKvq/5LtGWX3V1NcLDw+Hl5YW1a9eKlq1atUr4e9iwYairq0NiYqLeNEht5aZv+wL+iE0P2djY4P3330dxcTGkUimeP3+OyspK0Zzy8nJIpVLdBNhJd+/exalTpzB37tx258lkMgBAcXFxV4T1RrXURP1XMy/XSyqV4sGDB6LlTU1NePz4cbeoaUtzdPfuXWRlZYmOHmkik8nQ1NSEO3fudE2Ab1Dfvn3h4OAgPBe7e+1aXLhwAUVFRVpfi4B+1i8mJgbHjh3D2bNn0bt3b2G8I++XUqlU4+uzZZk+aCu/FjU1NQgNDUWPHj2QlpYGIyOjdrcnk8lw//59PHv27G2F3GHacnuZ+r6gq2vHDZIeqq2tRUlJCZydnTFixAgYGRnh9OnTwvKioiKUlpbCz89Ph1G+uuTkZDg6OiI8PLzdefn5+QAAZ2fnLojqzfLw8IBUKhXVq7q6Grm5uUK9/Pz8UFlZiWvXrglzzpw5A5VKJbwh6KuW5uj27ds4deoU7O3tta6Tn58PAwODVh9NdQf3799HRUWF8FzszrV7WVJSEkaMGAFfX1+tc/WpfkSEmJgYpKWl4cyZM/Dw8BAt78j7pZ+fH27evClqdFsafS8vr65JpA3a8gNevJ8EBwfD2NgYR48ebXUEUJP8/HzY2trq9AhgR3JTp74v6PLavfGvfbNX9vnnn1N2djbJ5XK6dOkSBQUFkYODAz148ICIiBYsWECurq505swZunr1Kvn5+ZGfn5+Oo341zc3N5OrqSnFxcaLx4uJiWrduHV29epXkcjllZGRQ3759aezYsTqKVLuamhrKy8ujvLw8AkBbtmyhvLw84ZdcGzZsIBsbG8rIyKAbN27QxIkTycPDg54+fSpsIzQ0lIYNG0a5ubl08eJFGjBgAM2YMUNXKQnay+358+c0YcIE6t27N+Xn55NCoRBuLb8guXz5Mm3dupXy8/OppKSE9u3bRz179qSoqCgdZ/ZCe/nV1NTQF198QTk5OSSXy+nUqVM0fPhwGjBgADU0NAjb0NfaEWl/bhIRVVVVkbm5Oe3YsaPV+vpev4ULF5K1tTVlZ2eLnn/19fXCHG3vl01NTTRkyBAKDg6m/Px8yszMpJ49e1J8fLwuUhLRll9VVRXJZDLy9vam4uJi0ZympiYiIjp69Cjt3r2bbt68Sbdv36a//e1vZG5uTqtXr9Zlalpz68i+oKtrxw2SHpg2bRo5OzuTsbEx9erVi6ZNm0bFxcXC8qdPn9KiRYvI1taWzM3N6eOPPyaFQqHDiF/diRMnCAAVFRWJxktLS2ns2LFkZ2dHJiYm1L9/f1q+fDlVVVXpKFLtzp49SwBa3aKjo4noxU/9V61aRU5OTmRiYkKBgYGt8q6oqKAZM2aQpaUlWVlZ0ezZs6mmpkYH2Yi1l5tcLte4DACdPXuWiIiuXbtGMpmMrK2tydTUlAYNGkRff/21qMHQpfbyq6+vp+DgYOrZsycZGRmRm5sbzZs3T/STYiL9rR2R9ucmEdGuXbvIzMyMKisrW62v7/Vr6/mXnJwszOnI++WdO3coLCyMzMzMyMHBgT7//HPRz+R1RVt+bdUXAMnlciJ6cdqJoUOHkqWlJVlYWJCvry/t3LmTmpubdZcYac+to/uCrqyd5L+BM8YYY4yx/+LvIDHGGGOMqeEGiTHGGGNMDTdIjDHGGGNquEFijDHGGFPDDRJjjDHGmBpukBhjjDHG1HCDxBhjjDGmhhskxphemzVrFiIiIoT7AQEBWLZsmc7i0Wbs2LH4/vvvdR3Ga5s+fTo2b96s6zAY0xlukBhjAICHDx9i4cKFcHV1hYmJCaRSKUJCQnDp0qU3sn31Rqezjhw5gvXr13d6/YCAAEgkEuHm5OSEKVOm4O7du8KcO3fuiObY29sjODgYeXl57W776NGjKC8vx/Tp0zsd3+twd3fHtm3bWo2vXbsWQ4cOfaVtrVy5El999RWqqqreTHCMdTPcIDHGAACTJ09GXl4e9u7di3/96184evQoAgICUFFRoevQROzs7NCjR4/X2sa8efOgUChQVlaGjIwM3Lt3D7///e9bzTt16hQUCgVOnDiB2tpahIWFtbpS/Mu2b9+O2bNnw8Cg+7+1DhkyBP369cO+fft0HQpjOtH9X8WMsddWWVmJCxcuYOPGjfjNb34DNzc3jBo1CvHx8ZgwYQIAYM6cOfjoo49E6zU2NsLR0RFJSUkAgB9++AHe3t4wMzODvb09goKCUFdXh7Vr12Lv3r3IyMgQjspkZ2cDAG7evIlx48YJ68yfPx+1tbVtxqr+EduzZ88QFxeHPn36wMTEBP379xfiaYu5uTmkUimcnZ3xq1/9CjExMbh+/Xqrefb29pBKpRg5ciS++eYblJeXIzc3V+M2Hz58iDNnzmD8+PGicYlEgl27duGjjz6Cubk5Bg0ahJycHBQXFyMgIAAWFhYYPXo0SkpKhHVajvjs2bMHrq6usLS0xKJFi9Dc3IxNmzZBKpXC0dERX331Vbt5tqWpqQlLly6FjY0N7O3tERcXh+jo6FZH+MaPH48DBw506jEY6+64QWKMwdLSEpaWlkhPT8ezZ880zpk7dy4yMzOhUCiEsWPHjqG+vh7Tpk2DQqHAjBkzMGfOHNy6dQvZ2dmYNGkSiAhffPEFpk6ditDQUCgUCigUCowePRp1dXUICQmBra0trly5gsOHD+PUqVOIiYnpcOxRUVFITU3F9u3bcevWLezatQuWlpYdXv/x48c4dOgQZDJZu/PMzMwAAM+fP9e4/OLFi0IDpG79+vWIiopCfn4+Bg4ciJkzZ+LTTz9FfHw8rl69CiJqlXNJSQmOHz+OzMxMpKamIikpCeHh4bh//z7OnTuHjRs3YuXKlW02bO3ZuHEj9u/fj+TkZFy6dAnV1dVIT09vNW/UqFH4+eef23xOMPZOeyuXwGWMdTs//PAD2drakqmpKY0ePZri4+OpoKBANMfLy4s2btwo3B8/fjzNmjWLiF5cCR4A3blzR+P2o6OjaeLEiaKx7777jmxtbam2tlYY+/HHH8nAwICUSqXG9fz9/Sk2NpaIiIqKiggAZWVldThPf39/MjIyIgsLCzI3NycA9P777wtXQyciksvlBIDy8vKIiOjJkyf08ccfk6WlpRCXuq1bt1Lfvn1bjQOglStXCvdzcnIIACUlJQljqampZGpqKtxfs2YNmZubU3V1tTAWEhJC7u7uoquye3p6UkJCgnDfzc2NjI2NycLCQnQzMjIiX19fYZ6TkxMlJiYK95uamsjV1bVVfQoKCtqtKWPvMj6CxBgD8OI7SGVlZTh69ChCQ0ORnZ2N4cOHIyUlRZgzd+5cJCcnAwDKy8tx/PhxzJkzBwDg6+uLwMBAeHt7Y8qUKdi9ezeePHnS7mPeunULvr6+sLCwEMbGjBkDlUqFoqIirTHn5+fD0NAQ/v7+r5RrZGQk8vPzUVBQgIsXL6J///4IDg5GTU2NaN7o0aNhaWkJW1tbFBQU4ODBg3ByctK4zadPn8LU1FTjMh8fH+HvlvW9vb1FYw0NDaiurhbG3N3dRd+1cnJygpeXl+j7TU5OTnjw4IHosZYvX478/HzRbcGCBcLyqqoqlJeXY9SoUcKYoaEhRowY0SrulqNm9fX1GvNi7F3GDRJjTGBqaorf/va3WLVqFS5fvoxZs2ZhzZo1wvKoqCj8+9//Rk5ODvbt2wcPDw98+OGHAF7sZLOysnD8+HF4eXnh22+/haenJ+Ry+VuLt2UH/qqsra3Rv39/9O/fH2PGjEFSUhJu376NgwcPiuYdPHgQBQUFePLkCUpKSvC73/2uzW06ODi02RAaGRkJf0skkjbHVCqVxnVa5mgae3mdljhacmu52dnZtRl3ex4/fgwA6NmzZ6fWZ6w74waJMdYmLy8v1NXVCfft7e0RERGB5ORkpKSkYPbs2aL5EokEY8aMwZdffom8vDwYGxsjLS0NAGBsbIzm5mbR/EGDBqGgoED0GJcuXYKBgQE8PT21xuft7Q2VSoVz5869TpowNDQE8OIo0Mv69OmDfv36wcbGRus2hg0bBqVSqfWoma5ZW1vDyckJV65cEcaam5s1fkm9sLAQvXv3hoODQ1eGyJhe4AaJMYaKigqMGzcO+/btw40bNyCXy3H48GFs2rQJEydOFM2dO3cu9u7di1u3biE6OloYz83Nxddff42rV6+itLQUR44cwcOHD4UvLbu7u+PGjRsoKirCo0eP0NjYiMjISJiamiI6OhqFhYU4e/YslixZgj/84Q9tfpT1Mnd3d0RHR2POnDlIT0+HXC5HdnY2Dh061O569fX1UCqVUCqVKCgowMKFC2Fqaorg4OBO/PdeGDZsGBwcHN7YeaPepiVLliAhIQEZGRkoKipCbGwsnjx5IhzJanHhwoXX+p8w1p29p+sAGGO6Z2lpCZlMhq1bt6KkpASNjY3o06cP5s2bhxUrVojmBgUFwdnZGYMHD4aLi4swbmVlhfPnz2Pbtm2orq6Gm5sbNm/ejLCwMAAvzj2UnZ2NkSNHora2FmfPnkVAQABOnDiB2NhYfPDBBzA3N8fkyZOxZcuWDse+Y8cOrFixAosWLUJFRQVcXV1bxaxu9+7d2L17NwDA1tYWPj4++Omnnzp01KothoaGmD17Nvbv39/qdAj6Ji4uDkqlElFRUTA0NMT8+fMREhIiHEkDgIaGBqSnpyMzM1OHkTKmOxIiIl0HwRjrPmpra9GrVy8kJydj0qRJug5HryiVSgwePBjXr1+Hm5ubrsPpMJVKhUGDBmHq1KnCWcp37NiBtLQ0nDx5UsfRMaYbfASJMdYhKpUKjx49wubNm2FjYyOcQJL9P6lUiqSkJJSWlup1g3T37l2cPHkS/v7+ePbsGf7yl79ALpdj5syZwhwjIyN8++23OoySMd3iI0iMsQ65c+cOPDw80Lt3b6SkpCAwMFDXIbFOunfvHqZPn47CwkIQEYYMGYINGzZg7Nixug6NMb3BDRJjjDHGmBr+FRtjjDHGmBpukBhjjDHG1HCDxBhjjDGmhhskxhhjjDE13CAxxhhjjKnhBokxxhhjTA03SIwxxhhjarhBYowxxhhTww0SY4wxxpia/wNYNlOyC19GMQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dbp = pd.concat([synthetic_data_df_gan.DBP, test.DBP], axis=1)\n",
        "dbp.columns = ['DBP_Synthetic', 'DBP_Real']\n",
        "dbp.plot(kind='kde')\n",
        "plt.xlabel('Diastolic BP (mmHg)')\n",
        "plt.title('Diastoli BP Distribution of Real vs Synthetic Test Data');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "I744Tz28kPbW",
        "outputId": "62a6a397-2fec-4ebf-fae1-f12bf7c5bced"
      },
      "execution_count": 77,
      "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": [
        "gend = pd.concat([synthetic_data_df_gan.GENDER, test.GENDER.reset_index()], axis=1).drop('index', axis=1)\n",
        "gend.columns = ['GENDER_Synthetic', 'GENDER_Real']\n",
        "len( gend [ gend.GENDER_Real == gend.GENDER_Synthetic]) / len(gend)\n",
        "\n",
        "gen_real_ct = gend.GENDER_Real.value_counts()\n",
        "gen_syn_ct = gend.GENDER_Synthetic.value_counts()\n",
        "gen_ct = pd.concat([gen_real_ct, gen_syn_ct], axis=1)\n",
        "gen_ct.columns = ['Real', 'Synthetic']\n",
        "gen_ct.plot(kind='bar', rot= 0)\n",
        "plt.title('Gender Distribution Real vs Synthetic Test Data');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "8KrSqaAUlMAy",
        "outputId": "8a5d6acd-4202-460c-c12f-68622cc0373e"
      },
      "execution_count": 78,
      "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": [],
      "metadata": {
        "id": "oRJTEr1qkPV5"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**RESULTS**:\n",
        "\n",
        " The GAN model used a single hidden layer (with 100 nodes for the generator and 150 nodes for the discriminator), and 10 latent variable size.  The model **training showed a torsade pattern illustrating the adversarial method** between the training of the generative and discriminator components.  It can be considered that 'convergence' was reached at around Epoch 100 - 200.\n",
        "\n",
        "The **synthetic data generated from the Test set showed a distribution nearly equivalent with the Real Test set**.  (Note: the distribution was more equivalent at lesser Epochs, but a higher Epoch was used here to demonstrate the adversarial training).  This shows the the **model training was reasonable and is able to generalize to new data**.\n",
        "\n",
        "This shows that **GAN can be applied to generate synthetic tabular data containing both continuous and categorical variables**."
      ],
      "metadata": {
        "id": "jGKAhLaUjvQG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**COMPARING RESULTS BETWEEN VAE AND GAN:**\n",
        "\n",
        "**VAE reached convergence faster.  Both VAE and GAN and had similar distributions between real and synthetic data** with this small dataset.  \n",
        "\n",
        "Due to the continuous improvement in training, it can be hypothesized that GAN may be more useful for more complex and bigger datasets."
      ],
      "metadata": {
        "id": "V7YcIBwVqQWH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TECHNICAL CONCLUSION:**\n",
        "\n",
        "VAE and GAN generative models can generate reasonable synthetic tabular data containing heterogenous variables.  \n",
        "\n"
      ],
      "metadata": {
        "id": "W9V-_fTxq7A0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**REAL-WORLD APPLICATION:**\n",
        "\n",
        "This will be useful in medical research to alleviate privacy restrictions.  It will enable data sharing between different institutions which will lead to data that is more representative of the whole population, particularly for rare diseases."
      ],
      "metadata": {
        "id": "9JLYMf_QsaET"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Acknowledgment:* ChatGPT and Gemini were used to help elaborate the models and fix bugs."
      ],
      "metadata": {
        "id": "DDUX5cCVye_f"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "c0vrY3avqMXt"
      },
      "execution_count": 79,
      "outputs": []
    }
  ]
}